2216
Annex VII  GlossaryAVII1.5°C pathway  See Pathways .
Ablation (of glaciers, ice sheets, or snow cover)  See Mass 
balance/budget (of glaciers or ice sheets) .
Abrupt change  A change in the system that is substantially 
faster than the typical rate of the changes in its history. See also 
Abrupt climate change and Tipping point.
Abrupt climate change  A large-scale abrupt change in the 
climate system that takes place over a few decades or less, persists (or is anticipated to persist) for at least a few decades and causes 
substantial impacts in human and/or natural systems. See also Abrupt 
change and Tipping point.
Accumulation (of glaciers, ice sheets, or snow cover)  See 
Mass balance/budget (of glaciers or ice sheets).
Active layer  Layer of ground above permafrost subject to annual 
thawing and freezing.
Adaptation  In human systems, the process of adjustment to 
actual or expected climate and its effects, in order to moderate harm

or exploit beneficial opportunities. In natural systems, the process 
of adjustment to  actual climate and its effects; human intervention 
may facilitate adjustment to expected climate  and its effects. 
See  also Adaptation options, Adaptive capacity and Maladaptive 
actions (Maladaptation).
Adaptation options  The array of strategies and measures 
that are available and appropriate for addressing adaptation. They 
include a wide range of actions that can be categorized as structural, institutional, ecological or behavioural.
Adaptive capacity  The ability of systems, institutions, humans 
and other organisms to adjust to potential damage, to take advantage 
of opportunities, or to respond to consequences (MA, 2005).
Added value  Improvement of the representation of some climatic 
aspects by one methodology compared to another methodology. For instance, downscaling a coarse resolution global climate model may 
improve the representation of regional climate in complex terrain.

Adjustments (in relation to effective radiative 
forcing)  The response to an agent perturbing the climate system  
that is driven directly by the agent, independently of any change 
in global surface temperature. For example, carbon dioxide and 
aerosols, by altering internal heating and cooling rates within the atmosphere, can each cause changes to cloud cover and other variables thereby producing an effective radiative forcing even in the absence of any surface warming or cooling. Adjustments are usually rapid in the sense that they begin to occur right away, 
before climate feedbacks which are driven by global surface 
warming (although some adjustments may still take significant time 
to proceed to completion, for example those involving vegetation or ice sheets).
Adjustment time  See Response time or adjustment time.
Advection  Transport of water or air along with its properties

(e.g., temperature, chemical tracers) by winds or currents. Regarding the general distinction between advection and convection, the 
former describes transport by large-scale motions of the atmosphere or ocean, while convection describes the predominantly vertical, 
locally induced motions.
Aerosol  A suspension of airborne solid or liquid particles, with 
typical particle size in the range of a few nanometres to several tens of micrometres and atmospheric lifetimes  of up to several 
days in the troposphere and up to years in the stratosphere. The 
term aerosol, which includes both the particles and the suspending gas, is often used in this report in its plural form to mean ‘aerosol particles’. Aerosols may be of either natural or anthropogenic origin 
in the troposphere; stratospheric aerosols mostly stem from volcanic 
eruptions. Aerosols can cause an effective radiative forcing directly

through scattering and absorbing radiation (aerosol–radiation interaction), and indirectly by acting as cloud condensation nuclei or 
ice nucleating particles that affect the properties of clouds ( aerosol–
cloud interaction), and upon deposition on snow- or ice-covered surfaces. Atmospheric aerosols may be either emitted as primary particulate matter or formed within the atmosphere from gaseous precursors (secondary production). Aerosols may be composed of sea salt, organic carbon, black carbon (BC) , mineral species (mainly 
desert dust), sulphate, nitrate and ammonium or their mixtures. See also Short-lived climate forcers (SLCFs). 
Aerosol effective radiative forcing (ERFari+aci)  See 
Aerosol–radiation interaction.
Aerosol optical depth (AOD)  Wavelength-dependent aerosol 
optical depth is a measure of the aerosol contribution to extinction of 
top-of-the-atmosphere solar intensity measured at the ground. AOD 
is unitless.
Fine-mode aerosol optical depth

Aerosol optical depth due to aerosol particles smaller than 1 μm 
in radius.
Aerosol–cloud interaction  A process by which a perturbation 
to aerosol affects the microphysical properties and evolution of 
clouds through the aerosol role as cloud condensation nuclei or ice nuclei, particularly in ways that affect radiation or precipitation; such 
processes can also include the effect of clouds and precipitation on aerosol. The aerosol perturbation can be anthropogenic or come from some natural source. The radiative forcing from such interactions has 
traditionally been attributed to numerous indirect aerosol effects, 
but in this report, only two levels of radiative forcing (or effect) 
are distinguished:
Effective radiative forcing (or effect) due to aerosol–cloud interactions 
(ERFaci)
 
The final radiative forcing (or effect) from the aerosol perturbation, 
including the adjustments to the initial change in droplet or crystal

formation rate. These adjustments include changes in the strength of 
convection, precipitation efficiency, cloud fraction, lifetime or water 
content of clouds, and the formation or suppression of clouds in 
remote areas due to altered circulations.
Instantaneous radiative forcing (or effect) due to aerosol–cloud 
interactions (IRFaci)  
The radiative forcing (or radiative effect, if the perturbation is 
internally generated) due to the change in number or size distribution 
of cloud droplets or ice crystals that is the proximate result of an

2217
Glossary Anne x VIIAVIIaerosol perturbation, with other variables (in particular total cloud 
water content) remaining equal. In liquid clouds, an increase in cloud 
droplet concentration and surface area would increase the cloud 
albedo . This effect is also known as the cloud albedo effect, first 
indirect effect, or Twomey effect. It is a largely theoretical concept 
that cannot readily be isolated in observations or comprehensive 
process models due to the ubiquity of adjustments.
See also Aerosol–radiation interaction .
Aerosol–radiation interaction An interaction of aerosol  
directly with radiation produces radiative effects. In this report, two 
levels of radiative forcing  (or effect) are distinguished:
Aerosol effective radiative forcing (ERFari+aci) 
The total effective radiative forcing  due to both aerosol –cloud and 
aerosol–radiation interactions is denoted aerosol effective radiative 
forcing (ERFari+aci).

Effective radiative forcing (or effect) due to aerosol–radiation 
interactions (ERFari) 
The final radiative forcing  (or effect) from the aerosol  perturbation, 
including  adjustments to the initial change in radiation. These 
adjustments include changes in cloud caused by the impact of 
the radiative heating on convective or larger-scale atmospheric 
circulations, traditionally known as semi-direct aerosol forcing 
(or effect).
Instantaneous radiative forcing (or effect) due to aerosol–radiation 
interactions (IRFari) 
The radiative forcing  (or radiative effect, if the perturbation is 
internally generated) of an aerosol  perturbation due directly to 
aerosol–radiation interactions, with all environmental variables 
remaining unaffected. Traditionally known in the literature as the 
direct aerosol forcing (or effect).
See also Aerosol–cloud interaction .
Afforestation Conversion to forest  of land that historically has

not contained forests. [Note: For a discussion of the term forest and 
related terms such as afforestation, reforestation  and deforestation , 
see the 2006 IPCC Guidelines for National Greenhouse Gas Inventories 
and their 2019 Refinement, and information provided by the United 
Nations Framework Convention on Climate Change (IPCC, 2006, 
2019; UNFCCC, 2021a, b).] See also Deforestation , Reforestation , 
Anthropogenic removals  and Carbon dioxide removal (CDR) .
Agreement In this Report, the degree of agreement within the 
scientific body of knowledge on a particular finding is assessed 
based on multiple lines of evidence  (e.g., mechanistic understanding, 
theory, data, models, expert judgement) and expressed qualitatively 
(Mastrandrea et al., 2010). See also Confidence , Likelihood , 
Uncertainty  and Evidence .
Agricultural and ecological drought See Drought .
Air mass A widespread body of air, the approximately 
homogeneous properties of which (i) have been established while

that air was situated over a particular region  of the Earth’s surface, 
and (ii) undergo specific modifications while in transit away from the 
source region (AMS, 2021).Air pollution Degradation of air quality with negative effects 
on human health or the natural or built environment due to the 
introduction, by natural processes or human activity, into the 
atmosphere  of substances (gases, aerosol ) which have a direct 
(primary pollutants) or indirect (secondary pollutants) harmful effect. 
See also Short-lived climate forcers (SLCFs) .
Airborne fraction The fraction of total carbon dioxide (CO 2) 
emissions (from fossil fuels  and land-use change ) remaining in 
the atmosphere .
Albedo The proportion of sunlight ( solar radiation ) reflected by 
a surface or object, often expressed as a percentage. Clouds, snow 
and ice usually have high albedo; soil surfaces cover the albedo range 
from high to low; vegetation in the dry season and/or in arid zones

can have high albedo, whereas photosynthetically active vegetation 
and the ocean  have low albedo. The Earth’s planetary albedo changes 
mainly through changes in cloudiness and of snow, ice, leaf area and 
land cover .
Alkalinity See Total alkalinity .
Altimetry A technique for measuring the height of the Earth’s 
surface with respect to the geocentre of the Earth within a defined 
terrestrial reference frame (geocentric sea level). See also Geocentric 
sea level change .
Annular modes Hemispheric scale patterns of atmospheric 
variability characterized by opposing and synchronous fluctuations 
in sea level pressure between the polar caps and mid-latitudes, 
with  a structure exhibiting a high degree of zonal symmetry, and 
with no real preferred time scales ranging from days to decades. In 
each hemisphere, these fluctuations reflect changes in the latitudinal 
position and strength of the mid-latitude jets and associated storm

tracks. Annular modes are defined as the leading mode of variability 
of extratropical sea level pressure or geopotential heights and are 
known as the Northern Annular Mode (NAM)  and Southern Annular 
Mode (SAM)  in the two hemispheres, respectively.
Northern Annular Mode (NAM) 
A see-saw latitudinal fluctuation in Northern Hemisphere sea level 
pressure or geopotential height between the Arctic and the mid-
latitudes. The NAM has some links with the stratospheric polar 
vortex  and is related to the fluctuation in strength and latitude of the 
mean westerlies. Its variance is maximum in winter and its pattern 
has a strong regional expression in the North Atlantic, being strongly 
correlated with the North Atlantic Oscillation  index. The NAM is also 
known as the Arctic Oscillation (AO). In its positive phase, the NAM is 
characterized by anomalously low pressure over the Arctic and high 
pressure over the mid-latitudes/subtropics, with a strengthening of

the zonally averaged westerly winds on their polar flank that confines 
colder air across the Arctic. The negative NAM phase is characterized 
by a more distorted wind pattern and jet meanders that increase 
storminess in the mid-latitude regions. See Section AIV.2.1 in Annex 
IV of the AR6 WGI report.
Southern Annular Mode (SAM) 
The leading mode of climate variability  of Southern Hemisphere sea 
level pressure and geopotential height, which is associated with the 
strength and latitudinal shifts in the mid- to high-latitudes westerly 
wind belt. The SAM is also known as the Antarctic Oscillation (AAO).

2218
Annex VII  GlossaryAVII
A positive SAM phase is defined as lower-than-normal pressures 
over the polar regions and higher-than-normal pressures in the 
southern mid-latitudes, with a contraction towards Antarctica and 
strengthening of the westerly wind belt. The negative SAM phase 
exhibits positive high-latitude pressure anomalies, negative mid-
latitude pressure anomalies and a weaker westerly flow expanded 
towards the equator. See Section AIV.2.2 in Annex IV of the AR6 WGI 
report. See also Annular modes .
Anomaly The deviation of a variable from its value averaged over 
a reference period .
Antarctic amplification See Polar amplification .
Antarctic Ice Sheet (AIS) See Ice sheet .
Antarctic oscillation (AAO) See Southern Annular Mode (SAM)  
(under Annular modes ).
Anthropocene A proposed new geological epoch resulting from 
significant human-driven changes to the structure and functioning of 
the Earth System, including the climate system . Originally proposed

in the Earth system science community in 2000, the proposed new 
epoch is undergoing a formalization process within the geological 
community based on the stratigraphic evidence  that human activities 
have changed the Earth system to the extent of forming geological 
deposits with a signature that is distinct from those of the Holocene , 
and which will remain in the geological record. Both the stratigraphic 
and Earth system approaches to defining the Anthropocene consider 
the mid-20th century to be the most appropriate starting date 
(Steffen et al., 2016), although others have been proposed and 
continue to be discussed. The Anthropocene concept has already 
been informally adopted by diverse disciplines and the public to 
denote the substantive influence of humans on the Earth system.
Anthropogenic Resulting from or produced by human activities.
Anthropogenic emissions Emissions of greenhouse gases 
(GHGs) , precursors  of GHGs and aerosols  caused by human activities.

These activities include the burning of fossil fuels , deforestation , land 
use and land-use changes  (LULUC), livestock production, fertilization, 
waste management, and industrial processes. See also Anthropogenic  
and Anthropogenic removals .
Anthropogenic removals The withdrawal of greenhouse gases 
(GHGs)  from the atmosphere  as a result of deliberate human 
activities. These include enhancing biological sinks  of CO 2 and using 
chemical engineering to achieve long-term removal and storage. 
Carbon dioxide capture and storage (CCS), which alone does not 
remove CO 2 from the atmosphere, can help reduce atmospheric CO 2 
from industrial and energy-related sources if it is combined with 
bioenergy production (BECCS), or if CO 2 is captured from the air 
directly and stored (DACCS). [Note: In the 2006 IPCC Guidelines for 
national GHG Inventories (IPCC, 2006), which are used in reporting of 
emissions to the UNFCCC, ‘anthropogenic’ land-related GHG fluxes

are defined as all those occurring on ‘managed land’, i.e., ‘where 
human interventions and practices have been applied to perform 
production, ecological or social functions’. However, some removals 
(e.g., removals associated with CO 2 fertilization and N  deposition) 
are not considered as ‘anthropogenic’, or are referred to as ‘indirect’ 
anthropogenic effects, in some of the scientific literature assessed in this report. As a consequence, the land-related net GHG emission 
estimates from global models included in this report are not necessarily 
directly comparable with land use, land-use change and forestry 
(LULUCF) estimates in national GHG Inventories.] See also Carbon 
dioxide removal (CDR) , Afforestation , Enhanced weathering , Ocean 
alkalinization/Ocean alkalinity enhancement  and  Reforestation .
Anthropogenic subsidence Downward motion of the land 
surface induced by anthropogenic  drivers (e.g., loading, extraction

of hydrocarbons and/or groundwater, drainage, mining activities) 
causing sediment compaction or subsidence/deformation of the 
sedimentary sequence, or oxidation of organic material, thereby 
leading to relative sea level rise .
Apparent hydrological sensitivity (ηa) The change in global 
mean precipitation per degree Celsius of global mean surface 
air temperature (GSAT)  change with units of % per °C, although 
it can also be calculated as W m–2 per °C. See also Hydrological 
sensitivity ( η).
Arctic amplification See Polar amplification .
Arctic oscillation (AO) See Northern Annular Mode (NAM)  
Arid zone Areas where vegetation growth is severely constrained 
due to limited water availability. For the most part, the native 
vegetation of arid zones is sparse. There is high rainfall variability, 
with annual averages below 300 mm. Crop farming in arid zones 
requires irrigation.
Aridity The state of a long-term climatic feature characterized

by low average precipitation or available water in a region . Aridity 
generally arises from widespread persistent atmospheric subsidence 
or anticyclonic conditions, and from more localized subsidence in the 
lee side of mountains (adapted from Gbeckor-Kove, 1989; Türkeş, 
1999). See also Drought .
Artificial ocean upwelling (AOUpw) A potential carbon 
dioxide removal (CDR)  method that aims to artificially pump up 
cooler, nutrient-rich waters from deep in the ocean  to the surface. 
The aim is to stimulate phytoplankton activity and thereby increase 
ocean CO 2 uptake.
Assets Natural or human-made resources that provide current 
or future utility, benefit, economic or intrinsic value to natural or 
human systems.
Atlantic Equatorial Mode See Atlantic Zonal Mode (AZM)  
under Tropical Atlantic Variability (TAV) .
Atlantic Meridional Mode (AMM) See Tropical Atlantic 
Variability (TAV) .
Atlantic Meridional Overturning Circulation (AMOC) See 
Meridional overturning circulation (MOC) .

Atlantic Multi-decadal Oscillation (AMO) See Atlantic Multi-
decadal Variability (AMV) .
Atlantic Multi-decadal Variability (AMV) Large-scale 
fluctuations observed from one decade to the next in a variety of 
instrumental records and proxy  reconstructions over the entire 
North Atlantic ocean and surrounding continents. Fingerprints of

2219
Glossary Anne x VIIAVII
AMV can be found at the surface ocean , which is characterized by 
swings in basin-scale sea surface temperature  anomalies reflecting 
the interaction with the atmosphere . The positive phase of the 
AMV is characterized by anomalous warming over the entire North 
Atlantic, with the strongest amplitude in the subpolar gyre and along 
sea ice margin zones in the Labrador Sea and Greenland/Barents 
Sea  and in the subtropical North Atlantic basin to a lower extent. 
In the AR6 WGI report, the term AMV is preferred to Atlantic Multi-
decadal Oscillation (AMO)  used in previous IPCC reports because 
there is no preferred time scale of decadal variability  as the term 
oscillation would indirectly imply. See Section AIV.2.7 in Annex IV of 
the AR6 WGI report.
Atlantic Niño See Atlantic Zonal Mode (AZM)  under Tropical 
Atlantic Variability (TAV) .
Atlantic Zonal Mode (AZM) See Tropical Atlantic Variability 
(TAV) .

Atmosphere The gaseous envelope surrounding the Earth, 
divided into five layers – the troposphere  which contains half of 
the Earth’s atmosphere, the stratosphere , the mesosphere, the 
thermosphere and the exosphere, which is the outer limit of the 
atmosphere. The dry atmosphere consists almost entirely of nitrogen 
(78.1% volume mixing ratio) and oxygen (20.9% volume mixing 
ratio), together with a number of trace gases, such as argon (0.93% 
volume mixing ratio), helium and radiatively active greenhouse gases 
(GHGs)  such as carbon dioxide (CO 2) (0.04% volume mixing ratio), 
methane (CH 4  ), nitrous oxide (N 2O) and ozone (O 3). In addition, 
the atmosphere contains the GHG water vapour (H 2O), whose 
concentrations are highly variable (0–5% volume mixing ratio) as the 
sources ( evapotranspiration ) and sinks (precipitation) of water vapour 
show large spatio-temporal variations, and atmospheric temperature 
exerts a strong constraint on the amount of water vapour an air

parcel can hold. The atmosphere also contains clouds and aerosols . 
See also Hydrological cycle , Stratosphere  and Troposphere .
Atmosphere–ocean general circulation model 
(AOGCM) See General circulation model (GCM) .
Atmospheric boundary layer The atmospheric layer adjacent 
to the Earth’s surface that is affected by friction against that boundary 
surface, and possibly by transport of heat and other variables across 
that surface (AMS, 2021). The lowest 100 m of the boundary layer 
(about 10% of the boundary layer thickness), where mechanical 
generation of turbulence is dominant, is called the surface boundary 
layer or surface layer.
Atmospheric lifetime See Lifetime .
Atmospheric rivers (ARs) Long, narrow (up to a few hundred 
km wide), shallow (up to a few km deep) and transient corridors of 
strong horizontal water vapour transport that are typically associated 
with a low-level jet stream ahead of the cold front of an extratropical 
cyclone (ETC)  (Ralph et al., 2018).

Attribution Attribution is defined as the process of evaluating 
the relative contributions of multiple causal factors to a change or 
event with an assessment of confidence .
Australian and Maritime Continent monsoon 
(AusMCM) See Global monsoon .Autotrophic respiration Respiration  by photosynthetic (see 
photosynthesis ) organisms (e.g., plants and algae).
Avalanche A mass of snow, ice, earth or rocks, or a mixture of 
these, falling down a mountainside.
Barystatic See Sea level change (sea level rise/sea level fall) .
Basal lubrication Reduction of friction at the base of an ice sheet  
or glacier  due to lubrication by meltwater. This can allow the glacier 
or ice sheet to slide over its base. Meltwater may be produced by 
pressure-induced melting, friction or geothermal heat, or surface 
melt may drain to the base through holes in the ice.
Baseline/reference See Reference scenario  (under Scenario ) and 
Reference period .

Baseline scenario See Reference scenario  (under Scenario ).
Bifurcation point See Tipping point .
Biodiversity Biodiversity or biological diversity means the 
variability among living organisms from all sources including, among 
other things, terrestrial, marine and other aquatic ecosystems , and the 
ecological complexes of which they are part; this includes diversity 
within species, between species, and of ecosystems (UN, 1992). See 
also Ecosystem .
Bioenergy with carbon dioxide capture and storage 
(BECCS) Carbon dioxide capture and storage (CCS)  technology 
applied to a bioenergy facility. Note that depending on the total 
emissions of the BECCS supply chain, carbon dioxide (CO 2) can be 
removed from the atmosphere . See also Carbon dioxide capture 
and storage (CCS) , Anthropogenic removals  and Carbon dioxide 
removal (CDR) .
Biogenic volatile organic compounds (BVOCs) See Volatile 
organic compounds (VOCs) .
Biogeophysical potential See Mitigation potential .

Biological (carbon) pump A series of ocean  processes through 
which inorganic carbon (as carbon dioxide, CO 2) is fixed as organic 
matter by photosynthesis  in sunlit surface water and then transported 
to the ocean interior, and possibly the sediment, resulting in the 
storage of carbon.
Biomass Organic material excluding the material that is fossilized 
or embedded in geological formations. Biomass may refer to the 
mass of organic matter in a specific area (ISO, 2014). 
Biosphere (terrestrial and marine) The part of the Earth 
system comprising all ecosystems  and living organisms, in the 
atmosphere , on land (terrestrial biosphere) or in the oceans  (marine 
biosphere), including derived dead organic matter, such as litter, soil 
organic matter and oceanic detritus.
Bipolar seesaw (also inter-hemispheric seesaw, inter-
hemispheric asymmetry, hemispheric asymmetry)  A 
phenomenon in which temperature changes in the Northern and

Southern hemispheres are related but out of phase, generally 
inferred to represent a change in the magnitude or sign of net 
heat transport across the equator. Originally called hemispheric 
asymmetry and linked to changes in thermohaline overturning

2220
circulation on multi-millennial scales (Mix et al., 1986), later named 
bipolar seesaw and applied to millennial scales (Broecker, 1998) with 
a similar thermohaline mechanism (Stocker and Johnsen, 2003). 
See also Meridional overturning circulation (MOC) and Deglacial or 
deglaciation or glacial termination .
Black carbon (BC) A relatively pure form of carbon, also known 
as soot, arising from the incomplete combustion of fossil fuels, biofuel, 
and biomass. It only stays in the atmosphere  for days or weeks. BC 
is a climate forcing  agent with strong warming effect, both in the 
atmosphere and when deposited on snow or ice. See also Aerosol  
and Atmosphere .
Blocking Associated with persistent, slow-moving high-pressure 
systems that obstruct the prevailing westerly winds in the middle 
and high latitudes and the normal eastward progress of extratropical 
transient storm systems. It is an important component of the intra-

seasonal climate variability  in the extratropics and can cause 
long-lived weather conditions such as cold spells in winter and 
summer  heatwaves .
Blue carbon Biologically driven carbon fluxes and storage in 
marine systems that are amenable to management. Coastal blue 
carbon focuses on rooted vegetation in the coastal zone, such as tidal 
marshes, mangroves and seagrasses. These ecosystems  have high 
carbon burial rates on a per unit area basis and accumulate carbon 
in their soils and sediments. They provide many non-climatic benefits 
and can contribute to ecosystem -based adaptation. If degraded or 
lost, coastal blue carbon ecosystems are likely to release most of their 
carbon back to the atmosphere . There is current debate regarding 
the application of the blue carbon concept to other coastal and non-
coastal processes and ecosystems, including the open ocean . See 
also Sequestration .
Brewer–Dobson circulation The meridional overturning

circulation of the stratosphere  transporting air upward in the tropics, 
poleward to the winter hemisphere, and downward at polar and 
subpolar latitudes. The Brewer–Dobson circulation is driven by the 
interaction between upward propagating planetary waves and the 
mean flow.
Burden The total mass of a substance of concern in the atmosphere .
Business as usual (BAU) The term business as usual scenario 
has been used to describe a scenario  that assumes no additional 
policies beyond those currently in place and that patterns of socio-
economic development are consistent with recent trends. The term 
is now used less frequently than in the past.  See also Reference 
scenario  (under  Scenario ).
13C Stable isotope  of carbon having an atomic weight of 
approximately 13. Measurements of the ratio of 13C/12C in carbon 
dioxide (CO 2) molecules are used to infer the importance of different 
carbon cycle  and climate  processes and the size of the terrestrial 
carbon reservoir .

14C Unstable isotope  of carbon having an atomic weight of 
approximately 14 and a half-life of about 5700 years. It is often used 
for dating purposes going back some 40 kyr. Its variation in time is 
affected by the magnetic fields of the Sun and Earth, which influence 
its production from cosmic rays.Calcification The process of biologically precipitating calcium 
carbonate minerals to create organism shells, skeletons, otoliths, or 
other body structures. The chemical equation describing calcification 
is Ca2+ (aq) + 2HCO 3– (aq) → CaCO 3(s) + CO 2 + H 2O. Aragonite and 
calcite are two common crystalline forms of biologically precipitated 
calcium carbonate minerals that have different solubilities.
Calving (of glaciers or ice sheets) The breaking off of 
discrete pieces of ice from a glacier , ice sheet  or an ice shelf  into 
lake or seawater, producing icebergs . This is a form of mass loss from 
an ice body.
Canopy temperature The temperature within the canopy of

a vegetation structure.
Carbon budget Refers to two concepts in the literature: (i) 
an assessment of carbon cycle sources  and sinks  on a global 
level, through the synthesis of evidence for fossil fuel  and cement 
emissions, emissions and removals associated with land use  and 
land-use change , ocean  and natural land sources and sinks of 
carbon dioxide (CO 2), and the resulting change in atmospheric CO 2 
concentration. This is referred to as the global carbon budget; (ii) 
the maximum amount of cumulative net global anthropogenic  CO 2 
emissions that would result in limiting global warming  to a given 
level with a given probability, taking into account the effect of other 
anthropogenic climate forcers. This is referred to as the total carbon 
budget when expressed starting from the pre-industrial  period, and 
as the remaining carbon budget when expressed from a recent 
specified date.
Note 1: Net anthropogenic CO 2 emissions are anthropogenic CO 2

emissions minus anthropogenic CO 2 removals. See also Carbon 
dioxide removal (CDR) .
Note 2: The maximum amount of cumulative net global anthropogenic 
CO 2 emissions is reached at the time that annual net anthropogenic 
CO 2 emissions reach zero.
Note 3: The degree to which anthropogenic climate forcers other than 
CO 2 affect the total carbon budget and remaining carbon budget 
depends on human choices about the extent to which these forcers 
are mitigated and their resulting climate  effects.
Note 4: The notions of a total carbon budget and remaining carbon 
budget are also being applied in parts of the scientific literature and 
by some entities at regional, national, or sub-national levels. The 
distribution of global budgets across individual different entities 
and emitters depends strongly on considerations of equity and other 
value judgements.
Carbon cycle The flow of carbon (in various forms, e.g., as 
carbon dioxide (CO 2), carbon in biomass, and carbon dissolved in

the ocean  as carbonate and bicarbonate) through the atmosphere , 
hydrosphere , terrestrial and marine biosphere  and lithosphere . In this 
report, the reference unit for the global carbon cycle is GtCO 2 or GtC 
(one Gigatonne = 1 Gt = 1015 grams; 1 GtC corresponds to 3.664 
GtCO 2). See also Ocean carbon cycle .
Carbon dioxide (CO 2) A naturally occurring gas, CO 2 is also 
a by-product of burning fossil fuels  (such as oil, gas and coal), of 
burning biomass , of land-use  change  (LUC) and of industrial 
processes (e.g., cement production). It is the principal anthropogenic

2221
greenhouse gas (GHG)  that affects the Earth’s radiative balance. It 
is the reference gas against which other GHGs are measured and 
therefore has a  global warming potential (GWP)  of 1. 
Carbon dioxide (CO 2) fertilization The increase of plant 
photosynthesis  and water-use efficiency in response to increased 
atmospheric carbon dioxide (CO 2) concentration. Whether this 
increased photosynthesis translates into increased plant growth 
and carbon storage on land depends on the interacting effects of 
temperature, moisture and nutrient availability.
Carbon dioxide capture and storage (CCS) A process in 
which a relatively pure stream of carbon dioxide (CO 2) from industrial 
and energy-related sources is separated (captured), conditioned, 
compressed and transported to a storage location for long-term 
isolation from the atmosphere . Sometimes referred to as carbon 
capture and storage. See also Bioenergy with carbon dioxide capture

and storage (BECCS) , Sequestration , Anthropogenic removals  and 
Carbon dioxide removal (CDR) .
Carbon dioxide removal (CDR) Anthropogenic  activities 
removing carbon dioxide (CO 2) from the atmosphere and durably 
storing it in geological, terrestrial, or ocean  reservoirs, or in products. 
It includes existing and potential anthropogenic enhancement of 
biological or geochemical CO 2 sinks  and direct air carbon dioxide 
capture and storage (DACCS), but excludes natural CO 2 uptake 
not directly caused by human activities. See also Anthropogenic 
removals , Afforestation , Enhanced weathering , Ocean alkalinization/
Ocean alkalinity enhancement , Reforestation , Bioenergy with carbon 
dioxide capture and storage (BECCS)  and Carbon dioxide capture and 
storage (CCS) .
Carbon neutrality Condition in which anthropogenic  CO 2 
emissions associated with a subject are balanced by anthropogenic 
CO 2 removals. The subject can be an entity such as a country,

an  organization, a district or a commodity, or an activity such as 
a service and an event. Carbon neutrality is often assessed over the life 
cycle including indirect (‘scope 3’) emissions, but can also be limited 
to the emissions and removals, over a specified period, for which the 
subject has direct control, as determined by the relevant scheme.
Note 1: Carbon neutrality and net zero CO 2 emissions  are overlapping 
concepts. The concepts can be applied at global or sub-global scales 
(e.g., regional, national and sub-national). At a global scale, the terms 
carbon neutrality and net zero CO 2 emissions are equivalent. At sub-
global scales, net zero CO 2 emissions is generally applied to emissions 
and removals under direct control or territorial responsibility of the 
reporting entity, while carbon neutrality generally includes emissions 
and removals within and beyond the direct control or territorial 
responsibility of the reporting entity. Accounting rules specified by

GHG programmes or schemes can have a significant influence on the 
quantification of relevant CO 2 emissions and removals.
Note 2: In some cases, achieving carbon neutrality may rely on the 
supplementary use of offsets to balance emissions that remain after 
actions by the reporting entity are taken into account.
See also Greenhouse gas neutrality  and Net zero CO 2 emissions .
Carbon sequestration See Sequestration .
Carbon sink See Sink.Carbon source See Source .
Carbon–climate feedback See Climate–carbon cycle feedback .
Carbonaceous aerosol Aerosol  consisting predominantly of 
organic substances and black carbon .
Carbonate counter pump See Carbonate pump .
Carbonate pump Ocean carbon fixation through the biological 
formation of carbonates, primarily by plankton that generate 
bio-mineral particles that sink to the ocean  interior, and possibly 
the sediment. It is also called carbonate counter-pump, since the

formation of calcium carbonate (CaCO 3) is accompanied by the release 
of carbon dioxide (CO 2) to surrounding water and subsequently to the 
atmosphere .
Catchment An area that collects and drains precipitation.
Cenozoic Era The third and current geological Era, which began 
66.0 Ma. It comprises the Paleogene, Neogene and Quaternary  
Periods.
Central Pacific El Niño See El Niño–Southern Oscillation (ENSO) .
Chaotic A dynamical system  such as the climate system , governed 
by non-linear deterministic equations, may exhibit erratic or chaotic 
behaviour in the sense that very small changes in the initial state of 
the system lead to large and apparently unpredictable changes in its 
temporal evolution. Such chaotic behaviour limits the predictability  
of the state of a non-linear dynamical system at specific future times, 
although changes in its statistics may still be predictable given 
changes in the system parameters or boundary conditions.

Charcoal Material resulting from charring of biomass , usually 
retaining some of the microscopic texture typical of plant tissues; 
chemically it consists mainly of carbon with a disturbed graphitic 
structure, with lesser amounts of oxygen and hydrogen.
Chlorofluorocarbons (CFCs) An organic compound that 
contains chlorine, carbon, hydrogen and fluorine and is used for 
refrigeration, air conditioning, packaging, plastic foam, insulation, 
solvents or aerosol propellants. Because they are not destroyed in 
the lower atmosphere , CFCs drift into the upper atmosphere  where, 
given suitable conditions, they lead to ozone (O 3 ) depletion. They 
are some of the greenhouse gases (GHGs)  covered under the 1987 
Montreal Protocol , as a result of which manufacturing of these 
gases has been phased out, and they are being replaced by other 
compounds, including hydrofluorocarbons (HFCs) .
Chronology Arrangement of events according to dates or times 
of occurrence.

Cirrus cloud thinning (CCT) See Solar radiation modification 
(SRM) .
Clathrate (methane) A partly frozen slushy mix of methane  gas 
and ice, usually found in sediments.
Clausius–Clapeyron equation/relationship The thermodynamic 
relationship between temperature and the vapour pressure of 
a substance in which two phases of the substance are in equilibrium 
(e.g., liquid water and water vapour). For gases such as water vapour,

2222
this relation gives the increase in equilibrium (or saturation) vapour 
pressure per unit change in air temperature.
Climate Climate in a narrow sense is usually defined as the average 
weather, or more rigorously as the statistical description in terms of 
the mean and variability of relevant quantities over a period of time 
ranging from months to thousands or millions of years. The classical 
period for averaging these variables is 30 years, as defined by the 
World Meteorological Organization (WMO). The relevant quantities 
are most often surface variables such as temperature, precipitation 
and wind. Climate in a wider sense is the state, including a statistical 
description, of the climate system .
Climate change A change in the state of the climate  that can be 
identified (e.g., by using statistical tests) by changes in the mean and/
or the variability of its properties and that persists for an extended

period, typically decades or longer. Climate change may be due to 
natural internal processes or external forcings  such as modulations 
of the solar cycles, volcanic eruptions and persistent anthropogenic  
changes in the composition of the atmosphere  or in land use . Note 
that the United Nations Framework Convention on Climate Change 
(UNFCCC) , in its Article 1, defines climate change as: ‘a change of 
climate which is attributed directly or indirectly to human activity 
that alters the composition of the global atmosphere and which is 
in addition to natural climate variability observed over comparable 
time periods’. The UNFCCC thus makes a distinction between climate 
change attributable to human activities altering the atmospheric 
composition and climate variability  attributable to natural causes. See 
also Climate variability , Detection and attribution , Global warming  
and Ocean acidification (OA) .
Climate change commitment Climate change commitment

is defined as the unavoidable future climate change  resulting from 
inertia in the geophysical and socio-economic systems. Different 
types of climate change commitment are discussed in the literature 
(see subterms). Climate change commitment is usually quantified 
in terms of the further change in temperature, but it includes other 
future changes, for example in the hydrological cycle , in extreme 
weather events , in extreme climate events, and in sea level.
Constant composition commitment 
The constant composition commitment is the remaining climate 
change  that would result if atmospheric composition, and hence 
radiative forcing , were held fixed at a given value. It results from the 
thermal inertia of the ocean  and slow processes in the cryosphere  
and land surface.
Constant emissions commitment 
The constant emissions commitment is the committed climate change  
that would result from keeping anthropogenic emissions  constant.
Zero emissions commitment

The zero emissions commitment is an estimate of the subsequent 
global warming  that would result after anthropogenic emissions  
are set to zero. It is determined by both inertia in physical climate 
system  components ( ocean , cryosphere , land surface) and carbon 
cycle  inertia. In its widest sense it refers to emissions of each 
climate forcer, including greenhouses gases , aerosols  and their 
precursors . The climate response to this can be complex due 
to the different time scale of response of each climate forcer. A  specific subcategory of zero emissions commitment is the Zero 
CO 2 Emissions Commitment which refers to the climate system 
response to CO 2 emissions after setting these to net zero. The  
CO 2-only definition is of specific use in estimating remaining 
carbon budgets .
Climate extreme (extreme weather or climate event) The 
occurrence of a value of a weather or climate  variable above (or 
below) a threshold value near the upper (or lower) ends of the range

of observed values of the variable. By definition, the characteristics 
of what is called extreme weather  may vary from place to place in an 
absolute sense. When a pattern of extreme weather persists for some 
time, such as a season, it may be classified as an extreme climate 
event, especially if it yields an average or total that is itself extreme 
(e.g., high temperature, drought , or heavy rainfall over a season). For 
simplicity, both extreme weather events and extreme climate events 
are referred to collectively as ‘climate extremes’.
Climate feedback An interaction in which a perturbation in one 
climate  quantity causes a change in a second, and the change in 
the second quantity ultimately leads to an additional change in the 
first. A negative feedback is one in which the initial perturbation is 
weakened by the changes it causes; a positive feedback is one in 
which the initial perturbation is enhanced. The initial perturbation

can either be externally forced or arise as part of internal variability . 
See also Climate–carbon cycle feedback , Cloud feedback  and Ice–
albedo feedback .
Climate feedback parameter A way to quantify the radiative 
response of the climate system  to a change induced by a  radiative 
forcing . It is quantified as the change in net energy flux at the top of 
atmosphere for a given change in annual global surface temperature. 
It has units of  W m–2 °C–1.
Climate forecast See Climate prediction .
Climate index A time series constructed from climate  variables 
that provides an aggregate summary of the state of the climate 
system . For example, the difference between sea level pressure in 
Iceland and the Azores provides a simple yet useful historical North 
Atlantic Oscillation (NAO)  index. Because of their optimal properties, 
climate indices are often defined using principal components – 
linear combinations of climate variables at different locations that

have maximum variance subject to certain normalization constraints 
(e.g., the Northern Annular Mode (NAM) and Southern Annular 
Mode (SAM)  indices, which are principal components of Northern 
Hemisphere and Southern Hemisphere gridded pressure anomalies, 
respectively).  Definitions of observational indices for Modes of 
climate variability  can be found in Annex IV of the AR6 WGI report. 
Climate indicator Measures of the climate system , including 
large-scale variables and climate proxies . See also Climate metrics .
Key climate indicators 
Key indicators constitute a finite set of  distinct variables that may 
collectively point to important overall changes in the climate 
system  of broad societal relevance across the atmospheric, oceanic, 
cryospheric and biospheric domains, with land as an implicit cross-
cutting theme. Taken together, these indicators would be expected 
to both have changed and continue to change in the future in a

2223
coherent and consistent manner.  See Cross-Chapter Box 2.2, Table 1 
in the AR6 WGI report.
Climate information  Information about the past, current 
state or future of the climate system that is relevant for mitigation, 
adaptation and risk management . It may be tailored or ‘co -produced’ 
for specific contexts, taking into account users’ needs and values.
Climate metrics  Measures of aspects of the overall climate 
system response to radiative forcing, such as equilibrium climate 
sensitivity (ECS) , transient climate response (TCR) , transient climate 
response to cumulative CO 2 emissions (TCRE)  and the airborne 
fraction of anthropogenic carbon dioxide. See also Greenhouse gas 
emission metric, Climate indicator and Key climate indicators  (under 
Climate indicator).
Climate model  A qualitative or quantitative representation of 
the climate system based on the physical, chemical and biological

properties of its components, their interactions and feedback 
processes and accounting for some of its known properties. The 
climate system can be represented by models of varying complexity; that is, for any one component or combination of components, a spectrum or hierarchy of models can be identified, differing in such aspects as the number of spatial dimensions, the extent to which 
physical, chemical or biological processes are explicitly represented, 
or the level at which empirical parametrizations are involved. There is an evolution towards more complex models with interactive chemistry and biology. Climate models are applied as a research tool to study 
and simulate the climate and for operational purposes, including 
monthly, seasonal and interannual climate predictions. See also Earth 
system model (ESM), Earth system model of intermediate complexity 
(EMIC), Energy balance model (EBM) , Simple climate model (SCM) ,

Regional climate model (RCM) , Dynamic global vegetation model 
(DGVM), General circulation model (GCM) and Emulators.
Climate pattern  A set of spatially varying coefficients obtained 
by ‘projection’ (regression) of climate variables onto a  climate index  
time series. When the climate index is a principal component, the 
climate pattern is an eigenvector of the covariance matrix, referred to 
as an empirical orthogonal function (EOF) in climate science.
Climate prediction  A climate prediction or climate forecast is the 
result of an attempt to produce (starting from a particular state of the climate system) an estimate of the actual evolution of the climate in 
the future, for example, at seasonal, interannual or decadal time scales. Because the future evolution of the climate system may be highly sensitive to initial conditions, has chaotic elements and is subject to natural variability, such predictions are usually probabilistic in nature.

Climate projection  Simulated response of the climate system to 
a scenario of future emissions or concentrations of greenhouse gases 
(GHGs) and aerosols and changes in land use, generally derived using 
climate models. Climate projections are distinguished from climate 
predictions by their dependence on the emission/concentration/
radiative forcing scenario used, which is in turn based on assumptions 
concerning, for example, future socio-economic and technological developments that may or may not be realized.
Climate response  A general term for how the climate system 
responds to a radiative forcing.Climate sensitivity  The change in the surface temperature  
in response to a change in the atmospheric carbon dioxide (CO 2) 
concentration or other radiative forcing. See also Climate feedback 
parameter.
Earth system sensitivity

The equilibrium surface temperature response of the coupled atmosphere–ocean–cryosphere–vegetation–carbon cycle system to a doubling of the atmospheric carbon dioxide (CO
2) concentration 
is referred to as Earth system sensitivity. Because it allows ice sheets 
to adjust to the external perturbation, it may differ substantially from 
the equilibrium climate sensitivity  derived from coupled atmosphere–
ocean models.
Effective equilibrium climate sensitivity  
An estimate of the surface temperature response to a doubling of 
the atmospheric  carbon dioxide (CO 2) concentration that is evaluated 
from model output or observations for evolving non-equilibrium conditions. It is a measure of the strengths of the  climate feedbacks at 
a particular time and may vary with  forcing history and climate state, 
and therefore may differ from equilibrium climate sensitivity.
Equilibrium climate sensitivity (ECS)  
The equilibrium (steady state) change in the  surface temperature

following a doubling of the atmospheric carbon dioxide (CO 2) 
concentration from pre-industrial conditions.
Transient climate response (TCR)  
The surface temperature response for the hypothetical scenario 
in which atmospheric carbon dioxide (CO 2) increases at 1% yr–1 
from pre-industrial to the time of a  doubling of atmospheric CO 2 
concentration (year 70).
Transient climate response to cumulative CO 2 emissions (TCRE)  
The transient surface temperature change per unit cumulative carbon 
dioxide (CO 2) emissions, usually 1000 GtC. TCRE combines both 
information on the airborne fraction of cumulative CO 2 emissions 
(the fraction of the total CO 2 emitted that remains in the atmosphere, 
which is determined by carbon cycle processes) and on the transient 
climate response (TCR).
Climate services  Climate services involve the provision of climate 
information in such a way as to assist decision-making. The service

includes appropriate engagement from users and providers, is based 
on scientifically credible information and expertise, has an effective 
access mechanism and responds to user needs (Hewitt et al., 2012).
Climate simulation ensemble  A group of parallel model 
simulations characterizing historical climate conditions, climate 
predictions, or climate projections. Variation of the results across 
the ensemble members may give an estimate of modelling-based 
uncertainty. Ensembles made with the same model but different initial conditions characterize the uncertainty associated with internal climate variability, whereas multi-model ensembles 
including simulations by several models also include the effect of 
model differences. Perturbed parameter ensembles, in which model 
parameters are varied in a systematic manner, aim to assess the uncertainty resulting from internal model specifications within a single model. Remaining sources of uncertainty unaddressed with

model ensembles are related to systematic model errors or biases,

2224
which may be assessed from systematic comparisons of model 
simulations with observations wherever available.
Climate system The global system consisting of five major 
components: the atmosphere , the hydrosphere , the cryosphere , the 
lithosphere  and the biosphere  and the interactions between them. 
The climate system changes in time under the influence of its own 
internal dynamics and because of external forcings  such as volcanic 
eruptions, solar variations, orbital forcing , and anthropogenic  
forcings such as the changing composition of the atmosphere and 
land-use  change .
Climate threshold A limit within the climate system  (or its 
forcing ) beyond which the behaviour of the system is qualitatively 
changed. See also Abrupt climate change  and Tipping point .
Climate variability Deviations of climate  variables from 
a given mean state (including the occurrence of extremes, etc.) at

all spatial and temporal scales beyond that of individual weather 
events. Variability may be intrinsic, due to fluctuations of processes 
internal to the climate system  (internal variability ), or extrinsic, due 
to variations in natural or anthropogenic external forcing  (forced 
variability). See also Climate change  and Modes of climate variability .
Decadal variability 
Decadal variability refers to climate variability  on decadal time scales. 
See also Pacific Decadal Variability (PDV) , Atlantic Multi-decadal 
Oscillation/Variability (AMO/AMV)  and Pacific Decadal Oscillation 
(PDO)  (under Pacific Decadal Variability (PDV) ).
Internal variability 
Fluctuations of the climate dynamical system when subject to 
a constant or periodic external forcing  (such as the annual cycle). See 
also Climate variability .
Natural variability 
Natural variability refers to climatic fluctuations that occur without 
any human influence, that is, internal variability  combined with

the response to external natural factors such as volcanic eruptions, 
changes in solar activity and, on longer time scales, orbital effects 
and plate tectonics. See also Orbital forcing .
Climate velocity The speed at which isolines of a specified 
climate  variable travel across landscapes or seascapes due to 
changing climate. For example, climate velocity for temperature is the 
speed at which isotherms move due to changing climate (km yr–1) and 
is calculated as the temporal change in temperature (°C yr–1) divided 
by the current spatial gradient in temperature (°C km–1). It can be 
calculated using additional climate variables such as precipitation or 
can be based on the climatic niche of organisms.
Climate–carbon cycle feedback A climate feedback  involves 
changes in the properties of the land and ocean carbon cycle  in 
response to climate change . In the ocean , changes in oceanic 
temperature and circulation could affect the atmosphere –ocean

carbon dioxide (CO 2) flux; on the continents, climate change  could 
affect plant photosynthesis  and soil microbial respiration  and hence 
the flux of CO 2 between the atmosphere and the land biosphere .
Climatic impact-driver (CID) Climatic impact-drivers (CIDs) are 
physical climate system  conditions (e.g., means, events, extremes) 
that affect an element of society or ecosystems . Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, 
neutral or a mixture of each across interacting system elements and 
regions . See also Risk, Hazard  and Impacts (consequences, outcomes) .
Cloud condensation nuclei (CCN) The subset of aerosol  
particles that serve as an initial site for the condensation of liquid 
water, which can lead to the formation of cloud droplets, under typical 
cloud formation conditions. The main factor that determines which 
aerosol  particles are CCN at a given supersaturation is their size.

Cloud feedback A climate feedback  involving changes in any 
of the properties of clouds as a response to a change in the local 
or global surface temperature. Understanding cloud feedbacks and 
determining their magnitude and sign requires an understanding of 
how a change in climate  may affect the spectrum of cloud types, 
the cloud fraction and height, the radiative properties of clouds, and 
finally the Earth’s radiation budget. 
Cloud radiative effect The radiative effect of clouds relative to 
the identical situation without clouds.
Cloud-resolving models (CRMs) Numerical models that are 
that are of high enough resolution  and have the necessary physics to 
represent the dynamical and physical processes of cloud formation.
CMIP6 See Coupled Model Intercomparison Project (CMIP) .
CO 2 equivalent (CO 2-eq) emission The amount of carbon 
dioxide (CO 2) emission that would have an equivalent effect on 
a  specified key measure of climate change , over a specified time

horizon, as an emitted amount of another greenhouse gas (GHG)  or 
a mixture of other GHGs. For a mix of GHGs, it is obtained by summing 
the CO 2-equivalent emissions of each gas. There are various ways and 
time horizons to compute such equivalent emissions (see greenhouse 
gas emission metric ). CO 2-equivalent emissions are commonly used 
to compare emissions of different GHGs, but should not be taken 
to imply that these emissions have an equivalent effect across all 
key measures of climate change. [Note: Under the Paris Rulebook 
(Decision 18/CMA.1, annex, paragraph 37), parties have agreed to 
use GWP-100 values from the IPCC AR5 or GWP-100 values from a 
subsequent IPCC Assessment Report to report aggregate emissions 
and removals of GHGs. In addition, parties may use other metrics 
to report supplemental information on aggregate emissions and 
removals of GHGs.]
Coast The land near to the sea. The term ‘coastal’ can refer to that

land (e.g., as in ‘coastal communities’), or to that part of the marine 
environment that is strongly influenced by land-based processes. Thus, 
coastal seas are generally shallow and near-shore. The landward and 
seaward limits of the coastal zone are not consistently defined, either 
scientifically or legally. Thus, coastal waters can either be considered 
as equivalent to territorial waters (extending 12 nautical miles/22.2 
km from mean low water), or to the full Exclusive Economic Zone, or 
to shelf seas, with less than 200 m water depth.
Common era (CE) CE (Common Era) and BCE (Before the 
Common Era) are alternative names for AD (Anno Domini) and BC 
(Before Christ) in the Gregorian international standard calendar-year 
system. CE/BCE are preferred in an international context because 
they are neutral with respect to religion. The numbering of calendar

2225
years is the same under both terminologies. The CE began in year 
AD 1 and extends to the present day. 
Compatible emissions Earth system models  that simulate 
the land and ocean carbon cycle  can calculate carbon dioxide 
(CO 2) emissions that are compatible with a given atmospheric CO 2 
concentration trajectory. The compatible emissions over a given period 
of time are equal to the increase of carbon over that same period of 
time in the sum of the three active reservoirs : the atmosphere , the 
land and the ocean .
Compound events See Compound weather/climate events .
Compound weather/climate events The terms ‘compound 
events’, ‘compound extremes’ and ‘compound extreme events’ are 
used interchangeably in the literature and this report and refer to the 
combination of multiple drivers and/or hazards  that contributes to 
societal and/or environmental risk (Zscheischler et al., 2018).
Concentrations scenario See Scenario .

Confidence The robustness of a finding based on the type, 
amount, quality and consistency of evidence  (e.g., mechanistic 
understanding, theory, data, models, expert judgement) and on the 
degree of agreement  across multiple lines of evidence. In this report, 
confidence is expressed qualitatively (Mastrandrea et al., 2010).
Constant composition commitment See Climate change 
commitment .
Constant emissions commitment See Climate change 
Convection Vertical motion driven by buoyancy forces arising 
from static instability, usually caused by near-surface cooling or 
increases in salinity in the case of the ocean  and near-surface 
warming or cloud-top radiative cooling in the case of the atmosphere . 
In the atmosphere, convection gives rise to cumulus clouds and 
precipitation and is effective at both scavenging and vertically 
transporting chemical species. In the ocean, convection can carry 
surface waters to deep within the ocean.

Convection-permitting models See Cloud-resolving models 
(CRMs) .
Coral bleaching Loss of coral pigmentation through the loss of 
intracellular symbiotic algae (known as zooxanthellae) and/or loss 
of their pigments.
Coral reef An underwater ecosystem  characterised by structure-
building stony corals. Warm-water coral reefs occur in shallow seas, 
mostly in the tropics, with the corals (animals) containing algae 
(plants) that depend on light and relatively stable temperature 
conditions. Cold-water coral reefs occur throughout the world, 
mostly at water depths of 50–500 m. In both kinds of reef, living 
corals frequently grow on older, dead material, predominantly made 
of calcium carbonate (CaCO 3). Both warm- and cold-water coral reefs 
support high biodiversity  of fish and other groups and are considered 
to be especially vulnerable to climate change .
Coupled Model Intercomparison Project (CMIP) A climate  
modelling activity from the World Climate Research Programme

(WCRP), which coordinates and archives climate model  simulations based on shared model inputs by modelling groups from around 
the world. The CMIP Phase 3 (CMIP3) multi-model dataset includes 
projections using Special Report on Emissions Scenarios (SRES) 
scenarios. The CMIP Phase 5 (CMIP5) dataset includes projections 
using the Representative Concentration Pathways (RCP) . The CMIP6 
phase involves a suite of common model experiments as well as an 
ensemble of CMIP-endorsed Model Intercomparison Projects (MIPs).
Cryosphere The components of the Earth system at and below 
the land and ocean  surface that are frozen, including snow cover, 
glaciers , ice sheets , ice shelves , icebergs , sea ice , lake ice, river ice, 
permafrost  and seasonally frozen ground .
Cumulative emissions The total amount of emissions released 
over a specified period of time. See also Carbon budget  and Transient 
climate response to cumulative CO 2 emissions (TCRE)  (under Climate 
sensitivity ).

Dansgaard–Oeschger events (D-O events) Millennial-scale 
events first characterized in Greenland ice cores  as abrupt warming 
from a cold stadial  state to a warmer interstadial  state, followed by 
a return to a cold stadial state (Dansgaard et al., 1993), and traced in 
the ocean  via deposits of ice-rafted sand grains (Bond and Lotti, 1995). 
Named after Willi Dansgaard and Hans Oeschger by Bond and Lotti 
(1995). An example of a D–O event during the most recent deglacial  
transition is the Bølling–Allerød interstadial. Warm D–O events in 
Greenland are associated with cooling events in Antarctica (Blunier 
and Brook, 2001) through ocean thermohaline circulation  (Stocker 
and Johnsen, 2003). See also Bipolar seesaw (also interhemispheric 
seesaw, interhemispheric asymmetry, hemispheric asymmetry) .
Data assimilation Mathematical method used to combine 
different sources of information in order to produce the best possible

estimate of the state of a system. This information usually consists 
of observations of the system and a numerical model of the system 
evolution. Data assimilation techniques are used to create initial 
conditions for weather forecast models and to construct reanalyses  
describing the trajectory of the climate system  over the time period 
covered by the observations.
Dead zones Extremely hypoxic  (i.e., low-oxygen) areas in oceans  
and lakes, caused by excessive nutrient input from human activities 
coupled with other factors that deplete the oxygen required to 
support many marine organisms in bottom and near-bottom water.
Decadal predictability Refers to the notion of predictability  
of the climate system  on a decadal time scale. See also Climate 
prediction , Predictability  and Decadal prediction .
Decadal prediction A climate prediction  on decadal time scales. 
See also Predictability  and Decadal predictability .
Decadal variability See Climate variability .

Deep uncertainty See Uncertainty .
Deforestation Conversion of forest  to non-forest. [Note: For 
a discussion of the term forest and related terms such as afforestation , 
reforestation  and deforestation, see the 2006 IPCC Guidelines for 
National Greenhouse Gas Inventories and their 2019 Refinement, and 
information provided by the United Nations Framework Convention

2226
on Climate Change (IPCC, 2006, 2019; UNFCCC, 2021a, b).] See also 
Afforestation  and Reforestation .
Deglacial or deglaciation or glacial termination The period 
of transition from glacial  conditions at the end of a glacial period 
to interglacial  conditions characterized by a reduction in land ice 
volume. Gradual changes can be punctuated by abrupt changes  
linked to stadial /interstadial  events and bipolar seesaw  aspect. The 
last deglacial transition occurred between about 18,000 and 11,000 
years ago. It encompasses rapid events such as Meltwater Pulse 1A 
(MWP-1A)  and millennial-scale fluctuations such as the Younger 
Dryas . See also Glacial–interglacial cycles  and Ice age .
Detection Detection of change is defined as the process of 
demonstrating that climate  or a system affected by climate has 
changed in some defined statistical sense, without providing a reason 
for that change. An identified change is detected in observations if its

likelihood  of occurrence by chance due to internal variability  alone is 
determined to be small, for example, <10%.
Detection and attribution See Detection  and Attribution .
Diatoms Microscopic (2–200 µm) unicellular photosynthetic 
algae that live in surface waters of lakes, rivers and oceans  and form 
shells of opal. In the global ocean, marine diatom species distribution 
is primarily driven by nutrient availability. On regional scales, their 
species distribution in ocean sediment cores can be related to past 
sea surface temperatures  (Abrantes et al., 2013).
Dimensions of integration In IPCC AR6, concepts used to 
synthesize the knowledge of climate change  across not just the 
physical sciences, but also across impacts , adaptation  and mitigation  
research. The concept of ‘dimensions of integration’ includes 
(i)  emission and concentration scenarios  underlying the climate 
change projections  assessed in this report, (ii) levels of  projected

global mean temperature change and (iii) total amounts of cumulative 
carbon emissions for projections.
Direct (aerosol) effect See Aerosol–radiation interaction .
Direct air capture (DAC) Chemical process by which a pure 
carbon dioxide (CO 2) stream is produced by capturing CO 2 from the 
ambient air. See also Anthropogenic removals  and Carbon dioxide 
removal (CDR) .
Disaster A ‘serious disruption of the functioning of a community 
or a society at any scale due to hazardous events interacting with 
conditions of exposure, vulnerability and capacity, leading to one or 
more of the following: human, material, economic and environmental 
losses and impacts’ (UNGA, 2016). See also Exposure , Hazard , Risk 
and Vulnerability .
Discharge (of ice) See Mass balance/budget (of glaciers or ice 
sheets) .
Dissolved inorganic carbon The combined total of different 
types of non-organic carbon in (seawater) solution, comprising

carbonate (CO 32–), bicarbonate (HCO 3–), carbonic acid (H 2CO 3) and 
carbon dioxide (CO 2).
Diurnal temperature range (DTR) The difference between the 
maximum and minimum temperature during a 24-hour period.Dobson unit (DU) A unit to measure the total amount of ozone  
in a vertical column above the Earth’s surface (total column ozone). 
The number of Dobson units is the thickness in units of 10–5 m that 
the ozone  column would occupy if compressed into a layer of uniform 
density at a pressure of 1013 hPa and a temperature of 0°C. One DU 
corresponds to a column of ozone containing 2.69 × 1020 molecules 
per square metre. A typical value for the amount of ozone in a column 
of the Earth’s atmosphere , although very variable, is 300 DU.
Downscaling A method that derives local- to regional-scale 
information from larger-scale models or data analyses. Two main 
methods exist: dynamical downscaling and empirical/statistical

downscaling. The dynamical method uses the output of regional 
climate models , global models with variable spatial resolution , or 
high-resolution global models. The empirical/statistical methods are 
based on observations and develop statistical relationships that link 
the large-scale atmospheric variables with local/regional climate 
variables. In all cases, the quality of the driving model remains an 
important limitation on quality of the downscaled information. The 
two methods can be combined, for example, applying empirical/
statistical downscaling to the output of a regional climate model 
consisting of a dynamical downscaling of a global climate model.
Drought An exceptional period of water shortage for existing 
ecosystems  and the human population (due to low rainfall, high 
temperature, and/or wind). See also Plant evaporative stress .
Agricultural and ecological drought 
Depending on the affected biome: a period with abnormal soil moisture

deficit, which results from combined shortage of precipitation and 
excess evapotranspiration , and during the growing season impinges 
on crop production or ecosystem  function in general.
Hydrological drought 
A period with large runoff  and water deficits in rivers, lakes and 
reservoirs.
Meteorological drought 
A period with an abnormal precipitation deficit.
Dynamic global vegetation model (DGVM) A model that 
simulates vegetation development and dynamics through space and 
time, as driven by climate  and other environmental changes.
Dynamical downscaling See Downscaling .
Dynamical system A process or set of processes whose evolution 
in time is governed by a set of deterministic physical laws. The climate 
system  is a dynamical system.
Early Eocene Climatic Optimum (EECO) The EECO is a period 
of geological time that occurred about 53 to 49 million years ago, 
during the Eocene Epoch. Continental positions at this time were

somewhat different to present due to tectonic plate movements. 
Geological data indicate that the EECO was a period of relatively 
high atmospheric CO 2 concentrations (about 1150–2500 ppmv) 
and relative warmth ( global mean surface temperature  was about 
10–18°C above the 1850–1900 reference), and polar ice sheets  
were absent.
Earth system model (ESM) A coupled atmosphere–ocean  
general circulation model (AOGCM)  in which a representation of

2227
the carbon cycle  is included, allowing for interactive calculation 
of atmospheric carbon dioxide (CO 2) or compatible emissions . 
Additional components (e.g., atmospheric chemistry, ice sheets , 
dynamic vegetation, nitrogen cycle, but also urban or crop models) 
may be included. See also Earth system model of intermediate 
complexity (EMIC) .
Earth system model of intermediate complexity 
(EMIC) EMICs represent climate  processes at a lower resolution  
or in a simpler, more idealized fashion than an Earth system model 
(ESM) .
Earth’s energy budget encompasses the major energy flows 
of relevance for the climate system : the top-of-atmosphere energy 
budget; the surface energy budget; changes in the global energy 
inventory and internal flows of energy within the climate system that 
characterize the climate state. 
Top-of-atmosphere energy budget 
Comprises the energy fluxes associated with incoming solar radiation ,

reflected solar radiation and emitted thermal radiation. Typical units: 
W m–2. 
Surface energy budget 
Comprises the exchanges of heat at the surface of the Earth associated 
with both radiative and non-radiative processes. Typical units: W m–2. 
Global energy inventory 
Quantifies the excess energy absorbed or lost by the Earth system 
(ocean , land, atmosphere  and cryosphere ), mostly in the form of heat, 
associated with radiative forcing  of the climate . Typical units: Joules. 
Global energy budget  
For a given time period, the global energy budget expresses the 
balance between change in the global energy inventory, the time-
integrated effective radiative forcing  and time-integrated radiative 
response of the climate system . Typical units: Joules.
See also Earth’s energy imbalance .
Earth’s energy imbalance The persistent and positive 
(downward) net top of atmosphere energy flux associated with 
greenhouse gas forcing  of the climate system . See also Earth’s energy

budget  and Radiative response (of the climate system) .
Earth system sensitivity See Climate sensitivity .
Effective equilibrium climate sensitivity See Climate 
sensitivity .
East Antarctic Ice Sheet (EAIS) See Ice sheet .
East Asian monsoon (EAsiaM) See Global monsoon .
Eastern boundary upwelling systems (EBUS) Eastern 
boundary upwelling systems (EBUS) are located at the eastern 
(landward) edges of major ocean  basins in both hemispheres, where 
equatorward winds drive upwelling currents that bring cool, nutrient-
rich (and often oxygen-poor) waters from the deep ocean to the 
surface near the coast.
Eastern Pacific El Niño See El Niño–Southern Oscillation (ENSO) .
Economic potential See Mitigation potential .Ecosystem A functional unit consisting of living organisms, their 
non-living environment and the interactions within and between 
them. The components included in a given ecosystem and its spatial 
boundaries depend on the purpose for which the ecosystem is

defined: in some cases, they are relatively sharp, while in others they 
are diffuse. Ecosystem boundaries can change over time. Ecosystems 
are nested within other ecosystems, and their scale can range from 
very small to the entire biosphere . In the current era, most ecosystems 
either contain people as key organisms or are influenced by the 
effects of human activities in their environment. 
Effective radiative forcing (ERF) See Radiative forcing , 
Aerosol effective radiative forcing (ERFari+aci)  (under Aerosol–
radiation interaction ), Effective radiative forcing (or effect) due to 
aerosol–cloud interactions (ERFaci)  (under Aerosol–cloud interaction ) 
and Effective radiative forcing (or effect) due to aerosol–radiation 
interactions (ERFari)  (under Aerosol–radiation interaction ).
Ekman transport The total transport resulting from a balance 
between the Coriolis force and the frictional stress due to the action 
of the wind on the ocean  surface.

El Niño See El Niño–Southern Oscillation (ENSO) .
El Niño–Southern Oscillation (ENSO) The term El Niño was 
initially used to describe a warm-water current that periodically flows 
along the coast of Ecuador and Peru, disrupting the local fishery. It has 
since become identified with warming of the tropical Pacific Ocean 
east of the dateline. This oceanic event is associated with a fluctuation 
of a global-scale tropical and subtropical surface pressure pattern 
called the Southern Oscillation. This coupled atmosphere –ocean  
phenomenon, with preferred time scales of two to about seven years, 
is known as the El Niño–Southern Oscillation (ENSO). The warm and 
cold phases of ENSO are called El Niño and La Niña, respectively. ENSO 
is often measured by the surface pressure anomaly difference between 
Tahiti and Darwin and/or the sea surface temperatures  in the central 
and eastern equatorial Pacific. This phenomenon has a great impact

on the wind, sea surface temperature and precipitation patterns in the 
tropical Pacific. It has climatic effects throughout the Pacific region 
and in many other parts of the world through global teleconnections . 
See Section AIV.2.3 in Annex IV of the AR6 WGI report.
Central Pacific El Niño 
An El Niño event in which sea surface temperature  anomalies are 
stronger in the central equatorial Pacific than in the east. Also known 
as a Modoki  El Niño event.
Eastern Pacific El Niño 
largest in the eastern tropical Pacific.
Electromagnetic spectrum Wavelength, frequency or energy 
range of all electromagnetic radiation. In terms of solar radiation , the 
spectral irradiance is the power arriving at the Earth per unit area, 
per unit wavelength.
Elevation-dependent warming (EDW) Characteristic of many 
regions where mountains are located, in which past and/or future

surface air temperature changes vary neither uniformly nor linearly 
with elevation. In many cases, warming is enhanced within or above 
a certain elevation range.

2228
Emergence (of the climate signal) Emergence of a climate 
change  signal or trend refers to when a change in climate  (the ‘signal’) 
becomes larger than the amplitude of natural or internal variations 
(defining the ‘noise’), This concept is often expressed as a ‘signal-
to-noise’ ratio and emergence occurs at a defined threshold of this 
ratio (e.g., S/N > 1 or 2). Emergence can refer to changes relative to 
a historical or modern baseline (usually at least 20 years long) and 
can also be expressed in terms of time ( time of emergence ) or in terms 
of a global warming level. Emergence is also used to refer to a time 
when we can expect to see a response to reducing greenhouse gas 
(GHG)  emissions (emergence with respect to mitigation ). Emergence 
can be estimated using observations and/or model simulations. See 
also Time of emergence (ToE) .
Emergent constraint An attempt to reduce the uncertainty in

climate projections , using an ensemble of Earth system models (ESMs)  
to relate a specific feedback or future change to an observation of the 
past or current climate  (typically some trend, variability or change 
in variability).
Emission factor/Emissions intensity A coefficient that 
quantifies the emissions or removals of a gas per unit activity. 
Emission factors are often based on a sample of measurement data, 
averaged to develop a representative rate of emission for a given 
activity level under a given set of operating conditions.
Emission pathways See Pathways .
Emissions See Cumulative emissions , Anthropogenic emissions , 
Fossil fuel emissions , Non-CO 2 emissions and radiative forcing  and 
Negative greenhouse gas emissions . See also Emissions scenario  
(under Scenario ), and Emission pathways.
Emulation Reproducing the behaviour of complex, process-
based models (namely, Earth system models, ESMs ) via simpler

approaches, using either emulators  or simple climate models (SCMs) . 
The computational efficiency of emulating approaches opens new 
analytical possibilities given that ESMs take a lot of computational 
resources for each simulation. See also Emulators  and Simple climate 
model (SCM) .
Emulators A broad class of heavily parametrized models (‘simple 
climate models’), statistical methods like neural networks, genetic 
algorithms or other artificial intelligence approaches designed to 
reproduce the responses of more complex, process-based Earth 
system models (ESMs) . The main application of emulators is to 
extrapolate insights from ESMs and observational constraints 
to a larger set of emission scenarios . See also Emulation  and Simple 
climate model (SCM) .
Energy balance model (EBM)  An energy balance model is 
a  simplified climate model that is typically used as an emulator 
of climate to analyse the energy budget of the Earth to compute

changes in the climate . In its simplest form, there is no explicit spatial 
dimension, and the model then provides an estimate of the changes 
in globally averaged temperature computed from the changes 
in radiation. This zero-dimensional energy balance model can be 
extended to a one-dimensional or two-dimensional model if changes 
to the energy budget with respect to latitude, or both latitude and 
longitude, are explicitly considered.Energy balance The difference between the total incoming and 
total outgoing energy. If this balance is positive, warming occurs; if 
it is negative, cooling occurs. Averaged over the globe and over long 
time periods, this balance must be zero. Because the climate system  
derives virtually all its energy from the Sun, zero balance implies that, 
globally, the absorbed solar radiation , that is, incoming solar radiation  
minus reflected solar radiation  at the top of the atmosphere  and

outgoing longwave radiation  emitted by the climate system  are equal.
Energy budget (of the Earth) The Earth is a physical system 
with an energy budget that includes all gains of incoming energy and 
all losses of outgoing energy. The Earth’s energy budget is determined 
by measuring how much energy comes into the Earth system from 
the Sun, how much energy is lost to space, and accounting for 
the remainder on Earth and its atmosphere . Solar radiation  is the 
dominant source of energy into the Earth system. Incoming solar 
energy may be scattered and reflected by clouds and aerosols  or 
absorbed in the atmosphere. The transmitted radiation is then either 
absorbed or reflected at the Earth’s surface. The average albedo  of 
the Earth is about 0.3, which means that 30% of the incident solar 
energy is reflected into space, while 70% is absorbed by the Earth. 
Radiant solar or shortwave energy is transformed into sensible heat,

latent energy (involving different water states), potential energy, and 
kinetic energy before being emitted as infrared radiation . With the 
average surface temperature of the Earth of about 15°C (288 K), 
the main outgoing energy flux is in the infrared part of the spectrum. 
See also Sensible heat flux  and Latent heat flux .
Enhanced weathering A proposed method to increase the 
natural rate of removal of carbon dioxide (CO 2) from the atmosphere  
using silicate and carbonate rocks. The active surface area of these 
minerals is increased by grinding, before they are actively added to 
soil, beaches or the open ocean . See also Carbon dioxide removal 
(CDR)  and Anthropogenic removals .
Ensemble A collection of comparable datasets that reflect 
variations within the bounds of one or more sources of uncertainty  
and that, when averaged, can provide a more robust estimate 
of underlying behaviour. Ensemble techniques are used by the

observational, reanalysis  and modelling communities. See also 
Climate simulation ensemble .
Equilibrium and transient climate experiment An   
equilibrium climate experiment is a climate model  experiment in 
which the model is allowed to fully adjust to a change in radiative 
forcing . Such experiments provide information on the difference 
between the initial and final states of the model, but not on the time-
dependent response. If the forcing is allowed to evolve gradually 
according to a prescribed emissions scenario , the time-dependent 
response of a climate model may be analysed. Such an experiment is 
called a transient climate experiment.
Equilibrium climate sensitivity (ECS) See Climate sensitivity .
Equilibrium line The spatially averaged boundary at a given 
moment, usually chosen as the seasonal mass budget  minimum at 
the end of summer, between the region on a glacier  where there 
is a net annual loss of ice mass (ablation area) and that where there is

a net annual gain ( accumulation  area). The altitude of this boundary 
is referred to as equilibrium line altitude (ELA).

2229
Equivalent carbon dioxide (CO 2) emission See CO 2 equivalent 
(CO 2-eq) emission .
Eutrophication Over-enrichment of water by nutrients such as 
nitrogen and phosphorus. It is one of the leading causes of water 
quality impairment. The two most acute symptoms of eutrophication 
are hypoxia  (or oxygen depletion) and harmful algal blooms.
Evaporation The physical process by which a liquid (e.g., water) 
becomes a gas (e.g., water vapour).
Evapotranspiration The combined processes through which 
water is transferred to the atmosphere  from open water and ice 
surfaces, bare soil, and vegetation that make up the Earth’s surface.
Potential evapotranspiration  The potential rate of water loss from 
wet soils and from plant surfaces, without any limits imposed by the 
water supply.
Evidence Data and information used in the scientific process to 
establish findings. In this report, the degree of evidence reflects the

amount, quality and consistency of scientific/technical information 
on which the Lead Authors are basing their findings. See also 
Agreement , Confidence , Likelihood  and Uncertainty .
Exposure The presence of people; livelihoods ; species or 
ecosystems ; environmental functions, services, and resources; 
infrastructure; or economic, social, or cultural assets in places and 
settings that could be adversely affected.
Extended concentration pathways (ECPs) See Representative 
concentration pathways (RCPs)  (under Pathways ).
External forcing External forcing refers to a forcing  agent 
outside the climate system  causing a change in the climate system. 
Volcanic eruptions, solar variations and changes in Earth’s orbit, as 
well as anthropogenic  changes in the composition of the atmosphere 
or in land use are external forcings. See also Orbital forcing .
Extratropical cyclone (ETC) Any cyclonic-scale storm that is not

a tropical cyclone . Usually refers to a mid- or high-latitude migratory 
storm system formed in regions of large horizontal temperature 
variations. Sometimes called extratropical storm or extratropical low.
Extratropical jets Extratropical jets are wind maxima in the 
upper troposphere  marking zones of baroclinic instability. Anomalies 
in the position of these jets are often associated with storms, blocking , 
and weather extremes.
Extreme climate event See Climate extreme (extreme weather 
or climate event) .
Extreme coastal water level (ECWL) See Extreme sea 
level (ESL) .
Extreme sea level (ESL) The occurrence of an exceptionally 
low or high local sea surface height, arising from (a combination 
of) short-term phenomena (e.g., storm surges , tides and waves). 
Relative sea level changes  affect extreme sea levels directly by 
shifting the mean water levels and indirectly by modulating the 
propagation of tides, waves and/or surges due to increased water

depth. In addition, extreme sea levels can be influenced by changes 
in the frequency, tracks or strength of weather systems and 
storms, or due to anthropogenically induced changes such as the modification of coastlines or dredging. In turn, changes in any or all 
of the contributions to extreme sea levels may lead to long–term 
relative sea level changes. Alternate expressions for ESL may be used 
depending on the processes resolved.
Extreme still water level (ESWL) refers to the combined contribution 
of relative sea level change, tides and storm-surges. Wind-waves 
also contribute to coastal sea level via three processes: infragravity 
waves (lower frequency gravity waves generated by the wind waves), 
wave setup (time-mean sea level elevation due to wave energy 
dissipation), and swash (vertical displacement up the shore-face 
induced by individual waves). Extreme total water level (ETWL) is the 
ESWL plus wave setup. When considering coastal impacts, swash is

also important, and Extreme coastal water level (ECWL) is used. See 
also Storm surge  and Sea level change (sea level rise/sea level fall) .
Extreme still water level (ESWL) See Extreme sea level (ESL) .
Extreme total water level (ETWL) See Extreme sea level (ESL) .
Extreme weather event An event that is rare at a particular 
place and time of year. Definitions of ‘rare’ vary, but an extreme 
weather event would normally be as rare as or rarer than the 10th 
or 90th percentile of a probability density function estimated from 
observations. By definition, the characteristics of what is called 
extreme weather may vary from place to place in an absolute sense. 
See also Climate extreme (extreme weather or climate event) .
Extreme/heavy precipitation event An extreme/heavy 
precipitation event is an event that is of very high magnitude with a 
very rare occurrence at a particular place. Types of extreme precipitation

may vary depending on its duration (hourly, daily or multi-days (e.g., 5 
days)) though all of them qualitatively represent high magnitude. The 
intensity of such events may be defined with a block maxima approach 
such as annual maxima or with a peaks over threshold approach, such 
as rainfall above the 95th or 99th percentile at a particular place.
Faculae Bright patches on the Sun. The area covered by faculae is 
greater during periods of high solar activity .
Feedback See Climate feedback .
Fine-mode aerosol optical depth See Aerosol optical depth 
(AOD) .
Fingerprint The climate response pattern in space and/or time to 
a specific forcing  is commonly referred to as a fingerprint. The spatial 
patterns of sea level response to melting of glaciers  or ice sheets  (or 
other changes in surface loading) are also referred to as fingerprints. 
Fingerprints are used to detect the presence of this response in 
observations and are typically estimated using forced climate model

simulations. See also Detection  and attribution .
Fire weather Weather conditions conducive to triggering 
and sustaining wildfires, usually based on a set of indicators and 
combinations of indicators including temperature, soil moisture , 
humidity, and wind. Fire weather does not include the presence or 
absence of fuel load.
Firn Snow that has survived at least one ablation  season but has 
not been transformed to glacier  ice. Its pore space is at least partially 
interconnected, allowing air and water to circulate. Firn densities 
typically are 400–830 kg m–3.

2230
Fitness-for-purpose The suitability of a model (or other resource, 
such as a dataset or method) for a particular task, such as quantifying 
the contribution of increased greenhouse gas  concentrations to 
recent changes in global mean surface temperature  or projecting 
changes in drought  frequency in a region under a given scenario . 
Assessment of a model’s fitness-for-purpose can be informed both by 
how the model represents relevant physical processes and by how it 
scores on relevant performance metrics.
Flaring Open air burning of waste gases and volatile liquids, 
through a chimney, at oil wells or rigs, in refineries or chemical plants, 
and at landfills.
Flood The overflowing of the normal confines of a stream or other 
water body, or the accumulation of water over areas that are not 
normally submerged. Floods can be caused by unusually heavy rain, 
for example during storms and cyclones. Floods include river (fluvial)

floods, flash floods, urban floods, rain (pluvial) floods, sewer floods, 
coastal  floods and glacial lake outburst floods (GLOFs) .
Flux A movement (a flow) of matter (e.g., water vapour, particles), 
heat or energy from one place to another, or from one medium (e.g., 
land surface) to another (e.g., atmosphere).
Foraminifera Single-celled, sand-sized marine organisms 
(protists) that possess a hard test mainly composed of agglutinated 
walls (detrital grains glued together with organic cement) or calcium 
carbonate (predominantly calcite). They are used to reconstruct 
a range of (paleo)environmental variables such as salinity, 
temperature, oxygenation, oxygen isotope composition and organic 
and nutrient flux.
Forcing See Radiative forcing .
Forest A vegetation type dominated by trees. Many definitions 
of the term forest are in use throughout the world, reflecting 
wide differences in biogeophysical conditions, social structure

and economics. [Note: For a discussion of the term forest in the 
context of National GHG inventories, see the 2006 IPCC Guidelines 
for National GHG Inventories and their 2019 Refinement, and 
information provided by the United Nations Framework Convention 
Afforestation , Deforestation  and Reforestation .
Fossil fuel emissions Emissions of greenhouse gases (GHGs)  
(in particular carbon dioxide (CO 2)), other trace gases and aerosols  
resulting from the combustion of fuels from fossil carbon deposits 
such as oil, gas and coal.
Fossil fuels Carbon-based fuels from fossil hydrocarbon deposits, 
including coal, oil and natural gas.
Free atmosphere The atmospheric layer that is negligibly 
affected by friction against the Earth’s surface, and which is above 
the atmospheric boundary layer .
Frozen ground Soil or rock in which part or all of the pore water 
consists of ice. See also Active layer  and Permafrost .

General circulation The large-scale motions of the atmosphere  
and the ocean  as a consequence of differential heating on a rotating 
Earth. General circulation contributes to the energy balance  of the 
system through transport of heat and momentum.General circulation model (GCM) A numerical representation 
of the atmosphere–ocean–sea ice  system based on the physical, 
chemical and biological properties of its components, their 
interactions and feedback processes. General circulation models 
are used for weather forecasts, seasonal to decadal prediction , and 
climate projections . They are the basis of the more complex Earth 
system models (ESMs) . See also Climate model .
Geocentric sea level change See Sea level change (sea level 
rise/sea level fall) .
Geoid The equipotential surface having the same geopotential at 
each latitude and longitude around the world (geodesists denote this 
potential W0) that best approximates the mean sea level. It is the

surface of reference for measurement of altitude. In practice, several 
variations of definitions of the geoid exist depending on the way the 
permanent tide (the zero-frequency gravitational tide due to the Sun 
and Moon) is considered in geodetic studies.
Geostrophic winds or currents A wind or current that is in 
balance with the horizontal pressure gradient and the Coriolis force, 
and thus is outside of the influence of friction. Thus, the wind or 
current is directly parallel to isobars and its speed is proportional to 
the horizontal pressure gradient.
Glacial isostatic adjustment (GIA) The ongoing changes 
in gravity, rotation and viscoelastic solid Earth deformation (GRD)  
in response to past changes in the distribution of ice and water 
on Earth’s surface. On a time scale of decades to tens of millennia 
following mass redistribution, Earth’s mantle flows viscously as it 
evolves toward isostatic equilibrium, causing solid Earth movement

and geoid  changes, which can result in regional-to-local sea level 
variations. See also Sea level change (sea level rise/sea level fall) .
Glacial lake outburst flood (GLOF)/Glacier lake 
outburst A sudden release of water from a glacier lake, including 
any of the following types – a glacier-dammed lake, a pro-glacial 
moraine-dammed lake or water that was stored within, under or on 
the glacier .
Glacial or glaciation A period characterized by the establishment 
of expanded ice sheets  and glaciers , and associated with global mean 
sea level (GMSL) substantially lower than present; generally coincides 
with even-numbered marine isotope stages . Glacial intervals were 
interrupted by interglacial  intervals. The Last Glacial Maximum (LGM) 
is a specific interval within the most recent glaciation, when ice sheets 
were near their global maximum volume (Clark et al., 2009; Gowan 
et al., 2021) and GMSL was nearly at its lowest level (Lambeck et al.,

2014; Yokoyama et al., 2018). Local or regional glacial maxima may 
be diachronous, for example ranging from about 29,000 years ago 
and 16,000 years ago. For purposes of global synthesis, IPCC AR6 
adopts a practical chronostratigraphic definition of LGM of 23,000–
19,000 years BP (before 1950; chronozone level 1 of Mix et al., 2001). 
For modelling purposes, LGM is defined by the model time step 
nearest to the centre of this interval, 21,000 years ago (Kageyama 
et al., 2017). See also Deglacial or deglaciation or glacial termination , 
Glacial–interglacial cycles , Ice age  and Interglacial or interglaciation .
Glacial termination See Deglacial or deglaciation or glacial 
termination .

2231
Glacial–interglacial cycles Phase of the Earth’s history marked 
by large changes in continental ice volume and global sea level. 
See also Glacial or glaciation , Deglacial or deglaciation or glacial 
termination , Interglacial or interglaciation  and Ice age .
Glaciated State of a surface that was covered by glacier  ice in the 
past, but not at present. See also Glacierized .
Glacier A perennial mass of ice, and possibly firn and snow, 
originating on the land surface by accumulation and compaction of 
snow and showing evidence of past or present flow. A glacier typically 
gains mass by accumulation  of snow and loses mass by ablation . 
Land ice masses of continental size (>50,000 km2) are referred to as 
ice sheets  (Cogley et al., 2011).
Outlet glacier  A glacier , usually between rock walls, that is part of, 
and drains, an ice sheet . See also Ice stream .
Glacierized A surface that is currently covered by glacier  ice. See 
also Glaciated .

Global carbon budget See Carbon budget .
Global dimming Global dimming refers to the observed 
widespread reduction in the amount of solar radiation  received at 
the Earth’s surface from the 1950s to the 1980s, with an increase in 
anthropogenic  aerosol emissions appearing to have contributed. This 
was followed by a partial recovery since the 1990s (‘brightening’), 
particularly in industrialized areas, coincident with a reduction in 
anthropogenic aerosol  emissions.
Global mean sea level (GMSL) change See Sea level change 
(sea level rise/sea level fall) .
Global mean surface air temperature (GSAT) Global 
average of near-surface air temperatures over land, oceans  and 
sea ice . Changes in GSAT are often used as a measure of global 
temperature change in climate models . See also Global mean surface 
temperature (GMST) .
Global mean surface temperature (GMST) Estimated 
global average of near-surface air temperatures over land and sea

ice, and sea surface temperature (SST)  over ice-free ocean  regions, 
with changes normally expressed as departures from a value over 
a specified reference period . See also Global mean surface air 
temperature (GSAT) .
Global monsoon The global monsoon (GM) is a global-scale 
solstitial mode that dominates the annual variation of tropical and 
sub-tropical precipitation and circulation. The GM domain is defined 
as the area where the annual range of precipitation (local summer 
minus winter mean precipitation rate) is greater than 2.5 mm day–1, 
following on from the definition as in Kitoh et al. (2013). Further details 
on how the GM is defined, used and related to regional monsoons 
throughout the Report are provided by Annex V in the AR6 WGI report.
Australian and Maritime Continent monsoon (AusMCM) 
The Australian–Maritime Continent monsoon (AusMCM) occurs 
during December–January–February, with the large-scale shift of the

Inter-tropical Convergence Zone into the Southern Hemisphere and 
covering northern Australia and the Maritime Continent up to 10°N. 
The AusMCM is characterized by the seasonal reversal of prevailing 
easterly winds to westerly winds and the onset of periods of active convection  and heavy rainfall. Over northern Australia, the monsoon 
season generally lasts from December to March and is associated 
with west to north-westerly inflow of moist winds, producing 
convection and heavy precipitation. Over the Maritime Continent, 
the main rainy season south of the equator is centred on December 
to February with north-westerly monsoon flow at low levels. Further 
details on how AusMCM is defined and used throughout the Report 
are provided in Annex V.
East Asian monsoon (EAsiaM) 
The East Asian monsoon (EAsiaM) is the seasonal reversal in wind 
and precipitation occurring over East Asia, including eastern China, 
Japan and the Korean peninsula. In contrast to the other monsoons

it extends quite far north, out of the tropical belt, and it is largely 
influenced by subtropical systems and by disturbances from the mid-
latitudes. The EAsiaM manifests during boreal summer with warm 
and wet southerly winds, but also during boreal winter with cold 
and dry northerly winds. In late April/early May, rainfall onsets in 
the central Indochina Peninsula, and in mid-June the rainy season 
arrives over East Asia with the formation of the Meiyu front along 
the Yangtze River valley, Changma in Korea and Baiu in Japan. In 
July, the monsoon advances up to North China, the Korean peninsula 
and central Japan. During boreal winter, strong north-westerlies 
manifest over north and north-east China, Korea and Japan, while 
strong north-easterlies arrive along the coast of East Asia. Further 
details on how EAsiaM is defined and used throughout the Report 
North American monsoon (NAmerM) 
The North American monsoon (NAmerM) is a regional-scale

atmospheric circulation system with increases in summer 
precipitation over northwestern Mexico and southwest United States. 
The monsoonal characteristics of the region include a pronounced 
annual maximum of precipitation in boreal summer (June–July–
August) accompanied by a surface low pressure system and an 
upper-level anticyclone, although seasonal reversal of the surface 
winds is primarily limited to the northern Gulf of California. Further 
details on how NAmerM is defined and used throughout the Report 
South American monsoon (SAmerM) 
The South American monsoon (SAmerM) is a regional circulation 
characterized by inflow of low-level winds from the Atlantic to South 
America, including Brazil, Peru, Bolivia and northern Argentina, 
associated with the development of surface pressure gradients (and 
intense precipitation) during austral summer (December–January–
February). During September–October–November, areas of intense

convection  migrate from northwestern South America to the south. 
Associated with this regime, an upper-tropospheric anticyclone 
(a.k.a. the Bolivian High) forms over the Altiplano region during the 
monsoon onset. The SAmerM then retreats during March–April–May 
with a northeastward migration of the convection. Further details 
on how SAmerM is defined and used throughout the Report are 
provided in Annex V.
South and South East Asian monsoon (SAsiaM) 
The South and South East Asian monsoon (SAsiaM) is characterized 
by pronounced seasonal reversals of wind and precipitation. The 
SAsiaM region extends across vast geographical areas and several 
countries, including India, Bangladesh, Nepal, Myanmar, Sri Lanka,

2232
Pakistan, Thailand, Laos, Cambodia, Vietnam and the Philippines. The 
SAsiaM starts in late May/early June and progresses towards the 
northeast, ending in late September/early October. During the core 
monsoon season, maxima of SAsiaM precipitation are located over 
the west coast, north-east and central north India, Myanmar and 
Bangladesh, whereas minima are located over north-west and south-
eastern India, western Pakistan, and south-eastern and northern Sri 
Lanka. Further details on how SAsiaM is defined and used throughout 
the Report are provided in Annex V.
West African monsoon (WAfriM) 
The West African monsoon (WAfriM) is a seasonal reversal in wind 
and precipitation whose domain includes Benin, Burkina-Faso, 
northern Cameroon, Cape Verde, northern Central African Republic, 
Chad, Gambia, Ghana, Guinea, Guinea Bissau, Ivory Coast, Liberia, 
Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo. The

WAfriM is characterized by the northward progression from May to 
September of moist low-level south-westerlies from the Gulf of Guinea. 
In May and June, rainfall essentially remains along the Guinean coast 
with a maximum occurring near 5°N, followed by a sudden decrease 
of rainfall, marking the ‘short dry season‘ in the Guinean coast and 
the monsoon onset in the Sahel. Then rainfall continues to progress 
northward up to about 18–20°N, with a maximum near 12°N in late 
August/September, until it retreats starting from October towards 
the Guinean coast for a second maximum. Further details on how 
WAfriM is defined and used throughout the Report are provided in 
Annex V.
Global surface temperature See Global mean surface 
temperature (GMST)  and Global mean surface air temperature 
(GSAT) . See also Global warming .
Global warming Global warming refers to the increase in global 
surface temperature  relative to a baseline reference period , averaging

over a period sufficient to remove interannual variations (e.g., 20 
or 30 years). A common choice for the baseline is 1850–1900 (the 
earliest period of reliable observations with sufficient geographic 
coverage), with more modern baselines used depending upon the 
application. See also Climate change  and Climate variability .
Global warming potential (GWP) An index measuring the 
radiative forcing  following an emission of a unit mass of a given 
substance, accumulated over a chosen time horizon, relative to that 
of the reference substance, carbon dioxide (CO 2). The GWP thus 
represents the combined effect of the differing times these substances 
remain in the atmosphere  and their effectiveness in causing radiative 
forcing. See also Lifetime  and Greenhouse gas emission metric .
Gravitational, rotational and deformational (GRD) 
effects See Sea level change (sea level rise/sea level fall) .
Gravity Recovery and Climate Experiment (GRACE) A pair

of satellites that measured the Earth’s gravity field anomalies from 
2002 to 2017. These fields have been used, among other things, to 
study mass changes of the polar ice sheets  and glaciers .
Greenhouse effect The infrared radiative effect of all infrared-
absorbing constituents in the atmosphere . Greenhouse gases (GHGs) , 
clouds, and some  aerosols  absorb terrestrial radiation  emitted by the 
Earth’s surface and elsewhere in the atmosphere. These substances 
emit infrared radiation  in all directions, but, everything else being equal, the net amount emitted to space is normally less than would 
have been emitted in the absence of these absorbers because of 
the decline of temperature with altitude in the troposphere  and the 
consequent weakening of emission. An increase in the concentration 
of GHGs increases the magnitude of this effect; the difference is 
sometimes called the enhanced greenhouse effect. The change in

a GHG concentration because of anthropogenic emissions  contributes 
to an instantaneous radiative forcing. Earth’s surface temperature 
and troposphere  warm in response to this forcing , gradually restoring 
the radiative balance at the top of the atmosphere.
Greenhouse gas emission metric A simplified relationship 
used to quantify the effect of emitting a unit mass of a given 
greenhouse gas  on a specified key measure of climate change . 
A relative GHG emission metric expresses the effect from one gas 
relative to the effect of emitting a unit mass of a reference GHG on 
the same measure of climate change. There are multiple emission 
metrics, and the most appropriate metric depends on the application. 
GHG emission metrics may differ with respect to (i) the key measure 
of climate change they consider, (ii) whether they consider climate 
outcomes for a specified point in time or integrated over a specified 
time horizon, (iii) the time horizon over which the metric is applied,

(iv) whether they apply to a single emission pulse, emissions sustained 
over a period of time, or a combination of both, and (v)  whether 
they consider the climate effect from an emission compared to the 
absence of that emission or compared to a reference emissions level 
or climate state.
Notes: Most relative GHG emission metrics (such as the global 
warming potential (GWP) , global temperature change potential 
(GTP), global damage potential, and GWP*) use carbon dioxide (CO 2) 
as the reference gas. Emissions of non-CO 2 gases, when expressed 
using such metrics, are often referred to as ‘carbon dioxide equivalent’ 
emissions. A metric that establishes equivalence regarding one key 
measure of the climate system  response to emissions does not imply 
equivalence regarding other key measures. The choice of a metric, 
including its time horizon, should reflect the policy objectives for 
which the metric is applied.
Greenhouse gas neutrality Condition in which metric-

weighted anthropogenic greenhouse gas (GHG)  emissions associated 
with a subject are balanced by metric-weighted anthropogenic 
GHG removals. The subject can be an entity such as a country, an 
organization, a district or a commodity, or an activity such as a 
service or an event. GHG neutrality is often assessed over the life 
cycle, including indirect (‘scope 3’) emissions, but can also be limited 
subject has direct control, as determined by the relevant scheme. 
The quantification of GHG emissions and removals depends on the 
GHG emission metric chosen to compare emissions and removals of 
different gases, as well as the time horizon chosen for that metric.
Note 1: GHG neutrality and net zero GHG emissions are overlapping 
(e.g., regional, national and sub-national). At a global scale, the terms

greenhouse gas neutrality and net zero greenhouse gas emissions are 
equivalent. At sub-global scales, net zero greenhouse gas emissions 
is generally applied to emissions and removals under direct control 
or territorial responsibility of the reporting entity, while greenhouse 
gas neutrality generally includes emissions and removals within and

2233
beyond the direct control or territorial responsibility of the reporting 
entity. Accounting rules specified by GHG programmes or schemes 
can have a significant influence on the quantification of relevant 
emissions and removals.
Note 2. Under the Paris Rulebook (Decision 18/CMA.1, annex, 
paragraph 37), parties have agreed to use GWP100 values from the 
IPCC AR5 or GWP100 values from a subsequent IPCC Assessment 
Report to report aggregate emissions and removals of GHGs. In 
addition, parties may use other metrics to report supplemental 
information on aggregate emissions and removals of GHGs.
Note 3: In some cases, achieving greenhouse gas neutrality may rely 
on the supplementary use of offsets to balance emissions that remain 
after actions by the reporting entity are taken into account. 
See also Carbon neutrality , Greenhouse gas emission metric  and Net 
zero greenhouse gas emissions .
Greenhouse gases (GHGs) Gaseous constituents of the

atmosphere , both natural and anthropogenic , that absorb and emit 
radiation at specific wavelengths within the spectrum of radiation 
emitted by the Earth’s  surface, by the atmosphere itself, and by 
clouds. This property causes the greenhouse effect . Water vapour 
(H2O), carbon dioxide (CO 2), nitrous oxide (N 2O), methane (CH 4 ) and 
ozone (O 3) are the primary GHGs in the Earth’s atmosphere. Human-
made GHGs include sulphur hexafluoride (SF 6 ), hydrofluorocarbons 
(HFCs) , chlorofluorocarbons (CFCs)  and perfluorocarbons (PFCs); 
several of these are also O 3-depleting (and are regulated under the 
Montreal Protocol ). See also Well-mixed greenhouse gas .
Greenland Ice Sheet (GrIS) See Ice sheet .
Gross Primary Production (GPP) See Primary production .
Ground-level ozone Atmospheric ozone (O 3) is formed naturally 
or from human-emitted precursors  near Earth’s surface, thus affecting 
human health, agriculture and ecosystems . Ozone is a greenhouse

gas (GHG) , but ground-level ozone, unlike stratospheric ozone, 
also directly affects organisms at the surface. Ground-level ozone 
is sometimes referred to as tropospheric ozone, although much of 
the troposphere  is well above the surface and thus does not directly 
expose organisms at the surface.
Grounding line The junction between a glacier  or ice sheet  and 
an ice shelf ; the place where ice starts to float. This junction normally 
occurs over a zone, rather than at a line.
Gyre Basin-scale ocean  horizontal circulation pattern with slow 
flow circulating around the ocean basin, closed by a strong and 
narrow (100 to 200 km wide) boundary current on the western side. 
The subtropical gyres in each ocean are associated with high pressure 
in the centre of the gyres; the subpolar gyres are associated with low 
pressure.
Hadley cell See Hadley circulation .
Hadley circulation A direct, thermally driven overturning cell in

the atmosphere  consisting of poleward flow in the upper troposphere , 
subsiding air into the subtropical anticyclones, return flow as part of 
the trade winds near the surface, and with rising air near the equator 
in the so-called Inter-tropical Convergence Zone .Halocarbons A collective term for the group of partially 
halogenated organic species, which includes the chlorofluorocarbons 
(CFCs) , hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons 
(HFCs) , halons, methyl chloride and methyl bromide. Many of the 
halocarbons have large global warming potentials . The chlorine and 
bromine-containing halocarbons are also involved in the depletion of 
the ozone layer .
Halocline A layer in the oceanic water column in which salinity 
changes rapidly with depth. Generally, saltier water is denser and 
lies below less salty water. In some high-latitude oceans  the surface 
waters may be colder than the deep waters, and the halocline is

responsible for maintaining water column stability and isolating the 
surface waters from the deep waters.
Halosteric See Sea level change (sea level rise/sea level fall) .
Halosteric sea level change See Sea level change (sea level 
Hazard The potential occurrence of a natural or human-induced 
physical event or trend that may cause loss of life, injury, or other 
health impacts , as well as damage and loss to property, infrastructure, 
livelihoods , service provision, ecosystems  and environmental 
resources. See also Impacts (consequences, outcomes)  and Risk.
Heat index A measure of how hot the air feels to the human body. 
The index is mainly based on surface air temperature and relative 
humidity ; thus it reflects the combined effect of high temperature and 
humidity on human physiology and provides a relative indication of 
potential health risks.
Heat stress A range of conditions in, for example, terrestrial

or aquatic organisms when the body absorbs excess heat during 
overexposure to high air or water temperatures or thermal radiation. 
In aquatic water-breathing animals, hypoxia and acidification can 
exacerbate vulnerability  to heat. Heat stress in mammals (including 
humans) and birds, both in air, is exacerbated by a detrimental 
combination of ambient heat, high humidity and low wind speeds, 
causing regulation of body temperature to fail.
Heatwave A period of abnormally hot weather, often defined with 
reference to a relative temperature threshold, lasting from two days 
to months. Heatwaves and warm spells have various and, in some 
cases, overlapping definitions. See also Marine heatwave , Blocking , 
Heat index  and Heat stress .
Heavy precipitation event See Extreme/heavy precipitation 
event .
Heinrich event Distinct layers of coarse-grained sediments 
comprised of ice-rafted debris identified across marine sediment cores

in the North Atlantic. These sedimentary layers are closely associated 
with millennial-scale cooling events in the North Atlantic and a 
distinct pattern of global temperature and hydrological changes that 
are largely consistent with evidence for a slowdown, or even near-
collapse, of the Atlantic Meridional Overturning Circulation (AMOC)  
during these times.
Heterotrophic respiration The conversion of organic matter to 
carbon dioxide (CO 2) by organisms other than autotrophs.

2234
Holocene The current interglacial  geological epoch, the second 
of two epochs within the Quaternary  Period, the preceding being 
the Pleistocene . The International Commission on Stratigraphy (ICS) 
defines the start of the Holocene Epoch at 11,700 years before 2000 
(Walker et al., 2019) spanning the interval from 11,700 yr to the 
present day. Together with the subadjacent Pleistocene, it comprises 
the Quaternary System/Period. The Holocene record contains diverse 
geomorphological, biological, climatological and archaeological 
evidence, within sequences that are often continuous and extremely 
well-preserved at decadal, annual and even seasonal resolution. 
As a consequence, the Holocene is perhaps the most intensively   
studied series/epoch within the entire Geological Time Scale. Yet 
until recently little attention had been paid to a formal subdivision of 
the Holocene. Here we describe an initiative by the Subcommission

on Quaternary Stratigraphy (SQS. It encompasses the mid-Holocene 
(MH), the 1000-year-long interval centred at 6000 years before 1950; 
a period of long-standing focus for climate modelling, with enhanced 
seasonality in the Northern Hemisphere and decreased seasonality in 
the Southern Hemisphere. The early part of the Holocene is marked by 
the late stages of deglaciation  of Pleistocene land ice, sea level rise, 
and the occurrence of warm phases that affected different regions at 
different times, often referred to as the ‘Holocene Thermal Maximum’. 
In addition, the epoch includes the post-glacial interval, which began 
approximately 7000 years ago when the fundamental features of the 
modern climate system  were essentially in place, as the influence of 
remnant Pleistocene ice sheets  waned. See also Anthropocene .
Holocene Thermal Maximum (HTM) See Holocene .
Human influence on the climate system Human-driven 
activities that lead to changes in the climate system  due to

perturbations of the Earth’s energy budget (also called anthropogenic 
forcing ). Human influence results from emissions of greenhouse 
gases , aerosols , ozone-depleting substances (ODSs) , and land-use 
change . See also Anthropogenic , Anthropogenic emissions  and 
Anthropogenic removals .
Human system Any system in which human organizations 
and institutions play a major role. Often, but not always, the term 
is synonymous with society or social system. Systems such as 
agricultural systems, urban systems, political systems, technological 
systems and economic systems are all human systems in the sense 
applied in this Report.
Hurricane See Tropical cyclone .
Hydroclimate Part of the climate  pertaining to the hydrology of 
a region .
Hydrofluorocarbons (HFCs) A type of greenhouse gas (GHG) , 
HFCs are organic compounds that contain fluorine, carbon and 
hydrogen atoms and they are produced commercially as a substitute

for chlorofluorocarbons (CFCs) . They are mainly used in refrigeration 
and semiconductor manufacturing.
Hydrological cycle The cycle in which water evaporates from the 
ocean  and the land surface, is carried over the Earth in atmospheric 
circulation as water vapour, condenses to form clouds, precipitates 
over the ocean and land as rain or snow, which on land can be 
intercepted by trees and vegetation, potentially accumulating as 
snow or ice, provides runoff on the land surface, infiltrates into soils, recharges groundwater, discharges into streams, and ultimately, flows 
into the oceans as rivers, polar glaciers  and ice sheets , from which it 
will eventually evaporate again. The various systems involved in the 
hydrological cycle are usually referred to as hydrological systems.
Hydrological drought See Drought .
Hydrological sensitivity (η) The linear change in global 
mean precipitation per degree Celsius of global mean surface air

temperature (GSAT)  change once precipitation changes related to 
fast atmospheric and land surface adjustments to radiative forcings  
have occurred. Units are % per °C although it can also be calculated 
as W m–2 per °C. See also Apparent hydrological sensitivity ( ηa ).
Hydrosphere The component of the climate system  comprising 
liquid surface and subterranean water, such as in oceans , seas, rivers, 
freshwater lakes, underground water, wetlands , etc.
Hypoxic Conditions of low dissolved oxygen in shallow water 
ocean  and freshwater environments. There is no universal threshold 
for hypoxia. A value around 60 μmol kg–1 has commonly been used 
for some estuarine systems, although this does not necessarily 
directly translate into biological impacts. Anoxic conditions occur 
where there is no oxygen present at all. See also Eutrophication .
Hypsometry The distribution of land or ice surface as a function 
of altitude.
Ice age An informal term for a geological period characterized

by a long-term reduction in the temperature of the Earth’s climate , 
resulting in the presence or expansion of ice sheets  and glaciers . 
Among the Earth’s ice ages is the current Quaternary  Period, 
characterized by alternating glacial  and interglacial  intervals. See 
also Deglacial or deglaciation or glacial termination  and Glacial–
interglacial cycles .
Ice core A cylinder of ice drilled out of a glacier  or ice sheet  
to determine the physical properties of the ice body and to gain 
information on past changes in climate  and composition of the 
atmosphere  that are preserved in the ice or in air trapped in the ice.
Ice sheet An ice body originating on land that covers an area of 
continental size, generally defined as covering >50,000 km2, and 
that has formed over thousands of years through accumulation  and 
compaction of snow. An ice sheet flows outward from a high central 
ice plateau with a small average surface slope. The margins usually

slope more steeply, and most ice is discharged  through fast-flowing 
ice streams or outlet glaciers , often into the sea or into ice shelves  
floating on the sea. There are only two ice sheets in the modern 
world, one on Greenland and one on Antarctica. The latter is divided 
into the East Antarctic Ice Sheet (EAIS), the West Antarctic Ice Sheet 
(WAIS) and the Antarctic Peninsula Ice Sheet. During glacial  periods, 
there were other ice sheets.
Ice shelf A floating slab of ice originating from land of considerable 
thickness extending from the coast (usually of great horizontal extent 
with a very gently sloping surface), resulting from the flow of ice 
sheets , initially formed by the accumulation of snow, and often filling 
embayments in the coastline of an ice sheet. Nearly all ice shelves are 
in Antarctica, where most of the ice discharged  into the ocean  flows 
via ice shelves.

2235
Ice stream A stream of ice with strongly enhanced flow that is 
part of an ice sheet . It is often separated from surrounding ice by 
strongly sheared, crevassed margins.
Ice–albedo feedback A climate feedback  involving changes 
in the Earth’s surface albedo . Snow and ice have an albedo much 
higher (up to ~0.8) than the average planetary albedo (~0.3). With 
increasing temperatures, it is anticipated that snow and ice extent 
will decrease, the Earth’s overall albedo will decrease and more solar 
radiation  will be absorbed, warming the Earth further.
Iceberg Large piece of freshwater ice broken off from a glacier  
or an ice shelf  during calving  and floating in open water (at least 
5 m height above sea level). Smaller pieces of floating ice known as 
‘bergy bits’ (less than 5 m above sea level) or ‘growlers’ (less than 
2 m above sea level) can originate from glaciers or ice shelves, or from

the breaking up of a large iceberg. Icebergs can also be classified by 
shape, most commonly being either tabular (steep sides and a flat 
top) or non-tabular (varying shapes, with domes and spires) (NOAA, 
2021). In lakes, icebergs can originate by breaking off shelf ice, which 
forms through freezing of a lake surface.
Impacts The consequences of realized risks on natural and human 
systems, where risks result from the interactions of climate-related 
hazards  (including extreme weather/climate events), exposure , and 
vulnerability . Impacts generally refer to effects on lives, livelihoods , 
health and well-being, ecosystems  and species, economic, social 
and cultural assets, services (including ecosystem services), and 
infrastructure. Impacts may be referred to as consequences or 
outcomes and can be adverse or beneficial. See also Adaptation , 
Exposure , Hazard , Vulnerability  and Risk.
Incoming solar radiation See Insolation .

Indian Ocean basin (IOB) mode A mode of interannual 
variability characterized by a temporal alternation of basin-wide 
warming and cooling of the Indian Ocean sea surface. It mostly 
develops in response to El Niño–Southern Oscillation (ENSO) , but 
often persists after ENSO’s equatorial eastern Pacific signal has 
dissipated. The IOB affects atmospheric circulation, temperature, and 
precipitation in South, South East, and East Asia as well as Africa, and 
modulates tropical cyclone  activity in the north-western Pacific. See 
Section AIV.2.4 in Annex IV of the AR6 WGI report. See also Modes of 
climate variability  and Indian Ocean Dipole (IOD) .
Indian Ocean Dipole (IOD) A mode of interannual variability that 
features an east–west dipole of sea surface temperature  anomalies 
in the tropical Indian Ocean. Its positive phase shows concurrent sea 
surface cooling off Sumatra and Java and warming off Somalia in the

west, combined with anomalous surface easterlies along the equator, 
while the opposite anomalies are seen in the negative phase. The IOD 
typically develops in boreal summer and matures in boreal autumn 
and controls part of the rainfall interannual variability in Australia, 
South Eastern Asia and Eastern Africa. See Section AIV.2.4 in Annex 
IV of the AR6 WGI report. See also Indian Ocean Basin (IOB) mode .
Indirect aerosol effect See Aerosol–cloud interaction .
Indirect land-use change (iLUC) See Land-use change (LUC) .
Industrial revolution A period of rapid industrial growth with 
far-reaching social and economic consequences, beginning in Britain during the second half of the 18th century and spreading to Europe 
and later to other countries including the United States. The invention 
of the steam engine was an important trigger of this development. 
The industrial revolution marks the beginning of a strong increase

in the use of fossil fuels , initially coal, and hence emission of carbon 
dioxide (CO 2).
Infrared radiation See Terrestrial radiation .
Initial condition ensemble (ICE) See Climate simulation 
ensemble .
Insolation The amount of solar radiation  reaching the Earth by 
latitude and by season measured in W m–2. Usually, insolation refers 
to the radiation arriving at the top of the atmosphere . Sometimes it is 
specified as referring to the radiation arriving at the Earth’s surface. 
See also Orbital forcing  and Total solar irradiance (TSI) .
Instantaneous radiative forcing (or effect) due to aerosol–
cloud interactions (IRFaci) See Aerosol–cloud interaction .
radiation interactions (IRFari) See Aerosol–radiation 
interaction .
Integrated assessment model (IAM) Models that integrate 
knowledge from two or more domains into a single framework. They 
are one of the main tools for undertaking integrated assessments.

One class of IAM used with respect to climate change mitigation  may 
include representations of: multiple sectors of the economy, such as 
energy, land use  and land-use change ; interactions between sectors; 
the economy as a whole; associated greenhouse gas (GHG)  emissions 
and sinks ; and reduced representations of the climate system . This 
class of model is used to assess linkages between economic, social 
and technological development and the evolution of the climate 
system. Another class of IAM additionally includes representations of 
the costs associated with climate change impacts , but includes less 
detailed representations of economic systems. These can be used to 
assess impacts and mitigation in a cost–benefit framework and have 
been used to estimate the social cost of carbon.
Inter-decadal Pacific Oscillation (IPO) See Pacific Decadal 
Variability (PDV) .
Inter-tropical Convergence Zone (ITCZ) The Inter-tropical

Convergence Zone is an equatorial zonal belt of low pressure, strong 
convection  and heavy precipitation near the equator where the 
north-east trade winds meet the south-east trade winds. This band 
moves seasonally. See also South Pacific Convergence Zone (SPCZ) .
Interglacial or interglaciation A globally warm period lasting 
thousands of years between glacial  periods within an ice age . 
Generally coincides with odd-numbered marine isotope stages (MIS)  
when mean sea level was close to present. The Last Interglacial (LIG) 
occurred between about 129 and 116 ka (thousand years) before 
present (defined as 1950) although the warm period started in some 
areas a few thousand years earlier. In terms of MIS, interglaciations  
are defined as the interval between the midpoint of the preceding 
termination and the onset of the next glaciation. The LIG coincides 
with MIS 5e.  The present interglaciation, the Holocene , started at

11,700 years before 2000 CE, although global mean sea level did not 
approach its present position until roughly 7000 years ago. See also

2236
Deglacial or deglaciation or glacial termination , Glacial-interglacial 
cycles , Glacial or glaciation  and Ice age .
Internal climate variability See Internal variability  (under 
Climate variability ).
Interstadial or interstade A brief period of regional climatic 
warming during a  glacial  or interglacial  interval, often characterized 
by transient glacial retreats. Interstadials are generally of short 
duration (hundreds to a few thousand years) compared to glacial or 
interglacial intervals (lasting many thousands to tens of thousands 
of years). One example of a regional interstadial event is based 
on millennial scale warming recorded by oxygen  isotope   ratios in 
Greenland  ice cores , the so called “Greenland Interstadials” (Johnsen 
et al., 1992). See also Stadial or stade .
Irreversibility A perturbed state of a dynamical system  is defined 
as irreversible on a given time scale if the recovery from this state due

to natural processes takes substantially longer than the time scale of 
interest. See also Tipping point .
Isostatic or Isostasy Isostasy refers to the response of the 
Earth to changes in surface load. It includes the deformational and 
gravitational response. This response is elastic on short time scales, 
as in the Earth– ocean  response to recent changes in mountain 
glaciation, or viscoelastic on longer time scales, as in the response to 
the last deglaciation  following the Last Glacial Maximum .
Isotopes Atoms of the same chemical element that have the 
same the number of protons but differ in the number of neutrons. 
Some proton–neutron configurations are stable (stable isotopes), 
others are unstable undergoing spontaneous radioactive decay 
(radioisotopes). Most elements have more than one stable isotope. 
Isotopes can be used to trace transport processes or to study 
processes that change the isotopic ratio. Radioisotopes provide, in

addition, time information that can be used for radiometric dating. 
See also 13C and 14C.
Key climate indicators See Climate indicator .
Kriging Kriging is a method of interpolation (normally spatial 
interpolation when used with atmospheric or oceanographic data) 
in which the interpolated values are estimated using a Gaussian 
process governed by prior covariances.
La Niña See El Niño–Southern Oscillation (ENSO) .
Land The terrestrial portion of the biosphere that comprises the 
natural resources (soil, near-surface air, vegetation and other biota, 
and water), the ecological processes, topography, and human 
settlements and infrastructure that operate within that system 
(UNCCD, 1994; FAO, 2007).
Land cover The biophysical coverage of land (e.g., bare soil, rocks, 
forests , buildings and roads or lakes). Land cover is often categorized 
in broad land-cover classes (e.g., deciduous forest, coniferous forest, 
mixed forest, grassland, bare ground). [Note: In some literature, land

cover and land use  are used interchangeably, but the two represent 
distinct classification systems. For example, the land cover class 
woodland can be under various land uses such as livestock grazing, 
recreation, conservation, or wood harvest.]Land–cover change Change from one land cover  class to 
another, due to change in land use  or change in natural conditions 
(Pongratz et al., 2018). See also Land-use change (LUC) .
Land surface air temperature (LSAT) The near-surface air 
temperature over land, typically measured at 1.25–2 m above the 
ground using standard meteorological equipment.
Land use The total of arrangements, activities and inputs applied 
to a parcel of land. The term land use is also used in the sense of 
the social and economic purposes for which land is managed (e.g., 
grazing, timber extraction, conservation and city dwelling). In 
national greenhouse gas (GHG)  inventories, land use is classified

according to the IPCC land-use categories of forest land, cropland, 
grassland, wetlands, settlements, other lands (see the 2006 IPCC 
Guidelines for National GHG Inventories and their 2019 Refinement 
for details (IPCC, 2006, 2019)).
Land-use change (LUC) The change from one land use  category 
to another. Note that in some scientific literature, land-use change 
encompasses changes in land-use categories as well as changes 
in land management. See also Afforestation,  Deforestation  and 
Reforestation .
Indirect land-use change (iLUC)  Land-use change outside the 
area of focus that occurs as a consequence of change in use or 
management of land within the area of focus, such as through market 
or policy drivers. For example, if agricultural land is diverted to biofuel 
production, forest  clearance may occur elsewhere to replace the 
former agricultural production. See Land-use change (LUC) .
Land water storage (LWS) Land water storage (LWS) includes

all surface water, soil moisture , groundwater storage and snow, but 
excludes water stored in glaciers  and ice sheets . Changes in LWS can 
be caused either by direct human intervention in the water cycle (e.g., 
storage of water in reservoirs by building dams in rivers, groundwater 
extraction from groundwater reservoirs for consumption and 
irrigation, or deforestation ) or by climate  variations (e.g., changes in 
the amount of water in endorheic lakes and wetlands , the canopy, 
the soil, the permafrost  and the snowpack). Land water storage 
changes caused by climate variations may also be indirectly affected 
by anthropogenic  influences. See also Sea level change (sea level rise/
sea level fall) .
Lapse rate The rate of change of an atmospheric variable, usually 
temperature, with height. The lapse rate is considered positive when 
the variable decreases with height.
Large-scale The climate system  involves process interactions

from the micro- to the global-scale. Any threshold for defining ‘large-
scale’ is arbitrary. Understanding of large-scale climate variability  
and change requires knowledge of both the response to external 
forcings  and the role of internal variability . Many external forcings 
have substantial hemispheric or continental scale variations. Modes 
of climate variability  are driven by ocean -basin-scale processes. Thus 
we define large-scale to include ocean-basin and continental scales 
as well as hemispheric and global scales.
Last deglacial transition See Deglacial or deglaciation or 
glacial termination  and Younger Dryas .

2237
Last Glacial Maximum (LGM) See Glacial or glaciation .
Last Interglacial (LIG) See Interglacial or interglaciation .
Last millennium The interval of the Common Era (CE)  between 
1001 and 2000 CE. Encompasses the Little Ice Age, a roughly defined 
period characterized by multiple expansions of mountain glaciers  
worldwide, the timing of which differs among regions but generally 
occurred between 1400 CE and 1900 CE. The last millennium also 
mostly encompasses the Medieval Warm Period (also called the 
Medieval Climate Anomaly), a roughly defined period of relatively 
warm conditions or other climate  excursions such as extensive 
drought , the timing and magnitude of which differ among regions, 
but generally occurred between 900 and 1400 CE. Transient climate 
model  experiments by the Paleoclimate Modelling Intercomparison 
Project (PMIP) for the last millennium extend from 850–1849 CE.
Latent heat flux The turbulent flux of heat from the Earth’s

surface to the atmosphere  that is associated with evaporation  or 
condensation of water vapour at the surface; a component of the 
surface energy budget. See also Sensible heat flux .
Lifetime Lifetime is a general term used for various time scales 
characterizing the rate of processes affecting the concentration of 
trace gases. The following lifetimes may be distinguished:
Response time or adjustment time (T a ) 
Response time or adjustment time (T a) is the time scale characterizing 
the decay of an instantaneous pulse input into the reservoir . The term 
adjustment time is also used to characterize the adjustment of the 
mass of a reservoir following a step change in the source  strength. 
Half-life or decay constant is used to quantify a first-order exponential 
decay process. See Response time or adjustment time  for a different 
definition pertinent to climate  variations.
The term lifetime is sometimes used, for simplicity, as a surrogate for 
adjustment time.

In simple cases, where the global removal of the compound is directly 
proportional to the total mass of the reservoir, the adjustment time 
equals the turnover time : T = T a. An example is CFC-11, which is 
removed from the atmosphere  only by photochemical processes in 
the stratosphere . In more complicated cases, where several reservoirs 
are involved or where the removal is not proportional to the total 
mass, the equality T = T a no longer holds. 
Carbon dioxide (CO 2) is an extreme example. Its turnover time 
is only about 4 years because of the rapid exchange between the 
atmosphere and the ocean  and terrestrial biota. However, a large part 
of that CO 2 is returned to the atmosphere within a few years. The 
adjustment time of CO 2 in the atmosphere is determined from the 
rates of removal of carbon by a range of processes with time scales 
from months to hundreds of thousands of years.  As a result, 15 to 
40% of an emitted  CO 2 pulse will remain in the atmosphere longer

than 1,000 years, 10 to 25% will remain about ten thousand years, 
and the rest will be removed over several hundred thousand years.  
In the case of methane (CH 4 ), the adjustment time is different from 
the turnover time because the removal is mainly through a chemical 
reaction with the hydroxyl radical (OH), the concentration of which 
itself depends on the CH 4 concentration. Therefore, the CH 4 removal 
rate S is not proportional to its total mass M.Turnover time (T)  (also called global atmospheric lifetime) is the 
ratio of the mass M of a reservoir  (e.g., a gaseous compound in 
the atmosphere ) and the total rate of removal S from the reservoir : 
T  =  M/S. For each removal process, separate turnover times can 
be defined. In soil carbon biology, this is referred to as mean 
residence time.
Light-absorbing particles Light-absorbing particles (LAP), for 
example, black carbon (BC) , brown carbon and dust, are particles

that absorb solar radiation  and convert it into internal energy, thus 
raising the particle’s temperature and emitting thermal-infrared 
radiation that is selectively absorbed by the surrounding medium. 
LAP affect the energy balance of the atmosphere  and clouds, and 
when deposited on snow and ice, they reduce snow/ice albedo, 
increasing heating and accelerating melting. These particles have a 
warming effect on climate .
Likelihood The chance of a specific outcome occurring, where 
this might be estimated probabilistically. Likelihood is expressed in 
this report using a standard terminology (Mastrandrea et al., 2010).  
See also Agreement , Confidence , Evidence  and Uncertainty .
Lithosphere The upper layer of the solid Earth, both continental 
and oceanic, which comprises all crustal rocks and the cold, mainly 
elastic part of the uppermost mantle. Volcanic activity, although part 
of the lithosphere , is not considered as part of the climate system , but

acts as an external forcing  factor.
Livelihood The resources used and the activities undertaken in 
order for people to live. Livelihoods are usually determined by the 
entitlements and assets to which people have access. Such assets can 
be categorized as human, social, natural, physical or financial.
Local sea level change See Sea level change (sea level rise/sea 
level fall) .
Long-lived greenhouse gases (LLGHGs) A set of well-mixed 
greenhouse gases  with long atmospheric lifetimes . This set of 
compounds includes carbon dioxide (CO 2) and nitrous oxide (N 2O), 
together with some halogenated compounds. They have a warming 
effect on climate . These compounds accumulate in the atmosphere  at 
decadal to centennial time scales, and their effect on climate hence 
persists for decades to centuries after their emission. On time scales 
of decades to a century, already emitted emissions of long-lived 
climate forcers can only be abated by greenhouse gas removal.

Longwave radiation See Terrestrial radiation .
Low-likelihood, high impact outcomes Outcomes/events 
whose probability of occurrence is low or not well known (as in the 
context of deep uncertainty ) but whose potential impacts  on society 
and ecosystems  could be high. To better inform risk assessment  and 
decision-making, such low- likelihood  outcomes are considered if 
they are associated with very large consequences and may therefore 
constitute material risks, even though those consequences do not 
necessarily represent the most likely outcome.
Madden–Julian Oscillation (MJO) The largest mode of 
tropical atmospheric intra-seasonal variability with typical periods 
ranging from 20 to 90 days. The MJO corresponds to planetary-
scale disturbances of pressure, wind and deep convection  moving 
predominantly eastward along the equator. As it progresses, the MJO

2238
is associated with the temporal alternation of large-scale enhanced 
and suppressed rainfall, with maximum loading over the Indian 
and western Pacific oceans, although influences of the MJO can 
be tracked over the Atlantic/Africa in dynamical fields. See Section 
AIV.2.8 in Annex IV of the AR6 WGI report.
Maladaptive actions (Maladaptation) Actions that may lead 
to increased risk of adverse climate -related outcomes, including via 
increased greenhouse gas (GHG)  emissions, increased vulnerability  
to climate change , or diminished welfare, now or in the future. 
Maladaptation is usually an unintended consequence.
Marine cloud brightening (MCB) See Solar radiation 
modification (SRM) .
Marine heatwave A period during which water temperature 
is abnormally warm for the time of the year relative to historical 
temperatures, with that extreme warmth persisting for days to 
months. The phenomenon can manifest in any place in the ocean  and

at scales of up to thousands of kilometres. See also Heatwave .
Marine ice cliff instability (MICI) A hypothetical mechanism 
of an ice cliff failure. In case a marine-terminated ice sheet  loses its 
buttressing ice shelf , an ice cliff can be exposed. If the exposed ice 
cliff is tall enough (about 800 m of the total height, or about 100 
m of the above-water part), the stresses at the cliff face exceed the 
strength of the ice, and the cliff fails structurally in repeated calving  
events. See also Marine ice sheet instability (MISI) .
Marine ice sheet instability (MISI) A mechanism of 
irreversible  (on the decadal to centennial time scale) retreat of a 
grounding line  for the marine-terminating glaciers , in case the glacier 
bed slopes towards the ice sheet  interior. See also Marine ice cliff 
instability (MICI) .
Marine isotope stage (MIS) Geological periods of alternating 
glacial  and interglacial  conditions, each typically lasting tens of

thousands of years as inferred from the oxygen isotope  composition 
of microfossils from deep sea sediment cores. MIS numbers 
increase back in time from the present, which is MIS 1. Even-
number MISs coincide with glacial periods, and odd-numbered MISs 
are interglacials.
Marine-based ice sheet An ice sheet  containing a substantial 
region that rests on a bed lying below sea level and whose perimeter 
is in contact with the ocean . The best known example is the West 
Antarctic Ice Sheet.
Mass balance/budget (of glaciers or ice sheets) Difference 
between the mass input ( accumulation ) and the mass loss ( ablation ) 
of an ice body (e.g., a glacier  or ice sheet ) over a stated time period, 
which is often a year or a season. Surface mass balance refers to the 
difference between surface accumulation and surface ablation.
Ablation (of glaciers, ice sheets, or snow cover) 
All processes that reduce the mass of a glacier , ice sheet , or snow

cover . The main processes are melting, and for glaciers also calving  
(or, when the glacier nourishes an ice shelf , discharge of ice  across 
the grounding line ), but other processes such as sublimation and loss 
of wind-blown snow can also contribute to ablation. Ablation also 
refers to the mass lost by any of these processes.Accumulation (of glaciers, ice sheets, or snow cover) 
All processes that add to the mass of a glacier , an ice sheet , or snow 
cover . The main process of accumulation is snowfall. Accumulation 
also includes deposition of hoar, freezing rain, other types of solid 
precipitation, gain of wind-blown snow, avalanching, and basal 
accumulation (often beneath floating ice).
Discharge (of ice) 
Rate of the flow of ice through a vertical section of a glacier  
perpendicular to the direction of the flow of ice. Often used to refer 
to the loss of mass at marine-terminating glacier fronts (mostly

calving  of icebergs  and submarine melt), or to mass flowing across 
the grounding line  of a floating ice shelf .
Mean sea level The surface level of the ocean  at a particular 
point averaged over an extended period of time such as a month 
or year. Mean sea level is often used as a national datum to which 
heights on land are referred.
Megacity Urban agglomerations with 10 million inhabitants 
or more.
Meltwater Pulse 1A (MWP-1A) A particular interval of rapid 
global sea level rise between about 14,700 and 14,300 years 
ago, associated with the end of the last ice age  and attributed to 
freshwater flux to the ocean  from accelerated melting of ice sheets  
and glaciers . First defined based on oxygen isotope  data (Duplessy 
et al., 1981), and later shown to be reflected by high rates of sea 
level rise (Fairbanks, 1989). See also Deglacial or deglaciation or 
glacial termination .
Meridional overturning circulation (MOC) Meridional

(north–south) overturning circulation in the ocean  quantified by 
zonal (east–west) sums of mass transports in depth or density 
layers. In the North Atlantic, away from the subpolar regions, the 
MOC (which is in principle an observable quantity) is often identified 
with the thermohaline circulation (THC), which is a conceptual and 
incomplete interpretation. The MOC is also driven by wind, and can 
also include shallower overturning cells such as occur in the upper 
ocean in the tropics and subtropics, in which warm (light) waters 
moving poleward are transformed to slightly denser waters and 
subducted equatorward at deeper levels.
Atlantic Meridional Overturning Circulation (AMOC) 
The main current system in the South and North Atlantic Oceans. 
AMOC transports warm upper- ocean  water northwards and cold, 
deep water southwards, as part of the global ocean circulation system. 
Changes in the strength of AMOC can affect other components of the 
climate system .

Meteorological drought See Drought .
Methane (CH 4) The greenhouse gas methane is the major 
component of natural gas and associated with all hydrocarbon fuels. 
Significant anthropogenic emissions  also occur as a result of animal 
husbandry and paddy rice production. Methane is also produced 
naturally where organic matter decays under anaerobic conditions, 
such as in wetlands . Under future global warming , there is potential 
for increased methane emissions from thawing permafrost , wetlands  
and sub-sea gas hydrates. See also Short-lived climate forcers (SLCFs) .
Microclimate Local climate  at or near the Earth’s surface.

2239
Microwave sounding unit (MSU) A microwave sounder on 
U.S. National Oceanic and Atmospheric Administration (NOAA) polar 
orbiter satellites that estimates the temperature of thick layers of the 
atmosphere  by measuring the thermal emission of oxygen molecules 
from a complex of emission lines near 60 GHz. A series of nine MSUs 
began making this kind of measurement in late 1978. Beginning in 
mid-1998, a follow-on series of instruments, the Advanced Microwave 
Sounding Units (AMSUs), began operation.
Mid-Holocene (MH) See Holocene .
Mid-Pliocene Warm Period (MPWP) See Pliocene .
Mineralization/Remineralization The conversion of an 
element from its organic form to an inorganic form as a result 
of microbial decomposition. In nitrogen mineralization, organic 
nitrogen from decaying plant and animal residues (proteins, nucleic 
acids, amino sugars and urea) is converted to ammonia (NH 3) and 
ammonium (NH 4+) by biological activity.

Mitigation (of climate change) A human intervention to 
reduce emissions or enhance the sinks  of greenhouse gases .
Mitigation pathways See Pathways .
Mitigation potential The quantity of net greenhouse gas  
emission reductions that can be achieved by a given mitigation  
option relative to specified emission baselines. [Note: Net greenhouse 
gas emission reductions is the sum of reduced emissions and/or 
enhanced sinks .] See also Sequestration potential .
Biogeophysical potential 
The mitigation potential constrained by biological, geophysical and 
geochemical limits and thermodynamics, without taking into account 
technical, social, economic and/or environmental considerations.
Economic potential 
The portion of the technical potential for which the social benefits 
exceed the social costs, taking into account a social discount rate and 
the value of externalities.
Technical potential 
The mitigation potential constrained by biogeophysical limits as well as

availability of technologies and practices. Quantification of technical 
potentials takes into account primarily technical considerations, 
but social, economic and/or environmental considerations are 
occasionally also included, if these represent strong barriers for the 
deployment of an option.
Mitigation scenario See Scenario .
Mixing ratio See Mole fraction or mixing ratio .
Model ensemble See Climate simulation ensemble . See 
also Ensemble .
Model initialization A climate prediction  typically proceeds 
by integrating a climate model  forward in time from an initial state 
that is intended to reflect the actual state of the climate system . 
Available observations of the climate system are assimilated into the 
model. Initialization is a complex process that is limited by available 
observations, observational errors and, depending on the procedure 
used, may be affected by uncertainty  in the history of climate forcing .

The initial conditions will contain errors that grow as the forecast progresses, thereby limiting the time period over which the forecast 
will be useful.
Model spread The range or spread in results from climate models , 
such as those assembled for Coupled Model Intercomparison Project 
Phase 6 (CMIP6). Does not necessarily provide an exhaustive and 
formal estimate of the uncertainty  in feedbacks , forcing  or projections  
even when expressed numerically, for example, by computing 
a standard deviation of the models’ responses. In order to quantify 
uncertainty, information from observations, physical constraints and 
expert judgement must be combined, using a statistical framework.
Modes of climate variability Recurrent space-time structures 
of natural variability  of the climate system  with intrinsic spatial 
patterns, seasonality and time scales. Modes can arise through the 
dynamical characteristics of the atmospheric circulation but also

through coupling between the ocean  and the atmosphere , with some 
interactions with land surfaces and sea ice . Many modes of variability 
are driven by internal climate processes and are a critical potential 
source of climate predictability  on sub-seasonal to decadal time 
scales. See Annex IV of the AR6 WGI report. See also Annular modes , 
Tropical Atlantic Variability (TAV) , Indian Ocean Dipole (IOD) , Indian 
Ocean Basin (IOB) mode , Pacific Decadal Variability (PDV) , Pacific 
Decadal Oscillation (PDO)  (under Pacific Decadal Variability (PDV) ), 
El Niño–Southern Oscillation (ENSO) , North Atlantic Oscillation 
(NAO) , Northern Annular Mode (NAM)  (under Annular modes ), 
Southern Annular Mode (SAM)  (under Annular modes ), Atlantic 
Meridional Mode (AMM)  (under Tropical Atlantic Variability (TAV) ), 
Atlantic Zonal Mode (AZM)  (under Tropical Atlantic Variability (TAV) ), 
Madden–Julian Oscillation (MJO) , Atlantic Multi-decadal Variability

(AMV)  and Inter-decadal Pacific Oscillation (IPO)  (under Pacific 
Decadal Variability (PDV) ).
Mole fraction or mixing ratio Mole fraction, or mixing ratio, is 
the ratio of the number of moles of a constituent in a given volume 
to the total number of moles of all constituents in that volume. It is 
usually reported for dry air. Typical values for well-mixed greenhouse 
gases  are in the order of μmol mol–1 (parts per million: ppm), nmol 
mol–1 (parts per billion: ppb), and fmol mol–1 (parts per trillion: ppt). 
Mole fraction differs from volume mixing ratio, often expressed in 
ppmv, etc., by the corrections for non-ideality of gases. This correction 
is significant relative to measurement precision for many greenhouse 
gases  (Schwartz and Warneck, 1995).
Monsoon See Global monsoon .
Montreal Protocol The Montreal Protocol on Substances that 
Deplete the Ozone Layer was adopted in Montreal in 1987, and 
subsequently adjusted and amended (including London (1990),

Copenhagen (1992), Vienna (1995), Montreal (1997), Beijing (1999) 
and Kigali (2016)). It controls the consumption and production of 
chlorine- and bromine-containing chemicals that destroy stratospheric 
ozone (O 3 ), such as chlorofluorocarbons (CFCs) , methyl chloroform, 
carbon tetrachloride and many others. Since the Kigali Amendment in 
2016, hydrofluorocarbons (HFCs) , which were used as alternatives to 
ozone-depleting substances (ODSs) ,  have been targeted for a phase-
down due to their climate  effect as greenhouse gases (GHGs) .
Multi-model ensemble (MME) See Climate simulation 
ensemble . See also Ensemble .

2240
Narrative See Storyline . See also Pathways .
Natural systems The dynamic physical, physicochemical  and 
biological components of the Earth system that would operate 
independently of human activities.
Natural variability See Climate variability .
Near-surface permafrost See Permafrost .
Negative greenhouse gas emissions Removal of greenhouse 
gases (GHGs)  from the atmosphere  by deliberate human activities, 
that is, in addition to the removal that would occur via natural carbon 
cycle  or atmospheric chemistry processes. See also Carbon dioxide 
removal (CDR) , Net negative greenhouse gas emissions , Net zero CO 2 
emissions  and Net zero greenhouse gas emissions .
Net negative greenhouse gas emissions A situation of 
net negative greenhouse gas emissions is achieved when metric-
weighted anthropogenic greenhouse gas (GHG)  removals exceed 
metric-weighted anthropogenic GHG emissions. Where multiple

GHG are involved, the quantification of net emissions depends on 
the metric chosen to compare emissions of different gases (such as 
global warming potential , global temperature change potential, and 
others, as well as the chosen time horizon). See also Net zero CO 2 
emissions , Net zero greenhouse gas emissions , Negative greenhouse 
gas emissions , Carbon dioxide removal (CDR)  and Greenhouse gas 
emission metric .
Net primary production (NPP) See Primary production .
Net zero CO 2 emissions Condition in which anthropogenic 
carbon dioxide (CO 2) emissions are balanced by anthropogenic CO 2 
removals over a specified period. 
Note: Carbon neutrality and net zero CO 2 emissions are overlapping 
(e.g., regional, national and sub-national). At a global scale, the 
terms carbon neutrality and net zero CO 2 emissions are equivalent. 
At sub-global scales, net zero CO 2 emissions is generally applied

to emissions and removals under direct control or territorial 
responsibility of the reporting entity, while carbon neutrality 
generally includes emissions and removals within and beyond the 
direct control or territorial responsibility of the reporting entity. 
Accounting rules specified by GHG programmes or schemes can 
have a significant influence on the quantification of relevant CO 2 
See also Net zero greenhouse gas emissions  and Carbon neutrality .
Net zero greenhouse gas emissions Condition in which 
metric-weighted anthropogenic greenhouse gas (GHG)  emissions 
are balanced by metric-weighted anthropogenic GHG removals over 
a specified period. The quantification of net zero GHG emissions 
depends on the GHG emission metric chosen to compare emissions 
and removals of different gases, as well as the time horizon chosen 
for that metric. 
Note 1: GHG neutrality and net zero GHG emissions are overlapping

concepts. The concept of net zero GHG emissions can be applied 
at global or sub-global scales (e.g., regional, national and sub-
national). At a global scale, the terms GHG neutrality and net zero 
GHG emissions are equivalent. At sub-global scales, net zero GHG emissions is generally applied to emissions and removals under 
direct control or territorial responsibility of the reporting entity, 
while GHG neutrality generally includes anthropogenic emissions 
and anthropogenic removals within and beyond the direct control 
or territorial responsibility of the reporting entity. Accounting rules 
specified by GHG programmes or schemes can have a significant 
influence on the quantification of relevant emissions and removals.
Note 2: Under the Paris Rulebook (Decision 18/CMA.1, annex, 
Report to report aggregate emissions and removals of GHGs. In

See also Net zero CO 2 emissions , Greenhouse gas emission metric  
and Greenhouse gas neutrality) .
Nitrogen deposition Nitrogen deposition is defined as the 
nitrogen transferred from the atmosphere  to the Earth’s surface by 
the processes of wet deposition and dry deposition.
Nitrous oxide (N 2O) The main anthropogenic  source of N 2O, 
a greenhouse gas (GHG), is agriculture (soil and animal manure 
management), but important contributions also come from sewage 
treatment, fossil fuel  combustion, and chemical industrial processes. 
N2O is also produced naturally from a wide variety of biological 
sources in soil and water, particularly microbial action in wet 
tropical  forests .
Non-CO 2 emissions and radiative forcing Non-CO 2 emissions 
included in this report are all anthropogenic emissions  other than

carbon dioxide (CO 2) that result in radiative forcing . These include 
short-lived climate forcers , such as methane (CH 4 ), some fluorinated 
gases, ozone (O 3 ) precursors, aerosols  or aerosol precursors , such as 
black carbon  and sulphur dioxide, respectively, as well as long-lived 
greenhouse gases , such as nitrous oxide (N 2O) or other fluorinated 
gases. The radiative forcing associated with non-CO 2 emissions and 
changes in surface albedo  (e.g., resulting from land-use change ) is 
referred to as non-CO 2 radiative forcing.
Non-linearity A process is called non-linear when there is no 
simple proportional relation between cause and effect. The climate 
system  contains many such non-linear processes, resulting in 
a system with potentially very complex behaviour. Such complexity 
may lead to abrupt climate change  and tipping points .
Non-methane volatile organic compounds (NMVOCs) See 
Volatile organic compounds (VOCs) .
Non-overshoot pathways See Pathways .

North American monsoon (NAmerM) See Global monsoon .
Northern Annular Mode (NAM) See Annular modes .
North Atlantic Oscillation (NAO) The leading mode of large-
scale atmospheric variability in the North Atlantic basin characterized 
by alternating (see-saw) variations in sea level pressure or 
geopotential height between the Azores High in the subtropics and 
the Icelandic Low in the mid- to high latitudes, with some northward 
extension deep into the Arctic. It is associated with fluctuations in the 
strength and latitudinal position of the main westerly winds across

2241
a vast North Atlantic–Europe domain, and thus with fluctuations in 
the embedded extratropical cyclones  and associated frontal systems 
leading to strong teleconnection  over the entire North Atlantic 
adjacent continents. The positive and negative phases of the NAO 
show similar characteristics described for the Northern Annular Mode 
(NAM) . See Section AIV.2.1 in Annex IV of the AR6 WGI report.
Northern polar vortex See Stratospheric polar vortex .
Ocean The interconnected body of saline water that covers 71% of 
the Earth’s surface, contains 97% of the Earth’s water and provides 
99% of the Earth’s biologically habitable space. It includes the 
Arctic, Atlantic, Indian, Pacific and Southern Oceans, as well as their 
marginal seas and coastal waters.
Ocean acidification (OA) A reduction in the pH of the ocean , 
accompanied by other chemical changes (primarily in the levels of 
carbonate and bicarbonate ions), over an extended period, typically

decades or longer, which is caused primarily by uptake  of carbon 
dioxide (CO 2) from the atmosphere , but can also be caused by other 
chemical additions or subtractions from the ocean. Anthropogenic  
OA refers to the component of pH reduction that is caused by human 
activity (IPCC, 2011, p. 37).
Ocean alkalinization/Ocean alkalinity enhancement A 
proposed carbon dioxide removal (CDR)  method that involves 
deposition of alkaline minerals or their dissociation products at the 
ocean  surface. This increases surface total alkalinity , and may thus 
increase ocean carbon dioxide (CO 2) uptake and ameliorate surface 
ocean acidification . See also Anthropogenic removals .
Ocean carbon cycle The ocean carbon cycle  is the set of processes 
that exchange carbon between various pools  within the ocean , as 
well as between the atmosphere , Earth’s interior, cryosphere , and the 
sea-floor. See also Carbon cycle .
Ocean deoxygenation The loss of oxygen in the ocean . It results

from ocean warming, which reduces oxygen solubility and increases 
oxygen consumption and stratification , thereby reducing the mixing 
of oxygen into the ocean interior.  Deoxygenation can also be 
exacerbated by the addition of excess nutrients in the coastal  zone.
Ocean dynamic sea level change See Sea level change (sea 
level rise/sea level fall) .
Ocean fertilization A proposed carbon dioxide removal (CDR)  
method that relies on the deliberate increase of nutrient supply to 
the near-surface ocean  with the aim of sequestering  additional CO 2 
from the atmosphere  through biological production. Methods include 
direct addition of micro-nutrients or macro-nutrients. To be successful, 
the additional carbon needs to reach the deep ocean where it has the 
potential to be sequestered on climatically relevant time scales.  See 
also Anthropogenic removals  and Carbon dioxide removal (CDR) .
Ocean heat uptake efficiency This is a measure (W m–2 °C–1) of

the rate at which heat storage by the global ocean  increases as global 
surface temperature rises. It is a useful parameter for climate change 
simulations  in which the radiative forcing  is changing monotonically, 
when it can be compared with the climate feedback parameter  to 
gauge the relative importance of radiative response and ocean heat 
uptake  in determining the rate of climate change . It can be estimated from such an experiment as the ratio of the rate of increase of ocean 
heat content to the surface temperature change.
Ocean stratification See Stratification .
Orbital forcing Orbital forcing  is the influence of slow, systematic 
and predictable changes in orbital parameters (eccentricity, obliquity 
and precession of the equinox) on incoming solar radiation  
(insolation ), especially its latitudinal and seasonal distribution. It is 
an external forcing  and a key driver of glacial–interglacial cycles .
Organic aerosol Component of the aerosol  that consists of

organic compounds, mainly carbon, hydrogen, oxygen and lesser 
amounts of other elements.
Outgoing longwave radiation Net outgoing radiation in the 
infrared part of the spectrum at the top of the atmosphere.
Outlet glacier See Glacier .
Overshoot pathways See Pathways .
Oxygen minimum zone (OMZ) The midwater layer (200–
1000 m) in the open ocean  in which oxygen saturation is the lowest 
in the ocean. The degree of oxygen depletion depends on the largely 
bacterial consumption of organic matter, and the distribution of the 
OMZs is influenced by large-scale ocean circulation. In coastal oceans, 
OMZs extend to the shelves and may also affect benthic ecosystems .
Ozone (O 3) The triatomic form of oxygen, and a gaseous 
atmospheric  constituent. In the troposphere , O 3 is created both 
naturally and by photochemical reactions involving gases resulting 
from human activities (e.g., smog). Tropospheric O 3 acts as 
a greenhouse gas (GHG) . In the stratosphere , O 3 is created by the

interaction between solar ultraviolet radiation and molecular oxygen 
(O2). Stratospheric O 3 plays a dominant role in the stratospheric 
radiative balance. Its concentration is highest in the ozone layer . See 
also Ground-level ozone , Ozone hole , Ozone-depleting substances 
(ODSs) , Ozonesonde  and Short-lived climate forcers (SLCFs) .
Ozone layer A layer of Earth’s stratosphere  that absorbs most 
of the Sun’s ultraviolet radiation. It contains high concentrations of 
ozone (O 3 ) in relation to other parts of the atmosphere , although still 
small in relation to other gases in the stratosphere . The ozone layer 
contains less than 10 parts per million of ozone, while the average 
ozone concentration in Earth’s atmosphere as a whole is about 
0.3 parts per million. The ozone layer is mainly found in the lower 
portion of the stratosphere, from approximately 15 to 35 kilometres 
(9.3 to 21.7 miles) above Earth, although its thickness varies

seasonally and geographically. See also Ozone hole  and Ozone-
depleting substances (ODSs) .
Ozone-depleting substances (ODSs) Man-made gases that 
destroy ozone (O 3 ) once they reach the ozone layer  in the stratosphere . 
Ozone-depleting substances include: chlorofluorocarbons (CFCs) , 
hydrochlorofluorocarbons (HCFCs), hydrobromofluorocarbons 
(HBFCs), halons, methyl bromide, carbon tetrachloride and methyl 
chloroform. They are used as refrigerants in commercial, home and 
vehicle air conditioners and refrigerators, foam blowing agents, 
components in electrical equipment, industrial solvents, solvents 
for cleaning (including dry cleaning), aerosol spray propellants and 
fumigants. See also Ozone layer , Ozone (O 3 ) and Stratospheric ozone .

2242
Ozonesonde An ozonesonde is a radiosonde measuring ozone 
(O3 ) concentrations. The radiosonde is usually carried on a weather 
balloon and transmits measured quantities by radio to a ground-
based receiver.
Pacific Decadal Oscillation (PDO) See Pacific Decadal 
Pacific Decadal Variability (PDV) Coupled decadal-to-inter-
decadal variability of the atmospheric circulation and underlying 
ocean  that is typically observed over the entire Pacific Basin 
beyond the  El Niño–Southern Oscillation (ENSO)  time scale. In the 
AR6 WGI report, PDV encapsulates the Pacific Decadal Oscillation 
(PDO) , the South Pacific Decadal Oscillation (SPDO), tropical Pacific 
decadal variability (also called decadal ENSO), and the Inter-decadal 
Pacific Oscillation (IPO). Typically, the positive phase of the PDV is 
characterized by anomalously high sea surface temperatures  in 
the central-eastern tropical Pacific that extend to the extratropical

North and South Pacific along the American coasts, encircled to the 
west by cold sea surface anomalies  in the mid-latitude North and 
South Pacific. The negative phase is accompanied by sea surface 
temperature anomalies of the opposite sign. Those sea surface 
temperature anomalies are linked to anomalies in atmospheric and 
oceanic circulation throughout the whole Pacific Basin. The PDV is 
associated with decadal modulations in the relative occurrence of 
El Niño and La Niña. See Section AIV.2.6 in Annex IV of the AR6 
WGI report.
Inter-decadal Pacific Oscillation (IPO) 
An equatorially symmetric pattern of sea surface temperature  
variability at decadal-to-inter-decadal time scales. While the Pacific 
Decadal Oscillation (PDO)  and its South Pacific counterpart, the 
South Pacific Decadal Oscillation (SPDO), are considered as physically 
distinct modes, the tropical Pacific decadal–inter-decadal variability 
can drive both the PDO and SPDO, forming the IPO as a synchronized

pan-Pacific variability. Its spatial pattern of sea surface temperature 
anomalies  is similar to that of the El Niño–Southern Oscillation 
(ENSO) , but with a broader meridional extent in the tropical signal 
and more weights in the extratropics compared to the tropics. In 
the AR6 WGI report, it is encapsulated within the definition and 
description of Pacific Decadal Variability (PDV) . See also Section 
AIV.2.6 in Annex IV of the AR6 WGI report.
Pacific Decadal Oscillation (PDO) 
The leading mode of variability obtained from decomposition in 
empirical orthogonal function of sea surface temperature  over the 
North Pacific north of 20°N, and characterized by a strong decadal 
component. The positive phase of the PDO features a dipole of sea 
surface temperature anomalies  in the North Pacific, with a cold lobe 
near the centre of the basin and extending westward along the 
Kuroshio, encircled by warmer conditions along the coast of North

America and in the subtropics. A positive PDO is accompanied by 
an intensified Aleutian Low and an associated cyclonic circulation 
enhancement leading to teleconnections  over the continents adjacent 
to the North Pacific. In the AR6 WGI report, the PDO is encapsulated 
within the definition and description of Pacific Decadal Variability 
(PDV) . See also Section AIV.2.6 in Annex IV of the AR6 WGI report.
Pacific-North American (PNA) pattern An atmospheric large-
scale wave pattern featuring a sequence of tropospheric high and low pressure anomalies  stretching from the subtropical west Pacific 
to the east coast of North America.
Palaeocene–Eocene Thermal Maximum (PETM) The PETM is 
a transient event that occurred between 55.9 and 55.7 million years 
ago. Continental positions at this time were somewhat different to 
present due to tectonic plate movements. Geological data indicate 
that the PETM was characterised by a warming ( global mean surface

temperature  rose to about 4°C–7°C warmer than the preceding mean 
state), and an increase in atmospheric CO 2 (from about 900 to about 
2000 ppmv). In addition, ocean pH and oxygen content decreased; 
many deep-sea species went extinct and tropical coral reefs  diminished.
Paleoclimate Climate  during periods prior to the development 
of measuring instruments, including historic and geologic time, for 
which only proxy  climate records are available.
Parameterization In climate models , this term refers to the 
technique of representing processes that cannot be explicitly 
resolved at the spatial or temporal resolution  of the model (sub-grid 
scale processes) by relationships between model-resolved larger-
scale variables and the area- or time-averaged effect of such sub-grid 
scale processes.
Pathways The temporal evolution of natural and/or human 
systems towards a future state. Pathway concepts range from sets

of quantitative and qualitative scenarios  or narratives  of potential 
futures to solution-oriented decision-making processes to achieve 
desirable societal goals. Pathway approaches typically focus on 
biophysical, techno-economic, and/or socio-behavioural trajectories 
and involve various dynamics, goals, and actors across different 
scales. See also Scenario  and Scenario storyline  (under Storyline ).
1.5°C pathway 
A pathway of emissions of greenhouse gases  and other climate 
forcers that provides an approximately one-in-two to two-in-three 
chance, given current knowledge of the climate response, of global 
warming  either remaining below 1.5°C or returning to 1.5°C by 
around 2100 following an overshoot.
Emission pathways 
Modelled trajectories of global  anthropogenic emissions   over the 
21st century.
Mitigation pathways 
A temporal evolution of a set of  mitigation scenario   features, such 
as greenhouse gas (GHG)  emissions and socio-economic development.

Non-overshoot pathways 
Pathways that stay below a specified concentration,  forcing , or global 
warming level during a specified period of time (e.g., until 2100).
Overshoot pathways 
Pathways that first exceed a specified concentration, forcing , or global 
warming level, and then return to or below that level again before 
the end of a specified period of time (e.g., before 2100). Sometimes 
the magnitude and likelihood  of the overshoot is also characterized. 
The overshoot duration can vary from one pathway to the next, 
but in most overshoot pathways in the literature and referred to as 
overshoot pathways in the AR6, the overshoot occurs over a period 
of at least one decade and up to several decades.

2243
Representative Concentration Pathways (RCPs) 
Scenarios  that include time series of emissions  and concentrations 
of the full suite of greenhouse gases (GHGs)  and aerosols  and 
chemically active gases, as well as land use /land cover  (Moss et 
al., 2010). The word representative signifies that each RCP provides 
only one of many possible scenarios that would lead to the specific 
radiative forcing  characteristics. The term pathway emphasises 
that not only the long-term concentration levels are of interest, but 
also the trajectory taken over time to reach that outcome (Moss et 
al., 2010).
RCPs usually refer to the portion of the concentration pathway 
extending up to 2100, for which integrated assessment models  
produced corresponding emission scenarios . Extended concentration 
pathways describe extensions of the RCPs from 2100 to 2300 
that were calculated using simple rules generated by stakeholder

consultations, and do not represent fully consistent scenarios. Four 
RCPs produced from integrated assessment models were selected 
from the published literature and used in the Fifth IPCC Assessment, 
and are also used in this Assessment for comparison, spanning 
the range from approximately below 2°C warming to high (>4°C) 
warming best-estimates by the end of the 21st century: RCP2.6, 
RCP4.5 and RCP6.0 and RCP8.5.
• RCP2.6: One pathway where radiative forcing peaks at 
approximately 3 W m–2 and then declines to be limited at 2.6 W 
m–2 in 2100 (the corresponding Extended Concentration Pathway, 
or ECP , has constant emissions after 2100).
• RCP4.5 and RCP6.0: Two intermediate stabilization pathways 
in which radiative forcing is limited at approximately 4.5 W m–2 
and 6.0 W m–2 in 2100 (the corresponding ECPs have constant 
concentrations after 2150).
• RCP8.5: One high pathway which leads to >8.5 W m–2 in 2100 
(the corresponding ECP has constant emissions after 2100 until

2150 and constant concentrations after 2250).
See also Coupled Model Intercomparison Project (CMIP)  and Shared 
Socio-economic Pathways (SSPs)  (under Pathways ).
Shared Socio-economic Pathways (SSPs) 
Shared Socio-economic Pathways (SSPs) have been developed 
to complement the Representative Concentration Pathways 
(RCPs) . By design, the RCP emission and concentration pathways 
were stripped of their association with a certain socio-economic 
development. Different levels of emissions and climate change  
along the dimension of the RCPs can hence be explored against 
the backdrop of different socio-economic development pathways 
(SSPs) on the other dimension in a matrix. This integrative SSP-RCP 
framework is now widely used in the climate impact  and policy 
analysis literature, where climate projections  obtained under the 
RCP scenarios are analysed against the backdrop of various SSPs. 
As several emissions updates were due, a new set of emissions

scenarios was developed in conjunction with the SSPs. Hence, the 
abbreviation SSP is now used for two things: On the one hand 
SSP1, SSP2, …, SSP5 are used to denote the five socio-economic 
scenario families. On the other hand, the abbreviations SSP1-1.9, 
SSP1-2.6, …, SSP5-8.5 are used to denote the newly developed 
emissions scenarios that are the result of an SSP implementation within an integrated assessment model . Those SSP scenarios are 
bare of climate policy assumption, but in combination with so-
called shared policy assumptions (SPAs), various approximate 
radiative forcing  levels of 1.9, 2.6, …, or 8.5 W m–2 are reached by 
the end of the century, respectively.
Pattern scaling Techniques used to represent the spatial 
variations in climate  at a given increase in global mean surface air 
temperature (GSAT)  are referred to as ‘pattern scaling’.
Peat Soft, porous or compressed, sedimentary deposit of which

a substantial portion is partly decomposed plant material with high 
water content in the natural state (up to about 90%).
Peatlands Peatland is a land where soils are dominated by peat.
Percentile A partition value in a population distribution that 
a given percentage of the data values are below or equal to. The 50th 
percentile corresponds to the median of the population. Percentiles 
are often used to estimate the extremes of a distribution. For example, 
the 90th (10th) percentile may be used to refer to the threshold for 
the upper (lower) extremes.
Permafrost Ground (soil or rock, and included ice and organic 
material) that remains at or below 0°C for at least two consecutive 
years (Harris et al., 1988). Note that permafrost is defined via 
temperature rather than ice content and, in some instances, may 
be ice-free.
Near-surface permafrost 
Permafrost within about 3–4 m of the ground surface. The depth 
is not precise, but describes what commonly is highly relevant for

people and ecosystems . Deeper permafrost is often progressively 
less ice-rich and responds more slowly to warming than near-surface 
permafrost. The presence or absence of near-surface permafrost is 
not the only significant metric of permafrost change, and deeper 
permafrost may persist when near-surface permafrost is absent.
Permafrost degradation 
Decrease in the thickness and/or areal extent of permafrost.
Permafrost thaw 
Progressive loss of ground ice in permafrost, usually due to input of 
heat. Thaw can occur over decades to centuries over the entire depth 
of permafrost ground, with impacts occurring while thaw progresses. 
During thaw, temperature fluctuations are subdued because energy 
is transferred by phase change between ice and water. After the 
transition from permafrost to non-permafrost, ground can be 
described as thawed.
Perturbed parameter ensemble See Climate simulation 
ensemble .
pH A dimensionless measure of the acidity of a dilute solution

(e.g., seawater) based on the activity, or effective concentration, of 
hydrogen ions (H+) in the solution. pH is measured on a logarithmic 
scale where pH = –log 10(H+). Thus, a pH decrease of 1 unit corresponds 
to a 10-fold increase in the acidity, or the activity of H+.
Phenology The relationship between biological phenomena that 
recur periodically (e.g., development stages, migration) and climate  
and seasonal changes.

2244
Photosynthesis The production of carbohydrates in plants, algae 
and some bacteria using the energy of light.  Carbon dioxide (CO 2) is 
used as the carbon source.
Physical climate storyline See Storyline .
Piacenzian warm period See Pliocene .
Plankton Free-floating organisms living in the upper layers 
of aquatic systems. Their distribution and migration are primarily 
determined by water currents. A distinction is made between 
phytoplankton, which depend on photosynthesis  for their energy 
supply, and zooplankton, which feed on phytoplankton, other 
zooplankton, and bacterioplankton.
Plant evaporative stress Plant evaporative stress in both 
crops and natural vegetation can result from the combination of 
a high atmospheric evaporative demand and limited available water 
to supply this demand by means of evapotranspiration , further 
enhancing agricultural and ecological drought .
Pleistocene The Pleistocene Epoch is the earlier of two epochs in

the Quaternary  System, extending from 2.59 Ma to the beginning of 
the Holocene  at approximately 11.7 ka.
Pliocene The Pliocene Epoch is the more recent of two epochs of 
the Neogene Period within the Cenozoic Era . It extends from 5.33 Ma 
to the beginning of the Pleistocene  Epoch at 2.59 Ma. The Neogene 
Period precedes the current geological period, the Quaternary  Period, 
which is one of several ice ages  that have occurred during Earth’s 
geological history. It encompasses the mid-Pliocene warm period 
(MPWP), also known as the Piacenzian warm period, which occurred 
from approximately 3.3 to 3.0 Ma. The MPWP , in turn, encompasses 
the interglacial  episode, marine isotope stage (MIS)  KM5c, which 
peaked at 3.205 Ma, when orbital forcing  was similar to modern 
(Haywood et al., 2016).
Polar amplification Polar amplification describes the 
phenomenon where surface temperature change at high latitudes 
exceeds the global average surface temperature change. The terms

Arctic amplification or Antarctic amplification are used when 
describing the phenomenon occurring at one of the poles.
Pollen analysis A technique of both relative dating and 
environmental reconstruction , consisting of the identification 
and counting of pollen types preserved in peat, lake sediments and 
other deposits.
Pool, carbon and nitrogen A reservoir   in the Earth system 
where elements, such as carbon and nitrogen, reside in various 
chemical forms for a period of time. See also Reservoir , Sequestration, 
Sequestration potential, Sink, Source and Uptake .
Post-glacial period See Holocene .
Potential evapotranspiration See Evapotranspiration .
Pre-industrial (period) The multi-century period prior to the 
onset of large-scale industrial activity around 1750. The reference 
period  1850–1900 is used to approximate pre-industrial global mean 
surface temperature (GMST) . See also Industrial revolution .
Precipitable water The total amount of atmospheric water

vapour in a vertical column of unit cross-sectional area. It is commonly expressed in terms of the height of the water if completely condensed 
and collected in a vessel of the same unit cross section.
Precursors Atmospheric compounds that are not greenhouse 
gases (GHGs)  or aerosols , but that have an effect on GHG or aerosol 
concentrations by taking part in physical or chemical processes 
regulating their production or destruction rates.
Predictability The extent to which future states of a system 
may be predicted based on knowledge of current and past states 
of the system. Because knowledge of the climate system ’s past and 
current states is generally imperfect, as are the models that utilize 
this knowledge to produce a climate prediction , and because the 
climate system  is inherently non-linear  and chaotic , predictability of 
the climate system is inherently limited. Even with arbitrarily accurate 
models and observations, there may still be limits to the predictability

of such a non-linear system (AMS, 2021). See also Climate prediction  
and Prediction quality/skill .
Prediction quality/skill Measures of the success of a prediction 
against observationally based information. No single measure can 
summarize all aspects of forecast quality, and a suite of metrics  is 
considered. Metrics will differ for forecasts given in deterministic and 
probabilistic form. See also Climate prediction  and Predictability .
Primary production The synthesis of organic compounds 
by plants and microbes, on land or in the ocean , primarily by 
photosynthesis  using light and carbon dioxide (CO 2) as sources 
of energy and carbon respectively. It can also occur through 
chemosynthesis, using chemical energy, for example, in deep 
sea vents.
Gross primary production (GPP) 
The total amount of carbon fixed by photosynthesis  over a specified 
time period.
Net primary production (NPP) 
The amount of carbon fixed by photosynthesis  minus the amount lost

by respiration  over a specified time period.
Probability density function (PDF) A probability density 
function is a function that indicates the relative chances of occurrence 
of different outcomes of a variable. The function integrates to unity 
over the domain for which it is defined and has the property that 
the integral over a sub-domain equals the probability that the 
outcome of the variable lies within that sub-domain. For example, 
the probability that a temperature anomaly  defined in a particular 
way is greater than zero is obtained from its PDF by integrating 
the PDF over all possible temperature anomalies greater than zero. 
Probability density functions that describe two or more variables 
simultaneously are similarly defined.
Process-based model Theoretical concepts and computational 
methods that represent and simulate the behaviour of real-world 
systems derived from a set of functional components and their

interactions with each other and the system environment, through 
physical and mechanistic processes occurring over time.
Projection A potential future evolution of a quantity or set of 
quantities, often computed with the aid of a model. Unlike predictions, 
projections are conditional on assumptions concerning, for example, 
future socio-economic and technological developments that may

2245
or may not be realized. See also Climate projection , Pathways  
and Scenario .
Proxy A proxy climate indicator  is any biophysical property of 
materials formed during the past that is interpreted to represent 
some combination of climate-related variations back in time. 
Climate-related data derived in this way are referred to as proxy data, 
and time series of proxy data are proxy records. Examples of proxy 
types include pollen assemblages, tree ring  widths, speleothem and 
coral geochemistry, and various data derived from marine sediments 
and glacier  ice. Proxy data can be calibrated to provide quantitative 
climate information .
Proxy records See Proxy .
Quasi-Biennial Oscillation (QBO) A near-periodic oscillation of 
the equatorial zonal wind between easterlies and westerlies in the 
tropical stratosphere  with a mean period of around 28 months. The 
alternating wind maxima descend from the base of the mesosphere

down to the tropopause  and are driven by wave energy that 
propagates up from the troposphere .
Quaternary The Quaternary Period is the last of three periods that 
make up the Cenozoic Era  (66 Ma to present), extending from 2.58 Ma 
to the present, and includes the Pleistocene  and Holocene  Epochs.
Radiative forcing The change in the net, downward minus 
upward, radiative flux (expressed in W m–2) due to a change in 
an external  driver of climate change , such as a change in the 
concentration of carbon dioxide (CO 2), the concentration of volcanic 
aerosols  or the output of the Sun. The stratospherically adjusted 
radiative forcing is computed with all tropospheric properties held 
fixed at their unperturbed values, and after allowing for stratospheric 
temperatures, if perturbed, to readjust to radiative-dynamical 
equilibrium.  Radiative forcing is called instantaneous if no change in 
stratospheric temperature is accounted for. The radiative forcing once

both stratospheric and tropospheric adjustments are accounted for is 
termed the effective radiative forcing.
Radiative response (of the climate system)  The net top-of-
atmosphere radiative flux that opposes a change in radiative forcing  
as a result of climate feedbacks . Typical units: W m–2. See also Earth’s 
energy budget  and Climate feedback parameter .
Rapid dynamical change (of glaciers or ice sheets) Changes 
in glacier  or ice sheet  mass controlled by changes in flow speed and 
discharge  rather than by accumulation  or ablation . This can result in 
a rate of mass change larger than that due to any imbalance between 
accumulation and ablation. Rapid dynamical change may be initiated 
by a climatic trigger, such as incursion of warm ocean  water beneath 
an ice shelf , or thinning of a grounded tide-water terminus, which 
may lead to reactions within the glacier system that may result in 
rapid ice loss.
Reanalysis Reanalyses are created by processing past

meteorological or oceanographic data using fixed state-of-the-
art weather forecasting or ocean  circulation models with data 
assimilation  techniques. They are used to provide estimates of 
variables such as historical atmospheric temperature and wind or 
oceanographic temperature and currents, and other quantities. Using 
fixed data assimilation avoids effects from the changing analysis system that occur in operational analyses. Although continuity is 
improved, global reanalyses still suffer from changing coverage and 
biases in the observing systems.
Reasons for concern (RFCs) Elements of a classification 
framework, first developed in the IPCC Third Assessment Report, 
which aims to facilitate judgements about what level of climate 
change  may be dangerous (in the language of Article 2 of the 
UNFCCC; UNFCCC, 1992) by aggregating risks from various sectors, 
considering hazards , exposures , vulnerabilities , capacities to adapt, 
and the resulting impacts .

Reconstruction (of climate variable) Approach to 
reconstructing the past temporal and spatial characteristics 
of a  climate  variable from predictors. The predictors can be 
instrumental data if the reconstruction is used to infill missing data 
or proxy  data if it is used to develop paleoclimate  reconstructions. 
Various techniques have been developed for this purpose: linear 
multivariate regression-based methods and non-linear Bayesian 
and analogue methods.
Reference period A time period of interest, or a period over 
which some relevant statistics are calculated. A reference period can 
be used as a baseline period or as a comparison to a baseline period. 
Baseline period 
A time period against which differences are calculated (e.g., expressed 
as anomalies relative to a baseline). 
Reference scenario See Scenario .
Reforestation Conversion to forest  of land that has previously 
contained forests but that has been converted to some other use.

[Note: For a discussion of the term forest and related terms such 
as afforestation , reforestation and deforestation , see the 2006 
IPCC Guidelines for National Greenhouse Gas Inventories and 
their 2019 Refinement, and information provided by the United 
2019; UNFCCC, 2021a, b).] See also Afforestation , Deforestation , 
Region Land and/or ocean  area characterized by specific 
geographical and/or climatological features. The climate  of a region 
emerges from a multi-scale combination of its own features, remote 
influences from other regions, and global climate conditions.
Regional climate model (RCM) A climate model  at higher 
resolution  over a limited area. Such models are used in downscaling  
global climate  results over specific regional domains.
Regional sea level change See Sea level change (sea level rise/

Relative humidity The ratio of actual water vapour pressure to 
that at saturation with respect to liquid water or ice at the same 
temperature. See also Specific humidity .
Relative sea level (RSL) change See Sea level change (sea level 
Remaining carbon budget See Carbon budget .
Representative Concentration Pathways (RCPs) See 
Pathways .

2246
Reservoir A component or components of the climate system  
where a greenhouse gas (GHG)  or a precursor  of a greenhouse gas is 
stored (UNFCCC Article 1.7 (UNFCCC, 1992)). See also Pool, carbon 
and nitrogen , Sequestration , Sequestration potential, Sink, Source 
and Uptake .
Resilience The capacity of interconnected social, economic 
and ecological systems to cope with a hazardous event, trend or 
disturbance, responding or reorganizing in ways that maintain their 
essential function, identity and structure. Resilience is a positive 
attribute when it maintains capacity for adaptation , learning and/
or transformation (Arctic Council, 2016). See also Hazard , Risk 
and Vulnerability .
Resolution In climate models , this term refers to the physical 
distance (metres or degrees) between each point on the grid used to 
compute the equations. Temporal resolution refers to the time step 
or time elapsed between each model computation of the equations.

Respiration The process whereby living organisms convert 
organic matter to carbon dioxide (CO 2), releasing energy and 
consuming molecular oxygen.
Response time or adjustment time In the context of climate  
variations, the response time or adjustment time is the time needed 
for the climate system  or its components to re-equilibrate to a new 
state, following a forcing  resulting from external processes. It is 
very different for various components of the climate system. The 
response time of the troposphere  is relatively short, from days to 
weeks, whereas the stratosphere  reaches equilibrium on a time 
scale of typically a few months. Due to their large heat capacity, 
the oceans  have a much longer response time: typically decades, 
but up to centuries or millennia. The response time of the strongly 
coupled surface–troposphere system is, therefore, slow compared 
to that of the stratosphere, and mainly determined by the oceans.

The biosphere  may respond quickly (e.g., to droughts ), but also very 
slowly to imposed changes. 
In the context of lifetimes , response time or adjustment time (T a) is 
the time scale characterizing the decay of an instantaneous pulse 
input into the reservoir . See Response time or adjustment time (T a ) 
under Lifetime .
Return period An estimate of the average time interval between 
occurrences of an event (e.g., flood or extreme rainfall) of (or below/
above) a defined size or intensity.
Return value The highest (or, alternatively, lowest) value of 
a given variable, on average occurring once in a given period of time 
(e.g., in 10 years).
Risk The potential for adverse consequences for human or 
ecological systems, recognizing the diversity of values and objectives 
associated with such systems. In the context of climate change , 
risks can arise from potential impacts  of climate change as well as 
human responses to climate change. Relevant adverse consequences

include those on lives, livelihoods , health and well-being, economic, 
social and cultural assets and investments, infrastructure, services 
(including ecosystem services), ecosystems  and species.
In the context of climate change impacts, risks result from dynamic 
interactions between climate-related hazards  with the exposure  and vulnerability  of the affected human or ecological system to the 
hazards. Hazards, exposure and vulnerability may each be subject 
to uncertainty in terms of magnitude and likelihood  of occurrence, 
and each may change over time and space due to socio-economic 
changes and human decision-making (see also risk management , 
adaptation and mitigation ).
In the context of climate change responses, risks result from the 
potential for such responses not achieving the intended objective(s), 
or from potential trade-offs with, or negative side-effects on, other 
societal objectives, such as the Sustainable Development Goals

(SDGs)  (see also  risk trade-off ). Risks can arise, for example, from 
uncertainty  in implementation, effectiveness or outcomes of climate 
policy, climate-related investments, technology development or 
adoption, and system transitions. See also Hazard  and Impacts 
(consequences, outcomes) .
Risk assessment The qualitative and/or quantitative scientific 
estimation of risks. See also Risk management  and Risk perception .
Risk framework A common framework for describing and 
assessing risk across all three Working Groups is adopted to promote 
clear and consistent communication of risks and to better inform risk 
assessment  and decision-making related to climate change .
Risk management Plans, actions, strategies or policies to reduce 
the likelihood  and/or magnitude of adverse potential consequences, 
based on assessed or perceived risks. See also Risk assessment  
and Risk perception .
Risk perception The subjective judgement that people make

about the characteristics and severity of a risk. See also Risk 
assessment  and Risk management .
Risk trade-off The change in the portfolio of risks that occurs when 
a countervailing risk is generated (knowingly or inadvertently) by an 
intervention to reduce the target risk (Wiener and Graham, 2009).
River discharge See Streamflow .
Rock glacier A debris landform (mass of rock fragments and finer 
material that contains either an ice core or an ice-cemented matrix) 
generated by a former or current gravity-driven creep of permafrost  
in mountain slopes (Harris et al., 1988; Giardino et al., 2011; IPA-RG, 
2020). It is detectable in the landscape due to the occurrence of (i) 
a steep slope delimiting the terminal part, (ii) generally well-defined 
lateral margins in a continuation of the front, and (iii) transversal 
or longitudinal ridges and furrows (ridge and furrow topography). 
These are geomorphological indicators of the occurrence of

permafrost conditions. Although it is an ice storage feature, it is not 
a type of glacier  since it does not originate at the surface by the 
recrystallization of snow.
Runoff The flow of water over the surface or through the 
subsurface, which typically originates from the part of liquid 
precipitation and/or snow/ice melt that does not evaporate, transpire 
or refreeze, and returns to water bodies.
Sampling uncertainty See Uncertainty .
Scenario A plausible description of how the future may develop 
based on a coherent and internally consistent set of assumptions 
about key driving forces (e.g., rate of technological change (TC),

2247
prices) and relationships. Note that scenarios are neither predictions 
nor forecasts, but are used to provide a view of the implications of 
developments and actions. See also Pathways  and Scenario storyline  
(under  Storyline ).
Baseline scenario 
See Reference scenario  (under Scenario ).
Concentrations scenario 
A plausible representation of the future development of atmospheric 
concentrations of substances that are radiatively active (e.g., 
greenhouse gases (GHGs) , aerosols , tropospheric ozone ), plus 
human-induced land–cover changes  that can be radiatively active 
via albedo  changes, and often used as input to a climate model  to 
compute climate projections .
Emissions scenario 
A plausible representation of the future development of emissions of 
substances that are radiatively active (e.g., greenhouse gases (GHGs) 
or aerosols ), plus human-induced land-cover  changes  that can be

radiatively active via albedo  changes,  based on a coherent and 
internally consistent set of assumptions about driving forces (such 
as demographic and socio-economic development, technological 
change, energy and land use ) and their key relationships. 
Concentration scenarios , derived from emission scenarios, are often 
used as input to a climate model  to compute climate projections .
Mitigation scenario 
A plausible description of the future that describes how the (studied) 
system responds to the implementation of mitigation  policies and 
measures.
Reference scenario 
Scenario used as starting or reference point for a comparison between 
two or more scenarios. 
[Note 1: In many types of climate change  research, reference 
scenarios reflect specific assumptions about patterns of socio-
economic development and may represent futures that assume 
no climate policies or specified climate policies, for example those

in place or planned at the time a study is carried out. Reference 
scenarios may also represent futures with limited or no climate 
impacts  or adaptation , to serve as a point of comparison for futures 
with impacts and adaptation. These are also referred to as baseline 
scenarios in the literature.
Note 2: Reference scenarios can also be climate policy or impact 
scenarios, which in that case are taken as a point of comparison 
to explore the implications of other features, for example, of delay, 
technological options, policy design and strategy or to explore 
the effects of additional impacts and adaptation beyond those 
represented in the reference scenario.
Note 3: The term business as usual scenario has been used to describe 
a scenario that assumes no additional policies beyond those currently 
in place and that patterns of socio-economic development are 
consistent with recent trends. The term is now used less frequently 
than in the past.

Note 4: In climate change attribution  or impact attribution research, 
reference scenarios may refer to counterfactual historical scenarios assuming no anthropogenic greenhouse gas (GHG)  emissions (climate 
change attribution) or no climate change (impact attribution).]
Socio-economic scenario 
A scenario that describes a plausible future in terms of population, 
gross domestic product (GDP), and other socio-economic factors 
relevant to understanding the implications of climate change .
Scenario storyline See Storyline .
Sea ice Ice found at the sea surface that has originated from 
the freezing of seawater. Sea ice may be discontinuous pieces (ice 
floes) moved on the ocean  surface by wind and currents (pack ice), 
or a motionless sheet attached to the coast  (land-fast ice). Sea ice 
concentration is the fraction of the ocean covered by ice. Sea ice less 
than one year old is called first-year ice. Perennial ice is sea ice that

survives at least one summer. It may be subdivided into second-year 
ice and multi-year ice, where multi-year ice has survived at least 
two summers.
Sea ice area (SIA) 
Sea ice area is the area covered by sea ice. In contrast to sea ice 
extent , it is a linear measure of sea ice coverage that does not depend 
on grid resolution.
Sea ice concentration 
Sea ice concentration is the fraction of the ocean  covered by ice.
Sea ice extent (SIE)  Sea ice extent is calculated for gridded data 
products as the total area of all grid cells with sea ice concentration  
above a given threshold, usually 15 %. It hence is a grid-dependent, 
non-linear measure of sea ice coverage.
Sea level change (sea level rise/sea level fall) Change to 
the height of sea level, both globally and locally ( relative sea level  
change) at seasonal, annual, or longer time scales due to (i) a change 
in ocean  volume as a result of a change in the mass of water in the

ocean (e.g., due to melt of glaciers  and ice sheets ), (ii) changes in 
ocean volume as a result of changes in ocean water density (e.g., 
expansion under warmer conditions), (iii) changes in the shape of the 
ocean basins and changes in the Earth’s gravitational and rotational 
fields, and (iv) local subsidence or uplift of the land. Global mean sea 
level (GMSL) change  resulting from change in the mass of the ocean 
is called barystatic. The amount of barystatic sea level change due 
to the addition or removal of a mass of water is called its sea level 
equivalent (SLE) . Sea level changes, both globally and locally, resulting 
from changes in water density are called steric. Density changes 
induced by temperature changes only are called thermosteric, while 
density changes induced by salinity changes are called halosteric. 
Barystatic and steric sea level changes do not include the effect of 
changes in the shape of ocean basins induced by the change in the

ocean mass and its distribution. See also Vertical land motion (VLM) , 
Land water storage , Glacial isostatic adjustment (GIA) , Extreme sea 
level (ESL)  and Storm surge .
Geocentric sea level change 
The change in local mean sea surface height with respect to the 
terrestrial reference frame; it is the sea level change observed with 
instruments from space. See also  Altimetry .

2248
Global mean sea level (GMSL) change 
The increase or decrease in the volume of the ocean  divided by 
the ocean surface area. It is the sum of changes in ocean density 
through temperature changes (global mean thermosteric sea level 
change ) and changes in the ocean mass as a result of changes in the 
cryosphere  or land water storage  (barystatic sea level change).
Gravitational, rotational and deformational (GRD) effects 
Changes in Earth gravity, Earth rotation and viscoelastic solid Earth 
deformation (GRD) result from the redistribution of mass between 
terrestrial ice and water reservoirs and the ocean . Contemporary 
terrestrial mass loss leads to elastic solid Earth uplift and a nearby 
relative sea level fall (for a single source of terrestrial mass loss this 
is within ~2000 km, for multiple sources the distance depends on 
the interaction of the different relative sea level patterns). Farther

away (more than ~7000 km for a single source of terrestrial mass 
loss), relative sea level rises more than the global average, due (to 
first order) to gravitational effects. Earth deformation associated 
with adding water to the oceans and a shift of the Earth’s rotation 
axis towards the source of terrestrial mass loss leads to second-order 
effects that increase spatial variability of the pattern globally. GRD 
effects due to the redistribution of ocean water within the ocean 
itself are referred to as self-attraction and loading effects.
Halosteric sea level change 
Halosteric sea level change occurs as a result of salinity variations: 
higher salinity leads to higher density and decreases the volume per 
unit of mass. Although both processes can be relevant on regional to 
local scales, only thermosteric changes impact the global mean sea 
level (GMSL) change , whereas the global mean halosteric change is 
negligible (Gregory et al., 2019).
Local sea level change

Change in sea level relative to a datum (such as present-day mean 
sea level ) at spatial scales smaller than 10 km.
Ocean dynamic sea level change 
Change in mean sea level relative to the geoid  associated with 
circulation and density-driven changes in the ocean . Ocean dynamic 
sea level change is regionally varying but by definition has a zero 
global mean and conventionally is inverse-barometer corrected (i.e., the 
effect of the hydrostatic depression of the sea surface by atmospheric 
pressure changes is removed). Changes in ocean currents occur due to 
variations in heating and cooling, variability in winds and changes in 
seasonally to annually averaged air temperature and humidity.
Regional sea level change 
Change in sea level relative to a datum (such as present-day mean 
sea level ) at spatial scales of about 100 km.
Relative sea level (RSL) change 
The change in local mean sea surface height (SSH) relative to the

local solid surface, that is, the sea floor, as measured by instruments 
that are fixed to the Earth’s surface, such as tide gauges . This 
reference frame is used when considering coastal impacts , hazards  
and adaptation  needs.
Steric sea level change 
Steric sea level change is caused by changes in ocean  density and 
is  composed of thermosteric sea level change  and halosteric sea 
level change .Thermosteric sea level change 
Thermosteric sea level change (where thermosteric sea level rise may 
also be referred to as thermal expansion) occurs as a result of changes 
in ocean  temperature: increasing temperature reduces ocean density 
and increases the volume per unit of mass.
Sea level equivalent (SLE) The SLE of a mass of water, ice, or 
water vapour is that mass, converted to a volume using a density of 
1000 kg m–3, and divided by the present-day ocean  surface area of 
3.625 × 1000 m2. Thus, 362.5 Gt of water mass added to the ocean

correspond to 1 mm of global mean sea level rise. 
Sea level rise (SLR) See Sea level change (sea level rise/sea 
level fall) .
Sea surface temperature (SST) The subsurface bulk 
temperature in the top few metres of the ocean , measured by ships, 
buoys and drifters. From ships, measurements of water samples 
in buckets were mostly switched in the 1940s to samples from 
engine intake water. Satellite measurements of skin temperature 
(uppermost layer; a fraction of a millimetre thick) in the infrared or 
the top centimetre or so in the microwave are also used, but must be 
adjusted to be compatible with the bulk temperature.
Semi-direct (aerosol) effect See Aerosol–radiation interaction .
Semi-empirical model Model in which calculations are based 
on a combination of observed associations between variables and 
theoretical considerations relating variables through fundamental 
principles (e.g., conservation of energy). For example, in sea level

studies, semi-empirical models refer specifically to transfer functions 
formulated to project future global mean sea level (GMSL) change , 
or contributions to it, from future global surface temperature  change 
or radiative forcing .
Sensible heat flux The turbulent or conductive flux of heat from 
the Earth’s surface to the atmosphere  that is not associated with 
phase changes of water; a component of the surface energy budget. 
See also Latent heat flux .
Sequestration The process of storing carbon in a carbon pool. 
See also Pool, carbon and nitrogen , Reservoir , Sequestration potential, 
Sink, Source and Uptake .
Sequestration potential The quantity of greenhouse gases  that 
can be removed from the atmosphere  by anthropogenic  enhancement 
of sinks  and stored in a pool. See Mitigation potential  for different 
subcategories of sequestration potential. See also Pool, carbon and 
nitrogen , Reservoir , Sequestration , Source  and Uptake .

Shared policy assumptions (SPAs) See Shared Socio-economic 
Pathways (SSPs)  (under Pathways ).
Shared Socio-economic Pathways (SSPs) See Pathways .
Short-lived climate forcers (SLCFs) A set of chemically 
reactive compounds with short (relative to carbon dioxide (CO 2)) 
atmospheric lifetimes  (from hours to about two decades) 
but characterized by different physiochemical properties and 
environmental effects. Their emission or formation has a significant 
effect on radiative forcing  over a period determined by their 
respective atmospheric lifetimes. Changes in their emissions can also 
induce long-term climate  effects via, in particular, their interactions

2249
with some biogeochemical cycles. SLCFs are classified as direct or 
indirect, with direct SLCFs exerting climate effects through their 
radiative forcing and indirect SLCFs being the precursors  of other 
direct climate forcers. Direct SLCFs include methane (CH 4 ), ozone 
(O3 ), primary aerosols  and some halogenated species. Indirect 
SLCFs are precursors of ozone or secondary aerosols. SLCFs can be 
cooling or warming through interactions with radiation and clouds. 
They are also referred to as near-term climate forcers. Many SLCFs 
are also air pollutants. A subset of exclusively warming SLCFs is 
also referred to as short-lived climate pollutants (SLCPs), including 
methane, ozone, and black carbon (BC) .
Short-lived climate pollutants (SLCP) See Short-lived climate 
forcers (SLCFs) .
Shortwave radiation See Solar radiation .
Significant wave height The average trough-to-crest height of

the highest one-third of the wave heights (sea and swell) occurring in 
a particular time period.
Simple climate model (SCM) A broad class of lower-
dimensional models of the energy balance , radiative transfer, carbon 
cycle,  or a  combination of such physical components. SCMs are 
also suitable for performing emulations  of climate -mean variables 
of Earth system models (ESMs) , given that their structural flexibility 
can capture both the parametric and structural uncertainties across 
process-oriented ESM responses. They can also be used to test 
consistency across multiple lines of evidence  with regard to climate 
sensitivity  ranges, transient climate responses (TCRs) , transient 
climate response to cumulative CO 2 emissions (TCREs)  and carbon 
cycle feedbacks . See also Emulators  and Earth system model of 
intermediate complexity (EMIC) .
Sink Any process, activity or mechanism which removes 
a greenhouse gas , an aerosol  or a precursor  of a greenhouse gas

from the atmosphere  (UNFCCC Article 1.8 (UNFCCC, 1992)). See also 
Pool, carbon and nitrogen , Reservoir , Sequestration , Sequestration 
potential, Source and Uptake .
Small Island Developing States (SIDS) Small Island 
Developing States (SIDS), as recognized by the United Nations 
OHRLLS (UN Office of the High Representative for the Least 
Developed Countries, Landlocked Developing Countries and Small 
Island Developing States), are a distinct group of developing countries 
facing specific social, economic and environmental vulnerabilities 
(UN-OHRLLS, 2011). They were recognized as a special case both for 
their environment and development at the Rio Earth Summit in Brazil 
in 1992. Fifty-eight countries and territories are presently classified 
as SIDS by the UN OHRLLS, with 38 being UN member states and 
20 being Non-UN Members or Associate Members of the Regional 
Commissions (UN-OHRLLS, 2018).
Snow cover Snow cover refers to all the snow that has

accumulated on the ground at a given time (UNESCO/IASH/
WMO, 1970).
Snow cover duration (SCD) 
How long snow continuously  remains on the land surface, or the 
period between snow-on and snow-off dates.Snow cover extent (SCE) 
The areal extent of snow covered ground.
Snow water equivalent (SWE) 
The depth of liquid water that would result if a mass of snow melted 
completely.
Socio-economic scenario See Scenario .
Soil moisture Water stored in the soil in liquid or frozen form. 
Root-zone soil moisture is of most relevance for plant activity.
Soil temperature The temperature of the soil. This can be 
measured or modelled at multiple levels within the depth of the soil.
Solar activity General term collectively describing a variety of 
magnetic phenomena on the Sun such as sunspots , faculae  (bright 
areas), and flares (emission of high-energy particles). It varies on 
time scales from minutes to millions of years. The solar cycle , with an

average duration of 11 years, is an example of a quasi-regular change 
in solar activity.
Solar cycle (11-year) A quasi-regular modulation of solar activity  
with varying amplitude and a period of between 8 and 14 years.
Solar radiation Electromagnetic radiation emitted by the Sun 
with a spectrum close to that of a black body with a temperature of 
5770 K. The radiation peaks in visible wavelengths. When compared 
to the terrestrial radiation  it is often referred to as shortwave 
radiation. See also Insolation  and Total solar irradiance (TSI) .
Solar radiation modification (SRM) Refers to a range of 
radiation modification measures not related to greenhouse gas 
(GHG) mitigation  that seek to limit global warming . Most methods 
involve reducing the amount of incoming solar radiation  reaching 
the surface, but others also act on the longwave radiation  budget by 
reducing optical thickness and cloud lifetime.
Cirrus cloud thinning (CCT)

One of several radiation modification approaches to counter the 
warming caused by greenhouse gases (GHGs) . In this approach, it 
is proposed to reduce the amount of cirrus clouds by injecting ice 
nucleating substances in the upper troposphere . The reduction in 
cirrus clouds is expected to increase the amount of longwave cooling 
to space resulting in a planetary cooling. Although cirrus cloud 
thinning primarily affects the longwave radiation  budget of our 
planet, it is often identified as one of the solar radiation modification 
(SRM)  approaches in the literature.
Marine cloud brightening (MCB) 
One of several solar radiation modification (SRM) approaches to 
increase the planetary albedo . In this approach, it is proposed to 
inject sea salt aerosols  into persistent marine low clouds. This is 
expected to increase the cloud droplet concentration of these clouds 
and their reflectivity.
Stratospheric aerosol injection (SAI)

increase the planetary albedo . In the approach, it is proposed to 
inject highly reflective aerosols  such as sulphates into the lower 
stratosphere . This is expected to increase the fraction of solar 
radiation  deflected to space resulting in a planetary cooling.

2250
Solubility pump A physicochemical process that transports 
dissolved inorganic carbon  from the ocean ’s surface to its interior. The 
solubility pump is primarily driven by the solubility of carbon dioxide 
(CO 2) (with more CO 2 dissolving in colder water) and the large-scale, 
thermohaline patterns of ocean circulation.
Source Any process or activity which releases a greenhouse gas , 
an aerosol  or a precursor  of a greenhouse gas into the atmosphere  
(UNFCCC Article 1.9 (UNFCCC, 1992)). See also Pool, carbon and 
nitrogen , Reservoir , Sequestration , Sequestration potential, Sink 
and Uptake .
South American monsoon (SAmerM) See Global monsoon .
South and Southeast Asian monsoon (SAsiaM) See 
Global monsoon .
Southern Annular Mode (SAM) See Annular modes .
South Pacific Convergence Zone (SPCZ) A band of low-
level convergence, cloudiness and precipitation ranging from the 
west Pacific warm pool south-eastwards towards French Polynesia.

It is one of the most significant features of subtropical Southern 
Hemisphere climate . It shares some characteristics with the Inter-
tropical Convergence Zone (ITCZ) , but is more extratropical in nature, 
especially east of the International Date Line.
Southern Oscillation See El Niño–Southern Oscillation (ENSO) .
Specific humidity The specific humidity specifies the ratio of 
the mass of water vapour to the total mass of moist air. See also 
Relative humidity .
Stadial or stade A brief period of regional climatic cooling during 
a glacial  or interglacial  interval, often characterized by transient 
glacial advances. Stadials are generally of short duration (hundreds 
to a few thousand years) compared to glacial or interglacial 
intervals (lasting many thousands to tens of thousands of years). 
One example of a regional stadial event is based on millennial scale 
cooling recorded by oxygen isotope  ratios in Greenland ice cores ,

the so called “Greenland Stadials” (Johnsen et al., 1992). See also 
Interstadial or interstade .
Statistical downscaling See Downscaling .
Steric sea level change See Sea level change (sea level rise/
Storm surge The temporary increase, at a particular locality, 
in the height of the sea due to extreme meteorological conditions 
(low atmospheric pressure and/or strong winds). The storm surge is 
defined as being the excess above the level expected from the tidal 
variation alone at that time and place. See also Sea level change (sea 
level rise/sea level fall)  and Extreme sea level (ESL) .
Storm tracks Originally, a term referring to the tracks of 
individual cyclonic weather systems, but now often generalized 
to refer to the main regions  where the tracks of extratropical 
disturbances occur as sequences of low (cyclonic) and high 
(anticyclonic) pressure systems.
Storyline A way of making sense of a situation or a series of

events through the construction of a set of explanatory elements. 
Usually, it is built on logical or causal reasoning. In climate  research, the term storyline is used both in connection to scenarios  as related 
to a future trajectory of the climate and human systems  or to a 
weather or climate event. In this context, storylines can be used 
to describe plural, conditional possible futures or explanations 
of a current situation, in contrast to single, definitive futures or 
explanations.
Physical climate storyline 
A self-consistent and plausible unfolding of a physical trajectory of 
the climate system , or a weather or climate event, on time scales 
from hours to multiple decades (Shepherd et al., 2018). Through this, 
storylines explore, illustrate and communicate uncertainties  in the 
climate system  response to forcing  and in internal variability .
Scenario storyline 
A narrative description of a scenario  (or  family of scenarios),

highlighting the main scenario characteristics, relationships between 
key driving forces and the dynamics of their evolution.
Stratification Process of forming of layers of ( ocean ) water 
with different properties such as salinity, density and temperature 
that act as barrier for water mixing. The strengthening of near-
surface stratification generally results in warmer surface waters, 
decreased oxygen levels in deeper water, and intensification of ocean 
acidification (OA)  in the upper ocean.
Stratosphere The highly stratified region of the atmosphere 
above the tropopause , extending to about 50 km altitude. See 
also Troposphere .
Stratospheric aerosol injection (SAI) See Solar radiation 
Stratosphere–troposphere exchange (STE) Stratosphere –
troposphere  exchange (STE) is understood as the flux of air or trace 
constituents across the tropopause , including both directions: the 
stratosphere to troposphere transport (STT) and troposphere to

stratosphere transport (TST). STE is one of the key factors controlling 
the budgets of ozone , water vapour and other substances in both the 
troposphere  and the lower stratosphere .
Stratospheric ozone Stratospheric ozone describes the ozone 
(O3 ) that resides in the stratosphere , the region of the atmosphere  
which exists between 10 and 50 kilometres above the surface of the 
earth. Ninety percent of total-column ozone resides in the stratosphere. 
See also Ozone layer  and Ozone-depleting substances (ODSs) .
Stratospheric polar vortex A large-scale region of cold air 
poleward of approximately 60 degrees that is contained by a strong 
westerly jet from the tropopause  (8–10 km) to the stratopause 
(50–60 km) and that forms in each hemisphere during the winter 
half-year. Planetary waves can temporarily disrupt the vortex, 
producing easterly winds and rapid warming over polar regions in the 
stratosphere , and leading to substantial weakening or breakdown of 
the vortex.

Stratospheric sounding unit (SSU) A three-channel infrared 
sounder on operational U.S. National Oceanic and Atmospheric 
Administration (NOAA) polar-orbiting satellites. The three channels 
are used to determine profiles of temperature in the stratosphere 
(AMS, 2021).

2251
Streamflow Water flow within a river channel, for example, 
expressed in m3 s–1. A synonym for river discharge.
Subduction Ocean  process in which surface waters enter the 
ocean interior from the surface mixed layer through Ekman pumping 
and lateral advection . The latter occurs when surface waters are 
advected to a region where the local surface layer is less dense and 
therefore must slide below the surface layer, usually with no change 
in density.
Sudden stratospheric warming (SSW) A phenomena of rapid 
warming in the stratosphere  at high latitudes (sometimes more than 
50°C in 1–2 days) that can cause breakdown of stratospheric polar 
vortices .
Sulphur hexafluoride (SF 6) SF6, a greenhouse gas (GHG) , is 
mainly used in heavy industry to insulate high-voltage equipment 
and to assist in the manufacturing of cable-cooling systems and 
semiconductors.
Sunspots Dark areas on the Sun where strong magnetic fields

reduce the convection, causing a temperature reduction of about 
1500 K compared to the surrounding regions. The number of sunspots 
is higher during periods of higher solar activity  and varies in particular 
with the solar cycle .
Surface air temperature See Land surface air temperature 
(LSAT)  and Global mean surface air temperature (GSAT) .
Surface mass balance (SMB) See Mass balance/budget (of 
glaciers or ice sheets) .
Surface temperature See Global mean surface air temperature 
(GSAT) , Global mean surface temperature (GMST) , Land surface air 
temperature (LSAT)  and Sea surface temperature (SST) .
Surprises A class of risk that can be defined as low- likelihood  but 
well-understood events and events that cannot be predicted with 
current understanding (see Section 1.4.4.3 in AR6 WGI Chapter 1).
Swash See Extreme sea level (ESL) .
Talik A layer or body of unfrozen ground in a permafrost  area 
due to a local anomaly in thermal, hydrological, hydrogeological or

hydrochemical conditions (IPA, 2005).
Technical potential See Mitigation potential .
Teleconnection Association between climate  variables at 
widely separated, geographically fixed locations related to each 
other through physical processes and oceanic and/or atmospheric 
dynamical pathways. Teleconnections can be caused by several 
climate phenomena, such as Rossby wave-trains, mid-latitude jet and 
storm track  displacements, fluctuations of the Atlantic Meridional 
Overturning Circulation (AMOC) , fluctuations of the Walker 
circulation , etc. They can be initiated by modes of climate variability , 
thus providing the development of remote climate anomalies  at 
various temporal lags. See also Teleconnection pattern .
Teleconnection pattern Spatial structure of climate anomalies  
that are linked to each other through  teleconnection  processes or 
that are the large-scale  fingerprint of  modes of climate variability .

Teleconnection patterns can be visualized using correlation and/or 
regression maps of  climate  variables with some  climate indices  (i.e., those derived from the temporal variation of the main modes of 
climate variability). They can also be obtained from principal 
component analysis, singular value decomposition/maximum 
covariance analysis, clustering based on spatial recurrence 
criteria, etc. See also Section Atlas.3.1 of the AR6 WGI report and 
Teleconnection .
Temperature overshoot Exceedance of a specified global 
warming level, followed by a decline to or below that level during 
a specified period of time (e.g., before 2100). Sometimes the 
magnitude and likelihood  of the overshoot is also characterized. 
The overshoot duration can vary from one pathway  to the next, but 
in most overshoot pathways  in the literature and as referred to as 
overshoot pathways in the AR6, the overshoot occurs over a period

of at least one decade and up to several decades. See also Pathways .
Terrestrial radiation Radiation emitted by the Earth’s surface, 
the atmosphere  and clouds. It is also known as thermal infrared or 
longwave radiation and is to be distinguished from the near-infrared 
radiation that is part of the solar spectrum. Infrared radiation, in 
general, has a distinctive range of wavelengths (spectrum) longer 
than the wavelength of the red light in the visible part of the spectrum. 
The spectrum of terrestrial radiation is almost entirely distinct from 
that of shortwave or solar radiation  because of the difference in 
temperature between the Sun and the Earth–atmosphere system.
Thermal expansion See Steric sea level change  (under Sea level 
change (sea level rise/sea level fall) ).
Thermocline The layer of maximum vertical temperature gradient 
in the ocean , lying between the surface ocean and the abyssal ocean.

In subtropical regions, its source waters are typically surface waters 
at higher latitudes that have subducted (see Subduction ) and moved 
equatorward. At high latitudes, it is sometimes absent, replaced by 
a halocline , which is a layer of maximum vertical salinity gradient.
Thermohaline circulation (THC) See Meridional overturning 
circulation (MOC) .
Thermokarst Process by which characteristic landforms result 
from thawing of ice-rich permafrost  or melting of massive ice 
(IPA, 2005).
Thermosteric See Sea level change (sea level rise/sea level fall) .
Tide gauge A device at a coastal or deep-sea location that 
continuously measures the level of the sea with respect to the 
adjacent land. Time averaging of the sea level so recorded gives the 
observed secular changes of the relative sea level.
Time of emergence (ToE) Time when a specific anthropogenic  
signal related to climate change  is statistically detected to emerge

from the background noise of natural climate variability  in a reference 
period , for a specific region  (Hawkins and Sutton, 2012). See also 
Emergence (of the climate signal) .
Tipping element A component of the Earth system that is 
susceptible to a tipping point .
Tipping point A critical threshold beyond which a system 
reorganizes, often abruptly and/or irreversibly. See also Tipping 
element , Irreversibility  and Abrupt change .

2252
Total alkalinity Total Alkalinity (AT) is a measurable parameter of 
the seawater acid–base system which, when expressed in micromoles 
per kilogram of seawater, is a conservative variable both on mixing 
and for changes in temperature and/or pressure. Changes in total 
alkalinity in the oceans  can result from a variety of biogeochemical 
processes that affect the acid–base composition of the seawater 
itself. However, its value is not affected by the exchange of carbon 
dioxide  gas between seawater and the atmosphere . Measurements of 
total alkalinity can thus be used to help study these biogeochemical 
processes and can also be used to help calculate the state of the 
seawater acid–base system. Total alkalinity is most commonly 
measured using an acidimetric titration technique that determines 
how much acid is required to titrate a seawater sample to a specified 
equivalence point.
Total carbon budget See Carbon budget .

Total solar irradiance (TSI) The total amount of solar radiation  
in watts per square metre received outside the Earth’s atmosphere  
on a surface normal to the incident radiation and at the Earth’s mean 
distance from the Sun. Reliable measurements of solar radiation can 
only be made from space, and the precise record extends back only 
to 1978. Variations of a few tenths of a percent are common, usually 
associated with the passage of sunspots  across the solar disk. The 
solar cycle  variation of TSI is of the order of 0.1% (AMS, 2021). See 
also Insolation .
Total water level See Extreme sea level (ESL) .
Trace gas A minor constituent of the atmosphere , next to 
nitrogen and oxygen that together make up 99% of all volume. 
The most important trace gases contributing to the greenhouse 
effect  are carbon dioxide (CO 2), ozone (O 3 ), methane (CH 4 ), nitrous 
oxide (N 2O), perfluorocarbons (PFCs), chlorofluorocarbons (CFCs) ,

hydrofluorocarbons (HFCs) , sulphur hexafluoride (SF 6 ) and water 
vapour (H 2O).
Transient climate response (TCR) See Climate sensitivity .
Transient climate response to cumulative CO 2 emissions 
(TCRE) See Climate sensitivity .
Tree rings Concentric rings of secondary wood evident in a cross 
section of the stem of a woody plant. The difference between the 
dense, small-celled late wood of one season and the wide-celled 
early wood of the following spring enables the age of a tree to be 
estimated, and the ring widths or density can be related to climate  
parameters such as temperature and precipitation.
Tropical Atlantic modes See Tropical Atlantic Variability (TAV) .
Tropical Atlantic Variability (TAV) A generic term to describe 
the climate variability  of the tropical Atlantic which is dominated 
at interannual to decadal time scales by two main climate modes: 
the Atlantic Zonal Mode (AZM)  and the Atlantic Meridional Mode

(AMM) . The Atlantic Zonal Mode, also commonly referred to as the 
Atlantic Niño or Atlantic equatorial mode, is associated with sea 
surface temperature  anomalies near the equator, peaking in the 
eastern basin, while the Atlantic meridional mode is characterized 
by an inter-hemispheric gradient of sea surface temperature 
and wind anomalies. Both modes are associated with significant 
teleconnections  over Africa and South America.Atlantic Meridional Mode (AMM)  The Atlantic Meridional Mode 
(AMM) refers to the interannual to decadal variability  of the cross-
equatorial sea surface temperature  gradients and surface wind 
anomalies in the tropical Atlantic. It modulates the strength and 
latitudinal shifts of the Inter-tropical Convergence Zone (ITCZ) , 
which impacts regional rainfall over Northeast Brazil and Atlantic 
hurricane  activity. See Section AIV.2.5 in Annex IV of the AR6 
Atlantic Zonal Mode (AZM)  An equatorial coupled mode in the

Atlantic similar to El Niño–Southern Oscillation (ENSO)  in the Pacific, 
and therefore sometimes referred to as the Atlantic Niño. The AZM 
is associated with sea surface temperature  anomalies near the 
equatorial Atlantic and rainfall disturbances over the African monsoon 
domain. Its variations are mostly observed in the interannual scale. It 
is called also Atlantic equatorial mode. See Section AIV.2.5 in Annex 
Tropical cyclone The general term for a strong, cyclonic-scale 
disturbance that originates over tropical oceans . Distinguished from 
weaker systems (often named tropical disturbances or depressions) 
by exceeding a threshold wind speed. A tropical storm is a tropical 
cyclone with one-minute average surface winds between 18 and 
32 m s–1. Beyond 32 m s–1, a tropical cyclone is called a hurricane, 
typhoon, or cyclone, depending on geographic location.
Tropopause The boundary between the troposphere  and the

stratosphere . It ranges from 8–9 km at high latitudes to 15–16 km 
in the tropics.
Troposphere The lowest part of the atmosphere , below the 
tropopause , where clouds and weather phenomena occur. In the 
troposphere, temperatures generally decrease with height. See 
also Stratosphere .
Tropospheric ozone See Ozone (O 3 ) and Ground-level ozone .
Tundra A treeless biome characteristic of polar and alpine regions.
Turnover time (T) See Lifetime .
Typhoon See Tropical cyclone .
Typological domains See Typological regions .
Typological regions Regions of the Earth that share one or more 
specific features (known as ‘typologies’), such as geographic location 
(e.g., coastal), physical processes (e.g., monsoons ), and biological 
(e.g., coral reefs , tropical forests ), geological (e.g., mountains) or 
anthropogenic  (e.g., megacities ) formation, and for which it is useful 
to consider the common  climate features. Typological regions are

smaller than climatic zones (e.g., a mountain region) and can be 
discontinuous (e.g., a group of megacities affected by the urban heat 
island  effect, or monsoon regions).
Uncertainty A state of incomplete knowledge that can result 
from a lack of information or from disagreement about what is 
known or even knowable. It may have many types of sources, 
from imprecision in the data to ambiguously defined concepts 
or  terminology, incomplete understanding of critical processes, or 
uncertain projections  of human behaviour. Uncertainty can therefore 
be represented by quantitative measures (e.g., a  probability density 
function ) or by qualitative statements (e.g., reflecting the judgement

2253
of a team of experts) (see Moss and Schneider, 2000; IPCC, 2004; 
Mastrandrea et al., 2010). See also Confidence and Likelihood.
Deep uncertainty  
A situation of deep uncertainty exists when experts or stakeholders do not know or cannot agree on: (1) appropriate conceptual models that describe relationships among key driving forces in a system; 
(2) the probability distributions used to represent uncertainty about
key variables and parameters; and/or (3) how to weigh and valuedesirable alternative outcomes (Lempert et al., 2003).
Interpolation uncertainty  
Uncertainty arising from a statistical or physical model-based interpolation of a field between available estimates to create a more spatio-temporally complete estimate.
Sampling uncertainty  
Uncertainty arising from incomplete or uneven availability of measurements in either space or time or both.
Trend estimates uncertainty

Uncertainty arising from data fitting to a time-series with potential non-linear and autorogressive character.
United Nations Framework Convention on Climate Change 
(UNFCCC)  The UNFCCC was adopted in May 1992 and opened 
for signature at the 1992 Earth Summit in Rio de Janeiro. It entered 
into force in March 1994 and as of September 2020 had 197 Parties 
(196 States and the European Union). The Convention’s ultimate 
objective is the ‘stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system’ (UNFCCC, 1992). The provisions of the Convention are pursued and implemented by two further 
treaties: the Kyoto Protocol and the Paris Agreement.
Uptake  The transfer of substances (such as carbon) or energy (e.g., 
heat) from one compartment of a system to another; for example, 
in the Earth system from the atmosphere to the ocean or to the

land. See also Pool, carbon and nitrogen , Reservoir, Sequestration, 
Sequestration potential, Sink and Source.
Upwelling region  A region of an ocean where cold, typically 
nutrient-rich waters well up from the deep ocean.
Urban heat island (UHI)  The relative warmth of a city compared 
with surrounding rural areas, associated with heat trapping due to the close proximity of tall buildings, the heat-absorbing properties 
of urban building materials, reduced ventilation, and heat generated 
directly from human activities. See also Urbanization.
Urbanization  In the WGI report, urbanization is used to mean the 
process of soil sealing with the change of natural land cover to built environment and urban areas, together with its associated albedo 
changes, and increased surface runoff and elevated warming. See 
also Urban heat island (UHI).
Ventilation  The exchange of ocean properties with the

atmospheric surface layer such that property concentrations are brought closer to equilibrium values with the atmosphere (AMS, 
2021), and the processes that propagate these properties into the ocean interior.Vertical land motion (VLM)  The change in height of the land 
surface or the sea floor and can have several causes in addition to elastic deformation associated with contemporary changes in 
gravity, rotation and viscoelastic solid Earth deformation (GRD)  and 
viscoelastic deformation associated with glacial isostatic adjustment 
(GIA). Subsidence (sinking of the land surface or sea floor) can, for 
instance, occur through compaction of alluvial sediments in deltaic regions, removal of fluids such as gas, oil, and water, or drainage 
of peatlands. Tectonic deformation of the Earth’s crust can occur as 
a result of earthquakes and volcanic eruptions. See also Sea level 
change (sea level rise/sea level fall).
Very short-lived halogenated substances (VSLSs)  Very

short-lived halogenated substances (VSLSs) are considered to include source gases (very short-lived halogenated substances present in 
the atmosphere in the form they were emitted from natural and 
anthropogenic sources), halogenated product gases arising from 
source gas degradation, and other sources of tropospheric inorganic halogens. VSLSs have tropospheric lifetimes of around 0.5 years 
or less.
Volatile organic compounds (VOCs)  Important class of organic 
chemical air pollutants that are volatile at ambient air conditions. Other terms used to represent VOCs are hydrocarbons (HCs), reactive 
organic gases (ROGs) and non-methane volatile organic compounds 
(NMVOCs). NMVOCs are major contributors – together with nitrogen oxides (NO
x), and carbon monoxide (CO) – to the formation of 
photochemical oxidants such as ozone (O 3 ).
Biogenic volatile organic compounds (BVOCs)

Organic gas-phase compounds emitted from terrestrial and aquatic ecosystems that are critical in ecology and plant physiology, from abiotic and biotic stress functions to integrated components of 
metabolism. BVOCs are important in atmospheric chemistry as 
precursors for ozone (O
3 ) and secondary organic aerosol formation. 
Other terms used to represent BVOCs are hydrocarbons (HCs), reactive organic gases (ROGs) and non-methane volatile organic 
compounds (NMVOCs).
Vulnerability  The propensity or predisposition to be adversely 
affected. Vulnerability encompasses a variety of concepts and 
elements including sensitivity or susceptibility to harm and lack of 
capacity to cope and adapt. See also Exposure, Hazard and Risk.
Walker circulation  Direct thermally driven zonal overturning 
circulation in the atmosphere over the tropical Pacific Ocean, with 
rising air in the western and sinking air in the eastern Pacific.
Warm spell  See Heatwave. 
Water cycle  See Hydrological cycle.

Water mass  A body of ocean water with identifiable properties 
(temperature, salinity, density, chemical tracers) resulting from its 
unique formation process. Water masses are often identified through 
a vertical or horizontal extremum of a property such as salinity. North 
Pacific Intermediate Water (NPIW) and Antarctic Intermediate Water (AAIW) are examples of water masses.
Water security  ‘The capacity of a population to safeguard 
sustainable access to adequate quantities of acceptable quality water for sustaining livelihoods, human well-being, and socio-

2254
economic development, for ensuring protection against water-borne 
pollution and water-related disasters, and for preserving ecosystems 
in a climate of peace and political stability’ (UN-Water, 2013).
Wave run-up See Extreme sea level (ESL) .
Wave setup See Extreme sea level (ESL) .
Weathering The gradual removal of atmospheric carbon dioxide 
(CO 2) through dissolution of silicate and carbonate rocks. Weathering 
may involve physical processes (mechanical weathering) or chemical 
activity (chemical weathering).
Well-mixed greenhouse gas A greenhouse gas (GHG)  that has 
an atmospheric lifetime  long enough (greater than several years) to 
be homogeneously mixed in the troposphere , and as such the global 
average mixing ratio can be determined from a network of surface 
observations. For many well-mixed greenhouse gases, measurements 
made in remote regions differ from the global mean by <15%.
West African monsoon (WAfriM) See Global monsoon .

West Antarctic Ice Sheet (WAIS) See Ice sheet .
Wetland Land that is covered or saturated by water for all or part 
of the year (e.g., peatland ).
Younger Dryas The period from approximately 12.9 to 11.7 ka 
(thousand years before 1950), during the last deglacial transition , 
characterized by a temporary return to colder conditions in many 
locations, especially around the North Atlantic. See also Stadial  and 
Last deglacial transition .
Zero emissions commitment See Climate change commitment .

150
Chapter 1 Framing, Context, and Methods1Executive Summary
Working Group I (WGI) of the Intergovernmental Panel on Climate 
Change (IPCC) assesses the current evidence on the physical science 
of climate change, evaluating knowledge gained from observations, 
reanalyses, paleoclimate archives and climate model simulations, 
as well as physical, chemical and biological climate processes. 
This chapter sets the scene for the WGI Assessment, placing it in the 
context of ongoing global and regional changes, international policy 
responses, the history of climate science and the evolution from 
previous IPCC assessments, including the Special Reports prepared 
as part of this Assessment Cycle. This chapter presents key concepts 
and methods, relevant recent developments, and the modelling and 
scenario framework used in this Assessment.
Framing and Context of the WGI Report
The WGI contribution to the IPCC Sixth Assessment Report (AR6)

assesses new scientific evidence relevant for a world whose 
climate system is rapidly changing, overwhelmingly due to 
human influence.  The five IPCC assessment cycles since 1990 have 
comprehensively and consistently laid out the rapidly accumulating 
evidence of a changing climate system, with the Fourth Assessment 
Report (AR4, 2007) being the first to conclude that warming of 
the climate system is unequivocal. Sustained changes have been 
documented in all major elements of the climate system, including 
the atmosphere, land, cryosphere, biosphere and ocean. Multiple 
lines of evidence indicate the unprecedented nature of recent large-
scale climatic changes in the context of all human history, and that 
these changes represent a millennial-scale commitment for the slow-
responding elements of the climate system, resulting in continued 
worldwide loss of ice, increase in ocean heat content, sea level rise 
and deep ocean acidification. {1.2.1, 1.3, Box 1.2, Appendix 1.A}

Since the IPCC Fifth Assessment Report (AR5), the international 
policy context of IPCC reports has changed. The UN Framework 
Convention on Climate Change (UNFCCC, 1992) has the overarching 
objective of preventing ‘dangerous anthropogenic interference 
with the climate system’. Responding to that objective, the Paris 
Agreement (2015) established the long-term goals of ‘holding the 
increase in global average temperature to well below 2°C above pre-
industrial levels and pursuing efforts to limit the temperature increase 
to 1.5°C above pre-industrial levels’ and of achieving ‘a balance 
between anthropogenic emissions by sources and removals by sinks 
of greenhouse gases in the second half of this century’. Parties to 
the Agreement have submitted Nationally Determined Contributions 
(NDCs) indicating their planned mitigation and adaptation strategies. 
However, the NDCs submitted as of 2020 are insufficient to reduce

greenhouse gas emissions enough to be consistent with trajectories 
limiting global warming to well below 2°C above pre-industrial levels 
(high confidence ). {1.1, 1.2}
This report provides information of potential relevance to the 
2023 global stocktake. The five-yearly stocktakes called for in the 
Paris Agreement will evaluate alignment among the Agreement’s 
long-term goals, its means of implementation and support, and evolving global efforts in climate change mitigation (efforts to limit 
climate change) and adaptation (efforts to adapt to changes that 
cannot be avoided). In this context, WGI assesses, among other 
topics, remaining cumulative carbon emissions budgets for a range 
of global warming levels, effects of long-lived and short-lived climate 
forcers, observed climate changes and their attribution to human 
forcing, and projected changes in sea level and climate extremes. 
{Cross-Chapter Box 1.1}
Understanding of the fundamental features of the climate

system is robust and well established. Scientists in the 19th century 
identified the major natural factors influencing the climate system. 
They also hypothesized the potential for anthropogenic climate 
change due to carbon dioxide (CO 2) emitted by fossil fuel combustion. 
The principal natural drivers of climate change, including changes in 
incoming solar radiation, volcanic activity, orbital cycles, and changes 
in global biogeochemical cycles, have been studied systematically 
since the early 20th  century. Other major anthropogenic drivers, 
such as atmospheric aerosols (fine solid particles or liquid droplets), 
land-use change and non-CO 2 greenhouse gases, were identified 
by the 1970s. Since systematic scientific assessments began in the 
1970s, the influence of human activity on the warming of the climate 
system has evolved from theory to established fact. Past projections 
of global surface temperature and the pattern of warming are

broadly consistent with subsequent observations ( limited evidence , 
high agreement ), especially when accounting for the difference in 
radiative forcing scenarios used for making projections and the 
radiative forcings that actually occurred. {1.3.1–1.3.6}
Global surface temperatures increased by about 0.1°C ( likely  
range –0.1°C to +0.3°C, medium confidence ) between the period 
around 1750 and the 1850–1900 period, with anthropogenic 
factors responsible for a warming of 0.0°C–0.2°C ( likely  range, 
medium confidence ). This assessed change in temperature before 
1850–1900 is not included in the AR6 assessment of global warming 
to date, to ensure consistency with previous IPCC assessment reports, 
and because of the lower confidence in the estimate. There was likely  
a net anthropogenic forcing of 0.0 –0.3 W m–2 in 1850 –1900 relative 
to 1750 ( medium confidence ), with radiative forcing from increases in 
atmospheric greenhouse gas concentrations being partially offset by

anthropogenic aerosol emissions and land-use change. Net radiative 
forcing from solar and volcanic activity is estimated to be smaller 
than ±0.1 W m–2 for the same period. {Cross-Chapter Box 1.2, 1.4.1, 
Cross-Chapter Box 2.3}
Natural climate variability can temporarily obscure or 
intensify anthropogenic climate change on decadal time 
scales, especially in regions with large internal interannual-
to-decadal variability. At the current level of global warming, 
an observed signal of temperature change relative to 
the 1850–1900 baseline has emerged above the levels of 
background variability over virtually all land regions ( high 
confidence ). Both the rate of long-term change and the amplitude of 
interannual (year-to-year) variability differ between global, regional 
and local scales, between regions and across climate variables, 
thus influencing when changes become apparent. Tropical regions 
have experienced less warming than most others, but also exhibit

151
Framing, Context, and Methods Chapter 11smaller interannual variations in temperature. Accordingly, the signal 
of change is more apparent in tropical regions than in regions with 
greater warming but larger interannual variations ( high confidence ). 
{1.4.2, FAQ 1.2}
AR6 has adopted a  unified framework of climate risk, 
supported by an increased focus in WGI on low-likelihood, high-
impact outcomes.  Systematic risk framing is intended to aid the 
formulation of effective responses to the challenges posed by current 
and future climatic changes and to better inform risk assessment and 
decision-making. AR6 also makes use of the ‘storylines’ approach, 
which contributes to building a  robust and comprehensive picture 
of climate information, allows for a more flexible consideration and 
communication of risk, and can explicitly address low-likelihood, 
high-impact outcomes. {1.1.2, 1.4.4, Cross-Chapter Box 1.3}
The construction of climate change information and

communication of scientific understanding are influenced 
by the values of the producers, the users and their broader 
audiences. Scientific knowledge interacts with pre-existing 
conceptions of weather and climate, including values and beliefs 
stemming from ethnic or national identity, traditions, religion or lived 
relationships to land and sea ( high confidence ). Science has values 
of its own, including objectivity, openness and evidence-based 
thinking. Social values may guide certain choices made during the 
construction, assessment and communication of information ( high 
confidence ). {1.2.3, Box 1.1}
Data, Tools and Methods Used across the WGI Report
Capabilities for observing the physical climate system have 
continued to improve and expand overall, but some reductions 
in observational capacity are also evident ( high confidence ). 
Improvements are particularly evident in ocean observing networks 
and remote-sensing systems, and in paleoclimate reconstructions

from proxy archives. However, some climate-relevant observations 
have been interrupted by the discontinuation of surface stations 
and radiosonde launches, and delays in the digitisation of records. 
Further reductions are expected to result from the COVID-19 
pandemic. In  addition, paleoclimate archives such as mid-latitude 
and tropical glaciers, as well as modern natural archives used for 
calibration (e.g.,  corals and trees), are rapidly disappearing due 
to a  host of pressures, including increasing temperatures ( high 
confidence ). {1.5.1}
Reanalyses have improved since AR5 and are increasingly 
used as a  line of evidence in assessments of the state and 
evolution of the climate system ( high confidence ). Reanalyses, 
where atmosphere or ocean forecast models are constrained by 
historical observational data to create a  climate record of the 
past, provide consistency across multiple physical quantities and 
information about variables and locations that are not directly

observed. Since AR5, new reanalyses have been developed with 
various combinations of increased resolution, extended records, more 
consistent data assimilation, estimation of uncertainty arising from 
the range of initial conditions, and an improved representation of the ocean. While noting their remaining limitations, the WGI report uses 
the most recent generation of reanalysis products alongside more 
standard observation-based datasets. {1.5.2, Annex 1}
Since AR5, new techniques have provided greater confidence 
in attributing changes in climate and weather extremes 
to climate change. Attribution is the process of evaluating the 
relative contributions of multiple causal factors to an observed 
change or event. This includes the attribution of the causal factors 
of changes in physical or biogeochemical weather or climate 
variables (e.g.,  temperature or atmospheric CO 2) as done in WGI, 
or of the impacts of these changes on natural and human systems

(e.g., infrastructure damage or agricultural productivity), as done in 
WGII. Attributed causes include human activities (such as emissions 
of greenhouse gases and aerosols, or land-use change), and changes 
in other aspects of the climate, or natural or human systems.  
{Cross-Working Group Box 1.1}
The latest generation of complex climate models has an 
improved representation of physical processes, and a  wider 
range of Earth system models now represent biogeochemical 
cycles. Since AR5, higher-resolution models that better capture 
smaller-scale processes and extreme events have become 
available.  Key model intercomparisons supporting this Assessment 
include the Coupled Model Intercomparison Project Phase 6 (CMIP6) 
and the Coordinated Regional Climate Downscaling Experiment 
(CORDEX), for global and regional models respectively. Results 
using CMIP Phase 5  (CMIP5) simulations are also assessed. Since 
AR5, large ensemble simulations, where individual models perform

multiple simulations with the same climate forcings, are increasingly 
used to inform understanding of the relative roles of internal 
variability and forced change in the climate system, especially 
on regional scales. The  broader availability of ensemble model 
simulations has contributed to better estimations of uncertainty 
in projections of future change ( high confidence ). A  broad set of 
simplified climate models is assessed and used as emulators to 
transfer climate information across research communities, such as for 
evaluating impacts or mitigation pathways consistent with certain 
levels of future warming. {1.4.2, 1.5.3, 1.5.4, Cross-Chapter Box 7.1}
Assessments of future climate change are integrated within 
and across the three IPCC Working Groups through the use of 
three core components: scenarios, global warming levels, and 
the relationship between cumulative CO 2 emissions and global 
warming. Scenarios have a long history in the IPCC as a method for

systematically examining possible futures. A new set of illustrative 
scenarios that cover the range of possible future developments of 
anthropogenic drivers of climate change found in the literature, 
derived from the Shared Socio-economic Pathways (SSPs), is used 
to synthesize knowledge across the physical sciences and impact, 
adaptation and mitigation research. The core set of SSP scenarios 
used in the WGI report, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and 
SSP5-8.5, cover a broad range of emissions pathways, including new 
low-emissions pathways. They start in 2015 and include scenarios 
with high and very high greenhouse gas (GHG) emissions (SSP3-7.0 
and SSP5-8.5) and CO 2 emissions that roughly double from current 
levels by 2100 and 2050, respectively; scenarios with intermediate

152
Chapter 1 Framing, Context, and Methods1GHG emissions (SSP2-4.5) and CO 2 emissions remaining around 
current levels until the middle of the century; and scenarios with very 
low and low GHG emissions and CO 2 emissions declining to net zero 
around or after 2050, followed by varying levels of net negative CO 2 
emissions (SSP1-1.9, SSP1-2.6). Emissions vary between scenarios 
depending on socio-economic assumptions, levels of climate change 
mitigation and, for aerosols and non-methane ozone precursors, 
air pollution controls. Alternative assumptions may result in similar 
emissions and climate responses, but the socio-economic assumptions 
and the feasibility or likelihood of individual scenarios are not part of 
this assessment, which focuses on the climate response to possible, 
prescribed emissions futures. Levels of global surface temperature 
change (global warming levels), which are closely related to a range

of hazards and regional climate impacts, also serve as reference 
points within and across IPCC Working Groups. Cumulative carbon 
emissions, which have a  nearly linear relationship to increases 
in global surface temperature, are also used. {1.6.1–1.6.4,  
Cross-Chapter Box 1.5, Cross-Chapter Box 11.1}

153
Framing, Context, and Methods Chapter 111.1 Report and Chapter Overview
The role of the Intergovernmental Panel on Climate Change (IPCC) 
is to critically assess the scientific, technical and socio-economic 
information relevant to understanding the physical science and impacts 
of human-induced climate change and natural variations, including 
the risks, opportunities and options for adaptation and mitigation. 
This task is performed through a comprehensive assessment of the 
scientific literature. The robustness of IPCC assessments stems from 
the systematic consideration and combination of multiple lines of 
independent evidence. In addition, IPCC reports undergo one of the 
most comprehensive, objective, open and transparent review and 
revision processes ever employed for science assessments.
Starting with the First Assessment Report (FAR; IPCC, 1990a) the 
IPCC assessments have been structured into three Working Groups.

Working Group I (WGI) assesses the physical science basis of climate 
change, Working Group II (WGII) assesses associated impacts, 
vulnerability and adaptation options, and Working Group III (WGIII) 
assesses mitigation response options. Each report builds on the 
earlier comprehensive assessments by incorporating new research 
and updating previous findings. The volume of knowledge assessed 
and the cross-linkages between the three Working Groups have 
substantially increased over time.
As part of its Sixth Assessment Cycle, from 2015 to 2022, the IPCC 
is producing three Working Group Reports, three targeted Special 
Reports, a  Refinement to the 2006 IPCC Guidelines for National 
Greenhouse Gas Inventories, and a Synthesis Report. The AR6 Special 
Reports covered the topics of Global Warming of 1.5°C (SR1.5; 
IPCC, 2018), Climate Change and Land (SRCCL; IPCC, 2019a) and 
The Ocean and Cryosphere in a  Changing Climate (SROCC; IPCC,

2019b). The SR1.5 and SRCCL are the first IPCC reports jointly 
produced by all three Working Groups. This evolution towards a more 
integrated assessment reflects a  broader understanding of the 
interconnectedness of the multiple dimensions of climate change.
1.1.1 The AR6 WGI Report
The Sixth Assessment Report (AR6) of the IPCC marks more than 
30  years of global collaboration to describe and understand, 
through expert assessments, one of the defining challenges of the 
21st century: human-induced climate change. Since the inception of 
the IPCC in 1988, our understanding of the physical science basis 
of climate change has advanced markedly. The amount and quality of 
instrumental observations and information from paleoclimate archives 
have substantially increased. Understanding of individual physical, 
chemical and biological processes has improved. Climate model 
capabilities have been enhanced, through the more realistic treatment

of interactions among the components of the climate system, and 
improved representation of the physical processes, in line with the 
increased computational capacities of the world’s supercomputers.
This Report assesses both observed changes, and the components 
of these changes that are attributable to anthropogenic influence 
(i.e.,  human-induced), distinguishing between anthropogenic and naturally forced changes (Chapter 3, Sections 1.2.1.1 and 1.4.1, and 
the Cross-Working Group Box on Attribution). The core assessment 
conclusions from previous IPCC reports are confirmed or strengthened 
in this report, indicating the robustness of our understanding of the 
primary causes and consequences of anthropogenic climate change.
The WGI contribution to AR6 is focused on physical and biogeochemical 
climate science information, with particular emphasis on regional 
climate changes. These are relevant for mitigation, adaptation

and risk assessment in the context of complex and evolving policy 
settings, including the Paris Agreement, the global stocktake, 
the Sendai Framework and the Sustainable Development Goals 
(SDGs) Framework.
The core of this report consists of 12 chapters plus the Atlas 
(Figure  1.1), which can together be grouped into three categories 
(excluding this framing chapter):
Large-scale Information (Chapters 2, 3 and 4).  These chapters 
assess climate information from global to continental or ocean-basin 
scales. Chapter 2 presents an assessment of the changing state of 
the climate system, including the atmosphere, biosphere, ocean and 
cryosphere. Chapter 3 continues with an assessment of the human 
influence on this changing climate, covering the attribution of observed 
changes, and introducing the fitness-for-purpose approach for the 
evaluation of climate models used to conduct the attribution studies. 
Finally, Chapter 4 assesses climate change projections, from the near

Process Understanding Large-Scale Information Regional Information
Framing, Context, MethodsSummary for Policymakers 
Technical Summary
Interactive AtlasWGIWGII
SYRWGIII
Changing State of the 
Climate System
Human Influence on 
the Climate System
Future Global ClimateGlobal Carbon & other 
Biogeochemical 
Cycles
Short-Lived Climate 
Forcers
The Earth’s Energy 
Budget
Ocean, Cryosphere, 
and Sea Level ChangeWater Cycle ChangesLinking Global to 
Regional Climate
Weather and Climate 
Extreme Events
Climate Information 
for Risk Assessment
Regional Climate 
Change
12
3
410
11
12
Atlas5
6
7
8
9
Figure 1.1 | The structure of the AR6 WGI Report.  Shown are the three pillars of 
the AR6 WGI, its relation to the WGII and WGIII contributions, and the cross-working-
group AR6 Synthesis Report (SYR).

154
Chapter 1 Framing, Context, and Methods1to the long term, including climate change beyond 2100, as well as 
the potential for abrupt and ‘low-likelihood, high-impact’ outcomes.
Process Understanding (Chapters 5, 6, 7, 8 and 9). These five  
chapters provide end-to-end assessments of fundamental Earth 
system processes and components: the carbon budget and 
biogeochemical cycles (Chapter  5), short-lived climate forcers 
and their links to air quality (Chapter 6), the Earth’s energy budget 
and climate sensitivity (Chapter  7), the water cycle (Chapter  8), 
and the ocean, cryosphere and sea level changes (Chapter  9). All 
these chapters provide assessments of observed changes, including 
relevant paleoclimatic information and understanding of processes 
and mechanisms as well as projections and model evaluation.
Regional Information (Chapters  10, 11, 12 and Atlas). New 
knowledge on climate change at regional scales is reflected in this

report with four chapters covering regional information. Chapter 10 
provides a framework for assessment of regional climate information, 
including methods, physical processes, an assessment of observed 
changes at regional scales, and the performance of regional models. 
Chapter 11 addresses extreme weather and climate events, including 
temperature, precipitation, flooding, droughts and compound events. 
Chapter 12 provides a comprehensive, region-specific assessment of 
changing climatic conditions that may be hazardous or favourable 
(hence influencing climate risk) for various sectors to be assessed 
in WGII. Lastly, the Atlas assesses and synthesizes regional climate 
information from the whole report, focussing on the assessments of 
mean changes in different regions and on model assessments for 
the regions. It also introduces the online Interactive Atlas, a novel 
compendium of global and regional climate change observations and

projections. It includes a visualization tool, which combines various 
warming levels and scenarios on multiple scales of space and time.Embedded in the chapters are Cross-Chapter Boxes  that highlight 
cross-cutting issues. Each chapter also includes an Executive 
Summary (ES),  and several Frequently Asked Questions (FAQs) . 
To enhance traceability and reproducibility of report figures and 
tables, detailed information on the input data used to create them, 
as well as links to archived code, are provided in the Input Data 
Tables  in chapter Supplementary Material . Additional metadata 
on  the  model input datasets is provided via the report website 
(https://www.ipcc.ch/assessmen t-report/ar6/ ).
The AR6 WGI Report includes a  Summary for Policymakers (SPM)  
and a  Technical Summary (TS) . The integration among the three 
IPCC Working Groups is strengthened by the inclusion of the Cross-
Working-Group Glossary .
1.1.2 Rationale for the New AR6 WGI Structure and

Its Relation to the Previous AR5 WGI Report
The AR6 WGI report, as a result of its scoping process, is structured 
around topics such as large-scale information, process understanding 
and regional information (Figure 1.1). This represents a rearrangement 
relative to the structure of the WGI contribution to the IPCC Fifth 
Assessment Report (AR5; IPCC, 2013a), as summarized in Figure 1.2. 
The AR6 approach aims at a  greater visibility of key knowledge 
developments that are potentially relevant for policymakers, 
including climate change mitigation, regional adaptation planning 
based on a risk management framework, and the global stocktake.
Two key subjects presented separately in AR5, paleoclimate and model 
evaluation, are now distributed among multiple AR6 WGI chapters. 
Various other cross-cutting themes are also distributed throughout 
this Report. A summary of these themes and their integration across 
chapters is described in Table 1.1.

Table 1.1 | Cross-cutting themes in AR6 WGI, and the main chapters that deal with them.  Bold numbers in the table indicate the chapters that have extensive coverage.
Thematic Focus Main Chapters; Additional Chapters
Aerosols 2, 6, 7, 8, 9, 10, 11;  3, 4, Atlas
Atmospheric Circulation 3, 4, 8;  2, 5, 10, 11
Biosphere 2, 3, 5, 11, Cross-Chapter Box 5.1;  1, 4, 6, 8
Carbon Dioxide Removal (CDR) 4, 5; 8
Cities and Urban Aspects 10, 11, 12;  2, 8, 9, Atlas
Climate Services 12, Atlas, Cross-Chapter Box 12.2; 1, 10
Climatic Impact-Drivers 12, Annex VI;  1, 9, 10, 11, Atlas
CO 2 Concentration Levels 1, 2, 5, Cross-Chapter Box 1.1; 12, Atlas
Coronavirus Pandemic (COVID-19) Cross-Chapter Box 6.1; 1
Cryosphere 2, 3, 9; 1, 4, 8, 12, Atlas
Deep Uncertainty 9; 4, 7, 8, Cross-Chapter Box 11.2, Cross-Chapter Box 12.1
Detection and Attribution 3, 10, 11, Cross-Working Group Box: Attribution;  5, 6, 8, 9, 12, Atlas
Emergence 1, 10, 12; 8, 11

Extremes and Abrupt Change 11, 12;  1, 5, 7, 8, 9, 10, Atlas, Cross-Chapter Box 12.1
Global Warming Hiatus Cross-Chapter Box 3.1;  10, 11

155
Framing, Context, and Methods Chapter 11
1. Introduction
2. Observations: Atmosphe re & Surface
3. Observations: Ocean
4. Observations: Cryosphere
5. Information from Paleoclimate Archives
6. Carbon & Other Biogeochemical Cycles
7. Clouds & Aerosols 
8. Anthropogenic & Natural Radiative Forcing
9. Evaluation of Climate Models
10. Detection & Attribution of Climate Change
11. Near-Term Climate Change
13. Sea Level Chan ge
14. Climate Phenomena & Regional ClimateObservations
& Paleoclima te
Information
Process
Understanding
From Forcing to 
Attributio n of 
Climate Change
Future Climate 
Change & 
Predictability 
Integration
Annex I Atlas
Distributed over Chapters 1, 3, 9, 10,
11, Atlas; 5, 6, 7, 8  
Expande d in regional Chapters
10, 11, 12, AtlasFraming Chapter 1 
Process chapter 5 
Process Chapter 9 
AR5 WGI Chapter Related information in AR6 WGI 
12. Long-Term Climate ChangeAR5 WGI Cha pter 
categories 
Mostly large-scale Chapters 3, 4; 
(somme elements in process and

regional chapters)Mostly large-scale Chapter 2; 
(somme elements in process and 
regional chapters)
Distributed over Chapters 1, 2;
3, 5, 7, 8, 9, Atlas, Box 11.3
Reorganised in process Chapters 
(Aerosols in 6; Clouds in 8; Radiative 
forcing in 7)
Figure 1.2 | Main relations between AR5 WGI and AR6 WGI chapters.  The left-hand column shows the AR5 WGI chapter categories. The central column lists the AR5 
WGI chapters, with the colour code indicating their relation to the AR6 WGI structure shown in Figure 1.1: Large-Scale Information (purple), Process Understanding (gold), 
Regional Information (light blue) and Whole-Report Information (dark blue). AR5 WGI chapters depicted in white have their topics distributed over multiple AR6 WGI chapters 
and categories. The right-hand column explains where to find related information in the AR6 WGI report.Thematic Focus Main Chapters; Additional Chapters
Land Use 5; 2, 7, 8, 10, 11
Limits of Habitability 9, 12;  11

Low-Likelihood, High-Impact/High Warming 1, 4, 11; 7, 8, 9, 10, Cross-Chapter Box 1.1, Cross-Chapter Box 1.3, Cross-Chapter Box 4
Model Evaluation 1, 3, 9, 10, 11, Atlas; 5, 6, 8
Modes of Variability 1, 2, 3, 4, 8, 9, Annex IV; 7, 10, 11, 12, Atlas
Monsoons 8; 3, 4, 9, 10, 11, 12, Atlas
Natural Variability 1, 2, 3, 4, 9, 11;  5, 8, 10
Ocean 3, 5, 9;  1, 2, 4, 7, 12, Atlas
Paleoclimate 1, 2; 3, 5, 7, 8, 9, Atlas, Box 11.3
Polar Regions 9, 12, Atlas;  2, 3, 7, 8
Radiative Forcing 7; 1, 2, 6, 11
Regional Case Studies 10, 11, Atlas; 12, Box 8.1, Box 11.4, Cross-Chapter Box 12.2
Risk 1, 11, 12, Cross-Chapter Box 1.3; 4, 5, 9, Cross-Chapter Box 12.1
Sea Level 9, 12;  1, 2, 3, 4, 7, 8, 10, 11, Atlas
Short-Lived Climate Forcers (SLCFs) 6, 7; 1, 2, 4, Atlas
Solar Radiation Modification (SRM) 4, 5; 6, 8
Tipping Points 5, 8, 9; 4, 11, 12, Cross-Chapter Box 12.1
Values and Beliefs 1, 10; 12
Volcanic Forcing 2, 4, 7, 8;  1, 3, 5, 9, 10, Annex III
Water Cycle 8, 11;  2, 3, 10, Box 11.1

156
Chapter 1 Framing, Context, and Methods11.1.3 Integration of AR6 WGI Assessments 
With Other Working Groups
Integration of assessments across the chapters of the WGI Report, 
and with WGII and WGIII, occurs in a number of ways, including work 
on a  common Glossary, risk framework (Cross-Chapter Box  1.3), 
scenarios and projections of future large-scale changes, and the 
presentation of results at various global warming levels (Section 1.6).
Chapters 8 to 12, and the Atlas, cover topics also assessed by WGII 
in several areas, including regional climate information and climate-
related risks. This approach produces a more integrated assessment 
of impacts of climate change across Working Groups. In particular, 
Chapter 10 discusses the generation of regional climate information 
for users, the co-design of research with users, and the translation 
of information into the user context (in particular directed towards

WGII). Chapter 12 provides a direct bridge between physical climate 
information (climatic impact-drivers) and sectoral impacts and risk, 
following the chapter organization of the WGII Assessment. Notably, 
Cross-Chapter Box  12.1 draws a  connection to representative key 
risks and Reasons for Concern (RFC).
The science assessed in Chapters 2 to 7, such as the carbon budget, 
short-lived climate forcers (SLCFs) and emissions metrics, are topics 
in common with WGIII, and relevant for the mitigation of climate 
change. This includes a  consistent presentation of the concepts of 
carbon budget and net zero emissions targets within chapters, in 
order to support integration in the Synthesis Report. Emissions-driven 
emulators (simple climate models), summarized in Cross-Chapter Box 7.1, are used to approximate large-scale climate responses of 
complex Earth System Models (ESMs) and have been used as tools to 
explore the expected global surface air temperature (GSAT) response

to multiple scenarios consistent with those assessed in WGI for the 
classification of scenarios in WGIII. Chapter 6 provides information 
about the impact of climate change on global air pollution, relevant 
for WGII, including Cross-Chapter Box 6.1 on the implications of the 
recent coronavirus pandemic (COVID-19) for climate and air quality. 
Cross-Chapter Box  2.3 in Chapter  2  presents an integrated cross-
Working Group discussion of global temperature definitions, with 
implications for many aspects of climate change science.
In addition, Chapter 1 sets out a shared terminology on cross-cutting 
topics, including climate risk, attribution and storylines, as well as 
an introduction to emissions scenarios, global warming levels and 
cumulative carbon emissions as an overarching topic for integration 
across all three Working Groups.
All these integration efforts are aimed at enhancing the bridges 
and ‘handshakes’ among Working Groups, enabling the final cross-

Working Group exercise of producing the integrated Synthesis Report.
1.1.4 Chapter Preview
The main purposes of this chapter are: (i) to set the scene for the 
WGI Assessment and to place it in the context of ongoing global 
changes, international policy processes, the history of climate science 
and the evolution from previous IPCC assessments, including the 
Chapter 1: Framing, context, and methods Chapter 1: Quick guide
Boxes Cross-chapter boxes Cross-working 
group boxSection 1.1
Report and chapter 
overview
Section 1.2
Where we are 
nowSection 1.3
How we got 
here
Paleo, abrupt change and tipping points
 1.2.1 | 1.3.2 | 1.4.4  | FAQ 1.3History of climate science
1.3Global warming levels
1.6.2Climate models, CMIP and CORDEX
1.3.4 | 1.5.3 | 1.5.4
Regions
1.4.5
Scenarios and storylines
1.3.5 | 1.4.4 | 1.6.1 | CC Box 1.4
Variability, emergence and uncertainty
1.4.2 | 1.4.3 | FAQ 1.1 | FAQ 1.2Baseline periods
1.4.1 | CC Box 1.2
Section 1.4
Foundations 
and conceptsSection 1.5

Major developments 
since AR5Section 1.6
Dimensions of 
integration
Section 1.7
Final remarks
Box 1.1
Calibrated 
uncertainty 
languageBox 1.2
Special 
Reports 
key findingsBox 1.3
Emission 
metrics Box 1.4
Net zero, carbon 
dioxide removal 
and temperatureCC Box 1.1
Global 
stocktakeCC Box 1.2
Pre–1850 
temperaturesCC Box 1.3
Risk framingCC Box 1.4
Scenarios
AttributionChapter 1 introduces the WGI contribution to AR6, its framing and context, 
and the methods used in the report.Key topics and corresponding sub-sections
FAQs
Figure 1.3 | Visual guide to Chapter 1.

157
Framing, Context, and Methods Chapter 11Special Reports prepared as part of the Sixth Assessment Cycle; 
(ii) to describe key concepts and methods, relevant developments 
since AR5, and the modelling framework used in this Assessment; 
and (iii) together with the other chapters of this report, to provide 
context and support for the WGII and WGIII contributions to AR6, 
particularly on climate information to support mitigation, adaptation 
and risk management.
The chapter comprises seven sections (Figure  1.3). Section  1.2 describes 
the present state of Earth’s climate, in the context of reconstructed 
and observed long-term changes and variations caused by natural 
and anthropogenic factors. It also provides context for the present 
Assessment by describing recent changes in international climate 
change governance and fundamental scientific values. The evolution 
of knowledge about climate change and the development of earlier

IPCC assessments are presented in Section 1.3. Approaches, methods 
and key concepts of this Assessment are introduced in Section 1.4. 
New developments in observing networks, reanalyses, modelling 
capabilities and techniques since AR5 are discussed in Section 1.5. The 
three main ‘dimensions of integration’ across Working Groups in AR6, 
that is, emissions scenarios, global warming levels and cumulative 
carbon emissions, are described in Section 1.6. The Chapter closes 
with a discussion of opportunities and gaps in knowledge integration 
in Section 1.7.
1.2 Where We Are Now
The IPCC Sixth Assessment Cycle occurs in the context of increasingly 
apparent climatic changes observed across the physical climate 
system. Many of these changes can be attributed to anthropogenic 
influences, with impacts on natural and human systems. The AR6 also 
occurs in the context of efforts in international climate governance 
such as the Paris Agreement, which sets a  long-term goal to hold

the increase in global average temperature to ‘well below 2°C above 
pre-industrial levels, and to pursue efforts to limit the temperature 
increase to 1.5°C above pre-industrial levels, recognizing that this 
would significantly reduce the risks and impacts of climate change.’ 
This section summarizes key elements of the broader context 
surrounding the assessments made in the present report.
1.2.1 The Changing State of the 
Physical Climate System
The WGI contribution to AR5 (AR5 WGI; IPCC, 2013a) assessed 
that ‘warming of the climate system is unequivocal’, and that since 
the 1950s, many of the observed changes are unprecedented over 
decades to millennia. Changes are evident in all components  of 
the climate system: the atmosphere and the ocean have warmed, 
amounts of  snow and ice have diminished, sea level has risen, 
the ocean has acidified and its oxygen content has declined, and 
atmospheric  concentrations of greenhouse gases (GHGs) have

increased (IPCC, 2013b). This Report documents that, since the AR5, 
changes to the state of the physical and biogeochemical climate 
1 Note that GMST and GSAT are physically distinct but closely related quantities (Section 1.4.1 and Cross-Chapter Box 2.3).system have continued, and these are assessed in full in later 
chapters. Here, we summarize changes to a  set of key large-scale 
climate indicators over the modern era (1850 to present). We also 
discuss the changes in relation to the longer-term evolution of the 
climate. These ongoing changes throughout the climate system form 
a key part of the context of the present Report.
1.2.1.1 Recent Changes in Multiple Climate Indicators
The physical climate system comprises all processes that combine to 
form weather and climate. The early chapters of this report broadly 
organize their assessments according to overarching realms: the 
atmosphere, the biosphere, the cryosphere (surface areas covered

by frozen water, such as glaciers and ice sheets), and the ocean. 
Elsewhere in the report, and in previous IPCC assessments, the land is 
also used as an integrating realm that includes parts of the biosphere 
and the cryosphere. These overarching realms have been studied 
and measured in increasing detail by scientists, institutions and 
the general public since the 18th  century, throughout the era of 
instrumental observation (Section 1.3). Today, observations include 
those taken by numerous land surface stations, ocean surface 
measurements from ships and buoys, underwater instrumentation, 
satellite and surface-based remote sensing, and in situ atmospheric 
measurements from aeroplanes and balloons. These instrumental 
observations are combined with paleoclimate reconstructions and 
historical documentations to produce a  highly detailed picture 
of the past and present state of the whole climate system, and to

allow assessments about rates of change across the different realms 
(Chapter 2 and Section 1.5).
Figure  1.4 documents that the climate system is undergoing 
a  comprehensive set of changes. It shows a  selection of key 
indicators of change through the instrumental era that are 
assessed and presented in the subsequent chapters of this report. 
Annual mean values are shown as stripes, with colours indicating 
their value. The transitions from one colour to another over 
time illustrate how conditions are shifting in all components of 
the climate system. For these particular indicators, the observed 
changes go beyond the yearly and decadal variability of the 
climate system. In this Report, this is termed an ‘emergence’ of the 
climate signal (Section 1.4.2 and FAQ 1.2).
Warming of the climate system is most commonly presented 
through the observed increase in global mean surface temperature 
(GMST). Taking a  baseline of 1850–1900, GMST change until

present (2011–2020) is 1.09°C [0.95 to 1.20] °C (Section  2.3 and 
Cross-Chapter Box 2.3). This evolving change has been documented 
in previous assessment reports, with each reporting a  higher 
total global temperature change (Section  1.3 and Cross-Chapter 
Box  1.2). The  total change in global surface air temperature 
(GSAT) (Section  1.4.1 and Cross-Chapter Box  2.3) attributable 
to anthropogenic activities is assessed to be consistent with the 
observed change in GSAT (Section 3.3).1

158
Chapter 1 Framing, Context, and Methods1
Similarly, atmospheric concentrations of a  range of GHGs are 
increasing. Carbon dioxide (CO 2, shown in Figure 1.4 and Figure 1.5a, 
found in AR5 and earlier reports to be the current strongest driver of 
anthropogenic climate change), has increased from 285.5 ± 2.1 ppm 
in 1850 to 409.9  ±  0.4  ppm in 2019; concentrations of methane 
(CH 4), and nitrous oxide (N 2O) have increased as well (Sections 2.2 
and 5.2, and Annex V). These observed changes are assessed 
to be in line with known anthropogenic and natural emissions, 
when accounting for observed and inferred uptake by land, ocean 
and biosphere respectively (Section  5.2), and are a  key source of 
anthropogenic changes to the global energy balance (or radiative 
forcing; Sections 2.2 and 7.3).
The hydrological (or water) cycle is also changing and is assessed 
to be intensifying, through a higher exchange of water between the

surface and the atmosphere (Sections  2.3 and 8.3). The resulting 
regional patterns of changes to precipitation are, however, different 
from surface temperature change, and interannual variability is larger, 
as illustrated in Figure 1.4. Annual land area mean precipitation in 
the Northern Hemisphere temperate regions has increased, while the 
subtropical dry regions have experienced a decrease in precipitation 
in recent decades (Section 2.3).
The cryosphere is undergoing rapid changes, with increased melting 
and loss of frozen water mass in most regions. This includes all 
frozen parts of the globe, such as terrestrial snow, permafrost, sea 
ice, glaciers, freshwater ice, solid precipitation, and the ice sheets 
covering Greenland and Antarctica (Chapter 9; SROCC, IPCC, 2019b). 
Figure 1.4 illustrates how, globally, glaciers have been increasingly

losing mass for the last fifty years. The total glacier mass in the most recent decade (2010–2019) was the lowest since the beginning of 
the 20th century (Sections 2.3 and 9.5).
The global ocean has warmed unabatedly since at least 1970 
(Sections 1.3, 2.3 and 9.2; SROCC, IPCC, 2019b). Figure 1.4 shows 
how the averaged ocean heat content is steadily increasing, with 
a total increase of [0.28 to 0.55] yottajoule (YJ; 1024 joule) between 
1971 and 2018 (Section 9.2). In response to this ocean warming, as 
well as to the loss of mass from glaciers and ice sheets, the global 
mean sea level (GMSL) has risen by 0.20 [0.15 to 0.25] metres 
between 1900 and 2018. GMSL rise has accelerated since the late 
1960s (see Section 9.6).
Overall, the changes in these selected climatic indicators have progressed 
beyond the range of natural year-to-year variability (Chapters  2, 3, 
8 and 9, and Sections 1.2.1.2 and 1.4.2). The indicators presented in

Figure 1.4 document a broad set of concurrent and emerging changes 
across the physical climate system. All indicators shown here, along with 
many others, are further presented in the coming chapters, together 
with a rigorous assessment of the supporting scientific literature. Later 
chapters (Chapters 10, 11, 12 and Atlas) present similar assessments 
at the regional level, where observed changes do not always align with 
the global mean picture shown here.
1.2.1.2 Long-Term Perspectives on 
Anthropogenic Climate Change
Paleoclimate archives (e.g., ice cores, corals, marine and lake sediments, 
speleothems, tree rings, borehole temperatures, soils) permit the 
reconstruction of climatic conditions before the instrumental era. 
Annual averages
Grey indicates that data are not available
Figure  1.4 |  Changes are occurring throughout the climate system.  Left: Main realms of the climate system: atmosphere, biosphere, cryosphere and ocean.

Right:  Six key indicators of ongoing changes since 1850, or the start of the observational or assessed record, through 2018. Each stripe indicates the global (except for 
precipitation which shows two latitude band means), annual mean anomaly for a single ye ar, relative to a multi-year baseline (except for CO2 concentration and glacier mass 
loss, which are absolute values). Grey indicates that data are not available. Datasets and baselines used are: (i) CO2: Antarctic ice cores (Lüthi et al., 2008; Bereiter et al., 
2015) and direct air measurements (Tans and Keeling, 2020) (see Figure 1.5 for details); (ii) precipitation: Global Precipitation Climatology Centre (GPCC) V8 (updated from 
Becker et al., 2013), baseline 1961–1990 using land areas only with latitude bands 33°N–66°N and 15°S–30°S; (iii) glacier mass loss: Zemp et al. (2019); (iv) global surface

air temperature (GMST): HadCRUT5 (Morice et al., 2021), baseline 1961–1990; (v) sea level change: (Dangendorf et al., 2019), baseline 1900–1929; (vi) ocean heat content 
(model–observation hybrid): Zanna et al. (2019), baseline 1961–1990. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

159
LIGLGMMHLIGLGMMHLIGLGMMH
-1012345
m-8-6-4-20246810
°C1002003004005006007008009001000150020002500
2281-230001 3
SSP5-8.5SSP1-2.6
SSP5-8.5SSP3-7.0SSP2-4.5SSP1-2.6SSP1-1.9
2081-21001234SSP5-8.5SSP3-7.0SSP2-4.5SSP1-2.6SSP1-1.9
-1012345
1850 1900 1950 2000
Year CE1850 1900 1950 20001850 1900 1950 2000
800 600 400 200 0
Time before 1850
(thousands of years)-150-120-90-60-3003060m-8-6-4-20246810°C
800 600 400 200 0150020002500
1002003004005006007008009001000 ppm
(c) Global mean sea level  (b) Global surface temperature(a) Atmospheric CO2 concentration   Climate Model Projections
2081-2100               2281-2300Data since 1850
 
(relative to 1850-1900)
(relative to.1850-1900)Paleoclimatic data of the past 800,000 years 
 ppm1850-1900 1995-2014
1850-1900 1995-2014

Figure 1.5 | Long-term context of anthropogenic climate change based on selected paleoclimatic reconstructions over the past 800,000 years (800 kyr) 
for three key indicators: atmospheric CO 2 concentrations, global mean surface temperature (GMST), and global mean sea level (GMSL).

160
This establishes an essential long-term context for the climate change 
of the past 150  years and the projected changes in the 21st  century 
and beyond (Chapter  3; IPCC, 2013a; Masson-Delmotte et al., 2013). 
Figure 1.5 shows reconstructions of three key indicators of climate change 
over the past 800,000 years (800 kyr)2 – atmospheric CO 2 concentrations, 
global mean surface temperature (GMST) and global mean sea level 
(GMSL) – comprising at least eight complete glacial–interglacial cycles 
(EPICA Community Members, 2004; Jouzel et al., 2007), which are largely 
driven by oscillations in the Earth’s orbit and consequent feedbacks 
on multi-millennial time scales (Berger, 1978; Laskar et al., 1993). The 
dominant cycles  – recurring approximately every 100 kyr  – can be 
found imprinted in the natural variations of these three key indicators. 
Before industrialisation, atmospheric CO 2 concentrations varied between

174  ppm and 300  ppm, as measured directly in air trapped in ice at 
Dome Concordia, Antarctica (Bereiter et al., 2015; Nehrbass-Ahles et al., 
2020). Relative to 1850–1900 CE, the reconstructed GMST changed in 
the range of –6°C to +1°C across these glacial–interglacial cycles (see 
Chapter 2, Section 2.3.1 for an assessment of different paleo-reference 
periods). GMSL varied between about –130 m during the coldest glacial 
maxima and +5 to +25 m  during the warmest interglacial periods 
(Chapter 2; Spratt and Lisiecki, 2016). They represent the amplitudes of 
natural, global-scale climate variations over the last 800 kyr prior to the 
influence of human activity. Further climate information from a variety of 
paleoclimatic archives is assessed in Chapters 2, 5, 7 and 9.
Paleoclimatic information also provides a  long-term perspective 
on rates of change of these three key indicators. In high-resolution 
reconstructions from polor ice cores, the rate of increase in

atmospheric CO 2 observed over 1919–2019 CE is one order of 
magnitude higher than the fastest CO 2 fluctuations documented 
during the Last Glacial Maximum and the last deglacial transition 
(Marcott et al., 2014, see Chapter 2, Section 2.2.3.2.1). Current multi-
decadal GMST exhibit a higher rate of increase than over the past 
2  kyr (Section  2.3.1.1.2; PAGES 2k Consortium, 2019), and in the 
20th  century GMSL rise was faster than during any other century 
over the past 3 kyr (Section 2.3.3.3).
Paleoclimate reconstructions also shed light on the causes of these 
variations, revealing processes that need to be considered when 
projecting climate change. The paleorecords show that sustained 
2 As old as the longest continuous climate records, which are based on the ice core from EPICA Dome Concordia, Antarctica. Polar ice cores are the only paleoclimatic archive providing direct

information on past greenhouse gas concentrations.changes in global mean temperature of a few degrees Celsius are 
associated with increases in sea level of several tens of metres 
(Figure  1.5). During two extended warm periods (interglacials) of 
the last 800 kyr, sea level is estimated to have been at least six 
metres higher than today (Chapter  2; Dutton et al., 2015). During 
the last interglacial, sustained warmer temperatures in Greenland 
preceded the peak of sea level rise (Figure 5.15 in Masson-Delmotte 
et al., 2013). The paleoclimate record therefore provides substantial 
evidence directly linking warmer GMST to substantially higher GMSL.
GMST will remain above present-day levels for many centuries even 
if net CO 2 emissions are reduced to zero, as shown in simulations 
with coupled climate models (Section  4.7.1; Plattner et al., 2008; 
Section 12.5.3 in Collins et al., 2013; Zickfeld et al., 2013; MacDougall

et al., 2020). Such persistent warm conditions in the atmosphere 
represent a  multi-century commitment to long-term sea level rise, 
summer sea ice reduction in the Arctic, substantial ice-sheet melting, 
potential ice-sheet collapse, and many other consequences in all 
components of the climate system (Section 9.4 and Figure 1.5; Clark 
et al., 2016; Pfister and Stocker, 2016; H. Fischer et al., 2018).
Paleoclimate records also show centennial- to millennial-scale 
variations, particularly during the ice ages, which indicate rapid or 
abrupt changes of the Atlantic Meridional Overturning Circulation 
(AMOC; Section  9.2.3.1) and the occurrence of a ‘bipolar seesaw’ 
(opposite-phase surface temperature changes in both hemispheres; 
Section  2.3.3.4.1; Stocker and Johnsen, 2003; EPICA Community 
Members, 2006; WAIS Divide Project Members et al., 2015; Lynch-
Stieglitz, 2017; Pedro et al., 2018; Weijer et al., 2019). This process

suggests that instabilities and irreversible changes could be triggered 
if critical thresholds are passed (Section  1.4.4.3). Several other 
processes involving instabilities are identified in climate models 
(Drijfhout et al., 2015), some of which may now be close to critical 
thresholds (Section 1.4.4.3; see also Chapters 5, 8 and 9 regarding 
tipping points; Joughin et al., 2014).
Based on Figure 1.5, the reconstructed, observed and projected ranges 
of changes in the three key indicators can be compared. By the first 
decade of the 20th  century, atmospheric CO 2 concentrations had 
already moved outside the reconstructed range of natural variation Figure  1.5 (continued): (a) Measurements of CO 2 in air enclosed in Antarctic ice cores (Lüthi et al. , 2008; Bereiter et al., 2015 [a compilation]; uncertainty 
±1.3 ppm; see Sections 2.2.3 and 5.1.2 for an assessment) and direct air measurements  (Tans and Keeling, 2020; uncertainty ±0.12 ppm). Projected CO 2 concentrations

for five Shared Socio-economic Pathways (SSP) scenarios are indicated by dots on the right-hand side of each panel (grey background; (Meinshausen et al., 2020; SSPs are 
described in Section 1.6). (b) Reconstruction of GMST from marine paleoclimate proxies (light-grey line: Snyder (2016); dark grey line: Hansen et al. (2013); see Section 2.3.1 
for an assessment). Observed and reconstructed temperature changes since 1850 are the AR6 assessed mean (referenced to 1850–1900; Box TS.3; 2.3.1.1); dots/whiskers 
on the right-hand panels (grey background) indicate the projected mean and ranges of warming derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP-
based (2081–2100) and Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC7; 2300) simulations (Tables 4.5 and 4.9). (c) Sea level changes

reconstructed from a  stack of oxygen isotope measurements on seven ocean sediment cores (Spratt and Lisiecki, 2016; see Chapter  2, Section  2.3.3.3 and Chapter  9, 
Section 9.6.2 for an assessment). The sea level record from 1850–1900 is from Kopp et al. (2016), while the 20th century record is an updated ensemble estimate of GMSL 
change (Palmer et al., 2021; Sections 2.3.3.3 and 9.6.1.1). Dots/whiskers on the right-hand panels of the figure (grey background) indicate the projected median and ranges 
derived from SSP-based simulations (2081–2100: Table 9.9; 2300: Section 9.6.3.5). Best estimates (dots) and uncertainties (whiskers), as assessed in Chapter 2, are included 
in the left and middle panels for each of the three indicators and selected paleo-reference periods used in this report (CO 2: Table 2.1; GMST: Section 2.3.1.1 and Cross-Chapter

Box 2.3, Table 1; GMSL: Sections 2.3.3.3 and 9.6.2. See also Cross-Chapter Box 2.1). Selected paleo-reference periods: LIG – Last Interglacial; LGM – Last Glacial Maximum; 
MH – mid-Holocene (Cross-Chapter Box 2.1, Table 1). The non-labelled best estimate in panel (c) corresponds to the sea level high-stand during Marine Isotope Stage 11, about 
410 ka (410,000 years ago; Section 9.6.2). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

161
Framing, Context, and Methods Chapter 11over the past 800 kyr. On the other hand, GMST and GMSL were higher 
than today during several interglacials of that period (Sections 2.3.1 
and 2.3.3, and Figure 2.34). Projections for the end of the 21st century, 
however, show that GMST will have moved outside of its natural range 
within the next few decades, except for the strong mitigation scenarios 
(Section  1.6). There is a  risk that GMSL may potentially leave the 
reconstructed range of natural variations over the next few millennia 
(Section 9.6.3.5; Clark et al., 2016; SROCC, IPCC, 2019b). In addition, 
abrupt changes can not be excluded (Section 1.4.4.3).
An important time period in the assessment of anthropogenic climate 
change is the last 2 kyr. Since AR5, new global datasets have been 
produced that aggregate aggregating local and regional paleorecords 
(PAGES 2k Consortium, 2013, 2017, 2019; McGregor et al., 2015;

Tierney et al., 2015; Abram et al., 2016; Hakim et al., 2016; Steiger 
et al., 2018; Brönnimann et al., 2019b). Before the global warming 
that began around the mid-19th century (Abram et al., 2016), a slow 
cooling in the Northern Hemisphere from roughly 1450–1850 CE is 
consistently recorded in paleoclimate archives (PAGES 2k Consortium, 
2013; McGregor et al., 2015). While this cooling, primarily driven by 
an increased number of volcanic eruptions (Section 3.3.1; PAGES 2k 
Consortium, 2013; Owens et al., 2017; Brönnimann et al., 2019b), 
shows regional differences, the subsequent warming over the past 
150 years exhibits a global coherence that is unprecedented in the 
last 2 kyr (Neukom et al., 2019).
The rate, scale and magnitude of anthropogenic changes in the 
climate system since the mid-20th century suggested the definition 
of a new geological epoch: the Anthropocene (Crutzen and Stoermer, 
2000; Steffen et al., 2007), referring to an era in which human

activity is altering major components of the Earth system and leaving 
measurable imprints that will remain in the permanent geological 
record (Figure  1.5; IPCC, 2018). These alterations include not only 
climate change itself, but also chemical and biological changes in 
the Earth system such as rapid ocean acidification due to uptake 
of anthropogenic CO 2, massive destruction of tropical forests, 
a  worldwide loss of biodiversity and the sixth mass extinction of 
species (Hoegh-Guldberg and Bruno, 2010; Ceballos et al., 2017; 
IPBES, 2019). According to the key messages of the last global 
assessment of the Intergovernmental Science-Policy Platform on 
Biodiversity and Ecosystem Services (IPBES, 2019), climate change is 
a ‘direct driver that is increasingly exacerbating the impact of other 
drivers on nature and human well-being’, and ‘the adverse impacts 
of climate change on biodiversity are projected to increase with 
increasing warming.’

1.2.2 The Policy and Governance Context
The contexts of both policymaking and societal understanding about 
climate change have evolved since AR5 was published (2013–2014). 
Increasing recognition of the urgency of the climate change threat, 
along with still-rising emissions and unresolved issues of mitigation 
and adaptation, including aspects of sustainable development, 
poverty eradication and equity, have led to new policy efforts. This 
section summarizes these contextual developments and how they 
have shaped, and been used during the preparation of this Report.1.2.2.1 IPCC reports and the UN Framework Convention on 
Climate Change (UNFCCC)
The IPCC First Assessment Report (FAR, IPCC, 1990a) provided the 
scientific background for the establishment of the UNFCCC (UNFCCC, 
1992), which committed parties to negotiate ways to ‘prevent 
dangerous anthropogenic interference with the climate system’ (the 
ultimate objective of the UNFCCC). The Second Assessment Report

(SAR, IPCC, 1996) informed governments in negotiating the Kyoto 
Protocol (1997), the first major agreement focusing on mitigation 
under the UNFCCC. The Third Assessment report (TAR, IPCC, 
2001a) highlighted the impacts of climate change and the need for 
adaptation, and introduced the treatment of new topics such as policy 
and governance in IPCC reports. The Fourth and Fifth Assessment 
Reports (AR4, IPCC, 2007a; AR5, IPCC, 2013a) provided the scientific 
background for the second major agreement under the UNFCCC: the 
Paris Agreement (2015), which entered into force in 2016.
1.2.2.2 The Paris Agreement (PA)
Parties to the PA commit to the goal of limiting global average 
temperature increase to ‘well below 2°C above pre-industrial levels, 
and to pursue efforts to limit the temperature increase to 1.5°C 
in order to significantly reduce the risks and impacts of climate 
change’. In AR6, as in many previous IPCC reports, observations and

projections of changes in global temperature are expressed relative 
to 1850–1900 as an approximation for pre-industrial levels (Cross-
Chapter Box 1.2).
The PA further addresses mitigation (Article  4) and adaptation to 
climate change (Article 7), as well as loss and damage (Article 8), 
through the mechanisms of finance (Article  9), technology 
development and transfer (Article 10), capacity-building (Article 11) 
and education (Article 12). To reach its long-term temperature goal, 
the PA recommends ‘achieving a  balance between anthropogenic 
emissions by sources and removals by sinks of greenhouse gases in 
the second half of this century’, a state commonly described as ‘net 
zero’ emissions (Article 4) (Section 1.6 and Box 1.4). Each Party to the 
PA is required to submit a Nationally Determined Contribution (NDC) 
and pursue, on a voluntary basis, domestic mitigation measures with 
the aim of achieving the objectives of its NDC (Article 4).

Numerous studies of the NDCs submitted since adoption of the PA 
in 2015 (Fawcett et al., 2015; UNFCCC, 2015, 2016; Lomborg, 2016; 
Rogelj et al., 2016, 2017; Benveniste et al., 2018; Gütschow et al., 
2018; UNEP , 2019) conclude that they are insufficient to meet the 
Paris temperature goal. In the present IPCC Sixth Assessment Cycle, 
a  Special Report on Global Warming of 1.5°C (SR1.5, IPCC, 2018) 
found, with high agreement , that current NDCs ‘are not in line with 
pathways that limit warming to 1.5°C by the end of the century.’ The 
PA includes a ratcheting mechanism designed to increase the ambition 
of voluntary national pledges over time. Under this mechanism, NDCs 
will be communicated or updated every five years. Each successive 
NDC will represent a ‘progression beyond’ the ‘then current’ NDC and 
reflect the ‘highest possible ambition’ (Article 4). These updates will 
be informed by a five-yearly periodic review including the Structured

Expert Dialogue (SED), as well as a  ‘global stocktake’, to assess

162
Chapter 1 Framing, Context, and Methods1collective progress toward achieving the PA long-term goals. These 
processes will rely upon the assessments prepared during the IPCC 
Sixth Assessment Cycle (e.g.,  Cross-Chapter Box  1.1; Schleussner 
et al., 2016b).
1.2.2.3 The Structured Expert Dialogue (SED)
Since AR5, the formal dialogue between the scientific and policy   
communities has been strengthened through a  new science–  
policy interface, the Structured Expert Dialogue (SED). The SED was 
established by UNFCCC to support the work of its two subsidiary 
bodies, the Subsidiary Body for Scientific and Technological Advice 
(SBSTA) and the Subsidiary Body for Implementation (SBI). The first 
SED aimed to ‘ensure the scientific integrity of the first periodic review’ 
of the UNFCCC, the 2013–2015 review. The Mandate of the periodic 
review is to ‘assess the adequacy of the long-term (temperature) 
goal in light of the ultimate objective of the convention’ and the

‘overall progress made towards achieving the long-term global goal, 
including a consideration of the implementation of the commitments 
under the Convention.’The SED of the first periodic review (2013–2015) provided an 
important opportunity for face-to-face dialogue between decision 
makers and experts on review themes, based on ‘the best available 
scientific knowledge, including the assessment reports of the IPCC.’ 
That SED was instrumental in informing the long-term global goal of 
the PA and in providing the scientific argument for the consideration 
of limiting warming to 1.5°C warming (UNFCCC, 2015; Fischlin, 
2017). The SED of the second periodic review, initiated in the second 
half of 2020, focuses on, among other things, ‘enhancing Parties’ 
understanding of the long-term global  goal and the scenarios 
towards achieving it in the light of the ultimate objective of the 
Convention’. The second SED provides a  formal venue for the

scientific and the policy communities to discuss the requirements 
and benchmarks to achieve the ‘long-term temperature goal’ (LTTG) 
of 1.5°C and well below 2°C global warming. The discussions also 
concern the associated timing of net zero emissions targets and the 
different interpretations of the PA LTTG, including the possibility 
of overshooting the 1.5° C warming level before returning to it by 
means of negative emissions (e.g.,  Section  1.6; Schleussner and 
Fyson, 2020). The second periodic review is planned to continue until 
November 2022 and its focus includes the review of the progress 
made since the first review, while minimising ‘possible overlaps’ and 
profiting from ‘synergies with the global stocktake’.
Cross-Chapter Box 1.1 | The WGI Contribution to AR6 and Its Potential Relevance for the 
Global Stocktake
Contributing Authors:  Malte Meinshausen (Australia/Germany), Gian-Kasper Plattner (Switzerland), Aïda Diongue-Niang (Senegal),

Francisco J. Doblas-Reyes (Spain), David Frame (New Zealand), Nathan P . Gillett (Canada), Helene T. Hewitt (United Kingdom), 
Richard G. Jones (United Kingdom), Hong Liao (China), Jochem Marotzke (Germany), James Renwick (New Zealand), Joeri Rogelj 
(United Kingdom, Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Claudia Tebaldi (United States of America), 
Blair Trewin (Australia)
The global stocktake under the Paris Agreement (PA) evaluates the collective progress of countries’ actions towards 
attaining the Agreement’s purpose and long-term goals every five years.  The first global stocktake is due in 2023, and then 
every five years thereafter, unless otherwise decided by the Conference of the Parties. The purpose and long-term goals of the PA 
are captured inter alia in Article 2: to ‘strengthen the global response to the threat of climate change, in the context of sustainable

development and efforts to eradicate poverty, including by’: mitigation3 specifically, ‘holding the increase in the global average 
temperature to well below 2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C above 
pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change’;  adaptation , that is, 
‘increasing the ability to adapt to the adverse impacts of climate change and foster climate resilience and low greenhouse gas (GHG) 
emissions development, in a manner that does not threaten food production’; and means of implementation and support , that is, 
‘making finance flows consistent with a pathway towards low GHG emissions and climate-resilient development.’
The PA further specifies that the stocktake shall be undertaken in a ‘comprehensive and facilitative manner, considering mitigation,

adaptation and the means of implementation and support, and in the light of equity and the best available science’ (Article 14).
3 The labels of ‘mitigation’, ‘adaptation’ and ‘means of implementation and support’ are provided here for guidance only, with no presumption about the actual legal content of the paragraphs and 
to what extent they encompass  mitigation, adaptation and means of implementation in its entirety.

163
Framing, Context, and Methods Chapter 11Cross-Chapter Box 1.1 (continued)
The sources of input envisaged for the global stocktake include the ‘latest reports of the Intergovernmental Panel on Climate Change’ 
as a central source of information.4 The global stocktake is one of the key formal avenues for scientific inputs into the UNFCCC and PA 
negotiation process alongside, for example, the Structured Expert Dialogues (SEDs) under the UNFCCC (Section 1.2.2).5
The WGI Assessment provides a  wide range of information with potential relevance for the global stocktake, 
complementing the IPCC AR6 Special Reports, the contributions from WGII and WGIII and the Synthesis Report. This 
includes the state of GHG emissions and concentrations, the current state of the climate, projected long-term warming levels under 
different scenarios, near-term projections, the attribution of extreme events, and remaining carbon budgets. Cross-Chapter Box 1.1,

Table 1 provides pointers to the in-depth material that WGI has assessed and that may be relevant for the global stocktake.
The following tabular overview of potentially relevant information from the WGI contribution for the global stocktake is 
structured into three sections: the current state of the climate, the long-term future, and the near-term. These sections and 
their order align with the three questions of the Talanoa dialogue, launched during COP23, based on the Pacific concept of talanoa6: 
‘Where are we’, ‘Where do we want to go’  and ‘ How do we get there?’
Cross-Chapter Box 1.1, Table 1 | WGI assessment findings and their potential relevance for the global stocktake.  The table combines information 
assessed in this report that could potentially be relevant for the global stocktake process. Section 1 focuses on the current state of the climate and its recent

past. Section 2 focuses on long-term projections in the context of the PA’s 1.5°C and 2.0°C goals and on progress towards net zero greenhouse gas emissions. 
Section 3 considers challenges and key insights for mitigation and adaptation in the near term from a WGI perspective. Further information on potential relevance of 
the aspects listed here in terms of, for example, impacts and socio-economic aspects can be found in the WGII and WGIII reports
Section 1: State of the Climate – ‘Where are we?’
WGI Assessment to inform about past changes in the climate system, current climate and committed changes
Question Chapter/Section Potential Relevance and Explanatory Remarks
How much warming have 
we observed in global mean 
surface air temperatures?Cross-Chapter Box 1.2; 
Cross-Chapter Box 2.3;
2.3.1.1, especially 2.3.1.1.3Knowledge about the current warming relative to pre-industrial levels allows us to quantify the remaining

distance to the PA goal of keeping global mean temperatures well below 2°C above pre-industrial levels or 
pursue best efforts to limit warming to 1.5°C above pre-industrial levels. Many of the Report’s findings are 
provided against a proxy for pre-industrial temperature levels, with Cross-Chapter Box 1.2 examining the 
difference between pre-industrial levels and the 1850–1900 period.
How much has 
the ocean warmed?2.3.3.1; 7.2; Box 7.2; 
9.2.1.1; Box 9.1 A warming ocean can affect marine life (e.g., coral bleaching) and is also one of the main contributors 
to long-term sea level rise (thermal expansion). Marine heatwaves can accentuate the impacts of ocean 
warming on marine ecosystems. Also, knowing the heat uptake of the ocean helps to better understand 
the response of the climate system and hence helps to project future warming.
How much have land 
areas warmed and how 
has precipitation changed?2.3.4; 5.4.3; 5.4.8;

8.2.1; 8.2.3; 8.5.1A stronger than global-average warming over land, combined with changing precipitation patterns,  
and/or increased aridity in some regions (like the Mediterranean) can severely affect land ecosystems 
and species distributions, the terrestrial carbon cycle, and food production systems. Amplified warming 
in the Arctic can enhance permafrost thawing, which in turn can result in overall stronger anthropogenic 
warming (a positive feedback loop). Intensification of heavy precipitation events can cause more severe 
impacts related to flooding.
How did the sea ice 
area change in recent 
decades in both the 
Arctic and Antarctic?2.3.2.1.1; 2.3.2.1.2;
9.3; Cross-Chapter 
Box 10.1; 12.4.9Sea ice area influences mass and energy (ice albedo, heat and momentum) exchange between the 
atmosphere and the ocean, and its changes in turn impact polar life, adjacent land and ice masses and

complex dynamical flows in the atmosphere. The loss of a year-round sea ice cover in the Arctic can severely 
impact Arctic ecosystems, affect the livelihood of First Nations in the Arctic, and amplify Arctic warming 
with potential consequences for the warming of the surrounding permafrost regions and ice sheets.
How much have atmospheric 
CO 2 and other GHG 
concentrations increased?2.2.3; 2.2.4; 5.1.1; 
5.2.2; 5.2.3; 5.2.4The main human influence on the climate is via combustion of fossil fuels and CO 2 emissions related 
to land-use change: the principal causes of increased CO 2 concentrations since the pre-industrial period. 
Historical observations indicate that current atmospheric concentrations are unprecedented within at least 
the last 800 kyr. An understanding of historical fossil fuel emissions and carbon cycle interactions, as well 
as methane (CH 4) and nitrous oxide (N 2O) sinks and sources, are crucial for better estimates of future GHG

emissions compatible with the PA’s long-term goals.
4 Paragraph 37b in 19/CMA.1 in FCCC/PA/CMA/2018/3/Add.2, pursuant decision 1/CP .21, paragraph 99 of the adoption of the PA in FCCC/CP/2015/10/Add.1, available at: https://unfccc.int/
documents/193408 .
5 Decision 5/CP .25, available at: https://unfccc.int/sites/default/files/resource/cp2 019_13a01E.pdf .
6 Decision 1/CP .23, in FCCC/CP/2017/L.13, available at https://unfccc.int/resource/docs/2017/cop 23/eng/l13.pdf .

164
Chapter 1 Framing, Context, and Methods1Cross-Chapter Box 1.1 (continued)
How much did sea level 
rise in past centuries and 
how large is the long-
term commitment?2.3.3.3;
9.6.1; 9.6.2; FAQ 9.1; 
Box 9.1; 9.6.3; 9.6.4Sea level rise is a comparatively slow consequence of a warming world. Historical warming committed 
the world already to long-term sea level rise that is not reversed in even the lowest emissions scenarios 
(such as 1.5°C), which come with a commitment to a multi-metre sea level rise. Regional sea level change 
near coastlines differs from global mean sea level change due to vertical land movement, ice mass 
changes and ocean dynamical changes.
How much has the ocean 
acidified and how much

oxygen has it lost?2.3.4.3; 2.3.4.2; 5.3Ocean acidification is affecting marine life, especially organisms that build calciferous shells and structures 
(e.g., coral reefs). Together with less oxygen in upper ocean waters and increasingly widespread oxygen 
minimum zones, and in addition to ocean warming, this poses adaptation challenges for coastal and marine 
ecosystems and their services, including seafood supply.
How much of the observed 
warming was due to 
anthropogenic influences?3.3.1To monitor progress toward the PA’s long-term goals it is important to know how much of the observed 
warming is due to human activities. Chapter 3 assesses human-induced warming in global mean near-
surface air temperature for the decade 2010–2019, relative to 1850–1900 with associated uncertainties, 
based on detection and attribution studies. This estimate can be compared with observed estimates of

warming for the same decade reported in Chapter 2, and is typically used to calculate carbon budgets 
consistent with remaining below a particular temperature threshold.
anthropogenic influence 
changed other aspects of the 
climate system?3.3.2; 3.3.3; 3.4; 3.5; 3.6; 
3.7; 8; 10.4; 12Climate change impacts are driven by changes in many aspects of the climate system, including changes 
in the water cycle, atmospheric circulation, ocean, cryosphere, biosphere and modes of variability. To better 
plan climate change adaptation it is relevant to know which observed changes have been driven by 
human influence.
How much are 
anthropogenic emissions 
contributing to changes in 
the severity and frequency 
of extreme events?1.5; Cross-Chapter Box 1.3; 
Cross-Chapter Box 3.2; 9.6.4; 
11.3–11.8; 12.3Adaptation challenges are often accentuated in the face of extreme events, including floods, droughts,

bushfires and tropical cyclones. For agricultural management, infrastructure planning, and designing for 
climate resilience it is relevant to know whether extreme events will become more frequent in the near 
future. In that respect it is important to understand whether observed extreme events are part of a natural 
background variability or caused by past anthropogenic emissions. This attribution of extreme events is 
therefore key to understanding current events, as well as to better project the future evolution of these 
events, such as temperature extremes, heavy precipitation, floods, droughts, extreme storms and compound 
events, and extreme sea level. Also, loss and damage events are often related to extreme events, which 
means that future disasters can be fractionally attributed to past human emissions.
Section 2: Long-Term Climate Futures – ‘Where do we want to go?’
WGI Assessment to inform how long-term climate change could unfold depending on chosen emissions futures

Question Chapter Potential Relevance and Explanatory Remarks
How are climate model 
projections used to 
project the range of 
future global and regional 
climate changes?3.8.2; Cross-Chapter Box 3.1; 
Box 4.1; 10.3; 10.4; 12.4The scientific literature provides new insights in a developing field of scientific research regarding 
evaluating model performance and weighting. This can lead to more constrained projection ranges 
for a given scenario and some variables, which take into account the performance of climate models 
and interdependencies among them. These techniques have a strong relevance to quantifying future 
uncertainties, for example regarding the likelihood of the various scenarios exceeding the PA’s long-term 
temperature goals of 1.5°C or 2°C.
If emissions scenarios 
are pursued that achieve 
mitigation goals by 
2050, what will be the 
difference in climate over 
the 21st century compared 
to emissions scenarios 
where no additional climate

policies are implemented?1.2.2; 4.6; FAQ 4.2; 
Chapters 9 and 
11; 12.4; Atlas;
Interactive AtlasEstimating the scale and timing of mitigation compatible with the PA’s long-term goals requires an 
understanding of the climate system response to a change in anthropogenic emissions. The new generation 
of scenarios spans the response space from very low emissions scenarios (SSP1-1.9) under the assumption 
of accelerated and effective climate policy implementation, to very high emissions scenarios in the absence 
of additional climate policies (SSP3-7.0 or SSP5-8.5). It can be informative to place current NDCs and 
their emissions mitigation pledges within this low- and high-end scenario range, that is, in the context of 
intermediate-high emissions scenarios (RCP4.5, RCP6.0 or SSP4-6.0). Climate response differences between 
those future intermediate or high emissions scenarios and those compatible with the PA’s long-term

temperature goals can help inform policymakers about the corresponding adaptation challenges.

165
What is the climatic effect 
of net zero GHG emissions 
and a balance between 
anthropogenic sources 
and anthropogenic sinks?Box 1.4; 4.7.2; 
5.2.2–5.2.4; 7.6Understanding the long-term climate effect of global emissions levels, including the effect of net zero 
emissions targets adopted by countries as part of their long-term climate strategies, can be important 
when assessing whether the collective level of mitigation action is consistent with the long-term 
goals of the PA. Understanding the dynamics of natural sources of CO 2, CH 4 and N 2O is a fundamental 
prerequisite to derive climate projections. Net zero GHG emissions, that is, the balance between

anthropogenic sources and anthropogenic sinks of CO 2 and other GHGs, will halt human-induced 
global warming and/or lead to slight reversal below peak warming levels. Net zero CO 2 emissions 
will approximately lead to a stabilization of CO 2-induced global warming.
What is the remaining 
carbon budget that 
is consistent with 
the PA’s long-term 
temperature goals?5.5The remaining carbon budget provides an estimate of how much CO 2 can still be emitted into the 
atmosphere by human activities while keeping GMST to a specific warming level. It thus provides key 
geophysical information about emissions limits consistent with limiting global warming to well below 
2°C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5°C. Remaining 
carbon budgets can be seen in the context of historical CO 2 emissions to date. The concept of the transient 
climate response to cumulative CO 2 emissions (TCRE) indicates that one tonne of CO 2 has the same effect

on global warming irrespective of whether it is emitted in the past, today, or in the future. In contrast, 
the global warming from short-lived climate forcers (SLCFs) is dependent on their rate of emission 
rather than their cumulative emissions.
What is our current 
knowledge on the ‘Reasons 
for Concern’ related to the 
PA’s long-term temperature 
goals and higher 
warming levels?Cross-Chapter Box 12.1; 
individual domains are 
discussed in 2.3.3; 3.5.4; 
4.3.2; 5.3; 8.4.1; 9.4.2, 9.5; 
Chapters 11 and 12Synthesis information on projected changes in indices of climatic impact-drivers feeds into different 
Reasons for Concern. Where possible, an explicit transfer function between different warming levels and 
indices quantifying characteristics of these hazards is provided, or the difficulties in doing so documented. 
Those indices include Arctic sea ice area in September; global average change in ocean acidification;

volume of glaciers or snow cover; ice volume change for the West Antarctic Ice Sheet (WAIS) and Greenland 
Ice Sheet (GrIS); Atlantic Meridional Overturning Circulation (AMOC) strength; amplitude and variance 
of El Niño–Southern Oscillation (ENSO) mode (Niño 3.4 index); and weather and climate extremes.
What are the climate effects 
and air pollution co-benefits 
of rapid decarbonisation due 
to the reduction of  
co-emitted short-lived 
climate forcers (SLCFs)?6.6.3; 6.7.3; Box 6.2Understanding to what degree rapid decarbonization strategies bring about reduced air pollution due to 
reductions in co-emitted SLCFs can help inform considerations of integrated and/or complementary policies, 
with synergies for pursuing the PA goals, the World Health Organization (WHO) air quality guidelines and 
the Sustainable Development Goals (SDGs).
What are the equilibrium 
climate sensitivity (ECS), the 
transient climate response 
(TCR), and transient climate 
response to CO 2 emissions

(TCRE) and what do these 
indicators tell us about 
expected warming over 
the 21st century under 
various scenarios?Box 4.1; 5.4; 5.5.1; 7.5ECS measures the long-term global mean warming in response to doubling CO 2 concentrations from  
pre-industrial levels, while TCR also takes into account the inertia of the climate system and is an indicator 
for the near- and medium-term warming. TCRE is similar to TCR, but asks the question of what is the 
implied warming in response to cumulative CO 2 emissions (rather than CO 2 concentration changes). 
The higher the ECS, TCR or TCRE, the lower are the GHG emissions that are consistent with the PA’s  
long-term temperature goals.
What is the Earth’s energy 
imbalance and why 
does it matter?7.2.2The current global energy imbalance implies that one can expect additional warming before the Earth’s 
climate system attains equilibrium with the current level of concentrations and radiative forcing.

Note though, that future warming commitments can be different depending on how future concentrations 
and radiative forcing change.
What are the regional 
and long-term changes 
in precipitation, 
evaporation and runoff?8.4.1; 8.5; 8.6; 10.4; 10.6; 
11.4; 11.9; 11.6; 11.7; 12.4; 
Atlas; Interactive AtlasChanges in regional precipitation – in terms of both extremes and long-term averages – are important 
for estimating adaptation challenges. Projected changes of precipitation minus evaporation (P–E) 
are closely related to surface water availability and drought probability. Understanding water cycle 
changes over land, including seasonality, variability and extremes, and their uncertainties, is important 
to estimate a broad range of climate impacts and adaptation, including food production, water supply 
and ecosystem functioning.

166
Are we committed to 
irreversible sea level rise 
and what is the expected 
sea level rise by the end 
of the century if we pursue 
strong mitigation or high 
emissions scenarios?4.7.2; 9.6.3; 9.6.4; 12.4;
Interactive AtlasUnlike many regional climate responses, global mean sea level (GMSL) keeps rising, even in the lowest 
emissions scenarios and is not halted when warming is halted. This is due to the long time scales on 
which ocean heat uptake, glacier melt and ice sheets react to temperature changes. Tipping points and 
thresholds in polar ice sheets need to be considered. Thus, sea level rise commitments and centennial-scale

irreversibility of ocean warming and sea level rise are important for future impacts under even the lowest 
of the emissions scenarios.
Can we project future 
climate extremes under 
various global warming 
levels in the long term?Chapter 11; 12.4;
Interactive AtlasProjections of future extreme weather and climate events and their regional occurrence, including at 
different global warming levels, are important for adaptation and disaster risk reduction. The attribution 
of these extreme events to natural variability and human-induced changes can be of relevance for both 
assessing adaptation challenges and issues of loss and damage.
What is the current 
knowledge of potential 
surprises, abrupt changes, 
tipping points and low-
likelihood, high-impact 
outcomes related to 
different levels of future 
emissions or warming?1.4.4; 4.7.2; 4.8; 5.4.8; 
Box 5.1; 8.5.3.2; 8.6.2; 
Box 9.4; 11.2.4; Cross-
Chapter Box 4.1; Cross-

Chapter Box 12.1From a risk perspective, it is useful to have information about lower-probability events and system changes, 
if they have the potential to result in high impacts, given the dynamic interactions between climate-related 
hazards and socio-economic drivers (i.e., exposure and vulnerability of the affected human or ecological 
systems). Examples include permafrost thaw, CH 4 clathrate feedbacks, ice-sheet mass loss and ocean 
turnover circulation changes, all of which can accelerate warming globally or yield particular regional 
responses and impacts.
Section 3: The Near Term – ‘How do we get there?’
WGI Assessment to inform near-term adaptation and mitigation options
Questions Chapter Potential Relevance and Explanatory Remarks
What are projected key 
climate indices under 
low, intermediate and 
high emissions scenarios 
in the near term, that is, 
the next 20 years?4.3; 4.4; FAQ 4.1, 10.6; 12.3;

Atlas; Interactive AtlasMuch of the near-term information and comparison to historical observations allows us to quantify 
the climate adaptation challenges for the next decades as well as the opportunities to reduce climate 
change by pursuing lower emissions. For this time scale both the forced changes and the internal 
variability are important.
How can the climate 
benefit of mitigating 
emissions of different 
GHGs be compared?7.6For mitigation challenges, it is important to compare efforts to reduce emissions of CO 2 versus emissions 
of other climate forcers, such as short-lived CH 4 or long-lived N 2O. Global warming potentials (GWPs), 
which are used in the UNFCCC and in emissions inventories, are updated and various other metrics 
are also investigated in this Report. While the NDCs of Parties to the PA, emissions inventories under 
the UNFCCC, and various emissions trading schemes work on the basis of GWP-weighted emissions,

some recent discussion in the scientific literature also considers projecting temperatures induced 
by SLCFs on the basis of emissions changes, not emissions per se.
Do mountain glaciers 
shrink, currently and in the 
near future, in regions that 
are currently dependent 
on them for seasonal 
freshwater supply?2.3.2.3; 8.4.1;
9.5; Cross-Chapter 
Box 10.4; 12.4:
Atlas.5.2.2;
Atlas.5.3.2;
Atlas.6.2; Atlas.9.2Mountain glaciers and seasonal snow cover often feed downstream river systems during the melting period, 
and can be an important source of freshwater. Changing river discharge can pose adaptation challenges. 
Melting mountain glaciers are among the main contributors to observed GMSL rise.
What are the capacities 
and limitations in the 
provision of regional climate 
information for adaptation 
and risk management?Cross-Chapter Box 1.3; 10.5; 
10.6; Box 10.2; Cross-
Chapter Box 10.4; 11.9; 12.6;

Cross-Chapter Box 12.1Challenges for adaptation and risk management are predominantly local, even if globally interlinked. There 
are a number of approaches used in the production of regional climate information for adaptation purposes 
focusing on regional scales. All of them consider a range of sources of data and knowledge that are distilled 
into, at times contextual, climate information. A wealth of examples can be found in this Report, including 
assessments of extremes and climatic impact-drivers, and attribution at regional scales. Specific regions 
and case studies for regional projections are considered, like the Sahel and West African monsoon drought 
and recovery, the southern Australian rainfall decline, and the Caribbean small island summer drought, 
and regional projections are discussed for Cape Town, the Mediterranean region and Hindu Kush Himalaya.

167
How important are 
reductions in short-lived 
climate forcers compared 
to the reduction of CO 2 and 
other long-lived GHGs?6.1; 6.6; 6.7; 7.6While most of the radiative forcing which causes climate change comes from CO 2 emissions, short-lived 
climate forcers also play an important role in the anthropogenic effect on climate change. Many aerosol 
species, especially SO4, tend to cool the climate and mask some GHG-induced warming, so reductions 
in these SLCFs would have a warming effect. On the other hand, many short-lived species themselves exert 
a warming effect, including black carbon and CH 4, the second most important anthropogenic GHG (in terms

of current radiative forcing). Notably, the climate response to aerosol emissions has a strong regional 
pattern and is different from that of GHG-driven warming.
What are potential co-
benefits and side effects of 
climate change mitigation?5.6.2; 6.1; 6.7.5The reduction of fossil fuel-related emissions often goes hand-in-hand with a reduction of air pollutants, 
such as aerosols and ozone. Reductions will improve air quality and result in broader environmental 
benefits (reduced acidification, eutrophication, and often tropospheric ozone recovery). More broadly, 
various co-benefits are discussed in WGII and WGIII, as well as co-benefits and side effects related to 
certain mitigation actions, like increased biomass use and associated challenges to food security and 
biodiversity conservation.
What large near-
term surprises could 
result in particular 
adaptation challenges?1.4; 4.4.4; Cross-Chapter 
Box 4.1; 8.5.2; 11.2.4;

Cross-Chapter Box 12.1Surprises can come from a range of sources: from incomplete understanding of the climate system, 
from surprises in emissions of natural (e.g., volcanic) sources, or from disruptions to the carbon cycle 
associated with a warming climate (e.g., methane release from permafrost thawing, tropical forest 
dieback). There could be large natural variability in the near term; or also accelerated climate change 
due to a markedly more sensitive climate than previously thought. When the next large explosive 
volcanic eruption will happen is unknown. The largest volcanic eruptions over the last few hundred 
years led to substantial but temporary cooling, including precipitation changes.
1.2.2.4 Sustainable Development Goals (SDGs)
Many interactions among environmental problems and development 
are addressed in the United Nations 2030 Agenda for Sustainable 
Development and its Sustainable Development Goals. The 2030

Agenda, supported by the finance-oriented Addis Ababa Action 
Agenda (UN DESA, 2015), calls on nations to ‘take the bold and 
transformative steps which are urgently needed to shift the world 
onto a sustainable and resilient path.’ The 2030 Agenda recognizes 
that ‘climate change is one of the greatest challenges of our time and 
its adverse impacts undermine the ability of all countries to achieve 
sustainable development.’ SDG 13 deals explicitly with climate 
change, establishing several targets for adaptation, awareness-
raising and finance. Climate and climate change are also highly 
relevant to most other SDGs, and UNFCCC is acknowledged as the 
main forum to negotiate the global response to climate change. 
For example, both long-lived GHGs (through mitigation decisions), 
and SLCFs (through air quality), are relevant to SDG 11 (sustainable 
cities and communities). Chapter  6  assesses the effects of SLCFs 
on climate and the implications of changing climate for air quality,

including opportunities for mitigation relevant to the SDGs (Box 6.2). 
Also, the UN Conference on Housing and Sustainable Development 
established a New Urban Agenda (United Nations, 2017) envisaging 
cities as part of the solutions for sustainable development, climate 
change adaptation and mitigation.1.2.2.5 The Sendai Framework for Disaster Risk 
Reduction (SFDRR)
The Sendai Framework for Disaster Risk Reduction is a non-binding 
agreement to reduce risks associated with disasters of all scales, 
frequencies and onset rates caused by natural or human-made 
hazards, including climate change. The SFDRR outlines targets and 
priorities for action including ‘understanding disaster risk’, along 
the dimensions of vulnerability, exposure of persons and assets, 
and hazard characteristics. Chapter 12 assesses climate information 
relevant to regional impact and risk assessment, with a  focus on 
climate hazards and other aspects of climate that influence society

and ecosystems and makes the link with Working Group II. AR6 
adopts a  consistent risk- and solution-oriented framing (Cross-
Chapter Box  1.3) that calls for a  multidisciplinary approach and 
cross-Working Group coordination in order to ensure integrative 
discussions of major scientific issues associated with integrative risk 
management and sustainable solutions (IPCC, 2017).
1.2.2.6 The Intergovernmental Science-Policy Platform 
on Biodiversity and Ecosystem Services (IPBES)
Efforts to address climate change take place alongside and in the 
context of other major environmental problems, such as biodiversity 
loss. IPBES, established in 2012, builds on the IPCC model of 
a science–policy interface and assessment. The Platform’s objective 
is to ‘strengthen the science–policy interface for biodiversity

168
Chapter 1 Framing, Context, and Methods1and ecosystem services for the conservation and sustainable 
use of biodiversity, long-term human well-being and sustainable 
development’ (UNEP , 2012). The SROCC (IPCC, 2019b) and SRCCL 
(IPCC, 2019a) assessed the relations between changes in biodiversity 
and in the climate system. The rolling work programme of IPBES up 
to 2030 will address interlinkages among biodiversity, water, food 
and health. This assessment will use a nexus approach to examine 
interlinkages between biodiversity and the above-mentioned issues, 
including climate change mitigation and adaptation. Furthermore, 
IPBES and IPCC will directly collaborate on biodiversity and climate 
change under the rolling work programme.
Addressing climate change alongside other environmental problems, 
while simultaneously supporting sustainable socio-economic 
development, requires a  holistic approach. Since AR5, there is

increasing attention on the need for coordination among previously 
independent international agendas, and a  recognition that climate 
change, disaster risk, economic development, biodiversity conservation 
and human well-being are tightly interconnected. The  current 
COVID-19 pandemic provides an example of the need for such 
interconnection, with its widespread impacts on economy, society and 
environment (e.g., Shan et al., 2021). Cross-Chapter Box 6.1 assesses 
the consequences of the COVID-19 lockdowns for emissions of GHGs 
and SLCFs, and related implications for the climate. Another example 
of the interconnected nature of these issues is the close link between 
SLCF emissions, climate change and air quality concerns (Chapter 6). 
Emissions of halocarbons have previously been successfully regulated 
under the Montreal Protocol and its Kigali Amendment. This has 
been achieved in an effort to reduce ozone depletion that has also

modulated other anthropogenic climate influence (Estrada et al., 
2013; Wu et al., 2013). In the process, emissions of some SLCFs were 
jointly regulated to reduce environmental and health impacts from air 
pollution (e.g., Gothenburg Protocol; Reis et al., 2012). Considering the 
recognized importance of SLCFs in climate change processes, the IPCC 
decided in May 2019 to approve that the IPCC Task Force on National 
Greenhouse Gas Inventories produces an IPCC Methodology Report on 
SLCFs to develop guidance for national SLCF inventories.
The evolving governance context since AR5 challenges the IPCC to 
provide policymakers and other actors with information relevant for 
both adaptation to and mitigation of climate change, and for the loss 
and damage induced.
1.2.3 Linking Science and Society: Communication, 
Values, and the IPCC Assessment Process
This section assesses how the process of communicating climate 
information has evolved since AR5. It summarizes key issues regarding

scientific uncertainty addressed in previous IPCC assessments and 
introduces the IPCC calibrated uncertainty language. Next it discusses 
the role of values in problem-driven, multidisciplinary science 
assessments such as this one. The section introduces climate services 
and how climate information can be tailored for greatest utility in 
specific contexts, such as the global stocktake. Finally, we briefly 
evaluate changes in media coverage of climate information since AR5, 
including the increasing role of Internet sources and social media.1.2.3.1 Climate Change Understanding, Communication 
and Uncertainties
Responses to climate change are facilitated when leaders, 
policymakers, resource managers and their constituencies share 
a  basic understanding of the causes, effects, and possible future 
course of climate change (SR1.5, IPCC, 2018; SRCCL, IPCC, 2019a). 
Achieving shared understanding is complicated, since scientific

knowledge interacts with pre-existing conceptions of weather and 
climate that have built up in diverse world cultures over centuries, 
and which are often embedded in strongly held values and beliefs 
stemming from ethnic or national identities, traditions, religions, and 
lived relationships to weather, land and sea (Van Asselt and Rotmans, 
1996; Rayner and Malone, 1998; Hulme, 2009, 2018; Green et al., 
2010; Jasanoff, 2010; Orlove et al., 2010; Nakashima et al., 2012; 
Shepherd and Sobel, 2020). These diverse, more local understandings 
can both contrast with and enrich the planetary-scale analyses of 
global climate science ( high confidence ).
Political cultures also give rise to variation in how climate science 
knowledge is interpreted, used and challenged (Leiserowitz, 2006; 
Oreskes and Conway, 2010; Brulle et al., 2012; Dunlap and Jacques, 
2013; Mahony, 2014, 2015; Brulle, 2019). A  meta-analysis of 
87  studies carried out between 1998 and 2016 (62 USA national,

16 non-USA national, 9 cross-national) found that political orientation 
and political party identification were the second most important 
predictors of views on climate change after environmental values 
(McCright et al. 2016). Ruiz et al. (2020) systematically reviewed 
34 studies of non-US nations or clusters of nations and 30 studies 
of the USA alone. They found that in the non-US studies, ‘changed 
weather’ and ‘socio-altruistic values’ were the most important 
drivers of public attitudes. For the USA case, by contrast, political 
affiliation and the influence of corporations were most important. 
Widely varying media treatment of climate issues also affects public 
responses (Section 1.2.3.4). In summary, environmental and socio-
altruistic values are the most significant influences on public opinion 
about climate change globally, while political views, political party 
affiliation, and corporate influence also had strong effects, especially 
in the USA ( high confidence ).

Furthermore, climate change itself is not uniform. Some regions face 
steady, readily observable change, while others experience high 
variability that masks underlying trends (Section 1.4.1); most regions 
are subject to hazards, but some may also experience benefits, 
at least temporarily (Chapters 11, 12 and Atlas). This non-uniformity 
may lead to wide variation in public climate change awareness and 
risk perceptions at multiple scales (Howe et al., 2015; Lee et al., 2015). 
For example, short-term temperature trends, such as cold spells or 
warm days, have been shown to influence public concern (Hamilton 
and Stampone, 2013; Zaval et al., 2014; Bohr, 2017).
Given these manifold influences and the highly varied contexts 
of climate change communication, special care is required when 
expressing findings and uncertainties, including IPCC assessments 
that inform decision making. Throughout the IPCC’s history, all three

Working Groups have sought to explicitly assess and communicate 
scientific uncertainty (Le Treut et al., 2007; Cubasch et al., 2013).

169
Framing, Context, and Methods Chapter 11Over time, the IPCC has developed and revised a  framework to 
treat uncertainties consistently across assessment cycles, reports, 
and Working Groups through the use of calibrated language (Moss 
and Schneider, 2000; IPCC, 2005). Since its First Assessment Report 
(FAR; IPCC, 1990a), the IPCC has specified terms and methods for communicating authors’ expert judgments (Mastrandrea and Mach, 
2011). During the AR5 cycle, this calibrated uncertainty language was 
updated and unified across all Working Groups (Mastrandrea et al., 
2010, 2011). Box 1.1 summarizes this framework as it is used in AR6.
Box 1.1 | Treatment of Uncertainty and Calibrated Uncertainty Language in AR6
The AR6 follows the approach developed for AR5 (Box 1.1, Figure 1), as described in the ‘Guidance Notes for Lead Authors of the IPCC 
Fifth Assessment Report on Consistent Treatment of Uncertainties’ (Mastrandrea et al., 2010). The uncertainty Guidance Note used

in AR6 clarifies the relationship between the qualitative description of confidence and the quantitative representation of uncertainty 
expressed by the likelihood scale. The calibrated uncertainty language emphasizes traceability of the assessment throughout the 
process. Key chapter findings presented in each chapter’s Executive Summary are supported in the chapter text by a summary of the 
underlying literature that is assessed in terms of evidence and agreement, confidence, and also likelihood, if applicable.
In all three Working Groups, author teams evaluate underlying scientific understanding and use two metrics to communicate the 
degree of certainty in key findings. These metrics are:
1. Confidence:  a qualitative measure of the validity of a finding, based on the type, amount, quality and consistency of evidence 
(e.g., data, mechanistic understanding, theory, models, expert judgment) and the degree of agreement.

2. Likelihood: a quantitative measure of uncertainty in a finding, expressed probabilistically (e.g., based on statistical analysis of 
observations or model results, or both, and expert judgement by the author team or from a formal quantitative survey of expert 
views, or both).
Throughout IPCC reports, the calibrated language indicating a formal confidence assessment is clearly identified by italics  (e.g.,  medium 
confidence ). Where appropriate, findings can also be formulated as statements of fact without uncertainty qualifiers.
Box.1.1, Figure 1 (adapted from Mach et al., 2017) shows the idealized step-by-step process by which IPCC authors assess scientific 
understanding and uncertainties. It starts with the evaluation of the available evidence and agreement (steps 1–2). The following 
summary terms are used to describe the available evidence: limited, medium,  or robust ; and the degree of agreement: low, medium,  or

high. Generally, evidence is most robust when there are multiple, consistent, independent lines of high-quality evidence.
If the author team concludes that there is sufficient evidence and agreement, the level of confidence can be evaluated. In this 
step, assessments of evidence and agreement are combined into a single metric (steps 3–5). The assessed level of confidence is 
expressed using five qualifiers: very low, low, medium, high,  and very high . Step 4 depicts how summary statements for evidence and 
agreement relate to confidence levels. For a given evidence and agreement statement, different confidence levels can be assigned 
depending on the context, but increasing levels of evidence and degrees of agreement correlate with increasing confidence. When 
confidence in a finding is assessed to be low, this does not necessarily mean that confidence in its opposite is high,  and vice versa.

Similarly, low confidence  does not imply distrust in the finding; instead, it means that the statement is the best conclusion based on 
currently available knowledge. Further research and methodological progress may change the level of confidence in any finding in 
future assessments.
If the expert judgement of the author team concludes that there is sufficient confidence and quantitative/probabilistic evidence, 
assessment conclusions can be expressed with likelihood statements (steps 5–6). Unless otherwise indicated, likelihood statements 
are related to findings for which the authors’ assessment of confidence is high or very high . Terms used to indicate the assessed 
likelihood of an outcome include: virtually certain : 99–100% probability, very likely : 90–100%, likely : 66–100%, about as likely as not : 
33–66%, unlikely : 0–33%, very unlikely : 0–10%, exceptionally unlikely : 0–1%. Additional terms ( extremely likely : 95–100%, more

likely than not  >50–100%, and extremely unlikely  0–5%) may also be used when appropriate.
Likelihood can indicate probabilities for single events or broader outcomes. The probabilistic information may build from statistical 
or modelling analyses, other quantitative analyses, or expert elicitation. The framework encourages authors, where appropriate, to 
present probability more precisely than can be done with the likelihood scale, for example with complete probability distributions or 
percentile ranges, including quantification of tails of distributions, which are important for risk management (Sections 1.2.2 and 1.4.4; 
Mach et al., 2017). In some instances, multiple combinations of confidence and likelihood are possible to characterize key findings

170
Considerable critical attention has focused on whether applying 
the IPCC framework effectively achieves consistent treatment of 
uncertainties and clear communication of findings to users (Shapiro 
et al., 2010; Adler and Hirsch Hadorn, 2014). Specific concerns include, for example, the transparency and traceability of expert judgements 
underlying the assessment conclusions (Oppenheimer et al., 2016) 
and the context-dependent representations and interpretations 
of probability terms (Budescu et al., 2009, 2012; Janzwood, 2020). Box 1.1 (continued)
4. Evaluate confidence based on
evidence and agreement 1. What evidence exists?
2. Evaluate evidence
QuantityQuality
ConsistencyType
and scientific agreement
Statistics
ModelsExperiments 6. Evaluate likelihood
Evidence (type, amount, quality , consistency)tnemeergA
Very low confidenceVery high confidenceHigh agreement
Robust evidence
Low agreement
Limited evidence

5. Suf ficient confidence and quantitative or 
probabilistic evidence? Medium confidence
No YesLow agreement
Robust evidenceHigh agreement
Medium agreement
Medium evidence
High confidence
Low confidenceOutcome probability
Virtually certain
Very likely
Likely
About as likely as not
Unlikely
Very unlikely
Exceptionally unlikely99-100% 
90-100% 
66-100%
33-66% 
0-33% 
0-10%
0-1% Extremely unlikely 0-5% More likely than not >50-100% Extremely likely 95-100% Evaluation and communication of degree of certainty in AR6 findings 
Observations
LikelihoodTheoryLikelihood Ranges
No Yes5. Suf ficient confidence and quantitative or
probabilistic evidence?  Statement of factCertain/factLikelihood outcome66%
90%
Likely range
Very likely rangeProbability
LikelyVery likelyExtremely likelyCumulative
probability
66%90%95%
Examples of assessments
Assessed evidence and agreement
Past projections of global temperature and the pattern of 
warming are broadly consistent with subsequent

observations (limited evidence, high agreement) 
{1.3.6}.Assessed confidence 
The probability of low-likelihood, high impact outcomes 
increases with higher global warming levels (high 
confidence ). {SPM.C.3.2}
The last time global surface temperature was sustained at 
or above 2.5°C higher than 1850–1900 was over 3 million 
years ago (medium confidence ). {SPM.B.1.1} 
There is low confidence  in long-term (multi-decadal to 
centennial) trends in the frequency of all-category tropical 
cyclones. {SPM.A.3.4}   Assessed likelihood
It is virtually certain that hot extremes (including 
heatwaves) have become more frequent and more 
intense across most land regions since the 1950s... 
{SPM.A.3.1}
Based on multiple lines of evidence, the very likely 
range of equilibrium climate sensitivity is between 2°C 
(high confidence) and 5°C (medium confidence). The 
AR6 assessed best estimate is 3°C with a likely range

of 2.5°C to 4°C (high confidence)... {SPM.A.4.4}     3. Suf ficient evidence and
agreement to evaluate
confidence?   
Assessed fact
It is unequivocal that human influence has warmed the 
atmosphere, ocean and land. Widespread and rapid 
changes in the atmosphere, ocean, cryosphere and 
biosphere have occurred. {SPM.A.1}
Box 1.1, Figure 1 | The IPCC AR6 approach for characterizing understanding and uncertainty in assessment findings.  This diagram illustrates the 
step-by-step process authors use to evaluate and communicate the state of knowledge in their assessment (Mastrandrea et al., 2010). Authors present evidence/
agreement, confidence, or likelihood terms with assessment conclusions, communicating their expert judgments accordingly. Example conclusions drawn from Report 
are presented in the box at the bottom of the figure. Figure adapted from Mach et al. (2017) .

For example, a  very likely  statement might be made with high confidence , whereas a  likely  statement might be made with very high 
confidence . In these instances, the author teams consider which statement will convey the most balanced information to the reader.
Throughout this WGI Report, unless stated otherwise, uncertainty is quantified using 90% uncertainty intervals. The 90% uncertainty 
interval, reported in square brackets [x to y], is estimated to have a 90% likelihood of covering the value that is being estimated. 
The range encompasses the median value and there is an estimated 10% combined likelihood of the value being below the lower end 
of the range (x) and above its upper end (y). Often the distribution will be considered symmetric about the corresponding best estimate 
(as in the illustrative example in the figure), but this is not always the case. In this report, an assessed 90% uncertainty interval is

referred to as a ‘ very likely range’. Similarly, an assessed 66% uncertainty interval is referred to as a ‘ likely range’.

171
Framing, Context, and Methods Chapter 11Budescu et al. (2014) surveyed 25 samples in 24 countries (a total 
of 10,792 individual responses), finding that even when shown IPCC 
uncertainty guidance, lay readers systematically misunderstood 
IPCC likelihood statements. When presented with a ‘high likelihood’ 
statement, they understood it as indicating a lower likelihood than 
intended by the IPCC authors. Conversely, they interpreted ‘low 
likelihood’ statements as indicating a  higher likelihood than 
intended. In another study, British lay readers interpreted uncertainty 
language somewhat differently from IPCC guidance, but Chinese 
lay people reading the same uncertainty language translated into 
Chinese differed much more in their interpretations (Harris et al., 
2013). Further, even though it is objectively more probable that wide 
uncertainty intervals will encompass true values, wide intervals were 
interpreted by lay people as implying subjective uncertainty or lack

of knowledge on the part of scientists (Løhre et al., 2019). Mach 
et al. (2017) investigated the advances and challenges in approaches 
to expert judgment in AR5. Their analysis showed that the shared 
framework increased the overall comparability of assessment 
conclusions across all Working Groups and topics related to climate 
change, from the physical science basis to resulting impacts, risks, and 
options for response. Nevertheless, many challenges in developing 
and communicating assessment conclusions persist, especially for 
findings drawn from multiple disciplines and Working Groups, for 
subjective aspects of judgements, and for findings with substantial 
uncertainties (Adler and Hirsch Hadorn, 2014). In summary, the 
calibrated language cannot entirely prevent misunderstandings, 
including a tendency to systematically underestimate the probability 
of the IPCC’s higher-likelihood conclusions and overestimate the

probability of the lower-likelihood ones ( high confidence ). However, 
a  consistent and systematic approach across Working Groups to 
communicate the assessment outcomes is an important characteristic 
of the IPCC.
Some suggested alternatives are impractical, such as always 
including numerical values along with calibrated language (Budescu 
et al., 2014). Others, such as using positive instead of negative 
expressions of low-to-medium probabilities, show promise but were 
not proposed in time for adoption in AR6 (Juanchich et al., 2020). This 
report therefore retains the same calibrated language used in AR5 
(Box 1.1). Like previous reports, AR6 also includes FAQs that express 
its chief conclusions in plain language designed for lay readers.
The framework for communicating uncertainties does not allow 
for indicating cases where ‘deep uncertainty’ is identified in the 
assessment (Adler and Hirsch Hadorn, 2014). The definition of deep

uncertainty in IPCC assessments has been described in the context 
of SROCC (IPCC, 2019b; Box 5 in Abram et al., 2019): a situation of 
deep uncertainty exists when experts or stakeholders do not know 
or cannot agree on: (i) appropriate conceptual models that describe 
relationships among key driving forces in a system; (ii) the probability 
distributions used to represent uncertainty about key variables and 
parameters; and/or (iii) how to weigh and value desirable alternative 
outcomes (Cross-Chapter Box  1.2 and Annex VII: Glossary; Abram 
et al., 2019). Since AR5, ‘storylines’ or ‘narratives’ approaches have 
been used to address issues related to deep uncertainty, for example 
low-likelihood events that would have high impact if they occurred, 
to better inform risk assessment and decision making (Section 1.4.4). Chapter  9  (Section  9.2.3) notes deep uncertainty in long-term 
projections for sea level rise, and in processes related to marine ice-

sheet instability and marine ice cliff instability.
1.2.3.2 Values, Science and Climate Change Communication
As noted above, values  – fundamental attitudes about what 
is important, good, and right  – play critical roles in all human 
endeavours, including climate science. In AR5, Chapters 3 and 4 of 
the WGIII Assessment addressed the role of cultural, social and ethical 
values in climate change mitigation and sustainable development 
(Fleurbaey et al., 2014; Kolstad et al., 2014). These values include 
widely accepted concepts of human rights, enshrined in international 
law, that are relevant to climate impacts and policy objectives (Hall 
and Weiss, 2012; Peel and Osofsky, 2018; Setzer and Vanhala, 2019). 
Specific values  – human life, subsistence, stability, and equitable 
distribution of the costs and benefits of climate impacts and policies – 
are explicit in the texts of the UNFCCC and the PA (Breakey et al.,

2016; Dooley and Parihar, 2016). Here we address the role of values 
in how scientific knowledge is created, verified and communicated. 
Chapters 10, 12 and Cross-Chapter Box 12.2 address how the specific 
values and contexts of users can be addressed in the co-production 
of climate information.
The epistemic (knowledge-related) values of science include 
explanatory power, predictive accuracy, falsifiability, replicability, 
and justification of claims by explicit reasoning (Popper, 1959; 
Kuhn, 1977). These are supported by key institutional values, 
including openness, ‘organized scepticism’, and objectivity or 
‘disinterestedness’ (Merton, 1973), operationalized as well-defined 
methods, documented evidence, publication, peer review, and 
systems for institutional review of research ethics (COSEPUP , 2009; 
Elliott, 2017). In recent decades, open data, open code and scientific 
cyber-infrastructure (notably the Earth System Grid Federation,

a partnership of climate modelling centers dedicated to supporting 
climate research by providing secure, web-based, distributed access 
to climate model data) have facilitated scrutiny from a larger range 
of participants, and FAIR data stewardship principles  – making 
data Findable, Accessible, Interoperable and Reusable (FAIR)  – are 
being mainstreamed in many fields (Wilkinson et al., 2016). Climate 
science norms and practices embodying these scientific values and 
principles include the publication of data and model code, multiple 
groups independently analysing the same problems and data, model 
intercomparison projects (MIPs), explicit evaluations of uncertainty, 
and comprehensive assessments by national academies of science 
and the IPCC.
The formal Principles Governing IPCC Work (1998, amended 2003, 
2006, 2012, 2013) specify that assessments should be ‘comprehensive, 
objective, open and transparent.’ The IPCC assessment process seeks

to achieve these goals in several ways: by evaluating evidence and 
agreement across all relevant peer-reviewed literature, especially that 
published or accepted since the previous assessment; by maintaining 
a  traceable, transparent process that documents the reasoning, 
data and tools used in the assessment; and by maximizing the 
diversity of participants, authors, experts, reviewers, institutions and 
communities represented, across scientific discipline, geographical

172
Chapter 1 Framing, Context, and Methods1location, gender, ethnicity, nationality and other characteristics. 
The multi-stage review process is critical to ensure an objective, 
comprehensive and robust assessment, with hundreds of scientists, 
other experts and governments providing comments to a series of 
drafts before the report is finalized.
Social values are implicit in many choices made during the 
construction, assessment and communication of climate science 
information (Heymann et al., 2017; Skelton et al., 2017). Some 
climate science questions are prioritized for investigation, or given 
a specific framing or context, because of their relevance to climate 
policy and governance. One example is the question of how the 
effects of a 1.5°C global warming would differ from those of a 2°C 
warming, an assessment specifically requested by Parties to the PA. 
The SR1.5 (2018) explicitly addressed this issue ‘within the context of

sustainable development; considerations of ethics, equity and human 
rights; and the problem of poverty’ (Chapters 1 and 5; see also Hoegh-
Guldberg et al., 2019) following the outcome of the approval of the 
outline of the Special Report by the IPCC during its 44th Session 
(Bangkok, Thailand, 17–20 October 2016). Likewise, particular metrics 
are sometimes prioritized in climate model improvement efforts 
because of their practical relevance for specific economic sectors or 
stakeholders. Examples include reliable simulation of precipitation in 
a specific region, or attribution of particular extreme weather events 
to inform rebuilding and future policy (Chapters 8 and 11; Intemann, 
2015; Otto et al., 2018; James et al., 2019). Sectors or groups whose 
interests do not influence research and modelling priorities may thus 
receive less information in support of their climate-related decisions 
(Parker and Winsberg, 2018).

Recent work also recognizes that choices made throughout the 
research process can affect the relative likelihood of false alarms 
(overestimating the probability and/or magnitude of hazards) or 
missed warnings (underestimating the probability and/or magnitude 
of hazards), known respectively as Type I and Type II errors. Researchers 
may choose different methods depending on which type of error they 
view as most important to avoid, a  choice that may reflect social 
values (Douglas, 2009; Knutti, 2018; Lloyd and Oreskes, 2018). This 
reflects a fundamental trade-off between the values of reliability and 
informativeness. When uncertainty is large, researchers may choose to 
report a wide range as very likely , even though it is less informative 
about potential consequences. By contrast, high-likelihood statements 
about a  narrower range may be more informative, yet also prove 
less reliable if new evidence later emerges that widens the range.

Furthermore, the difference between narrower and wider uncertainty 
intervals has been shown to be confusing to lay readers, who often 
interpret wider intervals as less certain (Løhre et al., 2019).
1.2.3.3 Climate Information, Co-production 
and Climate Services
In AR6, ‘climate information’ refers to specific information about the 
past, current or future state of the climate system that is relevant for 
mitigation, adaptation and risk management. Cross-Chapter Box 1.1 
is an example of climate information at the global scale. It provides 
climate change information with potential relevance for the global 
stocktake, and indicates where in AR6 this information may be found.Responding to national and regional policymakers’ needs for 
tailored information relevant to risk assessment and adaptation, 
AR6 emphasizes assessment of regional information more than 
earlier reports. Here the phrase ‘regional climate information’ refers

to predefined reference sets of land and ocean regions; various 
typological domains (such as mountains or monsoons); temporal 
frames including baseline periods as well as near term (2021–2040), 
medium term (2041–2060) and long term (2081–2100); and global 
warming levels (Chapters 10 and 12, Sections 1.4.1 and 1.4.5, and 
Atlas). Regional climate change information is constructed from 
multiple lines of evidence including observations, paleoclimate 
proxies, reanalyses, attribution of changes and climate model 
projections from both global and regional climate models 
(Sections 1.5.3 and 10.2–10.4). The constructed regional information 
needs to take account of user context and values for risk assessment, 
adaptation and policy decisions (Sections 1.2.3 and 10.5).
As detailed in Chapter 10, scientific climate information often requires 
‘tailoring’ to meet the requirements of specific decision-making 
contexts. In a study of the UK Climate Projections 2009 (UKCP09)

project, researchers concluded that climate scientists struggled to 
grasp and respond to users’ information needs because they lacked 
experience interacting with users, institutions and scientific idioms 
outside the climate science domain (Porter and Dessai, 2017). 
Economic theory predicts the value of ‘polycentric’ approaches 
to climate change informed by specific global, regional and local 
knowledge and experience (Ostrom, 1996, 2012). This is confirmed 
by numerous case studies of extended, iterative dialogue among 
scientists, policymakers, resource managers and other stakeholders 
to produce mutually understandable, usable, task-related information 
and knowledge, policymaking and resource management around the 
world (Lemos and Morehouse, 2005; Lemos et al., 2012, 2014, 2018; 
see Vaughan and Dessai, 2014 for a critical view). The SR1.5 (2018) 
assessed that ‘education, information, and community approaches, 
including those that are informed by indigenous knowledge and

local knowledge, can accelerate the wide-scale behaviour changes 
consistent with adapting to and limiting global warming to 1.5°C. 
These approaches are more effective when combined with other 
policies and tailored to the motivations, capabilities and resources 
of specific actors and contexts ( high confidence ).’ These extended 
dialogic co-production and education processes have thus been 
demonstrated to improve the quality of both scientific information 
and governance ( high confidence ) (Section  10.5 and Cross 
Chapter Box 12.2).
Since AR5, climate services have increased at multiple levels (local, 
national, regional and global) to aid decision-making of individuals 
and organizations and to enable preparedness and early climate 
change action. These services include appropriate engagement from 
users and providers, are based on scientifically credible information 
and producer and user expertise, have an effective access mechanism,

and respond to the users’ needs (Glossary; Hewitt et al., 2012). 
A Global Framework for Climate Services (GFCS) was established in 
2009 by the World Meteorological Organization (WMO) in support 
of these efforts (Hewitt et al., 2012; Lúcio and Grasso, 2016). 
Climate services are provided across sectors and time scales, from 
sub-seasonal to multi-decadal, and support co-design and co-

173
Framing, Context, and Methods Chapter 11production processes that involve climate information providers, 
resource managers, planners, practitioners and decision makers 
(Brasseur and Gallardo, 2016; Trenberth et al., 2016; C.D.  Hewitt 
et al., 2017). For example, they may provide high-quality data on 
temperature, rainfall, wind, soil moisture and ocean conditions, as 
well as maps, risk and vulnerability analyses, assessments, and future 
projections and scenarios. These data and information products may 
be combined with non-meteorological data, such as agricultural 
production, health trends, population distributions in high-risk areas, 
road and infrastructure maps for the delivery of goods, and other 
socio-economic variables, depending on users’ needs (WMO, 2020a). 
Cross-Chapter Box 12.2 illustrates the diversity of climate services 
with three examples from very different contexts.
The current landscape of climate services is assessed in detail

in Chapter  12 (Section  12.6), with a  focus on multi-decadal 
time scales relevant for climate change risk assessment. Other 
information relevant to improving climate services for decision-
making includes the assessment of methods to construct regional 
information (Chapter  10), as well as projections at the regional 
level (Atlas) relevant for impact and risk assessment in different 
sectors (Chapter 12).
1.2.3.4 Media Coverage of Climate Change
Climate services focus on users with specific needs for climate 
information, but most people learn about climate science findings 
from media coverage. Since AR5, research has expanded on how 
mass media report climate change and how their audiences respond 
(Dewulf, 2013; Jaspal and Nerlich, 2014; Jaspal et al., 2014). 
For  example, in five European Union (EU) countries, television 
coverage of AR5 used ‘disaster’ and ‘opportunity’ as its principal 
themes, but virtually ignored the ‘risk’ framing introduced by

AR5 WGII (Painter, 2015) and now extended by the AR6 (Cross-
Chapter Box 1.3). Other studies show that people react differently 
to climate change news when it is framed as a  catastrophe (Hine 
et al., 2016), as associated with local identities (Sapiains et al., 
2016), or as a social justice issue (Howell, 2013). Similarly, audience 
segmentation studies show that responses to climate change vary 
between groups of people with different, although not necessarily 
opposing, views on this phenomenon (e.g.,  Maibach et al., 2011; 
Sherley et al., 2014; Detenber et al., 2016). In Brazil, two studies 
have shown the influence of mass media on the high level of public 
climate change concern in that country (Rodas and Di Giulio, 2017; 
Dayrell, 2019).  In the USA, analyses of television network news show 
that climate change receives minimal attention, is most often framed 
in a political context, and largely fails to link extreme weather events

to climate change using appropriate probability framing (Hassol 
et al., 2016). However, recent evidence suggests that Climate Matters 
(an Internet resource to help US television weather forecasters link 
weather to climate change trends) may have had a positive effect 
on public understanding of climate change (Myers et al., 2020). 
Also, some media outlets have recently adopted and promoted 
terms and phrases stronger than the more neutral ‘climate change’ 
and ‘global warming’, including ‘climate crisis’, ‘global heating’, 
and ‘climate emergency’ (Zeldin-O’Neill, 2019). Google searches on 
those terms, and on ‘climate action’, increased 20-fold in 2019, when large social movements such as School Strikes for Climate gained 
worldwide attention (Thackeray et al., 2020). We thus assess that 
specific characteristics of media coverage play a major role in climate 
understanding and perception ( high confidence ), including how IPCC 
assessments are received by the general public.

Since AR5, social media platforms have dramatically altered the mass-
media landscape, bringing about a shift from uni-directional transfer 
of information and ideas to more fluid, multi-directional flows 
(Pearce et al., 2019). A survey covering 18 Latin American countries 
(StatKnows-CR2, 2019) found that the main sources of information 
about climate change mentioned were the Internet (52% of mentions), 
followed by social media (18%). There are well-known challenges 
with social media, such as misleading or false presentations of 
scientific findings, incivility that diminishes the quality of discussion 
around climate change topics, and ‘filter bubbles’ that restrict 
interactions to those with broadly similar views (Anderson and 
Huntington, 2017). However, at certain moments (such as at the 
release of the AR5 WGI report), Twitter studies have found that more 
mixed, highly-connected groups existed, within which members

were less polarized (Pearce et al., 2014; Williams et al., 2015). Thus, 
social media platforms may in some circumstances support dialogic 
or co-production approaches to climate communication. Because the 
contents of IPCC reports speak not only to policymakers, but also to 
the broader public, the character and effects of media coverage are 
important considerations across Working Groups.

174
Chapter 1 Framing, Context, and Methods11.3 How We Got Here: The Scientific Context
Scientific understanding of the climate system’s fundamental features 
is robust and well established. This section briefly presents the major 
lines of evidence in climate science (Figure  1.6). It illustrates their 
long history and summarizes key findings from the WGI contribution 
to AR5, referencing previous IPCC assessments for comparison, 
where relevant. Box  1.2 summarizes major findings from three 
Special Reports already released during the IPCC Sixth Assessment 
Cycle. This chapter’s Appendix 1A summarizes the principal findings 
of all six IPCC WGI Assessment Reports, including the present Report, 
in a single table for ease of reference.1.3.1 Lines of Evidence: Instrumental Observations
Instrumental observations of the atmosphere, ocean, land, biosphere 
and cryosphere underpin all understanding of the climate system.

This section describes the evolution of instrumental data for major 
climate variables at Earth’s land and ocean surfaces, at altitude in 
the atmosphere, and at depth in the ocean. Many data records exist, 
of varying length, continuity and spatial distribution; Figure 1.7 gives 
a schematic overview of temporal coverage.
Instrumental weather observation at the Earth’s surface dates to 
the invention of thermometers and barometers in the 17th century. 
1822
Fourier greenhouse 
analogy: atmosphere 
retains heat1896
Arrhenius models climate 
sensitivity to CO2
1840
Agassiz hypothesizes ice 
ages in Earth's past1897
Chamberlin describes 
geological carbon cycle1956
Plass models radiative 
absorption by CO21990
IPCC 
First Assessment 
Report1957
Revelle & Suess 
suggest oceans not 
absorbing all 
anthropogenic CO22000s
Earth System Models 
with coupled carbon 
cycles1852
Dove maps 
isotherms for 
most of globe1872
HMS 
Challenger: first 
deep ocean 
measurements1940s
Systematic

radiosonde 
observations begin
1957
Keeling begins 
continuous CO2 
measurements
2015
Satellites detect 
enhanced greenhouse 
effect at surface1999
Vostok 420,000-year 
ice core
2000s
Ocean data to 2000 m from 
Argo floats
1856
Foote suggests CO2 is a 
greenhouse gas1931
Hulburt 
calculates 4°C 
sensitivity
1967
Manabe & Wetherald model 
CO2 with H2O feedback2021
IPCC AR6
Energy and sea 
level budgets 
closed1861
Tyndall demonstrates 
infrared absorption by 
various gases1900
Ocean temperature 
measurements to 150 m1905
First near-global 
climate 
observing 
network
1969
First deep ice 
core (Greenland)2001
Satellites detect enhanced 
greenhouse effect at top of 
atmosphere1978
Continuous 
satellite 
temperature 
measurements 
begin2010
Ice sheet mass 
loss established
1938
Callendar suggests 
CO2 affecting global 
temperatures1979
Charney report begins 
scientific consensus on 
CO2 greenhouse effect1820 1850 1900 1950 2000 2020
CO2 and the enhanced greenhouse effect18502020
20201817

Humboldt first 
isotherm map
1933
Kincer notes 
warming at many 
individual locations1981
Last Glacial 
Maximum 
reconstruc -
tionObservations
1820 1850 1900 1950 2000 20201850
18502020
2020400
300350
275325375425 (ppm)1°
0.5°
0°
 (°C)  
Ice coresDirect measurmentsGlobal mean surface temperature anomaly relative to 1850-1900
Atmospheric CO2 concentrations
Figure 1.6 | Climate science milestones, between 1817 and 2021.  Top: Milestones in observations. Middle:  Curves of global surface air temperature (GMST) anomaly 
relative to 1850–1900, using HadCRUT5 (Morice et al., 2021); atmospheric CO 2 concentrations from Antarctic ice cores (Lüthi et al., 2008; Bereiter et al., 2015); direct air 
measurements from 1957 onwards (see Figure 1.4 for details; Tans and Keeling, 2020). Bottom:  Milestones in scientific understanding of the CO 2-enhanced greenhouse effect. 
Further details on each milestone are available in Section 1.3, and in Chapter 1 of AR4 (Le Treut et al., 2007).

175
Framing, Context, and Methods Chapter 11National and colonial weather services built networks of surface 
stations in the 19th  century. By the mid-19th  century, semi-
standardized naval weather logs recorded winds, currents, 
precipitation, air pressure, and temperature at sea, initiating the 
longest continuous quasi-global instrumental record (Maury, 1849, 
1855, 1860). Because the ocean covers over 70% of global surface 
area and constantly exchanges energy with the atmosphere, both air 
and sea surface temperatures (SST) recorded in these naval logs are 
crucial variables in climate studies. Dove (1853) mapped seasonal 
isotherms over most of the globe. By 1900, a patchy weather data-
sharing system reached all continents except Antarctica. Regular 
compilation of climatological data for the world began in 1905 with 
the Réseau Mondial (Air Ministry – Meteorological Office, 1921), and 
similar compilations  – the World Weather Records (Clayton, 1927)

and Monthly Climatic Data for the World (est. 1948)  – have been 
published continuously since their founding.
Land and ocean surface temperature data have been repeatedly 
evaluated, refined and extended (Section 1.5.1). As computer power 
increased and older data were recovered from handwritten records, 
the number of surface station records used in published global land 
temperature time series grew. A  pioneering study for 1880–1935 
used fewer than 150 stations (Callendar, 1938). A benchmark study 
of 1880–2005 incorporated 4300 stations (Brohan et al., 2006). 
A  study of the 1753–2011 period included previously unused 
station data, for a  total of 36,000 stations (Rohde et al., 2013); 
recent versions of this dataset comprise over 40,000 land stations 
(Rohde and Hausfather, 2020). Several centres, including the National 
Oceanic and Atmospheric Administration (NOAA), Hadley, and Japan 
Meteorological Agency (JMA), produce SST datasets independently

calculated from instrumental records. In the 2000s, adjustments 
for bias due to different measurement methods (buckets, engine 
intake thermometers, moored and drifting buoys) resulted in major 
improvements of SST data (Thompson et al., 2008), and these 
improvements continue (Huang et al., 2017; Kennedy et al., 2019). 
SST  and land-based data are incorporated into global surface 
temperature datasets calculated independently by multiple research 
groups, including NOAA, NASA, Berkeley Earth, Hadley-CRU, JMA, and 
China Meteorological Administration (CMA). Each group aggregates 
the raw measurement data, applies various adjustments for non-
climatic biases such as urban heat-island effects, and addresses 
unevenness in geospatial and temporal sampling with various 
techniques (see Section 2.3.1.1.3 and Table 2.4 for references). Other 
research groups provide alternative interpolations of these datasets 
using different methods (e.g., Cowtan and Way, 2014; Kadow et al.,

2020). Using the then available global surface temperature datasets, 
AR5 WGI assessed that the GMST increased by 0.85°C from 1880 
to 2012 and found that each of the three decades following 1980 
was successively warmer at the Earth’s surface than any preceding 
decade since 1850 (IPCC, 2013b). Marine air temperatures, especially 
those measured during nighttime, are increasingly also used to 
examine variability and long-term trends (e.g., Rayner et al., 2006; 
Kent et al., 2013; Cornes et al., 2020; Junod and Christy, 2020). 
Cross-Chapter Box 2.3 discusses updates to the global temperature 
datasets, provides revised estimates for the observed changes and 
considers whether marine air temperatures are changing at the 
same rate as SSTs.Data at altitude came initially from scattered mountain summits, 
balloons and kites, but the upper troposphere and stratosphere 
were not systematically observed until radiosonde (weather balloon)

networks emerged in the 1940s and 1950s. These provide the 
longest continuous quasi-global record of the atmosphere’s vertical 
dimension (Stickler et al., 2010). New methods for spatial and temporal 
homogenisation (intercalibration and quality control) of radiosonde 
records were introduced in the 2000s (Sherwood et al., 2008, 2015; 
Haimberger et al., 2012). Since 1978, Microwave Sounding Units 
(MSU) mounted on Earth-orbiting satellites have provided a second 
high-altitude data source, measuring temperature, humidity, ozone, 
and liquid water throughout the atmosphere. Over time, these satellite 
data have required numerous adjustments to account for such factors 
as orbital precession and decay (Edwards, 2010). Despite repeated 
adjustments, however, marked differences remain in the temperature 
trends from surface, radiosonde, and satellite observations; between 
the results from three research groups that analyse satellite data

(University of Alabama in Huntsville (UAH), Remote Sensing Systems 
(RSS), and NOAA); and between modelled and satellite-derived 
tropospheric warming trends (Thorne et al., 2011; Santer et al., 2017). 
These differences are the subject of ongoing research (Maycock 
et al., 2018). In the 2000s, Atmospheric Infrared Sounder (AIRS) and 
radio occultation (GNSS-RO) measurements provided new ways to 
measure temperature at altitude, complementing data from the MSU. 
GNSS-RO is a new independent, absolutely calibrated source, using 
the refraction of radio-frequency signals from the Global Navigation 
Satellite System (GNSS) to measure temperature, pressure and water 
vapour (Section 2.3.1.2.1; Foelsche et al., 2008; Anthes, 2011).
Heat-retaining properties of the atmosphere’s constituent gases were 
closely investigated in the 19th century. Foote (1856) measured solar 
heating of CO 2 experimentally and argued that higher concentrations

in the atmosphere would increase Earth’s temperature. Water vapour, 
ozone, CO 2 and certain hydrocarbons were found to absorb longwave 
(infrared) radiation, the principal mechanism of the greenhouse effect 
(Tyndall, 1861). Nineteenth-century investigators also established the 
existence of a natural biogeochemical carbon cycle. Carbon dioxide 
emitted by volcanoes is removed from the atmosphere through 
a combination of silicate rock weathering, deep-sea sedimentation, 
oceanic absorption, and biological storage in plants, shellfish, and 
other organisms. On multi-million-year time scales, the compression 
of fossil organic matter is stored as carbon as coal, oil and natural gas 
(Chamberlin, 1897, 1898; Ekholm, 1901).
Arrhenius (1896) calculated that a  doubling of atmospheric CO 2 
would produce warming of 5°C–6°C, but in 1900 new measurements 
seemed to rule out CO 2 as a  greenhouse gas due to overlap with 
the absorption bands of water vapour (Ångström, 1900; Very and

Abbe, 1901). Further investigation and more sensitive instruments 
later overturned Ångström’s conclusion (Fowle, 1917; Callendar, 
1938). Nonetheless, the major role of CO 2 in the energy balance of 
the atmosphere was not widely accepted until the 1950s (Callendar, 
1949; Plass, 1956, 1961; Manabe and Möller, 1961; Weart, 2008; 
Edwards, 2010). Revelle and Keeling established CO 2 monitoring 
stations in Antarctica and Hawaii during the 1957–1958 International 
Geophysical Year (Revelle and Suess, 1957; Keeling, 1960). These 
stations have tracked rising atmospheric CO 2 concentrations from

176
Chapter 1 Framing, Context, and Methods1315  ppm in 1958 to 414  ppm in 2020. Ground-based monitoring 
of other GHGs followed. The Greenhouse Gases Observing Satellite 
(GOSat) was launched in 2009, and two Orbiting Carbon Observatory 
satellite instruments have been in orbit since 2014.
The AR5 WGI highlighted ‘the other CO 2 problem’ (Doney et al., 2009), 
that is, ocean acidification caused by the absorption of some 20–30% 
of anthropogenic CO 2 from the atmosphere and its conversion to 
carbonic acid in seawater. The AR5 WGI assessed that the pH of 
ocean surface water has decreased by 0.1 since the beginning of 
the industrial era ( high confidence ), indicating approximately a 30% 
increase in acidity (IPCC, 2013b).
With a  heat capacity about 1000 times greater than that of the 
atmosphere, Earth’s ocean stores the vast majority of energy 
retained by the planet. Ocean currents transport the stored heat 
around the globe and, over decades to centuries, from the surface

to its greatest depths. The ocean’s thermal inertia moderates 
faster changes in radiative forcing on land and in the atmosphere, reaching full equilibrium with the atmosphere only after hundreds 
to thousands of years (Yang and Zhu, 2011). The earliest subsurface 
measurements in the open ocean date to the 1770s (Abraham et al., 
2013). From 1872–76, the research ship HMS Challenger  measured 
global ocean temperature profiles at depths up to 1700 m along its 
cruise track. By 1900, research ships were deploying instruments 
such as Nansen bottles and mechanical bathythermographs 
(MBTs) to develop profiles of the upper 150 m in areas of interest 
to navies and commercial shipping (Abraham et al., 2013). Starting 
in 1967, eXpendable BathyThermographs (XBTs) were deployed by 
scientific and commercial ships along repeated transects to measure 
temperature to 700 m  (Goni et al., 2019). Ocean data collection 
expanded in the 1980s with the Tropical Ocean Global Experiment

(TOGA; Gould, 2003). Marine surface observations for the globe, 
assembled in the mid-1980s in the International Comprehensive 
Ocean-Atmosphere Data Set (ICOADS; Woodruff et al., 1987, 2005), 
were extended to 1662–2014 using newly recovered marine records 
and metadata (Woodruff et al., 1998; Freeman et al., 2017). The Argo 
submersible float network, developed in the early 2000s, provided 
(a) Instrumental observations
(b) Paleoclimate
Figure 1.7 | Schematic of temporal coverage of (a) selected instrumental climate observations and (b) selected paleoclimate archives.  The satellite era 
began in 1979 CE. The width of the taper gives an indication of the amount of available records.

177
Framing, Context, and Methods Chapter 11the first systematic global measurements of the 700–2000 m layer. 
Comparing the HMS Challenger  data to data from Argo submersible 
floats revealed global subsurface ocean warming on the centennial 
scale (Roemmich et al., 2012). The AR5 WGI assessed with high 
confidence  that ocean warming accounted for more than 90% of the 
additional energy accumulated by the climate system between 1971 
and 2010 (IPCC, 2013b). In comparison, warming of the atmosphere 
corresponds to only about 1% of the additional energy accumulated 
over that period (IPCC, 2013a). Chapter 2 summarizes the ocean heat 
content datasets used in AR6 (Section 2.3.3.1 and Table 2.7).
Water expands as it warms. This thermal expansion, along with glacier 
mass loss, were the dominant contributors to GMSL rise during the 
20th century ( high confidence ) according to AR5 (IPCC, 2013b). Sea 
level can be measured by averaging across tide gauges, some of which

date to the 18th century. However, translating tide gauge readings 
into GMSL is challenging, since their spatial distribution is limited to 
continental coasts and islands, and their readings are relative to local 
coastal conditions that may shift vertically over time. Satellite radar 
altimetry, introduced operationally in the 1990s, complements the 
tide gauge record with geocentric measurements of GMSL at much 
greater spatial coverage (Katsaros and Brown, 1991; Fu et al., 1994). 
The AR5 WGI assessed that GMSL rose by 0.19 [0.17 to 0.21] m over 
the period 1901–2010, and that the rate of sea level rise increased 
from 2.0 [1.7 to 2.3] mm yr–1 in 1971–2010 to 3.2 [2.8 to 3.6] mm yr–1 
from 1993–2010. Warming of the ocean very likely  contributed 0.8 
[0.5 to 1.1] mm yr–1 of sea level change during 1971–2010, with the 
majority of that contribution coming from the upper 700 m (IPCC, 
2013b). Chapter 2 (Section 2.3.3.3) assesses current understanding

of the extent and rate of sea level rise, past and present.
Satellite remote sensing also revolutionized studies of the cryosphere 
(Sections  2.3.2 and 9.3–9.5), particularly near the poles, where 
conditions make surface observations very difficult. Satellite mapping 
and measurement of snow cover began in 1966, with land and 
sea ice observations following in the mid-1970s. Yet prior to the 
Third Assessment Report, researchers lacked sufficient data to tell 
whether the Greenland and Antarctic ice sheets were shrinking or 
growing. Through a combination of satellite and airborne altimetry 
and gravity measurements, and improved knowledge of surface 
mass balance and perimeter fluxes, a  consistent signal of ice loss 
for both ice sheets was established by the time of AR5 (Shepherd 
et al., 2012). After 2000, satellite radar interferometry revealed rapid 
changes in surface velocity at ice-sheet margins, often linked to

reduction or loss of ice shelves (Scambos et al., 2004; Rignot and 
Kanagaratnam, 2006). Whereas sea ice area and concentration have 
been continuously monitored since 1979 via microwave imagery, 
datasets for ice thickness emerged later from upward sonar profiling 
by submarines (Rothrock et al., 1999) and radar altimetry of sea ice 
freeboards (Laxon et al., 2003). A  recent reconstruction of Arctic 
sea ice extent back to 1850 found no historical precedent for the 
Arctic sea ice minima of the 21st century (Walsh et al., 2017). Glacier 
length has been monitored for decades to centuries; internationally 
coordinated activities now compile worldwide glacier length and 
mass balance observations (World Glacier Monitoring Service, Zemp 
et al., 2015), global glacier outlines (Randolph Glacier Inventory, 
Pfeffer et al., 2014), and ice thickness data for about 1100 glaciers (Glacier Thickness Database (GlaThiDa), Gärtner-Roer et al., 2014).

In summary, these data allowed AR5 WGI to assess that over the 
last two decades, the Greenland and Antarctic ice sheets have been 
losing mass, glaciers have continued to shrink almost worldwide, 
and Arctic sea ice and Northern Hemisphere spring snow cover have 
continued to decrease in extent ( high confidence ) (IPCC, 2013b).
1.3.2 Lines of Evidence: Paleoclimate
With the gradual acceptance of evidence for geological ‘deep time’ in 
the 19th century came investigation of fossils, geological strata, and 
other evidence pointing to large shifts in the Earth’s climate, from ice 
ages to much warmer periods, across thousands to billions of years. 
This awareness set off a search for the causes of climatic changes. 
The  long-term perspective provided by paleoclimate studies is essential 
to understanding the causes and consequences of natural variations 
in climate, as well as crucial context for recent anthropogenic climatic

change. The reconstruction of climate variability and change over 
recent millennia began in the 1800s (Brückner, 1890; Stehr and von 
Storch, 2000; Coen, 2018, 2020). In brief, paleoclimatology reveals 
the key role of CO 2 and other greenhouse gases in past climatic 
variability and change, the magnitude of recent climate change in 
comparison to past glacial–interglacial cycles, and the unusualness 
of recent climate change (Section 1.2.1.2 and Cross-Chapter Box 2.1; 
Tierney et al., 2020a). FAQ 1.3 provides a plain-language summary of 
its importance.
Paleoclimate studies reconstruct the evolution of Earth’s climate 
over hundreds to billions of years using pre-instrumental historical 
archives, indigenous knowledge, and natural archives left behind 
by geological, chemical and biological processes (Figure  1.7). 
Paleoclimatology covers a  wide range of temporal scales, ranging 
from the human historical past (decades to millennia) to geological

deep time (millions to billions of years). Paleoclimate reference 
periods are presented in Cross-Chapter Box 2.1.
Historical climatology aids near-term paleoclimate reconstructions 
using media such as diaries, almanacs and merchant accounts that 
describe climate-related events such as frosts, thaws, flowering 
dates, harvests, crop prices and droughts (Lamb, 1965, 1995; Le Roy 
Ladurie, 1967; Brázdil et al., 2005). Meticulous records by Chinese 
scholars and government workers, for example, have permitted 
detailed reconstructions of China’s climate back to 1000 CE, and even 
beyond (Louie and Liu, 2003; Ge et al., 2008). Climatic phenomena 
such as large-scale, regionally and temporally distributed warmer 
and cooler periods of the past 2000 years were reconstructed from 
European historical records (Lamb, 1965, 1995; Le Roy Ladurie, 1967; 
Neukom et al., 2019).
Indigenous and local knowledge has played an increasing role

in historical climatology, especially in areas where instrumental 
observations are sparse. Peruvian fishermen named the periodic 
El  Niño warm current in the Pacific, which was linked by later 
researchers to the Southern Oscillation (Cushman, 2004). Inuit 
communities have contributed to climatic history and community-
based monitoring across the Arctic (Riedlinger and Berkes, 2001;

178
Chapter 1 Framing, Context, and Methods1Gearheard et al., 2010). Indigenous Australian knowledge of climatic 
patterns has been offered as a complement to sparse observational 
records (Green et al., 2010; Head et al., 2014), such as those of sea-
level rise (Nunn and Reid, 2016). Ongoing research seeks to conduct 
further dialogue, utilize indigenous and local knowledge as an 
independent line of evidence complementing scientific understanding, 
and analyse their utility for multiple purposes, especially adaptation 
(Laidler, 2006; Alexander et al., 2011; IPCC, 2019c). Indigenous and 
local knowledge is used most extensively by IPCC WGII.
Certain geological and biological materials preserve evidence of past 
climate changes. These ‘natural archives’ include corals, trees, glacier 
ice, speleothems (stalactites and stalagmites), loess deposits (dust 
sediments), fossil pollen, peat, lake sediment and marine sediment

(Stuiver, 1965; Eddy, 1976; Haug et al., 2001; Wang et al., 2001; Jones 
et al., 2009; Bradley, 2015). By the early 20th  century, laboratory 
research had begun to use tree rings to reconstruct precipitation 
and the possible influence of sunspots on climatic change (Douglass, 
1914, 1919, 1922). Radiocarbon dating, developed in the 1940s 
(Arnold and Libby, 1949), allows accurate determination of the age of 
carbon-containing materials from the past 50,000 years; this dating 
technique ushered in an era of rapid progress in paleoclimate studies.
On longer time scales, tiny air bubbles trapped in polar ice sheets 
provide direct evidence of past atmospheric composition, including 
CO 2 levels (Petit et al., 1999), and the 18O isotope in frozen precipitation 
serves as a  proxy marker for temperature (Dansgaard, 1954). 
Sulphate deposits in glacier ice and as ash layers within sediment 
record major volcanic eruptions, providing another mechanism for

dating. The first paleoclimate reconstructions used an almost 100-kyr 
ice core taken at Camp Century, Greenland (Dansgaard et al., 1969; 
Langway Jr, 2008). Subsequent cores from Antarctica extended this 
climatic record to 800 kyr (EPICA Community Members, 2004; Jouzel, 
2013). Comparisons of air contained in these ice samples against 
measurements from the recent past enabled AR5 WGI to assess that 
atmospheric concentrations of CO 2, methane (CH 4), and nitrous oxide 
(N2O) had all increased to levels unprecedented in at least the last 
800,000 years (Figure 1.5; IPCC, 2013b).
Global reconstructions of sea surface temperature were developed 
from material contained in deep-sea sediment cores (CLIMAP Project 
Members et al., 1976), providing the first quantitative constraints for 
model simulations of ice-age climates (e.g., Rind and Peteet, 1985). 
Paleoclimate data and modelling showed that the Atlantic Ocean

circulation has not been stable over glacial–interglacial time periods, 
and that many changes in ocean circulation are associated with 
abrupt transitions in climate in the North Atlantic region (Ruddiman 
and McIntyre, 1981; Broecker et al., 1985; Boyle and Keigwin, 1987; 
Manabe and Stouffer, 1988).
By the early 20th century, cyclical changes in insolation due to the 
interacting periodicities of orbital eccentricity, axial tilt and axial 
precession had been hypothesized as a chief pacemaker of ice age–
interglacial cycles on multi-millennial time scales (Milankovitch, 
1920). Paleoclimate information derived from marine sediment 
provides quantitative estimates of past temperature, ice volume and 
sea level over millions of years (Figure 1.5; Emiliani, 1955; Shackleton and Opdyke, 1973; Siddall et al., 2003; Lisiecki and Raymo, 2005; 
Past Interglacials Working Group of PAGES, 2016). These estimates 
have bolstered the orbital cycles hypothesis (Hays et al., 1976;

Berger, 1977, 1978). However, paleoclimatology of multi-million to 
billion-year periods reveals that CH 4, CO 2, continental drift, silicate 
rock weathering and other factors played a greater role than orbital 
cycles in climate changes during ice-free ‘hothouse’ periods of Earth’s 
distant past (Frakes et al., 1992; Bowen et al., 2015; Zeebe et al., 2016).
The AR5 WGI (IPCC, 2013b) used paleoclimatic evidence to put 
recent warming and sea level rise in a  multi-century perspective 
and assessed that 1983–2012 was likely  to have been the warmest 
30-year period of the last 1400 years in the Northern Hemisphere 
(medium confidence ). The AR5 also assessed that the rate of sea level 
rise since the mid-19th century has been larger than the mean rate 
during the previous two millennia ( high confidence ).
1.3.3 Lines of Evidence: Identifying Natural 
and Human Drivers
The climate is a  globally interconnected system driven by solar

energy. Scientists in the 19th century established the main physical 
principles governing Earth’s temperature. By 1822, the principle of 
radiative equilibrium (the balance between absorbed solar radiation 
and the energy Earth re-radiates into space) had been articulated, 
and the atmosphere’s role in retaining heat had been likened to 
a greenhouse (Fourier, 1822). The primary explanations for natural 
climate change – greenhouse gases, orbital factors, solar irradiance, 
continental position, volcanic outgassing, silicate rock weathering, 
and the formation of coal and carbonate rock – were all identified by 
the late 19th century (Fleming, 1998; Weart, 2008).
The natural and anthropogenic factors responsible for climate 
change are known today as radiative ‘drivers’ or ‘forcers’. The net 
change in the energy budget at the top of the atmosphere, resulting 
from a  change in one or more such drivers, is termed ‘radiative

forcing’ (RF; Glossary) and measured in watts per square metre (W 
m–2). The total radiative forcing over a given time interval (often since 
1750) represents the sum of positive drivers (inducing warming) and 
negative ones (inducing cooling). Past IPCC reports have assessed 
scientific knowledge of these drivers, quantified their range for the 
period since 1750, and presented the current understanding of how 
they interact in the climate system. Like all previous IPCC reports, AR5 
assessed that total radiative forcing has been positive at least since 
1850–1900, leading to an uptake of energy by the climate system, 
and that the largest single contribution to total radiative forcing is 
the rising atmospheric concentration of CO 2 since 1750 (Chapter 7, 
and Cross-Chapter Box 1.2; IPCC, 2013a).
Natural drivers include changes in solar irradiance, ocean currents, 
naturally occurring aerosols, and natural sources and sinks of

radiatively active gases such as water vapour, CO 2, CH 4, and 
sulphur dioxide (SO 2). Detailed global measurements of surface-
level solar irradiance were first conducted during the 1957–1958 
International Geophysical Year (Landsberg, 1961), while top-of-
atmosphere irradiance has been measured by satellites since 1959

179
Framing, Context, and Methods Chapter 11(House  et al.,  1986). Measured changes in solar irradiance have 
been small and slightly negative since about 1980 (Matthes et al., 
2017). Water vapour is the most abundant radiatively active gas, 
accounting for about 75% of the terrestrial greenhouse effect, 
but because its residence time in the atmosphere averages just 
8–10 days, its atmospheric concentration is largely governed by 
temperature (van der Ent and Tuinenburg, 2017; Nieto and Gimeno, 
2019). As a result, non-condensing GHGs with much longer residence 
times serve as ‘control knobs’, regulating planetary temperature, 
with water vapour concentrations as a feedback effect (Lacis et al., 2010, 2013). The most important of these non-condensing gases 
is CO 2 (a positive driver), released naturally by volcanism at about 
637 MtCO 2 yr–1 in recent decades, or roughly 1.6% of the 37 GtCO 2 
emitted by human activities in 2018 (Burton et al., 2013; Le Quéré

et al., 2018). Absorption by the ocean and uptake by plants and soils 
are the primary natural CO 2 sinks on decadal to centennial time 
scales (Section 5.1.2 and Figure 5.3).
Aerosols (tiny airborne particles) interact with climate in numerous 
ways, some direct (e.g., reflecting solar radiation back into space) and 
Changes in global land temperature (60°S-60°N) relative to a 1901–1930 baseline (°C)
1870 1880 1890 1900 1910 1920 1930 1940 1950-0.6-0.4-0.200.20.40.6Global temperature anomaly (°C)Callendar (1938)
Callendar (1961)
Osborn et al. (2021)
Year(a) Callendar (1938)
(b) Comparing Callendar (1938, 1961) with CRUTEM5 (Osborn et al. 2021)
Figure 1.8 | G.S. Callendar’s estimates of global land temperature variations and their possible causes.  (a) The original figure from Callendar (1938), using 
measurements from 147 surface stations for 1880–1935, showing: (top)  ten-year moving departures from the mean of 1901–1930 (°C), with the dashed line representing his

estimate of the ‘CO 2 effect’ on temperature rise, and (bottom)  annual departures from the 1901–1930 mean (°C). (b) Comparing the estimates of global land (60°S–60°N) 
temperatures tabulated in Callendar (1938, 1961) with a modern reconstruction (CRUTEM5, Osborn et al., 2021) for the same period, following Hawkins and Jones (2013). 
Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

180
Chapter 1 Framing, Context, and Methods1others indirect (e.g., cloud droplet nucleation); specific effects may 
cause either positive or negative radiative forcing. Major volcanic 
eruptions inject SO 2 (a negative driver) into the stratosphere, creating 
aerosols that can cool the planet for years at a time by reflecting some 
incoming solar radiation. The history and climatic effects of volcanic 
activity have been traced through historical records, geological 
traces, and observations of major eruptions by aircraft, satellites and 
other instruments (Dörries, 2006). The negative RF of major volcanic 
eruptions was considered in the First Assessment Report (FAR; IPCC, 
1990a). In subsequent assessments, the negative RF of smaller 
eruptions has also been considered (e.g., Cross-Chapter Box 4.1 in 
Chapter 4 of this Report; Section 2.4.3 in IPCC, 1996). Dust and other 
natural aerosols have been studied since the 1880s (e.g.,  Aitken,

1889; Ångström, 1929, 1964; Twomey, 1959), particularly in relation 
to their role in cloud nucleation, an aerosol indirect effect whose 
RF may be either positive or negative depending on such factors 
as cloud altitude, depth and albedo (Stevens and Feingold, 2009; 
Boucher et al., 2013).
Anthropogenic drivers of climatic change were hypothesized as early as 
the 17th century, with a primary focus on forest clearing and agriculture 
(Grove, 1995; Fleming, 1998). In the 1890s, Arrhenius was first to 
calculate the effects of increased or decreased CO 2 concentrations on 
planetary temperature, and Högbom estimated that worldwide coal 
combustion of about 500 Mt yr–1 had already completely offset the 
natural absorption of CO 2 silicate rock weathering (Högbom, 1894; 
Arrhenius, 1896; Berner, 1995; Crawford, 1997). As coal consumption 
reached 900 Mt yr–1 only a  decade later, Arrhenius wrote that 
anthropogenic CO 2 from fossil fuel combustion might eventually

warm the planet (Arrhenius, 1908). In 1938, analysing records from 
147  stations around the globe, Callendar calculated atmospheric 
warming over land at 0.3°C–0.4°C from 1880–1935 and attributed 
about half of this warming to anthropogenic CO 2 (Figure 1.8; Callendar, 
1938; Fleming, 2007; Hawkins and Jones, 2013).
Studies of radiocarbon (14C) in the 1950s established that increasing 
atmospheric CO 2 concentrations were due to fossil fuel combustion. 
Since all the 14C once contained in fossil fuels long ago decayed 
into non-radioactive 12C, the CO 2 produced by their combustion 
reduces the overall concentration of atmospheric 14C (Suess, 1955). 
Related work demonstrated that while the ocean was absorbing 
around 30% of anthropogenic CO 2, these emissions were also 
accumulating in the atmosphere and biosphere (Section 1.3.1 and 
Chapter  5, Section  5.2.1.5). Further work later established that 
atmospheric oxygen levels were decreasing in inverse relation to the

anthropogenic CO 2 increase, because combustion of carbon consumes 
oxygen to produce CO 2 (Chapters 2 and 6; Keeling and Shertz, 1992; 
IPCC, 2013a). Revelle and Suess (1957) famously described fossil fuel 
emissions as a ‘large scale geophysical experiment’, in which ‘within 
a  few centuries we are returning to the atmosphere and ocean 
the concentrated organic carbon stored in sedimentary rocks over 
hundreds of millions of years.’ The 1960s saw increasing attention 
to other radiatively active gases, especially ozone (O 3; Manabe and 
Möller, 1961; Plass, 1961). Methane and nitrous oxide (N 2O) were 
not considered systematically until the 1970s, when anthropogenic 
increases in those gases were first noted (Wang et al., 1976). In the 
1970s and 1980s, scientists established that synthetic halocarbons (see Glossary), including widely used refrigerants and propellants, 
were extremely potent greenhouse gases (Sections  2.2.4.3 and

6.2.2.9; Ramanathan, 1975). When these chemicals were also 
found to be depleting the stratospheric ozone layer, they were 
stringently and successfully regulated on a global basis by the 1987 
Montreal Protocol on the Ozone Layer and successor agreements 
(Parson, 2003).
Radioactive fallout from atmospheric nuclear weapons testing 
(1940s–1950s) and urban smog (1950s–1960s) first provoked 
widespread attention to anthropogenic aerosols and ozone in the 
troposphere (Edwards, 2012). Theory, measurement and modelling 
of these substances developed steadily from the 1950s (Hidy, 2019). 
However, the radiative effects of anthropogenic aerosols did not 
receive sustained study until around 1970 (Bryson and Wendland, 
1970; Rasool and Schneider, 1971), when their potential as cooling 
agents was recognized (Peterson et al., 2008). The US Climatic 
Impact Assessment Program (CIAP) found that proposed fleets of 
supersonic aircraft, flying in the stratosphere, might cause substantial

aerosol cooling and depletion of the ozone layer, stimulating efforts 
to understand and model stratospheric circulation, atmospheric 
chemistry, and aerosol radiative effects (Mormino et al., 1975; Toon 
and Pollack, 1976). Since the 1980s, aerosols have increasingly been 
integrated into comprehensive modelling studies of transient climate 
evolution and anthropogenic influences, through treatment of volcanic 
forcing, links to global dimming and cloud brightening, and their 
influence on cloud nucleation and other properties (e.g., thickness, 
lifetime and extent), and precipitation (e.g.,  Hansen et al., 1981; 
Charlson et al., 1987, 1992; Albrecht, 1989; Twomey, 1991).
The FAR (1990) focused attention on human emissions of CO 2, CH 4, 
tropospheric O 3, chlorofluorocarbons (CFCs), and N 2O. Of these, at 
that time only the emissions of CO 2 and CFCs were well measured, 
with methane sources known only ‘semi-quantitatively’ (IPCC,

1990a). The FAR assessed that some other trace gases, especially 
CFCs, have global warming potentials hundreds to thousands of 
times greater than CO 2 and CH 4, but are emitted in much smaller 
amounts. As a  result, CO 2 remains by far the most important 
positive anthropogenic driver, with CH 4 next most significant 
(Section 1.6.3); anthropogenic methane stems from such sources as 
fossil fuel extraction, natural gas pipeline leakage, agriculture and 
landfills. In 2001, increased greenhouse forcing attributable to CO 2, 
CH 4, O 3, CFC-11 and CFC-12 was detected by comparing satellite 
measurements of outgoing longwave radiation measurements taken 
in 1970 and in 1997 (Harries et al., 2001). AR5 assessed that the 40% 
increase in atmospheric CO 2 contributed most to positive RF since 
1750. Together, changes in atmospheric concentrations of CO 2, CH 4, 
N2O and halocarbons from 1750–2011 were assessed to contribute 
a positive RF of 2.83 [2.26 to 3.40] W m–2 (IPCC, 2013b).

All IPCC reports have assessed the total RF as positive when 
considering all sources. However, due to the considerable variability 
of both natural and anthropogenic aerosol loads, FAR characterized 
total aerosol RF as ‘highly uncertain’ and was unable even to 
determine its sign (positive or negative). Major advances in 
quantification of aerosol loads and their effects have taken place 
since then, and IPCC reports since 1992 have consistently assessed

181
Framing, Context, and Methods Chapter 11total forcing by anthropogenic aerosols as negative (IPCC, 1992, 
1995a, 1996). However, due to their complexity and the difficulty 
of obtaining precise measurements, aerosol effects have been 
consistently assessed as the largest single source of uncertainty 
in estimating total RF (Stevens and Feingold, 2009; IPCC, 2013a). 
Overall, AR5 assessed that total aerosol effects, including cloud 
adjustments, resulted in a negative RF of –0.9 [–1.9 to −0.1] W m−2 
(medium confidence ), offsetting a substantial portion of the positive 
RF resulting from the increase in GHGs ( high confidence ) (IPCC, 
2013b). Chapter 7 provides an updated assessment of the total and 
per-component RF for the WGI contribution to AR6.
1.3.4 Lines of Evidence: Understanding 
and Attributing Climate Change
Understanding the global climate system requires both theoretical 
understanding and empirical measurement of the major forces

and  factors that govern the transport of energy and mass (air, water and  
water vapour) around the globe; the chemical and physical properties 
of the atmosphere, ocean, cryosphere and land surfaces; and the 
biological and physical dynamics of natural ecosystems, as well as 
the numerous feedbacks (both positive and negative) among these 
processes. Attributing climatic changes or extreme weather events 
to human activity (Cross-Working Group Box: Attribution) also 
requires an understanding of the many ways that human activities 
may affect the climate, along with statistical and other techniques 
for separating the ‘signal’ of anthropogenic climate change from the 
‘noise’ of natural climate variability (Section 1.4.2). This inter- and 
trans-disciplinary effort requires contributions from many sciences.
Due to the complexity of many interacting processes, ranging 
in scale from the molecular to the global, and occurring on time

scales from seconds to millennia, attribution makes extensive use 
of conceptual, mathematical, and computer simulation models. 
Modelling allows scientists to combine a vast range of theoretical 
and empirical understanding from physics, chemistry and other 
natural sciences, producing estimates of their joint consequences 
as simulations of past, present or future states and trends (Nebeker, 
1995; Edwards, 2010, 2011).
In addition to radiative transfer (discussed above in Section 1.3.3), 
forces and factors such as thermodynamics (energy conversions), 
gravity, surface friction, and the Earth’s rotation govern the planetary-
scale movements or ‘circulation’ of air and water in the climate 
system. The scientific theory of climate began with Halley (1686), 
who hypothesized vertical atmospheric circulatory cells driven by 
solar heating, and Hadley (1735), who showed how the Earth’s 
rotation affects that circulation. Ferrel (1856) added the Coriolis

force to existing theory, explaining the major structures of the global 
atmospheric circulation. In aggregate, prevailing winds and ocean 
currents move energy poleward from the equatorial regions where 
the majority of incoming solar radiation is received.
Climate models provide the ability to simulate these complex 
circulatory processes, and to improve the physical theory of climate 
by testing different mathematical formulations of those processes. Since controlled experiments at planetary scale are impossible, 
climate simulations provide one important way to explore the 
differential effects and interactions of variables such as solar 
irradiance, aerosols and GHGs. To assess their quality, models or 
components of models may be compared with observations. For this 
reason, they can be used to attribute observed climatic effects to 
different natural and human drivers (Hegerl et al., 2011). As early 
as Arrhenius (1896), simple mathematical models were used to

calculate the effects of doubling atmospheric carbon dioxide over pre-
industrial concentrations (approximately 550 ppm vs approximately 
275 ppm respectively). In the early 20th century Bjerknes formulated 
the Navier–Stokes equations of fluid dynamics for motion of the 
atmosphere (Bjerknes, 1906; Bjerknes et al., 1910), and Richardson 
(1922) developed a system for numerical weather prediction based 
on these equations. When electronic computers became available 
in the late 1940s, the methods of Bjerknes and Richardson were 
successfully applied to weather forecasting (Charney et al., 1950; 
Nebeker, 1995; Harper, 2008).
In the 1960s similar approaches to modelling the weather were used 
to model the climate, but with much longer runs than daily forecasting 
(Smagorinsky et al., 1965; Manabe and Wetherald, 1967). Simpler 
statistical and one- and two-dimensional modelling approaches 
continued in tandem with the more complex general circulation

models (GCMs; Manabe and Wetherald, 1967; Budyko, 1969; Sellers, 
1969). The first coupled atmosphere–ocean model (AOGCM) with 
realistic topography appeared in 1975 (Bryan et al., 1975; Manabe 
et al., 1975). Rapid increases in computer power enabled higher 
resolutions, longer model simulations, and the inclusion of additional 
physical processes in GCMs, such as aerosols, atmospheric chemistry, 
sea ice, and snow.
In the 1990s, AOGCMs were state of the art. By the 2010s, Earth 
system models (ESMs, also known as coupled carbon-cycle climate 
models) incorporated land surface, vegetation, the carbon cycle, 
and other elements of the climate system. Since the 1990s, some 
major modelling centres have deployed ‘unified’ models for 
both weather prediction and climate modelling, with the goal 
of a  seamless modelling approach that uses the same dynamics, 
physics and parameterisations at multiple scales of time and space 
(Section  10.1.2; Cullen, 1993; Brown et al., 2012; NRC, 2012;

WMO, 2015). Because weather forecast models make short-term 
predictions that can be frequently verified, and improved models are 
introduced and tested iteratively on cycles as short as 18 months, this 
approach allows major portions of the climate model to be evaluated 
as a  weather model and more frequently improved. However, all 
climate models exhibit biases of different degrees and types, and 
the practice of ‘tuning’ parameter values in models to make their 
outputs match variables such as historical warming trajectories has 
generated concern throughout their history (Section 1.5.3.2; Randall 
and Wielicki, 1997; Edwards, 2010; Hourdin et al., 2017). Overall, 
AR5 WGI assessed that climate models had improved since previous 
reports (IPCC, 2013b).
Since climate models vary along many dimensions, such as grid 
type, resolution, and parameterizations, comparing their results 
requires special techniques. To address this problem, the climate

182
Chapter 1 Framing, Context, and Methods1modelling community developed increasingly sophisticated model 
intercomparison projects (MIPs; Gates et al., 1999; Covey et al., 
2003). MIPs prescribe standardized experiment designs, time periods, 
output variables or observational reference data to facilitate direct 
comparison of model results. This aids in diagnosing the reasons for 
biases and other differences among models, and furthers process 
understanding (Section  1.5). Both the CMIP3 and CMIP5 model 
intercomparison projects included experiments testing the ability 
of models to reproduce 20th-century global surface temperature 
trends both with and without anthropogenic forcings. Although some 
individual model runs failed to achieve this (Hourdin et al., 2017), the 
mean trends of multi-model ensembles did so successfully (Meehl 
et al., 2007a; Taylor et al., 2012). When only natural forcings were 
included (creating the equivalent of a ‘control Earth’ without human

influence), similar multi-model ensembles could not reproduce the 
observed post-1970 warming at either global or regional scales 
(Edwards, 2010; Jones et al., 2013). The GCMs and ESMs compared 
in CMIP6 (used in this Report) offer more explicit documentation and 
evaluation of tuning procedures (Section 1.5; Schmidt et al., 2017; 
Burrows et al., 2018; Mauritsen and Roeckner, 2020).
The FAR (IPCC, 1990a) concluded that while both theory and models 
suggested that anthropogenic warming was already well underway, 
its signal could not yet be detected in observational data against the 
‘noise’ of natural variability (see also Section 1.4.2; and Barnett and 
Schlesinger, 1987). Since then, increased warming and progressively 
more conclusive attribution studies have identified human activities 
as the ‘dominant cause of the observed warming since the mid-
20th  century’ (IPCC, 2013b). ‘Fingerprint’ studies seek to detect

specific observed changes – expected from theoretical understanding 
and model results – that could not be explained by natural drivers 
alone, and to attribute statistically the proportion of such changes 
that is due to human influence. These include global-scale surface 
warming, nights warming faster than days, tropospheric warming 
and stratospheric cooling, a rising tropopause, increasing ocean heat 
content, changed global patterns of precipitation and sea level air 
pressure, increasing downward longwave radiation, and decreasing 
upward longwave radiation (Hasselmann, 1979; Karoly et al., 1994; 
Schneider, 1994; Santer et al., 1995, 2013; Hegerl et al., 1996, 1997; 
Gillett et al., 2003; Santer, 2003; Zhang et al., 2007; Stott et al., 2010; 
Davy et al., 2017; Mann et al., 2017). The Cross-Working Group Box 
on Attribution outlines attribution methods and uses from across 
AR6, now including event attribution (specifying the influence of

climate change on individual extreme events such as floods, or on 
the frequency of classes of events such as tropical cyclones). Overall, 
the evidence for human influence has grown substantially over time 
and from each IPCC report to the next.
A key indicator of climate understanding is whether theoretical climate 
system budgets or ‘inventories’, such as the balance of incoming and 
outgoing energy at the surface and at the top of the atmosphere, 
can be quantified and balanced observationally. The global energy 
budget, for example, includes energy retained in the atmosphere, 
upper ocean, deep ocean, ice, and land surface. Church et al. (2013) 
assessed in AR5 with high confidence  that independent estimates of 
effective radiative forcing (ERF), observed heat storage, and surface 
warming combined to give an energy budget for the Earth that is consistent with the AR5 WGI assessed likely  range of equilibrium 
climate sensitivity (ECS) [1.5°C to 4.5°C] to within estimated

uncertainties (on ECS, see Section  1.3.5; IPCC, 2013a). Similarly, 
over the period 1993–2010, when observations of all sea level 
components were available, AR5 WGI assessed the observed global 
mean sea level rise to be consistent with the sum of the observed 
contributions from ocean thermal expansion (due to warming) 
combined with changes in glaciers, the Antarctic and Greenland ice 
sheets, and land-water storage ( high confidence ). Verification that 
the terms of these budgets balance over recent decades provides 
strong evidence for our understanding of anthropogenic climate 
change (Cross-Chapter Box 9.1).
The Appendix to Chapter  1  (Appendix 1A) lists the key detection 
and attribution statements in the Summaries for Policymakers of 
WGI reports since 1990. The evolution of these statements over 
time reflects the improvement of scientific understanding and the 
corresponding decrease in uncertainties regarding human influence.

The Second Assessment Report (SAR) stated that ‘the balance of 
evidence suggests a discernible human influence on global climate’ 
(IPCC, 1995b). Five years later, the Third Assessment Report (TAR) 
concluded that ‘there is new and stronger evidence that most 
of the warming observed over the last 50  years is attributable to 
human activities’ (IPCC, 2001b). The AR4 further strengthened 
previous statements, concluding that ‘most of the observed increase 
in global average temperatures since the mid-20th century is very 
likely  due to the observed increase in anthropogenic greenhouse 
gas concentrations’ (IPCC, 2007b). The AR5 assessed that a human 
contribution had been detected in: changes in warming of the 
atmosphere and ocean; changes in the global water cycle; reductions 
in snow and ice; global mean sea level rise; and changes in some 
climate extremes. The AR5 concluded that ‘it is extremely likely  that 
human influence has been the dominant cause of the observed

warming since the mid-20th century’ (IPCC, 2013b).
1.3.5 Projections of Future Climate Change
It was recognized in IPCC AR5 that information about the near term 
was increasingly relevant for adaptation decisions. In response, AR5 
WGI made a specific assessment for how global surface temperature 
was projected to evolve over the next two decades, concluding that 
the change for the period 2016–2035 relative to 1986–2005 will likely 
be in the range of 0.3°C–0.7°C ( medium confidence ), assuming no 
major volcanic eruptions or secular changes in total solar irradiance 
(IPCC, 2013b). The AR5 was also the first IPCC assessment report 
to assess ‘decadal predictions’ of the climate, where the observed 
state of the climate system was used as a starting point for forecasts 
several years ahead. The AR6 examines updates to these decadal 
predictions (Section 4.4.1).
The assessments and predictions for the near-term evolution of global

climate features are largely independent of future CO 2 emissions 
pathways. However, AR5 WGI assessed that limiting climate change 
in the long-term future will require substantial and sustained 
reductions of GHG emissions (IPCC, 2013b). This assessment results 
from decades of research on understanding the climate system and

183
Framing, Context, and Methods Chapter 11its perturbations, and projecting climate change into the future. Each 
IPCC report has considered a range of emissions scenarios, typically 
including a scenario in which societies choose to continue on their 
present course, as well as several others reflecting socio-economic 
and policy responses that may limit emissions and/or increase the 
rate of CO 2 removal from the atmosphere. Climate models are used 
to project the outcomes of each scenario. However, future human 
climate influence cannot be precisely predicted because GHG and 
aerosol emissions, land use, energy use and other human activities 
may change in numerous ways. Common emissions scenarios used in 
the WGI contribution to AR6 are detailed in Section 1.6.
Based on model results and steadily increasing CO 2 concentrations 
(Bolin and Bischof, 1970; SMIC, 1971; Meadows et al., 1972), 
concerns about future ‘risk of effects on climate’ were addressed in

Recommendation 70 of the Stockholm Action Plan, resulting from 
the 1972 United Nations Conference on the Human Environment 
(UN, 1973). Numerous other scientific studies soon amplified these 
concerns (summarized in Schneider (1975) and Williams (1978); see 
also Nordhaus (1975, 1977). In 1979, a US National Research Council 
(NRC) group led by Jule Charney reported on the ‘best present 
understanding of the carbon dioxide/climate issue for the benefit of 
policymakers’, initiating an era of regular and repeated large-scale 
assessments of climate science findings.The 1979 Charney NRC report estimated ECS at 3°C, stating the range 
as 2°C–4.5°C, based on ‘consistent and mutually supporting’ model 
results and expert judgment (NRC, 1979). ECS is defined in IPCC 
assessments as the global surface air temperature (GSAT) response 
to CO 2 doubling (from pre-industrial levels) after the climate has 
reached equilibrium (stable energy balance between the atmosphere

and ocean). Another quantity, transient climate response (TCR), was 
later introduced as the change in GSAT, averaged over a  20-year 
period, at the time of CO 2 doubling in a scenario of concentration 
increasing at 1% per year. Calculating ECS from historical or 
paleoclimate temperature records, in combination with energy 
budget models, has produced estimates both lower and higher than 
those calculated using GCMs and ESMs; in this Report, these are 
assessed in Chapter 7, Section 7.5.2.
ECS is typically characterized as most relevant on centennial time 
scales, while TCR was long seen as a more appropriate measure of 
the 50–100-year response to gradually increasing CO 2. However, 
recent studies have raised new questions about how accurately both 
quantities are estimated by GCMs and ESMs (Grose et al., 2018; 
Meehl et al., 2020; Sherwood et al., 2020). Further, as climate models 
evolved to include a full-depth ocean, the time scale for reaching full

equilibrium became longer and new methods to estimate ECS had to 
be developed (Gregory et al., 2004; Meehl et al., 2020; Meinshausen 
et al., 2020). Because of these considerations, as well as new 
estimates from observation-based, paleoclimate, and emergent-
Table 1.2 | Estimates of equilibrium climate sensitivity (ECS) and transient climate response (TCR) from successive major scientific assessments since 
1979.  No likelihood statements are available for reports prior to 2001 because those reports did not use the IPCC calibrated uncertainty language. The assessed range of 
ECS differs from the range derived from general circulation model (GCM) and Earth system model (ESM) results because assessments take into account other evidence, other 
types of models, and expert judgment. The AR6 definition of ECS differs from previous reports, now including all long-term feedbacks except those associated with ice sheets.

AR6 estimates of ECS are derived primarily from process understanding, historical observations and emergent constraints, informed by (but not based on) GCM and ESM model 
results. CMIP6 is the 6th phase of the Coupled Model Intercomparison Project (Section 7.5.5 and Box 7.1).
AssessmentECS Range Derived from 
GCM and ESM Results (°C)Assessed Range of ECS (°C)Assessed Central  
estimate of ECS (°C)Assessed Range of TCR (°C)
NAS 1979 (NRC, 1979) 2.0–3.5 1.5–4.5 3.0
NAS 1983 (NRC, 1983) 2.0–3.5 1.5–4.5 3.0
Villach 1985 (WMO/
UNEP/ICSU, 1986)1.5–5.5 1.5–4.5 3.0
IPCC FAR 1990 (IPCC, 1990a) 1.9–5.2 1.5–4.5 2.5
IPCC 1992 Supplementary 
Report (IPCC, 1992)1.7–5.4 1.5–4.5 2.5 Discussed but not assessed
IPCC 1994 Radiative Forcing 
report (IPCC, 1995a)not given 1.5–4.5 2.5
IPCC SAR (IPCC, 1996) 1.9–5.2 1.5–4.5 2.5 Discussed but not assessed
IPCC TAR (IPCC, 2001a) 2.0–5.1 1.5–4.5 ( likely ) 2.5 1.1–3.1
IPCC AR4 (IPCC, 2007a) 2.1–4.4 2.0–4.5 ( likely ) 3.0 1.0–3.0

IPCC AR5 (IPCC, 2013a) 2.1–4.7 1.5–4.5 ( likely ) not given 1.0–2.5
World Climate 
Research Programme 
(Sherwood et al., 2020)Models not used in estimate2.6–3.9 (66% uncertainty 
interval, likely )
2.3–4.7 (90% uncertainty 
interval, very likely )not given Not given
IPCC AR6 20211.8–5.6 (CMIP6). Not used 
directly in assessing ECS 
range (Chapter 7).2.5–4.0 (likely )
2.0-5.0 ( very likely ) 3.0 1.4–2.2 ( likely )

184
Chapter 1 Framing, Context, and Methods1constraints studies (Sherwood et al., 2020), the AR6 definition of ECS 
has changed from previous reports; it now includes all feedbacks 
except those associated with ice sheets. Accordingly, unlike previous 
reports, the AR6 assessments of ECS and TCR are not based primarily 
on GCM and ESM model results (see Section 7.5.5 and Box. 7.1 for 
a full discussion).
Today, other sensitivity terms are sometimes used, such as ‘transient 
climate response to emissions’ (TCRE, defined as the ratio of warming 
to cumulative CO 2 emissions in a  CO 2-only simulation) and ‘Earth 
system sensitivity’ (ESS), which includes multi-century Earth system 
feedbacks such as changes in ice sheets.
Table 1.2 shows estimates of ECS and TCR for major climate science 
assessments since 1979. The table shows that despite some variation 
in the range of GCM and (for the later assessments) ESM results, 
expert assessment of ECS changed little between 1979 and the

present Report. Based on multiple lines of evidence, AR6 has narrowed 
the likely  range of ECS to 2.5°C–4.0°C (Chapter 7, Section 7.5.5).
The AR5 WGI assessed that there is a close relationship of cumulative 
total emissions of CO 2 and GMST response that is approximately 
linear (IPCC, 2013b). This finding implies that continued emissions 
of CO 2 will cause further warming and changes in all components of 
the climate system, independent of any specific scenario or pathway. 
Scenario-based climate projections using the Representative 
Concentration Pathways (RCPs) assessed in AR5 WGI result in 
continued warming over the 21st century in all scenarios except 
a  strong climate change mitigation scenario (RCP2.6). Similarly, 
under all RCP scenarios, AR5 assessed that the rate of sea level rise 
over the 21st  century will very likely  exceed that observed during 
1971–2010 due to increased ocean warming and increased loss of

mass from glaciers and ice sheets. Further increases in atmospheric 
CO 2 will also lead to further uptake of carbon by the ocean, which will 
increase ocean acidification. By the mid-21st century the magnitudes 
of the projected changes are substantially affected by the choice of 
scenario. The set of scenarios used in climate change projections 
assessed as part of AR6 is discussed in Section 1.6.
From the close link between cumulative emissions and warming it 
follows that any given level of global warming is associated with 
a total budget of GHG emissions, especially CO 2 as it is the largest long-
lived contributor to radiative forcing (Allen et al., 2009; Collins et al., 
2013; Rogelj et al., 2019). Higher emissions in earlier decades imply 
lower emissions later on to stay within the Earth’s carbon budget. 
Stabilizing the anthropogenic influence on global surface temperature 
thus requires that CO 2 emissions and removals reach net zero once the

remaining carbon budget is exhausted (Cross-Chapter Box 1.4).
Past, present and future emissions of CO 2 therefore commit the world to 
substantial multi-century climate change, and many aspects of climate 
change would persist for centuries even if emissions of CO 2 were 
stopped immediately (IPCC, 2013b). According to AR5, a large fraction 
of this change is essentially irreversible on a multi-century to millennial 
time scale, barring large net removal (‘negative emissions’) of CO 2 from 
the atmosphere over a  sustained period through as yet unavailable 
technological means (Chapters 4 and 5l; IPCC, 2013a, 2018). However, significant reductions of warming due to short-lived climate forcers 
(SLCFs) could reduce the level at which temperature stabilizes once CO 2 
emissions reach net zero, and also reduce the long-term global warming 
commitment by reducing radiative forcing from SLCFs (Chapter 5).
In summary, major lines of evidence  – observations, paleoclimate,

theoretical understanding and natural and human drivers – have been 
studied and developed for over 150  years. Methods for projecting 
climate futures have matured since the 1950s and attribution 
studies since the 1980s. We conclude that understanding of the 
principal features of the climate system is robust and well established.
1.3.6 How do Previous Climate Projections Compare 
with Subsequent Observations?
Many different sets of climate projections have been produced over 
the past several decades, so it is valuable to assess how well those 
projections have compared against subsequent observations. Consistent 
findings build confidence in the process of making projections for the 
future. For example, Stouffer and Manabe (2017) compared projections 
made in the early 1990s with subsequent observations. They found that 
the projected surface pattern of warming, and the vertical structure of 
temperature change in both the atmosphere and ocean, were realistic.

Rahmstorf et al. (2007, 2012) examined projections of global surface 
temperature and GMSL assessed by TAR and AR4 and found that 
the global surface temperature projections were in good agreement 
with the subsequent observations, but that sea level projections 
were underestimates compared to subsequent observations. The 
AR5 WGI also examined earlier IPCC assessment reports to evaluate 
their projections of how global surface temperature and GMSL would 
change (Cubasch et al., 2013) with similar conclusions.
Although these studies generally showed good agreement between 
past projections and subsequent observations, this type of analysis is 
complicated because the scenarios of future radiative forcing used in 
earlier projections do not precisely match the actual radiative forcings 
that subsequently occurred. Mismatches between the projections 
and subsequent observations could be due to incorrectly projected 
radiative forcings (e.g.,  aerosol emissions, GHG concentrations or

volcanic eruptions that were not included), an incorrectly modelled 
response to those forcings, or both. Alternatively, agreement 
between projections and observations could be fortuitous due to 
a  compensating balance of errors, for example, too low climate 
sensitivity but too strong radiative forcings.
One approach to partially correct for mismatches between the 
forcings used in the projections and the forcings that actually occurred 
is described by Hausfather et al. (2020). Model projections of global 
surface temperature and estimated radiative forcings were taken 
from several historical studies, along with the baseline ‘no-policy’ 
scenarios from the first four IPCC assessment reports. These model 
projections of temperature and radiative forcing are then compared 
to (i) the observed change in temperature through time over the 
projection period, and (ii) the observed change in temperature 
relative to the observationally estimated radiative forcing over the

projection period (Figure 1.9; data from Hausfather et al., 2020).

185
YearGlobal temperature change (°C)
1970 1980 1990 2000 2010 2020-0.20.00.20.40.60.81.01.2
Radiative Forcing (W/m²)Global temperature change (°C)
-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0-0.20.00.20.40.60.81.01.2Observations Manabe 1970 Rasool and Schneider 1971
Broecker 1975 Nordhaus 1977 Hansen 1981
Hansen 1988 FAR 1990 Manabe and Stouffer 1993
SAR 1995 TAR 2001 AR4 2007
Figure 1.9 | Assessing past projections of global temperature change.  (Top)  Projected temperature change post-publication on a temperature vs time (1970–2020) 
and (bottom)  temperature vs radiative forcing (1970–2017) basis for a  selection of prominent climate model projections (taken from Hausfather et al., 2020). Model 
projections (using global surface air temperature, GSAT) are compared to temperature observations (using global mean surface temperature, GMST) from HadCRUT5 (black)

and anthropogenic forcings (through 2017) from Dessler and Forster (2018), and have a baseline generated from the first five years of the projection period. Projections shown 
are: Manabe (1970), Rasool and Schneider (1971), Broecker (1975), Nordhaus (1977), Hansen et al. (1981, H81), Hansen et al. (1988, H88), Manabe and Stouffer (1993), along 
with the Energy Balance Model (EBM) projections from FAR, SAR and TAR, and the multi-model mean projection using CMIP3 simulations of the Special Report on Emissions 
Scenarios (SRES) A1B scenario from AR4. H81 and H88 show most expected scenarios 1 and B, respectively. See Hausfather et al. (2020) for more details of the projections. 

186
Although this approach has limitations when the modelled forcings 
differ greatly from the forcings subsequently experienced, they 
were generally able to project actual future global warming when 
the mismatches between forecast and observed radiative forcings 
are accounted for. For example, Scenario B  presented in Hansen 
et al. (1988) projected around 50% more warming than has been 
observed during the 1988–2017 period, but this is largely because 
it overestimated subsequent radiative forcings. Similarly, while FAR 
(IPCC, 1990a) projected a higher rate of global surface temperature 
warming than has been observed, this is largely because it 
overestimated future GHG concentrations: FAR’s projected increase in 
total anthropogenic forcing between 1990 and 2017 was 1.6 W m–2, 
while the observational estimate of actual forcing during that 
period is 1.1 W m–2 (Dessler and Forster, 2018). Under these actual

forcings, the change in temperature in FAR aligns with observations 
(Hausfather et al., 2020).
In addition to global surface temperature, past regional projections 
can be evaluated. For example, FAR (IPCC, 1990a) presented a series 
of temperature projections for 1990–2030 for several regions around the world. Regional projections were given for the best estimate of 1.8°C 
of global warming by 2030, compared to a baseline of 1850–1900, and 
were assigned low confidence . The FAR also suggested that regional 
temperature changes should be scaled by –30% to +50% to account 
for the uncertainty in projected global warming.
The regional projections presented in FAR are compared to 
the observed temperature change in the period since 1990 
(Figure  1.10), following Grose et al. (2017). Subsequent observed 
temperature change has tracked within the FAR projected range 
for the best estimate of regional warming in the Sahel, South Asia

and southern Europe. Temperature change has tracked at or below 
this range for the central North America and Australia regions, 
yet remains within the range reduced by 30% to generate FAR’s 
lower global warming estimate. This is consistent with the smaller 
observed estimate of radiative forcing compared to the FAR central 
estimate. Note that the projections assessed in Chapter  4  of this 
Report suggest that global temperatures will be around 1.2°C–1.8°C 
above 1850–1900 levels by 2030, a range which is also lower than 
the FAR central estimate.
1970 1980 1990 2000 2010 2020 2030-1012345Temperature relative to 1850–1900 (°C) First Assessment Report (IPCC FAR) regional temperature projections
Southern EuropeBest estimate for 1.8°C Global Warming 
Estimate scaled for range of Global Warming
5 Observed datasets
Observed Trends 1990–2020 - extrapolated
Southern Europe
Central North AmericaSahel
Southern AsiaAustraliaBest estimate
Scaled
Extrapolated obs. trend

Figure 1.10 | Range of projected temperature change for 1990–2030 for various regions defined in IPCC First Assessment Report (FAR). The left-hand 
panel shows the FAR projections (IPCC, 1990a)  for southern Europe, with the darker blue shade representing the range of projected change given for the best estimate of 1.8°C 
global warming by 2030 compared with pre-industrial levels, and the fainter blue shade showing the range scaled by –30% to +50% for lower and higher estimates of global 
warming. Blue lines show the regionally averaged observations from five global temperature gridded datasets, and blue dashed lines show the linear trends in those datasets for 
1990–2020 extrapolated to 2030. Observed datasets are: HadCRUT5, Cowtan and Way, GISTEMP , Berkeley Earth and NOAA GlobalTemp. The inset map shows the definition

of the FAR regions used. The right-hand  panel shows projected temperature changes by 2030 for the various FAR regions, compared to the extrapolated observational trends, 
following Grose et al. (2017). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

187
Box 1.2 | Special Reports in the IPCC Sixth Assessment Cycle: Key Findings
The Sixth Assessment Cycle started with three Special Reports. The Special Report on Global Warming of 1.5°C (SR1.5, IPCC, 2018), 
invited by the Parties to the UNFCCC in the context of the Paris Agreement, assessed current knowledge on the impacts of global 
warming of 1.5°C above pre-industrial levels and related global greenhouse gas (GHG) emissions pathways. The Special Report on 
Climate Change and Land (SRCCL, IPCC, 2019a) addressed GHG fluxes in land-based ecosystems, land use and sustainable land 
management in relation to climate change adaptation and mitigation, desertification, land degradation and food security. The Special 
Report on the Ocean and Cryosphere in a Changing Climate (SROCC, IPCC, 2019b) assessed new literature on observed and projected 
changes of the ocean and the cryosphere, and their associated impacts, risks and responses.

The SR1.5 and SRCCL were produced through a collaboration between the three IPCC Working Groups, SROCC by only Working 
Groups I and II. Here we focus on key findings relevant to the physical science basis covered by WGI.
Observations of climate change
The SR1.5 estimated with high confidence  that human activities caused a  global warming of approximately 1°C between the 
1850–1900 period and 2017. For the period 2006–2015, observed global mean surface temperature (GMST7) was 0.87°C ± 0.12°C 
higher than the average over the 1850–1900 period ( very high confidence ). Anthropogenic global warming was estimated to be 
increasing at 0.2 ± 0.1°C per decade ( high confidence ) and likely  matches the level of observed warming to within ±20%. The SRCCL 
found with high confidence  that over land, mean surface air temperature increased by 1.53°C ± 0.15°C between 1850–1900 and

2006–2015, or nearly twice as much as the global average. This observed warming has already led to increases in the frequency and 
intensity of climate and weather extremes in many regions and seasons, including heat waves in most land regions ( high confidence ), 
increased droughts in some regions ( medium confidence ), and increases in the intensity of heavy precipitation events at the global 
scale ( medium confidence ). These climate changes have contributed to desertification and land degradation in many regions ( high 
confidence ). Increased urbanization can enhance warming in cities and their surroundings (heat island effect), especially during heat 
waves ( high confidence ), and intensify extreme rainfall ( medium confidence ).
With respect to the ocean, SROCC assessed that it is virtually certain  that the ocean has warmed unabated since 1970 and has taken

up more than 90% of the excess heat contributed by global warming. The rate of ocean warming has likely  more than doubled since 
1993. Over the period 1982–2016, marine heatwaves have very likely  doubled in frequency and are increasing in intensity ( very high 
confidence ). In addition, the surface ocean acidified further ( virtually certain ) and loss of oxygen occurred from the surface to a depth 
of 1000 m  ( medium confidence ). The Report expressed medium confidence  that the Atlantic Meridional Overturning Circulation 
(AMOC) weakened in 2004–2017 relative to 1850–1900.
Concerning the cryosphere, SROCC reported widespread continued shrinking of nearly all components. Mass loss from the Antarctic 
Ice Sheet tripled over the period 2007–2016 relative to 1997–2006, while mass loss doubled for the Greenland Ice Sheet ( likely , 
medium confidence ). The Report concludes with very high confidence  that due to the combined increased loss from the ice sheets,

global mean sea level (GMSL) rise has accelerated ( extremely likely ). The rate of recent GMSL rise (3.6 ± 0.5 mm yr–1 for 2006–2015) is 
about 2.5 times larger than for 1901–1990. The report also found that Arctic sea ice extent has very likely  decreased for all months of 
the year since 1979 and that September sea ice reductions of 12.8 ± 2.3% per decade are likely  unprecedented for at least 1000 years. 
Feedbacks from the loss of summer sea ice and spring snow cover on land have contributed to amplified warming in the Arctic ( high 
confidence ), where surface air temperature likely  increased by more than double the global average over the last two decades. By 
contrast, Antarctic sea ice extent overall saw no statistically significant trend for the period 1979–2018 ( high confidence ).
7 Box 1.2 reproduces the temperature metrics as they appeared in the respective SPMs of the Special Reports. In AR6 long-term changes of GMST (global mean surface temperature) and GSAT

(global surface air temperature) are considered to be equivalent, differing in uncertainty estimates only (Cross-Chapter Box 2.3).Overall, there is medium confidence  that past projections of global 
temperature are consistent with subsequent observations, especially 
when accounting for the difference in radiative forcings used and 
those which actually occurred ( limited evidence, high agreement ). 
The FAR regional projections are broadly consistent with subsequent observations, allowing for regional-scale climate variability and 
differences in projected and actual forcings. There is medium 
confidence  that the spatial warming pattern has been reliably 
projected in past IPCC reports ( limited evidence, high agreement ).

188
Chapter 1 Framing, Context, and Methods1Box 1.2 (continued)
The SROCC assessed that anthropogenic climate change has increased observed precipitation ( medium confidence ), winds ( low 
confidence ), and extreme sea level events ( high confidence ) associated with some tropical cyclones. It also found evidence for an 
increase in the annual global proportion of Category 4 or 5 tropical cyclones in recent decades ( low confidence ).
Drivers of climate change
The SRCCL stated that the land is simultaneously a source and sink of CO 2, due to both anthropogenic and natural drivers. It estimates 
with medium confidence  that agriculture, forestry and other land use (AFOLU) activities accounted for around 13% of CO 2, 44% of 
CH 4, and 82% of N 2O emissions from human activities during 2007–2016, representing 23% (12.0 ± 3.0 GtCO 2 equivalent yr–1) of 
the total net anthropogenic emissions of GHGs. The natural response of land to human-induced environmental change – such as

increasing atmospheric CO 2 concentration, nitrogen deposition and climate change – caused a net CO 2 sink equivalent of around 29% 
of total CO 2 emissions ( medium confidence ); however, the persistence of the sink is uncertain due to climate change ( high confidence ).
The SRCCL also assessed how changes in land conditions affect global and regional climate. It found that changes in land cover have 
led to both a net release of CO 2, contributing to global warming, and an increase in global land albedo, causing surface cooling. 
However, the report estimated that the resulting net effect on globally averaged surface temperature was small over the historical 
period ( medium confidence ).
The SROCC found that the carbon content of Arctic and boreal permafrost is almost twice that of the atmosphere ( medium confidence ), 
and assessed medium evidence  with low agreement that thawing northern permafrost regions are currently releasing additional 
net CH 4 and CO 2.

Projections of climate change
The SR1.5 concluded that global warming is likely  to reach 1.5°C between 2030 and 2052 if it continues to increase at the current rate 
(high confidence ). However, even though warming from anthropogenic emissions will persist for centuries to millennia and will cause 
ongoing long-term changes, past emissions alone are unlikely  to raise global surface temperature to 1.5°C above 1850–1900 levels.
The SR1.5 also found that reaching and sustaining net zero anthropogenic CO 2 emissions and reducing net non-CO 2 radiative forcing 
would halt anthropogenic global warming on multi-decadal time scales ( high confidence ). The maximum temperature reached is then 
determined by (i) cumulative net global anthropogenic CO 2 emissions up to the time of net zero CO 2 emissions ( high confidence ) and (ii) 
the level of non-CO 2 radiative forcing in the decades prior to the time that maximum temperatures are reached ( medium confidence ).

Furthermore, climate models project robust differences in regional climate characteristics between the present day and a  global 
warming of 1.5°C, and between 1.5°C and 2°C, including mean temperature in most land and ocean regions and hot extremes in most 
inhabited regions ( high confidence ). There is medium confidence  in robust differences in heavy precipitation events in several regions 
and the probability of droughts in some regions.
The SROCC projected that global-scale glacier mass loss, permafrost thaw, and decline in snow cover and Arctic sea ice extent will 
continue in the period 2031–2050 due to surface air temperature increases ( high confidence ). The Greenland and Antarctic ice sheets 
are projected to lose mass at an increasing rate throughout the 21st century and beyond ( high confidence ). Sea level rise will also 
continue at an increasing rate. For the period 2081–2100 with respect to 1986–2005, the likely  ranges of GMSL rise are projected

at 0.26–0.53 m for RCP2.6 and 0.51–0.92 m for RCP8.5. For the RCP8.5 scenario, projections of GMSL rise by 2100 are higher by 
0.1 m than in AR5 due to a larger contribution from the Antarctic Ice Sheet ( medium confidence ). Extreme sea level events that 
occurred once per hundred years in the recent past are projected to occur at least once per year at many locations by 2050, especially 
in tropical regions, under all RCP scenarios ( high confidence ). According to SR1.5, by 2100 GMSL rise would be around 0.1 m lower 
with 1.5°C global warming compared to 2°C ( medium confidence ). If warming is held to 1.5°C, GMSL will still continue to rise well 
beyond 2100, but at a slower rate and a lower magnitude. However, instability and/or irreversible loss of the Greenland and Antarctic 
ice sheets, resulting in a multi-metre rise in sea level over hundreds to thousands of years, could be triggered at 1.5°C–2°C of global

warming ( medium confidence ). According to SROCC, sea level rise in an extended RCP2.6 scenario would be limited to around 1 m in 
2300 ( low confidence ) while under RCP8.5 multi-metre sea level rise is projected by then ( medium confidence ).
The SROCC projected that over the 21st century, the ocean will transition to unprecedented conditions, with increased temperatures 
(virtually certain ), further acidification ( virtually certain ), and oxygen decline ( medium confidence ). Marine heatwaves are projected to 
become more frequent ( very high confidence ) as are extreme El Niño and La Niña events ( medium confidence ). The AMOC is projected

189
1.4 AR6 Foundations and Concepts
The AR6 WGI builds on previous assessments using well established 
foundations and concepts. This section highlights some of the cross-
cutting methods applied in the climate change literature and topics 
discussed repeatedly throughout this Report. First, the choices related 
to ‘baselines’, or ‘reference periods’, are highlighted (Section 1.4.1), 
including a specific discussion on the pre-industrial baseline used in 
AR6 WGI (Cross-Chapter Box 1.2). The relationships between long-
term trends, climate variability and the concept of ‘emergence of 
changes’ (Section  1.4.2) and the sources of uncertainty in climate 
simulations (Section  1.4.3) are discussed next. The topic of low-
likelihood outcomes, storylines, abrupt changes and surprises follows 
(Section  1.4.4), including a  description of AR6 WGI risk framing 
(Cross-Chapter Box 1.3). The Cross-Working Group Box on Attribution

describes attribution methods, including those for extreme events. 
Various sets of geographical regions used in later chapters are also 
defined and introduced (Section 1.4.5).
1.4.1 Baselines, Reference Periods and Anomalies
Several baselines or reference periods are used consistently 
throughout AR6 WGI. Baseline refers to a  period against which 
differences are calculated, whereas reference period is used more 
generally to indicate a  time period of interest, or a  period over 
which some relevant statistics are calculated (Glossary). Variations 
in observed and simulated climate variables over time are often 
presented as ‘anomalies’, that is, the differences relative to a baseline, 
rather than using the absolute values. This is done for several reasons.First, anomalies are often used when combining data from multiple 
locations, because the absolute values can vary over small spatial scales 
which are not densely observed or simulated, whereas anomalies are

representative for much larger scales (e.g., for temperature; Hansen 
and Lebedeff, 1987). Since their baseline value is zero by definition, 
anomalies are also less susceptible to biases arising from changes 
in the observational network. Second, the seasonality in different 
climate indicators can be removed using anomalies to more clearly 
distinguish variability from long-term trends.
Third, different datasets can have different absolute values for the 
same climate variable that should be removed to allow effective 
comparisons of variations over time. This is often required when 
comparing climate simulations with each other, or when comparing 
simulations with observations, as simulated climate variables are 
also affected by model bias that can be removed when they are 
presented as anomalies. It can also be required when comparing 
observational datasets or reanalyses (Section 1.5.2) with each other, 
due to systematic differences in the underlying measurement system

(Figure 1.11). Understanding the reasons for any absolute difference 
is important, but whether the simulated absolute value matters when 
projecting future change will depend on the variable of interest. For 
example, there is not a strong relationship between climate sensitivity 
of a model (which is an indicator of the degree of future warming) 
and the simulated absolute global surface temperature (Mauritsen 
et al., 2012; Hawkins and Sutton, 2016).Box 1.2 (continued)
to weaken during the 21st century ( very likely ), but a collapse is deemed very unlikely  (albeit with medium confidence  due to known 
biases in the climate models used for the assessment).
Emissions pathways to limit global warming
The SR1.5 focused on emissions pathways and system transitions consistent with 1.5°C global warming over the 21st century. Building 
upon the understanding from AR5 WGI of the quasi-linear relationship between cumulative net anthropogenic CO 2 emissions since

1850–1900 and maximum global mean temperature, the Report assessed the remaining carbon budgets compatible with the 1.5°C 
or 2°C warming goals of the Paris Agreement. Starting from year 2018, the remaining carbon budget for a one-in-two (50%) chance 
of limiting global warming to 1.5°C is about 580 GtCO 2, and about 420 GtCO 2 for a two-in-three (66%) chance ( medium confidence).
At  constant 2017 emissions, these budgets would be depleted by about the years 2032 and 2028, respectively. Using GMST 
instead of GSAT gives estimates of 770 GtCO 2 and 570 GtCO 2, respectively ( medium confidence ). Each budget is further reduced by 
approximately 100 GtCO 2 over the course of this century when permafrost and other less well represented Earth system feedbacks 
are taken into account.
It is concluded that all emissions pathways with no or limited overshoot of 1.5°C imply that global net anthropogenic CO 2 emissions

would need to decline by about 45% from 2010 levels by 2030, reaching net zero around 2050, together with deep reductions in 
other anthropogenic emissions, such as methane and black carbon. To limit global warming to below 2°C, CO 2 emissions would have 
to decline by about 25% by 2030 and reach net zero around 2070.

190
HISTORICAL AND SSP1-2.6 SIMULATIONS
1850 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090121314151617Absolute global  
temperature  (°C)CMIP6 Jones et al. (1999)
Berkeley ERA-5
1850-1900 baseline
Near-term Mid-term Long-term
1850 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 20900123anomaly  (°C)Global temperature variations and baseline choices
1995-2014
baseline
1850 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090-1012Global temperature
anomaly  (°C)Global temperature
Figure 1.11 | Choice of baseline matters when comparing observations and model simulations.  Global mean surface air temperature (GSAT, grey) from a range of 
CMIP6 historical simulations (1850–2014; 25 models) and SSP1-2.6 (2015–2100) using absolute values (top)  and anomalies relative to two different baselines: 1850–1900

(middle)  and 1995–2014 (bottom) . An estimate of GSAT from a reanalysis (ERA-5, orange, 1979–2020) and an observation-based estimate of global mean surface air 
temperature (GMST) (Berkeley Earth, black, 1850–2020) are shown, along with the mean GSAT for 1961–1990 estimated by Jones et al. (1999), light blue shading (14.0°C 
± 0.5°C). Using the more recent baseline (bottom) allows the inclusion of datasets which do not include the periods of older baselines. The middle and bottom panels have 
scales which are the same size but offset. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

191
Framing, Context, and Methods Chapter 11For some variables, such as precipitation, anomalies are often 
expressed as percentages in order to more easily compare changes 
in regions with very different climatological means. However, 
for situations where there are important thresholds (e.g.,  phase 
transitions around 0°C) or for variables which can only take 
a particular sign or be in a fixed range (e.g., sea ice extent or relative 
humidity), absolute values are normally used.
The choice of a  baseline period has important consequences for 
evaluating both observations and simulations of the climate, 
for  comparing observations with simulations, and for presenting 
climate projections. There is usually no perfect choice of baseline 
as many factors have to be considered and compromises may be 
required (Hawkins and Sutton, 2016). It is important to evaluate the 
sensitivity of an analysis or assessment to the choice of the baseline.

For example, the collocation of observations and reanalyses within the 
model ensemble spread depends on the choice of the baseline, and 
uncertainty in future projections of climate is reduced if using a more 
recent baseline, especially for the near term (Figure 1.11). The length 
of an appropriate baseline or reference period depends on the  variable being considered, the rates of change of the variable and the 
purpose of the chosen period, but is usually 20 to 50  years long.  
The World Meteorological Organization (WMO) uses 30-year periods 
to define ‘climate normals’, which indicate conditions expected to be 
experienced in a given location.
For AR6 WGI, the period 1995–2014 is used as a baseline to calculate 
the changes in future climate using model projections and also as 
a ‘modern’ or ‘recent past’ reference period when estimating past 
observed warming. The equivalent period in AR5 was 1986–2005, and 
in SR1.5, SROCC and SRCCL it was 2006–2015. The primary reason for

the different choice in AR6 is that 2014 is the final year of the historical 
CMIP6 simulations. These simulations subsequently assume different 
emissions scenarios and so choosing any later baseline end date 
would require selecting a particular emissions scenario. For certain 
assessments, the most recent decade possible (e.g., 2010–2019 or 
2011–2020, depending on the availability of observations) is also 
used as a reference period (Cross-Chapter Box 2.3).
Figure  1.12 shows changes in observed global mean surface 
temperature (GMST) relative to 1850–1900 and illustrates 
for period
around 17500.00°C (1850 –1900)0.36°C (1961–1990)
(0.23-0.44)0.58°C (1980–1999)
(0.45-0.68)0.69°C (1986–2005)
(0.54-0.79)0.85°C (1995–2014)
(0.69-0.95)1.09°C (2011-2020)
(0.95-1.20)
0.23°C (1900–1999)
(0.10-0.31)1.26°C (2020)
(1.12-1.37)Observed global mean surface temperature change
Relative to 1850–1900 using four datasets

1860 1880 1900 1920 1940 1960 1980 2000 2020–0.200.20.40.60.811.21.4GMST change  (°C)
Figure 1.12 | Global warming over the instrumental period.  Observed global mean surface temperature (GMST) from four datasets, relative to the average temperature 
of 1850–1900 in each dataset (see Cross-Chapter Box 2.3 and Section 2.3.1.1 for more details). The shaded grey band indicates the assessed likely range for the period around 
1750 (Cross-Chapter Box 1.2). Different reference periods are indicated by the coloured horizontal lines, and an estimate of total GMST change up to that period is given, 
enabling a translation of the level of warming between different reference periods. The reference periods are all chosen because they have been used in AR6 or previous IPCC 
assessment reports. The value for the 1981–2010 reference period, used as a ‘climate normal’ period by the World Meteorological Organization, is the same as the 1986–2005

reference period shown. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

192
Chapter 1 Framing, Context, and Methods1observed global warming levels for a  range of reference periods 
that are either used in AR6 or were used in previous IPCC reports. 
This allows changes to be calculated between different periods 
and compared to previous assessments. For example, AR5 assessed 
the change in GMST from the 1850–1900 baseline to 1986–2005 
reference period as 0.61 [0.55  to  0.67]  °C, whereas it is now 
assessed to be 0.69 [0.52 to 0.82] °C using improved GMST datasets 
(Cross-Chapter Box 2.3).
The commonly used metric for global surface warming tends to be 
GMST but, as shown in Figure 1.11, climate model simulations tend to 
use global surface air temperature (GSAT). Although GMST and GSAT 
are closely related, the two measures are physically distinct. GMST is 
a combination of land surface air temperature (LSAT) and sea surface 
temperature (SST), whereas GSAT is surface air temperatures over

land, ocean and ice. A  key development in AR6 is the assessment 
that long-term changes in GMST and GSAT differ by at most 10% in either direction, with low confidence  in the sign of any differences 
(see Cross Chapter Box 2.3 for details). 
Three future reference periods are used in AR6 WGI for presenting 
projections: near term  (2021–2040), mid-term  (2041–2060) and 
long-term  (2081–2100; Figure  1.11). In AR6, 20-year reference 
periods are considered long enough to show future changes in 
many variables when averaging over ensemble members of multiple 
models, and short enough to enable the time dependence of changes 
to be shown throughout the 21st century. Projections with alternative 
recent baselines (such as 1986–2005 or the current WMO climate-
normal period of 1981–2010) and a wider range of future reference 
periods are presented in the Interactive Atlas. Note that ‘long term’ 
is also sometimes used in a more general sense to refer to durations

of centuries to millennia when examining past climate, as well as 
future climate change beyond the year 2100. Cross-Chapter Box 2.1 
discusses the paleo-reference periods used in AR6.
Cross-Chapter Box 1.2 | Changes in Global Temperature Between 1750 and 1850
Contributing Authors:  Ed Hawkins (United Kingdom), Paul Edwards (United States of America), Piers Forster (United Kingdom), 
Darrell S. Kaufman (United States of America), Jochem Marotzke (Germany), Malte Meinshausen (Australia/Germany), Maisa Rojas 
(Chile), Bjørn H. Samset (Norway), Peter Thorne (Ireland/United Kingdom)
The Paris Agreement aims to limit global temperatures to specific thresholds ‘above pre-industrial levels’. In AR6 WGI, as in previous 
IPCC reports, observations and projections of changes in global temperature are generally expressed relative to 1850–1900 as an 
approximate pre-industrial state (SR1.5, IPCC, 2018). This is a pragmatic choice based upon data availability considerations, though

both anthropogenic and natural changes to the climate occurred before 1850. The remaining carbon budgets, the chance of crossing 
global temperature thresholds, and projections of extremes and sea level rise at a  particular level of global warming can all be 
sensitive to the chosen definition of the approximate pre-industrial baseline (Millar et al., 2017b; Schurer et al., 2017; Pfleiderer et al., 
2018; Rogelj et al., 2019; Tokarska et al., 2019). This Cross-Chapter Box assesses the evidence on change in radiative forcing and global 
temperature from the period around 1750 to 1850–1900; variations in the climate before 1750 are discussed in Chapter 2.
Although there is some evidence for human influence on climate before 1750 (e.g., Ruddiman and Thomson, 2001; Koch et al., 2019), 
the magnitude of the effect is still disputed (Section 5.1.2.3; e.g., Joos et al., 2004; J. Beck et al., 2018), and most studies analyse

the human influence on climate over the industrial period. Historically, the widespread use of coal-powered machinery started the 
Industrial Revolution in Britain in the late 18th century (Ashton, 1997), but the global effects were small for several decades. In line 
with this, previous IPCC assessment reports considered changes in radiative forcing relative to 1750, and temperature changes were 
often reported relative to the ‘late 19th century’. The AR5 and SR1.5 made the specific pragmatic choice to approximate pre-industrial 
global temperatures by using the average of the 1850 –1900 period, when permanent surface observing networks emerged that 
provide sufficiently accurate and continuous measurements on a near-global scale (Sections 1.3.1 and 2.3.1.1), and because model 
simulations of the historical period used 1850 as their start date. For the same reasons, to ensure continuity with previous assessments,

and because of larger uncertainties and lower confidence in climatic changes before 1850 than after, AR6 makes the same choice to 
approximate pre-industrial global temperatures by using the the average of the 1850–1900 period.
Here we assess improvements in our understanding of climatic changes in the period 1750–1850. Anthropogenic influences on 
climate between 1750 and 1900 were primarily increased anthropogenic GHG and aerosol emissions, and changes in land use. 
Between 1750 and 1850 atmospheric CO 2 levels increased from about 278 ppm to about 285 ppm (equivalent to around 3 years 
of current rates of increase; Chapter 2, Section 2.2.3), corresponding to about 55 GtCO 2 in the atmosphere. Estimates of emissions 
from fossil fuel burning (about 4 GtCO 2, Boden et al., 2017) cannot explain the pre-1850 increase, so CO 2 emissions from land-use 
changes are implicated as the dominant source. The atmospheric concentration of other GHGs also increased over the same period,

and there was a cooling influence from other anthropogenic radiative forcings (such as aerosols and land-use changes), but with 
a  larger uncertainty than for GHGs (Sections  2.2.6 and 7.3.5.2, and Cross-Chapter Box  1.2, Figure  1; e.g.,  Carslaw et al., 2017;

193
Framing, Context, and Methods Chapter 11Cross-Chapter Box 1.2 (continued)
Owens et al., 2017; Hamilton et al., 2018). It is likely  that there was a net anthropogenic forcing of 0.0 –0.3 Wm–2 in 1850 –1900 relative 
to 1750 ( medium confidence ). The net radiative forcing from changes in solar activity and volcanic activity in 1850 –1900, compared 
to the period around 1750, is estimated to be smaller than ±0.1 W m–2, but note there were several large volcanic eruptions between 
1750 and 1850 (Cross-Chapter Box 1.2, Figure 1).
Several studies since AR5 have estimated changes in global temperatures following industrialisation and before 1850. Hawkins 
et al. (2017) used observations, radiative forcing estimates and model simulations to estimate the warming from 1720–1800 until 
1986–2005 and assessed a  likely range of 0.55°C–0.80°C, slightly broader than the equivalent range starting from 1850–1900

(0.6°C–0.7°C). From proxy evidence, PAGES 2k Consortium (2019) found that GMST for 1850–1900 was 0.02 [–0.22 to +0.16] °C 
warmer than the 30-year period centred on 1750. Schurer et al. (2017) used climate model simulations of the last millennium to 
estimate that the increase in GHG concentrations before 1850 caused an additional likely range of 0.0°C –0.2°C global warming 
when considering multiple reference periods. Haustein et al. (2017) implies an additional warming of around 0.05°C attributable 
to human activity from 1750 to 1850–1900, and the AR6 emulator (Section 7.3.5.3) estimates the likely range of this warming to 
be 0.04°C–0.14°C.
Combining these different sources of evidence, we assess that from the period around 1750 to 1850–1900 there was a change in 
global temperature of around 0.1 [–0.1 to +0.3] °C ( medium confidence ), with an anthropogenic component in a  likely range of 
0.0°C–0.2°C ( medium confidence ).

-1.5-1.0-0.50.00.51.01.52.02.53.0Radiative forcing (Wm-2)1850–1900 baseline
GHG
NATURAL
OTHER
ANTHROPOGENIC0.33 (0.28 to 0.37) Wm-2
0.02 (-0.04 to 0.09) Wm-2
-0.15 (-0.33 to -0.03) Wm-2Total anthropogenic forcing between
1750 and the 1850–1900 baseline is
  likely between 0.0-0.3 Wm-2
1750 1800 1850 1900 1950 2000 2019
Cross-Chapter Box 1.2, Figure 1 | Changes in radiative forcing from 1750–2019.  The radiative forcing estimates from the AR6 emulator (Cross-Chapter 
Box 7.1) are split into GHG, other anthropogenic (mainly aerosols and land use) and natural forcings, with the average over the 1850–1900 baseline shown for each. 
1.4.2 Variability and Emergence  
of the Climate Change Signal
Climatic changes since the pre-industrial era are a combination of 
long-term anthropogenic changes and natural variations on time 
scales from days to decades. The relative importance of these two

factors depends on the climate variable or region of interest. Natural 
variations consist of both natural radiatively forced trends (e.g., due 
to volcanic eruptions or solar variations) and ‘internal’ fluctuations of 
the climate system which occur even in the absence of any radiative 
forcings. The internal ‘modes of variability’, such as the El Niño–
Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), 
are discussed further in Annex IV.1.4.2.1 Climate Variability Can Influence Trends 
Over Short Periods
Natural variations in both weather and longer time scale phenomena 
can temporarily mask or enhance any anthropogenic trends 
(e.g.,  Deser et al., 2012; Kay et al., 2015). These effects are more 
important on small spatial and temporal scales but can also occur on 
the global scale (Cross-Chapter Box 3.1).
Since AR5, many studies have examined the role of internal variability 
through the use of ‘large ensembles’. Each such ensemble consists of

many different simulations by a single climate model for the same 
time period and using the same radiative forcings. These simulations 
differ only in their phasing of the internal climate variations (also

194
see Section  1.5.4.2). A  set of illustrative examples using one such 
large ensemble (Maher et al., 2019) demonstrates how variability 
can influence trends on decadal time scales (Figure 1.13). The long-
term anthropogenic trends in this set of climate indicators are clearly 
apparent when considering the ensemble as a whole (grey shading), 
and all the individual ensemble members have very similar trends 
for ocean heat content (OHC), which is a robust estimate of the total 
energy stored in the climate system (e.g., Palmer and McNeall, 2014). 
However, the individual ensemble members can exhibit very different 
decadal trends in global surface air temperature (GSAT), UK summer 
temperatures, and Arctic sea ice variations. More specifically, for 
a representative 11-year period, both positive and negative trends 
can be found in all these surface indicators, even though the long-

term trend is for increasing temperatures and decreasing sea ice. 
Periods in which the long-term trend is substantially masked or 
enhanced for more than 20 years are also visible in these regional 
examples. This highlights the fact that observations are expected 
to exhibit short-term trends which are larger or smaller than the 
long-term trend or that differ from the average projected trend from 
climate models, especially on continental spatial scales or smaller 
(Cross-Chapter Box  3.1). The actual observed trajectory can be 
considered as one realization of many possible alternative worlds 
that experienced different weather; this is also demonstrated by the construction of ‘observation-based large ensembles’, which 
are alternate possible realizations of historical observations that 
retain the statistical properties of observed regional weather 
(e.g., McKinnon and Deser, 2018).
1.4.2.2 The Emergence of the Climate Change Signal

In the 1930s it was noted that temperatures were increasing at both 
local and global scales (Figure  1.8; Kincer, 1933; Callendar, 1938). 
At the time it was unclear whether the observed changes were 
part of a  longer-term trend or a  natural fluctuation; the ‘signal’ 
had not yet clearly emerged from the ‘noise’ of natural variability. 
Numerous studies have since focused on the emergence of changes 
in temperature using instrumental observations (e.g., Madden and 
Ramanathan, 1980; Wigley and Jones, 1981; Mahlstein et al., 2011, 
2012; Lehner and Stocker, 2015; Lehner et al., 2017) and paleo-
temperature data (e.g., Abram et al., 2016).
Since the IPCC Third’s Assessment Report in 2001, the observed 
signal of climate change has been unequivocally detected at the 
global scale (Section 1.3), and this signal is increasingly emerging 
from the noise of natural variability on smaller spatial scales and

in a range of climate variables (FAQ 1.2). In this Report emergence Ocean Heat Content change
Member 1
Member 2
Member 3
Ensemble spread (5-95%)
Linear trends (2011–2021)2000 2010 2020 2030-0.100.10.20.3OHC (0-2000m) change  (YJ)Global temperature change
2000 2010 2020 2030-0.4-0.200.20.40.60.8Temperature change  (°C)UK summer temperature
2000 2010 2020 203013141516
Annual Arctic sea ice volume
2000 2010 2020 20309101112131415161718Volume  (1000km3)Natural variations can temporarily mask or enhance anthropogenic changes in climate
September Arctic sea ice extent
2000 2010 2020 203034567Extent  (million km2) Temperature (°C)Natural climate variations can temporarily mask or 
enhance anthropogenic climatic changes over a 
decade or more, especially for smaller regions and 
shorter averaging periods.Simulated examples of different possible climate 
trajectories.

Figure 1.13 | Simulated changes in various climate indicators under historical and RCP4.5 scenarios using the MPI ESM Grand Ensemble.  The grey shading 
shows the 5–95% range from the 100-member ensemble. The coloured lines represent individual example ensemble members, with linear trends for the 2011–2021 period 
indicated by the dashed lines. Changes in ocean heat content (OHC) over the top 2000 m represents the integrated signal of global warming (left) . The top row  shows surface 
air temperature-related indicators (annual GSAT change and UK summer temperatures) and the bottom row  shows Arctic sea ice-related indicators (annual ice volume and 
September sea ice extent). For smaller regions and for shorter time-period averages the variability increases and simulated short-term trends can temporarily mask or enhance

anthropogenic changes in climate. Data from Maher et al. (2019). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

195
Framing, Context, and Methods Chapter 11Total temperature change (°C)
0 0.5 1.0 1.5 2.0 2.5Year-to-year variability (°C)
0 0.2 0.4 0.6 0.8 1
Global warming level of emergence (°C)
0 0.2 0.4 0.6 0.8 1Change-to-variability ratio
01234 5
North America
1860 1890 1920 1950 1980 2010-1012Temperature change (°C)Northern Europe
1860 1890 1920 1950 1980 2010-1012East Asia
1860 1890 1920 1950 1980 2010-1012
Northern South America
1860 1890 1920 1950 1980 2010-1012Temperature change (°C)Tropical Africa
1860 1890 1920 1950 1980 2010-1012Observed changes in temperature have emerged in most regions
Australasia
1860 1890 1920 1950 1980 2010-1012 Observed 
changes relative 
to an 1850–1900 
baselineYear-to-year 
variability 
(1σ & 2σ)
Figure 1.14 | The observed emergence of changes in temperature.  (Top left)  The total change in temperature estimated for 2020 relative to 1850–1900 (following

Hawkins et al., 2020), showing the largest warming occurring in the Arctic. (Top right)  The amplitude of estimated year-to-year variations in temperature. (Middle  left) 
The ratio of the observed total change in temperature and the amplitude of temperature variability (the ‘signal-to-noise (S/N) ratio’), showing that the warming is most apparent 
in the tropical regions (also see FAQ 1.2). (Middle right)  The global warming level at which the change in local temperature becomes larger than the local year-to-year 
variability. The bottom panels show time series of observed annual mean surface air temperatures over land in various example regions, as indicated by the boxes in the top-left 
panel. The 1 and 2 standard deviations ( σ) of estimated year-to-year variations for that region are shown by the pink shaded bands. Observed temperature data from Berkeley

Earth (Rohde and Hausfather, 2020). Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

196
Chapter 1 Framing, Context, and Methods1of a  climate change signal or trend refers to when a  change in 
climate (the ‘signal’) becomes larger than the amplitude of natural 
or internal variations (defining the ‘noise’). This concept is often 
expressed as a ‘signal-to-noise’ ratio (S/N) and emergence occurs at 
a defined threshold of this ratio (e.g., S/N >1 or 2). Emergence can be 
estimated using observations and/or model simulations and can refer 
to changes relative to a historical or modern baseline (Section 12.5.2 
and Glossary). The concept can also be expressed in terms of time 
(the ‘time of emergence’; Glossary) or in terms of a global warming 
level (Section  11.2.5; Kirchmeier-Young et al., 2019) and is also 
used to refer to a time when we can expect to see a response of 
mitigation activities that reduce emissions of GHGs or enhance 
their sinks (emergence with respect to mitigation; Section 4.6.3.1). 
Whenever possible, emergence should be discussed in the context

of a clearly defined level of S/N or other quantification, such as ‘the 
signal has emerged at the level of S/N >2’, rather than as a simple 
binary statement. For an extended discussion, see Chapter  10 
(Section 10.4.3).
Related to the concept of emergence is the detection of change 
(Chapter  3). Detection of change is defined as the process of 
demonstrating that some aspect of the climate, or a system affected 
by climate, has changed in some defined statistical sense, often using 
spatially aggregating methods that try to maximize S/N, such as 
‘fingerprints’ (e.g., Hegerl et al., 1996), without providing a reason 
for that change. An identified change is detected in observations if its 
likelihood of occurrence by chance due to internal variability alone is 
determined to be small, for example, <10% (Glossary).
An example of observed emergence in surface air temperatures is 
shown in Figure 1.14. Both the largest changes in temperature and

the largest amplitude of year-to-year variations are observed in the 
Arctic, with lower latitudes showing less warming and smaller year-to-
year variations. For the six example regions shown in Figure 1.14, the 
emergence of changes in temperature is more apparent in Northern 
South America, East Asia and Central Africa, than for northern North 
America or Northern Europe. This pattern was predicted by Hansen 
et al. (1988) and noted in subsequent observations by Mahlstein et al. 
(2011) (Sections 10.3.4.3 and 12.5.2). Overall, tropical regions show 
earlier emergence of temperature changes than at higher latitudes 
(high confidence ).
Since AR5, the emergence of projected future changes has also 
been extensively examined, in variables including surface air 
temperature (Hawkins and Sutton, 2012; Kirtman et al., 2013; 
Tebaldi and Friedlingstein, 2013), ocean temperatures and salinity 
(Banks and Wood, 2002), mean precipitation (Giorgi and Bi, 2009;

Maraun, 2013), drought (Orlowsky and Seneviratne, 2013), extremes 
(Diffenbaugh and Scherer, 2011; Fischer et al., 2014; King et al., 2015; 
Schleussner and Fyson, 2020), and regional sea level change (Lyu 
et al., 2014). The concept has also been applied to climate change 
impacts such as effects on crop growing regions (Rojas et al., 2019). 
In AR6, the emergence of oceanic signals such as regional sea 
level change and changes in water mass properties is assessed in 
Chapter 9 (Section 9.6.1.4); emergence of future regional changes is 
assessed in Chapter 10 (Section 10.4.3); the emergence of extremes 
as a  function of global warming levels is assessed in Chapter  11 (Section  11.2.5); and the emergence of climatic impact-drivers for 
AR6 regions and many climate variables is assessed in Chapter 12 
(Section 12.5.2).
Although the magnitude of any change is important, regions which 
have a larger signal of change relative to the background variations

will potentially face greater risks than other regions, as they will 
see unusual or novel climate conditions more quickly (Frame et al., 
2017). As in Figure 1.14, the signal of temperature change is often 
smaller in tropical countries, but their lower amplitude of variability 
means they may experience the effects of climate change earlier 
than the mid-latitudes. In addition, these tropical countries are 
often among the most exposed, due to large populations (Lehner 
and Stocker, 2015), and often more vulnerable (Harrington et al., 
2016; Harrington and Otto, 2018; Russo et al., 2019). Higher levels 
of exposure and vulnerability increase the risk from climate-
related impacts (Cross-Chapter Box 1.3). The rate of change is also 
important for many hazards (e.g., Loarie et al., 2009). Providing more 
information about changes and variations on regional scales, and 
the associated attribution to particular causes (Cross-Working Group

Box: Attribution), is therefore important for adaptation planning.
1.4.3 Sources of Uncertainty in Climate Simulations
When evaluating and analysing simulations of the physical climate 
system, several different sources of uncertainty need to be considered 
(e.g., Hawkins and Sutton, 2009; Lehner et al., 2020). Broadly, these 
sources are: uncertainties in radiative forcings (both those observed 
in the past and those projected for the future); uncertainty in the 
climate response to particular radiative forcings; internal and 
natural variations of the climate system (which may be somewhat 
predictable); and interactions among these sources of uncertainty.
Ensembles of climate simulations (Section  1.5.4.2), such as 
those produced as part of the sixth phase of the Coupled Model 
Intercomparison Project (CMIP6), can be used to explore these 
different sources of uncertainty and estimate their magnitude. 
Relevant experiments with climate models include both historical

simulations constrained by past radiative forcings, and projections 
of future climate which are constrained by specified drivers, such 
as GHG concentrations, emissions, or radiative forcings. (The term 
‘prediction’ is usually reserved for estimates of the future climate 
state which are also constrained by the observed initial conditions of 
the climate system, analogous to a weather forecast.)
1.4.3.1 Sources of Uncertainty
1.4.3.1.1 Radiative forcing uncertainty
Future radiative forcing is uncertain due to as-yet-unknown societal 
choices that will determine future anthropogenic emissions; this is 
considered ‘scenario uncertainty’. The RCP and SSP scenarios, which 
form the basis for climate projections assessed in this Report, are 
designed to span a plausible range of future pathways (Section 1.6) 
and can be used to estimate the magnitude of scenario uncertainty, but 
the real world may also differ from any one of these example pathways.

197
Framing, Context, and Methods Chapter 11Uncertainties also exist regarding past emissions and radiative 
forcings. These are especially important for simulations of paleoclimate 
time periods, such as the Pliocene, Last Glacial Maximum or the last 
millennium, but are also relevant for the CMIP historical simulations 
of the instrumental period since 1850. In particular, historical 
radiative forcings due to anthropogenic and natural aerosols are less 
well constrained by observations than the GHG radiative forcings. 
There is also uncertainty in the size of large volcanic eruptions (and 
in the location for some that occurred before around 1850), and the 
amplitude of changes in solar activity, before satellite observations. 
The role of historical radiative forcing uncertainty was considered 
previously (Knutti et al., 2002; Forster et al., 2013) but, since AR5, 
specific simulations have been performed to examine this issue,

particularly for the effects of uncertainty in anthropogenic aerosol 
radiative forcing (e.g.,  Jiménez-de-la-Cuesta and Mauritsen, 2019; 
Dittus et al., 2020).
1.4.3.1.2 Climate response uncertainty
Under any particular scenario (Section 1.6.1), there is uncertainty in 
how the climate will respond to the specified emissions or radiative 
forcing combinations. A  range of climate models is often used to 
estimate the range of uncertainty in our understanding of the key 
physical processes and to define the ‘model response uncertainty’ 
(Sections 1.5.4 and 4.2.5). However, this range does not necessarily 
represent the full ‘climate response uncertainty ’ in how the climate 
may respond to a particular radiative forcing or emissions scenario. 
This is because, for example, the climate models used in CMIP 
experiments have structural uncertainties not explored in a typical 
multi-model exercise (e.g., Murphy et al., 2004) and are not entirely

independent of each other (Section 1.5.4.8; Masson and Knutti, 2011; 
Abramowitz et al., 2019); there are small spatial-scale features which 
cannot be resolved; and long time-scale processes or tipping points 
are not fully represented. Section 1.4.4 discusses how some of these 
issues can still be considered in a risk assessment context. For some 
metrics, such as equilibrium climate sensitivity (ECS), the CMIP6 
model range is found to be broader than the very likely  range assessed 
by combining multiple lines of evidence (Sections 4.3.4 and 7.5.6).
1.4.3.1.3 Natural and internal climate variations
Even without any anthropogenic radiative forcing, there would still 
be uncertainty in projecting future climate because of unpredictable 
natural factors such as variations in solar activity and volcanic 
eruptions. For projections of future climate, such as those presented 
in Chapter  4, the uncertainty in these factors is not normally

considered. However, the potential effects on the climate of large 
volcanic eruptions (Cross-Chapter Box 4.1; Zanchettin et al., 2016; 
Bethke et al., 2017) and large solar variations (Feulner and Rahmstorf, 
2010; Maycock et al., 2015) are studied. On longer time scales, orbital 
effects and plate tectonics also play a role.
Further, even in the absence of any anthropogenic or natural changes 
in radiative forcing, Earth’s climate fluctuates on time scales from 
days to decades or longer. These ‘internal’ variations, such as those 
associated with modes of variability (e.g.,  ENSO, Pacific Decadal 
Variability (PDV), or Atlantic Multi-decadal Variability (AMV); Annex IV) are unpredictable on time scales longer than a few years 
ahead and are a source of uncertainty for understanding how the 
climate might become in a particular decade, especially regionally. 
The increased use of ‘large ensembles’ of complex climate model

simulations to sample this component of uncertainty is discussed 
above in Section 1.4.2.1 and further in Chapter 4.
1.4.3.1.4 Interactions between variability and radiative forcings
It is plausible that there are interactions between radiative forcings 
and climate variations, such as influences on the phasing or 
amplitude of internal or natural climate variability (Zanchettin, 2017). 
For example, the timing of volcanic eruptions may influence Atlantic 
Multi-decadal Variability (e.g., Otterå et al., 2010; Birkel et al., 2018) 
or ENSO (e.g.,  Maher et al., 2015; Khodri et al., 2017; Zuo et al., 
2018), and anthropogenic aerosols may influence decadal modes of 
variability in the Pacific (e.g., Smith et al., 2016). In addition, melting 
of glaciers and ice caps due to anthropogenic influences has been 
speculated to increase volcanic activity (e.g., a specific example for 
Iceland is discussed in Swindles et al., 2018).
1.4.3.2 Uncertainty Quantification

Not all of these listed sources of uncertainty are of the same type. 
For example, internal climate variations are an intrinsic uncertainty 
that can be estimated probabilistically, and could be more precisely 
quantified, but cannot usually be reduced. However, advances in 
decadal prediction offer the prospect of narrowing uncertainties in 
the trajectory of the climate for a few years ahead (Section 4.2.3; 
e.g., Meehl et al., 2014; Yeager and Robson, 2017).
Other sources of uncertainty, such as model response uncertainty, 
can in principle be reduced, but are not amenable to a frequency-
based interpretation of probability, and Bayesian methods to 
quantify the uncertainty have been considered instead (e.g., Tebaldi, 
2004; Rougier, 2007; Sexton et al., 2012). The scenario uncertainty 
component is distinct from other uncertainties, given that future 
anthropogenic emissions can be considered as the outcome of a set 
of societal choices (Section 1.6.1).

For climate model projections it is possible to approximately quantify 
the relative amplitude of various sources of uncertainty (e.g., Hawkins 
and Sutton, 2009; Lehner et al., 2020). A range of different climate 
models are used to estimate the model response uncertainty to 
a  particular emissions pathway, and multiple pathways are used 
to estimate the scenario uncertainty. The unforced component 
of internal variability can be estimated from individual ensemble 
members of the same climate model (Section  1.5.4.8; e.g.,  Deser 
et al., 2012; Maher et al., 2019).
Figure 1.15 illustrates the relative size of these different uncertainty 
components using a ‘cascade of uncertainty’ (Wilby and Dessai, 2010), 
with examples shown for global mean temperature, Northern South 
American annual temperatures and East Asian summer precipitation 
changes. For global mean temperature, the role of internal variability 
is small, and the total uncertainty is dominated by emissions scenario

and model response uncertainties. Note that there is considerable

198
overlap between individual simulations for different emissions 
scenarios, even for the mid-term (2041–2060). For example, the 
slowest-warming simulation for SSP5-8.5 produces less mid-term 
warming than the fastest-warming simulation for SSP1-1.9. For the 
long term, emissions scenario uncertainty becomes dominant.
The relative uncertainty due to internal variability and model 
uncertainty increases for smaller spatial scales. In the regional 
example shown in Figure 1.15 for changes in temperature, the same 
scenario and model combination has produced two simulations 
which differ by 1°C in their projected 2081–2100 averages due solely 
to internal climate variability. For regional precipitation changes, 
emissions scenario uncertainty is often small relative to model 
response uncertainty. In the example shown in Figure 1.15, the SSPs 
overlap considerably, but SSP1-1.9 shows the largest precipitation

change in the near term, even though global mean temperature 
warms the least; this is due to differences between regional aerosol 
emissions projected in this and other scenarios (Wilcox et al., 2020). 
These cascades of uncertainty would branch out further if applying the 
projections to derive estimates of changes in hazard (e.g., Wilby and 
Dessai, 2010; Halsnæs and Kaspersen, 2018; Hattermann et al., 2018).1.4.4 Considering an Uncertain Future
Since AR5 there have been developments in how to consider and 
describe future climate outcomes which are considered possible but 
very unlikely, highly uncertain, or potentially surprising. To examine 
such futures there is a  need to move beyond the usual likely  or 
very likely  assessed ranges and consider low-likelihood outcomes, 
especially those that would result in significant impacts if they 
occurred (e.g., Sutton, 2018; Sillmann et al., 2021). This section briefly 
outlines some of the different approaches used in the AR6 WGI.

1.4.4.1 Low-Likelihood Outcomes
In the AR6, certain low-likelihood outcomes are described and 
assessed because they may be associated with high levels of 
risk, and  the greatest risks may not be associated with the most 
likely outcome. The aim of assessing these possible futures is to 
better inform risk assessment and decision-making. Two types are 
considered: (i) low-likelihood high-warming (LLHW) scenarios, which 
describe the climate in a  world with very high climate sensitivity; 
and (ii) low-likelihood, high-impact outcomes that have a  low Change relative to 1850–1900  (°C)Global mean temperature
Ensemble
membersIndividual model
ensemble meansSSPs multi-
model means
2041–2060
Change relative to 1995–2014  (°C)Northern South America temperatureCascade of uncertainties in climate projections2081–2100
Change relative to  1995–2014  (%)East Asia JJA rainfall
Ensemble

model meansDifferent sources of uncertainty dominate the total uncertainty in projections for different variables, regions and time periods
8.5
 2.6
1.9
 7.0
 4.5
1 2 3 4 5 6 78.5
 2.6
1.9
 7.0
 4.5
0 1 2 3 4 5 6 7 88.5
2.6
 1.9
7.0
4.5
-5 0 5 10 15 20 25
Figure 1.15 | The ‘cascade of uncertainties’ in CMIP6 projections. Changes in:  GSAT (left) ; Northern South America temperature (middle) ; and East Asia summer 
(June–July–August, JJA) precipitation (right) . These are shown for two time periods: 2041–2060 (top)  and 2081–2100 (bottom) . The SSP–radiative forcing combination is 
indicated at the top of each cascade at the value of the multi-model mean for each scenario. This branches downwards to show the ensemble mean for each model, and further 
branches into the individual ensemble members, although often only a single member is available. These diagrams highlight the relative importance of different sources of

uncertainty in climate projections, which varies for different time periods, regions and climate variables. See Section 1.4.5 for the definition of the regions used. Further details 
on data sources and processing are available in the chapter data table (Table 1.SM.1).

199
likelihood of occurring, but would cause large potential impacts on 
societies or ecosystems.
An illustrative example of how low-likelihood outcomes can produce 
significant additional risks is shown in Figure  1.16. The Reasons 
for Concern (RFCs) produced by the IPCC AR5 WGII define the 
additional risks due to climate change at different global warming 
levels. These have been combined with Chapter  4  assessments of 
projected global temperature for different emissions scenarios (SSPs; 
Section  1.6), and Chapter  7  assessments about ECS. For example, 
even following an intermediate emissions scenario could result in 
high levels of additional risk if ECS is at the upper end of the very 
likely  range. However, not all possible low-likelihood outcomes 
relate to ECS, and AR6 considers these issues in more detail than 
previous IPCC assessment reports (see Table 1.1 and Section 1.4.4.2 
for some examples).
1.4.4.2 Storylines

As societies are increasingly experiencing the impacts of climate 
change-related events, the climate science community is developing 
climate information tailored for particular regions and sectors. There 
is a  growing focus on explaining and exploring complex physical 
chains of events or on predicting climate under various future 
socio-economic developments. Since AR5, ‘storylines’ or ‘narratives’ approaches have been used to better inform risk assessment and 
decision-making, to assist understanding of regional processes, and 
represent and communicate climate projection uncertainties more 
clearly. The aim is to help build a cohesive overall picture of potential 
climate change pathways that moves beyond the presentation of 
data and figures (Glossary; Fløttum and Gjerstad, 2017; Moezzi et al., 
2017; Dessai et al., 2018; T.G. Shepherd et al., 2018).
In the broader IPCC context, the term ‘scenario storyline’ refers to

a narrative description of one or more scenarios, highlighting their 
main characteristics, relationships between key driving forces and 
the dynamics of their evolution (e.g., emissions of short-lived climate 
forcers assessed in Chapter  6  are driven by ‘scenario storylines’; 
see Section  1.6). The AR6 WGI is mainly concerned with ‘physical 
climate storylines’. A  physical climate storyline is a  self-consistent 
and plausible physical trajectory of the climate system, or a weather 
or climate event, on time scales from hours to multiple decades 
(T.G. Shepherd et al., 2018). This approach can be used to constrain 
projected changes or specific events on specified explanatory 
elements such as projected changes of large-scale indicators 
(Box  10.2). For example, Hazeleger et al. (2015) suggested using 
‘tales of future weather’, blending numerical weather prediction with 
a  climate projection to illustrate the potential behaviour of future

high-impact events (also see Hegdahl et al., 2020). Several studies 
describe how possible large changes in atmospheric circulation Schematic ECS likelihood
consistent with AR6
Likely rangeLow-likelihood
high warming
Low-likelihood
low warming
Relative likelihood0123456Equilibrium climate sensitivity (°C)Risks to unique and threatened systems
SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0SSP5-8.5High ECS Mid-range ECS
2025 2045 2065 2085
YearLow ECSRisks from distribution of impacts
YearRisks
High
Moderate
UndetectedVery
highUndefinedReasons For Concern (from AR5)
SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0SSP5-8.5
Figure 1.16 | Illustrating concepts of low-likelihood outcomes.  Left:  schematic likelihood distribution consistent with the IPCC AR6 assessments that equilibrium 
climate sensitivity (ECS) is likely in the range 2.5°C to 4.0°C, and very likely  between 2.0°C and 5.0°C (Chapter 7). ECS values outside the assessed very likely  range are

designated low-likelihood outcomes in this example (light grey). Middle  and right-hand columns:  additional risks due to climate change for 2020–2090 using the Reasons 
For Concern (RFCs, see IPCC, 2014b), specifically RFC1 describing the risks to unique and threatened systems and RFC3 describing risks from the distribution of impacts (O’Neill 
et al., 2017b; Zommers et al., 2020). The projected changes of GSAT used are the 95%, median and 5% assessed ranges from Chapter 4 for each SSP (top, middle and bottom); 
these are designated High ECS, Mid-range ECS and Low ECS respectively. The ‘burning-ember’ risk spectrum of graduated colours is usually associated with levels of committed 
GSAT change; instead, this illustration associates the risk spectrum with the GSAT temperature reached in each year from 2020 to 2090. Note that this illustration does not

include the vulnerability aspect of each SSP scenario. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

200
Chapter 1 Framing, Context, and Methods1would affect regional precipitation and other climate variables, and 
discuss the various climate drivers that could cause such a circulation 
response (James et al., 2015; Zappa and Shepherd, 2017; Mindlin 
et al., 2020). Physical climate storylines can also help frame the 
causal factors of extreme weather events (Shepherd, 2016) and then 
be linked to event attribution (Section  11.2.2 and Cross-Working 
Group Box: Attribution).
Storyline approaches can be used to communicate and contextualize 
climate change information in the context of risk for policymakers 
and practitioners (Box  10.2; e.g.,  de Bruijn et al., 2016; Dessai 
et al., 2018; Scott et al., 2018; Jack et al., 2020). They can also help 
in assessing risks associated with LLHI events (Weitzman, 2011; 
Sutton, 2018), because they consider the ‘physically self-consistent 
unfolding of past events, or of plausible future events or pathways’

(Shepherd et al., 2018), which would be masked in a  probabilistic 
approach. These aspects are important as the greatest risk need 
not be associated with the highest-likelihood outcome, and in fact 
will often be associated with low-likelihood outcomes. The storyline 
approach can also acknowledge that climate-relevant decisions in 
a risk-oriented framing will rarely be taken on the basis of physical climate change alone; instead, such decisions will normally take into 
account socio-economic factors as well (Shepherd, 2019).
In the AR6 WGI Assessment Report, these different storyline 
approaches are used in several places (see Table 1.1). Chapter 4 uses 
a  storyline approach to assess the upper tail of the distribution 
of global warming levels (the storylines of high global warming 
levels) and their manifestation in global patterns of temperature 
and precipitation changes. Chapter  9  uses a  storyline approach to

examine the potential for, and early warning signals of, a high-end 
sea level scenario, in the context of deep uncertainty related to our 
current understanding of the physical processes that contribute to 
long-term sea level rise. Chapter  10 assesses the use of physical 
climate storylines and narratives as a way to explore uncertainties 
in regional climate projections, and to link to the specific risk and 
decision context relevant to a  user, for developing integrated and 
context-relevant regional climate change information. Chapter  11 
uses the term storyline in the framework of extreme event attribution. 
Chapter 12 assesses the use of a storylines approach with narrative 
elements for communicating climate (change) information in the 
context of climate services (Cross-Chapter Box 12.2).
Cross-Chapter Box 1.3 | Risk Framing in IPCC AR6
Contributing Authors: Andy Reisinger (New Zealand), Maisa Rojas (Chile), Aïda Diongue-Niang (Senegal), Maarten K. van Aalst

(The  Netherlands), Mathias Garschagen (Germany), Mark Howden (Australia), Margot Hurlbert (Canada), Katharine Mach 
(United States of America), Sawsan Khair Elsied Abdel Rahim Mustafa (Sudan), Brian O’Neill (United States of America), Roque Pedace 
(Argentina), Jana Sillmann (Norway/Germany), Carolina Vera (Argentina), David Viner (United Kingdom)
The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; 
IPCC, 2012)  presented a framework for assessing risks from climate change, which linked hazards (due to changes in climate) with 
exposure and vulnerability (Cardona et al., 2012). This framework was further developed by AR5 WGII (IPCC, 2014b), while AR5 WGI 
focussed only on the hazard component of risk. As part of AR6, a cross-Working Group process expanded and refined the concept of

risk to allow for a consistent risk framing to be used across the three IPCC Working Groups (IPCC, 2019b; Box 2 in Abram et al., 2019; 
Reisinger et al., 2020).
In this revised definition, risk is defined as:
The potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives 
associated with such systems. In the context of climate change, risks can arise from potential impacts of climate change 
as well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health 
and well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), 
ecosystems and species.
In the context of climate change impacts, risks result from dynamic interactions between climate-related hazards with the 
exposure and vulnerability of the affected human or ecological system to the hazards. Hazards, exposure and vulnerability

may each be subject to uncertainty in terms of magnitude and likelihood of occurrence, and each may change over time and 
space due to socio-economic changes and human decision-making (see also risk management, adaptation and mitigation).
In the context of climate change responses, risks result from the potential for such responses not achieving the intended 
objective(s), or from potential trade-offs with, or negative side-effects on, other societal objectives, such as the Sustainable 
Development Goals (SDGs) (see also risk trade-off). Risks can arise, for example, from uncertainty in implementation, 
effectiveness or outcomes of climate policy, climate-related investments, technology development or adoption, and 
system transitions.

201
Framing, Context, and Methods Chapter 11Cross-Chapter Box 1.3 (continued)
The following concepts are also relevant for the definition of risk (Glossary):
Exposure:  The presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; 
or economic, social, or cultural assets in places and settings that could be adversely affected.
Vulnerability:  The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements 
including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.
Hazard:  The potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health 
impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources.

Impacts:  The consequences of realized risks on natural and human systems, where risks result from the interactions of climate-related 
hazards (including extreme weather/climate events), exposure, and vulnerability. Impacts generally refer to effects on lives, livelihoods, 
health and well-being, ecosystems and species, economic, social and cultural assets, services (including ecosystem services), and 
infrastructure. Impacts may be referred to as consequences or outcomes and can be adverse or beneficial.
Risk in AR6 WGI
The revised risk framing clarifies the role and contribution of WGI to risk assessment. ‘Risk’ in IPCC terminology applies only to human 
or ecological systems, not to physical systems on their own.
Climatic impact-drivers (CIDs): CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element 
of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral or a mixture

of each across interacting system elements and regions.
In AR6, WGI uses the term ‘climatic impact-drivers’ to describe changes in physical systems rather than ‘hazards’, because the term 
hazard already assumes an adverse consequence. The terminology of ‘climatic impact-driver’ therefore allows WGI to provide a more 
value-neutral characterization of climatic changes that may be relevant for understanding potential impacts, without pre-judging 
whether specific climatic changes necessarily lead to adverse consequences, as some could also result in beneficial outcomes 
depending on the specific system and associated values. Chapter 12 and the Atlas assess and provide information on climatic impact-
drivers for different regions and sectors to support and link to the WGII assessment of the impacts and risks (or opportunities) related 
to the changes in the climatic impact-drivers. Although CIDs can lead to adverse or beneficial outcomes, focus is given to CIDs

connected to hazards, and hence inform risk.
‘Extremes’ are a category of CID, corresponding to unusual events with respect to the range of observed values of the variable. 
Chapter 11 assesses changes in weather and climate extremes, their attribution and future projections.
As examples of the use of this terminology, the term ‘flood risk’ should not be used if it only describes changes in the frequency and 
intensity of flood events (a hazard); the risk from flooding to human and ecological systems is caused by the flood hazard, the exposure 
of the system affected (e.g., topography, human settlements or infrastructure in the area potentially affected by flooding) and the 
vulnerability of the system (e.g., design and maintenance of infrastructure, existence of early warning systems). As another example, 
climate-related risk to food security can arise from both potential climate change impacts and responses to climate change and can

be exacerbated by other stressors. Drivers for risks related to climate change impacts include climatic impact- drivers (e.g., drought, 
temperature extremes, humidity) mediated by other climatic impact-drivers (e.g., increased CO 2 fertilization of certain types of crops 
may help increase yields), the potential for indirect climate-related impacts (e.g., pest outbreaks triggered by ecosystem responses to 
weather patterns), exposure of people (e.g., how many people depend on a particular crop) and vulnerability or adaptability (how able 
are affected people to substitute other sources of food, which may be related to financial access and markets). 
Information provided by WGI may or may not be relevant to understand risks related to climate change responses. For example, the 
risk to a company arising from emissions pricing, or the societal risk from reliance on an unproven mitigation technology, is not directly

dependent on actual or projected changes in climate but arise largely from human choices. However, WGI climate information may be 
relevant to understand the potential for maladaptation, such as the potential for specific adaptation responses not achieving the desired 
outcome or having negative side effects. For example, WGI information about the range of sea level rise can help inform understanding 
of whether coastal protection, accommodation, or retreat would be the most effective risk management strategy in a particular context.

202
1.4.4.3 Abrupt Change, Tipping Points and Surprises
An ‘abrupt change’  is defined in this report as a change that takes 
place substantially faster than the rate of change in the recent history 
of the affected component of a  system (Glossary). In some cases, 
abrupt change occurs because the system state actually becomes 
unstable, such that the subsequent rate of change is independent of 
the forcing. We refer to this class of abrupt change as a ‘tipping point’ , 
defined as a critical threshold beyond which a system reorganizes, 
often abruptly and/or irreversibly (Glossary; Lenton et al., 2008). 
Some of the abrupt climate changes and climate tipping points 
discussed in this Report could have severe local climate responses, 
such as extreme temperature, droughts, forest fires, ice-sheet loss 
and collapse of the thermohaline circulation (Sections 4.7.2, 5.4.9, 
8.6 and 9.2.3).

There is evidence of abrupt changes in Earth’s history, and some of 
these events have been interpreted as tipping points (Dakos et al., 
2008). Some of these are associated with significant changes in the 
global climate, such as deglaciations in the Quaternary (past 2.5 
million years) and rapid warming at the Palaeocene–Eocene Thermal 
Maximum (around 55.5 million years ago; Bowen et al., 2015; Hollis 
et al., 2019). Such events changed the planetary climate for tens to 
hundreds of thousands of years, but at a rate that is actually much 
slower than projected anthropogenic climate change over this 
century, even in the absence of tipping points.
Such paleoclimate evidence has even fuelled concerns that 
anthropogenic GHGs could tip the global climate into a permanent 
hot state (Steffen et al., 2018). However, there is no evidence of such 
non-linear responses at the global scale in climate projections for 
the next century, which indicates a near-linear dependence of global

temperature on cumulative GHG emissions (Sections 1.3.5, 5.5 and 
7.4.3.1). At the regional scale, abrupt changes and tipping points, such 
as Amazon rainforest dieback and permafrost collapse, have occurred 
in projections with Earth System Models (Section  4.7.3; Drijfhout et al., 2015; Bathiany et al., 2020). In such simulations, tipping points 
occur in narrow regions of parameter space (e.g., CO 2 concentration 
or temperature increase), and for specific climate background states. 
This makes them difficult to predict using Earth system models (ESMs) 
relying on parmeterizations of known processes. In some cases, 
it is possible to detect forthcoming tipping points through time-
series analysis that identifies increased sensitivity to perturbations 
as the tipping point is approached (e.g.,  ‘critical slowing-down’, 
Scheffer et al., 2012).
Some suggested climate tipping points prompt transitions from one 
steady state to another (Figure 1.17). Transitions can be prompted

by perturbations such as climate extremes which force the system 
outside of its current well of attraction in the stability landscape; 
this is called noise-induced tipping (Figure  1.17a,b; Ashwin et al., 
2012). For example, the tropical forest dieback seen in some ESM 
projections is accelerated by longer and more frequent droughts over 
tropical land (Good et al., 2013).
Alternatively, transitions from one state to another can occur if 
a  critical threshold is exceeded; this is called ‘bifurcation tipping’ 
(Figure  1.17c,d; Ashwin et al., 2012). The new state is defined as 
‘irreversible’ on a  given time scale if the recovery from this state 
takes substantially longer than the time scale of interest, which is 
decades to centuries for the projections presented in this report. 
A well-known example is the modelled irreversibility of the ocean’s 
thermohaline circulation in response to North Atlantic changes such 
as freshwater input from rainfall and ice-sheet melt (Rahmstorf

et al., 2005; Alkhayuon et al., 2019), which is assessed in detail in 
Chapter 9 (Section 9.2.3).
The tipping point concept is most commonly framed for systems 
in which the forcing changes relatively slowly. However, this is not 
the case for most scenarios of anthropogenic forcing projected for 
the 21st century. Systems with inertia lag behind rapidly increasing 
forcing, which can lead to the failure of early warning signals or Cross-Chapter Box 1.3 (continued)
From a  WGI perspective, low-likelihood, high-impact outcomes and the concept of deep uncertainty are also relevant for risk assessment.
Low-likelihood, high-impact (LLHI) outcomes:  Outcomes/events whose probability of occurrence is low or not well known 
(as in the context of deep uncertainty) but whose potential impacts on society and ecosystems could be high. To better inform risk 
assessment and decision-making, such low-likelihood outcomes are considered if they are associated with very large consequences

and may therefore constitute material risks, even though those consequences do not necessarily represent the most likely outcome.
The AR6 WGI Report provides more detailed information about these types of events compared to AR5 (Table 1.1, Section 1.4.4).
Recognizing the need for assessing and managing risk in situations of high uncertainty, SROCC advanced the treatment of situations 
with deep uncertainty (Section 1.2.3; IPCC, 2019b; Box 5 in Abram et al., 2019). A situation of deep uncertainty exists when experts or 
stakeholders do not know or cannot agree on: (i) appropriate conceptual models that describe relationships among key driving forces 
in a system; (ii) the probability distributions used to represent uncertainty about key variables and parameters; and/or (iii) how to 
weigh and value desirable alternative outcomes (Abram et al., 2019). The concept of deep uncertainty can complement the IPCC 
calibrated uncertainty language and thereby broaden the communication of risk.

203
even the possibility of temporarily overshooting a bifurcation point 
without provoking tipping (Ritchie et al., 2019).
‘Surprises’ are a class of risk that can be defined as low-likelihood 
but well-understood events: they are events that cannot be predicted 
with current understanding. The risk from such surprises can be 
accounted for in risk assessments (Parker and Risbey, 2015). Examples 
relevant to climate science include: a  series of major volcanic 
eruptions or a nuclear war, either of which would cause substantial 
planetary cooling (Robock et al., 2007; Mills et al., 2014); significant 
21st century sea level rise due to marine ice sheet instability (MISI; 
Box 9.4); the potential for collapse of the stratocumulus cloud decks 
(Schneider et al., 2019) or other substantial changes in climate 
feedbacks (Section  7.4); and unexpected biological epidemics among humans or other species, such as the COVID-19 pandemic

(Cross-Chapter Box 6.1; Forster et al., 2020; Le Quéré et al., 2020). 
The discovery of the hole in the ozone layerwas also a surprise even 
though some of the relevant atmospheric chemistry was known at 
the time. The term ‘unknown unknowns’ (Parker and Risbey, 2015) 
is also sometimes used in this context to refer to events that cannot 
be anticipated with present knowledge or were of an unanticipated 
nature before they occurred.Bifurca tionNoise- induced
TimeClimate sta teTimeClimate sta te
(c)(d)(a)(b)
Climate sta teClimate sta teStabil ity
landsc apeStabil ity
landsc ape
Figure 1.17 | Illustration of two types of tipping points: noise-induced (a, b) and bifurcation (c, d).  (a) and (c) are example time-series (coloured lines) through 
the tipping point, with solid-black lines indicating stable climate states (e.g., low or high rainfall) and dashed lines representing the boundary between stable states. (b) and

(d) are stability landscapes, which provide an intuitive understanding of the different types of tipping point. The ‘valleys’ represent different climate states the system can occupy, 
with ‘hilltops’ separating the stable states. The resilience of a climate state is implied by the depth of the valley. The current state of the system is represented by a ball. Both 
scenarios assume that the ball starts in the left-hand valley (dashed-black lines) and then through different mechanisms dependent on the type of tipping transitions to the 
right-hand valley (coloured lines). Noise-induced tipping events (a, b), for instance drought events causing sudden dieback of the Amazon rainforest, develop from fluctuations 
within the system. The stability landscape in this scenario remains fixed and stationary. A series of perturbations in the same direction, or one large perturbation, are required to

force the system over the hilltop and into the alternative stable state. Bifurcation tipping events (c, d), such as a collapse of the thermohaline circulation in the Atlantic Ocean 
under climate change, occur when a critical level in the forcing is reached. Here the stability landscape is subjected to a change in shape. Under gradual anthropogenic forcing 
the left-hand valley begins to shallow and eventually vanishes at the tipping point, forcing the system to transition to the right-hand valley.

204
Chapter 1 Framing, Context, and Methods1Cross-Working Group Box | Attribution
Contributing Authors: Pandora Hope (Australia), Wolfgang Cramer (France/Germany), Gregory M. Flato (Canada), Katja Frieler 
(Germany), Nathan P . Gillett (Canada), Christian Huggel (Switzerland), Jan Minx (Germany), Friederike Otto (United Kingdom/
Germany), Camille Parmesan (France, United Kingdom/United States of America), Joeri Rogelj (United Kingdom/Belgium), Maisa Rojas 
(Chile), Sonia I. Seneviratne (Switzerland), Aimée B.A. Slangen (The Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), 
Maarten K. van Aalst (The Netherlands), Robert Vautard (France), Xuebin Zhang (Canada)
Introduction
Changes in the climate system are becoming increasingly apparent, as are the climate-related impacts on natural and human systems. 
Attribution is the process of evaluating the contribution of one or more causal factors to such observed changes or events. Typical

questions addressed by the IPCC include: ‘To what extent is an observed change in global temperature induced by anthropogenic GHG 
and aerosol concentration changes, or influenced by natural variability?’ and ‘What is the contribution of climate change to observed 
changes in crop yields, which are also influenced by changes in agricultural management?’ Changes in the occurrence and intensity of 
extreme events can also be attributed, addressing questions such as: ‘Have human GHG emissions increased the likelihood or intensity 
of an observed heatwave?’
This Cross-Working Group Box briefly describes why attribution studies are important. It also describes some new developments in the 
methods used in those studies and provides recommendations for interpretation.
Attribution studies serve to evaluate and communicate linkages associated with climate change, for example: between the human-

induced increase in GHG concentrations and the observed increase in air temperature or extreme weather events (AR6 WGI 
Chapters 3, 10 and 11); or between observed changes in climate and changing species distributions and food production (AR6 WGII 
Chapters 2 and others, summarized in WGII Chapter 16; e.g., Verschuur et al., 2021); or between climate change mitigation policies 
and atmospheric GHG concentrations (AR6 WGI Chapter 5; AR6 WGIII Chapter 14). As such, they support numerous statements made 
by the IPCC (AR6 WGI Section 1.3 and Appendix 1A; IPCC, 2013b, 2014b).
Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate protection policies (AR6 WGI 
Section 4.6.3; e.g., Nauels et al., 2019; Banerjee et al., 2020), inform and constrain projections (WGI Section 4.2.3; Gillett et al., 2021; 
Ribes et al., 2021) or inform the loss and damages estimates and potential climate litigation cases by estimating the costs of climate

change (Huggel et al., 2015; Marjanac et al., 2017; Frame et al., 2020). These findings can thus inform mitigation decisions as well as 
risk management and adaptation planning (e.g., CDKN, 2017).
Steps towards an attribution assessment
The unambiguous framing of what changes are being attributed to what causes is a crucial first step for an assessment (Easterling 
et al., 2016; Hansen et al., 2016; Stone et al., 2021), followed by the identification of the possible and plausible drivers of change and 
the development of a hypothesis or theory for the linkage (Cross-Working Group Box: Attribution, Figure 1). The next step is to clearly 
define the indicators of the observed change or event and note the quality of the observations. There has been significant progress in 
the compilation of fragmented and distributed observational data, broadening and deepening the data basis for attribution research

(WGI Section 1.5; e.g., Poloczanska et al., 2013; Ray et al., 2015; Cohen et al., 2018). The quality of the observational record of drivers 
should also be considered (e.g., volcanic eruptions: WGI Section 2.2.2). Impacted systems also change in the absence of climate 
change; this baseline and its associated modifiers – such as agricultural developments or population growth – need to be considered, 
alongside the exposure and vulnerability of people depending on these systems.
There are many attribution approaches, and several methods are detailed below. In physical and biological systems, attribution often 
builds on the understanding of the mechanisms behind the observed changes and numerical models are used, while in human systems 
other methods of evidence-building are employed. Confidence in the attribution can be increased if more than one approach is used

and the model is evaluated as fit-for-purpose (WGI Section 1.5, WGI Section 3.8, WGI Section 10.3.3.4 ; Hegerl et al., 2010; Vautard 
et al., 2019; Otto et al., 2020; Philip et al., 2020) . The final step includes appropriate communication of the attribution assessment and 
the accompanying confidence in the result (e.g., Lewis et al., 2019).
Attribution methods
Attribution of changes in atmospheric greenhouse gas concentrations to anthropogenic activity
The AR6 WGI Chapter 5 presents multiple lines of evidence that unequivocally establish the dominant role of human activities in the 
growth of atmospheric CO 2, including through analysing changes in atmospheric carbon isotope ratios and the atmospheric O 2–N 2

205
Framing, Context, and Methods Chapter 11Cross-Working Group Box (continued)
ratio (WGI Section 5.2.1.1). Decomposition approaches can be used to attribute emissions underlying those changes to various drivers 
such as population, energy efficiency, consumption or carbon intensity (Hoekstra and van den Bergh, 2003; Raupach et al., 2007; Rosa 
and Dietz, 2012). Combined with attribution of their climate outcomes, the attribution of the sources of GHG emissions can inform 
the attribution of anthropogenic climate change to specific countries or actors (Matthews, 2016; Otto et al., 2017; Skeie et al., 2017; 
Nauels et al., 2019), and in turn inform discussions on fairness and burden sharing (WGIII Chapter 14).
Attribution of observed climate change to anthropogenic forcing
Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to anthropogenic and natural

forcings (WGI Section 1.3.4; e.g., Hegerl et al., 2010; Bindoff et al., 2013). The most established method is to identify the ‘fingerprint’ 
of the expected space-time response to a particular climate forcing agent such as the concentration of anthropogenically induced 
GHGs or aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual forcing agents to 
an observed change (e.g., Gillett et al., 2021). New statistical approaches have been applied to better account for internal climate 
variability and the uncertainties in models and observations (WGI Section 3.2; e.g., Naveau et al., 2018; Santer et al., 2019) . There 
are many other approaches, for example, global mean sea level change has been attributed to anthropogenic climate forcing by 
attributing the individual contributions from, for example, glacier melt or thermal expansion, while also examining which aspects of

the observed change are inconsistent with internal variability (WGI Sections 3.5.2 and 9.6.1.4).
Specific regional conditions and responses may simplify or complicate attribution on those scales. For example, some human forcings, 
such as regional land-use change or aerosols, may enhance or reduce regional signals of change (WGI Sections 10.4.2, 11.1.6 and 
11.2.2; Lejeune et al., 2018; Undorf et al., 2018; Boé et al., 2020; Thiery et al., 2020) . In general, regional climate variations are larger 
than the global mean climate, adding additional uncertainty to 
attribution (e.g., in regional sea level change, WGI Section 9.6.1). 
These statistical limitations may be reduced by ‘process-based 
attribution’, focusing on the physical processes known to 
influence the response to external forcing and internal variability 
(WGI Section 10.4.2).
Attribution of weather and climate events to anthropogenic forcing
New methods have emerged since AR5 to attribute the change

in likelihood or characteristics of weather or climate events or 
classes of events to underlying drivers (WGI Sections 10.4.1 and 
11.2.2; NA SEM, 2016; Stott et al., 2016; Jézéquel et al., 2018; 
Wehner et al., 2018; Wang et al., 2021) . Typically, historical 
changes, simulated under observed forcings, are compared 
to a  counterfactual climate simulated in the absence of 
anthropogenic forcing. Another approach examines facets of the 
weather and thermodynamic status of an event through process-
based attribution (WGI Chapter  11 and Section  10.4.1; Hauser 
et al., 2016; Shepherd et al., 2018; Grose et al., 2019) . Events 
where attributable human influence have been found include hot 
and cold temperature extremes (including some with widespread 
impacts), heavy precipitation, and certain types of droughts 
and tropical cyclones (AR6 WGI Section 11.9; e.g., Vogel et al., 
2019; Herring et al., 2021). Event attribution techniques have

sometimes been extended to ‘end-to-end’ assessments from 
climate forcing to the impacts of events on natural or human 
systems (Otto, 2017).
Attribution of observed changes in natural or human systems to 
climate-related drivers
The attribution of observed changes to climate-related drivers 
across a  diverse set of sectors, regions and systems is part of 
each chapter in the WGII contribution to AR6 and is synthesized 
in WGII Chapter  16 (Section  16.2). The number of attribution Attribution in the IPCC
The process of evaluating the contribution of one or more causal 
factors to observed changes or events
Identify what you 
want to evaluate: observed 
change or event; consider 
its framing/indicators
Gather observations
Develop hypothesis 
or theory of possible and 
plausible causes Attribution assessment
1
2
345
System/method to test 
the hypothesis or theory. 
Apply process understanding, 
counterfactual,  evaluate 
methods and models, consider

constraintsEvaluate and communicate causes
Monitor anthropogenic influence
Inform projections, 
policies and actionPurposes
Cross-Working Group Box: Attribution, Figure 1 | Schematic of the 
steps to develop an attribution assessment, and the purposes of 
such assessments. Methods and systems used to test the attribution 
hypothesis or theory include: model-based fingerprinting; other 
model-based methods; evidence-based fingerprinting; process-
based approaches; empirical or decomposition methods; and the 
use of multiple lines of evidence. Many of the methods are based on the 
comparison of the observed state of a system to a hypothetical counterfactual 
world that does not include the driver of interest to help estimate the causes of 
the observed response.

206
1.4.5 Climate Regions Used in AR6
1.4.5.1 Defining Climate Regions
The AR5 assessed regional-scale detection and attribution and 
assessed key regional climate phenomena and their relevance for 
future regional climate projections. This report shows that past 
and future climate changes and extreme weather events can be 
substantial on local and regional scales (Chapters 8–12 and Atlas), 
where they may differ considerably from global trends, not only in 
intensity but even in the direction of change (e.g., Fischer et al., 2013).
Although the evolution of global climate trends emerges as the 
net result of regional phenomena, average or aggregate estimates 
often do not reflect the intensity, variability and complexity of 
regional climate changes (Stammer et al., 2018; Shepherd, 2019). 
A fundamental aspect of the study of regional climate changes is the 
definition of characteristic climate zones, clusters or regions, across

which the emergent climate change signal can be properly analysed 
and projected (see Atlas). Suitable sizes and shapes of such zones 
strongly depend not only on the climate variable and process of 
interest, but also on relevant multi-scale feedbacks.
There are several approaches to the classification of climate 
regions. When climate observation data was sparse and limited, the aggregation of climate variables was implicitly achieved through the 
consideration of biomes, giving rise to the traditional vegetation-
based classification of Köppen (1936). In the last decades, the 
substantial increases in climate observations, climate modelling, and 
data processing capabilities have allowed new approaches to climate 
classification, for example through interpolation of aggregated 
global data from thousands of stations (Peel et al., 2007; Belda 
et al., 2014; Beck et al., 2018) or through data-driven approaches 
applied to delineate ecoregions that behave in a coherent manner

in response to  climate variability (Papagiannopoulou et al., 2018). 
Experience shows that each method has strengths and weaknesses 
through trade-offs between detail and convenience. For instance, 
a  very detailed classification, with numerous complexly shaped 
regions derived from a  large set of variables, may be most useful 
for the evaluation of climate models (Rubel and Kottek, 2010; Belda 
et al., 2015; Beck et al., 2018) and climate projections (Feng et al., 
2014; Belda et al., 2016). In contrast, geometrically simple regions 
are often best suited for regional climate modelling and downscaling 
(e.g.,  the Coordinated Regional Climate Downscaling Experiment 
(CORDEX) domains; Section 1.5.3; Giorgi and Gutowski, 2015).
1.4.5.2 Types of Regions Used in AR6
IPCC’s recognition of the importance of regional climates can be 
traced back to its First Assessment Report (FAR; IPCC, 1990a), where Cross-Working Group Box (continued)

studies on climate change impacts has grown substantially since AR5, generally leading to higher confidence levels in attributing the 
causes of specific impacts. New studies include the attribution of changes in socio-economic indicators such as economic damages 
due to river floods (e.g., Schaller et al., 2016; Sauer et al., 2021), the occurrence of heat-related human mortality (e.g., Vicedo-Cabrera 
et al., 2018; Sera et al., 2020) or economic inequality (e.g., Diffenbaugh and Burke, 2019).
Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations 
from remote sensing, long-term in situ observations, and monitoring efforts, teamed with local knowledge, process understanding 
and empirical or dynamical modelling (WGII Section  16.2; Stone et al., 2013; Cramer et al., 2014). The attribution of a  change in

a natural or human system (e.g., wild species, natural ecosystems, crop yields, economic development, infrastructure or human health) 
to changes in climate-related systems (i.e., climate, ocean acidification, permafrost thawing or sea level rise) requires accounting for 
other potential drivers of change, such as technological and economic changes in agriculture affecting crop production (Hochman 
et al., 2017; Butler et al., 2018), changes in human population patterns and vulnerability affecting flood- or wildfire-induced damages 
(Huggel et al., 2015; Sauer et al., 2021), or habitat loss driving declines in wild species (IPBES, 2019). These drivers are accounted 
for by estimating a  baseline condition that would exist in the absence of climate change. The baseline might be stationary and be 
approximated by observations from the past, or it may change over time and be simulated by statistical or process-based impact models

(WGII Section 16.2; Cramer et al., 2014) . Assessment of multiple independent lines of evidence, taken together, can provide rigorous 
attribution when more  quantitative approaches are not available (Parmesan et al., 2013). These include paleodata, physiological 
and ecological experiments, natural ‘experiments’ from very long-term datasets indicating consistent responses to the same climate 
trend/event, and ‘fingerprints’ in species’ responses that are uniquely expected from climate change (e.g. poleward range boundaries 
expanding and equatorial range boundaries contracting in a coherent pattern worldwide;  Parmesan and Yohe, 2003) . Meta-analyses 
of species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of climate 
change at an appropriate scale for attribution to anthropogenic climate change (Parmesan and Yohe, 2003; Parmesan et al., 2013).

Impact attribution does not always involve attribution to anthropogenic climate forcing. However, a  growing number of studies 
include this aspect (e.g., Frame et al. (2020) for the attribution of damages induced by Hurricane Harvey; or Diffenbaugh and Burke 
(2019) for the attribution of economic inequality between countries; or Schaller et al. (2016) for flood damages).

207
Framing, Context, and Methods Chapter 11climate projections for 2030 were presented for five sub-continental 
regions (see Section 1.3.6 for an assessment of those projections). 
In subsequent reports, there has been a growing emphasis on the 
analysis of regional climate, including two special reports: one on 
regional impacts (IPCC, 1998) and another on extreme events (SREX, IPCC, 2012). A  general feature of previous IPCC reports is 
that the number and coverage of climate regions vary according to 
the subject and across Working Groups. Such varied definitions have 
the advantage of optimizing the results for a particular application 
(e.g., national boundaries are crucial for decision-making, but they 
SAfri
SAmer M
SAsiaMS. & S.E. Asian Monsoon 
WAfriMAusMCM Australian-Ma ritime Continent Monsoon 
EAsiaM
EqAmer    
NAmerM(a) AR6 Referenc e Land and Ocean Regions ( entire report) 
(b) Ty pological Regions (ex ample: monsoon domains, Chapter 8) NPO N. Pacif ic Ocean

NSAN. South Amer ica
NWNN.W. North America
NWSN.W. South America
NZNew Zealand
RAR Russian Arctic
RFE Russian Far East
SAH Sahar a
SAM South Amer ican Monsoon
SAO S. Atlantic Ocean 
SAS South Asia
SAU S. Australia
SCAS. Central America
SEA S.E. Asia
SEAF S.E. Africa
SES S.E. South America
SIO South Indic Ocean
SOO Souther n Ocean
SPO S. Pacific Ocean 
SSA S. South America
SWSS.W. South America
TIBTibetan Plateau
WAFW. Africa
WANW. Antar ctica
WCAW. Central Asia
WCEW. & Central Europe
WNAW. North America
WSAFW. Southern Africa
WSBW. SiberiaARO Arctic Ocean
ARPArabian Peninsula
ARSArabian Sea
BOB Bay of Beng al
CAFCentral Africa
CARCaribbean
CAUC. Austra lia
CNA C. North America
EAN E. Antarctica
EAO Equator ial Atlantic Ocean
EAS E. Asia
EAU E. Australia
ECAE. Central Asia
EEU E. Europe
EIOEquatorial Indic O cean
ENAE. North Amer ica
EPO Equatorial Pacific Ocean
ESAF E. Southern Africa
ESB E. Siberia
GIC Greenland/Iceland
MDGMadag ascar
MEDMediterranean
NAON. Atlantic Ocean

NAUN. Austra lia
NCA N. Central America
NEAF N.E. Africa
NENN.E. North America
NESN.E. South Amer ica
NEUN. Europe
(c) Continental Regions 
Caribbean–Small Islands
East Europe–Asi a
European Arctic
North American A rctic
North Central America
Small Isl andsMain region types used in AR6 WGI
NAme rM
EqAmer
SAmerMWAfriM
SAfri
AusMCMEAsiaMSAsiaM
E. Asian Monsoon
Equator ial Amer ica
N. Amer ican Monsoon
S. Africa
S. Amer ican Monsoon
W. African Monsoon
Figure 1.18 | Main region types used in this report.  (a) AR6 WGI Reference Set of Land and Ocean Regions (Iturbide et al., 2020), consisting of 46 land regions and 15 
ocean regions, including 3 hybrid regions (CAR, MED, SEA) that are both land and ocean regions. Abbreviations are explained to the right of the map. Notice that RAR, SPO, 
NPO and EPO extend beyond the 180º meridian, therefore appearing at both sides of the map (indicated by dashed lines). A comparison with the previous reference regions of

AR5 WGI (IPCC, 2013a) is presented in the Atlas. (b) Example of typological regions: monsoon domains (see Chapter 8). Abbreviations are explained to the right of the map. 
The black contour lines represent the global monsoon zones, while the coloured regions denote the regional monsoon domains. The two stippled regions (EqAmer and SAfri) do 
receive seasonal rainfall, but their classification as monsoon regions is still under discussion. (c) Continental Regions used mainly in Chapter 12 and the Atlas. Stippled zones 
define areas that are assessed in both regions (e.g., the Caribbean is assessed as Small Islands and also as part of Central America). Small Islands are ocean regions containing 
small islands with consistent climate signals and/or climatological coherence.

208
Chapter 1 Framing, Context, and Methods1rarely delimit distinctive climate regions), whereas variable definitions 
of regions may have the disadvantage of hindering multidisciplinary 
assessments and comparisons between studies or Working Groups.
In this Report, regional climate change is primarily addressed through 
the introduction of four classes of regions (unless otherwise explicitly 
mentioned and justified). The first two are the unified WGI Reference 
Sets of (i) Land Regions and (ii) Ocean Regions, which are used 
throughout the Report. These are supplemented by additional sets 
of (iii) Typological Regions – used in Chapters 5, 8–12 and Atlas – 
and (iv) Continental Regions, which are mainly used for linking 
Chapters 11, 12 and Atlas with Working Group II (Figure 1.18). All 
four classes of regions are defined and described in detail in the 
Atlas. Here we summarize their basic features.
The Reference Sets of Land and Ocean Regions are polygonal,

sub-continental domains, defined through a  combination of 
environmental, climatic and non-climatic (e.g., pragmatic, technical, 
historical) factors, in accordance with the literature and climatological 
reasoning based on observed and projected future climate. Merging 
the diverse functions and purposes of the regions assessed in the 
literature into a common reference set implies a certain degree of 
compromise between simplicity, practicality and climate consistency. 
For instance, Spain is fully included in the Mediterranean (MED) 
Reference Region, but is one of the most climatically diverse 
countries in the world. Likewise, a  careful comparison of panels 
a and b of Figure 1.18 reveals that the simplified southern boundary 
of the Sahara (SAH) Reference Region slightly overlaps the northern 
boundary of the West African Monsoon Typological Region. As 
such, the resulting Reference Regions are not intended to precisely

represent climates, but rather to provide simple domains suitable for 
regional synthesis of observed and modelled climate and climate 
change information (Iturbide et al., 2020). In particular, CMIP6 model 
results averaged over Reference Regions are presented in the Atlas.
The starting point for defining the AR6 Reference Sets of Land Regions 
was the collection of 26 regions introduced in SREX (IPCC, 2012). 
The SREX collection was then revised, reshaped, complemented and 
optimized to reflect the recent scientific literature and observed 
climate-change trends, giving rise to the novel AR6 Reference Set of 
46 Land Regions. Additionally, AR6 introduces a new Reference Set 
of 15 Ocean Regions (including 3 Hybrid Regions that are treated as 
both: land and ocean), which complete the coverage of the whole 
Earth (Iturbide et al., 2020).
Particular aspects of regional climate change are described by 
specialized domains called Typological Regions (Figure 1.18b). These

regions cover a  wide range of spatial scales and are defined by 
specific features, called typologies. Examples of typologies include: 
tropical forests, deserts, mountains, monsoon regions and megacities, 
among others. Typological Regions are powerful tools to summarize 
complex aspects of climate defined by a  combination of multiple 
variables. For this reason, they are used in many chapters of AR6 WGI 
and WGII (e.g., Chapters 8–12 and Atlas).
Finally, consistency with WGII is also pursued in Chapters  11, 
12 and the Atlas through the use of a  set of Continental Regions (Figure 1.18c), based on the nine continental domains defined in AR5 
WGII Part B (Hewitson et al., 2014). These are classical geopolitical 
divisions of Africa, Asia, Australasia, Europe, North America, Central 
and South America, plus Small Islands, Polar Regions, and the 
Ocean. In AR6 WGI, five hybrid zones (Caribbean–Small Islands, East 
Europe–Asia, European Arctic, North American Arctic, and Northern

Central America) are also identified, which are assessed in more 
than one Continental Region. Additional consistency with WGIII is 
pursued in Chapter  6  through the use of sub-continental domains 
which essentially form a  subset of the Continental Set of Regions 
(Figure 1.18c and Section 6.1).
1.5 Major Developments 
and Their Implications
This section presents a selection of key developments since AR5 of 
the capabilities underlying the lines of evidence used in the present 
report: observational data and observing systems (Section  1.5.1); 
new developments in reanalyses (Section  1.5.2); climate models 
(Section  1.5.3); and modelling techniques, comparisons and 
performance assessments (Section 1.5.4). For brevity, we focus on the 
developments that are of particular importance to the conclusions 
drawn in later chapters, though we also provide an assessment of 
potential losses of climate observational capacity.
1.5.1 Observational Data and Observing Systems

Progress in climate science relies on the quality and quantity of 
observations from a range of platforms: surface-based instrumental 
measurements, aircraft, radiosondes and other upper-atmospheric 
observations, satellite-based retrievals, ocean observations, and 
paleoclimatic records. An historical perspective to these types of 
observations is presented in Section 1.3.1.
Observed large-scale climatic changes assessed in Chapter  2, 
attribution of these changes in Chapter 3, and regional observations 
of specific physical or biogeochemical processes presented in other 
Chapters, are supported by improvements in observational capacity 
since AR5. Attribution assessments can be made at a higher likelihood 
level than in AR5, due in part to the availability of longer observational 
datasets (Chapter  3). Updated assessments are made based on 
new and improved datasets, for example of global temperature 
change (Cross-Chapter Box  2.3) or regional climate information

(Section 10.2). Of particular relevance to the AR6 assessment are the 
Essential Climate Variables (ECVs; Hollmann et al., 2013; Bojinski et al., 
2014), and Essential Ocean Variables (EOVs; Lindstrom et al., 2012), 
compiled by the Global Climate Observing System (GCOS; WMO, 
2016), and the Global Ocean Observing System (GOOS), respectively. 
These variables include physical, chemical and biological variables 
or groups of linked variables, and underpin ‘headline indicators’ 
(a selected set of essential parameters representing the state of the 
climate system) for climate monitoring (Trewin et al., 2021).
We highlight below the key advances in observational capacity since 
AR5, including major expansions of existing observational platforms

209
Framing, Context, and Methods Chapter 11as well as new and/or emerging observational platforms that play 
a key role in AR6. We then discuss potential near-term losses in key 
observational networks due to climate change or other adverse 
human-caused influence.
1.5.1.1 Major Expansions of Observational Capacity
1.5.1.1.1 Atmosphere, land and hydrological cycle
Satellites provide observations of a large number of key atmospheric 
and land-surface variables, ensuring sustained observations over 
wide areas. Since AR5, such observations have expanded to include 
satellite retrievals of atmospheric CO 2 via the NASA Orbiting Carbon 
Observatory satellites (OCO-2 and OCO-3; Eldering et al., 2017), 
following on from similar efforts employing the Greenhouse Gases 
Observing Satellite (GOSat; Yokota et al., 2009; Inoue et al., 2016). 
By combining remote sensing and in situ measurements, knowledge 
of fluxes between the atmosphere and land surface has improved

(Rebmann et al., 2018). FLUXNET ( https://fluxnet.org/ ) has been 
providing eddy covariance measurements of carbon, water, and 
energy fluxes between the land and the atmosphere, with some of 
the stations operating for over 20 years (Pastorello et al., 2017), while 
the Baseline Surface Radiation Network (BSRN) has been maintaining 
high-quality radiation observations since the 1990s (Ohmura et al., 
1998; Driemel et al., 2018).
Observations of the composition of the atmosphere have been 
further improved through expansions of existing surface observation 
networks (Bodeker et al., 2016; De Mazière et al., 2018) and through 
in situ measurements such as aircraft campaigns (Sections  2.2, 
5.2 and Section  6.2). Examples of expanded networks include the 
Aerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS; 
Pandolfi et al., 2018), which focuses on short-lived climate forcers, 
and the Integrated Carbon Observation System (ICOS), which

allows scientists to study and monitor the global carbon cycle 
and GHG emissions (Colomb et al., 2018). Examples of recent 
aircraft observations include the Atmospheric Tomography Mission 
(ATom), which has flown repeatedly along the north–south axis of 
both the Pacific and Atlantic oceans, and the continuation of the 
In-service Aircraft for a  Global Observing System (IAGOS) effort, 
which measures atmospheric composition from commercial aircraft 
(Petzold et al., 2015).
Two distinctly different but important remote-sensing systems can 
provide information about temperature and humidity since the 
early 2000s. Global navigation satellite systems (e.g.,  GPS), radio 
occultation and limb soundings provide information, although only 
data for the upper troposphere and lower stratosphere are suitable 
to support climate change assessments (Angerer et al., 2017; 
Scherllin-Pirscher et al., 2017; Gleisner et al., 2020; Steiner et al.,

2020). These measurements complement those from the Atmospheric 
Infrared Sounder (AIRS; Chahine et al., 2006). AIRS has limitations 
in cloudy conditions, although these limitations have been partly 
solved using new methods of analysis (Blackwell and Milstein, 2014; 
Susskind et al., 2014). These new data sources now have sufficiently 
long records to strengthen the analysis of atmospheric warming in 
Chapter 2 (Section 2.3.1.2).Assessments of the hydrological cycle in Chapters  2  and 8  are 
supported by longer time series and new developments. Examples 
are new satellites (McCabe et al., 2017) and measurements of water 
vapour using commercial laser absorption spectrometers and water 
vapour isotopic composition (Steen-Larsen et al., 2015; Zannoni 
et al., 2019). Data products of higher quality have been developed 
since AR5, such as the multi-source weighted ensemble precipitation 
(Beck et al., 2017) and multi-satellite terrestrial evaporation

products(Fisher et al., 2017). Longer series are available for satellite-
derived global inundation data (Prigent et al., 2020). Observations 
of soil moisture are now available via the Soil Moisture and Ocean 
Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) satellite 
retrievals, filling critical gaps in the observation of hydrological 
trends and variability over land (Dorigo et al., 2017). Similarly, the 
Gravity Recovery and Climate Experiment GRACE and GRACE-
FO satellites (Tapley et al., 2019) have provided key constraints 
on groundwater variability and trends around the world (Frappart 
and Ramillien, 2018). The combination of new observations with 
other sources of information has led to updated estimates of heat 
storage in inland waters (Vanderkelen et al., 2020), contributing to 
revised estimates of heat storage on the continents (Section 7.2.2.3; 
von Schuckmann et al., 2020).
The ongoing collection of information about the atmosphere as it

evolves is supplemented by the reconstruction and digitization 
of data about past conditions. Programmes aimed at recovering 
information from sources such as handwritten weather journals and 
ships’ logs continue to make progress, and are steadily improving 
spatial coverage and extending our knowledge backward in time. 
For example, Brönnimann et al. (2019a) has recently identified 
several thousand sources of climate data for land areas in the pre-
1890 period, with many from the 18th  century. The vast majority 
of these data are not yet contained in international digital data 
archives, and substantial quantities of undigitized ships’ weather 
log data exist for the same period (Kaspar et al., 2015). Since AR5 
there has been a growth of ‘citizen science’ activities, making use 
of volunteers to rapidly transcribe substantial quantities of weather 
observations. Examples of projects include: oldWeather.org  and 
SouthernWeatherDiscovery.org  (both of which used ship-based

logbook sources); the DRAW project (Data Rescue: Archival and 
Weather, which recovered land-based station data from Canada); 
WeatherRescue.org  (land-based data from Europe); JungleWeather.org  
(data from the Congo); and the Climate History Australia project 
(data from Australia; e.g.,  Park et al., 2018; Hawkins et al., 2019). 
Undergraduate students have also been recruited to successfully 
digitize rainfall data in Ireland (Ryan et al., 2018). Such observations 
are an invaluable source of weather and climate information for the 
early historical period that continues to expand the digital archives 
(e.g., Freeman et al., 2017) which underpin observational datasets 
used across several Chapters.
1.5.1.1.2 Ocean
Observations of the ocean have expanded significantly since AR5, 
with expanded global coverage of in situ ocean temperature and 
salinity observations, in situ ocean biogeochemistry observations, 
and satellite retrievals of a variety of EOVs. Many recent advances

210
Chapter 1 Framing, Context, and Methods1are extensively documented in a  compilation by Lee et al. (2019). 
Below we discuss those most relevant for the current assessment.
Argo is a global network of nearly 4000 autonomous profiling floats 
(Roemmich et al., 2019), delivering detailed constraints on the 
horizontal and vertical structure of temperature and salinity across 
the global ocean. Argo has greatly expanded since AR5, including 
biogeochemistry and measurements deeper than 2000 m  (Jayne 
et al., 2017), and the longer time series enable more rigorous climate 
assessments of direct relevance to estimates of ocean heat content 
(Sections  2.3.3.1 and 7.2.2.2). Argo profiles are complemented by 
animal-borne sensors in several key areas, such as the seasonally ice-
covered sectors of the Southern Ocean (Harcourt et al., 2019).
Most basin-scale arrays of moored ocean instruments have 
expanded since AR5, providing decades-long records of the ocean

and atmosphere properties relevant for climate, such as the El Niño–
Southern Oscillation (Chen et al., 2018), deep convection (de Jong 
et al., 2018) or transports through straits (Woodgate, 2018). Key 
basin-scale arrays include transport-measuring arrays in the Atlantic 
Ocean, continuing (McCarthy et al., 2020) or newly added since AR5 
(Lozier et al., 2019), supporting the assessment of regional ocean 
circulation (Section  9.2.3). Tropical ocean moorings in the Pacific, 
Indian and Atlantic oceans include new sites, improved capability for 
real-time transmission, and new oxygen and CO 2 sensors (Bourlès 
et al., 2019; Hermes et al., 2019; Smith et al., 2019).
A decade of observations of sea-surface salinity is now available via 
the SMOS and SMAP satellite retrievals, providing continuous and 
global monitoring of surface salinity in the open ocean and coastal 
areas for the first time (Section  9.2.2.2; Vinogradova et al., 2019; 
Reul et al., 2020).

The global network of tide gauges, complemented by a  growing 
number of satellite-based altimetry datasets, allows for more robust 
estimates of global and regional sea level rise (Sections  2.3.3.3 
and 9.6.1.3). Incorporating vertical land motion derived from the 
Global Positioning System (GPS), the comparison with tide gauges 
has allowed the correction of a drift in satellite altimetry series over 
the period 1993–1999 (Watson et al., 2015; Chen et al., 2017), thus 
improving our knowledge of the recent acceleration of sea level rise 
(Chapter  2, Section  2.3.3.3). These datasets, combined with Argo 
and observations of the cryosphere, allow a consistent closure of the 
global mean sea level budget (Cross-Chapter Box 9.1; WCRP Global 
Sea Level Budget Group, 2018).
1.5.1.1.3 Cryosphere
For the cryosphere, there has been much recent progress in 
synthesizing global datasets covering larger areas and longer time 
periods from multi-platform observations. For glaciers, the Global

Terrestrial Network for Glaciers, which combines data on glacier 
fluctuations, mass balance and elevation change with glacier outlines 
and ice thickness, has expanded and provided input for assessing 
global glacier evolution and its role in sea level rise (Sections 2.3.2.3 
and 9.5.1; Zemp et al., 2019). New data sources include archived 
and declassified aerial photographs and satellite missions, and high-resolution (10 m or less) digital elevation models (Porter et al., 2018; 
Braun et al., 2019).
Improvements have also been made in the monitoring of permafrost. 
The Global Terrestrial Network for Permafrost (GTN-P; Biskaborn 
et al., 2015) provides long-term records of permafrost temperature 
and active layer thickness at key sites to assess their changes over 
time. Substantial improvements to our assessments of large-scale 
snow changes come from intercomparison and blending of several 
datasets, for snow water equivalent (Mortimer et al., 2020) and

snow cover extent (Mudryk et al., 2020), and from bias corrections 
of combined datasets using in situ data (Sections 2.3.2.5 and 9.5.2; 
Pulliainen et al., 2020).
The value of gravity-based estimates of changes in ice-sheet mass 
has increased, as the time series from the GRACE and GRACE-FO 
satellites  – homogenized and absolutely calibrated  – is close to 
20 years in length. The European Space Agency’s (ESA’s) Cryosat-2 
radar altimetry satellite mission has continued to provide 
measurements of the changes in the thickness of sea ice and the 
elevation of the Greenland and Antarctic ice sheets (Tilling et al., 
2018). Other missions include NASA’s Operation IceBridge, collecting 
airborne remote-sensing measurements to bridge the gap between 
ICESat (Ice, Cloud and land Elevation Satellite) and the upcoming 
ICESat-2 laser altimetry missions. Longer time series from multiple 
missions have led to considerable advances in understanding the

origin of inconsistencies between the mass balances of different 
glaciers and reducing uncertainties in estimates of changes in 
the Greenland and Antarctic ice sheets (Bamber et al., 2018; A. 
Shepherd et al., 2018; Shepherd et al., 2020). Last, the first observed 
climatology of snowfall over Antarctica was obtained using the cloud/
precipitation radar onboard NASA’s CloudSat (Palerme et al., 2014).
1.5.1.1.4 Biosphere
Satellite observations have recently expanded to include data on the 
fluorescence of land plants as a measure of photosynthetic activity 
via the Global Ozone Monitoring Experiment (GOME; Guanter et al., 
2014; Yang et al., 2015) and OCO-2 satellites (Sun et al., 2017). 
Climate data records of leaf area index (LAI), characterizing the area 
of green leaves per unit of ground area, and the fraction of absorbed 
photosynthetically active radiation (FAPAR) – an important indicator 
of photosynthetic activity and plant health (Gobron et al., 2009) – are

now available for over 30 years (Claverie et al., 2016). In addition, 
key indicators such as fire disturbances/burned areas are now 
retrieved via satellite (Chuvieco et al., 2019). In the US, the National 
Ecological Observational Network (NEON) provides continental-scale 
observations relevant to the assessment of changes in aquatic and 
terrestrial ecosystems via a wide variety of ground-based, airborne, 
and satellite platforms (Keller et al., 2008). All these long-term 
records reveal range shifts in ecosystems (Section 2.3.4).
The ability to estimate changes in global land biomass has improved 
due to the use of different microwave satellite data (Liu et al., 2015) 
and in situ forest census data and co-located lidar, combined with 
the Moderate Resolution Imaging Spectroradiometer (MODIS; 
Baccini et al., 2017). This has allowed for improved quantification

211
Framing, Context, and Methods Chapter 11of land temperature (Duan et al., 2019), carbon stocks and human-
induced changes due to deforestation (Chapter  2, Section  2.2.7). 
Time series of Normalized Difference Vegetation Index (NDVI) 
from MODIS and other remote-sensing platforms is widely applied 
to assess the effects of climate change on vegetation in drought-
sensitive regions (Atampugre et al., 2019). New satellite imaging 
capabilities for meteorological observations, such as the advanced 
multispectral imager aboard Himawari-8 (Bessho et al., 2016), also 
allow for improved monitoring of challenging quantities such as 
seasonal changes of vegetation in cloudy regions (Section 2.3.4.3; 
Miura et al., 2019).
In the ocean, efforts are underway to coordinate observations of 
biologically relevant EOVs around the globe (Muller-Karger et al., 
2018; Canonico et al., 2019) and to integrate observations across 
disciplines (e.g., the Global Ocean Acidification Observing Network,

GOA-ON; Tilbrook et al., 2019). A  large number of coordinated 
field campaigns during the 2015/2016 El Niño event enabled 
the collection of short-lived biological phenomena such as coral 
bleaching and mortality caused by a months-long ocean heatwave 
(Hughes et al., 2018); beyond this event, coordinated observations of 
coral reef systems are increasing in number and quality (Obura et al., 
2019). Overall, globally coordinated efforts focused on individual 
components of the biosphere (e.g., the Global Alliance of Continuous 
Plankton Recorder Surveys, GACS; Batten et al., 2019) contribute to 
improved knowledge of the ways in which marine ecosystems are 
changing (Section 2.3.4.2).
Given widespread evidence for decreases in global biodiversity in 
recent decades  – and that these decreases are related to climate 
change and other forms of human disturbance (IPBES, 2019) – a new 
international effort to identify a set of Essential Biodiversity Variables

(EBVs) is underway (Pereira et al., 2013; Navarro et al., 2017).
In summary, the observational coverage of ongoing changes to the 
climate system is improved at the time of AR6, relative to what was 
available for AR5 ( high confidence ).
1.5.1.1.5 Paleoclimate
Major paleoreconstruction efforts completed since AR5 include 
a  variety of large-scale, multi-proxy temperature datasets and 
associated reconstructions spanning the last 2000  years (PAGES 
2k Consortium, 2017, 2019; Neukom et al., 2019), the Holocene 
(Kaufman et al., 2020), the Last Glacial Maximum (Cleator et al., 2020; 
Tierney et al., 2020b), the mid-Pliocene Warm Period (McClymont 
et al., 2020), and the Early Eocene Climatic Optimum (Hollis et al., 
2019). Newly compiled borehole data (Cuesta-Valero et al., 2019), 
as well as advances in statistical applications to tree ring data, 
result in more robust reconstructions of key indices such as Northern

Hemisphere temperature over the last millennium (e.g., Wilson et al., 
2016; Anchukaitis et al., 2017). Such reconstructions provide a new 
context for recent warming trends (Chapter 2) and serve to constrain 
the response of the climate system to natural and anthropogenic 
forcing (Chapters 3 and 7).Ongoing efforts have expanded the number of large-scale, tree 
ring-based drought reconstructions that span the last centuries 
to millennium at annual resolution (Chapter  8; Cook et al., 2015; 
Stahle et al., 2016; Aguilera-Betti et al., 2017; Morales et al., 2020). 
Likewise, stalagmite records of oxygen isotopes have increased in 
number, resolution and geographic distribution since AR5, providing 
insights into regional-to-global-scale hydrological change over the 
last centuries to millions of years (Chapter  8; Cheng et al., 2016; 
Denniston et al., 2016; Comas-Bru and Harrison, 2019). A new global 
compilation of water isotope-based paleoclimate records spanning

the last 2000 years (PAGES Iso2K) lays the groundwork for quantitative 
multi-proxy reconstructions of regional- to global-scale hydrological 
and temperature trends and extremes (Konecky et al., 2020).
Recent advances in the reconstruction of climate extremes – aside 
from temperature and drought – include expanded datasets of past 
El Niño–Southern Oscillation extremes (Section  2.4.2; e.g.,  Barrett 
et al., 2018; Freund et al., 2019; Grothe et al., 2020) and other modes 
of variability (Hernández et al., 2020), hurricane activity (e.g., Burn 
and Palmer, 2015; Donnelly et al., 2015), jet stream variability (Trouet 
et al., 2018) and wildfires (e.g., Taylor et al., 2016).
New datasets as well as recent data compilations and syntheses 
of sea level over the last millennia (Kopp et al., 2016; Kemp et al., 
2018), the last 20 kyr (Khan et al., 2019), the last interglacial period 
(Section 2.3.3.3: Dutton et al., 2015), and the Pliocene (Cross-Chapter

Box  2.4; Dumitru et al., 2019; Grant et al., 2019) help constrain 
sea level variability and its relationship to global and regional 
temperature variability, and to estimates of contributions to sea level 
change from different sources on centennial to millennial time scales 
(Section 9.6.2).
Reconstructions of paleo ocean pH (Section 2.3.3.5) have increased 
in number and accuracy, providing new constraints on ocean pH 
across the last centuries (e.g.,  Wu et al., 2018), the last glacial 
cycles (e.g.,  Moy et al., 2019), and the last several million years 
(e.g., Anagnostou et al., 2020). Such reconstructions inform processes 
and act as benchmarks for Earth system models of the global carbon 
cycle over the recent geologic past (Section 5.3.1), including previous 
high-CO 2 warm intervals such as the Pliocene (Cross-Chapter Box 2.4). 
Particularly relevant to such investigations are reconstructions of 
atmospheric CO 2 (Honisch et al., 2012; Foster et al., 2017) that span

the past millions to tens of millions of years.
Constraints on the timing and rates of past climate changes have 
improved since AR5. Analytical methods have increased the precision 
and reduced sample-size requirements for key radiometric dating 
techniques, including radiocarbon (Gottschalk et al., 2018; Lougheed 
et al., 2018) and uranium–thorium dating (Cheng et al., 2013). More 
accurate ages of many paleoclimate records are also facilitated 
by recent improvements in the radiocarbon calibration datasets 
(IntCal20, Reimer et al., 2020). A  recent compilation of global 
cosmogenic nuclide-based exposure dates (Balco, 2020b) allows for 
a  more rigorous assessment of the evolution of glacial landforms 
since the Last Glacial Maximum (Balco, 2020a).

212
Chapter 1 Framing, Context, and Methods1Advances in paleoclimate data assimilation (Section  10.2.3.2) 
leverage the expanded set of paleoclimate observations to create 
physically consistent gridded fields of climate variables for data-
rich intervals of interest (e.g.,  over the last millennium, (Hakim 
et al., 2016) or last glacial period (Cleator et al., 2020; Tierney et al., 
2020b)). Such efforts mirror advances in our understanding of the 
relationship between proxy records and climate variables of interest, 
as formalized in so-called proxy system models (e.g., Tolwinski-Ward 
et al., 2011; Dee et al., 2015; Dolman and Laepple, 2018).
Overall, the number, temporal resolution and chronological accuracy 
of paleoclimate reconstructions have increased since AR5, leading 
to improved understanding of climate system processes (or Earth 
system processes) ( high confidence ).
1.5.1.2 Threats to Observational Capacity or Continuity

The lockdowns and societal outcomes arising from the COVID-19 
pandemic pose a  new threat to observing systems. For example, 
WMO and UNESCO-IOC (Intergovernmental Oceanographic 
Commission) published a summary of the changes to Earth system 
observations during COVID-19 (WMO, 2020b). Fewer aircraft flights 
(down 75–90% in May 2020, depending on region) and ship transits 
(down 20% in May 2020) mean that onboard observations from 
those networks have reduced in number and frequency (James 
et al., 2020; Ingleby et al., 2021). Europe has deployed more 
radiosonde soundings to account for the reduction in data from air 
traffic. Fewer ocean observing buoys were deployed during 2020, 
and reductions have been particularly prevalent in the tropics and 
Southern Hemisphere. The full consequences of the pandemic, and 
responses to it, will come to light over time. Estimates of the effect 
of the reduction in aircraft data assimilation on weather forecasting

skill are small (James et al., 2020; Ingleby et al., 2021), potentially 
alleviating concerns about veracity of future atmospheric reanalyses 
of the COVID-19 pandemic period.
Surface-based networks have reduced in their coverage or range of 
variables measured due to COVID-19 and other factors. Over land, 
several factors, including the ongoing transition from manual to 
automatic observations of weather, have reduced the spatial coverage 
of certain measurement types, including rainfall intensity, radiosonde 
launches and pan evaporation, posing unique risks to datasets used 
for climate assessment (WMO, 2017; Lin and Huybers, 2019). Ship-
based measurements, which are important for ocean climate and 
reanalyses through time (Smith et al., 2019), have been in decline 
due to the number of ships contributing observations. There has also 
been a decline in the number of variables recorded by ships, but an 
increase in the quality and time-resolution of others (e.g., sea level

pressure, Kent et al., 2019).
Certain satellite frequencies are used to detect meteorological 
features that are vital to climate change monitoring. These can be 
disturbed by certain radio communications (Anterrieu et al., 2016), 
although scientists work to remove noise from the signal (Oliva 
et al., 2016). For example, water vapour in the atmosphere naturally 
produces a weak signal at 23.8 gigahertz (GHz), which is within the 
range of frequencies of the 5G cellular communications network (Liu et al., 2021). Concern has been raised about potential leakage 
from 5G network transmissions into the operating frequencies of 
passive sensors on existing weather satellites, which could adversely 
influence their ability to remotely observe water vapour in the 
atmosphere (Yousefvand et al., 2020).
Threats to observational capacity also include the loss of natural 
climate archives that are disappearing as a  direct consequence of

warming temperatures. Ice-core records from vulnerable alpine 
glaciers in the tropics (Permana et al., 2019) and the mid-latitudes 
(Gabrielli et al., 2016; Winski et al., 2018; Moreno et al., 2021) 
document more frequent melt layers in recent decades, with glacial 
retreat occurring at a rate and geographic scale that is unusual in 
the Holocene (Solomina et al., 2015). The scope and severity of coral 
bleaching and mortality events have increased in recent decades 
(Hughes et al., 2018), with profound implications for the recovery 
of coral climate archives from new and existing sites. An observed 
increase in the mortality of larger, long-lived trees over the last century 
is attributed to a  combination of warming, land-use change, and 
disturbance (e.g., McDowell et al., 2020). The ongoing loss of these 
natural, high-resolution climate archives endanger an end in their 
coverage over recent decades, given that many of the longest

monthly- to annually-resolved paleoclimate records were collected 
in the 1960s to 1990s (e.g., the PAGES2K database as represented 
in PAGES 2k Consortium, 2017). This gap presents a barrier to the 
calibration of existing decades-to-centuries-long records needed to 
constrain past temperature and hydrology trends and extremes.
Historical archives of weather and climate observations contained 
in ships’ logs, weather diaries, observatory logbooks and other 
sources of documentary data also risk being lost, for example 
to natural disasters or accidental destruction. These archives 
include measurements of temperature (air and sea surface), 
rainfall, surface pressure, wind strength and direction, sunshine 
amount, and many other variables back into the 19th  century. 
While internationally coordinated data-rescue efforts are focused 
on recovering documentary sources of past weather and climate 
data (e.g., Allan et al., 2011), no such coordinated efforts exist

for vulnerable paleoclimate archives. Furthermore, oral traditions 
about local and regional weather and climate from indigenous 
peoples represent valuable sources of information, especially when 
used in combination with instrumental climate data (Makondo 
and Thomas, 2018), but are in danger of being lost as indigenous 
knowledge-holders pass away.
In summary, while the quantity, quality and diversity of climate 
system observations have grown since AR5, the loss or potential loss 
of several critical components of the observational network is also 
evident ( high confidence ).
1.5.2 New Developments in Reanalyses
Reanalyses are usually the output of a  model (e.g.,  a  numerical 
weather prediction model) constrained by observations using data 
assimilation techniques, but the term has also been used to describe 
observation-based datasets produced using simpler statistical

213
Framing, Context, and Methods Chapter 11methods and models (Annex I: Observational Products). This section 
focuses on the model-based methods and their recent developments.
Reanalyses complement datasets of observations in describing 
changes through the historical record and are sometimes considered 
as ‘maps without gaps’ because they provide gridded output in 
space and time, often global, with physical consistency across 
variables on sub-daily time scales, and information about sparsely 
observed variables (such as evaporation; Hersbach et al., 2020). They 
can be globally complete, or regionally focussed and constrained 
by boundary conditions from a global reanalysis (Section 10.2.1.2). 
They can also provide feedback about the quality of the observations 
assimilated, including estimates of biases and critical gaps for some 
observing systems.
Many early reanalyses are described in Box 2.3 of Hartmann et al.

(2013). These were often limited by the underlying model, the data 
assimilation schemes and observational issues (Thorne and Vose, 
2010; Zhou et al., 2018). Observational issues include the lack of 
underlying observations in some regions, changes in the observational 
systems over time (e.g.,  spatial coverage, introduction of satellite 
data), and time-dependent errors in the underlying observations or in 
the boundary conditions, which may lead to stepwise biases in time. 
The assimilation of sparse or inconsistent observations can introduce 
mass or energy imbalances (Valdivieso et al., 2017; Trenberth et al., 
2019). Further limitations and some efforts to reduce the implications 
of these observational issues are detailed below.
The methods used in the development of reanalyses have progressed 
since AR5 and, in some cases, this has important implications for 
the information they provide on how the climate is changing. Annex

I  includes a  list of reanalysis datasets used in AR6. Recent major 
developments in reanalyses include the assimilation of a wider range 
of observations, higher spatial and temporal resolution, extensions 
further back in time, and greater efforts to minimize the influence of 
a temporally varying observational network.
1.5.2.1 Atmospheric Reanalyses
Extensive improvements have been made in global atmospheric 
reanalyses since AR5. The growing demand for high-resolution 
data has led to the development of higher-resolution atmospheric 
reanalyses, such as the Modern-Era Retrospective Analysis for 
Research and Applications, version 2 (MERRA-2; Gelaro et al., 2017) 
and ERA5 (Hersbach et al., 2020). There is a  focus on ERA5 here 
because it has been assessed as of high enough quality to present 
temperature trends alongside more traditional observational datasets 
(Section 2.3.1.1) and is also used in the Interactive Atlas.

Atmospheric reanalyses that were assessed in AR5 are still being 
used in the literature, and results from ERA-Interim (about 80 km 
resolution, production stopped in August 2019; Dee et al., 2011), the 
Japanese 55-year Reanalysis (JRA-55; Ebita et al., 2011; Kobayashi 
et al., 2015; Harada et al., 2016) and Climate Forecast System 
Reanalysis (CFSR; Saha et al., 2010) are assessed in AR6. Some 
studies still also use the NCEP/NCAR reanalysis, particularly because 
it extends back to 1948 and is updated in near-real time (Kistler et al., 2001). Older reanalyses have a number of limitations, which 
have to be accounted for when assessing the results of any study 
that uses them.
ERA5 provides hourly atmospheric fields at about 31 km resolution 
on 137 levels in the vertical, as well as land-surface variables and 
ocean waves. It is available from 1979 onwards and is updated in 
near-real time, with plans to extend back to 1950. A  10-member

ensemble is also available at coarser resolution, allowing uncertainty 
estimates to be provided (e.g.,  Section  2.3). MERRA-2 includes 
many updates from the earlier version, including the assimilation of 
aerosol observations, several improvements to the representation 
of the stratosphere, including ozone, and improved representations 
of cryospheric processes. All of these improvements increase the 
usefulness of these reanalyses (Section 7.3; Hoffmann et al., 2019).
Models of atmospheric composition and emissions sources and 
sinks allow the forecast and reanalysis of constituents such as O 3, 
carbon monoxide (CO), nitrogen oxides (NOx) and aerosols. The 
Copernicus Atmosphere Monitoring Service (CAMS) reanalysis shows 
improvement against earlier atmospheric composition reanalyses, 
giving greater confidence for its use to study trends and evaluate 
models (Section 7.3; e.g., Inness et al., 2019).
The intercomparison of reanalyses with each other, or with earlier

versions, is often done for particular variables or aspects of the 
simulation. ERA5 is assessed as the most reliable reanalysis for 
climate trend assessment (Section  2.3). Compared to ERA-Interim, 
the ERA5 forecast model and assimilation system, as well as the 
availability of improved reprocessing of observations, resulted in 
relatively smaller errors when compared to observations, including 
a better representation of global energy budgets, radiative forcing 
from volcanic eruptions (e.g., Mt. Pinatubo: Allan et al., 2020), the 
partitioning of surface energy (Martens et al., 2020), and wind (Kaiser-
Weiss et al., 2015, 2019; Borsche et al., 2016; Scherrer, 2020). In 
ERA5, higher resolution means a better representation of Lagrangian 
motion convective updrafts, gravity waves, tropical cyclones, and 
other meso- to synoptic-scale features of the atmosphere (Hoffmann 
et al., 2019; Martens et al., 2020). Low-frequency variability is found

to be generally well represented and, from 10 hPa downwards, 
patterns of anomalies in temperature match those from the ERA-
Interim, MERRA-2 and JRA-55 reanalyses. Inhomogeneities in the 
water cycle have also been reduced (Hersbach et al., 2020).
Precipitation is not usually assimilated in reanalyses and, depending 
on the region, reanalysis precipitation can differ from observations 
by more than the observational error (Zhou and Wang, 2017; Sun 
et al., 2018; Alexander et al., 2020; Bador et al., 2020), although 
these studies did not include ERA5. Assimilation of radiance 
observations from microwave imagers which, over ice-free ocean 
surfaces, improve the analysis of lower-tropospheric humidity, 
cloud liquid water and ocean-surface wind speed have resulted 
in improved precipitation outputs in ERA5 (Hersbach et al., 2020). 
Global averages of other fields, particularly temperature, from ERA-
Interim and JRA-55 reanalyses continue to be consistent over the last

20 years with surface observational data sets that include the polar 
regions (Simmons and Poli, 2015), although biases in precipitation

214
Chapter 1 Framing, Context, and Methods1and radiation can influence temperatures regionally (Zhou et al., 
2018). The global average surface temperature from MERRA-2 is far 
cooler in recent years than temperatures derived from ERA-Interim 
and JRA-55, which may be due to the assimilation of aerosols and 
their interactions (Section 2.3).
A number of regional atmospheric reanalyses (Section  10.2.1.2) 
have been developed, such as COSMO-REA (Wahl et al., 2017), and 
the Australian Bureau of Meteorology Atmospheric high-resolution 
Regional Reanalysis for Australia (BARRA; Su et al., 2019). Regional 
reanalyses can add value to global reanalyses due to the lower 
computational requirements, and can allow multiple numerical 
weather prediction models to be tested (e.g.,  Kaiser-Weiss et al., 
2019). There is some evidence that these higher-resolution reanalyses 
better capture precipitation variability than global lower-resolution

reanalyses (Jermey and Renshaw, 2016; Cui et al., 2017). They are 
further assessed in Section 10.2.1.2 and used in the Interactive Atlas.
In summary, the improvements in atmospheric reanalyses, and the 
greater number of years since the routine ingestion of satellite data 
began, relative to AR5, mean that there is increased confidence in 
using atmospheric reanalysis products alongside more standard 
observation-based datasets in AR6 ( high confidence ).
1.5.2.2 Sparse Input Reanalyses of the Instrumental Era
Although reanalyses such as ERA5 take advantage of new observational 
datasets and present a great improvement in atmospheric reanalyses, 
the issues introduced by the evolving observational network remain. 
Sparse input reanalyses, where only a  limited set of reliable and 
long-observed records are assimilated, address these issues, with 
the limitation of fewer observational constraints. These efforts are 
sometimes called centennial-scale reanalyses. One example is the

atmospheric 20th century Reanalysis (Compo et al., 2011; Slivinski 
et al., 2021) which assimilates only surface and sea-level pressure 
observations, and is constrained by time-varying observed changes 
in atmospheric constituents, prescribed sea surface temperatures and 
sea ice concentration, creating a reconstruction of the weather over 
the whole globe every three hours for the period 1806–2015. The 
ERA-20C atmospheric reanalysis (covering 1900–2010; Poli et al., 
2016) also assimilates marine wind observations, and CERA-20C is 
a centennial-scale reanalysis that assimilates both atmospheric and 
oceanic observations for the 1901–2010 period (Laloyaux et al., 
2018). These centennial-scale reanalyses are often run as ensembles 
that provide an estimate of the uncertainty in the simulated 
variables over space and time. Slivinski et al. (2021) conclude that 
the uncertainties in surface circulation fields in version 3  of the

20th  century Reanalysis are reliable and that there is also skill in 
its tropospheric reconstruction over the 20th  century. Long-term 
changes in other variables, such as precipitation, also agree well with 
direct observation-based datasets (Sections 2.3.1.3 and 8.3.2.8).
1.5.2.3 Ocean Reanalyses
Since AR5, ocean reanalyses have improved due to: increased 
model resolution (Zuo et al., 2017; Lellouche et al., 2018; Heimbach 
et al., 2019); improved physics (Storto et al., 2019); improvements in the atmospheric forcing from atmospheric reanalyses 
(see  Section  1.5.2.1.3); and improvements in the data quantity 
and quality available for assimilation (e.g.,  Lellouche et al., 2018; 
Heimbach et al., 2019), particularly due to Argo observations (Annex 
I; Zuo et al., 2019).
The first Ocean Reanalyses Intercomparison project (ORA-IP; 
Balmaseda et al., 2015) focussed on the uncertainty in key climate 
indicators, such as ocean heat content (Palmer et al., 2017),

thermosteric sea level (Storto et al., 2017, 2019), salinity (Shi 
et al., 2017), sea ice extent (Chevallier et al., 2017), and the AMOC 
(Karspeck et al., 2017). Reanalysis uncertainties occur in areas of 
inhomogeneous or sparse observational data sampling, such as for 
the deep ocean, the Southern Ocean, and western boundary currents 
(Lellouche et al., 2018; Storto et al., 2019). Intercomparisons have 
also been dedicated to specific variables such as mixed-layer depths 
(Toyoda et al., 2017), eddy kinetic energy, globally (Masina et al., 
2017) and in the polar regions (Uotila et al., 2019). Karspeck et al. 
(2017) found disagreement in the AMOC variability and strength in 
reanalyses over observation-sparse periods, whereas Jackson et al. 
(2019) reported a lower spread in AMOC strength across an ensemble 
of ocean reanalyses of the recent period (1993–2010), linked to 
improved observation availability for assimilation. Reanalyses also

have a larger spread of ocean heat uptake than data-only products 
and can produce spurious overestimates of heat uptake (Palmer 
et al., 2017), which is important in the context of estimating climate 
sensitivity (Storto et al., 2019). The ensemble approach for ocean 
reanalyses provides another avenue for estimating uncertainties 
across ocean reanalyses (Storto et al., 2019).
While there are still limitations in their representation of oceanic 
features, ocean reanalyses add value to products based only on 
observation, and are used to inform assessments in AR6 (Chapters 2, 
3, 7 and 9). Reanalyses of the atmosphere or ocean alone may not 
account for important atmosphere–ocean coupling, motivating the 
development of coupled reanalyses (Laloyaux et al., 2018; Schepers 
et al., 2018; Penny et al., 2019), but these are not assessed in AR6.
1.5.2.4 Reanalyses of the Pre-Instrumental Era
Longer reanalyses that extend further back in time than the

beginning of the instrumental record are being developed. They 
include the complete integration of paleoclimate archives and newly 
available early instrumental data into extended reanalysis datasets. 
Such integration leverages ongoing development of climate models 
that can simulate paleoclimate records in their units of analysis 
(i.e., oxygen isotope composition, tree ring width, etc.), in many cases 
using physical climate variables as input for so-called proxy system 
models (Evans et al., 2013; Dee et al., 2015). Ensemble Kalman filter 
data assimilation approaches allow for combining paleoclimate 
data and climate model data to generate annually resolved fields 
(Last Millenium Reanalysis, Hakim et al., 2016; Tardif et al., 2019) 
or even monthly fields (Franke et al., 2017). This allows for a greater 
understanding of decadal variability (Parsons and Hakim, 2019) and 
greater certainty around the full range of the frequency and severity

of climate extremes. This, in turn, allows for better-defined detection 
of change. It also helps to identify the links between biogeochemical

215
Framing, Context, and Methods Chapter 11cycles, ecosystem structure and ecosystem functioning, and 
to provide initial conditions for further model experiments or 
downscaling (Chapter 2).
1.5.2.5 Applications of Reanalyses
The developments in reanalyses described above mean that they 
are now used across a  range of applications. In AR6, reanalyses 
provide information for fields and in regions where observations 
are limited. There is growing confidence that modern reanalyses can 
provide another line of evidence in describing recent temperature 
trends (Tables 2.4 and 2.5). As their spatial resolution increases, the 
exploration of fine-scale extremes in both space and time becomes 
possible (e.g., wind; Kaiser-Weiss et al., 2015). Longer reanalyses can 
be used to describe the change in the climate over the last 100 to 
1000 years. Reanalyses have been used to help post-process climate 
model output, and drive impact models; however, they are often bias

adjusted first (Cross-Chapter Box  10.2; e.g., Weedon et al., 2014). 
Copernicus Climate Change Service (C3S) provides a bias-adjusted 
dataset for global land areas based on ERA5 called WFDE5 (Cucchi 
et al., 2020) which, combined with ERA5 information over the ocean 
(W5E5; Lange, 2019), is used as the AR6 Interactive Atlas reference 
for the bias adjustment of model output.
The growing interest in longer-term climate forecasts (from seasonal 
to multi-year and decadal) means that reanalyses are now more 
routinely being used to develop the initial state for these forecasts, 
such as for the Decadal Climate Prediction Project (DCPP; Boer et al., 
2016). Ocean reanalyses are now being used routinely in the context 
of climate monitoring, (e.g.,  the Copernicus Marine Environment 
Monitoring Service Ocean State Report; von Schuckmann et al., 2019).
In summary, reanalyses have improved since AR5 and can 
increasingly be used as a  line of evidence in assessments of the

state and evolution of the climate system ( high confidence ). 
Reanalyses provide consistency across multiple physical quantities, 
and information about variables and locations that are not directly 
various combinations of increased resolution, extended records, 
more consistent data assimilation, estimation of uncertainty arising 
from the range of initial conditions, and an improved representation 
of the atmosphere or ocean system. While noting their remaining 
limitations, this Report uses the most recent generation of reanalysis 
products alongside more standard observation-based datasets.
1.5.3 Climate Models
A wide range of numerical models is widely used in climate science to 
study the climate system and its behaviour across multiple temporal 
and spatial scales. These models are the main tools available to 
look ahead into possible climate futures under a range of scenarios

(Section  1.6). Global Earth system models (ESMs) are the most 
complex models that contribute to AR6. At the core of each ESM is 
a GCM (general circulation model) representing the dynamics of the 
atmosphere and ocean. ESMs are complemented by regional models 
(Section 10.3.1) and by a hierarchy of models of lower complexity. This section summarizes major developments in these different types 
of models since AR5. Past IPCC reports have made use of multi-model 
ensembles generated through various phases of the World Climate 
Research Programme (WCRP) Coupled Model Intercomparison 
Project (CMIP). Analysis of the latest CMIP Phase 6 (CMIP6; Eyring 
et al., 2016) simulations constitute a key line of evidence supporting 
this Assessment Report (Section  1.5.4). The key characteristics of 
models participating in CMIP6 are listed in Annex II: Models.
1.5.3.1 Earth System Models
Earth system models are mathematical formulations of the natural

laws that govern the evolution of climate-relevant systems: 
atmosphere, ocean, cryosphere, land, and biosphere, as well as the 
carbon cycle (Flato, 2011). They build on the fundamental laws of 
physics (e.g.,  Navier–Stokes or Clausius–Clapeyron equations) or 
empirical relationships established from observations and, when 
possible, they are constrained by fundamental conservation laws 
(e.g., mass and energy). The evolution of climate-relevant variables 
is computed numerically using high-performance computers (André 
et al., 2014; Balaji et al., 2017), on three-dimensional discrete grids 
(Staniforth and Thuburn, 2012). The spatial (and temporal) resolution 
of these grids in both the horizontal and vertical directions determines 
which processes need to be parameterized or whether they can be 
explicitly resolved. Developments since AR5 in model resolution, 
parameterizations and modelling of the land and ocean biosphere 
and of biogeochemical cycles are discussed below.

1.5.3.1.1 Model grids and resolution
The horizontal resolution and the number of vertical levels in ESMs 
is generally higher in CMIP6 than in CMIP5 (Figure  1.19). Global 
models with finer horizontal grids better represent many aspects of 
the circulation of the atmosphere (Gao et al., 2020; Schiemann et al., 
2020) and ocean (Bishop et al., 2016; Storkey et al., 2018), bringing 
improvements in the simulation of the global hydrological cycle 
(Roberts et al., 2018). CMIP6 includes a dedicated effort (HighResMIP , 
Haarsma et al., 2016) to explore the effect of higher horizontal 
resolution, such as ~50 km, ~25 km and even ~10 km (Section 1.5.4.2 
and Annex II, Table AII.6). Improvements are documented in the 
highest-resolution coupled models used for HighResMip (Hewitt 
et al., 2017; Roberts et al., 2019). Flexible grids allowing spatially 
variable resolution in the atmosphere (McGregor, 2015; Giorgetta 
et al., 2018) and in the ocean (Wang et al., 2014; Petersen et al.,

2019) are more widely used than at the time of the AR5.
The number of vertical levels in the atmosphere of global models 
has increased (Figure 1.19), partly to enable simulations to include 
higher levels in the atmosphere and better represent stratospheric 
processes (Charlton-Perez et al., 2013; Kawatani et al., 2019). Half 
the modelling groups now use ‘high-top’ models with a  top level 
above the stratopause (a pressure of about 1 hPa). The number of 
vertical levels in the ocean models has also increased in order to 
achieve finer resolution over the water column and especially in the 
upper mixed layer and to better resolve the diurnal cycle (Section 3.5 
and Annex II; Bernie et al., 2008).

216
Despite the documented progress of higher resolution, the 
model evaluation carried out in subsequent chapters shows that 
improvements between CMIP5 and CMIP6 remain modest at the 
global scale (Section  3.8.2; Bock et al., 2020). Lower resolution 
alone does not explain all model biases, for example, a low blocking 
frequency (Davini and D’Andrea, 2020) or a  wrong shape of the 
Intertropical Convergence Zone (Tian and Dong, 2020). Model 
performance depends on model formulation and parameterizations 
as much as on resolution (Chapters 3, 8 and 10).1.5.3.1.2 Representation of physical and chemical 
processes in ESMs
Atmospheric models include representations of physical processes 
such as clouds, turbulence, convection and gravity waves that are 
not fully represented by grid-scale dynamics. The CMIP6 models 
have undergone updates in some of their parameterization schemes 
compared to their CMIP5 counterparts, with the aim of better

representing the physics and bringing the climatology of the models 
closer to newly available observational datasets. Most notable 
developments are to schemes involving radiative transfer, cloud 
(km)
 (km)(km)
(a)
(c)
(b)
(d)
Evolution of model resolution from AR5 to AR6(km)
Figure  1.19 |  Resolution of the atmospheric and oceanic components of global climate models participating in CMIP5, CMIP6 and HighResMIP:  
(a, b) horizontal resolution (km), and (c, d) number of vertical levels.  Darker-colour circles indicate high-top models (in which the top of the atmosphere is above 
50 km). The crosses are the median values. These models are documented in Annex II. Note that duplicated models in a modelling group are counted as one entry when their 
horizontal and vertical resolutions are the same. For HighResMIP , one atmosphere–ocean coupled model with the highest resolution from each modelling group is used. The

horizontal resolution (rounded to 10 km) is the square root of the surface area of the Earth divided by the number of grid points, or the area of the ocean surface divided by the 
number of surface ocean grid points, for the atmosphere and ocean, respectively.

217
Framing, Context, and Methods Chapter 11microphysics, and aerosols, in particular a more explicit representation 
of the aerosol indirect effects through aerosol-induced modification 
of cloud properties. Broadly, aerosol–cloud microphysics has been 
a  key topic for the aerosol and chemistry modelling communities 
since AR5, leading to improved understanding of the climate 
influence of short-lived climate forcers, but they remain the single 
largest source of spread in ESM calculations of climate sensitivity 
(Meehl et al., 2020), with numerous parameterization schemes in 
use (Section 6.4; Gettelman and Sherwood, 2016; Zhao et al., 2018; 
Gettelman et al., 2019). The treatment of droplet size and mixed-
phase clouds (liquid and ice) was found to lead to changes in the 
climate sensitivity (Glossary) of some models between AR5 and AR6 
(Section  7.4; Bodas-Salcedo et al., 2019; Gettelman et al., 2019; 
Zelinka et al., 2020).

The representation of ocean and cryosphere processes  has also 
evolved significantly since CMIP5.  The explicit representation of 
ocean eddies, due to increased grid resolution (typically, from 1° 
to ¼°), is a  major advance in a  number of CMIP6 ocean model 
components (Hewitt et al., 2017). Advances in sea ice models have 
been made, for example through correcting known shortcomings in 
CMIP5 simulations, in particular the persistent underestimation of 
the rapid decline in summer Arctic sea ice extent (Rosenblum and 
Eisenman, 2016, 2017; Turner and Comiso, 2017; Notz and Stroeve, 
2018). The development of glacier and ice-sheet models has been 
motivated and guided by an improved understanding of key physical 
processes, including grounding line dynamics, stratigraphy and 
microstructure evolution, sub-shelf melting, and glacier and ice-
shelf calving, among others (Faria et al., 2014, 2018; Hanna et al., 
2020). The resolution of ice-sheet models has continuously increased,

including the use of nested grids, sub-grid interpolation schemes, 
and adaptive mesh approaches (Cornford et al., 2016), mainly for 
a  more accurate representation of grounding-line migration and 
data assimilation (Pattyn, 2018). Ice-sheet models are increasingly 
interactively coupled with global and regional climate models, 
accounting for the height–mass-balance feedback (Vizcaino et al., 
2015; Le clec’h et al., 2019), and enabling a better representation of 
ice-ocean processes, in particular for the Antarctic Ice Sheet (Asay-
Davis et al., 2017).
Sea level rise is caused by multiple processes acting on multiple time 
scales: ocean warming, glaciers and ice-sheet melting, change in 
water storage on land, and glacial isostatic adjustment (Box 9.1) but 
no single model can represent all these processes (Section 9.6). In 
this Report, the contributions are computed separately (Figure 9.28) 
and merged into a  common probabilistic framework and updated

from AR5 (Section 9.6; Church et al., 2013; Kopp et al., 2014).
Another notable development since AR5 is the inclusion of stochastic 
parameterizations of sub-grid processes in some comprehensive 
climate models (Sanchez et al., 2016). Here, the deterministic 
differential equations that govern the dynamical evolution of the 
model are complemented by knowledge of the stochastic variability 
in unresolved processes. While not yet widely implemented, the 
approach has been shown to improve the forecasting skill of weather 
models, to reduce systematic biases in global models (Berner et al., 
2017; Palmer, 2019) and to influence simulated climate sensitivity 
(Strommen et al., 2019).1.5.3.1.3 Representation of biogeochemistry, 
including the carbon cycle
Since AR5, more sophisticated land-use and land-cover change 
representations in ESMs have been developed to simulate the effects 
of land management on surface fluxes of carbon, water and energy

(Lawrence et al., 2016), although the integration of many processes 
(e.g.,  wetland drainage, fire as a  management tool) remains 
a  challenge (Pongratz et al., 2018). The importance of nitrogen 
availability to limit the terrestrial carbon sequestration has been 
recognized (Section  5.4; Zaehle et al., 2014) and so an increasing 
number of models now include a prognostic representation of the 
terrestrial nitrogen cycle and its coupling to the land carbon cycle 
(Jones et al., 2016; Arora et al., 2020), leading to a  reduction in 
uncertainty for carbon budgets (Section 5.1; Jones and Friedlingstein, 
2020). As was the case in CMIP5 (Ciais et al., 2013), the land surface 
processes represented vary across CMIP6 models, with at least 
some key processes (fire, permafrost carbon, microbes, nutrients, 
vegetation dynamics, plant demography) absent from any particular 
ESM land model (Table  5.4). Ocean biogeochemical models have

evolved to enhance the consistency of the exchanges between ocean, 
atmosphere and land, through riverine input and dust deposition 
(Stock et al., 2014; Aumont et al., 2015). Other developments include 
flexible plankton stoichiometric ratios (Galbraith and Martiny, 2015), 
improvements in the representation of nitrogen fixation (Paulsen 
et al., 2017), and the limitation of plankton growth by iron (Aumont 
et al., 2015). Due to the long time scale of biogeochemical processes, 
how the models are initialized (spun up) strategies has been shown 
to affect their performance in AR5 (Séférian et al., 2016).
1.5.3.2 Model Tuning and Adjustment
When developing climate models, choices have to be made in a number 
of areas. Besides model formulation and resolution, parameterizations 
of unresolved processes also involve many choices as, for each of 
these, several parameters can be set. The acceptable range for these 
parameters is set by mathematical consistency (e.g.,  convergence

of a  numerical scheme), physical considerations (e.g.,  energy 
conservation), observations, or a  combination of factors. Model 
developers choose a set of parameters that both falls within this range 
and mimics observations of individual processes or their statistics.
An initial set of such choices is usually made by (often extensive) 
groups of modellers working on individual components of the Earth 
system (e.g.,  ocean, atmosphere, land or sea ice). As components 
are assembled to build an ESM, the choices are refined so that the 
simulated climate best represents a number of pre-defined climate 
variables, or ‘tuning targets’. When these are met the model is 
released for use in intercomparisons such as CMIP . Tuning targets 
can be one of three types: mean climate; regional phenomena and 
features; or historical trends (Hourdin et al., 2017). One example of 
such a goal is that when the simulated climate system receives energy

from the sun in accordance with what we observe today, the resulting 
mean equilibrium temperature should also be close to observations. 
Whether tuning should be performed to facilitate accurate simulation 
of long-term trends such as changes in global mean temperature 
over the historical era, or rather be performed for each process

218
Chapter 1 Framing, Context, and Methods1independently such that all collective behaviour is emergent, is an 
open question (Schmidt et al., 2017; Burrows et al., 2018).
Each modelling group has its own strategy and, after AR5, a survey 
was conducted to understand the tuning approach used in 23 CMIP5 
modelling centres. The results are discussed in Hourdin et al. (2017), 
which stresses that the behaviour of ESMs depends on the tuning 
strategy. An important recommendation is that the calibration steps 
that lead to particular model tuning should be carefully documented. 
In CMIP6 each modelling group now describes the three levels of 
tuning, both for the complete ESM and for the individual components 
(available at https://explore.es-doc.org  and in the published model 
descriptions, Annex II: Models). The most important global tuning 
target for CMIP6 models is the net top-of-the-atmosphere (TOA) heat 
flux and its radiative components. Other global targets include: the

decomposition of the energy fluxes at TOA into a clear sky component 
and a component due to the radiative effect of clouds, global mean air 
and ocean temperature, sea ice extent, sea ice volume, glacial mass 
balance, and the global root mean square error of precipitation. The TOA 
heat flux balance is achieved using a diversity of approaches, usually 
unique to each modelling group. Adjustments are made for parameters 
associated with uncertain or poorly constrained processes (Schmidt 
et al., 2017), for example the aerosol indirect effects, adjustments to 
ocean albedo, marine dimethyl sulfide (DMS) parameterization, or 
cloud properties (Mauritsen and Roeckner, 2020).Regional tuning targets include: the AMOC, the Southern Ocean 
circulation, and temperature profiles in ocean basins (Golaz et al., 
2019; Sellar et al., 2019); regional land properties and precipitations 
(Mauritsen et al., 2019; Yukimoto et al., 2019) ; latitudinal distribution

of radiation (Boucher et al., 2020); spatial contrasts in TOA radiative 
fluxes or surface fluxes; and stationary waves in the Northern 
Hemisphere (Schmidt et al., 2017; Yukimoto et al., 2019).
Even with some core commonalities of approaches to model tuning, 
practices can differ, such as the use of initial drift from initialized 
forecasts, the explicit use of the transient observed record for the 
historical period, or the use of the present-day radiative imbalance at 
the TOA as a tuning target rather than an equilibrated pre-industrial 
balance. The majority of CMIP6 modelling groups report that they 
do not tune their model for the observed trends during the historical 
period (23 out of 29 groups), nor for ECS (25 out of 29). ECS and 
TCR are thus emergent properties for a  large majority of models. 
The effect of tuning on model skill and ensemble spread in CMIP6 is 
further discussed in Section 3.3.
1.5.3.3 From Global to Regional Models

The need for accurate climate information at the regional scale is 
increasing (Section  10.1). High-resolution global climate models, 
such as those taking part in HighResMIP , provide more detailed 
Hamburg
MPIVictoria
CCCMa
Livermore
E3SM
Boulder
NCAR
Princeton
NOAA-GFDL
New York
NASA-GISSBergen
NCC
Bremerhaven
AWI
8 EU Cities
EC-Earth
Exeter 
MOHC
Paris
IPSL
Toulouse
CNRM-CERFACSPune
CCCR-IITM Nanjing
NUISTBologna
CMCCQingdao
FIO-QLNM
Seoul
SNU
Seogwipo
NIMS-KMA
Tsukuba
MRI
Tokyo
MIROC
Taipei
AS-RCEC
Melbourne
CSIRO/BOMMoscow
INMBeijing
BCC             CAMS
BNU             THU
CAS
City CORDEX Institution Participation to: Cordex partner: CMIP 1 CMIP 2 CMIP 3 CMIP 5 CMIP 6
Figure 1.20 | World map showing the increased diversity of modelling centres contributing to CMIP and CORDEX.  Climate models are often developed by

international consortia. One such consortium, EC-Earth, is shown as an example under the label 8 EU Cities  (involving SMHI, Sweden; KNMI, The Netherlands; DMI, Denmark; 
AEMET, Spain; Met Éireann, Ireland; CNR-ISAC, Italy; Instituto de Meteorologia, Portugal; and FMI, Finland). There are too many such collaborations to display all of them on 
this map. More complete information about institutions contributing to CORDEX and CMIP6 is found in Annex II.

219
Framing, Context, and Methods Chapter 11information at the regional scale (Roberts et al., 2018). However, 
due to the large computational resources required by these models, only a  limited number of simulations per model are available. 
In  addition to CMIP global models, regional information can be 
derived using regional climate models (RCMs) and downscaling techniques, presented in Chapter  10 and the Atlas. RCMs are dynamical models, similar to GCMs, that simulate a limited region and are forced with boudary conditions from a  global simulation, 
often correcting for biases (Section 10.3, Cross-Chapter Box 10.2 and 
Annex II). This approach allows the use of a higher resolution within the chosen domain, and thus better represents important drivers of regional climate such as mountain ranges, land management and 
urban effects. RCMs resolving atmospheric convection explicitly are

now included in intercomparisons (Coppola et al., 2020) and are used in Chapters 10, 11 and 12. Other approaches, such as statistical downscaling, are also used to generate regional climate projections (Section 10.3; Maraun and Widmann, 2018).
The number of climate centres or consortia that carry out global 
climate simulations and projections has grown from 11 in the first CMIP to 19 in CMIP5 and 28 for CMIP6 (Section  1.5.4.2 and Annex II). Regional climate models participating in the Coordinated 
Regional Downscaling Experiment (CORDEX) are more diverse than 
the global ESMs (Section 1.5.4.3 and Annex II) and engage an even wider international community (Figure 1.20).
1.5.3.4  Models of Lower Complexity
Earth system models of intermediate complexity  (EMICs) 
complement the model hierarchy and fill the gap between conceptual, simple climate models and complex GCMs or ESMs (Claussen 
et al., 2002). EMICs are simplified; they include processes in a more

parameterized, rather than explicitly calculated, form and generally have lower spatial resolution compared to the complex ESMs. As a result, EMICs require much less computational resource and can be integrated for many thousands of years without supercomputers 
(Hajima et al., 2014). The range of EMICs used in climate change 
research is highly heterogeneous, ranging from zonally averaged or mixed-layer ocean models coupled to statistical-dynamical models of the atmosphere, to low-resolution three-dimensional ocean 
models coupled to simplified dynamical models of the atmosphere. 
An increasing number of EMICs include interactive representations of the global carbon cycle, with varying levels of complexity and numbers of processes considered (Plattner et al., 2008; Zickfeld et al., 2013; MacDougall et al., 2020). Given the heterogeneity of 
the EMIC community, modellers tend to focus on specific research

questions and develop individual models accordingly. As for any type of models assessed in this Report, the set of EMICs undergoes thorough evaluation and fit-for-purpose testing before being applied to address specific climate aspects.
EMICs have been used extensively in past IPCC reports, providing 
long-term integrations on paleoclimate and future time scales, including stabilization pathways and a  range of commitment scenarios, with perturbed physics ensembles and sensitivity studies, 
or with simulations targeting the uncertainty in global climate–
carbon cycle systems (e.g., Meehl et al., 2007b; Collins et al., 2013). More recently, a number of studies have pointed to the possibility of systematically different climate responses to external forcings in 
EMICs and complex ESMs (Frölicher and Paynter, 2015; Pfister and

Stocker, 2017, 2018) that need to be considered in the context of this report. For example, Frölicher and Paynter (2015) showed that EMICs have a higher simulated realized warming fraction (i.e., the TCR/ECS ratio) than CMIP5 ESMs and speculated that this may bias 
the temperature response to zero carbon emissions. But, in a recent 
comprehensive multi-model analysis of the zero CO
2 emissions 
commitment, MacDougall et al. (2020) did not find any significant differences between EMICs and ESMs in committed temperatures 
90  years after halting emissions. While some EMICs contribute to 
parts of the CMIP6-endorsed MIPs, a coordinated EMICs modelling effort similar to those carried out for AR4 (Plattner et al., 2008) and AR5 (Eby et al., 2013; Zickfeld et al., 2013) is not in place for IPCC AR6; however, EMICs are assessed in a number of chapters. For example, 
Chapters 4 and 5 use EMICs in the assessment of long-term climate

change beyond 2100 (Section  5.5); zero-emissions commitments, overshoot and recovery (Section 4.7); consequences of CO
2 removal 
(CDR) on the climate system and the carbon cycle (Sections 4.6 and 5.6); and long-term carbon cycle–climate feedbacks (Section 5.4).
Physical emulators and simple climate models make up a broad 
class of heavily parametrized models designed to reproduce the responses of the more complex, process-based models, and provide rapid translations of emissions, via concentrations and radiative 
forcing, into probabilistic estimates of changes to the physical climate 
system. The main application of emulators is to extrapolate insights from ESMs and observational constraints to a larger set of emissions scenarios (Cross-Chapter Box  7.1). The computational efficiency of 
various emulating approaches opens new analytical possibilities,

given that ESMs take a  lot of computational resources for each simulation. The applicability and usefulness of emulating approaches are however constrained by their skill in capturing the global mean climate responses simulated by the ESMs (mainly limited to global 
mean or hemispheric land/ocean temperatures) and by their ability 
to extrapolate skilfully outside the calibrated range.
The terms ‘emulator’ and ‘simple climate model’ (SCM) are different, 
although they are sometimes used interchangeably. SCM refers to 
a  broad class of lower-dimensional models of the energy balance, 
radiative transfer, carbon cycle, or a  combination of such physical components. SCMs can also be tuned to reproduce the calculations of climate-mean variables of a  given ESM, assuming that their structural flexibility can capture both the parametric and structural 
uncertainties across process-oriented ESM responses. When run in

this setup, they are termed emulators. Simple climate models do not have to be run in ‘emulation’ mode, though, as they can also be used to test consistency across multiple lines of evidence with regard to 
ranges in ECS, TCR, TCRE and carbon cycle feedbacks (Chapters 5 and 
7). Physical emulation can also be performed with very simple parameterizations (‘one-or-few-line climate models’), statistical methods like neural networks, genetic algorithms, or other artificial intelligence approaches, where the emulator behaviour is explicitly 
tuned to reproduce the response of a given ESM or model ensemble 
(Chapters 4, 5 and 7).

220
Chapter 1 Framing, Context, and Methods1Current emulators and SCMs include the generic impulse response 
model outlined in Chapter  8  of AR5 (AR5-IR; Supplementary 
Material 8.SM.11 of Myhre et al., 2013), two-layer models (Held 
et al., 2010; Rohrschneider et al., 2019; Nicholls et al., 2020), and 
higher-complexity approaches that include upwelling, diffusion and 
entrainment in the ocean component (e.g.,  MAGICC Version 5.3 
(Raper et al., 2001; Wigley et al., 2009); Version 6/7 (Meinshausen 
et al., 2011a);  OSCAR  (Gasser et al., 2017); CICERO SCM  (Skeie et al., 
2017); FaIR (Millar et al., 2017a; Smith et al., 2018); and a range of 
statistical approaches (Schwarber et al., 2019; Beusch et al., 2020b). 
An example of recent use of an emulator approach is an early 
estimate of the climate implications of the COVID-19 lockdowns 
(Cross-Chapter Box 6.1; Forster et al., 2020).
Since AR5, simplified climate models have been developed further, and

their use is increasing. Different purposes motivating development 
include: being as simple as possible for teaching purposes (e.g., a two-
layer energy balance model); being as comprehensive as possible to 
allow for propagation of uncertainties across multiple Earth system 
domains (MAGICC and others); or focusing on higher-complexity 
representation of specific domains (e.g.,  OSCAR). The common 
theme motivating many models is to improve parameterizations 
that reflect the latest findings in complex ESM interactions – such 
as the nitrogen cycle addition to the carbon cycle, or tropospheric 
and stratospheric ozone exchange – with the aim of emulating their 
global mean temperature response. Also, within the simple models 
that have a  rudimentary representation of spatial heterogeneity 
(e.g., four-box simple climate models), the ambition is to represent heterogeneous forcers such as black carbon more adequately (Stjern

et al., 2017), provide an appropriate representation of the forcing–
feedback framework (e.g., Sherwood et al., 2015), investigate new 
parameterizations of ocean heat uptake, and implement better 
representations of volcanic aerosol-induced cooling (Gregory 
et al., 2016a).
MAGICC (Wigley et al., 2009; Meinshausen et al., 2011a) and 
FaIR (Smith et al., 2018) were used in IPCC SR1.5 (IPCC, 2018) to 
categorize mitigation pathways into classes of scenarios that peak 
near 1.5°C, overshoot 1.5°C, or stay below 2°C. The SR1.5 (Rogelj 
et al., 2018b) concluded that there was high agreement  on the 
relative temperature response of pathways, but medium agreement  
on the precise absolute magnitude of warming, introducing a level of 
imprecision in the attribution of a single pathway to a given category.
In this Report, there are two notable uses of simple climate 
models. One is the connection between the assessed range of

ECS in Chapter  7, and the projections of future global surface air 
temperature (GSAT) change in Chapter 4, which is done via a two-
layer model based on Held et al. (2010). It is also used as input to 
sea level projections in Chapter 9. The other usage is the transfer of 
Earth system assessment knowledge to WGIII, via a set of models 
(MAGICC, FaIR, CICERO-SCM) specifically tuned to represent the WGI 
assessment. For an overview of the uses, and an assessment of the 
related Reduced Complexity Model Intercomparison Project (RCMIP), 
see Nicholls et al. (2020) and Cross-Chapter Box 7.1.
Box 1.3 | Emissions Metrics in AR6 WGI
Emissions metrics compare the radiative forcing, temperature change, or other climate effects arising from emissions of CO 2 against 
those from emissions of non-CO 2 radiative forcing agents (such as CH 4 or N 2O). They have been discussed in the IPCC since the First

Assessment Report and are used as a means of aggregating emissions and removals of different gases and placing them on a common 
(‘CO 2 equivalent’, or ‘CO 2-eq’) scale.
AR5 included a thorough assessment of common pulse emissions metrics, and how these address various indicators of future climate 
change (Myhre et al., 2013). Most prominently used are the global warming potentials (GWPs), which integrate the calculated radiative 
forcing contribution following an idealized pulse (or one-time) emission, over a chosen time horizon (IPCC, 1990a), or the global 
temperature change potential (GTP), which considers the contribution of emissions to the global-mean temperature at a specific time 
after emission. Yet another metric is the global precipitation change potential (GPP), used to quantify the precipitation change per unit 
mass of emission of a given forcing agent (Shine et al., 2015).
As an example of usage, the Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37] states that

Each Party shall use the 100-year time-horizon global warming potential (GWP) values from the IPCC Fifth Assessment Report, 
or 100-year time-horizon GWP values from a subsequent IPCC assessment report as agreed upon by the ‘Conference of the 
Parties serving as the meeting of the Parties to the Paris Agreement’ (CMA), to report aggregate emissions and removals of 
GHGs, expressed in CO 2-eq. Each Party may in addition also use other metrics (e.g., global temperature potential) to report 
supplemental information on aggregate emissions and removals of GHGs, expressed in CO 2-eq.
Since AR5, improved knowledge of the radiative properties, lifetimes and other characteristics of emitted species, and the response of 
the climate system, have led to updates to the numerical values of a range of metrics (Table 7.15). Another key development is a set 
of metrics that compare a pulse emission of CO 2 (as considered by GWP and GTP) to step-changes of emission rates for short-lived

components (i.e., also considering emissions trends). Termed GWP* (which also includes a pulse component) and combined global

221
1.5.4 Modelling Techniques, Comparisons 
and Performance Assessments
Numerical models, however complex, cannot be a  perfect 
representation of the real world. Results from climate modelling 
simulations constitute a key line of evidence for the present Report, 
which requires considering the limitations of each model simulation. 
This section presents recent developments in techniques and 
approaches to robustly extract, quantify and compare results from 
multiple, independent climate models, and how their performance 
can be assessed and validated.
1.5.4.1 Model ‘Fitness-for-Purpose’
A key issue addressed in this Report is whether climate models 
are adequate or ‘fit’ for purposes of interest, that is, whether they 
can be used to successfully answer particular research questions, 
especially about the causes of recent climate change and the future 
evolution of climate (e.g.,  Parker, 2009; Notz, 2015; Knutti, 2018;

Winsberg, 2018). Assessment of a  model’s fitness-for-purpose can 
be informed both by how the model represents relevant physical 
processes and by relevant performance metrics  (Baumberger 
et al., 2017; Parker, 2020). The processes and metrics that are 
most relevant can vary with the question of interest. For example, 
a  question about changes in deep-ocean circulation compared 
with a  question  about  changes  in  regional precipitation (Notz, 
2015; Gramelsberger et al., 2020). New model-evaluation 
tools (Section  1.5.4.5) and  emergent constraint methodologies 
(Section 1.5.4.7) can also aid the assessment of fitness-for-purpose, 
especially in conjunction with process understanding (Klein and 
Hall, 2015; Knutti, 2018). The broader availability of large model 
ensembles may allow for novel tests of fitness that better account 
for natural climate variability (Section 1.5.4.2). Fitness-for-purpose of 
models used in this Report is discussed in Chapter 3 (Section 3.8.4)

for the global scale, in Chapter 10 (Section 10.3) for regional climate, 
and in the other chapters for the process level.Typical strategies for enhancing the fitness-for-purpose of a model 
include increasing resolution in order to explicitly simulate key 
processes, improving relevant parameterizations, and careful tuning. 
Changes to a  model that enhance its fitness for one purpose can 
sometimes decrease its fitness for others, by upsetting a pre-existing 
balance of approximations. When it is unclear whether a model is 
fit for a purpose of interest, there is often a closely related purpose 
for which the evidence of fitness is clearer. For example, it might 
be unclear whether a  model is fit for providing highly accurate 
projections of precipitation changes in a region, but reasonable to 
think that the model is fit for providing projections of precipitation 
changes that cannot yet be ruled out (Parker, 2009). Such information

about plausible or credible changes can be useful to inform 
adaptation. Note that challenges associated with assessing models’ 
fitness-for-purpose need not prevent reaching conclusions with high 
confidence if there are multiple other lines of evidence supporting 
those same conclusions.
1.5.4.2 Ensemble Modelling Techniques
A key approach in climate science is the comparison of results from 
multiple model simulations with each other and against observations. 
These simulations have typically been performed by separate models 
with consistent boundary conditions and prescribed emissions or 
radiative forcings, as in the Coupled Model Intercomparison Project 
phases (CMIP , Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring 
et al., 2016). Such multi-model ensembles (MMEs) have proven 
highly useful in sampling and quantifying model uncertainty, within 
and between generations of climate models. They also reduce the

influence on projections of the particular sets of parametrizations 
and physical components simulated by individual models. The primary 
usage of MMEs is to provide a well-quantified model range, but when 
used carefully they can also increase confidence in projections (Knutti 
et al., 2010). Presently, however, many models also share provenance 
(Masson and Knutti, 2011) and may have common biases that should 
be acknowledged when presenting and building on MME-derived 
conclusions (Section 1.5.4.6; Boé, 2018; Abramowitz et al., 2019).Box 1.3 (continued)
temperature change potential (CGTP), these metrics allow the construction of a  near-linear relationship between global surface 
temperature change and cumulative CO 2 and CO 2-eq emissions of both short- and long-lived forcing agents (Allen et al., 2016; Cain 
et al., 2019; Collins et al., 2020). For example, the temperature response to a sustained methane reduction has a similar behaviour to

the temperature response to a pulse CO 2 removal (or avoided emission).
In this Report, recent scientific developments underlying emissions metrics, as relevant for WGI, are assessed in full in Section 7.6. 
In particular, see Box 7.3, which discusses the choice of metric for different usages, and Section 7.6.1, which treats the challenge 
of comparing the climate implication of emissions of short-lived and long-lived compounds. Also, the choice of metric is of key 
importance when defining and quantifying net zero GHG emissions (Box 1.4 and Section 7.6.2). Chapter 6 applies metrics to attribute 
GSAT change to short-lived climate forcer (SLCF) and long-lived GHG emissions from different sectors and regions (Section 6.6.2).
The metrics assessed in this Report are also used, and separately assessed, by WGIII. See Cross-Chapter Box  2  and Annex B  in 
Chapter 2 of the WGIII contribution to AR6.

222
Chapter 1 Framing, Context, and Methods1Since AR5, an increase in computing power has made it possible 
to investigate simulated internal variability and to provide 
robust estimates of forced model responses, using large initial 
condition  ensembles (ICEs), also referred to as single model 
initial  condition large ensembles (SMILEs). Examples using GCMs 
or ESMs that support assessments in AR6 include the CESM 
Large Ensemble (Kay et al., 2015), the MPI Grand Ensemble 
(Maher et al., 2019), and the CanESM2 large ensembles (Kirchmeier-
Young et al., 2017). Such ensembles employ a single GCM or ESM 
in a  fixed configuration, but starting from a  variety of different 
initial states. In some experiments, these initial states only differ 
slightly. As the climate system is chaotic, such tiny changes in initial 
conditions lead to different evolutions for the individual realizations 
of the system as a  whole. Other experiments start from a  set of

well-separated ocean initial conditions to sample the uncertainty in 
the circulation state of the ocean and its role in longer-time scale 
variations. These two types of ICEs have been referred to as ‘micro’ 
and ‘macro’ perturbation ensembles respectively (Hawkins et al., 
2016). In support of this Report, most models contributing to CMIP6 
have produced ensembles of multiple realizations of their historical 
and scenario simulations (Chapters 3 and 4).
Recently, the ICE technique has been extended to atmosphere-only 
simulations (Mizuta et al., 2017), single-forcer influences such as 
volcanic eruptions (Bethke et al., 2017), regional modelling (Mote 
et al., 2015; Fyfe et al., 2017; Schaller et al., 2018; Leduc et al., 2019), 
and to attribution of extreme weather events using crowdsourced 
computing ( climateprediction.net ; Massey et al., 2015).
ICEs can also be used to evaluate climate model parameterizations,

if models are initialized appropriately (Phillips et al., 2004; Williams 
et al., 2013), mostly within the framework of seamless weather and 
climate predictions (e.g.,  Palmer et al., 2008; Hurrell et al., 2009; 
Brown et al., 2012). Initializing an atmospheric model in hindcast 
mode and observing the biases as they develop permits testing of the 
parameterized processes, by starting from a known state rather than 
one dominated by quasi-random short-term variability (Williams 
et al., 2013; Ma et al., 2014; Vannière et al., 2014). However, single-
model initial-conditions ensembles cannot cover the same degrees of 
freedom as a multi-model ensemble, because model characteristics 
substantially affect model behaviour (Flato et al., 2013).
A third common modelling technique is the perturbed parameter 
ensemble (PPE; note that the abbreviation also sometimes refers to 
the sub-category ‘perturbed physics ensemble’). These methods are

used to assess uncertainty based on a single model, with individual 
parameters perturbed to reflect the full range of their uncertainty 
(Murphy et al., 2004; Knutti et al., 2010; Lee et al., 2011; Shiogama 
et al., 2014). Statistical methods can then be used to detect which 
parameters are the main causes of uncertainty across the ensemble. 
PPEs have been used frequently in simpler models, such as EMICs, and 
are being applied to more complex models. A caveat of PPEs is that the 
estimated uncertainty will depend on the specific parameterizations 
of the underlying model and may well be an underestimation of 
the ‘true’ uncertainty. It is also challenging to disentangle forced 
responses from internal variability using a PPE alone.Together, the three ensemble methods (MMEs, ICEs, PPEs) allow 
investigation of climate model uncertainty arising from internal 
variability, initial and internal boundary conditions, model 
formulations and parameterizations (Parker, 2013). Figure  1.21

illustrates the different ensemble types. Recent studies have also 
started combining multiple ensemble types or using ensembles in 
combination with statistical analytical techniques. For example, 
Murphy et al. (2018) combine MMEs and PPEs to give a  fuller 
assessment of modelling uncertainty. Wagman and Jackson (2018) 
use PPEs to evaluate the robustness of MME-based emergent 
constraints. Sexton et al. (2019) study the robustness of ICE 
(a) Multi−model ensemble (MME)
One simulation of the time evolution of Q per model, with internal variabiliy
Model 1
Model 2
Model 3
Baseline
Time
Time
TimeQuantity Q Quantity Q Quantity Q(b) Initial condition ensemble (ICE)
Multiple simulations of Q from one model, differing by initial conditions only
Model 1
10 realizations
(c) Perturbed parameter ensemble (PPE)
Systematic perturbation of parameter(s) affecting Q, within one model
Model 1
Parameter set 1
Parameter set 2
Parameter set 3(etc.)

Figure  1.21 |  Illustration of common types of model ensemble, simulating the 
time evolution of a quantity Q (such as global mean surface temperature).  
(a) Multi-model ensemble, where each model has its own realization of the processes 
affecting Q, and its own internal variability around the baseline value (dashed line). 
The multi-model mean (black) is commonly taken as the ensemble average. (b) Initial 
condition ensemble, where several realizations from a single model are compared. 
These differ only by minute (‘micro’) perturbations to the initial conditions of the 
simulation, such that over time, internal variability will progress differently in each 
ensemble member. (c) Perturbed physics ensemble, which also compares realizations 
from a  single model, but where one or more internal parameters that may affect 
the simulations of Q are systematically changed to allow for a quantification of the

impact of those quantities on the model results. Additionally, each parameter set may 
be taken as the starting point for an initial condition ensemble. In this figure, each set 
has three ensemble members.

223
Framing, Context, and Methods Chapter 11approaches by identifying parameters and processes responsible for 
model errors at the two different time scales.
Overall, we assess that increases in computing power and the 
broader availability of larger and more varied ensembles of model 
simulations have contributed to better estimations of uncertainty in 
projections of future change ( high confidence ). Note, however, that 
despite their widespread use in climate science today, the cost of 
the ensemble approach in human and computational resources, and 
the challenges associated with the interpretation of multi-model 
ensembles, has been questioned (Palmer and Stevens, 2019; Touzé-
Peiffer et al., 2020).
1.5.4.3 The Sixth Phase of the Coupled Model 
Intercomparison Project (CMIP6)
The Coupled Model Intercomparison Project (CMIP) provides 
a  framework to compare the results of different GCMs or ESMs 
performing similar experiments. Since its creation in the mid-1990s, it

has evolved in different phases, involving all major climate modelling 
centres in the world (Figure 1.20). The results of these phases have 
played a key role in previous IPCC reports, and the present Report 
assesses a  range of results from CMIP5 that were not published 
until after the AR5, as well as the first results of the 6th phase of 
Clouds /
Circulation
Ocean / 
Land / IceRegional 
phenomena
Scenarios
Decadal
predictionImpacts
Geo-
engineeringCarbon
cycleCharacterizing
forcingPaleo 
Land useChemistry /
AerosolsDECK
LUMIP
CDRMIP , GeoMIPC4MIPAerChemMIPGMMIP , 
HighResMIP,
PAMIP
DCPPPMIPCFMIP / DynVarMIP
OMIP, FAFMIP / 
LS3MIP / SIMIP
ISMIP6
CORDEX,
VIACS AB
ScenaioMIPRFMIP, DAMIP, 
VolMIPCMIP6 Historical SimulationStandardization, coordination 
Infrastructure, documentation
Figure  1.22 |  Structure of CMIP6, the 6th phase of the Coupled Model 
Intercomparison Project.  The centre shows the common DECK (Diagnostic,

Evaluation and Characterization of Klima) and historical experiments that all 
participating models must perform. The outer circles show the topics covered by the 
endorsed (red) and other MIPs (orange). See Table 1.3 for explanation of the MIP 
acronyms. Figure is adapted from Eyring et al. (2016).
Table 1.3 | CMIP6-Endorsed MIPs, their key references, and where they are used or referenced throughout this Report.
CMIP6-Endorsed  
MIP NameLong Name Key References Used in Chapters
AerChemMIP Aerosols and Chemistry Model Intercomparison Project Collins et al. (2017) 4, 6, Atlas
C4MIP Coupled Climate Carbon Cycle Model Intercomparison Project Jones et al. (2016) 4, 5, Atlas
CDRMIP The Carbon Dioxide Removal Model Intercomparison Project Keller et al. (2018) 4, 5, Atlas
CFMIP Cloud Feedback Model Intercomparison Project Webb et al. (2017) 4, 7, Atlas
CORDEX Coordinated Regional Climate Downscaling Experiment Gutowski Jr. et al. (2016) 4, 8, 9, 10, 11, 12, Atlas

DAMIP Detection and Attribution Model Intercomparison Project Gillett et al. (2016) 3, 10, Atlas
DCPP Decadal Climate Prediction Project Boer et al. (2016) 4, 8, Atlas
DynVarMIP Dynamics and Variability Model Intercomparison Project Gerber and Manzini (2016) Atlas
FAFMIP Flux-Anomaly-Forced Model Intercomparison Project Gregory et al. (2016b) 9, Atlas
GeoMIP Geoengineering Model Intercomparison Project Kravitz et al. (2015) 4, 5, 8, 12, Atlas
GMMIP Global Monsoons Model Intercomparison Project Zhou et al. (2016) 2, 3, 4, 10, Atlas
HighResMIP High Resolution Model Intercomparison Project Haarsma et al. (2016) 3, 8, 9, 10, 11, Atlas
ISMIP6 Ice Sheet Model Intercomparison Project for CMIP6 Nowicki et al. (2016) 3, 7, 9, Atlas
LS3MIP Land Surface, Snow and Soil Moisture van den Hurk et al. (2016) 3, 9, 11, Atlas
LUMIP Land Use Model Intercomparison Project Lawrence et al. (2016) 4, 6, Atlas
OMIP Ocean Model Intercomparison Project Griffies et al. (2016); Orr et al. (2017) 3, 9, Atlas

PAMIP Polar Amplification Model Intercomparison Project D.M. Smith et al. (2019) 10, Atlas
PMIP Paleoclimate Modelling Intercomparison ProjectHaywood et al. (2016); Jungclaus 
et al. (2017); Otto-Bliesner et al. (2017); 
Kageyama et al. (2018)2, 3, 7, 8, 9, 10, Atlas
RFMIP Radiative Forcing Model Intercomparison Project Pincus et al. (2016) 6, 7, Atlas
ScenarioMIP Scenario Model Intercomparison Project O’Neill et al. (2016) 4, 5, 6, 9, 10, 12, Atlas
SIMIP Sea Ice Model Intercomparison Project Notz et al. (2016) 4, 9, 12, Atlas
VIACS AB Vulnerability, Impacts, Adaptation and Climate Services Advisory Board Ruane et al. (2016) 12, Atlas
VolMIP Volcanic Forcings Model Intercomparison Project Zanchettin et al. (2016) 4, 8, Atlas

224
Chapter 1 Framing, Context, and Methods1CMIP (CMIP6; Eyring et al., 2016). The CMIP6 experiment design is 
somewhat different from previous phases. It now consists of a limited 
set of DECK (Diagnostic, Evaluation and Characterization of Klima) 
simulations and an historical simulation that must be performed by 
all participating models, as well as a wide range of CMIP6-Endorsed 
model intercomparison projects (MIPs) covering specialized topics 
(Figure 1.22; Eyring et al., 2016). Each MIP activity consists of a series 
of model experiments, documented in the literature (Table 1.3) and in 
an online database ( es-doc.org ; Annex II; Pascoe et al., 2020).
The CMIP DECK simulations form the basis for a range of assessments 
and projections in the following chapters. As in CMIP5, they consist of: 
a ‘pre-industrial’ control simulation (piControl, where ‘pre-industrial’ 
is taken as fixed 1850 conditions in these experiments); an idealized,

abrupt quadrupling of CO 2 concentrations relative to piControl 
(to estimate equilibrium climate sensitivity); a 1% per year increase 
in CO 2 concentrations relative to piControl (to estimate the transient 
climate response); and a  transient simulation with prescribed sea-
surface temperatures for the period 1979–2014 (termed ‘AMIP’ for 
historical reasons). In addition, all participating models perform 
a  historical simulation for the period 1850–2014. For the latter, 
common CMIP6 forcings are prescribed (Cross-Chapter Box  1.4, 
Table  2). Depending on the model setup, these include emissions 
and concentrations of short-lived species (Hoesly et al., 2018; Gidden 
et al., 2019), long-lived GHGs (Meinshausen et al., 2017), biomass 
burning emissions (van Marle et al., 2017), global gridded land-use 
forcing data (Ma et al., 2020), solar forcing (Matthes et al., 2017), 
and stratospheric aerosol data from volcanoes (Zanchettin et al.,

2016). The methods for generating gridded datasets are described 
in Feng et al. (2020). For AMIP simulations, common sea surface 
temperatures (SSTs) and sea ice concentrations (SICs) are prescribed. 
For simulations with prescribed aerosol abundances (i.e.,  not 
calculated from emissions), optical properties and fractional changes 
in cloud droplet effective radius are generally prescribed in order to 
provide a more consistent representation of aerosol forcing relative 
to earlier CMIP phases (Fiedler et al., 2017; Stevens et al., 2017). 
For models without ozone chemistry, time-varying gridded ozone 
concentrations and nitrogen deposition are also provided (Checa-
Garcia et al., 2018).
Beyond the DECK and the historical simulations, the CMIP6-Endorsed 
MIPs aim to investigate how models respond to specific forcings, 
their potential systematic biases, their variability, and their responses 
to detailed future scenarios such as the Shared Socio-economic

Pathways (SSPs; Section 1.6). Table 1.3 lists the 23 CMIP6-Endorsed 
MIPs and key references. Results from a range of these MIPs, and 
many others outside of the most recent CMIP6 cycle, will be assessed 
in the following chapters (also shown in Table  1.3). References to 
all the CMIP6 datasets used in the report are found in Annex II, 
Table AII.10.
1.5.4.4 Coordinated Regional Downscaling 
Experiment (CORDEX)
The Coordinated Regional Downscaling Experiment (CORDEX; 
Gutowski Jr. et al., 2016) is an intercomparison project for regional 
models and statistical downscaling techniques, coordinating simulations on common domains and under common experimental 
conditions in a  similar way to the CMIP effort. Dynamical and 
statistical downscaling techniques can provide higher-resolution 
climate information than is available directly from global climate 
models (Section  10.3). These techniques require evaluation and 
quantification of their performance before they can be considered

appropriate as usable regional climate information or be used in 
support of climate services. CORDEX simulations have been provided 
by a range of regional downscaling models for 14 regions, together 
covering much of the globe (Figure Atlas.7), and they are used 
extensively in the AR6 WGI Atlas (Atlas.1.4 and Annex II).
In support of AR6, CORDEX has undertaken a  new experiment 
(CORDEX-CORE) in which regional climate models downscale 
a common set of global model simulations, performed at a coarser 
resolution, to a spatial resolution spanning from 12–25 km over most 
of the CORDEX domains (Box Atlas.1). CORDEX-CORE represents 
an improved level of coordinated intercomparison of downscaling 
models (Remedio et al., 2019).
1.5.4.5 Model Evaluation Tools
For the first time in CMIP , a  range of comprehensive evaluation 
tools are now available that can run alongside the commonly used 
distributed data platform – Earth System Grid Federation (ESGF; see

Annex II) – to produce comprehensive results as soon as the model 
output is published to the CMIP archive.
For instance, the Earth System Model Evaluation Tool (ESMValTool; 
Eyring et al., 2020; Lauer et al., 2020; Righi et al., 2020) is used by 
a number of chapters. It is an open-source community software tool 
that includes a large variety of diagnostics and performance metrics 
relevant for coupled Earth system processes, such as for the mean, 
variability and trends, and it can also examine emergent constraints 
(Section 1.5.4.7). ESMValTool also includes routines provided by the 
WMO Expert Team on Climate Change Detection and Indices for the 
evaluation of extreme events (Min et al., 2011; Sillmann et al., 2013) 
and diagnostics for key processes and variability. Another example 
of an evaluation tool is the CLIVAR 2020 ENSO metrics package 
(Planton et al., 2021).
These tools are used in several chapters of this report for the creation

of the figures that show CMIP results. Together with the Interactive 
Atlas, they allow for traceability of key results, and an additional level 
of quality control on whether published figures can be reproduced. 
It also provides the capability to update published figures with, as 
much as possible, the same set of models in all figures, and to assess 
model improvements across different phases of CMIP (Section 3.8.2).
These new developments are facilitated by the definition of common 
formats for CMIP model output (Balaji et al., 2018) and the availability 
of reanalyses and observations in the same format as CMIP output 
(obs4MIPs; Ferraro et al., 2015). The tools are also used to support 
routine evaluation at individual model centres and simplify the 
assessment of improvements in individual models or generations 
of model ensembles (Eyring et al., 2019). Note, however, that while 
tools such as ESMValTool can produce an estimate of overall model

225
Framing, Context, and Methods Chapter 11performance, dedicated model evaluation still needs to be performed 
when analysing projections for a particular purpose, such as assessing 
changing hazards in a given region. Such evaluation is discussed in 
the next section, and in greater detail in later chapters of this Report.
1.5.4.6 Evaluation of Process-Based Models 
Against Observations
Techniques used for evaluating process-based climate models against 
observations were assessed in AR5 (Flato et al., 2013), and have 
progressed rapidly since (Eyring et al., 2019). The most widely used 
technique is to compare climatologies (long-term averages of specific 
climate variables) or time series of simulated (process-based) model 
output with observations, considering the observational uncertainty. 
A further approach is to compare the results of process-based models 
with those from statistical models. In addition to a comparison of

climatological means, trends and variability, AR5 already made 
use of a  large set of performance metrics for a  quantitative 
evaluation of the models.
Since AR5, a  range of studies has investigated model agreement 
with observations well beyond large-scale mean climate properties 
(e.g., Bellenger et al., 2014; Covey et al., 2016; Pendergrass and Deser, 
2017; Goelzer et al., 2018; Beusch et al., 2020a), providing information 
on the performance of recent model simulations across multiple 
variables and components of the Earth system (e.g.,  Anav et al., 
2013; Guan and Waliser, 2017). Based on such studies, this Report 
assesses model improvements across different CMIP DECK, CMIP6 
historical and CMIP6-Endorsed MIP simulations, and of differences 
in model performance between different classes of models, such as 
high- versus low-resolution models (see e.g., Section 3.8.2).
In addition, process- or regime-oriented evaluation of models has

been expanded since AR5. By focusing on processes, causes of 
systematic errors in the models can be identified and insights can 
be gained as to whether a mean state or trend is correctly simulated 
and for the right reasons. This approach is commonly used for the 
evaluation of clouds (e.g., Williams and Webb, 2009; Konsta et al., 
2012; Bony et al., 2015; Dal Gesso et al., 2015; Jin et al., 2017), 
dust emissions (e.g.,  Parajuli et al., 2016; Wu et al., 2016) as well 
as aerosol–cloud (e.g., Gryspeerdt and Stier, 2012) and chemistry–
climate (SPARC, 2010) interactions. Process-oriented diagnostics 
have also been used to evaluate specific phenomena such as the 
El Niño–Southern Oscillation (ENSO; Guilyardi et al., 2016), the 
Madden–Julian Oscillation (MJO; Ahn et al., 2017; Jiang et al., 2018), 
Southern Ocean clouds (Hyder et al., 2018), monsoons (Boo et al., 
2011; James et al., 2015) and tropical cyclones (Kim et al., 2018).

Instrument simulators provide estimates of what a  satellite would 
see if looking down on the model-simulated planet, and improve the 
direct comparison of modelled variables such as clouds, precipitation 
and upper tropospheric humidity with observations from satellites 
(e.g., Kay et al., 2011; Klein et al., 2013; Cesana and Waliser, 2016; 
Konsta et al., 2016; Jin et al., 2017; Chepfer et al., 2018; Swales 
et al., 2018; Zhang et al., 2018). Within the framework of the Cloud 
Feedback Model Intercomparison Project (CFMIP) contribution to 
CMIP6 (Webb et al., 2017), a  new version of the Cloud Feedback Model Intercomparison Project Observational Simulator (COSP; 
Swales et al., 2018) has been released which makes use of a collection 
of observation proxies or satellite simulators. Related approaches 
in this rapidly evolving field include simulators for Arctic Ocean 
observations (Burgard et al., 2020) and measurements of aerosol

observations along aircraft trajectories (Watson-Parris et al., 2019).
In this Report, model evaluation is performed in the individual 
chapters, rather than in a separate chapter as was the case for AR5. 
This applies to the model types discussed above, and also to dedicated 
models of subsystems that are not (or not yet) part of usual climate 
models, for example, glacier or ice-sheet models (Annex II). Further 
discussions are found in Chapter 3 (attribution), Chapter 5 (carbon 
cycle), Chapter 6 (short-lived climate forcers), Chapter 8 (water cycle), 
Chapter  9  (ocean, cryosphere and sea level), Chapter  10 (regional 
scale information) and the Atlas (regional models).
1.5.4.7 Emergent Constraints on Climate Feedbacks, 
Sensitivities and Projections
An emergent constraint is the relationship between an uncertain 
aspect of future climate change and an observable feature of the 
Earth System, evident across an ensemble of models (Allen and

Ingram, 2002; Mystakidis et al., 2016; Wenzel et al., 2016; Hall et al., 
2019; Winkler et al., 2019). Complex Earth system models (ESMs) 
simulate variations on time scales from hours to centuries, telling 
us how aspects of the current climate relate to its sensitivity to 
anthropogenic forcing. Where an ensemble of different ESMs 
displays a  relationship between a  short-term observable variation 
and a  longer-term sensitivity, an observation of the short-term 
Long-term intermodel uncertainty
Observable trend or variationEarth system sensitivity
Observed 
valuesConstrained 
valuesEmergent relationship
Figure 1.23 | The principle of emergent constraints.  An ensemble of models 
(blue dots) defines a relationship between an observable mean, trend or variation 
in the climate (x-axis) and an uncertain projection, climate sensitivity or feedback 
(y-axis). An observation of the x-axis variable can then be combined with the model-

derived relationship to provide a tighter estimate of the climate projection, sensitivity 
or feedback on the y-axis. Figure adapted from Eyring et al. (2019).

226
Chapter 1 Framing, Context, and Methods1variation in the real world can be converted, via the model-based 
relationship, into an ‘emergent constraint’ on the sensitivity. This is 
shown schematically in Figure 1.23 (see Glossary; Eyring et al., 2019).
Emergent constraints use the spread in model projections to estimate the 
sensitivities of the climate system to anthropogenic forcing, providing 
another type of ensemble-wide information that is not readily available 
from simulations with one ESM alone. As emergent constraints depend on 
identifying those observable aspects of the climate system that are most 
related to climate projections, they also help to focus model evaluation on 
the most relevant observations (Hall et al., 2019). However, there is a  chance 
that indiscriminate data-mining of the multi-dimensional outputs from 
ESMs could lead to spurious correlations (Caldwell et al., 2014; Wagman 
and Jackson, 2018) and less-than-robust emergent constraints on future

changes (Bracegirdle and Stephenson, 2013). To avoid this, emergent 
constraints need to be tested ‘out of sample’ on parts of the dataset 
that were not included in its construction (Caldwell et al., 2018) and  
should also always be based on sound physical understanding 
and mathematical theory (Hall et al., 2019). Their conclusions should also 
be reassessed when a new generation of MMEs becomes available, such 
as CMIP6. As an example, Chapter 7 (Section 7.5.4) discusses and assesses 
recent studies where equilibrium climate sensitivities (ECS) diagnosed in 
a multi-model ensemble are compared with the same models’ estimates 
of an observable quantity, such as post-1970s global warming or 
tropical sea surface temperatures of past climates like the Last Glacial 
Maximum or the Pliocene. Assessments of other emergent constraints 
appear throughout later chapters, such as Chapter  4  (Section  4.2.5), 
Chapter 5 (Section 5.4.6) and Chapter 7 (Section 7.5.4).

1.5.4.8 Weighting Techniques for Model Comparisons
Assessments of climate model ensembles have commonly assumed 
that each individual model is of equal value (‘model democracy’) 
and when combining simulations to estimate the mean and variance 
of quantities of interest, they are typically unweighted (Haughton 
et al., 2015). This practice has been noted to diminish the influence 
of models exhibiting a  good match with observations (Tapiador 
et al., 2020). However, exceptions to this approach exist, notably 
AR5 projections of sea ice, which only selected a few models which 
passed a model performance assessment (Collins et al., 2013), and 
more studies on this topic have appeared since AR5 (e.g., Eyring et al., 
2019). Ensembles are typically sub-selected by removing either poorly 
performing model simulations (McSweeney et al., 2015) or model 
simulations that are perceived to add little additional information, 
typically where multiple simulations have come from the same

model. They may also be weighted based on model performance.
Several recent studies have attempted to quantify the effect of various 
strategies for selection or weighting of ensemble members based 
on some set of criteria (Haughton et al., 2015; Olonscheck and Notz, 
2017; Sanderson et al., 2017). Model weighting strategies have been 
further employed since AR5 to reduce the spread in climate projections 
for a given scenario by using weights based on one or more model 
performance metrics (Wenzel et al., 2016; Knutti et al., 2017; Sanderson 
8 Note that the 5–95% is a  very likely  range (see Box 1.1 on the use of calibrated uncertainty language in AR6), though if this is purely a multi-model likelihood range, it is generally treated as likely, 
in the absence of other lines of evidence.et al., 2017; Lorenz et al., 2018; Liang et al., 2020). However, models 
may share representations of processes, parameterization schemes,

or even parts of code, leading to common biases. The models may 
therefore not be fully independent, calling into question inferences 
derived from multi-model ensembles (Abramowitz et al., 2019). 
Emergent constraints (Section  1.5.4.5) also represent an implicit 
weighting technique that explicitly links present performance to future 
projections (Bracegirdle and Stephenson, 2013).
Concern has been raised about the large extent to which code is 
shared within the CMIP5 multi-model ensemble (Sanderson et al., 
2015a). Boé (2018) showed that a clear relationship exists between 
the number of components shared by climate models and how similar 
the simulations are. The resulting similarities in behaviour need to be 
accounted for in the generation of best-estimate multi-model climate 
projections. This has led to calls to move beyond equally-weighted 
multi-model means towards weighted means that take into account 
both model performance and model independence (Sanderson et al.,

2015b, 2017; Knutti et al., 2017). Model independence has been 
defined in terms of performance differences within an ensemble 
(Masson and Knutti, 2011; Knutti et al., 2013, 2017, Sanderson 
et al., 2015a, b, 2017; Lorenz et al., 2018). However, this definition 
is sensitive to the choice of variable, observational dataset, metric, 
time period, and region, and a  performance-ranked ensemble has 
been shown to sometimes perform worse than a random selection 
(Herger et al., 2018a). The adequacy of the constraint provided by the 
data and experimental methods can be tested using a ‘calibration-
validation’ style partitioning of observations into two sets (Bishop and 
Abramowitz, 2013), or a ‘perfect model approach’ where one of the 
ensemble members is treated as the reference dataset and all model 
weights are calibrated against it (Bishop and Abramowitz, 2013; 
Wenzel et al., 2016; Knutti et al., 2017; Sanderson et al., 2017; Herger

et al., 2018a, b). Sunyer et al. (2014) use a Bayesian framework to 
account for model dependencies and changes in model biases. Annan 
and Hargreaves (2017) provides a statistical, quantifiable definition of 
independence that is independent of performance-based measures.
The AR5 quantified uncertainty in CMIP5 climate projections by 
selecting one realization per model per scenario, and calculating 
the 5–95% range of the resulting ensemble (Box 4.1) and the same 
strategy is generally still used in AR6. Broadly, the following chapters 
take the CMIP6 5–95% ensemble range as the likely  uncertainty 
range for projections,8 with no further weighting or consideration 
of model ancestry and as long as no universal, robust method for 
weighting a multi-model projection ensemble is available (Box 4.1). 
A  notable exception to this approach is the assessment of future 
changes in global surface air temperature (GSAT), which also draws

on the updated best estimate and range of equilibrium climate 
sensitivity assessed in Chapter 7. For a thorough description of the 
model-weighting choices made in this Report, and the assessment 
of GSAT, see Chapter  4  (Box  4.1). Model selection and weighting 
in downscaling approaches for regional assessment is discussed in 
Chapter 10 (Section 10.3.4).

227
Framing, Context, and Methods Chapter 111.6 Dimensions of Integration: Scenarios, 
Global Warming Levels and Cumulative 
Carbon Emissions
This section introduces three ways to synthesize climate 
change  knowledge across topics and chapters. These ‘dimensions 
of integration’ include (i) emissions and concentration scenarios 
underlying the climate change projections assessed in this 
Report,  (ii)  levels of global mean surface warming relative to the 
1850–1900 baseline (‘global warming levels’), and (iii) cumulative 
carbon emissions (Figure 1.24). All three dimensions can, in principle, 
be used to synthesize physical science knowledge across WGI, and 
also across climate change impacts, adaptation, and mitigation 
research. Scenarios, in particular, have a  long history of serving 
as a  common reference point within and across IPCC Working 
Groups and research communities. Similarly, cumulative carbon 
emissions and global warming levels provide key links between WGI

assessments and those of the other WGs; these two dimensions 
frame the cause–effect chain investigated by WGI. The closest links 
to WGIII are the emissions scenarios, as WGIII considers drivers of 
emissions and climate change mitigation options. The links to WGII 
are the geophysical climate projections from the Earth system 
models, which are often used as the starting point in the literature on 
climate impacts and adaptation.This section is structured as follows: first, the scenarios used in AR6 
are introduced and discussed in relation to scenarios used in earlier 
IPCC assessments (Section 1.6.1). Cross-Chapter Box 1.4 provides an 
overview of the new set of illustrative scenarios and how they are 
used in this report. Next, the two additional dimensions of integration 
are introduced: global warming levels (Section 1.6.2) and cumulative 
CO 2 emissions (Section 1.6.3). Net zero emissions are discussed in 
Box 1.4. The relation between global warming levels and scenarios is

further assessed in Cross-Chapter Box 11.1 in Chapter 11.
1.6.1 Scenarios
A scenario is a description of how the future may develop, based on 
a coherent and internally consistent set of assumptions about key 
drivers including demography, economic processes, technological 
innovation, governance, lifestyles, and relationships among these 
driving forces (Section 1.6.1.1; IPCC, 2000; Rounsevell and Metzger, 
2010; O’Neill et al., 2014). Scenarios can also be defined by 
geophysical driving forces only, such as emissions or abundances of 
GHGs, aerosols, and aerosol precursors or land-use patterns. Scenarios 
are not predictions; instead, they provide a ‘what-if’ investigation of 
the implications of various developments and actions (Moss et al., 
2010). WGI investigates potential future climate change principally 
by assessing climate model simulations using emissions scenarios

Figure 1.24 | The dimensions of integration across chapters and Working Groups in the IPCC AR6 Assessment.  This Report adopts three explicit dimensions of 
integration to integrate knowledge across chapters and Working Groups. The first dimension is scenarios; the second dimension is global mean warming levels relative to pre-
industrial levels; and the third dimension is cumulative CO 2 emissions. For the scenarios, illustrative 2100 end-points are also indicated (white circles). Further details on data 
sources and processing are available in the chapter data table (Table 1.SM.1).
1.52.03.04.0
Reasons for Concern related to 
global-mean temperatures 2. Global warming levels
 3. Cumulative CO2 emissions 1. Scenarios
History
Cumulative CO2 emissions (GtCO2)
Global-mean surface air temperatures (°C)
2100
2100
2100
2100
2020

228
Chapter 1 Framing, Context, and Methods1originating from the WGIII community (Section 1.6.1.2). The scenarios 
used in this WGI Report cover various hypothetical ‘baseline 
scenarios’ or ‘reference futures’ that could unfold in the absence of 
any – or any additional – climate policies (Glossary). These ‘reference 
scenarios’ originate from a comprehensive analysis of a wide array 
of socio-economic drivers, such as population growth, technological 
development, and economic development, and their broad spectrum 
of associated energy, land use and emissions implications (Riahi et al., 
2017). With direct policy relevance to the Paris Agreement’s 1.5°C 
and ‘well below’ 2°C goals, this Report also assesses climate futures 
where the effects of additional climate change mitigation action are 
explored, i.e., so-called mitigation scenarios (for a broader discussion 
of scenarios and futures analysis, see Cross-Chapter Box 1, Table 1 in 
SRCCL, IPCC, 2019a).

For this Report, the main emissions, concentration and land-use 
scenarios considered are a  subset of scenarios recently developed 
using the Shared Socio-economic Pathways framework (SSPs; 
Section  1.6.1.1 and Cross-Chapter Box  1.4; Riahi et al., 2017). 
Initially, the term ‘SSP’ described five broad narratives of future socio-economic development only (O’Neill et al., 2014). However, at least in 
the WGI community, the term ‘SSP scenario’ is now more widely used 
to refer directly to future emissions and concentration scenarios that 
result from combining these socio-economic development pathways 
with climate change mitigation assumptions. These are assessed in 
detail in WGIII (AR6 WGIII Chapter 3) and in Cross-Chapter Box 1.4, 
Table 1 in this chapter.
This Report uses a  core set of five illustrative SSP scenarios 
to assist  cross-Chapter integration and cross-Working Group 
applications: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5

(Cross-Chapter Box  1.4, Table  1). These scenarios span a  wide 
range of plausible societal and climatic futures from potentially 
below 1.5°C best-estimate warming to over 4°C warming by 2100 
(Figure  1.25). The  set of five SSP scenarios includes those in ‘Tier 
1’ simulations of the CMIP6 ScenarioMIP intercomparison project 
(Section  1.5.4; O’Neill et al., 2016) that participating climate 
modelling groups were asked to prioritize (SSP1-2.6, SSP2-4.5, 
SSP3-7.0 and SSP5-8.5), plus the low emissions scenario SSP1-1.9. 
SSP1-1.9 is used in combination with SSP1-2.6 to explore differential 
1800
 1900 1950 2000
 2050
0123456
2100 Today 1750PAGES 2k reconstructionGMST or GSAT warming relative to 1850-1900 (°C)Consolidated GMST (Chapter 2)Possible 
futures 
Very high
SSP5-8.5
High
SSP3-7.0
Intermediate
SSP2-4.5
Low
SSP1-2.6
Very low
SSP1-1.9
1850Historical global mean 
surface temperatures
1995-2014 warming
1850-1900GMST
GSATPeaking
Assessed
GSAT range
(Chapter 4)
5%

95%CO2 emissions:
Halving
Net-zero½
0
½½
½0
0
Figure 1.25 | Global mean surface air temperature (GSAT) illustrated as warming stripes from blue (cold) to red (warm) over three different time periods.  
From 1750–1850 based on PAGES 2K reconstructions (PAGES 2k Consortium, 2017, 2019); from 1850–2018 showing the composite GSAT time series assessed in Chapter 2; 
and from 2020 onwards using the assessed GSAT projections for each Shared Socio-economic Pathway (SSP) (from Chapter 4). For the projections, the upper end of each arrow 
aligns with the colour corresponding to the 95th percentile of the projected temperatures and the lower end aligns with the colour corresponding to the 5th percentile of the 
projected temperature range. Projected temperatures are shown for five scenarios from ‘very low’ SSP1-1.9 to ‘very high’ SSP5-8.5 (see Cross-Chapter Box 1.4 for more details

on the scenarios). For illustrative purposes, natural variability has been added from a single CMIP6 Earth system model (MRI ESM2). The points in time when total CO 2 emissions 
peak; reach halved levels of the peak; and reach net zero emissions are indicated with arrows, ‘½’ and ‘0’ marks, respectively. Further details on data sources and processing 
are available in the chapter data table (Table 1.SM.1).

229
Framing, Context, and Methods Chapter 11outcomes of approximately 1.5°C and 2.0°C warming relative to 
pre-industrial levels, relevant to the Paris Agreement goals. Further 
SSP scenarios are used in this report to assess specific aspects of, for 
example, air pollution policies in Chapter 6 (Cross-Chapter Box 1.4). 
In addition, the previous generation of Representative Concentration Pathways (RCPs) is also used in this Report when assessing future 
climate change (Section 1.6.1.3 and Cross-Chapter Box 1.4, Table 1).
Climatic changes over the 21st century (and beyond) are projected 
and assessed in subsequent chapters, using a broad range of climate 
Global-mean temperature rel. 1850 –1900 (˚C)
500 1000
Year CE1500 2000 2100 23002˚C
1.5˚CGlobal-mean temperature
Future 
projectionsParis Agreement goals
rel. pre-industrial0 500 1000 1500 2000
5001000150020002500
100020003000
267278 ppmppmppmppmppmppm
645
Atmospheric concentrations
300400500Year CE
Nitrous oxide MethaneCarbon dioxide

ppbppb
ppb
ppb
ppb
ppb
ppbppb
00˚C 0˚C5˚C 5˚C10˚C 10˚C
Figure 1.26 | Historical and projected future concentrations of carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2O) and global mean temperatures 
(GMST).  GMST temperature reconstructions over the last 2000 years were compiled by the PAGES 2k Consortium (2017, 2019) (grey line, with 95% uncertainty range), joined 
by historical GMST time series assessed in Chapter 2 (black line) – both referenced against the 1850–1900 period. Future GSAT temperature projections are from CMIP6 ESM 
models across all concentration-driven SSP scenario projections (Chapter 4). The discontinuity around year 2100 for CMIP6 temperature projections results from the fact that not 
all ESM models ran each scenario past 2100. The grey vertical band indicates the future 2015–2300 period. The concentrations used to drive CMIP6 Earth system models are

derived from ice core, firn and instrumental datasets (Meinshausen et al., 2017) and projected using an emulator (Cross-Chapter Box 7.1; Meinshausen et al., 2020). The colours 
of the lines indicate the SSP scenarios used in this Report (Cross-Chapter Box 1.4, Figure 1). Further details on data sources and processing are available in the chapter data 
table (Table 1.SM.1).

230
Chapter 1 Framing, Context, and Methods1models, conditional on the various SSP scenarios. The projected future 
changes can then be put into the context of longer-term paleoclimate 
data and historical observations, showing how the higher emissions 
and higher concentration scenarios diverge further from the range of 
climate conditions that ecosystems and human societies experienced 
in the past 2000  years in terms of global mean temperature and 
other key climate variables (Figures 1.26 and 1.5).
While scenarios are a key tool for integration across IPCC Working 
Groups, they also allow the integration of knowledge among 
scientific communities and across time scales. For example, 
agricultural yield, infrastructure and human health impacts of 
increased drought frequency, extreme rainfall events and hurricanes 
are often examined in isolation. New insights on climate impacts in 
WGII can be gained if compound effects of multiple cross-sectoral

impacts are considered across multiple research communities under 
consistent scenario frameworks (Section 11.8; Leonard et al., 2014; 
Warszawski et al., 2014). Similarly, a synthesis of WGI knowledge 
on sea level rise contributions is enabled by a consistent application 
of future scenarios across all specialized research communities, 
such as ice-sheet mass balance analyses, glacier loss projections 
and thermosteric change from ocean heat uptake (Chapter  9; 
e.g. Kopp et al., 2014).
In addition to the comprehensive SSP scenario set and the RCPs, 
multiple idealized scenarios and time-slice experiments using 
climate models are assessed in this Report. Idealized scenarios refer 
to experiments where, for example, CO 2 concentrations are increased 
by 1% per year, or instantly quadrupled. Such idealized experiments 
have been extensively used in previous model intercomparison 
projects and constitute the core ‘DECK’ set of model experiments of

CMIP6 (Section 1.5.4). They are, for example, used to diagnose the 
patterns of climate feedbacks across the suite of models assessed in 
this Report (Chapter 7).
In the following section, we further introduce the SSP scenarios and 
how they relate to the Shared Socio-economic Pathways framework 
(Section  1.6.1.1); describe the scenario generation process 
(Section 1.6.1.2); and provide a historical review of scenarios used in 
IPCC assessment reports (Section 1.6.1.3); before briefly discussing 
questions of scenario likelihood, scenario uncertainty and the use of 
scenario storylines (Section 1.6.1.4).
1.6.1.1 Shared Socio-economic Pathways
The Shared Socio-economic Pathways SSP1 to SSP5 describe a range 
of plausible trends in the evolution of society over the 21st century. 
They were developed in order to connect a wide range of research 
communities (Nakicenovic et al., 2014) and consist of two main 
elements: a set of qualitative, narrative storylines describing societal

futures (O’Neill et al., 2017a) and a  set of quantified measures 
of development at aggregated and/or spatially resolved scales. 
Each pathway is an internally consistent, plausible and integrated 
description of a  socio-economic future, but these socio-economic 
futures do not account for the effects of climate change, and no new 
climate policies are assumed. The SSPs’ quantitative projections of 
socio-economic drivers include population, gross domestic product (GDP) and urbanization (Dellink et al., 2017; Jiang and O’Neill, 2017; 
Samir and Lutz, 2017). By design, the SSPs differ in terms of the socio-
economic challenges they present for climate change mitigation and 
adaptation (Rothman et al., 2014; Schweizer and O’Neill, 2014) and 
the evolution of these drivers within each SSP reflects this design. 
Broadly, the five SSPs represent ‘sustainability’ (SSP1), a ‘middle-of-
the-road’ path (SSP2), ‘regional rivalry’ (SSP3), ‘inequality’ (SSP4),

and ‘fossil fuel-intensive’ development (SSP5; Cross-Chapter Box 1.4, 
Figure 1; O’Neill et al., 2017a). More specific information on the SSP 
framework and the assumptions underlying the SSPs will be provided 
in the IPCC WGIII report (WGIII Chapter  3; see also Box SPM.1 in 
SRCCL (IPCC, 2019d)).
The SSP narratives and drivers were used to develop scenarios of 
energy use, air pollution control, land use, and GHG emissions 
developments using integrated assessment models (IAMs; Riahi et al., 
2017; Rogelj et al., 2018a). An IAM can derive multiple emissions 
futures for each socio-economic development pathway, assuming 
no new mitigation policies or various levels of additional mitigation 
action (in the case of reference scenarios and mitigation scenarios, 
respectively; Riahi et al., 2017). By design, the evolution of drivers 
and emissions within the SSP scenarios do not take into account the 
effects of climate change.

The SSPX-Y scenarios and the RCP scenarios are categorized similarly, 
by reference to the approximate radiative forcing levels each one 
entails at the end of the 21st century. For example, the ‘1.9’ in the 
SSP1-1.9 scenario stands for an approximate radiative forcing level of 
1.9 W m–2 in 2100. The first number (X) in the ‘SSPX-Y’ acronym refers 
to one of the five shared socio-economic development pathways 
(Cross-Chapter Box 1.4, Figure 1 and Table 1.4).
This SSP scenario categorization, focused on end-of-century radiative 
forcing levels, reflects how scenarios were conceptualized until 
recently, namely, to reach a particular climate target in 2100 at the 
lowest cost and irrespective of whether the target was exceeded over 
the century. More recently, and in particular since IPCC SR1.5 report 
focused attention on peak warming scenarios (Rogelj et al., 2018b), 
scenario development started to explicitly consider peak warming,

cumulative emissions and the amount of net negative emissions 
(Rogelj et al., 2018b; Fujimori et al., 2019).
The SSP scenarios can be used for either emissions- or concentration-
driven model experiments (Cross-Chapter Box 1.4). ESMs can be run 
with emissions and concentrations data for GHGs and aerosols and 
land-use or landcover maps and calculate levels of radiative forcing 
internally. The radiative forcing labels of the RCP and SSP scenarios, 
such as ‘2.6’ in RCP2.6 or SSP1-2.6, are thus approximate labels 
for the year 2100 only. The actual global mean effective radiative 
forcing varies across ESMs due to different radiative transfer 
schemes, uncertainties in aerosol–cloud interactions, and different 
feedback mechanisms, among other reasons. Nonetheless, using 
approximate radiative forcing labels is advantageous because it 
establishes a clear categorization of scenarios, with multiple climate 
forcings and different combinations in those scenarios summarized in

a single number. The classifications according to cumulative carbon 
emissions (Section  1.6.3) and global warming level (Section  1.6.2

231
and Cross-Chapter Box  7.1 on emulators) complement those 
forcing labels.
A key advance of the SSP scenarios relative to the RCPs is a wider 
span of assumptions on future air-quality mitigation measures, and 
hence emissions of short-lived climate forcers (SLCFs; Rao et al., 
2017; Lund et al., 2020). This allows for a more detailed investigation 
into the relative roles of GHG and SLCF emissions in future global 
and regional climate change, and hence the implications of policy 
choices. For instance, SSP1-2.6 builds on an assumption of stringent 
air-quality mitigation policy, leading to rapid reductions in particle 
emissions, while SSP3-7.0 assumes slow improvements, with 
pollutant emissions over the 21st  century comparable to current 
levels (Figure 6.19 and Cross-Chapter Box 1.4, Figure 2).
One limitation of the SSP scenarios used for CMIP6 and in this Report 
is that they reduce emissions from all the major ozone-depleting

substances controlled under the Montreal Protocol (CFCs, halons, 
and hydrochlorofluorocarbons (HCFCs)) uniformly, rather than 
representing a  fuller range of possible high- and low-emissions 
futures (UNEP , 2016). Hydrofluorocarbon (HFC) emissions, on the 
other hand, span a wider range within the SSPs than in the RCPs 
(Cross-Chapter Box 1.4, Figure 2).
The SSP scenarios and previous RCP scenarios are not directly 
comparable. First, the gas-to-gas compositions differ; for example, 
the SSP5-8.5 scenario has higher CO 2 concentrations but lower CH 4 
concentrations compared to RCP8.5. Second, the projected 21st-
century trajectories may differ, even if they result in the same radiative 
forcing by 2100. Third, the overall effective radiative forcing (Chapter 7) may differ, and tends to be higher for the SSPs compared to RCPs 
that share the same nominal stratospheric-temperature-adjusted 
radiative forcing label. The stratospheric-temperature-adjusted

radiative forcings of the SSPs and RCPs, however, remain relatively 
close, at least by 2100 (Tebaldi et al., 2021). In summary, differences 
in, for example, CMIP5 RCP8.5 and CMIP6 SSP5-8.5 ESM outputs, are 
partially due to different scenario characteristics rather than different 
ESM characteristics only (Section 4.6.2).
When investigating various mitigation futures, WGIII goes beyond 
the core set of SSP scenarios assessed in WGI (SSP1-1.9, SSP1-2.6, 
etc.) to consider the characteristics of more than 1000 scenarios 
(Cross-Chapter Box  7.1). In addition, while staying within the 
framework of socio-economic development pathways (SSP1 to 
SSP5), WGIII also considers various mitigation possibilities through 
so-called illustrative pathways (IPs). These illustrative pathways 
help to highlight key narratives in the literature concerning various 
technological, social and behavioural options for mitigation, various 
timings for implementation, or varying emphasis on different GHG

and land-use options. Just as with the SSPX-Y scenarios considered 
in this Report, these illustrative pathways can be placed in relation to 
the matrix of SSP families and approximate radiative forcing levels in 
2100 (Cross-Chapter Box 1.4, Figure 1; IPCC WGIII, Chapter 3).
No likelihood is attached to the scenarios assessed in this report, 
and the feasibility of specific scenarios in relation to current trends 
is best informed by the WGIII contribution to AR6. In the scenario 
literature, the plausibility of the high emissions levels underlying 
scenarios such as RCP8.5 or SSP5-8.5 has been debated in light of 
recent developments in the energy sector (Section 1.6.1.4).Table 1.4 | Overview of different RCP and SSP acronyms as used in this report.
Scenario Acronym Description
‘SSPX ’ with X standing for the Shared 
Socio-economic Pathway family (1–5)The Shared Socio-economic Pathway family, i.e., the socio-economic developments with storylines regarding (among other things) GDP ,

population, urbanization, economic collaboration, and human and technological development projections that describe different future 
worlds in the absence of climate change and additional climate policy (O’Neill et al., 2014). The quantification of energy, land use and 
emissions implications in those storylines is not part of the SSPX narratives, but follows in a second step in which their climate outcomes 
are defined. This second step is dependent upon the integrated assessment model (IAM) that is used for this quantification (see SSPX-Y 
below; Riahi et al., 2017).
‘RCPY ’ with Y standing for approximate 
radiative forcing level in 2100, at levels 2.6, 
4.5, 6.0 or 8.5.Representative Concentration Pathways (RCPs; Moss et al., 2010; van Vuuren et al., 2011). These are GHG concentrations (Meinshausen 
et al., 2011b) , aerosol emissions (Lamarque et al., 2011) and land use-pattern time series (Hurtt et al., 2011) derived from several IAMs.

The pathways were originally generated from specific sets of socio-economic drivers, but these are no longer considered. Instead, these 
RCP emissions and concentrations time series are used in combination with a range of socio-economic futures (see SSPX-RCPY below). 
For example, the CMIP5 intercomparison (assessed in IPCC AR5; IPCC, 2013a) developed climate futures based on these emissions 
and concentrations pathways from the RCPs.
The SSP and RCP combination
‘SSPX-RCPY ’ with X and Y as above.Combination of the SSP Socio-Economic Pathway X with climate futures stemming from GCMs, AOGCMs or Earth system model 
runs that used the RCPY . This combination is widely used in the impact literature assessed by WGII (see for example the Special Issue 
on SSPs by van Vuuren et al. (2014) and the large literature collection in the International Committee On New Integrated Climate

change assessment Scenarios database (ICONICS, 2021). These SSPX-RCPY scenarios differ from the SSPX-Y group (see below) in that 
the respective socio-economic futures (SSPXs) and emissions and concentrations futures (RCPYs) were developed separately before 
being used in combination.
‘SSPX-Y ’ with X and Y as above.SSPX-Y is the abbreviation for a scenario, where X is the numbering of the SSP socio-economic family (1 to 5) that was used to 
develop the emissions pathway, and Y indicates the approximate radiative forcing value reached by 2100. The SSPX-Y scenarios 
span the nominal range from 1.9 to 8.5 W m–2. A range of different IAMs were used to quantify the SSPX-Y scenarios, but 
each IAM quantified both the socio-economic futures (energy use, land use, population etc.) and various emissions futures 
within the same IAM modelling framework, thus enhancing the consistency between the socio-economic backgrounds and

their resulting emissions futures. In contrast, the SSPX-RCPY framework combines the SSP socio-economic futures and RCP 
emissions and concentrations futures at random (see above). For more details, see Section 1.6.1.1.

232
Chapter 1 Framing, Context, and Methods1Cross-Chapter Box 1.4 | The SSP Scenarios as Used in Working Group I (WGI)
Contributing Authors:  Jan S. Fuglestvedt (Norway), Celine Guivarch (France), Christopher Jones (United Kingdom), Malte 
Meinshausen (Australia/Germany), Zebedee R. J. Nicholls (Australia), Gian-Kasper Plattner (Switzerland), Keywan Riahi (Austria), Joeri 
Rogelj (United Kingdom/Belgium), Sophie Szopa (France), Claudia Tebaldi (United States of America), Anne-Marie Treguier (France), 
and Detlef van Vuuren (The Netherlands)
The nine new SSP emissions and concentrations scenarios (SSP1-1.9 to SSP5-8.5; Cross-Chapter Box 1.4, Table 1) offer unprecedented 
detail of input data for climate model simulations. They allow for a more comprehensive assessment of climate drivers and responses 
than has previously been available, in particular because some of the scenarios’ time series, (e.g., pollutants, emissions or changes

in land use and land cover), are more diverse in the SSP scenarios than in the RCPs used in AR5 (Cross-Chapter Box 1.4, Figure 2; 
e.g., Chuwah et al., 2013).
The core set of five illustrative SSP scenarios – SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 – was selected in this Report to 
align with the objective that the new generation of SSP scenarios should fill certain gaps identified in the RCPs. For example, a scenario 
assuming reduced air-pollution control and thus higher aerosol emissions was missing from the RCPs. Likewise, nominally the only ‘no-
additional-climate-policy’ scenario in the set of RCPs was RCP8.5. The new SSP3-7.0 ‘no-additional-climate-policy’ scenario fills both 
these gaps. A very strong mitigation scenario in line with the 1.5°C goal of the Paris Agreement was also missing from the RCPs, and the 
SSP1-1.9 scenario now fills this gap, complementing the other strong mitigation scenario SSP1-2.6. The five core SSPs were also chosen

to ensure some overlap with the RCP levels for radiative forcing at the year 2100 (specifically 2.6, 4.5, and 8.5; O’Neill et al., 2016; Tebaldi 
et al., 2021), although effective radiative forcings are generally higher in the SSP scenarios compared to the equivalently named RCP 
pathways (Section 4.6.2 and Cross-Chapter Box 1.4, Figure 1). In theory, running scenarios with similar radiative forcings would permit 
analysis of the CMIP5 and CMIP6 outcomes for pairs of scenarios (e.g., RCP8.5 and SSP5-8.5) in terms of varying model characteristics 
rather than differences in the underlying scenarios. In practice, however, there are limitations to this approach (Sections 1.6.1.1 and 4.6.2).
Today
2020
Global-mean temperature (°C) 
2050 2100SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0Low NTCFSSP5-8.5
SSP5-3.4-OSSSP4-6.0
SSP4-3.4
Mitigation
SSP1
SustainabilitySSP2
Middle of the 
RoadSSP3
Regional 
RivalrySSP4
InequalitySSP5
Fossil-fueled 
developmentRCPs
Previous 
Scenarios

MitigationIndicator for warmingNo additional
climate policy
reference 
scenario
Mitigation 
scenarios
Additional
SSP scenarios
Previous RCP 
scenariosApproximate 2100 radiative forcing label (W/m2)SSP2
RoadSSP3
Regional 
development
Five main 
investigated 
in WG1
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/aSSP1
Sustainability
8.5
7.0
6.0
4.5
3.4
2.6
1.9Shared socio-economic pathways
SSP3-7.0
-lowNTCF
SSPX-YRCP8.5
RCP6.0
RCP4.5
RCP2.6
RCPYSSP4-6.0
SSP4-3.4SSP5-3.4
-OSSSP3-7.0
SSP2-4.5SSP5-8.5
SSP1-2.6
SSP1-1.9SSPX-Y
Cross-Chapter Box 1.4, Figure 1 | The SSP scenarios used in this Report, their indicative temperature evolution and radiative forcing categorization, 
and the five socio-economic storylines upon which they are built.  The core set of scenarios used in this report – i.e., SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0

and SSP5-8.5 – is shown together with an additional four SSPs that are part of ScenarioMIP , as well as previous RCP scenarios. In the left-hand panel , the indicative 
temperature evolution is shown (adapted from Meinshausen et al., 2020) . The black stripes on the respective scenario family panels on the left-hand side indicate 
a larger set of IAM-based SSP scenarios that span the scenario range more fully, but are not used in this report. The SSP–radiative forcing matrix is shown on the 
right-hand panel , with the SSP socio-economic narratives shown as columns and the indicative radiative forcing categorization by 2100 shown as rows. Note that 
the descriptive labels for the five SSP narratives refer mainly to the reference scenario futures without additional climate policies. For example, SSP5 can accommodate 
strong mitigation scenarios leading to net zero emissions; these do not match a ‘fossil-fuelled development’ label. Further details on data sources and processing are

233
Framing, Context, and Methods Chapter 11Cross-Chapter Box 1.4 (continued)
Cross-Chapter Box 1.4, Table 1 | Overview of SSP scenarios used in this report.  The middle column briefly describes the SSP scenarios and the right-hand 
column indicates the previous RCP scenarios that most closely match that SSP’s assessed global surface air temperature (GSAT) trajectory. RCP scenarios are generally 
found to result in larger modelled warming for the same nominal radiative forcing label (Section 4.6.2.2). The five core SSP scenarios used most commonly in this report 
are highlighted in bold . Further SSP scenarios are used where they allow assessment of specific aspects, e.g., air pollution policies in Chapter 6 (SSP3-7.0-lowNTCF). 
RCPs are used in this report wherever the relevant scientific literature makes substantial use of regional or domain-specific model output that is based on these previous

RCP pathways, such as sea level rise projections in Chapter 9 (Section 9.6.3.1) or regional climate aspects in Chapters 10 and 12. See Chapter 4 (Section 4.3.4) for the 
GSAT assessment for the SSP scenarios and Section 4.6.2.2 for a comparison between SSPs and RCPs in terms of both radiative forcing and global surface temperature.
SSPX-Y  
ScenarioDescription From an Emissions/Concentrations and Temperature  
 Perspective (Table 4.2)Closest RCP Scenarios
SSP1-1.9Holds warming to approximately 1.5°C above 1850–1900 in 2100 after slight overshoot 
(median) and implied net zero CO 2 emissions around the middle of the century.Not available. No equivalently low RCP scenario exists.
SSP1-2.6Stays below 2.0°C warming relative to 1850–1900 (median) with implied net zero CO 2 
emissions in the second half of the century.RCP2.6, although RCP2.6 might be cooler for the same 
model settings.
SSP4-3.4A scenario between SSP1-2.6 and SSP2-4.5 in terms of end-of-century radiative forcing. It does

not stay below 2.0°C in most CMIP6 runs (Chapter 4) relative to 1850–1900.No 3.4 level of end-of-century radiative forcing was 
available in the RCPs. Nominally SSP4-3.4 sits between 
RCP 2.6 and RCP 4.5, although SSP4-3.4 might be more 
similar to RCP4.5. Also, in the early decades of the 
21st century, SSP4-3.4 is close to RCP6.0, which featured 
lower radiative forcing than RCP4.5 in those decades.
SSP2-4.5Scenario approximately in line with the upper end of aggregate NDC emissions levels by 2030 
(Sections 1.2.2 and 4.3; SR1.5, (IPCC, 2018), Box 1). CO 2 emissions remaining around current levels 
until the middle of the century. The SR1.5 assessed temperature projections for NDCs to be between 
2.7°C and 3.4°C by 2100 (Section 1.2.2; SR1.5 (IPCC, 2018); Cross-Chapter Box 11.1), corresponding 
to the upper half of projected warming under SSP2-4.5 (Chapter 4). New or updated NDCs 
by the end of 2020 did not significantly change the emissions projections up to 2030, although

more countries adopted 2050 net zero targets in line with SSP1-1.9 or SSP1-2.6. The SSP2-4.5 
scenario deviates mildly from a ‘no-additional-climate-policy’ reference scenario, resulting in a best-
estimate warming around 2.7°C by the end of the 21st century relative to 1850–1900 (Chapter 4).RCP4.5 and, until 2050, also RCP6.0. Forcing in the 
latter was even lower than RCP4.5 in the early decades 
of the 21st century.
SSP4-6.0The end-of-century nominal radiative forcing level of 6.0 W m–2 can be considered  
a ‘no-additional-climate-policy’ reference scenario, under SSP1 and SSP4 socio-economic 
development narratives.RCP6.0 is nominally closest in the second half of 
the century, although global mean temperatures are 
estimated to be generally lower in RCPs compared to 
SSPs. Furthermore, RCP6.0 features lower warming than 
SSP4-6.0, as it has very similar temperature projections 
compared to the nominally lower RCP4.5 scenario 
in the first half of the century.

SSP3-7.0An intermediate-to-high reference scenario resulting from no additional climate policy under the 
SSP3 socio-economic development narrative. CO 2 emissions roughly double from current levels 
by 2100. SSP3-7.0 has particularly high non-CO 2 emissions, including high aerosols emissions.Between RCP6.0 and RCP8.5, although SSP3-7.0 
non-CO 2 emissions and aerosols are higher than in 
any of the RCPs.
SSP3-7.0-
lowNTCFA variation of the intermediate-to-high reference scenario SSP3-7.0 but with mitigation of CH 4 
and/or short-lived species such as black carbon and other short-lived climate forcers (SLCF). 
Note that variants of SSP3-7.0-lowNTCF differ in terms of whether CH 4 emissions are reduceda 
(Sections 4.4 and 6.6).SSP3-7.0-lowNTCF is between RCP6.0 and RCP8.5, 
as RCP scenarios generally incorporated a narrow 
and comparatively low level of SLCF emissions across 
the range of RCPs.
SSP5-3.4-OS

(Overshoot)A mitigation-focused variant of SSP5-8.5 that initially follows unconstrained emissions growth 
in a fossil fuel-intensive setting until 2040 and then implements the largest net negative CO 2 
emissions of all SSP scenarios in the second half of 21st century to reach SSP1-2.6 forcing levels 
in the 22nd century. Used to consider reversibility and strong overshoot scenarios in, or example, 
Chapters 4 and 5.Not available. Initially, until 2040, similar to RCP8.5.
SSP5-8.5A high-reference scenario with no additional climate policy. CO 2 emissions roughly double 
from current levels by 2050. Emissions levels as high as SSP5-8.5 are not obtained by 
integrated assessment models (IAMs) under any of the SSPs other than the fossil-fuelled 
SSP5 socio-economic development pathway.RCP8.5, although CO 2 emissions under SSP5-8.5 are 
higher towards the end of the century (Cross-Chapter 
Box 1.4, Figure 2). CH 4 emissions under SSP5-8.5 are

lower than under RCP 8.5. When used with the same 
model settings, SSP5-8.5 may result in slightly higher 
temperatures than RCP8.5 (Section 4.6.2).
a The AerChemMIP variant of SSP3-7.0-lowNTCF (Collins et al., 2017) only reduced aerosol and ozone precursors compared to SSP3-7.0, not methane. 
The SSP3-7.0-lowNTCF variant by the integrated assessment models also reduced methane emissions (Gidden et al., 2019), which creates differences between 
SSP3-7.0-lowNTCF and SSP3-7.0 also in terms of methane concentrations and some fluorinated gas concentrations that have OH related sinks (Meinshausen et al., 2020).

234
Chapter 1 Framing, Context, and Methods1Cross-Chapter Box 1.4 (continued)
Cross-Chapter Box 1.4, Table 2 | Overview of key climate forcer datasets used as input by ESMs for historical and future SSP scenario experiments.  
The data is available from the Earth System Grid Federation (ESGF , 2021) described in Eyring et al. (2016).
Climate Forcer Description
CO 2 Emissions (emissions-
driven runs only)Harmonized historical and future gridded emissions of anthropogenic CO 2 emissions (Hoesly et al., 2018; Gidden et al., 2019)  
are used instead of the prescribed CO 2 concentrations. See Chapter 4 (Section 4.3.1).
Historical and Future 
GHG ConcentrationsGHG surface air mole fractions of 43 species, including CO 2, CH 4, N 2O, HFCs, PFCs, halons, HCFCs, CFCs, sulphur hexafluoride 
(SF 6), ammonia (NF 3), including latitudinal gradients and seasonality from year 1 to 2500 (Meinshausen et al., 2017, 2020).
Land-Use Change and

Management PatternsGlobally gridded land use- and land cover-change datasets (Hurtt et al., 2020; Ma et al., 2020)
Biomass Burning EmissionsHistorical fire-related gridded emissions, including sulphur dioxide (SO 2), nitrogen oxides (NO x), carbon monoxide (CO), black 
carbon (BC), organic carbon (OC), NH 3, non-methane volatile organic compounds (NMVOCs), relevant to concentration-driven 
historical and future SSP scenario runs (van Marle et al., 2017).
Stratospheric and Tropospheric Ozone Historical and future ozone dataset, also with total column ozone (CCMI, 2021).
Reactive Gas EmissionsGridded global anthropogenic emissions of reactive gases and aerosol precursors, including CO, SO x, CH 4, NO x, NMVOCs, or NH 3 
(Hoesly et al., 2018; Feng et al., 2020).
Solar ForcingRadiative and particle input of solar variability from 1850 through to 2300 (Matthes et al., 2017). Future variations in solar 
forcing also reflect long-term multi-decadal trends.

Volcanic ForcingHistorical stratospheric aerosol climatology (Thomason et al., 2018), with the mean stratospheric volcanic aerosol prescribed 
in future projections.
In contrast to stylized assumptions about the future evolution of emissions (e.g., a linear phase-out from year A to year B), these 
SSP scenarios are the result of a detailed scenario generation process (Sections 1.6.1.1 and 1.6.1.2). While IAMs produce internally 
consistent future-emissions time series for CO 2, CH 4, N 2O, and aerosols for the SSP scenarios (Riahi et al., 2017; Rogelj et al., 2018a), 
these emissions scenarios are subject to several processing steps for harmonization (Gidden et al., 2018) and in-filling (Lamboll et al., 
2020), before also being complemented by several datasets so that ESMs can run these SSPs (Durack et al., 2018; Tebaldi et al., 2021). 
Although five scenarios are the primary focus of WGI, a total of nine SSP scenarios have been prepared with all the necessary detail

to drive the ESMs as part of the CMIP6 (Cross-Chapter Box 1.4, Figure 1 and Table 2).
ESMs are driven by either emissions or concentrations scenarios. Inferring concentration changes from emissions time series requires 
using carbon cycle and other gas cycle models. To aid comparability across ESMs, and in order to allow participation of ESMs that do 
not have coupled carbon and other gas cycle models in CMIP6, most of the CMIP6 ESM experiments are so-called ‘concentration-
driven’ runs, with concentrations of CO 2, CH 4, N 2O and other well-mixed GHGs prescribed in conjunction with aerosol emissions, 
ozone changes and effects from human-induced land-cover changes that may be radiatively active via albedo changes (Cross-Chapter 
Box 1.4, Figure 2). In these concentration-driven climate projections, the uncertainty in projected future climate change resulting from

our limited understanding of how the carbon cycle and other gas cycles will evolve in the future is not captured. For example, when 
deriving the default concentrations for these scenarios, permafrost and other carbon cycle feedbacks are considered using default 
settings, with a single time series prescribed for all ESMs (Meinshausen et al., 2020). Thus, associated uncertainties (Joos et al., 2013; 
Schuur et al., 2015) are not considered.
The so-called ‘emissions-driven’ experiments (Jones et al., 2016) use the same input datasets as concentration-driven ESM experiments, 
except that they use CO 2 emissions rather than concentrations (Chapter 5 and Section 4.3.1). In these experiments, atmospheric CO 2 
concentrations are calculated internally using the ESM interactive carbon cycle module and thus differ from the prescribed default CO 2 
concentrations used in the concentration-driven runs. In the particular case of SSP5-8.5, the emissions-driven runs are assessed to add

no significant additional uncertainty to future global surface air temperature (GSAT) projections (Section 4.3.1). However, generally, 
when assessing uncertainties in future climate projections, it is important to consider which elements of the cause–effect chain, 
from emissions to the resulting climate change, are interactively included as part of the model projections, and which are externally 
prescribed using default settings.

235
0˚ 100˚EDifferences in gridded emissions for year 2000 (CMIP6 minus CMIP5) SSP and RCP emissionsSSP and RCP concentrations
100015002000250030003500CH4 Surface atmospheric 
mole fraction (ppb)
N2O Surface atmospheric
 mole fraction (ppb)
320340360380400420440
0200400600800CH4 Emissions (Mt CH4/yr)
0510152025N2O Emissions (Mt N2O/yr)
05101520253035OC Emissions (Mt OC/yr)
020406080100120Sulfur Emissions (Mt SO2/yr)
020406080100120140160NOx Emissions (Mt NOx/yr)
050100150200250VOC Emissions (Mt VOC/yr)
0˚
100˚W50˚N
50˚s0˚50˚N
50˚s
-1000-40-20020406080100120140
-5000500100015002000 2020 2040 2060 2080 2100024681012
40050060070080090010001100CO2 Surface atmospheric 
mole fraction (ppm)
-20020406080100120CO2 Emissions (Gt CO2/yr)
0246810BC Emissions (Mt BC/yr)
020406080NH3 Emissions (Mt NH3/yr)
1416SF6 Emissions (kt SF6/yr)
2000 2020 2040 2060 2080 21000255075100125150175200PFC Emissions 
(Mt CO2eq / yr GWP-100 AR4)

2000 2020 2040 2060 2080 2100012345HFC Emissions 
(Gt CO2eq GWP-100 AR4/ yr)SO2 (Gg SO2/yr)
BC (Gg C/yr)
(a) CO₂ (b) CH₄ (c) N₂O
(d) CO₂ (e) CH₄ (f) N₂O
(g) BC (h) OC (i) SO₂
(j) NH₃ (k) NOx (l) VOC
(m) SF₆
(p) SO₂(n) PFCs(o) HFCs
0˚ 100˚W 100˚E
SSP5-8.5
SSP3-7.0
SSP2-4.5
RCP2.6
SSP1-2.6
SSP1-1.9
RCP8.5
RCP6.0
RCP4.5
SSP5-8.5
SSP3-7.0
SSP2-4.5
RCP2.6
SSP1-2.6
SSP1-1.9
RCP8.5
RCP6.0
RCP4.5
SSP5-8.5
SSP3-7.0
SSP2-4.5
RCP2.6
SSP1-2.6
SSP1-1.9
RCP8.5
RCP6.0
RCP4.5
SSP5-8.5
SSP3-7.0
SSP2-4.5
RCP2.6
SSP1-2.6
SSP1-1.9
RCP8.5
RCP6.0
RCP4.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP5-8.5
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP3-7.0
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
SSP2-4.5
RCP2.6
RCP2.6
RCP2.6
RCP2.6
RCP2.6
RCP2.6
RCP2.6
RCP2.6
RCP2.6
RCP2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-2.6
SSP1-1.9
SSP1-1.9
SSP1-1.9
SSP1-1.9

SSP1-1.9
SSP1-1.9
SSP1-1.9
SSP1-1.9
SSP1-1.9
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP8.5
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP6.0
RCP4.5
RCP4.5
RCP4.5
RCP4.5
RCP4.5
RCP4.5
RCP4.5
RCP4.5
RCP4.5
RCP4.5
SSP5-8.5
SSP3-7.0
SSP2-4.5
RCP2.6
SSP1-2.6
SSP1-1.9
RCP8.5
RCP6.0
RCP4.5
SSP1-1.9
SSP1-2.6(q) BC
Cross-Chapter Box  1.4, Figure  2 | Comparison between the Shared Socio-economic Pathways (SSP) scenarios and the Representative 
Concentration Pathway (RCP) scenarios in terms of their CO 2, CH 4 and N 2O atmospheric concentrations (a–c), and their global emissions of CO 2, 
CH 4, N 2O, black carbon (BC), organic carbon (OC), sulphur dioxide (SO 2), ammonia (NH 3), nitrogen oxides (NOx), volatile organic compounds 
(VOC), sulphur hexafluoride (SF6), perfluorocarbons (PFCs), and hydrofluorocarbons (HFCs) (d–o).

236
1.6.1.2 Scenario Generation Process for CMIP6
The scenario generation process involves research communities 
linked to all three IPCC Working Groups (Figure 1.27). It generally 
starts in the scientific communities associated with WGII and WGIII 
with the definition of new socio-economic scenario storylines (IPCC, 
2000; O’Neill et al., 2014) that are quantified in terms of their 
drivers  – i.e.,  GDP , population, technology, energy and land use  – 
and their resulting emissions (Riahi et al., 2017). Then, numerous 
complementation and harmonization steps are necessary for datasets 
within the WGI and WGIII science communities, including gridding 
emissions of anthropogenic short-lived forcers, providing open 
biomass-burning emissions estimates, preparing land-use patterns, 
aerosol fields, stratospheric and tropospheric ozone, nitrogen 
deposition datasets, solar irradiance and aerosol optical property

estimates, and observed and projected GHG concentration time series (documented for CMIP6 through input4mips; Cross-Chapter 
Box 1.4, Table 2; Durack et al., 2018).
Once these datasets are completed, ESMs are run in coordinated 
model intercomparison projects in the WGI science community, 
using standardized simulation protocols and scenario data. The most 
recent example of such a coordinated effort is the CMIP6 exercise 
(Section  1.5.4; Eyring et al., 2016) with, in particular, ScenarioMIP 
(O’Neill et al., 2016). The WGI science community feeds back climate 
information to WGIII via climate emulators (Cross-Chapter Box 7.1) 
that are updated and calibrated with the ESMs’ temperature 
responses and other lines of evidence. Next, this climate information 
is used to compute several high-level global climate indicators 
(e.g., atmospheric concentrations, global temperatures) for a much 
wider set of hundreds of scenarios that are assessed as part of the

IPCC WGIII Assessment (WGIII Annex C). The outcomes from climate 
models run under the different scenarios are then used to calculate Cross-Chapter Box 1.4 (continued)
Cross-Chapter Box 1.4, Figure 2 (continued):  Also shown are gridded emissions differences for SO 2 (p) and black carbon (q) for the year 2000 between the 
input emissions datasets that underpinned the CMIP5 and CMIP6 model intercomparisons. Historical emissions estimates are provided in black in panels (d–o) . The 
range of concentrations and emissions investigated under the RCP pathways is shaded grey. Panels (p) and (q) adapted from Figure 7 in Hoesly et al. (2018). Further 
details on data sources and processing are available in the chapter data table (Table 1.SM.1).
Drivers: 
GDP, Population, 
Technology...Emissions:
CO2, CH4, N2O, HFCs, 
aerosols, landuse patternstime time
Concentrations: 
CO2, CH4, N2O, HFCs, ..
ozone, and aerosol loadings. time
Climate Projections 
(incl. radiative forcing, temperatures,

precipitation, snow cover, sea ice, sea level, etc.) time time
Species-level and Human impacts
(incl. infectious diseases, heat stress, 
species distributions, insurance losses etc. )  time
CauseModels Data
Effect
WGIII
WGIWGII
Scenario generation process 
Geophysical impacts
(incl. river runoff, coastal erosion, 
ecosystem shifts, wildfire modelling, etc.) Integrated 
Assessement 
Models
Emission 
    harmonisation 
Reduced complexity 
and gas cycle modelsGeophysical 
Impact ModelsSpecies, human and 
socio-economic 
impact models
Ecosystem and 
biodiversity models Classification of scenarios against climate targets
ActionCalibration / EmulationDefining shared 
socio-economic
pathways
AdaptationEarth System and 
General Circulation Models 
Figure 1.27 | A simplified illustration of the scenario generation process, involving the scientific communities represented in the three IPCC Working

Groups. The circular set of arrows at the top indicates the main set of models and workflows used in the scenario generation process, with the lower level indicating the datasets.

237
the evolution of climatic impact-drivers (Chapter  12), and utilized 
by impact researchers together with exposure and vulnerability information, in order to characterize risk to human and natural 
systems from future climate change. The climate impacts associated 
with these scenarios or different warming levels are then assessed as part of WGII reports (Figure 1.27).
1.6.1.3  History of Scenarios within the IPCC
Scenario modelling experiments have been a  core element of physical climate science since the first transient simulations with a general circulation model in 1988 (Section 1.3; Hansen et al., 1988). 
Scenarios and modelling experiments assessed in IPCC reports have

evolved over time, which provides a ‘history of how the future was seen’. The starting time for the scenarios moves as actual emissions supersede earlier emissions assumptions, while new scientific insights into the range of plausible population trends, behavioural changes 
and technology options and other key socio-economic drivers of 
emissions also emerge (see WGIII; Leggett et al., 1992; IPCC, 2000; Moss et al., 2010; Riahi et al., 2017). Many different sets of climate projections have been produced over the past several decades, using 
different sets of scenarios. Here, we compare those earlier scenarios 
against the most recent ones.
Climate science research involving scenarios necessarily follows 
a  series of consecutive steps (Figure  1.27). As each step waits for 
input from the preceding one, delays often occur that result in the

impact literature basing its analyses on earlier scenarios than those most current in the climate change mitigation and climate system literature. It is therefore important to provide an approximate comparison across the various scenario generations (Chapter  4, 
Figure 1.28, and Cross-Chapter Box 1.4, Table 1).
The first widely used set of IPCC emissions scenarios was the 
IS92 scenarios in 1992 (Leggett et al., 1992). Apart from reference scenarios, IS92 also included a  set of stabilization scenarios, the 
so-called ‘S’ scenarios. Those ‘S’ pathways were designed to lead 
to CO
2 stabilization levels such as 350 ppm or 450 ppm. By 1996, 
those latter stabilization levels were complemented in the scientific literature by alternative trajectories that assumed a delayed onset of 
climate change mitigation action (Figure 1.28; Wigley et al., 1996).
By 2000, the IPCC Special Report on Emissions Scenarios (SRES)

produced the SRES scenarios (IPCC, 2000), albeit without assuming any climate policy-induced mitigation. The four broad groups of 
SRES scenarios (scenario ‘families’) – A1, A2, B1 and B2 – were the 
first scenarios to emphasize socio-economic scenario storylines, and also first to emphasize other GHGs, land-use change and aerosols. Figure  1.28 |  Comparison of the range of fossil fuel and industrial CO 2 
emissions from scenarios used in previous assessments up to AR6.  Previous 
assessments are the IS92 scenarios from 1992 (top), the Special Report on Emissions 
Scenarios (SRES) scenarios from the year 2000 (second panel), the Representative 
Concentration Pathway (RCP) scenarios designed around 2010 (third panel) and 
the Shared Socio-economic Pathways (SSP) scenarios (fourth panel). In addition, 
historical emissions are shown (black line; Figure  5.5); a  more complete set of 
scenarios is assessed in SR1.5 (bottom); (Huppmann et al., 2018). Further details on

data sources and processing are available in the chapter data table (Table 1.SM.1).
05101520253035
-505101520253035
1900 1940 1980 2020 2060 2100-505101520253035SSP3-7.0
Low NTCF
SSP4-3.4SSP4-6.0
SSP2-4.5
SSP5-3.4
-OS
SR1.5
database scenariosSSP1-1.9SSP1-2.6SSP3-7.0SSP5-8.5IS92a
IS92b
IS92dIS92e
IS92f
IS92c
RCP-2.6RCP-4.5RCP-6.0RCP-8.5A1BA1FI
A1TA2
B1B2
Fossil & Industrial Carbon Dioxide Emissions (GtC yr-1)1992: IS92 Scenarios
2000: SRES Scenarios
2010: RCP Pathways
2018: SSP Scenarios
2018: SR1.5 Database of Scenarios

238
Chapter 1 Framing, Context, and Methods1Represented by three scenarios for the high-growth A1 scenario 
family, those 6 SRES scenarios (A1FI, A1B, A1T, A2, B1, and B2) can 
still sometimes be found in today’s climate impact literature. The void 
of missing climate change mitigation scenarios was filled by a range 
of community exercises, including the so-called ‘post-SRES scenarios’ 
(Swart et al., 2002).
The RCP scenarios (van Vuuren et al., 2011) then broke new ground 
by providing low-emissions pathways that implied strong climate 
change mitigation, including an example with negative CO 2 emissions 
on a large scale, namely RCP2.6. As shown in Figure 1.28, the upper 
end of the scenario range has not substantially shifted. Building 
on the SRES multi-gas scenarios, the RCPs include time series of 
emissions and concentrations of the full suite of GHGs, aerosols and 
chemically active gases, as well as land use and land cover (Moss

et al., 2010). The word ‘representative’ signifies that each RCP is 
radiative forcing characteristics. The term ‘pathway’ emphasizes that 
not only the long-term concentration levels are of interest, but also 
the trajectory taken over time to reach that outcome (Moss et al., 
2010). RCPs usually refer to the concentration pathway extending to 
2100, for which IAMs produced corresponding emissions scenarios. 
Four RCPs produced from IAMs were selected from the published 
literature and are used in AR5 as well as in this report, spanning 
approximately the range from below 2°C warming to high (above 
4°C) warming best-estimates by the end of the 21st century: RCP2.6, 
RCP4.5 and RCP6.0 and RCP8.5 (Cross-Chapter Box  1.4, Table  1). 
Extended Concentration Pathways (ECPs) describe extensions of 
the RCPs from 2100 to 2300 that were calculated using simple rules

generated by stakeholder consultations; these do not represent fully 
consistent scenarios (Meinshausen et al., 2011b).
By design, the RCP emissions and concentrations pathways were 
originally developed using particular socio-economic development 
pathways, but those are no longer considered (Moss et al., 2010). 
The different levels of emissions and climate change represented in 
the RCPs can hence be explored against the backdrop of different 
socio-economic development pathways (SSP1 to SSP5; Section 1.6.1.1 
and Cross-Chapter Box  1.4). This integrative SSP-RCP framework 
(‘SSPX-RCPY’ in Table 1.4) is now widely used in the climate impact 
and policy analysis literature (e.g., ICONICS, 2021; Green et al., 2020; 
O’Neill et al., 2020), where climate projections obtained under the 
Considering various levels of future emissions and climate change for 
each socio-economic development pathway was an evolution from

the previous SRES framework (IPCC, 2000), in which socio-economic 
and emissions futures were closely aligned.
The new set of scenarios (SSP1-1.9 to SSP5-8.5) now features 
a higher top level of CO 2 emissions (SSP5-8.5 compared to RCP8.5), 
although the most significant change is again the addition of a very 
low climate change mitigation scenario (SSP1-1.9, compared to the 
previous low scenario, RCP2.6). Also, historically, none of the previous 
scenario sets featured a  scenario that involves a  very pronounced 
peak-and-decline emissions trajectory, but SSP1-1.9 does so now. 
The full set of nine SSP scenarios now includes a  high-aerosol-
emissions scenario (SSP3-7.0). The RCPs featured more uniformly low aerosol trajectories across all scenarios (Cross-Chapter Box 1.4, 
Figure 2). More generally, the SSP scenarios feature a later peak of 
global emissions for the lower scenarios, simply as a consequence of 
historical emissions not having followed the trajectory projected by

previous low scenarios (Figure 1.28).
Over the last decades, discussions around scenarios have often 
focussed on whether recent trends make certain future scenarios 
more or less probable or whether all scenarios are too high or too 
low. When the SRES scenarios first appeared, the debate was often 
whether the scenarios were overestimating actual world emissions 
developments (e.g., Castles and Henderson, 2003). With the strong 
emissions increase throughout the 2000s, that debate then shifted 
towards the question of whether the lower future climate change 
mitigation scenarios were rendered unfeasible (Pielke et al., 2008; 
van Vuuren and Riahi, 2008). Historical emissions between 2000 and 
2010 approximately track the upper half of SRES and RCP projections 
(Figure 1.28). More generally, the global fossil fuel and industrial CO 2 
emissions of recent decades tracked approximately the middle of 
the projected scenario ranges (Figure 1.28), although with regional

differences (Pedersen et al., 2020).
1.6.1.4 The Likelihood of Reference Scenarios,  
Scenario Uncertainty and Storylines
In general, no likelihood is attached to the scenarios assessed in this 
Report. The use of different scenarios for climate change projections 
allows the exploration of ‘scenario uncertainty’ (Section 1.4.4; SR1.5; 
Collins et al., 2013). Scenario uncertainty is fundamentally different 
from geophysical uncertainties, which result from limitations in the 
understanding and predictability of the climate system (Smith and 
Stern, 2011). In scenarios, by contrast, future emissions depend to 
a large extent on the collective outcome of choices and processes 
related to population dynamics and economic activity, or on choices 
that affect a given activity’s energy and emissions intensity (Jones, 
2000; Knutti et al., 2008; Kriegler et al., 2012; van Vuuren et al., 
2014). Even if identical socio-economic futures are assumed, the

associated future emissions still face uncertainties, since different 
experts and model frameworks diverge in their estimates of future 
emissions ranges (Ho et al., 2019).
When exploring various climate futures, scenarios with no, or no 
additional, climate policies are often referred to as ‘baseline’ or 
‘reference scenarios’ (Section 1.6.1.1 and Glossary). Among the five 
core scenarios used most in this report, SSP3-7.0 and SSP5-8.5 are 
explicit ‘no-climate-policy’ scenarios (Cross-Chapter Box 1.4, Table 1; 
Gidden et al., 2019), assuming a carbon price of zero. These future 
‘baseline’ scenarios are hence counterfactuals that include fewer 
climate policies compared to ‘business-as-usual’ scenarios  – given 
that ‘business-as-usual’ scenarios could be understood to imply 
a continuation of existing climate policies. Generally, future scenarios 
are meant to cover a broad range of plausible futures, due, for example

to unforeseen discontinuities in development pathways  (Raskin 
and Swart, 2020), or to large uncertainties in underlying long-
term projections of economic drivers (Christensen et al., 2018). 
However, the likelihood of high-emissions scenarios such as RCP8.5 
or SSP5-8.5 is considered low in light of recent developments in

239
Framing, Context, and Methods Chapter 11the  energy sector (Hausfather and Peters, 2020a, b). Studies that 
consider possible future emissions trends in the absence of additional 
climate policies, such as the recent IEA 2020 World Energy Outlook 
‘stated policy’ scenario (IEA, 2020), project approximately constant 
fossil fuel and industrial CO 2 emissions out to 2070, approximately 
in line with the intermediate RCP4.5, RCP6.0 and SSP2-4.5 scenarios 
(Hausfather and Peters, 2020b) and the 2030 global emissions levels 
that are pledged as part of the Nationally Determined Contributions 
(NDCs) under the Paris Agreement (Section  1.2.2; Fawcett et al., 
2015; Rogelj et al., 2016; UNFCCC, 2016; IPCC, 2018). On the other 
hand, the default concentrations aligned with RCP8.5 or SSP5-8.5 
and resulting climate futures derived by ESMs could be reached by 
lower emissions trajectories than RCP8.5 or SSP5-8.5. That is because

the uncertainty range on carbon cycle feedbacks includes stronger 
feedbacks than assumed in the default derivation of RCP8.5 and 
SSP5-8.5 concentrations (Section 5.4; Ciais et al., 2013; Friedlingstein 
et al., 2014; Booth et al., 2017).
To address long-term scenario uncertainties, scenario storylines 
(or  ‘narratives’) are often used (see Section  1.4.4 for a  more 
general discussion on ‘storylines’, also covering ‘physical climate 
storylines’; Rounsevell and Metzger, 2010; O’Neill et al., 2014). 
Scenario storylines are descriptions of a  future world, and the 
related large-scale socio-economic development pathways towards 
that world that are deemed plausible within the current state of 
knowledge and historical experience (Section 1.2.3; WGIII). Scenario 
storylines attempt to ‘stimulate, provoke, and communicate visions of 
what the future could hold for us’ (Rounsevell and Metzger, 2010) in 
settings where either limited knowledge or inherent unpredictability

in social systems prevent a  forecast or numerical prediction. 
Scenario storylines have been used in previous climate research, 
and they are the explicit or implicit starting point of any scenario 
exercise, including for the SRES scenarios (IPCC, 2000) and the SSPs 
(e.g., O’Neill et al., 2017a).
Recent technological or socio-economic trends might be informative 
for bounding near-term future trends, for example, if technological 
progress renders a  mitigation technology cheaper than previously 
assumed. However, short-term emissions trends alone do not 
generally rule out an opposite trend in the future (van Vuuren et al., 
2010). The ranking of individual RCP emissions scenarios from the 
IAMs with regard to emissions levels is different for different time 
horizons, for example, 2020 compared with longer-term emissions 
levels. For example, the strongest climate change mitigation scenario, 
RCP2.6, was in fact the second highest CO 2 emissions scenario (jointly

with RCP4.5) before 2020 in the set of RCPs and the strong global 
emissions decline in RCP2.6 only followed after 2020. Implicitly, 
this scenario feature was cautioning against the assumption that 
short-term trends predicate particular long-term trajectories. This is 
also the case in relation to the COVID-19 related drop in 2020 
emissions. Potential changes in underlying drivers of emissions, such 
as those potentially incentivized by COVID-19 recovery stimulus 
packages, are more significant for longer-term emissions than the 
short-term deviation from recent emissions trends (Cross-Chapter 
Box 6.1 on COVID-19).1.6.2 Global Warming Levels
The global mean surface temperature change, or ‘global warming 
level’ (GWL), is a ‘dimension of integration’ that is highly relevant 
across scientific disciplines and socio-economic actors. First, global 
warming levels relative to pre-industrial conditions are the quantity 
in which the 1.5°C and ‘well below 2°C’ Paris Agreement goals were

formulated. Second, global mean temperature change has been 
found to be almost-linearly related to a number of regional climate 
effects (Mitchell et al., 2000; Mitchell, 2003; Tebaldi and Arblaster, 
2014; Seneviratne et al., 2016; Li et al., 2020; Seneviratne and Hauser, 
2020). Even where non-linearities are found, some regional climate 
effects can be considered to be almost scenario-independent for 
a given level of warming (Sections 4.2.4, 4.6.1, 8.5.3 and 10.4.3.1, 
and Cross-Chapter Box  11.1). Finally, the evolution of aggregated 
impacts with warming levels has been widely used and embedded 
in the assessment of the ‘Reasons for Concern’ (RFC) in IPCC WGII 
(Smith et al., 2009; IPCC, 2014a). The RFC framework was further 
expanded in SR1.5 (2018), SROCC (2019) and SRCCL (2019) by 
explicitly describing the differential impacts of half-degree warming 
steps (Section 1.4.4 and Cross-Chapter Box 12.1; cf. King et al., 2017).

In this Report, the term ‘global warming level’ refers to the 
categorization of global and regional climate change, associated 
impacts, emissions and concentrations scenarios by GMST relative 
to 1850–1900, which is the period used as a proxy for pre-industrial 
levels (Cross-Chapter Box 11.1). By default, GWLs are expressed in 
terms of global surface air temperature (GSAT; Section  1.4.1 and 
Cross-Chapter Box 2.3).
As SR1.5 concluded, even half-degree global mean temperature 
steps carry robust differences in climate impacts (Chapter 11; SR1.5, 
IPCC, 2018; Schleussner et al., 2016a; Wartenburger et al., 2017). This 
Report adopts half-degree warming levels, which allows integration 
for climate projections, impacts, adaptation challenges and mitigation 
challenges within and across the three WGs. The core set of GWLs – 
1.5°C, 2.0°C, 3.0°C and 4.0°C – are highlighted (Chapters 4, 8, 11, 
12 and Atlas). Given that much impact analysis is based on previous

scenarios, (i.e., RCPs or SRES), and climate change mitigation analysis 
is based on new emissions scenarios in addition to the main SSP 
scenarios, these GWLs assist in the comparison of climate states 
across scenarios and in the synthesis across the broader literature.
The transient and equilibrium states of certain global warming levels 
can differ in their climate impacts (IPCC, 2018; King et al., 2020). 
Climate impacts in a ‘transient’ world relate to a scenario in which 
the world is continuing to warm. On the other hand, climate impacts 
at the same warming levels can also be estimated from equilibrium 
states after a  (relatively) short-term stabilization by the end of 
the 21st  century or at a  (near-)equilibrium state after a  long-term 
(multi-decadal to multi-millennial) stabilization. Different methods to 
estimate these climate states come with challenges and limitations 
(Section  4.6.1 and Cross-Chapter Box  11.1).  First,  information

can be drawn from GCM or ESM simulations that ‘pass through’ 
the respective warming levels (as used and demonstrated in the 
Interactive Atlas), also called ‘epoch’ or ‘time-shift’ approaches 
(Sections  4.2.4 and 4.6.1; Herger et al., 2015; James et al., 2017;

240
Chapter 1 Framing, Context, and Methods1Tebaldi and Knutti, 2018). Information from transient simulations can 
also be used through an empirical scaling relationship (Seneviratne 
et al., 2016, 2018; Wartenburger et al., 2017) or using ‘time sampling’ 
approaches, as described in James et al. (2017). Second, information 
can be drawn from large ESM ensembles with prescribed SST at 
particular global warming levels (Mitchell et al., 2017), although an 
underrepresentation of variability can arise when using prescribed 
SST temperatures (E.M. Fischer et al., 2018).
In order to fully derive climate impacts, warming levels will need to be 
complemented by additional information, such as their associated CO 2 
concentrations (e.g., fertilization or ocean acidification), composition 
of the total radiative forcing (aerosols compared with GHGs, with 
varying regional distributions) or socio-economic conditions (e.g., to 
estimate societal impacts). More fundamentally, while a  global

warming level is a good proxy for the state of the climate (Cross-
Chapter Box 11.1), it does not uniquely define a change in global or 
regional climate state. For example, regional precipitation responses 
depend on the details of the individual forcing mechanisms that 
caused the change (Samset et al., 2016); on whether the temperature 
level is stabilized or transient (King et al., 2020; Zappa et al., 2020); 
on the vertical structure of the troposphere (Andrews et al., 2010); 
and, in particular, on the global distribution of atmospheric aerosols 
(Frieler et al., 2012). Another aspect is how Earth system components 
with  century-to-millennial response time scales, such as long-term 
sea level rise or permafrost thaw, are affected by global mean 
warming. For example, sea level rise 50 years after a 1°C warming 
will be lower than sea level rise 150  years after that same 1°C 
warming (Chapter 9).
Also, forcing or response patterns that vary in time can create

differences in regional climates for the same global mean warming 
level, or can create non-linearities when scaling patterns from one 
warming level to another (King et al., 2018), depending on whether 
near-term transient climate, end of the century, equilibrium climate 
or climate states after an initial overshoot are considered.
In spite of these challenges, and thanks to recent methodological 
advances in quantifying or overcoming them, global warming 
levels provide a  robust and useful integration mechanism. They 
allow knowledge from various domains within WGI and across 
the three WGs to be integrated and communicated (Cross-Chapter 
Box 11.1). In this report, Chapters 4, 8, 11, 12 and the Atlas provide 
information specific to certain warming levels, highlighting the 
regional differences, but also the approximate scalability of regional 
climate change, that can arise from even a 0.5°C shift in global mean 
temperatures. Furthermore, building on WGI insights into physical

climate system responses (Cross-Chapter Box  7.1), WGIII will use 
peak and end-of-century global warming levels to classify a broad 
set of scenarios.1.6.3 Cumulative Carbon Dioxide Emissions
The AR5 WGI (IPCC, 2013a) and SR1.5 (IPCC, 2018) highlighted 
the near-linear relationship between cumulative carbon emissions 
and global mean warming (Sections 1.3 and 5.5). This implies that 
continued CO 2 emissions will cause further warming and changes 
in all components of the climate system, independent of any 
specific scenario or pathway. This is captured in the TCRE concept, 
which relates CO 2-induced global mean warming to cumulative 
carbon emissions (Chapter 5). This Report thus uses cumulative CO 2 
emissions to compare the climate response across scenarios, and 
to categorize emissions scenarios (Figure  1.29). The advantage of 
using cumulative CO 2 emissions is that it is an inherent emissions 
scenario characteristic rather than an outcome of the scenario-

based projections, where uncertainties in the cause–effect chain – 
from emissions to atmospheric concentrations to temperature 
change – are important.
There is also a  close relationship between cumulative total GHG 
emissions and cumulative CO 2 emissions for scenarios in the 
SR1.5 scenario database (Figure 1.29; IPCC, 2018). The dominance 
of CO 2 compared to other well-mixed GHGs (Figure  1.29 and 
Section 5.2.4) allows policymakers to make use of the carbon budget 
concept (Section  5.5) in a  policy context, in which GWP-weighted 
combinations of multiple GHGs are used to define emissions targets. 
A caveat is that cumulative GWP-weighted CO 2 equivalent emissions 
over the next decades do not yield exactly the same temperature 
outcomes as the same amount of cumulative CO 2 emissions, because 
atmospheric perturbation lifetimes of the various GHGs differ. While 
carbon budgets are not derived using GWP-weighted emissions

baskets but rather by explicit modelling of non-CO 2-induced warming 
(Section  5.5 and Cross-Chapter Box  7.1), the policy frameworks 
based on GWP-weighted emissions baskets can still make use of the 
insights from remaining cumulative carbon emissions for different 
warming levels.
The same cumulative CO 2 emissions could lead to a slightly different 
level of warming over time (Box  1.4). Rapid emissions followed 
by steep cuts and potentially net negative emissions would be 
characterized by a  higher maximum warming and faster warming 
rate, compared with the same cumulative CO 2 emissions spread 
over a longer period. As further explored in the WGIII assessment, 
one potential limitation when presenting emissions pathway 
characteristics in cumulative emissions budget categories is that path 
dependencies and lock-in effects (e.g.  today’s decisions regarding 
fossil fuel-related infrastructure) play an important role in long-term

mitigation strategies (Davis et al., 2010; Luderer et al., 2018). Similarly, 
high emissions early on might imply strongly net negative emissions 
(Minx et al., 2018) later on to reach the same target envelope for 
cumulative emissions and temperature by the end of the century 
(Box  1.4). This report explores options to address some of those 
potential issues from a WGI perspective (Sections 5.5.2 and 5.6.2).

241
Atmospheric CO2 concentrations (ppm)
2100 2200 2300278.300400500600700800900100011001200130014001500160017001800190020002100220023002400
2100 2200 2300
1800 1900 2000 2100 2200 2300012345678910111213Effective Radiative Forcing (W/ m-2)0 500 1000 1500 2000 2500 3000 35000500100015002000250030003500400045005000
 
Cumulative CO2 emissions (GtCO2) from 2013 to 2050 Cumulative GHG emissions (GtCO2-eq)
from 2013 to 2050 (weighted with GWP-100 AR4) Relationship between cumulative CO2 and 
cumulative GHG emissions in scenarios
CO₂              CH4, N2O 
     and other 
WMGHGs
SR1.5 database scenarios (n=411)
CO₂CO₂CO₂CO₂CH₄
Other
N₂O
CO₂
Figure 1.29 | The role of CO2 in driving future climate change in comparison to other greenhouse gases (GHGs).  The GHGs included here are CH 4, N 2O, and

40 other long-lived, well-mixed GHGs. The blue shaded area indicates the approximate forcing exerted by CO 2 in Shared Socio-economic Pathways (SSP) scenarios, ranging from 
very low SSP1-1.9 to very high SSP5-8.5 (Chapter 7). The CO 2 concentrations under the SSP1-1.9 scenarios reach approximately 350 ppm after 2150, while those of SSP5-8.5 
exceed 2000 ppm CO 2 in the longer term (up to year 2300). Similar to the dominant radiative forcing share at each point in time (lower area plots), cumulative GWP-100-
weighted GHG emissions happen to be closely correlated with cumulative CO 2 emissions, allowing policymakers to make use of the carbon budget concept in a policy context 
with multi-gas GHG baskets as it exhibits relatively low variation across scenarios with similar cumulative emissions until 2050 (inset panel) . Further details on data sources 
and processing are available in the chapter data table (Table 1.SM.1).

242
Chapter 1 Framing, Context, and Methods1Box 1.4 | The Relationships Between 'Net Zero' Emissions, Temperature Outcomes  
and Carbon Dioxide Removal
Article 4 of the Paris Agreement sets an objective to ‘achieve a balance between anthropogenic emissions by sources and removals by 
sinks of greenhouse gases’ (Section 1.2). This box addresses the relationship between such a balance and the corresponding evolution 
of global surface temperature, with or without the deployment of large-scale carbon dioxide removal (CDR), using the definitions of 
‘net zero CO 2 emissions’ and ‘net zero greenhouse gas (GHG) emissions’ of the AR6 Glossary (Annex VII).
‘Net zero CO 2 emissions’ is defined in AR6 as the condition in which anthropogenic CO 2 emissions are balanced by anthropogenic CO 2 
removals over a specified period. Similarly, ‘net zero GHG emissions’ is the condition in which metric-weighted anthropogenic GHG

emissions are balanced by metric-weighted anthropogenic GHG removals over a  specified period. The quantification of net zero 
GHG emissions thus depends on the GHG emissions metric chosen to compare emissions of different gases, as well as the time horizon 
chosen for that metric. (For a broader discussion of metrics, see Box 1.3 and Section 7.6, and WGIII Cross-Chapter Box 2.)
Technical notes expanding on these definitions can be found as part of their respective entries in the Glossary. The notes clarify the 
relation between ‘net zero’ CO 2 and GHG emissions and the concept of carbon and GHG neutrality, and the metric usage set out in 
the Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37].
A global net zero level of CO 2, or GHG, emissions will be achieved when the sum of anthropogenic emissions and removals across all 
countries, sectors, sources and sinks reaches zero. Achieving net zero CO 2 or GHG emissions globally, at a given time, does not imply

that individual entities (i.e., countries, sectors) have to reach net zero emissions at that same point in time, or even at all (see WGIII, 
TS Box 4 and Chapter 3).
Net zero CO 2 and net zero GHG emissions differ in their implications for the subsequent evolution of global surface temperature. Net 
zero CO 2 emissions result in approximately stable CO 2-induced warming, but overall warming will depend on any further warming 
contribution of non-CO 2 GHGs. The effect of net zero GHG emissions on global surface temperature depends on the GHG emissions 
metric chosen to aggregate emissions and removals of different gases. For GWP100 (the metric in which Parties to the Paris Agreement 
have decided to report their aggregated emissions and removals), net zero GHG emissions would generally imply a peak in global 
surface temperature, followed by a gradual decline (Section 7.6.2; see also Section 4.7.1 regarding the zero emissions commitment).

However, other anthropogenic factors, such as aerosol emissions or land use-induced changes in albedo, may still affect the climate.
The definitions of net zero CO 2 and GHG should also be seen in relation to the various CDR methods discussed in the context of 
climate change mitigation (see Section 5.6, which also includes an assessment of the response of natural sinks to CDR), and how it is 
employed in scenarios used throughout the WGI and WGIII reports (Section 1.6.1; see also WGIII Chapters 3, 7 and 12.)
For virtually all scenarios assessed by the IPCC, CDR is necessary to reach both global net zero CO 2 and net zero GHG emissions, to 
compensate for residual anthropogenic emissions. This is in part because for some sources of CO 2 and non-CO 2 emissions, abatement 
options to eliminate them have not yet been identified. For a given scenario, the choice of GHG metric determines how much net CDR

is necessary to compensate for residual non-CO 2 emissions, in order to reach net zero GHG emissions (Section 7.6.2).
If CDR is further used to go beyond net zero, to a situation with net-negative CO 2 emissions (i.e., where anthropogenic removals 
exceed anthropogenic emissions), anthropogenic CO 2-induced warming will decline. A further increase of CDR, until a situation with 
net zero or even net-negative GHG emissions is reached, would increase the pace at which historical human-induced warming is 
reversed after its peak (SR1.5, IPCC, 2018). Net negative anthropogenic GHG emissions may become necessary to stabilize the global 
surface temperature in the long term, should climate feedbacks further affect natural GHG sinks and sources (Chapter 5).
CDR can be achieved through a  number of measures (Section  5.6; SRCCL , IPCC, 2019a) . These include additional afforestation,

reforestation, soil carbon management, biochar, direct air capture and carbon capture and storage (DACCS), and bioenergy with 
carbon capture and storage (BECCS; de Coninck et al., 2018, SR1.5 Ch4; Minx et al., 2018; see also WGIII Chapters  7  and 12). 
Differences between land use, land-use change and forestry (LULUCF) accounting rules, and scientific bookkeeping approaches for CO 2 
emissions and removals from the terrestrial biosphere, can result in significant differences between the amount of CDR that is reported 
in different studies (Grassi et al., 2017). Different measures to achieve CDR come with different risks, negative side effects and 
potential co-benefits – also in conjunction with sustainable development goals – that can inform choices around their implementation 
(Section 5.6; Fuss et al., 2018; Roe et al., 2019). Technologies to achieve direct large-scale anthropogenic removals of non-CO 2 GHGs

are speculative at present (Yoon et al., 2009; Ming et al., 2016; Kroeger et al., 2017; Jackson et al., 2019) .

243
Framing, Context, and Methods Chapter 111.7 Final Remarks
The assessment in this Report is based on a rapidly growing body 
of new evidence from the peer-reviewed literature. Recently, 
scientific climate change research has doubled in output every 
5–6 years; the majority of publications deal with issues related to 
the physical climate system (Burkett et al., 2014; Haunschild et al., 
2016). The sheer volume of published, peer-reviewed literature on 
climate change presents a challenge to comprehensive, robust and 
transparent assessment.
The enhanced focus on regional climate in AR6 WGI further 
expands the volume of literature relative to AR5, including non-
English language publications sometimes presented as reports 
(‘grey’ literature), particularly on topics such as regional observing 
networks and climate services. These factors enhance the challenge 
of discovering, accessing and assessing the relevant literature.

The international, multilingual author teams of IPCC AR6, combined 
with the open expert-review process, help to minimize these concerns, 
but they remain a challenge.
Despite the key role of CMIP6 in this Report (Section 1.5), the number 
of studies evaluating its results and modelling systems remains 
relatively limited. At the time of publication, additional model 
results are still becoming available. This reflects the need for close 
temporal alignment of the CMIP cycle with the IPCC assessment 
process, and the growing complexity of coordinated international 
modelling efforts.
Indigenous and local knowledge includes information about past 
and present climate states. However, assessing this knowledge, and 
integrating it with the scientific literature, remains a challenge to be 
met. This lack of assessment capability and integration leads to most 
WGI chapters still not including indigenous and local knowledge in 
their assessment findings.

Spatial and temporal gaps in both historical and current observing 
networks, and the limited extent of paleoclimatic archives, have 
always posed a challenge for IPCC assessments. A relative paucity of 
long-term observations is particularly evident in Antarctica and in the 
depths of the ocean. Knowledge of previous cryospheric and oceanic 
processes is therefore incomplete. Sparse instrumental temperature 
observations prior to the industrial revolution make it difficult to 
uniquely characterize a ‘pre-industrial’ baseline, although this Report 
extends the assessment of anthropogenic temperature change 
further back in time than previous assessment cycles (Chapter 7 and 
Cross-Chapter Box 1.2).
Common, integrating scenarios can never encompass all possible 
events that might induce radiative forcing in the future (Section 1.4). 
These may include large volcanic eruptions (Cross-Chapter Box 4.1), 
the consequences of a  major meteorite, smoke plumes following

a conflict involving nuclear weapons, extensive geoengineering, or 
a major pandemic (Cross-Chapter Box 1.6). Scenario-related research 
also often focuses on the 21st century. Post-2100 climate changes 
are not covered as comprehensively, and their assessment is limited. 
Those long-term climate changes, potentially induced by forcing over the 21st century (as in the case of sea level rise), are nevertheless 
relevant for decision-making.
At the time of publication, the consequences of the COVID-19 
pandemic on emissions, atmospheric abundances, radiative forcing 
and the climate (Cross-Chapter Box  6.1), and on observations 
(Section  1.5.1), are not yet fully evident. Their assessment in this 
Report is thus limited.
Acknowledgements
We thank Alejandro Cearreta (UPV/EHU, Spain) for his invaluable 
contribution to the Glossary.

244
Chapter 1 Framing, Context, and Methods1Frequently Asked Questions
FAQ 1.1 |  Do We Understand Climate Change Better Now Compared to When the IPCC Started?
Yes, much better. The first IPCC report, released in 1990, concluded that human-caused climate change would 
soon become evident, but could not yet confirm that it was already happening. Today, evidence is overwhelming 
that the climate has indeed changed since the pre-industrial era and that human activities are the principal cause 
of that change. With much more data and better models, we also understand more about how the atmosphere 
interacts with the ocean, ice, snow, ecosystems and land surfaces of the Earth. Computer climate simulations 
have also improved dramatically, incorporating many more natural processes and providing projections at much 
higher resolutions.
Since the first IPCC report in 1990, large numbers of new instruments have been deployed to collect data in the

air, on land, at sea and from outer space. These instruments measure temperature, clouds, winds, ice, snow, 
ocean currents, sea level, soot and dust in the air, and many other aspects of the climate system. New satellite 
instruments have also provided a wealth of increasingly fine-grained data. Additional data from older observing 
systems and even hand-written historical records are still being incorporated into observational datasets, and 
these datasets are now better integrated and adjusted for historical changes in instruments and measurement 
techniques. Ice cores, sediments, fossils, and other new evidence from the distant past have taught us much 
about how Earth’s climate has changed throughout its history.
Understanding of climate system processes has also improved. For example, in 1990 very little was known about 
how the deep ocean responds to climate change. Today, reconstructions of deep-ocean temperatures extend as

far back as 1871. We now know that the oceans absorb most of the excess energy trapped  by greenhouse gases 
and that even the deep ocean is warming up. As another example, in 1990, relatively little was known about 
exactly how or when the gigantic ice sheets of Greenland and Antarctica would respond to warming. Today, 
much more data and better models of ice-sheet behaviour reveal unexpectedly high melt rates that will lead to 
major changes within this century, including substantial sea level rise (FAQ 9.2).
The major natural factors contributing to climate change on time scales of decades to centuries are volcanic 
eruptions and variations in the sun’s energy output. Today, data show that changes in incoming solar energy 
since 1900 have contributed only slightly to global warming, and they exhibit a slight downward trend since the 
1970s. Data also show that major volcanic eruptions have sometimes cooled the entire planet for relatively short

periods of time (typically several years) by erupting aerosols (tiny airborne particles) high into the atmosphere.
The main human causes of climate change are the heat- absorbing greenhouse gases released by fossil fuel 
combustion, deforestation, and agriculture, which warm the planet; and aerosols such as sulphate from burning 
coal, which have a short-term cooling effect that partially counteracts human-caused warming. Since 1990, we 
have more and better observations of these human factors as well as improved historical records, resulting in 
more precise estimates of human influence on the climate system (FAQ 3.1).
While most climate models in 1990 focused on the atmosphere, using highly simplified representations of oceans 
and land surfaces, today’s Earth system simulations include detailed models of oceans, ice, snow, vegetation and 
many other variables. An important test of models is their ability to simulate Earth’s climate over the period

of instrumental records (since about 1850). Several rounds of such testing have taken place since 1990, and 
the testing itself has become much more rigorous and extensive. As a group and at large scales, models have 
predicted the observed changes well in these tests (FAQ 3.3). Since there is no way to do a controlled laboratory 
experiment on the actual Earth, climate model simulations can also provide a kind of ‘alternate Earth’ to test 
what would have happened without human influence. Such experiments show that the observed warming 
would not have occurred without human influence.
Finally, physical theory predicts that human influence on the climate system should produce specific patterns 
of change, and we see those patterns in both observations and climate simulations. For example, nights are 
warming faster than days, less heat is escaping to space, and the lower atmosphere (troposphere) is warming but

the upper atmosphere (stratosphere) has cooled. These confirmed predictions are all evidence of changes driven 
primarily by increases in GHG concentrations rather than natural causes.

245
Framing, Context, and Methods Chapter 11FAQ 1.1 (continued)
?
Yes. Between 1990 and 2021, observations, models and climate und erstanding improved, while the dominant 
role of human influence in global warming was confirmed.
Climate models Global Global Regional1990
IPCC
First 
Assessment2021
IPCC
Sixth
Assessment
Human influence on climate
Global warming since late 1800s
Land surface temperature
Geological records
Global ocean heat content0.3–0.6°CEnergy budget Open
(inconsistent estimates)
5 million years (temperature)
5 million years (sea level)
160,000 years (CO2)
1955–1981 (two regions)Established fact
0.95–1.20°C
Up to 40,000 stations (1750–2020)Closed
(inputs = outputs + retained energy)
Sea level budget Open
(inconsistent estimates)Closed
(sum of contributions = observed sea level rise)
65 million years (temperature)
50 million years (sea level)
450 milion years (CO2)
1871–2018 (global)
Satellite remote sensing Temperature, snow cover ,

Earth radiation budgetTemperature, cryosphere, Earth radiation budget, CO 2, 
sea level, clouds, aerosols, land cove r, many others
State of the art
Typical model resolution
Major elements500 km
Circulating atmosphere and ocean 100 km
Circulating atmosphere and ocean 25–50 km
Radiative transfer
Land physics
Sea iceRadiative transfer
Sea ice
Atmospheric chemistry
Land use/cover
Land and ocean biogeochemistry
Aerosol and cloud interationsGeneral circulation models Earth system 
modelsHigh-resolution 
models1887 stations (1861–1990)SuspectedFAQ 1.1: Do we understand climate change better than when the IPCC started?
FAQ 1.1, Figure 1 | Sample elements of climate understanding, observations and models as assessed in the IPCC First Assessment Report 
(1990) and Sixth Assessment Report (2021).  Many other advances since 1990, such as key aspects of theoretical understanding, geological records and

attribution of change to human influence, are not included in this figure because they are not readily represented in this simple format. Fuller explanations of 
the history of climate knowledge are available in the introductory chapters of the IPCC Fourth and Sixth assessment reports.

246
FAQ 1.2 |  Where Is Climate Change Most Apparent?
The signs of climate change are unequivocal at the global scale and are increasingly apparent on smaller spatial 
scales. The high northern latitudes show the largest temperature increase, with clear effects on sea ice and 
glaciers. The warming in the tropical regions is also apparent because the natural year-to-year variations in 
temperature there are small. Long-term changes in other variables such as rainfall and some weather and climate 
extremes have also now become apparent in many regions.
It was first noticed that the planet’s land areas were warming in the 1930s. Although increasing atmospheric 
carbon dioxide (CO 2) concentrations were suggested as part of the explanation, it was not certain at the time 
whether the observed warming was part of a long-term trend or a natural fluctuation: global warming had not

yet become apparent. But the planet continued to warm, and by the 1980s the changes in temperature had 
become obvious or, in other words, the signal  had emerged .
Imagine you had been monitoring temperatures at the same location for the past 150 years. What would you 
have experienced? When would the warming have become noticeable in your data? The answers to these 
questions depend on where on the planet you are.
Observations and climate model simulations both demonstrate that the largest long-term warming trends are in 
the high northern latitudes and the smallest warming trends over land are in tropical regions. However, the year-
to-year variations in temperature are smallest in the tropics, meaning that the changes there are also apparent, 
relative to the range of past experiences (FAQ 1.2, Figure 1).
Changes in temperature also tend to be more apparent over land areas than over the open ocean and are often

most apparent in regions which are more vulnerable to climate change. It is expected that future changes will 
continue to show the largest signals at high northern latitudes, but with the most apparent warming in the 
tropics. The tropics also stand to benefit the most from climate change mitigation in this context, as limiting 
global warming will also limit how far the climate shifts relative to past experience.
Changes in other climate variables have also become apparent at smaller spatial scales. For example, changes 
in average rainfall are becoming clear in some regions, but not in others, mainly because natural year-to-year 
variations in precipitation tend to be large relative to the magnitude of the long-term trends. However, extreme 
rainfall is becoming more intense in many regions, potentially increasing the impacts from inland flooding (FAQ 
8.2). Sea levels are also clearly rising on many coastlines, increasing the impacts of inundation from coastal storm

surges, even without any increase in the number of storms reaching land. A decline in the amount of Arctic sea 
ice is apparent, both in the area covered and in its thickness, with implications for polar ecosystems.
When considering climate-related impacts, it is not necessarily the size of the change that is most important. 
Instead, it can be the rate of change or it can also be the size of the change relative to the natural variations 
of the climate to which ecosystems and society are adapted. As the climate is pushed further away from past 
experiences and enters an unprecedented state, the impacts can become larger, along with the challenge of 
adapting to them.
How and when a long-term trend becomes distinguishable from shorter-term natural variations depends on the 
aspect of climate being considered (e.g., temperature, rainfall, sea ice or sea level), the region being considered,

the rate of change, and the magnitude and timing of natural variations. When assessing the local impacts from 
climate change, both the size of the change and the amplitude of natural variations matter.

247
Framing, Context, and Methods Chapter 11FAQ 1.2 (continued)
FAQ 1.2: Where is climate change most apparent?
Temperature changes are most apparent in regions with smaller natural variations.
Estimation of:
2 standard deviations of natural year-to-year variations
1 standard deviation of natural year-to-year variations
Low latitudes (e.g. Tropical South America) High latitudes (e.g. mid-North America)Temperature change since 1850-1900 (°C)2.0
1.5
1.0
0.5
0
-0.5
-1.02.0
1.5
1.0
0.5
0
-0.5
-1.0
1860 1890 1920 1950 1980 2010 1860 1890 1920 1950 1980 2010Smaller trend that 
deviates earlier 
from past conditions°C
FAQ 1.2, Figure 1  | Observed variations in regional temperatures since 1850  (data from Berkeley Earth).  Regions in high latitudes, such as 
mid-North America (40°N–64°N, 140°W–60°W, left), have warmed by a larger amount than regions at lower latitudes, such as tropical South America

(10°S–10°N, 84°W–16°W, right ), but the natural variations are also much larger at high latitudes (darker and lighter shading represents 1 and 2 standard 
deviations, respectively, of natural year-to-year variations). The signal of observed temperature change emerged earlier in tropical South America than mid-
North America even though the changes were of a smaller magnitude. (Note that those regions were chosen because of the longer length of their observational 
record; see Figure 1.14 for more regions).

248
FAQ 1.3 |  What Can Past Climate Teach Us About the Future?
In the past, the Earth has experienced prolonged periods of elevated greenhouse gas concentrations that caused 
global temperatures and sea levels to rise. Studying these past warm periods informs us about the potential long-
term consequences of increasing greenhouse gases in the atmosphere.
Rising greenhouse gas concentrations are driving profound changes to the Earth system, including global 
warming, sea level rise, increases in climate and weather extremes, ocean acidification, and ecological shifts 
(FAQ 2.2 and FAQ 7.1). The vast majority of instrumental observations of climate began during the 20th century, 
when greenhouse gas emissions from human activities became the dominant driver of changes in Earth’s 
climate (FAQ 3.1).
As scientists seek to refine our understanding of Earth’s climate system and how it may evolve in coming decades

to centuries, past climate states provide a wealth of insights. Data about these past states help to establish the 
relationship between natural climate drivers and the history of changes in global temperature, global sea levels, 
the carbon cycle, ocean circulation, and regional climate patterns, including climate extremes. Guided by such 
data, scientists use Earth system models to identify the chain of events underlying the transitions between past 
climatic states (FAQ 3.3). This is important because during present-day climate change, just as in past climate 
changes, some aspects of the Earth system (e.g., surface temperature) respond to changes in greenhouse gases 
on a time scale of decades to centuries, while others (e.g., sea level and the carbon cycle) respond over centuries 
to millennia (FAQ 5.3). In this way, past climate states serve as critical benchmarks for climate model simulations,

improving our understanding of the sequences, rates, and magnitude of future climate change over the next 
decades to millennia.
Analyzing previous warm periods caused by natural factors can help us understand how key aspects of the 
climate system evolve in response to warming. For example, one previous warm-climate state occurred roughly 
125,000  years ago, during the Last Interglacial period, when slight variations in the Earth’s orbit triggered 
a sequence of changes that caused about 1°C–2°C of global warming and about 2–8 m of sea level rise relative 
to the 1850–1900, even though atmospheric carbon dioxide concentrations were similar to 1850–1900 values 
(FAQ 1.3, Figure 1). Modelling studies highlight that increased summer heating in the higher latitudes of the 
Northern Hemisphere during this time caused widespread melting of snow and ice, reducing the reflectivity of

the planet and increasing the absorption of solar energy by the Earth’s surface. This gave rise to global-scale 
warming, which led in turn to further ice loss and sea level rise. These self-reinforcing positive feedback  cycles  are 
a pervasive feature of Earth’s climate system, with clear implications for future climate change under continued 
greenhouse gas emissions. In the case of sea level rise, these cycles evolved over several centuries to millennia, 
reminding us that the rates and magnitude of sea level rise in the 21st century are just a fraction of the sea level 
rise that will ultimately occur after the Earth system fully adjusts to current levels of global warming.
Roughly 3 million years ago, during the Pliocene Epoch, the Earth witnessed a prolonged period of elevated 
temperatures (2.5°C–4°C higher than 1850–1900) and higher sea levels (5–25 m  higher than 1850–1900), in

combination with atmospheric carbon dioxide concentrations similar to those of the present day. The fact that 
Pliocene atmospheric carbon dioxide concentrations were similar to the present, while global temperatures and 
sea levels were significantly higher, reflects the difference between an Earth system that has fully adjusted to 
changes in natural drivers (the Pliocene) and one where greenhouse gases concentrations, temperature, and sea 
level rise are still increasing (present day). Much about the transition into the Pliocene climate state – in terms of 
key causes, the role of cycles that hastened or slowed the transition, and the rate of change in climate indicators 
such as sea level – remain topics of intense study by climate researchers, using a combination of paleoclimate 
observations and Earth system models. Insights from such studies may help to reduce the large uncertainties

around estimates of global sea level rise by 2300, which range from 0.3 m to 3 m above 1850–1900 (in a low-
emissions scenario) to as much as 16 m higher than 1850–1900 (in a very high-emissions scenario that includes 
accelerating structural disintegration of the polar ice sheets).
While present-day warming is unusual in the context of the recent geologic past in several different ways 
(FAQ 2.1), past warm climate states present a  stark reminder that the long-term adjustment to present-day 
atmospheric carbon dioxide concentrations has only just begun. That adjustment will continue over the coming 
centuries to millennia.

249
Framing, Context, and Methods Chapter 11FAQ 1.3 (continued) 
266-282 ppm*
1135ppm
445ppmFAQ 1.3: What can the past tell us about the future?
Past warm periods inform about the potential consequences of rising greenhouse gases in the atmosphere.
After adjustment Ongoing Distant 
pastPresent
*Triggered by changes in the Earth’s orbit, which redistributed incoming solar energy between seasons and latitudesFuture
emission scenarios
LowHigh
Atmospheric CO2
Fast response
Decades to centuries
Global temperature 
relative to 1850–1900
Slow response
Centuries to millennia
Global sea level 
relative to 1850–1900+0.5-1.5°C*360-420 ppm
291 ppm397 ppm
+5-25m
+5-10m*
0m+0.2m+0.8-1.2mPliocene
3 Million years agoLast interglacial*
125,000 years ago1850-1900 2011-2020 2100
+2.5-4.0°C
+1.1°C
0°C+3.3-5.7°C
+1.3-2.4°C
+0.5-0.8m
FAQ 1.3, Figure 1 | Comparison of past, present and future.  Schematic of atmospheric carbon dioxide concentrations, global temperature, and global

sea level during previous warm periods as compared to 1850–1900, present-day (2011–2020), and future (2100) climate change scenarios corresponding to 
low-emissions scenarios (SSP1-2.6; lighter colour bars) and very high-emissions scenarios (SSP5-8.5; darker colour bars).

290
Chapter 2  Changing State of the Climate System2
Executive Summary
Chapter 2 assesses observed large-scale changes in climate system 
drivers, key climate indicators and principal modes of variability. 
Chapter 3 considers model performance and detection/attribution, 
and Chapter 4 covers projections for a subset of these same indicators 
and modes of variability. Collectively, these chapters provide the basis 
for later chapters, which focus upon processes and regional changes. 
Within Chapter 2, changes are assessed from in situ and remotely 
sensed data and products and from indirect evidence of longer-term 
changes based upon a diverse range of climate proxies. The time-
evolving availability of observations and proxy information dictate 
the periods that can be assessed. Wherever possible, recent changes 
are assessed for their significance in a longer-term context, including 
target proxy periods, both in terms of mean state and rates of change.

Changes in Climate System Drivers
Climate system drivers lead to climate change by altering the Earth’s 
energy balance. The influence of a climate driver is described in terms 
of its effective radiative forcing (ERF), measured in W m–2. Positive 
ERF values exert a warming influence and negative ERF values exert 
a cooling influence (Chapter 7).
Present-day global concentrations of atmospheric carbon 
dioxide (CO 2) are at higher levels than at any time in at least 
the past two million years ( high confidence ). Changes in ERF 
since the late 19th century are dominated by increases in 
concentrations of greenhouse gases and trends in aerosols; 
the net ERF is positive and changing at an increasing rate since 
the 1970s ( medium confidence ). {2.2, 7.2, 7.3}
Change in ERF from natural factors since 1750 is negligible in 
comparison to anthropogenic drivers ( very high confidence ). 
Solar activity since 1900 was high but not exceptional compared

to the past 9000 years ( high confidence ). The average magnitude 
and variability of volcanic aerosol forcing since 1900 have not been 
unusual compared to the past 2500 years ( medium confidence ). 
{2.2.1, 2.2.2}
In 2019, concentrations of CO 2, methane (CH 4) and nitrous 
oxide (N 2O) reached levels of 409.9 (±0.4) parts per million 
(ppm), 1866.3 (±3.3) parts per billion (ppb) and 332.1 (±0.4) 
ppb, respectively. Since 1850, these well-mixed greenhouse 
gases (GHGs) have increased at rates that have no precedent 
on centennial time scales in at least the past 800,000 years.  
Concentrations of CO 2, CH 4, and N 2O increased from 1750 to 2019 by 
131.6 ± 2.9 ppm (47.3%), 1137 ± 10 ppb (156%), and 62 ± 6 ppb 
(23.0%) respectively. These changes are larger than those between 
glacial and interglacial periods over the last 800,000 years for CO 2 
and CH 4 and of comparable magnitude for N 2O (very high confidence ).

The best estimate of the total ERF from CO 2, CH 4 and N 2O in 2019 
relative to 1750 is 2.9 W m–2, an increase of 12.5% from 2011. ERF 
from halogenated components in 2019 was 0.4 W m–2, an increase of 
3.5% since 2011. {2.2.3, 2.2.4, 7.3.2}Tropospheric aerosol concentrations across the Northern 
Hemisphere mid-latitudes increased from 1700 to the last 
quarter of the 20th century, but have subsequently declined 
(high confidence ). Aerosol optical depth (AOD) has decreased 
since 2000 over Northern Hemisphere mid-latitudes and Southern 
Hemisphere mid-latitude continents, but increased over South 
Asia and East Africa ( high confidence ). These trends are even more 
pronounced in AOD from sub-micrometre aerosols for which the 
anthropogenic contribution is particularly large. The best-estimate of 
aerosol ERF in 2019 relative to 1750 is –1.1 W m–2. {2.2.6, 7.3.3}
Changes in other short-lived gases are associated with an

overall positive ERF ( medium confidence ). Stratospheric ozone 
has declined between 60°S and 60°N by 2.2% from the 1980s to 
2014–2017 ( high confidence ). Since the mid-20th century, tropospheric 
ozone has increased by 30–70% across the Northern Hemisphere 
(medium confidence ). Since the mid-1990s, free tropospheric ozone 
increases were 2–7% per decade in the northern mid-latitudes ( high 
confidence ), 2–12% per decade in the tropics ( high confidence ) and 
<5% per decade in southern mid-latitudes ( medium confidence ). The 
best estimate of ozone column ERF (0.5 W m–2 relative to 1750) is 
dominated by changes in tropospheric ozone. Due to discrepancies 
in satellite and in situ records, there is low confidence  in estimates of 
stratospheric water vapour change. {2.2.5, 7.3.2}
Biophysical effects from historical changes in land use have 
an overall negative ERF ( medium confidence ). The best-estimate 
ERF from the increase in global albedo is –0.15 W m–2 since 1700 and

–0.12 W m–2 since 1850 ( medium confidence ). {2.2.7, 7.3.4}
Changes in Key Indicators of Global Climate Change
Observed changes in the atmosphere, oceans, cryosphere and 
biosphere provide unequivocal evidence of a world that has 
warmed. Over the past several decades, key indicators of the 
climate system are increasingly at levels unseen in centuries 
to millennia, and are changing at rates unprecedented in at 
least the last 2000 years ( high confidence ). Temperatures as high 
as during the most recent decade (2011–2020) exceed the warmest 
centennial-scale range reconstructed for the present interglacial, 
around 6,500 years ago [0.2°C–1°C relative to 1850–1900] ( medium 
confidence ). The next older warm period is the last interglacial 
when the multi-centennial temperature range about 125,000 years 
ago [0.5°C–1.5°C relative to 1850–1900] encompassed the recent 
decade values ( medium confidence ). {2.3}
GMST increased by 0.85 [0.69 to 0.95] °C between 1850–1900

and 1995–2014 and by 1.09 [0.95 to 1.20] °C between 
1850–1900 and 2011–2020. From 1850–1900 to 2011–2020, 
the temperature increase over land (1.59 [1.34 to 1.83] °C) 
has been faster than over the oceans (0.88 [0.68 to 1.01] °C).  
GMST in the first two decades of the 21st century (2001–2020) was 
0.99 [0.84–1.10] °C higher than 1850–1900. Each of the last four 
decades has successively been warmer than all preceding decades 
since 1850. Over the last 50 years, observed GMST has increased at 
a rate unprecedented in at least the last 2000 years ( high confidence ).

291
Changing State of the Climate System Chapter 22
The increase in GMST since the mid-19th century was preceded by 
a slow decrease that began in the mid-Holocene (around 6500 years 
ago) ( medium confidence ). {2.3.1.1, Cross-Chapter Box 2.1}
Changes in GMST and global surface air temperature (GSAT) 
over time differ by at most 10% in either direction  ( high 
confidence ), and the long-term changes in GMST and GSAT are 
presently assessed to be identical.  There is expanded uncertainty 
in GSAT estimates, with the assessed change from 1850–1900 to 
1995–2014 being 0.85 [0.67 to 0.98] °C. {Cross-Chapter Box 2.3}
The troposphere has warmed since at least the 1950s, and it is 
virtually certain  that the stratosphere has cooled.  In the Tropics, 
the upper troposphere has warmed faster than the near-surface since 
at least 2001, the period over which new observational techniques 
permit more robust quantification ( medium confidence ). It is virtually

certain  that the tropopause height has risen globally over 1980–2018, 
but there is low confidence  in the magnitude. {2.3.1.2}
Changes in several components of the global hydrological 
cycle provide evidence for overall strengthening since at least 
1980 ( high confidence ). However, there is low confidence  
in comparing recent changes with past variations due to 
limitations in paleoclimate records at continental and global 
scales.  Global land precipitation has likely  increased since 1950, 
with a faster increase since the 1980s ( medium confidence ). Near-
surface specific humidity has increased over both land ( very likely ) 
and the oceans ( likely ) since at least the 1970s. Relative humidity has 
very likely  decreased over land areas since 2000. Global total column 
water vapour content has very likely  increased during the satellite 
era. Observational uncertainty leads to low confidence  in global 
trends in precipitation minus evaporation and river runoff. {2.3.1.3}

Several aspects of the large-scale atmospheric circulation have 
likely  changed since the mid-20th century, but limited proxy 
evidence yields low confidence  in how these changes compare 
to longer-term climate. The Hadley circulation has likely  widened 
since at least the 1980s, and extratropical storm tracks have likely  
shifted poleward in both hemispheres. Global monsoon precipitation 
has likely  increased since the 1980s, mainly in the Northern Hemisphere 
(medium confidence ). Since the 1970s, near-surface winds have 
likely  weakened over land. Over the oceans, near-surface winds likely  
strengthened over 1980–2000, but divergent estimates lead to low 
confidence  in the sign (direction) of change thereafter. It is likely  that 
the northern stratospheric polar vortex has weakened since the 1980s 
and experienced more frequent excursions toward Eurasia. {2.3.1.4}
Current Arctic sea ice coverage levels are the lowest since at

least 1850 for both annual mean and late-summer values ( high 
confidence ) and for the past 1000 years for late-summer values 
(medium confidence ). Between 1979 and 2019, Arctic sea ice area 
has decreased in both summer and winter, with sea ice becoming 
younger, thinner and more dynamic ( very high confidence ). Decadal 
means for Arctic sea ice area decreased from 6.23 million  km2 in 
1979–1988 to 3.76 million km2 in 2010–2019 for September and 
from 14.52 to 13.42 million km2 for March. Antarctic sea ice area has 
experienced little net change since 1979 ( high confidence ), with only minor differences between sea ice area decadal means for 1979–1988 
(2.04 million km2 for February, 15.39 million km2 for September) and 
2010–2019 (2.17 million km2 for February, 15.75  million km2 for 
September). {2.3.2.1}
Changes across the terrestrial cryosphere are widespread, with 
several indicators now in states unprecedented in centuries to

millennia ( high confidence ). Reductions in spring snow cover extent 
have occurred across the Northern Hemisphere since at least 1978 
(very high confidence ). With few exceptions, glaciers have retreated 
since the second half of the 19th century and have continued to 
retreat at increased rates since the 1990s ( very high confidence ); this 
behaviour is unprecedented in at least the last 2000 years ( medium 
confidence ). Greenland Ice Sheet (GrIS) mass loss has increased 
substantially since 2000 ( high confidence ). The Greenland Ice Sheet 
was smaller than at present during the Last Interglacial period ( high 
confidence ) and the mid-Holocene ( high confidence ). The Antarctic Ice 
Sheet (AIS) lost mass between 1992 and 2020 ( very high confidence ), 
with an increasing rate of mass loss over this period ( medium 
confidence ). Although permafrost persists in areas of the Northern 
Hemisphere where it was absent prior to 3000 years ago, increases in

temperatures in the upper 30 m over the past three to four decades 
have been widespread ( high confidence ). {2.3.2}
Global mean sea level (GMSL) is rising, and the rate of GMSL 
rise since the 20th century is faster than over any preceding 
century in at least the last three millennia ( high confidence ). 
Since 1901, GMSL has risen by 0.20 [0.15 to 0.25] m, and the 
rate of rise is accelerating.  The average rate of sea level rise 
was 1.3 [0.6 to 2.1] mm yr –1 between 1901 and 1971, increasing 
to 1.9 [0.8 to 2.9] mm yr –1 between 1971 and 2006, and further 
increasing to 3.7 [3.2 to 4.2] mm yr –1 between 2006 and 2018 
(high confidence ). Further back in time, there is medium confidence  
that GMSL was within –3.5 to +0.5 m ( very likely ) of present during 
the mid-Holocene (6000 years ago), 5 to 10 m ( likely ) higher during 
the Last Interglacial (125,000 years ago), and 5 to 25 m ( very likely ) 
higher during the mid-Pliocene Warm Period (MPWP) (3.3 million

years ago). {2.3.3.3}
Recent ocean changes are widespread, and key ocean 
indicators are in states unprecedented for centuries to 
millennia ( high confidence ). Since 1971, it is virtually certain  that 
global ocean heat content has increased for the upper (0–700 m) 
layer, very likely  for the intermediate (700–2000 m) layer and likely  
below 2000 m, and is currently increasing faster than at any point 
since at least the last deglacial transition (18 to 11 thousand years 
ago) ( medium confidence ). It is virtually certain  that large-scale near-
surface salinity contrasts have intensified since at least 1950. The 
Atlantic Meridional Overturning Circulation (AMOC) was relatively 
stable during the past 8000 years ( medium confidence ) but declined 
during the 20th century ( low confidence ). Ocean pH has declined 
globally at the surface over the past four decades ( virtually certain ) 
and in all ocean basins in the ocean interior ( high confidence ) over

the past 2–3 decades. A long-term increase in surface open ocean 
pH occurred over the past 50 million years ( high confidence ), and 
surface ocean pH as low as recent times is uncommon in the last 
2 million years ( medium confidence ). Deoxygenation has occurred in

292
most open ocean regions during the mid 20th to early 21st centuries 
(high confidence ), with decadal variability ( medium confidence ). 
Oxygen minimum zones are expanding at many locations ( high 
confidence ). {2.3.3}
Changes in the marine biosphere are consistent with large-scale 
warming and changes in ocean geochemistry ( high confidence ). 
The ranges of many marine organisms are shifting towards the 
poles and towards greater depths ( high confidence ), but a minority 
of organisms are shifting in the opposite directions. This mismatch 
in responses across species means that the species composition of 
ecosystems is changing ( medium confidence ). At multiple locations, 
various phenological metrics for marine organisms have changed 
in the last 50 years, with the nature of the changes varying with 
location and with species ( high confidence ). In the last two decades,

the concentration of phytoplankton at the base of the marine food 
web, as indexed by chlorophyll concentration, has shown weak and 
variable trends in low and mid-latitudes and an increase in high 
latitudes ( medium confidence ). Global marine primary production 
decreased slightly from 1998–2018, with increasing production in 
the Arctic ( medium confidence ). {2.3.4.2}
Changes in key global aspects of the terrestrial biosphere 
are consistent with large-scale warming ( high confidence ). 
Over the last century, there have been poleward and upslope shifts 
in the distributions of many land species ( very high confidence ) as 
well as increases in species turnover within many ecosystems ( high 
confidence ). Over the past half century, climate zones have shifted 
poleward, accompanied by an increase in the length of the growing 
season in the Northern Hemisphere extratropics and an increase in 
the amplitude of the seasonal cycle of atmospheric CO 2 above 45°N

(high confidence ). Since the early 1980s, there has been a global-scale 
increase in the greenness of the terrestrial surface ( high confidence ). 
{2.3.4.1, 2.3.4.3}
During the mid-Pliocene warm period (MPWP, 3.3 to 3.0 million 
years ago) slowly changing large-scale indicators reflect 
a  world that was warmer than present, with CO 2 similar to 
current levels.  CO 2 levels during the MPWP were similar to present 
for a sustained period, within a range of 360–420 ppm ( medium 
confidence ). Relative to the present, GMST, GMSL and precipitation 
rate were all higher, the Northern Hemisphere latitudinal temperature 
gradient was lower, and major terrestrial biomes were shifted 
northward ( very high confidence ). There is high confidence  that 
cryospheric indicators were diminished and medium confidence  
that the Pacific longitudinal temperature gradient weakened and 
monsoon systems strengthened. {2.3, Cross-Chapter Box 2.4, 9.6.2}

Inferences from past climate states based on proxy records can 
be compared with climate projections over coming centuries 
to place the range of possible futures into a longer-term 
context.  There is medium confidence  in the following mappings 
between selected paleo periods and future projections: during the 
Last Interglacial, GMST is estimated to have been 0.5°C–1.5°C 
warmer than the 1850–1900 reference for a sustained period, which 
overlaps the low end of the range of warming projected under 
SSP1-2.6, including its negative-emissions extension to the end of the 23rd century [1.0°C to 2.2°C]. During the mid-Pliocene Warm 
Period, the GMST estimate [2.5°C to 4.0°C] is similar to the range 
projected under SSP2-4.5 for the end of the 23rd century [2.3°C to 
4.6°C]. GMST estimates for the Miocene Climatic Optimum [5°C to 
10°C] and Early Eocene Climatic Optimum [10°C to 18°C], about 
15 and 50 million years ago, respectively, overlap with the range

projected for the end of the 23rd century under SSP5-8.5 [6.6°C to 
14.1°C]. {Cross-Chapter Box 2.1, 2.3.1, 4.3.1.1, 4.7.1.1}
Changes in Modes of Variability
Since the late 19th century, major modes of climate variability 
show no sustained trends but do exhibit fluctuations in 
frequency and magnitude at inter-decadal time scales, with 
the notable exception of the Southern Annular Mode, which 
has become systematically more positive ( high confidence ). 
There is high confidence  that these modes of variability have 
existed for millennia or longer, but low confidence  in detailed 
reconstructions of most modes prior to direct instrumental 
records.  Both polar annular modes have exhibited strong positive 
trends toward increased zonality of mid-latitude circulation over 
multi-decadal periods, but these trends have not been sustained for 
the Northern Annular Mode since the early 1990s ( high confidence ). 
For tropical ocean modes, a sustained shift beyond multi-centennial

variability has not been observed for El Niño–Southern Oscillation 
(medium confidence ), but there is limited evidence  and low agreement 
about the long-term behaviour of other tropical ocean modes. Modes 
of decadal and multi-decadal variability over the Pacific and Atlantic 
oceans exhibit no significant trends over the period of observational 
records ( high confidence ). {2.4}

293
Changing State of the Climate System  Chapter 22
2.1 Introduction
This chapter assesses the evidence basis for large-scale past changes 
in selected components of the climate system. As such, it combines 
much of the assessment performed in Chapters 2 through  5 of 
the Fifth Assessment Report (AR5) WGI contribution (IPCC, 2013) 
that, taken together, supported a finding of unequivocal recent 
warming of the climate system. The Sixth Assessment Report 
(AR6) WGI Report structure differs substantially from that in AR5 
(Section  1.1.2). This  chapter focuses upon observed changes in 
climate system drivers and changes in key selected large-scale 
indicators of climate change and in important modes of variability 
(Cross-Chapter Box 2.2), which allow for an assessment of changes 
in the global climate system in an integrated manner. This chapter 
is complemented by Chapters 3 and 4, which respectively consider 
model assessment/detection and attribution, and future climate

projections for subsets of these same indicators and modes. It does 
not consider changes in observed extremes, which are assessed in 
Chapter 11. The chapter structure is outlined in the visual abstract 
(Figure 2.1). 
Use is made of paleoclimate, in situ, ground- and satellite-based 
remote sensing, and reanalysis data products where applicable 
(Section 1.5). All observational products used in the chapter are 
detailed in Annex I, and information on data sources and processing 
for each figure and table can be found in the associated chapter Table 
2.SM.1 available as an electronic supplement to the chapter.  Use 
of common periods ranging from 56 million years ago through to the recent past is applied to the extent permitted by available data 
(Section 1.4.1 and Cross-Chapter Box 2.1). In all cases, the narrative 
proceeds from as far in the past as the data permit through to the 
present. Each sub-section starts by highlighting the key findings

from AR5 and any relevant AR6-cycle Special Reports (SROCC, 
SR1.5, SRCCL), and then outlines the new evidence-basis arising 
from a combination of: (i) new findings reported in the literature, 
including new datasets and new versions of existing datasets; 
and (ii) recently observed changes, before closing with a  new 
summary assessment. 
Trends, when calculated as part of this assessment, have wherever 
possible been calculated using a common approach following 
that adopted in Box 2.2 of Chapter 2 of AR5 (Hartmann et  al., 
2013). In addition to trends, consideration is also made of 
changes between various time slices/periods in performing the 
assessment (Section 1.4.1 and Cross-Chapter Box 2.1). Statistical 
significance of trends and changes are assessed at the two-tailed 
90% confidence ( very likely ) level unless otherwise stated. Limited 
use is also made of published analyses that have employed a range 
of methodological choices. In each such case the method/metric

is stated.
There exist a variety of inevitable and, in some cases, irreducible 
uncertainties in performing an assessment of the observational 
evidence for climate change. In some instances, a combination of 
sources of uncertainty is important. For example, the assessment 
of global surface temperature over the instrumental record in Figure 2.1 | Visual guide to Chapter 2. 
Chapter 2: Changing state of the climate system Chapter 2: Quick guide
Cross-chapter boxesSection 2.1
Section 2.2
Changes in climate drivers
Section 2.3
Changes in large-scale 
climate
Well-mixed greenhouse gases
2.2.3Surface temperature
2.3.1.1 Global hydrological cycle
2.3.1.3
Sea ice coverage and thickness
2.3.2.1 Ice sheet mass and extent
2.3.2.4
Ocean temperature, heat content and thermal expansion
2.3.3.1
Sea level
2.3.3.3El Niño–Southern Oscillation (ENSO)
2.4.2
2.3.1
Atmosphere 
and surface2.3.2
Cryosphere2.3.3
Ocean2.3.4
Biosphere
Section 2.4
Changes in modes of variability2.3.5

Synthesis of evidence 
for past changes
Section 2.5
CC Box 2.1
Paleoclimate reference periodsCC Box 2.2
Large-scale indicators of climate changeCC Box 2.3
New estimates of global warmingCC Box 2.4
Climate of the mid-Pliocene warm periodChapter 2 provides evidence for large-scale past changes in selected 
components of the climate system.Key topics and corresponding sub-sections
FAQs

294
Cross-Chapter Box 2.1 | Paleoclimate Reference Periods in the Assessment Report
Contributing Authors: Darrell S. Kaufman (United States of America), Kevin D. Burke (United States of America), Samuel Jaccard 
(Switzerland), Christopher Jones (United Kingdom), Wolfgang Kiessling (Germany), Daniel J. Lunt (United Kingdom), Olaf Morgenstern 
(New Zealand/Germany), John W. Williams (United States of America)
Over the long evolution of the Earth’s climate system, several periods have been extensively studied as examples of distinct 
climate states. This Cross-Chapter Box places multiple paleoclimate reference periods into the unifying context of Earth’s long-term 
climate history, and points to sections in the report with additional information about each period. Other reference periods, including 
those of the industrialized era, are described in Section 1.4.1.

The reference periods represent times that were both colder and warmer than present, and periods of rapid climate change, many 
with informative parallels to projected climate (Cross-Chapter Box 2.1, Table 1). They are used to address a wide variety of questions 
related to natural climate variations in the past (FAQ 1.3). Most of them are used as targets to evaluate the performance of climate 
models under different climate forcings (Section 3.8.2), while also providing insight into the ocean-atmospheric circulation changes 
associated with various radiative forcings and geographical changes. 
Global mean surface temperature (GMST) is a key indicator of the changing state of the climate system. Earth’s mean temperature 
history during the current geological era (Cenozoic, beginning 66 Ma (66 million years ago)) can be broadly characterized as follows 
(Cross-Chapter Box 2.1, Figure 1): (i) transient warming during the first 15 Myr (15 million years) of the Cenozoic, punctuated by

the Paleocene–Eocene Thermal Maximum; (ii) a long-term cooling over tens of millions of years beginning around 50 Ma, driven by 
(among other factors) the slow drift of tectonic plates, which drove mountain building, erosion and volcanism, and reconfigured ocean 
passages, all of which ultimately moved carbon from the atmosphere to other reservoirs and led to the development of the Antarctic 
Ice Sheet (AIS) about 35–30 Ma; (iii) the intensification of cooling by climate feedbacks involving interactions among tectonics, ice 
albedo, ocean circulation, land cover and greenhouse gases, causing ice sheets to develop in the Northern Hemisphere (NH) by about 
3 Ma; (iv) glacial-interglacial fluctuations paced by slow changes in Earth’s astronomical configuration (orbital forcing) and modulated 
by changes in the global carbon cycle and ice sheets on time scales of tens to hundreds of thousands of years, with particular

prominence during the last 1 Myr; (v) a transition with both gradual and abrupt shifts from the Last Glacial Maximum to the present 
interglacial epoch (Holocene), with sporadic ice-sheet breakup disrupting ocean circulation; (vi) continued warming followed by minor 
cooling following the mid-Holocene, with superposed centennial- to decadal-scale fluctuations caused by volcanic activity, among 
other factors; (vii) recent warming related to the build-up of anthropogenic greenhouse gases (Sections 2.2.3 and 3.3.1).
GMST estimated for each of the reference periods based on proxy evidence (Section 2.3.1.1) can be compared with climate projections 
over coming centuries to place the range of possible futures into a longer-term context (Cross-Chapter Box 2.1, Figure 1). Here, the 
very likely range of GMST for the warmer world reference periods are compared with the very likely range of GSAT projected for

the end the 21st century (2080–2100; Table 4.5) and the likely  range for the end of the 23rd century (2300; Table 4.9) under multiple 
Shared Socio-economic Pathway (SSP) scenarios. From this comparison, there is medium confidence in the following: GMST estimated 
for the warmest long-term period of the Last Interglacial about 125 ka (125,000 years ago; 0.5°C–1.5°C relative to 1850–1900) 
overlaps with the low end of the range of temperatures projected under SSP1-2.6 including its negative emissions extension to the end 
of the 23rd century (1.0°C  to 2.2°C). GMST estimated for a period of prolonged warmth during the mid-Pliocene Warm Period about 
3 Ma [2.5°C to 4.0°C] is similar to temperatures projected under SSP2-4.5 for the end of the 23rd century (2.3°C to 4.6°C). GMST 
estimated for the Miocene Climatic Optimum [5°C to 10°C] and Early Eocene Climatic Optimum [10°C to 18°C], about 15 and 50 Ma,

respectively, overlap with the range projected for the end of the 23rd century under SSP5-8.5 (6.6°C to 14.1°C) .Section  2.3.1.1.3 considers a combination of observational-dataset 
and trend-estimate uncertainties. Furthermore, estimates of parametric 
uncertainty are often not comprehensive in their consideration of 
all possible factors and, when such estimates are constructed in 
distinct manners, there are often significant limitations to their direct 
comparability (Hartmann et al., 2013, their Box 2.1).

295
Cross-Chapter Box 2.1 (continued)
Cross-Chapter Box 2.1, Table 1 | Paleo-reference periods, listed from oldest to youngest.  See ‘AR6 Sections’ (right-hand column) for literature citations 
related to each ‘Sketch of the climate state.’ See WGII (Chapters 1, 2 and 3) for citations related to paleontological changes. See Interactive Atlas for simulated climate 
variables for MPWP , LIG, LGM and MH.
Period Age/YearaSketch of the Climate State (Relative to 1850–1900), and Model Experiment 
Protocols  (italic ). Values for large-scale climate indicators including global temperature, 
sea level and atmospheric CO 2 are shown in Figure 2.34.AR6 Sections
(partial list)
Paleocene–Eocene 
Thermal Maximum 
(PETM)55.9–55.7 Ma 
(million years 
ago)A geologically rapid, large-magnitude warming event at the start of the Eocene when a large pulse 
of carbon was released to the ocean-atmosphere system, decreasing ocean pH and oxygen content.

Terrestrial plant and animal communities changed composition, and species distributions shifted poleward. 
Many deep-sea species went extinct and tropical coral reefs diminished. DeepMIP  (Lunt et al., 2017)2.2.3.1
2.3.1.1.1
5.1.2.1
5.3.1.1
7.5.3.4
Early Eocene Climatic 
Optimumb (EECO)53–49 MaProlonged ‘hothouse’ period with atmospheric CO 2 concentration >1000 ppm, similar to SSP5-8.5 
end-of-century values. Continental positions were somewhat different to present due to tectonic plate 
movements; polar ice was absent and there was more warming at high latitudes than in the equatorial 
regions. Near-tropical forests grew at 70°S, despite seasonal polar darkness. DeepMIP , about 50 Ma 
(Lunt et al., 2017, 2021) 2.2.3.1
2.3.1.1.1
7.4.4.1.2
7.5.3.4
7.5.6
Miocene Climatic 
Optimumb (MCO)16.9–14.7 MaProlonged warm period with atmospheric CO 2 concentrations 400–600 ppm, similar to SSP2-4.5 end-

of-century values. Continental geography was broadly similar to modern. At times, Arctic sea ice may 
have been absent, and the AIS was much smaller or perhaps absent. Peak in Cenozoic reef development.  
MioMIP1 , Early and Middle Miocene (Steinthorsdottir et al., 2021)2.2.3.1
2.3.1.1.1
Mid-Pliocene Warm 
Period (MPWP)3.3–3.0 Ma Warm period when atmospheric CO 2 concentration was similar to present (Cross-Chapter Box 2.4). The 
Arctic was much warmer, but tropical temperatures were only slightly warmer. Sea level was higher than 
present. Treeline extended to the northern coastline of the NH continents. Also called, ‘Piacenzian warm 
period.’ PMIP4  midPliocene-eoi400 , 3.2 Ma (Haywood et al., 2016, 2020)CCB2.4 
7.4.4.1.2
7.5.3.3
8.2.2.2
9.6.2
Last Interglacial (LIG)129–116 ka 
(thousand 
years ago)Most recent interglacial period, similar to mid-Holocene, but with more pronounced seasonal insolation

cycle. Northern high latitudes were warmer, with reduced sea ice. Greenland and West Antarctic ice sheets 
were smaller and sea level was higher. Monsoon was enhanced. Boreal forests extended into Greenland 
and subtropical animals such as Hippopotamus  occupied Britain. Coral reefs expanded latitudinally and 
contracted equatorially. PMIP4  lig127k , 127 ka (Otto-Bliesner et al., 2017, 2021) 2.2.3.2
2.3.1.1.1
2.3.3.3
9.2.2.1
9.6.2
Last Glacial Maximum 
(LGM)23–19 kaMost recent glaciation when global temperatures were lower, with greater cooling toward the poles. 
Ice sheets covered much of North America and north-west Eurasia, and sea level was commensurately 
lower. Atmospheric CO 2 was lower; more carbon was sequestered in the ocean interior. Precipitation 
was generally lower over most regions; the atmosphere was dustier, and ranges of many plant species 
contracted into glacial refugia; forest extent and coral reef distribution was reduced worldwide.

PMIP4lgm , 21 ka (Kageyama et al., 2017, 2021a) 2.2.3.2
2.3.1.1.1
3.3.1.1
3.8.2.1
5.1.2.2
7.4.4.1.2
7.5.3.1
8.3.2.4
9.6.2 
Last Deglacial Transition 
(LDT)18–11 kaWarming that followed the Last Glacial Maximum, with decreases in the extent of the cryosphere in both 
polar regions. Sea level, ocean meridional overturning circulation, and atmospheric CO 2 increased during 
two main steps. Temperate and boreal species ranges expanded northwards. Community turnover was 
large. Megafauna populations declined or went extinct. 2.2.3.2
5.1.2.2
5.3.1.2
8.6.1
9.6.2
Mid-Holocene (MH) 6.5–5.5 kaMiddle of the present interglacial when the CO 2 concentration was similar to the onset of the 
industrial era, but the orbital configuration led to warming and shifts in the hydrological cycle, especially 
NH monsoons. Approximate time during the current interglacial and before the onset of major industrial 
activities when GMST was highest. Biome-scale loss of North African grasslands caused by weakened

monsoons and collapses of temperate tree populations linked to hydroclimate variability. PMIP4 mid-
Holocene , 6 ka (Otto-Bliesner et al., 2017; Brierley et al., 2020) 2.3.1.1.2
2.3.2.4
2.3.3.3
3.3.1.1
3.8.2.1
8.3.2.4
8.6.2.2
9.6.2
Last millenniumc850–1850 CEClimate variability during this period is better documented on annual to centennial scales than during 
previous reference periods. Climate changes were driven by solar, volcanic, land cover, and anthropogenic 
forcings, including strong increases in greenhouse gasses since 1750. PMIP4 past1000, 850–1849 CE 
(Jungclaus et al., 2017)2.3.1.1.2
2.3.2.3
8.3.1.6
8.5.2.1
Box 11.3
a CE: Common Era; ka: thousands of years ago; Ma: millions of years ago.
b The word ‘optimum’ is traditionally used in geosciences to refer to the warmest interval of a geologic period. 
c The terms ‘Little Ice Age’ and ‘Medieval Warm Period’ (or ‘Medieval Climate Anomaly’) are not used extensively in this report because the timing of these episodes is

not well defined and varies regionally. Since AR5, new proxy records have improved climate reconstructions at decadal scale across the last millennium. Therefore, the 
dates of events within these two roughly defined periods are stated explicitly when possible.

296
Cross-Chapter Box 2.1, Figure 1 |  Global mean surface temperature (GMST) over the past 60 million years (60 Myr) relative to 1850–1900 shown 
on three time scales.  Information about each of the nine paleo reference periods (blue font) and sections in AR6 that discuss these periods are listed in Cross-Chapter 
Box 2.1 Table 1. Grey horizontal bars at the top mark important events. Characteristic uncertainties are based on expert judgement and are representative of the 
approximate midpoint of their respective time scales; uncertainties decrease forward in time. GMST estimates for most paleo reference periods (Figure 2.34) overlap with 
this reconstruction, but take into account multiple lines of evidence. Future projections span the range of global surface air temperature best estimates for SSP1–2.6 and

SSP5–8.5 scenarios described in Section 1.6. Range shown for 2100 is based on CMIP6 multi-model mean for 2081–2100 from Table 4.5; range for 2300 is based upon 
an emulator and taken from Table 4.9. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1). 
2.2 Changes in Climate Drivers
This section assesses the magnitude and rates of changes in both 
natural and anthropogenically mediated climate drivers over 
a range of time scales. First, changes in insolation (orbital and solar; 
Section 2.2.1), and volcanic stratospheric aerosol (Section 2.2.2) are 
assessed. Next, well-mixed greenhouse gases (GHGs; CO 2, N 2O and 
CH 4) are covered in Section 2.2.3, with climate feedbacks and other 
processes involved in the carbon cycle assessed in Chapter 5. The 
section continues with the assessment of changes in halogenated 
GHGs (Section 2.2.4), stratospheric water vapour, stratospheric

and tropospheric ozone (Section 2.2.5), and tropospheric aerosols 
(Section  2.2.6). Short-lived climate forcers (SLCFs), their precursor 
emissions and key processes are assessed in more detail in Chapter 6. 
Section 2.2.7 assesses the effect of historical land cover change 
on climate, including biophysical and biogeochemical processes. 
Section 2.2.8 summarizes the changes in the Earth’s energy balance 
since 1750 using the comprehensive assessment of effective radiative forcing (ERF) performed in Section 7.3. For some SLCFs 
with insufficient spatial or temporal observational coverage, ERFs are 
based on model estimates, but also reported here for completeness 
and context. Tabulated global mixing ratios of all well-mixed GHGs 
and ERFs from 1750–2019 are provided in Annex III.
2.2.1 Solar and Orbital Forcing
The AR5 assessed solar variability over multiple time scales, concluding 
that total solar irradiance (TSI) multi-millennial fluctuations over the

past 9 kyr were <1 W m–2, but with no assessment of confidence 
provided. For multi-decadal to centennial variability over the last 
millennium, AR5 emphasized reconstructions of TSI that show 
little change (<0.1%) since the Maunder Minimum (1645–1715) 
when solar activity was particularly low, again without providing 
a confidence level. The AR5 further concluded that the best estimate 
of radiative forcing due to TSI changes for the period 1750–2011

297
was 0.05–0.10 W m–2 (medium confidence ), and that TSI very likely 
changed by –0.04 [–0.08 to 0.00] W m–2 between 1986 and 2008. 
Potential solar influences on climate due to feedbacks arising from 
interactions with galactic cosmic rays are assessed in Section 7.3.4.5. 
Slow periodic changes in the Earth’s orbit around the Sun mainly 
cause variations in seasonal and latitudinal receipt of incoming solar 
radiation. Precise calculations of orbital variations are available for 
tens of millions of years (Berger and Loutre, 1991; Laskar et al., 2011). 
The range of insolation averaged over boreal summer at 65°N was 
about 83 W m−2 during the past million years, and 3.2 W m−2 during 
the past millennium, but there was no substantial effect upon global 
average radiative forcing (0.02 W m–2 during the past millennium).
A new reconstruction of solar irradiance extends back 9 kyr based

upon updated cosmogenic isotope datasets and improved models 
for production and deposition of cosmogenic nuclides (Poluianov 
et al., 2016), and shows that solar activity during the second half of 
the 20th century was in the upper decile of the range. TSI features 
millennial-scale changes with typical magnitudes of 1.5 [1.4 to 2.1] 
W m–2 (C.-J. Wu et  al., 2018). Although stronger variations in the 
deeper past cannot be ruled out completely (Egorova et al., 2018; 
Reinhold et al., 2019), there is no indication of such changes having 
happened over the last 9 kyr.Recent estimates of TSI and spectral solar irradiance (SSI) for 
the past millennium are based upon updated irradiance models 
(e.g., Egorova et al., 2018; C.-J. Wu et al., 2018) and employ updated 
and revised direct sunspot observations over the last three centuries 
(Clette et al., 2014; Chatzistergos et al., 2017) as well as records of 
sunspot numbers reconstructed from cosmogenic isotope data prior

to this (Usoskin et  al., 2016). These reconstructed TSI time series 
(Figure  2.2a) feature little variation in TSI averaged over the past 
millennium. The TSI between the Maunder Minimum (1645–1715) 
and second half of the 20th century increased by 0.7–2.7 W m–2 
(Jungclaus et al., 2017; Egorova et al., 2018; Lean, 2018; C.-J. Wu et al., 
2018; Lockwood and Ball, 2020; Yeo et al., 2020). This TSI increase 
implies a change in ERF of 0.09–0.35 W m–2 (Section 7.3.4.4).
Estimation of TSI changes since 1900 (Figure 2.2b) has further 
strengthened, and confirms a small (less than about 0.1  W  m–2) 
contribution to global climate forcing (Section 7.3.4.4). New 
reconstructions of TSI over the 20th century (Lean, 2018; C.-J. Wu 
et  al., 2018) support previous results that the TSI averaged over 
the solar cycle very likely  increased during the first seven decades 
of the 20th century and decreased thereafter (Figure 2.2b). TSI did

not change significantly between 1986 and 2019. Improved insights 
(Krivova et al., 2006; Yeo et al., 2015, 2017; Coddington et al., 2016) 
show that variability in the 200–400 nm UV range was greater than 
previously assumed. Building on these results, the forcing proposed 
by Matthes et  al. (2017) has a 16% stronger contribution to TSI 
Figure  2.2 | Time series of solar and volcanic forcing for the past 2500 years (a, c) and since 1850 (b, d). (a)  Total solar irradiance (TSI) reconstruction 
(10-year running averages) recommended for CMIP6/PMIP4 millennial experiments based on the radiocarbon dataset before 1850 (blue) scaled to the CMIP6 historical forcing 
after 1850 (purple). (b) TSI time series (six-month running averages) from CMIP6 historical forcing as inferred from sunspot numbers (blue), compared to CMIP5 forcing based

on (red) and an update to CMIP6 by a TSI composite (orange). (c) Volcanic forcing represented as reconstructed stratospheric aerosol optical depth (SAOD; as presented in 
Section 7.3.4.6) at 550 nm. Estimates covering 500 BCE to 1900 CE (green) and 1850–2015 (blue). (d) SAOD reconstruction from CMIP6 (v 4) (blue), compared to CMIP5 
forcing (red). Note the change in y-axis range between panels (c) and (d). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).Jungclaus et al. (2017)
Matthes et al. (2017)
Lean (2000)
Matthes et al. updated      Toohey and Sigl (2017)
Luo (2018)
    Luo (2018)
Sato et al. (1993)Stratospheric AOD Stratospheric AODSolar ERF (W m−2) Total Solar Irradiance (W m−2) Total Solar Irradiance (W m−2) (a)
(b)(c)
(d)
Volcanic ERF (W m−2) Volcanic ERF (W m−2) Solar ERF (W m−2) Solar and volcanic forcing over the last 2500 years
1360.513611361.513621362.5
−500 0 500 1000 1500−0.0500.050.10.150.2

1850 1900 1950 20000.2
−0.0500.050.10.15[1 Wm−2
1 Wm−200.10.30.40.5
0.2
0 −2−4−6−8−10
−500 0 500 1000 1500
1850 1900 1950 200000.050.10.15
0   −0.5−1  −1.5−2  −2.52000 2000

298
variability in this wavelength range compared to the forcing used in 
the 5th Phase of the Coupled Model Intercomparison Project (CMIP5).
To conclude, solar activity since the late 19th century was relatively 
high but not exceptional in the context of the past 9 kyr ( high 
confidence ). The associated global mean ERF is in the range of –0.06 
to +0.08 W m–2 (Section 7.3.4.4).
2.2.2 Volcanic Aerosol Forcing
The AR5 concluded that, on interannual time scales, the radiative 
effects of volcanic aerosols are a dominant natural driver of climate 
variability, with the greatest effects occurring within the first 
2–5 years following a strong eruption. Reconstructions of radiative 
forcing by volcanic aerosols used in the Paleoclimate Modelling 
Intercomparison Project Phase III (PMIP3) simulations and in AR5 
featured short-lived perturbations of a range of magnitudes, with

events of greater magnitude than –1 W m–2 (annual mean) occurring 
on average every 35–40 years, although no associated assessment 
of confidence was given. This section focuses on advances in 
reconstructions of stratospheric aerosol optical depth (SAOD), 
whereas Chapter 7 focuses on the ERF of volcanic aerosols, and 
Chapter 5 assesses volcanic emissions of CO 2 and CH 4; tropospheric 
aerosols are discussed in Section 2.2.6. Cross-Chapter Box 4.1 
undertakes an integrative assessment of volcanic effects including 
potential for 21st century effects.
Advances in analysis of sulphate records from the Greenland Ice Sheet 
(GrIS) and AIS have resulted in improved dating and completeness of 
SAOD reconstructions over the past 2.5 kyr (Sigl et al., 2015), a more 
uncertain extension back to 10 ka (Kobashi et al., 2017; Toohey and 
Sigl, 2017), and a better differentiation of sulphates that reach high 
latitudes via stratospheric (strong eruptions) versus tropospheric

pathways (A. Burke et  al., 2019; Gautier et  al., 2019). The PMIP4 
volcanic reconstruction extends the period analysed in AR5 by 1 kyr 
(Figure 2.2c; Jungclaus et  al., 2017) and features multiple strong 
events that were previously misdated, underestimated or not detected, 
particularly before about 1500 CE. The period between successive 
large volcanic eruptions (Negative ERF greater than –1 W m–2), ranges 
from 3–130 years, with an average of 43 ± 7.5 years between such 
eruptions over the past 2.5 kyr (data from Toohey and Sigl, 2017). The 
most recent such eruption was that of Mt Pinatubo in 1991. Century-
long periods that lack such large eruptions occurred once every 
400 years on average. Systematic uncertainties related to the scaling 
of sulphate abundance in glacier ice to radiative forcing have been 
estimated to be about 60% (Hegerl et al., 2006). Uncertainty in the 
timing of eruptions in the proxy record is ± 2 years (95% confidence

interval) back to 1.5 ka and ± 4 years before (Toohey and Sigl, 2017).
SAOD averaged over the period 950–1250 CE (0.012) was lower 
than for the period 1450–1850 CE (0.017) and similar to the 
period  1850–1900 (0.011). Uncertainties associated with these 
inter-period differences are not well quantified but have little effect 
because the uncertainties are mainly systematic throughout the record. 
Over the past 100 years, SAOD averaged 14% lower than the mean of the previous 24 centuries (back to 2.5 ka), and well within the range 
of centennial-scale variability (Toohey and Sigl, 2017).
Direct observations of volcanic gas-phase sulphur emissions (mostly 
SO 2), sulphate aerosols, and their radiative effects are available from 
a variety of sources (Kremser et  al., 2016). New estimates of SO 2 
emissions from explosive eruptions have been derived from satellite 
(beginning in 1979) and in situ measurements (Höpfner et  al.,

2015; Carn et al., 2016; Neely III and Schmidt, 2016; Brühl, 2018). 
Satellite observations of aerosol extinction after recent eruptions 
have uncertainties of about 15–25% (Vernier et al., 2011; Bourassa 
et al., 2012). Additional uncertainties occur when gaps in the satellite 
records are filled by complementary observations or using statistical 
methods (Thomason et al., 2018). Merged datasets (Thomason et al., 
2018) and sparse ground-based measurements (Stothers, 1997) allow 
for volcanic forcing estimates back to 1850. In contrast to the CMIP5 
historical volcanic forcing datasets (Ammann et al., 2003), updated 
time series (Figure 2.2d; Luo, 2018) feature a more comprehensive 
set of optical properties including latitude-, height- and wavelength-
dependent aerosol extinction, single scattering albedo and asymmetry 
parameters. A series of small-to-moderate eruptions since 2000 
resulted in perturbations in SAOD of 0.004–0.006 (Andersson et al., 
2015; Schmidt et al., 2018).

To conclude, strong individual volcanic eruptions cause multi-annual 
variations in radiative forcing. However, the average magnitude 
and variability of SAOD and its associated volcanic aerosol forcing 
since 1900 are not unusual in the context of at least the past 2.5 kyr 
(medium confidence ).
2.2.3 Well-mixed Greenhouse Gases (WMGHGs)
Well-mixed greenhouse gases generally have lifetimes of more than 
several years. The AR5 assigned medium confidence  to the values 
of atmospheric CO 2 concentrations (mixing ratios) during the warm 
geological periods of the early Eocene and Pliocene. It concluded 
with very high confidence  that, by 2011, the mixing ratios of CO 2, 
CH 4, and N 2O in the atmosphere exceeded the range derived from 
ice cores for the previous 800 kyr, and that the observed rates of 
increase of the greenhouse gases were unprecedented on centennial 
timescales over at least the past 22 kyr. It reported that over

2005–2011 atmospheric burdens of CO 2, CH 4, and N 2O increased, 
with 2011 levels of 390.5 parts per million (ppm), 1803.2 parts per 
billion (ppb) and 324.2 ppb, respectively. Increases of CO 2 and N 2O 
over 2005–2011 were comparable to those over 1996–2005, while 
CH 4 resumed increasing in 2007, after remaining nearly constant over 
1999–2006. A comprehensive process-based assessment of changes 
in CO 2, CH 4, and N 2O is undertaken in Chapter 5.
2.2.3.1 CO 2 During 450 Ma to 800 ka
Isotopes from continental and marine sediments using improved 
analytical techniques and sampling resolution have reinforced the 
understanding of long-term changes in atmospheric CO 2 during the 
past 450 Myr (Table 2.1 and Figure 2.3). In particular, for the last 
60 Myr, sampling resolution and accuracy of the boron isotope proxy

299
in ocean sediments has improved (Penman et al., 2014; Anagnostou 
et al., 2016, 2020; Chalk et al., 2017; Gutjahr et al., 2017; Babila et al., 
2018; Dyez et al., 2018; Raitzsch et al., 2018; Sosdian et al., 2018; 
Henehan et  al., 2019, 2020; de la Vega et  al., 2020; Harper et  al., 
2020), the understanding of the alkenone CO 2 proxy has increased 
(e.g.,  Badger et  al., 2019; Stoll et  al., 2019; Y . Zhang et  al., 2019; 
Zhang et al., 2020; Rae et al., 2021) and new phytoplankton proxies 
have been developed and applied (e.g.,  Witkowski et  al., 2018). 
Understanding of the boron isotope CO 2 proxy has improved since 
AR5 with studies showing very good agreement between boron-
CO 2 estimates and co-existing ice core CO 2 (Hönisch and Hemming, 
2005; Foster, 2008; Henehan et al., 2013; Chalk et al., 2017; Raitzsch 
et al., 2018; see Figure 2.3c). Such independent validation has proven

difficult to achieve with the other available CO 2 proxies (e.g., Badger 
et al., 2019; Da et al., 2019; Stoll et al., 2019; Y . Zhang et al., 2019). 
Remaining uncertainties in these ocean sediment based proxies 
(Hollis et al., 2019) partly limit the applicability of the alkenone δ13C 
and boron δ11B proxies beyond the Cenozoic, although new records 
are emerging, for example, Jurikova et al. (2020). CO 2 estimates from 
the terrestrial CO 2 proxies, such as stomatal density in fossil plants 
and δ13C of palaeosol carbonates, are available for much of the 
last 420 Myr. Given the low sampling density, relatively large CO 2 
uncertainty, and high age uncertainty (relative to marine sediments) of the terrestrial proxies, preference here is given to the marine based 
proxies (and boron in particular) where possible. 
Levels were close to 1750 values during at least one prolonged 
interval during the Carboniferous and Permian (350–252 Ma).

During the Triassic (251.9–201.3 Ma), atmospheric CO 2 mixing ratios 
reached a maximum of between 2000–5000 ppm (200–220  Ma). 
During the PETM (56 Ma) CO 2 rapidly rose from about 900 ppm 
to about 2000 ppm (Table 2.1; Schubert and Jahren, 2013; Gutjahr 
et al., 2017; Anagnostou et al., 2020) in 3–20 kyr (Zeebe et al., 2016; 
Gutjahr et al., 2017; Turner, 2018). Estimated multi-millennial rates 
of CO 2 accumulation during this event range from 0.3–1.5 PgC yr –1 
(Gingerich, 2019), at least 4–5 times lower than current centennial 
rates (Section 5.3.1.1). Based on boron and carbon isotope data, 
supported by other proxies (Hollis et  al., 2019), atmospheric CO 2 
during the EECO (50 Ma) was between 1150 and 2500 ppm ( medium 
confidence ), and then gradually declined over the last 50 Myr at a 
long-term rate of about 16 ppm Myr –1 (Figure 2.3). The last time the 
CO 2 mixing ratio was as high as 1000 ppm (the level reached by some

high emissions scenarios by 2100; Annex III) was prior to the Eocene-
Oligocene transition (33.5 Ma; Figure 2.3) that was associated with 
the first major advance of the AIS (Pearson et  al., 2009; Pagani 
et al., 2011; Anagnostou et al., 2016; Witkowski et al., 2018; Hollis 
Table 2.1 | Concentration (mixing ratios) and, where applicable, century time-scale rate of change of atmospheric CO 2 based on multiple datasets for 
target paleoclimate reference (Cross-Chapter Box 2.1, and Figure 2.34) and selected other periods.  Modern data are from Section 2.2.3.3 and Annex III. ‘AR6’ 
denotes best estimates assessed in this report and propagated to Figure 2.34. Units for the rate of change are given only for centennial periods characterized by rapid changes. 
Confidence  levels are very high  for instrumentally derived concentrations, high for values derived from air in glacier ice (back to LIG), medium  for values supported by multiple

proxy types (MPWP , EECO), and low for values from a single sedimentary proxy type (PETM). ‘ →’ indicates transition from the beginning to the end of the time interval. 
Uncertainties for Modern are based on 2019 estimates. Last Millennium rate of range shows lowest and highest values attained during this period; LDT shows highest rate of 
change. N/A indicates that values are not available. See chapter data table for bibliographic citation and auxiliary information for each dataset (Table 2.SM.1).
Reference Period CO 2 Concentration (ppm) and Dataset DetailsRate of Change  
(ppm per century)
Modern
(1995–2014)359.6 to 360.4 → 396.7 to 397.5 (AR6) 192.3 to 198.3a (AR6)
Last 100 years (1919–2019) 302.8 to 306.0 → 409.5 to 410.3 (AR6) 103.9 to 107.1 (AR6)
Approximate pre-industrial baseline 
(1850–1900; see Cross-Chapter Box 1.2)283.4 to 287.6 → 294.8 to 298.0 (AR6);
284.3b → 295.7b (CMIP6)16.5 to 27.1a (AR6)
22.8b,a (CMIP6)

Last millennium (1000–1750) 278.0 to 285.0 (AR6; average of WAIS Divide, Law Dome and EDML core data) –6.9 ~ 4.7b (Law Dome);  
–1.9 ~ 3.2b (EDML);  
–5.2 ~ 4.2b (WAIS Divide)
MH 260.1 to 268.1 (Dome C; CMIP6) N/A
LDT 193.2b → 271.2b (AR6); 195.2b → 265.3b (Dome C); 191.2b → 277.0b (WAIS Divide) 9.6b (WAIS Divide); 
7.1b (Dome C)
LGM 188.4 to 194.2 (AR6); 190.5 to 200.1 (WAIS Divide); 186.8 to 202.0 (Byrd); 184.9 to 193.1 (Dome C);  
180.5 to 192.7 (Siple Dome); 190b (PMIP6); 174.2 to 205.8 ( δ11B proxy)N/A
LIG265.9 to 281.5 (AR6); 259.4 to 283.8 (Vostok); 266.2 to 285.4 (Dome C); 275b (PMIP4) 282.2 to 305.8  
(δ11B proxy)N/A
MPWP (KM5c) 360 to 420 (AR6) N/A
EECO 1150 to 2500 (AR6) N/A
PETM 800 to 1000 → 1400 to 3150 (AR6) 4 to 42 (AR6)
a Centennial rate of change estimated by extrapolation of data from a shorter time period. The values (x to y) represent very likely  ranges (90% CIs).
b Data uncertainty is not estimated.

300
et  al., 2019). The compilation of Foster et  al. (2017) constrained 
CO 2 concentration to between 290 and 450 ppm during the MPWP , 
based primarily on the boron-isotope data reported by Martínez-
Botí et al. (2015b), consistent with the AR5 range of 300–450 ppm. 
A more recent high-resolution boron isotope-based study revealed 
that CO 2 cycled during the MPWP from about 330 to about 390 ppm 
on orbital timescales, with a mean of about 370 ppm (de la Vega 
et al., 2020). Although data from other proxy types (e.g., stomatal 
density or δ13C of alkenones) have too low resolution to resolve the 
orbital-related variability of CO 2 during this interval (e.g., Kürschner 
et al., 1996; Stoll et al., 2019) there is general agreement among 
the different proxy types with the boron-derived mean (e.g., Stoll 
et al., 2019). High-resolution sampling (about 1 sample per 3 kyr)

with the boron-isotope proxy indicates mean CO 2 mixing ratios for 
the Marine Isotope Stage KM5c interglacial were 360–420 ppm 
(medium confidence ) (de la Vega et al., 2020). Following the MPWP , the atmospheric CO 2 mixing ratio generally 
decreased at a rate of about 30 ppm Myr –1. It is very likely  that CO 2 
levels as high as the present were not experienced in the last 2 Myr 
(Hönisch et al., 2009; Bartoli et al., 2011; Martínez-Botí et al., 2015a; 
Chalk et al., 2017; Dyez et al., 2018; Da et al., 2019; Stoll et al., 2019). 
Related to the shift of glacial-interglacial cycle frequency from 40 to 
100 kyr at 0.8–1.2 Ma, there was a decrease of glacial-period CO 2 
(Chalk et  al., 2017; Dyez et  al., 2018). These boron isotope-based 
CO 2  results agree with available records based on ancient ice 
exposed near the surface of the AIS (Yan et  al., 2019), however, 
direct comparison is limited due to a lack of ancient ice cores with

sufficiently continuous stratigraphy (Higgins et al., 2015; Brook and 
Buizert, 2018).
To conclude, there is high confidence  that average EECO and MPWP 
(KM5c) CO 2 concentrations were higher than those preceding 
industrialization at 1150–2500 ppm and 360–420 ppm, respectively. 
Although there is some uncertainty due to the non-continuous nature 
of marine sediment records, the last time atmospheric CO 2 mixing 
ratio was as high as present was very likely  more than 2 Ma.
2.2.3.2 Glacial–Interglacial WMGHG Fluctuations from 800 Ka
Since AR5, the number of ice cores for the last 800 kyr has increased 
and their temporal resolution has improved (Figure 2.4), especially for 
the last 60 kyr and when combined with analyses of firn air, leading 
to improved quantification of greenhouse gas concentrations prior to 
the mid-20th century.
2.2.3.2.1 Carbon dioxide (CO 2)
Records of CO 2 from the AIS formed during the last glacial period

and the LDT show century-scale fluctuations of up to 9.6 ppm 
(Ahn et al., 2012; Ahn and Brook, 2014; Marcott et al., 2014; Bauska 
et al., 2015; Rubino et al., 2019). Although these rates are an order 
of magnitude lower than those directly observed over 1919–2019 CE 
(Section 2.2.3.3.1), they provide information on non-linear responses 
of climate-biogeochemical feedbacks (Section 5.1.2). Multiple 
records for 0–1850 CE show CO 2 mixing ratios of 274–285  ppm. 
Offsets among ice core records are about 1%, but the long-term 
trends agree well and show coherent multi-centennial variations of 
about 10 ppm (Ahn et al., 2012; Bauska et al., 2015; Rubino et al., 
2019). Multiple records show CO 2 concentrations of 278.3 ± 2.9 ppm 
in 1750 and 285.5 ± 2.1 ppm in 1850 (Siegenthaler et  al., 2005; 
MacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015). 
CO 2 concentration increased by 5.0 ± 0.8 ppm during 970–1130 CE,

followed by a decrease of 4.6 ± 1.7 ppm during 1580–1700 CE. 
The greatest rate of change over the CE prior to 1750 is observed 
at about 1600 CE, and ranges from –6.9 to +4.7 ppm per century in 
multiple high-resolution ice core records (Siegenthaler et al., 2005; 
MacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015; 
Rubino et  al., 2019). Although ice core records present low-pass 
filtered time series due to gas diffusion and gradual bubble close-
off in the snow layer over the ice sheet (Fourteau et al., 2020), the 
rate of increase since 1850 CE (about 125 ppm increase over about 
170 years) is far greater than implied for any 170-year period by ice 
core records that cover the last 800 ka ( very high confidence ). CO 2 (ppm)(b)450    400     350    300     250     200     150    100 00100020003000(a)
Age (millions of years ago)
55    50     45    40     35    30    25     20    15    10     55001000150020002500

3500       3000      2500      2000     1500      1000       500          0  150250350450(c)
CO 2 (ppm)Age (millions of years ago)
Age (thousands of years ago)100ppm
100ppmLOESS Fit CO 2 (ppm)δ13C-paleosols
Stomata
δ11B-foraminifera
δ13C-phytane
δ13C-alkenone
100ppm
Ant.Ice CoreEvolution of atmospheric CO 2
PETM                 EECO MCO      MPWP
MPWP MPT
50
0
Figure 2.3 | The evolution of atmospheric CO 2 through the last 450 
million years (450 Myr). The periods covered are 0–450 Ma (a), 0–58 Ma (b), 
and 0–3500 ka (c), reconstructed from continental rock, marine sediment and ice 
core records. Note different time scales and axes ranges in panels (a), (b) and (c). Dark 
and light green bands in (a) are uncertainty envelopes at 68% and 95% uncertainty, 
respectively. 100 ppm in each panel is shown by the marker in the lower right-hand 
corner to aid comparison between panels. In panel (b) and (c) the major paleoclimate

reference periods (CCB2.1) have been labelled, and in addition: MPT (Mid Pleistocene 
Transition), MCO (Miocene Climatic Optimum). Further details on data sources and 
processing are available in the chapter data table (Table 2.SM.1).

301
2.2.3.2.2 Methane (CH 4)
CH 4 concentrations over the past 110 kyr are higher in the Northern 
Hemisphere (NH) than in the Southern Hemisphere (SH), but closely 
correlated on centennial and millennial timescales (Buizert et  al., 
2015). On glacial to interglacial cycles, approximately 450 ppb 
oscillations in CH 4 concentrations have occurred (Loulergue et  al., 
2008). On millennial timescales, most rapid climate changes observed 
in Greenland and other regions are coincident with rapid CH 4 changes 
(Buizert et al., 2015; Rhodes et al., 2015, 2017). The variability of CH 4 on 
centennial timescales during the early Holocene does not significantly 
differ from that of the late Holocene prior to about 1850 (Rhodes et al., 
2013; Yang et al., 2017). The LGM concentration was 390.5 ± 6.0 ppb 
(Kageyama et  al., 2017). The global mean concentrations during 
0–1850 CE varied between 625 and 807 ppb. High-resolution ice core

records from Antarctica and Greenland exhibit the same trends with 
an inter-polar difference of 36–47 ppb (Sapart et al., 2012; L. Mitchell 
et al., 2013). There is a long-term positive trend of about 0.5 ppb per 
decade during the CE until 1750 CE. The most rapid CH 4 changes 
prior to industrialization were as large as 30–50  ppb on multi-
decadal timescales. Global mean CH 4 concentrations estimated from 
Antarctic and Greenland ice cores are 729.2 ± 9.4 ppb in 1750 and 
807.6 ± 13.8 ppb in 1850 (L. Mitchell et al., 2013).
2.2.3.2.3 Nitrous oxide (N 2O)
New records show that N 2O concentration changes are associated 
with glacial-interglacial transitions (Schilt et  al., 2014). The most 
rapid change during the last glacial termination is a 30 ppb increase 
in a 200-year period, which is an order of magnitude smaller 
than the modern rate (Section 2.2.3.3). During the LGM, N 2O was 
208.5 ± 7.7 ppb (Kageyama et al., 2017). Over the Holocene the lowest

value was 257 ± 6.6 ppb during 6–8 ka, but millennial variation is 
not clearly detectable due to analytical uncertainty and insufficient 
ice core quality (Flückiger et al., 2002; Schilt et al., 2010). Recently 
acquired high-resolution records from Greenland and Antarctica for 
the last 2 kyr consistently show multi-centennial variations of about 
5–10 ppb (Figure 2.4), although the magnitudes vary over time 
(Ryu  et  al., 2020). Three high temporal resolution records exhibit 
a short-term minimum at about 600 CE of 261 ± 4 ppb (MacFarling 
Meure et al., 2006; Ryu et al., 2020). It is very likely  that industrial 
N2O increase started before 1900 CE (Machida et al., 1995; Sowers, 
2001; MacFarling Meure et al., 2006; Ryu et al., 2020). Multiple ice 
cores show N 2O concentrations of 270.1 ± 6.0 ppb in 1750 and 
272.1 ± 5.7 ppb in 1850 (Machida et al., 1995; Flückiger et al., 1999; 
Sowers, 2001; Rubino et al., 2019; Ryu et al., 2020).
2.2.3.3 Modern Measurements of WMGHGs

In this section and for calculation of ERF , surface global averages are 
determined from measurements representative of the well-mixed lower 
troposphere. Global averages that include sites subject to significant 
anthropogenic activities or influenced by strong regional biospheric 
emissions are typically larger than those from remote sites, and require 
weighting accordingly (Table 2.2). This section focusses on global mean 
mixing ratios estimated from networks with global spatial coverage, 
and updated from the CMIP6 historical dataset (Meinshausen et al., 
2017) for periods prior to the existence of global networks.Evolution of well-mixed greenhouse gases
800350400
0 004 002 600200250300
200
300240280320
292296
280284288
272276CO2 (ppm)
2000
1600
1200
800
400CH4(ppb)
9001000
700800
600290
270280
250260Thousands of years before 2000CO2
N2OCH4
1600 0 400 800 1200
Year (CE)N2O (ppb)(a)
(b)
268
2000409.9CH4(ppb)
200 ppb260280
200220240
160180
21 01 81 61 41 02

Thousands of years before 2000WAIS Divide
Dome C
LDT LGMCO2 (ppm)
Law Dome
EDMLWAIS DivideCO2
Law Dome
WAIS Divide
NEEM Law Dome N2OCH4
Styx EURO GISP2 H15CO2 (ppm) N2O (ppb)332.120 ppm
20 ppm
200 ppb
20 ppb
20 ppb1866.320 ppm
Figure 2.4 | Atmospheric well-mixed greenhouse gas (WMGHG) concentrations 
from ice cores. (a)  Records during the last 800 kyr with the Last Glacial Maximum 
(LGM) to Holocene transition as inset. (b) Multiple high-resolution records over 
the CE. The horizontal black bars in panel (a) inset indicate LGM and Last Deglacial 
Termination (LDT) respectively. The red and blue lines in (b) are 100-year running 
averages for CO 2 and N 2O concentrations, respectively. The numbers with vertical 
arrows in (b) are instrumentally measured concentrations in 2019. Further details on 
data sources and processing are available in the chapter data table (Table 2.SM.1).

302
Table 2.2 | Atmospheric global annual mean mixing ratios (dry-air mole fraction) for well-mixed greenhouse gases.  The table provides observed values for 
2011 and 2019, and relative changes since 2011, for selected well-mixed, radiatively important gases (ERF >0.001 W m–2), estimated from various measurement networks 
or compilations. Units are parts per million (ppm) for CO 2, parts per billion (ppb) for CH 4 and N 2O, parts per trillion (ppt) for all other gases. Time series since 1750, data for 
additional gases, references, and network information can be found in Annex III and the corresponding electronic supplement. Further details on data sources and processing 
are available in the chapter data table (Table 2.SM.1).
Species Lifetime, 
AR6, ERF2011 2019 Change Network Species Lifetime,
AR6, ERF2011 2019 Change Network
CO 2
 # 390.5 409.9 (0.17) 5.0% NOAA*a
HCFC-2211.9 212.6 246.8 (0.5) 16.1% NOAA*

409.9 (0.4) 389.7 409.5 (0.37) 5.1% SIO 246.8 (0.6) 213.7 246.7 (0.4) 15.5% AGAGE*
2.156 390.2 409.6 (0.31) 5.0% CSIRO 0.053 209.0 244.1 (3.0) 22.0% UCI
390.9 410.5 (0.30) 5.0% WMO
HCFC-141b9.4 21.3 24.4 (0.1) 14.4% NOAA*
390.9     CMIP6 24.4 (0.3) 21.4 24.3 (0.1) 13.7% AGAGE*
CH 4
 9.1–11.8 1803.1 1866.6 (1.0) 3.5% NOAA* 0.004 20.8 26.0 (0.3) 25.0% UCI
1866.3 (3.3) 1803.6 1866.1 (2.0) 3.5% AGAGE*
HCFC-142b18 20.9 22.0 (0.1) 5.3% NOAA*
0.544 1791.8 1860.8 (3.5) 3.9% UCI 22.3 (0.4) 21.5 22.5 (0.1) 5.0% AGAGE*
1802.3 1862.5 (2.4) 3.3% CSIRO 0.004 21.0 22.8 (0.2) 8.6% UCI
1813 1877 (3) 3.5% WMO
HFC-134a 14 62.7 107.8 (0.4) 72% NOAA*
1813.1     CMIP6 107.6 (1.0) 62.8 107.4 (0.2) 71% AGAGE*
N2O
 116–109 324.2 331.9 (0.2) 2.4% NOAA* 0.018 63.4 107.6 (1.7) 70% UCI
332.1 (0.4) 324.7 332.3 (0.1) 2.4% AGAGE*
HFC-125  30 10.1 29.1 (0.3) 187% NOAA*
0.208 324.0 331.6 (0.3) 2.3% CSIRO 29.4 (0.6) 10.4 29.7 (0.1) 186% AGAGE*
324.3 332.0 (0.2) 2.4% WMO 0.007 
324.2     CMIP6

HFC-23228 24.1 32.4 (0.1) 35% AGAGE*
CFC-12102 526.9 501.5 (0.3) –4.8% NOAA* 32.4 (0.1)
503.1 (3.2) 529.6 504.6 (0.2) –4.7% AGAGE* 0.006 
0.180 525.3 508.4 (2.5) –3.2% UCI
HFC-143a51 11.9 23.8 (0.1) 100% NOAA*
CFC-1152 237.2 226.5 (0.2) –4.5% NOAA* 24.0 (0.4) 12.1 24.2 (0.1) 100% AGAGE*
226.2 (1.1) 237.4 225.9 (0.1) –4.8% AGAGE* 0.004 
0.066 237.9 224.9 (1.3) –5.5% UCI
HFC-325.4 4.27 19.2 (0.3) 350% NOAA*
CFC-11393 74.5 69.7 (0.1) –6.4% NOAA* 20.0 (1.4) 5.15 20.8 (0.2) 304% AGAGE*
69.8 (0.3) 74.6 69.9 (0.1) –6.3% AGAGE* 0.002 
0.021 74.9 70.0 (0.5) –6.5% UCI
CF450,000 79.0 85.5 (0.1) 8.2% AGAGE*
CFC-114189 16.36 16.28 (0.03) –0.5% AGAGE* 85.5 (0.2)
16.0 (0.05) 0.005 
0.005 
C2F6 10,000 4.17 4.85 (0.01) 16.3% AGAGE*
CFC-115540 8.39 8.67 (0.02) 3.3% AGAGE* 4.85 (0.1)
8.67 (0.02) 0.001 
0.002 
SF6 About 1000 7.32 9.96 (0.02) 36.1% NOAA*
CCl 4 32 86.9 78.4 (0.1) –9.8% NOAA* 9.95 (0.01) 7.28 9.94 (0.02) 36.5% AGAGE*
77.9 (0.7) 85.3 77.3 (0.1) –9.4% AGAGE* 0.006

0.013 87.8 77.7 (0.7) –11.5% UCI
AGAGE: Advanced Global Atmospheric Gases Experiment; SIO: Scripps Institution of Oceanography; NOAA: National Oceanic and Atmospheric Administration, Global 
Monitoring Laboratory; UCI: University of California, Irvine; CSIRO: Commonwealth Scientific and Industrial Research Organization, Aspendale, Australia; WMO: World 
Meteorological Organization, Global Atmosphere Watch, CMIP6 (Climate Model Intercomparison Project Phase 6). Mixing ratios denoted by AR6 are representative of the 
remote, unpolluted troposphere, derived from one or more measurement networks (denoted by *). Minor differences between 2011 values reported here and in the previous 
Assessment Report (AR5) are due to updates in calibration and data processing. ERF in 2019 is taken from Table 7.5, and the difference with the AR5 assessment reflects

updates in the estimates of AR6 global mixing ratios and updated radiative calculations. Uncertainties, in parenthesis, are estimated at 90% confidence interval. Networks use 
different methods to estimate uncertainties. Some uncertainties have been rounded up to be consistent with the number of decimal places shown. Lifetime is reported in years: 
# indicates multiple lifetimes for CO 2. For CH 4 and N 2O the two values represent total atmospheric lifetime and perturbation lifetime.

303
2.2.3.3.1 Carbon dioxide (CO 2)
There has been a positive trend in globally averaged surface CO 2 
mixing ratios since 1958 (Figure 2.5a), that reflects the imbalance 
of sources and sinks (Section 5.2). The growth rate has increased 
overall since the 1960s (Figure 2.5a inset), while annual growth rates 
have varied substantially, for example, reaching a peak during the 
strong El Niño events of 1997–1998 and 2015–2016 (Bastos et al., 
2013; Betts et al., 2016). The average annual CO 2 increase from 2000 
through 2011 was 2.0 ppm yr –1 (standard deviation 0.3 ppm yr –1), 
similar to what was reported in AR5. From 2011 through 2019 it was 
2.4 ppm yr –1 (standard deviation 0.5 ppm yr –1), which is higher than 
that of any comparable time period since global  measurements 
began. Global networks consistently show that the globally averaged 
annual mean CO 2 has increased by 5.0% since 2011, reaching

409.9 ± 0.4 ppm in 2019 (NOAA measurements). Further assessment 
of changing seasonality is undertaken in Section 2.3.4.1.
2.2.3.3.2 Methane (CH 4)
The globally averaged surface mixing ratio of CH 4 in 2019 was 
1866.3 ± 3.3 ppb, which is 3.5% higher than 2011, while observed 
increases from various networks range from 3.3–3.9% (Table 2.2 
and Figure 2.5b). There are marked growth rate changes over the 
period of direct observations, with a decreasing rate from the late-
1970s through the late-1990s, very little change in concentrations 
from 1999–2006, and resumed increases since 2006. Atmospheric 
CH 4 fluctuations result from complex variations of sources and sinks. 
A detailed discussion of recent methane trends and our understanding 
of their causes is presented in Cross-Chapter Box 5.2.
2.2.3.3.3 Nitrous oxide (N 2O)
The AR5 reported 324.2 ± 0.1 ppb for global surface annual mean 
N2O in 2011; since then, it has increased by 2.4% to 332.1 ± 0.4 ppb

in 2019. Independent measurement networks agree well for both 
the global mean mixing ratio and relative change since 2011 
(Table 2.2). Over 1995–2011, N 2O increased at an average rate of 
0.79 ± 0.05 ppb yr –1. The growth rate has been higher in recent years, 
amounting to 0.96 ± 0.05 ppb yr –1 from 2012 to 2019 (Figure 2.5c 
and Section 5.2.3.5).
2.2.3.4 Summary of Changes in WMGHGs
In summary, CO 2 has fluctuated by at least 2000 ppm over the last 
450 Myr ( medium confidence ). The last time CO 2 concentrations were 
similar to the present-day was over 2 Ma ( high confidence ). Further, 
it is certain that WMGHG mixing ratios prior to industrialization were 
lower than present-day levels and the growth rates of the WMGHGs 
from 1850 are unprecedented on centennial timescales in at least 
the last 800 kyr. During the glacial-interglacial climate cycles over 
the last 800 kyr, the concentration variations of the WMGHG were

50–100 ppm for CO 2, 210–430 ppb for CH 4 and 60–90 ppb for N 2O. 
Between 1750–2019 mixing ratios increased by 131.6 ± 2.9 ppm 
(47%), 1137 ± 10 ppb (156%), and 62 ± 6 ppb (23%), for CO 2, 
CH 4, and N 2O, respectively ( very high confidence ). Since 2011 (AR5) 
mixing ratios of CO 2, CH 4, and N 2O have further increased by 19 ppm, NOAA
CSIRO
AGAGECH 4 (ppb)Modern direct measurements of 
well-mixed GHGsCO 2 (ppm)NOAA
CSIRO
SIO400
380
360
340
320
300
1960 1970 1980 1990 2000 2010 2020
1980 1990 2000 2010 2020
1980 1990 2000 2010 20201960 1980 2000 2020ppm/yr4
3
2
1
0
(b) CH 4
(c) N 2ONOAA
CSIRO
AGAGE
UCI1800
1700
1600
1500
330
320
310
300N2O (ppb)1990 1980 2000 2020 201020
10
0ppb/yr
1990 1980 2000 2020 2010ppb/yr1.4
1.21.0
0.8
0.6
0.4
0.2(a) CO 2
Figure 2.5 | Globally averaged dry-air mole fractions of greenhouse gases. 
(a) CO 2 from SIO, CSIRO, and NOAA/GML  (b) CH 4 from NOAA, AGAGE, CSIRO, and 
UCI; and (c) N2O from NOAA, AGAGE, and CSIRO (Table 2.2). Growth rates, calculated

as the time derivative of the global means after removing seasonal cycle are shown 
as inset figures. Note that the CO 2 series is 1958–2019 whereas CH 4, and N 2O are 
1979–2019. Units are parts per million (ppm) or parts per billion (ppb). Further details 
on data are in Annex III, and on data sources and processing are available in the 
chapter data table (Table 2.SM.1).

304
63 ppb, and 7.7 ppb, reaching in 2019 levels of 409.9 (± 0.4) ppm, 
1866.3 (±  3.3)  ppb, and 332.1 (± 0.4) ppb, respectively. By 2019, 
the combined ERF (relative to 1750) of CO 2, CH 4 and N 2O was 
2.9 ± 0.5 W m–2 (Table 2.2; Section 7.3.2).
2.2.4 Halogenated Greenhouse Gases (CFCs, HCFCs, 
HFCs, PFCs, SF6 and others)
This category includes ozone depleting substances (ODS), their 
replacements, and gases used industrially or produced as by-products. 
Some have natural sources (Section 6.2.2.4). The AR5 reported 
that atmospheric abundances of chlorofluorocarbons (CFCs) were 
decreasing in response to controls on production and consumption 
mandated by the Montreal Protocol on Substances that Deplete 
the Ozone Layer and its amendments. In contrast, abundances of 
both hydrochlorofluorocarbons (HCFCs, replacements for CFCs) and 
hydrofluorocarbons (HFCs, replacements for HCFCs) were increasing.

Atmospheric abundances of perfluorocarbons (PFCs), SF 6, and NF 3 
were also increasing.
Further details on ODS and other minor greenhouse gases can be found 
in the Scientific Assessment of Ozone Depletion: 2018 (Engel et al., 2018; 
Montzka et al., 2018b). Updated mixing ratios of the most radiatively 
important gases (ERF >0.001 W m–2) are reported in Table  2.2, and 
additional gases (ERF <0.001 W m–2) are shown in Annex III.
2.2.4.1 Chlorofluorocarbons (CFCs)
Atmospheric abundances of most CFCs have continued to decline since 
2011 (AR5). The globally-averaged abundance of CFC-12 decreased 
by 25 ppt (4.8%) from 2011 to 2019, while CFC-11 decreased by 
about 11 ppt (4.7%) over the same period (Table 2.2 and Figure 2.6). 
Atmospheric abundances of some minor CFCs (CFC-13, CFC-115, 
CFC-113a) have increased since 2011 (Annex III), possibly related to 
use of HFCs (Laube et al., 2014). Overall, as of 2019 the ERF from CFCs

has declined by 9 ± 0.5% from its maximum in 2000, and 4.7 ± 0.6% 
since 2011 (Table 7.5).
While global reporting indicated that CFC-11 production had 
essentially ceased by 2010, and the atmospheric abundance of CFC-11 
is still decreasing, emissions inferred from atmospheric observations 
began increasing in 2013–2014 and remained elevated for 5–6 years, 
suggesting renewed and unreported production (Montzka et  al., 
2018a, 2021; Rigby et al., 2019; Park et al., 2021). The global lifetimes 
of several ozone-depleting substances have been updated (SPARC, 
2013), in particular for CFC-11 from 45 to 52 years. 
2.2.4.2 Hydrochlorofluorocarbons (HCFCs)
The atmospheric abundances of the major HCFCs (HCFC-22, 
HCFC-141b, HCFC-142b), primarily used in refrigeration and foam 
blowing, are increasing, but rates of increase have slowed in recent 
years (Figure 2.6). Global mean mixing ratios (Table 2.2) showed 
good concordance at the time of AR5 for the period 2005–2011. For

the period 2011–2019, the UCI network detected larger increases 
in HCFC-22, HCFC-141b, and HCFC-142b compared to the NOAA and AGAGE networks. Reasons for the discrepancy are presently 
unverified, but could be related to differences in sampling locations 
in the networks (Simpson et al., 2012). Emissions of HCFC-22, derived 
from atmospheric data, have remained relatively stable since 2012, 
while those of HCFC-141b and HCFC-142b have declined (Engel 
et  al., 2018). Minor HCFCs, HCFC-133a and HCFC-31, have been 
detected in the atmosphere (currently less than 1 ppt) and may be 
unintentional by-products of HFC production (Engel et al., 2018).
2.2.4.3 Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs), 
Sulphur Hexafluoride (SF 6) and Other Radiatively 
Important Halogenated Gases
Hydrofluorocarbons (HFCs) are replacements for CFCs and HCFCs. 
The atmospheric abundances of many HFCs increased between 2011 
and 2019. HFC-134a (mobile air conditioning, foam blowing, and

domestic refrigerators) increased by 71 % from 63 ppt in 2011 to 
107.6 ppt in 2019 (Table 2.2). The UCI network detected a slightly 
smaller relative increase (53%). HFC-23, which is emitted as a by-
product of HCFC-22 production, increased by 8.4 ppt (35%) over 500
400
300
200
100
0
250
200
150
100
50
0
25
20
15
10
5
0Changes in global mean atmospheric mixing ratios
 of halogenated GHGs
CFC-12 
HCFC-22 
Halons CFC-11 
CCl4
SF6
SO2F2CFC-113
PFCsHFC-134a
HCFC-141b
HCFC-142bCH3CCl3
CH2Cl2other HFCs
NF3(a)
25 ppt
25 ppt
25 ppt(b)
(c)ppt ppt ppt
1950 1960 1970 1980 1990 2000 2010 2020
Figure 2 .6 | Global mean atmospheric mixing ratios of select ozone-depleting 
substances and other greenhouse gases.  Data shown are based on the CMIP6 
historical dataset and data from NOAA and AGAGE global networks. PFCs include CF 4, 
C2F6, and C 3F8, and c-C4F8; Halons include halon-1211, halon-1301, and halon-2402; 
other HFCs include HFC-23, HFC-32, HFC-125, HFC-143a, HFC-152a, HFC-227ea,

HFC-236fa, HFC-245fa, and HFC-365mfc, and HFC-43-10mee. Note that the y-axis 
range is different for  (a), (b) and (c) and a 25 parts per trillion (ppt) yardstick is given 
next to each panel to aid interpretation. Further data are in Annex III and details on 

305
2011–2019. HFC-32 used as a substitute for HCFC-22, increased at 
least by 300%, and HFC-143a and HFC-125 showed increases 
of 100% and 187%, respectively. While the ERF of HFC-245fa is 
currently <0.001 W m–2, its atmospheric abundance doubled since 
2011 to 3.1 ppt in 2019 (Annex III). In contrast, HFC-152a is showing 
signs of stable (steady-state) abundance. 
Other radiatively important gases with predominantly anthropogenic 
sources also continue to increase in abundance. SF 6, used in electrical 
distribution systems, magnesium production, and semi-conductor 
manufacturing, increased from 7.3 ppt in 2011 to 10.0  ppt in 2019 
(+36%). Alternatives to SF 6 or SF 6-free equipment for electrical systems 
have become available in recent years, but SF 6 is still widely in use in 
electrical switch gear (Simmonds et al., 2020). The global lifetime of 
SF6 has been revised from 3200 years to about 1000  years (Kovács

et al., 2017; Ray et al., 2017) with implications for climate emissions 
metrics (Section 7.6.2). NF 3, which is used in the semi-conductor 
industry, increased 147% over the same period to 2.05 ppt in 2019. 
Its contribution to ERF remains small, however, at 0.0004 W m–2. The 
atmospheric abundance of SO 2F2, which is used as a fumigant in place 
of ozone-depleting methyl bromide, reached 2.5 ppt in 2019, a 46% 
increase from 2011. Its ERF also remains small at 0.0005 W m–2. 
The global abundance of CCl 4 continues to decline, down about 
9.6% since 2011. Following a revision of the global lifetime from 
26 to 32  years, and discovery of previously unknown sources 
(e.g.,  biproducts of industrial emissions), knowledge of the CCl 4 
budget has improved. There is now better agreement between top-
down emissions estimates (based on atmospheric measurements) 
and industry-based estimates (Engel et al., 2018). Halon-1211, mainly

used for fire suppression, is also declining, and its ERF dropped below 
0.001 W m–2 in 2019. While CH 2Cl2 has a short atmospheric lifetime 
(6 months), and is not well-mixed, its abundance is increasing and its 
ERF is approaching 0.001 W m–2.
Perfluorocarbons CF 4 and C 2F6, which have exceedingly long global 
lifetimes, showed modest increases from 2011 to 2019. CF 4, which 
has both natural and anthropogenic sources, increased 8.2% to 
85.5 ppt, and C 2F6 increased 16.3% to 4.85 ppt. c–C4F8, which is used 
in the electronics industry and may also be generated during the 
production of polytetrafluoroethylene (PTFE, also known as ‘Teflon’) 
and other fluoropolymers (Mühle et al., 2019), has increased 34% 
since 2011 to 1.75 ppt, although its ERF remains below 0.001 W m–2. 
Other PFCs, present at mixing ratios <1 ppt, have also been quantified 
(Droste et al., 2020; see Annex III).
2.2.4.4 Summary of Changes in Halogenated Gases

In summary, by 2019 the ERF of halogenated GHGs had increased 
by 3.5% since 2011, reflecting predominantly a decrease in 
the atmospheric mixing ratios of CFCs and an increase in their 
replacements. However, average annual ERF growth rates associated 
with halogenated gases since 2011 are a factor of seven lower than 
in the 1970s and 1980s. Direct radiative forcings from CFCs, HCFCs, 
HFCs, and other halogenated greenhouse gases were 0.28, 0.06, 
0.04, and 0.03 W  m–2 respectively, totalling 0.41 ± 0.07 W m–2 in 
2019 (see Table 7.5). 2.2.5 Other Short-lived Gases
2.2.5.1 Stratospheric Water Vapour
The AR5 assessed low confidence  in stratospheric water vapour (SWV) 
trends based on substantial seasonal and interannual variability in 
satellite data from 1992 to 2011. The 1980–2010 record of balloon-
borne frost point hygrometer measurements over Boulder, Colorado 
(40°N), showed an average net increase of 1.0 ± 0.2 ppm (27 ± 6%) 
in the 16–26 km layer.

Since AR5, bias-adjusted spatially comprehensive SWV measurements 
by different satellite sensors were merged to form continuous records 
(Hegglin et al., 2014; Froidevaux et al., 2015; Davis et al., 2016). These 
indicate no net global increase of SWV in the lower stratosphere since 
the late 1980s. Hegglin et al. (2014) reported a latitudinal dependence 
of SWV trends and suggested that the upward trend over Boulder 
should not be considered representative of the global stratosphere, 
while Lossow et al. (2018) showed insignificant differences between 
SWV trends at Boulder and those for the 35–45°N zonal mean from 
1980 to 2010 using model simulations and satellite observations. 
Recent studies of dynamical influences on SWV (Eguchi et  al., 
2015; Evan et  al., 2015; Tao et  al., 2015; Konopka et  al., 2016; 
Diallo et  al., 2018; Garfinkel et  al., 2018) have demonstrated that 
the quasi-biennial oscillation (QBO), El Niño–Southern Oscillation

(ENSO), Sudden Stratospheric Warming (SSW) events and possibly 
also Pacific Decadal Variability (PDV; W. Wang et  al., 2016), can 
significantly influence SWV abundance and the tropical cold point 
tropopause temperatures that largely control water vapour entering 
the stratosphere. It has also been shown that the convective lofting 
of ice can moisten the lower stratosphere over large regions (Dessler 
et al., 2016; Anderson et al., 2017; Avery et al., 2017). Near-global 
observations of SWV have revealed unusually strong and abrupt 
interannual changes, especially in the tropical lower stratosphere. 
Between December 2015 and November 2016, the tropical mean SWV 
anomaly at 82 hPa dropped from 0.9 ± 0.1 ppm to –1.0 ± 0.1 ppm, 
accompanied by highly anomalous QBO-related dynamics in the 
tropical stratosphere (P .A. Newman et al., 2016; Tweedy et al., 2017) 
and the transition of ENSO from strong El Niño to La Niña conditions

(Davis et al., 2017). The tropical mean SWV anomaly then rose sharply 
to 0.7 ± 0.1 ppm in June 2017 as warm westerlies returned to the 
tropical lower stratosphere and ENSO neutral conditions prevailed 
(Davis et al., 2017). 
In summary, in situ measurements at a single mid-latitude location 
indicate about a 25% net increase in stratospheric water vapour 
since 1980, while merged satellite data records since the late 1980s 
suggest little net change. Recent studies of dynamical influences 
on SWV have highlighted their substantial roles in driving large 
interannual variability that complicates trend detection. There 
thus continues to be low confidence in trends of SWV over the 
instrumental period. Disregarding dynamic influences on SWV, an 
ERF of 0.05 ±  0.05 W m–2 is estimated for SWV produced by CH 4 
oxidation (Section 7.3.2.6), unchanged from AR5.

306
2.2.5.2 Stratospheric Ozone
The AR5 assessed that it was certain that global stratospheric ozone 
from the mid-1990s to 2011 was nearly constant and about 3.5% 
lower than in the reference period 1964–1980. Most of the declines 
occurred prior to the mid-1990s. 
Global annual mean total ozone (Figure 2.7) significantly declined 
by about 3.5% during the 1980s and the early 1990s and by 2.5% 
over 60°S–60°N (near-global). Then, during 2000–2017, both 
global and near-global concentrations increased slightly, but not 
significantly, all in line with production and consumption limits of 
ODS regulated under the Montreal Protocol and its amendments. 
Near-global 2014–2017 mean total ozone is about 2.2% below the 
pre-ozone depletion 1964–1980 average (Braesicke et  al., 2018). 
At  southern and northern mid-latitudes, declines are 5.5% and 
3.0% compared to the 1964–1980 average respectively. Total ozone

remained practically unchanged in the tropics (Braesicke et al., 2018). 
Emission of ODS started before 1980 and some estimates suggest that as much as 40% of the long-term ozone loss occurred between 
1960 and 1980 (Shepherd et  al., 2014), lowering the 1964–1980 
baseline values by about 1% (outside the polar regions), a value 
close to observational uncertainties. The world’s longest record of 
total ozone measurements from Arosa, Switzerland, initiated in 1926, 
does not show any substantial long-term changes before about 1980 
(Staehelin et al., 2018). 
ERF depends strongly on the altitude of ozone changes. Two 
stratospheric regions are mainly responsible for long-term changes 
outside the polar regions. In the upper stratosphere (35–45 km), 
there was a strong decline (about 10%) from the start of observations 
in 1979 up to the mid-1990s and a subsequent increase by about 
4% to present (SPARC/IO3C/GAW, 2019). In the lower stratosphere

(20–25 km), there also was a statistically significant decline (7–8%) 
up to the mid-1990s, followed by stabilization or a small further 
decline (Ball et al., 2018, 2019), although the natural variability is 
too strong to make a conclusive statement (Chipperfield et al., 2018).
280285290295Dobson Units (DU)(a) Near-global (60°S-60°N)
320330340350(DU)
255260265270DU
300310320330DU(b) Northern Hemisphere (35°N-60°N)
(c) Tropics (20°S-20°N)1964-1980
(d) Southern Hemisphere (35°S-60°S)
1970 1980 1990 2000 2010
200250300350400450500DUNorthern hemisphere - March
(60°N-90°N)WOUDCSBUV V8.6/OMPS NASASBUV V8.6/OMPS NOAAGOME/SCIA GSGGOME/SCIA/OMI GTOMSR2
(e) Polar regions 
Southern hemisphere - October
(60°S-90°S)
Mean total ozone in six regions 
1970 1980 1990 2000 2010275
20201970 1980 1990 2000 2010 20201970 1980 1990 2000 2010 2020
20202020

Figure  2.7 | Time series of annual mean total column ozone from 1964–2019.  Values are in Dobson Units (DU), a good proxy for vertically integrated stratospheric 
ozone. Time series are shown for (a) near-global domain; (b–d)  three zonal bands; and (e) polar (60°–90°) total ozone in March (Northern Hemisphere) and October (Southern 
Hemisphere): the months when polar ozone losses usually are largest. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

307
The strongest ozone loss in the stratosphere continues to occur in 
austral spring over Antarctica (ozone hole) with emergent signs of 
recovery after 2000 (Langematz et al., 2018). Interannual variability 
in polar stratospheric ozone is driven by large scale winds and 
temperatures, and, to a lesser extent, by the stratospheric aerosol 
loading and the solar cycle. This variability is particularly large in the 
Arctic, where the largest depletion events, comparable to a typical 
event in the Antarctic, occurred in 2011 (Manney et  al., 2011; 
Langematz et  al., 2018) and again in 2020 (Manney et  al., 2020; 
Grooß and Müller, 2021). Further details on trends and ERF can be 
found in Sections 6.3.2 and 7.3.2.5. 
In summary, compared to the 1964–1980 average, stratospheric ozone 
columns outside polar regions (60°S–60°N) declined by about 2.5% 
over 1980–1995, and stabilized after 2000, with 2.2% lower values in

2014–2017. Large ozone depletions continue to appear in spring in 
the Antarctic and, in particularly cold years, also in the Arctic. Model-
based estimates disagree on the sign of the ERF due to stratospheric 
ozone changes, but agree that it is much smaller in magnitude than 
that due to tropospheric ozone changes (Section 7.3.2.5).
2.2.5.3 Tropospheric Ozone
The AR5 assessed medium confidence  in large-scale increases of 
tropospheric ozone at rural surface sites across the NH (1970–2010), 
and in a doubling of European surface ozone during the 20th 
century, with the increases of surface ozone in the SH being of 
low confidence . Surface ozone likely  increased in East Asia, but 
levelled off or decreased in the eastern USA and western Europe. 
Free tropospheric trends (1971–2010) from ozonesondes and aircraft 
showed positive trends in most, but not all, assessed regions, and 
for most seasons and altitudes. This section focuses on large scale

ozone changes; chemical and physical processes and regional 
changes in tropospheric ozone are assessed in Section 6.3.2.1 and 
Section 7.3.2.5 assesses radiative forcing.
Prior to 1850 ozone observations do not exist, but a recent analysis 
using clumped-isotope composition of molecular oxygen (18O18O in O 2) 
trapped in polar firn and ice, combined with atmospheric chemistry 
model simulations, constrains the global tropospheric ozone increase 
to less than 40% between 1850 and 2005, with most of this increase 
occurring between 1950 and 1980 (Yeung et  al., 2019). Recently, 
the Tropospheric Ozone Assessment Report identified and evaluated 
60 records of surface ozone observations collected at rural locations 
worldwide between 1896 and 1975, which were based on a range of 
measurement techniques with potentially large uncertainties (Tarasick 
et al., 2019). They found that from the mid-20th century (1930s to 
the early 1970s) to 1990–2014, rural surface ozone increased by

30–70% across the northern extra-tropics. This is smaller than the 
100% 20th-century increase reported in AR5, which relied on far 
fewer measurement sites, all in Europe. In the northern tropics limited 
low-elevation historical data (1954–1975) provide no clear indication 
of surface ozone increases (Tarasick et al., 2019). However, similar to 
the northern mid-latitude increases, lower-free tropospheric ozone 
at Mauna Loa, Hawaii increased by approximately 50% from the 
late 1950s to present (Cooper et  al., 2020). Historical observations 
are too limited to draw conclusions on surface ozone trends in the SH tropics and mid-latitudes since the mid-20th century, with 
tropospheric ozone exhibiting little change across Antarctica (Tarasick 
et  al., 2019; Cooper et  al., 2020). Based on reliable UV absorption 
measurements at remote locations (surface and lower troposphere), 
ozone trends since the mid-1990s varied spatially at northern mid-

latitudes, but increased in the northern tropics (2–17%; 1–6 ppbv 
per decade; (Cooper et al., 2020; Gaudel et al., 2020). Across the SH 
these more recent observations are too limited to determine zonal 
trends (e.g., tropics, mid-latitudes, high latitudes).
The earliest observations of free tropospheric ozone (1934–1955) 
are available from northern mid-latitudes where limited data 
indicate a tropospheric column ozone increase of 48 ± 30% up to 
1990–2012 (Tarasick et al., 2019). Starting in the 1960s, records from 
ozonesondes show no significant changes in the free troposphere 
over the Arctic and mid-latitude regions of Canada, but trends are 
mainly positive elsewhere in the northern mid-latitudes (Oltmans 
et  al., 2013; Cooper et  al., 2020). Tropospheric column and free 
tropospheric trends since the mid-1990s based on commercial aircraft, 
ozonesonde observations and satellite retrievals (Figure 2.8b,c), are 
overwhelmingly positive across the northern mid-latitudes (2–7%;

1–4 ppbv per decade) and tropics (2–14%; 1–5 ppbv per decade), with 
the largest increases (8–14%; 3–6 ppbv per decade) in the northern 
tropics in the vicinity of southern Asia and Indonesia. Observations 
in the SH are limited, but indicate average tropospheric column 
ozone increases of 2–12% (1–5 ppbv) per decade in the tropics 
(Figure 2.8c), and weak tropospheric column ozone increases (<5%, 
<1 ppbv per decade) at mid-latitudes (Cooper et al., 2020). Above 
Antarctica, mid-tropospheric ozone has increased since the late 20th 
century (Oltmans et al., 2013). The total ozone ERF from 1750 to 2019 
best estimate is assessed as 0.47 W m–2 (Section 7.3.2.5) and this is 
dominated by increases in the troposphere. The underlying modelled 
global tropospheric ozone column increase (Skeie et al., 2020) from 
1850 to 2010 of 40–60%, is somewhat higher than the isotope 
based upper-limit of Yeung et al. (2019). At mid-latitudes (30°–60°N)

model increases of 30–40% since the mid-20th century are broadly 
consistent with observations.
In summary, limited  available isotopic evidence constrains the 
global tropospheric ozone increase to less than 40% between 1850 
and 2005 ( low confidence ). Based on sparse historical surface/low 
altitude data tropospheric ozone has increased since the mid-20th 
century by 30–70% across the NH ( medium confidence ). Surface/low 
altitude ozone trends since the mid-1990s are variable at northern 
mid-latitudes, but positive in the tropics [2 to 17% per decade] 
(high confidence ). Since the mid-1990s, free tropospheric ozone 
has increased by 2–7% per decade in most regions of the northern 
mid-latitudes, and 2–12% per decade in the sampled regions of the 
northern and southern tropics ( high confidence ). Limited coverage 
by surface observations precludes identification of zonal trends in 
the SH, while observations of tropospheric column ozone indicate

increases of less than 5% per decade at southern mid-latitudes 

308
2.2.6 Aerosols
The AR5 assessed large-scale aerosol optical depth (AOD) trends 
over 2000–2009, concluding that there was low confidence  in 
a  global trend, but that AOD very likely  decreased from 1990 
onwards over Europe and the eastern USA, and increased since 2000 
over eastern and southern Asia. The ERF associated with aerosol–
radiation interactions for 2011 (relative to 1750) was estimated to 
be –0.45  ±  0.5 W m–2 and of aerosol–cloud interaction estimated 
as –0.45 [–1.2 to 0.0] W m–2. Aerosol ERF uncertainty was assessed 
as the largest contributor to the overall ERF uncertainty since 1750. 
This section assesses the observed large-scale temporal evolution 
of tropospheric aerosols. Aerosol-related processes, chemical and 
physical properties, and links to air quality, are assessed in Chapter 6. 
An in-depth assessment of aerosol interactions with radiation and 
clouds is provided in Section 7.3.3.

Aerosol proxy records of improved temporal resolution and quality 
are now available (Kylander et al., 2016; Stevens et al., 2016, 2018; 
Jacobel et al., 2017; Dornelas et al., 2018; Middleton et al., 2018), 
which further advance synthesis of new global compilations of 
aerosol loadings (Lambert et al., 2015; Albani et al., 2016). Estimates 
of the glacial/interglacial ratio in global dust deposition are within 
the range of 2–4 (Albani et  al., 2015; Lambert et  al., 2015). New 
reconstructions indicate a ratio of 3–5 for the glacial/interglacial loadings for mid- and high-latitude ocean of both hemispheres 
(Lamy et al., 2014; Martinez-Garcia et al., 2014; Serno et al., 2015). 
Improved quantification of changes in dust deposition from North 
Africa and North Atlantic sediment records confirms dust deposition 
rates lower by a factor 2–5 during the African Humid Period (10–5 ka) 
compared to the late Holocene (McGee et  al., 2013; Albani et  al.,

2015; Middleton et al., 2018; Palchan and Torfstein, 2019). During the 
Holocene, biogenic emissions and volcanic activity drove significant 
variability (up to one order of magnitude) in sulphate concentrations 
(Schüpbach et al., 2018). 
Ice cores allow for estimation of multi-centennial trends in mid- and 
high-latitude aerosol deposition, including those for sulphate 
and black carbon (Figure 2.9a,b). Sulphate in ice cores increased 
by a factor of 8 from the end of the 19th century to the 1970s in 
continental Europe, by a factor of 4 from the 1940s to the 1970s 
in Russia, and by a factor of 3 from the end of the 19th century to 
1950 in the Arctic (Svalbard). In all regions studied, concentrations 
have declined by about a factor of 2 following their peak (around 
1970 in Europe and Russia, and 1950 in the Arctic). Strong increases 
of black carbon (BC) were observed in the 20th century over Europe, 
Russia, Greenland (primarily originating from emissions from North

America), and in the Arctic (Svalbard). South America exhibits a small 
positive trend (Figure 2.9). BC concentrations in various Antarctic ice 
cores were below 1 ng g–1 without a clear trend. Ozone trend (ppbv per decade) Ozone trend (ppbv per decade) Ozone trend (ppbv per decade)(a) Surface and lower troposphere (b) Mid- and upper troposphere (c) Tropospheric column averageSurface and tropospheric ozone trends
TOST ozonesondes: 1998–2012
  Sat1 1979–2016 (TOMS, OMI/MLS)
  Sat2 1995–2015 (GOME, SCIAMACHY, OMI,
    GOME-2A, GOME-2B) 
  Sat3 1995–2015 (GOME, SCIAMACHY, GOME-II)TOST
TOST
TOST
TOSTSat1Sat1Sat3Sat2
−3−2−1012345678 −3−2−1012345678 −3−2−1012345678low elevation
high elevation
IAGOS aircraftIAGOS aircraft, mid-trop.,      1994–2016
IAGOS aircraft, upper trop.,   1994–2013
Hilo ozonesondes, mid-trop., 1994–2018
p <= 0.05
0.05 < p <= 0.10
0.10 < p < 0.34
p >= 0.34−90−80−70−60−50−40−30−20−100102030405060708090Latitude
  Satellite products:

Figure 2 .8 | Surface and tropospheric ozone trends. (a) Decadal ozone trends by latitude at 28 remote surface sites and in the lower free troposphere (650 hPa, about 
3.5 km) as measured by IAGOS aircraft above 11 regions. All trends are estimated for the time series up to the most recently available year, but begin in 1995 or 1994. Colours 
indicate significance (p-value) as denoted in the in-line key. See Figure 6.5 for a depiction of these trends globally.  (b) Trends of ozone since 1994 as measured by IAGOS aircraft 
in 11 regions in the mid-troposphere (700–300 hPa; about 3–9 km) and upper troposphere (about 10–12 km), as measured by IAGOS aircraft and ozonesondes. (c) Trends 
of average tropospheric column ozone mixing ratios from the TOST composite ozonesonde product and three composite satellite products based on TOMS, OMI/MLS (Sat1),

GOME, SCIAMACHY , OMI, GOME-2A, GOME-2B (Sat2), and GOME, SCIAMACHY , GOME-II (Sat3). Vertical bars indicate the latitude range of each product, while horizontal lines 
indicate the very likely  uncertainty range. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

309
Spatially resolved trends of AOD derived from Aqua/Terra MISR and 
MODIS instruments over 2000–2019 range between –2% and +2% 
per year (Figure 2.9c). Ground-based solar attenuation networks 
help to constrain and improve the satellite-derived retrievals of 
AOD, and trends derived from the AERONET network (Figure 2.9c,d) 
corroborate satellite results (Georgoulias et  al., 2016; Wei et  al., 
2019; Bauer et al., 2020; H. Yu et al., 2020) in particular for declines 
over Europe (Stjern et al., 2011; Cherian et al., 2014; Li et al., 2014) 
and the USA (Li et al., 2014; Jongeward et al., 2016). The tendency in 
AOD over East Asia reversed from positive (2000–2010) to negative 
(since 2010) (Sogacheva et al., 2018; Filonchyk et al., 2019; Ma et al., 
2019; Samset et al., 2019). Over southern Asia, however, AOD from 
satellite (MODIS/MISR) and AERONET retrievals show continuing

increases (Li et al., 2014; Zhao et al., 2017), with similar trends from 
UV-based aerosol retrievals from the Ozone Monitoring Instrument 
(OMI) on the Aura satellite (Dahutia et  al., 2018; Hammer et  al., 
2018). A comparison of MODIS and MISR radiometric observations 
with the broadband CERES satellite instrument (Corbett and 
Loeb, 2015) showed that drifts in calibration are unlikely to affect 
the satellite derived trends. CERES shows patterns for clear-sky 
broadband radiation consistent with the aerosol spatio-temporal 
changes (Loeb et al., 2018; Paulot et al., 2018). Satellite-derived trends are further supported by in situ regional surface 
concentration measurements, operational since the 1980s (sulphate) 
and 1990s (PM2.5) from a global compilation (Collaud Coen et al., 
2020) of networks over Europe (Stjern et al., 2011), North America 
(Jongeward et  al., 2016), and China (Zheng et  al., 2018). Collaud 
Coen et al. (2020) report from surface observations across the NH

mid-latitudes that aerosol absorption coefficients decreased since 
the first decade of the 21st century.
Anthropogenic aerosol is predominantly found in the fraction of 
particles with radii <1 µm that comprise the fine-mode AOD (AODf; 
Figure 2.9d; Kinne, 2019). A significant decline in AODf of more than 
1.5% per year from 2000 to 2019 has occurred over Europe and 
North America, while there have been positive trends of up to 1.5% 
per year over Southern Asia and East Africa. The global-scale trend in 
AODf of –0.03% per year (Figure 2.9) is significant. The results are 
consistent with trend estimates from an aerosol reanalysis (Bellouin 
et al., 2020), and the trends in satellite-derived cloud droplet number 
concentrations are consistent with the aerosol trends (Cherian and 
Quaas, 2020). Cloudiness and cloud radiative properties trends 
are, however, less conclusive possibly due to their large variability 
(Norris  et  al., 2016; Cherian and Quaas, 2020). Further details on

aerosol-cloud interactions are assessed in Section 7.3.3.2.
(c) Trend in aerosol optical depth (2000 –2019) (d) Trend in fine-mode aerosol optical depth (2000 –2019)
1.6−0.8 0 0.8 1.6
Percent per year−1.6−0.8 0 0.8 1.6
Percent per year(a) Non-sea salt sulfate (b) Refractory black carbon
Arctic
Russia
Europe
South America
AntarcticaArctic
Greenland
Russia
Eastern Europe
Europe
Antarctica800
600
400
200
0
1700 1750 1800 1850 1900 1950 2000 1700 1750 1800 1850 1900 1950 200012
9
6
3
0ng g−1
ng g−1Changes in aerosol loadings
Non significantSignificant Colour
Figure 2.9  | Aerosol evolution from ice-core measurements.  Changes are shown as 10-year averaged time series  (a, b)  and trends in remote-sensing aerosol optical 
depth (AOD) and AODf (c, d). (a)  Concentrations of non-sea salt (nss) sulphate (ng g–1). (b) Black carbon (BC) in glacier ice from the Arctic (Lomonosovfonna), Russia (Belukha),

Europe (Colle Gnifetti), South America (Illimani), Antarctica (stacked sulphate record, and BC from the B40 core), and BC from Greenland (stacked rBC record from Greenland 
and eastern Europe (Elbrus)). (c) Linear trend in annual mean AOD retrieved from satellite data for the 2000–2019 period (% yr –1). The average trend from MODerate Resolution 
Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) is shown. Trends are calculated using OLS regression with significance assessed 
following AR(1) adjustment after Santer et al. (2008). Superimposed are the trends in annual-mean AOD from the AERONET surface sunphotometer network for 2000–2019. 
(d) Linear trend in 2000–2019 as in (c), but for fine-mode AOD, AODf, and using only MISR over land. ‘×’ marks denote non-significant trends. Further details on data sources 
and processing are available in the chapter data table (Table 2.SM.1).

310
To conclude, atmospheric aerosols sampled by ice cores, influenced 
by northern mid-latitude emissions, show positive trends from 1700 
until the last quarter of the 20th century and decreases thereafter 
(high confidence ), but there is low confidence  in observations of 
systematic changes in other parts of the world in these periods. 
Satellite data and ground-based records indicate that AOD exhibits 
predominantly negative trends since 2000 over NH mid-latitudes and 
SH continents, but increased over South Asia and East Africa ( high 
confidence ). A globally deceasing aerosol abundance is thus assessed 
with medium confidence . This implies increasing net positive ERF , 
since the overall negative aerosol ERF has become smaller.
2.2.7 Land Use and Land Cover
The AR5 assessed that land use change very likely  increased the 
Earth’s albedo with a radiative forcing of –0.15 (± 0.10) W m–2.

AR5 also assessed that a net cooling of the surface, accounting for 
processes that are not limited to the albedo, was about as likely as not . 
The SRCCL concluded with medium confidence that the biophysical 
effects of land cover change (mainly increased albedo) had a cooling 
effect on surface temperatures. The SRCCL also concluded with very 
high confidence that the biogeochemical effects of land cover change 
(i.e., GHG emissions) resulted in a mean annual surface warming.
Much of the global land surface has been modified or managed to 
some extent by human activities during the Holocene. Reconstructions 
based on pollen data indicate that natural vegetation probably 
covered most of the Earth’s ice-free terrestrial surface until roughly 
the mid-Holocene (Marquer et  al., 2017; Harrison et  al., 2020; 
F . Li et al., 2020). Reconstructions based on pollen, archaeological, 
and historical data indicate deforestation at the regional scale

since at least 6 ka (Marquer et  al., 2017; Stephens et  al., 2019; 
Harrison et al., 2020; F . Li et al., 2020). From a global perspective, 
land-use forcing datasets (Lawrence et  al., 2016) estimate that 
changes in land use (and related deforestation) were small on the 
global scale until the mid-19th century and accelerated markedly 
thereafter, with larger uncertainties prior to industrialization (Kaplan 
et  al., 2017). Since the early 1980s, about 60% of all land cover 
changes have been associated with direct human activities, with 
spatial patterns emphasizing the regional character of land use 
and land management, including tropical deforestation, temperate 
afforestation, cropland intensification, and increased urbanization 
(Song et al., 2018; Zeng et al., 2018). At present, nearly three-quarters 
of the ice-free terrestrial surface is under some form of human use 
(Venter et al., 2016; Erb et al., 2017), particularly in agriculture and 
forest management.

The impact of historical land-cover change on global climate is 
assessed with model simulations that consider multiple climate and 
biophysical processes (e.g.,  changes in albedo, evapotranspiration, 
and roughness) and/or biogeochemical processes (e.g.,  changes in 
atmospheric composition such as carbon release from deforestation). 
The dominant biophysical response to land cover changes is albedo, 
which is estimated (using a MODIS albedo product and a historical 
land-use harmonization product) to have increased gradually prior to 
the mid-19th century and then strongly through the mid-20th century, with a slightly slower rise thereafter (Ghimire et  al., 2014). Recent 
radiative forcing estimates arising from biophysical processes generally 
fall at the lower end of the AR5 assessed range. For instance, based on 
historical simulations from 13 CMIP6 models, C.J. Smith et al. (2020) 
estimated that the ERF from surface albedo changes (including snow

cover and leaf area) was –0.08 [–0.22 to +0.06] W m–2 since 1850. 
Similarly, based on simulations from 13 CMIP5 models, Lejeune et al. 
(2020) estimated the radiative forcing from transitions between trees, 
crops, and grasslands was –0.11 [–0.16 to +0.04] W m–2 since 1860. 
Andrews et  al. (2017) identified an ERF of –0.40 W  m–2 since 
1860, ascribing much of the effect to increases in albedo (including 
the unmasking of underlying snow cover); notably, however, the 
analysis was based on a single model with a known tendency 
to overestimate the ERF (Collins et  al., 2011). Ward et  al. (2014) 
examined the combined effects of biophysical and biogeochemical 
processes, obtaining an RF of 0.9 ± 0.5 W m–2 since 1850 that was 
driven primarily by increases in land-use related GHG emissions from 
deforestation and agriculture (Ward and Mahowald, 2015). According 
to a large suite of historical simulations, the biophysical effects of

changes in land cover (i.e., increased surface albedo and decreased 
turbulent heat fluxes) led to a net global cooling of 0.10°C ± 0.14°C 
at the surface (SRCCL). Available model simulations suggest that 
biophysical and biogeochemical effects jointly may have contributed 
to a small global warming of 0.078°C ± 0.093°C at the surface over 
about the past two centuries (SRCCL), with a potentially even larger 
warming contribution over the Holocene as a whole (He et al., 2014).
In summary, biophysical effects from historical changes in land use 
have an overall negative ERF ( medium confidence ). The best-estimate 
ERF from the increase in global albedo is –0.15 W m–2 since 1700 
and –0.12 W m–2 since 1850 ( medium confidence ) (Section 7.3.4.1). 
Biophysical effects of land-use change likely resulted in a net 
global cooling of about 0.1°C since 1750 ( medium confidence ) 
(Section 7.3.5.3).
2.2.8 Effective Radiative Forcing (ERF) Exerted 
by the Assessed Climate Drivers

The AR5 concluded that changes in climate drivers over the industrial 
period corresponded to a positive ERF which increased more rapidly 
after 1970 than before. There was very high confidence in the positive 
ERF due to WMGHG, with CO 2 the single largest contributor. The AR5 
concluded that there was high confidence that aerosols have offset 
a substantial portion of the WMGHG forcing. 
This section reports the evolution in ERF with respect to 1750 
as assessed in Section 7.3 and relies on the observed changes in 
climate drivers as assessed in Section 2.2 wherever possible, and 
models otherwise. The ERF is assessed using the methods and details 
described in Section 7.3.1 and includes, in addition to the radiative 
forcing, the rapid adjustments, especially implied by clouds. The time 
series are shown in Figure 2.10.
Increasing TSI (Section 2.2.1) implies a small ERF of less than 
0.1 W m–2 between 1900 and 1980. TSI varies over the 11-year solar

cycle with ERF of order ± 0.1 W m–2 in the assessed period. Strong

311
volcanic eruptions (Section 2.2.2) with periods of strong negative 
ERF lasting 2–5 years in duration occurred in the late 19th and early 
20th centuries. There followed a relatively quiescent period between 
about 1920 and 1960, and then three strong eruptions in 1963, 
1982 and 1991, and only small-to-moderate eruptions thereafter 
(Schmidt et al., 2018). 
The atmospheric concentrations of WMGHGs (Section 2.2.3) have 
continuously increased since the early 19th century, with CO 2 
contributing the largest share of the positive ERF . Compared to 
the last two decades of the 20th century, the growth rate of CO 2 in the 
atmosphere increased in the 21st century, showed strong fluctuations 
for CH 4, and was about constant for N 2O. Mixing ratios of the most 
abundant CFCs declined (Section 2.2.4). Mixing  ratios of HCFCs 
increased, but growth rates are starting to decelerate. Mixing ratios

of HFCs and some other human-made components are increasing 
(Section 2.2.4). The ERF for CO 2 alone is stronger than for all the other 
anthropogenic WMGHGs taken together throughout the industrial period, and its relative importance has increased in recent years 
(Figures 2.10 and 7.6).
Among the gaseous short-lived climate forcers (Chapter 6 and 
Sections 2.2.5 and 7.3; excluding CH 4 here), ozone (O 3) is the 
component with the largest (positive) ERF . Concentrations from 
direct observations have increased since the mid-20th century and, 
mostly based on models, this extends to since 1750. Other gaseous 
short-lived climate forcers have small contributions to total ERF .
The net effect of aerosols (Sections 2.2.6 and 6.4) on the radiation 
budget, including their effect on clouds, and cloud adjustments, as 
well as the deposition of black carbon on snow (Section 7.3.4.3), 
was negative throughout the industrial period ( high confidence ).

The net effect strengthened (becoming more negative) over most 
of the 20th  century, but more likely than not  weakened (becoming 
less negative) since the late 20th century. These trends are reflected 
in measurements of surface solar radiation (Section 7.2.2.3) and the 
W m−2
Rate of change of anthropogenic ERF
1750 1800 1850 1900 1950 2000Tropospheric Aerosol
Other anthropogenic
Volcanic
Solar
TotalCarbon dioxide (CO2)
Methane (CH4)
Nitrous oxide (N2O)
Ozone (O3)
Halogenated gases
0.5
0.4
0.3
0.2
0.1
0.0
W m−2  per decadeChanges in effective radiative forcings (ERF)
−4−202
Figure 2.1 0 | Temporal evolution of effective radiative forcing (ERF) related to the drivers assessed in Section 2.2.  ERFs are based upon the calculations 
described in Chapter 7, of which the global annual mean, central assessment values are shown as lines and the 5 to 95% uncertainty range as shading (Section 7.3,

see Figures 7.6 to 7.8 for more detail on uncertainties). The inset plot shows the rate of change (linear trend) in total anthropogenic ERF (total without TSI and volcanic ERF) 
for 30-year periods centred at each dot. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

312
Earth’s energy imbalance (Section 7.2.2.1). The relative importance 
of aerosol forcing compared to other forcing agents has decreased 
globally in the most recent 30 years ( medium confidence ) and the 
reduction of the negative forcing in the 21st century enhances 
the overall positive ERF .
Land use and land cover changes (Section 2.2.7) over the industrial 
period introduce a negative radiative forcing by increasing the surface 
albedo. This effect increased since 1750, reaching current values of 
about –0.20 W m–2 (medium confidence ). This ERF value is taken from 
Section 7.3.4.1 and is different from the assessment in Section 2.2.7 
in that it also includes the effect of irrigation. It also includes uncertain rapid adjustments and thus there is low confidence  in its magnitude. 
Biogeochemical feedbacks can be substantial (Section 5.4) and are 
not included in ERF .

In conclusion, the net ERF due to all observed changes in climate drivers 
is positive, except for short periods (up to a few years in duration) 
following moderate to large volcanic eruptions, and has grown in 
magnitude since the late 19th century. The rate of change likely  has 
increased in the last 30 years, since CO 2 concentrations increased at an 
increasing rate due to growing CO 2 emissions ( very likely ), and since 
the aerosol forcing became less negative ( more likely than not ). 
Cross-Chapter Box 2.2 | Large-scale Indicators of Climate Change
Contributing Authors:  Veronika Eyring (Germany), Nathan P . Gillett (Canada), Sergey K. Gulev (Russian Federation), Jochem Marotzke 
(Germany), June-Yi Lee (Republic of Korea), Peter W. Thorne (Ireland/United Kingdom)
Chapters 2, 3 and 4 assess the current evidence basis for climatic changes, their causes, and their potential future under different

possible emissions pathways using a combination of observations and state-of-the-art Earth system models (ESMs). The assessment in 
these chapters focuses on selected large-scale indicators and modes as defined in this Box. These indicators and modes of variability 
taken together characterize overall changes to the climate system as a whole. 
Defining ‘large scale’
Understanding of large-scale climate variability and change requires knowledge of both the response to forcings and the role of 
internal variability. Many forcings have substantial hemispheric or continental scale variations. Modes of climate variability are 
generally driven by ocean basin scale processes. The climate system involves process interactions from the micro- to the global-scale 
and as such, any threshold for defining ‘large-scale’ is arbitrary, but, within these chapters on the basis of these considerations

large-scale is defined to include ocean basin and continental scales as well as hemispheric and global scales. 
Defining a key set of climate indicators
Key climate indicators should constitute a finite set of distinct variables and/or metrics that may collectively point to important overall 
changes in the climate system that provide a synthesis of climate system evolution and are of broad societal relevance. Key indicators 
have been selected across the atmospheric, oceanic, cryospheric and biospheric domains, with land as a cross-cutting component. These 
indicators, and their use across Chapters 2, 3 and 4, as well as the broader report, are summarized in Cross-Chapter Box 2.2, Table 1. 
All selected indicators are Essential Climate Variables as defined by the Global Climate Observing System (Bojinski et al., 2014).
Atmospheric indicators
Monitoring surface temperatures is integral to the negotiations of UNFCCC and the global mean temperature goals of the Paris

Agreement. Upper-air temperatures are a key indicator of different causal mechanisms underlying climate change and underpinned 
the very first conclusion in the Second Assessment Report of a ‘discernible human influence’ (Santer et al., 1996). To ascertain large-
scale changes of and human influence on the global hydrological cycle (which includes terrestrial and oceanic components), a small 
subset of indicators across this cycle are chosen: ocean and land precipitation–evaporation (P–E), global precipitation, total column 
water vapour, surface humidity (specific and relative), and global river runoff. Chapter 8 performs a substantive and holistic assessment 
of a much broader range of components. Finally, a warming world may be accompanied by a change in large-scale circulation patterns 
linked through energy/mass/momentum constraints such as the extent and strength of the Hadley circulation (HC), monsoon systems,

and/or the position and strength of the sub-tropical and polar jets. 2.3 Changes in Large-scale Climate

313
Cross-Chapter Box 2.2 (continued)
Cross-Chapter Box 2.2, Table 1 | Summary of the large-scale indicators used across Chapters 2 through 4 and their principal applications in 
remaining chapters.  Indicators are sub-divided by Earth-system domain and their inclusion in individual chapters is indicated by a blue dot. The list of additional 
chapters is limited to those where the variable is a principal consideration. 
Selected Large-scale Indicator of Climate ChangeUsed in Chapters Additional Regional or Process-based 
Assessments Undertaken in Chapters 2 3 4
Atmosphere and surface
Surface and upper air temperatures    7, 10, 11, 12, Atlas
Hydrological cycle components (surface humidity, precipitable water vapour, 
precipitation, runoff, Precipitation–Evaporation)   8, 10, 11, 12, Atlas
Atmospheric circulation (sea level pressure and winds, Hadley/Walker circulation,

global monsoons, blocking, storm tracks and jets, sudden stratospheric warmings)   8, 10, 11, 12
Cryosphere
Sea ice extent/area, seasonality and thickness    9, 12, Atlas
Terrestrial snow cover   8, 9, 12, Atlas
Glacier mass and extent   8, 9, 12
Ice-sheet mass and extent   9, 12, Atlas
Terrestrial permafrost temperature and active layer thickness  5, 9, 12
Ocean
Temperature/ocean heat content    5, 7, 9, 12, Atlas
Salinity   8, 9, 12
Sea level    9, 12
Circulation    5, 9
pH and deoxygenation    5, 12, Atlas
Biosphere
Seasonal cycle of CO 2   5, 12
Marine biosphere (distribution of marine biota, primary production, phenology) 
Terrestrial biosphere (distribution of terrestrial biota, global greening/browning, 
growing season)  5, 12
Cryospheric indicators
Changes in ice sheets are indicators of the longest-term impacts of climate change and associated with changes in global and

regional sea level. Seasonal snow cover has many implications for mid- to high-latitude regions (albedo, hydrological cycle, etc.) with 
impacts on biospheric components of the system. Changes in sea ice extent, seasonality and thickness have potential impacts for 
hemispheric-scale circulation (Cross-Chapter Box 10.1). Changes in glacier mass balance contribute to changes in sea level but also 
have substantial implications for water supply for a substantial proportion of the global population. Finally, changes in permafrost and 
the seasonally thawed active layer have substantial implications in mid- to high-latitudes and have been hypothesized to be important 
in potential feedbacks through degassing of WMGHGs as the permafrost thaws.
Oceanic indicators
Most of the energy imbalance (Box 7.2) in the climate system is taken up by the ocean, resulting in changes in ocean temperature and

heat content. Salinity changes indicate broad-scale hydrological cycle and circulation changes. Global-mean sea-level change is a key 
indicator of the impacts of both global warming and changes in global ice volume. Furthermore, it is integral to assessing the global 
energy budget (Cross-Chapter Box 9.1). The oceanic overturning circulation redistributes heat, carbon, oxygen and salinity within the 
ocean. Declines in ocean pH result from air-sea exchange of carbon dioxide and loss of ocean oxygen results from ocean warming; 
both lead to changes in marine ecosystems.

314
Biospheric indicators
The seasonal cycle of CO 2 is an integrated measure of the biogeochemical activity across the global biosphere. Changes in marine 
and terrestrial ecosystems can also be observed directly at large scales. For small, free-floating organisms such as phytoplankton, the 
dynamics can be rapid in nature, whereas on land slower changes in plant assemblages may occur, with commensurate changes in 
altitude and latitude of the tree-line. Lengthening of the growing season and the associated changes in phenology, distribution and 
abundance of species would be expected in most of the extratropics. Biospheric indicators and their impacts are assessed in much 
greater detail in WGII Chapters 2 and 3.
Defining a selection of modes of variability
Many modes of climate variability affect global, hemispheric or regional climate across a range of timescales. Conversely, their

behaviour may be influenced by global climate change. Modes were selected for inclusion that: (i) have effects at large spatial scales; 
and (ii) have substantial potential to modify interannual to multi-decadal climate. The selected modes are considered in multiple 
chapters (Cross-Chapter Box 2.2, Table 2) and are defined in Annex IV.
Cross-Chapter Box 2.2, Table 2 | Summary of the modes of variability used across Chapters 2 through 4 and their principal applications in 
remaining chapters.  Inclusion in each of Chapters 2 through 4 is indicated by a blue dot of the relevant table cell. The list of remaining chapters is limited to those 
where the mode of variability is a principal consideration of that chapter and is not intended to be exhaustive.
Selected Mode of VariabilityUsed in Chapters Additional Regional or Process-based Assessments 
Undertaken in Chapters 2 3 4
El Niño–Southern Oscillation    8
Indian Ocean Basin and dipole modes    8, 10

Atlantic Multi-decadal Variability    8, 10
Pacific Decadal Variability    8, 10
Annular modes (NAO/NAM, SAM)    8, 10
Atlantic Meridional and Zonal Modes    8
2.3.1 Atmosphere and Earth’s Surface
2.3.1.1 Surface Temperatures
2.3.1.1.1 Temperatures of the deep past (65 Ma to 8 ka)
This assessment of the paleo reference periods (Cross-Chapter Box 2.1) 
draws from studies based mostly or entirely on indirect observational 
evidence from geological archives (i.e.,  proxy records) rather than 
reconstructions that rely more heavily on modelled parameters and 
those based on deep-ocean temperatures (e.g.,  Köhler et  al., 2015; 
Friedrich et  al., 2016). In contrast to AR5, temperature estimates 
from climate models are not included in the assessed values for 
paleo reference periods in this chapter. The AR5 concluded that the 
reconstructed GMST during the PETM was 4°C–7°C warmer than 
pre-PETM mean climate ( low confidence ), and that the EECO and the

MPWP were 9°C–14°C and 1.9°C–3.6°C warmer than pre-industrial, 
respectively ( medium confidence ). The GMST during the LIG was 
assessed at 1°C–2°C warmer than pre-industrial (medium confidence ), 
whereas SROCC narrowed the range to 0.5°C–1.0°C warmer, but did 
not state a confidence level. The AR5 further concluded that it was 
very likely  that the LGM was 3°C–8°C colder than pre-industrial, and 
likely  that the maximum rate of global warming during the subsequent 
deglacial period was 1°C–1.5°C kyr –1.For the PETM, new reconstructions agree with those assessed by AR5. 
A major new compilation of proxy temperature data (Hollis et  al., 
2019) analysed using multiple statistical approaches (Inglis et  al., 
2020) indicates that GMST was 10°C–25°C (90% range) warmer 
than 1850–1900, or about 5°C warmer relative to the pre-PETM 
state. A related synthesis study also estimates that PETM warmed 
by 5°C (no uncertainty assigned; Zhu et al., 2019). A recent benthic

isotope compilation (Westerhold et al., 2020) transformed to GMST 
based on the formulation by J. Hansen et al. (2013; Cross-Chapter 
Box 2.1, Figure 1), and adjusted to 1850–1900 by adding 0.36°C, 
shows an increase of GMST by about 10°C during the PETM. This 
reflects the expected higher variability at single sites that were used 
to splice together the composite time series, compared to the globally 
averaged composite time series of Zachos et al. (2008). The latter was 
originally used by J. Hansen et al. (2013) to reconstruct GMST, and 
is the preferred representation of the global average bottom water 
conditions, despite its less well-refined chronology.
For the EECO, new GMST reconstructions fall at the high end of the range 
assessed by AR5. These include estimates of 7°C–18°C (90% range; 
Inglis et al., 2020) and 12°C–18°C (95% range; Zhu et al., 2019) warmer 
than 1850–1900, and 10°C–16°C warmer than 1995–2014 ‘recent

past’ conditions (2 standard error range; Caballero and Huber, 2013).

315
Together, they indicate that GMST was 10°C–18°C warmer during the 
EECO compared with 1850–1900 ( medium confidence ). 
The AR5 did not assess the GMST for the MCO. Reconstructions 
based on data from multiple study sites include estimates of about 
4°C (uncertainty range not specified; You et al., 2009) and 5°C–10°C 
(2 standard error range; Goldner et al., 2014) warmer than 1850–1900. 
Together, these studies indicate that GMST was 4°C–10°C warmer 
during the MCO ( medium confidence ).
For the MPWP , new proxy-based estimates of global sea surface 
temperatures (SST) are about 2.0°C–3.5°C warmer than 1850–1900, 
depending on which proxy types are included in the analysis (Foley 
and Dowsett, 2019; McClymont et al., 2020). On the basis of model-
derived relationships between land versus sea surface temperatures 
under different climate states (Figure 3.2b), the increase in GMST is

estimated to have been roughly 15% greater than the increase in 
global SST. Therefore, GMST during the MPWP is estimated to have 
been 2.5°C–4.0°C warmer than 1850–1900 ( medium confidenc e).
For the LIG (Cross-Chapter Box 2.1, Figure 1, and Figure 2.11), 
a major new compilation of marine proxy data (Turney et al., 2020) 
from 203 sites indicates that the average SST from 129–125 ka was 
1.0°C ± 0.2°C (2 SD) warmer than 1850–1900 (reported relative to 
1981–2010 and adjusted here by 0.8°C). These temperatures represent 
the time of peak warmth, which may not have been synchronous 
among these sites. This compares with two other SST estimates 
for 125 ka of 0.5°C ± 0.3°C (± 2 SD) warmer at 125 ka relative to 
1870–1889 (Hoffman et al., 2017), and about 1.4°C (no uncertainty 
stated) warmer at 125 ka relative to 1850–1900 (Friedrich and 
Timmermann, 2020; reported relative to 10–5 ka and adjusted here 
by 0.4°C; Kaufman et  al., 2020a). The average of these post-AR5

global SST anomalies is 1°C. Commensurately (Figure 3.2b), GMST 
is estimated to have been roughly 1.1°C above 1850–1900 values, 
although this value could be too high if peak warmth was not globally 
synchronous (Capron et al., 2017). A further estimate of peak GMST 
anomalies of 1.0°C–3.5°C (90% range; adjusted here to 1850–1900 
by adding 0.2°C) based on 59 marine sediment cores (Snyder, 2016) is 
considerably warmer than remaining estimates and is therefore given 
less weight in the final assessment. The warmest millennium of the 
LIG GMST reconstruction in J. Hansen et  al. (2013) is 1.5°C above 
1850–1900. In summary, GMST during the warmest millennia of the 
LIG (within the interval of around 129–125 ka) is estimated to have 
reached 0.5°C–1.5°C higher values than the 1850–1990 reference 
period ( medium confidence ). 
New GMST reconstructions for the LGM fall near the middle 
of  AR5’s very likely  range, which was based on a combination of

proxy reconstructions and model simulations. Two of these new 
reconstructions use marine proxies to reconstruct global SST that 
were scaled to GMST based on different assumptions. One indicates 
that GMST was 6.2 [4.5 to 8.1] °C cooler than the late Holocene 
average (Snyder, 2016), and the other, 5.7°C ± 0.8°C (2 SD) cooler 
than the average of the first part of the Holocene (10–5 ka) (Friedrich 
and Timmermann, 2020). A third new estimate (Tierney et al., 2020) 
uses a much larger compilation of marine proxies along with a data-
assimilation procedure, rather than scaling, to reconstruct a GMST of 6.1°C ± 0.4°C (2 SD) cooler than the late Holocene. Assuming that the 
1850–1900 reference period was 0.2°C and 0.4°C cooler than the late 
and first part of the Holocene, respectively (Kaufman et al., 2020a), 
the midpoints of these three new GMST reconstructions average 
–5.8°C relative to 1850–1900. The coldest multi-century period of the

LGM in the J. Hansen et al. (2013) reconstruction is 4.3°C colder than 
1850–1900. This compares to land- and SST-only estimates of about 
–6.1°C ± 2°C and –2.2°C ± 1°C, respectively (2 SD), which are based 
on AR5-generation studies that imply a warmer GMST than more 
recent reconstructions (Figure 1c in Harrison et al., 2015; Figure 7 in 
Harrison et  al., 2016). A major new pollen-based data-assimilation 
reconstruction averages 6.9°C cooler over northern extratropical 
land (Cleator et al., 2020). LGM temperature variability on centennial 
scales was about four times higher globally than during the Holocene, 
and even greater at high latitudes (Rehfeld et al., 2018). In summary, 
GMST is estimated to have been 5°C–7°C lower during the LGM 
(around 23–19 ka) compared with 1850–1900 ( medium confidence ). 
For the LDT (Cross-Chapter Box 2.1, Figure 1), no new large-scale 
studies have been published since AR5 (Shakun et  al., 2012) to

further assess the rate of GMST change during this period of rapid 
global warming (estimated at 1°C–1.5°C per kyr). The reconstruction 
of Shakun et  al. (2012) was based primarily on SST records and 
therefore underrepresents the change in GMST during the LDT. 
Temperature over Greenland increased by about ten times that rate 
during the centuries of most rapid warming (Jansen et al., 2020).
2.3.1.1.2 Temperatures of the post-glacial period (past 7000 years)
The AR5 did not include an assessment of large-scale temperature 
estimates for the MH, although it assigned high confidence  to the 
long-term cooling trend over mid- to high-latitudes of the Northern 
Hemisphere (NH) during the 5 kyr that preceded recent warming. For 
average annual NH temperatures, the period 1983–2012 was assessed 
as very likely  the warmest 30-year period of the past 800 years ( high 
confidence ) and likely  the warmest 30-year period of the past 1.4 kyr

(medium confidence ); the warm multi-decadal periods prior to the 
20th century were unsynchronized across regions, in contrast to 
the warming since the mid-20th century ( high confidence ), although 
only sparse information was available from the SH. 
This section concerns the Holocene period prior to industrialization 
when GMST was overall highest. Whereas SR1.5 focussed upon the 
‘Holocene thermal maximum’ when regional temperatures were 
up to 1°C higher than 1850–1900, though peak warming occurred 
regionally at different times between around 10 and 5 ka greatly 
complicating interpretation. A multi-method reconstruction (Kaufman 
et al., 2020a) based on a quality-controlled, multi-proxy synthesis of 
paleo-temperature records from 470 terrestrial and 209 marine sites 
globally (Kaufman et al., 2020b) indicates that the median GMST of 
the warmest two-century-long interval was 0.7 [0.3 to 1.8] °C warmer 
than 1800–1900 (which averaged 0.03°C colder than 1850–1900;

PAGES 2k Consortium, 2019), and was centred around 6.5 ka. This is 
similar to Marcott et al. (2013), which is based on a smaller dataset 
(73 sites) and different procedures to estimate a maximum warmth 
of 0.8°C ± 0.3°C (2 SD) at around 7.0 ka, adjusted here by adding 
0.3°C to account for differences in reference periods. These may be

316
Changes in surface temperature
1.5
0.01.0
0.5
−0.5
−10,000 −6000 −2000
Last interglacialLatest decade
Age: Agricultural Historical Industrial
Annual Resolution: Centuries DecadesYear (BCE)Global surface temperature 
relative to 1850–1900 ( °C)
1000 1400 1800 1900 2000
Year (CE)
Mid-HoloceneThe latest decade was warmer than any multi-century period
  after the last interglacial, around 125,000 years ago
Over the last 50 years, global temperature
has increased at a rate unprecedented in
at least the last 2000 years(a) Global surface temperatures are more likely than not unprecedented in the past 125,000 years
Kaufman et al.
Pages 2k
AR6 mean
−0.6 −0.4 −0.2 −0.1 0.0 0.6 0.4 0.2 0.11981–2020
Trend (°C per decade)1900–1980(b) Warming accelerated after the 1970s, but not all regions 
are warming equally(c) Temperatures have increased faster over 
land than over the oceans
1.52.0
1.0
0.5
0.0Land
Oceans1.59
0.88
−0.51.0
0.5
0.0
−0.5−0.5

1850 1900 1950 20001850 1900 1950 2000
1.0
0.5
0.0Annual and decadal averages
Each of the last four decades has in 
turn been warmer than any decade 
that preceded it since 1850.HadCRUT .5.0
Kadow et al.NOAAGlobalTemp
Berkeley EarthGlobal surface temperature relative to 1850–1900 (°C) 
Figure 2.11 | Earth’s surface temperature history with key findings annotated within each panel. (a)  GMST over the Holocene divided into three time scales: 
(i) 12 kyr–1 kyr in 100-year time steps; (ii) 1000–1900 CE, 10-year smooth; and (iii) 1900–2020 CE (from panel (c)). Median of the multi-method reconstruction (bold 
lines), with 5th and 95th percentiles of the ensemble members (thin lines). Vertical bars are the assessed medium confidence  ranges of GMST for the Last Interglacial and 
mid-Holocene (Section 2.3.1.1). The last decade value and very likely  range arises from Section 2.3.1.1.3. (b) Spatially resolved trends (°C per decade) for HadCRUTv5 over

(upper map) 1900–1980, and (lower map) 1981–2020. Significance is assessed following AR(1) adjustment after Santer et al. (2008), ‘×’ marks denote non-significant trends. 
(c) Temperature from instrumental data for 1850–2020, including (upper panel) multi-product mean annual time series assessed in Section 2.3.1.1.3 for temperature over the 
oceans (blue line) and temperature over the land (red line) and indicating the warming to the most recent 10 years; and annually (middle panel) and decadally (bottom panel) 
resolved averages for the GMST datasets assessed in Section 2.3.1.1.3. The grey shading in each panel shows the uncertainty associated with the HadCRUT5 estimate (Morice 
et al., 2021). All temperatures relative to the 1850–1900 reference period. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

317
underestimates because averaging inherently smoothed proxy records 
with uncertain chronologies reduces the variability in the temperature reconstruction (e.g.,  Dolman and Laepple, (2018) for sedimentary 
archives). However, the general coincidence between peak warmth 
and astronomically driven boreal summer insolation might reflect a bias toward summer conditions (Liu et al., 2014; Hou et al., 2019; Bova et  al., 2021), suggesting that the estimate is too high. This possibility is supported by AR5-generation proxy data focusing on 6 ka 
(Harrison et al., 2014), the long-standing MH modelling target (Cross-
Chapter  Box 2.1), that indicate surface temperatures for land and ocean were indistinguishable from ‘pre-industrial’ climate (Figure 1c in Harrison et al., 2015; Figure 7 in Harrison et al., 2016). In contrast, 
the GMST estimate from the multi-method global reconstruction

(Kaufman et al., 2020a) for the millennium centred on 6 ka is only about 0.1°C colder than the warmest millennium. 
Taking all lines of evidence into account, the GMST averaged over 
the warmest centuries of the current interglacial period (sometime 
between around 6 and 7 ka) is estimated to have been 0.2°C–1.0°C higher than 1850–1900 (medium confidence). It is therefore more 
likely than not that no multi-centennial interval during the post-glacial period was warmer globally than the most recent decade 
(which was 1.1°C warmer than 1850–1900; Section 2.3.1.1.3); the LIG 
(129–116 ka) is the next most recent candidate for a period of higher global temperature. Zonally averaged mean annual temperature reconstructions (Routson et al., 2019) indicate that MH warmth was most pronounced north of 30°N latitude, and that GMST subsequently 
decreased in general, albeit with multi-century variability, with greater 
cooling in the NH than in the SH (Kaufman et al., 2020a).

The temperature history of the last millennium and the methods 
used to reconstruct it have been studied extensively, both prior 
to and following AR5, as summarized recently by Smerdon and Pollack (2016) and Christiansen and Ljungqvist (2017). New regional (e.g., Shi et al., 2015; Stenni et al., 2017; Werner et al., 2018), global ocean (McGregor et  al., 2015), quasi-hemispheric (Neukom et  al., 
2014; Schneider et  al., 2015; Anchukaitis et  al., 2017), and global 
(Tardif et al., 2019) temperature reconstructions, and new regional proxy data syntheses (Lüning et al., 2019a, b) have been published, extending back 1–2 kyr. In addition, a major new global compilation of multiproxy, annually resolved paleo-temperature records for the 
CE (PAGES 2k Consortium, 2017) has been analysed using a variety

of statistical methods for reconstructing temperature (PAGES 2k Consortium, 2019). The median of the multi-method GMST reconstruction from this synthesis (Figure 2.11a) generally agrees with the AR5 assessment, while affording more robust estimates of 
the following major features of GMST during the CE: (i) an overall 
millennial-scale cooling trend of –0.18 [–0.28 to 0.00] °C kyr
 –1 prior 
to 1850; (ii) a multi-centennial period of relatively low temperature beginning around the 15th century, with GMST averaging –0.03 
[–0.30 to 0.06] °C between 1450 and 1850 relative to 1850–1900; 
(iii) the warmest multi-decadal period occurring most recently; and(iv) the rate of warming during the second half of the 20th century(from instrumental data) exceeding the 99th percentile of all 51-yeartrends over the past 2 kyr. Moreover, the new proxy data compilation
shows that the warming of the 20th century was more spatially

uniform than any other century-scale temperature change of theCE (medium confidence) (Neukom et al., 2019). A new independenttemperature reconstruction extending back to 1580 is based on an
expanded database of subsurface borehole temperature profiles,
along with refined methods for inverse modelling (Cuesta-Valeroet al., 2021). The borehole data, converted to GMST based on themodelled relation between changes in land versus sea surfacetemperature outlined previously, indicate that average GMST for
1600–1650 was 0.12°C colder than 1850–1900, which is similar to
the PAGES 2k reconstruction (0.09°C colder), although both estimates are associated with relatively large uncertainties (0.8°C (95% range)and 0.5°C (90% range), respectively). 
To conclude, following approximately 6 ka, GMST generally decreased,

culminating in the coldest multi-century interval of the post-glacial period (since 8 ka), which occurred between around 1450 and 1850 (high confidence). This multi-millennial cooling trend was reversed in the mid-19th century. Since around 1950, GMST has increased at an 
observed rate unprecedented for any 50-year period in at least the 
last 2000 years (high confidence).
Cross-Chapter Box 2.3 | New Estimates of Global Warming to Date, and Key Implications
Contributing Authors:   Peter W. Thorne (Ireland/United Kingdom), Blair Trewin (Australia), Richard P . Allan (United Kingdom), 
Richard Betts (United Kingdom), Lea Beusch (Switzerland), Chris Fairall (United States of America), Piers Forster (United Kingdom), Baylor Fox-Kemper (United States of America), Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Nathan P . Gillett (Canada), 
Ed Hawkins (United Kingdom), Christopher Jones (United Kingdom), Elizabeth Kent (United Kingdom), Svitlana Krakovska (Ukraine),

Elmar Kriegler (Germany), Jochem Marotzke (Germany), H. Damon Matthews (Canada), Thorsten Mauritsen (Germany/Denmark), Anna Pirani (Italy), Joeri Rogelj (United Kingdom, Austria/Belgium), Steven K. Rose (United States of America), Bjørn H. Samset (Norway), Sonia I. Seneviratne (Switzerland), Claudia Tebaldi (United States of America), Andrew Turner (United Kingdom), Russell S. Vose (United States of America), Rachel Warren (United Kingdom) 
This Cross-Chapter Box presents the AR6 WGI assessment of observed global warming and describes improvements and updates since 
AR5 and subsequent Special Reports. The revised estimates result from: the availability of new and revised observational datasets; the occurrence of recent record warm years; and the evaluation of the two primary metrics used to estimate global warming in past IPCC 
reports: ‘Global mean surface temperature’ (GMST) and ‘Global surface air temperature’ (GSAT). Implications for threshold crossing

times, remaining carbon budgets and impacts assessments across AR6 WGs are discussed.

318
Cross-Chapter Box 2.3 (continued)
Dataset innovations
Since AR5, all major datasets used for assessing observed temperature change based upon GMST have been updated and improved 
(Section 2.3.1.1.3). A number of new products have also become available, including new datasets (e.g., Berkeley Earth, Rohde and 
Hausfather, 2020) and new interpolations based on existing datasets (e.g., Cowtan and Way, 2014 and Kadow et al., 2020). These 
various estimates are not fully independent. 
Improvements in global temperature datasets since AR5 have addressed two major systematic issues. First, new SST datasets (Huang 
et al., 2017; Kennedy et al., 2019) address deficiencies previously identified in AR5 relating to the shift from predominantly ship-based 
to buoy-based measurements; these improvements result in larger warming trends, particularly in recent decades. Second, all datasets

now employ interpolation to improve spatial coverage. This is particularly important in the Arctic, which has warmed faster than the 
rest of the globe in recent decades (Atlas 5.9.2.2); under-sampling of the Arctic leads to a cool bias in recent decades (Simmons et al., 
2017; Benestad et al., 2019). Some datasets are now spatially complete (Cowtan and Way, 2014; Kadow et al., 2020) while others 
have expanded spatial coverage (Lenssen et al., 2019; Rohde and Hausfather, 2020; Morice et al., 2021; Vose et al., 2021). Several 
interpolation methods have been benchmarked against test cases (e.g., Lenssen et al., 2019), and comparisons with reanalyses further 
confirm the value of such interpolation (Simmons et al., 2017). It is extremely likely that interpolation produces an improved estimate 
of the changes in GMST compared to ignoring data-void regions.

Overall, dataset innovations and the availability of new datasets have led to an assessment of increased GMST change relative to the 
directly equivalent estimates reported in AR5 (Cross-Chapter Box 2.3, Table 1 and Figure 1).
Effects of warming since AR5 and choice of metrics of global mean temperature change
Each of the six years from 2015 to 2020 has likely  been warmer than any prior year in the instrumental record. GMST for the decade 
2011–2020 has been 0.19 [0.16 to 0.22] °C warmer than 2003–2012, the most recent decade used in AR5 (Cross-Chapter Box 2.3, 
Figure 1). A linear trend has become a poorer representation of observed change over time since most of the sustained warming has 
occurred after the 1970s (Cross-Chapter Box 2.3, Figure 1) and all values since 2012 are at least 0.2°C above a linear trendline for 
1850–2020. For this reason, the primary method used to assess observed warming in this report is the change in temperature from

1850–1900 to the most recent decade (2011–2020) or the recent past (1995–2014), replacing the trend-based methods used in AR5 
and earlier assessments. The effect of this change from trend-based to change-based metrics is currently relatively minor at –0.03°C 
(<5%) for the most recent decade, but this may not remain the case in future ( high confidence ).
Observed changes in global mean temperature since the pre-industrial era
AR5 used 1850–1900 as an approximate pre-industrial baseline for global temperature change, whilst using an earlier pre-industrial 
baseline of 1750 for radiative forcings. Cross-Chapter Box 1.2 assesses that there was an observed GMST change from the period 
around 1750 to 1850–1900 of around 0.1°C ( likely  range –0.1 to +0.3°C, medium confidence ). This additional global temperature 
change before 1850–1900 is not included when making AR6 assessments on global warming to date, global temperature threshold

crossing times, or remaining carbon budgets to ensure consistency with previous ARs.
Addressing the non-equivalence of GMST and GSAT 
GMST is a combination of land surface air temperatures (LSAT) and SSTs, whereas GSAT is a combination of LSAT and marine air 
temperatures (MATs). Although GMST and GSAT are closely related, the two measures are physically distinct. The implications have 
become more apparent since AR5 (Merchant et al., 2013; Cowtan et al., 2015; Simmons et al., 2017; IPCC, 2018 (SR1.5); Richardson 
et  al., 2018), and it has been shown (Rubino et  al., 2020) that MAT and SST can show distinct multi-decadal-scale trends and 
patterns of interannual variability. Although SR1.5 used GMST for observational-based and GSAT for model-based headline warming 
statements, they noted the importance of the difference for their assessment (SR1.5 Section 1.2.1.1). The SR1.5 used information

from CMIP5 models to estimate a GSAT equivalent from observation-based GMST for certain applications such as remaining carbon 
budgets. The following subsections assess available lines of evidence related to the equivalence between GMST and GSAT. 
Physical understanding
A well-understood physical constraint on the vertical gradient between the air and sea surface temperature is that it is approximately 
proportional to the turbulent sensible heat flux in the atmospheric surface layer (Chor et al., 2020). Similarly, the latent heat flux scales 
with the vertical humidity gradient and, in the global mean and in most oceanic regions, the latent heat flux is substantially larger than 
the sensible heat flux (Sections 7.2.1 and 9.2.1.3). If GSAT were to warm faster than GMST, the sensible surface heat flux would respond 
so as to reduce this difference. However, it is the sum of the sensible, latent, and radiative heat fluxes that controls GMST, so the sensible

319
heat flux effect cannot be considered in isolation. Attempts to further constrain the combination of fluxes (e.g., Lorenz et al., 2010; 
Siler et al., 2019) rely on parameterizations or output from Earth system models (ESMs) or reanalyses and so are not considered 
independent. Apart from the above global considerations, regional and seasonal effects such as changes to the frequency and intensity 
of storms, sea state, cloudiness, sea ice cover, vegetation and land use may all affect the GSAT to GMST difference, either directly or 
by altering the relationships between gradients and energy fluxes. These changing energy flux relationships are monitored through 
observing the stratification of the upper ocean (Section 9.2.1.3) and the response of upper ocean processes (Cross-Chapter Box 5.3) in

ESMs and reanalyses, but such monitoring tasks rival the observational challenge of directly observing SSTs and 2 m air temperature 
under a wide range of conditions. In summary, because of the lack of physical constraints and the complexity of processes driving 
changes in the GSAT to GMST temperature differences, there is no simple explanation based on physical grounds alone for how this 
difference responds to climate change.
Direct observational evidence
There is currently no regularly updated, entirely observation-based dataset for GSAT. The best available observations of near-surface 
air temperature over ocean are datasets of night-time marine air temperature (NMAT; e.g., Cornes et al., 2020; Junod and Christy, 
2020), though spatial coverage is less extensive than for SST. Night-time measurements are used to avoid potential biases from 
daytime heating of ship superstructures. Kennedy et al. (2019) show little difference between HadNMAT2 and HadSST4 between

1920 and 1990, but a warming of SST relative to NMAT manifesting as a step change of 0.05°C–0.10°C in the early 1990s, which 
may reflect an actual change, the impact of increasingly divergent spatial coverage between SST and MAT measurements, or 
unresolved structural uncertainties in one or both datasets. This leads to NMAT warming around 10% more slowly than SST over 
the last century. In contrast, Junod and Christy (2020) find NMAT trends which are 8–17% larger than those for SST in the ERSSTv4 
and HadISST datasets for the period 1900 to 2010, but 11–15% smaller than the SST trends for the same datasets from 1979 to 
2010. However, ERSSTv4 uses NMAT data as a basis for homogeneity adjustment so is not fully independent. Kent and Kennedy 
(2021) note sensitivity to methodological choices in comparisons but find that NMAT is warming more slowly than SST products

over most periods considered. Rubino et al. (2020) exploit tropical Pacific moored buoy arrays, available since the early 1980s, and 
find differences in NMAT and SST anomalies, which are sensitive to the choice of period and show spatio-temporal ENSO-related 
(Annex IV) signals in the differences. 
Overall, with medium evidence  and low agreement , available observational products suggest that NMAT is warming less than SST 
by up to 15%. Given that these ocean observations cover roughly two thirds of the globe, this implies that GMST is warming up to at 
most 10% faster than GSAT. Substantial uncertainty remains and the effect is highly sensitive to the choice of both time period and 
choice of NMAT and SST observational products to compare. Observed NMAT warming faster than observed SST cannot be precluded.
CMIP model-based evidence
CMIP historical simulations and projections agree that GSAT increases faster than GMST, the reverse of what is indicated by many

marine observations. Several studies approximate the approach used to derive GMST from observations by blending SST over open 
ocean and SAT over land and sea ice from model output (Cowtan et al., 2015; Richardson et al., 2018; Beusch et al., 2020; Gillett 
et al., 2021). Cowtan et al. found that trends in GSAT are of the order of 9% larger than for GMST in CMIP5, based on data from 
1850–2100 (historical + RCP8.5), if anomalies are blended and sea ice is allowed to vary over time (Cowtan et al., 2015). Broadly 
consistent numbers are found for both CMIP5 and CMIP6, across a range of SSP and RCP scenarios and time periods (Richardson 
et al., 2018; Beusch et al., 2020; Gillett et al., 2021). Blending monthly anomalies and allowing sea ice to vary, the change in GSAT 
for 2010–2019 relative to 1850–1900 is 2–8% larger than spatially-complete GMST in CMIP6 historical and SSP2-4.5 simulations

(Gillett et al., 2021), and 6–12% larger in CMIP5 historical and RCP2.6 and 8.5 simulations for 2007–2016 relative to 1861–1880 
(Richardson et al., 2018). However, a true like-for-like comparison to observational products is challenging because methodological 
choices have a large impact on the relationship between modelled GMST and GSAT and none of these studies fully reproduces the 
methods used to derive estimates of GMST in recent observational datasets, which use various ways to infill areas lacking in situ 
observations (Jones, 2020). 
Marine boundary layer behaviour and parameterizations in all CMIP models are based upon Monin-Obukhov similarity theory 
(e.g.,  Businger et  al., 1971), which informs assumptions around gradients in the near-surface boundary layer dependent upon 
temperature, wind speed and humidity. This leaves open the possibility of a common model bias, while Druzhinin et al. (2019) also

point to departures of temperature profiles from theoretical predictions under certain conditions. There remain inadequacies in 
understanding and modelling of key processes (Edwards et al., 2020), and biases in the representation of the absolute SST-MAT 
difference have been identified in climate models and reanalyses (Găinuşă-Bogdan et al., 2015; Zhou et al., 2020).

320
Reanalysis-based evidence
Simmons et al. (2017) found that in JRA-55 and ERA-Interim (following an adjustment to account for an apparent discontinuity), GSAT 
increased 2–4% faster than GMST over the period 1979–2016. In atmospheric reanalyses, SST is given as a lower boundary condition 
from an observed globally interpolated product (such as HadISST; Rayner et al., 2003) whereas the air temperature is reliant upon 
model parameterizations and assimilated observations that do not include MAT observations (Simmons et al., 2017), thereby limiting 
their capability to constrain differences in GMST and GSAT trends. Furthermore, it is unclear what the lack of dynamic coupling at the 
ocean-atmosphere interface might imply for the representativeness of reanalysis-based estimates. 
Representation of surface temperatures in sea ice regions

There is a significant issue in areas where sea ice melts or grows, where the quantity used in observational-based GMST estimates 
switches between air temperature and sea surface temperature. This primarily affects analyses combining SAT anomalies over 
land and ice with SST anomalies over ocean. In areas where sea ice has recently melted, the climatological value changes from an 
air-temperature based estimate to an SST estimate based upon the freezing point of seawater (–1.8°C). This switch in climatology to, 
in general, a warmer climatology, leads to a bias towards reduced warming in anomalies compared with analyses based on absolute 
temperatures. Richardson et al. (2018) found this underestimation to amount to approximately 3% of observed warming in historical 
model simulations. Given the projected future sea ice losses, the effect will grow in future ( low confidence ), with potential effects of

the order of 0.1°C in the second half of the 21st century under high warming scenarios, although with some uncertainty arising from 
the large spread of sea ice loss in model projections (Tokarska et al., 2019). 
Cross Chapter Box 2.3, Table 1 | Summary of key observationally based global warming estimates (in °C) to various reference periods in the 
present report and selected prior reports (AR5 WGI and SR1.5) and their principal applications (see Section 1.4.1 for further information on 
reference periods).  Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).
Reference 
PeriodAR6 GMST 
(°C)AR6 GSATa 
(°C)AR5 and/or SR1.5  (italics ) – 
Only Where Reported (°C)Principal Use of This Period in this Report 
and Previous Reports
1850–1900 to 
2011–20201.09  
[0.95 to 1.20]1.09  
[0.91 to 1.23]  Warming to present in AR6 WGI
2010–20191.06  
[0.92 to 1.17]1.06

[0.88 to 1.21]  Attributable warming assessment period in AR6 WGI
2006–20191.03  
[0.89 to 1.14]1.03  
[0.86 to 1.18] AR6 WGI warming estimate as a line of evidence for energy budget 
constraints to estimate ECS and TCR
2006–20150.94  
[0.79 to 1.04]0.94  
[0.76 to 1.08]0.87 [0.75 to 0.99] – GMST
0.97 [0.85 to 1.09] – GSATbWarming to date in SR1.5 
2003–20120.90  
[0.74 to 1.00]0.90  
[0.72 to 1.03]0.78 [0.72 to 0.85] Warming to date in AR5 WGI 
2001–20200.99  
[0.84 to 1.10]0.99  
[0.81to 1.14]Warming to first two decades of 21st century
1995–20140.85  
[0.69 to 0.95]0.85  
[0.67 to 0.98]  Warming to recent past in AR6 WGI
1986–20050.69  
[0.54 to 0.79]0.69  
[0.52 to 0.82]0.61 [0.55 to 0.67]cWarming to recent past in AR5 WGI. This difference is used to report in 
this box the implications of the AR6 historical global surface temperature

assessment in a way that is directly comparable to the AR5 estimate.
1961–19900.36  
[0.23 to 0.44]0.36  
[0.22 to 0.45] Warming to reference period recommended by WMO for national-level data 
sets used for climate change assessment (included in the AR6 WGI Atlas)
1880–2012 
OLS trend0.92  
[0.68 to 1.17]  0.85 [0.65 to 1.06]Warming trend to date in AR5 WGI Summary for Policymakers and AR5 
Synthesis Report
a As the uncertainty in the relationship between GMST and GSAT changes is independent of the uncertainty in the assessed change in GMST, these uncertainties are 
combined in quadrature.
b The SR1.5 derived a GSAT estimate by taking the CMIP5 ensemble mean GSAT change of 0.99°C, sub-sampling to HadCRUTv4.6, noting the offset in trends 
(0.84°C HadCRUT4 observed GMST vs. 0.86°C modelled GMST) and adjusting by this to arrive at an estimate of 0.97°C change in GSAT. The likely uncertainty range 
of ±0.12°C was not further adjusted.

c Note that the AR5 approach for the change from 1850–1900 to both 1986–2005 and 2003–2012 was based upon one dataset (HadCRUT4) and its parametric 
uncertainty estimates are known to underestimate the true uncertainty.

321
Summary of lines of evidence
GMST and GSAT are physically distinct. There is high confidence  that long-term changes in GMST and GSAT differ by at most 10% in 
either direction. However, conflicting lines of evidence from models and direct observations combined with limitations in theoretical 
understanding lead to low confidence  in the sign of any difference in long-term trends. The very likely  range of estimated historical 
GMST warming is combined with the assessed ± 10% uncertainty in the relationship between GMST and GSAT changes to infer 
a GSAT equivalent, accounting for any possible real-world physical difference. Improvements in understanding may yield a robust 
basis to apply a scaling-factor to account for the difference in future assessments.
Mapping between AR5 and AR6 Assessments

The AR5 assessed estimate for historical warming between 1850–1900 and 1986–2005 is 0.61 [0.55 to 0.67] °C. The equivalent in 
AR6 is 0.69 [0.54 to 0.79] °C, and the 0.08 [-0.01 to 0.12] °C difference is an estimate of the contribution of changes in observational 
understanding alone (Cross-Chapter Box 2.3, Table 1). The exact value of this contribution depends upon the metric being compared 
(GMST/GSAT, the method used to calculate a trend or change between two periods, the exact reference period used), with the 
best estimates (with the exception of the SR1.5 GSAT estimate) falling between 0.07°C and 0.12°C. The choice of 1850–1900 to 
1986–2005 as the basis is due to the widespread use of this period across AR5 and SR1.5 in several contexts. The AR6-assessed 
GMST warming between 1850–1900 and 2011–2020 is 1.09 [0.95 to 1.20] °C. An AR5-equivalent assessment using this estimated

difference in observational understanding is thus 1.01 [0.94 to 1.08] °C. These updates and improvements in observational datasets 
affect other quantities that derive from the assessment of GSAT warming, including estimates of remaining carbon budgets and 
estimates of crossing times of 1.5°C and 2°C of global warming (see Cross Chapter Box 2.3, Table 1).
Updates to estimated Global Warming Level (GWL) crossing times
The updated estimate of historical warming is one contribution to the revised time of projected crossing of the threshold of 1.5°C 
global warming in comparison with SR1.5, but is not the only reason for this update. The AR6 assessment of future change in GSAT 
(Table 4.5) results in the following threshold-crossing times, based on 20-year moving averages. The threshold-crossing time is defined 
as the midpoint of the first 20-year period during which the average GSAT exceeds the threshold. During the near term (2021–2040),

a 1.5°C GSAT increase relative to the average over the period 1850–1900 is very likely  to occur in scenario SSP5-8.5, likely to occur in 
scenarios SSP2-4.5 and SSP3-7.0, and more likely than not  to occur in scenarios SSP1-1.9 and SSP1-2.6. In all scenarios assessed here 
except SSP5-8.5, the central estimate of crossing the 1.5°C global warming level lies in the early 2030s. This is in the early part of the 
likely range (2030–2052) assessed in SR1.5, which assumed continuation of the then-reported warming rate; this estimated rate has 
been confirmed in AR6 (Section 3.3.1). Roughly half of this difference arises from the higher diagnosed historical warming in AR6. The 
other half arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than 
was assessed as ‘current’ in SR1.5 ( medium confidence ). When considering scenarios similar to SSP1-1.9 instead of linear extrapolation,

the SR1.5 estimate of when 1.5°C global warming is crossed is close to the central estimate reported here (SR1.5, Table 2.SM.12). 
Implications for assessment of emissions scenarios and remaining carbon budgets
To estimate the global warming implications of emissions scenarios, AR5 and SR1.5 combined estimates of observed GMST changes 
from 1850–1900 to 1986–2005 (Cross-Chapter Box 2.3, Table 1) with GSAT projections of subsequent warming. AR6 undertakes three 
changes to this approach. First, the AR6 assessment of improved observational records is used. Second, the recent past baseline period 
is updated from 1986–2005 to 1995–2014, and, third, historical estimates are expressed in GSAT instead of GMST for consistency 
of historical estimates with future projections. The updated estimates of warming to date in AR6 lead to higher estimates of future 
warming, all else being equal. The temperature classification of emissions scenarios in the WGIII report adopts the definition of

temperature classes as introduced in SR1.5, and assigns emissions scenarios to these classes based on their AR6 assessed GSAT 
outcomes (Cross-Chapter Box 7.1; WGIII Annex C.II.2.4). 
In both AR5 and SR1.5, remaining carbon budgets were expressed as a function of GSAT warming, while also highlighting the 
implications of using historical warming estimates expressed in GMST. The AR5 reported total carbon budgets for GSAT warming 
relative to 1861–1880. The AR5 Synthesis Report (SYR) also includes remaining carbon budget estimates based on AR5 WGIII 
scenario projections that use the method for AR5 scenario projections described above. The SR1.5 integrated several methodological 
advancements to estimate remaining carbon budgets and reported budgets for additional GSAT warming since the 2006–2015 period, 
estimating, following the application of an adjustment (Richardson et al., 2016, Table 1.1, SR1.5) to GMST, that 0.97°C (± 0.12°C) of

GSAT warming occurred historically between 1850–1900 and 2006–2015. The AR6 assessment, above, leads to an estimate of 0.94°C 
of warming between 1850–1900 and 2006–2015. All other factors considered equal, the AR6 estimate thus implies that 0.03°C 
more warming is considered for remaining carbon budgets compared to SR1.5. Combining this 0.03°C value with the SR1.5 transient

322
climate response to cumulative emissions of CO 2 (TCRE) translates into remaining carbon budgets about 70 [40–140] GtCO 2 larger 
compared to SR1.5 on a like-for-like basis. Meanwhile, on the same like-for-like basis, updates to historical observational products 
would reduce remaining carbon budgets reported in AR5 SYR based on WGIII scenario projections by about 180 [120 to 370] GtCO 2. 
Box 5.2 provides a further overview of updates to estimates of the remaining carbon budget since AR5. 
(a) Change in assessed historical global surface temperature estimates since AR5 
(b) Assessed global surface temperature anomalies
Anomaly relative to 1961–1990 mean ( °C)
Difference AR6−AR5 (°C)AR5 assessment
AR6 assessmentUpdates AR5 to AR6
Data product updates
Linear reg. to difference
Additional years
GMST to GSAT
Period around 1750
18500.0 0.5 1.0 1.5
1870 1890 1910 1930 1950 1970 1990 2010AR5
AR6
AR6−AR51.0

0.8
0.6
0.4
0.2
0.0
−0.2
−0.4
−0.6
−0.8
−1.00.2
0.0
−0.2Assessed change (°C)
Cross-chapter Box 2.3, Figure 1 | Changes in assessed historical surface temperature changes since AR5. (a) Summary of the impact of various steps 
from AR5 assessment  warming-to-date number for 1880–2012 using a linear trend fit to the AR6 assessment based upon the difference between 1850–1900 and 
2011–2020. Whiskers provide 90% ( very likely ) ranges. AR6 assessment in addition denotes additional warming since the period around 1750 (Cross-Chapter Box 
1.2). (b) Time series of the average of assessed AR5 series (orange, faint prior to 1880 when only HadCRUT4 was available) and AR6 assessed series (blue) and their 
differences (offset) including an illustration of the two trend fitting metrics used in AR5 and AR6. Further details on data sources and processing are available in the 

323
Implications for assessment of impacts and adaptation
The assessment of global warming to date now being larger than previously assessed has no consequence on the assessment of past 
climate impacts, nor does it generally imply that projected climate impacts are now expected to occur earlier. The implications are 
mainly that the level of warming associated with a particular impact has been revised. This has very limited practical implications 
for the assessment of the benefits of limiting global warming to specific levels, as well as for the urgency of adaptation action. For 
example, impacts that occurred in the period 1986–2005 were previously associated with a GMST increase of 0.61°C relative to 
1850–1900, relative to AR5 estimates. These impacts are now instead associated with a GMST increase of 0.69°C, relative to the

assessment in this Report. The impacts themselves have not changed. Similarly, the impacts previously associated with a GMST or 
GSAT increase of 1.5°C will now generally be associated with a slightly different global warming level. This is because projections of 
future warming and its impacts relative to 1850–1900 are normally made by adding projected warming from a recent past baseline 
to an estimate of the observed warming from 1850–1900, as in AR5 and SR1.5. 
Most of the previously projected impacts and risks associated with global warming of 1.5°C have therefore not changed and are 
still associated with the same level of future warming (0.89°C) relative to 1986–2005. With this warming now estimated as 0.08°C 
larger than in AR5, the future impacts previously associated with 1.5°C warming are now associated with 1.58°C warming. Similarly, 
the impacts now associated with 1.5°C warming would have previously been associated with 1.42°C warming. There are exceptions

where impacts studies have used a baseline earlier than 1986–2005 (e.g., King et al., 2017), for which the new estimate of the 
historical warming would mean an earlier occurrence of the projected impacts. However, even in these cases, the ostensible difference 
in impacts associated with a 0.08°C difference in global mean temperature will be small in comparison with the uncertainties. There 
are also substantial uncertainties in regional climate changes and the magnitude of climate impact-drivers projected to occur with 
global warming of 1.5°C (Betts et al., 2018; Seneviratne et al., 2018). Furthermore, the time of reaching global warming of 1.5°C is 
subject to uncertainties of approximately ±10 years associated with uncertainties in climate sensitivity, and ±3 to 4 years associated 
with the different SSP forcing scenarios (Section 4.3.4, Table 4.5, and see discussion above).

There is therefore high confidence  that assessment of the magnitude and timing of impacts-related climate quantities at 1.5°C is 
not substantially affected by the revised estimate of historical global warming. The assessment of the implications of limiting global 
warming to 1.5°C compared to 2°C will also remain broadly unchanged by the updated estimate of historical warming, as this depends 
on the relative impacts rather than the absolute impacts at any specific definition of global temperature anomaly ( high confidence ). 
2.3.1.1.3 Temperatures during the instrumental period – surface
The AR5 concluded that it was certain that GMST had increased 
since the late 19th century. Total warming in GMST was assessed 
as 0.85 [0.65 to 1.06] °C over 1880–2012, while the change from 
1850–1900 to 2003–2012 was assessed at 0.78 [0.72 to 0.85] °C, 
and from 1850–1900 to 1986–2005 at 0.61 [0.55 to 0.67] °C. The 
SR1.5 reported warming of GMST from 1850–1900 to 2006–2015

of 0.87°C, with an 1880–2012 trend of 0.86°C and an 1880–2015 
trend of 0.92°C. The SRCCL concluded that since the pre-industrial 
period, surface air temperature over land areas has risen nearly twice 
as much as the global mean surface temperature ( high confidence ). 
Since AR5, there have been substantial improvements in the 
availability of instrumental archive data both over the ocean and 
on land. A new version of the International Comprehensive Ocean-
Atmosphere Dataset (ICOADS Release 3.0, Freeman et  al., 2017) 
comprises over 450 million in situ marine reports and incorporates 
newly digitized data, increasing coverage in data sparse regions and 
times (e.g., polar oceans and World War I). The International Surface 
Temperature Initiative released a much improved collection of 
fundamental land surface air temperature records (Rennie et al., 2014) comprising more than 35,000 station records. These advances, both

of which have substantially improved spatial coverage, have reduced 
uncertainties in assessments of both land and marine data. 
Marine domain
For SST analyses, three products – HadSST4 (1850–present, Kennedy 
et  al., 2019), ERSSTv5 (1850–present, Huang et  al., 2017) and 
COBE SST2 (1880–present, (Hirahara et al., 2014) – now have bias 
adjustments applied throughout the record. The new SST datasets 
account for two major issues previously identified in AR5: that 
globally averaged buoy SSTs are about 0.12°C cooler than ship-
based SSTs (Kennedy et al., 2011; Huang et al., 2015), and that SSTs 
from ship engine room intakes may have biases for individual ships 
depending upon the sensor set-up (Kent and Kaplan, 2006) but have 
an overall warm bias when globally aggregated (Kennedy et al., 2019). 
The first issue primarily affects data since 1990, when buoys began to 
increasingly contribute to the observation network (Woodruff et al.,

2011), and the second issue has its largest effect from the 1940s 
to the 1970s. From the standpoint of uncertainty, ERSSTv4 (W. Liu 
et  al., 2015; Huang et  al., 2016) and subsequent versions (Huang 
et al., 2017), and HadSST4 have estimates presented as ensembles 
that sample parametric uncertainty. Comparisons  between these

324
independently-derived analyses and the assessed uncertainties 
(Kennedy, 2014; Kent et al., 2017) show unambiguously that global 
mean SST increased since the start of the 20th century, a conclusion 
that is insensitive to the method used to treat gaps in data coverage 
(Kennedy, 2014). 
A number of recent studies also corroborate important components of 
the SST record (Hausfather et al., 2017; Kent et al., 2017; Cowtan et al., 
2018; Kennedy et al., 2019). In particular, ATSR SST satellite retrievals 
(Merchant et al., 2012; Berry et al., 2018), the near-surface records 
from hydrographical profiles (Gouretski et  al., 2012; Huang et  al., 
2018), and coastal observations (Cowtan et al., 2018) have all been 
shown to be broadly consistent with the homogenized SST analyses. 
Hausfather et al. (2017) also confirmed the new estimate of the rate 
of warming seen in ERSSTv4 since the late 1990s through comparison

with independent SST data sources such as Argo floats and satellite 
retrievals. Nevertheless, dataset differences remain in the mid-20th 
century when there were major, poorly-documented, changes in 
instrumentation and observational practices (Kent et  al., 2017), 
particularly during World War II, when ship observations were limited 
and disproportionately originated from US naval sources (Thompson 
et al., 2008). Kennedy et al. (2019) also identify differences between 
the new HadSST4 dataset and other SST datasets in the 1980s and 
1990s, indicating that some level of structural uncertainty remains 
during this period, whilst Chan et al. (2019) and Davis et al. (2019) 
document residual uncertainties in the early and later 20th century 
records respectively. 
Historically, SST has been used as a basis for global temperature 
assessment on the premise that the less variable SST data provides 
a better estimate of marine temperature changes than marine

air temperature (MAT) (Kent and Kennedy, 2021). However, MAT 
products are used to adjust SST biases in the NOAA SST product 
because they are assessed to be more homogeneous (Huang 
et  al., 2017). Observational datasets exist for night-marine air 
temperature (NMAT) (e.g., Cornes et al., 2020; Junod and Christy, 
2020; Rayner et al., 2020) and there are methods to adjust daytime 
MATs (Berry et al., 2004), but there is to date no regularly updated 
dataset which combines MAT with temperatures over land. MAT 
datasets are more sparse in recent decades than SST datasets as 
marine datasets have become increasingly dependent on drifting 
buoys (Centurioni et al., 2019) which generally measure SST but not 
MAT, and there are almost no recent winter MAT data south of 40°S 
(Swart et  al., 2019). However, the situation reverses in the 19th 
century with a greater prevalence of MAT than SST measurements 
available in the ICOADS data repository (Freeman et  al., 2017,

2019; Kent and Kennedy, 2021).
Land domain
The GHCNMv4 dataset (Menne et al., 2018) includes many more land 
stations than GHCNMv3, arising from the databank efforts of Rennie 
et al. (2014), and calculates a 100-member parametric uncertainty 
ensemble drawing upon the benchmarking analysis of Williams et al. 
(2012), as well as accounting for sampling effects. A new version of 
the CRUTEM dataset (CRUTEMv5, Osborn et al., 2021) has increased 
data completeness and additional quality control measures. A new 
global land dataset, the China Land Surface Air Temperature (CLSAT) dataset (Xu et al., 2018) has higher network density in some regions 
(particularly Asia) than previously existing datasets. Global trends 
derived from CLSAT are generally consistent with those derived from 
other land datasets through 2014 (Xu et al., 2018).
The AR5 identified diurnal temperature range (DTR) as a substantial 
knowledge gap. The most recent analysis of Thorne et al. (2016a, b)

compared a broad range of gridded estimates of change in DTR, 
including a new estimate derived from the ISTI databank release 
using the pairwise homogenization algorithm used to create 
GHCNMv4, and estimates derived from Vose et al. (2005), HadEX2 
(Donat et al., 2013a), HadGHCND (Donat et al., 2013b), GHCNDEX 
(Donat et al., 2013b), Berkeley Earth (Rohde et al., 2013), and CRU TS 
(Harris et al., 2014). The analysis highlighted substantial ambiguity 
in pre-1950 estimates arising from sparse data availability. After 
1950 estimates agreed that DTR had decreased globally with most 
of that decrease occurring over the period 1960–1980. A subsequent 
DTR analysis using CLSAT further confirmed this behaviour (X. Sun 
et al., 2018). 
No recent literature has emerged to alter the AR5 finding that it is 
unlikely  that any uncorrected effects from urbanization (Box 10.3), or 
from changes in land use or land cover (Section 2.2.7), have raised

global Land Surface Air Temperature (LSAT) trends by more than 
10%, although larger signals have been identified in some specific 
regions, especially rapidly urbanizing areas such as eastern China 
(Y . Li et al., 2013; Liao et al., 2017; Z. Shi et al., 2019). There is also no 
clear indication that site-specific data homogeneity issues have had 
any significant impact on global trends since the early 20th century; 
there is more uncertainty in the 19th century, mainly arising from 
a lack of standardization of instrument shelters, which has been 
largely accounted for in data from central Europe (Jones et al., 2012), 
but less so elsewhere. 
Combined data products
At the time of AR5 a limitation of conventional datasets was the lack 
of coverage, especially in high latitudes, which although recognized 
as an issue (Simmons et al., 2010) had not been addressed in most 
products. Interpolation involves the statistical imputation of values

across regions with limited data and can add both systematic and 
random uncertainties (Lenssen et al., 2019). Cowtan and Way (2014) 
applied a kriging-based method to extend existing datasets to polar 
regions, while Kadow et al. (2020) used an artificial intelligence-based 
method, and Vaccaro et  al. (2021) used gaussian random Markov 
fields, for the same purpose, although only Kadow et al. (2020) uses 
the most recent generation of datasets as its base. The Berkeley Earth 
merged product (Rohde and Hausfather, 2020), HadCRUT5 (Morice 
et al., 2021) and NOAA GlobalTemp-Interim (Vose et al., 2021) all 
include interpolation over reasonable distances across data sparse 
regions which results in quasi-global estimates from the late 1950s 
when continuous Antarctic observations commenced. Interpolated 
datasets with substantial coverage of high latitudes show generally 
stronger warming of GMST than those with limited data in polar

regions (Vose et al., 2021), and their strong warming at high northern 
latitudes is consistent with independent estimates from reanalyses 
(Simmons et al., 2017; Lenssen et al., 2019) and satellites (Cowtan 
and Way, 2014). Given the spatial scales of surface temperature

325
variations and the verification of the methods, it is extremely likely  
that interpolation results in a less-biased estimate of the actual 
global temperature change than ignoring regions with limited or no 
data.
In total there are five conventional datasets which meet spatial 
coverage requirements and draw from the most recent generation 
of SST analyses, four of which have sufficient data in the 1850–1900 
period to allow an assessment of changes from that baseline 
(Table 2.3). A fifth dataset is added to the assessment for changes 
over land areas. Datasets share SST and LSAT data products and 
in several cases differ solely in the post-processing interpolation 
applied meaning that there are far fewer methodological degrees 
of freedom than implied by a straight count of the number of 
available estimates.
Estimates of GMST have also benefitted from improved estimation

of parametric uncertainties. New versions of three long-standing 
products from NASA GISTEMP v4 (Lenssen et  al., 2019), NOAA 
GlobalTempv5 (B. Huang et  al., 2019b) and HadCRUT5 (Morice 
et  al.,  2021) are all now available as ensemble estimates. These 
ensembles each account for a variety of systematic and random 
uncertainty effects in slightly different ways, giving broadly similar 
results, which are incorporated into the present assessment, with the 
total uncertainty generally declining up until the mid-20th century as 
data coverage improves.
Another significant development has been the incorporation of 
reanalysis products (Section 1.5.2) into operational monitoring 
of GSAT. It was reported in AR5 that various reanalyses were broadly 
consistent with conventional surface datasets in the representation 
of trends since the mid-20th century. Since that time, Simmons et al. 
(2017) found that the ERA-Interim (Dee et  al., 2011) and JRA-55

(Kobayashi et al., 2015) reanalyses continued to be consistent, over 
the last 20 years, with those surface datasets which fully represented the polar regions. GSAT trends from ERA5 reanalysis (Hersbach 
et  al., 2020) are also broadly consistent with GMST trends from 
conventional surface datasets. However, the MERRA-2 reanalysis 
(Gelaro et  al., 2017) GSAT spuriously cooled sharply relative to 
ERA-Interim and JRA-55 in about 2007 (Funk et al., 2019). Since the 
early 2000s, analyses of surface temperature, from which near-surface 
temperature may be derived, have also been available from various 
satellites (Famiglietti et  al., 2018; Prakash et  al., 2018; Susskind 
et  al., 2019), which have the potential to improve assessments of 
temperature changes over data-sparse regions. 
Most land areas in the extratropical Northern Hemisphere (NH) have 
warmed faster than the GMST average over both the 1900–2020 
and 1980–2020 periods (Figure 2.11b), although at more regional

scales, particularly in data sparse regions, considerable uncertainty 
is introduced by sometimes large differences in trends between 
different LSAT datasets (Rao et  al., 2018). Temperatures averaged 
over land areas globally have warmed by 1.59 [1.34 to 1.83] °C 
from 1850–1900 to 2011–2020, substantially higher than the SST 
warming of 0.88 [0.68 to 1.01] °C. The four conventional surface 
temperature products which meet all criteria to be included in the 
final assessment (Table 2.4) agree that each of the last four decades 
has consecutively been the warmest globally since the beginning of 
their respective records (Figure 2.11c and Table 2.4). Each of the six 
years 2015 to 2020 has very likely been at least 0.9°C warmer than 
the 1850–1900 average.
To conclude, from 1850–1900 to 1995–2014, GMST increased by 
0.85 [0.69 to 0.95] °C, to the first two decades of the 21st century 
(2001–2020) by 0.99 [0.84 to 1.10] °C, and to the most recent

decade (2011–2020) by 1.09 [0.95 to 1.20] °C. Each of the last four 
decades has in turn been warmer than any decade that preceded it 
since 1850. Temperatures have increased faster over land than over 
the oceans since 1850–1900, with warming to 2011–2020 of 1.59 
[1.34 to 1.83] °C versus 0.88 [0.68 to 1.01] °C, respectively.
Table 2.3 |  Principal characteristics of GMST in situ data products considered in AR6 WGI, highlighting interdependencies in underlying land and SST 
products and whether inclusion criteria are met.
DatasetPeriod of 
RecordLand 
ComponentSST 
ComponentEnsemble 
Uncertainties?Meets all Inclusion 
Criteria?Principal Reference
HadCRUT5 1850–2020 CRUTEM5 HadSST4 Yes Yes Morice et al. (2021)
NOAA GlobalTemp – Interim 1850–2020 GHCNv4 ERSSTv5 Yes, on earlier version Yes Vose et al. (2021)
Berkeley Earth 1850–2020 Berkeley HadSST4 No Yes Rohde and Hausfather (2020)
Kadow et al. 1850–2020 CRUTEM5 HadSST4 No Yes Kadow et al. (2020)

China – MST 1856–2020 CLSAT ERSSTv5 No Land only Sun et al. (2021)
GISTEMP 1880–2020 GHCNv4 ERSSTv5 Yes Post-1880 only Lenssen et al. (2019)
Cowtan and Way 1850–2020 CRUTEM4 HadSST3 Yes No Cowtan and Way (2014)
Vaccaro et al. 1850–2020 CRUTEM4 HadSST3 No No Vaccaro et al. (2021)

326
Table 2 .4 | Observed increase (°C) in GMST and underlying LSAT and SST estimates in various datasets.  Numbers in square brackets indicate 5–95% confidence 
ranges. Trend values are calculated with ordinary least squares following Santer et al. (2008) and expressed as a total change over the stated period. Datasets considered in 
this table are those with data for at least 90% of global grid points in each year from 1960 onwards. GMST and SST are shown only for data sets which use air temperature (as 
opposed to climatological SST values) over sea ice. Changes from an 1850–1900 baseline are calculated only for those datasets which have data in at least 80% of years over 
1850–1900. GMST values for each year are calculated as the mean of hemispheric means for the NH and SH, while LSAT and SST values are calculated from hemispheric means

weighted according to the proportion of land (ocean) in the two hemispheres. This may vary from the methods used by individual data set providers in their own reporting. 
Products which meet all criteria to be included in the final assessment and contribute to the average are shown in italics. Further details on data sources and processing are 
available in the chapter data table (Table 2.SM.1).
Diagnostic/
Dataset1850–1900 to 
1995–2014 (°C)1850–1900 to 
2001–2020 (°C)1850–1900 to 
2011–2020 (°C)Trend
1880–2020 (°C)Trend
1960–2020 (°C)Trend
1980–2020 (°C)
HadCRUT5GMST0.87 
[0.81 to 0.94]1.01 
[0.94 to 1.09]1.12 
[1.06 to 1.18]1.10 
[0.89 to 1.32]1.04
[0.93 to 1.14]0.76
[0.65 to 0.87]
LSAT 1.23 
[1.06 to 1.38]1.44 
[1.26 to 1.59]1.55 
[1.39 to 1.70]1.43
[1.16 to 1.70]1.50
[1.33 to 1.67]1.20
[1.04 to 1.36]
SST0.73 
[0.69 to 0.78]0.85 
[0.81 to 0.90]0.94 
[0.90 to 0.99]1.03
[0.80 to 1.25]0.90
[0.80 to 0.99]0.62
[0.51 to 0.72]
NOAA GlobalTemp – 
InterimGMST 0.76 0.91 1.021.06

[0.80 to 1.32]1.01
[0.90 to 1.11]0.75
[0.63 to 0.87]
LSAT 1.34 1.55 1.691.58
[1.32 to 1.84]1.54
[1.40 to 1.68]1.19
[1.04 to 1.35]
SST 0.53 0.65 0.750.85
[0.59 to 1.12]0.79
[0.69 to 0.89]0.57
[0.44 to 0.70]
GISTEMP v4GMST1.07
[0.80 to 1.34]1.05
[0.94 to 1.16]0.79
[0.67 to 0.90]
LSAT1.48
[1.19 to 1.78]1.56
[1.40 to 1.72]1.23
[1.07 to 1.39]
SST0.91
[0.65 to 1.17]0.84
[0.74 to 0.95]0.61
[0.49 to 0.72]
Berkeley EarthGMST 0.89 1.03 1.141.17
[0.94 to 1.40]1.09
[1.00 to 1.19]0.79
[0.68 to 0.90]
LSAT 1.28 1.49 1.601.50
[1.25 to 1.76]1.51
[1.36 to 1.66]1.16
[1.00 to 1.32]
SST 0.73 0.85 0.961.04
[0.81 to 1.26]0.93
[0.84 to 1.01]0.64
[0.54 to 0.74]
China-MST LSAT 1.18 1.38 1.491.48
[1.21 to 1.75]1.48
[1.31 to 1.65]1.16
Kadow et al.GMST 0.86 1.00 1.091.15
[0.95 to 1.35]1.01
[0.92 to 1.10]0.73
[0.63 to 0.82]
LSAT 1.29 1.49 1.611.60
[1.37 to 1.82]1.46
[1.30 to 1.61]1.14
[0.99 to 1.30]
SST 0.69 0.80 0.880.97
[0.78 to 1.16]0.83
[0.76 to 0.90]0.56
[0.48 to 0.65]
Cowtan-WayGMST0.82

[0.75 to 0.89]0.96 
[0.89 to 1.03]1.04 
[0.97 to 1.11]1.03
[0.84 to 1.22]0.94
[0.82 to 1.07]0.77
[0.67 to 0.87]
LSAT 1.23 1.43 1.541.42
[1.15 to 1.68]1.48
[1.31 to 1.65]1.20
SST 0.66 0.76 0.840.88
[0.71 to 1.05]0.73
[0.61 to 0.84]0.61
[0.52 to 0.69]
Vaccaro et al.GMST 0.76 0.89 0.970.99
[0.81 to 1.17]0.89
[0.77 to 1.00]0.72
[0.63 to 0.81]
LSAT 1.15 1.35 1.471.40
[1.13 to 1.67]1.47
[1.29 to 1.64]1.21
[1.06 to 1.36]
SST 0.60 0.70 0.770.82
[0.67 to 0.97]0.66
[0.55 to 0.76]0.53
[0.44 to 0.61]
ERA5GSAT0.78
[0.64 to 0.92]
LSAT1.21
[1.02 to 1.40]
Average – GMST 0.85 0.99 1.09 1.11 1.04 0.76
Average – LSAT 1.27 1.47 1.59 1.50 1.51 1.18
Average – SST 0.67 0.79 0.88 0.96 0.86 0.60

327
2.3.1.2 Temperatures During the Instrumental Period – 
Free Atmosphere
The AR5 reported that it was virtually certain  that tropospheric 
temperatures have risen, and stratospheric temperatures fallen, since 
the mid-20th century, but that assessments of the rate of change 
and its vertical structure had only medium confidence  in the NH 
extratropics and low confidence  elsewhere. In particular there was 
low confidence  in the vertical structure of temperature trends in the 
upper tropical troposphere.
2.3.1.2.1 Dataset developments
There have been updated radiosonde estimates from the University 
of Vienna (RAOBCORE and RICH; Haimberger et al., 2012) and a new 
dataset from the State University of New York (UAHRD, Zhou et al., 
2020). There are new versions of AMSU products from the University 
of Alabama in Huntsville (UAHv6.0; Spencer et al., 2017) and Remote 
Sensing Systems (RSSv4.0; Mears and Wentz, 2017). These updates

have led to convergence in the lower stratosphere layer (Maycock 
et  al., 2018); in particular, the move to UAHv6.0 has addressed 
homogeneity issues identified by Seidel et al. (2016), although residual 
differences remain (Christy et  al., 2018). Reanalyses products had 
identified limitations near the 300 hPa level where the contribution 
of aircraft observations has increased rapidly in recent years (Dee 
et al., 2011; Gelaro et al., 2017), leading to identified biases (Dee and 
Uppala, 2009), that have been addressed in ERA5 (Hersbach et al., 
2020). Modern reanalyses are generally well aligned with radiosonde 
and satellite observations in the middle and lower troposphere and 
lower stratosphere. A new operational mid- and upper-stratospheric 
dataset (STAR) has been developed by Zou and Qian (2016), merging 
the previous 1979–2006 SSU dataset (Zou et  al., 2014) with a 
dataset from 1998 onwards drawn from relevant AMSU channels

(Wang and Zou, 2014). Further stratospheric satellite-based datasets 
from various combinations of satellites have been developed by 
McLandress et al. (2015) and Randel et al. (2016).
New assessments of free-atmosphere temperature are available 
through radio occultation (RO) and Atmospheric Infrared Sounder 
(AIRS) products which begin in the early 2000s (Section 1.5.1.1). Global 
Navigation Satellite System (GNSS)-RO datasets have been compared 
against AMSU data records, finding almost identical trends (Khaykin 
et al., 2017). Comparison of RO with collocated radiosondes, Vaisala 
RS90/92 and GCOS Reference Upper Air Network data (RS92-GDP; 
Dirksen et  al., 2014), show very good correspondence with global 
annual mean differences of less than 0.2°C in the upper troposphere 
and lower stratosphere. Radiosonde daytime radiation biases were 
identified at higher altitudes (Ladstädter et al., 2015; Ho et al., 2017). 
The stability of RO makes this data a useful comparator for AMSU

(Chen and Zou, 2014) and radiosondes (Ho et al., 2017; Tradowsky 
et al., 2017), as well as anchoring post-2006 reanalyses datasets and 
improving their consistency in the lower and middle stratosphere (Long 
et al., 2017; Ho et al., 2020). The effective vertical resolution of RO 
measurements in the upper troposphere and lower stratosphere was 
found to be up to 100 m at the tropical tropopause (Zeng et al., 2019a), 
which is favourable for resolving atmospheric variability (Scherllin-
Pirscher et  al., 2012; Wilhelmsen et  al., 2018; Stocker  et  al.,  2019). Temperature trends in RO products are most consistent with each other 
and with other observations between 8 km and 25 km (Ho et al., 2012; 
Steiner et al., 2013, 2020a). The uncertainty increases above 25 km for 
the early RO period, for which data are based on the single-satellite 
CHAMP mission, but data at higher altitudes become more reliable 
for later missions based on advanced receivers (Steiner et al., 2020a),

along with the application of corrections for ionospheric effects 
(Danzer et al., 2020). The uncertainty due to the changing number of 
observations is reduced by correcting for the sampling uncertainty in 
RO climatological fields (e.g., Scherllin-Pirscher et al., 2011). For AIRS, 
thus far, stability of the instrument has been constrained to less than 
0.03°C per decade for selected window channels in a comparison to 
SSTs measured by ocean buoys (Aumann et al., 2019). Trends were 
inter-compared with trends in RO data and reanalysis data to assess 
systematic uncertainties (Leroy et al., 2018).
2.3.1.2.2 Assessment of trends
Warming has continued in the lower troposphere according to all 
radiosonde, reanalyses and satellite datasets, with a rate over 
1980–2019 similar to surface warming rates (Table 2.5; c.f. Table 2.4). 
Radiosonde-based products generally show greater warming rates 
for 1980–2019 than satellite-based products and reanalyses. They

also extend further back to the 1950s and trends since quasi-global 
coverage around 1960 also show warming (Table 2.5). Trends in RO 
and AIRS data, supported by radiosonde datasets, exhibit a warming 
trend in most of the mid- to upper- troposphere at all non-polar 
latitudes over 2002–2019. These also exhibit faster warming rates 
in the tropics in the upper troposphere than those observed at or 
near the surface (Figure 2.12); with the lowermost stratosphere also 
warming while above it is cooling. There is some spread between 
different data types in the tropics near the 15km level, although 
these differences are reduced to near zero if a subset of radiosonde 
data, using only high-quality instruments, is used (Steiner et  al., 
2020b). AMSU tropical middle troposphere data also show that 
warming rates are near or above those in the lower troposphere, but 
they are measuring much broader layers which greatly complicates 
interpretation (Steiner et al., 2020b).

Temperatures averaged through the full lower stratosphere (roughly 
10–25 km) have decreased over 1980–2019 in all data products, 
with the bulk of the decrease prior to 2000. The decrease holds 
even if the influence of the El Chichon (1982) and Pinatubo (1991) 
volcanic eruptions on the trend, found by Steiner et al. (2020a) to 
have increased the 1979–2018 cooling trend by 0.06°C per decade, 
is removed. Most datasets show no significant or only marginally 
significant trends over 2000–2019, and the results of Philipona et al. 
(2018) show weak increases over 2000–2015 in the very lowermost 
stratosphere sampled by radiosondes.
The STAR dataset shows cooling in the middle and upper stratosphere 
with a trend of –0.56°C ± 0.16°C per decade for the mid-stratosphere 
and –0.62°C ± 0.29°C per decade for the upper stratosphere over 
1980–2019, although both cooling rates have slowed substantially 
since the mid-1990s. The overall post-1980 trend is reduced in

magnitude by about 0.10°C per decade at both levels if the influences 
of the El Chichon and Pinatubo eruptions, and the solar cycle, are

328
removed (Zou and Qian, 2016). The results obtained by McLandress 
et al. (2015) for 1980–2012, Randel et al. (2016) for 1979–2015, and 
Maycock et al. (2018) for 1979–2016 are broadly consistent with this.
A rise in the tropopause height of 40 to 120 m per decade between 
1981 and 2015 was determined from both radiosonde and reanalysis 
datasets (Xian and Homeyer, 2019). Local studies (e.g.,  Tang et al., 2017; 
X. Chen et al., 2019) found stronger trends in some regions near the 
subtropical jet linked to tropical expansion (Section 2.3.1.4.1). Whilst 
Seidel and Randel (2006) found that the tropopause height was more 
closely coupled with temperatures in the  stratosphere than those 
in the troposphere, it is not yet clear whether the rate of increase in  tropopause height has experienced a similar recent slowdown 
to that of the cooling of the lower stratosphere, as short-period

trends are typically inconclusive due to significant natural variability 
(Scherllin-Pirscher et al., 2021). RO data (Gao et al., 2015) indicate 
little change in tropopause height over the short period from 2006 
to 2014, but a warming below the tropopause is observed over 2002 
to 2019 (Figure 2.12). 
In summary, the troposphere has warmed since the mid-20th century. 
There is medium confidence  that temperatures in the tropical upper 
troposphere have warmed faster than those at the surface since 2001, 
but low confidence  in changes prior to 2001. It is virtually certain  
Upper air temperature trends
−0.5 0.0 0.5 −0.5 0.0 0.5 −0.5 0.0 0.5
Temperature trend (°C per decade)−0.5 0.0 0.5(b) Global (1980–2019) (c) Global (2002–2019) (d) Tropics (1980–2019) (e) Tropics (2002–2019) RO (ROM SAF)
 RO (UCAR/NOAA)
 RO (WEGC) AIRS
RAOBCORE
RICH
 ERA5.1
50
100
200
300
400
500
700
90050
100
200
300
400
500
700
900Altitude (km)
Pressure (hpa)50
100
200
300
400
500
700
90025
20
15
10
5
025
20
15

10
5
025
20
15
10
5
00510152025
−70−50−30 10−10 30 50 70  
25
20
15
10
5
050
100
200
300
400
500
700
900Latitude °N Altitude (km)Radio occultation
Modern reanalysisInfrared satellite
Radiosonde (a) RO temperature trend  (ROM SAF), 2002–2019
−0.55−0.45−0.35−0.25−0.15−0.05 0.55 0.45 0.35 0.25 0.15 0.05
°C per decade
Figure 2.1 2 | Temperature trends in the upper air. (a) Zonal cross-section of temperature anomaly trends (2007–2016 baseline) for 2002–2019 in the upper troposphere 
and lower stratosphere region. The climatological tropopause altitude is marked by a grey line. Significance is not indicated due to the short period over which trends are shown, 
and because the assessment findings associated to this figure relate to difference between trends at different heights, not the absolute trends. (b, c)  Trends in temperature at

various atmospheric heights for 1980–2019 and 2002–2019 for the near-global (70°N–70°S) domain. (d, e)  as for (b, c) but for the tropical (20°N–20°S) region. Further details 
on data sources and processing are available in the chapter data table (Table 2.SM.1).

329
that the lower stratosphere has cooled since the mid-20th century. 
However, most datasets show that lower stratospheric temperatures 
have stabilized since the mid-1990s with no significant change over 
the last 20 years. It is likely  that middle and upper stratospheric 
temperatures have decreased since 1980, but there is low confidence  
in the magnitude. It is virtually certain  that the tropopause height has 
risen over 1980–2019 but there is low confidence  in the magnitude 
of this rise, or whether the rate of change has reduced commensurate 
with stabilized lower stratospheric temperatures.
2.3.1.3 Global Hydrological Cycle
This section focuses on large-scale changes in a subset of components 
of the hydrological cycle (Cross-Chapter Box 2.2). Chapter 8 undertakes 
a holistic assessment of changes in the hydrological cycle integrating 
observations, modelling and theoretical understanding, while Chapter 11

assesses hydrological cycle extremes such as droughts and floods. 2.3.1.3.1 Paleo perspective of the global hydrological cycle
The AR5 assessed large-scale indicators of terrestrial paleo 
hydroclimate, including as part of its assessment of paleo floods and 
droughts, but did not assess proxy evidence for paleo hydroclimate 
indicators over continental and larger scales. The paleoclimate 
evidence assessed in AR5 was broadly consistent with global 
hydroclimate scaling with temperature: warmer periods were wetter 
(e.g., the Pliocene; increased precipitation) with colder periods being 
drier (e.g., the LGM; decreased precipitation). 
Substantial limitations exist in reconstructing the global hydrological 
cycle prior to the Quaternary, particularly during the Eocene, due to 
the lack of high-resolution proxy records and their sparsity. Spatial 
heterogeneity complicates identification of wetting and drying 
signals during the PETM and the EECO, with paleo data and model

simulations suggesting an intensified global hydrological cycle 
(Carmichael et al., 2016, 2017; Hyland et al., 2017; West et al., 2020), 
in particular an increased specific humidity (Winnick et  al., 2015; 
van Dijk et al., 2020). Conditions wetter than present were inferred for 
the MPWP (Cross Chapter Box 2.4), with intensified Asian monsoons 
(An et al., 2015) but with nevertheless drier conditions over tropical 
and subtropical SH locations (Pontes et  al., 2020). A new global 
reconstruction of hydroclimate proxies for the LIG points to stronger 
boreal precipitation compared to 1850–1900 over high latitudes 
and especially over monsoon areas, with a  more heterogeneous 
signal for the SH (Scussolini et al., 2019). This heterogeneity is also 
present in the tropics, characterized by large zonal differences in 
precipitation change due to the variations in the intensity of Walker 
circulation (Section 2.3.1.4.1). Available records indicate reduced

global vegetation cover and abundant atmospheric dust deposition 
during the LGM (increased aridity), particularly over the tropics and 
high latitudes (Lamy et  al., 2014; Újvári et  al., 2017). This agrees 
with models and moisture-sensitive proxies, suggesting an overall 
decrease in global precipitation during the LGM relative to recent 
decades, albeit with regional-scale heterogeneity (Cao et al., 2019). 
Despite lower global precipitation amounts, research since AR5 has 
identified a wetting of mid-latitudes during the LGM (Putnam and 
Broecker, 2017; Lowry and Morrill, 2018; Morrill et al., 2018), thereby 
complicating the characterization of the LGM as a  relatively ‘dry’ 
period. Low evaporation rates and increased top-soil moisture during 
the LGM may have contributed to elevated levels of large closed-
basin lakes located in the 30°–45° latitudinal belts (Putnam and 
Broecker, 2017; Scheff et al., 2017), such as the south-west United

States (e.g., Ibarra et al., 2018), southern Australia (Petherick et al., 
2013; Fitzsimmons et al., 2015; Sniderman et al., 2019) and Patagonia 
(e.g., Quade and Kaplan, 2017). 
New analyses suggest that during the Holocene, the NH mid-
latitudes became increasingly wet, in phase with the strength of 
the latitudinal temperature and insolation gradients (Shuman and 
Marsicek, 2016; Routson et al., 2019). Nevertheless, there was also 
considerable spatial heterogeneity and variability on centennial to 
millennial timescales (Newby et  al., 2014; Shuman and Marsicek, 
2016; H. Zhang et  al., 2018; Liefert and Shuman, 2020). The NH 
tropics and many regions of the SH deep tropics experienced 
wetting up until the early to mid-Holocene but drying thereafter Table 2.5 | Observed change (°C) in free atmospheric temperatures in various 
datasets, for the lower tropospheric and lower stratospheric layers.  Numbers

in square brackets indicate 5–95% confidence ranges. Trend values are calculated 
with ordinary least squares following (Santer et al., 2008) and are expressed as a total 
change over the stated period. Further details on data sources and processing are 
DatasetTrend 
1960–2019Trend 
1980–2019Trend 
2000–2019
Lower troposphere
RAOBCORE1.08
[0.94 to 1.23]0.74
[0.57 to 0.91]0.52
[0.26 to 0.78]
RICH1.20
[1.06 to 1.34]0.79
[0.63 to 0.96]0.53
[0.28 to 0.77]
UAHRD0.97
[0.80 to 1.13]0.91
[0.76 to 1.05]0.53
[0.35 to 0.72]
UAH0.51
[0.37 to 0.65]0.29
[0.07 to 0.50]
RSS0.79
[0.66 to 0.92]0.41
[0.24 to 0.58]
ERA5.10.68
[0.52 to 0.84]0.55
[0.34 to 0.75]
Average 1.08 0.74 0.47
Lower stratosphere
RAOBCORE–1.37
[–1.80 to –0.93]–1.00
[–1.56 to –0.45]–0.05
[–0.20 to 0.09]
RICH–1.45
[–1.99 to –0.92]–1.19
[–1.95 to –0.42]0.02
[–0.20 to 0.23]
UAHRD−1.25
[−1.51 to −0.98]−0.79
[−1.16 to −0.43]−0.11
[−0.25 to 0.03]
UAH–1.14
[–1.61 to –0.67]–0.24

[–0.37 to –0.12]
RSS–0.90
[–1.37 to –0.43]–0.14
[–0.26 to –0.03]
STAR–0.97
[–1.45 to –0.49]–0.17
[–0.29 to –0.04]
ERA5.1–1.19
[–1.87 to –0.50]–0.01
[–0.13 to 0.10]
Average –1.36 –1.03 –0.10

330
(Shanahan et al., 2015; Nash et al., 2016; Muñoz et al., 2017; Quade 
et al., 2018). Evidence  for the SH is limited , with a wetting trend 
during the Holocene in low latitudes of South America (Kanner et al., 
2013; Mollier-Vogel et  al., 2013) and parts of the African tropics 
(Schefuß et  al., 2011; Chevalier and Chase, 2015) but a drying 
tendency over southern Australia and New Zealand (van den Bos 
et al., 2018; Barr et al., 2019) and South America (Quade and Kaplan, 
2017; Moreno et al., 2018).
For the CE, new proxy records have led to the creation of continental 
drought atlases (Cook et al., 2015; Palmer et al., 2015; Stahle et al., 2016; 
Morales et al., 2020) and millennial reanalyses (Steiger et al., 2018; 
Tardif et al., 2019). These reconstructions highlighted the occurrence 
of multi-decadal regional mega-droughts in the NH before 1600 CE, 
particularly during 800–1200 CE, with a predominance of wet periods

after 1700 CE (Cook et al., 2015; Rodysill et al., 2018; Shuman et al., 
2018). In the SH, much of South America and the African tropics 
experienced a reduction of precipitation during 900–1200 CE and 
a wetting peak during 1500–1800 CE (Tierney et al., 2015; Nash et al., 
2016; Fletcher et al., 2018; Lüning et al., 2018; Campos et al., 2019), 
with an opposite pattern in southern subtropical Africa (Woodborne 
et al., 2015; Lüning et al., 2018). Large multi-decadal variability was 
documented over Australia and New Zealand during the 800–1300 CE 
period, followed by a well-defined wet period during 1500–1800 CE 
(Barr et al., 2014; Evans et al., 2019). 
To summarize, since AR5 there has been considerable progress in 
detecting the variations of the global hydrological cycle prior to the 
instrumental period. There are indications from multiple sources of 
a wetting trend during the Holocene, particularly for the NH and parts

of the SH tropics ( medium confidence ). Hydroclimate during the CE 
is dominated by regional variability, generally precluding definitive 
statements on changes at continental and larger scales, with a general 
reduction of mega-drought occurrences over the last about 500 years 
(medium confidence ). Availability of proxy data for assessing Holocene 
hydroclimate variability is biased towards the NH, with medium 
evidence  but low agreement  for the assessment of SH changes. 
2.3.1.3.2 Surface humidity
The AR5 reported very likely  widespread increases in near-surface air 
specific humidity since the 1970s, abating from around 2000 to 2012 
(medium confidence ). This abatement resulted in a recent decline in 
relative humidity over the land.
Near surface humidity has been monitored using in-situ data 
(e.g., NOCSv2.0; Berry and Kent, 2011), satellite-derived estimations 
(e.g.,  HOAPS3, Liman et  al., 2018; J-OFURO3, Tomita et  al., 2019),

global gridded products such as HadISDH (Willett et al., 2014, 2020), 
and reanalyses (e.g.,  ERA5, JRA-55 and 20CRv3). In-situ based 
humidity products suffer from uncertainties over poorly sampled 
regions particularly in the SH (Berry and Kent, 2011; Kent et  al., 
2014; Willett et al., 2014). There is general consensus in the inter-
annual variability and sign of trends implying high confidence  in 
increasing specific humidity since the 1970s and decreasing relative 
humidity since 2000, particularly over land (Simmons et  al., 2010; 
Willett et al., 2014, 2020). Since 2012, specific humidity over land and ocean has remained well above the 1973–2019 average and 
reached record or near-record values (Figure 2.13b), with the strong 
2015–2016 El  Niño event boosting surface moisture levels (Byrne 
and O’Gorman, 2018). The abatement from around 2000 to 2012 
reported in AR5 has not persisted. This is consistent with increases

in total column water vapour (Section 2.3.1.3.3) and a resumption of 
rapid warming in surface temperatures (Section 2.3.1.1.3). The global 
averaged relative humidity however has remained depressed since 
2000 (Figure 2.13d; Simmons et al., 2010; Willett et al., 2014, 2020; 
Dunn et al., 2017; Vicente-Serrano et al., 2018). 
Since 1973, increases in specific humidity have been widespread 
and significant across the majority of the land and ocean regions 
where observations are available (Figure 2.13a). In contrast, trends 
in relative humidity show distinct spatial patterns with generally 
increasing trends over the higher latitudes and the tropics and 
generally decreasing trends over the sub-tropics and mid-latitudes, 
particularly over land areas (Figure 2.13c). Near-surface specific 
humidity over the oceans has increased since the 1970s according to 
several in-situ, satellite and reanalysis data records (Kent et al., 2014;

Robertson et al., 2020; Willett et al., 2020). According to the HadISDH 
product, increases in specific humidity and decreases in relative 
humidity are significant particularly over the NH mid-latitudes 
(Figure 2.13a,c). Poor data coverage over the SH south of 20°S does 
not allow for the robust assessment of trends. Sources of uncertainty 
include the initial measurement accuracy, homogenization over land, 
observational height at ships and instrument bias adjustment over 
ocean, and sparse spatio-temporal sampling (Prytherch et al., 2015; 
Roberts et al., 2019; Willett et al., 2020).
In summary, observations since the 1970s show a very likely  increase 
in near surface specific humidity over both land and oceans. A very 
likely  decrease in relative humidity has occurred over much of the 
global land area since 2000, particularly over mid-latitude regions of 
the NH, with increases at northern high latitudes. 
2.3.1.3.3 Total column water vapour (TCWV)

The AR5 concluded that total column water vapour (TCWV) very likely  
increased since the 1970s, at a rate that was overall consistent with 
the Clausius-Clapeyron relationship (about 7% per °C) given the 
observed increase in atmospheric temperature.
Records prior to the instigation of quasi-global coverage by radiosondes 
require the use of statistical relationships to infer TCWV from historical 
SST observations or the evaluation of centennial-scale reanalysis 
products (Smith and Arkin, 2015). These approaches reveal two 
periods of positive trends, one from 1910 to 1940 and the other from 
1975 onwards (Zhang et al., 2013; Mieruch et al., 2014; Shi et al., 
2018), concurrent with periods of positive SST trends (Figure 2.11). 
Potential sources of errors in the SST-based estimation of TCWV 
include both uncertainties in historical SST and uncertainties in the 
parameters that define the relationship between the variables (Smith

and Arkin, 2015). Trends based on 20CRv2c, ERA-20C and ERA-20CM 
indicate an increase in TCWV over much of the global ocean since 
the beginning of the 20th century, particularly over the tropics (Bordi 
et al., 2015; Smith and Arkin, 2015; Poli et al., 2016). TCWV trends

331
estimated since the middle of the 20th century from radiosonde 
observations show significant increases over North America and 
large portions of Eurasia, while decreases are restricted to Australia, 
eastern Asia and the Mediterranean region (Y . Zhang et al., 2018). 
Overall, there is a significant increase in TCWV over global land areas 
since 1979 (Chen and Liu, 2016).
Since the late 1970s a range of satellite missions permit a quasi-global 
assessment of TCWV. Several satellite products provide water vapour 
retrievals based upon distinct spectral domains, in addition to products 
from radiosondes, reanalyses and GNSS radio occultation. The GEWEX 
Water Vapour Assessment (G-VAP) provided an intercomparison of 
several TCWV data records, with global coverage but limited timespan 
(Schröder et al., 2018). The various global products generally exhibit 
a positive trend since 1979 (Figure 2.14; Allan et al., 2014; Mieruch

et al., 2014; Schröder et al., 2016; J. Wang et al., 2016), most evident 
over the tropics (Gu and Adler, 2013; Chen and Liu, 2016; Mears et al., 
2018; Wang and Liu, 2020; Salamalikis et  al., 2021). The existence 
of apparent breakpoints in several products, which are generally 
coincident with changes in the observing system, lead to trend 
estimates that are not in line with theoretical expectations imposed by 
the Clausius-Clapeyron relationship (Schröder et al., 2019), although 
other factors such as regional moisture divergence/convergence could account for the observed TCWV-temperature scaling. Substantial 
potential inhomogeneities affect trend estimates based on satellite, 
reanalysis and merged products in particular over Central Africa, the 
Sahara and central South America (Schröder et al., 2016, 2019; J. Wang 
et al., 2016). Moreover, data gaps in observations from ground-based 
GNSS receivers and radiosondes lead to low confidence  in TCWV

estimation in these regions. Figure 2.13 | Changes in surface humidity. (a) Trends in surface specific humidity over 1973–2019. Trends are calculated using OLS regression with significance assessed 
following AR(1) adjustment after Santer et al. (2008); ‘×’ marks denote non-significant trends). (b) Global average surface specific humidity annual anomalies (1981–2010 base 
period). (c) as (a) but for the relative humidity.  (d) as (b) but for the global average surface relative humidity annual anomalies. Further details on data sources and processing 
Changes in global mean total column water vapour
1.5
1.0
0.5
0.0
−0.5
−1.0
−1.5
1985 1995 2005 2015 1980 1990 2000 2010 2020Anomaly (mm)ERA5
20CRv3JRA55
REMSS v7NVAP-M
HOAPS v4
Figure 2.1 4 | Time series of global mean total column water vapour annual 
anomalies (mm) relative to a 1988–2008 base period.  Further details on data

sources and processing are available in the chapter data table (Table 2.SM.1).
Non significantSignificant ColourChanges in Surface Humidity
Trend (g kg−1 per decade)−0.5 0.5 0
−2 0
Trend (percent RH per decade)−1 1 2(b) Global average surface q annual anomalies  
(d) Global average surface RH annual anomalies  1980 1990 2000 2010 2020JRA55ERA520CRv3
HadISDH
1980 1990 2000 2010 20200.2
0
−0.40.4
−0.2(g kg−1) (Percent RH)0.5
0
−1.01.0
−0.5(a) Trends in surface specific humidity (q)
(c) Trends in surface relative humidity (RH)

332
In summary, positive trends in global total column water vapour 
are very likely  since 1979 when globally representative direct 
observations began, although uncertainties associated with changes 
in the observing system imply medium confidence  in estimation of the 
trend magnitudes. Low confidence  in longer-term trends arises from 
uncertainties in the SST-TCWV relationship and current centennial 
scale reanalyses, particularly during the first half of the 20th century.
2.3.1.3.4 Global precipitation
The AR5 concluded that there was low confidence  in precipitation 
change averaged over global land areas prior to 1950, and medium 
confidence  thereafter with no significant global trends. There was 
a likely  overall increase in precipitation in the well-sampled NH 
mid-latitudes, with high confidence  after 1951.
In situ precipitation records over land extend back for centuries in

a few locations, and to the early to mid-20th century quasi-globally. 
Datasets differ in their input data, completeness of records, period 
covered, and the gridding procedures applied, which, given spatial 
clustering and the small spatial scales of precipitation, results in 
differences in global and regional estimates of precipitation changes 
(Q. Sun et al., 2018; Nogueira, 2020). The spatial variability of observed 
long-term trends (1901–2019) based on GPCC V2020 and CRU 
TS 4.04 (Figure 2.15a,b) indicates significant increases in precipitation 
mainly over eastern North America, northern Eurasia, southern South 
America and north-western Australia. Decreases are strongest across 
tropical western and equatorial Africa and southern Asia. The temporal 
evolution of global annual land precipitation anomalies exhibits 
little consistency between GPCC V2020, CRU TS 4.04 and GHCNv4 
datasets, especially prior to 1950, that is associated with limitations

in data coverage (Figure 2.15c; Wu et al., 2013; Shen et al., 2014; Gu 
and Adler, 2015). These disagreements between datasets prior to the 
1950s result in differences in trend estimates over global land (Table 
2.6). A qualitative consistency in decadal and interdecadal variations 
between the products is only observed since the 1950s, with primarily 
positive land precipitation anomalies during the 1950s, 1970s and 
during 2000 to 2019 (Figure 2.15c). 
Several satellite-based precipitation datasets improve the 
representation of the spatio-temporal changes since the late 20th 
century. Some of these are based exclusively on satellite data 
(e.g., CMORPH, Joyce et al., 2004; GSMaP , Okamoto et al., 2005), with 
others being combinations of in situ observations, reanalyses and 
satellite retrievals (e.g., CMAP , Xie and Arkin, 1997; TRMM 3B43 V7, 
Huffman et al., 2007 ; PERSIANN-CDR, Ashouri et al., 2015; CHIRPS,

Funk et al., 2015; GPCP V2.3, Adler et al., 2018). These can be affected 
by systematic and random uncertainties due to inhomogeneities 
in the satellite-derived precipitation and station data and the 
uncertainties of blending algorithms (Hegerl et al., 2015; Q. Sun et al., 
2018). The spatial coverage of these products is near-global, with 
available estimations formally covering 60°S–60°N with decreasing 
quality from low to high latitudes, depending on the sensors and 
algorithms used (Hu et al., 2019). A detailed description of the most 
relevant satellite products is provided in section 10.2.1.1.Recent trends (1980–2019) for GPCC V2020, CRU TS 4.04 and GPCP 
V2.3 show significant increases in land precipitation over tropical 
Africa, the eastern portions of Europe and North America, central Asia 
and the Maritime Continent (Figure 2.14d–f). Significant decreases 
are observed over central South America, western North America,

northern Africa and the Middle East. A detailed assessment of the 
recent regional precipitation trends using the same datasets can 
be found in the Atlas. Global trends for 1980–2019 show a general 
increase in annual precipitation over land, which is particularly marked 
for CRU TS 4.04 and GHCNv4 (Table 2.6). These changes have been 
accompanied by a strengthening of precipitation seasonality over 
tropical land areas, although with broad spread between different 
satellite-based (GPCP , MSWEP_V1.2, PERSIANN-CDR) and in situ 
gridded datasets (GPCC, CRU TS; Chou et al., 2013; Li et al., 2016; 
Tan et al., 2020). Increasing trends since 1980, in contrast to longer-
term declining trends since 1901, are particularly evident over much 
of Africa, while more widespread negative trends were observed over 
much of southern South America in the more recent period (Atlas 7.2; 
Knutson and Zeng, 2018). A faster recent increase in precipitation

over global land is inferred comparing the precipitation trends over 
1960–2019 with 1980–2019 (Table 2.6). Over the global ocean, the 
comparison between precipitation datasets is compromised by the 
different measurement periods, as well as the spatial coverage of the 
available products (Adler et al., 2017; Nguyen et al., 2018; Jaber and 
Abu-Allaban, 2020; Nogueira, 2020), limiting the ability to assess the 
sign and magnitude of precipitation trends. The GPCPv2.3 database 
(Adler et  al., 2017, 2018) exhibits an increase of 2.94 mm yr –1 per 
decade over 1980–2019, principally due to the trends over the Indian 
ocean and in the tropical western Pacific (Figure 2.15f). The regional 
patterns of recent trends are consistent with the documented increase 
in precipitation over tropical wet regions and the decrease over 
dry areas, estimated through GPCP v2.2 data (Liu and Allan, 2013; 
Trammell et al., 2015; Kao et al., 2017; Polson and Hegerl, 2017).

In summary, globally averaged land precipitation has likely  increased 
since the middle of the 20th century ( medium confidence ), with low 
confidence  in trends prior to 1950. A faster increase in global land 
precipitation was observed since the 1980s ( medium confidence ), 
with large interannual variability and regional heterogeneity. 
Over the global ocean there is low confidence in the estimates of 
precipitation trends, linked to uncertainties in satellite retrievals, 
merging procedures and limited in situ observations.
Table 2. 6 | Globally averaged trend estimates over land and 90% confidence 
intervals for annual precipitation for each time series in Figure 2.15c over 
three periods all ending in 2019.  Trends are calculated using OLS regression with 
significance assessed after Santer et al. (2008). Further details on data sources and 
DatasetTrends in annual precipitation (mm yr –1 per decade)

1901–2019 1960–2019 1980–2019
GPCCv2020 1.01a ± 0.99 1.67 ± 3.23 5.60 ± 6.38
CRU TS 4.04 0.57 ± 2.08 0.17 ± 3.12 5.75a ± 5.09
GHCNv4 3.19a ± 1.48 5.03a ± 4.87 11.06a ± 9.17
GPCPv2.3 5.41a ± 5.20
a Trend values significant at the 10% level.

333
2.3.1.3.5 Precipitation minus evaporation
The AR5 concluded that the pattern of precipitation minus evaporation 
(P–E) over the ocean had been enhanced since the 1950s ( medium 
confidence ). Saline surface waters had become saltier, while the 
relatively fresh surface waters had become fresher. The inferred 
changes in P–E were consistent with the observed increased TCWV, 
although uncertainties in the available products prevented identifying 
robust trends.Estimating global-scale trends in P–E using direct observations 
alone is challenging due to limited evaporation measurements and 
inhomogeneities in satellite-derived precipitation and evaporation 
datasets (Hegerl et al., 2015; López et al., 2017). Hence, the assessment 
of global P–E trends is generally performed using reanalyses, although 
changes in the observing system imply considerable uncertainty

(Skliris et al., 2014). Since the second half of the 20th century, several 
reanalyses and observational datasets have shown increases in P–E 
over global land, although 75% of land areas exhibit no significant 
-60 -40 -20      0       20     40     60
Trend (mm yr-1 per decade)-150 -100 -50      0      50    100   15050
2575
0
–50
–75–25(mm yr -1)
30
20
10
0
–30–20–10
1900      1920    1940      1960      1980     2000    2020CRU TS 4.04 GPCC V2020 GHCN V4
GPCP V2.3(c) Global land precipitation anomalies (1891–2019)
(mm yr -1)
Significant Colour Non significant(f) GPCP V2.3 1980–2019
Trend (mm yr-1 per decade)(b) GPCC V2020 1901–2019 (e) GPCC V2020 1980–2019(a) CRU TS 4.04 1901–2019 (d) CRU TS 4.04 1980–2019Changes in observed precipitation
Figure 2.15  | Changes in observed precipitation. (a, b)  Spatial variability of observed precipitation trends over land for 1901–2019 for two global in-situ products. Trends

are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘×’ marks denote non-significant trends). (c) Annual time 
series and decadal means from 1891 to date relative to a 1981–2010 climatology (note that different products commence at distinct times). (d, e)  as (a, b), but for the periods 
starting in 1980. (f) is for the same period for the globally complete merged GPCP v2.3 product. Further details on data sources and processing are available in the chapter 
data table (Table 2.SM.1).

334
changes and both internal variability and observational uncertainty 
are substantial (Greve et al., 2014; Robertson et al., 2016). The recently 
released ERA5 (Hersbach et  al., 2020) showed improvements in the 
representation of tropical precipitation, although it overestimates 
global precipitation trends in comparison to ERA-Interim and GPCP 
(Nogueira, 2020), and suffers from temporal changes in the annual 
balance between precipitation and evaporation (Hersbach et al., 2020). 
The spatial pattern of P–E trends over 1980–2019 (Figure 2.16a) are 
largely consistent with the trends in the GPCP v2.3 precipitation dataset 
(Figure 2.15f and Section 2.3.1.3.4) and agrees in sign with the trends 
from other reanalyses such as JRA-55 and MERRA-2 (L. Yu et al., 2020). 
A variety of reanalysis products exhibit diverse temporal evolutions 
of P–E (Figure 2.16b–d). Globally MERRA-2, ERA20C and ERA20CM

exhibit little change whereas JRA-55, ERA5 and 20CRv3 all 
imply long-term changes (Figure 2.16d). A potential limitation in 
estimating P–E from some reanalysis products is readily apparent 
when considering the temporal evolution of global P–E from CFSR 
and MERRA (Figure 2.16d) which both exhibit strong discontinuities 
over the global ocean in the late 1990s. Over global land as a whole, precipitation exceeds evaporation (P–E >0) for all the reanalysis 
products (Figure 2.16c), with decreasing trends in P–E for ERA5 and 
JRA-55 and increasing trends for MERRA-2 and CFSR. The P–E over 
the global ocean is negative (evaporation exceeding precipitation) 
for most reanalyses (Figure 2.16d), with declining trends in ERA5 
and MERRA-2 dominated by trends in evaporation (Bosilovich et al., 
2017; Hersbach et al., 2020) (Figure 2.16d). The recent increase in 
ocean evaporation was also documented for several reanalyses (Craig

et al., 2017) and in satellite data (Andersson et al., 2011; Robertson 
et  al., 2014), although with considerable differences between 
available estimates (Chandanpurkar et al., 2017; L. Yu et al., 2020). 
An alternative indirect approach to estimate P–E changes is based on 
near-surface ocean salinity (Section 2.3.3.2), which is partially driven 
by the freshwater flux at the ocean surface. The near-surface salinity 
trends are more spatially coherent compared to those revealed by 
P–E estimates from reanalyses, with an intensification of the water 
cycle over oceans, especially in subtropical regions (Durack et  al., 
2012; Skliris et al., 2014; L. Yu et al., 2020). However, the precise rate 
of water cycle intensification implied by salinity trends is sensitive to 
methodological choices (e.g., Skliris et al., 2016; Zika et al., 2018).
Changes in precipitation minus evaporation 
(a) Spatial pattern of ERA5 P-E trends of 1980 –2019

Trend (mm yr−1 per decade)–200 0 100 200 100 Non significantSignificant Colour
(b) Global (c) Land (d) Ocean
0.4
0.2
0.0
–0.2P-E (mm/day)1.4
1.2
1.0
0.8
0.6
0.4
1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 20100.2
0.0
–0.2
–0.4
–0.6
20CRv3 ERA5 ERA20CM MERRA CFSR ERA20C JRA-55 MERRA-2
Figure 2. 16 | Changes in precipitation minus evaporation. (a)  Trends in precipitation minus evaporation (P–E) between 1980 and 2019. Trends are calculated using OLS 
regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘×’ marks denote non-significant trends). Time series of (b) global, (c) land-only and 
(d) ocean-only average annual P–E (mm day–1). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

335
In conclusion, observational uncertainty yields low confidence   in 
globally averaged trends in P–E over the 20th century, with a spatial 
pattern dominated by precipitation changes over land and by 
evaporation increases over the ocean. Different reanalyses disagree 
on the sign of long-term changes in the global mean P–E.
2.3.1.3.6 Streamflow
The AR5 concluded that there was low confidence  in a positive trend 
in global river discharge during the 20th century. It noted that many 
of the largest rivers with long term streamflow records have been 
impacted by non-climatic human influences such as dam construction 
or land-use change.
River discharge is monitored widely, although gaps remain at 
a subcontinental scale over central Asia and Africa (Wei et al., 2020). 
Substantial recent efforts have been made to generate new global 
streamflow datasets, consolidating observations from many stream

gauges to create streamflow indices (Do et al., 2018; Gudmundsson 
et al., 2018) and gridded products using neural networks (Barbarossa 
et al., 2018) or combinations between observations and reanalyses 
(Suzuki et al., 2018; Ghiggi et al., 2019). 
Human intervention on river discharge linked to increases in 
evapotranspiration and some reduction of intra-annual streamflow 
variability (Jaramillo and Destouni, 2015; Chai et  al., 2020) might 
affect the detection of trends in extreme daily streamflow events 
(Do et al., 2017; Gudmundsson et al., 2019). However, these activities 
have a minor impact on annual streamflow compared to climate 
variations (Dai et  al., 2009; Alkama et  al., 2013). Available global 
studies post-1950 generally concur that there have been more rivers 
experiencing decreases than increases in runoff (Do et  al., 2017; 
Su et al., 2018; Gudmundsson et al., 2019; X. Shi et al., 2019). Most 
of the rivers have not experienced statistically significant changes

in streamflow, and when globally aggregated there is no significant 
change (Dai and Zhao, 2017). Global streamflow variability is strongly 
modulated by ENSO and PDV, with below-normal global streamflow 
as a response to El Niño events and vice-versa during La  Niña 
episodes (Dai, 2016; Liang et al., 2016; Kim, 2019). The response of 
streamflow to changes in precipitation associated with ENSO and 
PDV has heterogeneous regional patterns at subcontinental scales 
(Section  8.3.2.9.1). No significant trends are found for reanalysis-
based discharge estimates over 1993 to 2015 (Chandanpurkar 
et  al., 2017). Uncertainties in global streamflow trends arise 
predominantly from changes in instrumentation, gauge restoration, 
recalibration of rating curves, flow regulation or channel engineering 
(Alkama et al., 2011; Gudmundsson et al., 2018; Ghiggi et al., 2019). 
In summary, the sign of global streamflow trends remains uncertain,

with slightly more globally gauged rivers experiencing significantly 
decreasing flows than significantly increasing flows since the 1950s 
(low confidence ).
2.3.1.4 Atmospheric Circulation
This section focuses on large-scale changes in a subset of 
components of the atmospheric circulation (Cross-Chapter Box 2.2). Chapter 8 assesses large-scale as well as regional aspects of 
circulation components and their impact on the hydrological cycle, 
while Chapter 11 assesses the association of circulation changes and 
variability with extreme events.
2.3.1.4.1 The Hadley and Walker circulations
The AR5 reported low confidence  in trends in the strength of the 
Hadley circulation (HC) and the Walker circulation (WC) due 
to uncertainties in available reanalysis datasets and the large 
interannual-to-decadal variability of associated circulation patterns. 
However, AR5 indicated a likely  widening of the tropical belt since the

1970s, albeit with large uncertainty in the magnitude of this change. 
There was high confidence  that the post-1990s strengthening of 
the Pacific WC reversed its weakening observed from the mid-19th 
century to the 1990s.
Paleo reconstructions of rainfall and trade winds extending over the 
last 100 kyr show an intensification of the NH HC concurrently with 
a weakening of the SH HC and a southward shift of the inter tropical 
convergence zone (ITCZ) during Heinrich stadials (Deplazes et  al., 
2013; McGee et al., 2018; Stríkis et al., 2018; Wendt et al., 2019). An 
intensification of the HC associated with conditions similar to La Niña 
(northward migrations of both the ITCZ and the SH westerlies) was 
found in reconstructions for the MH (McGee et al., 2014; Mollier-Vogel 
et  al., 2019). Changes in insolation from the mid to late Holocene 
favoured a southward migration in the position of the ITCZ and 
the descending branch of the HC in the NH, approaching its current

width and position (Wirth et al., 2013; Thatcher et al., 2020). Tree ring 
chronologies from the NH mid-latitudes over the last 800 years show 
that the northern edge of the HC tended to migrate southward during 
positive phases of ENSO and PDV, with northward shifts during negative 
phases (Alfaro-Sánchez et al., 2018). Between 1400 and 1850 CE the 
HC over both hemispheres and the ITCZ were displaced southward, 
consistent with occurrence of drought conditions in several NH regions 
(Wirth et  al., 2013; Burn and Palmer, 2014; Lechleitner et  al., 2017; 
Alfaro-Sánchez et al., 2018; Flores-Aqueveque et al., 2020). Moreover, 
several proxy records showed not only inter-hemispheric shifts in the 
ITCZ but a contraction of the tropical belt during 1400–1850 CE, which 
followed an expansion during 950–1250 CE (Denniston et al., 2016; 
Griffiths et al., 2016).
From centennial-scale reanalyses, Liu et  al. (2012) and D’Agostino

and Lionello (2017) found divergent results on HC extent over the 
last 150 years, although with unanimity upon an intensification of 
the SH HC. A substantial discrepancy between HC characteristics 
in centennial-scale reanalyses and in ERA-Interim (D’Agostino and 
Lionello, 2017) since 1979 yields significant questions regarding 
their ability to capture changes in HC behaviour. Taken together 
with the existence of apparent non-climatic artefacts in the datasets 
(Nguyen et al., 2015), this implies low confidence  in changes in the 
extent and intensity of HC derived from centennial-scale reanalyses. 
However, using multiple observational datasets and centennial-scale 
reanalyses, Bronnimann et  al. (2015) identified a  southward shift 
in the NH HC edge from 1945 to 1980 of about 0.25° latitude per 
decade, consistent with observed changes in global land monsoon 
precipitation (Section 2.3.1.4.2).

336
Since AR5 several studies based upon a range of metrics and 
different reanalyses products have suggested that the annual 
mean HC extent has shifted poleward at an approximate rate 
of 0.1°–0.5° latitude per decade over the last about 40 years 
(Allen and Kovilakam, 2017; Davis and Birner, 2017; Grise et al., 
2018; Staten et al., 2018, 2020; Studholme and Gulev, 2018; Grise 
and Davis, 2020). The observed widening of the annual mean HC, 
revealed by a variety of metrics, is primarily due to poleward shift 
of the Northern Hemisphere HC. There have been stronger upward 
trends in the NH extent of HC after 1992 (Figure 2.17a). The 
estimated magnitude of the recent changes based on modern-era 
reanalyses is not as large as that in AR5, due to apparent biases 
in older-generation reanalyses (Grise et al., 2019). Moreover, large 
interannual variability leads to uncertainties in estimates of long-

term changes (Nguyen et al., 2013; Garfinkel et al., 2015b; Seviour 
et al., 2018; Staten et al., 2018), particularly for the NH given its 
zonal asymmetries (Staten et al., 2020; Wang et al., 2020). These 
large-scale features of the HC based on reanalyses agree with 
estimates revealed from the Integrated Global Radiosonde Archive 
(IGRA) during 1979–2012 (Lucas and Nguyen, 2015; Mathew 
et al., 2016). Recent trends based on reanalyses indicate a larger 
seasonal widening in the HC for summer and autumn in each 
hemisphere, although the magnitude of changes in HC extent is 
strongly dependent on dataset and metrics used (Grise et al., 2018; 
Y . Hu et al., 2018; Staten et al., 2018). The shifts in the HC position 
were accompanied by a narrowing ITCZ over the Atlantic and Pacific 
basins, with no significant change in its location and increases in 
the precipitation intensity (Byrne et al., 2018).Trends in the HC intensity since 1979 differ between reanalyses,

although there is a tendency toward HC intensification (Figure 2.17b; 
Nguyen et al., 2013; Chen et al., 2014; D’Agostino and Lionello, 2017; 
R. Huang et al., 2019), which is more marked in the NH than the SH 
(Studholme and Gulev, 2018). However, the ability of reanalyses to 
represent the HC strength has been questioned due to inaccurate 
representation of latent heating distribution, which is directly related 
to tropical convection and influences the HC dynamics (Chemke and 
Polvani, 2019; Mathew and Kumar, 2019).
Paleo evidence during the LGM indicates a weaker WC over the Indian 
Ocean (DiNezio et  al., 2018; Windler et  al., 2019) with a  stronger 
Pacific WC (DiNezio and Tierney, 2013). During the Holocene, 
a  transition from a strong WC located more westward during the 
Early-to-Mid Holocene towards a weak and eastward shifted WC 
during the late Holocene was inferred from proxy records from the 
Pacific Warm Pool and South East Asia (Barr et al., 2019; Dang et al.,

2020; Griffiths et al., 2020), in concurrence with changes in ENSO 
activity (Section 2.4.2). Reconstructions for the CE showed weakened 
WC during 1000–1250 and since 1850, with an intensified circulation 
during 1500–1850 CE (Xu et al., 2016; Deng et al., 2017). 
Considering instrumental records, there is considerable interdecadal 
variability in the strength of the WC, resulting in time-period dependent 
magnitude and even sign of trends (Carilli et al., 2015; Bordbar et al., 
2017; Hou et al., 2018), with some studies reporting weakening over 
the 20th century (e.g.,  Power and Kociuba, 2011; Liu et  al., 2019), 
while others reported strengthening (Z. Li et  al., 2020), particularly 
30°N
28°N
26°N
24°NChanges in tropical atmospheric circulationLatitude (°)
Intensity ( 1011 kgs −1)(b) Hadley Cell Intensity (a) Hadley Cell Extent
SH 24°S
26°S
28°S
30°S1.4
1.2
1.0
0.8
1.4
1.2
1.0
0.8

1980      1985     1990    1995     2000     2005     2010    2015 1980      1985     1990    1995     2000    2005     2010     2015
MERRA-2 ERA5 ERA-Interim JRA-55NH
Figure  2.17 | Time series of the annual mean Northern Hemisphere (NH, top curves) and Southern Hemisphere (SH, bottom curves) Hadley cell extent (a) 
and Hadley cell intensity (b) since 1979.  Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

337
over the last 30–40 years (e.g., Hu et al., 2013; L’Heureux et al., 2013; 
Yim et al., 2017). Based on estimation of changes in mid-tropospheric 
velocity from changes in observed cloud cover, Bellomo and Clement 
(2015) suggest a weakening and eastward shift of the WC over 
1920–2010, however the robustness of this signal is questionable due 
to high uncertainty in the ship-reported cloud data used before 1954. 
Using centennial-scale 20CR reanalysis Tseng et  al. (2019) showed 
that the vertical westerly wind shear over the western Pacific does 
not indicate any long-term change during 1900–1980, but shows 
a marked increase since the 1980s that is not present in ERA-Interim 
and JRA-55, again calling into question the ability of centennial-
scale reanalyses to capture tropical circulation changes. Recent 
strengthening together with a westward shift of the WC (Bayr et al.,

2014; Ma and Zhou, 2016) was identified across several reanalysis 
products and observational datasets, and using different metrics for 
quantifying WC. Nevertheless, satellite observations of precipitation 
and analyses of upper tropospheric humidity suggest substantially 
weaker strengthening of the WC than implied by reanalyses 
(Chung et al., 2019). This recent strengthening in the WC is associated 
with enhanced precipitation in the tropical western Pacific, anomalous 
westerlies in the upper troposphere, strengthened downwelling in the 
central and eastern tropical Pacific, and anomalous surface easterlies 
in the western and central tropical Pacific (Dong and Lu, 2013; 
McGregor et al., 2014; Choi et al., 2016). Positive trends in sea level 
pressure over the eastern Pacific and concurrent negative trends over 
the Indonesian region result in a pattern implying a shift towards a La 
Niña-like WC regime, with strengthening of the Pacific Trade Winds

mainly over 1979–2012 (L’Heureux et al., 2013; England et al., 2014; 
Sohn et al., 2016; Zhao and Allen, 2019). Seasonal assessment of the 
WC showed significant changes in the vertical westerly wind shear 
over the Pacific during the austral summer and autumn implying 
a strengthening (Clem et al., 2017).
In summary, there has been a likely  widening of the Hadley circulation 
since the 1980s, mostly due to its extension in the NH, although 
there is only medium confidence  in the extent of the changes. This 
has been accompanied by a strengthening of the Hadley circulation, 
particularly in the NH ( medium confidence ). There is low confidence  
in the estimation of long-term trends in the strength of the Walker 
circulation, which are time period dependent and subject to dataset 
uncertainties. Trends since 1980 are better characterized and consistent 
with a very likely strengthening that resembles a La Niña-like Walker

circulation and a westward shift of the Walker circulation, although 
with medium confidence  in the magnitude of the changes, arising from 
the differences between satellite observations and reanalysis products.
2.3.1.4.2 Global monsoon (GM) changes
The AR5 reported a weakening of the global monsoon (GM) circulation 
as well as a decrease of global land monsoon rainfall over the second 
half of the 20th century. Nevertheless, there was low confidence  in the 
observed circulation trends due to uncertainties in reanalysis products 
and in the definition of the monsoon area. From a paleo perspective, 
AR5 only assessed regional monsoon changes.
New research based on high-resolution proxies reinforces previous 
findings on the influence of orbital cycles on GM variability on  millennial time scales. The intensity of the monsoon systems 
is generally out of phase between hemispheres, being associated 
with the precession cycle (about 21–23 kyr) (An et al., 2015; P .X.

Wang et al., 2017; Seth et al., 2019), with intensified NH monsoon 
systems during precession minima (Toucanne et al., 2015; Wagner 
et al., 2019). The eccentricity forcing (about 100 kyr cycle) shows 
stronger GM during interglacial periods (P .X. Wang et  al., 2014, 
2017; An et al., 2015; Mohtadi et al., 2016). Changes in obliquity 
(about 41 kyr cycle) modify the strength of monsoon systems, with 
increased summer monsoon rainfall when obliquity is maximal (Y . 
Liu et al., 2015b; Mohtadi et al., 2016). Millennial scale variability in 
GM during the LDT was also linked to the occurrences of Heinrich 
stadials, resulting in weakened NH monsoons and intensified SH 
monsoons (An et al., 2015; P .X. Wang et al., 2017; Margari et al., 
2020). 
An intensification of the NH monsoons in the early to mid-Holocene 
with increased precipitation and regional expansions of rainfall areas 
identified through a variety of proxy records is shown by Biasutti

et  al. (2018) and P .X. Wang et  al. (2017). The response for the SH 
monsoons during this period indicates a weakening in both summer 
and winter precipitation (P .X. Wang et al., 2014, 2017; Sachs et al., 
2018). A decline in GM precipitation and a retraction of the northern 
fringes of monsoon areas was inferred from the mid-Holocene 
onwards, with some regions experiencing wetter conditions during 
the mid to late Holocene compared with present and a strengthening 
of the SH monsoons (P .X. Wang et al., 2014, 2017; Sachs et al., 2018). 
For the CE, GM reconstructions exhibit inter-hemispheric contrast 
during the period 950–1250 CE, with intensified NH monsoons and 
weakened SH monsoons, and the opposite pattern during 1400–1850 
CE (P .X. Wang et al., 2014; An et al., 2015).
Direct observations highlight that the GM land precipitation, 
particularly over the NH, experienced a slight increase from 1900 
through the early 1950s, followed by an overall decrease from the

1950s to the 1980s, and then an increase to present (Kitoh et al., 
2013; B. Wang et  al., 2018, 2021; X. Huang et  al., 2019b). This 
highlights the existence of multi-decadal variations in the NH 
monsoon circulation patterns and precipitation intensity (Wang et al., 
2013; P .X. Wang et al., 2014, 2017; Monerie et al., 2019). An overall 
increase in monsoon precipitation during extended boreal summer 
(JJAS) over the NH since 1979 is revealed by GPCP (Deng et  al., 
2018; Han et al., 2019) and CMAP for 1980–2010 (Jiang et al., 2016). 
SH summer monsoon behaviour is dominated by strong interannual 
variability and large regional differences (Kitoh et al., 2013; Lin et al., 
2014; Jiang et  al., 2016; Kamae et  al., 2017; Deng et  al., 2018; 
Han et al., 2019), with no significant trends reported by GPCP and 
CMAP (Deng et al., 2018). Uncertainty predominantly arises from the 
observed increase in tropical precipitation seasonality (Feng et  al.,

2013) and the estimation of GM precipitation over the ocean areas, 
leading to a large apparent spread across datasets (Kitoh et al., 2013; 
Kamae et al., 2017). 
In summary, observed trends during the last century indicate that the 
GM precipitation decline reported in AR5 has reversed since the 1980s, 
with a likely  increase mainly due to a significant positive trend in the NH 
summer monsoon precipitation ( medium confidence ). However, GM

338
precipitation has exhibited large multi-decadal variability over the last 
century, creating low confidence  in the existence of centennial-length 
trends in the instrumental record. Proxy reconstructions show a  likely  
NH monsoons weakening since the mid-Holocene, with opposite 
behaviour for the SH monsoons. 
2.3.1.4.3 Extratropical jets, storm tracks, and blocking
The AR5 reported a likely  poleward shift of storm tracks and jet 
streams since the 1970s from different datasets, variables and 
approaches. These trends were consistent with the HC widening and 
the poleward shifting of the circulation features since the 1970s. 
There was low confidence  in any large-scale change in blocking. 
Proxy records consistent with modelling results imply a southward 
shift of the storm tracks over the North Atlantic during the LGM 
(Raible et al., 2021). A variety of proxies are available for the changes

in the position of the extratropical jets/westerlies during the Holocene. 
Recent syntheses of moisture-sensitive proxy records indicate 
drier-than-present conditions over mid-latitudes of western North 
America (Hermann et  al., 2018; Liefert and Shuman, 2020) during 
the MH, which together with a weakened Aleutian Low (Bailey et al., 
2018) implies that the winter North Pacific jetstream was shifted 
northward. A synthesis of lines of evidence from the SH indicates 
that the westerly winds were stronger over 14–5 ka, followed by 
regional asymmetry after 5 ka (Fletcher and Moreno, 2012). There 
is no consensus on the shifts of the SH westerlies with some studies 
implying poleward migrations (Lamy et al., 2010; Voigt et al., 2015; 
Turney et al., 2017; Anderson et al., 2018) and others suggesting an 
equatorward shift (Kaplan et al., 2016) in the MH.
During 950–1400 CE, hydroclimate indicators suggest a northward

shift of Pacific storm tracks over North America (McCabe-Glynn et al., 
2013; Steinman et al., 2014) which was comparable in magnitude 
to that over 1979–2015 (J. Wang et al., 2017a). Storm tracks over 
the North Atlantic-European sector shifted northward as indicated 
by multi-proxy indicators over the North Atlantic (Wirth  et  al., 
2013; Orme et al., 2017) and Mediterranean (Roberts et al., 2012). 
Reconstructed westerly winds in the SH suggest a poleward shift 
(Lamy et al., 2010; Schimpf et al., 2011; Goodwin et al., 2014; Koffman 
et al., 2014; Moreno et al., 2018), with latitudinal change comparable 
to that during recent decades (Swart and Fyfe, 2012; Manney and 
Hegglin, 2018). 
Multiple reanalyses show that since 1979 the subtropical jet 
wind speeds have generally increased in winter and decreased 
in summer in both hemispheres, but the trends are regionally 
dependent (Pena-Ortiz et al., 2013; Manney and Hegglin, 2018; S.H.

Lee et  al., 2019). Over NH mid-latitudes, the summer zonal wind 
speeds have weakened in the mid-troposphere (Francis and Vavrus, 
2012; Coumou et  al., 2014, 2015; Haimberger and Mayer, 2017). 
Meanwhile there are indications of enhanced jetstream meandering 
in boreal autumn at the hemispheric scale (Francis and Vavrus, 2015; 
Di Capua and Coumou, 2016), whereas the regional arrangement of 
meandering depends on the background atmospheric state (Cohen 
et al., 2020). These meandering trends, however, are sensitive to the 
metrics used (Screen and Simmonds, 2013; Hassanzadeh et al., 2014; Cattiaux  et  al., 2016; Vavrus, 2018). Hypothesized links to Arctic 
warming are assessed in Cross-Chapter Box 10.1. 
Multiple reanalyses and radiosonde observations show an increasing 
number of extratropical cyclones over the NH since the 1950s 
(Chang  and Yau, 2016; X.L. Wang et  al., 2016). The positive trends 
are generally consistent among reanalyses since 1979, though with

considerable spread (Tilinina et  al., 2013; X.L. Wang et  al., 2016). 
In  recent decades the number of deep extratropical cyclones has 
increased over the SH (Section 8.3.2.8.1 and Figure 8.12; Reboita et al., 
2015; X.L. Wang et  al., 2016), while the number of deep cyclones 
has decreased in the NH in both winter and summer (Neu et  al., 
2013; Coumou et al., 2015; Chang et al., 2016; J. Wang et al., 2017a; 
Gertler and O’Gorman, 2019). The regional changes for different 
intensity extratropical cyclones are assessed in Section 8.3.2.8.1. 
The  assessment of trends is complicated by strong interannual 
to decadal variability, sensitivity to dataset choice and resolution 
(Tilinina et al., 2013; Lucas et al., 2014; X.L. Wang et al., 2016; Pepler 
et al., 2018; Rohrer et al., 2018) and cyclone identification/tracking 
methods (Neu et al., 2013; Grieger et al., 2018). Thus there is overall 
low confidence  for recent changes in global extratropical storm tracks.

A consistent poleward shift of the tropospheric extratropical jets 
since 1979 is reported by multiple reanalyses (Figure 2.18; Davis and 
Rosenlof, 2012; Davis and Birner, 2013; Pena-Ortiz et al., 2013; Manney 
and Hegglin, 2018), and radiosonde winds (Allen et al., 2012). This 
is generally consistent with the previously reported shifts retrieved 
from satellite temperature observations (Fu and Lin, 2011; Davis and 
Rosenlof, 2012). After the 1960s the magnitude of meridional shifts 
in extratropical jets over both the North Atlantic and North Pacific 
in August is enhanced compared to multi-century variability (Trouet 
et  al., 2018). Despite some regional differences (Woollings et  al., 
2014; Norris et al., 2016; J. Wang et al., 2017a; Xue and Zhang, 2017; 
Ma and Zhang, 2018; Melamed-Turkish et al., 2018), overall poleward 
deflection of storm tracks in boreal winter over both the North Atlantic 
and the North Pacific was identified during 1979–2010 (Tilinina et al.,

2013). Over the SH extra-tropics there is a similarly robust poleward 
shift in the polar jet since 1979 (Pena-Ortiz et al., 2013; Manney and 
Hegglin, 2018; WMO, 2018), although after 2000 the December–
January–February (DJF) tendency to poleward shift of the SH jet 
stream position ceased (Banerjee et al., 2020). The general poleward 
movement in midlatitude jet streams (Lucas et al., 2014) is consistent 
with the expansion of the tropical circulation (Section  2.3.1.4.1). 
The changes of extratropical jets and westerlies are also related to the 
annular modes of variability (Section 2.4 and Annex IV).
Robust trends in blocking have only been found in certain regions 
and specific seasons during recent decades. Increases in blocking 
frequency have occurred over low-latitude regions in the North 
Atlantic in boreal winter (Davini et al., 2012), the South Atlantic in 
austral summer (Dennison et al., 2016) and the southern Indian Ocean

in austral spring (Schemm, 2018). Over the subpolar North Atlantic 
sustained periods of positive Greenland blocking were identified 
during 1870–1900 and from the late 1990s to 2015 (Hanna et al., 
2015). Further analysis of association of Greenland blocking with the 
NAM is provided in Section 2.4.1.1. Meanwhile, a reduced blocking 
frequency has been found over winter in Siberia (Davini et al., 2012)

339
and the south-western Pacific in austral spring (Schemm, 2018). 
Over eastern European Russia and western Siberia (40°E–100°E) a 
tendency towards longer blocking events was reported by Luo et al. 
(2016) for 2000–2013 and by Tyrlis et  al. (2020) for 1979–2017. 
Inter-annual variance in the number of blocking events over the SH 
(Oliveira et  al., 2014) and North Atlantic (Kim and Ha, 2015) has 
enhanced. Blocking events and their trends are sensitive to choice 
of datasets, calculation periods and methods (Cheung et al., 2013; 
Barnes et al., 2014; Pepler et al., 2018; Rohrer et al., 2018; Woollings 
et  al., 2018b; Kononova and Lupo, 2020). As a result, hemispheric 
and global trends in blocking frequency have overall low confidence.  
In summary, the total number of extratropical cyclones has likely  
increased since the 1980s in the NH ( low confidence ), but with

fewer deep cyclones particularly in summer. The number of strong 
extratropical cyclones has  likely  increased in the SH ( medium 
confidence ). The extratropical jets and cyclone tracks have likely  been 
shifting poleward in both hemispheres since the 1980s with marked 
seasonality in trends ( medium confidence ). There is low confidence  in shifting of extratropical jets in the NH during the mid-Holocene and 
over 950–1400 CE to latitudes that likely  were similar to those since 
1979. There is low confidence  in observed global-scale changes in the 
occurrence of blocking events.
2.3.1.4.4 Surface wind and sea level pressure
The AR5 concluded that surface winds over land had generally 
weakened. The confidence  for both land and ocean surface wind 
trends was low owing to uncertainties in datasets and measures 
used. Sea level pressure (SLP) was assessed to have likely  decreased 
from 1979–2012 over the tropical Atlantic and increased over large

regions of the Pacific and South Atlantic, but trends were sensitive to 
the period analysed. 
Terrestrial in situ wind datasets have been updated and the quality-
control procedures have been improved, with particular attention to 
homogeneity and to better retaining true extreme values (Dunn et al., 
2012, 2014, 2016). Global mean land wind speed (excluding Australia) Figure 2.1 8 | Trends in ERA5 zonal-mean zonal wind speed.  Shown are  (a) DJF (December–January–February); (b) MAM (March–April–May); (c) JJA (June–July–August); 
and (d) SON (September–October–November). Climatological zonal winds during the data period are shown in solid contour lines for westerly winds and in dashed lines for 
easterly. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘×’ marks denote non-significant trends).

Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).1000 850 700 600 500 400 300 200 150 100
1010
10102020
2020
30
3040Pressure (hPa)DJF (a) (b)
(c) (d)1000 850 700 600 500 400 300 200 150 100
1010
101020
2020
30MAM
1000 850 700 600 500 400 300 200 150 100
−10
1010
10102020
203030
40Pressure (hPa)JJA
1010
1010
2020
2030
30SON
Trend (ms-1 per decade)−1.5−1.0−0.5 1.5
90°S 60°S 30°S 0° 30°N 60°N 90°N90°S 60°S 30°S 0° 30°N 60°N 90°N
1.0 0.5 0Trends in ERA5 zonal mean wind speed 1979 –2018

340
from HadISD for 1979–2018 shows a reduction (stilling) of 0.063 m s–1 
per decade (Azorin-Molina et al., 2019). Trends are broadly insensitive 
to the subsets of stations used. Although the meteorological 
stations are unevenly distributed worldwide and sparse in South 
America and Africa, the majority exhibit stilling particularly in the 
NH (Figure 2.19). Regionally, strong decreasing trends are reported 
in central Asia and North America (–0.106 and –0.084 m s–1 per 
decade respectively) during 1979–2018 (McVicar et al., 2012; Vautard 
et al., 2012; J. Wu et al., 2018; Azorin-Molina et al., 2019). This stilling 
tendency has reversed after 2010 and the global mean surface winds 
have strengthened (Zeng et  al., 2019b; Azorin-Molina et  al., 2020), 
although the robustness of this reversal is unclear given the short 
period and interannual variability (Kousari et al., 2013; Kim and Paik, 
2015; Azorin-Molina et al., 2019).

Over the ocean, datasets demonstrate considerable disagreement in 
surface wind speed trends and spatial features (Kent et  al., 2013). 
Global ocean surface winds from NOCv2.0 demonstrate upward 
trends of about 0.11 m s–1 per decade (1979–2015) with somewhat 
smaller trends from WASwind for 1979–2011 (Azorin-Molina et al., 
2017, 2019). The trends are consistent until 1998, but diverge 
thereafter. Both ERA5 and JRA-55 reanalyses show consistently increasing global marine wind speeds over 1979–2015, though 
flattening since 2000, whereas MERRA-2 agrees until 1998, but then 
exhibits increased variability and an overall decrease in the last two 
decades (Azorin-Molina et al., 2019). This agrees with estimates by 
Sharmar et al. (2021) showing upward ocean wind trends from 1979 
to 2000 which are consistent in ERA-Interim, ERA5 and MERRA-2, 
but disagree with CFSR trends for the same period. Over 2000–2019 
all reanalyses show diverging tendencies. An updated multiplatform

satellite database (comprising data from altimeters, radiometers, and 
scatterometers) from 1985–2018 shows small increases in mean wind 
speed over the global ocean, with the largest increase observed in the 
Southern Ocean (Young and Ribal, 2019), consistent with signals in 
ERA-Interim, ERA5 and MERRA-2 (Sharmar et al., 2021). Overall, most 
products suggest positive trends over the Southern Ocean, western 
North Atlantic and the tropical eastern Pacific since the early 1980s. 
The modern era reanalyses exhibit SLP increases over the SH 
subtropics with stronger increases in austral winter over 1979–2018. 
Over the NH, SLP increased over the mid-latitude Pacific in boreal 
winter and decreased over the eastern subtropical and mid-latitude 
North Atlantic in boreal summer. Discrepancies in the low-frequency 
variations during the first half of the 20th century exist in the

Figure 2 .19 | Trends in surface wind speed. (a) Station observed winds from the integrated surface database (HadISD v2.0.2.2017f); (b) Cross-Calibrated Multi-Platform 
wind product; (c) ERA5; and (d) wind speed from the Objectively Analyzed Air-Sea Heat Fluxes dataset, release 3 (OAFLUX, release 3). White areas indicate incomplete or 
missing data. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008); ‘×’ marks denote non-significant 
trends. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).(a) HadISD (b) CCMP 
(c) ERA5 (d) OAFlux 
 
Trends in surface wind speed 1988–2017
Trends (ms−1 per decade) Non significantSignificant Colour 0 −0.4 −0.2 0.2 0.4

341
centennial-scale reanalysis products (Befort et  al., 2016). Overall, 
modern reanalysis datasets support the AR5 conclusion that 
there is no clear signal for trends in the strength and position of 
the  permanent and quasi-permanent pressure centres of action 
since the 1950s. Instead, they highlight multi-decadal variations. 
Large-scale SLP is strongly associated with the changes in modes of 
variability (Section 2.4 and Annex IV).
In summary, since the 1970s a worldwide weakening of surface wind 
has likely  occurred over land, particularly marked in the NH, with low 
confidence  in a recent partial recovery since around 2010. Differences 
between available wind speed estimates lead to low confidence  in 
trends over the global ocean as a whole but with most estimates 
showing strengthening globally over 1980–2000 and over the last 
four decades in the Southern Ocean, western North Atlantic and the 
tropical eastern Pacific.

2.3.1.4.5 Stratospheric polar vortex and sudden warming events
The AR5 assessed changes in the polar vortices and reported a  likely  
decrease in the lower-stratospheric geopotential heights over  Antarctica 
in spring and summer at least since 1979. 
Multiple definitions for the polar vortex strength and sudden 
stratospheric warming (SSW) events have been proposed and 
compared (Butler et al., 2015; Palmeiro et al., 2015; Waugh et al., 2017; 
Butler and Gerber, 2018), and new techniques identifying daily vortex 
patterns and SSWs have been developed (D.M. Mitchell et al., 2013; 
Kretschmer et al., 2018). Errors in reanalysis stratospheric winds were 
assessed and discrepancies in stratospheric atmospheric circulation 
and temperatures between reanalyses, satellites and radiosondes have 
been reported (D.M. Mitchell et al., 2013; Duruisseau et al., 2017). 
The northern stratospheric polar vortex has varied intra-seasonally

and with altitude during recent decades. Multiple reanalysis and 
radiosonde datasets show that the midwinter lower stratospheric 
geopotential height (150 hPa) over the polar region north of 60°N 
has increased significantly since the early 1980s (Bohlinger et al., 
2014; Garfinkel et al., 2017). This signal extends to the middle and 
upper stratosphere. In January-February zonal winds north of 60°N 
at 10 hPa have been weakening (Kim et al., 2014; Kretschmer et al., 
2018). Daily atmospheric circulation patterns over the northern 
polar stratosphere exhibit a decreasing frequency of strong vortex 
events and commensurate increase in more-persistent weak events, 
which largely explains the observed significant weakening of the 
vortex during 1979–2015 (Kretschmer et al., 2018). The northern 
polar vortex has weakened in early winter but strengthened during 
late winter (Bohlinger et al., 2014; Garfinkel et al., 2015a, 2017; Ivy

et al., 2016; Seviour, 2017; Kretschmer et al., 2018). In the middle 
and upper stratosphere, a strengthening trend of the northern 
polar vortex during DJF has occurred since 1998, contrasting the 
weakening trend beforehand (D. Hu et al., 2018). The position of the 
polar vortex also has long-term variations, exhibiting a persistent 
shift toward Northern Siberia and away from North America in 
February over the period 1979–2015 (Zhang et al., 2016; J. Zhang 
et  al., 2018). Multiple measures show similar location changes 
(Seviour, 2017). Sudden stratospheric warming (SSW), a phenomenon of rapid 
stratospheric air temperature increases (sometimes by more than 
50°C in 1–2 days), is tightly associated with the reversal of upper 
stratospheric zonal winds, and a resulting collapse or substantial 
weakening of the stratospheric polar vortex (Butler et  al., 2015; 
Butler and Gerber, 2018) and on average occurs approximately

6 times per decade in the NH winter (Charlton et al., 2007; Butler 
et  al., 2015). The SSW record from all modern reanalyses is very 
consistent. There is a higher occurrence of major midwinter SSWs 
in the 1980s and 2000s with no SSW events during 1990–1997 
(Reichler et al., 2012; Butler et al., 2015). An assessment of multi-
decadal variability and change in SSW events is sensitive to both 
chosen metric and methods (Palmeiro et al., 2015). Due to the lack of 
assimilation of upper air data, the centennial-scale reanalyses do not 
capture SSW events, even for the most recent decades (Butler et al., 
2015, 2017) and hence cannot inform on earlier behaviour. There 
has been considerably less study of trends in the SH stratosphere 
polar vortex strength despite the interest in the ozone hole and the 
potential impact of the SH stratosphere polar vortex strength on it. 
The occurrence of SSW events in the SH is not as frequent as in the

NH, with only 3 documented events in the last 40 years (Shen et al., 
2020).
In summary, it is likely that the northern lower stratospheric polar 
vortex has weakened since the 1980s in midwinter, and its location 
has shifted more frequently toward the Eurasian continent. The short 
record and substantial decadal variability yields low confidence  in 
any trends in the occurrence of SSW events in the NH winter and such 
events in the SH are rare.
2.3.2 Cryosphere
of the cryosphere (Cross-Chapter Box 2.2). Chapter 9 undertakes 
a holistic assessment of past and possible future changes and 
understanding of key processes in the cryosphere, including those at 
regional scales, integrating observations, modelling and theoretical 
understanding, while, here in chapter 2, the focus is on past large-
scale, observation-based cryospheric changes. 
2.3.2.1 Sea Ice Coverage and Thickness
2.3.2.1.1 Arctic sea ice

The AR5 reported that the annual mean Arctic sea-ice extent (SIE) 
very likely  decreased by 3.5–4.1% per decade between 1979 and 
2012 with the summer sea-ice minimum (perennial sea ice) very 
likely  decreasing by 9.4–13.6% per decade. This was confirmed by 
SROCC reporting the strongest reductions in September (12.8 ± 2.3% 
per decade; 1979–2018) and stating that these changes were likely  
unprecedented in at least 1 kyr ( medium confidence ). The spatial 
extent had decreased in all seasons, with the largest decrease for 
September ( high confidence ). The AR5 reported also that the average 
winter sea ice thickness within the Arctic Basin had likely  decreased 
by between 1.3 m and 2.3 m from 1980 to 2008 ( high confidence ), 
consistent with the decline in multi-year and perennial ice extent.

342
The SROCC stated further that it was virtually certain  that Arctic sea 
ice had thinned, concurrent with a shift to younger ice. Lower sea ice 
volume in 2010–2012 compared to 2003–2008 was documented in 
AR5 ( medium confidence ). There was high confidence  that, where the 
sea ice thickness had decreased, the sea-ice drift speed had increased. 
Proxy records are used in combination with modelling to assess Arctic 
paleo sea ice conditions to the extent possible. For the Pliocene, 
limited  proxy evidence  of a reduced sea ice cover compared to 
‘modern’ winter conditions (Knies et al., 2014; Clotten et al., 2018) 
and model simulations of a largely ice-free Arctic Ocean during 
summer (Howell et al., 2016; Feng et al., 2019; F . Li et al., 2020) imply 
medium confidence  that the Arctic Ocean was seasonally ice covered. 
Over the LIG, sparse proxy reconstructions (Stein et al., 2017; Kremer

et al., 2018) and proxy evidence from marine sediments (Kageyama 
et al., 2021b) provide medium confidence  of perennial sea ice cover. 
Over the past 13 kyr proxy records suggest extensive sea-ice 
coverage during the Younger Dryas (at the end of the LDT), followed 
by a decrease in sea ice coverage during the Early Holocene, and 
increasing sea-ice coverage from the MH to the mid-15th century 
(De Vernal et al., 2013; Belt et al., 2015; Cabedo-Sanz et al., 2016; 
Armand et al., 2017; Belt, 2018). There is limited evidence  that the 
Canadian Arctic had less multiyear sea ice during the Early Holocene 
than today (Spolaor et al., 2016). For more regional details on paleo 
arctic sea ice see Section 9.3.1.1. 
Pan-Arctic SIE conditions (annual means and late summer) during the 
last decade were unprecedented since at least 1850 (Figure 2.20a; 
Walsh et al., 2017, 2019; Brennan et al., 2020), while, as reported 
in SROCC, there remains medium confidence  that the September

(late  summer) Arctic sea ice loss during the last decade was 
unprecedented during the past 1 kyr. Sea-ice charts since 1850 
(Walsh et al., 2017, 2019) suggest that there was no significant trend 
before the 1990s, but the uncertainty of these estimates is large and 
could mask a trend, a possibility illustrated by Brennan et al. (2020), 
who found a loss of Arctic sea ice between 1910 and 1940 in an 
estimate based on a data assimilation approach.
There has been a continuing decline in SIE and Arctic sea ice area 
(SIA) in recent years (Figure 2.20a). To reduce grid-geometry 
associated biases and uncertainties (Notz, 2014; Ivanova et al., 2016; 
Meier and Stewart, 2019) SIA is used in addition to, or instead of SIE 
herein (see also section 9.3.1). A record-low Arctic SIA since the start 
of the satellite era (1979) occurred in September 2012 (Figure 2.20a). 
Decadal SIA means based on the average of three different satellite

products decreased from 6.23 to 3.76 million km2 for September and 
14.52 to 13.42 million km2 for March SIA (Figure 2.20a). Initial SIA 
data for 2020 (OSISAF) are within the range of these recent decadal 
means or slightly below (Figure 2.20a). SIA has declined since 1979 
across the seasonal cycle (Figure 9.13). Most of this decline in SIA has 
occurred after 2000, and is superimposed by substantial interannual 
variability. The sharp decline in Arctic summer SIA coincides with 
earlier surface melt onset (Mortin et  al., 2016; Bliss et  al., 2017), 
later freeze-up, and thus a longer ice retreat and open water period 
(Stammerjohn et al., 2012; Parkinson, 2014; Peng et al., 2018).Over the past two decades, first-year sea ice has become more 
dominant and the oldest multiyear ice (older than 4 years) which 
in March 1985 made up 33% of the Arctic sea-ice cover, has nearly 
disappeared, making up 1.2% in March 2019 (Perovich et  al.,

2020). The loss of older ice is indicative of a thinning overall of ice 
cover (Tschudi et  al., 2016), but also the remaining older ice has 
become thinner (E. Hansen et al., 2013). Since in situ ice thickness 
measurements are sparse, information about ice thickness is mainly 
based on airborne and satellite surveys. Records from a combination NASA team  
NASA bootstrap  
OSISAF    Mean 1979 –1988
Mean 2010 –2019
1850 –1978  range  (Arctic only)(b)
Antarctic sea ice area (million km2)18
16
14
12
6
4
2
0September
February
1980 1990 2010 2020 2000March18
16
14
12
6
4
2(a)
0SeptemberArctic sea ice area (million km2)
1980 1990 2010 2020 2000Changes in Arctic and Antarctic sea ice area
Figure 2.2 0 | Changes in Arctic and Antarctic sea ice area. (a) Three time 
series of Arctic sea-ice area (SIA) for March and September from 1979 to 2020 
(passive microwave satellite era). In addition, the range of SIA from 1850–1978 is

indicated by the vertical bar to the left. (b) Three time series of Antarctic sea ice 
area for September and February (1979–2020). In both (a) and (b), decadal means 
for the three series for the first and most recent decades of observations are shown 
by horizontal lines in grey (1979–1988) and black (2010–2019). SIA values have 
been calculated from sea ice concentration fields. Available data for 2020 (OSISAF) is 
shown in both (a) and (b). Further details on data sources and processing are available 
in the chapter data table (Table 2.SM.1).

343
of different platforms show for the central and western Arctic 
Ocean (Arctic Ocean north of Canada and Alaska) negative trends 
since the mid-1970s (Lindsay and Schweiger, 2015; Kwok, 2018), 
with a particularly rapid decline during the 2000s, which coincided 
with a large loss of multiyear sea ice. Direct observations from 2004 
and 2017 indicate a decrease of modal ice thickness in the Arctic 
Ocean north of Greenland by 0.75 m, but with little thinning between 
2014 and 2017 (Haas et al., 2017). This agrees with data based on 
satellite altimetry and airborne observations, showing no discernible 
thickness trend since 2010 (Kwok and Cunningham, 2015; Kwok, 
2018; Kwok and Kacimi, 2018; see Figure 2.21). However, sea-ice 
thickness derived from airborne and spaceborne data is still subject 
to uncertainties imposed by snow loading. For radar altimeters, 
insufficient penetration of radar signal into the snowpack results in

overestimation of ice thickness (e.g., Ricker et al., 2015; King et al., 
2018; Nandan et al., 2020). Negative trends in ice thickness since the 
1990s are also reported from the Fram Strait in the Greenland Sea, 
and north of Svalbard (E. Hansen et al., 2013; Renner et al., 2014; 
King et al., 2018; Rösel et al., 2018; Spreen et al., 2020). Thickness 
data collected in the Fram Strait originate from ice exported from 
the interior of the Arctic Basin and are representative of a larger 
geographical area upstream in the transpolar drift. A reduction of 
survival rates of sea ice exported from the Siberian shelves by 15% 
per decade has interrupted the transpolar drift and affected the long-
range transport of sea ice (Krumpen et al., 2019). The thinner and on 
average younger ice has less resistance to dynamic forcing, resulting 
in a more dynamic ice cover (Hakkinen et  al., 2008; Spreen et  al.,

2011; Vihma et al., 2012; Kwok et al., 2013).The SROCC noted the lack of continuous records of snow on sea 
ice. Nevertheless in recent decades, more snow on sea ice has been 
observed in the Atlantic sector in the Arctic than in the western Arctic 
Ocean (Webster et al., 2018). Previously, Warren et al. (1999) showed 
that over 1954–1991 there were weak trends towards declining snow 
depth on sea ice in the Pacific sector. Recent observations indicate 
a substantial thinning of the spring snowpack in the western Arctic 
(Cavalieri et al., 2012; Brucker and Markus, 2013; Kurtz et al., 2013; 
Laxon et al., 2013; Webster et al., 2018). In contrast, thick snow over 
Arctic sea ice in the Atlantic sector north of Svalbard (snow thickness 
around 0.4 m or more) has been observed in the 1970s and since the 
1990s (Rösel et al., 2018), but data are too sparse to detect trends. 
In summary, over 1979–2019 Arctic SIA has decreased for all months,

with the strongest decrease in summer ( very high confidence ). 
Decadal means for SIA decreased from the first to the last decade 
in that period from 6.23 to 3.76 million km2 for September, and from 
14.52 to 13.42 million km2 for March. Arctic sea ice has become 
younger, thinner and faster moving ( very high confidence ). Snow 
thickness on sea ice has decreased in the western Arctic Ocean 
(medium confidence ). Since the Younger Dryas at the end of the LDT, 
proxy indicators show that Arctic sea ice has fluctuated on multiple 
time scales with a decrease in sea ice coverage during the Early 
Holocene and an increase from the MH to the mid-15th century. 
Current pan-Arctic sea ice coverage levels (annual mean and late 
summer) are unprecedentedly low since 1850 ( high confidence),  and 
with medium confidence  for late summer for at least the past 1 kyr. 
2.3.2.1.2 Antarctic sea ice
The AR5 reported a small but significant increase in the total annual

mean Antarctic SIE that was very likely  in the range of 1.2–1.8% per 
decade between 1979 and 2012 (0.13–0.20 million km2 per decade) 
(very high confidence ), while SROCC reported that total Antarctic sea 
ice coverage exhibited no significant trend over the period of satellite 
observations (1979–2018) ( high confidence ). The SROCC noted that 
a significant positive trend in mean annual sea ice cover between 
1979 and 2015 had not persisted, due to three consecutive years of 
below-average sea ice cover (2016–2018). The SROCC stated also 
that historical Antarctic sea ice data from different sources indicated a 
decrease in overall Antarctic sea ice cover since the early 1960s, but was 
too small to be separated from natural variability ( high confidence ).
There is only limited evidence  from predominantly regional paleo 
proxies for the evolution of Southern Ocean sea ice before the 
instrumental record and estimates are not available for all proxy

target periods (Section 9.3.2). Proxies from marine sediments for 
intervals preceding and following the MPWP indicate open water 
conditions with less sea ice than modern conditions (Taylor-Silva and 
Riesselman, 2018; Ishino and Suto, 2020). During the LGM, proxies 
indicate that austral winter sea ice coverage reached the polar ocean 
front (e.g., Nair et al., 2019). More recently, sea ice coverage appears 
to have fluctuated substantially throughout the Holocene (e.g., for the 
western Amundsen Sea, Lamping et al., 2020). At the beginning of the 
CE, regional summer sea ice coverage in the north-western Ross Sea 
was lower than today (Tesi et al., 2020). Crosta et al. (2021) suggest, 
based on different proxies, four different phases with 7–10 months Error bars/Shading = 1 S.E.
1975 1985 1995 2005 2015 Thickness (m) 
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 
Regression Analysis
(Submarine record)ICEsat CS-2
70 N
80 N
0 E
Submarine transects (Feb-Mar)
Submarine transects (Oct-Nov)

ICESat/CryoSat-2 (Feb-Mar) 
ICESat/CryoSat-2 (Oct-Nov)
Operation Icebridge
(Mar-Apr) first-year ice
EM surveys (North Pole)
Changes in Arctic sea ice thickness
Figure  2.21 | Arctic sea ice thickness changes (means) for autumn (red/
dotted red) and winter (blue/dotted blue).  Shadings (blue and red) show 
1 standard error (S.E.) ranges from the regression analysis of submarine ice thickness 
and expected uncertainties in satellite ice thickness estimates. Data release area of 
submarine data ice thickness data is shown in inset. Satellite ice thickness estimates 
are for the Arctic south of 88°N. Thickness estimates from more localized airborne/
ground electromagnetic surveys near the North Pole (diamonds) and from Operation 
IceBridge (circles) are shown within the context of the larger scale changes in the 
submarine and satellite records. Further details on data sources and processing are 
available in the chapter data table (Table 2.SM.1).

344
periods of sea ice occurrence per year in the Antarctic region off Adelie 
Land during the CE, where each phase was several hundred years long. 
More recent sea ice reconstructions are based on diverse sources 
including whaling records (de La Mare, 1997, 2009; Cotté and Guinet, 
2007), old ship logbooks (Ackley et  al., 2003; Edinburgh and Day, 
2016), and ice core records (Curran et al., 2003; Abram et al., 2010; 
Sinclair et  al., 2014), amongst other methods (e.g.,  Murphy et  al., 
2014). These reconstructions, in combination with recent satellite-
based observations indicate: (i) a decrease in summer SIE across all 
Antarctic sectors since the early- to mid-20th century; (ii) a decrease 
in winter SIE in the East Antarctic and Amundsen-Bellingshausen 
Seas sectors starting in the 1960s; and (iii) small fluctuations in 
winter SIE in the Weddell Sea over the 20th century (Hobbs et al.,

2016a, b). There are also ice-core indications that the pronounced 
Ross Sea increase dates back to the mid-1960s (Sinclair et al., 2014; 
Thomas and Abram, 2016). While there is reasonable broad-scale 
concurrence across these estimates, the uncertainties are large, there 
is considerable interannual variability, and reconstructions require 
further validation (Hobbs et  al., 2016a, b). New reconstructions 
(Thomas et  al., 2019) from Antarctic land ice cores show that SIE 
in the Ross Sea had increased between 1900 and 1990, while the 
Bellingshausen Sea had experienced a decline in SIE; this dipole 
pattern is consistent with satellite-based observations from 1979 
to 2019 (Parkinson, 2019), but the recent rate of change then has 
been larger. Records of Antarctic SIE for the late 19th and early 
20th centuries (Edinburgh and Day, 2016), show SIE comparable 
with the satellite era, although with marked spatial heterogeneity 
(e.g., Thomas et al., 2019).

Early Nimbus satellite visible and infrared imagery from the 1960s 
(Meier et  al., 2013; Gallaher et  al., 2014) indicate higher overall 
SIE compared to 1979–2013 (Hobbs et  al., 2016a, b), but with 
large uncertainties and poorly quantified biases (NA SEM, 2017). 
The continuous satellite passive-microwave record shows that there 
was a modest increase in overall Antarctic SIA of 2.5% ± 0.2% per 
decade (1 standard error over 1979–2015; Comiso et  al., 2017). 
For overall ice coverage and for this period, positive long-term trends 
were most pronounced during austral autumn advance (Maksym, 
2019), being moderate in summer and winter, and lowest in spring 
(Holland, 2014; Turner et al., 2015; Hobbs et al., 2016a, b; Comiso 
et al., 2017). Since 2014, overall Antarctic SIE (and SIA) has exhibited 
major fluctuations from record-high to record-low satellite era 
extents (Massonnet et al., 2015; Reid and Massom, 2015; Reid et al.,

2015; Comiso et  al., 2017; Parkinson, 2019). After setting record-
high extents each September from 2012 through 2014, Antarctic SIE 
(and SIA) dipped rapidly in mid-2016 and remained predominantly 
below average through 2019 (Reid et al., 2020). For the most recent 
decade of observations (2010–2019), the decadal means of three 
SIA products (Figure 2.20b) were 2.17 million km2 for February and 
15.75  million km2 for September, respectively. The corresponding 
levels for the means for the first decade of recordings (1979–1988) 
were 2.04 million km2 for February and 15.39 million km2 for 
September indicating little overall change. Initial SIA data for 2020 
(OSISAF) show SIA for September above, and for February slightly 
below the recent decadal means (Figure 2.20b). The 2020 September 
level (OSISAF) remains below the levels observed over 2012–2014.In summary, Antarctic sea ice has experienced both increases and 
decreases in SIA over 1979–2019, and substantively lower levels

since 2016, with only minor differences between decadal means 
of SIA for the first (for February 2.04 million km2, for September 
15.39 million km2) and last decades (for February 2.17 million km2, 
for September 15.75 million km2) of satellite observations ( high 
confidence ). There remains low confidence  in all aspects of Antarctic 
sea ice prior to the satellite era owing to a paucity of records that are 
highly regional in nature and often seemingly contradictory.
2.3.2.2 Terrestrial Snow Cover
The AR5 concluded that snow cover extent (SCE) had decreased in the 
NH, especially in spring ( very high confidence ). For 1967–2012, the 
largest change was in June and March–April SCE very likely  declined. 
No trends were identified for the SH due to limited records and large 
variability. The SROCC concluded with high confidence  that Arctic 
June SCE declined between 1967 and 2018 and in nearly all mountain 
regions, snow cover declined in recent decades.

Analysis of the combined in situ observations (Brown, 2002) and the 
multi-observation product (Mudryk et al. 2020) indicates that since 
1922, April SCE in the NH has declined by 0.29 million km2 per decade, 
with significant interannual variability (Figure 2.22) and regional 
differences (Section 9.5.3.1). The limited pre-satellite era data does 
not allow for a similar assessment for the entire spring-summer 
period. Assessment of SCE trends in the NH since 1978 indicates that 
for the October to February period there is substantial uncertainty 
in trends with the sign dependent on the observational product. 
Analysis using the NOAA Climate Data Record shows an increase 
in October to February SCE (Hernández-Henríquez et  al., 2015; 
Kunkel et al., 2016) while analyses based on satellite borne optical 
sensors (Hori et  al., 2017) or multi-observation products (Mudryk 
et al., 2020) show a negative trend for all seasons (Section 9.5.3.1

and Figure 9.23). The greatest declines in SCE have occurred during 
boreal spring and summer, although the estimated magnitude is 
dataset dependent (Rupp et al., 2013; Estilow et al., 2015; Bokhorst 
et al., 2016; Thackeray et al., 2016; Connolly et al., 2019). 
2025303540
1920 1940 1960 1980 2000 2020April SCE (million km2)Northern Hemisphere April Snow Cover Extent (SCE)
SCE from Brown (2000)
Multi-observation dataset (Mudryk et al., 2020)
Figure 2 .22 | April snow cover extent (SCE) for the Northern Hemisphere 
(1922–2018).  Shading shows very likely  range. The trend over the entire 1922–2018 
period (black line) is –0.29 (± 0.07) million km2 per decade. Further details on data 

345
There has been a commensurate decrease in the snow-cover duration 
and persistence, particularly in higher latitudes due to earlier 
spring melt and, in some cases, later autumn onset of snow cover 
(Chen et al., 2015; Derksen et al., 2015; Hori et al., 2017; Hammond 
et al., 2018). Arctic snow-cover duration has decreased by 2–4 days 
per decade since the 1970s (Brown et al., 2017). Significant decreases 
in snow-cover duration have been documented over western Eurasia 
since 1978 (Hori et al., 2017). 
For the NH, maximum snow depth has generally decreased since 
the 1960s, with more robust trends for North America and greater 
uncertainty for Eurasia (Kunkel et al., 2016). Several satellite-based 
passive microwave and other products indicate general declines in 
pre-melt snow water equivalent since 1981 although there is regional 
and inter-dataset variability (Brown et al., 2017; Jeong et al., 2017;

Marty et  al., 2017; Mortimer et  al., 2020; Mudryk et  al., 2020; 
Pulliainen et al., 2020, Section 9.5.3). 
In summary, substantial reductions in spring snow cover extent have 
occurred in the NH since 1978 ( very high confidence ) with limited 
evidence  that this decline extends back to the early 20th century. 
Since 1981 there has been a general decline in NH spring snow water 
equivalent ( high confidence ).
2.3.2.3 Glacier Mass
The AR5 concluded with high confidence  that, during the Holocene, 
glaciers were at times smaller than at the end of the 20th century. 
The AR5 stated further with very high confidence  that most glaciers 
had been shrinking since the mid-1800s, and the mass loss from 
all glaciers worldwide very likely  increased from 1970 to 2009. The 
SROCC reported a globally coherent picture of continued glacier 
recession in recent decades ( very high confidence ) based on in situ

and satellite observations of changes in glacier area, length and mass, although there were considerable inter-annual and regional 
variations. Between 2006 and 2015 the global glacier mass change 
assessed by SROCC was –278 ± 113 Gt yr –1. 
Two recent global reviews on glaciers over the Holocene (Solomina 
et al., 2015) and the past 2 kyr (Solomina et al., 2016) summarize 
the chronologies of respectively 189 and 275 glaciers. The former 
shows that glaciers retreated during the LDT and retracted to their 
minimum extent between 8 ka and 6 ka. Except for some glaciers 
in the SH and tropics, glaciers expanded thereafter, reaching their 
maximum extent beyond their present-day margins during the mid-
15th to late 19th centuries CE. With few exceptions, glacier margins 
worldwide have retreated since the 19th century, with the rate of 
retreat and its global character since the late 20th century being 
unusual in the context of the Holocene (Solomina et  al.,  2016,

Figure 2.23a). However, the areal extents of modern glaciers 
in most places in the NH are still larger than those of the early 
and/or middle Holocene (Solomina et al., 2015). When considering 
Holocene and present glaciers extents, it is important to account 
for the relatively long adjustment time of glaciers (often referred to 
as response time; Section 9.5.1.3); the majority of modern glaciers 
are currently out of equilibrium with current climate, even without 
further global warming (Mernild et al., 2013; Christian et al., 2018; 
Marzeion et al., 2018; Zekollari et al., 2020). The size of glaciers 
during other periods warmer than the Early to Mid-Holocene, such 
as the MPWP and LIG, is largely unknown because the deposits 
marking previous extents were in almost all cases over-ridden 
by later glaciations. For Arctic glaciers, different regional studies 
consistently indicate that in many places glaciers are now smaller

than they have been in millennia (Lowell et al., 2013; Miller et al., 
2013, 2017; Harning et al., 2016, 2018; Schweinsberg et al., 2017, 
2018; Pendleton et al., 2019).
Mountain glacier advance and annual mass change
Mass change (Gt yr -1) 
−700−600−500−400−300−200−1000100200
1960 1980 2000 2020Zemp et al. (2019/2020)
Wouters et al. (2019)
SROCC
Hugonnet et al. (2021)80100120
60
40
20
0Number of glaciers advancing
0 500 1000 1500 2000(a) (b)
Figure 2.23  | Mountain glacier advance and annual mass change. (a) Number of a finite selection of surveyed glaciers that advanced during the past 2000 years. 
(b) Annual and decadal global glacier mass change (Gt yr –1) from 1961 until 2018. In addition, mass change mean estimates are shown. Ranges show the 90% confidence 
interval. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

346
New glacier outline (RGI Consortium, 2017) and glacier mass 
compilations (Zemp et al., 2019, 2020; Ciracì et al., 2020; Hugonnet 
et  al., 2021) improve, refine and update the quantification of 
glacier areal and mass changes based on observations from in situ 
and remote sensing data. Observations between the 1960s and 
2019 indicate that mass loss has increased over recent decades 
(Figure 2.23b). The overall global glacier mass loss rate has increased 
from 240  ± 9 Gt yr –1 over 2000–2009 to 290 ± 10 Gt yr –1 over 
2010–2019 (Hugonnet et al., 2021), confirming that the last decade 
exhibits the most negative glacier mass balance since the beginning 
of the observational record. Observations are in general consistent 
with trends revealed by global glacier mass change modelling for 
almost the entire 20th century (1901–1990) implying an estimated 
mass loss (without uncharted glaciers (Parkes and Marzeion, 2018)

and excluding peripheral glaciers of Greenland and Antarctica) of very 
likely  210 ± 90 Gt yr –1 and very likely  170 ± 80 Gt yr –1 for the period 
1971–2019 (Marzeion et al., 2015; Section 9.5.1 and Table 9.5).
In summary, there is very high confidence  that, with few 
exceptions, glaciers worldwide have retreated since the second 
half of the 19th century, and continue to retreat. The current global 
character of glacier mass loss is highly unusual (almost all glaciers 
simultaneously receding) in the context of at least the last 2 kyr 
(medium confidence ). Glacier mass loss rates have increased since 
the 1970s ( high confidence ). Although many surveyed glaciers are 
currently more extensive than during the MH ( high confidence ), 
they generally are in disequilibrium with respect to current climate 
conditions and hence are committed to further ice loss. 
2.3.2.4 Ice-sheet Mass and Extent
During glacial periods, ice sheets were more extensive and the state

of knowledge on their paleo-reconstruction can be found in recent 
publications (e.g.,  Stokes et  al., 2015; Batchelor et  al., 2019). This 
section focuses only on the large-scale aspects of those ice sheets, 
Greenland and Antarctic, that still exist today. 
2.3.2.4.1 Greenland Ice Sheet (GrIS)
The AR5 concluded the volume of the Greenland Ice Sheet (GrIS) was 
reduced compared to present during periods of the past few million 
years that were globally warmer than present ( high confidence ). 
It reported that the GrIS had lost ice during the prior two decades 
(very high confidence ), that the ice loss had occurred in several sectors, 
and that high rates of mass loss had both expanded to higher elevations 
(high confidence) and very likely  accelerated since 1992. The SROCC 
concluded that it was extremely lik ely that ice loss increased through 
the early 21st century. The SROCC also found that summer melting 
rate had increased since the 1990s to a rate unprecedented over the

last 350 years (very high confidence ), being two to five times greater 
than the pre-industrial rates ( medium confidence ).
Details of the history of the GrIS fluctuations during warm 
interglacials continue to be elucidated. Oscillations over the past 
7.5 Myr, including the Pliocene and through the glacial – interglacial 
cycles of the Pleistocene are not well-constrained, but most studies 
indicate that Greenland was at least partially glaciated over this time with extended periods when it was predominantly deglaciated 
(Bierman et al., 2016; Schaefer et al., 2016). Geological evidence and 
modelling studies suggest periods of glacial intensification during the 
Pliocene at 4.9 Myr, 4.0 Myr, 3.6 Myr and 3.3 Myr (De Schepper et al., 
2014; Bierman et al., 2016; Bachem et al., 2017). Retreat of the GrIS 
occurred during the MPWP and GrIS extent was reduced compared 
to today with some studies suggesting that the ice sheet was limited

to the highest elevations (De Schepper et al., 2014; Koenig et al., 2015; 
Haywood et al., 2016; Blake-Mizen et al., 2019). There is apparent 
glacial intensification following the MPWP , 2.75–2.72 Myr (Nielsen 
and Kuijpers, 2013; De Schepper et  al., 2014; Blake-Mizen et  al., 
2019; Knutz et al., 2019). Several studies agree that during the LIG 
the total GrIS extent was likely  less than present day (Section 9.4.1, 
Figure 9.17) with the total mass loss ranging from 0.3–6.2 m sea 
level equivalent (SLE), although timing and magnitude of this mass 
loss are not well constrained (Helsen et al., 2013; Stone et al., 2013; 
Vasskog et al., 2015; Goelzer et al., 2016; Sinclair et al., 2016; Yau 
et al., 2016; Clark et al., 2020). During the LGM, the GrIS reached 
a peak ice volume greater than present (2–5 m SLE), as revealed by 
the limited number of available geological records (Simpson et al., 
2009; Lecavalier et al., 2014; Batchelor et al., 2019).

Recent studies of marine and lake sediments, glacier ice, and 
geomorphic features show that the GrIS retreated rapidly during the 
early Holocene but halted periodically, with a complex ice-margin 
chronology (Carlson et al., 2014; Larsen et al., 2014, 2015; Young and 
Briner, 2015; Briner et al., 2016; Young et al., 2020). It is probable that 
its total volume during 8–3 ka was smaller than today (Larsen et al., 
2015; Young and Briner, 2015; Briner et al., 2016), but uncertainties 
exist regarding precisely when the minimum MH extent and volume 
was reached, due to uncertainties in reconstructions. The GrIS then 
re-advanced reaching its maximum extent in most places during 
1450–1850 CE, although the timing and extent of this maximum 
differed by sector (Larsen et al., 2015; Briner et al., 2016).
Greenland-wide estimates of mass change based on direct observations 
were limited prior to 1992 at the time of AR5 (Kjeldsen et al., 2015).

Combined records based on airborne observations, model-based 
estimates and geodetic approaches indicate an average mass loss of 
75 ± 29.4 Gt yr –1 for 1900–1983 (Kjeldsen et al., 2015). Integration 
of proxies and modelling indicates that the last time the rate of mass 
loss of the GrIS was plausibly similar to 20th century rates was during 
the early Holocene (Buizert et al., 2018; Briner et al., 2020).
Since AR5, a combination of remote sensing, in situ observations and 
modelling has provided new insights regarding surface processes and 
their contribution to recent GrIS mass changes (AMAP , 2017; van den 
Broeke et al., 2017; Bamber et al., 2018; Mouginot et al., 2019; IMBIE 
Consortium, 2020; Khan et al., 2020). Estimates of total ice loss during 
the post-1850 period (Kjeldsen et al., 2015) and recent observations 
show that the rate of loss has increased since the beginning of the 
21st century (IMBIE Consortium, 2020; Sasgen et al., 2020; Velicogna

et al., 2020) (Section 9.4.1.1 and Figures 2.24 and 9.17).
The GrIS lost 4890 [4140 to 5640] Gt (SLE 13.5 [11.4 to 15.6] mm) 
of ice between 1992 and 2020 (Section 9.4.1 and Figure 2.24; IMBIE 
Consortium, 2020). The ice sheet was close to mass balance in the 1990s,

347
but increases in mass loss have occurred since (Bamber et al., 2018; 
WCRP Global Sea Level Budget Group, 2018; Mouginot et al., 2019; 
IMBIE Consortium, 2020). The rate of ice-sheet (including peripheral 
glaciers) mass loss rose from 120 [70  to  170]  Gt  yr –1 (SLE  0.33 
[0.18  to  0.47] mm yr –1) in 1901–1990 to 330 [290 to 370] Gt  yr –1 
(SLE  0.91 [0.79 to 1.02] mm yr –1) for 2006–2018 (Section  9.4.1, 
Table 9.5).
In summary, the GrIS was smaller than present during the MPWP 
(medium confidence ), LIG ( high confidence ) and the MH ( high 
confidence ). GrIS mass loss began following a peak volume attained 
during the 1450–1850 period and the rate of loss has increased 
substantially since the turn of the 21st century ( high confidence ).
2.3.2.4.2 Antarctic Ice Sheet (AIS)
The AR5 reported that there was high confidence  that the Antarctic 
Ice Sheet (AIS) was losing mass. The average ice mass loss from

Antarctica was 97 [58 to 135] Gt yr –1 (GMSL equivalent of 0.27 
[0.16 to 0.37] mm yr –1) over 1993–2010, and 147 [74 to 221] Gt yr –1 
(0.41 [0.20 to 0.61] mm yr –1) over 2005–2010. These assessments 
included the Antarctic peripheral glaciers. The AR5 reported with high 
confidence  that the volume of the West Antarctic Ice Sheet (WAIS) 
was reduced during warm periods of the past few million years. The 
SROCC concluded that over 2006–2015, the AIS lost mass at an 
average rate of 155 ± 19 Gt yr –1 (very high confidence ). The SROCC 
also stated that it is virtually certain  that the Antarctic Peninsula 
and WAIS combined have cumulatively lost mass since widespread 
measurements began in 1992, and that the rate of loss has increased 
since around 2006.
Process understanding and, to some extent, paleoclimate records 
show that changes in parts of the AIS can occur over multi-century 
time scales (<2kyr; Sections 9.4.2.3 and 9.6.2; e.g.,  Dowdeswell

et al., 2020). Based on physical understanding, paleo evidence and 
numerical simulations, it is very likely  that the AIS has been smaller 
than today during at least some past warm climates (such as MCO 
and LIG), in particular the WAIS (Figure 9.18; Golledge et al., 2014; de Boer et al., 2015; DeConto and Pollard, 2016; Levy et al., 2016). 
Results from sediment studies suggest a smaller AIS during the 
MPWP compared with current levels, with main differences in the 
WAIS (Section 9.6.2; SROCC, IPCC, 2019; Bertram et  al., 2018; 
Shakun et  al., 2018). Marine sediments indicate that during the 
Pleistocene repeated ungrounding and loss of large marine-based 
parts of the AIS occurred during interglacial periods, with at least 
seven transitions between floating and grounded ice in the Ross 
Sea during the last 780 kyr (McKay et al., 2012) and at least three 
reductions in ice volume in the Wilkes Basin during the last 500 kyr

(Wilson et al., 2018). Proxies, modelling and process understanding 
(Rohling et  al.,  2019; Clark et  al., 2020) indicate that the AIS was 
smaller during the LIG than present. 
Geological evidence has been used to reconstruct Holocene glacial 
fluctuations of the ice sheet margin and lowerings of its surface, 
which occurred at different times in different places, as recently 
reviewed by Noble et al. (2020). In West Antarctica, marine sediments 
below the ice sheet (Kingslake et al., 2018) corroborate a previous 
glacial isostatic adjustment modelling study (Bradley et al., 2015), 
which suggests that ice had retreated behind the present grounding 
line prior to about 10 ka, and then readvanced. Geophysical imaging 
indicates a readvance in this area around 6 ± 2 ka (Wearing and 
Kingslake, 2019). Other studies from the region conclude that ice-
sheet retreat and thinning was fastest from 9 to 8 ka (Johnson 
et al., 2014; McKay et al., 2016; Spector et al., 2017), or millennia

later, during the MH (Hein et al., 2016; Johnson et al., 2019), with 
indications of a subsequent readvance (Venturelli et al., 2020). In 
East Antarctica, rapid ice-sheet thinning occurred between around 
9 and 5 ka (Jones et  al., 2015), consistent with previous work 
indicating that the ice sheet in many regions was at or close to 
its current position by 5 ka (Bentley et  al., 2014). Overall, during 
the MH, the AIS was retreating, but remained more extensive than 
present, while some parts of the ice sheet might have been smaller 
than now ( low confidence ). 
Improved estimates of surface mass balance (SMB) in Antarctica 
from 67  ice core records do not show any substantial changes in 
accumulation rates over most of Antarctica since 1200 CE (Frezzotti 
et al., 2013). The SMB growth rate in Antarctica is estimated to be 
7.0 ± 0.1 Gt per decade between 1800 and 2010 and 14.0 ± 1.8 Gt 
per decade since 1900 (Thomas et  al., 2017). For the period

1979–2000, an insignificant Antarctic-wide negative SMB trend has 
been estimated (Medley and Thomas, 2019). The Antarctic Ice Sheet 
lost 2670 [1800 to 3540]  Gt (SLE 7.4 [5.0 to 9.8]  mm) of ice between 
1992 and 2020. The rate of ice-sheet (including peripheral glaciers) 
mass loss rose from 0 [–36 to +40] Gt yr –1 (SLE  0.0 [–0.10 to 
0.11]  mm  yr –1) in 1901–1990 to 192 [145 to 239]  Gt yr –1 (SLE 
0.54 [0.47 to 0.61] mm yr –1) for 2006–2018 (Section 9.4.2, Figure 2.24, 
and Table 9.5). Within quantified uncertainties, this estimate agrees 
with other recent estimates (Rignot et al., 2019; B. Smith et al., 2020; 
Velicogna et al., 2020). There is therefore very high confidence  that 
the AIS has been losing mass over 1992–2020 (Section 9.4.2.1 and 
Figure 2.24). Major contributions to recent AIS changes arise from 
West Antarctica and Wilkes Land in East Antarctica (Rignot et  al., 
2019). For the East Antarctic most studies suggest that the mass

Changes in Antarctic and Greenland Ice Sheet mass
Antarctic iceIce mass change (Gton)
2000 2005 2010 2020 2015 1995Greenland ice0
−1000
−2000
−3000
−4000
−5000
Figure 2. 24 | Cumulative Antarctic Ice Sheet (AIS) and Greenland Ice Sheet 
(GrIS) mass changes.  Values shown are in gigatons and come from satellite-based 
measurements (IMBIE Consortium, 2018, 2020) for the period 1992–2020. The 
estimated uncertainties, very likely  range, for the respective cumulative changes are 
shaded. Further details on data sources and processing are available in the chapter 

348
balance is not significantly different from zero (Bamber et al., 2018; 
IMBIE Consortium, 2018; Mohajerani et al., 2018; Rignot et al., 2019).
In summary, the AIS has lost mass between 1992 and 2020 ( very high 
confidence ), and there is medium confidence  that this mass loss has 
increased. During the MPWP and LIG, the ice sheet was smaller than 
present ( medium confidence ). There is low confidence  as to  whether 
the total mass of the ice sheet was larger or smaller around 6 ka 
compared to now.
2.3.2.5 Terrestrial Permafrost
The AR5 concluded that in most regions and at most monitoring 
sites permafrost temperatures since the 1980s had increased ( high 
confidence ). Negligible change was observed at a few sites, mainly 
where permafrost temperatures were close to 0°C, with slight cooling 
at a limited number of sites. The AR5 also noted positive trends in 
active layer thickness (ALT; the seasonally thawed layer above the

permafrost) since the 1990s for many high latitude sites ( medium 
confidence ). The SROCC concluded permafrost temperatures have 
increased to record high levels since the 1980s ( very high confidence ) 
with a recent increase by 0.29°C ± 0.12°C from 2007 to 2016 
averaged across polar and high mountain regions globally.
Permafrost occurrence during the Pliocene has been inferred from 
pollen in lake sediments in NE Arctic Russia and permafrost-vegetation 
relationships which indicate that permafrost was absent during the 
MPWP in this region (Brigham-Grette et al., 2013; Herzschuh et al., 
2016). Analysis of speleothem records in Siberian caves, indicates 
that permafrost was absent in the current continuous permafrost 
zone at 60°N at the start of the 1.5 Ma record, with aggradation 
occurring around 0.4 Ma (Vaks et al., 2020). There are indications of 
extensive permafrost thaw during subsequent interglacials especially

further south in the current permafrost zone (Vaks et  al., 2013). 
Reconstruction of permafrost distribution during the LGM indicates 
that permafrost was more extensive in exposed areas (Vandenberghe 
et al., 2014). In non-glaciated areas of the North American Arctic there 
is permafrost that survived the LIG (French and Millar, 2014). Trends 
and timing of permafrost aggradation and thaw over the last 6 kyr in 
peatlands of the NH were recently summarized (Hiemstra, 2018; Treat 
and Jones, 2018). Three multi-century periods (ending 1000 Before the 
Common Era (BCE), 500 CE and 1850 CE) of permafrost aggradation, 
associated with neoglaciation periods are inferred resulting in more 
extensive permafrost in peatlands of the present-day discontinuous 
permafrost zone, which reached a peak approximately 250 years ago, 
with thawing occurring concurrently with post 1850 warming (Treat 
and Jones, 2018). Although permafrost persists in peatlands at the

southern extent of the permafrost zone where it was absent prior 
to 3 ka, there has been thawing since the 1960s (James et al., 2013; 
B.M. Jones et al., 2016; Holloway and Lewkowicz, 2020).
Records of permafrost temperature measured in several boreholes 
located throughout the northern polar regions indicate general 
warming of permafrost over the last 3–4 decades (Figure 2.25), 
with marked regional variations (Romanovsky et al., 2017a, b, 2020; 
Biskaborn et al., 2019). Recent (2018–2019) permafrost temperatures 
in the upper 20–30 m layer (at depths where seasonal variation is minimal) were the highest ever directly observed at most sites 
(Romanovsky et al., 2020), with temperatures in colder permafrost 
of northern North America being more than 1°C higher than they 
were in 1978. Increases in temperature of colder Arctic permafrost 
are larger (average 0.4°C–0.6°C per decade) than for warmer 
(temperature >–2°C) permafrost (average 0.17°C per decade) of

sub-Arctic regions (Figures 2.25, 9.22). 
Increases in permafrost temperature over the last 10–30 years of 
up to 0.3°C per decade have been documented at depths of about 
20 m in high elevation regions in the NH (European Alps, the Tibetan 
Plateau and some other high elevation areas in Asia; G. Liu et al., 
2017; Cao et al., 2018; Biskaborn et al., 2019; Noetzli et al., 2020; 
Zhao et al., 2020). In Antarctica, where records are limited and short 
(most <10 years) trends are less evident (Noetzli et al., 2019).
Assessment of trends in ALT is complicated by considerable ALT 
interannual variability. For example, in north-western North America 
during the extreme warm year of 1998, ALT was greater than in 
prior years. Although ALT decreased over the following few years, 
it has generally increased again since the late 2000s (Duchesne 
et  al., 2015; Romanovsky et  al., 2017b, 2020). However, at some 
sites there has been little change in ALT due to ground subsidence

that accompanies thaw of ice-rich permafrost (Streletskiy et  al., 
2017; O’Neill et al., 2019). In the European and Russian Arctic there 
has been a broad-scale increase in ALT during the 21st century 
(Streletskiy et al., 2015; Romanovsky et al., 2020). In high elevation 
areas in Europe and Asia, increases in ALT have occurred since the 
mid-1990s (Y . Liu et al., 2017; Cao et al., 2018; Noetzli et al., 2019, 
2020; Zhao et al., 2020). Limited and shorter records for Antarctica 
show marked interannual variability and no apparent trend with 
ALT being relatively stable or decreasing at some sites since 2006 
(Hrbáček et al., 2018).−2−101
1970 1980 1990 2000 2010 2020Changes in permafrost temperature 
 Int Alaska and Central Mackenzie Valley NWT
 (n = 10, mean T = -1.37°C)  
 Beaufort-Chukchi Sea
 (n = 9, mean T = -6.23°C) 
Baffin Bay Davis Strait
(n = 7, mean T = -10.95°C) Barents
(n = 11, mean T = -2.15°C)Temperature anomaly relative to IPY baseline (°C)

Figure 2.25 | Changes in permafrost temperature.  Average departures of 
permafrost temperature (measured in the upper 20–30 m) from a baseline established 
during International Polar Year (2007–2009) for Arctic regions. Further details on data 

349
Observations of ground subsidence and other landscape change 
(e.g.,  thermokarst, slope instability) since the middle of the 
20th  century in the Arctic associated with ground ice melting 
have been documented in several studies and provide additional 
indications of thawing permafrost (Séjourné et  al., 2015; Liljedahl 
et al., 2016; Borge et al., 2017; Kokelj et al., 2017; Nitze et al., 2017; 
Streletskiy et al., 2017; Derksen et al., 2019; Farquharson et al., 2019; 
Lewkowicz and Way, 2019; O’Neill et al., 2019; see Section 9.5.2.1). 
In mountain areas, destabilization and acceleration of rock glacier 
complexes that may be associated with warming permafrost have 
also been observed (Eriksen et al., 2018; Marcer et al., 2019).
In summary, increases in permafrost temperatures in the upper 30 m 
have been observed since the start of observational programs over 
the past three to four decades throughout the permafrost regions

(high confidence ). Limited evidence  suggests that permafrost was 
less extensive during the MPWP ( low confidence ). Permafrost that 
formed after 3ka still persists in areas of the NH, but there are 
indications of thaw after the mid-1800s ( medium confidence ).
2.3.3 Ocean
This section focuses on large-scale changes in a subset of physical 
components of the ocean (Cross-Chapter Box 2.2). Chapter 7 assesses 
the role of the ocean in Earth system heating and evaluates the Earth’s 
energy budget. Chapter 9 undertakes a holistic assessment of changes 
in the ocean integrating observations, modelling and theoretical 
understanding. Chapter 11 assesses extremes such as marine heat 
waves and storm surges. SSTs are assessed in Section 2.3.1.1 as they 
constitute a critical component of GMST estimation. 
2.3.3.1 Ocean Temperature, Heat Content 
and Thermal Expansion
AR5 assessed that since 1971, global ocean warming was virtually

certain  for the upper 700 m and likely  for the 700–2000 m layer. 
The SROCC reported linear warming trends for the 0–700 m and 
700–2000 m layers of the ocean, respectively, of 4.35 ± 0.8 and 2.25 
± 0.64 ZJ yr –1 over 1970–2017; 6.28 ± 0.48 and 3.86 ± 2.09 ZJ yr –1 
over 1993–2017; and 5.31 ± 0.48 and 4.02 ± 0.97 ZJ yr –1 over 2005–
2017. Both AR5 and SROCC assessed that the ocean below 2000 
m had likely  warmed since 1992. The SROCC reported global mean 
thermosteric sea level (ThSL) rise, associated with thermal expansion 
of the ocean, with a trend of 0.89 ± 0.05 mm yr –1 for 1970–2015; 
1.36 ± 0.40 mm yr –1 for 1993–2015; and 1.40 ± 0.40 mm yr –1 for 
2006–2015, and also reported that the rate of ocean warming over 
1993–2017 had likely  more than doubled since 1969–1992.
New ocean heat content (OHC) reconstructions derived from paleo 
proxies (Bereiter et  al., 2018; Baggenstos et  al., 2019; Shackleton 
et al., 2019; Gebbie, 2021) indicate that the global ocean warmed by

2.57°C ± 0.24°C, at an average rate of about 0.3°C ka–1 (equivalent 
to an OHC change rate of 1.3 ZJ yr –1) from the LGM (about 20 ka) to 
the early Holocene (about 10 ka; Section 9.2.2.1 and Figure 9.9). Over 
the LDT, ocean warming occurred in two stages, offset by some heat 
loss during the Antarctic Cold Reversal (14.58–12.75 ka). Only during a short period of rapid warming at the end of the Younger Dryas 
(12.75–11.55 ka) were rates comparable to those observed since 
the 1970s (Bereiter et al., 2018; Shackleton et al., 2019). Ice cores 
imply a small decrease in the global mean ocean temperature during 
the early Holocene (<0.4°C) (Bereiter et al., 2018; Baggenstos et al., 
2019). Sediment cores from the equatorial Pacific and Atlantic Ocean 
(0–1000 m) indicate a stronger regional cooling (compared to mean 
ocean temperature) of 1.0°C ± 0.7°C to 1.8°C ± 0.4°C from the early/
mid-Holocene to ca.1750 CE (Rosenthal et al., 2013, 2017; Morley

et al., 2014; Kalansky et al., 2015). Sediment cores from the western 
equatorial Pacific suggest 0.8°C ± 0.1°C higher temperatures in 
the upper 700 m of the ocean during 950–1100 CE compared to 
1400–1750 CE. These changes are consistent with a global estimate 
derived from combined surface and subsurface ocean temperature 
proxy records (PAGES 2k Consortium, 2013; McGregor et al., 2015). 
A combined study of model and observational data further confirmed 
these results, treating temperature as a passive tracer (Gebbie and 
Huybers, 2019) and addressing the role of circulation dynamics 
(Scheen and Stocker, 2020). Collectively, the proxy records indicate 
a global OHC decrease of about 400 ± 70 ZJ (about 170 ± 100 ZJ 
in the Pacific) in the upper 700 m between 950–1100 CE and 
1400–1750 CE, and also suggest that the deep Pacific is still adjusting 
to this cooling (Rosenthal et al., 2013), partially offsetting the global

increase since 1750 CE (Gebbie and Huybers, 2019; Gebbie, 2021).
For the instrumental era, since AR5 and SROCC, new and updated 
OHC and ThSL observation-based analyses (Johnson et  al., 2020; 
von Schuckmann et al., 2020) enhance an existing large ensemble 
of direct and indirect OHC estimates (Figure 2.26), although some 
rely to varying degrees upon information from ocean-climate models. 
Direct estimates benefit from improved: bias adjustments (e.g., Cheng 
et  al., 2018; Leahy et  al., 2018; Palmer et  al., 2018; Ribeiro et  al., 
2018; B. Wang et  al., 2018; Bagnell and DeVries, 2020; Gouretski 
and Cheng, 2020); interpolation methods (Kuusela and Stein, 2018; 
Su et  al., 2020); and characterization of sources of uncertainty 
(e.g.,  Good, 2017; Wunsch, 2018; Allison et  al., 2019; Garry et  al., 
2019; Meyssignac et al., 2019; Palmer et al., 2021), including those 
originating from forced and intrinsic ocean variability (Penduff et al.,

2018). After 2006 direct OHC estimates for the upper 2000 m layer 
benefit from the near-global ARGO array with its superior coverage 
over 60°S–60°N (Roemmich et al., 2019). Indirect estimates include 
OHC and ThSL series inferred from satellite altimetry and gravimetry 
since 2003 (Meyssignac et al., 2019), the passive uptake of OHC (ThSL) 
at centennial timescales inferred from observed SST anomalies, and 
time-invariant circulation processes from an ocean state estimation 
(e.g., Zanna et al., 2019). Resplandy et al. (2019) estimate the rate 
of global OHC uptake over 1991–2016 from changes in atmospheric 
composition and physical relationships based on CMIP5 model 
simulations. The uncertainties are broader than from direct estimates 
but the estimate is qualitatively consistent.
Collectively, the new and updated analyses strengthen AR5 and 
SROCC findings of a sustained increase in global OHC (Figure 2.26 
and Table 2.7) and associated ThSL rise. Larger warming rates are

observed in the upper 700 m compared to deeper layers, with 
more areas exhibiting significant warming than significant cooling 
(Johnson and Lyman, 2020). There is an improved consistency among

350
available estimates of OHC rates in the upper 2000 m since 2006. 
Cheng et al. (2020), von Schuckmann et al. (2020) and Johnson et al. 
(2020) have further confirmed that the central estimates of rates of 
OHC change in the upper 2000 m depths have increased after 1993 
and particularly since 2010 (Section 3.5.1.3 and Figures 2.26 and 
3.26), although uncertainties are large (Table 2.7). Ocean reanalyses 
support findings of continued upper ocean warming (Balmaseda 
et al., 2013; von Schuckmann et al., 2018; Meyssignac et al., 2019), 
albeit with higher spread than solely observational estimates, 
particularly in the poorly sampled deep ocean below 2000 m (Storto 
et al., 2017; Palmer et al., 2018). In summary, current multi-decadal to centennial rates of OHC 
gain are greater than at any point since the last deglaciation 
(medium confidence ). At multi-centennial timescales, changes in OHC

based upon proxy indicators demonstrate a tight link with surface 
temperature changes during the last deglaciation ( high confidence ), 
as well as during the Holocene and CE ( low confidence ). It is likely  the 
global ocean has warmed since 1871, consistent with the observed 
increase in sea surface temperature. It is virtually certain  that OHC 
increased between 1971 and 2018 in the upper 700 m and very 
likely  in the 700–2000 m layer, with high confidence  since 2006. It is 
likely  the OHC below 2000 m has increased since 1992. Confidence in 
the assessment of multi-decadal OHC increase is further strengthened 
by consistent closure of both global sea level and energy budgets 
(Section 7.2.2.2, Box 7.2, Cross-Chapter Box 9.1).
1850 1900 1950 2000500
400
300
200
0250500
400
350
300450Global ocean heat content (ZJ)Full-depth
100
2000 2005 2010 2020 2015
CSIRO
ISAS-15
NOC
CORAv5.2
CSIRO-BOAIPRCJAMSTEC
Scripps
KvS11
Cheng17 Ish17v7.3EN4
Lev12
PMEL
Su20-OPEN LEGOS
Desb17

GCOS20Table 2.7Zanna et al. 2019Changes in ocean heat content
0-2000 m(a) (b)
Figure 2.2 6 | Changes in ocean heat content (OHC).  Changes are shown over (a) full depth of the ocean from 1871–2019 from a selection of indirect and direct 
measurement methods. The series from Table 2.7 is shown in solid black in both (a) and (b) (see Table 2.7 caption for details). (b) as (a) but for 0–2000 m depths only and 
reflecting the broad range of available estimates over this period. For further details see chapter data table (Table 2.SM.1).

351
2.3.3.2 Ocean Salinity 
The AR5 concluded that subtropical regions of high salinity (where 
evaporation dominates over precipitation) had become more saline, 
while regions of low salinity (mostly in the tropics and high latitudes) had very likely  become fresher since the 1950s, both at the near-
surface, and in the ocean interior along ventilation pathways. From 
1950 to 2008, the mean surface contrast between high- and low-
salinity regions increased by 0.13 [0.08 to 0.17] (PSS-78, UNESCO/
ICES/SCOR/IAPSO, 1981). Across basins, the Atlantic Ocean had Table 2.7 |  Rates of global ocean heat content (OHC) and global mean thermosteric sea level (ThSL) change for four depth integrations over different 
periods.  For the period up to 1971, the assessment for all depth layers is based on Zanna et al. (2019). From 1971 onwards, consistent with AR5, Domingues et al. (2008, updated)

is the central estimate for 0–700 m along with uncertainty from a five-member ensemble (Domingues et al., 2008, updated; Levitus et al., 2012; Good et al., 2013; Cheng et al., 
2017; Ishii et al., 2017), following the approach of Palmer et al. (2021). Similarly, Ishii et al. (2017) is the central estimate for 700–2000 m with uncertainty based on a 3-member 
ensemble (Levitus et al., 2012; Cheng et al., 2017; Ishii et al., 2017). For depths below 2000 m, both central estimate and uncertainty are from Purkey and Johnson (2010, 
updated). In cases when OHC estimates do not have a ThSL counterpart (e.g., Good et al., 2013; Cheng et al., 2017), OHC was converted into ThSL using the average linear 
regression coefficients for 0–700 m and 700–2000 m from all available ensemble members. For consistency with the energy and sea-level budgets presented in Chapters 7 and

9, reported rates are based on the difference between the first and last annual mean value in each period (Palmer et al., 2021, Box 7.2, Cross-Chapter Box 9.1). N/A indicates 
not applicable. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).
Depth PeriodOHC Rate  
(ZJ y –1) ThSL Rate  
(mm yr –1)Relative Full Ocean Depth Contribution 
OHC ThSL
0–700 m1901–19902.50
[1.16 to 3.85]0.31
[0.16 to 0.45]81% 86%
1901–20183.11
[2.18 to 4.04]0.40
[0.30 to 0.50]66% 74%
1971–20185.14
[3.46 to 6.82]0.71
[0.51 to 0.90]61% 70%
1993–20186.06
[4.56 to 7.55]0.89
[0.69 to 1.10]58% 68%
2006–20186.28
[4.06 to 8.50]0.91
[0.51 to 1.31]54% 65%
700–2000 m1901–19900.50
[–0.59 to 1.60]0.04
[–0.07 to 0.16]16% 11%
1901–20181.26
[0.43 to 2.09]0.11
[0.02 to 0.19]27% 20%
1971–20182.62
[2.04 to 3.20]0.23
[0.16 to 0.31]31% 23%
1993–20183.31
[2.40 to 4.22]0.30
[0.19 to 0.41]32% 23%
2006–20184.14
[2.41 to 5.86]0.36
[0.15 to 0.58]36% 26%
>2000 m1901–19900.07

[0.02 to 0.12]0.01
[0.00 to 0.01]2% 3%
1901–20180.32
[0.18 to 0.46]0.03
[0.02 to 0.05]7% 6%
1971–20180.66
[0.33 to 0.99]0.07
[0.03 to 0.10]8% 7%
1993–20181.15
[0.58 to 1.72]0.12
[0.06 to 0.18]11% 9%
2006–20181.15
[0.06 to 0.18]10% 9%
Full-depth1901–19903.08
[1.36 to 4.79]0.36
[0.17 to 0.54]N/A N/A
1901–20184.68
[3.45 to 5.92]0.54
[0.40 to 0.68]N/A N/A
1971–20188.42
[6.08 to 10.77]1.01
[0.73 to 1.29]N/A N/A
1993–201810.52
[7.76 to 13.28]1.31
[0.95 to 1.66]N/A N/A
2006–201811.57
[7.20 to 15.94]1.39
[0.74 to 2.05]N/A N/A

352
become saltier and the Pacific and Southern Oceans had freshened 
(very likely ). 
Prior to the instrumental record, reconstructions of near-surface 
salinity change are accomplished by combining isotopic and 
elemental proxy data from microfossil plankton shells and skeletons 
preserved in deep-sea sediments. These data highlight changes in 
the salinity contrast between the Pacific and Atlantic oceans during 
past glacials (Broecker, 1989; Keigwin and Cook, 2007; Costa et al., 
2018) and for repeated episodes of increased subtropical salinity 
(Schmidt et al., 2004, 2006) and subpolar freshening (Cortijo et al., 
1997; Thornalley et  al., 2011) in the North Atlantic ocean. These 
episodes were associated with disruptions to the large-scale deep 
ocean circulation (Buizert et  al., 2015; Henry et  al., 2016; Lynch-
Stieglitz, 2017). Further quantification of paleo salinity changes

is complicated by incomplete understanding of proxy-salinity 
relationships and the relative influence of atmospheric and ocean 
processes across regions and paleo periods (Rohling, 2007; LeGrande 
and Schmidt, 2011; Holloway et al., 2016; Conroy et al., 2017).
Since AR5, new and extended multi-decadal analyses have 
strengthened the observational support for increased contrast 
between high and low near-surface salinity regions and inter-basin 
contrast since the mid-20th century (Section 9.2.2.2; Durack and 
Wijffels, 2010; Good et  al., 2013; Skliris et  al., 2014; Aretxabaleta 
et  al., 2017; Cheng et  al., 2020). These analyses employ different 
statistical algorithms for interpolation, and only Cheng et al. (2020) 
use CMIP5 model simulations to constrain observation-based signals 
in data-sparse regions.
The 1950–2019 trends reveal near-surface freshening of the northern 
and western Warm (and fresh) Pool of the Pacific and increased

salinity maxima in the subtropical Atlantic, strengthening the inter-
basin contrast (Figure 2.27a). There are indications that the subpolar 
freshening and subtropical salinification of the Atlantic ocean may 
extend back to at least 1896 (Friedman et  al., 2017). Over recent 
decades, new observations from Argo floats and ocean reanalyses 
provide general support that changes in the global patterns of near-
surface salinity contrast are broadly associated with an intensification 
of the hydrological cycle (Sections 2.3.1.3.5 and 8.3.1.1). However, 
this assessment is complicated by changing observational techniques 
(Section 1.5.1), temporally and spatially inhomogeneous sampling 
and uncertainties in interpolation algorithms and the substantial 
influence of modes of natural variabiltity and ocean circulation 
processes over interannual timescales (Skliris et  al., 2014; Durack, 
2015; Grist et al., 2016; Aretxabaleta et al., 2017; Vinogradova and

Ponte, 2017; Liu et al., 2020). Following AR5, based on the updated 
analysis from Durack and Wijffels (2010) which infills in situ gaps 
to recover large-scale patterns the mean salinity contrast between 
high- and low- near-surface salinity regions increased by 0.14 
[0.07 to 0.20] from 1950 to 2019.
Changes in the global patterns of near-surface salinity contrast 
are transferred to the ocean interior via ventilation pathways 
(Figure 2.27b). Large scale similarities in subsurface salinity changes 
across observational estimates point to decreasing (increasing) 
salinity in regions where salinity is lower (higher) than the global average, with freshening in subpolar regions and salinification in the 
subtropical gyres (Durack and Wijffels, 2010; Good et al., 2013; Skliris 
et  al., 2014; Durack, 2015; Aretxabaleta et  al., 2017; Cheng et  al., 
2020). Regional changes in salinity are assessed in Section 9.2.2.2.

In summary, it is virtually certain  that since 1950 near-surface high 
salinity regions have become more saline, while low salinity regions 
have become fresher, and it is very likely  that this extends to the ocean 
interior along ventilation pathways. Across basins, it is very likely  that 
the Atlantic has become saltier and the Pacific and Southern oceans 
have freshened. The differences between high-salinity and low-
salinity regions are linked to an intensification of the hydrological 
cycle ( medium confidence ). 
34.535
Changes in salinity (1950–2019), PSS-78 per decadeChanges in Ocean Salinity
−0.05(a)
(b)−0.04−0.03−0.02−0.01 00.010.020.030.040.05
0
100
200
300
400
500
70°S 50°S30°S 10°S 10°N30°N 50°N 70°N750
1000
1250
1500
1750
2000
−0.03 −0.02 −0.01 0 0.01 0.02 0.03
Changes in salinity (1950–2019), PSS-78 per decadePressure/depth (dbar/m)
Significant Colour
Non significant
Figure 2.2 7 | Changes in ocean salinity.  Estimates of salinity trends using

a total least absolute differences fitting method for (a) global near-surface salinity 
(SSS) changes and (b) global zonal mean subsurface salinity changes. Black contours 
show the associated climatological mean salinity (either near-surface (a) or subsurface 
(b)) for the analysis period (1950–2019). Both panels represent changes in Practical 
Salinity Scale 1978 [PSS-78], per decade. In both panels green denotes freshening 
regions and orange/brown denotes regions with enhanced salinities (‘×’ marks 
denote non-significant changes). Further details on data sources and processing are 

353
2.3.3.3 Sea Level
The AR5 concluded based on proxy and instrumental data that the 
rate of global mean sea level (GMSL) rise since the mid-19th century 
was larger than the mean rate during the previous two millennia 
(high confidence ). The SROCC reported with high confidence  that 
GMSL increases were 1.5 [1.1 to 1.9] mm yr –1 for 1902–2010 
(with an acceleration rate between –0.002 and +0.019 mm yr –2), 
2.1  [1.8  to  2.3] mm yr –1 for 1970–2015, 3.2 [2.8 to 3.5] mm yr –1 
for 1993–2015 and 3.6 [3.1 to 4.1] mm yr –1 for 2006–2015. AR5 
reported that GMSL during the LIG was, over several thousand years, 
between 5 and 10 m higher than 1985–2004 ( medium confidence ) 
whereas SROCC concluded it was virtually certain  that GMSL 
exceeded current levels ( high confidence ), and reached a peak that 
was likely  6–9 m higher than today, but did not exceed 10 m ( medium

confidence ). The AR5 concluded with high confidence  that there were 
two intra-LIG GMSL peaks and that the millennial-scale rate during 
these periods exceeded 2 mm yr –1. The AR5 had high confidence  that 
GMSL during the MPWP did not exceed 20 m above present. Based 
on new understanding, SROCC placed the upper bound at 25 m but 
with low confidence .
The Earth was largely ice free during the EECO (Cramer et al., 2011; 
Miller et al., 2020, Section 9.6.2), and complete loss of current land 
ice reservoirs would raise GMSL by 65.6 ± 1.8 m (Morlighem et al., 
2017, 2020; Farinotti et al., 2019). Given that GMSL change must be 
due to some combination of transient land ice growth and changes 
in terrestrial water storage, additional global mean thermosteric 
sea-level increase of 7 ± 2 m (Fischer et al., 2018) implies a peak 
EECO GMSL of 70–76 m ( low confidence ). Changes in ocean basin 
size driven by plate tectonics contributed a comparable amount to

global mean geocentric sea level in the Eocene, but are definitionally 
excluded from GMSL assessment (Wright et al., 2020).
For the MPWP , several studies of coastal features have provided 
additional quantitative sea-level estimates of: 5.6–19.2 m from 
Spain (Dumitru et al., 2019), approximately 14 m from South Africa 
(Hearty et  al., 2020), 15 m from the United States (Moucha and 
Ruetenik, 2017), and 25 m from New Zealand (Grant et al., 2019). 
Thus, consistent with SROCC, GMSL during the MPWP was higher 
than present by 5–25 m ( medium confidence ).
Reconstructions of GMSL from marine oxygen isotopes in foraminifera 
shells show variations of more than 100 m over intervals of 10–100 kyr 
during glacial-interglacial cycles of the Quaternary (Shackleton, 1987; 
McManus et  al., 1999; Waelbroeck et  al., 2002; Miller et  al., 2020). 
Correction for past temperatures and a calibration for ice-volume 
changes implies uncertainty estimates of ± 10–13 m (1 SD) (Grant

et al., 2014; Shakun et al., 2015; Spratt and Lisiecki, 2016). A recent 
marine oxygen-isotope-based GMSL reconstruction (Spratt and Lisiecki, 
2016) agrees with previous reconstructions, while focusing on the past 
800 kyr (Figure 2.28). It shows that GMSL during the Holocene was 
among the highest over this entire interval, and was surpassed only 
during the LIG (Marine Isotope Stage (MIS 5e)) and MIS 11 ( medium 
confidence ); however, relatively brief (about 2 kyr) highstands during 
other interglacial periods might be obscured by dating limitations.Few sites globally have well-preserved MIS 11 sea-level indicators 
(Dutton et al., 2015). As reported in AR5, Raymo and Mitrovica (2012) 
used glacial isostatic adjustment models to correct the elevation of 
MIS 11 sea-level proxies from Bermuda and Bahamas to estimate 
a peak MIS 11 GMSL between 6 and 13 m above present-day. This 
agrees with the elevation of 13 ± 2 m for the MIS 11 subtidal–intertidal

transition in South Africa (Roberts et  al., 2012). A  revised glacial 
isostatic adjustment at this location resulted in a peak GMSL estimate 
of 8–11.5 m (Chen et al., 2014). In light of these data, and the review 
by Dutton et al. (2015), the AR5 estimate of 6–13 m for MIS 11 remains 
the best available ( medium confidence ).
Recent studies have highlighted uncertainties in estimates of GMSL 
during the LIG, including the extent of GMSL variability (Capron et al., 
2019). Vertical land motions (Austermann et al., 2017) are starting to 
be considered quantitatively (e.g., Stephenson et al., 2019), but are 
still bounded by large uncertainties. The distribution and thickness 
of pre-LIG ice sheets (Dendy et al., 2017; Rohling et al., 2017) and 
isostasy driven by sediment loading since the LIG (Pico, 2020) 
add further uncertainty. In light of these recent studies and previous 
assessments, there is medium confidence  that peak GMSL during the

LIG was likely  between 5 and 10 m higher than modern. Relative 
sea-level estimates from some sites (e.g., Bahamas and Seychelles) 
report ephemeral, metre-scale fluctuations (Vyverberg et al., 2018). 
Different generations of LIG reef growth at other sites (e.g., Yucatan 
Peninsula, Western Australia) suggest the occurrence of sudden 
accelerations in GMSL change (Blanchon et al., 2009; O’Leary et al., 
2013). However, other sites (e.g.,  South Australia, Mediterranean), 
indicate that LIG sea level was substantially stable (T.-Y . Pan et al., 
2018; Polyak et  al., 2018). In addition, there are uncertainties 
in the interpretation of local relative sea level from some GMSL 
reconstructions (Barlow et  al., 2018). Therefore, low confidence  is 
assigned to any GMSL rate of change estimated within the LIG.
New geological proxies and glacial isostatic adjustment (GIA) 
modelling studies confirm that, at the LGM, GMSL was 125–134 m

below present (Lambeck et al., 2014; Yokoyama et al., 2018). During 
the LDT, GMSL rose from approximately –120 m to –50 m, implying an 
average rate of about 10 mm yr –1 (Lambeck et al., 2014). The fastest 
rise occurred during Meltwater Pulse 1A, at about 14.6–14.3  ka 
(Deschamps et al., 2012; Sanborn et al., 2017), when GMSL rose by 
between 8 m and 15 m ( medium confidence ) (J. Liu et al., 2016) at an 
average rate of 24–44 mm yr –1. 
Recent GIA modelling studies tuned to both near- and far-field relative 
sea level (RSL) data yield MH GMSL estimates of –3.8 to –1.0  m 
(Lambeck et al., 2014; Peltier et al., 2015; Bradley et al., 2016; Roy 
and Peltier, 2017). Estimates from relatively stable locations where 
the effects of GIA are small and relatively insensitive to parameters 
defining Earth rheology, and where RSL is expected to approximate 
GMSL to within about 1 m (e.g., Milne and Mitrovica, 2008), suggest 
that RSL was between about –6 to +1.5 m at around 6 ka at multiple

locations (Camoin et al., 1997; Braithwaite et al., 2000; Frank et al., 
2006; Montaggioni and Faure, 2008; Vacchi et al., 2016; Khan et al., 
2017; Hibbert et al., 2018). The assessment of GMSL change at 6 kyr 
is challenging considering the proportionately large GIA effect (Kopp 
et al., 2016), insufficient resolution of marine geochemical proxies

354
(δ18O, Mg/Ca) and uncertainties in the contribution of the Antarctic 
Ice Sheet during the MH (Section 2.3.2.4). The possibility that GMSL 
was at least somewhat higher than present cannot be excluded.
For the last 3 kyr, GMSL has been estimated from global databases of 
sea-level proxies, including numerous densely-sampled high-resolution salt-marsh records with decimetre scale vertical resolution and sub-
centennial temporal resolution (Kopp et al., 2016; Kemp et al., 2018). 
Over the last about 1.5 kyr, the most prominent century-scale GMSL 
trends include average maximum rates of lowering and rising of 
–0.7 ± 0.5 mm yr –1 (2 SD) over 1020–1120 CE, and 0.3 ± 0.5 (2 SD) 
over 1460–1560, respectively. Between 1000 and 1750 CE, GMSL is 
 
 Spratt & Lisieck 2016
 Kemp et al. 2018
 Church & White 2011
 Ray & Douglas 2011
 Jevrejeva et al. 2014
 Wenzel & Schroter 2014
 Hay et al. 2015
 Dangendorf et al. 2017
 Frederikse et al. 2018

Dangendorf et al. 2019
 Frederikse et al. 2020 AVISO
 EU CMEMS
 CSIRO
 CU (Nerem et al. 2018)
 ESA (Legeais et al. 2018)
 NASA (Beckley et al. 2017)
 NOAA
 LEGOS (Blazquez et al. 2018)
 Palmer et al. 2021 (1901–1993)
 WCRP GSLB group 2018 (1993–2018)50
0
−50
−100
−150
0.25
0.2
0.15
0.1
0.05
00.24
0.23
0.22
0.21
0.2
0.19
0.18
0.17
0.16
0.15
0.140.3
0.25
0.2
0.15
0.1
0.05
0
−0.05
−0.1Changes in global mean sea level
(a) Last 800 kyr
(c) Since 1850(d) Recent(b) Last 2500 yearsGlobal mean sea level (m) Global mean sea level (m)Global mean sea level (m) Global mean sea level (m)
2000 2010 20201850 1900 1950 2000−500 0 500 1000 1500 2000MIS11 LIG
LGMMH
Thousands of years before 2000Year (CE)
Figure 2.2 8 | Changes in global mean sea level. (a)  Reconstruction of sea-level from ice core oxygen isotope analysis for the last 800 kyr. For target paleo periods (CCB2.1)

and MIS11 the estimates based upon a broader range of sources are given as box whiskers. Note the much broader axis range (200 m) than for later panels (tenths of metres). 
(b) Reconstructions for the last 2500 years based upon a range of proxy sources with direct instrumental records superposed since the late 19th century. (c) Tide-gauge and, more 
latterly, altimeter-based estimates since 1850. The consensus estimate used in various calculations in Chapters 7 and 9 is shown in black. (d) The most recent period of record from 
tide-gauge and altimeter-based records. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

355
estimated to have been within the range of about –0.11 to +0.09 m 
relative to 1900 (Kemp et al., 2018). This was followed by a sustained 
increase of GMSL that began between 1820 and 1860 and has 
continued to the present day. New analyses demonstrate that it is very 
likely  that GMSL rise over the 20th century was faster than over any 
preceding century in at least 3 kyr (Kopp et al., 2016; Kemp et al., 2018) 
(Figure 2.28). 
Since SROCC, two new tide gauge reconstructions of 20th century 
GMSL change have been published, although both rely upon CMIP 
models to varying degrees (Figure 2.28). Frederikse et al. (2020) used 
a ‘virtual station’ method and a probabilistic framework to estimate 
GMSL change and its uncertainties since 1900. Dangendorf et al. (2019) 
combined a Kalman Smoother (Hay et al., 2015) with Reduced Space 
Optimal Interpolation (Church and White, 2011; Ray and Douglas,

2011) in an effort to better represent both the long-term GMSL change 
while preserving information on sea-level variability. In  addition, 
new ensemble-based methods for quantifying GMSL change have 
been presented that account for both structural and parametric 
uncertainty (Palmer et al., 2021). Altimeter time series of GMSL change 
(Figure 2.28) have been extended to 2019/2020 but bias adjustments 
(Watson et al., 2015; Beckley et al., 2017; Dieng et al., 2017; Ablain 
et al., 2019; Legeais et al., 2020) did not change since SROCC. 
Based on the ensemble approach of Palmer et  al. (2021) and an 
updated WCRP Global Sea Level Budget Group (2018) assessment 
(Figure 2.28) GMSL rose at a rate of 1.32 [0.58 to 2.06] mm yr  –1 for the 
period 1901–1971, increasing to 1.87 [0.82 to 2.92] mm yr  –1 between 
1971 and 2006, and further increasing to 3.69 [3.21 to 4.17] mm yr  –1 
for 2006–2018 ( high confidence ). The average rate for 1901–2018

was 1.73 [1.28 to 2.17] mm yr –1 with a total rise of 0.20 [0.15 to 
0.25] m (Table 9.5). The acceleration rate  very likely  is 0.094 [0.082 to 
0.115] mm yr –2 for 1993–2018 (WCRP Global Sea Level Budget Group, 
2018, updated), consistent with other estimates (Watson et al., 2015; 
X. Chen et al., 2017; Nerem et al., 2018; WCRP Global Sea Level Budget 
Group, 2018; Ablain et al., 2019; Legeais et al., 2020). For the period 
1902–2010 the updated tide gauge reconstructions published since 
SROCC also show a robust acceleration over the 20th century and 
the ensemble estimate of Palmer et al. (2021) gives a value of 0.0053 
[0.0042 to 0.0073] mm yr –2, based on an unweighted quadratic fit.
In summary, GMSL is rising, and the rate of GMSL rise since the 
20th century is faster than over any preceding century in at least the 
last three millennia ( high confidence ). Since 1901, GMSL has risen by 
0.20 [0.15 to 0.25] m at an accelerating rate. Further back in time, there

is medium confidence  that GMSL was within –3.5 to +0.5 m ( very likely 
range) of present during the MH, 5–10 m higher ( likely range ) during 
the LIG, and 5–25 m higher ( very likely  range) during the MPWP .
2.3.3.4 Ocean Circulation
2.3.3.4.1 Atlantic Meridional Overturning Circulation (AMOC)
The AR5 concluded that there was no evidence of a trend in the 
AMOC during the period of instrumental observations. However, AR5 
also stressed insufficient evidence to support a finding of change 
in the heat transport of the AMOC. SROCC assessed that there was emerging evidence in sustained observations, both in situ (2004–2017) 
and revealed from SST-based reconstructions, that the AMOC had 
weakened during the instrumental era relative to 1850–1900 ( medium 
confidence ), although there were insufficient data to quantify the 
magnitude of the weakening. SROCC also concluded with low 
confidence an increase of the Southern Ocean upper cell overturning

circulation. SROCC also reported with medium confidence  that the 
production of Antarctic Bottom Water had decreased since the 1950s 
consistent with a decreased lower cell overturning circulation, and 
potentially modulating the strength of the AMOC.
On multi-millennial timescales, proxy evidence indicates that the 
AMOC varied repeatedly in strength and vertical structure. During 
the last glacial period, particularly around the LGM, AMOC was 
estimated to be shallower than present, although there is continued 
debate about the magnitude of the shoaling (Lynch-Stieglitz et al., 
2007; Gebbie, 2014), and whether this change was associated with 
a weaker overturning (Ritz et al., 2013; Menviel et al., 2017; Muglia 
et al., 2018). There are indications that substantial variations in AMOC 
were associated with abrupt climate changes during the glacial 
intervals, including Dansgaard-Oeschger and Heinrich events (14–
70 ka) (McManus et al., 2004; Böhm et al., 2015; Henry et al., 2016;

Lynch-Stieglitz, 2017). During these millennial-scale oscillations, 
weakened AMOC was associated with dramatic cooling in the NH 
and warming in the SH (Buizert et al., 2015; Henry et al., 2016), while 
hemispheric changes of opposite sign accompanied strengthened 
AMOC. After the final demise of the Laurentide ice sheet about 8 
ka, the mean overall strength of AMOC has been relatively stable 
throughout the rest of the Holocene compared to the preceding 100 
kyr (Hoffmann et al., 2018; Lippold et al., 2019). There are however 
indications of episodic variations in AMOC during the Holocene 
(Bianchi and McCave, 1999; Oppo et  al., 2003; Thornalley et  al., 
2013; Ayache et al., 2018), and past interglacial intervals (Galaasen 
et  al., 2014, 2020; Hayes et  al., 2014; Mokeddem et  al., 2014; H. 
Huang et al., 2020). Over the last 3 kyr, there are indications that 
AMOC variability was potentially linked to decreasing production of

Labrador Sea Water (LSW), one of the water masses contributing to 
AMOC (Alonso-Garcia et  al., 2017; Moffa-Sánchez and Hall, 2017; 
Moffa-Sánchez et al., 2019).
Numerous proxy records collectively imply that AMOC is currently at 
its weakest point in the past 1.6 ka (Rahmstorf et al., 2015; Caesar 
et al., 2018, 2021; Thibodeau et al., 2018; Thornalley et al., 2018). 
Caesar et al. (2021) analyse a compilation of various available indirect 
AMOC proxies from marine sediments, in situ-based reconstructions 
and terrestrial proxies, which show a decline beginning in the late 
19th century and over the 20th century superimposed by large 
decadal variability in the second half of the 20th century. Indirect 
reconstructions of AMOC components based on coastal sea level 
records in the western North Atlantic (Ezer, 2013; McCarthy et al., 
2015; Piecuch, 2020) show an AMOC decline since the late 1950s, 
with only a short period of recovery during the 1990s.

However, other studies highlight that proxy records do not show such 
clear signals (Moffa-Sánchez et  al., 2019), and the use of SST- and 
coastal sea level-based proxies of AMOC places uncertainties on these 
results (Little et al., 2019; Jackson and Wood, 2020; Menary et al., 2020).

356
For instance, SSTs are additionally influenced by atmospheric and 
non-AMOC related ocean variability (Josey et al., 2018; Keil et al., 2020; 
Menary et al., 2020), while sea level responds to a variety of factors 
(e.g., atmospheric pressure and local winds) independent of the AMOC 
(Woodworth et al., 2014; Piecuch and Ponte, 2015; Piecuch et al., 2016). 
Finally, large decadal variability is present in many reconstructions and 
obscures estimation of the long-term trend over the 20th century (Ezer, 
2013; McCarthy et  al., 2015; Yashayaev and Loder, 2016; Thornalley 
et al., 2018; Caesar et al., 2021). It is also noted that the proxy reported 
AMOC decline, beginning in the late 19th century, is not supported by 
model-based evidence (Sections 3.5.4.1 and 9.2.3.1).
Since the 1980s, multiple lines of observational evidence for AMOC 
change exist. Ship-based hydrographic estimates of AMOC as

far back as the 1980s show no overall decline in AMOC strength 
(Fu  et  al., 2020; Worthington et  al., 2021). Direct indications from 
in-situ observations report a –2.5 ± 1.4 Sv change between 1993 and 
2010 across the OVIDE section, superimposed on large interannual to 
decadal variability (Mercier et al., 2015). At 41°N and 26°N, a decline 
of –3.1 ± 3.2 Sv per decade and –2.5 ± 2.1 Sv per decade respectively 
has been reported over 2004–2016 (Baringer et  al., 2018; Smeed 
et al., 2018). However, Moat et al. (2020) report an increase in AMOC 
strength at 26°N over 2009–2018. Recent time series of moored 
observations at 11ºS (Hummels et  al., 2015), 34°S (Meinen et  al., 
2018; Kersalé et al., 2020), and between 57 and 60ºN (Lozier et al., 
2019) are currently too short to permit robust conclusions about 
changes. The directly observed AMOC weakening since 2004, while 
significant, is over too short a period to assess whether it is part of

a longer term trend or dominated by decadal-scale internal variability 
(Smeed et al., 2014; Collins et al., 2019; Moat et al., 2020). Notably an 
increase and subsequent decline in the 1990s is present in estimates 
of AMOC and associated heat transport constructed from reanalyses 
or auxiliary data (Section 9.2.3.1; Frajka-Williams, 2015; Jackson 
et al., 2016; Trenberth and Fasullo, 2017; Jackson and Wood, 2020).
Repeated full depth in situ measurements report that deep 
convection – a major driver for AMOC – has recently returned to the 
Labrador Sea, particularly in 2015 (Yashayaev and Loder, 2016; Rhein 
et al., 2017), and to the Irminger Sea (de Jong and de Steur, 2016; 
Gladyshev et al., 2016; de Jong et al., 2018) following an extended 
period with weak convection since 2000. An associated strengthening 
of the outflow from the Labrador Sea has not been observed (Zantopp 
et al., 2017; Lozier et al., 2019), while strengthening of the AMOC is

tentative (Desbruyères et al., 2019; Moat et al., 2020). A long-term 
increase of the upper overturning cell in the Southern Ocean since 
the 1990s can be assessed with low confidence , and there is medium 
confidence  of a decrease in Antarctic bottom water (AABW) volume 
and circulation, which has potential implications for the strength of 
the AMOC (Section 9.2.3.2).
In summary, proxy-based reconstructions suggest that the AMOC 
was relatively stable during the past 8 kyr ( medium confidence ), 
with a weakening beginning since the late 19th century ( medium 
confidence ), but due to a lack of direct observations, confidence  in 
an overall decline of AMOC during the 20th century is low . From the 
mid-2000s to mid-2010s, the directly observed weakening in AMOC (high confidence ) cannot be distinguished between decadal-scale 
variability or a long-term trend ( high confidence ).
2.3.3.4.2 Western boundary currents and inter-basin exchanges

Both AR5 and SROCC reported that western boundary currents 
(WBCs) have undergone an intensification, warming and poleward 
expansion, except for the Gulf Stream and the Kuroshio, but did 
not provide confidence statements. The AR5 reported with medium  
to high confidence  intensification of the North Pacific subpolar 
gyre, the South Pacific subtropical gyre, and the subtropical cells, 
along with an expansion of the North Pacific subtropical gyre 
since the 1990s. It  was pointed out that these changes are likely  
predominantly due to interannual-to-decadal variability, and in the 
case of the subtropical cells represent a reversal of earlier multi-
decadal changes. SROCC concluded that it was unlikely  that there 
has been a statistically significant net southward movement of the 
mean Antarctic Circumpolar Current (ACC) position over the past 
20 years, in contrast to AR5, where this change had been assessed 
with medium confidenc e.

The intensity of the Kuroshio current system in the north-west Pacific 
varied in conjunction with the glaciation cycles over the last 1 Myr, 
with some limited glacial-interglacial variability in position (Jian et al., 
2000; Gallagher et al., 2015). The Agulhas current has strengthened 
substantially during the warming associated with deglaciations 
of the past 1 Myr (Peeters et  al., 2004; Bard and Rickaby, 2009; 
Martínez-Méndez et  al., 2010; Marino et  al., 2013; Ballalai et  al., 
2019). According to sediment core analyses, the Agulhas leakage 
varied by about 10 Sv during major climatic transitions over the past 
640 kyr (Caley et al., 2014). Available data suggests that there was 
relatively little change in the net flow of the ACC during the LGM, 
with no consensus on the sign of changes (McCave et al., 2013; Lamy 
et al., 2015; Lynch-Stieglitz et al., 2016), except at one location at 
the northern edge of the Drake Passage where a 40% decrease of

transport had been reported (Lamy et al., 2015). Longer time series 
from the northern entrance to Drake Passage suggest a consistent 
transport variability of 6–16% through glacial climate cycles, with 
higher current speeds during interglacial times and reduced current 
speeds during glacial intervals (Toyos et al., 2020). Inferred variability 
in the size and strength of the North Atlantic subpolar gyre was 
substantial, and included rapid changes on millennial time scales 
during both interglacial and glacial intervals over the last 150 kyr 
(Born and Levermann, 2010; Mokeddem et al., 2014; Irvalı et al., 2016; 
Mokeddem and McManus, 2016). North Atlantic – Arctic exchange 
has also varied in the past, with indications of an increasing inflow of 
Atlantic waters into the Arctic during the late Holocene (Ślubowska 
et al., 2005) with an acceleration to the recent inflow that is now the 
largest of the past 2 kyr (Spielhagen et al., 2011).

A latitudinal shift of subtropical/subpolar gyres on the order of 
0.1 ± 0.04° per decade is derived by an indirect method using remote 
sensing data during 1993–2018 (Yang et al., 2020). Direct observations 
show a systematic poleward migration of WBCs (Wu  et  al., 2012; 
Yang et al., 2016, 2020; Bisagni et al., 2017). However, they do not 
support an intensification of WBCs, with a weakening, broadening, 
or little change reported for the Kuroshio (Y .-L. Wang et  al., 2016;

357
Wang and Wu, 2018; Collins et  al., 2019), Gulf Stream (McCarthy 
et  al., 2018; Collins et  al., 2019; Dong et  al., 2019; Andres et  al., 
2020), Agulhas (Beal and Elipot, 2016; Elipot and Beal, 2018) and 
East Australian (Sloyan and O’Kane, 2015) currents. The Gulf Stream 
has recently reversed a long-term poleward migration (Bisagni 
et al., 2017). Multidecadal variability of the strength and position of 
WBCs (Hsin, 2015; Bisagni et al., 2017; McCarthy et al., 2018) and 
short records from direct observations obscure the detection of any 
long-term trends (Yang et al., 2020).
The Pacific to Arctic exchange at the Bering Strait plays a minor role in 
the total Arctic exchange with the global ocean, which has increased 
from 0.8 Sv to 1.0 Sv over 1990–2015 (Woodgate, 2018). For Atlantic-
Arctic exchange, major branches of Atlantic Water inflow from the 
North Atlantic into the Arctic across the Greenland–Scotland Ridge

have remained stable since the mid-1990s (Berx et al., 2013; Hansen 
et  al., 2015; Jochumsen et  al., 2017; Østerhus et  al., 2019), with 
only the smaller pathway of Atlantic Water north of Iceland showing 
a strengthening trend during 1993–2018 (Casanova-Masjoan et al., 
2020), but with associated heat transport strengthening through 
the 1990s (Rossby et al., 2020; Tsubouchi et al., 2021). The Arctic 
outflow remained broadly stable from the mid-1990s to the mid 
2010s (Østerhus et al., 2019). 
The heat and mass transport of the Indonesian throughflow  (ITF) 
shows substantial variability at seasonal to decadal time scales 
(Zhuang et al., 2013; Q.-Y . Liu et al., 2015; Susanto and Song, 2015; 
Feng et al., 2017, 2018; M. Li et al., 2018; Sprintall et al., 2019; Xie 
et al., 2019). Q.-Y . Liu et al. (2015) reported an increasing trend in 
the ITF geostrophic transport of 1 Sv per decade over 1984–2013, 
consistent with direct estimates (Sprintall et al., 2014), and results

from reanalyses (M. Li et al., 2018), and this appears to be linked to 
multi-decadal scale variability rather than a long-term trend (Kosaka 
and Xie, 2013; England et al., 2014; Lee et al., 2015). 
Southern Ocean circulation changes are assessed in SROCC (Meredith 
et al., 2019), and are confirmed and synthesized in Section 9.2.3.2 
which shows that there is no indication of ACC transport change, 
and that it is unlikely  that the mean meridional position of the ACC 
has moved southward in recent decades.
In summary, over the past 3–4 decades, the WBC strength is highly 
variable ( high confidence ), and WBCs and subtropical gyres have 
shifted poleward since 1993 ( medium confidence ). Net Arctic Ocean 
volume exchanges with the other ocean basins remained stable over 
the mid-1990s to the mid-2010s ( high confidence ). There is high 
confidence that the ITF shows strong multi-decadal scale variability 
since the 1980s.2.3.3.5 Ocean pH

The AR5 assessed with high confidence that the pH of the ocean 
surface had decreased since preindustrial times, primarily as a 
result of ocean uptake of CO 2. SROCC concluded that the global 
ocean absorbed 20–30% of total CO 2 emissions since the 1980s, 
with virtually certain  ocean surface pH decline. The SROCC assessed 
a rate of surface pH decline of 0.017–0.027 pH units per decade 
across a range of time series of pH observations longer than 15 
years. The decline in surface open ocean pH was assessed by SROCC 
as having very likely  already emerged from background natural 
variability for more than 95% of the global surface open ocean.
Understanding of changes in surface pH at paleo time-scales has 
increased since AR5 (Clarkson et  al., 2015; Foster and Rae, 2016; 
Zeebe et al., 2016; Gutjahr et al., 2017; Sosdian et al., 2018; Henehan 
et al., 2019; Anagnostou et al., 2020; Harper et al., 2020; Müller et al.,

2020). Over the last 65 million years there have been several intervals 
when the pH of surface waters varied concurrently with climate 
change such as during the PETM, EECO, and MCO (Figure  2.29a 
and Section 5.3.1.1). However, only during the PETM is the change 
sufficiently well-constrained to allow for a direct comparison with 
recent and current trends (Kirtland Turner, 2018). This event was 
associated with profound perturbations of the global carbon cycle, 
ocean warming, deoxygenation and a surface ocean pH decrease 
likely  ranging from 0.15 to 0.30 units (Penman et al., 2014; Gutjahr 
et al., 2017; Babila et al., 2018) – a rate that was likely  at least an 
order of magnitude slower than today (Cui et al., 2011; Bowen et al., 
2015; Frieling et al., 2016; Zeebe et al., 2016; Gutjahr et al., 2017; 
Kirtland Turner, 2018; Gingerich, 2019).
Paleo evidence suggests that surface ocean pH has gradually 
increased over the last 50 Myr (Anagnostou et  al., 2016, 2020;

Sosdian et al., 2018) (Figure 2.29a). Global mean surface pH values 
as low as observed during recent decades are uncommon in the 
last 2 Myr (Figure 2.29b) (Martínez-Botí et al., 2015a; Chalk et al., 
2017; Dyez et  al., 2018; Sosdian et  al., 2018), and have not been 
experienced in at least the last 25 kyr (Figure 2.29c; Palmer and 
Pearson, 2003; Foster, 2008; Palmer et al., 2010; Henehan et al., 2013; 
Kirschke et al., 2013; Martínez-Botí et al., 2015a; Naik et al., 2015; 
Ezat et al., 2017; Gray et al., 2018; Shao et al., 2019). The magnitude 
of pH change during the Pleistocene glacial–interglacial cycles was 
0.1–0.15 pH units  – similar to recent changes in the modern era 
(Figure 2.29c and Section 5.3.1.2; Hönisch et al., 2009; Chalk et al., 
2017; Shao et al., 2019). Maximum rates of pH change during the 
LDT, inferred from changes in atmospheric CO 2 recorded in ice cores 
(Marcott et al., 2014) and the established relationships between pH

and CO 2 changes and the boron isotope proxy (Hain et al., 2018), 
reached –0.02 pH units per century at about 11.7 ka, about 14.8 ka 
and about 16.3 ka, as previously sequestered CO 2 was transferred 
from the ocean interior to the subsurface ocean (Martínez-Botí et al., 
2015a; Jaccard et al., 2016; Rae et al., 2018).

358
Since the 1980s, the global ocean has experienced a decline in 
surface pH of 0.016 ± 0.006 pH units per decade based on indirect 
pH products (Figure 2.29d; Lauvset et  al., 2015; Hurd et  al., 2018; 
IPCC,  2019; Gehlen et  al., 2020) that agrees with the decline of 
0.017–0.025  pH units per decade assessed in SROCC from direct 
time-series measurements of pH. Section 5.3.2.2 assesses a decline 
that ranges from 0.01 to 0.026 pH units per decade for the tropical and 
subtropical open ocean areas, and 0.003–0.026 pH units per decade 
for the polar and subpolar open ocean regions by using time series 
and ship-based datasets from the surface ocean CO 2 measurement 
network (Bakker et al., 2016; Gehlen et al., 2020; Gregor and Gruber, 
2021). There is general consensus that global surface ocean pH trends 
over the past two decades have exceeded the natural background

variability (Lauvset et  al., 2015; Bindoff et  al., 2019; Gehlen et  al., 
2020). However, for some areas sparse data coverage, and large 
year-to-year variations hinders the detection of long-term surface 
ocean pH trends; for example in the Southern Ocean (Lauvset et al., 
2015; Bindoff et al., 2019) and in the Arctic Ocean (Lauvset et al., 2015; 
Bindoff et al., 2019; Meredith et al., 2019).
For subsurface pH changes, estimates arise from direct ship 
measurements from repeated hydrography programs (Carter et  al., 
2019), indirect estimates of pH through calcite and aragonite saturation 
horizons (Osborne et al., 2020; Ross et al., 2020), and the very recent 
biogeochemical Argo floats equipped with pH sensors (Claustre 
et  al.,  2020). Global subsurface pH has decreased over the past 20 
to 30 years, with signals observed to at least 1000 m depths (Lauvset 
et  al., 2020). Global findings are supplemented by regional findings

from the Pacific Ocean (Carter et al., 2019; Ross et al., 2020); the South 
Atlantic (Salt et  al., 2015) and Southern Ocean (Jones et  al., 2017); 
the North Atlantic Ocean and along the AMOC (Woosley et al., 2016; 
Perez et al., 2018), the Arctic Ocean (Qi et al., 2017) and marginal seas 
(C.-T.A. Chen et al., 2017). Further details are given in Section 5.3.3.1.
To conclude, it is virtually certain  that surface open ocean pH has 
declined globally over the last 40 years by 0.003–0.026 pH per 
decade, and a decline in the ocean interior has been observed in 
all ocean basins over the past 2–3 decades ( high confidence ). 
A  long-term increase in surface open ocean pH occurred over the 
past 50 Myr ( high confidence ), and surface open ocean pH as low 
as recent times is uncommon in the last 2 Myr ( medium confidence ). 
There is very high confidence  that open ocean surface pH is now the 
lowest it has been for at least 26 kyr and current rates of pH change

are unprecedented since at least that time. 
2.3.3.6 Ocean Deoxygenation
SROCC concluded that there has very likely  been a net loss of 
oxygen over all ocean depths since the 1960s linked to global ocean 
deoxygenation at a range of 0.3–2.0%, and that the oxygen levels in the 
global upper 1000 m of the ocean had decreased by 0.5–3.3% during 
1970–2010 ( medium confidence ), alongside an expansion of oxygen 
minimum zones (OMZ) by 3–8%. For the surface ocean (0–100 m) 
and the thermocline at 100–600 m, the very likely range of oxygen 
decline was assessed to be 0.2–2.1% and 0.7–3.5%, respectively. 
Multidecadal rates of deoxygenation showed variability throughout 
the water column and across ocean basins ( high confidence ).
3.53.02.52.01.51.00.50.07.98.08.18.28.3(b)pH
pH
2420 128407.98.08.18.28.3(c)60 50 40 30 20 10 07.57.77.98.18.3(a)
Age (million years ago)pH
PETMpanel (b)
panel (c)
16
Age (thousands of years)2019
global
meanno data
Age (million years ago)EECO MCO
0.1
0.1 0.1

MPWPLIGLGM
glacial
interglacial LGM LDT MH
Modern surface ocean pH(d)
Low latitude estimates
Global estimates
1980 1985 1990 1995 2000 2005 2010 2015 20208.0258.0508.0758.1008.1258.150 pH0.1BATS
HOT
CMEMS
Ocean-SODALow latitude surface ocean pH over the last 65 
million years
Figure 2.29 | Low-latitude surface ocean pH over the last 65 million years 
(65 Myr). (a)  Low-latitude (30°N–30°S) surface ocean pH over the last 65 Myr, 
reconstructed using boron isotopes in foraminifera. (b) as (a) but for the last 3.5 Myr. 
Double headed arrow shows the approximate magnitude of glacial-interglacial pH 
changes. (c) Multisite composite of surface pH. In (a, b, c) uncertainty is shown at 95% 
confidence as a shaded band. Relevant paleoclimate reference periods (CCB2.1) have 
been labelled. Period windows for succeeding panels are shown as horizontal black 
lines in (a) and (b). (d) Estimated low-latitude surface pH from direct observations

(BATS, HOT) and global mean pH (65°S–65°N) from two indirect estimates (CMEMS, 
OCEAN-SODA). Further details on data sources and processing are available in the 

359
Since AR5 evidence for changes in oxygen content based on new 
proxy reconstructions has increased  (Hoogakker et  al., 2015, 2018; 
Gottschalk et al., 2016, 2020; Anderson et al., 2019). Paleo records 
point to past periods of reduced oceanic oxygen levels during the late 
Permian (about 250 Ma), the Jurassic and the Cretaceous (180–100 
Ma), alongside associated global scale disturbances of the global 
carbon cycle. Emerging studies for the Cenozoic Era suggest more 
stable ocean oxygenation conditions throughout the interval on 
million-year time scales (X. Wang et al., 2016). Sedimentary proxy data 
indicate, however, that the seemingly stable Cenozoic was punctuated 
by transient, widespread deoxygenation during the PETM (Dickson 
et  al., 2012; Winguth et  al., 2012; Remmelzwaal et  al., 2019), with 
parts of the ocean reaching anoxic levels (Section 5.3.1.1; Yao et al.,

2018). Since the LGM, there was an overall emergence and expansion 
of low-oxygen waters into the ocean intermediate depths as a result 
of rapid warming and a reorganization of the global overturning 
circulation (Galbraith and Jaccard, 2015). The maximum expansion 
of oxygen-depleted waters during the LDT occurred coincidently with 
rapid warming in the NH at 14.7–12.9 ka (Jaccard and Galbraith, 
2012; Moffitt et al., 2015; Hoogakker et al., 2018). Deep (>1500 m) 
ocean oxygen levels increased by 100–150 µmol kg–1 since the LGM, 
reaching modern oxygen levels at about 10 ka (Section 5.3.1.2; 
Hoogakker et al., 2015; Gottschalk et al., 2016; Anderson et al., 2019). 
New findings for ocean oxygen content since SROCC are limited 
to regional scale assessments. The magnitude of change is difficult 
to compare across regions to arrive at a global assessment due to 
differences in depth range, time period, baseline climatology,

methodology, and particularly the use of different units. To facilitate 
comparisons, data are presented as change per decade, and 
conversions of the SROCC global mean percentage of oxygen decline 
estimates are provided as a loss of 3.2 μmol kg−1 in the upper 1000 m 
of the global ocean (1.93%), 2.0 μmol kg−1 (0.8 μmol kg−1 per decade) 
in the upper 1000 m of the global ocean (1.93%), and 2.0  μmol 
kg−1 (0.5 μmol kg−1 per decade) in the entire water column (1.15%) 
between 1970 and 2010 (Bindoff et al., 2019). Oxygen change also 
shows decadal variability (Ito et al., 2016; Stramma et al., 2020) that 
can influence estimates of trends. 
Subsurface (100–400 m) oxygen in the California Current system is 
estimated to have declined by 24 ± 2 μmol kg−1 (1.0 μmol kg−1 per 
decade) between 1993 and 2018, a rate similar to the global upper 
1000 m average (Bindoff et  al., 2019). In some locations, however, 
the magnitude of oxygen loss substantially exceeds global averages

(Queste et al., 2018; Bronselaer et al., 2020; Cummins and Ross, 2020; 
Stramma et  al., 2020). For example, a decline in oxygen content of 
11.7 ± 3.5% in the upper 4000 m, including a decline of 20.4 ± 7.2% 
in the upper 1550 m, is reported in the North Pacific over the period 
1958–2018 (Cummins and Ross, 2020) (equivalent to 2.3 μmol kg−1 per 
decade in the upper 1550 m and 2.0 μmol kg−1 decade− 1 throughout the 
4000 m water column). In some regions of the Southern Ocean south 
of 65°S oxygen in the upper 2000 m has declined by 60 μmol kg−1 
(~52  μmol kg−1 per decade) based on comparisons of 2014–2019 
and 1985–2005 observations (Bronselaer et  al., 2020). Within some 
OMZs regions of the Indian ocean, oxygen has declined from 6–12 to 
<2 μmol kg−1 between the 1960s and 2015–2016 (Bristow et al., 2017; 
Al-Said et al., 2018; Queste et al., 2018).Findings since SROCC provide further support that the volume of 
severely oxygen-depleted water has expanded in some locations

of the global ocean (Section 5.3.3.2). For example, vertical expansion 
of low oxygen zones is reported in the North Pacific at a rate of 
3.1 ± 0.5 m yr –1 (Ross et al., 2020), and suboxic waters have increased 
by 20% at a rate of about 19 m per decade from 1982–2010 in the 
Arabian Sea (Al-Said et al., 2018; Lachkar et al., 2019), and expanded 
off the coast of Mexico (Sánchez-Velasco et al., 2019).
In summary, episodes of widespread and long-lasting (100 ka scales) 
open-ocean deoxygenation were related to warm climate intervals of 
the Permian-Cretaceous, with conditions becoming generally better 
oxygenated as the climate cooled over the course of the Cenozoic ( high 
confidence ). The largest expansions of oxygen depleted waters over 
the past 25 ka were strongly linked to rapid warming rates ( medium 
confidence ). Open-ocean deoxygenation has occurred in most regions 
of the open ocean during the mid-20th to early 21st centuries ( high

confidence ), and shows decadal variability ( medium confidence ). 
Evidence further confirms SROCC that OMZs are expanding at many 
locations ( high confidence ).
2.3.4 Biosphere
This section is limited to a few biological indicators to demonstrate the 
close links between the biosphere and physical forcing. In selecting 
the indicators, we focussed on (i) those that are observable at large 
(global) spatial scales and over long (two decades or more) temporal 
scales, using standardized and consistent procedures; and (ii) those 
that are illustrative of the influence of the physical system on the 
biological realm. Chapters 2 and 3 of AR6 WGII undertake a more 
holistic assessment of biospheric impacts.
2.3.4.1 Seasonal Cycle of Atmospheric CO 2
The AR5 noted that because CO 2 uptake by photosynthesis occurs 
only during the growing season, the greater land mass in the NH 
imparts a characteristic ‘sawtooth’ seasonal cycle in atmospheric

CO 2. The SRCCL similarly stated that due to strong seasonal patterns 
of growth, NH terrestrial ecosystems are largely responsible for 
the seasonal variations in global atmospheric CO 2 concentrations. 
Neither AR5 nor SRCCL made a confidence statement about observed 
changes in the amplitude of the seasonal cycle of CO 2. 
In situ observations of CO 2 generally depict a rising amplitude of the 
seasonal cycle over the past half century, especially north of about 
45°N (Figure 2.30). For example, an amplitude increase of 6 ± 2.6% 
per decade has been observed at the Barrow surface observatory in 
Alaska over 1961–2011 (Graven et  al., 2013), with slightly slower 
increases thereafter. Aircraft data north of 45°N exhibit an amplitude 
increase of 57 ± 7% at 500 mb versus an increase of 26 ± 18% for 
35°N–45°N between field campaigns in 1958–1961 and 2009–2011 
(Graven et  al., 2013). Increases in amplitude for the period

1980–2012 are apparent at eight surface observatories north of 50°N 
(Piao et al., 2018), related primarily to a larger drawdown in June 
and July. Trends in seasonal cycle amplitude at lower latitudes are 
smaller (if present at all); for instance, the increase at the Mauna Loa

360
observatory in Hawaii since the early 1960s is only about half as 
large as at Barrow (Graven et al., 2013), and only one other low-
latitude observatory has a significant increase from 1980–2012 (Piao 
et al., 2018). There is a weak signal of an increase in amplitude at the 
Sinhagad observatory in western India in recent years (Chakraborty 
et al., 2020). Generally speaking, larger increases in the Arctic and 
boreal regions are indicative of changes in vegetation and carbon 
cycle dynamics in northern ecosystems (Forkel et al., 2016), though 
increased carbon uptake can also result from other factors such as 
warmer- and wetter-than-normal conditions. 
Recent satellite-based, global-scale estimates of seasonal variations 
in atmospheric CO 2 for the period 2003–2018 show that the seasonal 
variations in the SH are out of phase with those in the NH (Reuter 
et  al., 2020), which is consistent with the phenological shifts in

primary productivity between hemispheres. The net effect of the 
phase shift between the two hemispheres is to dampen the amplitude 
of the global average seasonal cycle. These integrated results also 
show that the amplitude of the oscillations has been increasing in 
the SH, from about 2009, but comparison with data from Baring head 
suggests that periods of high seasonal oscillation had occurred at 
that location in the SH prior to 1995.
In summary, there is very high confidence that the amplitude of the 
seasonal cycle of atmospheric CO 2 has increased at mid-to-high NH 
latitudes since the early 1960s. The observed increase is generally 
consistent with greater greening during the growing season and an 
increase in the length of the growing season over the high northern 
latitudes. Similarly, globally-integrated results from the SH also show 
an increase in seasonal amplitude of atmospheric CO 2 signal, from 
around 2009 to 2018 ( low confidence ).
2.3.4.2 Marine Biosphere

2.3.4.2.1 Large-scale distribution of marine biota
SROCC pointed out that long-term global observations of many 
key ocean variables, including phytoplankton, have not reached the 
density and accuracy necessary for detecting change. But SROCC 
noted the good comparability between short time-scale single-sensor 
ocean-colour products and a longer time-scale, climate-quality 
time series of multi-sensor, inter-sensor-bias-corrected, and error-
characterized, global data on chlorophyll-a concentration in the 
surface layers of the ocean. With respect to oligotrophic gyres, 
AR5 WGII concluded that the oligotrophic subtropical gyres of the 
Atlantic and Pacific Oceans are expanding and that they indicate 
declining phytoplankton stocks in these waters ( limited evidence, 
low agreement ). With respect to distributions of marine organisms, 
AR5 WGII reported range shifts of benthic, pelagic, and demersal 
species and communities ( high confidence ), though the shifts were 
not uniform.

Phytoplankton are responsible for marine primary production 
through photosynthesis; they are a major player in the ocean carbon 
cycle. They have a high metabolic rate and respond fast to changes in 
environmental conditions (light, temperature, nutrients, mixing), and 
as such, serve as a key indicator for change in marine ecosystems. J M MJ SNJ M M J
J M MJ SNJ M M JChanges in seasonal CO2
Barrow (71.3°N)
Barrow
Mauna Loa1961–63
2017–19
1958–63
2017–19Mauna Loa (19.5 °N)
1960 1970 1980 1990 2000 2010 2020Seasonal amplitude (ppm) Detrended CO2 (ppm)(a)20
19
18
17
16
15
14
13
8
7
6
5
6
4
2
0
−2
−4
−610
5
0
−5
−10
−15(b)
(c)
Figure 2.3 0 | Changes in the amplitude of the seasonal cycle of CO 2. 
(a) Observed peak-to-trough seasonal amplitude given by the day of year of downward 
zero crossing, of CO 2 concentration at Barrow (71°N, blue) and Mauna Loa (20°N, black). 
Seasonal CO 2 cycles observed at (b) Barrow and (c) Mauna Loa for the 1961–1963 or

1958–1963 and 2017–2019 time periods. The first six months of the year are repeated. 
Reprinted with permission from AAAS. Further details on data sources and processing 

361
Concentration of chlorophyll-a, the major photosynthetic pigment 
in all phytoplankton, is often used as a measure of phytoplankton 
biomass. As primary producers, they are also food for organisms at 
higher trophic levels. The multi-sensor time series of chlorophyll-a 
concentration has now been updated (Sathyendranath et  al., 
2019) to cover 1998–2018. Figure 2.31 shows that global trends in 
chlorophyll-a for the last two decades are insignificant over large 
areas of the global ocean (von Schuckmann et al., 2019), but some 
regions exhibit significant trends, with positive trends in parts of the 
Arctic and the Antarctic waters (>3% yr –1), and both negative and 
positive trends (within ± 3% yr –1) in parts of the tropics, subtropics 
and temperate waters. The interannual variability in chlorophyll-a data in many regions is strongly tied to indices of climate variability

(Section 2.4 and Annex IV) and changes in total concentration are 
typically associated with changes in phytoplankton community 
structure (e.g., Brewin et al., 2012; Racault et al., 2017b). Variability 
in community structure related to El Niño has, in turn, been linked to 
variability in fisheries, for example in the catch of anchovy ( Engraulis 
ringens ) in the Humboldt current ecosystem (Jackson et al., 2011).
Since AR5 WGII, analysis of a longer time series of ocean-colour data 
(1998–2012) has shown (Aiken et al., 2017) that the expansion of 
the low nutrient part of the North Atlantic oligotrophic gyre was 
significant, at 0.27 × 106 km2 per decade, but that the rate was 
much lower than that reported earlier by Polovina et  al. (2008). 
Furthermore, Aiken et al. (2017) reported no significant trend in the 
oligotrophic area of the South Atlantic Gyre. With the time series 
extended to 2016, von Schuckmann et al. (2018) reported that since

2007, there was a general decreasing trend in the areas of the North 
and South Pacific oligotrophic Gyres, while the North and South 
Atlantic oligotrophic Gyres remained stable, with little change in 
area, consistent with Aiken et al . (2017). The changing sign of trends 
in the areal extent of the oligotrophic gyres with increase in the 
length of the time series raises the possibility that these changes 
arise from interannual to multi-decadal variability. The time series 
of ocean-colour data is too short to discern any trend that might be 
superimposed on such variability. 
Similarly, there is limited consistent and long-term information on 
large-scale distributions of marine organisms at higher trophic levels. 
But there are increased indications since AR5 and SROCC that the 
distributions of various higher trophic-level organisms are shifting 
both polewards and to deeper levels (Edwards et al., 2016; Haug et al.,

2017; Atkinson et  al., 2019; Lenoir et  al., 2020; Pinsky et  al., 2020), 
mostly consistent with changes in temperature. However observations 
also show a smaller set of counter-intuitive migrations towards 
warmer and shallower waters, which could be related to changes 
in phenology and in larval transport by currents (Fuchs et al., 2020). 
There are also strengthening indications of greater representation by 
species with warm-water affinity in marine communities, consistent 
with expectations under observed warming (Burrows et  al., 2019). 
There are indications that pre-1850 CE plankton communities are 
different from their modern counterparts globally (Jonkers et al., 2019). 
Indicators of geographical distributions of species (mostly from coastal 
waters) suggest that the rates at which some species are leaving or 
arriving at an ecosystem are variable, leading to changes in community 
composition (Blowes et al., 2019), with likely greater representation of

warm-water species in some locations (Burrows et al., 2019). 
In summary, there is high confidence  that the latitudinal and depth 
limits of the distribution of various organisms in the marine biome 
are changing. There is medium confidence  that there are differences 
in the responses of individual species relative to each other, such that 
the species compositions of ecosystems are changing. There is medium 
confidence  that chlorophyll concentration in the surface shows weak 
negative and positive trends in parts of low and mid latitudes, and 
weak positive trends in some high-latitude areas. There is  medium 
confidence  that the large-scale distribution of the oligotrophic 
gyre provinces is subject to significant inter-annual variations, but Figure 2. 31 | Phytoplankton dynamics in the ocean. (a)  Climatology of 
chlorophyll-a concentration derived from ocean-colour data (1998–2018); (b) Linear

trends in chlorophyll concentration. Trends are calculated using OLS regression with 
significance assessed following AR(1) adjustment after Santer et al. (2008) (‘×’ marks 
denote non-significant changes). (c) Histogram of linear trends in chlorophyll 
concentration, after area weighting and with per-pixel uncertainty estimates based 
on comparison with in situ data. Further details on data sources and processing are 
(mg Chl m-3)10−210−1100101(a) Clim atology: Chlor ophyll concent rationPhytoplankton dynamics in the ocean
(c) Chlorophy ll concentration: Histogram of trends−4                 −2                   0                   2                   4(b) Linear t rends: Chlorophy ll concentration
108
107
106
105
104
103
−20  −15       −10         −5           0           5  10  15         20Area (km-2)
(Percent change yr-1) (Percent change yr-1) 
All trends
Significant trends (p < 0.1)Non significantSignificant Colour

362
low confidence in the long-term trends in the areal extent of these 
provinces because of insufficient length of direct observations.
2.3.4.2.2  Marine primary production
SROCC expressed low confidence  in satellite-based estimates of 
trends in marine primary production, citing insufficient length of the time series and lack of corroborating in situ measurements and 
independent validation time series. The report also cites significant 
mismatches in absolute values and decadal trends in primary production when different satellite-based products are compared.
Recent model-based results with assimilation of satellite data (Gregg 
and Rousseaux, 2019), show global annual mean marine primary 
production of around 38 (±1.13) PgC yr
 –1 over 1998–2015. This new 
result lies towards the low end of values reported in earlier, satellite-based, studies (range 36.5–67 PgC yr
 –1, reported in Sathyendranath

et  al. (2020)). Reconciling the results of Gregg and Rousseaux (2019) with earlier satellite-based studies leads to a mean of 
47 (±7.8) PgC yr
 –1. There is a strong correlation between interannual 
regional variability in marine primary production and climate 
variability (Racault et al., 2017b; Gregg and Rousseaux, 2019). The 
increase in primary production in the Arctic has been associated 
with retreating sea ice and with increases in nutrient supply and chlorophyll concentration (Lewis et al., 2020). Gregg and Rousseaux (2019) reported a decreasing trend in marine primary production, of –0.8 PgC (–2.1%) per decade globally. There is low confidence in this
trend because of the small number of studies and the short length ofthe time series (<20 years).
In conclusion, there is low confidence because of the small number of 
recent studies and the insufficient length of the time series analysed 
that marine primary production is 47 (± 7.8) PgC yr
 –1. A small decrease

in productivity is evident globally for the period 1998–2015, but regional changes are larger and of opposing signs (low confidence). 
2.3.4.2.3  Marine phenology 
Phenology is the study of the timing of important events in the annual life cycle of organisms (plants or animals; see also Annex VII: Glossary). The AR5 WGII noted that the timing of various seasonal 
biological events in the ocean had advanced by more than four days 
per decade over the previous 50-year period and concluded that there was high confidence in observed changes in the phenological metrics 
of marine organisms. The AR5 WGII further reported that, of those observations that showed a response, 81% of changes in phenology, 
distribution and abundance were consistent with anticipated 
responses to climate warming according to theoretical expectations, corroborated by updates in SROCC. The consequent current and future impacts on interactions between species, including competition and

predator-prey dynamics, were noted with high confidence.
There are additional indications that phenological metrics related to 
different species are changing, but not always in a similar manner. For example, many seabirds are breeding earlier, while others are 
breeding later (Sydeman et  al., 2015). Planktonic organisms in 
the North Atlantic are also responding differently to each other when subjected to the same environmental changes (Edwards and Richardson, 2004). Furthermore, different factors could be responsible for triggering phenological responses in different stages 
in the life cycle of a single organism (Koeller et al., 2009). The shift 
in the distribution of many benthic invertebrates on the North-west Atlantic shelf, including some commercially important shellfish, could be explained by phenology and larval transport, and the shift and contraction of species range have been associated with 
higher mortality (Fuchs et  al., 2020). Changes in phytoplankton

phenological indicators globally (Racault et al., 2012; Sapiano et al., 2012) have been linked to indicators of climate variability, such as the multivariate ENSO Index (Racault et al., 2017a), with responses 
varying across ecological provinces of the ocean (Longhurst, 2007). 
Phenological links between multiple components of an ecosystem 
have to be maintained intact, to retain system integrity. Since all higher pelagic organisms depend on phytoplankton for their food, 
either directly or indirectly, a match favours survival, and a mismatch 
is antagonistic to survival. Match represents synchronicity in the phenological events of both prey and predator. There are indications from ocean-colour data used in conjunction with fisheries data that the survival rate of various larger marine organisms depends on 
phenological metrics related to the seasonality of phytoplankton

growth. Such links have been demonstrated, for example, for haddock (Melanogrammus aeglefinus) in the North-west Atlantic (Platt et  al., 2003); northern shrimp in the North Atlantic (Koeller et al., 2009; Ouellet et al., 2011); sardine ( Sardinella aurita) off the 
Ivory coast (Kassi et  al., 2018); cod (Gadus morhua) and haddock (Melanogrammus aeglefinus ) larvae in the North-West Atlantic 
(Trzcinski et al., 2013); and oil sardine ( Sardinella longiceps ) off the 
south-west coast of India. Borstad et al. (2011) showed that fledgling 
production rate of rhinoceros auklets (Cerorhinca monocerata) 
on a  remote island in coastal north-eastern Pacific was related to seasonal values of chlorophyll-a biomass in the vicinity of the island. 
In summary, new in situ data as well as satellite observations 
strengthen AR5 and SROCC findings that various phenological metrics

for many species of marine organisms have changed in the last half century ( high confidence ), though many regions and many species 
of marine organisms remain under-sampled or even unsampled. The changes vary with location and with species (high confidence). There 
is a strong dependence of survival in higher trophic-level organisms 
(fish, exploited invertebrates, birds) on the availability of food at various stages in their life cycle, which in turn depends on phenologies of both ( high confidence ). There is a gap in our understanding of how 
the varied responses of marine organisms to climate change, from 
a phenological perspective, might threaten the stability and integrity 
of entire ecosystems. 
2.3.4.3  T errestrial Biosphere
2.3.4.3.1  Growing season and phenology changes
The AR5 WGII briefly discussed large-scale changes in the length of the growing season but made no confidence statement about observed trends. However, AR5 did conclude with high confidence that warming

363
Recent in situ analyses document increases in the length of the thermal 
growing season (i.e., the period of the year when temperatures are 
warm enough to support growth) over much of the extratropical land 
surface since at least the mid-20th century. Over the NH as a whole, 
an increase of about 2.0 days per decade is evident for 1951–2018 
(Dunn et  al., 2020), with slightly larger increases north of 45°N 
(Barichivich et al., 2013). Over North America, a rise of about 1.3 days 
per decade is apparent in the United States for 1900–2014 (Kukal 
and Irmak, 2018), with larger increases after 1980 (McCabe et  al., 
2015); likewise, all ecozones in Canada experienced increases from 
1950–2010 (Pedlar et  al., 2015). Growing season length in China increased by at least 1.0 days per decade since 1960 (Xia et  al., 
2018) and by several days per decade in South Korea since 1970

(Jung et al., 2015). In general, changes in phenological indicators are 
consistent with the increase in growing season length documented by 
instrumental data (Parmesan and Hanley, 2015). Several long-term, 
site-specific records illustrate the unusualness of recent phenological 
changes relative to interannual variability; for example, peak bloom 
dates for cherry blossoms in Kyoto, Japan have occurred progressively 
earlier in the growing season in recent decades, as have grape harvest 
dates in Beaune, France (Figure 2.32).
Changes in selected long-term phenological series
(a) Cherry blossom peak bloom in Kyoto, Japan
May-01
Apr-21
Apr-11
Apr-01
Apr-23
Apr-15
Apr-07
Mar-30
Apr-11
Mar-22
Mar-02
Feb-11
1950 1965 1980 1995 20101890 1920 1950 1980 20101400 1500 1600 1700 1800 1900 2000 1400 1500 1600 1700 1800 1900 2000
1830 1860 1890 1920 1950 1980
1965 1980 19952010
2010May-21May-31Jun-10Jun-20Apr-21May-02May-13May-24Aug-29Sep-18Oct-08Oct-28

(c) Spring phenology index in eastern China
(e) Grape harvest in Central Victoria, Australia
(f) Start of growing season in Tibetan Plateau, China(b) Grape harvest in Beaune, France
(d) Full flower of Piedmont species in Philadelphia, USA
Figure 2.32 | Phenological indicators of changes in growing season. (a)  Cherry blossom peak bloom in Kyoto, Japan; (b) grape harvest in Beaune, France; (c) spring 
phenology index in eastern China; (d) full flower of Piedmont species in Philadelphia, USA; (e) grape harvest in Central Victoria, Australia; (f) start of growing season in Tibetan 
Plateau, China. Red lines depict the 25-year moving average (top row) or the nine-year moving average (middle and bottom rows) with the minimum roughness boundary 
constraint of Mann (2004). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

364
Changes in the length of the photosynthetically active growing 
season (derived from the Normalized Difference Vegetation Index 
(NDVI)) are also evident over many land areas since the early 1980s. 
Increases of about 2.0 days per decade are apparent north of 45°N 
since the early 1980s (centred over mid-latitude Eurasia and north-
eastern North America), with indications of a reversal to a decline 
in season length starting in the early 2000s (Barichivich et al., 2013; 
Zhao et al., 2015; Garonna et al., 2016; Q. Liu et al., 2016; Park et al., 
2016). Satellite-based records suggest that most NH regions have 
experienced both an earlier start and a later end to the growing 
season, a finding supported by ground-based data (Piao et al., 2020). 
A number of studies also capture increases in growing season length 
over the Canadian Arctic (W. Chen et al., 2016), Fennoscandia (Høgda

et  al., 2013), most of Europe (Garonna et  al., 2014), and parts of 
sub-Saharan Africa (Vrieling et al., 2013). 
The general consistency between in situ and satellite estimates 
over the NH is noteworthy given that many factors independently 
contribute to uncertainty in observed changes. For example, there 
is no universally accepted definition of growing season length 
across in situ analyses; some define the growing season as the 
period based on a temperature threshold (e.g.,  5°C) whereas 
others use the frost-free period. Spatial and temporal coverage 
can also affect conclusions based upon in situ studies (Donat et al., 
2013b). For satellite analyses, uncertainties can be related to the 
satellite datasets themselves (e.g., satellite drift, sensor differences, 
calibration uncertainties, atmospheric effects); and to the methods 
for determining phenological metrics (e.g., start, end, and length of 
season; S. Wang et al., 2016).

In summary, based on multiple independent analyses of in situ, 
satellite, and phenological data, there is high confidence  that 
the length of the growing season has increased over much of the 
extratropical NH since at least the mid-20th century.
2.3.4.3.2 Terrestrial ecosystems
The AR5 WGII concluded that many terrestrial species have shifted 
their geographic ranges in recent decades ( high confidence ). Similarly, 
SRCCL assessed that many land species have experienced range size 
and location changes as well as altered abundances over recent 
decades ( high confidence ). SROCC noted that species composition 
and abundance have markedly changed in high mountain ecosystems 
in recent decades ( very high confidence ).
Paleoclimate reconstructions document large-scale biome shifts from 
the deep past through the Holocene (e.g., Hoogakker et al., 2016). 
The northernmost location of the treeline is a representative indicator

in this regard (Figure 2.34).  During the MPWP , boreal forest extended 
to the Arctic coast, with the northernmost treeline being about 4° 
to 10° latitude further north than at present; temperate forests and 
grasslands were also shifted poleward (with reduced tundra extent), 
while savannahs and woodlands were more expansive in Africa 
and Australia at the expense of deserts (Cross-Chapter Box  2.4, 
Figure  1b; Salzmann et  al., 2008, 2013; Sniderman et  al., 2016; 
Andrae et al., 2018). During the LGM, tundra and steppe expanded 
whereas forests were globally reduced in extent (Prentice et al., 2000; Binney et al., 2017), the northern treeline being about 17° to 23° 
latitude south of its present-day location in most areas. During the 
LDT, pervasive ecosystem transformations occurred in response to 
warming and other climatic changes (Nolan et  al., 2018; Fordham 
et al., 2020). By the MH, North Africa had experienced a widespread

conversion from grasslands to desert (Hoelzmann et al., 1998; Prentice 
et  al., 2000; Sha et  al., 2019), and the northernmost treeline had 
shifted poleward again to about 1° to 3° latitude north of its current 
location (MacDonald et al., 2000; Binney et al., 2009; Williams et al., 
2011). Over the past half century, there has been an increase in the 
spatial synchrony of annual tree growth across all continents that is 
unprecedented during the past millennium (Manzanedo et al., 2020). 
Elevated rates of vegetation change in the Holocene are consistent 
with climate variability (Shuman et al., 2019), intensified human land 
use (Fyfe et al., 2015; Marquer et al., 2017), and resulting increased 
ecosystem novelty (Finsinger et al., 2017; K.D. Burke et al., 2019).
Long-term ecological records capture extensive range shifts during 
the 20th and early 21st centuries (Lenoir and Svenning, 2015; 
Pecl  et  al., 2017). Research has been most extensive for North

America and western Eurasia, with fewer studies for central Africa, 
eastern Asia, South America, Greenland, and Antarctica (Lenoir and 
Svenning, 2015). Most documented changes are toward cooler 
conditions  – that is, poleward and upslope (Lenoir et  al., 2008; 
Harsch et  al., 2009; Elmendorf et  al., 2015; Parmesan and Hanley, 
2015; Evans and Brown, 2017). Notably, a large, quasi-global 
analysis (Chen et  al., 2011) estimated that many insect, bird, and 
plant species had shifted by 17 (±3) km per decade toward higher 
latitudes and 11 (±2) m per decade toward higher elevations since 
the mid-20th century, with changes in both the leading and trailing 
edges of species ranges (Rumpf et al., 2018). Over the past century, 
long-term ecological surveys also show that species turnover (i.e., the 
total number of gains and losses of species within an area) has 
significantly increased across a broad array of ecosystems (Dornelas

et al., 2014, 2019), including undisturbed montane areas worldwide 
(Gibson-Reinemer et al., 2015). Despite global losses to biodiversity, 
however, most local assemblages have experienced a change in 
biodiversity rather than a systematic loss (Pimm et al., 2014). With 
increased species turnover, the novelty of contemporary communities 
relative to historical baselines has risen (Hobbs et al., 2009; Radeloff 
et  al., 2015) due to greater spatial homogenization, mixtures of 
exotic and native species, altered disturbance regimes, and legacies 
of current or historic land use (Olden and Rooney, 2006; Schulte 
et al., 2007; Thompson et al., 2013; Goring et al., 2016). In general, 
terrestrial species have had lower rates of turnover than marine 
species (Dornelas et al., 2018; Blowes et al., 2019).
There are exceptions to the general pattern of poleward/upslope 
migration. For some species, various biotic and abiotic factors (such

as precipitation and land use) supersede the physiological effects of 
temperature (Vanderwal et  al., 2013; Gibson-Reinemer and Rahel, 
2015; Ordonez et al., 2016; Scheffers et al., 2016; Lenoir et al., 2020). 
For other species, poleward migration is slower than expectations 
from the observed temperature increases. Trees are one such example 
because of their long lifespan and gradual maturity (Renwick and 
Rocca, 2015); in fact, poleward advance is only evident at about 
half of the sites in a large global dataset of treeline dynamics for

365
1900-present (Harsch et  al., 2009). Furthermore, the northernmost 
extent of treeline at present (roughly 73°N) is actually somewhat 
south of its location in the MH (MacDonald et al., 2008) despite an 
expanding growing season in the extratropical NH since the mid-20th 
century (Section 2.3.4.3.1).
Consistent with species range shifts, SRCCL noted that there have 
been changes in the geographical distribution of climate zones. 
Poleward shifts in temperate and continental climates are evident 
across the globe over 1950–2010, with decreases in the area 
(and increases in the average elevation) of polar climates (Chan and 
Wu, 2015). Zonal changes towards higher latitudes in winter plant 
hardiness regions are apparent since the 1970s over the central 
and eastern USA, with elevational changes also being important 
in the western USA (Daly et  al., 2012). A clear northward shift in

winter plant hardiness zones is detectable across western Canada 
since 1930, with somewhat lesser changes in the south-eastern part 
of the country (McKenney et  al., 2014). A northward migration of 
agro-climate zones is also evident over Europe since the mid-1970s 
(Ceglar et al., 2019). In addition, a shift toward more arid climate 
zones is apparent in some areas, such as the Asian monsoon region 
(Son and Bae, 2015) as well as parts of South America and Africa 
(Spinoni et al., 2015).
In summary, there is very high confidence  that many terrestrial species 
have shifted their geographic ranges poleward and/or upslope over 
the past century, with increased rates of species turnover. There is 
high confidence that the geographical distribution of climate zones 
has shifted in many parts of the world. 
2.3.4.3.3 Global greening and browning
The AR5 WGII briefly discussed changes in global vegetation 
greenness derived from satellite proxies for photosynthetic activity.

Observed trends varied in their strength and consistency, and AR5 
thus made no confidence statement on observed changes. The SRCCL 
subsequently concluded that greening had increased globally over 
the past 2–3 decades ( high confidence ).
Vegetation index data derived from AVHRR and MODIS depicts 
increases in aspects of vegetation greenness (i.e.,  green leaf area 
and/or mass) over the past four decades (Piao et  al., 2020). NDVI 
increased globally from the early 1980s through the early 2010s 
(Y .  Liu et  al., 2015a). N. Pan et  al. (2018) found NDVI increases 
over about 70% of the Earth’s vegetated surface through 2013, 
and Osborne et  al. (2018) noted strong upward changes in NDVI 
in the circumpolar Arctic through 2016. Globally integrated Leaf 
Area Index (LAI) also rose from the early 1980s through at least 
the early 2010s (Zhu et al., 2016; Forzieri et al., 2017; Jiang et al., 
2017; Xiao et  al., 2017) and probably through near-present; for

example, C. Chen et  al. (2019) documented an LAI increase over 
one-third of the global vegetated area from 2000–2017. Although 
less frequently analysed for temporal trends, Fraction of Absorbed 
Photosynthetically Active Radiation (FAPAR) likewise increased 
over many global land areas (particularly China, India, and eastern 
Europe) in the past two decades (Figure 2.33; Forkel et  al., 2014; 
Gobron, 2018; Keenan and Riley, 2018). There are also documented changes in specific vegetation types, such as a  7% rise in global 
tree cover for 1982–2016 (Song et al., 2018) and an expansion of 
shrub extent in the Arctic tundra over 1982–2017 (Myers-Smith 
et al., 2020). The increased greening is largely consistent with CO 2 
fertilization at the global scale, with other changes being noteworthy 
at the regional level (Piao et al., 2020); examples include agricultural 
intensification in China and India (X. Chen et al., 2019; Gao et al.,

2019) and temperature increases in the northern high latitudes 
(Kong et al., 2017; Keenan and Riley, 2018) and in other areas such 
as the Loess Plateau in central China (Y . Wang et al., 2018). Notably, 
some areas (such as parts of Amazonia, central Asia, and the Congo 
basin) have experienced browning (i.e.,  decreases in green leaf 
area and/or mass) (Hoogakker et al., 2015; Gottschalk et al., 2016; 
Anderson et  al., 2019). Because rates of browning have exceeded 
rates of greening in some regions since the late 1990s, the increase in 
global greening has been somewhat slower in the last two decades 
(T.-Y . Pan et al., 2018). 
Global-scale linear trends differ substantially across products for the 
same periods and trend metrics used (Jiang et al., 2017). Several factors 
contribute to this large span in estimated changes. Remotely sensed 
vegetation products vary in their spatial and temporal completeness as

well as resolution and are sensitive to contamination from atmospheric 
composition, clouds, snow cover, and anisotropy, as well as orbital 
changes and sensor degradations (de Jong et  al., 2012; Zhu et  al., 
2016; Jiang et al., 2017; Xiao et al., 2017; N. Pan et al., 2018). Ground-
based measurements suitable for calibration and validation are scarce 
before 2000 (Xiao et al., 2017), and the recalibration of satellite records 
(e.g., as in from MODIS Collection 5 to 6) can affect trends (Piao et al., 
2020). It is possible that the increase in greenness over 2000–2015 
is larger than the increase in gross primary production (based on flux 
tower measurements and MODIS Collection 6 data) (L. Zhang et al., 
2018). Land use changes and altered disturbance regimes (e.g., floods, 
fires, diseases) may mask large-scale signals (Franklin et  al., 2016). 
In addition, there is a plethora of models for the identification of

Fractional change (%) over 1988–2019 <−2−1 012>3Trends in fraction of absorbed photosynthetically active radiation 
FAPAR from SeaWiFS; 
MERIS; MODIS TIP C6 (C) 
EC-JRC, 2020
Figure 2. 33 | Satellite-based trends in fraction of absorbed photosynthetically 
active radiation (per decade) for 1998–2019.  Trends are calculated using OLS 
regression with significance assessed following AR(1) adjustment after Santer et  al. 
(2008); ‘×’ marks denote non-significant trend). Unvegetated areas such as barren 
deserts (grey) and ice sheets (white) have no trend in FAPAR. Further details on data 

366
phenological metrics from satellite data as well as a variety of statistical 
techniques for analysing historical changes (S. Wang et al., 2016).
In summary, there is high confidence  that vegetation greenness 
(i.e., green leaf area and/or mass) has increased globally since the 
early 1980s. However, there is low confidence  in the magnitude of 
this increase owing to the large range in available estimates.
2.3.5 Synthesis of Evidence for Past Changes
Section 2.3 has assessed the observational evidence for changes in key 
indicators across the atmosphere, cryosphere, ocean and biosphere 
starting, where applicable, from paleoclimate proxy  records and coming up to the present day. This synthesis serves as an assessment 
of the evidence for change across the climate system as represented 
by the instrumental record and its unusualness in the longer-term 
context. Building upon previous sections assessing the observational

evidence for each key indicator individually, this section integrates 
the evidence across multiple indicators to arrive at a holistic and 
robust final assessment.
Climate has varied across a broad range of timescales (Figure 2.34). 
During the Cenozoic Era temperatures generally decreased over tens 
of millions of years, leading to the development of ice sheets. During 
the last two million years, climate has fluctuated between glacials 
and interglacials. Within the current Holocene interglacial and, with 
increasing detail in the CE, it is possible to reconstruct a history both 
−0.15 to +0.11
−7 ~ 517 to 27192 to 198
-1.1 ~ 0.70.4 to 0.62.9 to 3.6
0.5 to 1.5-0.14 ~ 0.240.66 to 1.00
0
−1.5 to 1.50.5 to 1.0
−0.05 to 0.03−0.03 to 0.000.15 to 0.25
278 to 285286  296360  397
4 to 4210
X to Y:
X  Y:
X ~ Y:very likely range, unless otherwise stated in FAIR data table
start to end of period, with no stated uncertainty
lowest and highest values, with no stated uncertainty24 to 44

2.5 to 4.0
10 to  2510 to 18−5 to −7−120  − 500.2 to 1.0
4 to 101 to 3
8 to 2−6 1
−23 to −17
70 to 765 to 255 to 10−134 to −125 −3.5 to 0.5
900  20001150 to 2500360 to 420266 to 282188 to 194193  271260 to 268Atmosphere
CO2 (ppm)CO2 
rate of change
(ppm/100 yr)Temperature 
relative to 
1850 –1900 (°C)Glacier extent 
1850 –1900Northern tree line 
1850 –1900 (°lat)Sea level relative 
to 1900 (m)Sea level rate
 of change 
(mm/yr or m/1000 yr)Cryosphere Biosphere Ocean
Paleocene-Eocene
 thermal maximum  
(55.9–55.7 Ma)
Colder
Lower sea level
More iceWarmer
Higher sea level
Less iceMinimum Maximum Pre-industrial Level
Insufficient data 
or not assessedEarly Eocene 
climate optimum 
(53–49 Ma)Mid-Pliocene 
warm period 
(3.3–3.0 Ma)Last Glacial 
Maximum 
(23–19 ka)
Last Interglacial 
(129–116 ka)Last Deglacial 
Transition
 (18–11 ka)Mid-Holocene 
(6.5–5.5 ka)Selected large-scale climate indicators from the Cenozoic era to the recent past
Last Millennium

(850–1850 CE)Approximate 
pre-industrial 
(1850–1900 CE)Recent past 
(1995–2014 CE) Younger
Older-0.15  to 0.11
Figure 2.34 | Selected large-scale climate indicators during paleoclimate and recent reference periods of the Cenozoic Era.  Values are based upon 
assessments carried out in this chapter, with confidence  levels ranging from low to very high . Refer to Cross-Chapter Box 2.1 for description of paleoclimate reference periods 
and Section 1.4.1 for recent reference periods. Values are reported as either the very likely  range (x to y), or best estimates from beginning to end of the reference period with 
no stated uncertainty (x → y), or lowest and highest values with no stated uncertainty (x ~ y). Temperature is global mean surface temperature. Glacier extent is relative and 
colour scale is inverted so that more extensive glacier extent is intuitively blue.

367
of more indicators of the climate system and, with increasing fidelity, 
the rates of change. Solely for the last 150 years or so are instrumental 
observations of globally distributed climate indicators available. 
However, only since the late 20th century have observational 
systems attained essentially global monitoring capabilities. The 
direct observations point unequivocally to rapid change across many 
indicators of the climate system since the mid-19th century. These are 
all consistent in indicating a world that has warmed rapidly. 
Assessing the long-term context of recent changes is key to 
understanding their potential importance and implications. The climate 
system consists of many observable aspects that vary over a very 
broad range of timescales. Some biogeochemical indicators of change 
such as atmospheric CO 2 concentrations  and ocean pH have shifted

rapidly and CO 2 concentrations  are currently at levels unseen in at least 
800 kyr (the period of continuous polar ice-core records) and very likely  
for millions of years. The GMST in the past decade is likely  warmer than it has been on a centennially-averaged basis in the CE and more likely 
than not  since the peak of the LIG. Many more integrative components 
of the climate system (e.g., glaciers, GMSL) are experiencing conditions 
unseen in millennia, whereas the most slowly responding components 
(e.g.,  ice-sheet extent, permafrost, tree line) are at levels unseen in 
centuries ( high confidence ). The rate at which several assessed climate 
indicators (e.g., GMSL, OHC, GSAT) have changed over recent decades 
is highly unusual in the context of preceding slower changes during the 
current post-glacial period ( high confidence ).
In summary, directly observed changes in the atmosphere, ocean, 
cryosphere and biosphere represent unequivocal evidence of

a warming world. Key climate indicators are now at levels not 
experienced for centuries to millennia. Since the late 19th century 
many indicators of the global climate system have changed at a rate 
unprecedented over at least the last two thousand years.
Cross-Chapter Box 2.4 | The Climate of the Pliocene (Around 3 Million Years Ago), When CO 2 
Concentrations Were Last Similar to Those of the Present Day
Contributing Authors: Alan M. Haywood (United Kingdom), Darrell S. Kaufman (United States of America), Nicholas R. Golledge 
(New Zealand/United Kingdom), Dabang Jiang (China), Daniel J. Lunt (United Kingdom), Erin L. McClymont (United Kingdom), 
Ulrich Salzmann (United Kingdom/Germany, United Kingdom), Jessica Tierney (United States of America)
Throughout this Report, information about past climate states is presented in the context of specific climate variables, processes or

regions. This Cross-Chapter Box focuses on a single paleoclimate reference period as an example of how proxy data, models and 
process understanding come together to form a more complete representation of a warm climate state that occurred during the 
relatively recent geologic past.
The Pliocene Epoch is one of the best-documented examples of a warmer world during which the slow responding components of 
the climate system were approximately in balance with concentrations of atmospheric CO 2, similar to present (e.g., Haywood et al., 
2016). It provides a means to constrain Earth’s equilibrium climate sensitivity (Section 7.5.3) and to assess climate model simulations 
(Section 7.4.4.1.2). During the Pliocene, continental configurations were similar to present (Cross-Chapter Box 2.4 Figure 1a), and 
many plant and animal species living then also exist today. These similarities increase reliability of paleo-environmental reconstructions

compared with those for older geological periods. Within the well-studied mid-Pliocene Warm Period (MPWP , also called the 
mid-Piacenzian Warm Period, 3.3–3.0 Ma), the interglacial period KM5c (3.212–3.187 Ma) has become a focus of research because 
its orbital configuration, and therefore insolation forcing, was similar to present (global mean insolation = –0.022 W m–2 relative to 
modern; Haywood et al., 2013), allowing for the climatic state associated with relatively high atmospheric CO 2 to be assessed with 
fewer confounding variables.
Major global climate indicators
During the KM5c interglacial, atmospheric CO 2 concentration was typically between 360 and 420 ppm (Section 2.2.3.1). New climate 
simulations of this interval from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) show a multi-model mean global 
surface air temperature of 3.2 [2.1 to 4.8] °C warmer than control simulations (Cross-Chapter Box 2.4, Figure 1a; Haywood et al., 2020).

This is consistent with proxy evidence for the broader MPWP , which indicates that global mean surface temperature was 2.5°C–4.0°C 
higher than 1850–1900 (Section 2.3.1.1.1). Global mean sea level was between 5 and 25 m higher than present (Section 2.3.3.3). 
Geological evidence (Section 2.3.2.4) and ice-sheet modelling (Section 9.6.2) indicate that both the Antarctic and Greenland Ice 
Sheets were substantially smaller than present (Cross-Chapter Box 2.4, Figure 1c). Attribution of sea level highstands to particular 
ice-sheet sources (Section 9.6.2) is challenging (DeConto and Pollard, 2016; Golledge, 2020), but improving (Berends et al., 2019; 
Grant et al., 2019).

368
Cross-Chapter Box 2.4 (continued)
Northern high latitudes
The latitudinal temperature gradient during the MPWP was reduced relative to present-day and the consistency between proxy and 
modelled temperatures has improved since AR5 (Section 7.4.4.1.2). Northern high latitude (>60°N) SSTs were up to 7°C higher than 
1850–1900 (Bachem et al., 2016; McClymont et al., 2020; Sánchez-Montes et al., 2020), and terrestrial biomes were displaced poleward 
(e.g., Dowsett et al., 2019) (Cross-Chapter Box 2.4, Figure 1b). Arctic tundra regions currently underlain by permafrost were warm 
enough to support boreal forests, which shifted northward by approximately 250 km in Siberia, and up to 2000 km in the Canadian 
Arctic Archipelago (Salzmann et al., 2013; Fletcher et al., 2017). The shift caused high-latitude surface albedo changes, which further

amplified the Pliocene global warming (Zhang and Jiang, 2014). Vegetation changes in north-east Siberia indicate that MPWP summer 
temperatures were up to 6°C higher than present day (Brigham-Grette et al., 2013). Farther south, modern boreal forest regions in 
Russia and eastern North America were covered with temperate forests and grasslands, whereas highly diverse, warm-temperate 
forests with subtropical taxa were widespread in central and eastern Europe (Cross-Chapter Box 2.4, Figure 1). While seasonal sea ice 
was present in the North Atlantic and Arctic oceans, its winter extent was reduced relative to present (Knies et al., 2014; Clotten et al., 
2018), and some models suggest that the Arctic was sea ice free during the summer (Howell et al., 2016; Feng et al., 2020). 
Tropical Pacific
The average longitudinal temperature gradient in the tropical Pacific was weaker during the Pliocene than during 1850–1900

(Section 7.4.4.2.2). Changes in Pacific SSTs and SST gradients had far-reaching impacts on regional climates through atmospheric 
teleconnections, affecting rainfall patterns in western North America (Burls and Fedorov, 2017; Ibarra et al., 2018). The reduced zonal 
SST gradient has led to suggestions that the Pliocene Pacific experienced a ‘permanent El Niño’ state (Molnar and Cane, 2002; Fedorov, 
2006). However, there is no direct geological evidence, nor support from climate models, that ENSO variability collapsed during the 
Pliocene. Although not located in the centre-of-action region for ENSO, Pliocene corals show temperature variability over 3–7 year 
timescales (Watanabe et  al., 2011). In addition, a multi-model intercomparison indicates that ENSO existed, albeit with reduced 
variability (Brierley, 2015). Thus, there is high confidence  that ENSO variability existed during the Pliocene.
Hydrological cycle

Vegetation reconstructions for the late Pliocene indicate regionally wetter conditions resulting in an expansion of tropical savannas 
and woodlands in Africa and Australia at the expense of deserts (Cross-Chapter Box 2.4, Figure 1b). PlioMIP2 climate models 
generally simulate higher rates of mean annual precipitation in the tropics and high latitudes, and a decrease in the subtropics, with 
a multi-model mean global increase of 0.19 [0.13–0.32] mm day–1 relative to control simulations (Cross-Chapter Box 2.4, Figure 1a; 
Haywood et al., 2020). Both simulations and limited  proxy evidence  indicate stronger monsoons in northern Africa, Asia, and northern 
Australia relative to present, but trends are uncertain in other monsoon regions (X. Li et al., 2018; Yang et al., 2018; X. Huang et al., 
2019a; R. Zhang et al., 2019). There is thus medium confidence  that monsoon systems were stronger during the Pliocene. Simulations

of MPWP climate show that global tropical cyclone intensity and duration increased during the MPWP (Yan et al., 2016); however, 
there is low confidence  in this result because inter-model variability is high.
Summary
During the MPWP (3.3–3.0 Ma) the atmospheric CO 2 concentration was similar to present, and the slow-response, large-scale 
indicators reflect a world that was warmer than present. With very high confidence , relative to present, global surface temperature, 
sea level, and precipitation rate were higher, NH latitudinal temperature gradient was lower, and major terrestrial biomes expanded 
poleward. With medium confidence  from proxy-based evidence alone (Section 2.3.2), combined with numerical modelling, analysis 
of the sea-level budget, and process understanding (Section 9.6.2), there is high confidence  that cryospheric indicators were 
diminished. There is medium confidence  that the Pacific longitudinal temperature gradient was weaker and monsoon systems

369
Cross-Chapter Box 2.4, Figure 1 | Climate indicators of the mid-Pliocene Warm Period (MPWP; 3.3–3.0 million years ago, Ma) from models 
and proxy data. (a)  Simulated surface air temperature (left) and precipitation rate anomaly (right) anomaly (relative to 1850–1900) from the Pliocene Model 
Intercomparison Project Phase 2 multi-model mean, including CMIP6 (n = 4) and non-CMIP6 (n = 12) models. Symbols represent site-level proxy-based estimates of 
sea-surface temperature for KM5c (n = 32), and terrestrial temperature (n = 8) and precipitation rate for the MPWP (n = 8). (b) Distribution of terrestrial biomes was 
considerably different during the Piacenzian Stage (3.6–2.6 Ma) (upper) compared with present-day (lower). Biome distributions simulated with a model (BIOME4)

in which Pliocene biome classifications are based on 208 locations, with model-predicted biomes filling spatial gaps, and the present day, with the model adjusted 
for CO 2 concentration of 324 parts per million (ppm). (c) Ice-sheet extent predicted using modelled climate forcing and showing where multiple models consistently 
predict the former presence or absence of ice on Greenland (n = 8 total) and Antarctica (n = 10 total). Further details on data sources and processing are available 
678910 3210678
Tropical forest
Savanna & dry woodland
Grassland & dry scrubland
DesertBoreal forest
Tundra
Ice
Warm temperate forestNumber of models in 
agreement that ice
was present
n = 8
Number of models in 
n = 10
Temperate forestClimate indicators of the mid-Pliocene Warm Period
(a) Surface air temperature and precipitation rate anomalies relative to 1850–1900

(b) Changes in vegetation from the Piacenzian to present day (c)−10−8−6−4−2 02468 10 0.0 −1.2−0.9−0.6−0.3 0.30.60.91.2
Present dayPiacenzian
 (3.6–2.6 Ma)
453210 45mm day-1°C

370
2.4 Changes in Modes of Variability
Modes of climate variability that are important for large-scale climate 
on interannual and longer timescales (Cross-Chapter Box 2.2) are 
assessed herein and defined and summarized in Annex IV. Though 
the modes of variability discussed here are assessed and classified on 
the basis of physical variables, it is important to recognize that the 
distribution and function of various components of the terrestrial and 
marine biospheres are modified in response to them. 
2.4.1 Annular Modes
2.4.1.1 Northern Annular Mode (NAM)/North Atlantic 
Oscillation (NAO)
The AR5 reported that the shift towards a positive NAO, a mode 
of variability in the North Atlantic that is closely related to the 
hemispheric-scale NAM, from the 1950s to the 1990s was largely 
reversed by more recent changes ( high confidence ). Moreover, 
periods of persistent negative or positive NAO states observed during

the latter part of the 20th century were not unusual, based on NAO 
reconstructions spanning the last half millennium ( high confidence ).
New multi-millennial-timescale NAO reconstructions are derived 
from marine and lake sediments, speleothems, tree rings and ice 
cores (Ortega et  al., 2015; Faust et  al., 2016; Sjolte et  al., 2018). 
NAO variability over the past 8 kyr suggests the presence of 
a  significant 1.5  kyr periodicity (Darby et  al., 2012). Positive NAO 
conditions dominated during the MH, while the prevailing NAO sign 
during the early and late Holocene was negative according to most 
reconstructions (Olsen  et  al., 2012; Ait Brahim et  al., 2019; Røthe 
et  al., 2019; Hernández et  al., 2020). For the CE, reconstructions 
developed since AR5 indicate no dominant NAO phase during 
1000–1300 CE (Jones et al., 2014; Baker et al., 2015; Ortega et al., 
2015; Lasher and Axford, 2019; Hernández et al., 2020), with either

negative (Baker et al., 2015; Faust et al., 2016; Mellado-Cano et al., 
2019) or a more variable phase of the NAO (Jones et al., 2014; Ortega 
et al., 2015; Sjolte et al., 2018; Cook et al., 2019) between 1400 and 
1850 CE. Several instrument-based NAO reconstructions extending 
back to the 17th and 18th centuries highlight the presence of multi-
decadal variations in the NAO phases (Cornes et al., 2013; Cropper 
et al., 2015), although these studies have limitations considering the 
seasonality of the centres of action and the locations of the stations 
used. Recent reconstructions of the large scale sea level pressure field 
yield more robust NAO analysis, showing a persistently negative NAO 
phase from the 1820s to the 1870s, with positive values dominating 
during the beginning of the 20th century followed by a declining 
trend over 1920–1970, with a recovery thereafter to a period of 
consistently high values between 1970 and the early 1990s (Delaygue

et al., 2019; Mellado-Cano et al., 2019). Based on the evaluation of 
several NAO reconstructions for recent centuries, Hernández et  al. 
(2020) highlighted that the strong positive NAO phases of the 1990s 
and early 21st century were not unusual. 
The predominantly positive phase during the 1990s was followed by 
partial reversal and a tendency towards stronger variability in boreal winter NAM and NAO since the late 1990s (Pinto and Raible, 2012; 
Hanna et  al., 2018). This is particularly evident in December NAO 
(Hanna et al., 2015) and NAM (Overland and Wang, 2015) indices, and 
is not unusual on multi-decadal time scales (Woollings et al., 2018a). 
Since the 1990s, a statistically significant summer NAO decline was 
reported, which is, to a lesser extent, also evident in the winter NAO, 
linked to an enhanced blocking activity over Greenland (Hanna et al., 
2015, 2016; Wachowicz et al., 2021). However, this was moderated by

persistent positive NAO values since 2015 (Annex IV.2.1). Based on 
observations and reanalysis datasets, multi-decadal variations were 
found for the NAM patterns: the Atlantic centre remained unchanged 
throughout 1920–2010, whereas the Pacific centre was stronger 
during 1920–1959 and 1986–2010 and weaker during 1960–1985 
(Gong et  al., 2018). Multidecadal changes were also observed in 
the position of the centres of action of the NAO (Moore et al., 2013; 
Zuo et al., 2016). 
In summary, positive trends for the NAM/NAO winter indices 
were observed between the 1960s and the early 1990s, but these 
indices have become less positive or even negative thereafter ( high 
confidence ). The NAO variability in the instrumental record was very 
likely not unusual in the millennial and multi-centennial context.
2.4.1.2 Southern Annular Mode (SAM)
The AR5 concluded that it was likely  that the SAM had become more 
positive since the 1950s and that this increase was unusual in the

context of the prior 400 years ( medium confidence ). Both AR5 and 
SROCC reported statistically significant trends in the SAM during the 
instrumental period for the austral summer and autumn. 
Several studies have attempted to reconstruct the evolution of the 
SAM during the Holocene using proxies of the position and strength 
of the SH zonal winds, although with no clear consensus regarding 
the timing and phase of the SAM (Hernández et al., 2020). The early 
Holocene was dominated by SAM positive phases (Moreno et  al., 
2018; Reynhout et  al., 2019), consistent with increasing westerly 
wind strength (Lamy et al., 2010), with some reconstructions showing 
significant centennial and millennial variability but no consistent trend 
after 5 ka (Hernández et  al., 2020). For the CE, enhanced westerly 
winds occurred over 0–1000 CE, as reflected in increased burning 
activity in Patagonia (Turney et al., 2016a) and tree ring records from

southern New Zealand (Turney et al., 2016b) imply a predominantly 
positive SAM phase. Pollen records and lake sediments from Tasmania, 
southern mainland Australia, New Zealand and southern South 
America, inferred the period of 1000 to 1400 CE to be characterized 
by anomalously dry conditions south of 40°S, implying a positive 
SAM (Moreno et al., 2014; Fletcher et al., 2018; Evans et al., 2019; 
Matley et al., 2020). Nevertheless, proxy reconstructions of the SAM 
based on temperature-sensitive records from tree rings, ice cores, 
lake sediments and corals spanning the mid-to-polar latitudes show 
alternating positive and negative phases (Figure 2.35).
Prolonged periods of negative SAM values were identified during the 
period 1400–1700 CE in several reconstructions (Figure 2.35, Villalba 
et al., 2012; Abram et al., 2014; Dätwyler et al., 2018), with a minimum 
identified during the 15th century (Hernández et al., 2020), although

371
some disagreements exist between proxy records before 1800 CE 
(Hessl et  al., 2017). Abram et  al. (2014) concluded that the mean 
SAM index during recent decades is at its highest levels for at least 
the last 1 kyr. Similarly, the summer SAM reconstruction by Dätwyler 
et al. (2018) indicates a strengthening over the last 60 years that is 
outside the very likely  range of the last millennium natural variability. 
The largest 30- and 50-year trends in the annual SAM index occurred 
at the end of the 20th century (after 1969 and 1950 respectively), 
indicating that the recent increase in the SAM is unprecedented in 
the context of at least the past three centuries (Yang and Xiao, 2018). 
Before the mid-1950s, SAM indices derived from station-based 
datasets, and centennial reanalyses show pronounced interannual 
and decadal variability but no significant trends, with low correlation

between SAM indices due to the diversity across different datasets 
and sensitivity to the definition used for the index calculation 
(Barrucand et  al., 2018; Schneider and Fogt, 2018; J. Lee et  al., 
2019). Various SAM indices exhibit significant positive trends 
since the 1950s, particularly during austral summer and autumn 
(Barrucand et al., 2018; Schneider and Fogt, 2018; J. Lee et al., 2019), 
unprecedented for austral summer over the last 150 years (J.M. Jones 
et al., 2016; Fogt and Marshall, 2020). This indicates a strengthening 
of the surface westerly winds around Antarctica, related to both the 
position and intensity of the subpolar jet in the SH (Section 2.3.1.4.3; 
Ivy et al., 2017; IPCC, 2019). The SAM trends have slightly weakened 
after about 2000 (Fogt and Marshall, 2020).
In summary, historical station-based reconstructions of the SAM 
show that there has been a robust positive trend in the SAM index,

particularly since 1950 and for the austral summer ( high confidence ). 
The recent positive trend in the SAM is likely  unprecedented in at least the past millennium, although medium confidence  arises due to 
the differences between proxy records before 1800 CE.
2.4.2 El Niño–Southern Oscillation (ENSO)
The AR5 reported with medium confidence  that ENSO-like variability 
existed, at least sporadically, during the warm background state of the 
Pliocene. It was also found ( high confidence ) that ENSO has remained 
highly variable during the last 7 kyr with no discernible orbital 
modulation. The AR5 concluded that large variability on interannual 
to decadal timescales, and differences between datasets, precluded 
robust conclusions on any changes in ENSO during the instrumental 
period. The SROCC reported epochs of strong ENSO variability 
throughout the Holocene, with no indications of a systematic trend in 
ENSO amplitude, but with some indication that the ENSO amplitude

over 1979–2009 was greater than at any point in the period from 
1590–1880 CE. It was also reported that the frequency and intensity 
of El Niño events in the period from 1951–2000 was high relative to 
1901–1950. 
Manucharyan and Fedorov (2014) found that ENSO-like variability 
has been present, at least sporadically, during epochs of millions 
of years (including the MPWP; Cross-Chapter Box 2.4), with proxy 
records indicating that this was the case even when cross-Pacific 
SST gradients were much weaker than present. There is substantial 
disagreement between proxy records for ENSO activity during 
the early Holocene (Zhang et  al., 2014; White et  al., 2018), and 
for ENSO activity and mean state at the LGM (Leduc et  al., 2009; 
Koutavas and Joanides, 2012; Sadekov et al., 2013; Ford et al., 2015, 
2018; Zhu  et  al., 2017; Tierney et  al., 2020). A number of studies 
(Cobb  et  al., 2013; H.  McGregor et  al., 2013; Carré et  al., 2014;

Emile-Geay et  al., 2016; Thompson et  al., 2017; Tian et  al., 2017; 
White et al., 2018; Grothe et al., 2019) have found that ENSO was 
substantially weaker than at present at various times in the mid-
Holocene within the period from 6 to 3 ka, with stronger decreases 
in variability revealed by remote proxies than by those close to the 
core region of ENSO activity. However, Karamperidou et al. (2015) 
find that weakening in ENSO-related variability in eastern Pacific 
proxies does not necessarily correspond to weakening in central 
Pacific proxies. Barrett et  al. (2018) concluded that multi-proxy 
reconstructions are more efficient at identifying eastern Pacific than 
central Pacific events. This suggests that a weakening of proxy-based 
signals may indicate an along-equatorial shift in ENSO activity rather 
than a weakening of ENSO during some periods. Following the period 
of weak ENSO variability in the mid-Holocene, a number of studies

find an increase in ENSO activity which, depending upon the study, 
commences between 4.4 and 3 ka (Koutavas and Joanides, 2012; 
Cobb et al., 2013; Zhang et al., 2014; S. Chen et al., 2016; Emile-Geay 
et al., 2016; Thompson et al., 2017; Du et al., 2021). 
Numerous studies (J. Li et  al., 2013; S. McGregor et  al., 2013; Rustic et  al., 
2015; Hope et al., 2017; Y . Liu et al., 2017) find substantial variability 
in ENSO activity on multi-decadal to centennial timescales over the 
last 500 to 1 kyr (Figure 2.36). Different proxies show a wide spread 
in the specific timing and magnitude of events in the pre-instrumental 
period (e.g., Dätwyler et al., 2019). Most investigators find that ENSO 
 (a) Reconstructed SAM varialbility Southern Annular Mode reconstructions over the last millenium
 (b) Observed SAM varialbility2
0
−2
−4
4
2
0
−2
−4
1900 1920 1940 1960 1980 2000 20201000 1200 1400 1600 1800 2000
20CRv2c
Marshall
NCEPFogtSummer 
Dätwyler et al., (2018)
Annual

Abram et al., (2014)Summer 
Villalba et al., (2012)SAM Index SAM Index
Figure 2.35  | Southern Annular Mode (SAM) reconstruction over the last 
millennium. (a)  SAM reconstructions as seven-year moving averages (thin lines) 
and 70-year LOESS filter (thick lines). (b) Observed SAM index during 1900–2019. 
Further details on data sources and processing are available in the chapter data table 
(Table 2.SM.1).

372
activity in recent decades was higher than the most recent centuries 
prior to the instrumental period. Grothe et al. (2019) also found that 
ENSO variance of the last 50 years was 25% higher than the average 
of the last millennium, and was substantially higher  than the 
average of the mid- to late-Holocene. S. McGregor et al., (2010, 2013) 
looked for common variance changes in pre-existing ENSO proxies, 
finding stronger ENSO variance for the 30-year period 1979–2009 
compared to any 30-year period within the timespan 1590–1880 CE. 
This finding also holds when adding more recently developed ENSO 
proxies (Figure 2.36). Koutavas and Joanides (2012), Ledru et  al. 
(2013) and Thompson et al. (2017) identify various periods within the 
range 1000 BCE to 1300 CE when ENSO activity was greater than 
in the following centuries, and more closely comparable to the mid-
20th century onwards behaviour.

Since AR5, updates to datasets used widely in prior ENSO assessments 
resulted in substantial and important revisions to observed tropical 
Pacific SST data (Section 2.3.1.1). In particular, ERSSTv4, and then ERSSTv5, addressed known SST biases in ERSSTv3 in the equatorial 
Pacific which affected the derived mean state and amplitude of indices 
based on that dataset (Huang et al., 2015). During the instrumental 
period, there is no robust indication of any significant century-scale 
trend in the east-west SST gradient across the equatorial Pacific 
Ocean, with periods when gradients have been stronger and weaker 
than the long-term average on decadal timescales, associated 
with a predominance of La Niña or El Niño events respectively. The 
frequency of El Niño and La Niña events is also subject to considerable 
decadal variability (e.g.,  Hu et  al., 2013) but with no indication of 
a long-term signal in the frequency of events. The ENSO amplitude

since 1950 has increased relative to the 1910–1950 period, as 
confirmed by independent proxy records (e.g.,  Gergis and Fowler, 
2009), the Southern Oscillation Index (SOI) (Braganza et al., 2009) and 
SSTs (e.g., Ohba, 2013; Yu and Kim, 2013), although there is a spread 
between different proxy and instrumental sources as to the magnitude 
of that increase (Figure 2.36). The El Niño events of 1982–1983, 
1997–1998 and 2015–2016 had the strongest anomalies in the Niño 
3.4 SST index since 1950. Their predominance was less evident from 
indices based on de-trended data such as the Oceanic Niño Index (ONI) 
(which still ranked them as the three strongest events since 1950, but 
only by a small margin), and in the SOI. B. Huang et al. (2019a) also 
note that analyses based upon buoy and Argo data, which are only 
available since the 1990s, are more capable of resolving strong events 
than analyses which do not include such data.

Prior to the 1950s, SST observations in the tropical Pacific were much 
sparser and hence uncertainties in Niño indices are much larger 
(B. Huang et al., 2020). SOI data and some newer SST-based studies 
show high ENSO amplitude, comparable to the post-1950 period, in 
the period from the mid-late 19th century to about 1910, but proxy 
indicators generally indicate that the late 19th and early 20th century 
were less active than the late 20th century (Figure 2.36). Yu and Kim’s 
(2013) implementation of the ONI found a number of events with the 
ONI above 1.5°C between 1888 and 1905, then no such events until 
1972, whilst the SOI indicates comparable or stronger events to the 
three strongest post-1950 events in 1896 and 1905. Giese and Ray 
(2011) also found a number of such events between 1890 and 1920 
in the SODA ocean reanalysis, corroborated further by B. Huang et al. 
(2020) and Vaccaro et  al. (2021), who found that the strength of

the 1877–1878 event was comparable with that of the 1982–1983, 
1997–1998 and 2015–2016 events. There have also been a number of 
strong La Niña events (e.g., 1973–1974, 1975–1976 and 2010–2011), 
with few clear analogues in the 1920–1970 period; the proxy-based 
analysis of McGregor et  al. (2010) indicates that the mid-1970s 
La Niña period was also extreme in a multi-centennial context. There 
is no indication that the frequency of high-amplitude events since the 
1970s reflects a long-term trend which can be separated from multi-
decadal variability, given apparent presence of several high-amplitude 
events in the late 19th and early 20th centuries, and the relatively 
large uncertainty in pre-1950 SST data in the tropical Pacific region. 
There is a distinction (Annex IV.2.3.1) between El Niño events 
centred in the eastern Pacific (‘Eastern Pacific’ (EP) or ‘classical’ 
events) and those centred in the Central Pacific (‘Central Pacific’ (CP)

or ‘Modoki’ events), which have different typical teleconnections 
(e.g., Ashok et al., 2007; Ratnam et al., 2014; Capotondi et al., 2015; (a)ENSO variance ratio - reconstructed 
ENSO variance ratio - historical(b)2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0Variance ratio Variance ratioProxy mean
Niño3.4
SOIProxy mean
Individual data sets
90th percentile
10th percentile
1910 1930 1950 1970 1990 20101200 1100 1300 1400 1500 1600 1700 1800 1900 2000
Figure 2.3 6 | Reconstructed and historical variance ratio of El Niño–
Southern Oscillation (ENSO). (a) 30-year running variance of the reconstructed 
annual mean Niño 3.4 or related indicators from various published reconstructions. 
(b) Variance of June–November Southern Oscillation Index (SOI) and April–March 
mean Niño 3.4 (1981–2010 base period) along with the mean reconstruction from 
(a). Further details on data sources and processing are available in the chapter data 
table (Table 2.SM.1).

373
Timmermann et  al., 2018). A number of studies, using a range of 
indicators, have found an increase in recent decades of the fraction 
of  CP El Niño events, particularly after 2000 (Yu and Kim, 2013; 
Lübbecke and McPhaden, 2014; Pascolini-Campbell et  al., 2015; 
Jiang and Zhu, 2018). Johnson (2013) found that the frequency of 
CP El Niño events had increased (although not significantly) over the 
1950–2011 period, being accompanied by a significant increase in 
the frequency of La Niña events with a warm (as opposed to cool) 
western Pacific warm pool. A coral-based reconstruction starting 
in 1600 CE (Freund et  al., 2019) found that the ratio of CP to EP 
events in the last 30 years was substantially higher than at any other 
time over the last 400 years. Variations in the proportion of CP and 
EP events have also been found in earlier periods, with Carré et al. 
(2014) finding a period of high CP activity around 7 ka.

There is no robust indication  of any changes in ENSO teleconnections 
over multi-centennial timescales (Hernández et al., 2020) despite multi-
decadal variability. Shi and Wang (2018) found that teleconnections 
with the broader Asian summer monsoon, including the Indian and 
the East Asian monsoon, were generally stable since the 17th century 
during the developing phase of the monsoon, and showed substantial 
decadal variability, but no clear trend, during the decaying phase. 
They also found that the weakening of teleconnections between the 
Indian summer monsoon and ENSO in recent decades had numerous 
precedents over the last few centuries. Räsänen et  al. (2016) also 
found substantial decadal variability, but little trend, in the strength 
of the relationship between ENSO and monsoon precipitation in 
South East Asia between 1650 and 2000. Dätwyler et  al. (2019) 
identified a number of multi-decadal periods with apparently changed

teleconnections at times over the last 400 years. 
In the instrumental period, teleconnections associated with ENSO 
are well known to vary on decadal to multi-decadal timescales 
(e.g., He et al., 2013; Lee and Ha, 2015; Ashcroft et al., 2016; Jin et al., 
2016; Wang et al., 2019). Yun and Timmermann (2018) found that 
decadal variations in teleconnections between ENSO and the 
Indian monsoon did not extend beyond what would be expected 
from a  stochastic process. Many observed decadal changes in 
teleconnections in the instrumental period are consistent with a shift 
to more central Pacific El Niño events (Evtushevsky et  al., 2018; 
Yeh et al., 2018; Yu and Sun, 2018; Zhao and Wang, 2019). Effects 
of the PDV (Kwon et  al., 2013; S. Wang et  al., 2014; Dong  et  al., 
2018) and the AMV (Kayano et  al., 2019) can also modulate 
ENSO teleconnections, and affect the frequency of CP versus EP 
events (Ashok et al., 2007). Chiodi and Harrison (2015) found that

teleconnections over the most recent decades are broadly consistent 
with those over the last 100 years. Variability in teleconnections can 
also occur on timescales longer than characteristic PDV timescales 
(e.g., Gallant et al., 2013). 
In summary, there is medium confidence  that both ENSO amplitude 
and the frequency of high-magnitude events since 1950 are higher 
than over the period from 1850 and possibly as far back as 1400, 
but low confidence  that they are outside the range of variability over 
periods prior to 1400, or higher than the average of the Holocene 
as a whole. Overall, there is no indication of a recent sustained shift 
in ENSO or associated features such as the Walker Circulation, or in teleconnections associated with these, being beyond the range of 
variability on decadal to millennial timescales. A high proportion of 
El Niño events in the last 20–30 years have been based in the central, 
rather than eastern Pacific, but there is low confidence  that this

represents a long-term change. 
2.4.3 Indian Ocean Basin and Dipole Modes
The AR5 did not provide an assessment of the Indian Ocean Dipole 
(IOD) records based on paleo reconstructions. For the instrumental 
era, AR5 reported that there were no trends in the IOD behaviour. 
However, the strength of the Indian Ocean Basin-wide (IOB) mode, 
quantified by the basin-scale averaged SST index, increased in all 
assessed periods except 1979–2012, but the AR5 neither quantified 
trends nor provided a confidence statement. 
For the LGM, enhanced equatorial Indian Ocean productivity 
in marine sediment records was associated with strengthened 
westerly jets, in line with a shallower central-western Indian Ocean 
thermocline and stronger negative IOD events (Punyu et al., 2014). 
The LGM Indian Ocean basin was substantially modified by the 
exposure of the tropical shelves (DiNezio et  al., 2018) and this 
has been associated with an Indian Ocean ‘El Niño‘ (Thirumalai

et  al., 2019). Wurtzel et  al. (2018) contend that during the LDT, 
including the Younger Dryas event, Indian Ocean precipitation did 
not mirror the zonal asymmetry observed in Indian Ocean SSTs 
in the Holocene based upon a speleothem record from Sumatra, 
but instead reflected shifts in moisture transport pathways and 
sources. Using Seychelles corals, representing the western pole 
of the IOD, spanning from the MH to present, Zinke et al. (2014) 
identified changes in seasonality, with the lowest seasonal SST 
range in the MH and then again around 2 ka, while the largest 
seasonal range occurred around 4.6 ka and then again during 
the near-present (1990–2003). Reconstructions from fossil corals 
for the eastern Indian Ocean point to stronger negative IOD SST 
anomalies due to the enhanced upwelling and cooling driven by 
a stronger monsoon with enhanced anomalous easterly winds in 
the eastern Indian Ocean during the MH (Abram et  al., 2020b).

Niedermeyer et al. (2014) from the analysis of stable isotopes in 
terrestrial plant waxes, suggest that the period 6.5 ka to 4.5 ka was 
characterized by an anomalously positive IOD mean state. During 
various parts of the Holocene, periods of a mean positive IOD-like 
state were associated with increased IOD variability, including 
events that exceed the magnitude of the strongest events during 
the instrumental period (Abram et al., 2020a).
From the coral δ18O record from the Seychelles over 1854–1994, 
Du  et  al. (2014) showed a 3–7 year dominant period associated 
with the IOB in response to ENSO forcing. They identified multi-
decadal variability in the IOB, with more active IOB phases during 
1870–1890, 1930–1955, and 1975–1992, while decadal variability in 
the IOB dominated during 1940–1975 (Du et al., 2014). Evidence  for 
changes in IOB characteristics during earlier periods (e.g., MH, LGM) 
is limited .

374
The role of decadal to multi-decadal variability has recently emerged 
as an important aspect of the IOD with many indications of the effects 
of Pacific Ocean processes on IOD variability through atmospheric 
and oceanic mechanisms (Dong et  al., 2016; Krishnamurthy and 
Krishnamurthy, 2016; Zhou et  al., 2017; Jin et  al., 2018). Positive 
events in the 1960s and 1990s were linked to a relatively shallow 
eastern Indian Ocean thermocline, and a primarily negative IOD 
state in the 1970s and 1980s was related to a deeper thermocline 
(Ummenhofer et al., 2016). Positive IOD events may have increased 
in frequency during the second half of the 20th century (Abram et al., 
2020a, b). Earlier observations of apparent changes in the frequency 
and/or magnitude of the IOD events are considered unreliable, 
particularly prior to the 1960s (Hernández et  al., 2020). Although the seasonal evolution and the type of ENSO (Section 2.4.2) may

influence the character of the IOD (Guo et al., 2015; Zhang et al., 
2015; Fan et al., 2016), the occurrence of some IOD events may be 
independent of ENSO (Sun et al., 2015). 
To summarize, there is low confidence  in any multi-decadal IOD 
variability trend in the instrumental period due to data uncertainties 
especially before the 1960s. In addition to data uncertainties, 
understanding of the IOB variability during the instrumental period is 
also limited by large-scale warming of the Indian Ocean. Neither the 
IOD nor the IOB have exhibited behaviour outside the range implied 
by proxy records ( low confidence ).
Interannual Climate Indices 
0.4
0.2
0.0
−0.2
−0.4
0.6
0.3
0.0
−0.3
−0.6
−0.9 2.0
1.0
0.0
−1.0
1.2
0.8
0.4
0.0
−0.4
−0.80.8
0.4
0.0
−0.4
−0.8
−1.2IOB (°C)
IOD (°C)Niño 3.4 (°C)
AMM (°C)AZM (°C)
1960 1980 2000 2020COBE  ERSST  HADI  KAPL  OISST

Figure 2.37 | Indices of interannual climate variability from 1950–2019 based upon several sea surface temperature data products.  Shown are the following 
indices from top to bottom: IOB mode, IOD, Niño3.4, AMM and AZM. All indices are based on area-averaged annual data (see Annex IV). Further details on data sources and 

375
2.4.4  Atlantic Meridional (AMM) and Zonal 
Modes (AZM)
The AR5 reported no changes in the Atlantic Meridional Mode 
(AMM) during the 20th century or shorter periods thereof. For the Atlantic Zonal Mode (AZM), also referred as the Atlantic Niño, 
the AR5 reported increases during the 1950–2012 period but neither 
assessed trends nor provided a confidence statement. The AR5 did not assess paleo evidence for the AZM and AMM.
Paleo-reconstructions of these two modes remain rather limited. 
Nonetheless, the interhemispheric cross-equatorial SST gradients linked 
to changes in ITCZ locations characteristic of the AMM has been found during the LGM, Heinrich Stadial 1, and the MH, with the largest shift occurring during HS1 (McGee et al., 2014). Similarly, the dipole-like SST 
pattern in the South Atlantic subtropics, which is related to the AZM

(Venegas et al., 1996; Morioka et al., 2011; Nnamchi et al., 2016, 2017; Lübbecke et al., 2018; Rouault et al., 2018; Foltz et al., 2019), has been reconstructed using SST proxies from marine sediment cores during the past 12 kyr (Wainer et al., 2014). The reconstructed index captures 
two significant cold events that occurred during the 12.9–11.6 ka and 
8.6–8.0 ka periods in the South Atlantic (Wainer et al., 2014). 
During the observational period the AZM and AMM (Figure 2.37) are 
related to the AMV largely controlling the interhemispheric gradient 
of the SST at decadal to multi-decadal timescales (Tokinaga and 
Xie, 2011; Polo et  al., 2013; Svendsen et  al., 2014a; Li et  al., 2015; Lübbecke et  al., 2018). The AZM interannual variability is enhanced (Lübbecke et  al., 2018; Foltz et  al., 2019), and is more strongly related to ENSO, during the negative phase of the AMV (Martín-Rey 
et al., 2014, 2018; Polo et al., 2015; Nnamchi et al., 2020). The AZM

displayed a persistent weakened variability over the 1960–2009 period associated with declined cold tongue upwelling (Tokinaga and Xie, 2011), which became pronounced since 2000 (Prigent et al., 2020a, 
b). Despite these multi-decadal fluctuations, there is limited evidence  
for any sustained change in the AMM (Chang et  al., 2011; Martín-
Rey et  al., 2018) and AZM (Martín-Rey et  al., 2018; Nnamchi et  al., 2020) during the instrumental period. The AZM and AMM interact on interannual timescales (Servain et al., 1999; Foltz and McPhaden, 
2010; Pottapinjara et al., 2019) leading in 2009 to extremes of both 
modes in which the negative phase of the AMM (Foltz et al., 2012; Burmeister et al., 2016) preceded an equatorial cold tongue cold event that was unprecedented in the prior 30 years (Foltz and McPhaden, 2010; Burmeister et al., 2016).
In summary, confidence is low in any sustained changes to the AZM 
and AMM variability in instrumental observations. There is very low

confidence in changes of the paleo AZM and AMM due to extremely 
limited availability of paleo reconstructions.
2.4.5  P acific Decadal Variability (PDV)
Pacific Decadal Variability (PDV) refers to the ocean-atmosphere climate variability over the Pacific Ocean at decadal-to-interdecadal time scales and is usually described by the Pacific Decadal Oscillation 
(PDO) or the Inter-decadal Pacific Oscillation (IPO) indices. The AR5 
and SROCC reported a large shift of the PDO in the late 1970s, with a  predominantly positive phase until the end of the 1990s, being mainly negative afterwards. There was no significant change assessed in the PDO during the instrumental period as a whole, and no confidence level was assigned. Changes in the pre-instrumental 
era PDO were not assessed in AR5. 
The existence of the PDV in the centuries prior to the instrumental 
period is evidenced by a variety of proxy records based on tree rings

(Biondi et al., 2001; D’Arrigo and Ummenhofer, 2015), corals (Felis 
et al., 2010; Deng et al., 2013; Linsley et al., 2015) and sediments (Lapointe et al., 2017; O’Mara et al., 2019). There is little coherence between the various paleo-proxy indices prior to the instrumental record, and neither these nor the instrumental records provide 
indications of a clearly defined spectral peak (Chen and Wallace, 2015; 
M. Newman et al., 2016; Henley, 2017; L. Zhang et al., 2018; Buckleyet al., 2019). For instance, spectral analysis from millennia length PDV reconstructions shows spectral peaks at multi-decadal, centennial and bi-centennial time scales (Beaufort and Grelaud, 2017), while 
only multi-decadal oscillations can be detected in the shorter (less

than 400 years into the past) paleoclimate reconstructions. A variety of proxies suggest a shift in the PDV from the early-mid Holocene, which was characterized by a persistently negative phase of the PDO (i.e., weak Aleutian Low), to the late Holocene, and more variable and 
more positive PDO (i.e., strong Aleutian Low) conditions. This shift at 
around 4.5 ka is also evident in the PDO periodicities, changing from bidecadal and pentadecadal variability in the early Holocene to only pentadecadal periodicities in the late Holocene (Hernández et  al., 
2020). Several proxy records indicate that the strengthening in the 
Aleutian Low inferred since the late 17th century is unprecedented over the last millennium (Z. Liu et al., 2017; Osterberg et al., 2017; Winski et al., 2017), in line with an increase in PDV low-frequency variability (Williams et al., 2017; Hernández et al., 2020).
The PDO and IPO indices are significantly correlated during the

instrumental period, showing regime shifts in the 1920s, 1940s, 1970s and around 1999. Positive PDV phases were observed from the 1920s to the mid-1940s and from the late 1970s to the late 1990s, while 
negative phases occurred from mid-1940s until the late 1970s, and 
since 1999 (Figure 2.38; Han et al., 2014; Chen and Wallace, 2015; M. Newman et al., 2016). The associated spatial patterns are quitesimilar, but the PDO pattern exhibits stronger SST anomalies in theextra-tropical North Pacific than the IPO (Chen and Wallace, 2015).
The strength and structure of the SST patterns also differ among the
periods (M. Newman et al., 2016).
Instrumental observations are sparse prior to 1950, and thus 
the fidelity of any PDV index derived for the second part of the 
19th  century and early decades of the 20th century is relatively low (Figure 2.38; Deng et  al., 2013; Wen et  al., 2014). This results in low agreement in the classification of the PDO/IPO phase among

several indices, even during recent years with the availability of high-
quality data. Nevertheless, the teleconnection patterns are robust 
regardless of the index used to characterize the PDO (McAfee, 2017). Analysis of time series of PDO and IPO highlights the (multi-) decadal nature  of this mode of variability with no significant trends, but highlights a recent switch from a positive to a negative phase since 
1999/2000 across all indicators (England et al., 2014; Henley, 2017).

376
In summary, the PDV in the instrumental record is dominated by 
(multi-)decadal-scale shifts between positive and negative phases 
over the last 150 years with no overall trend ( high confidence ). There 
is low confidence  in paleo-PDV reconstructions due to discrepancies 
among the various available time series in terms of phasing and 
timing. However, there is high confidence  in the occurrence of a shift 
from predominantly negative to positive PDO conditions from the 
middle to the late Holocene. 
2.4.6 Atlantic Multi-decadal Variability
The AR5 reported no robust changes in Atlantic Multi-decadal Variability 
(AMV) reconstructions based on paleo records due to low consistency 
between different AMV reconstructions prior to 1900. AR5 concluded 
that there have been no significant trends in the AMV index during the 
instrumental period and there was difficulty in interpreting the AMV

signal because of the long-term underlying SST warming trend. 
The AR5 conclusions about large uncertainties in AMV paleo 
reconstructions (Hernández et  al., 2020) have been reinforced by 
recent studies of tree rings (J. Wang et  al., 2017b), Greenland ice 
(Chylek et  al., 2012), and corals (Kilbourne et  al., 2014; Svendsen 
et al., 2014b; J. Wang et al., 2017b). The AMV exhibited a generally 
positive state over the first millennium of the CE (Mann et al., 2009; 
Singh et  al., 2018). Paleo reconstructions over the last millennium 
consistently show a negative AMV phase during 1400–1850 CE and 
a positive phase during 900–1200 CE (Mann et al., 2009; J. Wang 
et  al., 2017b; Singh et  al., 2018), consistent with warmer surface 
temperatures from tropical Atlantic records (Kilbourne et al., 2014). Instrumental observations show that AMV is characterized by 
basin-wide warm and cool periods with an average variation in

SST of about 0.4°C, but with larger variations in the North Atlantic 
subpolar gyre. Despite small differences in indices used to define the 
AMV (Annex IV), they all show warm periods occurring approximately 
between 1880–1900, 1940–1960, and from the mid-1990s to 
present, with cool periods in between (Figure 2.38) but no overall 
sustained change during the instrumental period (Booth et al., 2012; 
Gulev et al., 2013; Bellomo et al., 2018).
The oceanic changes are seen in salinity and temperature variations 
over the upper 3000 m of the North Atlantic (Polyakov et al., 2005; 
Keenlyside et al., 2015), and in sea level variations in the western 
North Atlantic along the Gulf Stream passage (McCarthy et  al., 
2015). The pattern and strength of the AMV differs among the 
periods (e.g.,  Svendsen et  al., 2014b; Reynolds et  al., 2018) and 
there are indications that there may have been a shift since 2005 
toward a negative phase of the AMV (Robson et al., 2016).

In summary, no sustained change in AMV indices has been 
observed over the instrumental period ( high confidence ). However, 
instrumental records may not be long enough to distinguish any 
oscillatory behaviour from trends in the AMV. There is low confidence  
in the paleo AMV reconstructions due to a paucity of high-resolution 
records. 
2.5 Final Remarks 
The assessment in this chapter is based upon an ever-expanding 
volume of available proxy and observational records and a growing 
body of literature. Since AR5, improvements have been made in all 
aspects of data collection, data curation, data provision and data 
analysis permitting improved scientific insights. The chapter has 
also benefitted from the availability of new products from a range 
of emerging, mainly space-based, observing capabilities and new 
generations of reanalysis products. However, a number of key 
challenges still remain which, if addressed, would serve to strengthen 
future reports.

• Development of new techniques and exploitation of existing 
and new proxy sources may help address challenges around 
the low temporal resolution of most paleoclimate proxy records, 
particularly prior to the Common Era, and ambiguities around 
converting paleoclimate proxy data into estimates of climate-
relevant variables. Conversions rely upon important proxy-specific 
assumptions and biases can be large due to limited accounting 
of seasonality, non-climatic effects, or the influence of multiple 
climate variables. These challenges currently limit the ability to 
ascertain the historical unusualness of recent directly observed 
climate changes for many indicators. {2.2, 2.3}
• Improved sharing of historical instrumental and proxy records 
(including metadata) along with significant efforts at data rescue 
of presently undigitized records would serve to significantly 
strengthen many aspects of the present assessment. Longer

observational time series help to better understand variability, 
and any underlying periodicity in climate indicators and climate Multi-decadal climate indices
0.6
0.3
0.0
−0.3
−0.6
0.6
0.3
0.0
−0.3
−0.6AMV (°C) PDV (°C)
1860 1890 1920 1950 1980 2010COBE  ERSST  HADI  KAPL 
Figure 2 .38 | Indices of multi-decadal climate variability from 1854–2019 
based upon several sea surface temperature data products.  Shown are the 
indices of the AMV and PDV based on area averages for the regions indicated in 
Annex IV . Further details on data sources and processing are available in the chapter 

377
forcers, especially for components of the climate system where 
the dominant response is on multi-decadal and longer time scales. Targeted rescue of early marine data records, especially 
from waters of the tropical oceans and the Southern Ocean, could 
help constrain modes of variability and important teleconnection changes in, for example, the hydrological cycle and patterns of global temperature change. {2.2, 2.3, 2.4}
•Contemporary observing systems have limitations in parts of theEarth system with limited accessibility or coverage, for examplecoastal and shallow ocean, polar regions, marine biosphere,
and the deep sea, leading to seasonal-dependency and regional
variability in data coverage and uncertainty which serve to limitconfidence in the present assessment of changes. {2.3}

•Contradictory lines of evidence exist between observations andmodels on the relationship between the rates of warming in GMST and GSAT, compounded by limitations in theoretical understanding. 
Improvements in air temperature datasets over the ocean and an
improved understanding of the representation of the lowermostatmosphere over the ocean in models would reduce uncertainty in assessed changes in GSAT. {Cross-Chapter Box 2.3}
The authors of Chapter 2 would like to extend their gratitude to Sara Veasey, Deborah Riddle, Deborah Misch (all NOAA NCEI, USA) and Robert Fawcett (BoM, Australia) for providing substantial 
support in the production and finalization of all figures. We wish 
to acknowledge Raphael Neukom (University of Bern, Switzerland) and Michael Bosilovich (NASA/GFSC, USA) for their assistance in the provision of data.

378
Frequently Asked Questions
FAQ 2.1 | The Earth’s Temperature Has Varied Before. How Is the Current Warming Any Different?
Earth’s climate has always changed naturally, but both the global extent and rate of recent warming are unusual. 
The recent warming has reversed a slow, long-term cooling trend, and research indicates that global surface 
temperature is higher now than it has been for millennia.
While climate can be characterized by many variables, temperature is a key indicator of the overall climate  state, and 
global surface temperature is fundamental to characterizing and understanding global climate change, including 
Earth’s energy budget. A rich variety of geological evidence shows that temperature has changed throughout 
Earth’s history. A variety of natural archives from around the planet, such as ocean and lake sediments, glacier

ice and tree rings, shows that there were times in the past when the planet was cooler, and times when it was 
warmer. While our confidence in quantifying large-scale temperature changes generally decreases the farther 
back in time we look, scientists can still identify at least four major differences between the recent warming and 
those of the past.
It’s warming almost everywhere.  During decades and centuries of the past 2000 years, some regions warmed 
more than the global average while, at the same time, other regions cooled. For example, between the 10th 
and 13th centuries, the North Atlantic region warmed more than many other regions. In contrast, the pattern of 
recent surface warming is globally more uniform than for other decadal to centennial climate fluctuations over 
at least the past two millennia.
It’s warming rapidly.  Over the past 2 million years, Earth’s climate has fluctuated between relatively warm

interglacial periods and cooler glacial periods, when ice sheets grew over vast areas of the northern continents. 
Intervals of rapid warming coincided with the collapse of major ice sheets, heralding interglacial periods such as 
the present Holocene Epoch, which began about 12,000 years ago. During the shift from the last glacial period 
to the current interglacial, the total temperature increase was about 5°C. That change took about 5000 years, 
with a maximum warming rate of about 1.5°C per thousand years, although the transition was not smooth. In 
contrast, Earth’s surface has warmed approximately 1.1°C since 1850–1900. However, even the best reconstruction 
of global surface temperature during the last deglacial period is too coarsely resolved for direct comparison with 
a period as short as the past 150 years. But for the past 2000 years, we have higher-resolution records that show

that the rate of global warming during the last 50 years has exceeded the rate of any other 50-year period.
Recent warming reversed a long-term global cooling trend.  Following the last major glacial period, global 
surface temperature peaked by around 6500 years ago, then slowly cooled. The long-term cooling trend was 
punctuated by warmer decades and centuries. These fluctuations were minor compared with the persistent and 
prominent warming that began in the mid-19th century when the millennial-scale cooling trend was reversed. 
It’s been a long time since it’s been this warm.  Averaged over the globe, surface temperatures of the past decade 
were probably warmer than when the long cooling trend began around 6500 years ago. If so, we need to look 
back to at least the previous interglacial period, around 125,000 years ago, to find evidence for multi-centennial 
global surface temperatures that were warmer than now.

Previous temperature fluctuations were caused by large-scale natural processes, while the current warming is 
largely due to human causes (see, for example, FAQ 1.3, FAQ 3.1). But understanding how and why temperatures 
have changed in the past is critical for understanding the current warming and how human and natural 
influences will interact to determine what happens in the future. Studying past climate changes also makes it 
clear that, unlike previous climate changes, the effects of recent warming are occurring on top of stresses that 
make humans and nature vulnerable to changes in ways that they have never before experienced (for example, 
see FAQ 11.2, FAQ12.3).

379
FAQ 2.1 (continued)
FAQ 2.1, Figure 1 | Evidence for the unusualness of recent warming.
FAQ 2.1: How is this global warming         
different to before?
Climate has always changed, but warming like that of recent 
decades has not been seen for millennia or longer.
It has been a long time 
since it has been this warmIt is warming rapidlyIt is warming almost everywhere
The warming reversed 
a long-term cooling

380
FAQ 2.2 | What Is the Evidence for Climate Change?
The evidence for climate change rests on more than just increasing surface temperatures. A broad range of 
indicators collectively leads to the inescapable conclusion that we are witnessing rapid changes to many aspects 
of our global climate. We are seeing changes in the atmosphere, ocean, cryosphere, and biosphere. Our scientific 
understanding depicts a coherent picture of a warming world.
We have long observed our changing climate. From the earliest scientists taking meteorological observations 
in the 16th and 17th centuries to the present, we have seen a revolution in our ability to observe and diagnose 
our changing climate. Today we can observe diverse aspects of our climate system from space, from aircraft 
and weather balloons, using a range of ground-based observing technologies, and using instruments that can

measure to great depths in the ocean. 
Observed changes in key indicators point to warming over land areas. Global surface temperature over land has 
increased since the late 19th century, and changes are apparent in a variety of societally relevant temperature 
extremes. Since the mid-1950s the troposphere (i.e., the lowest few km of the atmosphere) has warmed, and 
precipitation over land has increased. Near-surface specific humidity (i.e., water vapour) over land has  increased 
since at least the 1970s. Aspects of atmospheric circulation have also evolved since the mid-20th century, including 
a poleward shift of mid-latitude storm tracks.
Changes in the global ocean point to warming as well. Global average sea surface temperature has increased 
since the late 19th century. The heat content of the global ocean has increased since the 19th century, with more 
than 90% of the excess energy accumulated in the climate system being stored in the ocean. This ocean warming

has caused ocean waters to expand, which has contributed to the increase in global sea level in the past century. 
The relative acidity of the ocean has also increased since the early 20th century, caused by the uptake of carbon 
dioxide from the atmosphere, and oxygen loss is evident in the upper ocean since the 1970s.
Significant changes are also evident over the cryosphere – the portion of the Earth where water is seasonally or 
continuously frozen as snow or ice. There have been decreases in Arctic sea ice area and thickness and changes 
in Antarctic sea ice extent since the mid-1970s. Spring snow cover in the Northern Hemisphere has decreased 
since the late-1970s, along with an observed warming and thawing of permafrost (perennially frozen ground). 
The Greenland and Antarctic ice sheets are shrinking, as are the vast majority of glaciers worldwide, contributing 
strongly to the observed sea level rise.

Many aspects of the biosphere are also changing. Over the last century, long-term ecological surveys show that 
many land species have generally moved poleward and to higher elevations. There have been increases in green 
leaf area and/or mass (i.e., global greenness) since the early 1980s, and the length of the growing season has 
increased over much of the extratropical Northern Hemisphere since at least the mid-20th century. There is also 
strong evidence that various phenological metrics (such as the timing of fish migrations) for many marine species 
have changed in the last half century.
Change is apparent across many components of the climate system. It has been observed using a very broad range 
of techniques and analysed independently by numerous groups around the world. The changes are consistent in 
pointing to a climate system that has undergone rapid warming since the industrial revolution.

381
FAQ 2.2 (continued)
FAQ 2.2, Figure 1 | Synthesis of significant changes observed in the climate system over the past several decades. Upwards, downwards 
and circling arrows indicate increases, decreases and changes, respectively. Independent analyses of many components of the climate system that would be 
expected to change in a warming world exhibit trends consistent with warming. Note that this list is not comprehensive. 
Air temperature
(in the lowest
few km)Atmospheric circulation
Water 
vapour
Surface 
temperature
Species 
range shiftsOcean heat 
contentSea level
Sea surface 
temperature Species 
ranges shifts
Ocean 
acidification
Growing 
season lengthGlobal 
greenness
PrecipitationGlaciersIce sheets Snow coverPermafrost 
extent
Arctic sea iceFAQ 2.2: What is the evidence for climate change?
Taken together, observed changes in the 
climate system since the late 19th century 
reveal an unequivocally  warming world
AIR

425
Human Influence on the Climate System  Chapter 33
The evidence for human influence on recent climate change 
strengthened from the IPCC Second Assessment Report to the IPCC Fifth 
Assessment Report, and is now even stronger in this assessment. The 
IPCC Second Assessment Report (SAR, 1995) concluded ‘the balance 
of evidence suggests that there is a discernible human influence on 
global climate’. In subsequent assessments (TAR, 2001; AR4, 2007; and 
AR5, 2013), the evidence for human influence on the climate system 
was found to have progressively strengthened. The AR5 concluded 
that human influence on the climate system is  clear, evident from 
increasing greenhouse gas concentrations in the atmosphere, positive 
radiative forcing, observed warming, and physical understanding of 
the climate system. This chapter updates the assessment of human 
influence on the climate system for large-scale indicators of climate

change, synthesizing information from paleo records, observations and 
climate models. It also provides the primary evaluation of large-scale 
indicators of climate change in this Report, complemented by fitness-
for-purpose evaluation in subsequent chapters.
Synthesis Across the Climate System
atmosphere, ocean and land since pre-industrial times. Combining 
the evidence from across the climate system increases the level of 
confidence in the attribution of observed climate change to human 
influence and reduces the uncertainties associated with assessments 
based on single variables. Large-scale indicators of climate change in 
the atmosphere, ocean, cryosphere and at the land surface show clear 
responses to human influence consistent with those expected based 
on model simulations and physical understanding. {3.8.1}
For most large-scale indicators of climate change, the

simulated recent mean climate from the latest generation 
Coupled Model Intercomparison Project Phase 6 (CMIP6) 
climate models underpinning this assessment has improved 
compared to the Coupled Model Intercomparison Project 
Phase 5 (CMIP5) models assessed in AR5 ( high confidence ). 
Some differences from observations remain, for example in regional 
precipitation patterns. High-resolution models exhibit reduced 
biases in some but not all aspects of surface and ocean climate 
(medium confidence ), and most Earth system models, which include 
biogeochemical feedbacks, perform as well as their lower-complexity 
counterparts ( medium confidence ). The multi-model mean captures 
most aspects of observed climate change well ( high confidence ). The 
multi-model mean captures the proxy-reconstructed global-mean 
surface air temperature (GSAT) change during past high- and low-CO 2 
climates ( high confidence ) and the correct sign of temperature and

precipitation change in most assessed regions in the mid-Holocene 
(medium confidence ). The simulation of paleoclimates on continental 
scales has improved compared to AR5 ( medium confidence ), but 
models often underestimate large temperature and precipitation 
differences relative to the present day ( high confidence ). {3.8.2}
1 In this chapter, ‘greenhouse gases’ refers to well-mixed greenhouse gases.
2 In this chapter, ‘main driver’ means responsible for more than 50% of the change.Human Influence on the Atmosphere and Surface
The likely  range of human-induced warming in global-mean 
surface air temperature (GSAT) in 2010–2019 relative to 
1850–1900 is 0.8°C–1.3°C, encompassing the observed warming 
of 0.9°C–1.2°C, while the change attributable to natural forcings 
is only −0.1°C to +0.1°C.  The best estimate of human-induced 
warming is 1.07°C. Warming can now be attributed since 1850–1900, 
instead of since 1951 as done in AR5, thanks to a better understanding

of uncertainties and because observed warming is larger. The likely  
ranges for human-induced GSAT and global mean surface temperature 
(GMST) warming are equal ( medium confidence ). Attributing 
observed warming to specific anthropogenic forcings remains more 
uncertain. Over the same period, forcing from greenhouse gases1 
likely  increased GSAT by 1.0°C–2.0°C, while other anthropogenic 
forcings including aerosols likely  decreased GSAT by 0.0°C–0.8°C. It is 
very likely  that human-induced greenhouse gas increases were the 
main driver2 of tropospheric warming since comprehensive satellite 
observations started in 1979, and extremely likely  that human-induced 
stratospheric ozone depletion was the main driver of cooling in the 
lower stratosphere between 1979 and the mid-1990s. {3.3.1}
The CMIP6 model ensemble reproduces the observed 
historical global surface temperature trend and variability 
with biases small enough to support detection and attribution

of human-induced warming ( very high confidence ). The CMIP6 
historical simulations assessed in this report have an ensemble mean 
global surface temperature change within 0.2°C of the observations 
over most of the historical period, and observed warming is within the 
5–95% range of the CMIP6 ensemble. However, some CMIP6 models 
simulate a warming that is either above or below the assessed 5–95% 
range of observed warming. CMIP6 models broadly reproduce surface 
temperature variations over the past millennium, including the cooling 
that follows periods of intense volcanism ( medium confidence ). For 
upper air temperature, there is medium confidence  that most CMIP5 
and CMIP6 models overestimate observed warming in the upper 
tropical troposphere by at least 0.1°C per decade over the period 
1979 to 2014. The latest updates to satellite-derived estimates of 
stratospheric temperature have resulted in decreased differences

between simulated and observed changes of global mean temperature 
through the depth of the stratosphere ( medium confidence ). {3.3.1}
The slower rate of GMST increase observed over 1998–2012 
compared to 1951–2012 was a temporary event followed 
by a strong GMST increase ( very high confidence ). Improved 
observational datasets since AR5 show a larger GMST trend over 
1998–2012 than earlier estimates. All the observed estimates of 
the 1998–2012 GMST trend lie within the 10th–90th percentile 
range of CMIP6 simulated trends ( high confidence ). Internal 
variability, particularly Pacific Decadal Variability, and variations in 
solar and volcanic forcings partly offset the anthropogenic surface 
warming trend over the 1998–2012 period ( high confidence ). Global 
ocean heat content continued to increase throughout this period, 
indicating continuous warming of the entire climate system ( very

426
Chapter 3 Human Influence on the Climate System3
high confidence ). Since 2012, GMST has warmed strongly, with the 
past five years (2016–2020) being the warmest five-year period 
in the instrumental record since at least 1850 ( high confidence ). 
{Cross-Chapter Box 3.1, 3.3.1; 3.5.1}
It is likely  that human influence has contributed to3 moistening 
in the upper troposphere since 1979.  Also, there is medium 
confidence  that human influence contributed to a global increase 
in annual surface specific humidity, and medium confidence  that it 
contributed to a decrease in surface relative humidity over mid-latitude 
Northern Hemisphere continents during summertime. {3.3.2}
It is likely  that human influence has contributed to observed 
large-scale precipitation changes since the mid-20th century.  
New attribution studies strengthen previous findings of a detectable 
increase in Northern Hemisphere mid- to high-latitude land

precipitation ( high confidence ). Human influence has contributed to 
strengthening the zonal mean precipitation contrast between the wet 
tropics and dry subtropics ( medium confidence ). Yet, anthropogenic 
aerosols contributed to decreasing global land summer monsoon 
precipitation from the 1950s to 1980s ( medium confidence ). There 
is also medium confidence  that human influence has contributed 
to high-latitude increases and mid-latitude decreases in Southern 
Hemisphere summertime precipitation since 1979 associated with 
the trend of the Southern Annular Mode toward its positive phase. 
Despite improvements, models still have deficiencies in simulating 
precipitation patterns, particularly over the tropical ocean ( high 
confidence ). {3.3.2, 3.3.3, 3.5.2}
Human-induced greenhouse gas forcing is the main driver of 
the observed changes in hot and cold extremes on the global 
scale ( virtually certain ) and on most continents ( very likely ).

It is likely  that human influence, in particular due to greenhouse 
gas forcing, is the main driver of the observed intensification of 
heavy precipitation in global land regions during recent decades. 
There is high confidence  in the ability of models to capture the 
large-scale spatial distribution of precipitation extremes over land. 
The magnitude and frequency of extreme precipitation simulated 
by CMIP6 models are similar to those simulated by CMIP5 models 
(high confidence ). {Cross-Chapter Box 3.2}
It is likely  that human influence has contributed to the poleward 
expansion of the zonal mean Hadley cell in the Southern  
Hemisphere since the 1980s.  There is medium confidence  that 
the observed poleward expansion of the zonal mean Hadley cell in 
the Northern Hemisphere is within the range of internal variability. 
The causes of the observed strengthening of the Pacific Walker 
circulation since the 1980s are not well understood, and the observed

strengthening trend is outside the range of trends simulated in the 
coupled models ( medium confidence ). While CMIP6 models capture 
the general characteristics of the tropospheric large-scale circulation 
(high confidence ), systematic biases exist in the mean frequency of 
atmospheric blocking events, especially in the Euro-Atlantic sector, 
some of which reduce with increasing model resolution ( medium 
confidence ). {3.3.3}
3 In this chapter the phrase ‘human influence has contributed to’ an observed change means that the response to human influence is non-zero and consistent in sign with the observed change.Human Influence on the Cryosphere
It is very likely  that anthropogenic forcing, mainly due to 
greenhouse gas increases, was the main driver of Arctic sea 
ice loss since the late 1970s.  There is new evidence that increases 
in anthropogenic aerosols have offset part of the greenhouse 
gas-induced Arctic sea ice loss since the 1950s ( medium confidence ).

In the Arctic, despite large differences in the mean sea ice state, loss 
of sea ice extent and thickness during recent decades is reproduced 
in all CMIP5 and CMIP6 models ( high confidence ). By contrast, global 
climate models do not generally capture the small observed increase 
in Antarctic sea ice extent during the satellite era, and there is low 
confidence  in attributing the causes of this change. {3.4.1}
It is very  likely  that human influence contributed to the 
observed reductions in Northern Hemisphere spring snow cover 
since 1950.  The seasonal cycle in Northern Hemisphere snow cover is 
better reproduced by CMIP6 than by CMIP5 models ( high confidence ). 
Human influence was very likely  the main driver of the recent global, 
near-universal retreat of glaciers. It is very likely  that human influence 
has contributed to the observed surface melting of the Greenland 
Ice Sheet over the past two decades, and there is medium confidence

in an anthropogenic contribution to recent overall mass loss from the 
Greenland Ice Sheet. However, there is only limited evidence , with 
medium agreement , of human influence on Antarctic Ice Sheet mass 
balance through changes in ice discharge. {3.4.2, 3.4.3}
Human Influence on the Ocean
It is extremely likely  that human influence was the main driver 
of the ocean heat content increase observed since the 1970s, 
which extends into the deeper ocean ( very high confidence ). 
Since AR5, there is improved consistency between recent observed 
estimates and model simulations of changes in upper (<700 m) ocean 
heat content, when accounting for both natural and anthropogenic 
forcings. Updated observations and model simulations show 
that warming extends throughout the entire water column ( high 
confidence ), with CMIP6 models simulating 58% of industrial-era 
heat uptake (1850–2014) in the upper layer (0–700 m), 21% in 
the intermediate layer (700–2000 m) and 22% in the deep layer

(>2000 m). The structure and magnitude of multi-model mean ocean 
temperature biases have not changed substantially between CMIP5 
and CMIP6 ( medium confidence ). {3.5.1}
It is extremely likely  that human influence has contributed 
to observed near-surface and subsurface ocean salinity 
changes since the mid-20th century.  The associated pattern of 
change corresponds to fresh regions becoming fresher and salty 
regions becoming saltier ( high confidence ). Changes to the coincident 
atmospheric water cycle and ocean-atmosphere fluxes (evaporation 
and precipitation) are the primary drivers of the observed basin-scale 
salinity changes ( high confidence ). The observed depth-integrated 
basin-scale salinity changes have been attributed to human influence, 
with CMIP5 and CMIP6 models able to reproduce these patterns

427
only in simulations that include greenhouse gas increases ( medium 
confidence ). The basin-scale changes are consistent across models and 
intensify through the historical period ( high confidence ). The structure 
of the biases in the multi-model mean has not changed substantially 
between CMIP5 and CMIP6 ( medium confidence ). {3.5.2}
Combining the attributable contributions from glaciers, 
ice-sheet surface mass balance and thermal expansion, it is 
very likely  that human influence was the main driver of the 
observed global mean sea level rise since at least 1971.  Since 
AR5, studies have shown that simulations that exclude anthropogenic 
greenhouse gases are unable to capture the sea level rise due to 
thermal expansion (thermosteric) during the historical period 
and that model simulations that include all forcings (anthropogenic 
and natural) most closely match observed estimates. It is very likely

that human influence was the main driver of the observed global 
mean thermosteric sea level increase since 1970. {3.5.3, 3.5.1, 3.4.3}
While observations show that the Atlantic Meridional 
Overturning Circulation (AMOC) has weakened from the mid-
2000s to the mid-2010s ( high confidence ) and the Southern 
Ocean upper overturning cell has strengthened since the 
1990s ( low confidence ), observational records are too short 
to determine the relative contributions of internal variability, 
natural forcing, and anthropogenic forcing to these changes 
(high confidence ). No changes in Antarctic Circumpolar Current 
transport or meridional position have been observed. The mean zonal 
and overturning circulations of the Southern Ocean and the mean 
overturning circulation of the North Atlantic (the Atlantic Meridional 
Overturning Circulation, AMOC) are broadly reproduced by CMIP5 
and CMIP6 models. However, biases are apparent in the modelled

circulation strengths ( high confidence ) and their variability ( medium 
confidence ). {3.5.4}
Human Influence on the Biosphere
The main driver of the observed increase in the amplitude of 
the seasonal cycle of atmospheric CO 2 is enhanced fertilization 
of plant growth by the increasing concentration of atmospheric 
CO 2 (medium confidence ). However, there is only low confidence  
that this CO 2 fertilization has also been the main driver of observed 
greening because land management is the dominating factor in 
some regions. Earth system models simulate globally averaged 
land carbon sinks within the range of observation-based estimates 
(high confidence ), but global-scale agreement masks large regional 
disagreements. {3.6.1}
It is virtually certain  that the uptake of anthropogenic CO 2 
was the main driver of the observed acidification of the global 
surface open ocean.  The observed increase in CO 2 concentration

in the subtropical and equatorial North Atlantic since 2000 is likely  
associated in part with an increase in ocean temperature, a response 
that is consistent with the expected weakening of the ocean carbon 
sink with warming. Consistent with AR5 there is medium confidence  
that deoxygenation in the upper ocean is due in part to human influence. There is high confidence  that Earth system models simulate 
a realistic time evolution of the global mean ocean carbon sink. {3.6.2}
Human Influence on Modes of Climate Variability
It is very likely  that human influence has contributed to the 
observed trend towards the positive phase of the Southern 
Annular Mode (SAM) since the 1970s and to the associated 
strengthening and southward shift of the Southern 
Hemispheric extratropical jet in austral summer.  The influence of 
ozone forcing on the SAM trend has been small since the early 2000s 
compared to earlier decades, contributing to a weaker SAM trend

observed over 2000–2019 ( medium confidence ). Climate models 
reproduce the summertime SAM trend well, with CMIP6 models 
outperforming CMIP5 models ( medium confidence ). By  contrast, 
the cause of the Northern Annular Mode (NAM) trend towards its 
positive phase since the 1960s and associated northward shifts of 
the Northern Hemispheric extratropical jet and storm track in boreal 
winter is not well understood. Models reproduce the observed 
spatial features and variance of the SAM and NAM very well ( high 
confidence ). {3.3.3, 3.7.1, 3.7.2}
Human influence has not affected the principal tropical modes 
of interannual climate variability or their associated regional 
teleconnections beyond the range of internal variability ( high 
confidence ). Further assessment since AR5 confirms that climate and 
Earth system models are able to reproduce most aspects of the spatial 
structure and variance of the El Niño–Southern Oscillation and Indian

Ocean Basin and Dipole modes ( medium confidence ). However, despite 
a slight improvement in CMIP6, some underlying processes are still 
poorly represented. In the Tropical Atlantic basin, which contains the 
Atlantic Zonal and Meridional modes, major biases in modelled mean 
state and variability remain. {3.7.3 to 3.7.5}
There is medium confidence  that anthropogenic and volcanic 
aerosols contributed to observed changes in the Atlantic 
Multi-decadal Variability (AMV) index and associated regional 
teleconnections since the 1960s, but there is low confidence  
in the magnitude of this influence.  There is high confidence  that 
internal variability is the main driver of Pacific Decadal Variability 
(PDV) observed since pre-industrial times, despite some modelling 
evidence for potential human influence. Uncertainties remain in 
quantification of the human influence on AMV and PDV due to 
brevity of the observational records, limited model performance in

reproducing related sea surface temperature (SST) anomalies despite 
improvements from CMIP5 to CMIP6 ( medium confidence ), and 
limited process understanding of their key drivers. {3.7.6, 3.7.7}

428
3.1 Scope and Overview
This chapter assesses the extent to which the climate system has 
been affected by human influence and to what extent climate models 
are able to simulate observed mean climate, changes and variability. 
This assessment is the basis for understanding what impacts of 
anthropogenic climate change may already be occurring and informs 
our confidence in climate projections. Moreover, an understanding 
of the amount of human-induced global warming to date is key to 
assessing our status with respect to the Paris Agreement goals of 
holding the increase in global average temperature to well below 
2°C above pre-industrial levels and pursuing efforts to limit the 
temperature increase to 1.5°C (UNFCCC, 2016).
The evidence of human influence on the climate system has 
strengthened progressively over the course of the previous five IPCC 
assessments, from the Second Assessment Report that concluded ‘the

balance of evidence suggests a discernible human influence on climate’ 
through to the Fifth Assessment Report (AR5) which concluded that ‘it 
is extremely likely  that human influence caused more than half of the 
observed increase in global mean surface temperature (GMST) from 
1951 to 2010’ (see also Sections 1.3.4 and 3.3.1.1). The AR5 concluded 
that climate models had been developed and improved since the 
Fourth Assessment Report (AR4) and were able to reproduce many 
features of observed climate. Nonetheless, several systematic biases 
were identified (Flato et al., 2013). This chapter additionally builds on 
the assessment of attribution of global temperatures contained in the 
IPCC Special Report on Global Warming of 1.5°C (SR1.5; IPCC, 2018), 
assessments of attribution of changes in the ocean and cryosphere in the IPCC Special Report on the Ocean and Cryosphere in a Changing 
Climate (SROCC; IPCC, 2019b), and assessments of attribution of

changes in the terrestrial carbon cycle in the IPCC Special Report on 
Climate Change and Land (SRCCL, IPCC, 2019a).
This chapter assesses the evidence for human influence on 
observed large-scale indicators of climate change that are described 
in Cross-Chapter Box  2.2 and assessed in Chapter  2. It takes 
advantage of the longer period of record now available in many 
observational datasets. The assessment of the human-induced 
contribution to observed climate change requires an estimate of the 
expected response to human influence, as well as an estimate of the 
expected climate evolution due to natural forcings and an estimate of 
variability internal to the climate system (internal climate variability). 
For this we need high quality models, primarily climate and Earth 
system models. Since AR5, a new set of coordinated model results 
from the World Climate Research Programme (WCRP) Coupled Model 
Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016a) has

become available. Together with updated observations of large-scale 
indicators of climate change (Chapter  2), CMIP simulations are 
a key resource for assessing human influence on the climate system. 
Pre-industrial control and historical simulations are of most relevance 
for model evaluation and assessment of internal variability, and these 
simulations are evaluated to assess fitness-for-purpose for attribution, 
which is the focus of this chapter (see also Section 1.5.4). This chapter 
provides the primary evaluation of large-scale indicators of climate 
change in this Report, and is complemented by other fitness-for-
purpose evaluations in subsequent chapters. CMIP6 also includes an 
extensive set of idealized and single forcing experiments for attribution 
Chapter 3: Human influence on the climate system Chapter 3: Quick guide
Cross-chapter boxesSection 3.1
Scope and overview
Section 3.2
MethodsCarbon cycle
3.6.1 | 3.6.2 | 3.8.2
El Niño–Southern Oscillation  (ENSO)
3.7.3

Multivariate evaluation and attribution of climate change
3.8.1 | 3.8.2 
Ocean temperature and heat content
3.5.1
Precipitation, humidity and streamflow
3.3.2 | 3.8.2 | CC Box 3.2
Surface temperature
3.3.1.1 | 3.8.2 | CC Box 3.1 | CC Box 3.2 
Sea ice
3.4.1 | 3.8.2 
Sea level
3.5.3Section 3.8
Synthesis
CC Box 3.1
Early 21st century warming CC Box 3.2
ExtremesChapter 3 assesses human influence on the climate system and evaluates 
climate models on large scales.Key topics and corresponding sub-sections
Human 
influence 
on the...Section 3.3
...atmosphere 
and surfaceSection 3.4
...cryosphere
Section 3.7
...modes 
of variabilitySection 3.5
...oceanSection 3.6
...biosphere 
FAQs
Figure 3.1 |  Visual guide to Chapter 3.

429
(Eyring et al., 2016a; Gillett et al., 2016). In addition to the assessment 
of model performance and human influence on the climate system 
during the instrumental era up to the present-day, this chapter also 
includes evidence from paleo-observations and simulations over past 
millennia (Kageyama et al., 2018).
Whereas in previous IPCC assessment reports the comparison of 
simulated and observed climate change was done separately in a 
model evaluation chapter and a chapter on detection and attribution, 
in AR6 these comparisons are integrated together. This has the 
advantage of allowing a single discussion of the full set of explanations 
for any inconsistency in simulated and observed climate change, 
including missing forcings, errors in the simulated response to forcings, 
and observational errors, as well as an assessment of the application 
of detection and attribution techniques to model evaluation. Where

simulated and observed changes are consistent, this can be interpreted 
both as supporting attribution statements, and as giving confidence in 
simulated future change in the variable concerned (see also Box 4.1). 
However, if a model’s simulation of historical climate change has 
been tuned to agree with observations, or if the models used in an 
attribution study have been selected or weighted on the basis of the 
realism of their simulated climate response, this information would 
need to be considered in the assessment and any attribution results 
correspondingly tempered. An integrated discussion of evaluation and 
attribution supports such a robust and transparent assessment.
This chapter starts with a brief description of methods for detection 
and attribution of observed changes in Section 3.2, which builds on 
the more general introduction to attribution approaches in the Cross-
Working Group Box on Attribution in Chapter  1. In this chapter we

assess the detection of anthropogenic influence on climate on large 
spatial scales and long temporal scales, a concept related to, but 
distinct from, that of the emergence of anthropogenically-induced 
climate change from the range of internal variability on local scales 
and shorter time scales (Section 1.4.2.2). The following sections 
address the climate system component by component, in  each 
case assessing human influence and evaluating climate  models’ 
simulations of the relevant aspects of climate and climate  
change. This chapter assesses the evaluation and attribution of global, 
hemispheric, continental and ocean basin-scale indicators of climate 
change in the atmosphere and at the Earth’s surface (Section 3.3), 
cryosphere (Section 3.4), ocean (Section 3.5), and biosphere (Section 
3.6), and the evaluation and attribution of modes of variability 
(Section  3.7), the period of slower warming in the early 21st century

(Cross-Chapter Box 3.1) and large-scale changes in extremes (Cross-
Chapter Box 3.2). Model evaluation and attribution on sub-continental 
scales are not covered here, since these are assessed in the Atlas and 
in Chapter 10, and extreme event attribution is not covered since it is 
assessed in Chapter 11. Section 3.8 assesses multivariate attribution 
and integrative measures of model performance based on multiple 
variables, as well as process representation in different classes of 
models. The chapter structure is summarized in Figure 3.1.
3.2 Methods
New methods for model evaluation that are used in this chapter 
are described in Section 1.5.4. These include new techniques for process-based evaluation of climate and Earth system models against 
observations that have rapidly advanced since the publication of AR5 
(Eyring et al., 2019) as well as newly developed CMIP evaluation 
tools that allow a more rapid and comprehensive evaluation of the

models with observations (Eyring et al., 2016a, b).
In this chapter, we use the Earth System Model Evaluation Tool 
(ESMValTool, Eyring et al., 2020; Lauer et al., 2020; Righi et al., 2020) 
and the NCAR Climate Variability Diagnostic Package (CVDP , Phillips 
et al., 2014) that is included in the ESMValTool to produce most of 
the figures. This ensures traceability of the results and provides an 
additional level of quality control. The ESMValTool code to produce 
the figures in this chapter was released as open source software at 
the time of the publication of this Report (see details in the Chapter 
Data Table, Table SM.3.1). Figures in this chapter are produced either 
using one ensemble member from each model, or using all available 
ensemble members and weighting each simulation by 1/( NM i), where 
N is the number of models and Mi is the ensemble size of the ith 
model, prior to calculating means and percentiles. Both approaches

ensure that each model used is given equal weight in the figures, and 
details on which approach is used are provided in the figure captions.
An introduction to recent developments in detection and attribution 
methods in the context of this Report is provided in the Cross-
Working Group Box on Attribution in Chapter 1. Here we discuss new 
methods and improvements applicable to the attribution of changes in 
large-scale indicators of climate change which are used in this chapter.
3.2.1 Methods Based on Regression
Regression-based methods, also known as fingerprinting methods, 
have been widely used for detection of climate change and attribution 
of the change to different external drivers. Initially, these methods were 
applied to detect changes in global surface temperature, and were 
then extended to other climate variables at different time and spatial 
scales (e.g., Hegerl et al., 1996; Hasselmann, 1997; Allen and Tett,

1999; Gillett et al., 2003b; Zhang et al., 2007; Min et al., 2008a, 2011). 
These approaches are based on multivariate linear regression and 
assume that the observed change consists of a linear combination of 
externally forced signals plus internal variability, which generally holds 
for large-scale variables (Hegerl and Zwiers, 2011). The regressors 
are the expected space–time response patterns to different climate 
forcings (fingerprints), and the residuals represent internal variability. 
Fingerprints are usually estimated from climate model simulations 
following spatial and temporal averaging. A regression coefficient 
which is significantly greater than zero implies that a detectable 
change is identified in the observations. When the confidence interval 
of the regression coefficient includes unity and is inconsistent with 
zero, the magnitude of the model simulated fingerprints is assessed 
to be consistent with the observations, implying that the observed

changes can be attributed in part to a particular forcing. Variants 
of linear regression have been used to address uncertainty in the 
fingerprints due to internal variability (Allen and Stott, 2003) as well 
as structural model uncertainty (Huntingford et al., 2006).
In order to improve the signal-to-noise ratio, observations and 
model-simulated responses are usually normalized by an estimate 
of internal variability derived from climate model simulations. This

430
procedure requires an estimate of the inverse covariance matrix of 
the internal variability, and some approaches have been proposed 
for more reliable estimation of this (Ribes et al., 2009). A signal can 
be spuriously detected due to too-small noise, and hence simulated 
internal variability needs to be evaluated with care. Model-simulated 
variability is typically checked through comparing modelled variance 
from unforced simulations with the observed residual variance using 
a standard residual consistency test (Allen and Tett, 1999), or an 
improved one (Ribes and Terray, 2013). Imbers et al. (2014) tested 
the sensitivity of detection and attribution results to different 
representations of internal variability associated with short-memory 
and long-memory processes. Their results supported the robustness 
of previous detection and attribution statements for the global mean

temperature change but they also recommended the use of a wider 
variety of robustness tests.
Some recent studies focused on the improved estimation of the 
scaling factor (regression coefficient) and its confidence interval. 
Hannart et al. (2014) described an inference procedure for scaling 
factors which avoids making the assumption that model error and 
internal variability have the same covariance structure. An integrated 
approach to optimal fingerprinting was further suggested in which all 
uncertainty sources (i.e., observational error, model error, and internal 
variability) are treated in one statistical model without a preliminary 
dimension reduction step (Hannart, 2016). Katzfuss et al. (2017) 
introduced a similar integrated approach based on a  Bayesian 
model averaging. On the other hand, DelSole et al. (2019) suggested 
a bootstrap method to better estimate the confidence intervals of 
scaling factors even in a weak-signal regime. It is notable that some

studies do not optimize fingerprints, as uncertainty in the covariance 
introduces a further layer of complexity, but results in only a limited 
improvement in detection (Polson and Hegerl, 2017).
Another fingerprinting approach uses pattern similarity between 
observations and fingerprints, in which the leading empirical 
orthogonal function obtained from the time-evolving multi-model 
forced simulation is usually defined as a fingerprint (e.g., Santer 
et al., 2013; Marvel et al., 2019; Bonfils et al., 2020). Observations and 
model simulations are then projected onto the fingerprint to measure 
the degree of spatial pattern similarity with the expected physical 
response to a given forcing. This projection provides the signal time 
series, which is in turn tested against internal variability, as estimated 
from long control simulations. As a way to extend this pattern-based 
approach to a high-dimensional detection variable at daily time

scales, Sippel et al. (2019, 2020) proposed using the relationship 
pattern with a global climate change metric as a fingerprint. To solve 
the high-dimensional regression problem which makes regression 
coefficients not well constrained, they incorporated a statistical 
learning technique based on a regularized linear regression, which 
optimizes a global warming signal by giving lower weight to regions 
with large internal variability.
3.2.2 Other Probabilistic Approaches
Considering the difficulty in accounting for climate modelling 
uncertainties in the regression-based approaches, Ribes et al. (2017) 
introduced a new statistical inference framework based on an additivity assumption and likelihood maximization, which estimates 
climate model uncertainty based on an ensemble of opportunity and 
tests whether observations are inconsistent with internal variability 
and consistent with the expected response from climate models. The

method was further developed by Ribes et al. (2021), who applied 
it to narrow the uncertainty range in the estimated human-induced 
warming. Hannart and Naveau (2018), on the other hand, extended 
the application of standard causal theory (Pearl, 2009) to the context 
of detection and attribution by converting a time series into an event, 
and calculating the probability of causation, an approach which 
maximizes the causal evidence associated with the forcing. On the 
other hand, Schurer et al. (2018) employed a Bayesian framework 
to explicitly consider climate modelling uncertainty in the optimal 
regression method. Application of these approaches to attribution of 
large-scale temperature changes supports a dominant anthropogenic 
contribution to the observed global warming.
Climate change signals can vary with time and discriminant analysis 
has been used to obtain more accurate estimates of time-varying 
signals, and has been applied to different variables such as seasonal

temperatures (Jia and DelSole, 2012) and the South Asian monsoon 
(Srivastava and DelSole, 2014). The same approach was applied 
to separate aerosol forcing responses from other forcings (X. Yan 
et al., 2016) and results using climate model output indicated 
that detectability of the aerosol response is maximized by using 
a  combination of temperature and precipitation data. Paeth et al. 
(2017) introduced a detection and attribution method applicable for 
multiple variables based on a discriminant analysis and a Bayesian 
classification method. Finally, a systematic approach has been 
proposed to translating quantitative analysis into a description of 
confidence in the detection and attribution of a climate response 
to anthropogenic drivers (Stone and Hansen, 2016).
Overall, these new fingerprinting and other probabilistic methods for 
detection and attribution as well as efforts to better incorporate the 
associated uncertainties have addressed a number of shortcomings in

previously applied detection and attribution techniques. They further 
strengthen the confidence in attribution of observed large-scale 
changes to a combination of external forcings as assessed in the 
following sections.
3.3 Human Influence on the Atmosphere 
and Surface
3.3.1 Temperature
3.3.1.1 Surface Temperature
Surface temperature change is the aspect of climate in which the 
climate research community has had most confidence over past IPCC 
assessment reports. This confidence comes from the availability of longer 
observational records compared to other indicators, a large response to 
anthropogenic forcing compared to variability in the global mean, and 
a strong theoretical understanding of the key thermodynamics driving 
its changes (Collins et al., 2010; Shepherd, 2014). The AR5 assessed 
that it was extremely likely  that human activities had caused more than

431
half of the observed increase in global mean surface temperature from 
1951 to 2010, and virtually certain  that internal variability alone could 
not account for the observed global warming since 1951 (Bindoff et al., 
2013). The AR5 also assessed with very high confidence  that climate 
models reproduce the general features of the global-scale annual 
mean surface temperature increase over 1850–2011 and with high 
confidence  that models reproduce global and Northern Hemisphere 
temperature variability on a wide range of time scales (Flato et al., 
2013). This section assesses the performance of the new generation 
CMIP6 models (see Table AII.5) in simulating the patterns, trends, and 
variability of surface temperature, and the evidence from detection 
and attribution studies of human influence on large-scale changes in 
surface temperature.
3.3.1.1.1 Model evaluation

To be fit for detecting and attributing human influence on globally-
averaged surface temperatures, climate models need to represent, based 
on physical principles, both the response of surface temperature  to 
external forcings and the internal variability in surface temperature 
over various time scales. This section assesses the performance of 
those aspects in the latest generation CMIP6 climate models. See 
Section 3.8 for evaluation at continental scales, Chapter 10 for model 
evaluation in the context of regional climate information, and the Atlas 
for region-by-region assessments of model performance.
Reconstructions of past temperature from paleoclimate proxies 
(Section 2.3.1.1 and Cross-Chapter Box  2.1) have been used to 
evaluate modelled past climate temperature change patterns. The 
AR5 found that CMIP5 (Taylor et al., 2012) models were able to 
reproduce the large-scale patterns of temperature during the Last

Glacial Maximum (LGM) (Flato et al., 2013) and simulated a polar 
amplification broadly consistent with reconstructions for warm 
(Pliocene and Eocene) and cold (LGM) periods (Masson-Delmotte 
et al., 2013). Since AR5, a better understanding of temperature 
proxies and their uncertainties and in some cases the forcing applied 
to model simulations has led to better agreement between models 
and reconstructions over a wide range of past climates. For the 
Pliocene and Eocene warm periods, understanding of uncertainties in 
temperature proxies (Hollis et al., 2019; McClymont et al., 2020) and 
the boundary conditions used in climate simulations (Haywood et al., 
2016; Lunt et al., 2017) has improved, and some models now agree 
better with temperature proxies for these time periods compared 
to models assessed in AR5 (Sections 7.4.4.1.2, 7.4.4.2.2 and Cross-
Chapter Box 2.4; Zhu et al., 2019; Haywood et al., 2020; Lunt et al.,

2021). For the Last Interglacial (LIG), improved temporal resolution of 
temperature proxies (Capron et al., 2017) and better appreciation of 
the importance of freshwater forcing (Stone et al., 2016) have clarified 
the reasons behind apparent model-data inconsistencies. Regional 
LIG temperature responses simulated by CMIP6 are within the 
uncertainty ranges of reconstructed temperature responses, except 
in regions where unresolved changes in regional ocean circulation, 
meltwater, or vegetation changes may cause model mismatches 
(Otto-Bliesner et al., 2021). For the LGM, the CMIP5 and CMIP6 
ensembles compare similarly to new sea surface temperature (SST) 
and surface air temperature (SAT) proxy reconstructions (Figure 3.2a; 
Cleator et al., 2020; Tierney et al., 2020b). The very cold CMIP6 LGM simulation by the Community Earth System Model Version 2.1 
(CESM2.1) is an exception related to the high equilibrium climate

sensitivity (ECS) of that model (Section 7.5.6; Kageyama et al., 2021a; 
Zhu et al., 2021). Figure 3.2a illustrates the wide range of simulated 
global LGM temperature responses in both ensembles. CMIP6 models 
tend to underestimate the cooling over land, but agree better with 
oceanic reconstructions. For the mid-Holocene, the regional biases 
found in CMIP5 simulations are similar to those in pre-industrial and 
historical simulations (Harrison et al., 2015; Ackerley et al., 2017), 
suggesting common causes. CMIP5 models underestimate Arctic 
warming in the mid-Holocene (Yoshimori and Suzuki, 2019). CMIP6 
models simulate a mid-latitude, subtropical, and tropical cooling 
compared to the pre-industrial period, whereas temperature proxies 
indicate a warming (see Section 2.3.1.1.2; Brierley et al., 2020; 
Kaufman et al., 2020), although accounting for seasonal effects in the 
proxies may reduce the discrepancy (Bova et al., 2021). Over the past

millennium, reconstructed and simulated temperature anomalies, 
internal variability, and forced response agree well over Northern 
Hemisphere continents, but those statistics disagree strongly in 
the Southern Hemisphere, where models seem to overestimate 
the response (PAGES 2k-PMIP3 group, 2015). That disagreement is 
partly explained by the lower quality of the reconstructions in the 
Southern Hemisphere, but model and/or forcing errors may also 
contribute (Neukom et al., 2018). Figure  3.2b shows that land/sea 
warming contrast behaves coherently in model simulations across 
multiple periods, with a slight non-linearity in land warming due to 
a smaller contribution of snow cover to temperature response in 
warmer climates. A multivariate assessment of paleoclimate model 
simulations is carried out in Section 3.8.2.
For the historical period, AR5 assessed with very high confidence  
that CMIP5 models reproduced observed large-scale mean surface

temperature patterns, although errors of several degrees appear in 
elevated regions, like the Himalayas and Antarctica, near the edge 
of the sea ice in the North Atlantic, and in upwelling regions. This 
assessment is updated here for the CMIP6 simulations. Figure  3.3 
shows the annual mean surface air temperature at 2 m for the 
CMIP5 and CMIP6 multi-model means, both compared to the fifth 
generation European Centre for Medium-Range Weather Forecasts 
(ECMWF) atmospheric reanalysis (ERA5; Section 1.5.2) for the 
period 1995–2014. The distribution of biases is similar in CMIP5 and 
CMIP6 models, as already noted by several studies (Crueger et al., 
2018; Găinuşă-Bogdan et al., 2018; Kuhlbrodt et al., 2018; Lauer 
et al., 2018). Arctic temperature biases seem more widespread in 
both ensembles than assessed at the time of AR5. The fundamental 
causes of temperature biases remain elusive, with errors in clouds 
(Lauer et al., 2018), ocean circulation (Kuhlbrodt et al., 2018), winds

(Lauer et al., 2018), and surface energy budget (Hourdin et al., 2015; 
Séférian et al., 2016; Găinuşă-Bogdan et al., 2018) being frequently 
cited candidates. Increasing horizontal resolution shows promise for 
decreasing long-standing biases in surface temperature over large 
regions (Bock et al., 2020). Panels e and f of Figure 3.3 show that 
biases in the mean High-Resolution Model Intercomparison Project 
(HighResMIP , Haarsma et al., 2016) models (see also Table AII.6) are 
smaller than those in the mean of the corresponding lower-resolution 
versions of the same models simulating the same period (see also 
Section 3.8.2.2). However, the bias reduction is modest (Palmer and

432
 (c) Volca nic forcing and reconstruct ed and modelled G MST over the past m illennium151050510152025Temperature anomaly overland (°C)Last Glacial Maximum
mid-HoloceneLast Interglacialmid-Piacenzian Warm PeriodEarly Eocene Clima tic Optimum
1pctCO2abrupt4xCO2Time periods :
Instrumental
Reconstr uctionObser vation s:CMI P 6
CMI P5
non-CMI PMulti-model mean s:CMIP6
CMIP5
non-CMIPIndiv idual models:Fit to  data:
Temperature anomaly over oceans (°C)Temperature anomaly over oceans on reconstruction data pts (°C)Temperature anomaly over land on reconstruction data pts (°C)
-10 0 10 20(b) G lobal temperature anomaly ov er 
land and ocean for a range of climates(a) Last Glacial Maximum reconstruct ed and  modelled 
land and ocean t ropica l tem perature anomaly  
Figure 3.2 |  Changes in surface temperature for different paleoclimates.  (a) Comparison of reconstructed and modelled surface temperature anomalies for the Last

Glacial Maximum over land and ocean in the Tropics (30°N–30°S). Land-based reconstructions are from Cleator et al. (2020). Ocean-based reconstructions are from Tierney 
et al. (2020b). Model anomalies are calculated as the difference between Last Glacial Maximum and pre-industrial control simulations of the PMIP3 and PMIP4 ensembles, 
sampled at the reconstruction data points. (b) Land–sea contrast in global mean surface temperature change for different paleoclimates. Small symbols show individual model 
simulations from the CMIP5 and CMIP6 ensembles. Large symbols show ensemble means and assessed values. (c) Upper panel shows time series of volcanic radiative forcing, 
in W m−2, as used in the CMIP5 (Gao et al., 2008; Crowley and Unterman, 2013; see also Schmidt et al., 2011) and CMIP6 (850 CE to 1900 CE from Toohey and Sigl (2017),

1850–2015 from Luo (2018)). The forcing was calculated from the stratospheric aerosol optical depth at 550 nm shown in Figure 2.2. Lower panel shows time series of global 
mean surface temperature anomalies, in °C, with respect to 1850–1900 for the CMIP5 and CMIP6 past1000 simulations and their historical continuation simulations. Simulations 
are coloured according to the volcanic radiative forcing dataset they used. The median reconstruction of temperature from PAGES 2k Consortium (2019) is shown in black, the 
5–95% confidence interval is shown by grey lines and the grey envelopes show the 1st, 5th, 15th, 25th, 35th, 45th, 55th, 65th, 75th, 85th, 95th, and 99th percentiles. All data 
in both panels are band-passed filtered, where frequencies longer than 20 years have been retained. Further details on data sources and processing are available in the chapter 
data table (Table 3.SM.1).

433
Stevens, 2019). In addition, the biases of the limited number of models 
participating in HighResMIP are not entirely representative of overall 
CMIP6 biases, especially in the Southern Ocean, as  indicated by 
comparing panels b and f of Figure 3.3.
The AR5 assessed with very high confidence  that models reproduce 
the general history of the increase in global-scale annual mean 
surface temperature since the year 1850, although AR5 also reported 
that an observed reduction in the rate of warming over the period 
1998–2012 was not reproduced by the models (Cross-Chapter 
Box 3.1; Flato et al., 2013). Figure 3.2c and Figure 3.4 show time series 
of anomalies in annually and globally averaged surface temperature 
simulated by CMIP5 and CMIP6 models for the past millennium 
and the period 1850 to 2020, respectively, with the baseline set 
to 1850–1900 (see Section 1.4.1). As also indicated by Figure 3.4,

the spread in simulated absolute temperatures is large (Palmer and 
Stevens, 2019). However, the discussion is based on temperature 
anomaly time series instead of absolute temperatures because our 
focus is on evaluation of the simulation of climate change in these 
models, and also because anomalies are more uniformly distributed 
and are more easily deseasonalized to isolate long-term trends (see 
Section 1.4.1). CMIP6 models broadly reproduce surface temperature 
variations over the past millennium, including the cooling that follows 
periods of intense volcanism ( medium confidence ) (Figure  3.2c). 
Simulated GMST anomalies are well within the uncertainty range 
of temperature reconstructions ( medium confidence ) since about 
the year 1300, except for some short periods immediately following 
large volcanic eruptions, for which simulations driven by different 
forcing datasets disagree (Figure 3.2c). Before the year 1300, larger

disagreements between models and temperature reconstructions 
are expected because forcing and temperature reconstructions are 
increasingly uncertain further back in time, but specific causes have 
not been identified conclusively (Ljungqvist et al., 2019; PAGES 2k 
Consortium, 2019) ( medium confidence ). For the historical period, 
results for CMIP6 shown in Figure 3.4 suggest that the qualitative 
history of surface temperature increase is well reproduced, including 
the increase in warming rates beginning in the 1960s and the 
temporary cooling that follows large volcanic eruptions.
Although virtually all CMIP6 modelling groups report improvements 
in their model’s ability to simulate current climate compared to the 
CMIP5 version (Gettelman et al., 2019; Golaz et al., 2019; Mauritsen 
et al., 2019; Swart et al., 2019; Voldoire et al., 2019b; T. Wu et al., 
2019b; Bock et al., 2020; Boucher et al., 2020; Dunne et al., 2020), it

does not necessarily follow that the simulation of temperature trends 
is also improved (Bock et al., 2020; Fasullo et al., 2020). The CMIP6 
multi-model ensemble encompasses observed warming and the 
multi-model mean tracks those observations within 0.2°C over most 
of the historical period. Figure 3.4 confirms the findings of Papalexiou 
et al. (2020), who highlighted based on 29 CMIP6 models that most 
models replicate the period of slow warming  between 1942 and 
1975 and the late twentieth century warming (1975–2014). The 
CMIP6 multi-model mean is cooler over the period 1980–2000 than 
both observations and CMIP5 (Figure  3.4; Bock et al., 2020; Flynn 
and Mauritsen, 2020; Gillett et al., 2021). Biases of several tenths 
of a degree in some CMIP6 models over that period may be due 
to an overestimate in aerosol radiative forcing (Sections 6.3.5 and 7.3.3, and Figure 6.8; Andrews et al., 2020; Dittus et al., 2020; Flynn

and Mauritsen, 2020). Papalexiou et al. (2020), Tokarska et al. (2020) 
and Stolpe et al. (2021) all report that CMIP6 models on average 
overestimate warming from the 1970s or 1980s to the 2010s, 
although quantitative conclusions depend on which observational 
dataset is compared against (see also Table 2.4). However, Figure 3.4, 
which includes a larger number of models than available to those 
studies, indicates that the CMIP6 multi-model mean tracks observed 
warming better than the CMIP5 multi-model mean after the year 
2000. The CMIP6 multi-model mean GSAT warming between 1850–
1900 and 2010–2019 and associated 5–95% range is 1.09 [0.66 
to 1.64]  °C. Cross-Chapter Box  2.3 assessed GSAT warming over 
the same period at 1.06 [0.88 to 1.21] °C. So some CMIP6 models 
simulate a warming that is smaller than the assessed observed range, 
and other CMIP6 models simulate a warming that is larger. That 
overestimated warming may be an early symptom of overestimated

ECS in some CMIP6 models (Section 7.5.6; Meehl et al., 2020; Schlund 
et al., 2020), and has implications for projections of GSAT changes 
(Chapter 4; Liang et al., 2020; Nijsse et al., 2020; Tokarska et al., 2020; 
Ribes et al., 2021). In some models, a large ECS and a strong aerosol 
forcing lead to too large a mid-20th century cooling followed by 
overestimated warming rates in the late 20th century when aerosol 
emissions decrease (Golaz et al., 2019; Flynn and Mauritsen, 2020). 
Temperature biases are driven by both model physics and prescribed 
forcing, which is a challenge for model development.
Chylek et al. (2020) argue that CMIP5 models overestimate the 
temperature response to volcanic eruptions. Lehner et al. (2016), 
Rypdal (2018) and Stolpe et al. (2021) point instead to missed 
compensating effects on surface temperature change associated 
with internal variability in the El Niño–Southern Oscillation (ENSO)

or the Atlantic Multi-decadal Oscillation (AMO). An alternative view 
sees those ENSO and AMO responses as expressions of changes in 
climate feedbacks driven by the geographical pattern of SST changes 
(Andrews et al., 2018). At least one model is able to reproduce such 
pattern effects (Gregory and Andrews, 2016). Errors in the volcanic 
forcing prescribed in simulations, including for CMIP6 (Rieger et al., 
2020), also introduce differences with the observed temperature 
response, independently of the quality of the model physics. In 
addition, comparisons of the modelled temperature  response  
to large eruptions over the past millennium to temperature 
reconstructions based on tree rings show a much better agreement 
(Lücke et al., 2019; F . Zhu et al., 2020) than comparisons to the annual, 
multi-temperature proxy reconstructions shown in Figure 3.2c. These 
considerations, and Figures 3.2c and 3.4, suggest that CMIP6 models

do not systematically overestimate the cooling that follows large 
volcanic eruptions (see also Cross-Chapter Box 4.1).
When interpreting model simulations of historical temperature change, 
it is important to keep in mind that some models are tuned towards 
representing the observed trend in global mean surface temperature 
over the historical period (Hourdin et al., 2017). In Figure  3.4 the 
CMIP6 models that are documented to have been tuned to reproduce 
observed warming, typically by tuning aerosol forcing or factors 
that influence the model’s ECS, are marked with an asterisk. Such 
tuning of a model can strongly impact its temperature projections 
(Mauritsen and Roeckner, 2020). However, Bock et al. (2020) reported

434
No robust bias Robust bias Conflicting signals Colour No robust model improvement
Figure 3.3 | Annual mean near-surface (2 m) air temperature (°C) for the period 1995–2014. (a)  Multi-model (ensemble) mean constructed with one realization of 
the CMIP6 historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the fifth 
generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5). (c) Multi-model mean of the root mean square 
error calculated over all months separately and averaged, with respect to the climatology from ERA5. (d) Multi-model mean bias defined as the difference between the CMIP6 
multi-model mean and the climatology from ERA5. The difference between the multi-model mean of (e) high-resolution and (f) low-resolution simulations of four HighResMIP

models and the climatology from ERA5 is also shown. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of 
models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal, 
where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater 
than the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. 
Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding 
low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

435
that there is no statistically significant difference in multi-model 
mean GSAT between the models that had been tuned based on 
observed warming compared to those which had not. Moreover, 
only two of thirteen models used for the Detection and Attribution 
Model Intercomparison Project (DAMIP) simulations on which CMIP6 
attribution studies are based were tuned towards historical warming 
(Bock et al., 2020; Gillett et al., 2021). Further, tuning is done on 
globally averaged quantities, so does not substantially change the 
spatio-temporal pattern of response on which many regression-based attribution studies are based (Bock et al., 2020). Therefore, we 
assess with high confidence  that the tuning of a small number of 
CMIP6 models to observed warming has not substantially influenced 
attribution results assessed in this chapter.
The reliance of detection and attribution studies on climate models

(see  Section 3.2) requires that those models simulate realistic 
statistics of internal variability on multi-decadal time scales. An 
incorrect estimate of variability in models would affect confidence 
****Reference period
Reference periodGlobal mean surface air temperature
****
*Figure 3.4 |  Observed and simulated time series of the anomalies in annual and global mean surface air temperature (GSAT).  All anomalies are differences 
from the 1850–1900 time-mean of each individual time series. The reference period 1850–1900 is indicated by grey shading. (a) Single simulations from CMIP6 models (thin 
lines) and the multi-model mean (thick red line). Observational data (thick black lines) are from the Met Office Hadley Centre/Climatic Research Unit dataset (HadCRUT5), 
and are blended surface temperature (2 m air temperature over land and sea surface temperature over the ocean). All models have been subsampled using the HadCRUT5

observational data mask. Vertical lines indicate large historical volcanic eruptions. CMIP6 models which are marked with an asterisk are either tuned to reproduce observed 
warming directly, or indirectly by tuning equilibrium climate sensitivity. Inset: GSAT for each model over the reference period, not masked to any observations. (b) Multi-model 
means of CMIP5 (blue line) and CMIP6 (red line) ensembles and associated 5th to 95th percentile ranges (shaded regions). Observational data are HadCRUT5, Berkeley Earth, 
National Oceanic and Atmospheric Administration NOAAGlobalTemp-Interim and Kadow et al. (2020). Masking was done as in (a). CMIP6 historical simulations were extended 
with SSP2-4.5 simulations for the period 2015–2020 and CMIP5 simulations were extended with RCP4.5 simulations for the period 2006–2020. All available ensemble

members were used (see Section 3.2). The multi-model means and percentiles were calculated solely from simulations available for the whole time span (1850–2020). Figure 
is updated from Bock et al. (2020), their Figures 1 and 2. CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ . Further details on data sources and processing are available 
in the chapter data table (Table 3.SM.1).

436
in the conclusions from detection and attribution. The AR5 found 
that CMIP5 models simulate realistic variability in global-mean 
surface temperature on decadal time scales, with variability on 
multi-decadal time scales being more difficult to evaluate because 
of the short observational record (Flato et al., 2013). Since AR5, new 
work has characterized the contributions of variability in different 
ocean areas to SST variability, with tropical modes of variability like 
ENSO dominant on time scales of five to ten years, while longer time 
scales see the variance maxima move poleward to the North Atlantic, 
North Pacific, and Southern oceans (Monselesan et al., 2015). There 
may, however, be sizeable, two-way interdependencies between 
ENSO and sea surface temperature variability in different basins 
(Kumar et al., 2014; Cai et al., 2019), and ENSO’s influence on global

surface temperature variability may not be confined only to decadal 
time scales (Triacca et al., 2014). Studies based on large ensembles 
of 20th and 21st century climate change simulations confirm that 
internal variability has a substantial influence on global warming 
trends over periods shorter than 30–40 years (Kay et al., 2015; Dai 
and Bloecker, 2019). Although the equatorial Pacific seems to be the 
main source of internal variability on decadal time scales, Brown 
et al. (2016a) linked diversity in modelled oceanic convection, sea 
ice, and energy budget in high-latitude regions to overall diversity in 
modelled internal variability.
Interest in internal variability since the publication of AR5 stems in 
part from its importance in understanding the slower global surface temperature warming over the early 21st century (see Cross-Chapter 
Box 3.1). Evidence coming mostly from paleo studies is mixed on 
whether CMIP5 models underestimate decadal and multi-decadal

variability in global mean temperature. Schurer et al. (2013) found 
good agreement between internal variability derived from paleo 
reconstructions, estimated as the fraction of variance that is not 
explained by forced responses, and modelled variability, although 
the subset of CMIP5 models they used may have been associated 
with larger variability than the full CMIP5 ensemble. PAGES 2k 
Consortium (2019) found that the largest 51-year trends in both 
reconstructions of global mean temperature and fully forced climate 
simulations over the period 850 to 1850 were almost identical. Zhu 
et al. (2019) showed agreement in the modelled and reconstructed 
temporal spectrum of global surface temperatures on annual to 
multi-millennial time scales. However, they suggest that decadal- to 
centennial variability is partly forced by slow orbital changes that 
predate the last millennium. This is consistent with Gebbie and 
Huybers (2019), who showed that the deep ocean has been out

of equilibrium over that period. Laepple and Huybers (2014) found 
good agreement between modelled and proxy-derived decadal 
ocean temperature variability, but underestimates of variance by 
models by at least a factor of ten at centennial time scales because 
models underestimate the difference between the warm and cold 
periods of the last millennium. Parsons et al. (2020) found that some 
CMIP6 models exhibit much higher multi-decadal variability in GSAT 
than CMIP5 models, with indications that variability in these models Figure 3.5 | The standard deviation of annually averaged zonal-mean near-surface air temperature. This is shown for four detrended observed temperature 
datasets (HadCRUT5, Berkeley Earth, NOAAGlobalTemp-Interim and Kadow et al. (2020), for the years 1995-2014) and 59 CMIP6 pre-industrial control simulations (one

ensemble member per model, 65 years) (after Jones et al., 2013). For line colours see the legend of Figure 3.4. Additionally, the multi-model mean (red) and standard deviation 
(grey shading) are shown. Observational and model datasets were detrended by removing the least-squares quadratic trend. Further details on data sources and processing are 
available in the chapter data table (Table 3.SM.1).

437
is also higher than that from proxy reconstructions. CMIP6 models 
may not share the underestimation by CMIP5 models of variability 
in decadal to multi-decadal modes of variability, such as Pacific 
Decadal Variability (Section 3.7.6; England et al., 2014; Thompson 
et al., 2014; Schurer et al., 2015) and Atlantic Multi-decadal 
Variability (AMV), which may be partly forced, (see Section 3.7.7) 
but this assessment is limited by the small number of available 
studies. For the Southern Hemisphere, Hegerl et al. (2018) found an 
instance of internal variability in the early 20th century larger than 
that modelled, but indicated that could be an observational issue. Friedman et al. (2020) found biases in interhemispheric SST contrast 
in some models that may be consistent with underestimated cooling 
after early-20th century eruptions or underestimated Pacific Decadal

Variability, but could also be due to an imperfect separation between 
internal variability and forced signal in the observations. Figure 3.2c, 
updated from PAGES 2k Consortium (2019), compares modelled 
temperatures to reconstructions over the last millennium. It indicates 
that models reproduce the observed variability well, at least for the 
time scales between 20 and 50 years that paleo reconstructions 
typically resolve and that the figure represents. In summary, decadal 
Simulated variability of GSA T versus observed changesGSAT  anomaly (°C)
GSAT  anomaly (°C)
Figure 3.6 |  Simulated internal variability of global surface air temperature (GSAT) versus observed changes. (a)  Time series of five-year running mean GSAT 
anomalies in 45 CMIP6 pre-industrial control (unforced) simulations. The 10 most variable models in terms of five-year running mean GSAT are coloured according to the legend

on Figure 3.4. (b) Histograms of GSAT changes in CMIP6 historical simulations (extended by using SSP2-4.5 simulations) from 1850–1900 to 2010–2019 are shown by pink 
shading in (c), and GSAT changes between the average of the first 51 years and the average of the last 20 years of 170-year overlapping segments of the pre-industrial control 
simulations shown in (a) are shown by blue shading. GMST changes in observational datasets for the same period are indicated by black vertical lines. (c) Observed GMST 
anomaly time series relative to the 1850–1900 average. Black lines represent the five-year running means while grey lines show unfiltered annual time series. Further details 
on data sources and processing are available in the chapter data table (Table 3.SM.1).

438
GMST variability simulated in CMIP6 models spans the range of 
residual decadal variability in large-scale reconstructions ( medium 
evidence , low agreement ).
In addition, new literature suggests that anthropogenic forcing 
itself may locally increase or decrease variability in surface 
temperatures (Screen et al., 2014; Qian and Zhang, 2015; Brown 
et al., 2017; Park et al., 2018; Santer et al., 2018; Weller et al., 
2020). These studies imply limitations in the use of pre-industrial 
control simulations to quantify the role of unforced variability over 
the historical period. Some recent attribution studies (Gillett et al., 
2021; Ribes et al., 2021) have estimated variability from ensembles 
of forced simulations instead, which would be expected to resolve 
any such changes in variability.
Figure  3.5 shows the standard deviation of zonal-mean surface 
temperature in CMIP6 pre-industrial control simulations and observed

temperature datasets. Results are consistent with those based 
on CMIP5 models, which showed the largest model spread where 
variability is also large, in the tropics and mid- to high latitudes (Flato 
et al., 2013). Modelled variability is within a factor two of observed 
variability over most of the globe. The apparent overestimation of 
high latitude variability in models compared to observations may 
be due to interpolation and infilling over data sparse high latitude 
regions in the observational products shown here (Jones, 2016).
The previous paragraph took an ensemble-mean view of model 
performance, but individual models disagree on unforced variability. 
Figure 3.6 illustrates the large differences in GSAT variability in unforced 
CMIP6 pre-industrial control simulations, following the method of 
Parsons et al. (2020). Surface temperatures in pre-industrial conditions 
are especially variable in the ten models highlighted in Figure  3.6a,

and some models substantially exceed the variability seen in CMIP5 
models (Parsons et al., 2020). Figure 3.6b shows that the distribution 
of warming trends simulated by CMIP6 models in historical simulations 
is clearly distinct from that simulated in unforced pre-industrial control 
simulations. Still, the unforced variability of the five most variable 
models approaches half that observed over the historical period 
under anthropogenically forced conditions (Figure 3.6c; Parsons et al., 
2020; Ribes et al., 2021). For  the Centre National de la Recherche 
Météorologique (CNRM) models, which are among the most variable, 
the large, low-frequency variability is attributed to strong simulated 
Atlantic Multi-decadal Variability (Séférian et al., 2019; Voldoire et al., 
2019b), which is difficult to rule out because of the short observational 
record (Section 3.7.7; Cassou et al., 2018). But, importantly, patterns 
of temperature variability simulated by even the most variable models

differ from the pattern of forced temperature change (Parsons et al., 
2020). Taken together, this discussion and Figures  3.2, 3.5 and 3.6 
indicate that the statistics of internal variability in models compare 
well in most cases to observational estimates and temperature proxy 
reconstructions, though some CMIP6 models appear to have higher 
multi-decadal variability than CMIP5 models or proxy reconstructions. 
When used in attribution studies, models with overestimated 
variability would increase estimated uncertainties and make results 
statistically conservative.In summary, there is high confidence  that CMIP6 models reproduce 
observed large-scale mean surface temperature patterns and internal 
variability as well as their CMIP5 predecessors, but with little evidence 
for reduced biases. CMIP6 models also reproduce historical GSAT 
changes similarly to their CMIP5 counterparts ( medium confidence ). 
However, in spite of model imperfections, there is very high confidence

that biases in surface temperature trends and variability simulated 
by the CMIP5 and CMIP6 ensembles are small enough to support 
detection and attribution of human-induced warming.
3.3.1.1.2 Detection and attribution
Looking at periods preceding the instrumental record, AR5 assessed 
with high confidence  that the 20th century annual mean surface 
temperature warming reversed a 5000-year cooling trend in Northern 
Hemisphere mid- to high latitudes caused by orbital forcing, and 
attributed the reversal to anthropogenic forcing with high confidence 
(see also Section 2.3.1.1). Since AR5, the combined response to solar, 
volcanic and greenhouse gas forcing was detected in all Northern 
Hemisphere continents (PAGES 2k-PMIP3 group, 2015) over the 
period 864 to 1840. In contrast, the effect of those forcings was not 
detectable in the Southern Hemisphere (Neukom et al., 2018). Global 
and Northern Hemisphere temperature changes from reconstructions

over this period have been attributed mostly to volcanic forcing 
(Schurer et al., 2014; McGregor et al., 2015; Otto-Bliesner et al., 2016; 
PAGES 2k Consortium, 2019; Büntgen et al., 2020), with a smaller 
role for changes in greenhouse gas forcing, and solar forcing playing 
a minor role (Schurer et al., 2014; PAGES 2k Consortium, 2019).
Focusing now on warming over the historical period, AR5 assessed 
that it was extremely likely  that human influence was the dominant 
cause of the observed warming since the mid-20th century, and 
that it was virtually certain  that warming over the same period 
could not be explained by internal variability alone. Since AR5 many 
new attribution studies of changes in global surface temperature 
have focused on methodological advances (see also Section 3.2). 
Those advances include better accounting for observational and 
model uncertainties, and internal variability (Ribes and Terray, 2013;

Hannart, 2016; Ribes et al., 2017; Schurer et al., 2018); formulating 
the attribution problem in a counterfactual framework (Hannart and 
Naveau, 2018); and reducing the dependence of the attribution on 
uncertainties in climate sensitivity and forcing (Otto et al., 2015; 
Haustein et al., 2017, 2019). Studies now account for uncertainties 
in the statistics of internal variability, either explicitly (Hannart, 2016; 
Hannart and Naveau, 2018; Ribes et al., 2021) or implicitly (Ribes and 
Terray, 2013; Schurer et al., 2018; Gillett et al., 2021), thus addressing 
concerns about over-confident attribution conclusions. Accounting for 
observational uncertainty increases the range of warming attributable 
to greenhouse gases by only 10 to 30% (Jones and Kennedy, 2017; 
Schurer et al., 2018). While some attribution studies estimate 
attributable changes in globally-complete GSAT (Schurer et al., 2018; 
Gillett et al., 2021; Ribes et al., 2021), others attribute changes in

observational GMST, but this makes little difference to attribution 
conclusions (Schurer et al., 2018). Moreover, based on a synthesis of 
observational and modelling evidence, Cross-Chapter Box 2.3 assesses 
that the current best estimate of the scaling factor between GMST and 
GSAT is one, and therefore attribution studies of GMST and GSAT are

439
here treated together in deriving assessed warming ranges. Studies 
also increasingly validate their multi-model approaches using imperfect 
model tests (Schurer et al., 2018; Gillett et al., 2021; Ribes et al., 2021). 
Alternative techniques, based purely on statistical or econometric 
approaches, without the need for climate modelling, have also been 
applied (Estrada et al., 2013; Stern and Kaufmann, 2014; Dergiades 
et al., 2016) and match the results of physically-based methods. The 
larger range of attribution techniques and improvements to those 
techniques increase confidence in the results compared to AR5.
In contrast, studies published since AR5 indicate that closely 
constraining the separate contributions of greenhouse gas changes 
and aerosol changes to observed temperature changes remains 
challenging. Nonetheless, attribution of warming to greenhouse gas

forcing has been found as early as the end of the 19th century (Schurer 
et al., 2014; Owens et al., 2017; PAGES 2k Consortium, 2019). Hegerl 
et al. (2019) found that volcanism cooled global temperatures by 
about 0.1°C between 1870 and 1910, then a lack of volcanic activity 
warmed temperatures by about 0.1°C between 1910 and 1950, with anthropogenic aerosols cooling temperatures throughout the 
20th century, especially between 1950 and 1980 when the estimated  
range of aerosol cooling was about 0.1°C to 0.5°C. Jones et al. (2016) 
attributed a warming of 0.87 to 1.22°C per century over the period 
1906 to 2005 to greenhouse gases, partially offset by a cooling of 
−0.54°C to −0.22°C per century attributed to aerosols. But they also 
found that detection of the greenhouse gas or the aerosol signal 
often fails, because of uncertainties in modelled patterns of change 
and internal variability. That point is illustrated by Figure 3.7, which

shows two- and three-way fingerprinting regression coefficients for 13 
CMIP6 models and the corresponding attributable warming ranges, 
derived using HadCRUT4 (Gillett et al., 2021). Regression coefficients 
with an uncertainty range that includes zero mean that detection has 
failed. Models with regression coefficients significantly less than one 
significantly overpredict the temperature response to the corresponding 
forcing. Conversely, models with regression coefficients significantly 
greater than one underpredict the response to these forcings. While 
estimates of warming attributable to anthropogenic influence derived 
using individual models are generally consistent, estimates of warming (a) (b)
(d) (c)
Figure 3.7 |  Regression coefficients and corresponding attributable warming estimates for individual CMIP6 models.  Upper panels show regression coefficients

based on a two-way regression (left)  and three-way regression (right) , of observed five-year mean, globally averaged, masked and blended surface temperature (HadCRUT4) 
onto individual model response patterns, and a multi-model mean, labelled ‘Multi’. Anthropogenic, natural, greenhouse gas, and other anthropogenic (aerosols, ozone, land-use 
change) regression coefficients are shown. Regression coefficients are the scaling factors by which the model responses must be multiplied to best match observations. 
Regression coefficients consistent with one indicate a consistent magnitude response in observations and models, and regression coefficients significantly greater than zero 
indicate a detectable response to the forcing concerned. Lower panels show corresponding observationally-constrained estimates of attributable warming in globally-complete

GSAT for the period 2010–2019, relative to 1850–1900, and the horizontal black line shows an estimate of observed warming in GSAT for this period. Figure is adapted from 
Gillett et al. (2021), their Extended Data Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

440
attributable to greenhouse gases and aerosols separately based on 
individual models are not all consistent, and detection of the aerosol 
influence fails more often than that of greenhouse gases. Hence, 
results of recent studies emphasize the need to use multi-model 
means to better constrain estimates of GSAT changes attributable to 
greenhouse gas and aerosol forcing (Schurer et al., 2018; Gillett et al., 
2021; Ribes et al., 2021).
Figure 3.8 compares attributable changes in globally complete GSAT 
for the period 2010–2019 relative to 1850–1900 from three detection 
and attribution studies, two of which use CMIP6 multi-model means 
(Gillett et al., 2021; Ribes et al., 2021), and an estimate based on 
assessed effective radiative forcing and transient and equilibrium 
climate sensitivity (see Section 7.3.5.3). The reference period 
1850–1900 is used to assess attributable temperature changes

because this is when the earliest gridded surface temperature 
records start, this is when the CMIP6 historical simulations start, this is the earliest base period used in attribution literature, and 
this is a reference period used in IPCC SR1.5 and earlier reports. 
It should, however, be noted that Cross-Chapter Box  1.2 assesses 
with medium confidence  that there was an anthropogenic warming 
with a likely  range of 0.0°C–0.2°C between 1750 and 1850–1900. 
Figure  3.8 also shows the GSAT changes directly simulated in 
response to these forcings in thirteen CMIP6 models. In spite of their 
different methodologies and input datasets, the three attribution 
approaches yield very similar results, with the anthropogenic 
attributable warming range encompassing observed warming, and 
the natural attributable warming being close to zero. The warming 
driven by greenhouse gas increases is offset in part by cooling due 
to other anthropogenic forcing agents, mostly aerosols, although

uncertainties in these contributions are larger than the uncertainty in 
the net anthropogenic warming, as discussed above. Estimates based 
on physical understanding of forcing and ECS made by Chapter 7 are 
close to estimates from attribution studies, despite being the products Chapter 7
Figure 3.8 |  Assessed contributions to observed warming, and supporting lines of evidence.  Shaded bands show assessed likely ranges of temperature change 
in GSAT, 2010–2019 relative to 1850–1900, attributable to net human influence, well-mixed greenhouse gases, other human forcings (aerosols, ozone, and land-use change), 
natural forcings, and internal variability, and the 5–95% range of observed warming. Bars show 5–95% ranges based on (left to right) Haustein et al. (2017), Gillett et al. (2021) 
and Ribes et al. (2021), and crosses show the associated best estimates. No 5–95% ranges were provided for the Haustein et al. (2017) greenhouse gas or other human forcings

contributions. The Ribes et al. (2021) results were updated using a revised natural forcing time series, and the Haustein et al. (2017) results were updated using HadCRUT5. 
The Chapter 7 best estimates and ranges were derived using assessed forcing time series and a two-layer energy balance model as described in Section 7.3.5.3. Coloured 
symbols show the simulated responses to the forcings concerned in each of the models indicated. Further details on data sources and processing are available in the chapter 

441
of a different approach. This agreement enhances confidence in the 
magnitude and causes of attributable surface temperature warming.
The AR5 found high confidence  for a major role for anthropogenic 
forcing in driving warming over each of the inhabited continents, 
except for Africa where they found only medium confidence  
because of limited data availability (Bindoff et al., 2013). At the 
hemispheric scale, Friedman et al. (2020) and Bonfils et al. (2020) detected an anthropogenically forced response of inter-hemispheric 
contrast in surface temperature change, which has a complex time 
evolution but shows the Northern Hemisphere cooling relative 
to the Southern Hemisphere until around 1975 but then warming 
after that. Bonfils et al. (2020) attribute the Northern Hemisphere 
reversal to a combination of reduced aerosol forcing and greenhouse 
gas induced warming of Northern Hemisphere land masses.

Friedman et al. (2020) found that CMIP5 models simulate the 
Figure 3.9 |  Global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations.  Time series are 
shown for CMIP6 historical anthropogenic and natural (brown), natural-only (green), greenhouse gas only (grey) and aerosol only (blue) simulations (thick lines show multi-
model means and shaded regions show the 5th to 95th percentile ranges) and for HadCRUT5 (black). All models have been subsampled using the HadCRUT5 observational data 
mask. Temperature anomalies are shown relative to 1950–2010 for Antarctica and relative to 1850–1900 for other continents. CMIP6 historical simulations are extended using 
the SSP2-4.5 scenario simulations. All available ensemble members were used (see Section 3.2). Regions are defined by Iturbide et al. (2020). Further details on data sources 
and processing are available in the chapter data table (Table 3.SM.1).

442
correct sign of the inter-hemispheric contrast when forced with all 
forcings but underestimate its magnitude. Figure 3.9 shows global 
surface temperature change in CMIP6 all-forcing and natural-only 
simulations globally, averaged over continents, and separately over 
land and ocean surfaces. All-forcing simulations encompass observed 
temperature changes for all regions, while natural-only simulations 
fail to do so in recent decades except in Antarctica, based on the 
annual means shown. As stated above, warming results from 
a partial offset of greenhouse gas warming by aerosol cooling. That 
offset is stronger over land than ocean. Regionally, models show 
a large range of possible temperature responses to greenhouse gas 
and aerosol forcing, which complicates single-forcing attribution. 
A more detailed discussion of regional attribution can be found in 
Section 10.4. Over global land surfaces, Chan and Wu (2015) used

CMIP5 simulations to attribute a warming trend of 0.3 (2.5%–97.5% 
confidence interval: 0.2–0.36) °C per decade to anthropogenic 
forcing, with natural forcing only contributing 0.05 (0.02–0.06) °C 
per decade. Accounting for unsampled sources of uncertainty and the 
availability of only a single study, their result suggests that it is very 
likely  that human influence is the main driver of warming over land.
In summary, since the publication of AR5, new literature has 
emerged that better accounts for methodological and climate model 
uncertainties in attribution studies (Ribes et al., 2017; Hannart and 
Naveau, 2018) and that concludes that anthropogenic warming 
is approximately equal to observed warming over the 1951–2010 
period. The IPCC SR1.5 reached the same conclusion for 2017 relative 
to 1850–1900 based on anthropogenic warming and associated 
uncertainties calculated using the method of Haustein et al. (2017).

Moreover, the improved understanding of the causes of the apparent 
slowdown in warming over the beginning of the 21st century and 
the difference in simulated and observed warming trends over this 
period (Cross-Chapter Box 3.1) further improve our confidence in the 
assessment of the dominant anthropogenic contribution to observed 
warming. In deriving our assessments, these considerations are 
balanced against new literature that raises questions about the ability 
of some models to simulate variability in surface temperatures over 
a  range of time scales (Laepple and Huybers, 2014; Parsons et al., 
2017; Friedman et al., 2020), and the finding that some CMIP6 models 
exhibit substantially higher multi-decadal internal variability than 
that seen in CMIP5, which remains to be fully understood (Parsons 
et al., 2020; Ribes et al., 2021). Further, uncertainties in simulated 
aerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting

in very diverse spatial responses of different climate models to aerosol forcing, and inter-model differences in the historical global 
mean temperature evolution and in diagnosed cooling attributable to 
aerosols (Figure 3.8). Moreover, like previous generations of coupled 
model simulations, historical and single forcing CMIP6 simulations 
follow a common experimental design (Eyring et al., 2016a; Gillett 
et al., 2016) and are thus all driven by the same common set of 
forcings, even though these forcings are uncertain. Hence, forcing 
uncertainty is not directly accounted for in most of the attribution and 
model evaluation studies assessed here, although this limitation can 
to some extent be addressed by comparing with previous generation 
multi-model ensembles or individual model studies using different 
sets of forcings.
The IPCC SR1.5 best estimate and likely  range of anthropogenic 
attributable GMST warming was 1.0 ± 0.2°C in 2017 with respect

to the period 1850–1900. Here, the best estimate is expressed 
in terms of GSAT and is calculated as the average of the three 
estimates shown in Figure 3.9, yielding a value of 1.07°C. Ranges 
for attributable GSAT warming are derived by finding the smallest 
ranges with a precision of 0.1°C which span all of the 5–95% ranges 
from the attribution studies shown in Figure 3.9. These ranges are 
then assessed as likely  rather than very likely  because the studies 
may underestimate the importance of the structural limitations of 
climate models, which probably do not represent all possible sources 
of internal variability; use too simple climate models, which may 
underestimate the role of internal variability; or underestimate model 
uncertainty, especially when using model ensembles of limited size 
and inter-dependent models, for example through common errors in 
forcings across models, as discussed above. This leads to a likely range

for anthropogenic attributable warming in 2010–2019 relative to 
1850–1900 of 0.8 to 1.3°C in terms of GSAT. This range encompasses 
the best estimate and very likely  range of observed GSAT warming of 
1.06 [0.88 to 1.21] °C over the same period (Cross-Chapter Box 2.3). 
There is medium confidence  that the best estimate and likely ranges 
of attributable warming expressed in terms of GMST are equal to 
those for GSAT (Cross-Chapter Box 2.3). Repeating the process for 
other time periods leads to the best estimates and likely  ranges listed 
in Table 3.1. GSAT change attributable to natural forcings is −0.1 to 
+0.1°C. The likely  range of GSAT warming attributable to greenhouse 
gases is assessed in the same way to be 1.0 to 2.0°C while the GSAT 
change attributable to aerosols, ozone and land-use change is −0.8 
to 0.0°C. Progress in attribution techniques allows the important 
advance of attributing observed surface temperature warming since

1850–1900, instead of since 1951 as was done in AR5.
Table  3.1 |  Estimates of warming in GSAT attributable to human influence for different periods in °C, all relative to the 1850–1900 base period.  
Uncertainty ranges are 5–95% ranges for individual studies and likely ranges for the assessment. The results shown in the table use the methods described in the three studies 
indicated, but applied to additional periods and the warming trend. Ribes et al. (2021) results were updated using a corrected natural forcing time series, and Haustein et al. 
(2017) results were updated to use HadCRUT5.
1986–2005 1995–2014 2006–2015 2010–2019Warming Rate 
2010–2019
Ribes et al. (2021) 0.65 (0.52 to 0.77) 0.82 (0.69 to 0.94) 0.94 (0.8 to 1.08) 1.03 (0.89 to 1.17) 0.23 (0.18 to 0.29)
Gillett et al. (2021) 0.63 (0.32 to 0.94) 0.84 (0.63 to 1.06) 0.98 (0.74 to 1.22) 1.11 (0.92 to 1.30) 0.35 (0.30 to 0.41)

Haustein et al. (2017) 0.73 (0.58 to 0.82) 0.88 (0.75 to 0.98) 0.98 (0.87 to 1.10) 1.06 (0.94 to 1.22) 0.23 (0.19 to 0.35)
Assessment 0.68 (0.3 to 1.0) 0.85 (0.6 to 1.1) 0.97 (0.7 to 1.3) 1.07 (0.8 to 1.3) 0.2 (0.1 to 0.3)

443
The IPCC AR5 assessed the likely range of the contribution of internal 
variability to GMST warming to be −0.1 to +0.1°C over the period 
1951–2010. Since then, several studies have downplayed the 
contribution of internal modes of variability to global temperature 
variability, often by arguing for a forced component to those internal modes (Mann et al., 2014; Folland et al., 2018; Haustein et al., 2019; Liguori et al., 2020). Haustein et al. (2017) found a 5–95% confidence interval of −0.09°C to +0.12°C for the contribution of 
internal variability to warming between 1850–1879 and 2017. 
Ribes et al. (2021) imply a contribution of internal variability of −0.02°C ± 0.16°C to warming between 2010–2019 and 1850–1900, assuming independence between errors in the observations and in
the estimate of the forced response. Based on these studies, but
allowing for unsampled sources of error, we assess the likely range

of the contribution of internal variability to GSAT warming between2010–2019 and 1850–1900 to be −0.2°C to +0.2°C.
The IPCC SR1.5 gave a likely range for the human-induced warming rate 
of 0.1°C to 0.3°C per decade in 2017, with a best estimate of 0.2°C per 
decade (Allen et al., 2018). Table 3.1 lists the estimates of attributable anthropogenic warming rate over the period 2010–2019 based on the three studies that underpin the assessment of GSAT warming (Haustein 
et al., 2017; Gillett et al., 2021; Ribes et al., 2021). Estimates from 
Haustein et al. (2017), based on observed warming, and Ribes et al. (2021), based on CMIP6 simulations constrained by observed warming, are in good agreement. The Gillett et al. (2021) estimate, also based on CMIP6 models, corresponds to a larger anthropogenic attributable 
warming rate, because of a smaller warming rate attributed to natural

forcing than in Ribes et al. (2021). This disagreement does not support a decrease in uncertainty compared to the SR1.5 assessment. So the range for anthropogenic attributable surface temperature warming 
rate of 0.1°C to 0.3°C per decade is again assessed to be likely, with a 
best estimate of 0.2°C per decade.
3.3.1.2  Upper -air Temperature
Chapter 2 assessed that the troposphere has warmed since at least 
the 1950s, that it is virtually certain that the stratosphere has cooled, and that there is medium confidence that the upper troposphere 
in the tropics has warmed faster than the near-surface since at 
least 2001 (Section 2.3.1.2). The AR5 assessed that anthropogenic forcings, dominated by greenhouse gases, likely contributed to the 
warming of the troposphere since 1961 and that anthropogenic forcings, dominated by the depletion of the ozone layer due to 
ozone-depleting substances, very likely contributed to the cooling

of the lower stratosphere since 1979. Since AR5, understanding of observational uncertainties in the radiosonde and satellite data has improved with more available data and longer coverage, and 
differences between models and observations in the tropical 
atmosphere have been investigated further.
3.3.1.2.1  T ropospheric temperature
The AR5 assessed with low confidence that most, though not 
all, CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) models overestimated the observed warming trend in the tropical 
troposphere during the satellite period 1979–2012, and that a third to a half of this difference was due to an overestimate of the SST trend 
during this period (Flato et al., 2013). Since AR5, additional studies based on CMIP5 and CMIP6 models show that this warming bias in tropospheric temperatures remains. Recent studies have investigated 
the role of observational uncertainty, the model response to external

forcings, the influence of the time period considered, and the role of biases in SST trends in contributing to this bias.
Several studies since AR5 have continued to demonstrate an 
inconsistency between simulated and observed temperature trends in the tropical troposphere, with models simulating more warming than observations (Mitchell et al., 2013, 2020; Santer et al., 2017a, b; McKitrick and Christy, 2018; Po-Chedley et al., 2021). Santer et al. 
(2017b) used updated and improved satellite retrievals to investigate 
model performance in simulating the tropical mid- to upper-troposphere trends, and removed the influence of stratospheric cooling by regression. These factors were found to reduce the size of the discrepancy in mid- to upper-tropospheric temperature trends between 
models and observations over the satellite era, but a discrepancy

remained. Santer et al. (2017a) found that during the late 20th century, the discrepancies between simulated and satellite-derived mid- to upper-tropospheric temperature trends were consistent with internal variability, while during most of the early 21st century, simulated 
tropospheric warming was significantly larger than observed, which 
they related to systematic deficiencies in some of the external forcings used after year 2000 in the CMIP5 models. However, in CMIP6, differences between simulated and observed upper-tropospheric 
temperature trends persist despite updated forcing estimates (Mitchell 
et al., 2020). Figure 3.10 shows that CMIP6 models forced by combined anthropogenic and natural forcings overestimate temperature trends compared to radiosonde data (Haimberger et al., 2012) throughout the tropical troposphere (Mitchell et al., 2020). Over the 1979–2014 
period, models are more consistent with observations in the lower

troposphere, and least consistent in the upper troposphere around 200 hPa, where biases exceed 0.1°C per decade. Several studies using CMIP6 models suggest that differences in climate sensitivity may be an important factor contributing to the discrepancy between the 
simulated and observed tropospheric temperature trends (McKitrick 
and Christy, 2020; Po-Chedley et al., 2021), though it is difficult to deconvolve the influence of climate sensitivity, changes in aerosol forcing and internal variability in contributing to tropospheric warming biases (Po-Chedley et al., 2021). Another study found that the absence 
of a hypothesized negative tropical cloud feedback could explain half 
of the upper troposphere warming bias in one model (Mauritsen and Stevens, 2015).
Mitchell et al. (2013) and Mitchell et al. (2020) found a smaller

discrepancy in tropical tropospheric temperature trends in models forced with observed SSTs (see also Figure  3.10a), and CMIP5 models and observations were found to be consistent below 150 hPa when viewed in terms of the ratio of temperature trends aloft to 
those at the surface (Mitchell et al., 2013). Flannaghan et al. (2014) 
and Tuel (2019) showed that most of the tropospheric temperature trend difference between CMIP5 models and the satellite-based observations over the 1970–2018 period is due to respective differences in SST warming trends in regions of deep convection, 
and Po-Chedley et al. (2021) showed that CMIP6 models with a more

444
realistic SST simulation in the central and eastern Pacific show 
a better performance than other models. Though systematic biases 
still remain, this indicates that the bias in tropospheric temperature 
warming in models is in part linked to surface temperature warming 
biases, especially in the lower troposphere.
In summary, studies continue to find that CMIP5 and CMIP6 
model simulations warm more than observations in the tropical 
mid- and upper-troposphere over the 1979–2014 period (Mitchell 
et al., 2013, 2020; Santer et al., 2017a, b; Suárez-Gutiérrez et al., 
2017; McKitrick and Christy, 2018), and that overestimated surface 
warming is partially responsible (Mitchell et al., 2013; Po-Chedley 
et al., 2021). Some studies point to forcing errors in the CMIP5 
simulations in the early 21st century as a possible contributor 
(Mitchell et al., 2013; Sherwood and Nishant, 2015; Santer et al.,

2017a), but CMIP6 simulations use updated forcing estimates yet 
generally still warm more than observations. Although accounting 
for internal variability and residual observational errors can reconcile 
models with observations to some extent (Suárez-Gutiérrez et al., 
2017; Mitchell et al., 2020), some studies suggest that climate 
sensitivity also plays a role (Mauritsen and Stevens, 2015; McKitrick 
and Christy, 2020; Po-Chedley et al., 2021). Hence, we assess with 
medium confidence  that CMIP5 and CMIP6 models continue to 
overestimate observed warming in the upper tropical troposphere 
over the 1979–2014 period by at least 0.1°C per decade, in part because of an overestimate of the tropical SST trend pattern over 
this period.
The AR5 assessed as likely that anthropogenic forcings, dominated 
by greenhouse gases, contributed to the warming of the troposphere 
since 1961 (Bindoff et al., 2013). Since then, there has been further

progress in detecting and attributing tropospheric temperature 
changes. Mitchell et al. (2020) used CMIP6 models to find that the 
main driver of tropospheric temperature changes is greenhouse gases. 
Previous detection of the anthropogenic influence on tropospheric 
warming may have overestimated uncertainties: Pallotta and Santer 
(2020) found that CMIP5 climate models overestimate the observed 
natural variability in global mean tropospheric temperature on time 
scales of 5–20 years. Nevertheless, Santer et al. (2019) found that 
stochastic uncertainty is greater for tropospheric warming than 
stratospheric cooling because of larger noise and slower recovery time 
from the Mount Pinatubo eruption in the troposphere. The detection 
time of the anthropogenic signal in the tropospheric warming can be 
affected by both the model climate sensitivity and the model response 
to aerosol forcing. Volcanic forcing is also important, as models that

do not consider the influence of volcanic eruptions in the early 21st 
century overestimate the observed tropospheric warming since 1998 
(Santer et al., 2014). Changes in the amplitude of the seasonal cycle 
of tropospheric temperatures have also been attributed to human 
influence. Santer et al. (2018) found that satellite data and climate 
Temperature trend (°C/decade) Temperature trend (°C/decade) Temperature trend (°C/decade)
Figure 3.10 |  Observed and simulated tropical mean temperature trends through the atmosphere.  Vertical profiles of temperature trends in the tropics (20°S–20°N) 
for three periods: (a) 1979–2014, (b) 1979–1997 (ozone depletion era) and (c) 1998–2014 (ozone stabilization era). The black lines show trends in the Radiosonde Innovation 
Composite Homogenization (RICH) 1.7 (long dashed) and Radiosonde Observation Correction using Reanalysis (RAOBCORE) 1.7 (dashed) radiosonde datasets (Haimberger

et al., 2012), and in the ERA5/5.1 reanalysis (solid). Grey envelopes are centred on the RICH 1.7 trends, but show the uncertainty based on 32 RICH-observations members of 
version 1.5.1 of the dataset, which used version 1.7.3 of the RICH software but with the parameters of version 1.5.1. ERA5 was used as reference for calculating the adjustments 
between 2010 and 2019, and ERA-Interim was used for the years before that. Red lines show trends in CMIP6 historical simulations from one realization of each of 60 models. 
Blue lines show trends in 46 CMIP6 models that used prescribed, rather than simulated, sea surface temperatures (SSTs). Figure is adapted from Mitchell et al. (2020), their 
Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

445
models driven by anthropogenic forcing show consistent amplitude 
increases at mid-latitudes in both hemispheres, amplitude decreases 
at high latitudes in the Southern Hemisphere, and small changes in 
the tropics.
In summary, these studies confirm the dominant role of human activities 
in tropospheric temperature trends. We therefore assess that it is very 
likely  that anthropogenic forcing, dominated by greenhouse gases, 
was the main driver of the warming of the troposphere since 1979.
3.3.1.2.2 Stratospheric temperature
The AR5 concluded that the CMIP5 models simulated a generally 
realistic evolution of lower-stratospheric temperatures (Bindoff et al., 
2013; Flato et al., 2013), which was better than that of the CMIP3 
models, in part because they generally include time-varying ozone 
concentrations, unlike many of the CMIP3 models. Nonetheless, it was 
noted that there was a tendency for the simulations to underestimate

stratospheric cooling compared to observations. Bindoff et al. 
(2013) concluded that it was very likely  that anthropogenic forcing, 
dominated by stratospheric ozone depletion by chemical reactions 
involving trace species known as ozone-depleting substances (ODS), 
had contributed to the cooling of the lower stratosphere since 1979. 
Increased greenhouse gases cause near-surface warming but cooling 
of stratospheric temperatures.
For the lower stratosphere, a debate has been ongoing since AR5 
between studies finding that models underestimate the cooling 
of stratospheric temperature (Santer et al., 2017b), in part because of  
underestimated stratospheric ozone depletion (Eyring et al., 2013; 
Young et al., 2013), and studies finding that lower stratospheric 
temperature trends are within the range of observed trends (Young 
et al., 2013; Maycock et al., 2018). Different observational data and 
different time periods explain the different conclusions. Aquila et al.

(2016) used forced chemistry-climate models with prescribed SST to 
investigate the influence of different forcings on global stratospheric 
temperature changes. They found that in the lower stratosphere, 
the simulated cooling trend due to increasing greenhouse gases 
was roughly constant over the satellite era, while changes in ODS 
concentrations amplified that stratospheric cooling trend during 
the era of increasing ozone depletion up until the mid-1990s, with 
a  flattening of the temperature trend over the subsequent period 
over which stratospheric ozone has stabilized (Section 2.2.5.2). 
Mitchell et al. (2020) showed that while models simulate realistic 
trends in tropical lower-stratospheric temperature over the whole 
1979–2014 period when compared with radiosonde data, they 
tend to overestimate the cooling trend over the ozone depletion era (1979–1997) and underestimate it over the ozone stabilization era

(1998–2014; Figure 3.10b,c). They speculate that those disagreements 
are due to poor representations of stratospheric ozone forcing.
Upper stratospheric temperature changes were not assessed in 
the context of attribution or model evaluation in AR5, but this is 
an area where there has been considerable progress over recent 
years (Section  2.3.1.2.1). Simulated temperature changes in 
chemistry-climate models show good consistency with the reprocessed 
dataset from NOAA STAR but are less consistent with the revised 
UK Met Office record (Karpechko et al., 2018). The latter still shows 
stronger cooling than simulated in chemistry-climate models (Maycock 
et al., 2018). Reanalyses, which assimilate AMSU and SSU datasets, 
indicate an upper-stratospheric cooling from 1979 to 2009 of about 
3°C at 5 hPa and 4°C at 1 hPa that agrees well with the cooling in 
simulations with prescribed SST and using CMIP5 forcings (Simmons

et al., 2014). Mitchell (2016) used regularized optimal fingerprinting 
techniques to carry out an attribution analysis of annual mid- to upper-
stratospheric temperature in response to external forcings. They found 
that anthropogenic forcing has caused a cooling of approximately 
2°C–3°C in the upper stratosphere over the period of 1979–2015, with 
greenhouse gases contributing two thirds of this change and ozone 
depletion contributing one third. They found a large upper-stratospheric 
temperature change in response to volcanic forcing (0.4°C–0.6°C 
for Mount Pinatubo) but that change is still smaller than the lower-
stratospheric signal. Aquila et al. (2016) found that the cooling of the 
middle and upper stratosphere after 1979 is mainly due to changes in 
greenhouse gas concentrations. Volcanic eruptions and the solar cycle 
were found not to affect long-term stratospheric temperature trends 
but to have short-term influences.

In summary, based on the latest updates to satellite observations of 
stratospheric temperature, we assess that simulated and observed 
trends in global mean temperature through the depth of the 
stratosphere are more consistent than based on previous datasets, 
but some differences remain ( medium confidence ). Studies published 
since AR5 increase our confidence in the simulated stratospheric 
temperature response to greenhouse gas and ozone changes, and 
support an assessment that it is extremely likely  that stratospheric 
ozone depletion due to ozone-depleting substances was the main 
driver of the cooling of the lower stratosphere between 1979 and 
the mid-1990s, as expected from physical understanding. Similarly, 
revised observations and new studies support an assessment that it 
is extremely likely  that anthropogenic forcing, both from increases in 
greenhouse gas concentrations and depletion of stratospheric ozone

due to ozone-depleting substances, was the main driver of upper-
stratospheric cooling since 1979.
Cross-Chapter Box 3.1 | Global Surface Warming Over the Early 21st Century
Contributors:  Christophe Cassou (France), Yu Kosaka (Japan), John C. Fyfe (Canada), Nathan P . Gillett (Canada), Ed Hawkins 
(United Kingdom), Blair Trewin (Australia)
The AR5 found that the rate of global mean surface temperature (GMST) increase inferred from observations over the 1998–2012 
period was lower than the rate of increase over the 1951–2012 period, and lower than the ensemble mean increase in historical 
simulations from CMIP5 climate models extended by Representative Concentration Pathway (RCP) scenario simulations beyond 2005

446
 
Cross-Chapter Box 3.1 (continued)
(Flato et al., 2013). This apparent slowdown of surface global warming compared to the 62-year rate was assessed with medium 
confidence  to have been caused in roughly equal measure by a cooling contribution from internal variability and a reduced trend in 
external forcing (particularly associated with solar and volcanic forcing) in AR5 based on expert judgement (Flato et al., 2013). In AR5 
it was assessed that almost all CMIP5 simulations did not reproduce the observed slower warming, and that there was medium 
confidence  that the trend difference from the CMIP5 ensemble mean was to a substantial degree caused by internal variability 
with possible contributions from forcing error and model response uncertainty. This Cross-Chapter Box assesses new findings from 
observational products and statistical and physical models on trends over the 1998–2012 period considered in AR5.

Updated observational and reanalyses datasets and comparison with model simulations
Since AR5, there have been version updates and new releases of most observational GMST datasets (Cross-Chapter Box 2.3). All the 
updated products now available consistently find stronger positive trends for 1998–2012 than those assessed in AR5 (Cowtan and Way, 
2014; Karl et al., 2015; Hausfather et al., 2017; Medhaug et al., 2017; Simmons et al., 2017; Risbey et al., 2018). Simmons et al. (2017) 
reported that the 1998–2012 GMST trends in the updated observational and reanalysis datasets available at that time ranged from 
0.06°C to 0.14°C per decade, compared with the 0.05°C per decade on average reported in AR5, while the latest data products reported 
in Chapter 2 Table 2.4 show GMST or global mean near-surface air temperature (GSAT) trends over that period ranging from 0.12°C to

0.14°C per decade. The lowest trend in Simmons et al. (2017) is from HadCRUT4, now superseded by HadCRUT5, which shows a trend 
of 0.12°C per decade. The upward revision is mainly due to improved sea surface temperature (SST) datasets and infilling of surface 
temperature in locations with missing records in observational products, mainly in the Arctic (see Cross-Chapter Box 2.3 for details).
With these updates, all the observed trends assessed here lie within the 10th–90th percentile range of the simulated trends in the 
CMIP5 and CMIP6 simulations (Cross-Chapter Box 3.1, Figure 1a). This result is insensitive to whether model GSAT (based on surface 
air temperature) or GMST (based on a blend of surface air temperature over land and sea ice and SST over open ocean) is used, and to 
whether or not masking with the observational data coverage is applied. Therefore, the observed 1998–2012 trend is consistent with

both the CMIP5 or CMIP6 multi-model ensemble of trends over the same period ( high confidence ).
Internal variability
All the observation-based GMST and GSAT trends are lower than the multi-model mean GMST and GSAT trends of both CMIP5 and 
CMIP6 for 1998–2012 (Cross-Chapter Box 3.1, Figure 1a). This suggests a possible cooling contribution from internal variability during 
this period. This is supported by initialized decadal hindcasts, which account for the phase of the multi-decadal modes of variability 
(Sections 3.7.6  and 3.7.7 ), and which reproduce observed global mean SST and GSAT trends better than uninitialized historical 
simulations (Guemas et al., 2013; Meehl et al., 2014).
Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT 
fluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015;

Steinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (El Niño-like) to negative (La Niña-like) phases during the 
slow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower 
decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed 
radiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; Meehl et al., 2011, 2013, 2014; Maher et al., 
2014; Middlemas and Clement, 2016), suggesting the influence of PDV. This is confirmed by statistical models with the PDV-GSAT 
relationship estimated from observations and model simulations (Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017), 
selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations

over the slow warming period (Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is 
constrained to follow the observations (Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015). 
Part of the PDV trend may have been driven by anthropogenic aerosols (Smith et al., 2016); however, this result is model-dependent, 
and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble (Section 3.7.6). It is also notable 
that there is large uncertainty in the magnitude of the PDV influence on GSAT across models (Deser et al., 2017a; C.-Y . Wang et al., 
2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern 
Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular

Mode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Saffioti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean–Warm 
Land (COWL) pattern (Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large 
part of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019).
Such internally-driven variation of decadal GSAT trends is not unique to the 1998–2012 period (Section 1.4.2.1; Lovejoy, 2014; Roberts 
et al., 2015; Dai and Bloecker, 2019). Due to the nature of internal variability, surface temperature changes over the 1998–2012 period

447
 
are regionally- and seasonally-varying (Cross-Chapter Box 3.1, Figure 1c; Trenberth et al., 2014; Zang et al., 2019). Further, there was no 
slowdown in the increasing occurrence of hot extremes over land (Kamae et al., 2014; Seneviratne et al., 2014; Imada et al., 2017). Thus, 
the internally-driven slowdown of GSAT increase does not correspond to slowdown of warming everywhere on the Earth’s surface.
Updated forcing
CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations 
for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no 
net effect of updated anthropogenic aerosols is found on GSAT trends (Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018),

natural forcing by moderate volcanic eruptions in the 21st century (Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014) 
and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle (Lean, 2018) yield a negative 
contribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed 
and CMIP5 trends, as shown based on EMIC simulations (Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical 
models (Schmidt et al., 2014; Lean, 2018), and process-based climate models (Santer et al., 2014). However, in a single climate 
model study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and 
anthropogenic aerosols) available when the study was done made no significant difference to the 1998–2012 GMST trend from that

obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role (Outten et al., 
2015). In the multi-model ensemble mean, the 1998–2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter 
Box 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 
than CMIP5 (Section 7.5.6). To summarize, while there is medium confidence  that natural forcing that was missing in CMIP5 
contributed to the difference of observed and simulated GMST trends, confidence  remains low in the quantitative contribution of 
net forcing updates.
Energy budget and heat redistribution
The early 21st century slower warming was observed in atmospheric temperatures, but the heat capacity of the atmosphere is very 
small compared to that of the ocean. Although there is noticeable uncertainty among observational products (H. Su et al., 2017) and

observation quality changes through time, global ocean heat content continued to increase during the slower surface warming period 
(very high confidence ), at a rate consistent with CMIP5 and CMIP6 historical simulations (Sections 2.3.3.1, 3.5.1.3  and 7.2.2.2). 
There is high confidence  that the Earth’s energy imbalance was larger in the 2000s than in the 1985–1999 period (Section 7.2.2.1), 
consistent with accelerating ocean heat uptake in the past two decades (Section 3.5.1.3 ). Internal decadal variability is mainly associated 
with redistribution of heat within the climate system (X.H. Yan et al., 2016; Drijfhout, 2018) while associated top of the atmosphere 
radiation anomalies are weak (Palmer and McNeall, 2014). Heat redistribution in the top 350 m of the Indian and Pacific Oceans has 
been found to be the main contributor to reduced surface warming during the slower surface warming period (Lee et al., 2015; Nieves

et al., 2015; F . Liu et al., 2016), consistent with the simulated signature of PDV (England et al., 2014; Maher et al., 2018a; Gastineau 
et al., 2019). Below 700 m, enhanced heat uptake over the slower surface warming period was observed mainly in the North Atlantic and 
Southern Ocean (Chen and Tung, 2014), though whether this was a response to forcing or a unique signature of the slow GMST warming 
has been questioned (W. Liu et al., 2016).
Summary and implications
With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper 
understanding of mechanisms, there is very high confidence  that the slower GMST and GSAT increase inferred from observations in 
the 1998–2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic 
warming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reflected in the

continued warming of the global ocean ( very high confidence ) and in the continued rise of hot extremes over land ( medium confidence ). 
Considering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown (Hedemann 
et al., 2017; Power et al., 2017); rather, it should be interpreted as due to a combination of several factors (Huber and Knutti, 2014; 
Schmidt et al., 2014; Medhaug et al., 2017).
A major El Niño event in 2014–2016 led to three consecutive years of record annual GMST with unusually strong heat release from 
the North-western Pacific Ocean (Yin et al., 2018), which marked the end of the slower warming period (Hu and Fedorov, 2017; J. Su 
et al., 2017; Cha et al., 2018). The past five-year period (2016–2020) is the hottest five-year period in the instrumental record up to 
2020 ( high confidence ). This rapid warming was accompanied by a PDV shift toward its positive phase (J. Su et al., 2017; Cha et al.,

2018). A higher rate of warming following the 1998–2012 period is consistent with the predictions in AR5 Box 9.2 (Flato et al., 2013) 
and with a statistical prediction system (Sévellec and Drijfhout, 2018). Initialized decadal predictions show higher GMST trends in the 
early 2020s compared to uninitialized simulations (Thoma et al., 2015; Meehl et al., 2016a).

448
 
While some recent studies find that internal decadal GSAT variability may become weaker under GSAT warming, associated in part 
with reduced amplitude PDV (Section 4.5.3.5; Brown et al., 2017), the weakening is small under a realistic range of warming. A 
large volcanic eruption would temporarily cool GSAT (Cross-Chapter Box 4.1). Thus, there is very high confidence  that reduced and 
increased GMST and GSAT trends at decadal time scales will continue to occur in the 21st century (Meehl et al., 2013; Roberts et al., 
2015; Medhaug and Drange, 2016). However, such internal or volcanically forced decadal variations in GSAT trend have little effect on 
centennial warming (England et al., 2015; Cross-Chapter Box 4.1).
Cross-Chapter Box 3.1, Figure 1 |  15-year trends of global surface temperature for 1998–2012 and 2012–2026. (a, b)  GSAT and GMST trends for

1998–2012 (a) and 2012–2026 (b). Histograms are based on GSAT in historical simulations of CMIP6 (red shading, extended by SSP2-4.5) and CMIP5 (grey shading; 
extended by RCP4.5). Filled and open diamonds at the top represent multi-model ensemble means of GSAT and GMST trends, respectively. Diagonal lines show histograms 
of HadCRUT5.0.1.0. Triangles at the top of (a) represent GMST trends from Berkeley Earth, GISTEMP , Kadow et al. (2020) and NOAAGlobalTemp-Interim, and the GSAT 
trend from ERA5. Selected CMIP6 members whose 1998–2012 trends are lower than the HadCRUT5.0.1.0 mean trend are indicated by purple shading (a) and (b). In (a), 
model GMST and GSAT, and ERA5 GSAT are masked to match HadCRUT data coverage. (c–d)  Trend maps of annual near-surface temperature for 1998–2012 based on 
HadCRUT5.0.1.0 mean (c), and composited surface air temperature trends of subsampled CMIP6 simulations (d) with GSAT trends in the purple shaded area in (a). In

(c), cross marks indicate trends that are not significant at the 10% level based on t-tests with serial correlation taken into account. The ensemble size used for each of the 
histograms and the trend composite is indicated at the top right of each of the panels (a, b, d). Model ensemble members are weighted with the inverse of the ensemble 
size of the same model, so that each model is equally weighted. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

449
3.3.2 Precipitation, Humidity and Streamflow
3.3.2.1 Paleoclimate Context
A fact hindering detection and attribution studies in precipitation 
and other hydrological variables is the large internal variability of 
these fields relative to the anthropogenic signal. This low signal-to-
noise ratio hinders the emergence of the anthropogenic signal from 
natural variability. Moreover, the sign of the change depends on 
location and time of the year. Paleoclimate records provide valuable 
context for observed trends in the 20th and 21st century and assist 
with the attribution of these trends to human influence (see also 
Section 2.3.1.3.1). By nature, hydrological proxy data represent 
regional conditions, but taken together can represent large-scale 
patterns. As an example of how paleorecords have helped assessing 
the origin of changes, we consider some, mainly subtropical, regions

which have experienced systematic drying in recent decades (see also 
Section 8.3.1.3). Paleoclimate simulations of monsoons are assessed 
in Section 3.3.3.2.
Records of tree ring width have provided evidence that recent 
prolonged dry spells in the Levant and Chile are unprecedented in the 
last millennium ( high confidence ) (Cook et al., 2016a; Garreaud et al., 
2017). East Africa has also been drying in recent decades (Rowell et al., 
2015; Hoell et al., 2017), a trend that is unusual in the context of the sedimentary paleorecord spanning the last millennium (Tierney et al., 
2015). This may be a signature of anthropogenic forcing but cannot  
yet be distinguished from natural variability (Hoell et al., 2017; Philip 
et al., 2018). Likewise, tree rings indicate that the 2012–2014 drought 
in the south-western United States was exceptionally severe in the 
context of natural variability over the last millennium, and may have

been exacerbated by the contribution of anthropogenic temperature 
rise ( medium confidence ) (Griffin and Anchukaitis, 2014; Williams 
et al., 2015). Furthermore, Williams et al. (2020) used a combination 
of hydrological modelling and tree-ring reconstructions to show that 
the period from 2000 to 2018 was the driest 19-year span in south-
western North America since the late 1500s. Nonetheless, tree rings 
also indicate the presence of prolonged megadroughts in western 
North America throughout the last millennium that were more severe 
than 20th and 21st century events ( high confidence ) (Cook et al., 2004, 
2010, 2015). These were associated with internal variability (Coats 
et al., 2016; Cook et al., 2016b) and indicate that large-magnitude 
changes in the water cycle may occur irrespective of anthropogenic 
influence (see also McKitrick and Christy, 2019).
Paleoclimate records also allow for model evaluation under 
conditions different from present-day. The AR5 concluded that

models can successfully reproduce to first-order patterns of past 
precipitation changes during the Last Glacial Maximum (LGM) and 
mid-Holocene, though simulated precipitation changes during the 
Northern EuropeW. & C. EuropeMediterraneanSahara/Sahel West Africa4002000200400600800mm yr1Precipitation change in the Mid-Holocene
PMIP3 models
AWI-ESM-1-1-LR
CESM2
EC-Earth3-LR
FGOAL S-f3-LFGOAL S-g3
GISS-E2-1-G
HadGEM3-GC31- LL
INM-CM4-8
IPSL-CM6A-LR
MIROC-ES2LMPI-ESM1-2-LR
MRI-ESM2-0
NESM3
NorESM1-F
NorESM2-LM
UofT-CCSM-4Reconstr uctions
Figure 3.11 |  Comparison between simulated annual precipitation changes and pollen-based reconstructions in the mid-Holocene (6000 years ago).  The 
area-averaged changes relative to the pre-industrial control simulations over five regions (Iturbide et al., 2020) as simulated by CMIP6 models (individually identifiable, one

ensemble member per model) and CMIP5 models (blue) are shown, stretching from the tropics to high-latitudes. All regions contain multiple quantitative reconstructions of 
changes relative to present day; their interquartile range are shown by boxes and with whiskers for their full range excluding outliers. Figure is adapted from Brierley et al. (2020). 
Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

450
mid-Holocene tended to be underestimated (Flato et al., 2013). 
Further analysis of CMIP5 models confirmed these results but 
has also revealed systematic offsets from the paleoclimate record 
(DiNezio and Tierney, 2013; Hargreaves and Annan, 2014; Harrison 
et al., 2014, 2015; Bartlein et al., 2017; Scheff et al., 2017; Tierney 
et al., 2017). Harrison et al. (2014) concluded that CMIP5 models 
do not perform better in simulating rainfall during the LGM and 
mid-Holocene than earlier model versions despite higher resolution 
and complexity. However, prescribing changes in vegetation and dust 
was found to improve the match to the paleoclimate record (Pausata 
et al., 2016; Tierney et al., 2017) suggesting that vegetation feedbacks 
in the CMIP5 models may be too weak ( low confidence ) (Hopcroft 
et al., 2017). Brierley et al. (2020) compared the latitudinal gradient 
of annual precipitation changes in the European–African sector

simulated by CMIP6 models for the mid-Holocene with pollen-based 
reconstructions and showed that models generally reproduce the 
direction of changes seen in the reconstructions (Figure 3.11). They 
do not show a robust signal in area averaged rainfall over most 
European regions where quantitative reconstructions exist, which is not incompatible with reconstructions. Over the Sahara/Sahel and 
West Africa regions, where reconstructions suggest positive anomalies 
during the mid-Holocene, both CMIP5 and CMIP6 models also 
simulate a rainfall increase, but it is much weaker (see also Section 
3.3.3.2). Overall, however, large discrepancies remain between 
simulations and reconstructions.
Liu et al. (2018) evaluated the soil moisture changes that occurred 
during the LGM and concluded that the multi-model median from 
CMIP5 is consistent with available paleo-records in some regions, 
but not in others. CMIP5 models accurately reproduce an increase

in moisture in the western United States, related to an intensified 
winter storm track and decreased evaporative demand (Oster et al., 
2015; Ibarra et al., 2018; Lora, 2018). On the other hand, CMIP5 
models show a wide variety of responses in the tropical Indo-Pacific 
region, with only a few matching the pattern of change inferred from 
the paleoclimate record (DiNezio and Tierney, 2013; DiNezio et al., 
2018). The variable response across models is related to the effect 
of the exposure of the tropical shelves during glacial times, which 
CMIP5
CMIP6
RSS
ERA5
Figure 3.12 |  Total column water vapour trends (% per decade) for the period 1988–2019 averaged over the near-global oceans (50°S–50°N).  The figure 
shows satellite data (RSS) and ERA5.1 reanalysis, as well as CMIP5 (blue) and CMIP6 (red) historical simulations. All available ensemble members were used (see Section 3.2).

Fits to the model trend probability distributions were performed with kernel density estimation. Figure is updated from Santer et al. (2007). Further details on data sources and 
processing are available in the chapter data table (Table 3.SM.1).

451
variously intensifies or weakens convection in the rising branch of 
the Walker cell, depending on model parameterization (DiNezio 
et al., 2011). For the Last Interglacial, CMIP6 models reproduce 
the proxy-based increased precipitation relative to pre-industrial in the  
North African, South Asian and North American regions, but not in 
Australia (Scussolini et al., 2019).
In summary, there is medium confidence  that CMIP5 and CMIP6 
models can reproduce broad aspects of precipitation changes during 
paleo reference periods, but large discrepancies remain. Further 
assessment of model performance and comparison between CMIP5 
and CMIP6 during past climates can be found in Section 3.8.2.1.
3.3.2.2 Atmospheric Water Vapour
The AR5 concluded that an anthropogenic contribution to increases 
in specific humidity is found with medium confidence at and near the 
surface. A levelling off of atmospheric water vapour over land in

the last two decades that needed better understanding, and remaining 
observational uncertainties, precluded a more confident assessment 
(Bindoff et al., 2013). Sections 4.5.1.3 and 8.3.1.4 show that there 
have been significant advances in the understanding of the processes 
controlling land surface humidity. In particular, there has been a focus 
on the role of oceanic moisture transport and land-atmosphere 
feedbacks in explaining the observed trends in relative humidity.
Water vapour is the most important natural greenhouse gas and its 
amount is expected to increase in a global warming context leading 
to further warming. Particularly important are changes in the upper 
troposphere because there water vapour regulates the strength of 
the water-vapour feedback (Section 7.4.2.2). CMIP5 models have 
been shown to have a wet bias in the tropical upper troposphere 
and a dry bias in the lower troposphere, with the former bias and

model spread being larger than the latter (Jiang et al., 2012; Tian 
et al., 2013). Tian et al. (2013) also showed that in comparison to the 
AIRS specific humidity, CMIP5 models have the well-known double 
Inter-tropical Convergence Zone (ITCZ) bias in the troposphere from 
1000 hPa to 300 hPa, especially in the tropical Pacific. Water vapour 
biases in models are dominated by errors in relative humidity 
throughout the troposphere, which are in turn closely related to 
errors in large scale circulation; temperature errors dominate near 
the tropopause (Takahashi et al., 2016). Section 7.4.2.2 discusses 
this topic in more detail for CMIP6 models. However, Schröder et al. 
(2019) show that the majority of well-established water vapour 
records are affected by inhomogeneity issues and thus should 
be used with caution (see also Section 2.3.1.3.3). A comparison 
of trends in column water vapour path for 1998–2019 in satellite

data, a reanalysis, CMIP5 and CMIP6 simulations averaged over 
the near-global ocean reveals that while on average model trends 
are higher than those in observations and a reanalysis, the latter lie 
within the multi-model range (Figure 3.12).
The detection and attribution of tropospheric water vapour changes 
can be traced back to Santer et al. (2007), who used estimates of 
atmospheric water vapour from the satellite-based Special Sensor 
Microwave Imager (SSM/I) and from CMIP3 historical climate 
simulations. They provided evidence of human-induced moistening of the troposphere, and found that the simulated human fingerprint 
pattern was detectable at the 5% level by 2002 in water vapour 
satellite data (from 1988 to 2006). The observed changes matched the 
historical simulations forced by greenhouse gas changes and other 
anthropogenic forcings, and not those due to natural variability alone. 
Then, Santer et al. (2009) repeated this study with CMIP5 models,

and found that the detection and attribution conclusions were not 
sensitive to model quality. These results demonstrate that the human 
fingerprint is governed by robust and basic physical processes, such 
as the water vapour feedback. Finally, Chung et al. (2014) extended 
this line of research by focusing on the global-mean water vapour 
content in the upper troposphere. Using satellite-based observations 
and sets of CMIP5 climate simulations run under various climate-
forcing options, they showed that the observed moistening trend of the 
upper troposphere over the 1979–2005 period could not be explained 
by internal variability alone, but is attributable to a combination of 
anthropogenic and natural forcings. This increase in water vapour is 
accompanied by a reduction in mid-tropospheric relative humidity 
and clouds in the subtropics and mid-latitude in both models and 
observations related to changes in the Hadley cell (Section 3.3.3.1.1; 
Lau and Kim, 2015).

Dunn et al. (2017) confirmed earlier findings that global mean surface 
relative humidity increased between 1973 and 2000, followed by 
a steep decline (also reported in Willett et al., 2014) until 2013, and 
specific humidity correspondingly increased and then remained 
approximately constant (see also Section 2.3.1.3.2), with none of the 
CMIP5 models capturing this behaviour. They noted biases in the mean 
state of the  CMIP5 models’ surface relative humidity (and ascribed 
the failure to the representation of land surface processes and their 
response to CO 2 forcing), concluding that these biases preclude any 
detection and attribution assessment. On the other hand, Byrne and 
O’Gorman (2018) showed that the positive trend in specific humidity 
continued in recent years and can be detected over land and ocean 
from 1979 to 2016. Moreover, they provided a theory suggesting that 
the increase in annual surface temperature and specific humidity as well

as the decrease in relative humidity observed over land are linked to 
warming over the neighbouring ocean. They also pointed out that the 
negative trend in relative humidity over land regions is quite uncertain 
and requires further investigation. A recent study has also identified 
an anthropogenically-driven decrease in relative humidity over the 
Northern Hemisphere mid-latitude continents in summer between 1979 
and 2014, which was underestimated by CMIP5 models (Douville and 
Plazzotta, 2017). Furthermore, in a modelling study Douville et al. (2020) 
showed that this decrease in boreal summer relative humidity over mid-
latitudes is related not only to global ocean warming, but also to the 
physiological effect of CO 2 on plants in the land surface model.
In summary, we assess that it is likely that human influence has 
contributed to moistening in the upper troposphere since 1979. 
Also, there is medium confidence  that human influence contributed

to a global increase in annual surface specific humidity, and medium 
confidence  that it contributed to a decrease in surface relative humidity 
over mid-latitude Northern Hemisphere continents during summertime.

452
Chapter 3 Human Influence on the Climate System33.3.2.3 Precipitation
AR5 concluded that there was medium confidence  that human 
influence had contributed to large-scale precipitation changes over 
land since 1950, including an increase in the Northern Hemisphere 
mid- to high latitudes. Moreover, AR5 concluded that observational 
uncertainties and challenges in precipitation modelling precluded 
a more confident assessment (Bindoff et al., 2013). Overall, it found 
that large-scale features of mean precipitation in CMIP5 models were 
in modest agreement with observations, but there were systematic 
errors in the tropics (Flato et al., 2013).
Since AR5, X. Li et al. (2016b) found that CMIP5 models simulate the 
large scale patterns of annual mean land precipitation and seasonality 
well, as well as reproducing qualitatively the observed zonal mean 
land precipitation trends for the period 1948–2005: models capture

the drying trends in the tropics and at 45°S and the wetting trend in 
the Northern Hemisphere mid- to high latitudes, but the amplitudes 
of the changes are much smaller than observed. Land precipitation 
was found to show enhanced seasonality in observations (Chou 
et al., 2013), qualitatively consistent with the simulated response to 
anthropogenic forcing (Dwyer et al., 2014). However, models do not 
appear to reproduce the zonal mean trends in the magnitude of the 
seasonal cycle over the period 1948–2005, nor the two-dimensional 
distributions of trends of annual precipitation and seasonality over 
land, but differences may be explainable by internal variability 
(X. Li et al., 2016b). However, observed trends in seasonality depend 
on data set used (X. Li et al., 2016b; Marvel et al., 2017), and Marvel 
et al. (2017) found that observed changes in the annual cycle phase 
are consistent with model estimates of forced changes. These

phase changes are mainly characterized by earlier onset of the wet 
season on the equatorward flanks of the extratropical storm tracks, 
particularly in the Southern Hemisphere. Box 8.2 assesses regional 
changes in water cycle seasonality.
The CMIP5 models have also been shown to adequately simulate 
the mean and interannual variability of the global monsoon 
(Section 3.3.3.2), but maintain the double ITCZ bias in the equatorial 
Pacific (Lee and Wang, 2014; Tian, 2015; Ni and Hsu, 2018). Despite 
the ITCZ bias, CMIP5 models have been used to detect in reanalysis 
a southward shift in the ITCZ prior to 1975, followed by a northward 
shift in the ITCZ after 1975, in response to forced changes in 
inter-hemispheric temperature contrast (Sections 3.3.1.1 and 8.3.2.1, 
and Figure 8.11; Bonfils et al., 2020; Friedman et al., 2020). CMIP5 
models perform better than CMIP3 models, in particular regarding the 
global monsoon domain and intensity (Lee and Wang, 2014).

In observations at time scales less than a day intermittent rainfall 
fluctuations dominate variability, but CMIP5 models systematically 
underestimate them (Covey et al., 2018). Moreover, as noted in 
previous generation models, CMIP5 models produce rainfall too early 
in the day (Covey et al., 2016). Also, models overpredict precipitation 
frequency but have weaker intensity, although comparison with 
observed datasets is complex as there are large differences in intensity 
among them (Herold et al., 2016; Pendergrass and Deser, 2017; 
Trenberth et al., 2017). Regarding trends in precipitation intensity, 
models have also been shown to reproduce the compensation between increasing heavy precipitation and decreasing light to 
moderate rainfall (Thackeray et al., 2018b), a  characteristic found 
in the observational record (Gu and Adler, 2018). Regional model 
performance is further assessed in Chapter  8 and the Atlas, while 
precipitation extremes are considered in Chapter 11.

The simulation of annual mean rainfall patterns in the CMIP6 models  
reveals minor improvements compared to those in CMIP5 
models (Figure 3.13). The persistent biases include the double ITCZ in 
the tropical Pacific (seen as bands of excessive rainfall on both sides 
of the equatorial Pacific in Figure 3.13b,d) and the southward-shifted 
ITCZ in the equatorial Atlantic, which have been linked to the meridional 
pattern of SST bias (S. Zhou et al., 2020) and the reduced sensitivity of 
precipitation to local SST (Good et al., 2021). Tian and Dong (2020) also 
found that all three generations of CMIP models share similar systematic 
annual mean precipitation errors in the tropics, but that the double 
ITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3 
and CMIP5 models. They also found some improvement in the overly 
intense Indian ocean ITCZ and the too dry South American continent 
except over the Andes. Fiedler et al. (2020) identified improvements in

the tropical mean spatial correlations and root mean square error of 
the climatology as well as in the day-to-day variability, but found little 
change across CMIP phases in the double ITCZ bias and diurnal cycle. 
The CMIP6 models reproduce better the domain and intensity of the 
global monsoon (see Section 3.3.3.2). Moreover, CMIP6 models better 
represent the storm tracks (Priestley et al., 2020; also Section 3.3.3.3), 
thereby reducing the precipitation biases in the North Atlantic and 
mid-latitudes of the Southern Hemisphere (Figure 3.13b,d). As a result, 
pattern correlations between simulated and observed annual mean 
precipitation range between 0.80 and 0.92 for CMIP6 models, compared 
to a range of 0.79 to 0.88 for CMIP5 (Bock et al., 2020). This relative 
improvement may be related to increased model resolution, as found 
when comparing biases in the mean of the HighResMIP models with 
the mean of the corresponding lower-resolution versions of the same

models (see Figure 3.13e,f), particularly in the tropics and extratropical 
storm tracks. Consistent with this, a recent study using several coupled 
models showed that increasing the atmospheric resolution leads to 
a strong decrease in the precipitation bias in the tropical Atlantic ITCZ 
(see further discussion in Section 3.8.2.2; Vannière et al., 2019). Based 
on these results we assess that despite some improvements, CMIP6 
models still have deficiencies in simulating precipitation patterns, 
particularly over the tropical ocean ( high confidence ).
Recent studies comparing observations and CMIP5 simulations have 
shown that tropical volcanic eruptions induce a significant reduction 
in global precipitation, particularly over the wet tropics, including the 
global monsoon regions (Iles and Hegerl, 2014; Paik and Min, 2017; 
Paik et al., 2020a). Reconstructions and modelling studies also suggest 
a distinct remote influence of volcanic forcing such that large volcanoes

erupting in one hemisphere can enhance monsoon precipitation in the 
other hemisphere (F . Liu et al., 2016; Zuo et al., 2019). The climatic effect 
of volcanic eruptions is further assessed in Cross-Chapter Box 4.1.
An intensification of the wet–dry zonal mean patterns, consisting of 
the wet tropical and mid-latitude bands becoming wetter, and the dry 
subtopics becoming drier is expected in response to greenhouse gas 
and ozone changes (Section 8.2.2.2). However, detecting these changes

453
Figure 3.13 |  Annual-mean precipitation rate (mm day–1) for the period 1995–2014. (a)  Multi-model (ensemble) mean constructed with one realization of the CMIP6 
historical experiment from each model. (b) Multi-model mean bias, defined as the difference between the CMIP6 multi-model mean and the precipitation analysis from the 
Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and 
averaged with respect to the precipitation analysis from GPCP version 2.3. (d) Multi-model mean bias, calculated as the difference between the CMIP6 multi-model mean and 
the precipitation analysis from GPCP version 2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e) high resolution and (f) low-

resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version 2.3. Uncertainty is represented using the advanced approach. No overlay 
indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines 
indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting 
signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For more information on the advanced 
approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least

three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

454
is complicated by model errors in locating the main features of rainfall 
patterns. To deal with this issue, Marvel and Bonfils (2013) identified 
in each CMIP5 historical simulation the latitudinal peaks and troughs 
of the rainfall latitudinal patterns, measured the amplification and shift 
of these patterns in a pattern-based fingerprinting study, and found 
that the simultaneous amplification and shift in zonal precipitation 
patterns are detectable in Global Precipitation Climatology Project 
(GPCP) observations over the 1979–2012 period. Similarly, Bonfils 
et al. (2020) found that the intensification of wet–dry zonal patterns 
identified in CMIP5 historical simulations is detectable in reanalyses 
over the 1950–2014 period (see also Figure 8.11).
Based on long-term island precipitation records, Polson et al. (2016) 
identified significant increases in precipitation in the tropics and

decreases in the subtropics, which are consistent with those simulated 
by the CMIP5 models. Moreover, results from Polson and Hegerl (2017) 
give support to an intensification of the water cycle according to the wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as 
well. Other studies suggest that this paradigm does not necessarily hold 
over dry regions where moisture is limited (see also Section 8.2.2.1; 
Greve et al., 2014; Kumar et al., 2015). Polson and Hegerl (2017) 
explained this discrepancy by taking into account the seasonal and 
interannual movement of the regions (Allan, 2014). A follow-up study 
using CMIP6 models also found that the observed strengthening 
contrast of precipitation over wet and dry regions was detectable, 
although the increase was significantly larger in observations than in 
the multi-model mean. The change was attributed to a combination 
of anthropogenic and natural forcings, with anthropogenic forcings

detectable in multi-signal analyses (Figure 3.14; Schurer et al., 2020).
Global land precipitation has likely  increased since the middle of the 
20th century ( medium confidence ), while there is low confidence  
in trends in land data prior to 1950 and over the ocean during the 
satellite era due to disagreement between datasets (Section 2.3.1.3.4). Figure 3.14 |  Wet (a) and dry (b) region tropical mean (30°S–30°N) annual precipitation anomalies.  Observed data are shown with black lines (GPCP), ERA5 
reanalysis is shown in grey, single model simulations are shown with light blue/red lines (CMIP6), and multi-model mean results are shown with dark blue/red lines (CMIP6). Wet 
and dry region annual anomalies are calculated as the running mean over 12 months relative to a 1988–2020 base period. The regions are defined as the wettest third and driest

third of the surface area, calculated for the observations and for each model separately for each season (following Polson and Hegerl, 2017). Scaling factors (c, d)  are calculated 
for the combination of the wet and dry region mean, where the observations, reanalysis and all the model simulations are first standardized using the mean standard deviation of 
the pre-industrial control simulations. Two total least squares regression methods are used: noise in variables (following Polson and Hegerl, 2017) which estimates a best estimate 
and a 5–95% confidence interval using the pre-industrial controls (circle and thick green line) and the pre-industrial controls with double the variance (thin green line); and 
a bootstrap method (DelSole et al., 2019) (5–95% confidence interval shown with a purple line and best estimate with a circle). Panel (c) shows results for GPCP and panel (d)

for ERA5. Figure is adapted from Schurer et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

455
Figure  3.15a shows the time evolution of the global mean land 
precipitation since 1950, as well as the trend during the period. Adler 
et al. (2017) found no significant trend in the global mean precipitation 
during the satellite era, consistent with model simulations (Wu 
et al., 2013) and physical understanding of the energy budget (Section 
8.2.1). This has been suggested to be due to the negative effect of 
anthropogenic sulphate aerosol that opposed the positive influence of 
rising global mean temperatures due to greenhouse gases (Salzmann, 
2016; Richardson et al., 2018). The precipitation change expected from 
ocean warming is also partly offset by the fast atmospheric adjustment 
to increasing greenhouse gases (Section 8.2.1). Over the ocean, the 
negligible trend may be due to the cancelling effects of CO 2 and 
aerosols (Richardson et al., 2018).
A gridpoint based analysis of annual precipitation trends over

land regions since 1901 (Knutson and Zeng, 2018) comparing observed and simulated trends found that detectable anthropogenic 
increasing trends have occurred prominently over many mid- to 
high-latitude regions of the Northern Hemisphere and subtropics 
of the Southern Hemisphere. The observed trends in many cases are 
significantly stronger than modelled in the CMIP5 historical runs for 
the 1901–2010 period (though not for 1951–2010), which may be 
due to disagreement between observed datasets (Section 2.3.1.3.4), 
and/or suggest possible deficiencies in models.
The observed precipitation increase in the Northern Hemisphere high 
latitudes over the period 1966–2005 was attributed to anthropogenic 
forcing by a study using CMIP5 models (Wan et al., 2015) supporting 
the AR5 assessment. Initial results from CMIP6 also support the role 
of anthropogenic forcing in the precipitation increase observed in 
Northern Hemisphere high latitudes (see Figure 3.15c): the observed

positive trend detected for the band 60°N–90°N can only be 
° ° ° °
° ° ° °GHCN Climatology 1950-2014Figure 3.15 |  Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c–f)  Evolution of global and zonal average annual 
mean precipitation (mm day–1) over areas of land where there are observations, expressed relative to the base period of 1961–1990, simulated by CMIP6 models (one ensemble 
member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading 
shows the 5–95% confidence interval of the individual model simulations. The data is smoothed using a low pass filter. Observations from three different datasets are included: 
gridded values derived from Global Historical Climatology Network (GHCN version 2) station data, updated from Zhang et al. (2007), data from the Global Precipitation Climatology

Product (GPCP L3 version 2.3, Adler et al. (2003)) and from the Climate Research Unit (CRU TS4.02, Harris et al. (2014)). Also plotted are boxplots showing interquartile and 5–95% 
ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each 
observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day–1) of GHCN version 2 for the years 1950–2014 over land areas used 
to compute the plots. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

456
Chapter 3 Human Influence on the Climate System3reproduced when anthropogenic forcing is included, although models 
tend to simulate overall a larger positive trend. A similar positive trend, 
but less significant, is also detected between 30°N–60°N, while in the 
southern mid-latitudes no trend is simulated (see Figure 3.15d, f).
For the Southern Hemisphere extratropics, Solman and Orlanski (2016) 
found that the observed summertime rainfall increase over high 
latitudes and decrease over mid-latitudes over the period 1979–2010 
are quasi-zonally symmetric and related to changes in eddy activity. The 
latter were in turn found to be associated with the poleward shift of 
the westerlies due mostly to ozone depletion. Positive rainfall trends 
in the subtropics, particularly over south-eastern South America (see 
also Section 10.4.2.2) and northern and central Australia, have been also 
attributed to stratospheric ozone depletion (Kang et al., 2011; Gonzalez

et al., 2014) and greenhouse gases (Vera and Díaz, 2015; Saurral et al., 
2019). During austral winter, wetting at high latitudes and drying at 
mid-latitudes are not zonally homogenous, due to both changes in eddy 
activity and increased lower troposphere humidity. Solman and Orlanski 
(2016) associated these climate changes with increases in greenhouse 
gas concentration levels. Recently, Blazquez and Solman (2017) have 
shown that CMIP5 models represent very well the dynamical forcing 
and the frequency of frontal precipitation in the Southern Hemisphere 
winter extratropics, but the amount of precipitation due to fronts is 
overestimated. Chapters 10 and 11 validate in more detail the simulation 
of fronts in climate models (Sections 10.3.3.4.4 and 11.7.2.3).
Over the ocean, observations show coherent large-scale patterns of 
fresh ocean regions becoming fresher and salty ocean regions saltier 
across the globe, which has been related through modelling studies

to changes in precipitation minus evaporation and is consistent with 
the wet-gets-wetter, dry-gets-drier paradigm (see Sections 3.5.2.2 
and 8.2.2.1; Durack et al., 2012, 2013; Skliris et al., 2014; Durack, 
2015; Hegerl et al., 2015; Levang and Schmitt, 2015; Zika et al., 2015; 
Grist et al., 2016; Cheng et al., 2020).
Overall, studies published since AR5 provide further evidence of an 
anthropogenic influence on precipitation, and therefore we now assess 
that it is likely  that human influence has contributed to large-scale 
precipitation changes observed since the mid-20th century. New 
attribution studies strengthen previous findings of a detectable increase 
in mid to high latitude land precipitation over the Northern Hemisphere 
(high confidence ). There is medium confidence  that human influence 
has contributed to a strengthening of the zonal mean wet tropics-dry 
subtropics contrast, and that tropical rainfall changes follow the wet-

gets-wetter, dry-gets-drier paradigm. There is also medium confidence  
that ozone depletion has increased precipitation over the southern high 
latitudes and decreased it over southern mid-latitudes during austral 
summer. Owing to observational uncertainties and inconsistent results 
between studies, we conclude that there is low confidence  in the 
attribution of changes in the seasonality of precipitation.
3.3.2.4 Streamflow
Streamflow is to-date the only variable of the terrestrial water 
cycle with enough in-situ  observations to allow for detection and 
attribution analysis at continental to global scales. Based on evidence 
from a few formal detection and attribution studies, particularly on the timing of peak streamflow, and the qualitative evaluation of 
studies reporting on observed and simulated trends, AR5 concluded 
that there is medium confidence  that anthropogenic influence on 
climate has affected streamflow in some middle and high latitude

regions (Bindoff et al., 2013). The AR5 also noted that observational 
uncertainties are large and that often only a limited number of 
models were considered.
Section 2.3.1.3.6 assesses that there have not been significant trends 
in global average streamflow over the last century, though regional 
trends have been observed, driven in part by internal variability. Only 
a limited number of studies have systematically compared observed 
streamflow trends at continental to global scales with changes 
simulated by global circulation models in a detection and attribution 
setting. H. Yang et al. (2017) did not find a significant correlation 
between observed runoff changes and changes simulated in CMIP5 
models in most grid cells, consistent with the assessment that observed 
changes are dominated by internal variability. In  a  pan-European 
assessment, Gudmundsson et al. (2017) attributed the spatio-
temporal pattern of decreasing streamflow in southern Europe and

increasing streamflow in northern Europe to anthropogenic climate 
change, but also concluded that additional effects of human water 
withdrawals could not be excluded. Focussing on continental runoff 
between 1958 and 2004, Alkama et al. (2013) found a significant 
change only when using reconstructed data over all rivers, and a 
large uncertainty in the estimate of the global streamflow trend due 
to opposing changes over different continents. Gedney et al. (2014) 
detected the influence of aerosols on streamflow in North America 
and Europe, with aerosols having driven an increase in streamflow 
due to reduced evaporation (see Section 8.3.1.5 for details on 
processes). There is also evidence for a detectable anthropogenic 
contribution toward earlier winter-spring streamflows in the north-
central US (Kam et al., 2018) and in western Canada (Najafi et al., 
2017). From a model evaluation perspective, Sheffield et al. (2013)

reported that CMIP5 models reproduce spatial variations in runoff in 
North America well, though they tend to underestimate it.
Recently, Gudmundsson et al. (2021) performed a global detection 
and attribution study on streamflow and found that some regions are 
drying and others are wetting. Moreover, the simulated streamflow 
trends are consistent with observations only if externally forced 
climate change is considered, and the simulated effects of water and 
land management cannot reproduce the observed trends. The effects 
of volcanic eruptions in driving reduced streamflow have also been 
detected in the wet tropics (Iles and Hegerl, 2015; Zuo et al., 2019).
In summary, there is medium confidence  that anthropogenic climate 
change has altered local and regional streamflow in various parts 
of the world and that the associated global-scale trend pattern is 
inconsistent with internal variability. Moreover, human interventions

and water withdrawals, while affecting streamflow, cannot explain 
the observed spatio-temporal trends ( medium confidence ).

457
Human Influence on the Climate System  Chapter 33Cross-Chapter Box 3.2 | Human Influence on Large-scale Changes in Temperature 
and Precipitation Extremes
Contributors:  Nathan P . Gillett (Canada), Seung-Ki Min (Republic of Korea), Krishnan Raghavan (India), Ying Sun (China),  
Xuebin Zhang (Canada)
Understanding how temperature and precipitation extremes have changed at large scales and the causes of these changes is an 
important part of our overall assessment of human influence on the climate system. Chapter 11 assesses changes in extremes and 
their causes, while this Cross-Chapter Box summarizes relevant assessments and supporting evidence in Chapters 8 and 11 and relates 
changes in extremes to mean changes on global and continental scales.
Attribution of temperature extremes
One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and

global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. 
Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature (Seneviratne 
et al., 2016; Kharin et al., 2018). It is thus no surprise that changes in temperature extremes are consistent with warming mean 
temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to 
human influence (Section 3.3.1), and the connection between changes in mean and extreme temperatures, it is to be expected that 
anthropogenic forcing has also influenced temperature extremes.
Chapter 11 assesses that there is high confidence  that climate models can reproduce the mean state and overall warming of temperature 
extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture

observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time 
periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of 
temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes 
in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand 
panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum 
temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic 
forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean

temperature (Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes 
globally and over most continents over the 1951–2015 period (Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol 
influences are less clearly separable in observed changes in cold extremes.
Chapter 11 assesses that it is virtually certain  that human-induced greenhouse gas forcing is the main contributor to the observed 
increase in the likelihood and severity of hot extremes and the observed decrease in the likelihood and severity of cold extremes on 
global scales, and very likely  that this applies on most continents.
Attribution of precipitation extremes
An important piece of evidence supporting the SREX and AR5 assessment that there is medium confidence that anthropogenic forcing 
has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence

for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in 
atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly 
associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is 
supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the 
increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models 
project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation 
is projected to decrease.
The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models

(high confidence ), and there is high confidence  in the ability of models to capture the large-scale spatial distribution of precipitation 
extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering 
changes in precipitation intensity averaged over all wet days, there is high confidence  that daily mean precipitation intensities have 
increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely  that 
such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also 
finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation 
than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

458
Chapter 3 Human Influence on the Climate System3 
Cross-Chapter Box 3.2 (continued)
panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily 
precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with 
simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on 
extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the 
long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the 
influence of anthropogenic aerosol and natural forcings (Dong et al., 2020; Paik et al., 2020b).
Chapter 11 assesses that it is likely  that human influence, in particular due to greenhouse gas forcing, is the main driver of the

observed intensification of heavy precipitation in global land regions during recent decades (Section 11.4.4).
Cross-Chapter Box 3.2, Figure 1 |  Comparison of observed and simulated changes in global mean temperature and precipitation extremes.  Time 
series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation 
(Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with 
natural and human forcing (top)  and natural forcing only (bottom) . For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 
scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in

brackets represent the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Grid cells with more 
than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means, 
while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model (Section 3.2). Anomalies are 
relative to 1961–1990 means. Figure is updated from Seong et al. (2021), their Figure 3 and Paik et al. (2020b), their Figure 3. Further details on data sources and 
processing are available in the chapter data table (Table 3.SM.1).Climate extremes indices

459
Human Influence on the Climate System  Chapter 333.3.3 Atmospheric Circulation
3.3.3.1 The Hadley and Walker Circulations
The tropical tropospheric circulation features meridional and zonal 
overturning circulations, called Hadley and Walker circulations. In the 
zonal mean, the downwelling branch of the Hadley circulation cell 
is located in the subtropics and is often used as an indicator of the 
meridional extent of the tropics. In the equatorial zonal-vertical 
section, the major rising branch of the Walker circulation is located 
over the Maritime continent with secondary ascending regions over 
northern South America and Africa. The zonal component of the 
surface trade winds over most of the equatorial Pacific and Atlantic 
is associated with the Walker circulation. This section assesses the 
zonal-mean Hadley cell extent and the Pacific Walker circulation 
strength. Regional and water cycle aspects of these circulations are 
assessed in more detail in Section 8.3.2.

AR5 found medium confidence  that the depletion of stratospheric 
ozone had contributed to Hadley cell widening in the Southern 
Hemisphere in austral summer (Bindoff et al., 2013). It also noted 
that in contrast to a simulated weakening in response to greenhouse 
gas forcing, the Walker circulation had actually strengthened since 
the early 1990s, precluding any detection of human influence.
3.3.3.1.1 Hadley cell extent
Grise et al. (2019) found that a metric based on surface zonal winds, 
which are well constrained by surface observations, best compares 
reanalyses with CMIP5 models. With this method and new reanalysis 
products, the CMIP5 historical simulations exhibit comparable mean 
states and variability of the subtropical edge latitude of the Hadley 
cells to those observed (Grise et al., 2019).
Chapter 2 assesses that there has very likely  been a widening of the 
Hadley circulation since the 1980s (Section 2.3.1.4.1). The CMIP5

(Davis and Birner, 2017; Grise et al., 2018) and CMIP6 (Grise and 
Davis, 2020) historical simulation ensembles span the observed 
trends of the zonal-mean Hadley cell edges since the 1980s 
(Figure 3.16a–c). Studies based on CMIP5 models find a contribution 
from human influence to the observed widening trend, especially in 
the Southern Hemisphere (Gerber and Son, 2014; Staten et al., 2018, 
2020; Grise et al., 2019; Jebri et al., 2020), which is confirmed based 
on CMIP6 (Figure 3.16b,c; Grise and Davis, 2020).
In the annual mean, internal variability, including Pacific Decadal 
Variability (PDV; Annex IV.2.6), contributed to the observed 
zonal-mean Hadley cell expansion since 1980 comparably with 
human influence (Allen et al., 2014; Allen and Kovilakam, 2017; 
Mantsis et al., 2017; Amaya et al., 2018; Grise et al., 2018). Indeed, 
the ensemble-mean expansion in historical simulations is significantly 
weaker than in most of the reanalyses shown in Figure  3.16a–c,

while the Atmospheric Model Intercomparison Project (AMIP) 
simulations forced by observed SSTs (Figure 3.16a–c) show stronger 
trends than historical coupled simulations on average (Nguyen et al., 
2015; Davis and Birner, 2017; Grise et al., 2018). The human-induced 
change has not yet clearly emerged out of the internal variability range in the Northern Hemisphere (Quan et al., 2018; Grise et al., 
2019), whereas the trend in the annual-mean Southern Hemisphere 
edge is outside the 5th–95th percentile range of internal variability 
in CMIP6 in three out of the four reanalyses (Figure 3.16b). For the 
Southern Hemisphere summer when the simulated human influence 
is strongest, the 1981–2000 trend in three out of the four reanalyses 
falls outside the 5th–95th percentile range of internal variability 
(Figure 3.16c; L. Tao et al., 2016; Grise et al., 2018, 2019).
In CMIP5 simulations, greenhouse gas increases and, in austral summer,

stratospheric ozone depletion, contribute to the Southern Hemisphere 
expansion (Gerber and Son, 2014; Nguyen et al., 2015; L. Tao et al., 
2016; Y .H. Kim et al., 2017), but the ozone influence is not significant 
in available CMIP6 simulations (Figure 3.16b–c). Since the 2000s, the 
stabilization or slight recovery of stratospheric ozone (Section 2.2.5.2) 
is consistent with the smaller observed trends (Banerjee et al., 2020). 
While many CMIP5 models under-represent the magnitude of the PDV, 
implying potential overconfidence on the detection of human influence 
on the Hadley cell expansion, this is less the case for the CMIP6 models 
(Section 3.7.6). However, the mechanism underlying the Hadley cell 
expansion remains unclear (Staten et al., 2018, 2020), precluding 
a process-based validation of the simulated human influence.
3.3.3.1.2 Walker circulation strength
CMIP5 models reproduce the mean state of the Walker circulation

with reasonable fidelity, evidenced by the spatial pattern correlations 
of equatorial zonal mass stream function between models and 
observations being larger than 0.88 (Ma and Zhou, 2016). CMIP5 
historical simulations on average simulate a significant weakening 
of the Pacific Walker circulation over the 20th century (DiNezio et al., 
2013; Sandeep et al., 2014; Kociuba and Power, 2015), which is 
also seen in CMIP6 (Figure 3.16d). This weakening is accompanied 
by a reduction of convective activity over the Maritime Continent 
and an enhancement over the central equatorial Pacific (DiNezio 
et al., 2013; Sandeep et al., 2014; Kociuba and Power, 2015). 
In the CMIP6 simulations, greenhouse gas forcing induces this 
weakening (Figure 3.16d), which is consistent with theories based 
on radiative-convective equilibrium (Vecchi et al., 2006; Vecchi and 
Soden, 2007) and thermodynamic air-sea coupling (Xie et al., 2010), 
but inconsistent with a theory highlighting the ocean dynamical

effect which suggests a strengthening in response to greenhouse 
gas increases (Clement et al., 1996; Seager et al., 2019; see also 
Section 7.4.4.2.1). Seager et al. (2019) attributed this inconsistency 
to equatorial Pacific SST biases in the models (Section  3.5.1.2.1). 
However, observational and reanalysis datasets disagree on the sign 
of trends in the Walker Circulation strength over the 1901–2010 
period (Figure 3.16d), and Section 2.3.1.4.1 assesses low confidence  
in observed long-term Walker Circulation trends. The observational 
uncertainty remains high in the trends since the 1950s (Tokinaga 
et al., 2012; L’Heureux et al., 2013), though both CMIP5 and CMIP6 
historical simulations span trends of all but one observational data 
set (Figure  3.16e). For this period, external influence simulated 
in CMIP6 is insignificant due to a partial compensation of forced 
responses to greenhouse gases and aerosols and large internal

decadal variability (Figure  3.16e). It is notable that while AMIP 
simulations on average show strengthening over both the periods,

460
Chapter 3 Human Influence on the Climate System3those simulations are forced by one reconstruction of SST, which 
itself is subject to uncertainty before the 1970s (Deser et al., 2010; 
Tokinaga et al., 2012).
Observational SST products indicate that the equatorial zonal SST 
gradient from the western to the eastern equatorial Pacific has 
strengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical 
simulations on average simulate a weakening, large ensemble 
simulations span the observed strengthening since the 1950s 
(Watanabe et al., 2021) suggesting an important contribution from 
internal variability. Coats and Karnauskas (2017) also find that the 
anthropogenic influence on the SST gradient is yet to emerge out of 
internal variability even on centennial time scales.Trends since the 1980s in in-situ and satellite observations and 
reanalyses  exhibit strengthening of the Pacific Walker circulation

and SST gradient (Section 2.3.1.4.1 and Figure 3.16f; L’Heureux et al.,  
2013; Boisséson et al., 2014; England et al., 2014; Kociuba and 
Power, 2015; Ma and Zhou, 2016). AMIP simulations reproduce this 
strengthening (Figure 3.16d; Boisséson et al., 2014; Ma and Zhou, 2016), 
indicating a dominant role of SST changes. However, all reanalysis 
trends lie outside the 5–95% range of simulated CMIP6 historical 
Walker circulation trends over this period (Figure 3.16f), consistent with 
CMIP5 results (England et al., 2014; Kociuba and Power, 2015). This 
may be in part caused by the underestimation of the PDV magnitude 
especially in CMIP5 models (Section 3.7.6 ; Kociuba and Power, 2015; 
Chung et al., 2019), but also suggests a potential error in simulating the 
Figure 3.16 |  Model evaluation and attribution of changes in Hadley cell extent and Walker circulation strength. (a–c)  Trends in subtropical edge latitude of the

Hadley cells in (a) the Northern Hemisphere for 1980–2014 annual means and (b, c)  Southern Hemisphere for (b) 1980–2014 annual means and (c) 1980/81–1999/2000 
December–January–February means. Positive values indicate northward shifts. (d–f)  Trends in the Pacific Walker circulation strength for (d) 1901–2010, (e) 1951–2010 and 
(f) 1980–2014. Positive values indicate strengthening. Based on CMIP5 historical (extended with RCP4.5), CMIP6 historical, AMIP , pre-industrial control, and single forcing 
simulations along with HadSLP2 and reanalyses. Pre-industrial control simulations are divided into non-overlapping segments of the same length as the other simulations. White 
boxes and whiskers represent means, interquartile ranges and 5th and 95th percentiles, calculated after weighting individual members with the inverse of the ensemble size of

the same model, so that individual models are equally weighted (Section 3.2). The filled boxes represent the 5–95% confidence interval on the multi-model mean trends of the 
models with at least three ensemble members, with dots indicating the ensemble means of individual models. The edge latitude of the Hadley cell is where the surface zonal 
wind velocity changes sign from negative to positive, as described in the Appendix of Grise et al. (2018). The Pacific Walker circulation strength is evaluated as the annual mean 
difference of sea level pressure between 5°S–5°N, 160°W–80°W and 5°S–5°N, 80°E–160°E. Further details on data sources and processing are available in the chapter data 
table (Table 3.SM.1).

461
Human Influence on the Climate System  Chapter 33forced changes of the Walker circulation. Specifically, anthropogenic 
and volcanic aerosol changes over this period may have driven 
a strengthening (DiNezio et al., 2013; Takahashi and Watanabe, 2016; 
Hua et al., 2018). This aerosol influence may be indirect via Atlantic 
Multi-decadal Variability (AMV; Annex IV.2.7) through inter-basin 
teleconnections (McGregor et al., 2014; Chikamoto et al., 2016; 
Kucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017), 
which may be underestimated in models due to SST biases in the 
equatorial Atlantic (Section 3.5.1.2.2; McGregor et al., 2018). Note 
also the large uncertainty in aerosol influence on the Walker circulation 
(Kuntz and Schrag, 2016; Hua et al., 2018; Oudar et al., 2018), which is 
also seen in CMIP6 (Figure 3.16f).
Paleoclimate data from the Pliocene epoch suggest that there 
was a reduction in the zonal SST gradient in the tropical Pacific

under a similar CO 2 concentration as today (Section 7.4.4.2.2 and 
Cross-Chapter Box 2.4). Tierney et al. (2019) found that this weaker 
gradient compared to pre-industrial, which suggests a weaker Walker 
circulation, is captured by climate models under Pliocene CO 2 levels, 
in agreement with the CMIP6 response to greenhouse gas forcing 
(Figure 3.16d), though the magnitude of this effect varies strongly 
between models (Corvec and Fletcher, 2017).
3.3.3.1.3 Summary
It is likely  that human influence has contributed to the poleward 
expansion of the zonal mean Hadley cell in the Southern Hemisphere 
since the 1980s. This assessment is supported by studies since AR5, 
which consistently find human influence from greenhouse gas 
increases on the expansion, with additional influence from ozone 
depletion in austral summer. For the strong ozone depletion period 
of 1981–2000, human influence is detectable in the summertime 
poleward expansion in the Southern Hemisphere ( medium

confidence ). By contrast, there is medium confidence that the 
expansion of the zonal mean Hadley cell in the Northern Hemisphere 
is within the range of internal variability, with contributions from 
PDV and other internal variability. The causes of the observed 
strengthening of the Pacific Walker circulation over the 1980–2014 
period are not well understood, since the observed strengthening 
trend is outside the range of variability simulated in the coupled 
models ( medium confidence ). Large observational uncertainty, lack 
of understanding of the mechanism underlying the poleward Hadley 
cell expansion, and contradicting theories on the greenhouse gas 
influence and uncertainty in the aerosol influence on the Walker 
circulation strength, limit confidence in these assessments.
3.3.3.2 Global Monsoon
Monsoons are seasonal transitions of regimes in atmospheric 
circulation and precipitation with the annual cycle of solar insolation,

in association with redistribution of moist static energy (Wang and 
Ding, 2008; P .X. Wang et al., 2014; Biasutti et al., 2018). The global 
monsoon can be defined to encompasses all monsoon systems based 
on precipitation contrast in the solstice seasons (Wang and Ding, 
2008; Figure 3.17). All regional monsoons are intimately connected 
to the global tropical atmospheric overturning by mass (Trenberth et al., 2000), momentum and energy budgets (Biasutti et al., 2018; 
Geen et al., 2020). Assessments of regional monsoon changes are 
made in Sections 8.3.2.4, 10.4.2.1 and 10.6.3.
AR5 assessed that CMIP5 models simulated monsoons better than 
CMIP3 models but that biases remained in domains and intensity 
(high confidence ) (Flato et al., 2013). There were no detection and 
attribution assessment statements on the decreasing trend of global 
monsoon precipitation over land from the 1950s to the 1980s or 
the increasing trend of global monsoon precipitation afterwards.

In the paleoclimate context, it was determined with high confidence  
that orbital forcing produces strong interhemispheric rainfall variability 
evident in multiple types of proxies (Masson-Delmotte et al., 2013).
Paleoclimate proxy evidence shows that the global monsoon has 
varied with orbital forcing and greenhouse gases (Section 2.3.1.4.2; 
Mohtadi et al., 2016; Seth et al., 2019). These large-magnitude 
intensifications and weakenings in the global monsoon involved 
in some cases orders-of-magnitude changes in precipitation locally 
(Harrison et al., 2014; Tierney et al., 2017). Paleoclimate modelling 
and limited data from past climate states with high CO 2 suggest 
that precipitation intensifies in the monsoon domain under elevated 
greenhouse gases, providing context for present and future trends 
(Passey et al., 2009; Haywood et al., 2013; Zhang et al., 2013b). In 
model simulations of the mid-Pliocene, when globally averaged

temperature was higher than present day, precipitation was larger 
in West African, South Asian and East Asian monsoons than under 
pre-industrial conditions, consistent with proxy evidence (Zhang 
et al., 2015; Sun et al., 2016, 2018; Corvec and Fletcher, 2017; 
X. Li et al., 2018). Prescott et al. (2019) and R. Zhang et al. (2019) 
find an important role for orbital forcing and CO 2 in the mid-Pliocene 
monsoon expansion and intensification. Models are also able to 
capture interhemispherically contrasting monsoon changes in the 
Last Interglacial in response to orbital forcing and greenhouse gases, 
with wetter West African and Asian monsoons and a drier South 
American monsoon as seen in proxies (Govin et al., 2014; Gierz 
et al., 2017; Pedersen et al., 2017). In overall agreement with proxy 
evidence, a model with transient forcing simulates wetting and drying 
respectively of the Southern and Northern Hemisphere monsoons

during the last deglaciation, with an important contribution from 
Atlantic Meridional Overturning Circulation (AMOC) slowdown 
(Otto-Bliesner et al., 2014; Mohtadi et al., 2016).
During the mid-Holocene, global monsoons were stronger especially 
in the Northern Hemisphere with an expansion of the West African 
monsoon domain in response to orbital forcing (Biasutti et al., 2018; 
Section 2.3.1.4.2). Simulations of the mid-Holocene with CMIP5 
and CMIP6 models qualitatively capture the stronger Northern 
Hemisphere monsoon (Jiang et al., 2015; Brierley et al., 2020), mainly 
driven by atmospheric circulation changes (D’Agostino et al., 2019). 
However, the models underestimate the monsoon expansion found 
in proxy reconstructions (Perez-Sanz et al., 2014; Harrison et al., 
2015; Tierney et al., 2017), which may be linked to mean biases in 
the monsoon domain (Brierley et al., 2020) and may be improved 
by imposing vegetation and dust changes (Pausata et al., 2016). The

462
Chapter 3 Human Influence on the Climate System3the mid-Holocene (D’Agostino et al., 2020), consistent with proxy 
evidence (Section 2.3.1.4.2). These studies indicate that models 
can qualitatively reproduce past global monsoon changes seen in 
proxies, though issues remain in quantitatively reproducing proxy 
observations. Studies of last millennium simulations show that 
simulated global monsoon precipitation increases with global mean 
temperature, while changes in monsoon circulation and hemispheric 
monsoon precipitation depend on forcing sources (Liu et al., 2012; 
Chai et al., 2018). Compared to greenhouse gas and solar variations, 
volcanic forcing is more effective in changing the global monsoon 
precipitation over the last millennium (Chai et al., 2018).
Reproducing monsoons in terms of domain, precipitation amount, 
and timings of onset and retreat over the historical period also 
remains difficult. While CMIP5 historical simulations broadly capture

global monsoon domains and intensity based on summer and winter 
precipitation differences, they underestimate the extent and intensity 
of East Asian and North American monsoons while overestimating 
them over the tropical western North Pacific (Lee and Wang, 2014; 
M. Yan et al., 2016). B. Wang et al. (2020) reported that CMIP6 
models simulate the global monsoon domain and precipitation better 
(Figure 3.17a,b), albeit with biases in annual mean precipitation and the timings of onset and withdrawal of the Southern Hemisphere 
monsoon. Notable inter-model differences were identified in 
CMIP5, with the multi-model ensemble mean outperforming 
individual models (Lee and Wang, 2014). Common biases were 
identified across CMIP5 models in moist static energy and upper-
tropospheric temperature associated with the South Asian summer 
monsoon, which may arise from overly smoothed model topography 
(Boos and Hurley, 2012). However, in atmospheric models with

increasing resolution approaching 20 km, improvements in monsoon 
precipitation are not universal across regions and models, and overall 
improvements are unclear (Johnson et al., 2016; Ogata et al., 2017; 
L. Zhang et al., 2018b) .
In instrumental records, global summer monsoon precipitation 
intensity (measured by summer precipitation averaged over the 
monsoon domain) decreased from the 1950s to 1980s, followed 
by an increase (Section 2.3.1.4.2 and Figure 3.17c), arising mainly 
from variations in Northern Hemispheric land monsoons. A CMIP5 
multi-model study by Y . Zhang et al. (2018) found that observed 
1951–2004 trends of the global and Northern Hemisphere summer 
land monsoon precipitation intensity are well captured by historical 
simulations, and CMIP6 models show similar results for global land 
Figure 3.17 | Model evaluation of global monsoon domain, intensity, and circulation. (a, b)  Climatological summer-winter range of precipitation rate, scaled by

annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations 
for 1979–2014. The region enclosed by red lines is the monsoon domain based on the definition by Wang and Ding (2008). (c, d)  Five-year running mean anomalies of (c) global 
land monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average 
for 1979–2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 
hPa levels averaged over 0°–20°N, from 120°W eastward to 120°E in Northern Hemisphere summer (Wang et al., 2013; m s–1) in CMIP5 historical and RCP4.5 simulations,

and CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May to September is defined as Northern Hemisphere summer and 
Southern Hemisphere winter, and November to March is defined as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations 
are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of 
the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data 

463
Human Influence on the Climate System  Chapter 33summer monsoon precipitation (Figure 3.17c). However, the 1960s 
peak in the Northern Hemisphere summer monsoon circulation 
is outside the 5th–95th percentile range of CMIP5 and CMIP6 
historical simulations for two out of three reanalyses (Figure 3.17d). 
Modelling studies show that greenhouse gas increases act to 
enhance Northern Hemisphere summer monsoon precipitation 
intensity (Liu et al., 2012; Polson et al., 2014; Chai et al., 2018; L. 
Zhang et al., 2018b). Since the mid-20th century, however, modelling 
studies show that this effect was overwhelmed by the influence of 
anthropogenic aerosols in CMIP5 (Polson et al., 2014; Guo et al., 
2015; Y . Zhang et al., 2018; Giannini and Kaplan, 2019) and in 
CMIP6 (T. Zhou et al., 2020). Weakening of the monsoon circulation 
and reduction of moisture availability are important in this aerosol 
influence (T. Zhou et al., 2020). Besides these human influences,

the global monsoon is sensitive to internal variability and natural 
forcing including ENSO and volcanic aerosols on interannual 
time scales and PDV and AMV on decadal to multi-decadal time 
scales (Wang et al., 2013, 2018; F . Liu et al., 2016; Jiang and Zhou, 
2019; Zuo et al., 2019); though AMV in the 20th century may 
have been partly driven by aerosols, see Section 3.7.7. Indeed, 
AMIP simulations better reproduce the observed multi-decadal 
variations of the global monsoon precipitation and circulation 
(Figure 3.17c,d). Y . Zhang et al. (2018) find that the multi-model 
ensemble mean trend of global land monsoon precipitation in 
historical simulations, dominated by anthropogenic aerosol forcing 
contributions, emerges out of the 90% range of internally-driven 
trends in pre-industrial control simulations. However, it should 
be noted that CMIP5 models tend to under-represent the PDV 
magnitude (Section 3.7.6), suggesting potential overconfidence

in the detection of the forced signal. An observed enhancement 
in global summer monsoon precipitation since the 1980s is 
accompanied by an intensification of the Northern Hemisphere 
summer monsoon circulation (Figure  3.17c,d). These trends 
appear to be at the extreme of the range of the CMIP6 historical 
simulation ensemble but are well captured by AMIP simulations 
(Figure 3.17c,d). While the precipitation increase is consistent with 
greenhouse gas forcing, the circulation intensification is opposite 
to the simulated response to greenhouse gas forcing, and these 
enhancements have been attributed to PDV and AMV (Wang et al., 
2013; Kamae et al., 2017).
In summary, while greenhouse gas increases acted to enhance the global 
land monsoon precipitation over the 20th century ( medium confidence ), 
consistent with projected future enhancement (Section 4.5.1.5), this 
tendency was overwhelmed by anthropogenic aerosols from the 1950s

to the 1980s, which contributed to weakening of global land summer 
monsoon precipitation intensity for this period ( medium confidence ). 
There is medium confidence  that the intensification of global monsoon 
precipitation and Northern Hemisphere summer monsoon circulation 
since the 1980s is dominated by internal variability. These assessments 
are supported respectively by multi-model detection and attribution 
studies which find an important role for anthropogenic aerosols 
in the weakening trend, and studies that identify a role for AMV 
and PDV in inducing the Northern Hemisphere summer monsoon 
circulation enhancement since the 1980s. Supported by multi-model 
simulations that are qualitatively consistent with proxy evidence, there is high confidence  that orbital forcing contributed to higher 
Northern Hemisphere monsoon precipitation in the mid-Pliocene and 
mid-Holocene than pre-industrial. While CMIP5 models can capture

the domain and precipitation intensity of the global monsoon, biases 
remain in their regional representations, and they are unsuccessful 
in quantitatively reproducing changes in paleo reconstructions ( high 
confidence ). CMIP6 models reproduce the domain and precipitation 
intensity of the global monsoon observed over the instrumental 
period better than CMIP5 models ( medium confidence ). However, 
CMIP5 and CMIP6 models fail to fully capture the variations of the 
Northern Hemisphere summer monsoon circulation (Figure 3.17d), but 
there is low confidence  in this assessment due to a lack of evidence in 
the literature.
3.3.3.3 Extratropical Jets, Storm Tracks and Blocking
Extratropical jets are wind maxima in the upper troposphere which 
are often associated with storms, blocking, and weather extremes. 
Blocking refers to long-lived, stationary high-pressure systems that 
are often associated with a poleward displacement of the jet, causing

cold spells in winter and heatwaves in summer (e.g., Sousa et al., 
2018). Sections 2.3.1.4.3, 8.3.2.7, and 11.7.2 discuss these features 
in more detail.
AR5 concluded that models were able to capture the general 
characteristics of extratropical cyclones and storm tracks, although 
it also noted that most models underestimated cyclone intensity, 
that biases in cyclone frequency were linked to biases in sea surface 
temperatures, and that resolution can play a significant role in the 
quality of the simulation of storms (Flato et al., 2013). Similarly, AR5 
found with high confidence  that simulation of blocking was improved 
with increases in resolution. The AR5 did not specifically assess changes 
in Southern Hemisphere storm track characteristics or blocking.
Since AR5, new research using CMIP5 and CMIP6 models has confirmed 
that increasing the model resolution improves the simulation of 
cyclones and blocking in all seasons albeit with some exceptions and

caveats (Zappa et al., 2013; Davini et al., 2017; Schiemann et al., 2017, 
2020; Davini and D’Andrea, 2020; Priestley et al., 2020). New research 
also finds that model performance with respect to the simulation 
of cyclones and that of blocking events are correlated (Zappa et al., 
2014), suggesting biases in both are aspects of the same underlying 
problems in models (Figure  3.18). In the North Pacific basin the 
annual mean blocking frequency is now well simulated compared to 
earlier evaluations, but substantial errors in the blocking frequency 
remain in the Euro-Atlantic sector (Figure 3.18; Dunn-Sigouin and 
Son, 2013; Davini and D’Andrea, 2016, 2020; Mitchell et al., 2017; 
Woollings et al., 2018b). While there is a resolution dependence 
in the size of this bias, even at very high resolution blocking in the 
Euro-Atlantic sector remains underestimated (Schiemann et al., 2017), 
and there is evidence of a compensation of errors as the resolution is

increased (Davini et al., 2017). Davini and D’Andrea (2020) show that 
while the simulation of blocking improves with increasing resolution 
in CMIP3, CMIP5, and CMIP6 models, other factors contribute to 
biases, particularly to the underestimation of Euro-Atlantic blocking 
(Schiemann et al., 2020). The persistence of blocking events, typically

464
Chapter 3 Human Influence on the Climate System3underestimated, has not improved from CMIP5 to CMIP6 (Schiemann 
et al., 2020). Section 10.3.3.3 discusses the implications of the biases 
discussed here for regional climate.
For the North Pacific storm track CMIP6 simulations exhibit large 
remaining underestimations of cyclone frequencies during summer 
(June to August), which for the low-resolution models have essentially 
remained unchanged versus CMIP5, and there is only a small resolution 
dependence of this bias (Priestley et al., 2020). During winter (December 
to February), both CMIP5 and CMIP6 models tend to place the North 
Pacific storm track too far equatorward (M. Yang et al., 2018; Priestley 
et al., 2020), leading to an overestimation of cyclones between 30°N 
and 40°N in the Pacific and an underestimation to the north of 
this. Both low- and high-resolution models show this pattern, but 
low-resolution models generally simulate fewer cyclones throughout

the North Pacific (Priestley et al., 2020).
In winter, the North Atlantic storm track remains displaced to the 
south and east in many models (Harvey et al., 2020), leading to 
underestimation of cyclone frequencies near the North American coast 
and overestimation in the eastern North Atlantic. Higher-resolution 
CMIP6 models perform slightly better in this regard than low-
resolution models. In summer (June to August), cyclone frequencies 
throughout the extratropical North Atlantic, which were substantially 
underestimated in CMIP5, have improved in CMIP6 high-resolution 
models. In low-resolution CMIP6 models, the problem is essentially 
unchanged (Priestley et al., 2020); this is associated with generally 
underestimated variability of sea level pressure in CMIP models 
(Harvey et al., 2020).
For the Southern Hemisphere (not considered in AR5), Priestley 
et al. (2020) find considerable improvement in the placement of 
the Southern Ocean storm track during summer (December to

February) in CMIP6 models versus CMIP5, consistent with a more 
realistic annual mean surface wind maximum latitude in CMIP6 
than in CMIP5 (Goyal et al., 2021). Relative to CMIP5, both low- and 
high-resolution CMIP6 models have increased track densities south 
of about 55°S and decreased track densities between about 40°S 
and 55°S, in better agreement with observations than CMIP5 models 
(Parsons et al., 2016; Patterson et al., 2019). CMIP5 models and high-
resolution CMIP6 models simulate a storm track that is positioned 
too far equatorward, although the bias is smaller in the high-
resolution models. By contrast, the low-resolution CMIP6 models 
simulate a storm track that is slightly too far poleward on average 
(Priestley et al., 2020). In winter (June to August), the biases found in 
CMIP5 are only slightly improved in CMIP6, with models continuing 
to underestimate the broad maximum cyclone track density in the

south-eastern Indian Ocean and overestimate the minimum density 
in the south-western South Pacific (Priestley et al., 2020).
There is only one contiguous blocking region in the Southern 
Hemisphere, with the blocking frequency maximizing in the South 
Pacific and minimizing in the southern Indian Ocean regions (Parsons 
et al., 2016; Patterson et al., 2019). CMIP5 simulations agree relatively 
well with ERA-Interim in this region regarding the distribution of 
blocking events (Parsons et al., 2016). Individual models exhibit 
considerable biases in the blocking frequency; however only in austral summer do Patterson et al. (2019) find a systematic, multi-model 
underestimation of the blocking frequency in and around the Tasman 
Sea. The blocking frequency is anticorrelated with the amplitude 
of the SAM. Ozone depletion, through stratosphere-troposphere 
coupling, may have caused an increase in the blocking frequency in

the South Atlantic sector (Dennison et al., 2016); this finding requires 
confirmation using a multi-model approach.
In addition to inadequate resolution, blocking and storm track biases 
in both hemispheres also result from mean state biases, in particular, 
biases related to the parameterization of orographic effects and to 
the misrepresentation of the Gulf Stream SST front (Anstey et al., 
2013; Berckmans et al., 2013; Davini and D’Andrea, 2016; O’Reilly 
et al., 2016a; Pithan et al., 2016; Schiemann et al., 2017). Nonetheless 
overall SST biases have been suggested to have only a weak relevance 
to blocking (Davini and D’Andrea, 2016).
Section 2.3.1.4.3 assesses that the total number of extratropical 
cyclones has likely  increased since the 1980s in the Northern 
Hemisphere ( low confidence ), but with fewer deep cyclones 
particularly in summer. This observed reduction in cyclone activity 
by about 4% per decade in the Northern Hemisphere in summer

(Chang et al., 2016; Section 2.3.1.4.3) may be associated with 
human-induced warming. CMIP5 historical simulations generally 
reproduce a reduction but underestimate its magnitude (Chang 
et al., 2016). Furthermore, feedback mechanisms associated with 
clouds may be responsible for substantial inter-model spread (Chang 
et al., 2016; Voigt and Shaw, 2016). In boreal winter, recent studies 
have suggested a potential influence of the rapid Arctic warming on 
observed intensification of Northern Hemisphere storm track activity 
in the past few decades, while other studies question this possibility 
(Cross-Chapter Box 10.1).
Section 2.3.1.4.3 assesses that the extratropical jets and cyclone 
tracks have likely  shifted poleward in both hemispheres since the 
1980s with marked seasonality in trends ( medium confidence ). For 
the Southern Hemisphere, studies using CMIP5 and other models 
imply that both ozone depletion and increasing greenhouse gases

have caused substantial atmospheric circulation change since 
the 1960s when concentrations of ozone-depleting substances 
started to increase (Eyring et al., 2013; Iglesias-Suarez et al., 
2016; Karpechko et al., 2018; Son et al., 2018). In particular, ozone 
depletion, during austral summer, has been linked to a poleward 
shift of the westerly jet and Southern Hemisphere circulation zones 
and a southward expansion of the tropics (Kang et al., 2011), which 
is associated with a strengthening trend of the Southern Annular 
Mode (SAM; Section 3.7.2). This has been well reproduced by 
climate models with prescribed historical ozone concentration or 
interactive ozone chemistry (Gerber and Son, 2014; Son et al., 2018; 
Figure 3.19).
In summary, there is low confidence  that an observed decrease in 
the frequency of Northern Hemisphere summertime extratropical 
cyclones is linked to anthropogenic influence. In the Southern

Hemisphere, there is high confidence  that human influence, in the 
form of ozone depletion, has contributed to the observed poleward 
shift of the jet in austral summer, while confidence  is low for

465
Figure 3.19 |  Long-term mean (thin black contours) and linear trend (colour) of zonal mean December–January–February zonal winds from 1985 to 2014 
in the Southern Hemisphere.  The figure shows (a) ERA5 and (b) the CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero 
long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modified 
from Eyring et al. (2013), their Figure 12. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).
Figure 3.18 | Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December–January–February–
March – DJFM) for the years 1979–2000.  Results are shown for the ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and

shaded ranges show corresponding 5–95% ranges constructed with one realization from each model. Figure is adapted from Davini and D’Andrea (2020), their Figure 12 and 
following the D’Andrea et al. (1998) definition of blocking. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

466
Chapter 3 Human Influence on the Climate System3human influence on historical blocking activity. The low confidence  
statements are due to the limited number of studies available. The 
shift of the Southern Hemisphere jet is correlated with modulations 
of the SAM (Section 3.7.2). There is medium confidence  in model 
performance regarding the simulation of the extratropical jets, storm 
track and blocking activity, with increased resolution sometimes 
corresponding to better performance, but important shortcomings 
remain, particularly for the Euro-Atlantic sector of the Northern 
Hemisphere. Nonetheless, synthesizing across Sections 3.3.3.1–
3.3.3.3, there is high confidence  that CMIP6 models capture the 
general characteristics of the tropospheric large-scale circulation.
3.3.3.4 Sudden Stratospheric Warming Activity
Sudden stratospheric warmings (SSWs) are stratospheric weather 
events associated with anomalously high temperatures at high

latitudes persisting from days to weeks. Section 2.3.1.4.5 discusses 
the definition and observational aspects of SSWs. SSWs are often 
associated with anomalous weather conditions, for example, winter 
cold spells, in the lower atmosphere (e.g., Butler et al., 2015; Baldwin 
et al., 2021).
Seviour et al. (2016) found that stratosphere-resolving CMIP5 
models, on average, reproduce the observed frequency of vortex 
splits (one form of SSWs) but with a wide range of model-specific 
biases. Models that produce a better mean state of the polar vortex 
also tend to produce a more realistic SSW frequency (Seviour 
et al., 2016). The mean sea level pressure anomalies occurring in 
CMIP5 model simulations when an SSW is underway, however, 
differ substantially from those in reanalyses (Seviour et al., 
2016). Unlike stratosphere-resolving models, models with limited 
stratospheric resolution, which make up more than half of the 
CMIP5 ensemble, underestimate the frequency of SSWs (Osprey

et al., 2013; J. Kim et al., 2017). Taguchi (2017) found a general 
underestimation in CMIP5 models of the frequency of ‘major’ SSWs 
(which are associated with a break-up of the polar vortex), an 
aspect of an under-representation in those models of dynamical 
variability in the stratosphere. Wu and Reichler (2020) found that 
finer vertical resolution in the stratosphere and a model top above 
the stratopause tend to be associated with a more realistic SSW 
frequency in CMIP5 and CMIP6 models.
Some studies find an increase in the frequency of SSWs under 
increasing greenhouse gases (e.g., Schimanke et al., 2013; Young 
et al., 2013; J. Kim et al., 2017). However, this behaviour is not 
robust across ensembles of chemistry-climate models (Mitchell et al., 
2012; Ayarzagüena et al., 2018; Rao and Garfinkel, 2021). There is 
an absence of studies specifically focusing on simulated trends in 
SSWs during recent decades, and the short record and substantial

decadal variability yields low confidence  in any observed trends in 
the occurrence of SSW events in the Northern Hemisphere winter 
(Section 2.3.1.4.5). Such an absence of a trend and large variability 
would also be consistent with a recent reconstruction of SSWs 
extending back to 1850, based on sea level pressure observations 
(Domeisen, 2019), although this time series has limitations as it is not 
based on direct observations of SSWs.In summary, an anthropogenic influence on the frequency or other 
aspects of SSWs has not yet been robustly detected. There is low 
confidence in the ability of models to simulate any such trends over 
the historical period because of large natural interannual variability 
and also due to substantial common biases in the simulated mean 
state affecting the simulated frequency of SSWs.
3.4 Human Influence on the Cryosphere
3.4.1 Sea Ice
3.4.1.1 Arctic Sea Ice
The AR5 concluded that ‘anthropogenic forcings are very likely  to have

contributed to Arctic sea ice loss since 1979’ (Bindoff et al., 2013), based 
on studies showing that models can reproduce the observed decline only 
when including anthropogenic forcings, and formal attribution studies. 
Since the beginning of the modern satellite era in 1979, Northern 
Hemisphere sea ice extent has exhibited significant declines in all 
months with the largest reduction in September (see Section 2.3.2.1.1, 
and Figures 3.20 and 3.21 for more details on observed changes). The 
recent Arctic sea ice loss during summer is unprecedented since 1850 
(high confidence ), but as in AR5 and SROCC there remains only medium 
confidence that the recent reduction is unique during at least the past 
1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1). 
CMIP5 models also simulate Northern Hemisphere sea ice loss over the 
satellite era but with large differences among models (e.g., Massonnet

et al., 2012; Stroeve et al., 2012). The envelope of simulated ice loss 
across model simulations encompasses the observed change, although 
observations fall near the low end of the CMIP5 and CMIP6 distributions 
of trends (Figure 3.20). CMIP6 models on average better capture the 
observed Arctic sea ice decline, albeit with large inter-model spread. Notz 
et al. (2020) found that CMIP6 models better reproduce the sensitivity 
of Arctic sea ice area to CO 2 emissions and global warming than earlier 
CMIP models although the models’ underestimation of this sensitivity 
remains. Davy and Outten (2020) also found that CMIP6 models can 
simulate the seasonal cycle of Arctic sea ice extent and volume better 
than CMIP5 models. For the assessment of physical processes associated 
with changes in Arctic sea ice, see Section 9.3.1.1.
Since AR5, there have been several new detection and attribution 
studies on Arctic sea ice. While the attribution literature has mostly used

sea ice extent (SIE), it is closely proportional to sea ice area (SIA; Notz, 
2014), which is assessed in Chapters 2 and 9 and shown in Figures 3.20 
and 3.21. Kirchmeier-Young et al. (2017) compared the observed time 
series of the September SIE over the period 1979–2012 with those from 
different large ensemble simulations which provide a robust sampling 
of internal climate variability (CanESM2, CESM1, and CMIP5) using an 
optimal fingerprinting technique. They detected anthropogenic signals 
which were separable from the response to natural forcing due to solar 
irradiance variations and volcanic aerosol, supporting previous findings 
(Figure 3.21; Min et al., 2008b; Kay et al., 2011; Notz and Marotzke, 
2012; Notz and Stroeve, 2016). Using selected CMIP5 models and 
three independently derived sets of observations, Mueller et al. (2018) 
detected fingerprints from greenhouse gases, natural, and other 
anthropogenic forcings simultaneously in the September Arctic SIE over

467
Human Influence on the Climate System  Chapter 33the period 1953–2012. They further showed that about a quarter of the 
greenhouse gas induced decrease in SIE has been offset by an increase 
due to other anthropogenic forcing (mainly aerosols). Similarly, Gagné 
et al. (2017b) suggested that the observed increase in Arctic sea 
ice concentration over the 1950–1975 period was primarily due to 
the cooling contribution of anthropogenic aerosol forcing based on 
single model simulations. Gagné et al. (2017a) identified a detectable 
increase in Arctic SIE in response to volcanic eruptions using CMIP5 
models and four observational datasets. Polvani et al. (2020) suggested 
that ozone depleting substances played a substantial role in the Arctic 
sea ice loss over the 1955–2005 period.
Differences in sea ice loss among the models (Figure  3.20) have 
been attributed to a number of factors (see also Section 9.3.1.1). 
These factors include the late 20th century simulated sea ice state

(Massonnet et al., 2012), the magnitude of changing ocean heat 
transport (Mahlstein and Knutti, 2011), and the rate of global warming 
(e.g., Gregory et al., 2002; Mahlstein and Knutti, 2012; Rosenblum and Eisenman, 2017). Sea ice thermodynamic considerations 
indicate that the magnitude of sea ice variability and loss depends 
on ice thickness (Bitz, 2008; Massonnet et al., 2018) and  hence 
the climatology simulated by different models may influence their 
simulated sea ice trends ( medium confidence ), as indicated by the 
regression lines in Figure 3.20.
An important consideration in comparing Arctic sea ice loss in 
models and observations is the role of internal variability ( medium 
confidence ). Using ensemble simulations from a single model, 
Kay et al. (2011) suggested that internal variability could account 
for about half of the observed September ice loss. More recently, 
large ensemble simulations have been performed with many more

ensemble members (Kay et al., 2015). These enable a more robust 
characterization of internal variability in the presence of forced 
anthropogenic change. Using such large ensembles, some studies 
discussed the influence of internal variability on Arctic sea ice trends 
(Swart et al., 2015). Song et al. (2016) also compared the trends in the 
Figure 3.20 |  Mean (x-axis) and trend (y-axis) of Arctic sea ice area (SIA) in September (left) and Antarctic SIA in February (right) for 1979–2017 from 
CMIP5 (upper) and CMIP6 (lower) models.  All individual models (ensemble means) and the multi-model mean values are compared with the observations (OSISAF , NASA 
Team, and Bootstrap; see Figure 9.13). Solid line indicates a linear regression slope with corresponding correlation coefficient (r) and p-value provided. Note the different scales

used on the y-axis for Arctic and Antarctic SIA. Results remain essentially the same when using sea ice extent (SIE; not shown). Further details on data sources and processing 
are available in the chapter data table (Table 3.SM.1).

468
Chapter 3 Human Influence on the Climate System3forced and unforced simulations using multiple climate models and 
found that internal variability explains about 40% of the observed 
September sea ice melting trend, supporting previous studies (Stroeve 
et al., 2012). Based on the large ensembles of CESM1 and CanESM2, 
the September Arctic sea ice extent variance first increases and then 
decreases as SIE declines from its pre-industrial value (Kirchmeier-
Young et al., 2017; Mueller et al., 2018) consistent with previous work 
(Goosse et al., 2009), but neither study found a strong sensitivity of 
detection and attribution results to the change in variability. Further 
work has indicated that internally-driven summer atmospheric 
circulation trends with enhanced atmospheric ridges over Greenland 
and the Arctic Ocean, which project on the negative phase of the 
North Atlantic Oscillation (Section 3.7.1), play an important role

in the observed Arctic sea ice loss (Hanna et al., 2015; Ding et al., 
2017). A fingerprint analysis using the CESM large ensemble suggests 
that this internal variability accounts for 40–50% of the observed 
September Arctic sea ice decline (Ding et al., 2019; England et al., 
2019). Internally-generated decadal tropical variability and associated 
atmospheric teleconnections were suggested to have contributed to 
the changing atmospheric circulation in the Arctic and the associated 
rapid sea ice decline from 2000 to 2014 (Meehl et al., 2018).
Some recent studies evaluated the human contribution to recent 
record minimum SIE events in the Arctic. Analysing CMIP5 simulations, 
Zhang and Knutson (2013) found that the observed 2012 record low 
in September Arctic SIE is inconsistent with internal climate variability 
alone. Based on several large ensembles, Kirchmeier-Young et al. 
(2017) concluded that the observed 2012 SIE minimum cannot be

reproduced in a simulation excluding human influence. Fučkar et al. 
(2016) showed that climate change contributed to the record low 
March Arctic SIE in 2015, which was accompanied by the record 
minimum SIE in the Sea of Okhotsk (Paik et al., 2017).
Based on the new attribution studies since AR5, we conclude that it 
is very likely  that anthropogenic forcing mainly due to greenhouse 
gas increases was the main driver of Arctic sea ice loss since 
1979. Increases in anthropogenic aerosols have offset part of the 
greenhouse gas induced Arctic sea ice loss since the 1950s ( medium 
confidence ). Despite large differences in the mean sea ice state in 
the Arctic, Arctic sea ice loss is captured by all CMIP5 and CMIP6 
models. Nonetheless, large inter-model differences in the Arctic sea 
ice decline remain, limiting our ability to quantify forced changes and 
internal variability contributions.
3.4.1.2 Antarctic Sea Ice

AR5 concluded that ‘there is low confidence in the attribution of the 
observed increase in Antarctic SIE since 1979’ (Bindoff et al., 2013) due 
to the limited understanding of the external forcing contribution as well 
as the role of internal variability. Based on a difference between the 
first and last decades, Antarctic sea ice cover exhibited a small increase 
in summer and winter over the 1979–2017 period (Section 2.3.2.1.2, 
and Figures 3.20 and 3.21). However, these changes are not statistically 
significant and starting in late 2016, anomalously low sea ice area 
has been observed (Section 2.3.2.1.2). The mean hemispheric sea ice 
changes result from much larger, but partially compensating, regional 
changes with increases in the western Ross Sea and Weddell Sea and declines in the Bellingshausen and Amundsen Seas (Hobbs et al., 
2016). Observed regional trends have been particularly large in austral 
autumn (see Section 2.3.2.1.2, and also Section 9.3.2.1 for more details

of regional changes and related physical processes). Starting in austral 
spring of 2016, the ice extent decreased strongly (Turner et al., 2017) and 
has since remained anomalously low (Figure 3.21 and Figure 2.20). This 
decrease has been associated with anomalous atmospheric conditions 
associated with teleconnections from warming in the eastern Indian 
Ocean and a negative Southern Annular Mode (Chenoli et al., 2017; 
Stuecker et al., 2017; Schlosser et al., 2018; Meehl et al., 2019; Purich 
and England, 2019; G. Wang et al., 2019). A decadal-scale warming of 
the near-surface ocean that resulted from strengthened westerlies may 
also have contributed to and helped to sustain the sea ice loss (Meehl 
et al., 2019). Before satellites and on even longer time scales, very 
limited observational data and proxy coverage leads to low confidence 
in all aspects of Antarctic sea ice (Sections 2.3.2.1.2 and 9.3.2.1).
CMIP5 climate models generally simulate Antarctic sea ice loss over

the satellite era since 1979 (Mahlstein et al., 2013; Turner et al., 2013) 
in contrast to the observed change, and CMIP6 models also simulate 
Antarctic ice loss (Roach et al., 2020; Figure 3.20 and 3.21). A number 
of studies have suggested that this discrepancy may be in part due 
to the role of internal variability in the observed change (Mahlstein 
et al., 2013; Polvani and Smith, 2013; Zunz et al., 2013; Meehl et al., 
2016c; Turner et al., 2016), including teleconnections associated with 
tropical Pacific variability (Meehl et al., 2016c) and changing surface 
conditions resulting from multi-decadal ocean circulation variations 
(Singh et al., 2019). However, when the spatial pattern is considered, 
trends in the summer and autumn (from 1979–2005) appear outside 
the range of internal variability (Hobbs et al., 2015). This suggests 
that the models may exhibit an unrealistic simulation of the Antarctic 
sea ice forced response or the internal variability of the system.

Discrepancies among the models in simulated sea ice variability 
(Zunz et al., 2013), the sea ice climatological state (Roach et al., 
2018), upper ocean temperature trends (Schneider and Deser, 2018), 
Southern Hemisphere westerly wind trends (Purich et al., 2016), or 
the sea ice response to Southern Annular Mode variations (Ferreira 
et al., 2014; Holland et al., 2017; Kostov et al., 2017; Landrum et al., 
2017) may all play some role in explaining these differences with the 
observed trends. Increased fresh water fluxes caused by mass loss of 
the Antarctic Ice Sheet (either by melting at the front of ice shelves 
or via iceberg calving) have been suggested as a possible mechanism 
driving the multi-decadal Antarctic sea ice expansion (Bintanja et al., 
2015; Pauling et al., 2016) but there is a lack of consensus on this 
mechanism’s impacts (Pauling et al., 2017). A recent study based on 
a decadal prediction system suggests that initializing the state of

the Antarctic Bottom Water cell allows the system to reproduce the 
observed Antarctic sea ice increase (Zhang et al., 2017), consistent 
with the suggestion that multi-decadal variability associated with 
variations in deep convection has contributed to the observed 
increase in Antarctic sea ice since 1979 (Latif et al., 2013; Zhang 
et al., 2017; L. Zhang et al., 2019) (see also Section 9.3.2.1).
There have been several studies that aimed to identify causes of the  
observed Antarctic SIE changes. Gagné et al. (2015) assessed 
the consistency of observed and simulated changes in Antarctic SIE for 
an extended period using recovered satellite-based estimates, and found

469
Figure 3.21 |  Seasonal evolution of observed and simulated Arctic (left) and Antarctic (right) sea ice area (SIA) over 1979–2017.  SIA anomalies relative to the 
1979–2000 means from observations (OBS from OSISAF, NASA Team, and Bootstrap, top)  and historical (ALL, middle)  and natural only (NAT, bottom)  simulations 
from CMIP5 and CMIP6 models. These anomalies are obtained by computing non-overlapping three-year mean SIA anomalies for March (February for Antarctic SIA), June, 
September, and December separately. CMIP5 historical simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 historical simulations are 
extended by using SSP2-4.5 scenario simulations after 2014. CMIP5 NAT simulations end in 2012. Numbers in brackets represent the number of models used. The multi-model

mean is obtained by taking the ensemble mean for each model first and then averaging over models. Grey dots indicate multi-model mean anomalies stronger than inter-
model spread (beyond ± 1 standard deviation). Results remain very similar when based on sea ice extent (SIE – not shown). Units: 106 km2. Further details on data sources and 
processing are available in the chapter data table (Table 3.SM.1) and in the caption to Figure 9.13.

470
Chapter 3 Human Influence on the Climate System3that the observed trends since the mid-1960s are not inconsistent 
with model simulated trends. Studies based on the satellite period 
also indicate that the observed trends are largely within the range of 
simulated internal variability (Hobbs et al., 2016). A few distinct factors 
that led to the weak signal-to-noise ratio in Antarctic SIE trends have 
been further identified, which include large multi-decadal variability 
(Monselesan et al., 2015), the short observational record (e.g., Abram 
et al., 2013), and the limited model performance at representing 
the complex Antarctic climate system as discussed above (Bintanja 
et al., 2013; Uotila et al., 2014). The short period of comprehensive 
satellite observations, beginning in 1979, makes it challenging to set 
the observed increase between 1979 and 2015, or the subsequent 
decrease, in a long-term context, and to assess whether the difference

in trend between observations and models, which mostly simulate 
long-term decreases, is systematic or a rare expression of internal 
variability on decadal to multi-decadal time scales.
In conclusion, the observed small increase in Antarctic sea ice extent 
during the satellite era is not generally captured by global climate 
models, and there is low confidence  in attributing the causes of 
the change.
3.4.2 Snow Cover
Seasonal snow cover is a defining climate feature of the northern 
continents. It is therefore of considerable interest that climate 
models correctly simulate this feature. It is discussed in more detail in 
Section 9.5.3, and observational aspects of snow cover are assessed 
in Section 2.3.2.2.
The AR5 noted the strong linear correlation between Northern 
Hemisphere snow cover extent (SCE) and annual-mean surface air 
temperature in CMIP5 models. It was assessed as likely  that there 
had been an anthropogenic contribution to observed reductions in

Northern Hemisphere snow cover since 1970 (Bindoff et al., 2013). 
The AR5 assessed that CMIP5 models reproduced key features of 
observed snow cover well, including the seasonal cycle of snow cover 
over northern regions of Eurasia and North America, but had more 
difficulties in more southern regions with intermittent snow cover. 
The AR5 also found that CMIP5 models underestimated the observed 
reduction in spring snow cover over this period (Figure  3.22; see 
also Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria-
Otín and Zolina, 2020). This behaviour has been linked to how 
the snow-albedo feedback is represented in models (Thackeray 
et al., 2018a). The CMIP5 multi-model ensemble has been shown to 
represent the snow-albedo feedback more realistically than CMIP3, 
although models from some individual modelling centres have not 
improved or have even got worse (Thackeray et al., 2018a). There is

still a systematic overestimation of the albedo of boreal forest covered 
by snow (Thackeray et al., 2015; Y . Li et al., 2016). Consequently, the 
snow albedo feedback might have been overestimated by CMIP5 
models (Section 9.5.3; Xiao et al., 2017).
CMIP6 models improve on CMIP5 models in producing slightly 
increased SCE versus CMIP5, correcting the low bias in CMIP5 
(Mudryk et al., 2020). The linear relationship noted above between GSAT and SCE also exists in CMIP6 (Mudryk et al., 2020). Like CMIP5, 
the CMIP6 models capture the negative trend in spring snow cover 
that has occurred in recent decades (Figure  3.22). However, the 
median CMIP6 model now produces slightly stronger post-1981 
declines in the March to April mean SCE than the CMIP5 median 
(Mudryk et al., 2020). Until about 1980, the models produce a 
generally stable March to April SCE, but after that a substantial 
decline, reaching a loss of about 2 × 106 km2 in 2012–2017 relative

to the 1971–2000 average. Compared to earlier studies which found 
that models underestimate observed trends for the 1979–2005 
period (Brutel-Vuilmet et al., 2013), both CMIP5 and CMIP6 models 
5th-95th percentile 
range
range
Figure 3.22 |  Time series of Northern Hemisphere March–April mean snow 
cover extent (SCE) from observations, CMIP5 and CMIP6 simulations.  The 
observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), 
Mudryk et al. (2020), and GLDAS2. CMIP5 (top)  and CMIP6 (bottom)  simulations 
of the response to natural plus anthropogenic forcing are shown in brown, natural 
forcing only in green, and the pre-industrial control simulation range is presented in 
blue. Five-year mean anomalies are shown for the 1923–2017 period with the x-axis 
representing the centre years of each five-year mean. CMIP5 all forcing simulations 
are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing

simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading 
indicates 5th–95th percentile ranges for CMIP5 and CMIP6 all and natural forcings 
simulations, and solid lines are ensemble means, based on all available ensemble 
members with equal weight given to each model (Section 3.2). The blue vertical bar 
indicates the mean 5th–95th percentile range of pre-industrial control simulation 
anomalies, based on non-overlapping segments. The numbers in brackets indicate the 
number of models used. Anomalies are relative to the average over 1971–2000. For 
models, SCE is restricted to grid cells with land fraction ≥50%. Greenland is excluded 
from the total area summation. Figure is modified from Paik and Min (2020), their 
Figure 1. Further details on data sources and processing are available in the chapter 

471
Human Influence on the Climate System  Chapter 33show improved agreement with the observations over the period to 
2017 (Figure 3.22). One remaining concern is a failure of most CMIP6 
models to correctly represent the relationship between snow cover 
extent and snow mass, reflecting too slow seasonal increases and 
decreases of SCE in the models (Mudryk et al., 2020).
Several CMIP5 and CMIP6 based studies have consistently 
attributed the observed Northern Hemisphere spring SCE changes 
(Section 2.3.2.2) to anthropogenic influences (Rupp et al., 2013; Najafi 
et al., 2016; Paik and Min, 2020), with the observed changes being 
found to be inconsistent with natural variability alone. Similarly, spring 
snow thickness (Snow Water Equivalent) changes on the scale of the 
Northern Hemisphere have been attributed to greenhouse gas forcing 
(Jeong et al., 2017). Using individual forcing simulations from multiple 
CMIP6 models, Paik and Min (2020) detected greenhouse gas influence

in the observed decrease of early spring SCE between 1925 and 2019, 
which was found to be separable from the responses to other forcings.
In summary, it is very likely that anthropogenic influence contributed 
to the observed reductions in Northern Hemisphere springtime snow 
cover since 1950. CMIP6 models better represent the seasonality 
and geographical distribution of snow cover than CMIP5 simulations 
(high confidence ). Both CMIP5 and CMIP6 models simulate strong 
declines in spring SCE during recent years, in general agreement 
with observations, causing the multi-model mean decreasing trend 
in spring SCE to now better agree with observations than in earlier 
evaluations. Evidence has yet to emerge that interactions between 
vegetation and snow, found problematic in CMIP5, have improved in 
CMIP6 models (Section 9.5.3). Such deficiencies in the representation 
of snow in climate models mean there is medium confidence  in the

simulation of snow cover over the northern continents in CMIP6 model 
simulations. The models consistently link snow extent to surface air 
temperature (Figure 9.24). With warming of near-surface air linked to 
anthropogenic influence, and particularly to greenhouse gas increases 
(Section 3.3.1.1), this provides additional evidence that reductions in 
snow cover are also caused by human activity.
3.4.3 Glaciers and Ice Sheets
While Chapter  9 (Sections 9.4 and 9.5) discusses process 
understanding for glaciers and ice sheets, as well as evaluation of 
global and regional-scale glacier and ice-sheet models, our focus 
here is on the attribution of large-scale changes in glaciers and ice 
sheets. Land ice in the form of glaciers has been included in CMIP 
climate and Earth system models as components of the land surface 
models for many years. However, their representation is simplified 
and is omitted altogether in the less complex modelling systems. In

CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice 
area fraction, a component of land surface models, was defined as 
a time-independent quantity, and in most model configurations was 
preset at the simulation initialization as a permanent land feature. 
In CMIP6 considerable progress has been made in improving and 
evaluating the representation of modelled land ice. For glaciers, 
an example is the expansion of the Joint UK Land Environment 
Simulator (JULES) land surface model to enable elevated tiles, and 
hence more accurately simulate the altitudinal atmospheric effects on glaciers (Shannon et al., 2019). Moreover, standalone glacier models 
have now been systematically compared in GlacierMIP (Hock et al., 
2019a; Marzeion et al., 2020). The Antarctic and Greenland Ice Sheets 
were absent in global climate models that pre-date CMIP6 (Eyring 
et al., 2016a), however some preliminary analyses that used results

from CMIP5 to drive standalone ice-sheet models were included 
in AR5 (Church et al., 2013a). For the first time in CMIP , the latest 
CMIP6 phase includes a coordinated effort to simulate temporally 
evolving ice sheets within the Ice Sheet Model Intercomparison 
Project (ISMIP6; Box  9.3; Nowicki et al., 2016). Our understanding 
of aspects of the global water storage contained in glaciers and ice 
sheets, and their contribution to sea-level rise, has improved since 
AR5 and SROCC (Hock et al., 2019b; Meredith et al., 2019) both in 
models and observations (see assessment of observations and model 
evaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1; 
Antarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections 
2.3.2.3 and 9.5.1).
3.4.3.1 Glaciers
Glaciers are defined as perennial surface land ice masses 
independent of the Antarctic and Greenland Ice Sheets (Sections 9.5 
and 2.3.2.3). The AR5 assessed that anthropogenic influence had

likely  contributed to the retreat of glaciers observed since the 1960s 
(Bindoff et al., 2013), based on a high level of scientific understanding 
and robust estimates of observed mass loss, internal variability, and 
glacier response to climatic drivers. The SROCC (Hock et al., 2019b) 
concluded that atmospheric warming was very likely  the primary 
driver of glacier recession.
Simulations of glacier mass changes under climate change rely on 
glacier models driven by climate model output, often in collaborative 
research efforts such as GlacierMIP (Hock et al., 2019a; Marzeion 
et al., 2020). The GlacierMIP project is a systematic coordinated 
modelling effort designed to further understanding of glacier loss 
using global models. While the low resolution and remaining biases 
of climate model-derived boundary forcing data is a limitation, the 
release of the Randolph Glacier Inventory (Pfeffer et al., 2014; RGI 
Consortium, 2017) has supported more sophisticated, systematic and

comprehensive modelling of glaciers worldwide (Hock et al., 2019a).
A regional study considering 85 Northern Hemisphere glacier systems 
concluded that there is a discernible human influence on glacier mass 
balance, with glacier model simulations driven by CMIP5 historical 
and greenhouse gas-only simulations showing a glacier mass loss, 
whereas those driven by natural-only forced simulations showed a net 
glacier growth (Hirabayashi et al., 2016). In addition, a study of the 
role of climate change in glacier retreat using a simple mass-balance 
model for 37 glaciers worldwide, concluded that observed length 
changes would not have occurred without anthropogenic climate 
change, with observed length variations exceeding those associated 
with internal variability by several standard deviations in many 
cases (Roe et al., 2017). Roe et al. (2021) used the same model to 
estimate that at least 85% of cumulative glacier mass loss since

1850 is attributable to anthropogenic influence. While Marzeion 
et al. (2014) found that anthropogenic influence contributed only 
25 ± 35% of glacier mass loss for the period 1851–2010, their

472
Chapter 3 Human Influence on the Climate System3naturally-forced simulations exhibited a substantial negative mass 
balance, which Roe et al. (2021) argued is unrealistic. Moreover, 
Marzeion et al. (2014) estimated that anthropogenic influence 
contributed 69 ± 24% of glacier mass loss for the period 1991 to 
2010, consistent with a progressively increasing fraction of mass loss 
attributable to anthropogenic influence found by Roe et al. (2021).
In summary, considering together the SROCC assessment that 
atmospheric warming was very likely  the primary driver of glacier 
recession, the results of Roe et al. (2017, 2021) and our assessment of 
the dominant role of anthropogenic influence in driving atmospheric 
warming (Section 3.3.1), we conclude that human influence is very 
likely  the main driver of the near-universal retreat of glaciers globally 
since the 1990s.
3.4.3.2 Ice Sheets
3.4.3.2.1 Greenland Ice Sheet
The AR5 assessed that it is likely  that anthropogenic forcing

contributed to the surface melting of the Greenland Ice Sheet since 
1993 (Bindoff et al., 2013). The SROCC did not directly assess the 
attribution of Greenland Ice Sheet change to anthropogenic forcing, 
but it did assess with medium confidence  that summer melting of the 
Greenland Ice Sheet has increased to a level unprecedented over at 
least the last 350 years, which is two-to-fivefold the pre-industrial 
level (see also Trusel et al., 2018).
Section 2.3.2.4.1 assesses that Greenland Ice Sheet mass loss began 
in the latter half of the 19th century and that the rate of loss has 
increased substantially since the turn of the 21st century ( high 
confidence ), and also notes that integration of proxy evidence and 
modelling indicates that the last time the rate of mass loss was similar 
to the 20th century rate was during the early Holocene. Models of 
Greenland Ice Sheet evolution are evaluated in detail in Section

9.4.1.2, which assesses that there is overall medium confidence in 
these models. Model evaluation of surface mass balance changes 
over the Greenland Ice Sheet, including regional aspects, is also 
assessed in Atlas.11.2.3.
Detection and attribution studies of change in the Greenland Ice 
Sheet remain challenging (Kjeldsen et al., 2015; Bamber et al., 
2019). This is in part due to the short observational record (Shepherd 
et al., 2012, 2018, 2020; Bamber et al., 2018; Cazenave et al., 2018; 
Mouginot et al., 2019; Rignot et al., 2019) and the challenges this 
poses to the evaluation of modelling efforts (Section 9.4.1.2). The 
latter require not only dynamic ice-sheet models, but also appropriate 
atmospheric and oceanic conditions to use as a boundary forcing 
to drive the models (Nowicki and Seroussi, 2018; Barthel et al., 
2020). Nonetheless, new literature since AR5 finds that ice-sheet 
mass balance calculations using reanalysis-driven regional model

simulations of surface mass balance are found to agree well with 
the observed decrease in ice-sheet mass over the past twenty years 
(Fettweis et al., 2020; Sasgen et al., 2020; Tedesco and Fettweis, 
2020), consistent with earlier studies (Flato et al., 2013). These 
studies also show that the exceptional melt events observed in 2012 
and 2019 were associated with exceptional atmospheric conditions (Sasgen et al., 2020; Tedesco and Fettweis, 2020). These results 
support the finding that increased surface melting is associated with 
warming, although atmospheric circulation anomalies, including 
the summer North Atlantic Oscillation (NAO) and variations in 
snowfall play an important role in driving interannual variations 
(Section 9.4.1.1; Sasgen et al., 2020; Tedesco and Fettweis, 2020). 
Further, a coupled ice-sheet-climate model study found emergence 
of decreased surface mass balance prior to the present day in coastal

locations in Greenland, which dominate the integrated surface mass 
balance (Fyke et al., 2014), suggesting that observed variations 
in surface mass balance in these regions might be expected to be 
distinguishable from internal variability. A CMIP6 simulation of the 
historical period showed stable Greenland surface mass balance up 
to the 1990s, after which it declined due to increased melt and runoff, 
consistent with a downscaled reanalysis (van Kampenhout et al., 
2020). Further, all experts surveyed in a structured expert judgement 
exercise examining the causes of the increase in mass loss from 
the Greenland Ice Sheet over the last two decades (Bamber et al., 
2019) concluded that external forcing was responsible for at least 
50% of the mass loss. A comparison of Greenland Ice Sheet mass 
loss trends from observations and AR5 model projections for the 
period 2007–2017 found that the magnitude of the observed surface

mass balance trends was at the top of the AR5 assessed range, 
while mass loss due to changing ice dynamics was near the centre 
of the AR5 range (Slater et al., 2020), providing further evidence of 
consistent anthropogenically-forced mass loss trends in models and 
observations.
Drawing together the evidence from the continued and strengthened 
observed mass loss, the agreement between anthropogenically 
forced climate simulations and observations, and historical and paleo 
evidence for the unusualness of the observed rate of surface melting 
and mass loss, we assess that it is very likely  that human influence has 
contributed to the observed surface melting of the Greenland Ice Sheet 
over the past two decades, and that there is medium confidence  in an 
anthropogenic contribution to recent overall mass loss from Greenland.
3.4.3.2.2 Antarctic Ice Sheet
AR5 assessed that there was low confidence  in attributing the causes

of the observed mass loss from the Antarctic Ice Sheet since 1993 
(Bindoff et al., 2013). The SROCC assessed that there is medium 
agreement  but limited evidence  of anthropogenic forcing of Antarctic 
mass balance through both surface mass balance and glacier 
dynamics. It further assessed that Antarctic ice loss is dominated by 
acceleration, retreat and rapid thinning of the major West Antarctic 
Ice Sheet outlet glaciers ( very high confidence ), driven by melting 
of ice shelves by warm ocean waters ( high confidence ). Based on 
updated observations, Chapter  2 assesses that there is very high 
confidence  that the Antarctic Ice Sheet lost mass between 1992 and 
2017, and that there is medium confidence  that this mass loss has 
accelerated. Models of Antarctic Ice Sheet evolution are evaluated 
in detail in Section 9.4.2.2, which assesses that there is medium 
confidence in many ice-sheet processes in Antarctic Ice Sheet models,

but low confidence in the ocean forcing affecting basal melt rates. 
CMIP5 and CMIP6 models perform similarly in their simulation of 
Antarctic surface mass balance (Section 9.4.2.2, Gorte et al., 2020).

473
Human Influence on the Climate System  Chapter 33Model evaluation of surface mass balance over the Antarctic Ice 
Sheet, including regional aspects, is also assessed in Atlas.11.1.3.
Ice discharge around the West Antarctic Ice Sheet is strongly 
influenced by variability in basal melt (Jenkins et al., 2018; Hoffman 
et al., 2019), in particular at decadal and longer time scales (Snow 
et al., 2017). Basal melt rate variability can be induced by wind-driven 
ocean current changes, which may partly be of anthropogenic origin 
via greenhouse gas forcing (Holland et al., 2019). Moreover, ice 
discharge losses from the Antarctic Ice Sheet over the 2007–2017 
period are close to the centre of the model-based range projected 
in AR5 (Slater et al., 2020). However, expert opinion differs as to 
whether recent Antarctic ice loss from the West Antarctic Ice Sheet 
has been driven primarily by external forcing or by internal variability,

and there is no consensus (Bamber et al., 2019). Anthropogenic 
influence on the Antarctic surface mass balance, which is expected 
to partially compensate for ice discharge losses through increases in 
snowfall, is currently masked by strong natural variability (Previdi and 
Polvani, 2016; Bodart and Bingham, 2019), and observations suggest 
that it has been close to zero over recent years (see further discussion 
in Section 9.4.2.1; Slater et al., 2020).
Overall, there is medium agreement  but limited evidence  of 
anthropogenic influence on Antarctic mass balance through changes 
in ice discharge.
3.5 Human Influence on the Ocean
The global ocean plays an important role in the climate system, as it 
is responsible for transporting and storing large amounts of heat 
(Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2) 
and carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the 
atmosphere. Therefore, accurate ocean simulation in climate models

is essential for the realistic representation of the climatic response 
to anthropogenic warming, including rates of warming, sea level 
rise and carbon uptake, and the representation of coupled modes of 
climate variability.
Ocean model development has advanced considerably since AR5 
(Section 1.5.3.1). Ongoing model developments since AR5 have 
focused on improving the realism of the simulated ocean in coupled 
models, with horizontal resolutions increasing to 10–100 km (from 
about 200 km in CMIP5), and increased vertical resolutions in 
many modelling systems of 0–1 m for near-surface levels (from the 
highest resolution of 10 m in CMIP5). These developments are aimed 
at improving the representation of the diurnal cycle and coupling 
to the atmosphere (e.g., Bernie et al., 2005, 2007, 2008). General 
improvements to simulated ocean fidelity in response to increasing 
resolution are expected (Hewitt et al., 2017), and the effects of model

resolution on the fidelity of ocean models are discussed in more 
detail in Sections 9.2.2 and 9.2.4.
In this section we assess the global and basin-scale properties of 
the simulated ocean, with a focus on evaluation of the realism 
of simulated ocean properties, and the detection and attribution of  
human-induced changes in the ocean over the period of observational coverage. Observed changes to ocean temperature (Section 2.3.3.1), 
salinity (Section 2.3.3.2), sea level (Section 2.3.3.3) and ocean 
circulation (Section 2.3.3.4) are reported in Chapter  2.  A more 
process-based assessment of ocean changes, alongside the 
assessment of variability and changes in ocean properties with 
spatial scales smaller than ocean basins, is presented in Chapter 9.
3.5.1 Ocean Temperature
Ocean temperature and ocean heat content are key physical variables 
considered for climate model evaluation and are primary indicators 
of a changing ocean climate. This section assesses the performance

of climate models in representing the mean state ocean temperature 
and heat content (Section 3.5.1.1), with a particular focus on the 
tropical oceans given the importance of air-sea coupling in these 
areas (Section 3.5.1.2). This is followed by an assessment of detection 
and attribution studies of changes in ocean temperature and heat 
content (Section 3.5.1.3). Changes in global surface temperature are 
assessed in Section 3.3.1.1.
3.5.1.1 Sea Surface and Zonal Mean Ocean 
Temperature Evaluation
In CMIP3 and CMIP5 models, large SST biases were found in the 
mid- and high latitudes (Flato et al., 2013). In CMIP6, the Northern 
Hemisphere mid-latitude surface temperature biases appear to be 
marginally improved in the multi-model mean when contrasted to 
CMIP5 despite large biases remaining in a few models (Figures 3.23a 
and 3.24). There is a decreased spread of the zonal mean SST error 
between 50°N and 30°S, relative to CMIP5 (Figure  3.24a). On the

other hand, the Southern Ocean’s warm surface temperature bias 
remains (Figure 3.23a; Beadling et al., 2020), and is on average larger 
in CMIP6 than in CMIP5 models (Figures 3.23a and 3.24). This warm 
bias is often associated with persistent overlying atmospheric cloud 
biases (Hyder et al., 2018). Several other large biases also appear 
to remain largely unresolved in CMIP6, particularly warm biases in 
excess of 1°C along the equatorial eastern continental boundaries of 
the tropical Atlantic and Pacific Oceans (Figure 3.23a).
Overall, the simulated and observed trends in SST patterns are 
generally consistent for the historical period (Olonscheck et al., 
2020). The CMIP6 models generally represent the observed pattern of 
trends better than the CMIP5 models, and observed trends fall within 
the range of simulated trends over a larger area for CMIP6 models 
than for CMIP5 models (Olonscheck et al., 2020).
The CMIP5 multi-model mean zonally averaged subsurface ocean

temperature showed warm biases between 200 m and 2000 m 
(mid-depth) over most latitudes, with exceptions in the Southern 
Ocean (>60°S, 100–2000 m) and upper (0–400 m) Arctic Ocean. Cold 
biases were simulated near the surface (0–200 m) at most latitudes 
(Flato et al., 2013). CMIP6 biases are broadly consistent with those 
reported in CMIP5 for the near-surface (<200 m) and mid-depth 
(200–2000 m) ocean (Voldoire et al., 2019b; Beadling et al., 2020; 
Y . Zhu et al., 2020). The warm bias begins between 100 and 400 m 
depth in all three basins, however, it is most prominent in the Atlantic

474
Ocean, with a maximum magnitude in the equatorial latitudes, as 
in CMIP5 (Figure  3.25). In the Pacific, the large warm biases are 
mostly seen in the subtropical regions (30°N–60°N and 30°S–60°S). 
The cool near surface tropical bias is most prominent in the Pacific 
Ocean and also present in the Atlantic, with a smaller magnitude 
(Figure 3.25). Relative to CMIP5, the most prominent difference is an 
increase to the mid-depth (300–2000 m) warm bias in CMIP6 and a 
change in sign of the bias from cold to warm for the Southern Ocean 
mid-depth (>60°S) from CMIP5 to CMIP6 (Figure 3.25). Compared 
to CMIP3 and CMIP5, there is improved agreement between most 
CMIP6 models and observations in their representation of the 
zonal mean temperature of the upper 100 m of the Southern Ocean 
(Beadling et al., 2020).
Focusing on the deep ocean (>2000 m), the CMIP6 ensemble mean

shows a prominent and consistent warm bias (Figure 3.25), in all basins 
except the equatorial and northern Pacific, which contrasts to a cold 
bias seen in CMIP5 (Flato et al., 2013). We note that while an updated 
observational temperature dataset is used in this assessment (WOA09 
was used in AR5, while WOA18, 1981–2010 is used in AR6), the deep-
ocean warm bias remains and is approaching double the magnitude 
(about 0.5°C) of the equivalent CMIP5 multi-model mean bias, a feature 
which is particularly prominent in the Atlantic and southern Indian No robust biasRobust bias
Conflicting signal sColour
Figure 3.23 |  Multi-model mean bias of (a) sea surface temperature and (b) near-surface salinity, defined as the difference between the CMIP6 multi-model 
mean and the climatology from the World Ocean Atlas 2018.  The CMIP6 multi-model mean is constructed with one realization of 46 CMIP6 historical experiments

for the period 1995–2014 and the climatology from the World Ocean Atlas 2018 is an average over all available years (1955–2017). Uncertainty is represented using the 
advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree 
on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed 
lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on sign of change. For 
more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table 
(Table 3.SM.1).
Figure 3.24 | Biases in zonal mean and equatorial sea surface temperature

(SST) in CMIP5 and CMIP6 models.  CMIP6 (red), CMIP5 (blue) and HighResMIP 
(green) multi-model mean (a) zonally averaged SST bias; (b) equatorial SST bias; 
and (c) equatorial SST compared to observed mean SST (black line) for 1979–1999. 
The inter-model 5th and 95th percentiles are depicted by the respective shaded 
range. Model climatologies are derived from the 1979–1999 mean of the historical 
simulations, using one simulation per model. The Hadley Centre Sea Ice and Sea Surface 
Temperature version 1 (HadISST) (Rayner et al., 2003) observational climatology for 
1979–1999 is used as the reference for the error calculation in (a) and (b); and for 
observations in (c). Further details on data sources and processing are available in the 
chapter data table (Table 3.SM.1).

475
Oceans. Increased horizontal resolution as well as the choice of the 
vertical coordinate are reported to partly improve these biases in some 
models (Adcroft et al., 2019; Rackow et al., 2019; Hewitt et al., 2020).
Since AR5, there has been growing evidence that the representation 
of mean surface and deeper ocean temperatures in coupled climate 
models can be improved by increasing the horizontal resolution both 
in the ocean and the atmosphere (e.g., Small et al., 2014; Hewitt et al., 
2016; Iovino et al., 2016; Roberts et al., 2019). At an ocean resolution 
of around 1°, which is typical of CMIP6 models, some processes are 
parameterized rather than explicitly resolved, leading to a compromise 
in their dynamical representation. An increase in the model resolution 
allows for processes to be explicitly resolved, and can for example,

enhance the simulation of eddies, thus improving simulated vertical eddy transport, and reducing temperature drifts in the deeper ocean 
(Griffies et al., 2015; von Storch et al., 2016). For some models, the mean 
absolute error in ocean temperature below 500 m is smaller in the high 
resolution version compared to the low resolution version, particularly 
in eddy-active regions such as the North Atlantic (Rackow et al., 2019). 
Increasing the horizontal resolution of individual climate models often 
leads to an overall decrease in the surface temperature biases over 
regions where they persisted through earlier CMIP generations, such 
as the central and western equatorial Pacific, as well as the North 
and tropical Atlantic (Figure 3.3e; Roberts et al., 2019; Hewitt et al., 
2020). Despite this, as a group the four HighResMIP models included 
in Figures 3.3e and 3.24 do not on average show smaller SST biases 
than the CMIP6 multi-model mean, demonstrating the importance of

factors other than resolution in contributing to SST biases.Potential temperature and salinity bias for ocean basins (1981-2010)
Potential temperature diﬀerence (°C)
CMIP6 - WOA18Salinity diﬀerence (PSS-78)
CMIP6 - WOA18
Figure 3.25 | CMIP6 potential temperature and salinity biases for the global ocean, Atlantic Ocean, Pacific Ocean and Indian Ocean.  Shown in colour are the 
time-mean differences between the CMIP6 historical multi-model climatological mean and observations, zonally averaged for each basin (excluding marginal and regional seas). 
The observed climatological values are obtained from the World Ocean Atlas 2018 (WOA18, 1981–2010; Prepared by the Ocean Climate Laboratory, National Oceanographic 
Data Center, Silver Spring, MD, USA), and are shown as labelled black contours for each of the basins. The simulated annual mean climatologies for 1981 to 2010 are calculated

from available CMIP6 historical simulations, and the WOA18 climatology utilized synthesized observed data from 1981 to 2010. Output from a total of 30 available CMIP6 
models is used for the temperature panels (left column) and 28 models for the salinity panels (right column). Potential temperature units are °C and salinity units are the Practical 
Salinity Scale 1978 [PSS-78]. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

476
Chapter 3 Human Influence on the Climate System3In summary, there is little improvement in the multi-model mean 
sea surface and zonal mean ocean temperatures from CMIP5 to 
CMIP6 ( medium confidence ). Nevertheless, the CMIP6 models show 
a somewhat more realistic pattern of SST trends ( low confidence ).
3.5.1.2 Tropical Sea Surface Temperature Evaluation
3.5.1.2.1 Tropical Pacific Ocean
In CMIP5, mean state biases in the tropical Pacific Ocean including the 
excessive equatorial cold tongue, erroneous mean thermocline depth 
and slope along the equator remained but were improved relative 
to CMIP3 (Flato et al., 2013). Misrepresentation of the interaction 
between the atmosphere and ocean via the Bjerknes feedback and 
vertical mixing parameterizations, and a bias in winds were among 
the suggested reasons for the persistent biases (Li et al., 2014; Zhu 
and Zhang, 2018). Moving to CMIP6, a reduction of the cold bias in

the equatorial cold tongue in the central Pacific is found on average 
in the CMIP6 models (Figure  3.24b; Grose et al., 2020; Planton 
et al., 2021), however, this reduced bias is not statistically significant 
when considered across the multi-model ensemble (Planton et al., 
2021). It is also noteworthy that the longitude of the 28°C isotherm 
is closer to observed in CMIP6 than in CMIP5, with a coincident 
reduction in the CMIP6 inter-model standard deviation (Grose et al., 
2020). The latter result implies that there is an improvement in the 
representation of the tropical Pacific mean state in CMIP6 models. 
Comparison of biases in individual HighResMIP models with biases 
in lower resolution versions of the same models indicates that there 
is no consistent improvement in SST biases in most of the equatorial 
Pacific with resolution (Figure 3.3e; Bock et al., 2020).
3.5.1.2.2 Tropical Atlantic Ocean
Fundamental features such as the mean zonal SST gradient in the

tropical Atlantic were not reproduced in CMIP5 models. Studies 
have proposed that weaker than observed alongshore winds, 
underestimation of stratocumulus clouds, coarse model resolution, and 
insufficient oceanic cooling due to a deeper thermocline depth and 
weak vertical velocities at the base of the mixed layer in the eastern 
basin, underpinned these tropical Atlantic SST gradient biases (Hourdin 
et al., 2015; Richter, 2015). The SST gradient biases still remain in 
CMIP6. On average the cold bias in the western part of the basin is 
reduced while the warm bias in the eastern part has slightly increased 
(Figure 3.24b,c; Richter and Tokinaga, 2020). Several CMIP6 models, 
however, display large reductions in biases of the zonal SST gradient, 
such that the eastern equatorial Atlantic warm SST bias and associated 
westerly wind biases are mostly eliminated in these models (Richter 
and Tokinaga, 2020). The high resolution (HighResMIP) CMIP6 models

show a better representation of the zonal SST gradient (Figure 3.24b,c), 
but some lower resolution models also perform well, suggesting that 
resolution is not the only factor responsible for biases in Tropical 
Atlantic SST (Richter and Tokinaga, 2020).
3.5.1.2.3 Tropical Indian Ocean
The tropical Indian Ocean mean state is reasonably well simulated both 
in CMIP5 and CMIP6 (Figure 3.24b,c). However, CMIP5 models show a large spread in the thermocline depth, particularly in the equatorial 
part of the basin (Saji et al., 2006; Fathrio et al., 2017b), which has 
been linked to the parameterization of the vertical mixing and the wind 
structure, leading to a misrepresentation of the ventilation process in 
some models (Schott et al., 2009; Richter, 2015; Shikha and Valsala, 
2018). A common problem with the CMIP5 models is therefore a warm 
bias in the subsurface, mainly at depths around the thermocline, which 
is also apparent in the CMIP6 models (Figure 3.25g).

In the CMIP6 multi-model mean, the western tropical Indian Ocean 
shows a slightly larger warm bias compared to CMIP5 (Figure 3.24 b,c), 
which in part could be related to excessive supply of warm water 
from the Red Sea (Grose et al., 2020; Semmler et al., 2020). The 
HighResMIP models show decreases in SST bias across the Indian 
Ocean with increasing resolution (Figure  3.3e; Bock et al., 2020), 
though as a group the SST biases in the HighResMIP models are no 
smaller than those of the full CMIP6 ensemble.
3.5.1.2.4 Summary
In summary, the structure and magnitude of multi-model mean ocean 
and CMIP6 ( medium confidence ). Although biases remain in the latest 
generation models, the broad consistency between the observed and 
simulated basin-scale ocean properties suggests that CMIP5 and CMIP6 
models are appropriate tools for investigating ocean temperature

and ocean heat content responses to forcing. This also provides 
high confidence  in the utility of CMIP-class models for detection and 
attribution studies, for both ocean heat content (Section 3.5.1.3) and 
thermosteric sea level applications (Section 3.5.3.2).
3.5.1.3 Ocean Heat Content Change Attribution
The ocean plays an important role as the Earth’s primary energy store. 
The AR5 and SROCC assessed that the ocean accounted for more than 
90% of the Earth’s energy change since the 1970s (Rhein et al., 2013; 
Bindoff et al., 2019). These assessments are consistent with recent 
studies assessed in Section 7.2 and Cross-Chapter Box  9.1, which 
find that 91% of the observed change in Earth’s total energy from 
1971 to 2018 was stored in the ocean (von Schuckmann et al., 2020). 
The AR5 concluded that anthropogenic forcing has very likely  made 
a substantial contribution to ocean warming above 700 m, whereas 
below 700  m, limited measurements restricted the assessment of

ocean heat content changes in AR5 and prevented a robust comparison 
between observations and models (Bindoff et al., 2013).
With the recent increase in ocean sampling by Argo to 2000 m 
(Roemmich et al., 2015; Riser et al., 2016; von Schuckmann et al., 
2016) and the resulting improvements in estimates of ocean heat 
content (Abraham et al., 2013; Balmaseda et al., 2013; Durack et al., 
2014b; Cheng et al., 2017; von Schuckmann et al., 2020), a more 
quantitative assessment of the global ocean heat content changes 
that extends into the intermediate ocean (700–2000 m) over the 
more recent period (from 2005 to the present; Durack et al., 2018) 
can be performed. Observed ocean heat content changes are 
discussed in Section 2.3.3.1, where it is reported that it is virtually 
certain  that the global upper ocean (0–700 m) and very likely that

477
the global intermediate ocean (700–2000 m) warmed substantially 
from 1971 to the present. Further, ocean layer warming contributions 
are reported as 61% (0–700 m), 31% (700–2000 m) and 8% 
(>2000 m) for the 1971 to 2018 period (Table  2.7). CMIP5 model 
simulations replicate this partitioning fairly well for the industrial-era 
(1865 to 2017) throughout the upper (0–700 m, 65%), intermediate 
(700–2000 m, 20%) and deep (>2000 m, 15%) layers (Gleckler et al., 
2016; Durack et al., 2018). The corresponding warming percentages 
for the multi-model mean of a subset of CMIP6 simulations over the 
1850–2014 period are 58% for the upper, 21% for the intermediate, 
and 22% for the deep-ocean layers (Figure 3.26). These results are 
consistent with SROCC which assessed that it is virtually certain  that 
both the upper and intermediate ocean warmed from 2004 to 2016, 
with an increased rate of warming since 1993 (Bindoff et al., 2019).

The spatial distribution of these changes for different ocean depths 
is assessed in Section 9.2.2.1.
The multi-model means of both CMIP5 and CMIP6 historical 
simulations forced with time varying natural and anthropogenic 
forcing shows robust increases in ocean heat content in the upper 
(0–700 m) and intermediate (700–2000 m) ocean ( high confidence ) 
(Figure 3.26; Cheng et al., 2016, 2019; Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 2019). Temporary (<10 years) surface 
and subsurface cooling during and after large volcanic eruptions 
is also captured in the upper-ocean, and global mean ocean heat 
content (Balmaseda et al., 2013). The ocean heat content increase is 
also reflected in the corresponding ocean thermal expansion which is 
a leading contributor to global mean sea level rise (Sections 3.5.3.2 
and 9.2.4, and Box 9.1).
For the period 1971–2014, the rate of ocean heat uptake for the 
global ocean in the CMIP6 models is about 6.43 [2.08–8.66]

ZJ yr–1, with the upper, intermediate and deeper layers respectively 
accounting for 68%, 16% and 16% of the full depth global heat 
uptake (Figure  3.26). Overall, the simulated ocean heat content 
changes are consistent with the updated and improved observational 
analyses, within the very likely  uncertainty range defined for each 
(see also Section 2.3.3.1, Table 2.7; Domingues et al., 2008; Purkey 
and Johnson, 2010; Levitus et al., 2012; Good et al., 2013; Cheng 
et al., 2017; Ishii et al., 2017; Zanna et al., 2019) as well as with the 
ocean components of total Earth heating assessed in Section 7.2.2.2, 
Table  7.1. Nevertheless, large uncertainties remain, particularly in 
the deeper layers due to the poor temporal and spatial sampling 
coverage, particularly in the Atlantic, Southern and Indian Oceans Ocean Heat Content  (ZJ)Global Ocean Heat Content

Figure 3.26 |  Global ocean heat content in CMIP6 simulations and observations.  Time series of observed (black) and simulated (red) global ocean heat content 
anomalies with respect to 1995–2014 for the full ocean depth (left-hand panel) ; upper layer: 0–700 m (top right-hand panel) ; intermediate layer: 700–2000 m (middle 
right-hand panel) ; and the abyssal ocean: >2000 m (bottom right-hand panel) . The best estimate observations (black solid line) for the period of 1971–2018, along 
with very likely  ranges (black shading) are from Section 2.3.3.1. For the models (1860–2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model 
mean values (red solid line) after averaging across simulations for each independent model. The very likely  ranges in the simulations are shown in red shading. Simulation drift

has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units 
are zettajoules (ZJ; 1021 joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

478
(Garry et al., 2019). The very likely  ranges of the simulated trends 
for the full ocean depth and below 2000 m fall within the very 
likely  range of observed uptake during the last two decades. In the 
intermediate layer, the multi-model ensemble mean mostly stays 
above the observed 5th–95th percentile range before the year 
2000, and below that range after 2000. For the upper ocean, some 
individual model realizations show a reduced ocean heat content 
increase during the 1970s and 1980s, which is then compensated 
by a greater warming than the observations from the early 1990s. 
These discrepancies have been linked with a temporary increase in 
the Southern Ocean deep water formation rate, as well as with the 
models’ strong aerosol cooling effects and high equilibrium climate 
sensitivity (see also Section 7.5.6 and Box 7.2; Andrews et al., 2019, 
2020; Golaz et al., 2019; Dunne et al., 2020; Winton et al., 2020).

Nevertheless, simulations show that the rate of ocean heat uptake 
has doubled in the past few decades, when contrasted to the rate 
over the complete 20th century (Figure 3.26), with over a third of 
the accumulated heat stored below 700 m (Cheng et al., 2016, 2019; 
Gleckler et al., 2016; Durack et al., 2018). The Southern Ocean shows 
the strongest ocean heat uptake that penetrates to deeper layers 
(Section 9.2.3.2), whereas ocean heat content increases in the Pacific 
and Indian Oceans largely occur in the upper layers (Bilbao et al., 
2019).
Since AR5, the attribution of ocean heat content increases to 
anthropogenic forcing has been further supported by more detection 
and attribution studies. These studies have shown that contributions 
from natural forcing alone cannot explain the observed changes in 
ocean heat content in either the upper or intermediate ocean layers, 
and a response to anthropogenic forcing is clearly detectable in ocean

heat content (Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 
2019). Moreover, a response to greenhouse gas forcing is detectable 
independently of the response to other anthropogenic forcings 
(Bilbao et al., 2019; Tokarska et al., 2019), which has offset part of 
the greenhouse gas induced warming. Further evidence is provided 
by the agreement between observed and simulated changes in 
global thermal expansion associated with the ocean heat content 
increase when both natural and anthropogenic forcings are included 
in the simulations (Section 3.5.3.2), though internal variability plays a 
larger role in driving basin-scale thermosteric sea level trends (Bilbao 
et al., 2015). Over the Southern Ocean, warming is detectable over 
the late 20th century and is largely attributable to greenhouse gases 
(Swart et al., 2018; Hobbs et al., 2021), while other anthropogenic 
forcings such as ozone depletion have been shown to mitigate the

warming in some of the CMIP5 simulations (Swart et al., 2018; Hobbs 
et al., 2021). The use of the mean temperature above a fixed isotherm 
rather than fixed depth further strengthens a robust detection of the 
anthropogenic response in the upper ocean (Weller et al., 2016), and 
better accounting for internal variability in the upper ocean (Rathore 
et al., 2020), helps explain reported hemispheric asymmetry in ocean 
heat content change (Durack et al., 2014b).
In summary, there is strong evidence for an improved understanding 
of the observed global ocean heat content increase. It is extremely 
likely  that human influence was the main driver of the ocean heat 
content increase observed since the 1970s, which extends into the deeper ocean ( very high confidence ). Updated observations, like 
model simulations, show that warming extends throughout the 
entire water column ( high confidence ).
3.5.2 Ocean Salinity
While ocean assessments have primarily focused on temperature

changes, improved observational salinity products since the early 
2000s have supported more assessment of long-term ocean salinity 
change and variability from AR4 (Bindoff et al., 2007) to AR5 across 
both models and observations (Flato et al., 2013; Rhein et al., 2013). 
The AR5 assessed that it was very likely  that anthropogenic forcings 
have made a discernible contribution to surface and subsurface 
ocean salinity changes since the 1960s. The SROCC augmented these 
insights, noting that observed high latitude freshening and warming 
have very likely  made the surface ocean less dense with a stratification 
increase of between 2.18% and 2.42% from 1970 to 2017 (Bindoff 
et al., 2019). A recent observational analysis has expanded on these 
assessments, suggesting a very marked summertime density contrast 
enhancement across the mixed layer base of 6.2–11.6% per decade, 
driven by changes in temperature and salinity, which is more than six

times larger than previous estimates (Sallée et al., 2021). An idealized 
ocean modelling study suggests that the enhanced stratification can 
account for a third of the salinity enhancement signal since 1990 
(Zika et al., 2018). Thus, there has been an expansion of observed 
global- and basin-scale salinity change assessment literature since 
AR5, with many new studies reproducing the key patterns of long-
term salinity change reported in AR5 (Rhein et al., 2013), and 
linking these through modelling studies to coincident changes in 
evaporation–precipitation patterns at the ocean surface (Sections 
2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2).
Unlike SSTs, simulated sea surface salinity (SSS) does not provide 
a direct feedback to the atmosphere. However, some recent work 
has identified indirect radiative feedbacks through sea-salt aerosol 
interactions (Ayash et al., 2008; Amiri-Farahani et al., 2019; Z. Wang 
et al., 2019) that can act to strengthen tropical cyclones, and increase

precipitation (Balaguru et al., 2012, 2016; Grodsky et al., 2012; Reul 
et al., 2014; Jiang et al., 2019). The absence of a direct feedback is one 
of the primary reasons why salinity simulation is difficult to constrain 
in ocean modelling systems, and why deviations from the observed 
near-surface salinity mean state between models and observations 
are often apparent (Durack et al., 2012; Shi et al., 2017).
3.5.2.1 Sea Surface and Depth-profile Salinity Evaluation
When compared to the routine assessment of simulated SST, 
simulated SSS has not received the same research attention at 
global- to basin-scales. For CMIP3, there was reasonable agreement 
between the basin-scale patterns of salinity, with a comparatively 
fresher Pacific when contrasted to the salty Atlantic, and basin 
salinity maxima features aligning well with the corresponding 
atmospheric evaporation minus precipitation field (Durack et al., 
2012). Similar features are also reproduced in CMIP5 along with

realistic variability in the upper layers, but less variability than 
observations at 300 m and deeper, especially in the poorly sampled

479
Antarctic region (Pierce et al., 2012). In a regional study, only 
considering the Indian Ocean, CMIP5 SSS was assessed and it 
was shown that model biases were primarily linked to biases in 
the precipitation field, with ocean circulation biases playing a 
secondary role (Fathrio et al., 2017a). The sea surface salinity bias 
in CMIP6 models is shown in Figure 3.23b.
For the first time in AR5, alongside global zonal mean temperature, 
global zonal mean salinity bias with depth was assessed for 
the CMIP5 models. This showed a strong upper ocean (<300 m) 
negative salinity (fresh) bias of order 0.3 PSS-78, with a tendency 
toward a positive salinity (salty) bias (<0.25 PSS-78) in the Northern 
Hemisphere intermediate layers (200–3000 m) (Flato et al., 2013). 
These biases are also present in CMIP6, albeit with slightly smaller 
magnitudes (Figure  3.25). Here we expand the global zonal mean

bias assessment to consider the three independent ocean basins 
individually, which allows for an assessment as to which basin 
biases are dominating the global zonal mean. The basin with the 
most pronounced biases is the Atlantic, with a strong upper ocean 
(<300 m) fresh bias, of order 0.3 PSS-78 just like the global zonal 
mean, and a marked subsurface salinity bias that exceeds 0.5 PSS-78 
in equatorial waters between 400–1000 m.
The Pacific Ocean shares the strongest similarity to the global bias, 
with a similar upper ocean (<300 m) fresh bias. Lower magnitude 
positive salinity biases (about 0.3 PSS-78) are also present in both 
hemispheres between 200 and 3000 m, and deeper in the Southern 
Hemisphere (Figure 3.25). The Indian Ocean shows similar features 
to the Southern Hemisphere Pacific, with a marked upper ocean 
(<500 m) fresh bias of order 0.3 PSS-78, and a strong near-surface 
positive bias of order 0.4 PSS-78 associated with the Arabian Sea 
(Figure 3.25).

For the Southern Ocean in CMIP5, considerable fresh biases 
exist through the water column, and are most pronounced in the 
ventilated layers representing the subtropical mode and intermediate 
water masses (Sallée et al., 2013). A fresh bias in upper and 
intermediate layers of comparable magnitude is also seen in CMIP6 
(Figure 3.25). The structure of the biases in the CMIP6 multi-model 
mean (which averages across many simulations with differing 
subsurface geographies and differing Southern Ocean salinity 
biases (Beadling et al., 2020)) is similar to that evident in the CMIP5 
multi-model mean, but with slightly smaller magnitudes. The Arctic 
Ocean also on average exhibits a surface-enhanced fresh bias in the 
upper ocean (Figure 3.25), which is much larger than its Southern 
Hemisphere counterpart.
In summary, the structure of the salinity biases in the multi-model 
mean has not changed substantially between CMIP5 and CMIP6

(medium confidence ), though there is limited evidence  that the 
magnitude of subsurface biases has been reduced. Biases are 
sufficiently small to provide confidence in the utility of CMIP-class 
models for detection and attribution of ocean salinity.3.5.2.2 Salinity Change Attribution
AR5 concluded that it was very likely  that anthropogenic forcings 
had made a discernible contribution to surface and subsurface ocean 
salinity changes since the 1960s (Bindoff et al., 2013; Rhein et al., 
2013). It highlighted that the spatial patterns of salinity trends, and 
the mean fields of salinity and evaporation minus precipitation are all 
similar, with an enhancement to Atlantic Ocean salinity and freshening 
in the Pacific and Southern Oceans. Since AR5 all subsequent work 
on assessing observed and modelled salinity changes has confirmed 
these results.
Considerable changes to observed broad- or basin-scale ocean 
near-surface salinity fields have been reported (see Section 2.3.3.2),

and these have been linked to changes in the evaporation minus 
precipitation patterns at the ocean surface through model simulations, 
typically expressing a pattern of change where climatological mean 
fresh regions become fresher and corresponding salty regions 
becoming saltier (Durack et al., 2012, 2013; Zika et al., 2015; Lago 
et al., 2016; Skliris et al., 2016, 2018; Cheng et al., 2020), also broadly 
present in the CMIP6 multi-model mean (Figure 3.27). At basin-scales, 
Observed and modelled near-surface salinity trends
Observations (1950–2019)
CMIP6 historical multi-model mean (1950–2014)
-0.05   -0.04    -0.03     -0.02    -0.01      0.00     0.01     0.02      0.03    0.04      0.05   
PSS-78 decade-1
Near-surface climatological mean salinity, PSS-78
Figure 3.27 |  Maps of multi-decadal salinity trends for the near-surface 
ocean.  Units are Practical Salinity Scale 1978 [PSS-78] per decade. (Top)  The best

estimate (Section 2.3.3.2) observed trend (1950–2019, Durack and Wijffels, 2010). 
(Bottom)  Simulated trend from the CMIP6 historical experiment multi-model mean 
(1950–2014). Black contours show the climatological mean salinity in increments of 
0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are 

480
the depth-integrated effect of mean salinity changes as captured in 
halosteric sea level for the top 0 to 2000 m has also been assessed 
based on observational products, and these results mirror near-surface 
patterns in the CMIP5 and CMIP6 models, with most areas that are 
becoming fresher at the surface exhibiting increases in halosteric sea 
level, and areas becoming saltier exhibiting decreases (Durack et al., 
2014a; Figure  3.28). Further investigations using observations and 
models together have tied the long-term patterns of surface and 
subsurface salinity changes to coincident changes to the evaporation minus precipitation field over the ocean (Durack et al., 2012, 2013; 
Durack, 2015; Levang and Schmitt, 2015; Zika et al., 2015, 2018; Grist 
et al., 2016; Lago et al., 2016; Cheng et al., 2020), however the rate of 
these changes through time continues to be an active area of active

research (Skliris et al., 2014; Zika et al., 2015, 2018; Cheng et al., 
2020; Sallée et al., 2021).
Climate change detection and attribution studies have considered 
salinity, with the first of these assessed in AR5 (Bindoff et al., 2013). Since 
Figure 3.28 |  Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations.  Units are mm yr–1. In the right-hand column , 
three observed maps of 0 to 2000 m halosteric sea level trends are shown: top (D&W)  from Durack and Wijffels (2010), 1950–2019, updated; upper-middle (EN4)  from 
Good et al. (2013), 1950–2019, updated; and lower-middle (Ishii)  from Ishii et al. (2017), 1955–2019, updated. Bottom-right:  the CMIP6 historical multi-model mean 
(1950–2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In the left-hand column ,

basin-integrated halosteric (top)  and thermosteric (bottom)  trends for the Atlantic and Pacific, the two largest ocean basins, are shown, where Pacific anomalies are presented 
on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-
model mean shown as a dark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the 
multi-model mean as a dark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

481
that time, the positive detection conclusions (Stott et al., 2008; Pierce 
et al., 2012; Terray et al., 2012) have been supported by a number of 
more recent and independent assessments which have reproduced the 
multi-decadal basin-scale patterns of change in observations and models 
(Figures 3.27 and 3.28; Durack et al., 2014a; Durack, 2015; Levang and 
Schmitt, 2015; Skliris et al., 2016). Observed depth-integrated basin 
responses, contrasting the Pacific and Atlantic basins (freshening Pacific 
and enhanced salinity Atlantic) were also shown to be replicated in most 
historical (natural and anthropogenically forced) simulations, with 
this basin contrast absent in CMIP5 and CMIP6 natural-only simulations 
that exclude anthropogenic forcing (Durack et al., 2014a; Figure 3.28).
While observational sparsity considerably limits quantification of 
regional changes, a recent study by Friedman et al. (2017) assessed

salinity changes in the Atlantic Ocean from 1896 to 2013 and 
confirmed the pattern of mid-to-low latitude enhanced salinity 
and  high latitude North Atlantic freshening over this period exists 
even after accounting for the effects of the NAO and AMO.
Considering the bulk of evidence, it is extremely likely  that human 
influence has contributed to observed near-surface and subsurface 
salinity changes across the globe since the mid-20th century. All 
available multi-decadal assessments have confirmed that the 
associated pattern of change corresponds to fresh regions becoming 
fresher and salty regions becoming saltier ( high confidence ). CMIP5 
and CMIP6 models are only able to reproduce these patterns 
in simulations that include greenhouse gas increases ( medium 
confidence ). Changes to the coincident atmospheric water cycle and 
ocean-atmosphere fluxes (evaporation and precipitation) are the 
primary drivers of the basin-scale observed salinity changes ( high

confidence ). This result is supported by all available observational 
assessments, along with a growing number of climate modelling 
studies targeted at assessing ocean and water cycle changes. 
The basin-scale changes are consistent across models and intensify 
on centennial scales from the historical period through to the 
projections of future climate ( high confidence ).
3.5.3 Sea Level
In keeping with the scope of this chapter, this section addresses 
global and basin-scale sea level changes, whereas regional and local 
sea level changes are assessed in Section 9.6. In AR5, the observed 
sea level budget was closed by considering all contributing factors 
including ocean warming, mass contributions from terrestrial 
storage, glaciers, and the Antarctic and Greenland ice sheets 
(Church et al., 2013b). The SROCC found that the observed global 
mean sea level (GMSL) rise is consistent within uncertainties with 
the sum of the estimated observed contributions for 1993–2015

and 2006–2015.
3.5.3.1 Sea Level Evaluation
The current generation of climate models do not fully resolve many 
of the components required to close the observed sea level budget, 
such as glaciers, ice sheets and land water storage (see Section 9.6 
and Box 9.1). Consequently, most CMIP-based analyses of sea level change have focused on thermosteric sea level changes (i.e., thermal 
expansion due to warming) and ocean dynamic sea level change, 
both of which are simulated in the CMIP5-generation of models. 
The improved agreement between modelled thermal expansion and 
observed estimates during the historical period led the SROCC to 
assess a high confidence  level in the simulated thermal expansion 
using climate models and high confidence  in their ability to project 
future thermal expansion.
Since CMIP5 models do not include all necessary components of sea 
level change, this gap has been bridged by using offline models (for

glacier melt and ice-sheet surface mass balance) driven by reanalyses 
and model output. Some studies have used offline mass inputs to 
account for dynamic ice-sheet and terrestrial contributions. Slangen 
et al. (2017) and Meyssignac et al. (2017) suggested including 
corrections to several contributions to sea level changes including to 
the Greenland surface mass balance and glacier contributions, based 
on differences between CMIP5-driven model results and reanalysis-
driven results. This helps close the gap between models and 
observations for the 20th century globally, as well as providing better 
agreement with tide gauge observations in terms of interannual and 
multi-decadal variability at the regional scale.
In CMIP6, ice sheets (see Sections 3.4.3.2 and 9.4) are included for 
the first time in ISMIP6 (Nowicki et al., 2016). There is also scope 
for  new insights into terrestrial water contributions from land

surface (and sub-surface) modelling in the Land Surface, Snow and 
Soil moisture Model Intercomparison Project (LS3MIP; van den Hurk 
et al., 2016). In parallel, the GlacierMIP project (Hock et al., 2019a; 
Marzeion et al., 2020; see Sections 3.4.3.1 and 9.5) is also underway, 
and has provided more quantitative guidance and a comprehensive 
assessment of the uncertainties and best estimates of the current and 
future contributions of glaciers to the sea level budget.
3.5.3.2 Sea Level Change Attribution
The SROCC concluded with high confidence  that the dominant 
cause of GMSL rise since 1970 is anthropogenic forcing. Prior to 
that, AR5 had concluded that it is very likely  that there has been 
a  substantial contribution from anthropogenic forcings to GMSL 
rise since the 1970s. Since AR5, several studies have identified 
a human contribution to observed sea level change resulting from 
a warming climate as manifest in thermosteric sea level change and

the contribution from melting glaciers and ice sheets.
For the global mean thermosteric sea level change, Slangen et al. 
(2014) showed the importance of anthropogenic forcings (combined 
greenhouse gas and aerosol forcings) for explaining the magnitude 
of the observed changes between 1957 and 2005, considering the 
full depth of the ocean and natural forcings in order to capture the 
variability (see also Figure 3.29). Over the 1950–2005 period, Marcos 
and Amores (2014) found that human influence explains 87% of 
the 0–700 m global thermosteric sea level rise. Both thermosteric 
and regional dynamic patterns of sea level change in individual 
forcing experiments from CMIP5 were considered by Slangen et al. 
(2015) who showed that responses to anthropogenic forcings are 
significantly different from both internal variability and inter-model

482
differences and that although greenhouse gas and anthropogenic 
aerosol forcings produce opposite GMSL responses, there are 
differences in the response on regional scales. Based on these 
studies, we conclude that it is very likely  that anthropogenic forcing 
was the main driver of the observed global mean thermosteric sea 
level change since 1970.
In an attribution study of the sea-level contributions of glaciers, 
Marzeion et al. (2014) found that between 1991 and 2010, the 
anthropogenic fraction of global glacier mass loss was 69 ± 24% (see 
also Section 3.4.3.1). Slangen et al. (2016) considered all quantifiable 
components of the GMSL budget and showed that anthropogenically 
forced changes account for 69 ± 31% of the observed sea level rise 
over the period 1970 to 2005, whereas natural forcings combined 
with internal variability have a much smaller effect – only contributing

9 ± 18% of the change over the same period. These studies indicate 
that about 70% of the combined change in glaciers, ice-sheet surface 
mass balance and thermal expansion since 1970 can be attributed 
to anthropogenic forcing, and that this percentage has increased 
over the course of the 20th century. Detection studies on GMSL 
change in the 20th century (Becker et al., 2014; Dangendorf et al., 
2015) found that observed total GMSL change in the 20th century 
was inconsistent with internal variability. Dangendorf et al. (2015) 
determined that for 1900 to 2011 at least 45% of GMSL change is 
human-induced. A study that developed a semi-empirical model to 
link sea-level change to observed GMST change concluded that at 
least 41% of the 20th century sea-level rise would not have happened 
in the absence of the century’s increasing GMST and that there was a 95% probability that by 1970 GMSL was higher than that which

would have occurred in the absence of increasing GMST (Kopp et al., 
2016). Richter et al. (2020) compared modelled sea level change 
with the satellite altimeter observations from 1993 to 2015; a period 
short enough that internal variability can dominate the spatial 
pattern of change. They found that when GMSL is not removed, 
model simulated zonally averaged sea level trends are consistent 
with altimeter observations globally as well as in each ocean basin 
and much larger than might be expected from internal variability. 
Using spatial correlation, Fasullo and Nerem (2018) showed that the 
satellite altimeter trend pattern is already detectable.
We note that current detection and attribution studies do not 
yet include all processes that are important for sea-level change 
(see Section 9.6). However, based on the body of literature available, 
we conclude that the main driver of the observed GMSL rise since 
at least 1971 is very likely  anthropogenic forcing. The assessed

period starts in 1971 for consistency with observations assessed in 
Cross-Chapter Box 9.1.
Figure 3.29 | Simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the 
baseline period 1850–1900.  Historical simulations are shown in brown, natural only in green, greenhouse gas only in grey, and aerosol only in blue (multi-model means 
shown as thick lines, and shaded ranges between the 5th and 95th percentile). The best estimate observations (black solid line) for the period of 1971–2018, along with very 
likely ranges (black shading) are from Section 2.3.3.1 and are shifted to match the multi-model mean of the historical simulations for the 1995–2014 period. Further details on 
data sources and processing are available in the chapter data table (Table 3.SM.1).

483
3.5.4 Ocean Circulation
Circulation of the ocean, whether it be wind or density driven, plays 
a prominent role in the heat and freshwater transport of the Earth 
system (Buckley and Marshall, 2016). Thus, its accurate representation 
is crucial for the realistic representation of water mass properties, and 
replication of observed changes driven by atmosphere-land-ocean 
coupling. Here, we assess the ability of CMIP models to reproduce 
the observed large-scale ocean circulation, along with assessment 
of the detection and attribution of any anthropogenically-driven 
changes. We also note that the process-based understanding of these 
circulation changes and circulation changes occurring at smaller 
scales is assessed in Section 9.2.3.
3.5.4.1 Atlantic Meridional Overturning Circulation (AMOC)
The Atlantic Meridional Overturning Circulation (AMOC) represents 
a large-scale flow of warm salty water northward at the surface

and a return flow of colder water southward at depth. As such, 
its mean state plays an important role in transporting heat in the 
climate system, while its variability can act to redistribute heat (see 
Sections 2.3.3.4.1 and 9.2.3.1 for more details). Paleo-climatic and 
model evidence suggest that changes in AMOC strength have played 
a prominent role in past transitions between warm and cool climatic 
phases (e.g., Dansgaard et al., 1993; Ritz et al., 2013).
The AR5 concluded that while climate models suggested that an 
AMOC slowdown would occur in response to anthropogenic forcing, 
the short direct observational AMOC record precluded it from being 
used to support this model finding. Chapter  2 reports with high 
confidence , a weakening of the AMOC was observed in the mid-2000s 
to the mid-2010s, while again also noting that the observational 
record was too short to determine whether this is a significant 
trend or a manifestation of decadal and multi-decadal variability

(Section  2.3.3.4.1). Indirect evidence of AMOC weakening since at 
least the 1950s is also presented, but confidence in this longer-term 
decrease was low (Section 2.3.3.4.1).
Despite the additional six years or so of observations since AR5, the 
evaluation of the AMOC in models continues to be severely hampered 
by  the geographically sparse and temporally short observational 
record. The longest continuous observational estimates of the AMOC 
are based on measurements taken at 26°N by the RAPID-MOCHA 
array (Smeed et al., 2018). Basic evaluation of the AMOC at 26°N 
shows that the CMIP5 and CMIP6 multi-model mean overturning 
strength is comparable with RAPID (Reintges et al., 2017; Weijer et al., 
2020), but the model range is large (12–29 sverdrups (Sv)) for CMIP5 
(Zhang and Wang, 2013); and 10–31 Sv for CMIP6 (Weijer et al., 2020) 
(Figure 3.30a). It is noted that deviations of AMOC strength in CMIP5 
models have been related to global-scale sea surface temperature

biases (C. Wang et al., 2014). Both coupled and ocean-only models 
also underestimate the depth of the AMOC cell (Danabasoglu et al., 
2014; Weijer et al., 2020; Figure 3.30a). Paleo-climatic evidence has 
also raised questions regarding the accuracy of the representation of 
the strength and depth of the modelled AMOC during past periods 
(Otto-Bliesner et al., 2007; Muglia and Schmittner, 2015). Overall, 
however, both the CMIP5 and CMIP6 model ensembles simulate the general features of the AMOC mean state reasonably well, but there is 
a large spread in the latitude and depth of the maximum overturning, 
and the maximum AMOC strength (Figure 3.30a).
The short length of the observed time-series (RAPID has measured the 
AMOC since 2004), sparse observations, observational uncertainties 
(Sinha et al., 2018), as well as significant observed variability on 
interannual and longer time scales, makes comparison with modelled

AMOC variability challenging. RAPID observations show that the 
overturning at 26°N was 2.9 Sv weaker in the multi-year average of 
2008–2012 relative to 2004–2008 and 2.5 Sv weaker in 2012–2017 
relative to 2004–2008 (Smeed et al., 2014, 2018) (see also Section 
2.3.3.4.1). As expected, this weakening was accompanied by 
a  significant reduction in northward heat transport (Bryden et al., 
2020). CMIP5 and CMIP6 models produce a forced weakening of the 
AMOC over the 2012–2017 period relative to 2004–2008, but at 
26°N the multi-model mean response is substantially weaker than 
the observed AMOC decline over the same period. The discrepancy 
between the modelled multi-model mean (i.e., the forced response) 
and the RAPID observed AMOC changes has led studies to suggest 
that the observed weakening over 2004–2017 is largely due to 
internal variability (Yan et al., 2018). However, comparison of 
observed RAPID AMOC variability with modelled variability also

reveals that most CMIP5 models appear to underestimate the 
interannual and decadal time scale AMOC variability (Roberts et al., 
2014; Yan et al., 2018), and, although the overall variance is larger 
in CMIP6 than in CMIP5, similar results are found analysing the 
CMIP6 models (Figure 3.30b,c). It is currently unknown why most 
models underestimate this AMOC variability, or whether they are 
underestimating the internal or externally forced components. 
This underestimation of AMOC variability may also have potential 
implications for detection and attribution, the relationship between 
AMOC and AMV (see Section 3.7.7), and near-term predictions. 
There is also emerging evidence, based on analysis of freshwater 
transports, that the AMOC in CMIP5-era models is too stable, largely 
due to systematic biases in ocean salinity (W. Liu et al., 2017; 
Mecking et al., 2017). Such a  systematic bias may potentially be 
linked with the underestimation of both simulated AMOC internal

variability through eddy-mean flow interactions that are poorly 
represented in standard CMIP-class model resolution (Leroux et al., 
2018), and externally forced change.
As reported in Section 2.3.3.4.1, estimates of AMOC since at least 
1950, which are generated from observed surface temperatures 
or sea surface height, suggest the AMOC weakened through the 
20th century ( low confidence ) (Ezer et al., 2013; Caesar et al., 2018). 
Over the same period, the CMIP5 multi-model mean showed no 
significant net forced response in AMOC (Cheng et al., 2013). However, 
a significant forced change is simulated in the CMIP6 multi-model 
mean, where a clear increase of the AMOC is seen over the 1940–1985 
period (Figure 3.30e; Menary et al., 2020). Although there is general 
agreement that the influence of greenhouse gases acts to a weaken 
the modelled AMOC (Delworth and Dixon, 2006; Caesar et al., 2018), 
changes in solar, volcanic and anthropogenic aerosol emissions can

lead to temporary changes in AMOC on decadal- to multi-decadal 
time scales (Delworth and Dixon, 2006; Menary et al., 2013; Menary 
and Scaife, 2014; Swingedouw et al., 2017; Undorf et al., 2018b).

484
As  such, the simulated net forced response in AMOC is a balance 
between the different forcing factors (Section 9.2.3.1; Delworth and 
Dixon, 2006; Menary et al., 2020). The differing AMOC response of 
CMIP5 and CMIP6 models during the historical period has been 
associated with stronger aerosol effective radiative forcing in the 
CMIP6 models (Menary et al., 2020), such that the aerosol-induced 
AMOC increase during the 1940–1985 period overcomes the 
greenhouse gas induced decline (Figure  3.30e). However, models 
simulate a range of anthropogenic aerosol effective radiative forcing 
and a range of historical AMOC trends in CMIP6 (Menary et al., 2020) 
and there remains considerable uncertainty over the realism of the 
CMIP6 AMOC response during the 20th century (Figure 3.30d–f) due 
to disagreement among the differing lines of evidence. For example,

ocean reanalysis (Jackson et al., 2019) and forced ocean model simulations (Robson et al., 2012; Danabasoglu et al., 2016), which 
show AMOC changes that are broadly consistent with the CMIP6 
response, appear to disagree with observational estimates of AMOC 
over the historical period (Ezer et al., 2013; Caesar et al., 2018). It is 
noted, however, that the relatively short length of the forced ocean 
simulations and ocean reanalysis precludes a comparable assessment 
of 20th century trends. Furthermore, despite the similar AMOC 
evolution seen in forced ocean model simulations and the CMIP6 
models, it is unclear whether the same underlying mechanisms are 
responsible for the changes.
In summary, models do not support robust assessment of the 
role of anthropogenic forcing in the observed AMOC weakening

between the mid-2000s and the mid-2010s, which is assessed Figure 3.30 |  Observed and CMIP6 simulated AMOC mean state, variability and long-term trends. (a)  AMOC meridional stream function profiles at 26.5°N from 
the historical CMIP5 (1860–2004) and CMIP6 (1860–2014) simulations compared with the mean maximum overturning depth (horizontal grey line) and magnitude (vertical grey 
line) from the RAPID observations (2004–2018). The distributions of model ranges of AMOC maximum magnitude and depth are respectively displayed near the x- and y-axis. (b) 
Distributions of overlapping eight-year AMOC trends from individual CMIP6 historical simulations (pink box plots) are plotted along with the combined distributions of all available 
CMIP5 (blue boxplot) and CMIP6 (red boxplot) models. For reference, the observed eight-year trend calculated between 2004 and 2012 is also shown as a horizontal grey line

(following Roberts et al., 2014). (c) Distributions of interannual AMOC variability from individual CMIP6 model historical simulations, along with the combined distributions of all 
available CMIP5 and CMIP6 models. Interannual variability in models and observations is estimated as annual mean (April–March) differences, and the horizontal grey line is the 
observed value for 2009/2010 minus 2008/2009 (following Roberts et al., 2014). (d–f)  Distributions of linear AMOC trends calculated over various time periods (see panel titles) 
in CMIP6 simulations forced with: greenhouse gas forcing only (GHG), natural forcing only (NAT), anthropogenic aerosol forcing only (AER) and all forcing combined (Historical; 
HIST). (a–f) Boxes indicate the 25th to 75th percentile range, whiskers indicate 1st and 99th percentiles in (a-c) and 5th and 95th percentile in (d-f), and dots indicate outliers,

while the horizontal black line is the multi-model mean trend. In (d–f) the multi-model mean trend is also written above each distribution. The multi-model distributions in (a–c) 
were produced with one historical ensemble member per model for which the AMOC variable was available (listed), while those in (d–f) were produced with the detection and 
attribution simulation datasets utilized by Menary et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

485
to have occurred with high confidence  in Section 2.3.3.4.1, as 
the changes are outside of the range of modelled AMOC trends 
(regardless of whether they are forced or internally generated) in 
most models. Thus, we have low confidence  that anthropogenic 
forcing has influenced the observed changes in AMOC strength 
in the post-2004 period. In addition, there remains considerable 
uncertainty over the realism of the CMIP6 AMOC response during 
the 20th century due to disagreement among the differing lines 
of observational and modelled evidence (i.e., historical AMOC 
estimates, ocean reanalysis, forced ocean simulations and 
historical CMIP6 simulations). Thus, we have low confidence  that 
anthropogenic forcing has had a significant influence on changes 
in AMOC strength during the 1860–2014 period.
3.5.4.2 Southern Ocean Circulation
The Southern Ocean circulation provides the principal connections

between the world’s major ocean basins through the circulation of 
the Antarctic Circumpolar Current (ACC), while also largely controlling 
the connection between the deep and upper layers of the global 
ocean circulation, through its upper and lower overturning cells.
The assessment of observations presented in Sections 2.3.3.4.2 
and 9.2.3.2 reports that there is no evidence of an ACC transport 
change, and it is unlikely  that the mean meridional position of the 
ACC has moved southward in recent decades (Sections 2.3.3.4.2 
and 9.2.3.2). This is despite observations of surface wind displaying 
an intensification and southward shift (Section 2.4.1.2). There 
is low confidence  in an observed intensification of upper ocean 
overturning in the Southern Ocean and there is medium confidence  
for a slowdown of the Antarctic Bottom Water circulation and 
commensurate Antarctic Bottom Water volume decrease since the 
1990s (Section 9.2.3.2). Section 9.2.3.2 presents new evidence, since

SROCC, which assessed with medium confidence that the lower 
cell can episodically increase as a response to climatic anomalies, 
temporally counteracting the forced tendency for reduced bottom 
water formation.
The modelled strength of the ACC clearly improved from CMIP3, in 
which the models tended to underestimate the strength of the ACC, 
to CMIP5 (Meijers et al., 2012). This improvement in the realism of 
ACC strength continues from CMIP5 to CMIP6, with the modelled ACC 
strength converging toward the magnitude of observed estimates of 
net flow through the Drake Passage (Beadling et al., 2020). There is,  
however, a small number of models that still display an ACC that 
is much weaker than that observed, while several models also display 
much more pronounced ACC decadal variability than that observed 
(Beadling et al., 2020). The increased realism of the ACC was at least 
partly related to noted improvements in all metrics of the Southern

Ocean’s surface wind stress forcing (Beadling et al., 2020). The most 
notable wind stress forcing improvements were found in the strength 
and the latitudinal position of the zonally-averaged westerly wind 
stress maximum (Beadling et al., 2020; Bracegirdle et al., 2020). 
While the two-cell structure of the overturning circulation appears 
to be well captured by CMIP5 models (Sallée et al., 2013; Russell 
et al., 2018), they tend to underestimate the intensity of the lower 
cell overturning, and overestimate the intensity of the upper cell overturning (Sallée et al., 2013). As the lower overturning cell is 
closely related to Antarctic Bottom Water formation and deep 
convection, both fields also display substantial errors in CMIP5 
models (Heuzé et al., 2013, 2015). CMIP6 climate models show 
clear improvements compared to CMIP5 in their representation of 
Antarctic Bottom Water, which suggests an improved representation 
of the lower overturning cell (Heuzé, 2021).

Despite notable improvements of CMIP6 models compared to CMIP5 
models, inherent limitations in the representation of important 
processes at play in the Southern Ocean’s horizontal and vertical 
circulation remain (Section 9.2.3.2). For instance, Southern Ocean 
mesoscale eddies are largely parameterized in the current generation 
of climate models and, despite their small spatial scales, they are 
a key element for establishing the ACC and upper overturning cell, as 
well as for their future evolution under changing atmospheric forcing 
(Kuhlbrodt et al., 2012; Downes and Hogg, 2013; Gent, 2016; Downes 
et al., 2018; Poulsen et al., 2018). The absence of ice-sheet coupling 
in the CMIP6 model suite is another important limitation, as basal 
meltwater and calving can influence the circulation, particularly the 
lower cell of the Southern Ocean (Bronselaer et al., 2018; Golledge 
et al., 2019; Lago and England, 2019; Jeong et al., 2020; Moorman

et al., 2020). We note that early development of global climate 
models with interactive ice-shelf cavities has begun and is showing 
potential to be developed (Jeong et al., 2020).
In summary, while there have been improvements across successive 
CMIP phases (from CMIP3 to CMIP6) in the representation of 
the Southern Ocean circulation, such that the mean zonal and 
overturning circulations of the Southern Ocean are now broadly 
reproduced, substantial observational uncertainty and climate 
model challenges preclude attribution of Southern Ocean circulation 
changes ( high confidence ).
3.6 Human Influence on the Biosphere
3.6.1 Terrestrial Carbon Cycle
The AR5 did not make attribution statements on changes in global 
carbon sinks. The IPCC Special Report on Climate Change and Land 
(SRCCL) assessed with high confidence that global vegetation 
photosynthetic activity has increased over the last 2–3 decades 
(Jia et al., 2019). That increase was attributed to direct land use

and management changes, as well as to CO 2 fertilization, nitrogen 
deposition, increased diffuse radiation and climate change ( high 
confidence ). The AR5 assessed with high confidence  that CMIP5 
Earth System Models (ESMs) simulate the global mean land and 
ocean carbon sinks within the range of observation-based estimates 
(Flato et al., 2013). The IPCC SRCCL, however, noted the remaining 
shortcomings of carbon cycle schemes in ESMs (Jia et al., 2019), 
which for example do not properly incorporate thermal responses of 
respiration and photosynthesis, and frequently omit representations 
of permafrost thaw (Comyn-Platt et al., 2018), the nitrogen cycle 
(R.Q. Thomas et al., 2015) and its influence on vegetation dynamics 
(Jeffers et al., 2015), the phosphorus cycle (Fleischer et al., 2019), and 
accurate implications of carbon store changes for a range of land use

486
and land management options (Erb et al., 2018; Harper et al., 2018) 
(see Sections 5.2.1.4.1 and 5.4, Figure 5.24 and Table 5.4 for details).
This section considers three main large-scale indicators of climate 
change relevant to the terrestrial carbon cycle: atmospheric 
CO 2 concentration, atmosphere-land CO 2 fluxes, and leaf area 
index. These indicators were chosen because they have been 
the target of attribution studies. Other indicators, like land use 
and management, and wildfires, relate to human influence 
but are discussed in Chapter  5. Chapter  7 discusses energetic 
consequences of changes in the terrestrial carbon cycle in 
Section 7.4.2.5.2. CMIP5 and CMIP6 ESMs are most often run 
with prescribed observed historical changes in atmospheric 
CO 2 concentration and diagnose CO 2 emissions consistent 
with these. Such calculations require that the models simulate

realistic changes in the terrestrial carbon cycle over the historical  
period, as changes to land carbon stores will influence the size 
of CO 2 emissions consistent with prescribed CO 2 pathways, and 
associated remaining carbon budgets (Section 5.5). Such testing of 
existing models is needed while also recognising there are process 
representations still requiring inclusion.
Since AR5, atmospheric inversion studies have further tested 
or constrained models, while new datasets have been used to constrain specific parts of the terrestrial carbon cycle such as plant 
respiration (Huntingford et al., 2017). Figure 3.31 compares historical 
emissions-driven CMIP6 simulations of global mean atmospheric CO 2 
concentration and net ocean and land carbon fluxes to the assessed 
CO 2 concentration and fluxes from the Global Carbon Project 
(Friedlingstein et al., 2019). For 2014, the CMIP6 models simulate 
a range of CO 2 concentrations centred around the observed value of

397 ppmv, with a range of 381 to 412 ppmv. GSAT anomalies simulated 
over the historical period are very similar in models that simulate or 
prescribe changes in atmospheric CO 2 concentrations (Figures 3.31b 
and 3.4a). Most models simulate realistic temporal evolution of the 
global net ocean and land carbon fluxes, although model spread is 
larger over land (Figure 3.31c,d; see also Sections 3.6.2 and 5.4.5.2, 
and Figure  5.24). Although literature published soon after AR5 
highlighted the importance of representing nitrogen limitation on 
plant growth (Peng and Dan, 2015; R.Q. Thomas et al., 2015), more 
recent studies note that models without nitrogen limitation can still 
be consistent with the latest estimates of historical carbon cycle 
changes (Arora et al., 2020; Meyerholt et al., 2020). Uncertainties 
in the photosynthetic response to atmospheric CO 2 concentrations 
at global scales, shifts in carbon allocation and turnover, land-use

change (Hoffman et al., 2014; Wieder et al., 2019), and water 
limitation are also important influences on land carbon fluxes.
Figure 3.31 |  Evaluation of historical emissions-driven CMIP6 simulations for 1850–2014.  Observations (black) are compared to simulations of global mean 
(a) atmospheric CO 2 concentration (ppmv), with observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL; 
Dlugokencky and Tans, 2020); (b) surface air temperature anomaly (°C) with respect to the 1850–1900 mean, with observations from HadCRUT4 (Morice et al., 2012); 
(c) land carbon uptake (PgC yr–1), (d) ocean carbon uptake (PgC yr–1), both with observations from the Global Carbon Project (GCP; Friedlingstein et al., 2019) and grey shading 
indicating the observational uncertainty. Land and ocean carbon uptakes are plotted using a 10-year running mean for better visibility. The ocean uptake is offset to 0 in 1850

to correct for pre-industrial riverine-induced carbon fluxes. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

487
All models and observational estimates agree that interannual 
variability in net CO 2 uptake is much larger over land than over 
the ocean. Studies demonstrate that regional variations in both 
the trends and the yearly strength of the terrestrial carbon sink are 
considerable. Land carbon uptake is dominated by the extratropical 
northern latitudes (see also Section 5.4.5.3 and Figure  5.25; Ciais 
et al., 2019) because the tropics may have become a net source of 
carbon (Baccini et al., 2017). At local to regional scales, the dominant 
driver of yearly sink strength variations is water availability, but at 
continental to global scales, temperature anomalies are the dominant 
driver (Section 5.2.1.4.2; Jung et al., 2017). The major role of levels 
of water stored in the ground in influencing land-atmosphere CO 2 
exchange has also been confirmed through simultaneous analysis

of satellite gravimetry and atmospheric CO 2 levels (Humphrey et al., 
2018). When considered globally, simulated land and ocean carbon 
sinks fall within the range of observation-based estimates with high 
confidence . But there is also high confidence that that apparent 
success arises for the wrong reasons, as models underestimate the 
Northern Hemisphere carbon sink, as discussed in Section 5.4.5.3.
The seasonal cycle in atmospheric CO 2, which is driven by the 
drawdown of carbon by photosynthesis on land during the summer 
and release by respiration during the winter, has increased in 
amplitude since the start of systematic monitoring (Figure 3.32; see 
also Section 2.3.4.1). This trend, which is larger at higher latitudes of 
the Northern Hemisphere, was first reported by Keeling et al. (1996) 
and has continued. Changes in vegetation productivity have also 
been observed, as well as longer growing seasons (Park et al., 2016).

However, a slow down of the increasing trend has been noted, linked to 
a slow down of both vegetation greening and growing-season length 
increases (Buermann et al., 2018; Z. Li et al., 2018; K. Wang et al., 
2020). Figure 3.32 shows that CMIP6 terrestrial carbon cycle models 
partially capture the increasing amplitude of the seasonal cycle of the 
land carbon sink, also seen in observational reconstructions. However, 
the identification of the human influence that contributes most 
strongly to these changes in the seasonal cycle is debated.
Proposed causes of the trend in the amplitude of the seasonal cycle 
of CO 2, and its amplification at higher latitudes, include increases in 
the summer productivity and/or increases in the magnitude of winter 
respiration of northern ecosystems (Barichivich et al., 2013; Graven 
et al., 2013; Forkel et al., 2016; Wenzel et al., 2016), increases in 
productivity throughout the Northern Hemisphere by CO 2 fertilization,

and increases in the productivity of agricultural crops in northern 
mid-latitudes (Gray et al., 2014; Zeng et al., 2014). Recent studies 
have attempted to quantify the different contributions by comparing 
atmospheric CO 2 observations with ensembles of land surface 
model simulations. Piao et al. (2017) found that CO 2 fertilization of 
photosynthesis is the main driver of the increase in the amplitude 
of the seasonal cycle of atmospheric CO 2 but noted that climate 
change drives the latitudinal differences in that increase. North of 
40°N, Bastos et al. (2019) also found CO 2 fertilization to be the most 
likely driver, with warming at northern high latitudes contributing 
a decrease in amplitude, in contrast to earlier conclusions (Graven 
et al., 2013; Forkel et al., 2016), and agricultural and land use 
changes making only a small contribution. For temperate regions

of the Northern Hemisphere, K. Wang et al. (2020) found that the importance of CO 2 fertilization is decreased by drought stress, but 
also found only a small contribution from agricultural and land use 
changes. However, many global models do not include nitrogen 
fertilization, changes to crop cultivars or irrigation effects, with the 
latter associated with deficiencies in simulated terrestrial water 
cycling (H. Yang et al., 2018). All these factors influence the capability 
of models to simulate accurately the seasonal cycle in atmosphere-
land CO 2 exchanges. Model comparisons to the atmospheric CO 2 
concentration record for Barrow, Alaska, suggest that models 
underestimate current levels of carbon fixation (Winkler et al., 
2019) and have deficiencies in their phenological representation of 
greenness levels, particularly for autumn (Z. Li et al., 2018). Based on 
these studies and noting the uncertainty in the processes ultimately

driving changes in atmospheric CO 2 seasonal cycles (Section 5.2.1.4), 
we assess with medium confidence  that fertilization by anthropogenic 
increases in atmospheric CO 2 concentrations is the main driver of the 
increase in the amplitude of the seasonal cycle of atmospheric CO 2.
Detection and attribution methods have been applied to leaf area 
index, which represents ‘greenness’ and general photosynthetic 
productivity (see Section 2.3.4.3). Nitrogen deposition and land 
cover change trends remain small compared to variability, so 
attributing changes in leaf area index to those processes is difficult. 
Using three satellite products and ten land models, Zhu et al. (2016) 
found increases in leaf area index (greening) over 25–50% of global 
vegetated areas, and they attributed 70% of this greening to CO 2 
fertilization, although they found that land use change can dominate 
regionally. This is consistent with the attribution study of observed

greening of Mao et al. (2016), and with Mao et al. (2013) who found 
that CO 2 fertilization was the dominant cause of enhanced vegetation 
growth, with latitudinal changes in leaf area index explained by the 
larger land surface warming in the Northern Hemisphere. These 
conclusions are also consistent with those of Zhu et al. (2017), 
who found a dominant role for CO 2 fertilization in driving leaf area 
index changes in an attribution study in which land models were 
first weighted by performance. However, Chen et al. (2019) has 
challenged these results by showing that greening in India and China 
was driven by land-use change.
Leaf area index increases attributed to CO 2 fertilization are due 
to a direct raised physiological response. However, for drylands, 
CO 2-induced stomatal closure may act to conserve soil moisture and 
thereby indirectly drive higher photosynthesis through higher water 
use efficiency (Lu et al., 2016). In models with nitrogen deposition,

there is evidence that this simulated effect also influences leaf area 
index trends, however, because of a lack of literature based on 
large-scale land simulations including both nutrient limitation and 
crop intensification, it is not yet possible to make an attribution 
statement about their individual roles in leaf area index changes.
In summary, Earth system models simulate globally averaged land 
carbon sinks within the range of observation-based estimates 
(high confidence ), but global-scale agreement masks large 
regional disagreements. Based on new studies that attribute 
changes in atmospheric CO 2 seasonal cycle to CO 2 fertilization, 
albeit counteracted by other factors, combined with the medium 
confidence  that models represent the processes driving changes in

488
the seasonal cycle, we assess that there is medium confidence that 
CO 2 fertilization is the main driver of the increase in the amplitude of 
the seasonal cycle of atmospheric CO 2. Based on available literature, 
CO 2 fertilization has been the main driver of the observed greening 
trend, but there is only low confidence  in this assessment because 
of ongoing debate about the relative roles of CO 2 fertilization, high 
latitude warming, and land management, and the low number of 
models that represent the whole suite of processes involved.
3.6.2 Ocean Biogeochemical Variables
Since CMIP5, there has been a general increase in ocean horizontal and 
vertical grid resolution in ocean model components (Arora et al., 2020; 
Séférian et al., 2020). The latter of these developments is particularly 
significant for projections of ocean stressors as it directly affects the

representation of stratification. Updates in the representation of ocean 
biogeochemical processes between CMIP5 and CMIP6 have typically 
involved an increase in model complexity. Specific developments have 
been the more widespread inclusion of micronutrients, such as iron, 
variable stoichiometric ratios, more detailed representation of lower 
trophic levels including bacteria and the cycling and sinking of organic 
matter. CMIP6 biogeochemical model performance is generally an 
improvement on that of the parent CMIP5 generation of models 
(Séférian et al., 2020). The global representation of present-day air-sea carbon fluxes and surface chlorophyll concentrations show moderate 
improvements between CMIP5 and CMIP6. Similar improvements are 
seen in the representation of subsurface oxygen concentrations in 
most ocean basins, while the representation of surface macronutrient 
concentrations in CMIP6 is shown to have improved with respect

to silicic acid but declined slightly with respect to nitrate. Model 
representation of the micronutrient iron has not improved substantially 
since CMIP5, but many more models are capable of representing iron. 
In addition, a comparison of the carbon concentration and carbon 
climate feedbacks shows no significant change between CMIP5 and 
CMIP6 (Arora et al., 2020).
Since AR5, research has also focused on the detection and attribution 
of regional patterns in ocean biogeochemical change relating to 
interior deoxygenation, air-sea CO 2 flux, and ocean carbon uptake and 
associated acidification. Characterization of flux variability requires 
understanding of the suite of physical and biological processes 
including transport, heat fluxes, interior ventilation, biological 
production and gas exchange which can have very different controls 
on seasonal versus interannual time scales in both the North Pacific 
(Ayers and Lozier, 2012) and North Atlantic (Breeden and McKinley,

2016). In the Southern Ocean, models have difficulty reproducing 
the observed seasonal cycle and interannual variability, making 
attribution particularly challenging (Lovenduski et al., 2016; Mongwe 
et al., 2016, 2018).
The AR5 concluded that oxygen concentrations have decreased in 
the open ocean since 1960 and such decreases can be attributed 
in part to human influence with medium confidence . The decrease 
in ocean oxygen content in the upper 1000 m, between 1970 and 
2010, is further confirmed in SROCC ( medium confidence ), with 
the oxygen minimum zone expanding in volume (see also Section 
5.3.3.2). Observed oxygen declines over the last several decades 
(Stendardo and Gruber, 2012; Stramma et al., 2012; Schmidtko et al., 
2017) match model estimates in the surface ocean (Oschlies et al., 
2017) but are much larger than model derived estimates in the 
interior (Bopp et al., 2013; Cocco et al., 2013). Some of this difference

has been interpreted as due to a lack of representation of coastal 
eutrophication in these models (Breitburg et al., 2018), but much of 
it remains unexplained. This disparity is particularly apparent in the 
eastern Pacific oxygen minimum zone, where some CMIP5 models 
showed increasing trends whereas observations show a strong 
decrease (Cabré et al., 2015). However, proxy reconstructions suggest 
that over the last century the ocean may have in fact undergone 
increases in oxygen in the most oxygen poor regions (Deutsch et al., 
2014). As discussed in Section 5.3.1, ocean oxygen went through 
wide oscillations on multi-centennial time scales through the last 
deglaciation, with abrupt warming resulting in loss of oxygen in 
subsurface waters of the North Pacific (Praetorius et al., 2015). The 
global upper ocean oxygen inventory is negatively correlated with 
ocean heat content with a regression coefficient comparable to that

found in ocean models (Ito et al., 2017). Variability and trends in the 
observed upper ocean oxygen concentration are mainly driven by 
the apparent oxygen utilization component with small contributions 
from oxygen solubility, suggesting that changing ocean circulation, 
mixing, and/or biochemical processes, rather than thermally 
induced solubility effects may be the main drivers of observed Figure 3.32 |  Relative change in the amplitude of the seasonal cycle of global 
land carbon uptake in the historical CMIP6 simulations from 1961–2014.  Net 
biosphere production estimates from 19 CMIP6 models (red), the data-led reconstruction 
JMA-TRANSCOM (Maki et al., 2010; dotted) and atmospheric CO 2 seasonal cycle 
amplitude changes from observations (global as dashed line, Mauna Loa Observatory 
(MLO) (Dlugokencky et al., 2020) in bold black). Seasonal cycle amplitude is calculated 
using the curve fit algorithm package from the National Oceanic and Atmospheric

Administration Earth System Research Laboratory (NOAA ESRL). Relative changes are 
referenced to the 1961–1970 mean and for short time series adjusted to have the same 
mean as the model ensemble in the last 10 years. Interannual variation was removed with 
a nine-year Gaussian smoothing. Shaded areas show the one sigma model spread (grey) 
for the CMIP6 ensemble and the one sigma standard deviation of the smoothing (red) for 
the CO 2 MLO observations. Inset: average seasonal cycle of ensemble mean net biosphere 
production and its one sigma model spread for 1961–1970 (orange dashed line, light 
orange shading) and 2005–2014 (solid green line, green shading). Further details on 
data sources and processing are available in the chapter data table (Table 3.SM.1).

489
deoxygenation. The spatial distribution of the ocean deoxygenation 
in the interior of the ocean as well as over coastal areas is further assessed in Section 5.3.
As one of the most commonly observed surface parameters, the 
partial pressure of CO
2 has been the topic of considerable detection 
and attribution work. In North Atlantic subtropical and equatorial biomes, warming has been shown to be a significant and persistent 
contributor to the observed increase in the partial pressure of 
CO
2 since the mid -2000s with long -term warming leading to 
a reduction in ocean carbon uptake (Fay and McKinley, 2013), and with both the partial pressure of CO
2 and associated carbon uptake 
demonstrating strong predictability as a function of interannual to decadal climate state (H. Li et al., 2016; Li and Ilyina, 2018). In 
the Southern Ocean however, detection and attribution of surface 
trends in the partial pressure of CO

2 has proven more elusive and 
dependent on methodology, with some studies suggesting that Southern Ocean carbon uptake slowed from about 1990 to 2006 
and subsequently strengthened from 2007 to 2010 (Lovenduski 
et al., 2008; Fay et al., 2014; Ritter et al., 2017). Other studies have suggested that poor representation of the seasonal cycle in the Southern Ocean may confound the models’ ability to represent changes in the partial pressure of CO
2 in the Southern Ocean (Nevison 
et al., 2016; Mongwe et al., 2018).
Section 5.2.1.3 assesses that both observational reconstructions 
based on the partial pressure of CO 2 and ocean biogeochemical models 
show a quasi-linear increase in the ocean sink of anthropogenic CO 2 
from 1.0 ± 0.3 PgC yr–1 to 2.5 ± 0.6 PgC yr–1 between 1960–1969 and 
2010–2019 in response to global CO 2 emissions (high confidence). 
During the 1990s, the global net flux of CO 2 into the ocean is 
estimated to have weakened to 0.8  ±  0.5  PgC yr–1 while in 2000

and thereafter, it is estimated to have strengthened considerably to rates of 2.0 ± 0.5 PgC yr
–1, associated with changes in SST, the 
surface concentration of dissolved inorganic carbon and alkalinity, and decadal variations in atmospheric forcing (Landschützer et al., 
2016, see also Section 5.2).
Ocean acidification is one of the most detectible metrics of 
environmental change and was well covered in AR5, in which it was assessed that the uptake of anthropogenic CO
2 had very likely 
resulted in acidification of surface waters (Bindoff et al., 2013). Since then, observations and simulations of multi-decadal trends in 
surface carbon chemistry have increased in robustness. The evidence 
on ocean pH decline had further strengthened in SROCC with good agreement found between CMIP5 models and observations and an assessment that the ocean was continuing to acidify in response to ongoing carbon uptake (Bindoff et al., 2019). An observed decrease

in global surface open ocean pH is assessed in Section 2.3.3.5 to 
be virtually certain to have occurred with a rate of 0.003–0.026 per 
decade for the past 40 years. The ocean acidification has occurred not only in the surface layer but also in the interior of the ocean (Sections 
2.3.3.5 and 5.3.3). Rates have been observed to be between −0.015 
and −0.020 per decade in mode and intermediate waters of the North Atlantic through the combined effect of increased anthropogenic and remineralized carbon (Ríos et al., 2015) and acidification has been observed down to 3000 m in the deep water formation regions (Perez et al., 2018). There has also been considerable improvement in detection and attribution of anthropogenic CO
2 versus eutrophication-
based acidification in coastal waters (Wallace et al., 2014).
The increased evidence in recent studies supports an assessment that 
it is virtually certain that the uptake of anthropogenic CO 2 was the

main driver of the observed acidification of the global surface open ocean. The observed increase in acidification over the North Atlantic 
subtropical and equatorial regions since 2000 is likely associated 
in part with an increase in ocean temperature, a response which 
corresponds to the expected weakening of the ocean carbon sink with warming. Due to strong internal variability, systematic changes in carbon uptake in response to climate warming have not been observed 
in most other ocean basins at present. We further assess, consistent 
with AR5 and SROCC, that deoxygenation in the upper ocean is due in part to anthropogenic forcing, with medium confidence. There is 
high confidence that Earth system models simulate a realistic time 
evolution of the global mean ocean carbon sink.
3.7 Human Influence on Modes 
of Climate Variability
This section assesses model evaluation and attribution of changes

in the modes of climate variability listed in Cross-Chapter Box 2.2, Table 2. The structure of the modes is described in Annex IV, observed 
changes in the modes and associated teleconnections are assessed 
in Section 2.4, and the role of the modes in shaping regional climate is assessed in Section 10.1.3.2.
3.7.1  North Atlantic Oscillation and Northern 
Annular Mode
The Northern Annular Mode (NAM; also known as the Arctic Oscillation) is an oscillation of atmospheric mass between the Arctic and northern mid-latitudes, analogous to the Southern Annular Mode 
(SAM; Section 3.7.2). It is the leading mode of variability of sea-level 
pressure in the northern extratropics but also has a clear fingerprint through the troposphere up to the lower stratosphere, with maximum expression in boreal winter (Kidston et al., 2015). The North Atlantic Oscillation (NAO) can be interpreted as the regional expression of the 
NAM and captures most of the related variance in the troposphere over

a broad North Atlantic/Europe domain. Indices measuring the state of the NAO correlate highly with those of the NAM, and teleconnection patterns for both modes are rather similar (Feldstein and Franzke, 
2006). A detailed description of the NAM and the NAO as well as 
their associated teleconnection over land is given in Annex IV.2.1.
AR5 found that while models simulated correctly most of the 
spatial properties of the NAM, substantial inter-model differences 
remained in the details of the associated teleconnection patterns 
over land (Flato et al., 2013). The AR5 reported that most models did not reproduce the observed positive trend of the NAO/NAM indices during the second half of the 20th century. It was unclear to what extent this failure reflected model shortcomings and/or if 
the observed trend could be simply related to pronounced internal

490
climate variability. The AR5 accordingly did not make an attribution 
assessment for the NAO/NAM.
New studies since AR5 continue to find that CMIP5 models reproduce 
the spatial structure and magnitude of the NAM reasonably well (Lee 
and Black, 2013; Zuo et al., 2013; Davini and Cagnazzo, 2014; Ying 
et al., 2014; Ning and Bradley, 2016; Deser et al., 2017b; Gong et al., 
2017) although the North Pacific SLP anomalies remain generally too 
strong (Zuo et al., 2013; Gong et al., 2017) and the subtropical North 
Atlantic lobe of SLP anomalies conversely too weak (Ning and Bradley, 
2016) in many models. Such overall biases noted in both CMIP3 
and CMIP5 (Davini and Cagnazzo, 2014) persist in CMIP6 historical 
simulations, even though the multi-model multi-member ensemble 
mean spatial correlation between modelled and observed NAM is 
slightly higher (Figure 3.33a,d,g). Regarding the NAO, the majority

of CMIP5 models very successfully simulate its spatial structure (Lee 
et al., 2019) and its associations with extratropical jet, storm track 
and blocking variations over a broad North-Atlantic/Europe domain 
(Davini and Cagnazzo, 2014) and over land through teleconnections 
(Volpi et al., 2020). The good performance of the models is confirmed 
in CMIP6 with a marginal improvement of the averaged observation-
model spatial correlation (Figure  3.33b,e,h) and better skill based 
on other evaluation metrics (Fasullo et al., 2020). The slight 
underestimation of the SLP anomalies related to the NAO centres 
of actions over the Azores and Greenland–Iceland–Norwegian Seas 
remain unchanged compared to CMIP5.
CMIP5 models with a model top within the stratosphere seriously 
underestimate the amplitude of the variability of the wintertime 
NAM expression in the stratosphere, in contrast to CMIP5 models 
which extend well above the stratopause (Lee and Black, 2015).

However, even in the latter models, the stratospheric NAM events, 
and their downward influence on the troposphere, are insufficiently 
persistent (Charlton-Perez et al., 2013; Lee and Black, 2015). 
Increased vertical resolution does not show any significant added 
value in reproducing the structure and magnitude of the tropospheric 
NAM (Lee and Black, 2013) nor in the NAO predictability as assessed 
in a seasonal prediction context with a multi-model approach (Butler 
et al., 2016). On the other hand, there is mounting evidence that 
a correct representation of the Quasi Biennal Oscillation, extratropical 
stratospheric dynamics (the polar vortex and sudden stratospheric 
warmings), and related troposphere-stratosphere coupling, as well as 
their interplay with ENSO, are important for NAO/NAM timing (Scaife 
et al., 2016; Karpechko et al., 2017; Domeisen, 2019; Domeisen et al., 
2019), in spite of underestimated troposphere–stratosphere coupling

found in models compared to observations (O’Reilly et al., 2019b).
The observed trend of the NAM and NAO indices is positive in 
winter when calculated from the 1960s (Section 2.4.1.1) but it 
includes large multi-decadal variability, which means that the 
nature of the trend should be interpreted with caution (Gillett et al., 
2013). The multi-model multi-member ensemble mean of the trend 
estimated from historical simulations over that period is very close 
to zero for both CMIP5 and CMIP6 (Figures 3.33j,k and 3.34a). Even 
if one cannot rule out that 1958–2014 was an exceptional period 
of variability, the observational estimates of the wintertime NAO 
trend lie outside the 5th–95th percentile range of the distribution of trends in the CMIP6 historical simulations, and the observed NAM 
trends over the same period lie above the 90th percentile. There is 
a tendency for the CMIP5 models to systematically underestimate

the level of multi-decadal versus interannual variability of the winter 
NAO and jet stream compared to observations (X. Wang et al., 2017; 
Bracegirdle et al., 2018; Simpson et al., 2018). Results from CMIP6 
(Figure 3.33j,k) and over the 1958–2019 period (Figure 3.34a) confirm 
this conclusion and seriously question the ability of the models to 
simulate long-term fluctuations of the NAO/NAM, independently of 
its forced or internal origins.
Dedicated SST-forced stand-alone atmospheric model experiments 
(AMIP) suggest that ocean forcing appears to play a role in decadal 
variability of the NAO and associated fluctuations in the strength 
of the jet (Woollings et al., 2015). In particular, Atlantic and Indian 
Ocean SST anomalies (Fletcher and Cassou, 2015; Baker et al., 2019; 
Douville et al., 2019; Dhame et al., 2020) may have contributed 
to the long-term positive trend of the winter NAO/NAM over the 
20th  century, but there is only low confidence  in such a causal

relationship because of the limitation of the imposed SST approach 
in AMIP and the uncertainties in observed SST trends among datasets 
used as forcing of the atmospheric model. The representation of 
the NAM and NAO spatial structure is slightly improved in AMIP 
ensembles (Figure 3.33g,h), which also produce slightly larger trends 
than the historical simulations for the NAO, but not for the NAM.
When calculated over the most recent two decades, the wintertime 
NAM/NAO trend is weakly negative since the mid-1990s (Hanna 
et al., 2015). Recent studies based on observations (Gastineau and 
Frankignoul, 2015) and dedicated modelling experiments (Davini 
et al., 2015; Peings and Magnusdottir, 2016) suggest that the recent 
dominance of negative NAM/NAO could be partly related to the latest 
shift of the Atlantic Multi-decadal Variability (AMV) to a warm phase 
(Sections 2.4.4 and 3.7.7). Some recent modelling studies also find

that the Arctic sea ice decline might be partly responsible for more 
recurrent negative NAM/NAO (Peings and Magnusdottir, 2013; B.M. 
Kim et al., 2014; Nakamura et al., 2015), while other studies do not 
robustly identify such responses in models (see also Cross-Chapter 
Box 10.1).
In contrast to winter, the observed trend of the NAO index over 
1958–2014 is overall negative in summer and is associated with 
more recurrent blocking conditions over Greenland, in particular 
since the mid-1990s, thus contributing to the acceleration of melting 
of the Arctic sea ice (Section 3.4.1.1) and Greenland Ice Sheet 
(Section 3.4.3.2; Fettweis et al., 2013; Hanna et al., 2015; Ding et al., 
2017). The origin of the negative trend of the summer NAO has 
not been clearly identified, and is hypothesized to be the result of 
combined influences (Lim et al., 2019), though trends in summertime 
NAO should also be interpreted with caution because of the presence

of strong multi-decadal variability. The recent observed negative NAO 
prevalence and related blocking over Greenland is not present in any 
of the CMIP5 models (Hanna et al., 2018).
Regarding the influence of external forcings since pre-industrial 
times, AR5 noted that CMIP5 models tend to show an increase in the 
NAM in response to greenhouse gas increases (Bindoff et al., 2013).

491
Figure 3.33 |  Model evaluation of NAM, NAO and SAM in boreal winter.  Regression of Mean Sea Level Pressure (MSLP) anomalies (in hPa) onto the normalized 
principal component (PC) of the leading mode of variability obtained from empirical orthogonal decomposition of the boreal winter (December–February) MSLP poleward of 
20°N for the observed Northern Annular Mode (NAM, a) , over 20°N–80°N, 90°W–40°E for the North Atlantic Oscillation as shown by the black sector (NAO, b) , and poleward 
of 20°S for the Southern Annular Mode (SAM, c)  for the JRA-55 reanalysis. Cross marks indicate regions where the anomalies are not significant at the 10% level based on a 
t-test. The period used to calculate the NAO/NAM is 1958–2014 but 1979–2014 for the SAM. (d–f)  Same but for the multi-model ensemble (MME) mean from CMIP6 historical

simulations. Models are weighted in compositing to account for differences in their respective ensemble size. Diagonal lines show regions where less than 80% of the runs 
agree in sign. (g–i)  Taylor diagrams summarizing the representation of the modes in models and observations following Lee et al. (2019) for CMIP5 (light blue) and CMIP6 (red) 
historical simulations. The reference pattern is taken from JRA-55 (a–c). The ratio of standard deviation to that of the reference observations (radial distance), spatial correlation 
(radial angle) and resulting root-mean-squared errors (solid isolines) are given for individual ensemble members (crosses) and for other observational products (ERA5 and NOAA 
20CR version 3, black dots). Coloured dots stand for weighted multi-model mean statistics for CMIP5 (blue) and CMIP6 (light red) as well as for AMIP simulations from CMIP6

(orange). (j–l) Histograms of the trends built from all individual ensemble members and all the models (brown bars). Vertical lines in black show all the observational estimates. 
The orange, light red, and light blue lines indicate the weighted multi-model mean of CMIP6 AMIP , CMIP6 and CMIP5 historical simulations, respectively. Further details on data 
sources and processing are available in the chapter data table (Table 3.SM.1).

492
Based on the CMIP5 historical ensemble, Gillett and Fyfe (2013) 
however showed that such a trend is not significant in all seasons. 
A multi-model assessment of eight CMIP5 models found a NAM 
increase in response to greenhouse gases, but no robust influence of 
aerosol changes (Gillett et al., 2013). As for ozone depletion, there is 
no robust detectable influence on long-term trends of the NAO/NAM 
(Karpechko et al., 2018) in contrast to the SAM (Section 3.7.2), but 
there are indications that extreme Arctic ozone depletion events and 
their surface expression are linked to an anomalously strong NAM 
episodes (Calvo et al., 2015; Ivy et al., 2017). However, the direction 
of causality here is not clear.
Conclusions on external forcing influences on the NAM are supported 
by CMIP6 results based on single forcing ensembles (Figure 3.34a). 
Positive trends are found in historical simulations over 1958–2019

in boreal winter and are mainly driven by greenhouse gas increases. 
No significant trends are simulated in response to anthropogenic 
aerosols, stratospheric ozone or natural forcing. Albeit weak and not statistically significant, the sign of the multi-model mean forced 
response due to natural forcing is consistent with the observed 
reduction of solar activity since the 1980s (Section 2.2.1) whose 
influence would have favoured the negative phase of wintertime 
NAM/NAO based on the fingerprint of the nearly periodical 11-year 
solar cycle extracted from models (Scaife et al., 2013; Andrews et al., 
2015; Thiéblemont et al., 2015) or observations (Gray et al., 2016; 
Lüdecke et al., 2020). But such an NAO response to solar forcing 
remains highly uncertain and controversial, being contradicted by 
longer proxy records over the last millennium (Sjolte et al., 2018) 
and modelling evidence (Gillett and Fyfe, 2013; Chiodo et al.,

2019). For all seasons and for all individual forcings, uncertainties 
remain in the estimation of the forced response in the NAM trend as 
evidenced by considerable model spread (Figure 3.34a) and because 
the simulated forced component has small amplitude compared 
to internal variability.Figure 3.34 |  Attribution of observed seasonal trends in the annular modes to forcings.  Simulated and observed trends in NAM indices over 1958–2019 (a) and 
in SAM indices over 1979–2019 (b) and over 2000–2019 (c) for boreal winter (December–February average; DJF) and summer (June–August average; JJA). The indices are 
based on the difference of the normalized zonally averaged monthly mean sea level pressure between 35°N and 65°N for the NAM and between 40°S and 65°S for the SAM 
as defined in Jianping and Wang (2003) and Gong and Wang (1999), respectively; the unit is decade–1. Ensemble mean, interquartile ranges and 5th and 95th percentiles are

represented by empty boxes and whiskers for pre-industrial control simulations and historical simulations. The number of ensemble members and models used for computing 
the distribution is given in the upper-left legend. Grey lines show observed trends from the ERA5 and JRA-55 reanalyses. Multi-model multi-member ensemble means of the 
forced component of the trends as well as their 5–95% confidence intervals assessed from t-statistics, are represented by filled boxes, based on CMIP6 individual forcing 
simulations from DAMIP ensembles; greenhouse gases in brown, aerosols in light blue, stratospheric ozone in purple and natural forcing in green. Models with at least three 
ensemble members are used for the filled boxes, with black dots representing the ensemble means of individual models. Further details on data sources and processing are 

493
Despite new efforts since AR5 to reconstruct the NAO beyond the 
instrumental record, it is still very challenging to assess the role 
of external forcings in the apparent multi-decadal to centennial 
variability present throughout the last millennium. Large uncertainties 
remain in the reconstructed NAO index that are sensitive to the 
types of proxies and statistical methods (Trouet et al., 2012; Ortega 
et al., 2015; Anchukaitis et al., 2019; Cook et al., 2019; Hernández 
et al., 2020; Michel et al., 2020) and reconstructed NAO variations 
are often not reproduced using pseudo-proxy approaches in models 
(Lehner et al., 2012; Landrum et al., 2013). At low frequency, it 
remains challenging to evaluate if the observed or reconstructed 
signal corresponds to an actual change in the NAO intraseasonal to 
interannual intrinsic properties or rather to a change in the mean

background atmospheric circulation changes projecting on a specific 
phase of the mode. Consequently, conflicting results emerge in the 
attribution of reconstructed long-term variations in the NAO to solar 
forcing, whose influence thus remains controversial (Gómez-Navarro 
and Zorita, 2013; Moffa-Sánchez et al., 2014; Ortega et al., 2015; 
Ait Brahim et al., 2018; Sjolte et al., 2018; Xu et al., 2018). Influences 
from major volcanic eruptions appear to be more robust (Ortega et al., 
2015; Swingedouw et al., 2017) even if some modelling experiments 
question the amplitude of the response, which mostly projects on 
the positive phase of the NAM/NAO (Bittner et al., 2016). The 
forced response is dependent on the strength, seasonal timing and 
location of the eruption but may also depend on the mean climate 
background state (Zanchettin et al., 2013) and/or the phases of the 
main modes of decadal variability such as the AMV (Section 3.7.7; 
Ménégoz et al., 2018).

Finally, there is some evidence of an apparent signal-to-noise 
problem referred to as ‘paradox’ in seasonal and decadal hindcasts 
of the NAO over the period 1979–2018 (Scaife and Smith, 2018), 
which suggests that the NAO response to external forcing, SST or 
sea ice anomalies could be too weak in models. The weakness of the 
signal has been related to troposphere-stratosphere coupling which 
is too intermittent (O’Reilly et al., 2019b) and to chronic model biases 
in the persistence of NAO/NAM daily regimes, which is critically 
underestimated in coupled models (Strommen and Palmer, 2019; 
Zhang and Kirtman, 2019), and which does not exhibit significant 
improvement when model resolution is increased (Fabiano et al., 
2020). Note, however, that the apparent signal-to-noise problem may 
be dependent on the period analysed over the 20th century, which 
questions its interpretation as a general characteristic of coupled 
models (Weisheimer et al., 2020).

In summary, CMIP5 and CMIP6 models are skilful in simulating the 
spatial features and the variance of the NAM/NAO and associated 
teleconnections ( high confidence ). There is limited evidence  for 
a significant role for anthropogenic forcings in driving the observed 
multi-decadal variations of the NAM/NAO from the mid 20th century. 
Confidence in attribution is low: (i) because there is a large spread in the 
modelled forced responses which is overwhelmed anyway by internal 
variability; (ii) because of the apparent signal-to-noise problem; and (iii) 
because of the chronic inability of models to produce a range of trends 
which encompasses the observed estimates over the last 60 years.3.7.2 Southern Annular Mode
The Southern Annular Mode (SAM) consists of a meridional 
redistribution of atmospheric mass around Antarctica (Figure 3.33c,f), 
associated with a meridional shift of the jet and surface westerlies 
over the Southern Ocean. SAM indices are variously defined as the

difference in zonal-mean sea level pressure or geopotential height 
between middle and high latitudes or via a principal-component 
analysis (Annex IV.2.2). Observational aspects of the SAM are 
assessed in Section 2.4.1.2.
AR5 assessed that CMIP5 models have medium performance in 
reproducing the SAM with biases in pattern (Flato et al., 2013). It 
also concluded that the trend of the SAM toward its positive phase in 
austral summer since the mid-20th century is likely  to be due in part 
to stratospheric ozone depletion, and there was medium confidence 
that greenhouse gases have also played a role (Bindoff et al., 2013). 
Based on proxy reconstructions, AR5 found with medium confidence 
that the positive SAM trend since 1950 was anomalous compared 
to the last 400 years (Masson-Delmotte et al., 2013).
Additional research has shown that CMIP5 models reproduce 
the spatial structure of the SAM well, but tend to overestimate its

variability in austral summer at interannual time scales, although this 
variability is within the observational uncertainty (Figure  3.33c,f,i; 
Zheng et al., 2013; Schenzinger and Osprey, 2015). This is related 
to the models’ tendency to simulate slightly more persistent SAM 
anomalies in summer compared to reanalyses (Schenzinger and 
Osprey, 2015; Bracegirdle et al., 2020). This may be due in part to 
too weak a  negative feedback from tropospheric planetary waves 
(Simpson et al., 2013). CMIP6 models show improved performance 
in reproducing the spatial structure and interannual variance of the 
SAM in summer based on Lee et al. (2019) diagnostics (Figure 3.33i), 
with a better match of its trend with reanalyses over 1979–2014 
(Figure 3.33l), more realistic persistence and improved positioning of 
the westerly jet, which in CMIP5 models on average is located too far 
equatorward (Bracegirdle et al., 2020; Grose et al., 2020). In CMIP5, it

is also found that models which extend above the stratopause tend 
to simulate stronger summertime trends in the late 20th century 
than their counterparts with tops within the stratosphere (Rea 
et al., 2018; Son et al., 2018), though other differences between 
these sets of models, such as additional physical processes operating 
in the stratosphere or interactive ozone chemistry, may have also 
affected these results (Gillett et al., 2003a; Sigmond et al., 2008; Rea 
et al., 2018). At the surface, Ogawa et al. (2015) demonstrate with an 
atmospheric model the importance of sharp mid-latitude SST gradients 
for stratospheric ozone depletion to affect the SAM in summer. These 
studies imply that the well resolved stratosphere combined with finer 
ocean horizontal resolution has contributed to the stronger simulated 
trends in CMIP6 than in CMIP5.
CMIP6 historical simulations capture the observed positive trend of 
the summertime SAM when calculated from the 1970s to the 2010s

(Figure 3.34b). J.L. Thomas et al. (2015) found that the chance of the 
observed 1980–2004 trend occurring only due to internal variability is 
less than 10% in many of the CMIP5 models, and results from CMIP6 
models suggest that the chance of the 1979–2019 trend being due

494
to internal variability could be even lower (Figure 3.34b). Although 
paleo-reconstructions of the SAM index are uncertain and vary in 
terms of long-term trends (Section 2.4.1.2), new reconstructions 
show that the 60-year summertime SAM trend since the mid-20th 
century is outside the 5th–95th percentile range of the trends in the 
pre-industrial variability, which matches the trend range of CMIP5 
pre-industrial control simulations well (Dätwyler et al., 2018).
In general agreement with AR5, new research continues to indicate 
that both stratospheric ozone depletion and increasing greenhouse 
gases have contributed to the trend of the SAM during austral summer 
toward its positive phase in recent decades (Solomon and Polvani, 
2016), with the ozone depletion influence dominating (Gerber and 
Son, 2014; Son et al., 2018). In CMIP6 historical simulations there are

significant positive SAM trends over the 1979–2019 period in austral 
summer, although the contribution from ozone forcing evaluated 
with the four available models is not significant (Figure 3.34b). Three 
of these models share the same standard prescribed ozone forcing 
and produce significantly positive SAM trends over an extended 
period (1957–2019). The fourth model, MRI-ESM2-0, has the option 
of interactive ozone chemistry. Its ozone-only experiment is forced 
by prescribed ozone derived from its own historical simulations 
and produces a negative SAM trend associated with weak ozone 
depletion (Morgenstern et al., 2020). Morgenstern et al. (2014) and 
Morgenstern (2021) find an indirect influence of greenhouse gases 
on the SAM via induced ozone changes in coupled chemistry-climate 
simulations, which differ from the prescribed ozone simulations shown 
in Figure 3.34b. Since about 1997, the effective abundance of ozone-

depleting halogen has been decreasing in the stratosphere (WMO, 
2018), leading to a stabilization or even a reversal of stratospheric 
ozone depletion (Sections 2.2.5.2 and 6.3.2.2). The ozone stabilization 
and slight recovery since about 2000 may have caused a pause in the 
summertime SAM trend (Figure 3.34c; Saggioro and Shepherd, 2019; 
Banerjee et al., 2020), although some influence from internal variability 
cannot be ruled out. While some studies find an anthropogenic aerosol 
influence on the summertime SAM (Gillett et al., 2013; Rotstayn, 
2013), recent studies with larger multi-model ensembles find that 
this effect is not robust (Steptoe et al., 2016; Choi et al., 2019), 
consistent with CMIP6 single forcing ensembles (Figure 3.34). In the 
CMIP5 simulations, volcanic stratospheric aerosol has a  significant 
weakening effect on the SAM in autumn and winter (Cross-Chapter 
Box  4.1; Gillett and Fyfe, 2013), but there is no evidence that this

effect leads to a significant multi-decadal trend since the late 20th 
century. Beyond external forcing, Fogt et al. (2017) show a significant 
association of tropical SST variability with the summertime SAM 
trend since the mid-20th century in agreement with Lim et al. (2016), 
who, however, demonstrate that such a teleconnection between the 
summertime SAM and El  Niño–Southern Oscillation (Annex IV.2.3), 
found in observations, is missing in many CMIP5 models.
On longer time scales, last millennium experiments from CMIP5 
models fail to capture multicentennial variability evident in the 
reconstructions for the pre-industrial era (Abram et al., 2014; Dätwyler 
et al., 2018), which is also the case in those from available CMIP6 
models (Figure  3.35). However, there is large uncertainty among 
reconstructions (Section  2.4.1.2). It is therefore unclear whether this

disagreement reflects this observational uncertainty, whether forcings such as variations in the imposed insolation may be too weak, whether 
models are insufficiently sensitive to such variations, or whether internal 
variability including that associated with tropical Pacific variability 
is under-represented (Abram et al., 2014). The explanation could be 
a combination of all these factors. However, despite the aforementioned 
limitations of the reconstructions, Section 2.4.1.2 assesses that 
the recent positive trend in the SAM is likely  unprecedented in at 
least the past millennium ( medium confidence ). CMIP5 and CMIP6 
last-millennium simulations only capture the present anomalous state 
during the final decades of the simulations which are dominated by 
human influence; this state is also outside the range of simulated 
variability characteristic of pre-industrial times.
In summary, it is very likely  that anthropogenic forcings have

contributed to the observed trend of the summer SAM toward its 
positive phase since the 1970s. This assessment is supported by 
further model studies that confirm the human influence on the 
summertime SAM with improved models since AR5. While ozone 
depletion contributed to the trend from the 1970s to the 1990s 
(medium confidence ), its influence has been small since 2000, 
leading to a weaker summertime SAM trend over 2000–2019 
(medium confidence ). Climate models reproduce the spatial structure 
of the summertime SAM observed since the late 1970s well ( high 
confidence ). CMIP6 models reproduce the spatiotemporal features 
and recent multi-decadal trend of the summertime SAM better than 
CMIP5 models ( medium confidence ). However, there is a large spread 
in the intensity of the SAM response to ozone and greenhouse gas 
changes in both CMIP5 and CMIP6 models ( high confidence ), which 
Figure 3.35 |  Southern Annular Mode (SAM) indices in the last millennium.

(a) Annual-mean SAM reconstructions by Abram et al. (2014) and Dätwyler et al. (2018). 
(b) The annual-mean SAM index defined by Gong and Wang (1999) in CMIP5 and 
CMIP6 last millennium simulations extended by historical simulations. All indices are 
normalized with respect to 1961–1990 means and standard deviations. Thin lines and 
thick lines show seven-year and 70-year moving averages, respectively. Further details 

495
limits the confidence in the assessment of the ozone contribution 
to the observed trends. CMIP5 and CMIP6 models do not capture 
multicentennial variability of the SAM found in proxy reconstructions 
(low confidence ). This confidence level reflects that it is unclear 
whether this is due to a model or an observational shortcoming.
3.7.3 El Niño–Southern Oscillation (ENSO)
The El  Niño–Southern Oscillation (ENSO), which is generated via 
seasonally modulated interactions between the tropical Pacific ocean 
and atmosphere, influences severe weather, rainfall, river flow and 
agricultural production over large parts of the world (McPhaden 
et al., 2006). In fact, the remote climate influence of ENSO is so large 
that knowledge of its current phase and forecasts of its future phase 
largely underpin many seasonal rainfall and temperature forecasts 
worldwide (Annex IV.2.3).

AR5 noted that there have been clear improvements in the simulation of 
ENSO through previous generations of CMIP models (Flato et al., 2013), 
such that many CMIP5 models displayed behaviour that was qualitatively 
similar to that of the observed ENSO (Guilyardi et al., 2012). However, 
systematic errors were identified in the models’ representation of the 
tropical Pacific mean state and aspects of their interannual variability 
that affect quantitative comparisons. The AR5 assessment of ENSO 
concluded that the considerable observed inter-decadal modulations 
in ENSO amplitude and spatial pattern were largely consistent with 
unforced model simulations. Thus, there was low confidence  in the role 
of a human-induced influence in these (Bindoff et al., 2013).
Observed ENSO amplitude, which is measured by the standard 
deviation of SST anomalies in a central equatorial Pacific region often 
referred to as the Nino 3.4 region, along with the lifecycle of events, are

both reasonably well reproduced by most CMIP5 and CMIP6 models 
(Figure 3.36; Bellenger et al., 2014; Planton et al., 2021). The average 
CMIP5 model ENSO amplitude is slightly lower than that observed, 
while the average CMIP6 model ENSO amplitude is slightly higher than 
observed (Figure 3.36). The ENSO amplitude of the individual models, 
however, is highly variable across CMIP5 and CMIP6 models with many 
displaying either more or less variability than observed (Stevenson, 
2012; Grose et al., 2020; Planton et al., 2021).
ENSO events are often synchronized to the seasonal cycle in the 
observations, as the associated SST anomalies tend to peak in 
boreal winter (November to January) and be at their weakest in the 
boreal spring (March to April) (Harrison and Larkin, 1998; Larkin 
and Harrison, 2002). The majority of CMIP5 and CMIP6 models 
broadly reproduce the seasonality of ENSO SST variability in the 
central equatorial Pacific (Taschetto et al., 2014; Abellán et al., 2017;

Grose et al., 2020; Planton et al., 2021) (Figure  3.37). However, 
CMIP5 models, while displaying an improvement on CMIP3 models, 
appear to under-represent the magnitude of the seasonal variance 
modulation (Bellenger et al., 2014). This under-representation of 
seasonal variance modulation continues in CMIP6 models, which 
display no statistically significant difference in this behaviour when 
compared to CMIP5 models (Planton et al., 2021) (Figure 3.37).Observations show strong multi-decadal modulation of ENSO variance 
throughout the 20th century, with the most recent period displaying 
larger variability while the mid-century displayed relatively low ENSO 
variability (Figure 2.36; Li et al., 2013; McGregor et al., 2013; Hope 
et al., 2017). As assessed in Section 2.4.2, ENSO amplitude since 1950 
is higher than over the pre-industrial period from 1850 as far back 
as 1400 ( medium confidence ), but there is low confidence  that it is

higher than the variability over periods prior to 1400. This reported 
variance increase suggests that external forcing plays a role in the 
ENSO variance changes (Hope et al., 2017). However, large ensembles 
of single model or multiple model simulations do not find strong trends 
in ENSO variability over the historical period, suggesting that external 
forcing has not yet modulated ENSO variability with a magnitude that 
exceeds the range of internal variability (Hope et al., 2017; Maher et al., 
2018b; Stevenson et al., 2019). This is consistent with the Chapter 2 
assessment that there is no clear evidence for a recent sustained shift 
in ENSO beyond the range of variability on decadal to millennial time 
scales (Section 2.4.2). CMIP5 and CMIP6 models show a decrease in 
ENSO variance in the mid-Holocene (Brown et al., 2020), though not to 
the extent seen in paleo-proxy records (Emile-Geay et al., 2016). This 
suggests that both modelled and observed ENSO respond to changes

in external forcing, but not necessarily in the same manner.
Most CMIP5 and CMIP6 models are found to represent the general 
structure of observed SST anomalies during ENSO events well (Kim 
and Yu, 2012; Taschetto et al., 2014; Brown et al., 2020; Grose et al., 
2020). However, the majority of CMIP5 models display SST anomalies 
that: i) extend too far to the west (Taschetto et al., 2014; Capotondi 
et al., 2015); and ii) have meridional widths that are too narrow (Zhang 
and Jin, 2012) compared to the observations. CMIP6 models display 
a statistically significant improvement in the longitudinal representation 
of ENSO SST anomalies relative to CMIP5 models (Planton et al., 2021), 
however, systematic biases in the zonal extent and meridional width 
remain in CMIP6 models (Fasullo et al., 2020; Planton et al., 2021). The 
ENSO phase asymmetry, where observed strong El Niño events are larger 
and have a shorter duration than strong La Niña events (Ohba and Ueda,

2009; Frauen and Dommenget, 2010), is also under-represented in both 
CMIP5 and CMIP6 models (Zhang and Sun, 2014; Planton et al., 2021). 
In this instance, both CMIP5 and CMIP6 models typically display El Niño 
events that have a longer duration than those observed, La Niña events 
that have a similar duration to those observed, and there is very little 
asymmetry in the duration of El Niño and La Niña phases (Figure 3.36). 
Roberts et al. (2018) find an improvement in amplitude asymmetry 
in a HighResMIP model, but the under-representation remains.
The continuum of El  Niño events are typically stratified into two 
types (often termed ‘flavours’), Central Pacific and East Pacific, where 
the name denotes the location of the events’ largest SST anomalies 
(Annex IV.2.3; Capotondi et al., 2015). As discussed in Section 2.4.2, 
the different types of events tend to produce distinct teleconnections 
and climatic impacts (e.g., Taschetto et al., 2020). The characteristics of

El Niño events of these two flavours in CMIP5 were generally comparable 
to the observations (Taschetto et al., 2014). CMIP6 models, however, 
display a statistically significant improvement in the representation of 
this ENSO event-to-event SST anomaly diversity when compared with 
CMIP5 models (Planton et al., 2021). In addition to this ENSO event 
diversity, the short observational record also displays an increase in

496
Figure 3.36 |  Life cycle of (left) El Niño and (right) La Niña events in observations (black) and historical simulations from CMIP5 (blue; extended with 
RCP4.5) and CMIP6 (red).  An event is detected when the December ENSO index value in year zero exceeds 0.75 times its standard deviation for 1951–2010. (a, b)  Composites 
of the ENSO index (°C). The horizontal axis represents month relative to the reference December (the grey vertical bar), with numbers in parentheses indicating relative years. 
Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. (c, d)  Mean durations (months) of El Niño and La Niña events defined as 
number of months in individual events for which the ENSO index exceeds 0.5 times its December standard deviation. Each dot represents an ensemble member from the model

indicated on the vertical axis. The boxes and whiskers represent multi-model ensemble means, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6. The 
CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of each panel. The multi-model ensemble means and percentile values 
are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their 
ensemble sizes. The ENSO index is defined as the SST anomaly averaged over the Niño 3.4 region (5°S–5°N, 170°W–120°W). All results are based on five-month running 
mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

497
Figure  3.37 |  ENSO seasonality in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red) for 
1951–2010. (a)  Climatological standard deviation of the monthly ENSO index (SST anomaly averaged over the Niño 3.4 region; °C). Shading and lines represent 5th–95th 
percentiles and multi-model ensemble means, respectively. (b) Seasonality metric, which is defined for each model and each ensemble member as the ratio of the ENSO index 
climatological standard deviation in November–January (NDJ) to that in March–May (MAM). Each dot represents an ensemble member from the model indicated on the vertical 
axis. The boxes and whiskers represent the multi-model ensemble means, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6 individually. The CMIP5 and

CMIP6 multi-model ensemble means and observational values are indicated at the top right of the panel. The multi-model ensemble means and percentile values are evaluated 
after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. 
All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available 

498
the number of the Central Pacific-type events in recent decades (Ashok 
et al., 2007; McPhaden et al., 2011), which has also been identified 
as unusual in the context of the last 500–800 years based on recent 
paleo-climatic reconstructions (Section 2.4.2; Y . Liu et al., 2017; Freund 
et al., 2019). However, the short observational record combined with observational (L’Heureux et al., 2013) and paleo-climatic reconstruction 
uncertainties preclude firm conclusions being made about the 
long-term changes in the occurrence of different El Niño event types. 
Initial analysis with a selected number of CMIP3 models suggested 
that there may be a forced component to this recent prominence of 
ENSO teleconnections in boreal winter (Dec.-Feb.)Figure 3.38 |  Model evaluation of ENSO teleconnection for near surface air temperature and precipitation in boreal winter (December–January–February).

Teleconnections are identified by linear regression with the Niño 3.4 SST index based on Extended Reconstructed Sea Surface Temperature (ERSST) version 5 over the period 
1958–2014. Maps show observed patterns for temperature from the Berkeley Earth dataset over land and from ERSST version 5 over ocean (°C, top) and for precipitation from 
GPCC over land (shading, mm day–1) and GPCP worldwide (contours, period: 1979–2014). Distributions of regression coefficients (grey histograms) are provided for a subset of 
AR6 reference regions defined in Atlas.1.3 for temperature (top)  and precipitation (bottom) . All fields are linearly detrended prior to computation. Multi-model multi-member 
ensemble means are indicated by thick vertical black lines. Blue vertical lines show three observational estimates of temperature, based on Berkeley Earth, GISTEMP and CRUTS

datasets, and two observational estimates of precipitation, based on GPCC and CRUTS datasets. Further details on data sources and processing are available in the chapter 

499
Central Pacific-type events (Yeh et al., 2009), but analysis since then 
suggests that this behaviour is (i) consistent with that expected from 
internal variability (Newman et al., 2011); and (ii) not apparent across 
the full CMIP5 ensemble of historical simulations (Taschetto et al., 
2014). Analysis of single-model large ensembles suggests that changes 
to ENSO event type in response to historical radiative forcing are not 
significant (e.g., Stevenson et al., 2019). These same results, however, 
also suggest that multiple forcings can have significant influences 
on ENSO type and that the net response will depend on the accurate 
representation of the balance of these forcings (Stevenson et al., 2019).
The climatic effects of ENSO outside the tropical Pacific largely arise 
through atmospheric teleconnections that are induced by ENSO-driven 
changes in deep tropical atmospheric convection and heating (Yeh

et al., 2018). The teleconnections to higher latitudes are forced by 
waves that propagate into the extratropics (Hoskins and Karoly, 
1981) and respectively excite the Pacific-North American pattern 
(Horel and Wallace, 1981) and Pacific-South American pattern (Karoly, 
1989; Irving and Simmonds, 2016) in the Northern and Southern 
Hemispheres. Given the influence of these teleconnections on climate 
and extremes around the globe, it is important to understand how 
well they are reproduced in CMIP models. What has also become clear 
is that spatial correlations of ENSO’s teleconnections calculated over 
relatively short periods (<100 years) may not be the most effective 
way to assess these relationships (Langenbrunner and Neelin, 2013; 
Perry et al., 2020). This is because the spatial patterns are significantly 
affected by internal atmospheric variability on relatively short time 
scales (<100 years; Batehup et al., 2015; Perry et al., 2020). However,

looking at simplified metrics like the agreement in the sign of the 
teleconnections (Langenbrunner and Neelin, 2013), regional average 
teleconnection strength over land (Perry et al., 2020), or a combination 
of both (Power and Delage, 2018) provides a more robust depiction 
of the teleconnection representation. Examining sign agreement for 
the teleconnection patterns, ensembles of CMIP5 AMIP simulations 
display broad spatial regions with high sign agreement with the 
observations, suggesting that the model ensemble is producing 
useful information regarding the teleconnected precipitation signal 
(Langenbrunner and Neelin, 2013). Looking at regional averages of 
CMIP5 historical simulations, Power and Delage (2018) show that 
the average coupled model teleconnection pattern reproduces the 
sign of the observed teleconnections in the majority of the 25 regions 
analysed. The sign agreement between the observed teleconnection

and the multi-model mean teleconnection remains strong in CMIP6 
(18 out of 20 displayed regions; Figure 3.38), and the observed DJF 
(December–January–February) teleconnection strength falls within 
the modelled range in all of the displayed regions for temperature 
and precipitation. Note, however, that while there is broad agreement 
in ENSO teleconnections between CMIP6 models and observations 
during DJF (e.g., Fasullo et al., 2020), there are regions and seasons 
where the modelled teleconnection strength is outside the observed 
range (Chen et al., 2020).
Most CMIP5 and CMIP6 models exhibit ENSO behaviour during the 
historical period that, to first order, is qualitatively similar to that of 
the observed ENSO. Many studies are now delving deeper into the 
models to understand if they are accurately producing the dynamics 
driving ENSO and its initiation (Jin et al., 2006; Bellenger et al., 2014; Vijayeta and Dommenget, 2018; Bayr et al., 2019; Planton et al.,

2021). For both CMIP3 and CMIP5, diagnostics of ENSO event growth 
appear to show that the models, while producing ENSO variability 
that is qualitatively similar to that observed, do not represent the 
balance of the underlying dynamics well. The atmospheric Bjerknes 
feedback is too weak in the majority of models, while the surface heat 
flux feedback is also too weak in the majority of models. The former 
restricts event growth, while the latter restricts event damping, 
which when combined allow most models to produce variability in a 
range that is consistent with the observations (Bellenger et al., 2014; 
S.T.  Kim et al., 2014; Vijayeta and Dommenget, 2018; Bayr et al., 
2019). Analysis of ENSO representation in a subset of CMIP6 models 
by Planton et al. (2021) suggests that these issues remain.
To conclude, ENSO representation in CMIP5 models displayed 
a significant improvement from the representation of ENSO variability

in CMIP3 models, which displayed much more intermodel spread in 
standard deviation, and stronger biennial periodicity (Guilyardi et al., 
2012; Flato et al., 2013). In general, there has been no large step 
change in the representation of ENSO between CMIP5 and CMIP6, 
however, CMIP6 models appear to better represent some key ENSO 
characteristics (e.g., Brown et al., 2020; Planton et al., 2021). The 
instrumental record and paleo-proxy evidence through the Holocene 
all suggest that ENSO can display considerable modulations in 
amplitude, pattern and period (see also Section 2.4.2). For the 
period since 1850, there is no clear evidence for a sustained shift 
in ENSO index beyond the range of internal variability. However, 
paleo-proxy evidence indicates with medium confidence  that ENSO 
variability since 1950 is greater than at any time between 1400 and 
1850 (Section 2.4.2). Coupled models display large changes of ENSO

behaviour in the absence of external forcing changes, and little-to-no 
variance sensitivity to historical anthropogenic forcing. Thus, there is 
low confidence  that anthropogenic forcing has led to the changes of 
ENSO variability inferred from paleo-proxy evidence.
Chapter 2 reports low confidence  that the apparent change from East 
Pacific- to Central Pacific-type El  Niño events that occurred in the 
last 20–30 years was representative of a long term change. While 
some climate models do suggest external forcing may affect the 
El Niño event type, most climate models suggest that what has been 
observed is well within the range of natural variability. Thus, there is 
low confidence  that anthropogenic forcing has had an influence on 
the observed changes in El Niño event type.
3.7.4 Indian Ocean Basin and Dipole Modes
The Indian Ocean Basin (IOB) and Dipole (IOD) modes are the two 
leading modes of interannual SST variability over the tropical Indian

Ocean, featuring basin-wide warming/cooling and an east–west 
dipole of SST anomalies, respectively (Annex IV.2.4). The IOD mode 
is anchored to boreal summer to autumn by the air–sea feedback, 
and often develops in concert with ENSO. Driven by matured ENSO 
events, the IOB mode peaks in boreal spring and often persists 
into the subsequent summer. Similar patterns of Indian Ocean SST 
variability also dominate its decadal and longer time scale variability 
(Han et al., 2014b).

500
AR5 concluded that models show high and medium performance 
in reproducing the IOB and IOD modes, respectively ( medium 
confidence ), with difficulty in reproducing the persistence of the 
IOB and the pattern and magnitude of the IOD (Flato et al., 2013). 
There was low confidence  that changes in the IOD were detectable or 
attributable to human influence (Bindoff et al., 2013).
Since AR5, CMIP5 model representation of these modes has been 
analysed in detail, finding that most of the models qualitatively 
reproduce the spatial and seasonal features of the IOB and IOD modes 
(Chu et al., 2014; Liu et al., 2014; W. Tao et al., 2016). Improvements in 
simulating the IOB mode since CMIP3 have been identified in reduced 
multi-model mean biases and inter-model spread (W. Tao et al., 2016). 
CMIP5 models overall capture the transition from the IOD to IOB 
modes during an ENSO event (W. Tao et al., 2016). The IOB mode is

forced in part through a cross-equatorial wind–evaporation–SST 
feedback triggered by ENSO-forced anomalous ocean Rossby waves 
that propagate to the shallow climatological thermocline dome in the 
tropical south-western Indian Ocean (Du et al., 2009). Consistently, 
models with a deeper climatological thermocline dome produce 
a weaker and less persistent IOB mode (G. Li et al., 2015a; Zheng et al., 
2016). The deep thermocline bias remains in the ensemble mean of 
CMIP5 models due to a common surface easterly wind bias over the 
equatorial Indian Ocean (Lee et al., 2013) associated with weaker South 
Asian summer monsoon circulation (G. Li et al., 2015b). However, the 
influence of this systematic bias may be compensated by other biases, 
resulting in a realistic IOB magnitude (W. Tao et al., 2016). Halder et al. 
(2021) found that CMIP6 models reproduce the IOB mode reasonably 
well, but did not evaluate the progress since CMIP5.

By contrast, the IOD magnitude is overestimated by CMIP5 models on 
average, though with noticeable improvements from CMIP3 models 
(Liu et al., 2014). The overestimation of the IOD magnitude remains 
in most of 34 CMIP6 models examined in McKenna et al. (2020) with 
worsening on average in July and August. A too steep climatological 
thermocline slope along the equator due to the surface easterly wind 
bias in boreal summer and autumn contributes to this IOD magnitude 
bias through an excessively strong Bjerknes feedback in CMIP5 (Liu 
et al., 2014; G. Li et al., 2015b; Hirons and Turner, 2018). The surface 
easterly bias and associated east–west SST gradient bias are not 
improved in CMIP6 (Long et al., 2020; Section 3.5.1.2.3), suggesting 
that the thermocline bias also remains. McKenna et al. (2020) 
additionally find degradation in the positive-negative asymmetry 
of the IOD but an improvement in IOD frequency in a subset of

CMIP6 models compared to CMIP5. In terms of teleconnections, the 
equatorial surface easterly wind bias also affects the IOD-associated 
moisture transport anomalies toward tropical eastern Africa 
(Hirons and Turner, 2018) where the IOD is associated with strong 
precipitation anomalies in boreal autumn (Annex IV.2.4). CMIP5 
and CMIP6 models capture the IOD teleconnection to Southern and 
Central Australian precipitation although it is weaker on average than 
observed, with no clear improvements from CMIP5 to CMIP6 (Grose 
et al., 2020). Strong IOD events could also influence the Northern 
Hemisphere extratropical circulation in winter and in particular the 
NAM (Section 3.7.1), based on interference between forced Rossby 
waves emerging from the Indian Ocean and climatological stationary 
waves (Fletcher and Cassou, 2015). The record positive phase of the NAO/NAM in winter 2019–2020 assessed over the instrumental 
era has been accordingly linked to the record IOD event of autumn

2019 (Hardiman et al., 2020), which has been associated with the 
devastating record fire season in Australia (Wang and Cai, 2020).
The observed Indian Ocean basin-average SST increase on multi-decadal 
and centennial time scales is well represented by CMIP5 historical 
simulations, and has been attributed to the effects of greenhouse gases 
offset in part by the effects of anthropogenic aerosols mainly through 
aerosol-cloud interactions (Dong and Zhou, 2014; Dong et al., 2014b). 
The observed SST trend is larger in the western than eastern tropical 
Indian Ocean, which leads to an apparent upward trend of the IOD 
index, but this trend is statistically insignificant (Section 2.4.3). CMIP5 
models capture this warming pattern, which may be associated with 
Walker circulation weakening over the Indian Ocean due to greenhouse 
gas forcing (Dong and Zhou, 2014). However, strong internal decadal 
IOD-like variability and observational uncertainty preclude attribution

(Cai et al., 2013; Han et al., 2014b; Gopika et al., 2020). Such a positive 
IOD-like change in equatorial zonal SST gradient suggests an increase 
in the frequency of extreme positive events (Cai et al., 2014) and 
skewness (Cowan et al., 2015) of the IOD mode. While there is some 
evidence of an increase in frequency of positive IOD events during the 
second half of the 20th century, the current level of IOD variability is 
not unprecedented in a proxy reconstruction for the last millennium 
(Section 2.4.3; Abram et al., 2020). Besides, the IOD magnitude in 
the late 20th century is not significantly different between CMIP5 
simulations forced by historical and natural-only forcings, though 
this conclusion is based on only five selected ensemble members 
that realistically reproduce statistical features of the IOD (Blau and 
Ha, 2020). While selected CMIP5 models show weakening (Thielke 
and Mölg, 2019) and seasonality changes (Blau and Ha, 2020)

in IOD-induced rainfall anomalies in tropical eastern Africa, no 
comparison with observational records has been made. Likewise, 
while a strengthening tendency of the ENSO-IOB mode correlation 
and resultant intensification of the IOB mode are found in historical or 
future simulations in selected CMIP5 models (Hu et al., 2014; Tao et al., 
2015), such a change has not been detected in observational records.
After linear detrending, Pacific Decadal Variability (PDV; Annex IV.2.6; 
Section 3.7.6) has been suggested as a driver of decadal to 
multi-decadal variations in the IOB mode (Dong et al., 2016). However, 
correlation between the PDV and a decadal IOB index, defined from 
linearly detrended SST, changed from positive to negative during the 
1980s (Han et al., 2014a). The increase in anthropogenic forcing and 
recovery from the eruptions of El Chichón in 1982 and Pinatubo in 
1991 may have overwhelmed the PDV influence, and explain this

change (Dong and McPhaden, 2017; L. Zhang et al., 2018a). However, 
the low statistical degrees of freedom hamper clear detection of 
human influence in this correlation change.
To summarize, there is medium confidence  that changes in the 
interannual IOD variability in the late 20th century inferred from 
observations and proxy records are within the range of internal 
variability. There is no evidence of anthropogenic influence on the 
interannual IOB. On decadal- to multi-decadal time scales, there is 
low confidence  that human influence has caused a reversal of the   
correlation between PDV and decadal variations in the IOB mode.

501
The low confidence  in this assessment is due to the short observational 
record, limited number of models used for the attribution, lack of model 
evaluation of the decadal IOB mode, and uncertainty in the contribution 
from volcanic aerosols. Nevertheless, CMIP5 models have medium 
overall performance in reproducing both the interannual IOB and 
IOD modes, with an apparently good performance in reproducing the 
IOB magnitude arising from compensation of biases in the formation 
process, and overly high IOD magnitude due to the mean state bias 
(high confidence ). There is no clear improvement in the simulation of 
the IOD from CMIP5 to CMIP6 models, though there is only medium  
confidence  in this assessment, since only a subset of CMIP6 models 
have been examined. There is no evidence for performance changes 
in simulating the IOB from CMIP5 to CMIP6 models.
3.7.5 Atlantic Meridional and Zonal Modes

The Atlantic Zonal Mode (AZM), often referred to as the Atlantic 
Equatorial Mode or Atlantic Niño, and the Atlantic Meridional Mode 
(AMM) are the two leading basin-wide patterns of interannual to 
decadal variability in the tropical Atlantic. Akin to ENSO in the Pacific, 
the term Atlantic Niño is broadly used to refer to years when the 
SSTs in the tropical eastern Atlantic basin along the cold tongue are 
significantly warmer than the climatological average. The AMM is 
characterized by anomalous cross-equatorial gradients in SST. Both 
modes are associated with altered strength of the Inter-tropical 
Convergence Zone (ITCZ) and/or latitudinal shifts in the ITCZ, 
which locally affect African and American monsoon systems and 
remotely affect tropical Pacific and Indian Ocean variability through 
inter-basins teleconnections. A detailed description of both AZM and 
AMM, as well as their associated teleconnection over land, is given 
in Annex IV.2.5

AR5 mentioned the considerable difficulty in simulating both 
Atlantic Niño and AMM despite some improvements in CMIP5 for 
some models (Flato et al., 2013). Severe biases in mean state and 
variance for both SST and atmospheric dynamics including rainfall 
(e.g.,  a  double ITCZ) as well as teleconnections were reported. 
The AR5 highlighted the complexity of the tropical Atlantic biases, 
which were explained by multiple factors both in the ocean 
and atmosphere.
Since AR5, further analysis of the major persistent biases in models 
has been reported (Xu et al., 2014; Jouanno et al., 2017; Y . Yang 
et al., 2017; Dippe et al., 2018; Lübbecke et al., 2018; Voldoire 
et al., 2019a). Errors in equatorial and basin wide trade winds, 
cloud cover and ocean vertical mixing and dynamics both locally 
and in remote subtropical upwelling regions, key thermodynamic 
ocean–atmosphere feedbacks, and tropical land–atmosphere 
interaction have been shown to be detrimental to the representation

of both the Atlantic Niño and AMM leading to poor teleconnectivity 
over land (Rodríguez-Fonseca et al., 2015; Wainwright et al., 2019) 
and between tropical basins (Ott et al., 2015).
Despite some improvements (Richter et al., 2014; Nnamchi et al., 
2015), biases in the mean state are so large that the mean east–west 
temperature gradient at the equator along the thermocline remains opposite to observed in two thirds of the CMIP5 models 
(Section 3.5.1.2.2), which clearly affects the simulation of the Atlantic 
Niño and associated dynamics (Muñoz et al., 2012; Ding et al., 2015; 
Deppenmeier et al., 2016). The interhemispheric SST gradient is also 
systematically underestimated in models, with a too cold mean state 
in the northern part of the tropical Atlantic ocean and too warm 
conditions in the South Atlantic basin. The seasonality is poorly 
reproduced and the wind–SST coupling is weaker than observed 
so that altogether, and despite AMM-like variability in 20th century

climate simulations, AMM is not the dominant Atlantic mode in all 
CMIP5 models (Liu et al., 2013; Amaya et al., 2017). These biases in 
mean state translate into biases in modelling the mean ITCZ (Flato 
et al., 2013). Similar biases were found in experiments using CMIP5 
models but with different climate background states, such as Last 
Glacial Maximum, mid-Holocene and future scenario simulations 
(Brierley and Wainer, 2018). Analyses of CMIP6 show encouraging 
results in the representation of Atlantic Niño and AMM modes of 
variability in terms of amplitude and seasonality. Some models now 
display reduced biases in the spatial structure of the modes and related 
explained variance but persistent errors still remain on average in the 
timing of the modes and in the coupled nature of the modes, that is, 
the strength of the link between ocean (SST, mixed layer depth) and 
atmospheric (wind) anomalies (Richter and Tokinaga, 2020), as well

as in the Atlantic Ocean equatorial east–west temperature gradient 
(Section 3.5.1.2.2, Figure 3.24).
There are some recent indications that increasing model resolution 
both vertically and horizontally, in the ocean and atmospheric 
component (Richter, 2015; Small et al., 2015; Harlaß et al., 2018), 
could partly alleviate some tropical Atlantic biases in mean state 
(Section 3.5.1.2.2), seasonality, interannual- to decadal-variability 
and associated teleconnectivity over land, such as with the West 
African monsoon (Steinig et al., 2018). Results from CMIP6 tend to 
confirm that increasing resolution is not the unique way to address 
the biases in the tropical Atlantic (Richter and Tokinaga, 2020). For 
instance, the inclusion of a stochastic physics scheme has a nearly 
equivalent effect in the improvement of the mean number and the 
strength distribution of tropical Atlantic cyclones (Vidale et al., 2021).
Section 2.4.4 assess that there is low confidence  in any sustained

changes to the AZM and AMM variability in instrumental 
observations. Moreover, any attribution of possible human influence 
on the Atlantic modes and associated teleconnections is limited 
by the poor fidelity of CMIP5 and CMIP6 models in reproducing 
the mean tropical Atlantic climate, its seasonality and variability, 
despite hints of some improvement in CMIP6, as well as other 
sources of uncertainties related to limited process understanding 
in the observations (Foltz et al., 2019), the response of the tropical 
Atlantic climate to anthropogenic aerosol forcing (Booth et al., 
2012; Zhang et al., 2013a) and the presence of strong multi-decadal 
fluctuations related to AMV (Section 3.7.7) and cross-tropical basin 
interactions (Martín-Rey et al., 2018; Cai et al., 2019). The fact that 
most models poorly represent the climatology and variability of the 
tropical Atlantic combined with the short observational record makes

it difficult to place the recent observed changes in the context of 
internal multi-annual variability versus anthropogenic forcing.

502
In summary, based on CMIP5 and CMIP6 results, there is no robust 
evidence that the observed changes in either the Atlantic Niño or 
AMM modes and associated teleconnections over the second half of 
the 20th century are beyond the range of internal variability or have 
been influenced by natural or anthropogenic forcing. Considering 
the physical processes responsible for model biases in these modes, 
increasing resolution in both ocean and atmosphere components may 
be an opportunity for progress in the simulation of the tropical Atlantic 
changes as evidenced by some individual model studies (Roberts et al., 
2018), but this needs confirmation from a multi-model perspective.
3.7.6 Pacific Decadal Variability
Pacific Decadal Variability (PDV) is the generic term for the modes of 
variability in the Pacific Ocean that vary on decadal to inter-decadal 
time scales. PDV and its related teleconnections encompass the

Pacific Decadal Oscillation (PDO; Mantua et al., 1997; Zhang et al., 
1997; Mantua and Hare, 2002), and an anomalous SST pattern in 
the North Pacific, as well as a broader structure associated with 
Pacific-wide SSTs termed the Inter-decadal Pacific Oscillation (IPO; 
Power et al., 1999; Folland et al., 2002; Henley et al., 2015). Since the 
PDO and IPO indices are highly correlated, this section assesses them 
together as the PDV (Annex IV.2.6).
AR5 mentioned an overall limited  level of evidence for both CMIP3 
and CMIP5 evaluation of the Pacific modes at inter-decadal time 
scales, leading to low confidence  statements about the models’ 
performance in reproducing PDV (Flato et al., 2013) and similarly 
low confidence  in the attribution of observed PDV changes to human 
influence (Bindoff et al., 2013).
The implication of PDV in the observed slowdown of the GMST 
warming rate in the early 2000s (Cross-Chapter Box 3.1) has triggered

considerable research on decadal climate variability and predictability 
since AR5 (Meehl et al., 2013, 2016b; England et al., 2014; Dai et al., 
2015; Steinman et al., 2015; Kosaka and Xie, 2016; Cassou et al., 
2018). Many studies find that the broad spatial characteristics of 
PDV are reasonably well represented in unforced climate models 
(Newman et al., 2016; Henley, 2017) and in historical simulations 
in CMIP5 and CMIP6 (Figure 3.39), although there is sensitivity to 
the methodology used to remove the externally-forced component 
of the SST (Bonfils and Santer, 2011; Xu and Hu, 2018). Compared 
with CMIP3 models, CMIP5 models exhibit overall slightly better 
performance in reproducing PDV and associated teleconnections 
(Polade et al., 2013; Joshi and Kucharski, 2017), and also smaller 
inter-model spread (Lyu et al., 2016). CMIP6 models on average show 
slightly improved reproduction of the PDV spatial structure than

CMIP5 models (Figure 3.39a–c; Fasullo et al., 2020). SST anomalies in 
the subtropical South Pacific lobe are, however, too weak relative to 
the equatorial and North Pacific lobes in CMIP5 pre-industrial control 
and historical simulations (Henley et al., 2017), a bias that remains 
in CMIP6 (Figure 3.39b).
Biases in the PDV temporal properties and amplitude are present in 
CMIP5 (Cheung et al., 2017; Henley, 2017). While model evaluation 
is severely hampered by short observational records and incomplete observational coverage before satellite measurements started, the 
duration of PDV phases appears to be shorter in coupled models 
than in observations, and correspondingly the ratio of decadal to 
interannual variance is underestimated (Figure 3.39e,f; Henley et al., 
2017). This apparent bias may be associated with overly biennial 
behaviour of Pacific trade wind variability and related ENSO activity, 
leaving too weak variability on decadal time scales (Kociuba and

Power, 2015). ENSO influence on the extratropical North Pacific 
Ocean at decadal time scales is also very diverse among both CMIP3 
and CMIP5 models, being controlled by multiple factors (Nidheesh 
et al., 2017). In terms of amplitude, the variance of the PDV index 
after decadal filtering is significantly weaker in the concatenated 
CMIP5 ensemble than the three observational estimates used in 
Figure  3.39e ( p <0.1 with an F-test). Consequently, the observed 
PDV fluctuations over the historical period often lie in the tails of the 
model distributions (Figure 3.39e,f). Even if one cannot rule out that 
the observed PDV over the instrumental era represents an exceptional 
period of variability, it is plausible that the tendency of the CMIP5 
models to systematically underestimate the low frequency variance 
is due to an incomplete representation of decadal-scale mechanisms 
in these models. This situation is slightly improved in CMIP6 historical

simulations but remains a concern (Fasullo et al., 2020). The results of 
McGregor et al. (2018) suggest that the under-representation of the 
variability stems from Atlantic mean SST biases (Section 3.5.1.2.2) 
through inter-basin coupling.
While PDV is primarily understood as an internal mode of variability 
(Si and Hu, 2017), there are some indications that anthropogenically 
induced SST changes project onto PDV and have contributed to its 
past evolution (Bonfils and Santer, 2011; Dong et al., 2014a; Boo 
et al., 2015; Xu and Hu, 2018). However, the level of evidence is 
limited  because of the difficulty in correctly separating internal versus 
externally forced components of the observed SST variations, and 
because it is unclear whether the dynamics of the PDV are operative 
in this forced SST change pattern. Over the last two to three decades 
which encompass the period of slower GMST increase (Cross-Chapter

Box 3.1), Smith et al. (2016) found that anthropogenic aerosols have 
driven part of the PDV change toward its negative phase. A  similar 
result is shown in Takahashi and Watanabe (2016) who found 
intensification of the Pacific Walker circulation in response to aerosol 
forcing (Section 3.3.3.1.2). Indeed, CMIP6 models simulate a negative 
PDV trend since the 1980s (Figure 3.39f), which is much weaker than 
internal variability. However, a response to anthropogenic aerosols is 
not robustly identified in a large ensemble of a model (Oudar et al., 
2018), across CMIP5 models (Hua et al., 2018), or in idealized model 
simulations (Kuntz and Schrag, 2016). Alternatively, inter-basin 
teleconnections associated with the warming of the North Atlantic 
Ocean related to the mid-1990s phase shift of the AMV (McGregor et al., 
2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; 
Ruprich-Robert et al., 2017), and also warming in the Indian Ocean

(Luo et al., 2012; Mochizuki et al., 2016), could have favoured a PDV 
transition to its negative phase in the 2000s. Considering the possible 
influence of external forcing on Indian Ocean decadal variability 
(Section 3.7.4) and AMV (Section 3.7.7), any such human influence 
on PDV would be indirect through changes in these ocean basins, 
and then imported to the Pacific via inter-basin coupling. However, 
this human influence on AMV, and how consistently such inter-basin

503
processes affect PDV phase shifts, are uncertain. Other modelling 
studies find that anthropogenic aerosols can influence the PDV (Verma 
et al., 2019; Amiri-Farahani et al., 2020; Dow et al., 2020). It is however 
unclear whether and how much those forcings contributed to the 
observed variations of PDV. In CMIP6 models, the temporal correlation 
of the multi-model ensemble mean PDV index with its observational 
counterpart is insignificant and negligible (Figure  3.39f), suggesting 
that any externally-driven component in historical PDV variations was 
weak. Lastly, the multi-model ensemble mean computed from CMIP6 
historical simulations shows slightly stronger variation than the CMIP5 
counterpart, suggesting a greater simulated influence from external 
forcings in CMIP6. Still, the fraction of the forced signal to the total 
PDV is very low (Figure  3.39f), in contrast to AMV (Section 3.7.7).

Consistently, Liguori et al. (2020) estimate that the variance fraction of the externally-driven to total PDV is up to only 15% in a multi-model 
large ensemble of historical simulations. These findings support an 
assessment that PDV is mostly driven by internal variability since 
the pre-industrial era. The sensitivity of ensemble-mean PDV trends 
to the ensemble size (Oudar et al., 2018), and the dominance of the 
ensemble spread over the ensemble mean in the 60-year trend of 
the equatorial Pacific zonal SST gradient in large ensemble simulations 
(Watanabe et al., 2021), also support this statement.
In CMIP5 last millennium simulations, there is no consistency in 
temporal variations of PDV across the ensemble (Fleming and 
Anchukaitis, 2016). This supports the notion that PDV is internal 
in nature. However, this issue remains difficult to assess because 
paleoclimate reconstructions of PDV have too poor a level of 
Low model 
agreement (<80%)High model 
agreement (≥80%)Colour

Not significant
at the 10% level Significant Colour
Figure 3.39 |  Model evaluation of the Pacific Decadal Variability (PDV). (a, b)  Sea surface temperature (SST) anomalies (°C) regressed onto the Tripole Index (TPI; 
Henley et al., 2015) for 1900–2014 in (a) ERSST version 5 and (b) CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the 
inverse of the model ensemble size. A 10-year low-pass filter was applied beforehand. Cross marks in (a) represent regions where the anomalies are not significant at the 10% 
level based on a t-test. Diagonal lines in (b) indicate regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the PDV 
pattern in CMIP5 (each ensemble member is shown as a cross in light blue, and the weighted multi-model mean as a dot in dark blue), CMIP6 (each ensemble member is shown

as a cross in red, and the weighted multi-model mean as a dot in orange) and observations over 40°S–60°N and 110°E–70°W. The reference pattern is taken from ERSST 
version 5 and black dots indicate other observational products: Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST version 1) and Centennial in 
situ Observation-Based Estimates of Sea Surface Temperature version 2 (COBE-SST2). (d) Autocorrelation of unfiltered annual TPI at lag one year and 10-year low-pass filtered 
TPI at lag 10 years for observations over 1900–2014 (horizontal lines), 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations 
over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but showing standard deviation of the unfiltered and filtered TPI (°C). Boxes and whiskers

show weighted multi-model means, interquartile ranges and 5th and 95th percentiles. (f) Time series of the 10-year low-pass filtered TPI (°C) in ERSST version 5, HadISST 
version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean 
for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile ranges across ensemble members. The 5–95% confidence 
interval for the CMIP6 multi-model mean is given in thin dashed lines. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

504
agreement for a rigorous model evaluation in past millennia 
(Henley, 2017).
To conclude, there is high confidence  that internal variability has been 
the main driver of the PDV since pre-industrial times, despite some 
modelling evidence for potential external influence. This assessment 
is supported by studies based on large ensemble simulations that 
found the dominance of internally-driven PDV, and the CMIP6-based 
assessment (Figure  3.39). As such, PDV is an important driver of 
decadal internal climate variability which limits detection of human 
influence on various aspects of decadal climate change on global 
to regional scales ( high confidence ). Model evaluation of PDV is 
hampered by short observational records, spatial incompleteness of 
observations before the satellite observation era, and poor agreement 
among paleoclimate reconstructions. Despite the limitations of these

model-observation comparisons, CMIP5 models, on average, simulate 
broadly realistic spatial structures of the PDV, but with a clear bias 
in the South Pacific ( medium confidence ). CMIP5 models also very 
likely  underestimate PDV magnitude. CMIP6 models tend to show 
better overall performance in spatial structure and magnitude of 
PDV, but there is low confidence  in this assessment due to the lack 
of literature.
3.7.7 Atlantic Multi-decadal Variability
Atlantic Multi-decadal Variability (AMV) refers to a climate mode 
representing basin-wide multi-decadal fluctuations in surface 
temperatures in the North Atlantic (Figure 3.40a,f), with teleconnections 
particularly pronounced over the adjacent continents and the Arctic. 
The AMV phenomenon is usually assessed through SST anomalies 
averaged over the entire North Atlantic basin, hereafter the AMV 
index, but it is associated with many physical processes including 
three-dimensional ocean circulation, such as AMOC fluctuations

(Section 3.5.4.1), gyre adjustments, and salt and heat transport in the 
entire North Atlantic and subarctic Atlantic basins. The AMV, together 
with the  PDV, has been shown to have modulated GSAT on multi-
decadal time scales since pre-industrial times (Cross-Chapter Box 3.1; 
T. Wu et al., 2019a; Li et al., 2020). A detailed description of the AMV as 
well as its associated teleconnection over land is given in Annex IV.2.7.
AR5 assessed, based on climate models, that the AMV was primarily 
internally-driven alongside some contribution from external forcings 
(mainly anthropogenic aerosols) over the late 20th century (Bindoff et 
al., 2013; Flato et al., 2013). But AR5 also concluded that models show 
medium performance in reproducing the observed AMV, with difficulties 
in simulating the time scale, the spatial structure and the coherency 
between all the physical processes involved (Flato et al., 2013).
Climate models analysed since AR5 continue to simulate AMV-like

variability as part of their internal variability. This statement is mostly 
based on CMIP5 pre-industrial control and historical simulations 
(Wouters et al., 2012; Schmith et al., 2014; Menary et al., 2015; 
Ruprich-Robert and Cassou, 2015; Brown et al., 2016b; Chen et al., 
2016; Kim et al., 2018a) and is also true for the CMIP6 models 
(Menary et al., 2018; Voldoire et al., 2019b). Models also continue 
to support links to a wide array of remote climate influences through atmospheric teleconnections (Martin et al., 2014; Ruprich-Robert 
et al., 2017, 2018; Monerie et al., 2019; Qasmi et al., 2020; Ruggieri 
et al., 2021). Even if debate remains (Clement et al., 2015; Cane 
et al., 2017; Mann et al., 2020), there is now stronger evidence for 
a crucial role of oceanic dynamics in internal AMV that is primarily 
linked to the AMOC and its interplay with the NAO (Zhang et al., 
2013a; Müller et al., 2015; O’Reilly et al., 2016b, 2019a; Delworth

et al., 2017; Zhang, 2017; Sun et al., 2019; Kim et al., 2020). However, 
considerable diversity in the spatio-temporal properties of the 
simulated AMV is found in both pre-industrial control and historical 
CMIP5 experiments (Zhang and Wang, 2013; Wills et al., 2019). Such 
model diversity is presumably associated with the wide range of 
coupled processes associated with AMV (Baker et al., 2017; Woollings 
et al., 2018a) including large-scale atmospheric teleconnections and 
regional feedbacks relating to tropical clouds, Arctic sea ice in the 
subarctic basins and Saharan dust, whose relative importance and 
interactions across time scales are specific to each model (Martin 
et al., 2014; Brown et al., 2016b).
Additional studies since AR5 corroborate that CMIP5-era models 
tend to underestimate many aspects of observed AMV and its SST 
fingerprint. On average, the duration of modelled AMV episodes is 
too short, the magnitude of AMV is too weak and its basin-wide

SST spatial structure is limited by the poor representation of the 
link between the tropical North Atlantic and the subpolar North 
Atlantic/Nordic seas (Martin et al., 2014; Qasmi et al., 2017). Such 
mismatches between observed and simulated AMV (Figure 3.40c–e) 
have been associated with intrinsic model biases in both mean state 
(Menary et al., 2015; Drews and Greatbatch, 2016) and variability 
in the ocean and overlying atmosphere. For instance, compared to 
available observational data CMIP5 models underestimate the ratio 
of decadal to interannual variability of the main drivers of AMV, 
namely the AMOC, NAO and related North Atlantic jet variations 
(Section 3.7.1; Bracegirdle et al., 2018; Kim et al., 2018b; Simpson 
et al., 2018; Yan et al., 2018), which has strong implications for the 
simulated temporal statistics of AMV, AMV-induced teleconnections 
(Ault et al., 2012; Menary et al., 2015) and AMV predictability.

The increase of AMV variance in CMIP6 models (stronger magnitude 
and longer duration) seems to be explained by the enhanced 
variability in the subpolar North Atlantic SST (Figure 3.40b,c), which 
is particularly pronounced in some models, associated with greater 
variability in the AMOC (Section 3.5.4.1; Voldoire et al., 2019a; Boucher 
et al., 2020) and greater GMST multi-decadal variability (Section 3.3.1 
and Figure 3.40c–f; Voldoire et al., 2019b; Parsons et al., 2020). The 
decadal variance in SST in the subpolar North Atlantic seems now 
to be slightly overestimated in CMIP6 compared to observational 
estimates, while the AMV-related tropical SST anomalies remain 
weaker in line with CMIP5 (Figure 3.40b). The mechanisms producing 
the tropical-extratropical relationship at decadal time scales 
remain poorly understood despite stronger evidence since AR5 for 
the importance of the subpolar gyre SST anomalies in generating

tropical changes through atmospheric teleconnection (Caron 
et al., 2015; Ruprich-Robert et al., 2017; Kim et al., 2020). Significant 
discrepancies remain in the simulated AMV spatial pattern when 
historical simulations are compared to multivariate observations (Yan 
et al., 2018; Robson et al., 2020).

505
There is additional evidence since AR5 that external forcing has been 
playing an important role in shaping the timing and intensity of 
the observed AMV since pre-industrial times (Bellomo et al., 2018; 
Andrews et al., 2020). The time synchronisation between observed 
and multi-model mean AMV SST indices is significant in both CMIP5 
and CMIP6 historical simulations, while the explained variance of 
the forced response in CMIP6 appears stronger (Figure 3.40d–f). The 
competition between greenhouse gas warming and anthropogenic 
sulphate aerosol cooling has been proposed to be particularly 
important over the latter half of the 20th century (Booth et al., 2012; 
Steinman et al., 2015; Murphy et al., 2017; Undorf et al., 2018a; 
Haustein et al., 2019). The latest observed AMV shift from the cold 
to the warm phase in the mid-1990s at the surface ocean is well

captured in the CMIP6 forced component and may be associated with the lagged response to increased AMOC due to strong anthropogenic 
aerosol forcing over 1955–1985 (Menary et al., 2020) in combination 
with the rapid response through surface flux processes to declining 
aerosol forcing and increasing greenhouse gas influence since then. 
However, natural forcings may have also played a significant role. For 
instance, volcanic forcing has been shown to contribute in part to 
the cold phases of the AMV-related SST anomalies observed in the 
20th century (Terray, 2012; Bellucci et al., 2017; Swingedouw et al., 
2017; Birkel et al., 2018). Over the last millennium, natural forcings 
including major volcanic eruptions and fluctuations in solar activity 
are thought to have driven a larger fraction of the multi-decadal 
variations in the AMV than in the industrial era, with some interplay 
with internal processes (Otterå et al., 2010; Knudsen et al., 2014;

Moffa-Sánchez et al., 2014; J. Wang et al., 2017; Malik et al., 2018; 
Low model 
Figure 3.40 |  Model evaluation of Atlantic Multi-decadal Variability (AMV). (a, b)  Sea surface temperature (SST) anomalies (°C) regressed onto the AMV index 
defined as the 10-year low-pass filtered North Atlantic (0°–60°N, 80°W–0°E) area-weighted SST* anomalies over 1900–2014 in (a) ERSST version 5 and (b) the CMIP6 
multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of each model’s ensemble size. The asterisk denotes that the global 
mean SST anomaly has been removed at each time step of the computation. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level

based on a t-test. Diagonal lines in (b) show regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the AMV pattern 
in CMIP5 (each ensemble member is shown as a cross in light blue, and the weighted multi-model mean is shown as a dot in dark blue), CMIP6 (each ensemble member is 
shown as a cross in red, and the weighted multi-model mean is shown as a dot in orange) and observations over [0°–60°N, 80°W–0°E]. The reference pattern is taken from 
ERSST version 5 and black dots indicate other observational products (HadISST version 1 and COBE-SST2). (d) Autocorrelation of unfiltered annual AMV index at lag one 
year and 10-year low-pass filtered AMV index at lag 10 years for observations over 1900–2014 (horizontal lines), 115-year chunks of pre-industrial control simulations (open

boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but showing standard deviation of the unfiltered 
and filtered AMV indices (°C). Boxes and whiskers show the weighted multi-model means, interquartile ranges and 5th and 95th percentiles. (f) Time series of the AMV index 
(°C) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are 
the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile ranges obtained from 
all ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is shown by the thin dashed line. Further details on data sources and processing are 

506
Mann et al., 2021), but other studies question the role of natural 
forcings over this period (Zanchettin et al., 2014; Lapointe et al., 
2020).
Model evaluation of the AMV phenomenon remains difficult because 
of short observational records (especially of detailed process-based 
observations), the lack of stationarity in the variance, spatial patterns 
and frequency of the AMV assessed from modelled SST (Qasmi et al., 
2017), difficulties in estimating the forced signals in both historical 
simulations and observations (Tandon and Kushner, 2015), and 
because of probable interplay between internally and externally-driven 
processes (Watanabe and Tatebe, 2019). Furthermore, models 
simulate a large range of historical anthropogenic aerosol forcing 
(Smith et al., 2020) and questions often referred to as signal-to-noise 
paradox have been raised concerning the models’ ability to correctly

simulate the magnitude of the response of AMV-related atmospheric 
circulation phenomena, such as the NAO (Section 3.7.1), to both 
internally and externally generated changes (Scaife and Smith, 2018). 
Related methodological and epistemological uncertainties also call 
into question the relevance of the traditional basin-average SST 
index to assessing the AMV phenomenon (Zanchettin et al., 2014; 
Frajka-Williams et al., 2017; Haustein et al., 2019; Wills et al., 2019).
To summarize, results from CMIP5 and CMIP6 models together 
with new statistical techniques to evaluate the forced component 
of modelled and observed AMV, provide robust evidence  that 
external forcings have modulated AMV over the historical period. 
In particular, anthropogenic and volcanic aerosols are thought to 
have played a role in the timing and intensity of the negative (cold) 
phase of AMV recorded from the mid-1960s to mid-1990s and 
subsequent warming ( medium confidence ). However, there is low

confidence  in the estimated magnitude of the human influence. 
The limited level of confidence is primarily explained by difficulties 
in accurately evaluating model performance in simulating AMV. 
The evaluation is severely hampered by short instrumental records 
but also, equally importantly, by the lack of detailed and coherent 
long-term process-based observations (for example of the AMOC, 
aerosol optical depth, surface fluxes and cloud changes), which limit 
our process understanding. In addition, studies often rely solely on 
simplistic SST indices that may be hard to interpret (Zhang et al., 
2016) and may mask critical physical inconsistencies in simulations 
of the AMV compared to observations (Zhang, 2017).
3.8 Synthesis Across Earth 
System Components
3.8.1 Multivariate Attribution of Climate Change
The AR5 concluded that human influence on the climate system is 
clear (IPCC, 2013), based on observed increasing greenhouse gas

concentrations in the atmosphere, positive radiative forcing, observed 
warming, and physical understanding of the climate system. The AR5 
also assessed that it was virtually certain  that internal variability alone 
could not account for observed warming since 1951 (Bindoff et al., 
2013). Evidence has grown since AR5 that observed changes since 
the 1950s in many parts of the climate system are attributable to anthropogenic influence. So far, this chapter has focused on examining 
individual aspects of the climate system in separate sections. The results 
presented in Sections 3.3 to 3.7 substantially strengthen our assessment 
of the role of human influence on climate since pre-industrial times. 
In this section we look across the whole climate system to assess to 
what extent a physically consistent picture of human induced change 
emerges across the climate system (Figure 3.41).
The observed global surface air temperature warming of 0.9°C to

1.2°C in 2010–2019 is much greater than can be explained by internal 
variability ( likely  –0.2°C to +0.2°C) or natural forcings ( likely  –0.1°C 
to +0.1°C) alone, but consistent with the assessed anthropogenic 
warming ( likely  0.8°C to 1.3°C; Section 3.3.1.1). It is very likely  
that human influence is the main driver of warming over land 
(Section 3.3.1.1). Moreover, the atmosphere as a whole has warmed 
(Table 7.1), and it is very likely  that human-induced greenhouse gas 
increases were the main driver of tropospheric warming since 1979 
(Section 3.3.1.2). It is virtually certain  that greenhouse gas forcing 
was the main driver of the observed changes in hot and cold extremes 
over land at the global scale (Cross-Chapter Box 3.2).
As might be expected from a warming atmosphere, moisture in the 
troposphere has increased and precipitation patterns have changed. 
Human influence has likely  contributed to the observed changes in

humidity and precipitation (Section 3.3.2). It is likely  that human 
influence, in particular due to greenhouse gas forcing, is the main 
driver of the observed intensification of heavy precipitation in global 
land regions during recent decades (Cross-Chapter Box  3.2). The 
pattern of ocean salinity changes indicate that fresh regions are 
becoming fresher and that salty regions are becoming saltier as 
a result of changes in ocean-atmosphere fluxes through evaporation 
and precipitation ( high confidence ) making it extremely likely  
that human influence has contributed to observed near-surface 
and subsurface salinity changes since the mid-20th century 
(Section  3.5.2.2). Taken together, this evidence indicates a human 
influence on the water cycle.
It is very likely  that human influence was the main driver of Arctic 
sea ice loss since the late 1970s (Section 3.4.1.1), and very likely  that 
it contributed to the observed reductions in Northern Hemisphere

springtime snow cover since 1950 (Section 3.4.2). Human influence 
was very likely  the main driver of the recent global, near-universal 
retreat of glaciers and it is very likely  that it contributed to the 
observed surface melting of the Greenland Ice Sheet over the past 
two decades (Section 3.4.3.2.1). It is extremely likely that human 
influence was the main driver of the ocean heat content increase 
observed since the 1970s (Section 3.5.1.3), and very likely  that 
human influence was the main driver of the observed GMSL rise since 
at least 1970 (Section 3.5.3.2).
Combining the evidence from across the climate system 
(Sections 3.3–3.7) increases the level of confidence in the attribution 
of observed climate change to human influence and reduces the 
uncertainties associated with assessments based on a single variable. 
From this combined evidence, it is unequivocal that human influence 
has warmed the atmosphere, ocean and land components of the

507
Ocean heat content Near-surface air temperatur eNear-surface air temperature 
over landGlobal
Sea ice Near-surface air temperature
Precipitation
° °
Anthropogenic + natural
Natural
Figure 3.41 |  Summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system,  
for continental, ocean basin and larger scales.  Black lines show observations, brown lines and shading show the multi-model mean and 5th–95th percentile ranges for 
CMIP6 historical simulations including anthropogenic and natural forcing, and blue lines and shading show corresponding ensemble means and 5th–95th percentile ranges 
for CMIP6 natural-only simulations. Temperature time series are as in Figure 3.9, but with smoothing using a low pass filter. Precipitation time series are as in Figure 3.15 and

ocean heat content as in Figure 3.26. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

508
3.8.2 Multivariate Model Evaluation
Similar to the assessment of multivariate attribution of climate 
change in the previous section, this section covers the performance 
of the models across different variables (Sections 3.8.2.1) and 
different classes of models (Section 3.8.2.2). Here the focus is on 
a system-wide assessment using integrative measures of model 
performance that characterize model performance using multiple 
diagnostic fields derived from multi-model ensembles.
3.8.2.1 Integrative Measures of Model Performance
The purpose of this section is to use multivariate analyses to address 
how well models simulate present-day and historical climate. For 
every diagnostic field considered, model performance is compared 
to one or multiple observational references, and the quality of the 
simulation is expressed as a single number, for example a correlation

coefficient or a root mean square difference versus the observational 
reference. By simultaneously assessing different performance indices, 
model improvements can be quantified, similarities in behaviour 
between different models become apparent, and dependencies 
between various indices become evident (Gleckler et al., 2008; 
Waugh and Eyring, 2008).
AR5 found significant differences between models in the simulation 
of mean climate in the CMIP5 ensemble when measured against 
meteorological reanalyses and observations (Flato et al., 2013), see 
also Stouffer et al. (2017). The AR5 determined that for the diagnostic 
fields analysed, the models usually compared similarly against two 
different reference datasets, suggesting that model errors were 
generally larger than observational uncertainties or other differences 
between the observational references. In agreement with previous 
assessments, the CMIP5 multi-model mean generally performed

better than individual models (Annan and Hargreaves, 2011; Rougier, 
2016). The AR5 considered 13 atmospheric fields in its assessment 
for the instrumental period but did not assess multi-variate model 
performance in other climate domains (e.g., ocean, land, and sea ice). 
The AR5 found only modest improvement regarding the simulation 
of climate for two periods of the Earth’s history (the Last Glacial 
Maximum and the mid-Holocene) between CMIP5 and previous 
paleoclimate simulations. Similarly, for the modern period only 
modest, incremental progress was found between CMIP3 and 
CMIP5 regarding the simulation of precipitation and radiation. The 
representation of clouds also showed improvement, but remained 
a key challenge in climate modelling (Flato et al., 2013).
The type of multi-variate analysis of models presented in AR5 remains 
critical to building confidence for example in projections of climate 
change. It is expanded here to the previous-generation CMIP3 and

present-generation CMIP6 models and also to more variables and 
more climate domains, covering land and ocean as well as sea ice. 
The multi-variate evaluation of these three generations of models 
is performed relative to the observational datasets listed in Annex I, 
Table  AI.1. For many of these datasets, a rigorous characterization of the 
observational uncertainty is not available, see discussion in Chapter 2. 
Here, as much as possible, multiple independent observational datasets 
are used. Disagreements among them would cause differences in model scoring, indicating that observational uncertainties may be substantial 
compared to model errors. Conversely, similar scores against different 
observational datasets would suggest model biases may be larger than 
the observational uncertainty.
An analysis of a basket of 16 atmospheric variables (Figure 3.42a) 
assessed across CMIP3, CMIP5, and CMIP6 models but excluding

high-resolution models participating in HighResMIP , reveals the 
progress made between these three generations of models (Bock 
et al., 2020). Progress is evidenced by the increasing prevalence of 
blue colours (indicating a performance better than the median) for 
the more recent model versions. Additionally, a few CMIP6 models 
outperform the best-performing CMIP5 models. Progress is evident 
across all 16 variables. As noted in AR5, the models typically score 
similarly against both observational reference datasets, indicating 
that indeed uncertainties in these reference datasets are smaller 
than model biases. Several models and model families perform 
better compared to observational references than the median, across 
a majority of the climate variables assessed, and conversely some 
other models or model families compare more poorly against these 
reference datasets. Such a good correspondence across a range 
of diagnostic fields probing different aspects of climate enhances

confidence that the improved performances reflect progress in the 
physical realism of these simulations. An alternative explanation, 
that progress is due to a cancellation of errors achieved by model 
tuning, appears improbable given the large number of diagnostic 
fields involved here. However, several instances of poor model 
performance (red colours in Figure  3.42) still exist in the CMIP6 
ensemble. Family relationships (i.e.  various degrees of shared 
formulations; Knutti et al., 2013) between the models are apparent, 
for example, the GISS, GFDL, CESM, CNRM, and HadGEM/UKESM1/
ACCESS families score similarly across all atmospheric variables, 
both for the CMIP5 and CMIP6 generations. In the cases of CESM2/
CESM2(WACCM), CNRM-CM6-1/CNRM-ESM2-1, NorCPM1/
NorESM2-LM, and HadGEM3-GC31-LL/UKESM1-0-LL, the high-
complexity model scores as well or better than its lower-complexity 
counterpart, indicating that increasing complexity by adding Earth

system features, which by removing constraints could be expected to 
degrade a model’s performance, does not necessarily do so. Several 
high climate-sensitivity models (Section 7.5; Meehl et al., 2020), in 
particular CanESM5, CESM2, CESM2-WACCM, HadGEM3-GC31-LL, 
and UKESM1-0-LL, score well against the benchmarks. In accordance 
with AR5 and earlier assessments, the multi-model mean, with some 
notable exceptions, is better than any individual model (Annan and 
Hargreaves, 2011; Rougier, 2016).
Regarding model performance for the ocean and the cryosphere 
(Figure  3.42b), it is apparent that for many models there are 
substantial differences between the scores for Arctic and Antarctic 
sea ice concentration. This might suggest that it is not sea ice physics 
directly that is driving such differences in performance but rather 
other influences, such as differences in geography, the role of large 
ice shelves (which are absent in the Arctic), or large-scale ocean

dynamics. As for atmospheric variables, progress is evident also 
across all four ocean and ten land variables from CMIP5 to CMIP6.

509
Figure 3.42 |  Relative space–time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP simulations (1980–1999) compared to observational datasets. (a)  CMIP3, 
CMIP5, and CMIP6 for 16 atmospheric variables (b) CMIP5 and CMIP6 for 10 land variables and four ocean/sea-ice variables. A relative performance measure is displayed, with blue shading indicating better and red shading indicating 
worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and an additional data set (upper left triangle). Reference/
additional datasets are from top to bottom in (a): ERA5/NCEP , GPCP-SG/GHCN, CERES-EBAF , CERES-EBAF , CERES-EBAF , CERES-EBAF , JRA-55/ERA5, ESACCI-SST/HadISST, ERA5/NCEP , ERA5/NCEP , ERA5/NCEP , ERA5/NCEP , ERA5/NCEP ,

ERA5/NCEP , AIRS/ERA5, ERA5/NCEP and in (b): CERES-EBAF , CERES-EBAF , CERES-EBAF , CERES-EBAF , LandFlux-EVAL, Landschuetzer2016/ JMA-TRANSCOM; MTE/FLUXCOM, LAI3g, JMA-TRANSCOM, ESACCI-SOILMOISTURE, HadISST/
ATSR, HadISST, HadISST, ERA-Interim. White boxes are used when data are not available for a given model and variable. Figure is updated and expanded from Bock et al. (2020), their Figure 5 CC BY 4.0 https://creativecommons.org/
licenses/by/4.0/ . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

510
In summary, CMIP6 models perform generally better for a basket of 
variables covering mean historical climate across the atmosphere, 
ocean, and land domains than previous-generation and older models 
(high confidence ). Earth System models characterized by additional 
biogeochemical feedbacks often perform at least as well as related 
more constrained, lower-complexity models lacking these feedbacks 
(medium confidence ). In many cases, the models score similarly 
against both observational references, indicating that model errors 
are usually larger than observational uncertainties ( high confidence ). 
Moreover, synthesizing across Sections 3.3–3.7, we assess that the 
CMIP6 multi-model mean captures most aspects of observed climate 
change well ( high confidence ).
Using centred pattern correlations (quantifying pattern similarity 
on a scale of –1 to 1, with 1 expressing perfect similarity and 0 no

relationship) for selected fields, AR5 documented improvements 
between CMIP3 and CMIP5 in surface air temperature, outgoing 
longwave radiation, and precipitation (Figure  9.6 of Flato et al., 
2013). Little further progress between CMIP3 and CMIP5 was found 
for fields that were already quite well simulated in CMIP3 (such 
as surface air temperature and outgoing longwave radiation). For 
precipitation, the spread reduced because the worst-performing 
models improved. The shortwave cloud radiative effect remained 
relatively poorly simulated with significant inter-model spread 
(e.g.,  Calisto et al., 2014). This comparison of centred pattern 
correlations is designed to help determine the quality of simulation 
of different diagnostics relative to each other, and also to examine 
progress between generations of models. Figure  3.43 shows the 
centred pattern correlations for 16 variables for CMIP3, CMIP5 and 
CMIP6 models. In the ensemble averages, CMIP6 performs better

than CMIP5 and CMIP3 for near-surface temperature, precipitation, 
mean sea-level pressure, and many other variables. For the variables 
shown, the uncertainties in observational datasets, in particular for precipitation and northward wind at 850 hPa, remain substantial 
relative to mean model errors (see grey dots in Figure 3.43).
In addition to the multivariate assessments of simulations of the 
recent historical period, simulations of selected periods of the Earth’s 
more distant history can be used to benchmark climate models by 
exposing them to climate forcings that are radically different from 
the present and recent past (Harrison et al., 2015, 2016; Kageyama 
et al., 2018; Tierney et al., 2020a). These time periods provide an out-
of-sample test of models because they are not in general used in the 
process of model development. They encompass a range of climate 
drivers, such as volcanic and solar forcing for the Last Millennium,

orbital forcing for the mid-Holocene and Last Interglacial, and changes 
in greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm 
Period, and early Eocene (Sections 2.2 and 2.3). These drivers led to 
climate changes, including in surface temperature (Section 2.3.1.1) 
and the hydrological cycle (Section 2.3.1.3.1), which are described 
by paleoclimate proxies that have been synthesized to support 
evaluations of models on a global and regional scale. However, the 
more sparse, indirect, and regionally incomplete climate information 
available from paleo-archives motivates a different form of the 
multivariate analysis of simulations covering these periods versus the 
equivalent for the historical period, as described below.
AR5 found that reconstructions and simulations of past climates both 
show similar responses in terms of large-scale patterns of climate 
change, such as polar amplification (Flato et al., 2013; Masson-

Delmotte et al., 2013). However, for several regional signals (e.g., 
the north–south temperature gradient in Europe and regional 
precipitation changes), the magnitude of change seen in the proxies 
relative to the pre-industrial period was underestimated by the models. 
When benchmarking CMIP5/PMIP3 models against reconstructions 
of the mid-Holocene and LGM, AR5 found only a slight improvement 
compared with earlier model versions across a range of variables. For 
Pattern correlation with observational referencecorrelation
Specific
Humidity
400 hPaNear-Surface
Air TemperaturePrecipitation TOA 
Outgoing
Shortwave
RadiationTOA 
Outgoing
Longwave
RadiationTOA SW
Cloud Rad
EffectTOA LW
Cloud Rad
EffectSea Level
PressureTemperature
850 hPaTemperature
200 hPaEastward
 Wind
850 hPaEastward
 Wind
200 hPaNorthward
 Wind
850 hPaNorthward
 Wind
200 hPaGeopotential
Height
500 hPa1.0
0.8
0.4
0.2
0CMIP6
CMIP5
CMIP3
Additional observations
Surface
Temperature

Figure 3.43 |  Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999.  Results are 
shown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model 
ensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF , 
CERES-EBAF , CERES-EBAF , CERES-EBAF , JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and 
additional observational datasets (from left to right: NCEP , GHCN, -, -, -, -, ERA5, HadISST, NCEP , NCEP , NCEP , NCEP , NCEP , NCEP , ERA5, NCEP) are shown (solid grey circles) if

available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to a resolution of 4° in longitude and 
5° in latitude. Figure is updated and expanded from Bock et al. (2020), their Figure 7 CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ . Further details on data sources and 

511
the Last Interglacial, it was noted that the magnitude of observed 
annual mean warming in the Northern Hemisphere was only reached 
in summer in the models. For the mid-Pliocene Warm Period, it was 
noted that both proxies and models showed a polar amplification of 
temperature compared with the pre-industrial period, but a formal 
model evaluation was not carried out.
Since AR5, new simulation protocols have been developed in PMIP4 
(Kageyama et al., 2018), which are further described for the mid-
Holocene and the Last Interglacial by Otto-Bliesner et al. (2017), for 
the LGM by Kageyama et al. (2017), for the Pliocene by Haywood 
et al. (2016), and for the early Eocene by Lunt et al. (2017). These have resulted in new model simulations for these time periods (Brierley 
et al., 2020; Haywood et al., 2020; Kageyama et al., 2021a; Lunt et al., 
2021; Otto-Bliesner et al., 2021). These time periods span an assessed

temperature range of 20°C (Section 2.3.1.1), and for all periods the 
PMIP4 multi-model ensemble mean is within 0.5°C of the assessed 
range of GSAT (Figure 3.44a). Those time periods for which the multi-
model ensemble mean is outside the assessed range of GSAT, the 
mid-Holocene and the Last Interglacial, are primarily forced by orbital 
changes not greenhouse gas forcing, and as a result the forcing as 
well as the assessed and modelled response are relatively close to 
zero in the global annual mean. During these periods, climate change 
therefore is a consequence of more poorly understood Earth System 
Figure 3.44 |  Multivariate synopsis of paleoclimate model results compared to observational references.  Data-model comparisons for (a) GSAT anomalies for 
five PMIP4 periods and for regional features for the (b) mid-Holocene and (c) LGM periods, for PMIP3 and PMIP4 models. The results from CMIP6 models are shown as coloured

dots. In (a) the light orange shading shows the very likely  assessed ranges presented in Section 2.3.1.1. In (b) and (c), the regions and variables are defined as follows: North 
America (20°N–50°N, 140°W–60°W), Western Europe (35°N–70°N, 10°W–30°E) and West Africa (0°–30°N, 10°W–30°E); mean temperature of the coldest month (MTCO; 
°C), mean temperature of the warmest month (MTWA; °C), mean annual precipitation (MAP; mm yr–1). In (b) and (c) the ranges shown for the reconstructions (Bartlein et al. 
(2011) for mid-Holocene and Cleator et al. (2020) for LGM) are based on the standard error given at each site: the average and associated standard deviation over each area is 
obtained by computing 1000 times the average of randomly drawn values from the Gaussian distributions defined at each site by the reconstruction mean and standard error;

the light orange colour shows the ±1 standard deviation of these 1000 estimates. The dots on (b) and (c) show the average of the model output for grid points for which there 
are reconstructions. The ranges for the model results are based on an ensemble of 1000 averages over 50 years randomly picked in the model output time series for each region 
and each variable: the mean ± one standard deviation is plotted for each model. Figure is adapted from Brierley et al. (2020), their Figure S3 for the mid-Holocene; and from 
Kageyama et al. (2021b), their Figure 12 for the LGM. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

512
feedbacks acting on the response to orbital differences versus the 
present-day, affecting the seasonality of insolation.
Polar amplification in the LGM, mid-Pliocene Warm Period, and 
Early Eocene Climatic Optimum (EECO) simulations is assessed in 
Section 7.4.4.1.2. Here we focus on the mid-Holocene and the LGM, 
which have been a part of AMIP or CMIP through several assessment 
cycles, and as such serve as a reference to quantify regional model-data 
agreement from one IPCC assessment to another. We compare the 
results from 15 CMIP6 models using the PMIP4 protocol (CMIP6-PMIP4), 
with non-CMIP6 models using the PMIP4 protocol, with PMIP3 models, 
and with regional temperature and precipitation changes from proxies 
for the mid-Holocene (Figure  3.44b). For six out of seven variables 
shown, the CMIP6 multi-model mean captures the correct sign of the 
change. For five out of seven of them the CMIP6 ensemble mean is

within the reconstructed range. For the other two variables (changes in 
the mean temperature of the warmest month over North America and 
in the mean annual precipitation over West Africa) nearly all PMIP4 
and PMIP3 models are outside the reconstructed range. CMIP6 models 
show regional patterns of temperature changes similar to the PMIP3 
ensemble (Brierley et al., 2020), but the slight mid-Holocene cooling in 
PMIP4 compared with PMIP3, probably associated with lower imposed 
mid-Holocene carbon dioxide concentrations (Otto-Bliesner et al., 
2017), improves the regional model performance for summer and 
winter temperatures (Figure 3.44b). However, this cooling also results 
in a CMIP6 mid-Holocene GSAT that lies further from the assessed 
range (Figure 3.44a). All models show an expansion of the monsoon 
areas from the pre-industrial to the mid-Holocene simulations in the 
Northern Hemisphere, but this expansion in some cases is only large

enough to cancel out the bias in the pre-industrial control simulations 
(Section 3.3.3.2; Brierley et al., 2020). There is a slight improvement in 
representing the northward expansion of the West African monsoon 
region in PMIP4 compared with PMIP3 (Figures 3.11 and 3.44b).
Fourteen simulations of the LGM climate have been produced 
following the CMIP6-PMIP4 protocol using 11 models, five of 
which are from the latest CMIP6 generation. The multi-model-mean 
global cooling simulated by these models is close to that simulated 
by the CMIP5-PMIP3 ensemble, but the range of results is larger. 
The increase in the range is largely due to the inclusion of CESM2 
which simulates a much larger cooling than the other PMIP4 models 
(Figure  3.44a). This is consistent with its larger climate sensitivity 
(see also Section 3.3.1.1; Zhu et al., 2021). The other models on 
average also simulate slightly larger cooling in PMIP4 versus PMIP3

(Kageyama et al., 2017, 2021a). The PMIP4 multi-model mean is 
within the range of reconstructed regional averages for four out 
of seven regional variables; this is unchanged from PMIP3 but for 
different variables (Figure 3.44c). For all fields, the results of many 
individual models are outside the reconstructed range. For two 
variables out of seven (changes in the mean temperature of the 
warmest month and mean annual precipitation over Western Europe) 
no model is within the range of the reconstructions. This analysis is 
strengthened compared with the equivalent analysis in AR5 because 
it is based on larger and improved reconstructions (Cleator et al., 
2020). Most CMIP6-PMIP4 models simulate a slightly stronger AMOC 
in the LGM, but no strong deepening of the AMOC (Kageyama et al., 
2021a), while most other PMIP4 models simulate a strengthening and strong deepening of the AMOC, as was the case for the PMIP3 
models (Muglia and Schmittner, 2015; Sherriff-Tadano et al., 2018).

Only one model (CESM1.2) shows a shoaling of LGM AMOC which 
is consistent with reconstructions (Marzocchi and Jansen, 2017; 
Sherriff-Tadano et al., 2018).
17 PMIP4 models completed Last Interglacial simulations (Otto-Bliesner 
et al., 2021). The comparison to reconstructions is generally good, 
except for some discrepancies, such as for upwelling systems in 
the South East Atlantic or discrepancies which may result from 
local melting of remnant ice sheets absent in the Last Interglacial 
simulation protocol. All models simulate a decrease in Arctic sea ice 
in summer, commensurate with increased summer insolation, while 
some models even simulate a large or complete loss (Guarino et al., 
2020; Kageyama et al., 2021b). Sea ice reconstructions for the central 
Arctic are, however, too uncertain to evaluate this behaviour. The 
Last Interglacial simulations indicate a  clear relationship between 
simulated sea ice loss and model responses to increased greenhouse

gas forcing (Kageyama et al., 2021b; Otto-Bliesner et al., 2021).
Overall, the PMIP multi-model means agree very well (within 0.5°C 
of the assessed range) with GSAT reconstructed from proxies 
across multiple time periods, spanning a range from 6°C colder 
than pre-industrial (Last Glacial Maximum) to 14°C warmer than 
pre-industrial (Early Eocene Climate Optimum) ( high confidence ). 
During the orbitally-forced mid-Holocene, the CMIP6 multi-model 
mean captures the sign of the regional changes in temperature 
and precipitation in most regions assessed, and there have 
been some regional improvements compared to AR5 ( medium 
confidence ). The limited number of CMIP6 simulations of the 
LGM hinders model evaluation of the multi-model mean, but for 
both LGM and mid-Holocene, models tend to underestimate the 
magnitude of large changes ( high confidence ). Some long-standing 
model-data discrepancies, such as a dry bias in North Africa in the

mid-Holocene, have not improved in CMIP6 compared with PMIP3 
(high confidence ).
3.8.2.2 Process Representation in Different Classes of Models
Based on new scientific insights and newly available observations, 
many improvements have been made to models from CMIP5 to 
CMIP6, including changes in the representation of physics of the 
atmosphere, ocean, sea ice, and land surface. In many cases, changes 
in the detailed representation of cloud and aerosol processes have 
been implemented. The new generation of CMIP6 climate models 
also features increases in spatial resolution, as well as inclusion of 
additional Earth system processes and new components (see further 
details in Section 1.5.3.1 and in Tables AII.5 and AII.6). Such changes 
to model physics and resolution are often designed to improve the 
fitness-for-purpose of a model such as for projecting regional aspects 
of climate (Section 10.3) or to more fully represent feedbacks to make

the models more fit for long-term climate projections affected for 
example by carbon cycle feedbacks (see also Section 1.5.3.1).
Factors affecting model performance include resolution, the type of 
dynamical core (spectral, finite difference or finite volume), physics 
parameters and parameterisations, model structure, for example,

513
many of the coupled HighResMIP models (Haarsma et al., 2016) use 
the NEMO ocean model, affecting model diversity, and the range and 
degree of process realism (e.g., for aerosols, atmospheric chemistry 
and other Earth System components). This section particularly 
explores the influence of model resolution and of complexity on 
model performance (see also Section 8.5.1).
A key advance in CMIP6 compared to CMIP5 is the presence of 
high-resolution models that have participated in HighResMIP . 
Resolution alone can significantly affect a model’s performance, 
with some effects propagating to the global scale. Recent studies 
have shown that enhancing the horizontal resolution of models 
is seen to significantly affect aspects of large-scale circulation 
as well as improve the simulation of small-scale processes and 
extremes when compared to CMIP3 and CMIP5 models (see also

Section 11.4.3; Haarsma et al., 2016), with some models approaching 
10 km resolution in the atmosphere (Kodama et al., 2021) or ocean 
(Caldwell et al., 2019; Gutjahr et al., 2019; Roberts et al., 2019; Chang 
et al., 2020; Semmler et al., 2020).
As discussed in Section 3.3, CMIP6 models reproduce observed 
large-scale mean surface temperature patterns as well as their 
CMIP5 predecessors, but biases in surface temperature in the 
mean of HighResMIP models are smaller than those in the mean 
of the corresponding standard resolution CMIP6 configurations of 
the same models (Section 3.3.1.1 and Figure  3.3). The extent and 
causes of improvements due to increased horizontal resolutions 
in the atmosphere and ocean domains depend on the model 
(Kuhlbrodt et al., 2018; Roberts et al., 2018, 2019; Sidorenko et al., 
2019), although they typically involve better process representation 
(for  example of ocean currents and atmospheric storms) which

can lead to reduced biases in top of atmosphere radiation and 
cloudiness.  Precipitation has likewise improved in CMIP6 versus 
CMIP5 models, but biases remain. The high resolution (<25 km) 
class of models participating in HighResMIP compares regionally 
better against observations than the standard resolution CMIP6 
models (of order 100 km, Figure 3.13; Section 3.3.2), partly because 
of an improved representation of orographic (mountain-induced) 
precipitation which constitutes a major fraction of precipitation on 
land, but other processes also play an important role (Vannière et al., 
2019). However, there are also large parts of the tropical ocean where 
precipitation in high-resolution models is not improved compared to 
standard resolution CMIP6 models (Vannière et al., 2019).
Additionally, the representation of surface and deeper ocean mean 
temperature is improved in models with higher horizontal resolution 
(Sections 3.5.1.1 and 3.5.1.2) with systematic improvements in

coupled tropical Atlantic sea surface temperature and precipitation 
biases at higher resolutions (Roberts et al., 2019, single model; 
Vannière et al., 2019, multi-model), the North Atlantic cold bias (Bock 
et al., 2020, multi-model; Roberts et al., 2018, 2019; Caldwell et al., 
2019; all single models) as well as deep-ocean biases (Small et al., 
2014; Griffies et al., 2015; Caldwell et al., 2019; Gutjahr et al., 2019; 
Roberts et al., 2019; Chang et al., 2020, all single model studies). 
Atlantic ocean transports (heat and volume) are also generally 
improved compared to observations (Grist et al., 2018; Caldwell et al., 
2019; Docquier et al., 2019; Roberts et al., 2019, 2020c; Chang et al., 2020), as well as some aspects of air-sea interactions (P . Wu et al., 
2019, single model; Bellucci et al., 2021, multi-model). However, warm-
biased sea surface temperatures in the Southern Ocean are worse in 
comparison to standard resolution CMIP6 models (Bock et al., 2020).

The AR5 noted problems with the simulation of clouds in this region 
which were later attributed to a lack of supercooled liquid clouds 
(Bodas-Salcedo et al., 2016). Mesoscale ocean processes are critical 
to maintaining the Southern Ocean stratification and response to 
wind forcing (Marshall and Radko, 2003; Hallberg and Gnanadesikan, 
2006), and their explicit representation requires even higher ocean 
resolution (Hallberg, 2013). Similarly, atmospheric convection remains 
unresolved even in the highest-resolution climate models participating 
in HighResMIP . However, there is also evidence of improvements 
in the frequency, distribution and interannual variability of tropical 
cyclones in HighResMIP (Roberts et al., 2020a, b), particularly in the 
Northern Hemisphere (see further discussion in Section 11.7.1.3), 
and their interaction with the ocean (Scoccimarro et al., 2017, single 
model), as well as the global moisture budget (Vannière et al., 2019).

At higher resolution the track density of tropical cyclones is increased 
practically everywhere where tropical cyclones occur. Simulation of 
some climate extremes is shown to be improved at higher resolution 
including explosively developing extra-tropical cyclones (Vries et al., 
2019; Jiaxiang et al., 2020), blocking (Section 3.3.3.3; Fabiano et al., 
2020; Schiemann et al., 2020) and European extreme precipitation 
due to a better representation of the North Atlantic storm track (van 
Haren et al., 2015) and orographic boundary conditions (Schiemann 
et al., 2018).
In CMIP6 a number of Earth system models have increased the realism 
by which key biogeochemical aspects of the coupled Earth system are 
represented, affecting, for example, the carbon and nitrogen cycles, 
aerosols, and atmospheric chemistry (e.g., Cao et al., 2018; Gettelman 
et al., 2019; Lin et al., 2019; Mauritsen et al., 2019; Séférian et al.,

2019; Sellar et al., 2019; Sidorenko et al., 2019; Swart et al., 2019; 
Dunne et al., 2020; Seland et al., 2020; Wu et al., 2020; Ziehn et al., 
2020). In addition to increased process realism, the level of coupling 
between the physical climate and biogeochemical components of 
the Earth system has also been enhanced in some models (Mulcahy 
et al., 2020) as well as across different biogeochemical components 
(see Section 5.4 for a discussion and Table 5.4 for an overview). For 
example, the nitrogen cycle is now simulated in several ESMs (Zaehle 
et al., 2015; Davies-Barnard et al., 2020). This advance accounts 
for the fertilization effect nitrogen availability has on vegetation 
and carbon uptake, reducing uncertainties in the simulations of 
the carbon uptake responses to physical climate change (Section 
3.6.1) and to CO 2 increases (Arora et al., 2020), thus improving 
confidence in the simulated airborne fraction of CO 2 emissions

(Jones and Friedlingstein, 2020) and better constraining remaining 
carbon budgets (Section 5.5). Such advances also allow investigation 
of land-based climate change mitigation options (e.g., through 
changes in land management and associated terrestrial carbon 
uptake (Mahowald et al., 2017; Pongratz et al., 2018)) or interactions 
between different facets of the managed Earth system, such as 
interactions between mitigation efforts targeting climate warming 
and air quality (West et al., 2013). A number of developments also 
explicitly target improved simulation of the past.

514
Chapter 3 Human Influence on the Climate System3Further such ESM developments include: (i) Apart from the nitrogen 
cycle, extending terrestrial carbon cycle models to simulate 
interactions between the carbon cycle and other nutrient cycles, such 
as phosphorus, that are known to play an important role in limiting 
future plant uptake of CO 2 (Zaehle et al., 2015). (ii) Introducing 
explicit coupling between interactive atmospheric chemistry and 
aerosol schemes (Gettelman et al., 2019; Sellar et al., 2019), which 
has been shown to affect estimates of historical aerosol radiative 
forcing (Karset et al., 2018). Furthermore, interactive treatment 
of atmospheric chemistry in a full ESM supports investigation of 
interactions between climate and air quality mitigation efforts, such as 
in AerChemMIP (Collins et al., 2017), as well as interactions between 
stratospheric ozone recovery and global warming (Morgenstern 
et al., 2018). (iii) Coupling between components of Earth system

models has been extended to increase their utility for studying future 
interactions across the full Earth system, such as between ocean 
biogeochemistry and cloud-aerosol processes (Mulcahy et al., 2020), 
and vegetation and impacts on dust production (Kok et al., 2018), 
production of secondary organic aerosols (SOA, Zhao et al., 2017) 
and Equilibrium Climate Sensitivity (ECS), whereby enhanced CO 2 
fertilization of land vegetation causes changes in regional surface 
albedo (Andrews et al., 2019). Increased coupling between physical 
climate and biogeochemical processes in a single ESM, along with 
an increased number of interactively represented processes, such as 
permafrost thaw, vegetation, wildfires and continental ice sheets 
increases our ability to investigate the potential for abrupt and 
interactive changes in the Earth system (see Sections 4.7.3 and 5.4.9, 
and Box 5.1). Table 5.4 provides an overview of recent advances in 
representing the carbon cycle in ESMs.

In summary, both high-resolution and high-complexity models have 
been evaluated as part of CMIP6. In comparison with standard 
resolution CMIP6 models, higher resolution probed under the 
HighResMIP activity (Haarsma et al., 2016) improves aspects of the 
simulation of climate (particularly concerning sea surface temperature) 
but discrepancies remain and there are some regions, such as parts of 
the Southern Ocean, where currently attainable resolution produces 
inferior performance ( high confidence ). Such model behaviour can 
indicate deficiencies in model physics that are not simply associated 
with resolution. In several cases, high-complexity ESMs that include 
additional interactions between Earth system components and thus 
have potential for additional associated model errors nevertheless 
perform as well as their low-complexity counterparts, illustrating that 
interactively simulating these Earth System components as part of 
the climate system is now well established.

Graphic developers: Bettina K. Gier (Germany), Birgit Hassler 
(Germany), Soufiane Karmouche (Germany, Morocco), Seungmok Paik 
(Republic of Korea), Min-Gyu Seong (Republic of Korea)
Data providers: Leopold Haimberger (Austria), Lawrence Mudryk 
(Canada), Dirk Notz (Germany)

515
Human Influence on the Climate System  Chapter 33Frequently Asked Questions
FAQ 3.1 | How Do We Know Humans Are Responsible for Climate Change?
The dominant role of humans in driving recent climate change is clear. This conclusion is based on a synthesis of 
information from multiple lines of evidence, including direct observations of recent changes in Earth’s climate; 
analyses of tree rings, ice cores, and other long-term records documenting how the climate has changed in the 
past; and computer simulations based on the fundamental physics that governs the climate system.
Climate is influenced by a range of factors. There are two main natural drivers of variations in climate on time 
scales of decades to centuries. The first is variations in the sun’s activity, which alter the amount of incoming 
energy from the sun. The second is large volcanic eruptions, which increase the number of small particles (aerosols)

in the upper atmosphere that reflect sunlight and cool the surface–an effect that can last for several years 
(see also FAQ 3.2). The main human drivers of climate change are increases in the atmospheric concentrations 
of greenhouse gases and of aerosols from burning fossil fuels, land use and other sources. The greenhouse 
gases trap infrared radiation near the surface, warming the climate. Aerosols, like those produced naturally 
by volcanoes, on average cool the climate by increasing the reflection of sunlight. Multiple lines of evidence 
demonstrate that human drivers are the main cause of recent climate change.
The current rates of increase of the concentration of the major greenhouse gases (carbon dioxide, methane 
and nitrous oxide) are unprecedented over at least the last 800,000 years. Several lines of evidence clearly show 
that these increases are the results of human activities. The basic physics underlying the warming effect of

greenhouse gases on the climate has been understood for more than a century, and our current understanding 
has been used to develop the latest generation climate models (see FAQ 3.3). Like weather forecasting models, 
climate models represent the state of the atmosphere on a grid and simulate its evolution over time based on 
physical principles. They include a representation of the ocean, sea ice and the main processes important in 
driving climate and climate change.
Results consistently show that such climate models can only reproduce the observed warming (black line in 
FAQ 3.1, Figure 1) when including the effects of human activities (grey band in FAQ 3.1, Figure 1), in particular 
the increasing concentrations of greenhouse gases. These climate models show a dominant warming effect of 
greenhouse gas increases (red band, which shows the warming effects of greenhouse gases by themselves),

which has been partly offset by the cooling effect of increases in atmospheric aerosols (blue band). By contrast, 
simulations that include only natural processes, including internal variability related to El Niño and other similar 
variations, as well as variations in the activity of the sun and emissions from large volcanoes (green band in 
FAQ 3.1, Figure 1), are not able to reproduce the observed warming. The fact that simulations including only 
natural processes show much smaller temperature increases indicates that natural processes alone cannot explain 
the strong rate of warming observed. The observed rate can only be reproduced when human influence is added 
to the simulations.
Moreover, the dominant effect of human activities is apparent not only in the warming of global surface 
temperature, but also in the pattern of warming in the lower atmosphere and cooling in the stratosphere,

warming of the ocean, melting of sea ice, and many other observed changes. An additional line of evidence for 
the role of humans in driving climate change comes from comparing the rate of warming observed over recent 
decades with that which occurred prior to human influence on climate. Evidence from tree rings and other 
paleoclimate records shows that the rate of increase of global surface temperature observed over the past fifty 
years exceeded that which occurred in any previous 50-year period over the past 2000 years (see FAQ 2.1).
Taken together, this evidence shows that humans are the dominant cause of observed global warming over 
recent decades.

516
Chapter 3 Human Influence on the Climate System3FAQ 3.1 (continued)
FAQ 3.1: How do we know humans are causing climate change?
Observed warming (1850-2019) is only reproduced in simulations including human influence. 
ObservationsGlobal surface temperature change since 1850 
(°C)2.5
2.0
1.5
1.0
0.5
0
-0.5
-1.0
-1.5
1900 1950 2000 2020 1850Combined
(Human & natural causes)Greenhouse gases (human)
Aerosols (Human)Natural causes
FAQ 3.1, Figure 1 |  Observed warming (1850–2019) is only reproduced in simulations including human influence.  Global surface temperature 
changes in observations, compared to climate model simulations of the response to all human and natural forcings (grey band), greenhouse gases only 
(red band), aerosols and other human drivers only (blue band) and natural forcings only (green band). Solid coloured lines show the multi-model mean, 
and coloured bands show the 5–95% range of individual simulations.

517
FAQ 3.2 | What is Natural Variability and How Has it Influenced Recent Climate Changes?
Natural variability refers to variations in climate that are caused by processes other than human influence. 
It includes variability that is internally generated within the climate system and variability that is driven by 
natural external factors. Natural variability is a major cause of year-to-year changes in global surface climate and 
can play a prominent role in trends over multiple years or even decades. But the influence of natural variability 
is typically small when considering trends over periods of multiple decades or longer. When estimated over the 
entire historical period (1850–2020), the contribution of natural variability to global surface warming of –0.23°C 
to +0.23°C is small compared to the warming of about 1.1°C observed during the same period, which has been

almost entirely attributed to the human influence.
Paleoclimatic records (indirect measurements of climate that can extend back many thousands of years) and 
climate models all show that global surface temperatures have changed significantly over a wide range of time 
scales in the past. One of these reasons is natural variability , which refers to variations in climate that are either 
internally  generated within the climate system or externally  driven by natural changes. Internal natural variability 
corresponds to a redistribution of energy within the climate system (for example via atmospheric circulation  
changes similar to those that drive the daily weather) and is most clearly observed as regional, rather than 
global, fluctuations in surface temperature. External natural variability can result from changes in the Earth’s 
orbit, small variations in energy received from the sun, or from major volcanic eruptions. Although large orbital

changes are related to global climate changes of the past, they operate on very long time scales (i.e., thousands 
of years). As such, they have displayed very little change over the past century and have had very little influence 
on temperature changes observed over that period. On the other hand, volcanic eruptions can strongly cool the 
Earth, but this effect is short-lived and their influence on surface temperatures typically fades within a decade 
of the eruption.
To understand how much of observed recent climate change has been caused by natural variability (a process 
referred to as attribution), scientists use climate model simulations. When only natural factors are used to force 
climate models, the resulting simulations show variations in climate on a wide range of time scales in response to 
volcanic eruptions, variations in solar activity, and internal natural variability. However, the influence of natural

climate variability typically decreases as the time period gets longer, such that it only has mild effects on multi-
decadal and longer trends (FAQ 3.2, Figure 1).
Consequently, over periods of a couple of decades or less, natural climate variability can dominate the human-
induced surface warming trend – leading to periods with stronger or weaker warming, and sometimes even 
cooling (FAQ 3.2, Figure 1, left and centre). Over longer periods, however, the effect of natural variability is 
relatively small (FAQ 3.2, Figure 1, right). For instance, over the entire historical period (1850–2019), natural 
variability is estimated to have caused between –0.23°C and +0.23°C of the observed surface warming of 
about 1.1°C. This means that the bulk of the warming has been almost entirely attributed to human activities, 
particularly emissions of greenhouse gases (FAQ 3.1).
Another way to picture natural variability and human influence is to think of a person walking a dog. The path

of the walker represents the human-induced warming, while their dog represents natural variability. Looking at 
global surface temperature changes over short periods is akin to focusing on the dog. The dog sometimes moves 
ahead of the owner and other times behind. This is similar to natural variability that can weaken or amplify 
warming on the short term. In both cases it is difficult to predict where the dog will be or how the climate will 
evolve in the near future. However, if we pull back and focus on the slow steady steps of the owner, the path of 
the dog is much clearer and more predictable, as it follows the path of its owner. Similarly, human influence on 
the climate is much clearer over longer time periods.

518
Chapter 3 Human Influence on the Climate System3FAQ 3.2 (continued)
FAQ 3.2 What is natural variability and how has it influenced recent climate changes?
Natural variability can alter global temperature over short time scales (1 year to ~2 decades) but it has a minimal 
influence on longer time scales . Since 1850, natural variability (             ) has caused between -0.23°C and 0.23°C 
of global temperature change, compared to the warming of about 1.1°C observed (         ) over that period.
Annual (1 year) variations Decadal (10 year) variations Multi-decadal (30 year) variations
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.80.8
0.0
-0.80.8
0.0
-0.8Dominated by natural variability Less influenced by natural variability, 
but natural cooling or more intense
warming can still occurDominated by the human influenceGlobal surface temperature change (°C)
1950 1970 1990 2010 ‘20
Year1950 1970 1990 2010 ‘20
Year1950 1980 2010 ‘20
Year

FAQ 3.2, Figure 1 |  Annual (left), decadal (middle) and multi-decadal (right) variations in average global surface temperature.  The thick 
black line is an estimate of the human contribution to temperature changes, based on climate models, whereas the green lines show the combined effect 
of natural variations and human-induced warming, different shadings of green represent different simulations, which can be viewed as showing a range of 
potential pasts. The influence of natural variability is shown by the green bars, and it decreases on longer time scales. The data is sourced from the CESM1 
large ensemble.

519
FAQ 3.3 | Are Climate Models Improving?
Yes, climate models have improved and continue to do so, becoming better at capturing complex and small-
scale processes and at simulating present-day mean climate conditions. This improvement can be measured by 
comparing climate simulations against historical observations. Both the current and previous generations of 
models show that increases in greenhouse gases cause global warming. While past warming is well simulated by 
the new generation models as a group, some individual models simulate past warming that is either below or 
above what is observed. The information about how well models simulate past warming, as well as other insights 
from observations and theory, are used to refine this Report’s projections of global warming.
Climate models are important tools for understanding past, present and future climate change. They are

sophisticated computer programs that are based on fundamental laws of physics of the atmosphere, ocean, 
ice, and land. Climate models perform their calculations on a three-dimensional grid made of small bricks 
or ‘gridcells’ of about 100 km across. Processes that occur on scales smaller than the model grid cells (such 
as the transformation of cloud moisture into rain) are treated in a simplified way. This simplification is done 
differently in different models. Some models include more processes and complexity than others; some represent 
processes in finer detail (smaller grid cells) than others. Hence the simulated climate and climate change vary 
between models.
Climate modelling started in the 1950s and, over the years, models have become increasingly sophisticated as 
computing power, observations and our understanding of the climate system have advanced. The models used

in the IPCC First Assessment Report published in 1990 correctly reproduced many aspects of climate (FAQ 1.1). 
The actual evolution of the climate since then has confirmed these early projections, when accounting for the 
differences between the simulated scenarios and actual emissions. Models continue to improve and get better 
and better at simulating the large variety of important processes that affect climate. For example, many models 
now simulate complex interactions between different aspects of the Earth system, such as the uptake of carbon 
dioxide by vegetation on land and by the ocean, and the interaction between clouds and air pollutants. While 
some models are becoming more comprehensive, others are striving to represent processes at higher resolution, 
for example to better represent the vortices and swirls in currents responsible for much of the transport of heat 
in the ocean.

Scientists evaluate the performance of climate models by comparing historical climate model simulations to 
observations. This evaluation includes comparison of large-scale averages as well as more detailed regional and 
seasonal variations. There are two important aspects to consider: (i) how models perform individually and (ii) 
how they perform as a group. The average of many models often compares better against observations than any 
individual model, since errors in representing detailed processes tend to cancel each other out in multi-model 
averages.
As an example, FAQ 3.3 Figure 1 compares simulations from the three most recent generations of models (available 
around 2008, 2013 and 2021) with observations of three climate variables. It shows the correlation between 
simulated and observed patterns, where a value of 1 represents perfect agreement. Many individual models of

the new generation perform significantly better, as indicated by values closer to 1. As a group, each generation 
out-performs the previous generation: the multi-model average (shown by the longer lines) is progressively closer 
to 1. The vertical extent of the colored bars indicates the range of model performance across each group. The 
top of the bar moves up with each generation, indicating improved performance of the best performing models 
from one generation to the next. In the case of precipitation, the performance of the worst performing models 
is similar in the two most recent model generations, increasing the spread across models.
Developments in the latest generation of climate models, including new and better representation of physical, 
chemical and biological processes, as well as higher resolution, have improved the simulation of many aspects 
of the Earth system. These simulations, along with the evaluation of the ability of the models to simulate past

warming as well as the updated assessment of the temperature response to a doubling of CO 2 in the atmosphere, 
are used to estimate the range of future global warming (FAQ 7.3).

520
Chapter 3 Human Influence on the Climate System3FAQ 3.3 (continued)
CMIP6
CMIP5
CMIP3
1.0
0.9
0.8
0.7
0.6FAQ 3.3:  Are Climate Models Improving?
Yes, climate models have improved with increasing computer power and better understanding of climate processes.
Better model 
performance
Poorer model 
CorrelationMulti-model 
averageIndividual models
Smaller 
spread 
across 
models
Larger 
spread 
across 
models
Near-Surface
Air TemperaturePrecipitation Sea Level
PressureSkill of models at reproducing observations
FAQ 3.3, Figure  1 |  Pattern correlations between models and observations of three different variables: surface air temperature, 
precipitation and sea level pressure.  Results are shown for the three most recent generations of models, from the Coupled Model Intercomparison 
Project (CMIP): CMIP3 (orange), CMIP5 (blue) and CMIP6 (purple). Individual model results are shown as short lines, along with the corresponding ensemble

average (long line). For the correlations the yearly averages of the models are compared with the reference observations for the period 1980–1999, with 
1 representing perfect similarity between the models and observations. CMIP3 simulations performed in 2004-2008 were assessed in the IPCC Fourth 
Assessment, CMIP5 simulations performed in 2011–2013 were assessed in the IPCC Fifth Assessment, and CMIP6 simulations performed in 2018–2021 
are assessed in this Report.

555
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44
This chapter assesses simulations of future global climate change, 
spanning time horizons from the near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) out to the year 2300. 
Changes are assessed relative to both the recent past (1995–2014) 
and the 1850–1900 approximation to the pre-industrial period.
The projections assessed here are mainly based on a new range 
of scenarios, the Shared Socio-economic Pathways (SSPs) used 
in the Coupled Model Intercomparison Project Phase 6 (CMIP6).  
Among the SSPs, the focus is on the five scenarios SSP1 -1.9, SSP1 -2.6, 
SSP2 -4.5, SSP3 -7.0, and SSP5 -8.5. In the SSP labels, the first number 
refers to the assumed shared socio-economic pathway, and the second 
refers to the approximate global effective radiative forcing (ERF) in 2100. Where appropriate, this chapter also assesses new results from

CMIP5, which used scenarios based on Representative Concentration 
Pathways (RCPs). Additional lines of evidence enter the assessment, especially for change in globally averaged surface air temperature (GSAT) and global mean sea level (GMSL), while assessment for changes in other quantities is mainly based on CMIP6 results. Unless 
noted otherwise, the assessments assume that there will be no major 
volcanic eruption in the 21st  century. {1.6, 4.2.2, 4.3.2, 4.3.4, 4.6.2, Box 4.1, Cross-Chapter Box 4.1, Cross-Chapter Box 7.1, 9.6}
Assessed future change in GSAT is, for the first time in an IPCC report, explicitly constructed by combining scenario-based projections with observational constraints based on 
past simulated warming, as well as an updated assessment

of equilibrium climate sensitivity (ECS) and transient climate response (TCR). Climate forecasts initialized using recent observations have also been used for the period 2019–2028. The inclusion of additional lines of evidence has reduced the assessed 
uncertainty ranges for each scenario. {4.3.1, 4.3.4, 4.4.1, 7.5}
In the near term (2021–2040), a 1.5°C increase in the 20-year 
average of GSAT, relative to the average over the period 
1850–1900, is very likely
 to occur in scenario SSP5 -8.5, 
likely  to occur in scenarios SSP2 -4.5 and SSP3 -7.0, and more 
likely than not  to occur in scenarios SSP1 -1.9 and SSP1 -2.6. 
The  threshold-crossing time is defined as the midpoint of the  first 
20-year period during which the average GSAT exceeds the
threshold. In all scenarios assessed here except SSP5 -8.5, the central
estimate of crossing the 1.5°C threshold lies in the early 2030s. Thisis in the early part of the likely range (2030–2052) assessed in the

IPCC Special Report on Global Warming of 1.5°C (SR1.5), which
assumed continuation of the then-current warming rate; this rate has 
been confirmed in the AR6. Roughly half of this difference betweenassessed crossing times arises from a  larger historical warmingdiagnosed in AR6. The other half arises because for central estimatesof climate sensitivity, most scenarios show stronger warming over
the near term than was assessed as ‘current’ in SR1.5 ( medium
confidence
). When considering scenarios similar to SSP1 -1.9 insteadof linear extrapolation, the SR1.5 estimate of when 1.5°C global 
warming is crossed is close to the central estimate reported here. It is more likely than not
 that under SSP1 -1.9, GSAT relative to 
1850–1900 will remain below 1.6°C throughout the 21st  century, implying a potential temporary overshoot of 1.5°C global warming of no more than 0.1°C. If climate sensitivity lies near the lower end of

the assessed very likely range, crossing the 1.5°C warming threshold 
is avoided in scenarios SSP1 -1.9 and SSP1 -2.6 (medium confidence). 
{2.3.1, Cross-Chapter Box 2.3, 3.3.1, 4.3.4, Box 4.1, 7.5}
By 2030, GSAT in any individual year could exceed 1.5°C 
relative to 1850–1900 with a  likelihood between 40% and 60%, across the scenarios considered here (medium confidence).  Uncertainty in near-term projections of annual GSAT 
arises in roughly equal measure from natural internal variability and 
model uncertainty ( high  confidence). By contrast, near-term annual 
GSAT levels depend less on the scenario chosen, consistent with the 
IPCC Fifth Assessment Report (AR5) assessment. Forecasts initialized from recent observations simulate annual GSAT changes for the 
period 2019–2028 relative to the recent past that are consistent with 
the assessed very likely range (high confidence). {4.4.1, Box 4.1}
Compared to the recent past (1995–2014), GSAT averaged over

the period 2081–2100 is very likely to be higher by 0.2°C–1.0°C 
in the low-emissions scenario SSP1 -1.9 and by 2.4°C–4.8°C 
in the high-emissions scenario SSP5 -8.5. For the scenarios 
SSP1 -2.6, SSP2 -4.5, and SSP3 -7.0, the corresponding very likely  
ranges are 0.5°C–1.5°C, 1.2°C–2.6°C, and 2.0°C–3.7°C, respectively. The uncertainty ranges for the period 2081–2100 continue to be 
dominated by the uncertainty in ECS and TCR ( very high confidence ). 
Emissions-driven simulations for SSP5 -8.5 show that carbon-cycle 
uncertainty is too small to change the assessment of GSAT projections 
(high confidence). {4.3.1, 4.3.4, 4.6.2, 7.5}
The CMIP6 models project a  wider range of GSAT change than the assessed range (high confidence); furthermore, the CMIP6 GSAT increase tends to be larger than in CMIP5 (very high confidence ). About half of the increase in simulated 
warming has occurred because higher climate sensitivity is more

prevalent in CMIP6 than in CMIP5; the other half arises from higher 
ERF in nominally comparable scenarios (e.g., RCP8.5 and SSP5 -8.5; 
medium confidence ). In SSP1 -2.6 and SSP2 -4.5, ERF changes also 
explain about half of the changes in the range of warming ( medium 
confidence ). For SSP5 -8.5, higher climate sensitivity is the primary 
reason behind the upper end of the warming being higher than in 
CMIP5 (medium confidence). {4.3.1, 4.3.4, 4.6.2, 7.5.6}
While high-warming storylines – those associated with GSAT 
levels above the upper bound of the assessed very likely range – 
are by definition extremely unlikely, they cannot be ruled out. 
For SSP1 -2.6, such a high-warming storyline implies long-term 
(2081–2100) warming well above, rather than well below, 2°C (high confidence). Irrespective of scenario, high-warming storylines imply changes in many aspects of the climate system that exceed the 
patterns associated with the central estimate of GSAT changes by up

556
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4
It is virtually certain  that the average surface warming will 
continue to be higher over land than over the ocean and that 
the surface warming in the Arctic will continue to be more 
pronounced than the global average over the 21st  century. 
On average, the surface is expected to warm faster over land 
than over the ocean by a  factor of 1.5 ( likely range 1.4 to 1.7). 
The warming pattern likely  varies across seasons, with northern high 
latitudes warming more during boreal winter than summer ( medium 
confidence ). Regions with increasing or decreasing year-to-year 
variability of seasonal mean temperatures will likely  increase in their 
spatial extent. {4.3.1, 4.5.1, 7.4.4}
It is very likely  that long-term lower-tropospheric warming 
will be larger in the Arctic than in the global mean.  It is very 
likely  that global mean stratospheric cooling will be larger by the

end of the 21st century in a pathway with higher atmospheric CO 2 
concentrations. It is likely  that tropical upper tropospheric warming 
will be larger than at the tropical surface, but with an uncertain 
magnitude owing to the effects of natural internal variability and 
uncertainty in the response of the climate system to anthropogenic 
forcing. {4.5.1, 3.3.1.2}
Annual global land precipitation will increase over the 
21st  century as GSAT increases ( high confidence ). The likely  
range of change in globally averaged annual land precipitation 
during 2081–2100 relative to 1995–2014 is –0.2 to +4.7% 
in the low-emissions scenario SSP1-1.9 and 0.9–12.9% in 
the high-emissions scenario SSP5-8.5, based on all available 
CMIP6 models.  The corresponding likely  ranges are 0.0–6.6% in 
SSP1-2.6, 1.5–8.3% in SSP2-4.5, and 0.5–9.6% in SSP3-7.0. {4.3.1, 
4.5.1, 4.6.1, 8.4.1}
Precipitation change will exhibit substantial regional

differences and seasonal contrast as GSAT increases over the 
21st century ( high confidence ). As warming increases, a larger land 
area will experience statistically significant increases or decreases 
in precipitation ( medium confidence ). Precipitation will very likely  
increase over high latitudes and the tropical oceans, and likely  increase 
in large parts of the monsoon region, but likely decrease over large 
parts of the subtropics in response to greenhouse gas-induced 
warming. Interannual variability of precipitation over many land 
regions will increase with global warming ( medium confidence ). 
{4.5.1, 4.6.1, 8.4.1}
Near-term projected changes in precipitation are uncertain, 
mainly because of natural internal variability, model 
uncertainty, and uncertainty in natural and anthropogenic 
aerosol forcing ( medium confidence ).  In the near term, no 
discernible differences in precipitation changes are projected

between different SSPs ( high confidence ). The anthropogenic aerosol 
forcing decreases in most scenarios, contributing to increases in 
GSAT ( medium confidence ) and global mean land precipitation 
(low confidence ). {4.3.1, 4.4.1, 4.4.4, 8.5}In response to greenhouse gas-induced warming, it is likely  that 
global land monsoon precipitation will increase, particularly 
in the Northern Hemisphere, although Northern Hemisphere 
monsoon circulation will likely  weaken.  In the long term 
(2081–2100), monsoon rainfall change will feature a  north–south 
asymmetry characterized by a  greater increase in the Northern 
Hemisphere than in the Southern Hemisphere and an east–west 
asymmetry characterized by an increase in Asian-African monsoon 
regions and a  decrease in the North American monsoon  region 
(medium confidence ). Near-term changes in global monsoon 
precipitation and circulation are uncertain due to model uncertainty

and internal variability such as Atlantic Multi-decadal Variability 
and Pacific Decadal Variability ( medium  confidence ). {4.4.1, 4.5.1, 
8.4.1, 10.6.3}
It is likely  that at least one large volcanic eruption will occur 
during the 21st century. Such an eruption would reduce GSAT 
for several years, decrease global mean land precipitation, alter 
monsoon circulation, modify extreme precipitation, and change 
the profile of many regional climatic impact-drivers. A  low-
likelihood, high-impact outcome would be several large eruptions 
that would greatly alter the 21st century climate trajectory compared 
to SSP-based Earth system model projections. {Cross-Chapter Box 4.1}
Large-scale Circulation and Modes of Variability
In the near term, the forced change in Southern Annular 
Mode in austral summer is likely  to be weaker than observed 
during the late 20th century under all five SSPs assessed.  This 
is because of the opposing influence in the near- to mid-term from

stratospheric ozone recovery and increases in other greenhouse gases 
on the Southern Hemisphere summertime mid-latitude circulation 
(high confidence ). In the near term, forced changes in the Southern 
Annular Mode in austral summer are therefore likely  to be smaller 
than changes due to natural internal variability. {4.3.3, 4.4.3}
In the long term, the Southern Hemisphere mid-latitude jet 
is likely  to shift  poleward and strengthen under  SSP5-8.5 
relative to 1995–2014.  This is likely  to be accompanied by an 
increase in the Southern Annular Mode index in all seasons relative 
to 1995–2014. For SSP1-2.6, CMIP6 models project no robust change 
in the Southern Annular Mode index in the long term. It is  likely  that 
wind speeds associated with extratropical cyclones will strengthen 
in the Southern Hemisphere storm track for SSP5-8.5. {4.5.1, 4.5.3}
The CMIP6 multi-model ensemble projects a  long-term 
increase in the boreal wintertime Northern Annular Mode

index under the high-emissions scenarios of SSP3-7.0 and 
SSP5-8.5, but regional changes may deviate from a simple shift 
in the mid-latitude circulation.  Substantial uncertainty and thus 
low confidence  remain in projecting regional changes in Northern 
Hemisphere jet streams and storm tracks, especially for the North 
Atlantic basin in winter; this is due to large natural internal variability, 
the competing effects of projected upper- and lower-tropospheric 
temperature gradient changes, and new evidence of weaknesses in 
simulating past variations in North Atlantic atmospheric circulation

557
on seasonal-to-decadal time scales. One exception is the expected 
decrease in frequency of atmospheric blocking events over Greenland 
and the North Pacific in boreal winter in SSP3-7.0 and SSP5-8.5 
scenarios ( medium confidence ). {4.5.1}
Near-term predictions and projections of the sub-polar branch 
of the Atlantic Multi-decadal Variability (AMV) on the decadal 
time scale have improved in CMP6 models compared to CMIP5 
(high confidence ). This is likely  to be related to a more accurate 
response to natural forcing in CMIP6 models. Initialization contributes 
to the reduction of uncertainty and to predicting subpolar sea surface 
temperature. AMV influences on the nearby regions can be predicted 
over lead times of 5–8 years ( medium  confidence ). {4.4.3}
It is virtually certain  that the El Niño–Southern Oscillation 
(ENSO) will remain the dominant mode of interannual

variability in a warmer world.  There is no model consensus for 
a systematic change in intensity of ENSO sea surface temperature 
variability over the 21st century in any of the SSP scenarios assessed 
(medium confidence ). However, it is very likely  that ENSO rainfall 
variability, used for defining extreme El Niños and La Niñas, will 
increase significantly, regardless of amplitude changes in ENSO 
SST variability, by the second half of the 21st century in scenarios 
SSP2-4.5, SSP3-7.0, and SSP5-8.5. {4.3.3, 4.5.3, 8.4.2}
Cryosphere and Ocean
Under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, it is 
likely  that the Arctic Ocean in September, the month of annual 
minimum sea ice area, will become practically ice-free (sea 
ice area less than 1 million km2) averaged over 2081–2100 
and all available simulations.  Arctic sea ice area in March, the 
month of annual maximum sea ice area, also decreases in the future

under each of the considered scenarios, but to a much lesser degree 
(in percentage terms) than in September ( high confidence ). {4.3.2}
Under the five scenarios assessed, it is virtually certain  
that global mean sea level (GMSL) will continue to rise 
through the 21st century. For the period 2081–2100 relative to 
1995–2014, GMSL is likely  to rise by 0.46–0.74 m under SSP3-7.0 
and by 0.30–0.54 m under SSP1-2.6 ( medium confidence ). For the 
assessment of change in GMSL, the contribution from land-ice melt 
has been added offline to the CMIP6-simulated contributions from 
thermal expansion. {4.3.2. 9.6}
It is very likely  that the cumulative uptake of carbon by 
the ocean and by land will increase through to the end of the 
21st century. Carbon uptake by land shows greater increases but 
with greater uncertainties than for ocean carbon uptake. The fraction 
of emissions absorbed by land and ocean sinks will be smaller

under high emissions scenarios than under low emissions scenarios 
(high confidence ). Ocean surface pH will decrease steadily through 
the 21st  century, except for SSP1-1.9 and SSP1-2.6 where values 
decrease until around 2070 and then increase slightly to 2100 
(high confidence ). {4.3.2, 5.4}Climate Response to Emissions Reduction, Carbon Dioxide 
Removal and Solar Radiation Modification
If strong mitigation is applied from 2020 onward as reflected 
in SSP1-1.9, its effect on 20-year trends in GSAT would likely  
emerge during the near term (2021–2040), measured against 
an assumed non-mitigation scenario such as SSP3-7.0 and 
SSP5-8.5. However, the response of many other climate 
quantities to mitigation would be largely masked by internal 
variability during the near term, especially on the regional scale 
(high confidence ). The mitigation benefits for these quantities would 
emerge only later during the 21st century ( high confidence ). During

the near term, a small fraction of the surface can show cooling under 
all scenarios assessed here, so near-term cooling at any given location 
is fully consistent with GSAT increase ( high confidence ). Events 
of reduced and increased GSAT trends at decadal time scales will 
continue to occur in the 21st century but will not affect the centennial 
warming ( very high confidence ). {4.6.3, Cross-Chapter Box 3.1}
Because of the near-linear relationship between cumulative 
carbon emissions and GSAT change, the cooling or avoided 
warming from carbon dioxide removal (CDR) is proportional 
to the cumulative amount of CO 2 removed by CDR 
(high  confidence ). The climate system response to net negative 
CO 2 emissions is expected to be delayed by years to centuries. 
Net negative CO 2 emissions due to CDR will not reverse some 
climate change, such as sea level rise, at least for several centuries 
(high confidenc e). The climate effect of a sudden and sustained CDR

termination would depend on the amount of CDR-induced cooling 
prior to termination and the rate of background CO 2 emissions at the 
time of termination ( high confidence ). {4.6.3, 5.5, 5.6}
Solar radiation modification (SRM) could offset some of the 
effects of anthropogenic warming on global and regional climate, 
but there would be substantial residual and overcompensating 
climate change at the regional scale and seasonal time 
scale ( high  confidence ), and there is low confidence  in our 
understanding of the climate response to SRM, specifically 
at the regional scale.  Since AR5, understanding of the global and 
regional climate response to SRM has improved, due to modelling work 
with more sophisticated treatment of aerosol-based SRM options and 
stratospheric processes. Improved modelling suggests that multiple 
climate goals could be met simultaneously. A  sudden and sustained 
termination of SRM in a  high-emissions scenario such as SSP5-8.5

would cause a  rapid climate change ( high  confidence ). However, 
a gradual phase-out of SRM combined with emissions reductions and 
CDR would more  likely than not avoid larger rates of warming . {4.6.3}
Climate Change Commitment and Change Beyond 2100
Earth system modelling experiments since AR5 confirm that the 
zero CO 2 emissions commitment (the additional rise in GSAT 
after all CO 2 emissions cease) is small ( likely  less than 0.3°C in 
magnitude) on decadal time scales, but that it may be positive 
or negative. There is low confidence in the sign of the zero CO 2 
emissions commitment. Consistent with SR1.5, the central estimate is

558
taken as zero for assessments of remaining carbon budgets for global 
warming levels of 1.5°C or 2°C. {4.7.2, 5.5.2}.
Overshooting specific global warming levels such as 2°C 
has effects on the climate system that persist beyond 2100 
(medium confidence ). Under one scenario including a  peak and 
decline in atmospheric CO 2 concentration (SSP5-3.4-OS), some 
climate metrics such as GSAT begin to decline but do not fully reverse 
by 2100 to levels prior to the CO 2 peak ( medium confidence ). GMSL 
continues to rise in all models up to 2100 despite a reduction in CO 2 
to 2040 levels. {4.6.3, 4.7.1, 4.7.2}
Using extended scenarios beyond 2100, projections show likely  
warming by 2300, relative to 1850-1900, of 1.0°C-2.2°C for 
SSP1-2.6 and 6.6°C-14.1°C for SSP5-8.5.  By 2300, warming under 
the SSP5-3.4-OS overshoot scenario decreases from a peak around

year 2060 to a level very similar to SSP1-2.6. Precipitation over land 
continues to increase strongly under SSP5-8.5. GSAT projected for the 
end of the 23rd century under SSP2-4.5 ( likely  2.3°C–4.6°C higher 
than over the period 1850–1900) has not been experienced since the 
mid-Pliocene, about 3 million years ago. GSAT projected for the end 
of the 23rd century under SSP5-8.5 ( likely  6.6°C–14.1°C higher than 
over the period 1850–1900) overlaps with the range estimated for 
the Miocene Climatic Optimum (5°C–10°C higher) and Early Eocene 
Climatic Optimum (10°C–18°C higher), about 15 and 50 million 
years ago, respectively ( medium confidence ). {2.3.1.1, 4.7.1}

559
4.1 Scope and Overview of This Chapter
This chapter assesses simulations of future climate change, covering 
both near-term and long-term global changes. The chapter assesses 
simulations of physical indicators of global climate change, such as 
global surface air temperature (GSAT), global land precipitation, 
Arctic sea ice area (SIA), and global mean sea level (GMSL). 
Furthermore, the chapter covers indices and patterns of properties 
and circulation not only for mean fields but also for modes of 
variability that have global significance. The choice of quantities to 
be assessed is summarized in Cross-Chapter Box 2.2 and comprises 
a subset of the quantities covered in Chapters 2 and 3. This chapter 
provides consistent coverage from near-term to long-term global 
changes and provides the global reference for the later chapters 
covering important processes and regional change.

Essential input to the simulations assessed here is provided by future 
scenarios of concentrations or anthropogenic emissions of radiatively 
active substances; the scenarios represent possible sets of decisions 
by humanity, without any assessment that one set of decisions is 
more probable to occur than any other set (Section  1.6). As in 
previous assessment reports, these scenarios are used for projections 
of future climate using global atmosphere–ocean general circulation 
models (AOGCMs) and Earth system models (ESMs; Section 1.5.3); 
the latter include representation of various biogeochemical cycles 
such as the carbon cycle, the sulphur cycle, or ozone (e.g.,  Flato, 
2011; Flato et al., 2013). This chapter thus provides a comprehensive 
assessment of the future global climate response to different future 
anthropogenic perturbations to the climate system.
Every projection assessment is conditioned on a  particular forcing

scenario. If sufficient evidence is available, a  detailed probabilistic 
assessment of a physical climate outcome can be performed for each 
scenario separately. By contrast, there is no agreed-upon approach 
to assigning probabilities to forcing scenarios, to the point that it has 
been debated whether such an approach can even exist (e.g., Grübler 
and Nakicenovic, 2001; Schneider, 2001, 2002). Although there were 
some recent attempts to ascribe subjective probabilities to scenarios 
(e.g.,  Ho et  al., 2019; Hausfather and Peters, 2020), and although 
‘feasibility’ along different dimensions is an important concept in 
scenario research (see AR6 WGIII Chapter 3), the scenarios used for 
the model-based projections assessed in this chapter do not come 
with statements about their likelihood of actually unfolding in the 
future. Therefore, it is usually not possible to combine responses to 
individual scenarios into an overall probabilistic statement about

expected future climate. Exceptions to this limit in the assessment 
are possible only under special circumstances, such as for some 
statements about near-term climate changes that are largely 
independent of the scenario chosen (e.g., Section 4.4.1). Beyond this, 
no combination of responses to different scenarios can be assessed 
in this chapter but may be possible in future assessments.
A central element of this chapter is a  comprehensive assessment 
of the sources of uncertainty of future projections (Section  1.4.3). 
Uncertainty can be broken down into scenario uncertainty, model 
uncertainty involving model biases, uncertainty in simulated effective 
radiative forcing and model response, and the uncertainty arising from internal variability (Cox and Stephenson, 2007; Hawkins and 
Sutton, 2009). An additional source of projection uncertainty arises 
from possible future volcanic eruptions and future solar variability.

Assessment of uncertainty relies on multi-model ensembles such as the 
Coupled Model Intercomparison Project Phase 6 (CMIP6, Eyring et al., 
2016), single-model initial-condition large ensembles (e.g.,  Kay 
et al., 2015; Deser et al., 2020), and ensembles initialized from the 
observed climate state (decadal predictions, e.g., Smith et al., 2013a; 
Meehl et al., 2014; Boer et al., 2016; Marotzke et al., 2016). Ensemble 
evaluation methods include assessment of model performance and 
independence (e.g., Knutti et al., 2017; Boé, 2018; Abramowitz et al., 
2019); emergent and other observational constraints (e.g., Allen and 
Ingram, 2002; Hall and Qu, 2006; Cox et al., 2018); and the uncertainty 
assessment of equilibrium climate sensitivity and transient climate 
response in Chapter  7. Ensemble evaluation is assessed in Box  4.1 
through the inclusion of lines of evidence in addition to the projection 
ensembles, including implications for potential model weighting.

The uncertainty assessment in this chapter builds on one particularly 
noteworthy advance since the IPCC Fifth Assessment Report (AR5). 
Internal variability, which constitutes irreducible uncertainty over 
much of the time horizon considered here (Hawkins et  al., 2016; 
Marotzke, 2019), can be better estimated in models even under 
a  changing climate through the use of large initial-condition 
ensembles (Kay et  al., 2015). For many climate quantities and 
compared to the forced climate change signal, internal variability is 
dominant in any individual realization – including the one that will 
unfold in reality – in the near term (Kirtman et al., 2013; Marotzke 
and Forster, 2015), is substantial in the mid-term, and is still 
recognizable in the long term in many quantities (Deser et al., 2012a; 
Marotzke and Forster, 2015). This chapter will use the strengthened 
information on internal variability throughout.
The expanded treatment of uncertainty allows this chapter a more

comprehensive assessment of the benefits from mitigation than in 
previous IPCC reports, as well as the climate response to carbon 
dioxide removal (CDR) and solar radiation modification (SRM), and 
how to detect them against the backdrop of internal variability. 
Important advances have been made in the detection and attribution 
of mitigation, CDR, and SRM (Bürger and Cubasch, 2015; Lo et al., 
2016; Ciavarella et  al., 2017); exploring the ‘time of emergence’ 
(ToE; see Annex VII: Glossary) of responses to assumed emissions 
reductions (Tebaldi and Friedlingstein, 2013; Samset et  al., 2020) 
and the attribution of decadal events to forcing changes that 
reflect emissions reductions (Marotzke, 2019; Spring et  al., 2020; 
McKenna et al., 2021).
The question of the potential crossing of thresholds relative to global 
temperature goals (Geden and Loeschel, 2017) is intimately related 
to the benefits of mitigation; a prerequisite is an assessment of how

robustly magnitudes of warming can be defined (Millar et al., 2017). 
This chapter provides an update to the IPCC Special Report on Global 
Warming of 1.5°C (SR1.5, IPCC, 2018a) and constitutes a reference 
point for later chapters and AR6 WGIII on the effects of mitigation, 
including a robust uncertainty assessment.

560
The chapter is organized as follows (Figure  4.1). After Section  4.2 
on the methodologies used in the assessment, Section 4.3 assesses 
projected changes in key global climate indicators throughout the 
21st  century, relative to the period 1995–2014, which comprises 
the last 20 years of the historical simulations of CMIP6 (Eyring et al., 
2016) and hence the most recent past simulated with the observed 
atmospheric composition. The global climate indicators assessed 
include GSAT, global land precipitation, Arctic sea ice area (SIA), 
global mean sea level (GMSL), the Atlantic Meridional Overturning 
Circulation (AMOC), global mean ocean surface pH, carbon 
uptake by land and ocean, the global monsoon, the Northern and 
Southern Annular Modes (NAM and SAM), and the El Niño–Southern 
Oscillation (ENSO). Differently from the assessment for changes

in other quantities only based on the range of CMIP6 projections, 
additional lines of evidence enter the assessment for GSAT and GMSL 
change. For most results and figures based on CMIP6, one realization 
from each model (the first of the uploaded set) is used. Section 4.3 
finally synthesizes the assessment of GSAT change using multiple 
lines of evidence in addition to the CMIP6 projection simulations.
Section  4.4 covers near-term climate change, defined here as the 
period 2021–2040 and taken relative to the period 1995–2014. 
Section  4.4 focuses on global and large-scale climate indicators, 
including precipitation and circulation indices and selected modes 
of variability (see Cross-Chapter Box  2.2 and Annex IV: Modes  of 
Variability), as well as on the spatial distribution of warming. 
The  potential roles of short-lived climate forcers (SLCFs) and

volcanic eruptions on near-term climate change are also discussed. Section 4.4 synthesizes information from initialized predictions and 
non-initialized projections for the near-term change.
Section  4.5 then covers mid-term and long-term climate change, 
defined here as the periods 2041–2060 and 2081–2100, respectively, 
again relative to the period 1995–2014. The mid-term period is thus 
chosen as the twenty-year period following the short-term period 
and straddling the mid-century point, year 2050; it is during the 
mid-term that differences between scenarios are expected to emerge 
against internal variability. The long-term period is defined, as in 
AR5, as the 20-year period at the end of the century. Section 4.5 
assesses the same set of indicators as Section  4.4, as well as 
changes in internal variability and in large-scale patterns, both of 
which are expected to emerge in the mid- to long-term. The chapter

sub-division according to time slices (near term, mid-term, and long 
term) is thus to a large extent motivated by the different roles that 
internal variability plays in each period, compared to the expected 
forced climate-change signal.
Section  4.6 assesses the climate implications of climate policies, as 
simulated with climate models. First, Section 4.6 assesses patterns of 
climate change expected for various levels of GSAT rise including 1.5°C, 
2°C, 3°C, and 4°C, compared to the approximation to the pre-industrial 
period 1850–1900 to facilitate immediate connection to SR1.5 and 
the temperature goals specified in the Paris Agreement (UNFCCC, 
2016). Section 4.6 continues with climate goals, overshoot, and path-
dependence, as well as the climate response to mitigation, CDR, and 
SRM. Section 4.6 also covers the consistency between RCPs and SSPs.
Chapter 4: Future global climate Chapter 4: Quick guide
Cross-chapter boxesSection 4.1
Overview
Section 4.2
Methodology

Global surface temperature
4.3.1 | 4.3.4 | 4.4.1 | 4.5.1 | 4.7.1Arctic sea ice and global sea level
4.3.2 | 4.4.2 
Climate models and forcing scenarios
4.2.1 | 4.2.2 | Box 4.1
Global warming levels
4.3.4 | 4.6.1Global monsoons
4.4.1.4 | 4.5.1.5
Modes of variability
4.3.3 | 4.4.3 | 4.5.3Mitigation, CO2  removal and solar radiation modification
4.6.3 Commitment, irreversibility and abrupt change
4.7.1 | 4.7.2 
Section 4.3
Selected key indicators over the 
21st century
Section 4.6
Policy implicationsSection 4.7
Beyond 2100Section 4.4
Near-term 
(2021–2040)Section 4.5
Mid-term (2041–2060) and long-term 
(2081–2100)
Section 4.8
High warming 
storylines
Box 4.1
Ensemble evaluation and weightingCC Box 4.1
The climate effects of volcanic eruptionsChapter 4 assesses simulations of future global climate change over the near, 
mid- and long term and out to year 2300.Key topics and corresponding sub-sections
BoxesFAQs
Figure  4.1 | Visual guide to Chapter 4.

561
Section  4.7 assesses very long-term changes in selected global 
climate indicators, from 2100 to 2300. Section  4.7 continues with 
climate-change commitment and the potential for irreversibility and 
abrupt climate change. The chapter concludes with Section 4.8 on 
the potential for low-likelihood, high-impact storylines, followed by 
answers to three frequently asked questions (FAQs).
4.2 Methodology
4.2.1 Models, Model Intercomparison Projects, 
and Ensemble Methodologies
Similar to the approach used in AR5 (Flato et al., 2013), the primary 
lines of evidence of this chapter are comprehensive climate models 
(atmosphere–ocean general circulation models, AOGCMs) and Earth 
system models (ESMs); ESMs differ from AOGCMs by including 
representations of various biogeochemical cycles. We also build 
on results from ESMs of intermediate complexity (EMICs; Claussen

et  al., 2002; Eby et  al., 2013) and other types of models where 
appropriate. This chapter focuses on a particular set of coordinated 
multi-model experiments known as model intercomparison projects 
(MIPs). These frameworks recommend and document standards for 
experimental design for running AOGCMs and ESMs to minimize 
the chance of differences in results being misinterpreted. CMIP is an 
activity of the World Climate Research Programme (WCRP), and the 
latest phase is CMIP6 (Eyring et al., 2016). To establish robustness of 
results, it is vital to assess the performance of these models in terms 
of mean state, variability, and the response to external forcings. 
That evaluation has been undertaken using the CMIP6 ‘Diagnostic, 
Evaluation and Characterization of Klima’ (DECK) and historical 
simulations in Chapter 3 of this Report, which concludes that there 
is high confidence  that the CMIP6 multi-model mean captures most

aspects of observed climate change well (Section 3.8.3.1).
This chapter draws mainly on future projections referenced both against 
the period 1850–1900 and the recent past, 1995–2014, performed primarily under ScenarioMIP (O’Neill et  al., 2016). This  allows us to 
assess both dimensions of integration across scenarios (Section 4.3) 
and global warming levels (Section  4.6) as discussed in Chapter  1 
(Section 1.6). Other MIPs also target future scenarios with a focus on 
specific processes or feedbacks and are summarized in Table 4.1.
Multi-model ensembles provide the central focus of projection 
assessment. While single-model experiments have great value for 
exploring new results and theories, multi-model ensembles additionally 
underpin the assessment of the robustness, reproducibility, and 
uncertainty attributable to model internal structure and processes 
variability (Section  4.2.5; Hawkins and Sutton, 2009). Techniques

underlying the combination of evaluation and weighting that are 
applied in this chapter are synthesized in Box 4.1.
Climate model simulations can be performed in either ‘concentration-
driven’ or ‘emissions-driven’ configurations reflecting whether the 
CO 2 concentration is prescribed to follow a pre-defined pathway or 
is simulated by the Earth system models in response to prescribed 
emissions of CO 2 (Box 6.4, Ciais et al., 2013). The majority of CMIP6 
experiments are conducted in concentration-driven configurations in 
order to enable models without a  fully interactive carbon cycle to 
perform them, and throughout most of this chapter we present results 
from those simulations unless otherwise stated. Concentrations of 
other greenhouse gases are always prescribed. However, the SSP5-8.5 
scenario has also been performed in emissions-driven configuration 
(‘esm-ssp585’) by 10 ESMs, and in Section  4.3.1.1 we assess the 
impact on simulated climate over the 21st century.

Internal variability complicates the identification of forced climate 
signals, especially when considering regional climate signals over 
short time scales (up to a few decades), such as local trends over the 
satellite era (Hawkins and Sutton, 2009; Deser et al., 2012a; Xie et al., 
2015; Lovenduski et  al., 2016; Suárez-Gutiérrez et  al., 2017). Large 
initial-condition ensembles, where the same model is run repeatedly 
under identical forcing but with initial conditions varied through small 
perturbations or by sampling different times of a pre-industrial control 
Table 4.1 | Model Intercomparison Projects (MIPs) utilized in Chapter 4.
MIP/Experiment Usage Chapter/Section Reference
DECK, 1%, 4×CO2 Diagnosing climate sensitivityAssessed in Chapter 7
ECS and TCR used in GSAT assessmentEyring et al. (2016)
CMIP6 Historical Evaluation, baselineAssessed in Chapter 3,
used in Chapter 4 to cover reference periodEyring et al. (2016)

ScenarioMIP Future projections Used throughout Chapter 4 O’Neill et al. (2016)
AerChemMIP Aerosols and trace gases 4.4.4 Collins et al. (2017)
C4MIP CO 2 emissions-driven simulations 4.3.1 C.D. Jones et al. (2016a)
CDRMIP Carbon dioxide removal 4.6.3 Keller et al. (2018)
DCPP Near-term climate change 4.2.3, Box 4.1, 4.4 Boer et al. (2016)
GeoMIP Solar radiation modification 4.6.3 Kravitz et al. (2011)
PDRMIP Forcing dependence of precipitation 4.5.1 Myhre et al. (2017)
SIMIP Sea ice assessment 4.3 Notz et al. (2016)
ZECMIP Zero emissions commitment 4.7.1 Jones et al. (2019)
CMIP5 RCP scenario assessment 4.6.2, 4.7.1 Taylor et al. (2012)

562
run, have substantially grown in their use since AR5 (Deser  et  al., 
2012a; Kay et al., 2015; Rodgers et al., 2015; Hedemann et al., 2017; 
Stolpe et al., 2018; Maher et al., 2019). Such large ensembles have 
shown potential to quantify  uncertainty due to internal variability 
(Hawkins et al., 2016; McCusker et al., 2016; Sigmond and Fyfe, 2016; 
Lehner et al., 2017; McKinnon et al., 2017; Marotzke, 2019) and thereby 
extract the forced signal from the internal variability, which can be 
calibrated against observational data to improve the reliability of 
probabilistic climate projections over the near and mid-term (O’Reilly 
et al., 2020). Moreover, they allow the investigation of forced changes 
in internal variability (e.g., Maher et al., 2018). A key assumption is 
that a given model skilfully represents internal variability; structural 
uncertainty is not accounted for.

A complementary approach that represents structural uncertainty in 
a given model is stochastic physics (Berner et al., 2017). The approach has 
proven useful in representing structural uncertainty on seasonal climate 
time scales (Weisheimer et  al., 2014; Batté and Doblas-Reyes, 2015; 
MacLachlan et al., 2015). Stochastic physics can markedly improve the 
internal variability in a given model (Dawson and Palmer, 2015; Wang 
et al., 2016; Christensen et al., 2017; Davini et al., 2017; Watson et al., 
2017; Strømmen et al., 2018; Yang et al., 2019). Stochastic physics can 
also correct long-standing mean-state biases (Sanchez-Gomez et  al., 
2016) and can influence the predicted climate sensitivity (Christensen 
and Berner, 2019; Strommen et al., 2019; Meccia et al., 2020).
Perturbed-physics ensembles (Murphy et al., 2004) are also used to 
systematically account for parameter uncertainty in a given model. 
Uncertain model parameters are identified and ranges in their

values selected that conform to emergent observational constraints 
(see Section 1.5.4.2). These parameters are then changed between 
ensemble members to sample the effect of parameter uncertainty on 
climate (Piani et al., 2005; Sexton et al., 2012; Johnson et al., 2018; 
Regayre et  al., 2018). It is possible to weight ensemble members 
according to some performance metric or emergent constraint 
(e.g., Fasullo et al., 2015; Section 1.5.4.7) to improve the ensemble 
distribution (Box 4.1).
4.2.2 Scenarios
The AR5 drew heavily on four main scenarios, known as Representative 
Concentration Pathways (RCPs: Meinshausen et al., 2011; van Vuuren 
et  al., 2011), and simulation results from CMIP5 (Section  4.2.1; 
Taylor et al., 2012). The RCPs were labelled by the approximate radiative 
forcing reached at the year 2100, going from 2.6, 4.5, 6.0 to 8.5 W m–2.
This chapter draws on model simulations from CMIP6 (Eyring 
et  al.,  2016) using a  new range of scenarios based on Shared

Socio-economic Pathways (SSPs; O’Neill et al., 2016). The set of SSPs is 
described in detail in Chapter 1 (Section 1.6) and recognizes that global 
radiative forcing levels can be achieved by different pathways of CO 2, 
non-CO 2 greenhouse gases (GHGs), aerosols (Amann et al., 2013; Rao 
et al., 2017) and land use; the set of SSPs therefore establishes a matrix 
of global forcing levels and socio-economic storylines. ScenarioMIP 
(O’Neill et  al., 2016) identifies four priority (tier-1) scenarios that 
participating modelling groups are asked to perform, SSP1-2.6 for sustainable pathways, SSP2-4.5 for middle-of-the-road, SSP3-7.0 
for regional rivalry, and SSP5-8.5 for fossil fuel-rich development. This 
chapter focuses its assessment on these, plus the SSP1-1.9 scenario, 
which is directly relevant to the assessment of the 1.5°C Paris 
Agreement goal. Further, this chapter discusses these scenarios and 
their extensions past 2100 in the context of the very long-term climate

change in Section  4.7.1. Projections of short-lived climate forcers 
(SLCFs) are assessed in more detail in Chapter 6 (Section 6.7).
In presenting results and evidence, this chapter tries to be as 
comprehensive as possible. In tables we show multi-model mean 
change and 5–95% range for all five SSPs, while in time series 
figures we show multi-model mean change for all five SSPs but for 
clarity 5–95% range only for SSP1-2.6 and SSP3-7.0. Where maps are 
presented, due to space restrictions we focus on showing multi-model 
mean change for SSP1-2.6 and SSP3-7.0. SSP1-2.6 is preferred over 
SSP1-1.9 because the latter has far fewer simulations available. The 
high-end scenarios RCP8.5 or SSP5-8.5 have recently been argued 
to be implausible to unfold (e.g.,  Hausfather and Peters, 2020; see 
Chapter 3 of the AR6 WGIII). However, where relevant we show results 
for SSP5-8.5, for example to enable backwards compatibility with AR5,

for comparison between emissions-driven and concentration-driven 
simulations, and because there is greater data availability of daily 
output for SSP5-8.5. When presenting low-likelihood, high-warming 
storylines we also show results from the high-end SSP5-8.5 scenario.
ScenarioMIP simulations include advances in techniques to better 
harmonize with historical forcings relative to CMIP5. For example, 
projected changes in the solar cycle include long-term modulation 
rather than a repeating solar cycle (Matthes et al., 2017). Background 
natural aerosols are ramped down to an average historical level used 
in the control simulation by 2025 and background volcanic forcing 
is ramped up from the value at the end of the historical simulation 
period (2015) over 10 years to the same constant value prescribed 
for the pre-industrial control (piControl) simulations in the DECK, and 
then kept fixed – both changes are intended to avoid inconsistent

model treatment of unknowable natural forcing to affect the 
near-term projected warming.
Complete backward comparability between CMIP5 and CMIP6 scenarios 
cannot be established for detailed regional assessments, because the 
SSP scenarios include regional forcings – especially from land use and 
aerosols – that are different from the CMIP5 RCPs. Even at a global level, 
a quantitative comparison is challenging between corresponding SSP and 
RCP radiative forcing levels due to differing contributions to the forcing 
(Meinshausen et al., 2020) and evidence of differing model responses 
(Section 4.6.2.2; Wyser et al., 2020). The RCP scenarios assessed in AR5 
all showed similar, rapid reductions in SLCFs and emissions of SLCF 
precursor species over the 21st century; the CMIP5 projections hence 
did not sample a wide range of possible trajectories for future SLCFs 
(Chuwah et al., 2013). The SSP scenarios assessed in the AR6 offer more

scope to explore SLCF pathways as they sample a broader range of air 
quality policy options (Gidden et al., 2019) and relationships of CO 2 to 
non-CO 2 greenhouse gases (Meinshausen et al., 2020). Section 4.6.2.2 
assesses RCP and SSP differences. Other MIPs (see Section 4.2.1) have 
been designed to explicitly explore some of the implications of the 
different socio-economic storylines for a given radiative forcing level.

563
4.2.3 Sources of Near-term Information
This subsection describes the three main sources of near-term 
information used in Chapter 4. These are (i) the projections from the 
CMIP6 multi-model ensemble introduced in Section  4.2.1 (Eyring 
et  al., 2016; O’Neill et  al., 2016); (ii) observationally constrained 
projections (Gillett et  al., 2013; Stott et  al., 2013); and (iii) the 
initialized predictions contributed to CMIP6 from the Decadal 
Climate Prediction Project (DCPP; Boer et al., 2016). The projections 
under (i) and the observational constraints under (ii) are used for 
all time horizons considered in this chapter, whereas the initialized 
predictions under (iii) are relevant only in the near term.
Observationally constrained projections (Gillett et  al., 2013, 2021; 
Shiogama et al., 2016; Ribes et al., 2021) use detection and attribution

methods to attempt to reach consistency between observations and 
models and thus provide improved projections of near-term change. 
Notable advances have been made since AR5, for example the ability 
to observationally constrain estimates of Arctic sea ice loss for global 
warming of 1.5°C, 2.0°C, and 3.0°C above pre-industrial levels 
(Screen and Williamson, 2017; Jahn, 2018; Screen, 2018; Sigmond 
et  al., 2018). There is high confidence that these approaches can 
reduce the uncertainties involved in such estimates.
The AR5 was the first IPCC report to assess decadal climate predictions 
initialized from the observed climate state (Kirtman et al., 2013), and 
assessed with high confidence that these predictions exhibit positive 
skill for near-term average surface temperature information, globally 
and over large regions, for up to ten years. Substantially more experience 
in producing initialized decadal predictions has been gained since AR5;

the remainder of this subsection assesses the advances made.
Because the ‘memory’ that potentially enables prediction of multi-year 
to decadal internal variability resides mainly in the ocean, some systems 
initialize the ocean state only (e.g., Müller et al., 2012; Yeager et al., 
2018), whereas others incorporate observed information in the initial 
atmospheric states (e.g., Pohlmann et al., 2013; Knight et al., 2014) or 
other non-oceanic drivers that provide further sources of predictability 
(Alessandri et al., 2014; Weiss et al., 2014; Bellucci et al., 2015a).
Ocean initialization techniques generally use one of two strategies. 
Under full-field initialization, estimates of observed climate states 
are represented directly on the model grid. A potential drawback is 
that predictions initialized using the full-field approach tend to drift 
toward the biased climate preferred by the model (Smith et al., 2013a;

Bellucci et al., 2015b; Sanchez-Gomez et al., 2016; Kröger et al., 2018; 
Nadiga et al., 2019). Such drifts can be as large as, or larger than, 
the climate anomaly being predicted and may therefore obscure 
predicted climate anomalies (Kröger et al., 2018) unless corrected for 
through post-processing. By contrast, anomaly initialization reduces 
drifts by adding observed anomalies (i.e., deviations from mean 
climate) to the mean model climate (Pohlmann et al., 2013; Smith 
et al., 2013a; Thoma et al., 2015b; Cassou et al., 2018), but has the 
disadvantage that the model state is then further from the real world 
from the start of the prediction. For both approaches, unrealistic 
features in the observation data used for initialization may induce 
unrealistic transient behavior (Pohlmann et  al., 2017; Teng et  al., 2017; Nadiga et al., 2019), and non-linearity can reduce forecast skill 
(Chikamoto et al., 2019). As yet, neither of the initialization strategies

has been clearly shown to be superior (Hazeleger et  al., 2013; 
Magnusson et al., 2013; Smith et al., 2013a; Marotzke et al., 2016), 
although such comparisons may be sensitive to the model, region, 
and details of the initialization and forecast assessment procedures 
considered (Polkova et al., 2014; Bellucci et al., 2015b).
There is also a  wide range of techniques employed to assimilate 
observed information into models in order to generate suitable 
initial conditions (Polkova et  al., 2019). These range in complexity 
from simple relaxation towards observed time series of sea surface 
temperature (SST) (Mignot et  al., 2016) or wind stress anomalies 
(Thoma et al., 2015a, b), to relaxation toward three-dimensional ocean 
and sometimes atmospheric state estimates from various sources 
(e.g.,  Pohlmann et  al., 2013; Knight et  al., 2014; Dunstone et  al., 
2016), or hybrid relaxation combining surface and tri-dimensional

restoring as function of ocean basins and depth (Sanchez-Gomez 
et al., 2016), to sophisticated data assimilation methods such as the 
ensemble Kalman filter (Nadiga et al., 2013; Counillon et al., 2014, 
2016; Msadek et al., 2014; Karspeck et al., 2015; Brune et al., 2018; 
Cassou et  al., 2018; Polkova et  al., 2019), the four-dimensional 
ensemble-variational hybrid data assimilation (He et al., 2017, 2020) 
and the initialization of sea ice (Guemas et  al., 2016; Kimmritz 
et  al., 2018). In addition, decadal predictions necessarily consist 
of ensembles of forecasts to quantify uncertainty, as discussed in 
Section 4.2.1. A common way to generate an ensemble is through 
sets of initial conditions containing small variations that lead to 
different subsequent climate trajectories. A  variety of methods 
and assumptions has been employed to generate and filter initial-
condition ensembles for decadal prediction (e.g., Marini et al., 2016;

Kadow et al., 2017). As yet, there is no clear consensus as to which 
initialization and ensemble generation techniques are most effective, 
and evaluations of their comparative performance within a  single 
modelling framework are needed (Cassou et al., 2018).
A consequence of model imperfections and resulting model systematic 
errors is that estimates of these errors must be removed from the 
prediction to isolate the predicted climate anomaly and the phase 
of the decadal modes of climate variability (Sections 4.4.3.5 and 
4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7). Because of the 
tendency for systematic drifts to occur following initialization, bias 
corrections generally depend on time since the start of the forecast, 
often referred to as lead time. In practice, the lead-time-dependent 
biases are calculated using ensemble retrospective predictions, 
also known as hindcasts, and recommended basic procedures for

such corrections are provided in previous studies (Goddard et  al., 
2013; Boer et al., 2016). The biases are also dynamically corrected 
during hindcasts and predictions by incorporating the multi-year 
monthly mean analysis increments from the initialization into 
the initial condition at each integration step (Wang et al., 2013b). 
Besides mean climate as a function of lead time, further aspects of 
decadal predictions may be biased, such as the modes of variability 
(see Section 3.7 and Annex IV) upon which drift patterns are projected 
(Sanchez-Gomez et al., 2016), and additional correction procedures 
have thus been proposed to remove biases in representing long-term 
trends (Kharin et al., 2012; Kruschke et al., 2016; Balaji et al., 2018;

564
Pasternack et  al., 2018), as well as more general dependences of 
drift on initial conditions (Fučkar et al., 2014; Pasternack et al., 2018; 
Nadiga et al., 2019).
Many skill measures exist that describe different aspects of the 
correspondence between predicted and observed conditions, and no 
single metric should be considered exclusively. Important aspects of 
forecast performance captured by different skill measures include: (i) the 
ability to predict the sign and phases of the main modes of decadal 
variability and their regional fingerprint through teleconnections; 
(ii) the typical magnitude of differences between predicted and observed 
values, forecast reliability and resolution (Corti et  al., 2012); and 
(iii) whether the forecast ensemble appropriately represents uncertainty 
in the predictions. A framework for skill assessment that encompasses

each of these aspects of forecast quality has been proposed (Goddard 
et al., 2013). A new, process-based method to assess forecast skill in 
decadal predictions is to analyse how well a  specific mechanism is 
represented at each lead time (Mohino et al., 2016).
One additional aspect of forecast quality assessment is that estimated 
skill can be degraded by errors in observational datasets used for 
verification, in addition to errors in the predictions (Massonnet 
et al., 2016; Ferro, 2017; Karspeck et al., 2017; Juricke et al., 2018). 
This suggests that skill may tend to be underestimated, particularly 
for climate variables whose observational uncertainties are relatively 
large, and that the predictions themselves may prove useful for 
assessing the quality of observational datasets (Massonnet, 2019).
Skill assessments have shown that initialized predictions can 
out-perform their uninitialized counterparts (Doblas-Reyes et  al.,

2013; Meehl et al., 2014; Bellucci et al., 2015a; D.M. Smith et al., 2018, 
2019; Yeager et al., 2018), although such comparisons are sensitive 
to the region and variable considered, multi-model predictions are 
generally more skilful than individual models (Doblas-Reyes et  al., 
2013; D.M. Smith et al., 2013b, 2019). Considerable skill, especially for 
temperature, can be attributed to external forcings such as changes 
in GHG, aerosol concentrations, and volcanic eruptions. On a global 
scale, this contribution to skill has been found to exceed that from 
the prediction of internal variability except in the early stages (about 
one year for global SST) of the forecast (Corti et al., 2015; Sospedra-
Alfonso and Boer, 2020; Bilbao et al., 2021), though idealized potential 
skill measures and observations-based studies suggest that improving 
the prediction of internal variability could extend this crossover to 
longer lead times (Boer et  al., 2013; Årthun et  al., 2017). In some

cases, part of the skill arises from the ability of initialized predictions 
to capture observed transitions of major modes of climate variability 
(Meehl et al., 2016) such as the Pacific Decadal Variability (PDV) and 
the Atlantic Multi-decadal Variability (AMV; see Sections 4.4.3.5 
and 4.4.3.6, and Annex IV, Sections AIV.2.6 and AIV.2.7).
Initialized predictions of near-surface temperature are particularly 
skilful over the North Atlantic, a region of high potential and realized 
predictability (Keenlyside et  al., 2008; Pohlmann et  al., 2009; Boer 
et al., 2013; Yeager and Robson, 2017). Much of this predictability is 
associated with the North Atlantic subpolar gyre (Wouters et al., 2013), 
where skill in predicting ocean conditions is typically high (Hazeleger 
et al., 2013; Brune and Baehr, 2020) and shifts in ocean temperature and salinity potentially affecting surface climate can be predicted 
up to several years in advance (Robson et  al., 2012; Hermanson

et al., 2014), although such assessments remain challenging due to 
incomplete knowledge of the state of the ocean during the hindcast 
evaluation periods (Menary and Hermanson, 2018). A  substantial 
improvement of the sub-polar gyre SST prediction is found in CMIP6 
models, which is attributed to a  more accurate response to the 
AMOC-related delayed response to volcanic eruptions (Section 4.4.3; 
Borchert et al., 2021). A significant improvement GSAT prediction skill 
is also found over some land regions including East Asia (Monerie 
et al., 2018), Eurasia (Wu et al., 2019), Europe (Müller et al., 2012; 
D.M. Smith et al., 2019) and the Middle East (D.M. Smith et al., 2019).
Skill for multi-year to decadal precipitation forecasts is generally much 
lower than for temperature, although one exception is Sahel rainfall 
(Sheen et al., 2017), due to its dependence on predictable variations 
in North Atlantic SST through teleconnections (Annex IV; Martin and

Thorncroft, 2014a). Predictive skill on decadal time scales is found for 
extratropical storm-tracks and storm density (Kruschke et al., 2016; 
Schuster et  al., 2019), atmospheric blocking (Schuster et  al., 2019; 
Athanasiadis et al., 2020), the Quasi-Biennial Oscillation (QBO; Scaife 
et al., 2014; Pohlmann et al., 2019) and over the tropical oceans (tropical 
trans-basin variability; Chikamoto et al., 2015). In addition, decadal 
predictions with large ensemble sizes are able to predict multi-annual 
temperature (Peters et al., 2011; Sienz et al., 2016; Borchert et al., 2019; 
Sospedra-Alfonso and Boer, 2020), precipitation (Yeager et al., 2018; 
D.M. Smith et  al., 2019), and atmospheric circulation (Smith et  al., 
2020) anomalies over certain land regions, although the ensemble-
mean magnitudes are much weaker than observed. This discrepancy 
may be symptomatic of an apparent deficiency in climate models that

causes some predictable signal, such as that associated to the North 
Atlantic Oscillation (NAO; Section AIV.2.1), to be much weaker than 
in nature (Eade et al., 2014; Scaife and Smith, 2018; Strommen and 
Palmer, 2019; Smith et  al., 2020), while others, such as that linked 
to the SAM (Section AIV.2.2), are more consistent with observations 
(Byrne et al., 2019).
Evidence is accumulating that additional properties of the Earth 
system relating to ocean variability may be skilfully predicted on 
multi-annual time scales. These include levels of Atlantic hurricane 
activity (Smith et al., 2010; Caron et al., 2017), winter sea ice in the 
Arctic (Onarheim et al., 2015; Dai et al., 2020), drought and wildfire 
(Chikamoto et al., 2017; Paxian et al., 2019; Solaraju-Murali et al., 
2019), and variations in the ocean carbon cycle including CO 2 uptake 
(H. Li et al., 2016, 2019; Lovenduski et al., 2019; Fransner et al., 2020) 
and chlorophyll (Park et al., 2019).

In summary, despite challenges (Cassou et al., 2018), there is high 
confidence  that initialized predictions contribute information to 
near-term climate change for some regions over multi-annual 
to  decadal time scales. Furthermore, there are indications that 
initialized predictions can constrain near-term projections (Befort 
et  al., 2020). The clearest improvements through initialization 
are seen in the North Atlantic and related phenomena such as 
hurricane frequency, Sahel and European rainfall. By contrast, there 
is medium or low confidence that uncertainty is reduced for other 
climate variables.

565
4.2.4 Pattern Scaling
Projected climate change is typically represented in this chapter 
for specific future periods. One important source of uncertainty in 
projections presented for fixed future epochs (time-slabs/time-slices) 
is the underlying scenario used; another is the structural uncertainty 
associated with model climate sensitivity. Presenting projections and 
associated measures of uncertainty for specific periods (see Sections 4.4 
and 4.5) remains the most widely applied methodology towards 
informing climate change impact studies. It is becoming increasingly 
important from the perspective of climate change and mitigation 
policy, however, to present projections also as a function of the change 
in global mean temperature (i.e., global warming levels, GWLs). They 
are expressed either in terms of changes of global mean surface

temperature (GMST) or GSAT (see  Section  1.6.2 and Cross-Chapter 
Box 2.3). For example, the IPCC SR1.5 (Hoegh-Guldberg et al., 2018) 
assessed the regional patterns of warming and precipitation change for 
GMST increase of 1.5°C and 2°C above 1850–1900 levels. Techniques 
used to represent the spatial variations in climate at a given GWL are 
referred to as pattern scaling.
In the ‘traditional’ methodology as applied in AR5 (Collins et al., 2013), 
patterns of climate change in space are calculated as the product of the 
change in GSAT at a given point in time and a spatial pattern of change 
that is constant over time and the scenario under consideration, and 
which may or may not depend on a particular climate model (Allen 
and Ingram, 2002; Mitchell, 2003; Lambert and Allen, 2009; Andrews 
and Forster, 2010; Bony et al., 2013; Lopez et al., 2014). This approach 
assumes that external forcing does not affect the internal variability

of the climate system, which may be regarded a stringent assumption 
when taking into account decadal and multi-decadal variability (Lopez 
et  al., 2014) and the potential non-linearity of the climate change 
signal. Moreover, pattern scaling is expected to have lower skill for 
variables with large spatial variability (Tebaldi and Arblaster, 2014). 
Pattern scaling also fails to capture changes in boundaries that move 
poleward such as sea ice extent and snow cover (Collins et al., 2013), 
and temporal frequency quantities such as frost days that decrease 
under warming but are bounded at zero. Spatial patterns are also 
expected to be different between transient and equilibrium simulations 
because of the long adjustment time scale of the deep ocean.
Further developments of the AR5 approach have since explored the 
role of aerosols in modifying regional climate responses at a specific 
degree of global warming and also the effect of different GCMs and

scenarios on the scaled spatial patterns (Frieler et al., 2012; Levy et al., 
2013). Furthermore, the modified forcing-response framework (Kamae 
and Watanabe, 2012, 2013; Sherwood et al., 2015), which decomposes 
the total climate change into fast adjustments and slow response, 
identifies the fast adjustment as forcing-dependent and the slow 
response as forcing-independent, scaling with the change in GSAT.
For precipitation change, there is near-zero fast adjustment for solar 
forcing but suppression during the fast-adjustment phase for  CO 2 
and black-carbon radiative forcing (Andrews et al., 2009; Bala et al., 
2010; Cao et al., 2015). By contrast, the slow response in precipitation 
change is independent of the forcing. This indicates that pattern 
scaling is not expected to work well for climate variables that have a  large fast-adjustment component. Even in such cases, pattern 
scaling still works for the slow response component, but a correction

for the forcing-dependent fast adjustment would be necessary to 
apply pattern scaling to the total climate change signal. In a multi-
model setting, it has been shown that temperature change patterns 
conform better to pattern scaling approximation than precipitation 
patterns (Tebaldi and Arblaster, 2014).
Herger et al. (2015) have explored the use of multiple predictors for 
the spatial pattern of change at a given degree of global warming, 
following the approach of Joshi et al. (2013) that explored the role 
of the land–sea warming ratio as a second predictor. They found that 
the land–sea warming contrast changes in a  non-linear way with 
GSAT, and that it approximates the role of the rate of global warming 
in determining regional patterns of climate change. The inclusion 
of the land–sea warming contrast as the second predictor provides 
the largest improvement over the traditional technique. However, as 
pointed out by Herger et  al. (2015), multiple-predictor approaches

still cannot detect non-linearities (or internal variability), such as the 
apparent dependence of spatial temperature variability in the mid- to 
high latitudes on GSAT (e.g., Fischer and Knutti, 2014; Screen, 2014).
An alternative to the traditional pattern scaling approach is the 
time-shift method described by Herger et al. (2015) which is applied in 
this chapter (also called the epoch approach; see Section 4.6.1). When 
applied to a transient scenario such as SSP5-8.5, a future time-slab is 
referenced to a particular increase in the GSAT (e.g., 1.5°C or 2°C of 
global warming above pre-industrial levels). The spatial patterns that 
result represent a  direct scaling of the spatial variations of climate 
change at the particular level of global warming. An important 
advantage of this approach is that it ensures physical consistency 
between the different variables for which changes are presented 
(Herger et al., 2015). The internal variability does not have to be scaled

and is consistent with the GSAT change. Furthermore, the time-shift 
method allows for a partial comparison of how the rate of increase 
in GSAT influences the regional spatial patterns of climate change. 
For example, spatial patterns of change for global warming of 2°C 
can be compared across the SSP2-4.5 and SSP5-8.5 scenarios. Direct 
comparisons can also be obtained between variations in the regional 
impacts of climate change for the case where global warming stabilizes 
at, for instance, 1.5°C or 2°C of global warming, as opposed to the case 
where the GSAT reaches and then exceeds the 1.5°C or 2°C thresholds 
(Tebaldi and Knutti, 2018). An important potential caveat is that 
forcing mechanisms such as aerosol radiative forcing are represented 
differently in different models, even for the same SSP . This may imply 
different regional aerosol direct and indirect effects, implying different 
regional climate change patterns. Hence, it is important to consider the

variations in the forcing mechanisms responsible for a specific increase 
in GSAT towards understanding the uncertainty range associated with 
the variations in regional climate change. A minor practical limitation 
of this approach is that stabilization scenarios at 1.5°C or 2°C of global 
warming, such as SSP1-2.6, do not allow for spatial patterns of change 
to be calculated from these scenarios at higher levels of global warming, 
while it is possible in scenarios such as SSP5-8.5 (Herger et al., 2015).
In this chapter, the spatial patterns of change as a function of GWLs 
(defined in terms of the increase in the GSAT relative to 1850–1900)

566
are thus constructed using the time-shift approach, thereby 
accounting for various non-linearities and internal variability that 
influence the projected climate change signal. This implies a reliance 
on large ensemble sizes to quantify the role of uncertainties in 
regional responses to different degrees of global warming. The 
assessment in Section  4.6.1 also explores how the rate of global 
warming (as represented by different SSPs), aerosol effects, and 
transient as opposed to stabilization scenarios influence the spatial 
variations in climate change at specific levels of global warming.
4.2.5 Quantifying Various Sources of Uncertainty
The AR5 assessed with very high confidence  that climate models 
reproduce the general features of the global-scale annual mean surface 
temperature increase over the historical period, including the more

rapid warming in the second half of the 20th century, and the cooling 
immediately following large volcanic eruptions. Furthermore, because 
climate and Earth system models are based on physical principles, they 
were assessed in AR5 to reproduce many important aspects of observed 
climate. Both aspects were argued to contribute to our confidence in the 
models’ suitability for their application in quantitative future predictions 
and projections (Flato et al., 2013). This Report assesses (in Section 3.8.2) 
with high confidence  that for most large-scale indicators of climate 
change, the recent mean climate simulated by the latest generation 
climate models underpinning this assessment has improved compared 
to the models assessed in AR5, and with high confidence  that the 
multi-model mean captures most aspects of observed climate change 
well. These assessments form the foundation of applying climate and 
Earth system models to the projections assessed in this chapter. Where

appropriate, the assessment of projected changes is accompanied by an 
assessment of process understanding and model evaluation.
That said, fitness-for-purpose of the climate models used for long-term 
projections is fundamentally difficult to ascertain and remains an 
epistemological challenge (Parker, 2009; Frisch, 2015; Baumberger et al., 
2017). Some literature exists comparing previous IPCC projections to 
what has unfolded over the subsequent decades (Cubasch et al., 2013), 
and recent work has confirmed that climate models since around 1970 
have projected global surface warming in reasonable agreement with 
observations once the difference between assumed and actual forcing 
has been taken into account (Hausfather et al., 2020). However, the 
long-term perspective to the end of the 21st century or even out to 
2300 takes us beyond what can be observed in time for a standard 
evaluation of model projections, and in this sense the assessment of

long-term projections will remain fundamentally limited.
The spread across individual runs within a  multi-model ensemble 
represents the response to a  combination of different sources of 
uncertainties (Section 1.4.3), specifically: scenario uncertainties, climate 
response uncertainties (also referred to as model uncertainties) 
related to parametric and other structural uncertainties in the 
model representation of the climate system, and internal variability 
(e.g., Hawkins and Sutton, 2009; Kirtman et al., 2013). While the nature 
of these uncertainties was introduced in Section 1.4.3, this subsection 
assesses methods to disentangle different sources of uncertainties and 
quantify their contributions to the overall ensemble spread.As discussed extensively in AR5 (Collins et al., 2013), ensemble spread 
in projections performed with different climate models accounts for 
only part of the entire model uncertainty, even when considering the

uncertainty in the radiative forcing in projections (Vial et al., 2013) 
and forced response. The AR5 uncertainty characterisation (Kirtman 
et  al., 2013) followed Hawkins and Sutton (2009) and diagnosed 
internal variability through a high-pass temporal filter. This approach 
has deficiencies particularly if internal variability manifests on the 
multi-decadal time scales (Deser et al., 2012a; Marotzke and Forster, 
2015) and is classified as (model) response uncertainty instead of 
internal variability. Single-model initial-condition large ensembles 
revealed that the AR5 approach underestimates the role of internal 
variability uncertainty and overestimates the role of model uncertainty 
(Maher et al., 2018; Stolpe et al., 2018; Lehner et al., 2020) particularly 
at the local scale while yielding a  reasonable approximation for 
uncertainty separation for GSAT (Lehner et al., 2020).
Single-model initial-condition large ensembles thus represent a crucial

step towards a cleaner separation of model uncertainty and internal 
variability than available for AR5 (Deser et al., 2014, 2016; Saffioti et al., 
2017; Sippel et al., 2019; Milinski et al., 2020; von Trentini et al., 2020; 
Maher et al., 2021). Novel approaches have been proposed to further 
quantify internal variability in multi-model ensembles (Hingray and Saïd, 
2014; Evin et al., 2019; Hingray et al., 2019). For time horizons beyond 
the limit of decadal predictability (Branstator and Teng, 2010; Meehl 
et al., 2014; Marotzke et al., 2016), such as in the CMIP6 projections, 
the simulations are starting from random rather than assimilated 
initial conditions. Internal variability constitutes an uncertainty in the 
projection of the climate in a future period of 10 or 20 years that is 
irreducible, but can be precisely quantified for individual models using 
sufficiently large initial-condition ensembles (Fischer et al., 2013; Deser

et al., 2016, 2020; Hawkins et al., 2016; Pendergrass et al., 2017; Luo 
et al., 2018; Dai and Bloecker, 2019; Maher et al., 2019).
Uncertainties in emissions of greenhouse gases and aerosols that affect 
future radiative forcings are represented by selected SSP scenarios 
(Sections 1.6.1 and 4.2.2). In addition to emission uncertainties, 
SSPs represent uncertainties in land use changes (van Vuuren et al., 
2011; Ciais et al., 2013; O’Neill et al., 2016; Christensen et al., 2018). 
Additional uncertainty comes from climate carbon-cycle feedbacks 
and the residence time of atmospheric constituents, and are at least 
partly accounted for in emissions-driven simulations as opposed to 
concentration-driven simulations (Friedlingstein et al., 2014; Hewitt 
et al., 2016). The climate carbon-cycle feedbacks affect the transient 
climate response to cumulative CO 2 emissions (TCRE). Constraining 
this uncertainty is crucial for the assessment of remaining carbon

budgets consistent with global mean temperature levels (Millar 
et al., 2017; IPCC, 2018a) and is covered in Chapter 5 of this Report. 
Finally, there are uncertainties in future solar and volcanic forcing 
(Cross-Chapter Box 4.1).
The relative magnitude of model uncertainty and internal variability 
depends on the time horizon of the projection, location, spatial 
and temporal aggregation, variable, and signal strength (Rowell, 
2012; Fischer et  al., 2013; Deser et  al., 2014; Saffioti et  al., 2017; 
Kirchmeier-Young et al., 2019). New literature published after AR5 
systematically discusses the role of different sources of uncertainty

567
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44and shows that the relative contribution of internal variability is larger 
for short than for long projection horizons (Marotzke and Forster, 
2015; Lehner et al., 2020; Maher et al., 2021), larger for high latitudes 
than for low latitudes, larger for land than for ocean variables, larger 
at station level than for continental or global means, larger for annual 
maxima/minima than for multi-decadal means,  larger for dynamic 
quantities (and, by implication, precipitation) than for temperature 
(Fischer et al., 2014).
The method introduced by Hawkins and Sutton (2009) and applied to 
GSAT projections reveals that by the end of the 21st century, the fraction 
contribution of the climate model response uncertainty to the  total 
uncertainty is larger in CMIP6 than in CMIP5 whereas the relative 
contribution of scenario uncertainty is smaller (Lehner et  al., 2020).

This is the case even when sub-selecting pathways and scenarios that 
are most similar in CMIP5 and CMIP6, that is, the range from RCP2.6 
to RCP8.5 vs SSP1-2.6 to SSP5-8-5, respectively (Lehner et al., 2020). 
The larger range of response uncertainty is further consistent with the 
larger range of TCR and GSAT warming for a comparable pathway in 
CMIP6 than CMIP5 (Forster et al., 2020; Tokarska et al., 2020).
Some uncertainties are not, or only partially accounted for in the CMIP6 
experiments, such as uncertainties in natural forcings from solar 
and volcanic forcings, long-term Earth system feedbacks including 
land–ice feedbacks, groundwater feedbacks (Smerdon,  2017) or 
some long-term carbon-cycle feedbacks (Fischer et al., 2018). Where 
appropriate, this chapter uses results from non-CMIP ESMs or EMICs 
to assess the role of these feedbacks. Still other uncertainties – such 
as further pandemics, nuclear holocaust, global natural disaster

such  as tsunami or asteroid impact, or fundamental technological 
change such as fusion – are not accounted for at all.
4.2.6 Display of Model Agreement and Spread
Maps of multi-model mean changes provide an average estimate for 
the forced model climate response to a certain forcing. However, they 
do not include any information on the robustness of the response across 
models nor on the significance of the change with respect to unforced 
internal variability (Tebaldi et  al., 2011). Models can consistently 
show absence of significant change, in which case they should not be 
expected to agree on the sign of a change (e.g., Tebaldi et al., 2011; 
Knutti and Sedláček, 2013; Fischer et al., 2014). If a multi-model mean 
map of precipitation shows no change, it is unclear whether the models 
consistently project insignificant changes or whether projections span 
both significant increases and significant decreases. Several methods

have been proposed to distinguish significant conflicting signals from 
agreement on no significant change (Tebaldi et al., 2011; Knutti and 
Sedláček, 2013; McSweeney and Jones, 2013; Zappa et al., 2021). A set 
of different methods have been introduced in the literature to display 
model robustness and to put a climate change signal into the context 
of internal variability. Box 12.1 in AR5 provides a detailed assessment 
of different methods of mapping model robustness and Cross-Chapter 
Box Atlas.1 provides an update of recent proposals including the 
methods used in this Report.Most methods for quantifying robustness assume that only one 
realization from each model is applied. There are challenges that 
arise from having heterogeneous multi-model ensembles with many 
members for some models and single members for others (Olonscheck 
and Notz, 2017; Evin et  al., 2019). Furthermore, the methods that 
map model robustness usually ignore that sharing parametrizations

or entire components across coupled models can lead to substantial 
model interdependence (Fischer et  al., 2011; Kharin et  al., 2012; 
Knutti et al., 2013, 2017; Leduc et al., 2015; Sanderson et al., 2015, 
2017; Annan and Hargreaves, 2017; Boé, 2018; Abramowitz et  al., 
2019). This may lead to a  biased estimate of model agreement if 
a substantial fraction of models is interdependent. The methodologies 
and results in this literature since AR5 are higher in quality and 
clarity. However, quantifying and accounting for model dependence 
in a  robust way remains challenging (Abramowitz et  al.,  2019). 
Furthermore, absence of significant mean change in a certain climate 
variable does not imply absence of substantial impact, because 
there may be substantial change in variability, which is typically not 
mapped (McSweeney and Jones, 2013).
Chapter  4 uses the advanced approach, taking into account the 
sign and significance of the change (Cross-Chapter Box Atlas.1,

approach  C). Where not applicable, such as due to a  lack of the 
necessary model output, the simple method is used taking into account 
only agreement on the sign of the change across the multi-model 
ensemble (Cross-Chapter Box Atlas.1, approach B). The advanced 
approach is similar to the method used in AR5 but isolates conflicting 
signals as proposed in Zappa et  al. (2021). It  uses three mutually 
exclusive categories and distinguishes (i) areas with significant change 
and high model agreement (no overlay), (ii) areas with no change or no 
robust change (diagonal lines), and (iii) areas with significant change 
but low agreement  (crossed lines). Category (i) marks areas where 
the climate change signals likely  emerge from internal variability, 
where two-thirds or more of the models project changes greater than 
internal variability and 80% or more of the models agree on the sign 
of the change. Category (ii) marks areas where fewer than two-thirds

of the models project changes greater than internal variability, and 
category (iii) marks areas with significant but conflicting signals, where 
two-thirds or more of the models project changes greater than internal 
variability but less than 80% agree on the sign of the change.
In this chapter variability is defined as , where  
is the standard deviation of 20-year means in the pre-industrial 
control simulations (see Cross-Chapter Box, Atlas.1). Category (a) 
uses a definition very similar to the AR5 method for stippling, except 
that the model signal is compared to its corresponding internal rather 
than the multi-model mean variability, to account for the substantial 
model differences in pre-industrial internal variability (Parsons et al., 
2020). Changes smaller than internal variability can have potential 
impacts particularly if they persist over sustained periods such as 
several decades. Finally, even when changes do not exceed variability

at the grid point level they may exceed variability if aggregated over 
catchment basins, regions, or continents (Cross-Chapter Box Atlas.1). 
Maps of mean changes also ignore potential changes in variability 
addressed by a  more comprehensive assessment of changes in 
temperature variability (Section  4.5.1) and modes of internal 
variability (Section 4.4.3).

568
Box 4.1 | Ensemble Evaluation and Weighting
The AR5 used a pragmatic approach to quantify the uncertainty in CMIP5 GSAT projections (Collins et al., 2013). The multi-model 
ensemble was constructed by picking one realization per model per scenario. For most quantities, the 5–95% ensemble range was 
used to characterize the uncertainty, but the 5–95% ensemble range was interpreted as the 17–83% ( likely ) uncertainty range. 
The uncertainty was thus explicitly assumed to contain sources not represented by the model range. While straightforward and clearly 
communicated, this approach had several drawbacks.
i. The uncertainty breakdown into scenario uncertainty, model uncertainty, and internal variability (Cox and Stephenson, 2007; 
Hawkins and Sutton, 2009) in AR5 followed Hawkins and Sutton (2009) and diagnosed internal variability through a high-pass

temporal filter (Kirtman et al., 2013), but it has since become clear that even multi-decadal trends contain substantial internal 
variability relative to the forced response in many variables (e.g., Deser et al., 2012a, 2020; Marotzke and Forster, 2015; Lehner et al., 
2020); hence a more comprehensive approach is needed. 
ii. The uncertainty characterization ignores observation-based information about internal climate variability during the most recent 
past, such as is used in initialized predictions. While this may matter little for the long-term projections (Collins et al., 2013), it 
is very important for the near-term future (Kirtman et al., 2013). The AR5 included additional uncertainty quantification for the 
near-term projections (Kirtman et  al., 2013), leading to a  downward adjustment of assessed near-term GSAT change, which 
created an inconsistency in the transition from near-term to long-term GSAT assessment in AR5.

iii. The AR5 used the range of CMIP5 equilibrium climate sensitivity (ECS) side-by-side with the ECS  likely  range assessed from 
multiple lines of evidence (the CMIP5 ensemble, instrumental observations, and paleo-information; Collins et al., 2013). While the 
CMIP5 range in ECS and AR5 ECS likely  range did not differ much, the difference did create an inconsistency. Furthermore, AR5 
WGIII used the assessed  likely  range for ECS in their calculations of carbon budgets (IPCC, 2014), and these uncertainties matter 
a great deal when assessing remaining carbon budgets consistent with limiting global warming to 1.5°C above pre-industrial 
levels (Millar et al., 2017, 2018a, b; Rogelj et al., 2018b; Schurer et al., 2018). 
Another important consideration concerns the potential weighting of model contributions to an ensemble, based on model 
independence, model performance during the historical period, or both. Such model weighting (in fact, model selection) was performed

in AR5 for projections of Arctic sea ice (Collins et al., 2013), but that particular application has subsequently been shown by Notz 
(2015) to be contaminated by internal variability, making the resulting weighting questionable (Stroeve and Notz, 2015). For a general 
cautionary note, see Weigel et al. (2010). Approaches that take into account internal variability and model independence have been 
proposed since AR5 (Knutti et al., 2017; Boé, 2018; Abramowitz et al., 2019; Brunner et al., 2020).
There are hence good reasons for basing an assessment of future global climate on lines of evidence in addition to the projection 
simulations. However, despite some progress, no universal, robust method for weighting a multi-model projection ensemble is available, 
and expert judgement must be included, as it did for AR5, in the assessment of the projections. The default in this chapter follows

the AR5 approach for GSAT (Collins et al., 2013) and interprets the CMIP6 5–95% ensemble range as the likely  uncertainty range.
Additional lines of evidence enter the assessment particularly for the most important indicator of global climate change, GSAT. The CMIP6 
ensemble generally shows larger projected warming by the end of the 21st century, relative to the average over the period 1995–2014, 
than the CMIP5 ensemble (Section 4.3.1). The warming has increased in part because of models with higher ECS in CMIP6, compared to 
CMIP5 ( high confidence ) (e.g., Meehl et al., 2020; Tokarska et al., 2020; Zelinka et al., 2020; J. Zhu et al., 2020), and in part because of 
higher ERF in CMIP6 than in CMIP5 (e.g., Tebaldi et al., 2021, Section 4.6.2). Because change in several other important climate quantities 
scales with change in GSAT (Section 4.2.4), bringing in additional lines of evidence is particularly important for the GSAT assessment.

The Chapter 4 assessment uses information from the following sources: 
i. The CMIP6 multi-model ensemble (Eyring et al., 2016), augmented if appropriate by the CMIP5 ensemble (Taylor et al., 2012).
ii. Single-model large initial-condition ensembles (e.g., Kay et al., 2015; Sigmond and Fyfe, 2016; Maher et al., 2019) and combinations 
of control runs with CMIP transient simulations (e.g., Thompson et al., 2015; Olonscheck and Notz, 2017) to characterize internal 
variability. Several analyses using multiple large ensembles have recently become available and add robustness to the results 
(Maher et al., 2018, 2019, 2020, 2021; Deser et al., 2020; Lehner et al., 2020).
iii. Assessed best estimates, likely , and very likely  ranges of ECS and TCR, from process understanding, warming in the instrumental record, 
paleoclimates, and emergent constraints (Tables 7.13 and 7.14, and Section 7.5). The ECS and TCR ranges are converted into GSAT

ranges using as an emulator a two-layer energy balance model (EBM, e.g., Held et al., 2010) that is driven by the effective radiative

569
Box 4.1 (continued)
forcing (ERF) assessed in Chapter 7 (Cross-Chapter Box 7.1). Assuming for the ERF resulting from a doubling of the CO 2 concentration, 
∆F2 × CO2 = 4.0 W m–2 (close to the best estimate of 3.93 W m–2, Section 7.3), and using the so-called zero-layer approximation to 
the EBM (e.g., Marotzke and Forster, 2015; Jiménez-de-la-Cuesta and Mauritsen, 2019) permits a one-to-one translation of any 
pair of ECS and TCR into a pair of climate feedback parameter α and ocean heat uptake coefficient κε, using the simple equations 
α = –∆F2 × CO2  ECS–1 and κε = ∆F2 × CO2  TCR–1 – ∆F2 × CO2  ECS–1 (e.g., Jiménez-de-la-Cuesta and Mauritsen, 2019; see Chapter 7 for 
a detailed discussion). The results are displayed in Box 4.1, Figure 1 and are used in the synthesis GSAT assessment in Section 4.3.4.

iv. Model independence diagnosed a priori, based on shared model components for atmosphere, ocean, land surface, and sea ice of 
CMIP5 models (Boé, 2018). CMIP5 models have been re-sampled assuming that two models sharing either the atmosphere or the 
ocean component are effectively the same model (Maher et al., 2021). Downweighting CMIP5 models that share a component 
with another has substantial influence on diagnosed model agreement on change in ENSO (Maher et al., 2021), but has negligible 
influence (much less than 0.1°C) on the ensemble mean and range of GSAT change over the 21st century. No corresponding 
diagnosis exists yet for CMIP6 models, and no weighting based on a-priori independence is applied here.
v. Performance in simulating the past and a-posteriori independence based on comparison against observations (Knutti et al., 2017; 
Abramowitz et al., 2019). This approach has been applied to CMIP6-simulated GSAT and has led to a substantial reduction in

model range (Brunner et al., 2020; Liang et al., 2020; Section 4.3.4). The CMIP6-simulated Arctic sea ice area has been compared 
to the observed record, and models have been selected whose ensemble range across their individual realizations (Olonscheck and 
Notz, 2017) includes the observational range of uncertainty. A larger fraction of these selected simulations show an ice-free Arctic 
in September before 2050, compared to the entire CMIP6 ensemble (Notz and SIMIP Community, 2020; Section 4.3.2).
vi. A linear inverse method (kriging) has combined the entire GSAT record since 1850 with the CMIP6 historical simulations to 
produce constrained projections for the 21st  century; again the reduction in range has been substantial (Ribes et  al., 2021; 
Section 4.3.4; Section 4.3.4).
vii. Emergent constraints (e.g., Hall and Qu, 2006; Cox et al., 2018; Brient, 2020), which for the post-1970 warming have been applied

to the CMIP5 (Jiménez-de-la-Cuesta and Mauritsen, 2019) and CMIP6 ensembles (Nijsse et al., 2020; Tokarska et al., 2020) and 
have likewise led to a substantial reduction in GSAT ensemble range (Section 4.3.4). 
viii. Climate predictions initialized from recent observations (e.g., Kirtman et al., 2013) and the Decadal Climate Prediction Project 
(DCPP) contribution to CMIP6 (Boer et al., 2016; Smith et al., 2020; Sospedra-Alfonso and Boer, 2020). Initialized predictions for the 
period 2019–2028 exist for eight DCPP models and are used here (Box 4.1, Figure 1 and Section 4.4.1). The DCPP results have been 
drift-removed and referenced to the time-averaged hindcasts for 1995–2014 lead-year by lead-year, following (Kharin et al., 2012; 
Kruschke et al., 2016).
Box  4.1, Figure  1 shows annual mean GSAT simulated by CMIP6 models for both the historical period and forced

by scenario SSP2-4.5 until 2100, combined with various characterizations of uncertainty. First, internal variability is 
estimated with the 50-member ensemble simulated with CanESM5. The 5–95% ensemble range for annual mean GSAT 
in CanESM5 is slightly below 0.4°C; in other CMIP6 large ensembles this range is about 0.5°C (MIROC6, IPSL-CM6A) 
and slightly above 0.6°C (S-LENS/EC-Earth3). The CMIP5 large ensemble MPI-GE shows a  range of slightly below 0.5°C 
(Bengtsson and Hodges, 2019), in reasonable agreement with observed variability (Maher et  al., 2019). There is thus 
high confidence  in the CMIP6-simulated level of internal variability in annual mean GSAT, as displayed in Box  4.1, Figure  1.
Second, Section 7.5 very likely  ECS and TCR ranges are converted into GSAT ranges with the EBM as an emulator using, in this 
example, SSP2-4.5 radiative forcing information. Because the ECS and TCR assessments in Section 7.5 are based on multiple lines

of evidence and the EBM physics are well understood, there is likewise high confidence  in the EBM-emulated warming. Third, the 
initialized-forecast ensembles from eight CMIP6 DCPP models are shown in the inset, for the period 2019–2028. During this period, 
the initialized forecasts are consistent, within internal variability, with the EBM-emulated range, further adding to the high confidence  
in the assessed-GSAT range.
The constrained range of GSAT change is useful for quantifying uncertainties in changes of other climate quanties that scale well 
with GSAT change, such as September Arctic sea ice area, global mean precipitation, and many climate extremes (Cross-Chapter 
Box 11.1). However, there are also quantities that do not scale linearly with GSAT change, such as global mean land precipitation, 
atmospheric circulation, AMOC, and modes of variability, especially ENSO SST variability. Because we do not have robust scientific

evidence to constrain changes in other quantities, uncertainty quantification for their changes is based on CMIP6 projections and 
expert judgement. For the assessment for changes in GMSL, the contribution from land-ice melt has been added offline to the CMIP6 
simulated contributions from thermal expansion, consistent with Chapter 9 (Section 9.6).

570
4.3 Projected Changes in Global Climate 
Indices in the 21st Century
This section assesses the latest simulations of representative indicators 
of global climate change presented as time series and tabulated values 
over the 21st century and across the main realms of the global climate 
system. In the atmospheric realm (Section 4.3.1), we assess simulations 
of GSAT (Figure  4.2a) and global land precipitation (Figure  4.2b). 
Across the cryospheric, oceanic, and biospheric realms (Section 4.3.2), 
we assess simulations of Arctic SIA (Figure 4.2c), GMSL (Figure 4.2d), 
the AMOC, ocean and land carbon uptake, and pH. In Section 4.3.3 we 
assess simulations of several indices of climate variability, namely, the 
indices of the NAM, SAM, and ENSO. Finally, Section 4.3.4 assesses 
future GSAT change based on the CMIP6 ensemble in combination

with other lines of evidence. An assessment of projected changes in 
related global extreme indices can be found in Chapter 11.From the CMIP6 multi-model ensemble we consider historical 
simulations with observed external forcings to 2014 and extensions 
to 2100 based on the five high-priority scenarios. We use the first 
realization (‘r1’) contributed by each modelling group. In tabular form, 
we show ensemble-mean changes and uncertainties for the near-term 
(2021–2040), mid-term (2041–2060), and the long-term (2081–2100), 
relative to present-day (1995–2014) and the approximation to 
pre-industrial (1850–1900). Changes in precipitation over land 
near 1.5°C, 2.0°C, 3.0°C, and 4.0°C of global warming relative to 
1850–1900 are also assessed.Box 4.1 (continued)
Box 4.1 Figure 1 | CMIP6 annual mean global surface air temperature (GSAT) simulations and various contributions to uncertainty in the projections

ensemble.  The figure shows anomalies relative to the period 1995–2014 (left y-axis), converted to anomalies relative to 1850–1900 (right y-axis); the difference 
between the y-axes is 0.85°C (Cross-Chapter Box 2.3). Shown are historical simulations with 39 CMIP6 models (grey) and projections following scenario SSP2-4.5 
(dark yellow; thin lines: individual simulations; heavy line; ensemble mean; dashed lines: 5% and 95% ranges). The black curve shows the observations-based estimate 
(HadCRUT5; Morice et al., 2021). Light blue shading shows the 50-member ensemble CanESM5, such that the deviations from the CanESM5 ensemble mean have been 
added to the CMIP6 multi-model mean. The green curves are from the emulator and show the central estimate (solid) and very likely  range (dashed) for GSAT. The inset 
shows a cut-out from the main plot and additionally in light purple for the period 2019–2028 the initialized forecasts from eight models contributing to DCPP (Boer et al.,

2016); the deep-purple curve shows the average of the forecasts. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).DCPP
2014 2016 2018 2020 2022 2024 2026 2028 2030
1850 1900 1950 2000 2050 2100–20123CMIP6 historical
CMIP6SSP2 -4.5
EmulatorHadCRUT5
CanESM 5 internal variability
–101234GSAT anomaly relative to 1850-1900 (°C)
0.00.51.0
1.01.52.0
–1GSAT anomaly relative to 1995-2014 (°C)

571
4.3.1 Atmosphere
4.3.1.1 Surface Air Temperature
The AR5 assessed from CMIP5 simulations and other lines of 
evidence that GSAT will continue to rise over the 21st  century if 
greenhouse gas (GHG) concentrations continue increasing (Collins 
et al., 2013). The AR5 concluded that GSAT for 2081–2100, relative to 
1986–2005 will likely  be in the 5–95% range of 0.3°C–1.7°C under 
RCP2.6 and 2.6°C–4.8°C under RCP8.5. The corresponding ranges for 
the intermediate emissions scenarios with emissions peaking around 
2040 (RCP4.5) and 2060 (RCP6.0) are 1.1°C–2.6°C and 1.4°C–3.1°C, 
respectively. The AR5 further assessed that GSAT averaged over 
the period 2081–2100 are projected to likely  exceed 1.5°C above 1850–1900 for RCP4.5, RCP6.0 and RCP8.5 ( high  confidence ) and 
are likely  to exceed 2°C above 1850–1900 for RCP6.0 and RCP8.5 
(high confidence ). Global surface temperature changes above 2°C

under RCP2.6 were deemed unlikely  (medium confidence ).
Here, for continuity’s sake, we assess the CMIP6 simulations of GSAT 
in a fashion similar to the AR5 assessment of the CMIP5 simulations. 
From these, we compute anomalies relative to 1995–2014 and display 
the evolution of ensemble means and 5–95% ranges (Figure  4.2). 
We also use the ensemble mean GSAT difference between 1850–1900 
and 1995–2014, 0.82°C, to provide an estimate of the changes since 
1850–1900 (Figure 4.2, right axis). Finally, we tabulate the ensemble 
mean changes between 1995–2014 and 2021–2040, 2041–2060, 
and 2081–2100 respectively (Figure 4.2).
(a) Glob al temper ature cha nge
Near
termMid
termLong
termHistorica l51
SSP1-1.9 11
SSP1-2.6 30
SSP2-4.5 33
SSP3-7.0 26
SSP5-8.5 32
–20246Relative to 19 95-201 4 (ºC)
0246(b) Glob al land pr ecipitatio n change
501131322732

1950                     2000    2050  2100 1950                     2000    2050  21001950                     2000    2050  2100 1950                     2000    2050  2100–4–20246810Relative to 19 95-201 4 (%)
(c) Se ptember  Arctic sea  ice area
Practica llyseaicefree
36921241922
0246810 million km2(d) Glob al mean sea level  change
Observed
SSP1-1.9 assesse d
SSP1-2.6 assessed
SSP2-4.5 assesse d
SSP3-7.0 assessed
SSP5-8.5 assesse d
0.000.250.500.751.00Relative to 19 95-201 4 (m)
Relative to1850- 1900 (ºC)Figure  4.2 | Selected indicators of global climate change from CMIP6 historical and scenario simulations. (a)  Global surface air temperature changes relative 
to the 1995–2014 average (left axis) and relative to the 1850–1900 average (right axis; offset by 0.82°C, which is the multi-model mean and close to observed best estimate,

Cross-Chapter Box 2.1, Table 1). (b) Global land precipitation changes relative to the 1995–2014 average. (c) September Arctic sea ice area. (d) Global mean sea level (GMSL) 
change relative to the 1995–2014 average. (a), (b) and (d) are annual averages, (c) are September averages. In (a–c), the curves show averages over the CMIP6 simulations, 
the shadings around the SSP1-2.6 and SSP3-7.0 curves show 5–95% ranges, and the numbers near the top show the number of model simulations used. Results are derived 
from concentration-driven simulations. In (d), the barystatic contribution to GMSL (i.e., the contribution from land-ice melt) has been added offline to the CMIP6 simulated 
contributions from thermal expansion (thermosteric). The shadings around the SSP1-2.6 and SSP3-7.0 curves show 5–95% ranges. The dashed curve is the low confidence  and

low likelihood outcome at the high end of SSP5-8.5 and reflects deep uncertainties arising from potential ice-sheet and ice-cliff instabilities. This curve at year 2100 indicates 
1.7 m of GMSL rise relative to 1995–2014. More information on the calculation of GMSL is available in Chapter 9, and further regional details are provided in the Atlas. 
Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

572
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4The CMIP6 models show a  5–95% range of GSAT change for 
2081–2100, relative to 1995–2014, of 0.6°C–2.0°C under 
SSP1-2.6 where CO 2 concentrations peak between 2040 and 2060 
(see Table 4.2). The corresponding range under the highest overall 
emissions scenario  (SSP5-8.5) is 2.7°C–5.7°C. The ranges for the 
intermediate and high emissions scenarios (SSP2-4.5 and SSP3-7.0), 
where CO 2 concentrations increase to 2100, but less rapidly than 
SSP5-8.5, are 1.4°C–3.0°C and 2.2°C–4.7°C, respectively. The range 
for the lowest emissions scenario (SSP1-1.9) is 0.2°C–1.3°C.
In summary, the CMIP6 models show a general tendency toward larger 
long-term globally averaged surface warming than did the CMIP5 
models, for nominally comparable scenarios ( very high confidence ). 
In SSP1-2.6 and SSP2-4.5, the 5–95% ranges have remained similar

to the ranges in RCP2.6 and RCP4.5, respectively, but the distributions 
have shifted upward by about 0.3°C ( high confidence ). For SSP5-8.5 
compared to RCP8.5, the 5% bound of the distribution has hardly 
changed, but the 95% bound and the range have increased by about 
20% and 40%, respectively ( high confidence ). About half of the warming 
increase has occurred because of more models with higher climate 
sensitivity in CMIP6, compared to CMIP5; the other half of the warming 
increase arises from higher effective radiative forcing in nominally 
comparable scenarios ( medium confidence, see Section 4.6.2).
With regards to global warming levels (GWLs) of 1.5°C, 2.0°C and 
3.0°C, we note that there is unanimity across all of the CMIP6 model 
simulations that GSAT change relative to 1850–1900 will rise above: 
(i) 1.5°C following SSP2-4.5, SSP3-7.0, or SSP5-8.5 (on average 
around 2030); (ii) 2.0°C following either SSP3-7.0 or SSP5-8.5

(on average around 2043); and (iii) 3.0°C following SSP5-8.5 (on 
average around 2062). Under SSP1-1.9, 55% and 36% of the model 
simulations rise above 1.5°C and 2.0°C, respectively, while for 
SSP1-2.6 those percentages increase to 87% and 58%, respectively. 
Here, the time of GSAT exceedance is determined as the first year 
at which 21-year running averages of GSAT exceed the given GWL. In Section 4.3.4, these values are reassessed using CMIP6 ensemble 
in combination with other lines of evidence.
CMIP6 models project increases in area-weighted land, ocean, tropical 
(30°S–30°N), Arctic (67.7°N–90°N), and Antarctic (90°S–55°S) 
surface air temperature (Table 4.2). Consistent with AR5, and earlier 
assessments, CMIP6 models project that annual average surface air 
temperature will warm about 50% more over land than  over the 
ocean, and that the Arctic will warm about more than 2.5  times 
the global average (Section  4.5.1). For 2081–2100, relative to

1995–2014, the CMIP6 models show 5–95% ranges of warming 
over land of 0.3°C–2.0°C and 3.5°C–7.6°C following SSP1-1.9 and 
SSP5-8.5, respectively. The corresponding ranges for Arctic surface air 
temperature change are 0.5°C–6.6°C and 6.2°C–15.2°C, respectively.
The concentration-driven simulations presented above use 
a prescribed CO 2 pathway calculated by the MAGICC7.0 model using 
the CMIP6 emissions (Meinshausen et  al., 2020). This is compared 
here with the CO 2 concentration simulated by CMIP6 ESMs in 
response to the SSP5-8.5 emissions (Figure  4.3). The 1995–2014 
mean simulated CO 2 level is 375 ppm, very similar to the prescribed 
378 ppm, but the ESM 5–95% range is 357–391 ppm. By the end of 
the 21st century (2081–2100), the ESM mean is 953 ppm – below the 
prescribed CO 2 pathway (1004 ppm), but with a large 5–95% range of 
848–1045 ppm, which spans the prescribed concentration level. This 
result differs from CMIP5, which showed that ESMs typically simulated

CO 2 concentrations higher than the prescribed concentration-driven 
RCP pathways. Reduced spread in CMIP6 carbon cycle feedbacks 
compared to CMIP5 has been postulated to be due to the inclusion 
of nitrogen cycle processes in about half of CMIP6 ESMs (Arora et al., 
2020). This means that the CMIP6 spread in GSAT response to CO 2 
emissions is dominated by climate sensitivity differences between 
ESMs more than by carbon cycle differences ( high  confidence ) 
(Jones and Friedlingstein, 2020; Williams et al., 2020).
Table  4.2 | CMIP6 annual mean surface air temperature anomalies (°C). Displayed are multi-model averages and, in parentheses, the 5–95% ranges, for selected time 
periods, regions, and SSPs. The numbers of models used are indicated in Figure 4.2.
Time Period and Region SSP1-1.9 (°C) SSP1-2.6 (°C) SSP2-4.5 (°C) SSP3-7.0 (°C) SSP5-8.5 (°C)
Global: 2021–2040
Relative to 1995–2014
Relative to 1850–19000.7 (0.3, 1.1)
1.5 (1.1, 2.2)0.7 (0.4, 1.1)
1.6 (1.1, 2.2)0.7 (0.4, 1.2)

1.6 (1.0, 2.3)0.7 (0.5, 1.2)
1.6 (1.0, 2.4)0.8 (0.5, 1.3)
1.7 (1.2, 2.4)
Global: 2041–2060
Relative to 1850–19000.8 (0.3, 1.5)
1.7 (1.1, 2.4)1.0 (0.6, 1.6)
1.9 (1.2, 2.7)1.3 (0.8, 1.9)
2.1 (1.5, 3.0)1.4 (0.9, 2.3)
2.3 (1.6, 3.2)1.7 (1.2, 2.5)
2.6 (1.8, 3.4)
Global: 2081–2100
Relative to 1850–19000.7 (0.2, 1.5)
1.5 (1.0, 2.2)1.2 (0.6, 2.0)
2.0 (1.3, 2.8)2.0 (1.4, 3.0)
2.9 (2.1, 4.0)3.1 (2.2, 4.7)
3.9 (2.8, 5.5)4.0 (2.7, 5.7)
4.8 (3.6, 6.5)
Land: 2081–2100
Relative to 1995–20140.9 (0.3, 2.0) 1.5 (0.8, 2.6) 2.7 (1.7, 4.0) 4.1 (3.0, 6.2) 5.3 (3.5, 7.6)
Ocean: 2081–2100
Relative to 1995–20140.6 (0.1, 1.2) 1.0 (0.5, 1.8) 1.8 (1.2, 2.7) 2.7 (1.8, 4.0) 3.4 (2.3, 4.9)
Tropics: 2081–2100
Relative to 1995–20140.5 (0.1, 1.1) 1.0 (0.5, 1.6) 1.8 (1.2, 2.5) 2.7 (2.0, 4.0) 3.5 (2.4, 4.9)
Arctic: 2081–2100
Relative to 1995–20142.4 (0.5, 6.6) 3.3 (0.4, 7.5) 5.4 (2.8, 10.0) 7.7 (4.5, 13.4) 10.0 (6.2, 15.2)
Antarctic: 2081–2100

Relative to 1995–20140.5 (0.0, 1.1) 1.1 (0.1, 2.9) 1.9 (0.6, 3.2) 2.8 (1.3, 4.5) 3.6 (1.7, 5.6)

573
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44Simulated GSAT over 1995–2014, relative to 1850–1900 period, 
warms by very similar amounts in the two sets of simulations: 0.82°C 
(0.45–1.31) in emissions-driven compared with 0.75°C (0.53–1.09) 
in concentration-driven simulations. By the end of the 21st century, 
warming in emissions-driven simulations is very similar: 4.58°C 
(3.53–6.70), reflecting the slightly lower CO 2 concentration 
simulated by the ESMs compared with warming under the 
prescribed CO 2 pathway of 4.69°C (3.70–6.77). This difference in 
model-mean response is more than an order of magnitude smaller 
than the 5–95% spread across model projections. The spread in CO 2 
concentration, compared with the prescribed default concentration, 
leads to a very small increase by about 0.1°C in the spread of GSAT 
projections, but it is not possible to tell if this is a direct consequence

of the simulation configuration or internal variability of the model 
simulations. These differences due to experimental configuration 
would be smaller still under scenarios with lower CO 2 levels, and 
so we assess that results from concentration-driven and emissions-
driven configurations do not affect the assessment of GSAT 
projections ( high confidence ).
4.3.1.2 Precipitation
The AR5 assessed from CMIP5 projections that global mean 
precipitation over the 21st  century will increase by more than 
0.05 mm day–1 (about 2% of global precipitation) and 0.15 mm day–1 
(about 5% of global precipitation) under the RCP2.6 and RCP8.5 
scenarios, respectively (Collins et  al., 2013). These changes are 
generally in line with those from the CMIP6 simulations following 
SSP1-2.6 and SSP5-8.5 (Table 4.3).Unlike AR5, our focus here is on land rather than global precipitation 
because land precipitation has greater societal relevance. These are

displayed as percent changes relative to 1995–2014 (Figure  4.2b). 
Based on these results, we conclude that global land precipitation 
is larger during the period 2081–2100 than during the period 
1995–2014, under all scenarios considered here ( high confidence ) 
(Table  4.3). Global land precipitation for 2081–2100, relative to 
1995–2014, shows a 5–95% range of –0.2 to +4.7% under SSP1-1.9 
and 0.9–12.9% under SSP5-8.5, respectively. The corresponding ranges 
under the other emissions scenarios are 0.0–6.6% (SSP1-2.6), 1.5–
8.3% (SSP2-4.5), and 0.5–9.6% (SSP3-7.0). A detailed assessment of 
hydrological sensitivity, or change in precipitation per degree warming, 
can be found in Chapter 8 (Section 8.2.1).
For scenarios where unanimity across all of the model simulations 
that GSAT change relative to 1850–1900 rises above 1.5°C (SSP2-4.5, 
SSP3-7.0, or SSP5-8.5), the ensemble-mean change in global land 
precipitation from 1850–1900 until the time of exceedance is on

average about 1.6%. For scenarios with unanimous global warming 
above 2.0°C (SSP3-7.0, or SSP5-8.5) and 3.0°C (SSP5-8.5), the 
ensemble-mean increase in global land precipitation for those 
models that do exceed 2.0°C and 3.0°C is on average about 2.6% 
and 4.9%, respectively. On average under SSP1-1.9 and SSP1-2.6, 
the global land precipitation change for simulations where 
global warming exceeds 1.5°C and 2.0°C will be about 1.9% and 
3.0%, respectively.
(a) Atmospheric CO2 concentration (b) Global temperature change
1200
1000
800
600
400
200ppm
Relative to 1995 –2014 (°C)6
4
28
6
4
2
00
Relative to 1850 –1900 (°C)CMIP6 model mean
Concentration-driven
Individual modelConcentration-drivenEmissions-driven
1960 1980 2000 2020 2040 2060 2080 2100 1960 1980 2000 2020 2040 2060 2080 2100Figure  4.3 | Comparison of concentration-driven and emissions-driven simulation. (a) Atmospheric CO 2 concentration; (b) global surface air temperature from

models which performed SSP5-8.5 scenario simulations in both emissions-driven (blue) and concentration-driven (red) configurations. For concentration driven simulations, CO 2 
concentration is prescribed, and follows the red line in panel (a) in all models. For emissions-driven simulations, CO 2 concentration is simulated and can therefore differ for each 
model, blue lines in panel (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

574
Relative to 1995–2014, and across all of the scenarios considered 
here, CMIP6 models show greater increases in precipitation over land 
than either globally or over the ocean (Table  4.3; high confidence ). 
Over the Northern Hemisphere (NH) extratropics, the 5–95% changes 
in precipitation over land between 1995–2014 and 2021–2040, 
2041–2060, and 2081–2100, following SSP5-8.5, are 0.6–4.9%, 
1.5–8.8%, and 4.7–17.2%, respectively (Figure 4.4). At the other end of 
scenario spectrum, SSP1-1.9, the corresponding changes are 0.6–5.4%, 
0.6–7.3%, and 0.2–7.7%, respectively. By contrast, over the North 
Atlantic subtropics, precipitation decreases by about 10% following 
SSP3-7.0 and SSP5-8. There is no change in subtropical precipitation 
in the North Atlantic following SSP1-1.9, SSP1-2.6, or SSP2-4.5 ( high 
confidence ); thereby highlighting the potential limitations of pattern

scaling for regional hydrological changes (Section 8.5.3). The reasons for 
the opposing changes in these two regions are assessed in Chapter 8.4.3.2 Cryosphere, Ocean and Biosphere
4.3.2.1 Arctic Sea Ice
The AR5 assessed from CMIP5 simulations that there will be year-
round reductions of Arctic sea ice coverage by the end of this century 
(Collins et al., 2013). These range from 43% under RCP2.6 and 94% 
under RCP8.5 in September, and from 8% under RCP2.6 and 34% 
under RCP8.5 in March ( medium confidence ). Based on a five-member 
selection of CMIP5 models, AR5 further assessed that for RCP8.5, 
Arctic sea ice coverage in September will drop below 1 million km2 
and be practically ice free at some point between 2040 and 2060. 
The SROCC further assessed that the probability of an ice-free Arctic 
in September for stabilized global warming of 1.5°C and 2.0°C is

approximately 1% and 10–35%, respectively (IPCC, 2019).Table  4.3 | CMIP6 precipitation anomalies (%) relative to averages over 1995–2014 for selected future periods, regions and SSPs. Displayed are the 
multi-model averages across the individual models and, in parentheses, the 5 –95% ranges. Also shown are land precipitation anomalies at the time when global increase in 
GSAT relative to 1850–1900 exceeds 1.5°C, 2.0°C, 3.0°C, and 4.0°C, and the percentage of simulations for which such exceedances are true (to the right of the parentheses). 
Here, the time of GSAT exceedance is determined as the first year at which 21-year running averages of GSAT exceed the given threshold. Land precipitation percent anomalies 
are then computed as 21-year averages about the year of the first GSAT crossing. The numbers of models used are indicated in Figure 4.4.
Time Period and Region SSP1-1.9 (%) SSP1-2.6 (%) SSP2-4.5 (%) SSP3-7.0 (%) SSP5-8.5 (%)

Land2021–2040 2.4 (0.7, 4.1) 2.0 (–0.6, 3.6) 1.5 (–0.4, 3.6) 1.2 (–1.0, 3.4) 1.7 (–0.1, 4.1)
2041–2060 2.7 (0.6, 5.0) 2.8 (–0.4, 5.2) 2.7 (0.3, 5.2) 2.5 (–0.8, 5.1) 3.7 (–0.1, 6.9)
2081–2100 2.4 (–0.2, 4.7) 3.3 (0.0, 6.6) 4.6 (1.5, 8.3) 5.8 (0.5, 9.6) 8.3 (0.9, 12.9)
Global 2081–2100 2.0 (0.4, 4.2) 2.9 (1.0, 5.2) 4.0 (2.3, 6.7) 4.7 (2.3, 8.2) 6.5 (3.4, 10.9)
Ocean 2081–2100 1.9 (0.6, 4.1) 2.8 (1.1, 5.4) 3.8 (2.0, 6.8) 4.4 (2.1, 7.9) 6.0 (2.9, 10.5)
Land∆T > 1.5°C 2.0 (0.6, 4.4) 55 1.7 (–2.0, 6.9) 87 1.7 (–2.9, 6.2) 100 1.5 (–3.9, 6.6) 100 1.5 (–3.5, 6.4) 100
∆T > 2.0°C 3.8 (2.4, 5.8) 36 2.2 (–2.0, 4.6) 58 2.8 (–2.2, 8.1) 97 2.4 (–4.4, 7.7) 100 2.8 (–2.8, 8.3) 100
∆T > 3.0°C – (–, –) 0 – (–, –) 0 4.9 (1.5, 9.6) 54 4.3 (–4.4, 11.5) 97 4.9 (–2.6, 11.0) 100
∆T > 4.0°C – (–, –) 0 – (–, –) 0 4.2 (1.3, 6.3) 9 5.1 (–2.5, 11.1) 57 6.4 (–3.4, 15.0) 85
(a) E xtratropi cal prec ipitation cha nge
Northern Hem ispher e
Near
termMid
termLong
termHistorica l50
SSP1-2.6 31
SSP2-4.5 32

SSP3-7.0 27
-15-5515Relative to 19 95-201 4 (%)
-20-1001020(b) S ubtropi cal preci pitation chan ge 
North Atlantic
-20-1001020
1950                     2000    2050  2100 1950                     2000    2050  2100
Figure  4.4 | CMIP6 annual mean precipitation changes (%) from historical and scenario simulations. (a)  Northern Hemisphere extratropics (30°N–90°N).  
(b) North Atlantic subtropics (5°N–30°N, 80°W–0°). Changes are relative to 1995–2014 averages. Displayed are multi-model averages and, in parentheses, 5–95% ranges. 
The numbers inside each panel are the number of model simulations. Results are derived from concentration-driven simulations. Further details on data sources and processing 
are available in the chapter data table (Table 4.SM.1).

575
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44With regards to the model selection in AR5, model evaluation studies 
have since identified shortcomings of the CMIP5 models to match 
the observed distribution of sea ice thickness in the Arctic (Stroeve 
et al., 2014; Shu et al., 2015) and the observed evolution of albedo 
on seasonal scales (Koenigk et  al., 2014). It was also found that 
many models’ deviation from observed sea ice cover climatology 
cannot be explained by internal variability, whereas the models’ 
deviation from observed sea ice cover trend (over the satellite 
period) can often be explained by internal variability (Olonscheck 
and Notz, 2017). This hinders a  selection of models according to 
their simulated trends, which additionally has been shown to only 
have a  weak effect on the magnitude of simulated future trends 
(Stroeve and Notz, 2015).
Based on results from the CMIP6 models, we conclude that on

average the Arctic will become practically ice-free in September 
by the end of the 21st  century under SSP2-4.5, SSP3-7.0, and 
SSP5-8.5 ( high confidence ) (Figure 4.2c and Table 4.4). Also, in the 
CMIP6 models, Arctic SIA in March decreases in the future, but to 
a much lesser degree, in percentage terms, than in September ( high 
confidence ) (Table  4.4). A  more detailed assessment of projected 
Arctic and also Antarctic sea ice change can be obtained in Chapter 9 
(Section 9.3.1).
Studies focusing on the relationship of sea ice extent and changes in 
external drivers have consistently found a much-reduced likelihood 
of a  practically ice-free Arctic Ocean during summer for global 
warming of 1.5°C than for 2.0°C (Screen and Williamson, 2017; 
Jahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve,  2018; 
Sigmond  et  al.,  2018; Olson et  al., 2019). This is shown here in 
a  large initial-condition ensemble of observationally constrained

model simulations where GSAT are stabilized at 1.5°C, 2.0°C and 
3.0°C warming relative to 1850–1900 in the RCP8.5 scenario 
(Figure  4.5). Temperature stabilization is achieved by switching 
off all the anthropogenic emissions around the time that GSAT 
first reaches the stabilization thresholds. Simulations have been 
observationally constrained to correct for a model bias in simulated 
historical September sea ice extent. In these simulations, Arctic sea 
ice coverage in September is simulated, on average, to drop below 
1 million km2 around 2040, consistent with the AR5 set of assessed 
models (Sigmond et al., 2018). The individual model simulations, for 
which there are twenty for each stabilized temperature level, show that the probability of the Arctic becoming practically ice free at the 
end of the 21st century is significantly higher for 2°C warming than 
for 1.5°C warming above 1850–1900 levels ( high confidence ).
4.3.2.2 Global Mean Sea Level

The AR5 assessed from CMIP5 process-based simulations that 
the rate of GMSL rise during the 21st century will very likely  exceed 
the rate  observed during 1971–2010 for all RCP scenarios due to 
increases in ocean warming and loss of mass from glaciers and ice 
sheets (Church et  al., 2013). Further, AR5 concluded that for the 
period 2081–2100, compared to 1986–2005, GMSL rise is likely  
(medium confidence ) to be in the 5–95% range of projections 
from process-based models, which give 0.26–0.55 m for RCP2.6, 
Table 4. 4 | CMIP6 Arctic sea ice area for selected months, time periods, and across five SSPs.  Displayed are the multi-model averages across the individual models 
and, in parentheses, the 5 –95% ranges. The number of models used in these calculations are shown in Figure 4.2c.
Month and Time PeriodSSP1-1.9  
(106 km2)SSP1-2.6  
(106 km2)SSP2-4.5  
(106 km2)SSP3-7.0  
(106 km2)SSP5-8.5  
(106 km2)

September2021–2040 2.6 (1.1, 6.5) 2.7 (0.6, 6.4) 2.8 (0.7, 6.4) 3.1 (1.1, 6.4) 2.5 (0.4, 5.8)
2041–2060 2.2 (0.3, 6.5) 2.0 (0.2, 6.1) 1.7 (0.1, 5.6) 1.7 (0.1, 5.7) 1.2 (0.0, 5.2)
2081–2100 2.4 (0.2, 6.2) 1.7 (0.0, 6.0) 0.8 (0.0, 4.6) 0.5 (0.0, 3.3) 0.3 (0.0, 2.2)
March2021–2040 14.0 (11.4, 18.7) 14.9 (11.9, 25.8) 14.9 (11.9, 23.5) 15.0 (11.7, 27.3) 14.9 (11.9, 24.7)
2041–2060 13.8 (10.9, 18.3) 14.5 (10.9, 25.7) 14.3 (11.1, 23.3) 14.2 (10.5, 27.1) 13.9 (10.2, 24.5)
2081–2100 13.7 (10.9, 18.5) 14.2 (10.6, 25.7) 13.1 (9.5, 22.2) 11.8 (5.4, 25.5) 9.7 (3.1, 21.6)
Septem ber Arctic sea ice  extent
Historica l
RCP8.5
RCP8.5 ( 1.5ºC)
RCP8.5 ( 2.0ºC)
RCP8.5 ( 3.0ºC)
1950                         200 0 210001234567million  km2
2050Figure 4. 5 | Arctic sea ice extent in September in a large initial-condition 
ensemble of observationally-constrained simulations of an Earth system 
model (CanESM2).  The black and red curves are averages over twenty simulations

following historical forcings to 2015 and RCP8.5 extensions to 2100. The other curves 
are averages of over 20 simulations each after global surface air temperature has been 
stabilized at the indicated degree of global mean warming relative to 1850–1900. The 
bars to the right are the minimum to maximum ranges over 2081–2100 (Sigmond 
et  al., 2018). The horizontal dashed line indicates a  practically sea ice-free Arctic. 
(Table 4.SM.1).

576
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information40.32–0.63 m for RCP4.5, 0.33–0.63 m for RCP6.0, and 0.45–0.82 m 
for RCP8.5. For RCP8.5, the rise by 2100 is 0.52–0.98 m with a rate 
during 2081–2100 of 8–16 mm yr –1.
There have been substantial modelling advances since AR5, with 
most sea level projections corresponding to one of three categories: 
(i)  central-range projections, combining scenario-conditional 
probability distributions for the different contributions to estimate 
a  central range under different scenarios; (ii) probabilistic 
projections, which explicitly consider outcomes for a  wide range 
of likelihoods, including low-likelihood, high-impact outcomes; and 
(iii)  semi-empirical projections, based on statistical relationships 
between past GMSL changes and climate variables, which 
now calibrate individual contributions and are consistent with 
physical-model based estimates (Section 9.6.3).

Based on the assessment of the latest modelling information 
(Figure 4.2d and Section 9.6.3), we conclude that under the SSP3-7.0, 
the likely  range of GMSL change averaged over 2081–2100 relative 
to 1995–2014 is 0.46–0.74 m. Under SSP1-2.6, the likely  range 
over the long-term is 0.30–0.54 m. Further, in SSP2-4.5, SSP3-7.0, 
and SSP5-8.5, the rise in GMSL is projected to accelerate over the 
21st  century. A  detailed assessment of the processes contributing 
to these projected rises and accelerations in GMSL, together 
with a comparison to AR5 and SROCC, can be found in Chapter 9 
(Section 9.6.3). Projected changes in the thermosteric component of 
GMSL beyond 2300 are assessed in Section 4.7.1.
In summary, it is virtually certain  that under any one of the assessed 
SSPs, there will be continued rise in GMSL through the 21st century.
4.3.2.3 Atlantic Meridional Overturning Circulation
The AR5 assessed from CMIP5 simulations that the Atlantic Meridional

Overturning Circulation (AMOC) will very likely  weaken over the 
21st century, and the projected weakening of the AMOC is consistent 
with CMIP5 projections of an increase of high-latitude temperature 
and high-latitude precipitation, with both effects causing the surface 
waters at high latitudes to become less dense and therefore more 
stable (Collins et al., 2013).
Based on CMIP6 models, we find that over the 21st century, AMOC 
strength, relative to 1995–2014, shows a multi-model mean decrease 
in each of the SSP scenarios but with a  large spread across the 
individual simulations (Figure 4.6). We also note that the magnitude 
of the ensemble-mean strength decrease is approximately scenario 
independent up to about 2060 (Weijer et al., 2020). A more detailed 
assessment of these projected AMOC changes, and the mechanisms 
involved, can be found in Chapter 9 (Section 9.2.3).
In summary, we assess from the CMIP6 models that AMOC weakening

over the 21st century is very likely ; the rate of weakening is approximately 
independent of the emissions scenario ( high confidence ).Based on a  large initial condition ensemble of simulations with 
a  CMIP5 model (CanESM2) with emissions scenarios leading to 
stabilization of global warming of 1.5°C, 2.0°C, or 3.0°C relative 
to 1850–1900, AMOC continues to decline for 5–10 years after 
GSAT is effectively stabilized at the given GWL (Sigmond et  al., 
2020). This is followed by a  recovery of AMOC strength for about 
the next 150 years to a level that is approximately independent of 
the considered stabilization scenario. These results are replicated in 
simulations in a CMIP6 model (CanESM5) with emissions cessation 
after diagnosed CO 2 emissions reach 750 Gt, 1000 Gt, or 1500 Gt. 
These emissions levels lead to global warming stabilization at 1.5°C, 
2.0°C, or 3.0°C relative to 1850–1900. In summary, in these model

simulations the AMOC recovers over several centuries after the 
cessation of CO 2 emissions ( medium confidence ).
4.3.2.4 Ocean and Land Carbon Uptake
The AR5 concluded with very high confidence  that ocean carbon 
uptake of anthropogenic CO 2 will continue under all RCPs through the 
21st century, with higher uptake corresponding to higher concentration 
pathways. The future evolution of the land carbon uptake was assessed 
to be much more uncertain than for ocean carbon uptake, with a majority 
of CMIP5 models projecting a continued cumulative carbon uptake.
Atlantic m eridional overtu rning ci rculation
oo
ooooooooo
ooooooooo
ooooooooo
ooooooooo
Near
termMid
termLong
termHistorica l17
SSP1-1.9  2
SSP1-2.6 9
SSP2-4.5  9
SSP3-7.0  9
SSP5-8.5  9–10–505Relative to 19 95–201 4 (Sv)
1950                 2000   2050  2100Figure 4.6 | CMIP6 annual mean Atlantic Meridional Overturning Circulation 
(AMOC) strength change in historical and scenario simulations.  Changes are

relative to averages from 1995–2014. The curves show ensemble averages and the 
shadings the 5–95% ranges across the SSP1-2.6 and SSP3-7.0 ensembles. The circles 
to the right of the panel show the anomalies averaged from 2081–2100 for each 
of the available model simulations. The numbers inside the panel are the number of 
model simulations. Here, the strength of the AMOC is computed as the maximum 
value of annual mean ocean meridional overturning mass stream function in the 
Atlantic at 26°N. Results are from concentration-driven simulations. Further details 
on data sources and processing are available in the chapter data table (Table 4.SM.1).

577
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44Based on results from the CMIP6 models, we conclude that the flux 
of carbon from the atmosphere into the ocean increases continually 
through most of 21st  century in the two highest emissions 
and decreases continually under the other emissions scenarios 
(Figure 4.7a). The flux of carbon from the atmosphere to land shows 
a similar 21st century behaviour across the scenarios but with much 
higher year-to-year variation than ocean carbon flux (Figure 4.7b). 
A  more in-depth assessment and discussion of the mechanism 
involved can be found in Chapter 5 (Section 5.4.5).
In summary, we assess that the cumulative uptake of carbon by the 
ocean and by land will increase through the 21st century irrespective 
of the considered emissions scenarios except SSP1-1.9 ( very 
high confidence ).
4.3.2.5 Surface Ocean pH
The AR5 assessed from CMIP5 simulations that it is virtually certain

that increasing storage of carbon by the ocean under all four RCPs 
through to 2100 will increase ocean acidification in the future (Ciais 
et al., 2013). Specifically, AR5 reported that CMIP5 models project 
increased ocean acidification globally to 2100 under all RCPs, 
and that the corresponding model mean and model spread in the 
decrease in surface ocean pH from 1986–2005 to 2081–2100 would 
be 0.065 (0.06–0.07) for RCP2.6, 0.145 (0.14–0.15) for RCP4.5, 
0.203 (0.20–0.21) for RCP6.0 and 0.31 (0.30–0.32) for RCP8.5.
Based on results from the CMIP6 models we conclude that, except 
for the lower-emissions scenarios SSP1-1.9 and SSP1-2.6, ocean 
surface pH decreases monotonically through the 21st  century 
(high confidence ) (Figure 4.8).
(a) Atmosp here to ocea n carbon flux 
Near
termMid
termLong
termHistorica l18
SSP1-1.9  5
SSP1-2.6 12
SSP2-4.5 13
SSP3-7.0 12
SSP5-8.5 12
–3036912PgC/yr(b) Atmosp here to lan d carbon flux 
18511111010
–3036912PgC/yr

1950                     2000    2050  2100 1950                     2000    2050  2100Figure  4.7 | CMIP6 carbon uptake in historical and scenario simulations. (a)  Atmosphere to ocean carbon flux (PgC yr –1). (b) Atmosphere to land carbon flux 
(PgC yr –1). The curves show ensemble averages and the shadings show the 5–95% ranges across the SSP1-2.6 and SSP3-7.0 ensembles. The numbers inside each panel are 
the number of model simulations. The land uptake is taken as Net Biome Productivity (NBP) and so includes any modelled net land-use change emissions. Results are from 
concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).
Global su rface ocean pH
Near
termMid
termLong
termHistorica l 7
SSP1-1.9  2
SSP1-2.6 5
SSP2-4.5  6
SSP3-7.0 6
SSP5-8.5  4
7.67.77.87.98.08.1(no uni ts)
1950                     2000    2050  2100
Figure   4.8 | Global average surface ocean pH.  The shadings around the

SSP1-2.6 and SSP5-7.0 curves are the 5–95% ranges across those ensembles. 
The numbers inside each panel are the number of model simulations. Results are from 
concentration-driven simulations. Further details on data sources and processing are 
available in the chapter data table (Table 4.SM.1).

578
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information44.3.3 Modes of Variability
4.3.3.1 Northern and Southern Annular Modes
4.3.3.1.1 Northern Annular Mode
The Northern Annular Mode (NAM) is the leading mode of variability 
in the NH extratropical atmosphere (Section AIV.2.1). Throughout this 
chapter, we use a simple fixed latitude-based NAM index defined as 
the difference in SLP between 35°N and 65°N (Section AIV.2.1; Li and 
Wang, 2003). The NAM index computed from the latitudinal gradient 
in SLP is strongly correlated with variations in the latitudinal position 
and strength of the mid-latitude westerly jets, and with the spatial 
distribution of Arctic sea ice (Caian et al., 2018). Projected changes 
in the position and strength of the mid-latitude westerly jets, storm 
tracks, and atmospheric blocking in both hemispheres are assessed 
in Section 4.5.1.6. The AR5 referred to the NAM, and its synonym the

Arctic Oscillation (AO), through its regional counterpart, the North 
Atlantic Oscillation (NAO). Here, we use the term NAM to refer also 
to the AO and NAO (Section AIV.2.1), accepting that the AO and NAO 
are not identical entities.
We first summarize the assessment of past NAM changes and their 
attribution from Chapters 2 and 3 to put into context the future 
projections described here. Strong positive trends for the NAM/NAO 
indices were observed since 1960, which have weakened since the 
1990s ( high confidence ) (Section  2.4.1.1). The NAO variability in 
the instrumental record was likely  not unusual in the millennial and 
multi-centennial context (Section 2.4.1.1). Climate models simulate 
the gross features of the NAM with reasonable fidelity, including 
its interannual variability, but models tend to systematically 
underestimate the amount of multi-decadal variability of the NAM 
and jet stream compared to observations (Section  3.7.1; J. Wang

et  al., 2017b; Bracegirdle et  al., 2018; Simpson et  al., 2018), with 
the caveat of the observational record being relatively short to characterize decadal variability (Chiodo et  al., 2019). A  realistic 
simulation of the stratosphere and SST variability in the tropics and 
northern extratropics are important for a model to realistically capture 
the observed NAM variability. Despite some evidence from climate 
model studies that anthropogenic forcings influence the NAM, there 
is limited evidence  for a significant role for anthropogenic forcings in 
driving the observed multi-decadal variations of the NAM over the 
instrumental period (Section 3.7.1).
The AR5 assessed from CMIP5 simulations that the future boreal 
wintertime NAM is very likely to exhibit large natural variations and 
trends of similar magnitude to that observed in the past and is likely to 
become slightly more positive in the future (Collins et al., 2013). Based

on CMIP6 model results displayed in Figure  4.9a, we conclude that 
the boreal wintertime surface NAM is more positive by the end of the 
21st century under SSP3-7.0 and SSP5-8.5 ( high confidence ). For these 
high emissions scenarios, the 5–95% range of NAM index anomalies 
averaged from 2081–2100 are 0.3–3.8 hPa and 0.32–5.2  hPa, 
respectively. On the other hand, under neither of the lowest emissions 
scenarios, SSP1-1.9 and SSP1-2.6, does the NAM show a  robust 
change, by the end of the 21st century ( high confidence ).
Significant progress has been made since AR5 in understanding the 
physical mechanisms responsible for changes in the NAM, although 
uncertainties remain. It is now clear from the literature that the 
NAM response, and the closely-related response of the mid-latitude 
storm tracks, to anthropogenic forcing in CMIP5-era climate models 
is determined by a  ‘tug-of-war’ between two opposing processes

(Harvey et al., 2014; Shaw et al., 2016; Screen et al., 2018a): (i) Arctic 
amplification (Sections 4.5.1.1 and 7.4.4.1), which decreases the 
low-level meridional temperature gradient, reduces baroclinicity on 
the poleward flank of the eddy-driven jet, and shifts the storm tracks 
equatorward and leading to a  negative  NAM (see Box 10.1; Harvey et al., 
2015; Hoskins and Woollings, 2015; Peings et al., 2017; Screen et al., 
2018a); and (ii) enhanced warming in the tropical upper-troposphere, 
(a) No rthern  annul ar mod e (DJF)
Near
termMid
termLong
termHistorica l52
SSP1-2.6 34
SSP3-7.0 28
SSP5-8.5 35
–12–8–404812Relative to 199 5–2014 (hPa )(b) S outhern  annular mod e (DJF)
521134332835
–12–8–404812Relative to 199 5–2014 (hPa )

Figure 4 .9 | CMIP6 simulations of boreal winter (December –January–February, DJF) Annular Mode indices. (a) NAM and (b) SAM. The NAM is defined as the 
difference in zonal mean SLP at 35°N and 65°N (Li and Wang, 2003) and the SAM as the difference in zonal mean SLP at 40°S and 65°S (Gong and Wang, 1999). All anomalies 
are relative to averages from 1995–2014. The curves show multi-model ensemble averages over the CMIP6 r1 simulations. The shadings around the SSP1-2.6 and SSP3-7.0 
curves denote the 5–95% ranges of the ensembles. The numbers inside each panel are the number of model simulations. The results are for concentration-driven simulations. 
Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

579
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44due to GHG increases and associated water vapour and lapse rate 
feedbacks, which increases the upper-level meridional temperature 
gradient and causes a poleward shift of the storm tracks and a  positive  
NAM (Harvey et al., 2014; Vallis et al., 2015; Shaw, 2019). The large 
diversity in projected NAM changes in CMIP5 multi-model ensemble 
(Gillett and Fyfe, 2013) appears to be at least partly explained by the 
relative importance of these two mechanisms in particular models 
(Harvey et al., 2014, 2015; Vallis et al., 2015; McCusker et al., 2017; 
Oudar et  al., 2017). Models that produce larger Arctic amplification 
also tend to produce larger equatorward shifts of the mid-latitude jets 
and associated negative NAM responses (Barnes and Polvani, 2015; 
Harvey et al., 2015; Zappa and Shepherd, 2017; McKenna et al., 2018; 
Screen et al., 2018a; Zappa et al., 2018).

Another area of progress is new understanding the role of cloud 
radiative effects in shaping the mid-latitude circulation response 
to anthropogenic forcing. Through their non-uniform distribution of 
radiative heating, cloud changes can modify meridional temperature 
gradients and alter mid-latitude circulation and the annular modes in 
both hemispheres (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; 
Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 
2018; Albern et al., 2019; Voigt et al., 2019). In addition to the effects 
of changing upper and lower tropospheric temperature gradients on 
the NAM, progress has been made since AR5 in understanding the 
effect of simulated changes in the strength of the stratospheric polar 
vortex on winter NAM projections (Manzini et al., 2014; Zappa and 
Shepherd, 2017; Simpson et al., 2018).
4.3.3.1.2 Southern Annular Mode
The Southern Annular Mode (SAM) is the leading mode of large-scale

extratropical atmospheric variability in the Southern Hemisphere and 
influences most of the southern extratropics (Annex IV, Section AIV.2.2). 
In its positive phase, the SAM characterizes anomalously low pressure 
over the polar cap and high pressure in southern mid-latitudes (Marshall, 
2003). While there are some zonal asymmetries to the structure of the 
SAM (Section AIV.2.2), it is more symmetric than its NH counterpart 
(Fyfe et  al., 1999). Throughout this chapter, we use a  simple fixed 
latitude-based SAM index defined as the difference in zonal mean SLP 
between 40°S and 65°S (Gong and Wang, 1999; see Section AIV.2.2 for 
discussion of other SAM indices). Although the SAM is often used as 
a proxy for the location of the mid-latitude westerly wind belt, trends 
in the SAM can reflect a combination of changes in jet position, width, 
and strength. The changes in the Southern Hemisphere circulation 
associated with the SAM influence surface wind stress (Wang et al.,

2014) and hence affect the Southern Ocean.
Over the instrumental period, there has been a  robust positive 
trend in the SAM index, particularly since 1970 ( high confidence ) 
(Section  2.4.1.2). There is medium confidence  that the recent 
trend in the SAM is unprecedented in the past several centuries 
(Section 2.4.1.2). There is high confidence  that stratospheric ozone 
depletion and GHG increases have contributed to the positive SAM 
trend during the late 20th century, with ozone depletion dominating 
in austral summer, following the peak of the Antarctic ozone hole 
in September –October, and GHG increases dominating in other 
seasons  (Section  3.7.2). To capture the effects of stratospheric ozone changes on the SAM, climate models must include a realistic 
representation of ozone variations (Section 3.7.2). In models that do 
not explicitly represent stratospheric ozone chemistry, which includes 
the majority of the CMIP6 model ensemble, an ozone dataset is

prescribed. To properly capture the effects of ozone depletion and 
recovery on the stratosphere and surface climate, the prescribed 
ozone dataset must realistically capture observed stratospheric ozone 
trends with sufficiently high temporal resolution (Neely et al., 2014; 
Young et al., 2014). The CMIP6 experiment protocol recommended 
the use of a prescribed 4-D monthly mean ozone concentration field 
for models without stratospheric chemistry (Eyring et al., 2016).
The AR5 assessed that the positive trend in the austral summer/
autumn SAM observed since 1970 (see Section  2.4.1.2) is likely  to 
weaken considerably as stratospheric ozone recovers through the 
mid-21st  century, while in other seasons the SAM changes depend 
on the emissions scenario, with a larger increase in SAM for higher 
emissions scenarios. In CMIP6 models, the austral summer SAM 
is more positive by the end of the 21st century under SSP3-7.0 and

SSP5-8.5 (Figure  4.9b). On the other hand, under SSP1-1.9 and 
SSP1-2.6, the SAM is projected to be less positive, especially under 
SSP1-1.9 where the 5–95% ranges of anomalies relative to 1995–2014 
are –3.1 to 0.0 hPa averaged from 2081–2100. In summary, under 
the highest emissions scenarios in the CMIP6 models, the SAM in 
the austral summer becomes more positive through the 21st century 
4.3.3.2 El Niño–Southern Oscillation
The El Niño–Southern Oscillation (ENSO) is the most dominant mode 
of variability on interannual time scales and also the dominant source 
of seasonal climate predictability (Box  11.3 and Annex IV, 
Section AIV.2.3; Timmermann et  al., 2018). The AR5 assessed from 
CMIP5 simulations that ENSO variability will very likely  remain the 
dominant mode of interannual climate variability in the future, and 
that associated ENSO precipitation variability on regional scales is

likely  to intensify (Christensen et al., 2013). However, they assessed 
there was low confidence  in projected changes in ENSO variability in 
the 21st century due to a strong component of internal variability.
Among a range of indices proposed for representing ENSO, we use 
the most prominent one, the Niño 3.4 index, defined as the average 
equatorial SST or precipitation across the central equatorial Pacific 
(5°S–5°N, 170°W–120°W; Section AIV.2.3). Here, we consider the 
evolution of the amplitude of Niño 3.4 index for SST and precipitation 
over the 21st  century as projected by CMIP6 models. Analysis of 
CMIP6 models shows there is no robust model consensus on the 
forced changes in the amplitude of ENSO SST variability even under 
the high-emissions scenarios SSP3-7.0 and SSP5-8.5, but a significant 
increasing trend in the amplitude of ENSO precipitation variability is 
projected across the 21st century in the four SSPs (Figure 4.10). This

is broadly consistent with results from CMIP5 models (Christensen 
et al., 2013; Power et al., 2013; Cai et al., 2015; Chen et al., 2017; 
Wengel et al., 2018), recent studies with CMIP6 models (Brown et al., 
2020; Fredriksen et al., 2020; Freund et al., 2020; Yun et al., 2021), 
and large initial-condition ensemble experiments (Maher et al., 2018; 
Zheng et al., 2018; Haszpra et al., 2020).

580
It is therefore very likely  that the amplitude of ENSO rainfall variability 
will intensify in response to global warming over the 21st century 
although there is no robust consensus from CMIP6 climate models 
for a systematic change in amplitude of ENSO SST variability even in 
the high-emissions scenarios of SSP3-7.0 and SSP5-8.5.
4.3.4 Synthesis Assessment of Projected Change 
in Global Surface Air Temperature
GSAT change is assessed using multiple lines of evidence 
including the CMIP6 projection simulations out to year 2100. The 
assessment combines CMIP6 projections driven by SSP scenarios 
with observational constraints on simulated past warming (Box 4.1 and Figure 4.11a,b; Brunner et al., 2020; Liang et al., 2020; Nijsse 
et  al., 2020; Tokarska et  al., 2020; Ribes et  al., 2021), as well 
as the AR6-updated assessment of ECS and TCR in Section  7.5.

The approaches of (Liang et al., 2020; Tokarska et al., 2020; Ribes 
et al., 2021) have first been extended to all 20-year averaging periods 
between 2000 and 2100. For each 20-year period, the 5%, 50%, and 
95% percentile GSAT values of these three constrained CMIP6 results 
are averaged percentile by percentile (Figure 4.11c). Then, an emulator 
based on a two-layer energy balance model (e.g., Held et al., 2010) 
is driven by the Chapter 7-derived ERF . The emulator parameters are 
chosen such that the central estimate, lower bound of the very likely 
range, and upper bound of the  very likely  range of climate feedback 
parameter and ocean heat uptake coefficient take the values that 
map onto the corresponding combination of ECS (3°C, 2°C and 
5°C, respectively) and TCR (1.8°C, 1.2°C and 2.4°C, respectively) 
of Section 7.5 (see Box 4.1). As a final step, the constrained-CMIP6 
and the emulator-based 5%, 50%, and 95% percentile GSAT values

are averaged percentile by percentile (Figure 4.11c,d and Table 4.5). 
Constrained CMIP6 results and the ECS- and TCR-based emulator 
thus contribute one-half each to the GSAT assessment. Because the 
emulator results and (Ribes et al., 2021) represent the forced response 
only, and averaging over the other two individual estimates (Liang 
et al., 2020; Tokarska et al., 2020) further reduces the contribution 
from internal variability, the assessed GSAT time series are assumed 
to represent purely the forced response.
Averaged over the period 2081–2100, GSAT is very likely  to be higher 
than in the recent past (1995–2014) by 0.3°C–0.9°C in the low-
emissions scenario SSP1-1.9 and by 2.6°C–4.7°C in the high-emission 
scenario SSP5-8.5. For the scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0, 
the corresponding very likely  ranges are 0.6°C–1.4°C, 1.3°C–2.5°C, 
and 2.0°C–3.8°C, respectively (Figure  4.11 and Table  4.5). Because

the different approaches for estimating long-term GSAT change 
produce consistent results (Figure  4.11), there is high confidence 
in this assessment. These ranges of the long-term projected GSAT 
change generally correspond to AR5 ranges for related scenarios but 
the likelihood is increased to very likely  ranges, in contrast to the  likely  
ranges in AR5. Over the mid-term period 2041–2060, the very 
likely  GSAT ranges of SSP1-1.9 and SSP5-8.5 are almost completely 
distinct ( high confidence ) (Table 4.5; see also Section 4.3.1).
CMIP6 models project a wider range of GSAT change than the assessed 
range ( high confidence ) (Section 4.3.1). The CMIP6 models with a higher 
climate sensitivity simulate warming rates higher than assessed 
very likely  here (Section 4.3.1); these rates are very unlikely  but not 
impossible to occur and hence cannot be excluded. The implications of 
these very unlikely  warming rates for patterns of surface temperature

and precipitation change are assessed in Section 4.8.
For the near term, initialized decadal forecasts constitute another 
line of evidence over the period 2019–2028 (Box 4.1). The forecasts 
are consistent with the assessed GSAT very likely  range (Box  4.1, 
Figure 1), strengthening the confidence in the near-term assessment.
The assessed ranges of GSAT change can be converted to change 
relative to mean GSAT over the period 1850–1900 for a consistent 
comparison with AR5 (IPCC, 2013) and SR1.5 (IPCC, 2018a). 
(a) Ni no3.4 SS T ampl itude chang e
1950 2000 2050 21000.00.51.01.52.0Standar d deviati on ratio (w rt 1995 –2014)
(b) Ni no3.4 pr ecipitation  amplitud e change
1950 2000 2050 21000123Standar d deviatio n ratio (w rt 1995 –2014)Historica l (45)
SSP1-1.9 (1 3)
SSP1-2.6 (4 0)
SSP2-4.5 (4 0)
SSP3-7.0 (3 6)
SSP5-8.5 (4 3)
Historica l (44)
SSP1-2.6 (3 9)
SSP2-4.5 (3 9)

SSP5-8.5 (4 2)Figure 4.10 | Changes in amplitude of ENSO Variability.  Variability of (a) SST 
and (b) precipitation anomalies averaged over Niño 3.4 region for 1950–2014 
from CMIP6 historical simulations and for 2015–2100 from four SSPs. Thick lines 
stand for multi-model mean and shading is the 5–95% range across CMIP6 models 
for historical simulation (grey), SSP1-2.6 (blue) and SSP3-7.0 (pink), respectively. 
The amplitude of ENSO SST and rainfall variability is defined as the standard deviation 
of the detrended Niño 3.4-area averaged SST and rainfall index, respectively, over 
30-year running windows. The standard deviation in every single model is normalized 
by each model’s present-day standard deviation averaged from 1995 to 2014. 
The number of available models is listed in parentheses. This figure is adopted from 
Yun et al. (2021). Further details on data sources and processing are available in the 
chapter data table (Table 4.SM.1).

581
GSAT was warmer in 1995–2014 (recent past) than 1850–1900 by 
0.85 [0.67 to 0.98] °C. GSAT diagnosed for 1986–2005 (AR5 recent 
past) relative to 1850–1900 is 0.08°C higher than was diagnosed 
in AR5, due to methodological and dataset updates (Cross-Chapter 
Box 2.3, Table 1).The uncertainty in GSAT relative to 1850–1900 includes the very 
likely  ranges of assessed GSAT change relative to 1995–2014 
(depending on scenario and period, between 0.5°C and 2.4°C; 
Figure  4.11d and Table  4.5), the uncertainty in historical GSAT 
change from the mean over 1850–1900 to 1995–2014 (about 0.3°C; 
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.50123456(a)Unconstrain ed CMIP6
Liangetal.
Ribes etal.
Tokars ka etal.Liangetal.
Tokars ka etal.
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.50123456(b)Constrain ed CMIP6
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.50123456
(c)Synthesis
Constrain edCMIP 6

Emulator
2000–2019 2020–2039 2040–2059 2060–2079 2081–2100012345GSAT 2081–2100 relative to 1995–2014 (°C)(d)Assessed GSAT
SSP1 -1.9
SSP1 -2.6
SSP2 -4.5
SSP3 -7.0
SSP5 -8.5
1.5 °C123456
123456
123456
12345Relative to 1850–1900 (°C)GSAT 2081–2100 relative to 1995–2014 (°C) GSAT 2081–2100 relative to 1995–2014 (°C)
GSAT 2081–2100 relative to 1995–2014 (°C)
Relative to 1850–1900 (°C) Relative to 1850–1900 (°C)Relative to 1850–1900 (°C)
Figure 4.1 1 | Multiple lines of evidence for global surface air temperature (GSAT) changes for the long-term period, 2081–2100, relative to the average 
over 1995–2014, for all five priority scenarios.  The unconstrained CMIP6 5–95% ranges (coloured bars) in (a) differ slightly because different authors used different 
subsamples of the CMIP6 archive. The constrained CMIP6 5–95% ranges (coloured bars) in (b) are smaller than the unconstrained ranges in (a) and differ because of

different samples from the CMIP6 archive and because different observations and methods are used. In (c), the average of the ranges in (b) is formed (grey bars). Green bars in 
(c) show the emulator ranges, defined such that the best estimate, lower bound of the very likely  range, and upper bound of the  very likely  range of climate feedback parameter 
and ocean heat uptake coefficient take the values that map onto the corresponding values of ECS and TCR of Section 7.5 (see Box 4.1). The time series in (d) are constructed 
by taking the average of the constrained CMIP6 ranges and the emulator ranges. The y-axes on the right-hand side are shifted upward by 0.85°C, the central estimate of the 
observed warming for 1995–2014, relative to 1850–1900 (Cross-Chapter Box 2.3, Table 1). Further details on data sources and processing are available in the chapter data 
table (Table 4.SM.1).

582
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Cross-Chapter  Box  2.3), and the estimate of internal variability in 
20-year GSAT averages (5–95% range about 0.15°C, Box 4.1; Maher 
et  al., 2019). These uncertainties are assumed to be independent 
and are added in quadrature, meaning that the total uncertainty is 
only slightly larger than the dominating contribution by the GSAT 
change relative to 1995–2014 (Table 4.5). The addition is done by 
numerically sampling a normal distribution fitted to the 5%, 50% and 
95% percentiles of the internal variability, as well as sampling skew-
normal distributions (e.g., O’Hagan and Leonard, 1976) fitted to the 
5%, 50% and 95% percentiles of both historical warming and GSAT 
relative to 1995–2014. The result is a joint probability distribution of 
GSAT change and 20-year period.
Averaged over the period 2081–2100, GSAT is very likely  to be

higher than in the period 1850–1900 by 1.0°C–1.8°C in the low-
emissions scenarios SSP1-1.9 and by 3.3°C–5.7°C in the high-
emissions scenario SSP5-8.5. For the scenarios SSP1-2.6, SSP2-4.5, 
and SSP3-7.0, the corresponding very likely  ranges are 1.3°C–2.4°C, 
2.1°C–3.5°C, and 2.8°C–4.6°C, respectively (Table 4.5).
Time series of assessed GSAT change are now used to assess the time 
when certain thresholds of GSAT increases are crossed (Table 4.5). 
The threshold-crossing time is defined as the midpoint of the first 
20-year period during which the average GSAT exceeds the threshold. 
During the near term (2021–2040), a 1.5°C increase in the 20-year 
average of GSAT, relative to the average over the period 1850–1900, 
is very likely  to occur in scenario SSP5-8.5, likely to occur in scenarios 
SSP2-4.5 and SSP3-7.0, and more likely than not  to occur in scenarios 
SSP1-1.9 and SSP1-2.6. In all scenarios assessed here except SSP5-8.5,

the central estimate of crossing the 1.5°C threshold lies in the early 
2030s, in the early part of the likely range (2030 –2052) assessed in SR1.5, which assumed continuation of the then-current warming 
rate. Roughly half of this difference arises from a  larger historical 
warming diagnosed in AR6, while the other half arises because for 
central estimates of climate sensitivity, most scenarios show stronger 
warming over the near term than was estimated as ‘current’ in SR1.5 
(medium confidence ). The SR1.5 estimate with a  median of 0.2°C 
per decade has been confirmed in AR6 (Section 3.3.1); by contrast, 
the assessed GSAT change shows central-estimate rates over the 
period 2010 to 2035 that range from 0.21°C per decade under 
SSP1-1.9 to 0.30°C per decade under SSP5-8.5. When considering 
scenarios similar to SSP1-1.9 instead of linear extrapolation, the 
SR1.5 estimate of when 1.5°C global warming is crossed is close to

the central estimate reported here (SR1.5, Table 2.SM. 12). If ECS and 
TCR lie near the lower end of the assessed very likely  range, crossing 
the 1.5°C warming threshold is avoided in scenarios SSP1-1.9 
and SSP1-2.6 ( medium confidence ). It is more likely than not  that 
under SSP1-1.9, GSAT relative to 1850–1900 will remain below 
1.6°C throughout the 21st century, implying a potential temporary 
overshoot above 1.5°C of no more than 0.1°C. All statements about 
crossing the 1.5°C threshold assume that no major volcanic eruption 
occurs during the near term.
A warming level of 2°C in GSAT, relative to the period 1850–1900, 
is very likely  to be crossed in the mid-term period 2041–2060 under 
SSP5-8.5, likely  to be crossed in the mid-term period under SSP3-7.0, 
and more likely than not  to be crossed during the mid-term period 
under SSP2-4.5. During the entire 21st  century, a  warming level 
of 2°C in GSAT, relative to the period 1850–1900, will be crossed

under SSP5-8.5 and SSP3-7.0, will extremely likely  be crossed under 
SSP2-4.5, will unlikely  be crossed under SSP1-2.6, and will extremely 
unlikely  be crossed under SSP1-1.9.
Table 4. 5 | Assessment results for 20-year averaged GSAT change, based on multiple lines of evidence. The change is displayed in °C relative to the 1995–2014 
and 1850–1900 reference periods for selected time periods (near term 2021–2040, mid-term 2041–2060, and long term 2081–2100), and as the time when certain temperature 
thresholds are crossed, relative to the period 1850–1900. The recent reference period 1995–2014 was higher in GSAT than the period 1850–1900 by 0.85 [0.67 to 0.98] °C, 
(Cross-Chapter Box 2.3). The entries give both the central estimate and, in parentheses, the very likely  (5–95%) range. An entry of ‘n.c.’ means that the global warming threshold 
is ‘not crossed’ during the period 2021–2100.
Time Period SSP1-1.9 (°C) SSP1-2.6 (°C) SSP2-4.5 (°C) SSP3-7.0 (°C) SSP5-8.5 (°C)

Near Term: 2021–2040
Relative to 1850–19000.6 [0.4 to 0.9]
1.5 [1.2 to 1.7]0.6 [0.4 to 0.9]
1.5 [1.2 to 1.8]0.7 [0.4 to 0.9]
1.5 [1.2 to 1.8]0.8 [0.5 to 1.0]
1.6 [1.3 to 1.9]
Mid-term: 2041–2060
Relative to 1850–19000.7 [0.4 to 1.1]
1.6 [1.2 to 2.0]0.9 [0.5 to 1.3]
1.7 [1.3 to 2.2]1.1 [0.8 to 1.6]
2.0 [1.6 to 2.5]1.3 [0.9 to 1.7]
2.1 [1.7 to 2.6]1.5 [1.1 to 2.1]
2.4 [1.9 to 3.0]
Long Term: 2081–2100
Relative to 1995–2014 
Relative to 1850–19000.6 [0.2 to 1.0]
1.4 [1.0 to 1.8]0.9 [0.5 to 1.5]
1.8 [1.3 to 2.4]1.8 [1.2 to 2.6]
2.7 [2.1 to 3.5]2.8 [2.0 to 3.7]
3.6 [2.8 to 4.6]3.5 [2.4 to 4.8]
4.4 [3.3 to 5.7]
1.5°C
Relative to 1850–19002025–2044
[2013–2032 to n.c.]2023–2042
[2012–2031 to n.c.]2021–2040
[2012–2031 to 
2037–2056]2021–2040
[2013–2032 to 
2033–2052]2018–2037
[2011–2030 to 
2029–2048]
2°C
Relative to 1850–1900n.c.
[n.c. to n.c.]n.c.
[2031–2050 to n.c.]2043–2062
[2028–2047 to 
2075–2094]2037–2056

[2026–2045 to 
2053–2072]2032–2051
[2023–2042 to 
2044–2063]
3°C
[2061–2080 to n.c.]2066–2085
[2050–2069 to n.c.]2055–2074
[2042–2061 to 
2074–2093]
4°C
[2070–2089 to n.c.]2075–2094
[2058–2077 to n.c.]

583
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 444.4 Near-term Global Climate Changes
This section assesses changes in large-scale climate over the period 
2021–2040 and includes information from both projections and 
initialized decadal predictions. The structure is similar to Section 4.3. 
Unless noted otherwise, the assessment assumes that there will be 
no major volcanic eruption in the near term. The climatic effects of 
volcanic eruptions are assessed in Section 4.4.4 and Cross-Chapter 
Box 4.1; Section 4.4.4 also assesses the climate effects of short-lived 
climate forcers.
4.4.1 Atmosphere
4.4.1.1 Average Global Surface Air Temperature
The AR5 assessed that it is likely  that GSAT will increase in the 
range 0.3°C–0.7°C over the period 2016–2035 relative to 
1986–2005 ( medium confidence ), and that there were not large 
differences in the GSAT change among different RCPs in this period

(Kirtman et  al., 2013). The AR5 further assessed that it is more 
likely than not  that the mean GSAT for the period 2016–2035 will 
be more than 1°C above the mean for 1850–1900, and it is very 
unlikely  that it will be more than 1.5°C above the 1850–1900 mean 
(medium confidence ). It was shown that in the period 2016–2035, 
differences in GSAT across RCP scenarios for a single climate model 
are typically smaller than differences between climate models under 
a single RCP scenario, indicating that model structural uncertainty 
is larger than scenario uncertainty over that period (Hawkins and 
Sutton, 2009).
Near-term (2021–2040) GSAT changes relative to 1995–2014 
exhibit only minor dependence on SSP scenario, consistent with 
AR5 (Table 4.5). Averaged over the twenty years of the near term 
and across all scenarios, GSAT is very likely  to be higher than over 
1995–2014 by 0.4°C–1.0°C (Table 4.5), with most of the uncertainty

arising from that in ECS and TCR ( high confidence ) (Section 4.3.4; 
e.g.,  Lehner et  al., 2020). The assessed near-term warming is thus 
larger than in AR5 by 0.1°C to 0.2°C. This upward revision has the 
same magnitude as the ad-hoc downward adjustment to near-term 
projected GSAT change that was performed in AR5 (Box  4.1; 
Kirtman et al., 2013).
Averaged near-term GSAT is as likely as not  at least 1.5°C higher than 
during 1850–1900, across the five SSP scenarios used here (Table 4.5 
and Section 4.3.4). This much higher likelihood of near-term warming 
reaching 1.5°C than in AR5 arises both because surface warming has 
continued since AR5 (the period 1995–2014 was warmer by 0.16°C 
than 1986–2005; Cross-Chapter Box  2.3, Table  1), and because 
of methodological and dataset updates (the AR6 assessment of 
1986–2005 GSAT change relative to 1850–1900 is 0.08°C higher 
than in the AR5; Cross-Chapter Box 2.3, Table 1).

For annual mean GSAT, uncertainty in near-term projections arises 
in roughly equal measure from internal variability and model 
uncertainty ( high confidence ) (Box  4.1). Forecasts initialized from recent observations simulate GSAT changes for the period 2019–2028 
relative to the recent past that are consistent with the assessed 
very likely  range in annual mean GSAT ( high confidence ) (Box  4.1, 
Figure 1, and Table 4.5). Because annual mean GSAT shows a higher 
level of internal variability than the 20-year mean, individual years 
are expected to cross the 1.5°C earlier than the assessed GSAT does. 
For example, Smith et al. (2018) apply a multi-model decadal-forecast 
ensemble to assess the likelihood that global warming of 1.5°C higher 
than over 1850–1900 will be temporarily exceeded in the near future.
When we repeat the uncertainty quantification for GSAT as in 
Section  4.3.4 but with the corresponding higher level of internal

variability for annual instead of 20-year averages added in quadrature, 
we can estimate the likelihood that an individual year would 
cross the GSAT 1.5°C threshold. By 2030, GSAT in any individual 
year could exceed 1.5°C relative to 1850–1900 with a  likelihood 
between 40 and 60 percent, across the scenarios considered here 
(medium confidence ).
4.4.1.2 Spatial Patterns of Surface Warming
Consistent with AR5 and earlier assessments, Figure  4.12 shows 
for SSP1-2.6 and SSP3-7.0 that the largest warming occurs at high 
latitudes, particularly in boreal winter in the Arctic (Section 4.5.1.1), 
and larger warming over land than over the ocean (Section 4.5.1.1). In 
both scenarios, the increase in seasonal mean surface temperatures 
over many NH land regions exceeds 1°C relative to 1995–2014. In the 
near term, the two scenarios show surface temperature changes that 
are similar in magnitude. The trajectories for well-mixed GHGs, and as

a consequence the effective radiative forcing, in the scenarios have 
not yet diverged that much (O’Neill et al., 2016; Riahi et al., 2017). 
Based on the currently available CMIP6 models, regions that do not 
show robust warming in the near-term include the northern North 
Atlantic, parts of India, parts of North America and Eurasia in winter, 
and the subtropical eastern Pacific in the Southern Hemisphere.
The ERF patterns from aerosols and well-mixed GHGs are distinct 
(Chapter 7), and warming patterns therefore depend on the precise 
mix of forcing agents in the scenarios. The spatial efficacies – the 
change in surface temperature per unit ERF  – for CO 2, sulphate 
and black carbon aerosols and solar forcing have been recently 
evaluated in climate models (Modak et  al., 2016, 2018; Duan 
et  al., 2018; Modak and Bala, 2019; Richardson et  al., 2019). On 
average, the spatial patterns of near-surface warming are largely

similar for different external drivers (Xie et  al., 2013; Richardson 
et  al., 2019; Samset et  al., 2020), despite the patterns of forcing 
being different and despite the large spread across different models 
(Richardson et al., 2019).
Internal variability in near-surface temperature change is large 
in many regions, particularly in mid-latitudes and polar regions 
(Hawkins and Sutton, 2012). Projections from individual realizations 
can therefore exhibit divergent regional responses in the near-term 
in areas where the amplitude of a forced signal is relatively small 
compared to internal variability (Deser et al., 2012b, 2014, 2016).

584
4.4.1.3 Precipitation
The AR5 assessed that zonal mean precipitation will very likely  
increase in high and some of the mid latitudes and will more likely than 
not decrease in the subtropics. The AR5 further assessed that the 
near-term changes in precipitation are largely uncertain at regional 
scales, and much of the non-robustness in near-term projections is 
attributable to internal variability and model uncertainty.
The mean patterns of seasonal precipitation change in CMIP6 models 
are consistent with AR5, increasing at high latitudes, over oceanic 
regions, and in wet regions over the tropics; and decreasing in dry 
regions including large parts of the subtropics (Figure  4.13). The 
magnitude of projected changes in precipitation in the near term, 
especially on regional scales is small compared to the magnitude 
of internal variability (Section  10.4.3; Hawkins and Sutton, 2011,

2016; Hoerling et al., 2011; Deser et al., 2012b; Power et al., 2012). Analyses  of CMIP5, CMIP6, and single-model large-ensemble 
simulations show that for the uncertainty in near-term precipitation 
projections, model uncertainty and internal variability dominate 
while the scenario uncertainty is very small (Section 8.5; Lehner et al., 
2020). Based on large ensembles of climate change experiments, 
it was shown that internal variability decreases over time for 
both temperature and precipitation on decadal scales (Zhang and 
Delworth, 2018; Tebaldi et  al., 2021). The precipitation projections 
from CMIP6 models shows larger model uncertainty associated with 
the higher average transient climate response (Lehner et al., 2020).
The ‘wet get wetter, dry get drier’ paradigm, which has been used to 
explain the global precipitation pattern responding to global warming 
(Held and Soden, 2006), might not hold, especially over subtropical

land regions (Greve et  al., 2014; Feng and Zhang, 2015; Greve and 
Seneviratne, 2015). Over the tropical oceans, precipitation changes are 
largely driven by the pattern of SST changes (He et al., 2018), and in Seasonal mean temperature change
DJF SSP1-2.6 (2021-40) DJF SSP3-7.0 (2021-2040)
JJA SSP1-2.6 (2021-40) JJA SSP3-7.0 (2021-2040)
Color Robust significant change
No or no robust significant change
Conflicting signal     –3  –2.5  –2 –1.5  –1  –0.5  0   0.5   1   1.5   2    2.5   3 
°C39
39 3737
Figure  4 .12 | Near-term change of seasonal mean surface temperature.  Displayed are projected spatial patterns of CMIP6 multi-model mean change (°C) in 
(top)  December –January–February (DJF) and (bottom)  June–July–August (JJA) near-surface air temperature for 2021–2040 from SSP1-2.6 and SSP3-7.0 relative to 1995–2014.

The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that 
is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. 
Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. 
Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models 
agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

585
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44the subtropics, precipitation response is driven primarily by the fast 
adjustment to CO 2 forcing (He and Soden, 2017). In addition to the 
response to GHG forcing, forcing from natural and anthropogenic 
aerosols exert impacts on regional patterns of precipitation 
(Section  10.3.1; Ramanathan et  al., 2005; Bollasina et  al., 2011; 
Polson et al., 2014; Krishnan et al., 2016; L. Liu et al., 2018; Shawki 
et al., 2018). The large uncertainties in near-term regional precipitation 
projections arise due to the interplay between internal variability and 
anthropogenic external forcing (Endo et al., 2018; Wang et al., 2021). 
Uncertainties in future aerosol emissions scenarios contribute to 
uncertainties in regional precipitation projections (Wilcox et al., 2020). 
Aerosol changes induce a drying in the SH tropical band compensated

by wetter conditions in the NH counterpart (Acosta Navarro et al., 2017). 
The spatially uneven distribution of the aerosol forcing may also induce 
changes in tropical precipitation caused by shifts in the mean location 
of the intertropical convergence zone (ITCZ) (Hwang et al., 2013; Ridley 
et al., 2015; Voigt et al., 2017). Because of the large uncertainty in the 
aerosol radiative forcing and the dynamical response to the aerosol 
forcing there is medium confidence  in the impacts of aerosols on near-term projected changes in precipitation. Precipitation changes in the 
near term show seasonal amplification, precipitation increase in the 
rainy season and decrease in the dry season (Fujita et al., 2019).
Consistent with AR5, we conclude that projected changes of seasonal 
mean precipitation in the near term will increase at high latitudes. 
Near-term projected changes in precipitation are uncertain mainly

because of natural internal variability, model uncertainty, and uncertainty 
in natural and anthropogenic aerosol forcing ( medium confidence ).
4.4.1.4 Global Monsoon Precipitation and Circulation
The global monsoon is a forced response of the coupled atmosphere–
land–ocean system to the annual cycle of solar insolation and 
characterized by a  seasonal reversal of circulation and a  seasonal 
alternation of dry and wet conditions (Section 8.3.2, Figure 8.11 and 
Annex V). The global monsoon concept helps to dissect the mechanisms 
and controlling factors of monsoon variability at various temporal-
spatial scales (Wang and Ding, 2008; P .X. Wang et al., 2017).
Seasonal mean precipitation change
Conflicting signal        –50      –30      –20     –10        0        10       20       30       50
%39
3937
37

Figure  4.13 | Near-term change of seasonal mean precipitation.  Displayed are projected spatial patterns of CMIP6 multi-model mean change (%) in (top)   December –January–
February (DJF) and (bottom)  June–July–August (JJA) precipitation from SSP1-2.6 and SSP3-7.0 in 2021–2040 relative to 1995–2014. The number of models used is indicated 
in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show 
a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change 
or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals

where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on 
data sources and processing are available in the chapter data table (Table 4.SM.1).

586
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4In AR5, there was no specific assessment on global monsoon changes 
in the near term, but information can be derived from CMIP5 
projections of the spatial patterns of precipitation change. While 
the basic pattern of wet regions, including global monsoon regions, 
tending to get wetter and dry regions tending to get drier is apparent, 
large response uncertainty is evident in the substantial spread in 
the magnitude of projected change from different simulations. 
Over the global land monsoon regions, model uncertainty and 
internal variability together explain 99.7% of the fraction of total 
variance (Zhou et al., 2020), near-term projected multi-model mean 
precipitation changes are almost everywhere smaller than the 
estimated standard deviation of internal variability (Figure 4.13).
The global land monsoon precipitation index, defined as the area-

weighted precipitation rate in the global land monsoon domain, tends 
to increase in the near term under all five core SSPs (Figure 4.14a) (Chen 
et al., 2020), but changes are small compared to the intermodel spread 
in the historical period. The Northern Hemisphere summer monsoon 
circulation index, defined as the vertical shear of zonal winds between 
850 and 200 hPa averaged in a zone stretching from Mexico eastward 
to the Philippines (0°–20°N, 120°W–120°E), tends to decrease under 
four of the five SSP scenarios (Figure 4.14b), potentially offsetting 
monsoon precipitation increase. Projected changes in the global 
monsoon circulation are also uncertain, because they are influenced 
by internal variability such as AMV and PDV (see Section  3.3.3.2) 
and structural differences across models. In the near-term, for CMIP6 
projections (Figure 4.14a), the multi-model mean (5–95% range) of 
global land monsoon precipitation change is 1.9% (–0.4 to 4.9%),

1.6% (–1.0 to 5.2%), 1.3% (–1.7 to 3.7%), and 1.9% (–0.8 to 5.2%) 
under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively.
In summary, we assess that near-term changes in global monsoon 
precipitation and circulation will be affected by the combined effects 
of model uncertainty and internal variability, such as AMV and PDV, 
which together are larger than the forced signal ( medium confidence ).4.4.2 Cryosphere, Ocean and Biosphere
4.4.2.1 Arctic Sea Ice
The AR5 assessed that for RCP8.5, Arctic sea ice coverage in September 
will drop below 1 million km2, or become practically ice-free, at some 
point between 2040 and 2060 (Collins et al., 2013). Since AR5, there 
has been substantial progress in understanding the response of 
Arctic sea ice to near-term changes in external forcing. In particular, 
it is very likely that different trajectories of the near-term evolution 
of anthropogenic forcing cause distinctly different likelihood ranges

for very low sea ice coverage to occur over the next two decades 
(Notz and Stroeve, 2018). For example, there is an unlikely  drop of 
September Arctic sea ice coverage to below 1 million km2 before 2040 
for RCP 2.6, and a  likely  drop of September Arctic sea ice coverage 
to below 1 million km2 before 2040 for RCP 8.5 ( medium confidence 
given the single study). The much higher likelihood of a practically 
sea ice free Arctic Ocean during summer before 2040 in RCP8.5 
compared to RCP2.6 is consistent with related studies assessed in 
SROCC that find a substantially increased likelihood of an ice-free 
Arctic Ocean for 2.0°C compared to 1.5°C mean global warming 
relative to pre-industrial levels (Screen and Williamson, 2017; Jahn, 
2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond 
et al., 2018; Olson et al., 2019).
Based on results from CMIP6 models, we conclude that Arctic SIA will 
decrease in September in the near term (Figure 4.15, high confidence ).

In the case of 10-year trends ending in the near term, 79% of the 
simulations considered across all the core SSPs project decreasing 
Arctic sea ice area in September. Due to less of an influence from 
internal variability, this number rises to 98% in the case of 30-year 
trends. A more detailed assessment of near-term Arctic sea ice changes 
can be found in Chapter 9 (Section 9.3.1). A detailed assessment of 
Antarctic sea ice changes is in Chapter 9 (Section 9.3.2).
Figure 4.1 4 | Time series of global land monsoon precipitation and Northern Hemisphere summer monsoon (NHSM) circulation index anomalies. (a)  Global 
land monsoon precipitation index anomalies (unit: %) defined as the area-weighted mean precipitation rate in the global land monsoon domain (as defined by Wang et al. 
(2013a) for the CMIP6 historical simulation (1950–2014) and five SSPs (2015–2100). (b) Anomalies in NHSM circulation index (unit: m s–1), defined as the vertical shear of

zonal winds between 850 and 200 hPa averaged in a zone stretching from Mexico eastward to the Philippines (0°–20°N, 120°W–120°E; Wang et al., 2013a) for the CMIP6 
historical simulation and five SSPs. One realization is averaged from each model. Anomalies are shown relative to the present-day (1995–2014) mean. The curves show averages 
over the simulations, the shadings around the SSP1-2.6 and SSP3-7.0 curves show 5–95% ranges, and the numbers near the top show the number of model simulations used. 

587
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 444.4.2.2 Ocean and Land Carbon Flux
Ocean carbon flux is both a  key feature of the physical ocean in 
mitigating the rise of atmospheric CO 2 and a driver of changes in the 
ocean biosphere, including changes in ocean acidity. Based on results 
from CMIP6 models, we conclude that SSP2-4.5, SSP3-7.0, and 
SSP5-8.5 all clearly lead to increasing 10-, 20-, and 30-year trends in 
ocean carbon flux over the near term ( high confidence ) (Figure 4.16,). 
Increasing trends in ocean carbon flux are less obvious in the lower-
emissions scenarios. Ensemble-mean trends in land carbon flux over the near term are generally increasing, but these are unlikely  to be 
detected given a large component of terrestrial variability combined 
with model uncertainty. A more detailed assessment is in Chapter 5 
(Section 5.2.1).
In summary, it is likely  that ocean carbon flux will increase in the near

term under the higher emissions scenarios, while a large component 
of terrestrial variability makes it is unlikely  that an increase in land 
carbon flux will be detected over this period.
4.4.3 Modes of Variability
This subsection assesses the near-term evolution of the large-scale 
modes of climate variability. Assessment of the physical mechanisms 
and the individual feedbacks involved in the future change of each 
mode and their role on future regional climate variability are provided 
in Sections 8.4.2, 9.2.3 and 10.1.3, and Cross-Chapter Box 10.1.
4.4.3.1 Northern and Southern Annular Modes
4.4.3.1.1 The Northern Annular Mode
The AR5 assessed from CMIP5 simulations that there is only 
medium confidence in near-term projections of a northward shift 
of NH storm track and westerlies, and an associated increase in 
the NAM index, because of the large response uncertainty and 
the potentially large influence of internal variability. A tendency in

the near term towards a slightly more positive NAM in the three 
highest emissions scenarios during boreal fall, winter, and spring is 
apparent in Figure 4.17a. However, in general the projected near-
term multi-model mean change in the NAM is small in magnitude 
compared to the inter-model and/or multi-realization variability 
within the ensemble (Figure  4.17a; Deser et  al., 2012b, 2017; 
Barnes and Polvani, 2015).
Septem ber Arctic sea ice  area
SSP1-1.9 9
SSP1-2.6 21
SSP2-4.5 24
SSP3-7.0 19
SSP5-8.5 22
79%                        94%                        98%
Length of pe riod endi ng in 2021 -2040-3-2-1012(million squ are kms pe r decade)
10-year                 20-year                 30-year              Figure  4.15 | CMIP6 linear trends in September Arctic sea-ice area for 
10-year, 20-year, and 30-year periods ending in 2021–2040 following 
five SSPs. Plotted are the 5–95% ranges across the ensembles of simulations.

The numbers at the top of the plot are the number of model simulations in each 
SSP ensemble. The numbers near the bottom of the plot indicate the percentage 
of simulations across all the SSPs with decreasing sea-ice area. Results are from 
SSP1-1.9  5
SSP1-2.6 11
SSP2-4.5 11
SSP3-7.0 10
SSP5-8.5 10
10-year               20-year               30-year 10-year               20-year               30-year
Length of pe riod endi ng in 2021 -2040-4-20246(PgC /yr per deca de)
(b) Atmosp here to lan d carbon flux 
 5 111110 10
Figure  4.16 | CMIP6 trends in ocean and land carbon flux for 10-year, 20-year, and 30-year periods ending in 2021–2040. (a) Ocean carbon flux. (b) Land

carbon flux. Plotted are the 5–95% ranges across the ensembles of simulations, for five SSPs. The numbers at the top of the plots are the number of model simulations in each 
SSP ensemble.  Units are PgC yr –1 per decade. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

588
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4On seasonal to interannual time scales, there is new evidence since 
AR5 that initialized predictions show lower potential predictability 
for the boreal winter NAO than the correlation skill with respect 
to observations (Eade et  al., 2014; Baker et  al., 2018; Scaife and 
Smith, 2018; Athanasiadis et  al., 2020). This has been referred to 
in the literature as a ‘signal-to-noise paradox’ and means that very 
large ensembles of predictions are needed to isolate the predictable 
component of the NAO. While the processes that contribute to the 
predictability of the winter NAO on seasonal time scales may be 
distinct from the processes that drive multi-decadal trends, there is 
emerging evidence that initialized predictions also underrepresent the 
predictability of the winter NAO on decadal time scales (D.M. Smith 
et al., 2019). Post-processing and aggregation of initialized predictions

may therefore reveal significant skill for predicting the winter NAO on 
decadal time scales (Smith et al., 2020). Considering these new results 
since AR5, in the near-term it is likely  that any anthropogenic forced 
signal in the NAM will be of comparable magnitude or smaller than 
natural internal variability in the NAM ( medium confidence ).
4.4.3.1.2 The Southern Annular Mode
The AR5 assessed that it is likely  that increases in GHGs and the 
projected recovery of the Antarctic ozone hole will be the principal 
drivers of future SAM trends. Additionally, the positive trend in 
austral summer/autumn SAM observed over the past several decades 
(Section 2.4.1.2; Chapter 2 in AR5, Hartmann et al., 2013), is likely  
to weaken considerably as stratospheric ozone recovers through to 
the  mid-21st  century. The effects of ozone depletion and recovery 
on the SH circulation primarily occur in austral summer, while GHGs

influence the SH circulation year round (Gillett and Fyfe, 2013; Grise 
and Polvani, 2014b). Therefore, they are likely to be the dominant driver 
of projected circulation changes outside of austral summer (Gillett 
and Fyfe, 2013; Barnes et al., 2014; Solomon and Polvani, 2016). Based 
on current scenarios specifying future atmospheric decline of ozone 
depleting substances (WMO, 2011), chemistry-climate models project the Antarctic ozone hole in October to recover by around 2060 (WMO, 
2014, 2018; Dhomse et al., 2018). Observational evidence since AR5 
shows the onset of Antarctic ozone hole recovery (Solomon et  al., 
2016; WMO, 2018) that has been attributed to a pause in the summer 
SAM trend over the past couple of decades (Saggioro and Shepherd, 
2019; Banerjee et al., 2020). In austral summer, ozone recovery and 
increasing GHGs will have opposing effects on the SAM over the next 
several decades (Barnes et al., 2014).

Since AR5, there have been advances in understanding the role of 
internal climate variability for projected near-term SH circulation 
trends (Solomon and Polvani, 2016). A large initial-condition ensemble 
following the RCP4.5 emissions scenario shows a monotonic positive 
SAM trend in austral winter. In austral summer, the SAM trend over 
the first half of the 21st century is weaker compared to the strongly 
positive trend observed and simulated over the late 20th  century. 
In that model, the number of realizations required to identify 
a  detectable change in decadal mean austral winter SAM index 
from a year 2000 reference state decreased to below five by around 
2025–2030 (Solomon and Polvani, 2016). However, in December –
January–February (DJF) the same criterion is not met until the second 
half of the 21st century, owing to the near-term opposing effects of 
ozone recovery and GHGs on the austral-summer SAM. In austral

summer, forced changes in the SAM index in the near-term are 
therefore likely  to be smaller than changes due to internal variability 
(Figure 4.17b; Barnes et al., 2014; Solomon and Polvani, 2016).
CMIP6 models show a  tendency in the near-term towards a  more 
positive SAM index especially in the austral winter (June–July–
August, JJA; Figure 4.17b). In all seasons, the differences between 
the central estimates of the change in the SAM index for each SSP 
are much smaller than the inter-model ensemble spread. The number 
of CMIP6 realizations in Figure 4.17b is larger than the suggested 
threshold of five realizations needed to detect a significant near-term 
change in decadal-mean austral winter SAM index for a single CMIP5 
(a) No rthern  Annul ar Mod e
Season-2024Anom aly rela tive to 1995- 2014 (hP a)
(b) S outhern  Annul ar Mod e
1134332835

SON JJA MAM DJF SON JJA MAM DJF
Figure 4. 17 | CMIP6 Annular Mode index change (hPa) from 1995–2014 to 2021–2040. (a)  Northern Annular Mode (NAM); (b) Southern Annular Mode (SAM). 
The NAM is defined as the difference in zonal mean sea level pressure (SLP) at 35°N and 65°N (Li and Wang, 2003) and the SAM as the difference in zonal mean SLP at 40°S 
and 65°S (Gong and Wang, 1999). The shadings are the 5–95% ranges across the simulations. The numbers near the top of each panel are the numbers of model simulations 
in each SSP ensemble. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

589
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44model (Solomon and Polvani, 2016), and yet the 5–95% intervals on 
the CMIP6 ensemble spread encompass zero for all core SSPs. This 
suggests both internal variability and model uncertainty contribute 
to the CMIP6 ensemble spread in near-term SAM index changes. 
Based on these results, it is more likely than not  that in the near-term 
under all assessed SSP scenarios the SAM index would become more 
positive than in present-day in austral autumn, winter and spring.
An influence of forcing agents other than stratospheric ozone and 
GHGs, such as anthropogenic aerosols, on SAM changes over the 
historical period has been reported in some climate models (Rotstayn, 
2013), but the response across a larger set of CMIP5 models is not 
robust (Steptoe et  al., 2016) and depends on how tropospheric 
temperature responds to aerosols (Choi et al., 2019). This gives low

confidence  in the potential influence of anthropogenic aerosols on 
the SAM in the future.
4.4.3.2 El Niño–Southern Oscillation
The AR5 assessed that it is very likely  that the ENSO will remain 
the dominant mode of interannual variability in the future but did 
not specify its change in near term. A subset of CMIP5 models that 
simulate the ENSO Bjerknes index most realistically show an increase 
of ENSO SST amplitude in the near-term future and decline thereafter 
(Kim et al., 2014). However, detection of robust near-term changes of 
ENSO SST variability in response to anthropogenic forcing is difficult 
to achieve due to pronounced unforced low-frequency modulations 
of ENSO (Wittenberg, 2009; Maher et al., 2018; Wengel et al., 2018). 
Figure 4.10 in Section 4.3.3.2, using CMIP6 models, also shows no 
robust change in ENSO SST variability in the near term.
While there is no strong model consensus on the change in amplitude

of ENSO SST variability, the amplitude of ENSO-associated rainfall 
variability likely  increases (Power et al., 2013; Cai et al., 2015). Analysis 
of CMIP6 models shows a  slight increasing trend in amplitude of 
rainfall variability over Niño 3.4 region in the near term attributable to 
mean moisture increase, regardless of changes in ENSO SST variability 
(Figure 4.10). However, there are no distinguishable changes in the 
rainfall variability among five SSPs with significant model spread in 
the near term. Hence, no robust change in amplitude of ENSO SST 
and rainfall variability is expected in the near term although the 
rainfall variability slightly increases ( medium confidence ).
4.4.3.3 Indian Ocean Basin and Dipole Modes
Important modes of interannual climate variability with pronounced 
climate impacts in the Africa–Indo-Pacific areas of the globe are 
the Indian Ocean Dipole (IOD), which is closely related to, and

often coincides with, ENSO phases (Stuecker et al., 2017), and the 
Indian Ocean basin (IOB) mode. This is often described as a capacitor 
effect in response to ENSO (Xie et al., 2009; Du et al., 2013) and can 
feed back onto ENSO evolution (Cai et al., 2019). IOD and IOB are 
extensively described in Annex IV (Section AIV2.4).
The projected climate mean state changes in the tropical Indian 
Ocean resemble a  positive IOD state, with faster warming in the 
west compared to the east. This mean state change will potentially lead to a reduction in the amplitude of IOD events, albeit with no 
robust change in IOD frequency (Cai et al., 2014b). There is no robust 
evidence yet suggesting a cessation of IOD variability or a significant 
change in the IOB mode in the near-term.
4.4.3.4 Tropical Atlantic Modes
Interannual variability of the tropical Atlantic can be described in 
terms of two main climate modes: the Atlantic equatorial mode

and the Atlantic meridional mode (AMM; Annex IV, Section AIV2.5). 
The  Atlantic equatorial mode, also commonly referred to as the 
Atlantic Niño or Atlantic Zonal Mode, is associated with SST 
anomalies near the equator, peaking in the eastern basin, while the 
AMM is characterized by an inter-hemispheric gradient of SST and 
wind anomalies. Both modes are associated with changes in the 
ITCZ and related winds and exert a strong influence on the climate in 
adjacent and remote regions.
Despite considerable improvements in CMIP5 with respect to CMIP3, 
most CMIP5 models have difficulties in simulating the mean climate 
of the tropical Atlantic (Mohino et  al., 2019) and are not able to 
correctly simulate the main aspects of Tropical Atlantic Variability 
(TAV) and associated impacts. This is presumably the main reason 
why there is a lack of specific studies dealing with near-term changes 
in tropical Atlantic modes. Nevertheless, AR5 reported that the ocean

is more predictable than continental areas at the decadal time scale 
(Kirtman et al., 2013). In particular, the predictability in the tropical 
oceans is mainly associated with decadal variations of the external 
forcing component. Since the AMV affects the tropical Atlantic, 
near-term variations of the AMV can modulate the equatorial mode 
and the AMM as well as associated impacts.
There are no specific studies focusing on near-term changes in 
tropical Atlantic modes; nevertheless, decadal predictions show 
that although the North Atlantic stands out in most CMIP5 models 
as the primary region where skill might be improved because of 
initialization, encouraging results have also been found in the 
tropical Atlantic (Meehl et  al., 2014). The effect of initialization in 
the tropical Atlantic is not only visible in surface temperature but also 
in the subsurface ocean (Corti et al., 2015). In particular, initialization

improves the skill via remote ocean conditions in the North Atlantic 
subpolar gyre and tropical Pacific, which influence the tropical 
Atlantic through atmospheric teleconnections (Dunstone et al., 2011; 
Vecchi et  al., 2014; García-Serrano et  al., 2015). Improvements of 
some aspects of climate prediction systems (initialization techniques, 
large ensembles, increasing model resolution) have also led to skill 
improvements over the tropical Atlantic (Pohlmann et  al., 2013; 
Monerie et al., 2017; Yeager and Robson, 2017).
Recent studies have shown that the AMV can modulate not only 
the characteristics of the Atlantic Niños, but also their inter-basin 
teleconnections (Indian and Pacific). In particular, the Atlantic 
Niño–ENSO relationship is strongest during negative AMV phases 
(Martín-Rey et al., 2014; Losada and Rodríguez-Fonseca, 2016) when 
equatorial Atlantic SST variability is enhanced (Martín-Rey et al., 2017; 
Lübbecke et al., 2018).

590
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Based on CMIP5 and available CMIP6 results, we conclude that there 
is a lack of studies on the near-term evolution of TAV and associated 
teleconnections for a  comprehensive assessment. However, some 
studies show that despite severe model biases there are skilful 
predictions in the mean state of tropical Atlantic surface temperature 
several years ahead ( medium confidence ), though skill in simulated 
variability has not been assessed yet. Decadal changes in the Atlantic 
Niño spatial configuration and associated teleconnections might be 
modulated by the AMV, but there is limited evidence  and therefore  
low confidence  in these results.
4.4.3.5 Pacific Decadal Variability
Climate variability of the Pacific Ocean on decadal and inter-decadal 
time scales is described in terms of a number of quasi-oscillatory SST 
patterns such as the Pacific Decadal Oscillation (PDO; Mantua et al.,

1997) and the Inter-decadal Pacific Oscillation (IPO; Folland, 2002), 
which are referred to as the Pacific Decadal Variability (PDV; Newman 
et al., 2016). PDV comprises an inter-hemispheric pattern that varies 
at decadal to inter-decadal time scales (Figure  3.35). However, 
although the spatial domains to derive the IPO and PDO indices differ, 
and despite uncertainty related to trend removal and time-filtering 
(Newman et al., 2016; Tung et al., 2019), the IPO and PDO are highly 
correlated in time and they will be assessed together as the PDV 
(Annex IV, Section AIV.2.6).
The AR5 assessed that near-term predictions of PDV (then referred 
to as PDO or IPO) were largely model dependent (Mochizuki 
et  al., 2012; van Oldenborgh et  al., 2012), not robust to sampling 
of initialization start-dates, overall not statistically significant, 
and worse than persistence (Doblas-Reyes et  al., 2013), although 
some studies showed positive skill for PDV (Mochizuki et al., 2010;

Chikamoto et  al., 2013). The CMIP5 decadal-prediction ensemble 
yielded no prediction skill of SST over the key PDV centres of action 
in the Pacific Ocean, both at two-to-five-year and six-to-nine-year 
forecast averages (Doblas-Reyes et al., 2013; Guemas et al., 2013; 
Boer and Sospedra-Alfonso, 2019).
Since AR5, the processes causing the multi-decadal variability in the 
Pacific Ocean have become better understood (Newman et al., 2016; 
Henley, 2017). However, the relative importance of tropical and 
extratropical processes underlying PDV remains unclear; although it 
seems to be stochastically driven rather than self-excited (Liu, 2012; 
Liu and Di Lorenzo, 2018), the South Pacific being a  key region 
for the tropical branch of PDV (Chung et  al., 2019; Liguori and 
Di Lorenzo, 2019).
Because PDV represents not one, but many dynamical processes, it 
represents a challenge as a target for near-term climate predictions

and projections. The new generation of decadal forecast systems 
keeps showing poor (Shaffrey et al., 2017) to moderate (D.M. Smith 
et al., 2019) multi-year prediction skill for PDV, although the potential 
for forecasting capabilities is demonstrated in case studies (Meehl 
and Teng, 2012; Meehl et al., 2014). For the near-term, a transition of 
PDV from the negative phase (1999–2012) towards a positive phase 
is predicted in the coming years (2013–2022; Meehl et al., 2016).The PDV has been shown to influence the pace of global warming 
(Cross-Chapter Box 3.1), but the extent to which PDV is externally forced 
or internally generated (Mann et al., 2020) remains an open question, 
and there is still no robust evidence. Thus, there is low confidence  on 
how the PDV will evolve in the near-term (Bordbar et al., 2019).
4.4.3.6 Atlantic Multi-decadal Variability
The Atlantic Multi-decadal Variability (AMV) is a large-scale climate

mode accounting for the main fluctuations in North Atlantic SST on 
multi-decadal time scales (Section AIV.2.7). The AMV influences air 
temperatures and precipitation over adjacent and remote continents, 
and its undulations can partially explain the observed variations in 
the NH mean temperatures (Steinman et al., 2015). The origin of this 
variability is still uncertain. Ocean dynamics (e.g.,  changes in the 
AMOC), external forcing, and local atmospheric forcing all seem to 
play a role (Menary et al., 2015; Ruprich-Robert and Cassou, 2015; 
Brown et al., 2016; Cassou et al., 2018; Wills et al., 2019). Recent 
studies have discussed that the ocean dynamics play an active role 
in generating AMV (Oelsmann et al., 2020) and its interplay with the 
NAO (Vecchi et  al., 2017; R. Zhang et  al., 2019; Kim et  al., 2020), 
although natural and anthropogenic external forcing might be 
crucial in modulating its amplitude and timing (Bellucci et al., 2017;

Bellomo et  al., 2018; Andrews et  al., 2020; Borchert et  al., 2021; 
Mann et al., 2021; see Sections 3.7.7 and AIV.2.7).
The AR5 assessed with high confidence that initialized predictions can 
improve the skill for temperature over the North Atlantic, in particular 
in the sub-polar branch of AMV, compared to the projections, for 
the first five years (see AR5 WGI Figures 11.3 and 11.4). However, 
non-initialized predictions showed positive correlation over the 
same time-range as well, consistent with the notion that part of this 
variability is caused by external forcing (Section 3.7.7).
Since AR5, near-term initialized predictions, both multi-model (Bellucci 
et al., 2015a; García-Serrano et al., 2015; D.M. Smith et al., 2019) and 
single-model ensembles (Marotzke et al., 2016; Simpson et al., 2018; 
Yeager et al., 2018; Hermanson et al., 2020; Bilbao et al., 2021), confirm 
substantial skill in hindcasting North Atlantic SST anomalies on a time

range of eight to ten years. On the same time range, Borchert et al. 
(2021) show a  substantial improvement in the prediction of the 
subpolar gyre SST (the northern component of the AMV) in CMIP6 
models compared to CMIP5, in both initialized and non-initialized 
simulations. The higher skill of CMIP6 models can be attributed to 
a  more accurate response of SST variations in the subpolar gyre 
to  natural forcing, possibly originating from the AMOC-related 
delayed response to volcanic eruptions (Hermanson et al., 2020).
Initialization contributes to the reduction of uncertainty and to 
predicting subpolar SST amplitude (Borchert et al., 2021). Yet, skill in 
predicting the AMV is not always translated into equally successful 
predictions of temperature and precipitation over the nearby land 
and ocean regions (Langehaug et al., 2017). This might be related to 
systematic model errors in the simulation of the spatial and temporal

structure of the AMV and too weak associated teleconnections 
(Section 3.7.7), and also to the larger noise in regional land variables 
compared to the AMV index. However, AMV predictions can be

591
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44used as proxies to predict other variables such as precipitation over 
Western Europe and Eurasia and SAT over Mediterranean, Northern 
Europe and north-east Asia (Årthun et al., 2018; Borchert et al., 2019; 
Ruggieri et al., 2021) whose relationship with North Atlantic SST is 
robust in observations, but not well captured in climate models.
Encouraging results about the prediction of land precipitation linked 
to the warm AMV phase (Section 3.7.7 and Annex IV, Figure AIV.2.7) 
on a  two-to-nine-year time scale are reported in the multi-model 
study by D.M. Smith et  al. (2019). Positive correlations with 
observations are found in the Sahel, South America, the Maritime 
Continent. Analyses from large-ensemble decadal prediction systems 
such as the community Earth system model decadal prediction large 
ensemble (CESM-DPLE; Yeager et al., 2018) show an improvement

with respect to the CMIP5 decadal hindcasts (Martin and Thorncroft, 
2014b) in forecasting Sahel precipitation over three to seven years, 
which is consistent with the current understanding of AMV impact 
over Africa (Mohino et al., 2016; D.M. Smith et al., 2019). CESM-DPLE 
predicts drought conditions over the Sahel through 2020, which is 
compatible with a shift towards a negative phase of AMV as a result 
of a  weakening of the AMOC, advocated by a  number of studies 
(Hermanson et al., 2014; Robson et al., 2014; Yeager et al., 2015).
In summary, the confidence  in the predictions of AMV and its effects 
is medium . However, there is high confidence  that the AMV skill 
over five-to-eight-year lead times is improved by using initialized 
predictions, compared to non-initialized simulations.
4.4.4 Response to Short-lived Climate Forcers 
and Volcanic Eruptions
Mitigation of SLCFs affects future climate projections and could

alter the time of emergence of anthropogenic climate change 
signals. The AR5  assessed that emission reductions aimed at 
decreasing local air pollution could have a  near-term warming 
impact on climate ( high confidence ) (Kirtman et al., 2013). Because 
of their shorter lifetimes, reductions in emissions of SLCF species 
mainly influence near-term GSAT trends (Chalmers et  al., 2012; 
Shindell et  al., 2017; Shindell and Smith, 2019), but on decadal 
time scales the near-term response to even very large reductions 
in SLCFs may be difficult to detect in the presence of large internal 
climate variability (Samset et  al., 2020). The changes in SLCF 
emissions during the COVID-19 pandemic has resulted in a small 
net radiative forcing without a discernible impact on GSAT (Cross-
Chapter Box 6.1). SLCF mitigation also leads to a higher GSAT in 
the mid- to long-term (Smith and Mizrahi, 2013; Stohl et al., 2015; 
Hienola et al., 2018) and can influence peak warming during the

21st century (Rogelj et al., 2014; Hienola et al., 2018). This section 
focuses on the total effect of SLCF changes on GSAT projections 
in the SSP scenarios. A more detailed breakdown of the separate 
climate effects of SLCF species and precursor species can be found 
in Sections 6.7.2 and 6.7.3.
A model experiment based on the SSP3-7.0 scenario with aerosols, 
their precursors, and non-methane tropospheric ozone precursors 
set to SSP1-1.9 abundances (SSP3-7.0-lowSLCF-highCH4; Collins et al., 2017) shows a projected multi-model mean GSAT anomaly 
that is higher by 0.22°C at mid-century (2045-2054) compared 
to SSP3-7.0 (Figure 4.18; Allen et al., 2020), but this difference is 
smaller than the inter-model spread of the SSP3-7.0 projections 
based on the CMIP6 models. Note the SSP3-7.0-lowSLCF-highCH4 
experiment does not perturb methane from SSP3-7.0 concentrations. 
A modified SSP3-7.0-lowSLCF-lowCH4 scenario that also includes

methane mitigation shows a lower GSAT by mid-century compared 
to SSP3-7.0 (Allen et al., 2021).
Building on CMIP6 results for the effects of reducing SLCF 
emissions from a baseline of SSP3-7.0, the overall contribution of 
SLCFs to GSAT changes in the marker SSPs are now quantified using 
a simple climate model emulator. For consistency with Section 6.7.2 
and Figure  6.22, the basket of SLCF compounds considered 
includes aerosols, ozone, methane, black carbon on snow and 
hydrofluorocarbons (HFCs) with lifetimes of less than 50 years. In 
the five marker SSPs considered, the net effect of SLCFs contributes 
to a higher GSAT in the near, mid- and long term (Table 4.6 and 
Section  6.7.2). In the SSP1-1.9 and SSP1-2.6 scenarios, SLCFs 
contribute to a higher GSAT by a central estimate of around 0.3°C 
compared to 1995–2014 across the three-time horizons. In the 
long-term, the 0.3C warming due to SLCFs in SSP1-2.6 can be

compared to the assessed very likely  GSAT change for this period of 
0.5°C–1.5°C (Section 4.3.4 and Table 4.5). The SSP2-4.5, SSP3-7.0 
and SSP5-8.5 scenarios all show a larger SLCF effect on GSAT in 
the long term relative to the near term. In SSP3-7.0, the long-
term warming due to SLCFs by 0.7°C can be compared with the 
assessed very likely  GSAT anomaly for this period of 2.0°C –3.7°C 
(Section 4.3.4). In summary, it is very likely  that changes in SLCFs 
contribute to an overall warmer GSAT over the near, mid- and long 
term in the five SSP scenarios considered (Table 4.6, Section 6.7.2 
and Figure 6.22).
Global surface air temperature change
1995 2015 2035 2055–1012
012Relative to 1995–2014 (ºC)
Relative to 1850–1900 (ºC)SSP3-7.0-lowSLCF-highCH4 (9)SSP3-7.0 (9)
Historical (9)
Figure 4.18 | Influence of SLCFs on projected GSAT change.  Change is shown 
relative to the 1995–2014 average (left axis) and relative to the 1850–1900 average

(right axis). The comparison is for CMIP6 models for the AerChemMIP (Collins et al., 
2017) SSP3-7.0-lowSLCF-highCH4 experiment (red dashed; note in the original 
experiment protocol this is called SSP3-7.0-lowNTCF), where concentrations of short-
lived species are reduced compared to reference SSP3-7.0 scenario (red solid). Black 
shows the historical simulation until 2014 for the same 9 models as the projections. 
The curves show averages over the r1 simulations contributed to the CMIP6 exercise, 
the shadings around the historical and SSP3-7.0 curves shows 5–95% ranges and the 
numbers near the top show the number of model simulations.

592
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4In addition to effects on GSAT, SLCFs affect other aspects of the global 
climate system (Section 6.7.2). The additional warming at high northern 
latitudes associated with projected reductions in aerosol emissions 
over the 21st century leads to a more rapid reduction in Arctic sea 
ice extent in the RCP scenarios (Gagné et  al., 2015). Furthermore, 
mitigation of non-methane SLCFs in the SSP3-7.0-lowSLCF-highCH4 
scenario causes an increase in global mean precipitation, with larger 
regional changes in southern and eastern Asia (Allen et al., 2020).
The main uncertainties in climate effects of SLCFs in the future come 
from: (i) the uncertainty in anthropogenic aerosol ERF (Section 7.3.3); 
(ii) uncertainty in the relative emissions of different SLCFs that have 
warming and cooling effects in the current climate (Section 6.2); and

(iii) physical uncertainty including the efficacy of the climate response 
to SLCFs compared to long-lived GHGs (Marvel et al., 2016; Richardson 
et al., 2019). One example of physical uncertainty is that the shortwave 
radiative forcing from methane was neglected in previous calculations 
(Etminan et al., 2016; Collins et al., 2018), which affects understanding 
of present day and future methane ERF (Modak et al., 2018). Another 
example of physical uncertainty is projected changes in lightning-
NO x production, which contribute to future ozone radiative forcing 
(Banerjee et al., 2014, 2018; Finney et al., 2018).
Another factor that could substantially alter projections in the 
near-term would be the occurrence of a  large explosive volcanic 
eruption, or even a decadal to multi-decadal sequence of small-to-
moderate volcanic eruptions as witnessed over the early 21st century 
(Cross-Chapter Box 4.1; Santer et al., 2014). An eruption similar to

the last large tropical eruption, Mount Pinatubo in the Philippines 
in June 1991, is expected to cause substantial Northern Hemisphere 
(NH) cooling, peaking between 0.09°C and 0.38°C and lasting for 
three to five years, as indicated by climate model simulations over the 
past millennium (e.g., Jungclaus et al., 2010). Phase 3 of Paleoclimate 
Modelling Intercomparison Project (PMIP3) simulated a  significant 
NH cooling in response to individual volcanic events (peaks between 
0.1°C and 0.5°C, depending on model, during the first year after 
the eruption) that lasts for three to five years. On a regional scale, 
the double volcanic events that occurred in 536 and 540 CE resulted 
in a cooling of 2°C (Büntgen et al., 2016; Toohey et al., 2016).
Since AR5, there has been growing progress in understanding the 
climate impacts of volcanic eruptions. Volcanic forcing is regarded as 
the dominant driver of forced variability in preindustrial surface air

temperature (Schurer et al., 2013, 2014). Large eruptions in the tropics 
and high latitudes were primary drivers of interannual-to-decadal temperature variability in the Northern Hemisphere during the past 
2,500 years, with cooling persisting for up to ten years after some 
of the largest eruptive episodes (Sigl et al., 2015). Repeated clusters 
of volcanic eruptions can induce a net negative radiative forcing that 
results in a centennial- and global-scale cooling trend via a decline 
in mixed-layer oceanic heat content (McGregor et  al., 2015). The 
response to multi-decadal changes in volcanic forcing (representing 
clusters of eruptions) shows similar cooling in both simulations and 
reconstructions of NH temperature. Volcanic eruptions generally 
result in decreased global precipitation for up to a few years following 
the eruption (Iles and Hegerl, 2014, 2015; Man et  al., 2014), with 
climatologically wet regions drying and climatologically dry regions

wetting ( medium confidence ), which is opposite to the response 
under global warming (Held and Soden, 2006; Iles et al., 2013; Zuo 
et  al., 2019a, b). El Niño-like warming appears after large volcanic 
eruptions, as seen in both observations (Adams et al., 2003; McGregor 
et al., 2010; Khodri et al., 2017) and climate model simulations (Ohba 
et al., 2013; Pausata et al., 2015; Colose et al., 2016; Stevenson et al., 
2016; Khodri et al., 2017; Predybaylo et al., 2017; Zuo et al., 2018). 
The large tropical eruptions are coincident with positive Indian Ocean 
dipole events (Maher et al., 2015).
In AR5, uncertainty due to future volcanic activity was not considered 
in the assessment of the CMIP5 21st  century climate projections 
(Taylor  et  al., 2012; O’Neill et  al., 2016). Since AR5, there has been 
considerable progress in quantifying the impacts of volcanic eruptions 
on decadal climate prediction and longer-term climate projections

(Meehl et al., 2015; Swingedouw et al., 2015, 2017; Timmreck et al., 
2016; Bethke et al., 2017; Illing et al., 2018). By exploring 60 possible 
volcanic futures under RCP4.5, it has been demonstrated that the 
inclusion of time-varying volcanic forcing may enhance climate 
variability on annual-to-decadal time scales (Bethke et  al., 2017). 
Consistent with a  tropospheric cooling response, the change in 
ensemble spread in the volcanic cases is skewed towards lower GSAT 
relative to the non-volcanic cases (Cross-Chapter Box 4.1, Figure 1). 
In  these simulations with multiple volcanic forcing futures there is: 
(i) an increase in the frequency of extremely cold individual years; (ii) an 
increased likelihood of decades with negative GSAT trend (decades 
with negative GSAT trends become 50% more commonplace); 
(iii)  later anthropogenic signal emergence (the mean time at which 
the signal of global warming emerges from the noise of natural

climate variability is delayed almost everywhere) ( high confidence ); 
and (iv) a 10% overall reduction in global land monsoon precipitation 
and a 20% overall increase in the ensemble spread (Man et al., 2021).Table  4.6 | The net effect of SLCFs on GSAT change.  Changes in 20-year averaged GSAT relative to 1995–2014 for 2021–2040, 2041–2060, and 2081–2100 for the five 
marker SSP scenarios. Values give the median and, in parentheses, the 5–95% range calculated from a 2237-member ensemble of the two-layer emulator that is driven with 
the ERF projections, including uncertainties, described in Chapter 7 Supplementary Material 7.SM.1.4. The ensemble is constrained to assessed ranges of ECS, TCR, ocean heat 
content change, GSAT response, and carbon cycle metrics (Section 7.3.5; Chapter 7 Supplementary Material 7.SM.2.2). The GSAT contribution of individual forcer responses

use the difference between parallel runs of the constrained two-layer model with all anthropogenic forcing and all anthropogenic forcing with the component of interest 
(e.g., methane) removed (Chapter 7 Supplementary Material 7.SM.2.3). Values are given to one decimal place.
Near Term (2021–2040) 0.2 (0.1, 0.3) 0.2 (0.1, 0.3) 0.2 (0.1, 0.3) 0.2 (0.1, 0.3) 0.3 (0.2, 0.4)
Mid-Term (2041–2060) 0.2 (0.0, 0.4) 0.2 (0.0, 0.4) 0.3 (0.2, 0.4) 0.3 (0.2, 0.4) 0.5 (0.3, 0.7)
Long Term (2081–2100) 0.1 (-0.1, 0.4) 0.2 (0.0, 0.4) 0.3 (0.1, 0.6) 0.5 (0.4, 0.8) 0.7 (0.4, 1.0)

593
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44Cross-Chapter Box 4.1 | The Climate Effects of Volcanic Eruption
Contributing Authors:  Sarah L. Connors (France/United Kingdom), Amanda Maycock (United Kingdom), Peter W. Thorne (Ireland/
United Kingdom), Nicolas Bellouin (United Kingdom/France), Ingo Bethke (Norway/Germany), Deliang Chen (Sweden), Annalisa Cherchi 
(Italy), Alejandro Di Luca (Australia/Canada/Argentina), Piers Forster (United Kingdom), Nathan P . Gillett (Canada), Darrell S. Kaufmann 
(The United States of America), June-Yi Lee (Republic of Korea), Elizaveta Malinina (Canada/Russian Federation), Seung-Ki Min (Republic 
of Korea), Johannes Quaas (Germany), Alex C. Ruane (The United States of America), Jean-Baptiste Sallée (France), Sonia I. Seneviratne 
(Switzerland), Chris Smith (United Kingdom), Matthew Toohey (Canada, Germany/Canada), Andrew  Turner (United Kingdom), 
Cunde Xiao (China), Tianjun Zhou (China)

Before the industrial period, explosive volcanic eruptions were the largest source of forced climate variability globally on interannual to 
centennial time scales (Section 2.2). While usually omitted from scenarios used for future climate projections, as they are unpredictable, 
volcanic eruptions have the potential to influence future climate on multi-annual to decadal time scales and affect many climatic 
impact drivers (as defined in Sections 12.1 and 12.3). Since AR5, more comprehensive paleo evidence and observations, as well as 
improved modelling have advanced understanding of the climate response to past volcanic eruptions. Building on multiple chapter 
assessments, this box synthesizes how volcanic eruptions affect climate and considers implications of possible future events.
How frequent are volcanic eruptions?
Proxy records show that large volcanic eruptions with effective radiative forcing (ERF) more negative than –1 W m–2 occurred on

average twice a century throughout the last 2500 years, the most recent being Pinatubo in 1991 (Section 2.2.2). About eight larger 
eruptions (ERF stronger than –5 W m–2) also occurred during this period (Figure 2.2), notably Tambora about 1815 and Samalas 
about 1257. A Samalas-type eruption may occur one to two times per millennium on average (Newhall et al., 2018). Typically, three 
in every four centuries have experienced at least one eruption stronger than –1 W m–2 (Pinatubo or larger). The volcanic aerosol 
burden was 14% lower during the 20th century compared to the average of the preceding 24 centuries (Section 2.2.2), whereas the 
13th century was among the most volcanically active, with four eruptions exceeding that of Pinatubo-1991 (Sigl et al., 2015).
Past climate responses to volcanic activity
Major eruptions drive a range of climate system responses for several years depending upon whether the eruption occurs in the tropics

(stratospheric aerosol dispersion into both hemispheres) or the extratropics (dispersion into the hemisphere of eruption) owing to the 
Brewer-Dobson circulation. The climatic response also depends on the effective injection height, sulphur mass injected, and time of 
year of the eruption (Marshall et al., 2019, 2020). These factors determine the total mass, lifetime and optical properties of volcanic 
aerosol in the stratosphere and influence the stratospheric aerosol optical depth (SAOD). The ERF from volcanic stratospheric aerosol 
is assessed to be –20 ± 5 W m–2 per unit sAOD (Section 7.3.4.6).
Due to the direct radiative effect of volcanic stratospheric aerosols, large volcanic eruptions lead to an overall decrease of GSAT, which can 
extend to multi-decadal or century time scales in the case of clustered volcanism (Section 3.3.1.1; Schurer et al., 2013; McGregor et al.,

2015; Sigl et al., 2015; Kobashi et al., 2017; Zambri et al., 2017; Brönnimann et al., 2019; Neukom et al., 2019). Large eruptions also increase 
the frequency of extremely cold individual years and the likelihood of cooling trends occurring in individual decades (Cross-Chapter 
Box 3.1 and Section 4.4.4; Paik and Min, 2018). Re-dating of ice core chronologies now confirms that the coldest decades of the past 
approximately 2000 years are the outcome of volcanic eruptions (Sigl et al., 2015; Büntgen et al., 2016; Toohey et al., 2016; Neukom et al., 
2019). CMIP5 and CMIP6 models reproduce the decreased GSAT that follows periods of intense volcanism. New reconciliations between 
simulations and proxy-based reconstructions of past eruptions have been achieved through better Earth System Model representation 
of volcanic plume chemical compositions (Legrande et al., 2016; Marshall et al., 2020; F . Zhu et al., 2020). Yet, remaining disagreements

reflect differences in the volcanic forcing datasets used in the simulations ( medium confidence ) (Section 3.3.1.1 and Figure 3.2c).
Although incomplete, proxy records show large impacts upon contemporary society from eruptions such as 1257 Samalas and 1815 
Tambora, the latter resulting in ‘the year without a summer’ with multiple harvest failures across the Northern Hemisphere (e.g., Raible 
et al., 2016). Comparing CMIP5 multi-model simulations with observations has improved understanding of the hydrological responses 
to 20th century eruptions, particularly global land monsoon drying, and associated uncertainties (Section 3.3.2.3). Global mean land 
precipitation decreases for up to a few years following the eruption, with climatologically wet regions drying and dry regions wetting 
(Sections 3.3.2.3 and 4.4.4). Changes in monsoon circulations occur with a general weakening of tropical precipitation (Section 8.5.2.3)

and a decrease in extreme precipitation over global monsoon regions (Section 11.4.4). Monsoon precipitation in one hemisphere tends 
to be enhanced by eruptions occurring in the other hemisphere or reduced if they occur in the same hemisphere (Sections 3.3.2.3 
and 8.5.2.3). Volcanic eruptions have been linked to the onset of El Niño  followed by La Niña although this connection remains 
contentious (Adams et al., 2003; Bradley et al., 2003; McGregor et al., 2010; Khodri et al., 2017; F . Liu et al., 2018; Sun et al., 2019;

594
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Cross-Chapter Box 4.1 (continued)
Paik et al., 2020; Predybaylo et al., 2020). Volcanic activity could drive short-term (one-to-three-year) positive changes in the annual 
SAM index through modulations in the extratropical temperature gradient and wave driving of the polar stratosphere (Yang and Xiao, 
2018). In the cryosphere, Arctic sea ice extent increases for years to decades (Gagné et al., 2017a), and modelling indicates that sea 
ice/ocean feedbacks can prolong cooling long after volcanic aerosols are removed (Miller et al., 2012). On annual time scales, the ocean 
buffers the atmospheric response to volcanic eruptions by storing the cooling in the ocean subsurface, then feeding it back to the 
atmosphere. Large eruptions affect ocean heat content and thermosteric sea level over decadal-to-centennial scales (Section 9.2.2.1).
Potential implications on 21st century projections

Given the unpredictability of individual eruptions, volcanic forcing is prescribed as a constant background loading in CMIP6 models 
(Eyring et al., 2016). This means the effects of potential large volcanic eruptions are largely absent from model projections, and few studies 
have addressed the potential implications on 21st century warming. One study considered future scenarios with hypothetical volcanic 
eruptions consistent with levels of Common Era volcanic activity (Bethke et al., 2017) under RCP4.5 and found that climate projections 
could be substantially altered (Cross-Chapter Box 4.1, Figure 1). Although temporary, close to pre-industrial level temperatures could be 
experienced globally for a  few years after a  1257 Samalas-sized eruption. Several other key climate indicators are also changed substantially, 
consistent with evidence from past events. Bethke et al. (2017) suggest that an eruption early in the 21st century could delay the timing

of crossing 1.5°C global warming by several years. Clustered eruptions would have substantial impact upon GSAT evolution throughout 
the century (Cross-Chapter Box 4.1, Figure 1), and could have far-reaching implications, as observed for past eruptions. For near-term 
response options, decadal prediction models can update 21st-century projections once a volcanic eruption occurs (Timmreck et al., 2016).
Summary
It is likely  that at least one large eruption will occur during the 21st century. Such an eruption would reduce GSAT for several years, 
decrease global mean land precipitation, alter monsoon circulation, modify extreme precipitation, and change the profile of many 
regional climatic impact-drivers. A low-likelihood, high-impact outcome would be several large eruptions that would greatly alter the 
21st century climate trajectory compared to SSP-based ESM projections.

Cross-Chapter Box  4.1, Figure  1 | Potential impact of volcanic eruption on future global temperature change.  CMIP5 projections of possible 
21st-century futures under RCP4.5 after a 1257 Samalas magnitude volcanic eruption in 2044, from Bethke et al. (2017). (a) Volcanic ERF of the most volcanically 
active ensemble member, estimated from SAOD. (b) Annual mean global surface air temperature. Ensemble mean (solid) of future projections including volcanoes 
(blue) and excluding volcanoes (red) with 5–95% range (shading) and ensemble minima/maxima (dots); evolution of the most volcanically active member (black). 
Data created using a SMILE approach with NorESM1 in its CMIP5 configuration. See Sections 2.2.2 and 4.4.4 for more details. Further details on data sources and 
processing are available in the chapter data table (Table 4.SM.1).0.00.51.01.52.02.53.0Temperature above Pre-industrial (ºC)

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100(b)Impact of eruptions upon 21st Century GSAT projections−4
−8
−12ERF (W m−2)(a)Potential low likelihood high impact 21st Century volcanic future
5-95 %Without volcanoes
With volcanoes
Mean
Min/maxLowest ensemble model member

595
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 444.5 Mid- to Long-term Global 
Climate Change
4.5.1 Atmosphere
This section assesses how the global atmospheric indicators assessed 
in Section 4.3 manifest themselves in large-scale spatial patterns of 
atmospheric change in the mid-term (2041–2060) and long term 
(2081–2100). The patterns of change in any given future period 
represent a combination of unforced internal variability and a forced 
response including their interaction (Deser et al., 2016). The role of 
internal variability is much larger at the local to regional scale than 
in the global mean projections. We here assess multi-model mean 
patterns based on CMIP6 models without any weighting or emergent 
constraints. The mean represents an estimate of the forced response 
and is a more homogeneous pattern than the 20-year mean change

patterns in any individual model realization (Knutti et al., 2010).4.5.1.1 Near-surface Air Temperature
Patterns of near-surface air temperature changes show widespread 
warming by 2041–2060 and 2081–2100 (Figure  4.19) for all SSPs 
relative to 1995–2014. The area fraction experiencing warming 
increases with the level of global mean warming. As GSAT continues 
to increase, it is very likely  that by the middle and the end of the 
21st century most of the global land and ocean areas will be warmer 
than in 1995–2014 ( high confidence , Section 4.3.1.1).
The multi-model mean temperature change pattern (Figure  4.19) 
shows some robust key characteristics that are independent of the 
time horizon and scenario, such as a land–ocean warming contrast, 
amplified warming over the Arctic region, assessed below, or the 
comparatively small warming or even cooling in the North Atlantic 
subpolar gyre (Section  9.2.1.1). Furthermore, changes in aerosol

concentrations and land use and land management can have 
a direct imprint on the regional warming pattern (Bright et al., 2017; 
Annual mean temperature change
SSP1-2.6 (2041–60) SSP1-2.6 (2081–2100)
SSP3-7.0 (2041–60) SSP3-7.0 (2081–2100)38 38
35 35
No change or no robust signalRobust signal
Conflicting signalsColour
°C–6    –4    –3    –2    –1   –0.5    0    0.5    1      2      3      5      6
Figure 4.19 | Mid- and long-term change of annual mean surface temperature.  Displayed are projected spatial patterns of multi-model mean change in annual mean 
near-surface air temperature (°C) in 2041–2060 and 2081–2100 relative to 1995–2014 for (top)  SSP1-2.6 and (bottom)  SSP3-7.0. The number of models used is indicated in 
the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show

a change greater than the internal-variability threshold (see Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with 
no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of 
conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. 

596
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Kasoar et al., 2018). Note that the global average of the pattern 
shown in Figure  4.19 corresponds to CMIP6 multi-model mean 
GSAT warming (Section 4.3.1) and is thus somewhat warmer than 
the warming pattern consistent with the central estimate of the 
GSAT range assessed in Section  4.3.4. Since the regional mean 
warming scales well with global warming levels independent 
of the emissions scenario (Section  4.2.4), the key characteristics 
of the spatial pattern assessed here are largely independent of the 
difference between CMIP6 multi-model global mean and assessed 
global GSAT change.
4.5.1.1.1 Land–ocean warming contrast
It is virtually certain  that future average warming will be higher over 
land than over the ocean. Section 2.2.1 of SRCCL (G. Jia et al., 2019) 
assessed that it is certain that land temperatures have increased

more than global mean temperatures since the pre-industrial period. 
This so-called land–ocean warming contrast is a striking feature of 
observed trends (Lambert and Chiang, 2007; Byrne and O’Gorman, 
2018) and projected changes in surface-air temperature (Sutton 
et  al., 2007; Joshi and Gregory, 2008; Dong et  al., 2009; Lambert 
et al., 2011; Drost et al., 2012; Bayr and Dommenget, 2013; Byrne 
and O’Gorman, 2013b; Izumi et al., 2013; Joshi et al., 2013). Between 
1979 and 2016, average temperature over land increased by 42% 
more than over the ocean (Byrne and O’Gorman, 2018). A  similar 
warming contrast is found in CMIP5 projections though with large 
differences across models and latitudes (Sutton et al., 2007; Drost 
et al., 2012; Byrne and O’Gorman, 2013b; Joshi et al., 2013), which 
is also consistent with paleoclimate evidence (Izumi et  al., 2013; 
Schmidt et al., 2014). The ratio of land-to-ocean warming is greater

than one for almost all regions ( high confidence ) and is larger for 
dry subtropical continents (about 1.5) than for moist regions in the 
tropics and mid-latitudes (about 1.2; Byrne and O’Gorman, 2013a). 
Projected warming over land and ocean only is shown in Table 4.2 
for different scenarios, and the global average ratio of land-to-ocean 
warming in CMIP6 is 1.5 with a  likely range of 1.4 to 1.7, which is 
consistent with estimates based on CMIP5.
Since AR5, a robust physical understanding of the warming contrast 
been developed. A simple theory based on atmospheric dynamics and 
moisture transport shows that surface-air temperature and relative 
humidity over land are strongly coupled, and demonstrates that the 
warming contrast occurs because air over land is drier than over 
the ocean (Joshi et al., 2008; Byrne and O’Gorman, 2013a, b, 2018). 
The warming contrast causes land relative humidity to decrease 
(Byrne and O’Gorman, 2016, 2018; Chadwick et al., 2016) and this

feeds back on and strengthens the warming contrast. Differences 
in land-relative humidity responses across models are the primary 
cause of uncertainty in the land–ocean warming contrast (Byrne 
and O’Gorman, 2013b). These land-relative humidity changes are 
ultimately controlled by moisture transport between the land and 
ocean boundary layers (Byrne and O’Gorman, 2016; Chadwick et al., 
2016) and are also sensitive to characteristics of land surfaces that 
are challenging to model, including stomatal conductance and soil 
moisture (Berg et al., 2016; Zarakas et al., 2020).4.5.1.1.2 Polar amplification
It is very likely  that under all SSPs the warming in the Arctic will be 
more pronounced than in the global average over the 21st century. 
Since AR5 the understanding of the physical mechanisms driving 
polar amplification has improved.
The Arctic surface is projected to warm more than the global average 
over the 21st century, with annual-average Arctic warming of about

3°C (SSP1-2.6), 10°C (SSP3-7.0) and 12°C in (SSP5-8.5) by 2081–2100 
relative to 1995–2014 (Figure  4.19). This phenomenon, known as 
polar or Arctic amplification, is a ubiquitous feature of the response 
to GHG forcing simulated by climate models (Manabe and Wetherald, 
1975, 1980; Manabe and Stouffer, 1980; Robock, 1983; Hansen et al., 
1984; Manabe et al., 1991; Holland and Bitz, 2003; Winton, 2006; 
Pithan and Mauritsen, 2014) and has been observed over recent 
decades concurrent with Arctic sea ice loss (Section 2.3.2.1; Serreze 
and Barry, 2011). Based on robust scientific understanding and 
agreement across multiple lines of evidence (Section 7.4.4.1), there is 
high confidence  that the rate of Arctic surface warming will continue 
to exceed the global average over the 21st century.
A variety of mechanisms contribute to Arctic amplification 
(Section 7.4.4.1.1). While surface-albedo feedbacks associated with

the loss of sea ice and snow have long been known to play important 
roles (Arrhenius, 1896; Manabe and Stouffer, 1980; Robock, 1983; 
Hall, 2004), it is now recognized that temperature (lapse-rate and 
Planck) feedbacks also contribute to Arctic amplification through 
a less efficient longwave radiative damping to space with warming 
at high latitudes (Winton, 2006; Pithan and Mauritsen, 2014; Goosse 
et al., 2018; Stuecker et al., 2018). Increases in poleward atmospheric 
latent heat transport and oceanic heat transport also contribute to 
Arctic warming (Holland and Bitz, 2003; Bitz et al., 2006; Lee et al., 
2011, 2017; Alexeev and Jackson, 2013; Marshall et al., 2014, 2015; 
Woods and Caballero, 2016; Nummelin et al., 2017; Singh et al., 2017; 
Merlis and Henry, 2018; Oldenburg et al., 2018; Armour et al., 2019; 
Beer et al., 2020). Projected reduction in the strength of the AMOC 
over the 21st century is expected to reduce Arctic warming, but even

a strong AMOC reduction would not eliminate Arctic amplification 
entirely ( medium confidence ) (Liu et  al., 2017; Y . Liu et  al., 2018; 
Wen et al., 2018).
There remains substantial uncertainty in the magnitude of projected 
Arctic amplification (Smith et  al., 2020), with the Arctic warming 
ranging from two to four times the global average in models (Holland 
and Bitz, 2003; Nummelin et  al., 2017). This uncertainty primarily 
stems from different representations of polar surface-albedo, lapse-
rate, and cloud feedbacks, and from different projected poleward 
energy transport changes (Holland and Bitz, 2003; Crook et al., 2011; 
Mahlstein and Knutti, 2011; Pithan and Mauritsen, 2014; Bonan 
et al., 2018). The magnitude of Arctic amplification may also depend 
on the mix of radiative forcing agents (Najafi et al., 2015; Sand et al., 
2016; Stjern et al., 2019) such as the contribution of ozone depleting 
substances (Polvani et al., 2020). Tropospheric aerosol emissions tend

to reduce simulated Arctic warming over the middle of the 20th century 
(Gagné et al., 2017b) and consequently aerosol emission reductions 
in observations and SSP scenarios enhance simulated Arctic warming

597
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44over recent and future decades (Section  6.4.3; Gagné et  al., 2015; 
Acosta Navarro et al., 2016; Wobus et al., 2016; Wang et al., 2018).
Climate models project a weaker polar amplification in the SH than in 
the NH under transient warming (Figure 4.19). Model simulations (Hall, 
2004; Danabasoglu and Gent, 2009; Li et al., 2013) and paleoclimate 
proxies indicate polar amplification in both hemispheres near 
equilibrium, but generally with less warming in the Antarctic than 
the Arctic (Section 7.4.4.1.2). The primary driver of delayed warming 
of the southern high latitudes is the upwelling in the Southern Ocean 
and associated ocean heat uptake that is then transported away 
from Antarctica by northward flowing surface waters (Frölicher et al., 
2015; Marshall et al., 2015; Armour et al., 2016; W. Liu et al., 2018), 
although asymmetries in feedbacks between the poles also play

a role (Section 7.4.4.1.1). Changes in westerly surface winds over the 
Southern Ocean have the potential to affect the rate of sea-surface 
warming, but there is currently low confidence  in even the sign of 
the effect based on a diverse range of climate model responses to 
wind changes (Marshall et  al., 2014; Ferreira et  al., 2015; Kostov 
et al., 2017; Seviour et al., 2019). A substantial increase in freshwater 
input to the ocean from the Antarctic ice sheet could further slow the 
emergence of SH polar amplification by cooling the Southern Ocean 
surface (Bronselaer et  al., 2018; Golledge et  al., 2019; Schloesser 
et al., 2019), but this process is not represented in current climate 
models which lack dynamic ice sheets. Thus, while there is high 
confidence  that the SH high latitudes will warm by more than the 
tropics on centennial time scales, there is low confidence  that such 
a feature will emerge this century (Section 7.4.4.1).
Seasonal warming patterns

The warming pattern shows distinct seasonal characteristics. 
The  majority of models show a  stronger hemispheric winter than 
summer warming over land poleward of about 55°N and 55°S 
(Figure 4.20) and thereby a reduced amplitude of the temperature cycle (Dwyer et al., 2012; Donohoe and Battisti, 2013). On the other hand, 
over most of the subtropics and mid-latitudinal land regions except for 
parts of Asia, models project stronger warming in hemispheric summer 
than winter (Donohoe and Battisti, 2013; Santer et al., 2018), leading 
to an amplification of the seasonal cycle. This phenomenon has been 
studied particularly in the case of the amplified summer warming over 
the Mediterranean region (Seager  et  al., 2014a; Kröner  et  al., 2017; 
Brogli et al., 2019).
4.5.1.1.3 Changes in temperature variability
It has long been recognized that along with mean temperatures 
also variance and skewness of the temperature distribution may

be changing (Gregory and Mitchell, 1995; Mearns et  al., 1997). 
By amplifying or dampening changes in the tail of temperature 
distribution such changes are potentially highly relevant to extremes 
(Section  11.3.1) and pose a  serious challenge to adaptation 
measures. Changes in temperature variability can occur from diurnal 
to multi-decadal time scales and from the local to the global scale 
with potentially even opposing signals in different seasons and at 
the different spatial scales
Changes in GSAT variability are poorly understood. Based on model 
experiments it has been suggested that unforced variability of GSAT 
tends to decrease in a warmer world as a result of reduced albedo 
variability in high latitudes resulting from melting snow and sea ice 
(Huntingford et al., 2013; Brown et al., 2017), but confidence remains  
low and an observed change has not been detected. An assessment of 
changes in global temperature variability is inherently challenging due

to the interplay of unforced internal variability and forced changes.
Changes in tropical temperature variability may arise from changes 
in the amplitude of ENSO (Section  4.5.3.2). Over the extratropics, 
several studies have identified robust large-scale patterns of changes 
Warming difference JJA vs. DJF (SSP1-2.6) Warming difference JJA vs. DJF (SSP3-7.0)
°CColor High model agreement
Low model agreement      –4   –3   –2  –1.5 –1  –0.5   0   0.5   1    1.5   2     3     4
Figure 4 .20 | Difference of surface temperature change between June–July–August (JJA) and December –January–February (DJF). Displayed are spatial 
patterns of multi-model mean difference in projected warming in JJA minus warming in DJF in 2081–2100 relative to 1995–2014 for (left)  SSP1-2.6 and (right)  SSP3-7.0. 
Diagonal lines mark areas where fewer than 80% of the models agree on the sign of change, and no overlay where at least 80% of the models agree. Further details on data

sources and processing are available in the chapter data table (Table 4.SM.1).

598
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4in variability of annual and particularly seasonal mean temperature, 
including (i) a reduction in mid- to high-latitude winter temperature 
variability and (ii) an increase in summer temperature variability 
over land in the tropics and subtropics (Huntingford et  al., 2013; 
Holmes et  al., 2016; see Figure  4.21). The multi-ensemble average 
across seven single-model initial-condition large ensembles projects 
a consistent reduction in year-to-year December –January–February 
(DJF) variability around about 50°N–70°N and June–July–August 
(JJA) variability around 55°S–70°S along the edge of the sea ice- and 
snow-covered region (Figure 4.21). There is growing evidence that 
year-to-year and day-to-day temperature variability decreases in 
winter over northern mid- to high-latitudes (Fischer et  al., 2011; 
De Vries et al., 2012; Screen, 2014; Schneider et al., 2015; Holmes

et al., 2016; Borodina et al., 2017; Tamarin-Brodsky et al., 2020) which 
implies that the lowest temperatures rise more than the respective 
climatological mean temperatures ( medium confidence ). Over the 
NH, reduced high-latitude temperature variability is associated 
with disproportionally large warming in source region of cold-air 
advection due to Arctic amplification and land–sea contrast (De Vries 
et al., 2012; Screen, 2014; Holmes et al., 2016). It has further been 
argued that a reduction in snow and sea ice coverage from partly 
to completely snow- and ice-free ocean and land surface would 
substantially reduce cold-season temperature variability (Gregory and 
Mitchell, 1995; Fischer et al., 2011; Borodina et al., 2017) and lead to 
a shortening of the cold season and earlier onset of the warm season 
(Cassou and Cattiaux, 2016). Mid-latitudinal winter temperature 
variability is further affected by a complex interplay of a multitude

of processes including potential changes in atmospheric circulation, 
but there is low confiden ce in the dominant contribution of Arctic 
warming compared to other drivers (Cross-Chapter Box 10.1).In JJA, the multi-model average projects an increase in year-to-year 
JJA variability over Central Europe and North America (Figure 4.21). 
In particular an increase in daily to interannual summer temperature 
variability has been projected over central Europe as a result of larger 
year-to-year variability in soil moisture conditions varying between 
a  wet and dry regime and leading to enhanced land–atmosphere 
interaction (Seneviratne et  al., 2006; Fischer et  al., 2012; Holmes 
et  al., 2016). Furthermore, the amplified warming in the source 
regions of warm-air advection due to land–ocean warming contrast 
and amplified Mediterranean warming (Seager et al., 2014a; Brogli 
et  al., 2019), may lead to disproportionally strong warming of

the hottest days and summers and thereby increased variability. 
Enhanced temperature variability is further projected over some land 
regions in the subtropics and tropics (Bathiany et al., 2018).
In summary, there is medium confidence  that continued warming will 
regionally lead to increased and decreased year-to-year temperature 
variability in the extratropics and there is medium confidence  that 
year-to-year temperature variability will decrease over parts of the 
mid- to high- latitudes of the winter hemisphere.
4.5.1.2 Annual Mean Atmospheric Temperature
Section  12.4.3.2 of AR5 assessed that there is high confidence  in 
the overall pattern of projected end of 21st  century tropospheric 
temperature change and that it is very likely  that some of the largest 
warming will occur in the northern high latitudes. They further 
assessed that proportionately larger warming is likely to occur 
in the tropical upper troposphere than at the tropical surface, but

with medium confidence  owing to the relatively large observational 
Change in DJF temperature variability
30 25 20 15 -15 -20 -25 -30 10 5 0 -5 -10Change in JJA temperature variability
%Colour High model agreement
Low model agreement
Figure  4.21 | Percentage change in interannual variability of (left) December –January–February (DJF) and (right) June–July–August (JJA) mean 
temperature averaged across seven large initial condition ensembles.  Average changes across seven single-model, initial-condition large ensembles are shown for 
RCP8.5 in 2081–2100 (and where not available for 2080–2099) relative to 1995–2014. Standard deviations are calculated across all members of the large ensembles for every 
given year to avoid inflation due to the underlying trend and then averaged across the period. Changes are averaged across the ensembles MPI-GE (100 members, Maher

et al., 2019), CanESM2 (50 members, Kirchmeier-Young et al., 2017), NCAR-CESM (30 members, Kay et al., 2015), GFDL-CM3 (20 members, Sun et al., 2018), GFDL-ESM2M 
(30 members, Rodgers et al., 2015), CSIRO-Mk3-6-0 (30 members, Jeffrey et al., 2013) and EC-EARTH (16 members, Hazeleger et al., 2010). Also see Deser et al. (2020) for 
further information on those ensembles. Diagonal lines indicate areas with low model agreement where fewer than 80% of the models agree on the sign of the change, and 
no overlay areas with high model agreement where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available 
in the chapter data table (Table 4.SM.1).

599
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44uncertainties and contradictory analyses regarding model accuracy in 
simulating tropical upper tropospheric temperature trends.
CMIP6 projections show warming throughout the troposphere by the 
end of this century and a  mix of warming and cooling in the stratosphere 
depending on the emissions scenario (Figure 4.22). The patterns of 
tropospheric temperature change are highly consistent with those 
derived from earlier generations of climate models as assessed in 
AR5, AR4 and TAR. In SSP1-2.6, the multi-model mean warming 
remains below 3°C everywhere in the troposphere except near the 
surface in the Arctic; this is similar to the findings in AR5 based on 
CMIP5 models for RCP2.6. In SSP3-7.0, the zonal mean tropospheric 
warming is also largest in the tropical upper troposphere, reaching 
more than 5°C, and near the surface in the Arctic where warming

exceeds 8°C (Figure 4.22). It is likely  that the warmer projected GSAT 
in the unconstrained CMIP6 model ensemble contributes to larger 
warming in the tropical upper troposphere and in the Arctic lower 
troposphere. This assessment is based on the understanding of polar 
amplification assessed in Chapter  7 (Section  7.4.4.1), and at low 
latitudes is based on the understanding of moist convective processes 
as well as the relationship between CMIP5- and CMIP6-simulated 
surface temperatures and tropical upper tropospheric warming over 
the historical period (Section 3.3.1.2).
Projected stratospheric temperature trends are determined by 
a balance between the major radiative drivers from ozone recovery, 
rising CO 2 and other greenhouse gases (including stratospheric 
water vapour) (Maycock, 2016), as well as future changes in the 
Brewer –Dobson circulation, which can alter the latitudinal pattern of

stratospheric temperature trends (Fu et al., 2015, 2019). In the lower stratosphere, the CMIP6 models project a weak cooling in the inner 
tropics in SSP1-2.6 and a warming at other latitudes (Figure 4.22). 
There is enhanced lower stratospheric warming over the Antarctic 
pole owing to the effects of ozone hole recovery on polar temperatures 
(Maycock, 2016; Solomon et al., 2017). The projected strengthening of 
the Brewer –Dobson circulation in the future (Hardiman et al., 2014) 
also affects stratospheric temperature trends, with adiabatic cooling 
at low latitudes and warming in middle and high latitudes (Fu et al., 
2015, 2019). In SSP3-7.0, there is widespread cooling across much 
of the stratosphere, as expected from the higher GHG emissions, 
with a smaller warming in the Antarctic lower stratosphere. Owing 
to the importance of ozone recovery for the radiative balance of 
the stratosphere, future global and local stratospheric temperature

trends do not scale with projected GSAT change.
In summary, new results since AR5 do not generally alter the 
understanding of projected zonal mean atmospheric temperature 
changes. There is high confidence  in the overall pattern of projected 
tropospheric temperature changes given its robustness across many 
generations of climate models. It is further very likely  that projected 
long-term tropospheric warming will be larger than the global 
mean in the Arctic lower troposphere. It is likely  that tropical upper 
tropospheric warming will be larger than at the tropical surface, 
however with an uncertain magnitude owing to the potentially large 
role of natural internal variability and differences across models in 
the simulated free tropospheric temperature response to a  given 
forcing scenario (Section 3.3.1.2). It is very likely  that global mean 
stratospheric cooling will be larger by the end of the 21st century 
in a pathway with higher atmospheric CO 2 concentrations.

-5 -4 -3 -2 -1 0 1 2 3 4 5Color High model agreement
Low model agreementPressure (hPa)
Pressure (hPa)
Figure 4 .22 | Long-term change of annual and zonal mean atmospheric temperature.  Displayed are multi-model mean change in annual and zonal mean atmospheric 
temperature (°C) in 2081–2100 relative to 1995–2014 for (left)  SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. Diagonal 
lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where 80% or more of the models agree on the sign of the change. 

600
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information44.5.1.3 Near-surface Relative Humidity
The AR5 contrasted future changes in near-surface relative humidity 
(RH) over land and ocean, concluding with medium confidence  that 
reductions in near-surface RH over many land areas are likely . The 
decrease in near-surface RH over most land areas is associated with 
the larger warming rates over land than over the ocean and is termed 
the last-saturation-temperature constraint, as explained in AR5.
Since AR5, significant effort has been devoted to understanding the 
mechanisms for the decrease in near-surface land RH under global 
warming, and the relevance of RH changes for the land–sea warming 
contrast and the water cycle. For the near-surface RH decrease over 
land, both the moisture transport from the ocean and land–atmosphere 
feedback processes contribute. For changes in specific humidity over

land, the moisture transport from the ocean is dominant while the 
role of evapotranspiration is secondary (Byrne and O’Gorman, 2016; 
Chadwick et  al., 2016). Nevertheless, the changes in near-surface land RH are also strongly influenced by evapotranspiration, which is 
suppressed by the drying of soils and plant responses to increasing 
CO 2 related to stomatal closure under climate change (Byrne and 
O’Gorman, 2015; Berg et  al., 2016; Chadwick et  al., 2016; Swann 
et  al., 2016; Lemordant et  al., 2018). The combination of oceanic 
and continental influences can explain the spatially diverse trends 
in the near-surface RH over land in the observations for the recent 
decades, with a generally dominant negative trend at the global scale 
(Vicente-Serrano et  al., 2018). There is a  strong feedback between 
the near-surface land RH decrease and land–ocean warming contrast 
under future warming projections (Section 4.5.1.1).

Changes in land RH can modulate the response of the water cycle to 
global warming (Chadwick et al., 2013; Byrne and O’Gorman, 2015). 
Most CMIP5 models project higher precipitation associated with higher 
near-surface RH and temperature under climate change (Lambert 
et  al., 2017). Over land, the spatial gradients of fractional changes 
in near-surface RH contribute to a  drying tendency in  precipitation 
Seasonal mean relative humidity change
DJF SSP1-2.6 (2081–2100) DJF SSP3-7.0 (2081–2100)
JJA SSP1-2.6 (2081–2100) JJA SSP3-7.0 (2081–2100)
Colour Robust significant change
Conflicting signal          –8      –6      –4       –2           0         2         4          6         8
%3131 30
30
Figure  4.23 | Long-term changes in seasonal mean relative humidity.  Displayed are projected spatial patterns of multi-model mean change (%) in seasonal

(top)  December –January–February (DJF) and (bottom)  June–July–August (JJA) mean near-surface relative humidity in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 
and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from 
internal variability, that is, where at least 66% of the models show a change greater than the internal variability threshold (Section 4.2.6) and at least 80% of the models agree 
on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the 
internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but

fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

601
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44minus evapotranspiration with warming, which partly explains why 
the ‘wet gets wetter, dry gets drier’ paradigm does not hold over land 
(Byrne and O’Gorman, 2015). Terrestrial aridity is projected to increase 
over land, as manifested by a decrease in the ratio of precipitation to 
potential evapotranspiration, in which the decrease in near-surface 
land RH has a  contribution of about 35% in CMIP5 models under 
doubled CO 2 forcing (Fu and Feng, 2014). The aridity can be further 
amplified by the feedbacks of projected drier soils on land surface 
temperature, RH, and precipitation (Berg et al., 2016).
The CMIP6 multi-model ensemble projects general decreases in 
near-surface relative humidity over a  large fraction of land areas, 
but moderate increases over the ocean (Figure 4.23). The projected 
changes depend on emissions scenario and season. Changes in

near-surface RH under SSP1-2.6 are insignificant compared to 
natural variability. Under SSP3-7.0, during boreal summer, significant 
decreases relative to natural variability are projected in continental 
Europe and the Middle East, North America, South America and 
South Africa.
lead to decreased near-surface relative humidity over a large fraction 
of land areas, but moderate increases over the ocean. There is high 
confidence  that near-surface relative humidity will decrease over 
parts of the tropical and subtropical latitudes over land.
4.5.1.4 Precipitation
The AR5 assessed that changes in mean precipitation in a warmer 
world will exhibit substantial spatial variation. Also, the contrast of 
mean precipitation between dry and wet regions and between dry 
and wet seasons will increase over most of globe as temperatures 
increase. The general pattern of change indicates that high latitude

land masses are likely  to experience greater amounts of precipitation 
due to the increased specific humidity of the warmer troposphere as 
well as increased transport of water vapour from the tropics by the 
end of this century under the RCP8.5 scenario. Many mid-latitude 
and subtropical arid and semi-arid regions will likely  experience 
less precipitation, while many moist mid-latitude regions will likely  
experience more precipitation by the end of this century under the 
RCP8.5 scenario.
Since AR5, progress has been achieved in understanding changes 
in patterns and rates of precipitation with GSAT rise. The projected 
precipitation changes can be decomposed into a part that is related 
to atmospheric circulation referred to as dynamical component 
and a  part related to water vapour changes, the thermodynamic 
component. Based on process understanding and modelling (Fläschner 
et al., 2016; Samset et al., 2016), global mean precipitation will very

likely  increase by 1–3% per °C of GSAT warming (Section  8.2.1). 
The increase in atmospheric water vapour is a robust change under 
global warming, the sensitivity of global precipitation change to 
warming is smaller (2% per °C) as compared to water vapour change 
(7% per °C; Held and Soden, 2006). Global energy balance places 
a  strong constraint on the global mean precipitation (Allen and 
Ingram, 2002; Pendergrass and Hartmann, 2014; Myhre et al., 2018; 
Siler et  al.,  2019). Tropospheric radiative cooling constrains global precipitation (Pendergrass and Hartmann, 2014), leading to a slow 
SST-dependent response and a forcing-dependent rapid adjustment. 
Rapid adjustments account for large regional differences in 
hydrological sensitivity across multiple drivers (Samset et al., 2016; 
Myhre et  al., 2017). The rapid regional precipitation response to 
increased CO 2 is robust across models, implying that the uncertainty

in long-term changes is mainly associated with the response to 
SST-mediated feedbacks (Richardson et  al., 2016). Precipitation 
response to fast adjustments and slow temperature-driven responses 
are assessed in detail in Chapter 8 (Section 8.2.1).
The thermodynamic response to global warming is associated with 
a ‘wet get wetter’ mechanism, with enhanced moisture flux leading 
to subtropical dry regions getting drier and tropical and mid-latitude 
wet regions getting wetter (Held and Soden, 2006; Chou et al., 2009). 
Recent studies suggest that the dry-get-drier argument does not hold, 
especially over subtropical land regions (Greve et  al., 2014; Feng 
and Zhang, 2015; Greve and Seneviratne, 2015). The discrepancy 
may be partly arising due to differences in model climatologies 
and by change in the location of wet and dry regions (Polson and 
Hegerl, 2017). Over the 21st century, significant rate of precipitation

change is associated with a spatial stabilization and intensification 
of moistening and drying patterns (Chavaillaz et  al., 2016). In the 
tropics, weakening of circulation leads to a ‘wet gets drier, dry gets 
wetter’ pattern (Chadwick et  al., 2013). Climate model agreement 
for precipitation change in the tropics is lower than for other regions 
(Knutti and Sedláček, 2013; McSweeney and Jones, 2013). Sources 
of inter-model uncertainty in regional rainfall projections arise from 
circulation changes (Kent et al., 2015; Chadwick, 2016) and spatial 
shifts in convection and convergence, associated with SST pattern 
change and land–sea thermal contrast change (Kent et  al., 2015; 
Chadwick et  al., 2017) with a  secondary contribution from the 
response to direct CO 2 forcing (Chadwick, 2016). Factors governing 
changes in large-scale precipitation patterns are assessed in detail in 
Sections 8.2.2 and 10.4.1.
Long-term multi-model mean change in seasonal precipitation

(JJA and DJF) from CMIP6 models (Figure  4.24) shows substantial 
regional differences and seasonal contrast. Changes in seasonal 
precipitation under SSP1-2.6 are small compared to internal 
variability. Consistent with the AR5, patterns of precipitation 
change are very likely  to increase in the high latitudes especially 
during local winter and over tropical oceans under SSP3-7.0 ( high 
confidence ). CMIP6 projections show an increase in precipitation 
over larger parts of the monsoon regions and decreases in many 
subtropical regions including the Mediterranean, southern Africa 
and south-west Australia ( medium confidence ). The large-scale 
patterns of precipitation shown in Figure 4.24 are consistent with the 
patterns presented in Section 8.4.1.3. Precipitation changes exhibit 
strong seasonal characteristics (Box 8.2), and, in many regions, the 
sign of the precipitation changes varies with season. Precipitation

variability is projected to increase over a majority of global land area, 
as assessed in Chapter 8 (Section 8.4.1.3.3), over a wide range of 
time scales in response to warming (Pendergrass et al., 2017).

602
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Most of the projected changes in precipitation exhibit a  sharp 
contrast between land and ocean (Sections 8.2.1 and 8.4.1). Temperature-driven intensification of land-mean precipitation during 
the 20th  century has been masked by fast precipitation responses 
to anthropogenic sulphate and volcanic forcing (Allen and Ingram, 2002; Richardson et  al., 2018a). Based on the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), land-mean precipitation is expected to increase more rapidly with the projected 
decrease in sulphate forcing and continued warming, contributing to 
increased global mean precipitation (Table 4.3) and will be clearly observable by the mid-21st  century based on RCP4.5 and RCP8.5 scenarios (Richardson et al., 2018a).
Consistent with the findings of AR5, a  gradual increase in global 
mean precipitation is projected over the 21st  century with an

increase of approximately 2.9% (1.0–5.2%) under SSP1 -2.6 and 4.7% 
(2.3–8.2%) under SSP3 -7.0 during 2081–2100 relative to 1995–2014. The corresponding increase in annual mean global land precipitation is 
3.3% (0–6.6%), in the SSP1 -2.6 and 5.8% (0.5–9.6%) in the SSP3 -7.0 
(Table  4.3). CMIP6 models show greater increases in precipitation over land than either globally or over the ocean (high confidence).
Based on the assessment of CMIP6 models, we conclude that it 
is very likely that, in the long term, global mean land and global 
mean ocean precipitation will increase with increasing GSAT. Annual mean and global mean precipitation will very likely increase by 
1–3% per °C GSAT warming. The patterns of precipitation change 
will exhibit substantial regional differences and seasonal contrast as 
GSAT increases over the 21st century ( high confidence). Precipitation 
will very likely increase over high latitudes and the tropical ocean

and will likely increase in large parts of the monsoon regions. 
However, it is likely to decrease over the subtropics, including Mediterranean, southern Africa and south-west Australia, in response to GHG-induced warming.
Conflicting signal–50      –30      –20     –10       0        10       20        30       50
%38 35
35 38
Figure 4.24 | Long-term change of seasonal mean precipitation.  Displayed are projected spatial patterns of multi-model mean change (%) in (top)  December  –January–
February (DJF) and (bottom)  June–July–August (JJA) mean precipitation in 2081–2100 relative to 1995–2014, for (left) SSP1 -2.6 and (right) SSP3 -7.0. The number of models

used is indicated in the top right of the maps. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at 
least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal 
lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

603
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 444.5.1.5 Global Monsoon Precipitation and Circulation
The AR5 assessed changes of the global monsoon in the context 
of long-term trends across the 21st  century and the change by 
2081–2100. The AR5 showed growing evidence of improved skill 
of climate models in reproducing the climatological features of the 
global monsoon. Taken together with identified model agreement on 
future changes, the global monsoon precipitation, aggregated over all 
regional monsoon regions, is likely  to strengthen in the 21st century 
with increases in its area and intensity, while the monsoon circulation 
weakens. In all RCP scenarios, the global monsoon area is very likely 
to increase, and the global monsoon precipitation intensity is likely  to 
increase, resulting in a  very likely  increase in the global monsoon 
total precipitation, by 2081–2100 (Kitoh et al., 2013).

Since AR5, there has been progress in understanding physical 
mechanisms for the projected changes in global monsoon and 
quantifying the sources of uncertainty in projections. The increase in 
global monsoon precipitation under warming is primarily attributed 
to the increase of moisture convergence, which comes mainly from 
the thermodynamic effect due to increasing atmospheric moisture 
but is partly offset by reduced convergence (W. Zhang et  al., 
2019; Chen et  al., 2020). The dynamic effect, such as monsoon 
circulation changes, dominates regional differences in the projected 
monsoon  precipitation  changes (Chen et  al., 2020). Specifically, 
NH monsoon precipitation will increase more strongly than its 
SH counterpart, due to an increase in hemispheric temperature 
difference between the NH and SH, enhancement of the Hadley 
circulation, and atmospheric moistening, countered by stabilization 
of the troposphere (Lee and Wang, 2014). The seasonality of

global monsoon rainfall is projected to be enhanced in response to 
warming, featuring a greater wet–dry season contrast (Lee and Wang 
2014; Zhang et al. 2019). In addition, the interannual variability of 
global monsoon rainfall is projected to intensify mainly over land, 
with a strengthened relationship between global monsoon and ENSO 
(Hsu et al., 2013; Wang et al., 2020, 2021).
For the uncertainty in mean monsoon precipitation projections, the 
model uncertainty is the dominant contributor throughout the century 
and explains more than 70% of the inter-model variance during near 
term, mid-term, and long term. The contribution of internal variability 
is only important at the beginning in early decades, while scenario 
uncertainty becomes important at the end of the 21st  century. The 
sources of uncertainty for the mean and extreme monsoon precipitation 
mainly differ in the long-term projection, when the contribution of

scenario uncertainty is comparable to the model uncertainty for extreme 
precipitation (Zhou et al., 2020). Although the magnitude of internal 
variability differs between CMIP5 models and single-model, initial-
condition large ensembles, the impact is only evident in the beginning 
decades. For the mid- and long term, the magnitude difference does not 
alter that model uncertainty is the dominant source of uncertainty in 
the projections of global land monsoon precipitation (Zhou et al., 2020).
Based on the projections of changes in precipitation from CMIP6 
under the four SSPs, the global monsoon precipitation is likely  to 
strengthen in the 21st century with increases in its intensity, while 
NH summer monsoon circulation weakens (Figure 4.14). Global land monsoon precipitation will likely  increase by 1.3–2.4% per °C GSAT 
warming among the four scenarios considered here. In the  long 
term, the multi-model mean change (5–95% range of the available

41 projections) of global land monsoon precipitation index is 2.9% 
(–0.8  to +7.8%), 3.7% (–2.5 to +8.6%), 3.77% (–3.2  to  +8.1%), 
and 5.7% (–2.8 to +12.3%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and 
SSP5-8.5, respectively. This enhancement is caused by thermodynamic 
responses due to increased moisture, which is partly offset by 
dynamic responses due to a weakened circulation (Chen et al., 2020). 
The patterns of monsoon rainfall change in the mid- to long-term 
include a north–south asymmetry characterized by greater increase 
in the NH than the SH, and an East–West asymmetry characterized 
by enhanced Asian-African monsoons and weakened North American 
monsoon ( medium confidence ) (Lee and Wang, 2014; Mohtadi et al., 
2016; Pascale et al., 2017; Wang et al., 2021).
Based on the assessment of CMIP6 models, we conclude that it is likely  
that, in the mid- to long term, the global land monsoon precipitation 
will increase with GSAT rise despite a weakened monsoon circulation.

The global land monsoon precipitation will likely  increase by 1.3–2.4% 
per °C GSAT warming among the four scenarios. Monsoon precipitation 
responses depend on region and emissions scenario ( high confidence ).
4.5.1.6 Sea Level Pressure, Large-scale Atmospheric 
Circulation, Storm Tracks and Blocking
This subsection provides a  global overview of long-term changes 
in atmospheric dynamical features that is complementary to the 
regional assessment of links to the hydrological cycle in Chapter 8 
(Section 8.4.2), and assessment of the connections to extreme events 
in Chapter 11 (Section 11.7.2).
4.5.1.6.1 Sea level pressure
The AR5 assessed that mean sea level pressure is projected to decrease 
in high latitudes and to increase in mid-latitudes. Such a pattern is 
associated with a poleward shift in the storm track and an increase 
in the annular mode index. This broad pattern is also found in CMIP6 
models (Figure 4.25). Under SSP1-2.6, the pattern in sea level pressure

change resembles that for SSP3-7.0, but the amplitudes are small 
compared to internal variability in 20-year means (Figure 4.25). One 
exception is found in the SH mid-latitudes, where pressure robustly 
increases in SSP3-7.0 in both austral summer and winter, but shows 
no robust change in SSP1-2.6. This is likely  attributable to the larger 
GHG forcing in SSP3-7.0 compared to SSP1-2.6, which contributes 
to a poleward shift of the SH mid-latitude circulation and becomes 
relatively more important than the effect of ozone recovery which 
drives an equatorward shift in the circulation (see Section 4.5.3.1 on 
the Southern Annular Mode; Barnes and Polvani, 2013; Barnes et al., 
2014; Bracegirdle et al., 2020b). The poleward shift in SH mid-latitude 
circulation in SSP3-7.0 likely  contributes to the wetting trend at high 
southern latitudes (Figure 4.25).
As was found in AR5, several regional sea level pressure features

stand out from the zonal-mean change. Sea level pressure markedly 
decreases in north-eastern North America and north-eastern Asia in 
boreal winter. In boreal summer, sea level pressure robustly decreases

604
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4in the Mediterranean and the Middle-East, a decrease that has been 
linked to a large-scale heat low forced by the amplified warming of 
the region (Haarsma et al., 2009). It is likely  that sea level pressure 
will increase across the south-western North America and Central 
America in boreal summer under SSP3-7.0 due to an intensification 
of the eastern North Pacific subtropical summer high (Li et al., 2012) 
and a weakening of the North American monsoon (Section 4.5.1.5; 
Pascale et al., 2017; Wang et al., 2020). These changes in circulation 
are connected to drying across the eastern subtropical Pacific and 
Central America regions (Figure 4.24).
4.5.1.6.2 Zonal wind and westerly jets
Storm tracks and mid-latitude westerly jets are dynamically related 
aspects of mid-latitude circulation. The AR5 assessed that a poleward

shift of the SH westerlies and storm track is likely  by the end of the 
21st  century under RCP8.5 ( medium confidence ). In contrast, low 
confidence  was assessed for the storm-track response in the NH.Under both SSP1-2.6 and SSP3-7.0 there is a strengthening and lifting 
of the subtropical jets in both hemispheres (Figure 4.26), consistent 
with the response to large-scale tropospheric warming found in earlier 
generations of climate models (Collins et al., 2013). In the SH, GHG 
emissions tend to force a poleward shift of the jet, but this is opposed, 
particularly in austral summer, by the stratospheric ozone hole recovery 
(Barnes and Polvani, 2013; Barnes et  al., 2014; Bracegirdle et  al., 
2020b). Consistent with sea level pressure changes, CMIP6 models 
project a  strengthening and poleward shift of the SH jet in austral 
summer and winter under SSP3-7.0, but smaller and non-robust 
changes in SH mid-latitude zonal winds under SSP1-2.6 (Figure 4.26;

see also Section 4.5.3.1). CMIP6 models show an improved simulation 
of the SH jet stream latitude (Bracegirdle et al., 2020a; Curtis et al., 
2020). This has been linked to a reduction in the projected poleward 
shift of the SH jet in austral summer compared to the CMIP5 models 
(Curtis et al., 2020; Goyal et al., 2021), although differences in the 
pattern of SST response may also play a  role (Wood  et  al., 2020). 
In the NH extratropics, the changes in lower-tropospheric zonal-mean 
Seasonal mean sea level pressure change
JJA SSP1-2.6 (2081–2100) JJA SSP3-7.0 (2081–2100)37
3734
34
Conflicting signal      –6    –5    –4    –3   –2    –1      0      1     2       3      4      5      6
hPa
Figure 4.25 | Long-term change of seasonal-mean sea level pressure.  Displayed are projected spatial patterns of multi-model mean change in (top) December –January–

February (DJF) and (bottom)  June–July–August (JJA) mean sea level pressure (hPa) in 2081–2100 relative to 1995–2014, for (left)  SSP1-2.6 and (right)  SSP3-7.0. The number 
of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at 
lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed 
lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree

on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

605
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44zonal winds by the end of the century are generally smaller than in the 
SH. In boreal winter, there is a weak poleward shift of the NH zonal-
mean westerly jet maximum in SSP3-7.0.
CMIP5 and CMIP6 models show a  strong seasonal and regional 
dependence in the response to climate change of NH westerlies (Barnes 
and Polvani, 2013; Grise and Polvani, 2014b; Simpson et al., 2014; Zappa 
et al., 2015; Harvey et al., 2020; Oudar et al., 2020). CMIP5 projections 
indicate a  poleward shift of the westerlies in the North Atlantic in boreal summer, while the North Pacific jet weakens in this season 
(Simpson et al., 2014; Davini and D’Andrea, 2020; Harvey et al., 2020). 
There is a poleward shift in the westerlies in both the North Pacific and 
North Atlantic in Autumn (Barnes and Polvani, 2013; Simpson et al., 
2014). However, the shift of the westerlies is more uncertain in the

other seasons, particularly in the North Atlantic in winter (Simpson 
et al., 2014; Zappa and Shepherd, 2017). Here, the circulation response 
is not well described as a simple shift, since the North Atlantic jet tends 
to be squeezed on both its equatorward and poleward flanks, together 
Colour High model agreement
Pressure (hPa)Pressure (hPa)
Figure 4.26 | Long-term change of zonal-mean, zonal wind.  Displayed are  multi-model mean changes in (left)  boreal winter (December –January–February, DJF) and 
(right)  austral winter (June–July–August, JJA) zonal mean, zonal wind (m s–1) in 2081–2100 for (top) SSP1-2.6 and (bottom) SSP3-7.0 relative to 1995–2014. The 1995–2014 
climatology is shown in contours with spacing 10 m s–1. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay

where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

606
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4with an eastward extension into Europe (Li et al., 2018; Peings et al., 
2018; Simpson et al., 2019a; Harvey et al., 2020; Oudar et al., 2020). 
Simulations indicate that most of the changes in winter storminess 
over the Euro-Atlantic region will occur only after exceeding the 1.5°C 
warming level (Barcikowska et al., 2018).
Progress since AR5 has improved understanding of the climate change 
aspects that can drive these different, and potentially opposite, 
responses in the mid-latitude jets and storm tracks. A poleward shift 
of the jets and storm tracks is expected in response to an increase 
in the atmospheric stratification and in the upper-tropospheric 
equator-to-pole meridional temperature gradient, while it is opposed 
by the decrease in the meridional temperature gradient in the 
lower troposphere associated with the polar amplification of global

warming (Harvey et  al., 2014; Shaw et  al., 2016). Recent analyses 
have identified additional climate aspects that can drive mid-latitude 
jet changes, including patterns in sea surface warming (Mizuta 
et al., 2014; Langenbrunner et al., 2015; Ceppi et al., 2018; Wood et al., 
2020), land–sea warming contrast (Shaw and Voigt, 2015), loss of sea 
ice (Deser et al., 2015; Harvey et al., 2015; Screen et al., 2018b; Zappa 
et al., 2018), and changes in the strength of the stratospheric polar 
vortex (Manzini et al., 2014; Grise and Polvani, 2017; Simpson et al., 
2018; Ceppi and Shepherd, 2019). From an energetics perspective, 
the uncertainty in the response of the jet streams depends on the 
response of clouds, their non-spatially uniform radiative feedbacks 
shaping the meridional profile of warming (Ceppi et al., 2014; Voigt 
and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and 
Shepherd, 2017; Lipat et  al., 2018; Albern et  al., 2019; Voigt et  al.,

2019). Climate models seem to underestimate the forced component 
of the year-to-year variability in the atmospheric circulation, 
particularly in the North Atlantic sector (Scaife and Smith, 2018), 
which suggests some relevant dynamical processes may not be well 
represented. Whether and how this may affect long-term projections 
is unknown. In conclusion, due to the influence from competing 
dynamical drivers and the absence of observational evidence, there 
is medium confidence  in a projected poleward shift of the NH zonal-
mean low-level westerlies in autumn and summer and low confidence  
in the other seasons. There is also overall low confidence  in projected 
regional changes in the NH low-level westerlies, particularly for the 
North Atlantic basin in boreal winter.
The anthropogenic forced signal in extratropical atmospheric circulation 
may well be small compared to internal variability (Deser et al., 2012b,

2014) and, as assessed in AR5, there is generally low agreement  across 
models in many aspects of regional atmospheric circulation change 
particularly in the NH (Shepherd, 2014). The latter means that, in some 
regions, a multi-model average perspective of atmospheric circulation 
change represents a  small residual after averaging over large 
intermodel spread. This is in strong contrast to thermodynamic aspects 
of climate change, such as surface temperature change, for which 
model results are generally highly consistent (see, e.g., Figure 4.19). 
Furthermore, models share systematic biases in some aspects of 
extratropical atmospheric circulation such as mid-latitude jets, which 
can have complex implications for understanding forced changes 
(Simpson and Polvani, 2016). Given these issues, an emerging field 
of research since AR5 has focused on the development of ‘storylines’

for regional atmospheric circulation change (Shepherd,  2019). The storyline approach is grounded in the identification of a set of 
physical predictors of atmospheric circulation change, such as those 
described above (Harvey et al., 2014; Manzini et al., 2014; Shepherd 
et al., 2018), which act together to determine a specific outcome in 
the projected atmospheric circulation change. The consequences 
of multi-model spread in the physical predictors of atmospheric 
circulation change can be investigated, conditioned on a  specified 
level of global warming (see also Section 1.4.4.2 and Box 10.2; Zappa 
and Shepherd, 2017; Zappa, 2019; Mindlin et al., 2020).
4.5.1.6.3 Storm tracks
As stated in AR5, the number of extratropical cyclones (ETC) 
composing the storm tracks is projected to weakly decline in future 
projections, but by no more than a few percent change. The reduction 
is mostly located on the equatorward flank of the storm tracks, which

is associated with the Hadley cell expansion and a poleward shift in 
the mean genesis latitude of ETCs (Tamarin-Brodsky and Kaspi, 2017). 
Furthermore, the poleward propagation of individual ETCs is expected 
to increase with warming (Graff and LaCasce, 2014; Tamarin-Brodsky 
and Kaspi, 2017), thus contributing to a  poleward shift in the 
mid-latitude transient-eddy kinetic energy. The increased poleward 
propagation results from the strengthening of the upper tropospheric 
jet and increased cyclone-associated precipitation (Tamarin-Brodsky 
and Kaspi, 2017), which are robust aspects of climate change.
In the NH boreal winter, CMIP6 models show a  northward shift 
of the ETC density in the North Pacific, a  tripolar pattern in the 
North Atlantic, and a  weakening of the Mediterranean storm 
track (Figure  4.27a). CMIP6 models show overall low agreement  
on changes in ETC density in the North Atlantic in boreal winter

(Figure 4.27a). A poleward shift of the storm track is evident in the 
SH (Figure 4.27b), particularly in the Indian and Pacific Ocean sectors. 
CMIP6 models still feature long-standing biases in the representation 
of storm tracks; for example, the winter storm track into Europe is 
too zonal, though different measures of storm track activity indicate 
some improvements compared to the previous generations of models 
(Harvey et al., 2020; Priestley et al., 2020).
Regarding the dynamical intensity of the storm tracks (Section 11.7.2), 
the number of ETCs associated with intense surface wind speeds and 
undergoing explosive pressure deepening are projected to strongly 
decrease in the NH winter (Seiler and Zwiers, 2016; Chang, 2018). 
The weakening of surface winds of ETCs in the NH is attributed to 
the reduced low-level baroclinicity from SST and sea ice changes 
(Harvey et al., 2014; Seiler and Zwiers, 2016; J. Wang et al., 2017a).

There are, however, regional exceptions such as in the northern North 
Pacific, where explosive and intense ETCs are projected to increase 
in association with the poleward shift of the jet and increased upper-
level baroclinicity (Seiler and Zwiers, 2016). Eddy kinetic energy 
and intense cyclone activity are also projected to decrease in the 
NH summer in association with a  weakening of the jet (Lehmann 
et al., 2014; Chang et al., 2016). However, explosive cyclones tend 
to be too weak in climate models (Seiler and Zwiers, 2016; Priestley 
et al., 2020), though this bias seems to be reduced in high-resolution 
simulations (Jiaxiang et al., 2020). Furthermore, models may not fully 
capture the contribution of the future increase in mesoscale latent

607
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44heating to cyclone intensification (Li et al., 2014; Pfahl et al., 2015; 
Willison et al., 2015; Michaelis et al., 2017). In conclusion, there is 
only medium confidence  in the projected decrease in the frequency 
of intense NH ETCs.
In contrast to the Northern Hemisphere, the Southern Hemisphere 
shows an increase in the frequency of intense ETCs in CMIP5 models 
(Chang, 2017), and there is high confidence  that wind speeds 
associated with ETCs are expected to intensify in the SH storm 
track for high emissions scenarios. These changes in intensity are 
accompanied by an overall southward shift of the SH winter storm 
track (Figure 4.27b) due to the poleward shift in the upper-level jet 
and the increase in the meridional SST gradient linked to the slower 
warming of the Southern Ocean (Grieger et al., 2014).
Regardless of dynamical intensity changes, there is high confidence

that the number of ETCs associated with extreme precipitation 
is projected to increase with warming, due to the increased 
moisture-loading capacity of the atmosphere (Section 8.4.2; Yettella 
and Kay, 2017; Hawcroft et al., 2018). 
4.5.1.6.4 Atmospheric blocking
Blocking is associated with a class of quasi-stationary, high-pressure 
weather systems in the middle and high latitudes that disrupt the 
prevailing westerly flow. These events can persist for extended 
periods, such as a week or longer, and can cause long-lived extreme weather conditions, from heat waves in summer to cold spells in 
winter (see Section 11.7.2 for a detailed discussion of these features 
and Section 3.3.3.3 for the assessment of blocking biases in models 
simulations). The AR5 assessed with medium confidence  that the 
frequency of blocking would not increase under enhanced GHG 
concentrations, while changes in blocking intensity and persistence 
remained uncertain.

The CMIP5 projections suggest that the response of blocking 
frequency to climate change might be quite complex (Dunn-Sigouin 
et  al., 2013; Masato et  al., 2013). An eastward shift of winter 
blocking activity in the NH is indicated (Masato et al., 2013; Kitano 
and Yamada, 2016; Lee and Ahn, 2017; Matsueda and Endo, 2017) 
while during boreal summer, blocking frequency tends to decrease 
in mid-latitudes (Matsueda and Endo, 2017), with the exception of 
the eastern Europe–western Russia region (Masato et al., 2013). The 
projected decrease of blocking in boreal summer partially contrasts 
with the observed increase in Greenland blocking (Hanna et al., 2018; 
Davini and D’Andrea, 2020). However, as shown in Woollings et al. 
(2018), the spatial distribution and the magnitude of the suggested 
changes are sensitive to the blocking detection methods (Schwierz 
et al., 2004; Barriopedro et al., 2010; Davini et al., 2012). In the SH,

blocking frequency is projected to decrease in the Pacific sector 
during austral spring and summer. However, seasonal and regional 
changes are not totally consistent across the models (Parsons et al., 
2016), and, as assessed in Section 3.3.3.3, model biases might affect 
their response.Color High model agreement
Figure  4.27 | Changes in extratropical storm track density.  Displayed are projected spatial pattern of multi-model mean change of extratropical storm track density in 
winter (Northern Hemisphere December –January–Februrary, NH DJF , and Southern Hemisphere June–July–August, SH JJA) in 2080–2100 for SSP5-8.5 relative to 1979–2014 
based on 13 CMIP6 models. Diagonal lines indicate regions where fewer than 80% of the models agree on the sign of the change and no overlay where at least 80% of the

models agree on the sign of change. Units are number density per 5° spherical cap per month. Further details on data sources and processing are available in the chapter data 

608
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4To better understand the uncertainty in future blocking activity, 
a process-oriented approach has been proposed that aims to link 
blocking responses to different features of the global warming 
pattern. Upper-level tropical warming might be the key factor 
leading to a  reduced blocking, because of the strengthening 
of zonal winds (Kennedy et  al., 2016). The more controversial 
influence of near-surface Arctic warming might lead to an increased 
blocking frequency (Mori et  al., 2014; Francis and Vavrus, 2015) 
(see Chapter 10, Box 10.1).
Figure 4.28 shows a clear decrease in blocking activity over Greenland 
and North Pacific for SSP7.0 and SSP8.5. Models with the largest 
decrease in blocking frequency in boreal winter are those showing 
the smallest frequency bias during the historical period (Davini 
and D’Andrea, 2020). In conclusion, there is medium confidence

that the frequency of atmospheric blocking events over Greenland 
and the North Pacific will decrease in boreal winter in the SSP3-7.0 
and SSP5-8.5 scenarios.
4.5.2 Ocean
4.5.2.1 Ocean Temperature
Projections of long-term ocean thermal properties are assessed 
comprehensively in Chapter 9 (Sections 9.2.1.1 and 9.2.2.1) and are 
not covered here to avoid unnecessary overlap.4.5.2.2 Ocean Acidification
The model-simulated, long-term trend of ocean acidification is assessed 
in Section 4.3.2.5 and Chapter 5 (Section 5.3.4.1). It is virtually certain 
that surface ocean acidification will continue in response to the rise 
in atmospheric CO 2, and continued penetration of anthropogenic 
CO 2 from the surface to the deep ocean will acidify the ocean interior 
(Figure 4.29). By the end of this century, under SSP3-7.0, a pH reduction 
of about 0.3 is found at a  few hundred metres depth of the global 
ocean, with stronger acidification in the interior North Atlantic and the

mid- to high-latitude Southern Ocean. At a depth of about 1 km, a pH 
reduction of about 0.1 is found.
Projections with CMIP6 ESMs (Kwiatkowski et  al., 2020) show 
a surface pH decline of –0.16 ± 0.002 (±1 standard deviation) under 
SSP1-2.6 and –0.44 ± 0.005 under SSP5-8.5 from 1870–1899 to 
2080–2099. The high-latitude oceans, in particular the Arctic, show 
greater decline in pH and accelerated acidification (Terhaar et  al., 
2020). For the same period, model-projected bottom-water pH 
decline is –0.018 ± 0.001 under SSP1-2.6 and –0.030 ± 0.002 under 
SSP5-8.5. The projected large-scale surface ocean acidification will be 
primarily determined by the pathway of atmospheric CO 2, with weak 
dependence on change in climate ( high confidence ) (Section 5.3.4.1; 
Hurd et al., 2018). However, for a given atmospheric CO 2 scenario, 
uncertainty in projected ocean acidification increases with ocean 
depth because of model-simulated differences in ocean circulation

that transports anthropogenic CO 2 from the surface to bottom ocean 
(high confidence ) (Kwiatkowski et al., 2020). For example, projected 
surface pH fully separates between SSPs scenarios before 2050, but 
some overlap across SSPs is still found for projected bottom-water pH 
in 2080 (Kwiatkowski et al., 2020).Atmospheric Blo cking frequencies
3025252427 3 02468101214161820
CMIP6 OBSBlocking frequency (%)(a) Centr al Europe
CMIP6 OBSBlocking frequency (%)(b) Greenland
CMIP6 OBSBlocking frequency (%)(c) Nor th Pacific
OBS
histor ical
SSP1−2.6
SSP2−4.5
SSP3−7.0
SSP5−8.5
Figure  4.28 | Projected winter atmospheric blocking frequencies.  Box plot showing December –March atmospheric blocking frequencies from historical simulations 
over 1995–2014 and projections over 2081–2100, over (a) the Central European region (20°W–20°E, 45°N–65°N); (b) the Greenland region (65°W–20°W, 62.5°N–72.5°N);

(c) the North Pacific region (130°E–150°W, 60°N–75°N). Values show the percentage of blocked days per season following the (Davini et al., 2012) index. Median values are 
the thick black horizontal bar. The lower whiskers extend from the first quartile to the smallest value in the ensemble, and the upper whiskers extend from the third quartile 
to the largest value. The whiskers are limited to an upper bound that is 1.5 times the interquartile range (the distance between the third and first quartiles). Black dots show 
outliers from the whiskers. The numbers below each bar report the number of models included. Observationally-based values are obtained as the average of the ERA-Interim 
Reanalysis, the JRA-55 Reanalysis and the NCEP/NCAR Reanalysis. Adapted from Davini and D’Andrea (2020). Further details on data sources and processing are available in 
the chapter data table (Table 4.SM.1).

609
4.5.3 Modes of Variability
4.5.3.1 Northern and Southern Annular Modes
4.5.3.1.1 The Northern Annular Mode
wintertime NAM is very likely to exhibit natural variability and 
forced trends of similar magnitude to that observed in the historical 
period and is likely  to become slightly more positive in the future. 
Considerable uncertainty is related to physical mechanisms to explain 
the observed and projected changes in the NAM, but NAM trends are 
clearly closely connected to projected shifts in the mid-latitude jets 
and storm tracks.
NAM projections from climate models analysed since AR5 reveal 
broadly similar results to the late 21st century. CMIP6 models show 
a  positive ensemble-mean trend in most seasons and the higher 
emissions scenarios that is comparable to between-model or between-

realization variability (Figure  4.30a). The NAM generally becomes more positive by the end of the century except in boreal summer (JJA) 
when there is no change in the NAM in these simulations. In boreal 
winter (DJF) under SSP5-8.5, the central estimate is an increase in 
the NAM by almost 3 hPa in the long-term compared to 1995–2014. 
This can be compared to a multi-model mean interannual standard 
deviation in the winter NAM index of 3.4 hPa during the period 
1850–1900. We conclude with high confidence that in the mid- to 
long-term, the boreal wintertime surface NAM is more positive under 
SSP3-7.0 and SSP5-8.5, while under SSP1-1.9 and SSP1-2.6, the NAM 
does not show any robust change.
4.5.3.1.2 The Southern Annular Mode
The AR5 assessed it is likely  that the evolution of the SAM over the 
21st century will be primarily determined by the interplay between 
the effects of ozone recovery and changing GHG concentrations and

influence the SAM in opposing ways. Owing to the relative effects 
of these two drivers, CMIP5 model SAM and Southern Hemisphere 
circulation projections differed markedly across forcing scenarios 
and across seasons (Barnes and Polvani, 2013; Barnes et al., 2014). 
CMIP5 models simulated a  weak negative SAM trend in austral 
summer for RCP4.5 by the end of the century (F . Zheng et al., 2013), 
while for RCP8.5 they simulated a weak positive SAM trend in austral 
summer (F . Zheng et al., 2013). A substantial fraction of the spread in 
CMIP5 projections of the end of century SH summer jet shift under 
RCP8.5 may be attributable to differences in the simulated change in 
break-up of the stratospheric polar vortex, with models that produce 
a later break-up date showing a larger summertime poleward jet shift 
(Ceppi and Shepherd, 2019). For RCP2.6, the effect of ozone recovery 
on the SAM has been found to dominate over that of GHGs in austral

summer (Eyring et  al., 2013). In austral winter, the poleward shift 
of the SH circulation in CMIP5 models, and the associated increase 
in the SAM index, tends to be larger, on average, in higher forcing 
scenarios though with substantial inter-model spread (Barnes et al., 
2014). New research since the AR5 shows that the previous theory 
for the apparent relationship across models between the annual 
mean climatological SH jet position and the amplitude of forced SH 
jet shift (Kidston and Gerber, 2010) does not hold at seasonal time 
scales (Simpson and Polvani, 2016).
In most seasons, the SAM becomes more positive by the end of 
the century relative to 1995–2014 under SSP2-4.5, SSP3-7.0, and 
SSP5-8.5 (Figure 4.30b). Conversely, under SSP1-1.9 and SSP1-2.6, 
in most seasons the SAM index does not show a  robust change 
compared to 1995–2014 except in austral summer when it becomes 
significantly more negative. The greatest change in the SAM occurs

in austral winter, where CMIP6 models show an ensemble-mean 
increase in the SAM index of almost 5 hPa in SSP5-8.5. This can be 
compared to a  multi-model mean interannual standard deviation 
in the austral winter SAM index of 4.0 hPa during 1850–1900. In 
conclusion, there is high confidence that in high emissions scenarios 
(SSP3-7.0 and SSP5-8.5) the SAM becomes more positive in all 
seasons, while in the lowest scenario (SSP1-1.9) there is a  robust 
decrease in austral summer.Color High model agreement
Figure 4.2 9 | Long-term change of annual and zonal ocean pH.  Displayed 
are multi-model mean change in annual and zonal ocean pH in 2081–2100 relative 
to the mean of 1995–2014 for SSP1-2.6 and SSP3-7.0, respectively. Eleven CMIP6 
model results are used. Diagonal lines indicate regions where fewer than 80% of 
the models agree on the sign of the change and no overlay where at least 80% of the

models agree on the sign of change. Further details on data sources and processing 

610
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information44.5.3.2 El Niño–Southern Oscillation
The AR5 assessed that it is very likely  that the El Niño–Southern 
Oscillation (ENSO) will remain the dominant mode of interannual 
variability in the future. Moreover, due to increased moisture 
availability, the associated precipitation variability on regional scales 
was assessed to likely  intensify. An eastward shift in the patterns of 
temperature and precipitation variations in the North Pacific and North 
America related to El Niño and La Niña teleconnections was projected 
with medium confidence . The stability of teleconnections to other 
regional implications including those in Central and South America, 
the Caribbean, parts of Africa, most of Asia, Australia and most Pacific 
Islands were assessed to be uncertain (Christensen et al., 2013).
There is no consensus on changes in amplitude of ENSO SST

variability across CMIP iterations. The main factors driving the 
diversity of ENSO SST amplitude change in climate models are internal 
variability, SST-mean warming pattern, and model systematic biases. 
First, pronounced low-frequency modulations of ENSO exist even in 
unforced control simulations due to internal variability, which leads 
a large uncertainty in quantifying future ENSO changes (Wittenberg, 
2009; Vega-Westhoff and Sriver, 2017; Zheng et  al., 2018). Second, 
ENSO characteristics depend on the climate mean state of the tropical 
Pacific; however, ENSO can also influence the mean state through 
non-linear processes (Cai et al., 2015; Timmermann et al., 2018). The 
response of the tropical Pacific mean state to anthropogenic forcing 
is characterized by a  faster warming on the equator compared to 
the off-equatorial region, a faster warming of the eastern equatorial 
Pacific compared to the central tropical Pacific (e.g., El Niño-like mean

SST warming, see Section  7.4.4.2), and a  weakening of the Walker 
circulation in most models. Those models with a El Niño-like warming 
tend to project a  strengthening of ENSO SST variability whereas 
models with a La Niña-like warming tend to project a weakening of 
variability (Zheng et al., 2016; Kohyama and Hartmann, 2017; J. Wang 
et  al., 2017b; Cai et  al., 2018a; Fredriksen et  al., 2020). Third, how 
to take model biases into account leads to different ENSO changes. 
Kim et  al. (2014) suggested that a  subset of CMIP5 models that simulate linear ENSO stability realistically exhibit a  decrease in 
ENSO amplitude by the second half of the 21st century. However, an 
increase of ENSO SST variability has been projected when considering 
biases in ENSO pattern simulation by different models (Zheng et al., 
2016; Cai et al., 2018a). This highlights the importance of constraining 
tropical Pacific mean state changes in order to enhance confidence in

the projected response of ENSO.
There is also no robust consensus on changes in ENSO diversity. 
Several studies suggest that an increase in Eastern Pacific (EP)-ENSO 
events tends to be projected particularly in the models with an 
El Niño-like warming (Zheng et al., 2016; Cai et al., 2018a; Fredriksen 
et al., 2020). However, Freund et al. (2020) suggested that models 
with a  El Niño-like mean warming show a  tendency toward more 
Central Pacific (CP) events but fewer EP events compared to models 
with an La Niña-like warming in both CMIP5 and CMIP6 models.
Even though there is low agreement in simulated changes in ENSO 
SST variability, the majority of models project an increase in 
amplitude of ENSO rainfall variability attributable to the increase 
in mean SST and moisture in CMIP5 (Power et al., 2013; Watanabe 
et  al., 2014; Huang and Xie, 2015) and CMIP6 (Yun et  al., 2021). 
It is likely  that extreme El Niño events, accompanied by the eastern

equatorial Pacific rainfall exceeding the 5 mm day–1 rainfall threshold, 
will increase in intensity (Cai et al., 2014a, 2017). However, it has 
also been suggested that historical model biases over the equatorial 
Pacific cold tongue in CMIP5 may lead to the greater precipitation 
mean change and amplification of extreme ENSO-associated rainfall 
in CMIP5 (Stevenson et al., 2021).
There is limited intermodel agreement  on future changes in ENSO 
teleconnections largely depending on changes in the mean state and 
changes in ENSO properties (Yeh et al., 2018). Many CMIP5 and CMIP6 
models project that the centres of the extratropical teleconnection over 
North Pacific and North America will shift eastward in association with 
an eastward shift in tropical convective anomalies (Yeh et al., 2018; 
Fredriksen et  al., 2020). There is an indication that tropical cyclones 
Season-4-202468Anom aly rela tive to 199 5–2014 (hP a)

Season-4-202468Anomaly relative to 1995–2014 (hPa)
 DJF             MAM             JJA             SON  DJF             MAM             JJA             SON11
34SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.533
28
35Figure 4. 30 | CMIP6 Annular Mode index change from 1995–2014 to 2081–2100. (a)  Northern Annular Mode (NAM) and (b) Southern Annular Mode (SAM). 
The NAM is defined as the difference in zonal mean SLP at 35°N and 65°N (Li and Wang, 2003) and the SAM as the difference in zonal mean SLP at 40°S and 65°S (Gong and 
Wang, 1999). The shadings are the 5–95% ranges across the simulations. The numbers near the top are the numbers of model simulations in each SSP ensemble. Further details 

611
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44will become more frequent during future El Niño events (and less 
frequent during future La Niña events) by the end of the 21st century 
(Chand et  al., 2017), thus contributing to the projected increase in 
ENSO-associated hydro-climate impacts.
While CMIP6 models show no robust change in ENSO SST amplitude 
in the mid- and long-term period across all four SSPs, a  robust 
increase in ENSO rainfall amplitude is found particularly in SSP2-4.5, 
SSP3-7.0, and SSP5-8.5 (Figure 4.10). The changes in ENSO rainfall 
amplitude in the long-term future (2081–2100) relative to the recent 
past (1995–2014) are statistically significant at the 95% confidence.
To conclude, the forced change in ENSO SST variability is highly 
uncertain in CMIP5 and CMIP6 models ( medium confidence ). However, 
it is very likely  that ENSO-related rainfall variability will increase

significantly regardless of ENSO amplitude changes in the mid- and 
long-term future. It is likely  that the pattern of ENSO teleconnection 
over the North Pacific and North America will shift eastward.
4.5.3.3 Indian Ocean Basin and Dipole Modes
In the mid- to long-term, projected climate mean state changes in the 
tropical Indian Ocean are expected to resemble a positive IOD state, 
with faster warming in the west compared to the east (Cai et al., 2013; 
X.-T. Zheng et al., 2013). However, it was argued that this projected 
mean state change could be due to the large mean state biases in 
the simulated current climate and potentially not a realistic outcome 
(G. Li et al., 2016). Mean state biases also lead to lack of consensus 
on projected equatorial Indian Ocean SST variability and equatorial 
modes of climate variability independent of the IOD (DiNezio et al., 
2020). If mean state change will indeed resemble a  positive IOD

state, however, this would lead to a  reduction in the amplitude 
difference between positive and negative IOD events, but with no 
robust change in IOD frequency (Cai et al., 2013). For a small subset 
of CMIP5 models that simulate IOD events best, a  slight increase 
in IOD frequency was found under the CMIP5 RCP4.5 scenario 
(Chu et al., 2014).
However, it was also found that the frequency of extreme positive 
IOD events, which exhibit the largest climate impacts, might increase 
by a  factor of about three under the CMIP5 RCP8.5 scenario 
(Cai et al., 2014b). Partially consistent with the above result, a more 
recent study by Cai et al. (2021), based on CMIP5 RCP8.5 and CMIP6 
SSP5-8.5 simulations, shows a  robust increased SST variability of 
large positive IOD events, but a  decreased variability of moderate 
IOD events. An approximate doubling of these extreme positive IOD 
events was still found for global warming of 1.5°C warming above

pre-industrial levels, without a  projected decline thereafter (Cai 
et al., 2018b). These results depend, however, on the realism of the 
projected mean state change in the Indian Ocean (G. Li et al., 2016).
To conclude, the forced change in IOD in mid- and long-term 
future remains uncertain due to limited lines of evidence and its 
dependence on model mean biases. However, there is low confidence  
that the frequency of extreme positive IOD events will increase under 
the high-emissions scenario of SSP5-8.5.4.5.3.4 Tropical Atlantic Modes
The AR5 assessed that there is low confidence  in projected changes 
of the Tropical Atlantic Variability (TAV) because of the general failure 
of climate models to simulate main aspects of this variability such 
as the northward displaced ITCZ. The models that best represent the 
Atlantic meridional mode (AMM) show a weakening for future climate 
conditions. However, model biases in representation of Altantic Niños

strongly limit an assessment of future changes.
Long-term changes in TAVs and associated teleconnections are 
expected as a result of global warming, but large uncertainties exist 
due to the models’ systematic underestimation of the connection 
between PDV and Indo-Pacific SST variations (Lübbecke et al., 2018; 
Cai et al., 2019). Observational analyses show large discrepancies in 
SST and trade wind strength (Servain et al., 2014; Mohino and Losada, 
2015). Single-model sensitivity experiments show that Atlantic Niño 
characteristics at the end of 21st  century remain consistent with 
those of the 20th century, though changes in the climatological SSTs 
can lead to changes in the associated teleconnections (Mohino and 
Losada, 2015).
The weakening of the AMOC expected from global warming 
(see  Section  4.3.2.3) has been suggested to have an influence on 
the mean background state of tropical-Atlantic surface conditions,

thereby enhancing equatorial Atlantic variability and resulting in 
a stronger tropical Atlantic–ENSO teleconnection (see Section 3.7.5 
for a detailed discussion; Svendsen et al., 2014). A recent multi-model 
study, based on CMIP5, concluded that the TAV-Pacific teleconnection 
will weaken under global warming due to the increased thermal 
stability of the atmosphere (F . Jia et al., 2019). However, there is still 
a clear lack of model studies, and hence no robust evidence on the 
long-term evolution of TAV and associated teleconnections.
4.5.3.5 Pacific Decadal Variability
The AR5 assessed that there is low confidence  in projections of 
future changes in Pacific decadal variability (PDV) due to the inability 
of CMIP5 models to represent the connection between PDV and 
Indo-Pacific SST variations. Because the PDV appears to encompass 
the combined effects of different dynamical processes operating 
at different time scales, representation of PDV in climate models

remains a challenge (Section 3.7.6) and its long-term evolution under 
climate change uncertain.
In addition to uncertainty from the future evolution of the mechanisms 
that determined the PDV, it is also unclear how the background state 
in the Pacific Ocean will change due to time-varying radiative forcing, 
and how this change will interact with variability at interannual and 
low-frequency time scales (Fedorov et  al., 2020). Recent research 
suggests that the PDV will have a  weaker amplitude and higher 
frequency with global warming (Zhang and Delworth, 2016; Xu and 
Hu, 2017; Geng et al., 2019). The former appears to be associated 
with a decrease in SST variability and the meridional gradient over 
the Kuroshio-Oyashio region, with a reduction in North Pacific wind 
stress and meandering of the subpolar/subtropical gyre interplay 
(Zhang and Delworth, 2016). The latter is hypothesized to rely on

612
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4the enhanced ocean stratification and shallower mixed layers of 
a  warmer climate, which would increase the phase speed of the 
westward-propagating oceanic waves, hence shortening the decadal 
to inter-decadal component (Goodman and Marshall, 1999; Zhang 
and Delworth, 2016; Xu and Hu, 2017). The weakening of the PDV 
in a  warmer climate may reduce the internal variability of global 
mean surface temperature, to which PDV seems associated (Zhang 
et al., 1997; Kosaka and Xie, 2016; Geng et al., 2019). Thus, a weaker 
and higher frequency PDV could reduce the contribution of internal 
variability to the GSAT trend and eventually lead to a  reduced 
probability of surface-warming hiatus events.
In summary, based on CMIP5, there is medium confidence  that 
a weaker and higher frequency PDV is expected under global warming.
4.5.3.6 Atlantic Multi-decadal Variability

Based on paleoclimate reconstructions and model simulations, AR5 
assessed that AMV is unlikely  to change its behaviour in the future. 
However, AMV fluctuations over the coming decades are likely  
to influence regional climate, enhancing or offsetting some of the 
effects of global warming.
Recent proxy-derived reconstructions of AMV-related signals show 
persistent multi-decadal variability over the last three centuries 
(Kilbourne et al., 2014; Svendsen et al., 2014; Moore et al., 2017), 
up to the last millennium (Chylek et  al., 2011; Zhou et  al., 2016; 
J. Wang et  al., 2017b) and beyond (Knudsen et  al., 2011). This 
implies that in the past AMV properties were little affected by large 
climatic excursions.
AMV long-term changes under future warming scenarios have so 
far scarcely been investigated. A  study on the CMIP5 multi-model 
simulations under RCP8.5 scenario by (Villamayor et al., 2018) found 
no substantial differences in the simulated SST patterns (and  in

the related tropical rainfall response) when RCP8.5, historical and 
piControl simulations are compared. Such results suggest that the 
AMV is not expected to change under global warming. A  more 
recent single-model large ensemble study (Hand et al., 2020) shows 
a  pronounced change in the AMV pattern under global warming 
linked to a strong reduction of the mean AMOC and its variability. 
However, since a  superposition of multiple processes controls the 
AMV, as extensively discussed in Annex IV (Section AIV.2.7), in 
Chapter  3 (Section  3.7.7), and in Chapter  9 (Section  9.2.3.1), the 
length of the RCP8.5 simulations might be not sufficient to properly 
evaluate the respective weight and interplay of internal components 
and influences from external forcing on AMV projections.
In conclusion, on the basis of paleoclimate reconstructions and 
CMIP5 model simulations, there is low confidence that the AMV 
is not expected to change in the future.4.6 Implications of Climate Policy

4.6.1 Patterns of Climate Change for Specific Levels 
of Global Warming
This subsection provides an assessment of changes in climate 
at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the 
period 1850–1900 (Section  1.6.2), in particular a  discussion of 
the regional patterns of change in temperature (Section  4.6.1.1), 
precipitation (Section 4.6.1.2), and aspects of atmospheric circulation 
(Section  4.6.1.3). An assessment of changes in extreme weather 
events as a function of different levels of global warming is provided 
in Chapter  11, while corresponding analyses of regional climate 
change are provided in Chapter  12 and in the Atlas. This section 
builds upon assessments from AR5 (Bindoff et al., 2013; Christensen 
et al., 2013; Collins et al., 2013; Hartmann et al., 2013) and SR1.5 
(SR1.5; Hoegh-Guldberg et  al., 2018), as well as new literature 
related to projections of climate at 1.5°C, 2°C, and higher levels of 
global warming above pre-industrial levels.

Several methodologies have been applied to estimate the spatial 
patterns of climate change associated with a given level of global 
warming. These include performing model simulations under 
stabilisation scenarios designed to achieve a  specific level of 
global warming, the analysis of epochs identified within transient 
simulations that systematically exceed different thresholds of global 
warming (e.g., Mitchell et al., 2017; Dosio et al., 2018; Hoegh-Guldberg 
et al., 2018; Kjellström et al., 2018), and analysis based on statistical 
methodologies that include empirical scaling relationships (ESR; 
Schleussner et al., 2017; Dosio and Fischer, 2018; Seneviratne et al., 
2018) and statistical pattern scaling (e.g., Kharin et al., 2018). These 
different methodologies are assessed in some detail in Section 4.2.4 
(James et  al., 2017) and generally provide qualitatively consistent 
results regarding changes in the spatial patterns of temperature and

rainfall means and extremes (see Chapter 11) at different levels of 
global warming.
In this subsection, we present the projected patterns of climate change 
obtained following the epoch approach (also called the time-shift 
method, see Section 4.2.4) under the Tier 1 SSPs (SSP1-2.6, SSP2-4.5, 
SSP3-7.0 and SSP5-8.5). For each model simulation considered under 
each of these SSPs, 20-year moving averages of the global average 
atmospheric surface temperature are first constructed, then this time 
series is used to detect the first year during when GSAT exceeds the 
1.5°C, 2°C, 3°C and 4°C thresholds with respect to the 1850–1900 
(Cross-Chapter Box  11.1). The temperature thresholds are not 
exceeded in all the model simulations across the Tier 1 SSPs. That 
is, decreasing numbers of simulations are available for the analysis 
of patterns of change as the temperature threshold increases. For 
each simulation within which a  given temperature threshold is

exceeded, a 20-year global climatology is subsequently constructed 
to represent that level of global warming, centred on the year for 
which the threshold was first exceeded. The composite of all such 
climatologies across the Tier 1 SSPs and model simulations constitute 
the spatial patterns of change for a  given temperature threshold. 
Some of the complexities of scaling patterns of climate change with 
different levels of global warming are also discussed in the following

613
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44sections. These include overshoot versus stabilization scenarios and 
limitations of pattern scaling for strong mitigation and stabilization 
scenarios (Tebaldi and Arblaster, 2014). At least for the case of annual 
mean temperature and precipitation, strong evidence exists that even 
for strong mitigation and stabilization scenarios, patterns of change 
at lower levels of warming scale similarly to those reconstructed 
from transient simulations using either standard pattern-correlation 
or time-shift methodologies (Tebaldi and Knutti, 2018).
Pattern scaling performance based on scenario experiments is 
generally better for near-surface temperature than for precipitation 
(Ishizaki et  al., 2013). For precipitation, rapid adjustments due to 
different forcing agents must be accounted for (Richardson et  al., 
2016). Possible non-linear responses to different forcing levels are

also important (Good et al., 2015, 2016). Pattern scaling does not 
work as well at high forcing levels (Osborn et al., 2018). It is also 
important to distinguish the forced response from internal variability 
when comparing similar warming levels (Suárez-Gutiérrez et  al., 
2018). The purpose of this section is not to repeat the analysis for 
all the variables considered in Sections 4.4 and 4.5, but rather to 
show a selected number of key variables that are important from the 
perspective of understanding the response of the physical climate 
system to different levels of warming.
4.6.1.1 Temperature
Global warming of 1.5°C implies higher mean temperatures 
compared to 1850–1900, with generally higher warming over land 
compared to ocean areas ( virtually certain ) and larger warming in 
high latitudes compared to low latitudes (Figure 4.31). In addition, 
global warming of 2°C versus 1.5°C results in robust increases in

the mean temperatures in almost all locations, both on land and 
in the ocean ( virtually certain ), with subsequent further warming 
at almost all locations at higher levels of global warming ( virtually 
certain ) (Hoegh-Guldberg et al., 2018). For each particular level of 
global warming, relatively larger mean warming is projected for land 
regions ( virtually certain ) (see Figure 4.31; Christensen et al., 2013; 
Collins et al., 2013; Seneviratne et al., 2016). The projected changes 
at 1.5°C and 2°C global warming are consistent with observed 
historical global trends in temperature and their attribution to 
anthropogenic forcing (Chapter 3), as well as with observed changes 
under the recent global warming of 0.5°C (Schleussner et al., 2017; 
Hoegh-Guldberg et al., 2018). That is, spatial patterns of temperature 
changes associated with the 0.5°C difference in GMST warming 
between 1991–2010 and 1960–1979 (Schleussner et  al., 2017;

Hoegh-Guldberg et al., 2018) are consistent with projected changes 
under 1.5°C and 2°C of global warming.The largest increase in annual mean temperature is found in the 
high latitudes of the Northern Hemisphere across all levels of 
global warming ( virtually certain ) (Figure  4.31). This phenomenon 
peaks in the Arctic and is known as Arctic amplification, with the 
underlying physical mechanisms assessed in detail in Section 4.5.1 
and Chapter  7 (Section  7.4.4.1). For the CMIP6 ensemble average 
considered here, Arctic annual mean temperatures warm by a factor 
of 2.3, 2.5, 2.4 and 2.4 for 1.5°C, 2°C, 3°C and 4°C of global warming, 
respectively. That is, Arctic warming scales approximately linearly 
with GSAT. Generally, when Arctic amplification is considered across 
individual models, warming occurs at a factor of two to four times the 
global level of warming. It is unlikely  that warming in the Southern 
Hemisphere high latitudes in the 21st century will exceed the change

in GSAT, or that it will substantially exceed warming in the tropics, 
for GSAT change ranging between 1.5°C and 4°C (Figure 4.31 and 
Table  4.2). Correspondingly, there is low confidence  of Antarctic 
amplification occurring under transient, 21st century low mitigation 
scenarios (Table 4.2 and Section 7.4.4.1). The Antarctic continent is 
projected to warm at a higher rate than the mid-latitude Southern 
Ocean, however, at all levels of global warming (Figure  4.31). 
The relevant physical mechanisms that reduce the amplitude of polar 
amplification over Antarctica compared to the Arctic are assessed in 
detail in Section 4.5.1 and Chapter 7 (Section 7.4.4.1). In the Southern 
Hemisphere the strongest warming over land is to occur, at any given 
level of global warming, over the subtropical areas of South America, 
southern Africa and Australia ( high confidence ). The relatively 
strong warming in subtropical southern Africa may be attributed to

strong soil-moisture–temperature coupling and projected increased 
dryness under enhanced subsidence (Engelbrecht et al., 2015; Vogel 
et al., 2017). Across the globe, in the tropics, subtropics, and mid- 
to high latitudes, temperatures tend to scale linearly with the level 
of increase in GSAT and patterns of change are largely scenario 
independent ( high confidence ).

614
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4(b) Change at 2°C global warming
(d) Change at 4°C global warming(a) Change at 1.5°C global warming
(c) Change at 3°C global warming
°C-5 -4-3-2.5 -2-1.5 01.5 22.5 345
-4-3-2-1.5 -1-0.5 00.5 11.5 234
°C(e) Difference 2°C vs. 1.5°C (f) Difference 3°C vs. 1.5°C (g) Difference 4°C vs. 1.5°C 
Conflicting signalsColour153 137
90 48
Figure 4 .31 | Projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming.  Displayed are 
(a–d)  spatial patterns of change in annual average near-surface temperature at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900 and (e–g)  spatial 
patterns of differences in temperature change at 2°C, 3°C, and 4°C of global warming compared to 1.5°C of global warming. The number of models used is indicated in the top

right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability. That is, where at least 66% of the models show a change 
greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no 
robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals 
where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were 
assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are 

615
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 444.6.1.2 Precipitation
While global mean precipitation increases as GSAT rises with the 
very likely  range of 1–3% per 1°C ( high confidence ) (Sections 8.2.1 
and 8.4.1), patterns of precipitation change do not scale as 
linearly with GSAT increase. Nevertheless, common features of 
precipitation change in the multi-model mean across scenarios 
still exist for different levels of global warming (Figure  4.32). 
Precipitation will very likely  increase in the high latitudes and 
over tropical regions, and will likely  increase in large parts of the 
monsoon region, but are likely  to decrease over the subtropical 
regions, including the Mediterranean, southern Africa, parts of 
Australia and South America at all four levels of global warming. 
The increases and decreases in precipitation will amplify at higher 
levels of global warming ( high confidence ) (Figure 4.32). Changes

in extreme precipitation events under different levels of global 
warming are assessed in Chapter 11.The SR1.5 stated low confidence  regarding changes in global 
monsoons at 1.5°C versus 2°C of global warming, as well as 
differences in monsoon responses at 1.5°C versus 2°C. Generally, 
statistically significant changes in regional annual average 
precipitation are expected at a global mean warming of 2.5°C–3°C 
or more (Tebaldi et  al., 2015). Over the austral-winter rainfall 
regions of south-western South America, South Africa and Australia, 
projected decreases in mean annual rainfall show high agreement  
across models and a  strong climate change signal even under 
1.5°C of global warming, with further amplification of the signal 
at higher levels of global warming ( high confidence ) (Mindlin et al., 
2020). This is a signal evident in observed rainfall trends over these 
regions (Sections 2.3.1.3 and 8.3.1.6 ). Also, over the Asian monsoon

regions, increases in rainfall will occur at 1.5°C and 2°C of global 
warming (Chevuturi et  al., 2018). At warming levels of 1.5°C and 
2°C, the changes in global monsoons are strongly dependent on 
the modelling strategies used, such as fully coupled transient, fully 
coupled quasi-equilibrium, and atmosphere-only quasi-equilibrium 
(b) Change at 2°C global warming
(c) Change at 3°C global warming147 132
85 47
        -40      -30      -20      -10        0        10        20       30        40
%
Figur e 4.32 | Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global 
warming. Displayed are (a–d)  spatial patterns of change in annual precipitation at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900. No map

overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the 
internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant 
change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 
66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from 
a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the 

616
simulations. In particular, the differences of regional monsoon 
changes among model setups are dominated by strategy choices 
such as transient versus quasi-equilibrium set-up, prescription of SST, 
and treatment of aerosols (Zhang and Zhou, 2021).
The global and land area fractions with significant precipitation 
changes with global warming are shown in Figure 4.33. It is virtually 
certain  that average warming will be higher over land. As warming 
increases, a larger global and land area will experience statistically 
significant increases or decreases in precipitation ( medium confidence ). 
The increase of the area fraction with significant precipitation increase 
is larger over land than over the ocean, but the increase of the area 
fraction with significant precipitation decrease is larger over the ocean than over land (Figure 4.33). Precipitation variability in most climate

models increases over the global land area in response to warming 
(Pendergrass et al., 2017).
In summary, based on the assessment of CMIP6 models, there is high 
confidence that global mean precipitation will increase with increase 
in global mean surface temperature. Precipitation will very likely  
increase in the high latitudes and over tropical regions, likely  increase 
in large parts of the monsoon region, but will likely  decrease over 
the subtropical regions. There is high confidence that increases and 
decreases in precipitation will amplify over higher levels of global 
warming. As warming increases, there is medium confidence that 
a larger land area will experience statistically significant increases or 
decreases in precipitation.
4.6.1.3 Atmospheric Circulation
The AR5 reported that the application of pattern scaling to extract 
information on variables other than surface temperature and 
precipitation was relatively unexplored. Since AR5, new studies

have examined the relationship between projections of mid-latitude 
atmospheric circulation and GSAT both in terms of interpreting spread 
in responses across the CMIP5 multi-model ensemble (Grise and 
Polvani, 2014a, 2016) and to investigate variations in the circulation 
response as a  function of GSAT change over time within a  given 
forcing experiment (Grise and Polvani, 2017; Ceppi et al., 2018).
At a  fixed time horizon, the CMIP5 multi-model spread in GSAT 
explains only a small fraction of the spread in the shift of the Northern 
Hemisphere mid-latitude circulation due to an abrupt quadrupling in 
CO 2 (Grise and Polvani, 2016). The fraction of model spread explained 
by GSAT in the shift of the Southern Hemisphere circulation is larger, 
but still fairly small (Grise and Polvani, 2014a, 2016). At a fixed time 
horizon and for a  given emissions scenario, CMIP5 multi-model 
spread in storm track shifts, and the closely related mid-latitude jets,

can be better explained by multi-model spread in lower and upper 
level meridional temperature gradients than by GSAT (Harvey et al., 
2014; Grise and Polvani, 2016).
In the North Atlantic, North Pacific, and Southern Hemisphere, 
the transient response of the mid-latitude jets to forcing behaves 
non-linearly with GSAT (Grise and Polvani, 2017; Ceppi et al., 2018). 
This is a  consequence of the time-dependence of the relationship 
between radiative forcing and GSAT and the temporal evolution of 
SST patterns (Ceppi et al., 2018), with a potential seasonal component 
in the SH associated with polar stratospheric temperature changes 
(Grise and Polvani, 2017). Consequently, the epoch approach applied 
to a transient simulation of the 21st century will overestimate the 
mid-latitude circulation response in a stabilized climate. Dedicated 
time slice experiments simulating stabilized climates are therefore 
required to assess differences in mid-latitude circulation at given

levels of global warming (Li et al., 2018). A further complication in 
the SH is the competing influences of ozone recovery and increasing 
GHG concentrations on the austral-summer mid-latitude circulation 
during the first half the 21st  century (Barnes and Polvani, 2013; 
Barnes et  al., 2014). Using transient 21st  century experiments to 
diagnose changes in SH mid-latitude circulation at different levels 0 20 40 60 10 30 50Area fraction (%)
0 5 10 15 20 25Area fraction (%) Area fraction (%)Area fraction (%)Area fraction (%)
30
1 2 3 4 5
GSAT warming (°C)1 2 3 4 5
GSAT warming (°C)Area with significant wettening Area with significant drying1 2 3 4 5
GSAT warming (°C)0 20 40 60
0 5 10 15 20 25 30Land area with significant wettening Land area with significant drying
SSP1−2.6
SSP3−7.0
SSP5−8.5
SSP1−2.6
SSP3−7.0
SSP5−8.5
Figure  4 .33 | Area fraction of significant precipitation change at 1.5°C,

2°C, 3°C, and 4°C of global warming. Range of land fraction (top)  and global 
area fraction (bottom)  with significant precipitation increase (left-hand side)  and 
decrease (right-hand side)  in the projected annual precipitation change (%) at 
levels of global warming compared to the period 1850–1900. Values were assessed 
from a 20-year period at a given warming level from SSP1-2.6, SSP3-7.0 and SSP5-8.5 
in CMIP6. The solid line illustrates the CMIP6-multi model mean and the shaded band 
is the 5–95% range across models that reach a given level of warming. Further details 

617
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44of warming therefore confounds the effects of ozone recovery and 
GHG increases (Ceppi et al., 2018). Given these various limitations, 
we do not apply epoch analysis to assess mid-latitude atmospheric 
circulation changes and related annular modes of variability.
4.6.2 Climate Goals, Overshoot, and Path-Dependence
Many scenarios aiming at limiting warming by 2100 to 1.5°C involve 
overshoot – ERF temporarily exceeds a certain level before peaking 
and declining again (Annex VII: Glossary). To quantify the implications 
of any such overshoot, this subsection assesses reversibility of 
climate due to temporary overshoot of GSAT levels during the 
21st century, and implications for the use of carbon budgets. It also 
assesses differences in climate outcomes under different pathways, 
with a focus on comparing the SSPs used in CMIP6 with the RCPs 
used in CMIP5.4.6.2.1 Climate Change Under Overshoot

The SR1.5 (IPCC, 2018b) concluded with high confidence  that 
overshoot trajectories ‘result in higher impacts and associated 
challenges compared to pathways that limit global warming to 
1.5°C with no or limited overshoot’. The degree and duration of 
overshoot affects the risks and impacts likely to be experienced 
(Hoegh-Guldberg et al., 2018) and the emissions pathway required to 
achieve it (Akimoto et al., 2018). Consequences relating to ice sheets 
and climatic extremes have been found to be greater at 2°C of global 
warming than at 1.5°C (Schleussner et  al., 2016; Hoegh-Guldberg 
et al., 2018) but even on recovery to lower temperatures, these effects 
may not reverse. Overshoot has been found to lead to irreversible 
changes in thermosteric sea level (Tokarska and Zickfeld, 2015; Palter 
et al., 2018; Tachiiri et al., 2019), AMOC (Palter et al., 2018), ice sheets, 
and permafrost carbon (Sections 4.7.2 and 5.4.9) and to long-lasting

effects on ocean heat (Tsutsui et  al., 2006). Abrupt changes and 
CO2 (ppm) CO2 (ppm)
Figure 4. 34 | Simulated changes in climate indices for SSP5-3.4-OS plotted against atmospheric CO 2 concentration (ppm) from 480 up to 571 and back to 496 
by 2100. (a)  Global surface air temperature change; (b) Global land precipitation change; (c) September Arctic sea ice area change; (d) Global thermosteric sea level change. Plotted 
changes are relative to the 2034–2053 mean which has same CO 2 as 2081–2100 mean (shaded grey bar). Red lines denote changes during the period up to 2062 when CO 2 is 
rising, blue lines denote changes after 2062 when CO 2 is decreasing again. Thick line is multi-model mean; thin lines and shading show individual models and complete model range. 
Numbers in square brackets indicate number of models used in each panel. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

618
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4tipping points are not well understood, but the higher the warming 
level and the longer the duration of overshoot, the greater the risk of 
unexpected changes (Section 4.7.2). Non-reversal of the hydrological 
cycle has also been found in some studies with an increase in global 
precipitation following CO 2 decrease being attributed to a build-up of 
ocean heat (Wu et al., 2010), and to a fast atmospheric adjustment to 
CO 2 radiative forcing (Cao et al., 2011).
Global temperature is expected to remain approximately constant if 
emissions of CO 2 were to cease (Section 4.7.1.1), and so reductions 
in GSAT are only possible in the event of net negative global CO 2 
emissions. We assess here results from an overshoot scenario 
(SSP5-3.4-OS; O’Neill et al., 2016), which explores the implications 
of a peak and decline in forcing during the 21st century. Reversibility

under more extreme and idealized carbon dioxide removal (CDR) 
scenarios is assessed in Section 4.6.3. In SSP5-3.4-OS, CO 2 peaks 
at 571 ppm in the year 2062 and reverts to 497 ppm by 2100 – 
approximately the same level as in 2040. SSP5-3.4-OS has strong 
net negative emissions of CO 2, exceeding those in SSP1-2.6 
and SSP1-1.9 from 2070 onwards and reaching –5.5  PgC  yr –1 
(–20 GtCO 2 yr –1) by 2100. While this causes global mean temperature 
to decline, changes in climate have not fully reversed by 2100 under 
this reversal of CO 2 concentration (Figure  4.34). Quantities are 
compared for 2081–2100 relative to a 20-year period (2034–2053) 
of the same average CO 2. Differences between these two periods of 
the same CO 2 are: GSAT: 0.28 ± 0.30°C (mean ± standard deviation); 
global land precipitation: 0.026 ± 0.011 mm day–1; September 
Arctic sea ice area: –0.32 ± 0.53 million km2; thermosteric sea level: 
12 ± 0.8 cm. As assessed in Section  9.3.1.1, Arctic sea ice area

is linearly reversible with GSAT. Although these climate quantities 
are not fully reversible, the overshoot scenario results in reduced 
climate change compared with stabilisation or continued increase 
in greenhouse gases (Tsutsui et al., 2006; Palter et al., 2018; Tachiiri 
et al., 2019) ( high confidence ).
The transient climate response to cumulative CO 2 emissions, 
TCRE, allows climate policy goals to be associated with remaining 
carbon budgets as global temperature increase is near-linear with 
cumulative emissions (Section 5.5). Research since AR5 has shown 
that the concept of near-linearity of climate change to cumulative 
carbon emissions holds for measures other than just GSAT, such as 
regional climate (Leduc et al., 2016) or extremes (Harrington et al., 
2016; Seneviratne et  al., 2016). However, ocean heat and carbon 
uptake do exhibit path dependence, leading to deviation from the 
TCRE relationship for levels of overshoot above 300 PgC (Zickfeld

et al., 2016; Tokarska et al., 2019). Sea level rise, loss of ice sheets, 
and permafrost carbon release may not reverse under overshoot 
and recovery of GSAT and cumulative emissions (Section 4.7). TCRE 
remains a valuable concept to assess climate policy goals and how 
to achieve them but given the non-reversibility of different climate 
metrics with CO 2 and GSAT reductions, it has limitations associated 
with evaluating the climate response under overshoot scenarios and 
CO 2 removal ( medium confidence ).4.6.2.2 Consistency Between Shared Socio-economic 
Pathways and Representative Concentration Pathways
As CMIP5 and CMIP6 employed different scenario sets (RCPs and SSPs, 
respectively; see Section 1.6.1.1 and Cross-Chapter Box 1.4), we assess 
how much of the differences in projections are due to the scenario 
change and how much due to model changes. The CMIP6-simulated 
GSAT increases tend to be larger than in CMIP5, for nominally

comparable scenarios (Section 4.3.1; Tebaldi et al., 2021).
The radiative forcing labels on SSP and RCP scenarios is approximate 
and enables the multiple climate forcings within the scenario to be 
characterized by a  single number. While the scenarios are similar in 
terms of the stratospheric adjusted radiative forcing (Tebaldi et  al., 
2021), they differ more in their effective radiative forcing (ERF). The 
combination of component forcings (CO 2, non-CO 2 greenhouse gases, 
aerosols) within the scenario also differ (Meinshausen et  al., 2020). 
The ERF levels in the RCP and SSP scenarios have been calculated 
by sampling uncertainty in forcing from a  range of different GHG 
species and aerosols (see 7.SM.1.4 for details). Figure  4.35 shows 
the time evolution and 2081–2100 mean across the families of 
scenarios and how this affects projections of GSAT. That the ERFs differ 
between corresponding SSP and RCP scenarios makes a comparison

between  CMIP6 and CMIP5 projections challenging (Tebaldi et  al., 
2021). Wyser et  al. (2020) find the EC-Earth3-Veg model exhibits 
stronger radiative forcing and substantially greater warming under 
SSP5-8.5 than RCP8.5, and similar, but smaller additional warmings 
for SSP2-4.5 and SSP1-2.6 compared with RCP4.5 and RCP2.6, 
respectively. In addition to the global response, climate can vary 
regionally due to non-CO 2 components of forcing (Samset et al., 2016; 
Richardson et al., 2018a, b).
Emulators (Cross-Chapter Box 7.1) can be used to aid understanding 
of differences between generations of scenarios. The AR5 (Collins 
et  al., 2013) explored the differences between CMIP3 and CMIP5 
(their Figure  12.40). Here we use an emulator calibrated to AR6 
assessed GSAT ranges, thus eliminating the effect of differences 
in the model ensembles, to analyse the differences between 
SSP and RCP scenarios. MAGICC7.5 in its WGIII-calibrated setup

(see Cross-Chapter Box 7.1) projects differences in 2081–2100 mean 
warming between the RCP2.6 and SSP1-2.6 scenarios of around 
0.2°C, between RCP4.5 and SSP2-4.5 of around 0.3°C and between 
RCP8.5 and SSP5-8.5 of around 0.3°C (Figure  4.35b). The SSP 
scenarios also have a wider 5–95% range simulated by MAGICC7.5 
explaining about half of the increased range seen when comparing 
CMIP5 and CMIP6 models. Higher climate sensitivity is, though, the 
primary reason behind the upper end of the warming for SSP5-8.5 
reaching 1.5°C higher than the CMIP5 results. Compared with the 
differences between the CMIP5 and CMIP6 multi-model ensembles 
for the same scenario pairs (Table A6 in Tebaldi et  al., 2021), the 
higher ERFs of the SSP scenarios contribute approximately half of 
the warmer CMIP6 SSP outcomes ( medium confidence ).
In summary, there is medium confidence that about half of the warming 
increase in CMIP6 compared to CMIP5 is due to higher  climate

sensitivity in CMIP6 models; the other half arises from higher ERF in 
nominally comparable scenarios (e.g., RCP8.5 and SSP5-8.5).

619
4.6.3  Climate Response to Mitigation,  Carbon Dioxide 
Most strong-mitigation scenarios assume – in addition to emissions 
reductions  – some form of carbon dioxide removal (CDR). Anthropogenic activities that remove CO
2 from the atmosphere and 
durably store it in geological, terrestrial, or ocean reservoirs, or in products (see Glossary). The SR1.5 (Rogelj et  al., 2018b) assessed 
that all pathways that limit warming to 1.5°C by 2100 with no or 
limited overshoot use CDR. In the SSP class of scenarios, SSP1 -1.9 
is characterized by a rapid decline of net CO 2 emissions to zero by 
2050 and net negative CO 2 emissions in the second half of this 
century (O’Neill et al., 2016; Rogelj et al., 2018a), implying the use of 
CDR. The term ‘net CO 2 emissions’ refers to the difference between

anthropogenic CO 2 emissions and removal by CDR options, and ‘net 
negative CO 2 emissions’ imply a scenario where CO 2 removal exceeds 
emissions (van Vuuren et  al., 2011, 2016). The terms ‘negative emissions’ and ‘net negative emissions’ refer to and include all GHGs 
(see Glossary).
Climate change can be also offset by solar radiation modification 
(SRM) measures that modify the Earth’s radiation budget to reduce global warming (see Glossary). CDR and SRM approaches have been 
together referred to as ‘geoengineering’ or ‘climate engineering’ 
in the literature (The Royal Society, 2009; NRC, 2015a, b; Schäfer et  al., 2015). However, following SR1.5 (de Coninck et  al., 2018), these terms are inconsistently used in the literature, so that CDR 
and SRM are explicitly differentiated here. SRM contrasts with

climate change mitigation because it introduces a  ‘mask’ to the climate  change  problem by altering the Earth’s radiation budget, rather than attempting to address the root cause of the problem, which is the increase in GHGs in the atmosphere.
Section 4.6.3.1 assesses the emergence of the climate response to 
mitigation, which is reflected by the difference between high- and low-emissions scenarios. Section  4.6.3.2 then assesses the climate response to mitigation through CDR options, usually assumed 
against the background of some emissions scenario; note that the 
CDR options themselves are assessed in Chapter 5 (Section 5.6.2). Section 4.6.3.3 assesses the climate system response to SRM options. The biogeochemical implications of CDR and SRM are assessed in Chapter 5 (Sections 5.6.2 and 5.6.3, respectively). The importance of 
CDR for reaching net zero or negative CO
2 emissions in mitigation 
pathways is assessed in the AR6 WGIII report (Chapters 3, 4, 6, 7

and 12). The risks for and impacts on human and natural systems 
due to SRM are assessed in the AR6 WGII report (Chapter 16), and 
the international governance issues related to SRM and CDR are assessed in the AR6 WGIII report (Chapter 14).
4.6.3.1  Emergence of the Climate Response to Mitigation
Reducing GHG emissions will eventually slow and limit the degree of 
climate change relative to high-emissions scenarios such as SSP5 -8.5 
(very high confidence). Even when CO 2 emissions are reduced, 
however, atmospheric CO 2 concentrations continue to increase 
as long as emissions exceed removal by sinks (Millar et al., 2017). Surface warming would likewise initially continue under scenarios of 
decreasing emissions, resulting in a substantial lag between a peak 
in CO
2 emissions and peak warming ( high confidence ) (Ricke and 
Caldeira, 2014; Zickfeld and Herrington, 2015). The lag between peak emissions and warming depends on the emissions history

prior to the peak and also on the rate of the subsequent emissions 
reductions (Matthews, 2010; Ricke and Caldeira, 2014; Zickfeld and Herrington, 2015).
In addition to the lag between peak emissions and peak warming, 
the climate response to reduced emissions would be overlain by 
internal variability, which can amplify or attenuate the forced response. The resulting masking of differences between scenarios is illustrated in Figure 4.36 for GSAT trends over 2021–2040 (Maher 
et al., 2020). The overall trends conform to expectations in that most 
simulations show warming almost everywhere, especially under scenario RCP8.5 (Figure 4.36 bottom row). But any individual grid point can in principle show no warming or even cooling, even under 
(a) Ef fective Radiative Forcing
(b) Surface Air Temperature ChangeºC W m-2
Figure  4.35 | Comparison of RCPs and SSPs run by a single emulator to 
estimate scenario differences. Time series with 5–95% ranges and medians of

(a) effective radiative forcings, calculated as described in Annex 7.A.1; and (b) global 
surface air temperature projections relative to 1850–1900 for the RCP and SSP
scenarios from MAGICC 7.5. Note that the nameplate radiative forcing level refersto stratospheric adjusted radiative forcings in AR5-consistent settings (Tebaldi et al.,2021) while ERFs may differ. MAGICC7.5 is here run in the recommended setup forWGIII, prescribing observed GHG concentrations for the historical period and switching to emissions-driven runs in 2015. Further details on data sources and processing areavailable in the chapter data table (Table 4.SM.1).

620
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4RCP 8.5, over the near term (Figure 4.36, middle row). The magnitude 
of pointwise maximum and minimum temperature trends can be as 
large as 0.5°C per year (Figure 4.36, top and middle rows), exceeding 
possible trends in the global mean by one order of magnitude. While 
it is only a small fraction of the surface that simultaneously can show 
cooling, cooling at any given location is fully consistent with globally 
averaged surface warming over the near term ( high confidence , since 
the findings of Maher et al. (2020) are consistent across six different 
large initial-condition ensembles).
An important development since AR5 has been the quantification 
of when the climate response to mitigation can be expected to 
emerge from the background noise of internal variability (illustrated 
in Figure 4.36; see Section 1.4.2.2 and Glossary). A basic ambiguity

arises because once mitigation measures are in place, it is no longer possible to observe what the climate would have been without these 
measures, and any statement about emergence of the response to 
mitigation is contingent upon the assumed strength of mitigation in 
relation to an assumed (‘counterfactual’) no-mitigation scenario. Still, 
there is high agreement  on the emergence of the climate response 
to mitigation across a number of independent studies using different 
models and different statistical approaches.
Among global quantities, emergence of the response to differing CO 2 
emissions – representing differences between low- and high-emissions 
scenarios – is first expected to arise in global mean CO 2 concentrations, 
about 10 years after emissions pathways have started diverging ( high 
confidence ) (Tebaldi and Friedlingstein, 2013; Peters et  al., 2017; 
Schwartzman and Keeling, 2020; Spring et al., 2020). In these studies,

emergence is generally defined as the time at which the global mean 
MPI-RCP85 max trend
MPI-RCP85 min trend
MPI-RCP85 %trends > 0MPI-RCP26 max trend
MPI-RCP26 min trend
MPI-RCP26 %trends > 0CESM-RCP85 max trend
CESM-RCP85 min trend
-0.5 0 0.5
CESM-RCP85 %trends > 0
0 20 40 60 80 100
%°C/yr
Figure  4.36 | Masking of climate response to mitigation by internal variability in the near term.  Near-term (2021–2040) pointwise maximum (top row)  and 
pointwise minimum (middle row)  surface air temperature trends in the large initial-condition ensemble from MPI (left and centre columns) , and CESM (right-hand 
column)  models in the RCP2.6 (left-hand column)  and RCP8.5 scenarios (centre and right columns) . The percentage of ensemble members with a warming trend in 
the near term is shown in the bottom panels. Figure modified from Maher et al. (2020). Further details on data sources and processing are available in the chapter data table 

621
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44concentration first differs between mitigation and non-mitigation 
scenarios by more than two standard deviations of internal variability, 
although there are some methodological differences.
Emergence in GSAT would be delayed further, owing to the inertia in 
the climate system. Although not investigating emergence as defined 
here in AR6, Tebaldi et al. (2021) used a 20-year running-mean GSAT 
and compared pairwise either model-by-model or between CM IP6 
ensemble means from the core set of five scenarios assessed in 
this chapter. Differences by more than 0.1°C showed up in most 
cases in the near term,  with only some of the individual models 
and the comparisons of the closest scenarios showing a delay until 
the mid-term. Taking internal variability explicitly into account, Tebaldi 
and Friedlingstein (2013) and Samset et al. (2020) found emergence

of mitigation benefits in GSAT changes about 25–30 years after 
RCP2.6 emissions diverge from the higher-emissions trajectories 
in RCP4.5 and RCP8.5. Consistently, Marotzke (2019) found about 
one-third likelihood that a trend reduction in GSAT, over the period 
2021–2035 relative to 2005–2020, would be attributable to the 
emissions reductions implied by the difference between RCP2.6 and 
RCP4.5. Emergence of the GSAT response to mitigation of individual 
short-lived climate forcers (SLCFs) would likewise not occur until 
several decades after emissions trajectories diverge, owing to the 
relatively small influence of individual SLCFs on the total ERF (Samset 
et al., 2020), see also Section 4.4.4 and Figure 4.18.
In contrast to the earlier studies, emergence in GSAT within the 
near-term has recently been found by McKenna et  al. (2021) who 
investigated the likelihood that under the SSP scenarios GSAT trends 
will exceed the largest historical observed 20-year trends. They found

that under scenario SSP1-1.9, the 20-year GSAT trends would likely  be 
lower than in SSP3-7.0 and SSP5-8.5 within the near term. This earlier 
diagnosed time of emergence compared to Marotzke (2019), while 
using a similar statistical approach, presumably arose because of the 
longer-period trends (20 rather than 15 years) and the larger difference 
between emissions trajectories considered ( medium confidence ). 
Using 20-year temperature anomalies relative to 1995–2014 instead 
of 20-year trends yielded a low probability of emergence (McKenna 
et  al., 2021), consistent with the AR5 (Collins et  al., 2013; Kirtman 
et al., 2013; Tebaldi and Friedlingstein, 2013; Samset et al., 2020). It is 
not yet understood why GSAT trends appear to show faster emergence 
of mitigation benefits, compared to GSAT anomalies.
Emergence of mitigation benefits has been studied much less 
for quantities other than globally and annually averaged CO 2

concentration and surface temperature. Boreal-winter temperatures 
are more challenging for emergence, due to larger variability in 
boreal winter and adding a  decade to the time of emergence, 
whereas emergence times for boreal-summer averages are similar 
to the annual temperature averages (Tebaldi and Friedlingstein, 
2013). Emergence happens later at the regional scale, with a median 
time of emergence of 30–45 years after emissions paths separate in 
RCP2.6 relative to RCP4.5 and RCP8.5; a stricter requirement of 95% 
confidence level instead of median induces a delay of several decades, 
bringing time of emergence toward the end of the 21st century at 
regional scales (Tebaldi and Friedlingstein, 2013).Attribution to emissions reductions, for the case of RCP2.6 relative 
to RCP4.5, is not substantially more likely for 2021–2035 trends 
in upper-2000 m OHC than for GSAT (Marotzke, 2019), although 
OHC change is thought to be less susceptible to internal variability.

Furthermore, Marotzke (2019) found only around 10% likelihood 
of mitigation-benefit emergence during 2021–2035 for change 
in AMOC and September Arctic sea ice area. Tebaldi and Wehner 
(2018) showed that the differences in temperature extremes 
between RCP4.5 and RCP8.5 over all land areas become statistically 
significant by 2050. The seemingly contrasting result of Ciavarella 
et  al. (2017) that mitigation benefits arise earlier for climate 
extremes poses no contradiction, because Ciavarella et  al. (2017) 
did not look at emergence as defined here but at the extremes of 
a distribution, which differ between scenarios already at a time when 
the distributions are still largely overlapping.
In summary, if strong mitigation is applied from 2020 onward as 
reflected in SSP1-1.9, its effect on 20-year trends in GSAT would 
likely  emerge during the near term, measured against an assumed 
non-mitigation scenario such as SSP3-7.0 and SSP5-8.5. However,

the response of many other climate quantities to mitigation would 
be largely masked by internal variability during the near term, 
especially on the regional scale ( high confidence ). The mitigation 
benefits for these quantities would emerge only later during the 
21st century ( high confidence ). During the near term, a small fraction 
of the surface can show cooling under all scenarios assessed here, 
so near-term cooling at any given location is fully consistent with 
globally averaged surface warming ( high confidence ).
4.6.3.2 Climate Response to Mitigation by Carbon 
Dioxide Removal
CDR options include afforestation, soil carbon sequestration, bioenergy 
with carbon capture and storage (BECCS), wet land restoration, ocean 
fertilization, ocean alkalinisation, enhanced terrestrial weathering 
and direct air capture and storage (see Section 5.6.2 and Table 5.9 for 
a more complete discussion). Chapter 8 (Section 8.4.3) assesses the

implications of CDR for water cycle changes. The potential of different 
CDR options in terms of the amount of CO 2 removed per year from the 
atmosphere, costs, co-benefits and side effects of the CDR approaches 
are assessed in SR1.5 (de Coninck et al., 2018), the AR6 WGIII Report 
(see Chapters 7 and 12), and in several review papers (Fuss et  al., 
2018; Lawrence et al., 2018; Nemet et al., 2018). In the literature, CDR 
options are also referred to as ‘negative CO 2 emissions technologies’.
Deployment of CDR will lead to a reduction in atmospheric CO 2 levels 
only if uptake by sinks exceeds net CO 2 emissions. Hence, there could 
be a  substantial delay between the initiation of CDR and net CO 2 
emissions turning negative (van Vuuren et al., 2016), and the time to 
reach net negative CO 2 emissions and the evolution of atmospheric 
CO 2 and climate thereafter would depend on the combined pathways 
of anthropogenic CO 2 emissions, CDR, and natural sinks. The cooling

(or avoided warming) due to CDR would be proportional to the 
cumulative amount of CO 2 removed from the atmosphere by CDR 
(Tokarska and Zickfeld, 2015; Zickfeld et al., 2016), as implied by the 
near-linear relationship between cumulative carbon emissions and 
GSAT change (Section 5.5).

622
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Emissions pathways that limit globally averaged warming to 1.5°C 
or 2°C by the year 2100 assume the use of CDR approaches in 
combination with emissions reductions to follow net negative CO 2 
emissions trajectory in the second half of this century. For instance , 
in SR1.5, all analysed pathways limiting warming to 1.5°C by 2100 
with no or limited overshoot include the use of CDR to some extent to 
offset anthropogenic CO 2 emissions and the median of CO 2 removal 
across all scenarios was 730 GtCO 2 in the 21st  century (Rickels 
et al., 2018; Rogelj et al., 2018b). Affordable and environmentally 
and socially acceptable CDR options at scale well before 2050 are 
an important element of 1.5°C-consistent pathways especially in 
overshoot scenarios (de Coninck et  al., 2018). The required scale 
of removal by CDR can vary from 1–2 GtCO 2 yr –1 year from 2050

onwards to as much as 20 GtCO 2 yr –1 (Waisman et  al., 2019). 
In the SSP class of scenarios, net CO 2 emissions turn negative from 
around 2050 in SSP1-1.9 and around 2070 in SSP1-2.6 and in the 
overshoot scenario SSP5-3.4-OS (O’Neill et  al., 2016). Thus, CDR 
would play a pivotal role in limiting climate warming to 1.5°C or 
2°C (Minx et al., 2018). In stark contrast, however, two extensive 
reviews (Lawrence et al., 2018; Nemet et al., 2018) conclude that it 
is implausible that any CDR technique can be implemented at the 
scale needed by 2050.
When CDR is applied continuously and at scales as large as currently 
deemed possible, under RCP8.5 as the background scenario, the 
widely discussed CDR options such as afforestation, ocean iron 
fertilization and surface ocean alkalinisation are individually 
expected to be relatively ineffective, with limited (8%) warming 
reductions relative to the scenario with no CDR option (Keller et al.,

2014). Hence, the potential role that CDR will play in lowering 
the temperature in high-emissions scenarios is limited ( medium 
confidence ). The challenges involved in comparing the climatic 
effects of various CDR options has also been recognized in recent 
studies (Sonntag et  al., 2018; Mengis et  al., 2019). For instance, 
due to compensating processes such as biogeophysical effects of 
afforestation (warming from albedo decrease when croplands are 
converted to forests) more carbon is expected to be removed from 
the atmosphere by afforestation than by ocean alkalinisation to 
reach the same global mean cooling.
The climate response to CDR-caused net negative CO 2 emissions 
has been studied in Earth system models by prescribing idealized 
ramp-down of CO 2 concentrations (MacDougall, 2013; Zickfeld 
et al., 2016; Schwinger and Tjiputra, 2018), CO 2 concentrations of 
RCP scenarios that have net negative CO 2 emissions (C.D. Jones

et al., 2016b), and idealized net negative CO 2 emissions scenarios 
(Tokarska and Zickfeld, 2015). The Carbon Dioxide Removal Model 
Intercomparison Project (CDRMIP) uses multiple ESMs to explore 
the climate response, effectiveness of CO 2 removal, and challenges 
of CDR options (Keller et al., 2018). Idealized CDRMIP simulations 
increase CO 2 concentrations at 1% per year from the level in the 
pre-industrial control run (piControl) to 4×CO2  and subsequently 
decrease at the same rate to the piControl level. This section 
assesses the lag in climate response to CDR-caused negative 
emissions; climate ‘reversibility’ is assessed in Section  4.7.2. 
The  ramp-down phase, though unrealistic, represents the ‘net 
negative CO 2 emissions’ phase.Figure  4.37 illustrates the first results from CDRMIP (Keller et  al., 
2018). Other studies that use similar (Zickfeld et al., 2016; Schwinger 
and Tjiputra, 2018; Jeltsch-Thömmes et al., 2020) or other idealized

scenarios (MacDougall, 2013) or more realistic net negative CO 2 
emissions scenarios such as RCP2.6 (C.D. Jones et al., 2016b) and 
scenarios that limit warming to 2°C or less after different levels of 
overshoot (Tokarska and Zickfeld, 2015) arrive at similar conclusions. 
Changes in key climate variables substantially lag behind the decline 
in CO 2 (Figure 4.37). The precipitation increase at the beginning of 
the ramp-down phase agrees with the increase in precipitation for an 
abrupt decline in CO 2 (Cao et al., 2011). Notwithstanding a decline in 
atmospheric CO 2, global mean thermosteric sea level would continue 
to rise. When atmospheric CO 2 returns to the piControl level, global 
mean thermosteric sea level is higher than its value at peak CO 2 
(Figure 4.37), and it is likely  that thermosteric global sea level would 
not return to piControl levels for over 1000 years after atmospheric 
CO 2 is restored to piControl concentrations (Tokarska and Zickfeld,

2015; Ehlert and Zickfeld, 2018). Therefore, there is high confidence 
that sea level rise will not be reversed by CDR at least for several 
centuries Chapter  9 (Section  9.6.3.5). A  comparison of different 
models shows recovery of AMOC intensity during net negative CO 2 
emissions, but the results are model dependent – strengthening with 
an overshoot in most models (Jackson et al., 2014) and strengthening 
but not reaching the initial state in some models (Sgubin et al., 2015). 
The overall lag in response is qualitatively similar to the lagged 
climate system response in the overshoot scenario SSP5-34-OS where 
CO 2 rises until 2062 and decreases thereafter (Figure 4.34). The lag 
in climate response to CDR causes hysteresis between key climate 
variables such as temperature, precipitation, AMOC and sea level, 
and atmosphere CO 2 with the hysteresis characteristics dependent 
on the rate of CDR and climate sensitivity (MacDougall, 2013; Jeltsch-
Thömmes et al., 2020).

Termination of CDR refers to a sudden and sustained discontinuation 
of CDR deployment (see Section  4.6.3.3 for termination effects of 
SRM). The literature on the termination effects of CDR is limited, 
mostly considering scenarios where CDR implementation is explicit 
and does not result in net negative CO 2 emissions (Keller et  al., 
2014; González et  al., 2018). In simulations where CDR is applied 
on the RCP8.5 scenario at scales as large as currently deemed 
possible, the increase in global mean warming rates following CDR 
termination are relatively small in comparison to SRM termination 
(Keller et al., 2014). The exception is artificial ocean upwelling where 
surface cooling is mainly caused by bringing cold water from the 
deep ocean; upon termination this causes larger rates of surface 
warming (Oschlies et al., 2010). When background emissions are as 
high as in RCP8.5, termination of a  large global-scale application

of CDR such as ocean alkalinisation for multiple decades could also 
result in large regional warming rates (up to 0.15°C per year) that 
are comparable to those caused by termination of SRM (González 
et al., 2018). In such cases, large amounts of CO 2 would be removed 
from the atmosphere before termination, and termination would 
cause a temporal trajectory of atmospheric CO 2 that is parallel to the 
high-emissions scenario but from an atmosphere with much lower 
CO 2 levels. Because CO 2 radiative forcing is a logarithmic function of 
CO 2 concentration, large regional warming rates are simulated in such 
terminations. Thus, there is high confidence that the climate effect of

623
Figure 4 .37 | Delayed climate response to carbon dioxide removal (CDR)-caused net negative CO 2 emissions.  Multi-model simulated response in global and 
annual mean climate variables for a ramp-up followed by ramp-down of CO 2. Atmospheric CO 2 increases from the pre-industrial level at a rate of 1% yr –1 to 4×CO2, then 
decreases at the same rate to the pre-industrial level and then remains constant. The ramp-down phase represents the period of net negative CO 2 emissions. (a) Normalized 
ensemble mean anomaly of key variables as a function of year, including atmospheric CO 2, surface air temperature, precipitation, thermosteric sea level change (see Glossary), 
global sea ice area, Northern Hemisphere sea ice area in September, and Atlantic meridional overturning circulation (AMOC); (b) surface air temperature; (c) precipitation;

(d) September Arctic sea ice area; (e) AMOC; (f) thermosteric sea level; five-year running means are shown for all variables except the sea level change. In (b, f), red lines 
represent the phase of CO 2 ramp-up, blue lines represent the phase of CO 2 ramp-down, brown lines represent the period after CO 2 has returned to pre-industrial level, and black 
lines represent the multi-model mean. For all of the segments in (b, f), the solid coloured lines are CMIP6 models, and the dashed lines are other models (i.e., EMICs, CMIP5-era 
models). Vertical dashed lines indicate peak CO 2 and when CO 2 again reaches pre-industrial value. The number of CMIP6 and non-CMIP6 models used is indicated in each panel. 
The time series for the multi-model means (b, f) and the normalized anomalies (a) are terminated when data from all models are not available, in order to avoid the discontinuity

in the time series. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

624
CDR termination would  depend on the amount CO 2 removed by CDR 
prior to termination and the rate of background CO 2 emissions at the 
time of termination. See also Chapter 5, Table 5.9, which summarizes 
the termination effects of individual CDR options.
In summary, there is high confidence that, due to the near-linear 
relationship between cumulative carbon emissions and GSAT change, 
cooling or avoided warming due to a  CDR option would depend 
on the cumulative amount of CO 2 removed by that CDR option. 
The climate system response to the deployment of CDR is expected 
to be delayed by years (e.g.,  in temperature, precipitation, sea ice 
extent) to centuries (e.g.,  sea level and AMOC) ( high confidence ). 
The climate response to a  sudden and sustained CDR termination 
would depend on the amount of CDR-induced cooling prior to

termination and the rate of background CO 2 emissions at the time of 
termination ( high confidence ).
4.6.3.3 Climate Response to Solar Radiation Modification
Most SRM approaches, including stratospheric aerosol injection (SAI), 
marine cloud brightening (MCB), and surface albedo enhancements 
(Table 4.7), aim to cool the Earth by deflecting more solar radiation 
to space. Although cirrus cloud thinning (CCT) aims to cool the planet 
by increasing the longwave emission to space, it is included in the portfolio of SRM options (Table 4.7) for consistency with AR5 (Boucher 
et al., 2013) and SR1.5 (de Coninck et al., 2018). Other approaches 
such as injection of sulphate aerosols into the Arctic troposphere 
and sea ice albedo enhancements for moderating regional warming 
have also been suggested (MacCracken, 2016; Field et  al., 2018). 
As noted in SR1.5 (de Coninck et al., 2018), SRM is only considered as 
a potential supplement to deep mitigation, for example in overshoot

scenarios (MacMartin et al., 2018).
The AR5 assessed the climate response to, as well as risks and side 
effects of, several SRM options (Boucher et al., 2013) and concluded 
with high confidence  that SRM, if practicable, could substantially 
offset a  global temperature rise and partially offset some other 
impacts of global warming, but the compensation for the climate 
change caused by GHGs would be imprecise. The AR5 furthermore 
concluded that models consistently suggest that SRM would generally 
reduce climate differences compared to a world with elevated GHG 
concentrations and no SRM; however, there would also be residual 
regional differences in climate (e.g., temperature and rainfall) when 
compared to a climate without elevated GHGs. The AR5 concluded 
with high confidence  that scaling SRM to substantial levels would 
carry the risk that if the SRM were terminated for any reason, surface 
temperatures would increase rapidly (within a  decade or two) to

values consistent with the GHG forcing (Boucher et al., 2013).Table  4.7 | A summary of the various SRM approaches.
SRM ApproachProposed Mechanism and Associated 
Uncertainties of the SRM ApproachGlobal Mean Negative 
Radiative Forcing Potential 
and CharacteristicsKey Climate and 
Environmental EffectsReferences
Stratospheric 
Aerosol Injection 
(SAI)Injection of aerosols or their precursor gases 
into the stratosphere to scatter sunlight back 
to space; Aerosol types such as sulphates, 
calcium carbonate, and titanium dioxide have 
been proposed; large uncertainties associated 
with type of aerosol, aerosol radiative 
properties, microphysics, chemistry, stratospheric 
processes, and temporal and spatial strategy 
of aerosol injection.1–8 W m–2, depending on the 
amount and pattern of injection, 
and transport and growth of injected 
particles; compared to other SRM 
approaches, radiative forcing could 
be more homogenously distributed.Change in temperature and

precipitation pattern; precipitation 
reduction in some monsoon regions; 
decrease in direct and increase 
in diffuse sunlight at surface; 
stratospheric heating and changes 
to stratospheric dynamics and 
chemistry; potential delay in ozone 
hole recovery; changes in surface UV 
radiation; changes in crop yields.Visioni et al. (2017); 
Tilmes et al. (2018b); 
Simpson et al. (2019b)
Marine Cloud 
Brightening (MCB)Injection of sea salt or other types of aerosols 
to increase the albedo of marine stratocumulus 
clouds; regional option to reduce SST in hurricane 
formation regions and in coral reef areas; large 
uncertainties associated with cloud microphysics 
and aerosol–cloud-radiation interactions.1–5 W m–2, depending on the scale 
and amount of sea salt injection; 
heterogeneous radiative forcing.Change in land–sea contrast 
and precipitation patterns.Latham et al., (2012, 
2014); Ahlm et al. (2017); 
Stjern et al. (2018)
Cirrus Cloud

Thinning (CCT)Inject ice nuclei in the upper troposphere to 
reduce the lifetime and optical thickness of cirrus 
clouds to allow more longwave radiation to 
escape to space; large uncertainties associated 
with cirrus cloud formation processes, cirrus 
microphysics, and interaction with aerosol.1–2 W m–2, depending on cirrus 
microphysical response and seeding 
strategy; heterogeneous radiative 
forcing; loss in cirrus clouds could 
also cause significant shortwave 
forcing regionally; risk of overseeding 
and consequent warming.Changes in temperature and 
precipitation pattern; increase in 
solar radiation reaching surface.Storelvmo and 
Herger (2014); 
Jackson et al. (2016); 
Gasparini et al. (2020)
Surface-
Based Albedo 
ModificationIncrease ocean albedo by creating microbubbles; 
add reflective material to increase desert albedo; 
paint the roof of buildings white to increase roof 
reflectivity; increase albedo of agriculture land

via no-till farming or modifying crop albedo, add 
reflective material to increase sea ice albedo.Radiative forcing of a few W m–2 
might be achieved via increase 
in ocean and desert albedo, but 
the large-scale implementation is 
not feasible; less than 0.5 W m–2 
for white roof and crop albedo 
enhancement; heterogeneous 
radiative forcing.Change in land–sea contrast 
and precipitation pattern for 
ocean and desert albedo increase; 
more localized effect for white 
roofs, no-till farming, and crop 
albedo modification.Evans et al. (2010); 
Davin et al. (2014); 
Zhang et al. (2016); 
Field et al. (2018); 
Kravitz et al. (2018)

625
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44The SR1.5 (de Coninck et  al., 2018) assessed SRM in terms of its 
potential to limit warming to below 1.5°C in temporary overshoot 
scenarios and the associated impacts. It concluded that SAI could 
limit warming to below 1.5°C but that the climate response to SAI is 
uncertain and varies across climate models. Overall, the assessment 
concluded that the combined uncertainties related to SRM approaches, 
including technological maturity, limited physical understanding of the 
response to SRM, potential impacts, and challenges of governance, 
constrain potential deployment of SRM in the near future.
This subsection assesses the global and large-scale physical climate 
system response to SRM based on theoretical and modelling studies. 
There is no mature technology today to implement any of the 
SRM options assessed here. A short summary of the SRM options,

including the proposed mechanism of each SRM approach, radiative 
forcing potential, and key climate and environmental effects, is listed 
in Table 4.7. Chapter 5 (Section 5.6.3) assesses the biogeochemical 
implications of SRM, Chapter 6 (Section 6.4.6) assesses the potential 
ERF of the aerosol-based SRM options and Chapter 8 (Section 8.6.3) 
assesses the abrupt water cycle changes in response to initiation or 
termination of SRM. The risks to human and natural systems, impacts 
of SRM, ethics, and perceptions are assessed in the WGII Report 
(Chapter  16). Governance issues associated with SRM research 
and deployment are assessed in the WGII and WGIII Reports. The 
assessment of technical feasibility and engineering aspects of SRM is 
beyond the scope of this Report.
The AR5 assessed SRM modelling mainly based on idealized 
simulations that used solar constant reductions. Since then, more 
in-depth investigations into specific SRM approaches have been

conducted with more sophisticated treatment of aerosol–cloud–
radiative interactions and stratospheric dynamics and chemistry 
underlying SAI, MCB, and CCT. Another major development since AR5 
is the investigation into whether multiple climate policy goals may be 
met by optimally designed SRM strategies, including large-ensemble 
SAI simulations using multiple injection locations. There are large 
uncertainties in important SRM-related processes such as aerosol 
microphysics and aerosol–cloud–radiation interaction and hence the 
level of understanding is low.
As assessed in SR1.5 (de Coninck et al., 2018), most of the knowledge 
about SRM is based on idealized model simulations and some 
natural analogues. In addition to single-model studies, more results 
from the coordinated modelling work of Geoengineering Model 
Intercomparison Project (GeoMIP) have become available. GeoMIP 
was initiated at the time of AR5 (Kravitz et al., 2011, 2013a) and is

now in its second phase under the framework of CMIP6 (GEOMIP6, 
Kravitz et al., 2015). However, studies based on GeoMIP6 data are 
currently limited and hence the assessment on climate response to 
SRM here is derived mostly from GeoMIP literature together with 
studies with single models.
Simple calculations and climate modelling studies show that about 
2% extra solar irradiance reflected away from Earth or a  one 
percentage point increase in planetary albedo (0.31 to 0.32) would 
suffice to offset global mean warming from a doubling of the CO 2 
concentration (The Royal Society, 2009; Kravitz et al., 2013a, 2021). To offset the same amount of CO 2-induced GSAT increase, 
different levels of ERF are required for different methods of SRM 
(Schmidt et al., 2012; Chiodo and Polvani, 2016; Modak et al., 2016; 
Duan et  al.,  2018; Russotto and Ackerman, 2018; Krishnamohan 
et al., 2019; Zhao et al., 2021).
As assessed in AR5 (Boucher et al., 2013), abruptly introducing SRM to

fully offset global warming reduces temperature toward 1850–1900 
values with an e-folding time of only about five years (Matthews and 
Caldeira, 2007). A more realistic approach would be a slow ramp-up 
of SRM to offset further warming (MacCracken, 2016; Tilmes et al., 
2016). Modelling studies have consistently shown that SRM has the 
potential to offset some effects of increasing GHGs on global and 
regional climate, including the melting of Arctic sea ice (Berdahl et al., 
2014; Moore et al., 2014) and mountain glaciers (Zhao et al., 2017), 
weakening of Atlantic meridional overturning circulation (AMOC; Cao 
et al., 2016; Hong et al., 2017; Tilmes et al., 2020), changes in extremes 
of temperature and precipitation (Curry et al., 2014; Ji et al., 2018; 
Muthyala et  al., 2018), and changes in frequency and intensity of 
tropical cyclone (Moore et al., 2015; Jones et al., 2017).
The climate response to SRM depends greatly on the characteristics of

SRM implementation approaches. There could be substantial residual 
or overcompensating climate change at both the global and regional 
scales and seasonal time scales (Kravitz et  al., 2014; McCusker 
et al., 2015; Irvine et al., 2016; Fasullo et al., 2018; Jiang et al., 2019; 
Gertler et  al., 2020). This is because the climate response to SRM 
options is different from the response to GHG increase (Figure 4.38). 
For  instance, when global mean warming is offset by a  uniform 
reduction in incoming sunlight, there is residual warming in the high 
latitudes and overcooling in the tropics (Kravitz et al., 2013a; Kalidindi 
et al., 2015), and a reduction in tropical mean rainfall (Tilmes et al., 
2013). In simulations of stratospheric SO 2 injection, SRM diminishes 
the amplitude of the seasonal cycle of temperature at many high-
latitude locations, with warmer winters and cooler summers (Jiang 
et  al., 2019). Further, the rates of response could differ between

surface temperature and slow components in the climate system 
such as sea level rise (Irvine et  al., 2012; Jones et  al., 2018). SRM 
implemented at a moderate intensity, for example by offsetting half 
of the global warming, has the potential to reduce negative effects 
such as reduced precipitation that are associated with fully offsetting 
global mean warming (Irvine et al., 2019; Irvine and Keith, 2020).
For the same amount of global mean cooling achieved, the pattern 
of climate response would depend on SRM characteristics (Niemeier 
et al., 2013; Duan et al., 2018; Muri et al., 2018). This is illustrated 
in Figure 4.38 for temperature and precipitation change relative to 
a  high-CO 2 world for scenarios of CO 2 reduction, solar irradiance 
reduction, SAI, and MCB. The pattern differences for different 
methods are much larger for precipitation than for temperature. The 
pattern of climate change resulting from SRM is also different from

that resulting from CO 2 reduction (Figure 4.38). It is virtually certain  
that SRM approaches would not be able to precisely offset the GHG-
induced anthropogenic climate change at global and regional scales.
Because of different sensitivity of precipitation change to CO 2 and 
solar forcings (Myhre et  al., 2017), if shortwave-based SRM is used

626
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Correlation RMS
1.11
1.09
1.10
1.071.00
0.99
0.97
0.91SMR noitalerroC
5.49 1.00
5.10 0.93
6.71 0.61
7.38 0.63Temperatur e Change P er Deg ree
Global Cooling (°C/°C )Precipitation Change Per Deg ree
Global Cooling (%/°C )CO2 reduction Solar ReductionStratospheric
Sulfate AerosolsMarine Cloud
Brightening
Color High model ag reemen t
Low model agr eemen t–2.5       –2     –1.5      –1      –0.5    –0.25 –50    –30     –20     –10     –5         0        5        10      20       30      50
Figure  4.38 | Multi-model response per degree global mean cooling in temperature and precipitation in response to CO 2 forcing and SRM forcing. Top 
row shows the response to a CO 2 decrease, calculated as the difference between pre-industrial control simulation and abrupt4xCO 2 simulations where the CO 2 concentration

is quadrupled abruptly from the pre-industrial level (11-model average); second row  shows the response to a globally uniform solar reduction, calculated as the difference 
between  GeoMIP experiment G1 and abrupt4xCO 2 (11-model average); third row  shows the response to stratospheric sulphate aerosol injection, calculated as the 
difference between GeoMIP experiment G4 (a continuous injection of 5 Tg SO 2 year –1 at one point on the equator into the lower stratosphere against the RCP4.5 background 
scenario) and RCP4.5 (six-model average); and the bottom row  shows the response to marine cloud brightening, calculated as the difference between GeoMIP experiment 
G4cdnc (increase cloud droplet concentration number in marine low cloud by 50% over the global ocean against RCP4.5 background scenario) and RCP4.5 (eight-model

average). All differences (average of years 11–50 of simulation) are normalized by the global mean cooling in each scenario, averaged over years 11–50. Diagonal lines 
represent regions where fewer than 80% of the models agree on the sign of change. The values of correlation represent the spatial correlation of each SRM-induced temperature 
and precipitation change pattern with the pattern of change caused by a reduction of atmospheric CO 2. RMS (root mean square) is calculated based on the fields shown in the 
maps (normalized by global mean cooling). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

627
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44to fully offset GHG-induced global mean warming, there would 
be a  overcompensation of GHG-induced increase in global mean 
precipitation (Kravitz et  al., 2013a; Tilmes et  al., 2013; Irvine et  al., 
2016). Further, regional SRM approaches such as aerosol injections 
into the Arctic stratosphere are  likely  to remotely influence on tropical 
monsoon precipitation by shifting the mean position of ITCZ (Nalam 
et al., 2018). However, the shift could be avoided by simultaneously 
cooling the southern hemisphere (MacCracken et  al., 2013; Kravitz 
et  al., 2016; Nalam et  al., 2018). The SRM response of precipitation 
minus evapotranspiration (P–E) is found to be smaller than that 
of precipitation because of reduction in both precipitation and 
evapotranspiration (Tilmes et al., 2013; Nalam et al., 2018; Irvine et al., 
2019). Thus, global mean soil moisture could be effectively maintained,

though with significant regional variability (Cheng et al., 2019).
The Geoengineering Large Ensemble Project (GLENS) has investigated 
achieving multiple climate policy goals by adjusting the rate of 
stratospheric SO 2 injection at four different latitudes. GSAT, the 
inter-hemispheric temperature difference, and the equator-to-pole 
temperature gradient could be maintained simultaneously at the 
year-2020 level under RCP 8.5 (Tilmes et al., 2018a). The possibility of 
using SAI to simultaneously stabilize non-temperature metrics such 
as tropical precipitation and Arctic sea ice extent is also explored (Lee 
et al., 2020). Furthermore, the potential of achieving multiple climate 
policy goals by combining two SRM approaches is also examined 
in a few modelling studies, with low confidence in the outcome of 
combining various approaches and the related climate response 
(Boucher et al., 2017; Cao et al., 2017).
4.6.3.3.1 Stratospheric aerosol injection

Most SRM research has focused on stratospheric aerosol injection 
(SAI) and most SAI studies have assessed the effects of injection. Most 
research has focused on stratospheric aerosol injection (SAI): the 
injection of sulphate particles or its precursor gases such as SO 2, which 
would then be oxidized to H 2SO4. Injection of other types of aerosol 
particles, such as calcite (CaCO 3), titanium dioxide (TiO 2), aluminium 
oxide (Al 2O3), and engineered nanoparticles has also been proposed 
(Keith, 2010; Ferraro et al., 2011; Pope et al., 2012; Weisenstein et al., 
2015; A.C. Jones et  al., 2016; Keith et  al., 2016), but are much less 
studied compared to sulphate injection. The natural analogue for 
sulphate aerosol injection is major volcanic eruptions (Cross-Chapter 
Box  4.1). While volcanic eruptions are not perfect analogues for 
SAI (Robock et  al., 2013; Plazzotta et  al., 2018; Duan et  al., 2019), 
studies on climate impacts of past volcanic eruptions can inform on

the potential impact of stratospheric sulphate injection. For example, 
emergent constraints (Chapters 1 and 5) that relate the climate system 
response to volcanic eruptions can be used to reduce uncertainty of 
the land surface temperature response to SAI (Plazzotta et al., 2018).
The cooling potential of SAI using sulphate aerosols depends on 
many factors (Visioni et al., 2017) including the amount of injection 
(Niemeier and Timmreck, 2015), aerosol microphysics (Krishnamohan 
et al., 2020), the spatial and temporal pattern of injection (Tilmes et al., 
2017), response of stratospheric dynamics and chemistry (Richter 
Jadwiga et al., 2018), and aerosol effect on cirrus clouds (Visioni et al., 
2018). A negative radiative forcing of a few W m–2 (ranging from one to eight W m–2) could be achieved depending on the amount and 
location of SO 2 injected into the stratosphere (Aquila et  al., 2014; 
Pitari et al., 2014; Niemeier and Timmreck, 2015; Kravitz et al., 2017;

Kleinschmitt et al., 2018; Tilmes et al., 2018a). The simulated efficacy 
of SAI by emission of SO 2 (radiative forcing per mass of injection rate) 
generally decreases with the increase in injection rate because of the 
growth of larger particles (about 0.5 microns) through condensation 
and coagulation reducing the mass scattering efficiency (Niemeier 
and Timmreck, 2015; Kleinschmitt et  al., 2018). However, efficacy 
changes little for total injection rate up to about 25 Tg sulphur 
per year when SO 2 is injected at multiple locations simultaneously 
(Kravitz et  al., 2017; Tilmes et  al., 2018a). Differences in model 
representation of aerosol microphysics, evolution of particle size, 
stratospheric dynamics and chemistry, and aerosol microphysics–
radiation–circulation interactions all contribute to the uncertainty in 
simulated cooling efficiency of SAI. Compared to sulphate aerosols, 
injection of non-sulphate particles would result in different cooling

efficacy, but understanding is limited (Pope et al., 2012; Weisenstein 
et al., 2015; A.C. Jones et al., 2016).
Earlier modelling studies focused on the effect of equatorial sulphate 
injection that tends to overcool the tropics and undercool the poles. 
Compared to equatorial injection, off-equatorial injection at multiple 
locations shows a  closer resemblance to the baseline climate in 
many aspects, including temperature, precipitation, and sea ice 
coverage (Kravitz et  al., 2019). However, significant regional and 
seasonal residual and overcompensating climate change is reported, 
including regional shifts in precipitation, continued warming of 
polar oceans, and shifts in the seasonal cycle of snow depth and 
sea ice cover (Fasullo et al., 2018; Jiang et al., 2019; Simpson et al., 
2019b). By appropriately adjusting the amount, latitude, altitude, and 
timing of the aerosol injection, modelling studies suggest that SAI is

conceptually able  to achieve some desired combination of radiative 
forcing and climate response ( medium confidence ) (MacMartin et al., 
2017; Dai et al., 2018; Lee et al., 2020; Visioni et al., 2020b).
There is large uncertainty in the stratospheric response to SAI, and 
the change in stratospheric dynamics and chemistry would depend 
on the amount, size, type, location, and timing of injection. There is 
high confidence  that aerosol-induced stratospheric heating will play 
an important role in surface climate change (Simpson et  al., 2019b) 
by altering the effective radiative forcing (Krishnamohan et al., 2019), 
lower stratosphere stability (Ferraro and Griffiths, 2016), quasi-biennial 
oscillation (QBO) (Aquila et  al., 2014; Niemeier and Schmidt, 2017; 
Kleinschmitt et  al., 2018), polar vortexes (Visioni et  al., 2020a), and 
North Atlantic Oscillation (Jones et  al., 2021). Model simulations 
indicate stronger polar jets and weaker storm tracks and a poleward

shift of the tropospheric mid-latitude jets in response to stratospheric 
sulphate injections in the tropics (Ferraro et al., 2015; Richter Jadwiga 
et al., 2018), as the meridional temperature gradient is increased in the 
lower stratosphere by the aerosol-induced heating. The aerosol-induced 
warming would also offset some of the GHG-induced stratospheric 
cooling. Compared to equatorial injection, off-equatorial injection is 
likely to result in reduced change in stratospheric heating, circulation, 
and QBO (Richter Jadwiga et al., 2018; Kravitz et al., 2019). Stratospheric 
ozone response to sulphate injection is uncertain depending on the 
amount, altitude, and location of injection (WMO,  2018). It is  likely

628
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4that sulphate injection would cause a  reduction in polar column 
ozone concentration and delay the recovery of Antarctic ozone hole 
(Pitari et al., 2014; Richter Jadwiga et al., 2018; Tilmes et al., 2018b), 
which would have implications for UV radiation and surface ozone 
(Pitari et al., 2014; Xia et al., 2017; Richter Jadwiga et al., 2018; Tilmes 
et al., 2018b). Injection of non-sulphate aerosols is likely  to result in less 
stratospheric heating and ozone loss (Pope et al., 2012; Weisenstein 
et  al., 2015; Keith et  al., 2016). One side effect of SAI is increased 
sulphate deposition at surface. A  recent modelling study indicates 
that to maintain global temperature at 2020 levels under RCP 8.5, 
increased sulphate deposition from stratospheric sulphate injection 
could be globally balanced by the projected decrease in tropospheric

anthropogenic SO 2 emissions, but the spatial distribution of sulphate 
deposition would move from low to high latitudes (Visioni et al., 2020c).
4.6.3.3.2 Marine cloud brightening
Marine cloud brightening (MCB) involves injecting small aerosols 
such as sea salt into the base of marine stratocumulus clouds 
where the aerosols act as cloud condensation nuclei (CCN). In the 
absence of other changes, an increase in CCN would produce higher 
cloud droplet number concentration with reduced droplet sizes, 
increasing cloud albedo. Increased droplet concentration may also 
increase cloud water content and optical thickness, but recent studies 
suggest that liquid water path response to anthropogenic aerosols is 
weak due to the competing effects of suppressed precipitation and 
enhanced cloud water evaporation (Toll et al., 2019). An analogue 
for MCB are reflective, persistent ‘ship tracks’ observed after the 
passage of a  sea-going vessel emitting combustion aerosols into

susceptible clouds (Christensen and Stephens, 2011; Chen et  al., 
2012; Gryspeerdt et al., 2019). A recent study (Diamond et al., 2020) 
found a  substantial increase in cloud reflectivity from shipping in 
south-east Atlantic basin, suggesting that a  regional-scale test of 
MCB in stratocumulus-dominated regions could  be successful.
Modelling studies suggest that MCB has the potential to achieve 
a negative forcing of about 1 to 5 W m–2, depending on the deployment 
area and strategies of cloud seeding (Hill and Ming, 2012; Partanen 
et al., 2012; Alterskjær et al., 2013; Ahlm et al., 2017; Stjern et al., 
2018). Regional applications of MCB has also been suggested for 
offsetting severe impacts from tropical cyclones whose genesis is 
associated with higher SST (MacCracken, 2016; Latham et al., 2014) 
and for protecting coral reefs from higher SST (Latham et al., 2013). 
However, such regional approaches also involve large uncertainties

in the magnitude of the responses and consequences.
Several modelling studies suggest that the direct scattering effect by 
injected particles might also play an important role in the cooling effect 
of MCB, but the relative contribution of aerosol–cloud and aerosol–
cloud–radiation effect is uncertain (Partanen et  al., 2012; Kravitz 
et al., 2013b; Ahlm et al., 2017). Relative to the high-GHG climate, 
it is likely  that MCB would increase precipitation over tropical land 
due to the inhomogeneous forcing pattern of MCB over ocean and 
land ( medium confidence ) (Bala et al., 2011; Alterskjær et al., 2013; 
Niemeier et al., 2013; Ahlm et al., 2017; Muri et al., 2018; Stjern et al., 
2018). Because of the high level of uncertainty associated with cloud 
microphysics and aerosol–cloud–radiation interaction (Section 7.3), the climate response to MCB is as uncertain. Results from global 
climate models are subject to large uncertainty because of different

treatment of cloud microphysics and inadequate representation of 
sub-grid aerosol and cloud processes (Alterskjær and Kristjánsson, 
2013; Stuart et al., 2013; Connolly et al., 2014; Stjern et al., 2018). 
Sea salt deposition over land (Muri et al., 2015) and the effect of sea 
salt emission on atmospheric chemistry (Horowitz et al., 2020) are 
some of the potential side effects of MCB.
4.6.3.3.3 Cirrus cloud thinning
Cirrus clouds trap more outgoing thermal radiation than they 
reflect incoming solar radiation and thus have an overall warming 
effect  on the climate system (Mitchell and Finnegan, 2009). The 
aim of cirrus cloud thinning (CCT) is to reduce cirrus cloud optical 
depth by increasing the heterogeneous nucleation via seeding cirrus 
clouds with an optimal concentration of ice nucleating particles, 
which might cause larger ice crystals and rapid fallout, resulting in 
reduced lifetime and coverage of cirrus clouds (Muri et  al., 2014;

Gasparini et al., 2017; Lohmann and Gasparini, 2017; Gruber et al., 
2019). CCT  aims to achieve the opposite effect of contrails that 
increase cirrus cover and cause a small positive ERF (Section 7.3). 
A high-resolution modelling study of CCT over a limited area of the 
Arctic suggested that cirrus seeding causes a decrease in ice crystal 
number concentration and a reduction in mixed-phase cloud cover, 
both of which cause a cooling effect (Gruber et al., 2019).
Under present-day climate, cirrus clouds exerts a  net positive 
radiative forcing of about 5 W m–2 (Gasparini and Lohmann, 2016; 
Hong et al., 2016), indicating a maximum cooling potential of the 
same magnitude if all cirrus cloud were removed from the climate 
system. However, modelling results show a  much smaller cooling 
effect of CCT. For the optimal ice nuclei seeding concentration and 
globally non-uniform seeding strategy, a net negative cloud radiative

forcing of about 1 to 2 W m–2 is achieved (Storelvmo and Herger, 
2014; Gasparini et  al., 2020). A  few studies find that no seeding 
strategy could achieve a significant cooling effect, owing to complex 
microphysical mechanisms limiting robust climate responses to 
cirrus seeding (Penner et al., 2015; Gasparini and Lohmann, 2016). 
A higher than optimal concentration of ice nucleating particles could 
also result in over-seeding that increases rather than decreases cirrus 
optical thickness (Storelvmo et  al., 2013; Gasparini and Lohmann, 
2016). Thus, there is low confidence  in the cooling effect of CCT, due 
to limited understanding of cirrus microphysics, its interaction with 
aerosols, and the complexity of seeding strategy.
Relative to the high-GHG climate and for the same amount of global 
cooling, CCT is simulated to cause an increase in global precipitation 
compared to shortwave-based SRM options such as SAI and MCB

(Duan et al., 2018; Muri et al., 2018) because of the opposing effects of 
CCT and increased CO 2 on outgoing longwave radiation (Kristjánsson 
et  al., 2015; Jackson et  al., 2016). Combining SAI and CCT has 
suggested that GHG-induced changes in global mean temperature 
and precipitation can be simultaneously offset (Cao et al., 2017), but 
there is low confidence  in the applicability of this result to the real 
world owing to the large uncertainty in simulating aerosol forcing 
and the complex cirrus microphysical processes.

629
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 444.6.3.3.4 Surface-based albedo modification
Surface-based albedo modification could, in principle, achieve 
a negative radiative forcing of a few W m–2 by enhancing the albedo 
of the ocean surface (Gabriel et  al., 2017; Kravitz et  al., 2018). 
However, the technology does not exist today to increase ocean 
albedo at large scale. An increase in crop albedo or roof albedo in 
urban areas could help to reduce warming in densely populated and 
important agricultural regions, but the effect would be limited to 
local scales and ineffective at counteracting global warming (Crook 
et al., 2015; Zhang et al., 2016). Large changes in desert albedo could 
in principle result in substantial global cooling, but would severely 
alter the hydrological cycle (Crook et al., 2015).
In addition to above-mentioned SRM methods, a  number of local 
intervention methods have been proposed to limit the loss of

cryosphere, such as applying reflective materials over sea ice (Field 
et  al., 2018), pumping seawater on top of the ice surface (Desch 
et  al., 2017; Zampieri and Goessling, 2019), depositing a  massive 
amount of snow over ice sheets (Feldmann et al., 2019), and blocking 
warm seawater from reaching glaciers (J.C. Moore et  al., 2018). 
The  stabilization of ice sheets through local intervention methods 
would reduce sea level commitment (Section 9.6.3.5). However, these 
methods are subject to large uncertainty concerning their feasibility 
and effectiveness, and their effects would be largely localized.
4.6.3.3.5 Detectability of climate response to solar 
radiation modification
Internal variability could mask the response to solar radiation 
modification (SRM)-related forcing in the near term (Section 4.6.3.1). 
A detection of the global scale climate system response to stratospheric 
sulphate aerosol injection will  likely  require a  forcing of the size

produced by the 1991 Mount Pinatubo eruption (Robock et al., 2010). 
In model simulations of where 5 Tg SO 2 is injected into the stratosphere 
continuously (roughly one fourth of the 1991 Pinatubo eruption per 
year) under RCP 4.5, it is shown that, relative to the high-GHG world 
without SRM, the effect of SRM on global temperature and precipitation 
is detectable after one to two decades (Bürger and Cubasch, 2015; 
Lo et al., 2016) which is similar to the time scale for the emergence of 
GSAT trends due to strong mitigation (Section 4.6.3.1). The detection 
time is sensitive to detection methods and filtering techniques 
(Lo et  al., 2016). An  analysis using GLENS simulation (MacMartin 
et  al., 2019) compares response in temperature, precipitation, and 
precipitation minus evapotranspiration (P-E) between a climate state 
with GHG-induced 1.5°C global mean temperature change and that 
with the same global mean temperature but under RCP4.5 emissions

and a limited deployment of SO 2 injection. It is found that at grid-scale, 
difference in climate response between these two climate states are 
not detectable by the end of this century. However, for higher emissions 
scenarios of the RCP8.5 and correspondingly larger SRM deployment 
for maintaining the same global mean temperature change of 1.5°C, 
the regional differences are detectable before the end of the century. 
In addition to surface temperature and precipitation, observations of 
aerosol burden and temperature in the stratosphere via the deployment 
of stratospheric aerosol observing system might facilitate the detection 
of climate response to SAI.4.6.3.3.6 Climate response to termination of solar 
A hypothetical, sudden and sustained termination of SRM in a world 
with high GHG concentrations has been simulated to cause climate 
rebound effects such as rapid increase in global temperature,

precipitation, and sea level, and rapid reduction in sea ice area (Jones 
et al., 2013; McCusker et al., 2014; Crook et al., 2015; Muri et al., 
2018). Model simulations also show reduced precipitation over 
land areas in the first few years following termination, indicating 
general drying that would exacerbate the effects of rapid warming 
(McCusker et al., 2014). A sudden and sustained termination of SRM 
is also expected to weaken carbon sinks, accelerating atmospheric 
CO 2 accumulation and warming (Tjiputra et  al., 2016; Muri et  al., 
2018; Plazzotta et al., 2019). A gradual phase-out of SRM combined 
with mitigation and CDR could reduce the large warming rates 
from sudden SRM termination (MacMartin et  al., 2014; Keith and 
MacMartin, 2015; Tilmes et al., 2016), though this would be limited 
by how rapidly emission reductions can be scaled up (Ekholm and 
Korhonen, 2016).
4.6.3.3.7 Synthesis of the climate response to solar 

Modelling studies have consistently shown that SRM has the potential 
to offset some effect of increasing GHGs on global and regional 
climate ( high confidence ), but there would be substantial residual or 
overcompensating climate change at the regional scale and seasonal 
time scale ( high confidence ). Large uncertainties associated with 
aerosol–cloud–radiation interactions persist in our understanding 
of climate response to aerosol-based SRM options. For the same 
amount of global mean cooling, different SRM options would cause 
different patterns of climate change ( medium confidence ). Modelling 
studies suggest that it is conceptually possible to achieve multiple 
climate policy goals by optimally designed SRM strategies.
The effect of SRM options on global temperature and precipitation 
response would be detectable after one or two decades, which is 
similar to the time scale for the detection of strong mitigation. There

is high confidence that a sudden and sustained termination of a high 
level of SRM against a high-GHG background would cause a rapid 
increase in temperature at a rate that far exceeds that projected for 
climate change without SRM. However, a gradual phase-out of SRM 
combined with mitigation and CDR would more likely than not  avoid 
large rates of warming .
4.7 Climate Change Beyond 2100
This section assesses changes in climate beyond 2100. An advance 
since AR5 is the availability of ESM results for scenarios beyond 2100 
and for much longer stabilisation simulations compared with analysis 
predominantly based on Earth system models of intermediate 
complexity (EMICs) at the time of AR5 (e.g., Eby et al., 2013; Zickfeld 
et al., 2013). Long-term commitment of sea level rise due to thermal 
expansion and ice-sheet loss is assessed in Chapter 9 (Section 9.6.3.5 
and Figure  9.30). Here we assess projections of GSAT, global

630
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4precipitation, and Arctic sea ice. Uncertainties relating to potential 
long-term changes in AMOC are treated in Section 9.2.3.1.
On multi-century time scales it is common to explore changes that 
are due to long-term commitment. Here we differentiate between:
• Committed emissions due to infrastructure.  Infrastructure 
that causes greenhouse gas emissions cannot be changed straight 
away leading to a commitment from existing infrastructure that 
some emissions will continue for a  number of years into the 
future (Davis and Socolow, 2014; C.J. Smith et al., 2019). Further 
consideration of this aspect of commitment will be assessed 
by WGIII.
• Climate response to constant emissions.  Some of the scenario 
extensions beyond 2100 make assumptions about constant 
emissions (either positive or negative). Section 4.7.1 will assess 
changes in climate under scenario extensions beyond 2100.

• Committed climate change to constant atmospheric 
composition. There is widespread literature on how the climate 
continues to change after stabilisation of radiative forcing. 
This includes diagnosing the long-term climate response to 
a doubling of CO 2 (ECS, Chapter 7). Since AR5, more GCMs have 
run stabilized forcing simulations for many centuries allowing 
new insights into their very long-term behaviour (Section 7.4.3).
• Committed response to zero emissions. How climate would 
continue to evolve if all emissions ceased. The SR1.5 assessed 
changes in climate if emissions of all greenhouse gases and 
aerosols ceased. Section 4.7.2 assesses new results considering 
cessation of CO 2-only emissions which forms a significant term in 
calculating remaining carbon budgets.
• Irreversibility. Some changes do not revert if the forcing is 
removed, leaving a committed change to the system. Section 4.7.2

assesses changes in the Earth system which may be irreversible.• Abrupt changes.  If a  tipping point in the climate system is 
passed, then some elements may continue to respond if the 
forcing which caused them is removed. Section  4.7.2 assesses 
the potential for abrupt changes in the Earth system.
4.7.1 Commitment and Climate Change Beyond 2100
4.7.1.1 Climate Change Following Zero Emissions
The zero emissions commitment (ZEC) is the climate change 
commitment that would result, in terms of projected GSAT, from 
setting carbon dioxide (CO 2) emissions to zero. It is determined by both 
inertia in physical climate system components (ocean, cryosphere, 
land surface) and carbon cycle inertia (see Annex VII). In its widest 
sense it refers to emissions of all compounds including greenhouses 
gases, aerosols and their pre-cursors. A specific sub-category of zero 
emissions commitment is the zero CO 2 emissions commitment, which

refers to the climate system response to a cessation of anthropogenic 
CO 2 emissions excluding the impact of non-CO 2 forcers. Assessment 
of remaining carbon budgets requires an assessment of zero CO 2 
emissions commitment as well as of the transient climate response 
to cumulative carbon emissions (TCRE; Section 5.5.2).
There is an offset of continued warming following cessation 
of emissions by continued CO 2 removal by natural sinks ( high 
confidence ) (e.g.,  Matthews and Caldeira, 2008; Solomon et  al., 
2009; Joos et  al., 2013; Ricke and Caldeira, 2014). Some models 
continue warming by up to 0.5°C after emissions cease at 2°C of 
warming (Frölicher et al., 2014; Frölicher and Paynter, 2015; Williams 
et al., 2017), while others simulate little to no additional warming 
(Nohara et  al., 2015). In SR1.5, the available evidence indicated 
that past CO 2 emissions do not commit to substantial further 
Time (Years)−120−100−80−60−40−200Δ Atmospheric CO2 (ppm)

Time (Years)−1.0−0.8−0.6−0.4−0.20.00.20.40.6Zero Emissions Commitment (oC)(a) (b)
Figure 4. 39 | Zero emissions commitment (ZEC).  Changes in (a) atmospheric CO 2 concentration and (b) evolution of global surface air temperature (GSAT) following 
cessation of CO 2 emissions branched from the 1% per year experiment after emissions of 1000 Pg C (Jones et al., 2019; MacDougall et al., 2020). ZEC is the temperature 
anomaly relative to the estimated temperature at the year of cessation. ZEC 50 is the 20-year mean GSAT change centred on 50 years after the time of cessation (see Table 4.8) – 
this period is marked with the vertical dotted lines. Multi-model mean is shown as thick black line, individual model simulations are in grey. Further details on data sources and 
processing are available in the chapter data table (Table 4.SM.1).

631
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44warming (Allen et al., 2018). A ZEC close to zero was thus applied 
for the computation of the remaining carbon budget (Rogelj et al., 
2018b). However, the  available literature consisted of simulations 
from a  small number of models using a  variety of experimental 
designs, with some simulations showing a  complex evolution of 
temperature following cessation of emissions (e.g., Frölicher et al., 
2014; Frölicher and Paynter, 2015; Williams et al., 2017).
Here we draw on new simulations to provide an assessment of ZEC 
using multiple ESMs (Jones et  al., 2019) and EMICs (MacDougall 
et al., 2020). Figure 4.39 shows results from 20 models that simulate 
the evolution of CO 2 and the GSAT response following cessation of 
CO 2 emissions for an experiment where 1000 PgC is emitted during 
a 1% per year CO 2 increase. All simulations show a strong reduction in

atmospheric CO 2 concentration following cessation of CO 2 emissions 
in agreement with previous studies and basic theory that natural 
carbon sinks will persist. Therefore, there is very high confidence that 
atmospheric CO 2 concentrations would decline for decades if CO 2 
emissions cease.  Temperature evolution in the 100 years following 
cessation of emissions varies by model and across time scales, with 
some models showing declining temperature, others having ZEC close 
to zero, and others showing continued warming following cessation 
of emissions (Figure  4.39). The GSAT response depends on the 
balance of carbon sinks and ocean heat uptake (MacDougall et al., 
2020). The 20-year average GSAT change 50 years after the cessation 
of emissions (ZEC 50) is summarized in Table 4.8. The mean value of 
ZEC 50 is –0.079°C, with 5–95% range –0.34°C–0.28°C. There is no

strong relationship between ZEC 50 and modelled climate sensitivity (neither ECS nor TCR; MacDougall et al., 2020). It is therefore  likely  
that the absolute magnitude of ZEC 50 is less than 0.3°C, but we 
assess low confidence in the sign of ZEC on 50-year time scales. This 
is small compared with natural variability in GSAT.
4.7.1.2 Change in Global Climate Indices Beyond 2100
This subsection assesses changes in global climate indices out to 
2300 using extensions of the SSP scenarios (Meinshausen et al., 2020) 
and literature based on extensions to the RCP scenarios from CMIP5 
(Meinshausen et al., 2011), which differ from the SSPs despite similar 
labelling of global radiative forcing levels (Section 4.6.2). Meinshausen 
et al. (2020) describe the extensions to the SSP scenarios, which differ 
slightly from the ScenarioMIP documentation (O’Neill et al., 2016). 
A simplified approach across scenarios reduces emissions such that

after 2100, land-use CO 2 emissions are reduced to zero by 2150; any 
net negative fossil CO 2 emissions are reduced to zero by 2200, and 
positive fossil CO 2 emissions are reduced to zero by 2250. Non-CO 2 
fossil fuel emissions are also reduced to zero by 2250 while land-
use-related non-CO 2 emissions are held constant at 2100 levels. The 
extensions are created up to the year 2500, but ESM simulations 
have only been requested, as part of the CMIP6 protocol, to run 
to 2300. As a  result, unlike the RCP8.5 extension, SSP5-8.5 sees 
a decline in CO 2 concentration after 2250, but the radiative forcing 
level is similar, reaching approximately 12 W m–2 during most of the 
extension. Both SSP1-2.6 and SSP5-3.4-OS decrease radiative forcing 
after 2100. SSP5-3.4-OS is designed to return to the same level of 
forcing as SSP1-2.6 during the first half of the 22nd century. Because 
relatively few CMIP6 ESMs have submitted results beyond 2100,

GSAT projections using the MAGICC7 emulator (see Cross-Chapter 
Box 7.1) are also shown here.
Changes in climate at 2300 have impacts and commitments 
beyond this timeframe ( high confidence ). Sea level rise may 
exceed 2 m on millennial time scales even when warming is 
limited to 1.5°C–2°C, and tens of metres for higher warming levels 
(Table 9.10). Randerson et al. (2015) showed increasing importance 
on carbon cycle feedbacks of slow ocean processes, Mahowald 
et  al. (2017) showed the long-lasting legacy of land-use effects 
and J.K. Moore et al. (2018) show how changes in Southern Ocean 
winds affect nutrients and marine productivity well beyond 2300. 
Clark et al. (2016) show that physical and biogeochemical impacts 
of 21st century emissions have a potential committed legacy of at 
least 10,000 years.Table 4. 8 | The 20-year average GSAT change 50 years after the cessation 
of emissions (ZEC50).  Displayed are ZEC50 estimated from eleven ESMs (top) and 
nine EMICs (bottom).

Model ZEC 50 (°C)
ACCESS-ESM1.5 0.01
CanESM5 –0.14
CESM2 –0.31
CNRM-ESM2-1 0.06
GFDL-ESM2M –0.27
GFDL-ESM4 –0.21
GISS-E2-1-G –0.15
MIROC-ES2L –0.08
MPI-ESM1.2-LR –0.27
NorESM2-LM –0.33
UKESM1-0-LL 0.28
Bern3D-LPX 0.01
DCESS1.0 0.06
CLIMBER-2 –0.07
IAPRAS 0.28
LOVECLIM 1.2 –0.04
MESM 0.01
MIROC-lite –0.06
PLASM-GENIE –0.36
UVic ESCM 2.10 0.03

632
4.7.1.2.1 Global surface air temperature
Both CMIP6 and CMIP5 results show that global temperature beyond 
2100 is strongly dependent on scenario, and the difference in GSAT 
projections between high- and low-emissions scenarios continues to 
increase ( high confidence ). Under the extended RCP2.6 (Caesar et al., 
2013) and SSP1-2.6 scenarios, where CO 2 concentration and radiative 
forcing continue to decline beyond 2100, GSAT stabilizes during the 
21st century before decreasing and remaining below 2°C until 2300, 
except in some of the very high climate-sensitivity ESMs, which 
project GSAT to stay above 2°C by 2300 (Figure 4.40). Under RCP8.5, 
regional temperature changes above 20°C have been reported in 
multiple models over high-latitude land areas (Caesar et al., 2013; 
Randerson et  al., 2015). Non-CO 2 forcing and feedbacks remain

important by 2300 ( high confidence ). Randerson et al. (2015) found 
that 1.6°C of warming by 2300 came from non-CO 2 forcing alone 
in RCP8.5, and Rind et  al. (2018) show that regional forcing  from 
aerosols can have notable effects on ocean circulation on centennial time scales. High latitude warming led to longer growing seasons 
and increased vegetation growth in the CESM1 model (Liptak 
et al., 2017), and Burke et al. (2017) found that carbon release from 
permafrost areas susceptible to this warming may amplify future 
climate change by up to 17% by 2300.
Too few CMIP6 models performed the extension simulations to allow 
a  robust assessment of GSAT projection, and some of those which 
did had higher than average climate sensitivity values. Therefore, we 
base our assessment of GSAT projections (Table 4.9) on the MAGICC7 
emulator calibrated against assessed GSAT to 2100 (Section  4.3.4, 
Cross-Chapter Box 7.1). Because the emulator approach has not been

evaluated in depth up to 2300 in the same way as it has up to 2100 
(Cross-Chapter Box 7.1) we account for possible additional uncertainty 
by assessing the 5–95% range from MAGICC as likely  instead of very 
likely . It is therefore likely  that GSAT will exceed 2°C above that of 
the period 1850–1900 at the year 2300 in the extended SSP scenarios 
SSP2-4.5, SSP3-7.0 and SSP5-8.5  (Figure  4.40). For  SSP1-2.6 and 
Figure 4. 40 | Simulated climate changes up to 2300 under the extended SSP scenarios.  Displayed are (a) projected global surface air temperature (GSAT) change, 
relative to 1850–1900, from CMIP6 models (individual lines) and MAGICC7 (shaded plumes); (b) as (a) but zoomed in to show low-emissions scenarios; (c) global land 
precipitation change; and (d) September Arctic sea ice area. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

633
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44SSP1-1.9, mean warming at 2300 is 1.5°C and 0.9°C respectively. 
GSAT differences between SSP5-3.4-overshoot and SSP1-2.6 peak 
during the 21st century but decline to less than about 0.25°C after 
2150 ( medium confidence ).
To place the temperature projections for the end of the 23rd century 
into the context of paleo temperatures, GSAT under SSP2-4.5 ( likely  
2.3°C–4.6°C higher than over the period 1850–1900) has not been 
experienced since the Mid Pliocene, about three million years ago. 
GSAT projected for the end of the 23rd  century under SSP5-8.5 
(likely   6.6°C–14.1°C higher than over the period 1850–1900) 
overlaps with the range estimated for the Miocene Climatic Optimum 
(5°C–10°C higher) and Early Eocene Climatic Optimum (10°C–18°C 
higher), about 15 and 50 million years ago, respectively ( medium 
confidence ) (Chapter 2).
4.7.1.2.2 Global land precipitation

Global land precipitation will continue to increase in line with GSAT 
under high emissions scenarios ( medium confidence ). Precipitation 
changes over land show larger variability and a  less clear signal 
than global total precipitation. Caesar et  al. (2013) showed that 
under the CMIP5 extension simulations, HadGEM2-ES projected 
global land precipitation to remain roughly the same in RCP2.6, to 
increase by about 4% in RCP4.5 and to increase by about 7% in 
RCP8.5. Their results showed global precipitation increasing linearly 
with temperature while radiative forcing increases, but then more 
quickly if forcing is stabilized or reduced. This backs up findings of an 
intensification of the hydrological cycle following CO 2 decrease which 
has been attributed to a build-up of ocean heat (Wu et al., 2010), and 
to a fast atmospheric adjustment to CO 2 radiative forcing (Cao et al., 
2011). Figure  4.40 shows that global land precipitation increases

in CMIP6 models until 2300 for SSP5-8.5 but stabilizes in SSP1-2.6 
and SSP5-3.4-OS. SSP1-2.6 and SSP5-3.4-OS are not distinguishable 
in behaviour of projected global land precipitation after 2100.
4.7.1.2.3 Arctic sea ice
Chapter  9 assesses with high confidence  that on decadal and 
longer time scales, Arctic summer sea ice area will remain highly 
correlated with global mean temperature until the summer sea ice 
has vanished (Section  9.3.1.1). This means that Arctic sea ice will 
continue to decline in scenarios of continued warming but will begin 
to recover in scenarios where GSAT begins to decrease. Under the CMIP5 extension simulations, minimum (September) Arctic sea ice 
area began to recover for most models under RCP2.6 out to 2300, 
while RCP4.5 and RCP8.5 extensions became ice-free in September 
(Hezel et al., 2014; Bathiany et al., 2016). They also found increasingly 
strong winter responses under continued warming such that under

the RCP8.5 extension, the Arctic became ice-free nearly year-round 
by 2300. Consistent with the assessment in Section  9.3.1.1 that 
Arctic sea ice area is correlated with GSAT, CMIP6 projections to 2300 
show partial sea ice recovery by 2300 in SSP1-2.6 in line with GSAT 
(Figure 4.40), with one model (MRI-ESM2-0) showing near complete 
recovery to present-day values. SSP1-2.6 and SSP5-3.4-OS are not 
distinguishable in behaviour of Arctic sea ice in these models after 
2100. SSP5-8.5 remains ice-free in September up to 2300.
4.7.2 Potential for Abrupt and Irreversible 
Similar to AR5 and SROCC, AR6 defines an abrupt climate change as 
a large-scale abrupt change in the climate system that takes place 
over a few decades or less, persists (or is anticipated to persist) for at 
least a few decades and causes substantial impacts in human and/or 
natural systems (Glossary). Further, AR6 considers such a perturbed

state of a  dynamical system as irreversible on a  given time scale, 
if the recovery time scale from this state due to natural processes 
takes substantially longer than the time scale of interest (Glossary). 
The AR6 adopts the related definition of a tipping point as a critical 
threshold beyond which a system reorganizes, often abruptly and/
or irreversibly, and a tipping element as a component of the Earth 
system that is susceptible to a tipping point (Glossary). Tipping points 
may involve global or regional climate changes from one stable state 
to another stable state or to changes that occur faster than the rate 
of change of forcing (Alley et al., 2003) and include shifts from one 
equilibrium state to another and other responses of the climate system 
to external forcing (Section  1.2.4.2). While reversibility has been 
defined alternatively in the literature with respect to the response 
specifically to idealized CO 2 forcing and generally GSAT change, AR6

considers both definitions synonymous, because it has been widely 
demonstrated that the GSAT change is reversible in models with 
respect to CO 2 with a several-year lag (Boucher et al., 2012).
Abrupt and irreversible changes in the climate system are assessed 
across multiple chapters in AR6. This section provides a cross-chapter 
synthesis of these assessments as an update to Table  12.4 in AR5 
and Table 6.1 in SROCC. Understanding of abrupt climate change and 
irreversibility has advanced considerably since AR5 with many of the 
projected changes in proposed Tipping Elements having grown more 
confident (Table 4.10). Many aspects of the physical climate changes 
induced by GHG warming previously demonstrated to be reversible 
in a single model have been confirmed in multiple models (Boucher 
et al., 2012; Tokarska and Zickfeld, 2015) with others such as sea level 
rise or terrestrial ecosystems confirmed to continue to respond on long

time scales (Clark et al., 2016; Zickfeld et al., 2017; Pugh et al., 2018).
The Carbon Dioxide Removal Model Intercomparison Project 
(CDR-MIP; Keller et  al., 2018) comprises a  set of 1% ramp-up, 
ramp-down simulations aimed at establishing a  multi-model Table 4 .9 | Change of global surface air temperature at 2300.  Displayed are 
the median and 5–95% range of GSAT change at 2300 relative to 1850–1900 for the 
six scenarios used with MAGICC7.
Scenario Median (°C) 5–95% Range (°C)
SSP5-8.5 9.6 6.6–14.1
SSP3-7.0 8.2 5.7–11.8
SSP2-4.5 3.3 2.3–4.6
SSP5-3.4-OS 1.6 1.1–2.2
SSP1-2.6 1.5 1.0–2.2
SSP1-1.9 0.9 0.6–1.4

634
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4assessment of reversibility of Earth system components. Preliminary 
results from CDRMIP are presented in Section  4.6.3. Results from 
the SSP5-3.4-Overshoot scenario and other quantities of climate 
change at the same CO 2 level before and after overshoot are 
assessed in Section  4.6.2. Forcing reversal is followed by reversal 
of ocean surface and land temperature along with land and ocean 
precipitation, snow cover, and Arctic sea ice with a lag of a few years 
to decades (Table 4.10). Other tipping elements have much longer 
time scales of reversibility from decades to millennia. Drijfhout et al. 
(2015) provided an assessment of 13 regional mechanisms of abrupt 
change, finding abrupt changes in sea ice, oceanic flows, land ice, 
and terrestrial ecosystem response, although with little consistency 
among the models. The potential for abrupt changes in ice sheets,

the AMOC, tropical forests, and ecosystem responses to ocean 
acidification were also recently reviewed by (Good et  al., 2018). They found that some degree of irreversible loss of the West Antarctic 
Ice Sheet (WAIS) may have already begun, that tropical forests are 
adversely affected by drought, and rapid development of aragonite 
undersaturation at high latitudes affecting calcifying organisms.
New since AR5 is the fundamental recognition in SRCCL and in 
this Report (Chapter 5) that projected changes in forests strongly 
depend on the human disturbance and that tropical forest dieback 
in the absence of disturbance is largely driven by the increased 
potential for drought, while that in boreal forests includes both 
thermal and hydrological factors (Drijfhout et al., 2015). For some 
proposed tipping elements, the role of seasonal change has become 
better understood. For example, the lack of a tipping point in the

reduction of summer Arctic sea ice area (Stroeve and Notz, 2015) 
has been further substantiated. The role of abrupt change at the 
Table 4. 10 | Cross-chapter assessment updating AR5 and SROCC of components in the Earth system that have been proposed as susceptible to tipping 
points/abrupt change, irreversibility, projected 21st century change, and overall change in assessment from previous IPCC reports.  Also provided are 
confidence levels and, in parentheses, the main section(s) of this Report in which proposed tipping elements are assessed.
Earth System 
Component/Tipping 
ElementPotential Abrupt 
Climate Change?Irreversibility if Forcing 
Reversed (Time Scales 
Indicated)Projected 21st Century Change Under 
Continued WarmingChange in 
Global Monsoon 
(4.5.1.5; 8.6)Yes, under AMOC 
collapse, medium 
confidenceReversible within years to 
decades, medium confidenceMedium confidence  in global monsoon increase; medium

confidence  in Asian-African strengthening and North 
American weakeningMore lines of evidence 
than AR5
Tropical Forest  
(5.4.8; 8.6.2)Yes, low confidenceIrreversible for multi-decades,  
medium confidenceMedium confidence  of increasing vegetation carbon storage 
depending on human disturbanceMore confident rates 
than AR5
Boreal Forest  
(5.4.8)Yes, low confidenceIrreversible for multi-decades,  
medium confidenceMedium confidence  in offsetting lower latitude dieback and 
poleward extension depending on human disturbanceMore confident rates 
than AR5
Permafrost Carbon  
(5.4.8)Yes, high confidenceIrreversible for centuries,  
high confidenceVirtually certain  decline in frozen carbon; low confidence  
in net carbon changeMore confident rates 
than SROCC
Arctic Summer Sea Ice 
(4.3.2; 4.6.2.1; 9.3.1)No, high confidenceReversible within years to 
decades,  high confidenceLikely complete lossMore specificity than 
SROCC
Arctic Winter Sea Ice

(4.3.2; 9.3.1)Yes, high confidenceReversible within years to 
decades,  high confidenceHigh confidence  in moderate winter declinesMore specificity than 
SROCC
Antarctic Sea Ice  
(9.3.2)Yes, low confidence Unknown,  low confidence Low confidence  in moderate winter and summer declinesImproved CMIP6 
simulation
Greenland Ice Sheet  
(9.4.1)No, high confidenceIrreversible for millennia,  
high confidenceVirtually certain  mass loss under all scenariosMore lines of evidence 
than SROCC
West Antarctic Ice Sheet 
and Shelves (9.4.2; Box 9.4)Yes, high confidenceIrreversible for decades to 
millennia, high confidenceLikely  mass loss under all scenarios; deep uncertainty  
in projections for above 3°CAdded deep uncertainty 
at GWL >3°C
Global Ocean Heat Content 
(4.5.2.1; 4.6.2.1; 9.2.2; 
CCBox 7.1)No, high confidenceIrreversible for centuries,  
very high confidenceVery high confidence  oceans will continue to warmBetter consistency with 
ECS/TCR
Global Sea-Level Rise

(4.6.2.1; 4.6.3.2; 9.6.3.5; 
Box 9.4)Yes, high confidenceIrreversible for centuries,  
very high confidenceVery high confidence  in continued rise; deep uncertainty  
in projections above 3°CAdded deep uncertainty 
at GWL >3°C
AMOC  
(4.6.3.2; 8.6.1; 9.2.3.1)Yes, medium confidenceReversible within centuries, 
high confidenceVery likely decline; medium confidence of no collapseMore lines of evidence 
than SROCC
Southern MOC  
(9.2.3.2)Yes, medium confidenceReversible within decades to 
centuries,  low confidenceMedium confidence  in decrease in strengthMore lines of evidence 
than SROCC
Ocean Acidification 
(4.3.2.5; 5.4.2; 5.4.4)Yes, high confidenceReversible at surface; irreversible 
for centuries to millennia at 
depth, very high confidenceVirtually certain  to continue with increasing CO 2; 
likely polar aragonite undersaturationMore lines of evidence 
than SROCC
Ocean Deoxygenation 
(5.3.3.2)Yes, high confidenceReversible at surface; irreversible 
for centuries to millennia at

depth, medium confidenceMedium confidence  in deoxygenation rates and 
increased hypoxiaImproved CMIP6 

635
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44edges (Bathiany et al., 2020) has also been clarified, as has been 
the importance of distinguishing summer from winter mechanisms 
and associated abruptness, because ice area reduces gradually in 
summer, but not necessarily in winter (Bathiany et al., 2016). For 
other tipping elements including AMOC (Section  9.2.3.1), mixed 
layer depth (Section 9.2.1.3), and sea level rise (Section 9.6.3.5), 
an increase in the diversity of model structure and sensitivity to 
multiple factors has led to a better understanding of the complexity 
of the problem, with some increase in assessed uncertainty and an 
assessed deep uncertainty (Glossary) related to projected sea level 
rise with global warming levels above 3°C (Section 9.6.3.5). In still 
other cases such as Antarctic sea ice (Section 9.3.2) and Southern 
Ocean Meridional Overturning Circulation (MOC; Section 9.2.3.1),

uncertainty remains high. Finally, it has also been postulated that 
models may be prone to being too stable (Valdes, 2011) based on 
the limitations of models as well as other lines of evidence such 
paleo-evidence of abrupt events (Dakos et  al., 2008; Klus et  al., 
2018; Sime et al., 2019).
4.8 Low-likelihood, High-warming Storylines
Previous IPCC assessments have primarily assessed the projected 
likely  range of changes (e.g., Collins et al., 2013; see also Box 1.1). 
The focus on the likely  range partly results from the design of model 
intercomparison projects that are not targeted to systematically 
assess the upper and lower bounds of projections, which in principle 
would require a  systematic sampling of structural and parametric 
model uncertainties. The upper and lower bounds of model 
projections may further be sensitive to the missing representation 
of processes and to deep uncertainties about aspects of the climate 
system (Section 1.2.3.1).

However, a  comprehensive risk assessment requires taking into 
account high potential levels of warming whose likelihood is low, 
but potential impacts on society and ecosystems are high (Xu and 
Ramanathan, 2017; Sutton, 2018). Climate-related risks have been 
argued to increase with increasing levels of global warming even if 
their likelihood decreases (O’Neill et al., 2017). Thus, it has recently 
been argued that an assessment that is too narrowly focused on 
the likely  range potentially ignores the changes in the physical 
climate system associated with the highest risks (Sutton, 2018; 
see Section 1.4.4.1).
Given that the CMIP experiments can be considered ensembles of 
opportunity that are not designed for probabilistic assessments, 
alternative approaches such as physically plausible high-impact 
scenarios (Sutton, 2018) or storylines have been suggested to 
investigate the tail of the distribution (Lenderink et al., 2014; Zappa

and Shepherd, 2017; Kjellström et al., 2018; Shepherd et al., 2018; 
see Section  1.4.4). Such storylines informed by a  combination of 
process understanding, model evidence, and paleo information can 
be used for risk assessment and adaptation planning to test how 
well adaptation strategies would cope if the impacts of climate 
change were more severe than suggested by the likely  model range 
(Section  1.4.4). Note that by definition the lower bound of the 
likely  model range (Box  4.1) is equally likely as the upper bound. However,  low-warming storylines are not specifically assessed in 
this section to focus on storylines associated with highest risks. This 
section further focuses on storylines of high and very high global 
warming levels along with their manifestation in global patterns 
of temperature and precipitation changes. However, this does not 
account for the largest potential changes at regional levels, which

would require taking into account storylines of regional changes 
dependent on changes in atmospheric circulation, land–atmosphere 
interactions, and regional to local feedbacks.
This section adopts an approach suggested in Sutton (2018). Since 
changes in temperature and precipitation tend to increase with 
the level of warming (Section 4.6.1), low-likelihood, high-warming 
storylines are here illustrated for a level of warming consistent with 
the upper bound of the assessed very likely  range (Section 4.3.4) and 
for a level of warming above the very likely  range. ECS and TCR are 
the dominant sources of uncertainty in projections of future warming 
under moderate to strong emissions scenarios (Section 7.5.7). Thus, 
a very high level of warming may occur if ECS and TCR are close to 
or above the upper bound of the assessed very likely  range, which, 
to agree with historical trends, would require a  strong historical

aerosol cooling and/or strong SST pattern effects, combined with 
strong positive cloud feedback and substantial biases in paleoclimate 
temperature reconstructions, each of which are assessed as either 
unlikely  or very unlikely , though not ruled out (Section 7.5.5).
For SSP1-2.6, the warming consistent with the upper bound of the 
assessed very likely  range corresponds to a  warming of 1.5°C in 
2081–2100 relative to 1995–2014 and 2.4°C relative to 1850–1900 
(Section 4.3.4), a warming well above the 2°C warming level even in 
SSP1-2.6. Based on different lines of evidence, Figure 4.41 illustrates 
by how much such a  low-likelihood, high-warming storyline 
exceeds the warming pattern consistent with the assessed best 
estimate GSAT warming of 0.9°C relative to 1995–2014. The  first 
estimate  (Figure  4.41, second row) is based on the assumption 
that the multi-model mean temperature pattern scales linearly with 
global mean warming. While linear scaling provides an appropriate

approximation for changes in temperatures patterns at lower levels 
of warming (Section 4.2.4), this assumption cannot easily be tested 
for an extrapolation to higher levels of warming. Thus, a  second 
estimate (Figure 4.41, third row) is based on the average of the five 
models that simulate a GSAT warming most consistent with the upper 
bound of the assessed  very likely  range (Section 4.3.4 and Box 4.1; 
note some of the models share components). The two estimates 
for the annual mean temperature pattern for a  low-likelihood, 
high-warming storyline consistently show a  warming pattern that 
substantially exceeds the best estimate warming pattern in most 
regions except around the North Atlantic and the parts of the Arctic. 
Pattern scaling suggests more than 50% warming above the best 
estimate, with 2°C–3°C warming over much of Eurasia and North 
America and more than 4°C warming relative to 1995–2014 over the

Arctic (Figure 4.41c). The other approach based on five models shows 
less warming than the best estimate and even larger area of cooling 
in the North Atlantic but more warming than the best estimate over 
much of the tropical Pacific, Atlantic, around Antarctica and other the 
land regions (Figure 4.41e).

Figure  4.41 | High-warming storylines for changes in annual mean temperature. (a, b)  Changes in 2081–2100 relative to 1995–2014 consistent with the assessed 
best global surface air temperature (GSAT) estimate (0.9°C and 3.5°C relative to 1995–2014 for SSP1-2.6 and SSP5-8.5, respectively). The CMIP6 multi-model mean is linearly 
pattern-scaled to the best GSAT estimate. (c–h)  Annual mean warming above the best estimate, relative to panels (a) and (b) respectively; note the different colour bar in a high 
and very high-warming storyline for 2081–2100. (c, d)  Multi-model mean warming pattern scaled to very high GSAT level corresponding to the upper bound of the assessed 
very likely  range (4.8°C for SSP5-8.5 and 1.5°C for SSP1-2.6; see Section 4.3.4). (e, f)  Average of five models with high GSAT warming nearest to the upper estimate of the very

likely range (CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3 for SSP1-2.6 and ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-
1-HRfor SSP5-8.5); (g, h)  Average of four and five models, respectively (ACCESS-CM2, HadGEM3-GC31-LL, HadGEM3-GC31-MM, UKESM1-0-LL for SSP1-2.6 and CanESM5, 
CanESM5-CanOE, HadGEM3-GC31-LL: HadGEM3-GC31-MM, UKESM1-0-LL for SSP5-8.5) projecting very high GSAT warming exceeding the very likely  range. Further details 

637
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44For the high-emissions scenarios SSP3-7.0 and SSP5-8.5, 
a  high-warming storyline is associated with wide-spread warming 
that exceeds the already high best-estimate warming by another 
35–50%. For SSP5-8.5, this corresponds to a warming of 1°C–3°C in 
addition to the best estimate over most land regions, which implies 
more than 6°C relative to 1995–2014 over most extratropical land 
regions and Amazonia. Over large parts of the Arctic, annual mean 
temperatures increase by more than 10°C relative to 1995–2014 
in such a  high-warming storyline under SSP5-8.5. The two lines 
of evidence yield more consistent patterns for SSP5-8.5 than for 
SSP1-2.6, but there are substantial differences concerning whether 
the strongest warming above the best estimate occurs over the 
tropics or extratropical land regions.
While individual models project even stronger warming over

extratropical land regions (Figure 4.41 bottom row), their projected 
GSAT warming exceeds the assessed very likely  5–95% range and 
thus correspond to an extremely unlikely  (below 5% likelihood) 
storyline. While all the models consistent with such a storyline tend 
to overestimate the observed warming trend over the historical 
period (Brunner et al., 2020; Liang et al., 2020; Nijsse et al., 2020; 
Tokarska et al., 2020; Ribes et al., 2021), some of them show a good 
representation of several aspects of the present-day climate (Andrews 
et al., 2019; Sellar et al., 2019; Swart et al., 2019). Such a very high-
warming storyline implies widespread warming of more than 1.5°C 
and 3°C above the best-estimate warming pattern under SSP1-2.6 
and SSP5-8.5, respectively. Under SSP1-2.6, this corresponds to 
more than 3°C warming relative to 1995–2014 over land regions in 
the northern mid- to high latitudes and more than 6°C in the Arctic

(Figure 4.41g). Under SSP5-8.5, such a very high-warming storyline 
implies more than 8°C warming over parts of Amazonia and more 
than 6°C over most other tropical land regions (Figure 4.41h).
High-warming storylines are very likely  also associated with 
substantial changes in the hydrological cycle due to strong 
thermodynamic changes, which can be amplified or offset by 
dynamical changes (Emori and Brown, 2005; Seager et  al., 2014b; 
Chavaillaz et al., 2016; Kröner et al., 2017; Chen et al., 2019). Here 
the assessment of the hydrological cycle in high-warming storylines 
is limited to changes in annual mean precipitation, but changes in 
seasonal mean precipitation can be even stronger due to enhanced 
seasonality in many regions (Box 8.2).
Quantifying precipitation changes associated with high-warming 
storylines is challenging since models show the largest changes 
in precipitation over different regions (Sections 4.5.1 and 4.6.1).

In some areas, models project opposing signals in different seasons 
or a  combination of decreasing mean and increasing extreme 
precipitation (Kendon et  al., 2014; Ban et  al., 2015; Giorgi et  al., 
2016; Pendergrass et al., 2017). Models with the most pronounced 
GSAT warming are not necessarily associated with the strongest 
precipitation response in all regions, in part due to projected 
changes in atmospheric dynamics (Madsen et al., 2017; Zappa and 
Shepherd, 2017; Li et al., 2018).Different alternative estimates of changes in annual mean precipitation 
patterns consistent with high-warming levels are compared here. 
The first estimate (Figure 4.42b) is based on a linear pattern scaling of 
the multi-model mean precipitation pattern for SSP5-8.5 (Figure 4.42a) 
to be consistent with the upper bound of the assessed very likely  
GSAT range (see above). This estimate is reasonably consistent with 
the average response of the five models with GSAT warming most

consistent with the upper bound of the very likely  warming range 
(Figure 4.42c) except for Australia. Both estimates show about 30–40% 
larger changes in annual mean precipitation than the response pattern 
consistent with the best GSAT estimate. In a high-warming storyline, 
widespread increases of more than 30% occur in many regions north 
of 50°N and over parts of the tropics. Around the Mediterranean and 
other parts of the subtropics, a high-warming storyline is associated 
with a  reduction in annual mean precipitation of more than 30% 
depending on the season.
Both the multi-model mean and the pattern-scaled responses show 
a smoother pattern than in individual simulations (Tebaldi and Knutti, 
2007; Knutti et al., 2010), because the multi-model mean filters out 
internal variability and because model differences in the location 
of the largest change tend to cancel. Individual model simulations 
show opposing signs in precipitation change such as over parts of

Australia, the west coast of North America, parts of West Africa and 
India (Figure 4.42d), which tend to offset in the multi-model mean 
response. The spatial probability distribution of precipitation changes 
shows that areas of strong precipitation increase or decrease occur 
in all models (Figure  4.42g, see also Section  4.6.1). However, due 
to the spatial smoothing, the multi-model mean response shows 
a lower area fraction of drying than most of the individual models 
(Tebaldi and Knutti, 2007; Knutti et al., 2010). The five models with 
GSAT warming consistent with a high-warming storyline and the two 
models projecting GSAT warming exceeding the very likely  GSAT 
warming range show a  much larger area fraction of drying and 
somewhat larger fraction of strong precipitation increases than the 
multi-model mean (Figure 4.42b–d).
The high-warming storyline shown in Figure  4.42b,c does not 
correspond to an upper or lower estimate of annual precipitation

increase and decrease over individual locations, which in many 
regions may differ in the sign of the response (Figure  4.42e,f) 
due to differences in the model response and internal variability 
(Madsen et  al., 2017). Figure  4.42e,f illustrates upper and lower 
local estimates corresponding to the 5–95% model range of local 
uncertainties as opposed to the global-warming storylines. Note, 
however, that Figure  4.42e,f does not show a  physically plausible 
global precipitation response pattern, because information at the 
different grid points is taken from different model simulations.
Again, the manifestation of changes in the hydrological cycle for 
a  high-warming storyline is not limited to precipitation, but would 
substantially affect other variables such as soil moisture, runoff, 
atmospheric humidity, and evapotranspiration. The changes are also 
not limited to annual mean precipitation but may be stronger or weaker

for individual seasons and for precipitation extremes and dry spells.

639
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44Figure 4.42 (continued): (a)  Estimates for annual mean precipitation changes in 2081–2100, relative to 1995–2014, consistent with the best global surface air temperature 
(GSAT) estimate derived by linearly scaling the CMIP6 multi-model mean changes to a GSAT change of 3.5°C. (b, c)  Estimates for annual mean precipitation changes in 
2081–2100 relative 1995–2014 in a storyline representing a physically plausible high-global-warming level. (b) Multi-model-mean precipitation scaled to high-global-warming 
level (corresponding to 4.8°C, the upper bound of the very likely  range; see Section 4.3.4).  (c) Average of five models with GSAT warming nearest to the high level of 
warming (ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR) (d) Annual mean precipitation changes in four of the five individual model simulations

averaged in (c). (e, f)  Local upper estimate (95% quantile across models) and lower estimate (5% quantile across models) at each grid point. Information at individual grid 
points comes from different model simulations and illustrates local uncertainty range but should not be interpreted as a pattern. (g) Area fraction of changes in annual mean 
precipitation 2081–2100 relative to 1995–2014 for (i) all CMIP6 model simulations (thin black lines), (ii) models shown in (c) (red lines), and (iii) models showing very high 
warming above the models shown in (c) (dark red lines). The grey range illustrates the 5–95% range across CMIP6 models and the solid black line the area fraction of the 
multi-model mean pattern shown in (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).
While this assessment is limited to temperature and precipitation, 
such a  high-warming storyline would manifest itself also in other

climate variables (Sanderson et  al., 2011) assessed in this chapter 
such as Arctic sea ice, atmospheric circulation changes, and sea 
level rise (Ramanathan and Feng, 2008; Xu and Ramanathan, 2017; 
Steffen et al., 2018).
In summary, while high-warming storylines – those associated with 
global warming levels above the upper bound of the assessed very 
likely range  – are by definition extremely unlikely , they cannot be 
ruled out. For SSP1-2.6, such a  high-warming storyline implies 
warming well above rather than well below 2°C ( high confidence ). 
Irrespective of scenario, high-warming storylines imply changes 
in many aspects of the climate system that exceed the patterns 
associated with the best estimate of GSAT changes by up to more 
than 50% ( high confidence ).
The authors are deeply grateful to the WGI Bureau and Technical 
Support Unit for their support throughout the writing process.

Special thanks go to Anna Pirani, Sarah Connors, Clotilde Pean, Robin 
Matthews and Elisabeth  Lonnoy for their organizational support. 
Special thanks go to Michael Sigmond, Sophie Berger, Paolo Davini, 
Melissa Gomis and Tom Maycock for their contributions to figures 
and FAQs. We acknowledge outstanding contributions from our two 
chapter scientists, Sebastian Milinski and Kyung-Sook Yun. We thank 
Ziming Chen for contributing the analysis of CMIP6 models. Finally, 
we would like to thank all reviewers for their constructive comments.

640
Chapter 4  Futur e Global Climate: Scenario-based Projections and Near-term Information4Frequently Asked Questions
FAQ 4.1 | How Will the Climate Change Over the Next Twenty Years?
The parts of the climate system that have shown clear increasing or decreasing trends in recent decades will 
continue these trends for at least the next twenty years. Examples include changes in global surface temperature, 
Arctic sea ice cover, and global average sea level. However, over a period as short as twenty years, these trends are 
substantially influenced by natural climate variability, which can either amplify or attenuate the trend expected 
from the further increase in greenhouse gas concentrations.
Twenty years are a long time by human standards but a short time from a climate point of view. Emissions of 
greenhouse gases will continue over the next twenty years, as assumed in all the scenarios considered in this

Report, albeit with varying rates. These emissions will further increase concentrations of greenhouse gases in 
the atmosphere (see FAQ 4.2), leading to continued trends in global surface warming and other parts of the 
climate system, including Arctic sea ice and global average sea level (see FAQ 9.2). FAQ 4.1, Figure 1 shows that 
both global surface temperature rise and the shrinking of sea ice in the Arctic will continue, with little difference 
between high- and low-emissions scenarios over the next 20 years (that is, between the red and blue lines).
However, these expected trends will be overlain by natural climate variability (see FAQ 3.2). First, a major volcanic 
eruption might occur, such as the 1991 eruption of Mt. Pinatubo on the Philippines; such an eruption might cause 
a global surface cooling of a few tenths of a degree Celsius lasting several years. Second, both atmosphere and

ocean show variations that occur spontaneously, without any external influence. These variations range from 
localized weather systems to continent- and ocean-wide patterns and oscillations that change over months, years, 
or decades. Over a period of twenty years, natural climate variability strongly influences many climate quantities, 
when compared to the response to the increase in greenhouse gas concentrations from human activities. The effect 
of natural variability is illustrated by the very different trajectories that individual black, red or blue lines can 
take in FAQ 4.1, Figure 1. Whether natural variability would amplify or attenuate the human influence cannot 
generally be predicted out to twenty years into the future. Natural climate variability over the next twenty years 
thus constitutes an uncertainty that at best can be quantified accurately but that cannot be reduced.

Locally, the effect of natural variability would be much larger still. Simulations (not shown here) indicate that, 
locally, a cooling trend over the next twenty years cannot be ruled out, even under the high-emissions scenario – 
at a small number of locations on Earth, but these might lie anywhere. Globally, though, temperatures would 
rise under all scenarios.
In summary, while the direction of future change is clear for the two important climate quantities shown here – 
the global surface temperature and the Arctic sea ice area in September – the magnitude of the change is much 
less clear because of natural variability.

641
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44FAQ 4.1 (continued)
Global surface temperature change (ºC)
Sea ice area change (millions of km2)
(Arctic – September)-0.50.00.51.01.52.0
Year-6-4-202FAQ 4.1: How will climate change over 
the next 20 years? 
Current climatic trends  will continue in the next 2 decades 
but their exact magnitude cannot be predicted, because of 
natural variability.
Emission 
scenarioLow High
Historical
2000 2010 2020 2030 20402000 2010 2020 2030 2040
SSP3–7.0SSP1–2.6
FAQ 4.1, Figure  1 | Simulations over the period 1995–2040, encompassing the recent past and the next twenty years, of two important 
indicators of global climate change. (Top)  Global surface temperature, and (bottom) , the area of Arctic sea ice in September. Both quantities are

shown as deviations from the average over the period 1995–2014. The grey curves are for the historical period ending in 2014; the blue curves represent 
a low-emissions scenario (SSP1-2.6) and the red curves one high-emissions scenario (SSP3-7.0).

642
FAQ 4.2 | How Quickly Would We See the Effects of Reducing Carbon Dioxide Emissions?
The effects of substantial reductions in carbon dioxide emissions would not be apparent immediately, and 
the time required to detect the effects would depend on the scale and pace of emissions reductions. Under the 
lower-emissions scenarios considered in this Report, the increase in atmospheric carbon dioxide concentrations 
would slow visibly after about five to ten years, while the slowing down of global surface warming would be 
detectable after about twenty to thirty years. The effects on regional precipitation trends would only become 
apparent after several decades.
Reducing emissions of carbon dioxide (CO 2) – the most important greenhouse gas emitted by human activities –

would slow down the rate of increase in atmospheric CO 2 concentration. However, concentrations would only 
begin to decrease when net emissions approach zero, that is, when most or all of the CO 2 emitted into the 
atmosphere each year is removed by natural and human processes (see FAQ 5.1 and FAQ 5.3). This delay between 
a peak in emissions and a decrease in concentration is a manifestation of the very long lifetime of CO 2 in the 
atmosphere; part of the CO 2 emitted by humans remains in the atmosphere for centuries to millennia.
Reducing the rate of increase in CO 2 concentration would slow down global surface warming within a decade. 
But this reduction in the rate of warming would initially be masked by natural climate variability and might 
not be detected for a few decades (see FAQ 1.2, FAQ 3.2 and FAQ 4.1). Detecting whether surface warming has 
indeed slowed down would thus be difficult in the years right after emissions reductions begin.

The time needed to detect the effect of emissions reductions is illustrated by comparing low- and high-emissions 
scenarios (FAQ 4.2, Figure 1). In the low-emissions scenario (SSP1-2.6), CO 2 emissions level off after 2015 and begin 
to fall in 2020, while they keep increasing throughout the 21st century in the high-emissions scenario (SSP3-7.0). 
The uncertainty arising from natural internal variability in the climate system is represented by simulating each 
scenario ten times with the same climate model but starting from slightly different initial states back in 1850 
(thin lines). For each scenario, the differences between individual simulations are caused entirely by simulated 
natural internal variability. The average of all simulations represents the climate response expected for a given 
scenario. The climate history that would actually unfold under each scenario would consist of this expected

response combined with the contribution from natural internal variability and the contribution from potential 
future volcanic eruptions (the latter effect is not represented here).
FAQ 4.2, Figure 1 shows that the atmospheric CO 2 concentrations differ noticeably between the two scenarios 
about five to ten years after the emissions have begun to diverge in year 2015. In contrast, the difference in 
global surface temperatures between the two scenarios does not become apparent until later – about two to 
three decades after the emissions histories have begun to diverge in this example. This time would be longer if 
emissions were reduced more slowly than in the low-emissions scenario illustrated here and shorter in the case 
of stronger reductions. Detection would take longer for regional quantities and for precipitation changes, which 
vary more strongly from natural causes. For instance, even in the low-emissions scenario, the effect of reduced

CO 2 emissions would not become visible in regional precipitation until late in the 21st century.
In summary, it is only after a few decades of reducing CO 2 emissions that we would clearly see global temperatures 
starting to stabilize. By contrast, short-term reductions in CO 2 emissions, such as during the COVID-19 pandemic, 
do not have detectable effects on either CO 2 concentration or global temperature. Only sustained emissions 
reductions over decades would have a widespread effect across the climate system.

643
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44FAQ 4.2 (continued)
2015410440
2020 2025
FAQ 4.2: Detecting reduced CO2 emissions
Sustained reduction in carbon dioxide (CO2) emissions 
would become apparent in atmospheric concentration 
after 5–10 years and in the temperature after 20–30 years.
CO2 emissions (billion tonnes of CO2 per year)
CO2 concentration in the atmosphere (ppm)Year
SSP3–7.0
SSP1–2.6900
800
700
600
500
400
2020 2040 2060 2080 2100
Year
Global surface temperature (°C)
SSP3–7.0
SSP1–2.617.5
17.0
16.5
16.0
15.5
15.0
14.5
YearDivergence 
of scenarios
Divergence 
of scenariosSSP3–7.0
SSP1–2.6LowEmission 
scenarioHigh80
60
40
20
0
2020 2040 2060 2080 21002015
FAQ 4.2, Fig ure 1 | Observing the benefits of emissions reductions. (Top)  Carbon dioxide (CO 2) emissions, (middle)  CO 2 concentration in the atmosphere

and (bottom)  effect on global surface temperature for two scenarios: a low-emissions scenario (SSP1-2.6, blue) and a high-emissions scenario (SSP3-7.0). In the 
low-emissions scenario, CO 2 emissions begin to decrease in 2020 whereas they keep increasing throughout the 21st century in the high-emissions scenario. 
The thick lines are the average of the 10 individual simulations (thin line) for each scenario. Differences between individual simulations reflect natural variability.

644
FAQ 4.3 | At a Given Level of Global Warming, What Are the Spatial Patterns of Climate Change?
As the planet warms, climate change does not unfold uniformly across the globe, but some patterns of regional 
change show clear, direct and consistent relationships to increases in global surface temperature. The Arctic 
warms more than other regions, land areas warm more than the ocean surface, and the Northern Hemisphere 
more than the Southern Hemisphere. Precipitation increases over high latitudes, tropics and large parts of 
the monsoon regions, but decreases over the subtropics. For cases like these, we can infer the direction and 
magnitude of some regional changes – particularly temperature and precipitation changes – for any given level 
of global warming.
The intensity of climate change will depend on the level of global warming. It is possible to identify certain

patterns of regional climate change that occur consistently, but increase in amplitude, across increasing levels of 
global warming. Such robust spatial patterns of climate change are largely independent of the specific scenario 
(and pathway in time) that results in a given level of global warming. That is, as long as different scenarios result 
in the same global warming level, irrespective of the time when this level is attained in each scenario, we can 
infer the patterns of regional change that would result from this warming. When patterns of changes are robust, 
regional consequences can be assessed for all levels of global warming, for all future time periods, and for all 
scenarios. Temperature and precipitation show such robust patterns of changes that are particularly striking.
The high latitudes of the Northern Hemisphere are projected to warm the most, by two to four times the level

of global warming – a phenomenon referred to as Arctic amplification (FAQ 4.3, Figure 1, left). Several processes 
contribute to this high rate of warming, including increases in the absorption of solar radiation due to the loss 
of reflective sea ice and snow in a warmer world. In the Southern Hemisphere, Antarctica is projected to warm 
faster than the mid-latitude Southern Ocean, but the Southern Hemisphere high latitudes are projected to 
warm at a reduced amplitude compared to the level of global warming (FAQ 4.3, Figure 1, left). An important 
reason for the relatively slower warming of the Southern Hemisphere high latitudes is the upwelling of Antarctic 
deep waters that drives a large surface heat uptake in the Southern Ocean.
The warming is generally stronger over land than over the ocean, and in the Northern Hemisphere compared to 
the Southern Hemisphere, and with less warming over the central subpolar North Atlantic and the southernmost

Pacific. The differences are the result of several factors, including differences in how land and ocean areas 
absorb and retain heat, the fact that there is more land area in the Northern Hemisphere than in the Southern 
Hemisphere, and the influence of ocean circulation. In the Southern Hemisphere, robust patterns of relatively 
high warming are projected for subtropical South America, southern Africa, and Australia. The relatively strong 
warming in subtropical southern Africa arises from strong interactions between soil moisture and temperature 
and from increased solar radiation as a consequence of enhanced subsidence.
Precipitation changes are also proportional to the level of global warming (FAQ 4.3, Figure 1, right), although 
uncertainties are larger than for the temperature change. In the high latitudes of both the Southern and Northern 
Hemispheres, increases in precipitation are expected as the planet continues to warm, with larger changes

expected at higher levels of global warming (FAQ 4.3, Figure 1, right). The same holds true for the projected 
precipitation increases over the tropics and large parts of the monsoon regions. General drying is expected 
over the subtropical regions, particularly over the Mediterranean, southern Africa and parts of Australia, South 
America, and south-west North America, as well as over the subtropical Atlantic and parts of the subtropical 
Indian and Pacific Oceans. Increases in precipitation over the tropics and decreases over the subtropics amplify 
with higher levels of global warming.
Some regions that are already dry and warm, such as southern Africa and the Mediterranean, are expected to 
become progressively drier and drastically warmer at higher levels of global warming.
In summary, climate change will not affect all the parts of the globe evenly. Rather, distinct regional patterns of

temperature and precipitation change can be identified, and these changes are projected to amplify as the level 
of global warming increases.

645
Future Global Climate: Scenario-based Projections and Near-term Information  Chapter 44FAQ 4.3 (continued)
FAQ 4.3: Climate change and regional patterns
Climate change is not uniform and proportional to the level of global warming.
Precipitation will increase in high latitudes, the tropics 
and monsoon regions and decrease in the subtropicsWarming will be stronger in the Arctic, 
on land and in the Northern Hemisphere
Warmer
Wetter Drier+1.5°C
+3.0°C
1°C 3°C 5°C 0% -40% -20% 20% 40%
FAQ 4.3, Figure  1 | Regional changes in temperature (left) and precipitation (right) are proportional to the level of global warming, 
irrespective of the scenario through which the level of global warming is reached.  Surface warming and precipitation change are shown relative 
to the 1850–1900 climate, and for time periods over which the globally averaged surface warming is 1.5°C (top)  and 3°C (bottom) , respectively. Changes

presented here are based on 31 CMIP6 models using the high-emissions scenario SSP3-7.0.

676
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Executive Summary
It is unequivocal that the increases in atmospheric carbon dioxide 
(CO 2), methane (CH 4) and nitrous oxide (N 2O) since the pre-industrial 
period are caused by human activities. The accumulation of GHGs in 
the atmosphere is determined by the balance between anthropogenic 
emissions, anthropogenic removals, and physical-biogeochemical 
source and sink dynamics on land and in the ocean. This chapter 
assesses how physical and biogeochemical processes of the carbon 
and nitrogen cycles affect the variability and trends  of GHGs in 
the atmosphere as well as ocean acidification and deoxygenation. 
It identifies physical and biogeochemical feedbacks that have affected 
(or could affect) future rates of GHG accumulation in the atmosphere, 
and therefore, influence climate change and its impacts. This chapter 
also assesses the remaining carbon budget to limit global warming

within various goals, as well as the large-scale consequences of 
carbon dioxide removal (CDR) and solar radiation modification (SRM) 
on biogeochemical cycles. {Figures 5.1, 5.2}.
The Human Perturbation of the Carbon 
and Biogeochemical Cycles
Global mean concentrations for well-mixed GHGs (CO 2, 
CH 4  and  N 2O) in 2019 correspond to increases of about 
47%, 156%, and 23%, respectively, above the levels in 1750 
(representative of the pre-industrial era) ( high confidence ). 
Current atmospheric concentrations of the three GHGs are higher 
than at any point in the last 800,000  years, and in 2019 reached 
409.9 parts per million (ppm) of CO 2, 1866.3 parts per billion (ppb) 
of CH 4, and 332.1 ppb of N 2O (very high confidence ). Current CO 2 
concentrations in the atmosphere are also unprecedented in the last 
2 million years ( high confidence ). In the past 60 million years, there 
have been periods in Earth’s history when CO 2 concentrations were

significantly higher than at present, but multiple lines of evidence 
show that the rate at which CO 2 has increased in the atmosphere 
during 1900–2019 is at least 10 times faster than at any other 
time during the last 800,000 years ( high confidence ), and 4–5 times 
faster than during the last 56 million years ( low confidence ). {5.1.1, 
2.2.3; Figures 5.3, 5.4; Cross-Chapter Box 2.1}
Contemporary Trends of Greenhouse Gases 
It is unequivocal that the increase of CO 2, CH 4, and N 2O in 
the atmosphere over the industrial era is the result of human 
activities ( very high confidence ). This assessment is based on 
multiple lines of evidence including atmospheric gradients, isotopes, 
and inventory data. During the last measured decade, global average 
annual anthropogenic emissions of CO 2, CH 4, and N 2O, reached the 
highest levels in human history at 10.9 ± 0.9 petagrams of carbon 
per year (PgC yr–1, 2010–2019), 335–383 teragrams of methane per

year (TgCH 4 yr–1, 2008–2017), and 4.2–11.4 teragrams of nitrogen 
per year (TgN yr–1, 2007–2016), respectively ( high confidence ). 
{5.2.1, 5.2.2, 5.2.3, 5.2.4; Figures 5.6, 5.13, 5.15}.The CO 2 emitted from human activities during the decade 
of 2010–2019 (decadal average 10.9 ±  0.9  PgC yr–1) was 
distributed between three Earth system components: 46% 
accumulated in the atmosphere (5.1 ± 0.02 PgC yr–1), 23% was 
taken up by the ocean (2.5 ± 0.6 PgC yr–1) and 31% was stored 
by vegetation in terrestrial ecosystems (3.4 ±  0.9  PgC yr–1) 
(high confidence ). Of the total anthropogenic CO 2 emissions, the  
combustion of fossil fuels was responsible for 81–91%, with 
the  remainder being the net CO 2 flux from land-use change and 
land management (e.g., deforestation, degradation, regrowth after 
agricultural abandonment, and peat drainage). {5.2.1.2, 5.2.1.5; 
Table 5.1; Figures 5.5, 5.7, 5.12}
Over the past six decades, the average fraction

of  anthropogenic CO 2 emissions that has accumulated in 
the atmosphere (referred to as the airborne fraction) has 
remained nearly constant at approximately 44%. The ocean and 
land sinks of CO 2 have continued to grow over the past six decades 
in response to increasing anthropogenic CO 2 emissions ( high 
confidence) . Interannual and decadal variability of the regional and 
global ocean and land sinks indicate that these sinks are sensitive to 
climate conditions and therefore to climate change ( high confidence ). 
{5.2.1.1, 5.2.1.2, 5.2.1.3, 5.2.1.4; Figures 5.7, 5.8, 5.10}
Recent observations show that ocean carbon processes are 
starting to change in response to the growing ocean sink, 
and these changes are expected to contribute significantly to 
future weakening of the ocean sink under medium- to high-
emissions scenarios.  However, the effects of these changes are not 
yet reflected in a weakening trend of the contemporary (1960–2019)

ocean sink ( high confidence ). {5.1.2, 5.2.1.3, 5.3.2.1; Figures  5.8, 
5.20; Cross-Chapter Box 5.3}
Atmospheric concentration of CH 4 grew at an average rate 
of 7.6 ± 2.7 ppb yr–1 for the last decade (2010–2019), with 
a faster growth of 9.3 ± 2.4 ppb yr–1 over the last six years 
(2014–2019) ( high confidence ). The multi-decadal growth trend 
in atmospheric CH 4 is dominated by anthropogenic activities ( high 
confidence ), and the growth since 2007 is largely driven by emissions 
from both fossil fuels and agriculture (dominated by livestock) 
(medium confidence) . The interannual variability is dominated by 
El Niño–Southern Oscillation cycles, during which biomass burning 
and wetland emissions, as well as loss by reaction with tropospheric 
hydroxyl radical (OH) play an important role. {5.2.2; Figures  5.13, 
5.14; Table 5.2; Cross-Chapter Box 5.2}
Atmospheric concentration of N 2O grew at an average 
rate of 0.85 ±  0.03 ppb yr–1 between 1995 and 2019, with

a further increase to 0.95 ± 0.04 ppb yr–1 in the most recent 
decade (2010–2019).  This increase is dominated by anthropogenic 
emissions, which have increased by 30% between the 1980s and the 
most recent observational decade (2007–2016) ( high confidence ). 
Increased use of nitrogen fertilizer and manure contributed to about 
two-thirds of the increase during the 1980–2016 period, with the 
fossil fuels/industry, biomass burning, and wastewater accounting for 
much of the rest ( high confidence ). {5.2.3; Figures 5.15, 5.16, 5.17}

677
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Ocean Acidification and Ocean Deoxygenation
Ocean acidification is strengthening as a result of the ocean 
continuing to take up CO 2 from human-caused emissions ( very 
high confidence ). This CO 2 uptake is driving changes in seawater 
chemistry that result in the decrease of pH and associated reductions 
in the saturation state of calcium carbonate, which is a constituent 
of skeletons or shells of a variety of marine organisms. These trends of 
ocean acidification are becoming clearer globally, with a  very likely  
rate of decrease in pH in the ocean surface layer of 0.016 to 0.020 per 
decade in the subtropics and 0.002 to 0.026 per decade in subpolar 
and polar zones since the 1980s. Ocean acidification has spread 
deeper in the ocean, surpassing 2000 m depth in the northern North 
Atlantic and in the Southern Ocean. The greater projected pH declines

in Coupled Model Intercomparison Project Phase 6 (CMIP6) models 
are primarily a consequence of higher atmospheric CO 2 concentrations 
in the Shared Socio-economic Pathways (SSPs) scenarios than 
their Coupled Model Intercomparison Project Phase  5  (CMIP5) 
Representative Concentration Pathway (RCP) analogues. {5.3.2.2, 
5.3.3.1; 5.3.4.1; Figures 5.20, 5.21} 
Ocean deoxygenation is projected to continue to increase with 
ocean warming ( high confidence ). Earth system models (ESMs) 
project a 32–71% greater subsurface (100–600 m) oxygen decline, 
depending on the scenario, than reported in the Special Report on 
the Ocean and Cryosphere (SROCC) for the period 2080–2099. This is 
attributed to the effect of larger surface warming in CMIP6 models, 
which increases ocean stratification and reduces ventilation ( medium 
confidence ). There is low confidence  in the projected reduction of 
oceanic N 2O emissions under high-emissions scenarios because

of greater oxygen losses simulated in ESMs in CMIP6, uncertainties 
in the process of oceanic N 2O emissions, and a  limited number of 
modelling studies available. {5.3.3.2; 7.5}
Future Projections of Carbon Feedbacks 
on Climate Change
Oceanic and terrestrial carbon sinks are projected to continue 
to grow with increasing atmospheric concentrations of CO 2, 
but the fraction of emissions taken up by land and ocean is 
expected to decline as the CO 2 concentration increases ( high 
confidence ). ESMs suggest approximately equal global land and 
ocean carbon uptake for each of the SSP scenarios. However, the range 
of model projections is much larger for the land carbon sink. Despite 
the wide range of model responses, uncertainty in atmospheric CO 2 
by 2100 is dominated by future anthropogenic emissions rather than 
uncertainties related to carbon–climate feedbacks ( high confidence ). 
{5.4.5; Figure 5.25, 5.26}
Increases in atmospheric CO 2 lead to increases in land carbon

storage through CO 2 fertilization of photosynthesis and 
increased water use efficiency ( high confidence ). However, 
the overall change in land carbon also depends on land-use change 
and on the response of vegetation and soil to continued warming and 
changes in the water cycle, including increased droughts in some 
regions that will diminish the sink capacity. Climate change alone is expected to increase land carbon accumulation in the high latitudes 
(not including permafrost) and also to lead to a counteracting loss of 
land carbon in the tropics ( medium confidence , Figure 5.25). More 
than half of the latest CMIP6 ESMs include nutrient limitations on 
the  carbon cycle, but these models still project increasing tropical 
land carbon ( medium confidence ) and increasing global land carbon 
(high confidence ) through the 21st century. {5.4.1, 5.4.3, 5.4.5; 
Figure 5.27; Cross-Chapter Box 5.1}
Future trajectories of the ocean CO 2 sink are strongly

emissions-scenario dependent ( high confidence ). Emissions 
scenarios SSP4-6.0 and SSP5-8.5 lead to warming of the surface ocean 
and large reductions of the buffering capacity, which will slow the 
growth of the ocean sink after 2050. Scenario SSP1-2.6 limits further 
reductions in buffering capacity and warming, and the ocean sink 
weakens in response to the declining rate of increasing atmospheric 
CO 2. There is low confidence  in how changes in the biological pump 
will influence the magnitude and direction of the ocean carbon 
feedback. {5.4.2, 5.4.4, Cross-Chapter Box 5.3}
Beyond 2100, land and ocean may transition from being 
a  carbon sink to a  source under either very high emissions 
or net negative emissions scenarios, but for different reasons. 
Under very high emissions scenarios such as SSP5-8.5, ecosystem 
carbon losses due to warming lead the land to transition from 
a carbon sink to a source ( medium confidence ), while the ocean is

expected to remain a sink ( high confidence ). For scenarios in which 
CO 2 concentration stabilizes, land and ocean carbon sinks gradually 
take up less carbon as the increase in atmospheric CO 2 slows down. 
In scenarios with moderate net negative CO 2 emissions, and CO 2 
concentrations declining during the 21st century (e.g., SSP1-2.6), the 
land sink transitions to a net source in decades to a few centuries 
after CO 2 emissions become net negative, while the ocean remains 
a sink ( low confidence ). Under scenarios with large net negative CO 2 
emissions and rapidly declining CO 2 concentrations (e.g., SSP5-3.4-OS 
(overshoot)), both land and ocean switch from a sink to a transient 
source during the overshoot period ( medium confidence ). {5.4.10, 
5.6.2.1.2; Figures 5.30, 5.33}
Thawing terrestrial permafrost will lead to carbon release 
(high confidence ), but there is low confidence  in the timing, 
magnitude and the relative roles of CO 2 versus CH 4 as feedback

processes . CO 2 release from permafrost is projected to be 3–41 PgC 
per 1°C of global warming by 2100, based on an ensemble of models. 
However, the incomplete representation of important processes such 
as abrupt thaw, combined with weak observational constraints, 
only allow low confidence in both the magnitude of these estimates 
and in how linearly proportional this feedback is to the amount of 
global warming. It is very unlikely  that gas clathrates in terrestrial 
and subsea permafrost will lead to a detectable departure from the 
emissions trajectory during this century. {5.4.9; Box 5.1}
The net response of natural CH 4 and N 2O sources to future 
warming will be increased emissions ( medium confidence ). 
Key processes include increased CH 4 emissions from wetlands and 
permafrost thaw, as well as increased soil N 2O emissions in a warmer 
climate, while ocean N 2O emissions are projected to decline at

678
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5centennial time scale. The magnitude of the responses of each 
individual process and how linearly proportional these feedbacks 
are to the amount of global warming is known with low confidence 
due to incomplete representation of important processes in models 
combined with weak observational constraints. Models project that, 
over the 21st century, the combined feedback of 0.02–0.09  W m–2 °C–1 
is comparable to the effect of a  CO 2 release of 5–18 petagrams 
of carbon equivalent per °C (PgCeq °C–1) (low confidence ). {5.4.7, 
5.4.8; Figure 5.29}
The response of biogeochemical cycles to the anthropogenic 
perturbation can be abrupt at regional scales, and irreversible 
on decadal to century time scales ( high confidence ). The 
probability of crossing uncertain regional thresholds (e.g.,  high 
severity fires, forest dieback) increases with climate change

(high confidence ). Possible abrupt changes and tipping points in 
biogeochemical cycles lead to additional uncertainty in 21st century 
GHG concentrations, but these are very likely  to be smaller than the 
uncertainty associated with future anthropogenic emissions ( high 
confidence ). {5.4.9}
Remaining Carbon Budgets to Climate Stabilization
There is a  near-linear relationship between cumulative 
CO 2 emissions and the increase in global mean surface air 
temperature (GSAT) caused by CO 2 over the course of this 
century for global warming levels up to at least 2°C relative to 
pre-industrial ( high confidence ). Halting global warming would 
thus require global net anthropogenic CO 2 emissions to become zero. 
The ratio between cumulative CO 2 emissions and the consequent 
GSAT increase, which is called the transient climate response to 
cumulative emissions of CO 2 (TCRE), likely  falls in the 1.0°C–2.3°C 
per 1000 PgC range. The narrower range compared to the IPCC Fifth

Assessment Report (AR5) is due to a better integration of evidence 
across the science in this assessment. Beyond this century, there 
is low confidence  that the TCRE remains an accurate predictor of 
temperature changes in scenarios of very low or net negative CO 2 
emissions because of uncertain Earth system feedbacks that can 
result in further warming or a  path-dependency of warming as 
a function of cumulative CO 2 emissions. {5.4, 5.5.1}
Mitigation requirements over this century for limiting 
maximum warming to specific levels can be quantified using 
a  carbon budget that relates cumulative CO 2 emissions to 
global mean temperature increase ( high confidence ). For the 
period 1850–2019, a total of 655 ± 65 PgC (2390 ± 240 GtCO 2, likely  
range) of anthropogenic CO 2 has been emitted. Remaining carbon 
budgets (starting from 1 January 2020) for limiting warming to 1.5°C, 
1.7°C, and 2.0°C are 140 PgC (500 GtCO 2), 230 PgC (850 GtCO 2) and

370  PgC (1350  GtCO 2), respectively, based on the 50th percentile 
of TCRE. For the 67th percentile, the respective values are 110 PgC 
(400 GtCO 2), 190 PgC (700 GtCO 2) and 310 PgC (1150 GtCO 2). These 
remaining carbon budgets may vary by an estimated ±  60  PgC 
(220 GtCO 2) depending on how successfully future non-CO 2 emissions 
can be reduced. Since AR5 and the Special Report on Global Warming 
of 1.5°C (SR1.5), estimates have undergone methodological improvements, resulting in larger, yet consistent estimates. {5.5.2, 
5.6; Figure 5.31; Table 5.8}
Several factors affect the precise value of remaining carbon 
budgets, including estimates of historical warming, future 
emissions from thawing permafrost, and variations in 
projected non-CO 2 warming.  Remaining carbon budget estimates 
can increase or decrease by 150 PgC ( likely  range; 150 PgC equals 
550 GtCO 2) due to uncertainties in the level of historical warming,

and by an additional ±  60  PgC (±220 GtCO, likely  range) due to 
geophysical uncertainties surrounding the climate response to non-
CO 2 emissions such as CH 4, N 2O, and aerosols. Permafrost thaw is 
included in the estimates, together with other feedbacks that are 
often not captured by models. Despite the large uncertainties 
surrounding the quantification of the effects of additional Earth 
system feedback processes, such as emissions from wetlands and 
permafrost thaw, these feedbacks represent identified additional 
amplifying risk factors that scale with additional warming and mostly 
increase the challenge of limiting warming to specific temperature 
thresholds. These uncertainties do not change the basic conclusion 
that global CO 2 emissions would need to decline to at least net zero 
to halt global warming. {5.4, 5.5.2} 
Biogeochemical Implications of Carbon Dioxide Removal 
and Solar Radiation Modification
Land- and ocean-based carbon dioxide removal (CDR) methods

have the potential to sequester CO 2 from the atmosphere, 
but the benefits of this removal would be partially offset by 
CO 2 release from land and ocean carbon stores ( very high 
confidence ). The fraction of CO 2 removed that remains out of the 
atmosphere, a measure of CDR effectiveness, decreases slightly with 
increasing amount of removal ( medium confidence ) and decreases 
strongly if CDR is applied at lower CO 2 concentrations ( medium 
confidence ). {5.6.2.1; Figures 5.32, 5.33, 5.34}
The century-scale climate–carbon cycle response to a  CO 2 
removal from the atmosphere is not always equal and opposite 
to the response to a CO 2 emission ( medium confidence ). For 
simultaneously cumulative CO 2 emissions and removals of greater 
than or equal to 100 PgC, CO 2 emissions are 4 ± 3% more effective 
at raising atmospheric CO 2 than CO 2 removals are at lowering 
atmospheric CO 2. The asymmetry originates from state-dependencies

and non-linearities in carbon cycle processes and implies that an extra 
amount of CDR is required to compensate for a positive emission of 
a given magnitude to attain the same change in atmospheric CO 2. 
The net effect of this asymmetry on the global surface temperature 
is poorly constrained due to low agreement  between models ( low 
confidence ). {5.6.2.1; Figure 5.35}
Wide-ranging side effects of CDR methods have been 
identified that can either weaken or strengthen the carbon 
sequestration and cooling potential of these methods and 
affect the achievement of sustainable development goals 
(high confidence ). Biophysical and biogeochemical side effects 
of CDR methods are associated with changes in surface albedo,

679
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55the water cycle, emissions of CH 4 and N 2O, ocean acidification 
and marine ecosystem productivity ( high confidence ). These side 
effects and associated Earth system feedbacks can decrease carbon 
uptake and/or change local and regional climate, and in turn limit 
the CO 2 sequestration and cooling potential of specific CDR methods 
(medium confidence ). Deployment of CDR, particularly on land, 
can also affect water quality and quantity, food production and 
biodiversity, with consequences for the achievement of related 
sustainable development goals ( high confidence ). These effects 
are often highly dependent on local context, management regime, 
prior land use, and scale of deployment ( high confidence ). A wide 
range of co-benefits are obtained with methods that seek to restore 
natural ecosystems or improve soil carbon ( high confidence ). The

biogeochemical effects of terminating CDR are expected to be small 
for most CDR methods ( medium confidence ). {5.6.2.2; Figure 5.36; 
Cross-Chapter Box 5.1}
Solar radiation modification (SRM) would increase the global 
land and ocean CO 2 sinks ( medium confidence ) but would not 
stop CO 2 from increasing in the atmosphere, thus exacerbating 
ocean acidification under continued anthropogenic emissions 
(high confidence ). SRM acts to cool the planet relative to 
unmitigated climate change, which would increase the land sink by 
reducing plant and soil respiration and slow the reduction of ocean 
carbon uptake due to warming ( medium confidence ). SRM would not 
counteract or stop ocean acidification ( high confidence ). The sudden 
and sustained termination of SRM would rapidly increase global 
warming, with the return of positive and negative effects on the 
carbon sinks ( very high confidence ). {4.6.3; 5.6.3}

680
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.1 Introduction
The physical and biogeochemical controls of greenhouse gases 
(GHGs) is a  central motivation for this chapter, which identifies 
biogeochemical feedbacks that have led or could lead to a  future 
acceleration, slowdown or abrupt transitions in the rate of GHG 
accumulation in the atmosphere, and therefore of climate change. 
A  characterization of the trends and feedbacks lead to improved 
quantification for the remaining carbon budgets for climate 
stabilization, and the responses of the carbon cycle to atmospheric 
carbon dioxide removal (CDR), which is embedded in many of the 
mitigation scenarios, to achieve the goals of the Paris Agreement.
Changes in the abundance of well-mixed GHGs –  carbon dioxide 
(CO 2), methane (CH 4) and nitrous oxide (N 2O) – in the atmosphere 
play a large role in determining the Earth’s radiative properties and

its climate in the past, the present and the future (Chapters  2, 4, 
6 and 7). Since 1950, the increase in atmospheric GHGs has been the 
dominant cause of the human-induced climate change (Section 3.3). 
While the main driver of changes in atmospheric GHGs over the 
past 200 years relates to the direct emissions from human activities, 
the net accumulation of GHGs in the atmosphere is controlled by 
biogeochemical source-sink dynamics of carbon that exchange 
between multiple reservoirs on land, oceans and atmosphere. 
The combustion of fossil fuels and land-use change for the period 
1750–2019 released an estimated 700 ± 75 PgC (1 PgC = 1015 g of 
carbon) into the atmosphere, of which less than half remains in the 
atmosphere today (Sections  5.2.1.2; 5.2.1.5) (Friedlingstein et al., 2020). This emphasizes the central role of terrestrial and ocean CO 2 
sinks in regulating its atmospheric concentration (Ballantyne et al.,

2012; W. Li et al., 2016; Le Quéré et al., 2018a; Ciais et al., 2019; 
Gruber et al., 2019b; Friedlingstein et al., 2020).
The chapter covers three dominant GHGs in the human perturbation 
of the Earth’s radiation budget for which high-quality records exist: 
CO 2, CH 4 and N 2O (Figure 5.1).
Section 5.1 (this section) provides the time context on how unique 
current and future scenarios of GHGs atmospheric concentrations 
and growth rates are in the Earth’s history. It also introduces the 
main processes involved in carbon–climate feedbacks, followed by 
an assessment of what can be learned from the paleo record towards 
a  better understanding of contemporary and future GHGs–climate 
dynamics and their response to different mitigation trajectories.
Section  5.2 covers the state of the carbon cycle and other 
biogeochemical cycles, and global budgets of CO 2, CH 4 and N 2O for the 
industrial era (since 1750). The section emphasizes the last 60-year

period for which high-resolution observations are available and the 
most recent decade for comprehensive GHG budgets. Significant 
advances have taken place since the IPCC Fifth Assessment Report 
(AR5), particularly in constraining the annual-to-decadal variability 
of the ocean and land carbon sources and sinks, and in revealing the 
sensitivity of carbon pools to current and future climate changes. There 
has been an important increase in modelling capability of the three 
GHGs, for land and oceans, atmospheric and ocean observations, and 
Chapter 5: Carbon and biogeochemical cycles Chapter 5: Quick guide
Cross-chapter boxesSection 5.1
Introduction and the paleo context
Section 5.2
Historical trends, variability & 
budgets of
Paleo context
5.1.2 | 5.3.1Anthropogenic greenhouse gas emissions
5.2.1.1 | 5.2.2.2 | 5.2.3.2 | 5.2.4 
Historical and remaining greenhouse gas budgets
5.2.1.5 | 5.2.2.5 | 5.2.3.5 | 5.5.2
Ocean sinks, acidification and deoxygenation

5.2.1.3 | 5.3.2 | 5.3.3 Future projections
5.3.4 | 5.4.5 | 5.4.10CO2 and non-CO2 biogeochemical feedbacks
5.1.1 | Box 5.1 | 5.4.7 | 5.4.8 | 5.4.9
Mitigation, negative emissions and solar radiation modification
5.5 | 5.6Section 5.3
Ocean acidification and deoxygenation5.2.1 
CO25.2.2 
CH45.2.3 
N2OSection 5.2.4
Relative importance of 
CO2, CH4 and N2O
Section 5.5
Remaining carbon budgetsSection 5.6
Implications of CO2 removal and solar 
radiation modificationSection 5.4
Biogeochemical feedbacks on climate change
Section 5.7
Box 5.1
PermafrostBox 5.2
Carbon budget improvements 
and implicationsBox 5.3
Response to 
CO2 removalCC Box 5.1
Land Carbon-Water 
InteractionsCC Box 5.2
Drivers of atmospheric 
methane changeCC Box 5.3
Ocean 
carbon-heat nexusChapter 5 identifies biogeochemical feedbacks with potentially significant 
climatic effects.Key topics and corresponding sub-sections
BoxesFAQs
Figure 5.1 | Visual guide to Chapter 5.

681
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55remote sensing products that have enabled researchers to constrain 
the causes of the observed trends and variability.
Section 5.3 builds on the Special Report on the Ocean and Cryosphere 
(SROCC) covering the change in ocean acidification due to oceanic 
CO 2 uptake across the paleo, historical periods and future projections 
using Coupled Model Intercomparison Project Phase 6  (CMIP6), 
with consequences for marine life (assessed in the Sixth Assessment 
Report Working Group II, AR6 WGII) and biogeochemical cycles. The 
section also assesses changes in deoxygenation of the oceans due to 
warming, increased stratification of the surface ocean, and slowing 
of the meridional overturning circulation.
Section  5.4 covers the future projections of biogeochemical cycles 
and their feedbacks to the climate system fully utilizing the database 
of the concentration-driven CMIP6. Since AR5, Earth system models

(ESMs) have made progress towards including more complex carbon 
cycle and associated biogeochemical processes that enable exploring 
a  range of possible future carbon–climate feedbacks and their 
influences on the climate system. The section addresses uncertainties 
and limits of our models to predict future dynamics for GHG emissions 
trajectories, as well as new understanding on processes involved in 
carbon–climate feedbacks and the possibility for rapid and abrupt 
changes brought by non-linear dynamics.
Section  5.5 covers the development of the total and remaining 
carbon budgets to climate stabilization targets and the associated 
transient climate response to cumulative CO 2 emissions. The section 
shows the progress made since AR5 (IPCC, 2013a) and the Special 
Report on Global Warming of 1.5°C (IPCC, 2018), particularly on 
key components required to estimate the remaining carbon budget, 
including the transient response to cumulative emissions of CO 2, the

zero emissions commitment, the projected non-CO 2 warming, and 
the unrepresented Earth system feedbacks.
Section  5.6 assesses the impacts of CDR and solar radiation 
modification for the purpose of climate change mitigation on the 
global carbon cycle, building from the assessment in the IPCC Special 
Report on Climate Change and Land (SRCCL). It includes an overview 
of the major CDR options and potential collateral biogeochemical 
effects beyond the intended climate change mitigation strategies. 
The potential capacity to deliver atmospheric reductions and the 
socio-economic feasibility of such options are assessed in detail in 
AR6 working group III (WGIII).
Finally, Section 5.7 highlights the knowledge gaps as limits to the 
assessment. The assessment would have been strengthened had 
those gaps not existed.
5.1.1 The Physical and Biogeochemical Processes  
in Carbon–Climate Feedbacks
The influence of anthropogenic CO 2 emissions and emissions

scenarios on the carbon–climate system is the primary driver of 
ocean and terrestrial sinks as the major negative feedbacks that 
determine the atmospheric CO 2 levels, which then drive climate feedbacks through radiative forcing (Figure  5.2) (Friedlingstein 
et al., 2006; C.D. Jones et al., 2013; Jones and Friedlingstein, 2020). 
Biogeochemical feedbacks follow as an outcome of both carbon and 
climate forcing on the physics and the biogeochemical processes of 
the ocean and terrestrial carbon cycles (Figure 5.2) (Katavouta et al., 
2018; Williams et al., 2019; Jones and Friedlingstein, 2020). Together, 
these carbon–climate feedbacks can amplify or suppress climate 
change by altering the rate at which CO 2 builds up in the atmosphere 
through changes in the land and ocean sources and sinks (Figure 5.2; 
C.D. Jones et al., 2013; Raupach et al., 2014; Williams et al., 2019). 
These changes depend on the, often non-linear, interaction of the

drivers (CO 2 and climate) and processes in the ocean and land as well 
as the emissions scenarios (Figure 5.2; Sections 5.4 and 5.6) (Raupach 
et al., 2014; Schwinger et al., 2014; Williams et al., 2019). There is 
high confidence  that carbon–climate feedbacks and their century 
scale evolution play a critical role in two linked climate metrics that 
have significant climate and policy implications: (i) the fraction of 
anthropogenic CO 2 emissions that remains in the atmosphere, the 
so-called airborne fraction of CO 2 (AF; Section  5.2.1.2, Figures  5.2 
and 5.7, and FAQ 5.1); and (ii) the quasi-linear trend characteristic 
of the transient temperature response to cumulative CO 2 emissions 
(TCRE; Section 5.5; MacDougall, 2016; Williams et al., 2016; Jones 
and Friedlingstein, 2020) and other GHGs (CH 4 and N 2O). This chapter 
assesses the implications of these issues from the perspective 
of carbon cycle processes (Figure  5.2) in Section  5.2 (historical

and contemporary), Section  5.3 (changing carbonate chemistry), 
Section  5.4 (future projections), Section  5.5 (remaining carbon 
budget) and Section  5.6 (response to carbon dioxide removal and 
solar radiation modification).
The airborne fraction is an important constraint for adjustments 
in carbon–climate feedbacks and reflects the partitioning of CO 2 
emissions between reservoirs by the negative feedbacks, which 
were 31% on land and 23% in the ocean for the decade 2010–2019 
and also dominated the historical period (Figure  5.2; Table  5.1) 
(Friedlingstein et al., 2020). During the period 1959–2019, the 
airborne fraction has largely followed the growth in anthropogenic 
CO 2 emissions with a  mean of 44% and a  large interannual 
variability (Ballantyne et al., 2012; Ciais et al., 2019; Friedlingstein 
et al., 2020) (Section  5.2.1.2; Table  5.1). The negative feedback to 
CO 2 concentrations is associated with its impact on the air–sea and

air–land CO 2 exchange through strengthening of partial pressure of 
CO 2 (pCO 2) gradients as well as the internal processes that enhance 
uptake. Two of these key processes are the buffering capacity of the 
ocean and the CO 2 fertilization effect on gross primary production 
(Sections 5.4.1–5.4.4).
Positive and negative climate and carbon feedbacks involve: (i) fast 
processes on land and oceans at time scales from minutes to years, 
such as photosynthesis, soil respiration, net primary production, 
shallow ocean physics and air–sea fluxes; and (ii) slower processes 
taking from decades to millennia, such as changing ocean buffering 
capacity, ocean ventilation, vegetation dynamics, permafrost 
changes, peat formation and decomposition (Figure 5.2; Ciais et al., 
2013; Forzieri et al., 2017; Williams et al., 2019). Depending on the 
particular combination of driver process and response dynamics, 
they behave as positive or negative feedbacks that amplify or

682
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5dampen the magnitude and rates of climate change, respectively 
(Cox et al., 2000; Friedlingstein et al., 2003, 2006; Hauck and Völker, 
2015; Williams et al., 2019); red and turquoise arrows in Figure 5.2 
and Table 5.1).
Carbon cycle feedbacks co-exist with climate (heat and moisture) 
feedbacks (Cross-Chapter Boxes 5.1 and 5.3), which together drive 
contemporary (Section 5.2) and future (Section 5.4) carbon–climate 
feedbacks (Williams et al., 2019). The excess heat generated by 
radiative forcing from increasing concentration of atmospheric 
CO 2 and other GHGs is mostly taken up by the ocean (>90%) and 
the residual balance partitioned between atmospheric, terrestrial 
and ice melting (Cross-Chapter Box 9.2; Frölicher et al., 2015). The 
combined effect of these two large-scale negative feedbacks of CO 2 
and heat are reflected in the TCRE (Section 5.5 and Cross-Chapter

Box  5.3), which points to a  quasi-linear and quasi-emission-path 
independent relationship between cumulative emissions of CO 2 and global warming, which is used as the basis to estimate the remaining 
carbon budget (Section  5.5; MacDougall and Friedlingstein, 
2015; MacDougall, 2017; Bronselaer and Zanna, 2020; Jones and 
Friedlingstein, 2020). There is still low confidence  on the relative 
roles and importance of the ocean and terrestrial carbon processes 
on TCRE variability and uncertainty on centennial time scales 
(MacDougall, 2016; MacDougall et al., 2017; N.L.  Williams et al., 
2017; Katavouta et al., 2018, 2019; Jones and Friedlingstein, 2020) 
(Sections 5.5.1.1, 5.5.1.2).
The combined effects of climate and CO 2 concentration feedbacks 
on the global carbon cycle are projected by ESMs to modify both the 
processes and natural reservoirs of carbon on a regional and global 
scale that may result in positive feedbacks (red arrows in Figure 5.2),

which could weaken the major terrestrial and ocean sinks and disrupt 
the airborne fraction and TCRE under medium- to high-emissions 
scenarios (Section 5.4.5 and Figure 5.25).
- -+ +
+/- +/-
Climate
Geological storageOceanLandAtmospheric 
CO2 
Airborne 
fraction
Anthropogenic
CO2 
emissions
Fossil fuelsGreenhouse effect
Land-to-ocean carbon transfer
CO2 sequestrationAnthropogenic CO2 removal (CDR)Carbon feedbacksCarbon feedbacksClimate feedbacks
Strengthen air–sea CO2 gradient
Biological C-pump Plant growth
Nutrient Interations
Freshwater processes
Fire regimes & other 
disturbancesLand use changeOcean warming/freshening
Wind stress
Climate extreme eventsWarming
Water cycle changes 
Climate extreme events
Decrease 
solubility
Buffering decrease
Circulation change
Stratification change
DeoxygenationClimate feedbacks
Soil respiration
Permafrost thawing
Erosion
CO2 fertilisationAtmosphere
Active carbon cycle Active carbon cycleAnthropogenic CO2 removal (CDR)

Figure 5.2 | Key compartments, processes and pathways that govern historical and future CO 2 concentrations and carbon–climate feedbacks through 
the coupled Earth system.  The anthropogenic CO 2 emissions, including land-use change, are partitioned via negative feedbacks (turquoise dotted arrows) between the 
ocean (23%), the land (31%) and the airborne fraction (46%) of anthropogenic CO 2 that sets the changing CO 2 concentration in the atmosphere (2010–2019; Table 5.1). This 
regulates most of the radiative forcing that creates the heat imbalance that drives the climate feedbacks to the ocean (blue) and land (green). Positive feedbacks (red arrows) 
result from processes in the ocean and on land (red text). Positive feedbacks are influenced by both carbon-concentration and carbon–climate feedbacks simultaneously.

Additional biosphere processes have been included, but these have an as-yet-uncertain feedback impact (blue-dotted arrows). CO 2 removal from the atmosphere into the ocean, 
land and geological reservoirs, necessary for negative emissions, has been included (grey arrows). Although this schematic is built around CO 2 (the dominant greenhouse gas), 
some of the same processes also influence the fluxes of CH 4 and N 2O and the strength of the positive feedbacks from the terrestrial and ocean systems.

683
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 555.1.2 Paleo Trends and Feedbacks
Paleoclimatic proxy records extend beyond the variability of recent 
decadal climate oscillations and thus provide an independent 
perspective on feedbacks between climate and carbon cycle 
dynamics. According to reconstructions, these past changes were 
slower than the current anthropogenic ones, so they cannot provide 
an unequivocal comparison. Nonetheless, they can help appraise 
sensitivities and point towards potentially dominant mechanisms 
of change (Tierney et al., 2020) on (sub)centennial to (multi)
millennial time scales.
The AR5 (WGI, Chapter 5) concluded with medium  confidence  that 
atmospheric CO 2 concentrations reached 350–450  ppm during 
the mid-Pliocene (3.3–3.0  million years ago (Ma)), and possibly 
1000  ppm during the Early Eocene (52–48  Ma). The AR5 (WGI, 
Chapter 5) also concluded with very high confidence  that the current

rates of increases in CO 2, CH 4 and N 2O atmospheric concentrations 
were unprecedented with respect to the ice core record covering the 
last deglacial transition (LDT, 18–11 ka) and with medium confidence  
that the rate of change of the reconstructed GHG rise was also 
unprecedented compared to the lower resolution of the records of 
the past 800 kyr.5.1.2.1 Cenozoic Proxy CO 2 Record
Quantifying past changes in the rate of CO 2 accumulation in the 
atmosphere based on reconstructions using marine sediment 
proxies is complex as age model uncertainties, assumptions and 
shortcomings underlying proxy applications and sedimentary 
processes conspire to alter and confound rate estimates (Ajayi et al., 
2020). Differential sediment mixing and bioturbation contribute 
to smooth and attenuate proxy records (Hupp and Kelly, 2020), 
thereby tending to underestimate maximum rates of change (Kemp 
et al., 2015). Considering the extent to which uncertainties can affect

sediment-based rate estimates, and notwithstanding recent effort 
in minimizing their inherent contribution, there is generally low to 
medium confidence  in quantifying rates of change on a time scale less 
than a decade back thousands of years, and less than a millennium 
back millions of years in the past based on marine sediments.
In the past, atmospheric CO 2 concentrations reached much higher 
levels than present day (Cross-Chapter Box  2.1 and Figure  5.3). 
In  particular, the Paleocene–Eocene thermal maximum (PETM), 
55.9–55.7 Ma (Figure 5.3), provides some level of comparison with 
the current and projected anthropogenic increase in CO 2 emissions 
(Chapter 2). Atmospheric CO 2 concentrations increased from about 
900 to around 2000 ppm in 3–20 kyr as a result of geological carbon 
Figure 5.3 | Atmospheric CO 2 concentrations and growth rates for the past 60 million years (Myr) and projections to 2100.  (a) CO 2 concentrations data for

the period 60 Myr to the time prior to 800 kyr (left column) are shown as the LOESS Fit and 68% range (data from Chapter 2) (Foster et al., 2017). Concentrations from 1750 
and projections through 2100 are taken from Shared Socio-economic Pathways of IPCC AR6 (Meinshausen et al., 2017). (b) Growth rates are shown as the time derivative of 
the concentration time series. Inserts in (b) show growth rates at the scale of the sampling resolution. Further details on data sources and processing are available in the chapter 
data table (Table 5.SM.6).

684
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5release to the ocean–atmosphere system (Zeebe et al., 2016; Gutjahr 
et al., 2017; Cui and Schubert, 2018; Kirtland Turner, 2018). There 
is low to medium confidence in evaluations of the total amount 
of carbon released during the PETM, as proxy data constrained 
estimates vary from around 3000 to more than 7000  PgC, with 
methane hydrates, volcanic emissions, terrestrial and/or marine 
organic carbon, or some combination thereof, as the probable 
sources of carbon (Zeebe et al., 2009; Cui et al., 2011; Gutjahr et al., 
2017; Elling et al., 2019; S.M. Jones et al., 2019; Haynes and Hönisch, 
2020). Methane emissions related to hydrate/permafrost thawing 
and fossil carbon oxidation may have acted as positive feedbacks 
(Lunt et al., 2011; Armstrong McKay and Lenton, 2018; Lyons et al., 
2019), as the inferred increase in atmospheric CO 2 can only account

for approximately half of the reported warming (Zeebe et al., 2009). 
The estimated, time-integrated carbon input is broadly similar 
to  the RCP8.5 extension scenario, although CO 2 emissions rates 
(0.3–1.5 Pg yr–1) and by inference the rate of CO 2 accumulation in the 
atmosphere (4–42 ppm per century) during the PETM were at least 
4–5 lower than during the modern era (from 1995 to 2014; Table 2.1; 
Zeebe et al., 2016; Gingerich, 2019).
The last 50 Myr (50  million years) have been characterized by 
a  gradual decline in atmospheric CO 2 levels at a  rate of about 
16 ppm Myr–1 (Figure 5.3; Foster et al., 2017; Gutjahr et al., 2017). 
The exact cause of this long-term change in CO 2 remains uncertain, 
but may be related to an imbalance between long-term sources of 
CO 2 (volcanic outgassing) and long-term sinks (organic carbon burial 
and silicate weathering).
The most recent time interval when atmospheric CO 2 concentration

was as high as 1000  ppm (i.e.,  similar to the end of 21st century 
projection for the high-end emissions scenario RCP8.5) was around 
33.5 Ma, prior to the Eocene-Oligocene transition (Y .G. Zhang et al., 
2013; Anagnostou et al., 2016). Atmospheric CO 2 levels then reached 
a critical threshold (1000–750 ppm; DeConto et al., 2008) to allow 
for the development of permanent regional ice sheets on Antarctica, 
associated with changes in Southern Ocean hydrography, which 
would have increased deep ocean CO 2 storage (Leutert et al., 2020).
The most recent interval characterized by atmospheric CO 2 levels 
similar to modern (i.e.,  360–420  ppm) was the mid-Pliocene Warm 
Period (MPWP , 3.3–3.0 Ma; Martínez-Botí et al., 2015; de la Vega et al., 
2020) (Chapter 2). The relatively high atmospheric CO 2 concentration 
during the MPWP are related to vigorous ocean circulation and 
a rather inefficient marine biological carbon pump (Burls et al., 2017),

which would have reduced deep ocean carbon storage. After the 
MPWP , atmospheric CO 2 concentrations declined gradually at a rate 
of 30 ppm Myr–1 (Figure 5.3; de la Vega et al., 2020), as an increase in 
ocean stratification led to enhanced ocean carbon storage, allowing for 
major, sustained advances in Northern Hemisphere ice sheets, 2.7 Ma 
(Sigman et al., 2004; DeConto et al., 2008).
5.1.2.2 Glacial–Interglacial Greenhouse Gas Records
The Antarctic ice core record covering the past 800  kyr provides 
an important archive to explore the carbon–climate feedbacks 
prior to anthropogenic perturbations (Brovkin et al., 2016). Polar ice cores represent the only climatic archive from which past GHG 
concentrations can be directly measured. Major GHGs, CH 4, N 2O and 
CO 2 generally co-vary on orbital time scales (Loulergue et al., 2008; 
Lüthi et al., 2008; Schilt et al., 2010b; Chapter 2), with consistently 
higher atmospheric concentrations during warm intervals of the past,

pointing to a  strong sensitivity to climate (Figure  5.4). Modelling 
work suggests that the carbon cycle contributed to globalise 
and amplify changes in orbital forcing, which are pacing glacial–
interglacial climate oscillations (Ganopolski and Brovkin, 2017), 
with ocean biogeochemistry and physics, terrestrial vegetation, 
peatland, permafrost and exchanges with the lithosphere including 
chemical weathering, volcanic activity, sediment burial and marine 
calcium carbonate compensation all playing a role in modulating the 
concentration of atmospheric GHGs.
Since AR5, the number of ice core records and the temporal resolution 
of their data for the last 800 kyr have improved, in particular for the 
last 60  kyr. Additionally, the advent of isotopic measurements on 
GHGs extracted from air trapped in ice, allows for more robust source 
apportionments and inventory assessments. Therefore, the ensuing 
discussion focuses on these two specific aspects.

Major pre-industrial sources of CH 4 comprise wetlands (including 
subglacial environments) and biomass burning (Bock et al., 2010, 
2017; Lamarche-Gagnon et al., 2019; Kleinen et al., 2020). Pre-
industrial atmospheric N 2O concentrations were regulated by 
microbial production in marine and terrestrial environments and 
by photochemical removal in the stratosphere (Schilt et al., 2014; 
Battaglia and Joos, 2018b; H.  Fischer et al., 2019). Pre-industrial 
atmospheric CO 2 concentrations were largely regulated by exchange 
with exogenic terrestrial and ocean carbon reservoirs. The imbalance 
between geological sources and sinks in the ocean–atmosphere–land 
biosphere system additionally plays an important role in modulating 
the air–sea partitioning of the active carbon inventory on multi-
millennial time scales (Cartapanis et al., 2018).
Model-based estimates indicate that wetland CH 4 emissions were 
reduced by 24–40% during the Last Glacial Maximum (LGM) when

compared to pre-industrial, while CH 4 emissions related to biomass 
burning (wildfires) decreased by 35–75% (Valdes et al., 2005; Hopcroft 
et al., 2017; Kleinen et al., 2020). N 2O emissions decreased by about 
30% during the LGM based on data-constrained model estimates 
(Schilt et al., 2014; H.  Fischer et al., 2019) owing to a  combination 
of a  weaker hydrological cycle and a  generally better ventilated 
intermediate depth ocean relative to present, reducing (de)nitrification 
processes (Galbraith et al., 2013; H. Fischer et al., 2019).
During past ice ages, generally colder and drier climate conditions 
contributed to a  substantial decline of the land biosphere carbon 
inventory, in particular in boreal peatlands (–300  PgC; Treat et al., 
2019). Estimates assessing the glacial decrease in the global terrestrial 
biosphere carbon stock vary between –300 and –600 PgC (Ciais et al., 
2012; Peterson et al., 2014; Menviel et al., 2017; Kleinen et al., 2020),

possibly –850 PgC when accounting for ocean-sediment interactions 
and burial (Jeltsch-Thömmes et al., 2019), a considerable contraction 
when compared to the modern land biosphere stock. The large range 
of estimates reflects a  yet limited understanding of how carbon

685
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55cycle dynamics were altered by glacially perturbed nutrient fluxes and 
soil dynamics, as well as largely exposed shelf areas in the tropics as 
a result of lowered sea level. Recent estimates suggest deep-sea CO 2 
storage during the last ice age exceeded modern values by as much 
as 750 – 950 PgC (Skinner et al., 2015, 2017; Buchanan et al., 2016; 
Anderson et al., 2019; Gottschalk et al., 2020b). A  combination of 
increased CO 2 solubility associated with 2–3°C lower mean oceanic 
temperatures (Bereiter et al., 2018), increased the oceanic residence 
time of CO 2 (Skinner et al., 2017), altered oceanic alkalinity (Yu 
et al., 2010; Cartapanis et al., 2018). A generally more efficient marine 
biological carbon pump (BCP; Galbraith and Jaccard, 2015; J. Yu et al., 
2019; Galbraith and Skinner, 2020) enhanced the partition CO 2 into the 
ocean interior, (although the relative contribution of each mechanism

remains a matter of debate). Recent observationally constrained ESM 
results highlight that air–sea disequilibrium amplifies the effect of 
cooling and iron fertilization on glacial carbon storage (Khatiwala 
et al., 2019).
Ice core observations combined with model-based estimates thus 
reveal with high confidence  that both terrestrial and marine CH 4 and N 2O emissions were reduced under glacial climate conditions. 
Multiple lines of evidence indicate with high confidence  that 
enhanced storage of remineralized CO 2 in the ocean interior, owing 
to a combination of synergistic mechanisms, was sufficient to balance 
the removal of carbon from the atmosphere and the terrestrial 
biosphere reservoirs combined during the last ice age.
Vegetation regrowth and increased precipitation in wetland 
regions associated with the mid-deglacial Northern Hemisphere 
warming (referred to as the Bølling/Allerød (B/A) warm interval,

14.7–12.7 ka), in particular in the (sub)tropics, accounts for large 
increases in both CH 4 and N 2O emissions to the atmosphere 
(Baumgartner et al., 2014; Schilt et al., 2014; Bock et al., 2017; 
H. Fischer et al., 2019). Specifically, changes in CH 4 sources were 
steered by variations in vegetation productivity, source size area, 
temperatures and precipitation as modulated by insolation, local 
sea level changes and monsoon intensity (Bock et al., 2017; 
Kleinen et al., 2020). Changes in the CH 4 atmospheric sink term 
probably only played a secondary role in modulating atmospheric 
CH 4 inventories across the LDT (Hopcroft et al., 2017; Kleinen et al., 
Figure 5.4 | Atmospheric concentrations of CO 2, CH 4 and N 2O in air bubbles and clathrate crystals in ice cores (800,000 BCE to 1990 CE).  Note the variable

x-axis range and tick mark intervals for the three columns. Ice core data is over-plotted by atmospheric observations from 1958 to present for CO 2, from 1984 for CH 4 and from 
1994 for N 2O. The time-integrated, millennial-scale linear growth rates for different time periods (800,000–0 BCE, 0–1900 CE and 1900–2017 CE) are given in each panel. For 
the BCE period, mean rise and fall rates are calculated for the individual slopes between the peaks (interglacials) and troughs (glacial periods), which are given in the panels in 
left column. The data for BCE period are used from the Vostok, EPICA, Dome C and WAIS ice cores (Petit et al., 1999; Monnin, 2001; Pépin et al., 2001; Raynaud et al., 2005; 
Siegenthaler et al., 2005; Loulergue et al., 2008; Lüthi et al., 2008; Schilt et al., 2010a). The data after 0–yr CE are taken mainly from Law Dome ice core analysis (MacFarling

Meure et al., 2006). The surface observations for all species are taken from NOAA cooperative research network (Dlugokencky and Tans, 2019), where ALT, MLO and SPO stand 
for Alert (Canada), Mauna Loa Observatory, and South Pole Observatory, respectively. BCE = before current era, CE = current era. Further details on data sources and processing 
are available in the chapter data table (Table 5.SM.6).

686
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks52020) Geological emissions, related to the destabilization of fossil 
(radiocarbon-dead) CH 4 sources buried in continental margins 
as a  result of sudden warming, appear small (Bock et al., 2017; 
Petrenko et al., 2017; Dyonisius et al., 2020). Stable isotope analysis 
on N 2O extracted from Antarctic and Greenland ice reveal that 
marine and terrestrial emissions increased by 0.7 ±  0.3 and 1.7 
±  0.3 TgN, respectively, across the LDT (H.  Fischer et al., 2019). 
During abrupt Northern Hemisphere warmings, terrestrial emissions 
responded rapidly to the northward displacement of the Intertropical 
Convergence Zone (ITCZ) associated with the resumption of the 
Atlantic meridional overturning circulation (AMOC; H. Fischer et al., 
2019). About 90% of these step increases occurred rapidly, possibly 
in less than 200 years (H. Fischer et al., 2019). In contrast, marine

emissions increased more gradually, modulated by global ocean 
circulation reorganization.
The gradual increase in atmospheric CO 2 across the LDT was 
punctuated by three centennial 10–13 ppm increments, coeval with 
100–200 ppb increases in CH 4 (Marcott et al., 2014), reminiscent 
of similar oscillations reported for the last ice age associated with 
transient warming events (Dansgaard/Oeschger (DO) events; Ahn 
and Brook, 2014; Rhodes et al., 2017; Bauska et al., 2018) as well as 
previous deglacial transitions (Nehrbass-Ahles et al., 2020). The rate 
of change in atmospheric CO 2 accumulation during these transient 
events exceeds the averaged deglacial growth rates by at least 50% 
(Table 2.1, Figure 5.4). The early deglacial release of remineralized 
carbon from the ocean abyss coincided with the  resumption of 
Southern Ocean overturning circulation (Skinner  et al., 2010; 
Schmitt et al., 2012; Ferrari et al., 2014; Gottschalk et al., 2016,

2020a; Jaccard et al., 2016; Rae et al., 2018; Moy et al., 2019) and 
the concomitant reduction in the global efficiency of the marine 
BCP , associated, in part, with dwindling iron fertilization (Hain 
et al., 2010; Martínez-García et al., 2014; Jaccard et al., 2016) The 
two subsequent pulses, centred 14.8 and 12.9 ka, are associated 
with enhanced air–sea gas exchange in the Southern Ocean (T. Li 
et al., 2020), iron fertilization in the South Atlantic and North Pacific 
(Lambert et al., 2021) and rapid increase in soil respiration owing 
to the resumption of AMOC and associated southward migration 
of the ITCZ (Marcott et al., 2014; Bauska et al., 2018). Rapid 
warming of high northern latitudes contributed to thaw permafrost, 
possibly liberating labile organic carbon to the atmosphere (Köhler 
et al., 2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer 
et al., 2019). Ocean surface pH reconstructions indicate that the

ocean was oversaturated with respect to the atmosphere during 
the early, mid-LDT (Martínez-Botí et al., 2015b; Shao et al., 2019; 
Shuttleworth et al., 2021), suggesting that ocean sources at that 
time may have been larger than terrestrial sources. Over the course 
of the LDT, the decrease in Northern Hemisphere permafrost carbon 
stocks has been more than compensated by an increase in the 
carbon stocks of mineral soils, peatland and vegetation (Lindgren 
et al., 2018; Jeltsch-Thömmes et al., 2019). The land biosphere was, 
on average, a  net sink for atmospheric carbon and accumulated 
several hundred Gt of carbon over the LDT. Detailed investigations 
reveal that Antarctic air temperatures, and more generally Southern 
Hemisphere (30°S–60°S) proxy temperature reconstructions, led 
the rise in pCO 2 at the onset of the LDT, 18  ka ago, by several 
hundred years (Shakun et al., 2012; Chowdhry Beeman et al., 2019). Atmospheric CO 2 led reconstructed global average temperature by

several centuries (Shakun et al., 2012), corroborating the importance 
of CO 2 as an amplifier of orbitally driven warming. During the LDT, 
the phasing between Antarctic air temperature and atmospheric 
GHG concentration changes was nearly synchronous, yet variable, 
owing to the complex nature of the mechanisms modulating the 
global carbon cycle (Chowdhry Beeman et al., 2019). Mean ocean 
temperature reconstructions, based on noble gas extracted from 
Antarctic ice are closely correlated with Antarctic air temperature 
and pCO 2 records, emphasizing the role the Southern Ocean is 
playing in modulating global climate variability (Bereiter et al., 
2018; Baggenstos et al., 2019).
Enhanced mid-ocean ridge magmatism and/or hydrothermal activity 
modulated by sea level rise has recently been hypothesized to have 
contributed to the deglacial CO 2 rise (Crowley et al., 2015; Lund 
et al., 2016; Huybers and Langmuir, 2017; Stott et al., 2019b). While

geological carbon release may have affected the ocean’s radiocarbon 
budget (Ronge et al., 2016; Rafter et al., 2019; Stott et al., 2019a), 
model results suggest that the potential contribution of geological 
carbon sources to the atmosphere remained small (Roth and Joos, 
2012; Hasenclever et al., 2017).
Simulations of Earth models of intermediate complexity (EMIC) with 
coupled glacial–interglacial climate and the carbon cycle were able to 
reproduce first-order changes in the atmospheric CO 2 content for the 
first time in recent years (Ganopolski and Brovkin, 2017; Khatiwala 
et al., 2019). The most important processes accounting for the full 
deglacial CO 2 amplitude in the models include solubility changes, 
changes in oceanic circulation and marine carbonate chemistry. The 
effect of the terrestrial carbon cycle, variable volcanic outgassing 
and the temperature dependence on the oceanic remineralization 
length scale contribute less than 15 ppm CO 2 between the glacial

and interglacial intervals of the cycles. However, details in the 
simulated response of the marine carbon cycle and atmospheric CO 2 
concentrations to changes in ocean circulation depend to a  large 
degree on model parametrization (Gottschalk et al., 2019).
Independent paleoclimatic evidence suggests with high confidence  
that marine and terrestrial CH 4 and N 2O emissions are highly sensitive 
to climate on (sub)centennial time scales. Limited, yet internally 
consistent ice core measurements indicate with medium confidence  
that pulsed geologic CH 4 release from continental margins associated 
with warming remained negligible across the LDT. Multiple lines of 
evidence suggest with high confidence  that CO 2 was released from the 
ocean interior on centennial time scales during the LDT in response 
to, or associated with warming, contributing to the transition out of 
the last glacial stage to the current interglacial period.

Multiple lines of evidence inferred from marine sediment proxies 
indicate with low to medium confidence  that the millennial rates of 
CO 2 concentration change in the atmosphere during the last 56 Myr 
were at least four to five times lower than during the last century 
(Figure  5.3). In spite of uncertainties in ice core reconstructions 
related to delayed enclosure of air bubbles, which tend to smooth 
the records, there is high confidence  that the rates of atmospheric 
CO 2 and CH 4 change during the last century were at least 10 and 5

687
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55times faster, respectively, than the maximum centennial growth rate 
averages of those gases during the last 800 kyr (Fig. 5.4).
5.1.2.3 Holocene Changes
Atmospheric GHG concentrations were much less variable during 
the pre-industrial Holocene (from 11.7 ka to 1750 CE). Atmospheric 
CH 4 concentrations decreased at the beginning of the Holocene, 
consistent with a general weakening of boreal sources (Yang et al., 
2017; Beck et al., 2018) and further decline during the mid-Holocene 
owing to a reduction in Southern Hemisphere emissions concomitant 
with a  southward shift of the ITCZ (Singarayer et al., 2011; Beck 
et al., 2018). Atmospheric CH 4 concentrations increased about 5 ka, 
which prompted the hypothesis of an early anthropogenic influence 
related to land-use changes in South East Asia (Ruddiman et al., 
2016). However, stable isotope compositions on CH 4 extracted from

Greenland and Antarctic ice (Beck et al., 2018) reveal that natural 
emissions located in the southern tropics were responsible for the 
rise in atmospheric CH 4 concentrations, in line with model simulations 
(Singarayer et al., 2011) thus disputing the early anthropogenic 
influence on the global CH 4 budget. Atmospheric N 2O concentrations 
increased slightly (20 ppb) across the Holocene, associated with 
a gradual decline in its nitrogen stable isotope composition (H. Fischer 
et al., 2019). The combined signal is consistent with a small increase 
in terrestrial emissions, offset by a  reduction in marine emissions 
(Schilt et al., 2010b; H. Fischer et al., 2019).
The early Holocene decrease in CO 2 concentration by about 5 ppm 
(Schmitt et al., 2012) has been attributed to post-glacial regrowth 
in terrestrial biomass and a gradual increase in peat reservoirs over 
the Holocene, resulting in the sequestration of several hundred PgC

(Yu et al., 2010; Nichols and Peteet, 2019). Peat accumulation 
rates in boreal and temperate regions were higher under warmer 
summer conditions in the early to mid-Holocene (Loisel et al., 2014; 
Stocker et al., 2017). The 20 ppm gradual increase of atmospheric 
CO 2 starting 7  ka has been attributed to a  decrease in natural 
terrestrial biomass due to climate change, carbonate compensation 
and enhanced shallow water carbonate deposition (Menviel and 
Joos, 2012; Brovkin et al., 2016), consistent with stable carbon 
isotope measurements on CO 2 extracted from Antarctic ice (Elsig 
et al., 2009; Schmitt et al., 2012). These isotopic measurements do 
not support an early anthropogenic influence on atmospheric CO 2 
due to land-use change and forest clearing (Ruddiman et al., 2016). 
Recent paleoceanographic evidence suggests that remineralized 
carbon outgassing associated with increased Southern Ocean 
circulation and upwelling (Studer et al., 2018), possibly promoted

by stronger Southern Hemisphere westerly winds (Saunders et al., 
2018), could have additionally contributed to the late Holocene 
increase in atmospheric CO 2 concentrations. However, the role of 
these mechanisms remained insignificant in transient Holocene 
ESM simulations (Brovkin et al., 2019). Overall, as in AR5 (WGI, 
Chapter  5), there is medium confidence  in the key drivers of 
the  CO 2 increase between the early Holocene and the  beginning 
of the industrial era, yet there is low confidence  in the relative 
contributions of these drivers due to insufficient quantitative 
constraints on particular processes.5.2 Historical Trends, Variability and Budgets 
of CO 2, CH 4 and N 2O
This section assesses the trends and variability in atmospheric 
accumulation of the three main greenhouse gases (GHGs) –  CO 2, 
CH 4 and N 2O – their ocean and terrestrial sources and sinks as well 
as their budgets during the Industrial Era (1750–2019). Emphasis is

placed on the more recent contemporary period (1959–2019) where 
understanding is increasingly better constrained by atmospheric, 
ocean and land observations. The section also assesses our increased 
understanding of the anthropogenic forcing and processes driving 
the trends, as well as how variability at the seasonal to decadal 
scales provide insights on the mechanism governing long-term 
trends and emerging biogeochemical–climate feedbacks with their 
regional characteristics.
5.2.1 CO 2: Trends, Variability and Budget
5.2.1.1 Anthropogenic CO 2 Emissions
There are two anthropogenic sources of carbon dioxide (CO 2): fossil 
emissions and net emissions (including removals) resulting from 
land-use change and land management (also shown in this chapter 
as LULUCF: land use, land-use change, and forestry; in previous 
IPCC reports it has been termed forestry and other land use, FOLU). 
Fossil CO 2 emissions include the combustion of the fossil fuels

coal, oil and gas, covering all sectors of the economy (electricity, 
transport, industrial, and buildings), fossil carbonates such as in 
cement manufacturing, and other industrial processes such as the 
production of chemicals and fertilizers (Figure  5.5a). Fossil CO 2 
emissions are estimated by combining economic activity data and 
emissions factors, with different levels of methodological complexity 
(tiers) or approaches (e.g., IPCC Guidelines for National Greenhouse 
Gas Inventories). Several organizations or groups provide estimates 
of fossil CO 2 emissions, with each dataset having slightly different 
system boundaries, methods, activity data, and emissions factors 
(Andrew, 2020). Datasets cover different time periods, which can 
dictate the datasets and methods that are used for a  particular 
application. The data reported here is from an annually updated 
data source that combines multiple sources to maximise temporal

coverage (Friedlingstein et al., 2020). The uncertainty in global fossil 
CO 2 emissions is estimated to be ±5% (1 standard deviation).
Fossil CO 2 emissions have grown continuously since the beginning 
of the industrial era (Figure  5.5) with short intermissions due to 
global economic crises or social instability (Peters et al., 2012; 
Friedlingstein et al., 2020). In the most recent decade (2010–2019), 
fossil CO 2 emissions reached an average 9.6 ±  0.5  PgC yr–1 and 
were responsible for 86% of all anthropogenic CO 2 emissions. In 
2019, fossil CO 2 emissions were estimated to be 9.9 ± 0.5 PgC yr–1 
excluding carbonation (Friedlingstein et al., 2020), the highest on 
record. These estimates exclude the cement carbonation sink of 
around 0.2 PgC yr–1. Fossil CO 2 emissions grew at 0.9% yr–1 in the 
1990s, increasing to 3.0% yr–1 in the 2000s, and reduced to 1.2% 
from 2010 to 2019. The slower growth in fossil CO 2 emissions in

688
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5CO 2 emissions from coal use grew at 4.8% yr–1 in the 2000s, but 
slowed to 0.4% yr–1 in the 2010s. CO 2 emissions from oil use grew 
steadily at 1.1% yr–1 in both the 2000s and 2010s. CO 2 emissions 
from gas use grew at 2.5% yr–1 in the 2000s and 2.4% yr–1 in 2010s, 
but has shown signs of accelerated growth of 3% yr–1 since 2015 
(G.P . Peters et al., 2020). Direct CO 2 emissions from carbonates in 
cement production are around 4% of total fossil CO 2 emissions, 
and grew at 5.8% yr–1 in the 2000s but a slower 2.4% yr–1 in the 
2010s. The uptake of CO 2 in cement infrastructure (carbonation) 
offsets about one half of the carbonate emissions from current 
cement production (Friedlingstein et al., 2020). These results are 
robust across the different fossil CO 2 emissions datasets, despite 
minor differences in levels and rates, as expected given the

reported uncertainties (Andrew, 2020). During 2020, the COVID-19 
pandemic led to a rapid, temporary decline in fossil CO 2 emissions, 
estimated to be around 7% based on a synthesis of four estimates. 
(Cross-Chapter Box  6.1; Forster et al., 2020; Friedlingstein et al., 
2020; Le Quéré et al., 2020; Z. Liu et al., 2020).
The global net flux from land-use change and land management 
is composed of carbon fluxes from land-use conversions, land 
management and changes therein (Pongratz et al., 2018) and is 
equivalent to the LULUCF fluxes from the agriculture, forestry and 
other land use (AFOLU) sector (Jia et al., 2019). It consists of gross 
emissions (loss of biomass and soil carbon in clearing or logging, 
harvested product decay, emissions from peat drainage and 
burning, degradation) and gross removals (CO 2 uptake in natural 
vegetation regrowing after harvesting or agricultural abandonment, 
afforestation). The LULUCF flux relates to direct human interference

with terrestrial vegetation, as opposed to the natural carbon fluxes occurring due to interannual variability or trends in environmental 
conditions (in particular, climate, CO 2, and nutrient deposition) 
(Houghton, 2013).
Progress since AR5 and SRCCL (IPCC, 2019a) allows more accurate 
estimates of gross and net fluxes due to the availability of more 
models, model advancement in terms of inclusiveness of land-use 
practices, and advanced land-use forcings (Ciais et al., 2013; Klein 
Goldewijk et al., 2017; Hurtt et al., 2020). In addition, important 
terminological discrepancies were resolved. First, synergistic effects 
of land-use change and environmental changes have been identified 
as a key reason for the large discrepancies between model estimates 
of the LULUCF flux, explaining up to 50% of differences (Pongratz 
et al., 2014; Stocker and Joos, 2015; Gasser et al., 2020). Another 
reason for discrepancies relates to natural fluxes being considered

as part of the LULUCF flux when occurring on managed land in the 
United Nations Framework Convention on Climate Change (UNFCCC) 
national GHG inventories; these fluxes are considered part of the 
natural terrestrial sink in global vegetation models and excluded in 
bookkeeping models (Grassi et al., 2018). LULUCF fluxes following 
national GHG inventories or Food and Agriculture Organization 
of the United Nations (FAO) datasets, including recent estimates 
(Tubiello et al., 2021), are thus excluded from our global assessment, 
but their comparison against the academic approach is available 
elsewhere – at the global scale (Jia et al., 2019) and European level 
(Petrescu et al., 2020).
Land-use-related component fluxes can be verified by the growing 
databases of global satellite-based biomass observations in 
combination with information on remotely sensed land cover change. (a) Anthropogenic global CO2 emissions (b) Global CO2 emissions from land-use changePgC yr -1

PgC yr -1            (range)
Figure 5.5 | Global anthropogenic CO 2 emissions.  (a) Historical trends of anthropogenic CO 2 emissions (fossil fuels and net land-use change, including land management, 
called LULUCF flux in the main text) for the period 1870 to 2019, with ‘others’ representing flaring, emissions from carbonates during cement manufacture. Data sources: 
(Boden et al., 2017; IEA, 2017; Andrew, 2018; BP , 2018; Le Quéré et al., 2018a; Friedlingstein et al., 2020). (b) The net land-use change CO 2 flux (PgC yr–1) as estimated by three 
bookkeeping models and 16 Dynamic Global Vegetation Models (DGVMs) for the global annual carbon budget 2019 (Friedlingstein et al., 2020). The three bookkeeping models 
are from Hansis et al., 2015; Houghton and Nassikas, 2017; Gasser et al., 2020 and are all updated to 2019. Their average is used to determine the net land-use change flux in

the annual global carbon budget (black line). The DGVM estimates are the result of differencing a simulation with and without land-use changes run under observed historical 
climate and CO 2, following the Trendy v9 protocol ( https://sites.exeter.ac.uk/trendy/protocol/ ); they are used to provide an uncertainty range to the bookkeeping estimates 
(Friedlingstein et al., 2020). All estimates are unsmoothed annual data. Estimates differ in process comprehensiveness of the models and in definition of flux components 
included in the net land use change flux. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

689
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55However, they differ from bookkeeping and modelling with Dynamic 
Global Vegetation Models (DGVMs) in excluding legacy emissions 
from pre-satellite-era land-use change and land management, and 
neglecting soil carbon changes, often focusing on gross deforestation, 
not regrowth (Jia et al., 2019).
For the decade 2010–2019, average emissions were estimated at 
1.6 ± 0.7 PgC yr–1 (mean ± standard deviation, 1 sigma; Friedlingstein 
et al., 2020). A  likely general upward trend since 1850 is reversed 
during the second part of the 20th century (Figure  5.5b). Trends 
since the 1980s have low confidence  because they differ between 
estimates, which is related, among other things, to Houghton and 
Nassikas (2017) using a different land-use forcing than Hansis et al. 
(2015) and the DGVMs. Higher emissions estimates are expected 
from DGVMs run under transient environmental conditions compared

to bookkeeping estimates, because the DGVM estimate includes 
the loss of additional sink capacity. Because the transient setup 
requires a reference simulation without land-use change to separate 
anthropogenic fluxes from natural land fluxes, LULUCF estimates 
by DGVMs include the sink forests that would have developed in 
response to environmental changes on areas that in reality have been 
cleared (Pongratz et al., 2014). The agricultural areas that replaced 
these forests have a reduced residence time of carbon, lacking woody 
material, and thus provide a substantially smaller additional sink over 
time (Gitz and Ciais, 2003). The loss of additional sink capacity is 
growing in particular with atmospheric CO 2 and increases DGVM-
based LULUCF flux estimates relative to bookkeeping estimates over 
time (Figure 5.5).
Gross emissions are on average two to three times larger than the net 
flux from LULUCF , increasing from an average of 3.5 ± 1.2 PgC yr–1 for

the decade of the 1960s to an average of 4.4 ± 1.6 PgC yr–1 during 
2010–2019 (Friedlingstein et al., 2020). Gross removals partly 
balance these gross emissions to yield the net flux from LULUCF and 
increase from –2.0 ± 0.7 PgC yr–1 for the 1960s to –2.9 ± 1.2 PgC yr–1 
during 2010–2019. These large gross fluxes show the relevance of 
land management, such as harvesting or rotational agriculture, and 
the large potential to reduce emissions by halting deforestation 
and degradation.
More evidence on the pre-industrial LULUCF flux has emerged 
since AR5 in the form of new estimates of cumulative carbon losses 
until today, and of a better understanding of natural carbon cycle 
processes over the Holocene (Ciais et al., 2013). Cumulative carbon 
losses by land-use activities since the start of agriculture and forestry 
(pre-industrial and industrial era) have been estimated at 116 PgC 
based on global compilations of carbon stocks for soils (Sanderman

et al., 2017) with about 70  PgC of this occurring prior to 1750, 
and for vegetation as 447  PgC (inner quartiles of 42 calculations: 
375–525  PgC) (Erb et al., 2018). Emissions prior to 1750 can be 
estimated by subtracting the post-1750 LULUCF flux from Table 5.1 
from the combined soil and vegetation losses until today; they would 
then amount to 328 (161–501) PgC assuming error ranges are 
independent. A share of 353 (310–395) PgC from prior to 1800 has 
indirectly been suggested as the difference between net biosphere 
flux and terrestrial sink estimates, which is compatible with ice-core 
records due to a low airborne fraction of anthropogenic emissions in pre-industrial times (Erb et al., 2018; see also Section 5.1.2.3). Low 
confidence  is assigned to pre-industrial emissions estimates.
Since AR5, evidence emerged that the LULUCF flux might have been 
underestimated as DGVMs include anthropogenic land cover change,

but often ignore land management practices not associated with 
a change in land cover; land management is more widely captured 
by bookkeeping models through use of observation-based carbon 
densities (Ciais et al., 2013; Pongratz et al., 2018). Sensitivity studies 
show that practices such as wood and crop harvesting increase 
global net LULUCF emissions (Arneth et al., 2017) and explain about 
half of the cumulative loss in biomass (Erb et al., 2018).
5.2.1.2 Atmosphere
Atmospheric CO 2 concentration measurements in remote locations 
began in 1957 at the South Pole Observatory (SPO) and in 1958 
at Mauna Loa Observatory (MLO), Hawaii, USA (Keeling, 1960) 
(Figure  5.6a). Since then, measurements have been extended to 
multiple locations around the world (Bacastow et al., 1980; Conway 
et al., 1994; Nakazawa et al., 1997). In addition, high-density global 
observations of total column CO 2 measurements by dedicated GHG-

observing satellites began in 2009 (Yoshida et al., 2013; O’Dell 
et al., 2018). Annual mean CO 2 growth rates are observed to be 
1.56 ± 0.18 ppm yr–1 (average and range from 1 standard deviation 
of annual values) over the 61 years of atmospheric measurements 
(1959–2019), with the rate of CO 2 accumulation almost tripling 
from an average of 0.82 ±  0.29  ppm yr–1 during the decade of 
1960–1969 to 2.39 ± 0.37 ppm yr–1 during the decade of 2010–2019 
(Chapter 2). The latter agrees well with that derived for total column 
(XCO 2) measurements by the Greenhouse Gases Observing Satellite 
(GOSAT; Figure 5.6b). The interannual oscillations in monthly mean 
CO 2 growth rates (Figure 5.6b) show a close relationship with the 
El Niño–Southern Oscillation (ENSO) cycle (Figure 5.6b) due to the 
ENSO-driven changes in terrestrial and ocean CO 2 sources and sinks 
on the Earth’s surface (Section 5.2.1.4).
Multiple lines of evidence unequivocally establish the dominant

role of human activities in the growth of atmospheric CO 2. First, the 
systematic increase in the difference between the MLO and SPO 
records (Figure 5.6a) is caused primarily by the increase in emissions 
from fossil fuel combustion in industrialized regions that are situated 
predominantly in the Northern Hemisphere (Ciais et al., 2019). Second, 
measurements of the stable carbon isotope in the atmosphere (d13C–
CO 2) are more negative over time because CO 2 from fossil fuels 
extracted from geological storage is depleted in 13C (Figure  5.6c; 
Rubino et al., 2013; Keeling et al., 2017). Third, measurements of the 
d(O 2/N2) ratio show a declining trend because for every molecule of 
carbon burned, 1.17 to 1.98 molecules of oxygen (O 2) is consumed 
(Figure 5.6d; Ishidoya et al., 2012; Keeling and Manning, 2014). These 
three lines of evidence confirm unambiguously that the atmospheric 
increase of CO 2 is due to an oxidative process (i.e.,  combustion).

Fourth, measurements of radiocarbon (14C–CO 2) at sites around the 
world (Levin et al., 2010; Graven et al., 2017; Turnbull et al., 2017) 
show a continued long-term decrease in the 14C/12C ratio. Fossil fuels 
are devoid of 14C and therefore fossil fuel-derived CO 2 additions 
decrease the atmospheric 14C/12C ratio (Suess, 1955).

690
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Over the past six decades, the fraction of anthropogenic CO 2 
emissions that has accumulated in the atmosphere (referred to as 
airborne fraction) has remained near constant at approximately 
44% (Figure 5.7) (Ballantyne et al., 2012; Ciais et al., 2019; Gruber 
et al., 2019b; Friedlingstein et al., 2020). This suggests that the land 
and ocean CO 2 sinks have continued to grow at a  rate consistent 
with the growth rate of anthropogenic CO 2 emissions, albeit with 
large interannual and sub-decadal variability dominated by the land 
sinks (Figure 5.7).
Since AR5, an alternative observable diagnostic to the airborne 
fraction has been proposed to understand the trends in land and 
ocean sinks in response to its driving atmospheric CO 2 concentrations 
(Raupach et al., 2014; Bennedsen et al., 2019). It is the sink rate 
that is defined as the combined ocean and land sink flux per unit of

atmospheric excess of CO 2 above pre-industrial levels (Raupach et al., 
Figure 5.7 | Airborne fraction and anthropogenic (fossil fuel and land-use change) CO 2 emissions.  Data as in Section 5.2.1.1. The multivariate El Niño–Southern 
Oscillation (ENSO) index (shaded) and the major volcanic eruptions are marked along the x-axis. Further details on data sources and processing are available in the chapter 
data table (Table 5.SM.6).Figure 5.6 | Time series of CO 2 concentrations and related measurements 
in ambient air.  (a) Concentration time series and MLO-SPO difference, (b) growth 
rates, (c) 14C and 13C isotopes, and (d) O 2/N2 ratio. The data for Mauna Loa 
Observatory (MLO) and South Pole Observatory (SPO) are taken from the Scripps 
Institution of Oceanography (SIO)/University of California, San Diego (Keeling et al., 
2001). The global mean CO 2 are taken from National Oceanic and Atmospheric 
Administration (NOAA) cooperative network (as in Chapter 2), and Greenhouse Gases

Observing Satellite (GOSAT) monthly mean XCO 2 (mixing ratio) time series are taken 
from National Institute for Environmental Studies (Yoshida et al., 2013). CO 2 growth 
rates are calculated as the time derivative of deseasonalized time series (Nakazawa 
et al., 1997). The D(O 2/N2) are expressed in per meg units (= (FF/M) × 106, where 
FF = moles of O 2 consumed by fossil-fuel burning, M = 3.706 × 1019, total number 
of O 2 molecules in the atmosphere (Keeling and Manning, 2014). The 14CO 2 time 
series at Barring Head, Wellington, New Zealand (BHD) is taken from GNS Science 
and NIWA (Turnbull et al., 2017). The multivariate ENSO index (MEI) is shown as the 
shaded background in panel (b); (warmer shade indicates El Niño). Further details on 
data sources and processing are available in the chapter data table (Table 5.SM.6).
Atmospheric Carbon dioxide (CO 2) and Oxygen (O 2)

691
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 552014). The sink rate has declined over the past six decades, which 
indicates that the combined ocean and land sinks are not growing 
as fast as the growth in atmospheric CO 2 (Raupach et al., 2014; 
Bennedsen et al., 2019). Possible explanations for the sink rate decline 
are that the land and/or ocean CO 2 sinks are no longer responding 
linearly with CO 2 concentrations or that anthropogenic emissions 
are slower than exponential (Figure  5.7 and Sections  5.2.1.3 and 
5.2.1.4; Gloor et al., 2010; Raupach et al., 2014; Bennedsen et al., 
2019). In addition, both diagnostics are influenced by major climate 
modes (e.g., ENSO) and volcanic eruptions that contribute to high 
interannual variability (Gloor et al., 2010; Frölicher et al., 2013; 
Raupach et al., 2014), suggesting high sensitivity to future climate 
change. Uncertain land-use change fluxes (Section 5.2.1.2) influence

the robustness of the trends. Based on the airborne fraction (AF), 
it is concluded with medium confidence  that both ocean and land 
CO 2 sinks have grown consistent with the rising of anthropogenic 
emissions. Further research is needed to understand the drivers of 
changes in the CO 2 sink rate.
5.2.1.3 Ocean Carbon Fluxes and Storage
Since AR5 and SROCC, major advances in globally coordinated 
ocean CO 2 observations (Surface Ocean CO 2 Atlas, SOCAT; and 
Global Ocean Data Analysis Project, GLODAP), the harmonization 
of ocean and coastal-observation-based products, atmospheric and 
oceanic inversion models and forced global ocean biogeochemical 
models (GOBMs) have increased the level of confidence in the 
assessment of trends and variability of air–sea fluxes and storage of 
CO 2 in the ocean during the historical period (1960–2018; see also 
Supplementary Materials 5.SM.1; Ciais et al., 2013; Bakker et al., 
2016; Landschützer et al., 2016, 2020; Bindoff et al., 2019; DeVries

et al., 2019; Gregor et al., 2019; Gruber et al., 2019a, b; Tohjima et al., 
2019; Friedlingstein et al., 2020; Hauck et al., 2020; Olsen et al., 
2020). A major advance since SROCC is that, for the first time, all six 
published observational product fluxes used in this assessment, are 
made more comparable using a common ocean and sea ice cover 
area, integration of climatological coastal fluxes scaled to increasing 
atmospheric CO 2 and an ensemble mean of ocean fluxes calculated 
from three re-analysis wind products (Supplementary Materials 
5.SM.2; Landschützer et al., 2014, 2020; Rödenbeck et al., 2014; 
Zeng et al., 2014; Denvil-Sommer et al., 2019; Gregor et al., 2019; 
Iida et al., 2021). From a process point of view, the ocean uptake 
of anthropogenic carbon is a two-step set of abiotic processes that 
involves the exchange of CO 2, first across the air–sea boundary into 
the surface mixed layer, followed by its transport into the ocean

interior where it is stored for decades to millennia, depending on 
the depth of storage (Gruber et al., 2019b). Two definitions of air–
sea fluxes of CO 2 are used in this assessment for both observational 
products and models: S ocean is the global mean ocean CO 2 sink and 
Fnet denotes the net spatially varying CO 2 fluxes (Hauck et al., 2020). 
Adjustment of the mean global F net for the pre-industrial sea-to-air 
CO 2 flux associated with land-to-ocean carbon flux term makes F net 
comparable to S ocean (Jacobson et al., 2007; Resplandy et al., 2018; 
Hauck et al., 2020).
There are multiple lines of observational and modelling evidence that 
support with high confidence  the finding that, in the historical period (1960–2018), air–sea fluxes and storage of anthropogenic CO 2 are 
largely influenced by atmospheric CO 2 concentrations, physical 
ocean processes and physicochemical carbonate chemistry, which 
determines the unique properties of CO 2 in seawater (Chapter 9 and

Cross-Chapter Box  5.3; Wanninkhof, 2014; DeVries et al., 2017; 
McKinley et al., 2017, 2020, Gruber et al., 2019a, b; Hauck et al., 
2020). Here we assess three different approaches (Figures  5.8a,b 
and 5.9) that together provide high confidence  that, during the 
historical period (1960–2018), the ocean carbon sink (S ocean) and its 
associated ocean carbon storage have grown in response to global 
anthropogenic CO 2 emissions (Gruber et al., 2019a; Hauck et al., 
2020; McKinley et al., 2020).
5.2.1.3.1 Ocean carbon fluxes and storage: 
Global multi-decadal trends
In the first assessment approach, the mean global multi-decadal 
(1960–2019) trends in the ocean sink (S ocean) for CO 2 show a high 
degree of coherence across the nine GOBMs and six pCO 2-based 
observational product reconstructions (1987–2018) which, despite 
a  temporary slowdown (or ‘hiatus’)  in the 1990s, is also quasi-
linear over that period (Figure 5.8a; Gregor et al., 2019; Hauck et al.,

2020). This coherence between the GOBMs and observations-based 
reconstructions (1987–2018; r2=0.85) provides high confidence  
that the ocean sink (S ocean in Section  5.2.1.5) evaluated from 
GOBMs (1960–2019) grew quasi-linearly from 1.0 ±  0.3  PgC yr–1 
to 2.5 ± 0.6 PgC yr–1 between the decades 1960–1969 and 2010–
2019 in response to global CO 2 emissions (Figure  5.8a; Table  5.1; 
Friedlingstein et al., 2020; Hauck et al., 2020). The cumulative ocean 
CO 2 uptake (105  ±  20  PgC) is 23% of total anthropogenic CO 2 
emissions (450 ± 50 PgC) for the same period (Friedlingstein et al., 
2020).  Notwithstanding the high confidence  in the magnitude of 
the annual to decadal trends for S ocean, this assessment is moderated 
to medium  confidence by the low confidence  in the currently 
inadequately constrained uncertainties in the pre-industrial land-to-
ocean carbon flux, the uncertain magnitude of winter outgassing from

the Southern Ocean, and the uncertain effect of the ocean surface 
cool-skin, the effect of data sparsity, differences between wind 
products and the uncertain contribution from the changing land–
ocean continuum on global and regional fluxes (Jacobson et al., 2007; 
Resplandy et al., 2018; Roobaert et al., 2018; Bushinsky et al., 2019; 
Hauck et al., 2020; Watson et al., 2020; Gloege et al., 2021). However, 
both GOBMs and pCO 2-based observational products independently 
reveal a slowdown or ‘hiatus’ of the ocean sink in the 1990s, which 
provides a  valuable constraint for model verification and leads to 
greater confidence in the model outputs  (Figure 5.8a; Landschützer 
et al., 2016; Gregor et al., 2018; DeVries et al., 2019; Hauck et al., 
2020). A number of studies point to the role of the Southern Ocean in 
the global ‘1990s hiatus’ in air–sea CO 2 fluxes, but provide different 
process-based explanations linking ocean temperature, mixing and

meridional overturning circulation (MOC) responses to variability 
in large-scale climate systems, wind stress and volcanic activity, as 
well as the sensitivity of the air–sea CO 2 flux to small changes in 
the atmospheric forcing from anthropogenic CO 2 (Landschützer et al., 
2016; DeVries et al., 2017; Bronselaer et al., 2018; Gregor et al., 2018; 
Gruber et al., 2019a; Keppler and Landschützer, 2019; McKinley et al., 
2020; Nevison et al., 2020). Data sparsity in the Southern Ocean could

692
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5
also be a factor amplifying the global decadal perturbation of the 
1990s (Gloege et al., 2021). Therefore, while there is high confidence  
in the 1990s hiatus of the global ocean sink for anthropogenic CO 2, 
and that the Southern Ocean makes an observable contribution to 
it, there is still low confidence  in the attribution for the processes 
behind the 1990s hiatus (Section 5.2.1.3.2). Observed increases in 
the amplitude of the seasonal cycle of ocean pCO 2 and reductions in 
the mean global buffering capacity provide high confidence  that the 
growing CO 2 sink is also beginning to drive observable large-scale 
changes in ocean carbonate chemistry (Jiang et al., 2019). However, 
there is medium confidence  that these changes which, depending 
on the emissions scenario, could drive future ocean feedbacks, are 
still too small to emerge from the historical multi-decadal observed

growth rate of S ocean (Sections 5.1.2; 5.3.2 and 5.4.2, and Figure 5.8a; SROCC Section  5.2.2.3.2; Bates et al., 2014; Sutton et al., 2016; 
Fassbender et al., 2017; Landschützer et al., 2018; Jiang et al., 2019). 
A recent model-based study suggests that re-emergence of previously 
stored anthropogenic CO 2 is changing the buffering capacity of the 
mixed layer and reducing the ocean sink for anthropogenic CO 2 
during the historical period (Rodgers et al., 2020). This trend is 
not reflected in observations-based products (Figure  5.8a), so we 
attribute a  low confidence .
The second assessment approach makes use of six independent 
methods to constrain the mean decadal ocean sink over the period 
1990–2019 (Figure  5.8b). This provides a  multi-decadal advance 
on the 1990–1999 decadal constraint from (Denman et al., 2007) 
that has been widely used as a model constraint for GOBMs used 
for the global carbon budget (Hauck et al., 2020). The medium

confidence  attributed by this assessment of the global multi-decadal 
trend (Figure  5.8a) is further supported by the broad agreement 
in magnitude and trend of the decadal mean ocean CO 2 uptake 
with assessments that also include additional observations-based, 
independent methods such as ocean CO 2 inversion and atmospheric 
CO 2 and O 2/N2 measurements (Figure 5.8b; Supplementary Materials 
Tables 5.SM.1 and 5.SM.2).
Here we provide a third comparative assessment approach depicting 
the spatial coherence of ocean air–sea fluxes and storage rates 
of CO 2 as well as a quantitative assessment of both fluxes for the 
same period (1994–2007; Figure  5.9). Observation-based pCO 2 
flux products show that emissions of natural CO 2 occur mostly in 
the tropics and high-latitude Southern Ocean, and that the uptake 
and storage of anthropogenic CO 2 occurs predominantly in the mid-
latitudes (Chapter 9, Figure 5.9 and Cross-Chapter Box 5.3). Strong

ocean CO 2 sink regions are those in the mid-latitudes associated 
with the cooling of poleward flowing subtropical surface waters 
as well as equatorward flowing sub-polar surface waters, both of 
which contribute to the formation of Mode, Intermediate and Deep 
water masses that transport anthropogenic CO 2 into the ocean 
interior on time scales of decades to centuries in both hemispheres 
(Section 9.2.2.3 and Figure 5.9; DeVries, 2014; Gruber et al., 2019b; 
Wu et al., 2019). The mean decadal scale magnitude and uncertainties 
of S ocean from net air sea fluxes (F net) were calculated from an ensemble 
of six observational-based product reconstructions (Figure  5.9a) 
and the storage rates in the ocean interior derived from multiple 
ocean interior CO 2 datasets (Gruber et al., 2019b; Figure 5.9b). The 
cumulative CO 2 stored in the ocean interior from 1800 to 2007 has 
been estimated at 140 ±18 PgC (Gruber et al., 2019b). As reported in

SROCC (Section 5.2.2.3.1; IPCC, 2019b), the net ocean CO 2 storage 
between 1994–2007 was 29 ± 4 PgC, which corresponds to a mean 
storage of 26 ± 5% of anthropogenic CO 2 emissions for that period 
(Gruber et al., 2019b). The resulting net annual storage rate of 
anthropogenic CO 2, equivalent to S ocean for the period mid-1994 to 
mid-2007 is 2.2 ± 0.3 PgC yr–1, which is in very close agreement with 
the top-down air–sea flux estimate of S ocean of 2.1 ±  0.5  PgC yr–1 
from GOBMs and 1.9 ± 0.3PgC yr–1 from pCO 2-based observational 
products with the steady river carbon flux correction of 0.62 PgC yr–1 
for the same time period (Gruber et al., 2019b; Hauck et al., 2020). 
This close agreement between these independent ocean CO 2 sink 
estimates derived from air–sea fluxes and storage rates in the ocean 1960 1970 1980 1990 2000 2010–4–3–2–10
Ocean sink fluxes for 
anthropogenic CO2 (PgC yr-1) Comparative Trends in the Ocean Si nk for Anthropogenic CO2:

Models (1960-2018) and Observation -based Products (1987-2018)
Observation-based products Models
Atmospheric CO 2  inversionsGlobal ocean 
biogeochemical models
Observation-based productsAtmospheric CO 2 and 
O2/N2
Ocean carbon inversion
model Ocean carbon inventory 
change (b) Multi-method Decadal means of the Ocean Sink Fluxes 
     for Anthropogenic CO2(a) Comparitive trends in the ocean sink for anthropogenic CO2 
models (1960–2018) and observation-based products (1987–2018) 
Ocean uptake
1990–1999 1994 –2007 2000–2009 2007 –2016 2010–2019
PeriodsPgC yr -1–0.5
–1.0
–1.5
–2.0
–2.5
–3.0
–3.5
Figure 5.8 | Multi-decadal trends for the ocean sink of CO 2. (a) The multi-
decadal (1960–2019) trends in the annual ocean sink (S ocean) reconstructed from nine 
Global Ocean Biogeochemical Models (GOBM) forced with atmospheric re-analysis 
products (Hauck et al., 2020), six observationally based gap-filling products that

reconstructed spatial and temporal variability in the ocean CO 2 flux from sparse 
observations of surface ocean pCO 2 (Supplementary Materials 5.SM.2). The trends in 
Socean were calculated from the mean annual GOBM outputs, and the observational 
products were used to provide confidence in the GOBM assessments (r2=0.85). Thick 
lines represent the multi-model mean. Observationally based products have been 
corrected for pre-industrial river carbon fluxes (0.62 PgC yr–1) based on the average of 
estimates from Jacobson et al. (2007) and Resplandy et al. (2018). (b) Mean decadal 
constraints and their confidence intervals for global ocean sink (S ocean) of anthropogenic 
CO 2 using multiple independent or quasi-independent lines of evidence or methods 
for the period 1990–2019 (see Supplementary Materials Tables 5.SM.1 and 5.SM.2 
for magnitudes, uncertainties and published sources). Further details on data sources

and processing are available in the chapter data table (Table 5.SM.6).

693
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55interior support the medium confidence assessment  that the ocean 
anthropogenic carbon storage rates continue to be determined by the 
ocean sink (S ocean) in response to growing CO 2 emissions (Figure 5.9; 
McKinley et al., 2020).
5.2.1.3.2 Ocean carbon fluxes and storage: 
Regional and global variability
The intent of this assessment is to show how global variability can 
be regionally forced (Gregor et al., 2019; Landschützer et al., 2019; 
Hauck et al., 2020). Since AR5 and SROCC, advances in global 
ocean CO 2 flux products, GOBMs and atmospheric inversion models 
have strengthened confidence in the assessment of how ocean 
regions influence mean global variability and trends of ocean CO 2 
air–sea fluxes (F net; see Supplementary Materials Figure  5.SM.1; 
Ciais et al., 2013; Landschützer et al., 2014, 2015; Rödenbeck et al., 
2014; McKinley et al., 2017; Bindoff et al., 2019; Gregor et al., 2019;

Friedlingstein et al., 2020; Hauck et al., 2020). The coherence in the 
regional variability of the anomalies in F net from three independent 
lines of evidence support with high confidence  that the non-steady 
state global interannual-decadal variability of F net has clear regional 
influences (Gregor et al., 2019; Landschützer et al., 2019). The tropical 
oceans contribute the most to the global mean interannual variability 
(Supplementary Materials Figure 5.SM.1d). The high latitude oceans, 
particularly the Southern Ocean, contribute the most to the global-
scale decadal variability (Supplementary Materials Figure 5.SM5.1b,c; 
(Landschützer et al., 2016, 2019; Gregor et al., 2019; Gruber et al., 
2019a; Hauck et al., 2020). The influence of the Southern Ocean 
on the global mean decadal variability and the 1990s hiatus is 
supported by the highest regional–global correlation coefficients

(Supplementary Materials Figures  5.SM.1a,c). In contrast, the equatorial oceans’ influence on global mean F net has a low correlation 
because, notwithstanding the coherence in interannual variability, it 
does not show the same global mean trend of strengthening sink 
in response to growing global emissions (Supplementary Materials 
Figure 5.SM.1d; Gregor et al., 2019). All regions, except the equatorial 
ocean, contribute to varying extents to the multi-decadal trend of 
growth in the global ocean sink (Supplementary Materials Figure 5.
SM.1). Data sparseness in the high latitudes and the relatively short 
length of the observational records leads to low confidence  in the 
attribution of the processes that link regional–global variability to 
climate (Landschützer et al., 2019; Gloege et al., 2021).
Regional decadal-scale anomalies in the variability of ocean CO 2 
storage have also emerged, probably associated with changes in the

MOC, which may influence the global variability in F net (Chapter 9; 
DeVries et al., 2017). In the interior of the Indian and Pacific sectors 
of the Southern Ocean, and the North Atlantic, the increase in the 
CO 2 inventory from 1994 to 2007 was about 20% smaller than 
expected from the atmospheric CO 2 increase during the same period 
and the anthropogenic CO 2 inventory in 1994 (Sabine eta al., 2004; 
Gruber et al., 2019a). There is medium confidence  that the ocean 
CO 2 inventory strengthened again in the decade 2005–2015 (DeVries 
et al., 2017). In the North Atlantic, a low rate of anthropogenic CO 2 
storage at 1.9 ± 0.4 PgC per decade during the time period of 1989–
2003 increased to 4.4 ±  0.9  PgC per decade during 2003–2014. 
This is associated with changing ventilation patterns driven by the 
North Atlantic Oscillation (Woosley et al., 2016). In the Pacific sector 
of the Southern Ocean, the rate of anthropogenic CO 2 storage also

increased from 8.8 ± 1.1 (1 σ) PgC per decade during 1995–2005 to 
11.7 ± 1.1 PgC per decade during 2005–2015 (Carter et al., 2019). 
However, in the Subantarctic Mode Water of the Atlantic sector of 
(a) Net air–sea ﬂux (Fnet) of CO2 (1994–2007) (b) Rate of change in anthropogenic CO2 inventory (1994–2007)
gC m-2 yr-1gC m-2 yr-1–24   –16   –8      0      8     16    24 0     2      4     6     8    10   12   14   16    
Figure 5.9 | Comparative regional characteristics of the mean decadal (1994–2007) sea-air CO 2 flux (Fnet) and ocean storage of anthropogenic CO 2.  
(a) Regional source–sink characteristics for contemporary ocean air–sea CO 2 fluxes (F net) derived from the ensemble of six observation-based products using Surface Ocean CO 2 
Atlas (SOCAT)v6 observational dataset (Landschützer et al., 2014; Rödenbeck et al., 2014; Zeng et al., 2014; Bakker et al., 2016; Denvil-Sommer et al., 2019; Gregor et al., 2019;

Iida et al., 2021). Warm colours depict outgassing fluxes and black contours characterize the super-biomes defined from Fay and McKinley (2014) and adjusted by Gregor et al. 
(2019) also used to calculate the variability in regional flux anomalies (Supplementary Materials Figure 5.SM.1); (b) The regional characteristics of the storage fluxes of CO 2 in 
the ocean interior for the same period (Gruber et al., 2019b). The dots reflect ocean areas where the 1-sigma standard deviation of Fnet from the six observational-based product 
reconstructions is larger than the magnitude of the mean. This reflects source–sink transition areas where the mean Fnet is small and more strongly influenced by spatial and 
temporal variability across the products. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

694
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5the Southern Ocean, the storage rate of the anthropogenic CO 2 was 
rather lower after 2005 than before (Section 9.2.3.2; Tanhua et al., 
2017; Bindoff et al., 2019). These changes have been predominantly 
ascribed to the impact of changes in the MOC on the transport of 
anthropogenic CO 2 into the ocean interior due to regional climate 
variability, in addition to the increase in the atmospheric CO 2 
concentration (Section 9.2.3.1; Wanninkhof et al., 2010; Pérez et al., 
2013; DeVries et al., 2017, 2019; Gruber et al., 2019b; McKinley 
et al., 2020). However, the low frequency of carbon observations 
in the interior of the vast ocean leads to medium confidence  in the 
assessment of temporal variability in the rate of regional ocean CO 2 
storage and its controlling mechanisms.
In summary, multiple lines of observational and modelling evidence 
provide high confidence  in the finding that the ocean sink for

anthropogenic CO 2 has increased quasi-linearly over the past 60 years 
in response to growing global emissions of anthropogenic CO 2, with 
a  mean fraction of 23% of total emissions. The high confidence  
assessment is moderated to medium confidence  due to a  number 
of ocean CO 2 flux terms yet to be adequately constrained. Observed 
changes in the variability of ocean pCO 2 and observed reductions in 
changes in ocean carbonate chemistry. However, there is medium 
confidence  that these changes which, depending on the emissions 
scenario, could drive future ocean feedbacks, are still too small to 
emerge from the historical multi-decadal observed growth rate of S ocean.
5.2.1.4 Land CO 2 Fluxes: Historical and Contemporary 
Variability and Trends
5.2.1.4.1 Trend in land–atmosphere CO 2 exchange

The global net land CO 2 sink is assessed to have grown over the 
past six decades (Sarmiento et al., 2010; Ballantyne et al., 2017; Le 
Quéré et al., 2018b; Ciais et al., 2019; Friedlingstein et al., 2020) 
(high confidence ). Estimated as residual from the mass balance 
budget of fossil fuel CO 2 emissions minus atmospheric CO 2 growth 
and the ocean CO 2 sink, the global net land CO 2 sink (including both 
land CO 2 sink and net land-use change emissions) increased from 
0.3 ± 0.6 PgC yr–1 during the 1960s to 1.8 ± 0.8 PgC yr–1 during the 
2010s (Friedlingstein et al., 2020). An increasing global net land CO 2 
sink since the 1980s (Figure 5.10) was consistently suggested both 
by atmospheric inversions (e.g., Peylin et al., 2013) and by DGVMs 
(e.g.,  Sitch et al., 2015; Friedlingstein et al., 2019). The Northern 
Hemisphere contributes more to the net increase in the land CO 2 sink 
compared to the Southern Hemisphere (Ciais et al., 2019), and boreal

and temperate forests probably contribute the most (Tagesson et al., 
2020). Attributing an increased net land CO 2 sink to finer regional 
scales remains challenging, but inversions of satellite-based column 
CO 2 products that have emerged since AR5 are a promising tool to 
further constrain regional land-atmosphere CO 2 exchange (Ciais 
et al., 2013; Houweling et al., 2015; Reuter et al., 2017; O’Dell et al., 
2018; Palmer et al., 2019).
Carbon uptake by vegetation photosynthesis exerts a  first-order 
control over the net land CO 2 sink. Several lines of evidence show enhanced vegetation photosynthesis over the past decades ( medium 
to high confidence ) (Figure 5.10), including increasing satellite-derived 
vegetation greenness (e.g., see Chapter 2; Mao et al., 2016; Zhu et al., 
2016; Jia et al., 2019) and satellite-derived photosynthesis indicators 
(e.g., Badgley et al., 2017; Zhang et al., 2018), change in atmospheric

concentration of carbonyl sulphide (Campbell et al., 2017), enhanced 
seasonal CO 2 amplitude (Graven et al., 2013; Forkel et al., 2016), 
observation-driven inference of increasing photosynthesis CO 2 
uptake based mostly on enhanced water use efficiency (Cheng et al., 
2017), and DGVM simulated increase of photosynthesis CO 2 uptake 
(Anav et al., 2015).
Substantial progress has been made since AR5 on attributing change 
of the global net land CO 2 sink. Increasing global net land CO 2 sink 
since the 1980s is mainly driven by the fertilization effect from rising 
atmospheric CO 2 concentrations (Schimel et al., 2015; Sitch et al., 2015; 
Fernández-Martínez et al., 2019; O’Sullivan et al., 2019; Tagesson 
et al., 2020; Walker et al., 2021) ( medium confidence ). Increasing 
nitrogen deposition (de Vries et al., 2009; Devaraju et al., 2016; 
Huntzinger et al., 2017) or the synergy between increasing nitrogen 
deposition and atmospheric CO 2 concentration (O’Sullivan et al.,

2019) could have also contributed to the increasing global net land 
CO 2 sink. The effects of climate change alone on the global net land 
CO 2 sink is so divergent that even the signs (directions) of the effects 
are not the same across DGVMs (e.g., Huntzinger et al., 2017).
Lower fire emissions of CO 2 and enhanced vegetation carbon 
uptake due to reduced global burned area have contributed to the 
increasing global net land CO 2 sink in the recent decade (Arora 
and Melton, 2018; Yin et al., 2020) ( low to medium confidence ). 
Satellite observations reveal a declining trend in global burned area 
by about 20% over past two decades (Andela et al., 2017; Earl 
and Simmonds, 2018; Forkel et al., 2019), a trend most pronounced 
in regions like northern Africa (Forkel et al., 2019; Zubkova et al., 
2019; Bowman et al., 2020) and Mediterranean Europe (Turco et al., 
2016). However, burned area trends are highly heterogeneous

regionally with increasing trends reported in regions like western 
United States (Holden et al., 2018; Abatzoglou et al., 2019). Some 
regions (e.g.,  Amazon basin and Australia) experienced record-
breaking fire events in 2019 and 2020 (e.g.,  Boer et al., 2020), 
whose effects on burned area trends remain to be explored. The 
burned area trends were primarily attributed to both human-
induced climate change and human activities (Jolly et al., 2015; 
Andela et al., 2017; Holden et al., 2018; Turco et al., 2018; Teckentrup 
et al., 2019; Bowman et al., 2020), as well as changing frequency of 
lightning in the boreal region (Veraverbeke et al., 2017). In addition 
to changes in the burned area, fire dynamics could affect the trend 
in land-atmosphere CO 2 exchange indirectly through increasing 
concentration of air pollutants (see Section  6.3.4 for impacts 
of ozone and aerosol on the carbon cycle; Yue and Unger, 2018; 
Lasslop et al., 2019).

Significant uncertainties remain for the land CO 2 sink partition 
of processes due to challenges in reconciling multiple-scale 
evidence from experiments to the globe (Fatichi et al., 2019; Walker 
et al., 2021), due to large spatial and inter-model differences in 
diagnosing dominant driving factors affecting the net land CO 2 sink

695
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55
(Huntzinger et al., 2017; Fernández-Martínez et al., 2019), and due 
to model deficiency in process representations (He et al., 2016). 
Nitrogen dynamics, a major gap in DGVMs identified in AR5, have 
now been incorporated in about half of the DGVMs contributing to 
the carbon budget of the Global Carbon Project (GCP) (see Le Quéré 
et al. (2018a) for model characteristics) and a  growing number 
of ESMs (Arora et al., 2020). However, as the representations of carbon–nitrogen interactions vary greatly among models, large 
uncertainties remain on how nitrogen cycling regulates the 
response of ecosystem carbon uptake to higher atmospheric CO 2 
(Walker et al., 2015; Wieder et al., 2019; Davies-Barnard et al., 2020; 
Meyerholt et al., 2020; see Section 5.4.1). Fire modules have been 
incorporated into 10 of 16 DGVMs contributing to the global carbon

budget (Le Quéré et al., 2018a), and a growing number of models 
have representations of human ignitions and fire suppression 
processes (Rabin et al., 2017; Teckentrup et al., 2019). There are also 
growing DGVM developments to include management practices 
(Pongratz et al., 2018) and the effects of secondary forest regrowth 
(Pugh et al., 2019), though models still under-represent intensively 
managed ecosystems, such as croplands and managed forests 
(Guanter et al., 2014; Thurner et al., 2017). Processes that have 
not yet played a significant role in the land CO 2 sink of the past 
decades but can grow in importance, include permafrost (Box 5.1) 
and peatlands dynamics (Dargie et al., 2017; Gibson et al., 2019), 
have also been incorporated in some DGVMs (Koven et al., 2015b; 
Burke et al., 2017a; Guimberteau et al., 2018). Growing numbers 
and varieties of Earth observations are being jointly used to drive 
and benchmark models, helping to further identify missing key

processes or mechanisms that are poorly represented in the current 
generation of DGVMs (e.g., Collier et al., 2018).
5.2.1.4.2 Interannual variability in land–atmosphere CO 2 exchange
The AR5 stated that the interannual variability of the atmospheric 
CO 2 growth rate is dominated by tropical land ecosystems. A set of 
new satellite measurements applied to assess the variability of the 
tropical land carbon balance since AR5 (Ciais et al., 2013) confirm 
this statement, including satellite column CO 2 measurements, 
estimating the recent anomalous land–atmosphere CO 2 exchange 
induced by El Niño at continental scale (e.g.,  J.  Liu et al., 2017; 
Palmer et al., 2019), and L-band vegetation optical depth, 
estimating tropical above-ground biomass carbon stock changes 
(Fan et al., 2019). In addition, based on medium evidence  and 
medium agreement  between studies with DGVMs and atmospheric 
inversions, semi-arid ecosystems over the tropical zones have

a  larger contribution to interannual variability in global land–
atmosphere CO 2 exchange than moist tropical forest ecosystems 
(low to medium confidence ) (Poulter et al., 2014; Ahlstrom et al., 
2015; Piao et al., 2020).
Understanding the mechanisms driving interannual variability in 
the carbon cycle has the potential to provide insights into whether 
and to what extent the carbon cycle can affect the climate (carbon–
climate feedback), with particular interests over the highly climate-
sensitive tropical carbon cycle (e.g.,  Cox et al., 2013; X.  Wang 
et al., 2014; Fang et al., 2017; Jung et al., 2017; Humphrey et al., 
2018; Malhi et al., 2018; see Section 5.4). Consistent findings from 
studies with atmospheric inversions, satellite observations and 
DGVMs (e.g.,  Malhi et al., 2018; Rödenbeck et al., 2018) lead to 
high confidence  that the tropical net land CO 2 sink is reduced under 
warmer and drier conditions, particularly during El Niño events.

Interannual variations in tropical land-atmosphere CO 2 exchange 
are significantly correlated with anomalies of tropical temperature, 
water availability and terrestrial water storage (X. Wang et al., 2014; -2024Land CO2 Flux
(PgC yr-1) (PgC yr-1)(a) net land CO2 sink
sink source
-2-1012Standardized
anomaly(c) NIRv/CSIF anomaly
larger smaller-2-101NDVI anomaly
 (10-2NDVI unit)(b) Normalized Difference Vegetation Index
greener browner
1980 1985 1990 1995 2000 2005 2010 2015-14-70714GPP anomaly
(d) Gross Primary Production
more less
 CO2 mass balance  Model Satellite(-driven)
residual net land CO2 sink Inversion
DGVMsAVHRR MODIS
NIRv CSIF
Figure 5.10 | Trends of the net land CO 2 sink and related vegetation 
observations during 1980–2019.  (a) Net land CO 2 sink. The residual net land 
CO 2 sink is estimated from the global CO 2 mass balance (fossil fuel emissions minus 
atmospheric CO 2 growth rate and ocean CO 2 sink). Inversions indicate the net land

CO 2 sink estimated by an ensemble of four atmospheric inversions. Dynamic Global 
Vegetation Models (DGVMs) indicate the mean net land CO 2 sink estimated by 
17 dynamic global vegetation models driven by climate change, rising atmospheric 
CO 2, land-use change and nitrogen deposition change (for carbon-nitrogen models). 
The positive values indicate net CO 2 uptake from the atmosphere. (b) Normalized 
difference vegetation index (NDVI). The anomaly of global area-weighted NDVI 
observed by Advanced Very High Resolution Radiometer (AVHRR) and MODIS satellite 
sensors. AVHRR data are accessible during 1982–2016 and MODIS data are accessible 
during 2000–2018. (c) Near-infrared reflectance of vegetation (NIRv) and contiguous 
solar-induced chlorophyll fluorescence (CSIF). The standardized anomaly of area-
weighted NIRv during 2001–2018 (Badgley et al., 2017) and CSIF during 2000–2018 
(Zhang et al., 2018). (d) Gross primary production (GPP). The GPP from Cheng et al.

(2017), DGVMs and MODIS GPP product (MOD17A3). GPP from Cheng et al. (2017) 
is based on an analytical model driven by climate change, rising atmospheric CO 2, 
AVHRR leaf area index datasets and evapotranspiration datasets. GPP from DGVMs 
is the ensemble mean global GPP estimated by the same 17 DGVMs that provide the 
net land CO 2 sink estimates. Shaded area indicates 1– σ inter-model spread except for 
atmospheric inversions, whose ranges were used due to limited number of models. 
(Table 5.SM.6).

696
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Jung et al., 2017; Humphrey et al., 2018; Piao et al., 2020), whose 
relative contribution are difficult to separate due to covariations 
between these climatic factors. At continental scale, the dominant 
climatic driver of interannual variations of tropical land-atmosphere CO 2 exchange was temperature variations (Figure 5.11; Piao et al., 
2020), which could partly result from the spatial compensation of 
the water availability effects on land-atmospheric CO 2 exchange 
(Jung et al., 2017).
-2-1012
(a) GlobalEl Chichón PinatuboMore net
releaseMore net
uptake-0.5-0.2500.250.50 0.5 1Correlation with
temperature (** 99%,
* 95% significant)
**
**
-2-1012
(b) NorthMore net
uptake-0.5-0.2500.250.5
-2-1012
(c) TropicsLand CO²  flux anomalies (PgC yr-1)Land surface air temperature anomalies (oC)
More net
**
**
1980 1985 1990 1995 2000 2005 2010 2015-2-1012

(d) SouthMore net
uptake**
-0.5-0.2500.250.5
Land CO2 flux anomalies (models)
Inversion DGVMTemperature anomalies
CRUENSO intensity
Niño 3.4 index
Figure 5.11 | Interannual variation in detrended anomalies of the net land CO 2 sink and land surface air temperature during 1980–2019. Correlation 
coefficients between the net land CO 2 sink anomalies and temperature anomalies are show on the right bar plots. The net land CO 2 sink is estimated by four atmospheric 
inversions (blue) and 15 Dynamic Global Vegetation Models (DGVMs) (green), respectively (Friedlingstein et al., 2020). Solid blue and green lines show model mean detrended 
anomalies of the net land CO 2 sink. The ensemble mean of DGVMs is bounded by the 1– σ inter-model spread in each large latitude band (North 30°N–90°N, Tropics 
30°S–30°N, South 90°S–30°S) and the globe. The ensemble mean of atmospheric inversions is bounded by model spread. For each latitudinal band, the anomalies of the net

land CO 2 sink and temperature (orange) were obtained by removing the long-term trend and seasonal cycle. A 12-month running mean was taken to reduce high-frequency 
noise. The bars in the right panels show correlation coefficients between the net land CO 2 sink anomalies and temperature anomalies for each region. ** indicates P<0.01; 
* indicates P<0.05. The grey shaded area shows the intensity of El Niño–Southern Oscillation (ENSO) as defined by the Niño 3.4 index. Two volcanic eruptions (El Chichón and 
Mount Pinatubo) are indicated with light blue dashed lines. Temperature data are from the Climatic Research Unit (CRU), University of East Anglia (Harris et al., 2014). Anomalies 
were calculated following Patra et al. (2005), but using a 12-month low-pass filter and detrended to obtain interannual variations. Further details on data sources and processing 

697
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Cross-Chapter Box 5.1 | Interactions Between the Carbon and Water Cycles, Particularly Under 
Drought Conditions
Contributors: Josep G. Canadell (Australia), Philippe Ciais (France), Hervé Douville (France), Sabine Fuss (Germany), Robert Jackson 
(United States of America), Annalea Lohila (Finland), Shilong Piao (China), Sonia I.  Seneviratne (Switzerland), Sergio M. Vicente-
Serrano (Spain), Sönke Zaehle (Germany)
This box presents an assessment of interactions between the carbon and water cycles that influence the dynamics of the biosphere 
and its interaction with the climate system. It also highlights carbon–water trade-offs arising from the use of land-based climate 
change mitigation options. Individual aspects of the interactions between the carbon and water cycles are addressed in separate

chapters (Sections 5.2.1, 5.4.1, 8.2.3, 8.3.1, 8.4.1 and 11.6). The influence of wetlands and dams on methane emissions is assessed 
elsewhere (Sections 5.2.2, 5.4.7 and 8.3.1), as well as the consequences of permafrost thawing (Section 9.5.2 and Box 5.1) and/or 
increased flooding (Sections 8.4.1, 11.5 and 12.4) on wetland extent in the northern high latitudes and wet tropics.
Does elevated CO 2 alleviate the impacts of drought?
Increasing atmospheric CO 2 concentration enhances leaf photosynthesis and drives a partial closure of leaf stomata, leading to higher 
water-use efficiency (WUE) at the leaf canopy and ecosystem scales (Norby and Zak, 2011; De Kauwe et al., 2013; Fatichi et al., 2016; 
Knauer et al., 2017; Mastrotheodoros et al., 2017). Since AR5 (Box 6.3), a growing body of evidence from tree-ring and carbon isotopes 
further confirms an increase of plant water-use efficiency over decadal to centennial time scales, with some evidence for a stronger

enhancement of photosynthesis compared to stomatal reductions (Frank et al., 2015; Guerrieri et al., 2019; Adams et al., 2020).
Multiple lines of evidence suggest that WUE has increased in near proportionality to atmospheric CO 2 (high confidence ) at a rate 
generally consistent with Earth system models (ESMs), despite variation in the WUE response to CO 2 (De Kauwe et al., 2013; Frank 
et al., 2015; Keeling et al., 2017; Lavergne et al., 2019; Walker et al., 2021). Both field-scale CO 2 enrichment experiments and process 
models show the effect of physiologically induced water savings, particularly under water-limiting conditions (De Kauwe et al., 2013; 
Farrior et al., 2015; Lu et al., 2016; Roy et al., 2016). Plants can also benefit from reduced drought stress due to enhanced CO 2 
without ecosystem-scale water savings (Jiang et al., 2021). To some extent, this increased WUE offsets the effects of enhanced vapour

pressure deficit (VPD) on plant transpiration (Bobich et al., 2010; Creese et al., 2014; Jiao et al., 2019), but will have limited effect on 
ameliorating plant water stress during extreme drought events (Xu et al., 2016; Menezes-Silva et al., 2019; L. Liu et al., 2020), when 
leaf stomata are governed primarily by soil moisture (Roy et al., 2016).
Leaf stomata closure can have large effects on land freshwater availability because of reduced plant transpiration, leading in some 
regions to higher soil moisture and runoff (Roderick et al., 2015; Milly and Dunne, 2016; Y . Yang et al., 2019). However, increased water 
availability is often not realized because other CO 2 physiological effects that enhance ecosystem evapotranspiration might offset the 
gains. These effects include plant growth and leaf area expansion (Ainsworth and Long, 2005; Ukkola et al., 2016; McDermid et al.,

2021), lengthening of the vegetative growing season (Frank et al., 2015; Lian et al., 2021), and the effects of stomatal closure on 
near-surface atmosphere that leads to increased air temperature and VPDs (Berg et al., 2016; Vogel et al., 2018; Zhou et al., 2019; 
Grossiord et al., 2020).
ESMs show no consensus about the net hydrological response to physiological CO 2 effects. Some studies show water savings as 
a consequence of the CO 2 effects on leaf stomata closure (Swann et al., 2016; Lemordant et al., 2018), while other studies show 
that increased leaf area offsets the gains from increased WUE (Mankin et al., 2019). However, these projections are subject to ESM 
uncertainties to quantify transpiration (Lian et al., 2021), among them the correct representations of plant hydraulic architecture such 
as changes in xylem anatomical properties and deep rooting (Nie et al., 2013; L. Liu et al., 2020).

In conclusion, it is very likely that elevated CO 2 leads to increased WUE at the leaf level, concurrent with enhanced photosynthesis. 
Increased CO 2 concentrations alleviate the effects of water deficits on plant productivity ( medium confidence ) but there is low 
confidence  for its role under extreme drought conditions. There is low confidence  that increased WUE by vegetation will substantially 
reduce global plant transpiration and diminish the frequency and severity of soil moisture and streamflow deficits associated with the 
radiative effect of higher CO 2 concentrations.
How does drought affect the terrestrial CO 2 sink?
Water availability controls the spatial distribution of photosynthesis – gross primary productivity (GPP) – over a larger part of the globe 
(Beer et al., 2010) and, at local scale, drought decreases GPP more than respiration (Schwalm et al., 2012) over most ecosystem types.

This makes water availability a major climatic driver of variability in net ecosystem exchange (Jung et al., 2017; Humphrey et al., 2018).

698
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5In addition to suppressing photosynthesis, field evidence suggests that droughts reduce the land CO 2 sink, also through increasing 
forest mortality and promoting wildfire (Allen et al., 2015; Brando et al., 2019; Abram et al., 2021).
At the global scale, interannual variability in the atmospheric CO 2 growth rate and global-scale terrestrial water storage from satellite 
show that a  lower global net land CO 2 sink is associated with below-average terrestrial water storage (Humphrey et al., 2018). 
Atmospheric inversions based on surface and satellite column CO 2 measurements show significant carbon release during drought 
events in pan-tropic areas (Phillips et al., 2009; Gatti et al., 2014; J. Liu et al., 2017; Palmer et al., 2019). Regional extreme droughts in 
the mid-latitudes also decrease GPP and land CO 2 sink (Ciais et al., 2005; Wolf et al., 2016; W. Peters et al., 2020; Flach et al., 2021).

Droughts are not compensated by equivalent wet anomalies because of the non-linear response of the terrestrial carbon uptake to 
soil moisture (Green et al., 2019).
Uncertainties remain on the magnitude of sensitivity of the land carbon fluxes to droughts. Global studies indicate stronger control 
of soil moisture to variations in satellite proxies of GPP than VPD (Stocker et al., 2019; L. Liu et al., 2020). However, given that VPD 
increases exponentially with atmospheric warming, some studies suggest that VPD in stomatal regulation will become increasingly 
more important under a warmer climate (Novick et al., 2016; Grossiord et al., 2020). It is difficult to isolate the relative contributions of 
warmer temperature, higher VPD and lower soil moisture. This is because land-atmosphere feedbacks cause a simultaneous increase 
of plant evaporative demand and of root zone water deficit impairing plant root uptake (Berg et al., 2016). These physiological

responses can be further compounded by drought legacies (Anderegg et al., 2015), changes in structure and population dynamics due 
to forest mortality (McDowell et al., 2020), disturbances associated with drought (fire, insects damage; Anderegg et al., 2020) and 
possible trade-offs between resistance and resilience (X. Li et al., 2020). Nonetheless, ESMs suggest that increased drought effects 
under very high levels of global warming (about 4°C at the end of the 21st century) contribute to the reduced efficiency of the land 
sink (Green et al., 2019).
In conclusion, there is high confidence  that the global net land CO 2 sink is reduced on interannual scale when regional-scale 
reductions in water availability associated with droughts occur, particularly in tropical regions. There is also high confidence  that the 
global land sink will become less efficient due to soil moisture limitations and associated drought conditions in some regions for high-

emissions scenarios, specially under global warming above 4°C. However, there is low confidence  on how these water cycle feedbacks 
will play out in lower emissions scenarios (at 2°C global warming or lower) due to uncertainties in regional rainfall changes and the 
balance between the CO 2 fertilization effect, through WUE, and the radiative impacts of greenhouse gases.
What are the limits of carbon dioxide removal from a water cycle perspective?
Carbon dioxide removal (CDR) options based on terrestrial carbon sinks will require the appropriation of significant amounts of 
water at the landscape level. Most mitigation pathways that seek to limit global warming to 1.5°C or less than 2°C require the 
removal of about 30 to 300 GtC from the atmosphere by 2100 (Rogelj et al., 2018b). Bioenergy with carbon capture and storage 
(BECCS), and afforestation/reforestation are the dominant CDR options used in climate stabilization scenarios, implying large

requirements for land and water (Section 5.6; Beringer et al., 2011; Boysen et al., 2017b; Fajardy and Mac Dowell, 2017; Jans et al., 
2018; Séférian et al., 2018b; Yamagata et al., 2018; Stenzel et al., 2019). A review of freshwater requirements for irrigating biomass 
plantations shows a range between 15 and 1250 km3 per GtC of biomass harvest. This is equivalent to a water requirement of 
99–8250 km3 for the median BECCS deployment of around 3.3 GtC yr−1 (Smith et al., 2016) in <2°C-scenarios (Stenzel et al., 
2021), assuming that biomass is converted to electricity, which is substantially less efficient than converting biomass to heat. 
These large ranges are the result of different assumptions about the type of biomass and yield improvements, management, and 
land availability. The use of alternative feedstocks, such as wastes, residues and algae, would lead to smaller water requirements 
(Smith et al., 2019).

Most of the water consumed in BECCS is used to grow the feedstock, with carbon capture and storage constituting a smaller portion 
across all crops (Rosa et al., 2020), with an estimated evaporative loss of 260 km3 yr−1 for 3.3 GtC yr−1 (Smith et al., 2016). The 
same authors also estimate water use for CDR through afforestation at 1040 km3 yr−1 for 3.3 GtC yr−1, including interception and 
transpiration, adjusted for the original land cover’s water use. 
The impacts of different CDR options on the water cycle depend crucially on regional climate, prior land cover, and scale of deployment 
(Trabucco et al., 2008). Extensive irrigation for afforestation in drier areas will have larger downstream impacts than in wetter regions, 
with the difference in water use between the afforested landscapes and its previous vegetation determining the level of potential 
impacts on evapotranspiration and runoff (Jackson et al., 2005; Teuling et al., 2017). Afforestation and reforestation sometimes

699
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55enhances precipitation through atmospheric feedbacks such as increased convection, at least in the tropics (Ellison et al., 2017) and 
the increase in precipitation can, in some regions, even cancel out the increased evapotranspiration (Li et al., 2018). 
In conclusion, extensive deployment of BECCS and afforestation/reforestation will require larger amounts of freshwater resources 
than used by the previous vegetation, altering the water cycle at regional scales ( high confidence ). Consequences of high water 
consumption on downstream uses, biodiversity, and regional climate depend on prior land cover, background climate conditions, 
and scale of deployment ( high confidence ). Therefore, a regional approach is required to determine the efficacy and sustainability 
of CDR projects. 
Cross-Chapter Box 5.1 (continued)
5.2.1.5 CO 2 Budget
The global CO 2 budget (Figure  5.12) encompasses all natural

and anthropogenic CO 2 sources and sinks. Table  5.1 shows the 
perturbation of the global carbon mass balance between reservoirs 
since the beginning of the industrial era, circa 1750.
Since AR5 (Ciais et al., 2013), a number of improvements have led to the 
more constrained carbon budget presented here. Some new additions 
include: (i) the use of independent estimates for the residual carbon 
sink on natural terrestrial ecosystems (Le Quéré et al., 2018a); (ii) 
improvements in the estimates of emissions from cement production 
(Andrew, 2019) and the sink associated with cement carbonation (Cao 
et al., 2020); (iii) improved and new emissions estimates from forestry 
and other land use (Hansis et al., 2015; Gasser et al., 2020); (iv) the 
use of ocean observation-based sink estimates and a  revised river 
flux partition between hemispheres (Friedlingstein et al., 2020); and 
(v) the expansion of constraints from atmospheric inversions, based on

surface networks and the use of satellite retrievals. 
The budget, based on the annual assessment by the GCP 
(Friedlingstein et al., 2020), uses independent estimates of all major 
flux components: fossil fuel and carbonate emissions (E FOS), CO 2 fluxes from land use, land-use change, and forestry (E LULUCF), the growth rate 
of CO 2 in the atmosphere (G atm), and the ocean (S ocean) and natural 
land (S land) CO 2 sinks. An imbalance term (B Imb) is required to ensure 
mass balance of the source and sinks that have been independently 
estimated: E FOS +  E LULUCF =  G atm +  S ocean +  S land. +  B Imb. All estimates 
are reported with 1 standard deviation (±1 σ, 1 sigma) representing 
a likelihood of 68%.
Over the past decade (2010–2019), 10.9 ±  0.9  PgC yr–1 were 
emitted from human activities, which were distributed between 
three Earth system components: 46% accumulated in the 
atmosphere (5.1 ± 0.02 PgC yr–1), 23% was taken up by the ocean

(2.5 ± 0.6 PgC yr–1) and 31% was stored by vegetation in terrestrial 
ecosystems (3.4 ±  0.9  PgC yr–1) (Table  5.1). There is a  budget 
imbalance of 0.1  PgCyr–1 which is within the uncertainties of the 
other terms. Over the industrial era (1750–2019), the total cumulative 
CO 2 fossil fuel and industry emissions were 445 ± 20 PgC, and the 
LULUCF flux (= net land-use change in Figure 5.12) was 240 ± 70 PgC 
(medium confidence ). The equivalent total emissions (685 ± 75 PgC) 
was distributed between the atmosphere (285  ±  5  PgC), oceans 
(170 ± 20 PgC) and land (230 ± 60 PgC; Table 5.1), with a budget 
imbalance of 20  PgC.  This budget (Table  5.1) does not explicitly 
account for source/sink dynamics due to carbon cycling in the 
Table 5.1 | Global anthropogenic CO 2 budget accumulated since the Industrial Revolution (onset in 1750) and averaged over the 1980s, 1990s, 2000s,

and 2010s.  By convention, a negative ocean or land to atmosphere CO 2 flux is equivalent to a gain of carbon by these reservoirs. The table does not include natural exchanges 
(e.g., rivers, weathering) between reservoirs. Uncertainties represent the 68% confidence interval (Friedlingstein et al., 2020).
1750–2019
Cumulative
(PgC)1850–2019
(PgC)1980–1989
Mean Annual 
Growth Rate
 (PgC yr–1)1990–1999
(PgC yr–1)2000–2009
(PgC yr–1)2010–2019
(PgC yr–1)
Emissions
Fossil fuel combustion 
and cement production445 ± 20 445 ± 20 5.4 ± 0.3 6.3 ± 0.3 7.7 ± 0.4 9.4 ± 0.5
Net land-use change 240 ± 70 210 ± 60 1.3 ± 0.7 1.4 ± 0.7 1.4 ± 0.7 1.6 ± 0.7
Total emissions 685 ± 75 655 ± 65 6.7 ± 0.8 7.7 ± 0.8 9.1 ± 0.8 10.9 ± 0.9
Partition
Atmospheric increase 285 ± 5 265 ± 5 3.4 ± 0.02 3.2 ± 0.02 4.1 ± 0.02 5.1 ± 0.02
Ocean sink 170 ± 20 160 ± 20 1.7 ± 0.4 2.0 ± 0.5 2.1 ± 0.5 2.5 ± 0.6

Terrestrial sink 230 ± 60 210 ± 55 2.0 ± 0.7 2.6 ± 0.7 2.9 ± 0.8 3.4 ± 0.9
Budget imbalance 0 20 –0.4 –0.1 0 –0.1

700
land–ocean aquatic continuum comprising freshwaters, estuaries, 
and coastal areas. Natural and anthropogenic transfers of carbon 
from soils to freshwater systems are significant (2.4–5.1 PgC yr–1) 
(Regnier et al., 2013; Drake et al., 2018). Some of the carbon is 
buried in freshwater bodies (0.15 PgC) (Mendonça et al., 2017), and 
a  significant proportion returns to the atmosphere via outgassing 
from lakes, rivers and estuaries (Raymond et al., 2013; Regnier et al., 
2013; Lauerwald et al., 2015). The net export of carbon from the 
terrestrial domain to the open oceans is estimated to be 0.80 PgC yr–1 
(medium confidence) , based on the average of (Jacobson et al., 2007; Resplandy et al., 2018) and corrected to account for 0.2 PgC buried 
in ocean floor sediments. These terms are included in Figure  5.12. 
Inclusion of other smaller fluxes could further constrain the carbon

budget (Ito, 2019; Friedlingstein et al., 2020).
5.2.2 Methane (CH 4): Trends, Variability and Budget
Methane is a  much more powerful greenhouse gas than CO 2 
(Chapter 7) and participates in tropospheric chemistry (Chapter 6).   
Flux: Billion tonnes of Carbon per year (PgC/yr)
Stocks: Billion tonnes of Carbon (PgC)
Stocks Anthropogenic changeNatural AnthropogenicCarbon (CO2) Budget
Net 
land-use 
change1.6 0.1
2.5
12004501700
1750700
37,100580230 118-250.8 0.29.4
Volcanism
Permafrost Vegetation Soils
Coal Oil GasVegetation 
and soils-445
Fossil 
fuels
Ocean floor 
sedimentSurface ocean
Intermediate and deep seaDissolved 
organic 
carbonMarine 
biotaFossil fuels
(Coal, oil, gas)
Cement 
productionFreshwater Rivers
BurialExport from
soils to 
riversGross photosynthesisNet land fluxAtmosphere
Total respiration and fireOcean-atmosphere gas exchange
2.03.0
2.04054.6
27
264275
0.223
113 29111.1 25.6
54.0 25.5Net ocean flux
Rock 
weathering
900591+279+-5

+173+-2311.01.9   3.4 +- +-0.9 +-0.7 +-0.5 0.6    2.5 0.6Average increase 5.1+-0.02
0.3 1.5
Reserves
Figure 5.12 | Global carbon (CO 2) budget (2010–2019).  Yellow arrows represent annual carbon fluxes (in PgC yr–1) associated with the natural carbon cycle, estimated 
for the time prior to the industrial era, around 1750. Pink arrows represent anthropogenic fluxes averaged over the period 2010–2019. The rate of carbon accumulation in 
the atmosphere is equal to net land-use change emissions, including land management (called LULUCF in the main text) plus fossil fuel emissions, minus land and ocean net 
sinks (plus a small budget imbalance, Table 5.1). Circles with yellow numbers represent pre-industrial carbon stocks in PgC. Circles with pink numbers represent anthropogenic 
changes to these stocks (cumulative anthropogenic fluxes) since 1750. Anthropogenic net fluxes are reproduced from Friedlingstein et al. (2020). The relative change of gross

photosynthesis since pre-industrial times is based on 15 DGVMs used in Friedlingstein et al. (2020). The corresponding emissions by total respiration and fire are those required 
to match the net land flux, exclusive of net land-use change emissions which are accounted for separately. The cumulative change of anthropogenic carbon in the terrestrial 
reservoir is the sum of carbon cumulatively lost by net land-use change emissions, and net carbon accumulated since 1750 in response to environmental drivers (warming, rising 
CO 2, nitrogen deposition). The adjusted gross natural ocean–atmosphere CO 2 flux was derived by rescaling the value in Figure 1 of Sarmiento and Gruber (2002) of 70 PgC yr–1 
by the revised estimate of the bomb radiocarbon (14C) inventory in the ocean. The original bomb 14C inventory yielded an average global gas transfer velocity of 22 cm hr–1;

the revised estimate is 17cm hr–1 leading to 17/22*70=54. Dissolved organic carbon reservoir and fluxes from Hansell et al. (2009). Dissolved inorganic carbon exchanges 
between surface and deep ocean, subduction and obduction from Levy et al. (2013). Export production and flux from (Boyd et al., 2019). Net primary production (NPP) and 
remineralization in surface layer of the ocean from Kwiatkowski et al. (2020); Séférian et al. (2020). Deep ocean reservoir from Keppler et al. (2020). Anthropogenic carbon 
reservoir in the ocean is from Gruber et al. (2019b) extrapolated to 2015. Fossil fuel reserves are from BGR (2020); fossil fuel resources are 11,490 PgC for coal, 6,780 PgC for oil 
and 365 PgC for natural gas. Permafrost region stores are from Hugelius et al. (2014); Strauss et al. (2017); Mishra et al. (2021) (see also Box 5.1) and soil carbon stocks outside

of permafrost region from Batjes (2016); Jackson et al. (2017). Biomass stocks (range of seven estimates) are from Erb et al. (2018). Sources for the fluxes of the land–ocean 
continuum are provided in main text and adjusted within the ranges of the various assessment to balance the budget (Section 5.2.1.5).

701
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55The  CH 4 variability in the atmosphere is mainly the result of the 
net balance between the sources and sinks on the Earth’s surface 
and chemical losses in the atmosphere. Atmospheric transport 
evens out the regional CH 4 differences between different parts of 
the Earth’s atmosphere. The steady-state lifetime is estimated to 
be 9.1 ± 0.9 years (Section 6.3.1 and Table 6.2). About 90% of the 
loss of atmospheric CH 4 occurs in the troposphere by reaction with 
hydroxyl radical (OH), 5% by bacterial soil oxidation, and the rest 
5% by chemical reactions with OH, excited state oxygen (O1D), 
and atomic chlorine (Cl) in the stratosphere (Saunois et al., 2020). 
Methane has large emissions from natural and anthropogenic origins, 
but a clear demarcation of their nature is difficult because of the use 
and conversions of the natural ecosystem for human activities. The

largest natural sources are from wetlands, freshwater and geological 
process, while the largest anthropogenic emissions are from enteric 
fermentation and manure treatment, landfills and waste treatment, 
rice cultivation and fossil fuel exploitation (Table 5.2). In the past two 
centuries, CH 4 emissions have nearly doubled, predominantly human 
driven since 1900, and persistently exceeded the losses ( virtually 
certain ), thereby increasing the atmospheric abundance as evidenced 
from the ice core and firn air measurements (Ferretti et al., 2005; 
Ghosh et al., 2015).
This section discusses both bottom-up and top-down estimates of 
emissions and sinks. Bottom-up estimates are based on empirical 
upscaling of point measurements, emissions inventories and 
dynamical model simulations, while top-down estimates refer to 
those constrained by atmospheric measurements and chemistry-
transport models in inversion systems. Since AR5, a  larger suite

of atmospheric inversions using both in situ and remote sensing 
measurement have led to better understanding of the regional 
CH 4 sources (Cross-Chapter Box  5.2). New ice core measurements 
of 14C-CH 4 are used for estimating the geological sources of CH 4 
(Table 5.2). Compared to the SRCCL (IPCC, 2019a; Jia et al., 2019), 
we provide a whole atmospheric sources-sinks budget consisting of 
all emissions and losses.
5.2.2.1 Atmosphere
Since the start of direct measurements of CH 4 in the atmosphere in 
the 1970s (Figure 5.13), the highest growth rate was observed from 
1977 to 1986 at 18 ± 4 ppb yr–1 (multi-year mean and 1 standard 
deviation) (Rice et al., 2016). This rapid CH 4 growth followed the 
green revolution with increased crop production and a fast rate of 
industrialization that caused rapid increases in CH 4 emissions from 
ruminant animals, rice cultivation, landfills, oil and gas industry and 
coal mining (Ferretti et al., 2005; Ghosh et al., 2015; Crippa et al.,

2020). Due to increases in oil prices in the early 1980s, emissions 
from gas flaring declined significantly (Stern and Kaufmann, 1996). 
This explains the first reduction in CH 4 growth rates from 1985 to 
1990 (Steele et al., 1992; Chandra et al., 2021). Further emissions 
reductions occurred following the Mt Pinatubo eruption in 1991 
that triggered a reduction in CH 4 growth rate through a decrease in 
wetland emissions driven by lower surface temperatures due to the 
light scattering by aerosols (Bândă et al., 2016; Chandra et al., 2021). 
In the late 1990s through to 2006 there was a temporary pause in the 
CH 4 growth rate, with higher confidence on its causes than in AR5: emissions from the oil and gas sectors declined by about 10 Tg yr–1 
through the 1990s, and atmospheric CH 4 loss steadily increased 
(Dlugokencky et al., 2003; Simpson et al., 2012; Crippa et al., 2020; 
Höglund-Isaksson et al., 2020; Chandra et al., 2021). The methane

growth rate began to increase again at 7 ± 3 ppb yr–1 during 2007–
2016, the causes of which are highly debated since AR5 (Rigby et al., 
2008; Dlugokencky et al., 2011; Dalsøren et al., 2016; Nisbet et al., 
Figure 5.13 | Time series of CH 4 concentrations, growth rates and isotopic 
composition. (a)  CH 4 concentrations; (b) CH 4 growth rates; (c) d13-CH 4. Data 
from selected site networks operated by the National Oceanic and Atmospheric 
Administration (NOAA; Dlugokencky et al., 2003), Advanced Global Atmospheric 
Gases Experiment (AGAGE; Prinn et al., 2018) and Portland Airport (PDX, Portland 
State University; Rice et al., 2016). To maintain clarity, data from many other 
measurement networks are not included here, and all measurements are shown in the 
World Metereological Organization X2004ACH 4 global calibration standard. Global 
mean values of XCH 4 (total-column), retrieved from radiation spectra measured by

the Greenhouse Gases Observing Satellite (GOSAT) are shown in panels (a) and (b). 
Cape Grim Observatory (CGO; 41°S, 145°E) and Trinidad Head (THD; 41°N, 124°W) 
data are taken from the AGAGE network. NOAA global and northern hemispheric 
(NH) means for d13C are calculated from 10 and 6 sites, respectively. The PDX data 
adjusted to NH (period: 1977–2000) are merged with THD (period: 2001–2019) for 
CH 4 concentration and growth rate analysis, and PDX and NOAA NH means of d13C 
data are used for joint interpretation of long-term trends analysis. The multivariate 
El Niño–Southern Oscillation (ENSO) index (MEI) is shown in panel (b). Further details 
on data sources and processing are available in the chapter data table (Table 5.SM.6).

702
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks52016; Patra et al., 2016; Schaefer et al., 2016; Schwietzke et al., 2016; 
Turner et al., 2017; Worden et al., 2017; He et al., 2020); studies 
disagree on the relative contribution of thermogenic, pyrogenic 
and biogenic emission processes and variability in tropospheric 
OH concentration. The renewed CH 4 increase is accompanied by 
a reversal of d13C trend to more negative values post 2007; opposite 
to what occurred in the 200 years prior (Ferretti et al., 2005; Ghosh 
et al., 2015; Schaefer et al., 2016; Schwietzke et al., 2016; Nisbet et al., 
2019), suggesting an increasing contribution from animal farming, 
landfills and waste, and a slower increase in emissions from fossil 
fuel exploitation since the early 2000s (Patra et al., 2016; Jackson 
et al., 2020; Chandra et al., 2021). Atmospheric concentrations of 
CH 4 reached 1866.3 ppb in 2019 (Figure  5.14). A  comprehensive

assessment of the CH 4 growth rates over the past four decades is 
presented in Cross-Chapter Box 5.2.
5.2.2.2 Anthropogenic Methane (CH 4) Emissions
The positive gradient between CH 4 at Cape Grim, Australia (41°S) and 
Trinidad Head, USA (41°N), and the bigger difference between Trinidad 
Head and global mean CH 4 compared to that between global mean 
CH 4 and Cape Grim, strongly suggest that the Northern Hemisphere 
is the dominant origin of anthropogenic CH 4 emissions (Figure 5.13). 
The loss rate of CH 4 in troposphere does not produce a large positive 
north–south hemispheric gradient in CH 4 due to parity in hemispheric 
mean OH concentration (Patra et al., 2014), or in the case of greater OH 
concentrations in the northern rather than the Southern Hemisphere 
as simulated by the chemistry-climate models (Naik et al., 2013). Coal 
mining contributed about 35% of the total CH 4 emissions from all 
fossil fuel-related sources. Top-down estimates of fossil fuel emissions

(106 Tg yr–1) are smaller than bottom-up estimates (115  Tg yr–1) 
during 2008–2017 (Table  5.2). Inventory-based estimates suggest 
that CH 4 emissions from coal mining increased by 17 Tg yr–1 between 
the periods 2002–2006 and 2008–2012, with a dominant contribution 
from China (Peng et al., 2016; Crippa et al., 2020; Höglund-Isaksson 
et al., 2020). Inventory-based estimates suggest that CH 4 emissions 
from coal mining increased by 17 Tg yr–1 between the periods 2002–
2006 and 2008–2012, with a dominant contribution from China (Peng 
et al., 2016; Crippa et al., 2020; Höglund-Isaksson et al., 2020). Recent 
country statistics and detailed inventory-based estimates show that 
CH 4 emissions from coal mining in China declined between 2012 
and 2016 (Sheng et al., 2019; Gao et al., 2020), while atmospheric-
based estimates suggest a  continuation of CH 4 emissions growth 
but at a slower rate to the year 2015 (Miller et al., 2019) and 2016

(Chandra et al., 2021). Emissions from oil and gas extraction and 
use decreased in the 1980s and 1990s, but increased in the 2000s 
and 2010s (Dlugokencky et al., 1994; Stern and Kaufmann, 1996; 
Howarth, 2019; Crippa et al., 2020). The attribution to multiple CH 4 
sources using spatially aggregated atmospheric d13C data remained 
underdetermined to infer the global total emissions from the fossil 
fuel industry, biomass burning and agriculture (Rice et al., 2016; 
Schaefer et al., 2016; Schwietzke et al., 2016; Worden et al., 2017; 
Thompson et al., 2018).In the agriculture and waste sectors (Table 5.2), livestock production 
has the largest emissions source (109 Tg yr–1 in 2008–2017) 
dominated by enteric fermentation by about 90%. Methane is 
formed during the storage of manure, when anoxic conditions are 
developed (Hristov et al., 2013). Emissions from enteric fermentation 
and manure have increased gradually from about 87 Tg yr–1 in

1990–1999 to 109 Tg yr–1 in 2008–2017 mainly due to the increase 
in global total animal numbers. Methane production in livestock 
rumens (cattle, goats, sheep, water buffalo) are affected by the type, 
amount and quality of feeds, energy consumption, animal size, health 
and growth rate, meat and milk production rate, and temperature 
(Broucek, 2014; S.R.O. Williams et al., 2020; SRCCL Section  5.4.3). 
Waste management and landfills produced 64 Tg yr–1 in 2008–2017, 
with global emissions increasing steadily since the 1970s and, 
despite significant declines in the USA, western Europe and Japan 
(Crippa et al., 2020; Höglund-Isaksson et al., 2020).
Emissions from rice cultivation decreased from about 45 Tg yr–1 in the 
1980s to about 29 Tg yr–1 in the decade 2000–2009, but increased 
again slightly to 31 Tg yr–1 during 2008–2017, based on inventories 
data. However, ecosystem models showed a gradual increase with 
time due to climate change ( limited evidence, low agreement )

(Crippa et al., 2020; Höglund-Isaksson et al., 2020; Ito, 2020).
Biomass burning and biofuel consumption (including natural and 
anthropogenic processes) caused at least 30 Tg yr–1 emissions during 
2008–2017 and constituted up to about 5% of global anthropogenic 
CH 4 emissions. Methane emissions from open biomass burning 
decreased during the past two decades mainly due to reduction 
of burning in savanna, grassland and shrubland (van der Werf 
et al., 2017; Worden et al., 2017). There is recent evidence from the 
tropics that fire occurrence is non-linearly related to precipitation, 
implying that severe droughts will increase CH 4 emissions from fires, 
particularly from the degraded peatlands (Field et al., 2016).

703
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Table 5.2 | Global CH 4 budget. Sources and sinks of CH 4 for the two most recent decades for wich data is available, from bottom-up and top-down estimations (in Tg CH 4 yr–1). 
The data are updated from Saunois et al. (2020), for the bottom-up anthropogenic emissions (FAO, 2019; US EPA, 2019; Crippa et al., 2020; Höglund-Isaksson et al., 2020), 
top-down geological emissions (Schwietzke et al., 2016; Petrenko et al., 2017; Hmiel et al., 2020), and top-down sinks from seven selected inverse models. The means (min-max) 
with outliers removed from the range and the means are given. Outliers defined as >75th percentile + 3 × the interquartile range or <25th percentile – 3 × the interquartile 
range. The top-down budget imbalances are calculated for each model separately and averaged. Note also the round-off error for the sources and sinks, which sometimes leads

to last digit mismatch in the sums. For detailed information on datasets, see further details on data table 5.SM.6.
2000–2009  
(Tg CH 4 yr–1)2008–2017  
(Tg CH 4 yr–1)
Top-down Bottom-up Top-down Bottom-up
Sources
Natural sources 215 (176–243) 369 (245–484) 215 (183–248) 371 (245–488)
Wetlands 180 (153–196) 147 (102–178) 180 (159–199) 149 (102–182)
Other sources 35 (21–47) 222 (143–306) 36 (21–49) 222 (143–306)
Freshwater (lakes and rivers)   159 (117–212)
Wild animals   2 (1–3)
Termites   9 (3–15)
Geological (land and oceans)   23 (0–71) 45 (18–65)
Other oceanic (sea-air flux and hydrates)   6 (4–10)
Permafrost (excl. lakes and wetlands) 1 (0–1)
Anthropogenic sources 332 (312–347) 330 (309–350) 357 (336–375) 356 (335–383)
Agriculture and waste 206 (198–219) 195 (185–212) 221 (209–238) 208 (192–230)
Enteric fermentation and manure   103 (101–107) 109 (106–115)
Landfills and waste   60 (53–70) 64 (55–77)
Rice   29 (23–34) 31 (25–37)

Fossil fuels 101 (71–151) 100 (94–108) 106 (81–131) 115 (114–116)
Coal   29 (26–33) 38 (36–39)
Oil and gas   65 (60–72) 70 (68–73)
Transport   3 (1–8) 5 (1–11)
Industry   3 (0–6) 3 (1–5)
Biomass burning and biofuels 29 (23–35) 32 (24–44) 30 (22–36) 30 (22–39)
Biomass burning   19 (15–32) 17 (14–26)
Biofuels   10 (8–12) 10 (8–13)
Sinks
Total chemical loss 511 (502–515) 595 (489–749) 514 (474–529) 602 (496–754)
Tropospheric OH   553 (476–677) 560 (483–682)
Stratospheric loss   31 (12–37) 31 (12–37)
Tropospheric Cl   11 (1–35) 11 (1–35)
Soil uptake 34 (27–41) 30 (11–49) 37 (27–43) 30 (11–49)
Sum of sources 548 (524–560) 699 (554–834) 576 (550–589) 727 (581–872)
Sum of sinks 546 (533–556) 625 (500–798) 551 (501–572) 632 (507–803)
Imbalance 7 (4–11) 74   21 (18–26) 95  
Atmospheric growth rate (ppb yr–1) 2 ± 4 7 ± 3

704
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.2.2.3 Land Biospheric Emissions and Sinks
Freshwater wetlands are the single largest global natural source 
of CH 4 in the atmosphere, accounting for about 26% of the total 
CH 4 source ( robust evidence, medium agreement ). Progress has 
been made since AR5 (Ciais et al., 2013) in better constraining 
freshwater lake and river emissions and reducing double counting 
with wetland emissions. Bottom-up and top-down estimates for 
2008–2017 are 149 and 180 Tg yr–1, respectively, with a  top-
down uncertainty range of 159–199 Tg yr–1 (Table  5.2). The large 
uncertainties stem from challenges in mapping wetland area and 
temporal dynamics to landscape estimates, and in scaling methane 
production, transport and consumption processes that are measured 
with small chambers or flux towers (Pham-Duc et al., 2017). Both the 
top-down and bottom-up estimates presented in Table 5.2 indicate

little increase in wetland CH 4 emissions during the last three decades, 
with the new estimates being slightly smaller than in AR5 due to 
updated wetland maps and ecosystem model simulations (Melton 
et al., 2013; Poulter et al., 2017). Wetland emissions show strong 
interannual variability due to the changes in inundated land area, air 
temperature and microbial activity (Bridgham et al., 2013). Present 
terrestrial ecosystem model simulated CH 4 emissions variability does 
not produce strong correlation with the El Niño–Southern Oscillation 
(ENSO) cycle (Cross-Chapter Box 5.2, Figure 2), although observation 
evidence is emerging for lower CH 4 emissions during El Niños and 
greater emissions during La Niña (Pandey et al., 2017).
Trees in upland and wetland forests contribute to CH 4 emissions by 
abiotic production in the canopy, by the methanogenesis taking place 
in the stem, and by conducting CH 4 from soil into the atmosphere

(Covey and Megonigal, 2019). There is emerging evidence of the 
important role of trees in transporting and conducting CH 4 from 
soils into the atmosphere, especially in tropics (Pangala et al., 2017), 
whereas direct production of CH 4 by vegetation only has a  minor 
contribution ( limited evidence, high agreement ) (Bruhn et al., 2012; 
Covey and Megonigal, 2019). The contribution of trees in transporting 
CH 4 may further widen the gap between the bottom-up and top-down 
estimates in the global budget, particularly needing a re-assessment 
of emissions in the tropics and in forested wetlands of temperate and 
boreal regions (Pangala et al., 2017; Jeffrey et al., 2019; Welch et al., 
2019; Sjögersten et al., 2020).
Microbial methane uptake by soil comprises up to 5% (30 Tg yr–1) 
of the total CH 4 sink in 2008–2017 (Table  5.2). There is evidence 
from experimental and modelling studies of increasing soil microbial 
uptake due to increasing temperature (Yu et al., 2017), although

evidence also exists for decreasing CH 4 consumption, possibly linked 
to precipitation changes (Ni and Groffman, 2018). The estimate of 
global methane loss by microbial oxidation in upland soils has been 
lowered marginally by 4 Tg yr–1, compared to 34 Tg yr–1 in AR5, for 
the period 2000–2009. Termites, an infraorder of insects (Isoptera) 
found in almost all land masses, emitted about 9 Tg yr–1 of CH 4 in 
2000–2009. Increased emissions from insects and other anthropods 
are projected (Brune, 2018).5.2.2.4 Ocean and Inland Water Emissions and Sinks
In AR5, the ocean CH 4 emissions were reported together with 
geological emissions, summing up to 54 (33–75) Tg yr–1. Coastal 
oceans, fjords and mud volcanos are major sources of CH 4 in the 
marine environment, but CH 4 flux measurements are sparse. Saunois 
et al. (2020) estimate that the oceanic budget, including biogenic, 
geological and hydrate emissions from coastal and open ocean, is

6 (range 4–10) Tg yr–1 for the 2000s, which is in good agreement with 
an air–sea flux measurement-based estimate of 6–12 Tg yr–1 (Weber 
et al., 2019). When estuaries are included, the total oceanic budget 
is 9–22 Tg yr–1, with a mean value of 13 Tg yr–1. A recent synthesis 
suggests that CH 4 emissions from shallow coastal ecosystems, 
particularly from mangroves, can be as high as 5–6 Tg yr–1 (Al-Haj 
and Fulweiler, 2020). The reservoir emissions, including coastal 
wetlands and tidal flats, contribute up to 13 Tg yr–1 (Borges and Abril, 
2011; Deemer et al., 2016). Methane seepage from the Arctic shelf, 
possibly triggered by the loss of geological storage due to warming 
and thawing of permafrost and hydrate decomposition, has a wide 
estimated range of 0.0–17 Tg yr–1 (Shakhova et al., 2010, 2014, 2017; 
Berchet et al., 2016); advanced eddy covariance measurements put 
the best estimate at about 3 Tg yr–1 from the East Siberian Arctic

shelf (Thornton et al., 2020). The current flux is expected  to be a mix 
of pre-industrial and climate change-driven fluxes, CH 4 seepage is 
anticipated to increase in a warmer world (Dean et al., 2018).
All geological sources around the world, including the coastal oceans 
and fjords, are estimated to emit CH 4 in the range of 35–76 Tg yr–1 
(Etiope et al., 2019). There is evidence that the ventilation of geological 
CH 4 is likely  to be smaller than 15 Tg yr–1 (Petrenko et al., 2017; Hmiel 
et al., 2020). A lower geological CH 4 ventilation will reduce the gap 
between bottom-up and top-down estimates (Table 5.2), but widen 
the gap in the ratio of fossil fuel-derived sources to the biogenic 
sources for matching the D14C-CH 4 observations.
Inland water (lakes, rivers, streams, ponds, estuaries) emissions are 
proportionally the largest source of uncertainty in the CH 4 budget. 
Since AR5 (Ciais et al., 2013), the inland water CH 4 source has been

revised from 8–73 Tg yr–1 (1980s) to 117–212 Tg yr–1 (2000s) with 
the availability of more observational data and improved areal 
estimates (Bastviken et al., 2011; Deemer et al., 2016; Stanley 
et al., 2016; DelSontro et al., 2018; Saunois et al., 2020). However, 
it is difficult to estimate bottom-up CH 4 emissions, due to the large 
spatial and temporal variation in lake and river CH 4 fluxes (Wik et al., 
2016; Crawford et al., 2017; Natchimuthu et al., 2017), uncertainties 
in their global area (Allen and Pavelsky, 2018), a  relatively small 
number of observations, and varying measurement methods –  for 
example, those neglecting ebullition, varying upscaling methods, and 
lack of appropriate processes (Sanches et al., 2019; Engram et al., 
2020; L. Zhang et al., 2020). Accordingly, there is no clear accounting 
of inland waters in top-down budgets, which is the main reason 
for the large gap in bottom-up and top-down estimates of ‘other

sources’ in the CH 4 budget (Table  5.2). Despite recent progress in 
separating wetlands from inland waters, there is double-counting in

705
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55the bottom-up estimates of their emissions (Thornton et al., 2016a). 
Although there is evidence that regional human activities and 
global warming both increase inland water CH 4 emissions (Beaulieu 
et al., 2019), the increase in the decadal emissions since AR5 (Ciais 
et al., 2013) rather reflect improvements in the estimate ( medium 
confidence ), due to updates in the datasets and new upscaling 
approaches (Saunois et al., 2020).
5.2.2.5 Methane (CH 4) Budget
A summary of top-down and bottom-up estimates of CH 4 emissions 
and sinks for the period 2008–2017 is presented in Figure 5.14 (details 
in Table 5.2 and the associated text for the emissions). In addition to 
483-682 Tg yr–1 loss of CH 4 in the troposphere by reaction with OH, 
1–35 Tg yr–1 of CH 4 loss is estimated to occur in the lower troposphere 
due to Cl but are not included in the top-down models as shown in

Table 5.2 (Hossaini et al., 2016; Gromov et al., 2018; X. Wang et al., 
2019). The decadal mean CH 4 burden/imbalance increased at the rate 
of 30, 12, 7 and 21 Tg yr–1 in the 1980s (1980–1989), 1990s (1990–
1999), 2000s (2000–2009) and the most recent decade (2008–2017), 
respectively ( virtually certain ), as can be estimated from observed 
atmospheric growth rate (Cross-Chapter Box 5.2, Figure 1).Recent analysis using D14C-CH 4 in ice samples suggest that CH 4 
emissions from fossil fuel exploitation are responsible for 30% of 
total CH 4 emissions (Lassey et al., 2007; Hmiel et al., 2020), which is 
largely inconsistent with sectorial budgets where fossil fuel emissions 
add up to 20% only (Ciais et al., 2013). However, recent model 
simulations produce fairly consistent d13C-CH 4 values and trends, as 
observed in the atmospheric samples using 20% fossil fuel emissions 
fraction (Ghosh et al., 2015; Warwick et al., 2016; Fujita et al., 2020;

Strode et al., 2020). Further research is needed to clarify the relative 
roles of CH 4 emissions from fossil fuel exploitation and freshwater 
components. A key challenge is to accommodate the higher estimated 
emissions from these two components without a major increase in 
the sinks, in order to be consistent with the observed changes in the 
carbon and hydrogen isotopes.
Methane (CH4) Budget
Biomass 
burningLandfills 
and wasteRice 
cultivationLivestockFreshwaters Wetlands OceanStratospheric 
lossTropospheric 
chlorine lossTropospheric 
hydroxyl loss
11–493–1512–37 1–35 483–682
117–212 102–182 4–10 18–65
Fossil fuels Termites Oxidation 
in soilsAtmosphere
22–39 114–116 55–77 106–115 25–37
Geological 
sources
530,0002 - 8,000,000
511,000–1,513,000
Permafrost hydratesOcean hydrates
Gas reservesFlux: Million tonnes of CH4 per year (Tg CH4/yr)
Stocks: Million tonnes of Methane ( CH4)
Stocks (range)Natural Anthropogenic Both
Average increase
19+-82005 +3127 +-17
Anthropogenic change

Figure 5.14 | Global methane (CH 4) budget (2008–2017). Values and data sources as in Table  5.2 (in TgCH 4). The atmospheric stock is calculated from mean CH 4 
concentration, multiplying a factor of 2.75 ± 0.015 Tg ppb–1, which accounts for the uncertainties in global mean CH 4 (Chandra et al., 2021). Further details on data sources and 
processing are available in the chapter data table (Table 5.SM.6).

706
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Cross-Chapter Box 5.2 | Drivers of Atmospheric Methane Changes During 1980–2019
Contributors: Prabir K. Patra (Japan/India), Josep G. Canadell (Australia), Frank J. Dentener (European Union, The Netherlands), Xin 
Lan (United States of America/China), Vaishali Naik (United States of America)
The atmospheric methane (CH 4) growth rate has varied widely over the past three decades, and the causes have been extensively 
studied since AR5. The mean growth rate decreased from 15 ± 5 ppb yr–1 in the 1980s to 0.48 ± 3.2 ppb yr–1 during 2000–2006 
(the so-called quasi-equilibrium phase) and returned to an average rate of 7.6 ± 2.7 ppb yr–1 in the past decade (2010–2019) (based 
on data in Figure 5.14). Atmospheric CH 4 grew faster (9.3 ± 2.4 ppb yr–1) over the last six years (2014–2019) – a period with prolonged

El Niño conditions, which contributed to high CH 4 growth rates consistent with behaviour during previous El Niño events (Figure 5.14b). 
Because of large uncertainties in both the emissions and sinks of CH 4, it has been challenging to quantify accurately the methane 
budget and ascribe reasons for the growth over 1980–2019. In the context of CH 4 emissions mitigation, it is critical to understand if 
the changes in growth rates are caused by emissions from human activities or by natural processes responding to changing climate. 
If CH 4 continues to grow at rates similar to those observed over the past decade, it will contribute to decadal scale climate change and 
hinder the achievement of the long-term temperature goals of the Paris Agreement (Section 7.3.2.2; Nisbet et al., 2019).
Cross-Chapter Box 5.2, Figure 1 shows the decadal CH 4 budget derived from the Global Carbon Project (GCP)-CH 4 synthesis for 1980s,

1990s and 2000s (Kirschke et al., 2013), and for 2010–2017 (Saunois et al., 2020). The imbalance of the sources and sinks estimated 
by atmospheric inversions (dark blue bars) can be used to explain the changes in CH 4 concentration increase rates between the 
decades (Table 5.2).
Since AR5, many studies have discussed the role of different source categories in explaining the increase in CH 4 growth rate since 
2007 and a coincident decrease of d13C–CH 4 and dD–CH 4 isotopes (Figure 5.13; Rice et al., 2016). Both 13C and D are enriched in 
Cross-Chapter Box 5.2, Figure 1 | Methane sources and sinks for four decades from atmospheric inversions with the budget imbalance  (source–
sink; dark blue bars) (plotted on the left y-axis). Top-down analysis from Kirschke et al. (2013); Saunois et al. (2020). The global CH 4 concentration seen in the black

line (plotted on the right y-axis), representing National Oceanic and Atmospheric Administration (NOAA) observed global monthly mean atmospheric CH 4 in dry-air 
mole fractions for 1983–2019 (Chapter 2, Annex V). Natural sources include emissions from natural wetlands, lakes and rivers, geological sources, wild animals, 
termites, wildfires, permafrost soils, and oceans. Anthropogenic sources include emissions from enteric fermentation and manure, landfills, waste and wastewater, rice 
cultivation, coal mining, oil and gas industry, biomass and biofuel burning. The top-down total sink is determined from global mass balance that includes chemical 
losses due to reactions with hydroxyl (OH), atomic chlorine (Cl), and excited atomic oxygen (O1D), and oxidation by bacteria in aerobic soils (Table 5.2). Further details 

707
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55mass-weighted average source signatures for CH 4 emissions from thermogenic sources (e.g., coal mining, oil and gas industry) and 
pyrogenic (biomass burning) sources, and depleted in biogenic (e.g., wetlands, rice paddies, enteric fermentation, landfill and waste) 
sources. Proposed hypotheses for CH 4 growth (2007–2017) are inconclusive and vary from a concurrent decrease in thermogenic and 
increase in wetland and other biogenic emissions (Nisbet et al., 2016; Schwietzke et al., 2016), an increase in emissions from agriculture 
in the tropics (Schaefer et al., 2016), a concurrent reduction in pyrogenic emissions and an increase in thermogenic emissions (Worden 
et al., 2017), or an emissions increase from biogenic sources and a slower increase in emissions from thermogenic sources compared 
to inventory emissions (Patra et al., 2016; Thompson et al., 2018; Jackson et al., 2020; Chandra et al., 2021).

A few studies emphasize the role of chemical destruction by hydroxyl (OH; the primary sink of methane), in driving changes in 
the growth of atmospheric methane abundance, in particular after 2006 (Rigby et al., 2017; Turner et al., 2017). Studies applying 
three-dimensional atmospheric inversion (McNorton et al., 2018), simple multi-species inversion (Thompson et al., 2018), as well as 
empirical methods using a variety of observational constraints based on OH chemistry (Nicely et al., 2018; Patra et al., 2021), do not 
find trends in OH large enough to explain the methane changes post-2006. On the contrary, global chemistry–climate models based 
on fundamental principles of atmospheric chemistry and known emissions trends of anthropogenic non-methane short-lived climate 
forcers simulate an increase in OH over this period (Zhao et al., 2019; Stevenson et al., 2020; see Section 6.2.3). These contrasting lines

of evidence suggest that OH changes may have had a small moderating influence on methane growth since 2007 ( low confidence ).
Cross-Chapter Box 5.2 Figure 2 shows that modelled wetland emissions anomalies for all regions did not exhibit statistically significant 
trends ( high agreement between models, medium evidence ). Thus, the inter-decadal difference of total CH 4 emissions derived from 
inversion models and wetland emissions, arises mainly from anthropogenic activities. The time series of regional emissions suggest 
that progress towards atmospheric CH 4 quasi-equilibrium was primarily driven by reductions in anthropogenic (fossil fuel exploitation) 
emissions in Europe, Russia and temperate North America over 1988–2000. In the global totals, emissions equalled loss in the early 
2000s. The growth since 2007 is driven by increasing agricultural emissions from East Asia (1997–2017), West Asia (2005–2017),

Brazil (1988–2017) and Northern Africa (2005–2017), and fossil fuel exploitations in temperate North America (2010–2017; Lan et al., 
2019; Crippa et al., 2020; Höglund-Isaksson et al., 2020; Jackson et al., 2020; Chandra et al., 2021).
Cross-Chapter Box 5.2, Figure 2 | Anomalies in global and regional methane (CH 4) emissions for 1988–2017.  The map in the centre shows mean CH 4 
emissions for 2010–2016. Multi-model mean (line) and 1-s standard deviations (shaded) for 2000–2017 are shown for 9 surface CH 4 and 10 satellite XCH 4 inversions, 
and 22 wetland models or model variants that participated in GCP-CH 4 budget assessment (Saunois et al., 2020). The results for the period before 2000 are available 
from two inversions, one using 19 sites (Chandra et al., 2021; also used for the 2010–2016 mean emissions map) and one for global totals (Bousquet et al., 2006).

The long-term mean values for 2010–2016 (common for all GCP–CH 4 inversions), as indicated within each panel separately, are subtracted from the annual-mean 
time series for the calculation of anomalies for each region. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6). 
Cross-Chapter Box 5.2 (continued)

708
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5There is evidence from emissions inventories at country level and regional scale inverse modelling that CH 4 growth rate variability 
between 1988 and 2017 is closely linked to anthropogenic activities ( medium agreement ). Isotopic composition observations and 
inventory data suggest that concurrent emissions changes from both fossil fuels and agriculture are playing roles in the resumed CH 4 
growth since 2007 ( high confidence ). Shorter-term decadal variability is predominantly driven by the influence of El Niño–Southern 
Oscillation on emissions from wetlands and biomass burning (Cross-Chapter Box 5.2, Figure 2), and loss due to OH variations ( medium 
confidence ), but lacking quantitative contribution from each of the sectors. By synthesizing all available information regionally from

a priori (bottom-up) emissions, satellite and surface observations, including isotopic information, and inverse modelling (top-down), 
the capacity to track and explain changes in, and drivers of, natural and anthropogenic CH 4 regional and global emissions has 
improved since AR5, but fundamental uncertainties related to OH variations remain unchanged. 
5.2.3 N2O: Trends, Variability and Budget
In natural ecosystems, nitrous oxide (N 2O) is primarily produced as 
a by-product during the remineralization of organic matter via the 
primary processes of nitrification and denitrification (Butterbach-
Bahl et al., 2013; Voss et al., 2013). The net N 2O production is highly 
sensitive to local environmental conditions such as temperature, 
oxygen concentrations, pH and the concentrations of ammonium and 
nitrate, among others, causing strong variability of N 2O emissions in 
time and space, even at small scales. Changes in the atmospheric

abundance of N 2O result largely from the balance of the net N 2O 
sources on land and ocean, and the photochemical destruction of 
N2O in the stratosphere.
Since AR5 (WGI, Section  6.4.3), improved understanding of N 2O 
sources allows for a  more comprehensive assessement of the 
global N 2O budget (Table 5.3). This progress is based on extended 
atmospheric observations (Francey et al., 2003; Elkins et al., 2018; 
Prinn et al., 2018), improved atmospheric N 2O inversions (Saikawa 
et al., 2014; Thompson et al., 2019), updated and expanded 
inventories of N 2O sources (Winiwarter et al., 2018; Janssens-
Maenhout et al., 2019), as well as improved bottom-up estimate of 
freshwater, ocean and terrestrial sources (Martinez-Rey et al., 2015; 
Landolfi et al., 2017; Buitenhuis et al., 2018; Lauerwald et al., 2019; 
Maavara et al., 2019; Tian et al., 2019).
The human perturbation of the natural nitrogen cycle through the

use of synthetic fertilizers and manure, as well as nitrogen deposition 
resulting from land-based agriculture and fossil fuel burning has 
been the largest driver of the increase in atmospheric N 2O of 31.0 
±  0.5 ppb (10%) between 1980 and 2019 ( robust evidence , high 
agreement ) (Tian et al., 2020). The long atmospheric lifetime of N 2O 
implies that it will take more than a  century before atmospheric 
abundances stabilize after the stabilization of global emissions. 
The rise of atmospheric N 2O is of concern, not only because of its 
contribution to the anthropogenic radiative forcing (Chapter 7) but 
also because of the importance of N 2O in stratospheric ozone loss 
(Ravishankara et al., 2009; Fleming et al., 2011; W. Wang et al., 2014).5.2.3.1 Atmosphere
The tropospheric abundance of N 2O was 332.1 ± 0.4 ppb in 2019 
(Figure  5.15), which is 23% higher than pre-industrial levels of 
270.1 ± 6.0 ppb ( robust evidence, high agreement ). Current estimates

are based on atmospheric measurements with high accuracy and 
density (Francey et al., 2003; Elkins et al., 2018; Prinn et al., 2018), 
and pre-industrial estimates are based on multiple ice-core records  
(Section 2.2.3.2.3). The average annual tropospheric growth rate was 
0.85 ± 0.03 ppb yr–1 during the period 1995 to 2019 (Figure 5.15a). 
The atmospheric growth rate increased by about 20% between 
the decade 2000–2009 and the most recent decade of 2010–2019 
(0.95 ± 0.04 ppb yr–1) (robust evidence, high agreement ). The growth 
rate in 2010–2019 was also higher than during 1970–2000 (0.6–
0.8 ppb yr–1; Ishijima et al., 2007) and the 30-year period prior to 2011 
(0.73 ± 0.03 ppb yr–1), as reported by AR5. New evidence since AR5 
(WGI, Section 6.4.3) confirms that, in the tropics and subtropics, large 
interannual variations in the atmospheric growth rate are negatively 
correlated with the multivariate ENSO index (MEI) and associated

anomalies in land and ocean fluxes (Ji et al., 2019; Thompson et al., 
2019; S. Yang et al., 2020) (Figure 5.15a).
As assessed by SRCCL (IPCC, 2019a), combined firn, ice, air and 
atmospheric measurements show that the 15N/14N isotope ratio 
(robust evidence , high agreement ) and the predominant position of 
the 15N atom in atmospheric N 2O (limited evidence , low agreement ) 
in N 2O has changed since 1940 (Figure 5.15b, c) whereas they were 
relatively constant in the pre-industrial period (Ishijima et al., 2007; 
Park et al., 2012; Prokopiou et al., 2017, 2018). The SRCCL concluded 
that this change indicates a shift in the nitrogen-substrate available 
for denitrification, and the relative contribution of nitrification to 
the global N 2O source ( robust evidence , high agreement ), which are 
associated with increased fertilizer use in agriculture (Park et al., 
2012; Snider et al., 2015; Prokopiou et al., 2018).

Since AR5 (WGI, Section 6.4.3), the mean atmospheric lifetime of N 2O 
has been revised to 116 ± 9 years (Prather et al., 2015). The small 
negative feedback of the N 2O lifetime to increasing atmospheric 
N2O results in a slightly lower residence time (109 ± 10 years) of N 2O

709
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55perturbations compared with that assessed by AR5 (118–131 years) 
(Prather et al., 2015). The dominant N 2O loss occurs through 
photolysis and oxidation by O(1D) radicals in the stratosphere and 
amounts to approximately 13.1 (12.4–13.6) TgN yr–1 (Minschwaner 
et al., 1993; Prather et al., 2015; Tian et al., 2020).5.2.3.2 Anthropogenic N 2O Emissions
The AR5 (WGI, Section 6.4.3) and SRCCL (Section 2.3.3) concluded 
that agriculture is the largest anthropogenic source of N 2O 
emissions. Since SRCCL (2.3.3), a  new synthesis of inventory-
based and modelling studies shows that the widespread use of 
synthetic fertilizers and manure on cropland and pasture, manure 
management and aquaculture resulted in 3.8 (2.5–5.8) TgN yr–1 
(average 2007–2016) ( robust evidence, high agreement ) (Table 5.3; 
Winiwarter et al., 2018; FAO, 2019; Janssens-Maenhout et al., 2019;

Tian et al., 2020). Observations from field-measurements (Song et al., 
2018), inventories (Wang et al., 2020) and atmospheric inversions 
(Thompson et al., 2019) further corroborate the assessment of SRCCL 
that there is a non-linear relationship between N 2O emissions and 
nitrogen input, implying an increasing fraction of fertilizer lost as 
N2O with larger fertilizer excess ( medium evidence , high agreement ). 
Several studies using complementary methods indicate that 
agricultural N 2O emissions have increased by more than 45% since 
the 1980s ( high confidence ) (Figure  5.16 and Table  5.3; Davidson, 
2009; Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian 
et al., 2020), mainly due to the increased use of nitrogen fertilizer 
and manure. N 2O emissions from aquaculture are among the fastest 
rising contributors of N 2O emissions, but their overall magnitude is 
still small in the overall N 2O budget (Tian et al., 2020).

The principal non-agricultural anthropogenic sources of N 2O are 
industry, specifically chemical processing, wastewater, and the 
combustion of fossil fuels (Table  5.3). Industrial emissions of N 2O 
mainly due to nitric and adipic acid production have decreased 
in North America and Europe since the widespread installation 
of abatement technologies in the 1990s (Pérez-Ram ίrez et al., 
2003; Lee et al., 2011; Janssens-Maenhout et al., 2019). There is 
still considerable uncertainty in industrial emissions from other 
regions of the world with contrasting trends between inventories 
(Thompson et al., 2019). Globally, industrial emissions and emissions 
from fossil fuel combustion by stationary sources, such as power 
plants, as well as smaller emissions from mobile sources (e.g., road 
transport and  aviation) have remained nearly constant between 
the 1980s and 2007–2016 ( medium evidence , medium agreement ) 
(Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian et al.,

2020). Wastewater N 2O emissions, including those from domestic 
and industrial sources, have increased from 0.2 (0.1–0.3) TgN yr–1 
to 0.35 (0.2–0.5) TgN yr–1 between the 1980s and 2007–2016 (Tian 
et al., 2020).
Biomass burning from crop residue burning, grassland, savannah and 
forest fires, as well as biomass burnt in household stoves, releases N 2O 
during the combustion of organic matter. Updated inventories since 
AR5 (WGI, Section 6.4.3) result in a lower range of the decadal mean 
emissions of 0.6 (0.5–0.8) TgN yr–1 (van der Werf et al., 2017; Tian 
et al., 2020). The attribution of grassland, savannah or forest fires to 
natural or anthropogenic origins is uncertain, preventing a separation 
of the biomass burning source into natural and anthropogenic.
Figure 5.15 | Changes in atmospheric nitrous oxide (N 2O) and its 
isotopic composition since 1940.  (a) Atmospheric N 2O abundance (parts 
per billion, ppb) and growth rat (ppb yr–1); (b) δ15N of atmospheric N 2O; and (c)

alpha-site 15N–N 2O.  Estimates are based on direct atmospheric measurements in 
the Advanced Global Atmospheric Gases Experiment (AGAGE), Commonwealth 
Scientific and Industrial Research Organisation (CSIRO), and National Oceanic and 
Atmospheric Administration (NOAA) networks (Prinn et al., 2000, 2018; Francey et al., 
2003; Hall et al., 2007; Elkins et al., 2018), archived air samples from Cape Grim, 
Australia (Park et al., 2012), and firn air from the North Greenland Ice Core Project 
(NGRIP) Greenland and H72 Antarctica (Ishijima et al., 2007), Law Dome Antarctica 
(Park  et al.,  2012), as well as a  collection of firn ice samples from Greenland 
(Prokopiou et al., 2017, 2018). Shading in (a) is based on the multivariate El Niño–
Southern Oscillation (ENSO) index, with red indicating El Niño conditions (Wolter and 
Timlin, 1998). Further details on data sources and processing are available in the 
chapter data table (Table 5.SM.6).

710
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.2.3.3 Emissions from Ocean, Inland Water 
Bodies and Estuaries
Since AR5 (WGI, Section  6.4.3), new estimates of the global ocean 
N2O source derived from ocean biogeochemistry models are 3.4 (2.5–
4.3) TgN yr–1 for the period 2007–2016 (Figure 5.16; Manizza et al., 
2012; Suntharalingam et al., 2012; Martinez-Rey et al., 2015; Landolfi 
et al., 2017; Buitenhuis et al., 2018; Tian et al., 2020). This is slightly 
lower than climatological estimates from empirically based methods 
and surface ocean data syntheses (Bianchi et al., 2012; S. Yang et al., 
2020). Nitrous oxide processes in coastal upwelling zones continue 
to be poorly represented in global estimates of marine N 2O emissions 
(Kock et al., 2016), but may account for an additional 0.2–0.6 TgN yr–1 
of the global ocean source (Seitzinger et al., 2000; Nevison et al., 2004).
In the oxic ocean (>97% of ocean volume), nitrification is believed

to be the primary N 2O source (Freing et al., 2012). In sub-oxic ocean 
zones (Section  5.3), where denitrification prevails, higher N 2O 
yields and turnover rates make these regions potentially significant 
sources of N 2O (Arévalo-Martínez et al., 2015; Babbin et al., 2015; 
Ji et al., 2015). The relative proportion of ocean N 2O from oxygen-
minimum zones is highly uncertain (Zamora et al., 2012). Estimates 
derived from in situ sampling, particularly in the eastern tropical 
Pacific, suggest significant fluxes from these regions, and potentially account for up to 50% of the global ocean source (Codispoti, 2010; 
Arévalo-Martínez et al., 2015; Babbin et al., 2015). However, recent 
global-scale analyses estimate lower contributions (4–7%, Battaglia 
and Joos, 2018b; Buitenhuis et al., 2018). Further investigation 
is required to reconcile these estimates and provide improved 
constraints on the N 2O source from low-oxygen zones.

Atmospheric deposition of anthropogenic N on oceans can stimulate 
marine productivity and influence ocean emissions of N 2O.  New 
ocean model analyses since AR5 (WGI, 6.4.3), suggest a  relatively 
modest global potential impact of 0.01–0.32 TgN yr–1 (pre-industrial 
to present-day) equivalent to 0.5–3.3% of the global ocean N 2O 
source (Suntharalingam et al., 2012; Jickells et al., 2017; Landolfi  
et al., 2017). However, larger proportionate impacts are predicted in 
nitrogen-limited coastal and inland waters downwind of continental 
pollution outflow, such as the Northern Indian Ocean (Jickells et al., 
2017; Suntharalingam et al., 2019).
Inland waters and estuaries are generally sources of N 2O as 
a  result of nitrification and denitrification of dissolved inorganic 
nitrogen, however, they can serve as N 2O sinks in specific conditions 
(Webb et al., 2019). Since AR5 (WGI, 6.4.3), improved emissions 
factors, including their spatio-temporal scaling, and consideration

of transport within the aquatic system allows for better constraint 0 0.1 0.2 0.3 0.4 0.5 0.61860 1900 1950 20000.050.000.050.10 (b) Boreal North America
1860 1900 1950 20000.20.00.20.40.60.81.0 (c) Temperate North America
1860 1900 1950 20000.20.10.00.10.2 (d) Central America
1860 1900 1950 20000.40.20.00.20.4 (e) Tropical America
1860 1900 1950 20000.20.10.00.10.2 (f) Temperate South America
1860 1900 1950 20000.20.00.20.40.60.81.0 (g) Europe
1860 1900 1950 20000.20.10.00.10.20.30.4 (h) Northern Africa
1860 1900 1950 20000.20.10.00.10.2 (j) Central Africa
1860 1900 1950 20000.20.10.00.10.2 (l) Southern Africa
1860 1900 1950 20000.20.10.00.10.20.30.4 (k) North Asia
1860 1900 1950 20000.20.10.00.10.2 (i) West Asia
1860 1900 1950 20000.20.00.20.40.60.81.0 (m) South Asia
1860 1900 1950 20000.00.51.01.52.0 (n) East Asia
1860 1900 1950 20000.20.10.00.10.20.30.4 (o) Southeast Asia
1860 1900 1950 20000.20.10.00.10.20.30.4 (p) Oceania
c(0, 1)c(0, 1)Change in Nitrous Oxide ﬂux

(Tg (N N2O) yr1)      
Baseline mean
Range
Total mean
Range
c(0, 1)c(0, 1)(a) Average Nitrous Oxide ( N2O) ﬂux (2007 2016)
g (N 2ON) m2yr1no data
Figure 5.16 | Decadal mean nitrous oxide (N 2O) emissions for 2007–2016 and its change since 1850 based on process-model projections.  The total effect, 
including that from anthropogenic nitrogen additions (atmospheric deposition, manure addition, fertilizer use and land-use), is evaluated against the background flux driven 
by changes in atmospheric carbon dioxide (CO 2) concentration, and climate change. Fluxes are derived from the N 2O model intercomparison project ensemble of terrestrial 
biosphere models (Tian et al., 2019) and three ocean biogeochemical models (Landolfi et al., 2017; Battaglia and Joos, 2018a; Buitenhuis et al., 2018). Further details on data 
sources and processing are available in the chapter data table (Table 5.SM.6).

711
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55of these emissions (Murray et al., 2015; Hu et al., 2016; Lauerwald 
et al., 2019; Maavara et al., 2019; Kortelainen et al., 2020; Yao et al., 
2020). Despite uncertainties because of the side effects of canals and 
reservoirs on nutrient cycling, these advances permit attribution of 
a fraction of inland water N 2O emissions to anthropogenic sources 
(Tian et al., 2020), which contributes to the increased anthropogenic 
share of the global N 2O source in this report compared to AR5 (Ciais 
et al., 2013). As an indirect consequence of agricultural nitrogen use 
and waste-water treatment, the anthropogenic emissions from inland 
waters have increased by about a quarter (0.1 TgN yr–1) between the 
1980s and 2007–2016 (Tian et al., 2020).
5.2.3.4 Emissions and Sinks in Non-agricultural Land
Soils are the largest natural source of N 2O, arising primarily from

nitrogen processing associated with microbial nitrification and 
denitrification (Table 5.3; Butterbach-Bahl et al., 2013; Snider et al., 
2015). Under some conditions, soils can also act as a net sink of N 2O, 
but this effect is small compared to the overall source (Schlesinger, 
2013). Since AR5 (WGI, Section  6.4.3), improved global process-
based models (Tian et al., 2019) suggest a present-day source of 6.7 
(5.3–8.1) TgN yr–1 (2007–2016 average), which is consistent with 
the estimate in AR5. Process-based models and inventory-based 
methods show that increased N deposition has enhanced terrestrial 
N2O emissions by 0.8 (0.4–1.4 TgN  yr–1) relative to approximately 
pre-industrial times, and by 0.2 (0.1–0.2) TgN yr–1 between the 1980s 
and 2007–2016 ( limited evidence , medium agreement ) (Figure 5.16; 
Tian et al., 2019). This estimate is at the high end of the range

reported in AR5 (WGI, Section  6.4.3). Model projections further show that global warming has led to increased soil N 2O emissions 
of 0.8 (0.3–1.3) TgN yr–1 since approximately pre-industrial times, of 
which about half occurred since the 1980s ( limited evidence , high 
agreement ) (Tian et al., 2019, 2020).
The SRCCL assessed that deforestation and other forms of land-use 
change significantly alter terrestrial N 2O emissions through emission 
pulses following conversions, generally resulting in long-term 
reduced emissions in unfertilized ecosystems ( medium evidence, 
high agreement ). This conclusion is supported by a  recent study 
demonstrating that the deforestation-pulse effect is offset by the 
effect of reduced area of mature tropical forests (Tian et al., 2020).
Uncertainties remain in process-based models with respect to their 
ability to capture the complicated responses of terrestrial N 2O 
emissions to rain pulses, freeze–thaw cycles and the net consequences

of elevated levels of CO 2 accurately (Tian et al., 2019). Emerging 
literature suggests that permafrost thaw may contribute significantly 
to arctic N 2O emissions (Voigt et al., 2020), but these processes are 
not yet adequately represented in models and upscaling to large-
scale remains a significant challenge.
5.2.3.5 N2O Budget
The synthesis of bottom-up estimates of N 2O sources (Sections  5.2.3.2–
5.2.3.4 and Figure  5.17) yields a  global source of 17.0 (12.2 to 
23.5) TgN yr–1 for the years 2007–2016 (Table  5.3). This estimate 
is comparable to AR5, but the uncertainty range has been reduced 
primarily due to improved estimates of ocean and anthropogenic N 2O 
Nitrous Oxide (N2O) Budget
Agriculture Atmospheric 
deposition on 
landWastewaterStratospheric loss Atmospheric chemistry
-0.3
Fossil fuels 
and industryRivers, 
estuaries, 
coastal zoneAtmospheric 
deposition 
on oceanBiomass 
and 
biofuel Soils under 
natural

vegetationFlux: Million tonnes of N2O per year (TgN(N2O)/yr)
Stocks: Million tonnes of Nitrogen (TgN)
Atmosphere
0.2–1.2
2.5–4.3 0.3–0.4 4.9–6.5 0.5–0.8 2.5–5.8 0.4–1.4 -0.6–1.1 0.2–0.5 0.8–1.1 0.2–0.71293 +263+-16
12.4 –13.6
0.1–0.2Stocks Anthropogenic changeNatural Anthropogenic
Surface 
sinkOceansAverage increase
4.5 (4.3-4.6)Both
Figure 5.17 | Global nitrous oxide (N 2O) budget (2007–2016). Values and data sources as in Table 5.3. The atmospheric stock is calculated from mean N 2O concentration, 
multiplying a factor of 4.79 ± 0.05 Tg ppb–1 (Prather et al., 2012). Pool sizes for the other reservoirs are largely unknown. Further details on data sources and processing are 
available in the chapter data table (Table 5.SM.6).

712
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5sources. Since AR5 (WGI, Section 6.4.3), improved capacity to estimate 
N2O sources from atmospheric N 2O measurements by inverting 
models of atmospheric transport provides a new and independent 
constraint for the global N 2O budget (Saikawa et al., 2014; Thompson 
et al., 2019; Tian et al., 2020). The decadal mean source derived from 
these inversions is remarkably consistent with the bottom-up global 
N2O budget for the same period, however, the split between land and 
ocean sources based on atmospheric inversions is less constrained, 
yielding a smaller land source of 11.3 (10.2 to 13.2) TgN yr–1 and 
a  larger ocean source of 5.7 (3.4   to  7.2) TgN yr–1, respectively, 
compared to bottom-up estimates.
Supported by multiple studies and extensive observational evidence 
(Sections 5.2.3.2–5.2.3.4 and Figure 5.17), anthropogenic emissions 
contributed about 40% (7.3; uncertainty range: 4.2 to 11.4 TgN yr–1)

to the total N 2O source in 2007–2016 ( high confidence ). This 
estimate is larger than in AR5 (WGI, 6.4.3) due to a larger estimated 
effect of nitrogen deposition on soil N 2O emissions and the explicit 
consideration of the role of anthropogenic nitrogen in determining 
inland water and estuary emissions.Based on bottom-up estimates, anthropogenic emissions from 
agricultural nitrogen use, industry and other indirect effects have 
increased by 1.7 (1.0 to 2.7) TgN yr–1 between the decades 1980–
1989 and 2007–2016, and are the primary cause of the increase in the 
total N 2O source ( high confidence ). Atmospheric inversions indicate 
that changes in surface emissions, rather than in the atmospheric 
transport or sink of N 2O, are the cause for the increased atmospheric 
growth rate of N 2O (robust evidence, high agreement ) (Thompson 
et al., 2019). However, the increase of 1.6 (1.4 to 1.7) TgN yr–1 in 
global emissions between 2000–2005 and 2010–2015 based

on atmospheric inversions is somewhat larger than bottom-up 
estimates over the same period, primarily because of differences in 
the estimates of land-based emissions.
5.2.4 The Relative Importance of CO 2, CH 4, and N 2O
The total influence of anthropogenic greenhouse gases (GHGs) on the 
Earth’s radiative balance is driven by the combined effect of those 
gases, and the three most important – carbon dioxide (CO 2), methane Table 5.3 | Global N 2O budget (units TgN yr–1) averaged over the 1980s, 1990s, 2000s as well as the recent decade starting in 2007.  Uncertainties represent 
the assessed range of source/sink estimates. All numbers are reproduced from Tian et al. (2020) based on a compilation of inventories, bottom-up models, as well as atmospheric 
inversions. For detailed information on datasets, see Data Table 5.SM.6.
AR6 1980–1989
(TgN yr–1)AR6 1990–1999
(TgN yr–1)AR6 2000–2009
(TgN yr–1)AR6 (2007–2016)
(TgN yr–1)AR5 (2006–2011)
(TgN yr–1)
Bottom-up Budget

Anthropogenic sources
Fossil fuel combustion and Industry 0.9 (0.8 to 1.1) 0.9 (0.9 to 1.0) 1.0 (0.8 to 1.0) 1.0 (0.8 to 1.1) 0.7 (0.2 to 1.8)
Agriculture (incl. aquaculture) 2.6 (1.8 to 4.1) 3.0 (2.1 to 4.8) 3.4 (2.3 to 5.2) 3.8 (2.5 to 5.8) 4.1 (1.7 to 4.8)
Biomass and biofuel burning 0.7 (0.7 to 0.7) 0.7 (0.6 to 0.8) 0.6 (0.6 to 0.6) 0.6 (0.5 to 0.8) 0.7 (0.2 to 1.0)
Wastewater 0.2 (0.1 to 0.3) 0.3 (0.2 to 0.4) 0.3 (0.2 to 0.4) 0.4 (0.2 to 0.5) 0.2 (0.1 to 0.3)
Inland water, estuaries, coastal zones 0.4 (0.2 to 0.5) 0.4 (0.2 to 0.5) 0.4 (0.2 to 0.6) 0.5 (0.2 to 0.7)
Atmospheric nitrogen deposition on ocean 0.1 (0.1 to 0.2) 0.1 (0.1 to 0.2) 0.1 (0.1 to 0.2) 0.1 (0.1 to 0.2) 0.2 (0.1 to 0.4)
Atmospheric nitrogen deposition on land 0.6 (0.3 to 1.2) 0.7 (0.4 to 1.4) 0.7 (0.4 to 1.3) 0.8 (0.4 to 1.4) 0.4 (0.3 to 0.9)
Other indirect effects from CO 2, climate 
and land-use change0.1 (–0.4 to 0.7) 0.1 (–0.5 to 0.7) 0.2 (–0.4 to 0.9) 0.2 (–0.6 to 1.1)

Total anthropogenic 5.6 (3.6 to 8.7) 6.2 (3.9 to 9.6) 6.7 (4.1 to 10.3) 7.3 (4.2 to 11.4) 6.3 (2.6 to 9.2)
Natural sources and sinks
Rivers, estuaries, and coastal zones 0.3 (0.3 to 0.4) 0.3 (0.3 to 0.4) 0.3 (0.3 to 0.4) 0.3 (0.3 to 0.4) 0.6 (0.1 to 2.9)
Open oceans 3.6 (3.0 to 4.4) 3.5 (2.8 to 4.4) 3.5 (2.7 to 4.3) 3.4 (2.5 to 4.3) 3.8 (1.8 to 9.4)
Soils under natural vegetation 5.6 (4.9 to 6.6) 5.6 (4.9 to 6.5) 5.6 (5.0 to 6.5) 5.6 (4.9 to 6.5) 6.6 (3.3 to 9.0)
Atmospheric chemistry 0.4 (0.2 to 1.2) 0.4 (0.2 to 1.2) 0.4 (0.2 to 1.2) 0.4 (0.2 to 1.2) 0.6 (0.3 to 1.2)
Surface sink –0.01 (–0.3 to 0) –0.01 (–0.3 to 0) –0.01 (–0.3 to 0) –0.01 (–0.3 to 0) –0.01 (–1 to 0)
Total natural 9.9 (8.5 –12.2) 9.8 (8.3–12.1) 9.8 (8.2 –12.0) 9.7 (8.0 –12.0) 11.6 (5.5–23.5)
Total bottom-up source 15.5 (12.1 to 20.9) 15.9 (12.2 to 21.7) 16.4 (12.3 to 22.4) 17.0 (12.2 to 23.5) 17.9 (8.1 to 30.7)
Observed growth rate 3.7 (3.7 to 3.7) 4.5 (4.3 to 4.6) 3.6 (3.5 to 3.8)

Inferred stratospheric sink 12.9 (12.2-13.5) 13.1 (12.4–13.6) 14.3 (4.3 to 28.7)
Atmospheric inversion
Atmospheric loss 12.1 (11.4 to 13.3) 12.4 (11.7 to 13.3)
Total source 15.9 (15.1 to 16.9) 16.9 (15.9 to 17.7)
Imbalance 3.6 (2.2 to 5.7) 4.2 (2.4 to 6.4)

713
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55(CH 4), nitrous oxide (N 2O) – were discussed in the previous sections. 
This section compares the balance of the sources and sinks of these 
three gases and their regional net flux contributions to the radiative 
forcing. CO 2 has multiple residence times in the atmosphere – from one 
year to many thousands of years (Box 6.1 in Ciais et al., 2013) – and 
N2O has a mean lifetime of 116 years. They are both long-lived GHGs, 
while CH 4 has a lifetime of 9.1 years and is considered a short-lived 
GHG (see Chapter 2 for lifetime of GHGs, Chapter 6 for CH 4 chemical 
lifetime, and Chapter 7 for effective radiative forcing of all GHGs).
Figure 5.18 shows the contribution to radiative forcing of CO 2, CH 4, 
N2O, and the halogenated species since the 1900s to more recent decades. For the period 1960–2019, the relative contribution to the 
total effective radiative forcing (ERF) was 63% for CO 2, 11% for

CH 4, 6% for N 2O, and 17% for the halogenated species (Chapter 7; 
Figure 5.18). The systematic decline in the relative contribution to 
ERF for CH 4 since 1850 is caused by a slower increase rate of CH 4 
in the recent decades, at 6, 10 and 5 ppb yr–1 during 1850–2019, 
1960–2019 and 2000–2019, respectively, in comparison with the 
increasing rate of CO 2 (at 0.7, 1.6 and 2.2 ppm yr–1, respectively) 
and N 2O (at 0.4, 0.7 and 0.9 ppb yr–1, respectively; Figure  5.4). 
Owing to the shorter lifetime of CH 4, the effect of a reduction in 
the emissions increase rate on the ERF increase is evident at inter-
decadal time scales.
Atmospheric abundance of GHGs is proportional to their emissions-
loss budgets in the Earth’s environment. There are multiple metrics 
to evaluate the relative importance of different GHGs for the global 
atmospheric radiation budget and the socio-economic impacts 
(Section 7.6). Metrics for weighting emissions are further developed

in AR6 WGIII. Figure 5.19 shows the regional emissions of the three 
main GHGs. For North Asia, Europe, Temperate North America and 
West Asia, the most dominant GHG source is CO 2 (high confidence ) 
(Figure  5.19) while, for East Asia, South Asia, South East Asia, 
Tropical South America, Temperate North America and Central Africa, 
the source is CH 4 (Figure  5.19). The N 2O emissions are dominant 
in regions with intense use of nitrogen fertilizers in agriculture. 
Only boreal North America showed net sinks of CO 2, while close 
to flux neutrality is observed for North Asia, Southern Africa, and 
Australasia. Persistent emissions of CO 2 are observed for Tropical 
and South America, northern Africa, and South East Asia ( medium 
confidence ). The medium confidence  arises from large uncertainties 
in the estimated non-fossil fuel CO 2 fluxes over these regions due to 
the lack of high-quality atmospheric measurements.

Figure 5.18 | Contributions of carbon dioxide (CO 2), methane (CH 4), nitrous 
oxide (N 2O) and halogenated species to the total effective radiative 
forcing (ERF) increases in 2019 since 1850, 1960 and 2000, respectively.  
ERF data are taken from Annex III (based on calculations from Chapter 7). Note that 
the sum of the ERFs exceeds 100% because there are negative ERFs due to aerosols 
and clouds. Further details on data sources and processing are available in the chapter 
Figure 5.19 | Regional distributions of net fluxes of carbon dioxide (CO 2), methane (CH 4), nitrous oxide (N 2O) on the Earth’s surface. The region divisions, shown 
as the shaded map, are made based on ecoclimatic characteristics of the land. The fluxes include those from anthropogenic activities and natural causes that result from responses

to anthropogenic greenhouse gases and climate change (feedbacks) as in the three budgets shown in Sections 5.2.1.5, 5.2.2.5, and 5.2.3.5. The CH 4 and N 2O emissions are 
weighted by arbitrary factors of 50 and 500, respectively, for depiction by common y-axes. Fluxes are shown as the mean of the inverse models as available from Thompson et al. 
(2019); Friedlingstein et al. (2020); Saunois et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

714
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.3 Ocean Acidification and Deoxygenation
The surface ocean has absorbed a quarter of all anthropogenic CO 2 
emissions, mainly through physical–chemical processes (McKinley 
et al., 2016; Gruber et al., 2019b; Friedlingstein et al., 2020). Once 
dissolved in seawater, CO 2 reacts with water and forms carbonic acid, 
which in turn dissociates, leading to a decrease in the concentration 
of carbonate (CO 3–2) ions, and increasing both bicarbonate (HCO 3–) 
and hydrogen (H+) ion concentration. This process has caused a shift 
in the carbonate chemistry towards a  less basic state, commonly 
referred to as ‘ocean acidification’ (Caldeira and Wickett, 2003; 
Orr et al., 2005; Doney et al., 2009). Although the societal concern 
regarding ocean acidification is relatively recent (about the last 
20  years), the physical–chemical basis for the ocean absorption

(sink) of atmospheric CO 2 has been discussed much earlier by Revelle 
and Suess (1957). The AR5 and SROCC assessments were of robust 
evidence  that the H+ ion concentration is increasing in the surface 
ocean, thereby reducing seawater pH (= -log [H+]) (Section 2.3.4.1; Orr 
et al., 2005; Feely et al., 2009; Ciais et al., 2013; Bindoff et al., 2019), 
and there is high confidence  that ocean acidification is impacting 
marine organisms (Bindoff et al., 2019).
Ocean oxygen decline, or deoxygenation, is driven by changes in ocean 
ventilation and solubility (Bindoff et al., 2019). It is virtually certain  
that anthropogenic forcing has made a  substantial contribution to 
the ocean heat content increase over the historical period (Bindoff 
et al., 2019; IPCC, 2019c; Chapter  9, Section  2.3.3.1), strengthening 
upper water column stratification. Ocean warming decreases the 
solubility of dissolved oxygen in seawater, and it contributes to about

15% of the  dissolved oxygen decrease in the oceans according to 
estimates based on solubility and the recent SROCC assessment 
(medium confidence ), especially in sub-surface waters, between 
100–600 m depth (Helm et al., 2011; Schmidtko et al., 2017; Breitburg 
et al., 2018; Oschlies et al., 2018; SROCC, Section 5.3.1). Stratification 
reduces the ventilation flux into the ocean interior, contributing to 
most of the remaining ocean deoxygenation (Schmidtko et al., 2017; 
Breitburg et al., 2018; Section  3.6.2). Deoxygenation may enhance 
emissions of nitrous oxide, especially from oxygen minimum zones 
(OMZs) or hypoxic coastal areas (Breitburg et al., 2018; Oschlies et al., 
2018). Since SROCC (Bindoff et al., 2019), CMIP6 model simulation 
results agree with the reported 2% loss (4.8 ± 2.1 Pmoles O 2) in total 
dissolved oxygen in the upper ocean layer (100–600 m) for the 1970–
2010 period (Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017;

Kwiatkowski et al., 2020; Section  2.3.4.2). The response of marine 
organisms to the coupled effects of ocean warming, acidification and 
deoxygenation occur at different metabolic levels on different groups, 
and include respiratory stress and reduction of thermal tolerance 
(Gruber, 2011; Bindoff et al., 2019; IPCC, 2019c; Kawahata et al., 
2019). An assessment of these effects on marine biota is found in WGII 
AR6 Chapter 2.
This section assesses past events of ocean acidification and 
deoxygenation (Section  5.3.1), the historical trends and spatial 
variability for the upper ocean (Section 5.3.2) and the ocean interior 
(Section  5.3.3). Future projections for ocean acidification and the 
drivers in the coastal ocean are assessed in Sections  5.3.4 and 
5.3.5, respectively.5.3.1 Paleoclimate Context
5.3.1.1 Paleocene–Eocene Thermal Maximum
The Paleocene–Eocene Thermal Maximum (PETM) was an episode of 
global warming exceeding pre-industrial temperatures by 4°C–8°C

(McInerney and Wing, 2011; Dunkley Jones et al., 2013) that occurred 
55.9–55.7  Ma. The PETM involved a  large pulse of geologic CO 2 
released into the ocean–atmosphere system in 3–20 kyr (Zeebe et al., 
2016; Gutjahr et al., 2017; Kirtland Turner et al., 2017; Kirtland Turner, 
2018; Gingerich, 2019; Section 5.2.1.1). In response, observationally 
constrained model simulations report an increase in atmospheric 
CO 2 concentrations ranging from about 900  ppm to >2000  ppm 
(Chapter 2; Gutjahr et al., 2017; Cui and Schubert, 2018; Anagnostou 
et al., 2020). The PETM thus provides a test for our understanding of 
the ocean’s response to the increase in carbon (and heat) emissions 
over geologically short time scales.
A limited number of independent proxy records indicate that the 
PETM was associated with a  surface ocean pH decline ranging 
from 0.15 to 0.30 units (Penman et al., 2014; Gutjahr et al., 2017; 
Babila et al., 2018) . It was also accompanied by a rapid (<10 ka)

shallowing of the carbonate saturation horizon, resulting in 
the widespread dissolution of sedimentary carbonate, followed 
by a  gradual (100  kyr) recovery (Zachos et al., 2005; Bralower 
et al., 2018). The remarkable similarity among sedimentary records 
spanning a  wide range of ecosystems suggests with medium 
confidence  that the perturbation in the ocean carbonate saturation 
was global (Babila et al., 2018) and directly resulted from elevated 
atmospheric CO 2 levels. The degree of acidification is similar to the 
0.4 pH unit decrease projected for the end of the 21st century under 
RCP8.5 (Gattuso et al., 2015) and is estimated to have occurred at 
a rate about one order of magnitude slower than the current rate 
of ocean acidification (Zeebe et al., 2016). There is low confidence  
in the inferred rates of ocean acidification inherent to the range of 
uncertainties affecting rates estimates based on marine sediments 
(Section 5.1.2.1).

Recent model outputs and globally distributed geochemical data 
reveal with medium confidence  widespread ocean deoxygenation 
during the PETM (Dickson et al., 2012, 2014; Winguth et al., 2012; 
Chang et al., 2018; Remmelzwaal et al., 2019), with parts of the 
ocean potentially becoming drastically oxygen-depleted (anoxic; 
Yao et al., 2018; Clarkson et al., 2021). Deoxygenation affected the 
surface ocean globally (including the Arctic Ocean; Sluijs et al., 2006), 
due to vertical and lateral expansion of OMZs (Zhou et al., 2014) that 
resulted from warming and related changes in ocean stratification . 
Expansion of OMZs may have stimulated N 2O production through 
water-column (de)nitrification (Junium et al., 2018). The degree to 
which N 2O production impacted PETM warming, however, has not 
yet been established.
The feedbacks associated with recovery from the PETM are uncertain, 
yet could include drawdown associated with silicate weathering

(Zachos et al., 2005) and regrowth of terrestrial and marine organic 
carbon stocks (Bowen and Zachos, 2010; Gutjahr et al., 2017).

715
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 555.3.1.2 Last Deglacial Transition
The Last Deglacial Transition (LDT) is the best documented climatic 
transition in the past associated with a substantial atmospheric CO 2 
rise ranging from 190 to 265 ppm between 18–11 ka (Marcott et al., 
2014). The amplitude of the deglacial CO 2 rise is thus on the same 
order of magnitude as the increase since the Industrial Revolution.
Boron isotope ( δ11B) data suggest a 0.15–0.05 unit decrease in sea 
surface pH (Hönisch and Hemming, 2005; Henehan et al., 2013) 
across the LDT, an average rate of decline of about 0.002 units per 
century compared with the current rate of more than 0.1 units 
per century (Bopp et al., 2013; Gattuso et al., 2015). Planktonic 
foraminiferal shell weights decreased by 40% to 50% (Barker 
and Elderfield, 2002), and coccolith mass decreased by about 
25% (Beaufort et al., 2011) across the LDT.  Independent proxy

reconstructions thus highlight with high confidence  that pH values 
decreased as atmospheric CO 2 concentrations increased across the 
LDT. There is, however, low confidence  in the inferred rate of ocean 
acidification owing to multiple sources of uncertainties affecting 
rates estimates based on marine sediments (Section 5.1.2.1).
Geochemical and micropaleontological evidence suggest that 
intermediate-depth OMZs almost vanished during the Last Glacial 
Maximum (LGM) (Jaccard et al., 2014). However, multiple lines of 
evidence suggest with medium confidence  that the deep (>1500 m) 
ocean became depleted in O 2 (concentrations were possibly lower 
than 50 μmol kg–1) globally (Jaccard and Galbraith, 2012; Hoogakker 
et al., 2015, 2018; Gottschalk et al., 2016, 2020a; Anderson et al., 
2019) as a  combined result of sluggish ventilation of the ocean 
subsurface (Gottschalk et al., 2016, 2020a; Skinner et al., 2017) and

a generally more efficient marine biological carbon pump (Buchanan 
et al., 2016; Yamamoto et al., 2019; Galbraith and Skinner, 2020).
During the LDT, deep ocean ventilation increased as Antarctic Bottom 
Water (AABW) (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard 
et al., 2016) and subsequently the Atlantic meridional overturning 
circulation (McManus et al., 2004; Lippold et al., 2016) resumed, 
transferring previously sequestered remineralized carbon from the 
ocean interior to the upper ocean, and eventually the atmosphere 
(Skinner et al., 2010; Galbraith and Jaccard, 2015; Gottschalk et al., 
2016; Ronge et al., 2016, 2020; Sikes et al., 2016; Rae et al., 2018), 
contributing to the deglacial CO 2 rise. Intermediate depths lost 
oxygen as a result of sluggish ventilation and increasing temperatures 
(decreasing saturation). As the world emerged from the last Glacial 
period, OMZs underwent a large volumetric increase at the beginning

of the Bølling-Allerød (B/A), a  northern-hemisphere wide warming 
event, 14.7 ka (Jaccard and Galbraith, 2012; Praetorius et al., 2015) 
with deleterious consequences for benthic ecosystems (e.g., Moffitt 
et al., 2015). These observations indicate with high confidence  that 
the rate of warming, affecting the solubility of oxygen and upper 
water column stratification, coupled with changes in subsurface 
ocean ventilation, impose a  direct control on the degree of ocean 
deoxygenation, implying a  high sensitivity of ocean oxygen loss 
to warming. The expansion of OMZs contributed to a  widespread 
increase in water column (de)nitrification (Galbraith and Kienast, 
2013), which contributed substantially to enhanced marine N 2O emissions. Nitrogen stable isotope measurements on N 2O extracted 
from ice cores suggest that approximately one-third (of the order of 
0.7 ± 0.3TgN yr–1) of the deglacial increase in N 2O emissions relates

to oceanic sources (Schilt et al., 2014; H. Fischer et al., 2019).
5.3.2 Historical Trends and Spatial Characteristics 
in the Upper Ocean
5.3.2.1 Reconstructed Centennial Ocean Acidification Trends
Ocean pH time series are based on the reconstruction of coral 
boron isotope ratios ( δ11B). A majority of coral δ11B data have been 
generated from the western Pacific region with a few records from 
the Atlantic Ocean. Biweekly resolution paleo-pH records show 
monsoonal variation of about 0.5 pH unit in the South China Sea 
(Liu et al., 2014). Interannual ocean pH variability in the range of 
0.07–0.16 pH unit characterizes southwest Pacific corals that are 
attributed to El Niño–Southern Oscillation (ENSO) (H.C. Wu et al., 
2018) and river runoff (D’Olivo et al., 2015). Decadal (10-, 22- and 48-
year) ocean pH variations in the south-west Pacific have been linked 
to the Inter-decadal Pacific Oscillation, causing variations of up to

0.30 pH unit in the Great Barrier Reef (Pelejero et al., 2005; Wei et al., 
2009) but weaker (about 0.08 pH unit) in the open ocean (H.C. Wu 
et al., 2018). Decadal variations in the South China Sea pH changes 
of 0.10–0.20 have also been associated with the variation in the East 
Asian monsoon (Liu et al., 2014; Wei et al., 2015), as a weakening of 
the Asian winter monsoon leads to sluggish water circulation within 
the reefs, building up localised CO 2 concentration in the water due to 
calcification and respiration.
Since the beginning of the industrial period in the mid-19th century, 
coral δ11B-derived ocean pH has decreased by 0.06–0.24 pH unit in 
the South China Sea (Liu et al., 2014; Wei et al., 2015) and 0.12 pH 
unit in the south-west Pacific (H.C. Wu et al., 2018). Since the mid-
20th century, a distinct feature of coral δ11B records relates to ocean 
acidification trends, albeit having a wide range of values: 0.12–0.40 pH

unit in the Great Barrier Reef (Wei et al., 2009; D’Olivo et al., 2015), 
0.05–0.08 pH unit in the north-west Pacific (Shinjo et al., 2013) and 
0.04–0.09 pH unit in the Atlantic Ocean (Goodkin et al., 2015; Fowell 
et al., 2018). Concurrent coral carbon isotopic ( δ13C) measurements 
infer ocean uptake of anthropogenic CO 2 from the combustion of 
fossil fuel, based on the lower abundance of 13C in fossil fuel carbon. 
Western Pacific coral records show depleted δ13C trends since the 
late 19th century that are more prominent since the mid-20th century 
(high confidence ) (Pelejero et al., 2005; Wei et al., 2009; Shinjo et al., 
2013; Liu et al., 2014; Kubota et al., 2017; H.C. Wu et al., 2018).
Overall, many of the records show a  highly variable seawater pH 
underlaying strong imprints of internal climate variability ( high 
confidence ) and, in most instances, superimposed on a decreasing 
δ11B trend that is indicative of anthropogenic ocean acidification in

recent decades ( medium confidence ). The robustness of seawater 
pH reconstructions is currently limited by the uncertainty on the 
calibration of the δ11B proxy in different tropical coral species.

716
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.3.2.2 Observations of Ocean Acidification 
over Recent Decades
The SROCC (Section  5.2.2.3) indicated that it is virtually certain  
that  the ocean has undergone acidification globally in response 
to ocean CO 2 uptake, and concluded that pH in open ocean 
surface water has changed by a  virtually certain  range of –0.017 
to –0.027 pH units per decade since the late 1980s. Since SROCC, 
evidence of the progress of acidification across all regions of the 
oceans has been further strengthened by continued observations 
of seawater carbonate chemistry at ocean time series stations, 
and compiled shipboard studies providing temporally resolved and 
methodologically consistent datasets (Jiang et al., 2019) (Figure 5.20; 
Supplementary Material Table 5.SM.3; Section 2.3.3.5).
In the subtropical open oceans, decreases in pH have been reported 
with a  very likely  rate range from –0.016 to –0.019 pH units per

decade since 1980s, which equates to approximately 4 % increase 
in hydrogen ion concentration ([H+]) per decade. Accordingly, the 
saturation state Ω  (=[Ca2+][CO 32-]/Ksp) of seawater with respect to 
calcium carbonate mineral aragonite has been declining at rates 
ranging from –0.07 to –0.12 per decade (González-Dávila et al., 
2010; Feely et al., 2012; Bates et al., 2014; Takahashi et al., 2014; Ono et al., 2019; Bates and Johnson, 2020; Supplementary Material 
Table  5.SM.3). These rates are consistent with the rates expected 
from the transient equilibration with increasing atmospheric CO 2 
concentrations, but the variability of rate in decadal time scale has 
also been detected with robust evidence  (Ono et al., 2019; Bates 
and Johnson, 2020). In the tropical Pacific, its central and eastern 
upwelling zones exhibited a faster pH decline of –0.022 to –0.026 pH 
unit per decade due to increased upwelling of CO 2-rich sub-surface

waters in addition to anthropogenic CO 2 uptake (Sutton et al., 2014; 
Lauvset et al., 2015). By contrast, warm pools in the western tropical 
Pacific exhibited slower pH decline of –0.010 to –0.013 pH unit per 
decade (Supplementary Material Table 5.SM.3; Lauvset et al., 2015; 
Ishii et al., 2020). Observational and modelling studies (Nakano et al., 
2015; Ishii et al., 2020) consistently suggest that slower acidification 
in this region is attributable to the anthropogenic CO 2 taken up in the 
extratropics around a decade ago and transported to the tropics via 
shallow meridional overturning circulations.
In open subpolar and polar zones, the very likely  range (–0.003 to 
–0.026 pH unit per decade) and uncertainty (up to 0.010) observed 
in pH decline are larger than in the subtropics, reflecting the complex 
interplay between physical and biological forcing mechanisms 
(Olafsson et al., 2009; Midorikawa et al., 2012; Bates et al., 2014; 
pH8.00 8.03 8.06 8.09 8.12

Figure 5.20 | Multi-decadal trends of pH (Total Scale) in surface layer at various sites of the oceans and a global distribution of annual mean pH adjusted 
to the year 2000.  Time-series data of pH are from Dore et al. (2009), Olafsson et al. (2009), González-Dávila et al. (2010), Bates et al. (2014), Takahashi et al. (2014), 
Wakita et al. (2017), Merlivat et al. (2018), Ono et al. (2019), and Bates and Johnson (2020). Global distribution of annual mean pH have been evaluated from data of surface 
ocean pCO 2 measurements (Bakker et al., 2016; Jiang et al., 2019). Acronyms in panels: KNOT and K2 – Western Pacific subarctic gyre time series; HOT – Hawaii Ocean Time-
series; BATS – Bermuda Atlantic Time-series Study; DYFAMED – Dynamics of Atmospheric Fluxes in the Mediterranean Sea; ESTOC – European Station for Time-series in the

Ocean Canary Islands; CARIACO – Carbon Retention in a Colored Ocean Time-series. Further details on data sources and processing are available in the chapter data table 

717
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Takahashi et al., 2014; Lauvset et al., 2015; Wakita et al., 2017; Merlivat 
et al., 2018). Nevertheless, the high agreement  of pH decline among 
these available time-series studies leads to high confidence in the 
trend of acidification in these zones. In the Arctic Ocean, a temporally 
limited time series of carbonate chemistry measurements prevents 
drawing robust conclusions on ocean acidification trends. However, 
the carbonate saturation state (Ω) is generally low, and observational 
studies show with robust evidence  that the recent extensive melting 
of sea ice leading to enhanced air–sea CO 2 exchange, large freshwater 
inputs, together with river discharge and glacial drainage, as well 
as the degradation of terrestrial organic matter in seawater, result 
in the decline of  Ω  of aragonite to undersaturation (Bates et al., 
2009; Chierici and  Fransson, 2009; Yamamoto-Kawai et al., 2009;

Azetsu-Scott et al., 2010; Robbins et al., 2013; Fransson et al., 2015; 
Semiletov et al., 2016; Anderson et al., 2017; Qi et al., 2017; Beaupré-
Laperrière et al., 2020; Y . Zhang et al., 2020; SROCC Section 3.2.1.2.4, 
IPCC, 2019b). The low saturation state of aragonite (Ω about 1) has 
also been observed in surface waters of the Antarctic coastal zone 
associated with freshwater input from glaciers (Mattsdotter Björk 
et al., 2014) and upwelling of deep water (Hauri et al., 2015) as well 
as along eastern boundary upwelling systems (Feely et al., 2016).
Overall, in agreement with SROCC, it is virtually certain  from these 
observational studies that ocean surface waters undergo acidification 
globally with the CO 2 increase in the atmosphere. These sustained 
measurements over the past decades, and campaign studies of ocean 
carbonate chemistry, also highlight with robust evidence  that trends 
of acidification have been modulated by the variability and changes

in physical and chemical states of ocean, including those affected by 
the warming of the cryosphere, and need to be better understood.
5.3.3 Ocean Interior Change
5.3.3.1 Ocean Memory: Acidification in the Ocean Interior
Advances in observations and modelling for ocean physics and 
biogeochemistry and established knowledge of ocean carbonate 
chemistry show with very high confidence  that anthropogenic CO 2 
taken up into the ocean surface layer is further spreading into the 
ocean interior through ventilation processes, including vertical 
mixing, diffusion, subduction and meridional overturning circulations 
(Sections 2.3.3.5, 5.2.1.3 and 9.2.2.3; Sallée et al., 2012; Bopp et al., 
2015; Nakano et al., 2015; Iudicone et al., 2016; Toyama et al., 2017; 
Pérez et al., 2018; Gruber et al., 2019b) and is causing acidification in 
the ocean interior. The net change in oxygen consumption by aerobic 
respiration of marine organisms further influences acidification by

releasing CO 2 (Section  5.3.3.2; Chen et al., 2017; Breitburg et al., 
2018; Robinson, 2019).
Observations over past decades of basin-wide and global syntheses 
of ocean interior carbon show that the extent of acidification due to 
anthropogenic CO 2 invasion tends to diminish with depth ( very high 
confidence ) (Section 5.2.1.3.3 and Figure 5.21; Woosley et al., 2016; 
Carter et al., 2017; Lauvset et al., 2020). The regions of deep convection 
such as subpolar North Atlantic and Southern Ocean present the 
deepest acidification detections below 2000 m ( medium confidence ). Mid-latitudinal zones within the subtropical cells and tropical regions 
present a  relatively deep and shallow detection, respectively. A  pH 
decrease has also been observed on the Antarctic continental shelf 
(Hauck et al., 2010; Williams et al., 2015). Acidification is also underway 
in the subsurface to intermediate layers of the Arctic Ocean due to

the inflow of ventilated waters from the North Atlantic and the North 
Pacific (Qi et al., 2017; Ulfsbo et al., 2018).
A significant increase in acidification resulting from net metabolic CO 2 
release coupled with ocean circulation changes has been shown with 
high confidence  in large swathes of intermediate waters in the Pacific 
and Atlantic oceans (Dore et al., 2009; Byrne et al., 2010; Ríos et al., 
2015; Chu et al., 2016; Carter et al., 2017; Lauvset et al., 2020). For 
example, ocean circulation contributes a pH change of –0.013 ± 0.013 
to the overall observed change of –0.029 ± 0.014 for 1993–2013 at 
depths around 1000 m at 30°S–40°S in the South Atlantic ocean (Ríos 
et al., 2015). Long-term repeated observations in the North Pacific show 
a decline in dissolved oxygen (–4.0 μmol kg−1 per decade at maximum) 
being sustained in the intermediate water since the 1980s (Takatani 
et al., 2012; Sasano et al., 2015). The amplification of acidification

associated with the weakening ventilation is thought to have been 
occurring persistently. In contrast, for the North Pacific subtropical 
mode water, large decadal variability in pH and aragonite saturation 
state with amplitudes of about 0.02 and about 0.1, respectively, are 
superimposed on secular declining trends due to anthropogenic CO 2 
invasion (Oka et al., 2019). This is associated with the variability in 
ventilation due to the approximately 50% variation in the formation 
volume of the mode water that is forced remotely by the Pacific 
Decadal Oscillation (Qiu et al., 2013; Oka et al., 2015).
These trends of acidification in the ocean interior lead to high 
confidence  in shoaling of the saturation horizons of calcium carbonate 
minerals where Ω  = 1. In the Pacific Ocean where the aragonite 
saturation horizon is shallower (a few hundred metres to 1200 m; 
Figure 5.21c), the rate of its shoaling is in the order of 1–2 m yr–1

(Feely et al., 2012; Ross et al., 2020). In contrast, shoaling rates of 
4 m yr–1 to 1710 m for 1984–2008 and of 10–15 m yr–1 to 2250 m for 
1991–2016 have been observed in the Iceland sea and the Irminger 
sea, respectively (Olafsson et al., 2009; Pérez et al., 2018).
In summary, ocean acidification is spreading into the ocean interior. 
Its rates at depths are controlled by the ventilation of the ocean 
interior as well as anthropogenic CO 2 uptake at the surface, thereby 
diminishing with depth ( very high confidence ) (Figure 5.21). Variability 
in ocean circulation modulates the trend of ocean acidification at 
depths through the changes in ventilation and their impacts on 
metabolic CO 2 content. However, the large knowledge gap around 
ventilation changes leads to low confidence  in their impacts in many 
ocean regions (Sections 5.3.3.2; 9.2.2.3 and 9.3.2).
5.3.3.2 Ocean Deoxygenation and its Implications 
for Greenhouse Gases

As summarized in SROCC (Section  5.2.2.4), there is a  growing 
consensus that between 1970 and 2010 the open ocean has 
very likely  lost 0.5–3.3% of its dissolved oxygen in the upper 
1000 m depth (Section 2.3.3.6; Helm et al., 2011; Ito et al., 2017;

718
Schmidtko  et al.,  2017; Bindoff et al., 2019). Regionally, the 
equatorial and North Pacific, the Southern Ocean and the South 
Atlantic have shown the greatest oxygen loss of up to 30 mol m–2 per 
decade (Schmidtko et al., 2017). Warming – via solubility reduction 
and circulation changes – mixing and respiration are considered the 
major drivers, with 50% of the oxygen loss for the upper 1000 m of 
the global oceans attributable to the solubility reduction (Schmidtko 
et al., 2017). Climate variability also modifies the oxygen loss on 
interannual and decadal time scales especially for the tropical 
ocean OMZs (Deutsch et al., 2011, 2014; Llanillo et al., 2013) and 
the North Pacific subarctic zone (Whitney et al., 2007; Sasano et al., 
2018; Cummins and Ross, 2020). However, quantifying the oxygen

decline and variability and attributing them to processes in different regions remains challenging (Levin, 2018; Oschlies et al., 2018). 
Earth system models (ESMs) in CMIP5 and CMIP6 corroborate the 
decline in ocean oxygen, and project a continuing and accelerating 
decline with a  strong impact of natural climate variability under 
high-emissions scenarios (Bopp et al., 2013; Long et al., 2016; 
Kwiatkowski et al., 2020). However, CMIP5 models did not reproduce 
observed patterns for oxygen changes in the tropical thermocline, 
and generally simulated only about half the oxygen loss inferred 
from observations (Oschlies et al., 2018). CMIP6 models have 
a  more realistic simulated mean state of ocean biogeochemistry 
than CMIP5 models due to improved ocean physical processes 
and better representation of biogeochemical processes (Séférian 
et al., 2020). They also exhibit enhanced ocean warming as a result 
72ºN85ºN82ºN 40ºN 0º 45ºS 67ºS 45ºS 45ºN 0º 50ºS 20ºS 10ºS(b)(a)
(c)

0-0.5-0.6-1
-0.4
-0.3
-0.2
-0.1-0.7-0.8-0.9
0-0.05-0.1-0.15
ΔpH
ΔΩ arag
72ºN85ºN82ºN 40ºN 0º 45ºS 67ºS 45ºS 45ºN 0º 50ºS 20ºS 10ºS-6000-5000-4000-3000-2000-1000-800-600-400-200Depth (m)
-6000-5000-4000-3000-2000-1000-800-600-400-200Depth (m)
Figure 5.21 | Spread of ocean acidification from the surface into the interior of ocean since pre-industrial times. (a)  Map showing the three transects used to 
create the cross sections shown in (b) and (c); vertical sections of the changes in (b) pH and (c) saturation state of aragonite (Ω arag) between 1800–2002 due to anthropogenic 
CO 2 invasion (colour). Contour lines are their contemporary values in 2002. The red transect begins in the Nordic Seas and then follows the GO-SHIP lines A16 southward in 
the Atlantic Ocean, SR04 and S04P westward in the Southern Ocean, and P16 northward in the Pacific Ocean. The purple line follows the GO-SHIP line I09 southward in the

Indian Ocean. The green line on the smaller inset crosses the Arctic Ocean from the Bering Strait to North Pole along 175°W and from the North Pole to the Fram Strait along 
5°E (Lauvset et al.,  2020). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

719
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55of an increase in the equilibrium climate sensitivity (ECS) of 
CMIP6 relative to CMIP5 models, which contributes to increased 
stratification and reduced subsurface ventilation (Sections  4.3.1, 
4.3.4, 5.3.3.2, 7.4.2, 7.5.6, 9.2.1, and TS2.4). Consequently, CMIP6 
model ensembles reproduce the ocean deoxygenation trend 
of −0.30  to −1.52 mmol m−3 per decade between 1970–2010 
reported in SROCC (Section 5.2.2.4) with a very likely  range, and 
also project 32–71% greater subsurface (100–600  m) oxygen 
decline relative to their Representative Concentration Pathway 
(RCP) analogues in CMIP5, reaching to the likely  range of decline of 
6.4 ± 2.9 mmol m–3 under SSP1–2.6 and 13.3 ± 5.3 mmol m–3 under 
SSP5–8.5, from 1870–1899 to 2080–2099 (Kwiatkowski et al., 
2020). It is concluded that the oxygen content of subsurface ocean 
is projected to transition to historically unprecedented condition

with decline over the 21st century ( medium confidence ).
In oxygen-depleted waters, microbial processes (denitrification and 
anammox, i.e., anaerobic ammonium oxidation; Kuypers et al., 2005; 
Codispoti, 2007; Gruber and Galloway, 2008) remove fixed nitrogen, 
and when upwelled waters reach the photic zone, primary production 
becomes nitrogen-limited (Tyrrell and Lucas, 2002). However, in 
other oceanic regions, increased water-column stratification due 
to warming may reduce the amount of N 2O reaching the surface 
and thereby decrease N 2O flux to the atmosphere. Landolfi et al. 
(2017) suggest that, by 2100, under the RCP8.5 scenario, total N 2O 
production in the ocean may decline by 5% and N 2O emissions be 
reduced by 24% relative to the pre-industrial era due to decreased 
organic matter export and anthropogenic-driven changes in ocean 
circulation and atmospheric N 2O concentrations. Projected oxygen 
loss in the ocean is thought to result in an ocean-climate feedback

through changes in the natural emissions of GHGs ( low confidence ).
The areas with relatively rapid oxygen decrease include OMZs in the 
tropical oceans, where oxygen content has been decreasing at 
a rate of 0.9–3.4 µmol kg–1 per decade in the thermocline for the 
past five decades (Stramma et al., 2008). Low oxygen, low pH and 
shallow aragonite saturation horizons in the OMZs of the eastern 
boundary upwelling regions co-occur, affecting ecosystem structure 
(Chavez et al., 2008) and function in the water column, including the 
presently unbalanced nitrogen cycle (Paulmier and Ruiz-Pino, 2009). 
The coupling between upwelling, productivity, and oxygen depletion 
feeds back to biological productivity and the role of these regions as 
sinks or sources of climate active gases. When OMZ waters upwell and 
impinge on the euphotic zone, they release significant quantities of 
GHGs, including N 2O (0.81–1.35 TgN yr–1), CH 4 (0.27–0.38 TgCH 4 yr–1),

and CO 2 (yet to be quantified) to the atmosphere, exacerbating 
global warming (Paulmier et al., 2008; Naqvi et al., 2010; Kock 
et al., 2012; Arévalo-Martínez et al., 2015; Babbin et al., 2015; 
Farías et al., 2015). Modelling projections suggest a global decrease 
of 4–12% in oceanic N 2O emissions (from 3.71–4.03  TgN  yr–1 to  
3.54–3.56  TgN  yr–1) from 2005 to 2100 under RCP8.5, despite 
a  tendency to increased N 2O production in the OMZs, associated 
primarily with denitrification (Martinez-Rey et al., 2015). It is difficult 
to single out the contribution of nitrification and denitrification, 
which can occur simultaneously. A rigorous separation of these two 
processes would require more mechanistic parametrization, which 
has been hindered by the still large conceptual and parametric uncertainties (Babbin et al., 2015; Trimmer et al., 2016; Landolfi et al., 
2017). Furthermore, the correlation between N 2O and oxygen varies

with microorganisms present, nutrient concentrations, and other 
environmental variables (Voss et al., 2013).
In summary, total oceanic N 2O emissions were projected to decline 
by 4–12% from 2005–2100 (Martinez-Rey et al., 2015) and by 24% 
from the pre-industrial era to 2100 (Landolfi et al., 2017) under 
RCP8.5. However, there is low confidence  in the reduction in N 2O 
emissions to the atmosphere, because of large conceptual and 
parametric uncertainties, a limited number of modelling studies that 
explored this process, and greater oxygen losses simulated in CMIP6 
models than in CMIP5 models (Kwiatkowski et al., 2020).
5.3.4 Future Projections for Ocean Acidification
5.3.4.1 Future Projections with Earth System Models (ESMs)
Projections with CMIP5 ESMs, reported in AR5 (Section  6.4.4) 
and SROCC (Section  5.2.2.3; IPCC, 2019b), showed changes in 
global mean surface ocean pH from 1870–1899 to 2080–2099 of 
–0.14 ± 0.001 (inter-model standard deviation) under RCP2.6 and

–0.38 ±  0.005 under RCP8.5 with pronounced regional variability 
(Bopp et al., 2013; Hurd et al., 2018). They also projected faster pH 
declines in mode waters below seasonal mixed layers (Resplandy 
et al., 2013; Watanabe and Kawamiya, 2017) as has been observed in 
the Atlantic (Salt et al., 2015) and in the Pacific (Carter et al., 2019), 
because of the net CO 2 release by respiration and lowering CO 2 
buffering capacity of seawater. In these CO 2 concentration-driven 
simulations, the level of acidification in the surface ocean is primarily 
determined by atmospheric CO 2 concentration and regional seawater 
carbonate chemistry, thereby providing consistent projections across 
models. New projections with CMIP6 ESMs show greater surface pH 
decline of –0.16 ± 0.002 under the SSP1-2.6 and –0.44 ± 0.005 under 
SSP5-8.5 from 1870–1899 to 2080–2099 (Section 4.3.2.5 and Cross-
Chapter Box 5.3; Kwiatkowski et al., 2020). The greater pH declines

in CMIP6 are primarily a  consequence of higher atmospheric CO 2 
concentrations in SSPs than their CMIP5-RCP analogues (Kwiatkowski 
et al., 2020). Ocean acidification is also projected to occur with high 
confidence  in the Abyssal Bottom Waters in regions such as the 
northern North Atlantic and the Southern Ocean (Sulpis et al., 2019), 
with the rates of global mean pH decline of –0.018 ± 0.001 under 
SSP1-2.6 and –0.030 ±  0.002 under SSP5-8.5 from 1870–1899 to 
2080–2099 in CMIP6 (Kwiatkowski et al., 2020).
In surface ocean, changes in the amplitude of seasonal variations in 
pH are also projected to occur with high confidence. ESMs in CMIP6 
show +73 ±  12% increase in the amplitude of seasonal variation 
in hydrogen ion concentration ([H+]) but 10 ± 5% decrease in the 
seasonal variation in pH (= -log [H+]) from 1995–2014 to 2080–
2099 under SSP5-8.5. The simultaneous amplification of [H+] and 
attenuation of pH seasonal cycles is counterintuitive but is the

consequence of a  greater increase in the annual mean [H+] due 
to anthropogenic CO 2 invasion than the corresponding increase 
in its seasonal amplitude. These changes are consistent with the 
amplification/attenuation of the seasonal variation of +81 ±16% for

720
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5[H+] and –16 ±  7% for pH from 1990–1999 to 2090–2099 under 
RCP8.5 in CMIP5 (Kwiatkowski and Orr, 2018).
The signal of ocean acidification in surface ocean is large and is 
projected to emerge beyond the range of natural variability within 
the time scale of a decade in all ocean basins (Schlunegger et al., 
2019). There is high agreement  among modelling studies that the 
largest pH decline and large-scale undersaturation of aragonite 
in surface seawater start to occur first in polar oceans (Orr et al., 
2005; Steinacher et al., 2009; Hurd et al., 2018; Jiang et al., 2019). 
Under SSP5-8.5, the largest surface pH decline, exceeding 0.45 
between 1995–2014 and 2080–2099, occurs in the Arctic Ocean 
(Kwiatkowski et al., 2020). The freshwater input from sea ice melt 
is an additional factor leading to a  faster decline of aragonite 
saturation level than expected from the anthropogenic CO 2 uptake

(Yamamoto et al., 2012). The increase in riverine and glacial 
discharges that provide terrigenous carbon, nutrients and alkalinity 
as well as freshwater are the other factors modifying the rate of 
acidification in the Arctic Ocean. However, their impacts have been 
projected in a limited number of studies with extensive knowledge 
gaps and model simplifications leading to low confidence  in 
their impacts (Terhaar et al., 2019; Hopwood et al., 2020). In the 
Southern Ocean, the aragonite undersaturation starts in the 2030s 
in RCP8.5, and the area that experiences aragonite undersaturation 
for at least one month per year by 2100 is projected to be more 
than 95%. Under RCP2.6, short periods (less than one month) of 
aragonite undersaturation are expected to be found in less than 
2% of the area during this century (Sasse et al., 2015; Hauri et al., 
2016; Negrete-García et al., 2019). These long term projections are

modified at interannual time scales by large-scale climate modes 
(Ríos et al., 2015) such as the ENSO and the Southern Annular 
Mode (Conrad and Lovenduski, 2015). In other regions, acidification 
trends are influenced by a range of processes such as changes in 
ocean circulation, temperature, salinity, carbon cycling, and the 
structure of the marine ecosystem. As, at present, models do not 
resolve fine-scale variability of these processes, current projections 
do not fully capture the changes that the marine environment will 
experience in the future (Takeshita et al., 2015; Turi et al., 2016).
Overall, with the rise of atmospheric CO 2, the physics of CO 2 transfer 
across the air–sea interface, the carbonate chemistry in seawater, 
the trends of ocean acidification being observed in the past decades 
(Section 5.3.3.2) and modelling studies described in this section, it 
is virtually certain  that ocean acidification will continue to grow.

However, the magnitude and sign (direction) of many of ocean 
carbon–climate feedbacks are still poorly constrained (Matear and 
Lenton, 2014, 2018), leading to low confidence in their significant 
and long-lasting impacts on ocean acidification.
5.3.4.2 Reversal of Ocean Acidification by Carbon 
Dioxide Removal
Reversing the increase in atmospheric CO 2 concentrations through 
negative emissions (Section  5.6) will reverse ocean acidification 
at the sea surface ( high confidence ) but will not result in 
rapid amelioration of ocean acidification in the deeper ocean 
(Section 5.3.3.2). The ocean’s uptake of atmospheric CO 2 will start to decrease as atmospheric CO 2 decreases (Sections  5.4.5, 5.4.10 
and 5.6.2.1; Mathesius et al., 2015; Tokarska and Zickfeld, 2015). 
However, because of the long time scales of the ocean turnover that 
transfers CO 2 from the upper to the deep ocean, excess carbon will 
continue to accumulate in the deep ocean even after a decrease in

atmospheric CO 2 (Cao et al., 2014; Mathesius et al., 2015; Tokarska 
and Zickfeld, 2015; T. Li et al., 2020). There is thus high confidence  
that CO 2 emissions leave a long-term legacy in ocean acidification, 
and are therefore irreversible at multi-human generational scales, 
even with aggressive atmospheric CO 2 removal.
5.3.5 Coastal Ocean Acidification and Deoxygenation
The coastal ocean, from the shoreline to the isobath of 200  m, is 
highly heterogeneous due to the complex interplay between 
physical, biogeochemical and anthropogenic factors (Gattuso et al., 
1998; Chen and Borges, 2009; Dürr et al., 2011; Laruelle et al., 2014; 
McCormack et al., 2016). These areas, according to SROCC (Bindoff 
et al., 2019) are, with high confidence , already affected by ocean 
acidification and deoxygenation. This section assesses the drivers 
and spatial variability of acidification and deoxygenation based on 
new observations and data products.
5.3.5.1 Drivers

Observations and data products including models (Astor et al., 2013; 
Bakker et al., 2016; Kosugi et al., 2016; Vargas et al., 2016; Laruelle 
et al., 2017, 2018; Orselli et al., 2018; Roobaert et al., 2019; Cai et al., 
2020; H. Sun et al., 2020) confirm the strong spatial and temporal 
variability in the coastal ocean surface carbonate chemistry and sea-
air CO 2 fluxes ( high agreement , robust evidence ). The anthropogenic 
CO 2-induced acidification is either mitigated or enhanced through 
biological processes; primary production removes dissolved CO 2 
from the surface, and respiration adds CO 2 and consumes oxygen 
in the subsurface layers. The relative intensity of these processes is 
controlled by natural or anthropogenic eutrophication. Other drivers 
of variability include biological community composition, freshwater 
input from rivers or melting ice, sea ice cover and calcium carbonate 
precipitation/dissolution dynamics, coastal upwelling and regional

circulation, and seasonal surface cooling (Fransson et al., 2015, 
2017; Feely et al., 2018; Roobaert et al., 2019; Cai et al., 2020; Hauri 
et al., 2020; Monteiro et al., 2020b; H. Sun et al., 2020). Near-shore 
surface waters are often supersaturated with CO 2, regardless of the 
latitude, especially in highly populated areas receiving substantial 
amounts of domestic and industrial sewage (Chen and Borges, 2009). 
Nevertheless, thermal or haline-stratified eutrophic coastal areas 
may act as net atmospheric CO 2 sinks (Chou et al., 2013; Cotovicz 
Jr. et al., 2015). Continental shelves, excluding near-shore areas, 
act as CO 2 sinks at a  rate of 0.2 ±  0.02  PgC yr–1 (Laruelle et al., 
2014; Roobaert et al., 2019), considering ice-free areas only. Under 
increasing atmospheric CO 2 and eutrophication, such ecosystems 
would be more vulnerable to ecological and seawater chemistry 
changes, impacting the local economy.

Since AR5, (Ciais et al., 2013) and in agreement with SROCC (IPCC, 
2019b), there is now high agreement (robust evidence)  that coastal

721
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55ocean acidification, whether induced only by increasing atmospheric 
CO 2 or exacerbated by eutrophication or upwelling, has negative 
effects on specific groups of marine organisms such as reef-building 
corals, crabs, pteropods, and sessile fauna (AR6 WGII, Chapter  3; 
Dupont et al., 2010; Bindoff et al., 2019; Bednaršek et al., 2020; 
Osborne et al., 2020), especially when combined with stressors 
such as temperature and deoxygenation, and potentially increased 
bioavailability of toxic elements such as arsenic and copper (Millero 
et al., 2009; Boyd et al., 2015; Breitburg et al., 2018).
Since SROCC (Bindoff et al., 2019), there is further evidence that 
anthropogenic eutrophication via continental runoff and atmospheric 
nutrient deposition, and ocean warming are very likely  the main 
drivers of deoxygenation in coastal areas (Levin and Breitburg,

2015; Levin et al., 2015; Royer et al., 2016; Breitburg et al., 2018; 
Cocquempot et al.,  2019; Fagundes et al., 2020; Limburg et al., 
2020). Increasing intensity and frequency of wind-driven upwelling 
is responsible for longer and more intense coastal hypoxia, fuelled 
by organic matter degradation from primary production (medium 
to high agreement, medium evidence)  (Rabalais et al., 2010; 
Bakun  et al.,  2015; Varela et al., 2015; Fennel and Testa, 2019; 
Limburg et al., 2020). Locally, submarine groundwater discharge may 
enhance the eutrophication state ( low agreement , limited evidence , 
Luijendijk et al., 2020). Since AR5 (Ciais et al., 2013) and SROCC 
(Bindoff et al., 2019) new observations and model studies confirm the 
trends in increasing coastal hypoxia caused by eutrophication, ocean 
warming and changes in circulation (Claret et al., 2018; Dussin et al., 
2019; Limburg et al., 2020), as well as the ubiquitous impacts on

marine organisms and fisheries (AR6 WGII Chapter 3; Carstensen and 
Conley, 2019; Fennel and Testa, 2019; Osma et al., 2020). Following 
open ocean deoxygenation trends since the 1950s, more than 
700 coastal regions are being reported as hypoxic (dissolved oxygen 
concentration <2  mg O 2 L–1) (Limburg et al., 2020). Additionally, 
deoxygenation or increasing severe hypoxic periods in coastal areas 
may enhance the sea-to-air fluxes of N 2O and CH 4 especially through 
microbial-mediated processes in the water column–sediment 
interface ( medium agreement ) (Middelburg and Levin, 2009; Naqvi 
et al., 2010; Farías et al., 2015; Limburg et al., 2020).
5.3.5.2 Spatial Characteristics
There is high agreement (robust evidence ) that heterogeneity implies 
different responses of coastal regions to increasing atmospheric CO 2, 
decreasing seawater pH and calcium carbonate saturation state, and 
deoxygenation (Duarte et al., 2013; Regnier et al., 2013; Breitburg

et al., 2018; Laruelle et al., 2018; Carstensen and Duarte, 2019).
There is high agreement  that long-time series of observations 
utilizing standard methods are needed to distinguish the climate 
change signal in seawater carbonate chemistry from the natural 
variability of coastal sites (Duarte et al., 2013; Salisbury and Jönsson, 
2018; IOC, 2019; Sutton et al., 2019; Tilbrook et al., 2019; Turk et al., 
2019). Despite the increasing availability of data and sea–air CO 2 
flux budgets for the coastal ocean (Sections  5.3.5.1 and 5.2.3.1), 
additional long-term observations are required to constrain the 
global time of emergence of coastal acidification. There is high 
agreement (medium evidence ) that, for the coastal subtropical to temperate north-east Pacific and north-west Atlantic, the mean time 
of emergence for acidification is above two decades (Sutton et al., 
2019; Turk et al., 2019).
Observations and models predict an expansion and intensification

of low-pH deep water intrusions for the north-east Pacific coastal 
upwelling area ( high agreement , robust evidence ) (Hauri et al., 2013; 
Feely et al., 2016; Cai et al., 2020). Areas such as the California Current 
System are naturally exposed to intrusions of low-pH, high pCO 2sea 
deep waters from remineralization processes and anthropogenic CO 2 
intrusion (Feely et al., 2008, 2010, 2018; Chan et al., 2019; Lilly et al., 
2019; Cai et al., 2020).The eastern Pacific coastal upwelling displays 
seasonality in subsurface aragonite undersaturation as a consequence 
of the interplay between anthropogenic CO 2, respiration and 
intrusion  of upwelling waters (Feely et al., 2008, 2010, 2016, 2018; 
Hauri et al., 2013; Vargas et al., 2016; Chan et al., 2019; Lilly et al., 
2019). The  coastal south-east Pacific upwelling combined with low-
pH, low-alkalinity, organic matter-rich river inputs display extreme 
temporal variability in surface seawater pCO 2 and low aragonite

saturation (Vargas et al., 2016; Osma et al., 2020).
Temperate, non-upwelling coastal areas along the north-west 
Atlantic display a trend of decreasing seawater pH, mainly attributed 
to the combined effects of eutrophication and decreasing seawater 
buffering capacity ( high agreement , robust evidence ). Observations 
show an increasing north to south gradient of aragonite saturation 
state (Sutton et al., 2016; Fennel et al., 2019; Cai et al., 2020). Low 
alkalinity and total inorganic carbon concentration, combined with 
an ocean signal of acidification, diminishes the buffering capacity 
along the decreasing salinity gradient from the ocean to the coast. 
Models suggest that, in this area, the aragonite saturation is 
seasonally controlled by nutrient availability and primary production, 
supporting the finding that eutrophication is the main driver for 
exacerbating acidification (Cai et al., 2017, 2020). The coastal Gulf of

Mexico is facing a parallel increase in bottom water acidification and 
deoxygenation off the Mississippi Delta driven by eutrophication (Cai 
et al., 2011; Laurent et al., 2017; Fennel et al., 2019).
Many coastal tropical areas are under heavy anthropogenic 
eutrophication induced by the effluents from large cities, or 
receive large riverine inputs of freshwater, nutrients, and organic 
matter (such as Amazon, Mississippi, Orinoco, Congo, Mekong, or 
Changjiang rivers). Under strong eutrophication, often sub-surface 
and bottom waters present pH values lower than average surface 
open ocean (about 8.0) because increased respiration decreases pH 
(high agreement , robust evidence ), despite a  net atmospheric CO 2 
sink in shallow and vertically stratified coastal areas (Koné et al., 
2009; Wallace et al., 2014; Cotovicz Jr. et al., 2015, 2018; Fennel and 
Testa, 2019; Lowe et al., 2019; Section 5.3.5.1).
There is medium evidence  from observations and models that

the coastal north-western Antarctic Peninsula (Southern Ocean) 
will experience calcium carbonate undersaturation by 2060, 
considering that anthropogenic emissions reach an atmospheric CO 2 
concentration of about 500 pm at that date (Lencina-Avila et al., 
2018; Monteiro et al., 2020a). The synergies among warming, melt 
water, sea-air CO 2 equilibrium and circulation may, to some extent,

722
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5offset the coastal ocean acidification trends in Antarctica (Henley 
et al., 2020). In the coastal western Arctic Ocean, there is increasing 
robust evidence  that ocean acidification is driven by sea-air CO 2 fluxes 
and sea-ice melt, and increasing intrusions since the 1990s of low-
alkalinity Pacific water, lowering aragonite saturation state (Qi et al., 
2017, 2020; Cross et al., 2018). The Bering Sea (north-eastern Pacific) 
shows decreasing trends in calcium carbonate saturation, associated 
to the increasing atmospheric CO 2 uptake combined with riverine 
freshwater and carbon inputs ( high agreement , robust evidence ) 
(Pilcher et al., 2019; H. Sun et al., 2020).
The spatial distribution of hypoxic areas is highly heterogeneous 
in the coastal ocean, and there is high agreement, robust evidence  
that more severe hypoxia or anoxia is often associated with highly

populated coastal areas, or local circulation and upwelling, and 
seasonal stratification leading to an accumulation of organic matter 
in subsurface waters (Ciais et al., 2013; Rabalais et al., 2014; M. Li 
et al., 2016; Breitburg et al., 2018; Bindoff et al., 2019; SROCC 
Chapter 5). The causes and trends of coastal deoxygenation can only 
be assessed by making available long-term time series combined 
with regional modelling (Fennel and Testa, 2019), as in the California 
current system (Wang et al., 2017), the East China Sea (Chen et al., 
2007; Qian et al., 2017), the Namibian or along the north-western 
Atlantic shelves (Claret et al., 2018). Other coastal upwelling sites 
such as the Arabian Sea display seasonal hypoxia but no worsening 
trends (Gupta et al., 2016).
The Baltic Sea is the largest semi-enclosed sea where hypoxia is 
reported to have happened before the 1950s (Carstensen et al., 
2014; Rabalais et al., 2014; Łukawska-Matuszewska et al., 2019).

The frequency and volume of seawater inflow from the North Sea 
decreased after 1950, leading to an expansion of hypoxic areas from 
40,000 to 60,000 km² in combination with increasing eutrophication 
(Carstensen et al., 2014). From the available observations, there is 
robust evidence  that many areas in the Baltic Sea are experiencing 
deoxygenation despite efforts to reduce nutrient loads (Lennartz 
et al., 2014; Jokinen et al., 2018).
There is medium agreement (medium evidence ) that simply reducing 
anthropogenic nutrient inputs may lead to less severe coastal 
hypoxic conditions, as observed in the coastal north-western Adriatic 
Sea (Djakovac et al., 2015). However, low-oxygen sediments may 
remain a  long-term source of phosphorus and ammonium to the 
water column, and in this way fuelling primary production (Jokinen 
et al., 2018; Fennel and Testa, 2019; Limburg et al., 2020).
5.4 Biogeochemical Feedbacks 

This section covers biogeochemical feedbacks on climate change, which 
represent one of the largest sources of uncertainty in climate change 
projections. The relevant processes are discussed (Sections  5.4.1 
to 5.4.4), prior to discussing the simulation and projection of the 
carbon cycle in Earth system models (Section  5.4.5), emergent 
constraints on future projections (Section 5.4.6), non-CO 2 feedbacks (Section  5.4.7), combined feedback assessment (Section  5.4.8), 
possible biogeochemical abrupt changes (Section 5.4.9), long-term 
carbon cycle projections (Section 5.4.10), and near-term prediction of 
ocean and land carbon sinks (5.4.11).
5.4.1 Direct CO 2 Effect on Land Carbon Uptake
The AR5 (WGI, Box  6.3) and SRCCL (IPCC, 2019a) concluded with 
high confidence  that rising atmospheric CO 2 increases leaf-level 
photosynthesis. This effect is represented in all ESMs. New studies since 
AR5 add evidence that the leaf-level CO 2 fertilization is modulated

by acclimation of photosynthesis to long-term CO 2 exposure, 
growth temperature, seasonal drought, and nutrient availability, 
but these effects are not yet routinely represented in ESMs (Smith 
and Dukes, 2013; Baig et al., 2015; Kelly et al., 2016; Drake et al., 
2017; Jiang et al., 2020a). Cross-Chapter Box 5.1 assesses multiple 
lines of evidence, which suggest that the ratio of plant CO 2 uptake 
to water loss – plant water-use efficiency (WUE) – increases in near 
proportionality to atmospheric CO 2. Despite advances in the regional 
coverage of field experiments, observations of the consequences of 
CO 2 fertilization at ecosystem level are still scarce, in particular from 
outside the temperate zone (Song et al., 2019). New syntheses since 
AR5 corroborate that the effect of elevated CO 2 on plant growth and 
ecosystem carbon storage is generally positive ( high confidence ), 
but is modulated by temperature, water and nutrient availability

(Reich et al., 2014; Obermeier et al., 2017; Peñuelas et al., 2017; 
Hovenden et al., 2019; Song et al., 2019). Plant carbon allocation, 
changes in plant community composition, disturbance, and natural 
plant mortality are important processes affecting the magnitude of the 
response, but are currently poorly represented in models (De Kauwe 
et al., 2014; Friend et al., 2014; Reich et al., 2018; A.P . Walker et al., 
2019; K. Yu et al., 2019), and thus contribute strongly to uncertainty 
in ESM projections (Arora et al., 2020).
Field studies with elevated CO 2 have demonstrated that the initial 
stimulation of above-ground growth may decline if insufficient 
nutrients such as nitrogen or phosphorus are available (Finzi et al., 
2007; Norby et al., 2010; Hungate et al., 2013; Reich and Hobbie, 
2013; Talhelm et al., 2014; Terrer et al., 2018). Model-data syntheses 
have demonstrated that capturing the observed long-term effect

of elevated CO 2 depends on the ability of models to predict the 
effect of vegetation on soil biogeochemistry (Zaehle et al., 2014; 
Koven et al., 2015b; Medlyn et al., 2015; Walker et al., 2015). Meta-
analyses of CO 2 manipulation experiments point to increased soil 
microbial activity and accelerated turnover of soil organic matter 
(van Groenigen et al., 2017) as a result of increased below-ground 
carbon allocation by plants (Song et al., 2019), and increased root 
exudation or mycorrhizal activity due to enhanced plant nutrient 
requirements under elevated CO 2 (Drake et al., 2011; Terrer et al., 
2016; Meier et al., 2017). These effects are not considered in most 
ESMs. One global model that attempts to represent these processes 
suggests that elevated CO 2-related carbon accumulation is reduced 
in soils but increased in vegetation relative to more conventional 
models (Sulman et al., 2019).

723
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Our understanding of the effects of phosphorus limitation is less 
developed than for nitrogen, but a  growing body of literature 
suggests that it is just as important, particularly in regions with 
highly weathered soils (Wang et al., 2018; Terrer et al., 2019; Du 
et al., 2020). CO 2 experiments collectively show that soil phosphorus 
is an important constraint on the CO 2 fertilization effect on plant 
biomass (Terrer et al., 2019; Jiang et al., 2020a). For example, despite 
increases in photosynthesis after four years of CO 2 exposure, a free-
air CO 2 enrichment experiment in a phosphorus-limited mature forest 
ecosystem did not find an increase in biomass production (Jiang 
et al., 2020b). The lack of free-air CO 2 enrichment experiments in 
phosphorus-limited tropical forests limits our understanding of the 
role of phosphorus availability in constraining the CO 2 fertilization

effect globally (Norby et al., 2016; Fleischer et al., 2019). Models 
accounting for the effects of phosphorus availability, in addition to 
nitrogen, generally show an even stronger reduction of the response 
of ecosystem carbon storage to elevated CO 2 (Goll et al., 2012; Zhang 
et al., 2014; X. Yang et al., 2019). Insufficient data and uncertainties 
in the process formulation cause large uncertainty in the magnitude 
of this effect (Medlyn et al., 2016; Fleischer et al., 2019).
Consistent with AR5 (WGI, Section 6.4.2), the CO 2 fertilization effect  is 
the dominant cause for the projected increase in land carbon uptake 
between 1860 and 2100 in ESMs (Figures 5.26 and 5.27, and Table 5.5; 
Arora et al., 2020). In the CMIP6 ensemble, the increase of land carbon 
storage due to CO 2 fertilization is a global phenomenon but is strongest 
in the tropics (Figure 5.26). The resulting increase of productivity is a key 
driver of increases in vegetation and soil carbon storage. However,

consistent with earlier findings (Todd-Brown et al., 2013; Friend et al., 
2014; Hajima et al., 2014), processes affecting vegetation carbon-use 
efficiency and turnover, such as allocation changes, mortality, and 
vegetation structural changes, as well as the pre-industrial soil carbon 
turnover time, also play an important role (Arora et al., 2020).
As a  major advance since AR5 (WGI, Section  6.4.2), six out of 
11  models in the C4MIP-CMIP6 ensemble account for nitrogen 
cycle dynamics over land (Table  5.4). On average, these models 
exhibit a  25–30% lower CO 2 fertilization effect on land carbon 
storage, compared to models that do not account for nitrogen cycle 
dynamics (Figure 5.29 and Table 5.5). The only model in the C4MIP-
CMIP6 ensemble that explicitly represents the effect of P availability 
on  plant growth suggests the lowest carbon storage response 
to increasing CO 2 (Arora et al., 2020). The lower CO 2 effect due to

decreased nutrient availability is generally consistent with analyses 
of the implicit nutrient limitation in CMIP5 simulations (Wieder 
et al., 2015; Zaehle et al., 2015) and independent assessments by 
stand-alone land models (Zaehle et al., 2010; Wårlind et al., 2014; 
Zhang et al., 2014; Goll et al., 2017; Meyerholt et al., 2020). The 
simulated effects are generally consistent with expectations based 
on independent observations (Walker et al., 2021). However, the 
magnitude of nutrient feedbacks in these models is poorly constrained 
by observations, owing to the limited geographic distribution of 
available observations and the uncertain scaling of results obtained 
from manipulation experiments to transient system dynamics (Song 
et al., 2019; Wieder et al., 2019; Meyerholt et al., 2020).Our understanding of the various biological processes that affect 
the strength of the CO 2 fertilization effect on photosynthesis and

its impact on carbon storage in vegetation and soils, (in particular 
regarding the limitations imposed by nitrogen and phosphorus 
availability), has developed since AR5 (WGI, Box  6.2). Based on 
consistent behaviour across all CMIP6 ESMs, there is high confidence 
that CO 2 fertilization of photosynthesis acts as an important negative 
feedback on anthropogenic climate change, by reducing the rate at 
which CO 2 accumulates in the atmosphere. Since AR5 (WGI, Box 6.2), 
an increasing number of CMIP6 ESMs account for nutrient cycles. 
The consistent results found in their model projections suggests with 
high confidence  that limited nutrient availability will limit the CO 2 
fertilization effect (Arora et al., 2020). The magnitude of the direct 
CO 2 effect on land carbon uptake, and its limitation by nutrients, 
remains uncertain.
5.4.2 Direct CO 2 Effects on Projected 
Ocean Carbon Uptake
In AR5 (WGI, Section 6.4.2) there was high agreement  that CMIP5

ESMs project continued ocean CO 2 uptake through to 2100, with 
higher uptake corresponding to higher concentration or emissions 
pathways. There has been no significant change in the magnitude 
of the sensitivity of ocean carbon uptake to increasing atmospheric 
CO 2, or in the inter-model spread, between the CMIP5 and CMIP6 era 
(Arora et al., 2020). The analysis from emissions and concentration-
driven CMIP5 model projections show that the ocean sink stops 
growing beyond 2050 across all emissions scenarios (Section 5.4.5.3). 
CMIP6 models also show a similar time evolution of global ocean 
CO 2 uptake to CMIP5 models over the 21st century (Figure  5.25) 
with decreasing net ocean CO 2 uptake ratio to anthropogenic CO 2 
emissions under SSP5-8.5.
The projected weakening of ocean carbon uptake is driven by 
a  combination of decreasing carbonate buffering capacity and 
warming, which are positive feedbacks under weak to no mitigation

scenarios (SSP4 and 5). In high mitigation scenarios (SSP1-2.6), 
weakening ocean carbon uptake is driven by decreasing emissions 
(Cross-Chapter Box  5.3). The detailed understanding of carbonate 
chemistry in seawater that has accumulated over more than half 
a  century (e.g.,  Revelle and Suess, 1957; Egleston et al., 2010), 
provides high confidence  that the excess CO 2 dissolved in seawater 
leads to a non-linear reduction of the CO 2 buffering capacity, that 
is smaller dissolved inorganic carbon (DIC) increase with respect 
to pCO 2 increase along with the increase in cumulative ocean CO 2 
uptake. Recent studies (Katavouta et al., 2018; Jiang et al., 2019; Arora 
et al., 2020; Rodgers et al., 2020) suggest with medium confidence  
that the decrease in the ocean CO 2 uptake ratio to anthropogenic 
CO 2 emissions, under low to no mitigation scenarios over the 
21st century, is predominantly attributable to the ocean carbon-

concentration feedback through the reduction of the seawater CO 2 
buffering capacity, but with contributions from physical drivers such 
as warming and wind stress ( medium confidence ) and biological 
drivers ( low confidence ) (Sections 5.2.1.3.3 and 5.4.4).

724
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Projected increases in ocean DIC due to anthropogenic CO 2 uptake 
amplify the sensitivity of carbonate system variables to perturbations 
of DIC in the surface ocean, for example via the amplitude of the 
seasonal cycle of pCO 2, which impacts the mean annual air–sea fluxes 
(Hauck et al., 2015; Fassbender et al., 2018; Landschützer et al., 2018; 
SROCC, Section 5.2.2.3). A larger amplification of the surface ocean 
pCO 2 seasonality occurs in the subtropics where  pCO 2 seasonality is 
dominated by temperature seasonality, with the summer increase 
in the difference in pCO 2 between surface water and the overlying 
atmosphere reaching 3μatm per decade between 1990 and 2030 
under RCP8.5 (Schlunegger et al., 2019; Rodgers et al., 2020). In 
contrast, the impact of biological production on the seasonal cycle of 
pCO 2 in summer in the Southern Ocean strengthens the drawdown 
of CO 2 (Hauck et al., 2015).

Overall, there is medium confidence  on three outcomes in the ocean 
from projected CO 2 uptake under medium to high CO 2 concentration 
scenarios: (i) a weakening of the buffering capacity, which impacts 
the airborne fraction via the reduction of the ocean CO 2 buffering 
capacity due to cumulative ocean CO 2 uptake, which reduces 
the net ocean CO 2 uptake ratio to anthropogenic CO 2 emissions 
(Katavouta et al., 2018; Arora et al., 2020; Rodgers et al., 2020); 
(ii) an amplification of the seasonal cycle of CO 2 variables, which 
impacts both the ocean sink and ocean acidification (Hauck et al., 
2015); (iii) a decrease in the aragonite and calcite saturation levels in 
the ocean, which negatively impacts the calcification rates of marine 
organisms ( high confidence ) and forms a negative feedback on the 
uptake of CO 2 (McNeil and Sasse, 2016) (Cross-Chapter Box 5.3).
5.4.3 Climate Effect on Land Carbon Uptake
The AR5 assessed with medium confidence  that future climate

change will decrease land carbon uptake relative to the case with 
constant climate, but with a  poorly constrained magnitude (AR5 
WGI, Chapter  6, Executive Summary). Ongoing uncertainty in the 
magnitude and geographic pattern of the feedbacks (Section 5.4.5), 
continues to support a  medium confidence  assessment that future 
climate change will decrease land carbon uptake relative to the case 
with constant climate.
5.4.3.1 Plant Physiology
Plant productivity is highly dependent on local climate. In cold 
environments, warming has generally led to an earlier onset of the 
growing season, and with it an increase in early season vegetation 
productivity (e.g., Forkel et al., 2016). However, this trend is affected 
by the adverse effects of climate variability, and other emerging 
limitations on vegetation production by water, energy and nutrients, 
which may gradually reduce the effects of warming (Piao et al., 2017;

Buermann et al., 2018; Liu et al., 2019). At centennial time scales, 
boreal forest expansion may act as a  climate-driven carbon sink 
(Pugh et al., 2018).
In tropical and temperate environments, temperature simultaneously 
affects the metabolic rates of photosynthetic processes within leaf 
tissues, as well as the vapour pressure deficit that drives transpiration, its control by leaf stomata, and the resulting soil and plant tissue 
water content. Thus the direct effect of warming on photosynthesis 
can be positive, negative, or invariant depending on the environmental 
context (Lin et al., 2012; Yamori et al., 2014; Smith and Dukes, 2017; 
Grossiord et al., 2020). Observations and models suggest that the 
vapour pressure deficit effects are stronger than direct temperature 
effects on enzyme activities (Smith et al., 2020), and that acclimation 
of photosynthetic optimal temperature may mitigate productivity 
losses of tropical forests under climate change (Kattge and Knorr,

2007; Tan et al., 2017; Kumarathunge et al., 2019). Some models have 
begun to include these acclimation responses in photosynthesis and 
autotrophic respiration (Lombardozzi et al., 2015; Smith et al., 2015; 
Huntingford et al., 2017; Mercado et al., 2018).
5.4.3.2 Fire and Other Disturbances
The SRCCL assessed that climate change is playing an increasing role 
in determining wildfire regimes alongside human activity ( medium 
confidence ), with future climate variability expected to enhance the 
recurrence and severity of wildfires in many biomes, such as tropical 
rainforests ( high confidence ). Projections of increased fire weather in 
a warmer climate are widespread (Section 12.3.2.8) and may drive 
increased fire frequency and severity in several regions, including 
Arctic and boreal ecosystems (Gauthier et al., 2015; X.J.  Walker 
et al.,  2019), Mediterranean-type ecosystems (Turco et al., 2014; 
Jin et al., 2015), degraded tropical forests (Aragão et al., 2018), and

tropical forest-savanna transition zones (Lehmann et al., 2014).
Wildfire is included in some CMIP6 ESMs (Table  5.4) and is thus 
only partially represented in estimates of carbon–climate feedbacks 
from these models. The CMIP5 ESMs that include fire project an 
8–58% increase of fire carbon emissions under future scenarios, with 
higher emissions under higher warming scenarios; the ensemble 
spread is driven by differing factors such as population density, fire 
management, and other land-use processes (Kloster and Lasslop, 
2017). Fire dynamics in CMIP6 models, as evaluated in land-only 
configurations of CMIP6-generation land surface models, also show 
large variations but better agreement with observations (Teckentrup 
et al., 2019; Hantson et al., 2020; Lasslop et al., 2020).
Climate change also drives changes to vegetation composition 
and ecosystem carbon storage through other disturbances such 
as forest dieback that lead to biome shifts in tropical forests (Cox

et al., 2004; Jones et al., 2009; Brando et al., 2014; Le Page et al., 
2017; Zemp et al., 2017), and temperate and boreal regions (Joos 
et al., 2001; Lucht et al., 2006; Scheffer et al., 2012; Lasslop et al., 
2016). The AR5 assessed that large-scale loss of tropical forests due 
to climate change is unlikely (WGI, Section 6.4.9). Newer ecosystem 
modelling approaches that include a  greater degree of ecosystem 
heterogeneity and diversity show a reduced sensitivity of such forest 
dieback-type changes (Levine et al., 2016; Sakschewski et al., 2016), 
supporting the AR5 assessment (Section 5.4.9). Beyond such biome 
shifts, observations of tropical forests also show that increasing tree 
mortality rates within tropical forests may reduce carbon turnover 
times and storage (Brienen et al., 2015), that increased tree mortality 
rates in tropical forests and elsewhere are expected with increased 
temperatures and vapour pressure deficit (Cross-Chapter Box  5.1;

725
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Allen et al., 2015; McDowell et al., 2018; Grossiord et al., 2020), and 
that these processes are not well represented in ESMs (Powell et al., 
2013; Fisher et al., 2018). An ensemble of land models that includes 
ecological processes such as forest demography shows that changes 
to mortality may be a more important driver of carbon dynamics than 
changes to productivity (Friend et al., 2014).
Overall, climate change will force widespread increases in fire weather 
throughout the world (Section  12.3.2.8). Because of incomplete 
inclusion of fire in ESMs, a  separate compilation of fire-driven 
carbon–climate feedback estimates is shown in Figure 5.29, based 
on results from Eliseev et al. (2014a) and Harrison et al. (2018). There 
is low agreement  in magnitude and medium agreement  in sign which 
leads to an assessment of medium confidence  that fire represents

a positive carbon–climate feedback, but very low confidence  in the 
magnitude of that feedback. Other disturbances such as tree mortality 
will increase across several ecosystems ( medium agreement ) with 
decreased vegetation carbon ( medium confidence ). However, the 
lack of model agreement and key process representation in ESMs 
leads to a  low confidence  assessment in the projected magnitude 
of this feedback.
5.4.3.3 Soil Carbon
Changes to soil carbon stocks in response to climate change are 
a potentially strong positive feedback (Cox et al., 2000). Since AR5 
(WGI, Section 6.4.2), progress has been made in understanding soil 
carbon dynamics, and associated feedbacks. Advances include: (i) 
an increased understanding of and ability to quantify high-latitude 
soil carbon feedbacks (Box 5.1); (ii) increased understanding of the 
causes responsible for soil carbon persistence on long time scales, 
particularly the interactions between decomposers and soil organic

matter and mineral assemblages (Kleber et al., 2007; Schmidt 
et al., 2011; Luo et al., 2016); and (iii) increased understanding of 
soil carbon dynamics in subsurface layers (Hicks Pries et al., 2017; 
Balesdent et al., 2018).
CMIP6 ESMs predict losses of soil carbon with warming, which are 
larger than climate-driven vegetation carbon losses (Arora et al., 
2020). As in CMIP5 (Todd-Brown et al., 2013), there is also a large 
CMIP6 ensemble spread in climate-driven soil carbon changes, 
partially driven by a large spread in the current soil carbon stocks 
predicted by the models. In CMIP5 ESMs, much of the soil carbon 
losses with warming can be traced to decreased carbon inputs, 
with a  weaker contribution from changing soil carbon lifetimes 
due to faster decomposition rates (Koven et al., 2015b), which may 
be an artefact of the lack of permafrost carbon (Box 5.1). Isotopic 
constraints suggest that CMIP5 ESMs systematically overestimated

the transient sensitivity of soil 14C responses to atmospheric 
14C changes, implying that the models respond too quickly to 
changes in either inputs or turnover times, and that therefore the 
soil contribution to all feedbacks may be weaker than currently 
projected (He et al., 2016). Using natural gradients of soil carbon 
turnover as a constraint on long-term responses to warming suggests 
that both CMIP5 and CMIP6 ESMs may systematically underestimate 
the temperature sensitivity at high latitudes, and may overestimate 
the temperature sensitivity in the tropics (Koven et al., 2017; Wieder et al., 2018; Varney et al., 2020), although experimental soil warming 
in tropical forests suggest high sensitivity of decomposition to 
warming in those regions as well (Nottingham et al., 2020).
Peat soils, where thick organic layers build up due to saturated 
and anoxic conditions, represent another possible source of carbon 
to the atmosphere. Peats could dry, and decompose or burn as

a  result of climate change in both high (Chaudhary et al., 2020) 
and tropical (Cobb et al., 2017) latitudes, and in combination with 
anthropogenic drainage of peatlands (Warren et al., 2017). Peat 
carbon dynamics are not included in the majority of CMIP6 ESMs.  
 
Soil microbial dynamics shift in response to temperature, giving 
rise to complex longer-term trophic effects that are more complex 
than the short-term sensitivity of decomposition to temperature. 
Such responses are observed in response to long-term warming 
experiments (Melillo et al., 2017). While most CMIP6 ESMs do not 
include microbial dynamics, simplified global soil models that do 
include such dynamics show greater uncertainty in projections of 
soil carbon changes, despite agreeing more closely with current 
observations, than the linear models used in most ESMs (Wieder 
et al., 2013; Guenet et al., 2018).
In nutrient-limited ecosystems, prolonged soil warming can induce

a  fertilization effect through increased decomposition, which 
increases nutrient availability and thereby vegetation productivity 
(Melillo  et al., 2011). Models that include this process tend to 
show  a  weaker carbon–climate feedback than those that do not 
(Thornton et al., 2009; Zaehle et al., 2010; Wårlind et al., 2014; 
Meyerholt et al., 2020). In CMIP6, six out of 11 ESMs include 
a representation of the nitrogen cycle, and the mean of those models 
predicts a weaker carbon–climate feedback than the overall ensemble 
mean (Arora et al., 2020; Section  5.4.8). These models only partly 
account for the interactions of nutrient effects with other processes, 
such as shifts of vegetation zones under climate changes (Sakaguchi 
et al., 2016) leading to either changes in species composition or 
changes in plant tissue nutrient to carbon ratios (Thomas et al., 2015; 
Achat et al., 2016; Du et al., 2019).
The high agreement  and multiple lines of evidence that warming

increases decomposition rates lead to high confidence  that 
warming will, overall, result in carbon losses relative to a constant 
climate and contribute to the positive carbon–climate feedback 
(Section 5.4.8). However, the wide spread in ESM projections and the 
lack of model representation of key processes that may amplify or 
mitigate soil carbon losses on longer time scales (including microbial 
dynamics, permafrost, peatlands, and nutrients) lead to low confidence  
in the magnitude of global soil carbon losses with warming.

726
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Box 5.1 | Permafrost Carbon and Feedbacks to Climate
What is permafrost carbon and why should we be concerned about it?
Soils in the Arctic and other cold regions contain perennially frozen layers, known as permafrost. Soils in the northern permafrost region 
store a large amount of organic carbon, estimated at 1460–1600 PgC across surface soils and deeper deposits (Hugelius et al., 2014; 
Strauss et al., 2017; Mishra et al., 2021). Of that carbon, permafrost soils and deposits store 1070–1360 PgC, of which 300–400 PgC 
are in the first metre, and the rest at depth. The remaining 280–340 PgC are in permafrost-free soils within the permafrost region. 
These carbon deposits have accumulated over thousands of years due to the slow rates of organic matter decomposition in frozen 
and/or waterlogged soil layers, but these frozen soils are highly decomposable upon thaw (Schädel et al., 2014).

Is permafrost carbon already thawing and emitting greenhouse gases?
The permafrost region was a historic carbon sink over centuries to millennia ( high confidence ) (Loisel et al., 2014; Lindgren et al., 
2018). Currently though, thawing soils due to anthropogenic warming are losing carbon from the decomposition of old frozen organic 
matter, as found via carbon 14 (14C) signature of respiration at sites undergoing rapid permafrost thaw (Hicks Pries et al., 2013), of 
dissolved organic carbon in rivers draining watersheds with permafrost thaw (Vonk et al., 2015; Wild et al., 2019), and of methane 
(CH 4) produced in thawing lakes (Walter Anthony et al., 2016).
Despite accumulating evidence of increased carbon losses, it is difficult to scale up site- and ecosystem-level measurements to assess 
the net carbon balance over the entire permafrost region, due to the high spatial heterogeneity, the strong seasonal cycles, and the

difficulty in monitoring these regions consistently across the year. The Special Report on Ocean and the Cryosphere in a Changing 
Climte (SROCC) assessed with high confidence  that ecosystems in the permafrost region act as carbon sinks during the summer 
growing season, and that wintertime carbon losses are significant, consistent with a multi-decadal small increase in CO 2 emissions 
during early winter at Barrow, Alaska (Sweeney et al., 2016; Webb et al., 2016; Meredith et al., 2019). These findings have been further 
strengthened by recent comprehensive synthesis of in-situ wintertime flux observations that show large carbon losses during the non-
growing season (Natali et al., 2019). Increased autumn and winter respiration are a key large-scale fingerprint of top-down permafrost 
thaw predicted by ecosystem models (Parazoo et al., 2018). However, the length of these wintertime observational records is too short

to unequivocally determine whether winter carbon losses are higher now than they used to be. One study inferred a multi-year net CO 2 
source for the tundra in Alaska (Commane et al., 2017), which is equivalent to 0.3 PgC yr–1 when scaled up to the northern permafrost 
region ( low confidence ) (Meredith et al., 2019).
Since AR5, evidence of a more active carbon cycle in the northern high-latitude regions has also been observed through the increased 
amplitude of CO 2 seasonal cycles. However, the relative roles of local sources versus influence from mid-latitudes makes it difficult to infer 
changes to Arctic ecosystems from these observations (Graven et al., 2013; Forkel et al., 2016; Takata et al., 2017; Bruhwiler et al., 2021). 
Estimates of CO 2 fluxes with atmospheric inversion models showed an enhanced seasonal cycle amplitude but no significant trends in

annual total fluxes, in agreement with flux tower measurements over one decade (2004–2013) (Welp et al., 2016; Takata et al., 2017).
In addition to CO 2, CH 4 emissions from the northern permafrost region contribute to the global methane budget, but evidence as to 
whether these emissions have increased from thawing permafrost is mixed. The SROCC assigned low confidence  to the degree of 
recent additional CH 4 emissions from diverse sources throughout the permafrost region. These include observed regional lake area 
change, which suggest a 1.6–5 Tg CH 4 yr–1 increase over the last 50 years (Walter Anthony et al., 2016), ice-capped geological sources 
(Walter Anthony et al., 2012; Kohnert et al., 2017), and shallow Arctic Ocean shelves. The shallow subsea emissions are particularly 
uncertain due to the wide range of estimates (3 Tg CH 4 yr–1 (Thornton et al., 2016b) to 17 Tg CH 4 yr–1 (Shakhova et al., 2014)), and

the lack of a baseline with which to infer any changes; however, the upper half of this range in flux estimates is inconsistent with the 
atmospheric inversions constrained by the pan-Arctic CH 4 concentration measurements (Berchet et al., 2016).
Atmospheric measurements and inversions performed at the global and regional scales do not show any detectable trends in annual 
mean CH 4 emissions from the permafrost region over the past 30 years (Jackson et al., 2020; Saunois et al., 2020; Bruhwiler et al., 
2021), consistent with atmospheric measurements in Alaska that showed no significant annual trends, despite significant increase in air 
temperature (Sweeney et al., 2016). Atmospheric inversions and biospheric models do not show any clear trends in CH 4 emissions for 
wetland regions of the high latitudes during the period 2000–2016 (Patra et al., 2016; Poulter et al., 2017; Jackson et al., 2020; Saunois

et al., 2020). Large uncertainties on wetland extent and limited data constraints place low confidence  in these modelling approaches.
The SROCC also assessed with high confidence  that CH 4 fluxes have been under-observed due to their high variability at multiple scales 
in both space and time, and that there is a persistent mismatch between top-down and bottom-up methane budgets, with emissions 
calculated by upscaling ground observations typically higher than emissions inferred from large-scale atmospheric observations 
(Thornton et al., 2016a; Saunois et al., 2020).

727
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55In conclusion, there is high confidence  that the permafrost region has acted as a historic carbon sink over centuries to millennia, and 
high confidence  that some permafrost regions are currently net sources of CO 2. There is robust evidence  that some CH 4 emissions 
sources for some regions have increased over the past decades ( medium confidence ). For the northern permafrost-wide region, 
no multi-decadal trend has been detected on CO 2 and CH 4 fluxes but, given the low resolution and sparse observations of current 
observations and modelling sytems, we place low confidence  in this statement.
Since AR5, there have been new studies showing that permafrost thaw also leads to nitrous oxide (N 2O) release from soil (Abbott and 
Jones, 2015; Karelin et al., 2017; Wilkerson et al., 2019), a previously unaccounted source. However, this release is unquantified at the 
pan-Arctic scale.

What does the paleo record tell us?
Large areas of Alaska and Siberia are underlain by frozen, glacial-age, ice- and carbon-rich deposits, and many of these areas show 
evidence of thermokarst processes during Holocene warm periods. Rapid warming of high northern latitudes contributed to permafrost 
thaw, liberating labile organic carbon to the atmosphere (Köhler et al., 2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer et al., 
2019), supporting the vulnerability of these areas to further warming (Strauss et al., 2013, 2017).
Radiogenic and stable isotopic measurements on CH 4 trapped in Antarctic ice support the view that CH 4 emissions from fossil carbon 
reservoirs, including permafrost and methane hydrates, remained small in response to the deglacial warming. Mass-balance calculations 
reveal that geological CH 4 emissions have not exceeded 19 Tg yr–1, highlighting that the deglacial increase in CH 4 emissions was

predominantly related to contemporary CH 4 emissions from tropical wetlands and seasonally inundated floodplains (Bock et al., 2017; 
Petrenko et al., 2017; Dyonisius et al., 2020). Isotopic constraints on CO 2 losses from permafrost with warming after the Last Glacial 
Maximum (LGM) are weaker than for CH 4. While the biosphere as a whole held less carbon during the LGM than the pre-industrial, 
that change in stocks was smaller than the change in plant productivity, and so carbon losses at high latitudes may have been offset 
by increased tropical productivity in response to warming during the Last Deglacial Transition (LDT; Ciais et al., 2012). There is also 
paleoclimate evidence for processes that mitigate carbon losses with warming on longer time scales, such as longer-term carbon 
accumulation in lake deposits following thermokarst thaw (Walter Anthony et al., 2014), and long-term accumulation of carbon in

permafrost soils following LDT carbon loss (Lindgren et al., 2018), particularly in peatlands which accumulated carbon at a slow but 
persistent rate in warm paleoclimates (Treat et al., 2019).
In conclusion, several independent lines of evidence indicate that permafrost thaw did not release vast quantities of fossil CH 4 
associated with the transient warming events of the LDT. This suggests that large emissions of CH 4 from old carbon sources will not 
occur in response to future warming ( medium confidence ).
What level of emissions do we expect in the future?
Near-surface permafrost is projected to decrease significantly under future global warming scenarios ( high confidence ) (Section 9.5.2), 
thus creating the potential for releasing CO 2 and CH 4 to the atmosphere, and act as a positive carbon–climate feedback.
The processes that govern permafrost carbon loss are grouped into gradual and abrupt mechanisms. Gradual processes include the

deepening of the seasonally thawed active layer into perennially frozen permafrost layers and lengthening of the thawed season 
within the active layer, which increases the amount of organic carbon that is thawed and the duration of thaw. Abrupt thaw processes 
include ice-wedge polygon degradation, hillslope collapse, thermokarst lake expansion and draining, all of which are processes largely 
occurring in regions with very high soil carbon content (Olefeldt et al., 2016a, b). Abrupt thaw processes can contribute up to half of 
the total net greenhouse gas release from permafrost loss, the rest attributed to gradual thaw (Schneider von Deimling et al., 2015; 
Turetsky et al., 2020). Increased fire frequency and severity (Hu et al., 2010) also contributes to abrupt emissions and the removal of 
the insulating cover which leads to an acceleration of permafrost thaw (Genet et al., 2013). Ecological feedbacks can both mitigate

and amplify carbon losses: nutrient release from increased organic matter decomposition can drive vegetation growth that partially 
offsets soil carbon losses (Salmon et al., 2016), but also lead to biophysical feedbacks that further amplify warming (Myers-Smith 
et al., 2011).
Through the Coupled Model Intercomparison Project Phase 5 (CMIP5), Earth system models (ESMs) had not included permafrost carbon 
dynamics. This remains largely true in Coupled Model Intercomparison Project Phase 6 (CMIP6), with most models not representing 
permafrost carbon processes, a small number representing the active-layer thickening effect on decomposition (Table 5.4), and no 
ESMs representing thermokarst or fire-permafrost-carbon interactions. The CMIP6 ensemble mean predicts a negative carbon–climate 
feedback in the permafrost region. However, those that do include permafrost carbon show a positive carbon–climate feedback in the  
Box 5.1 (continued)

728
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5permafrost region (Figure 5.27). Given the current limited ESM capacity to assess permafrost feedbacks, estimates in this report are 
based on published permafrost-enabled land surface model results.
The SROCC assessed that warming under a high-emissions scenario (RCP8.5 or similar) would result in a loss of permafrost carbon by 
2100 of 10s to 100s of PgC, with a maximum estimate of 240 PgC and a best estimate of 92 ± 17 PgC (Meredith et al., 2019; SROCC, 
Figure 3.11). Under lower emissions scenarios, Schneider von Deimling et al. (2015) estimated permafrost feedbacks of 20–58 PgC of 
CO 2 by 2100 under an RCP2.6 scenario, and 28–92 PgC of CO 2 under an RCP4.5 scenario.
This new assessment, based on studies included in or published since SROCC (Schaefer et al., 2014; Koven et al., 2015c; Schneider von

Deimling et al., 2015; Schuur et al., 2015; MacDougall and Knutti, 2016a; Gasser et al., 2018; Yokohata et al., 2020), estimates that 
the permafrost CO 2 feedback per degree of global warming (Figure 5.29) is 18 [3.1 to 41, 5–95% range] PgC °C–1. The assessment 
is based on a  wide range of scenarios evaluated at 2100, and an assessed estimate of the permafrost CH 4-climate feedback at 
2.8 [0.7 to 7.3] PgCeq °C–1 (Figure 5.29). This feedback affects the remaining carbon budgets for climate stabilization and is included 
in their assessment (Section 5.5.2).
Beyond 2100, models suggest that the magnitude of the permafrost carbon feedback strengthens considerably over the period 
2100–2300 under a high-emissions scenario (Schneider von Deimling et al., 2015; McGuire et al., 2018). Schneider von Deimling et al. 
(2015) estimated that thawing permafrost could release 20–40 PgC of CO 2 in the period from 2100 to 2300 under an RCP2.6 scenario,

and 115–172 PgC of CO 2 under an RCP8.5 scenario. The multi-model ensemble (McGuire et al., 2018) projects a much wider range of 
permafrost soil carbon losses of 81–642 PgC (mean 314 PgC) for an RCP8.5 scenario from 2100 to 2300, and of a gain of 14 PgC to 
a loss of 54 PgC (mean loss of 17 PgC) for an RCP4.5 scenario over the same period.
Methane release from permafrost thaw (including abrupt thaw) under a  high-warming RCP8.5 scenario has been estimated at 
836–2614 Tg CH 4 over the 21st century and 2800–7400 Tg CH 4 from 2100–2300 (Schneider von Deimling et al., 2015), and as 
5300 Tg CH 4 over the 21st century and 16,000 Tg CH 4 from 2100–2300 (Turetsky et al., 2020). For RCP4.5, these numbers are 538–
2356 Tg CH 4 until 2100 and 2000–6100 Tg CH 4 from 2100–2300 (Schneider von Deimling et al., 2015), and 4100 Tg CH 4 until 2100 
and 10,000 Tg CH 4 from 2100–2300 (Turetsky et al., 2020).

A key uncertainty is whether permafrost carbon feedbacks scale roughly linearly with warming (Koven et al., 2015c), or instead scale at 
a greater (MacDougall and Knutti, 2016b; McGuire et al., 2018) or smaller rate (e.g., CH 4 emissions estimated by Turetsky et al., 2020). 
It also remains unclear whether the permafrost carbon pool represents a coherent global tipping element of the Earth system with a single 
abrupt threshold (Drijfhout et al., 2015) at a given level of global warming, or a local scale tipping point without abrupt thresholds when 
aggregated across the pan-Arctic region, as is suggested by recent model results (e.g., Koven et al., 2015a; McGuire et al., 2018).
In conclusion, thawing terrestrial permafrost will lead to carbon release under a warmer world ( high confidence ). However, there 
is low confidence  on the timing, magnitude and linearity of the permafrost climate feedback owing to the wide range of published

estimates and the incomplete knowledge and representation in models of drivers and relationships. It is projected that CO 2 released 
from permafrost will be 18 (3.1–41) PgC °C–1 by 2100, with the relative contribution of CO 2 vs CH 4 remaining poorly constrained. 
Permafrost carbon feedbacks are included among the under-represented feedbacks quantified in Figure 5.29. 
5.4.4 Climate Effects on Future Ocean Carbon Uptake
5.4.4.1 Physical Drivers of Future Ocean Carbon 
Uptake and Storage
The principal contribution to increasing global ocean carbon is the 
air–sea flux of CO 2, which changes the dissolved inorganic carbon 
(DIC) inventory (Section 5.4.2; Arora et al., 2020). The processes that 
influence the variability and trends of the ocean carbon–heat nexus 
are assessed in Cross-Chapter Box 5.3. Climate has three important

impacts on the ocean uptake of anthropogenic CO 2: (i) ocean warming reduces the solubility of CO 2, which increases pCO 2 and increases the 
stratification of the mixed layer, both acting as positive feedbacks 
weakening the ocean sink (Section  9.2.1 and Cross-Chapter 
Box 5.3; Arora et al., 2020); (ii) changing the temporal and spatial 
characteristics of wind stress and storms alters mixing – entrainment 
in, and across the bottom of, the mixed layer (Bronselaer et al., 2018); 
and (iii) warming and wind stress influence the large-scale meridional 
overturning circulation (MOC) circulation, which modifies the rate 
of ventilation, storage or outgassing of ocean carbon in the ocean 
interior (Section 5.2.3.1; Gruber et al., 2019b; Arora et al., 2020). The 
land-to-ocean riverine flux and the carbon burial in ocean sediments

729
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55play a  minor role ( low confidence ) (Arora et al., 2020). Based on 
high agreement of projections by coupled climate models, there is 
high confidence that the resultant climate–carbon cycle feedbacks 
are positive, but the extent of the ocean sink weakening is scenario 
dependent (Arora et al., 2020).
Regionally, the Southern Ocean is a  major sink of anthropogenic 
CO 2 (Figure  5.8a), although challenges in modelling its circulation 
and Antarctic sea ice transport (Sections 3.4.1.2, 9.2.3.2 and 9.3.2) 
generate uncertainty in the response of its sink to future carbon–
climate feedbacks. Increased freshwater input may cause a slowdown 
of the lower overturning circulation, leading to increased Southern 
Ocean biological carbon storage (Ito et al., 2015); alternatively, 
increased winds may intensify the overturning circulation, reducing 
the net CO 2 sink in the Southern Ocean (Bronselaer et al., 2018;

Saunders et al., 2018). On centennial time scales, there is thus low 
confidence  in the overall effect of intensifying winds in the Southern 
Ocean on CO 2 uptake.
5.4.4.2 Biological Drivers of Future Ocean Carbon Uptake
While physical drivers control the present-day anthropogenic carbon 
sink, biological processes are responsible for the majority of the 
vertical gradient in DIC (natural carbon storage). A  small fraction 
of the organic carbon fixed by primary production (PP) reaches the 
sea floor, where it can be stored in sediments on geological time 
scales, making the biological carbon pump (BCP) an important 
mechanism for very long-term CO 2 storage. Projected reductions in 
ocean ventilation (Section  9.2.1.4) would lengthen residence time 
and lead to DIC accumulating in the deep ocean due to organic 
carbon remineralization.
Since AR5 (Section  6.3.2.5.6), progress has been made in 
understanding the biological drivers of ocean carbon uptake in both

coupled climate models and observations (SROCC, Section 5.2.2.6). 
Here we focus on potential feedbacks between biological processes 
and climate. In CMIP5 models, the direction of modelled PP in response 
to increased atmospheric CO 2 concentration and climate warming 
was unclear (Taucher and Oschlies, 2011; Laufkötter et al., 2015). 
This remains the case in the CMIP6 models; inter-model uncertainty 
has increased in CMIP6 models, compared to CMIP5. The projected 
global multi-model mean change in PP in 13 models run under the 
SSP5−8.5 scenario is −3 ± 9% (2080–2099 mean values relative to 
1870–1899 ± the inter-model standard deviation; Kwiatkowski et al., 
2020). Under the low-emissions, high-mitigation scenario SSP1−2.6, 
the global change in PP is −0.56  ±  4%. Observations in the 
contemporary period provide little direct constraint on the modelled 
responses of PP to climate change, partly due to insufficiently long

records (Henson et al., 2016). However, there is some indication of an 
emergent constraint on changes in tropical PP based on interannual 
variability derived from remote sensing (Section 5.4.6; Kwiatkowski 
et al., 2017).
In CMIP5 models run under RCP8.5, particulate organic carbon (POC) 
export flux is projected to decline by 1–12% by 2100 (Taucher and 
Oschlies, 2011; Laufkoetter et al., 2015). Similar values are predicted 
in 18 CMIP6 models, with declines of 2.5–21.5% (median –14%) or 
0.2–2 GtC (median –0.8 GtC) between 1900 and 2100 under the SSP5-8.5 scenario. The mechanisms driving these changes vary widely 
between models due to differences in parametrization of particle 
formation, remineralization and plankton community structure.
Ocean warming reduces the vertical supply of nutrients to the 
upper ocean due to increasing stratification (Section 9.2.1.4) but may 
also act to alleviate seasonal light limitation. The projected effect is

to decrease PP at low latitudes and increase PP at high latitudes 
(Kwiatkowski et al., 2020). Future changes to dust deposition due to 
desertification (Mahowald et al., 2017), alterations to the nitrogen 
cycle (Section 5.3.3.2; SROCC, Section 5.2.3.1.2), and reducing sea 
ice cover (Ardyna and Arrigo, 2020) all have the potential to alter 
PP regionally. Higher ocean temperatures tend to result in higher 
metabolic rates, although respiration may increase more rapidly 
than PP (Boscolo-Galazzo et al., 2018; Brewer, 2019; Cavan et al., 
2019). Ocean warming and reduced PP are expected to result in 
lower zooplankton abundance, and the expansion of oxygen 
minimum zones (OMZs) may reduce the ability of zooplankton to 
remineralize POC, thus increasing the efficiency of the BCP and 
forming a negative climate feedback (Cavan et al., 2017). Increased 
microbial respiration due to warming may result in greater quantities 
of organic carbon transferred into the dissolved organic carbon

pool (Jiao et al., 2014; Legendre et al., 2015; Roshan and DeVries, 
2017) which, while increasing the residence time of carbon in the 
ocean, would ultimately reduce the sedimentary burial, and hence 
sequestration on geologic time scales (Olivarez Lyle and Lyle, 2006).
Most models project that smaller phytoplankton are favoured in 
future ocean conditions ( medium confidence ; Cabré et al., 2015; Fu 
et al., 2016; Flombaum et al., 2020) driven by warming water and/
or changing nutrient availability, which would alter the magnitude 
and efficiency of the BCP by altering the sinking speed, respiration 
rate and aggregation/fragmentation of sinking particles. There is low 
confidence  in the sign of the resulting feedback: regions in which 
small phytoplankton dominate may have a  more efficient pump, 
although the total amount of organic carbon reaching the sea floor 
is lower (Herndl and Reinthaler, 2013; Bach et al., 2016; Richardson,

2019). Alternatively, an increase in small phytoplankton could result 
in a less efficient pump, due either to a greater fraction of PP being 
processed through the upper ocean microbial loop (Jiao et al., 2014) 
or generation of slower sinking particles (Guidi et al., 2009; Leung 
et al., 2021). Variable phytoplankton stoichiometry is predicted to 
increase the amount of carbon stored via the BCP relative to the 
amount of PP , so that fixed stoichiometry models (as in CMIP5) may 
underestimate cumulative ocean carbon uptake to 2100 by 0.5–3.5% 
(2–15 PgC; RCP8.5 scenario; Kwiatkowski et al., 2020). Other climate 
effects such as deoxygenation or ocean acidification could also result 
in alterations to the magnitude and efficiency of the BCP (Krumhardt 
et al., 2019; Raven et al., 2021; Taucher et al., 2021).
Based on high agreement across multiple lines of evidence and 
physical understanding there is high confidence that feedbacks to

climate will arise from alterations to the magnitude and efficiency 
of the BCP changing PP , and the depth of remineralization. However, 
the complexity of the mechanisms involved in the export and 
remineralization of POC result in low confidence  in the magnitude 
and sign of biological feedbacks to climate. Nevertheless, improved 
model representation of PP and the BCP is required (which requires

730
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5better observational constraints), as the contribution of biological 
processes to CO 2 uptake is expected to become more significant with 
continued climate change (Hauck et al., 2015).
5.4.5 Carbon Cycle Projections in Earth System Models
This section summarizes future projections of land and ocean 
carbon sinks from the latest ESMs. ESMs are the basis for century 
time-scale projections (Chapter 4), and for detection and attribution 
studies (Chapter 3). These models aim to simulate the evolution of 
the carbon sources and sinks on land and in the ocean, in addition 
to the physical components of the climate system. ESMs include 
interactions between many of the processes and feedbacks described 
in Sections 5.4.1 to 5.4.4.
ESMs are now integral to the Coupled Model Intercomparison Project. 
Model output data from CMIP5 was analysed in AR5, while data from

CMIP6 forms the basis for the analysis presented in this subsection. The 
CMIP5 ESMs discussed in AR5 (WGI, Section 6.4.2) produced a wide 
range of projections of future CO 2 (Friedlingstein et al., 2014b) primarily 
associated with different magnitudes of carbon–climate and carbon-
concentration feedbacks (Arora et al., 2013), but also exacerbated by 
differences in the simulation of the net carbon release from land-use 
change (Brovkin et al., 2013). A  key deficiency of almost all CMIP5 
ESMs was the neglect of nutrient limitations on CO 2-fertilization of 
land plant photosynthesis (Section 5.4.1; Zaehle et al., 2015).
Some CMIP6 models considered in this report now include nitrogen 
limitations on land vegetation growth, along with many other added 
processes compared to CMIP5. Table 5.4 summarizes characteristics of the land and ocean carbon cycle models used in CMIP6 ESMs (Arora 
et al., 2020). In CMIP6, most ocean carbon cycle models (8 of 11) track

three or more limiting nutrients (most often nitrogen, phosphorus, 
silicon, iron), and include two or more phytoplankton types. More 
than half of the land carbon cycle models (6 of 11) now include an 
interactive nitrogen cycle, and almost half (5 of 11) represent forest 
fires. However, even for CMIP6, very few models explicitly represent 
vegetation dynamics (3 of 11) or permafrost carbon (2 of 11). Despite 
these remaining limitations, the carbon cycle components of CMIP6 
represent an advance on those in CMIP5, as they represent additional 
important processes (e.g.,  nitrogen limitations on the land carbon 
sink, and iron limitations on ocean ecosystems).
ESMs can be driven by anthropogenic CO 2 emissions (‘emissions-driven’ 
runs), in which case atmospheric CO 2 concentration is a  predicted 
variable; or by prescribed time-varying atmospheric concentrations 
(‘concentration-driven’ runs). In concentration-driven runs, simulated

land and ocean carbon sinks respond to the prescribed atmospheric 
CO 2 and resulting changes in climate, but do not feed back through 
changes in the atmospheric CO 2 concentration. Concentration-driven 
runs are used to diagnose the carbon emissions consistent with 
the Shared Socio-economic Pathways (SSPs) and other prescribed 
concentration scenarios (Section 5.5). In this subsection we specifically 
analyse results from concentration-driven ESM projections.
5.4.5.1 Evaluation of the Contemporary Carbon Cycle 
in Concentration-driven Runs
To give confidence in their projections, models need to be 
compared to the widest possible array of observational benchmarks. 
This is particularly the case for highly uncertain land carbon cycle 
Table 5.4 | Properties of the CMIP6 Earth system models (ESMs), focusing on the land and ocean carbon cycle components of these models (Arora

et al., 2020). Characteristics listed under each ESM are: number of vegetation carbon pools (veg C pools); number of soil and litter carbon pools (dead C pools); number of 
Plant Functional Types (PFTs); whether wildfire is represented (fire); whether vegetation dynamics is represented (dynamic veg); whether permafrost carbon is represented 
(permafrost C); whether the nitrogen cycle is represented (nitrogen cycle); the number of phytoplankton types (phytoplankton); the number of zooplankton types (zooplankton); 
and the list of ocean nutrients represented (limiting nutrients).
Modelling 
GroupCSIRO BCC CCCma CESM CNRM GFDL IPSL JAMSTEC MPINorESM2-
LMUK
ESMACCESS-
ESM1.5BCC-CSM2-
MRCanESM5 CESM2CNRM-
ESM2-1GFDL-ESM4IPSL-CM6A-
LRMIROC-
ES2LMPI-
ESM1.2-LRNorESM2-LM UKESM1-0-LL
Land carbon/biogeochemistry component
Model nameCABLE2.4
CASA-CNPBCC-AVIM2 CLASS-CTEM CLM5 ISBA-CTRIP LM4p1ORCHIDEE 
( 2)MATSIRO 
(phys)
VISIT-e 
(BGC)JSBACH3.2 CLM5 JULES-ES-1.0
Veg C pools 3 3 3 22 6 6 8 3 3 3 3

Dead C pools 6 8 2 7 7 4 3 6 18 7 4
PFTS 13 16 9 22 16 6 15 13 12 21 13
Fire No No No Yes Yes Yes No No Yes Yes No
Dynamic Veg No No No No No Yes No No Yes No Yes
Permafrost C No No No Yes No No No No No Yes No
Nitrogen cycle Yes No No Yes No No No Yes Yes Yes Yes
Ocean carbon/biogeochemistry component
Model name WOMBAT MOM4_L40 CMOC (biol) MARBLPISCESv2-
gasCOBALTv2 PISCES-v2 OECO2 HAMOCC6 HAMOCC5.1 MEDUSA-2.1
Phytoplankton 1 0 1 3 2 3 2 2 2 1 2
Zooplankton 1 0 1 1 2 3 2 1 1 1 2
Limiting 
nutrientsP, Fe P N N, P, Si, Fe N, P, Si, Fe N, P, Si, Fe N, P, Si, Fe N, P, Fe N, P, Si, FE N, P, Si, Fe N, Si, Fe

731
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55-1.64 -0.88 -1.13 0.17 -0.31 -0.38 -0.50 0.24 -0.23 1.34 -1.70 0.17 -0.74 1.45 1.56 0.26 0.92 1.40
-2.65 -0.42 0.44 -0.18 -0.49 -0.52 -0.57 0.17 0.70 0.15 -0.47 1.51 -1.24 0.58 -0.72 -0.83 0.97 0.87 1.00 1.70
-0.82 -0.99 -0.27 -1.02 0.64 -1.14 -0.62 -0.60 0.28 0.39 -1.08 1.09 0.65 0.43 -1.40 -1.01 0.82 1.05 1.41 2.20
-0.34 0.74 0.74 -0.14 -0.85 0.21 -1.98 0.22 -0.34 0.10 0.11 1.25 -0.88 1.29 -1.65 -1.81 1.11 -0.06 0.98 1.29
-3.66 -0.35 0.47 0.06 -0.67 -0.57 0.12 0.44 1.33 -0.07 -0.23 0.96 -0.17 -0.19 0.02 -0.05 0.47 0.99 -0.03 1.13
-0.02 -0.39 -0.38 -0.93 0.24 -0.98 -0.73 -0.71 -0.21 0.66 -1.20 1.60 0.12 0.42 -1.52 -1.24 1.40 0.40 1.49 1.99
-0.85 -0.20 0.80 -0.28 -1.12 -1.23 -1.67 0.45 0.65 -1.04 0.37 1.02 -0.39 1.19 -0.54 -1.63 0.63 0.92 1.48 1.45
-2.79 -0.45 0.47 0.50 -0.38 0.34 0.35 0.43 0.58 0.15 -0.08 0.95 -2.91 0.43 0.37 0.15 0.39 0.51 0.49 0.50

0.90 -0.56 -0.86 -0.24 -1.35 0.99 -0.01 -0.94 -1.54 0.81 0.59 0.51 -0.79 0.90 -0.21 -1.24 0.43 -0.94 1.34 2.21
-1.54 -0.36 -2.92 -0.74 1.53 -0.00 0.37 0.85 0.42 0.26 0.39 0.59 1.10 -0.87 0.21 0.69 0.09 -0.07Biomass
Burned area
Leaf Area Index
Soil Carbon
Net Ecosystem Exchange
Ecosystem Respiration
Carbon Dioxide
Global Net Carbon Balance
Runoﬀ
Latent Heat
Sensible Heat
Terrestrial Water Storage Anomaly
Permafrost
(b) Ocean Benchmarking Results
Ocean Ecosystems
Chlorophyll
Oxygen, surface
Ocean Nutrients
Nitrate, surface
Phosphate, surface
Silicate, surface
Ocean Carbon
TAlk, surface
DIC, surface
Ocean Physical Drivers
Mixed Layer Depth
Temperature, surface
Salinity, surface
Salinity, 200m
Salinity, 700m
Ocean Relationships
Relative Scale
Worse Value Missing Data or Error Better Valuebcc-csm1-1
CanESM2
CESM1-BGC
GFDL-ESM2G
IPSL-CM5A-LR
MIROC-ESM
MPI-ESM-LR
NorESM1-ME
HadGEM2-ES
BCC-CSM2-MR
CanESM5
CESM2
GFDL-ESM4
MIROC-ES2L
MPI-ESM1.2-LR

UKESM1-0-LL
Mean CMIP5
Mean CMIP6CMIP5 ESMs CMIP6 ESMs
Gross Primary Productivity(a) Land Benchmarking Results
Land Ecosystem & Carbon Cycle
Land Hydrology Cycle
Evaporative FractionEvapotranspiration
Temperature, 200m
Temperature, 700m
Vertical temperature gradient
Oxygen, surface/WOA2018
Nitrate, surface/WOA2018
Figure 5.22 | Overview scores for CMIP5 (left-hand side of table) and CMIP6 (right-hand side of table) Earth system models (ESMs), for multiple 
benchmarks against different datasets.  (a) Benchmarking of ESM land models; (b) benchmarking of ocean models. Scores are relative to other models within each 
benchmark row, with positive scores indicating a better agreement with observations. Models included are only those from institutions that participated in both CMIP5 and 
CMIP6 carbon cycle experiments, in order to trace changes from one ensemble to the next. CMIP5 models are labels in blue and CMIP6 in red. Further details on data sources

732
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5feedbacks (Arora et al., 2013; Friedlingstein et al., 2014b). Land 
models within ESMs can be compared to multiple different datasets 
that test different aspects of the models. These include fluxes, such as 
gross carbon uptake, and states, such as leaf area and carbon stocks, 
which influence carbon fluxes and are diagnostic of carbon turnover 
times. Comparisons can also be made between between carbon and 
water cycles and other aspects of the terrestrial carbon cycle. To 
provide these multiple orthogonal constraints, a model benchmarking 
system – the international land model benchmarking (ILAMB) – has 
been developed (Collier et al., 2018).
Figure 5.22 shows an overview set of land (Figure 5.22a) and ocean 
(Figure  5.22b) benchmarks applied to both the CMIP5 and CMIP6 
historical simulations. There is good evidence of an improvement in  
model performance from CMIP5 (in yellow) to CMIP6 (in green),

in both the land and ocean, based on these benchmarks. The mean of 
the CMIP6 land models outperforms or performs equivalently to the 
mean of the CMIP5 land models on all available metrics.
5.4.5.2 Evaluation of Historical Carbon Cycle Simulations 
This section evaluates concentration-driven historical simulations 
of changes in land and ocean cumulative carbon uptake, against 
observation-based estimates from the Global Carbon Project (GCP; 
Le Quéré et al., 2018a). For each model, common historical land-use 
changes were prescribed (Jones et al., 2016a).
Figure  5.23 shows global annual mean values from CMIP6 
concentration-driven runs for 1850 to 2014. The ocean carbon cycle 
models reproduce historical carbon uptake well, with the model range 
for the global ocean carbon sink in 2014 (2.3–2.7 GtC yr–1) clustering 
around the central GCP estimate of 2.6 ±  0.5 GtC yr–1. Simulated 
cumulative ocean carbon uptake (1850–2014) ranges from 110 to

166 GtC, with a model mean of 131 ± 17 PgC, which is lower than 
the GCP estimate of 150 ± 25 GtC (Figure 5.23a). This suggests that 
CMIP6 models may slightly underestimate historical ocean carbon 
uptake (Watson et al., 2020).The land carbon cycle components of historical ESM simulations 
show a larger range, with simulated cumulative land carbon uptake 
(1850–2014) spanning the range from –47 to +21 GtC, compared to 
the GCP estimate of –12 ± 50 GtC (Figure 5.23b). This range is due in 
part to the complications of simulating the difference between carbon 
uptake by intact ecosystems and the direct release of carbon due 
to land-use change (Hajima et al., 2020a). There is  high confidence  
that the land continues to dominate the overall uncertainty in the 
projected response of the global carbon cycle to climate change.
5.4.5.3 Evaluation of Latitudinal Distribution 
of Simulated Carbon Sinks
This distinction between the relatively high fidelity with which

the ocean carbon sink is simulated, and the much wider range of 
simulations of the land carbon sink, is also evident in the zonal 
distribution of the sinks (Figure  5.24). We compare the ESM 
simulations to estimates from three atmospheric inversion models: 
Copernicus Atmosphere Monitoring Service (CAMS; Chevallier 
et al., 2005), Carbon Tracker 2017 (Peters et al., 2007) and Model for 
Interdisciplinary Research on Climate Atmospheric Transport Model 
(MIROC-ATM4; Saeki and Patra, 2017). The ocean carbon cycle 
components of CMIP6 ESMs are able to simulate the tropical CO 2 
source and mid-latitude CO 2 sink, with relatively small model spread 
(Figure 5.24a). The CMIP6 ensemble (red wedge) simulates a larger 
ocean carbon sink at 50°N and a weaker sink in the Southern Ocean, 
than the inversion estimate, but with some evidence of a reduction in 
these residual errors compared to CMIP5 (blue wedge). The spread in

inversion fluxes arises primarily from differences in the atmospheric 
CO 2 measurement networks and from transport model uncertainties.
It has been previously noted that AR5 models tended to overestimate 
land uptake in the tropics and underestimate uptake in the northern 
mid-latitudes, compared to inversion estimates. The inclusion of 
nitrogen limitations on CO 2-fertilization within CMIP6 models was 
expected to reduce this discrepancy (Anav et al., 2013). There is indeed 
some evidence that the CMIP6 ensemble (red wedge in Figure 5.24b) 
captures the northern land carbon sink more clearly than CMIP5 
Figure 5.23 | CMIP6 Earth system model (ESM) concentration-driven historical simulations for 1850 to 2014, compared to observation-based estimates 
from the global carbon project (GCP).  (a) Cumulative ocean carbon uptake from 1850 (PgC); (b) cumulative land carbon uptake from 1850 (PgC). Only models that

simulate both land and ocean carbon fluxes are shown here. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

733
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55(blue wedge in Figure 5.24b), but there remains a tendency for the 
ESMs to place more of the global land carbon sink in the tropics than 
the mid-latitudes, compared to the inversion estimates. Based on 
a consistent signal across CMIP6 ESMs, there is medium confidence  
that land carbon cycle models continue to underestimate the 
Northern Hemisphere land carbon sink, when compared to estimates 
from atmospheric inversion (Ciais et al., 2019).
5.4.5.4  Coupled Climate–Carbon Cycle Projections
Land and ocean carbon uptake are driven primarily by increases in 
atmospheric CO 2 (Figure 5.25). As a result, the evolution of land and 
ocean carbon sinks differs significantly between the SSP scenarios. 
Under scenarios that have greater increases in atmospheric CO 2 (such 
as SSP5-8.5 and SSP3-7.0) the absolute values of the sinks are larger, 
but the fraction of implied emissions taken up by the sinks declines

through the 21st century. By contrast, scenarios that assume CO 2 
stabilization in the 21st century (such as SSP1-2.6 or SSP2-4.5), have 
smaller absolute sinks, but these sinks take up an increasing fraction 
of the implied emissions (Figure  5.25d). These general principles 
apply to the ocean and land carbon sinks.
The concentration-driven CMIP6 ESMs agree well on the evolution 
of the global ocean carbon sink through the 21st century for four 
SSP scenarios (Figure 5.25). The five-year ensemble mean ocean sink 
declines to 0.6 ± 0.2 GtC yr–1 by 2100 under SSP1-2.6, and peaks 
around 2080 at 5.4 ± 0.4 GtC yr–1 under SSP5-8.5. Cumulative ocean 
carbon uptake from 1850 is projected to saturate at approximately 
290 ± 30 GtC under SSP1-2.6, and to reach 520 ± 40 GtC by 2100 
under SSP5-8.5 (Figure 5.25e).
The ensemble mean changes in land and ocean sinks are qualitatively 
similar, but the land shows much higher interannual variability in

carbon uptake (Figure 5.25c) and also a much larger spread in the model projections of cumulative land carbon uptake (Figure 5.25f). The 
five-year ensemble mean net land carbon sink is projected to decline 
to 0.4 ± 1.0 GtC yr–1 by 2100 under SSP1-2.6, and to reach around 
5.6 ± 3.7 GtC yr–1 under SSP5-8.5 (Figure 5.25c). Cumulative net land 
150 ± 35 GtC under SSP1-2.6, and to reach 310 ± 130 GtC by 2100 
under SSP5-8.5. Significant uncertainty remains in the future of 
the global land carbon sink, but there has been a notable reduction 
in the model spread from CMIP5 to CMIP6.
Geographical patterns of carbon changes for four SSP scenarios are 
shown in Figure 5.26, with cleared areas (no diagonal lines) showing 
agreement on the sign of the change by at least 80% of the models. 
In all scenarios the ocean sink is strongest in the Southern Ocean and

North Atlantic. The land carbon sink occurs primarily where there are 
present-day forests. In the mid- and high-northern latitudes, a carbon 
sink is projected as a result of the combined impacts of increasing CO 2 
and warming (Section 5.4.5.5). Changes in land carbon storage in the 
tropics also depend strongly on the assumed rate of deforestation 
which varies in magnitude across the SSPs, from relatively low rates 
in SSP1-2.6 to relatively high rates in SSP3-7.0.
In summary, oceanic and terrestrial carbon sinks are projected 
to continue to grow with increasing atmospheric concentrations 
of CO 2, but the fraction of emissions taken up by land and ocean 
is expected to decline as the CO 2 concentration increases ( high 
confidence ). In the ensemble mean, ESMs suggest approximately 
equal global land and ocean carbon uptake for each of the SSP 
scenarios. However, the range of model projections is much 
larger for the land carbon sink. Despite the wide range of model

responses, uncertainty in atmospheric CO 2 by 2100 is dominated 
by future anthropogenic emissions rather than carbon–climate 
feedbacks ( high confidence ).
Figure 5.24 | Comparison of modelled zonal distribution of contemporary carbon sinks against atmospheric inversion estimates for 2000–2009: (a) ocean 
carbon uptake;  (b) net land uptake. Latitude runs from 90°S (i.e., –90°N) to 90°N. Positive uptake represents a carbon sink to ocean/land while negative uptake represents 
a carbon source. The land uptake is taken as net biome productivity (NBP) and so includes net land-use change emissions. The bands show the mean ±1 standard deviation 
across the available inversions (black bands, 3 models), CMIP5 Earth system models (ESMs) (blue bands, 12 models for the ocean, 12 models for the land), and CMIP6 ESMs 
(red bands, 11 models for ocean, 10 models for land). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

734
SSP5-8.5
SSP3-7.0
SSP2-4.5
SSP1-2.6
SSP5-8.5
SSP3-7.0
SSP2-4.5
SSP1-2.6
Historical runs
GCP Estimate
Figure 5.25 | Modelled evolution of the global land and ocean carbon sinks for 1900 to 2100 in concentration-driven CMIP6 Earth system model (ESM) 
scenario runs.  (SSP1-2.6: blue; SSP2-4.5: orange; SSP3-7.0: red; SSP5-8.5: brown): (a) prescribed atmospheric CO 2 concentrations; (b) five-year running mean ocean 
carbon sink (GtC yr–1); (c) five-year running mean net land carbon sink (GtC yr–1); (d) inferred cumulative sink fraction of emissions from 1850; (e) change in ocean carbon 
storage from 1850 (GtC); (f) change in land carbon storage from 1850 (GtC). Thick lines represent the ensemble mean of the listed ESM runs, and the error bars represents

± 1 standard deviation about that mean. The grey wedges represent estimates from the global carbon project (GCP), assuming uncertainties in the annual mean ocean 
and net land  carbon sinks of 0.5 GtC yr–1 and 1 GtC yr–1 respectively, and uncertainties in the changes in carbon stores (ocean, land and cumulative total emissions) of 
25 GtC. The net land carbon sink is taken as net biome productivity (NBP) and so includes any modelled net land-use change emissions. Further details on data sources and 
Colour
Figure 5.26 | Maps of net carbon changes under four Shared Socio-economic Pathway (SSP) scenarios, as evaluated from nine CMIP6 Earth system 
models.  Uncertainty is represented using the simple approach (see Cross-Chapter Box Atlas.1 for more information). No overlay indicates regions with high model agreement,

where ≥80% of models agree with the ensemble mean on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree with 
the ensemble mean on the sign of change. On land, this is calculated as the time integral of net biome productivity (NBP), for the ocean it is the time-integral of air–sea carbon 
dioxide (CO 2) gas flux anomalies relative to the pre-industrial. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

735
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 555.4.5.5 Linear Feedback Analysis
To diagnose the causes of the varying time-evolution of 
carbon  sinks,  the traditional linear feedback approach is 
adopted (Friedlingstein et al., 2003), as used previously to analyse 
C4MIP (Friedlingstein et al., 2006) and CMIP5 models (Arora et al., 
2013). Changes in land carbon storage ( ΔC L) and changes in ocean 
carbon storage ( ΔCo) are decomposed into contributions arising from 
warming ( ΔT) and increases in CO 2 (ΔCO 2):
where βL (βo) and γL (γo) are coefficients that represent the sensitivity 
of land (ocean) carbon storage to changes in CO 2 and global mean 
temperature respectively. This feedback formalism is one of several 
that have been proposed for analysing climate–carbon cycle 
feedbacks (Lade et al., 2018).
This quasi-equilibrium framework is scenario dependent because 
of the time scales associated with land and ocean carbon uptake,

as discussed in AR5 (WGI, Box 6.4). However, it is retained here for 
traceability with AR5. This approach has been used to define a number 
of emergent constraints on carbon cycle feedbacks (Section  5.4.6) 
and to reconstruct the transient climate response to cumulative CO 2 
emissions (TCRE) (Jones and Friedlingstein, 2020), as in Section 5.5. 
To minimize the confounding effect of the scenario dependence, β and γ values are diagnosed from idealized runs in which a 1% per 
year increase in atmospheric CO 2 concentration is prescribed, as for 
AR5 (WGI, Box  6.4; Arora et al., 2013). Values of β are calculated 
from ‘biogeochemical’ runs in which the prescribed CO 2 increases do 
not affect climate, and these are then used to isolate γ  values in 
fully coupled runs where both climate and CO 2 change (Friedlingstein 
et al., 2003).
∆"!=	%!	&"'"+	)!	&* 
Table 5.5 | Diagnosed global feedback parameters for CMIP6 ESMs based

on 1% per year runs to 4×CO 2 (Arora et al., 2020). The last two rows show the 
mean and standard deviation across the CMIP6 and CMIP5 models, respectively.
Land Feedback Factors Ocean Feedback Factors
Model NameβL 
(PgC 
ppm–1)γL 
(PgC K–1)βo 
(PgC p_uo 
c;hnjppm–1)γo 
(PgC K–1)
ACCESS-ESM1.5 0.37 –21.1 0.90 –23.8
CanESM5 1.28 16.0 0.77 –14.7
CESM2 0.90 –21.6 0.71 –10.9
CNRM-ESM2-1 1.36 –83.1 0.70 –9.4
IPSL-CM6A-LR 0.62 –8.7 0.76 –13.0
MIROC-ES2L 1.12 –69.6 0.73 –22.3
MPI-ESM1.2-LR 0.71 –5.2 0.77 –20.1
NOAA-GFDL-
ESM40.93 –80.1 0.84 –21.7
NorESM2-LM 0.85 –21.0 0.78 –19.6
UKESM1-0-LL 0.75 –38.4 0.75 –14.1
CMIP6 
Model Mean0.89 ± 0.30–33.3 ± 
33.80.77 ± 0.06 –16.9 ± 5.1
CMIP5 
Model Mean0.93 ± 0.49–57.9 ± 
38.20.82 ± 0.07 –17.3 ± 3.8
Figure 5.27 | Maps of carbon-concentration and carbon–climate feedback 
terms, as well as net carbon changes under the idealized 1% per year 
carbon dioxide (CO 2) scenario, as evaluated from CMIP6 Earth system

models (ESMs).  Shown are the model means from nine CMIP6 ESMs. Uncertainty 
is represented using the simple approach (see Cross-Chapter Box Atlas.1 for more 
information): No overlay indicates regions with high model agreement, where ≥80% 
of models agree with the ensemble mean on the sign of change; diagonal lines 
indicate regions with low model agreement, where <80% of models agree with the 
ensemble mean on the sign of change. Also shown are zonal-mean latitude profiles 
of land (green) and ocean (blue) feedbacks. On the land, the zonal mean feedback for 
the mean of the ensemble of models that include nitrogen is shown as dashed lines, 
and for carbon-only models as dash-dotted lines, and the carbon–climate feedback 
from one permafrost-carbon enabled ESM is shown as a dotted line. Carbon changes 
are calculated as the difference between carbon stocks at different times on land and 
for the ocean as the time integral of atmosphere–ocean CO 2 flux anomalies relative

to the pre-industrial. The denominator for gamma here is the global mean surface 
air temperature. Further details on data sources and processing are available in the 

736
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Table 5.5 shows the global land and global ocean values of β and γ for 
each of the CMIP6 ESMs (Arora et al., 2020). The last two rows show 
the ensemble means and standard deviation across the ensemble for 
CMIP6 and CMIP5. In both ensembles, the largest uncertainties are 
in the sensitivity of land carbon storage to CO 2 (βL) and the sensitivity 
of land carbon storage to temperature ( γL). The more widespread 
modelling of nitrogen limitations in CMIP6 was expected to lead 
to reductions in both of these feedback parameters. There is some 
evidence for that, with ensemble mean γL moving from –58 ±  38 
GtC K–1 to –33 ±  33 GtC K–1. Between CMIP5 and CMIP6, there 
are also reductions in ensemble mean βo (0.82 to 0.77 GtC ppm–1), 
βL (0.93 to 0.89 GtC ppm–1) and γo (–17.3 to –16.9 GtC K–1), but 
these are progressively less significant compared to the model 
spread in each case.

In these idealized 1% per year CO 2 runs, the CMIP6 models show 
reasonable agreement on the patterns of carbon uptake and also on 
the separate impacts of CO 2 increase and climate change (Figure 5.27). 
For the ensemble mean, increasing atmospheric CO 2 increases carbon 
uptake by the oceans, especially in the Southern Ocean and the North 
Atlantic Ocean, and on the land, especially in tropical and boreal 
forests (β, Figure  5.27a). Climate change further enhances land carbon storage in the boreal zone, but has a compensating negative 
impact on the carbon sink in tropical and subtropical lands, and in 
the North Atlantic Ocean ( γ, Figure  5.27b). Overall, the ensemble 
mean of the CMIP6 ESMs model indicates increasing carbon storage 
with CO 2 in almost all locations (Figure 5.27c).
5.4.6 Emergent Constraints to Reduce Uncertainties 
in Projections
Emergent constraints are based on relationships between observable

aspects of the current or past climate (such as trends or variability), 
and uncertain aspects of future climate change (such as the strength 
of particular feedbacks). These relationships are evident across 
an ensemble of models. When combined with an observational 
estimate of the trend or variability in the real climate, such emergent 
relationships can yield ‘emergent constraints’ on future climate 
change (Hall et al., 2019). At the time of AR5 (WGI, Section 9.8.3), 
there had been relatively few applications of the technique to 
constrain carbon cycle sensitivities, but there have been many studies 
published since (e.g., the summary in Cox, 2019). Figure 5.28 shows 
some key published emergent constraints on the carbon cycle in ESMs.
360 380 400500550600650700
5 10 15−200−150−100−50(b) Tropical climate-carbon feedback
Models
Observation
Emergent Constraint
0.00 0.01 0.02 0.03 0.041.21.31.41.51.6
0.02 0.04 0.06 0.080123456(d) High-latitude greening

−10 −5 0−8−6−4−20(e) Tropical Ocean PP
−0.5 0.0 0.5 1.04.55.05.56.0(f) Ocean Carbon UptakeCO2 at 2060 (ppmv)
Climate Impact, γLT (GtC/°C)ΔGPP ( 2xCO2-1xCO2)
Sensitivity climate change (%/°C)
Global Uptake 2090s (GtC/yr) GPP ( 2xCO2) / GPP (1xCO2)
CO2 at 2010 (ppmv) Sensitivity of CO2 growth-rate (GtC/yr/°C) Sensitivity of CO2 amplitude (ppmv/ppmv) 
Southern Ocean Uptake, current (GtC/yr) Sensitivity of ENSO variability (%/°C) LAI Sensitivity to CO2(a) Future CO2 under RCP8.5 (c) Extra-tropical CO2 fertilisation
Figure 5.28 | Examples of emergent constraints on the carbon cycle in Earth system models (ESMs), reproduced from previously published studies: (a) projected 
global mean atmospheric carbon dioxide (CO 2) concentration by 2060 under the RCP8.5 emissions scenario against the simulated CO 2 in 2010 (Friedlingstein et al., 2014b;

Hoffman et al., 2014); (b) sensitivity of tropical land carbon to warming ( γLT) against the sensitivity of the atmospheric CO 2 growth-rate to tropical temperature variability 
(Cox et al., 2013; Wenzel et al., 2014); (c) sensitivity of extratropical (30°N–90°N) gross primary production to a doubling of atmospheric CO 2 against the sensitivity of the 
amplitude of the CO 2 seasonal cycle at Kumkahi, Hawaii to global atmospheric CO 2 concentration (Wenzel et al., 2016); (d) change in high-latitude (30°N–90°N) gross 
primary production versus trend in high-latitude leaf area index or ‘greenness’ (Winkler et al., 2019); (e) sensitivity of the primary production of the Tropical Ocean to climate 
change versus its sensitivity to El Niño–Southern Oscillation (ENSO)-driven temperature variability (Kwiatkowski et al., 2017); (f) global ocean carbon sink in the 2090s versus

the current-day carbon sink in the Southern Ocean. In each case, a red dot represents a single ESM projection, the grey bar represents the emergent relationship between the 
y-variable and the x-variable, the blue bar represents the observational estimate of the x-axis variable, and the green bar represents the resulting emergent constraint on 
the y-axis variable. The thicknesses represent ± one standard error in each case. Figure after Cox (2019). Further details on data sources and processing are available in the 

737
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 555.4.7 Climate Feedbacks from CH 4 and N 2O
Sources and sinks of CH 4 and N 2O respond both directly and indirectly 
to atmospheric CO 2 concentration and climate change, and thereby 
give rise to additional biogeochemical feedbacks in the climate 
system, which may amplify or attenuate climate–carbon cycle 
feedbacks (Gasser et al., 2017; Lade et al., 2018; Denisov et al., 2019). 
Many of these of feedbacks are only partially understood, and thus 
were only partially addressed in AR5 (WGI, Sections 6.3.3, 6.3.4 and 
6.4.7). Since AR5, a growing body of estimates from ESMs, as well 
as independent modelling and observation-based studies, enable 
improved estimates of the associated feedbacks.
The goal of this section is to assess the climate feedback parameters 
α, as it is defined in Section 7.4.1.1, for CH 4 and N 2O biogeochemical 
feedbacks. The strength of the feedbacks is estimated in a  linear

framework (Gregory et al., 2009), using the radiative forcing equations 
for CO 2, CH 4 and N 2O (Etminan et al., 2016). In addition to estimates 
from ESMs, the feedback parameter α is estimated from independent 
estimates of surface emission climate sensitivities and atmospheric 
box models, following (Arneth et al., 2010; Thornhill et al., 2021). 
These assessed feedback parameters are used in Section 7.4.2.5.
The CH 4 feedbacks may arise from changing wetland emissions 
(including rice farming) and from sources that are expected to 
grow under climate change (e.g., related to permafrost thaw, fires, 
and freshwater bodies). CH 4 emissions from wetlands and landfills 
generally increase with warming due to enhanced decomposition 
with higher temperatures, thereby potentially providing a  positive 
CH 4 feedback on climate (Dean et al., 2018). The contribution of 
wetlands to interannual variability of atmospheric CH 4 is shaped by

the different impacts of temperature and precipitation anomalies 
on wetland emissions (e.g., during El Niño episodes) and therefore 
the relationship between climate anomalies and the wetland 
contribution to the CH 4 growth rate is complex (Pison et al., 2013; 
Nisbet et al., 2016; X.  Zhang et al., 2020). As assessed by SROCC 
(IPCC, 2019b), there is high agreement across model simulations 
that wetlands CH 4 emissions will increase in the 21st century, but 
low agreement  in the magnitude of the change (Denisov et al., 2013; 
Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017; Koffi 
et al., 2020). Climate change increases wetland emissions (Gedney 
et al., 2004, 2019; Volodin, 2008; Ringeval et al., 2011; Denisov et al., 
2013; Shindell et al., 2013) and gives rise to an estimated wetland 
CH 4–climate feedback of 0.03 ± 0.01 W m–2 °C–1 (mean ± 1 standard 
deviation;  limited evidence , high agreement ) (Arneth et al., 2010;

Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017). The 
effect of rising CO 2 on productivity, and therefore on the substrate 
for methanogenesis, can further increase the projected increase in 
wetland CH 4 emissions (Ringeval et al., 2011; Melton et al., 2013). 
Model projections accounting for the combined effects of CO 2 and 
climate change suggest a potentially larger climate feedback (0.01–
0.16 W m–2 °C–1) (limited evidence , low agreement ) (Gedney et al., 
2019; Thornhill et al., 2021). Methane release from wetlands depends 
on the nutrient availability for methanogenic and methanotrophic 
microorganisms that can further modify this feedback (Stepanenko 
et al., 2016; Donis et al., 2017; Beaulieu et al., 2019). Methane 
emissions from thermokarst ponds and wetlands resulting from permafrost thaw are estimated to contribute an additional CH 4-
climate feedback of 0.01 [0.003 to 0.04, 5–95% range] W m–2 °C–1 
(limited evidence, low agreement ).

Methane release from wildfires may increase by up to a factor of 1.5 
during the 21st century (Eliseev et al., 2014a, b; Kloster and Lasslop, 
2017). However, given the contemporary estimate for CH 4 from 
wildfires of no more than 16 TgCH 4 yr–1 (van der Werf et al., 2017; 
Saunois et al., 2020), this feedback is small, adding no more than 
40 ppb to the atmospheric CH 4 by the end of the 21st century ( medium 
confidence ). Methane emissions from pan-Arctic freshwater bodies is 
also estimated to increase by 16 TgCH 4 yr–1 in the 21st century (Tan 
and Zhuang, 2015). Emissions from subsea and permafrost methane 
hydrates are not expected to change substantially in the 21st century 
(Section 5.4.9.1.3).
Land biosphere models show high agreement that long-term 
warming will increase N 2O release from terrestrial ecosystems (Xu-
Ri et al., 2012; B.D.  Stocker et al., 2013; Zaehle, 2013; Tian et al., 
2019). A positive land N 2O climate feedback is consistent with paleo-

evidence based on reconstructed and modelled emissions during 
the last deglacial period (Schilt et al., 2014; H. Fischer et al., 2019; 
Joos et al., 2020). The response of terrestrial N 2O emissions to 
atmospheric CO 2 increase and associated warming is dependent on 
nitrogen availability (van Groenigen et al., 2011; Butterbach-Bahl 
et al., 2013; Tian et al., 2019). Model-based estimates do not account 
for the potentially strong emissions increases in boreal and  arctic 
ecosystems associated with future warming and permafrost thaw 
(Elberling et al., 2010; Voigt et al., 2017). There is medium confidence 
that the land N 2O climate feedback is positive, but low confidence in 
the magnitude (0.02 ± 0.01 W m–2 °C–1).
Climate change will also affect N 2O production in the ocean (Codispoti, 
2010; Freing et al., 2012; Bopp et al., 2013; Rees et al., 2016; Breider et al., 
2019). Model projections in the 21st century show a 4–12% decrease

in ocean N 2O emissions under RCP8.5 due to a combination of factors, 
including increased ocean stratification, decreased ocean productivity, 
and the impact of increasing atmospheric N 2O abundance on the air–
sea flux, corresponding to an ocean N 2O climate feedback of –0.008 
±  0.002  W m–2 °C–1 (limited evidence,  high agreement ) (Martinez-
Rey et al., 2015; Landolfi et al., 2017; Battaglia and Joos, 2018b). 
On millennial time scales, the ocean N 2O climate feedback may be 
positive, owing to ocean deoxygenation and long-term increases in 
remineralization (Battaglia and Joos, 2018b).
Based-on these studies, there is medium confidence  that the 
combined climate feedback parameter for CH 4 and N 2O is positive, 
but there is low confidence  in the magnitude of the estimate (0.05 
[0.02 to 0.09] W m–2 °C–1, 5–95% range).
5.4.8 Combined Biogeochemical Climate Feedback
This section assesses the magnitude of the combined biogeochemical

feedback in the climate system (Figure 5.29) by integrating evidence 
from: carbon-cycle projections represented in Earth system models 
(Section  5.4.5.5), independent estimates of CO 2 emissions due to

738
Figure 5.29 | Estimates of the biogeochemical climate feedback parameter ( α). The parameter α (W m−2 °C−1) for a feedback variable x is defined as αx = δN
δxdx
dT where 
δN
δx is the change in top-of-atmosphere energy balance in response to a change in x induced by a change in surface temperature ( T), as in Section 7.4.1.1. (a) Synthesis of 
biogeochemical feedbacks from panels (b) and (c). Orange (blue) bars correspond to positive (negative) feedbacks increasing (decreasing) radiative forcing at the top of the 
atmosphere. Bars denote the mean and the error bar represents the 5–95% range of the estimates; (b) carbon-cycle feedbacks as estimated by coupled carbon-cycle climate 
models in the CMIP5 (Arora et al., 2013) and CMIP6 (Arora et al., 2020) ensembles, where dots represent single model estimates, and filled (open) circles are those estimates

which do (not) include the representation of a terrestrial nitrogen cycle; (c) Estimates of other biogeochemical feedback mechanisms based on various modelling studies. Dots 
represent single estimates, and coloured bars denote the mean of these estimates with no weighting being made regarding the likelihood of any single estimate, and error bars 
the 5–95% range derived from these estimates. Results in panel (c) have been compiled from (a) Section 5.4.3.2 (Eliseev et al., 2014a; Harrison et al., 2018); (b) Section 5.4.3.3 
(Schneider von Deimling et al., 2012; Burke et al., 2013, 2017b; Koven et al., 2015a, c; MacDougall and Knutti, 2016b; Gasser et al., 2018; Kleinen and Brovkin, 2018), where 
the estimates from Burke et al., 2013 have been constrained as assessed in their study (c) Section 5.4.7 (Schneider von Deimling et al., 2012, 2015; Koven et al., 2015c; Turetsky

et al., 2020); (d) Section 5.4.7 (Arneth et al., 2010; Denisov et al., 2013; Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017); (f) Section 5.4.7 (Xu-Ri et al., 2012; 
B.D. Stocker et al., 2013; Zaehle, 2013; Tian et al., 2019); (g) Section 5.4.7 (Martinez-Rey et al., 2015; Landolfi et al., 2017; Battaglia and Joos, 2018b). (h) Section 6.3, Table 6.9 
mean and the 5–95%  range the assessed feedback parameter. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

739
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55permafrost thaw (Box  5.1) and fire (Section  5.4.3.2), natural CH 4 
and N 2O emissions (Section  5.4.7), and aerosol and atmospheric 
chemistry (Section  6.3.6). We derive a  physical climate feedback 
parameter α, as defined in Section 7.4.1.1, for CO 2-based feedbacks 
using the linear framework proposed by Gregory et al. (2009), using 
the radiative forcing equations for CO 2 (Etminan et al., 2016).
The climate feedback parameter for CO 2 (–1.13 ± 0.45  W m–2 °C–1, mean 
and 1  standard-deviation range) is dominated by the contribution 
of the CO 2-induced increase of ocean and land carbon 
storage (–1.46  ±  0.41  W m–2 °C–1, corresponding to a  βL+O of 
1.66 ± 0.31 PgC ppm–1), with smaller contributions from the carbon 
cycle’s response to climate (0.24 ± 0.17 W m–2 °C–1, corresponding 
to γL+O of –50 ±  34  PgC °C–1), and emissions from permafrost

thaw (0.09  [0.02  to 0.20] W  m–2 °C–1, corresponding to γ  of 
–18 [3 to 41] PgC °C–1, mean and 5–95% range) (Figure 5.29a). This 
estimate does not include an estimate of the fire-related CO 2 feedback 
(range: 0.01–0.06 W m–2 °C–1), as only limited evidence  was available 
to inform its assessment. The sum (mean and 5–95th  percentile 
range) of feedbacks from natural emissions of CH 4 including 
permafrost thaw, and N 2O (0.05 [0.02 to 0.09]  W  m–2  °C–1), and 
feedbacks from aerosol and atmospheric chemistry (–0.20 [–0.41 to 
0.01] W m–2 °C–1) leads to an estimate of the non-CO 2 biogeochemical 
feedback parameter of –0.15 [–0.36 to +0.06] W m–2 °C–1. There is 
low confidence  in the estimate of the non-CO 2 biogeochemical 
feedbacks, due to the large range in the estimates of α  for some 
individual feedbacks (Figure 5.29c), which can be attributed to the 
diversity in how models account for these feedbacks, limited process-

level understanding, and the existence of known feedbacks where 
there is insufficient evidence to assess the feedback strength.
CO 2 and non-CO 2 biogeochemical feedbacks are an important 
component of the assessment of TCRE and the remaining carbon 
budget (Section 5.5). The feedbacks of the carbon cycle of CO 2 and 
climate are implicitly taken account in the TCRE assessment, because 
they are represented in the various underlying lines of evidence. Other feedback contributions, such as the non-CO 2 biogeochemical 
feedback, can be converted into a  carbon-equivalent feedback 
term (γ; Section  5.4.5.5, 7.6) by reverse application of the linear 
feedback approximation (Gregory et al., 2009). The contributions 
of non-CO 2 biogeochemical feedbacks combine to a  linear 
feedback term of 30 ± 27 PgCeq °C–1 (1 standard deviation range, 
111 ± 98 Gt CO 2-eq °C–1), including a feedback term of –11 [–18 to 
–5]  PgCeq °C–1 (5–95% range, –40 [–62 to –18] Gt  CO 2-eq °C–1)

from natural CH 4 and N 2O sources.  The biogeochemical feedback 
from permafrost thaw leads to a combined linear feedback term of 
–21 ± 12 PgCeq °C–1 (1 standard deviation range –77 ± 44 Gt CO 2-
eq  °C–1). For the integration of these feedbacks in the assessment 
of the remaining carbon budget (Section  5.5.2), two individual 
non-CO 2 feedbacks (tropospheric ozone, and methane lifetime) are 
captured in the AR6-calibrated emulators (Box 7.1). Excluding those 
two contributions, the resulting combined linear feedback term for 
application in Section 5.5.2 is assessed at a reduction of 7 ± 27 PgCeq 
°C–1 (1 standard deviation range, –26 ± 97 PgCeq °C–1). For the same 
reasons as for the feedback terms expressed in W m–2 °C–1 (see above), 
there is overall low confidence  in the magnitude of these feedbacks.
5.4.9 Abrupt Changes and Tipping Points
The applicability of the linear feedback framework (Section 5.4.5.5)

suggests that large-scale biogeochemical feedbacks are approximately 
linear in the forcing from changes in CO 2 and climate. Nevertheless, 
regionally the biosphere is known to be capable of producing 
abrupt changes or even ‘tipping points’ (Higgins and Scheiter, 
2012; Lasslop et al., 2016). Abrupt change is defined as a  change 
in the system that is substantially faster than the typical rate of the 
changes in its history (Section 1.4.5). A related matter is a tipping 
point: a critical threshold beyond which a system reorganizes, often 
abruptly and/or irreversibly. Possible abrupt changes in the Earth 
system include those related to ecosystems and biogeochemistry 
(Lenton et al., 2008; Steffen et al., 2018): tropical and boreal forest 
Table 5.6 | Examples of possible biogeochemical abrupt changes and tipping points in the Earth system.  The fourth and sixth columns provide upper estimates

of the impact of each example on the evolution of atmospheric GHGs in the 21st century. These upper estimates are therefore very unlikely  but provide a useful comparison to 
the impact of direct anthropogenic emissions (currently 2.5 ppm yr–1).
Abrupt Change/
Tipping PointKey Region(s)Probability to 
Occur in the 
21st CenturyMaximum CO 2 or 
CH 4 Release in the 
21st CenturyPrincipal 
Development 
Time ScaleMaximum CO 2 
or CH 4 Rate of 
Change Over the 
21st Century(Ir)reversibility
Tropical 
forests dieback 
(Section 5.4.9.1.1)Amazon watershed Low<200 PgC as CO 2 
(Section 5.4.9.1.1; 
medium confidence )Multi-decadal CO 2: <0.5 ppm yr–1Irreversible at 
multi-decadal scale 
(medium confidence )
Boreal forests dieback 
(Sections 5.4.9.1.1, 
5.4.3.2)Boreal Eurasia 
and North AmericaLow<27 Pg 
(Section 5.4.9.1.2; 
medium confidence )Multi-decadal Small ( low confidence )Irreversible at 
Biogenic emissions 
from permafrost thaw

(Section 5.4.9.1.2)Pan-Arctic Highup to 240 PgC of CO 2 
and up to 5300 Tg of 
CH 4 (Section 5.4.8.1.2; 
low confidence )Multi-decadalCO 2: ≤1 ppm yr–1
CH 4: ≤10 ppb yr–1Irreversible at 
centennial time scales 
(high confidence )
Methane release 
from clathrates 
(Section 5.4.9.1.3)Oceanic shelf Very lowvery likely  small 
(Section 5.4.9.1.3)Multi-millennium CH 4: ≤0.2 ppb yr–1Irreversible at multi-
millennium time scales 

740
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5dieback; and release of greenhouse gases (GHGs) from permafrost 
and methane clathrates (Table 5.6). In this section we therefore focus 
on estimating upper limits on the possible impact of abrupt changes on  
the  evolution of atmospheric GHGs out to 2100, for comparison 
to the impact of direct anthropogenic emissions.
5.4.9.1 Assessment of Biogeochemical Tipping Points
5.4.9.1.1 Forest dieback
Published examples of abrupt biogeochemical changes in models 
include tropical rain forest dieback (Cox et al., 2004; Jones et al., 
2009; Brando et al., 2014; Le Page et al., 2017; Zemp et al., 2017), 
and temperate and boreal forest dieback (Joos et al., 2001; Lucht 
et al., 2006; Scheffer et al., 2012; Lasslop et al., 2016; Section 5.4.3). 
Such transitions may be related to: (i) large-scale changes in mean 
climate conditions crossing particular climate thresholds (Joos et al.,

2001; Cox et al., 2004; Lucht et al., 2006; Hirota et al., 2011; Scheffer 
et al., 2012; Le Page et al., 2017; Zemp et al., 2017); (ii) temperature 
and precipitation extremes (Staver et al., 2011; Higgins and Scheiter, 
2012; Scheffer et al., 2012; Pavlov, 2015; Zemp et al., 2017); or 
(iii) possible enhancement and intermittency in fire activity (Staver 
et al., 2011; Higgins and Scheiter, 2012; Lasslop et al., 2016; Brando 
et al., 2020). Simulated changes in forest cover are a combination 
of the effects of CO 2 on photosynthesis and water-use efficiency 
(Section 5.4.1), and the effects of climate change on photosynthesis, 
respiration and disturbance (Section  5.4.3). In ESMs, direct CO 2 
effects tend to enhance forest growth, but the impacts of climate 
change vary between being predominantly negative in the tropics 
and predominantly positive in the boreal zone (Figure 5.27).
Most ESMs project continuing carbon accumulation in tropical forests

as a result of direct CO 2 effects overwhelming the negative effects of 
climate change (Huntingford et al., 2013; Drijfhout et al., 2015; Boulton 
et al., 2017). In the real world, forests may be less vulnerable to climate 
changes than those modelled in ESMs because of the greater plant 
trait diversity, which confers additional resilience (Reyer et al., 2015; 
Levine et al., 2016; Sakschewski et al., 2016), and because of possible 
acclimation of vegetation to warming (Good et al., 2011, 2013; Lloret 
et al., 2012; Mercado et al., 2018). On the contrary, forests may be more 
vulnerable in the real world due to indirect climate change effects such 
as insect outbreaks and diseases not considered here (Section 5.4.3.2) 
or model limitations in representing the effects disturbances such as 
wildfire and droughts. In general, forests are most vulnerable when 
climate change is combined with increased rates of direct deforestation 
(Nobre et al., 2016; Le Page et al., 2017).

To estimate an upper limit on the impact of Amazon forest dieback 
on atmospheric CO 2, we consider the very unlikely  limiting case of 
negligible direct-CO 2 effects (Section  5.4.1). Emergent constraint 
approaches (Section 5.4.6) may be used to estimate an overall loss of 
tropical land carbon due to climate change alone, of around 50 PgC 
per °C of tropical warming (Cox et al., 2013; Wenzel et al., 2014). 
This implies an upper limit to the release of tropical land carbon of 
<200 PgC over the 21st century (assuming tropical warming of <4°C , 
and no CO 2-fertilization), which translates to dCO 2/dt <0.5 ppm yr–1.Boreal forest dieback is not expected to change the atmospheric CO 2 
concentration substantially because forest loss at the south is partly 
compensated by: (i) temperate forest invasion into previously boreal 
areas; and (ii) boreal forest gain at the north (Friend et al., 2014; 
Kicklighter et al., 2014; Schaphoff et al., 2016) ( medium confidence ).

An upper estimate of this magnitude, based on statistical modelling 
of climate change alone, is of 27 Pg vegetation carbon loss in 
the southern boreal forest, which is roughly balanced by gains in the 
northern zone (Koven, 2013). Carbon release from vegetation and 
soil due to wildfires in boreal regions (Eliseev et al., 2014b; Turetsky 
et al., 2015; X.J. Walker et al., 2019) is also not expected to change 
this estimate substantially because of its small present-day value of 
about 0.2 PgC yr–1 (van der Werf et al., 2017), and because of likely  
increases in precipitation in boreal regions (Section 4.5.1).
5.4.9.1.2 Biogenic emissions following permafrost thaw
There is large uncertainty in release of GHGs from permafrost in the 
21st century. The largest of these estimates implies tens to hundreds 
of gigatons of carbon released in the form of CO 2 (Box 5.1) and CH 4 
emissions up to 100 TgCH 4 yr–1 (Box 5.1). A carbon dioxide release of

such magnitude would lead to an increase in the CO 2 accumulation 
rate in the atmosphere of ≤1 ppm yr–1. These emissions develop at 
a multi-decadal time scale. Assuming a CH 4 lifetime in the atmosphere 
of the order of 10 years and the associated feedback parameter of 
1.34 ± 0.04 (Section 6.2.2.1), this would increase the atmospheric CH 4 
content by about 500 ppb over the century, corresponding to a rate of 
≤10 ppb yr–1. Irrespective of its origin, additional CH 4 accumulation of 
such a magnitude is not expected to modify the temperature response 
to anthropogenic emissions by more than a few tenths of a °C (Gedney 
et al., 2004; Eliseev et al., 2008; Denisov et al., 2013). Emissions from 
permafrost thawing are assessed in Box 5.1.
5.4.9.1.3 Methane release from clathrates
The total global clathrate reservoir is estimated to contain 1500–
2000  PgC (Archer et al., 2009; Ruppel and Kessler, 2017), held 
predominantly in ocean sediments, with only an estimated 20 PgC

in and under permafrost (Ruppel, 2015). The present-day CH 4 
release from shelf clathrates is <10 TgCH 4 yr–1 (Kretschmer et al., 
2015; Saunois et al., 2020). Despite polar amplification (Chapter 7), 
substantial releases from the permafrost-embedded subsea 
clathrates is very unlikely  (Minshull et al., 2016; Malakhova and 
Eliseev, 2017, 2020). This is consistent with an overall small release 
of CH 4 from the shelf clathrates during the last deglacial transition, 
despite large reorganizations in climate state (Bock et al., 2017; 
Petrenko et al., 2017; Dyonisius et al., 2020). The long time scales 
associated with clathrate destabilization makes it unlikely  that CH 4 
release from the ocean to the atmosphere will deviate markedly from 
the present-day value through the 21st century (Hunter et al., 2013), 
corresponding to no more than additional 20 ppb of atmospheric CH 4 
(i.e., <0.2 ppb  yr–1). Another possible source of CH 4 is gas clathrates

in deeper terrestrial permafrost and below it (Buldovicz et al., 2018; 
Chuvilin et al., 2018), which may have caused recent craters in the 
north of Russia (Arzhanov et al., 2016, 2020; Arzhanov and Mokhov, 
2017; Kizyakov et al., 2017, 2018). Land clathrates are formed at 
depths greater than 200  m  (Ruppel and Kessler, 2017; Malakhova

741
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55and Eliseev, 2020), which precludes a substantial response to global 
warming over the next few centuries and associated emissions.
Thus, it is very unlikely that CH 4 emissions from clathrates will 
substantially warm the climate system over the next few centuries.
5.4.9.2 Abrupt Changes Detected in Earth System 
Model Projections
Projecting abrupt changes is intrinsically difficult, because by 
definition abrupt changes occur in a small region of the parameter 
and/or forcing space. At the time of AR5 there was no available 
systematic study of abrupt changes or tipping points in ESMs. An 
analysis of ESMs since AR5 has identified a number of abrupt changes 
in the CMIP5 ensemble (Drijfhout et al., 2015; Bathiany et al., 2020). 
These include abrupt changes in tropical forests and high-latitude 
greening, permafrost thaw, and vegetation composition change

(Bathiany et al., 2020). Most modelled abrupt changes were detected 
in boreal and tundra regions, with few models showing Amazon 
forest dieback (Bathiany et al., 2020).
Based on the evidence presented in this section, we conclude that 
abrupt changes and tipping points in the biogeochemical cycles lead 
to additional uncertainty in 21st century GHG concentrations changes. 
However, these are very likely  to be small compared to the uncertainty 
associated with future anthropogenic emissions ( high confidence ).
5.4.10 Long-term Response Past 2100
The AR5 assessed with very high confidence  that the carbon cycle in 
the ocean and on land will continue to respond to climate change and 
rising atmospheric CO 2 concentrations created during the 21st century 
(WGI, Chapter  6, Executive Summary). Since AR5, experiments with 
the Community Earth System Model version 1  (CESM1) under the 
RCP8.5 extension scenario to 2300, suggest that both land and ocean

carbon–climate feedbacks strengthen in time, land and ocean carbon-
concentration feedbacks weaken, and the relative importance of ocean 
sinks versus land sinks increases (Randerson et al., 2015). Under high 
emissions scenarios, this relative strengthening of land carbon–climate 
feedbacks leads the terrestrial biosphere to shift from sink to source at 
some point after 2100 in all of the CMIP5 ESMs and CMIP5-era Earth 
system models of intermediate complexity (EMICs) (Tokarska et al., 
2016). The strengthening of land and ocean carbon–climate feedbacks 
projected beyond 2100 under high emissions scenarios offsets the 
declining climate sensitivity to incremental increases of CO 2, leading 
to a net strengthening of carbon cycle feedbacks, as measured by the 
gain parameter, from one century to the next (Randerson et al., 2015).
Figure  5.30 shows carbon cycle changes to 2300 under three SSP 
scenarios with long-term extensions: SSP5-8.5, SSP5-3.4-overshoot,

and SSP1-2.6, for four CMIP6 ESMs and one EMIC.  Under all three 
scenarios, all five models project a reversal of the terrestrial carbon cycle 
from a sink to a source. However, the reasons for these reversals under 
very high emissions and low/negative emissions are  very  different. 
Under the SSP5-8.5 scenario, the terrestrial carbon–climate   
feedback is projected to strengthen, while the carbon-concentration  feedbacks weaken after emissions peak at 2100, which together drives 
the land to become a net carbon source after 2100 (Tokarska et al., 
2016). The difference in both timing and magnitude of this transition 
across the ensemble, leads to an assessment of medium confidence  in 
the likelihood and low confidence  in the timing and strength, of the 
land transitioning from a net sink to a net source under such a scenario. 
Based on high agreement  across all available models, we assess with 
high confidence  that the ocean sink strength would weaken but not

reverse under a long-term high emissions scenario. In the SSP5-3.4-
overshoot scenario, both the terrestrial and ocean reservoirs act 
as transient carbon sources during the overshoot period, when net 
anthropogenic CO 2 emissions are negative and CO 2 concentrations 
are falling, and then revert to near-zero (land) or weak sink (ocean) 
fluxes after stabilization of atmospheric CO 2. The SSP1-2.6 scenario, 
characterized by lower peak CO 2 concentrations, a smaller overshoot, 
and much less carbon loss from land-use change, shows instead 
a  relaxation towards a  neutral biosphere on land, and a  sustained 
weak sink in the ocean (see also Section 5.6.2.2.1.2).
Figure 5.30 | Trajectories of carbon cycle dynamics for models beyond 
2100.  Shown are three scenarios: SSP5-8.5; SSP5-3.4-overshoot; and SSP1-2.6, 
from four ESMs (CanESM5, UKESM1, CESM2-WACCM, IPSL-CM6a-LR) and one EMIC 
(UVIC-ESCM, Mengis et al., 2020) for which extensions beyond 2100 are available.

Solid lines represent the median flux value across the ensemble, and shading 
represents 15th–85th percentiles across the ensemble. Further details on data sources 

742
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.4.11 Near-term Prediction of Ocean 
and Land Carbon Sinks
The AR5 (WGI, Section  11.3.2) assessed near-term  climate 
predictability based on ESMs initialized from the observed 
climate  state. Since AR5, a  growing number of prediction systems 
have been developed based on ESMs that include the ocean and 
land carbon cycle components. Predictability of key physical climate 
variables (assessed in Chapter  4) provides a  platform to establish 
predictive skill for interannual variations in the strength of the natural 
carbon sinks in response to internal climate variability. In most 
systems the carbon cycle components are only indirectly initialized 
and respond to the initialized climate variations (Li et al., 2019). This 
subsection synthesizes information on predictability of the land and 
ocean carbon sinks using both the idealized potential predictability 
and the actual predictability skill measures.

Longer-term memory residing in the ocean enables predictability 
of the ocean carbon sink (McKinley et al., 2017; Li and Ilyina, 2018). 
The predictive horizon of the globally integrated air–sea CO 2 fluxes 
has been assessed in perfect-model frameworks that are based on an 
idealized ensemble of simulations in which each ensemble member 
serves as a verification, while no observations are assessed. Perfect-
model studies provide an estimate of the upper range of potential 
predictability for the integrated air–sea CO 2 fluxes of about two years 
globally and up to a decade in some regions (Séférian et al., 2018a; 
Spring and Ilyina, 2020). Evidence is also emerging for predictive skill 
of the global air–sea CO 2 fluxes of up to six years based on prediction 
systems initialized with observed physical climate states (Ilyina et al., 
2021), with a potential for even longer-term regional predictability 
in some regions, including the North Atlantic and subpolar Southern

Ocean (H. Li et al., 2016; Lovenduski et al., 2019a).
Models suggest that predictability of the air–sea CO 2 flux is related 
to predictability of ocean biogeochemical state variables such as 
dissolved inorganic carbon (DIC) and total alkalinity (TA; Lovenduski 
et al., 2019a), as well as the mixed layer depth (H.  Li et al., 2016). 
Temperature variations largely control shorter-term predictability of 
the ocean carbon sink, while longer-term predictability is related to 
non-thermal drivers such as ocean circulation and biology (Li et al., 
2019). Although there is a substantial spatial heterogeneity, initialized 
predictions suggest stronger multi-year variations of the air–sea CO 2 
flux and generally tend to outperform uninitialized simulations on the 
global scale (Li et al., 2019). The predictive skill of air–sea CO 2 flux 
shows a  consistent spatial pattern in different models, despite the 
wide range of techniques used to assimilate observational information

(Regnier et al., 2013). ESM-based prediction systems also demonstrate 
predictability of other marine biogeochemical properties such as net 
primary production (Séférian et al., 2014; Yeager et al., 2018; Park et al., 
2019) and seawater pH (Brady et al., 2020).
Seasonal predictability of air-land CO 2 flux up to 6–8 months is driven 
by the state of El Niño–Southern Oscillation (ENSO) (Zeng et al., 
2008; Betts et al., 2018). Fewer land carbon initialized predictions 
are available from decadal prediction systems, yet they tend to 
outperform the uninitialized simulations in capturing the major year-
to-year variations, as indicated by higher correlations with the global carbon budget estimates. There is growing evidence that the potential 
predictive skill of air-land CO 2 flux is maintained out to a lead-time 
of two years (Lovenduski et al., 2019b); this predictability horizon 
is also supported by perfect model studies (Séférian et al., 2018a;

Spring and Ilyina, 2020). The origins of this interannual predictability 
are not yet fully understood. However, they seem to be associated 
with the oscillatory behaviour of ENSO (Séférian et al., 2014) and 
the drivers of terrestrial carbon flux predictability, such as ecosystem 
respiration and gross primary production (Lovenduski et al., 2019a). 
Initialized simulations suggest that observed variability in the land 
carbon sink is improved through initialization of prediction systems 
with the observed state of the physical climate.
The predictability horizon of variations in atmospheric CO 2 growth 
rate is not yet fully established in the literature. However, predictive 
skill of the land and ocean carbon sinks show a potential to establish 
predictability of variations in atmospheric CO 2 up to two years in 
advance in the initialized prediction systems, with an upper bound of 
up to three years in a perfect-model study (Spring and Ilyina, 2020);

this skill is primarily limited by the terrestrial carbon sink predictability.
5.5 Remaining Carbon Budgets
Science at the time of AR5 established a  near-linear relationship 
between cumulative emissions of CO 2 and the resulting global 
warming (Allen et al., 2009; Matthews et al., 2009; Meinshausen 
et al., 2009; Zickfeld et al., 2009; T.F . Stocker et al., 2013). The amount 
of global warming per unit of cumulated carbon dioxide emissions 
is called the transient climate response to cumulative CO 2 emissions 
(TCRE). This TCRE relationship is now used to estimate the 
amount of CO 2 emissions that would be consistent with limiting 
global warming to specific levels (Allen et al., 2009; Matthews 
et al., 2009, 2012;  Meinshausen et al., 2009; Zickfeld et al., 2009; 
M.  Collins  et al.,  2013; T.F .  Stocker et al., 2013; Knutti and Rogelj, 
2015; Rogelj et al., 2016, 2019; Goodwin et al., 2018). The remainder 
of CO 2 emissions that would be in line with limiting global warming

to a specific temperature level (while accounting for all other factors 
affecting global warming) can be estimated with the help of the TCRE 
and is referred to as the remaining carbon budget (Rogelj et al., 2019; 
Matthews et al., 2020). Section 5.5.1 first assesses the TCRE as one 
of the core concepts underlying the notion of a  remaining carbon 
budget, and Section 5.5.2 then integrates this with other contributing 
factors from across this assessment to provide a  consolidated 
assessment following the approach of the Special Report on Global 
Warming of 1.5°C (SR1.5) (Rogelj et al., 2018b). The historical carbon 
budget of CO 2 already emitted is assessed in Section 5.2.1.5.
5.5.1 Transient Climate Response to Cumulative 
Emissions of Carbon Dioxide (TCRE)
5.5.1.1 Contributing Physical Processes 
and Theoretical Frameworks
The processes that translate emissions of CO 2 into a  change in 
global temperature (terrestrial and oceanic carbon uptake, radiative

743
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55forcing from CO 2, and ocean heat uptake) are governed by complex 
mechanisms that all evolve in time (Sections 3.5, 4.3, 4.5, 5.4, and 
7.3, and Cross-Chapter Box 5.3; Gregory et al., 2009). Starting with 
an initial description in AR5 (M. Collins et al., 2013; T.F . Stocker et al., 
2013), a  body of literature has since expanded the understanding 
of physical mechanisms from which a  simple proportional 
relationship between cumulative emissions of CO 2 and change in 
global temperature arises – expressed in either global mean surface 
temperature (GMST) or global surface air temperature (GSAT).
Studies have focused on two key features of the transient climate 
response to cumulative CO 2 emissions (TCRE) relationship: (i) why 
the relationship is nearly constant in time (Goodwin et al., 2015; 
MacDougall and Friedlingstein, 2015; Williams et al., 2016; Ehlert

et al., 2017; Katavouta et al., 2018); and (ii) why, and under which 
conditions, the relationship is independent of the historical rate (or 
pathway) of CO 2 emissions (MacDougall, 2017; Seshadri, 2017).
There is increased confidence in the near-constancy of TCRE because of 
the variety of methods that have been used to examine this relationship: 
sensitivity studies with Earth system models of intermediate complexity 
(EMICs; Herrington and Zickfeld, 2014; Ehlert et al., 2017); theory-
based equations used to examine ESM and EMIC output (Goodwin 
et al., 2015; R.G. Williams et al., 2016, 2017b); and simple analytical 
models that capture aspects of the TCRE relationship (MacDougall and 
Friedlingstein, 2015). All studies agree that the near-constancy of the 
TCRE arises from compensation between the diminishing sensitivity 
of radiative forcing to CO 2 at higher atmospheric concentration and 
the diminishing ability of the ocean to take up heat and carbon at

higher cumulative emissions (Allen et al., 2009; Matthews et al., 2009; 
Frölicher and Paynter, 2015; Goodwin et al., 2015; Gregory et al., 2015; 
MacDougall and Friedlingstein, 2015; MacDougall, 2016; Tokarska 
et al., 2016; Ehlert et al., 2017).
The question of whether, and under which conditions, the TCRE 
relationship is independent of the historical rate of CO 2 emissions 
(also referred to as ‘pathway independence of TCRE’) has been 
examined by using simple mathematically tractable models 
(MacDougall, 2017; Seshadri, 2017). Based on the assumption that the cumulative fraction of carbon taken up by the terrestrial 
biosphere is constant, and that the climate feedback parameter and 
ocean heat uptake efficacy do not change in time, both studies agree 
that pathway independence is sensitive to the rate of CO 2 emissions, 
such that pathway independence is expected to break down at both 
very high and very low absolute CO 2 emissions rates (MacDougall,

2017; Seshadri, 2017). Note that, in pathways with strongly 
declining emissions, the cumulative sink fraction by the combined 
terrestrial biosphere and ocean is expected to increase (Figure 5.25). 
The studies also agree that no similar relationship analogous to TCRE 
can be expected for short-lived non-CO 2 forcers, for which the annual 
emissions are a  closer proxy for the implied warming (M.  Collins 
et al., 2013; Sections 6.4, 7.6). MacDougall (2017) suggests that two 
additional constraints are required to create pathway independence: 
first, the transport of heat and carbon into the deep ocean should be 
governed by processes with similar time scales; and second, the ratio 
of the net change in the atmospheric carbon pool to the net change 
in the ocean carbon pool should be close to the ratio of the enhanced 
longwave radiation to space (i.e.,  the radiative response of the 
surface) to ocean heat uptake. If these ratios are identical, then TCRE

would be completely path independent (MacDougall, 2017). If the 
ratios are close but not identical, TCRE would be only approximately 
path independent over a wide range of cumulative emissions (Cross-
Chapter Box 5.3; MacDougall, 2017).
The land carbon cycle does not appear to play a fundamental role 
in the origin of the linearity and path-independence of TCRE (Goodwin 
et al., 2015; MacDougall and Friedlingstein, 2015; Ehlert et al., 2017) 
but, in contrast to the ocean sink, dominates the uncertainty in the 
magnitude of TCRE by modulating the cumulative airborne fraction of 
carbon (Goodwin et al., 2015; Williams et al., 2016; Katavouta et al., 
2018; Jones and Friedlingstein, 2020). Some terrestrial carbon cycle 
feedbacks (such as the permafrost carbon feedback; Section 5.4.8, 
Box 5.1) have the potential to alter both the linearity and pathway 
independence of TCRE, if such feedbacks significantly contribute

carbon to the atmosphere (Sections 5.5.1.2.3 and 5.4.8, and Box 5.1; 
MacDougall and Friedlingstein, 2015). A  recent study also shows 
how the value of TCRE can depend on the effect of ocean ventilation 
modulating ocean heat uptake (Katavouta et al., 2019).
Cross-Chapter Box 5.3 | The Ocean Carbon–Heat Nexus and Climate Change Commitment
Contributors:  Pedro M.S. Monteiro (South Africa), Jean-Baptiste Sallée (France),  Piers Foster (United Kingdom), Baylor Fox-Kemper 
(United States of America), Helen T. Hewitt (United Kingdom), Masao Ishii (Japan), Joeri Rogelj (United Kingdom/Belgium), Kirsten 
Zickfeld (Canada/Germany)
Context
In the past 60  years, the ocean has taken up and stored 23 ±  5% of anthropogenic carbon emissions ( medium confidence ) 
(Section 5.2.1.3) as well as more than 90% of the heat that has accumulated in the Earth system (referred to as excess heat) since the

1970s (Sections 7.2.2, 9.2.2 and 9.2.3, and Box 7.2; Frölicher et al., 2015; Talley et al., 2016; Gruber et al., 2019b; Hauck et al., 2020). The 
interplay between heat and CO 2 uptake by the ocean has played a major role in slowing the rate of global warming, and also provides 
a first order influence in determining the unique properties of a metric of the coupled climate–carbon cycle response – transient 
climate response to cumulative CO 2 emissions (TCRE) – which is critical to setting the future remaining carbon emissions budget 
(Sections 5.5.1.3 and 5.5.4). This role of the ocean in the uptake of heat and anthropogenic CO 2 and related feedbacks is what we

744
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5term the ‘ocean carbon–heat nexus’. The ocean processes behind this nexus are important in shaping and understanding the near-
linear relationship between cumulative CO 2 emissions and global warming (TCRE) as well as the uncertainties in future projections 
of TCRE properties (Zickfeld et al., 2016; Bronselaer and Zanna, 2020; Jones and Friedlingstein, 2020), its path independence 
(MacDougall, 2017), and the warming commitment after cessation of greenhouse gas emissions – the zero emissions commitment 
(ZEC; Section 5.5.2; Zickfeld et al., 2016; Ehlert and Zickfeld, 2017). In this box, we assess the role of the ocean and its physical and 
chemical thermodynamic processes that shape these striking characteristics.
The role of the ocean in setting the coupled climate–carbon cycle response is threefold. First, the ocean and land carbon sinks together

set the airborne fraction (AF) of CO 2 in the atmosphere, which sets the radiative forcing that drives the additional heat in the atmosphere, 
most of which is taken up by the ocean (Sections 7.2 and 9.2; Katavouta et al., 2019; Williams et al., 2019). the land carbon sink does 
not appear to play an important role in determining the linearity and path-independence of TCRE (Section 5.5.1.1; Goodwin et al., 2015; 
MacDougall and Friedlingstein, 2015; Ehlert et al., 2017). Second, the ocean sets the thermal response through ocean heat uptake 
(Section 9.2; Frölicher et al., 2015; Bronselaer and Zanna, 2020). Third, there is a feedback within the ocean carbon–heat nexus as ocean 
warming, particularly under low or no mitigation scenarios, weakens the ocean sink of CO 2, which influences the AF , and hence the 
radiative forcing (Box 7.1; Williams et al., 2019). The near-linear relationship between cumulative CO 2 emissions and global warming

(TCRE) is thought to arise, to a large extent, from the compensation between the decreasing ability of the ocean to take up heat and 
CO 2 at higher cumulative CO 2 emissions, pointing to similar processes that determine ocean uptake of heat and carbon (Section 5.5.1.1; 
Goodwin et al., 2015; MacDougall and Friedlingstein, 2015; Williams et al., 2016; Zickfeld et al., 2016; Ehlert et al., 2017).
Processes that drive the ocean carbon–heat nexus and its change
The air–sea flux of heat and all gases across the ocean interface is driven by a common set of complex and turbulent diffusion and 
mixing processes that are difficult to observe (Sections 5.2.1.3 and 9.2.1.2; Wanninkhof et al., 2009; Wanninkhof, 2014; Cronin et al., 
2019; Watson et al., 2020). These processes are typically simplified into widely verified expressions that link the flux to wind stress, the

solubility and the gradient across the air–sea interface ( medium confidence ). Because the ocean has a higher heat capacity than the 
atmosphere (the heat capacity of the upper 100 m of the ocean is about 30 times larger than the heat capacity of the atmosphere), 
the partitioning of heat between the atmosphere and the ocean is primarily influenced by the temperature differences between air 
and seawater. Similarly, the unique seawater carbonate buffering capacity enables CO 2 to be stored in the ocean as dissolved salts, 
rather than just as dissolved gas; this increases the capacity of seawater to store CO 2 by two orders of magnitude beyond the solubility 
of CO 2 gas and approximates the partitioning ratio of heat between the atmosphere and the ocean (Section 9.2.2.1; Zeebe and Wolf-
Gladrow, 2009; Bronselaer and Zanna, 2020). The role of the biological carbon pump in influencing the ocean sink of anthropogenic

carbon into the ocean interior is assessed to be minimal during the historical period, but this may change, particularly in regional 
contexts, by 2100 ( medium confidence ) (Laufkötter et al., 2015; Kwiatkowski et al., 2020). Its role is important in the natural or pre-
industrial carbon cycle ( medium confidence ) (Henson et al., 2016).
Under climate change, the buffering capacity of the ocean decreases (increasing Revelle Factor), which reflects a decreasing capacity 
for the ocean to take up additional anthropogenic CO 2 and store it in the dissolved inorganic carbon reservoir (Egleston et al., 2010). 
In contrast to CO 2, there is no physical limitation that would reduce the ability of surface ocean temperature to equilibrate with the 
atmospheric temperature. However, both carbon and heat fluxes depend on air–sea heat fluxes that in turn depend on gradients of

characteristics at the air–sea interface. These gradients at the air–sea interface respond to ocean dynamics, such as the volume of 
the surface mixed-layer that equilibrates with the atmosphere, and ocean circulation that can flush the surface layer with water 
masses that have not equilibrated with the atmosphere for a long time. Limited recent evidence suggests that the effect of small-scale 
dynamics absent in climate and Earth system models might be locally important (Bachman and Klocker, 2020). In summary, changes 
in heat and carbon uptake by the ocean rely on a combination of unique chemical and shared physical processes, any of which have 
the potential to disrupt the coherence of heat and CO 2 change in the ocean.
Spatial pattern of air–sea fluxes and storage
Large-scale regional and global ocean circulation shape the spatial pattern of the uptake and storage of both CO 2 and heat (see

Figure 5.8 for carbon; Figure 9.6 for heat observations; Section 9.2; Frölicher et al., 2015; Bronselaer and Zanna, 2020). This coherence 
of spatial patterns driven by the large-scale ocean circulation has three aspects. First, notwithstanding interannual-decadal variability 
in heat and CO 2 uptake, there is a spatial coherence of the temporally integrated uptake at the air–sea boundary, particularly in the 
Southern Ocean (Cross-Chapter Box 5.3, Figure 1; Talley et al., 2016; Keppler and Landschützer, 2019; Auger et al., 2021). Second, the 
importance of the meridional overturning circulation in the subsequent storage of both heat and CO 2 in mode, intermediate and deep 
waters of the ocean interior (Section 9.2). Third, of particular note, the roles of the North Atlantic Ocean (Section 9.2.3.1) and the  
Cross-Chapter Box 5.3 (continued)

745
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Southern Ocean (Section 9.2.3.2) in linking the spatial pattern of air–sea fluxes, the storage of heat and carbon, and ultimately in 
understanding and predicting the sensitivity of the carbon-heat nexus to climate change (Frölicher et al., 2015; Thomas et al., 2018; 
Wu et al., 2019).
The role of the large-scale circulation in shaping these fluxes is to: (i) flush the ocean surface layer with deep waters that are relatively 
cold and with weak or no anthropogenic CO 2 and heat content because they have been isolated from the atmosphere for centuries; 
and (ii) transport the anthropogenic CO 2 and heat at depth, away from the atmosphere (Frölicher et al., 2015; Marshall et al., 2015; 
Armour et al., 2016). For instance, in the Southern Ocean, upwelled water masses take up a large amount of anthropogenic CO 2

and heat (Cross-Chapter Box  5.3, Figure  1), which are then exported northward by the circulation to be stored at depth in the 
Southern Hemisphere subtropical gyres (Cross-Chapter Box 5.3, Figure 1; Figure 9.7). In the North Atlantic, the signature of the Atlantic 
meridional overturning circulation (AMOC) is also clearly visible, with large amounts of heat and carbon being stored beneath the 
North Atlantic subtropical gyre at 1 km depth (Cross-Chapter Box 5.3, Figure 1). In summary, the net air–sea fluxes of anthropogenic 
CO 2 and heat depend on large-scale circulation, which is associated with upper ocean stratification, mixed-layer depth, and water-
mass formation, transport and mixing (Sections 9.1–9.3).
Changes in ocean processes and impact on the ocean carbon–heat nexus
Future projections of the ocean carbon–heat nexus in the second half of the 21st century, particularly those under weak or no

mitigation scenarios, are characterized by the strengthening of the two largest positive feedbacks: weakening surface ocean CO 2 
buffering capacity (increasing Revelle Factor) and warming that further reduces CO 2 solubility and strengthens ocean stratification, 
which reduces exchange between the ocean surface and interior (Jiang et al., 2019; Bronselaer and Zanna, 2020). These are offset 
by a growing but scenario-dependent negative feedback from increasing carbon and heat air–sea fluxes towards the ocean, due to 
increased atmospheric temperature and CO 2 concentrations (Talley et al., 2016; Jiang et al., 2019; McKinley et al., 2020). The Southern 
Ocean in particular is one of the regions where the projected feedback can be largest and where inter-model differences are strongest 
(Roy et al., 2011; Frölicher et al., 2015; Hewitt et al., 2016; Mongwe et al., 2018). These projected trends in ocean carbonate chemistry

(Section 5.4.2), together with surface ocean warming (Section 9.2.1.1), explain the slow down and long-term reduction of the ocean 
sink for anthropogenic CO 2 even as emissions continue to rise beyond 2050 under weak-to-no-mitigation scenarios (Figures 2.7.1 
and 5.25, and Technical Summary TS Box 7). Projected change in the North Atlantic and Southern Ocean overturning circulation 
also impact air–sea fluxes of heat and carbon. The very likely  decline in AMOC in the 21st century for all shared socio-economic 
pathways (SSP) scenarios (Section 9.2.3.1) tends to reduce heat and carbon uptake, resulting in a positive feedback. In contrast, in 
the Southern Ocean, the future 21st century projected increase in upper ocean overturning circulation ( low confidence ) – due to 
increasing wind forcing projected for all scenarios, except those with large mitigation (SSP1-2.6) – produces a negative feedback, with

increasing heat and carbon uptake and storage despite the increasing stratification and outgassing of natural CO 2 in the upwelling 
zone (Sections 9.2.3.2 and 5.2.1.3). 
Cross-Chapter Box 5.3, Figure 1 | CMIP6 multi-model mean of changes in zonally integrated (a) heat and (b) carbon storage in the ocean 
between the pre-industrial and the modern period.  Carbon corresponds to dissolved inorganic carbon. Data are shown for the upper 2000 m. The modern 
period is 1995–2014. Adapted from Frölicher et al. (2015).

746
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5In summary, a combination of unique chemical properties of seawater carbonate combined with shared physical ocean processes 
explain the coherence and scaling in the uptake and storage of both CO 2 and heat in the ocean, which is the basis for the carbon–
heat nexus ( high confidence ). In this way, the processes of the ocean carbon-heat nexus help understand the quasi-linear and path 
independence of properties of TCRE, which forms the basis for the zero emissions commitment (ZEC; Section 5.5) ( medium confidence ). 
Future projections under low or no mitigation indicate with high confidence  that carbon chemistry and warming will strengthen the 
positive feedback to climate change by reducing ocean carbon uptake, and medium confidence  that ocean circulation may partially 
compensate that positive feedback by slightly increasing anthropogenic carbon storage. Increasing ocean warming and stratification

may decrease exchanges between the surface and subsurface ocean, which could reduce the path independence of TCRE, though this 
effect can be partially counterbalanced regionally by increasing circulation associated with increasing winds ( low confidence ). 
5.5.1.2 Assessment of Limits of the TCRE Concept
5.5.1.2.1 Sensitivity to amount of cumulative CO 2 emissions
The AR5 indicated that the concept of a constant ratio of cumulative 
emissions of CO 2 to temperature was applicable to scenarios with 
increasing cumulative CO 2 emissions up to 2000  PgC (M.  Collins 
et al., 2013). Recent analyses added confidence to this insight 
(Herrington and Zickfeld, 2014; Steinacher and Joos, 2016) and 
showed some evidence of a potentially larger window of constant 
TCRE (Leduc et al., 2015; Tokarska et al., 2016). Using an analytical 
approach, MacDougall and Friedlingstein (2015) quantified 
a  window of constant TCRE –  defined as the range in cumulative

emissions over which the TCRE remains within 95% of its maximum 
value – as between 360 to 1560 PgC. However, models with a more 
sophisticated ocean representation suggest that TCRE could also 
remain constant for considerably larger quantities of cumulative 
emissions, up to at least 3000 PgC (Leduc et al., 2015; Tokarska et al., 
2016). Beyond this upper limit, studies are inconclusive, with some 
suggesting that TCRE will decrease (Leduc et al., 2015) and others 
indicating that the linearity would hold up to as much as 5000 PgC 
(Tokarska et al., 2016).
As cumulative emissions increase, weakening land and ocean carbon 
sinks increase the airborne fraction of CO 2 emissions (see Figure 5.25), 
but each unit increase in atmospheric CO 2 has a smaller effect on 
global temperature owing to the logarithmic relationship between 
CO 2 and its radiative forcing (Matthews et al., 2009; Etminan et al., 
2016). At high values of cumulative emissions, some models simulate

less warming per unit CO 2 emitted, suggesting that the saturation 
of CO 2 radiative forcing becomes more important than the effect of 
weakened carbon sinks (Herrington and Zickfeld, 2014; Leduc et al., 
2015). The behaviour of carbon sinks at high emissions levels remains 
uncertain, as models used to assess the limits of the TCRE show 
a large spread in net land carbon balance (Section 5.4.5), and most 
estimates did not include the effect of permafrost carbon feedbacks 
(Sections 5.5.1.2.3 and 5.4). The latter would tend to further increase 
the airborne fraction at high cumulative emissions levels, and could 
therefore extend the window of linearity to higher total amounts of 
emissions (MacDougall et al., 2015). Leduc et al. (2016) suggested 
further that a declining strength of snow and sea ice feedbacks in 
a  warmer world would also contribute to a  smaller TCRE at high amounts of cumulative emissions. However, Tokarska et al. (2016)

suggested that a  large decrease in TCRE for high cumulative 
emissions is only associated with some EMICs; in the four ESMs 
analysed in their study, the TCRE remained approximately constant 
up to 5000 PgC, owing to stronger declines in the efficiency of ocean 
heat uptake in ESMs compared to EMICs.
Overall, there is high agreement  between multiple lines of evidence 
(robust evidence ) resulting in high confidence  that TCRE remains 
constant for the domain of increasing cumulative CO 2 emissions 
until at least 1500  PgC, with medium confidence of it remaining 
constant up to 3000 PgC because of less agreement across available 
lines of evidence.
5.5.1.2.2 Sensitivity to the rate of CO 2 emissions
Global average temperature increase responds over a time scale of 
about 10 years following the emission of a 100 PgC pulse of CO 2 
(Joos et al., 2013; Ricke and Caldeira, 2014), with larger emission 
pulses associated with longer time scales and smaller pulses with

shorter ones (Joos et al., 2013; Matthews and Solomon, 2013; 
Zickfeld and Herrington, 2015). This behaviour is confirmed in other 
studies, including those that calculate the temperature response 
to an instantaneous doubling or quadrupling of atmospheric CO 2 
(Matthews et al., 2009; Gillett et al., 2013; Herrington and Zickfeld, 
2014; Leduc et al., 2015; Hajima et al., 2020b). These findings 
suggest that the TCRE is sensitive to the rate of emissions, but 
studies assessing this sensitivity have found diverging results. For 
example, an increase in TCRE and its surrounding uncertainty was 
reported for experiments that imply a  gradual decline in annual 
CO 2 emissions (Tachiiri et al., 2019). These studies suggest that, in 
most cases, TCRE would be expected to increase in scenarios with 
decreasing annual emissions rates. This increase in TCRE for annual 
CO 2 emissions declining towards zero can be the result of the zero

emissions commitment (ZEC) which is the amount of warming 
projected to occur following a complete cessation of emissions (see 
Section 4.7.1.1 for its assessment), as well as Earth system processes 
that are unrepresented in current TCRE estimates (Section 5.5.2.2.4) 
and other factors. When using TCRE to estimate CO 2 emissions 
consistent with a  specific maximum warming level, these factors 
have to be taken into account (see Figure  5.31). Combined with 
recent literature on the ZEC (MacDougall et al., 2020) and emissions

747
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55pathways (Huppmann et al., 2018) and noting the lack of literature 
that disentangles these various contributions, there is medium 
evidence  and high agreement  resulting in medium confidence  that 
the TCRE remains a good predictor of CO 2-induced warming when 
applied in the context of emissions reduction pathways, provided that 
ZEC and long-term Earth system feedbacks are adequately accounted 
for when emissions decline towards zero (see also Section 5.5.1.2.3).
5.5.1.2.3 Reversibility and Earth system feedbacks
There are relatively few studies that have assessed how the TCRE 
is expected to change in scenarios of declining emissions followed 
by net negative annual CO 2 emissions. Conceptually, the literature 
suggests that the small lag of about a decade between CO 2 emissions 
and temperature change (Ricke and Caldeira, 2014; Zickfeld and 
Herrington, 2015) would result in more warming at a given amount

of cumulative emissions in a scenario where that emissions level is 
first exceeded and then returned to by deploying negative emissions 
(referred to as an ‘overshoot’, as is often the case in scenarios that 
aim to limit radiative forcing in 2100 to 2.6 or 1.9 W m–2 (Riahi et al., 
2017; Rogelj et al., 2018a). Zickfeld et al. (2016) showed this to 
hold across a range of scenarios, with positive emissions followed 
by negative emissions, whereby the TCRE increased by about 10% 
across the transition from positive to negative emissions as a result 
of the thermal and carbon inertia of the deep ocean. However, CMIP6 
results for the SSP5-3.4-overshoot scenario show diverging trends 
across various ESMs (Figure 5.30). In an idealized CO 2-concentration-
driven setting, Tachiiri et al. (2019) also reported an increase in 
TCRE. Exploring pathways with emissions rates and overshoots closer 
to mitigation pathways considered over the 21st century (in this case

up to about 300 PgC), a recent emissions-driven EMIC experiment 
showed pathway independence of TCRE (Tokarska et al., 2019a). 
Furthermore, also in absence of net negative emissions, warming 
would not necessarily remain perfectly constant on time scales of 
centuries and millennia, but could decrease or increase (Frölicher and 
Paynter, 2015; R.G. Williams et al., 2017a; Hajima et al., 2020b). These 
additional changes in global mean temperature increase at various 
time scales are known as the ZEC (C.D. Jones et al., 2019; MacDougall 
et al., 2020), assessed in Section 4.7.1.1, and have to be integrated 
when using TCRE to estimate warming or remaining carbon budgets 
in overshoot scenarios.
The AR5-assessed (W.J. Collins et al., 2013) TCRE range was based 
in part on the ESMs available at the time, which did not include 
some potentially important Earth system feedbacks. Since then, 
a  number of studies have assessed the importance of permafrost

carbon feedbacks, in particular on remaining carbon budgets 
(MacDougall and Friedlingstein, 2015; MacDougall et al., 2015; 
Burke et al., 2017b; Gasser et al., 2018; Lowe and Bernie, 2018), 
a development highlighted and assessed in the IPCC Special Report 
on Global Warming of 1.5°C (Rogelj et al., 2018b). MacDougall and 
Friedlingstein (2015) reported a TCRE increase of about 15% when 
including permafrost carbon feedbacks. The overall linearity of the 
TCRE during the 21st century was not affected, but they also found 
that permafrost carbon feedbacks caused an increase in TCRE on 
multi-century time scales under declining CO 2 emissions rates. In 
addition, other processes that are not regarded, or are only partially considered in individual or all ESMs, could cause a further increase 
or decrease of TCRE (Matthews et al., 2020). These are discussed in 
detail in Section 5.4, but their quantitative effects on TCRE have not 
yet been explored by the literature.

Whether TCRE remains an accurate predictor of CO 2-induced 
warming when annual CO 2 emissions reach zero and are followed 
by net carbon dioxide removal (also referred to as TCRE reversibility) 
therefore hinges on contributions of slow components of the climate 
system that cause unrealized warming from past CO 2 emissions. Such 
slow components can arise from either physical climate (i.e., ocean 
heat uptake) or carbon cycle (i.e.,  ocean carbon uptake and 
permafrost carbon release) processes. The combined effect of these 
processes determines the magnitude and sign of the ZEC (MacDougall 
et al., 2020), which in turn impacts TCRE reversibility. As discussed in 
Section 4.7.1.1, recent model estimates of the ZEC suggest a range 
of ±0.19°C centred on zero (MacDougall et al., 2020). This suggests 
low agreement among models as to the reversibility of the TCRE in 
response to net-negative CO 2 emissions. Furthermore, most models

used for ZEC assessments to date do not represent permafrost carbon 
processes, although understanding their contribution is essential to 
quantify the TCRE contribution. There is therefore limited evidence  
that quantifies the impact of permafrost carbon feedbacks on the 
reversibility of TCRE, leading to low confidence  that the TCRE remains 
an accurate predictor of temperature changes in scenarios of net-
negative CO 2 emissions on time scales of more than a half a century.
5.5.1.3 Estimates of TCRE
The AR5 (M. Collins et al., 2013) assessed that the TCRE is likely  to 
fall in the range of 0.8°C –2.5°C per 1000 PgC (or per exagrams of 
carbon, EgC–1) for cumulative emissions up to 2000 PgC, based on 
multiple lines of evidence. These include estimates based on ESMs 
of varying complexity (Matthews et al., 2009; Gillett et al., 2013; 
Zickfeld et al., 2013), simple climate modelling approaches (Allen 
et al., 2009; Rogelj et al., 2012) or observational constraints and

attributable warming (Gillett et al., 2013).
Since AR5, new studies have further expanded the evidence base for 
estimating the value of TCRE. These studies rely on ESMs or EMICs, 
observational constraints and concepts of attributable warming, 
or theoretically derived equations (see Table  5.7 for an overview). 
Several studies have endeavoured to partition the uncertainty in 
the value of TCRE into constituent sources. For example, TCRE can 
be decomposed into terms of TCR and the airborne fraction of 
anthropogenic CO 2 emissions over time (Allen et al., 2009; Matthews 
et al., 2009). These two terms are assessed individually (see Section 5.4 
and Chapter 7, respectively) and allow the integration of evidence 
assessed elsewhere in the report into the assessment of TCRE 
(Section 5.5.1.4). Further studies use a variety of methods, including 
analysing the outputs from CMIP5 (R.G. Williams et al., 2017b) or 
CMIP6 (Arora et al., 2020; Jones and Friedlingstein, 2020), conducting

perturbed parameter experiments with a single model (MacDougall 
et al., 2017), Monte-Carlo methods applied to a simple climate model 
(Spafford and Macdougall, 2020), or observations and estimates of 
the contribution of CO 2 and non-CO 2 forcers (Matthews et al., 2021). 
All of the studies agree that uncertainty in climate sensitivity – either

748
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Table 5.7 | Overview of results from studies estimating the transient climate response to cumulative CO 2 emissions (TCRE).  GSAT = Global mean surface air 
temperature increase, SAT = surface air temperature (e.g., over land only), SST = sea surface temperature, ECS = equilibrium climate sensitivity. Studies that do not isolate the 
CO 2-induced warming contribution in their TCRE estimates are not included.
StudyTCRE Range  
(°C per 1000 PgC)Notes
Studies available at the time of IPCC AR5
Matthews et al. (2009) 1–2.1 5–95% range; GSAT; C4MIP model range
Allen et al. (2009) 1.4–2.5 5–95% range; blended global mean SAT and SSTs (no infilling of coverage gaps); simple model
Zickfeld et al. (2009) 1.5 Best estimate; GSAT, EMIC
Williams et al. (2012) 0.8–1.9 Range consistent with 2°C to 4.5°C ECS; GSAT

Rogelj et al. (2012) About 1–25–95% range; historical constraint on GMST increase, but other constraints on GSAT increase  
MAGICC model calibrated to C4MIP model range and 2°C–4.5°C likely ECS
Zickfeld et al. (2013) 1.4–2.5; mean: 1.9 Model range; GSAT, EMICs
Eby et al. (2013) 1.1–2.1; mean: 1.6 Model range; GSAT, EMICs
Gillett et al. (2013) 0.8–2.4 Model range; GSAT, CMIP5 ESMs
Gillett et al. (2013) 0.7–2.05–95% range; blended global mean SAT and SSTs; observationally constrained estimates of historical 
warming and emissions
IPCC AR5  
M. Collins et al. (2013)0.8–2.5 Assessed likely range; multiple lines of evidence; mixed definition of global average temperature increase
Studies published since IPCC AR5
Tachiiri et al. (2015) 0.3–2.4 5–95% range; blended global mean SAT and SSTs; JUMP-LCM model perturbed physics ensemble (EMIC)
Tachiiri et al. (2015) 1.1–1.75–95% range; blended global mean SAT and SSTs; observationally constrained JUMP-LCM perturbed 
physics ensemble

Goodwin et al. (2015) 1.1 ± 0.55–95% range; theoretically derived TCRE equation constrained by surface warming, radiative forcing, 
and historic ocean and land carbon uptake from IPCC AR5
Millar et al. (2017a) 1.0–2.55 to 95% range; blended global mean SAT and SSTs (HadCRUT4); observationally constrained probabilistic 
setup of simple climate model
Steinacher and Joos (2016) 1.0–2.7; median: 1.7 5–95% range; GSAT, observationally constrained BERN3D-LPJ EMIC
MacDougall et al. (2017) 0.9–2.5; mean: 1.7 5–95% range; GSAT, emulation of 23 CMIP5 ESMs
Ehlert et al. (2017) 1.2–2.1 Model range; GSAT, UVIC EMIC with varying ocean mixing parameters
R.G. Williams et al. (2017b) 1.4–2.1; mean: 1.8 1-sigma range; GSAT, diagnosed from 10 CMIP5 ESMs
Millar and Friedlingstein (2018) 0.9–2.6; best estimate: 1.35–95% range; blended global mean SAT and SSTs (Cowtan and Way, 2014); detection attribution 
with observational constraints

Millar and Friedlingstein (2018) Best estimate: 1.5 Blended global mean SAT and SSTs (Berkeley Earth); detection attribution with observational constraints
Millar and Friedlingstein (2018) Best estimate: 1.2Blended global mean SAT and SSTs (Cowtan and Way, 2014); detection attribution with observational 
constraints, with updated historical CO 2 emissions (Le Quéré et al., 2018b)
C.J. Smith et al. (2018) 1.0–2.25–95% range; blended global mean SAT and SSTs (Cowtan and Way, 2014); observationally constrained 
probabilistic setup of simple climate model
Matthews et al. (2021) 1.0–2.2; median: 1.55–95% range; blended global mean SAT and SSTs; human-induced warming (Haustein et al., 2017) based 
on an average of three full coverage datasets; observationally constrained estimate using the current non-CO 2 
fraction of total anthropogenic forcing
Arora et al. (2020)1.3–2.4; mean: 
1.8; median: 1.65Model range; GSAT, diagnosed CO 2 emissions in CMIP6 ESMs

R.G. Williams et al. (2020) 1.2–2.1; mean: 1.6 1-sigma range; GSAT, diagnosed CO 2 emissions in 9 CMIP6 ESMs
Jones and Friedlingstein (2020) 1.2–2.7; median: 1.85–95% range; GSAT; estimate based on decomposition presented in (Jones and Friedlingstein, 2020) 
with ranges of carbon cycle feedback parameters from CMIP6 (Arora et al., 2020), see Section 5.4.
Spafford and Macdougall (2020)1.1–2.9; mean: 1.9;  
median: 1.85–95% range; ratio of land SAT and SST; probabilistic assessment of with a zero-dimensional ocean 
diffusive model
Cross-AR6 lines of evidence
Transient Climate Response (TCR) 
and Airborne Fraction (AF)1.0–2.3; median: 1.65–95% range; GSAT; TCR–AF decomposition-based estimate using the assessed range of TCR 
(Section 7.5, 1.8°C median with 0.4°C 1-sigma range) and an airborne fraction of 53 ± 6% (1-sigma range)
Overall assessment
IPCC AR61.0–2.3;

best estimate: 1.65Likely range; GSAT; based on combination of cross-AR6 lines of evidence (Section 5.5.1.4); normally distributed

749
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55equilibrium climate sensitivity (ECS) or transient climate response 
(TCR) – is among the most important contribution to uncertainty in 
TCRE, with uncertainty in the strength of the land carbon feedback 
and ocean heat uptake or ventilation having also been identified as 
crucial to uncertainty in TCRE (Matthews et al., 2009; Gillett et al., 
2013; Ehlert et al., 2017; MacDougall et al., 2017; R.G.  Williams 
et al., 2017a, 2020; Katavouta et al., 2019; Arora et al., 2020; Jones 
and Friedlingstein, 2020; Spafford and Macdougall, 2020). Finally, 
internal variability has been shown to affect the maximum accuracy 
of TCRE estimates by ±0.1°C per 1000 PgC (5–95% range; Tokarska 
5.5.1.4 Combined Assessment of TCRE
Studies differ in how they define TCRE, in the methods they use, and 
their assumptions, such as the assumed climate sensitivity distribution

or the choice of metrics of global temperature change (e.g., GMST 
or GSAT, see Table 5.7). This makes TCRE estimates from individual 
studies difficult to compare. The combined assessment of TCRE 
therefore takes advantage of the well-established decomposition 
of TCRE in two factors: the TCR and the AF (Section  5.5.1.3). This 
provides a  TCRE assessment range for CO 2-induced warming 
at the time of doubling CO 2 concentrations that builds on the 
broader Working Group 1 assessment. Expert judgement based on 
the airborne fraction range found in CMIP6 models (Arora et al., 
2020; Jones and Friedlingstein, 2020) suggest a value of 53% with 
a 1-sigma range of ±6%, which is double the sigma range based on 
the spread of CMIP6 models only. Combining this range with the 
AR6 TCR assessment (Section 7.5, best estimate 1.8°C, 1.4°C–2.2°C 
likely and 1.2°C–2.4°C very likely range) results in a 5–95% range of 
1.0–2.3°C per 1000 PgC (0.27°C–0.63°C per 1000 GtCO 2). Based on

expert judgement that accounts for the incomplete coverage of all 
Earth system components, this results in a consolidated assessment 
that TCRE would fall likely in the range of 1.0–2.3°C per 1000 PgC, 
with a best estimate of 1.65°C per 1000 PgC (0.45°C per 1000 GtCO 2). 
Warming here reflects the human-induced GSAT increase and assumes 
a normal distribution. Some studies using observational constraints 
support a lognormal shape for the TCRE distribution (Spafford and 
Macdougall, 2020), but such a distribution is currently not supported 
by the combined assessment of TCR and airborne fraction. Finally, 
this assessed TCRE range needs to be considered in combination with 
the ZEC (Section 4.7.1.1) when estimating the CO 2-induced warming 
of low-emissions scenarios.
5.5.2  Remaining Carbon Budget Assessment
Estimates of remaining carbon budgets consistent with holding global 
warming below a specific temperature threshold depend on a range

of factors which are increasingly being studied and quantified. These 
factors include: (i) well-understood methodological and definitional 
choices (Sections  5.5.2.1 and 5.5.2.2; Friedlingstein et al., 2014a; 
Rogelj et al., 2016, 2018b); and (ii) a set of contributing factors such 
as historical warming, the TCRE and its limitations, the ZEC (the 
amount of warming projected to occur following a complete cessation 
of emissions; see Section 4.7.1.1), as well as contributions of non-
CO 2 climate forcers (Section 5.5.2.2; MacDougall and Friedlingstein, 2015; Rogelj et al., 2015a, b; MacDougall, 2016; Simmons and 
Matthews, 2016; Ehlert et al., 2017; Matthews et al., 2017, 2021; 
Millar et al.,  2017b; Goodwin et al., 2018; Mengis et al., 2018; 
Pfleiderer et al., 2018; Tokarska et al., 2018; Cain et al., 2019). These 
contributing factors are integrated in an overarching assessment of 
remaining carbon budgets for limiting global average warming to

levels ranging from 1.5°C to 2.5°C relative to pre-industrial levels 
provided in Section  5.5.2.3. Box  5.2 provides an overview of the 
methodological advances since AR5 (W.J. Collins et al., 2013).
5.5.2.1 Framework and Earlier Approaches
The AR6 Glossary (Annex VII) defines remaining carbon budgets 
as the maximum amount of cumulative net global anthropogenic 
CO 2 emissions expressed from a  recent specified date that would 
result in limiting global warming to a  given level with a  given 
probability, taking into account the effect of other anthropogenic 
climate forcers, consistent with the definition used inSR1.5 (Rogelj 
et al., 2018b). Studies, however, apply a variety of definitions that 
result in published remaining carbon budget estimates informing to 
cumulative emissions at the time when global-mean temperature 
increase would reach, exceed, avoid, or peak at a  given warming 
level with a given probability (M. Collins et al., 2013; T.F . Stocker et al.,

2013; Clarke et al., 2014; Friedlingstein et al., 2014a; IPCC, 2014; 
Rogelj et al., 2016; Millar et al., 2017b).
This section provides an assessment of remaining carbon budgets 
consistent with the AR6 Glossary definition (Annex VII). Given that 
some feedbacks are time dependent, the values in this section 
apply to limiting warming over the 21st century, consistent with 
recent studies highlighting the usefulness of time-limited carbon 
budgets (Sanderson, 2020). Irrespective of the exact definition of 
the remaining carbon budget, the finding that higher cumulative 
CO 2 emissions lead to higher temperatures implies that annual net 
CO 2 emissions have to decline to close to zero in order to halt global 
warming, whether at 1.5°C, 2°C or another level (Allen et al., 2018).
Two approaches were used in AR5 to determine carbon budgets 
(M. Collins et al., 2013; T.F . Stocker et al., 2013; Clarke et al., 2014; 
IPCC, 2014; Rogelj et al., 2016). Working Group I (WGI) reported

threshold exceedance budgets (TEB) that correspond to the amount 
of cumulative CO 2 emissions at the time a  specific temperature 
threshold is exceeded, with a  given probability in a  particular 
greenhouse-gas and aerosol (pre-cursor) emissions scenario 
(M. Collins et al., 2013; IPCC, 2013b; T.F . Stocker et al., 2013). WGI 
also reported TEBs for the hypothetical case that only CO 2 would be 
emitted by human activities (M. Collins et al., 2013; IPCC, 2013b; 
T.F . Stocker et al., 2013). The AR5 Working Group III used threshold 
avoidance budgets (TAB) that correspond to the cumulative CO 2 
emissions over a  given time period of a  subset of greenhouse-
gas and aerosol (precursor) emissions scenarios in which global-
mean temperature increase stays below a  specific temperature 
threshold with at least a  given probability (Clarke et al., 2014). 
The AR5 synthesis report used TABs defined until the time of peak 
warming over the 21st century (IPCC, 2014). Drawbacks have been

identified for TEBs and TABs (Rogelj et al., 2016). TABs provide 
an estimate of the cumulative CO 2 emissions under pathways

750
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5that have as a  common characteristic the fact that they do not 
exceed a  specific global warming threshold. However, the actual 
level of maximum warming can vary between pathways, leading 
to an unnecessary and poorly constrained spread in TAB estimates 
(Rogelj et al., 2016). Therefore, the TAB approach typically does 
not result in accurate projections of the remaining carbon budget. 
One drawback of TEBs is that they provide an estimate of the 
cumulative CO 2 emissions at the time global warming crosses 
a given threshold of interest in a specific emissions scenario – for 
example, most of the standard scenarios used in climate change 
research, such as the Representative Concentration Pathways 
(RCPs) or Shared Socio-economic Pathways (SSPs), exceed global 
warming of 1.5°C or 2°C (see Cross-Chapter Box 1.5) (M. Collins 
et al., 2013; T.F .  Stocker et al., 2013; Friedlingstein et al., 2014a;

Millar et al., 2017b). Because of potential variations in non-CO 2 
warming at that point in time, or potential lags of about a decade 
in CO 2 warming (Joos et al., 2013; Ricke and Caldeira, 2014; Rogelj 
et al., 2015a, 2016, 2018b; Zickfeld and Herrington, 2015) TEBs also 
do not provide a precise estimate of the remaining carbon budget 
for limiting warming to a specific level.Since the publication of AR5 (M.  Collins et al., 2013), several 
new approaches have been proposed that provide a  solution 
to the identified limitations of TABs and TEBs. Most of these 
approaches indirectly rely on the concept of TCRE (Section 5.5.1), 
for example, because they estimate modelled cumulative CO 2 
emissions until a  temperature threshold is crossed and use this 
budget to infer insights for pathways that attempt to limit warming 
to below this threshold and thus need to follow a different path 
(Friedlingstein et al., 2014a; Matthews et al., 2017; Millar et al.,

2017b; Goodwin et al., 2018; Tokarska and Gillett, 2018). In this 
report, the assessment framework of SR1.5 for remaining carbon 
budgets is applied (Rogelj et al., 2018b, 2019). This framework allows 
integration of multiple lines of evidence to assess the contributions 
of five components that together result in a consolidated assessment 
of the remaining carbon budget (TCRE, historical human-induced 
warming, non-CO 2 warming, the ZEC, and adjustments due to 
additional Earth system feedbacks, see Section  5.5.2.2). It builds 
on the advances in estimating remaining carbon budgets or 
related quantities that have been published since AR5 (Rogelj 
et al., 2015a; Haustein et al., 2017; Matthews et al., 2017, 2021;  0 1000 2000 3000 4000 5000 6000 7000
Cumulative carbon dioxide emissions since 1850 (GtCO2)-101234567Global mean temperature increase since 1850–1900 (°C - GSAT)0 200 400 600 800 1000 1200 1400 1600 1800 2000Cumulative carbon emissions since 1850 (PgC)

Historical observations
Scenarios:
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5Human-induced warming assessmentHuman-induced warming estimate
Assessed TCRE range
historical
human-induced 
warming
(Section 5.5.2.2.2) 
unrepresented Earth system
feedbacks (Section 5.5.2.2.5)
Cumulative CO2 emissions from today (GtCO2)0TCRE
(Section 5.5.1.4)Temperature increase since preindustrial levels (°C)
remaining
allowable warmingzero emissions commitment (ZEC, Section 5.5.2.2.4)
non-CO2 contribution (Section 5.5.2.2.3) 
remaining
carbon budgetglobal warming limit of interest
0
Figure 5.31 | Illustration of relationship between cumulative emissions of carbon dioxide (CO 2) and global mean surface air temperature (GSAT) increase 
(left) and conceptual schematic of the assessment of the remaining carbon budget from its constituting components (right).  Carbon budgets consistent

with various levels of additional warming are provided in Table 5.8 and should not be read from the illustrations in either panel. Left-hand panel: Historical data (thin black line 
data) shows historical CO 2 emissions as reported in Friedlingstein et al. (2020) together with the assessed global mean surface air temperature increase from 1850–1900 as 
assessed in Chapter 2 (Box 2.3, GSAT). The orange-brown range with its central line shows the estimated human-induced share of historical warming (Haustein et al., 2017). 
The vertical orange-brown line shows the assessed range of historical human-induced warming for the 2010–2019 period relative to 1850–1900 (Chapter 3). The grey cone 
shows the assessed range for the transient climate response to cumulative CO 2 emissions (TCRE) assessed to fall likely in the 1.0°C–2.3°C per 1000 PgC range (Section 5.5.1.4),

starting from 2015. Thin coloured lines show CMIP6 simulations for the five scenarios of the AR6 core set (SSP1-1.9, sky blue; SSP1-2.6, dark blue; SSP2-4.5, yellow; SSP3-7.0, 
red; SSP5-8.5, maroon), starting from 2015. Diagnosed carbon emissions (Arora et al., 2020) are complemented with estimated land-use change emissions for each respective 
scenario (Gidden et al., 2019). Coloured areas show the Chapter 4 assessed very likely range of GSAT projections and thick coloured central lines the median estimate, 
for each respective scenario. These projections are expressed relative to the cumulative CO 2 emissions that are available for emissions-driven CMIP6 ScenarioMIP experiments for 
each respective scenario (Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019). Right-hand panel: schematic illustration of assessment of remaining carbon budget based

on multiple lines of evidence. The remaining allowable warming is estimated by combining the global warming limit of interest with the assessed historical human induced 
warming (Section 5.5.2.2.2), the assessed future potential non-CO 2 warming contribution (Section 5.5.2.2.3) and the zero emissions commitment (Section 5.5.2.2.4). Note that 
contributions in the right-hand panel are illustrative and contributions are not to scale. For example, the central ZEC estimate was assessed to be zero. The remaining allowable 
warming (vertical blue bar) is subsequently combined with the assessed TCRE (Sections 5.5.1.4 and 5.5.2.2.1) and contribution of unrepresented Earth system feedbacks in 
models used to estimate ZEC and TCRE (Section 5.5.2.2.5) to provide an assessed estimate of the remaining carbon budget (horizontal blue bar; see Table 5.8). Further details 

751
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Millar et al., 2017b; Gasser et al., 2018; Lowe and Bernie, 2018; 
Tokarska et al., 2018; Nicholls et al., 2020).
Recent studies suggest further changes to this framework by 
including non-linear adjustments to the TCRE contribution (Nicholls 
et al., 2020), or including non-CO 2 forcers in different ways by 
accounting for their different forcing effects (Matthews et al., 2021). 
Figure  5.31 provides a  conceptual schematic of how the various 
individually assessed contributions are combined into a consolidated 
assessment of the remaining carbon budget. Together with estimates 
of historical CO 2 emissions to date (Section 5.2.1), these remaining 
carbon budgets provide the overall amount of cumulative CO 2 
emissions consistent with limiting global warming to specific levels. 
A comparison with the approach applied in AR5 (M. Collins et al.,

2013; Clarke et al., 2014) is available in SR1.5 Section 2.2.2 (Rogelj 
et al., 2018b) as well as Box 5.2.
5.5.2.2 Assessment of Individual Components
Remaining carbon budgets are assessed through the combination of 
five separate components (Forster et al., 2018; Rogelj et al., 2018b). 
Each component is discussed and assessed separately in the sections 
below, based on all available lines of evidence. Box 5.1 details the 
differences compared to AR5 and SR1.5 estimates (W.J. Collins et al., 
2013; Rogelj et al., 2018b).
5.5.2.2.1 TCRE
The first and central component for estimating remaining carbon 
budgets is the TCRE. Based on the assessment in Section 5.5.1.4, an 
assessed likely range for TCRE of 1.0°C–2.3°C per 1000  PgC with 
a normal distribution is used.
5.5.2.2.2 Historical warming
Advances in methods to estimate remaining carbon budgets have 
shown the importance of applying an estimate of historical warming

to date that is as accurate as possible (Millar et al., 2017b; Tokarska 
and Gillett, 2018). This becomes particularly important when 
assessing remaining carbon budgets for global warming levels 
that are relatively close to present-day warming, such as a 1.5°C 
or 2°C levels (Rogelj et al., 2018b). Also shown to be important is 
the definition of global average temperature by which historical 
warming is estimated (Cross-Chapter Box  2.3; Cowtan and Way, 
2014; Allen et al., 2018; Pfleiderer et al., 2018; Richardson et al., 
2018; Tokarska et al., 2019b), as is the correct isolation of human-
induced global warming (Haustein et al., 2017; Allen et al., 2018) to 
remove the effect of internal variability. Based on the assessment 
in Section 3.3 (Table 3.1), here we apply an assessed best-estimate 
of historical warming expressed as an increase in GSAT of 1.07°C 
(0.8–1.3°C, likely range) between 1850–1900 and 2010–2019. 
This choice implies global coverage and is consistent with AR5

where carbon budgets were reported in GSAT (M.  Collins et al., 
2013; T.F . Stocker et al., 2013), SR1.5 where GSAT was the central 
metric for remaining carbon budgets (Rogelj et al., 2018b), and 
recent studies that highlight how GSAT enables an easy translation 
with AR5 (Tokarska et al., 2019b). The  use of other historical reference periods (Cross-Chapter Box 1.2) or temperature metrics 
and updated data products (Cross-Chapter Box  2.3) can result 
in a  different estimated historical warming and thus a  changed 
remaining carbon budget.
5.5.2.2.3 Non-CO 2 warming contribution
Non-CO 2 emissions contribute either cumulatively (N 2O, and other 
long-lived climate forcers) or in proportion to their annual emissions 
(CH 4 and other short-lived climate forcers) to global warming, and 
thus also affect estimates of remaining carbon budgets by reducing 
the amount of warming that could still result from CO 2 emissions

(Meinshausen et al., 2009; Friedlingstein et al., 2014a; Knutti and 
Rogelj, 2015; Rogelj et al., 2015a, 2016; R.G. Williams et al., 2016, 
2017b; Matthews et al., 2017; Collins et al., 2018; Mengis et al., 2018; 
Tokarska et al., 2018; Zickfeld et al., 2021). The size of this contribution 
has been estimated both implicitly (Meinshausen et al., 2009; 
Friedlingstein et al., 2014a; Rogelj et al., 2016; Matthews et al., 2017; 
Mengis et al., 2018; Tokarska et al., 2018) and explicitly (Rogelj et al., 
2015a, 2018b; Collins et al., 2018; Matthews et al., 2021) by varying 
the assumptions of non-CO 2 emissions and associated warming. 
Internally consistent evolutions of future CO 2 and non-CO 2 emissions 
allow for derivation of non-CO 2 warming contributions consistent 
with global CO 2 emissions reaching net zero levels, and therewith 
capping maximum future CO 2 emissions (Smith and Mizrahi, 2013; 
Clarke et al., 2014; Huppmann et al., 2018; Rogelj et al., 2018b;

Matthews et al., 2021). Pathways that reflect such development 
typically show a stabilization or decline in non-CO 2 radiative forcing 
and warming at, and after the time of, global CO 2 emissions reaching 
net zero levels, as illustrated in the scenario database underlying 
SR1.5 (Huppmann et al., 2018; Rogelj et al., 2018b).
The impact of non-CO 2 emissions on remaining carbon budgets 
is  assessed with emulators (Meinshausen et al., 2009; Millar 
et al., 2017b; Gasser et al., 2018; Goodwin et al., 2018; Rogelj et al., 
2018b; C.J. Smith et al., 2018; Matthews et al., 2021) that incorporate 
synthesized climate and carbon-cycle knowledge (Cross-Chapter 
Box 7.1). The estimated implied non-CO 2 warming can subsequently 
be applied to reduce the remaining allowable warming for estimating 
the remaining carbon budget (Figure  5.31; Rogelj et al., 2018b, 
2019). Alternative methods estimate the non-CO 2 fraction of total

anthropogenic forcing (Matthews et al., 2021), or do not correct for 
non-CO 2 warming directly. The latter methods instead consider CO 2 
and non-CO 2 warming together to define a CO 2-forcing equivalent 
carbon budget from which eventual non-CO 2 contributions expressed 
in CO 2-forcing-equivalent emissions have to be subtracted to obtain 
a  remaining carbon budget (Jenkins et al., 2018; Matthews et al., 
2020). These studies also use emulators to invert a specified evolution 
of non-CO 2 forcing to a  corresponding amount of equivalent CO 2 
emissions (Matthews et al., 2020), or alternatively use empirical 
relationships linking changes in non-CO 2 greenhouse gas emissions 
to warming (Cain et al., 2019). Methods to express non-CO 2 emissions 
in CO 2 equivalence are assessed in Section 7.6, yet their applicability 
and related uncertainties for remaining carbon budgets have not yet 
been covered in-depth in the literature.

752
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Application of the SR1.5 method (Forster et al., 2018; Rogelj et al., 
2018b) with AR6-calibrated emulators (Box 7.1) suggests a median 
additional non-CO 2 warming contribution at the time global CO 2 
emissions reach net zero levels of about 0.1°C–0.2°C relative to 
2010–2019. Uncertainty surrounding this range due to geophysical 
uncertainties such as non-CO 2-forcing uncertainties and TCR is of the 
order of ±0.1°C. Differences in the choices of mitigation strategies 
considered in low-emissions scenarios (Huppmann et al., 2018) result 
in a potential additional variation around the central range of at least 
±0.1°C (spread across scenarios, referred to as non-CO 2 scenario 
uncertainty in Table 5.8).
5.5.2.2.4 Adjustments due to the zero emissions commitment
Use of TCRE for estimating remaining carbon budgets needs to 
consider the zero emissions commitment (ZEC), the potential

additional warming after a complete cessation of net CO 2 emissions. 
Based on the ZEC assessment presented in Section 4.7.1.1, the ZEC’s 
central value is taken to be zero with a  likely  range of ±0.19°C, 
noting that it might either increase or decrease after half a century. 
ZEC uncertainty is assessed for a time frame of half a century, as this 
most appropriately reflects the time between stringent mitigation 
pathways reaching net zero CO 2 emissions and the end of the 
century. For shorter time horizons, a similar central zero value applies, 
but with a smaller range (MacDougall et al., 2020). Experiments that 
ramped up and down emissions following a bell-shaped trajectory 
(MacDougall and Knutti, 2016a) show that when annual CO 2 
emissions decline to zero at a pace consistent with those currently 
assumed in mitigation scenarios (Huppmann et al., 2018; Rogelj 
et al., 2018b), the ZEC will already be realized to a large degree at the

time of reaching net zero CO 2 emissions (MacDougall et al., 2020).
5.5.2.2.5 Adjustments for additional Earth system feedbacks
Section  5.5.1.2 highlighted recent literature describing potential 
impacts of Earth system feedbacks that have typically not been 
included in standard ESMs (MacDougall and Friedlingstein, 
2015; Schneider von Deimling et al., 2015; Schädel et al., 2016; Burke 
et al., 2017b; Mahowald et al., 2017; Comyn-Platt et al., 2018; Gasser 
et al., 2018; Lowe and Bernie, 2018), the most important of which 
is carbon release from thawing permafrost. The SR1.5 estimated 
unrepresented Earth system processes to result in a  reduction 
of remaining carbon budgets of up to 100  GtCO 2 over the course 
of this century, and more thereafter (Rogelj et al., 2018b). Here 
this assessment is updated based on the Earth system feedback 
assessment of Section  5.4.8 and synthesized in Figure  5.29 by 
applying the reverse method by Gregory et al. (2009).

The assessment in Section 5.4 and Box 5.1 highlights the different 
nature, magnitude and uncertainties surrounding additional 
Earth system feedback. The remaining carbon budgets reported in 
Table 5.8 account for these feedbacks, including corrections due to 
permafrost CO 2 and CH 4 feedbacks as well as those due to aerosol 
and atmospheric chemistry (Section 5.4.8). Two of these additional 
feedbacks (tropospheric ozone and methane lifetime feedbacks) 
are included in the projections of non-CO 2 warming carried out 
with AR6-calibrated emulators (Box  7.1). The remainder of these independent Earth system feedbacks combine to a  feedback of 
about 7  ± 27  PgC  K–1 (1-sigma range, or 26 ±  97  GtCO 2 °C–1). 
Overall, Section  5.4.8 assessed there to be low confidence in the 
exact magnitude of these feedbacks and they represent identified 
additional amplifying factors that scale with additional warming, and 
mostly increase the challenge of limiting global warming to or below

specific temperature levels.
5.5.2.3 Remaining Carbon Budget
The combination of the five components assessed in 
Sections  5.5.2.2.1–5.5.2.2.5 allows for an overall assessment of 
the remaining carbon budget in line with different levels of global 
average warming, as documented in SR1.5 (Rogelj et al., 2018b). The 
overall assessment of remaining carbon budgets (Table 5.8) reflects 
the uncertainty in TCRE quantification and provides estimates of the 
uncertainties surrounding the contributions of each of the respective 
further components. A  formal combination of all uncertainties is 
not possible because they are not all independent, or because they 
represent choices rather than probabilistic uncertainties (Matthews 
et al., 2021). In light of all uncertainties related to TCRE, non-CO 2 
forcing and response, the level of non-CO 2 mitigation, and historical 
warming, there is a  small probability that the remaining carbon

budget for limiting warming to 1.5°C since the 1850–1900 period 
is effectively zero. However, applying best estimate values for all but 
uncertainties in Earth system feedbacks and TCRE, the remaining 
carbon budgets in line with the Paris Agreement are generally small 
yet not zero (see Table 5.8).
There is robust evidence  supporting the concept of TCRE as well 
as high confidence  in the range of historical human-induced 
warming. Combined with the assessed uncertainties in the Earth 
system’s response to non-CO 2 emissions and less well-established 
quantification of some of the effect of non-linear Earth system 
feedbacks, this leads to medium confidence  being assigned 
to  the assessed remaining carbon budget estimates while noting 
the  identified and assessed uncertainties and potential variations. 
The reported values are applicable to warming and cumulative 
emissions over the 21st century. For climate stabilization beyond the

21st century, this confidence would decline to very low confidence  
due to uncertainties in Earth system feedbacks and the ZEC.  For 
estimates of total carbon budgets in line with limiting global 
warming to a specific level, an estimate of historical CO 2 emissions 
should be added to the remaining carbon budget values reported 
in Table  5.8. Historical CO 2 emissions between 1850 and 2019 
have been estimated at about 655 ±  65  PgC (1-sigma range, or 
2390 ± 240 GtCO 2, see Table 5.1), while since 1 January 2015, an 
additional 57  PgC (210  GtCO 2) has been emitted until the end of 
2019 (Friedlingstein et al., 2020).

753
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Table 5.8 | The assessed remaining carbon budget and corresponding uncertainties.  Assessed estimates are provided for additional human-induced warming 
expressed as global average surface air temperature since the recent past (2010–2019), which likely amounted to 0.8 to 1.3 with a best estimate of 1.07°C relative to 
1850–1900 (Table 3.1 in Chapter 3).
Additional 
Warming 
Since 
2010–
2019aWarming 
Since  
1850–
1900aRemaining Carbon Budgetb 
starting from 1 January 2020 and subject to 
variations and uncertainties quantified in the columns 
on the rightScenario 
VariationGeophysical Uncertainties
°C °CPercentiles of TCREc,d 
PgC (GtCO 2)Non-CO 2 
scenario 
variationeNon-CO 2 
forcing and 
response 
uncertaintyfHistorical 
uncertaintyaZero 
emissions 
commitment 
(ZEC)
uncertaintygRecent 
emissions 
uncertaintyh
17th 33rd 50th 67th 83rdPgC 
(GtCO 2)PgC 
(GtCO 2)PgC

(GtCO 2)
0.23 1.3 100 (400) 60 (250) 40 (150) 30 (100) 10 (50)
Values can vary by at least ±60 PgC (±220 GtCO 2) 
due to choices related to non-CO 2 emissions mitigation
due to uncertainty in the warming reponse to future  
non-CO 2 emissions
±150 PgC (±550 GtCO 2)
±115 PgC (±420 GtCO 2)
±6 PgC (±20 GtCO 2)
0.33 1.4 180 (650) 120 (450) 90 (350) 70 (250) 50 (200)
0.43 1.5 250 (900) 180 (650) 140 (500) 110 (400) 80 (300)
0.53 1.6330 
(1200)230 (850) 180 (650) 150 (550) 110 (400)
0.63 1.7400 
(1450)290 
(1050)230 (850) 190 (700) 150 (550)
0.73 1.8470 
(1750)350 
(1250)280 
(1000)230 (850) 180 (650)
0.83 1.9550 
(2000)400 
(1450)320 
(1200)270 
(1000)210 (800)
0.93 2620 
(2300)460 
(1700)370 
(1350)310 
(1150)250 (900)
1.03 2.1700 
(2550)510 
(1900)420 
(1500)350 
(1250)280 
(1050)
1.13 2.2770 
(2850)570 
(2100)460 
(1700)390 
(1400)310 
(1150)
1.23 2.3850 
(3100)630 
(2300)510 
(1850)430 
(1550)350 
(1250)
1.33 2.4920 
(3350)680

(2500)550 
(2050)470 
(1700)380 
(1400)
a Human-induced global surface air temperature increase between 1850–1900 and 2010–2019 is assessed at 0.8–1.3°C ( likely range; Chapter 3) with a best estimate of 
1.07°C. Warming reflects changes in GSAT, as TCRE and other estimates are GSAT-based. Combined with a central estimate of TCRE (1.65°C EgC–1) the uncertainty in historical 
human-induced GSAT warming results in a potential variation of remaining carbon budgets of ±150 PgC or ±550 GtCO 2.
b Historical CO 2 emissions between 1850 and 2019 have been estimated at about 655 ± 65 PgC ( likely range, or 2390 ± 240 GtCO 2, see Table 5.1). Note that 57 PgC 
(210 GtCO 2) have been emitted from the middle of the 2010–2019 reference period (2015) until the end of 2019 (Friedlingstein et al., 2020).
c TCRE: transient climate response to cumulative CO 2 emissions, assessed to fall likely between 1.0–2.3°C EgC–1 with a normal distribution. PgC values are rounded to the

nearest 10; GtCO 2 values to the nearest 50. For comparison, assuming a lognormal distribution with a 1.0–2.3°C EgC–1 central 66% range instead of a normal distribution 
would increase remaining carbon budgets at the 17th, 33rd, 50th, 67th, and 83rd percentile with 3%, 10%, 12%, 9%, 2%, respectively. Future non-CO 2 contributions in 
these remaining carbon budget estimates are based on the scenarios assessed in the SR1.5 report and estimated as the median quantile regression of non-CO 2 warming since 
2010–2019 relative to total additional warming since 2010–2019 at the time scenarios reach net-zero CO 2 emissions (Forster et al., 2018; Huppmann et al., 2018; Rogelj et al., 
2018b).
d Additional Earth system feedbacks are included in the remaining carbon budget estimates as discussed in Section 5.5.2.2.5. The tropospheric ozone and methane lifetime

contributions are included through the non-CO 2 warming projections by the AR6-calibrated Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) 
emulator, while the remaining feedbacks are assessed totalling a combined feedback of magnitude 7 ± 27 PgC K–1 (1-sigma range, or 26 ± 97 GtCO 2 °C–1).
e Variations due to different scenario assumptions related to the future evolution of non-CO 2 emissions in mitigation scenarios reaching net zero CO 2 emissions (Huppmann 
et al., 2018; Rogelj et al., 2018b) of at least ±0.1°C (spread across scenarios). Combined with a central estimate of TCRE (1.65°C EgC–1) this results in at least ±60 PgC or 
±220 GtCO 2. This spread reflects the variation in the underlying scenario ensemble but is not a formal likelihood. WGIII will re-assess the potential for non-CO 2 mitigation based 
on literature since SR1.5.

f Remaining carbon budget variation due to geophysical uncertainty in forcing and temperature response of non-CO 2 emissions of the order of ±0.1°C, very likely range (5–95%) 
of non-CO 2 response (Section 5.5.2.2.3). Combined with a central estimate of TCRE (1.65°C EgC–1) this results in at least ±60 PgC or ±220 GtCO 2.
g The variation due to the ZEC is estimated for a central TCRE value of 1.65°C EgC–1 and a 1-sigma ZEC range of 0.19°C. In real-world pathways, the magnitude of this effect 
will depend on the pace of CO 2 emissions reductions to net zero.
h Historical emissions uncertainty reflects the ±10% uncertainty in the historical emissions estimate since 1 January 2015.

754
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5Box 5.2 | Implications of Methodological Advancements in Estimating the Remaining Carbon 
Budget since the IPCC’s Fifth Assessment Report (AR5)
Methodological advancements since AR5 (M. Collins et al., 2013; IPCC, 2013b, 2014; T.F . Stocker et al., 2013; Clarke et al., 2014) 
result in an updated and strengthened assessment of remaining carbon budgets. Methods and approaches at the time of AR5 are 
described in Section  5.5.2.1. Since AR5, strengths and weaknesses of various approaches have been more clearly articulated in 
the literature (e.g., in Rogelj et al., 2016; Millar et al., 2017b; Tokarska and Gillett, 2018; Matthews et al., 2020), resulting in a new 
consolidated framework applied in SR1.5 (Rogelj et al., 2018b, 2019) that is also used in AR6. This framework incorporates five 
methodological advancements compared to AR5, the implications of which are discussed in this box.

First, publications since AR5 applied methods that limit the effect of uncertainties in historical, diagnosed emissions in coupled Earth 
system models (ESMs) on estimates of the remaining carbon budget (Millar et al., 2017b; Tokarska and Gillett, 2018). These new 
methods express remaining carbon budget estimates relative to a recent reference period instead of relative to the pre-industrial 
period (Millar et al., 2017b; Tokarska et al., 2019b). Estimates of the full carbon budget since the pre-industrial period can still be 
obtained by adding estimates of historical CO 2 emissions (Table 5.1) to the estimates in Table 5.8. This methodological update resulted, 
all other aspects being equal, in median estimates of remaining carbon budgets being about 350–450 GtCO 2 larger compared to AR5 
(IPCC, 2014; Millar et al., 2017b).
At the time of AR5, Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) provided global surface air temperature

(GSAT) projections for the representative concentration pathways (Meinshausen et al., 2011c), which were used to determine carbon 
budgets while taking into account the effects of non-CO 2 forcers (T.F .  Stocker et al., 2013). Their use came with two recognized 
limitations: first, the model spread of the CMIP5 represents an ensemble of opportunity with limited statistical value (Tebaldi and 
Knutti, 2007); and second, the evolution of non-CO 2 emissions as a function of cumulative CO 2 emissions can differ markedly between 
high and low emissions pathways (Meinshausen et al., 2011c; Friedlingstein et al., 2014a; Rogelj et al., 2016; Matthews et al., 2017). 
Solutions to these two limitations have been published since AR5 and represent the second and third methodological improvement 
compared to AR5.
The reliance on an ensemble of opportunity (i.e., a serendipitous collection of scenario data from a variety of sources and studies) is

avoided by methodologically separating the assessment of future warming contributions of non-CO 2 emissions from the spread in 
transient climate response to cumulative CO 2 emissions (TCRE; Section 5.5.2; Rogelj et al., 2018b, 2019). This facilitates the explicit 
representation of TCRE uncertainty by a formal distribution, in this case a normal distribution with a 1.0–2.3°C 1000 PgC–1 1–sigma 
range (Section  5.5.1.4). The effect of this methodological advance can be estimated from a  direct comparison of the frequency 
distribution of TCRE in CMIP5 models that were used in AR5, and the formal TCRE distribution used in AR6, but is limited in precision. 
For estimates of the remaining carbon budget in line with limiting warming to 1.5°C or 2°C relative to pre-industrial levels, this 
improvement is estimated to lead to a reduction of budgets of the order of about 100 GtCO 2 between AR5 and AR6.

The third methodological improvement is a more direct estimation of the warming contribution of non-CO 2 emissions, consistent with 
pathways that bring global CO 2 emissions down to net zero. Instead of deriving this contribution implicitly from the CMIP5 ensemble, 
climate emulators (Meinshausen et al., 2011b; C.J. Smith et al., 2018; Schwarber et al., 2019) that are calibrated to the combined 
AR6 assessment (Cross-Chapter Box 7.1) are used to estimate the non-CO 2 contribution across a wide variety of stringent mitigation 
scenarios (Huppmann et al., 2018). The specific relative effect of this advance compared to AR5 is not calculable because CMIP5 data 
does not isolate non-CO 2 from CO 2-induced warming.
The fourth and fifth methodological advancements are to explicitly account for the zero emissions commitment (ZEC; Section 5.5.2.2.4) 
and adjust estimates for Earth system feedbacks that are typically not represented in Earth system models (ESMs; Section 5.5.2.2.5).

The central estimate of the assessed ZEC used in SR1.5 and AR6 is zero (Section 4.7.1.1). ZEC uncertainties are reported separately 
(Table 5.8), and the additional consideration of ZEC therefore does result in a better understanding but not in a net shift of central 
estimates of the remaining carbon budget compared to AR5. Furthermore, AR5 did not explicitly account for Earth system feedbacks 
not represented in ESMs. The SR1.5 assessed that they could reduce the remaining carbon budgets by about 100 GtCO 2 over centennial 
time scales. This assessment has been updated in AR6, including a wider range of biogeochemical feedbacks and new evidence 
(Section 5.5.2.2.5). Some of these feedbacks are captured in the estimation of non-CO 2 warming (see below), while the combined 
effect of remaining positive and negative feedbacks is assessed to reduce the remaining carbon budget estimates by 7 ± 27 PgC K–1 
(1-sigma range, or 26 ± 97 GtCO 2 °C–1) compared to AR5.

Between SR1.5 and AR6, each of the five components described in Section  5.5.2.1 and Figure  5.31 have been re-assessed (see 
Sections 5.5.2.2.1–5.5.2.2.5). Their updated assessments in turn affect the assessment of the remaining carbon budget. The new

755
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55and narrower assessment of TCRE in AR6 compared to SR1.5 ( likely range of 1.0°C–2.3°C EgC–1 compared to 0.8°C–2.5°C EgC–1, 
respectively, with the same central estimate) leads to no change in median estimates and about a 50 and 100 GtCO 2 increase in 
remaining carbon budgets estimates at the 67th percentile in AR6 compared to SR1.5 for 1.5°C and 2°C of global warming, respectively.
For historical warming, SR1.5 used GSAT increase between 1850–1900 and 2006–2015 of 0.97°C as its main starting point, while 
also providing values for other temperature metrics. Remaining carbon budgets were expressed starting from 1 January 2018 by 
accounting for historical emissions emitted from 1 January 2011 until the end of 2017. AR6 uses anthropogenic (human-induced) 
warming until the 2010–2019 period, which is assessed at the 0.8-1.3°C range, with a best estimate of 1.07°C (Table 3.1), and

subsequently accounts for historical emissions from 1  January 2015 until the end of 2019 to express remaining carbon budget 
estimates from 1 January 2020 onwards. The human-induced warming between the 1850–1900 and 2006–2015 periods used in SR1.5 
was assessed by AR6 at 0.97°C (Table 3.1). In a like-with-like comparison, the combined effect of data and methodological updates 
in historical warming estimates thus results in no shift in estimated remaining carbon budgets between SR1.5 and AR6. However, the 
emissions of the years passed since SR1.5 reduce the remaining carbon budget by about 85 GtCO 2. Note that AR6 also updated its 
GSAT assessment for total warming between the 1850–1900 and 2006–2015 periods, reporting 0.94°C of warming. On a like-with-
like basis, this would have resulted in slightly larger remaining carbon budgets compared to SR1.5 (Cross-Chapter Box 2.3).

The non-CO 2 contribution to future warming in emissions scenarios (Huppmann et al., 2018) is re-assessed with AR6-calibrated 
emulators, in this case MAGICC7 (Cross-Chapter Box 7.1; Meinshausen et al., 2009, 2011a, 2020). The re-assessment of non-CO 2 
warming with MAGICC7 results in a relationship that closely matches the average relationship applied in SR1.5 (shown in Section 2.
SM.1.1.2 in Forster et al., 2018), and therefore does not change estimates of the remaining carbon budget relative to SR1.5. The 
median ZEC assessment remained the same between SR1.5 and AR6, and therefore does not change the median remaining carbon 
budget estimates. Finally, as indicated above, AR6 expanded the assessment of Earth system feedbacks compared to SR1.5 and 
included it in its central remaining carbon budget estimates. Some feedbacks are accounted for through the non-CO 2 warming

estimate (Section 5.5.2.2.5), while the remainder combines to reduce the median remaining carbon budget estimates for 1.5°C and 
2°C of warming by about 10 to 20 GtCO 2, respectively, compared to SR1.5.
All methodological improvements and new evidence combined result in median and 67th percentile remaining carbon budget 
estimates for limiting warming to 1.5°C being about 300–350 GtCO 2 larger compared to an assessment that would use the evidence 
and methods available at the time of the AR5. For limiting warming to 2°C, the difference is about 400–500 GtCO 2. Since SR1.5, fewer 
key advancements had to be integrated. In a like-with-like comparison, the combined effects of all AR6 updates result in median 
remaining carbon budget estimates for limiting warming to 1.5°C and 2°C being the same and about 60 GtCO 2 smaller, respectively, 
in AR6 compared to SR1.5. At the 67th percentile, remaining carbon budget estimates for limiting warming to 1.5°C and 2°C are about

40 to 60 GtCO 2 larger, respectively, mainly as a result of a narrower assessed TCRE range. 
Box 5.2 (continued)
5.6 Biogeochemical Implications of 
Carbon Dioxide Removal and Solar 
Radiation Modification
5.6.1 Introduction
Carbon dioxide removal (CDR) refers to anthropogenic activities that 
seek to remove CO 2 from the atmosphere and durably store it in 
geological, terrestrial or ocean reservoirs, or in products (Glossary). 
CO 2 is removed from the atmosphere by enhancing biological or 
geochemical carbon sinks or by direct capture of CO 2 from air and 
storage. Solar radiation modification (SRM) refers to the intentional, 
planetary-scale modification of the Earth’s radiative budget with 
the aim of limiting global warming. Most proposed SRM methods 
involve reducing the amount of incoming solar radiation reaching 
the surface, but others also act on the longwave radiation budget by 
reducing optical thickness and cloud lifetime (Glossary). SRM does

not fall within the IPCC definitions of mitigation and adaptation (Glossary). CDR and SRM are referred to as ‘geoengineering’ in some 
of the literature, and are considered separately in this report.
This section assesses the implications of CDR and SRM for 
biogeochemical cycles. CDR has received growing interest as an 
important mitigation option in emissions scenarios consistent with 
meeting the Paris Agreement climate goals (SR1.5, SRCCL). The 
climate effects of CDR and SRM are assessed in Chapter  4, and 
a  detailed assessment of the socio-economic dimensions of these 
options is presented in AR6 WGIII, Chapters 7 and 12.
5.6.2 Biogeochemical Responses to Carbon 
Dioxide Removal (CDR)
The scope of this section is to assess the general and methods-specific 
effects of CDR on the global carbon cycle and other biogeochemical 
cycles. The focus is on Earth system feedbacks that either amplify

756
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5or reduce carbon sequestration potentials of specific CDR methods, 
and determine their effectiveness in reducing atmospheric CO 2 and 
mitigating climate change. Technical carbon sequestration potentials 
of CDR methods are assessed on a qualitative scale; a comprehensive 
quantitative assessment is left to the AR6 Working Group III Report 
(Chapters 7 and 12). Biogeochemical and biophysical side effects of 
CDR methods are assessed here, while the co-benefits and trade-
offs for biodiversity, water and food production are briefly discussed 
for completeness, but a  comprehensive assessment is left to WGII 
(Chapters 2 and 5) and WGIII (Chapters 7 and 12). The assessment 
in this chapter emphasizes literature published since the AR5 WGI 
report (Chapter  6) for the assessment of the global carbon cycle

response to CDR, and literature published since SR1.5 (Chapter  4; IPCC, 2018), SRCCL (Chapter  6, IPCC, 2019a) and SROCC (Bindoff 
et al., 2019) for the assessment of potentials and side effects of 
specific CDR methods. Emerging literature on deliberate methane 
removal is also briefly discussed.
In this chapter, CDR methods are categorised by the carbon 
cycle processes that result in CO 2 removal: (i) enhanced net 
biological production and storage by land ecosystems; (ii) enhanced 
net biological production and storage in the open and coastal 
ocean; (iii) enhanced geochemical processes on land and in the 
ocean; and (iv) direct air capture and storage by chemical processes. 
A  subset of CDR methods that restore or sustainably manage 
natural or modified ecosystems while providing human well-being 
Table 5.9 | Characteristics of carbon dioxide removal (CDR) methods.  Termination effects refer to the possible effects of a hypothetical, sudden and sustained

termination of the CDR method.
CategoryMethods
(Section Where 
the Method is 
Assessed)Nature of 
CO 2 Removal 
Process/
Storage 
FormDescriptionTime Scale of 
Carbon StorageFactors that Affect 
Carbon Storage 
Time ScaleTermination Effects
Enhanced 
biological 
production and 
storage on land 
(in vegetation, 
soils or geologic 
formations)Afforestation, 
reforestation and 
forest management
(5.6.2.2.1)Biological/
organicStore carbon in trees and 
soils by planting, restoring 
or managing forestsDecades to centuries 
(Cooper, 1983)Disturbances 
(e.g., fires, pests), 
extreme weatherNone
Soil carbon 
sequestration
organicUse agricultural management 
practices to improve 
soil carbon storageDecades to centuries
(Dignac et al., 2017)Soil and crop 
managementNone
Biochar
organicBurn biomass at high 
temperature under anoxic 
conditions to form biochar 
and add to soilsDecades to centuries 
(Campbell et al., 2018)Fire None
Peatland restoration

organicStore carbon in soil by creating 
or restoring peatlandsDecades to centuries
(Harenda et al., 2018)Peatland drainage, 
fire, drought, land-
use changeNone
Bioenergy with carbon 
capture and storage 
(BECCS)
inorganicProduction of energy from plant 
biomass combined with carbon 
capture and storagePotentially permanent –  
analogous to direct 
air carbon capture 
with carbon storage 
(DACCS) (Szulczewski et 
al., 2012)Leakage None
Enhanced 
storage in coastal 
and open oceanOcean fertilization
(5.6.2.2.2)Biological/
organicFertilize upper ocean with micro 
(Fe) and macronutrients (N, P) 
to increase phytoplankton 
photosynthesis and biomass 
and deep ocean carbon storage 
through the biological pumpDecades to millennia
(Oschlies et al., 2010a; 
Robinson et al., 2014)Ocean stratification 
and circulation 
(Robinson et al., 
2014); efficiency of 
carbon sequestration 
in deep ocean (Yoon

et al., 2018)Uncertain (Keller et al., 
2014)
Artificial ocean 
upwelling
organicPump nutrient-rich deep ocean 
water to the surface to increase 
carbon uptake and storage 
through the biological pump.Centuries to millennia 
(Oschlies et al., 2010b)Ocean circulation; 
dissolved inorganic 
carbon content 
of upwelled 
waters (Oschlies et al., 
2010b)Warming beyond 
temperatures 
experienced if artificial 
ocean upwelling had 
not been deployed 
(Keller et al., 2014)
Restoration of 
vegetated coastal 
ecosystems 
(“blue carbon”)
organicManage coastal ecosystems to 
increase net primary production 
and store carbon in sedimentsDecades to centuries if 
functional integrity of 
ecosystem maintained 
(Mcleod et al., 2011)Land-use change of 
coastal ecosystems; 
extreme weather 
(e.g., heatwaves); 
sea level change 
(NASEM, 2019)None

757
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55and biodiversity benefits are also referred to as natural or nature-
based solutions (Glossary; Griscom et al., 2017, 2020; Fargione 
et al., 2018). CDR methods commonly discussed in the literature are 
summarized in Table 5.9. Other CDR options have been suggested, 
but there is insufficient literature for an assessment. These include 
ocean biomass burial, ocean downwelling, removal of CO 2 from 
seawater with storage, and cloud alkalinization (Keller et al., 
2018a; GESAMP , 2019).
5.6.2.1 Global Carbon Cycle Responses to CDR
This subsection assesses evidence about the response of the global 
carbon cycle to CDR from idealized model simulations which 
assume that CO 2 is removed from the atmosphere directly and 
stored permanently in the geologic reservoir, which is analogous to 
direct air carbon capture with carbon storage (DACCS) (Table 5.9). 
The carbon cycle response to specific land and ocean-based CDR

methods is assessed in Section  5.6.2.2.2. At the time of AR5 
there were very few studies about the global carbon cycle response 
to CDR. Based on these studies and general understanding of the 
carbon cycle, AR5 WGI Chapter 6 assessed that it is virtually certain  
that deliberate removal of CO 2 from the atmosphere will be partially 
offset by outgassing of CO 2 from the ocean and land carbon sinks. 
Low confidence  was placed on any quantification of effects. Since 
AR5 WGI Chapter 6, several studies have investigated the carbon 
cycle response to CDR in idealized ‘pulse’ removal simulations, 
whereby a specified amount of CO 2 is removed instantly from the 
atmosphere, and scenario simulations with CO 2 emissions and 
removals following a plausible trajectory. In addition, a dedicated 
carbon dioxide removal model intercomparison project was 
initiated (CDRMIP; Keller et al., 2018b) which includes a  range

of CDR experiments from idealized simulations to simulations of  deployment of specific CDR methods (afforestation and 
ocean alkalinization).
This subsection assesses three aspects of the climate–carbon cycle 
response to CDR: the time-dependent behaviour of CO 2 fluxes 
in scenarios with CDR, the effectiveness of CDR in drawing down 
atmospheric CO 2 and cooling global mean temperature, and the 
symmetry of the climate–carbon cycle response to positive and 
negative CO 2 emissions.CategoryMethods
Process/
Storage 
Enhanced 
geochemical 
processes on land 
and in oceanEnhanced weathering
(5.6.2.2.3)Geochemical/
inorganicSpread alkaline minerals 
on land to chemically remove 
atmospheric CO 2 in reactions 
that form solid minerals 
(carbonates and silicates) 
that are stored in soils

or in the ocean10,000 to 106 years 
(Fuss et al., 2018)Storage in soils or 
ocean (Fuss et al., 
2018)None
Ocean alkalinization
inorganicIncreased CO 2 uptake via 
increased alkalinity by 
deposition of alkaline minerals 
(e.g., olivine).10,000 to 100,000 years 
(Keller, 2019)Carbonate chemistry; 
ocean stratification 
(Keller, 2019)Higher rates of 
warming and 
acidification than if 
alkalinization had not 
begun (under a high 
emissions scenario) 
(González et al., 2018)
ChemicalDirect air carbon 
capture with 
storage (DACCS)
(5.6.2.2.4)Chemical/
inorganicDirect removal of CO 2 from air 
through chemical adsorption, 
absorption or mineralization, 
and storage underground, in 
deep ocean or in long-lasting 
usable materialsPotentially permanent Leakage None

758
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5(c) Net positive CO2 emissions with CDR
Geological? ?(d) Net negative CO2 emissions 
GeologicalLand Ocean(b) Industrial era CO2 emissions
GeologicalOcean Land(a) Unperturbed carbon cycle fluxes
GeologicalMajor natural carbon exchanges
Natural carbon exchanges with the 
geological  reservoir
Carbon flux change due to 
anthropogenic activities
Carbon dioxide removal pathway
Carbon reservoir size increase
Carbon reservoir size decrease 
due to natural carbon redistribution
Carbon reservoir size increase due 
to storage
due to deliberate carbon removal
Land OceanLand Ocean
Box 5.3, Figure 1| Schematic representation of carbon fluxes between atmosphere, land, ocean and geological reservoirs.  Different system conditions

are shown: (a) an unperturbed Earth system; and changes in carbon fluxes for (b) an Earth system perturbed by fossil fuel carbon dioxide (CO 2) emissions; (c) an Earth 
system in which fossil fuel CO 2 emissions are partially offset by carbon dioxide removal (CDR); (d) an Earth system in which CDR exceeds CO 2 emissions from fossil 
fuels (‘net negative’ CO 2 emissions). Carbon fluxes depicted in (a) (solid and dashed black lines) also occur in (b–d). The question mark in the land-to-ocean carbon 
flux perturbation in (c) and (d) indicates that the effect of CDR on this flux is unknown. Note that box sizes do not scale with the size of carbon reservoirs. Adapted 
from Keller et al. (2018a). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).Box 5.3 | Carbon Cycle Response to CO 2 Removal from the Atmosphere
During the industrial era, CO 2 emitted by the combustion of fossil fuels and land-use change has been redistributed between

atmosphere, land, and ocean carbon reservoirs due to carbon cycle processes (Box 5.3, Figure 1b and Figure 5.13). Over the past 
decade (2010–2019), 46% of the emitted CO 2 remained in the atmosphere, 23% was taken up by the ocean, and 31% by the terrestrial 
biosphere (Section 5.2.1.5). When carbon dioxide removal (CDR) is applied during periods in which human activities are net CO 2 
sources to the atmosphere, and the amount of emissions removed by CDR is smaller than the net source (net positive CO 2 emissions), 
CDR acts to reduce the net emissions (Box 5.3 Figure 1c). In this scenario, part of the CO 2 emissions in the atmosphere are removed 
by the land and ocean sinks, as has been the case historically.
When CDR removes more CO 2 emissions than human activities emit (net negative CO 2 emissions), and atmospheric CO 2 declines, the 
land and ocean sinks initially continue to take up CO 2 from the atmosphere. This is because carbon sinks, particularly the ocean, exhibit

inertia and continue to respond to the prior trajectory of rising atmospheric CO 2 concentration. After some time, which is determined 
by the magnitude of the removal and the rate and amount of CO 2 emissions prior to the CDR application, land and ocean carbon 
reservoirs begin to release CO 2 into the atmosphere, making CDR less effective (Box 5.3, Figure 1d).

759
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 555.6.2.1.1 Carbon cycle response to instantaneous CDR
Idealized ‘pulse’ removal Earth system model simulations are useful 
for understanding the carbon cycle response to CDR.  Figure  5.32 
illustrates the response of atmospheric CO 2, land and ocean carbon 
sinks to an instantaneous CO 2 removal applied from a pre-industrial 
equilibrium state. Following CO 2 removal from the atmosphere, 
the atmospheric CO 2 concentration declines rapidly at first and then rebounds (Figure  5.32a). This rebound is due to CO 2 release 
by the terrestrial biosphere and the ocean in response to declining 
atmospheric CO 2 levels (Figure 5.32b,c; M. Collins et al., 2013). For 
the model simulations shown in Figure 5.32, 23 ± 6% (mean ± 1 
standard deviation) of the 100  PgC removed remains out of the 
atmosphere 80–100  years after the instantaneous removal. The 
remainder is offset by CO 2 outgassing from the land (49 ±  12%)

and ocean (29 ± 7%). While the direction of the CO 2 flux is robust 
across models, the relative contribution of the outgassing from land 
and ocean reservoirs to the atmospheric CO 2 rebound after removal 
varies. These results corroborate the high confidence  placed by AR5 
WGI (Chapter 6) on the partial compensation of CO 2 removal from 
the atmosphere by CO 2 outgassing from the land and ocean. Due 
to disagreement between models, the magnitude of this outgassing 
and the relative contribution of land and ocean fluxes remains 
low confidence .
5.6.2.1.2 Carbon cycle response over time in scenarios with CDR
Since AR5 WGI (Chapter  6), studies with ESMs have explored 
the land and ocean carbon sink response to scenarios with CO 2 
emissions gradually declining during the 21st century. As CDR 
and other mitigation activities are ramped up, CO 2 emissions in 
these scenarios reach net zero and, as removals exceed emissions,

become net negative. Studies exploring the carbon sink response 
to such scenarios (e.g., RCP2.6, SSP1-2.6) show that, when net CO 2 
emissions are positive but start to decline, uptake of CO 2 by the land 
and ocean begins to weaken (compare land and ocean CO 2 fluxes 
in panels a and b of Figure 5.33; Tokarska and Zickfeld, 2015; Jones 
et al., 2016b). During the first decades after CO 2 emissions become 
net negative, both the ocean and land carbon sinks continue to take 
up CO 2, albeit at a lower rate. For the land carbon sink, the sink-to-
source transition occurs decades to a  century after CO 2 emissions 
become net negative (Figure 5.33c). The ocean remains a sink of CO 2 
for centuries after emissions become net negative (Figure 5.33c–e; 
Section 5.4.9; Figure 5.30). Whether the transition to source occurs 
at all, the timing of the transition and the magnitude of the CO 2 
source are determined by the magnitude of the removal and the rate

and amount of net CO 2 emissions prior to emissions becoming net 
negative ( medium confidence ) (Tokarska and Zickfeld, 2015; Jones 
et al., 2016b). For scenarios with large amounts of CO 2 removal, such 
as SSP5-3.4-overshoot, the land source is larger than for SSP1-2.6 
and the ocean also turns into a source (Section 5.4.10, Figure 5.30). 
While the qualitative response to scenarios with net-negative 
emissions is largely robust across models, the timing of the sink-to-
source transition and the magnitude of the CO 2 source vary between 
models, particularly for the land sink. Due to low agreement  between 
models, there is low confidence  in the timing of the sink-to-source 
transition and the magnitude of the CO 2 source in scenarios with 
net-negative CO 2 emissions.240260280ppmMIROC-ES2L
CanESM5
GFDL-ESM4
UVic ESCM CanESM5-CanOENorESM2-LM
ACCESS-ESM1-5(a)  Atmospheric CO2 concentration 
0 20 40 60 80 1000 20 40 60 80 100

-80-60-40-200PgC(b)  Land carbon change
Years since pulse removal-50-40-30-20-100PgC(c)  Ocean carbon change
Figure 5.32 | Carbon cycle response to instantaneous carbon dioxide (CO 2) 
removal from the atmosphere. (a)  Atmospheric CO 2 concentration; (b) change 
in land carbon reservoir; (c) change in ocean carbon reservoir. Results are shown for 
simulations with seven CMIP6 Earth system models and the University of Victoria Earth 
System Climate Model (UVic ESCM) of intermediate complexity forced with 100 PgC 
instantaneously removed from the atmosphere. The ‘pulse’ removal is applied from 
a  model state in equilibrium with a  pre-industrial atmospheric CO 2 concentration 
(CDRMIP experiment CDR-pi-pulse; Keller et al., 2018b). Changes in land and ocean 
carbon reservoirs are calculated relative to a pre-industrial control simulation. Data 
for the UVic ESCM is from Zickfeld et al. (2021). Further details on data sources and

760
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.6.2.1.3 Removal effectiveness of CDR
It is well understood that land and ocean carbon fluxes are sensitive 
to the level of atmospheric CO 2 and climate change and differ under 
varied future scenarios (Section  5.4). It is therefore important to 
establish to what extent the removal effectiveness of CDR –  here 
defined as the fraction of total CO 2 removed that remains out of 
the atmosphere –  is dependent on the scenario in which CDR is 
applied. Different metrics have been proposed to quantify the 
removal effectiveness of CDR (Tokarska and Zickfeld, 2015; Jones 
et al., 2016b; Zickfeld et al., 2016). One is the airborne fraction (AF) 
of cumulative CO 2 emissions, defined in the same way as for positive 
emissions (i.e., as the fraction of total CO 2 emissions remaining in the 
atmosphere), with its use extended to periods of declining and net

negative CO 2 emissions. This metric, however, has not proven to be 
useful to quantify the removal effectiveness of CDR in simulations 
where CDR is applied from a trajectory of increasing atmospheric CO 2 
concentration. This is because it measures the carbon cycle response 
to CDR as well as to the prior atmospheric CO 2 trajectory (Tokarska 
and Zickfeld, 2015; Jones et al., 2016b). A  more useful metric is 
the perturbation airborne fraction (PAF; Jones et al., 2016b), which 
measures the AF of the perturbation (in this case the CO 2 removal) 
relative to a reference scenario (Tokarska and Zickfeld, 2015; Jones 
et al., 2016b). The advantage of this metric is that it isolates the 
response to a CO 2 removal from the response to atmospheric CO 2 
prior to when the removal is applied. A disadvantage is that the PAF 
cannot be calculated from a  single model simulation, but instead requires a  reference simulation to evaluate the effect of the CO 2

removal. When CDR is applied from an equilibrium state, the PAF and 
AF are equivalent measures.
In scenario simulations and idealized simulations with instantaneous 
CO 2 removals applied from an equilibrium state, the removal 
effectiveness of CDR is found to be slightly dependent on the rate 
and amount of CDR (Tokarska and Zickfeld, 2015; Jones et al., 
2016b; Zickfeld et al., 2021), and to be strongly dependent on the 
emissions scenario from which CDR is applied (Jones et al., 2016b; 
Zickfeld et al., 2021). The fraction of CO 2 removed remaining out of 
the atmosphere decreases slightly for larger removals and decreases 
strongly when CDR is applied from a lower background atmospheric 
CO 2 concentration (Figure  5.34), due to state dependencies and 
climate–carbon cycle feedbacks that lead to a  stronger overall 
response to CO 2 removal (Zickfeld et al., 2021). Based on the high 
agreement  between studies, we assess with medium confidence  that

the removal effectiveness of CDR is only slightly dependent on the 
rate and magnitude of removal and is smaller at lower background 
atmospheric CO 2 concentrations. Simulations with Earth system 
models of intermediate complexity (EMIC) with instantaneous CO 2 
removal from different equilibrium initial states suggest that the 
smaller removal effectiveness of CDR at lower background CO 2 
levels results in greater cooling per unit CO 2 removed (Zickfeld et al., 
2021). However, there is low confidence  in the robustness of this 
result as climate sensitivity has been shown to exhibit opposite state 
dependence in EMICs and ESMs (Section 7.4.3.1).
Net CO2 emissions 
and removals
Net land flux
Net ocean fluxCO2 in atmosphereDifferent emission stages in SSP1-2.6 scenario, characterized by:
(a) Large net positive
    CO2 emissions
    (emissions exceed 
     removals)  (b) Small net positive
     CO2 emissions
     (emissions exceed
      removals)  (c) Net negative

(removals exceed
      emissions) (d) Net negative
     (removals exceed
      emissions) (e) Net zero
    and removalsNo
significant
change
within
this period
2000 2050 2100 2150 2200 2250 2300550
500
450
400
350
300Carbon dioxide concentration (ppm)
Figure 5.33 | Carbon sink response in a scenario with net carbon dioxide (CO 2) removal from the atmosphere. Shown are CO 2 flux components from concentration-
driven Earth system model (ESM) simulations during different emissions stages of SSP1-2.6 and its long-term extension: (a) Large net positive CO 2 emissions; (b) small net 
positive CO 2 emissions; (c), (d) net negative CO 2 emissions; (e) net zero CO 2 emissions. Positive flux components act to raise the atmospheric CO 2 concentration, whereas 
negative components act to lower the CO 2 concentration. Net CO 2 emissions, land and ocean CO 2 fluxes represent the multi-model mean and standard deviation (error bar) of

four ESMs (CanESM5, UKESM1, CESM2-WACCM, IPSL-CM6a-LR) and one Earth system model of intermediate complexity (UVic ESCM; Mengis et al., 2020). Net CO 2 emissions 
are calculated from concentration-driven ESM simulations as the residual from the rate of increase in atmospheric CO 2 and land and ocean CO 2 fluxes. Fluxes are accumulated 
over each 50-year period and converted to concentration units (ppm). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

761
5.6.2.1.4 Symmetry of carbon cycle response to positive 
and negative CO 2 emissions
It is commonly assumed that the climate–carbon cycle response 
to a  negative CO 2 emission (i.e.,  removal from the atmosphere) 
is equal in magnitude and opposite in sign to the response to 
a  positive CO 2 emission of equal magnitude –  that is, symmetric. 
If the response were symmetric, a  positive CO 2 emission could be 
offset by a  negative emission of equal magnitude. This subsection 
assesses the symmetry in the coupled climate–carbon cycle response in model simulations with positive and negative CO 2 emission pulses 
applied from a pre-industrial climate state. Simulations with seven 
CMIP6 ESMs and the UVic Earth System Climate Model (ESCM) of 
intermediate complexity suggest that the carbon cycle response is 
asymmetric for pulse emissions/removals of ±100 PgC (Figure 5.35).

For all models, the fraction of CO 2 remaining in the atmosphere 
after an emission is larger than the fraction of CO 2 remaining out 
of the atmosphere after a  removal (by 4  ± 3%; mean ±  standard 
deviation). In other words, an emission of CO 2 into the atmosphere 
is more effective at raising atmospheric CO 2 than an equivalent 
CO 2 removal is at lowering it. Sensitivity experiments with the UVic 
ESCM suggest that the asymmetry increases for larger amounts of 
emissions/removals and is insensitive to the background atmospheric 
CO 2 concentration from which the emissions/removals are applied 
(Figure  5.35). This asymmetry in the atmospheric CO 2 response 
originates from asymmetries in the land and ocean carbon fluxes 
due to non-linearities in the carbon cycle response to CO 2 and 
temperature (Section  5.4) (Zickfeld et al., 2021). Given medium 
evidence  and high agreement , there is medium confidence  in the

sign of the asymmetry of the carbon cycle response to positive and 
negative CO 2 emissions. The sign of the symmetry of the temperature 
response differs between models, with three out of seven examined 
ESMs showing a smaller temperature response to a 100 PgC emission 
than to an equivalent CO 2 removal. Therefore, there is low confidence  
in the sign of the asymmetry of the temperature response to positive 
and negative CO 2 emissions.
Figure 5.34 | Removal effectiveness of carbon dioxide removal (CDR).  
(a) Fraction of carbon dioxide (CO 2) remaining out of the atmosphere for idealized 
model simulations with CDR applied instantly (pulse removals) from climate states in 
equilibrium with different atmospheric CO 2 concentration levels (one to four times 
the pre-industrial atmospheric CO 2 concentration; shown on the horizontal axis). The 
fraction is calculated 100 years after pulse removal. The black triangle and error bar

indicate the multi-model mean and standard deviation for the seven Earth system 
models shown in Figure 5.32 forced with a 100 PgC pulse removal. Other symbols 
illustrate results with the UVic ESCM model of intermediate complexity for different 
magnitudes of pulse removals (triangles: –100  PgC; circles: –500  PgC; squares: 
–1000 PgC). Data for the UVic ESCM is from Zickfeld et al. (2021). (b) Perturbation 
airborne fraction (see text for definition) for model simulations where CDR is applied 
from four Representative Concentration Pathways (RCPs) (shown on the horizontal 
axis in terms of their cumulative CO 2 emissions during 2020–2099). Symbols indicate 
results for four CDR scenarios, which differ in terms of the magnitude and rate of CDR 
(see Jones et al. (2016b) for details). Results are based on simulations with the Hadley 
Centre Simple Climate-Carbon Model and are shown for the year 2100. Data from

Jones et al. (2016b). Further details on data sources and processing are available in 
the chapter data table (Table 5.SM.6).
Figure 5.35 | Asymmetry in the atmospheric carbon dioxide (CO 2) response 
to CO 2 emissions and removals. Shown are the fractions of total CO 2 emissions 
remaining in the atmosphere (right-hand side) and CO 2 removals remaining out of 
the atmosphere (left-hand side) 80–100 after a  pulse emission/removal. Triangles 
and green circles denote results for seven Earth system models (ESMs) and the UVic 
ESCM model of intermediate complexity forced with ±100 PgC pulses applied from 
a  pre-industrial state (1 ×  CO 2) (Carbon Dioxide Removal Model Intercomparison 
Project (CDRMIP) experiment CDR-pi-pulse; Keller et al., 2018b). Yellow circles and 
diamonds indicate UVic ESCM results for CO 2 emissions/removals applied at 1.5 times 
(1.5 × CO 2) and 2 times (2 × CO 2) the pre-industrial CO 2 concentration, respectively.

Pulses applied from a 2 × CO 2 state span the magnitude ±100 PgC to ±500 PgC.   
UVic ESCM data is from Zickfeld et al. (2021). Further details on data sources and 

762
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks55.6.2.2 Effects of Specific CDR Methods on Biogeochemical 
Cycles and Climate
The AR5 WGI (Chapter  6) discussed the CDR methods, their  implications 
and unintended side effects on carbon cycle and climate, including 
their time scales and potentials. Since then, three IPCC special 
reports (SR) have been published. First, SR1.5 Chapter  4  (IPCC, 
2018) assessed the potentials and current understanding, including 
the side effects, of bioenergy with carbon capture and storage 
(BECCS), afforestation/reforestation, soil carbon sequestration, 
biochar, enhanced weathering, ocean alkalinization, direct air carbon 
capture with storage (DACCS) and ocean fertilization. Second, SRCCL 
Chapter  6  (IPCC, 2019a) assessed the potentials, co-benefits and 
trade-offs of land-based mitigation options. It assessed with high 
confidence  that land-based CDR options do not sequester carbon

indefinitely, except for peatland restoration. Multiple co-benefits 
were identified in the deployment of CDR options, many of them 
with a  potential to make positive contributions to sustainable 
development, enhancement of ecosystem functions and services 
and other societal goals. However, their potential was concluded to 
be context specific, and limits were identified in their contribution 
to global mitigation, such as competition for land. The third report, 
the Special Report on Ocean and the Cryosphere in a  Changing 
Climate (SROCC) Chapter 5 (IPCC, 2019b), assessed the potential of 
marine options for climate change mitigation. It concluded that the 
feasibility of open ocean fertilization and alkalinization approaches 
were negligible, due to their inconclusive influence on ocean carbon 
storage on long time scales, due to the unintended side effects on 
marine ecosystems, and the associated governance challenges. The

assessment of blue carbon ecosystems concluded that they could 
contribute only minimally to atmospheric CO 2 reduction globally, 
but emphasized that the benefits of protecting and restoring coastal 
blue carbon extend beyond climate change mitigation (SROCC 
Section 5.5.12).
5.6.2.2.1 Land-based biological CDR methods
Biological CDR methods, introduced in Table  5.9, seek to increase 
carbon storage on land by enhancing net primary productivity and/or 
reducing CO 2 sources to the atmosphere.
Forest-based methods include afforestation, reforestation, and forest 
management (Table 5.9). Building on previous work that emphasized 
the global potentials of various options, more recent advances have 
focused on the limits of those global potentials in light of ecological 
and climate risks that can threaten the long-term permanence of 
carbon stocks (Boysen et al., 2017b; Anderegg et al., 2020). Some 
of those risks arise from droughts, fires, insect outbreaks, diseases,

erosion, and other disturbances (Thompson et al., 2009).
Sustainable forest management can help to manage some of these 
vulnerabilities, while in some cases it can increase and maintain 
forest sinks through harvest, transfer of carbon to wood products 
and their use to store carbon and substitute emissions-intensive 
construction materials (Churkina et al., 2020). Forest genomics 
techniques can increase the success of both reforestation and conservation initiatives, accelerating breeding for tree health and 
productivity (Isabel et al., 2020).
In response to increasing risks to permanence of carbon stocks of some 
types of afforestation practices and the competition for land, there 
has been an increasing recognition that secondary forest regrowth 
and restoration of degraded forests and non-forest ecosystems can 
play a  large role in carbon sequestration ( high confidence ). The 
rational for this focus builds on their high carbon stocks and rates of

sequestration (Griscom et al., 2017; Lewis et al., 2019; Maxwell et al., 
2019; Pugh et al., 2019), high resilience to disturbances (Dymond 
et al., 2014; Messier et al., 2019), and additional benefits such as 
enhanced biodiversity (Strassburg et al., 2020).
The global sequestration potential of forestation varies substantially 
depending on the scenario-assumptions of available land and of 
background climate (AR6 WGIII, Section 7.5). Afforestation of native 
grasslands, savannas, and open-canopy woodlands leads to the 
undesirable loss of unique natural ecosystems with rich biodiversity, 
carbon storage and other ecosystem services (Veldman et al., 2015; 
IPBES, 2018). Comprehensive approaches to assess the effectiveness 
of land-based carbon removal options need to be based on the 
whole carbon cycle, covering both carbon stocks and flows, and 
establishing the links between human activities and their impacts on 
the biosphere and atmosphere (Keith et al., 2021).

A range of mechanisms could enhance CO 2 sequestration of forest-
based methods under future scenarios, including CO 2 fertilization, 
soil carbon enrichment due to enhanced litter input, or the 
northward shift of the tree line in future climate projection (Bathiany 
et al., 2010; Sonntag et al., 2016; Boysen et al., 2017b; Harper et al., 
2018). There is low confidence  in the net direction of feedbacks of 
afforestation on global mean temperature. The feedbacks are highly 
region dependent. For instance, afforestation at high latitudes 
would decrease albedo and increase local warming, while at low 
latitudes,  the cooling effect of enhanced evapotranspiration could 
exceed the warming effect due to albedo decrease (Pearson et al., 
2013; W. Zhang et al., 2013; Jia et al., 2019, SRCCL, Section 2.6.1). 
Both afforestation and reforestation affect the hydrological cycle 
through increased volatile organic compound (VOC) emissions and

cloud albedo (Teuling et al., 2017; Kalliokoski et al., 2020), enhanced 
precipitation (Ellison et al., 2017) and increased transpiration, with 
potential effects on runoff and, especially in dry regions, on water 
supply (Figure 5.36 and Cross-Chapter Box 5.1; Farley et al., 2005; 
Smith et al., 2016; Krause et al., 2017; Teuling et al., 2019). Forest-
based methods can either raise or lower N 2O emissions, depending 
on tree species, previous land use, soil type and climatic factors 
(low confidence ) (Figure 5.36 and Supplementary Materials, Table 5.
SM.4; Benanti et al., 2014; Chen et al., 2019; McDaniel et al., 2019). 
Afforestation will decrease biodiversity if native species are replaced 
by monocultures ( high confidence ), while there is medium confidence  
that biodiversity is improved when forests are introduced into land 
areas with degraded soils or intensive monocultures, or where 
native species are re-introduced into managed land (Figure  5.36,

Supplementary Materials Table 5.SM.4; Hua et al., 2016; Williamson 
and Bodle, 2016; P . Smith et al., 2018; Holl and Brancalion, 2020).

763
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Soil carbon losses from human agriculture accounted for about 
116 PgC in the last 12,000 years (Section 5.2.1.2; Sanderman et al., 
2017). With best management practices, two-thirds of these losses 
may be recoverable, setting a  theoretical maximum of 77  PgC 
that can be sequestered in soils. Methods to increase soil carbon 
content may be applied to the restoration of marginal or degraded 
land (Paustian et al., 2016; Smith, 2016), but may also be used in 
traditional agricultural lands. A  simple practice is to increase the 
input of carbon to the soil by selecting appropriate varieties or 
species with greater root mass (Kell, 2011) or higher yields and net 
primary production (NPP) (Burney et al., 2010). In addition, improved 
agricultural practices also increase soil carbon content. These 
include  using crop  rotation cycles, increasing the amount of crop

residues, using crop cover to prevent periods of bare soil (Poeplau 
and Don, 2015; Griscom et al., 2017), optimizing grazing (Henderson 
et al., 2015) and residue management (Wilhelm et al., 2004), using 
irrigation (Campos et al., 2020), employment of low-tillage or no-
tillage (W. Sun et al., 2020), agroforestry, cropland nutrient recycling, 
and avoiding grassland conversion (Paustian et al., 2016; Fargione 
et al., 2018). With medium confidence,  methods that seek soil 
carbon sequestration will diminish nitrous oxide (N 2O) emissions and 
nutrient leaching, and improve soil fertility and biological activity 
(Figure  5.36; Tonitto et al., 2006; Fornara et al., 2011; Paustian 
et al., 2016; Smith et al., 2016; SRCCL, Section 2.6.1.3, IPCC, 2019a). 
However, if improved soil carbon sequestration practices involve 
higher fertilization rates, N 2O emissions would increase (Gu et al., 
2017). Some soil carbon sequestration methods, such as cover crops

and crop diversity, can increase biodiversity ( medium confidence ) 
(Paustian et al., 2016; P . Smith et al., 2018).
Biochar is produced by burning biomass at high temperatures under 
anoxic conditions (pyrolysis) and, when added to soils, can increase 
soil carbon stocks and fertility for decades to centuries (Woolf et al., 
2010; Lehmann et al., 2015). Biochar application improves many soil 
qualities and increases crop yield ( medium confidence ) (Ye et al., 2020; 
SRCCL, Chapter 4.9.5), particularly in already degraded or weathered 
soils (Woolf et al., 2010; Lorenz and Lal, 2014; Jeffery et al., 2016), 
increases soil water holding capacity ( medium  confidence ) (Karhu 
et al., 2011; Liu et al., 2016; B.M.C. Fischer et al., 2019; Verheijen et al., 
2019) and evapotranspiration ( low confidence ) (B.M.C. Fischer et al., 
2019). The use of biochar reduces nutrient losses ( low confidence ) 
(Woolf et al., 2010), enhances fertilizer nitrogen use efficiency and

improves the bioavailability of phosphorus (Figure  5.36; Clough 
et al., 2013; Shen et al., 2016; Z. Liu et al., 2017). Biochar addition 
may decrease methane (CH 4) emissions in inundated and acid soils 
such as rice fields ( low confidence ) (Jeffery et al., 2016; Huang et al., 
2019; S. Wang et al., 2019; S. Yang et al., 2019). In non-inundated, 
neutral soils, CH 4 uptake from the atmosphere is suppressed after 
biochar application ( low confidence ) (Jeffery et al., 2016), and soil 
N2O emissions decline ( medium confidence ) (Cayuela et al., 2014; 
Kammann et al., 2017). The potential risks of introducing harmful 
contaminants into the soil environment are not well understood 
(Lorenz and Lal, 2014). With low confidence , application of biochar 
can have co-benefits for soil microbial biodiversity (P . Smith et al., 
2018), while the potential trade-offs for biodiversity are due to land

requirements (Tisserant and Cherubini, 2019).Peatlands are less extensive than forests, croplands and grazing 
lands, yet per unit area, they hold high carbon stocks (Griscom et al., 
2017). Peatland restoration relies on back-conversion or building 
of high-carbon-density soils through flooding –  that is, rewetting 
(Leifeld et al., 2019). High water level and anoxic conditions are 
prerequisites for restoring by returning drained and/or degraded 
peatlands back to their natural state as CO 2 sinks, but restoration 
also results in enhanced CH 4 emissions which are similar to or higher 
than the pre-drainage fluxes ( high confidence ) (Koskinen et al., 2016; 
Wilson et al., 2016a; Hemes et al., 2019; Renou-Wilson et al., 2019; 
Holl et al., 2020). In a  multi-decadal time frame, the reduction in 
CO 2 emissions from rewetting more than compensates for the 
initial increase in radiative forcing due to enhanced CH 4 emissions

(Günther et al., 2020). Rewetting drained peatlands will decrease 
N2O emissions ( medium confidence ) (Wilson et al., 2016b; H.  Liu 
et al., 2020; Tiemeyer et al., 2020). Restored wetlands and peatlands 
act as buffer zones that provide infiltration and nutrient retention 
and offer protection to water quality (Daneshvar et al., 2017; Lundin 
et al., 2017), particularly in nutrient-loaded agricultural catchments. 
Peatland restoration can also recover much of the original biodiversity 
(medium confidence ) (Meli et al., 2014; P . Smith et al., 2018).
The concept of BECCS rests on the premise that bioenergy production 
is carbon neutral – that is, as much CO 2 is sequestered when growing 
biomass as feedstock as is released by its combustion. If these 
emissions are also captured and stored, the net effect is removal of 
CO 2 from the atmosphere (Fuss et al., 2018). Sequestration potentials 
from BECCS depend strongly on the feedstock, climate, and

management practices (Beringer et al., 2011; Kato and Yamagata, 
2014; Heck et al., 2016; Smith et al., 2016; Krause et al., 2017). 
If woody bioenergy plants replace marginal land, net carbon uptake 
increases, enriching soil carbon (Don et al., 2012; Heck et al., 2016; 
Boysen et al., 2017a, b). However, replacing carbon-rich ecosystems 
with herbaceous bioenergy plants could deplete soil-carbon stocks 
and reduce the additional sink capacity of standing forests (Don et al., 
2012; Harper et al., 2018). Furthermore, wood-based BECCS may not 
be carbon negative in the first decades, initially emitting more CO 2 
than sequestering (Sterman et al., 2018). BECCS has several trade-
offs to deal with, including possible threats to water supply and soil 
nutrient deficiencies ( medium confidence ) (SRCCL Chapters 2 and 6, 
and Cross-Chapter Box 5.1; Smith et al., 2016; Krause et al., 2017; de 
Coninck et al., 2018; Heck et al., 2018; Roy et al., 2018). Deployment

of BECCS at the scales envisioned by many 1.5°C–2.0°C mitigation 
scenarios could threaten biodiversity and require large land areas, 
competing with afforestation, reforestation and food security 
(Anderson and Peters, 2016; P . Smith et al., 2018). Additional risks 
and side effects are related to geologic carbon storage (Fuss et al., 
2018; see also Section 5.6.2.2.4).
In conclusion, land-based CDR methods that rely on enhanced net 
biological uptake and storage of carbon, have a  wide range of 
biogeochemical and biophysical side effects. These side effects can 
(directly or indirectly) strengthen or weaken the climate change 
mitigation effect of a  given method, or affect water quality and 
quantity, food supply and biodiversity (Figure  5.36). With the 
exception of weakened ocean carbon sequestration, there is low 
confidence  in the Earth system feedbacks of these methods. Most

764
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5methods are associated with a  range of biogeochemical and 
biophysical side effects and co-benefits and trade-offs, but these 
prior land use, and scale ( high confidence ). Highest co-benefits are 
obtained with methods that seek to restore natural ecosystems and 
improve soil carbon sequestration (Figure 5.36) while highest trade-
off possibilities (symmetry with the highest co-benefits) occur for 
reforestation or afforestation with monocultures and BECCS, again 
with strong dependence on scale and context ( medium confidence ).
5.6.2.2.2 Ocean-based biological CDR methods
Both ocean biological and physical processes drive the CO 2 exchange 
between the ocean and atmosphere. However, the ocean physical 
processes that remove CO 2 from the atmosphere, such as large-
scale circulation, cannot be feasibly altered, so ocean CDR methods

focus on increasing the productivity of ocean ecosystems, and 
subsequent sequestration of carbon (GESAMP , 2019). There has 
been no change to the assessment of SROCC (SROCC Section 5.5.1): 
there is low confidence  that nutrient addition to the open ocean, 
either through artificial ocean upwelling or iron fertilization, could 
contribute to climate change mitigation, due to its inconclusive 
effect on carbon sequestration and risks of adverse side effects on 
marine ecosystems (Figure 5.36, Table 5.9; Supplementary Materials 
Text 5.SM.3 and Table 5.SM.4; AR6 WGIII Chapter 12; Gattuso et al., 
2018; Boyd and Vivian, 2019; Feng et al., 2020). In addition, ocean 
fertilization is currently prohibited by the London Protocol (Dixon 
et al., 2014; GESAMP , 2019).
Restoration of vegetated coastal ecosystems (sometimes referred 
to as ‘blue carbon’ –  see Glossary) refers to the potential for 
increasing carbon sequestration by plant growth and burial

of organic carbon in the soil of coastal wetlands (including salt 
marshes and mangroves) and seagrass ecosystems. Wider usage of 
the term blue carbon occurs in the literature, for example, including 
seaweeds (macroalgae), shelf sea sediments and open ocean 
carbon exchanges. However, such systems are less amenable to 
management, with many uncertainties relating to the permanence 
of their carbon stores (Windham-Myers et al., 2018; Lovelock and 
Duarte, 2019; SROCC, Section 5.5.1.1).
Coastal wetlands and seagrass meadows store significant amounts 
of carbon and are among the most productive ecosystems per unit 
area (Griscom et al., 2017, 2020; Ortega et al., 2019; Serrano et al., 
2019). These rates could be reduced in the future, since these habitats 
are vulnerable to changing conditions, such as temperature, salinity, 
sediment supply, storm severity and continued coastal development 
(Bindoff et al., 2019; NASEM, 2019). These ecosystems are under

threat from anthropogenic conversion and degradation and are being 
lost at rates between 0.7% and 7% per annum with consequent CO 2 
emissions (e.g., Atwood et al., 2017; Howard et al., 2017; Hamilton 
and Friess, 2018; Sasmito et al., 2019). Although sea level rise might 
lead to greater carbon sequestration in coastal wetlands (Rogers 
et al., 2019), there is high confidence that the frequency and intensity 
of marine heatwaves will increase (Cross-Chapter Box 9.1; Frölicher 
and Laufkötter, 2018; Laufkötter et al., 2020),which poses a  more immediate threat to the integrity of coastal carbon stocks (Smale 
et al., 2019). Blue carbon restoration seeks to increase the rate of 
carbon sequestration, although restoration may be challenging, 
because of ongoing use of coastal land for human settlement, 
conversion to agriculture and aquaculture, shoreline hardening and 
port development.
Biogeochemical factors affecting reliable quantification of the

climatic benefits of coastal vegetation include the variable production 
of CH 4 and N 2O by such ecosystems (Adams et al., 2012; Keller, 2018; 
Rosentreter et al., 2018), uncertainties regarding the provenance 
of the carbon that they accumulate (Macreadie et al., 2019), and 
the release of CO 2 by biogenic carbonate formation in seagrass 
ecosystems (Kennedy et al., 2018). While coastal habitat restoration 
potentially provides significant mitigation of national emissions for 
some countries (Taillardat et al., 2018; Serrano et al., 2019), the global 
sequestration potential of blue carbon approaches is <0.02 PgC yr–1 
(medium confidence ) (Figure 5.36; SROCC, Section 5.5.1.2; Griscom 
et al., 2017; Gattuso et al., 2018; GESAMP , 2019; NASEM, 2019).
5.6.2.2.3 Geochemical CDR methods
Enhanced weathering (EW) is based on naturally occurring 
weathering processes of silicate and carbonate rocks, removing CO 2 
from the atmosphere. Weathering is accelerated by spreading ground

rocks on soils, coasts or oceans. EW increases the alkalinity and pH 
of natural waters, helps dampen ocean acidification and increases 
ocean carbon uptake (Beerling et al., 2018). The dissolution of 
minerals stimulates biological productivity of croplands (Hartmann 
et al., 2013; Beerling et al., 2018), but can also liberate toxic trace 
metals (such as nickel, chromium, copper) into soil or water bodies 
(Keller et al., 2018a; Strefler et al., 2018). EW can also contribute 
to freshwater salinization as a  result of increased salt inputs and 
cation exchange in watersheds, and so adversely affecting drinking 
water quality ( low confidence ) (Kaushal et al., 2018). With a  medium 
confidence , amendment of soils with minerals will have lower N 2O 
emissions (Kantola et al., 2017; Blanc-Betes et al., 2020) but will not 
have a marked effect on evapotranspiration or albedo (Fuss et al., 
2018; de Oliveira Garcia et al., 2020). The mining of minerals can

cause adverse impacts on biodiversity, however, the use of waste 
materials such as concrete demolition or steel slags for EW can reduce 
the need for mining (Renforth, 2019). The spreading of minerals on 
land has a neutral impact on biodiversity (P . Smith et al., 2018).
Ocean alkalinization, via the deposition of alkaline minerals 
(e.g., olivine) or their dissociation products (e.g., quicklime) at the 
ocean surface, can increase surface total alkalinity and thus increase 
CO 2 uptake and storage (Glossary; Supplementary Material Text 
5.SM.3; AR6 WGIII Chapter 12; GESAMP , 2019; Keller, 2019). Ocean 
alkalinization ameliorates surface ocean acidification ( high confidence ) 
(Hauck et al., 2016; Tran et al., 2020), but there are also negative side 
effects on the marine ecosystem, most of which are poorly understood 
or quantified (Figure 5.36 and Supplementary Materials Table 5.SM.4; 
Bach et al., 2019). Although ocean alkalinization could potentially

sequester large amounts of carbon (≥1  PgC yr–1; Figure  5.36; and 
Supplementary Materials Table 5.SM.5) there is no new evidence to

765
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55revisit the SROCC (SROCC Section 5.5.1.2.4) conclusion that there is 
low confidence  that ocean alkalinization is a viable climate change 
mitigation approach.
5.6.2.2.4 Chemical CDR methods
Direct air carbon capture with carbon storage (DACCS) is a  combination 
of two techniques, direct capture of CO 2 from ambient air (DAC) and 
carbon storage. DAC entails contacting the air, capturing the CO 2 
on a liquid solvent or solid sorbent, and regenerating the solvent or 
sorbent. Different DAC methods have been proposed, which differ 
by the chemical process used to capture the CO 2 and to recover it 
from the sorbent or solvent (NASEM, 2019). The captured CO 2 may 
be either stored geologically as a high-pressure gas or sequestered 
by a mineral carbonation process. Storage is potentially permanent 
in both pressurised gas and mineral form (Fuss et al., 2018). DACCS

has significant requirements of energy and, (depending on the type 
of technology), water and materials (Smith et al., 2016; NASEM, 
2019). Compared to other CDR methods, it has a small land footprint 
(Smith et al., 2016; NASEM, 2019). Side effects of DACCS include 
CO 2-depleted air leaving the air contactor, which could have adverse 
effects on crop and ecosystem productivity, and VOC emissions 
(NASEM, 2019). Additional risks and side effects are related to the 
high pressure at which CO 2 is stored in geologic formations (Fuss 
et al., 2018). DACCS is assessed in detail in WGIII Chapter 12.
5.6.2.2.5 Methane removal
Proposals to remove CH 4 from the atmosphere are emerging 
(de Richter et al., 2017; Jackson et al., 2019). CH 4 removal methods 
seek to capture CH 4 directly from ambient air, similarly to DACCS 
for CO 2 using, for example, zeolite trapping, but instead of storing 
it, CH 4 would be chemically oxidized to CO 2 (Jackson et al., 2019).

Methane can be also removed microbially by supporting naturally 
occurring processes, such as by enhancing the soil microbial uptake 
through afforestation (J. Wu et al., 2018) or by directing the venting 
air from a  cow barn into the soil bed of a  nearby greenhouse, 
utilizing microbial CH 4 oxidation (Nisbet et al., 2020). Microbial CH 4 
oxidation could also be used for removal of CH 4 leaked from point 
sources by building biocatalytic polymers which include methane-
oxidizing enzymes (Blanchette et al., 2016). Methane removal is, 
however, still in its infancy and the available literature is insufficient 
for an assessment.

766
CDR methods
Afforestation, reforestation 
and forest management
Soil carbon sequestration
Biochar
(BECCS)Peatland restoration
Blue carbon 
Direct air carbon capture 
and storage (DACCS)Ocean alkalinisationArtificial ocean upwellingEarth system feedbacks
Land C sequestration
Cooling
N
2O emissions
CH
4 emissions
Ocean acidificationSide effects
Strengthening
Weakening
Strengthening or weakening
Neutral
No evidenceNeutralEvapotranspiration
Albedo
Cold water upwelling
Water quality
Water quantity
Food supply
BiodiversityCharacteristics of carbon dioxide 
removal (CDR) methods
Biogeochemical effects Biophysical effects Co-benefits / Trade-offsIncrease
Decrease
Increase or decreaseNo evidenceTrade-offs 
Co-benefitsVOC emissionsCo-benefits or trade-offsMarine NPPOcean deoxygenationNot applicableOcean C sequestration

Enhanced weatheringOcean iron fertilisation Low Medium HighConfidence
Large >3 GtCO2 yr -1 Moderate 0.3-3 GtCO2 yr -1 Small <0.3 GtCO2 yr -1 
Decades 
to
centuries
Centuries
to 
millenia
10 to 100
thousand
years
Potentially
permanentTimescale of 
carbon storageBiogeophysical or technical 
sequestration potential
Figure 5.36 | Characteristics of carbon dioxide removal (CDR) methods, ordered according to the time scale of carbon storage.  The first column shows biogeophysical (for open-ocean methods) or technical (for all other 
methods) sequestration potentials (i.e., the sequestration potentials constrained by biological, geophysical, geochemical limits and thermodynamics and, for technical potentials, availability of technologies and practices; technical potentials

for some methods also consider social or environmental factors if these represent strong barriers for deployment; see Glossary, Annex VII), classified into low (<0.3 GtCO 2 yr–1), moderate (0.3−3 GtCO 2 yr–1) and large (>3 GtCO 2 yr–1) (details 
underlying this classification are provided in Supplementary Materials Table 5.SM.5). The other columns show Earth system feedbacks that deployment of a given CDR method would have on carbon sequestration and climate, along with 
biogeochemical, biophysical, and other side effects of a given method. Earth system feedbacks do not include the direct effect of CO 2 sequestration on atmospheric CO 2, only secondary effects. For Earth system feedbacks, the colours indicate 
whether the feedbacks strengthen or weaken carbon sequestration and the climate cooling effect of a given CDR method. For biogeochemical and biophysical side effects the colours indicate whether the deployment of a CDR method

increases or decreases the magnitude of the effect, whereas for co-benefits and trade-offs the colour indicates whether deployment of a CDR method results in beneficial (co-benefits) or adverse side effects (trade-offs) for water quality and 
quantity, food production and biodiversity. The details and references underlying the Earth system feedback and side effect assessment are provided in Supplementary Materials Table 5.SM.4. Further details on data sources and processing are 

767
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 555.6.3 Biogeochemical Responses to Solar Radiation 
Modification (SRM)
This section assesses the possible consequences of solar radiation 
modification (SRM) on the biosphere and global biogeochemical 
cycles. The SRM options and the physical climate response to SRM is 
assessed in detail in Section 4.6.3 and Table 4.7. Section 6.3.6 assesses 
the potential effective radiative forcing of aerosol-based SRM 
options and Section 8.6.3 assesses the abrupt water cycle changes in response to initiation or termination of SRM. Most literature on 
the biogeochemical responses to SRM focuses on stratospheric 
aerosol injection (SAI), and only a  few studies have investigated 
the biogeochemical responses to marine cloud brightening (MCB) 
and cirrus cloud thinning (CCT). At the time of AR5, there were only 
a few studies on the biogeochemical responses to SRM. The main

assessment of AR5 (Ciais et al., 2013) was that SRM will not interfere 
with the direct biogeochemical effects of increased CO 2, such as 
ocean acidification and CO 2 fertilization, but could affect the carbon 
−30−20−100102030405060
SRM
2020−2069Termination
2070−2089Change in cum ulativ e CO2 uptake (PgC)
Multi-model mean
Multi-model meanBNU−ESM
CanESM2
CNRM−ESM1
HadGEM2−ES
MIROC−ESM
MIROC−ESM−CHEM
BNU−ESM
CanESM2
CNRM−ESM1
MIROC−ESM
MIROC−ESM−CHEMLand, biosphere
OceanEffect of SRM on land and ocean CO2 uptake
Figure 5.37 | Cumulative carbon dioxide (CO 2) uptake by land and ocean carbon sinks in response to stratospheric sulphur dioxide (SO2) injection. Results 
are shown for a scenario with 50-year (2020−2069) continuous stratospheric SO 2 injection at a rate of 5 Tg yr–1 appplied to a RCP4.5 baseline scenario (GeoMIP experiment G4;

Kravitz et al., 2011), followed by termination in year 2070. Anomalies are shown relative to RCP4.5 for the multi-model ensemble mean and for each of six Earth system models 
(ESMs) over the 50-year period of stratospheric SO 2 injection (left-hand side), and over 20 years after termination of SO 2 injection (right-hand side). Adapted from Plazzotta et al. 
(2019). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

768
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5cycle through climate–carbon feedbacks. Overall, AR5 concluded that 
the level of confidence on the effects of SRM on carbon and other 
biogeochemical cycles is very low (Ciais et al., 2013). Since AR5, more 
modelling work has been conducted to examine various aspects of 
the global biogeochemical cycle responses to SRM. 
5.6.3.1 Effects of SRM on the Carbon Cycle
Relative to a  high-greenhouse gas (GHG) world without solar 
radiation modification (SRM), SRM would affect the carbon cycles 
through changes in sunlight, climate (e.g., temperature, precipitation, 
soil moisture, ocean circulation), and atmospheric chemistry 
(e.g.,  ozone; Section  4.6.3.3; Cao, 2018). Net SRM effects on the 
carbon cycle, relative to a world without SRM, depend on the change 
of individual factors, and interactions among them.
SRM-mediated sunlight changes directly affect the carbon cycle. In

particular, SAI would reduce the sunlight reaching the Earth’s surface, 
but also increase the fraction of sunlight that is diffuse. These changes 
in the quantity and quality of the sunlight have opposing effects 
on the photosynthesis of land plants. On their own, reductions in 
photosynthetically active radiation (PAR) will reduce photosynthesis. 
However, diffuse light is more effective than direct light in accessing 
the light-limited leaves within plant canopies, leading to the so-called 
‘diffuse-radiation’ fertilization effect (Mercado et al., 2009). The 
estimated balance between the negative impacts of reducing PAR 
and the positive impacts of increasing diffuse fraction differ between 
models (Kalidindi et al., 2015; Xia et al., 2016; C.-E. Yang et al., 2020) 
and across different ecosystems. The change in the absolute amount 
of direct and diffuse radiation could also depend on the height of 
the additional sulphate aerosol layer in the stratosphere and the

hygroscopic growth of aerosols (Krishnamohan et al., 2019, 2020). 
SRM-mediated cooling also affects the terrestrial carbon cycle. 
Relative to a high-GHG world without SRM, the simulated responses 
of net primary production (NPP) to SRM differ widely between 
models, such that even the sign of global mean change is uncertain 
(Glienke et al., 2015). SRM-induced cooling would decrease NPP at 
high latitudes by reducing the length of the growing season (Glienke 
et al., 2015). At low latitudes, the NPP response to SRM-induced 
cooling is sensitive to the effect of nitrogen limitation (Glienke et al., 
2015; Duan et al., 2020). SRM-induced cooling tends to increase NPP 
in models without the nitrogen cycle because of reduced heat stress. 
However, in models including the nitrogen cycle, this is counteracted 
by reductions in NPP because of reductions in nitrogen mineralization 
and nitrogen availability (Glienke et al., 2015). SRM-induced

changes in the hydrological cycle (Section 8.6.3), including changes 
in evapotranspiration, precipitation, and soil moisture, also pose 
strong constraints on the vegetation response (Dagon and Schrag, 
2019). For the same amount of global mean cooling, different SRM 
options, such as SAI, MCB, and CCT, would have different effects on 
gross primary production (GPP) and NPP because of different spatial 
patterns of temperature, available sunlight and hydrological cycle 
changes (Section 4.6.3.3) (Duan et al., 2020). Modelling studies show 
that SRM-induced cooling would reduce plant and soil respiration 
(Tjiputra et al., 2016; Cao and Jiang, 2017; Muri et al., 2018; 
C.-E. Yang et al., 2020). Despite the large uncertainty in modelled 
NPP response, existing modelling studies consistently show that SRM would increase the global land carbon sink relative to a  high-CO 2 
world without SRM ( high confidence ).
Based on available evidence, SRM with elevated CO 2 would increase

global mean NPP and carbon storage on land relative to an unperturbed 
climate, mainly because of CO 2 fertilization of photosynthesis ( high 
confidence ) (Glienke et al., 2015; Tjiputra et al., 2016; Dagon and 
Schrag, 2019; Duan et al., 2020; C.-E. Yang et al., 2020). However, the 
amount of increase is uncertain as it depends on the extent to which 
CO 2 fertilization of land plants is limited by nutrient availability.
Relative to a  high-CO 2 world without SRM, SRM would also  have 
compensating effects on crop yields. SRM is expected to 
have  a  positive impact on crop yields by diminishing heat stress 
(Pongratz et al., 2012). However, reductions in light availability will 
produce a  counteracting reduction in crop yields, especially if the 
crop type does not benefit appreciably from diffuse-light fertilization 
(Proctor et al., 2018). The balance between these effects varies 
markedly across crop types and regions, from projected increases

in maize production in China (Xia et al., 2014) to reductions in 
groundnut yields in parts of India (Yang et al., 2016). Because of 
these diverging results from a limited set of studies, there is overall 
low confidence  in the effect of SRM on crop yields.
Consistent with the AR5 assessment, there is high confidence  that 
SRM would not mitigate CO 2-induced ocean acidification (Ciais 
et al., 2013). Some studies even suggest an acceleration of deep-
ocean acidification as a result of ocean circulation change (Tjiputra 
et al., 2016; Lauvset et al., 2017). There are large differences in the 
simulated spatial pattern of ocean NPP change in response to SRM, 
which depends strongly on the SRM method that is considered 
(Partanen et al., 2016; Lauvset et al., 2017).
5.6.3.2 Consequences of SRM and its Termination 
on Atmospheric CO 2 Burden
Modelling studies consistently show that, relative to a  high-CO 2 
world without SRM, SRM-induced cooling (Section  4.6.3.3) would

reduce plant and soil respiration, and also reduce the negative 
effects of warming on ocean carbon uptake (Tjiputra et al., 2016; Xia 
et al., 2016; Cao and Jiang, 2017; Jiang et al., 2018; Muri et al., 2018; 
Sonntag et al., 2018; Plazzotta et al., 2019; C.-E. Yang et al., 2020). 
A multi-model study (Plazzotta et al., 2019) indicates that, relative 
to a  high-CO 2 concentration world without SRM, stratospheric 
sulphur dioxide (SO 2) injection increases the allowable CO 2 emissions 
by enhancing CO 2 uptake by both land and ocean (Figure  5.37). 
As a result of enhanced global carbon uptake, SRM would reduce the 
burden of atmospheric CO 2 (high confidence ). However, the amount 
of SRM-induced reduction in atmospheric CO 2 depends on the future 
emissions scenario and modelled oceanic and terrestrial carbon 
sinks, which differ widely between models (Tjiputra et al., 2016; Xia 
et al., 2016; Cao and Jiang, 2017; Muri et al., 2018). Models that

include the terrestrial nitrogen cycle usually report a much smaller 
reduction of atmospheric CO 2 in response to SRM than models 
without the nitrogen cycle, mainly because nitrogen limitation leads 
to a weaker terrestrial carbon sink (Tjiputra et al., 2016; Muri et al., 
2018; C.-E. Yang et al., 2020). Large-scale application of SAI is  found

769
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55to reduce the rate of release of CO 2 and CH 4 from permafrost thaw 
(Lee et al., 2019; Chen et al., 2020).
A hypothetical sudden and sustained termination of SRM would 
cause a rapid increase in global warming that would pose great risks to biodiversity (A.  Jones et al., 2013; McCusker et al., 2014; Trisos et al., 2018) (Section  4.6.3.3). It would also weaken carbon sinks, accelerating atmospheric CO
2 accumulation and inducing further 
warming (Figure 5.37; Matthews and Caldeira, 2007; Tjiputra et al., 2016; Muri et al., 2018; Plazzotta et al., 2019). However, a scenario with 
gradual phase-out of SRM under emissions reduction could reduce 
the large negative effect of sudden SRM termination (MacMartin et al., 2014; Keith and MacMartin, 2015; Tilmes et al., 2016), though this would be limited by how rapidly emissions reductions can be scaled-up (Ekholm and Korhonen, 2016).

5.6.3.3  Consequences of SRM on other Biogeochemical Cycles
SAI is found to reduce global average surface ozone concentration 
(Xia et al., 2017) mainly as a result of aerosol-induced reduction in stratospheric ozone at polar regions, resulting in reduced transport 
of ozone from the stratosphere (Pitari et al., 2014; Tilmes et al., 
2018). The reduction in surface ozone, together with alteration to ultraviolet (UV) radiation, would have important implications for vegetation response (Xia et al., 2017). A  modelling study shows that sea salt aerosol injection for MCB would reduce hydroxyl (OH) 
concentration and increase CH
4 lifetime, and hence, have potential 
implications for surface ozone pollution (Horowitz et al., 2020). It 
has also been reported that the use of SAI to limit global mean 
warming from 2°C to 1.5°C would reduce fire weather in many 
areas (Burton et al., 2018).
5.6.3.4  Synthesis of Biogeochemical Responses to SRM

SRM would alter the global carbon cycle through SRM-induced climate effect, such as changes in sunlight, temperature, precipitation, and ocean circulation. Compared to a high-CO
2 world without SRM, SRM 
would enhance the net uptake of CO 2 by the terrestrial biosphere 
and ocean, thus acting to reduce atmospheric CO 2 (high confidence). 
SRM would also affect surface ozone, UV radiation, and atmospheric chemistry. Due to complex interplays between SRM-induced changes 
in direct and diffuse sunlight, temperature, the coupling of water-
carbon-nitrogen cycles, and atmospheric chemistry, there is large 
uncertainty in the overall response of the terrestrial biosphere response to SRM. Thus, the level of confidence on the effect of SRM 
on carbon and other biogeochemical cycles is low.5.7 Final Remarks
Key research developments to further strengthen the confidence levels in AR7 include the following:
Contemporary Greenhouse Gases (GHGs) Trends 
and Attribution

•Further constrain the CO 2, CH 4 and N 2O fluxes from land use,
land-use change and forestry (including gross fluxes), and fossil
fuels. Improving spatial resolution and representations of land
management, such as forestry, grazing and cropping.
•Improve representation of the variability and trends in the
transport of carbon through the land–ocean continuum, whichhas implications for partitioning the land and ocean CO
2 sinks.
•Improve understanding of the controls over the airborne franction and sinks rates, their trends, and future dynamics.
•Fill gaps in space and time for ocean CO 2 and ancillary physical
and biogeochemical observations at the ocean surface andinterior to reduce the biases and uncertainties in the variability
and trends for air–sea fluxes and inventory changes, particularly
for the Arctic and the Southern Ocean.
•Reduce uncertainties of CH 4 emissions from wetlands and inland
waters, which are the largest source term in the global CH 4

budget and proportionally have the largest uncertainty, to betterunderstand future CH
4-climate feedbacks.
•Reduce uncertainties in atmospheric transport models used toestimate regional sources and sinks of GHGs as independentevidence from that of ground and inventory estimates.
Ocean Acidification and Deoxygenation
•Improve observations for the interplay between carbonatechemistry and a  variety of biogeochemical and physicalprocesses, including eutrophication and freshwater inflow in
coastal zones to increase the robustness of future assessments
of ocean acidification.
•Improve our understanding of changes in water mass ventilationassociated with climate change and variability to gainfurther insights into future trends in ocean acidification and
deoxygenation in the ocean interior.
Biogeochemical Feedbacks on Climate Change
•Improve understanding and representation in Earth system

models of changes in land carbon storage and associatedcarbon–climate feedbacks including: better treatment of the CO
2
fertilization, nutrient-limitations, soil organic matter stabilizationand turnover; land-use change; large-scale and fine-scalepermafrost carbon; plant growth, mortality, and competition
dynamics; plant hydraulics; and disturbance processes.
•Improve observations and process understanding of CH 4 and
N2O source responses to climate, specifically from wetlands
and permafrost thaw.

770
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5• Improve observations and process understanding of ocean N 2O 
source responses to oxygen loss and climate, particularly in 
the oxygen minimum zones of the tropical oceans and eastern 
boundary upwelling regions.
• Improve understanding of the sensitivity of ocean carbon–
climate feedbacks to physical processes that are not yet resolved 
by the ocean domain in ESMs.
• Improve understanding of the processes affecting the efficiency, 
climate sensitivity and emerging feedbacks in the ocean carbon 
cycle via the biological carbon pump to constrain future global 
ocean feedbacks.
Remaining Carbon Budget to Climate Stabilization
• Improve understanding of the sign and magnitude of a possible 
zero emissions commitment (ZEC). The ZEC affects estimates 
of carbon budgets derived from the transient climate response 
to cumulative emissions of CO 2 (TCRE), but not TCRE itself.

ZEC is particularly relevant once global CO 2 emissions decline 
towards net zero.
• Better constraint of the airborne fraction to reduce the spread in 
TCRE assessment.
• Account for time scales of Earth system feedbacks over time 
for increased accuracy of mitigation needs once global CO 2 
emissions reach near-zero levels.
Carbon Dioxide Removal (CDR) and Solar Radiation 
• Run large-scale and long-term experiments and assessments to 
explore: the regional feasibility of CDR methods; whether they 
present an actual and verifiable negative regional carbon balance; 
and whether they result in adverse unintended consequences.
• Improve understanding of the effectiveness of CDR methods to 
lower atmospheric CO 2 and reduce warming, taking into account 
Earth system feedbacks.

771
Global Carbon and Other Biogeochemical Cycles and Feedbacks  Chapter 55Frequently Asked Questions
FAQ 5.1 | Is the Natural Removal of Carbon From the Atmosphere Weakening?
For decades, about half of the carbon dioxide (CO 2) that human activities have emitted to the atmosphere 
has been taken up by natural carbon sinks in vegetation, soils and oceans. These natural sinks of CO 2 have 
thus roughly halved the rate at which atmospheric CO 2 concentrations have increased, and therefore slowed 
down global warming. However, observations show that the processes underlying this uptake are beginning to 
respond to increasing CO 2 in the atmosphere and climate change in a way that will weaken nature’s capacity 
to take up CO 2 in the future. Understanding of the magnitude of this change is essential for projecting how the 
climate system will respond to future emissions and emissions reduction efforts.
Direct observations of CO 2 concentrations in

the atmosphere, which began in 1958, show that the 
atmosphere has only retained roughly half of the CO 2 
emitted by human activities, due to the combustion of 
fossil fuels and land-use change such as deforestation 
(FAQ 5.1, Figure 1). Natural carbon cycle processes on 
land and in the oceans have taken up the remainder 
of these emissions. These land and ocean removals or 
‘sinks’ have grown largely in proportion to the increase 
in CO 2 emissions, taking up 31% (land) and 23% 
(ocean) of the emissions in 2010–2019, respectively 
(FAQ 5.1, Figure 1). Therefore, the average proportion 
of yearly CO 2 emissions staying in the atmosphere 
has remained roughly stable at 44% over the last six 
decades, despite continuously increasing CO 2 emissions 
from human activities.
On land, it is mainly the vegetation that captures CO 2 
from the atmosphere through plant photosynthesis , 
which ultimately accumulates both in vegetation and 
soils.  As more CO 2 accumulates in the atmosphere,

plant carbon capture increases through the CO 2 
fertilization effect  in regions where plant growth is not 
limited by, for instance, nutrient availability. Climate 
change affects the processes responsible for the uptake 
and release of CO 2 on land in multiple ways. Land CO 2 
uptake is generally increased by longer growing seasons 
due to global warming in cold regions and by nitrogen 
deposition in nitrogen-limited regions. Respiration by 
plants and soil organisms, natural disturbances such 
as fires, and human activities such as deforestation all 
release CO 2 back into the atmosphere. The combined 
effect of climate change on these processes is to weaken 
the future land sink. In particular, extreme temperatures 
and droughts as well as permafrost thaw (see FAQ 5.2) 
tend to reduce the land sink regionally. In the ocean, 
FAQ 5.1, Figure  1 | Atmospheric carbon dioxide (CO 2) and natural 
carbon sink s. (Top)  Global emissions of CO 2 from human activities and the

growth rate of CO 2 in the atmosphere; (middle)  the net land and ocean CO 2 
removal (natural sinks); and  (bottom)  the fraction of CO 2 emitted by human 
activities remaining in atmosphere from 1960 to 2019. Lines are the five 
years running mean, error bars denote the uncertainty of the mean estimate. 
See Table 5.SM.6 for more information on the data underlying this figure.FAQ 5.1: Is natural removal of carbon 
from the atmosphere weakening?
No, natural carbon sinks have taken up a near constant 
fraction of our carbon dioxide (CO2) emissions over the 
last six decades. However, this fraction is expected to 
decline in the future if CO2 emissions continue to increase.
Natural sinks
CO 2 remaining in the atmosphere50
40
30
20
10
0
15
10
5
01960 1980 2000 2020
1960 1980 2000 2020
100%
80
60
40
20
0
1960 1980
Years2000 2020Billion tonnes of 
 CO2 per yearBillion tonnes of 
CO2 per yearProportion of emitted CO2remaining in the atmosphereHuman
caused 
emissions
Atmospheric

772
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5several factors control how much CO 2 is captured: the difference in CO 2 partial pressure between the atmosphere 
and the surface ocean; wind speeds at the ocean surface; the chemical composition of seawater (that is, its 
buffering capacity ), which affects how much CO 2 can be taken up; and the use of CO 2 in photosynthesis by 
seawater microalgae. The CO 2-enriched surface ocean water is transported to the deep ocean in specific zones 
around the globe (such as the Northern Atlantic and the Southern Ocean), effectively storing the CO 2 away from 
the atmosphere for many decades to centuries. The combined effect of warmer surface ocean temperatures on 
these processes is to weaken the future ocean CO 2 sink. 
The ocean carbon sink is better quantified than the land sink, thanks to direct ocean and atmospheric carbon

observations. The land carbon sink is more challenging to monitor globally, because it varies widely, even 
regionally. There is currently no direct evidence that the natural sinks are slowing down, because observable 
changes in the fraction of human emissions stored on land or in oceans are small compared to year-to-year 
and decadal variations of these sinks. Nevertheless, it is becoming more obvious that atmospheric and climate 
changes are affecting the processes controlling the land and ocean sinks.
Since the land and ocean sinks respond to the rise in atmospheric CO 2 and to human-induced global warming, 
the absolute amount of CO 2 taken up by land and ocean will be affected by future CO 2 emissions. This also implies 
that, if countries manage to strongly reduce global CO 2 emissions, or even remove CO 2 from the atmosphere, 
these sinks will take up less CO 2 because of the reduced human perturbation of the carbon cycle. Under future

high-warming scenarios, it is expected that the global ocean and land sinks will stop growing in the second 
half of the century as climate change increasingly affects them. Thus, the total amount of CO 2 emitted to the 
atmosphere and the responses of the natural CO 2 sinks will both determine what efforts are required to limit 
global warming to a certain level (see FAQ 5.4), underscoring how important it is to understand the evolution of 
these natural CO 2 sinks.FAQ 5.1 (continued)

773
FAQ 5.2 | Can Thawing Permafrost Substantially Increase Global Warming?
In the Arctic, large amounts of organic carbon are stored in permafrost – ground that remains frozen throughout 
the year. If significant areas of permafrost thaw as the climate warms, some of that carbon may be released 
into the atmosphere in the form of carbon dioxide or methane, resulting in additional warming. Projections 
from models of permafrost ecosystems suggest that future permafrost thaw will lead to some additional 
warming – enough to be important, but not enough to lead to a ‘runaway warming’ situation, where permafrost 
thaw leads to a dramatic, self-reinforcing acceleration of global warming.
The Arctic is the biggest climate-sensitive carbon pool on Earth, storing twice as much carbon in its frozen soils,

or permafrost , than is currently stored in the atmosphere. As the Arctic region warms faster than anywhere else 
on Earth, there are concerns that this warming could release greenhouse gases to the atmosphere and therefore 
significantly amplify climate change.
The carbon in the permafrost has built up over thousands of years, as dead plants have been buried and 
accumulated within layers of frozen soil, where the cold prevents the organic material from decomposing. As 
the Arctic warms and soils thaw, the organic matter in these soils begins to decompose rapidly and return to the 
atmosphere as either carbon dioxide or methane, which are both important greenhouse gases. Permafrost can 
also thaw abruptly in a given place, due to melting ice in the ground reshaping Arctic landscapes, lakes growing 
and draining, and fires burning away insulating surface soil layers. Thawing of permafrost carbon has already

been observed in the Arctic, and climate models project that much of the shallow permafrost (<3 m depth) 
throughout the Arctic would thaw under moderate to high amounts of global warming (2°C–4°C).
While permafrost processes are complex, they are beginning to be included in models that represent the 
interactions between the climate and the carbon cycle. The projections from these permafrost carbon models show 
a wide range in the estimated strength of a carbon–climate vicious circle, from both carbon dioxide and methane, 
equivalent to 14–175 billion tonnes of carbon dioxide released per 1°C of global warming. By comparison, in 
2019, human activities have released about 40 billion tonnes of carbon dioxide into the atmosphere. This has two 
implications. First, the extra warming caused by permafrost thawing is strong enough that it must be considered 
when estimating the total amount of remaining emissions permitted to stabilize the climate at a given level of

global warming (i.e., the remaining carbon budget, see FAQ 5.4). Second, the models do not identify any one 
amount of warming at which permafrost thaw becomes a ‘tipping point’ or threshold in the climate system 
that would lead to a runaway global warming. However, models do project that emissions would continuously 
increase with warming, and that this trend could last for hundreds of years.
Permafrost can also be found in other cold places (e.g., mountain ranges), but those places contain much less 
carbon than in the Arctic. For instance, the Tibetan plateau contains about 3% as much carbon as is stored in 
the Arctic. There is also concern about carbon frozen in shallow ocean sediments. These deposits are known as 
methane hydrates  or clathrates , which are methane molecules locked within a cage of ice molecules. They formed 
as frozen soils that were flooded when sea levels rose after the last ice age. If these hydrates thaw, they may

release methane that can bubble up to the surface. The total amount of carbon in permafrost-associated 
methane hydrates is much less than the carbon in permafrost soils. Global warming takes millennia to penetrate 
into the sediments beneath the ocean, which is why these hydrates are still responding to the last deglaciation. 
As a result, only a small fraction of the existing hydrates could be destabilised during the coming century. Even 
when methane is released from hydrates, most of it is expected to be consumed and oxidised into carbon dioxide 
in the ocean before reaching the atmosphere. The most complete modelling of these processes to date suggests 
a release to the atmosphere at a rate of less than 2% of current human-induced methane emissions.
Overall, thawing permafrost in the Arctic appears to be an important additional source of heat-trapping gases 
to the atmosphere, more so than undersea hydrates. Climate and carbon cycle models are beginning to consider

permafrost processes. While these models disagree on the exact amount of the heat-trapping gases that will be 
released into the atmosphere, they agree that: (i) the amount of such gases released from permafrost will increase 
with the amount of global warming; and (ii) the warming effect of thawing permafrost is significant enough to 
be considered in estimates of the remaining carbon budgets for limiting future warming.

774
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5FAQ 5.2, Figure 1 | The Arctic permafrost is a big pool of carbon that is sensitive to climate change.  (Left) Quantity of carbon stored in the 
permafrost, to 3 m depth (NCSCDv2 dataset) and (right)  area of permafrost vulnerable to abrupt thaw (Circumpolar Thermokarst Landscapes dataset).
FAQ5.2:
Can thawing permafrost substantially increase global temperatures?
Carbon stored in the  Arctic permafrost
Permafrost vulnerable to abrupt thaw
+
+North Pole
Arctic circleNorth Pole
Arctic circleThe thawing of frozen ground in the Arcrtic will release carbon that will amplify global warming but this will not lead 
to runaway warming.
0 1000 2000 km
0 20 40 60 80+Kg of organic carbon per m2FAQ 5.2 (continued)

775
FAQ 5.3 | Could Climate Change Be Reversed By Removing Carbon Dioxide From the Atmosphere?
Deliberate removal of carbon dioxide (CO 2) from the 
atmosphere could reverse (i.e.,  change the direction 
of) some aspects of climate change. However, this 
will only happen if it results in a net reduction in the 
total amount of CO 2 in the atmosphere, that is, if 
deliberate removals are larger than emissions. Some 
climate change trends, such as the increase in global 
surface temperature, would start to reverse within 
a  few years. Other aspects of climate change would 
take decades (e.g.,  permafrost thawing) or centuries 
(e.g., acidification of the deep ocean) to reverse, and 
some, such as sea level rise, would take centuries to 
millennia to change direction.
The term negative carbon dioxide (CO 2) emissions  
refers to the removal of CO 2 from the atmosphere by

deliberate human activities, in addition to removals 
that occur naturally, and is often used as synonymous 
with carbon dioxide remova l. Negative CO 2 emissions 
can compensate for the release of CO 2 into the 
atmosphere by human activities. They could be 
achieved by strengthening natural CO 2 sequestration 
processes on land (e.g., by planting trees or through 
agricultural practices that increase the carbon content 
of soils) and/or in the ocean (e.g.,  by restoration of 
coastal ecosystems) or by removing CO 2 directly from 
the atmosphere. If CO 2 removals are greater than 
human-caused CO 2 emissions globally, emissions are 
said to be net negative . It should be noted that CO 2 
removal technologies are unable, or not yet ready, to 
achieve the scale of removal that would be required to 
compensate for current levels of emissions, and most 
have undesired side effects.
In the absence of deliberate CO 2 removal, the CO 2 
concentration in the atmosphere (a measure of

the amount of CO 2 in the atmosphere) results from 
a  balance between human-caused CO 2 release and 
the removal of CO 2 by natural processes on land and 
in the ocean (natural ‘carbon sinks’; see FAQ 5.1). 
If  CO 2 release exceeds removal by carbon sinks, the 
CO 2 concentration in the atmosphere would increase;  
FAQ 5.3: Could climate change be reversed 
by removing CO2 from the atmosphere?
Removing more carbon dioxide (CO2) from the atmosphere 
than is emitted into it could reverse some aspects of climate 
change, but some changes would continue in their current 
direction for decades to millennia.
Atmospheric CO2 (ppm)
CO2 peak600
550
500
450
400
350
300
250
1900 2000 2100 2200 2300
YES, BUT YEARS
Global surface 
change (°C)2.5
2.0
1.5
1.0
0.5
0.0
-0.5
YES, BUT DECADES
Permafrost 
area change 
(millions of km2 )
Ocean thermal 
expansion (m)1
0
-1
-2
-3
-4
-5
-6
-7
-8
NO, CENTURIES, MILLENNIA
0.6
0.5
0.4
0.3
0.2
0.1

0.0
1900 2000 2100
Year2200 2300FAQ 5.3, Figure 1 | Changes in aspects of climate change in response 
to a  peak and decline in the atmospheric CO 2 concentration  (top 
panel).  The vertical grey dashed line indicates the time of peak CO 2 
concentration in all panels. It shows that the reversal of global surface warming 
lags the decrease in the atmospheric CO 2 concentration by a few years, the 
reversal of permafrost area decline lags the decrease in atmospheric CO 2 by 
decades, and ocean thermal expansion continues for several centuries. The 
quantitative information in the figure (i.e., numbers on vertical axes) is not to 
be emphasized as it results from simulations with just one model and will be 
different for other models. The qualitative behaviour, however, can be expected 
to be largely model independent.

776
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5if CO 2 release equals removal, the atmospheric CO 2 concentration would stabilize; and if CO 2 removal exceeds 
release, the CO 2 concentration would decline. This applies in the same way to net CO 2 emissions – that is, the sum 
of human-caused releases and deliberate removals.
If the CO 2 concentration in the atmosphere starts to go down, the Earth’s climate would respond to this change 
(FAQ 5.3, Figure 1). Some parts of the climate system take time to react to a change in CO 2 concentration, so 
a  decline in atmospheric CO 2 as a  result of net negative emissions would not lead to immediate reversal of 
all climate change trends. Recent studies have shown that global surface temperature starts to decline within 
a few years following a decline in atmospheric CO 2, although the decline would not be detectable for decades

due to natural climate variability (see FAQ 4.2). Other consequences of human-induced climate change, such as 
reduction in permafrost area, would take decades; yet others, such as warming, acidification and oxygen loss 
of the deep ocean, would take centuries to reverse following a decline in the atmospheric CO 2 concentration. 
Sea level would continue to rise for many centuries to millennia, even if large deliberate CO 2 removals were 
successfully implemented.
‘Overshoot’ scenarios are a  class of future scenarios that are receiving increasing attention, particularly in 
the context of ambitious climate goals, such as the global warming limits of 1.5°C or 2°C included in the Paris 
Agreement. In these scenarios, a slow rate of reduction in emissions in the near term is compensated by net 
negative CO 2 emissions in the later part of this century, which results in a temporary breach or ‘overshoot’ of

a given warming level. Due to the delayed reaction of several climate system components, it follows that the 
temporary overshoot would result in additional climate changes compared to a scenario that reaches the goal 
without overshoot. These changes would take decades to many centuries to reverse, with the reversal taking 
longer for scenarios with larger overshoot.
Removing more CO 2 from the atmosphere than is emitted into it would indeed begin to reverse some aspects 
of climate change, but some changes would still continue in their current direction for decades to millennia. 
Approaches capable of large-scale removal of CO 2 are still in the state of research and development or unproven 
at the scales of deployment necessary to achieve a  net reduction in atmospheric CO 2 levels. CO 2 removal 
approaches, particularly those deployed on land, can have undesired side effects on water, food production 
and biodiversity.FAQ 5.3 (continued)

777
FAQ 5.4 | What Are Carbon Budgets?
There are several types of carbon budgets. Most often, the term refers to the total net amount of carbon dioxide 
(CO 2) that can still be emitted by human activities while limiting global warming to a specified level (e.g., 1.5°C 
or 2°C above pre-industrial levels). This is referred to as the ‘remaining carbon budget’. Several choices and value 
judgements have to be made before it can be unambiguously estimated. When the remaining carbon budget is 
combined with all past CO 2 emissions to date, a ‘total carbon budget’ compatible with a specific global warming 
limit can also be defined. A third type of carbon budget is the ‘historical carbon budget’, which is a scientific way 
to describe all past and present sources and sinks of CO 2.
The term remaining carbon budget  is used to describe the total net amount of CO 2 that human activities can

still release into the atmosphere while keeping global warming to a specified level, like 1.5°C or 2°C relative 
to pre-industrial temperatures. Emissions of CO 2 from human activities are the main cause of global warming. 
A remaining carbon budget can be defined because of the specific way CO 2 behaves in the Earth system. That 
is, global warming is roughly linearly proportional to the total net amount of CO 2 emissions that are released 
into the atmosphere by human activities – also referred to as cumulative anthropogenic CO 2 emissions. Other 
greenhouse gases behave differently and have to be accounted for separately.
The concept of a  remaining carbon budget implies that, to stabilize global warming at any particular level, 
global emissions of CO 2 need to be reduced to net zero levels at some point. ‘Net zero CO 2 emissions’ describes 
a  situation where all the anthropogenic emissions of CO 2 are counterbalanced by deliberate anthropogenic

removals so that, on average, no CO 2 is added or removed from the atmosphere by human activities. Atmospheric 
CO 2 concentrations in such a situation would gradually decline to a long-term stable level as excess CO 2 in the 
atmosphere is taken up by ocean and land sinks (see FAQ 5.1). The concept of a remaining carbon budget also 
means that, if CO 2 emissions reductions are delayed, deeper and faster reductions are needed later to stay within 
the same budget. If the remaining carbon budget is exceeded, this will result in either higher global warming 
or a need to actively remove CO 2 from the atmosphere to reduce global temperatures back down to the desired 
level (see FAQ 5.3).
Estimating the size of remaining carbon budgets depends on a set of choices. These choices include: (1) the global 
warming level that is chosen as a limit (for example, 1.5°C or 2°C relative to pre-industrial levels); (2) the probability

with which we want to ensure that warming is held below that limit (for example, a one-in-two, two-in-three, 
or higher chance), and (3) how successful we are in limiting emissions of other greenhouse gases that affect the 
climate, such as methane or nitrous oxide. These choices can be informed by science, but ultimately represent 
subjective choices. Once these choices have been made, to estimate the remaining carbon budget for a given 
temperature goal, we can combine knowledge about: how much our planet has warmed already; the amount 
of warming per cumulative tonne of CO 2; and the amount of warming that is still expected once global net 
CO 2 emissions are brought down to zero. For example, to limit global warming to 1.5°C above pre-industrial 
levels with either a  one-in-two (50%) or two-in-three (67%) chance, the remaining carbon budgets amount 
to 500 and 400 billion tonnes of CO 2, respectively, from 1 January 2020 onward (FAQ 5.4, Figure 1). Currently,

human activities are emitting around 40 billion tonnes of CO 2 into the atmosphere in a single year.
The remaining carbon budget depends on how much the world has already warmed to date. This past warming 
is caused by historical emissions, which are estimated by looking at the historical carbon budget  – a scientific way 
to describe all past and present sources and sinks of CO 2. It describes how the CO 2 emissions from human activities 
have redistributed across the various CO 2 reservoirs of the Earth system. These reservoirs are the ocean, the land 
vegetation, and the atmosphere (into which CO 2 was emitted). The share of CO 2 that is not taken up by the 
ocean or the land, and that thus increases the concentration of CO 2 in the atmosphere, causes global warming. 
The historical carbon budget tells us that, of the about 2560 billion tonnes of CO 2 that were released into the

atmosphere by human activities between the years 1750 and 2019, about a quarter were absorbed by the ocean 
(causing ocean acidification) and about a third by the land vegetation. About 45% of these emissions remain in 
the atmosphere (see FAQ 5.1). Adding these historical CO 2 emissions to estimates of remaining carbon budgets 
allows an estimate of the total carbon budget  consistent with a specific global warming level.

778
Chapter 5  Global Carbon and Other Biogeochemical Cycles and F eedbacks5In summary, determining a remaining carbon budget – that is, how much CO 2 can be released into the atmosphere 
while stabilizing global temperature below a chosen level – is well understood but relies on a set of choices. 
However, it is clear that, for limiting warming below 1.5°C or 2°C, the remaining carbon budget from 2020 
onwards is much smaller than the total CO 2 emissions released to date.
FAQ 5.4, Figure 1 | Various types of carbon budgets.  Historical cumulative carbon dioxide (CO 2) emissions determine to a large degree how much the 
world has warmed to date, while the remaining carbon budget indicates how much CO 2 could still be emitted while keeping warming below specific temperature 
thresholds. Several factors limit the precision with which the remaining carbon budget can be estimated. Therefore, estimates need to specify the probability with

which they aim at limiting warming to the intended target level (e.g., limiting warming to 1.5°C with a 67% probability).
This remaining carbon budget 
can increase or decrease depending on how deeply we reduce greenhouse gases 
other than CO
2 FAQ 5.4: What are Carbon Budgets?
The term carbon budget is used in several ways. Most often the term refers to the total net amount of carbon dioxide 
(CO2) that can still be emitted by human activities while limiting global warming to a specified level. 
Historical budget
GtCO2 already emitted between 1750–2019
Remaining carbon budget
GtCO2 in line with keeping 
global warming to  1.5°C or 2°C
1.5°C
2°C2560  
GtCO2
+/-220
GtCO2(500) GtCO2
50% Probability 67%
(1350) GtCO2 1150 (Gt = billion tonnes)400FAQ 5.4 (continued)

819
Short-lived Climate Forcers  Chapter 66Executive Summary
Short‑lived climate forcers (SLCFs) affect climate and are, in most 
cases, also air pollutants. They include aerosols (sulphate, nitrate, 
ammonium, carbonaceous aerosols, mineral dust and sea spray), 
which are also called particulate matter (PM), and chemically 
reactive gases (methane, ozone, some halogenated compounds, 
nitrogen oxides, carbon monoxide, non‑methane volatile organic 
compounds, sulphur dioxide and ammonia). Except for methane and 
some halogenated compounds whose lifetimes are about a decade 
or more, SLCF abundances are spatially highly heterogeneous since 
they only persist in the atmosphere from a few hours to a couple 
of months.  SLCFs are either radiatively active or influence the 
abundances of radiatively active compounds through chemistry 
(chemical adjustments), and their climate effect occurs predominantly 
in the first two decades after their emission or formation. They can

have either a  cooling or warming effect on climate, and they also 
affect precipitation and other climate variables. Methane and some 
halogenated compounds are included in climate treaties, unlike 
the other SLCFs that are nevertheless indirectly affected by climate 
change mitigation since many of them are often co‑emitted with 
CO 2 in combustion processes. This chapter assesses the changes, in 
the past and in a selection of possible futures, of the emissions and 
abundances of individual SLCFs primarily on global to continental 
scales, and how these changes affect the Earth’s energy balance 
through radiative forcing and feedback in the climate system. 
The  attribution of climate and air ‑quality changes to emissions 
sectors and regions, and the effects of SLCF mitigations defined for 
various environmental purposes, are also assessed. 
Recent Evolution in Short-lived Climate Forcer (SLCF) 
Emissions and Abundances 
Over the last decade (2010–2019), strong shifts in the

geographical distribution of emissions have led to changes 
in atmospheric abundances of highly variable SLCFs ( high 
confidence ). Evidence from satellite and surface observations 
shows strong regional variations in trends of ozone (O 3), 
aerosols and their precursors ( high confidence ). In particular, 
tropospheric columns of nitrogen dioxide (NO 2) and sulphur dioxide 
(SO 2) continued to decline over North America and Europe ( high 
confidence ), and to increase over Southern Asia ( medium confidence ), 
but have declined over Eastern Asia ( high confidence ). Global 
carbon monoxide (CO) abundance has continued to decline ( high 
confidence ). The concentrations of hydrofluorocarbons (HFCs) are 
increasing ( high confidence ). Global carbonaceous aerosol budgets 
and trends remain poorly characterized due to limited observations, 
but sites representative of background conditions have reported 
multi‑year declines in black carbon (BC) over several regions of the

Northern Hemisphere. {6.2, 6.3, 2.2.4, 2.2.5, 2.2.6}
There is no significant trend in the global mean tropospheric 
concentration of hydroxyl (OH) radical – the main sink for many 
SLCFs, including methane (CH 4) – from 1850 up to around 1980 
(low confidence ) but OH has remained stable or exhibited 
a positive trend since the 1980s ( medium confidence ). Global OH cannot be measured directly and is inferred from Earth system and 
chemistry–climate models (ESMs, CCMs) constrained by emissions 
and from observationally constrained inversion methods. There is 
conflicting information from these methods for the 1980–2014 
period. ESMs and CCMs concur on a positive trend since 1980 (about 
a  9% increase over 1980–2014) and there is medium confidence  
that this trend is mainly driven by increases in global anthropogenic 
(human‑caused) nitrogen oxide (NO x) emissions and decreases in 
anthropogenic CO emissions. The observation‑constrained methods

suggest either positive trends or the absence of trends based on 
limited evidence  and medium agreement. Future changes in global 
OH, in response to SLCF emissions and climate change, will depend 
on the interplay between multiple offsetting drivers of OH. {6.3.6 and 
Cross‑Chapter Box 5.1}
Effect of SLCFs on Climate and Biogeochemical Cycles 
Over the historical period, changes in aerosols and their 
effective radiative forcing (ERF) have primarily contributed to 
a surface cooling, partly masking the greenhouse gas-driven 
warming ( high confidence ). Radiative forcings induced by aerosol 
changes lead to both local and remote temperature responses ( high 
confidence ). The temperature response preserves the south to north 
gradient of the aerosol ERF – hemispherical asymmetry – but is more 
uniform with latitude and is strongly amplified towards the Arctic 
(medium confidence ). {6.4.1, 6.4.3}
Since the mid-1970s, trends in aerosols and their precursor

emissions have led to a shift from an increase to a decrease of 
the magnitude of the negative globally averaged net aerosol 
ERF ( high confidence ). However, the timing of this shift varies by 
continental‑scale region and has not occured for some finer regional 
scales. The spatial and temporal distribution of the net aerosol ERF 
from 1850 to 2014 is highly heterogeneous, with stronger magnitudes 
in the Northern Hemisphere ( high confidence ). {6.4.1}
For forcers with short lifetimes (e.g.,  months) and not 
considering chemical adjustments, the response in surface 
temperature occurs strongly as soon as a  sustained change 
in emissions is implemented, and that response continues to 
grow for a few years, primarily due to thermal inertia in the 
climate system ( high confidence ). Near its maximum, the response 
slows down but will then take centuries to reach equilibrium ( high 
confidence ). For SLCFs with longer lifetimes (e.g., a decade), a delay

equivalent to their lifetimes is appended to the delay due to thermal 
inertia ( high confidence ). {6.6.1}
Over the 1750–2019 period, changes in SLCF emissions, 
especially of methane, NO x and SO 2, have substantial effects 
on effective radiative forcing (ERF) ( high confidence ). The 
net global emissions‑based ERF of NO x is negative and that of 
non‑methane volatile organic compounds (NMVOCs) is positive, in 
agreement with the AR5 Assessment ( high confidence ). For methane, 
the emissions‑based ERF is twice as high as the abundance‑based ERF 
(high confidence ) attributed to chemical adjustment mainly via ozone 
production. SO 2 emissions changes make the dominant contribution

820
Chapter 6 Short-lived Climate Forcers6to the ERF from aerosol–cloud interactions ( high confidence ). 
Over the 1750–2019 period, the contributions from the emitted 
compounds to changes in global surface air temperature (GSAT) 
broadly match their contributions to the ERF ( high confidence ). Since 
a peak in emissions‑induced SO 2 ERF has already occurred recently 
and since there is a delay in the full GSAT response, changes in SO 2 
emissions have a slightly larger contribution to GSAT change than 
CO 2 emissions, relative to their respective contributions to ERF . {6.4.2, 
6.6.1 and 7.3.5} 
Reactive nitrogen, ozone and aerosols affect terrestrial 
vegetation and the carbon cycle through deposition and 
effects on large-scale radiation ( high confidence ). However, 
the magnitude of these effects on the land carbon sink, ecosystem 
productivity and hence their indirect CO 2 forcing remain uncertain 
due to the difficulty in disentangling the complex interactions

between the individual effects.  As such, these effects are assessed 
to be of second order in comparison to the direct CO 2 forcing ( high 
confidence ), but effects of ozone on terrestrial vegetation could 
add a substantial (positive) forcing compared with the direct ozone 
forcing ( low confidence ). {6.4.4}
Climate feedbacks induced from changes in emissions, 
abundances or lifetimes of SLCFs mediated by natural 
processes or atmospheric chemistry are assessed to have an 
overall cooling effect ( low confidence ), that is, a total negative 
feedback parameter of –0.20 [–0.41 to +0.01] W m–2 °C−1. These 
non‑CO 2 biogeochemical feedbacks are estimated from ESMs, which 
have advanced since AR5 to include a consistent representation of 
biogeochemical cycles and atmospheric chemistry. However, process‑
level understanding of many chemical and biogeochemical feedbacks 
involving SLCFs, particularly natural emissions, is still emerging,

resulting in low confidence  in the magnitude and sign of most SLCF 
climate  feedback parameters. {6.2.2, 6.4.5} 
Future Projections for Air Quality Considering Shared 
Socio-economic Pathways (SSPs) 
Future air quality (in term of surface ozone and PM 
concentrations) on global to local scales will be primarily 
driven by changes in precursor emissions as opposed to 
climate change ( high confidence ) and climate change is 
projected to have mixed effects. A warmer climate is expected 
to reduce surface ozone in regions remote from pollution sources 
(high confidence ) but is expected to increase it by a few parts per 
billion over polluted regions, depending on ozone precursor levels 
(medium to high confidence ). Future climate change is expected to 
have mixed effects, positive or negative, with an overall low effect, 
on global surface PM and more generally on the aerosol global 
burden ( medium confidence ), but stronger effects are not excluded in

regions prone to specific meteorological conditions ( low confidence ). 
Overall, there is low confidence  in the response of surface ozone and 
PM to future climate change due to the uncertainty in the response 
of the natural processes (e.g., stratosphere–troposphere exchange, 
natural precursor emissions, particularly including biogenic volatile organic compounds, wildfire‑emitted precursors, land and marine 
aerosols, and lightning NO x) to climate change. {6.3, 6.5}
The SSPs span a  wider range of SLCF emissions than the 
Representative Concentration Pathways (RCPs), representing 
better the diversity of future options in air pollution 
management ( high confidence ). In the SSPs, the socio‑economic 
assumptions and climate change mitigation levels primarily drive 
future emissions, but the SLCF emissions trajectories are also 
steered by varying levels of air pollution control originating from 
the SSP narratives, independently from climate change mitigation.

Consequently, SSPs consider a  large variety of regional ambitions 
and effectiveness in implementing air pollution legislation and result 
in wider range of future air pollution levels and SLCF‑induced climate 
effects. {6.7.1}
Air pollution projections range from strong reductions in global 
surface ozone and PM (e.g., SSP1-2.6, with strong mitigation 
of both air pollution and climate change) to no improvement 
and even degradation (e.g., SSP3-7.0 without climate change 
mitigation and with only weak air pollution control) ( high 
confidence ). Under the SSP3‑7.0 scenario, PM levels are projected 
to increase until 2050 over large parts of Asia, and surface ozone 
pollution is projected to worsen over all continental areas through 
2100 ( high confidence ). Without climate change mitigation but with 
stringent air pollution control (SSP5‑8.5), PM levels decline through 
2100, but high methane levels hamper the decline in global surface

ozone at least until 2080 ( high confidence ). {6.7.1}
Future Projections of the Effect of SLCFs on GSAT in the 
Core SSPs 
In the next two decades, it is very likely  that the SLCF 
emissions changes in the WGI core set of SSPs will cause 
a  warming relative to 2019, whatever the SSPs, in addition 
to the warming from long-lived greenhouse gases. The net 
effect of SLCF and hydrofluorocarbon  (HFC) changes on 
GSAT across the SSPs  is a likely  warming of 0.06°C–0.35°C in 
2040 relative to 2019. Warming over the next two decades 
is quite similar across the SSPs due to competing effects of 
warming (methane, ozone) and cooling (aerosols) SLCFs . For 
the scenarios with the most stringent climate and air pollution 
mitigations (SSP1‑1.9 and SSP1‑2.6), the likely  near ‑term warming 
from the SLCFs is predominantly due to sulphate aerosol reduction, 
but this effect levels off after 2040. In the absence of climate change

policies and with weak air pollution control (SSP3‑7.0), the likely  
near‑term warming due to changes in SLCFs is predominantly due 
to increases in methane, ozone and HFCs, with smaller contributions 
from changes in aerosols. SSP5‑8.5 has the highest SLCF‑induced 
warming rates due to warming from methane and ozone increases 
and reduced aerosols due to stronger air pollution control compared 
to the SSP3‑7.0 scenario.  {6.7.2}  
At the end of the century, the large diversity of GSAT response 
to SLCF changes among the scenarios robustly covers the 
possible futures, as the scenarios are internally consistent

821
Short-lived Climate Forcers  Chapter 66and span a  range from very high to very low emissions.  In 
the scenarios without climate change mitigation (SSP3‑7.0 and 
SSP5‑8.5) the  likely  range of the estimated warming due to SLCFs 
in 2100 relative to 2019 is 0.4°C–0.9°C {6.7.3, 6.7.4}. In SSP3‑7.0 
there is a near ‑linear warming due to SLCFs of 0.08°C per decade, 
while for SSP5‑8.5 there is a  more rapid warming in the first half 
of the century. For the scenarios considering the most stringent 
climate and air pollution mitigations (SSP1‑1.9 and SSP1‑2.6), the 
reduced warming from reductions in methane, ozone and HFCs partly 
balances the warming from reduced aerosols, and the overall SLCF 
effect is a  likely  increase in GSAT of 0.0°C–0.3°C in 2100, relative 
to 2019. The SSP2‑4.5 scenario (with moderate climate change and 
air pollution mitigations) results in a  likely warming of 0.2°C–0.5°C 
in 2100 due to SLCFs, with the largest warming from reductions in

aerosols.  {6.7.3}
Potential Effects of SLCF Mitigation 
Over time scales of 10 to 20 years, the global temperature 
response to a year’s worth of current emissions of SLCFs is at 
least as large as that due to a year’s worth of CO 2 emissions 
(high confidence ). Sectors producing the largest SLCF-induced 
warming are those dominated by methane emissions: fossil 
fuel production and distribution, agriculture and waste 
management ( high confidence ). On these time scales, SLCFs 
with cooling effects can significantly mask the CO 2 warming in the 
case of fossil fuel combustion for energy and land transportation, 
or completely offset the CO 2 warming and lead to an overall net 
cooling in the case of industry and maritime shipping (prior to the 
implementation of the revised fuel‑sulphur limit policy for shipping 
in 2020) ( medium confidence ). Ten years after a one‑year pulse of 
present‑day aviation emissions, SLCFs induce strong but short‑lived

warming contributions to the GSAT response ( medium confidence ), 
while CO 2 both gives a warming effect in the near term and dominates 
the long‑term warming impact ( high-confidence ). {6.6.1, 6.6.2}
The effects of SLCFs decay rapidly over the first few decades 
after pulse emission. Consequently, on time scales longer 
than about 30 years, the net long-term global temperature 
effects of sectors and regions are dominated by CO 2 (high 
confidence ). The global mean temperature response following 
a climate change mitigation measure that affects emissions of both 
short‑ and long‑lived climate forcers depends on their atmospheric 
decay times, how fast and for how long the emissions are reduced, 
and the inertia in the climate system. For SLCFs including methane, 
the rate of emissions drives the long‑term global temperature effect, 
as opposed to CO 2 for which the long‑term global temperature effect 
is controlled by the cumulative emissions. About 30 years or more

after a one‑year emission pulse occurs, the sectors contributing the 
most to global warming are industry, fossil fuel combustion for energy 
and land transportation, essentially through CO 2 (high confidence ). 
Current emissions of SLCFs, CO 2 and N 2O from Eastern Asia and North 
America are the largest regional contributors to additional net future 
warming on both short ( medium confidence ) and long time scales 
(high confidence ). {6.6.1, 6.6.2}At present, emissions from the residential and commercial 
sectors (fossil and biofuel use for cooking and heating) and 
the energy sector (fossil fuel production, distribution and 
combustion) contribute the most to the world population’s 
exposure to anthropogenic fine PM ( high confidence ), 
whereas emissions from the energy and land transportation 
sectors contribute the most to ozone exposure ( medium to  
high confidence ). The contribution of different sectors to PM varies

across regions, with the residential sector being the most important 
in Southern Asia and Africa, agricultural emissions dominating in 
Europe and North America, and industry and energy production 
dominating in Central and Eastern Asia, Latin America and the 
Middle East. Energy and industry are important PM 2.5 contributors in 
most regions, except Africa ( high confidence ). Sector contributions to 
surface ozone concentrations are similar for all regions. {6.6.2}
Assuming implementation and efficient enforcement of both 
the Kigali Amendment to the Montreal Protocol on Ozone 
Depleting Substances and current national plans to limit 
emissions (as in SSP1-2.6), the effects of HFCs on GSAT, relative 
to 2019, would remain below +0.02°C from 2050 onwards 
versus about +0.04°C to +0.08°C in 2050 and +0.1°C to +0.3°C 
in 2100 considering only national HFC regulations decided 
prior to the Kigali Amendment (as in SSP5-8.5) ( medium

confidence ). Further improvements in the efficiency of refrigeration 
and air ‑conditioning equipment during the transition to low‑global‑
warming‑potential refrigerants would bring additional greenhouse 
gas reductions ( medium confidence ) resulting in benefits for climate 
change mitigation and to a lesser extent for air quality due to reduced 
air pollutant emissions from power plants . {6.6.3, 6.7.3} 
Future changes in SLCFs are expected to cause additional 
warming. This warming is stable after 2040 in scenarios leading 
to lower global air pollution as long as methane emissions 
are also mitigated, but the overall warming induced by SLCF 
changes is higher in scenarios in which air quality continues to 
deteriorate (induced by growing fossil fuel use and limited air 
pollution control) ( high confidence ). If a strong air pollution control 
resulting in reductions in anthropogenic aerosols and non‑methane

ozone precursors was considered in SSP3‑7.0, it would lead to a  likely  
additional near ‑term global warming of 0.08  [0.00  to  0.10]  °C in 
2040. An additional concomitant methane mitigation (consistent 
with SSP1’s stringent climate change mitigation policy implemented 
in the SSP3 world) would not only alleviate this warming but would 
turn this into a cooling of 0.07 °C with a  likely  range of [–0.02 to 
+0.14]  °C (compared with SSP3‑7.0 in 2040) . Across the SSPs, the 
collective reduction of methane, ozone precursors and HFCs can 
make a difference of 0.2°C with a  very likely  range of [0.1 to 0.4] °C 
in 2040 and 0.8°C with a  very likely  range of [0.5 to 1.3] °C at the 
end of the 21 st century (comparing SSP3‑7.0 and SSP1‑1.9), which is 
substantial in the context of the Paris Agreement. Sustained methane 
mitigation, wherever it occurs, stands out as an option that combines 
near‑ and long‑term gains on surface temperature ( high confidence )

and leads to air ‑quality benefits by reducing surface ozone levels 
globally ( high confidence ). {6.6.3, 6.7.3, 4.4.4 }

822
Chapter 6 Short-lived Climate Forcers6Rapid decarbonization strategies lead to air-quality 
improvements but are not sufficient to achieve, in the near 
term, air-quality guidelines set for fine PM by the World Health 
Organization (WHO), especially in parts of Asia and in some 
other highly polluted regions ( high confidence ). Additional 
methane and BC mitigation would contribute to offsetting the 
additional warming associated with SO 2 reductions that would 
accompany decarbonization ( high confidence ). Strong air pollution 
control as well as strong climate change mitigation, implemented 
separately, lead to large reductions in exposure to air pollution by the 
end of the century ( high confidence ). Implementation of air pollution 
controls, relying on the deployment of existing technologies, leads 
more rapidly to air quality benefits than climate change mitigation 
(high confidence ), which requires systemic changes. However,

in both cases, significant parts of the population are projected to 
remain exposed to air pollution exceeding the WHO guidelines ( high 
confidence ). Additional policies envisaged to attain Sustainable 
Development Goals (SDGs; e.g.,  access to clean energy, waste 
management) bring complementary SLCF reduction. Only strategies 
integrating climate, air quality, and development goals are found to 
effectively achieve multiple benefits. {6.6.3, 6.7.3, Box 6.2 }
Implications of COVID-19 Restrictions for Emissions,  
Air Quality and Climate 
Emissions reductions associated with COVID-19 containment 
led to a discernible temporary improvement of air quality in 
most regions, but changes to global and regional climate are 
undetectable above internal variability. Global anthropogenic 
NO x emissions decreased by a maximum of about 35% in April 2020 
(medium confidence ). There is high confidence  that, with the 
exception of surface ozone, these emissions reductions have

contributed to improved air quality in most regions of the world. 
Global fossil CO 2 emissions decreased by 7% (with a  range of  
5.8–13.0%) in 2020 relative to 2019, largely due to reduced emissions 
from the transportation sector ( medium confidence ). Overall, the net 
ERF , relative to ongoing trends, from COVID‑19 restrictions was likely 
small and positive for 2020 (less than 0.2 W m–2), thus temporarily 
adding to the total anthropogenic climate influence, with positive 
forcing from aerosol changes dominating over negative forcings 
from CO 2, NO x and contrail cirrus changes. Consistent with this small 
net radiative forcing, and against a  large component of internal 
variability, Earth system model simulations show no detectable effect 
on global or regional surface temperature or precipitation ( high 
confidence ). {Cross‑Chapter Box 6.1}

823
Short-lived Climate Forcers  Chapter 666.1 Introduction 
Short‑lived climate forcers (SLCFs) are a  set of chemically and 
physically reactive compounds with atmospheric lifetimes typically 
shorter than two decades but differing in terms of physiochemical 
properties and environmental effects. SLCFs can be classified as 
direct or indirect, with direct SLCFs exerting climate effects through 
their radiative forcing and indirect SLCFs being precursors of direct 
climate forcers. Direct SLCFs include methane (CH 4), ozone (O 3), short‑
lived halogenated compounds, such as hydrofluorocarbons (HFCs), 
hydrochlorofluorocarbons (HCFCs), and aerosols. Indirect SLCFs 
include nitrogen oxides (NO x), carbon monoxide (CO), non‑methane 
volatile organic compounds (NMVOCs), sulphur dioxide (SO 2), and 
ammonia (NH 3). Aerosols consist of sulphate (SO2
4 –), nitrate (NO–
3 ), ammonium (NH+
4), carbonaceous aerosols (e.g.,  black carbon (BC),

organic aerosols (OA)), mineral dust, and sea spray (see Table 6.1) 
and can be present as internal or external mixtures and at sizes from 
nano‑meters to tens of micro‑meters. SLCFs can be emitted directly 
from natural systems and anthropogenic sources (primary) or can be 
formed by reactions in the atmosphere (secondary; Figure 6.1). 
6.1.1 Importance of SLCFs for Climate and Air Quality
The atmospheric lifetime determines the spatial and temporal 
variability, with most SLCFs showing high variability, except 
methane and many HCFCs and HFCs that are also well‑mixed (as 
a consequence methane is discussed together with other well‑mixed 
Sources and processes contributing to SLCFs and 
their effects on the climate system
O3 O3O(1D)
OHPAN
HO2HNO3NO2
NO
AIR POLLUTION
  EMISSIONS FROM NATURAL SYSTEMS CLIMATE SYSTEM CHANGES
RADIATIVE FORCING 
ANTHROPOGENIC EMISSIONS ATMOSPHERIC COMPOSITION

atmospheric chemistry and physicsabsorption scattering albedo changestemperature weather patterns precipitation cryosphereclimate feedback
air pollution effects
Figure 6.1 | Sources and processes leading to atmospheric short-lived climate forcer (SLCF) burden and their interactions with the climate system.  Both 
direct and indirect SLCFs and the role of atmospheric processes for the lifetime of SLCFs are depicted. Anthropogenic emissions sectors illustrated are: fossil fuel exploration, 
distribution and use; biofuel production and use; waste; transport; industry; agricultural sources; and open biomass burning. Emissions from natural systems include those from 
open biomass burning, vegetation, soil, ocean, lightning and volcanoes. SLCFs interact with solar or terrestrial radiation, surface albedo, and cloud or precipitation systems.

The radiative forcing due to individual SLCFs can be either positive or negative. Climate change induces changes in emissions from most natural systems as well as from some 
anthropogenic emissions sectors (e.g., agriculture) leading to a climate feedback (purple arrows). Climate change also influences atmospheric chemistry processes, such as 
chemical reaction rates or via circulation changes, thus affecting atmospheric composition leading to a climate feedback. Air pollutants influence emissions from terrestrial 
vegetation, including agriculture (grey arrow).

824
Chapter 6 Short-lived Climate Forcers6greenhouse gases (GHGs) in Chapters 2, 5, and 7). In contrast to well‑
mixed GHGs, such as CO 2, methane and some HFCs, the radiative 
forcing effects of most SLCFs are largest at regional scales and 
climate effects predominantly occur in the first two decades after their emissions or formation. However, changes in their emissions 
can also induce long‑term climate effects, for instance by altering 
some biogeochemical cycles.  Therefore, the temporal evolution of 
radiative effects of SLCFs follows that of emissions, that is, when SLCF 
Table 6.1 | Overview of SLCFs of interest for Chapter 6.  For each SLCF , its source types, lifetime in the atmosphere, and associated radiatively active agent is given. Source 
type can be primary (emitted) and/or secondary (formed through multiple atmospheric mechanisms). Unless otherwise noted, the stated lifetime refers to tropospheric lifetime.*

Climate effect of increased SLCFs is indicated as ‘+’ for warming and ‘–’ for cooling. ‘Direct’ is used for SLCFs exerting climate effects through their radiative forcing and 
‘Indirect’ for SLCFs which are precursors affecting the atmospheric burden of other climatically active compounds. Other processes through which SLCFs affect climate are listed 
where applicable. The World Health Organization (WHO) guidelines for air quality (AQ) are given, where applicable, to show which SLCFs are regulated for air-quality purposes. 
Compounds Source Type Lifetime Direct Indirect Climate 
ForcingOther Effects 
on ClimateaWHO AQ 
Guidelinesb 
CH 4 Primary~9 years 
~12 years  
(perturbation time)CH 4 O3, H2O, CO 2 +
 Noc
O3 Secondary Hours to weeks O3CH 4, secondary 
organic and sulphate 
aerosols+Ecosystems100 μg m–3 
8‑hour mean
NO x (= NO + NO 2) Primary Hours to daysO3, nitrate aerosols, 
CH 4+/– Ecosystems40 μg m–3 
annual mean  
200 μg m–3 
1‑hour mean
COPrimary +

Secondary 1 to 4 months O3, CH 4+ No
NMVOCs**Primary + 
SecondaryHours to monthsO3, CH 4,  
organic aerosols+/– No
SO 2 PrimaryDays (trop.)  
to weeks (strat.)Sulphate and nitrate 
aerosols,  O3–  Ecosystems20 μg m–3 
24‑hour mean 
500 μg m–3 
10‑minute mean
NH 3 Primary Hours Ammonium  
Sulphate,  
Ammonium Nitrate– Ecosystems No
HCFCs Primary Months to years HCFCs O3 +/– Noc
HFCs Primary Days to years HFCs +  Noc
Halons and Methylbromide Primary YearsHalons and 
MethylbromideStratospheric O 3 +/– Noc
Very Short-lived Halogenated 
Species (VSLSs)Primary Less than 6 months O3 – Noc
Sulphate aerosols  Secondary Minutes to weeks Sulphate  –Clouds
Ecosystemsas part of PMd
Nitrate aerosols  Secondary Minutes to weeks Nitrate –Clouds
Carbonaceous Aerosols Primary + 
SecondaryMinutes to Weeks BC, OA +/–Cryo, Clouds
Sea spray Primary Day to week Sea spray –Clouds

Mineral dust Primary Minutes to Weeks Mineral dust +/–Cryo Cloud
* For lifetimes reported in this table, it is assumed that the compounds are uniformly mixed throughout the troposphere, however, this assumption is unlikely for compounds 
with lifetimes <1 year and, therefore, the reported values should be viewed as approximations (Prather et al., 2001). 
** Some NMVOCs are biogenic volatile organic compounds (BVOCs).
 a Clouds: effect on clouds through aerosol–cloud interactions, Ecosystems: effect on ecosystems through changes in radiation and deposition, Cryo: effect on planetary albedo 
through deposition on snow and ice; b Krzyzanowski and Cohen (2008); c regulated through Kyoto/Montreal protocols; d For Particulate Matter with diameter <2.5 µm (PM 2.5): 
10 µg m–3 annual mean or 25 µg m–3 24-hour mean (99th percentile) and for Particulate Matter with diameter <10 µm (PM 10): 20 µg m–3 annual mean or 50 µg m–3 24-hour 
mean (99th percentile).

825
Short-lived Climate Forcers  Chapter 66emissions decline to zero their atmospheric abundance and radiative 
effects decline towards zero. The total influence of individual SLCF 
emissions on radiative forcing and climate includes their effects 
on the abundances of other forcers through chemistry (chemical 
adjustments).
SLCFs can affect climate by interacting with radiation or by 
perturbing other components of the climate system (e.g.,  the 
cryosphere and carbon cycle through deposition, or the water cycle 
through modifications of cloud properties via cloud condensation 
nuclei or ice nuclei). SLCFs can have either net warming or net 
cooling effects on climate. In addition to altering the Earth’s radiative 
balance, many SLCFs are also air pollutants with adverse effects 
on human health and ecosystems. SLCFs are of interest for climate 
policies (e.g.,  methane, HFCs), and are regulated as air pollutants 
(e.g., aerosols, ozone) or because of their deleterious influence on

stratospheric ozone (e.g., HCFCs). The list of SLCFs assessed in this 
chapter and their effects are provided in Table 6.1. 
As depicted in Figure  6.1, emissions of SLCFs are governed 
by anthropogenic activities and sources from natural systems 
(see Section 6.2 for details). Atmospheric chemistry in this context 
is both a  source and a  sink of SLCFs. For instance, ozone and 
secondary aerosols are exclusively formed through atmospheric 
mechanisms (Sections 6.3.2 and 6.3.5 respectively). The hydroxyl 
(OH) radical, the most important oxidizing agent in the troposphere, 
acts as a  sink for SLCFs by reacting with them and thereby 
influencing their lifetime (Section  6.3.6). Through SLCF radiative 
forcing and feedbacks (Section 6.4), key climate parameters, such as 
temperature, hydrological cycle and weather patterns are perturbed. 
Climate change also influences air quality (Section 6.5). As depicted 
in Figure 6.1, SLCFs affect both climate and air quality, hence SLCF

mitigation has linkages to both issues (Section 6.6). Socio‑economic 
narratives including air ‑quality policies determine future projections 
of SLCFs in the five core Shared Socio‑economic Pathways (SSPs): 
SSP1‑1.9, SSP1‑2.6, SSP2‑4.5, SSP3‑7.0, and SSP5‑8.5 (described 
in Chapter 1), and in addition, a subset of SSP3 scenarios make it 
possible to isolate the effect of various SLCF mitigation trajectories 
on climate and air quality (Section 6.7).
6.1.2 Treatment of SLCFs in Previous Assessments
Although ozone, aerosols and their precursors have been considered 
in previous IPCC assessment reports, AR5 considered SLCFs as 
a  specific category of climate‑relevant compounds but referred 
to them as near ‑term climate forcers (NTCFs; Myhre et al., 2013) . 
In AR5, the linkages between air quality and climate change were 
also considered in a  more detailed and quantitative way than in 
previous reports (Kirtman et al., 2013; Myhre et al., 2013).

The AR5 WGI assessed radiative forcings for short‑lived gases, 
aerosols, aerosol precursors and aerosol–cloud interactions as well 
as the evolution of confidence levels in the forcing mechanisms from 
SAR to AR5. Whereas the forcing mechanisms for ozone and aerosol–
radiation interactions were estimated to be characterized with high 
confidence , the ones induced by aerosols through other processes remained of very low to low confidence . The AR5 also reported that 
forcing agents such as aerosols and ozone are highly heterogeneous 
spatially and temporally, and these patterns affect global and 
regional temperature responses as well as other aspects of climate 
response such as the hydrologic cycle (Myhre et al., 2013) .
The AR5 WGI also evaluated the air quality‑climate interaction 
through the projected trends of surface ozone and PM 2.5. Kirtman 
et al. (2013) concluded with high confidence  that the response of air

quality to climate‑driven changes is more uncertain than the response 
to emissions‑driven changes, and also that locally higher surface 
temperatures in polluted regions will trigger regional feedbacks in 
chemistry and local emissions that will increase peak levels of ozone 
and PM 2.5 (medium confidence ).
In the IPCC Special Report on Global Warming of 1.5°C (SR1.5; 
Allen et al., 2018a), Rogelj et al. (2018a) state that the evolution 
of methane and SO 2 emissions strongly influences the chances 
of limiting warming to 1.5°C, and that, considering mitigation 
scenarios to limit warming to 1.5°C or 2°C, a weakening of aerosol 
cooling would add to future warming in the near term, but can be 
tempered by reductions in methane emissions ( high confidence ). In 
addition, as some SLCFs are co‑emitted alongside CO 2, especially 
in the energy and transport sectors, low CO 2 scenarios, relying on 
decline of fossil fuel use, can result in strong abatement of some

cooling and warming SLCFs (Rogelj et al., 2018a). On the other hand, 
specific reductions of the warming SLCFs (methane and BC) would, 
in the short term, contribute significantly to the efforts of limiting 
warming to 1.5°C. Reductions of BC and methane would have 
substantial co‑benefits, improving air quality and therefore limiting 
effects on human health and agricultural yields. This would, in 
turn, enhance the institutional and socio‑cultural feasibility of such 
actions in line with the United Nations’ Sustainable Development 
Goals (SDGs; Coninck et al., 2018).
Following SR1.5, the IPCC Special Report on Climate Change and 
Land (SRCCL; IPCC, 2019a) took into consideration the emissions 
on land of three major SLCFs: mineral dust, carbonaceous aerosols 
(BC and OA) and biogenic volatile compounds (BVOCs) (Jia et al., 
2019). The SRCCL concluded that: (i) there is no agreement about 
the direction of future changes in mineral dust emissions; (ii) fossil

fuel and biomass burning, and secondary organic aerosols (SOA) 
from natural BVOC emissions are the main global sources of 
carbonaceous aerosols whose emissions are expected to increase in 
the near future due to possible increases in open biomass burning 
and increase in SOA from oxidation of BVOCs ( medium confidence ); 
and (iii) BVOCs are emitted in large amounts by forests and they are 
rapidly oxidized in the atmosphere to form less volatile compounds 
that can condense and form SOA, and in a warming planet, BVOC 
emissions are expected to increase but magnitude is unknown and 
will depend on future land‑use change, in addition to climate ( limited 
evidence , medium agreement ).
Finally, the IPCC Special Report on the Ocean and Cryosphere in 
a Changing Climate (SROCC; IPCC, 2019b) discussed the effects of 
BC deposition on snow and glaciers, concluding that there is high 
confidence that darkening of snow through the deposition of BC

826
Chapter 6 Short-lived Climate Forcers6and other light‑absorbing particles enhances snowmelt in the Arctic 
(Meredith et al., 2019), but that there is limited evidence and low 
agreement that long‑term changes in glacier mass of high mountain 
areas are linked to light‑absorbing particles (Hock et al., 2019).
6.1.3 Chapter Roadmap
Figure 6.2 presents the Chapter 6 roadmap. 
Specific aspects of SLCFs can also be found in other chapters of this 
report: the evolution of ozone, HFCs and aerosols, as well as the long‑
term evolution of methane, dust and volcanic aerosols are discussed  
in Chapter  2; near ‑term climate projections and SLCFs are  
discussed in Chapter 4; the global budget of methane is addressed in 
Chapter 5; aerosol–cloud and aerosol–precipitation interactions are 
treated in Chapters 7 and 8, respectively; the global radiative forcing 
of SLCFs is assessed in Chapter  7; some aspects of downscaling 
methodology in climate modelling concerning SLCFs are discussed

in Chapter 10. The WGII report assesses how climate change affects 
air pollution and its impacts on human health and the WGIII report 
assesses the role of SLCFs in abatement strategies and their cost‑
effectiveness, the implications of mitigation efforts on air pollution 
as well as the articulation between air pollution policies and 
GHG mitigation. This chapter discusses air quality from a global point of view with 
a  focus on surface ozone and particulate matter concentrations. 
Local and indoor air pollution, as well as the effect of air pollution 
on health, are beyond the scope of this chapter. This Assessment 
is mainly based on results and studies relying on global models or 
observation datasets operated through global networks or from 
satellites. Global chemistry‑climate models enable the quantification 
of changes in background concentrations, such as changes in 
surface ozone due to large‑scale changes in climate or methane,

by considering comprehensively the physiochemical processes 
(Box 6.1). In addition, climate effects are often non‑linear responses 
to concentrations which already respond non‑linearly to emissions, 
with per ‑mass unit effects often larger in pristine than in polluted 
regions, justifying the relevance of global models. However, specific 
aspects of urban air quality cannot be captured by global models 
and require high‑resolution models that reproduce the temporal and 
spatial variability of emissions and abundances necessary to precisely 
account for the non‑linearity of the chemistry and the sensitivity of 
local air pollution to its drivers. Consequently, the sectoral analysis 
in Section  6.6 and the mitigation effects in Section  6.7 cannot be 
directly applied for local air ‑quality planning. 
Due to their short lifetimes, SLCF trends and effects are strongly 
related to the localization and evolution of the emissions sources.

To better link the drivers of emissions evolution and SLCFs, Chapter 6 
makes use of regions defined by the WGIII in most of the analysis. 
An exception is made for the effect of SLCFs on the climate, for which 
analysis relies on WGI Atlas regions.
Chapter 6: Short-lived climate forcers Chapter 6: Quick guide
Cross-chapter boxesSection 6.1
Importance of short-lived climate forcers (SLCFs) for climate and air quality Historical and future evolution of SLCF concentrations
6.3 | 6.7.1 | (and Chapter 5 for methane)
SLCF radiative forcing
6.4.1 | 6.4.2 | (and Chapter 7)Oxidizing capacity
6.3.6Interactions between SLCFs and clouds and precipitation
6.4.3 | (and Chapters 7 and 8)
Solar radiation modification
6.4.6 | (and Chapters 4 and 5)Connections to air quality
6.5 | 6.6.2 | 6.6.3 | 6.7.2 | CC Box 6.1 | Box 6.2
Sectoral attribution
6.6.2Implications of socio-economic and emissions pathways
6.6.3 | 6.7
Box 6.1
SLCFs: From process-level studies 
to global modelsBox 6.2

SLCFs mitigation and sustainable 
development goalsCC Box 6.1
COVID-19 and air qualityChapter 6 discusses the evolution of reactive compounds and their effects 
on climate and global air quality.Key topics and corresponding sub-sections
BoxesSection 6.2
SLCF 
emissionsSection 6.3
SLCF atmospheric 
abundanceSection 6.4
SLCF radiative forcing 
and climate effects
Section 6.5
Implications of changing 
climate on air quality
Section 6.8
PerspectivesSection 6.6
Air quality and climate response to 
SLCF mitigation 
Section 6.7
Future projections of SLCFs, air quality and climate 
response in SSP scenarios
FAQs
Figure 6.2 |  Visual guide to Chapter 6.  See Section 6.1.3 for additional description of the chapter.

827
Short-lived Climate Forcers  Chapter 666.2 Global and Regional Temporal Evolution 
of SLCF Emissions
SLCF emissions originate from a  variety of sources driven by 
anthropogenic activities and natural processes. The natural sources 
include vegetation, soil, fire, lightning, volcanoes and oceans. 
Changes in SLCF emissions from natural systems occur either due 
to human activities, such as land‑use change, or due to global 
changes. Their sensitivity to climate change thus induces climate 
feedbacks (see Section 6.4.5 for a quantification of these feedbacks). 
This section reviews the current understanding of historical emissions 
for anthropogenic, natural, and open biomass burning sources. 
A detailed discussion of methane sources, sinks, trends are provided 
in Chapter 5, Section 5.2.2. 
6.2.1 Anthropogenic Sources
Estimates of global anthropogenic (human‑caused) SLCF emissions 
and their historical evolution that were used in AR5 (CMIP5; Lamarque

et al., 2010) have been revised for use in CMIP6 (Hoesly et al., 2018). 
The update considered new data and assessment of the impact 
of environmental policies, primarily regarding air pollution control 
(R. Wang et al., 2014; S.X. Wang et al., 2014; Montzka et al., 2015; 
Crippa et al., 2016; Turnock et al., 2016; Klimont et al., 2017a; Zanatta 
et al., 2017; Prinn et al., 2018) . Additionally, Hoesly et al. (2018) have 
extended estimates of anthropogenic emissions back to 1750 and 
developed an updated and new set of spatial proxies allowing for more 
differentiated (source sector ‑wise) gridding of emissions (Feng et al., 
2020). The CMIP6 emissions inventory has been developed with the 
Community Emissions Data System (CEDS) that improves upon existing 
inventories with a  more consistent and reproducible methodology, 
similar to approaches used in, for example, the EDGAR database 
(Crippa et al., 2016) and the GAINS model (Amann et al., 2011; Klimont

et al., 2017a; Höglund‑Isaksson et al., 2020) where emissions of all 
compounds are consistently estimated using the same emissions drivers 
and propagating individual components (activity data and emissions 
factors) separately to capture fuel and technology trends affecting 
emissions trajectories over time. This contrasts with the approach used 
to establish historical emissions for CMIP5 where different datasets 
available at the time were combined. The CMIP6 exercise is based on 
the first release of the CEDS emissions dataset (version 2017‑05‑18, 
sometimes referred to hereafter as CMIP6 emissions) whose main 
features regarding SLCFs are described hereafter. The CEDS has been 
and will be regularly updated and extended; the recent update of the 
CEDS (Hoesly et al., 2019) and consequences for this Assessment is 
discussed when necessary. Some details on how SLCF emissions 
have been represented in scenarios used by IPCC assessments can be

found in Chapter 1 (Section 1.6.1 and Cross‑Chapter Box 1.4 and in 
Section 6.7.1.1).
For most of the SLCF species, the global and regional anthropogenic 
emissions trends developed for CMIP6 for the period 1850–2000 are 
not substantially different from those used in CMIP5 (Figures 6.18 
and 6.19) despite the different method used to derive them. Hoesly 
et al. (2018, CEDS) developed independent time series capturing 
trends in fuel use, technology and level of control, whereas CMIP5 combined different emissions datasets. However, for the period 
after 1990, the CMIP6 dataset shows for all species, except for 
SO 2, CO, and (since 2011) for NO x, a  different trend than CMIP5 
(i.e., continued strong growth of emissions driven primarily by 
developments in Asia (Figure 6.19)). The unprecedented growth of 
emissions from Eastern and Southern Asia since 2000 changed the 
global landscape of emissions, making Asia the dominant SLCF source

region (Figures  6.3 and 6.19). The Representative Concentration 
Pathways (RCP) scenarios used in AR5 started from the year 2000 
(van Vuuren et al., 2011) and did not capture the SLCF emissions 
which actually occurred until 2015. The CEDS inventory (Hoesly et al., 
2018) includes improved representation of these trends and the 
estimate for 2014. These findings have been largely supported by 
several independent emissions inventory studies and remote‑sensing 
data analysis. However, for the last decade the decline of Asian 
emissions of SO 2 and NO x appears underestimated while growth 
of BC and OC emissions in Asia and Africa seems overestimated in 
CMIP6, compared to most recent regional evaluations (Klimont et al., 
2017a; Zheng et al., 2018b; Elguindi et al., 2020; Kanaya et al., 2020; 
McDuffie et al., 2020), which are largely considered in the updated 
release of the CEDS (Hoesly et al., 2019). Consequently, global CMIP6

anthropogenic emissions for 2014 are likely overestimated by about 
10% for SO 2 and NO x and by about 15% for BC and OC. 
For SO 2, independent emissions inventories and observational 
evidence show that on a  global scale strong growth of Asian 
emissions has been countered by reduction in North America and 
Europe (Reis et al., 2012; Amann et al., 2013; Crippa et al., 2016; 
Aas et al., 2019) . However, Chinese emissions declined by nearly 
70% between about 2006 and 2017  (high confidence ) (Silver et al., 
2018; Zheng et al., 2018b; Mortier et al., 2020; Tong et al., 2020) . 
The estimated reduction in China contrasts with continuing strong 
growth of SO 2 emissions in Southern Asia (Figure 6.19). In 2014, over 
80% of anthropogenic SO 2 emissions originated from power plants 
and industry, with Asian sources contributing more than 50% of the 
total (Figure 6.3).
Global emissions of NO x have been growing in spite of the

successful reduction of emissions in North America, Europe, Japan 
and Korea (Crippa et al., 2016; Turnock et al., 2016; Miyazaki 
et al., 2017; Jiang et al., 2018), partly driven by continuous efforts 
to strengthen the emissions standards for road vehicles in most 
countries (Figures 6.18 and 6.19). In many regions, an increase in 
vehicle fleet as well as non‑compliance with emissions standards 
(Anenberg et al., 2017, 2019; Jonson et al., 2017; Jiang et al., 
2018) , growing aviation (Grewe et al., 2019; Lee et al., 2021) and 
demand for energy, and consequently a  large number of new 
fossil fuel power plants, have more than compensated for these 
reductions. Since about 2011, global NO x emissions appear to 
have stabilized or slightly declined ( medium confidence ) but the 
global rate of decline has been underestimated in the CEDS, as 
recent data suggest that emissions reductions in China were larger 
than included in the CEDS (Figure  6.19 and Hoesly et al., 2018).

Recent bottom‑up emissions estimates (Zheng et al., 2018b) largely 
confirm what has been shown in satellite data (F . Liu et al., 2016; 
Miyazaki et al., 2017; Silver et al., 2018): a strong decline of NO 2 
column over eastern China ( high confidence ) (Section  6.3.3.1).

828
Chapter 6 Short-lived Climate Forcers6At a  global level, the estimated CEDS CO emissions trends are 
comparable to NO x, which has been confirmed by several inverse 
modelling studies (Section 6.3.3.2). The transport sector (including 
international shipping and aviation) was the largest anthropogenic 
source of NO x (about 50% of the total) and also contributed over 
25% of CO emissions in 2014; Asia represented 50% and North 
America and Europe about 20% of global total NO x and CO 
emissions (Figure 6.3).
Oil production‑distribution and transport sectors have dominated 
anthropogenic NMVOC emissions for most of the 20th century 
(Hoesly et al., 2018) and still represent a large share (Figure 6.3). 
Efforts to control transport emissions (i.e., increasing stringency 
of vehicle emissions limits) were largely offset by the fast growth 
of emissions from chemical industries and solvent use, as well as 
from fossil fuel production and distribution, resulting in continued

growth of global anthropogenic NMVOC emissions since 1900 
(high confidence ) (Figure 6.18). Since AR5, there is high confidence  
that motor vehicle NMVOC emissions have sharply declined in 
North America and Europe in the last decades (Rossabi and Helmig, 
2018), for example, by about an order of magnitude in major US 
cities since 1990 (Bishop and Haugen, 2018; McDonald et al., 
2018). Increasing (since 2008) oil‑ and gas‑extraction activities in 
North America lead to a strong growth of NMVOC emissions ( high 
confidence ) as shown by analysis of ethane column data (Franco 
et al., 2016), but absolute emission amounts remain uncertain 
(Pétron et al., 2014; Tzompa‑Sosa et al., 2019). In Eastern Asia, 
there is medium confidence  in a decreasing trend of motor vehicle 
emissions, suggested by ambient measurements in Beijing since 
2002 (Wang et al., 2015) and by bottom‑up estimates (Zheng et al., 
2018b), and a  decrease in residential heating emissions due to

declining coal and biofuel use since 2005 (Zheng et al., 2018b; M. 
Li et al., 2019). However, total anthropogenic NMVOC emissions 
have increased steadily in China since the mid‑20th century, 
largely due to the growing importance of the solvent‑use and 
industrial sectors ( medium evidence , high agreement ) (Sun et al., 
2018; Zheng et al., 2018b; M. Li et al., 2019) . Resulting changes 
in the NMVOC speciated emissions might be underestimated in 
the current regional and global inventories. For  example, in the 
USA, a recent study suggested an emergent shift in urban NMVOC 
sources from transportation to chemical products (i.e., household 
chemicals, personal care products, solvents, etc.), which is not in 
accordance with emissions inventories currently used (McDonald 
et al., 2018). In many European regions and cities, wood burning has 
been increasingly used for residential heating, partly for economic 
reasons and because it is considered CO 2‑neutral (Athanasopoulou

et al., 2017); in situ measurements in several cities, including Paris, 
suggest that wood burning explains up to half of the NMVOC 
emissions during winter (Kaltsonoudis et al., 2016; Languille et al., 
2020). Due to the vast heterogeneity of sources and components 
of NMVOCs, uncertainty in regional emissions and trends is higher 
than for most other components.
Emissions of carbonaceous aerosols (BC, OC) have been steadily 
increasing and their emissions have almost doubled since 1950 
(medium confidence ) (Hoesly et al., 2018). Before 1950, North 
America and Europe contributed about half of the global total but successful introduction of diesel particulate filters on road 
vehicles (Fiebig et al., 2014; Robinson et al., 2015; Klimont et al., 
2017a) and declining reliance on solid fuels for heating brought in 
large reductions ( high confidence ) (Figure  6.19). Currently, global 
carbonaceous aerosol emissions originate primarily from Asia and

Africa (Bond et al., 2013; Hoesly et al., 2018; Elguindi et al., 2020; 
McDuffie et al., 2020), representing about 80% of the global total 
(high confidence ) (Figure 6.3). Consideration, in CMIP6, of emissions 
from kerosene lamps and gas flaring, revised estimates for open 
burning of waste, regional coal consumption, and new estimates for 
Russia (Stohl et al., 2013; Huang et al., 2015; Huang and Fu, 2016; 
Kholod et al., 2016; Conrad and Johnson, 2017; Evans et al., 2017; 
Klimont et al., 2017a) resulted in over 15% higher global emissions 
of OC and BC than in the CMIP5 estimates for the first decade of 
the 21st century (Figure  6.18). However, the continued increase 
of BC emissions over Eastern Asia after 2005, estimated in CMIP6 
(Figure 6.19), has been questioned recently as a steady decline of 
BC concentrations was measured in the air masses flowing out from 
the east coast of China (Kanaya et al., 2020), which has been also

estimated in recent regional bottom‑up and top‑down inventories 
(Zheng et al., 2018a; Elguindi et al., 2020; McDuffie et al., 2020) . 
Since AR5, confidence in emissions estimates and trends in North 
America and Europe has increased, but high uncertainties remain for 
Asia and Africa, despite their major contribution to global emissions. 
The size distribution of emitted species, of importance for climate 
and health impacts, remains uncertain and the CEDS inventory 
does not provide such information. Overall, a factor two uncertainty 
in global estimates of BC and OC emissions remains, with post‑
2005 emissions overestimated in Asia ( high confidence ) and Africa 
(medium confidence ). 
Bottom‑up global emissions estimates of methane (Lamarque 
et al., 2010; Hoesly et al., 2018; Janssens‑Maenhout et al., 2019; 
Höglund‑Isaksson et al., 2020) for the last two decades are higher 
than top‑down assessments (e.g.,  Saunois et al., 2016, 2020) but

trends from the two methods are similar and indicate continued 
growth ( high confidence ). Larger discrepancies exist at the sectoral 
and regional levels, notably for coal mining (Peng et al., 2016; 
Miller et al., 2019) and the oil and gas sector due to the growth of 
unconventional production and higher loss estimates (Section 5.2.2; 
Franco et al., 2016; Alvarez et al., 2018; Dalsøren et al., 2018). 
Agricultural production (livestock and mineral nitrogen fertilizer 
application) is the primary source of ammonia in the atmosphere 
with more than half of present‑day emissions originating in Asia 
(Hoesly et al., 2018; Figure 6.3, EC‑JRC/PBL, 2020; Vira et al., 2020) . 
NH 3 emissions are estimated to have grown strongly since 1850, 
especially since 1950, driven by continuously increasing livestock 
production, widespread application of mineral nitrogen fertilizers, 
and lack of action to control ammonia ( high confidence ) (Erisman

et al., 2008; Riddick et al., 2016; Hoesly et al., 2018; Fowler et al., 
2020) . The trends estimated in CMIP5 and CMIP6 are similar, while 
in absolute terms, CMIP6 has somewhat higher emissions as it 
includes emissions from wastewater and human waste that were 
largely missing in CMIP5 (Hoesly et al., 2018). CMIP6 has improved 
spatial and temporal distribution of emissions (Lamarque et al., 
2013a) relying on the EDGAR v4.3 database and Paulot et al. (2014),

829
Short-lived Climate Forcers  Chapter 66but important uncertainties remain for regionally specific temporal 
patterns (Riddick et al., 2016; Liu et al., 2019; Feng et al., 2020; 
Vira et al., 2020) . The continuing increase in global NH 3 emissions 
is driven primarily by growing livestock and crop production in Asia 
while emissions in the USA and Europe remain about constant or 
have slightly declined in the last decade (Hoesly et al., 2018). Recent 
satellite and ground observations support trends estimated in CMIP6 
dataset (Section 6.3.3.4). To summarize, there are significant differences in spatial and temporal 
patterns of SLCF emissions across global regions (Figure 6.18). Until 
the 1950s, the majority of SLCF emissions were associated with 
fossil fuel use (SO 2, NO x, NMVOCs, CO) and about half of BC and OC 
originated from North America and Europe (Lamarque et al., 2010; 
Hoesly et al., 2018). Since the 1990s a large redistribution of emissions

was associated with strong economic growth in Asia and declining 
emissions in North America and Europe due to air ‑quality legislation 
Figure 6.3 | Relative regional and sectoral contributions to the present day (year 2014) anthropogenic emissions of short-lived climate forcers (SLCFs).  
Emissions data are from the Community Emissions Data System (CEDS; Hoesly et al., 2018). Emissions are aggregated into the following sectors: fossil fuel production and 
distribution (coal mining, oil and gas production, upstream gas flaring, gas distribution networks), fossil fuel combustion for energy (power plants), residential and commercial 
(fossil and biofuel use for cooking and heating), industry (combustion and production processes, solvent-use losses from production and end use), transport (road and off-road 
vehicles), shipping (including international shipping), aviation (including international aviation), agriculture (livestock and crop production), waste management (solid waste,

including landfills and open trash burning, residential and industrial waste water), and other. Further details on data sources and processing are available in the chapter data 
table (Table 6.SM.3).

830
Chapter 6 Short-lived Climate Forcers6and the declining capacity of energy‑intensive industry; currently more 
than 50% of anthropogenic emissions of each SLCF species (including 
methane and NH 3) originates from Asia (Figure  6.3; Amann et al., 
2013; Bond et al., 2013; Fiore et al., 2015; Crippa et al., 2016, 2018; 
Klimont et al., 2017a; Hoesly et al., 2018) . The dominance of Asia for 
SLCF emissions is corroborated by growing remote‑sensing capacity 
that has been providing an independent evaluation of estimated 
pollution trends in the last decade (Duncan et al., 2013; Lamsal et al., 
2015; Luo et al., 2015; Fioletov et al., 2016; Geddes et al., 2016; Irie 
et al., 2016; Krotkov et al., 2016; Wen et al., 2018) .
Since AR5, the quality and completeness of activity and emission‑
factor data and applied methodology, including spatial allocation 
together with independent satellite‑derived observations, have 
improved, raising confidence in methods used to derive emissions.

There is high confidence  in the sign of global trends of SLCF emissions 
until the year 2000. However, only medium confidence  for the rate 
of change in the two last decades, owing primarily to uncertainties 
in the actual application of reduction technologies in fast‑growing 
economies of Asia. At a  regional level, bottom‑up derived SLCF 
emissions trends, and magnitudes in regions with strong economic 
growth and changing air ‑quality regulation, are highly uncertain but 
can be better constrained with top‑down methods (Section 6.3). For 
most SLCF species, there is high confidence  in trends and magnitudes 
for affluent countries from the Organisation for Economic Co‑ 
operation and Development (OECD) regions where accurate and 
detailed information about drivers of emissions exists; medium 
confidence  is assessed for regional emissions of NH 3, methane and 
NMVOC. 
6.2.2 Emissions by Natural Systems
This section assesses our current understanding of SLCF emissions

by natural systems. Many naturally occurring emission processes in 
the Earth system have been perturbed by the growing influence of 
human activities either directly (e.g., deforestation, agriculture) or 
via human‑induced atmospheric CO 2 increase and climate change, 
and therefore cannot be considered as purely natural emissions. 
The  temporal evolution and spatial distribution of natural SLCF 
emissions are highly variable and their estimates rely on models 
with rather uncertain parametrizations for production mechanisms. 
For  some SLCFs, the natural processes by which emissions occur 
are also not well understood. In the following sections, we assess 
the level of confidence in present‑day SLCF emissions by natural 
systems, in their perturbation since the pre‑industrial period and 
their sensitivity to future changes. When available, the assessment 
also includes estimates from the CMIP6 model ensemble. Note that

volcanic SO 2 emissions are discussed in Section 2.2.2 and natural 
sources of methane and N 2O are assessed in Sections 5.2.2 and 
5.2.3.
6.2.2.1 Lightning NO x
Lightning contributes approximately 10% of the total NO x emissions 
(Murray, 2016). Since lightning NO x (LNO x) is predominantly released 
in the upper troposphere, it has a disproportionately large impact on ozone and OH, and on the lifetime of methane compared with surface 
NO x emissions. Whereas the global spatial and temporal distribution 
of lightning flashes can be characterized thanks to satellite‑borne 
and ground sensors (Virts et al., 2013; Cecil et al., 2014), constraining 
the amount of NO x produced per flash (Miyazaki et al., 2014; Medici 
et al., 2017; Nault et al., 2017; Marais et al., 2018; D.J. Allen et al., 
2019; Bucsela et al., 2019)  and its vertical allocation (Koshak et al., 
2014; Medici et al., 2017) has been more elusive. 
Atmospheric Chemistry and Climate Model Intercomparison

Project (ACCMIP) models in CMIP5 used a range of LNO x between 
1.2–9.7  TgN y−1 (Lamarque et al., 2013b). In CMIP6, the corresponding 
LNO x range is between 3.2–7.6 TgN y−1 (Griffiths et al., 2021). 
All CMIP6 models (as well as most models included in CMIP5, Young 
et al., 2013) apply a parametrization that relates cloud‑top height 
to lightning intensity (Price and Rind, 1992), projecting an increase 
in LNO x in a warmer world in the range of 0.27–0.61 TgN yr−1 °C–1 
(Thornhill et al., 2021a). However, models using parametrizations 
based on convection (Grewe et al., 2001), updraft mass flux (Allen 
and Pickering, 2002) or ice flux (Finney et al., 2016a) show either 
much less sensitivity or a  negative response (Finney et al., 2016b, 
2018; Clark et al., 2017). 
In summary, the total present‑day global lightning NO x emissions are 
still estimated to be within a factor of two. There is high confidence  
that LNO x are perturbed by climate change; however, there is low

confidence  in the sign of the change due to fundamental uncertainties 
in parametrizations.  
6.2.2.2 NO x Emissions from Soils
Soil NO x (SNO x) emissions occur in connection with complex 
biogenic/microbial nitrification and denitrification processes (Ciais 
et al., 2013), which in turn are sensitive – in a non‑linear manner – 
to temperature, precipitation, soil moisture, carbon and nutrient 
content, and the biome itself (e.g., Hudman et al., 2012). Global SNO x 
estimates, based on observationally constrained chemistry‑transport 
model and vegetation model studies, show a broad range between 
4.7–16.8 TgN yr–1 (Young et al., 2018). This estimate is generally 
larger than the current source strength used in CMIP6 simulations, 
which is prescribed using an early empirical estimate, typically scaled 
to about 5 TgN yr–1 (Yienger and Levy, 1995). 
By the end of the 21st century, the overall nitrogen fixation in non‑
agricultural ecosystems could be 40% larger than in 2000, due to

increased enzyme activity with growing temperatures, but the 
emission rates of NO (and N 2O) could be dominated by changes in 
precipitation patterns and evapotranspiration fluxes (Fowler et al., 
2015). Current Earth system models (ESMs) incorporate biophysical 
and biogeochemical processes only to a  limited extent (Jia et al., 
2019), precluding adequate climate sensitivity studies for SNO x. 
Hence, while the current strength source of soil NO x has been 
better constrained over the last decade, adequate representations 
of SNO x and how it escapes from the canopy, which could provide 
quantitative estimates of climate‑driven changes in SNO x, are still 
missing in ESMs.

831
Short-lived Climate Forcers  Chapter 666.2.2.3 Vegetation Emissions of Organic Compounds 
A wide range of BVOCs are emitted from vegetation with the 
dominant compounds being isoprene and monoterpenes but also 
including sesquiterpenes, alkenes, alcohols, aldehydes and ketones. 
The photooxidation of BVOC emissions plays a  fundamental role 
in atmospheric composition by controlling the regional and global 
budgets of ozone and organic aerosols, and impacting the lifetime 
of methane and other reactive components (Arneth et al., 2010b; 
Heald and Spracklen, 2015). Substantial uncertainty exists across 
different modelling frameworks for estimates of global total BVOC 
emissions and individual compound emissions (Messina et al., 2016). 
Global isoprene emissions estimates differ by a factor of two from 
300–600 TgC yr–1 and global monoterpene emissions estimates by 
a factor of five from 30–150 TgC yr–1 (Messina et al., 2016). A main

driver of the uncertainty ranges is the choice of basal emissions rates 
assigned to different plant functional types in the model; however, 
the smaller uncertainty range for isoprene than for monoterpenes is 
not fully understood (Arneth et al., 2008). The evaluation of global 
BVOC emissions is challenging because of poor measurement data 
coverage in many regions and the lack of year ‑round measurements 
(Unger et al., 2013). Several observational approaches have been 
developed in the past few years to improve understanding of BVOC 
emissions, including indirect methods such as the measurement of 
the OH loss rate in forested environments (Yang et al., 2016) and 
application of the variability in satellite formaldehyde concentrations 
(Palmer et al., 2006; Barkley et al., 2013; Stavrakou et al., 2014). 
Direct space‑borne isoprene retrievals using infrared radiance (IR) 
measurements have very recently become available (Fu et al., 2019;

Wells et al., 2020). Collectively these approaches have identified 
weaknesses in the ability of the parametrizations in global models 
to reproduce BVOC emissions hotspots (Wells et al., 2020). However, 
none of the current observational approaches have yet been able to 
reduce the uncertainty ranges in global emissions estimates.
At the plant level, BVOC emissions rates and composition depend 
strongly on plant species with plants tending to emit either isoprene 
or monoterpenes but not both. Photosynthetic activity is a  main 
driver of isoprene and monoterpene production. Therefore, radiation 
and temperature, along with leaf‑water status, phenological state 
and atmospheric CO 2 mixing ratio, affect emissions directly (on the 
leaf scale) and indirectly (via plant productivity; Guenther et al., 2012; 
Loreto et al., 2014; Niinemets et al., 2014) . CO 2 directly influences 
the isoprene‑synthesis process, with inhibition under increasing

atmospheric CO 2 (Rosenstiel et al., 2003; Possell et al., 2005; 
Wilkinson et al., 2009). Direct CO 2 inhibition has been observed for 
some monoterpene compounds (Loreto et al., 2001; Llorens et al., 
2009). Severe/long‑term water stress may reduce emissions whilst 
mild/short‑term water stress may temporarily amplify or maintain 
BVOC emissions to protect plants against ongoing stress (Peñuelas 
and Staudt, 2010; Potosnak et al., 2014; Genard‑Zielinski et al., 2018). 
Furthermore, observations in the Amazon indicate that the chemical 
composition of monoterpene emissions could also change under 
elevated temperature conditions (Jardine et al., 2016). In addition, all 
these processes are investigated over short time scales but the long‑
term response of BVOC emissions depends on how the vegetation itself responds to the altered climate state (including temperature 
and water stress).
Global BVOC emissions are highly sensitive to environmental changes

including changes in climate, atmospheric CO 2, and vegetation 
composition and cover changes in natural and managed lands. Recent 
global modelling studies agree that global isoprene emissions have 
declined since the pre‑industrial period, driven predominantly by 
anthropogenic land‑use and land‑cover change (LULCC) with results 
converging on a 10–25% loss of isoprene emissions between 1850 
and the present day (Lathière et al., 2010; Unger, 2013, 2014; Acosta 
Navarro et al., 2014; Heald and Geddes, 2016; Hantson et al., 2017; 
Hollaway et al., 2017; Scott et al., 2017). The historical evolution of 
monoterpene and sesquiterpene emissions is less well studied and 
there is no robust consensus on even the sign of the change (Acosta 
Navarro et al., 2014; Hantson et al., 2017). Future global isoprene 
and monoterpene emissions depend strongly on the climate and 
land‑use scenarios considered (Hantson et al., 2017; Szogs et al.,

2017). BVOC emissions will be sensitive to future land‑based climate 
change mitigation strategies including afforestation and bioenergy, 
with impacts of bioenergy depending on the choice of crops (Szogs 
et al., 2017). 
Most CMIP6 models use overly simplistic parametrizations and 
project an increase in global BVOC emissions in response to warming 
temperatures (Turnock et al., 2020). This good agreement actually 
reflects the lack of diversity in BVOC‑emissions parametrizations in 
global models that do not fully account for the complex processes 
influencing emissions that are discussed above.
Overall, we assess that historical global isoprene emissions declined 
between the pre‑industrial period and the present day by 10–25% 
(low confidence ) but historical changes in global monoterpenes and 
sesquiterpenes are too uncertain to provide an assessment. Future 
changes in BVOCs depend strongly on the evolution of climate and

land use and are strongly sensitive to land‑based climate change 
mitigation strategies. However, the net response of BVOC emissions 
is uncertain due to the complexity of processes that are hard to 
constrain observationally and are considered with various degrees 
of details in models.
6.2.2.4 Land Emissions of Dust Particles
The emission of dust particles into the atmosphere results from 
a natural process, namely saltation bombardment of the soil by large 
wind‑blown particles, such as sand grains, and from disintegration 
of saltating particle clusters (Kok et al., 2012). The occurrence 
and intensity of dust emissions are controlled by soil properties, 
vegetation and near ‑surface wind, making dust emissions sensitive 
to climate change and LULCC (Jia et al., 2019). In addition, dust can 
be directly emitted through human activities, such as agriculture, 
off‑road vehicles, building construction and mining, and indirectly

emitted through hydrological changes due to human actions such as 
water diversion for irrigation (e.g., Ginoux et al., 2012). Estimates of 
the anthropogenic fraction of global dust vary from less than 10% to 
over 60% suggesting that the human contribution to the global dust

832
Chapter 6 Short-lived Climate Forcers6budget is quite uncertain (Ginoux et al., 2012; Stanelle et al., 2014; 
Xi and Sokolik, 2016). Reconstruction of global dust (deposition) 
from paleo records indicate factor of two to four changes between 
the different climate regimes in the glacial and interglacial periods 
(Section 2.2.6). 
An extremely limited number of studies have explored the evolution 
of global dust sources since pre‑industrial times (Mahowald et al., 
2010; Stanelle et al., 2014). A modelling study estimated a  25% 
increase in global dust emissions between the late 19th century and 
the present, due to agricultural land expansion and climate change 
(Stanelle et al., 2014). CMIP5 models were unable to capture the 
observed variability of annual and longer time scales in North African 
dust emissions (Evan et al., 2014), however, more recent ESMs with 
process‑based dust emissions schemes that account for changes in

vegetation and climate in a more consistent manner, better match 
the observations (Kok et al., 2014; Evans et al., 2016). Feedbacks 
between the global dust cycle and the climate system (Section 6.4.5) 
could account for a substantial fraction of the total aerosol feedbacks 
in the climate system with an order of magnitude enhancement 
on a  regional scale (Kok et al., 2018). In  summary, there is high 
confidence  that atmospheric dust source and loading are sensitive to 
changes in climate and land use, however, there is low confidence  in 
quantitative estimates of dust emission response to climate change. 
6.2.2.5 Oceanic Emissions of Marine Aerosols and Precursors
Oceans are a  significant source of marine aerosols that influence 
climate directly by scattering and absorbing solar radiation or 
indirectly through the formation of cloud condensation nuclei 
(CCN) and ice nucleating particles (INPs). Marine aerosols consist

of primary sea‑spray particles and secondary aerosols produced by 
the oxidation of emitted precursors, such as dimethylsulphide (DMS) 
and numerous other BVOCs. Sea‑spray particles, composed of sea 
salt and primary organic aerosols (POA), are produced by wind‑
induced wave breaking as well as the direct mechanical disruption 
of waves. The understanding of sea‑spray emissions has increased 
substantially over the last five years, however, the knowledge of 
formation pathways and factors influencing their emissions continue 
to have large uncertainties (Forestieri et al., 2018; Saliba et al., 2019). 
The emission rate of sea‑spray particles is predominantly controlled 
by wind speed. Since AR5, the influence of other factors, including 
sea surface temperature, wave history and salinity is increasingly 
evident (Callaghan et al., 2014; Grythe et al., 2014; Ovadnevaite et al., 
2014; Salter et al., 2014; Barthel et al., 2019) . Marine POA, often the

dominant submicron component of sea spray, are emitted as a result 
of oceanic biological activity, however the biological processes by 
which these particles are produced remain poorly characterized 
contributing to large uncertainties in global marine POA emissions 
estimates (Tsigaridis et al., 2014; Cravigan et al., 2020; Hodzic et al., 
2020) . Furthermore, the particle size and chemical composition of 
sea‑spray particles, and how these evolve in response to changing 
climate factors and dynamic oceanic biology, continue to have 
large uncertainties. 
DMS, the largest natural source of sulphur in the atmosphere, is 
produced by marine phytoplankton and is transferred from ocean water to the atmosphere due to wind‑induced mixing of surface 
water. DMS oxidizes to produce sulphate aerosols and contributes 
to the formation of CCN. Since AR5, the range in global DMS flux 
estimates reduced from 10–40 TgS yr–1 to 9–34 TgS yr–1 with a  very

likely  range of 18–24 TgS yr–1 based on sea‑surface measurements 
and satellite observations (Lana et al., 2011). DMS production, 
and consequently emissions, have been shown to respond to 
multiple stressors, including climate warming, eutrophication, and 
ocean acidification. However, large uncertainties in process‑based 
understanding of the mechanisms controlling DMS emissions, from 
physiological to ecological, limit our knowledge of past variations 
and our capacity to project future changes.
Overall, there is low confidence  in the magnitude and changes in 
marine aerosol emissions in response to shifts in climate and marine 
ecosystem processes.
6.2.2.6 Open Biomass Burning Emissions 
Emissions from open biomass burning (including forest, grassland, 
peat fires and agricultural waste burning) represent about 30%, 10%, 
15% and 40% of present‑day global emissions of CO, NO x, BC and 
OC, respectively (van Marle et al., 2017; Hoesly et al., 2018). Wildfires

also play an important role in several atmospheric chemistry–climate 
feedback mechanisms (Bowman et al., 2009; Fiore et al., 2012) and 
fire events occurring near populated areas induce severe air pollution 
episodes (Marlier et al., 2020; Rooney et al., 2020; Yu et al., 2020).
For the last two decades, model‑based emissions estimates are 
constrained by remote‑sensing capacity to detect active fires and 
area burned. In AR5, biomass burning emissions were derived from 
a satellite product (Lamarque et al., 2010). Since then, improvements 
in detection of small fires has enhanced the agreement with higher ‑
resolution and ground‑based data on burned area in several regions 
(Randerson et al., 2012; Mangeon et al., 2015), especially for areas 
subjected to agricultural waste burning (Chuvieco et al., 2016, 2019). 
The updated emissions factors and the contribution of forest versus 
savanna fires lead to significantly higher global emissions of NO x

and lower emissions of OC and CO in CMIP6, compared with CMIP5. 
A recent compilation and assessment of emissions factors (Andreae, 
2019) indicates that the emissions factors from Akagi et al. (2011), 
primarily used to produce the CMIP6 datasets, differ by ±50% for 
CO, OC, BC and NO x, depending on the biome, and would imply, for 
example, up to 10–30% higher OC and BC emissions from tropical 
forest fires. 
The historical (pre‑satellite era) dataset for CMIP6 considers advances 
in knowledge of past fire dynamics (new fire proxy datasets, such as 
charcoal in sediments and levoglucosan in ice cores) and visibility 
records from weather stations (Marlon et al., 2016; van Marle et al., 
2017). At a  global level, CMIP5 and CMIP6 emissions trends are 
similar, however, there are substantial differences at the regional 
level, especially for the USA, South America (south of Amazonia) and 
Southern Hemisphere Africa (van Marle et al., 2017).

Globally, the CMIP5 estimates (Lamarque et al., 2010), indicated 
a  gradual decline of open biomass burning emissions from 1920

833
Short-lived Climate Forcers  Chapter 66to about 1950 and then steady, and stronger than CMIP6, increase 
towards 2000. In contrast, CMIP6 biomass burning emissions 
(van Marle et al., 2017) increase only slightly over 1750–2015 – they 
peak during the 1990s after which they decrease gradually, which is 
consistent with the assessment of fire trends in Chapter 5. Therefore, 
the CMIP6 evolution has a smaller difference between pre‑industrial 
and present‑day emissions than CMIP5, resulting in a lower radiative 
forcing of biomass burning SLCFs, possibly leading to a lower effect 
on climate (van Marle et al., 2017). 
Climate warming, especially through change in temperature and 
precipitation, will generally increase the risk of fire (Jia et al., 2019, 
see also Chapter 12) and can also affect the fire injection and plume 
height (Veira et al., 2016), but occurrence of fires and their emissions 
in the future strongly depends on anthropogenic factors, such as

population density, land use and fire management (Veira et al., 
2016). Consequently, future emissions vary widely with increases 
and decreases amongst the SSP scenarios due to different land‑use 
change scenarios. 
In summary, there has been an improvement in the knowledge of 
biomass burning emissions by reducing key uncertainties highlighted 
in AR5. However, systematic assessment of remaining uncertainties 
is limited, with a lower limit of uncertainties due to emissions factors 
of 30%, and larger uncertainties due to burning‑activity estimates, 
especially at regional level. Overall, a  medium confidence  in current 
global biomass burning SLCF emissions and their evolution over the 
1  The word ‘concentration’ is used to denote abundances in terms of mixing ratio for most species unless specified.satellite era is assessed. There is low to medium confidence  in SLCF 
emissions from biomass burning from the pre‑industrial period to the

1980s,  which rely on the incorporation of several proxy data, with 
limited spatial representativeness. Nevertheless, uncertainties in 
the absolute value of pre‑industrial emissions remain high, limiting 
confidence in radiative forcing estimates. 
6.3 Evolution of Atmospheric  
SLCF Abundances
This section assesses the evolution of atmospheric abundance1 of 
SLCFs since AR5 based on observations and modelling, our knowledge 
of SLCF burden and distribution, and our understanding of the trends 
over longer time scales. In addition to emissions (Section  6.2), 
atmospheric chemistry (gas and heterogeneous chemistry), deposition 
(including wet and dry removal), and transport processes play a major 
role in determining the atmospheric distribution, budget and lifetime 
of SLCFs. The distribution and lifetime of SLCFs are further influenced 
by the modulation of chemical and physical processes in response 
to a changing climate. Therefore, the time evolution of atmospheric

abundance of SLCFs is characterized by many complex non‑linear 
interactions occurring at varying temporal and spatial scales. For this 
Assessment, global‑scale, long‑term measurements are employed 
only for a  few gaseous SLCFs while for most short‑lived species 
regional‑scale observations and global models are relied upon. 
Box 6.1 | Atmospheric Abundance of SLCFs: From Process-level Studies to Global  
Chemistry–Climate Models 
Changes in the atmospheric distribution of SLCFs determine their radiative forcing, and climate and air ‑quality impacts. This box 
provides an overview of how process‑level understanding of the distribution and evolution of chemical compounds is derived and 
where uncertainties come from. 
Process‑level understanding of tropospheric gas and aerosol chemistry developed through laboratory and simulation chamber 
experiments, as well as quantum chemical theory, is used to generate chemical mechanisms. Atmospheric simulation chambers are

designed to identify the chemical pathways and quantify reaction kinetics in isolation from atmospheric transport, deposition and 
emission processes. Ideally the chemical regimes studied are representative for ambient atmospheric complexity and concentrations 
(e.g.,  McFiggans et al., 2019). Recently, quantum chemical theory has advanced to a level that it can provide kinetic and product 
information in a parameter range not possible with laboratory experiments (Vereecken et al., 2015) . Iterative and interlinked use of 
simulation chamber and quantum chemical theory has led to improved knowledge of chemical mechanisms (Peeters et al., 2009, 
2014; Nguyen et al., 2010; Fuchs et al., 2013). For application in chemistry–climate models (CCMs), the chemical mechanisms need 
to be computationally efficient, requiring simplifications. Such simplifications include reduced hydrocarbon representations, the

application of lumping techniques (one compound or a chemical structure representing a family of compounds, for example, as done 
for parametrizing SOA formation) and/or the implementation of artificial operators representing key steps of the chemistry (Emmerson 
and Evans, 2009; Xia et al., 2009; Stockwell et al., 2020). Additionally, aerosol microphysical processes (nucleation, coagulation, 
condensation, evaporation and sedimentation) that determine the evolution of aerosol number concentrations and size particle 
distribution are represented in parametrized forms in global models with varying levels of complexity (Mann et al., 2014).
A wide range of in situ and remotely sensed observations are used to characterize atmospheric chemical composition. Measurements 
made routinely as part of long‑term monitoring programmes are particularly useful for assessing long‑term trends and variability, and

spatial distributions (Sections 2.2, 6.3 and 7.3.3), while intensive field campaigns provide a more comprehensive view of atmospheric

834
Chapter 6 Short-lived Climate Forcers6 
Box 6.1 (continued)
composition at a specific location for a limited time, facilitating an improved process‑level understanding. Retrieval of atmospheric 
concentrations from satellites, in particular, has been tremendously useful for providing global continuous coverage, although the 
retrievals themselves depend on prior information of atmospheric composition usually derived from models. Over the last decade 
or so, observations of atmospheric concentrations have been combined with information from global chemistry–climate models to 
produce global assimilation and forecasting systems with the purpose of producing chemical reanalysis or improving model inputs 
(i.e., emissions or boundary conditions) and forecasts (Miyazaki et al., 2015; Randles et al., 2017; Inness et al., 2019). 
Global three‑dimensional CCMs (Box 6.1, Figure 1) represent the full coupling of chemistry with climate physics (e.g.,  Morgenstern

et al., 2017) with different levels of complexity (e.g., interactive aerosols with or without tropospheric and/or stratospheric chemistry). 
Methane concentrations are typically prescribed or constrained to observations while emissions of other SLCFs (or their precursors) are 
either prescribed or calculated interactively in the current generation of CCMs (Collins et al., 2017) . CCMs, now part of Earth system 
models (ESMs), are applied extensively to simulate the distribution and evolution of chemical compounds on a variety of spatial and 
temporal scales to improve current knowledge, make future projections and investigate global scale chemistry–climate interactions 
and feedbacks (Section  3.8.2.2). CCMs are also used to interpret observations to disentangle the processes that drive observed 
variability and trends. Some aspects of air quality, such as diurnal peaks or local threshold violations, strong gradient in chemical

regimes and coupling between processes cannot be captured by relatively coarse spatial resolution (>50 km) global CCMs (Markakis 
et al., 2014) and necessitate subsequent downscaling modelling exercises.
The skill of CCMs is typically assessed by their ability to reproduce observed abundance, trends and variability of chemical compounds. 
However, uncertainty remains large because of observation limitations (errors and uncertainties, spatial and temporal coverage), 
Model 
generated
meteorological
variabilityComplex chemistry 
and microphysics 
mechanismsReduced lumped 
chemistry and
microphysics 
mechanisms 
In situ and remote observations
Ground-based, balloon, 
      ship, aircraft, and 
satellite measurements
Model complexity ParameterizationsSpecies and 
  processes      Laboratory and theoretical researchParameterizations
and lumping
Chemical complexityReaction kinetic 
experiments 
simulation chambers 
quantum chemical 
theory
  Reaction rates 
product identity
product yield

Model output - 
observation 
comparisonCO2 , volcanoes,
solar cycle, etc.(Optionally)
     offline 
   methaneChemistry-climate models
Dynamics ChemistryRadiation
Model
improvement Knowledge exchange between laboratory/theoretical studies, 
observations observations and global chemistry-climate models
Box 6.1, Figure 1 | Knowledge exchange between laboratory/theoretical studies, observations and global climate–chemistry models (CCMs) to 
inform our understanding of short-lived climate forcers (SLCFs).

835
Short-lived Climate Forcers  Chapter 66 
model parametrizations (e.g.,  chemical mechanisms, photolysis schemes, parametrizations for mixing and convective transport, 
and deposition), model input parameters (e.g., reaction rate constants, emissions) and an incomplete understanding of the physical 
and chemical processes that determine SLCF distributions (Brasseur and Jacob, 2017; Young et al., 2018). CCMs can therefore not 
capture every aspect of atmospheric chemical composition, but are expected to represent, as faithfully as possible, the sensitivity 
of chemical compounds to their drivers (e.g.,  anthropogenic emissions). Models are evaluated in multiple ways to identify their 
strengths and weaknesses in explaining the evolution of SLCF abundances. For example, CCM simulations are performed in the 
nudged or offline meteorology mode, that is, driven by observed or reanalysed meteorology rather than in the free‑running mode,

for consistent comparison of modelled chemical composition with observations for a specific time period (Dameris and Jöckel, 2013). 
However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric 
physics and/or chemistry (Orbe et al., 2018; Chrysanthou et al., 2019). Chemical mechanisms implemented in CCMs are evaluated 
and intercompared to assess their skill in capturing relevant chemistry features (e.g., Brown‑Steiner et al., 2018). The multi‑model 
ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations 
of SLCFs related to structural uncertainty and internal variability (Naik et al., 2013; Shindell et al., 2013; Young et al., 2013; Turnock 
et al., 2020) . However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range.

This assessment draws upon results from single‑model studies and recent multi‑model intercomparisons (e.g., AeroCom, CCMI), in 
particular those endorsed by CMIP6 (see Table 1.3), which then allows for the full consideration of robustness and uncertainty due 
to model structures and processes. Based on the collective information provided in this body of literature, the CMIP6 multi‑model 
ensemble is largely fit‑for ‑purpose of evaluating the influence of SLCFs on radiative forcing, climate and non‑CO 2 biogeochemical 
feedbacks. Additionally, CMIP6 models are fit for capturing the global air pollution response to changes in emissions and meteorology, 
but have difficulty in simulating the mean state (Turnock et al., 2020). The set of CMIP6 simulations has been used to update the 
relations between emissions and surface temperature at the heart of the emulators (Cross‑Chapter Box 7.1) and update emissions

metrics (Section 7.6). Emulators and emissions metrics are used in this chapter (Sections 6.6 and 6.7) to assess more specifically the 
effect of the individual SLCFs for each sector and region, which would be of prohibitive computing cost with CCMs. CCMs are also 
used to build global source‑receptor models which use relations between surface concentrations and emissions. Such a model is used 
to assess the impact of various mitigation policies on air quality (Sections 6.5 and 6.7).
6.3.1 Methane (CH 4)
The global mean surface mixing ratio of methane has increased by 
156% since 1750 (Section  2.2.3.4 and Annex III). Since AR5, the 
methane mixing ratio has increased by about 3.5% from 1803 ± 2 ppb 
in 2011 to 1866 ± 3 ppb in 2019 (Section 2.2.3.3.2) largely driven by 
anthropogenic activities as assessed in Chapter 5 (Section 5.2.2 and 
Cross‑Chapter Box 5.2). 
An assessment of the global methane budget is provided in Chapter 5,

while this section assesses methane atmospheric lifetime and 
perturbation time (Prather et al., 2001). The AR5 based its assessment 
of methane lifetime on Prather et al. (2012). The methane chemical 
lifetime due to tropospheric OH, the primary sink of methane, was 
assessed to be 11.2 ± 1.3 years constrained by surface observations 
of methyl chloroform (MCF), and lifetimes due to stratospheric loss,2 
tropospheric halogen loss and soil uptake were assessed to be 
150 ± 50 years, 200 ± 100 years, and 120 ± 24 years, respectively 
(Myhre et al., 2013). Considering the full range of individual lifetimes, 
the total methane lifetime was assessed in AR5 to be 9.25 ± 0.6 years.
2 Prather et al. (2012) report lifetime due to stratospheric loss and soil uptake as 120 ± 24 years and 150 ± 50 years, respectively, and they were inadvertently swapped in AR5 with no effect on 
the total methane lifetime. The global chemical methane sink, essentially due to tropospheric

OH, required to calculate the chemical lifetime is estimated by either 
bottom‑up global CCMs and ESMs (BU) or top‑down observational 
inversion methods (TD). BU global models represent the coupled 
chemical processes and feedbacks that determine the chemical sinks 
but show large diversity in their estimates, particularly the tropospheric 
OH sink (Y . Zhao et al., 2019; Stevenson et al., 2020). TD  inversion 
methods, on the contrary, provide independent observational 
constraints on the methane sink due to tropospheric OH over large 
spatio‑temporal scales, but are prone to observational uncertainties 
and do not account for the chemical feedbacks on OH (Prather and 
Holmes, 2017; Naus et al., 2019). The central estimate of mean chemical 
methane loss over the period 2008–2017 varied from 602 [minimum 
and maximum range of 507–803] Tg yr–1 from BU chemistry–climate 
models in the Chemistry–Climate Modelling Initiative (CCMI) to

514 [474–529] Tg yr–1 from TD inverse modelling (Section 5.2.2 and 
Table 5.2). The smaller range in the TD estimate (11%) results from the 
use of a common climatological mean OH distribution (Saunois et al., 
2020; Zhao et al., 2020a), while the larger range in the BU estimate (49%)  
reflects the diversity in OH concentrations from different chemical

836
Chapter 6 Short-lived Climate Forcers6mechanisms implemented in the global models (Y . Zhao et al., 2019). 
See Section 6.3.6 for further discussion on the conflicting information 
on OH from CCMs/ESMs and TD inversion approaches. Further work is 
required to reconcile differences between BU and TD estimates of the 
chemical methane sink. 
The present‑day BU methane chemical lifetime shows a larger spread 
than that in the TD estimates (Table 6.2) in line with the spread seen 
in the sink estimates. The spread in the methane lifetime calculated 
by three CMIP6 ESMs is narrower and is enclosed within the spread 
of the BU CCMI model ensemble. Based on the consideration that the 
small imbalance in total methane sources versus sinks derived from 
TD estimates is close to the observed atmospheric methane growth 
rate (Table 5.2), the TD values are assessed to be the best estimates 
for this assessment. The relative uncertainty (± 1 standard deviation)

is taken to be the same as that in AR5, that is, 11.8%, 33% and 10% 
for chemical, soil and total lifetime, respectively. The central estimate 
of the total atmospheric methane lifetime assessed here is the same 
as that in AR5. 
The methane perturbation lifetime ( τpert) is defined as the e‑folding 
time it takes for the methane burden to decay back to its initial value 
after being perturbed by a change in methane emissions. Perturbation 
lifetime is longer than the total atmospheric lifetime of methane, as an 
increase in methane emissions decreases tropospheric OH, which in 
turn increases the lifetime and therefore the methane burden (Prather, 
1994; Fuglestvedt et al., 1996; Holmes et al., 2013; Holmes, 2018). 
Since perturbation lifetime relates changes in emissions to changes 
in burden, it is used to determine the emissions metrics assessed in 
Chapter  7 (Section  7.6). The perturbation lifetime is related to the

atmospheric lifetime as τpert = f * τtotal where f is the feedback factor 
and is calculated as f = 1/(1‑s), where s = δ (ln τtotal)/δ (ln[CH 4]) (Prather 
et al., 2001). Since there are no observational constraints for either 
τpert or f, these quantities are derived from CCMs or ESMs. AR5 used 
f = 1.34 ± 0.06 based on a combination of multi‑model (mostly CTMs 
and a few CCMs) estimates (Holmes et al., 2013). A recent model study 
explored new aspects of methane feedbacks finding that the strength 
of the feedback, typically treated as a constant, varies in space and 
time but will in all likelihood remain within 10% over the 21st century 
(Holmes, 2018). For this Assessment, the value of f is assessed to be 
1.30 ± 0.07 based on a six‑member ensemble of AerChemMIP ESMs (Thornhill et al., 2021b). This f value is slightly smaller but within the 
range of the AR5 value. This results in an overall perturbation methane 
lifetime of 11.8 ± 1.8 years, within the range of the AR5 value of

12.4 ± 1.4 years. The methane perturbation lifetime assessed here is 
used in the calculation of emissions metrics in Section 7.6. 
6.3.2 Ozone (O 3)
6.3.2.1 Tropospheric Ozone 
About 10% of the total atmospheric ozone column resides in the 
troposphere. The ozone forcing on climate strongly depends on its 
vertical and latitudinal distribution in the troposphere. The lifetime 
of ozone in the troposphere ranges from a  few hours in polluted 
urban regions to up to few months in the upper troposphere. 
Observed tropospheric ozone concentrations range from less than 
10 ppb over the tropical Pacific Ocean to as much as 100 ppb in 
the upper troposphere and more than 100 ppb downwind of major 
ozone precursor emissions regions. An ensemble of five CMIP6 
models including whole atmospheric chemistry and interactive 
ozone has been shown to simulate consistently the present‑day 
ozone distribution (north to south and latitudinal gradients) and its

seasonal variability when compared with observations from sondes, 
background surface stations and satellite products (Griffiths et al., 
2021). The biases, whose magnitude is similar to AR5, are lower than 
15% against climatological seasonal cycles from ozonesondes with 
an overestimate in the Northern Hemisphere and an underestimate 
in the Southern Hemisphere (Griffiths et al., 2021). The CMIP6 multi‑
model ensemble estimate of the global mean lifetime of ozone for 
present‑day conditions is 25.5 ± 2.2 days (Griffiths et al., 2021), which 
is within the range of previous multi‑model estimates (Stevenson 
et al., 2006; Young et al., 2013), indicating a  high level of confidence .
The AR5 assessed the tropospheric ozone burden to be 337 ± 23 Tg for 
the year 2000 based on the ACCMIP ensemble of model simulations 
(Myhre et al., 2013). Multiple satellite products, ozonesondes and 
CCMs are used to estimate tropospheric ozone burden (Table 6.3).

Satellite products provide lower ‑bound values as they exclude regions 
under polar night conditions (Gaudel et al., 2018). The tropospheric 
ozone burden values from multi‑model exercises are within the range Table 6.2 | Methane lifetime due to chemical losses, soil uptake and total atmospheric lifetime based on CMIP6 multi-model analysis, and bottom-up 
and top-down methane budget estimates in Table 5. 2. Bottom-up and top-down methane lifetimes are calculated using the central estimates of the respective sinks for 
the mean 2008–2017 period in Table 5.2 together with the mean 2008–2017 global methane concentration of 1815 ppb (see Annex III) converted to methane burden using 
a fill-factor of 2.75 Tg/ppb from Prather et al. (2012). Values in parentheses show the minimum and maximum range.
StudyTotal Chemical Lifetime 
(years)Soil Lifetime 
(years)Total Atmospheric Lifetime
(years)Number of Models/  
Inversions
Stevenson et al. (2020)a8.3 (8.1–8.6)b160 8.0 (7.7–8.2) 3 CMIP6 ESMs

Bottom‑up (based on Table 5.2) 8.3 (6.2–9.8) 166 (102–453) 8.0 (6.3–10.0) 7 CCMI CCMs/CTMs
Top‑down (based on Table 5.2) 9.7 (9.4–10.5) 135 (116–185) 9.1 (8.7–10.0) 7 inversion systems
AR6 assessed value c9.7 ± 1.1 135 ± 44 9.1 ± 0.9Based on top‑down with 
uncertainty estimate from AR5
a Mean over 2005–2014
b Does not include lifetime due to tropospheric halogen loss
c Uncertainties indicate ±1 standard deviation

837
Short-lived Climate Forcers  Chapter 66of the observational estimates despite different definitions of the 
tropopause for multi‑model estimates which can lead to differences 
of about 10% on the ozone‑burden model estimates (Griffiths et al., 
2021). Weighted by their number of members, CMIP6 and CCMI 
multi‑model estimates and observational estimates of tropospheric 
ozone burden in about the year 2010, lead to an assessment of the 
tropospheric ozone burden of 347 ± 28 Tg for 2010. 
The tropospheric ozone budget is controlled by chemical production and 
loss, by stratospheric–tropospheric exchange (STE), and by deposition 
at the Earth’s surface, whose magnitude are calculated by CCMs 
(Table 6.3). Despite the high agreement  of the model ensemble mean 
with observational estimates in the present‑day tropospheric ozone 
burden, the values of individual budget terms can vary widely across 
models in CMIP6, consistent with previous model intercomparison

experiments (Young et al., 2018). Furthermore, single‑model studies 
have shown that the halogen chemistry, which is typically neglected 
from model chemistry schemes in CCMs, may have a notable impact on 
the ozone budget, as halogens, particularly of marine origin, take part 
in efficient ozone‑loss catalytic cycles in the troposphere (Saiz‑Lopez 
et al., 2012; Sarwar et al., 2015; Sherwen et al., 2016) .
Because of the heterogeneous distribution of ozone, limited 
observations or proxies do not provide accurate information about 
the global pre‑industrial abundance, posing a  challenge to the 
estimation of the historical evolution of tropospheric ozone. Therefore, 
global CCMs complemented by observations are relied upon for 
estimating the long‑term changes in tropospheric ozone. The AR5 
concluded that anthropogenic changes in ozone precursor emissions

are unequivocally responsible for the increase in tropospheric ozone between 1850 and the present (Myhre et al., 2013). Based on limited 
isotopic evidence, Chapter  2 assesses that the global tropospheric 
ozone increased by less than 40% between 1850 and 2005 ( low Table 6.3 | Global tropospheric ozone budget terms and burden based on multi-model estimates and observations for present conditions.  All uncertainties 
quoted as ± 1 standard deviation. Values of tropospheric ozone burden with asterisk indicate average over the latitudinal zone 60°N–60°S. STE = stratospheric–tropospheric 
exchange.
PeriodBurden
(Tg)Production
Tg yr–1Loss
Tg yr–1Deposition
Tg yr–1STE
Tg yr–1 Number of Models/Reference
Models
~2000
time slice (1995–2004)347 ± 30 4510 ± 566 3948 ± 379 846 ± 44 284 ± 193CMIP6a (5 Earth system models for 
burden and 4 models for budget terms)
(Griffiths et al., 2021)~2010 
time slice (2005–2014)356 ± 31 4708 ± 589 4122 ± 399 863 ± 40 277 ± 201
~2000341 ± 31

(309 ± 31)*CCMIb (9 models)  
(Archibald et al., 2020)
2010345 ± 30
(314 ± 29)*
~2000 340 ± 34 4937 ± 656 4442 ± 570 996 ± 203 535 ± 161TOARc (based on 32–49 models 
participating in inter ‑model 
comparisons and single‑model studies)
(Young et al., 2018)
2010–2014 338 ± 6TOSTd, IASIe‑FORLI, and IASI‑SOFRID
(Gaudel et al., 2018)
2010–2014 302 ± 12* TOST, IASI‑FORLI, IASI‑SOFRID, OMIf/
MLS, OMI‑SAO and OMI‑RAL 
a CMIP6: Coupled Model Intercomparison Project Phase 6; b CCMI: Chemistry–Climate Model Initiative; c TOAR Tropospheric Ozone Assessment Report; d TOST Trajectory-mapped 
Ozonesonde dataset for the Stratosphere and Troposphere; e IASI Infrared Atmospheric Sounding Interferometer; f OMI Ozone Monitoring Instrument.
Figure  6.4 | Time evolution of global annual mean tropospheric ozone 
burden (in Tg) from 1850 to 2100.  Multi-model means for CMIP6 historical 
experiment (1850–2014) from UKESM1-LL-0, CESM2-WACCM, MRI-ESM2-0, GISS-

E2.1-G and GFDL-ESM4 and for ScenarioMIP SSP3-7.0 experiment (2015–2100) are 
represented with their inter-model standard deviation (±1 standard deviation, shaded 
areas). Observation-based global tropospheric ozone burden estimate (from Table 6.3) 
is for 2010–2014. Tropospheric Ozone Assessment Report (TOAR) multi-model mean 
value (from Table 6.3) is for 2000 with a ±1 standard deviation error-bar. Atmospheric 
Chemistry and Climate Model Intercomparison Project (ACCMIP) multi-model means are 
for 1850, 1930, 1980 and 2000 time slices with ±1 standard deviation error-bars. The 
troposphere is masked by the tropopause pressure calculated in each model using the 
WMO thermal tropopause definition. Further details on data sources and processing are 
available in the chapter data table (Table 6.SM.3).

838
Chapter 6 Short-lived Climate Forcers6confidence ) (Section  2.2.5.3). The CMIP6 models are in line with 
this increase of tropospheric ozone with an ensemble‑mean value 
of 109  ± 25 Tg (model range) from 1850–1859 to 2005–2014 
(Figure 6.4). This increase is higher than the AR5 value of 100 ± 25 Tg 
from 1850–2010 due to higher ozone precursor emissions in CMIP6. 
However, the AR5 and CMIP6 values are close when considering the 
reported uncertainties. The uncertainties are equivalent in CMIP6 and 
AR5 despite enhanced inclusion of coupled processes in the CMIP6 
ESMs (e.g., biogenic NMVOC emissions or interactive stratospheric 
ozone chemistry). 
Since the mid‑20th century, the CMIP6 model ensemble shows 
a higher global trend (Figure 6.4). Since the mid‑1990s, the trends 
are better documented by observations (Section  2.2.5.3) and 
indicate spatial heterogeneity. In particular, in situ observations 
at remote surface sites and in the lower free troposphere indicate

positive trends that are far more common than negative trends, 
especially in the northern tropics and across Southern and Eastern 
Asia (Figure  6.5). The CMIP6 ensemble and observations largely 
agree on the magnitude of the global positive trend since 1997 
(0.82 ± 0.13 Tg yr–1 in the model ensemble; 0.70 ± 0.15 Tg yr–1 in the 
ozonesonde dataset; 0.83 ± 0.85 Tg yr–1 in the satellite ensemble) and 
qualitatively reproduce positive trends in the Southern Hemisphere 
(Griffiths et al., 2021). More analyses are needed for evaluation in 
other parts of the world to assess the skill of the recent ensemble 
based on CMIP6 emissions. In summary, there is high confidence in the estimated present‑
day (about 2010) global tropospheric ozone burden based on an 
ensemble of models and observational estimates (347 ± 28 Tg), but 
there is medium confidence among the individual models for their 
estimates of the tropospheric ozone‑related budget terms.

Evidence from successive multi‑model intercomparisons and the 
limited isotopic evidence agree on the magnitude of the increase 
of the tropospheric ozone burden from 1850 to the present day in 
response to anthropogenic changes in ozone precursor emissions 
corroborating AR5 findings. This increase is assessed to be 109 ± 25  Tg 
(medium confidence ). The CMIP6 model ensemble shows a constant 
global increase since the mid‑20th century whose rate is consistent 
with that derived from observations since the mid‑1990s. 
6.3.2.2 Stratospheric Ozone 
Ninety percent of the total column ozone (TCO) resides in the 
stratosphere. The chemical lifetime of ozone in the stratosphere 
ranges from less than a  day in the upper stratosphere to several 
months in the lower stratosphere (Bekki and Lefevre, 2009). Global 
stratospheric ozone trends based on observations are assessed in 
Chapter 2 (Section 2.2.5.2). The CMIP6 model ensemble shows that

global TCO has slightly changed from 1850–1960 (Keeble et al., 2021). 
The rapid decline in the 1970s and 1980s due to halogenated ozone‑
depleting substances (ODSs, as assessed in Section  2.2.5.2 from 
observations) until the end of the 1990s, followed by a slight increase 
since then, is captured by the models (Keeble et al., 2021). Overall, 
the observed climatology patterns and annual cycle amplitudes are 
well represented in the CMIP6 ensemble mean. The CMIP6 ensemble 
overestimates the observed TCO values by up to 6% (10–20 Dobson 
Units (DU)) globally in the NH and SH mid‑latitudes, and in the 
tropics, but the trend in these regions is well captured between 1960 
and 2014. However, there is poor agreement between the individual 
CMIP6 models in the pre‑industrial period and throughout the 
historical period, with model TCO values spread across a range of 
about 60 DU. The global stratospheric ozone column decreased by 
14.3 ± 8.7 DU from 1850–2014 (Keeble et al., 2021).

Model simulations attribute about half of the observed upper ‑
stratospheric ozone increase after 2000 to the decline of ODS since 
the late 1990s while the other half of the ozone increase is attributed 
to the slowing of gas‑phase ozone destruction cycles due to cooling 
of the upper stratosphere by increasing GHGs (Aschmann et al., 
2014; Oberländer ‑Hayn et al., 2015). 
In summary, global stratospheric ozone column has decreased from 
pre‑industrial period to present day in response to the ODS‑induced 
ozone rapid decline in the 1970s and 1980s, followed by slow, and 
still incomplete, recovery. There is medium confidence  that global 
stratospheric ozone column has changed by 14.3 ± 8.7 DU between 
1850 and 2014. 
Decadal ozone trends since 1994
P-value: p ≥ 0.34 0.1 < p < 0.34 0.05 < p < 0.1 p ≤ 0.05Station type: surface altitude > 1500m airborne
Trends (ppb per decade):
-4       0       4
Figure 6.5 | Decadal tropospheric ozone trends  since 1994.  Trends are shown

at 28 remote and regionally representative surface sites (Cooper et al., 2020) and in 
11 regions of the lower free troposphere (650 hPa, about 3.5 km) as measured by 
In-Service Aircraft for a Global Observing System (IAGOS) above Europe, north-eastern 
USA, south-eastern USA, western North America, north-east China, South East Asia, 
southern India, the Persian Gulf, Malaysia/Indonesia, the Gulf of Guinea and northern 
South America (Gaudel et al., 2020). High-elevation surface sites are >1500 m above 
sea level. All trends end with the most recently available year but begin in 1995 
or 1994. The sites and datasets are the same as those used in Figure 2.8, further 
details on data sources and processing are available in the Chapter  2 data table 

839
Short-lived Climate Forcers  Chapter 666.3.3 Precursor Gases
6.3.3.1 Nitrogen Oxides (NO x)
The distribution of tropospheric NO x is highly variable in space and 
time owing to its short lifetime coupled with highly heterogeneous 
emission and sink patterns. NO x undergoes chemical processing, 
including the formation of nitric acid (HNO 3), nitrate (NO–
3 ), and 
organic nitrates (e.g., alkyl nitrate and peroxyacyl nitrate), atmospheric 
transport, and deposition. Despite challenges in retrieving quantitative 
information from satellite observations (Duncan et al., 2014; Lin et al., 
2015; Lorente et al., 2017; Silvern et al., 2018), improved accuracy and 
resolution of satellite‑derived tropospheric NO 2 columns over the past 
two decades have advanced understanding of the global distribution, 
long‑term trends and source attribution of NO x. Long‑term average 
tropospheric NO 2 column based on multiple satellite‑borne instruments

(Figure 6.6a) reveals the highest NO 2 levels over the most populated, 
urbanized and industrialized regions of the world corresponding to high 
NO x emissions source regions (Krotkov et al., 2016; Georgoulias et al., 
2019). Enhanced but highly variable NO 2 columns are also associated 
with biomass‑burning regions as well as areas influenced by lightning 
activity (Miyazaki et al., 2014; Tanimoto et al., 2015).
Observational constraints derived from the isotopic composition 
of atmospheric nitrate inferred from ice cores provide evidence of 
increasing anthropogenic NO x sources since pre‑industrial times 
(Hastings et al., 2009; Geng et al., 2014). Global NO x emissions trends 
in bottom‑up inventories (Section 6.2.1) as well as model simulations 
of nitrogen deposition (Lamarque et al., 2013a) are in qualitative 
agreement with these observational constraints. CMIP6 ESMs exhibit 
stable NO x burden over the first half of the 20th century and then

a sharp increase driven by a factor of three increase in emissions, 
however, the magnitude of this increase remains uncertain due to 
poor observational constraints on pre‑industrial concentrations of 
NO x (Griffiths et al., 2021).
The AR5 reported NO 2 decreases by 30–50% in Europe and North 
America, and increases by more than a  factor of two in Asia, over 
the 1996–2011 period based on satellite observations (Hartmann 
et al., 2013). Extension of this analysis covering the time period up to 
2015 reveals that NO 2 has continued to decline over the USA, Western 
Europe and Japan (Schneider et al., 2015; Duncan et al., 2016; Krotkov 
et al., 2016) because of effective fossil fuel NO x emissions controls 
(Section 6.2), although this rate of decline has slowed down post‑2011 
(Jiang et al., 2018). Satellite observations also reveal a 32% decline in 
NO 2 column over China after peaking in 2011 (Figure 6.6b), consistent

with declining NO x emissions (Section 6.2) due to the implementation 
of emissions‑control strategies (de Foy et al., 2016; Irie et al., 2016; 
F . Liu et al., 2016) . Over Southern Asia, tropospheric NO 2 levels have 
grown rapidly with increases of 50% during 2005–2015, largely 
driven by hotspot areas in India experiencing rapid expansion of 
the power sector (Duncan et al., 2016; Krotkov et al., 2016).  Further 
analysis indicates that many parts of India have also undergone 
a  reversal in NO 2 trends since 2011 that has been attributed to 
a combination of factors, including a slowdown in economic growth, 
implementation of cleaner technologies, non‑linear NO x chemistry, 
and meteorological variability (Georgoulias et al., 2019). Satellite data reveals spatially heterogeneous NO 2 trends over the Middle East with 
an overall increase over 2005–2010 and a decrease over large parts 
of the region after 2011–2012. The reasons for trend reversal within

individual areas are diverse, including warfare, imposed sanctions, 
and air ‑quality controls (Lelieveld et al., 2015a; Georgoulias et al., 
2019). Satellite‑derived tropospheric NO 2 levels over Africa and Latin 
America do not show a clear trend; both increasing and decreasing 
trends are observed over large agglomerations in these regions since 
the early 2000s (Schneider et al., 2015; Duncan et al., 2016). 
In summary, global tropospheric NO x abundance has increased from 
1850–2015 ( high confidence ). Satellite observations of tropospheric 
NO x indicate strong regional variations in trends over 2005–2015. 
There is high confidence  that NO 2 has declined over the USA and 
Western Europe since the mid‑1990s and increased over China until 
2011. NO 2 trends have reversed (declining) over China beginning in 
2012 and NO 2 has increased over Southern Asia by 50% since 2005 
Figure 6.6 | Long-term climatological mean (a) and time evolution (b) of

tropospheric nitrogen dioxide (NO 2) vertical column density.  Data are from 
the merged GOME/SCIAMACHY/GOME-2 (TM4NO2A version 2.3) dataset for the 
period 1996–2016 (Georgoulias et al., 2019). Time evolution of NO 2 column shown in   
panel (b) is normalized to the fitted 1996 levels for the 10 regions shown as boxes 
in  panel (a). Further details on data sources and processing are available in the 
chapter data table (Table 6.SM.3).

840
Chapter 6 Short-lived Climate Forcers6Table 6.4 | Summary of the global CO trends based on model estimates and observations.  
Analysis Period Trends: Regions Reference/Methodology
Global/Hemispheric
2003–2015 –0.86% yr–1 Flemming et al. (2017)  
Model assimilating MOPITT
2002–2013 –1.4% yr–1Gaubert et al. (2017)
2002–2018 –0.50 ± 0.3% yr–1: 60°N–60°S (MOPITT)
–0.56 ± 0.3% yr–1; –0.61 ± 0.2% yr–1: 0°–60°N
–0.35 ± –0.3% yr–1; ‑0.33±0.3% yr–1: 0°–60°SBuchholz et al. (2021)
Satellite Observations
MOPITT; AIRS
2000–2017 –0.32 ± 0.05% yr–1Zheng et al. (2019)
MOPITT
2003–2014around –2.5 to 0.5 ppb yr–1: Northern Hemisphere  
around –0.5 to 0 ppb yr–1: Southern HemisphereFlemming et al. (2017) NOAA Carbon Cycle  
Cooperative Global Air Sampling Network
2001–2013  –2.19 to –0.80 ppb yr–1: Northern Hemisphere (Upper Troposphere/Tropopause Layer)Cohen et al. (2018)  
IAGOS Airborne
Pacific/Tropics
2004–2013

(Spring Mean) –2.9 ± 2.6 ppb yr–1: Mauna Loa (19.54°N, 155.58°W)Gratz et al. (2015)  
Ground‑based
2004–2013 
(Spring Mean) –2.6 ± 1.8 ppb yr–1: Sand Island Midway (28.21°N, 177.38°W)Gratz et al. (2015)  
Europe
1996–2006 –0.45 ± 0.16% yr–1: Jungfraujoch (46.6°N, 8.0°E)
–1.00 ± 0.24% yr–1: Zugspitze (47.4°N, 11.0°E)
–0.62 ± 0.19% yr–1: Harestua (60.2°N, 10.8°E)
0.61 ± 0.16% yr–1: Kiruna (67.8°N, 20.4°E)Angelbratt et al. (2011)  
 2001–2011
May to Sep–3.1 ± 0.30 ppb yr–1: Pico Mt. Obs (38.47°N, 28.40°W)  
–1.4 ± 0.20 ppb yr–1: Mace Head, IrelandKumar et al. (2013)  
2002–2018 –‑0.89 ± 0.1% yr–1: Europe (45°N–55°N, 0°E–15°E)Buchholz et al. (2021)
MOPITT 
2001–2010–2.5 ppb yr–1: Thompson Farm (43.11°N, 70.95°W)
–2.3 ppb yr–1: Mt. Washington (44.27°N, 71.30°W)
+2.8 ppb yr–1: Castle Springs (43.75°N, 71.35°W)
–3.5 ppb yr–1: Pack Monadnock (42.86°N, 71.88°W)
–2.8 ppb yr–1: Whiteface Mountain (44.40°N, 73.90°W)

–4.3 ppb yr–1: Pinnacle State Park (42.09°N, 77.21°W)Zhou et al. (2017)  
2004–2013
(Spring Mean)–3.2 ± 2.9 ppb yr–1: Mt. Bachelor Observatory Gratz et al. (2015)  
2004–2012
(Spring Mean)–2.8 ± 1.8 ppb yr–1: Shemya Island (55.21°N, 162.72°W)Gratz et al. (2015)  
2002–2018 –0.85 ± 0.1%yr–1: Eastern USA (35°N–40°N, –95°E–75°E)Buchholz et al. (2021)
MOPITT
Asia
2005–2018  –0.46 ± 0.14% yr–1: Eastern AsiaZheng et al. (2018a)
WDCGG Ground‑based
2005–2018  –0.41 ± 0.09% yr–1: Eastern AsiaZheng et al. (2018a)
MOPITT
2002–2018–1.18 ± 0.3% yr–1: (Northeast China 30°E–40°E, 110°E–123°E)  
–0.28 ± 0.2% yr–1: (North India 20°N–30°N, 70°E–95°E)Buchholz et al. (2021)
MOPITT

841
Short-lived Climate Forcers  Chapter 666.3.3.2 Carbon Monoxide (CO)
About half of the atmospheric CO burden is due to its direct emissions 
and the remainder is due to the atmospheric oxidation of methane 
and NMVOCs. Reaction with OH is the primary sink of CO with 
a smaller contribution from dry deposition.
Since AR5, advances in satellite retrievals (e.g.,  Worden et al., 
2013; Warner et al., 2014; Buchholz et al., 2021) , ground‑based 
column observations (e.g.,  Zeng et al., 2012; Té et al., 2016) , 
airborne platforms (e.g.,  Cohen et al., 2018; Petetin et al., 2018), 
surface measurement networks (e.g., Andrews et al., 2014; Schultz 
et al., 2015; Prinn et al., 2018; Pétron et al., 2019)  and assimilation 
products (e.g.,  Deeter et al., 2017; Flemming et al., 2017; Zheng 
et al., 2019) have resulted in better characterization of the present‑
day atmospheric CO  distribution. Typical annual mean surface 
CO concentrations range from around 120 ppb in the Northern

Hemisphere to around 40 ppb in the Southern Hemisphere (Pétron 
et al., 2019). The sub‑regional patterns in CO reflect the distribution of 
emissions sources. Seasonal hotspots are linked to areas of biomass 
burning in tropical South America, equatorial Africa, South East Asia 
and Australia. A study using data assimilation techniques estimates 
a global mean CO burden of 356 ± 27 Tg over the 2002–2013 period 
(Gaubert et al., 2017). 
Global models generally capture the global spatial distribution 
of the observed CO concentrations but have regional biases of 
up to 50% (e.g.,  Emmons et al., 2020; Horowitz et al., 2020). 
Despite updated emissions datasets, the global multi‑model and 
single‑model simulations persistently underestimate observed CO 
concentrations at northern high and mid‑latitudes as well as in the 
Southern Hemisphere, but with smaller biases compared with that 
in the Northern Hemisphere (Naik et al., 2013; Stein et al., 2014;

Monks et al., 2015; Strode et al., 2015). Models are biased high in the 
tropics, particularly over highly polluted areas in India and Eastern 
Asia (Strode et al., 2016; Yarragunta et al., 2017).
Estimates of global CO burden simulated by global models generally 
fall within the range of that derived from data assimilation 
techniques, though the spread across the models is large (Naik et al., 
2013; Stein et al., 2014; Zeng et al., 2015; Myriokefalitakis et al., 
2016) . There is a large diversity in model‑simulated CO budget driven 
by uncertainties in CO sources and sinks, particularly those related to 
in situ production from NMVOCs and loss due to reaction with OH 
(Stein et al., 2014; Zeng et al., 2015; Myriokefalitakis et al., 2016). 
Global CO budget analysis from a multi‑model ensemble for more 
recent years, including results from the CMIP6 model runs, are not 
yet available. 
Reconstructions of CO concentrations based on limited ice‑core

samples in the Northern Hemisphere high latitudes suggest CO mole 
fractions of about 145 ppb in the 1950s, which rose by 10–15 ppb 
in the mid‑ 1970s, and then declined by about 30–130 ppb by 2008 
(Petrenko et al., 2013). The negative trends since the 1990s are often 
attributed to emissions regulations from road transportation in North 
America and Europe. Due to limited observations prior to the satellite 
era, long‑term global CO trends are based on estimates from models. An increase of global CO burden of about 50% for the year 2000 
relative to 1850 is found in CMIP6 (Griffiths et al., 2021). 
The AR5 reported a  global CO decline of about 1% yr–1 based on 
satellite data from 2002–2010, but biases in instruments rendered 
low confidence  in this trend. The AR5 also indicated a  small CO 
decrease from in situ networks but did not provide quantitative 
estimates. New analysis of CO trends performed since AR5 and based

on different observational platforms and assimilation products show 
a decline globally and over most regions during the last one to two 
decades with varying amplitudes partly depending on the period of 
analysis (Table 6.4). Inversion‑based analysis attributes the global CO 
decline during the past two decades to decreases in anthropogenic 
and biomass‑burning CO emissions despite probable increase in 
atmospheric CO chemical production (Gaubert et al., 2017; Jiang 
et al., 2017; Zheng et al., 2019). Furthermore, Buchholz et al. (2021) 
report a  slowdown in global CO decline in 2010–2018 compared 
to 2002–2010, although the magnitude and sign of this change in 
the trend varies regionally. Global models prescribed with emissions 
inventories developed prior to the CMIP6 inventory capture the 
declining observed CO trends over North America and Europe but 
not over Eastern Asia (Strode et al., 2016). CMIP6 models driven by

CMIP6 emissions simulate a negative trend in global CO burden over 
the 1990–2020 period (Griffiths et al., 2021), however the simulated 
trends have not yet been evaluated against observations. 
In summary, our understanding of present‑day global CO distribution 
has increased since AR5 with newer and improved observations and 
reanalysis. There is high confidence  that global CO burden is declining 
since 2000. Evidence from observational CO reanalysis suggests 
this decline is driven by reductions in anthropogenic CO emissions, 
however this is yet to be corroborated by global ESM studies with the 
most recent emissions inventories. 
6.3.3.3 Non‑Methane Volatile Organic Compounds (NMVOCs) 
NMVOCs encompass thousands of compounds with lifetimes from 
hours to days to months, and abundances and chemical composition 
highly variable with respect to space and time. Although the biogenic 
source (Section  6.2.2) dominates the global NMVOC budget,

anthropogenic activities are the main driver of long‑term trends in 
the abundance of many compounds.
Information on the global distribution of individual NMVOCs is scarce, 
except for the less reactive compounds having lifetimes of several 
days to months. Based on measurements from polar firn air samples 
and ground‑based networks, AR5 reported that the abundances of 
the predominantly anthropogenic light alkanes (C 2‑C5) increased 
until 1980 and declined afterwards. The decline was attributed to 
air‑quality emissions controls and to fugitive emissions decreases 
following the collapse of the Soviet Union (Simpson et al., 2012). 
Since AR5, scarce ground‑based measurements have shown that the 
decline in C 2‑C3 alkanes ended around 2008 and their abundances 
are since growing again, which is primarily attributed to increasing 
North American emissions (Section  6.2.1). Furthermore, since AR5 
the evolution of ethane levels during the past millennium was made

accessible by analysis of ice‑core samples (Nicewonger et al., 2016).

842
Chapter 6 Short-lived Climate Forcers6The large observed interpolar ratio of ethane in pre‑industrial times 
(3.9) corroborates a  large geologic source of ethane previously 
put forward by (Etiope and Ciccioli, 2009), and narrows down its 
likely global magnitude (Nicewonger et al., 2018) ( low to medium 
confidence ). The incorporation of geologic emissions in CCMs is 
not yet systematic though a one‑model study has shown improved 
agreement of the results with observations (Dalsøren et al., 2018). 
Formaldehyde (HCHO) is a short‑lived, high‑yield product of NMVOC 
oxidation, and formaldehyde column data from satellite instruments 
can therefore inform on trends in anthropogenic NMVOC abundances 
over very industrialized regions. The AR5 reported significant positive 
trends in formaldehyde between 1997 and 2009 over northeastern 
China (4% yr–1) and negative trends over northeastern US cities. 
Since AR5, there is robust evidence and high agreement  of an upward

trend of HCHO over eastern China, though large regional disparities 
exist in the trends (De Smedt et al., 2015; Shen et al., 2019) with 
a possible negligible or decreasing trend over Beijing and the Pearl 
River Delta. In other world regions, in particular North America, there 
is limited to medium evidence for significant changes in the HCHO 
columns, except in regions where the trend is particularly strong 
(e.g., the Houston area: –2.2% yr–1 over 2005–2014) and the Alberta 
oil sands (+3.8% yr–1; Zhu et al., 2017). Over the northeastern USA, 
even the sign of the trend differs between studies (De Smedt et al., 
2015; Zhu et al., 2017)  for reasons that are unclear. 
In summary, after a decline between 1980 and 2008, abundances of 
light NMVOCs have increased again over the Northern Hemisphere 
due to the extraction of oil and gas in North America ( high 
confidence ). Trends in satellite HCHO observations, used as a proxy of

anthropogenic NMVOC over industrialized areas, show a significant 
positive trend over eastern China ( high confidence ) but also indicate 
large regional disparities in the magnitude of the trends over China 
and even in their signs over North America.
6.3.3.4 Ammonia (NH 3) 
Ammonia is the most abundant alkaline gas in the atmosphere. 
Its present‑day source is dominated by livestock and crop production 
(Section  6.2). Ammonia reacts with nitric acid and sulphuric acid 
to produce ammonium sulphate and ammonium nitrate, which 
contribute to the aerosol burden (Section 6.3.5.2), promotes aerosol 
nucleation by stabilizing sulphuric acid clusters (Kirkby et al., 2011), 
and contributes to nitrogen deposition (Section 6.4.4; Sheppard et al., 
2011; Flechard et al., 2020). Trends in NH 3 were not assessed in AR5.
Considerable expansion of satellite (Clarisse et al., 2009; Shephard 
and Cady‑Pereira, 2015; Warner et al., 2016)  and ground‑based

observations (Miller et al., 2014; Y . Li et al., 2016; Pan et al., 2018) has 
improved our understanding of the spatial distribution and seasonal to 
interannual variability of ammonia, and advanced its representations 
in models (e.g., Zhu et al., 2015). Regionally, peak NH 3 concentrations 
are observed over large agricultural (e.g.,  northern India, the USA 
Midwest and Central Valley) and biomass‑burning regions, in good 
qualitative agreement with emissions inventories (Van Damme 
et al., 2015, 2018) . However, several large agricultural and industrial 
hotspots have been found to be missing or greatly underestimated in emissions inventories (Van Damme et al., 2018) . NH 3 exhibits a strong 
vertical gradient, with a  maximum in the boundary layer (Schiferl 
et al., 2016), and can be transported into the upper troposphere and 
lower stratosphere (UTLS), particularly in the Asian Monsoon region, 
as indicated by observations (Froyd et al., 2009; Höpfner et al., 2016,

2019) and theoretical considerations (Ge et al., 2018) . There is a large 
range in the present‑day NH 3 burden (from 0.04–0.7 TgN) simulated 
by CCMs, highlighting deficiencies in the process‑level representation 
of NH 3 in current global models (Bian et al., 2017). The underestimate 
of surface NH 3 concentrations (Bian et al., 2017) further highlights such 
deficiencies and the limitations in comparing site‑specific observations 
with relatively coarse‑resolution models.
Observations show that NH 3 concentration has been increasing 
in recent decades in the USA (Butler et al., 2016; Warner et al., 2016; 
Yu et al., 2018) , Western Europe (van Zanten et al., 2017; Warner et al., 
2017; Wichink Kruit et al., 2017; Tang et al., 2018) , and China (Warner 
et al., 2017; M. Liu et al., 2018). This trend has been attributed to 
a combination of increasing ammonia emissions (Sutton et al., 2013; 
Fowler et al., 2015) and decreases in the chemical reaction of NH 3

with nitric and sulphuric acids associated with reductions in  SO 2 
and NO x emissions whose rate depends on the region (Warner et al., 
2017; Yao and Zhang, 2019). Over longer time scales, CCMs simulate 
an increase of the NH 3 burden by a factor of two to seven since pre‑
industrial conditions (Xu and Penner, 2012; Hauglustaine et al., 2014). 
In summary, progress has been made in the understanding of the 
spatio‑temporal distribution of ammonia, though representation of 
NH 3 remains rather unsatisfactory due to process‑level uncertainties. 
Evidence from observations and models suggests that ammonia 
concentrations have been increasing over recent decades due to 
emissions and chemistry. There is high confidence  that the global NH 3 
burden has increased considerably from the pre‑industrial period to the 
present day, although the magnitude of the increase remains uncertain.
6.3.3.5  Sulphur Dioxide (SO 2) 
The AR5 did not assess trends in SO 2 concentrations. Trends in SO 2

abundances are consistent with the overall anthropogenic emissions 
changes as presented in Section  6.2 and Figure  6.18. Long‑term 
surface‑based in situ observations in North America and Europe 
show reductions of more than 80% since the measurements began 
around 1980 (Table 6.5). Europe had the largest reductions in the first 
part of the period while the highest reduction came later in North 
America. Observed trends are qualitatively reproduced by global 
and regional models over North America and Europe during the 
period 1990–2015 for which emissions changes are well quantified 
(Table 6.5; Aas et al., 2019).
In situ observations over other parts of the world are scattered. 
However, the limited in situ observations in Eastern Asia indicate an 
increase in atmospheric SO 2 up to around 2005 and then a decline 
(Aas et al., 2019). This is confirmed by satellite observations (Krotkov 
et al., 2016), which further reveal a rapid decline in SO 2 since around

2012 or 2013 (Krotkov et al., 2016; Zheng et al., 2018b). In India, on 
the other hand, SO 2 levels have doubled between 2005 and 2015 
(Krotkov et al., 2016).

843
Short-lived Climate Forcers  Chapter 66In summary, surface and satellite observations indicate strong 
regional variations in trends of atmospheric SO 2 abundance. The SO 2 
concentrations in North America and Europe have declined over 
1980–2015 with slightly stronger reductions in North America 
(70 ± 20%) than in Europe (58 ± 32%) over 2000–2015, though 
Europe had larger reductions than the US in the prior decade (1990–
2000). In Asia, the SO 2 trends are more scattered, though there is 
medium confidence  that there was a strong increase up to around 
2005, followed by a  steep decline in China, while over India, the 
concentrations are increasing steadily.
6.3.4 Short-lived Halogenated Species 
Halogenated species are emitted in the atmosphere in the form 
of the synthetically produced chlorofluorocarbons (CFCs), halons, 
hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and 
others. Their historical global abundances are provided in Annex III

and discussed in Chapter 2 (Section 2.2.4 and Table 2.3). In summary, 
for the period 2011–2019, the abundance of total chlorine from 
HCFCs has continued to increase in the atmosphere with decreased 
growth rates; total tropospheric bromine from halons and methyl 
bromide continued to decrease while abundances of most currently 
measured HFCs increased significantly, consistent with expectations 
based on the ongoing transition away from the use of ODSs. Here, 
emphasis is given on the very short‑lived halogenated species 
(VSLSs). The trends for these species were not discussed in IPCC AR5. VSLSs are halogenated substances with atmospheric lifetimes less 
than half a  year. While longer ‑lived ODSs account for most of the 
present‑day stratospheric halogen loading, there is robust evidence  
that VSLSs contribute to stratospheric bromine and chlorine 
(Carpenter et al., 2014; Elvidge et al., 2015a; Hossaini et al., 2015 ), 
thus also contributing to stratospheric ozone depletion.

Of the atmospheric VSLSs, brominated and iodinated species 
are predominantly of oceanic origin, while chlorinated species 
have significant additional anthropogenic sources (Carpenter 
et al., 2014; Hossaini et al., 2015). Global mean chlorine from the 
VSLSs has increased in the troposphere from about 91  ppt in 
2012 to about 110  ppt in 2016 (Engel et al., 2018). This increase 
is mostly due to dichloromethane (CH 2Cl2), a  species that has 
predominantly anthropogenic sources reflected by three‑times higher 
concentrations in the Northern Hemisphere than in the Southern 
Hemisphere (Hossaini  et al., 2017). The upward dichloromethane 
trend is corroborated by upper ‑tropospheric aircraft data over 
the period 1998–2014 (Elvidge et al., 2015b; Oram et al., 2017). 
The observations from the surface networks show that the abundance 
of dichloromethane continued to increase until 2019 (Annex III), 
although the accuracy of global abundance of VSLSs is limited by the

scarce coverage by networks. No long‑term changes of the bromine‑
containing VSLSs have been observed (Engel et al., 2018).Table 6.5 | Summary of changes or trends in atmospheric abundance of sulphur dioxide (SO 2) and sulphate (SO42–) aerosols based on in situ and satellite 
observations.  
Analysis Period Trends in SO 2 Trends in Particulate SO42–Reference
Global Models/Assimilated Models
1990–2000–8.54 ± 1.40% yr–1 (EU, 43 sites)
–2.63 ± 0.30% yr–1 (NA, 53 sites)–5.23 ± 1.17% yr–1 (EU, 41 sites)
–1.94 ± 0.43% yr—(NA 101 sites)Aas et al. (2019)
2000–2015–0.41 ± 0.92% yr–1 (EA, 19 sites) 
–4.86 ± 1.31% yr–1 (EU, 47 sites)
–4.40 ± 0.93% yr–1 (NA, 77 sites)0.02 ± 0.91% yr–1 (EA, 13 sites)
–3.26 ± 0.85% yr–1 (EU, 36 sites)
–3.18 ± 0.66% yr–1 (NA, 218 sites)Aas et al. (2019)
Ground-based In Situ Observations
1980–1990–5.03 ± 2.04% yr–1 (EU, 20 sites)
–2.5% yr–1 (US)–2.56 ± 3.10% yr–1 (EU, 16 sites)
–1.80 ± 4.09% yr–1 (US SO42– in precipitation, 78 sites)Aas et al. (2019)
US EPAa

1990–2000–7.56 ± 1.81% yr–1 (EU, 43 sites)
–3.27 ± 1.69% yr–1 (NA, 53 sites)–5.16 ± 2.11% yr–1 (EU, 41 sites)
–2.08 ± 1.44% yr–1 (NA, 101 sites)Aas et al. (2019)
2000–2015–0.14 ± 5.32% yr–1 (EA, 19 sites)
–3.89 ± 2.16% yr–1 (EU, 47 sites)
–4.69 ± 1.35% yr–1 (NA, 77 sites)2.68 ± 9.41% yr–1 (EA, 13 sites)
–2.67 ± 2.03% yr–1 (EU, 36 sites)
–3.15 ± 1.30% yr–1 (NA, 218 sites)Aas et al. (2019)
Change Based on Satellite Observations
2005–2015 ca –80% (Eastern US) Krotkov et al. (2016)
2005–2015 ca –60% (Eastern EU) Krotkov et al. (2016)
2005–2015 200 ± 50% (India) Krotkov et al. (2016)
2005 (and 2012) 
–2015ca –50% (The North China Plain) Krotkov et al. (2016)
a https://www.epa.gov/air-trends/sulfur-dioxide-trends

844
Chapter 6 Short-lived Climate Forcers66.3.5 Aerosols 
This section assesses trends in the atmospheric distribution of 
aerosols and improvements in relevant physical and chemical 
processes. The observed large‑scale temporal evolution of aerosols 
is assessed in Section 2.2.6. Since AR5, long‑term measurements of 
aerosol mass concentrations from regional global surface networks 
have continued to expand and provide information on the distribution 
and trends in aerosols (Figure 6.7). There is large spatial variability in 
aerosol mass concentration, expressed as PM 2.5, dominant aerosol 
type and aerosol composition, consistent with the findings in AR5. 
Remote‑sensing instruments provide a larger ‑scale view of aerosol 
distributions and trends than ground‑based monitoring networks by 
retrieving the aerosol optical depth (AOD), which is indirectly related 
to aerosol mass concentrations. AOD is the column‑integrated

measure of extinction of the solar intensity due to aerosols at a given 
wavelength, and is therefore relevant to the estimation of the 
radiative forcing of aerosol–radiation interactions (Section 7.3.3.1). Models participating in Phase III of the AeroCom intercomparison 
project were found to underestimate present‑day AOD by about 
20% (Gliß et al., 2021), although different remote‑sensing estimates 
obtain different estimates of global mean AOD. Gliß et al. (2021) also 
highlight the considerable diversity in the simulated contribution of 
various aerosol types to total AOD. However, models simulate regional 
trends in AODs that agree well, when expressed as percentage change, 
with ground‑ (Mortier et al., 2020; Gliß et al., 2021)  and satellite‑
based (Cherian and Quaas, 2020; Gliß et al., 2021) observations. 
AOD  trends simulated by CMIP6 models are more consistent with 
satellite‑derived trends than CMIP5 models for several sub‑regions,

thanks to improved emissions estimates (Cherian and Quaas, 2020). 
All CMIP6 models simulate a positive trend in global mean AOD from 
1850, with a  strong increase after the 1950s coinciding with the 
massive increase in anthropogenic SO 2 emissions (Figure 6.8). Global 
mean AOD increases have slowed since 1980, or even reversed in 
some models, as a result of a compensation between SO 2 emissions 
decreases over the USA and Europe in response to air ‑quality 
Figure 6.7 | Distribution of PM 2.5 composition mass concentration (in μg m–3) for the major PM 2.5 aerosol components.  Those aerosol components are sulphate, 
nitrate, ammonium, sodium, chloride, organic carbon and elemental carbon. The central world map depicts the intermediate-level regional breakdown of observations (10 
regions) following the IPCC Sixth Assessment Report Working Group III (AR6 WGIII). Monthly averaged PM 2.5 aerosol component measurements are from: (i) the Environmental

Protection Agency (EPA) network which include 211 monitor sites primarily in urban areas of North America during 2000–2018 (Solomon et al., 2014), (ii) the Interagency 
Monitoring of Protected Visual Environments (IMPROVE) network during 2000–2018 over 198 monitoring sites representative of the regional haze conditions over North 
America, (iii) the European Monitoring and Evaluation Programme (EMEP) network over 70 monitoring in Europe and (eastern) Eurasia during 2000–2018, (iv) the Acid 
Deposition Monitoring Network in Eastern Asia (EANET) network with 39 (18 remote, 10 rural, 11 urban) sites in Eurasia, Eastern Asia, South East Asia and Developing Pacific, 
and Asia-Pacific Developed during 2001–2017, (v) the global Surface Particulate Matter Network (SPARTAN) during 2013–2019 with sites primarily in highly populated regions

around the world (i.e., North America, Latin America and Caribbean, Africa, Middle East, Southern Asia, Eastern Asia, South East Asia and Developing Pacific; Snider et al., 2015, 
2016), and (vii) individual observational field campaign averages over Latin America and Caribbean, Africa, Europe, Eastern Asia, and Asia-Pacific Developed (Celis et al., 2004; 
Feng et al., 2006; Bourotte et al., 2007; Fuzzi et al., 2007; Mariani and de Mello, 2007; Molina et al., 2007, 2010; Favez et al., 2008; Mkoma, 2008; Aggarwal and Kawamura, 
2009; Mkoma et al., 2009; de Souza et al., 2010; Li et al., 2010; Martin et al., 2010; Radhi et al., 2010; Weinstein et al., 2010; Batmunkh et al., 2011; Gioda et al., 2011; Pathak 
et al., 2011; F . Zhang et al., 2012; Cho and Park, 2013; Zhao et al., 2013; Wang et al., 2019; Kuzu et al., 2020). Further details on data sources and processing are available in 
the chapter data table (Table 6.SM.3).

845
Short-lived Climate Forcers  Chapter 66controls since the mid‑1980s, and increases over Asia. From about 
2000, global mean AOD stabilized in the models, driven by soaring 
emissions in Southern Asia and declining emissions in Eastern Asia 
(Section  6.2.1). Trends after around 2010 are difficult to assess 
from CMIP6 models because the historical simulations end at 2014. 
Nevertheless, the strong decline in anthropogenic SO 2 emissions over 
Eastern Asia since 2011 is underestimated in the CMIP6 emissions 
database (Hoesly et al., 2018), indicating that the observed AOD 
change over Eastern Asia may not be captured accurately by CMIP6 
models (Wang et al., 2021). While all CMIP6 models simulate the 
increase of AOD between 1850 and 2014 there is strong inter ‑model 
diversity in the simulated AOD change since 1850 ranging from 
0.01 (15%) to 0.08 (53%) in 2014. Some models therefore lie outside 
the 68% confidence interval of 0.02 (15%) to 0.04 (or 30%) for

global AOD change in 2005–2015 compared to 1850, estimated by 
Bellouin et al. (2020) based on observational and model (excluding 
CMIP6) lines of evidence. In addition to the horizontal distribution 
of aerosols documented by AOD, their number size distribution, 
vertical distribution, optical properties, hygroscopicity, ability to act 
as CCN, chemical composition, mixing state and morphology are key 
elements to assess their climate effect (Section 6.4). 
6.3.5.1 Sulphate (SO 42–)
Sulphate aerosols (or sulphate‑containing aerosols) are emitted 
directly or formed in the atmosphere by gas‑ and aqueous‑phase 
oxidation of precursor sulphur gases, including SO 2, DMS and carbonyl 
sulphide (OCS), emitted from anthropogenic and natural sources 
(Section 6.2). Sulphate aerosols influence climate forcing directly by 
either scattering solar radiation or absorbing longwave radiation, 
and indirectly by influencing cloud micro‑ and macrophysical

properties and precipitation (Boucher et al., 2013; Myhre et al., 2013). 
Additionally, sulphate aerosols and sulphate deposition have a large 
impact on air quality and ecosystems (Reis et al., 2012). The majority 
of sulphate particles are formed in the troposphere, however, SO 2 and 
other longer ‑lived natural precursors, such as OCS, transported into 
the stratosphere, contribute to the background stratospheric aerosol layer (Kremser et al., 2016). SO 2 emissions from volcanic eruptions are 
a significant source of stratospheric sulphate loading (see Chapter 2 
for reconstruction of stratospheric aerosol optical depth and 
Chapter  7 for radiative forcing of volcanic aerosols). Furthermore, 
studies suggest sulphate contributions from anthropogenic SO 2 
emissions transported into the stratosphere could have a consequent 
impact on radiative forcing (Myhre et al., 2004; Yu et al., 2016). 
However, there is significant uncertainty in the relative importance of

this stratospheric sulphate source (Kremser et al., 2016).
Process understanding of sulphate production pathways from SO 2 
emissions has seen some progress since AR5. More specifically, 
many global climate models now have a more complete description 
of chemical reactions such that oxidant levels (including ozone) 
are better described, include a  pH‑dependence of SO 2 oxidation 
(e.g., Kirkevåg et al., 2018; Bauer et al., 2020) , and implement explicit 
descriptions of ammonium and nitrate aerosol components, which 
may influence the partitioning of sulphate (Bian et al., 2017; Lund 
et al., 2018a) . The pH influences the heterogeneous chemistry as well 
as the physical properties of the aerosols, and this topic has been 
a subject of growing interest since AR5 (Cheng et al., 2016; Freedman 
et al., 2019; Nenes et al., 2020). Increases in cloudwater pH have 
been shown to significantly increase the radiative forcing due to 
sulphate aerosols (Turnock et al., 2019).

Sulphate is removed from the atmosphere by dry deposition and 
wet scavenging, and these processes depend on the characteristics 
of the Earth’s surface, and the intensity, frequency and amount of 
precipitation (Boucher et al., 2013). Even though there have been 
some improvements since AR5, representation of atmospheric 
transport and of wet scavenging and related cloud processes remains 
a key source of uncertainty in the simulated aerosol distribution and 
lifetime, with further consequences for the sulphate forcing estimates 
(Kristiansen et al., 2016; Lund et al., 2018a). There are also still 
relatively large uncertainties in the emission height used in models 
affecting the simulated aerosol distribution (Yang et al., 2019a) .
Based on long‑term surface‑based in situ observations, AR5 
reported a  strong decline in sulphate aerosols in Europe and the 
USA over 1990–2009, with the largest decreases occurring before

2000 in Europe and post‑2000 in the USA. Since AR5, atmospheric 
measurements in conjunction with model results have provided 
insights into the spatial and temporal distribution of sulphate and 
sulphur deposition (Vet  et al., 2014; Tan et al., 2018; Aas et al., 
2019). The in situ observations in North America and Europe reveal 
substantial reduction since the measurements started around 
1980, though the trends have not been linear through this period 
(Table 6.5). Several regional studies agree with these trend estimates 
for Europe (Banzhaf et al., 2015; Theobald et al., 2019)  and North 
America (Sickles II and Shadwick, 2015; Paulot et al., 2016) . Further, 
the concentrations of primary emitted SO 2 (Section  6.3.3.5) show 
greater decreases than secondary sulphate aerosols over these 
regions due to a  combination of higher oxidation rate (hence 
more SO 2 converted to SO 42–) and increased dry deposition rate of

SO 2 (Fowler et al., 2009; Banzhaf et al., 2015) . In situ observations 
over other parts of the world are scattered (Figure  6.7), and the 
lack of observations makes it too uncertain to quantify regional 
Figure 6.8 | Time evolution of changes in global mean aerosol optical depth 
(AOD) at 550 nm.  The year of reference is 1850. Data are shown from individual 
Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. 
Each time series corresponds to the ensemble mean of realizations done by each model. 
Simulation results from years including major volcanic eruptions (e.g., Novarupta, 1912; 
Pinatubo, 1991), are excluded from the analysis for models encompassing the 
contribution of stratospheric volcanic aerosols to total AOD. Further details on data 
sources and processing are available in the chapter data table (Table 6.SM.3).

846
Chapter 6 Short-lived Climate Forcers6representative trends (Hammer  et al., 2018). However, limited in 
situ observations in Eastern Asia indicate an increase in atmospheric 
sulphate up to around 2005 and then a decline (Aas et al., 2019), 
which is confirmed by satellite observations of SO 2 (Section 6.3.3.5). 
In India, on the other hand, satellite observations indicate a rapid 
increase in the SO 2 levels (Krotkov et al., 2016), and long‑term 
measurements of sulphate in precipitation in India further provide 
evidence of an increasing trend from 1980–2010 (Bhaskar and 
Rao, 2017; Aas et al., 2019). Further improvements in global trend 
assessments are expected with new integrated reanalysis products 
from the Earth‑system data assimilation projects (Randles et al., 
2017; Inness et al., 2019). 
Indirect evidence of decadal trends in the atmospheric loading of 
sulphur are provided by Alpine ice cores, mainly influenced by

European sources (Engardt et al., 2017), and ice cores from Svalbard 
(Samyn et al., 2012) and Greenland (Patris et al., 2002; Iizuka et al., 
2018) influenced by sources in Europe and North America. These 
show similar patterns with a  weak increase from the end of the 
19th  century up to around 1950, followed by a  steep increase up 
to around 1980, and then a significant decrease over the next two 
decades (see also Section 2.2.6). This general trend is consistent with 
the emissions of SO 2 in North America and Europe (Figures 6.18 and 
6.19; Hoesly et al., 2018) .
Global and regional models qualitatively reproduce observed trends 
over North America and Europe for the period 1990–2015 for which 
emissions changes are generally well quantified (Aas et al., 2019; 
Mortier et al., 2020), building confidence in the relationship between 
emissions, concentration, deposition and radiative forcing derived 
from these models. However, the models seem to systematically

underestimate sulphate (Bian et al., 2017; Lund et al., 2018a) and 
AOD (Lund et al., 2018a; Gliß et al., 2021), and there are quite 
large differences in the models’ distribution of the concentration 
fields of sulphate driven by differences in the representation of 
photochemical production and sinks of aerosols. One global model 
study also highlighted biases in simulated sulphate trends over the 
2001–2015 period over eastern China due to uncertainties in the 
CEDS anthropogenic SO 2 emissions trends (Paulot et al., 2018a) .
In summary, there is high confidence  that the global tropospheric 
sulphate burden increased from 1850 to around 2005, but there 
are large regional differences in the magnitude. Sulphate aerosol 
concentrations in North America and Europe have declined over 1980–
2015 with slightly stronger reductions in North America (47 ± 20%) 
than in Europe (40 ± 30%) over 2000–2015, though Europe had 
larger reductions in the prior decade (1990–2000; 52 ± 21% and

21 ± 14% respectively for Europe and North America). In Asia, the 
trends are more scattered, though there is medium confidence  
that there was a  strong increase up to around 2005, followed by 
a  steep decline in China, while over India, the concentrations are 
increasing steadily.
6.3.5.2 Ammonium (NH 4+) and Nitrate Aerosols (NO 3–)
Ammonium sulphate and ammonium nitrate aerosols are formed 
when NH 3 reacts with nitric acid (HNO 3) and sulphuric acid (H 2SO 4), produced in the atmosphere by the oxidation of NO x and SO 2 
respectively. Ammonium nitrate is formed only after H 2SO 4 is fully 
neutralized. NH 4+ and NO 3– aerosols produced via these gas‑to‑particle 
reactions are a major fraction of fine‑mode particles (with diameter 
<1µm) affecting air quality and climate. Coarse‑mode nitrate, 
formed by the heterogeneous reaction of nitric acid with dust and 
sea salt, dominates the overall global nitrate burden, but has little

radiative effect (Hauglustaine et al., 2014; Bian et al., 2017). Trends in 
ammonium (NH 4+) and nitrate (NO 3–) were not assessed in AR5.
Global model present‑day estimates of the global NH 4+ burden range 
from 0.1–0.6 TgN (Bian et al., 2017). Models generally simulate 
surface NH 4+ concentrations better than surface NH 3 concentrations 
(Bian et al., 2017), which reflects its thermodynamic control by SO 42– 
rather than NH 3 (Shi et al., 2017). The concomitant increases of NH 3, 
SO 2, and NO x emissions (see Section  6.2) have led to a  factor of 
three to nine increase in the simulated NH 4+ burden from 1850–2000 
(Hauglustaine et al., 2014; Lund et al., 2018a), driven primarily by 
ammonium sulphate (70–90%). The increases in the NH 3 and NH 4+ 
burdens are indirectly supported by the observed increase of NH 4+ 
concentration in ice cores in mid‑ to high latitudes (Kang et al., 2002; 
Kekonen et al., 2005; Lamarque et al., 2013; Iizuka et al., 2018) .

Ammonium nitrate is semi‑volatile, which results in complex spatial 
and temporal patterns in its concentrations (Putaud et al., 2010; 
Hand et al., 2012a; H. Zhang et al., 2012), reflecting variations in 
its precursors, NH 3 and HNO 3, as well as SO 42–, non‑volatile cations, 
temperature and relative humidity (Nenes et al., 2020) . High relative 
humidity and low temperature as well as elevated fine particulate 
matter loading (Huang et al., 2014; Petit et al., 2015; H. Li et al., 2016; 
Sandrini et al., 2016)  favour nitrate production. Measurements reveal 
the high contribution of NO 3– to surface PM 2.5 (>30%) in regions with 
elevated regional NO x and NH 3 emissions, such as the Paris area 
(Beekmann et al., 2015; Zhang et al., 2019), northern Italy (Masiol 
et al., 2015; Ricciardelli et al., 2017), Salt Lake City (Kuprov et al., 
2014; Franchin et al., 2018), the North China Plains (Guo et al., 
2014; Chen et al., 2016) and New Delhi  (Pant et al., 2015). Recent

observations also show that ammonium nitrate contributes to the 
Asian tropopause aerosol layer (Vernier et al., 2018; Höpfner et al., 
2019). Model diversity in simulating the present‑day global fine‑mode 
NO 3– burden is large with two multi‑model intercomparison studies 
reporting estimates in the range of 0.14–1.88 Tg and 0.08–0.93 Tg 
respectively (Bian et al., 2017; Gliß et al., 2021). Models differ in their 
estimates of the global tropospheric nitrate burden by up to a factor 
of 13 with differences remaining nearly the same across the CMIP5 
and CMIP6 generation of models (Bian et al., 2017; Gliß et al., 2021). 
While regional patterns in the concentration of fine‑mode NO 3– are 
qualitatively captured by models, the simulation of fine‑mode NO 3– 
is generally worse than that of NH 4+ or SO 42– (Bian et al., 2017). This 
can be partly attributed to the semi‑volatile nature of ammonium 
nitrate and biases in the simulation of its precursors (Heald et al.,

2014; Paulot et al., 2016), including the sub‑grid scale heterogeneity 
in NO x and NH 3 emissions (Zakoura and Pandis, 2018). 
Models indicate that the burden of fine‑mode NO 3– has increased 
by a factor of two to five from 1850–2000 (Xu and Penner, 2012; 
Hauglustaine et al., 2014; Lund et al., 2018a), an increase that has

847
Short-lived Climate Forcers  Chapter 66accelerated between 2001 and 2015 (Lund et al., 2018a; Paulot et al., 
2018b). The sensitivity of NO 3– to changes in NH 3, SO 42–, and HNO 3 
is determined primarily by aerosol pH, temperature, and aerosol 
liquid water (Guo et al., 2016, 2018; Weber et al., 2016; Nenes et al., 
2020). In regions where aerosol pH is high, changes in NO 3– follow 
changes in NO x emissions, consistent with the observed increase of 
ammonium nitrate in northern China from 2000–2015 (Wen et al., 
2018) and its decrease in the US Central Valley (Pusede et al., 2016). 
In contrast, the decrease in SO 2 emissions in the south‑east USA has 
caused little change in NO 3– from 1998–2014 as nitric acid largely 
remains in the gas phase due to highly acidic aerosols (Weber et al., 
2016; Guo et al., 2018). 
In summary, there is high confidence  that the NH 4+ and NO 3– burdens 
have increased from the pre‑industrial period to the present day,

although the magnitude of the increase is uncertain especially 
for NO 3–. The sensitivity of NH 4+ and NO 3– to changes in NH 3, H 2SO 4 
and HNO 3 is well understood theoretically. However, it remains 
challenging to represent in models, in part because of uncertainties 
in the simulation of aerosol pH, and only a minority of ESMs consider 
nitrate aerosols in CMIP6.
3 The terms EC and BC are operationally defined on the basis of the methodology used for their quantification (i.e., thermal refractivity and light absorption, respectively) and often  
used interchangeably.
4 The carbon mass fraction of OA is termed as OC, a conversion factor between 1.4 to 1.6 from OC to OA is typically assumed.6.3.5.3 Carbonaceous Aerosols 
Carbonaceous aerosols are black carbon (BC)3, which is soot 
made almost purely of carbon, and organic aerosols4 (OA), which 
also contain hydrogen and oxygen and can be of both primary 
(POA) or secondary (SOA) origin. BC and a  fraction of OA called

brown carbon (BrC) absorb solar radiation. The various components 
of carbonaceous aerosols have different optical properties, so the 
knowledge of their partition, mixing, coating and ageing is essential 
to assess their climate effect (Section 7.3.3.1.2). 
Carbonaceous aerosols receive attention in the scientific and policy 
arena due to their radiative forcing, and their sizeable contribution 
to PM in an air ‑quality context (Rogelj et al., 2014b; Harmsen et al., 
2015; Shindell et al., 2016; Haines et al., 2017; Myhre et al., 2017) . 
BC  exerts a  positive forcing, but the forcing from carbonaceous 
aerosol as a  whole is negative (Bond et al., 2013; Thornhill et al., 
2021b). On average, carbonaceous aerosols account for 50–70% 
of PM with a  diameter lower than 1 µm in polluted and pristine 
areas (Zhang et al., 2007; Carslaw et al., 2010; Andreae et al., 2015; 
Monteiro dos Santos et al., 2016; Chen et al., 2017) .

Table 6.6 | Summary of the regional carbonaceous aerosol trends at background observation sites.
Species Analysis Period Change/Trends References
BC1990–2009Arctic Sites (Alert, Barrow, Ny‑Alesund)
−2% yr−1Sharma et al. (2013)
1970–2010Finland (Kevo remote site)
−1.8% yr–1Dutkiewicz et al. (2014)
2005–2014Germany (rural site)
−2% yr–1Kutzner et al. (2018)
2009–2016United Kingdom (Harwell rural site)
−8% yr–1Singh et al. (2018)
2009–2019Japan (Fukue Island)
−5.8 ± 1.5% yr–1Kanaya et al. (2020)
2009–2015India (Darjeeling mountain site)
−5% yr–1Sarkar et al. (2019)
OA 2001–2015USA (IMPROVE sites east of 100°W)
−2% yr–1Malm et al. (2017)
Total Carbon  
(EC + OC) 1990–2010USA (IMPROVE sites)
Western USA: −4 to −5% yr–1
Eastern USA: −1 to −2% yr–1Hand et al. (2013)
2002–2010Spain (Montseny rural site)
−5% yr–1Querol et al. (2013)

848
Chapter 6 Short-lived Climate Forcers6An extensive review on BC (Bond et al., 2013) discussed limitations in 
inferring its atmospheric abundance and highlighted inconsistencies 
between different terminology and related measurement techniques 
(Petzold et al., 2013; Sharma et al., 2017). Due to a lack of global 
observations, AR5 only reported declining total carbonaceous aerosol 
trends from the USA and a declining BC trend from the Arctic based 
on data available up to 2008. Since AR5, the number of observation 
sites has grown worldwide (Figure  6.7) but datasets suitable for 
global trend analyses remain limited (Reddington et al., 2017; 
Laj et al., 2020). Locally, studies based on observations from rural and 
background sites have reported decreasing surface carbonaceous 
aerosol trends in the Arctic, Europe, the USA, Japan and India 
(Table 6.6). Increases in carbonaceous aerosol concentrations in some 
rural sites of the western USA have been associated with wildfires

(Hand et al., 2013; Malm et al., 2017). Long‑term OA observations 
are scarce, so their trends outside of the USA are difficult to assess. 
Ice‑core analysis has provided insight into carbonaceous aerosol 
trends predating the satellite and observation era over the Northern 
Hemisphere (Section 2.2.6, Figure 2.9b). 
Knowledge of carbonaceous aerosol atmospheric abundance 
continues to rely on global models due to a  lack of global‑scale 
observations. For BC, models agree within a  factor of two with 
measured surface mass concentrations in Europe and North America, 
but underestimate concentrations at the Arctic surface by one to two 
orders of magnitude, especially in winter and spring (Lee et al., 2013; 
Lund et al., 2018a). For OA, AeroCom models underestimate surface 
mass concentrations by a factor of two over urban areas, as their low 
horizontal resolution prevents them from resolving local pollution 
peaks (Tsigaridis et al., 2014; Lund et al., 2018a). Models agree

within a  factor of two with OA surface concentrations measured 
at remote sites, where surface concentrations are more spatially 
uniform (Tsigaridis et al., 2014). 
BC and OA lifetimes are estimated to be 5.5 days ± 35% and 6.0 
days ± 29% (median ± 1 standard deviation), respectively, based 
on an ensemble of 14 models (Gliß et al., 2021). Disagreement 
in  simulated lifetime leads to horizontal and vertical variations in 
predicted carbonaceous aerosol concentrations, with implications for 
radiative forcing (Samset et al., 2013; Lund et al., 2018b). Airborne 
campaigns have provided valuable vertical‑profile measurements 
of carbonaceous aerosol concentrations (Schwarz et al., 2013; 
Freney et al., 2018; Hodgson et al., 2018; Schulz et al., 2019; D. Zhao 
et al., 2019; Morgan et al., 2020). Compared to those measurements, 
models tend to transport BC too high in the atmosphere, suggesting 
that lifetimes are not larger than 5.5 days (Samset et al., 2013; Lund

et al., 2018b). Newly developed size‑dependent wet‑scavenging 
parametrization for BC (Taylor et al., 2014; Schroder et al., 2015; 
Ohata et al., 2016; G. Zhang et al., 2017; Ding et al., 2019; Moteki 
et al., 2019; Motos et al., 2019) may lead to decreased BC lifetimes 
and improve agreement with observed vertical profiles. 
Simulated BC burdens show a  large spread among models 
(Gliß  et al., 2021), despite using harmonised primary emissions, 
because of differences in BC removal efficiency linked to different 
treatment of ageing and mixing, particularly in strong source regions. 
The multi‑model median BC burden for the year 2010 from Gliß et al. (2021), based on 14 AeroCom models, is 0.131 ± 0.047 Tg 
(median  ± 1 standard deviation). The range encompasses values 
reported by independent single‑model estimates (Huang et al., 2013; 
Lee et al., 2013; Sharma et al., 2013; Q. Wang et al., 2014; Tilmes 
et al., 2019) . 
Simulated OA burdens also show a  large spread among global

models, with Gliß et al. (2021) reporting a  multi‑model median of 
1.91 ± 0.65 Tg for the year 2010. The large spread reflects the wide 
range in the complexity of the OA parametrizations, particularly for 
SOA formation, as well as in the primary OA emissions (Tsigaridis 
et al., 2014; Gliß et al., 2021). The uncertainties are particularly large 
in model estimates of SOA production rates, which vary between 
10 and 143 Tg yr–1 (Tsigaridis et al., 2014; Hodzic et al., 2016; Tilmes 
et al., 2019). While the level of complexity in the representation of OA 
in global models has increased since AR5 (Shrivastava et al., 2017; 
Hodzic et al., 2020), limitations in process‑level understanding of 
the formation, ageing and removal of organic compounds lead to 
uncertainties in the global model predictions of global OA burden 
and distribution as well as the relative contribution of POA and 
SOA to OA. Jo et al. (2016) estimated that BrC contributes about

20% of total OA burden. That would give BrC a burden similar to 
that of BC ( low confidence ), enhancing the overall forcing exerted by 
carbonaceous aerosol absorption (Zhang et al., 2020).
In summary, the lack of global‑scale observations of carbonaceous 
aerosols, the complexity of processes influencing them, and the large 
spread in their simulated global budget and burdens means that 
there is only low confidence  in the quantification of the present‑day 
atmospheric distribution of individual components of carbonaceous 
aerosols. Global trends in carbonaceous aerosols cannot be 
characterized due to limited observations, but sites representative of 
background conditions have reported multi‑year declines in BC over 
several regions of the Northern Hemisphere.
6.3.6 Implications of SLCF Abundances  
for Atmospheric Oxidizing Capacity 
The atmospheric oxidising capacity is determined primarily by 
tropospheric hydroxyl (OH) radical and to a smaller extent by NO 3

radical, ozone, hydrogen peroxide (H 2O2) and halogen radicals. OH 
is the main sink for many SLCFs, including methane, halogenated 
compounds (HCFCs and HFCs), CO and NMVOCs, controlling their 
lifetimes and consequently their abundance and climate influence. 
OH‑initiated oxidation of methane, CO and NMVOCs in the presence 
of NO x leads to the production of tropospheric ozone. OH also 
contributes to the formation of aerosols from oxidation of SO 2 to 
sulphate and NMVOCs to secondary organic aerosols. The evolution 
of the atmospheric oxidising capacity of the Earth driven by human 
activities and natural processes is, therefore, of significance for 
climate and air ‑quality concerns. 
The main source of tropospheric OH is the photoexcitation of 
tropospheric ozone that creates an electronically excited oxygen atom 
which reacts with water vapour producing OH. A secondary source of 
importance for global OH is the recycling of peroxy radicals formed by

849
Short-lived Climate Forcers  Chapter 66the reaction of OH with reduced and partly oxidized species, including 
methane, CO and NMVOCs. In polluted air, NO x emissions control the 
secondary OH production, while in pristine air it occurs via other 
mechanisms involving, in particular, isoprene (Lelieveld et al., 2016; 
Wennberg et al., 2018). Knowledge of the effect of isoprene oxidation 
on OH recycling has evolved tremendously over the past decade, 
facilitating mechanistic explanation of elevated OH concentrations 
observed in locations characterised by low NO x levels (Hofzumahaus 
et al., 2009; Paulot et al., 2009; Peeters et al., 2009, 2014; Fuchs et al., 
2013) . Since AR5, the inclusion of improved chemical mechanisms 
in some CTMs suggest advances in understanding of the global OH 
budget, however, these improvements have yet to be incorporated in 
CMIP6‑generation ESMs. 
As a  result of the complex photochemistry, tropospheric OH

abundance is sensitive to changes in SLCF emissions as well as 
climate. Increases in methane, CO and NMVOCs reduce OH while 
increases in water vapour and temperature, incoming solar radiation, 
NO x and tropospheric ozone enhance OH. The OH level thus responds 
to climate change and climate variability via its sensitivity to 
temperature and water vapour, as well as the influence of climate 
on natural emissions (e.g.,  wetland methane emissions, lightning 
NO x, BVOCs, fire emissions) with consequent feedbacks on climate 
(Section 6.4.5). Climate modes of variability, like El Niño–Southern Oscillation, also contribute to OH variability via changes in lightning 
NO x emissions and deep convection (Turner et al., 2018) , and fire 
emissions (Rowlinson et al., 2019). 
Global‑scale OH observations are non‑existent because of its 
extremely short lifetime (around 1 second) and therefore global 
OH abundance and its time variations are either inferred from

atmospheric measurements of methyl chloroform (MCF; Prinn et al., 
2018 and references therein) or derived from global atmospheric 
chemistry models (Lelieveld et al., 2016) . The AR5 reported small 
interannual OH variations in the 2000s based on atmospheric 
inversions of MCF observations (within ±5%) and global CCMs and 
CTMs (within ±3%) (Ciais et al., 2013). 
Since AR5, there is much closer agreement in the estimates of 
interannual variations in global mean OH derived from atmospheric 
inversions, empirical reconstruction, and global CCMs and ESMs, 
with an estimate of 2–3% over the 1980–2015 period (Table 6.7). 
While the different methodologies agree on the occurrence of small 
interannual variations, there is much debate over the longer ‑term 
global OH trend. Two studies using multi‑box model inversions of 
MCF and methane observations suggest large positive and negative 
trends since the 1990s in global mean OH (Rigby et al., 2017;

Turner et al., 2017), however, both find that observational 
Table 6.7 | Summary of global OH trends and interannual variability from studies post 2010.  
Reference Time Period OH Trends and IAV Approach
Inversion and Empirical Methods Based on Observations
Montzka et al. (2011) 1998–2007 2.3 ± 1.3% (IAV) 3D inversion
Ciais et al. (2013) 2000s within ±5% (IAV) AR5 based on inversions
McNorton et al. (2016) 1993–2011 ±2.3% (IAV) Box‑model inversion
Rigby et al. (2017) 1980–2014 10% increase from the late 1990s–2004; 10% decrease from 2004–2014 Box‑model inversion
Turner et al. (2017) 1983–2015 about 7% increase in 1991–2001; 7% decrease in 2003–2016 Box‑model inversion 
Nicely et al. (2018) 1980–2015 1.6% (IAV) Empirical reconstruction
McNorton et al. (2018) 2003–2015 1.8 ± 0.4% decrease 3D inversion
Naus et al. (2019) 1994–2015 3.8 ± 3.2% increase Box‑model inversion 
Patra et al. (2021) 1996–2015 2–3% IAV, no trend 3D inversion
Global CTMs, CCMs and ESMs
John et al. (2012)1860–2005

1980–20006% decrease
About 3% increaseCCM
Holmes et al. (2013) 1997–2009 0.7–1.1% (IAV) Multi‑model CTMs
Ciais et al. (2013) 2000s within ±3% (IAV) AR5 based on CCMs
Murray et al. (2013) 1998–2006 increasing trend 3D CTM
Naik et al. (2013) 1980–2000
1850–20003.5 ± 2.2 % increase
−0.6 ± 8.8%Multi‑model CCMs/CTMs
Murray et al. (2014) 1770s–1990s 5.3% increase CCM
Dalsøren et al. (2016) 1970–2012 8% increase 3D CTM
Gaubert et al. (2017) 2002–2013 7% increaseCCM with assimilated satellite 
CO observations
Y . Zhao et al. (2019)1960–2010
1980–20001.9 ± 1.2 % (IAV)
4.6 ± 2.4 % increaseMulti‑model CCMs/CTMs
Stevenson et al. (2020)1980–2014
1850–19809% increase
no trendMulti‑model ESMs

850
Chapter 6 Short-lived Climate Forcers6constraints are weak, such that a wide range of multi‑annual OH 
variations are possible. Indeed, Naus et al. (2019) find an overall 
positive global OH trend over the past two decades (Table  6.7) 
after accounting for uncertainties and biases in atmospheric MCF 
and methane inversions, confirming the weakness in observational 
constraints for deriving OH trends. Global ESMs, CCMs and CTMs 
exhibit increasing global OH after 1980 contrary to the lack of 
trend derived from some atmospheric inversions and empirical 
reconstructions (Table 6.7). In particular, a three‑member ensemble 
of ESMs participating in the AerChemMIP/CMIP6 agrees that global 
OH has increased since 1980 by around 9% (Figure 6.9) with an 
associated reduction in methane lifetime (Stevenson et al., 2020). 
This positive OH trend is in agreement with the OH increase of about

7% derived by assimilating global‑scale satellite observations of CO over the 2002–2013 period (with CO declining trends) into 
a CCM (Section 6.3.4; Gaubert et al., 2017). Multi‑model sensitivity 
analysis suggests that increasing OH since 1980 is predominantly 
driven by changes in anthropogenic SLCF emissions with the 
complementary influence of increasing NO x and decreasing CO 
emissions (Stevenson et al., 2020). 
Over paleo time scales, proxy‑based observational constraints from 
methane and formaldehyde suggest tropospheric OH to be a factor 
of two to four lower in the Last Glacial Maximum (LGM) relative 
to pre‑industrial levels, though these estimates are highly uncertain 
(Alexander and Mickley, 2015) . Global models, in contrast, exhibit no 
change in tropospheric OH (and consequently in methane lifetime)

at the LGM relative to the pre‑industrial period (Murray et al., 2014; Figure 6.9 | Time evolution of global annual mean tropospheric hydroxyl (OH) over the historical period , expressed as a percentage anomaly relative to 
the mean over 1998–2007.  (a) Results from three CMIP6 models, including UKESM1-0LL (green), GFDL-ESM4 (blue), and CESM2-WACCM (red), are shown; the shaded 
light green and light red bands show mean over multiple ensemble members for UKESM1-0LL (3) and CESM2-WACCM (3) models, respectively with the multi-model mean 
anomalies shown in thick black line. (b) Multi-model mean OH anomalies for the 1980–2014 period compared with those derived from observational-based inversions from 
Montzka et al., (2011); Rigby et al., (2017); Turner et al., (2017); Nicely et al., (2018); Naus et al., (2019); Patra et al., (2021) in the zoomed box. Further details on data sources 
and processing are available in the chapter data table (Table 6.SM.3).

851
Short-lived Climate Forcers  Chapter 66Quiquet et al., 2015) , however, the sign and magnitude of OH changes 
are sensitive to model predictions of changes in natural emissions, 
including lightning NO x and BVOCs, and model representation of 
isoprene oxidation chemistry (Achakulwisut et al., 2015; Hopcroft 
Regarding change since the pre‑industrial era, at the time of the 
AR5, the ensemble mean of 17 global models participating in 
ACCMIP indicated little change in tropospheric OH from 1850–2000. 
This was due to the competing and finally offsetting changes in 
factors enhancing or reducing OH with a consequent small decline 
in methane lifetime (Naik et al., 2013; Voulgarakis et al., 2013). 
However, there was large diversity in both the sign and magnitude 
of past OH changes across the individual models attributed to the 
disparate implementation of chemical and physical processes (Nicely

et al., 2017; Wild et al., 2020). Analysis of historical simulations from 
three CMIP6 ESMs indicates little change in global mean OH from 
1850 to about 1980 (Stevenson et al., 2020) . However, there is no 
observational evidence of changes in global OH since 1850 up to the 
early 1980s to evaluate the ESMs. 
In summary, global mean tropospheric OH does not show a significant 
trend from 1850 up to around 1980 ( low confidence ). There is 
conflicting information from global models constrained by emissions 
versus observationally constrained inversion methods over the 1980–
2014 period. A positive trend since 1980 (about 9% increase over 
1980–2014) is a  robust feature among ESMs and CCMs and there 
is medium confidence  that this trend is mainly driven by increases in 
global anthropogenic NO x emissions and decreases in CO emissions. 
There is  limited evidence  and medium agreement for positive trends 
or absence of trends inferred from observation‑constrained methods.

Overall, there is medium confidence  that global mean OH has remained 
stable or exhibited a positive trend since the 1980s. 
6.4 SLCF Radiative Forcing 
and Climate Effects
The radiative forcing on the climate system introduced by SLCFs is 
distinguished from that of long‑lived greenhouse gases (LLGHGs) by 
the diversity of forcing mechanisms for SLCFs, and the challenges 
of constraining these mechanisms via observations and of inferring 
their global forcings from available data. Chapter  7 assesses the 
global estimates of effective radiative forcing (ERF) due to SLCF 
abundance changes. This section assesses the characteristics 
(e.g.,  spatial patterns, temporal evolution) of forcings, emissions‑
based SLCF forcings, climate response and feedbacks due to SLCFs 
relying primarily on results from CMIP6 models. Additionally, the 
ERFs for several aerosol‑based forms of solar –radiation modification 
(SRM) are discussed in Section 6.4.6.

Forcing and climate response due to changes in SLCFs are typically 
estimated from global models that vary in their representation of 
the various chemical, physical and radiative processes (Box  6.1) 
affecting the causal chain from SLCF emissions to climate response 
(Figure  6.1). The AR5 noted that the representation of aerosol 
processes varied greatly in CMIP5 models and that it remained unclear what level of sophistication is required to properly quantify 
aerosol effects on climate (Boucher et al., 2013). Since the AR5, 
Ekman (2014) found that the CMIP5 models with the most complex 
representations of aerosol impacts on cloud microphysics had 
the largest reduction in biases in surface temperature trends. 
CMIP6‑generation CCMs that simulate aerosol and cloud‑size 
distributions better represent the effect of a  volcanic eruption 
on lower atmosphere clouds than a  model with aerosol‑mass 
only (Malavelle et al., 2017). This highlights the need for skilful

simulation of conditions underlying aerosol–cloud interactions, 
such as the distribution, transport and properties of aerosol 
species, in addition to the interactions themselves (Chapter  7). 
In advance of CMIP6, representations of aerosol processes and 
aerosol–cloud interactions in ESMs have generally become more 
comprehensive ( Meehl et al., 2020; Gliß et al., 2021; Thornhill 
et al., 2021b ; see also Section 1.5), with enhanced links to aerosol 
emissions and gas‑phase chemistry. Many CMIP6 models (Annex 
II: Table AII.5) now simulate aerosol number size distribution, in 
addition to mass distribution, which is a prerequisite for accurately 
simulating number concentrations of CCN (Bellouin et al., 2013), 
while some CMIP6 models use prescribed aerosol optical properties 
to constrain aerosol forcing (e.g., Stevens et al., 2017). Hence, the 
range of complexity in aerosol modeling noted in CMIP5 is still 
present in the CMIP6 ensemble. Although simulated CCN have

been compared to surface (Fanourgakis et al., 2019) and aircraft 
(Reddington et al., 2017) measurements, with mixed results, 
the lack of global coverage limits confidence in the evaluations. 
Evaluations of AOD have been more wide‑ranging (Section 6.3.5; 
Gliß et al., 2021) but are less relevant to aerosol–cloud interactions 
as they do not allow the evaluation of vertical profiles, aerosol‑
cloud overlap regions, aerosol type or number. Nevertheless, biases 
in simulated patterns and trends in AOD, alongside biases in cloud 
fractions (Vignesh et al., 2020), likely affect quantifications of the 
aerosol–cloud interactions. 
In summary, CMIP6 models generally represent more processes 
that drive aerosol–cloud interactions than the previous generation 
of climate models, but there is only medium confidence  that those 
enhancements improve their fitness for the purpose of simulating 
radiative forcing due to aerosol–cloud interactions because only a few

studies have identified the level of sophistication required to do so. 
In addition, the challenge of representing the small‑scale processes 
involved in aerosol–cloud interactions, and a lack of relevant model‑
data comparisons, does not allow a  quantitative assessment of 
the progress of the models from CMIP5 to CMIP6 in simulating the 
underlying conditions relevant for aerosol–cloud interactions at this 
time. 
6.4.1 Historical Estimates of Regional Short-lived 
Climate Forcing 
The highly heterogeneous distribution of SLCF abundances 
(Section 6.3) translates to strong heterogeneity in the spatial pattern 
and temporal evolution of forcing and climate responses due to 
SLCFs. This section assesses the spatial patterns of the current forcing 
due to aerosols and their historical evolution by region.

852
Chapter 6 Short-lived Climate Forcers6In AR5, the confidence  in the spatial patterns of aerosol and ozone 
forcing was lower than that for the global mean because of the large 
spread in the regional distribution simulated by global models, and 
was assessed as medium . The AR5 assessment was based on aerosol 
and ozone RFs, and aerosol ERFs (with fixed SSTs) from ACCMIP and 
a small sample of CMIP5 models (Myhre et al., 2013; Shindell et al., 
2013). For this assessement, the spatial distribution of aerosol ERF 
due to human‑induced changes in aerosol concentrations over 1850–
2014 is quantified based on results from a seven‑member ensemble 
of CMIP6 ESMs including interactive gas and aerosol chemistry 
analysed in AerChemMIP . There is insufficient information to estimate 
the spatial patterns of ozone ERF from CMIP6, however, the spatial 
patterns in SLCF ERF are dominated by that from aerosol ERF over 
most regions (e.g.,  Shindell et al., 2015). The aerosol ERF includes

contributions from both direct aerosol–radiation (ERFari) and indirect 
aerosol–cloud interactions (ERFaci; Section 7.3.3), and is computed 
as the difference between radiative fluxes from simulations with 
time‑evolving aerosol and their precuror emissions, and identical 
simulations but with these emissions held at their 1850 levels (Collins 
et al., 2017). Both simulations are driven by time‑evolving sea surface 
temperatures (SSTs) and sea ice from the respective coupled model 
historical simulation, and therefore, differ from ERFs computed using 
fixed pre‑industrial SST and sea ice fields (Section  7.3.1), but the 
effect of this difference is generally small (Forster et al., 2016). A 
correction for land surface temperature change (Section 7.3.1) is not 
available from these data to explicitly quantify the contribution from 
adjustments. The ESMs included here used the CMIP6 anthropogenic 
and biomass‑burning emissions for ozone and aerosol precursors

but varied in their representation of the natural emissions, chemistry 
and climate characteristics contributing to spread in the simulated 
concentrations (Section 6.3) and resulting forcings, partly reflecting 
uncertainties in the successive processes (Thornhill et al., 2021b).
The geographical distribution of the ensemble‑mean aerosol ERF 
over the 1850–2014 period is highly heterogeneous (Figure 6.10a) in 
agreement with AR5. Negative ERF is greatest over and downwind of 
most industrialized regions in the Northern Hemisphere and to some 
extent over tropical biomass‑burning regions, with robust signals. 
The largest negative forcing occurs over Eastern Asia and Southern 
Asia, followed by Europe and North America, reflecting the changes 
in anthropogenic aerosol emissions in recent decades (Section 6.2). 
Positive ERF  over high albedo areas, including cryosphere, deserts and 
clouds, also found in AR5 and attributed to absorbing aerosols, are

not robust across the small CMIP6 ensemble applied here. Regionally 
aggregated shortwave (SW) and longwave (LW) components of 
the aerosol ERF  exhibit similar large variability across regions 
(Figure 6.10b). The SW flux changes come from aerosol–radiation and 
aerosol–cloud interactions while the small positive LW flux changes 
come from aerosol–cloud interactions (related to liquid‑water path 
changes (Section 7.3.2.2). These spatial patterns in aerosol ERF are 
similar to the patterns reported in AR5. 
Time evolution of 20‑year means of regional net aerosol ERF shows 
that the regions are divided into two groups depending on whether 
the mean ERF attains its negative peak value in the 1970s–1980s 
(e.g., Europe, North America) or in the late 1990s–2000s (e.g., Asia, 
South America; Figure 6.11). Qualitatively, this shift in the distribution of ERF trends is consistent with the regional long‑term trends in 
aerosol precursor emissions (Section 6.2; Figures 6.18 and 6.19) and

their abundances (Section  6.3). However, at finer regional scales, 
there are regions where sulphate aerosols are still following an 
upward trend (e.g., Southern Asia; Section 6.3.5) implying that the 
trends in ERF may not have shifted for these regions. The continental‑
scale ERF  trends are also in line with the satellite‑observed AOD 
trends assessed in Section 2.2.6. Global mean ERF reaches maximum 
(a) Net effective radiative forcing due to aerosols
(b) Mean regional effective radiative forcing due to aerosols
–10.5      –7.5       –4.5    –1.5          1.5         4.5         7.5       10.5
–12       –10       –8         –6         –4        –2          0          2          4
W m-2W m-2
Figure 6.10 | Multi-model mean effective radiative forcings (ERFs) over 
the recent-past (1995-2014) induced by aerosol changes since 1850.  Panel

(a) shows the spatial distribution of the net ERF with area-weighted global mean 
ERF shown at the lower right corner. Uncertainty is represented using the advanced 
approach: no overlay indicates regions with robust signal, where ≥66% of models 
show change greater than variability threshold and ≥80% of all models agree on sign 
of change; diagonal lines indicate regions with no change or no robust signal, where 
<66% of models show a change greater than the variability threshold; crossed lines 
indicate regions with conflicting signal, where ≥66% of models show change greater 
than variability threshold and <80% of all models agree on sign of change. For more 
information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. 
Panel (b) shows the mean shortwave and longwave ERF for each of the 14 regions 
defined in the Atlas. Violins in panel (b) show the distribution of values over regions

where ERFs are significant. ERFs are derived from the difference between top of the 
atmosphere (TOA) radiative fluxes for Aerosol Chemistry Model Intercomparison 
Project (AerChemMIP) experiments histSST  and histSST-piAer  (Collins et al., 2017) 
averaged over 1995–2014 (Box 1.4, Chapter 1). The results come from seven Earth 
system models: MIROC6, MPI-I-ESM-1-2-HAM, MRI-ESM2-0, GFDL-ESM4, GISS-E2-
1-G, NorESM2-LM and UKESM-0-LL. These data can be seen in the Interactive Atlas. 
(Table 6.SM.3).

853
Short-lived Climate Forcers  Chapter 66negative values in the mid‑1970s and its magnitude gradually 
decreases thereafter. This weakening of the negative forcing since 
1990 agrees with findings that attribute this to a  reduction in 
global mean SO 2 emissions combined with an increase in global 
BC (Myhre et al., 2017). Uncertainties in model‑simulated aerosol 
ERF distribution and trends can result from inter ‑model variations 
in the representation of aerosol–cloud interactions and aerosol 
microphysical processes as also demonstrated by Bauer et al. (2020). 
In summary, the spatial and temporal distribution of the net aerosol 
ERF from 1850–2014 is highly heterogeneous ( high confidence ). 
Globally, there has been a  shift from increase to decrease of the 
negative net aerosol ERF driven by trends in aerosol and their 
precursor emissions ( high confidence ). However, the timing of this 
shift varies by continental‑scale region and has not occurred for some

finer regional scales. 
6.4.2 Emissions-based Radiative Forcing and Effect  
on Global Surface Air Temperature (GSAT)
The ERFs attributable to emissions versus concentrations for several 
SLCFs including ozone and methane are different. A concentration 
change, used to assess the abundance ‑based ERF , results from  the 
changes in emissions of multiple species and subsequent chemical 
reactions. The corollary is that the perturbation of a single emitted 
compound can induce subsequent chemical reactions and affect the 
concentrations of several climate forcers (chemical adjustments); 
this  is what is accounted for in emissions‑based ERF . Due to non‑
linear chemistry (Section  6.3) and non‑linear aerosol–cloud 
interactions (Section  7.3.3.2), the ERF attributed to the individual 
species cannot be precisely defined and can only be estimated 
through model simulations. For example, the ERF attributed to

methane emissions, which includes indirect effects through ozone formation and oxidation capacity with feedbacks on the methane 
lifetime, depend non‑linearly on the concentrations of NO x, CO and 
NMVOCs. This means that the results from the model simulations 
depend to some extent on the chosen methodology. In AR5 (based 
on Shindell et al., 2009; Stevenson et al., 2013) the attribution was 
done by removing the anthropogenic emissions of individual species 
one  by one from a  control simulation for present‑day conditions. 
Further, only the radiative forcings, and not the ERF (mainly including 
the effect of aerosol–cloud interactions) were attributed to the 
emitted compounds.
Since AR5, the emissions estimates have been revised and extended 
for CMIP6 (Hoesly et al., 2018), the models have been further 
developed, the period has been extended (1750–2019, versus 1750–
2011 in AR5) and the experimental setup for the model simulations

has changed (Collins et al., 2017), making a  direct comparison of 
results difficult. Figure  6.12a shows the global and annual mean 
ERF attributed to emitted compounds over the period 1750–2019 
based on AerChemMIP simulations (Thornhill et al., 2021b) where 
anthropogenic emissions or concentrations of individual species 
were perturbed from 1850 to 2014 levels (methodology described in 
Supplementary Material 6.SM.1). 
The ERF based on primary CO 2 emissions is slightly lower than the 
abundance‑based estimate (Section 7.3.2.1) because the abundance‑
based ERF combines the effect of primary CO 2 emissions and a small 
additional secondary contribution from atmospheric oxidation of 
methane, CO, and NMVOCs (4%) of fossil origin, consistent with 
AR5 findings.
Ozone‑depleting substances, such as N 2O and halocarbons, cause 
a  reduction in stratospheric ozone, which affects ozone and OH 
production in the troposphere through ultraviolet radiation changes

(and thus affect methane). They also have indirect effects on aerosols 
and clouds (Karset et al., 2018), since changes in oxidants induce 
changes in the oxidation of aerosol precursors. 
The net ERF from N 2O emissions is estimated to be 0.24 [0.13 to 0.34] 
W m–2, which is very close to the abundance‑based estimate of 
0.21 W m–2 (Section  7.3.2.3). The indirect contributions from N 2O 
are relatively minor with negative (methane‑lifetime) and positive 
(ozone‑and‑cloud) effects nearly compensating each other. Emissions 
of halogenated compounds, including CFCs and HCFCs, were assessed 
as very likely causing a net‑positive ERF in the AR5. However, recent 
studies (Morgenstern et al., 2020; O’Connor et al., 2021; Thornhill 
et al., 2021b) find strong adjustments in Southern Hemisphere 
aerosols and clouds, such that the very likely  range in the emission‑
based ERF for CFC + HCFCs + HFCs now also include negative values.

For methane emissions, in addition to their direct effect, there are 
indirect positive ERFs from methane enhancing its own lifetime, 
causing ozone production, enhancing stratospheric water vapour, 
and influencing aerosols and the lifetimes of HCFCs and HFCs (Myhre 
et al., 2013; O’Connor et al., 2021). The ERF from methane emissions 
is considerably higher than the ERF estimate resulting from its 
abundance change. The central estimate with the very likely  range is 
1.19 [0.81 to 1.58] W m–2 for the emissions‑based estimate compared 
Figure  6.11 | Time evolution of 20-year multi-model mean averages of 
the annual area-weighted mean regional net effective radiative forcings 
(ERFs) due to aerosols  for each of the 14 major regions in the Atlas, and 
global mean, using the models and model experiments as in Figure 6. 10. 

854
Chapter 6 Short-lived Climate Forcers6with 0.54 W m–2 for the abundance‑based estimate (Section 7.3.5). 
The abundance‑based ERF estimate for methane results from 
contributions of its own emissions and the effects of several other 
compounds, some decreasing methane lifetime, notably NO x, which 
importantly reduce the methane abundance‑based ERF . Emissions of 
CO and NMVOCs both indirectly contribute to a positive ERF through 
enhancing ozone production in the troposphere and increasing 
the methane lifetime. For CO and NMVOCs of fossil origin there is 
also a 0.07 W m–2 contribution to CO 2 from their oxidation. The very 
likely  total ERF of CO and NMVOCs emissions is estimated to be 
0.44 [0.22 to 0.67] W m–2. 
NO x causes a  positive ERF through enhanced tropospheric ozone 
production and a negative ERF through enhanced OH concentrations 
that reduce the methane lifetime. There is also a small negative ERF 
contribution through the formation of nitrate aerosols, although

only three of the AerChemMIP models include nitrate aerosols. The 
best estimate of the net ERF from changes in anthropogenic NO x 
emissions is –0.27 [–0.55 to 0.01] W m–2. The magnitude is somewhat 
greater than the AR5 estimate (–0.15 [–0.34 to +0.02] W m–2) but 
with a similar level of uncertainty. The difference between AR6 and 
AR5 estimates is possibly due to the different modeling protocols 
(see Supplementary Material: 6.SM.1).Anthropogenic emissions of SO 2 lead to the formation of sulphate 
aerosols and a negative ERF through aerosol–radiation and aerosol–
cloud interactions. The emissions‑based ERFaci, which was not 
previously considered in AR5, is now included. The estimated ERF is 
thus considerably more negative than the AR5 estimate with a  radiative 
forcing of –0.4 W m–2, despite the decline of ERF due to aerosols since 
2011 (Section 7.3.3.1.3, Figure 6.12a). SO 2 emissions are estimated 
to contribute to a negative ERF of –0.94 [–1.63 to –0.25] W m–2, with

–0.23 W m–2 from aerosol–radiation interactions and –0.70 W m–2 
from aerosol–cloud interactions. Emissions of NH 3 lead to formation 
of ammonium‑nitrate aerosols with an estimated ERF of –0.03 W m–2. 
The best estimate for the ERF due to emissions of BC is reduced from 
the AR5, and is now estimated to be 0.11 [–0.20 to 0.42] W  m–2 
with an uncertainty also including negative values. As discussed in 
Section  7.3.3.1.2, a  significant portion of the positive BC forcing 
from aerosol–radiation interactions is offset by negative atmospheric 
adjustments due to cloud changes, as well as lapse rate and 
atmospheric water vapour changes, resulting in a smaller positive net 
ERF for BC compared with AR5. The large range in the forcing estimate 
stems from variation in the magnitude and sign of atmospheric 
adjustments across models and is related to the differences in the 
model treatment of different processes affecting BC (e.g.,  ageing,

mixing) and its interactions with clouds and cryosphere (Section 7.3.3; 
Thornhill et al., 2021b). The emissions‑based ERF for organic carbon 
aerosols is –0.21 [–0.44 to +0.02] W m–2, a weaker estimate compared 
with AR5 attributed to stronger absorption by OC (Section 7.3.3.1.2).(a) (b)
Figure 6.12 | Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions 
between 1750 to 2019 based on CMIP6 models (Thornhill et al., 2021b).  ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical 
formulae in section 7.3.2, H 2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in 
which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in

Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) 
and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). ‘Cloud’ includes cloud adjustments 
(semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO 2, organic carbon and 
black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ (Section 7.3.3). For GSAT estimates, time series (1750–2019) for the ERFs have been 
estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean

temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 
°C–1. Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 
and 7.7, respectively. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

855
Short-lived Climate Forcers  Chapter 66The emissions‑based contributions to GSAT change (Figure  6.12b) 
were not assessed in AR5, but with the ERF from aerosol–cloud 
interactions attributed to the emitted compounds there is now 
a better foundation for this assessment. The contribution to emissions‑
based ERF at 2019 (Figure 6.12a) is scaled by the historical emissions 
(over the period 1750–2019) of each compound to reconstruct 
the historical time series of ERF . An impulse response function (Cross‑
Chapter Box 7.1, Supplementary Material 7.SM5.2) is then applied 
to obtain the contribution of SLCF emissions to the GSAT response. 
Due  to the non‑linear chemical and physical processes described 
above relating emissions to ERF , and the additional non‑linear 
relations between ERF and GSAT, these emissions‑based estimates 
of GSAT responses strongly depend on the methodology applied to 
estimate ERF and GSAT (Supplementary Material 6.SM.2). Therefore,

the relative contribution of each compound through its primary 
emissions versus secondary formation or destruction (e.g.,  for 
methane emissions its ozone versus methane contributions), by 
construction (omitting the non‑linear processes), will be equal for 
ERF and GSAT. Uncertainties in the GSAT response are estimated 
using the assessed range of the equilibrium climate sensitivity (ECS) 
from Chapter 7 of this report. For most of the emitted compounds the 
uncertainty in the GSAT response is dominated by the uncertainty in 
the relationship between emissions and the ERF .
The contributions from the emitted compounds to GSAT broadly follow 
their contributions to the ERF , mainly because their evolution over the 
past decades have been relatively similar and slow enough compared 
to their lifetimes to be reflected similarly in their ERF and GSAT despite 
the delay of the GSAT response to ERF changes (Section  6.6.1).

However, for some SLCFs (e.g., SO 2) that have been reduced globally, 
their contribution to GSAT change is slightly higher compared with that 
of CO 2 than their relative contribution to ERF because the peak in their 
ERF change has already occurred (Section 6.4.1) whereas the peak of 
their GSAT effect started to decline recently (Figure 7.9). This is due 
to the inertia of the climate system delaying the full response of GSAT 
to a change in forcing (Figure 6.15). 
In summary, emissions of SLCFs, especially methane, NO x and SO 2, 
have substantial effects on effective radiative forcing (ERF) ( high 
confidence ). The net global emissions‑based ERF of NO x is negative 
and that of NMVOCs is positive, in agreement with the AR5 assessment 
(high confidence ). For methane, the emissions‑based ERF is twice as 
high as the abundance‑based ERF ( high confidence ). SO 2 emissions 
make the dominant contribution to the ERF associated with the

aerosol–cloud interaction ( high confidence ). The contributions from 
the emitted compounds to GSAT broadly follow their contributions to 
the ERF ( high confidence ). However, due to the inertia of the climate 
system delaying the full GSAT response to a  change in forcing, 
the contribution to GSAT change due to SO 2 emissions is slightly 
higher compared with that due to CO 2 emissions (than their relative 
contributions to ERF) because the peak in emission‑induced SO 2 ERF 
has already occurred.6.4.3 Climate Responses to SLCFs
This section briefly discusses the climate response to SLCFs, in 
particular to changes in aerosols, and gathers complementary 
information and assessments from Chapters 3, 7, 8 and 10. 
Warming or cooling atmospheric aerosols, such as BC and sulphate, 
can affect temperature and precipitation in distinct ways by 
modifying the shortwave and longwave radiation, the lapse rate 
of the troposphere, and influencing cloud microphysical properties

(Section  10.1.4.1.4, Box  8.1). An important distinction between 
scattering and absorbing aerosols is the opposing nature of their 
influences on circulation, clouds and precipitation, besides surface 
temperature as evident from the contrasting regional climate 
responses to regional aerosol emissions (e.g., Lewinschal et al., 2019; 
Sand et al., 2020; also see Chapters 8 and 10). 
On the global scale, as assessed in Chapter 3, anthropogenic aerosols 
have likely  cooled GSAT since 1850–1900 driven by the negative 
aerosol forcing, while it is extremely likely  that human‑induced 
stratospheric ozone depletion has primarily driven stratospheric cooling 
between 1979 and the mid‑1990s. Multiple modelling studies support 
the understanding that present‑day emissions of SO 2, a  precursor 
for sulphate aerosols, are the dominant driver of near ‑ surface air 
temperature responses in comparison to BC or OC even though, for

some regions, BC forcing plays a key role (Baker et al., 2015; Samset 
et al., 2016; Stjern et al., 2017; Zanis et al., 2020) . Further, there is 
high confidence  that the aerosol‑driven cooling has led to detectable 
large‑scale water ‑cycle changes since at least the mid‑20th century 
as assessed in Chapter  8. The overall effect of surface cooling from 
anthropogenic aerosols is to reduce global precipitation and alter large‑
scale atmospheric circulation patterns ( high confidence ), primarily 
driven by the cooling effects of sulphate aerosols (Section  8.2.1).  
In addition, there is high confidence  that darkening of snow through 
the deposition of black carbon and other light‑absorbing particles 
enhances snowmelt (Section 7.3.4.3; SROCC Chapter 3).
In AR5, there was low confidence  in the overall understanding of 
climate response to spatially varying patterns of forcing, though 
there was medium  to high confidence  in some regional climate

responses, such as the damped warming of the NH and shifting of 
the ITCZ from aerosols, and positive feedbacks enhancing the local 
response from high‑latitude snow and ice albedo changes. Since 
AR5, the relationship between inhomogeneous forcing and climate 
response is better understood, providing further evidence of the 
climate influence of SLCFs (aerosols and ozone in particular) on 
global to regional scales (Collins et al., 2013; Shindell et al., 2015; 
Aamaas et al., 2017; Kasoar et al., 2018; Persad and Caldeira, 2018; 
Wilcox et al., 2019) which differ from the relatively homogeneous 
spatial influence from LLGHGs. 
Large geographical variations in aerosol ERFs (Section 6.4.1) affect 
global and regional temperature responses (Myhre et al., 2013; 
Shindell et al., 2015). Multi‑model CMIP6 ensemble‑mean results 
(Figure  6.13) show cooling over almost all areas of the globe in 
response to increases of aerosol and their precursor emissions from

1850 to the recent past (1995–2014). While the ERF has hotspots,

856
Chapter 6 Short-lived Climate Forcers6the temperature response is more evenly distributed in line with the 
results of CMIP5 models including the temperature response to ozone 
changes (Shindell et al., 2015). The ensemble‑mean global mean 
surface temperature decreases by 0.66°C ± 0.51°C while decreasing 
by 0.97°C ± 0.54°C for the Northern Hemisphere and 0.34°C ± 0.2°C 
for the Southern Hemisphere. The zonal‑mean temperature response 
is negative at all latitudes ( high confidence ) and becomes more 
negative with increasing latitude, with a maximum ensemble‑mean 
decrease of around 2.7°C at northern polar latitudes. The zonal‑
mean response is not directly proportional to the zonal‑mean forcing, 
especially in the Arctic where the temperature response is cooling 
while the local ERF is positive (Figure 6.10). This is consistent with 
prior studies showing that the Arctic, in particular, is highly sensitive 
to forcing at NH mid‑latitudes (e.g.,  Shindell and Faluvegi, 2009;

Sand et al., 2013a) and with results from CMIP5 models (more on the 
Arctic below; Shindell et al., 2015). Thus, there is high confidence  that 
the temperature response to aerosols is more asymmetric than the 
response to WMGHGs and negative at all latitudes.
The asymmetric aerosol and greenhouse gas forcing on regional‑
scale climate responses have also been assessed to lead to 
contrasting effects on precipitation in Chapter  8. The asymmetric 
historical radiative forcing due to aerosols led to a southward shift in 
the tropical rain belt ( high confidence ) and contributed to the Sahel 
drought from the 1970s to the 1980s ( high confidence ). Furthermore, 
the asymmetry of the forcing led to contrasting effects in monsoon 
precipitation changes over West Africa, Southern Asia and Eastern 
Asia over much of the mid‑20th century due to GHG‑induced 
precipitation increases counteracted by anthropogenic aerosol‑

induced decreases ( high confidence ) (see Section 8.3 and Box 8.1). The Arctic region is warming considerably faster than the rest of 
the globe (Atlas 11.2.2) and, generally, studies indicate that this 
amplification of the temperature response toward the Arctic has 
an important contribution from local and remote aerosol forcing 
(Stjern et al., 2017; Westervelt et al., 2018).  Several studies indicate 
that changes in long‑range transport of sulphate and BC from 
northern mid‑latitudes can potentially  explain a significant fraction 
of Arctic warming since the 1980s (e.g., Navarro et al., 2016; Breider 
et al., 2017; Ren et al., 2020) . Modelling studies show that changes 
in mid‑latitude aerosols have influenced Arctic climate by changing 
the radiative balance through aerosol–radiation and aerosol–cloud 
interactions, and enhancing poleward heat transport (Navarro et al., 
2016; Ren et al., 2020). Idealized aerosol‑perturbation studies have

shed further light on the sensitivity of Arctic temperature response to 
individual aerosol species. Studies show relatively large responses in 
the Arctic to BC perturbations and reveal the importance of remote 
BC forcing by rapid adjustments (Sand et al., 2013b; Stjern et al., 
2017; L. Liu et al., 2018; Yang et al., 2019b) . Perturbations in SO 2 
emissions over major emitting regions in the Northern Hemisphere 
have been shown to produce the largest Arctic temperature responses 
(Kasoar et al., 2018; Lewinschal et al., 2019). 
The effects of changes in aerosols on local and remote changes in 
temperature, circulation and precipitation are sensitive to a number 
of model uncertainties affecting aerosol sources, transformation 
and resulting radiative efficacy. Therefore, regional climate effects 
in global model studies must be interpreted with caution. When 
investigating the climate response to regional aerosol emissions,

such uncertainties are likely to be confounded even further by 
the variability between models in regional climate and circulation 
Conflicting signalsColour–4.2      –2.8     –1.4         0        1.4        2.8       4.2       5.6
(°C)(°C)–3            –2            –1             0             1              2             3(a) Surface air temperature response due to aerosols (b) Zonal mean change in surface air temperature 
due to aerosolsLatitude (°N)75
50
25
0
–25
–50
–75
Figure 6.13 | Multi-model mean surface air temperature response over the recent past (1995–2014) induced by aerosol changes since 1850.  Calculation is 
based on the difference between CMIP6 ‘historical’ and AerChemMIP ‘hist-piAer’ experiments averaged over 1995–2014, where (a) is the spatial pattern of the annual mean

surface air temperature response, and (b) is the mean zonally averaged response. Model means are derived from the years 1995–2014. Uncertainty is represented using the 
advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than variability threshold and ≥80% of all models agree on 
sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines 
indicate regions with conflicting signal, where ≥66% of models show change greater than variability threshold and <80% of all models agree on sign of change. For more 
information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1. AerChemMIP models MIROC6, MRI-ESM2-0, NorESM2-LM, GFDL-ESM4, GISS-E2-1-G 
and UKESM1-0-LL are used in the analysis. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

857
Short-lived Climate Forcers  Chapter 66patterns, leading to greater inter ‑model spread at regional scales 
than at a global scale (Baker et al., 2015; Kasoar et al., 2016). 
In summary, over the historical period, changes in aerosols and 
their ERF have primarily contributed to cooling, partly masking 
the human‑induced warming ( high confidence ). Radiative forcings 
induced by aerosol changes lead to both local and remote changes in 
temperature ( high confidence ). The temperature response preserves 
hemispheric asymmetry of the ERF but is more latitudinally uniform 
with strong amplification of the temperature response towards the 
Arctic ( medium confidence ). 
6.4.4 Indirect Radiative Forcing Through Effects 
of SLCFs on the Carbon Cycle 
Deposition of reactive nitrogen (Nr; i.e., NH 3 and NO x) increases the 
plant productivity and carbon sequestration in N‑limited forests and 
grasslands, and also in open and coastal waters and open ocean. Such

inadvertent fertilization of the biosphere can lead to eutrophication 
and reduction in biodiversity in terrestrial and aquatic ecosystems. 
The AR5 assessed that it is likely that Nr deposition over land currently 
increases natural CO 2 sinks, in particular in forests, but the magnitude 
of this effect varies between regions (Ciais et al., 2013). Increasing 
Nr deposition or the synergy between increasing Nr deposition 
and atmospheric CO 2 concentration could have contributed to the 
increasing global‑net land CO 2 sink (Section 5.2.1.4.1). 
Ozone uptake itself damages photosynthesis and reduces plant 
growth with consequences for the carbon and water cycles 
(Ainsworth et al., 2012; Emberson et al., 2018). The AR5 concluded 
there was robust evidence  of the effect of ozone on plant physiology 
and subsequent alteration of the carbon storage, but considered 
insufficient quantification of and a lack of systematic incorporation

of the ozone effect in carbon‑cycle models as a limitation to assess 
the terrestrial carbon balance (Ciais et al., 2013). Since AR5 several 
more ESMs have incorporated interactive ozone‑vegetation damage 
resulting in an increase in evidence to support the influence of ozone 
on the land carbon cycle. The new modelling studies tend to focus 
on ozone effects on plant productivity rather than the land carbon 
storage and agree that ozone‑induced gross‑primary productivity 
(GPP) losses are largest today in the eastern USA, Europe and eastern 
China, ranging from 5–20% on the regional scale ( low confidence ) 
(Yue and Unger, 2014; Lombardozzi et al., 2015; Yue et al., 2017; 
Oliver et al., 2018). There is medium evidence  and high agreement  
based on observational studies and models that ozone‑vegetation 
interactions further influence the climate system, including water and 
carbon cycles by affecting stomatal control over plant transpiration of

water vapour between the leaf surface and atmosphere (Wittig et al., 
2007; Sun et al., 2012; VanLoocke et al., 2012; Lombardozzi et al., 
2013; Hoshika et al., 2015; Arnold et al., 2018). While some modelling 
studies suggest that the unintended Nr deposition fertilization effect 
in forests may potentially offset the ozone‑induced carbon losses 
(Felzer et al., 2007; de Vries et al., 2017), complex interactions have 
been observed between ozone and Nr deposition to ecosystems that 
have not yet been included in ESMs. For some plants, the effects 
of increasing ozone on root biomass become more pronounced as Nr deposition increased, and the beneficial effects of Nr on root 
development were lost at higher ozone treatments (Mills et al., 
2016). Reducing uncertainties in ozone vegetation damage effects 
on the carbon cycle requires improved information on the sensitivity 
of different plant species to ozone, and measurements of ozone

dose‑response relationships for tropical plants, which are currently 
lacking. Surface ozone effect on the land carbon sink and indirect CO 2 
forcing, therefore, remains uncertain. Collins et al. (2010) showed that 
adding in the effects of surface ozone on vegetation damage and 
reduced uptake of CO 2 added about 10% to the methane emissions 
metrics and could change the sign of the NO x metrics. However, this 
estimate has to be considered as an upper limit due to limitation of 
the paramaterization used by Sitch et al. (2007)  considering more 
recent knowledge and is thus not included in the current metrics 
(Section 7.6.1.3).
Tropospheric aerosols influence the land and ocean ecosystem 
productivity and the carbon cycle through changing physical climate 
and meteorology (Jones, 2003; Cox et al., 2008; Mahowald, 2011; 
Unger et al., 2017) and through changing deposition of nutrients 
including nitrogen, sulphur, iron and phosphorous (Mahowald

et al., 2017; Kanakidou et al., 2018). There is robust evidence  and 
high agreement  from field (Oliveira et al., 2007; Cirino et al., 2014; 
Rap et al., 2015; X. Wang et al., 2018) and modelling (Mercado et al., 
2009; Strada and Unger, 2016; Lu et al., 2017; Yue et al., 2017) studies 
that aerosols affect plant productivity through increasing the diffuse 
fraction of downward shortwave radiation, although the magnitude 
and importance to the global land carbon sink is controversial. At large 
scales the dominant effect of aerosols on the carbon cycle is likely  
a global cooling effect of the climate ( medium confidence ) (Jones, 
2003; Mahowald, 2011; Unger et al., 2017). We  assess that these 
interactions between aerosols and the carbon cycle are currently too 
uncertain to constrain quantitatively the indirect CO 2 forcing.
In summary, reactive nitrogen, ozone and aerosols affect terrestrial 
vegetation and the carbon cycle through deposition and effects on

large‑scale radiation ( high confidence ) but the magnitude of these 
effects on the land carbon sink, ecosystem productivity and indirect 
CO 2 forcing remain uncertain due to the difficulty in disentangling 
the complex interactions between the effects. As such, we assess the 
effects to be of second order in comparison to the direct CO 2 forcing 
(high confidence ) but, at least for ozone, it could add a substantial 
(positive) forcing compared with its direct forcing ( low confidence ). 
6.4.5 Non-CO 2 Biogeochemical Feedbacks 
Climate change‑induced changes in atmospheric composition and 
forcing due to perturbations in natural processes constitute an 
Earth system feedback amplifying (positive feedback) or diminishing 
(negative feedback) the initial climate perturbation (Ciais et al., 2013; 
Heinze et al., 2019). Quantification of these biogeochemical feedbacks 
is important to allow for a better estimate of the expected effects

of emissions reduction policies for mitigating climate change and 
the effect on the allowable global carbon budget (Lowe and Bernie, 
2018). Biogeochemical feedbacks due to changes in the carbon cycle 
are assessed in Section  5.4.5, while physical and biogeophysical

858
Chapter 6 Short-lived Climate Forcers6climate feedbacks are assessed in Section 7.4.2. Additionally, non‑CO 2 
biogeochemical feedbacks  due to climate‑driven changes in methane 
sources and N 2O sources and sinks are assessed in Section  5.4.7. 
The goal of this section is to estimate the feedback parameter 
(α  as defined in section  7.4.1.1) from climate‑induced changes in 
atmospheric abundances or lifetimes of SLCFs mediated by natural 
processes or atmospheric chemistry. These non‑CO 2 biogeochemical 
feedbacks act on time scales of years to decades and have important 
implications for climate sensitivity and emissions‑abatement policies. 
The feedback parameter is quantified entirely from ESMs that 
expand the complexity of CCMs by coupling the physical climate and 
atmospheric chemistry to land and ocean biogeochemistry. In AR5, 
α for non‑CO 2 biogeochemical feedbacks was estimated from an 
extremely limited set of modelling studies with much less confidence

associated with the estimate. Since AR5, ESMs have advanced to 
include more feedback processes, facilitating a relatively more robust 
assessment of α. CMIP6 ESMs participating in AerChemMIP performed 
coordinated sets of experiments (Collins et al., 2017) facilitating the 
consistent estimation of α (Thornhill et al., 2021a) and we rely on this 
multi‑model analysis for the best estimates (Table 6.8). Considering 
the consistent methodology, the assessed central values and 5–95% 
ranges for α are based on the AerChemMIP estimates. The full range 
of model uncertainty is not captured in AerChemMIP because of the 
relatively small ensemble size, therefore estimates from studies using 
other models or with different protocols are discussed to reinforce or 
critique these values. 
Climate–sea-spray feedback:  Sea‑spray emissions from ocean 
surfaces influence climate directly or indirectly through the formation 
of CCN as discused in Section 6.2.1.2. They are sensitive to SST and

sea ice extent, as well as to wind speed, and are therefore expected 
to feedback on climate (Struthers et al., 2013). However, there are 
large uncertainties in the strength of climate feedback from sea‑
spray aerosols because of the diversity in the model representation 
of emissions (many represent sea‑salt emissions only) and their 
functional dependence on environmental factors noted above, in situ 
atmospheric chemical and physical processes affecting the sea‑spray 
lifetime, and aerosol–cloud interactions (Struthers et al., 2013; Soares 
et al., 2016; Nazarenko et al., 2017). Additional work is needed to 
identify how sea‑spray and POA emissions respond to shifts in ocean 
biology and chemistry in response to warming, ocean acidification 
and changes in circulation patterns (Cochran et al., 2017) , and 
affect CCN and INP formation (DeMott et al., 2016). AerChemMIP 
models, representing only the sea‑salt emissions, agree that the

sea‑salt‑climate feedback is negative, however there is a large range 
in the feedback parameter indicating large uncertainties (Table 6.8).
Climate–DMS feedback:  Dimethyl sulphide (DMS) is produced 
by marine phytoplankton and is emitted to the atmosphere where 
it can lead to the subsequent formation of sulphate aerosol and 
CCN (Section  6.2.2.5). Changes in DMS emissions from ocean 
could feedback on climate through their response to changes in 
temperature, solar radiation, ocean mixed‑layer depth, sea ice extent, 
wind speed, nutrient recycling or shifts in marine ecosystems due to 
ocean acidification and climate change, or atmospheric processing 
of DMS into CCN (Heinze et al., 2019). Models with varying degrees 
of representation of the relevant biogeochemical processes and effects on DMS fluxes produce diverging estimates of changes in 
DMS emissions strength under climate change resulting in large 
uncertainties in the DMS–sulphate–cloud albedo feedback (Bopp  et al.,

2004; Kloster et al., 2007; Gabric et al., 2013). In AR5, the climate‑
DMS feedback parameter was estimated to be –0.02  W m–2 °C–1 based 
on a single model. Since AR5, new modelling studies using empirical 
relationships between pH and total DMS production find that global 
DMS emissions decrease due to combined ocean acidification and 
climate change, leading to a  strong positive climate feedback (Six 
et al., 2013; Schwinger et al., 2017). However, another study argues 
for a much weaker positive feedback globally due to complex and 
compensating regional changes in marine ecosystems (S. Wang et al., 
2018). The AerChemMIP multi‑model analysis suggests small positive 
feedback (Table 6.8), consistent with these recent studies, but with 
large uncertainties in the magnitude of α. 
Climate–dust feedback:  Mineral dust is the most abundant aerosol 
type in the atmosphere, when considering aerosol mass, and affects 
the climate system by interacting with both longwave and shortwave

radiation as well as contributing to the formation of CCN and 
INP . Because dust emissions are sensitive to climate variability 
(e.g.,  through changes in the extent of arid land; Section  6.2.2.4), 
it has been hypothesized that the climate‑dust feedback could be 
an important feedback loop in the climate system. Since AR5, an 
improved understanding of the shortwave absorption properties 
of dust as well as a  consensus that dust particles are larger than 
previously thought has led to a  revised understanding that the 
magnitude of radiative forcing due to mineral dust is small (Kok et al., 
2017; Ryder et al., 2018). A recent study notes that global models 
underestimate the amount of coarse dust in the atmosphere and 
accounting for this limitation raises the possibility that dust emissions 
warm the climate system (Adebiyi and Kok, 2020). Model predictions 
of dust emissions in response to future climate change range from an

increase (Woodward et al., 2005) to a decrease (Mahowald and Luo, 
2003), thus leading to high uncertainties on the sign of the climate‑
dust feedback. Since AR5, Kok et al. (2018) estimated the direct 
dust‑climate feedback parameter, from changes in the dust direct 
radiative effect only, to be in the range –0.04 to +0.02 W m–2 °C–1. 
The assessed central value and the 5–95% range of the climate‑dust 
feedback parameter based on AerChemMIP ensemble (Table  6.8) 
is within the range of the published estimate, however both the 
magnitude and sign of α are model‑dependent. 
Climate–ozone feedback:  Changes in ozone concentrations in 
response to projected climate change have been shown to lead to 
a  potential climate‑atmospheric chemistry feedback. Chemistry–
climate models consistently project a  decrease in lower tropical 
stratospheric ozone levels due to enhanced upwelling of ozone‑
poor tropospheric air associated with surface warming‑driven

strengthening of the Brewer‑Dobson circulation (Bunzel and 
Schmidt, 2013). Further, models project an increase in middle and 
extratropical stratospheric ozone due to increased downwelling 
through the strengthened Brewer ‑Dobson circulation (Bekki et al., 
2013; Dietmüller et al., 2014). These stratospheric ozone changes 
induce a  net‑negative global mean ozone radiative feedback 
(Dietmüller et al., 2014). Tropospheric ozone shows a  range of 
responses to climate with models generally agreeing that warmer

859
Short-lived Climate Forcers  Chapter 66climate will lead to decreases in the tropical lower troposphere 
owing to increased water vapour, and increases in the subtropical 
to mid‑latitude upper troposphere due to increases in lightning 
and stratosphere‑to‑troposphere transport (Stevenson et al., 2013). 
A small positive feedback is estimated from climate‑induced changes 
in global mean tropospheric ozone (Dietmüller et al., 2014) while 
a small negative feedback is estimated by Heinze et al. (2019) based 
on the model results of Stevenson et al. (2013). Additionally, these 
ozone feedbacks induce a  change in stratospheric water vapour 
amplifying the feedback due to stratospheric ozone (Stuber et al., 
2001). Since AR5, several modelling studies have estimated the 
intensity of meteorology‑driven ozone feedbacks on climate from 
either combined tropospheric and stratospheric ozone changes or 
separately with contrasting results. One study suggests no change

(Marsh et al., 2016), while other studies report reductions of ECS 
ranging from 7–8% (Dietmüller et al., 2014; Muthers et al., 2014) 
to 20% (Nowack et al., 2015). The estimate of this climate‑ozone 
feedback parameter is very strongly model‑dependent with values 
ranging from –0.13 to –0.01 W m–2 °C–1 though there is agreement 
that it is negative. The assessed central value and the 5–95% range of 
climate‑ozone feedback parameter based on AerChemMIP ensemble 
is within the range of these published estimates, but closer to the 
lower bound. This climate‑ozone feedback factor does not include 
the feedback on ozone from lightning changes which is discussed 
separately below. 
Climate–BVOC feedback: BVOCs, such as isoprene and 
terpenes, are produced by land vegetation and marine plankton 
(Sections 6.2.2.3 and 6.2.2.5). Once in the atmosphere, BVOCs and 
their oxidation products lead to the formation of secondary organic

aerosols (SOA) exerting a  negative forcing, and increased ozone 
concentrations and methane lifetime exerting a  positive forcing. 
BVOC emissions are suggested to lead to a climate feedback in part 
because of their strong temperature dependence observed under 
present‑day conditions (Kulmala et al., 2004; Arneth et al., 2010a). 
Their response to future changes in climate and CO 2 levels remains 
uncertain (Section 6.2.2.3). Estimates of the climate‑BVOC feedback 
parameter are typically based on global models which vary in their 
level of complexity of emissions parametrization, BVOC speciation, 
the mechanism of SOA formation and the interaction with ozone 
chemistry (Thornhill et al., 2021a). Since AR5, observational studies 
(Paasonen et al., 2013) and models (Scott et al., 2018) estimate the 
feedback due to biogenic SOA (via changes in BVOC emissions) to 
be in the range of about –0.06 to –0.01 W m–2 °C–1. The assessed

central estimate of the climate‑BVOC feedback parameter based on 
the AerChemMIP ensemble suggests that climate‑induced increases 
in SOA from BVOCs will lead to a  strong cooling effect that will 
outweigh the warming from increased ozone and methane lifetime, 
however the uncertainty is large (Thornhill et al., 2021a). 
Climate–lightning NO x feedback: As discussed in Section 6.2.2.1, 
climate change influences lightning NO x emissions. Increases in 
lightning NO x emissions will not only increase tropospheric ozone 
and decrease methane lifetime but also increase the formation 
of sulphate and nitrate aerosols, via oxidant changes, offsetting 
the positive forcing from ozone. The response of lightning NO x to 
climate change remains uncertain and is highly dependent on the parametrization of lightning in ESMs (Section 6.2.1.2; Finney et al., 
2016b; Clark et al., 2017). AerChemMIP multi‑model ensemble mean 
estimate a net‑negative climate feedback from increases in lightning

NO x in a warming world (Thornhill et al., 2021a). All AerChemMIP 
models use a  cloud‑top height lightning parametrization that 
predicts increases in lightning with warming. However, a  positive 
climate‑lightning NO x feedback cannot be ruled out because of 
the dependence of the response to lightning parametrizations as 
discussed in Section 6.2.2.1. 
Climate–methane lifetime feedback:  Warmer and wetter climate 
will lead to increases in OH and oxidation rates leading to reduced 
atmospheric methane lifetime  – a  negative feedback (Naik et al., 
2013; Voulgarakis et al., 2013). Furthermore, since OH is in turn 
removed by methane, the climate‑methane lifetime feedback will be 
amplified (Section 6.3.1; Prather, 1996) . Based on the multi‑model 
results of Voulgarakis et al. (2013), α for climate‑methane lifetime 
is estimated to be –0.030 ± 0.01 W m−2 °C−1 by Heinze et al. (2019). 
The assessed central value of α based on the AerChemMIP ensemble

is within the range of this estimate but with greater uncertainty 
(Thornhill et al., 2021a). 
Climate–fire feedback: Wildfires are a  major source of SLCF 
emissions (Section  6.2.2.6). Climate change has the potential to 
enhance fire activity (Sections 12.4 and 5.4.3.2) thereby enhancing 
SLCF emissions leading to feedbacks. Climate‑driven increases in fire 
could potentially lead to offsetting feedback from increased ozone 
and decreased methane lifetime (due to increases in OH) leaving 
the feedback from aerosols to dominate with an uncertain net 
effect (e.g., Landry et al., 2015). The AR5 assessment of climate‑fire 
feedbacks included a value of α due to fire aerosols to be in the range 
of –0.03 to + 0.06 W m−2 °C−1 based on Arneth et al. (2010a). A recent 
study estimates climate feedback due to fire aerosols to be greater 
than that due to BVOCs, with a value of α equal to –0.15 (–0.24 to 
–0.05) W m−2 °C−1 (Scott et al., 2018). Clearly, the assessment of fire‑

related non‑CO 2 biogeochemical feedbacks is very uncertain because 
of limitations in the process understanding of the interactions 
between climate, vegetation and fire dynamics, and atmospheric 
chemistry and their representation in the current generation 
ESMs. Some AerChemMIP ESMs include the representation of fire 
dynamics but do not activate their interaction with atmospheric 
chemistry. Given the large uncertainty and lack of information from 
AerChemMIP ESMs, we do not include a quantitative assessment of 
climate‑fire feedback for AR6. 
In summary, climate‑driven changes in emissions, atmospheric 
abundances or lifetimes of SLCFs are assessed to have an  
overall cooling effect, that is, a  negative feedback parameter of 
–0.20  [–0.41  to + 0.01] W m−2 °C−1, thereby reducing climate 
sensitivity (Section 7.4.2.5.1). This net feedback parameter is obtained 
by summing the assessed estimates for the individual feedback given

in Table 6.8. Confidence  in the magnitude and the sign of most of the 
individual as well as the total non‑CO 2 biogeochemical feedbacks 
remains low as evident from the large range in the value of α. This 
large uncertainty is attributed to the diversity in model representation 
of the relevant chemical and biogeochemical processes based on 
limited process‑level understanding.

860
Chapter 6 Short-lived Climate Forcers66.4.6 ERF by Aerosols in Proposed Solar 
Radiation Modification 
Solar radiation modification (SRM; Sections 4.6.3.3 and 8.6.3) has 
the potential to exert a  significant ERF on the climate, mainly by 
affecting the SW component of the radiation budget (e.g., Caldeira 
et al., 2013; NRC, 2015; Lawrence et al., 2018). The possible ways 
and the extent to which the most commonly discussed options may 
affect radiative forcing is addressed in this section. Side effects of 
SRM on stratospheric ozone and changes in atmospheric transport 
due to radiative heating of the lower stratosphere are discussed in 
Section 4.6.3.3. 
Stratospheric aerosol injections (SAI) have the potential to achieve 
a  high negative global ERF , with maximum ERFs ranging from 
–5  to  –2  W m–2 (Niemeier and Timmreck, 2015; Weisenstein 
et al., 2015; Niemeier and Schmidt, 2017; Kleinschmitt et al., 2018). 
The  magnitude of the maximum achievable ERF depends on the

chosen aerosol type and mixture, internal structure and size, or 
precursor gas (e.g., SO 2), as well as the injection strategy (latitude, 
altitude, magnitude and season of injections), plume dispersal, 
model representation of aerosol microphysics, and ambient aerosol 
concentrations (Rasch et al., 2008; Robock et al., 2008; Pierce et al., 
2010; Weisenstein et al., 2015; Laakso et al., 2017; MacMartin et al., 
2017; Dai et al., 2018; Kleinschmitt et al., 2018; Vattioni et al., 2019; 
Visioni et al., 2019) . For sulphur, the radiative forcing efficiency is 
of around –0.1 to –0.4 W  m–2/(TgS yr–1) (Niemeier and Timmreck, 
2015; Weisenstein et al., 2015; Niemeier and Schmidt, 2017). 
Different manufactured aerosols, such as ZrO 2, TiO 2 and Al 2O3, have 
different ERF efficiencies compared to sulphate (Ferraro et al., 2011; 
Weisenstein et al., 2015; Dykema et al., 2016; Jones et al., 2016) .

The aerosol size distribution influences the optical properties of an aerosol layer, and hence the ERF efficiency, which also depends 
on the dispersion, transport, and residence time of the aerosols. 
For marine cloud brightening  (MCB), seeded aerosols may affect both 
cloud microphysical and macrophysical properties (Section 7.3.3.2). 
By principle, MCB relies on ERFaci through the so‑called Twomey 
effect (Twomey, 1977), but ERFari may be of equal magnitude 
as shown in studies that consider spraying of sea salt outside 
tropical marine cloud areas (Jones and Haywood, 2012; Partanen 
et al., 2012; Alterskjaer and Kristjánsson, 2013; Ahlm et al., 2017). 
The maximum negative ERF estimated from modelling is within the 
range of –5.4 to –0.8 W m–2 (Latham et al., 2008; Rasch et al., 2009; 
Jones et al., 2011; Partanen et al., 2012; Alterskjaer and Kristjánsson, 
2013) . For dry sea salt, the ERF efficiency is estimated to be within

the range of –3 to –10 W m–2/(Pg yr–1), when emitted over tropical 
oceans in  ESMs in the Geoengineering Intercomparison Project 
(GeoMIP; Ahlm et al., 2017). Cloud‑resolving models reveal the 
complex behaviour and response of stratocumulus clouds to seeding, 
in that the ERF efficiency depends on meteorological conditions, 
and the ambient aerosol composition, where lower background 
particle concentrations may increase the ERFaci efficiency (Wang 
et al., 2011). Seeding could suppress precipitation formation and 
drizzle, and hence increase the lifetime of clouds, preserving their 
cooling effect (Ferek et al., 2000). In contrast, cloud lifetime could 
be decreased by making the smaller droplets more susceptible to 
evaporation. Modelling studies have shown that a  positive ERFaci  
(warming) could also result from seeding clouds with too large 
aerosols (Pringle et al., 2012; Alterskjaer and Kristjánsson, 2013). 
These individual and combined processes are not well understood,

and may have a limited representation in models, or counteracting 
errors (Mülmenstädt and Feingold, 2018), lending  low to medium 
confidence  to the ERF estimates.Table 6.8 | Assessed central estimates and the very likely  ranges (5–95%) of non-CO 2 biogeochemical feedback parameter ( αx) based on the AerChemMIP 
ensemble estimates (Thornhill et al. , 2021a).  As in Section 7.4.1.1, αx (W m−2 °C−1) for a feedback variable x is defined as αx = δN
δxdx
dT where δN
δx is the change in TOA energy 
balance in response to a change in x induced by a change in surface temperature ( T). The 5–95% range is calculated as mean ± standard deviation × 1.645 for each feedback. 
The level of confidence in these estimates is low owing to the large intermodel spread. Published estimates of αx are also shown for comparison. 
Non-CO 2 Biogeochemical 
Climate Feedback ( x)Number of 
AerChemMIP 
ModelsAssessed Central Estimate 
and Very Likely Range of 
Feedback Parameter ( αx)

W m−2 °C−1Published Estimates of αx
W m−2 °C−1
Sea salt 6 –0.049 [–0.13 to +0.03] –0.08 (Paulot et al., 2020)
DMS 3 0.005 [0.0 to 0.01] –0.02 (Ciais et al., 2013)
Dust 6 –0.004 [–0.02 to +0.01] –0.04 to +0.02 (Kok et al., 2018)
Ozone 4 –0.064 [–0.08 to ‑0.04]–0.015 (Dietmüller et al., 2014), –0.06 (Muthers et al., 2014, stratospheric ozone 
changes only), –0.01 (Marsh et al., 2016, stratospheric ozone changes only), 
–0.13 (Nowack et al., 2015, stratospheric ozone and water vapour changes), 
–0.007 ± 0.009 (Heinze et al., 2019, tropospheric ozone changes only)
BVOC 4 –0.05 [–0.22 to +0.12]–0.06 (Scott et al., 2017, aerosol effects only), –0.01 (Paasonen et al., 2013; 
indirect aerosol effects only), 0–0.06 (Ciais et al., 2013)
Lightning 4 –0.010 [–0.04 to +0.02]
Methane lifetime 4 –0.030 [–0.12 to +0.06] –0.30 ± 0.01 (Heinze et al., 2019)
Total non‑CO 2 Biogeochemical 
feedbacks assessed in this chapter –0.200 [–0.41 to +0.01] 0.0 ± 0.15 (Sherwood et al., 2020)

861
Short-lived Climate Forcers  Chapter 66Modelled ERFaci associated with cirrus cloud thinning (CCT) cover 
a wide range in the literature, and the maximum are of the order of 
–0.8 to –3.5 W m–2, though they are of low confidence , with some 
studies using more simplified representations (Mitchell and Finnegan, 
2009; Storelvmo et al., 2013; Kristjánsson et al., 2015; Jackson et al., 
2016; Muri et al., 2018; Gasparini et al., 2020). ERFaci for CCT is mainly 
affected by particle seeding concentrations, with an optimum around 
20 L–1, according to limited evidence  from models (Storelvmo et al., 
2013). Seeding leading to higher particle concentrations could lead 
to a warming (Storelvmo et al., 2013; Penner et al., 2015; Gasparini 
and Lohmann, 2016). The lack of representation of processes related 
to, for example, heterogeneous and homogeneous freezing and their 
prevalence, is a dominant source of uncertainty in ERF estimates, in 
addition to less research activity.

In summary, the aerosol and cloud microphysics involved with SRM 
are not well understood, notably due to insufficient (and varying 
degrees of) representation of relevant processes in models. ERF of up 
to several W m–2 is reported in the literature, with SAI at the higher 
end and CCT with lower potentials, though it remains a challenge to 
establish ERF potentials and efficacies with confidence. Modelling 
studies have been published with more sophisticated treatment of 
SRM since AR5, but the uncertainties, such as cloud–aerosol radiation 
interactions, remain large ( high confidence ).
6.5 Implications of Changing Climate on AQ
Air pollutants can be impacted by climate change through physical 
changes affecting meterorological conditions, chemical changes 
affecting their lifetimes, and biological changes affecting their natural 
emissions (Kirtman et al., 2013). Changes in meteorology affect air 
quality directly through modifications of atmospheric transport

patterns (e.g.,  occurrence and length of atmospheric blocking 
episodes, ventilation of the polluted boundary layer), extent of mixing 
layer and stratosphere–troposphere exchange (STE) for surface ozone 
(von Schneidemesser et al., 2015), and through modifications of the 
rate of reactions that generate secondary species in the atmosphere. 
Changing precipitation patterns in a  future climate also influence 
the wet removal efficiency, in particular for atmospheric aerosols 
(Hou  et al., 2018). Processes at play in non‑CO 2 biogeochemical 
feedbacks (Section  6.4.5) are also involved in the perturbation of 
atmospheric pollutants (Section 6.2.2).
This section relies on observational studies performed by analysing 
the correlation between specific meteorological conditions projected 
to occur more frequently in the future and surface pollutants, and 
global‑ and regional‑scale modelling studies considering solely 
climate change in the future. We also assess the surface ozone and

PM 2.5 changes based on CMIP6 models analysed in AerChemMIP , 
considering climate change in isolation with emissions in 2050 from 
SSP3‑7.0 scenario (Section 6.7.1). Air quality being highly variable in 
space and time, the use of regional atmospheric chemistry models is 
necessary to characterize the effect of future climate on air quality 
properly. However, difficulties for such assessment arise from the 
5  Baseline ozone is defined as the observed ozone at a site when it is not influenced by recent, locally emitted or anthropogenically produced pollution (Jaffe et al., 2018).need for long simulations that include complex chemistry–natural 
system interactions with high computational cost, in addition 
to the difficulty related to the regionalization of climate change 
(Section  10.3.1.2). Changes in the occurrence of weather patterns 
influencing air pollution (e.g.,  anticyclonic stagnation conditions, 
transport pathways from pollution sources, convection) due to

climate change are assessed in Chapters 4 and 11. 
6.5.1 Effect of Climate Change on Surface Ozone
The AR5 assessed with high confidence  that in unpolluted regions, 
higher water vapour abundances and temperatures in a  warmer 
climate would enhance ozone chemical destruction, leading to lower 
baseline5 surface ozone levels (Kirtman et al., 2013). In polluted 
regions, AR5 assessed with medium confidence  that higher surface 
temperatures will trigger regional feedbacks in chemistry and local 
emissions that will increase surface ozone and the intensity of surface 
ozone peaks. 
The response of surface ozone to climate‑induced Earth system 
changes is complex due to counteracting effects. Studies considering 
the individual effects of climate‑driven changes in specific precursor 
emissions or processes show increases in surface ozone under 
warmer atmosphere for some processes. This is indeed the case for 
enhanced STE and stratospheric ozone recovery (Sekiya and Sudo,

2014; Hess  et al., 2015; Banerjee et al., 2016; Meul et al., 2018; 
Morgenstern et al., 2018; Akritidis et al., 2019) or the increase of 
soil NO x emissions (Wu et al., 2008; Romer et al., 2018), which can 
each lead to 1 to 2 ppb increase in surface ozone. Other processes, 
in particular deposition or those related to emissions from natural 
systems (Section  6.2.2) are expected to play a  key role in future 
surface ozone and even the occurrence of pollution events (e.g., in the 
case of wildfires) but their effects are difficult to quantify in isolation.
Since the AR5, several studies have investigated the net effect of 
climate change on surface ozone, based on either global or regional 
model projections. A systematic and quantitative comparison of the 
ozone change, however, is difficult due to the variety of models with 
different complexities in the representation of natural emissions, 
chemical mechanisms and physical processes, as well as the surface

ozone metrics applied for analysis. Ozone response to climate change 
has been shown to be particularly sensitive to model representation 
of processes like BVOC emissions, deposition, and isoprene chemistry  
(Squire et al., 2015; Val Martin et al., 2015; Schnell et al., 2016; 
Pommier et al., 2018) . More robust protocols are now used more 
commonly comprising, notably, longer simulations necessary to 
separate change from interannual variability (Barnes et al., 2016; 
Lacressonnière et al., 2016; Garcia‑Menendez et al., 2017) . However, 
the amplitude of climate change penalty on ozone over polluted 
regions may be different in high‑resolution (regional‑ and urban‑
scale) models in comparison to coarse‑resolution global models, 
because a number of controlling processes are resolution‑dependent 
including for example, local emissions and sensitivity to the chemical

862
Chapter 6 Short-lived Climate Forcers6regime (NMVOC limited versus NO x limited; Lauwaet et al., 2014; 
Markakis et al., 2014, 2016).
Consistent with AR5 findings, global mean surface ozone 
concentration decreases range from 0.69 ± 0.16 ppb to 
2.28 ± 0.24 ppb  due to the dominating role of ozone destruction 
by water vapour in a  four ‑member ensemble of CMIP6 ESM for 
surface warmings of 1.5°C–2.5°C (Figure  6.14). This decrease is 
driven by the ozone decrease over oceans, especially in the tropics 
(decrease of 1–5 ppb) and large parts of the continental unpolluted 
regions. The sensitivity of annual mean surface ozone to the level 
of surface warming over these remote areas varies spatially from 
–2 to –0.2 ppb oC–1 (Supplementary Material Figure 6.SM.1).Over ozone‑producing regions of the world, such as in North America, 
Europe and Eastern Asia, AR5 and post‑AR5 model studies project 
a general increase of surface ozone levels (climate change penalty

on ozone) in a  future warmer climate particularly during summer 
(Fu and Tian, 2019) . However, in current regional models, using 
more robust protocols, this increase of surface ozone, attributable 
to climate change, is of lower magnitude than in previous estimates 
(Lacressonnière et al., 2016; Garcia‑Menendez et al., 2017). Climate 
change enhances the efficiency of precursor emissions to generate 
surface ozone in polluted regions (Schnell et al., 2016), and thus the 
magnitude of this effect will depend on the emissions considered in 
the study (present or future, and mitigated or not; Colette et al., 2015; 
Fiore et al., 2015) . 
Low model agreementHigh model agreement Color±
(d) ozone change at 2.5°C (-2.28 ± 0.24 ppb) (c) ozone change at 2.0°C (-1.70 ± 0.15 ppb)(b) ozone change at 1.5°C (-1.19 ± 0.10 ppb) (a) ozone change at 1.0°C (-0.69 ± 0.16 ppb)

Figure 6.14 | Multi-model annual mean change in surface O 3 (ppb) concentrations at different warming levels.  Changes are shown for (a) 1.0°C, (b) 1.5°C, (c) 
2.0°C and (d) 2.5°C increases in global mean surface air temperature. CMIP6 models include GFDL-ESM4, GISS-E2-1-G, MRI-ESM2-0 and UKESM1-0-LL. For each model, the 
change in surface O 3 is calculated as the difference between two AerChemMIP experiments – one with evolving future emissions and sea surface temperatures (SSTs) under 
the SSP3-7.0 scenario and the other with the same setup but with fixed present-day SSTs. The difference is calculated as a 20-year mean in surface O 3 around the year when 
the temperature threshold in each model is exceeded. The multi-model change in global annual mean surface O 3 concentrations with ± 1 standard deviation are shown within

parentheses. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, three out of four models agree on sign of change; 
diagonal lines indicate regions with low model agreement, where three out of four models agree on sign of change. For more information on the simple approach, please refer 
to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

863
Short-lived Climate Forcers  Chapter 66Considering anthropogenic emissions of precursors globally higher 
than the current emissions (SSP3‑7.0 in 2050; Figure 6.20), the CMIP6 
ensemble confirms the surface ozone penalty due to climate change  
over regions close to anthropogenic pollution sources or close to 
natural emissions sources of ozone precursors (e.g., biomass‑burning 
areas), with a penalty of a few ppb for the annual mean, proportional 
to warming levels (Figure  6.14). This rate ranges regionally from  
0.2–2 ppb °C–1 (Supplementary Material Figure  6.SM.1). The CMIP6 
ESMs show this consistently for South East Asia (in line with Hong et al. 
(2019) and Schnell et al. (2016)) and for India (in line with Pommier 
et al., 2018) as well as in parts of Africa and South America, close to 
enhanced BVOC emissions (at least three out of four ESMs agree on 
the sign of change). The results are mixed in polluted regions of Europe

and US because of lower anthropogenic precursor emissions which 
leads to a very low sensitivity of surface ozone to climate change (–0.5 
ppb °C–1 to 0.5 ppb °C–1; Supplementary Material Figure 6.SM.1) and 
thus the ESMs can disagree on sign of changes for a given warming 
level. This heterogeneity in the results is also found in regional studies 
over North America (Gonzalez‑Abraham et al., 2015; Val Martin et al., 
2015; Schnell et al., 2016; He et al., 2018; Nolte et al., 2018; Rieder 
et al., 2018)  or over Europe (Colette et al., 2015; Lacressonnière et al., 
2016; Schnell et al., 2016; Fortems‑Cheiney et al., 2017) .
Overall, warmer climate is expected to reduce surface ozone in 
unpolluted regions as a result of greater water vapour abundance 
accelerating ozone chemical loss ( high confidence ). Over regions 
with high anthropogenic and/or natural ozone precursor emissions, 
there is prevailing evidence that climate change will introduce

a surface O 3 penalty increasing with increasing warming levels (with 
a  magnitude ranging regionally from 0.2–2 ppb °C–1) (medium to 
high confidence ). Yet, there are uncertainties in processes affected 
in a warmer climate which can impact and modify future baseline 
and regional/local surface ozone levels. The response of surface 
ozone to future climate change through stratosphere–troposphere 
exchange, soil NO x emissions and wildfires is positive ( medium 
confidence ). In addition, there is low confidence  in the magnitude 
of the effect of climate change on surface ozone through biosphere 
interactions (natural methane, non‑methane BVOC emissions and 
ozone deposition) and lightning NO x emissions. 
6.5.2 Impact of Climate Change on Particulate Matter
Changes in concentration and chemistry of particulate matter (PM) in 
a changing climate depend in a complex manner on the response of 
the multiple interactions of changes in emissions, chemical processes,

deposition and other factors (e.g.,  temperature, precipitation, 
circulation patterns). These changes are difficult to assess and, at 
the time of AR5, no confidence level  was attached to the overall 
impact of climate change on PM 2.5 (Kirtman et al., 2013). Possible 
changes induced by climate change may concern both atmospheric 
concentration levels and chemical composition.
Higher temperatures increase the reaction rate of gaseous SO 2 to 
particulate sulphate conversion but also favour evaporation of 
particulate ammonium nitrate (Megaritis et al., 2013). Also, higher 
temperatures are expected to affect BVOC emissions (e.g., Pacifico et al., 2012) that would influence SOA concentrations, although this 
effect has been questioned by more recent evidence (B. Wang et al., 
2018; Z. Zhao et al., 2019). More generally, climate change will also 
affect dust concentration levels in the atmosphere (Section 6.2.2.4)

and the occurrence of forest fires, both very large sources of aerosols 
to the global troposphere (Section 6.2.2.6).
Wet deposition constitutes the main sink for atmospheric 
PM (Allen et al., 2016, 2019; Xu and Lamarque, 2018). In particular, 
precipitation frequency has a  higher effect on PM wet deposition 
than precipitation intensity (Hou et al., 2018). PM is also sensitive 
to wind speed and atmospheric stability conditions emphasizing 
the importance of stagnation episodes and low planetary boundary 
layer heights for increasing PM atmospheric concentrations (Porter 
et al., 2015).
At the global scale, depending on its magnitude, the warming leads 
either to a small increase in global mean PM concentration levels 
(about 0.21 µg m–3 in 2100 for RCP8.5), mainly controlled by sulphate 
and organic aerosols or a small decrease (–0.06 µg m–3 for RCP2.6, 
Westervelt et al. (2016) and Xu and Lamarque (2018)). On the other

hand, Xu and Lamarque (2018) and Allen et al. (2016, 2019) found an 
increase of aerosol burden and PM surface concentration throughout 
the 21st Century, attributed to a decrease in wet‑removal flux despite 
the overall projected increase in global precipitation, on the ground 
of an expected shift of future precipitation towards more frequent 
heavy events. Based only on three models, the CMIP6 ensemble 
shows that for most land areas, there is low agreement  between 
models on the sign of the effect of climate change on annual mean 
PM 2.5 (Supplementary Material Figure 6.SM.2).
Due to the typical atmospheric lifetime of PM in the atmosphere, 
of the order of a few days, most studies dealing with the future PM 
concentration levels have a regional character and concern mainly 
Europe (Megaritis et al., 2013; Lacressonnière et al., 2016, 2017; 
Lemaire et al., 2016; Cholakian et al., 2019), the USA (Penrod et al.,

2014; Fiore et al., 2015; Gonzalez‑Abraham et al., 2015; Shen et al., 
2017; He et al., 2018; Nolte et al., 2018), Southern and Eastern Asia 
(Jiang et al., 2013; Nguyen et al., 2019) and India (Pommier et al., 
2018). No studies are available for other areas of the world.
Changes in the chemical composition of PM as a result of future climate 
change can also be an important issue for the effects of PM on human 
health and the environment, but only sparse data are available in the 
literature on this and the results are, as yet, inconclusive (Im et al., 
2012; Jiang et al., 2013; Megaritis et al., 2013; Gonzalez‑Abraham 
et al., 2015; Gao et al., 2018; He et al., 2018; Cholakian et al., 2019).
Overall, there is medium confidence  (medium evidence , high 
agreement ) in a  small effect, positive or negative, on PM global 
burden due to climate change. 
6.5.3 Impact of Climate Change on Extreme Pollution 
Extreme air pollution is identified as the concentration of an air

pollutant that is above a given threshhold value (high concentration

864
Chapter 6 Short-lived Climate Forcers6or a high percentile) as the sensitivity of peak values to meteorological 
conditions can be different from sensitivity of the median or mean 
(Porter et al., 2015). The AR5 assessed with medium confidence  
that uniformly higher temperatures in polluted environments will 
trigger regional feedbacks in chemistry and local emissions that will 
increase peak ozone and PM pollution, but assessed low confidence  
in projecting changes in meteorological blocking associated with 
these extreme episodes. 
Meteorological conditions, such as heatwaves, temperature 
inversions and atmospheric stagnation episodes favour air quality 
extremes and are influenced by changing climate (Fiore et al., 2015). 
The body of literature on the connection between climate change and 
extreme anthropogenic pollution episodes is essentially based on 
correlation and regression applied to observation reanalysis but the

metrics and methodologies differ making quantitative comparisons 
difficult. Many emission processes in the natural systems are sensitive 
to temperature, and bursts of emissions as a  reponse to extreme 
weather, as in the case of wildfires in dry conditions (Bondur et al., 
2020; Xie et al., 2020) can occur, which would then add to the risk 
of extreme air pollution but are not sufficiently constrained to be 
quantitatively assessed.
Since AR5, published studies provide augmented evidence for 
the connections between extreme ozone and PM pollution events 
and high temperatures, especially long‑lasting heatwaves, whose 
frequency is increasing due to a warming climate (Lelieveld et al., 
2014; Porter et al., 2015; Hou and Wu, 2016; Jing et al., 2017; Schnell 
and Prather, 2017; Sun et al., 2017; H. Zhang et al., 2017). However, 
the relationship between air pollution and individual meteorological 
parameters is exaggerated because of covariation on synoptic time

scales (Fiore et al., 2015). For example, heatwaves are often associated 
with clear skies and stagnation, making clear attribution to specific 
meteorological variables complicated. In Asia, future changes in 
winter conditions have also been shown to favour more particulate 
pollution (Cai et al., 2017; Zou et al., 2017). The relationship between 
the occurrence of stagnation episodes and high concentrations 
of ozone and PM 2.5 has been shown to be regionally and metric 
dependant (Oswald et al., 2015; Sun et al., 2017; Kerr and Waugh, 
2018; Schnell et al., 2018; Garrido‑Perez et al., 2019).
The increase of frequency, duration and intensity of heatwaves 
is extremely likely  on all continents for different future warming 
levels (Section 11.3.5, Table 11.2). However, there is low confidence  
in projected changes in storm tracks, jets and blocking, and thus 
their influence on extreme temperatures in the mid‑latitudes 
(Section 11.3.1).

In conclusion, there is still medium confidence  that climate‑
driven changes in meterorological conditions, such as heatwaves 
or stagnations, will favour extreme air pollution episodes over 
highly polluted areas, however, the relationship between these 
meteorological conditions and high concentrations of ozone and 
PM 2.5 have been shown to be regionally and metric dependant.6.6 Air Quality and Climate Response 
to SLCF Mitigation
Long‑lived greenhouse gas (LLGHG) emissions reductions are 
typically motivated by climate change mitigation policies, whereas 
SLCF reductions mostly result from air pollution control and climate 
policies (FAQ6.2), as well as policies focusing on achieving UN 
Sustainable Development Goals (SDGs; Box 6.2). The management 
of several SLCFs (BC, methane, tropospheric ozone and HFCs) is 
considered in the literature as a  fast‑response, near ‑term measure 
to curb climate change, while reduction of emissions of LLGHGs

is an essential measure for mitigating long‑term climate warming 
(Shindell et al., 2012, 2017b; Shoemaker et al., 2013; Rogelj et al., 
2014b; Lelieveld et al., 2019). Note that the term short‑lived climate 
pollutants (SLCPs), referring only to warming SLCFs, has been used 
within the policy arena. The SR1.5 report states that limiting warming 
to 1.5°C to achieve Paris Agreement goals, implies net‑zero CO 2 
emissions around 2050 and concurrent deep reductions in emissions 
of non‑CO 2 forcers, particularly methane (Rogelj et al., 2018a). 
In addition, several SLCFs are key air pollutants or precursors of fine 
particulate matter (PM 2.5) and tropospheric ozone, and therefore 
subject to control driven by air ‑quality targets. 
Policies addressing the reduction of either SLCFs or LLGHGs, often 
prioritize mitigation of emissions from specific anthropogenic 
sources, such as energy production, industry, transportation,

agriculture, waste management and residential fuel use. The choice 
of the targetted sector and chosen measures will determine the 
ratios of emitted SLCFs and LLGHGs. These changes in emissions of 
co‑emitted species will result in diverse responses driven by complex 
chemical and physical processes, and resulting climate perturbations. 
The understanding of the co‑benefits through sectoral mitigation 
efforts (as well as potential negative impacts) is essential to inform 
policymaking. 
The discussion of targeted SLCF policies and their role in climate 
change mitigation includes: critical evaluation of the climate  co‑
benefits (Smith and Mizrahi, 2013; Pierrehumbert, 2014; Rogelj et al., 
2014b; Strefler et al., 2014; M.R. Allen et al., 2016); modelling the 
potential of dedicated BC and methane reductions in association with 
or without climate policy (Harmsen et al., 2020a; Smith et al., 2020); 
quantifying individual or multi‑component mitigation in relation

to natural variability (Samset et al., 2020); warning about the risk 
of diversion of resources from targeted LLGHG policies, especially 
those targeting CO 2 (e.g., Shoemaker et al., 2013); and seeing it as 
an opportunity to strengthen commitment and accelerate action on 
LLGHGs (Victor et al., 2015; Aakre et al., 2018). 
Over the last decade, research on air quality–climate interactions and 
feedbacks has brought new attention from policy communities to the 
possibility of win‑win mitigation policies that could both improve air 
quality and mitigate climate change, possibly also reducing the cost 
of interventions (Anenberg et al., 2012; Shindell et al., 2012, 2017b; 
Schmale et al., 2014a, b; Sadiq et al., 2017; Fay et al., 2018; Harmsen 
et al., 2020b) . Haines et al. (2017)  and Shindell et al. (2017b) connect 
the measures to mitigate SLCFs with the achievements of some of 
the SDGs. Indeed, most studies on co‑benefits to date focus on the

865
Short-lived Climate Forcers  Chapter 66impacts of climate change mitigation strategies, in particular to meet 
Nationally Determined Contributions (NDCs) and/or to remain below 
specific global temperature targets, on air quality and human health 
(West et al., 2013; Rao et al., 2016; Shindell et al., 2016, 2017b; 
Zhang et al., 2016; Chang et al., 2017; Haines et al., 2017; Li et al., 
2018; Markandya et al., 2018; Williams et al., 2018; Xie et al., 2018; 
Lelieveld et al., 2019) . Such co‑benefits of climate change mitigation 
for air quality and human health can offset the costs of the climate 
measures (Saari et al., 2015; Li et al., 2018). A growing number of 
studies analyse the co‑benefits of current and planned air ‑quality 
policies on LLGHGs and global and regional climate change impacts 
(Lund et al., 2014; Akimoto et al., 2015; Lee et al., 2016; Maione et al., 
2016; Peng et al., 2017) .
This section assesses the effects of mitigating SLCFs, motivated

by various objectives, discussing temperature response time, 
temperature and air ‑quality attribution of SLCFs sources, and chosen 
mitigation approach. The air quality and climate effects of the 
measures to contain the spread of COVID‑19 in 2020 are discussed in 
Cross‑Chapter Box 6.1 at the end of this section.
6.6.1 Implications of Lifetime on Temperature 
Response Time Horizon
The effect over time on GSAT following a mitigation effort affecting 
emissions of LLGHGs or SLCFs depends on the lifetimes of the 
LLGHGs and SLCFs, their radiative efficiencies, how fast the emissions 
are reduced, how long reductions last (limited time or sustained 
reduction), and the inertia of the climate system itself. Mitigation of 
SLCFs is often implemented through new legislation or technology 
standards for the different emissions sectors and components, 
implying that reductions are sustained over time. 
It is often perceived that the full climatic response following

mitigation of SLCFs will occur almost immediately. However, the 
inertia of the climate system strongly modifies the short‑term and 
long‑term response. SLCFs with lifetimes shorter than the time scales 
for inter ‑hemisheric mixing (1–2 years) can cause a more spatially 
heterogeneous forcing than LLGHGs and thus different regional 
patterns of the climate response (Section  6.4.3). The temporal 
response in GSAT to a radiative forcing can be quantified using linear 
impulse response functions (Cross‑Chapter Box 7.1; Geoffroy et al., 
2013; Olivié and Peters, 2013; Smith et al., 2018) . Figure 6.15 shows 
the GSAT response for sustained step reduction in emissions of 
idealised SLCFs with different lifetimes. The response is relative to 
a  baseline with constant emissions, so effects of emissions before 
the step reduction is not shown. For SLCFs with lifetimes shorter 
than a  few years, the concentrations quickly reach a  new steady

state and the response time is primarily governed by the thermal 
inertia and thus the time scales of the climate system. For compounds 
with lifetime on the order of 10 years (e.g., methane), there is about 
a 10‑year delay in the response during the first decades, compared 
to compounds with lifetimes less than one year. However on longer 
time scales the response is determined solely by the time scales of 
the climate system itself. For CO 2 (dashed line in Figure  6.15) the 
temporal response is very different due to the long time scale for mixing into the deep ocean and therefore a substantial fraction of 
atmospheric CO 2 is only removed on millenium time scales. This 
means that for SLCFs including methane, the rate of emissions drives 
the long‑term stabilisation, as opposed to CO 2 where the long‑term 
effect is controlled by cumulative emissions (Allen et al., 2018b). 
Methods to compare rates of SLCF emissions with cumulative CO 2

emissions are discussed in Chapter 7 (Section 7.6.1.4).
As a  consequence, in idealized ESM studies that assume an 
instantaneous removal of all anthropogenic or fossil fuel‑related 
emissions, a rapid change in aerosol levels occurs leading to large 
increases in GSAT with the rate of warming lasting for several years. 
Similarly, the thermal inertia causes the pulse emissions (Figure 6.15) 
of SLCFs to have a  significant effect on surface temperature even 
after 10 years. 
In summary, for SLCFs with short lifetime (e.g., months), the response 
in surface temperature occurs strongly as soon as a sustained change 
in emissions is implemented and continues to grow for a  few 
years, primarily due to thermal inertia in the climate system ( high 
confidence ). Near its maximum, the response slows down but will 
then take centuries to reach equilibrium ( high confidence ). For SLCFs 
with longer lifetimes (e.g.,  a decade), a  delay equivalent to their

lifetimes comes in addition to the delay due the thermal inertia 
(high confidence ). 
Figure 6.15 | Global mean surface air temperature (GSAT) response to an 
abrupt reduction in emissions (at time t=0) of idealized climate forcing 
agents with different lifetimes.  All emissions are cut to give a radiative forcing 
of –1 W m–2 at a steady state (except for CO 2). In other words, if the yearly emissions 
are E 0 before the reduction, they will have a fixed lower value E year>0 = (E 0 – ΔE) for 
all succeeding years. For comparison, the GSAT response to a sustained reduction in 
CO 2 emissions resulting in an RF of –1 W m–2 in year 100 is included (dashed line). 
The temperature response is calculated using an impulse response function (Cross-
Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2 °C–1. Further 
details on data sources and processing are available in the chapter data table (Table 6.
SM.3).

866
Chapter 6 Short-lived Climate Forcers66.6.2 Attribution of Temperature and Air Pollution 
Changes to Emissions Sectors and Regions 
Assessment of the temperature response to source emissions 
sectors is important for identifying priority mitigation measures and 
designing efficient mitigation strategies. 
Temperature effects of emissions can be quantified for the historical 
contribution to the present temperature impact (Section 6.4.2), for 
idealized one‑year pulses of emissions or for continued (sustained) 
emissions at present levels and for changes during a specific time 
period, for emissions from future scenarios with various hypotheses, 
giving complementary information to feed mitigation strategies. 
The AR5 assessed the net global temperature impact of source 
emissions sectors from a  one‑year pulse (a single year’s worth) 
of year 2008 emissions and found that the largest contributors to 
warming on 50–100‑year time scales are the energy, industrial and

on‑road transportation sectors. Sectors that emit large amounts of 
methane (agriculture and waste management) and black carbon 
(residential biofuel) are important contributors to warming over 
short time horizons up to 20 years. Below, we discuss the effect on 
ERFs, temperature and air pollution of selected key sectors estimated 
to have large non‑CO 2 forcing, including agriculture, residential and 
commercial, and transport (aviation, shipping, land transportation).
6.6.2.1 Agriculture 
According to the SRCCL assessment (Jia et al., 2019), agriculture, 
forestry and other land use (AFOLU) are a  significant net source 
of GHG emissions ( high confidence ), with more than half of these 
emissions attributed to non‑CO 2 GHGs from agriculture. With respect 
to SLCFs, agricultural activities are major global sources of methane 
and NH 3 (Section  6.2.1). The agriculture sector exerts strong near ‑
term warming due to large methane emissions that is slightly

offset by a small cooling from secondary inorganic aerosols formed 
notably from the NH 3 emissions (Heald and Geddes, 2016; Lund 
et al., 2020). For present‑day emissions, agriculture is the second 
largest contributor to warming on short time scales but with a small 
persisting effect on surface temperature (+0.0012°C ± 0.00028°C) 
after a pulse of current emissions (Figure 6.16, see detailed description 
in Section  6.6.2.3.4; Lund et al., 2020). Aerosols produced from 
agricultural emissions, released after nitrogen fertilizer application 
and from animal husbandry, influence surface air quality and make 
an important contribution to surface PM 2.5 in many densely populated 
areas ( Figure 6.17; Lelieveld et al., 2015b; Bauer et al., 2016).
6.6.2.2 Residential and Commercial Cooking and Heating
The residential and commercial sector is associated with SLCF 
emissions of carbonaceous aerosols, CO and NMVOCs, SO 2 and NO x,

and can be split by fuel type (biofuel or fossil fuel) where residential 
fossil fuel is also associated with CO 2 and methane emissions 
(Section 6.2.1). 
The net effect of residential CO and NMVOC emissions is warming 
and that of SO 2 and NO x is cooling of the atmosphere.  However, the sign of the net global radiative effects of carbonaceous aerosols from 
the residential sector and solid‑fuel cookstove emissions (warming 
or cooling) is not well constrained based on evidence from recent 
global atmospheric modelling studies. Estimates of direct aerosol –
radiation and aerosol –cloud effects from the global residential 
sector range from –20 to +60 mW m–2 (Kodros et al., 2015) and 
–66 to +21 mW m–2 (Butt et al., 2016) and from –20 to +10 mW m–2 
(Kodros et al., 2015) and –52 to –16 mW m–2 (Butt et al., 2016), 
respectively.  Uncertainties are due to assumptions about the aerosol 
emissions masses, size distribution, aerosol optical properties

and mixing states (Section  6.3.5.3). Allowing BC to act as an INP 
in a  global model leads to a  much larger global forcing estimate 
from –275 to +154 mW m–2 with a large uncertainty range due to 
uncertainty in the plausible range of maximum freezing efficiency 
of BC (Huang et al., 2018). The residential biofuel sector is a major 
concern for indoor air quality (Bonjour et al., 2013). In addition, several 
atmospheric modelling studies find that this sector is also important 
for outdoor air quality and even a dominant source of population‑
weighted outdoor PM 2.5 in India and China (Lelieveld et al., 2015b; 
Silva et al., 2016; Spracklen et al., 2018; Reddington et al., 2019).
The net climate effect of a one‑year pulse of current emissions from the 
residential sector is warming in the near term of +0.0018°C  ±  0.00084°C 
from fossil fuel use and +0.0014°C ± 0.0012°C from biofuel use. Over 
a 100‑year time horizon, this warming is +0.0017°C ± 0.00017°C

and +0.0001°C ± 0.000079°C, respectively (Lund et al., 2020). This is 
due to the effects of BC, methane, CO and NMVOCs, which add to 
that of CO 2, but the uncertainty in the sign of carbonaceous aerosol 
net effects challenges overall quantitative understanding of this 
sector and leads to low confidence  in this assessment. Residential 
sector emissions are an important source of indoor and outdoor air 
pollution in Asia and globally ( high confidence ).
6.6.2.3 Transportation
6.6.2.3.1 Aviation
Aviation is associated with a range of SLCFs, in particular emissions 
of NO x and aerosol particles, alongside emissions of water vapour 
and CO 2. The largest SLCF effects are those from the formation 
of persistent condensation trails (contrails) and NO x emissions. 
Persistent contrails are ice‑crystal clouds, formed around aircraft 
soot particles (and water vapour from the engine), injected in 
ambient cold and ice‑supersaturated atmosphere, which can spread

and form contrail cirrus clouds. The ‘net NO x’ effect arises from the 
formation of tropospheric ozone, counterbalanced by the destruction 
of ambient methane and associated cooling effects of reductions 
in stratospheric water vapour and background ozone. The AR5 
assessed the radiative forcing from persistent linear contrails to be 
+0.01 [+0.005  to  +0.03] W m–2 for the year 2011, with medium 
confidence  (Boucher et al., 2013). The combined linear contrail and 
their subsequent evolution to contrail cirrus radiative forcing from 
aviation was assessed to be +0.05 [+0.02 to +0.15] W m–2, with low 
confidence . An additional forcing of +0.003 W m–2 due to emissions 
of water vapour in the stratosphere by aviation was also reported 
(Boucher et al., 2013). The aviation sector was also estimated to lead

867
Short-lived Climate Forcers  Chapter 66to a  net surface warming at 20‑ and 100‑year horizons following 
a  one‑year pulse emission. This net temperature response was 
determined by similar contributions from contrails, contrail cirrus and 
CO 2 over a 20‑year time horizon, and dominated by CO 2 in a 100‑year 
perspective (Figure 8.34 in AR5, Myhre et al., 2013). 
Our assessment is built upon Lee et al. (2021). Their study consists of 
an updated, comprehensive assessment of aviation climate forcing 
in terms of RF and ERF based on a large number of studies and the 
most recent air ‑traffic and fuel‑use datasets available (for 2018), 
new calculations and the normalization of values from published 
modelling studies, and combining the resulting best estimates via 
a Monte‑Carlo analysis. Lee et al. (2021) report a net aviation ERF 
for year ‑2018 emissions of +0.101 [0.055–0.145] W m–2 with major 
contributions from contrail cirrus (0.057 W m–2), CO 2 (0.034 W m–2)

and NO x (0.017 W m–2). Contrails and aviation‑induced cirrus yield 
the largest individual positive ERF followed by CO 2 and NO x emissions 
(Lee  et al. 2021). The confidence  level in ERF due to contrails 
and aviation‑induced cirrus is assessed to be low in Chapter  7 
(Section 7.3.4.2) due to potential missing processes. The formation 
and emission of sulphate aerosols yield a negative (cooling) term. 
SLCF forcing terms contribute about eight times more than CO 2 to 
the uncertainty in the aviation net ERF in 2018 (Lee et al., 2021). 
The largest uncertainty in assessing aviation climate effects is on the 
interactions of BC and sulphate aerosols on cirrus and mixed‑phase 
clouds, for which no best estimates of the ERFs were provided (Lee 
One of the most significant changes between AR5 and AR6 in terms 
of aviation SLCFs is the explicit calculation of a contrail cirrus ERF 
found to be 35% of the corresponding RF (Bickel et al., 2020),

confirming the studies indicating smaller efficacy of linear contrails 
(Ponater et al., 2005; Rap et al., 2010). The net‑NO x term is generally 
agreed to be a positive RF in the present day, although attribution 
in a non‑linear chemical system is problematic (Grewe et al., 2019), 
but Skowron et al. (2021) point out that the sign of net NO x term is 
dependent on background conditions and could be negative under 
certain future scenarios.
The best estimate ERFs from aviation (Lee et al., 2021) have been used 
to calculate aviation‑specific Absolute Global Temperature change 
Potential (AGTP) using the method described in Lund et al. (2020) 
and subsequently compute the effect of a one‑year pulse of aviation 
emissions on global mean surface temperature on a 10‑ and 100‑
year time horizon (Section 6.6.2.3.4 and Figure 6.16). The effect of 
contrail‑cirrus is most important for the estimated net‑GSAT response 
after the first decade, followed by similar warming contributions

from NO x and CO 2 emissions. At a  20‑year time horizon, the net 
contribution from aviation to GSAT has switched from a positive to 
a small negative effect (see Supplementary Material 6.SM.4). This is 
due to the combination of rapidly decaying contrail‑cirrus warming 
and the complex time variation of the net temperature response to 
NO x emissions, which changes sign between 10 and 20 years due 
to the balance between the positive short‑lived ozone forcing and 
negative forcing from changes in methane and methane‑induced 
changes in ozone and stratospheric water vapour. The net GSAT 
response to aviation emissions has previously been estimated to be positive on a 20‑year time horizon (AR5, Chapter 8; Lund et al., 2017). 
This difference in net GSAT after 20 years in AR5 compared to AR6, 
results primarily from a shorter time scale of the climate response in 
the underlying AGTP calculations in Lund et al. (2020), which means

the initial, strong impacts of the most short‑lived SLCFs, including the 
warming by contrail‑cirrus decay faster, in turn giving the net NO x 
effect a relatively higher importance after 20 years. On longer time 
horizons, the net GSAT response switches back to positive, as CO 2 
becomes the dominating warming contribution.
In summary, the net aviation ERF is assessed to be +0.1 W m–2 (±0.045) 
for the year 2018 ( low confidence) . This confidence level is largely 
a result of the fact that the SLCF‑related terms, which account for 
more than half (66%) of the net aviation ERF , are the most uncertain 
terms. The climate response to SLCF‑related aviation terms exhibits 
substantial spatio‑temporal heterogeneity in characteristics ( high 
confidence ). Overall, cirrus and contrail cirrus warming, as well as 
NO x‑induced ozone increase, induce strong but short‑lived warming 
contributions to the GSAT response 10 years after a one‑year pulse of

present‑day aviation emissions ( medium confidence ), while CO 2 both 
gives a warming effect in the near term and dominates the long‑term 
warming impact ( high confidence ).    
6.6.2.3.2 Shipping
Quantifying the effects of shipping on climate is particularly 
challenging because (i) the sulphate cooling impact is dominated 
by aerosol–cloud interactions and (ii) ship emissions contain NO x, 
SO x and BC, which lead to mixed particles. Previous estimates 
of the sulphate radiative effects from present‑day shipping 
span the range –47 to –8 mW m–2 (direct radiative effect) and 
–600 to –38 mW m–2 (indirect radiative effects) (Lauer et al., 2007; 
Balkanski et al., 2010; Eyring et al., 2010; Lund et al., 2012) . Partanen 
et al. (2013) reported a  global mean ERF for year ‑2010 shipping 
aerosol emissions of –390 mW m–2. The temperature change has 
been shown to be highly sensitive to the choice of aerosol–cloud 
parametrization (Lund  et al., 2012). One year of global present‑

day shipping emissions, not considering the impact of recent low 
sulphur fuel regulation (IMO, 2016), are estimated to cause net 
cooling in the near term (–0.0024°C ± 0.0025°C) and slight warming  
(+0.00033°C ± 0.00015°C) on a 100‑year horizon (Lund et al., 2020). 
Shipping is also of importance for air pollution in coastal areas along 
the major trade routes, especially in Europe and Asia (Corbett et al., 
2007; H. Liu et al., 2016, Figure 6.17; Jonson et al., 2020). Jonson 
et al. (2020) estimated that shipping is responsible for 10% or more 
of the controllable PM 2.5 concentrations and depositions of oxidised 
nitrogen and sulphur for many coastal countries. Widespread 
introduction of low‑sulphur fuels in shipping from 2020 (IMO, 2016) 
will lead to improved air quality and reduction in premature mortality 
and morbidity (Sofiev et al., 2018).
In summary, a year’s worth of present‑day global shipping emissions

(i.e., without the implementation of the 2020 clean fuel standards) 
cause a net global cooling (–0.0024 ± 0.0025°C) on 10–20 year time 
horizons ( high confidence ) but its magnitude is of low confidence .

868
Chapter 6 Short-lived Climate Forcers66.6.2.3.3 Land transportation
The on‑road and off‑road transportation sectors have a net warming 
impact on climate over all time scales (Berntsen and Fuglestvedt, 
2008; Fuglestvedt et al., 2008; Unger et al., 2010; Lund et al., 2020). 
A one‑year pulse of present‑day emissions has a  small net global 
temperature effect on short time scales (+0.0011°C ± 0.0045°C), 
predominantly driven by CO 2 and BC warming offset by NO x‑induced 
cooling through methane lifetime reductions (Lund et al., 2020).
The vehicle tailpipe emissions profiles of diesel and gasoline are 
distinctly different. Diesel air pollutant emissions are dominated by 
BC and NO x whereas gasoline air pollutant emissions are dominated 
by CO and NMVOCs, especially when distribution and upstream losses 
are considered. Thus, the net radiative effect of the on‑road vehicle 
fleets depends upon the share of different fuels used, in particular

gasoline and diesel (Lund et al., 2014; Huang et al., 2020). The net 
SLCF for year ‑2010 emissions from the global diesel vehicle fleet 
have been estimated to be +28 mW m–2 (Lund et al., 2014). Huang 
et al. (2020) estimated net global radiative effects of SLCFs (including 
aerosols, ozone, and methane) from the gasoline and diesel vehicle 
fleets in the year 2015 to be +13.6 and +9.4 mW m–2, respectively, 
with similar fractional contributions of SLCFs to the total global 
climate impact including CO 2 on the 20‑year time scale (14–15%).
There is consensus that on‑road transportation sector emissions, 
including gasoline and diesel, are important anthropogenic 
contributors to elevated surface ozone and PM 2.5 concentrations 
(Chambliss et al., 2014; Lelieveld et al., 2015b; Silva et al., 2016; 
Anenberg et al., 2019) . At a  global scale, land transportation has 
been estimated to be the dominant contributor to surface ozone

concentrations in populated areas (Silva et al., 2016) and ozone‑
induced vegetation damages (Section  6.4.4; Unger et al., 2020). 
Furthermore, it is now well established that real‑world diesel NO x 
emissions rates are substantially higher, the so‑called ‘excess NO x’, 
in all regional markets than in laboratory tests, worsening air quality 
(Anenberg et al., 2017; Jonson et al., 2017; Chossière et al., 2018) 
and contributing to slightly larger warming on the scale of years and 
smaller warming at the decadal scale (Tanaka et al., 2018). Excess 
NO x emissions from key global diesel markets are estimated at 
4.6 Tg yr–1 in 2015, with annual mean ozone and PM 2.5 increases of 
1 ppb and 1µg m–3 across large regions of Europe, India and China 
(Anenberg et al., 2017).
In summary, the present‑day global land‑based transport pulse 
emissions cause a  net global warming on all time scales ( high 
confidence ) and are detrimental to air quality ( high confidence ).

6.6.2.4 GSAT Response to Emissions Pulse  
of Current Emissions
Figure 6.16 presents the GSAT response to an idealized pulse of year ‑
2014 emissions of individual SLCF and LLGHG. The GSAT response 
is calculated for 11 sectors and 10 regions accounting for best 
available knowledge and geographical dependence of the forcing 
efficacy of different SLCFs (Lund et al., 2020). Two time horizons 
are shown (of 10  and 100 years) to represent near ‑ and long‑term effects (and a 20‑year horizon is presented in Supplementary 
Material Figure 6.SM.3). Other time‑horizon choices may affect the 
relative importance, and even sign in the case of the NO x effect, 
of the temperature response from some of the SLCFs, or be more 
relevant for certain applications. GSAT response is calculated using 
the concept of AGTP (Section 7.6.2.2). Further details of the AGTP 
emulator applied in Figure 6.16 are provided in Lund et al. (2020) 
and Supplementary Material 6.SM.4 (Section 7.6.1.2, Cross‑Chapter

Box 7.1 and Supplementary Material 7.SM.7.2). As discussed by Lund 
et al. (2020), the AGTP framework is primarily designed to study 
the relative importance of individual emissions and sources, but the 
absolute magnitude of temperature responses should be interpreted 
with care due to the linearity of the AGTP , which does not necessarily 
capture all the non‑linear effects of SLCFs emissions on temperature.
Differences in the mix of emissions result in net effects on GSAT 
that vary substantially, in both magnitude and sign, between sectors 
and regions. SLCFs contribute substantially to the GSAT effects of 
sectors on short time horizons (10–20 years) but CO 2 dominates on 
longer time horizons (Figure 6.16). As the effect of the SLCFs decays 
rapidly over the first few decades after emission, the net long‑term 
temperature effect is predominantly determined by CO 2. N 2O adds 
a  small contribution to the long‑term effect of agriculture. CO 2

emissions cause an important contribution to near ‑term warming 
that is not always fully acknowledged in discussions of LLGHGs and 
SLCFs (Lund et al., 2020). 
The global sectoral ranking for near ‑ and long‑term global 
temperature effects is similar to the AR5 assessment despite regional 
reductions in aerosol precursor emissions between 2008 and 2014. 
This report has applied updated climate policy metrics for SLCFs 
and treatment of aerosol–cloud interactions for SO 2, BC and OC 
(Lund et al., 2020). By far the largest 10‑year GSAT effects are from 
the energy production (fossil fuel mining and distribution), agriculture 
and waste management sectors ( high confidence ). Methane is the 
dominant contributor in the energy production, agriculture and 
waste management sectors. On the 10‑year time horizon, other net 
warming sectors are residential fossil fuel and energy combustion 
(dominated by CO 2) and aviation and residential biofuel (dominated

by SLCFs and cloud) ( medium confidence ). The total residential and 
commercial sector, including biofuel and fossil fuels, is the fourth 
largest contributor to warming globally on short time horizons of 
10–20 years. The energy combustion sector has considerable cooling 
from high emissions of SO 2 that result in a relatively small net GSAT 
temperature effect on short time horizons, despite the high CO 2 
emissions from this activity. On the 10‑year time horizon, global 
emissions from industry and shipping cause a  net cooling effect 
despite a considerable warming from CO 2 emissions. On the 100‑year 
time horizon, the net effects of agriculture and waste management are 
small, while energy combustion is the largest individual contributor 
to warming due to its high CO 2 emissions. The second largest driver 
of long‑term temperature change is industry, demonstrating the 
importance of non‑CO 2 emissions for shaping relative weight over

different time frames. Transport contributes a small net warming on 
the 10‑year time horizon that increases by a factor of three on the 
100‑year time horizon. In contrast, the aviation sector contribution to 
warming shrinks by about a factor of three between the 10‑ and 100‑

869
Short-lived Climate Forcers  Chapter 66year time horizons. Results for the 20‑year time horizon are provided 
in the Supplementary Material 6.SM.4. Compared to the 10‑year time 
horizon, there are some changes in ranking, especially of sectors and 
regions with a strong SO 2 contribution, which decays substantially 
between 10 and 20 years. Aviation is the sector with the most 
distinct difference between 10‑ and 20‑year time horizons, such that 
the net GSAT effect after 20 years becomes small but negative. This 
is due to a switch in sign for the NO x AGTP for this sector and the 
stronger effect of short‑lived ozone response over these two short‑
term horizons in the case of aviation compared with other sectors.
In terms of source regions, the largest contributions to net short‑
term warming are caused by emissions in Eastern Asia, Latin 
America and North America, followed by Africa, Eastern Europe, 
West‑Central Asia and South East Asia ( medium confidence ).

However, the relative contributions from individual species vary. 
In Eastern Asia, North America, Europe and Southern Asia, the effect 
of current emissions of cooling and warming SLCFs approximately 
balance in the near term and these regions cause comparable net 
warming effects on 10‑ and 100‑year time horizons (Figure  6.16). 
In Latin America, Africa, and South East Asia and Developing Pacific, 
methane and BC emissions are currently high while emissions of CO 2 
and cooling aerosols are low compared to other regions, resulting in 
a net warming effect after 10 years that is substantially higher than 
that of CO 2 alone. 
Overall, the global sectors that contribute the largest warming on 
short time scales are the methane‑dominated sources, that is energy 
production (fossil fuel mining and distribution), and agriculture 
and waste management ( high confidence ). On short time scales, 
other net warming sectors are residential fossil fuel and energy

combustion (dominated by CO 2), and aviation and residential biofuel 
(dominated by SLCFs) ( medium confidence ). On short time scales, 
global emissions from industry and shipping cause a  net cooling 
effect despite a  considerable warming from CO 2 emissions ( high 
confidence ). On longer time horizons, the sectors that contribute the 
largest warming are energy combustion and industry due to the large 
CO 2 emissions ( high confidence ). 
6.6.2.5 Source attribution of regional air pollution
The attribution of present‑day surface PM 2.5 and ozone concentrations 
to sectors and regions (Figure  6.17) is based on 2014 CMIP6 
emissions used in the TM5‑FASST model (Van Dingenen et al., 2018) 
that has been widely applied to analyse air quality in regional and 
global scenarios (e.g., Van Dingenen et al., 2009; Rao et al., 2016, 
2017; Vandyck et al., 2018; Harmsen et al., 2020b) . Regions with the 
largest year ‑2014 population‑weighted annual average surface PM 2.5

concentrations are Southern Asia, Eastern Asia and the Middle East. 
The dominant anthropogenic source of ambient PM 2.5 in Southern 
Asia are the residential and commercial sectors (biomass and coal 
fuel‑based cooking and heating) with secondary contributions from 
energy and industry. In Eastern Asia, the main anthropogenic sources 
of ambient PM 2.5 are energy, industry and residential sources. Natural 
sources, predominantly dust, are the most important PM 2.5 source in 
the Middle East, Africa and Eurasia, contributing about 40–70% of 
ambient annual average concentrations (Figure  6.17). Agriculture is an important contributor to ambient PM 2.5 in Europe and North 
America, while open biomass burning is a  major contributor in 
South East Asia and Developing Pacific, North America as well as 
Latin America. These results are consistent with several global and 
regional studies, where contribution of emissions sources to ambient

PM 2.5 or premature mortality was estimated at different scales 
(e.g., Guttikunda et al., 2014; Lelieveld et al., 2015b; Amann et al., 
2017; Qiao et al., 2018; Venkataraman et al., 2018; Wu et al., 2018) . 
Natural sources contribute more than 50% to surface ozone in all 
regions except Southern Asia and South East Asia. Southern Asia, 
Eastern Asia and the Middle East experience the highest surface 
ozone levels of all regions. For ozone, the anthropogenic sectoral 
attribution is more uniform across regions than for PM 2.5, except 
for Southern and South East Asia, where land transportation 
plays a  larger role, and Eastern Asia, where the most significant 
contribution is from energy and industry. Land transportation and 
energy are the most important contributors to ozone across many 
of the regions, with smaller contributions from agriculture, biomass 
burning, waste management and industry. Open biomass burning is

not a major contributor to surface ozone, except for in Africa, Latin 
America and South East Asia where its contribution is estimated at 
about 5–10% of anthropogenic sources. The relative importance of 
natural and anthropogenic emissions sources on surface ozone has 
been assessed in several studies (Uherek et al., 2010; Zare et al., 
2014; Mertens et al., 2020; Unger et al., 2020) and the results are 
comparable with the estimates of the TM5‑FASST used here.
Residential and commercial cooking and heating are among the 
most important anthropogenic sources of ambient PM 2.5, except in 
the Middle East and Asia‑Pacific Developed ( high confidence ) and 
agriculture is the dominant source in Europe and North America 
(medium confidence ). Energy and industry are important PM 2.5 
contributors in most regions, except Africa ( high confidence ). Energy 
and land transportation are the major anthropogenic sources of 
ozone across many world regions ( medium to high confidence ).

870
Chapter 6 Short-lived Climate Forcers6Effect of a one year pulse of present−day emissions on global surface temperature
−0.06 −0.05 −0.04 −0.03 −0.02 −0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Change in GSAT due to total anthropogenic emissions (°C)Response after 10 years (H=10)
Response after 100 years (H=100)
−0.010 −0.005 0.000 0.005 0.010 0.015
Change in GSAT by sector (°C) H=10
H=100Fossil fuel production
and distribution
Agriculture
Waste management
Residential and com−
mercial
Fossil fuel combustion
for energy
mercial (biofuel use only)
Land transportation
Open biomass burning
Industry
Shipping
AviationCO 2
CH4
N2O
BC
OC
SO2
NOx
CO
VOC
NH3
Avia−contrail
Avia−stratH2O
HFCs
 Net effect
5% to 95% range
Change in GSAT by region (°C) H=10
H=100Eastern Asia
Latin America and
Caribbean
Africa
Eurasia
Europe
Southeast Asia and
Developing Paciﬁc
Middle East
Southern Asia
Asia−Paciﬁc
Developed

Figure 6.16 | Global mean temperature response 10 and 100 years following one year of present-day (year 2014) emissions.  The temperature response 
is broken down by individual species and shown for total anthropogenic emissions (top), sectoral emissions (left) and regional emissions (right). Sectors and regions are 
sorted by (high-to-low) net temperature effect on the 10-year time scale. Error bars in the top panel show uncertainty (5–95% interval) in net temperature effect due to 
uncertainty in radiative forcing only (calculated using a  Monte Carlo approach and best estimate uncertainties from the literature  – see Lund et al. (2020) for details).  
CO 2 emissions are excluded from open biomass burning and residential biofuel use due to their unavailability in the Community Emissions Data System (CEDS) and uncertainties

around non-sustainable emission fraction. Emissions for 2014 originate from the CEDS (Hoesly et al., 2018), except for HFCs which are from Purohit et al. (2020), open biomass 
burning from van Marle et al. (2017), and aviation H 2O which is from Lee et al. (2021). The split of fossil fuel production and distribution and combustion for energy and 
residential and commercial fuel use into fossil fuel and biofuel components is obtained from the GAINS model (ECLIPSE version 6b dataset). Open biomass burning emissions are 
not included for the regions. Emissions are aggregated into fossil fuel production and distribution (coal mining, oil and gas production, upstream gas flaring and gas distribution 
networks), agriculture (livestock and crop production), fossil fuel combustion for energy (power plants), industry (combustion and production processes, solvent-use losses from

production and end use), residential and commercial (fossil fuel use for cooking and heating as well is HFCs leakage from A/C and refrigeration), waste management (solid 
waste, including landfills and open trash burning, residential and industrial waste water), transport (road and off-road vehicles, and HFC leakage from A/C and refrigeration 
equipment), residential and commercial (biofuels use for cooking and heating), open biomass burning (forest, grassland, savanna fires and agricultural waste burning), shipping 
(including international shipping), and aviation (including international aviation). Further details on data sources and processing are available in the chapter data table (Table 6.
SM.3).

871
Short-lived Climate Forcers  Chapter 666.6.3 Past and Current SLCF Reduction Policies  
and Future Mitigation Opportunities 
Several SLCF‑emissions reduction strategies have been explored in 
the literature or are already pursued as part of environmental and 
development policies, including air quality, waste management, 
energy poverty and climate change. The effects of various policies 
and strategies have been addressed in a limited number of modelling 
studies with different objectives that range from assessment of 
specific policies and their regional effects (UNEP and WMO, 2011; 
Shindell et al., 2012, 2017b; AMAP , 2015a, b; Haines et al., 2017; 
UNEP and CCAC, 2018; UNEP , 2019; Harmsen et al., 2020a ) to large‑
scale global‑scenario studies with varying levels of SLCF control 
(e.g.,  Sand et al., 2016; Rogelj et al., 2018b; Shindell and Smith, 
2019). They could be grouped into: 
• Projections of future SLCF emissions compatible with the climate

change mitigation trajectories investigated in the climate model 
intercomparison projects (respective RCP or SSP scenarios): in 
such scenarios, when climate change mitigation is considered, 
it is associated with a strong decrease of CO 2 emissions, largely 
relying on fossil fuel‑use reduction, along with proportional 
reductions in the co‑emitted SLCFs from combustion and methane 
from the production and distribution of fossil fuels. Depending on the carbon price and climate change mitigation target, further 
reduction of methane from waste and agriculture will also be 
part of such scenarios. The limitations of RCP scenarios (where 
continuous strengthening of air ‑quality legislation was assumed 
resulting in lack of futures where global and regional air 
quality deteriorates) for the analysis of air quality and potential 
for mitigation of SLCFs have been discussed in literature 
(e.g.,  Amann et al., 2013; von Schneidemesser et al., 2015).

SSP scenarios consider various levels of air pollution control, in 
accordance with their socio‑economic narrative, and thus cover 
a wider span of SLCF trajectories (Section 6.7). The economic cost 
of the implementation of these scenarios and their co‑benefits 
on air quality and SDGs are assessed in the AR6 WGIII Report 
(Chapter 3). 
• Projections of SLCF emissions assuming strong reduction of all air 
pollutants in the absence of climate change mitigation (e.g., the 
SSP3‑lowSLCF scenario): the latter is an idealized simulation of 
a very ambitious air ‑quality policy where the maximum technical 
potential of existing end‑of‑pipe technologies is explored in 
the SSP3‑7.0 scenario. Methane reduction can also be part of 
such sensitivity analysis, although methane reductions have not 
historically been motivated by air pollution concerns. 
• Projection of emissions targeting air quality or other development

priorities: anthropogenic emissions’ source‑structure and the Attribution of regional, population−weighted PM2.5 and ozone to sectors
Fraction of total (%) Eastern AsiaPM2.5 surface concentration
52.0Population−weighted regional mean (µg m-3)Latin America and
Caribbean10.4
North America  9.1
Eurasia 13.9
Developing Paciﬁc14.1
Europe 10.4
Africa 24.5
Middle East 43.3
Southern Asia 52.7
Developed12.0Energy Industry Land transportation Shipping Aviation Residential and commercial
Agriculture Waste management Open biomass burning Natural sources
Fraction of total (%) Ozone surface concentration
49.3Population−weighted regional mean (ppb)
33.4
35.8
33.8
41.8
35.5
37.5
46.1
59.4
39.0
Figure 6.17 | Emissions source-sector attribution of regional population-weighted mean concentrations of PM 2.5 and ozone for present-day emissions

(year 2014).  Regional concentrations and source apportionment are calculated with the TM5-FASST model (Van Dingenen et al., 2018) for the 2014 emissions data from the 
Community Emissions Data System (CEDS) (Hoesly et al., 2018) and van Marle et al. (2017) for open-biomass burning. Dust and sea salt contributions to PM 2.5 are monthly 
mean climatological averages over 2010–2018 from CAMS global reanalysis (EAC4) (Inness et al., 2019), generated using Copernicus Climate Change Service information 
(January 2020). Anthropogenic sectors are similar to those in Figures 6.2 and 6.16, except the grouping of fossil fuel production, distribution and combustion for energy under 
‘Energy’ and the grouping of use of fossil fuel and biofuel use for cooking and heating under ‘Residential and Commercial’. Further details on data sources and processing are 

872
Chapter 6 Short-lived Climate Forcers6level of exposure to pollution and subsequent effects varies 
significantly from one region to another. Therefore, air ‑quality 
policies, regional climate impact concerns and development 
priorities, as well as the consequent level of mitigation of 
particular SLCF species, will differ regionally and source‑wise with 
respect to the emissions sources, influence of inter ‑continental 
transport of pollution, and spatial physical heterogeneities (Lund 
et al., 2014; AMAP , 2015a; Sand et al., 2016; Turnock et al., 2016; 
Sofiev et al., 2018; WMO, 2018) . This is also the case at a finer 
local and regional scale where priorities and scope for SLCF 
mitigation will differ (e.g., Amann et al., 2017; UNEP , 2019). 
• Projections exploring mitigation potential for a particular source 
or SLCF: These studies focus on the assessment of SLCF reduction 
potential that can be realised with either existing and proven

technologies or extend the scope to include transformational 
changes needed to achieve further reduction (e.g.,  UNEP and 
WMO, 2011; Stohl et al., 2015; Velders et al., 2015; Purohit and 
Höglund‑Isaksson, 2017; Gómez‑Sanabria et al., 2018; UNEP , 
2019; Höglund‑Isaksson et al., 2020; Purohit et al., 2020) – several 
of these studies are subsequently used for parametrization of the 
models used to develop emissions scenarios (e.g., IAM models 
used in the IPCC process). 
In the following subsections, we assess the SLCF mitigation and its 
effects as identified in regional and global studies evaluating past 
and current air quality and other SLCF regulations (Sections 6.6.3.1, 
6.6.3.2 and 6.6.3.3). Development policies, independent from the 
CMIP6 assessment framework, including peer ‑reviewed studies and 
initiatives like the United Nations Environment Programme (UNEP), 
analysing win‑win climate, air ‑quality and SDG‑motivated strategies

are discussed in Section 6.6.3.4. Note that sensitivity studies where 
impacts of complete removal of particular species are analysed 
(e.g., Samset et al., 2018) are used sparingly in this assessment. While 
such analysis can be useful for assessing the effect of a zero‑emissions 
commitment (Chapter 4.7.1.1), they do not correspond to a realistic 
SLCF‑mitigation strategy with plausible pace of implementation and 
removal of co‑emitted species (Shindell and Smith, 2019). Discussion 
of climate and air ‑quality implications of SLCF reductions in SSP 
scenarios is provided in Section 6.7. 
6.6.3.1 Climate Response to Past AQ Policies
Air‑quality policies emerged several decades ago focusing on 
emissions mitigation, first driven by local‑ then by regional‑scale 
air‑quality and ecosystem‑damage concerns, that is, health impacts, 
acidification and eutrophication. They have made it possible to reduce 
or limit pollution exposure in many megacities or highly populated

regions, for example, in Los Angeles, Mexico City and Houston in North 
America (Parrish et al., 2011), Santiago in Chile (Gallardo et al., 2018), 
São Paulo in Brazil (Andrade et al., 2017), Europe (Reis et al., 2012; 
Crippa et al., 2016; Serrano et al., 2019), and over Eastern Asia during 
the last decade (Silver et al., 2018; Zheng et al., 2018b). However, very 
few studies have quantified the impact of these policies on climate. 
The AR5 concluded that air ‑quality control will have consequences on 
climate including strong regional variability, however, no estimates 
of impacts of specific air ‑quality policy were available. Since AR5, 
few studies have provided estimates of climate‑relevant indicators affected by significant air pollutant burden changes due to air ‑quality 
policy in selected regions. Turnock et al. (2016) estimated that the 
strong decrease in NO x, SO 2 and PM 2.5 emissions in Europe, induced 
by air ‑quality policies resulting in implementation of abatement

measures since the 1970s, have caused a  surface warming of 
+0.45°C ± 0.11°C and increase of precipitation +13 ± 0.8 mm yr–1 
over Europe, compared to the scenario without such policies. While 
the temperature increase is likely overestimated since the impact of 
the increase in ammonium nitrate was not considered in this study, 
the simulated European all‑sky TOA radiative effect of the European 
air pollutant mitigation over the period 1970–2009 is 2.5 times the 
change in global mean CO 2 radiative forcing over the same period 
(Myhre et al., 2013). Other studies found that the recent measures to 
reduce pollution over China have induced a decrease of aerosols and 
increase of ozone over east China (K. Li et al., 2019, 2020), resulting 
in an overall warming effect mainly due to the dominant effect of 
sulphate reductions in the period 2012–2017 (Dang and Liao, 2019). 
6.6.3.2 Recently Decided SLCF‑relevant Global Legislation

International shipping emissions regulation: from January 2020, 
a  new global standard, proposed by the International Maritime 
Organisation, limits the sulphur content in marine fuels to 
0.5%  against the previous 3.5% (IMO, 2016). This legislation is 
considered in the SSP5 and SSP2‑4.5 and with a delay of few years 
in SSP3‑lowSLCF , SSP1‑1.9, and SSP1‑2.6, and in other SSP‑emissions 
scenarios achieved by the mid‑21st century. This global measure 
aims to reduce the formation of sulphate (and consequently PM 2.5) 
and largely reduce the health exposure to PM 2.5, especially over 
India, east China and coastal areas of Africa, and the Middle East 
(Sofiev  et al., 2018). Sofiev et al. (2018) used a  high spatial‑and‑
temporal resolution chemistry climate model and estimated a  net 
total ERF of +71 mW m–2 associated with this measure and due to 
lower direct aerosol cooling (+3.9 mW m–2) and lower cloud albedo 
(+67 mW m–2). This value, which correponds to an 80% decrease of

the cooling effect of shipping induced by about 8 Tg of SO 2 of avoided 
emissions, is consistent with older estimates which considered 
similar reduction of emitted sulphur. However, there is considerable 
uncertainty in the indirect forcing since small changes in aerosols, 
acting as CCNs in a clean environment, can have disproportionally 
large effects on the radiative balance. Since sulphate is by far the 
largest component of the radiative forcing (Fuglestvedt et al., 2008) 
and of surface temperature effect (Figure 6.16) due to ship emissions 
over a short time scale, limiting the co‑emitted SLCFs can not offset 
the warming by sulphur reductions. The reduction of sulphur emissions 
from shipping is assessed to lead to a slight warming mainly due to 
aerosol–cloud interactions ( medium evidence , medium agreement ).
The Kigali Amendment (UNEP , 2016): with the adoption of the Kigali 
Amendment to the Montreal Protocol (UN, 1989) in 2016, parties

agreed to the phase‑down of HFCs, substances that are not ozone 
depleting but are climate‑forcing agents (Papanastasiou et al., 
2018). Baseline scenarios, in the absence of controls or only pre‑
Kigali national legislation, projected increased use and emissions 
of HFCs. All recent baseline projections are significantly higher than 
those used in the Representative Concentration Pathways (RCP) 
scenarios (Figure  6.18; Meinshausen et al., 2011). There is low

873
Short-lived Climate Forcers  Chapter 66confidence  that the high baseline (assuming absence of controls, 
lack of technical progress and high growth) as developed by Velders 
et al. (2009), resulting in additional warming of about 0.5°C by 
2100 (Xu et al., 2013; WMO, 2018), is plausible. Evolution of HFC 
emissions along the baselines consistent with Velders et al. (2009) 
and Velders et al. (2015) would result in a global average warming, 
due to HFCs, relative to 2000, of about 0.1°C–0.12°C by 2050 and  
0.35°C–0.5°C and 0.28°C–0.44°C by 2100, respectively (Xu  et al.,  
2013). The  baseline implementation considered in SSP5‑8.5 
(Section 6.7.1.1) is comparable to the lower bound of projections by 
Velders et al. (2015; Figure 6.18) and several other studies (Gschrey 
et al., 2011; Purohit and Höglund‑Isaksson, 2017; EPA, 2019; Purohit 
et al., 2020) and result in additional warming of 0.15°C–0.3°C by 
2100 ( medium confidence ) (Figure 6.22).

Efficient implementation of the Kigali Amendment and national and 
regional regulations has been projected to reduce global average 
warming in 2050 by 0.05°C–0.07°C (Klimont et al., 2017b; WMO, 
2018) and by 0.2°C–0.4°C in 2100 compared with the baseline 
(see Figure  2.20 of WMO, 2018). Analysis of SSP scenarios based 
on an emulator (Section  6.7.3) shows a  comparable mitigation 
potential of about 0.02°C–0.07°C in 2050 and about 0.1°C–0.3°C 
in 2100 (Figure  6.22, SSP5‑8.5 versus SSP1‑2.6). Furthermore, the 
energy efficiency improvements of cooling equipment alongside the 
transition to low‑global‑warming potential alternative refrigerants 
for refrigeration and air ‑conditioning equipment could potentially 
increase the climate benefits from the HFC phasedown under the 
Kigali Amendment (Shah et al., 2015; Höglund‑Isaksson et al., 
2017; Purohit and Höglund‑Isaksson, 2017; WMO, 2018) . Purohit 
et al. (2020) estimated that depending on the expected rate of

technological development, improving the energy efficiency of 
stationary cooling technologies and compliance with the Kigali 
Amendment could bring future global electricity savings of more than 
20% of the world’s expected electricity consumption beyond 2050 
or cumulative reduction of about 75–275 Gt CO 2‑eq over the period 
2018–2100 ( medium confidence ). This could potentially double the 
climate benefits of the HFC phase‑down of the Kigali Amendment as 
well as result in small air ‑quality improvements due to reduced air 
pollutant emissions from the power sector (i.e., 8–16% reduction of 
PM 2.5, SO 2 and NO x; Purohit et al., 2020).
6.6.3.3 Assessment of SLCF Mitigation Strategies  
and Opportunities 
There is a consensus in the literature that mitigation of SLCF emissions 
plays a central role in simultaneous mitigation of climate change, air 
quality and other development goals including SDG targets (UNEP 
and WMO, 2011; Shindell et al., 2012, 2017b; Rogelj et al., 2014b,

2018b; AMAP , 2015a; Haines et al., 2017; Klimont et al., 2017b; 
McCollum et al., 2018; Rafaj et al., 2018; UNEP and CCAC, 2018; UNEP , 
2019) . There is less agreement in the literature with respect to the 
actual mitigation potential (or its potential rate of implementation), 
necessary policies to trigger successful implementation, and resulting 
climate impacts. Most studies agree that climate policies, especially 
those aiming to keep warming below 1.5°C or 2°C, trigger large SLCF 
mitigation co‑benefits, (e.g., Rogelj et al., 2014b, 2018b), however, 
discussion of practical implementation of respective policies and SDGs has only started (Haines et al., 2017). Note that mitigation 
scenarios outside of the SSP framework are assessed here while 
those within the SSPs are assessed in Section 6.7.3. 
Focusing on air quality, specifically addressing aerosols, by introducing 
the best available technology reducing PM 2.5, SO 2 and NO x in most

Asian countries within the 2030–2050 time frame (a strategy that 
has indeed shown reduction in PM 2.5 exposure in China) comes, 
in many regions, short of national regulatory PM 2.5 concentration 
standards (often set at 35 µg  m–3 for annual mean; UNEP , 2019). 
Similarly, global studies (Rafaj et al., 2018; Amann et al., 2020) show 
that strengthening current air ‑quality policies, that address primarily 
aerosols and their precursors, will not enable the achievement of 
WHO air quality guidelines (annual average concentration of PM 2.5 
below 10 µg m–3) in many regions. 
A multi‑model study (four ESMs and six CTMs) found a consistent 
response to the removal of SO 2 emissions that resulted in a global 
mean surface temperature increase of 0.69°C (0.4°C–0.84°C). 
However, results are mixed for a  global BC‑focused deep SLCF 
reduction without SO 2 and methane mitigation which remain as in the 
baseline (see ECLIPSE in Figure 6.18). BC contributed about –0.022°C

temperature reduction for the decade 2041–2050 based on the 
assumption that mitigation of the non‑methane species contributed 
only about 10% of the global temperature reduction for the strategy 
where methane mitigation was also included (–0.22°C  ±  0.07°C; 
Stohl et al., 2015). These results are consistent with studies analysing 
similar strategies using emulators (e.g.,  Smith and Mizrahi, 2013; 
Rogelj et al., 2014b). Stohl et al. (2015) also analysed the impact of 
BC‑focused mitigation on air quality, estimating large‑scale regional 
reduction in PM 2.5 mean concentration from about 2% in Europe to 
20% over India for the decade 2041–2050. 
Local response to global reduction can be higher than the global 
temperature response, particularly for regions subjected to rapid 
changes. Hence, mitigation of rapid warming in the Arctic has been 
subject to an increasing number of studies (Sand et al., 2013b, 2016; 
Jiao et al., 2014; AMAP , 2015a, b; Mahmood et al., 2016; Christensen

et al., 2019) . Considering maximum technically feasible reductions 
(MTFR) for methane globally and an idealized strategy reducing key 
global anthropogenic sources of BC (about 80% reduction by 2030 
and sustained thereafter) and precursors of ozone was estimated to 
jointly bring a reduction of Arctic warming, averaged over the 2041–
2050 period, between 0.2°C and 0.6°C (AMAP , 2015a; Sand et al., 
2016). Stohl et al. (2015) have estimated that a global SLCF mitigation 
strategy (excluding further reduction of SO 2) would lead to about 
twice as high a temperature reduction (–0.44 (–0.39 to –0.49) °C) in 
the Arctic than the global response to such mitigation. 
While there is robust evidence  that air ‑quality policies resulting 
in reductions of aerosols and ozone can be beneficial for human 
health but can lead to ‘disbenefits’ for near ‑term climate change, the 
existence of such trade‑offs in response to climate change mitigation

policies is less certain (Shindell and Smith, 2019). Recent studies 
show that very ambitious but plausible gradual phasing out of fossil 
fuels in 1.5°C‑compatible pathways with little or no overshoot, lead 
to a near ‑term future warming of less than 0.1°C, when considering

874
Chapter 6 Short-lived Climate Forcers6associated emissions reduction of both warming and cooling species. 
This suggests that there may not be a strong conflict, at least at the 
global scale, between climate and air ‑quality benefits in the case of 
a worldwide transition to clean energy (Shindell and Smith, 2019; Smith 
et al., 2019). However, at the regional scale, the changes in spatially 
variable emissions and abundance changes might result in different 
responses, including implications for precipitation and monsoons 
(Chapter 8), especially over Southern Asia (e.g., Wilcox et al., 2020).
Decarbonization of energy supply and end‑use sectors is among key 
pillars of any ambitious climate change mitigation strategy and it 
would result in improved air quality owing to associated reduction of 
co‑emitted SLCF emissions (e.g., McCollum et al., 2013; Rogelj et al., 
2014b; Braspenning Radu et al., 2016; Rao et al., 2016; Stechow

et al., 2016; Lelieveld et al., 2019; Shindell and Smith, 2019). Regional 
studies (Lee et al., 2016; Shindell et al., 2016; Chen et al., 2018; Li et al., 
2018), where significant CO 2 reductions were assumed for 2030 and 
2050, show consistently reduced of PM 2.5 and ozone concentrations 
resulting in important health benefits. However, these improvements 
are not sufficient to bring PM 2.5 levels in agreement with the WHO air ‑
quality guidelines in several regions. Amann et al. (2020) and UNEP 
(2019) highlight that only the combination of strong air ‑quality, 
development and climate policies, including societal transformations, 
could pave the way towards the achievement of such a  target at 
a regional and global level. 
At a  global level, Rao et al. (2016) showed that climate policies, 
compatible with Copenhagen pledges and a long‑term CO 2 target of 
450 ppm, result in important air ‑quality benefits, reducing the share

of the global population exposed to PM 2.5 levels above the WHO 
Tier 1 standard (35 µg m–3) in 2030 from 21% to 5%. The impacts 
are similar to a  strong air ‑quality policy but still leave large parts 
of population, especially in Asia and Africa, exposed to levels well 
above the WHO air quality guideline level of 10 µg m–3. The latter can 
be partly alleviated by combining such climate policy with strong air ‑
quality policy. Shindell et al. (2018) analysed more ambitious climate 
change mitigation scenarios than Rao et al. (2016) and highlighted 
the opportunities to improve air quality and avert societal effects 
associated with warmer climate by accelerated decarbonization 
strategies. Most climate change mitigation strategies compatible with 
limiting global warming to well below 2°C rely on future negative CO 2 
emissions postponing immediate reduction. Alternatively, a  faster

decarbonization could allow the achievement of a 2°C goal without negative CO 2 emissions and, with currently known and effectively 
applied emissions‑control technologies, this would also have 
immediate and significant air ‑quality benefits, reducing premature 
deaths worldwide (Shindell et al., 2018). For  a  2°C‑compatible 
pathway, Vandyck et al. (2018) estimated 5% and 15% reduction 
in premature mortality due to PM 2.5 in 2030 and 2050, respectively, 
compared to reference scenarios.
There is robust evidence  that reducing atmospheric methane will 
benefit climate and improve air quality through near ‑surface ozone 
reduction (Fiore et al., 2015; Shindell et al., 2017a) and wide agreement 
that strategies reducing methane offer larger (and less uncertain) 
climate benefits than policies addressing BC (e.g., Smith and Mizrahi, 
2013; Rogelj et al., 2014b , 2018b ; Stohl et al., 2015;  Christensen 
et al., 2019; Shindell and Smith, 2019). SR1.5 (Rogelj et al., 2018b)

highlighted the importance of methane mitigation in limiting warming 
to 1.5ºC in addition to net zero CO 2 emissions by 2050. Implementation 
of the identified maximum technically feasible reductions (MTFR) 
potential for methane globally, estimated at nearly 50% reduction  
(or 205 Tg CH 4 in 2050) of anthropogenic emissions from the baseline, 
would lead to a reduction in warming, calculated as the differences 
between the baseline and MTFR scenario, for the 2036–2050 period 
of about 0.20°C ± 0.02°C globally (AMAP , 2015b). Plausible levels of 
methane mitigation, achieved with proven technologies, can increase 
the feasibility of achieving the Paris Agreement goal through slightly 
slowing down the pace of CO 2 reductions (but not changing the final 
CO 2 reduction goal) while this benefit is enhanced by the indirect 
effects of methane mitigation on ozone levels (Collins et al., 2018). 
Adressing methane mitigation appears even more important in view of

recently observed growth in atmospheric concentrations that is linked 
to increasing anthropogenic emissions (Section 5.2.2). 
Neither ambitious climate change policy nor air ‑quality abatement 
policy can automatically yield co‑benefits without integrated policies 
aimed at co‑beneficial solutions (Zusman et al., 2013; Schmale et al., 
2014a; Melamed et al., 2016), particularly in the energy generation 
and transport sectors (Rao et al., 2013; Thompson et al., 2016; Shindell 
et al., 2018; Vandyck et al., 2018) . Integrated policies are necessary 
to yield multiple benefits of mitigating climate change, improving air 
quality, protecting human health and achieving several SDGs.
Box 6.2 | SLCF Mitigation and Sustainable Development Goals (SDG) Opportunities
Striving to achieve air ‑quality and climate targets will bring significant SLCF reductions. These reductions contribute first and foremost

to the attainment of SDGs targeting improved human health and sustainable cities (SDGs 3 and 11), specifically related to PM 
exposure (goals 3.9 and 11.6; Lelieveld, 2017; Amann et al., 2020), but also access to affordable and clean energy, responsible 
consumption and production, and climate, as well as reducing nutrient losses and consequently protecting biodiversity (SDG 7, 12, 
13, 14 and 15; UNEP , 2019; Amann et al., 2020). Furthermore, declining SLCF emissions will result in reduced crop losses (SDG 2; zero 
hunger) due to decrease of ozone exposure (Feng and Kobayashi, 2009; Ainsworth et al., 2012; Emberson et al., 2018). 
However, the design of suitable policies addressing these SDGs can be difficult because of the complexity of linking emissions to 
impacts on human health, ecosystems, equity, infrastructure and costs. Beyond the fact that several species are co‑emitted, interlinkage

875
Short-lived Climate Forcers  Chapter 66In summary, there is high confidence  that effective decarbonization 
strategies could lead to air ‑quality improvements but are not sufficient 
to achieve, in the near term, air ‑quality WHO guideline values set for 
fine particulate matter, especially in parts of Asia and in some highly 
polluted regions. Additional policies (e.g.,  access to clean energy, 
waste management) envisaged to attain SDGs bring complementary SLCF reduction ( high confidence) . Sustained methane mitigation, 
wherever it occurs,  stands out as an option that combines near ‑ and 
long‑term gains on surface temperature ( high confidence ) and leads 
to an air pollution benefit by reducing ozone levels globally ( high 
confidence ). 
Box 6.2 (continued)
between species, such as through atmospheric chemistry, can weaken the benefit of emissions reduction efforts. An illustration lies

in the recent (2013–2017) reduction of aerosols over China (Silver et al., 2018; Zheng et al., 2018b) resulting from the strategy to 
improve air quality (‘Clean Air Action’); this has successfully reduced the level of PM 2.5 but has led to a concurrent increase in surface 
ozone, partly due to declining heterogeneous interactions of ozone precursors with aerosols (K. Li et al., 2019; Yu et al., 2019). This side 
effect on ozone has been addressed since then by amending the legislation to target NMVOC sources, especially solvent use. Complex 
interactions between anthropogenic and biogenic volatile compounds are also at play and reduction of certain SLCFs could possibly 
promote new particle formation from organic vapours (e.g., Lehtipalo et al., 2018). Finally, a recent example of this complexity is the 
mixed effects on ozone pollution induced by NO x decrease during the COVID‑19 pandemic (Cross‑Chapter Box 6.1). Thus, the climate

and air pollution effects of policies depend strongly on the choice of regulated compounds and the degree of reduction. Such policies 
have to be informed by strong science support, including for example multi‑model analyses such as HTAP (UNECE, 2010) and AMAP 
(AMAP , 2015a, b), based on global and regional CCMs. This is essential to capture the complexity and inform the policy development 
process.
In addition, pursuing SDG objectives, apparently decoupled from air pollution, such as improved waste management, access to clean 
energy, or improved agricultural practices, would also stimulate and lead to mitigation of SLCFs. Amann et al. (2020)  show that a global 
strategy to achieve the WHO air quality guidelines, cannot only rely on air pollution control but also on a combination of SDG‑aligned 
policies. Such actions would include energy efficiency improvements, increased use of renewables, reduction of methane from waste

management and agriculture, and CO 2 and methane due to lower fossil fuel consumption, resulting in climate co‑benefits. Consideration 
of SDGs including local air ‑quality co‑benefits, creates an opportunity to support and gain acceptance for ambitious climate change 
mitigation (Jakob and Steckel, 2016; Stechow et al., 2016; Vandyck et al., 2018). Such near ‑term policies targeting SDGs and air quality 
would enable longer ‑term transformations necessary to achieve climate goals (Chapter 17, WGIII).
Cross-Chapter Box 6.1 | Implications of COVID-19 Restrictions for Emissions, Air Quality  
and Climate
Coordinators: Astrid Kiendler ‑Scharr (Germany/Austria), John C. Fyfe (Canada)
Contributors: Josep G. Canadell (Australia), Sergio Henrique Faria (Spain/Brazil), Piers Forster (UK), Sandro Fuzzi (Italy), Nathan P . Gillett 
(Canada), Christopher Jones (UK), Zbigniew Klimont (Austria/Poland), Svitlana Krakovska (Ukraine), Prabir Patra (Japan/India),

Joeri Rogelj (Austria/Belgium), Bjørn Samset (Norway), Sophie Szopa (France), Izuru Takayabu (Japan), Hua Zhang (China)
In response to the outbreak of COVID‑19 (officially the severe acute respiratory syndrome–coronavirus 2 or SARS‑CoV ‑2), which was 
declared a pandemic on March 11 2020 by the World Health Organization (WHO), regulations were imposed by many countries to 
contain the spread of COVID‑19. Restrictions were implemented on the movement of people, such as closing borders or requiring 
the majority of population to stay at home, for periods of several months. This Cross‑Chapter Box assesses the influence of the 
COVID‑19 containment on short‑lived climate forcers (SLCFs) and long‑lived greenhouse gases (LLGHGs), and related implications for 
the climate. Note that this assessment was developed late in the AR6 WGI process and is based on the available emerging literature.
Emissions

Global fossil CO 2 emissions are estimated to have declined by 7% ( medium confidence ) in 2020 compared to 2019 emissions, with 
estimates ranging from 5.8% to 13.0% based on various combinations of data on energy production and consumption, economic 
activity and proxy activity data for emissions and their drivers (Forster et al., 2020; Friedlingstein et al., 2020; Le Quéré et al., 2020; Liu 
et al., 2020) . However, the concentration of atmospheric CO 2 continued to grow in 2020 compared to previous years (Dlugokencky and 
Tans, 2021). Given the large natural interannual variability

876
Cross-Chapter Box 6.1 (continued)
of CO 2 (Section 5.2.1), and the small expected impact of emissions in the CO 2 growth rate, there were no observed changes in CO 2 
concentration that could be attributed to COVID‑19 containment ( medium confidence ) (Chevallier et al., 2020; Tohjima et al., 2020). 
Global daily CO 2 emissions from fossil fuel sources had a maximum decline of 17% in early April, compared with the mean 2019 
levels, and coinciding with the global peak pandemic lockdown (Le Quéré et al., 2020). The reductions in CO 2 emissions in 2020 were 
dominated by the drop in emissions from surface transport followed, in order of absolute emissions reductions, by industry, power and 
aviation (Le Quéré et al., 2020; Liu et al., 2020). Residential emissions showed little change (Liu et al., 2020) or rose slightly (Forster

et al., 2020; Le Quéré et al., 2020). Aviation had the biggest relative drop in activity. CO 2 emissions due to land use (based on early and 
uncertain evidence on deforestation and forest fires) were higher than average in 2020 (Amador ‑Jiménez et al., 2020). 
Using similar methodologies, Forster et al. (2020) assembled activity data and emissions estimates for other greenhouse gases and 
aerosols and their precursors. Anthropogenic NO x emissions, which are largely from the transport sector, are estimated to have 
decreased by a maximum of 35% in April ( medium confidence ). Species whose emissions are dominated by other sectors, such as 
methane and NH 3 from agriculture, saw smaller reductions. 
Abundances and air quality
Owing to the short atmospheric lifetimes of SLCFs relevant to air quality, changes in their concentrations were detected within 
a few days after lockdowns had been implemented (e.g., Bauwens et al., 2020; Venter et al., 2020; Gkatzelis et al., 2021; Shi et al.,

2021) . The COVID‑19‑driven economic slowdown has illustrated how complex the relationship is between emissions and air pollutant 
concentrations due to non‑linearity in the atmospheric chemistry leading to secondary compound formation (Section 6.1, Box 6.1; 
Kroll et al., 2020).
Several studies have examined the effect of COVID‑19 containment on air quality, showing that multi‑year datasets with proper 
statistical/modelling analysis are required to discriminate the effects of meteorology from that of emissions reduction (Dhaka et al., 
2020; L. Li et al., 2020; Wang et al., 2020; Zhao et al., 2020b) . Accounting for meteorological influences and with an increasing 
stringency index, the median observed change in NO 2 decreased from –13% to –48%, and in PM 2.5 decreased from –10% to –33%, 
whereas the median change in ozone increased from 0% to 4% (Gkatzelis et al., 2021). The latter can be explained by the decrease of

NO emissions that titrate ozone in specific highly polluted areas, leading to the observed increase in surface ozone concentration in 
cities (Le et al., 2020; Sicard et al., 2020; Huang et al., 2021).
The temporary decrease of PM 2.5 concentrations should be put in perspective of the sustained reduction (estimated at 30–70%), which 
could be achieved by implementing policies addressing air quality and climate change (Section 6.6.3). Such sustained reductions can 
lead to multiple benefits and simultaneously achieve several SDGs (Section 6.6.3). These policies would also result in reduction of 
ground‑level ozone by up to 20% (Section 6.7.1.3).
Except for ozone, temporary improvement of air quality during lockdown periods was observed in most  regions of the world ( high 
confidence ), resulting from a combination of interannual meteorological variability and the impact of COVID‑19 containment measures

(high confidence ). Estimated air pollution reductions associated with lockdown periods are lower than what can be expected from 
integrated mitigation policy leading to lasting reductions ( medium confidence ).
Radiative forcings
COVID‑19‑related emissions changes primarily exerted effective radiative forcing (ERF) through reduced emissions rates of CO 2 and 
methane, altered abundance of SLCFs, notably ozone, NO 2 and aerosols, and through other changes in anthropogenic activities, 
notably a reduction in the formation of aviation‑induced cirrus clouds. 
Forster et al. (2020) combined the FaIR emulator (Cross‑Chapter Box 7.1) with emissions changes for a range of species, relative to 
a continuation of Nationally Determined Contributions (Rogelj et al., 2017). They found a negative ERF from avoided CO 2 emissions that 
strengthens through 2020 to –0.01 W m–2. During the spring lockdown, they found a peak positive ERF of 0.1 W m–2 from loss of aerosol‑

induced cooling, and a peak negative ERF of –0.04 W m–2 from reductions in tropospheric ozone (from reduced photochemical production 
via NO x). Overall, they estimated a net ERF of +0.05 W m–2 for spring 2020, declining to +0.025 W m–2 by the end of the year.

877
Low model agreement (<80%)High model agreement (≥80%) Colour
Cross-Chapter Box 6.1, Figure 1 | Emissions reductions and their effect on aerosols and climate in response to COVID-19.  Estimated reductions in 
emissions of CO 2, SO 2 and NO x are shown in panel (a) based on reconstructions using activity data (updated from Forster et al., 2020). Eight Earth system models 
(ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until 
August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. 
Panel  (b) shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel (c) shows ESM-simulated GSAT anomalies during 2020; curves

denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) 
is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, 
where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. 
For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.
Gettelman et al. (2021) extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol‑induced ERF ranging 
from 0.12 to 0.3 W m–2, depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m–2 from loss of contrail 
warming. Overall, they report a peak ERF of 0.04 to 0.2 W m–2, and a subsequent decline to around half the peak value. Two independent

ESM studies Weber et al. (2020) and Yang et al. (2020) found consistent results in time evolution and component contributions but 
included fewer forcing components.
The available studies are in broad agreement on the sign and magnitude of contributions to ERF from COVID‑19‑related emissions 
changes during 2020. The range in peak global mean ERF in spring 2020 was [0.025 to 0.2] W m–2 (medium confidence ), composed 
of a  positive forcing from aerosol–climate interactions of [0.1 to 0.3]  W  m–2, and negative forcings from CO 2 (–0.01 W m–2), 
NO x (–0.04 W m–2) and contrail cirrus (–0.04 W m–2) (limited evidence , medium agreement ). By the end of 2020, the ERF was at half 
the peak value ( medium confidence ). 
Climate responses
Changes in atmospheric composition due to COVID‑19 emissions reductions are not thought to have caused a detectable change 
in global temperature or rainfall in 2020 ( high confidence ). A large ensemble of Earth system model (ESM) simulations show an

ensemble average reduction in Aerosol Optical Depth (AOD) in some regions, notably Eastern and Southern Asia (Fyfe et al., 2021). 
This result is supported by observational studies finding decreases in optical depth in 2020 (Gkatzelis et al., 2021; Ming et al., 2021; 
van Heerwaarden et al., 2021), which may have contributed to observed increases in solar irradiance (van Heerwaarden et al., 2021) 
or solar clear ‑sky reflection (Ming et al., 2021).

878
Chapter 6 Short-lived Climate Forcers66.7 Future Projections of Atmospheric 
Composition and Climate Response 
in SSP Scenarios
This section assesses the 21st‑century projections of SLCF emissions, 
abundances and responses in terms of climate and air quality 
following the SSPs (Chapter  1, Section  1.6.1.3 and Cross‑Chapter 
Box 1.5; Riahi et al., 2017; Gidden et al., 2019). The future evolution 
of atmospheric abundances and the resulting climate and AQ 
responses is driven mainly by anthropogenic emissions and by natural 
emissions modulated by chemical, physical and biological processes 
as discussed in Sections 6.2 and 6.3. Like the RCP scenarios used in 
AR5, the SSP emissions scenarios consider only direct anthropogenic 
(including biomass burning) emissions and do not project natural 
emissions changes due to climate or land‑use changes; ESMs 
intrinsically consider these biogeochemical feedbacks to varying 
degrees (Section  6.4.5). We rely on future projections based on

CMIP6 ESMs with comprehensive representation of chemistry, 
aerosol microphysics and biospheric processes that participated 
in the ScenarioMIP (O’Neill et al., 2016) and AerChemMIP (Collins 
et al., 2017). However, due to the high computational costs of 
running coupled ESMs, they cannot be used for quantifying the 
contributions from individual species, regions and sectors, and across 
the scenarios. Therefore, reduced complexity models (Box  1.3 and 
Cross‑Chapter Box 7.1), which represent chemistry and complex ESM 
interactions in parametrized forms updated since the AR5, are also 
applied here. 
6.7.1 Projections of Emissions  
and Atmospheric Abundances 
6.7.1.1 SLCF Emissions and atmospheric abundances
The trajectory of future SLCF emissions is driven by the evolution of 
socio‑economic drivers described in Section  1.6.1.1 but dedicated, 
SSP‑specific, air pollution policy storylines can change the regional 
and global trends (Rao et al., 2017). Additionally, assumptions

about urbanization (Jiang and O’Neill, 2017) will affect the spatial 
distribution of emissions and consequently air quality.Growing urbanization worldwide has strongly modified the spatial 
distribution and intensity of SLCF emissions. The effect and extent 
of urbanization on air pollution and other emissions species are 
captured within Integrated Assessment Models (IAMs) at varying 
levels of complexity. In most cases, models use a  combination of 
proxies and assumptions of end‑use efficiency and technological 
improvement assumptions to estimate emissions arising from rural‑
to‑urban migration and population growth within cities, utilizing 
quantifications of urbanization for the SSPs (Jiang and O’Neill, 2017). 
In addition, spatial patterns of future rural and urban population 
growth, migration, and decline have been quantified for the SSPs 
using a  gravity model (Jiang and O’Neill, 2017). However, linking 
these spatial patterns with IAM regional emissions pathways is still

an ongoing area of study and has not yet been represented in spatial 
emissions estimates provided by IAMs (Riahi et al., 2017; Gidden 
et al., 2019; Feng et al., 2020). As described in Feng et al. (2020), 
spatial emissions estimates derived for CMIP6 are largely a product of 
existing spatial patterns of population, but do not vary dynamically in 
future patterns. To the extent urbanization is accounted for in gridded 
emissions, IAM native region resolution (varying, for example, from 
11 world regions to more than 30, depending on the model) provides 
urbanization‑based dynamics. Despite the interest of studying the 
effect of well‑planned, densely populated urban centres, which 
can help to maximize the benefits of agglomeration, by providing 
proximity to infrastructure and services, the opportunity for energy 
saving, and providing a frame for air ‑quality control, IAM realizations 
of SSPs are not sufficient to assess this effect. The  opportunities

and risks associated with this rapid urbanization for SLCF emissions 
and air quality are analysed in the Chapter 6 of the WGII report and 
Chapter 8 of the WGIII report.
All the RCP trajectories started in 2005 and relied on the assumption 
that economic growth will bring rapid strengthening of air pollution 
legislation, effectively reducing emissions of non‑methane SLCFs 
(e.g., Chuwah et al., 2013). While in the long‑term such trends are 
expected if more ambitious air pollution control goes on par with 
economic growth. The near ‑term developments, however, might be 
much more diverse across regions and species, as has been observed 
in the last three decades (Amann et al., 2013; Rafaj et al., 2014; Rafaj 
and Amann, 2018; Ru et al., 2018), especially in several fast‑growing 
economies, leading to the difference between CMIP6 historical Cross-Chapter Box 6.1 (continued)

Model simulations of the response to COVID‑19 emissions reductions indicate a small warming of global surface air temperature 
(GSAT) due to a decrease in sulphate aerosols (Forster et al., 2020; Fyfe et al., 2021), balanced by cooling due to an ozone decrease 
(Forster et al., 2020; Weber et al., 2020), black carbon decrease (Weber et al., 2020) and CO 2 decrease. It is noted that observational 
studies report little SO 2 change, at least locally near the surface (Shi et al., 2021), and do not correlate with emissions inventory‑based 
changes (Gkatzelis et al., 2021). One study suggests a small net warming while another using idealized simulations suggests a small 
cooling (Weber et al., 2020). Simulated GSAT and rainfall changes are unlikely to be detectable in observations ( high confidence ) 
(Samset et al., 2020; Fyfe et al., 2021). Multi‑model ESM simulations based on a realistic COVID‑19 containment forcing scenario

(Forster et al., 2020) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross‑Chapter 
Box 6.1).

879
Short-lived Climate Forcers  Chapter 66estimates for the post‑2000 period (Hoesly et al., 2018)  and those 
used in RCPs (Figure 6.18). Since several SLCFs are also air pollutants, 
the narrow range of the RCP emissions trajectories in the future 
allowed for only limited analysis of near ‑future air quality (Amann 
et al., 2013; Chuwah et al., 2013; von Schneidemesser et al., 2015) . 
However, the range of storylines in the SSPs lead to a wider range 
of assumed pollution‑control policies in the SSPs (Rao et al., 2017; 
Riahi et al., 2017). In SSP1 and SSP5, strong air ‑quality policies are 
assumed to minimize the adverse effects of pollution on population 
and ecosystems. In SSP2, a medium pollution control, with lower than 
current policy targets, is considered. Only weak, regionally varied, air 
pollution policies are applied in the SSP3 and SSP4. Additional climate 
policies introduced to reach defined radiative forcing targets will also

affect SLCF emissions. The SSP SLCF‑emissions trajectories (Rao et al., 
2017; Gidden et al., 2019) assume a long‑term coupling of economic 
growth and specific emissions indicators, such as sectoral emission densities. The pace of change varies across regions and SSPs resulting 
in a  wider range of future air pollutants evolution (Figure  6.18), 
reflecting the differences in assumed levels of air pollution controls 
across regions (Figure  6.19). At the end of the century, the range 
across the SSPs is about four times that of RCPs for SO 2 and NO x, two 
to four times for BC and NMVOCs, and up to three times for CO and 
OC, while indicating a slightly smaller range than RCPs for methane 
(Figure  6.18). The originally developed SSP scenarios (Rao et al., 
2017) have been harmonized with the CMIP6 historical emissions 
(Hoesly et al., 2018) and include updated SO 2 emissions to account 
for the recent decline in China (Gidden et al., 2019).

All SSP scenarios (Figure 6.18), except SSP3‑7.0, project decline in 
global total emissions for all SLCFs by the end of the 21st century, 
except for ammonia and for HFCs (more on HFCs below). Similar to 
RCPs, ammonia emissions continue to increase in most SSPs, except Velders (2015) high
Velders (2015) lo w
GAINS CLE
GAINS KA
GAINS MTFR
Figure 6.18 | Global anthropogenic and biomass burning short-lived climate forcer (SLCF) and CO 2 emissions from 1850 to 2100 and HFC emissions from 
1990 to 2100.  Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850–2014 are based on Hoesly et al. (2018) and van Marle et al. 
(2017); emissions for CMIP5 for the period 1850–2005 are from Lamarque et al. (2010); CO 2 emissions are from EDGAR database (Crippa et al., 2020); methane (CH 4) and

HFCs are from (Crippa et al., 2019); and air pollutants are from EC-JRC / PBL (2020), Höglund-Isaksson (2012) and Klimont et al. (2017a) for ECLIPSE. Projections originate 
from the Shared Socio-Economic Pathway (SSP) database (Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019); Representative Concentration Pathway (RCP) database 
(van Vuuren et al., 2011); GAINS (CLE – current legislation baseline, KA – Kigali Amendment, MTFR – maximum technical mitigation potential) for HFCs (Purohit et al., 2020; 
Velders et al. 2015); and, ECLIPSE (Stohl et al., 2015). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

880
Chapter 6 Short-lived Climate Forcers6
Figure 6.19 | Regional anthropogenic and biomass burning short-lived climate forcer (SLCF) emissions from 1850 to 2100.

881
Short-lived Climate Forcers  Chapter 66SSP1 and SSP2, accounting for the expected growth in food demand 
and a general lack of effective policies targeting agricultural emissions. 
Additionally, mitigation potential for NH 3 is generally smaller than 
for other species owing to fugitive and widely distributed sources 
(Pinder et al., 2007; Klimont and Winiwarter, 2015; Mohankumar 
Sajeev et al., 2018; Sajeev et al., 2018). Most significant changes of 
SLCF emissions in the near and long‑term compared to the present 
day are expected for SO 2. This is due to ever more stringent (and 
enforced) legislation in China’s power sector, extended recently to 
industrial sources (Zheng et al., 2018b; Tong et al., 2020), declining 
coal use in most SSPs, recently announced stricter emissions limits 
for the power sector in India, and reduction of the sulphur content of 
oil fuel used in international shipping from 2020 (IMO, 2016). For the

lower forcing targets (e.g., SSP1‑2.6), the SO 2 trajectories are similar 
to the RCPs resulting in over 50–90% decline by 2050 and 2100, 
respectively, while for the scenarios with no climate policies, the SSPs 
show a large spread even at the end of the century.
Until the mid‑21st century, SSP3‑7.0 and SSP5‑8.5 scenarios project 
no reduction in NO x emissions at the global level with decline in 
most OECD countries and Eastern Asia, driven by existing legislation 
in power, industry and transportation (e.g.,  Tong et al., 2020), 
and continued increase in the rest of the world (Figures  6.18 and 
6.19). Towards the end of the century, similar trends continue 
in SSP3‑7.0 while emissions in SSP5 decline strongly owing to 
faster technological progress and stronger air ‑quality action (Rao 
et al., 2017; Riahi et al., 2017). By 2100, the ‘Regional Rivalry’ (SSP3) 
scenario emissions of NO x (and most other SLCFs, except ammonia)

are typically twice as high as the next highest SSP projection, both at 
the global (Figure 6.18) and regional levels (Figure 6.19). In emissions 
pathways consistent with Paris Agreement goals (SSP1‑1.9 or  SSP1‑
2.6; Section 1.6.1), NO x drops, compared to 2015, by 50% in SSP1‑2.6 
and by 65% in SSP1‑1.9 by 2050, is reduced by about 70% by 2100, 
resulting in global emissions levels comparable to the 1950s and 
below the RCP range. In these pathways considering strong climate 
change mitigation, similar reductions are projected at the regional 
level, except in Africa (less than 50% decline) due to its high share of 
biomass emissions as well as strong growth in population and fossil 
fuel use. The trends in anthropogenic and biomass‑burning emissions 
for other ozone precursors (NMVOC, CO) are similar to that of NO x. 
An additional scenario, based on the SSP3‑7.0, has been designed 
specifically to assess the effect of a strong SLCF emissions abatement

and is called SSP3‑7.0‑lowNTCF in the literature (Collins et al., 2017; 
Gidden et al., 2019). It has been applied in the modelling studies 
(e.g.,  AerChemMIP) with or without consideration of additional 
methane reduction and we refer here to these scenarios, respectively, 
as SSP3‑7.0‑lowSLCF‑lowCH 4 or SSP3‑7.0‑lowSLCF‑highCH 4. In these 
scenarios, aerosols, their precursors, and non‑methane tropospheric 
ozone precursors are mitigated by applying the same emissions 
factors as in SSP1‑1.9. For global methane emissions, the range is similar for SSPs and RCPs 
over the entire century (Figure  6.18), with highest projections in 
SSP3‑7.0 (slightly below RCP8.5) estimating doubling of the current 
emissions and a reduction of about 75% by 2100 in scenarios consistent 
with 1.5°C to 2°C targets; similar to RCP2.6. At the regional level, the 
evolution of methane emissions in climate change mitigation scenarios 
is comparable to RCPs but there are significant differences for some

regions with respect to high CO 2 emissions scenarios. In  particular, 
projections for Eastern Asia differ significantly, the highest SSP3‑7.0 
is about half of the highest RCP by 2100 (Figure 6.19), which is due to 
much lower projections of coal use in China driven largely by efforts 
during the last decade to combat poor air quality. At the same time, 
the SSP scenarios without climate change mitigation project faster 
growth in methane emissions in Africa, the Middle East and Latin 
America (Figure  6.19) driven by developments in agriculture, the oil 
and gas sectors, and, especially in Africa, waste management. There 
are significant differences in the assessment and feasibility of rapid 
methane mitigation. Höglund‑Isaksson et al. (2020) review most 
recent studies and assess feasibility of rapid widespread mitigation, 
concluding that significant (over 50%) reductions are attainable but 
the feasibility of such reductions could be constrained in the short

term due to locked capital. This might have implications for near ‑term 
evolution assumed in, for example, SSP1‑1.9 or SSP3‑lowSLCF‑lowCH 4, 
where emissions drop very quickly due to fast decarbonization and 
reductions in agriculture. Such high reduction potential in agriculture 
has been also assumed in other studies (Lucas et al., 2007; Harmsen 
et al., 2020a) but is questioned by Höglund‑Isaksson et al. (2020) 
who indicate that widespread implementation (within decades) of 
policies bringing about institutional and behavioural changes would 
be important for transition towards very low methane emissions from 
livestock production.
Global emissions of carbonaceous aerosols are projected to decline 
in all SSP scenarios (Figure 6.18) except SSP3‑7.0. In that scenario, 
which also has much higher emissions than any of the RCPs, about 
half of the anthropogenic BC originates from cooking and heating on 
solid fuels, mostly in Asia and Africa (Figure 6.19), where only limited

progress in access to clean energy is achieved. Slow progress in 
improving waste management, high coal use in energy and industry, 
and no further progress in controlling diesel engines in Asia, Africa 
and Latin America contributes most of the remaining emissions, 
resulting in about 90% of anthropogenic BC emitted in the non‑
OECD world by 2100 in SSP3‑7.0. A similar picture emerges for OC 
but with greater contribution of the waste management sector and 
biomass burning, and lower impact of transportation and industry 
developments. Since scenarios compliant with Paris Agreement goals 
(SSP1‑1.9 or SSP1‑2.6; Section 1.6.1) include widespread access to 
clean energy already by 2050, the global and regional emissions of 
BC decline by 70–75% by 2050 and 80% by 2100 relayive to 2015. 
The decline in the residential sector (about 90% by 2050 and over 
95% by 2100) is accompanied by a  strong reduction in transport

(over 98%) and the decarbonization of the industry and energy sector. Figure 6.19 (continued): Emissions for the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 1850–2014 are based on Hoesly et al. (2018) and van Marle 
et al. (2017) and emissions for CMIP5 for the period 1850–2005 are from Lamarque et al. (2010). Projections originate from the Shared Socio- economic Pathway (SSP) database 
(Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019) and Representative Concentration Pathway (RCP) database (van Vuuren et al., 2011). Further details on data sources 

882
Chapter 6 Short-lived Climate Forcers6About 50% of remaining BC emissions in SSP1‑1.9 or SSP1‑2.6 are 
projected to originate from waste and open biomass burning of 
which open burning of waste represent a  significant part. Some 
studies suggest this might be pessimistic as, for example, efficient 
waste management (consistent with SDG goals) could potentially 
eliminate the open burning of solid residues (Gómez‑Sanabria et al., 
2018), which accounts for over 30% of BC emissions in SSP1‑1.9 
in 2050 or 2100.
The SSP scenarios draw on the HFC projections developed by Velders 
et al. (2015) considering, in climate change mitigation scenarios, the 
provisions of the Kigali Amendment (2016) to the Montreal Protocol 
leading to phase‑down of HFCs (Section  6.6.3.2; Papanastasiou 
et al., 2018). The SSP scenarios without climate change mitigation 
(e.g, SSP3‑7.0, SSP5‑8.5) show a range in HFC emissions of 3.2–5.3 
GtCO 2‑eq yr–1 in 2050 and about 4–7.2 GtCO 2‑eq yr–1 by 2100 while in

deep climate change mitigation scenarios (SSP1‑1.9 and SSP1‑2.6), 
consistent with the 1.5–2°C targets, they are expected to drop to 
0.1–0.35 GtCO 2‑eq yr–1 (Figure  6.18). In SSP1‑1.9, the extent of 
reduction and its pace is more ambitious than current estimates of 
the effect of the fully implemented and enforced Kigali Amendment 
(Figure 6.18; Höglund‑Isaksson et al., 2017; Purohit and Höglund‑
Isaksson, 2017). The best representation of the HFC emissions 
trajectories in the SSP framework compliant with the Kigali 
Amendment is the SSP1‑2.6 and the baseline (including only pre‑
Kigali national legislation; Section  6.6.3) is best represented by 
SSP5‑8.5 (Figure 6.18). However, since HFC emissions in SSPs were 
developed shortly after the Kigali Amendment had been agreed, 
none of these projections represents accurately the HFC emissions 
trajectory corresponding to the phase‑out emissions levels agreed 
to in the Kigali Amendment (Meinshausen et al., 2020), leading to

medium confidence  in the assessment of the benefits of the Kigali 
Amendment when using SSP projections for HFCs. 
The SSP SLCF trajectories reflect the effect of recent legislation and 
the assumed evolution thereof in the longer term, however, they do 
not necessarily reflect the full mitigation potential for several SLCFs, 
within particular SSPs (Figure 6.18), that could be achieved with air ‑
quality‑ or SDG‑targeted policies (Amann et al., 2013; Rogelj et al., 
2014a; Haines et al., 2017; Klimont et al., 2017b; Rafaj and Amann, 
2018; Shindell et al., 2018; Tong et al., 2020) . Such policies could 
bring more rapid mitigation of SLCFs, independent of the climate 
strategy (Section 6.6.3).
The projections of future SLCF abundances typically follow their 
emissions trajectories except for SLCFs that are formed from 
precursor reactions (e.g.,  tropospheric ozone) or are influenced by 
biogeochemical feedbacks (Sections 6.2.2 and 6.4.5). According to

multi‑model CMIP6 simulations, total column ozone (reflecting mostly 
stratospheric ozone) is projected to return to 1960s values by the 
middle of the 21st century under the SSP2‑4.5, SSP3‑7.0, SSP4‑3.4, 
SSP4‑6.0 and SSP5‑8.5 scenarios (Keeble et al., 2021). ESMs project 
increasing tropospheric ozone burden over the 2015–2100 period 
for the SSP3‑7.0 scenario (Figure 6.4; Griffiths et al., 2021), there is, 
however, a large spread in the magnitude of this increase reflecting 
structural uncertainties associated with the model representation of 
processes that influence tropospheric ozone. Sources of uncertainties in SLCF‑abundance projections include scenario uncertainties, or 
parametric and structural uncertainties in the model representation 
of the processes affecting simulated abundances with implications 
for radiative forcing and air quality. The evolution of methane 
abundances in SSP scenarios, for example, is derived from integrated

assessment models (IAMs) which do not include the effects from 
biogeochemical feedbacks (e.g., climate‑driven changes in wetland 
emissions; Meinshausen et al., 2020) introducing uncertainty. 
In summary, in SSPs, in addition to the socio‑economic development 
and climate change mitigation policies shaping the GHG emissions 
trajectories, the SLCF emissions trajectories are also steered by 
varying levels of air pollution control originating from SSP narratives 
and independent from climate change mitigation. Consequently, 
SSPs span a wider range of SLCF emissions than considered in the 
RCPs, better covering the diversity of future options in air pollution 
management and SLCF‑induced climate effects ( high confidence ). 
In addition to SSP‑driven emissions, the future evolution of SLCFs 
abundance is also sensitive to chemical and biogeochemical 
feedbacks involving SLCFs, particularly natural emissions, whose 
magnitude and sign are poorly constrained.

6.7.1.2 Future Evolution of Surface Ozone  
and PM Concentrations 
The projection of air ‑quality relevant abundances (surface ozone and 
PM 2.5) under the SSP scenarios are assessed here. Future changes in 
global and regional annual mean surface ozone and PM 2.5 driven by 
the evolution of emissions as well as by climate change have been 
quantified by CMIP6 models analysed in AerChemMIP (Allen et al., 
2020, 2021; Turnock et al., 2020). 
Surface ozone increases continuously until 2050 across most regions 
in SSP3‑7.0 and SSP5‑8.5, (Turnock et al., 2020), particularly over 
Eastern Asia, Southern Asia, the Middle East, Africa, South East Asia 
and Developing Pacific, where this increase can reach and even exceed 
5 ppb for annual mean averaged over land areas (Figure 6.20). After 
2050, surface ozone concentrations decrease in SSP5‑8.5, reaching 
levels below their 2005–2014 mean levels in most regions, but level 
off or continue to increase under SSP3‑7.0. The increase in surface

ozone in the SSP5‑8.5 scenario occurs despite an emissions decrease 
of several ozone precursors because the methane emissions increase 
until about 2080 in the absence of climate change mitigation. Ozone 
decreases over all regions in response to strong emissions mitigation 
in SSP1‑1.9 and SSP1‑2.6 (Turnock et al., 2020), with decreases of 
5  to 10 ppb as soon as 2030 in North America, Europe, Eurasia, 
Eastern Asia, the Middle East and Southern Asia in their annual 
means over land areas. In most regions surface ozone is reduced 
slightly or remains near present day values in the middle of the road 
scenario, SSP2‑4.5. In 2100, the largest differences in surface ozone 
changes across the scenarios occur for the Middle East, Southern 
Asia and Eastern Asia with  differences ranging up to 40 ppb between 
SSP3‑7.0 and SSP1‑1.9 at the end of the century. In each scenario, 
despite discrepancies in the magnitude of changes, especially over

North America, Europe, Eurasia, Eastern Asia and Southern Asia, the 
models are in high agreement  regarding the signs of the changes and 
are thus assessed as of high confidence .

883
Short-lived Climate Forcers  Chapter 66The strong abatement of ozone precursor emissions (except those of 
methane; SSP3‑7.0‑lowSLCF‑highCH 4) lead to a  decrease of  global 
average surface ozone by 15% (6 ppb) between 2015 and 2055 (Allen 
et al., 2020), and ozone decreases in all regions except Southern Asia. 
However, this decrease is twice as large when methane emissions are 
abated simultaneously (SSP3‑7.0‑lowSLCF‑lowCH 4), underlying the 
importance of methane emissions reduction as an important lever to 
reduce ozone pollution ( high confidence ) (Section 6.6.4). 
A decrease in surface PM 2.5 concentrations is estimated for SSP1‑1.9, 
SSP1‑2.6 and SSP2‑4.5 (Turnock et al., 2020) (Figure 6.21). A decrease 
in PM 2.5, is also projected in SSP5‑8.5, which does not consider any 
climate change mitigation but has a  strong air pollution control. 
The decrease is largest in the regions with the highest 2005–2014 
mean concentrations (the Middle East, Southern Asia and Eastern

Asia). Under the SSP3‑7.0 scenario, PM 2.5 is predicted to increase or 
remain at near present‑day values across Asia; regions where present‑
day concentrations are currently the highest. There is large model spread over regions with large natural aerosol sources, for example, 
in North Africa, where dust sources are important. The mitigation of 
non‑methane SLCFs in the SSP3‑7.0‑lowSLCF‑highCH 4 scenario is 
predicted to reduce PM 2.5 by 25% (in 2055, relative to the SSP3‑7.0 
scenario) over global land surface areas (Allen et al., 2020).
The magnitude of the annual mean change in surface ozone and PM 2.5 
for all the SSPs (accounting for both emissions and climate change) is 
greater than that expected from climate change in isolation (Turnock 
et al., 2020). The uncertainty in the projections comes from how 
natural emissions will respond to climate change. However, multiple 
lines of evidence (along with Sections 6.2.2, 6.5, and 6.7.1) provide

high confidence  (compared to medium  in AR5) that changes in 
emissions, and in particular in human‑induced emissions, will drive 
future air pollution levels rather than physical climate change. 
In summary, future air pollution levels are strongly driven by precursor 
emissions trajectories in the SSPs with substantial reductions in global North America Europe Eastern Asia Eurasia
Africa Southern A sia Middle East
Global Asia-P aci/f_ic DevelopedSouth-East A sia
and Developing P aci/f_ic Change in Sur face Ozone
36.7 +/- 7.1 38.9 +/- 7.8 33.7 +/- 7.4 46.0 +/- 7.3
27.3 +/- 5.9 38.1 +/- 6.3 50.1 +/- 5.6 54.1 +/- 7.4Latin America and Carribean
31.2 +/- 4.6 33.4 +/- 5.9 27.4 +/- 5.7
2020 20402060 2080 21002020 2040 2060 2080 2100 2020 2040 2060 2080 21002020 2040 2060 20802100
ssp126
ssp245
ssp370
ssp585ssp370-lowSL CF-highCH4
ssp370-lowSL CF-lowCH420
15
20
15
0
-5
-10
-15
-20(ppb )20
15
20
15
0
-5
-10
-15
-20(ppb )
20
15
20
15
-5
-10
-15
-20
(ppb )20
15
20
15
-5
-10
-15
-20
(ppb )
00

20
15
20
15
-5
-10
-15
-20
(ppb )
0
Figure 6.20 | Projected changes in regional annual mean surface ozone (O 3; ppb) from 2015 to 2100 in different shared socio-economic pathways (SSPs) . 
Each panel represents values averaged over the corresponding land area (except for ‘Global’) shown on the map in Figure 6.7. Solid coloured lines and shading indicate the 
multi-model mean and ±1 standard deviation across the available CMIP6 models (Turnock et al. 2020; Allen et al. 2021) for each scenario. Changes are relative to annual mean 
values calculated over the period 2005–2014 from the historical experiment as indicated in the top left of each regional panel along with ±1 standard deviation. For each model 
all available ensemble members are averaged before being used to calculate the multi-model mean. Ozone changes are also displayed in the Interactive Atlas. Further details 
on data sources and processing are available in the chapter data table (Table 6.SM.3).

884
Chapter 6 Short-lived Climate Forcers6surface ozone and PM (when air pollution and climate change are 
both strongly mitigated, e.g., SSP1‑2.6) to no improvement and even 
degradation ( high confidence ) (when no climate change mitigation 
and only weak air pollution control are considered, SSP3‑7.0). In 
the latter case, PM levels are estimated to increase until 2050 over 
large parts of Asia and surface ozone pollution worsens over all 
continental areas throughout the whole century ( high confidence ). 
In scenarios without climate change mitigation but with strong air 
pollution control (SSP5‑8.5), high methane levels hamper the decline 
in global surface ozone in the near term and only PM levels decrease 
(high confidence ).6.7.2 Evolution of Future Climate in Response  
to Changes in SLCF Emissions 
6.7.2.1 Effects of changes in SLCFs on ERF  
and Climate Response 
This section assesses how the different spatial and temporal evolution

of SLCF emissions in the SSPs affects the future global and regional 
ERFs, and GSAT and precipitation responses. In CMIP6, only a very 
limited set of simulations (all based on the SSP3‑7.0 scenario) have 
been carried out with coupled ESMs to specifically address the future 
role of SLCFs (Sections 4.3 and 4.4; Collins et al., 2017). Note that 
the ScenarioMIP simulations (Section 4.3) include the SLCF emissions 
(as shown in Figures 6.18 and 6.19), however, they cannot be used 
to quantify the effect of individual forcers. Coupled ESMs can in 
principle be used for this through a series of sensitivity simulations 
(e.g., Allen et al., 2020, 2021), but the amount of computer time 
Figure  6.21 | Future changes in regional five-year mean surface PM 2.5 from 2015 to 2100 in different shared socio-economic pathways (SSPs).

PM 2.5 stands for micrograms per cubic meter of aerosols with diameter less than 2.5 μm and is calculated by summing up individual aerosol mass components from each 
model as: black carbon + particulate organic matter + sulphate + 0.25 × sea salt + 0.1 × dust. Since not all CMIP6 models reported nitrate aerosol, it is not included here. 
See Figure 6.20 for further details. PM 2.5 changes are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data 

885
Short-lived Climate Forcers Chapter 66required has made this approach prohibitive across the full SSP range. 
Therefore, to quantify the contribution from emissions of individual 
forcers spanning the range of the SSP scenarios to GSAT response, 
the analysis is mainly based on estimates using a two‑layer emulator 
configuration derived from the medians of MAGICC7 and FaIRv1.6.2 
(Section 1.5.3.4, Cross‑Chapter Box 7.1 and Supplementary Material 
7.SM5.2). The contribution from SLCFs to changes in GSAT have been 
calculated based on the global mean ERF for the various components 
as assessed in Section  7.3.5, using the two‑layer emulator for the 
climate response. 
The projections of GSAT for a broad group of forcing agents (aerosols, 
methane, tropospheric ozone and HFCs with lifetimes lower than 
50 years) for the SSP scenarios show how much of the future 
warming or cooling (relative to 2019) can be attributed to the SLCFs

(Figure 6.22). Note that during the first two decades, some of these 
changes in GSAT are due to emissions before 2019, in particular 
for the longer ‑lived SLCFs such as methane and HFCs (Figure 6.15). 
The scenarios SSP3‑7.0‑lowSLCF‑highCH 4 and SSP3‑7.0‑lowSLCF‑
lowCH 4 are special cases of the SSP3‑7.0 scenario with strong, but 
realistic, reductions in non‑methane SLCFs and all SLCFs, respectively 
(Gidden et al., 2019).
As discussed in Sections 6.2, 6.3 and 6.4, there are uncertainties 
relating emissions of SLCFs to changes in abundance (Box 6.2) and 
further to ERF , in particular for aerosols and tropospheric ozone. 
Furthermore, there are uncertainties related to climate sensitivity, 
that is, the relation between ERF and change in GSAT. Uncertainties 
in the ERF are assessed in Chapter 7 and calibrated impulse response 
function also includes the assessed range (Box 7.1). There are also 
uncertainties related to the radiative efficacies of the different SLCFs

and time scales for the response, in particular for regional emissions 
(Schwarber et al., 2019; Yang et al., 2019b) that cannot be accounted 
for with the simple models used here. 
Historical emissions have been updated until 2019 (see 
Supplementary Material 7.SM.1.3.1) and used for ERF for calculating 
GSAT in Figure 6.22. The year 2019 has been chosen as the base year 
to be consistent with the attributed temperature changes since 1750 
(Figure 7.8). The warming attributed to SLCFs (methane, ozone and 
aerosols) over the last decade (Figure 7.8) constitutes about 30% of 
the peak SLCF‑driven warming in the most stringent scenarios (SSP1), 
in good agreement with Shindell and Smith (2019), and supported by 
the recent observed decline in AOD (Section 2.2.6). 
From 2019 and until about 2040, SLCFs and HFCs will contribute 
to increase GSAT in the WGI core set of SSP scenarios, with a  very 
likely  range of 0.04°C–0.41°C relative to 2019. The warming is most

pronounced in the strong mitigation scenarios (i.e., SSP1‑1.9 and 
SSP1‑2.6) due to rapid cuts in aerosols. In scenario SSP3‑7.0, there is 
no reduction of aerosols until mid‑century and it is the increases in 
methane and ozone that give a net warming in 2040. The warming 
is similar in magnitude to that in the SSP1‑scenarios, in which the 
reduction in aerosols is the main driver. Contributions to warming 
from methane, ozone, aerosols and HFCs make SSP5‑8.5 the scenario 
with the highest warming in 2040 and throughout the century. After about 2040, it is likely  that, across the scenarios, the net effect 
of the removal of aerosols is a further increase in GSAT. However, 
their contribution to the rate of change decreases towards the end 
of the century (from up to 0.2°C per decade before 2040 to about 
0.03°C per decade after 2040). After 2040, the changes in methane, 
HFCs and tropospheric ozone become equally important as the

changes in the aerosols for the GSAT trends. In the low‑emissions 
scenarios (SSP1‑1.9 and SSP1‑2.6), the contribution to warming from 
the SLCFs peaks around 2040 with a  very likely  range of 0.04°C to 
0.34°C. After the peak, the reduced warming from reductions in 
methane and ozone dominates, giving a best total estimate warming 
induced by SLCF and HFC changes of 0.12°C and 0.14°C respectively, 
in 2100, with a  very likely  range of –0.07°C to +0.45°C (Figure 6.22). 
However, in the longer term towards the end of the century there 
are very significant differences between the scenarios. In SSP3‑7.0 
while for SSP5‑8.5 there is a more rapid early warming. In SSP3‑7.0, 
the limited reductions in aerosols, but a steady increase in methane, 
HFCs and ozone lead to a nearly linear contribution to GSAT reaching 
a best estimate of 0.5°C in 2100. Contributions from methane and

ozone decrease towards 2100 in SSP5‑8.5, however the warming 
from HFCs still increase and the SSP5‑8.5 has the largest SLCF and 
HFC warming in 2100 with a best estimate of 0.6°C. In the SSP2‑4.5 
scenario, a reduction in aerosols contributes to about 0.3°C warming 
in 2100, while contributions from ozone and methane in this scenario 
are small.
The simplified approach used to estimate the contributions to GSAT in 
Figure 6.22 has been supplemented with ESM simulations driven by 
the two versions of the SSP3‑7.0‑lowSLCF scenario (Section 6.7.1.1). 
Results from five CMIP6 ESMs with fully interactive atmospheric 
chemistry and aerosols for the high‑methane scenario show (Allen 
et al., 2020, 2021) that reductions in emissions of air pollutants 
would lead to an additional increase in GSAT by 2055 relative to 2015 
compared to the standard SSP3‑7.0 scenario, with a best estimate 
of 0.23°C ± 0.05°C, and a corresponding increase in global mean

precipitation of 1.3 ± 0.17% (note that uncertainties from the work 
of Allen et al. here and elsewhere are reported as twice standard 
deviation). Including methane mitigation (SSP3‑7.0‑lowSLCF‑lowCH 4) 
would lead to a small increase in global precipitation (0.7 ± 0.1%) 
by mid‑century despite a decrease in GSAT (Section 6.7.3), which is 
related to the higher sensitivity of precipitation to sulphate aerosols 
than greenhouse gases (Section 8.2.1; Allen et al., 2021). 
Regionally inhomogeneous ERFs can lead to regionally dependent 
responses (Section  6.4.3). Mitigation of non‑methane SLCFs over 
the period 2015–2055 (SSP3‑7.0‑lowSLCF‑highCH 4 versus SSP3‑7.0) 
will lead to positive ERF over land regions (Allen et al., 2020) . There 
are large regional differences in the ERF from no significant trend 
over northern Africa to about 0.5 W m–2 decade–1 for Southern Asia. 
The differences are mainly driven by differences in the reductions

of sulphate aerosols. There is no strong correspondence between 
regional warming and the ERF trends. As expected, the sensitivity 
(temperature change per unit ERF) increases towards higher latitudes 
due to climate feedbacks and teleconnections. Regionally, the 
warming rates are higher over continental regions, with the highest 
increase in temperatures for Central and northern Asia and the Arctic

886
Figure 6.22 | Time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature 
(GSAT) across the WGI core set of Shared Socio-Economic Pathways (SSPs) . Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with 
lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. The GSAT changes are based on the assessed historic and future evolution of effective 
radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C 
for a doubling of atmospheric CO 2 (feedback parameter of –1.31 W m–2 °C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties

(5–95% ranges) for the GSAT change between 2019 and 2100. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

887
Short-lived Climate Forcers  Chapter 66in 2055 relative to 2015. The models agree on an increasing global 
mean trend in precipitation due to SLCFs, however precipitation 
trends over land are more uncertain (Allen et al., 2020), in agreement 
with the relationship between aerosol and precipitation trends 
assessed in Chapter 8.
ESM estimates of future concentrations of various SLCFs vary 
considerably even when using the same future emissions scenarios, 
which is related to sources of model structural uncertainty in 
the several physical, chemical and natural emissions model 
parametrizations. The general uncertainties in understanding and 
representing chemical and physical processes governing the life 
cycle of SLCFs (Box  6.1) necessarily also applies to simulations of 
future concentrations and ERF . In addition, how the models are able 
to simulate climate changes (i.e., circulation and precipitation) that 
affect the dispersion and removal of SLCFs constitute a  structural

uncertainty in the models. Also SLCF‑related climate feedbacks 
(e.g., NO x from lightning or BVOCs from vegetation; Section 6.4.5) 
add to the uncertainty. In the near term (2035–2040), it is unlikely  that differences in the 
socio‑economic developments and emissions controls induced 
by policies (as embedded in the SSPs) can lead to a  discernible 
difference in the net effect of changes of SLCFs on GSAT. This is 
because the inter ‑model spread in the estimated net effect of SLCFs 
on GSAT is as large as the difference between the scenarios due to 
the compensating effects of change in emissions leading to cooling 
and warming. However, in the longer term, there is high confidence  
that the net warming induced by changes in SLCFs will be lower in 
the scenario considering strong climate change mitigation (SSP1‑1.9 
and SSP1‑2.6 that include reductions in methane emissions) than in 
the high CO 2 emissions scenarios (SSP3‑7.0 and SSP5‑8.5).

6.7.2.2 Effect of Regional Emissions of SLCFs on GSAT
For SLCFs with lifetimes shorter than typical mixing times in the 
atmosphere (days to weeks), the effects on secondary forcing agents 
(e.g., tropospheric ozone, sulphate and nitrate aerosols) depend on 
where and when the emissions occur due to non‑linear chemical and 
Contribution from changes in anthropogenic regional emissions of SLCFs to GSAT changes
−0.10 −0.05 0.00 0.05 SSP1−2.6∆GSAT(2040−2020)
North America∆GSAT(2040−2020)
Europe∆GSAT(2040−2020)
Southern Asia∆GSAT(2040−2020)
Eastern Asia∆GSAT(2040−2020)
Developing Paciﬁc∆GSAT(2040−2020)
Asia−Paciﬁc Developed∆GSAT(2040−2020)
Africa∆GSAT(2040−2020)
Middle East∆GSAT(2040−2020)
Caribbean∆GSAT(2040−2020)
Eurasia
−0.10 −0.05 0.00 0.05 SSP2−4.5
∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC) ∆GSAT (oC)−0.10 −0.05 0.00 0.05 SSP3−7.0
−0.10 −0.05 0.00 0.05 0.10 SSP5−8.5

−0.1 0.0 0.1 0.2 0.3 ∆GSAT(2100−2020)
North America∆GSAT(2100−2020)
Europe∆GSAT(2100−2020)
Southern Asia∆GSAT(2100−2020)
Eastern Asia∆GSAT(2100−2020)
Developing Paciﬁc∆GSAT(2100−2020)
Asia−Paciﬁc Developed∆GSAT(2100−2020)
Africa∆GSAT(2100−2020)
Middle East∆GSAT(2100−2020)
Caribbean∆GSAT(2100−2020)
Eurasia 
−0.1 0.0 0.1 0.2 0.3 
∆GSAT (oC) 
−0.1 0.0 0.1 0.2 0.3  
−0.1 0.0 0.1 0.2 0.3 CH4 BC OC SO2 NOx CO NMVOC NH3
Figure 6.23 | Contribution from regional emissions of short-lived climate forcers (SLCFs) to changes in global surface air temperature (GSAT) in 2040 
(upper row) and 2100 (lower row), relative to 2020 for four Shared Socio-economic Pathways (SSPs).  Adapted from Lund et al. (2020). NO x, CO, and NMVOC 
account for the impact through changes in ozone and methane, NO x additionally includes the impact through formation of nitrate aerosols. BC, SO 2 and OC accounts for

the direct aerosol effect (aerosol–radiation interactions), as well as an estimate of the semi-direct effect for BC due to rapid adjustments and indirect effect (aerosol–cloud 
interactions) of sulphate aerosols. Regions are the same as shown in the map in Figure 6.7. Further details on data sources and processing are available in the chapter data 

888
Chapter 6 Short-lived Climate Forcers6physical processes. Also, the ERF following a change in concentrations 
depends on the local conditions (Sections 6.2, 6.3 and 6.4). 
While the emulators used for GSAT projections shown in Figure 6.22 do 
not take the regional perspective into account, the set of simulations 
performed within the Hemispheric Transport of Air Pollutants Phase 
2 (HTAP2) project (Galmarini et al., 2017) allows for this perspective. 
The results from the chemistry–transport model OsloCTM3 taking 
part in the HTAP2 have been used by Lund et al. (2020) to derive 
region‑specific absolute global warming potentials (AGTPs; cf. 
Aamaas et al., 2016) for each emitted SLCF and each HTAP2 region. 
With this set of AGTPs, Lund et al. (2020) estimate the transient 
response in GSAT to the regional anthropogenic emissions. There are 
important differences in the contributions to GSAT in 2040 and 2100 
(relative to 2020) between the regions and scenarios, mainly due to

the differences in the mixture of emitted SLCFs (Figure 6.23). There is 
overall good agreement between the total net contribution from all 
regions to GSAT and the estimate based on global ERF and the two‑
layer emulator (Figure 6.22). 
In the low‑ and medium‑emissions scenarios (SSP1‑2.6 and SSP2‑4.5), 
the warming effects induced by changes in SLCFs on GSAT are 
dominated by emissions in North America, Europe and Eastern Asia 
(Figure 6.23). In SSP1‑2.6 the emissions of all SLCFs in all regions 
decrease and the net effect of the changes in SLCFs from all of these 
three regions is an increase in GSAT of about 0.02°C (per region) 
in 2040 and about 0.04°C in 2100. For SSP2‑4.5, emissions of most 
SLCFs continue to increase in Southern Asia (Figure  6.19), leading 
to a net cooling in the near term (–0.03°C in 2040), while in 2100, 
North America, Europe, Eastern and Southern Asia all contribute 
to a warming, most pronounced from Eastern Asia (0.05°C). In the

SSP3‑7.0 scenario, the net effect induced by changes in SLCFs in all 
regions is an enhanced warming towards the end of the century, 
driven predominantly by change in methane. Africa is the region 
contributing the most to predicted global warming due to SLCF 
changes in 2100 (0.24°C).  In SSP5‑8.5, methane emissions increase 
in North America, Europe and Africa, while there is a  decrease 
in the Asian regions. For North America and Europe, the methane 
increase combined with a reduction in aerosol leads to highest net 
contribution to GSAT in this scenario (0.06°C and 0.04°C in 2100, 
respectively). The high growth in methane makes Africa the region 
with the largest contribution to future warming by SLCFs (0.18°C in 
2100 versus 2020) in this scenario. 
6.7.3 Effect of SLCF Mitigation in SSP Scenarios
Air‑quality policies lead to a decrease in emissions of both warming 
and cooling SLCFs. Here we assess the contribution of SLCFs to the

total warming (also including the LLGHGs) in the case of stringent 
SLCF mitigation to improve air quality in scenarios with continued 
high use of fossil fuels (e.g.,  SSP3‑7.0‑lowSLCF and SSP5‑8.5). 
Conversely, we also assess the effect on air quality of strategies 
aiming to mitigate air pollution or climate change under the SSP3‑7.0 
framework (using the SSP3‑7.0‑lowSLCF‑lowCH 4, SSP3‑7.0‑lowSLCF‑
highCH 4 and SSP3‑3.4 scenarios). As illustrated in Figure 2.2 of SR1.5 (Rogelj et al., 2018a), the total 
aerosol ERF change in stringent mitigation pathways is expected to 
be positive and to contribute to a warming since it is dominated by 
the effects from the phase‑out of SO2 (Figure 6.24, Section 6.7.2.2). 
Recent emissions inventories and observations of trends in AOD 
(Sections 2.2.6 and 6.2.1) show that it is very likely  that there have 
been reductions in global SO 2 emissions and in aerosol burdens over 
the last decade. Here, we use 2019 as the reference year rather than

the ‘Recent Past’ defined as the average over 1995–2014 (Section 4.1) 
in order to exclude the recent emissions reductions when discussing 
the different possible futures. 
The role of the different SLCFs, and also the net of all the SLCFs 
relative to the total warming in the scenarios, is quite different across 
the SSP scenarios varying with the summed levels of climate change 
mitigation and air pollution control (Figure  6.24). In the scenario 
without climate change mitigation but with strong air pollution 
control (SSP5‑8.5) all the SLCFs (methane, aerosols and tropospheric 
ozone) and the HFCs (with lifetimes less than 50 years) add to 
the warming, while in the strong climate change and air pollution 
mitigation scenarios (SSP1‑1.9 and SSP1‑2.6), the emissions controls 
act to reduce methane, ozone and BC, and these reductions thus 
contribute to cooling. In  all scenarios, except SSP3‑7.0, emissions 
controls lead to a  reduction of the aerosols relative to 2019,

causing a warming. However, the warming from aerosol reductions 
is stronger in the SSP1 scenarios (with best estimates of 0.21°C in 
2040 and 0.4°C in 2100 in SSP1‑2.6) because of higher emissions 
reductions from stronger decrease of fossil fuel use in these 
scenarios than in SSP5‑8.5 (0.13°C in 2040 and 0.22°C in 2100). The 
changes in methane abundance contribute a warming of 0.14°C in 
SSP5‑8.5, but a cooling of 0.14°C in SSP1‑2.6 by the end of the 21st 
century relative to 2019. Furthermore, under SSP5‑8.5, HFCs induce 
a warming of 0.06°C with a  very likely  range of [0.04 to 0.08] °C in 
2050 and 0.2 [0.1 to 0.3] °C by the end of the 21st century, relative 
to 2019, while under SSP1‑2.6, warming due to HFCs is negligible 
(below 0.02°C) ( high confidence ). This assessment relies on these 
estimates, which are based on updated ERFs and HFC lifetimes. It is in 
accordance with previous estimates (Section 6.6.3.2) of the efficiency

of the implementation of the Kigali Amendment and national 
regulations. It  is very likely  that under a  stringent climate and air 
pollution mitigation scenario (SSP1‑2.6), the warming induced by 
changes in methane, ozone, aerosols and HFCs towards the end of 
the 21st century, will be very low compared with the warming they 
would cause under the SSP5‑8.5 scenario (0.14°C in SSP1‑2.6 versus 
0.62°C in SSP5‑8.5). 
For the SSP3‑7.0‑lowSLCF‑highCH 4 and SSP3‑7.0‑lowSLCF‑lowCH 4 
scenarios, a  five‑ESM ensemble has been analysed relative to the 
standard SSP3‑7.0 scenario (Allen et al., 2020, 2021). For SSP3‑7.0‑
lowSLCF‑highCH 4, in which the methane emissions are as in the 
standard SSP3‑7.0 scenario, Allen et al. (2021) found an enhanced 
global mean surface warming of 0.23°C ± 0.05°C by mid‑century 
and 0.21°C ± 0.03°C by 2100 relative to the warming in the standard 
SSP3‑7.0 scenario. Also including strong mitigation of methane

emissions, the same models (Allen et al., 2021) find that the warming 
is offset resulting in a net cooling of 0.15°C ± 0.05°C at mid‑century

889
Short-lived Climate Forcers  Chapter 66(2050–2059) and 0.50°C ± 0.02°C at the end of the century (2090–
2099) relative to SSP3‑7.0.
There is robust evidence  and  high agreement that non‑methane 
SLCF mitigation measures, through reductions in aerosols and 
non‑methane ozone precursors to improve air quality (SSP3‑7.0‑
lowSLCF‑highCH 4 vs SSP3‑7.0), would lead to additional near ‑term 
warming with a range of 0.1°C–0.3°C. Methane mitigation that also 
reduces tropospheric ozone, stands out as an option that combines 
near‑ and long‑term gains on surface temperature ( high confidence) . 
With stringent but realistic methane mitigation (SSP3‑7.0‑lowSLCF‑
lowCH 4), it is  very likely  that warming (relative to SSP3‑7.0) from non‑
methane SLCFs can be offset (Figure 6.24; Allen et al., 2021). Due to 
the slower response to the methane mitigation, this offset becomes 
more robust over time and it is very likely  to be an offset after

2050. However, when comparing to the present day, it is unlikely  
that methane mitigation can fully cancel out the warming over the 
21st century from reduction of non‑methane cooling SLCFs.
The SSP3 storyline assumes ‘regional rivalry’ (Section 1.6.1.1) with 
weak air pollution legislation and no climate change mitigation, 
and is compared here against SSP3‑7.0‑lowSLCF‑lowCH 4 (strong 
air pollution control) and SSP3‑3.4 (the most ambitious climate 
policy feasible under the SSP3 narrative). In the SSP3‑3.4 scenario, 
all emissions follow the SSP3‑7.0 scenario until about 2030 and 
then deep and rapid cuts in fossil fuel use are imposed (Fujimori 
et al., 2017). In the case of climate change mitigation, such as in 
the SSP3‑3.4 scenario, the decrease of SLCF emissions is a co‑benefit 
from the targeted decrease of CO 2 (when SLCFs are co‑emitted), 
but also directly targeted as in the case of methane. For SLCFs, this means that emissions of aerosols and methane increase until 2030

and are reduced quickly thereafter (Fujimori et al., 2017). The effect 
on GSAT (relative to 2019) is shown in Figures 6.22 and 6.24. The net 
GSAT response to the SLCFs is dominated by the aerosols, with an 
initial cooling until 2030, then a fast rebound for 15 years followed 
by a  very moderate warming reaching 0.21°C in 2100. The ozone 
reduction causes a slight cooling (up to 0.06°C), in contrast to the 
warming in the SSP3‑7.0‑lowSLCF‑highCH 4 scenario in which the 
methane emissions increase until 2100.
To assess the effect of dedicated air ‑quality versus climate policy 
on air quality, PM 2.5 and ozone indicators were estimated for three 
SSP3 scenarios by applying a widely used approach for the analysis 
of air ‑quality implications for given emissions scenarios (Rao 
et al., 2017; Van Dingenen et al., 2018; Vandyck et al., 2018) and 
whose sensitivity of surface concentrations to emissions changes is 
comparable to that in the ESM ensemble (Supplementary Material

6.SM.5). The assessment shows that both strong air pollution control 
and strong climate change mitigation, implemented independently, 
lead to a large reduction of exposure to PM 2.5 and ozone by the end 
of the century ( high confidence ) (Figures 6.25 and 6.26). However, 
implementation of air pollution control, relying on the deployment 
of existing technologies, leads to benefits more rapidly than climate 
change mitigation ( high confidence ), which requires systemic changes 
and is thus implemented later in this scenario. Notably, under the 
underlying SSP3 context, significant parts of the population remain 
exposed to air quality exceeding the WHO guidelines for PM 2.5 over 
the whole century ( high confidence ), in particular in Africa, Eastern 
and Southern Asia and the Middle East, and for ozone only a small 
improvement in population exposure is expected in Africa and Asia.

Figure 6.24 | Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the 
WGI core set of Shared Socio-economic Pathways (SSPs) . Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), 
are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution 
of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity 
of 3.0°C for a doubling of atmospheric CO 2 (feedback parameter of –1.31 W m–2 °C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar)

includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs 
and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

890
Chapter 6 Short-lived Climate Forcers635 μg m–3
10 μg m–3Thresholds
for population
exposure
SSP3-3.4
Strong climate mitigation
Weak air pollution controlSSP3-7.0 low SLCF low CH4No climate mitigation
Strong air pollution controlSSP3-7.0
No climate mitigation
Weak air pollution control
Africa
Asia-Paciﬁc
developed
Eastern Asia
Europe
EurasiaPopulation
exposed (%)Population
weighted mean
concentration (μg m–3)Surface PM2.5
Latin America & 
Caribbean
South-East Asia & 
developping Paciﬁc
2015 2030 2050 2100 2015 2030 2050 2100 2015 2030 2050 210060
40
20
0100
80
60
40
20
060
40
20
060
40
20
060
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
0
60
40
20
060
40
20
060
40
20
060
40
20
060
40
20
060
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
0
60
40
20
0
100
80
60
40

20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
Figure 6.25 | Effect of dedicated air pollution or climate policy on population-weighted PM 2.5 concentrations (µg m–3) and share of population (%) 
exposed to different PM 2.5 levels across 10 world regions.  Thresholds of 10 µg m–3 and 35 µg m–3 represent the WHO air quality guideline and the WHO interim target 
1, respectively; WHO (2017). Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-lowSLCF (strong air pollution controls are

assumed), and a climate change mitigation scenario SSP3-3.4; details of scenario assumptions are discussed in Riahi et al. (2017) and Rao et al. (2017). Analysis performed 
with the TM5-FASST model (Van Dingenen et al., 2018) using emissions projections from the Shared economic Pathway (SSP) database (Riahi et al., 2017; Rogelj et al., 2018a; 
Gidden et al., 2019). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

891
Short-lived Climate Forcers  Chapter 66SSP3-3.4
Weak air pollution controlPopulation
exposed (%)Population weighted mean of annual average 
daily maximum 8-hourly O3 concentrations (ppb)Thresholds
exposure60 pbb
35 pbb
2015 2030 2050 2100 2015 2030 2050 2100 2015 2030 2050 2100Africa
developed
Europe
EurasiaLatin America & 
Caribbean
developping Paciﬁc100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40

20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0Surface O3
Figure 6.26 | Effect of dedicated air pollution or climate policy on population-weighted ozone concentrations (ppb) and share of population (%) exposed

to different ozone levels across 10 world regions.  Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-low SLCF (strong 
air pollution controls are assumed), and a climate change mitigation scenario (SSP3-3.4); details of scenario assumptions are discussed in Riahi et al. (2017) and Rao et al. 
(2017). Analysis performed with the TM5-FASST model (Van Dingenen et al., 2018) using emissions projections from the Socio-economic Pathway (SSP) database (Riahi et al., 
2017; Rogelj et al., 2018a; Gidden et al., 2019). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

892
Chapter 6 Short-lived Climate Forcers6Confidence levels here result from expert judgement on the whole 
chain of evidence.
In summary, the warming induced by SLCF changes is stable after 
2040 in the WGI core set of SSP scenarios associated with lower global 
air pollution as long as methane emissions are also mitigated, but 
the overall warming induced by SLCF changes is higher in scenarios 
in which air quality continues to deteriorate (caused by growing 
fossil fuel use and limited air pollution control) ( high confidence ). 
In the SSP3‑7.0 context, applying an additional strong air pollution 
control resulting in reductions in anthropogenic aerosols and non‑
methane ozone precursors would lead to an additional near ‑term 
global warming of 0.08 °C with a  very likely  range of [–0.05 to 0.25] 
°C (compared with SSP3‑7.0 for the same period). A simultaneous 
methane mitigation consistent with SSP1’s stringent climate change

mitigation policy implemented in the SSP3 world, could entirely 
alleviate this warming and even lead to a  cooling of 0.07°C with 
a very likely  range of [–0.08 to +0.18] °C  (compared with SSP3‑7.0 
for the same period) . Across the SSPs, the reduction of methane , 
ozone precursors and HFCs can make a 0.2 [0.1 to 0.4] °C difference 
on GSAT in 2040 and a 0.8 [0.5 to 1.3] °C difference at the end of the 
21st century (Figure 6.24), which is substantial in the context of the 
Paris Agreement. Sustained methane mitigation, wherever it occurs,  
stands out as an option that combines near ‑ and long‑term gains 
on surface temperature ( high confidence) and leads to air pollution 
benefits by reducing surface ozone level globally ( high confidence ). 
Strong air pollution control as well as strong climate change 
mitigation, implemented independently, lead to large reduction of the 
exposure to air pollution by the end of the century ( high confidence ).

Implementation of air pollution control, relying on the deployment of 
existing technologies, leads more rapidly to air ‑quality benefits than 
climate change mitigation which requires systemic changes but, in 
both cases, significant parts of the population remain exposed to air 
pollution exceeding the WHO guidelines ( high confidence ).
6.8 Perspectives
Ice‑core analyses can now inform trends for more SLCFs over the 
last millennium (such as light NMVOCs or CO) and more proxies are 
available to inform about past emissions. However, pre‑industrial 
levels of SLCFs are still relatively poorly constrained. In addition, 
recent trends in abundances of the various types of aerosols and 
of NMVOCs suffer from the scarcity of observation networks in 
various parts of the world, in particular in the Southern Hemisphere. 
Such  network development is necessary to record and understand 
the evolution of atmospheric composition. 
Assessment of future air pollution changes at the urban level

requires the use of a  high‑resolution models to properly account 
for non‑linearities in chemistry, specific urban structures and local 
meteorology as well as temporal and spatial variations in emissions 
and population exposure. To assess the relevance of air pollution 
reduction policies, regional air ‑quality models are necessary and 
are still not implemented in many developing countries. To properly 
apply such models, the quality of spatialized emissions inventories is essential, but the production of accurate emissions inventories 
remain a  challenge for lots of rapidly growing urban areas. The 
emissions reporting now planned in the official mandate of the Task 
Force on National Greenhouse Gas Inventories (TFI) can be a step 
in this direction if accompanied by efforts on spatial distribution of 
emissions.  An integrated modelling framework associating global and 
high‑resolution chemistry–transport models with shared protocols is

missing to allow a systematic assessment of future changes on air 
quality at this scale. 
In parallel, opportunities of progress may emerge from big‑data 
acquisition. Big data and their mining can inform practices related 
to emissions or can document pollution levels if associated with 
massive deployment of low‑cost sensors through citizen science. 
New generation satellite data will also give access to sub‑kilometre‑
scale air pollution observations.
A systematic emissions modeling framework is needed to assess the 
LLGHG emissions changes associated with SLCF reductions induced 
by air pollution control in the SSP framework. The SLCF‑mediated 
effects of large‑scale technology deployment to allow climate change 
mitigation, such as hydrogen energy production, carbon capture and 
storage though amine filters, or changes in agricultural practices 
to limit GHG emissions and/or produce bioenergy are also not 
considered in the emissions scenarios.

Since AR5, the complexity of ESMs has increased to include many 
chemical and biogeochemical processes. These processes are 
necessary to quantify non‑CO 2 biogeochemical feedbacks on the 
Earth system resulting from climate‑driven changes in atmospheric 
chemistry and SLCF emissions from natural systems and, in turn, the 
impact of SLCFs on biogeochemical cycles. Enhanced understanding 
of the biological, chemical and physical processes based on 
experimental and observational work has facilitated advances in the 
ESMs. However, assessment of non‑CO 2 biogeochemical feedbacks 
and SLCF effects on land and aquatic ecosystem productivity still 
remains challenging due to the multiple complex processes involved 
and limitations in observational constraints to evaluate the skill of 
ESMs in realistically simulating the processes. Advances will come 
from better understanding of the processes and mechanisms, in 
particular at the interfaces between components. The development

of high‑resolution ESMs will facilitate their evaluation against high‑ 
resolution observations.

893
Short-lived Climate Forcers  Chapter 66Frequently Asked Questions
FAQ 6.1 | What Are Short-lived Climate Forcers and How Do They Affect the Climate?
Short-lived climate forcers (SLCFs) are compounds such as methane and sulphate aerosols that warm or cool the 
Earth’s climate over shorter time scales – from days to years – than greenhouse gases like carbon dioxide, whose 
climatic effect lasts for decades, centuries or more. Because SLCFs do not remain in the atmosphere for very 
long, their effects on the climate are different from one region to another and can change rapidly in response 
to changes in SLCF emissions. As some SLCFs also negatively affect air quality, measures to improve air quality 
have resulted in sharp reductions in emissions and concentrations of those SLCFs in many regions over the few 
last decades.
The SLCFs include gases as well as tiny particles called aerosols , and they can have a warming or cooling effect

on the climate (FAQ 6.1, Figure 1). Warming SLCFs are either greenhouse gases (e.g., ozone or methane) or 
particles like black carbon (also known as soot), which warm the climate by absorbing energy and are sometimes 
referred to as short-lived climate pollutants.  Cooling SLCFs, on the other hand, are mostly made of aerosol 
particles (e.g.,  sulphate, nitrate and organic aerosols) that cool down the climate by reflecting away more 
incoming sunlight. 
Some SLCFs do not directly affect the climate but produce climate-active compounds and are referred to as 
precursors. SLCFs are emitted both naturally and as a result of human activities, such as agriculture or extraction 
of fossil fuels. Many of the human sources, particularly those involving combustion, produce SLCFs at the 
same time as carbon dioxide and other long-lived greenhouse gases. Emissions have increased since the start

of industrialization, and humans are now the dominant source for several SLCFs and SLCF precursors, such as 
sulphur dioxide (which produces sulphate aerosols) and nitrogen oxides (which produce nitrate aerosols and 
ozone), despite strong reductions over the last few decades in some regions due to efforts to improve air quality. 
The climatic effect of a  chemical compound in the atmosphere depends on two things: (i) how effective it 
is at cooling or warming the climate (its radiative efficiency ) and (ii) how long it remains in the atmosphere 
(its lifetime ). Because they have high radiative efficiencies, SLCFs can have a strong effect on the climate even 
though they have relatively short lifetimes of up to about two decades after emission. Today, there is a balance 
between warming and cooling from SLCFs, but this can change in the future.
The short lifetime of SLCFs constrains their effects in both space and time. First, of all the SLCFs, methane and

the short-lived halocarbons persist the longest in the atmosphere: up to two decades (FAQ 6.1, Figure 1). This is 
long enough to mix in the atmosphere and to spread globally. Most other SLCFs only remain in the atmosphere 
for a few days to weeks, which is generally too short for mixing in the atmosphere, sometimes even regionally. 
As a result, the SLCFs are unevenly distributed and their effects on the climate are more regional than those of 
longer-lived gases. Second, rapid (but sustained) changes in emissions of SLCFs can result in rapid climatic effects. 
In addition to the direct warming and cooling effects, SLCFs have many other consequences for the climate system 
and for air quality (see FAQ 6.2). For instance, deposition of black carbon on snow darkens its surface, which 
subsequently absorbs more solar energy, leading to more melting and more warming. Aerosols also modify the

properties of clouds, which has indirect cooling effects on the climate and causes changes in local rainfall (see 
FAQ 7.2). Climate models indicate that SLCFs have altered atmospheric circulation on local and even hemispheric 
scales (e.g., monsoons) as well as regional precipitation. For instance, recent observations show that regional 
weather is influenced by strong regional contrasts in the evolution of aerosol concentrations, particularly over 
South and East Asia. 
Although policies to limit climate change and discussions of the so-called remaining carbon budgets  primarily 
focus on carbon dioxide (see FAQ 5.4), SLCFs can significantly affect temperature changes. It is therefore important 
to understand how SLCFs work and to quantify their effects. Because reducing some of the SLCF emissions, such as 
methane, can simultaneously reduce warming effects and adverse effects on air quality as well as help attaining

Sustainable Development Goals, mitigation of SLCFs is often viewed as a favourable ‘win-win’ policy option.

894
Chapter 6 Short-lived Climate Forcers6FAQ 6.1 (continued)
FAQ 6.1: What are short-lived climate forcers and how do they affect the climate?
12 years
Weeks
1-20 yearsWarming/cooling  effect
Days
DaysShort lived climate forcers do not remain for very long in the atmosphere, thus an increase or decrease in their emissions 
rapidly affects the climate system.
Short-lived 
climate forcerMain human sources Lifetime 
in atmosphereRelative contribution to 
global temperature change
Black 
Carbon
Halocarbons
Organic 
aerosolsNitrate
SulphateMethane 
(CH4)
Ozone 
(O3)
Agriculture  Fossil fuels  Waste                   Industry         Residential           Transportation                 Refrigerants
FAQ 6.1, Figure 1 | Main short-lived climate forcers, their sources, how long they exist in the atmosphere, and their relative contribution to 
global surface temperature changes between 1750 and 2019 (area of the globe).  By definition this contribution depends on the lifetime, the warming/

cooling potential (radiative efficiency), and the emissions of each compound in the atmosphere. Blue indicates cooling and orange indicates warming. Note that, 
between 1750 and 2019, the cooling contribution from aerosols (blue diamonds and globe) was approximately half the warming contribution from carbon dioxide.

895
FAQ 6.2 | What Are the Links Between Limiting Climate Change and Improving 
Air Quality? 
Climate change and air quality are intimately linked. Many of the human activities that produce long-lived 
greenhouse gases also emit air pollutants, and many of these air pollutants are also ‘short-lived climate forcers’ 
that affect the climate. Therefore, many options for improving air quality may also serve to limit climate change 
and vice versa. However, some options for improving air quality cause additional climate warming, and some actions that address climate change can worsen air quality. 
Climate change and air pollution are both critical environmental issues that are already affecting humanity. 
In 2016, the World Health Organization attributed 4.2 million deaths worldwide every year to ambient (outdoor)

air pollution. Meanwhile, climate change impacts water resources, food production, human health, extreme events, coastal erosion, wildfires, and many other phenomena. 
Most human activities, including energy production, agriculture, transportation, industrial processes, waste 
management and residential heating and cooling, result in emissions of gaseous and particulate pollutants that 
modify the composition of the atmosphere, leading to degradation of air quality as well as to climate change. These air pollutants are also short-lived climate forcers – substances that affect the climate but remain in the 
atmosphere for shorter periods (days to decades) than long-lived greenhouse gases like carbon dioxide (see 
FAQ 6.1). While this means that the issues of air pollution and climate change are intimately connected, air pollutants and greenhouse gases are often defined, investigated and regulated independently of one another in 
both the scientific and policy arenas.

Many sources simultaneously emit carbon dioxide and air pollutants. When we drive our fossil fuel vehicles 
or light a fire in the fireplace, it is not just carbon dioxide or air pollutants that are emitted, but always both. It is therefore not possible to separate emissions into two clearly distinct groups. As a result, policies aiming at 
addressing climate change may have benefits or side effects for air quality, and vice versa. 
For example, some short-term ‘win-win’ policies that simultaneously improve air quality and limit climate change 
include the implementation of energy efficiency measures, methane capture and recovery from solid-waste management and the oil and gas industry, zero-emissions vehicles, efficient and clean stoves for heating and 
cooking, filtering of soot (particulate matter) for diesel vehicles, cleaner brick-kiln technology, practices that 
reduce burning of agricultural waste, and the eradication of burning of kerosene for lighting.

There are, however, also ‘win-lose’ actions. For example, wood burning is defined as carbon neutral because 
a tree accumulates the same amount of carbon dioxide throughout its lifetime as is released when wood from 
that tree is burned. However, burning wood can also result in significant emissions of air pollutants, including 
carbon monoxide, nitrogen oxides, volatile organic compounds, and particulate matter, that locally or regionally affect the climate, human health and ecosystems (FAQ 6.2, Figure 1). Alternatively, decreasing the amount of 
sulphate aerosols produced by power and industrial plants, and from maritime transport, improves air quality 
but results in a warming influence on the climate, because those sulphate aerosols contribute to cooling the atmosphere by blocking incoming sunlight. 
Air quality and climate change represent two sides of the same coin, and addressing both issues together could

lead to significant synergies and economic benefits while avoiding policy actions that mitigate one of the two 
issues but worsen the other.

896
Chapter 6 Short-lived Climate Forcers6FAQ 6.2 (continued)
FAQ 6.2: Limiting climate change and improving air quality?
Current actions to 
limit climate change
Reduce
IncreaseReduce
Climate benefit Crop benefits Health benefits Climate penaltyCurrent actions to 
improve air qualityWarming compounds
Cooling compounds
No direct climate effectClimate change and air quality are so intimately linked that addressing one issue can affect the other one.
Carbon 
dioxide 
(CO2)SulphateOrganic 
carbonNitrateNitrous 
oxide 
(N2O)
Halogenated 
compounds
Tropospheric 
Ozone (O3)Surface 
particulate 
matterMethane 
(CH4)
Volatile 
organic 
compounds Carbon 
monoxide 
(CO)
Nitrogen 
oxides 
(NOX)Black
Carbon
FAQ 6.2, Figure 1 |  Links between actions aiming to limit climate change and actions to improve air quality.  Greenhouse gases (GHGs) and aerosols

(orange and blue) can affect climate directly. Air pollutants (bottom) can affect human health, ecosystems and climate. All these compounds have common sources 
and sometimes interact with each other in the atmosphere which makes it impossible to consider them separately (dotted grey arrows).

925
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Executive Summary
This chapter assesses the present state of knowledge of Earth’s energy 
budget: that is, the main flows of energy into and out of the Earth system, and how these energy flows govern the climate response to 
a  radiative forcing. Changes in atmospheric composition and land 
use, like those caused by anthropogenic greenhouse gas emissions 
and emissions of aerosols and their precursors, affect climate through perturbations to Earth’s top-of-atmosphere energy budget. The effective radiative forcings (ERFs) quantify these perturbations, 
including any consequent adjustment to the climate system 
(but  excluding surface temperature response). How  the climate system responds to a given forcing is determined by climate feedbacks associated with physical, biogeophysical and biogeochemical 
processes. These feedback processes are assessed, as are useful

measures of global climate response, namely equilibrium climate sensitivity (ECS) and the transient climate response (TCR). This chapter also assesses emissions metrics, which are used to quantify how the climate response to the emissions of different greenhouse gases 
compares to the response to the emissions of carbon dioxide (CO
2). 
This chapter builds on the assessment of carbon cycle and aerosol 
processes from Chapters 5 and 6, respectively, to quantify non-CO 2 
biogeochemical feedbacks and the ERF for aerosols. Other chapters in this Report use this chapter’s assessment of ERF , ECS and TCR to help 
understand historical and future temperature changes (Chapters  3 
and 4, respectively), the response to cumulative emissions and the remaining carbon budget (Chapter  5), emissions-based radiative forcing (Chapter 6) and sea level rise (Chapter 9). This chapter builds on findings from the IPCC Fifth Assessment Report (AR5), the Special

Report on Global Warming of 1.5°C (SR1.5), the Special Report on 
the Ocean and Cryosphere in a Changing Climate (SROCC) and the Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse 
gas luxes in terrestrial ecosystems (SRCCL). Very likely ranges are 
presented unless otherwise indicated.
Earth’s Energy Budget
Since AR5, the accumulation of energy in the Earth system, quantified by changes in the global energy inventory for all components of the climate system, has become established 
as a robust measure of the rate of global climate change on 
interannual-to-decadal time scales.  Compared to changes in 
global surface air temperature (GSAT), the global energy inventory exhibits less variability, which can mask underlying climate trends.  
Compared to AR5, there is increased confidence in the quantification 
of changes in the global energy inventory due to improved

observational records and closure of the sea level budget. Energy will continue to accumulate in the Earth system until at least the end of the 21st  century, even under strong mitigation scenarios, 
and will primarily be observed through ocean warming and 
associated with continued sea level rise through thermal expansion (high confidence). {7.2.2, Box 7.2, Table 7.1, Cross-Chapter Box 9.1, Table 9.5, 9.2.2, 9.6.3}The global energy inventory increased by 282 [177 to 387] Zettajoules (ZJ; 10
21 Joules) for the period 1971–2006 and
152 [100 to 205] ZJ for the period 2006–2018.  This corresponds 
to an Earth energy imbalance of 0.50 [0.32 to 0.69] W m–2 for the
period  1971–2006, increasing to 0.79 [0.52 to 1.06] W m–2 for
the  period 2006–2018, expressed per unit area of Earth’s surface.Ocean heat uptake is by far the largest contribution and accounts for
91% of the total energy change. Compared to AR5, the contribution

from land heating has been revised upwards from about 3% toabout 5%. Melting of ice and warming of the atmosphere accountfor about 3% and 1% of the total change respectively. More
comprehensive analysis of inventory components and cross-validation 
of global heating rates from satellite and in situ observations leadto a  strengthened assessment relative to AR5 (high confidence). {Box 7.2, 7.2.2, Table 7.1, 7.5.2.3}
Improved quantification of effective radiative forcing, the climate 
system radiative response, and the observed energy increase in the Earth system for the period 1971–2018 demonstrate improved closure of the global energy budget compared to AR5.  Combining 
the likely range of ERF with the central estimate of radiative response

gives an expected energy gain of 340 [47 to 662] ZJ. Combining the likely range of climate response with the central estimate of ERF gives an expected energy gain of 340 [147 to 527] ZJ. Both estimates are consistent with an independent observation-based assessment of the global energy increase of 284 [96 to 471] ZJ, (very likely range) 
expressed relative to the estimated 1850–1900 Earth energy imbalance 
(high confidence). {7.2.2, Box 7.2, 7.3.5, 7.5.2}
Since AR5, additional evidence for a  widespread decline 
(or dimming) in solar radiation reaching the surface is found 
in the observational records between the 1950s and 1980s, with a  partial recovery (brightening) at many observational sites thereafter (high confidence). These trends are neither a local phenomenon nor a measurement artefact ( high confidence). 
Multi-decadal variation in anthropogenic aerosol emissions are thought to be a  major contributor ( medium confidence), but

multi-decadal variability in cloudiness may also have played a role. The downward and upward thermal radiation at the surface has increased in recent decades, in line with increased greenhouse 
gas concentrations and associated surface and atmospheric warming 
and moistening (medium confidence). {7.2.2}
Effective Radiative Forcing
For carbon dioxide, methane, nitrous oxide and 
chlorofluorocarbons, there is now evidence to quantify the effect on ERF of tropospheric adjustments (e.g., from changes in atmospheric temperatures, clouds and water vapour). 
The assessed ERF for a doubling of carbon dioxide compared 
to 1750 levels (3.93 ± 0.47 W m
–2) is larger than in AR5.  Effective 
radiative forcings (ERF), introduced in AR5, have been estimated for a larger number of agents and shown to be more closely related to the 
temperature response than the stratospheric-temperature adjusted

radiative forcing. For carbon dioxide, the adjustments include the physiological effects on vegetation (high confidence). {7.3.2}

926
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7The total anthropogenic ERF over the industrial era 
(1750–2019) was 2.72 [1.96 to 3.48] W m–2. This estimate 
has increased by 0.43 W m–2 compared to AR5 estimates for 
1750–2011.  This increase includes +0.34  W m–2 from increases 
in atmospheric concentrations of well-mixed greenhouse gases 
(including halogenated species) since 2011, +0.15 W m–2 from 
upwards revisions of their radiative efficiencies and +0.10 W m–2 
from re-evaluation of the ozone and stratospheric water vapour ERF . 
The 0.59 W m–2 increase in ERF from greenhouse gases is partly offset 
by a better-constrained assessment of total aerosol ERF that is more 
strongly negative than in AR5, based on multiple lines of evidence 
(high confidence ). Changes in  surface reflectance from land-use 
change, deposition of light-absorbing particles on ice and snow, and 
contrails and aviation-induced cirrus have also contributed to the

total anthropogenic ERF over the industrial era, with –0.20 [–0.30 to 
–0.10] W m–2 (medium confidence ), +0.08  [0  to 0.18] W  m–2 (low 
confidence ) and +0.06 [0.02  to 0.10]  W  m–2 (low  confidence ), 
respectively. {7.3.2, 7.3.4, 7.3.5}
Anthropogenic emissions of greenhouse gases and their 
precursors contribute an ERF of 3.84 [3.46 to 4.22] W m–2 
over the  industrial era (1750–2019). Most of this total 
ERF, 3.32  [3.03  to 3.61] W m–2, comes from the well-mixed 
greenhouse gases, with changes in ozone and stratospheric 
water vapour (from methane oxidation) contributing 
the remainder.  The ERF of greenhouse gases is composed of 
2.16  [1.90  to  2.41] W  m–2 from carbon dioxide, 0.54 [0.43  to 
0.65]  W m–2 from methane, 0.41 [0.33 to 0.49] W m–2 from halogenated 
species, and 0.21 [0.18 to 0.24] W m–2 from nitrous oxide. The ERF for 
ozone is 0.47 [0.24 to 0.71] W m–2. The estimate of ERF for ozone 
has increased since AR5 due to revised estimates  of precursor

emissions and better accounting for effects of tropospheric ozone 
precursors in the stratosphere. The estimated ERF for methane has 
slightly increased due to a combination of increases from improved 
spectroscopic treatments being somewhat offset by accounting for 
adjustments  (high confidence ). {7.3.2, 7.3.5}
Aerosols contribute an ERF of –1.3 [–2.0 to –0.6] W m–2 over 
the industrial era (1750–2014) ( medium confidence ). The ERF 
due to aerosol–cloud interactions (ERFaci) contributes most 
to the magnitude of the total aerosol ERF ( high confidence ) 
and is assessed to be –1.0 [–1.7 to –0.3] W m–2 (medium 
confidence ), with the remainder due to aerosol–radiation 
interactions (ERFari), assessed to be –0.3 [–0.6 to 0.0] W m–2 
(medium confidence ). There has been an increase in the estimated 
magnitude but a  reduction in the uncertainty of the total aerosol 
ERF relative to AR5, supported by a  combination of increased

process-understanding and progress in modelling and observational 
analyses. ERF estimates from these separate lines of evidence are 
now consistent with each other, in contrast to AR5, and support the 
assessment that it is virtually certain  that the total aerosol ERF is 
negative. Compared to AR5, the assessed magnitude of ERFaci has 
increased, while the magnitude of ERFari has decreased . The total 
aerosol ERF over the period 1750–2019 is less certain than the 
headline statement assessment. It is also assessed to be smaller 
in magnitude at –1.1 [–1.7 to –0.4] W m–2, primarily due to recent 
emissions changes ( medium confidence ). {7.3.3, 7.3.5, 2.2.6}Climate Feedbacks and Sensitivity
The net effect of changes in clouds in response to global 
warming is to amplify human-induced warming, that is, the 
net cloud feedback is positive ( high confidence ). Compared to 
AR5, major advances in the understanding of cloud processes

have increased the level of confidence and decreased the 
uncertainty range in the cloud feedback by about 50%.  
An assessment of the low-altitude cloud feedback over the subtropical 
oceans, which was previously the major source of uncertainty in the 
net cloud feedback, is improved owing to a combined use of climate 
model simulations, satellite observations, and explicit simulations 
of clouds, altogether leading to strong evidence that this type of 
cloud amplifies global warming. The net cloud feedback, obtained 
by summing the cloud feedbacks assessed for individual regimes, 
is 0.42 [–0.10 to +0.94] W m–2 °C–1. A net negative cloud feedback is 
very unlikely  (high confidence ). {7.4.2, Figure 7.10, Table 7.10}
The combined effect of all known radiative feedbacks (physical, 
biogeophysical, and non-CO 2 biogeochemical) is to amplify the 
base climate response, also known as the Planck temperature 
response ( virtually certain ). Combining these feedbacks with the

base climate response, the net feedback parameter based on process 
understanding is assessed to be –1.16 [–1.81 to –0.51] W m–2 °C–1, 
which is slightly less negative than that inferred from the overall ECS 
assessment. The combined water-vapour and lapse-rate feedback 
makes the largest single contribution to global warming, whereas 
the cloud feedback remains the largest contribution to overall 
uncertainty. Due to the state-dependence of feedbacks, as evidenced 
from paleoclimate observations and from models, the net feedback 
parameter will increase (become less negative) as global temperature 
increases. Furthermore, on long time scales the ice-sheet feedback 
parameter is very likely  positive, promoting additional warming on 
millennial time scales as ice sheets come into equilibrium with the 
forcing ( high confidence ). {7.4.2, 7.4.3, 7.5.7}
Radiative feedbacks, particularly from clouds, are expected 
to become less negative (more amplifying) on multi-decadal

time scales as the spatial pattern  of surface warming 
evolves, leading to an ECS that is higher than was inferred 
in AR5 based on warming over the instrumental record. 
This new understanding, along with updated estimates 
of historical temperature change, ERF, and Earth’s energy 
imbalance, reconciles previously disparate ECS estimates 
(high confidence ). However, there is currently insufficient evidence 
to quantify a  likely  range of the magnitude of future changes to 
current climate feedbacks. Warming over the instrumental record 
provides robust constraints on the lower end of the ECS range 
(high  confidence ), but owing to the possibility of future feedback 
changes it does not, on its own, constrain the upper end of the range, 
in contrast to what was reported in AR5. {7.4.4, 7.5.2, 7.5.3}
Based on multiple lines of evidence the best estimate of ECS is 
3°C, the likely  range is 2.5°C to 4°C, and the very likely  range

is 2°C to 5°C. It is virtually certain  that ECS is larger than 1.5°C. 
Substantial advances since AR5 have been made in quantifying ECS 
based on feedback process understanding, the instrumental record,

927
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity Chapter 77paleoclimates and emergent constraints. There is a  high level of 
agreement among the different lines of evidence. All lines of evidence 
help rule out ECS values below 1.5°C, but currently it is not possible 
to rule out ECS values above 5°C. Therefore, the 5°C upper end of 
the very likely  range is assessed to have medium confidence  and the 
other bounds have high confidence . {7.5.5}
Based on process understanding, warming over the instrumental 
record, and emergent constraints, the best estimate of TCR is 
1.8°C, the likely  range is 1.4°C to 2.2°C and the very likely  
range is 1.2°C to 2.4°C ( high confidence ). {7.5.5}
On average, Coupled Model Intercomparison Project Phase 6 
(CMIP6) models have higher mean ECS and TCR values than 
the Phase 5 (CMIP5) generation of models. They also have 
higher mean values and wider spreads than the assessed best

estimates and very likely  ranges within this Report.  These higher 
ECS and TCR values can, in some models, be traced to changes in 
extra-tropical cloud feedbacks that have emerged from efforts to 
reduce biases in these clouds compared to satellite observations 
(medium confidence ). The broader ECS and TCR ranges from CMIP6 
also lead the models to project a range of future warming that is wider 
than the assessed warming range, which is based on multiple lines of 
evidence. However, some of the high-sensitivity CMIP6 models are less 
consistent with observed recent changes in global warming and with 
paleoclimate proxy data than models with ECS within the very likely  
range. Similarly, some of the low-sensitivity models are less consistent 
with the paleoclimate data. The CMIP models with the highest ECS 
and TCR values provide insights into low-likelihood, high-impact 
outcomes, which cannot be excluded based on currently available

evidence ( high confidence ). {4.3.1, 4.3.4, 7.4.2, 7.5.6}
Climate Response
The total human-forced GSAT change from 1750 to 2019 
is calculated to be 1.29 [0.99 to 1.65] °C. This calculation 
is  an emulator-based estimate, constrained by the historic 
GSAT and ocean heat content changes from Chapter  2 and 
the ERF, ECS and TCR from this chapter.  The calculated GSAT 
change is composed of a  well-mixed greenhouse gas warming of 
1.58 [1.17  to 2.17] °C ( high confidence ), a  warming from ozone 
changes of 0.23  [0.11 to 0.39] °C ( high confidence ), a  cooling of 
–0.50 [–0.22 to –0.96] °C from aerosol effects ( medium confidence ), 
and a –0.06 [–0.15 to +0.01] °C contribution from surface reflectance 
changes from land-use change and light-absorbing particles on 
ice and snow ( medium confidence ). Changes in solar and volcanic 
activity are assessed to have together contributed a small change of 
–0.02 [–0.06 to +0.02] °C since 1750 ( medium confidence ). {7.3.5}

Uncertainties regarding the true value of ECS and TCR are 
the dominant source of uncertainty in global temperature 
projections over the 21st  century under moderate to high 
greenhouse gas emissions scenarios. For scenarios that reach 
net zero carbon dioxide emissions, the uncertainty in the 
ERF values of aerosol and other short-lived climate forcers 
contribute substantial uncertainty in projected temperature.  Global ocean heat uptake is a  smaller source of uncertainty in 
centennial-time scale surface warming ( high confidence ). {7.5.7}
The assessed historical and future ranges of GSAT change in this 
Report are shown to be internally consistent with the Report’s 
assessment of key physical-climate indicators: greenhouse gas 
ERFs, ECS and TCR.  When calibrated to match the assessed ranges 
within the assessment, physically based emulators can reproduce 
the best estimate of GSAT change over 1850–1900 to 1995–2014 to

within 5% and the very likely  range of this GSAT change to within 
10%. Two physically based emulators match at least two-thirds of the 
Chapter  4-assessed projected GSAT changes to within these levels 
of precision.  When used for multi-scenario experiments, calibrated 
physically based emulators can adequately reflect assessments 
regarding future GSAT from Earth system models and/or other lines of 
evidence ( high confidence ). {Cross-Chapter Box 7.1}
It is now well understood that the Arctic warms more quickly 
than the Antarctic due to differences in radiative feedbacks 
and ocean heat uptake between the poles, but that surface 
warming will eventually be amplified in both the Arctic and 
Antarctic ( high confidence ). The causes of this polar amplification 
are well understood, and the evidence is stronger than at the 
time of AR5, supported by better agreement between modelled 
and observed polar amplification during warm paleo time periods

(high confidence ). The Antarctic warms more slowly than the Arctic 
owing primarily to upwelling in the Southern Ocean, and even at 
equilibrium is expected to warm less than the Arctic. The rate of Arctic 
surface warming will continue to exceed the global average over this 
century ( high confidence ). There is also high confidence  that Antarctic 
amplification will emerge as the Southern Ocean surface warms on 
centennial time scales, although only low confidence  regarding 
whether this feature will emerge during the 21st century. {7.4.4}
The assessed global warming potentials (GWP) and global 
temperature-change potentials (GTP) for methane and nitrous 
oxide are slightly lower than in AR5 due to revised estimates 
of their lifetimes and updated estimates of their indirect 
chemical effects ( medium confidence ). The assessed metrics now 
also include the carbon cycle response for non-CO 2 gases. The carbon 
cycle estimate is lower than in AR5, but there is high confidence  in

the need for its inclusion and in the quantification methodology. 
Metrics for methane from fossil fuel sources account for the extra 
fossil CO 2 that these emissions contribute to the atmosphere and so 
have slightly higher emissions metric values than those from biogenic 
sources ( high confidence ). {7.6.1}
New emissions metric approaches such as GWP* and the 
combined-GTP (CGTP) are designed to relate emissions rates 
of short-lived gases to cumulative emissions of CO 2. These 
metric approaches are well suited to estimate the GSAT 
response from aggregated emissions of a  range of gases 
over time, which can be done by scaling the cumulative CO 2 
equivalent emissions calculated with these metrics by the 
transient climate response to cumulative emissions of CO 2. 
For a given multi-gas emissions pathway, the estimated contribution 
of emissions to surface warming is improved by using either these

928
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7new metric approaches or by treating short- and long-lived GHG 
emissions pathways separately, as compared to approaches that 
aggregate emissions of GHGs using standard GWP or GTP emissions 
metrics. By contrast, if emissions are weighted by their 100-year 
GWP or GTP values, different multi-gas emissions pathways with the 
same aggregated CO 2 equivalent emissions rarely lead to the same 
estimated temperature outcome ( high confidence ). {7.6.1, Box 7.3}
The choice of emissions metric affects the quantification of net 
zero GHG emissions and therefore the resulting temperature 
outcome after net zero emissions are achieved.  In general, 
achieving net zero CO 2 emissions and declining non-CO 2 radiative 
forcing would be sufficient to prevent additional human-caused 
warming. Reaching net zero GHG emissions as quantified by 
GWP-100 typically results in global temperatures that peak and

then decline after net zero GHGs emissions are achieved, though 
this outcome depends on the relative sequencing of mitigation of 
short-lived and long-lived species. In contrast, reaching net zero GHG 
emissions when quantified using new emissions metrics such as 
CGTP or GWP* would lead to approximate temperature stabilization 
(high confidence ). {7.6.2}

929
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity Chapter 777.1 Introduction, Conceptual Framework, 
and Advances Since the Fifth 
Assessment Report
This chapter assesses the major physical processes that affect the 
evolution of Earth’s energy budget and the associated changes in 
surface temperature and the broader climate system, integrating 
elements that were dealt with separately in previous reports.
The top-of-atmosphere (TOA) energy budget determines the net amount 
of energy entering or leaving the climate system. Its time variations 
can be monitored in three ways, using: (i) satellite observations of the 
radiative fluxes at the TOA; (ii) observations of the accumulation of 
energy in the climate system; and (iii) observations of surface energy 
fluxes. When the TOA energy budget is changed by a human or natural 
cause (a ‘radiative forcing’), the climate system responds by warming 
or cooling (i.e., the system gains or loses energy). Understanding

of changes in the Earth’s energy flows helps understanding of the 
main physical processes driving climate change. It also provides 
a fundamental test of climate models and their projections.
This chapter principally builds on the IPCC Fifth Assessment Report 
(AR5; Boucher, 2012; Church et al., 2013; M. Collins et al., 2013; Flato 
et al., 2013; Hartmann et al., 2013; Myhre et al., 2013b; Rhein et al., 
2013). It also builds on the subsequent IPCC Special Report on Global 
Warming of 1.5°C (SR1.5; IPCC, 2018), the Special Report  on 
the Ocean and Cryosphere in a  Changing Climate (SROCC; IPCC, 
2019a) and the Special Report on climate change, desertification, 
land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SRCCL; IPCC, 
2019b), as well as community-led assessments (e.g., Bellouin et al. 
(2020) covering aerosol radiative forcing and Sherwood et al. (2020) 
covering equilibrium climate sensitivity).

Throughout this chapter, global surface air temperature (GSAT) 
is used to quantify surface temperature change (Cross-Chapter 
Box  2.3 and Section  4.3.4). The total energy accumulation in 
the Earth system represents a  metric of global change that is 
complementary to GSAT but shows considerably less variability on 
interannual-to-decadal time scales (Section  7.2.2). Research and 
new observations since AR5 have improved scientific confidence in 
the quantification of changes in the global energy inventory and 
corresponding estimates of Earth’s energy imbalance (Section 7.2). 
Improved understanding of adjustments to radiative forcing and of 
aerosol–cloud interactions have led to revisions of forcing estimates 
(Section 7.3). New approaches to the quantification and treatment 
of feedbacks (Section 7.4) have improved the understanding of their 
nature and time-evolution, leading to a better understanding of how

these feedbacks relate to equilibrium climate sensitivity (ECS). This 
has helped to reconcile disparate estimates of ECS from different 
lines of evidence (Section 7.5). Innovations in the use of emissions 
metrics have clarified the relationships between metric choice 
and temperature policy goals (Section  7.6), linking this chapter to 
WGIII which provides further information on metrics, their use, and 
policy goals beyond temperature. Very likely  (5–95%) ranges are 
presented unless otherwise indicated. In particular, the addition of 
‘(one standard deviation)’ indicates that the range represents one 
standard deviation.
Chapter 7: The Earth’s energy budget Chapter 7: Quick guide
Cross-chapter boxesSection 7.1
Introduction and advances
Observations of Earth‘s energy budget
7.2 | FAQ 7.1
Polar amplification
7.4.4.1Aerosols
7.3.3 | 7.3.5 | FAQ 7.2
Clouds
7.3.3.2 | 7.4.2.4 | FAQ 7.2
Paleoclimate data
7.4.3.2 | 7.5.3
Synthesis of forcing, feedbacks and climate sensitivity

7.3.5 | 7.4.2.7 | 7.5.5 | FAQ 7.3Emissions metrics and climate policy
7.6.2
Box 7.1
Energy budget frameworkBox 7.2
Global energy budgetBox 7.2
Physical considerations in 
emissions-metric choiceCC Box 7.1
Physical emulation of Earth system modelsChapter 7 assesses the major physical processes affecting the evolution of Earth’s 
energy budget and the associated changes in temperature and the climate system.Key topics and corresponding sub-sections
BoxesSection 7.2
Energy budget
Section 7.3
Radiative forcingSection 7.4
Climate feedbacks
Section 7.6
MetricsSection 7.5
Climate sensitivity
FAQs(a)
Figure 7. 1 | Visual guide to Chapter 7.  Panel (a) Overview of the chapter.

930
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Assessment involves multiple 
lines of evidence from various 
sources:
Paleo records
ObservationsSatellites
Simulations and
theoryClimate feedbacks
Key Improvements
Section 7.4Climate sensitivity
Section 7.5Radiative forcing
Section 7.3Earth’s energy budget is influenced by:  
Volcanoes GHGs and aerosols from agriculture, 
industry and fossil-fuel combustionQuantification of effective radiative forcing for 
human and natural forcing agents: Key processes explored:
Cloud changes (feedback)
Ocean warming coupled
with atmospheric feedbacks
New elements in this Chapter relative to AR5
Advances in observing the energy budget show that Earth is warming everywhere,
from multiple lines of evidence
An improved radiative forcing concept from better understanding of adjustments
Improved understanding of cloud processes leads to a better constrained cloud feedback

New science shows that as the pattern of sea-surface temperature change evolves in time, 
feedbacks change, potentially affecting projections
A tighter constraint on equilibrium climate sensitivity is possible, leading to improved 
surface temperature projections
New emissions metric approaches account for the different warming implications of short-li -
ved and long-lived climate forcings
 Earth’s energy imbalance results from increases in 
greenhouse gases absorbing thermal radiation that would 
otherwise be emitted to space. This radiative forcing leads 
to an accumulation of energy in the Earth’s system
Evidence from: process 
understanding, historical 
warming, paleoclimates, 
and emergent constraints
Ocean (91%)Ice (3%)
Land (5%)Atmosphere (1%)
Earth’s energy budget encompasses 
the major energy flows of relevance 
for the climate system Section 7.2 
Cloud changes are an important 
feedback on how the Earth system 
responds to a radiative forcingIncoming 
solar 
radiation

Outgoing
Radiation
The contribution to the 
increase in Earth’s energy 
inventory is shown by %(b)
Figure 7.1 (continued):  Panel (b) Visual abstract of the chapter, illustrating why the Earth’s energy budget matters and how it relates to the underlying chapter assessment. 
The methods used to assess processes and key new findings relative to AR5 are highlighted. Upper schematic adapted from Von Schuckmann et al. (2020).

931
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity Chapter 77In Box  7.1 an energy budget framework is introduced, which 
forms the basis for the discussions and scientific assessment in the 
remainder of this chapter and across the Report. The framework 
reflects advances in the understanding of the Earth system response 
to climate forcing since the publication of AR5. A schematic of this 
framework and the key changes relative to the science reported in 
AR5 are provided in Figure 7.1.
A simple way to characterize the behaviour of multiple aspects of 
the climate system at once is to summarize them using global-scale 
metrics. This Report distinguishes between ‘climate metrics’ (e.g., ECS, 
TCR) and ‘emissions metrics’ (e.g., global warming potential, GWP , or 
global temperature-change potential, GTP), but this distinction is not 
definitive. Climate metrics are generally used to summarize aspects

of the surface temperature response (Box  7.1). Emissions metrics 
are generally used to summarize the relative effects of emissions of 
different forcing agents, usually greenhouse gases (GHGs; Section 7.6). 
The climate metrics used in this report typically evaluate how the 
Earth system response varies with atmospheric gas concentration or 
change in radiative forcing. Emissions metrics evaluate how radiative 
forcing or a key climate variable (such as GSAT) is affected by the 
emissions of a certain amount of gas. Emissions-related metrics are 
sometimes used in mitigation policy decisions such as trading GHG 
reduction measures and life cycle analysis. Climate metrics are useful 
to gauge the range of future climate impacts for adaptation decisions 
under a  given emissions pathway. Metrics such as the transient 
climate response to cumulative emissions of carbon dioxide (TCRE) 
are used in both adaptation and mitigation contexts: for gauging

future global surface temperature change under specific emissions 
scenarios, and to estimate remaining carbon budgets that are used 
to inform mitigation policies (Section 5.5).Given that TCR and ECS are metrics of GSAT response to 
a  theoretical doubling of atmospheric CO 2 (Box  7.1), they do not 
directly correspond to the warming that would occur under realistic 
forcing scenarios that include time-varying CO 2 concentrations and 
non-CO 2 forcing agents (such as aerosols and land-use changes). 
It has been argued that TCR, as a metric of transient warming, is 
more policy-relevant than ECS (Frame et al., 2006; Schwartz, 2018). 
However, as detailed in Chapter  4, both established and recent 
results (Forster et  al., 2013; Gregory et  al., 2015; Marotzke and 
Forster, 2015; Grose et al., 2018; Marotzke, 2019) indicate that TCR 
and ECS help explain variation across climate models both over the 
historical period and across a range of concentration-driven future

scenarios. In emission-driven scenarios the carbon cycle response 
is also important (Smith et  al., 2019). The proportion of variation 
explained by ECS and TCR varies with scenario and the time period 
considered, but both past and future surface warming depend on 
these metrics (Section 7.5.7).
Regional changes in temperature, rainfall, and climate extremes 
have been found to correlate well with the forced changes in GSAT 
within Earth System Models (ESMs; Section 4.6.1; Giorgetta et al., 
2013; Tebaldi and Arblaster, 2014; Seneviratne et  al., 2016). While 
this so-called ‘pattern scaling’ has important limitations arising from, 
for instance, localized forcings, land-use changes, or internal climate 
variability (Deser et al., 2012; Luyssaert et al., 2014), changes in GSAT 
nonetheless explain a substantial fraction of inter-model differences 
in projections of regional climate changes over the 21st  century 
(Tebaldi and Knutti, 2018). This Chapter’s assessments of TCR and

ECS thus provide constraints on future global and regional climate 
change (Chapters 4 and 11).
Box 7.1 | The Energy Budget Framework: Forcing and Response
The forcing and response energy budget framework provides a methodology to assess the effect of individual drivers of global surface 
temperature response, and to facilitate the understanding of the key phenomena that set the magnitude of this temperature response. 
The framework used here is developed from that adopted in previous IPCC reports (see Ramaswamy et al., 2019 for a discussion). 
Effective Radiative Forcing (ERF) , introduced in AR5 (Boucher et al., 2013; Myhre et al., 2013b) is more explicitly defined in this 
Report and is employed as the central definition of radiative forcing (Sherwood et al., 2015, Box 7.1, Figure 1a). The framework has also 
been extended to allow variations in feedbacks over different time scales and with changing climate state (Sections 7.4.3 and 7.4.4).

The global surface air temperature (GSAT) response to perturbations that give rise to an energy imbalance is traditionally approximated by 
the following linear energy budget equation, in which Δ N represents the change in the top-of-atmosphere (TOA) net energy flux, Δ F is an 
effective radiative forcing  perturbation to the TOA net energy flux, α is the net feedback parameter and Δ T is the change in GSAT :
ΔN = ΔF + α ΔT
ERF is the TOA energy budget change resulting from the perturbation, excluding any radiative response related to a change in GSAT 
(i.e., Δ T = 0). Climate feedbacks ( α) represent those processes that change the TOA energy budget in response to a given Δ T.
The effective radiative forcing, ERF (ΔF; units: W m–2) quantifies the change in the net TOA energy flux of the Earth system due to 
an imposed perturbation (e.g., changes in greenhouse gas or aerosol concentrations, in incoming solar radiation, or land-use change).

ERF is expressed as a change in net downward radiative flux at the TOA following adjustments in both tropospheric and stratospheric 
temperatures, water vapour, clouds, and some surface properties, such as surface albedo from vegetation changes, that are uncoupled to(Box 7.1, Equation 7.1)

932
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Box 7.1 (continued)
any GSAT change (Smith et al., 2018b). These adjustments affect the TOA energy balance and hence the ERF . They are generally assumed 
to be linear and additive (Section 7.3.1). Accounting for such processes gives an estimate of ERF that is more representative of the climate 
change response associated with forcing agents than stratospheric-temperature-adjusted radiative forcing (SARF) or the instantaneous 
radiative forcing (IRF; Section  7.3.1). Adjustments are processes that are independent of GSAT change, whereas feedbacks  refer to 
processes caused by GSAT change. Although adjustments generally occur on time scales of hours to several months, and feedbacks 
respond to ocean surface temperature changes on time scales of a year or more, time scale is not used to separate the definitions. ERF has

often been approximated as the TOA energy balance change due to an imposed perturbation in climate model simulations with sea surface 
temperature and sea-ice concentrations set to their pre-industrial climatological values (e.g., Forster et al., 2016). However, to match 
the adopted forcing–feedback framework, the small effects of any GSAT change from changes in land surface temperatures need to be 
removed from the TOA energy balance in such simulations to give an approximate measure of ERF (Box 7.1, Figure 1b and Section 7.3.1).
approximate ERF:fixed surface temperatures and sea-ice 
approximate ERF: land adjustment removed 
Earth’s energy imbalance (Wm-2)
Global-mean near surface air temperature change (ºC)annual values up to year 150 
annual values after year 150 effective radiative forcing (ERF) for 2xCO2:
energy imbalance at zero temperature change 
equilibrium climate sensitivity (ECS):
temperature change at zero energy imbalance climate feedback parameter ( α):

slope of line connecting ERF and ECS 
Example effective α: regression of first 150 years 
Example estimate of effective ECS 
0                           1                            2                            3 0                           1                            2                            34
2
04
2
0(b) Approximate estimates based on models or other analyses (a) Deﬁntions based on idealised 2xCO2 response 
Box 7.1, Figure 1 | Schematics of the forcing–feedback framework adopted within the assessment, following Equation 7.1.  The figure illustrates 
how the Earth’s top-of-atmosphere (TOA) net energy flux might evolve for a hypothetical doubling of atmospheric CO 2 concentration above pre-industrial levels, where 
an initial positive energy imbalance (energy entering the Earth system, shown on the y-axis) is gradually restored towards equilibrium as the surface temperature

warms (shown on the x-axis). (a) illustrates the definitions of effective radiative forcing (ERF) for the special case of a doubling of atmospheric CO 2 concentration, the 
feedback parameter and the equilibrium climate sensitivity (ECS). (b) illustrates how approximate estimates of these metrics are made within the chapter and how 
these approximations might relate to the exact definitions adopted in panel (a).
The feedback parameter, α (units: W m–2 °C–1) quantifies the change in net energy flux at the TOA for a given change in GSAT. Many 
climate variables affect the TOA energy budget, and the feedback parameter can be decomposed, to first order, into a sum of terms 
where x represents a variable of the Earth system that has a direct effect on the energy budget at the TOA. The sum of the feedback terms 
(i.e., α in Equation 7.1) governs Earth’s equilibrium GSAT response to an imposed ERF . In previous assessments, α and the related ECS

have been associated with a distinct set of physical processes (Planck response and changes in water vapour, lapse rate, surface albedo, 
and clouds; Charney et al., 1979). In this assessment, a more general definition of α and ECS is adopted such that they include additional 
Earth system processes that act across many time scales (e.g., changes in natural aerosol emissions or vegetation). Because, in our 
assessment, these additional processes sum to a near-zero value, including these additional processes does not change the assessed 
central value of ECS but does affect its assessed uncertainty range (Section 7.4.2). Note that there is no standardized notation or sign 
convention for the feedback parameter in the literature. Here the convention is used that the sum of all feedback terms (the net feedback 
parameter, α) is negative for a stable climate that radiates additional energy to space with a GSAT increase, with a more negative value

of α corresponding to a stronger radiative response and thus a smaller GSAT change required to balance a change in ERF (Equation 7.1). !=#$%
$&'&
'T
!

933
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Box 7.1 (continued)
A change in process x amplifies the temperature response to a forcing when the associated feedback parameter αx is positive (positive 
feedback) and dampens the temperature response when αx is negative (negative feedback). New research since AR5 emphasizes how 
feedbacks can vary over different time scales (Section 7.4.4) and with climate state (Section 7.4.3), giving rise to the concept of an 
‘effective feedback parameter’ that may be different from the equilibrium value of the feedback parameter governing ECS (Section 7.4.3).
The equilibrium climate sensitivity, ECS (units: °C), is defined as the equilibrium value of Δ T in response to a sustained doubling 
of atmospheric CO 2 concentration from a pre-industrial reference state. The value of ERF for this scenario is denoted by Δ F2xCO2, giving

ECS = –Δ F2xCO2/α from Equation 7.1 applied at equilibrium (Box 7.1, Figure 1a and Section 7.5). ‘Equilibrium’ refers to a steady state 
where Δ N averages to zero over a multi-century period. ECS is representative of the multi-century to millennial Δ T response to Δ F2xCO2, 
and is based on a CO 2 concentration change so any feedbacks that affect the atmospheric concentration of CO 2 do not influence 
its value. As employed here, ECS also excludes the long-term response of the ice sheets (Section 7.4.2.6) which may take multiple 
millennia to reach equilibrium, but includes all other feedbacks. Due to a number of factors, studies rarely estimate ECS or α at 
equilibrium or under CO 2 forcing alone. Rather, they give an  ‘effective feedback parameter’ (Section 7.4.1 and Box 7.1, Figure 1b) or 
an ‘effective ECS’ (Section 7.5.1 and Box 7.1, Figure 1b), which represent approximations to the true values of α or ECS. The ‘effective

ECS’ represents the equilibrium value of Δ T in response to a sustained doubling of atmospheric CO 2 concentration that would occur 
assuming the ‘effective feedback parameter’ applied at that equilibrium state. For example, a feedback parameter can be estimated 
from the linear slope of Δ N against Δ T over a set number of years within ESM simulations of an abrupt doubling or quadrupling 
of atmospheric CO 2 (2×CO 2 or 4×CO 2, respectively), and the ECS can be estimated from the intersect of this regression line with 
ΔN = 0  (Box 7.1, Figure 1b). To infer ECS from a given estimate of effective ECS necessitates that assumptions are made for how ERF 
varies with CO 2 concentration (Section 7.3.2) and how the slope of Δ N against Δ T relates to the slope of the straight line from ERF to 
ECS (Section 7.5 and Box 7.1, Figure 1b). Care has to be taken when comparing results across different lines of evidence to translate

their estimates of the effective ECS into the ECS definition used here (Section 7.5.5).
The transient climate response, TCR  (units: °C), is defined as the Δ T for the hypothetical scenario in which CO 2 increases at 1% yr –1 
from a pre-industrial reference state to the time of a doubling of atmospheric CO 2 concentration (year 70; Section 7.5). TCR is based 
on a CO 2 concentration change, so any feedbacks that affect the atmospheric concentration of CO 2 do not influence its value. It is 
a measure of transient warming accounting for the strength of climate feedbacks and ocean heat uptake. The transient climate 
response to cumulative emissions of carbon dioxide (TCRE)  is defined as the transient Δ T per 1000 Gt C of cumulative CO 2 
emissions increase since the pre-industrial period. TCRE combines information on the airborne fraction of cumulative CO 2 emissions

(the fraction of the total CO 2 emitted that remains in the atmosphere at the time of doubling, which is determined by carbon cycle 
processes) with information on the TCR. TCR is assessed in this chapter, whereas TCRE is assessed in Chapter 5 (Section 5.5).
7.2 Earth’s Energy Budget and its 
Changes Through Time
Earth’s energy budget encompasses the major energy flows of relevance 
for the climate system (Figure 7.2). Virtually all the energy that enters or 
leaves the climate system does so in the form of radiation at the TOA. 
The TOA energy budget is determined by the amount of incoming solar 
(shortwave) radiation and the outgoing radiation that is composed of 
reflected solar radiation and outgoing  thermal (longwave) radiation 
emitted by the climate system. In a steady-state climate, the outgoing 
and incoming radiative components are essentially in balance in the 
long-term global mean, although there are still fluctuations around this

balanced state that arise through internal climate variability (Brown 
et  al., 2014; Palmer and McNeall, 2014). However, anthropogenic 
forcing has given rise to a persistent imbalance in the global mean 
TOA radiation budget that is often referred to as Earth’s energy 
imbalance (e.g., Trenberth et al., 2014; von Schuckmann et al., 2016), 
which is a key element of the energy budget framework ( N; Box 7.1, 
Equation 7.1) and an important metric of the rate of global climate 
change (Hansen et al., 2005a; von Schuckmann et al., 2020). In addition to the TOA energy fluxes, Earth’s energy budget  al.o includes the 
internal flows of energy within the climate system, which characterize 
the climate state. The surface energy budget consists of the net solar 
and thermal radiation as well as the non-radiative components such as 
sensible, latent and ground heat fluxes (Figure 7.2, upper panel). It is 
a key driver of the global water cycle, atmosphere and ocean dynamics,

as well as a variety of surface processes.
7.2.1 Present-day Energy Budget
Figure 7.2 (upper panel) shows a schematic representation of Earth’s 
energy budget for the early 21st century, including globally averaged 
estimates of the individual components (Wild et al., 2015). Clouds are 
important modulators of global energy fluxes. Thus, any perturbations 
in the cloud fields, such as forcing by aerosol–cloud interactions 
(Section  7.3) or through cloud feedbacks (Section  7.4) can have 
a strong influence on the energy distribution in the climate system. 
To illustrate the overall effects that clouds exert on energy fluxes, 
Figure 7.2 (lower panel) also shows the energy budget in the absence

934
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7
       100   
     (97, 100 ) 
  
absorbed  
surface  up surface down surface         80 
          (75,89) 
 
absorbed 
atmosphere  
evaporation 
 sensible 
heat reﬂected incoming 
reﬂected 
surface 
greenhouse  
gases atmospheric  
window 
latent heat 340 
(340, 341) 
160 
(155,165) 239 
(237, 242) 
398 
(395, 400) 342 
(339, 347) imbalance 82 
(72, 85) 21 
(16, 24)       25 
      ( 23,26) 
0.7 
(0.5, 0.9)     down      185 
    surface  (180, 188 ) Units Wm-2 
All sky Solar Thermal 
outgoing  
  53 
(52, 55) 340 
(340, 341) 267 
(265, 270) 
398 
(395, 400) 314 
(312, 316) Clear sky 
 up surface down surface outgoing  
sensible 
heat reﬂected 
 33 
(31, 34)         73 
         (67,78) 
 
absorbed 
      247 
  (246, 249) 
  
absorbed  
surface 214 
(211, 218 ) 
evaporation  
 Units Wm-2 
Solar Thermal 
incoming 
atmospheric  
window 
gases reﬂected 
surface

Figure 7. 2 | Schematic representation of the global mean energy budget of the Earth (upper panel), and its equivalent without considerations of cloud 
effects (lower panel).  Numbers indicate best estimates for the magnitudes of the globally averaged energy balance components in W m–2 together with their uncertainty 
ranges in parentheses (5–95% confidence range), representing climate conditions at the beginning of the 21st century. Note that the cloud-free energy budget shown in the 
lower panel is not the one that Earth would achieve in equilibrium when no clouds could form. It rather represents the global mean fluxes as determined solely by removing 
the clouds but otherwise retaining the entire atmospheric structure. This enables the quantification of the effects of clouds on the Earth energy budget and corresponds to

the way clear-sky fluxes are calculated in climate models. Thus, the cloud-free energy budget is not closed and therefore the sensible and latent heat fluxes are not quantified 
in the lower panel. Figure adapted from Wild et al. (2015, 2019).

935
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77of clouds, with otherwise identical atmospheric and surface radiative 
properties. It has been derived by taking into account information 
contained in both in situ and satellite radiation measurements taken 
under cloud-free conditions (Wild et al., 2019). A comparison of the 
upper and lower panels in Figure  7.2 shows that without clouds, 
47  W m–2 less solar radiation is reflected back to space globally 
(53 ± 2 W m–2 instead of 100 ± 2 W m–2), while 28 W m–2 more thermal 
radiation is emitted to space (267 ± 3 W m–2 instead of 239 ± 3 W m–2). 
As a result, there is a 20 W m–2 radiative imbalance at the TOA in the 
clear-sky energy budget (Figure  7.2, lower panel), suggesting that 
the Earth would warm substantially if there were no clouds.
The AR5 (Church et al., 2013; Hartmann et al., 2013; Myhre et al., 2013b) 
highlighted the progress that had been made in quantifying the TOA

radiation budget following new satellite observations that became 
available in the early 21st  century (Clouds and the Earth’s Radiant 
Energy System, CERES; Solar Radiation and Climate Experiment, 
SORCE). Progress in the quantification of changes in incoming solar 
radiation at the TOA is discussed in Chapter  2 (Section  2.2). Since 
AR5, the CERES Energy Balance EBAF Ed4.0 product was released, 
which includes algorithm improvements and consistent input datasets 
throughout the record (Loeb et  al., 2018b). However, the overall 
precision of these fluxes (uncertainty in global mean TOA flux of 
1.7% (1.7 W m–2) for reflected solar and 1.3% (3.0 W m–2) for outgoing 
thermal radiation at the 90% confidence level) is not sufficient to 
quantify the Earth’s energy imbalance in absolute terms. Therefore, 
the CERES EBAF reflected solar and emitted thermal TOA fluxes were 
adjusted, within the estimated uncertainties, to ensure that the net

TOA flux for July 2005 to June 2015 was consistent with the estimated 
Earth’s energy imbalance for the same period based on ocean heat 
content (OHC) measurements and energy uptake estimates for the 
land, cryosphere and atmosphere (Section  7.2.2.2; Johnson et  al., 
2016; Riser et al., 2016). ESMs typically show good agreement with 
global mean TOA fluxes from CERES-EBAF . However, as some ESMs are 
known to calibrate their TOA fluxes to CERES or similar data (Hourdin 
et al., 2017), this is not necessarily an indication of model accuracy, 
especially as ESMs show significant discrepancies on regional scales, 
often related to their representation of clouds (Trenberth and Fasullo, 
2010; Donohoe and Battisti, 2012; Hwang and Frierson, 2013; J.-L.F . Li 
et al., 2013; Dolinar et al., 2015; Wild et al., 2015).
The radiation components of the surface energy budget are associated 
with substantially larger uncertainties than at the TOA, since they

are less directly measured by passive satellite sensors and require 
retrieval algorithms and ancillary data for their estimation (Raschke 
et al., 2016; Kato et al., 2018; Huang et al., 2019). Confidence in the 
quantification of the global mean surface radiation components has 
increased recently, as independent estimates now converge to within 
a few W m–2 (Wild, 2017). Current best estimates for downward solar 
and thermal radiation at Earth’s surface are approximately 185 W m–2 
and 342 W m–2, respectively (Figure 7.2). These estimates are based on 
complementary approaches that make use of satellite products from 
active and passive sensors (L’Ecuyer et  al., 2015; Kato et  al., 2018) 
and information from surface observations and Earth system models 
(ESMs; Wild et al., 2015). Inconsistencies in the quantification of the 
global mean energy and water budgets discussed in AR5 (Hartmann

et al., 2013) have been reconciled within the (considerable) uncertainty ranges of their individual components (Wild et al., 2013, 2015; L’Ecuyer 
et  al., 2015). However, on regional scales, the closure of the surface 
energy budgets remains a  challenge with satellite-derived datasets 
(Loeb et al., 2014; L’Ecuyer et al., 2015; Kato et al., 2016). Nevertheless, 
attempts have been made to derive surface energy budgets over land 
and ocean (Wild et al., 2015), over the Arctic (Christensen et al., 2016b), 
and over individual continents and ocean basins (L’Ecuyer et al., 2015; 
Thomas et al., 2020). Since AR5, the quantification of the uncertainties 
in surface energy flux datasets has improved. Uncertainties in global 
monthly mean downward solar and thermal fluxes in the CERES-EBAF 
surface dataset are, respectively, 10 W m–2 and 8 W m–2 (converted to 
5–95% ranges; Kato et al., 2018). The uncertainty in the surface fluxes

for polar regions is larger than in other regions (Kato et al., 2018) due 
to the limited number of surface sites and larger uncertainty in surface 
observations (Previdi et  al., 2015). The uncertainties in ocean mean 
latent and sensible heat fluxes are approximately 11 W m–2 and 5 W m–2 
(converted to 5–95% ranges), respectively (L’Ecuyer et al., 2015). A recent 
review of the latent and sensible heat flux accuracies over the period 
2000–2007 highlights significant differences between several gridded 
products over ocean, where root-mean-squared differences between 
the multi-product ensemble and data at more than 200  moorings 
reached up to 25 W m–2 for latent heat and 5 W m–2 for sensible heat 
(Bentamy et  al., 2017). This uncertainty stems from the retrieval of 
flux-relevant meteorological variables, as well as from differences in the 
flux parametrizations (Yu, 2019). Estimating the uncertainty in sensible

and latent heat fluxes over land is difficult because of the large temporal 
and spatial variability. The flux values over land computed with three 
global datasets vary by 10–20% (L’Ecuyer et al., 2015).
ESMs also show larger discrepancies in their surface energy fluxes than 
at the TOA due to weaker observational constraints, with a spread of 
typically 10–20 W m–2 in the global average, and an even greater 
spread at regional scales (J.-L.F . Li et al., 2013; Wild et al., 2013; Boeke 
and Taylor, 2016; Wild, 2017, 2020; C. Zhang et al., 2018). Differences 
in the land-averaged downward thermal and solar radiation in CMIP5 
ESMs amount to more than 30 and 40 W m–2, respectively (Wild et al., 
2015). However, in the global multi-model mean, the magnitudes of 
the energy budget components of the CMIP6 ESMs generally show 
better agreement with reference estimates than previous model 
generations (Wild, 2020).
In summary, since AR5, the magnitudes of the global mean energy

budget components have been quantified more accurately, not only at 
the TOA, but also at the Earth’s surface, where independent estimates 
of the radiative components have converged ( high confidence ). 
Considerable uncertainties remain in regional surface energy budget 
estimates as well as their representation in climate models.
7.2.2 Changes in Earth’s Energy Budget
7.2.2.1 Changes in Earth’s Top-of-atmosphere Energy Budget
Since 2000, changes in top-of-atmosphere (TOA) energy fluxes can 
be tracked from space using CERES satellite observations (Figure 7.3). 
The variations in TOA energy fluxes reflect the influence of internal 
climate variability, particularly that of El Niño–Southern Oscillation

936
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7(ENSO), in addition to radiative forcing of the climate system and 
climate feedbacks (Allan et al., 2014; Loeb et al., 2018b). For example, 
globally, the reduction in both outgoing thermal and reflected solar 
radiation during La Niña conditions in 2008/2009 led to an energy 
gain for the climate system, whereas enhanced outgoing thermal and 
reflected solar radiation caused an energy loss during the El Niños of 
2002/2003 and 2009/2010 (Figure 7.3; Loeb et al., 2018b). An ensemble 
of CMIP6 models is able to track the variability in global mean TOA fluxes observed by CERES, when driven with prescribed sea surface 
temperatures (SSTs) and sea ice concentrations (Figure 7.3; Loeb et al., 
2020). Under cloud-free conditions, the CERES record shows a near 
zero trend in outgoing thermal radiation (Loeb et al., 2018b), which – 
combined with an increasing surface upwelling thermal flux – implies

an increasing clear-sky greenhouse effect (Raghuraman et al., 2019). 
Conversely, clear-sky solar reflected TOA radiation in the CERES record 
covering March 2000 to September 2017 shows a decrease due to 
Figure 7 .3 | Anomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 (solid black lines) and various CMIP6 climate models 
(coloured lines) in terms of (a) reflected solar, (b) emitted thermal and (c) net TOA fluxes.  The multi-model means are additionally depicted as solid red lines. 
Model fluxes stem from simulations driven with prescribed sea surface temperatures (SSTs) and all known anthropogenic and natural forcings. Shown are anomalies of 12-month 
running means. All flux anomalies are defined as positive downwards, consistent with the sign convention used throughout this chapter. The correlations between the multi-model

means (solid red lines) and the CERES records (solid black lines) for 12-month running means are: 0.85 for the global mean reflected solar; 0.73 for outgoing thermal radiation; 
and 0.81 for net TOA radiation. Figure adapted from Loeb et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

937
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77reductions in aerosol optical depth in the Northern Hemisphere and 
sea ice fraction (Loeb et al., 2018a; Paulot et al., 2018).
An effort to reconstruct variations in net TOA fluxes back to 1985, 
based on a combination of satellite data, atmospheric reanalysis and 
high-resolution climate model simulations (Allan et  al., 2014; Liu 
et  al., 2020), exhibits strong interannual variability associated with 
the volcanic eruption of Mount Pinatubo in 1991 and the ENSO events 
before 2000. The same reconstruction suggests that Earth’s energy 
imbalance increased by several tenths of a W m–2 between the periods 
1985–1999 and 2000–2016, in agreement with the assessment of 
changes in the global energy inventory (Section 7.2.2.2, and Box 7.2, 
Figure  1). Comparisons of year-to-year variations in Earth’s energy 
imbalance estimated from CERES and independent estimates based

on ocean heat content change are significantly correlated with similar 
phase and magnitude (Johnson et al., 2016; Meyssignac et al., 2019), 
promoting confidence in both satellite and in situ-based estimates 
(Section 7.2.2.2).
In summary, variations in the energy exchange between Earth and space 
can be accurately tracked since the advent of improved observations 
since the year 2000 ( high confidence ), while reconstructions indicate 
that the Earth’s energy imbalance was larger in the 2000s than in the 
1985–1999 period ( high confidence ).
7.2.2.2 Changes in the Global Energy Inventory
The global energy inventory quantifies the integrated energy gain 
of the climate system associated with global ocean heat uptake, 
warming of the atmosphere, warming of the land, and melting of ice. 
Due to energy conservation, the rate of accumulation of energy in 
the Earth system (Section  7.1) is equivalent to the Earth energy 
imbalance (Δ N in Box  7.1, Equation 7.1). On annual and longer

time scales, changes in the global energy inventory are dominated 
by changes in global ocean heat content (OHC; Rhein et al., 2013; 
Palmer and McNeall, 2014; Johnson et al., 2016). Thus, observational 
estimates and climate model simulations of OHC change are critical 
to the understanding of both past and future climate change 
(Sections 2.3.3.1, 3.5.1.3, 4.5.2.1 and 9.2.2.1).
Since AR5, both modelling and observation-based studies have 
established Earth’s energy imbalance (characterized by OHC change) 
as a more robust metric of the rate of global climate change than 
GSAT on interannual-to-decadal time scales (Palmer and McNeall, 
2014; von Schuckmann et al., 2016; Wijffels et al., 2016; Cheng et al., 
2018; Allison et al., 2020). This is because GSAT is influenced by large 
unforced variations, for example linked to ENSO and Pacific Decadal 
Variability (Roberts et al., 2015; Yan et al., 2016; Cheng et al., 2018). 
Measuring OHC change more comprehensively over the full ocean

depth results in a higher signal-to-noise ratio and a time series that 
increases steadily over time (Box 7.2, Figure 1; Allison et al., 2020). 
In addition, understanding of the potential effects of historical ocean 
sampling on estimated global ocean heating rates has improved 
(Durack et al., 2014; Good, 2017; Allison et al., 2019) and there are 
now more estimates of OHC change available that aim to mitigate the 
effect of limited observational sampling in the Southern Hemisphere 
(Lyman and Johnson, 2008; Cheng et al., 2017; Ishii et al., 2017).The assessment of changes in the global energy inventory for 
the  periods 1971–2018, 1993–2018 and 2006–2018 draws upon 
the latest observational time series and the assessments presented 
in other chapters of this report. The estimates of OHC change come 
directly from the assessment presented in Chapter 2 (Section 2.3.3.1). 
The assessment of land and atmospheric heating comes from von

Schuckmann et al. (2020), based on the estimates of Cuesta-Valero 
et al. (2021) and Steiner et al. (2020), respectively. Heating of inland 
waters, including lakes, reservoirs and rivers, is estimated to account 
for <0.1% of the total energy change, and is therefore omitted from 
this assessment (Vanderkelen et al., 2020).  The cryosphere contribution 
from the melting of grounded ice is based on the mass-loss 
assessments presented in Chapter  9, Section  9.4.1 (Greenland 
Ice Sheet), Section  9.4.2 (Antarctic Ice Sheet) and Section  9.5.1 
(glaciers). Following AR5, the estimate of heating associated with 
loss of Arctic sea ice is based on a reanalysis (Schweiger et al., 2011), 
following the methods described by Slater et  al. (2021). Chapter  9 
(Section 9.3.2) finds no significant trend in Antarctic sea ice area over 
the observational record, so a zero contribution is assumed. Ice melt 
associated with the calving and thinning of floating ice shelves is

based on the decadal rates presented in Slater et al. (2021). For all 
cryospheric components, mass loss is converted to heat input using 
a latent heat of fusion of 3.34 × 105 J Kg–1 °C–1 with the second-order 
contributions from variations associated with ice type and warming 
of ice from sub-freezing temperatures disregarded, as in AR5. The net 
change in energy, quantified in Zettajoules (1 ZJ = 1021  Joules), 
is computed for each component as the difference between the 
first and last year of each period (Table  7.1). The uncertainties in 
the depth-interval contributions to OHC are summed to get the 
uncertainty in global OHC change. All other uncertainties are assumed 
to be independent and added in quadrature. 
For the period 1971–2010, AR5 (Rhein et al., 2013) found an increase 
in the global energy inventory of 274 [196 to 351] ZJ with a  93% 
contribution from total OHC change, approximately 3% for both ice 
melt and land heating, and approximately 1% for warming of the

atmosphere. For the same period, this Report finds an upwards revision 
of OHC change for the upper (<700 m depth) and deep (>700 m depth) 
ocean of approximately 8% and 20%, respectively, compared to AR5 
and a modest increase in the estimated uncertainties associated with 
the ensemble approach of Palmer et al. (2021). The other substantive 
change compared to AR5 is the updated assessment of land heating, 
with values approximately double those assessed previously, based 
on a  more comprehensive analysis of the available observations 
(von Schuckmann et al., 2020; Cuesta-Valero et al., 2021). The result 
of these changes is an assessed energy gain of 329 [224 to 434] ZJ 
for the period 1971–2010, which is consistent with AR5 within the 
estimated uncertainties, despite the systematic increase.
The assessed changes in the global energy inventory (Box  7.2, 
Figure 1, and Table 7.1) yields an average value for Earth’s energy

imbalance ( N in Box 7.1, Equation 7.1) of 0.57 [0.43 to 0.72] W m–2 
for the period 1971–2018, expressed relative to Earth’s surface 
area ( high  confidence ). The estimates for the periods 1993–2018 
and 2006–2018 yield substantially larger values of 0.72  [0.55  to 
0.89]  W m–2 and 0.79 [0.52 to 1.06] W m–2, respectively, consistent with 
the increased radiative forcing from GHGs ( high confidence ). For the

938
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7period 1971–2006, the total energy gain was 282 [177 to 387] ZJ, with 
an equivalent Earth energy imbalance of 0.50 [0.32 to 0.69] W m–2. 
To  put these numbers in context, the 2006–2018 average Earth 
system heating is equivalent to approximately 20 times the annual 
rate of global energy consumption in 2018.1
Consistent with AR5 (Rhein et al., 2013), this Report finds that ocean 
warming dominates the changes in the global energy inventory 
(high confidence ), accounting for 91% of the observed change for 
all periods considered (Table 7.1). The contributions from the other 
components across all periods are approximately 5% from land 
heating, 3% for cryosphere heating and 1% associated with warming 
of the atmosphere ( high confidence ). The assessed percentage 
contributions are similar to the recent study by von Schuckmann et al.

(2020) and the total heating rates are consistent within the assessed 
uncertainties. Cross-validation of heating rates based on satellite and 
in situ observations (Section 7.2.2.1), and closure of the global sea 
level budget using consistent datasets (Cross-Chapter Box 9.1 and 
Table 9.5), strengthen scientific confidence in the assessed changes 
in the global energy inventory relative to AR5.
7.2.2.3 Changes in Earth’s Surface Energy Budget
The AR5 (Hartmann et  al., 2013) reported pronounced changes 
in multi-decadal records of in situ observations of surface solar 
radiation, including a  widespread decline between the 1950s and 
1980s, known as ‘global dimming’, and a partial recovery thereafter, 
termed ‘brightening’ (Section 12.4). These changes have interacted 
with closely related elements of climate change, such as global and 
regional warming rates (Z. Li et al., 2016; Wild, 2016; Du et al., 2017; 
Zhou et al., 2018a), glacier melt (Ohmura et al., 2007; Huss et al.,

2009), the intensity of the global water cycle (Wild, 2012) and 
terrestrial carbon uptake (Mercado et  al., 2009). These observed 
changes have also been used as emergent constraints to quantify 
aerosol effective radiative forcing (Section 7.3.3.3).
Since AR5, additional evidence for dimming and/or subsequent 
brightening up to several percent per decade, based on direct surface 
observations, has been documented in previously less-studied areas 
1 https://ourworldindata.org/energy , accessed 13 April 2021.of the globe, such as Iran, Bahrain, Tenerife, Hawaii, the Taklaman 
Desert and the Tibetan Plateau (Elagib and Alvi, 2013; You et al., 2013; 
Garcia et al., 2014; Longman et al., 2014; Rahimzadeh et al., 2015). 
Strong decadal trends in surface solar radiation remain evident after 
careful data quality assessment and homogenization of long-term 
records (Sanchez-Lorenzo et  al., 2013, 2015; Manara et  al., 2015,

2016; Wang et al., 2015; Z. Li et al., 2016; Wang and Wild, 2016; Y . He 
et al., 2018; Yang et al., 2018). Since AR5, new studies on the potential 
effects of urbanization on solar radiation trends indicate that these 
effects are generally small, with the exception of some specific sites 
in Russia and China (Wang et al., 2014; Imamovic et al., 2016; Tanaka 
et al., 2016). Also, surface-based solar radiation observations have 
been shown to be representative over large spatial domains of up 
to several degrees latitude/longitude on monthly and longer time 
scales (Hakuba et al., 2014; Schwarz et al., 2018). Thus, there is high 
confidence  that the observed dimming between the 1950s and 1980s 
and the subsequent brightening are robust and do not arise from 
measurement artefacts or localized phenomena.
As noted in AR5 (Hartmann et al., 2013) and supported by recent studies, 
the trends in surface solar radiation are less spatially coherent since the

beginning of the 21st century, with evidence for continued brightening 
in parts of Europe and the USA, some stabilization in China and India, 
and dimming in other areas (Augustine and Dutton, 2013; Sanchez-
Lorenzo et al., 2015; Manara et al., 2016; Soni et al., 2016; Wang and 
Wild, 2016; Jahani et al., 2018; Pfeifroth et al., 2018; Yang et al., 2018; 
Schwarz et  al., 2020). The CERES-EBAF satellite-derived dataset of 
surface solar radiation (Kato et al., 2018) does not indicate a globally 
significant trend over the short period 2001–2012 (Zhang et al., 2015), 
whereas a statistically significant increase in surface solar radiation of 
+3.4 W m−2 per decade over the period 1996–2010 has been found 
in the Satellite Application Facility on Climate Monitoring (CM SAF) 
record of the geostationary satellite Meteosat, which views Europe, 
Africa and adjacent ocean (Posselt et al., 2014).
Since AR5, there is additional evidence that strong decadal changes

in surface solar radiation have occurred under cloud-free conditions, 
as shown for long-term observational records in Europe, USA, China, 
India and Japan (Xu et al., 2011; Gan et al., 2014; Manara et al., 2016; Table  7.1 | Contributions of the different components of the global energy inventory for the periods 1971–2018, 1993–2018 and 2006–2018 (Box 7.2 
and Cross-Chapter Box 9.1).  Energy changes are computed as the difference between annual mean values or year mid-points. The total heating rates correspond to Earth’s 
energy imbalance and are expressed per unit area of Earth’s surface.
Component1971–2018 1993–2018 2006–2018
Energy Gain (ZJ) % Energy Gain (ZJ) % Energy Gain (ZJ) %
Ocean
0–700 m
700–2000 m 
>2000 m396.0 [285.7 to 506.2]
241.6 [162.7 to 320.5] 
123.3 [96.0 to 150.5]
31.0 [15.7 to 46.4]91.0
55.6
28.3
7.1263.0 [194.1 to 331.9]
151.5 [114.1 to 188.9]
82.8 [59.9 to 105.6]
28.7 [14.5 to 43.0]91.0
52.4
28.6
10.0138.8 [86.4 to 191.3]
75.4 [48.7 to 102.0]

49.7 [29.0 to 70.4]
13.8 [7.0 to 20.6]91.1
49.5
32.6
9.0
Land 21.8 [18.6 to 25.0] 5.0 13.7 [12.4 to 14.9] 4.7 7.2 [6.6 to 7.8] 4.7
Cryosphere 11.5 [9.0 to 14.0] 2.7 8.8 [7.0 to 10.5] 3.0 4.7 [3.3 to 6.2] 3.1
Atmosphere 5.6 [4.6 to 6.7] 1.3 3.8 [3.2 to 4.3] 1.3 1.6 [1.2 to 2.1] 1.1
TOTAL 434.9 [324.5 to 545.3] ZJ 289.2 [220.3 to 358.1] ZJ 152.4 [100.0 to 204.9] ZJ
Heating Rate 0.57 [0.43 to 0.72] W m–20.72 [0.55 to 0.89] W m–20.79 [0.52 to 1.06] W m–2

939
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Soni et al., 2016; Tanaka et al., 2016; Kazadzis et al., 2018; J. Li et al., 
2018; Yang et al., 2019; Wild et al., 2021). This suggests that changes 
in the composition of the cloud-free atmosphere, primarily in aerosols, 
contributed to these variations, particularly since the second half of 
the 20th  century (Wild, 2016). Water vapour and other radiatively 
active gases seem to have played a minor role (Wild, 2009; Mateos 
et al., 2013; Posselt et al., 2014; Yang et al., 2019). For Europe and East 
Asia, modelling studies also point to aerosols as an important factor 
for dimming and brightening by comparing simulations that include 
or exclude variations in anthropogenic aerosol and aerosol-precursor 
emissions (Golaz et al., 2013; Nabat et al., 2014; Persad et al., 2014; 
Folini and Wild, 2015; Turnock et  al., 2015; Moseid et  al., 2020).

Moreover, decadal changes in surface solar radiation have often 
occurred in line with changes in anthropogenic aerosol emissions 
and associated aerosol optical depth (Streets et al., 2006; Wang and 
Yang, 2014; Storelvmo et al., 2016; Wild, 2016; Kinne, 2019). However, 
further evidence for the influence of changes in cloudiness on dimming 
and brightening is emphasized in some studies (Augustine and 
Dutton, 2013; Parding et al., 2014; Stanhill et al., 2014; Pfeifroth et al., 
2018; Antuña-Marrero et al., 2019). Thus, the contribution of aerosol 
and clouds to dimming and brightening is still debated. The relative 
influence of cloud-mediated aerosol effects versus direct aerosol 
radiative effects on dimming and brightening in a specific region may 
depend on the prevailing pollution levels (Section 7.3.3; Wild, 2016).
ESMs and reanalyses often do not reproduce the full extent of 
observed dimming and brightening (Wild and Schmucki, 2011; Allen

et al., 2013; Zhou et al., 2017a; Storelvmo et al., 2018; Moseid et al., 
2020; Wohland et al., 2020), potentially pointing to inadequacies in 
the representation of aerosol mediated effects or related emissions 
data. The inclusion of assimilated aerosol optical depth inferred from 
satellite retrievals in the MERRA2 reanalysis (Buchard et al., 2017; 
Randles et al., 2017) helps to improve the accuracy of the simulated 
surface solar radiation changes in China (Feng and Wang, 2019). 
However, non-aerosol-related deficiencies in model representations 
of clouds and circulation, and/or an underestimation of natural 
variability, could further contribute to the lack of dimming and 
brightening in ESMs (Wild, 2016; Storelvmo et al., 2018).
The AR5 reported evidence for an increase in surface downward 
thermal radiation based on different studies covering 1964 to 2008, in line with what would be expected from an increased radiative forcing

from GHGs and the warming and moistening of the atmosphere. 
Updates of the longest observational records from the Baseline 
Surface Radiation Network continue to show an increase at the 
majority of sites, in line with an overall increase predicted by ESMs 
of the order of 2 W m–2 per decade (Wild, 2016). Upward longwave 
radiation at the surface is rarely measured but is expected to have 
increased over the same period due to rising surface temperatures.
Turbulent fluxes of latent and sensible heat are also an important 
part of the surface energy budget (Figure 7.2). Large uncertainties 
in measurements of surface turbulent fluxes continue to prevent the 
determination of their decadal changes. Nevertheless, over the ocean, 
reanalysis-based estimates of linear trends from 1948–2008 indicate 
high spatial variability and seasonality. Increases in magnitudes of 
4 to 7 W m–2 per decade for latent heat and 2 to 3 W m–2 per decade

for sensible heat in the western boundary current regions are mostly 
balanced by decreasing trends in other regions (Gulev and Belyaev, 
2012). Over land, the terrestrial latent heat flux is estimated to have 
increased in magnitude by 0.09 W m–2 per decade from 1989–1997, 
and subsequently decreased by 0.13 W m–2 per  decade from 
1998–2005 due to soil-moisture limitation mainly in the Southern 
Hemisphere (derived from Mueller et al., 2013). These trends are small 
in comparison to the uncertainty associated with satellite-derived 
and in situ observations, as well as from land-surface models forced 
by observations and atmospheric reanalyses. Ongoing advances 
in remote sensing of evapotranspiration from space (Mallick 
et  al., 2016; Fisher et  al., 2017; McCabe et  al., 2017a, b), as well 
as terrestrial water storage (Rodell et al., 2018) may contribute to 
future constraints on changes in latent heat flux.
In summary, since AR5, multi-decadal decreasing and increasing trends

in surface solar radiation of up to several percent per decade have 
been detected at many more locations, even in remote areas. There is 
high confidence  that these trends are widespread, and not localized 
phenomena or measurement artefacts. The origin of these trends is 
not fully understood, although there is evidence that anthropogenic 
aerosols have made a substantial contribution ( medium confidence ). 
There is medium confidence that downward and upward thermal 
radiation has increased since the 1970s, while there remains low 
confidence  in the trends in surface sensible and latent heat.
Box 7.2 | The Global Energy Budget
This box assesses the present knowledge of the global energy budget for the period 1971–2018, that is, the balance between radiative 
forcing, the climate system radiative response and observations of the changes in the global energy inventory (Box 7.2, Figure 1a,d).

The net effective radiative forcing (ERF) of the Earth system since 1971 has been positive (Section 7.3 and Box 7.2, Figure 1b,e), mainly 
as a result of increases in atmospheric greenhouse gas concentrations (Sections 2.2.8 and 7.3.2). The ERF of these positive forcing 
agents have been partly offset by that of negative forcing agents, primarily due to anthropogenic aerosols (Section 7.3.3), which 
dominate the overall uncertainty. The net energy inflow to the Earth system from ERF for the period 1971–2018 is estimated to be 
937 ZJ (1 ZJ = 1021 J) with a  likely  range of 644 to 1259 ZJ (Box 7.2, Figure 1b).

940
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Box 7.2 (continued)
The ERF-induced heating of the climate system results in increased thermal radiation to space via the Planck response, but the picture 
is complicated by a variety of climate feedbacks (Section 7.4.2 and Box 7.1) that also influence the climate system radiative response 
(Box 7.2, Figure 1c). The total radiative response is estimated by multiplying the assessed net feedback parameter, α, from process-based 
evidence (Section 7.4.2 and Table 7.10) with the observed GSAT change for the period (Cross Chapter Box 2.3) and time-integrating 
(Box 7.2, Figure 1c). The net energy outflow from the Earth system associated with the integrated radiative response for the period 
1971–2018 is estimated to be 621 ZJ with a  likely range of 419 to 823 ZJ. Assuming a pattern effect (Section 7.4.4) on α of –0.5  W m–2 °C–1 
would lead to a systematically larger energy outflow by about 250 ZJ.

Box 7.2, Figure 1 | Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: (a) observations of 
changes in the global energy inventory; (b) integrated radiative forcing; and (c) integrated radiative response.  Black dotted lines indicate the central 
estimate with likely and very likely  ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial 
Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2 °C–1 (c). Background grey lines indicate equivalent heating 
rates in W m–2 per unit area of Earth’s surface. Panels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and 
integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change

in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under 
a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. Shading 
represents the very likely  range for observed energy change relative to pre-industrial levels and likely range for all other quantities. Forcing and response time series 
are expressed relative to a baseline period of 1850–1900. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).
Combining the likely range of integrated radiative forcing (Box  7.2, Figure  1b) with the central estimate of integrated radiative 
response (Box 7.2, Figure 1c) gives a central estimate and likely  range of 340 [47 to 662] ZJ (Box 7.2, Figure 1f). Combining the likely

range of integrated radiative response with the central estimate of integrated radiative forcing gives a  likely  range of 340 [147 to 
527] ZJ (Box 7.2, Figure 1f). Both calculations yield an implied energy gain in the climate system that is consistent with an independent 
observation-based assessment of the increase in the global energy inventory expressed relative to the estimated 1850–1900 
(b) (a) (c)
(f) (e) (d)

941
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Box 7.2 (continued)
Earth energy imbalance (Section 7.5.2 and Box 7.2, Figure 1a) with a central estimate and very likely  range of 284 [96 to 471] ZJ 
(high confidence ) (Box 7.2, Figure 1d; Table 7.1). Estimating the total uncertainty associated with radiative forcing and radiative 
response remains a scientific challenge and depends on the degree of correlation between the two (Box 7.2, Figure 1f). However, the 
central estimate of observed energy change falls well with the estimated likely  range, assuming either correlated or uncorrelated 
uncertainties. Furthermore, the energy budget assessment would accommodate a  substantial pattern effect (Section  7.4.4.3) 
during 1971–2018 associated with systematically larger values of radiative response (Box 7.2, Figure 1c), and potentially improved

closure of the global energy budget. For the period 1970–2011, AR5 reported that the global energy budget was closed within 
uncertainties ( high confidence ) and consistent with the likely  range of assessed climate sensitivity (Church et  al., 2013). This 
Report provides a  more robust quantitative assessment based on additional evidence and improved scientific understanding.
In addition to new and extended observations (Section 7.2.2), confidence in the observed accumulation of energy in the Earth system 
is strengthened by cross-validation of heating rates based on satellite and in situ observations (Section  7.2.2.1) and closure of 
the global sea level budget using consistent datasets (Cross-Chapter Box 9.1 and Table 9.5). Overall, there is high confidence  that 
the global energy budget is closed for 1971–2018 with improved consistency compared to AR5.
7.3 Effective Radiative Forcing
Effective radiative forcing (ERF) quantifies the energy gained or lost by

the Earth system following an imposed perturbation (for instance in 
GHGs, aerosols or solar irradiance). As such it is a fundamental driver 
of changes in the Earth’s TOA energy budget. ERF is determined by 
the change in the net downward radiative flux at the TOA (Box 7.1) 
after the system has adjusted to the perturbation but excluding the 
radiative response to changes in surface temperature. This section 
outlines the methodology for ERF calculations (Section  7.3.1) and 
then assesses the ERF due to greenhouse gases (Section  7.3.2), 
aerosols (Section 7.3.3) and other natural and anthropogenic forcing 
agents (Section 7.3.4). These are brought together in Section 7.3.5 
for an overall assessment of the present-day ERF and its evolution 
over the historical time period from 1750 to 2019. The same section 
also evaluates the surface temperature response to individual ERFs.
7.3.1 Methodologies and Representation 
in Models: Overview of Adjustments

As introduced in Box 7.1, AR5 (Boucher et al., 2013; Myhre et al., 
2013b) recommended ERF as a more useful measure of the climate 
effects of a  physical driver than the stratospheric-temperature-
adjusted radiative forcing (SARF) adopted in earlier assessments. 
The AR5 assessed that the ratios of surface temperature change to 
forcing resulting from perturbations of different forcing agents were 
more similar between species using ERF than SARF . ERF extended the 
SARF concept to account for not only adjustments to stratospheric 
temperatures, but also responses in the troposphere and effects on 
clouds and atmospheric circulation, referred to as ‘adjustments’. 
For more details see Box 7.1. Since circulation can be affected, these 
responses are not confined to the locality of the initial perturbation 
(unlike the traditional stratospheric-temperature adjustment).
This chapter defines ‘adjustments’ as those changes caused by the

forcing agent that are independent of changes in surface temperature, rather than defining a  specific time scale. The AR5 used the term 
‘rapid adjustment’, but in this assessment the definition is based on 
the independence from surface temperature rather than the rapidity. 
The definition of ERF in Box 7.1 aims to create a clean separation 
between forcing (energy budget changes that are not mediated by 
surface temperature) and feedbacks (energy budget changes that 
are mediated by surface temperature). This means that changes in 
land or ocean surface temperature patterns (for instance as identified 
by Rugenstein et al., 2016b) are not included as adjustments. As in 
previous assessments (Forster et al., 2007; Myhre et al., 2013b) ERFs 
can be attributed simply to changes in the forcing agent itself or 
attributed to components of emitted gases (Figure  6.12). Because 
ERFs can include chemical and biospheric responses to emitted

gases, they can be attributed to precursor gases, even if those gases 
do not have a direct radiative effect themselves. Similar chemical and 
biospheric responses to forcing agents can also be included in the 
ERF in addition to their direct effects.
Instantaneous radiative forcing (IRF) is defined here as the change 
in the net TOA radiative flux following a perturbation, excluding any 
adjustments. SARF is defined here as the change in the net radiative 
flux at TOA following a  perturbation including the response to 
stratospheric temperature adjustments. These differ from AR5 where 
these quantities were defined at the tropopause (Myhre et al., 2013b). 
The net IRF values will be different using the TOA definition. The net 
SARF values will be the same as with the tropopause definition, 
but will have a  different partitioning between the longwave and 
shortwave. Defining all quantities at the TOA enables consistency in 
breaking down the ERF into its component parts.

The assessment of ERFs in AR5 was preliminary because ERFs were 
only available for a few forcing agents, so for many forcing agents the 
Report made the assumption that ERF and SARF were equivalent. This 
section discusses the body of work published since AR5. This work has 
computed ERFs across many more forcing agents and models; closely 
examined the methods of computation; quantified the processes 
involved in causing adjustments; and examined how well ERFs predict

942
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7the ultimate temperature response. This work is assessed to have led 
to a much-improved understanding and increased confidence in the 
quantification of radiative forcing across the Report. These same 
techniques allow for an evaluation of radiative forcing within Earth 
system models (ESMs) as a key test of their ability to represent both 
historical and future temperature changes (Sections 3.3.1 and 4.3.4).
The ERF for a  particular forcing agent is the sum of the IRF and 
the contribution from the adjustments, so in principle this could 
be constructed bottom-up by calculating the IRF and adding in the 
adjustment contributions one-by-one or together. However, there 
is no simple way to derive the global tropospheric adjustment 
terms or adjustments related to circulation changes without using 
a comprehensive climate model (e.g., CMIP5 or CMIP6). There have

been two main modelling approaches used to approximate the ERF 
definition in Box 7.1. The first approach is to use the assumed linearity 
(Box 7.1, Equation 7.1) to regress the net change in the TOA radiation 
budget (Δ N) against change in global mean surface temperature 
(ΔT) following a step change in the forcing agent (Box 7.1, Figure 1; 
Gregory et  al., 2004). The ERF (Δ F) is then derived from Δ N when 
ΔT = 0. Regression-based estimates of ERF depend on the temporal 
resolution of the data used (Modak et al., 2016, 2018). For the first 
few months of a  simulation both surface temperature change and 
stratospheric-temperature adjustment occur at the same time, leading 
to misattribution of the stratospheric-temperature adjustment to the 
surface temperature feedback. Patterns of sea surface temperature 
(SST) change also affect estimates of the forcing obtained by regression 
methods (Andrews et  al., 2015). At multi-decadal time scales the

curvature of the relationship between net TOA radiation and surface 
temperature can also lead to biases in the ERF estimated from the 
regression method (Section 7.4; Armour et al., 2013; Andrews et al., 
2015; Knutti et al., 2017). The second modelling approach to estimate 
ERF is to set the Δ T term in Box 7.1 (Box 7.1, Equation 7.1) to zero. It is 
technically difficult to constrain land surface temperatures in ESMs 
(Shine et al., 2003; Ackerley and Dommenget, 2016; Andrews et al., 
2021), so most studies reduce the Δ T term by prescribing the SSTs 
and sea ice concentrations in a pair of ‘fixed-SST’ (fSST) simulations 
with and without the change in forcing agent (Hansen et al., 2005b). 
An approximation to ERF (Δ Ffsst) is then given by the difference in 
ΔNfsst between the simulations. The fSST method has less noise due 
to internal variability than the regression method. Nevertheless 
a 30-year fSST integration or 10 × 20-year regression ensemble needs

to be conducted in order to reduce the 5–95% confidence range to 
0.1 W m–2 (Forster et al., 2016).Neither the regression or fSST methods 
are practical for quantifying the ERF of agents with forcing magnitudes 
of the order of 0.1 W m–2 or smaller. The internal variability in the 
fSST method can be further constrained by nudging winds towards 
a  prescribed climatology (Kooperman et  al., 2012). This allows the 
determination of the ERF of forcing agents with smaller magnitudes 
but excludes adjustments associated with circulation responses 
(Schmidt et al., 2018). There are insufficient studies to assess whether 
these circulation adjustments are significant.
Since the near-surface temperature change over land, Δ Tland, is not 
constrained in the fSST method, this response needs to be removed 
for consistency with the Section 7.1 definition. These changes in the

near-surface temperature will also induce further responses in the tropospheric temperature and water vapour that should also be 
removed to conform with the physical definition of ERF . The radiative 
response to Δ Tland can be estimated through radiative transfer 
modelling in which a kernel, k, representing the change in net TOA 
radiative flux per unit of change in near-surface temperature change 
over land (or an approximation using land surface temperature), is 
precomputed (Smith et  al., 2018b, 2020b; Richardson et  al., 2019; 
Tang et al., 2019). Thus ERF ≈ Δ Ffsst – k ΔTland. Since k is negative 
this means that Δ Ffsst underestimates the ERF . For 2×CO 2, this 
underestimate is around 0.2 W m–2 (Smith et  al., 2018b, 2020b). 
There have been estimates of the corrections due to tropospheric 
temperature and water vapour (Tang et al., 2019; Smith et al., 2020b) 
showing additional radiative responses of comparable magnitude to

those directly from Δ Tland. An alternative to computing the response 
terms directly is to use the feedback parameter, α (Hansen et al., 2005b; 
Sherwood et al., 2015; Tang et al., 2019). This gives approximately 
double the correction compared to the kernel approach (Tang et al., 
2019). The response to land surface temperature change varies with 
location and even for GSAT change k is not expected to be the same 
as α (Section 7.4). One study where land surface temperatures are 
constrained in a model (Andrews et al., 2021) finds this constraint 
adds +1.0 W m–2 to Δ Ffsst for 4×CO 2, thus confirming the need for 
a  correction in calculations where this constraint is not applied. 
For this assessment the correction is conservatively based only on 
the direct radiative response kernel to Δ Tland as this has a  strong 
theoretical basis to support it. While there is currently insufficient 
corroborating evidence to recommend including tropospheric

temperature and water-vapour corrections in this assessment, it is 
noted that the science is progressing rapidly on this topic.
TOA radiative flux changes due to the individual adjustments can be 
calculated by perturbing the meteorological fields in a climate model’s 
radiative transfer scheme (partial radiative perturbation approach) 
(Colman, 2015; Mülmenstädt et al., 2019) or by using precomputed 
radiative kernels of sensitivities of the TOA radiation fluxes to 
changes in these fields (as done for near-surface temperature change 
above; Vial et al., 2013; Zelinka et al., 2014; Zhang and Huang, 2014; 
Smith et al., 2018b, 2020b). The radiative kernel approach is easier to 
implement through post-processing of output from multiple ESMs, 
whereas it is recognized that the partial radiation perturbation 
approach gives a more accurate estimate of the adjustments within 
the setup of a  single model and its own radiative transfer code.

There is little difference between using a radiative kernel from the 
same or a different model when calculating the adjustment terms, 
except for stratospheric temperature adjustments where it is 
important to have sufficient vertical resolution in the stratosphere 
in the model used to derive the kernel (Smith et al., 2018b, 2020a).
For comparison with offline radiative transfer calculations the SARFs 
can be approximated by removing the adjustment terms (apart from 
stratospheric temperature) from the ERFs using radiative kernels to 
quantify the adjustment for each meteorological variable. Kernel 
analysis by Chung and Soden (2015) suggested a  large spread in 
CO 2 SARF across climate models, but their analysis was based on 
regressing variables in a  coupled-ocean experiment rather than 
using a fSST approach which leads to a large spread due to natural 
variability (Forster et al., 2016). Adjustments computed from radiative

943
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77kernels are shown for seven different climate drivers (using a fSST 
approach) in Figure  7.4. Table  7.2 shows the estimates of SARF , 
ΔFfsst and ERF (corrected for land surface temperature change) for 
2×CO 2 from the nine climate models analysed in Smith et al. (2018b). 
The  SARF shows a  smaller spread over previous studies (Pincus 
et  al., 2016; Soden et  al., 2018) and most estimates are within 
10% of the multi-model mean and the assessment of 2×CO 2 SARF 
in Section 7.3.2 (3.75 W m–2). It is not possible from these studies to 
determine how much of this reduction in spread is due to convergence 
in the model radiation schemes or the meteorological conditions 
of the model base states; nevertheless the level of agreement in 
this and earlier intercomparisons gives medium confidence  in the 
ability of ESMs to represent radiative forcing from CO 2. The 4×CO 2

CMIP6 fSST experiments (Smith et  al., 2020b) in Table  7.2 include 
ESMs with varying levels of complexity in aerosols and reactive 
gas chemistry. The CMIP6 experimental setup allows for further 
climate effects of CO 2 (including on aerosols and ozone) depending 
on model complexity. The chemical effects are adjustments to CO 2 
but are not separable from the SARF in the diagnosis in Table 7.2. 
In these particular models, this leads to higher SARF than when only 
CO 2 varies, however there are insufficient studies to make a formal 
assessment of composition adjustments to CO 2.Table 7 .2 | SARF, Δ Ffsst, and ERF diagnosed from Earth system models for 
fixed-SST (fSST) CO 2 experiments.  2×CO 2 data taken from fixed atmospheric 
composition experiments (Smith et  al., 2018b). 4×CO 2 data taken from CMIP6 
experiments with interactive aerosols (and interactive gas phase chemistry in some; 
Smith et al., 2020b). The radiative forcings from the 4×CO 2 experiments are scaled by

0.476 for comparison with 2×CO 2 (Meinshausen et al., 2020). SARF is approximated 
by removing the (non-stratospheric-temperature) adjustment terms from the ERF . 
In Smith et al. (2018b), separation of temperature adjustments into tropospheric and 
stratospheric contributions is approximate based on a fixed tropopause of 100 hPa at 
the equator, varying linearly in latitude to 300 hPa at the poles. In Smith et al. (2020b), 
this separation is based on the model-diagnosed tropopause. ERF is approximated by 
removing the response to land surface temperature change from ΔFfsst. The confidence 
range is based on the inter-model standard deviation.
2×CO 2 
Experiments
(Smith et al., 
2018b)Stratospheric- 
temperature-
adjusted 
Radiative Forcing 
(SARF, W m–2)ΔFfsst 
(W m–2)Effective 
Radiative 
Forcing  
(ERF, W m–2)
HadGEM2-ES 3.45 3.37 3.58
NorESM1 3.67 3.50 3.70
GISS-E2-R 3.98 4.06 4.27
CanESM2 3.68 3.57 3.77
MIROC-SPRINTARS 3.89 3.62 3.82
NCAR-CESM1-CAM5 3.89 4.08 4.39

HadGEM3 3.48 3.64 3.90
IPSL-CM5A 3.50 3.39 3.61
MPI-ESM 4.27 4.14 4.38
NCAR-CESM1-CAM4 3.50 3.62 3.86
Multi-model 
mean and 5–95% 
confidence range3.73 ± 0.44 3.70 ± 0.44 3.93 ± 0.48
0.476 × 4×CO 2 
2020b)Stratospheric- 
adjusted 
Radiative 
Forcing  
ACCESS-CM2 3.56 3.78 3.98
CanESM5 3.67 3.62 3.82
CESM2 3.56 4.24 4.48
CNRM-CM6-1 3.99 3.81 4.01
CNRM-ESM2-1 3.99 3.77 3.94
EC-Earth3 3.85 4.04
GFDL-CM4 3.65 3.92 4.10
GFDL-ESM4 3.27 3.68 3.85
GISS-E2-1-G 3.78 3.50 3.69
HadGEM3-GC31-LL 3.61 3.85 4.07
IPSL-CM6A-LR 3.84 3.81 4.05
MIROC6 3.63 3.48 3.69
MPI-ESM1-2-LR 3.74 3.97 4.20
MRI-ESM2-0 3.76 3.64 3.80
NorESM2-LM 3.58 3.88 4.10
NorESM2-MM 3.62 3.99 4.22
UKESM1-0-LL 3.49 3.78 4.01
confidence range3.67 ± 0.29 3.80 ± 0.30 4.00 ± 0.32 Figure 7.4  | Radiative adjustments at top of atmosphere for seven different

climate drivers as a proportion of forcing.  Tropospheric temperature (orange), 
stratospheric temperature (yellow), water vapour (blue), surface albedo (green), 
clouds (grey) and the total adjustment (black) is shown. For the greenhouse gases 
(carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed 
as a  percentage of stratospheric-temperature-adjusted radiative forcing (SARF), 
whereas for aerosol, solar and volcanic forcing they are expressed as a percentage 
of instantaneous radiative forcing (IRF). Land surface temperature response (outline 
red bar) is shown, but included in the definition of forcing. Data from Smith et al. 
(2018b) for carbon dioxide and methane; Smith et al. (2018b) and Gray et al. (2009) 
for solar; Hodnebrog et al. (2020b) for nitrous oxide and CFC-12; Smith et al. (2020b) 
for aerosol, and Marshall et al. (2020) for volcanic. Further details on data sources and 
processing are available in the chapter data table (Table 7.SM.14).

944
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7ERFs have been found to yield more consistent values of GSAT change 
per unit forcing than SARF , that is, α shows less variation across different 
forcing agents (Rotstayn and Penner, 2001; Shine et al., 2003; Hansen 
et  al., 2005b; Marvel et  al., 2016; Richardson et  al., 2019). Having 
a consistent relationship between forcing and response is advantageous 
when making climate projections using simple models (Cross-Chapter 
Box 7.1) or emissions metrics (Section 7.6). The definition of ERF used in 
this assessment, which excludes the radiative response to land surface 
temperature changes, brings the  α values into closer agreement than 
when SARF is used (Richardson et al., 2019), although for individual 
models there are still variations, particularly for more geographically 
localized forcing agents. However, even for ERF , studies find that

α  is not identical across all forcing agents (Shindell, 2014; Shindell 
et al., 2015; Modak et al., 2018; Modak and Bala, 2019; Richardson 
et al., 2019). Section 7.4.4 discusses the effect of different SST response 
patterns on α. Analysis of the climate feedbacks (Kang and Xie, 2014; 
Gregory et  al., 2016, 2020; Marvel et  al., 2016; Duan et  al., 2018; 
Persad and Caldeira, 2018; Stuecker et al., 2018; Krishnamohan et al., 
2019) suggests a  weaker feedback (i.e., less-negative α) and hence 
larger sensitivity for forcing of the higher latitudes (particularly the 
Northern Hemisphere). Nonetheless, as none of these variations are 
robust across models, the ratio of 1/ α  from non-CO 2 forcing agents 
(with approximately global distributions) to that from doubling CO 2 is 
within 10% of unity.
In summary, this Report adopts an estimate of ERF based on the 
change in TOA radiative fluxes in the absence of GSAT changes.

This allows for a  theoretically cleaner separation between forcing 
and feedbacks in terms of factors respectively unrelated and related 
to GSAT change (Box  7.1). ERF can be computed from prescribed 
SST and sea ice experiments after removing the TOA energy budget 
change associated with the land surface temperature response. 
In  this assessment this is removed using a  kernel accounting only 
for the direct radiative effect of the land surface temperature 
response. To compare these results with sophisticated high spectral 
resolution radiative transfer models the individual tropospheric 
adjustment terms can be removed to leave the SARF . SARFs for 
2×CO 2 calculated by ESMs from this method agree within 10% with 
the more sophisticated models. The new studies highlighted above 
suggest that physical feedback parameters computed within this 
framework have less variation across forcing agents. There is high 
confidence  that an α based on ERF as defined here varies by less

(less than variation 10% across a range of forcing agents with global 
distributions), than α based on SARF . For geographically localized 
forcing agents there are fewer studies and less agreement between 
them, resulting in low confidence that ERF is a suitable estimator of 
the resulting global mean near-surface temperature response .
7.3.2 Greenhouse Gases
High spectral resolution radiative transfer models provide the most 
accurate calculations of radiative perturbations due to greenhouse 
gases (GHGs), with errors in the instantaneous radiative forcing (IRF) 
of less than 1% (Mlynczak et al., 2016; Pincus et al., 2020). They can 
calculate IRFs with no adjustments, or SARFs by accounting for the 
adjustment of stratospheric temperatures using a  fixed dynamical heating. It is not possible with offline radiation models to account 
for other adjustments. The high-resolution model calculations of 
SARF for carbon dioxide, methane and nitrous oxide have been

updated since AR5, which were based on Myhre et al. (1998). The 
new calculations include the shortwave forcing from methane and 
updates to the water vapour continuum (increasing the total SARF 
of methane by 25%) and account for the absorption band overlaps 
between carbon dioxide and nitrous oxide (Etminan et  al., 2016). 
The  associated simplified expressions, from a  re-fitting of the 
Etminan et al. (2016) results by Meinshausen et al. (2020), are given 
in Supplementary Material, Table 7.SM.1. The shortwave contribution 
to the IRF of methane has been confirmed independently (Collins 
et al., 2018). Since they incorporate known missing effects we assess 
the new calculations as being a  more appropriate representation 
than Myhre et al. (1998).
As described in Section  7.3.1, ERFs can be estimated using ESMs, 
however the radiation schemes in climate models are approximations 
to high spectral resolution radiative transfer models with variations

and biases in results between the schemes (Pincus et  al., 2015). 
Hence ESMs alone are not sufficient to establish ERF best estimates 
for the well-mixed GHGs (WMGHGs). This assessment therefore 
estimates ERFs from a combined approach that uses the SARF from 
radiative transfer models and adds the tropospheric adjustments 
derived from ESMs.
In AR5, the main information used to assess components of ERFs 
beyond SARF was from Vial et  al. (2013) who found a  near-zero 
non-stratospheric adjustment (without correcting for near-surface 
temperature changes over land) in 4×CO 2 CMIP5 model experiments, 
with an uncertainty of ±10% of the total CO 2 ERF . No calculations 
were available for other WMGHGs, so ERF was therefore assessed to 
be approximately equal to SARF (within 10%) for all WMGHGs.
The effect of WMGHGs in ESMs can extend beyond their direct 
radiative effects to include effects on ozone and aerosol chemistry and

natural emissions of ozone and aerosol precursors, and in the case of 
CO 2 to vegetation cover through physiological effects. In some cases 
these can have significant effects on the overall radiative budget 
changes from perturbing WMGHGs within ESMs (Myhre et al., 2013b; 
Zarakas et al., 2020; O’Connor et al., 2021; Thornhill et al., 2021a). 
These composition adjustments are further discussed in Chapter  6 
(Section 6.4.2).
7.3.2.1 Carbon Dioxide (CO 2)
The SARF for carbon dioxide (CO 2) has been slightly revised due to 
updates to spectroscopic data and inclusion of the absorption band 
overlaps between N 2O and CO 2 (Etminan et al., 2016). The formulae 
fitting to the Etminan et  al. (2016) results in Meinshausen et  al. 
(2020) are used. This increases the SARF due to doubling CO 2 slightly 
from 3.71 W m–2 in AR5 to 3.75 W m–2. Tropospheric responses to 
CO 2 in fSST experiments have been found to lead to an approximate

balance in their radiative effects between an increased radiative 
forcing due to water vapour, cloud and surface-albedo adjustments 
and a  decrease due to increased tropospheric temperature and 
land surface temperature response (Table  7.3; Vial et  al., 2013;

945
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Zhang  and  Huang, 2014; Smith et  al., 2018b, 2020b). The Δ Ffsst 
includes any effects represented within the ESMs on tropospheric 
adjustments due to changes in evapotranspiration or leaf area 
(mainly affecting surface and boundary-layer temperature, low-cloud 
amount, and albedo) from the CO 2-physiological effects (Doutriaux-
Boucher et al., 2009; Cao et al., 2010; T.B. Richardson et al., 2018). 
The effect on surface temperature (negative longwave response) is 
consistent with the expected physiological responses and needs to 
be removed for consistency with the ERF definition. The split between 
surface and tropospheric temperature responses was not reported in 
Vial et al. (2013) or Zhang and Huang (2014) but the total of surface 
and tropospheric temperature response agrees with Smith et  al. 
(2018b, 2020b), giving medium confidence  in this decomposition.

Doutriaux-Boucher et al. (2009) and Andrews et al. (2021) (using the 
same land surface model) find a 13% and 10% increase respectively 
in ERF due to the physiological responses to CO 2. The physiological 
adjustments are therefore assessed to make a substantial contribution 
to the overall tropospheric adjustment for CO 2 (high confidence ), 
but there is insufficient evidence to provide a quantification of the 
split between physiological and thermodynamic adjustments. These 
forcing adjustments due to the effects of CO 2 on plant physiology 
differ from the biogeophysical feedbacks due to the effects of 
temperature changes on vegetation discussed in Section  7.4.2.5. 
The adjustment is assumed to scale with the SARF in the absence of 
evidence for non-linearity. The tropospheric adjustment is assessed 
from Table 7.3 to be +5% of the SARF with an uncertainty of 5%, 
which is added to the Meinshausen et al. (2020) formula for SARF .

Due to the agreement between the studies and the understanding 
of the physical mechanisms there is medium confidence in the 
mechanisms underpinning the tropospheric adjustment, but  low 
confidence in its magnitude .The ERF from doubling CO 2 (2×CO 2) from the 1750 level (278 ppm; 
Section 2.2.3.3) is assessed to be 3.93 ± 0.47 W m–2 (high confidence ). 
Its assessed components are given in Table  7.4. The combined 
spectroscopic and radiative transfer modelling uncertainties give an 
uncertainty in the CO 2 SARF of around 10% or less (Etminan et al., 
2016; Mlynczak et al., 2016). The overall uncertainty in CO 2 ERF is 
assessed as ±12%, as the more uncertain adjustments only account 
for a small fraction of the ERF (Table 7.3). The 2×CO 2 ERF estimate 
is 0.2 W m–2 larger than using the AR5 formula (Myhre et al., 2013b) 
due to the combined effects of tropospheric adjustments which were 
assumed to be zero in AR5. CO 2 concentrations have increased from

278 ppm in 1750 to 410 ppm in 2019 (Section 2.2.3.3). The historical 
ERF estimate from CO 2 is revised upwards from the AR5 value of 
1.82 ± 0.38 W m–2 (1750–2011) to 2.16 ± 0.26 W m–2 (1750–2019) in 
this assessment, from a combination of the revisions described above 
(0.06 W m–2) and the 19 ppm rise in atmospheric concentrations 
between 2011 and 2019 (0.27 W m–2). The ESM estimates of 2×CO 2 
ERF (Table 7.2) lie within ±12% of the assessed value (apart from 
CESM2). The definition of ERF can also include further physiological 
effects – for instance on dust, natural fires and biogenic emissions 
from the land and ocean – but these are not typically included in the 
modelling setup for 2×CO 2 ERF .
7.3.2.2 Methane (CH 4)
The SARF for methane (CH 4) has been substantially increased 
due to updates to spectroscopic data and inclusion of shortwave 
absorption (Etminan et al., 2016). Adjustments have been calculated 
in nine climate models by Smith et al. (2018b). Since CH 4 is found

to absorb in the shortwave near infrared, only adjustments from 
those models including this absorption are taken into account. Table 7.3 | Adjustments to the top-of-atmosphere (TOA) carbon dioxide forcing due to changes in stratospheric temperature, surface and tropospheric 
temperatures, water vapour, clouds, and surface albedo, as a  fraction of the stratospheric-temperature-adjusted radiative forcing (SARF).  Effective 
radiative forcing (ERF) is defined in this Report as excluding the surface temperature response.
Percentage of SARF 
(source study)Surface 
Temperature Tropospheric 
Temperature Stratospheric
TemperatureSurface 
Albedo Water 
Vapour CloudsTroposphere
(Including 
Surface)Troposphere
(Excluding 
Surface)
Vial et al. (2013) –20% combined N/A 2% 6% 11% –1% N/A
Zhang and Huang (2014) –23% combined 26% N/A 6% 16% –1% N/A
Smith et al. (2018b) –6% –16% 30% 3% 6% 12% –1% +5%
Smith et al. (2020b) –6% –15% 35% 3% 6% 15% +3% +9%

Table 7. 4 | Assessed effective radiative forcing (ERF), stratospheric-temperature-adjusted radiative forcing (SARF) and tropospheric adjustments to 
2×CO 2 change since pre-industrial times compared to the AR5 assessed range (Myhre et al., 2013b).  Adjustments are due to changes in tropospheric temperatures, 
water vapour, clouds and surface albedo and land cover and are taken from Smith et al. (2018b) and assessed as a percentage of SARF (Table 7.3). Uncertainties are based on 
multi-model spread in Smith et al. (2018b). Note some of the uncertainties are anticorrelated, which means that they do not sum linearly.
2×CO 2 ForcingAR5
SARF/ERF 
(W m–2)SARF
(W m–2)Tropospheric 
Temperature 
Adjustment 
(W m–2)Water 
Vapour 
(W m–2)Cloud 
(W m–2)Surface 
Albedo and 
Land-cover 
(W m–2)Total 
(W m–2)ERF 
(W m–2)
2×CO 2 ERF components 3.71 3.75 –0.60 0.22 0.45 0.11 0.18 3.93
5–95% uncertainty ranges

as percentage of ERF10% (SARF)
20% (ERF)<10% ±6% ±4% ±7% ±2% ±7% ±12%

946
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7For these models  the adjustments act to reduce the ERF because 
the shortwave absorption leads to tropospheric heating and 
reductions in upper tropospheric cloud amounts. The adjustment 
is –14% ± 15%, which counteracts much of the increase in SARF 
identified by Etminan et al. (2016). Modak et al. (2018) also found 
negative forcing adjustments from a methane perturbation including 
shortwave absorption in the NCAR CAM5 model, in agreement with 
the above assessment. The uncertainty in the shortwave component 
leads to a higher radiative modelling uncertainty (14%) than for CO 2 
(Etminan et al., 2016). When combined with the uncertainty in the 
adjustment, this gives an overall uncertainty of ±20%. There is high 
confidence  in the spectroscopic revision but only medium confidence  
in the adjustment modification. CH 4 concentrations have increased

from 729 ppb in 1750 to 1866 ppb in 2019 (Section  2.2.3.3). 
The  historical ERF estimate from AR5 of 0.48 ± 0.10  W m–2 
(1750–2011) is revised to 0.54 ± 0.11 W m–2 (1750 to 2019) in this 
assessment from a combination of spectroscopic radiative efficiency 
revisions (+0.12 W m–2), adjustments (–0.08 W m–2) and the 63 ppb 
rise in atmospheric CH 4 concentrations between 2011 and 2019 
(+0.03 W m–2). As the adjustments are assessed to be small, there 
is high confidence  in the overall assessment of ERF from methane. 
Increased methane leads to tropospheric ozone production and 
increased stratospheric water vapour, so that an attribution of forcing 
to methane emissions gives a larger effect than that directly from the 
methane concentration itself. This is discussed in detail in Chapter 6 
(Section 6.4.2) and shown in Figure 6.12.
7.3.2.3 Nitrous oxide (N 2O)
The tropospheric adjustments to nitrous oxide (N 2O) have been

calculated from 5 ESMs as 7% ± 13% of the SARF (Hodnebrog et al., 
2020b). This value is therefore taken as the assessed adjustment, but 
with low confidence . The radiative modelling uncertainty is ±10% 
(Etminan et al., 2016), giving an overall uncertainty of ±16%. Nitrous 
oxide concentrations have increased from 270 ppb in 1750 to 332 ppb 
in 2019 (Section  2.2.3.3). The historical ERF estimate from N 2O is 
revised upwards from 0.17 ± 0.06 W m–2 (1750–2011) in AR5 to 
0.21 ± 0.03  W m–2 (1750–2019) in this assessment, of which 0.02  W m–2 
is due to the 7 ppb increase in concentrations, and 0.02 W m–2 to the 
tropospheric adjustment. As the adjustments are assessed to be small 
there remains high confidence  in the overall assessment.
Increased nitrous oxide leads to ozone depletion in the upper 
stratosphere which will make a  positive contribution to the 
direct ERF here (Section  6.4.2 and Figure  6.12) when considering 
emissions-based estimates of ERF .

7.3.2.4 Halogenated Species
The stratospheric-temperature-adjusted radiative efficiencies (SARF 
per ppb increase in concentration) for halogenated compounds 
are reviewed extensively in Hodnebrog et al. (2020a), an update to 
those used in AR5. Many halogenated compounds have lifetimes 
short enough that they can be considered short-lived climate forcers 
(SLCFs; Table 6.1). As such, they are not completely ‘well-mixed’ and 
their vertical distributions are taken into account when determining 
their radiative efficiencies. The World Meteorological Organization (WMO, 2018) updated the lifetimes of many halogenated compounds 
and these were used in Hodnebrog et al. (2020a).
The tropospheric adjustments to chlorofluorocarbons (CFCs), 
specifically CFC-11 and CFC-12, have been quantified as 13% ± 10% 
and 12% ± 14% of the SARF , respectively (Hodnebrog et al., 2020b). 
The assessed adjustment to CFCs is therefore 12% ± 13% with

low confidence  due to the lack of corroborating studies. There have 
been no calculations for other halogenated species so for these the 
tropospheric adjustments are therefore assumed to be 0 ±  13% 
with low confidence.  The radiative modelling uncertainties are 
14% and 24% for compounds with lifetimes greater than and less 
than five years, respectively (Hodnebrog et al., 2020a). The overall 
uncertainty in the ERFs of halogenated compounds is therefore 
assessed to be 19% and 26% depending on the lifetime. The ERF 
from CFCs is slowly decreasing, but this is compensated for by the 
increased forcing from the replacement species (HCFCs and HFCs). 
The ERF from HFCs has increased by 0.028 ± 0.05 W m–2. Thus, the 
concentration changes mean that the total ERF from halogenated 
compounds has increased since AR5 from 0.360 ± 0.036 W m–2 
to 0.408 ± 0.078 W m–2 (Table 7.5). Of this, 0.034 W m–2 is due to 
increased radiative efficiencies and tropospheric adjustments, and

0.014 W m–2 is due to increases in concentrations. As the adjustments 
are assessed to be small there remains high confidence  in the 
overall assessment.
Halogenated compounds containing chlorine and bromine lead 
to ozone depletion in the stratosphere which will reduce the 
associated ERF (Morgenstern et  al., 2020). Chapter  6 (Section  6.4 
and Figure 6.12) assesses the ERF contributions due to the chemical 
effects of reactive gases.
7.3.2.5 Ozone
Estimates of the pre-industrial to present-day tropospheric ozone 
radiative forcing are based entirely on models. The lack of pre-industrial 
ozone measurements prevents an observational determination. There 
have been limited studies of ozone ERFs (MacIntosh et  al., 2016; 
Xie et  al., 2016; Skeie et  al., 2020). Skeie et  al. (2020) found little 
net contribution to the ERF from tropospheric adjustment terms for 
1850–2000 change in ozone (tropospheric and stratospheric ozone

combined), although MacIntosh et al. (2016) suggested that increases 
in stratospheric or upper tropospheric ozone reduces high-cloud 
and increases low-cloud, whereas an increase in lower tropospheric 
ozone reduces low-cloud. Further studies suggest that changes in 
circulation due to decreases in stratospheric ozone affect Southern 
Hemisphere clouds and the atmospheric levels of sea salt aerosol 
that would contribute additional adjustments, possibly of comparable 
magnitude to the SARF from stratospheric ozone depletion (Grise 
et al., 2013, 2014; Xia et al., 2016, 2020). ESM responses to changes 
in ozone-depleting substances (ODS) in CMIP6 show a  much more 
negative ERF than would be expected from offline calculations 
of SARF (Morgenstern et  al., 2020; Thornhill et  al., 2021b) again 
suggesting a negative contribution from adjustments. However there 
is insufficient evidence available to quantify this effect.

947
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Without sufficient information to assess whether the ERFs differ 
from SARF , this assessment relies on offline radiative transfer 
calculations of SARF for both tropospheric and stratospheric 
ozone. Checa-Garcia et  al. (2018) found SARF of 0.30 W m–2 for 
changes in ozone (1850–1860 to 2009–2014). These were based 
on precursor emissions and ODS concentrations from the Coupled 
Chemistry Model Initiative (CCMI) project (Morgenstern et al., 2017). 
Skeie et al. (2020) calculated an ozone SARF of 0.41 ± 0.12 W m–2 
(1850–2010; from five climate models and one chemistry transport 
model) using CMIP6 precursor emissions and ODS concentrations 
(excluding models without fully interactive ozone chemistry and 
one model with excessive ozone depletion). The ozone precursor 
emissions are higher in CMIP6 than in CCMI, which explains much of 
the increase compared to Checa-Garcia et al. (2018) .

Previous assessments have split the ozone forcing into tropospheric 
and stratospheric components. This does not correspond to the division 
between ozone production and ozone depletion and is sensitive to 
the choice of tropopause ( high confidence ) (Myhre et  al., 2013b). 
The  contributions to total SARF in CMIP6 (Skeie et  al., 2020) are 
0.39 ± 0.07 and 0.02 ± 0.07 W m–2 for troposphere and stratosphere 
respectively (using a  150 ppb ozone tropopause definition). This 
small positive (but with uncertainty encompassing negative values) 
stratospheric ozone SARF is due to contributions from ozone 
precursors to lower stratospheric ozone and some of the CMIP6 models 
showing ozone depletion in the upper stratosphere, where depletion 
contributes a positive radiative forcing ( medium confidence ).
As there is insufficient evidence to quantify adjustments, for total 
ozone the assessed central estimate for ERF is assumed to be equal

to SARF ( low confidence ) and follows Skeie et al. (2020), since that 
study uses the most recent emissions data. The dataset is extended 
over the entire historical period following Skeie et  al. (2020), 
with a SARF for 1750–1850 of 0.03 W m–2 and for 2010–2018 of 
0.03 W m–2, to give 0.47 [0.24 to 0.70] W m–2 for 1750–2019. This 
maintains the 50% uncertainty (5–95% range) from AR5 which is 
largely due to the uncertainty in pre-industrial emissions (Rowlinson 
et al., 2020). There is also high confidence  that this range includes 
uncertainty due to the adjustments. The CMIP6 SARF is more positive 
than the AR5 value of 0.31 W m–2 for the period 1850–2011 (Myhre 
et al., 2013b) which was based on the Atmospheric Chemistry and 
Climate Intercomparison Project (ACCMIP; Shindell et al., 2013) . The 
assessment is sensitive to the assumptions on precursor emissions 
used to drive the models, which are larger in CMIP6 than ACCMIP .

In summary, although there is insufficient evidence to quantify 
adjustments, there is high confidence  in the assessed range of ERF 
for ozone changes over the 1750–2019 period, giving an assessed 
ERF of 0.47 [0.24 to 0.70] W m–2.7.3.2.6 Stratospheric Water Vapour
This section considers direct anthropogenic effects on stratospheric 
water vapour by oxidation of methane. Since AR5 the SARF from 
methane-induced stratospheric water vapour changes has been 
calculated in Winterstein et al., 2019, corresponding to 0.09 W m –2 
when scaling to 1850 to 2014 methane changes. This is marginally 
larger than the AR5 assessed value of 0.07 ± 0.05 W m–2 (Myhre 
et  al., 2013b). Wang and Huang (2020) quantified the adjustment 
terms to a stratospheric water vapour change equivalent to a forcing 
from a 2×CO 2 warming (which has a different vertical profile). They 
found that the ERF was less than 50% of the SARF due to high-cloud 
decrease and upper tropospheric warming. The assessed ERF is

therefore 0.05 ± 0.05 W m–2 with a lower limit reduced to zero and 
the central value and upper limit reduced to allow for adjustment 
terms. This still encompasses the two recent SARF studies. There is 
medium confidence  in the SARF from agreement with the recent 
studies and AR5. There is low confidence  in the adjustment terms.
Stratospheric water vapour may also change as an adjustment to 
species that warm or cool the upper troposphere–lower stratosphere 
region (Forster and Joshi, 2005; Stuber et al., 2005), in which case it 
should be included as part of the ERF for that compound. Changes in 
GSAT are also associated with changes in stratospheric water vapour 
as part of the water-vapour–climate feedback (Section 7.4.2.2).
7.3.2.7 Synthesis
The ERF of GHGs (excluding ozone and stratospheric water vapour) 
over 1750–2019 is assessed to be 3.32 ± 0.29 W m–2. It has 
increased by 0.49 W m–2 compared to AR5 (reference year 2011)

(high confidence ). Most of this has been due to an increase in CO 2 
concentration since 2011 [0.27 ± 0.03] W m–2, with concentration 
increases in CH 4, N 2O and halogenated compounds adding 0.02, 
0.02 and 0.01 W m–2 respectively (Table  7.5). Changes in the 
radiative efficiencies (including adjustments) of CO 2, CH 4, N 2O and 
halogenated compounds have increased the ERF by an additional 
0.15 W m–2 compared to the AR5 values ( high confidence ). Note 
that the ERFs in this section do not include chemical effects of 
GHGs on production or destruction of ozone or aerosol formation 
(Section 6.2.2). The ERF for ozone is considerably increased compared 
to AR5 due to an increase in the assumed ozone precursor emissions 
in CMIP6 compared to CMIP5, and better accounting for the effects 
of both ozone precursors and ODSs in the stratosphere. The ERF for 
stratospheric water vapour is slightly reduced. The combined ERF

from ozone and stratospheric water vapour has increased since AR5 
by 0.10 ± 0.50 W m–2 (high confidence ), although the uncertainty 
ranges still include the AR5 values.

948
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity77.3.3 Aerosols
Anthropogenic activity, and particularly burning of biomass 
and fossil fuels, has led to a  substantial increase in emissions of 
aerosols and their precursors, and thus to increased atmospheric 
aerosol concentrations since the pre-industrial era (Sections 2.2.6 
and 6.3.5, and Figure 2.9). This is particularly true for sulphate and 
carbonaceous aerosols (Section 6.3.5). This has in turn led to changes 
in the scattering and absorption of incoming solar radiation, and also 
affected cloud micro- and macro-physics and thus cloud radiative 
properties. Aerosol changes are heterogeneous in both space and 
time and have impacted not just Earth’s radiative energy budget but 
also air quality (Sections 6.1.1 and 6.6.2). Here, the assessment is 
focused exclusively on the global mean effects of aerosols on Earth’s

energy budget, while regional changes and changes associated with individual aerosol compounds are assessed in Chapter  6 
(Sections 6.4.1 and 6.4.2).
Consistent with the terminology introduced in Box  7.1, the ERF 
due to changes from direct aerosol–radiation interactions (ERFari) 
is equal to the sum of the instantaneous top-of-atmosphere (TOA) 
radiation change (IRFari) and the subsequent adjustments. Likewise, 
the ERF following interactions between anthropogenic aerosols and 
clouds (ERFaci, referred to as ‘indirect aerosol effects’ in previous 
assessment reports) can be divided into an instantaneous forcing 
component (IRFaci) due to changes in cloud droplet (and indirectly 
also ice crystal) number concentrations and sizes, and the subsequent 
adjustments of cloud water content or extent. While these changes 
are thought to be induced primarily by changes in the abundance

of cloud condensation nuclei (CCN), a  change in the number of Table 7 .5 | Present-day mole fractions in parts per trillion (pmol mol–1), except where specified, and effective radiative forcing (ERF, in W m–2) for 
the well-mixed greenhouse gases (WMGHGs).  Data taken from Chapter 2 (Section 2.2.3). The data for 2011 (the time of the AR5 estimates) are also shown. Some of the 
concentrations vary slightly from those reported in AR5 owing to averaging different data sources. Individual species are reported where 1750–2019 ERF is at least 0.001 W m–2. 
Radiative efficiencies for the minor gases are given in Supplementary Material, Table 7.SM.7. Uncertainties in the ERF for all gases are dominated by the uncertainties in the 
radiative efficiencies. Tabulated global mixing ratios of all WMGHGs and ERFs from 1750 to 2019 are provided in Annex III.
Concentration ERF with Respect to 1850 ERF with Respect to 1750
2019 2011 1850 1750 2019 2011 2019 2011

CO 2 (ppm) 409.9 390.5 285.5 278.3 2.012 ± 0.241 1.738 2.156 ± 0.259 1.882
CH 4 (ppb) 1866.3 1803.3 807.6 729.2 0.496 ± 0.099 0.473 0.544 ± 0.109 0.521
N2O (ppb) 332.1 324.4 272.1 270.1 0.201 ± 0.030 0.177 0.208 ± 0.031 0.184
HFC-134a 107.6 62.7 0.0 0.0 0.018 0.010 0.018 0.010
HFC-23 32.4 24.1 0.0 0.0 0.006 0.005 0.006 0.005
HFC-32 20.0 4.7 0.0 0.0 0.002 0.001 0.002 0.001
HFC-125 29.4 10.3 0.0 0.0 0.007 0.002 0.007 0.002
HFC-143a 24.0 12.0 0.0 0.0 0.004 0.002 0.004 0.002
SF6 10.0 7.3 0.0 0.0 0.006 0.004 0.006 0.004
CF4 85.5 79.0 34.0 34.0 0.005 0.004 0.005 0.004
C2F6 4.8 4.2 0.0 0.0 0.001 0.001 0.001 0.001
CFC-11 226.2 237.3 0.0 0.0 0.066 0.070 0.066 0.070
CFC-12 503.1 528.6 0.0 0.0 0.180 0.189 0.180 0.189
CFC-113 69.8 74.6 0.0 0.0 0.021 0.022 0.021 0.022
CFC-114 16.0 16.3 0.0 0.0 0.005 0.005 0.005 0.005
CFC-115 8.7 8.4 0.0 0.0 0.002 0.002 0.002 0.002
HCFC-22 246.8 213.2 0.0 0.0 0.053 0.046 0.053 0.046
HCFC-141b 24.4 21.4 0.0 0.0 0.004 0.003 0.004 0.003

HCFC-142b 22.3 21.2 0.0 0.0 0.004 0.004 0.004 0.004
CCl 4 77.9 86.1 0.0 0.0 0.013 0.014 0.013 0.014
Sum of HFCs (HFC-134a equivalent) 237.1 128.6 0.0 0.0 0.040 0.022 0.040 0.022
Sum of CFCs+HCFCs+other ozone 
depleting gases covered by the 
Montreal Protocol (CFC-12 equivalent)1031.9 1050.1 0.0 0.0 0.354 0.362 0.354 0.362
Sum of PFCs (CF 4 equivalent) 109.4 98.9 34.0 34.0 0.007 0.006 0.007 0.006
Sum of Halogenated species 0.408 ±0.078 0.394 0.408 ±0.078 0.394
Total 3.118 ±0.258 2.782 3.317 ±0.278 2.981

949
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77ice nucleating particles (INPs) in the atmosphere may also have 
occurred, and thereby contributed to ERFaci by affecting properties of 
mixed-phase and cirrus (ice) clouds. In the following, an assessment 
of IRFari and ERFari (Section  7.3.3.1) focusing on observation-
based (Section 7.3.3.1.1) as well as model-based (Section 7.3.3.1.2) 
evidence is presented. The same lines of evidence are presented 
for IRFaci and ERFaci in Section 7.3.3.2. These lines of evidence are 
then compared with TOA energy budget constraints on the total 
aerosol ERF (Section  7.3.3.3) before an overall assessment of the 
total aerosol ERF is given in Section  7.3.3.4. For the model-based 
evidence, all estimates are generally valid for 2014 relative to 1750 
(the time period spanned by CMIP6 historical simulations), while for 
observation-based evidence the assessed studies use slightly different

end points, but they all generally fall within a decade (2010–2020).
7.3.3.1 Aerosol–Radiation Interactions
Since AR5, deeper understanding of the processes that govern 
aerosol radiative properties, and thus IRFari, has emerged. Combined 
with new insights into adjustments to aerosol forcing, this progress 
has informed new observation- and model-based estimates of ERFari 
and associated uncertainties.
7.3.3.1.1 Observation-based lines of evidence
Estimating IRFari requires an estimate of industrial-era changes in 
aerosol optical depth (AOD) and absorption AOD, which are often 
taken from global aerosol model simulations. Since AR5, updates 
to methods of estimating IRFari based on aerosol remote sensing 
or data-assimilated reanalyses of atmospheric composition have 
been published. Ma et  al. (2014) applied the method of Quaas 
et  al. (2008) to updated broadband radiative flux measurements 
from CERES, MODIS-retrieved AODs, and modelled anthropogenic

aerosol fractions to find a clear-sky IRFari of −0.6 W m−2. This would 
translate into an all-sky estimate of about −0.3 W m−2 based on the 
clear-sky to all-sky ratio implied by Kinne (2019). Rémy et al. (2018) 
applied the methods of Bellouin et al. (2013a) to the reanalysis by 
the Copernicus Atmosphere Monitoring Service, which assimilates 
MODIS total AOD. Their estimate of IRFari varies between −0.5 W m–2 
and  −0.6  W  m−2  over the period 2003–2018, and they attribute 
those relatively small variations to  variability in biomass-burning 
activity.  Kinne (2019) provided updated monthly total AOD and 
absorption AOD climatologies, obtained by blending multi-model 
averages with ground-based sun-photometer retrievals, to find 
a best estimate of IRFari of −0.4 W m−2. The updated IRFari estimates 
above are all scattered around the midpoint of the IRFari range of 
−0.35 ± 0.5 W m−2 assessed by AR5 (Boucher et al., 2013).

The more negative estimate of Rémy et al. (2018) is due to neglecting 
a small positive contribution from absorbing aerosols above clouds 
and obtaining a  larger anthropogenic fraction than Kinne (2019). 
Rémy et al. (2018) also did not update their assumptions on black 
carbon anthropogenic fraction and its contribution to absorption to 
reflect recent downward revisions (Section 7.3.3.1.2). Kinne (2019) 
made those revisions, so more weight is given to that study to assess 
the central estimate of satellite-based IRFari to be only slightly 
stronger than reported in AR5 at –0.4 W m–2. While uncertainties in the anthropogenic fraction of total AOD remain, improved 
knowledge of anthropogenic absorption results in a slightly narrower 
very likely  range here than in AR5. The assessed best estimate and 
very likely  IRFari range from observation-based evidence is therefore 
–0.4 ± 0.4 W m–2, but with medium confidence due to the limited 
number of studies available .

7.3.3.1.2 Model-based lines of evidence
While observation-based evidence can be used to estimate IRFari, global 
climate models are needed to calculate the associated adjustments 
and the resulting ERFari, using the methods described in Section 7.3.1. 
A range of developments since AR5 affect model-based estimates 
of IRFari. Global emissions of most major aerosol compounds and 
their precursors are found to be higher in the current inventories, 
and with increasing trends. Emissions of the sulphate precursor 
SO 2 are a notable exception; they are similar to those used in AR5 
and approximately time-constant in recent decades (Hoesly et  al., 
2018). Myhre et  al. (2017) showed, in a  multi-model experiment, 
that the net result of these revised emissions is an IRFari trend that 
is relatively flat in recent years (post-2000), a finding confirmed by 
a single-model study by Paulot et al. (2018).
In AR5, the assessment of the black carbon (BC) contribution to IRFari

was markedly strengthened in confidence by the review by Bond et al. 
(2013), where a key finding was a perceived model underestimate 
of atmospheric absorption when compared to Aeronet observations 
(Boucher et  al., 2013). This assessment has since been revised 
considering: new knowledge on the effect of the temporal resolution 
of emissions inventories (Wang et al., 2016); the representativeness 
of Aeronet sites (Wang et  al., 2018); issues with comparing 
absorption retrieval to models (E. Andrews et  al., 2017); and the 
ageing (Peng et al., 2016), lifetime (Lund et al., 2018b) and average 
optical parameters (Zanatta et al., 2016) of BC. Consistent with these 
updates, Lund et al. (2018a) estimated the net IRFari in 2014 (relative 
to 1750) to be –0.17 W m–2, using CEDS emissions (Hoesly et al., 2018) 
as input to a chemical transport model. They attributed the weaker 
estimate relative to AR5 (–0.35 ± 0.5 W m–2; Myhre et al., 2013a)

to stronger absorption by organic aerosol, updated parametrization 
of BC absorption, and slightly reduced sulphate cooling. Broadly 
consistent with Lund et al. (2018a), another single-model study by 
Petersik et al. (2018) estimated an IRFari of –0.19 W m–2. Another 
single-model study by Lurton et al. (2020) reported a more negative 
estimate at –0.38 W m–2, but is given less weight here because 
the model lacked interactive aerosols and instead used prescribed 
climatological aerosol concentrations.
The above estimates support a  less negative central estimate and 
a slightly narrower range compared to those reported for IRFari from 
ESMs in AR5 of –0.35 [–0.6 to –0.13] W m–2. The assessed central 
estimate and very likely  IRFari range from model-based evidence 
alone is therefore –0.2 ± 0.2 W m–2 for 2014 relative to 1750, with 
medium confidence  due to the limited number of studies available. 
Revisions due to stronger organic aerosol absorption, further

developed BC parameterizations and somewhat reduced sulphate 
emissions in recent years.

950
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Since AR5 considerable progress has been made in the understanding 
of adjustments in response to a wide range of climate forcings, as 
discussed in Section 7.3.1. The adjustments in ERFari are principally 
caused by cloud changes, but also by lapse rate and atmospheric 
water vapour changes, all mainly associated with absorbing aerosols 
like BC. Stjern et  al. (2017) found that for BC, about 30% of the 
(positive) IRFari is offset by adjustments of clouds (specifically, an 
increase in low-clouds and decrease in high-clouds) and lapse rate, 
by analysing simulations by five Precipitation Driver Response Model 
Intercomparison Project (PDRMIP) models. Smith et  al. (2018b) 
considered more models participating in PDRMIP and suggested 
that about half the IRFari was offset by adjustments for BC, a finding 
generally supported by single-model studies (Takemura and Suzuki,

2019; Zhao and Suzuki, 2019). Thornhill et al. (2021b) also reported 
a negative adjustment for BC based on AerChemMIP (Collins et al., 
2017) but found it to be somewhat smaller in magnitude than those 
reported in Smith et al. (2018b) and Stjern et al. (2017). In contrast, 
Allen et al. (2019) found a positive adjustment for BC and suggested 
that most models simulate negative adjustment for BC because of 
a misrepresentation of aerosol atmospheric heating profiles.
Zelinka et  al. (2014) used the approximate partial radiation 
perturbation technique to quantify the ERFari in 2000 relative 
to 1860 in nine CMIP5 models; they estimated the ERFari 
(accounting for a small contribution from longwave radiation) to be 
–0.27 ± 0.35 W m–2. However, it should be noted that in Zelinka et al. 
(2014) adjustments of clouds caused by absorbing aerosols through 
changes in the thermal structure of the atmosphere (termed the 
semidirect effect of aerosols in AR5) are not included in ERFari but

in ERFaci. The corresponding estimate emerging from the Radiative 
Forcing Model Intercomparison Project (RFMIP , Pincus et al., 2016) 
is –0.25 ± 0.40 W m–2 (Smith et  al., 2020b), which is generally 
supported by single-model studies published since AR5 (Zhang et al., 
2016; Fiedler et al., 2017; Nazarenko et al., 2017; Zhou et al., 2017c, 
2018b; Grandey et al., 2018). A 5% inflation is applied to the CMIP5 
and CMIP6 fixed-SST derived estimates of ERFari from Zelinka et al. 
(2014) and Smith et al. (2020b) to account for land surface cooling 
(Table 7.6). Based on the above, ERFari from model-based evidence is 
assessed to be –0.25 ± 0.25 W m–2.
7.3.3.1.3 Overall assessment of IRFari and ERFari
The observation-based assessment of IRFari of –0.4 ± 0.4 W m–2 and 
the corresponding model-based assessment of –0.2 ± 0.2 W m–2 can 
be compared to the range of –0.45 to –0.05 W m–2 that emerged 
from a  comprehensive review in which an observation-based

estimate of anthropogenic AOD was combined with model-derived 
ranges for all relevant aerosol radiative properties (Bellouin et  al., 
2020). Based on the above, IRFari is assessed to be –0.25 ± 0.2 W m–2 
ERFari from model-based evidence is –0.25 ± 0.25 W m–2, which 
suggests a  small negative adjustment relative to the model-based 
IRFari estimate, consistent with the literature discussed in 
Section 7.3.3.1.2. Adding this small adjustment to our assessed IRFari 
estimate of –0.25 W m–2, and accounting for additional uncertainty in 
the adjustments, ERFari is assessed to –0.3 ± 0.3 ( medium confidence ). This assessment is consistent with the 5–95% confidence range for 
ERFari in Bellouin et al. (2020) of –0.71 to –0.14 W m–2, and notably 
implies that it is very likely  that ERFari is negative. Differences relative 
to Bellouin et al. (2020) reflect the range of estimates in Table 7.6 and 
the fact that an ERFari more negative than –0.6 W m–2 would require

adjustments that considerably augment the assessed IRFari, which is 
not supported by the assessed literature.
7.3.3.2 Aerosol–Cloud Interactions
Anthropogenic aerosol particles primarily affect water clouds by 
serving as additional cloud condensation nuclei (CCN) and thus 
increasing cloud drop number concentration (N d; Twomey, 1959). 
Increasing N d while holding liquid water content constant reduces cloud 
drop effective radius (r e), increases the cloud albedo, and induces an 
instantaneous negative radiative forcing (IRFaci). The  clouds are Table 7.6  | Present-day effective radiative forcing (ERF) due to changes 
in aerosol–radiation interactions (ERFari) and changes in aerosol–cloud 
interactions (ERFaci), and total aerosol ERF (ERFari+aci)  from GCM CMIP6 
(2014 relative to 1850; Smith et  al., 2020b and later model results) and CMIP5 
(year 2000 relative to 1860; Zelinka et al., 2014). CMIP6 results are simulated as part

of RFMIP (Pincus et al., 2016). An additional 5% is applied to the CMIP5 and CMIP6 
model results to account for land-surface cooling (Figure 7.4; Smith et al., 2020a).
ModelsERFari
(W m–2)ERFaci
(W m–2)ERFari+aci
(W m–2)
ACCESS-CM2 –0.24 –0.93 –1.17
ACCESS-ESM1-5 –0.07 –1.19 –1.25
BCC-ESM1 –0.79 –0.69 –1.48
CanESM5 –0.02 –1.09 –1.11
CESM2 +0.15 –1.65 –1.50
CNRM-CM6-1 –0.28 –0.86 –1.14
CNRM-ESM2-1 –0.15 –0.64 –0.79
EC-Earth3 –0.39 –0.50 –0.89
GFDL-CM4 –0.12 –0.72 –0.84
GFDL-ESM4 –0.06 –0.84 –0.90
GISS-E2-1-G (physics_version=1) –0.55 –0.81 –1.36
GISS-E2-1-G (physics_version=3) –0.64 –0.39 –1.02
HadGEM3-GC31-LL –0.29 –0.87 –1.17
IPSL-CM6A-LR –0.39 –0.29 –0.68
IPSL-CM6A-LR-INCA –0.45 –0.35 –0.80
MIROC6 –0.22 –0.77 –0.99
MPI-ESM-1-2-HAM +0.10 –1.40 –1.31
MRI-ESM2-0 –0.48 –0.74 –1.22
NorESM2-LM –0.15 –1.08 –1.23
NorESM2-MM –0.03 –1.26 –1.29
UKESM1-0-LL –0.20 –0.99 –1.19
CMIP6 average and 5–95% 
confidence range  
(2014 relative to 1850)–0.25 ± 0.40 –0.86 ± 0.57 –1.11 ± 0.38

CMIP5 average and 5–95% 
(2000 relative to 1860)–0.27 ± 0.35 –0.96 ± 0.55 –1.23 ± 0.48

951
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77thought to subsequently adjust by a slowing of the drop coalescence 
rate, thereby delaying or suppressing rainfall. Rain generally reduces 
cloud lifetime and thereby liquid water path (LWP , i.e., the vertically 
integrated cloud water) and/or cloud fractional coverage (Cf; Albrecht, 
1989), thus any aerosol-induced rain delay or suppression would 
be expected to increase LWP and/or Cf. Such adjustments could 
potentially lead to an ERFaci considerably larger in magnitude than 
the IRFaci alone. However, adding aerosols to non-precipitating clouds 
has been observed to have the opposite effect (i.e., a reduction in LWP 
and/or Cf) (Lebsock et  al., 2008; Christensen and Stephens, 2011). 
These findings have been explained by enhanced evaporation of the 
smaller droplets in the aerosol-enriched environments, and resultant 
enhanced mixing with ambient air, leading to cloud dispersal.

A small subset of aerosols can also serve as ice nucleating particles 
(INPs) that initiate the ice phase in supercooled water clouds, and 
thereby alter cloud radiative properties and/or lifetimes. However, 
the ability of anthropogenic aerosols (specifically BC) to serve 
as INPs in mixed-phase clouds has been found to be negligible in 
recent laboratory studies (e.g.,  Vergara-Temprado et  al., 2018). 
No  assessment of the contribution to ERFaci from cloud phase 
changes induced by anthropogenic INPs will therefore be presented.
In ice (cirrus) clouds (cloud temperatures less than –40°C), INPs can 
initiate ice crystal formation at relative humidity much lower than 
that required for droplets to freeze spontaneously. Anthropogenic 
INPs can thereby influence ice crystal numbers and thus cirrus cloud 
radiative properties. At cirrus temperatures, certain types of BC 
have in fact been demonstrated to act as INPs in laboratory studies

(Ullrich et al., 2017; Mahrt et al., 2018), suggesting a non-negligible 
anthropogenic contribution to INPs in cirrus clouds. Furthermore, 
anthropogenic changes to drop number also alter the number of 
droplets available for spontaneous freezing, thus representing 
a  second pathway through which anthropogenic emissions could 
affect cirrus clouds.
7.3.3.2.1 Observation-based evidence
Since AR5, the analysis of observations to investigate aerosol–cloud 
interactions has progressed along several axes: (i) The framework of 
forcing and adjustments introduced rigorously in AR5 has helped better 
categorize studies; (ii) the literature assessing statistical relationships 
between aerosol and cloud in satellite retrievals has grown, and 
retrieval uncertainties are better characterized; (iii) advances have 
been made to infer causality in aerosol–cloud relationships.
In AR5 the statistical relationship between cloud microphysical

properties and aerosol index (AI; AOD multiplied by Ångström 
exponent) was used to make inferences about IRFaci were assessed 
alongside other studies which related cloud quantities to AOD. 
However, it is now well-documented that the latter approach leads to 
low estimates of IRFaci since AOD is a poor proxy for cloud-base CCN 
(Penner et al., 2011; Stier, 2016). Gryspeerdt et al. (2017) demonstrated 
that the statistical relationship between droplet concentration and 
AOD leads to an inferred IRFaci that is underestimated by at least 
30%, while the use of AI leads to estimates of IRFaci to within ±20%, 
if the anthropogenic perturbation of AI is known.Further, studies assessed in AR5 mostly investigated linear relationships 
between cloud droplet concentration and aerosol (Boucher et  al., 
2013). Since in most cases the relationships are not linear, this leads 
to a  bias (Gryspeerdt et  al., 2016). Several studies did not relate

cloud droplet concentration, but cloud droplet effective radius, to the 
aerosol (Brenguier et al., 2000). This is problematic because in order to 
infer IRFaci, stratification by cloud LWP is required (McComiskey and 
Feingold, 2012). Where LWP positively co-varies with aerosol retrievals 
(which is often the case), IRFaci inferred from such relationships is 
biased towards low values. Also, it is increasingly evident that different 
cloud regimes show different sensitivities to aerosols (Stevens and 
Feingold, 2009). Averaging statistics over regimes thus biases the 
inferred IRFaci (Gryspeerdt et  al., 2014b). The AR5 concluded that 
IRFaci estimates tied to satellite studies generally show weak IRFaci 
(Boucher et al., 2013), but when correcting for the biases discussed 
above, this is no longer the case.
Since AR5, several studies assessed the global IRFaci from satellite 
observations using different methods (Table 7.7). All studies relied

on statistical relationships between aerosol and cloud quantities 
to infer sensitivities. Four studies inferred IRFaci by estimating the 
anthropogenic perturbation of N d (cloud drop number concentration). 
For this, Bellouin et  al. (2013b) and Rémy et  al. (2018) made use 
of regional-seasonal regressions between satellite-derived N d and 
AOD following Quaas et  al. (2008), while Gryspeerdt et  al. (2017) Table 7. 7 | Studies quantifying aspects of the global effective radiative 
forcing due to aerosol–cloud interactions ERFaci that are mainly based on 
satellite retrievals and were published since AR5.  All forcings/adjustments are 
presented as global annual mean values in W m–2. Most studies split the ERFaci into 
instantaneous radiative forcing (IRFaci) and adjustments in liquid water path (LWP) 
and cloud fraction (Cf) separately. All published studies only considered liquid clouds. 
Some studies assessed the IRFaci and the LWP adjustment together and called this

‘intrinsic forcing’ (Christensen et al., 2017) and the cloud fraction adjustment ‘extrinsic 
forcing’. Published uncertainty ranges are converted to 5–95% confidence intervals, 
and ‘n/a’ indicates that the study did not provide an estimate for the relevant IRF/ERF .
IRFaci  
(W m–2)Liquid Water 
Path (LWP) 
Adjustment  
Fraction (Cf) 
(W m–2)Reference
–0.6 ± 0.6 n/a n/a Bellouin et al. (2013b)
–0.4 [–0.2 to –1.0] n/a n/a Gryspeerdt et al. (2017)
–1.0 ± 0.4 n/a n/a McCoy et al. (2017b)
n/a n/a–0.5  
[–0.1 to –0.6]Gryspeerdt et al. (2016)
n/a +0.3 to 0.0 n/a Gryspeerdt et al. (2019)
–0.8 ± 0.7 n/a n/a Rémy et al. (2018)
–0.53
–1.14 [–1.72 to –0.84]
–1.2 to –0.6
–0.69 [–0.99 to –0.44]+0.15
n/a
n/a
n/an/a
n/a
n/a
n/aToll et al. (2019)
Hasekamp et al. (2019)
McCoy et al. (2020)
Diamond et al. (2020)
‘Intrinsic Forcing’ 
–0.5 ± 0.5 –0.5 ± 0.5 Chen et al. (2014)
–0.4 ± 0.3 n/a Christensen et al. (2016a)
–0.3 ± 0.4 –0.4 ± 0.5 Christensen et al. (2017)

952
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7used AI instead of AOD in the regression to infer IRFaci. McCoy 
et al. (2017b) instead used the sulphate-specific mass derived in the 
MERRA aerosol reanalysis that assimilated MODIS AOD (Rienecker 
et al., 2011). All approaches have in common the need to identify 
the anthropogenic perturbation of the aerosol to assess IRFaci. 
Gryspeerdt et  al. (2017) and Rémy et  al. (2018) used the same 
approach as Bellouin et  al. (2013b), while McCoy et  al. (2017b) 
used an anthropogenic fraction from the AEROCOM multi-model 
ensemble (Schulz et al., 2006). Chen et al. (2014), Christensen et al. 
(2016a) and Christensen et  al. (2017) derived the combination of 
IRFaci and the LWP adjustment to IRFaci (‘intrinsic forcing’ in their 
terminology). They relate AI and cloud albedo statistically and use the 
anthropogenic aerosol fraction from Bellouin et al. (2013b). This was

further refined by Hasekamp et  al. (2019) who used additional 
polarimetric satellite information over ocean to obtain a better proxy 
for CCN. They derived an IRFaci of –1.14 [–1.72 to –0.84] W m–2. 
The variant by Christensen et al. (2017) is an update compared to 
the Chen et al. (2014) and Christensen et al. (2016a) studies in that it 
better accounts for ancillary influences on the aerosol retrievals such 
as aerosol swelling and three-dimensional radiative effects. McCoy 
et  al. (2020) used the satellite-observed hemispheric difference in 
Nd as an emergent constraint on IRFaci as simulated by GCMs to 
obtain a  range of –1.2 to –0.6 W m–2 (95% confidence interval). 
Diamond et al. (2020) analysed the difference in clouds affected by 
ship emissions with unperturbed clouds and based on this inferred 
a global IRFaci of –0.69 [–0.99 to –0.44] W m–2.
Summarizing the above findings related to statistical relationships and

causal aerosol effects on cloud properties, there is high confidence  
that anthropogenic aerosols lead to an increase in cloud droplet 
concentrations. Taking the average across the studies providing IRFaci 
estimates discussed above and considering the general agreement 
among estimates (Table 7.7), IRFaci is assessed to be –0.7 ± 0.5 W m–2 
Multiple studies have found a positive relationship between cloud 
fraction and/or cloud LWP and aerosols (e.g.,  Nakajima et  al., 
2001; Kaufman and Koren, 2006; Quaas et  al., 2009). Since AR5, 
however, it has been documented that factors independent of 
causal aerosol–cloud interactions heavily influence such statistical 
relationships. These include the swelling of aerosols in the high 
relative humidity in the vicinity of clouds (Grandey et al., 2013) and the 
contamination of aerosol retrievals next to clouds by cloud remnants 
and cloud-side scattering (Várnai and Marshak, 2015; Christensen

et al., 2017). Stratifying relationships by possible influencing factors 
such as relative humidity (Koren et al., 2010) does not yield satisfying 
results since observations of the relevant quantities are not available 
at the resolution and quality required. Another approach to tackle 
this problem was to assess the relationship of cloud fraction with 
droplet concentration (Gryspeerdt et  al., 2016; Michibata et  al., 
2016; Sato et al., 2018). The relationship between satellite-retrieved 
cloud fraction and N d was found to be positive (Christensen et al., 
2016a, 2017; Gryspeerdt et al., 2016), implying an overall adjustment 
that leads to a more negative ERFaci. However, since retrieved N d is 
biased low for broken clouds this result has been called into question 
(Grosvenor et al., 2018). Zhu et al. (2018) proposed to circumvent 
this problem by considering N d of only continuous thick cloud covers, on the basis of which Rosenfeld et al. (2019) still obtained a positive

relationship between cloud fraction and N d relationship.
The relationship between LWP and cloud droplet number is debated. 
Most recent studies (primarily based on MODIS data) find negative 
statistical relationships (Michibata et al., 2016; Toll et al., 2017; Sato 
et al., 2018; Gryspeerdt et al., 2019), while Rosenfeld et al. (2019) 
obtained a  modest positive relationship. To increase confidence 
that observed relationships between aerosol emissions and cloud 
adjustments are causal, known emissions of aerosols and aerosol 
precursor gases into otherwise pristine conditions have been 
exploited. Ship exhaust is one such source. Goren and Rosenfeld 
(2014) suggested that both LWP and Cf increase in response 
to ship emissions, contributing approximately 75% to the total 
ERFaci in mid-latitude stratocumulus. Christensen and Stephens 
(2011) found that such strong adjustments occur for open-cell 
stratocumulus regimes, while adjustments are comparatively small

in closed-cell regimes. Volcanic emissions have been identified 
as another important source of information (Gassó, 2008). From 
satellite observations, Yuan et  al. (2011) documented substantially 
larger Cf, higher cloud tops, reduced precipitation likelihood, and 
increased albedo in cumulus clouds in the plume of the Kīlauea 
volcano in Hawaii. Ebmeier et al. (2014) confirmed the increased LWP 
and albedo for other volcanoes. In contrast, for the large Holuhraun 
eruption in Iceland, Malavelle et al. (2017) did not find any large-scale 
change in LWP in satellite observations. However, when accounting 
for meteorological conditions, McCoy et al. (2018) concluded that for 
cyclonic conditions, the extra Holuhraun aerosol did enhance LWP . 
Toll et al. (2017) examined a large sample of volcanoes and found 
a distinct albedo effect, but only modest LWP changes, on average. 
Gryspeerdt et  al. (2019) demonstrated that the negative LWP–N d

relationship becomes very small when conditioned on a  volcanic 
eruption, and therefore concluded that LWP adjustments are small 
in most regions. Similarly, Toll et al. (2019) studied clouds downwind 
of various anthropogenic aerosol sources using satellite observations 
and inferred an IRFaci of –0.52 W m–2 that was partly offset by 29% 
due to aerosol-induced LWP decreases.
Apart from adjustments involving LWP and Cf, several studies 
have also documented a  negative relationship between cloud-top 
temperature and AOD/AI in satellite observations (e.g., Koren et al., 
2005). Wilcox et al. (2016) proposed that this could be explained by 
black-carbon (BC) absorption reducing boundary-layer turbulence, 
which in turn could lead to taller clouds. However, it has been 
demonstrated that the satellite-derived relationships are affected 
by spurious co-variation (Gryspeerdt et al., 2014a), and it therefore 
remains unclear whether a systematic causal effect exists.

Identifying relationships between INP concentrations and cloud 
properties from satellites is intractable because the INPs generally 
represent a  very small subset of the overall aerosol population 
at any given time or location. For ice clouds, only a  few satellite 
studies have so far investigated responses to aerosol perturbations. 
Gryspeerdt et al. (2018) find a positive relationship between aerosol 
and ice crystal number for cold cirrus under strong dynamical forcing, 
which could be explained by an overall larger number of solution 
droplets available for homogeneous freezing in polluted regions.

953
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Zhao et al. (2018) conclude that the sign of the relationship between 
ice crystal size and aerosol depends on humidity. While these 
studies support modelling results finding that ice clouds do respond 
to anthropogenic aerosols (Section  7.3.3.2.2), no quantitative 
conclusions about IRFaci or ERFaci for ice clouds can be drawn based 
on satellite observations.
Only a handful of studies have estimated the LWP and Cf adjustments 
that are needed for satellite-based estimates of ERFaci. Chen et al. 
(2014) and Christensen et al. (2017) used the relationship between 
cloud fraction and AI to infer the cloud fraction adjustment. 
Gryspeerdt et al. (2017) used a similar approach but tried to account 
for non-causal coorelations between aerosols and cloud fraction by 
using N d as a mediating factor. These three studies together suggest 
a global Cf adjustment that augments ERFaci relative to IRFaci by

–0.5 ± 0.4 W m–2 (medium confidence ). For global estimates of the 
LWP adjustment, evidence is even scarcer. Gryspeerdt et al. (2019) 
derived an estimate of the LWP adjustment using a method similar 
to Gryspeerdt et al. (2016). They estimated that the LWP adjustment 
offsets 0–60% of the (negative) IRFaci (0.0 to +0.3 W m–2). Supporting 
an offsetting LWP adjustment, Toll et al. (2019) estimated a moderate 
LWP adjustment of 29% (+0.15 W m–2). The adjustment due to LWP is 
assessed to be small, with a central estimate and very likely  range of 
0.2 ± 0.2 W m–2, but with low confidence  due to the limited number 
of studies available.
Combining IRFaci and the associated adjustments in Cf and LWP 
(adding uncertainties in quadrature), considering only liquid-water 
clouds and evidence from satellite observations alone, the 
central estimate and very likely  range for ERFaci is assessed to be 
–1.0  ±  0.7 W  m–2 (medium confidence ). The confidence level and

wider range for ERFaci compared to IRFaci reflect the relatively large 
uncertainties that remain in the adjustment contribution to ERFaci.
7.3.3.2.2 Model-based evidence
As in AR5, the representation of aerosol–cloud interactions in ESMs 
remains a challenge, due to the limited representation of important 
sub-gridscale processes, from the emissions of aerosols and their 
precursors to precipitation formation. ESMs that simulate ERFaci 
typically include aerosol–cloud interactions in liquid stratiform clouds 
only, while very few include aerosol interactions with mixed-phase, 
convective and ice clouds. Adding to the spread in model-derived 
estimates of ERFaci is the fact that model configurations and 
assumptions vary across studies, for example when it comes to the 
treatment of oxidants, which influence aerosol formation, and their 
changes through time (Karset et al., 2018).
In AR5, ERFaci was assessed as the residual of the total aerosol ERF

and ERFari, as the total aerosol ERF was easier to calculate based 
on available model simulations (Boucher et  al., 2013). The central 
estimates of total aerosol ERF and ERFari in AR5 were –0.9 W m–2 and 
–0.45 W m–2, respectively, yielding an ERFaci estimate of –0.45 W m–2. 
This value is much less negative than the bottom-up estimate of 
ERFaci from ESMs presented in AR5 (–1.4 W m–2) and efforts have 
been made since to reconcile this difference. Zelinka et  al. (2014) 
estimated ERFaci to be –0.96 ± 0.55 W m–2 (including semi-direct effects, and with land-surface cooling effect applied), based on 
nine CMIP5 models (Table 7.6). The corresponding ERFaci estimate 
based on 17 RFMIP models from CMIP6 is slightly less negative 
at –0.86  ±  0.57 W m–2 (Table  7.6). Other post-AR5 estimates of 
ERFaci based on single-model studies are either in agreement with 
or slightly larger in magnitude than the CMIP6 estimate (Gordon

et al., 2016; Fiedler et al., 2017, 2019; Neubauer et al., 2017; Karset 
et al., 2018; Regayre et al., 2018; Zhou et al., 2018b; Golaz et al., 
2019; Diamond et al., 2020).
The adjustment contribution to the CMIP6 ensemble mean ERFaci 
is –0.20 W m–2, though with considerable differences between 
the models (Smith et  al., 2020b). Generally, this adjustment in 
ESMs arises mainly from LWP changes (e.g.,  Ghan et  al., 2016), 
while satellite observations suggest that cloud cover adjustments 
dominate and that aerosol effects on LWP are overestimated in ESMs 
(Bender et  al., 2019). Large-eddy-simulations also tend to suggest 
an overestimated aerosol effect on cloud lifetime in ESMs, but some 
report an aerosol-induced decrease in cloud cover that is at odds 
with satellite observations (Seifert et al., 2015). Despite this potential 
disagreement when it comes to the dominant adjustment mechanism, 
a  substantial negative contribution to ERFaci from adjustments is

supported both by observational and modelling studies.
Contributions to ERFaci from anthropogenic aerosols acting as INPs 
are generally not included in CMIP6 models. Two global modelling 
studies incorporating parametrizations based on recent laboratory 
studies both found a negative contribution to ERFaci (Penner et al., 2018; 
McGraw et al., 2020), with central estimates of –0.3 and –0.13 W m–2, 
respectively. However, previous studies have produced model estimates 
of opposing signs (Storelvmo, 2017). There is thus limited evidenc e and 
medium agreement  for a small negative contribution to ERFaci from 
anthropogenic INP-induced cirrus modifications ( low confidence ).
Similarly, aerosol effects on deep convective clouds are typically not 
incorporated in ESMs. However, cloud-resolving modelling studies 
support non-negligible aerosol effects on the radiative properties of 
convective clouds and associated detrained cloud anvils (Tao et al.,

2012). While global ERF estimates are currently not available for 
these effects, the fact that they are missing in most ESMs adds to the 
uncertainty range for the model-based ERFaci.
From model-based evidence, ERFaci is assessed to –1.0 ± 0.8 W m–2 
(medium confidence ). This assessment uses the mean ERFaci in 
Table 7.6 as a starting point, but further allows for a small negative 
ERF contribution from cirrus clouds. The uncertainty range is based on 
those reported in Table 7.6, but widened to account for uncertain but 
likely  non-negligible processes currently unaccounted for in ESMs.
7.3.3.2.3 Overall assessment of ERFaci
The assessment of ERFaci based on observational evidence alone 
(–1.0 ± 0.7 W m–2) is very similar to the one based on model evidence 
alone (–1.0 ± 0.8 W m–2), in strong contrast to what was reported 
in AR5. This reconciliation of observation-based and model-based 
estimates is the result of considerable scientific progress and reflects

954
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7estimates. The strong agreement between the two largely independent 
lines of evidence increases confidence in the overall assessment of the 
central estimate and very likely  range for ERFaci of –1.0 ± 0.7 W m–2 
(medium confidence ). The assessed range is consistent with but 
narrower than that reported by the review of Bellouin et al. (2020) of 
–2.65 to –0.07 W m–2. The difference is primarily due to a wider range 
in the adjustment contribution to ERFaci in Bellouin et  al. (2020), 
however adjustments reported relative to IRFaci ranging from 40% 
to 150% in that study are fully consistent with the ERFaci assessment 
presented here.
7.3.3.3 Energy Budget Constraints on the Total Aerosol ERF
Energy balance models of reduced complexity have in recent years 
increasingly been combined with Monte Carlo approaches to provide 
valuable ‘top-down’ (also called inverse) observational constraints

on the total aerosol ERF . These top-down approaches report ranges 
of aerosol ERF that are found to be consistent with the global mean 
temperature record and, in some cases, also observed ocean heat 
uptake. However, the total aerosol ERF is also used together with the 
historical temperature record in Section 7.5 to constrain equilibrium 
climate sensitivity (ECS) and transient climate response (TCR). Using 
top-down estimates as a separate line of evidence also for the total 
aerosol ERF would therefore be circular. Nevertheless, it is useful 
to examine the development of these estimates since AR5, and the 
degree to which these estimates are consistent with the upper and 
lower bounds of the assessments of total aerosol ERF (ERFari+aci).
When the first top-down estimates emerged (e.g., Knutti et al., 2002), 
it became clear that some of the early (‘bottom-up’) ESM estimates 
of total aerosol ERF were inconsistent with the plausible top-down

range. However, as more inverse estimates have been published, it 
has increasingly become clear that they too are model-dependent 
and span a wide range of ERF estimates, with confidence intervals 
that in some cases do not overlap (Forest, 2018). It has also become 
evident that these methods are sensitive to revised estimates of other 
forcings and/or updates to observational datasets. A recent review 
of 19 such estimates reported a mean of –0.77 W m–2 for the total 
aerosol ERF , and a 95% confidence interval of [–1.15 to –0.31] W m–2 
(Forest, 2018). Adding to that review, a  more recent study using 
the same approach reported an estimate of total aerosol ERF of 
–0.89 [–1.82 to –0.01] W m–2 (Skeie et al., 2018). However, in the 
same study, an alternative way of incorporating ocean heat content in 
the analysis produced a total aerosol ERF estimate of –1.34 [–2.20 to 
–0.46] W m–2, illustrating the sensitivity to the manner in which

observations are included. A new approach to inverse estimates took 
advantage of independent climate radiative response estimates from 
eight prescribed SST and sea ice-concentration simulations over the 
historical period to estimate the total anthropogenic ERF . From this 
a total aerosol ERF of –0.8 [–1.6 to +0.1] W m–2 was derived (valid 
for near-present relative to the late 19th  century). This range was 
found to be more invariant to parameter choices than earlier inverse 
approaches (Andrews and Forster, 2020).
Beyond the inverse estimates described above, other efforts 
have been made since AR5 to constrain the total aerosol ERF . For 
example, Stevens (2015) used a simple (one-dimensional) model to simulate the historical total aerosol ERF evolution consistent with 
the observed temperature record. Given the lack of temporally 
extensive cooling trends in the 20th-century record and the fact that 
the historical evolution of GHG forcing is relatively well constrained,

the study concluded that a  more negative total aerosol ERF than 
–1.0 W m–2 was incompatible with the historical temperature record. 
This was countered by Kretzschmar et al. (2017), who argued that 
the model employed in Stevens (2015) was too simplistic to account 
for the effect of geographical redistributions of aerosol emissions 
over time. Following the logic of Stevens (2015), but basing their 
estimates on a subset of CMIP5 models as opposed to a simplified 
modelling framework, Kretzschmar et al. argued that a total aerosol 
ERF as negative as –1.6 W  m–2 was consistent with the observed 
temperature record. Similar arguments were put forward by Booth 
et al. (2018), who emphasized that the degree of non-linearity of the 
total aerosol ERF with aerosol emissions is a central assumption in 
Stevens (2015).
The historical temperature record was also the key observational 
constraint applied in two additional studies (Rotstayn et al., 2015;

Shindell et al., 2015) based on a subset of CMIP5 models. Rotstayn 
et  al. (2015) found a  strong temporal correlation (>0.9) between 
the total aerosol ERF and the global surface temperature. They used 
this relationship to produce a best estimate for the total aerosol ERF 
of –0.97 W m–2, but with considerable unquantified uncertainty, in 
part due to uncertainties in the TCR. Shindell et al. (2015) came to 
a similar best estimate for the total aerosol ERF of –1.0 W m–2 and 
a 95% confidence interval of –1.4 to –0.6 W m–2 but based this on 
spatial temperature and ERF patterns in the models in comparison 
with observed spatial temperature patterns.
A separate observational constraint on the total ERF was proposed 
by Cherian et al. (2014), who compared trends in downward fluxes of 
solar radiation observed at surface stations across Europe (described 
in Section 7.2.2.3) to those simulated by a subset of CMIP5 models.

Based on the relationship between solar radiation trends and the 
total aerosol ERF in the models, they inferred a total aerosol ERF of 
–1.3 W m–2 and a standard deviation of ± 0.4 W m–2.
Based solely on energy balance considerations or other observational 
constraints, it is extremely likely  that the total aerosol ERF is negative 
(high confidence ), but extremely unlikely  that the total aerosol ERF is 
more negative than –2.0 W m–2 (high confidence ).
7.3.3.4 Overall Assessment of Total Aerosol ERF
In AR5 (Boucher et al., 2013), the overall assessment of total aerosol 
ERF (ERFari+aci) used the median of all ESM estimates published prior 
to AR5 of –1.5 [–2.4 to –0.6] W m–2 as a starting point, but placed more 
confidence in a subset of models that were deemed more complete 
in their representation of aerosol–cloud interactions. These models, 
which included aerosol effects on mixed-phase, ice and/or convective

clouds, produced a smaller estimate of –1.38 W m–2. Likewise, studies 
that constrained models with satellite observations (five in total), 
which produced a median estimate of –0.85 W m–2, were given extra 
weight. Furthermore, a longwave ERFaci of 0.2 W m–2 was added to 
studies that only reported shortwave ERFaci values. Finally, based on

955
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77higher resolution models, doubt was raised regarding the ability of 
ESMs to represent the cloud-adjustment component of ERFaci with 
fidelity. The expert judgement was therefore that aerosol effects 
on cloud lifetime were too strong in the ESMs, further reducing the 
overall ERF estimate. The above lines of argument resulted in a total 
aerosol assessment of –0.9 [–1.9 to –0.1] W m–2 in AR5.
Here, the best estimate and range is revised relative to AR5 (Boucher 
et al., 2013), partly based on updates to the above lines of argument. 
Firstly, the studies that included aerosol effects on mixed-phase 
clouds in AR5 relied on the assumption that anthropogenic black 
carbon (BC) could act as INPs in these clouds, which has since 
been challenged by laboratory experiments (Kanji et  al., 2017; 
Vergara-Temprado et al., 2018). There is no observational evidence of

appreciable ERFs associated with aerosol effects on mixed-phase and 
ice clouds (Section 7.3.3.2.1), and modelling studies disagree when it 
comes to both their magnitude and sign (Section 7.3.3.2.2). Likewise, 
very few ESMs incorporate aerosol effects on deep convective 
clouds, and cloud-resolving modelling studies report different 
effects on cloud radiative properties depending on environmental 
conditions (Tao et al., 2012). Thus, it is not clear whether omitting 
such effects from ESMs would lead to any appreciable ERF biases, 
or if so, what the sign of such biases would be. As a result, all ESMs 
are given equal weight in this assessment. Furthermore, there is now 
a considerably expanded body of literature which suggests that early 
modelling studies that incorporated satellite observations may have 
resulted in overly conservative estimates of the magnitude of ERFaci 
(Section 7.3.3.2.1). Finally, based on an assessment of the longwave

ERFaci in the CMIP5 models, the offset of +0.2 W m–2 applied in AR5 
appears to be too large (Heyn et al., 2017). As in AR5, there is still 
reason to question the ability of ESMs to simulate adjustments in 
LWP and cloud cover in response to aerosol perturbation, but it is 
not clear that this will result in biases that exclusively increase the 
magnitude of the total aerosol ERF (Section 7.3.3.2.2).
The assessment of total aerosol ERF here uses the following lines of 
evidence: satellite-based evidence for IRFari; model-based evidence 
for IRFari and ERFari; satellite-based evidence of IRFaci and ERFaci; 
and finally model-based evidence for ERFaci. Based on this, ERFari and 
ERFaci for 2014 relative to 1750 are assessed to be –0.3 ± 0.3 W m–2 
and –1.0 ± 0.7 W m–2, respectively. There is thus strong evidence 
for a substantive negative total aerosol ERF , which is supported by 
the broad agreement between observation-based and model-based

lines of evidence for both ERFari and ERFaci that has emerged since 
AR5 (Gryspeerdt et  al., 2020). However, considerable uncertainty 
remains, particularly with regards to the adjustment contribution 
to ERFaci, as well as missing processes in current ESMs, notably 
aerosol effects on mixed-phase, ice and convective clouds. This leads 
to a  medium confidence  in the estimate of ERFari+aci and a slight 
narrowing of the uncertainty range. Because the estimates informing 
the different lines of evidence are generally valid for approximately 
2014 conditions, the total aerosol ERF assessment is considered valid 
for 2014 relative to 1750.
Combining the lines of evidence and adding uncertainties in 
quadrature, the ERFari+aci estimated for 2014 relative to 1750 
is assessed to be –1.3 [–2.0 to –0.6] W m–2 (medium confidence ). The  corresponding range from Bellouin et  al. (2019) is –3.15 to 
–0.35 W m–2, thus there is agreement for the upper bound while the

lower bound assessed here is less negative. A  lower bound more 
negative than –2.0 W m–2 is not supported by any of the assessed 
lines of evidence. There is high confidence  that ERFaci contributes 
most (75–80%) to the total aerosol effect (ERFari+aci). In contrast 
to AR5 (Boucher et al., 2013), it is now virtually certain  that the total 
aerosol ERF is negative. Figure 7.5 depicts the aerosol ERFs from the 
different lines of evidence along with the overall assessments.
As most modelling and observational estimates of aerosol ERF have 
end points in 2014 or earlier, there is limited evidence  available for 
the assessment of how aerosol ERF has changed from 2014 to 2019. 
However, based on a general reduction in global mean AOD over this 
period (Section  2.2.6 and Figure  2.9), combined with a  reduction 
in emissions of aerosols and their precursors in updated emissions 
inventories (Hoesly et  al., 2018), the aerosol ERF is assessed to

have decreased in magnitude from about 2014 to 2019 ( medium 
confidence ). Consistent with Figure 2.10, the change in aerosol ERF 
from about 2014 to 2019 is assessed to be +0.2 W m–2, but with 
low confidence due to limited evidence . Aerosols are therefore 
assessed to have contributed an ERF of –1.1 [–1.7 to –0.4] W m–2 
over 1750–2019 ( medium confidence ).
Figure 7. 5 | Net aerosol effective radiative forcing (ERF) from different 
lines of evidence.  The headline AR6 assessment of –1.3 [–2.0 to –0.6] W m–2 
is highlighted in purple for 1750–2014 and compared to the AR5 assessment of 
–0.9  [–1.9 to –0.1] W m–2 for 1750–2011. The evidence comprising the AR6 
assessment is shown below this: energy balance constraints [–2 to 0 W m–2 with 
no best estimate]; observational evidence from satellite retrievals of –1.4 [–2.2 to 
–0.6] W m–2; and climate model-based evidence of –1.25 [–2.1 to –0.4] W m–2.

Estimates from individual CMIP5 (Zelinka et al., 2014) and CMIP6 (Smith et al., 2020b 
and Table 7.6) models are depicted by blue and red crosses respectively. For each 
line of evidence the assessed best-estimate contributions from ERFari and ERFaci 
are shown with darker and paler shading respectively. The observational assessment 
for ERFari is taken from the IRFari. Uncertainty ranges are represented by black bars 
for the total aerosol ERF and depict very likely  ranges. Further details on data sources 
and processing are available in the chapter data table (Table 7.SM.14).

956
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity77.3.4 Other Agents
In addition to the large anthropogenic ERFs associated with 
WMGHGs and atmospheric aerosols assessed in Sections 7.3.2 
and 7.3.3, land-use change, contrails and aviation-induced cirrus, 
and light-absorbing particles deposited on snow and ice have also 
contributed to the overall anthropogenic ERF and are assessed in 
Sections 7.3.4.1, 7.3.4.2 and 7.3.4.3. Changes in solar irradiance, 
galactic cosmic rays, and volcanic eruptions since pre-industrial 
times combined represent the natural contribution to the total 
(anthropogenic + natural) ERF and are discussed in Sections 7.3.4.4, 
7.3.4.5 and 7.3.4.6.
7.3.4.1 Land Use
Land-use forcing is defined as those changes in land-surface 
properties directly caused by human activity rather than by climate 
processes (see also Section  2.2.7). Land-use change affects the 
surface albedo. For example, deforestation typically replaces darker

forested areas with brighter cropland, and thus imposes a negative 
radiative forcing on climate, while afforestation and reforestation 
can have the opposite effect. Precise changes depend on the nature 
of the forest, crops and underlying soil. Land-use change also affects 
the amount of water transpired by vegetation (Devaraju et al., 2015). 
Irrigation of land directly affects evaporation (Sherwood et  al., 
2018), causing a  global increase of 32,500 m3 s−1 due to human 
activity. Changes in evaporation and transpiration affect the latent 
heat budget, but do not directly affect the top-of-atmosphere (TOA) 
radiative fluxes. The lifetime of water vapour is so short that the 
effect of changes in evaporation on the greenhouse contribution 
of water vapour are negligible (Sherwood et  al., 2018). However, 
evaporation can affect the ERF through adjustments, particularly 
through changes in low-cloud amounts. Land management affects

the emissions or removal of GHGs from the atmosphere (such as 
CO 2, CH 4, N 2O). These emissions changes have the greatest effect on 
climate (Ward et al., 2014), however they are already included in GHG 
inventories. Land-use change also affects the emissions of dust and 
biogenic volatile organic compounds (BVOCs), which form aerosols 
and affect the atmospheric concentrations of ozone and methane 
(Section 6.2.2). The effects of land use on surface temperature and 
hydrology were recently assessed in SRCCL (Jia et al., 2019).
Using the definition of ERF from Section  7.1, the adjustment in 
land-surface temperature is excluded from the definition of ERF , 
but changes in vegetation and snow cover (resulting from land-use 
change) are included (Boisier et al., 2013). Land-use change in the 
mid-latitudes induces a substantial amplifying adjustment in snow 
cover. Few climate model studies have attempted to quantify the

ERF of land-use change. T. Andrews et al. (2017) calculated a very 
large surface albedo ERF (–0.47 W m–2) from 1860 to 2005 in the 
HadGEM2-ES model, although they did not separate out the surface 
albedo change from snow cover change. HadGEM2-ES is known 
to overestimate the amount of boreal trees and shrubs in the 
unperturbed state (Collins et al., 2011) so will tend to overestimate 
the ERF associated with land-use change. The increases in dust in 
HadGEM2-ES contributed an extra –0.25 W m–2, whereas cloud cover 
changes added a small positive adjustment (0.15 W m–2) consistent with a  reduction in transpiration. A  multi-model quantification of 
land-use forcing in CMIP6 models (excluding one outlier) (Smith 
et  al., 2020b) found an IRF of –0.15  ± 0.12 W m–2 (1850–2014), 
and an ERF (correcting for land-surface temperature change) of 
–0.11 ± 0.09 W m–2. This shows a small positive adjustment term 
(mainly from a  reduction in cloud cover). CMIP5 models show an

IRF of –0.11  [–0.16 to –0.04] W m–2 (1850–2000) after excluding 
unrealistic models (Lejeune et al., 2020).
The contribution of land-use change to albedo changes has recently 
been investigated using MODIS and AVHRR to attribute surface albedo 
to geographically specific land-cover types (Ghimire et  al., 2014). 
When combined with a historical land-use map (Hurtt et al., 2011) 
this gives a SARF of –0.15 ± 0.01 W m–2 for the period 1700–2005, of 
which approximately –0.12 W m–2 is from 1850. This study accounted 
for correlations between vegetation type and snow cover, but not the 
adjustment in snow cover identified in T. Andrews et al. (2017).
The indirect contributions of land-use change through biogenic 
emissions is very uncertain. Decreases in BVOCs reduce ozone and 
methane (Unger, 2014), but also reduce the formation of organic 
aerosols and their effects on clouds (Scott et al., 2017). Adjustments 
through changes in aerosols and chemistry are model dependent

(Zhu et al., 2019b; Zhu and Penner, 2020), and it is not yet possible to 
make an assessment based on a limited number of studies.
The contribution of irrigation (mainly to low-cloud amount) is 
assessed as –0.05 [–0.1 to 0.05] W  m–2 for the historical period 
(Sherwood et al., 2018).
Because the CMIP5 and CMIP6 modelling studies are in agreement 
with Ghimire et  al. (2014), that study is used as the assessed 
albedo ERF . Adding the irrigation effect to this gives an overall 
assessment of the ERF from land-use change of –0.20 ± 0.10 W m–2 
(medium confidence ). Changes in ERF since 2014 are assumed to 
be small compared to the uncertainty, so this ERF applies to the 
period 1750–2019. The uncertainty range includes uncertainties in 
the adjustments.
7.3.4.2 Contrails and Aviation-induced Cirrus
ERF from contrails and aviation-induced cirrus is taken from the 
assessment of Lee et  al. (2020), at 0.057 [0.019 to 0.098] W m–2

in 2018 (see Section 6.6.2 for an assessment of the total effects of 
aviation). This is rounded up to address its low confidence  and the 
extra year of air traffic to give an assessed ERF over 1750–2019 of 
0.06 [0.02 to 0.10] W m–2. This assessment is given low confidence  due 
to the potential that processes missing from the assessment would 
affect the magnitude of contrails and aviation-induced cirrus ERF .
7.3.4.3 Light-absorbing Particles on Snow and Ice
In AR5, it was assessed that the effects of light-absorbing particles 
(LAPs) did probably not significantly contribute to recent reductions 
in Arctic ice and snow (Vaughan et  al., 2013). The SARF from 
LAPs on snow and ice was assessed to 0.04 [0.02 to 0.09] W m–2 
(Boucher et al., 2013), a range appreciably lower than the estimates

957
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77given in AR4 (Forster et  al., 2007). This effect was assessed to be 
low confidence  (medium evidence , low agreement ) (Table  8.5 in 
Myhre et al., 2013b).
Since AR5 there has been progress in the understanding of the 
physical state and processes in snow that govern the albedo 
reduction by black carbon (BC). The SROCC (IPCC, 2019a) assessed 
that there is high confidence  that darkening of snow by deposition 
of BC and other light-absorbing aerosol species increases the rate of 
snow melt (Section 2.2 in Hock et al., 2019; Section 3.4 in Meredith 
et al., 2019). C. He et al. (2018) found that taking into account both 
the non-spherical shape of snow grains and internal mixing of BC 
in snow significantly altered the effects of BC on snow albedo. The 
reductions of snow albedo by dust and BC have been measured and 
characterized in the Arctic, the Tibetan Plateau, and mid-latitude

regions subject to seasonal snowfall, including North America and 
northern and eastern Asia (Qian et al., 2015).
Since AR5, two further studies of global IRF from black carbon on 
snow deposition are available, with best estimates of 0.01 W m–2 
(Lin et  al., 2014) and 0.045 W m–2 (Namazi et  al., 2015). Organic 
carbon deposition on snow and ice has been estimated to contribute 
a  small positive IRF of 0.001 to 0.003 W m–2 (Lin et  al., 2014). 
No  comprehensive global assessments of mineral dust deposition 
on snow are available, although the effects are potentially large 
in relation to the total effect of LAPs on snow and ice forcing 
(Yasunari et al., 2015).
Most radiative forcing estimates have a  regional emphasis. 
The regional focus makes estimating a global mean radiative forcing 
from aggregating different studies challenging, and the relative 
importance of each region is expected to change if the global pattern

of emissions sources changes (Bauer et al., 2013). The lower bound 
of the assessed range of BC on snow and ice is extended to zero 
to encompass Lin et al. (2014), with the best estimate unchanged, 
resulting in 0.04 [0.00 to 0.09] W m–2. The efficacy of BC on snow 
forcing was estimated to be 2 to 4 times as large as for an equivalent 
CO 2 forcing as the effects are concentrated at high latitudes in the 
cryosphere (Bond et al., 2013). However, it is unclear how much of 
this effect is due to radiative adjustments leading to a higher ERF , 
and how much comes from a less negative feedback α due to the 
high-latitude nature of the forcing. To estimate the overall ERF , 
the IRF is doubled assuming that part of the increased efficacy is due 
to adjustments. This gives an overall assessed ERF of +0.08 [0.00 to 
0.18] W m–2, with low confidence .
7.3.4.4 Solar
Variations in the total solar irradiance (TSI) represent a  natural

external forcing agent. The dominant cycle is the solar 11-year 
activity cycle, which is superimposed on longer cycles (Section 2.2). 
Over the last three 11-year cycles, the peak-to-trough amplitude in 
TSI has differed by about 1 W m–2 between solar maxima and minima 
(Figure 2.2).
The fractional variability in the solar irradiance, over the solar cycle 
and between solar cycles, is much greater at short wavelengths in the 200–400 nanometre (nm) band than for the broad visible/
infrared band that dominates TSI (Krivova et al., 2006). The IRF can be 
derived simply by Δ TSI × (1 – albedo)/4 irrespective of wavelength, 
where the best estimate of the planetary albedo is usually taken to 
be 0.29 and Δ TSI represents the change in total solar irradiance 
(Stephens et al., 2015). (The factor 4 arises because TSI is per unit 
area of Earth  cross section presented to the Sun and IRF is per 
unit area of Earth’s surface). The adjustments are expected to be

wavelength dependent. Gray et al. (2009) determined a stratospheric 
temperature adjustment of –22% to spectrally resolved changes in 
the solar radiance over one solar cycle. This negative adjustment is 
due to stratospheric heating from increased absorption by ozone at 
the short wavelengths, increasing the outgoing longwave radiation 
to space. A multi-model comparison (Smith et al., 2018b) calculated 
adjustments of –4% due to stratospheric temperatures and –6% due 
to tropospheric processes (mostly clouds), for a change in TSI across 
the spectrum (Figure 7.4). The smaller magnitude of the stratospheric 
temperature adjustment is consistent with the broad spectral change 
rather than the shorter wavelengths characteristic of solar variation. 
A single-model study also found an adjustment that acts to reduce 
the forcing (Modak et  al., 2016). While there has not yet been 
a  calculation based on the appropriate spectral change, the –6%

tropospheric adjustment from Smith et al. (2018b) is adopted along 
with the Gray et  al. (2009) stratospheric temperature adjustment. 
The ERF due to solar variability over the historical period is therefore 
represented by 0.72 × Δ TSI × (1 – albedo)/4 using the TSI timeseries 
from Chapter 2 (Section 2.2.1).
The AR5 (Myhre et  al., 2013b) assessed solar SARF from around 
1750 to 2011 to be 0.05 [0.00 to 0.10] W m–2 which was computed 
from the seven-year mean around the solar minima in 1745 (being 
closest to 1750) and 2008 (being the most recent solar minimum). 
The inclusion of tropospheric adjustments that reduce ERF (compared 
to SARF in AR5) has a negligible effect on the overall forcing. Prior to 
the satellite era, proxy records are used to reconstruct historical solar 
activity. In AR5, historical records were constructed using observations 
of solar magnetic features. In this assessment historical time series

are constructed from radiogenic compounds in the biosphere and in 
ice cores that are formed from cosmic rays (Steinhilber et al., 2012).
In this assessment the TSI from the Paleoclimate Model 
Intercomparison Project Phase 4 (PMIP4) reconstruction is used 
(Section 2.2.1; Jungclaus et al., 2017). Proxies constructed from the 
14C and 10Be radiogenic records for the SATIRE-M model (Vieira et al., 
2011) and 14C record for the PMOD model (Shapiro et  al., 2011) 
for the 1745 solar minimum provide ERFs for 1745–2008 of –0.01, 
–0.02 and 0.00 W m–2 respectively. An independent dataset from the 
National Oceanic and Atmospheric Administration’s Climate Data 
Record (Coddington et  al., 2016; Lean, 2018) provides an ERF for 
1745–2008 of +0.03 W m–2. One substantially higher ERF estimate of 
+0.35 W m–2 derived from TSI reconstructions is provided by Egorova 
et  al. (2018). However, the estimate from Egorova et  al. (2018) 
hinges on assumptions about long-term changes in the quiet Sun

for which there is no observed evidence. Lockwood and Ball (2020) 
analysed the relationship between observed changes in cosmic ray 
fluxes and recent, more accurate, TSI data and derived ERF between 
–0.01 and +0.02 W m–2, and Yeo et al. (2020) modelling showed the

958
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7maximum possible ERF to be 0.26 ± 0.09 W m–2. Hence the Egorova 
et  al. (2018) estimate is not explicitly taken into account in the 
assessment presented in this section.
In contrast to AR5, the solar ERF in this assessment uses full solar 
cycles rather than solar minima. The pre-industrial TSI is defined as 
the mean from all complete solar cycles from the start of the 14C 
SATIRE-M proxy record in 6755 BCE to 1744 CE. The mean TSI from 
solar cycle 24 (2009–2019) is adopted as the assessment period for 
2019. The best estimate solar ERF is assessed to be 0.01 W m–2, using 
the 14C reconstruction from SATIRE-M, with a  likely  range of –0.06 to 
+0.08 W m–2 (medium confidence ). The uncertainty range is adopted 
from the evaluation of Lockwood and Ball (2020) using a  Monte 
Carlo analysis of solar activity from the Maunder Minimum to 2019

from several datasets, leading to an ERF of –0.12 to +0.15 W m–2. 
The Lockwood and Ball (2020) full uncertainty range is halved as the 
period of reduced solar activity in the Maunder Minimum had ended 
by 1750 ( medium confidence ).
7.3.4.5 Galactic Cosmic Rays
Variations in the flux of galactic cosmic rays (GCR) reaching the 
atmosphere are modulated by solar activity and affect new particle 
formation in the atmosphere through their link to ionization of 
the troposphere (Lee et  al., 2019). It has been suggested that 
periods of high GCR flux correlate with increased aerosol and 
CCN concentrations and therefore also with cloud properties 
(e.g., Dickinson, 1975; Kirkby, 2007).
Since AR5, the link between GCR and new particle formation has been 
more thoroughly studied, particularly by experiments in the CERN 
CLOUD chamber (Cosmics Leaving OUtdoor Droplets; Dunne et al., 
2016; Kirkby et al., 2016; Pierce, 2017). By linking the GCR-induced

new particle formation from CLOUD experiments to CCN, Gordon 
et  al. (2017) found that the CCN concentration for low-clouds 
differed by 0.2–0.3% between solar maximum and solar minimum. 
Combined with relatively small variations in the atmospheric ion 
concentration over centennial time scales (Usoskin et al., 2015), it 
is therefore unlikely that cosmic ray intensity affects present-day 
climate via nucleation (Yu and Luo, 2014; Dunne et al., 2016; Pierce, 
2017; Lee et al., 2019).
Studies continue to seek a relationship between GCR and properties 
of the climate system based on correlations and theory. Svensmark 
et  al. (2017) proposed a  new mechanism for ion-induced increase 
in aerosol growth rate and subsequent influence on the CCN 
concentration. The study does not include an estimate of the 
resulting effect on atmospheric CCN concentration and cloud 
radiative properties. Furthermore, Svensmark et  al. (2009, 2016)

find correlations between GCRs and aerosol and cloud properties 
in satellite and ground-based data. Multiple studies investigating 
this link have challenged such correlations (Kristjánsson et al., 2008; 
Calogovic et al., 2010; Laken, 2016).
AR5 concluded that the GCR effect on CCN is too weak to have any 
detectable effect on climate and no robust association was found 
between GCR and cloudiness (Boucher et  al., 2013). Published literature since AR5 robustly supports these conclusions with key 
laboratory, theoretical and observational evidence. There is high 
confidence  that GCRs contribute a  negligible ERF over the period 
1750–2019.
7.3.4.6 Volcanic Aerosols
There is large episodic negative radiative forcing associated with 
sulphur dioxide (SO  2) being ejected into the stratosphere from 
explosive volcanic eruptions, accompanied by more frequent smaller 
eruptions (Figure  2.2 and Cross-Chapter Box  4.1). From SO 2 gas,

reflective sulphate aerosol is formed in the stratosphere where it may 
persist for months to years, reducing the incoming solar radiation. 
The volcanic SARF in AR5 (Myhre et  al., 2013b) was derived by 
scaling the stratospheric aerosol optical depth (SAOD) by a factor of 
–25 W m–2 per unit SAOD from Hansen et al. (2005b). Quantification 
of the adjustments to SAOD perturbations from climate model 
simulations have determined a significant positive adjustment driven 
by a reduction in cloud amount (Figure 7.4; Marshall et al., 2020). 
Analysis of CMIP5 models provides a mean ERF of –20 W m–2 per 
unit SAOD (Larson and Portmann, 2016). Single-model studies with 
successive generations of Hadley Centre climate models produce 
estimates between –17 and –19 W m–2 per unit SAOD (Gregory et al., 
2016; Marshall et al., 2020), with some evidence that ERF may be non-
linear with SAOD for large eruptions (Marshall et al., 2020). Analysis

of the volcanically active periods of 1982–1985 and 1990–1994 using 
the CESM1(WACCM) aerosol–climate model provided an SAOD-
to-ERF relationship of –21.5 (± 1.1) W m–2 per unit SAOD (Schmidt 
et al., 2018). Volcanic SO 2 emissions may contribute a positive forcing 
through effects on upper tropospheric ice clouds, due to additional 
ice nucleation on volcanic sulphate particles (Friberg et  al., 2015; 
Schmidt et  al., 2018), although one observational study found no 
significant effect (Meyer et  al., 2015). Due to low agreement , the 
contribution of sulphate aerosol effects on ice clouds to volcanic ERF 
is not included in the overall assessment.
Non-explosive volcanic eruptions generally yield negligible global 
ERFs due to the short atmospheric lifetimes (a few weeks) of volcanic 
aerosols in the troposphere. However, as discussed in Section 7.3.3.2, 
the massive fissure eruption in Holuhraun, Iceland persisted for

months in 2014 and 2015 and did in fact result in a  marked and 
persistent reduction in cloud droplet radii and a  corresponding 
increase in cloud albedo regionally (Malavelle et al., 2017). This shows 
that non-explosive fissure eruptions can lead to strong regional and 
even global ERFs, but because the Holuhraun eruption occurred in 
Northern Hemisphere winter, solar insolation was weak and the 
observed albedo changes therefore did not result in an appreciable 
global ERF (Gettelman et al., 2015).
The ERF for volcanic stratospheric aerosols is assessed to be 
–20 ± 5 W m–2 per unit SAOD ( medium confidence ) based on the 
CMIP5 multi-model mean from the Larson and Portmann (2016) 
SAOD forcing efficiency calculations combined with the single-model 
results of Gregory et al. (2016), Schmidt et al. (2018) and Marshall 
et al. (2020). This is applied to the SAOD time series from Chapter 2 
(Section  2.2.2) to generate a  time series of ERF and temperature

response shown in Chapter 2 (Figure 2.2 and Figure 7.8, respectively).

959
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77The period from 500 BCE to 1749 CE, spanning back to the start of 
the record of Toohey and Sigl (2017), is defined as the pre-industrial 
baseline and the volcanic ERF is calculated using an SAOD anomaly 
from this long-term mean. As in AR5, a pre-industrial to present-day 
ERF assessment is not provided due to the episodic nature of 
volcanic eruptions.
7.3.5 Synthesis of Global Mean Radiative 
Forcing, Past and Future
7.3.5.1 Major Changes in Forcing since the IPCC 
Fifth Assessment Report
The AR5 introduced the concept of effective radiative forcing (ERF) 
and radiative adjustments, and made a preliminary assessment that 
the tropospheric adjustments were zero for all species other than 
the effects of aerosol–cloud interaction and black carbon. Since AR5, 
new studies have allowed for a tentative assessment of values for 
tropospheric adjustments to CO 2, CH 4, N2O, some CFCs, solar forcing,

and stratospheric aerosols, and to place a  tighter constraint on 
adjustments from aerosol–cloud interaction (Sections 7.3.2, 7.3.3 
and 7.3.4). In AR6, the definition of ERF explicitly removes the land-
surface temperature change as part of the forcing, in contrast to AR5 
where only sea surface temperatures were fixed. The ERF is assessed 
to be a better predictor of modelled equilibrium temperature change 
(i.e., less variation in feedback parameter) than SARF (Section 7.3.1).As discussed in Section 7.3.2, the radiative efficiencies for CO 2, CH 4 
and N 2O have been updated since AR5 (Etminan et al., 2016). There 
has been a  small (1%) increase in the stratospheric-temperature-
adjusted CO 2 radiative efficiency, and a +5% tropospheric adjustment 
has been added. The stratospheric-temperature-adjusted radiative 
efficiency for CH 4 is increased by approximately 25% ( high confidence ). 
The tropospheric adjustment is tentatively assessed to be –14% ( low

confidence ). A  +7% tropospheric adjustment has been added to 
the radiative efficiency for N 2O and +12% to CFC-11 and CFC-12 
(low confidence ).
For aerosols there has been a convergence of model and observational 
estimates of aerosol forcing, and the partitioning of the total aerosol 
ERF has changed. Compared to AR5 a  greater fraction of the ERF is 
assessed to come from ERFaci compared to the ERFari. It is now assessed 
as virtually certain  that the total aerosol ERF (ERFari+aci) is negative.
7.3.5.2 Summary ERF Assessment
Figure 7.6 shows the industrial-era ERF estimates for 1750 to 2019 
for the concentration change in different forcing agents. The assessed 
uncertainty distributions for each individual component are combined 
with a  100,000-member Monte Carlo simulation that samples the 
different distributions, assuming they are independent, to obtain 
the overall assessment of total present-day ERF (Supplementary

Material 7.SM.1). The corresponding emissions-based ERF figure is 
shown in Chapter 6 (Figure 6.12).
Figure 7. 6 | Change in effective radiative forcing (ERF) from 1750 to 2019 by contributing forcing agents (carbon dioxide, other well-mixed greenhouse 
gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, anthropogenic total, and solar).  
Solid bars represent best estimates, and very likely  (5–95%) ranges are given by error bars. Non-CO 2 WMGHGs are further broken down into contributions from methane (CH 4), 
nitrous oxide (N 2O) and halogenated compounds. Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down 
into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). For aerosols and solar, the 2019 single-year values are given (Table 7.8),

which differ from the headline assessments in both cases. Volcanic forcing is not shown due to the episodic nature of volcanic eruptions. Further details on data sources and 

960
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7The total anthropogenic ERF over the industrial era (1750–2019) is 
estimated as 2.72 [1.96 to 3.48] W m–2 (high confidence ) (Table 7.8 and 
Annex III) . This represents a 0.43 W m–2 increase over the assessment 
made in AR5 (Myhre et  al., 2013b) for the period 1750–2011. This 
increase is a result of compensating effects. Atmospheric concentration 
increases of GHGs since 2011 and upwards revisions of their forcing 
estimates have led to a 0.59 W m–2 increase in their ERF . However, the 
total aerosol ERF is assessed to be more negative compared to AR5, 
due to revised estimates rather than trends ( high confidence ).
Greenhouse gases, including ozone and stratospheric water vapour from 
methane oxidation, are estimated to contribute an ERF of 3.84 [3.46 to 
4.22] W m–2 over 1750–2019. Carbon dioxide continues to contribute 
the largest part (56 ± 16%) of this GHG ERF ( high confidence ).

As discussed in Section 7.3.3, aerosols have in total contributed an ERF 
of –1.1 [–1.7 to –0.4] W m–2 over 1750–2019 ( medium confidence ). Aerosol–cloud interactions contribute approximately 75–80% of 
this ERF with the remainder due to aerosol–radiation interactions 
(Table 7.8).
For the purpose of comparing forcing changes with historical 
temperature change (Section  7.5.2), longer averaging periods are 
useful. The change in ERF from the second half of the 19th century 
(1850–1900) compared with a  recent period (2006–2019) is 
+2.20 [1.53 to 2.91] W m–2, of which 1.71 [1.51 to 1.92] W m–2 is due 
to CO 2.
7.3.5.3 Temperature Contribution of Forcing Agents
The estimated contribution of forcing agents to the 2019 global 
surface air temperature (GSAT) change relative to 1750 is shown in 
Figure 7.7. These estimates were produced using the concentration-

derived ERF time series presented in Figure  2.10 and described Table 7.8  | Summary table of effective radiative forcing (ERF) estimates for AR6 and comparison with the four previous IPCC assessment reports. Prior to 
AR5 values are stratospheric-temperature-adjusted radiative forcing (SARF). For AR5 aerosol–radiation interactions (ari) and aerosol–cloud interactions (aci) are ERF; all other 
values assume ERF equals SARF . Ranges shown are 5–95%. Volcanic ERF is not added to the table due to the episodic nature of volcanic eruptions which makes it difficult 
to compare to the other forcing mechanisms. Solar ERF is based on total solar irradiance (TSI) and not spectral variation.
DriverGlobal Mean Effective Radiative Forcing (W m–2)
SAR
(1750–1993)TAR
(1750–1998)AR4
(1750–2005)AR5
(1750–2011)AR6
(1750–2019)Comment
CO 2 1.56 [1.33 to 1.79] 1.46 [1.31 to 1.61] 1.66 [1.49 to 1.83] 1.82 [1.63 to 2.01] 2.16 [1.90 to 2.41] Increases in concentrations. 
Changes to radiative 
efficiencies.

Inclusion of tropospheric 
adjustments.CH 4 0.47 [0.40 to 0.54 0.48 [0.41 to 0.55] 0.48 [0.43 to 0.53] 0.48 [0.43 to 0.53] 0.54 [0.43 to 0.65]
N2O 0.14 [0.12 to 0.16] 0.15 [0.14 to 0.16] 0.16 [0.14 to 0.18] 0.17 [0.14 to 0.20] 0.21 [0.18 to 0.24]
Halogenated species 0.26 [0.22 to 0.30] 0.36 [0.31 to 0.41] 0.33 [0.30 to 0.36] 0.36 [0.32 to 0.40] 0.41 [0.33 to 0.49]
Tropospheric ozone 0.4 [0.2 to 0.6] 0.35 [0.20 to 0.50] 0.35 [0.25 to 0.65] 0.40 [0.20 to 0.60]
0.47 [0.24 to 0.71]Revised precursor emissions. 
No tropospheric adjustment 
assessed. No troposphere–
stratosphere separation.Stratospheric ozone –0.1 [–0.2 to –0.05] –0.15 [–0.25 to –0.05] –0.05 [–0.15 to 0.05] –0.05 [–0.15 to 0.05]
water vapourNot estimated [0.01 to 0.03] 0.07 [0.02 to 0.1] 0.07 [0.02 to 0.12] 0.05 [0.00 to 0.10]Downward revision due 
to adjustments.
Aerosol–radiation

interactions–0.5 [–0.25 to –1.0] Not estimated –0.50 [–0.90 to –0.10] –0.45 [–0.95 to 0.05] –0.22 [–0.47 to 0.04]ERFari magnitude reduced by 
about 50% compared to AR5, 
based on agreement between 
observation-based and 
modelling-based evidence.
Aerosol–cloud 
interactions[–1.5 to 0.0]
(sulphate only)[–2.0 to 0.0]
(all aerosols)–0.7 [–1.8 to –0.3]
(all aerosols)–0.45 [–1.2 to 0.0] –0.84 [–1.45 to –0.25]ERFaci magnitude increased 
by about 85% compared to 
AR5, based on agreement 
between observation-based 
and modelling-based lines 
of evidence.
Land use Not estimated –0.2 [–0.4 to 0.0] –0.2 [–0.4 to 0.0] –0.15 [–0.25 to –0.05] –0.20 [–0.30 to –0.10] Includes irrigation.
Surface albedo (black + 
organic carbon aerosol 
on snow and ice)Not estimated Not estimated 0.10 [0.00 to 0.20] 0.04 [0.02 to 0.09] 0.08 [0.00 to 0.18]Increased since AR5 
to better account for 
temperature effects.
Combined contrails and

aviation-induced cirrusNot estimated [0.00 to 0.04] Not estimated 0.05 [0.02 to 0.15] 0.06 [0.02 to 0.10] Narrower range since AR5.
Total anthropogenic Not estimated Not estimated 1.6 [0.6 to 2.4] 2.3 [1.1 to 3.3] 2.72 [1.96 to 3.48]Increase due to GHGs, 
compensated slightly by 
aerosol ERFaci.
Solar irradiance 0.3 [0.1 to 0.5] 0.3 [0.1 to 0.5] 0.12 [0.06 to 0.30] 0.05 [0.0 to 0.10] 0.01 [–0.06 to 0.08]Revised historical TSI 
estimates and methodology.

961
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77in  Supplementary Material 7.SM.1.3. The resulting GSAT changes 
over time are shown in Figure 7.8. The historical time series of ERFs 
for the WMGHGs can be derived by applying the ERF calculations of 
Section 7.3.2 to the observed time series of WMGHG concentrations 
in Chapter 2 (Section 2.2).
These ERF timeseries are combined with a  two-layer emulator 
(Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2) using 
a  2237-member constrained Monte Carlo sample of both forcing 
uncertainty (by sampling ERF ranges) and climate response (by 
sampling ECS, TCR and ocean heat capacity ranges). The net model 
warming over the historical period is matched to the assessment 
of historical GSAT warming from 1850–1900 to 1995–2014 of 
0.85 [0.67 to 0.98] °C (Cross-Chapter Box 2.3) and ocean heat content 
change from 1971 to 2018 (Section  7.2.2.2). Therefore the model

gives the breakdown of the GSAT trend associated with different 
forcing mechanisms that are consistent with the overall GSAT 
change. The model assumes that there is no variation in feedback 
parameter across forcing mechanisms (Section 7.3.1) and variations 
in the effective feedback parameter over the historical record 
(Section 7.4.4). The distribution of ECS was informed by Section 7.5.5 
and chosen to approximately maintain the best estimate and likely /
very likely  ranges assessed in that section (see also Supplementary Material 7.SM.2). The TCR has an ensemble median value of 1.81°C, 
in good agreement with Section  7.5.5. Two error bars are shown 
in Figure  7.7. The dashed error bar shows the contribution of ERF 
uncertainty (as assessed in the subsections of Section 7.3) employing 
the best estimate of climate response with an ECS of 3.0°C. The solid 
bar is the total response uncertainty using the Section  7.5.5 assessment

of ECS. The uncertainty in the historical temperature contributions 
ofthe different forcing agents is mostly due to uncertainties in ERF , 
yet for the WMGHG the uncertainty is dominated by the climate 
response as its ERF is relatively well known (Figure 7.7). From the 
assessment of emulator responses in Cross-Chapter Box 7.1, there is 
high confidence  that calibrated emulators such as the one employed 
here can represent the historical GSAT change between 1850–1900 
and 1995–2014 to within 5% for the best estimate and 10% for the 
very likely  range (Supplementary Material, Table 7.SM.4). This gives 
high confidence  in the overall assessment of GSAT change for the 
response to ERFs over 1750–2019 derived from the emulator.
The total human forced GSAT change from 1750 to 2019 is calculated 
to be 1.29 [1.00 to 1.65] °C ( high confidence ). Although the total 
emulated GSAT change has high confidence , the confidence of the

individual contributions matches those given for the ERF assessment in 
the subsections of Section 7.3. The calculated GSAT change is comprised 
Figure 7. 7 | The contribution of forcing agents to 2019 temperature change relative to 1750 produced using the two-layer emulator (Supplementary 
Material 7.SM.2), constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1.  The results are from a 2237-member ensemble. 
Temperature contributions are expressed for carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and 
aviation-induced cirrus, aerosols, solar, volcanic, and total. Solid bars represent best estimates, and very likely  (5–95%) ranges are given by error bars. Dashed error bars show the 
contribution of forcing uncertainty alone, using best estimates of ECS (3.0°C), TCR (1.8°C) and two-layer model parameters representing the CMIP6 multi-model mean. Solid error

bars show the combined effects of forcing and climate response uncertainty using the distribution of ECS and TCR from Tables 7.13 and 7.14, and the distribution of calibrated 
model parameters from 44 CMIP6 models. Non-CO 2 WMGHGs are further broken down into contributions from methane (CH 4), nitrous oxide (N 2O) and halogenated compounds. 
Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down into contributions from aerosol–cloud interactions 
(ERFaci) and aerosol–radiation interactions (ERFari). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

962
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Cross-Chapter Box 7.1 | Physical Emulation of Earth System Models for Scenario Classification 
and Knowledge Integration in AR6
Contributors:  Zebedee R.J. Nicholls (Australia), Malte Meinshausen (Australia/Germany), Piers Forster (United Kingdom), Kyle Armour 
(United States of America), Terje Berntsen (Norway), William Collins (United Kingdom), Christopher Jones (United Kingdom), Jared Lewis 
(Australia/New Zealand), Jochem Marotzke (Germany), Sebastian Milinski (Germany), Joeri Rogelj (United Kingdom/Belgium), 
Chris Smith (United Kingdom)
Climate model emulators are simple physically based models that are used to approximate large-scale climate responses of complex 
Earth system models (ESMs). Due to their low computational cost they can populate or span wide uncertainty ranges that ESMs

cannot. They need to be calibrated to do this and, once calibrated, they can aid inter-ESM comparisons and act as ESM extrapolation 
tools to reflect and combine knowledge from ESMs and many other lines of evidence (Geoffroy et al., 2013a; Good et al., 2013; Smith 
et al., 2018a). In AR6, the term ‘climate model emulator’ (or simply ‘emulator’) is preferred over ‘simple’ or ‘reduced-complexity climate 
model’ to reinforce their use as specifically calibrated tools (Cross-Chapter Box 7.1, Figure 1). Nonetheless, simple physically basedof a WMGHG warming of 1.58 [1.17 to 2.17] °C ( high confidence ), 
a  warming from ozone changes of 0.23  [0.11  to  0.39]  °C ( high 
confidence ), and a cooling of –0.50 [–0.22 to –0.96] °C from aerosol 
effects ( medium confidence ). The aerosol cooling has considerable 
regional time dependence (Section 6.4.3) but has weakened slightly 
over the last 20 years in the global mean (Figures 2.10 and 7.8).

There is also a –0.06 [–0.15 to +0.01] °C contribution from surface 
reflectance changes which is dominated by land-use change ( medium 
confidence ). Changes in solar and volcanic activity are assessed to 
have together contributed a small change of –0.02 [–0.06 to +0.02] °C 
since 1750 ( medium confidence ).
The total (anthropogenic + natural) emulated GSAT between 
1850–1900 and 2010–2019 is 1.14 [0.89 to 1.45] °C, compared 
to the assessed GSAT of 1.06 [0.88 to 1.21] °C (Section 2.3.1 and 
Cross Chapter Box  2.3). The emulated response is slightly warmer 
than the observations and has a  larger uncertainty range. As the 
emulated response attempts to constrain to multiple lines of 
evidence (Supplementary Material 7.SM.2), only one of which is 
GSAT, they should not necessarily be expected to exactly agree. 
The larger uncertainty range in the emulated GSAT compared to the 
observations is reflective of the uncertainties in ECS, TCR and ERF

(particularly the aerosol ERF) that drive the emulator response.
The emulator gives a range of GSAT response for the period 1750 
to 1850–1900 of 0.09 [0.04 to 0.14] °C from anthropogenic ERFs. 
These results are used as a line of evidence for the assessment of this 
change in Chapter 1 (Cross-Chapter Box 1.2), which gives an overall 
assessment of 0.1°C [ likely range –0.1 to +0.3] °C.
Figure 7.8 presents the GSAT time series using ERF time series for 
individual forcing agents rather than their aggregation. It shows that 
for most of the historical period the long time scale total GSAT trend 
estimate from the emulator closely follows the CO 2 contribution. 
The GSAT estimate from non-CO 2 greenhouse gas forcing (from other 
WMGHGs and ozone) has been approximately cancelled out in the 
global average by a cooling GSAT trend from aerosols. However, since 
1980 the aerosol cooling trend has stabilized and may have started

to reverse, so that over the last few decades the long-term warming has been occurring at a  faster rate than would be expected due 
to CO 2 alone ( high confidence ) (see also Sections 2.2.6 and 2.2.8). 
Throughout the record, but especially prior to 1930, periods of 
volcanic cooling dominate decadal variability. These estimates 
of the forced response are compared with model simulations and 
attributable warming estimates in Chapter 3 (Section 3.3.1).
Figure 7. 8 | Attributed global surface air temperature change (GSAT) from 
1750 to 2019 produced using the two-layer emulator (Supplementary 
Material 7.SM.2), forced with ERF derived in this chapter (displayed in 
Figure 2.10) and climate response constrained to assessed ranges for key 
climate metrics described in Cross-Chapter Box 7.1.  The results shown are the 
medians from a 2237-member ensemble that encompasses uncertainty in forcing and 
climate response (year-2019 best estimates and uncertainties are shown in Figure 7.7

for several components). Temperature contributions are expressed for carbon dioxide 
(CO 2), methane (CH 4), nitrous oxide (N 2O), other well-mixed greenhouse gases 
(WMGHGs), ozone (O 3), aerosols, and other anthropogenic forcings, as well as total 
anthropogenic, solar, volcanic, and total forcing. Shaded uncertainty bands show very 
likely (5–95%)  ranges. Further details on data sources and processing are available in 
the chapter data table (Table 7.SM.14).

963
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Cross-Chapter Box 7.1 (continued)
climate models have a long history of use in previous IPCC reports (Section 1.5.3.4). Climate model emulators can include carbon and 
other gas cycles and can combine uncertainties along the cause–effect chain, from emissions to temperature response. AR5 (M. Collins 
et al., 2013) used the MAGICC6 emulator (Meinshausen et al., 2011a) in a probabilistic setup (Meinshausen et al., 2009) to explore 
the uncertainty in future projections. A simple impulse response emulator (Good et al., 2011) was also used to ensure a consistent 
set of ESM projections could be shown across a range of scenarios. Chapter 8 in AR5 WGI (Myhre et al., 2013b) employed a two-layer 
emulator for quantifying global temperature-change potentials (GTP). In AR5 WGIII (Clarke et al., 2014), MAGICC6 was also used

for the classification of scenarios, and in AR5 Synthesis Report (IPCC, 2014) this information was used to estimate carbon budgets. 
In SR1.5, two emulators were used to provide temperature projections of scenarios: the MAGICC6 model, which was used for the 
scenario classification, and the FaIR1.3 model (Millar et al., 2017; Smith et al., 2018a).
The SR1.5 found that the physically based emulators produced different projected non-CO 2 forcing and identified the largely 
unexplained differences between the two emulators used as a  key knowledge gap (Forster et  al., 2018). This led to a  renewed 
effort to test the skill of various emulators. The Reduced Complexity Model Intercomparison Project (RCMIP; Nicholls et al., 2020) 
found that the latest generation of the emulators can reproduce key characteristics of the observed changes in global surface air

temperature (GSAT) together with other key responses of ESMs (Cross-Chapter Box 7.1, Figure 1a). In particular, despite their reduced 
structural complexity, some emulators are able to replicate the non-linear aspects of ESM GSAT response over a range of scenarios. 
GSAT emulation has been more thoroughly explored in the literature than other types of emulation. Structural differences between 
emulation approaches lead to different outcomes and there are problems with emulating particular ESMs. In conclusion, there is 
medium confidence  that emulators calibrated to single ESM runs can reproduce ESM projections of the forced GSAT response to 
other similar emissions scenarios to within natural variability (Meinshausen et al., 2011b; Geoffroy et al., 2013a; Dorheim et al., 2020; 
Nicholls et al., 2020; Tsutsui, 2020), although larger differences can remain for scenarios with very different forcing characteristics.

For variables other than GSAT there has not yet been a comprehensive effort to evaluate the performance of emulators.
Application of emulators in AR6 WGI
Cross-Chapter Box 7.1 Table 1 shows the use of emulators within the WGI Report. The main use of emulation in the Report is to estimate 
GSAT change from effective radiative forcing (ERF) or concentration changes, where various versions of a two-layer energy budget 
emulator are used. The two-layer emulator is equivalent to a two-timescale impulse-response model (Supplementary Material 7.SM.2; 
Geoffroy et al., 2013b). Both a single configuration version and probabilistic forms are used. The emulator is an extension of the energy 
budget equation (Box 7.1, Equation 7.1) and allows for heat exchange between the upper- and deep-ocean layers, mimicking the 
ocean heat uptake that reduces the rate of surface warming under radiative forcing (Gregory, 2000; Held et al., 2010; Winton et al.,

2010; Armour, 2017; Mauritsen and Pincus, 2017; Rohrschneider et al., 2019). Although the same energy budget emulator approach 
is used, different calibrations are employed in various sections, to serve different purposes and keep lines of evidence as independent 
as possible. Chapter 9 additionally employs projections of ocean heat content from the Chapter 7 two-layer emulator to estimate the 
thermostatic component of future sea level rise (Section 9.6.3 and Supplementary Material 7.SM.2).
Cross-Chapter Box 7.1, Table 1 | Use of emulation within the WGI Report.
Section Application and Emulator Type Emulated Variables
Cross Chapter-Box 1.2 Estimate anthropogenic temperature change pre-1850, based on radiative forcing time series from Chapter 7. Uses 
the Chapter 7 calibrated two-layer emulator: a two-layer energy budget emulator, probabilistically calibrated to AR6 
ECS, TCR, historical warming and ocean heat uptake ranges, driven by the Chapter 7 concentration-based ERFs.GSAT

Section 3.3
Section 7.3Investigation of the historical temperature response to individual forcing mechanisms to complement detection and 
attribution results. Uses the Chapter 7 calibrated two-layer emulator.GSAT 
Box 4.1Understanding the spread in GSAT increase of CMIP6 models and comparison to other assessments; assessment of 
contributions to projected temperature uncertainty. Uses a two-layer emulator calibrated to the Chapter 7 ECS and 
TCR assessment driven by Chapter 7 best-estimate ERFs.GSAT
Section 4.6Emulators used to assess differences in radiative forcing and GSAT response between RCP and SSP scenarios. Uses 
the Chapter 7 ERF time series and the MAGICC7 probabilistic emissions-driven emulator for GSAT calibrated to the 
WGI assessment.ERF , GSAT
Section 4.7Emulator used for long-term GSAT projections (post-2100) to complement the small number of ESMs with data beyond 
2100. Uses the MAGICC7 probabilistic emissions-driven emulator calibrated to the WGI assessment.GSAT

Section 5.5Estimated non-CO 2 warming contributions of mitigation scenarios at the time of their net zero CO 2 emissions for 
integration in the assessment of remaining carbon budgets. Uses the MAGICC7 probabilistic emissions-driven emulator 
calibrated to the WGI assessment.GSAT

964
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Cross-Chapter Box 7.1 (continued)
Section 6.6
Section 6.7Estimated contributions to future warming from SLCFs across SSP scenarios based on ERF time series. Uses a single 
two-layer emulator configuration derived from the medians of MAGICC7 and FaIRv1.6.2 AR6 WG1 GSAT probabilistic 
responses and the best-estimate of ECS and TCR.GSAT
Section 7.5Estimating a process-based TCR from a process-based ECS. Uses a two-layer emulator in probabilistic form calibrated 
to process-based estimates from Chapter 7; a different calibration compared to the main Chapter 7 emulator.TCR
Section 7.6Deriving emissions metrics. Uses two-layer emulator configurations derived from MAGICC7 and FaIRv1.6.2 AR6 WG1 
probabilistic GSAT responses.GTPs and their 
uncertainties

Section 9.6Deriving global mean sea level projections. Uses the Chapter 7 calibrated two-layer emulator for GSAT and ocean heat 
content, where GSAT drives regional statistical emulators of ice sheets and glaciers.Sea level and ice loss
Section 11.2 and 
Cross-Chapter Box 11.1Regional patterns of response are compared to global mean trends. Assessed literature includes projections with 
a regional pattern scaling and variability emulator.Various regional 
information 
Emissions-driven emulators (as opposed to ERF-driven or concentration-driven emulators) are also used in the Report. In Chapter 4 
(Section 4.6) MAGICC7 is used to emulate GSAT beyond 2100 since its long-term response has been assessed to be fit-for-purpose 
to represent the behaviour of ESMs. In Chapter  5 (Section  5.5) MAGICC7 is used to explore the non-CO 2 GSAT contribution in 
emissions scenarios. In Chapter 6 and Chapter 7 (Section 7.6), two-layer model configurations are tuned to match the probabilistic

GSAT responses of FaIRv1.6.2 and MAGICC7 emissions-driven emulators. For Chapter 6 the two median values from FaIRv1.6.2 and 
MAGICC7 emulators are averaged and then matched to the best-estimate ECS of 3°C and TCR of 1.8°C (Tables 7.13 and 7.14) under 
the best-estimate ERF due to a doubling of CO 2 of 3.93 W m–2 (Table 7.4). For Section 7.6 a distribution of responses is used from the 
two emulators to estimate uncertainties in global temperature change potentials (GTP).
Emissions-driven emulators for scenario classification in AR6 WGIII
As in AR5 and SR1.5, emissions-driven emulators are used to communicate outcomes of the physical climate science assessment and 
uncertainties to quantify the temperature outcome associated with different emissions scenarios. In particular, the computational 
efficiency of these emulators allows the analysis of a large number of multi-gas emissions scenarios in terms of multiple characteristics,

e.g., year of peak temperature or 2030 emissions levels, in line with keeping global warming to below 1.5°C or 2.0°C.
Four emissions-driven emulators have been considered as tools for WGIII to explore the range of GSAT response to multiple scenarios 
beyond those assessed in WGI. The four emulators are CICERO-SCM (Skeie et al., 2017, 2021), FaIRv1.6.2 (Millar et al., 2017; Smith 
et al., 2018a), MAGICC7 (Meinshausen et al., 2009) and OSCARv3.1.1 (Gasser et al., 2017a, 2020). Each emulator’s probabilistic 
distribution has been calibrated to capture the relationship between emissions and GSAT change. The calibration is informed by 
the WGI assessed ranges of ECS, TCR, historical GSAT change, ERF , carbon cycle metrics and future warming projections under the 
(concentration-driven) SSP scenarios. The emulators are then provided as a tool for WGIII to perform a GSAT-based classification of

mitigation scenarios consistent with the physical understanding assessed in WGI. The calibration step reduced the emulator differences 
identified in SR1.5. Note that evaluation of both central and range estimates of each emulator’s probabilistic projections is important 
to assess the fitness-for-purpose for the classification of scenarios in WGIII, based on information beyond the central estimate of 
GSAT warming.
MAGICC7 and FaIRv1.6.2 emissions-based emulators are able to represent the WGI assessment to within small differences (defined 
here as within typical rounding precisions of ±5% for central estimates and ±10% for ranges) across more than 80% of metric ranges 
(Cross-Chapter Box 7.1, Table 2). Both calibrated emulators are consistent with assessed ranges of ECS, historical GSAT, historical 
ocean heat uptake, total greenhouse gas ERF , methane ERF and the majority of the assessed SSP warming ranges. FaIRv1.6.2 also

matches the assessed central value of TCRE and airborne fraction. Whereas, MAGICC7 matches the assessed TCR ranges as well as 
providing a closer fit to the SSP warming ranges for the lower-emissions scenarios. In the evaluation framework considered here, 
CICERO-SCM represents historical warming to within 2% of the assessed ranges and also represents future temperature ranges 
across the majority of the assessment, although it lacks the representation of the carbon cycle. In this framework, OSCARv3.1.1 is less 
able to represent the assessed projected GSAT ranges although it matches the range of airborne fraction estimates closely and the 
assessed historical GSAT likely  range to within 0.5%. Despite these identified limitations, both CICERO-SCM and OSCARv3.1.1 provide 
additional information for evaluating the sensitivity of scenario classification to model choice.

How emulators match the assessed ranges used for the evaluation framework is summarized here and in Table 2. The first is too-low 
projections for 2081–2100 under SSP1-1.9 (8% or 15% too low for the central estimate and 15% or 25% too low for the lower end in

965
the case of MAGICC7 or FaIRv1.6.2, respectively). The second is the representation of the aerosol ERF (both MAGICC7 and FaIRv1.6.2 
are greater than 8% less negative than the central assessed range and greater than 10% less negative for the lower assessed range), 
as energy balance models struggle to reproduce an aerosol ERF with a magnitude as strong as the assessed best estimate and still 
match historical warming estimates. Both emulators have medium to large differences compared to the TCRE and airborne fraction 
ranges (see notes beneath Cross-Chapter Box 7.1, Table 2). Finally, there is also a slight overestimate of the low end of the assessed 
historical GSAT range.
Cross-Chapter Box 7.1, Figure 1 | A comparison between the global surface air temperature (GSAT) response of various calibrated simple climate

models, assessed ranges and Earth system models (ESMs). (a)  and (b) compare the assessed historical GSAT time series (Section 2.3.1) with four multi-gas 
emulators calibrated to replicate numerous assessed ranges (panel (a); Cross-Chapter Box 7.1, Table 2) and also compares idealized CO 2-only concentration scenario 
response for one ESM (IPSL CM6A-LR) and multiple emulators which participated in RCMIP Phase 1 (Nicholls et al., 2020) calibrated to that single ESM (panel (b)). (c) 
and (d) compare this Report’s assessed ranges for GSAT warming (Box 4.1) under the multi-gas scenario SSP1-2.6 with the same calibrated emulators as in (a). For 
context, a range of CMIP6 ESM results are also shown (thin lines in (c) and open circles in (d)). Panel (b) adapted from Nicholls et al. (2020). Further details on data 
sources and processing are available in the chapter data table (Table 7.SM.14).
CMIP6 models

SSP1-2.6(c) Emulation of low emissions scenario SSP1-2.6 (d) Emulation SSP1-2.6 - period averages(a) Emulation of historical warming
SSP1-2.6 medians and 5-95% ranges: 
Assessed range Chapter 4
Calibrated emulators
CFMO
Assessed 
GSAT range
Chapter 4
CMIP6 
models
Emulators:
Scenario mode: 
c - concentration-driven
e - emissions-drivenC - Cicero-SCM
CFMOF - FaIRv1.6.2
FM
OCM - MAGICC7
O - OSCARv3.1.1
C - Cicero-SCM
F - FaIRv1.6.2
M - MAGICC7
FM
OCFM
OC
cc ccc
eecc ccc
eeccccc
ee
20001900 2000 2100 2200 2300 2400 2500
–0.500.51.01.5Global-mean surface air temperature (GSAT) 
relative to 1995–2014 ( °C)2.0
2020 2040 2060 2080 2100(b) Emulation of idealized CO2 long-term 
scenarios
abrupt 4xCO₂
abrupt 2xCO₂
abrupt 0.5xCO₂Global-mean surface air temperature (GSAT)
 relative to startyear/1850 (°C)
–4–20246810
AR5-IR-2box
AR5-IR-3box
Hector
Held-2layer-UoM
MAGICC7
MCE-v1.1
Individual CMIP6 ESM: 
IPSL-CM6A-LR r1i1p1f1

1860 1880 1900 1920 1940 1960 1980 2000 2020–0.50.00.51.01.52.0Global-mean Surface Air Temperature (GSAT) 
Assessed 
GSAT
Chapter 2
Assessed 
GSAT
Chapter 2CMIP6 
models
CMIP6 
models
2021–2040 2041–2060 2081-2100 2081–21001995–20141995–
2014
2021–2040 2041–2060 2081–2100
Cross-Chapter Box 7.1, Figure  1 | A comparison between the global surface air temperature (GSAT) response of various calibrated simple 
climate models, assessed ranges and Earth system models (ESMs). (a)  and (b) compare the assessed historical GSAT time series (Section 2.3.1) with four 
multi-gas emulators calibrated to replicate numerous assessed ranges (panel (a); Cross-Chapter Box 7.1, Table 2) and also compares idealized CO 2-only concentration 
scenario response for one ESM (IPSL CM6A-LR) and multiple emulators which participated in RCMIP Phase 1 (Nicholls et al., 2020) calibrated to that single ESM

(panel (b)). (c) and (d) compare this Report’s assessed ranges for GSAT warming (Box 4.1) under the multi-gas scenario SSP1-2.6 with the same calibrated emulators 
as in (a). For context, a range of CMIP6 ESM results are also shown (thin lines in (c) and open circles in (d)). Panel (b) adapted from Nicholls et al. (2020). Further 
details on data sources and processing are available in the chapter data table (Table 7.SM.14).

966
Overall, there is high confidence  that emulated historical and future ranges of GSAT change can be calibrated to be internally consistent 
with the assessment of key physical-climate indicators in this Report: greenhouse gas ERFs, ECS and TCR. When calibrated to match 
the assessed ranges of GSAT and multiple physical climate indicators, physically based emulators can reproduce the best estimate of 
GSAT change over 1850–1900 to 1995–2014 to within 5% and the very likely  range of this GSAT change to within 10%. MAGICC7 
and FaIRv1.6.2 match at least two-thirds of the Chapter 4 assessed projected GSAT changes to within these levels of precision.
Cross-Chapter Box 7.1, Table 2 | Percentage differences between the emulator value and the WGI assessed best estimate and range for key

metrics.  Values are given for four emulators in their respective AR6-calibrated probabilistic setups. Absolute values of these indicators are shown in Supplementary 
Material, Table 7.SM.4.
Emulator CICERO-SCM FaIRv1.6.2 MAGICC7 OSCARv3.1.1
Assessed Range Lower Central Upper Lower Central Upper Lower Central Upper Lower Central Upper
Key metrics
ECS (°C) 26% 2% –18% 3% –2% 1% –3% –1% –3% –8% –15% –22%
TCRE (°C per 1000 GtC)** 29% –7% –21% 37% 5% –5% 50% –8% –20%
TCR (°C) 15% –5% –3% 14% 0% 3% 6% 4% 9% 26% 1% –14%
Historical warming and Effective Radiative Forcing 
GSAT warming (°C)
1995–2014 rel. 1850–19002% 0% 0% 7% 3% 4% 7% 1% –1% –0% –8% –0%
Ocean heat content change (ZJ)*
1971–2018–24% –27% –29% 5% –4% –9% –1% –3% –6% –47% –39% 10%
Total Aerosol ERF (W m–2)
2005–2014 rel. 175036% 37% 10% 16% 12% 0% 10% 8% 8% 38% 15% –31%
GHG ERF (W m–2)
2019 rel. 17504% –5% –13% 1% 2% 1% 2% 1% –0% 1% 3% –3%
Methane ERF (W m–2)
2019 rel. 175031% 4% –13% 3% 3% 3% 0% –0% 3% 8% –1% –5%

Carbon Cycle metrics 
Airborne Fraction 1pctCO 2 
(dimensionless)*
2×CO 28% –3% –11% 12% 6% –1% 1% –0% 8%
4×CO 212% 1% –9% 15% 4% –6% 5% –1% –1%
Future warming (GSAT) relative to 1995–2014
SSP1-1.9 (°C)2021–2040 10% –4% 10% 3% 1% 11% 2% –0% 4% 12% –9% –25%
2041–2060 8% –9% 7% –11% –8% 6% –1% –1% 7% 12% –8% –31%
2081–2100 –12% –25% –2% –25% –15% 4% –15% –8% 3% 7% –10% –31%
SSP1-2.6 (°C)2021–2040 7% –5% 5% 2% 1% 8% –1% –2% –0% 9% –9% –28%
2041–2060 8% –6% 2% –2% –2% 5% 0% 1% 2% 15% –6% –28%
2081–2100 –2% –14% –5% –8% –7% 1% –6% –1% 1% 17% –9% –29%
SSP2-4.5 (°C)2021–2040 8% –5% 5% 7% –1% 2% 3% –3% –2% –5% –14% –30%
2041–2060 4% –4% 3% 1% –1% 2% 1% 1% 2% 8% –8% –28%
2081–2100 –1% –10% –3% –2% –3% 1% –2% 1% 3% 8% –4% –25%
SSP3-7.0 (°C)2021–2040 11% –4% 1% 14% 1% –1% 10% 1% –0% –5% –15% –29%
2041–2060 4% –5% –0% 6% 0% –1% 7% 4% 1% 7% –8% –26%
2081–2100 –0% –8% –3% 3% –1% –1% 6% 3% 6% 5% –6% –25%

SSP5-8.5 (°C)2021–2040 5% –7% 2% 9% 2% 4% 7% 1% 2% 1% –14% –30%
2041–2060 2% –8% –1% 4% 0% 4% 3% 2% 4% 10% –6% –24%
2081–2100 4% –7% –3% 6% –0% 1% 8% 4% 7% 9% –4% –25%

967
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity Chapter 77Cross-Chapter Box 7.1 (continued)
Notes.  Metrics calibrated against are equilibrium climate sensitivity, ECS (Section 7.5); transient climate response to cumulative CO 2 emissions, TCRE (Section 5.5); 
transient climate response, TCR (Section  7.5), historical GSAT change (Section  2.3); ocean heat uptake (Sections 7.2 and 2.3); effective radiative forcing, ERF 
(Section 7.3); carbon cycle metrics, namely airborne fractions of idealized CO 2 scenarios (taking the likely range as twice the standard deviation across the models 
analysed in Arora et al. (2020; see also Table 5.7, ‘cross-AR6 lines of evidence’ row); and GSAT projections under the concentration-driven SSP scenarios for the near 
term (2021–2040), mid-term (2041–2060) and long term (2081–2100) relative to 1995–2014 (Table 4.2). See Supplementary Material, Table 7.SM.4 for a version

of this table with the absolute values rather than percentage differences. The columns labelled ‘upper’ and ‘lower’ indicate 5–95% ranges, except for the variables 
demarcated with an asterisk or double asterisk (* or **), where they denote likely ranges from 17–83%. Note that the TCRE assessed range (**) is wider than the 
combination of the TCR and airborne fraction to account for uncertainties related to model limitations (Table 5.7) hence it is expected that the emulators are too 
narrow on this particular metric and/or too wide on TCR and airborne fraction. For illustrative purposes, the cells are coloured as follows: white cells indicate small 
differences (up to ±5% for the central value and +10% for the ranges), light blue and light yellow cells indicate medium differences (up to +10% and –10% for light 
blue and light yellow for central values, respectively; up to ±20% for the ranges) and darker cells indicate larger positive (blue) or negative (yellow) differences. Note

that values are rounded after the colours are applied.
7.4 Climate Feedbacks
The magnitude of global surface temperature change primarily 
depends on the strength of the radiative forcings and feedbacks, 
the latter defined as the changes of the net energy budget at the 
top-of-atmosphere (TOA) in response to a change in the GSAT (Box 7.1, 
Equation 7.1). Feedbacks in the Earth system are numerous, and it can 
be helpful to categorize them into three groups: (i) physical feedbacks; 
(ii) biogeophysical and biogeochemical feedbacks; and (iii) long-term 
feedbacks associated with ice sheets. The physical feedbacks (e.g., those 
associated with changes in lapse rate, water vapour, surface albedo, or 
clouds; Sections 7.4.2.1–7.4.2.4) and biogeophysical/biogeochemical 
feedbacks (e.g., those associated with changes in methane, aerosols, 
ozone, or vegetation; Section 7.4.2.5) act both on time scales that are 
used to estimate the equilibrium climate sensitivity (ECS) in models

(typically 150 years, see Box 7.1) and on longer time scales required 
to reach equilibrium. Long-term feedbacks associated with ice sheets 
(Section  7.4.2.6) are relevant primarily after several centuries or 
more. The feedbacks associated with biogeophysical/biogeochemical 
processes and ice sheets, often collectively referred to as Earth system 
feedbacks, had not been included in conventional estimates of the 
climate feedback (e.g., Hansen et al., 1984), but the former can now 
be quantified and included in the assessment of the total (net) climate 
feedback. Feedback analysis represents a formal framework for the 
quantification of the coupled interactions occurring within a complex 
Earth system in which everything influences everything else (e.g., Roe, 
2009). As  used here (as presented in Section  7.4.1), the primary 
objective of feedback analysis is to identify and understand the key 
processes that determine the magnitude of the surface temperature

response to an external forcing. For each feedback, the basic underlying 
mechanisms and their assessments are presented in Section 7.4.2.
Up until AR5, process understanding and quantification of feedback 
mechanisms were based primarily on global climate models. Since 
AR5, the scientific community has undertaken a wealth of alternative 
approaches, including observational and fine-scale modelling 
approaches. This has in some cases led to more constrained feedbacks 
and, on the other hand, uncovered shortcomings in global climate 
models, which are starting to be corrected. Consequently, AR6 achieves 
a more robust assessment of feedbacks in the climate system that is 
less reliant on global climate models than in earlier assessment reports.It has long been recognized that the magnitude of climate feedbacks 
can change as the climate state evolves over time (Manabe and 
Bryan, 1985; Murphy, 1995), but the implications for projected future

warming have been investigated only recently. Since AR5, progress 
has been made in understanding the key mechanisms behind this 
time- and state-dependence. Specifically, the state-dependence 
is assessed by comparing climate feedbacks between warmer and 
colder climate states inferred from paleoclimate proxies and model 
simulations (Section 7.4.3). The time-dependence of the feedbacks 
is evident between the historical period and future projections and 
is assessed to arise from the evolution of the surface warming pattern 
related to changes in zonal and meridional temperature gradients 
(Section 7.4.4).
7.4.1 Methodology of the Feedback Assessment
The global surface temperature changes of the climate system are 
generally analysed with the classical forcing–feedback framework as 
described in Box  7.1 (Equation 7.1). In this equation α  is the net 
feedback parameter (W m–2 °C–1). As surface temperature changes in

response to the TOA energy imbalance, many other climate variables 
also change, thus affecting the radiative flux at the TOA. The aggregate 
feedback parameter can then be decomposed into an approximate 
sum of terms α = Σx αx, where x is a vector representing variables 
that have a direct effect on the net TOA radiative flux N and  
Following the conventional definition, the physical climate feedbacks 
are here decomposed into terms associated with a vertically uniform 
temperature change (Planck response, P), changes in the water-vapour 
plus temperature lapse-rate (WV+LR), surface albedo (A) and 
clouds (C). The water-vapour plus temperature lapse rate feedback 
is further decomposed using two different approaches, one based 
on changes in specific humidity, the other on changes in relative 
humidity. Biogeochemical feedbacks arise due to changes in aerosols 
and atmospheric chemical composition in response to changes in

surface temperature, and Gregory et al. (2009) and Raes et al. (2010) 
show that they can be analysed using the same framework as for !!=#$
#%&%
&'

968
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7the physical climate feedbacks (Sections 5.4 and 6.4.5). Similarly, 
feedbacks associated with biogeophysical and ice-sheet changes can 
also be incorporated.
In global climate models, the feedback parameters αx in global 
warming conditions are often estimated as the mean differences in 
the radiative fluxes between atmosphere-only simulations in which 
the change in SST is prescribed (Cess et al., 1990), or as the regression 
slope of change in radiation flux against change in GSAT using 
atmosphere–ocean coupled simulations with abrupt CO 2 changes 
(abrupt4xCO2 ) for 150 years (Box 7.1; Gregory et al., 2004; Andrews 
et  al., 2012; Caldwell et  al., 2016). Neither method is perfect, but 
both are useful and yield consistent results (Ringer et al., 2014). In 
the regression method, the radiative effects of land warming are 
excluded from the ERF due to doubling of CO 2 (Section 7.3.2), which

may overestimate feedback values by about 15%. At the same time, 
the feedback calculated using the regression over years 1–150 ignores 
its state-dependence on multi-centennial time scales (Section 7.4.3), 
probably giving an underestimate of α  by about 10% (Rugenstein 
et al., 2019). These effects are both small and approximately cancel 
each other in the ensemble mean, justifying the use of regression over 
150 years as an approximation to feedbacks in ESMs.
The change of the TOA radiative flux N as a function of the change of 
a climate variable x (such as water vapour) is commonly computed 
using the ‘radiative kernel’ method (Soden et al., 2008). In this method, 
the kernel ∂N/∂x is evaluated by perturbing x within a radiation code. 
Then multiplying the kernel by d x/dT inferred from observations, 
meteorological analysis or GCMs produces a value of  αx.
Feedback parameters from lines of evidence other than global

models are estimated in various ways. For example, observational 
data combined with GCM simulations could produce an emergent 
constraint on a  particular feedback (Hall and Qu, 2006; Klein and 
Hall, 2015), or the observed interannual fluctuations in the global 
mean TOA radiation and the surface air temperature, to which the 
linear regression analysis is applied, could generate a direct estimate 
of the climate feedback, assuming that the feedback associated with 
internal climate variability at short time scales can be a surrogate 
of the feedback to CO 2-induced warming (Dessler, 2013; Loeb et al., 
2016). The assumption is not trivial, but can be justified given that the 
climate feedbacks are fast enough to occur at the interannual time 
scale. Indeed, a  broad agreement has been obtained in estimates 
of individual physical climate feedbacks based on interannual 
variability and longer climate change time scales in GCMs (Zhou

et al., 2015; Colman and Hanson, 2017). This means that the climate 
feedbacks estimated from the observed interannual fluctuations are 
representative of the longer-term feedbacks (decades to centuries). 
Care must be taken for these observational estimates because they 
can be sensitive to details of the calculation such as data sets and 
periods used (Dessler, 2013; Proistosescu et al., 2018). In particular, 
there would be a dependence of physical feedbacks on the surface 
warming pattern at the interannual time scale due, for example, to 
El Niño–Southern Oscillation. However, this effect both amplifies and 
suppresses the feedback when data include the positive and negative 
phases of the interannual fluctuation, and therefore the net bias will 
be small.In summary, the classical forcing–feedback framework has been 
extended to include biogeophysical and non-CO 2 biogeochemical 
feedbacks in addition to the physical feedbacks. It has also been used

to analyse seasonal and interannual-to-decadal climate variations in 
observations and ESMs, in addition to long-term climate changes 
as seen in abrupt4xCO2  experiments. These developments allow an 
assessment of the feedbacks based on a  larger variety of lines of 
evidence compared to AR5.
7.4.2 Assessing Climate Feedbacks
This section provides an overall assessment of individual feedback 
parameters, αx, by combining different lines of evidence from 
observations, theory, process models and ESMs. To achieve this, 
we review the understanding of the key processes governing the 
feedbacks, why the feedback estimates differ among models, studies or 
approaches, and the extent to which these approaches yield consistent 
results. The individual terms assessed are the Planck response 
(Section  7.4.2.1) and feedbacks associated with changes in water 
vapour and lapse rate (Section 7.4.2.2), surface albedo (Section 7.4.2.3),

clouds (Section 7.4.2.4), biogeophysical and non-CO 2 biogeochemical 
processes (Section 7.4.2.5), and ice sheets (Section 7.4.2.6). A synthesis 
is provided in Section 7.4.2.7. Climate feedbacks in CMIP6 models are 
then evaluated in Section  7.4.2.8, with an explanation of how they 
have been incorporated into the assessment.
7.4.2.1 Planck Response
The Planck response represents the additional thermal or longwave (LW) 
emission to space arising from vertically uniform warming of the surface 
and the atmosphere. The Planck response αP, often called the Planck 
feedback, plays a fundamental stabilizing role in Earth’s climate and has 
a value that is strongly negative: a warmer planet radiates more energy 
to space. A  crude estimate of αP can be made using the normalized 
greenhouse effect g̃, defined as the ratio between the greenhouse effect 
G and the upwelling LW flux at the surface (Raval and Ramanathan,

1989). Current estimates (Section 7.2, Figure 7.2) give G = 159 W m–2 and 
g̃ ≈ 0.4. Assuming g̃ is constant, one obtains for a surface temperature 
Ts = 288 K, αP = (g – 1) 4 σ T3s ≈ –3.3 W m–2 °C–1, where σ is the Stefan–
Boltzmann constant. This parameter αP is estimated more accurately 
using kernels obtained from meteorological reanalysis or climate 
simulations (Soden and Held, 2006; Dessler, 2013; Vial et  al., 2013; 
Caldwell et al., 2016; Colman and Hanson, 2017; Zelinka et al., 2020). 
Discrepancies among estimates primarily arise because differences 
in cloud distributions make the radiative kernels differ (Kramer et al., 
2019). Using six different kernels, Zelinka et  al. (2020) obtained 
a  spread of ±0.1 W m–2 °C–1 (one standard deviation). Discrepancies 
among estimates secondarily arise from differences in the  pattern of 
equilibrium surface temperature changes among ESMs. For the CMIP5 
and CMIP6 models this introduces a  spread of ±0.04  W  m–2 °C–1

(one  standard deviation). The multi-kernel and multi-model mean of 
αP is equal to –3.20 W m–2 °C–1 for the CMIP5 and –3.22 W m–2 °C–1 
for the CMIP6 models (Supplementary Material, Table 7.SM.5). Overall, 
there is high confidence  in the estimate of the Planck response, which is 
assessed to be αP  = –3.22 W m–2 °C–1 with a  very likely range of –3.4 to 
–3.0 W m–2 °C–1 and a  likely range  of –3.3 to –3.1 W m–2 °C–1.

969
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77The Planck temperature response Δ TP is the equilibrium temperature 
change in response to a forcing Δ F when the net feedback parameter 
is equal to the Planck response parameter: Δ TP = –ΔF / αP.
7.4.2.2 Water-vapour and Temperature Lapse-rate Feedbacks
Two decompositions are generally used to analyse the feedbacks 
associated with a  change in the water-vapour and temperature 
lapse-rate in the troposphere. As in any system, many feedback 
decompositions are possible, each of them highlighting a particular 
property or aspect of the system (Ingram, 2010; Held and Shell, 2012; 
Dufresne and Saint-Lu, 2016). The first decomposition considers 
separately the changes (and therefore feedbacks) in the lapse rate 
(LR) and specific humidity (WV). The second decomposition considers 
changes in the lapse rate assuming constant relative humidity (LR*) 
separately from changes in relative humidity (RH).

The specific humidity (WV) feedback, also known as the water-vapour 
feedback, quantifies the change in radiative flux at the TOA due to 
changes in atmospheric water vapour concentration associated with 
a change in global mean surface air temperature. According to theory, 
observations and models, the water vapour increase approximately 
follows the Clausius–Clapeyron relationship at the global scale 
with regional differences dominated by dynamical processes 
(Section 8.2.1; Sherwood et al., 2010a; Chung et al., 2014; Romps, 
2014; R. Liu et al., 2018; Schröder et al., 2019). Greater atmospheric 
water vapour content, particularly in the upper troposphere, results in 
enhanced absorption of LW and SW radiation and reduced outgoing 
radiation. This is a  positive feedback. Atmospheric moistening 
has been detected in satellite records (Section  2.3.1.3.3), it is 
simulated by climate models (Section  3.3.2.2), and the estimates

agree within model and observational uncertainty (Soden et  al., 
2005; Dessler, 2013; Gordon et  al., 2013; Chung et  al., 2014). 
The estimate of this feedback inferred from satellite observations is 
αWV = 1.85 ± 0.32 W m–2 °C–1 (R. Liu et al., 2018). This is consistent 
with the value αWV = 1.77 ± 0.20 W m–2 °C–1 (one standard deviation) 
obtained with CMIP5 and CMIP6 models (Zelinka et al., 2020).
The lapse-rate (LR) feedback quantifies the change in radiative flux 
at the TOA due to a non uniform change in the vertical temperature 
profile. In the tropics, the vertical temperature profile is mainly driven 
by moist convection and is close to a moist adiabat. The warming is 
larger in the upper troposphere than in the lower troposphere (Manabe 
and Wetherald, 1975; Santer et al., 2005; Bony et al., 2006), leading 
to a  larger radiative emission to space and therefore a  negative 
feedback. This larger warming in the upper troposphere than at

the surface has been observed over the last 20 years thanks to the 
availability of sufficiently accurate observations (Section 2.3.1.2.2). 
In the extratropics, the vertical temperature profile is mainly driven 
by a balance between radiation, meridional heat transport and ocean 
heat uptake (Rose et al., 2014). Strong winter temperature inversions 
lead to warming that is larger in the lower troposphere (Payne et al., 
2015; Feldl et al., 2017a) and a positive LR feedback in polar regions 
(Section 7.4.4.1; Manabe and Wetherald, 1975; Bintanja et al., 2012; 
Pithan and Mauritsen, 2014). However, the tropical contribution 
dominates, leading to a negative global mean LR feedback (Soden 
and Held, 2006; Dessler, 2013; Vial et al., 2013; Caldwell et al., 2016). The LR feedback has been estimated at interannual time scales 
using meteorological reanalysis and satellite measurements of 
TOA fluxes (Dessler, 2013). These estimates from climate variability

are consistent between observations and ESMs (Dessler, 2013; 
Colman and Hanson, 2017). The mean and standard deviation of 
this feedback under global warming based on the cited studies are 
αLR = –0.50 ± 0.20 W m–2 °C–1 (Dessler, 2013; Caldwell et al., 2016; 
Colman and Hanson, 2017; Zelinka et al., 2020).
The second decomposition was proposed by Held and Shell (2012) to 
separate the response that would occur under the assumption that 
relative humidity remains constant from that due to the change in 
relative humidity. The feedback is decomposed into three: (i) change 
in  water vapour due to an identical temperature increase at the 
surface and throughout the troposphere assuming constant relative 
humidity, which will be called the Clausius–Clapeyron (CC) feedback 
here; (ii) change in LR assuming constant relative humidity (LR*); 
(iii) change in relative humidity (RH). Since AR5 it has been clarified 
that by construction, the sum of the temperature lapse rate and specific

humidity (LR + WV) feedbacks is equal to the sum of the Clausius–
Clapeyron feedback, the lapse rate feedback assuming constant 
relative humidity, and the feedback from changes in relative humidity 
(that is, CC + LR* + RH). Therefore, each of these two sums may 
simply be referred to as the ‘water-vapour plus lapse-rate’ feedback.
The CC feedback has a large positive value due to well understood 
thermodynamic and radiative processes: αCC = 1.36 ± 0.04 W m–2 °C–1 
(one standard deviation; Held and Shell, 2012; Zelinka et al., 2020). 
The lapse-rate feedback assuming a constant relative humidity (LR*) in 
CMIP6 models has small absolute values ( αLR* = –0.10 ± 0.07  W m–2 °C–1 
(one standard deviation)), as expected from theoretical arguments 
(Ingram, 2010, 2013). It includes the pattern effect of surface warming 
that modulates the lapse rate and associated specific humidity 
changes (Po-Chedley et al., 2018b). The relative humidity feedback is

close to zero ( αRH = 0.00 ± 0.06 W m–2 °C–1 (one standard deviation)) 
and the spread among models is confined to the tropics (Sherwood 
et  al.,  2010b; Vial et  al., 2013; Takahashi et  al., 2016; Po-Chedley 
et al., 2018b). The change in upper tropospheric RH is closely related 
to model representation of current climate (Sherwood et al., 2010b; 
Po-Chedley et  al., 2019), and a  reduction in model RH biases is 
expected to reduce the uncertainty of the RH feedback. At interannual 
time scales, it has been shown that the change in RH in the tropics is 
related to the change of the spatial organization of deep convection 
(Holloway et al., 2017; Bony et al., 2020).
Both decompositions allow estimates of the sum of the lapse-rate and 
specific humidity feedbacks αLR+WV. The multi-kernel and multi-model 
mean of αLR+WV  is equal to 1.24 and 1.26 W m–2 °C–1 respectively 
for CMIP5 and CMIP6 models, with a  standard deviation of

0.10 W m–2 °C–1 (Zelinka et al., 2020). These values are larger than the 
recently assessed value of 1.15 W m–2 °C–1 by Sherwood et al. (2020) as 
a larger set of kernels, including those obtained from meteorological 
reanalysis, are used here.
Since AR5, the effect of the water vapour increase in the stratosphere 
as a  result of global warming has been investigated by different 
studies. This increase produces a positive feedback between 0.1 and

970
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity70.3 W m–2 °C–1 if the stratospheric radiative response is computed 
assuming temperatures that are adjusted with fixed dynamical 
heating (Dessler et al., 2013; Banerjee et al., 2019). However, various 
feedbacks reduce this temperature adjustment and the overall 
physical (water vapour, temperature and dynamical) stratospheric 
feedback becomes much smaller (0.0 to 0.1 W m–2 °C–1; Huang et al., 
2016, 2020; Li and Newman, 2020), with uncertainty arising from 
limitations of current ESMs in simulating stratospheric processes. 
The total stratospheric feedback is assessed at 0.05 ± 0.1 W m–2 °C–1 
(one standard deviation).
The combined ‘water-vapour plus lapse-rate’ feedback is positive. 
The  main physical processes that drive this feedback are well understood 
and supported by multiple lines of evidence including models, theory 
and observations. The combined ‘water-vapour plus lapse-rate’

feedback parameter is assessed to be αLR+WV  = 1.30 W m–2 °C–1, with 
a very likely range of 1.1 to 1.5 W m–2 °C–1 and a  likely range of 1.2 to 
1.4 W m–2 °C–1 with high confidence.
7.4.2.3 Surface-albedo Feedback
Surface albedo is determined primarily by reflectance at Earth’s surface, 
but also by the spectral and angular distribution of incident solar 
radiation. Changes in surface albedo result in changes in planetary 
albedo that are roughly reduced by two-thirds, owing to atmospheric 
absorption and scattering, with variability and uncertainty arising 
primarily from clouds (Bender, 2011; Donohoe and Battisti, 2011; Block 
and Mauritsen, 2013). Temperature change induces surface-albedo 
change through several direct and indirect means. In the present climate 
and at multi-decadal time scales, the largest contributions by far are 
changes in the extent of sea ice and seasonal snow cover, as these 
media are highly reflective and are located in regions that are close

to the melting temperature (Sections 2.3.2.1 and 2.3.2.2). Reduced 
snow cover on sea ice may contribute as much to albedo feedback as 
reduced extent of sea ice (Zhang et al., 2019). Changes in the snow 
metamorphic rate, which generally reduces snow albedo with warmer 
temperature, and warming-induced consolidation of light-absorbing 
impurities near the surface, also contribute secondarily to the albedo 
feedback (Flanner and Zender, 2006; Qu and Hall, 2007; Doherty et al., 
2013; Tuzet et al., 2017). Other contributors to albedo change include 
vegetation state (assessed separately in Section 7.4.2.5), soil wetness 
and ocean roughness.
Several studies have attempted to derive surface-albedo feedback 
from observations of multi-decadal changes in climate, but only 
over limited spatial and inconsistent temporal domains, inhibiting 
a purely observational synthesis of global surface-albedo feedback 
(αA). Flanner et al. (2011) applied satellite observations to determine

that the northern hemisphere (NH) cryosphere contribution 
to  global αA over the period 1979–2008 was 0.48 [ likely  range 
0.29  to 0.78]  W  m–2 °C–1, with roughly equal contributions from 
changes in land snow cover and sea ice. Since AR5, and over 
similar periods of observation, Crook and Forster (2014) found an 
estimate of 0.8  ±  0.3 W  m–2 °C–1 (one standard deviation) for the 
total NH extratropical surface-albedo feedback, when averaged 
over global surface area. For Arctic sea ice alone, Pistone et  al. 
(2014) and Cao et  al. (2015) estimated the contribution to global αA to be 0.31  ±  0.04  W m–2 °C–1 (one standard deviation) and 
0.31 ± 0.08  W m–2 °C–1 (one standard deviation), respectively, whereas 
Donohoe et al. (2020) estimated it to be only 0.16 ± 0.04 W m–2 °C–1 
(one standard deviation). Much of this discrepancy can be traced to 
different techniques and data used for assessing the attenuation of

surface-albedo change by Arctic clouds. For the NH land snow, Chen 
et  al. (2016) estimated that observed changes during 1982–2013 
contributed (after converting from NH temperature change to global 
mean temperature change) by 0.1 W m–2 °C–1 to global αA, smaller 
than the estimate of 0.24  W  m–2 °C–1 from Flanner et  al. (2011). 
The  contribution of the Southern Hemisphere (SH) to global αA is 
expected to be small because seasonal snow cover extent in the SH 
is limited, and trends in SH sea ice extent are relatively flat over much 
of the satellite record (Section 2.3.2).
CMIP5 and CMIP6 models show moderate spread in global αA, 
determined from century time scale changes  (Qu and Hall, 2014; 
Schneider et  al., 2018; Thackeray and Hall, 2019; Zelinka et  al., 
2020), owing to variations in modelled sea ice loss and snow cover 
response in boreal forest regions. The multi-model mean global-scale 
αA (from all contributions) over the 21st century in CMIP5 models

under the RCP8.5 scenario was derived by Schneider et  al. (2018) 
to be 0.40 ± 0.10 W m–2 °C–1 (one standard deviation). Moreover, 
they found that modelled αA does not decline over the 21st century, 
despite large losses of snow and sea ice, though a weakened feedback 
is apparent after 2100. Using the idealized abrupt4xCO2 , as for the 
other feedbacks, the estimate of the global-scale albedo feedback in 
the CMIP5 models is 0.35 ± 0.08 W m–2 °C–1 (one standard deviation; 
Vial et  al., 2013; Caldwell et  al., 2016). The CMIP6 multi-model 
mean varies from 0.3 to 0.5 W m–2 °C–1 depending on the kernel 
used (Zelinka et al., 2020). Donohoe et al. (2020) derived a multi-
model mean αA and its inter-model spread of 0.37 ± 0.19 W m–2 °C–1 
from the CMIP5 abrupt4xCO2 ensemble, employing model-specific 
estimates of atmospheric attenuation and thereby avoiding bias 
associated with use of a single radiative kernel.
The surface-albedo feedback estimates using centennial changes

have been shown to be highly correlated to those using seasonal 
regional changes for NH land snow (Qu and Hall, 2014) and Arctic 
sea ice (Thackeray and Hall, 2019). For the NH land snow, because 
the physics underpinning this relationship are credible, this opens the 
possibility to use it as an emergent constraint (Qu and Hall, 2014). 
Considering only the eight models whose seasonal cycle of albedo 
feedback falls within the observational range does not change 
the multi-model mean contribution to global αA (0.08 W m–2 °C–1) 
but decreases the inter-model spread by a  factor of two (from 
±0.03 to  ±0.015 W m–2 °C–1; Qu and Hall, 2014). For Arctic sea 
ice, Thackeray and Hall (2019) show that the seasonal cycle also 
provides an emergent constraint, at least until mid-century when the 
relationship degrades. They find that the CMIP5 multi-model mean 
of the Arctic sea ice contribution to αA is 0.13 W m–2 °C–1 and that

the inter-model spread is reduced by a factor of two (from ±0.04 to 
±0.02 W m–2  °C–1) when the emergent constraint is used. This model 
estimate is smaller than observational estimates (Pistone et al., 2014; 
Cao et al., 2015) except those of Donohoe et al. (2020). This can be 
traced to CMIP5 models generally underestimating the rate of Arctic 
sea ice loss during recent decades (Section 9.3.1; Stroeve et al., 2012;

971
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Flato et al., 2013), though this may also be an expression of internal 
variability, since the observed behaviour is captured within large 
ensemble simulations (Notz, 2015). CMIP6 models better capture the 
observed Arctic sea ice decline (Section 3.4.1). In the SH the opposite 
situation is observed. Observations show relatively flat trends in SH 
sea ice over the satellite era (Section 2.3.2.1) whereas CMIP5 models 
simulate a small decrease (Section 3.4.1). SH αA is presumably larger 
in models than observations but only contributes about one quarter 
of the global αA. Thus, we assess that αA estimates are consistent, at 
global scale, in CMIP5 and CMIP6 models and satellite observations, 
though hemispheric differences and the role of internal variability 
need to be further explored.
Based on the multiple lines of evidence presented above that

include observations, CMIP5 and CMIP6 models and theory, the 
global surface-albedo feedback is assessed to be positive with 
high confidence . The basic phenomena that drive this feedback are 
well understood and the different studies cover a  large variety of 
hypotheses or behaviours, including how the evolution of clouds 
affects this feedback. The value of the global surface-albedo 
feedback is assessed to be αA = 0.35 W m–2 °C–1, with a  very likely  
range from 0.10 to 0.60 W m–2 °C–1 and a  likely  range from 0.25 to 
0.45 W m–2 °C–1 with high confidence .
7.4.2.4 Cloud Feedbacks
7.4.2.4.1 Decomposition of clouds into regimes
Clouds can be formed almost anywhere in the atmosphere when 
moist air parcels rise and cool, enabling the water vapour to 
condense. Clouds consist of liquid water droplets and/or ice crystals, 
and these droplets and crystals can grow into larger particles of rain,

snow or drizzle. These microphysical processes interact with aerosols, radiation and atmospheric circulation, resulting in a highly complex 
set of processes governing cloud formation and life cycles that 
operate across a wide range of spatial and temporal scales.
Clouds have various types, from optically thick convective clouds 
to thin stratus and cirrus clouds, depending upon thermodynamic 
conditions and large-scale circulation (Figure 7.9). Over the equatorial 
warm pool and inter-tropical convergence zone (ITCZ) regions, high 
SSTs stimulate the development of deep convective cloud systems, 
which are accompanied by anvil and cirrus clouds near the tropopause 
where the convective air outflows. The large-scale circulation 
associated with these convective clouds leads to subsidence over 
the subtropical cool ocean, where deep convection is suppressed by 
a  lower tropospheric inversion layer maintained by the subsidence

and promoting the formation of shallow cumulus and stratocumulus 
clouds. In the extratropics, mid-latitude storm tracks control cloud 
formation, which occurs primarily in the frontal bands of extratropical 
cyclones. Since liquid droplets do not freeze spontaneously at 
temperatures warmer than approximately –40°C and ice nucleating 
particles that can aid freezing at warmer temperatures are scarce 
(see Section 7.3.3), extratropical clouds often consist both of super-
cooled liquid and ice crystals, resulting in mixed-phase clouds.
In the global energy budget at TOA, clouds affect shortwave (SW) 
radiation by reflecting sunlight due to their high albedo (cooling 
the climate system) and also longwave (LW) radiation by absorbing 
the  energy from the surface and emitting at a  lower temperature 
to space, that is, contributing to the greenhouse effect, warming 
the climate system. In general, the greenhouse effect of clouds

strengthens with height whereas the SW reflection depends on 
the cloud optical properties. The effects of clouds on Earth’s energy 
budget are measured by the cloud radiative effect (CRE), which 
is the difference in the TOA radiation between clear and all skies 
Equator 30º 60ºRising high clouds (+)
Surface warmingRising of Tropopause
Fewer anvil clouds (-)
Enhanced stability
DestabilizationFewer low clouds (+)Moreliquid from ice (-)Rising of tropopauseEnhanced stability
PoleMajor advances since AR5
 Comprehensive assessment of feedbacks in 
   different cloud regimes (cf. Table 7.9)
 Increased confidence of the positive low-cloud 
   amount feedback
 Improved understanding of the cloud phase 
   change feedback
Figure 7. 9 | Schematic cross section of diverse cloud responses to surface warming from the tropics to polar regions.  Thick solid and dashed curves indicate the

tropopause and the subtropical inversion layer in the current climate, respectively. Thin grey text and arrows represent robust responses in the thermodynamic structure to greenhouse 
warming, of relevance to cloud changes. Text and arrows in red, orange and green show the major cloud responses assessed with high, medium  and low confidence , respectively, 
and the sign of their feedbacks to the surface warming is indicated in the parenthesis. Major advances since AR5 are listed in the box. Figure adapted from Boucher et al. (2013).

972
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7(see Section 7.2.1). In the present climate, the SW CRE tends to be 
compensated by the LW CRE over the equatorial warm pool, leading 
to the net CRE pattern showing large negative values over the 
eastern part of the subtropical ocean and the extratropical ocean 
due to the dominant influence of highly reflective marine low-clouds.
In a  first attempt to systematically evaluate equilibrium climate 
sensitivity (ECS) based on fully coupled general circulation models 
(GCMs) in AR4, diverging cloud feedbacks were recognized as 
a dominant source of uncertainty. An advance in understanding the 
cloud feedback was to assess feedbacks separately for different cloud 
regimes (Gettelman and Sherwood, 2016). A  thorough assessment 
of cloud feedbacks in different cloud regimes was carried out in AR5 
(Boucher et al., 2013), which assigned high or medium confidence  for

some cloud feedbacks but low or no confidence  for others (Table 7.9). 
Many studies that estimate the net cloud feedback using CMIP5 
simulations (Vial et  al., 2013; Caldwell et  al., 2016; Zelinka et  al., 
2016; Colman and Hanson, 2017) show different values depending on 
the methodology and the set of models used, but often report a large 
inter-model spread of the feedback, with the 90% confidence interval 
spanning both weak negative and strong positive net feedbacks. 
Part of this diversity arises from the dependence of the model cloud 
feedbacks on the parametrization of clouds and their coupling to 
other sub-grid-scale processes (Zhao et al., 2015).
Since AR5, community efforts have been undertaken to understand 
and quantify the cloud feedbacks in various cloud regimes coupled 
with large-scale atmospheric circulation (Bony et al., 2015). For some 
cloud regimes, alternative tools to ESMs, such as observations, theory,

high-resolution cloud resolving models (CRMs), and large eddy 
simulations (LES), help quantify the feedbacks. Consequently, the net 
cloud feedback derived from ESMs has been revised by assessing 
the regional cloud feedbacks separately and summing them with 
weighting by the ratio of fractional coverage of those clouds over the 
globe to give the global feedback, following an approach adopted 
in Sherwood et al. (2020). This ‘bottom-up’ assessment is explained 
below with a  summary of updated confidence of individual cloud 
feedback components (Table 7.9). Dependence of cloud feedbacks on 
evolving patterns of surface warming will be discussed in Section 7.4.4 
and is not explicitly taken into account in the assessment presented 
in this section.
7.4.2.4.2 Assessment for individual cloud regimes
High-cloud altitude feedback
It has long been argued that cloud-top altitude rises under global 
warming, concurrent with the rising of the tropopause at all latitudes

(Marvel et al., 2015; Thompson et al., 2017). This increasing altitude 
of high-clouds was identified in early generation GCMs and the 
tropical high-cloud altitude feedback was assessed to be positive 
with high confidence  in AR5 (Boucher et al., 2013). This assessment 
is supported by a  theoretical argument called the ‘fixed anvil 
temperature mechanism’, which ensures that the temperature of the 
convective detrainment layer does not change when the altitude of 
high-cloud tops increases with the rising tropopause (Hartmann and 
Larson, 2002). Because the cloud-top temperature does not change 
significantly with global warming, cloud LW emission does not increase even though the surface warms, resulting in an enhancement 
of the high-cloud greenhouse effect (a positive feedback; Yoshimori 
et al. (2020)). The upward shift of high-clouds with surface warming 
is detected in observed interannual variability and trends in satellite

records for recent decades (Chepfer et al., 2014; Norris et al., 2016; 
Saint-Lu et  al., 2020). The observational detection is not always 
successful (Davies et al., 2017), but the cloud altitude shifts similarly in 
many CRM experiments (Khairoutdinov and Emanuel, 2013; Tsushima 
et al., 2014; Narenpitak et al., 2017). The high-cloud altitude feedback 
was estimated to be 0.5 W m–2 °C–1 based on GCMs in AR5, but is 
revised, using a recent re-evaluation that excludes aliasing effects by 
reduced low-cloud amounts, downward to 0.22  ±  0.12 W  m–2  °C–1 
(one standard deviation; Zhou et  al., 2014; Zelinka et  al., 2020). 
In conclusion, there is high confidence in the positive high-cloud 
altitude feedback simulated in ESMs as it is supported by theoretical, 
observational, and process modelling studies.
Tropical high-cloud amount feedback
Updrafts in convective plumes lead to detrainment of moisture at 
a  level where the buoyancy diminishes, and thus deep convective

clouds over high SSTs in the tropics are accompanied by anvil and 
cirrus clouds in the upper troposphere. These clouds, rather than 
the convective plumes themselves, play a  substantial role in the 
global TOA radiation budget. In the present climate, the net CRE 
of these clouds is small due to a cancellation between the SW and 
LW components (Hartmann et  al., 2001). However, high-clouds 
with different optical properties could respond to surface warming 
differently, potentially perturbing this radiative balance and therefore 
leading to a non-zero feedback.
A thermodynamic mechanism referred to as the ‘stability iris effect’ 
has been proposed to explain that the anvil cloud amount decreases 
with surface warming (Bony et  al., 2016). In this mechanism, 
a  temperature-mediated increase of static stability in the upper 
troposphere, where convective detrainment occurs, acts to balance 
a  weakened mass outflow from convective clouds, and thereby

reduce anvil cloud areal coverage (Figure 7.9). The reduction of anvil 
cloud amount is accompanied by enhanced convective aggregation 
that causes a  drying of the surrounding air and thereby increases 
the LW emission to space that acts as a  negative feedback (Bony 
et al., 2020). This phenomenon is found in many CRM simulations 
(Emanuel et al., 2014; Wing and Emanuel, 2014; Wing et al., 2020) 
and also identified in observed interannual variability (Stein et al., 
2017; Saint-Lu et al., 2020).
Despite the reduction of anvil cloud amount supported by several 
lines of evidence, estimates of radiative feedback due to high-cloud 
amount changes is highly uncertain in models. The assessment 
presented here is guided by combined analyses of TOA radiation and 
cloud fluctuations at interannual time scale using multiple satellite 
datasets. The observationally based local cloud amount feedback 
associated with optically thick high-clouds is negative, leading to

its global contribution (by multiplying the mean tropical anvil cloud 
fraction of about 8%) of –0.24 ± 0.05 W m–2 °C–1 (one standard 
deviation) for LW (Vaillant de Guélis et  al., 2018). Also, there is 
a  positive feedback due to increase of optically thin cirrus clouds 
in the tropopause layer, estimated to be 0.09 ± 0.09 W  m–2  °C–1

973
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77(one  standard deviation; Zhou et  al., 2014). The negative LW 
feedback due to reduced amount of thick high-clouds is partly 
compensated by the positive SW feedback (due to less reflection of 
solar radiation), so that the tropical high-cloud amount feedback 
is assessed to be equal to or smaller than their sum. Consistently, 
the net high-cloud feedback in the tropical convective regime, 
including a  part of the altitude feedback, is estimated to have 
the global contribution of –0.13 ± 0.06 W m–2 °C–1 (one standard 
deviation; Williams and Pierrehumbert, 2017). The negative cloud 
LW feedback is considerably biased in CMIP5 GCMs (Mauritsen and 
Stevens, 2015; Su et al., 2017; Li et al., 2019) and highly uncertain, 
primarily due to differences in the convective parametrization (Webb 
et al., 2015). Furthermore, high-resolution CRM simulations cannot

alone be used to constrain uncertainty because the results depend 
on parametrized cloud microphysics and turbulence (Bretherton 
et  al., 2014; Ohno et  al., 2019). Therefore, the tropical high-cloud 
amount feedback is assessed as negative but with low confidence 
given the lack of modelling evidence. Taking observational estimates 
altogether and methodological uncertainty into account, the global 
contribution of the high-cloud amount feedback is assessed to be 
–0.15 ± 0.2 W m–2 °C–1 (one standard deviation).
Subtropical marine low-cloud feedback
It has long been argued that the response of marine boundary-layer 
clouds over the subtropical ocean to surface warming was the largest 
contributor to the spread among GCMs in the net cloud feedback 
(Boucher et al., 2013). However, uncertainty of the marine low-cloud 
feedback has been reduced considerably since AR5 through combined 
knowledge from theoretical, modelling and observational studies

(Klein et al., 2017). Processes that control the low-clouds are complex 
and involve coupling with atmospheric motions on multiple scales, 
from the boundary-layer turbulence to the large-scale subsidence, 
which may be represented by a combination of shallow and deep 
convective mixing (Sherwood et al., 2014).
In order to disentangle the large-scale processes that cause the cloud 
amount either to increase or decrease in response to the surface 
warming, the cloud feedback has been expressed in terms of several 
‘cloud controlling factors’ (Qu et al., 2014, 2015; Zhai et al., 2015; 
Brient and Schneider, 2016; Myers and Norris, 2016; McCoy et al., 
2017a). The advantage of this approach over conventional calculation 
of cloud feedbacks is that the temperature-mediated cloud response 
can be estimated without using information of the simulated cloud 
responses that are less well-constrained than the changes in the 
environmental conditions. Two dominant factors are identified for

the subtropical low-clouds: a thermodynamic effect due to rising SST 
that acts to reduce low-cloud by enhancing cloud-top entrainment 
of dry air, and a  stability effect accompanied by an enhanced 
inversion strength that acts to increase low-cloud (Qu et al., 2014, 
2015; Kawai et  al., 2017). These controlling factors compensate 
with a  varying degree in different ESMs, but can be constrained 
by referring to the observed seasonal or interannual relationship 
between the low-cloud amount and the controlling factors in the 
environment as a  surrogate. The analysis leads to a  positive local 
feedback that has the global contribution of 0.14 to 0.36 W m–2 °C–1 
(Klein et  al., 2017), to which the feedback in the stratocumulus 
regime dominates over the feedback in the trade cumulus regime (Cesana et al., 2019; Radtke et al., 2021). The stratocumulus feedback 
may be underestimated because explicit simulations using LES show

a  larger local feedback of up to 2.5 W  m–2  °C–1, corresponding to 
the global contribution of 0.2 W m–2 °C–1 by multiplying the mean 
tropical stratocumulus fraction of about 8% (Bretherton, 2015). 
Supported by different lines of evidence, the subtropical marine 
low-cloud feedback is assessed as positive with high confidence . 
Based on the combined estimate using LESs and the cloud 
controlling factor analysis, the global contribution of the feedback 
due to marine low-clouds equatorward of 30° is assessed to be 
0.2 ± 0.16 W m–2 °C–1 (one standard deviation), for which the range 
reflects methodological uncertainties.
Land cloud feedback
Intensification of the global hydrological cycle is a  robust feature 
of global warming, but at the same time, many land areas in 
the subtropics will experience drying at the surface and in the 
atmosphere (Section  8.2.2). This occurs due to limited water 
availability in these regions, where the cloudiness is consequently

expected to decrease. Reduction in clouds over land is consistently 
identified in the CMIP5 models and also in a  GCM with explicit 
convection (Bretherton et al., 2014; Kamae et al., 2016a). Because 
low-clouds make up the majority of subtropical land clouds, this 
reduced amount of low-clouds reflects less solar radiation and leads 
to a positive feedback similar to the marine low-clouds. The mean 
estimate of the global land cloud  feedback in CMIP5 models is 
smaller than the marine low-cloud feedback, 0.08 ± 0.08 W m–2 °C–1 
(Zelinka et al., 2016). These values are nearly unchanged in CMIP6 
(Zelinka et al., 2020). However, ESMs still have considerable biases 
in the climatological temperature and cloud fraction over land, 
and the magnitude of this feedback has not yet been supported by 
observational evidence. Therefore, the feedback due to decreasing 
land clouds is assessed to be 0.08 ± 0.08 W m–2 °C–1 (one standard 
deviation) with low confidence .

Mid-latitude cloud amount feedback
Poleward shifts in the mid-latitude jets are evident since the 1980s 
(Section  2.3.1.4.3) and are a  feature of the large-scale circulation 
change in future projections (Section 4.5.1.6). Because mid-latitude 
clouds over the North Pacific, North Atlantic and Southern Ocean 
are induced mainly by extratropical cyclones in the storm tracks 
along the jets, it has been suggested that the jet shifts should be 
accompanied by poleward shifts in the mid-latitude clouds, which 
would result in a positive feedback through the reduced reflection 
of insolation (Boucher et al., 2013). However, studies since AR5 have 
revealed that this proposed mechanism does not apply in practice 
(Ceppi and Hartmann, 2015). While a poleward shift of mid-latitude 
cloud maxima in the free troposphere has been identified in satellite 
and ground-based observations (Bender et al., 2012; Eastman and 
Warren, 2013), associated changes in net CRE are small because

the responses in high and low-clouds to the jet shift act to cancel 
each other (Grise and Medeiros, 2016; Tselioudis et al., 2016; Zelinka 
et  al., 2018). This cancellation is not well captured in ESMs (Lipat 
et al., 2017), but the above findings show that the mid-latitude cloud 
feedback is not dynamically driven by the poleward jet shifts, which 
are rather suggested to occur partly in response to changes in high 
clouds (Y . Li et al., 2018).

974
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Thermodynamics play an important role in controlling extratropical 
cloud amount equatorward of about 50° latitude. Recent studies 
showed, using observed cloud controlling factors, that the mid-latitude 
low-cloud fractions decrease with rising SST, which also acts to 
weaken stability of the atmosphere unlike in the subtropics (McCoy 
et al., 2017a). ESMs consistently show a decrease of cloud amounts 
and a resultant positive SW feedback in the 30°–40° latitude bands, 
which can be constrained using observations of seasonal migration 
of cloud amount (Zhai et  al., 2015). Based on the qualitative 
agreement between observations and ESMs, the mid-latitude cloud 
amount feedback is assessed as positive with medium confidence. 
Following these emergent constraint studies using observations 
and CMIP5/6 models, the global contribution of net cloud amount

feedback over 30°–60° ocean areas, covering 27% of the globe, 
is assessed at 0.09  ± 0.1 W m–2 °C–1 (one standard deviation), in 
which the uncertainty reflects potential errors in models’ low-cloud 
response to changes in thermodynamic conditions.
Extratropical cloud optical depth feedback
Mixed-phase clouds that consist of both liquid and ice are dominant 
over the Southern Ocean (50°S–80°S), which accounts for 20% of the 
net CRE in the present climate (Matus and L’Ecuyer, 2017). It has been 
argued that the cloud optical depth (opacity) will increase over the 
Southern Ocean as warming drives the replacement of ice-dominated 
clouds with liquid-dominated clouds (Tan et al., 2019). Liquid clouds 
generally consist of many small cloud droplets, while the crystals 
in ice clouds are orders of magnitude fewer in number and much 
larger, causing the liquid clouds to be optically thicker and thereby 
resulting in a  negative feedback (Boucher et  al., 2013). However,

this phase-change feedback works effectively only below freezing 
temperature (Lohmann and Neubauer, 2018; Terai et al., 2019) and 
other processes that increase or decrease liquid water path (LWP) 
may also affect the optical depth feedback (McCoy et al., 2019).
Due to insufficient amounts of super-cooled liquid water in the 
simulated atmospheric mean state, many CMIP5 models overestimated 
the conversion from ice to liquid clouds with climate warming and 
the resultant negative phase-change feedback (Kay et  al., 2016a; 
Tan et al., 2016; Lohmann and Neubauer, 2018). This feedback can 
be constrained using satellite-derived LWP observations over the 
past 20 years that enable estimates of both long-term trends and 
the interannual relationship with SST variability (Gordon and Klein, 
2014; Ceppi et al., 2016; Manaster et al., 2017). The observationally-
constrained SW feedback ranges from –0.91 to –0.46 W m–2 °C–1 over

40°S–70°S depending on the methodology (Ceppi et al., 2016; Terai 
et al., 2016). In some CMIP6 models, representation of super-cooled 
liquid water content has been improved, leading to weaker 
negative optical depth feedback over the Southern Ocean closer 
to observational estimates (Bodas-Salcedo et  al., 2019; Gettelman 
et al., 2019). This improvement at the same time results in a positive 
optical depth feedback over other extratropical ocean where LWP 
decreased in response to reduced stability in those CMIP6 models 
(Zelinka et al., 2020). Given the accumulated observational estimates 
and an improved agreement between ESMs and observations, the 
extratropical optical depth feedback is assessed to be small negative 
with medium confidence.  Quantitatively, the global contribution of 
this feedback is assessed to have a value of –0.03 ± 0.05 W m–2 °C–1 (one standard deviation) by combining estimates based on observed 
interannual variability and the cloud controlling factors.

Arctic cloud feedback 
Clouds in polar regions, especially over the Arctic, form at low altitude 
above or within a stable to neutral boundary layer and are known to 
co-vary with sea ice variability beneath. Because the clouds reflect 
sunlight during summer but trap LW radiation throughout the year, 
seasonality plays an important role in cloud effects on Arctic climate 
(Kay et al., 2016b). AR5 assessed that Arctic low-cloud amount will 
increase in boreal autumn and winter in response to declining sea 
ice in a  warming climate, due primarily to an enhanced upward 
moisture flux over open water. The cloudier conditions during these 
seasons result in more downwelling LW radiation, acting as a positive 
feedback on surface warming (Kay and Gettelman, 2009). Over 
recent years, further evidence of the cloud contribution to the Arctic 
amplification has been obtained (Section 7.4.4.1; Goosse et al., 2018). 
Space-borne lidar (light detection and ranging) observations show

that the cloud response to summer sea ice loss is small and cannot 
overcome the cloud effect in autumn (Taylor et  al., 2015; Morrison 
et al., 2019). The seasonality of the cloud response to sea ice variability 
is reproduced in GCM simulations (Laîné et al., 2016; Yoshimori et al., 
2017). The agreement between observations and models indicates 
that the Arctic cloud feedback is positive at the surface. This leads 
to an Arctic cloud feedback at TOA that is likely  positive, but very 
small in magnitude, as found in some climate models (Pithan and 
Mauritsen, 2014; Morrison et al., 2019). The observational estimates 
are sensitive to the analysis period and the choice of reanalysis data, 
and a  recent estimate of the TOA cloud feedback over 60°N–90°N 
using atmospheric reanalysis data and CERES satellite observations 
suggests a regional value ranging from –0.3 to +0.5 W m–2 °C–1, which 
corresponds to a global contribution of –0.02 to +0.03 W m–2 °C–1

(R. Zhang et al., 2018). Based on the overall agreement between ESMs 
and observations, the Arctic cloud feedback is assessed to be small 
positive and has the value of 0.01 ± 0.05 W m–2 °C–1 (one standard 
deviation). The assessed range indicates that a negative feedback is 
almost as probable as a positive feedback, and the assessment that 
the Arctic cloud feedback is positive is therefore given low confidence .
7.4.2.4.3 Synthesis for the net cloud feedback
The understanding of the response of clouds to warming and 
associated radiative feedback has deepened since AR5 (Figure  7.9 
and FAQ 7.2). Particular progress has been made in the assessment of 
the marine low-cloud feedback, which has historically been a major 
contributor to the cloud feedback uncertainty but is no longer the 
largest source of uncertainty. Multiple lines of evidence (theory, 
observations, emergent constraints and process modelling) are now

available in addition to ESM simulations, and the positive low-cloud 
feedback is consequently assessed with high confidence .
The best estimate of net cloud feedback is obtained by summing 
feedbacks associated with individual cloud regimes and assessed 
to be αC = 0.42 W  m–2 °C–1. By assuming that the uncertainties 
of individual cloud feedbacks are independent of each other, 
their standard deviations are added in quadrature, leading to the 
likely  range of 0.12 to 0.72 W  m–2 °C–1 and the very likely  range

975
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77of –0.10 to +0.94 W m–2 °C–1 (Table 7.10). This approach potentially 
misses feedbacks from cloud regimes that are not assessed, but almost 
all the major cloud regimes were taken into consideration (Gettelman 
and Sherwood, 2016) and therefore additional uncertainty will be 
small. This argument is also supported by an agreement between 
the net cloud feedback assessed here and the net cloud feedback 
directly estimated using observations. The observational estimate, 
which is sensitive to the period considered and is based on two 
atmospheric reanalyses (ERA-Interim and MERRA) and TOA radiation 
budgets derived from the CERES satellite observations for the years 
2000–2010, is 0.54 ± 0.7 W m–2 °C–1 (one standard deviation; Dessler, 
2013). The observational estimate overlaps with the assessed range 
of the net cloud feedback. The assessed very likely  range is reduced

by about 50% compared to AR5, but is still wide compared to those 
of other climate feedbacks (Table 7.10). The largest contribution to 
this uncertainty range is the estimate of tropical high-cloud amount 
feedback which is not yet well quantified using models.
In reality, different types of cloud feedback may occur simultaneously in 
one cloud regime. For example, an upward shift of high-clouds associated 
with the altitude feedback could be coupled to an increase/decrease of 
cirrus/anvil cloud fractions associated with the cloud amount feedback. 
Alternatively, slowdown of the tropical circulation with surface warming 
(Section 4.5.3 and Figure 7.9) could affect both high and low-clouds 
so that their feedbacks are co-dependent. Quantitative assessments 
of such covariances require further knowledge about cloud feedback 
mechanisms, which will further narrow the uncertainty range.
In summary, deepened understanding of feedback processes in

individual cloud regimes since AR5 leads to an assessment of the 
positive net cloud feedback with high confidence . A small probability 
(less than 10%) of a net negative cloud feedback cannot be ruled 
out, but this would require an extremely large negative feedback 
due to decreases in the amount of tropical anvil clouds or increases 
in optical depth of extratropical clouds over the Southern Ocean; 
neither is supported by current evidence.
7.4.2.5 Biogeophysical and Non-CO 2 Biogeochemical Feedbacks
The feedbacks presented in the previous sections (Sections 7.4.2.1–
7.4.2.4) are directly linked to physical climate variables (for example 
temperature, water vapour, clouds, or sea ice). The central role of climate feedbacks associated with these variables has been recognized 
since early studies of climate change. However, in addition to these 
physical climate feedbacks, the Earth system includes feedbacks for 
which the effect of global mean surface temperature change on the

TOA energy budget is mediated through other mechanisms, such 
as the chemical composition of the atmosphere, or by vegetation 
changes. Among these additional feedbacks, the most important is 
the CO 2 feedback that describes how a change of the global surface 
temperature affects the atmospheric CO 2 concentration. In  ESM 
simulations in which CO 2 emissions are prescribed, changes in surface 
carbon fluxes affect the CO 2 concentration in the atmosphere, the 
TOA radiative energy budget, and eventually the global mean surface 
temperature. In ESM simulations in which the CO 2 concentration 
is prescribed, changes in the carbon cycle allow compatible CO 2 
emissions to be calculated, that is, the CO 2 emissions that are 
compatible with both the prescribed CO 2 concentration and the 
representation of the carbon cycle in the ESM. The CO 2 feedback 
is assessed in Chapter 5 (Section 5.4). The framework presented in 
this chapter assumes that the CO 2 concentration is prescribed, and

our assessment of the net feedback parameter, α, does not include 
carbon cycle feedbacks on the atmospheric CO 2 concentration 
(Section  7.1 and Box  7.1).  However, our assessment of α does 
include non-CO 2 biogeochemical feedbacks (including effects due to 
changes in atmospheric methane concentration; Section  7.4.2.5.1) 
and biogeophysical feedbacks (Section  7.4.2.5.2). A  synthesis of 
the combination of biogeophysical and non-CO 2 biogeochemical 
feedbacks is given in Section 7.4.2.5.3.
7.4.2.5.1 Non-CO 2 biogeochemical feedbacks
The chemical composition of the atmosphere (beyond CO 2 and water 
vapour changes) is expected to change in response to a  warming 
climate. These changes in greenhouse gases (methane, nitrous oxide 
and ozone) and aerosol amount (including dust) have the potential 
to alter the TOA energy budget and are collectively referred to as 
‘non-CO 2 biogeochemical feedbacks’. Methane (CH 4) and nitrous

oxide (N 2O) feedbacks arise partly from changes in their emissions 
from natural sources in response to temperature change; these are 
assessed in Chapter  5 (Section  5.4.7; see also Figure  5.29c). Here 
we exclude the permafrost CH 4 feedback (Section 5.4.9.1.2) because, 
although associated emissions are projected to increase under 
warming on multi-decadal to centennial time scales, on longer time Table 7.9  | Assessed sign and confidence level of cloud feedbacks in different regimes in AR5 and AR6.  For some cloud regimes, the feedback was not assessed 
in AR5, indicated by N/A.
Feedback AR5 AR6
High-cloud altitude feedback Positive ( high confidence ) Positive ( high confidence )
Tropical high-cloud amount feedback N/A Negative ( low confidence )
Subtropical marine low-cloud feedback N/A ( low confidence ) Positive ( high confidence )
Land cloud feedback N/A Positive ( low confidence )

Mid-latitude cloud amount feedback Positive ( medium confidence ) Positive ( medium confidence )
Extratropical cloud optical depth feedback N/A Small negative ( medium confidence )
Arctic cloud feedback Small positive ( very low confidence ) Small positive ( low confidence )
Net cloud feedback Positive ( medium confidence ) Positive ( high confidence )

976
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7scales these emissions would eventually substantially decline as the 
permafrost carbon pools were depleted (Schneider von Deimling 
et  al., 2012, 2015). This leaves the wetland CH 4, land N 2O, and 
ocean N 2O feedbacks, the assessed mean values of which sum to 
a  positive feedback parameter of +0.04 [0.02 to 0.06] W m–2 °C–1 
(Section  5.4.7). Other non-CO 2 biogeochemical feedbacks that are 
relevant to the net feedback parameter are assessed in Chapter 6 
(Section 6.4.5 and Table 6.8). These feedbacks are associated with 
sea salt, dimethyl sulphide, dust, ozone, biogenic volatile organic 
compounds, lightning, and CH 4 lifetime, and sum to a  negative 
feedback parameter of –0.20 [–0.41 to +0.01] W m–2 °C–1. The 
overall feedback parameter for non-CO 2 biogeochemical feedbacks 
is obtained by summing the Chapter 5 and Chapter 6 assessments,

which gives –0.16 [–0.37 to +0.05] W m–2 °C–1. However, there is 
low confidence  in the estimates of both the individual non-CO 2 
biogeochemical feedbacks as well as their total effect, as evident 
from the large range in the magnitudes of α from different studies, 
which can be attributed to diversity in how models account for these 
feedbacks and limited process-level understanding.
7.4.2.5.2 Biogeophysical feedbacks
Biogeophysical feedbacks are associated with changes in the spatial 
distribution and/or biophysical properties of vegetation, induced 
by surface temperature change and attendant hydrological cycle 
change. These vegetation changes can alter radiative fluxes directly 
via albedo changes, or via surface momentum or moisture flux 
changes and hence changes in cloud properties. However, the direct 
physiological response of vegetation to changes in CO 2, including 
changes in stomatal conductance, is considered part of the CO 2

effective radiative forcing rather than a feedback (Section 7.3.2.1). 
The time scale on which vegetation responds to climate change is 
relatively uncertain but can be from decades to hundreds of years 
(Willeit et al., 2014), and could occur abruptly or as a tipping point 
(Sections 5.4.9.1.1, 8.6.2.1 and 8.6.2.2); equilibrium only occurs 
when the soil system and associated nutrient and carbon pools 
equilibrate, which can take millennia (Brantley, 2008; Sitch et  al., 
2008). The overall effects of climate-induced vegetation changes may 
be comparable in magnitude to those from anthropogenic land-use 
and land-cover change (Davies-Barnard et al., 2015). Climate models 
that include a  dynamical representation of vegetation (e.g.,  Reick 
et al., 2013; Harper et al., 2018) are used to explore the importance 
of biogeophysical feedbacks (Notaro et  al., 2007; Brovkin et  al., 
2009; O’ishi et al., 2009; Port et al., 2012; Willeit et al., 2014; Alo and

Anagnostou, 2017; W. Zhang et al., 2018; Armstrong et al., 2019). 
In AR5, it was discussed that such model experiments predicted 
that expansion of vegetation in the high latitudes of the Northern 
Hemisphere would enhance warming due to the associated surface-
albedo change, and that reduction of tropical forests in response 
to climate change would lead to regional surface warming, due to 
reduced evapotranspiration (M. Collins et al., 2013), but there was no 
assessment of the associated feedback parameter. The SRCCL stated 
that regional climate change can be dampened or enhanced by 
changes in local land cover, but that this depends on the location and 
the season; however, in general the focus was on anthropogenic land-
cover change, and no assessment of the biogeophysical feedback 
parameter was carried out. There are also indications of a  marine biogeophysical feedback associated with surface-albedo change due

to changes in phytoplankton (Frouin and Iacobellis, 2002; Park et al., 
2015), but there is not currently enough evidence to quantitatively 
assess this feedback.
Since AR5, several studies have confirmed that a shift from tundra to 
boreal forests and the associated albedo change leads to increased 
warming in Northern Hemisphere high latitudes ( high confidence ) 
(Willeit et al., 2014; W. Zhang et al., 2018; Armstrong et al., 2019). 
However, regional modelling indicates that vegetation feedbacks may 
act to cool climate in the Mediterranean (Alo and Anagnostou, 2017), 
and in the tropics and subtropics the regional response is in general 
not consistent across models. On a global scale, several modelling 
studies have either carried out a feedback analysis (Stocker et al., 2013; 
Willeit et al., 2014) or presented simulations that allow a feedback 
parameter to be estimated (O’ishi et al., 2009; Armstrong et al., 2019),

in such a way that the physiological response can be accounted for 
as a  forcing rather than a  feedback. The central estimates of the 
biogeophysical feedback parameter from these studies range from 
close to zero (Willeit et al., 2014) to +0.13 W m–2 °C–1 (Stocker et al., 
2013). An additional line of evidence comes from the mid-Pliocene 
warm period (MPWP , Chapter 2, Cross-Chapter Box 2.1), for which 
paleoclimate proxies provide evidence of vegetation distribution 
and CO 2 concentrations. Model simulations that include various 
combinations of modern versus MPWP vegetation and CO 2 allow an 
associated feedback parameter to be estimated, as long as account 
is also taken of the orographic forcing (Lunt et al., 2010, 2012b). This 
approach has the advantage over pure modelling studies in that the 
reconstructed vegetation is based on (paleoclimate) observations, 
and is in equilibrium with the CO 2 forcing. However, there are

uncertainties in the vegetation reconstruction in regions with little 
or no proxy data, and it is uncertain how much of the vegetation 
change is associated with the physiological response to CO 2. This 
paleoclimate approach gives an estimate for the biogeophysical 
feedback parameter of +0.3 W m–2 °C–1.
Given the limited number of studies, we take the full range of estimates 
discussed above for the biogeophysical feedback parameter, and 
assess the very likely  range to be from 0.0 to +0.3 W m–2 °C–1, with 
a central estimate of +0.15 W m–2 °C–1 (low confidence ). Although this 
assessment is based on evidence from both models and paleoclimate 
proxies, and the studies above agree on the sign of the change, there is 
nonetheless limited evidence . Higher confidence could be obtained if 
there were more studies that allowed calculation of a biogeophysical 
feedback parameter (particularly from paleoclimates), and if the

partitioning between biogeophysical feedbacks and physiological 
forcing were clearer for all lines of evidence.
7.4.2.5.3 Synthesis of biogeophysical and non-CO 2 
biogeochemical feedbacks
The non-CO 2 biogeochemical feedbacks are assessed in 
Section  7.4.2.5.1 to be –0.16 [–0.37 to +0.05] W m–2 °C–1 and the 
biogeophysical feedbacks are assessed in Section  7.4.2.5.2 to be 
+0.15  [0.0 to +0.3] W  m–2 °C–1. The sum of the biogeophysical 
and non-CO 2 biogeochemical feedbacks is assessed to have 
a  central value of –0.01 W m–2 °C–1 and a  very likely  range from

977
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77–0.27  to  +0.25 W  m–2  °C–1 (Table  7.10). Given the relatively long 
time scales associated with the biological processes that mediate the 
biogeophysical and many of the non-CO 2 biogeochemical feedbacks, 
in comparison with the relatively short time scale of many of the 
underlying model simulations, combined with the small number of 
studies for some of the feedbacks, and the relatively small signals, this 
overall assessment has low confidence .
Some supporting evidence for this overall assessment can be 
obtained from the CMIP6 ensemble, which provides some pairs of 
instantaneous 4×CO 2 simulations carried out using related models, 
with and without biogeophysical and non-CO 2 biogeochemical 
feedbacks. This is not a  direct comparison because these pairs of 
simulations may differ by more than just their inclusion of these 
additional feedbacks; furthermore, not all biogeophysical and

non-CO 2 biogeochemical feedbacks are fully represented. However, 
a comparison of the pairs of simulations does provide a first-order 
estimate of the magnitude of these additional feedbacks. Séférian 
et  al. (2019) find a  slightly more negative feedback parameter in 
CNRM-ESM2-1 (with additional feedbacks) then in CNRM-CM6-1 
(a decrease of 0.02 W m–2 °C–1, using the linear regression method 
from years 10–150). Andrews et al. (2019) also find a slightly more 
negative feedback parameter when these additional feedbacks are 
included (a decrease of 0.04 W m–2 °C–1 in UKESM1 compared with 
HadGEM3-GC3.1). Both of these studies suggest a small but slightly 
negative feedback parameter for the combination of biogeophysical 
and non-CO 2 biogeochemical feedbacks, but with relatively large 
uncertainty given (i) interannual variability and (ii) that feedbacks 
associated with natural terrestrial emissions of CH 4 and N 2O were not 
represented in either pair.

7.4.2.6 Long-Term Radiative Feedbacks Associated 
with Ice Sheets
Although long-term radiative feedbacks associated with ice sheets are 
not included in our definition of ECS (Box 7.1), the relevant feedback 
parameter is assessed here because the time scales on which these 
feedbacks act are relatively uncertain, and the long-term temperature 
response to CO 2 forcing of the entire Earth system may be of interest.
Earth’s ice sheets (Greenland and Antarctica) are sensitive to climate 
change (Section  9.4; Pattyn et  al., 2018). Their time evolution is 
determined by both their surface mass balance and ice dynamic 
processes, with the latter being particularly important for the West 
Antarctic Ice Sheet. Surface mass balance depends on the net energy 
and hydrological fluxes at their surface, and there are mechanisms of 
ice-sheet instability that depend on ocean temperatures and basal 
melt rates (Section 9.4.1.1). The presence of ice sheets affects Earth’s

radiative budget, hydrology, and atmospheric circulation due to their 
characteristic high albedo, low roughness length, and high altitude, 
and they influence ocean circulation through freshwater input from 
calving and melt (e.g.,  Fyke et  al., 2018). Ice-sheet changes also 
modify surface albedo through the attendant change in sea level 
and therefore land area (Abe-Ouchi et al., 2015). The time scale for 
ice sheets to reach equilibrium is of the order of thousands of years 
(Clark et al., 2016). Due to the long time scales involved, it is a major 
challenge to run coupled climate–ice sheet models to equilibrium, and as a  result, long-term simulations are often carried out with 
lower complexity models, and/or are asynchronously coupled.
In AR5, it was described that both the Greenland and Antarctic ice 
sheets would continue to lose mass in a warming world (M. Collins 
et  al., 2013), with a  continuation in sea level rise beyond the year

2500 assessed as virtually certain . However, there was low confidence  
in the associated radiative feedback mechanisms, and as such, there 
was no assessment of the magnitude of long-term radiative feedbacks 
associated with ice sheets. That assessment is consistent with SROCC, 
wherein it was stated that ‘with limited published studies to draw 
from and no simulations run beyond 2100, firm conclusions regarding 
the net importance of atmospheric versus ocean melt feedbacks on 
the long-term future of Antarctica cannot be made.’
The magnitude of the radiative feedback associated with changes to 
ice sheets can be quantified by comparing the global mean long-term 
equilibrium temperature response to increased CO 2 concentrations in 
simulations that include interactive ice sheets with that of simulations 
that do not include the associated ice sheet–climate interactions 
(Swingedouw et al., 2008; Vizcaíno et al., 2010; Goelzer et al., 2011;

Bronselaer et  al., 2018; Golledge et  al., 2019). These simulations 
indicate that on multi-centennial time scales, ice-sheet mass loss leads 
to freshwater fluxes that can modify ocean circulation (Swingedouw 
et  al., 2008; Goelzer et  al., 2011; Bronselaer et  al., 2018; Golledge 
et al., 2019). This leads to reduced surface warming (by about 0.2°C 
in the global mean after 1000 years; Section 7.4.4.1.1; Goelzer et al., 
2011), although other work suggests no net global temperature 
effect of ice-sheet mass loss (Vizcaíno et  al., 2010). However, 
model simulations in which the Antarctic Ice Sheet is removed 
completely in a  paleoclimate context indicate a  positive global 
mean feedback on multi-millennial time scales due primarily to the 
surface-albedo change (Goldner et al., 2014a; Kennedy-Asser et al., 
2019); in Chapter 9 (Section 9.6.3) it is assessed that such ice-free 
conditions could eventually occur given 7°C–13°C of warming. This

net positive feedback from ice-sheet mass loss on long time scales 
is also supported by model simulations of the mid-Pliocene Warm 
Period (MPWP; Cross-chapter Box  2.1) in which the volume and 
area of the Greenland and West Antarctic ice sheets are reduced in 
model simulations in agreement with geological data (Chandan and 
Peltier, 2018), leading to surface warming. As such, overall, on multi-
centennial time scales the feedback parameter associated with ice 
sheets is likely  negative ( medium confidence ), but on multi-millennial 
time scales by the time the ice sheets reach equilibrium, the feedback 
parameter is very likely  positive ( high confidence ) (Table  7.10). 
However, a relative lack of models carrying out simulations with and 
without interactive ice sheets over centennial to millennial time scales 
means that there is currently not enough evidence to quantify the 
magnitude of these feedbacks, or the time scales on which they act.
7.4.2.7 Synthesis

Table  7.10 summarizes the estimates and the assessment of the 
individual and the net feedbacks presented in the above sections. 
The uncertainty range of the net climate feedback was obtained by 
adding standard deviations of individual feedbacks in quadrature, 
assuming that they are independent and follow the Gaussian

978
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7distribution. It is virtually certain  that the net climate feedback is 
negative, primarily due to the Planck temperature response, indicating 
that climate acts to stabilize in response to radiative forcing imposed 
to the system. Supported by the level of confidence associated with 
the individual feedbacks, it is also virtually certain  that the sum of 
the non-Planck feedbacks is positive. Based on Table  7.10 these 
climate feedbacks amplify the Planck temperature response by about 
2.8 [1.9 to 5.9] times . Cloud feedback remains the largest contributor 
to uncertainty of the net feedback, but the uncertainty is reduced 
compared to AR5. A  secondary contribution to the net feedback 
uncertainty is the biogeophysical and non-CO 2 biogeochemical 
feedbacks, which together are assessed to have a central value near 
zero and thus do not affect the central estimate of ECS. The net climate

feedback is assessed to be –1.16 W m–2 °C–1, likely  from –1.54  to 
–0.78 W m–2 °C–1, and very likely  from –1.81 to –0.51 W m–2 °C–1.
Feedback parameters in climate models are calculated assuming 
that they are independent of each other, except for a  well-known 
co-dependency between the water vapour (WV) and lapse rate (LR) 
feedbacks. When the inter-model spread of the net climate feedback is 
computed by adding in quadrature the inter-model spread of individual 
feedbacks, it is 17% wider than the spread of the net climate feedback 
directly derived from the ensemble. This indicates that the feedbacks in 
climate models are partly co-dependent. Two possible co-dependencies 
have been suggested (Huybers, 2010; Caldwell et  al., 2016). One is 
a negative covariance between the LR and longwave cloud feedbacks, 
which may be accompanied by a  deepening of the troposphere 
(O’Gorman and Singh, 2013; Yoshimori et al., 2020) leading both to

greater rising of high-clouds and a larger upper-tropospheric warming. 
The other is a  negative covariance between albedo and shortwave 
cloud feedbacks, which may originate from the Arctic regions: 
a reduction in sea ice enhances the shortwave cloud radiative effect 
because the ocean surface is darker than sea ice (Gilgen et al., 2018). 
This covariance is reinforced as the decrease of sea ice leads to an 
increase in low-level clouds (Mauritsen et  al., 2013). However, the 
mechanism causing these co-dependences between feedbacks is not 
well understood yet and a quantitative assessment based on multiple 
lines of evidence is difficult. Therefore, this synthesis assessment does 
not consider any co-dependency across individual feedbacks.The assessment of the net climate feedback presented above is 
based on a single approach (i.e., process understanding) and directly 
results in a value for ECS given in Section 7.5.1; this is in contrast to

the synthesis assessment of ECS in Section  7.5.5 which combines 
multiple approaches. The total (net) feedback parameter consistent 
with the final synthesis assessment of the ECS and Equation 7.1 
(Box 7.1) is provided there.
7.4.2.8 Climate Feedbacks in ESMs
Since AR5, many modelling groups have newly participated in CMIP 
experiments, leading to an increase in the number of models in 
CMIP6 (Section 1.5.4). Other modelling groups that contributed to 
CMIP5 also updated their ESMs for carrying out CMIP6 experiments. 
While some of the CMIP6 models share components and are 
therefore not independent, they are analysed independently when 
calculating climate feedbacks. This, and more subtle forms of model 
inter-dependence, creates challenges when determining appropriate 
model weighting schemes (Section 1.5.4). Additionally, it must be kept 
in mind that the ensemble sizes of the CMIP5 and CMIP6 models are 
not sufficiently large to sample the full range of model uncertainty.

The multi-model mean values of all physical climate feedbacks are 
calculated using the radiative kernel method (Section  7.4.1) and 
compared with the assessment in the previous sections (Figure 7.10). 
For CMIP models, there is a  discrepancy between the net climate 
feedback calculated directly using the time evolutions of Δ T and Δ N 
in each model and the accumulation of individual feedbacks, but it 
is negligibly small (Supplementary Material 7.SM.4). Feedbacks 
due to biogeophysical and non-CO 2 biogeochemical processes are 
included in some models but neglected in the kernel analysis. In AR6, 
biogeophysical and non-CO 2 biogeochemical feedbacks are explicitly 
assessed (Section 7.4.2.5).
All the physical climate feedbacks apart from clouds are very similar 
in the CMIP5 and CMIP6 model ensembles (see also Table 7.10). These 
values, where possible supported by other lines of evidence, are used 
for assessing feedbacks in Sections 7.4.2.1–7.4.2.3. A  difference

found between CMIP5 and CMIP6 models is the net cloud feedback, 
Table 7.1 0 | Synthesis assessment of climate feedbacks (central estimate shown in bold).  The mean values and their 90% ranges in CMIP5/6 models, derived using 
multiple radiative kernels (Zelinka et al., 2020) are also presented for comparison.
Feedback Parameter αx 
(W m–2 °C–1)CMIP5 GCMs CMIP6 ESMs AR6 Assessed Ranges
Mean and 
5–95% IntervalMean and 
5–95% IntervalCentral EstimateVery likely  
IntervalLikely  IntervalLevel of 
Confidence
Planck –3.20 [–3.3 to –3.1] –3.22 [–3.3 to –3.1] –3.22 –3.4 to –3.0 –3.3 to –3.1 high
WV+LR 1.24 [1.08 to 1.35] 1.25 [1.14 to 1.45] 1.30 1.1 to 1.5 1.2 to 1.4 high
Surface albedo 0.41 [0.25 to 0.56] 0.39 [0.26 to 0.53] 0.35 0.10 to 0.60 0.25 to 0.45 medium
Clouds 0.41 [–0.09 to 1.1] 0.49 [–0.08 to 1.1] 0.42 –0.10 to 0.94 0.12 to 0.72 high
Biogeophysical and non-CO 2 
biogeochemicalNot evaluated Not evaluated –0.01 –0.27 to 0.25 –0.16 to 0.14 low

Residual of kernel estimates 0.06 [–0.17 to 0.29] 0.05 [–0.18 to 0.28 ]
Net (i.e., relevant for ECS) –1.08 [–1.61 to –0.68] –1.03 [–1.54 to –0.62] –1.16 –1.81 to –0.51 –1.54 to –0.78 medium
Long-term ice-sheet 
feedbacks (millennial scale)>0.0 high

979
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77which is larger in CMIP6 by about 20%. This change is the major 
cause of less-negative values of the net climate feedback in CMIP6 
than in CMIP5 and hence an increase in modelled ECS (Section 7.5.1).
A remarkable improvement of cloud representation in some CMIP6 
models is the reduced error of the too-weak negative shortwave CRE 
over the Southern Ocean (Bodas-Salcedo et al., 2019; Gettelman et al., 
2019) due to a more realistic simulation of supercooled liquid droplets 
and associated cloud optical depths that were biased low commonly 
in CMIP5 models (McCoy et  al., 2014a, b). Because the negative 
cloud optical depth feedback occurs due to ‘brightening’ of clouds via 
phase change from ice to liquid cloud particles in response to surface 
warming (Cesana and Storelvmo, 2017), the extratropical cloud 
shortwave feedback tends to be less negative or even slightly positive

in models with reduced errors (Bjordal et al., 2020; Zelinka et al., 2020). 
The assessment of cloud feedbacks in Section  7.4.2.4 incorporates 
estimates from these improved ESMs. Yet, there still remain other 
shared model errors, such as in the subtropical low-clouds (Calisto 
et al., 2014) and tropical anvil clouds (Mauritsen and Stevens, 2015), 
hampering an assessment of feedbacks associated with these cloud 
regimes based only on ESMs (Section 7.4.2.4).
7.4.3 Dependence of Feedbacks on Climate Mean State
In the standard framework of forcings and feedbacks (Section 7.4.1 
and Box 7.1), the approximation is made that the strength of climate 
feedbacks is independent of the background global mean surface 
temperature. More generally, the individual feedback parameters, 
αx, are often assumed to be constant over a range of climate states, including those reconstructed from the past (encompassing a range 
of states warmer and colder than today, with varying continental

geographies) or projected for the future. If this approximation holds, 
then the equilibrium global surface temperature response to a fixed 
radiative forcing will be constant, regardless of the climate state 
to which that forcing is applied.
This approximation will break down if climate feedbacks are not 
constant, but instead vary as a function of, for example, background 
temperature (Roe and Baker, 2007; Zaliapin and Ghil, 2010; Roe and 
Armour, 2011; Bloch-Johnson et al., 2015), continental configuration 
(Farnsworth et al., 2019), or configuration of ice sheets (Yoshimori 
et al., 2009). If the real climate system exhibits this state-dependence, 
then the future equilibrium temperature change in response to 
large forcing may be different from that inferred using the standard 
framework, and/or different to that inferred from paleoclimates. Such 
considerations are important for the assessment of ECS (Section 7.5).

Climate models generally include representations of feedbacks that 
allow state-dependent behaviour, and so model results may also 
differ from the predictions from the standard framework.
In AR5 (Boucher et  al., 2013), there was a  recognition that climate 
feedbacks could be state-dependent (Colman and McAvaney, 2009), 
but modelling studies that explored this (e.g., Manabe and Bryan, 1985; 
Voss and Mikolajewicz, 2001; Stouffer and Manabe, 2003; Hansen et al., 
2005b) were not assessed in detail. Also in AR5 (Masson-Delmotte 
et  al.,  2013), it was assessed that some models exhibited weaker 
sensitivity to Last Glacial Maximum (LGM; Cross-Chapter Box  2.1) 
forcing than to 4×CO 2 forcing, due to state-dependence in shortwave 
cloud feedbacks.
Figure 7.1 0 | Global mean climate feedbacks estimated in abrupt4xCO2 simulations of 29 CMIP5 models (light blue) and 49 CMIP6 models (orange),

compared with those assessed in this Report (red).  Individual feedbacks for CMIP models are averaged across six radiative kernels as computed in Zelinka et al. (2020). 
The white line, black box and vertical line indicate the mean, 66% and 90% ranges, respectively. The shading represents the probability distribution across the full range of GCM/
ESM values and for the 2.5–97.5 percentile range of the AR6 normal distribution. The unit is W m–2 °C–1. Feedbacks associated with biogeophysical and non-CO 2 biogeochemical 
processes are assessed in AR6, but they are not explicitly estimated from general circulation models (GCMs)/Earth system models (ESMs) in CMIP5 and CMIP6. Further details 
on data sources and processing are available in the chapter data table (Table 7.SM.14).

980
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Here, recent evidence for state-dependence in feedbacks from 
modelling studies (Section  7.4.3.1) and from the paleoclimate 
record (Section  7.4.3.2) are assessed, with an overall assessment in 
Section 7.4.3.3. The focus is on temperature-dependence of feedbacks 
when the system is in equilibrium with the forcing; evidence for transient 
changes in the net feedback parameter associated with evolving spatial 
patterns of warming is assessed separately in Section 7.4.4.
7.4.3.1 State-dependence of Feedbacks in Models
There are several modelling studies since AR5 in which ESMs 
of varying complexity have been used to explore temperature 
dependence of feedbacks, either under modern (Hansen et al., 2013; 
Jonko et  al., 2013; Meraner et  al., 2013; Good et  al., 2015; Duan 
et al., 2019; Mauritsen et al., 2019; Rohrschneider et al., 2019; Stolpe

et al., 2019; Bloch-Johnson et al., 2020; Rugenstein et al., 2020) or 
paleo (Caballero and Huber, 2013; Zhu et al., 2019a) climate conditions, 
typically by carrying out multiple simulations across successive CO 2 
doublings. A  non-linear temperature response to these successive 
doublings may be partly due to forcing that increases more (or less) 
than expected from a purely logarithmic dependence (Section 7.3.2; 
Etminan et  al., 2016), and partly due to state-dependence in 
feedbacks; however, not all modelling studies have partitioned the 
non-linearities in temperature response between these two effects. 
Nonetheless, there is general agreement among ESMs that the net 
feedback parameter, α, increases (i.e., becomes less negative) as 
temperature increases from pre-industrial levels (i.e.,  sensitivity to 
forcing increases as temperature increases; e.g., Meraner et al., 2013; 
see Figure 7.11). The associated increase in sensitivity to forcing is,

in most models, due to the water vapour (Section 7.4.2.2) and cloud 
(Section  7.4.2.4) feedback parameters increasing with warming 
(Caballero and Huber, 2013; Meraner et al., 2013; Zhu et al., 2019a; 
Rugenstein et  al., 2020; Sherwood et  al., 2020). These changes are 
offset partially by the surface-albedo feedback parameter decreasing 
(Jonko et  al., 2013; Meraner et  al., 2013; Rugenstein et  al., 2020), 
as a consequence of a reduced amount of snow and sea ice cover 
in a much warmer climate. At the same time, there is little change 
in the Planck response (Section 7.4.2.1), which has been shown in one 
model to be due to competing effects from increasing Planck emission 
at warmer temperatures and decreasing planetary emissivity due to 
increased CO 2 and water vapour (Mauritsen et al., 2019). Analysis of 
the spatial patterns of the non-linearities in temperature response 
(Good et al., 2015) suggests that these patterns are linked to a reduced

weakening of the AMOC, and changes to evapotranspiration. The 
temperature dependence of α is also found in model simulations of 
high-CO 2 paleoclimates (Caballero and Huber, 2013; Zhu et al., 2019a). 
The temperature dependence is not only evident at very high CO 2 
concentrations in excess of 4×CO 2, but also apparent in the difference 
in temperature response to a  2×CO 2 forcing compared with to 
a 4×CO 2 forcing (Mauritsen et al., 2019; Rugenstein et al., 2020), and 
as such is relevant for interpreting century-scale climate projections.
Despite the general agreement that α increases as temperature 
increases from pre-industrial levels (Figure  7.11), other modelling 
studies have found the opposite (Duan et  al., 2019; Stolpe et  al., 
2019). Modelling studies exploring state-dependence in climates 
colder than today, including in cold paleoclimates such as the LGM, provide conflicting evidence of either decreased (Yoshimori

et al., 2011) or increased (Kutzbach et al., 2013; Stolpe et al., 2019) 
temperature response per unit forcing during cold climates compared 
to the modern era.
In contrast to most ESMs, the majority of Earth system models of 
intermediate complexity (EMICs) do not exhibit state-dependence, 
or have a  net feedback parameter that decreases with increasing 
temperature (Pfister and Stocker, 2017). This is unsurprising since 
EMICs usually do not include process-based representations of 
water-vapour and cloud feedbacks. Although this shows that care 
must be taken when interpreting results from current generation 
EMICs, Pfister and Stocker (2017) also suggest that non-linearities 
in feedbacks can take a long time to emerge in model simulations 
due to slow adjustment time scales associated with the ocean; 
longer simulations also allow better estimates of equilibrium 
warming (Bloch-Johnson et al., 2020). This implies that multi-century

simulations (Rugenstein et  al., 2020) could increase confidence in 
ESM studies examining state-dependence.
The possibility of more substantial changes in climate feedbacks, 
sometimes accompanied by hysteresis and/or irreversibility, has been 
suggested from some theoretical and modelling studies. It has 
been postulated that such changes could occur on a global scale and 
across relatively narrow temperature changes (Popp et al., 2016; von 
der Heydt and Ashwin, 2016; Steffen et al., 2018; Schneider et al., 
2019; Ashwin and von der Heydt, 2020; Bjordal et al., 2020). However, 
the associated mechanisms are highly uncertain, and as such there is 
low confidence  as to whether such behaviour exists at all, and in the 
temperature thresholds at which it might occur.
Overall, the modelling evidence indicates that there is medium 
confidence that the net feedback parameter, α, increases (i.e., becomes 
less negative) with increasing temperature (i.e., that sensitivity

to forcing increases with increasing temperature), under global 
surface background temperatures at least up to 40°C (Meraner 
et  al., 2013; Seeley and Jeevanjee, 2021), and medium confidence  
that this temperature dependence primarily derives from increases 
in the water-vapour and shortwave cloud feedbacks. This assessment 
is further supported by recent analysis of CMIP6 model simulations 
(Bloch-Johnson et al., 2020) in the framework of nonlinMIP (Good 
et  al., 2016), which showed that out of 10 CMIP6 models, seven 
of them showed an increase of the net feedback parameter with 
temperature, primarily due to the water-vapour feedback.
7.4.3.2 State-dependence of Feedbacks in 
the Paleoclimate Proxy Record
Several studies have estimated ECS from observations of the 
glacial–interglacial cycles of the last approximately 2 million years, 
and found a  state-dependence, with more-negative α (i.e.,  lower 
sensitivity to forcing) during colder periods of the cycles and

less-negative α during warmer periods (von der Heydt et al., 2014; 
Köhler et al., 2015, 2017; Friedrich et al., 2016; Royer, 2016; Snyder, 
2019); see summaries in Skinner (2012) and von der Heydt et  al. 
(2016). However, the nature of the state-dependence derived from 
these observations is dependent on the assumed ice-sheet forcing

981
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77(Köhler et  al., 2015; Stap et  al., 2019), which is not well known, 
due to a  relative lack of proxy indicators of ice-sheet extent and 
distribution prior to the LGM (Cross-Chapter Box 2.1). Furthermore, 
many of these glacial–interglacial studies estimate a  very strong 
temperature-dependence of α (Figure 7.11) that is hard to reconcile 
with the other lines of evidence, including proxy estimates from 
warmer paleoclimates. However, if the analysis excludes time periods 
when the temperature and CO 2 data are not well correlated, which 
occurs in general at times when sea level is falling and obliquity 
is decreasing, the state-dependence reduces (Köhler et  al., 2018). 
Despite these uncertainties, due to the agreement in the sign of the 
temperature-dependence from all these studies, there is medium 
confidence  from the paleoclimate proxy record that the net feedback

parameter, α, was less negative in the warm periods than in the cold 
periods of the glacial–interglacial cycles.
Paleoclimate proxy evidence from past high-CO 2 time periods much 
warmer than present (the early Eocene and Paleocene–Eocene 
Thermal Maximum, PETM; Cross-Chapter Box  2.1) show that the 
feedback parameter increases as temperature increases (Anagnostou 
et  al., 2016, 2020; Shaffer et  al., 2016). However, such 
temperature-dependence of feedbacks was not found in the warm 
Pliocene relative to the cooler Pleistocene (Martínez-Botí et al., 2015), 
although the temperature changes are relatively small at this time, 
making temperature-dependence challenging to detect given the 
uncertainties in reconstructing global mean temperature and forcing. 
Overall, the paleoclimate proxy record provides medium confidence  
that the net feedback parameter, α, was less negative in these past 
warm periods than in the present day.
7.4.3.3 Synthesis of State-dependence of Feedbacks

from Modelling and Paleoclimate Records
Overall, independent lines of evidence from models (Section 7.4.3.1) 
and from the paleoclimate proxy record (Section  7.4.3.2) lead to 
high confidence  that the net feedback parameter, α, increases 
(i.e., becomes less negative) as temperature increases; that is, 
that sensitivity to forcing increases as temperature increases 
(Figure  7.11). This temperature-dependence should be considered 
when estimating ECS from ESM simulations in which CO 2 is 
quadrupled (Section 7.5.5) or from paleoclimate observations from 
past time periods colder or warmer than today (Section  7.5.4). 
Although individual lines of evidence give only medium confidence , 
the overall high confidence comes from the multiple models that 
show the same sign of the temperature-dependence of α, the 
general agreement in evidence from the paleo proxy and modelling 
lines of evidence, and the agreement between proxy evidence from

both cold and warm past climates. However, due to the large range 
in estimates of the magnitude of the temperature-dependence of 
α across studies (Figure  7.11), a  quantitative assessment cannot 
currently be given, which provides a  challenge for including this 
temperature-dependence in emulator-based future projections 
(Cross-Chapter Box 7.1). Greater confidence in the modelling lines of 
evidence could be obtained from simulations carried out for several 
hundreds of years (Rugenstein et al., 2020), substantially longer than 
in many studies, and from more models carrying out simulations at 
multiple CO 2 concentrations. Greater confidence in the paleoclimate lines of evidence would be obtained from stronger constraints on 
atmospheric CO 2 concentrations, ice-sheet forcing, and temperatures, 
during past warm climates.
7.4.4 Relationship Between Feedbacks 
and Temperature Patterns
The large-scale patterns of surface warming in observations since

the 19th  century (Section  2.3.1) and climate model simulations 
(Section  4.3.1 and Figure  7.12a) share several common features. 
In particular, surface warming in the Arctic is greater than for the 
global average and greater than in the Southern Hemisphere (SH) 
high latitudes; and surface warming is generally greater over land 
than over the ocean. Observations and climate model simulations 
also show some notable differences. ESMs generally simulate 
a weakening of the equatorial Pacific Ocean zonal (east–west) SST 
gradient on multi-decadal to centennial time scales, with greater 
warming in the east than the west, but this trend has not been seen 
in observations (Section 9.2.1 and Figure 2.11b).
Chapter 4 (Section 4.5.1) discusses patterns of surface warming for 
21st-century climate projections under the Shared Socio-economic 
Pathways (SSP) scenarios. Chapter  9 (Section  9.2.1) assesses 
historical SST trends and the ability of coupled ESMs to replicate the

observed changes. Chapter 4 (Section 4.5.1) discusses the processes 
that cause the land to warm more than the ocean (land–ocean 
warming contrast). This section assesses process understanding of 
the large-scale patterns of surface temperature response from the Temperature-dependence of α from ESMs and paleoclimate proxies 
–10 0 10 20
Global mean temperature anomaly relative to pre-industrial (°C)–2.0–1.5–1.0–0.50.0Feedback parameter, α (Wm -2°C-1) 
–2.0–1.5–1.0–0.50.0
assessed very likely range of α
paleo proxies
ESM simulationsincreasing climate sensitivity
Figure 7. 11 | Feedback parameter,  α (W m–2 °C–1), as a function of global 
mean surface air temperature anomaly relative to pre-industrial, for ESM 
simulations (red circles and lines)  (Caballero and Huber, 2013; Jonko et al., 2013; 
Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; 
Stolpe et al., 2019; Zhu et al., 2019a), and derived from paleoclimate proxies

(grey squares and lines)  (von der Heydt et al., 2014; Anagnostou et al., 2016, 
2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Köhler et al., 2017; 
Snyder, 2019; Stap et al., 2019). For the ESM simulations, the value on the x-axis 
refers to the average of the temperature before and after the system has equilibrated 
to a forcing (in most cases a CO 2 doubling), and is expressed as an anomaly relative 
to an associated pre-industrial global mean temperature from that model. The light 
blue shaded square extends across the assessed range of α (Table 7.10) on the y-axis, 
and on the x-axis extends across the approximate temperature range over which the 
assessment of α is based (taken as from zero to the assessed central value of ECS; 
see Table 7.13). Further details on data sources and processing are available in the 
chapter data table (Table 7.SM.14).

982
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7perspective of a regional energy budget. It then assesses evidence 
from the paleoclimate proxy record for patterns of surface warming 
during past time periods associated with changes in atmospheric CO 2 
concentrations. Finally, it assesses how radiative feedbacks depend 
on the spatial pattern of surface temperature, and thus how they 
can change in magnitude as that pattern evolves over time, with 
implications for the assessment of ECS based on historical warming 
(Sections 7.4.4.3 and 7.5.2.1).
7.4.4.1 Polar Amplification
Polar amplification describes the phenomenon where surface 
temperature change at high latitudes exceeds the global average 
surface temperature change in response to radiative forcing of the 
climate system. Arctic amplification, often defined as the ratio of 
Arctic to global surface warming, is a ubiquitous emergent feature

of climate model simulations (Section  4.5.1 and Figure  7.12a; 
Holland and Bitz, 2003; Pithan and Mauritsen, 2014) and is also 
seen in observations (Section 2.3.1). However, both climate models 
and observations show relatively less warming of the SH high 
latitudes compared to the Northern Hemisphere (NH) high latitudes 
over the historical record (Section  2.3.1), a  characteristic that is 
projected to continue over the 21st  century (Section  4.5.1). Since 
AR5 there is a  much-improved understanding of the processes 
that drive polar amplification in the NH and delay its emergence 
in the SH (Section 7.4.4.1.1). Furthermore, the paleoclimate record 
provides evidence for polar amplification from multiple time periods 
associated with changes in CO 2 (Hollis et  al., 2019; Cleator et  al., 
2020; McClymont et  al., 2020; Tierney et  al., 2020b), and allows 
an evaluation of polar amplification in model simulations of these

periods (Section 7.4.4.1.2). Research since AR5 identifies changes in 
the degree of polar amplification over time, particularly in the SH, 
as a key factor affecting how radiative feedbacks may evolve in the 
future (Section 7.4.4.3).
7.4.4.1.1 Critical processes driving polar amplification
Several processes contribute to polar amplification under greenhouse 
gas forcing, including the loss of sea ice and snow (an amplifying 
surface-albedo feedback), the confinement of warming to near the 
surface in the polar atmosphere (an amplifying lapse-rate feedback), 
and increases in poleward atmospheric and oceanic heat transport 
(Pithan and Mauritsen, 2014; Goosse et al., 2018; Dai et al., 2019; 
Feldl et  al., 2020). Modelling and process studies since AR5 have 
led to an improved understanding of the combined effect of these 
different processes in driving polar amplification and how they differ 
between the hemispheres.

Idealized modelling studies suggest that polar amplification would 
occur even in the absence of any amplifying polar surface-albedo 
or lapse-rate feedbacks owing to changes in poleward atmospheric 
heat transport under global warming (Hall, 2004; Alexeev et al., 2005; 
Graversen and Wang, 2009; Alexeev and Jackson, 2013; Graversen 
et al., 2014; Roe et al., 2015; Merlis and Henry, 2018; Armour et al., 
2019). Poleward heat transport changes reflect compensating 
changes in the transport of latent energy (moisture) and dry-static 
energy (sum of sensible and potential energy) by atmospheric circulations (Alexeev et  al., 2005; Held and Soden, 2006; Hwang 
and Frierson, 2010; Hwang et al., 2011; Kay et al., 2012; Huang and 
Zhang, 2014; Feldl et al., 2017a; Donohoe et al., 2020). ESMs project 
that within the mid-latitudes, where eddies dominate the heat 
transport, an increase in poleward latent energy transport arises from

an increase in the equator-to-pole gradient in atmospheric moisture 
with global warming, with moisture in the tropics increasing more 
than at the poles as described by the Clausius–Clapeyron relation 
(Section  8.2). This change is partially compensated by a  decrease 
in dry-static energy transport arising from a  weakening of the 
equator-to-pole temperature gradient as the polar regions warm 
more than the tropics.
Energy balance models that approximate atmospheric heat transport 
in terms of a  diffusive flux down the meridional gradient of 
near-surface moist static energy (sum of dry-static and latent energy) 
are able to reproduce the atmospheric heat transport changes seen 
within ESMs (Flannery, 1984; Hwang and Frierson, 2010; Hwang 
et al., 2011; Rose et al., 2014; Roe et al., 2015; Merlis and Henry, 
2018), including the partitioning of latent and dry-static energy 
transports (Siler et  al., 2018b; Armour et  al., 2019). These models

suggest that polar amplification is driven by enhanced poleward 
latent heat transport and that the magnitude of polar amplification 
can be enhanced or diminished by the latitudinal structure of radiative 
feedbacks. Amplifying polar feedbacks enhance polar warming and 
in turn cause a decrease in the dry-static energy transport to high 
latitudes (Alexeev and Jackson, 2013; Rose et al., 2014; Roe et al., 
2015; Bonan et  al., 2018; Merlis and Henry, 2018; Armour et  al., 
2019; Russotto and Biasutti, 2020). Poleward latent heat transport 
changes act to favour polar amplification and inhibit tropical 
amplification (Armour et  al., 2019), resulting in a  strongly polar-
amplified warming response to polar forcing and a more latitudinally 
uniform warming response to tropical forcing within ESMs (Alexeev 
et al., 2005; Rose et al., 2014; Stuecker et al., 2018). The important 
role for poleward latent energy transport in polar amplification is

supported by studies of atmospheric reanalyses and ESMs showing 
that episodic increases in latent heat transport into the Arctic can 
enhance surface downwelling radiation and drive sea ice loss on 
sub-seasonal time scales (Woods and Caballero, 2016; Gong et al., 
2017; Lee et  al., 2017; B. Luo et  al., 2017), however this may be 
a smaller driver of sea ice variability than atmospheric temperature 
fluctuations (Olonscheck et al., 2019).
Regional energy budget analyses are commonly used to diagnose the 
relative contributions of radiative feedbacks and energy fluxes to polar 
amplification as projected by ESMs under increased CO 2 concentrations 
(Figure  7.12; Feldl and Roe, 2013; Pithan and Mauritsen, 2014; 
Goosse et al., 2018; Stuecker et al., 2018). These analyses suggest that 
a primary cause of amplified Arctic warming in ESMs is the latitudinal 
structure of radiative feedbacks, which warm the Arctic more than

the tropics (Figure  7.12b), and enhanced latent energy transport 
into the Arctic. That net atmospheric heat transport into the Arctic 
does not change substantially within ESMs, on average, under CO 2 
forcing (Figure 7.12b) reflects a compensating decrease in poleward 
dry-static energy transport as a response to polar amplified warming 
(Hwang et al., 2011; Armour et al., 2019; Donohoe et al., 2020). The 
latitudinal structure of radiative feedbacks primarily reflects that of

983
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77
(c) Tropics warming contributions (ºC)(b) Arctic warming contributions (ºC)
(d) Antarctic warming contributions (ºC)0Near-surface
air temperature
change (ºC)10
5
Arctic warming contributions from individual
radiative feedbacks (ºC)
Tropics warming contributions from individual
Antarctic warming contributions from individual
radiative feedbacks (ºC)(a) Contributions to regional warming in CMIP6 ESMs in response to CO2 quadrupling
moistdry
Antarctic warming
Effective radiative forcing
Ocean heat uptake
Atmospheric heat transport
Radiative feedbacksAntarctic warming
Radiative feedbacks
moist
moistdry0 2 4 6 8
0 2 4 6 8
0 2 4 6 8
Water-vapour + lapse-rate
Water-vapour
Lapse-rate
Surface-albedo
Net cloud
Water-vapour + lapse-rateWater-vapour
Net cloud

Net cloudAntarctic warming
Radiative feedbacks0 4 8 12 16
0 4 8 12 16
0 4 8 12 16
Figure 7.12  | Contributions of effective radiative forcing, ocean heat uptake, atmospheric heat transport, and radiative feedbacks to regional surface 
temperature changes at year 100 of abrupt4xCO2  simulations of CMIP6 Earth system models (ESMs).

984
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7the surface-albedo and lapse-rate feedbacks,  which preferentially 
warm the Arctic (Graversen et al., 2014; Pithan and Mauritsen, 2014; 
Goosse et al., 2018). Latitudinal structure in the lapse-rate feedback 
reflects weak radiative damping to space with surface warming in 
polar regions, where atmospheric warming is constrained to the 
lower troposphere owing to stably stratified conditions, and strong 
radiative damping in the tropics, where warming is enhanced in the 
upper troposphere owing to moist convective processes. This is only 
partially compensated by latitudinal structure in the water-vapour 
feedback (Taylor et al., 2013), which favours tropical warming (Pithan 
and Mauritsen, 2014). While cloud feedbacks have been found to 
play little role in Arctic amplification in CMIP5 models (Pithan and 
Mauritsen, 2014; Goosse et  al., 2018; Figure  7.12b), less-negative

cloud feedbacks at high latitude, as seen within some CMIP6 models 
(Zelinka et  al., 2020), tend to favour stronger polar amplification 
(Dong et al., 2020). A weaker Planck response at high latitudes, owing 
to less efficient radiative damping where surface and atmospheric 
temperatures are lower, also contributes to polar amplification (Pithan 
and Mauritsen, 2014). The effective radiative forcing of CO 2 is larger in 
the tropics than at high latitudes, suggesting that warming would be 
tropically amplified if not for radiative feedbacks and poleward latent 
heat transport changes (Figure 7.12b–d; Stuecker et al., 2018).
While the contributions to regional warming can be diagnosed within 
ESM simulations (Figure  7.12), assessment of the underlying role of 
individual factors is limited by interactions inherent to the coupled 
climate system. For example, polar feedback processes are coupled and 
influenced by warming at lower latitudes (Screen et al., 2012; Alexeev

and Jackson, 2013; Graversen et  al., 2014; Graversen and Burtu, 
2016; Rose and Rencurrel, 2016; Feldl et al., 2017a, 2020; Yoshimori 
et  al.,  2017; Garuba et  al., 2018; Po-Chedley et  al., 2018b; Stuecker 
et al., 2018; Dai et al., 2019), while atmospheric heat transport changes 
are in turn influenced by the latitudinal structure of regional feedbacks, 
radiative forcing, and ocean heat uptake (Hwang et al., 2011; Zelinka 
and Hartmann, 2012; Feldl and Roe, 2013; Huang and Zhang, 2014; 
Merlis, 2014; Rose et al., 2014; Roe et al., 2015; Feldl et al., 2017b; 
Stuecker et al., 2018; Armour et al., 2019). The use of different feedback 
definitions, such as a lapse-rate feedback partitioned into upper and 
lower tropospheric components (Feldl et  al., 2020) or including the 
influence of water vapour at constant relative humidity (Held and Shell, 
2012; Section 7.4.2), would also change the interpretation of which 
feedbacks contribute most to polar amplification.

The energy budget analyses (Figure 7.12) suggest that greater surface 
warming in the Arctic than the Antarctic under greenhouse gas forcing 
arises from two main processes. The first is large surface heat uptake 
in the Southern Ocean (Figure 7.12c) driven by the upwelling of deep waters that have not yet felt the effects of the radiative forcing; the 
heat taken up is predominantly transported away from Antarctica 
by northward-flowing surface waters (Section  9.2.1; Marshall 
et al., 2015; Armour et al., 2016). Strong surface heat uptake also 
occurs in the subpolar North Atlantic Ocean under global warming 
(Section 9.2.1). However, this heat is partially transported northward 
into the Arctic, which leads to increased heat fluxes into the Arctic 
atmosphere (Figure 7.12b; Rugenstein et al., 2013; Jungclaus et al., 
2014; Koenigk and Brodeau, 2014; Marshall et al., 2015; Nummelin 
et al., 2017; Singh et al., 2017; Oldenburg et al., 2018). The second

main process contributing to differences in Arctic and Antarctic 
warming is the asymmetry in radiative feedbacks between the poles 
(Yoshimori et al., 2017; Goosse et al., 2018). This primarily reflects the 
weaker lapse-rate and surface-albedo feedbacks and more-negative 
cloud feedbacks in the SH high latitudes (Figure  7.12). However, 
note the SH cloud feedbacks are uncertain due to possible biases in 
the treatment of mixed phase clouds (Hyder et al., 2018). Idealized 
modelling suggests that the asymmetry in the polar lapse-rate 
feedback arises from the height of the Antarctic Ice Sheet precluding 
the formation of deep atmospheric inversions that are necessary 
to produce the stronger positive lapse-rate feedbacks seen in the 
Arctic (Salzmann, 2017; Hahn et al., 2020). ESM projections of the 
equilibrium response to CO 2 forcing show polar amplification in both 
hemispheres, but generally with less warming in the Antarctic than

the Arctic (C. Li et al., 2013; Yoshimori et al., 2017).
Because multiple processes contribute to polar amplification, it is 
a robust feature of the projected long-term response to greenhouse 
gas forcing in both hemispheres. At the same time, contributions 
from multiple processes make projections of the magnitude of polar 
warming inherently more uncertain than global mean warming 
(Holland and Bitz, 2003; Roe et  al., 2015; Bonan et  al., 2018; 
Stuecker et al., 2018). The magnitude of Arctic amplification ranges 
from a  factor of two to four in ESM projections of 21st-century 
warming (Section  4.5.1). While uncertainty in both global and 
tropical warming  under greenhouse gas forcing is dominated by 
cloud feedbacks (Section  7.5.7; Vial et  al., 2013), uncertainty in 
polar warming arises from polar surface-albedo, lapse-rate, and 
cloud feedbacks, changes in atmospheric and oceanic poleward heat 
transport, and ocean heat uptake (Hwang et al., 2011; Mahlstein and

Knutti, 2011; Pithan and Mauritsen, 2014; Bonan et al., 2018).
The magnitude of polar amplification also depends on the type of 
radiative forcing applied (Section  4.5.1.1; Stjern et  al., 2019), with 
Chapter  6 (Section  6.4.3) discussing changes in sulphate aerosol 
emissions and the deposition of black carbon aerosols on ice and 
snow as potential drivers of amplified Arctic warming. The timing of Figure 7.12 (continued): (a)  Pattern of near-surface air temperature change. (b–d)  Contributions to net Arctic (>60°N), tropical (30°S–30°N), and Antarctic (<60°S) warming 
calculated by dividing regional-average energy inputs by the magnitude of the regional-average Planck response. The contributions from radiative forcing, changes in moist, 
dry-static, and total atmospheric energy transport, ocean heat uptake, and radiative feedbacks (orange bars) all sum to the value of net warming (grey bar). Inset shows regional

warming contributions associated with individual feedbacks, all summing to the total feedback contribution. Uncertainties (represented by black whiskers) show the interquartile 
range (25th and 75th percentiles) across models. The warming contributions (units of °C) for each process are diagnosed by calculating the energy flux (units of W m–2) that each 
process contributes to the atmosphere over a given region, either at the top-of-atmosphere or surface, then dividing that energy flux by the magnitude of the regional Planck 
response (around 3.2 W m–2 °C–1 but varying with region). By construction, the individual warming contributions sum to the total warming in each region. Radiative kernel methods 
(Section 7.4.1) are used to decompose the net energy input from radiative feedbacks into contributions from changes in atmospheric water vapour, lapse rate, clouds, and surface

albedo (Zelinka et al. (2020) using the Huang et al. (2017) radiative kernel). The CMIP6 models included are those analysed by Zelinka et al. (2020) and the warming contribution 
analysis is based on that of Goosse et al. (2018). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

985
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77the emergence of SH polar amplification remains uncertain due to 
insufficient knowledge of the time scales associated with Southern 
Ocean warming and the response to surface wind and freshwater 
forcing (Bintanja et al., 2013; Kostov et al., 2017, 2018; Pauling et al., 
2017; Purich et  al., 2018). ESM simulations indicate that freshwater 
input from melting ice shelves could reduce Southern Ocean warming 
by up to several tenths of a  °C over the 21st  century by increasing 
stratification of the surface ocean around Antarctica ( low confidence  
due to medium agreement  but limited evidence ) (Sections 7.4.2.6 
and 9.2.1, and Box 9.3; Bronselaer et al., 2018; Golledge et al., 2019; 
Lago and England, 2019). However, even a large reduction in the Atlantic 
Meridional Overturning Circulation (AMOC) and associated northward 
heat transport due, for instance, to greatly increased freshwater runoff

from Greenland would be insufficient to eliminate Arctic amplification 
(medium confidence  based on medium agreement  and medium 
evidence ) (Liu et al., 2017; Y . Liu et al., 2018; Wen et al., 2018).
Arctic amplification has a distinct seasonality with a peak in early 
winter (November to January) owing to sea ice loss and associated 
increases in heat fluxes from the ocean to the atmosphere resulting 
in strong near-surface warming (Pithan and Mauritsen, 2014; Dai 
et al., 2019). Surface warming may be further amplified by positive 
cloud and lapse-rate feedbacks in autumn and winter (Burt et  al., 
2016; Morrison et al., 2019; Hahn et al., 2020). Arctic amplification 
is weak in summer owing to surface temperatures remaining stable 
as excess energy goes into thinning the summertime sea ice cover, 
which remains at the melting point, or into the ocean mixed layer. 
Arctic amplification can also be interpreted through changes in

the surface energy budget (Burt et al., 2016; Woods and Caballero, 
2016; Boeke and Taylor, 2018; Kim et  al., 2019), however such 
analyses are complicated by the finding that a  large portion of 
the changes in downward longwave radiation can be attributed to 
the lower troposphere warming along with the surface itself (Vargas 
Zeppetello et al., 2019).
7.4.4.1.2 Polar amplification from proxies and models during 
past climates associated with CO 2 change
Paleoclimate proxy data provide observational evidence of large-scale 
patterns of surface warming in response to past forcings, and allow an 
evaluation of the modelled response to these forcings (Sections 3.3.1.1 
and 3.8.2.1). In particular, paleoclimate data provide evidence for 
long-term changes in polar amplification during time periods in which 
the primary forcing was a change in atmospheric CO 2, although data 
sparsity means that for some time periods this evidence may be limited

to a  single hemisphere or ocean basin, or the evidence may come 
primarily from the mid-latitudes as opposed to the polar regions. In 
this context, there has been a modelling and data focus on the Last 
Glacial Maximum (LGM) in the context of PMIP4 (Cleator et al., 2020; 
Tierney et al., 2020b; Kageyama et al., 2021), the mid-Pliocene Warm 
Period (MPWP) in the context of PlioMIP2 (Cross-Chapter Box  2.4; 
Salzmann et  al., 2013; Haywood et  al., 2020; McClymont et  al., 
2020), the Early Eocene Climatic Optimum (EECO) in the context of 
DeepMIP (Hollis et al., 2019; Lunt et al., 2021), and there is growing 
interest in the Miocene (Goldner et al., 2014b; Steinthorsdottir et al., 
2021; for definitions of time periods see Cross-Chapter Box  2.1). 
For all these time periods, in addition to the CO 2 forcing there are long-term feedbacks associated with ice sheets (Section 7.4.2.6), and 
in particular for the Early Eocene there is a forcing associated with

paleogeographic change (Farnsworth et al., 2019). However, because 
these non-CO 2 effects can all be included as boundary conditions in 
model simulations, these time periods allow an assessment of the 
patterns of modelled response to known forcing (although uncertainty 
in the forcing increases further back in time). Because these changes 
to boundary conditions can be complex to implement in models, 
and because long simulations (typically longer than 500 years) are 
required to approach equilibrium, these simulations have been carried 
out mostly by pre-CMIP6 models, with relatively few (or none for the 
Early Eocene) fully coupled CMIP6 models in the ensembles.
At the time of AR5, polar amplification was evident in proxy 
reconstructions of paleoclimate sea surface temperature (SST) 
and surface air temperature (SAT) from the LGM, MPWP and the 
Early Eocene, but uncertainties associated with proxy calibrations

(Waelbroeck et  al., 2009; Dowsett et  al., 2012; Lunt et  al., 2012a) 
and the role of orbital forcing (for the MPWP; Lisiecki and Raymo, 
2005) meant that the degree of polar amplification during these 
time periods was not accurately known. Furthermore, although some 
models (CCSM3; Winguth et al., 2010; Huber and Caballero, 2011) 
at that time were able to reproduce the strong polar amplification 
implied by temperature proxies of the Early Eocene, this was achieved 
at higher CO 2 concentrations (>2000 ppm) than those indicated by 
CO 2 proxies (<1500 ppm; Beerling and Royer, 2011).
Since AR5 there has been progress in improving the accuracy of proxy 
temperature reconstructions of the LGM (Cleator et al., 2020; Tierney 
et  al., 2020b), the MPWP (McClymont et  al., 2020), and the Early 
Eocene (Hollis et al., 2019) time periods. In addition, reconstructions 
of the MPWP have been focused on a short time slice with an orbit

similar to modern-day (isotopic stage KM5C; Haywood et al., 2013, 
2016b). Furthermore, there are more robust constraints on CO 2 
concentrations from the MPWP (Martínez-Botí et  al., 2015; de la 
Vega et  al., 2020) and the Early Eocene (Anagnostou et  al., 2016, 
2020). As such, polar amplification during the LGM, MPWP , and Early 
Eocene time periods can now be better quantified than at the time 
of AR5, and the ability of climate models to reproduce this pattern 
can be better assessed; model-data comparisons for SAT and SST for 
these three time periods are shown in Figure 7.13.
Since AR5, there has been progress in the simulation of polar 
amplification by paleoclimate models of the Early Eocene. Initial 
work indicated that changes to model parameters associated with 
aerosols and/or clouds could increase simulated polar amplification 
and improve agreement between models and paleoclimate data 
(Kiehl and Shields, 2013; Sagoo et  al., 2013), but such parameter

changes were not physically based. In support of these initial findings, 
a more recent (CMIP5) climate model, that includes a process-based 
representation of cloud microphysics, exhibits polar amplification 
in better agreement with proxies when compared to the models 
assessed in AR5 (Zhu et al., 2019a). Since then, some other CMIP3 
and CMIP5 models in the DeepMIP multi-model ensemble (Lunt 
et  al., 2021) have obtained polar amplification for the EECO that 
is consistent with proxy indications of both polar amplification and 
CO 2. Although there is a lack of tropical proxy SAT estimates, both

986
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Polar ampliﬁcation in paleo proxies and models of the EECO, MPWP, and LGM
SAT (EECO - preindustrial)
-18.0 -14.0 -10.0 -6.0 -2.0 2.0 6.0 10.0 14.0 18.0
temperature anomaly (°C)60N
30N
EQ
30S
60S
90W 0 90E
(a)SAT (MPWP - preindustrial)
30N
EQ
30S
60S
90W 0 90E
(b)SAT (preindustrial - LGM)
30N
EQ
30S
60S
90W 0 90E
(c)
90S 60S 30S EQ 30N 60N 90N
latitude-1001020304050temperature anomaly (oC)DeepMIP models zonal mean (N=5)
DeepMIP ensemble zonal mean
DeepMIP ensemble site mean
EECO proxy sites (N=47)
EECO proxy sites mean
(d)SAT (MPWP - preindustrial)
latitude-505101520temperature anomaly (oC)PlioMIP models zonal mean (N=17)
PlioMIP ensemble zonal mean
PlioMIP ensemble site mean

MPWP proxy sites (N=8)
MPWP proxy sites mean
(e)SAT (preindustrial - LGM)
latitude-505101520temperature anomaly (oC)PMIP4 models zonal mean (N=9)
PMIP4 ensemble zonal mean
PMIP4 ensemble site mean
NH proxy amp: 4.2 oCNH model amp: 4.1 oCLGM proxy sites (N=98)
LGM proxy sites mean
(f)
SST (EECO - preindustrial)
latitude-10010203040temperature anomaly (oC)DeepMIP models zonal mean (N=5)
SH proxy amp: 20.0 oCSH model amp: 4.1 oC
NH proxy amp: 4.5 oCNH model amp: 1.7 oCEECO proxy sites (N=19)
(g)SST (MPWP - preindustrial)
latitude0510temperature anomaly (oC)PlioMIP models zonal mean (N=17)
SH proxy amp: 0.9 oCSH model amp: 0.1 oC
NH proxy amp: 2.5 oCNH model amp: 1.5 oCMPWP proxy sites (N=32)
(h)SST (preindustrial - LGM)

latitude0510temperature anomaly (oC)PMIP4 models zonal mean (N=9)
SH proxy amp: 0.1 oCSH model amp: 0.3 oC
NH proxy amp: 0.7 oCNH model amp: 1.1 oCLGM proxy sites (N=227)
(i)
30N
EQ
30S
60S
90W 0 90E
(j)SST (MPWP - preindustrial)
-4.50 -3.50 -2.50 -1.50 -0.50 0.50 1.50 2.50 3.50 4.50
30N
EQ
30S
60S
90W 0 90E
(k)SST (preindustrial - LGM)
30N
EQ
30S
60S
90W 0 90E
(l)
Figure 7.13  | Polar amplification in paleo proxies and models of the Early Eocene Climatic Optimum (EECO), the Mid-Pliocene Warm Period (MPWP) 
and the Last Glacial Maximum (LGM).

987
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77proxies and DeepMIP models show greater terrestrial warming 
in the high latitudes than the mid-latitudes in both hemispheres 
(Figure 7.13a,d). SST proxies also exhibit polar amplification in both 
hemispheres, but the magnitude of this polar amplification is too low 
in the models, in particular in the south-west Pacific (Figure 7.13g,j).
For the MPWP , model simulations are now in better agreement with 
proxies than at the time of AR5 (Haywood et  al., 2020; McClymont 
et  al., 2020). In particular, in the tropics new proxy reconstructions 
of SSTs are warmer and in better agreement with the models, due in 
part to the narrower time window in the proxy reconstructions. There 
is also better agreement at higher latitudes (primarily in the North 
Atlantic), due in part to the absence of some very warm proxy SSTs 
due to the narrower time window (McClymont et  al., 2020), and in

part to a modified representation of Arctic gateways in the most recent 
Pliocene model simulations (Otto-Bliesner et  al., 2017), which have 
resulted in warmer modelled SSTs in the North Atlantic (Haywood 
et  al., 2020). Furthermore, as for the Eocene, improvements in the 
representation of aerosol–cloud interactions have also led to improved 
model-data consistency at high latitudes (Feng et al., 2019). Although 
all PlioMIP2 models exhibit polar amplification of SAT, due to the 
relatively narrow time window there are insufficient terrestrial proxies 
to assess this (Figure 7.13b,e). However, polar SST amplification in the 
PlioMIP2 ensemble mean is in reasonably good agreement with that 
from SST proxies in the Northern Hemisphere (Figure 7.13h,k).
The Last Glacial Maximum (LGM) also gives an opportunity to 
evaluate model simulation of polar amplification under CO 2 forcing, 
albeit under colder conditions than today (Kageyama et al., 2021).

Terrestrial SAT and marine SST proxies exhibit clear polar amplification 
in the Northern Hemisphere, and the PMIP4 models capture this well 
(Figure  7.13c,f,i,l), particularly for SAT. There is less proxy data in 
the mid- to high latitudes of the Southern Hemisphere, but here the 
models exhibit polar amplification of both SST and SAT. LGM regional 
model-data agreement is also assessed in Chapter 3 (Section 3.8.2).
Overall, the proxy reconstructions give high confidence  that there was 
polar amplification in the LGM, MPWP and EECO, and this is further 
supported by model simulations of these time periods (Figure  7.13; 
Zhu et al., 2019a; Haywood et al., 2020; Kageyama et al., 2021; Lunt 
et al., 2021). For both the MPWP and EECO, models are more consistent 
with the temperature and CO 2 proxies than at the time of AR5 ( high 
confidence ). For the LGM Northern Hemisphere, which is the region with the most data and the time period with the least uncertainty in

model boundary conditions, polar amplification in the PMIP4 ensemble 
mean is in good agreement with the proxies, especially for SAT ( medium 
confidence ). Overall, the confidence in the ability of models to accurately 
simulate polar amplification is higher than at the time of AR5, but 
a more complete model evaluation could be carried out if there were 
more CMIP6 paleoclimate simulations included in the assessment.
7.4.4.1.3 Overall assessment of polar amplification
Based on mature process understanding of the roles of poleward 
latent heat transport and radiative feedbacks in polar warming, 
a high degree of agreement across a hierarchy of climate models, 
observational evidence, paleoclimate proxy records of past climates 
associated with CO 2 change, and ESM simulations of those past 
climates, there is high confidence  that polar amplification is a robust 
feature of the long-term response to greenhouse gas forcing in both

hemispheres. Stronger warming in the Arctic than the global average 
has already been observed (Section  2.3.1) and its causes are well 
understood. It is very likely  that the warming in the Arctic will be 
more pronounced than the global average over the 21st century ( high 
confidence ) (Section 4.5.1.1). This is supported by models’ improved 
ability to simulate polar amplification during past time periods, 
compared with at the time of AR5 ( high confidence ); although this is 
based on an assessment of mostly non-CMIP6 models.
Southern Ocean SSTs have been slow to warm over the instrumental 
period, with cooling since about 1980 owing to a  combination of 
upper-ocean freshening from ice-shelf melt, intensification of surface 
westerly winds from ozone depletion, and variability in ocean 
convection (Section 9.2.1). This stands in contrast to the equilibrium 
warming pattern either inferred from the proxy record or simulated

by ESMs under CO 2 forcing. There is high confidence  that the SH high 
latitudes will warm more than the tropics on centennial time scales 
as the climate equilibrates with radiative forcing and Southern Ocean 
heat uptake is reduced. However, there is only low confidence  that 
this feature will emerge this century.
7.4.4.2 Tropical Pacific Sea Surface Temperature Gradients
Research published since AR5 identifies changes in the tropical 
Pacific Ocean zonal SST gradient over time as a key factor affecting 
how radiative feedbacks may evolve in the future (Section 7.4.4.3). Figure 7.13 (continued): Temperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’) are shown for the high-CO 2 EECO and MPWP 
time periods, and for the low-CO 2 LGM (expressed as pre-industrial minus LGM). (a), (b) and (c)  Modelled near-surface air temperature anomalies for ensemble-mean

simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy 
near-surface air temperature anomalies (coloured circles). (d), (e) and (f)  Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey 
vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a–c), light-red lines show the modelled temperature anomaly 
for the individual models that make up each ensemble (LGM, N=9; MPWP , N=17; EECO, N=5). Black dashed lines show the average of the proxy values in each latitude band: 
90°S–30°S, 30°S–30°N, and 30°N–90°N. Red dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies.

Black and red dashed lines are only shown if there are five or more proxy points in that band. Mean differences between the 90°S/N to 30°S/N and 30°S to 30°N bands are 
quantified for the models and proxies in each plot. Panels (g), (h) and (i)  are like panels (d–f) but for sea surface temperature (SST) instead of near-surface air temperature. 
Panels (j), (k) and (l)  are like panels (a–c) but for SST instead of near-surface air temperature. For the EECO maps – (a) and (j) – the anomalies are relative to the zonal mean of 
the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al. (2018), (c) and 
(f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where

there are multiple proxy estimations at a single site, a mean is taken. Model ensembles are (a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame 
paleogeography, and carried out under CO 2 concentrations within the range assessed in Table 2.2, are shown); (b), (e), (h) and (k) PlioMIP; and (c), (f), (i) and (l) PMIP4. Further 

988
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7There is now a much-improved understanding of the processes that 
govern the tropical Pacific SST gradient (Section 7.4.4.2.1) and the 
paleoclimate record provides evidence for its equilibrium changes 
from time periods associated with changes in CO 2 (Section 7.4.4.2.2).
7.4.4.2.1 Critical processes determining changes in tropical 
Pacific sea surface temperature gradients
A weakening of the equatorial Pacific Ocean east–west SST gradient, 
with greater warming in the east than the west, is a common feature 
of the climate response to greenhouse gas forcing as projected by 
ESMs on centennial and longer time scales (e.g., Figure 7.14b; see 
Section 4.5.1). There are thought to be several factors contributing to 
this pattern. In the absence of any changes in atmospheric or oceanic 
circulations, the east–west surface temperature difference is theorized

to decrease owing to weaker evaporative damping, and thus greater 
warming in response to forcing, where climatological temperatures 
are lower in the eastern Pacific cold tongue (Xie et  al., 2010; Luo 
et  al., 2015). Within atmospheric ESMs coupled to a  mixed-layer 
ocean, this gradient in damping has been linked to the rate of change 
with warming of the saturation specific humidity, which is set by the 
Clausius–Clapeyron relation (Merlis and Schneider, 2011). Gradients 
in low-cloud feedbacks may also favour eastern equatorial Pacific 
warming (DiNezio et al., 2009).
In the coupled climate system, changes in atmospheric and oceanic 
circulations will influence the east-west temperature gradient 
as well. It is expected that as global temperature increases and 
as  the east–west temperature gradient weakens, east–west sea 
level pressure gradients and easterly trade winds (characterizing 
the Walker circulation) will weaken as well (Sections 4.5.3, 8.2.2.2

and 8.4.2.3, and Figure 7.14b; Vecchi et al., 2006, 2008). This would, 
in turn, weaken the east–west temperature gradient through 
a reduction of equatorial upwelling of cold water in the east Pacific 
and a  reduction in the transport of warmer water to the western 
equatorial Pacific and Indian Ocean (England et al., 2014; Dong and 
McPhaden, 2017; Li et al., 2017; Maher et al., 2018).
Research published since AR5 (Burls and Fedorov, 2014b; Fedorov 
et  al., 2015; Erfani and Burls, 2019) has built on an earlier theory 
(Liu and Huang, 1997; Barreiro and Philander, 2008) linking the 
east–west temperature gradient to the north–south temperature 
gradient. In particular, model simulations suggest that a reduction in the 
equator-to-pole temperature gradient (polar amplification) increases 
the temperature of water subducted in the extra-tropics, which in turn 
is upwelled in the eastern Pacific. Thus, polar amplified warming, with

greater warming in the mid-latitudes and subtropics than in the deep 
tropics, is expected to contribute to the weakening of the east–west 
equatorial Pacific SST gradient on decadal to centennial time scales.
The transient adjustment of the equatorial Pacific SST gradient is 
influenced by upwelling waters which delay surface warming in the 
east since they have not been at the surface for years-to-decades to 
experience the greenhouse gas forcing. This ‘thermostat mechanism’ 
(Clement et al., 1996; Cane et al., 1997) is not thought to persist to 
equilibrium since it does not account for the eventual increase in 
temperatures of upwelled waters (Liu et al., 2005; Xie et al., 2010; Y . Luo  et  al., 2017) which will occur as the subducting waters in 
mid-latitudes warm by more than the tropics on average as polar 
amplification emerges. An individual CMIP5 ESM (GFDL’s ESM2M) 
has been found to exhibit a La Niña-like pattern of Pacific temperature

change through the 21st  century, similar to the SST trends seen 
over the historical record (Section  9.2.1 and Figure  7.14a), owing 
to a  weakening asymmetry between El Niño and La Niña events 
(Kohyama et al., 2017), but this pattern of warming may not persist 
to equilibrium (Paynter et al., 2018).
Since 1870, observed SSTs in the tropical western Pacific Ocean have 
increased while those in the tropical eastern Pacific Ocean 
have  changed less (Figure  7.14a and Section  9.2.1). Much of the 
resultant strengthening of the equatorial Pacific temperature gradient 
has occurred since about 1980 due to strong warming in the west and 
cooling in the east (Figure 2.11b) concurrent with an intensification 
of the surface equatorial easterly trade winds and Walker circulation 
(Sections 3.3.3.1, 3.7.6, 8.3.2.3 and 9.2, and Figures 3.16f and 3.39f; 
England et  al., 2014). This temperature pattern is also reflected in 
regional ocean heat content trends and sea level changes observed

from satellite altimetry since 1993 (Bilbao et al., 2015; Richter et al., 
2020). The observed changes may have been influenced by one or 
a combination of temporary factors including sulphate aerosol forcing 
(Smith et al., 2016; Takahashi and Watanabe, 2016; Hua et al., 2018), 
internal variability within the Indo-Pacific Ocean (Luo et al., 2012; Chung 
et al., 2019), teleconnections from multi-decadal tropical Atlantic SST 
trends (Kucharski et  al., 2011, 2014, 2015; McGregor et  al.,  2014; 
Chafik et  al., 2016; X. Li et  al., 2016; Kajtar et  al., 2017; Sun et  al., 
2017), teleconnections from multi-decadal Southern Ocean SST trends 
(Hwang et al., 2017), and coupled ocean–atmosphere dynamics which 
slow warming in the equatorial eastern Pacific (Clement et al., 1996; 
Cane et al., 1997; Seager et al., 2019). CMIP3 and CMIP5 ESMs have 
difficulties replicating the observed trends in the Walker circulation 
and Pacific Ocean SSTs over the historical record (Sohn et al., 2013;

Zhou et al., 2016; Coats and Karnauskas, 2017), possibly due to model 
deficiencies including insufficient multi-decadal Pacific Ocean SST 
variability (Laepple and Huybers, 2014; Bilbao et  al., 2015; Chung 
et al., 2019), mean state biases affecting the forced response or the 
connection between Atlantic and Pacific basins (Kucharski et al., 2014; 
Kajtar et al., 2018; Luo et al., 2018; McGregor et al., 2018; Seager et al., 
2019), and/or a misrepresentation of radiative forcing (Sections 9.2.1 
and 3.7.6). However, the observed trends in the Pacific Ocean SSTs 
are still within the range of internal variability as simulated by large 
initial condition ensembles of CMIP5 and CMIP6 models (Olonscheck 
et al., 2020; Watanabe et al., 2021). Because the causes of observed 
equatorial Pacific temperature gradient and Walker circulation trends 
are not well understood (Section 3.3.3.1), there is low confidence  in 
their attribution to anthropogenic influences (Section 8.3.2.3), while

there is medium confidence  that the observed changes have resulted 
from internal variability (Sections 3.7.6 and 8.2.2.2).
7.4.4.2.2 Tropical Pacific temperature gradients 
in past high-CO 2 climates
The AR5 stated that paleoclimate proxies indicate a reduction in 
the longitudinal SST gradient across the equatorial Pacific during the 
Mid-Pliocene Warm Period (MPWP; Masson-Delmotte et al., 2013;

989
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77see  Cross-Chapter Box  2.1 and Cross-Chapter Box  2.4 in this 
Report). This assessment was based on SST reconstructions 
between two sites situated very close to the equator in the heart 
of the western Pacific warm pool and eastern Pacific cold tongue, 
respectively. Multiple SST reconstructions based on independent 
paleoclimate proxies generally agreed that during the Pliocene the 
SST gradient between these two sites was reduced compared with 
the modern long-term mean (Wara et al., 2005; Dekens et al., 2008; 
Fedorov et al., 2013).
Since AR5, the generation of new SST records has led to a variety 
of revised gradient estimates, specifically the generation of a new 
record for the warm pool (Zhang et al., 2014), the inclusion of SST 
reconstructions from sites in the South China Sea as warm pool 
estimates (O’Brien et al., 2014; Zhang et al., 2014), and the inclusion

of several new sites from the eastern Pacific as cold tongue estimates 
(Zhang et al., 2014; Fedorov et al., 2015). Published estimates of the 
reduction in the longitudinal SST difference for the Late Pliocene, 
relative to either Late Quaternary (0–0.5 million years ago) or 
pre-industrial values, include 1°C to 1.5°C (Zhang et al., 2014), 0.1°C 
to 1.9°C (Tierney et al., 2019), and about 3°C (Ravelo et al., 2014; 
Fedorov et al., 2015; Wycech et al., 2020). All of these studies report 
a further weakening of the longitudinal gradient based on records 
extending into the Early Pliocene. While these revised estimates differ 
in magnitude due to differences in the sites and SST proxies used, 
they all agree that the longitudinal gradient was weaker, and this 
is supported by the probabilistic approach of Tierney et al. (2019). 
However, given that there are currently relatively few western 
equatorial Pacific records from independent site locations, and due

to uncertainties associated with the proxy calibrations (Haywood 
et  al., 2016a), there is only medium confidence  that the average 
longitudinal gradient in the tropical Pacific was weaker during the 
Pliocene than during the Late Quaternary.
To avoid the influence of local biases, changes in the longitudinal 
temperature difference within Pliocene model simulations are typically 
evaluated using domain-averaged SSTs within chosen east and west 
Pacific regions and as such there is sensitivity to methodology. Unlike 
the reconstructed estimates, longitudinal gradient changes simulated 
by the Pliocene Model Intercomparison Project Phase 1 (PlioMIP1) 
models do not agree on the change in sign and are reported as 
spanning approximately –0.5°C to +0.5°C by Brierley et  al. (2015) 
and approximately –1°C to +1°C by Tierney et  al. (2019). Initial 
PlioMIP  Phase 2 (PlioMIP2) analysis suggests responses similar to 
PlioMIP1 (Feng et al., 2019; Haywood et al., 2020). Models that include

hypothetical modifications to cloud albedo or ocean mixing are 
required to simulate the substantially weaker longitudinal differences 
seen in reconstructions of the Early Pliocene (Fedorov et  al., 2013; 
Burls and Fedorov, 2014a).
While more western Pacific warm pool temperature reconstructions 
are needed to refine estimates of the longitudinal gradient, several 
Pliocene SST reconstructions from the east Pacific indicate enhanced 
warming in the centre of the eastern equatorial cold tongue 
upwelling region (Liu et  al., 2019). This enhanced warming in the 
east Pacific cold tongue appears to be dynamically consistent with 
reconstruction of enhanced subsurface warming (Ford et al., 2015) and enhanced warming in coastal upwelling regions, suggesting 
that the tropical thermocline was deeper and/or less stratified during 
the Pliocene. The Pliocene data therefore suggest that the observed 
cooling trend over the last 60 years in parts of the eastern equatorial

Pacific (Section 9.2.1.1 and Figure 9.3; Seager et al., 2019), whether 
forced or due to internal variability, involves transient processes 
that are probably distinct from the longer-time scale process (Burls 
and Fedorov, 2014a, b; Luo et  al., 2015; Heede et  al., 2020) that 
maintained warmer eastern Pacific SST during the Pliocene.
7.4.4.2.3 Overall assessment of tropical Pacific sea surface 
temperature gradients under CO 2 forcing
The paleoclimate proxy record and ESM simulations of the MPWP , 
process understanding, and ESM projections of climate response to 
CO 2 forcing provide medium evidence  and a  medium agreement  and 
thus medium confidence  that equilibrium warming in response to 
elevated CO 2 will be characterized by a weakening of the east–west 
tropical Pacific SST gradient.
Overall the observed pattern of warming over the instrumental 
period, with a  warming minimum in the eastern tropical Pacific

Ocean (Figure 7.14a), stands in contrast to the equilibrium warming 
pattern either inferred from the MPWP proxy record or simulated 
by ESMs under CO 2 forcing. There is medium confidence  that the 
observed strengthening of the east–west SST gradient is temporary 
and will transition to a weakening of the SST gradient on centennial 
time scales. However, there is only low confidence  that this transition 
will emerge this century owing to a low degree of agreement across 
studies about the factors driving the observed strengthening of 
the east–west SST gradient and how those factors will evolve in 
the  future. These trends in tropical Pacific SST gradients reflect 
changes in the climatology, rather than changes in ENSO amplitude 
or variability, which are assessed in Chapter 4 (Section 4.3.3).
7.4.4.3 Dependence of Feedbacks on Temperature Patterns
The expected time-evolution of the spatial pattern of surface warming

in the future has important implications for values of ECS inferred 
from the historical record of observed warming. In particular, changes 
in the global top-of-atmosphere (TOA) radiative energy budget can be 
induced by changes in the regional variations of surface temperature, 
even without a change in the global mean temperature (Zhou et al., 
2016; Ceppi and Gregory, 2019). Consequently, the global radiative 
feedback, characterizing the net TOA radiative response to global 
surface warming, depends on the spatial pattern  of that warming. 
Therefore, if the equilibrium warming pattern under CO 2 forcing 
(similar to CMIP6 projections in Figure 7.12a) is distinct from that 
observed over the historical record or indicated by paleoclimate 
proxies (Sections 7.4.4.1 and 7.4.4.2), then ECS will be different 
from the effective ECS (Box 7.1) that is inferred from those periods. 
Accounting for the dependence of radiative feedbacks on the spatial

pattern of warming has helped to reconcile values of ECS inferred 
from the historical record with values of ECS based on other lines of 
evidence and simulated by climate models (Section 7.5.2.1; Armour, 
2017; Proistosescu and Huybers, 2017; Andrews et al., 2018) but has 
not yet been examined in the paleoclimate context.

990
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7This temperature ‘pattern effect’ (Stevens et  al., 2016) can result 
from both internal variability and radiative forcing of the climate 
system. Importantly, it is distinct from potential radiative feedback 
dependencies on the global surface temperature, which are assessed 
in Section 7.4.3. While changes in global radiative feedbacks under 
transient warming have been documented in multiple generations 
of climate models (Williams et al., 2008; Andrews et al., 2015; Ceppi 
and Gregory, 2017; Dong et  al., 2020), research published since 
AR5 has developed a much-improved understanding of the role of 
evolving SST patterns in driving feedback changes (Armour et  al., 
2013; Andrews et  al., 2015, 2018; Gregory and Andrews, 2016; 
Zhou et al., 2016, 2017b; Ceppi and Gregory, 2017; Haugstad et al., 
2017; Proistosescu and Huybers, 2017; Andrews and Webb, 2018;

Marvel et al., 2018; Silvers et al., 2018; Dong et al., 2019, 2020). This 
section assesses process understanding of the pattern effect, which 
is dominated by the evolution of SSTs. Section 7.5.2.1 describes how 
potential feedback changes associated with the pattern effect are 
important to interpreting ECS estimates based on historical warming.
The radiation changes most sensitive to warming patterns are those 
associated with low-cloud cover (affecting global albedo) and the 
tropospheric temperature profile (affecting thermal emission to 
space) (Ceppi and Gregory, 2017; Zhou et al., 2017b; Andrews et al., 
2018; Dong et  al., 2019). The mechanisms and radiative effects 
of these changes are illustrated in Figure  7.14a,b. SSTs in regions 
of deep convective ascent (e.g., in the western Pacific warm pool) 
govern the temperature of the tropical free troposphere and, in turn, 
affect low-clouds through the strength of the inversion that caps the

boundary layer (i.e., the lower-tropospheric stability) in subsidence 
regions (Wood and Bretherton, 2006; Klein et  al., 2017). Surface 
warming within ascent regions thus warms the free troposphere 
and increases low-cloud cover, causing an increase in emission 
of thermal radiation to space and a  reduction in absorbed solar 
radiation. In contrast, surface warming in regions of overall descent 
preferentially warms the boundary layer and enhances convective 
mixing with the dry free troposphere, decreasing low-cloud cover 
(Bretherton et al., 2013; Qu et al., 2014; Zhou et al., 2015). This leads 
to an increase in absorption of solar radiation but little change in 
thermal emission to space. Consequently, warming in tropical ascent 
regions results in negative lapse-rate and cloud feedbacks while 
warming in tropical descent regions results in positive lapse-rate 
and cloud feedbacks (Figure  7.14; Rose and Rayborn, 2016; Zhou

et al., 2017b; Andrews and Webb, 2018; Dong et al., 2019). Surface 
warming in mid-to-high latitudes causes a weak radiative response 
owing to compensating changes in thermal emission (Planck and 
lapse-rate feedbacks) and absorbed solar radiation (shortwave cloud 
and surface-albedo feedbacks; Rose and Rayborn, 2016; Dong et al., 
2019), however this compensation may weaken due to less-negative 
shortwave cloud feedbacks at high warming (Frey and Kay, 2018; 
Bjordal et al., 2020; Dong et al., 2020).
The spatial pattern of SST changes since 1870 shows relatively 
little warming in key regions of less-negative radiative feedbacks, 
including the eastern tropical Pacific Ocean and Southern Ocean 
(Sections 7.4.4.1 and 7.4.4.2, and Figures 2.11b and 7.14a). Cooling 
in these regions since 1980 has occurred along with an increase in 
the strength of the capping inversion in tropical descent regions, resulting in an observed increase in low-cloud cover over the tropical

eastern Pacific (Figure 7.14a; Zhou et al., 2016; Ceppi and Gregory, 
2017; Fueglistaler and Silvers, 2021). Thus, tropical low-cloud cover 
increased over recent decades even as global surface temperature 
increased, resulting in a  negative low-cloud feedback which is 
at odds with the positive low-cloud feedback expected for the 
pattern of equilibrium warming under CO 2 forcing (Section 7.4.2.4 
and Figure 7.14b).
(a) Atmospheric response to observed Pacific ocean warming
Descent
Observed sea-surface temperature trend over
1870– 2019 (ºC per century)
(b) Atmospheric response to projected Pacific ocean warming
Descent
profile
Decreased
low-cloud
cover
CMIP6 sea-surface temperature trend over years
1-150 after abrupt CO2 quadrupling (ºC per century)
Increased
low-cloud
coverAtmospheric
profile
Remote tropospheric warming
–0.6      –0.3        0        0.3       0.6
–2        –1          0          1          2
Weakened Walker

circulationStrengthened Walker
circulation
Increased
inversion
strength
Decreased
inversion
strengthAscent
Ascent
Figure  7.14 | Illustration of tropospheric temperature and low-cloud 
response to observed and projected Pacific Ocean sea surface temperature 
trends. (a)  Atmospheric response to linear sea surface temperature trend observed 
over 1870–2019 (HadISST1 dataset; Rayner et al., 2003).  (b) Atmospheric response 
to linear sea-surface temperature trend over 150 years following abrupt4xCO2 
forcing as projected by CMIP6 ESMs (Dong et al., 2020). Relatively large historical 
warming in the western tropical Pacific has been communicated aloft (a shift from 
grey to red atmospheric temperature profile), remotely warming the tropical free 
troposphere and increasing the strength of the inversion in regions of the tropics 
where warming has been slower, such as the eastern equatorial Pacific. In turn, an

increased inversion strength has increased the low-cloud cover (Zhou et al., 2016) 
causing an anomalously negative cloud and lapse-rate feedbacks over the historical 
record (Andrews et al., 2018; Marvel et al., 2018). Relatively large projected warming 
in the eastern tropical Pacific is trapped near the surface (shift from grey to red 
atmospheric temperature profile), decreasing the strength of the inversion locally. 
In turn, a decreased inversion strength combined with surface warming is projected to 
decrease the low-cloud cover, causing the cloud and lapse-rate feedbacks to become 
less negative in the future. Figure adapted from Mauritsen (2016). Further details on 
data sources and processing are available in the chapter data table (Table 7.SM.14).

991
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Andrews et al. (2018) analysed available CMIP5/6 ESM simulations 
(six in total) comparing effective feedback parameters diagnosed 
within atmosphere-only ESMs using prescribed historical SST and sea 
ice concentration patterns with the equilibrium feedback parameters 
as estimated within coupled ESMs (using identical atmospheres) 
driven by abrupt 4×CO 2 forcing. The atmosphere-only ESMs show 
pronounced multi-decadal variations in their effective feedback 
parameters over the last  century, with a  trend towards strongly 
negative values since about 1980 owing primarily to negative 
shortwave cloud feedbacks driven by warming in the western 
equatorial Pacific Ocean and cooling in the eastern equatorial 
Pacific Ocean (Zhou et al., 2016; Andrews et al., 2018; Marvel et al., 
2018; Dong et  al., 2019). Yet, all six models show a  less-negative

net feedback parameter under abrupt4xCO2 than for the historical 
period (based on regression since 1870 following Andrews et  al., 
2018). The average change in net feedback parameter between 
the historical period and the equilibrium response to CO 2 forcing, 
denoted here as α’, for these simulations is α’ = +0.6 W m–2 °C–1 
(+0.3 to +1.0 W m–2 °C–1 range across models; Figure 7.15b). These 
feedback parameter changes imply that the value of ECS may be 
substantially larger than that inferred from the historical record 
(Section  7.5.2.1). These findings can be understood from the fact 
that, due to a  combination of internal variability and transient 
response to forcing (Section 7.4.4.2), historical sea surface warming 
has been relatively large in regions of tropical ascent (Figure 7.14a), 
leading to an anomalously large net negative radiative feedback; 
however, future warming is expected to be largest in tropical

descent regions, such as the eastern equatorial Pacific, and at high 
latitudes (Sections 7.4.4.1 and 7.4.4.2 and Figure 7.14b), leading to 
a less-negative net radiative feedback and higher ECS.
A similar behaviour is seen within transient simulations of coupled 
ESMs, which project SST warming patterns that are initially 
characterized by relatively large warming rates in the western 
equatorial Pacific Ocean on decadal time scales and relatively large 
warming in the eastern equatorial Pacific and Southern Ocean 
on centennial time scales (Andrews et  al., 2015; Proistosescu 
and Huybers, 2017; Dong et  al., 2020). Recent studies based on 
simulations of 1%  yr –1 CO 2 increase ( 1pctCO 2) or abrupt4xCO2  as 
analogues for historical warming suggest characteristic values of 
α’ = +0.05 W m–2 °C–1 (–0.2 to +0.3 W m–2 °C–1 range across models) 
based on CMIP5 and CMIP6 ESMs (Armour 2017, Lewis and Curry 
2018, Dong et al. 2020). Using historical simulations of one CMIP6 ESM

(HadGEM3-GC3.1-LL), Andrews et al. (2019) find an average feedback 
parameter change of α’ = +0.2 W m–2 °C–1 (–0.2 to +0.6 W m–2 °C–1 
range across four ensemble members). Using historical simulations 
from another CMIP6 ESM (GFDL CM4.0), Winton et al. (2020) find 
an average feedback parameter change of α’ = +1.5 W m–2 °C–1 
(+1.2  to +1.7 W m–2 °C–1 range across three ensemble members). 
This value is larger than the α’ = +0.7 W m–2 °C–1 within GFDL CM4.0 
for historical CO 2 forcing only, suggesting that the value of α’ may 
depend on historical non-CO 2 forcings such as those associated with 
tropospheric and stratospheric aerosols (Marvel et al., 2016; Gregory 
et al., 2020; Winton et al., 2020).
The magnitude of the net feedback parameter change α’ found 
within coupled CMIP5 and CMIP6 ESMs is generally smaller than that found when prescribing observed warming patterns within 
atmosphere-only ESMs (Figure  7.15; Andrews et  al., 2018). This

arises from the fact that the forced spatial pattern of warming within 
transient simulations of most coupled ESMs are distinct from observed 
warming patterns over the historical record in key regions such as 
the equatorial Pacific Ocean and Southern Ocean (Sections 7.4.4.1 
and 7.4.4.2), while being more similar to the equilibrium pattern 
simulated under abrupt4xCO2 . However, historical simulations 
with HadGEM3-GC3.1-LL (Andrews et  al., 2019) and GFDL CM4.0 
(Winton et  al., 2020) show substantial spread in the value of 
α’  across ensemble members, indicating a  potentially important 
role for internal variability in setting the magnitude of the pattern 
effect over the historical period. Using the  100-member historical 
simulation ensemble of MPI-ESM1.1, Dessler et al. (2018) find that 
internal climate variability alone results in a 0.5 W m–2 °C–1 spread in 
the historical effective feedback parameter, and thus also in the value

of α’. Estimates of α’ using prescribed historical warming patterns 
provide a more realistic representation of the historical pattern effect 
because they account for the net effect of the transient response 
to historical forcing and internal variability in the observed record 
(Andrews et al., 2018).
The magnitude of α’, as quantified by ESMs, depends on the accuracy 
of both the projected patterns of SST and sea ice concentration 
changes in response to CO 2 forcing and the radiative response to 
those patterns (Andrews et al., 2018). Model biases that affect the 
long-term warming pattern (e.g.,  SST and relative humidity biases 
in the equatorial Pacific cold tongue as suggested by Seager et al., 
2019) will affect the value of α’. The value of α’ also depends on the 
accuracy of the historical SST and sea ice concentration conditions 
prescribed within atmosphere-only versions of ESMs to quantify 
the historical radiative feedback (Figure 7.15b). Historical SSTs are

particularly uncertain for the early portion of the historical record 
(Section 2.3.1), and there are few constraints on sea ice concentration 
prior to the satellite era. Using alternative SST datasets, Andrews 
et al. (2018) found little change in the value of α’ within two models 
(HadGEM3 and HadAM3), while Lewis and Mauritsen (2021) found 
a smaller value of α’ within two other models (ECHAM6.3 and CAM5). 
The sensitivity of results to the choice of dataset represents a major 
source of uncertainty in the quantification of the historical pattern 
effect using atmosphere-only ESMs that has yet to be systematically 
explored, but the preliminary findings of Lewis and Mauritsen (2021) 
and Fueglistaler and Silvers (2021) suggest that α’ could be smaller 
than the values reported in Andrews et al. (2018).
While there are not yet direct observational constraints on the 
magnitude of the pattern effect, satellite measurements of variations

in TOA radiative fluxes show strong co-variation with changing 
patterns of SSTs, with a  strong dependence on SST changes in 
regions of deep convective ascent (e.g., in the western Pacific warm 
pool; Loeb et al., 2018a; Fueglistaler, 2019). Cloud and TOA radiation 
responses to observed warming patterns in atmospheric models have 
been found to compare favourably with those observed by satellite 
(Section 7.2.2.1 and Figure 7.3; Zhou et al., 2016; Loeb et al., 2020). 
This observational and modelling evidence indicates the potential for 
a  strong pattern effect in nature that will only be negligible if the 
observed pattern of warming since pre-industrial levels persists to

992
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7equilibrium – an improbable scenario given that Earth is in a relatively 
early phase of transient warming and that reaching equilibrium would 
take multiple millennia (C. Li et  al., 2013). Moreover, paleoclimate 
proxies, ESM simulations, and process understanding indicate that 
strong warming in the eastern equatorial Pacific Ocean (with medium 
confidence ) and Southern Ocean (with high confidence ) will emerge 
on centennial time scales as the response to CO 2 forcing dominates 
temperature changes in these regions (Sections 7.4.4.1, 7.4.4.2 
and 9.2.1). However, there is low confidence  that these features, which 
have been largely absent over the historical record, will emerge this 
century (Sections 7.4.4.1, 7.4.4.2 and Section 9.2.1). This leads to high 
confidence  that radiative feedbacks will become less negative as the 
CO 2-forced pattern of surface warming emerges ( α’ > 0 W m–2 °C–1),

but low confidence  that these feedback changes will be realized this 
century. There is also substantial uncertainty in the magnitude of the 
net radiative feedback change between the present warming pattern 
and the projected equilibrium warming pattern in response to CO 2 
forcing owing to the fact that its quantification currently relies solely 
on ESM results and is subject to uncertainties in historical SST patterns. 
Thus, based on the pattern of warming since 1870, α’ is estimated to 
be in the range 0.0 to 1.0 W m–2 °C–1 but with a  low confidence  in 
the upper end of this range. A value of α’ = +0.5 ± 0.5 W m–2 °C –1 
is used to represent this range in Box 7.2 and Section 7.5.2, which 
respectively assess the implications of changing radiative feedbacks 
for Earth’s energy imbalance and estimates of ECS based on the 
instrumental record. The value of α’ is larger if quantified based on

the observed pattern of warming since 1980 (Figure 2.11b) which is more distinct from the equilibrium warming pattern expected under 
CO 2 forcing ( high confidence ) (similar to CMIP6 projections shown in 
Figure 7.12a; Andrews et al., 2018).
7.5 Estimates of ECS and TCR
Equilibrium climate sensitivity (ECS) and transient climate response 
(TCR) are metrics of the global surface air temperature (GSAT) 
response to forcing, as defined in Box  7.1. ECS is the magnitude 
of the long-term GSAT increase in response to a  doubling of 
atmospheric CO 2 concentration after the planetary energy budget is 
balanced, though leaving out feedbacks associated with ice sheets; 
whereas the TCR is the magnitude of GSAT increase at year 70 when 
CO 2 concentration is doubled in a 1% yr –1 increase scenario. Both 
are idealized quantities, but can be inferred from paleoclimate or 
observational records or estimated directly using climate simulations,

and are strongly correlated with the climate response in realistic 
future projections (Sections 4.3.4 and 7.5.7; Grose et al., 2018).
TCR is always smaller than ECS because ocean heat uptake acts to 
reduce the rate of surface warming. Yet, TCR is related to ECS across 
CMIP5 and CMIP6 models (Grose et al., 2018; Flynn and Mauritsen, 
2020) as expected since TCR and ECS are inherently measures 
of climate response to forcing; both depend on effective radiative 
forcing (ERF) and the net feedback parameter, α. The relationship 
between TCR and ECS is, however, non-linear and becomes more so -3 -2 -1 0-3-2-10
-3 -2 -1 0-3-2-10(b) Atmosphere-only ESMs using observed
historical warming patterns
Historical feedback α (W m-2 ºC-1)Abrupt4xCO2 feedback α (W m-2 ºC-1)
Historical feedback α (W m-2 ºC-1)Abrupt4xCO2 feedback α (W m-2 ºC-1)(a) CMIP6 ESMs under historical forcing or
idealized CO2 forcingRelationship between historical and abrupt4xCO2 net radiative feedback in ESMs
0
-1
-2

-3GFDL CM4.0
historicalHadGEM3-GC3.1-LL
historical0
-1
-2
-3
-3                      -2                      -1                        0 -3                      -2                      -1                        0
Figure 7.15  | Relationship between historical  and abrupt4xCO2  net radiative feedbacks in ESMs. (a)  Radiative feedbacks in CMIP6 ESMs estimated under 
historical forcing (values for GFDL CM4.0 and HadGEM3-CG3.1-LL from Winton et al. (2020) and Andrews et al. (2019), respectively); horizontal lines show the range across 
ensemble members. The other points show effective feedback values for 29 ESMs estimated using regression over the first 50 years of abrupt4xCO2 simulations as an analogue 
for historical warming (Dong et al., 2020). (b) Historical radiative feedbacks estimated from atmosphere-only ESMs with prescribed observed sea-surface temperature and

sea-ice concentration changes (Andrews et al., 2018) based on a linear regression of global top-of-atmosphere (TOA) radiation against global near-surface air temperature 
over the period 1870–2010 (pattern of warming similar to Figure 7.14a) and compared with equilibrium feedbacks in  abrupt4xCO2 simulations of coupled versions of the 
same ESMs (pattern of warming similar to Figure 7.14b). In all cases, the equilibrium feedback magnitudes are estimated as CO 2 ERF divided by ECS where ECS is derived 
from regression over years 1–150 of abrupt4xCO2 simulations (Box 7.1); similar results are found if the equilibrium feedback is estimated directly from the slope of the linear 
regression. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

993
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77for higher ECS values (Hansen et al., 1985; Knutti et al., 2005; Millar 
et  al., 2015; Flynn and Mauritsen, 2020; Tsutsui, 2020) owing to 
ocean heat uptake processes and surface temperature pattern effects 
temporarily reducing the rate of surface warming. When α is small 
in magnitude, and correspondingly ECS is large (recall that ECS is 
inversely proportional to α), these temporary effects are increasingly 
important in reducing the ratio of TCR to ECS.
Before AR6, the assessment of ECS relied on either CO 2-doubling 
experiments using global atmospheric models coupled with 
mixed-layer ocean or standardized CO 2-quadrupling ( abrupt4xCO2 ) 
experiments using fully coupled ocean–atmosphere models or 
Earth system models (ESMs). The TCR has similarly been diagnosed 
from ESMs in which the CO 2 concentration is increased at 1% yr –1

(1pctCO 2, an approximately linear increase in ERF over time) and is 
in practice estimated as the average over a 20-year period centred 
at the time of atmospheric CO 2 doubling, that is, year 70. In AR6, 
the assessments of ECS and TCR are made based on multiple lines 
of evidence, with ESMs representing only one of several sources 
of information. The constraints on these climate metrics are based 
on radiative forcing and climate feedbacks assessed from process 
understanding (Section  7.5.1), climate change and variability seen 
within the instrumental record (Section 7.5.2), paleoclimate evidence 
(Section 7.5.3), emergent constraints (Section 7.5.4), and a synthesis 
of all lines of evidence (Section 7.5.5). In AR5, these lines of evidence 
were not explicitly combined in the assessment of climate sensitivity, 
but as demonstrated by Sherwood et  al. (2020) their combination 
narrows the uncertainty ranges of ECS compared to that assessed

in AR5. ECS values found in CMIP6 models, some of which exhibit 
values higher than 5°C (Meehl et al., 2020; Zelinka et al., 2020), are 
discussed in relation to the AR6 assessment in section 7.5.6.
7.5.1 Estimates of ECS and TCR Based 
on Process Understanding
This section assesses the estimates of ECS and TCR based on process 
understanding of the ERF due to a  doubling of CO 2 concentration 
and the net climate feedback (Sections 7.3.2 and 7.4.2). This 
process-based assessment is made in Section 7.5.1.1 and applied to 
TCR in Section 7.5.1.2.
7.5.1.1 ECS Estimated Using Process-based 
Assessments of Forcing and Feedbacks
The process-based assessment is based on the global energy budget 
equation (Box 7.1, Equation 7.1), where the ERF (Δ F) is set equal to 
the effective radiative forcing due to a doubling of CO 2 concentration 
(denoted as Δ F2×CO2) and the climate state reaches a new equilibrium, 
that is, Earth’s energy imbalance averages to zero (Δ N = 0). ECS is

calculated as the ratio between the ERF and the net feedback 
parameter: ECS = –Δ F2×CO2/α. Estimates of Δ F2×CO2 and α are obtained 
separately based on understanding of the key processes that 
determine each of these quantities. Specifically, Δ F2×CO2 is estimated 
based on instantaneous radiative forcing that can be accurately 
obtained using line-by-line calculations, to which uncertainty due 
to adjustments are added (Section 7.3.2). The range of α is derived by aggregating estimates of individual climate feedbacks based not 
only on ESMs but also on theory, observations, and high-resolution 
process modelling (Section 7.4.2).
The effective radiative forcing of CO 2 doubling is assessed to be 
ΔF2×CO2 = 3.93 ± 0.47 W m–2 (Section 7.3.2.1), while the net feedback 
parameter is assessed to be  α = –1.16 ± 0.40 W m–2 °C–1 (Table 7.10), 
where the ranges indicate one standard deviation. These values are 
slightly different from those directly calculated from ESMs because

more information is used to assess them, as explained above. Assuming 
ΔF2×CO2 and α each follow an independent normal distribution, the 
uncertainty range of ECS can be obtained by substituting the respective 
probability density function into the expression of ECS (red curved bar 
in Figure 7.16). Since α is in the denominator, the normal distribution 
leads to a long tail in ECS towards high values, indicating the large 
effect of uncertainty in α in estimating the likelihood of a high ECS 
(Roe and Baker, 2007; Knutti and Hegerl, 2008).
The wide range of the process-based ECS estimate is not due 
solely to uncertainty in the estimates of Δ F2×CO2 and α, but is partly 
explained by the assumption that  ΔF2×CO2 and α are independent in 
this approach. In CMIP5 and CMIP6 ensembles,  ΔF2×CO2 and α are 
negatively correlated when they are calculated using linear regression 
in abrupt4xCO2  simulations ( r2 = 0.34; Andrews et al., 2012; Webb

et al., 2013; Zelinka et al., 2020). The negative correlation leads to 
compensation between the inter-model spreads of these quantities, 
thereby reducing the ECS range estimated directly from the models. 
If the process-based ECS distribution is reconstructed from probability 
distributions of Δ F2×CO2 and α assuming that they are correlated as in 
CMIP model ensembles, the range of ECS will be narrower by 14% 
(pink curved bar in Figure 7.16). If, however, the covariance between 
ΔF2×CO2 and α is not adopted, there is no change in the mean, but the 
wide range still applies.
A significant correlation between Δ F2×CO2 and α also occurs when 
the two parameters are estimated separately from atmospheric ESM 
fixed-SST experiments (Section  7.3.1) or fixed CO 2 concentration 
experiments (Section  7.4.1; Ringer et  al., 2014; Chung and Soden, 
2018). Hence the relationship is not expected to be an artefact of 
calculating the parameters using linear regression in abrupt4xCO2

simulations. A  possible physical cause of the correlation may be 
a  compensation between the cloud adjustment and the cloud 
feedback over the tropical ocean (Ringer et  al., 2014; Chung and 
Soden, 2018). It has been shown that the change in the hydrological 
cycle is a controlling factor for the low-cloud adjustment (Dinh and 
Fueglistaler, 2019) and for the low-cloud feedback (Watanabe et al., 
2018), and therefore the responses of these clouds to the direct CO 2 
radiative forcing and to the surface warming may not be independent. 
However, robust physical mechanisms are not yet established, 
and furthermore, the process-based assessment of the tropical 
low-cloud feedback is only indirectly based on ESMs given that 
physical processes which control the low-clouds are not sufficiently 
well-simulated in models (Section  7.4.2.4). For these reasons, the 
co-dependency between Δ F2×CO2 and α is assessed to have low 
confidence and, therefore, the more conservative assumption that

they are independent for the process-based assessment of ECS 
is retained.

994
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7In summary, the ECS based on the assessed values of Δ F2×CO2 and α is 
assessed to have a median value of 3.4°C with a  likely  range of 2.5 to 
5.1 °C and  very likely  range of 2.1 to 7.7 °C. To this assessed range of 
ECS, the contribution of uncertainty in α is approximately three times 
as large as the contribution of uncertainty in Δ F2×CO2.
7.5.1.2 Emulating Process-based ECS to TCR
ECS estimated using the ERF due to a doubling of CO 2 concentration 
and the net feedback parameter (ECS = –Δ F2×CO2/α) can be translated 
into the TCR so that both climate sensitivity metrics provide consistent 
information about the climate response to forcing. Here a two-layer 
energy budget emulator is used to transfer the process-based 
assessment of forcing, feedback, efficacy and heat uptake to TCR 
(Supplementary Material 7.SM.2.1 and Cross-Chapter Box 7.1). The

emulator can reproduce the transient surface temperature evolution in 
ESMs under 1pctCO 2 simulations and other climate change scenarios, 
despite the very low number of degrees of freedom (Held et al., 2010; 
Geoffroy et al., 2012, 2013a; Palmer et al., 2018). Using this model 
with parameters given from assessments in Sections 7.2, 7.3, and 7.4, 
TCR is assessed based on the process-based understanding.
In the two-layer energy balance emulator, additional parameters are 
introduced: heat capacities of the upper and deep ocean, heat uptake 
efficiency ( γ), and the so-called efficacy parameter ( ε) that represents the dependence of radiative feedbacks and heat uptake on the evolving SST 
pattern under CO 2 forcing alone (Section 7.4.4). In the real world, natural 
internal variability and aerosol radiative forcing also affect the efficacy 
parameter, but these effects are excluded for the current discussion.
The analytical solution of the energy balance emulator reveals

that the global surface temperature change to abrupt increase of 
the atmospheric CO 2 concentration is expressed by a  combination 
of a  fast adjustment of the surface components of the climate 
system and a  slow response of the deep ocean, with time scales 
of several years and several centuries, respectively (grey curve in 
Figure 7.17b). The equilibrium response of upper ocean temperature, 
approximating SST and the surface air temperature response, 
depends, by definition, only on the radiative forcing and the net 
feedback parameter. Uncertainty in α dominates (80–90%) the 
corresponding uncertainty range for ECS in CMIP5 models (Vial et al., 
2013), and also an increase of ECS in CMIP6 models (Section 7.5.5) 
is attributed by about 60–80% to a  change in α (Zelinka et  al., 
2020). For the range of TCR, the contribution from uncertainty in α is 
reduced to 50–60% while uncertainty in Δ F2×CO2 becomes relatively

more important (Geoffroy et al., 2012; Lutsko and Popp, 2019). TCR 
reflects the fast response occurring approximately during the first 
20 years in the abrupt4xCO2  simulation (Held et al., 2010), but the 
fast response is not independent of the slow response because there 
is a non-linear co-dependence between them (Andrews et al., 2015). 
The non-linear  relationship between ECS and TCR indicates that 
the probability of high TCR is not very sensitive to changes in the 
probability of high ECS (Meehl et al., 2020).
Considering an idealized time evolution of ERF (1% increase per year 
until CO 2 doubling and held fixed afterwards, see Figure  7.17a), 
the TCR defined by the surface temperature response at year 70 is 
derived by substituting the process-based ECS into the analytical 
solution of the emulator (Figure  7.17b, see also Supplementary 
Material 7.SM.2.1). When additional parameters in the emulator 
are prescribed by using CMIP6 multi-model mean values of those

estimates (Smith et al., 2020b), this calculation translates the range of 
ECS in Section 7.5.2.1 to the range of TCR. The transient temperature 
response, in reality, varies with different estimates of the ocean 
heat uptake efficiency ( γ) and efficacy ( ε). When the emulator was 
calibrated to the transient responses in CMIP5 models, it shows that 
uncertainty in heat capacities is negligible and differences in γ and 
ε explain 10–20% of the inter-model spread of TCR among GCMs 
(Geoffroy et al., 2012). Specifically, their product, κ = γε, appearing 
in a simplified form of the solution, that is, TCR ≅ –ΔF2×CO2/(α – κ), 
gives a  single parameter quantifying the damping effects of heat 
uptake (Jiménez-de-la-Cuesta and Mauritsen, 2019). This parameter 
is positive and acts to slow down the temperature response in 
a similar manner to the ‘pattern effect’ (Sections 7.4.4.3 and 7.5.2.1). 
The ocean heat uptake in nature is controlled by multiple processes

associated with advection and mixing (Exarchou et al., 2014; Kostov 
et al., 2014; Kuhlbrodt et al., 2015) but is simplified to be represented 
by a  single term of heat exchange between the upper and deep 
ocean in the emulator. Therefore, it is challenging to constrain 
γ and ε from process-based understanding (Section 7.5.2). Because 
the estimated values are only weakly correlated across models, 
the mean value and one standard deviation of κ are calculated as Figure  7.16  | Probability distributions of ERF to CO 2 doubling ( ΔF2×CO2 ; 
top) and the net climate feedback ( α; right), derived from process-
based assessments in Sections 7.3.2 and 7.4.2.  Central panel shows the joint 
probability density function calculated on a two-dimensional plane of ΔF2×CO2  and 
α (red), on which the 90% range shown by an ellipse is imposed to the background 
theoretical values of ECS (colour shading). The white dot, and thick and thin curves

inside the ellipse represent the mean, likely and very likely  ranges of ECS. An alternative 
estimation of the ECS range (pink) is calculated by assuming that ΔF2×CO2  and α have 
a  covariance. The assumption about the co-dependence between ΔF2×CO2  and α 
does not alter the mean estimate of ECS but affects its uncertainty. Further details on 

995
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77κ = 0.84 ± 0.38 W m–2 °C–1 (one standard deviation) by ignoring their 
covariance (the mean value is very similar to that used for Box 4.1, 
Figure  1; see Supplementary Material 7.SM.2.1). By incorporating 
this inter-model spread in κ, the range of TCR is widened by about 
10% (blue bar in Figure 7.17b). Yet, the dominant contribution to the 
uncertainty range of TCR arises from the net feedback parameter α, 
consistent with analyses of CMIP6 models (Williams et  al., 2020), 
and this assessment remains unchanged from AR5 stating that 
uncertainty in ocean heat uptake is of secondary importance.
In summary, the process-based estimate of TCR is assessed to have 
the central value of 2.0°C with the likely  range from 1.6 to 2.7 °C and 
the very likely  range from 1.3 to 3.1 °C ( high confidence ). The upper 
bound of the assessed range was slightly reduced from AR5 but can

be further constrained using multiple lines of evidence (Section 7.5.5).
7.5.2 Estimates of ECS and TCR Based 
on the Instrumental Record
This section assesses the estimates of ECS and TCR based on the 
instrumental record of climate change and variability with an 
emphasis on new evidence since AR5. Several lines of evidence 
are assessed including the global energy budget (Section  7.5.2.1), 
the use of simple climate models evaluated against the historical temperature record (Section 7.5.2.2), and internal variability in global 
temperature and TOA radiation (Section  7.5.2.3). Section  7.5.2.4 
provides an overall assessment of TCR and ECS based on these lines 
of evidence from the instrumental record.
7.5.2.1 Estimates of ECS and TCR Based 
on the Global Energy Budget
The GSAT change from 1850–1900 to 2006–2019 is estimated to be 
1.03 [0.86 to 1.18] °C (Cross-chapter Box 2.3). Together with estimates 
of Earth’s energy imbalance (Section 7.2.2) and the global ERF that

has driven the observed warming (Section  7.3), the instrumental 
temperature record enables global energy budget estimates of ECS 
and TCR. While energy budget estimates use instrumental data, they 
are not based purely on observations. A conceptual model typically 
based on the global mean forcing and response energy budget 
framework (Box 7.1) is needed to relate ECS and TCR to the estimates 
of global warming, ERF and Earth’s energy imbalance (Forster, 2016; 
Knutti et al., 2017). Moreover, ESM simulations partly inform estimates 
of the historical ERF (Section 7.3) as well as Earth’s energy imbalance 
in the 1850–1900 climate (the period against which changes are 
measured; Forster, 2016; Lewis and Curry, 2018). ESMs are also used 
to estimate uncertainty due the internal climate variability that may 
have contributed to observed changes in temperature and energy 
imbalance (e.g., Palmer and McNeall, 2014; Sherwood et al., 2020).

Research since AR5 has shown that global energy budget estimates 
of ECS may be biased low when they do not take into account how 
radiative feedbacks depend on the spatial pattern of surface warming 
(Section 7.4.4.3) or when they do not incorporate improvements in 
the estimation of global surface temperature trends which take better 
account of data-sparse regions and are more consistent in their 
treatment of surface temperature data (Section 2.3.1). Together with 
updated estimates of global ERF and Earth’s energy imbalance, these 
advances since AR5 have helped to reconcile energy budget estimates 
of ECS with estimates of ECS from other lines of evidence.
The traditional global mean forcing and response energy budget 
framework (Section 7.4.1 and Box 7.1; Gregory et al., 2002) relates 
the difference between the ERF (Δ F) and the radiative response to 
observed global warming ( αΔT) to the Earth’s energy imbalance (Δ N):

ΔN = αΔT + ΔF. Given the relationship ECS  =  –Δ F2×CO2/α, where Δ F2×CO2 
is the ERF from CO 2 doubling, ECS can be estimated from historical 
estimates of Δ T, ΔF, ΔN and Δ F2×CO2: ECS = Δ F2×CO2ΔT/(ΔF – Δ N). 
Since TCR is defined as the temperature change at the time of 
CO 2 doubling under an idealized 1% yr –1 CO 2 increase, it can be 
inferred from the historical record as: TCR  =  Δ F2×CO2  ΔT/ΔF, under 
the assumption that radiative forcing increases quickly compared to 
the adjustment time scales of the deep ocean, but slowly enough 
and over a sufficiently long time that the upper ocean is adjusted, 
so that Δ T and Δ N increases approximately in proportion to Δ F. 
Because Δ N is positive, TCR is always smaller than ECS, reflecting 
weaker transient warming than equilibrium warming. TCR is better 
constrained than ECS owing to the fact that the denominator of TCR, 
without the quantity Δ N, is more certain and further from zero than

is the denominator of ECS. The upper bounds of both TCR and ECS 
estimated from historical warming are inherently less certain than 
their lower bounds because Δ F is uncertain and in the denominator.
5
4
3
2
1
0
6
5
4
3
2
1
0ERF (W/m2)
0                                      70 YEAR                       1% increase2xCO2Temperature response (°C)uncertainty range
due to F, a and k6
5
4
3
2
1
0
ECS (℃)
TCR (℃)TCR likely (very likely) range: 1.6-2.7 ℃ (1.3-3.1 ℃)
Response to 2xCO2Process assessment of Transient Climate Response
(a) Idealized radiative forcing
(b) Emulated temperature response
0                                      70 YEAR                       
Figure  7. 17 | (a) Time evolution of the effective radiative forcing (ERF) 
to the CO 2 concentration increased by 1% per year until year 70 (equal 
to the time of doubling) and kept fixed afterwards (white line).  The likely 
and very likely  ranges of ERF indicated by light and dark orange have been assessed

in Section 7.3.2.1. (b) Surface temperature response to the CO 2 forcing calculated 
using the emulator with a given value of ECS, considering uncertainty in ΔF2×CO2, α, 
and κ associated with the ocean heat uptake and efficacy (white line). The likely and 
very likely  ranges are indicated by cyan and blue, respectively. For comparison, the 
temperature response to abrupt doubling of the CO 2 concentration is displayed by 
a grey curve. The mean, likely and very likely  ranges of ECS and TCR are shown at the 
right (the values of TCR also presented in the panel). Further details on data sources 

996
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7The traditional energy budget framework lacks a representation of 
how radiative feedbacks depend on the spatial pattern of warming. 
Thus, studies employing this framework (Otto et  al., 2013; Lewis 
and Curry, 2015, 2018; Forster, 2016) implicitly assume that the net 
radiative feedback has a constant magnitude, producing an estimate 
of the effective ECS (defined as the value of ECS that would occur 
if α does not change from its current value) rather than of the true 
ECS. As summarized in Section  7.4.4.3, there are now multiple 
lines of evidence providing high confidence  that the net radiative 
feedback will become less negative as the warming pattern evolves 
in the future (the pattern effect). This arises because historical 
warming has been relatively larger in key negative feedback regions 
(e.g.,  western tropical Pacific Ocean) and relatively smaller in key

positive feedback regions (e.g.,  eastern tropical Pacific Ocean and 
Southern Ocean) than is projected in the near-equilibrium response 
to CO 2 forcing (Section 7.4.4.3; Held et al., 2010; Proistosescu and 
Huybers, 2017; Dong et  al., 2019), implying that the true ECS will 
be larger than the effective ECS inferred from historical warming. 
This section first assesses energy budget constraints on TCR and the 
effective ECS based on updated estimates of historical warming, ERF , 
and Earth’s energy imbalance. It then assesses what these energy 
budget constraints imply for values of ECS once the pattern effect is 
accounted for.
Energy budget estimates of TCR and ECS have evolved in the 
literature over recent decades. Prior to AR4, the global energy 
budget provided relatively weak constraints, primarily due to large 
uncertainty in the tropospheric aerosol forcing, giving ranges of the 
effective ECS that typically included values above 10°C (Forster, 2016;

Knutti et  al., 2017). Revised estimates of aerosol forcing together 
with a larger greenhouse gas forcing by the time of AR5 led to an 
estimate of Δ F that was more positive and with reduced uncertainty 
relative to AR4. Using energy budget estimates and radiative forcing 
estimates updated to 2009, Otto et  al. (2013) estimated that TCR 
was 1.3 [0.9 to 2.0] °C, and that the effective ECS was 2.0 [1.2 to 
3.9] °C. This AR5-based energy budget estimate of ECS was lower 
than estimates based on other lines of evidence, leading AR5 to 
expand the assessed likely  range of ECS to include lower values 
relative to AR4. Studies since AR5 using similar global energy budget 
methods have produced similar or slightly narrower ranges for TCR 
and effective ECS (Forster, 2016; Knutti et al., 2017).
Energy budget estimates of TCR and ECS assessed here are based 
on improved observations and understanding of global surface 
temperature trends extended to the year 2020 (Section  2.3.1),

revised estimates of Earth’s energy imbalance (Section  7.2), and 
revised estimates of ERF (Section  7.3). Accurate, in situ-based 
estimates of Earth’s energy imbalance can be made from around 
2006 based on near-global ocean temperature observations 
from the ARGO array of autonomous profiling floats (Sections 2.3 
and 7.2). Over the period 2006–2018 the Earth’s energy imbalance 
is estimated to be 0.79 ± 0.27 W m–2 (Section 7.2) and it is assumed 
that this value is also representative for the period 2006–2019. 
Anomalies are taken with respect to the baseline period 1850–1900, 
although other baselines could be chosen to avoid major volcanic 
activity (Otto et al., 2013; Lewis and Curry, 2018). Several lines of 
evidence, including ESM simulations (Lewis and Curry, 2015), energy balance modelling (Armour, 2017), inferred ocean warming given 
observed SSTs using ocean models (Gebbie and Huybers, 2019; 
Zanna et  al., 2019), and ocean warming reconstructed from noble

gas thermometry (Baggenstos et  al., 2019) suggest a  1850–1900 
Earth energy imbalance of 0.2 ± 0.2 W m–2. Combined with estimates 
of internal variability in Earth’s energy imbalance, calculated using 
periods of equivalent lengths of years as used in unforced ESM 
simulations (Palmer and McNeall, 2014; Sherwood et al., 2020), the 
anomalous energy imbalance between 1850–1900 and 2006–2019 
is estimated to be Δ N = 0.59 ± 0.35 W m–2. GSAT change between 
1850–1900 and 2006–2019 is estimated to be Δ T = 1.03°C ± 0.20 °C 
(Cross-Chapter Box  2.3 and Box  7.2) after accounting for internal 
temperature variability derived from unforced ESM simulations 
(Sherwood et al., 2020). The ERF change between 1850–1900 and 
2006–2019 is estimated to be Δ F  =  2.20 [1.53 to 2.91] W  m–2 
(Section 7.3.5) and the ERF for a doubling of CO 2 is estimated to be 
ΔF2×CO2 = 3.93 ± 0.47 W m–2 (Section 7.3.2). Employing these values 
within the traditional global energy balance framework described

above (following the methods of Otto et al. (2013) and accounting 
for correlated uncertainties between Δ F and Δ F2×CO2) produces a TCR 
of 1.9 [1.3 to 2.7] °C and an effective ECS of 2.5 [1.6 to 4.8] °C. 
These TCR and effective ECS values are higher than those in the 
recent literature (Otto et al., 2013; Lewis and Curry, 2015, 2018) but 
are comparable to those of Sherwood et al. (2020) who also used 
updated estimates of observed warming, Earth’s energy imbalance, 
and ERF .
The trend estimation method applied to global surface temperature 
affects derived values of ECS and TCR from the historical record. 
In this Report, the effective ECS is inferred from estimates that 
use global coverage of GSAT to estimate the surface temperature 
trends. The GSAT trend is assessed to have the same best estimate 
as the observed global mean surface temperature (GMST), 
although the  GSAT trend is assessed to have larger uncertainty

(see Cross-Chapter Box 2.3). Many previous studies have relied on 
HadCRUT4 GMST estimates that used the blended observations 
and did not interpolate over regions of incomplete observational 
coverage such as the Arctic. As a result, the ECS and TCR derived 
from these studies has smaller ECS and TCR values than those 
derived from model-inferred estimates (M. Richardson et al., 2016, 
2018). The energy budget studies assessing ECS in AR5 employed 
HadCRUT4 or similar measures of GMST trends. As other lines of 
evidence in that report used GSAT trends, this could partly explain 
why AR5-based energy budget estimates of ECS were lower than 
those estimated from other lines of evidence, adding to the overall 
disparity in M. Collins et al. (2013). In this report, GSAT is chosen 
as the standard measure of global surface temperature to aid 
comparison with previous model- and process-based estimates of 
ECS, TCR and climate feedbacks (see Cross-Chapter Box 2.3).

The traditional energy budget framework has been evaluated within 
ESM simulations by comparing the effective ECS estimated under 
historical forcing with the ECS estimated using regression methods 
(Box 7.1) under abrupt4xCO2  (Andrews et al., 2019; Winton et al., 
2020). For one CMIP6 model (GFDL-CM4.0), the value of effective 
ECS derived from historical energy budget constraints is 1.8°C while 
ECS is estimated to be 5.0°C (Winton et al., 2020). For another model

997
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77(HadGEM3-GC3.1-LL) the effective ECS derived from historical energy 
budget constraints is 4.1°C (average of four ensemble members) while 
ECS is estimated to be 5.5°C (Andrews et al., 2019). These modelling 
results suggest that the effective ECS under historical forcing could be 
lower than the true ECS owing to differences in radiative feedbacks 
induced by the distinct patterns of historical and equilibrium warming 
(Section 7.4.4.3). Using GFDL-CM4, Winton et al. (2020) also find that 
the value of TCR estimated from energy budget constraints within 
a  historical simulation (1.3°C) is substantially lower than the true 
value of TCR (2.1°C) diagnosed within a  1pctCO 2 simulation owing to 
a combination of the pattern effect and differences in the efficiency 
of ocean heat uptake between historical and 1pctCO 2 forcing . This 
section next considers how the true ECS can be estimated from

the historical energy budget by accounting for the pattern effect. 
However, owing to limited evidence  this section does not attempt to 
account for these effects in estimates of TCR.
Research since AR5 has introduced extensions to the traditional 
energy budget framework that account for the feedback dependence 
on temperature patterns by allowing for multiple radiative 
feedbacks operating on different time scales (Armour et al., 2013; 
Geoffroy et  al., 2013a; Armour, 2017; Proistosescu and Huybers, 
2017; Goodwin, 2018; Rohrschneider et  al., 2019), by allowing 
feedbacks to vary with the spatial pattern or magnitude of ocean 
heat uptake (Winton et al., 2010; Rose et al., 2014; Rugenstein et al., 
2016a), or by allowing feedbacks to vary with the type of radiative 
forcing agent (Kummer and Dessler, 2014; Shindell, 2014; Marvel 
et al., 2016; Winton et al., 2020). A direct way to account for the 
pattern effect is to use the relationship ECS = –Δ F2×CO2/(α  + α’),

where α = (Δ N  – Δ F)/ΔT is the effective feedback parameter 
(Box 7.1) estimated from historical global energy budget changes 
and α’ represents the change in the feedback parameter between 
which can be estimated using ESMs (Section 7.4.4.3; Armour, 2017; 
Andrews et  al., 2018, 2019; Lewis and Curry, 2018; Dong et  al., 
2020; Winton et al., 2020).
The net radiative feedback change between the historical warming 
pattern and the projected equilibrium warming pattern in response to 
CO 2 forcing ( α’) is estimated to be in the range 0.0 to 1.0 W m–2 °C–1 
(Figure  7.15). Using the value α’ = +0.5 ± 0.5 W  m–2 °C  –1 to 
represent this range illustrates the effect of changing radiative 
feedbacks on estimates of ECS. While the effective ECS inferred from 
historical warming is 2.5 [1.6 to 4.8] °C, ECS = –Δ F2×CO2/(α + α’) 
is 3.5 [1.7  to 13.8] °C. For comparison, values of α’ derived from

the response to historical and idealized CO 2 forcing within coupled 
climate models (Armour, 2017; Lewis and Curry, 2018; Andrews et al., 
2019; Dong et al., 2020; Winton et al., 2020) can be approximated 
as α’ = +0.1 ± 0.3 W m–2 °C–1 (Section 7.4.4.3), corresponding to 
a value of ECS of 2.7 [1.7 to 5.9] °C. In both cases, the low end of 
the ECS range is similar to that of the effective ECS inferred using the 
traditional energy balance model framework that assumes α’ = 0, 
reflecting a weak dependence on the value of α’ when ECS is small 
(Armour, 2017; Andrews et al., 2018); the low end of the ECS range 
is robust even in the hypothetical case that α’ is slightly negative. 
However, the high end of the ECS range is substantially larger than 
that of the effective ECS and strongly dependent on the value of α’.The values of ECS obtained from the techniques outlined above 
are all higher than those estimated from both AR5 and recently

published estimates (M. Collins et al., 2013; Otto et al., 2013; Lewis 
and Curry, 2015, 2018; Forster, 2016). Four revisions made in this 
Report are responsible for this increase: (i) an upwards revision of 
historic global surface temperature trends from newly published 
trend estimates (Section  2.3.1); (ii) an 8% increase in the ERF for 
ΔF2×CO2 (Section  7.3.2); (iii) a  more negative central estimate of 
aerosol ERF , which acts to reduce estimates of historical ERF trends; 
and (iv) accounting for the pattern effect in ECS estimates. Values of 
ECS provided here are similar to those based on the historical energy 
budget found in Sherwood et al. (2020), with small differences owing 
to methodological differences and the use of different estimates of 
observed warming, Earth’s energy imbalance, and ERF .
Overall, there is high confidence  that the true ECS is higher than the 
effective ECS as inferred from the historical global energy budget,

but there is substantial uncertainty in how much higher because of 
limited evidence  regarding how radiative feedbacks may change in 
the future. While several lines of evidence indicate that α’ > 0, the 
quantitative accuracy of feedback changes is not known at this time 
(Section 7.4.4.3). Global energy budget constraints thus provide high 
confidence  in the lower bound of ECS which is not sensitive to the 
value of α’: ECS is extremely unlikely  to be less than 1.6°C. Estimates 
of α’ that are informed by idealized CO 2 forcing simulations of 
coupled ESMs (Armour, 2017; Lewis and Curry, 2018; Andrews et al., 
2019; Dong et al., 2020; Winton et al., 2020) indicate a median value 
of ECS of around 2.7°C while estimates of α’ that are informed by 
observed historical sea surface temperature patterns (Andrews et al., 
2018) indicate a  median value of ECS of around 3.5°C. Owing to 
large uncertainties in future feedback changes, the historical energy

budget currently provides little information about the upper end of 
the ECS range.
7.5.2.2 Estimates of ECS and TCR Based 
on Climate Model Emulators
Energy budget emulators are far less complex than comprehensive 
ESMs (Section  1.5.3 and Cross-Chapter Box  7.1). For example, an 
emulator could represent the atmosphere, ocean, and land using 
a small number of connected boxes (e.g., Goodwin, 2016), or it could 
represent the global mean climate using two connected ocean layers 
(e.g., Cross-Chapter Box 7.1 and Supplementary Material 7.SM.2). The 
numerical efficiency of emulators means that they can be empirically 
constrained by observations: a  large number of possible parameter 
values (e.g., feedback parameter, aerosol radiative forcing, and ocean 
diffusivity) are randomly drawn from prior distributions; forward 
integrations of the model are performed with these parameters and 
weighted against observations of surface or ocean warming, producing

posterior estimates of quantities of interest such as TCR, ECS and 
aerosol forcing (Section  7.3). Owing to their reduced complexity, 
emulators lack full representations of the spatial patterns of sea surface 
temperature and radiative responses to changes in those patterns 
(discussed in Section  7.4.4.3) and many represent the net feedback 
parameter using a  constant value. The ranges of ECS reported by 
studies using emulators are thus interpreted here as representative of 
the effective ECS over the historical record rather than of the true ECS.

998
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Improved estimates of ocean heat uptake over the past two decades 
(Section 7.2) have diminished the role of ocean diffusivity in driving 
uncertainty in ECS estimates, leaving the main trade-off between 
posterior ranges in ECS and aerosol radiative forcing (Forest, 2002; 
Knutti et al., 2002; Frame et al., 2005). The AR5 (Bindoff et al., 2013) 
assessed a variety of estimates of ECS based on emulators and found 
that they were sensitive to the choice of prior parameter distributions 
and temperature datasets used, particularly for the upper end of the 
ECS range, though priors can be chosen to minimize the effect on 
results (e.g., Lewis, 2013). Emulators generally produced estimates 
of effective ECS between 1°C and 5°C and ranges of TCR between 
0.9°C and 2.6°C. Padilla et al. (2011) use a simple global-average 
emulator with two time scales (Section  7.5.1.2; Supplementary

Material 7.SM.2) to estimate a TCR of 1.6 [1.3 to 2.6] °C. Using the 
same model, Schwartz (2012) finds TCR in the range 0.9°C–1.9°C 
while Schwartz (2018) finds that an effective ECS of 1.7°C provides 
the best fit to the historical global surface temperature record 
while also finding a  median aerosol forcing that is smaller than 
that assessed in Section  7.3. Using an eight-box representation 
of the atmosphere–ocean–terrestrial system constrained by 
historical warming, Goodwin (2016) found an effective ECS of 
2.4  [1.4 to 4.4] °C while Goodwin (2018) found effective ECS 
to be in the range 2°C–4.3°C when using a prior for ECS based on 
paleoclimate constraints.
Using an emulator comprised of Northern and Southern hemispheres 
and an upwelling-diffusive ocean (Aldrin et al., 2012), with surface 
temperature and ocean heat content datasets updated to 2014, 
Skeie et al. (2018) estimate a TCR of 1.4 [0.9 to 2.0] °C and a median

effective ECS of 1.9 [1.2 to 3.1] °C. Using a  similar emulator 
comprised of land and ocean regions and an upwelling-diffusive 
ocean, with global surface temperature and ocean heat content 
datasets up to 2011, Johansson et al. (2015) find an effective ECS of 
2.5 [2.0 to 3.2] °C. The estimate is found to be sensitive to the choice 
of dataset endpoint and the representation of internal variability 
meant to capture the El  Niño–Southern Oscillation and Pacific 
Decadal Variability. Differences between these two studies arise, 
in part, from their different global surface temperature and ocean 
heat content datasets, different radiative forcing uncertainty ranges, 
different priors for model parameters, and different representations 
of internal variability. This leads to different estimates of effective 
ECS, with the median estimate of Skeie et  al. (2018) lying below 
the 5–95% range of effective ECS from Johansson et  al. (2015).

Moreover, while the Skeie et al. (2018) emulator has a constant value 
of the net feedback parameter, the Johansson et al. (2015) emulator 
allows distinct radiative feedbacks for land and ocean, contributing 
to the different results.
The median estimates of TCR and effective ECS inferred from emulator 
studies generally lie within the 5–95% ranges of those inferred from 
historical global energy budget constraints (1.3 to 2.7 °C for TCR and 
1.6 to 4.8 °C for effective ECS). Their estimates would be consistent 
with still-higher values of ECS when accounting for changes in 
radiative feedbacks as the spatial pattern of global warming evolves 
in the future (Section 7.5.2.1). Cross-Chapter Box 7.1 and references 
therein show that four very different physically based emulators can 
be calibrated to match the assessed ranges of historical GSAT change, ERF , ECS and TCR from across the report. Therefore, the fact that the

emulator effective ECS values estimated from previous studies tend to 
lie at the lower end of the range inferred from historical global energy 
budget constraints may reflect that the energy budget constraints in 
Section 7.5.2.1 use updated estimates of Earth’s energy imbalance, 
GSAT trends and ERF , rather than any methodological differences 
between the lines of evidence. The ‘emergent constraints’ on ECS 
based on observations of climate variability used in conjunction with 
comprehensive ESMs are assessed in Section 7.5.4.1.
7.5.2.3 Estimates of ECS Based on Variability in Earth’s 
Top-of-atmosphere Radiation Budget
While continuous satellite measurements of top-of-atmosphere 
(TOA) radiative fluxes (Figure  7.3) do not have sufficient accuracy 
to determine the absolute magnitude of Earth’s energy imbalance 
(Section 7.2.1), they provide accurate estimates of its variations and 
trends since the year 2002 that agree well with estimates based on

observed changes in global ocean heat content (Loeb et al., 2012; 
Johnson et  al., 2016; Palmer, 2017). When combined with global 
surface temperature observations and simple models of global energy 
balance, satellite measurements of TOA radiation afford estimates 
of the net feedback parameter associated with recent climate 
variability (Tsushima and Manabe, 2013; Donohoe et  al., 2014; 
Dessler and Forster, 2018). These feedback estimates, derived from 
the regression of TOA radiation on surface temperature variability, 
imply values of ECS that are broadly consistent with those from 
other lines of evidence (Forster, 2016; Knutti et al., 2017). A history of 
regression-based feedbacks and their uncertainties is summarized in 
Bindoff et al. (2013), Forster (2016), and Knutti et al. (2017).
Research since AR5 has noted that regression-based feedback 
estimates depend on whether annual- or monthly-mean data are used

and on the choice of lag employed in the regression, complicating 
their interpretation (Forster, 2016). The observed lead–lag relationship 
between global TOA radiation and global surface temperature, and 
its dependence on sampling period, is well replicated within unforced 
simulations of ESMs (Dessler, 2011; Proistosescu et al., 2018). These 
features arise because the regression between global TOA radiation 
and global surface temperature reflects a blend of different radiative 
feedback processes associated with several distinct modes of 
variability acting on different time scales (Annex IV), such as monthly 
atmospheric variability and interannual El Niño–Southern Oscillation 
(ENSO) variability (Lutsko and Takahashi, 2018; Proistosescu et al., 
2018). Regression-based feedbacks thus provide estimates of 
the radiative feedbacks that are associated with internal climate 
variability (e.g.,  Brown et  al., 2014), and do not provide a  direct

estimate of ECS ( high confidence ). Moreover, variations in global 
surface temperature that do not directly affect TOA radiation may 
lead to a positive bias in regression-based feedback, although this 
bias appears to be small, particularly when annual-mean data are 
used (Murphy and Forster, 2010; Spencer and Braswell, 2010, 2011; 
Proistosescu et al., 2018). When tested within ESMs, regression-based 
feedbacks have been found to be only weakly correlated with values 
of ECS (Chung et al., 2010), although cloudy-sky TOA radiation fluxes 
have been found to be moderately correlated with ECS at ENSO time 
scales within CMIP5 models (Lutsko and Takahashi, 2018).

999
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Finding such correlations within models requires simulations that 
span multiple centuries, suggesting that the satellite record may 
not be of sufficient length to produce robust feedback estimates. 
However, correlations between regression-based feedbacks and 
long-term feedbacks have been found to be higher when focused on 
specific processes or regions, such as for the cloud- or water-vapour 
feedbacks (Section 7.4.2; Dessler, 2013; Zhou et al., 2015). Assessing 
the global radiative feedback in terms of the more stable relationship 
between tropospheric temperature and TOA radiation offers another 
potential avenue for constraining ECS. The ‘emergent constraints’ 
on ECS based on variability in the TOA energy budget are assessed 
in Section 7.5.4.1.
7.5.2.4 Estimates of ECS Based on the Climate 
Response to Volcanic Eruptions
A number of studies consider the observed climate response to volcanic

eruptions over the 20th  century (Section  3.3.1 and Cross-Chapter 
Box 4.1; Knutti et al., 2017). However, the direct constraint on ECS 
is weak, particularly at the high end, because the temperature 
response to short-term forcing depends only weakly on radiative 
feedbacks and because it can take decades of a sustained forcing 
before the magnitude of temperature changes reflects differences in 
ECS across models (Geoffroy et al., 2013b; Merlis et al., 2014). It is 
also a challenge to separate the response to volcanic eruptions from 
internal climate variability in the years that follow them (Wigley et al., 
2005). Based on ESM simulations, radiative feedbacks governing 
the global surface temperature response to volcanic eruptions can 
be substantially different than those governing long-term global 
warming (Merlis et al., 2014; Marvel et al., 2016; Ceppi and Gregory, 
2019). Estimates based on the response to volcanic eruptions agree

with other lines of evidence (Knutti et  al., 2017), but they do not 
constitute a direct estimate of ECS ( high confidence ). The ‘emergent 
constraints’ on ECS based on climate variability, including volcanic 
eruptions, are summarized in Section 7.5.4.1.
7.5.2.5 Assessment of ECS and TCR Based 
Evidence from the instrumental temperature record, including 
estimates using global energy budget changes (Section  7.5.2.1), 
climate emulators (Section 7.5.2.2), variability in the TOA radiation 
budget (Section  7.5.2.3), and the climate response to volcanic 
eruptions (Section 7.5.2.4) produce median ECS estimates that range 
between 2.5°C and 3.5°C, but a best estimate value cannot be given 
owing to a strong dependence on assumptions about how radiative 
feedbacks will change in the future. However, there is robust 
evidence  and high agreement  across the lines of evidence that ECS is

extremely likely greater than 1.6°C ( high confidence ). There is robust 
evidence  and medium agreement  across the lines of evidence that 
ECS is very likely  greater than 1.8°C and likely greater than 2.2°C 
(high confidence ). These ranges of ECS correspond to estimates 
based on historical global energy budget constraints (Section 7.5.2.1) 
under the assumption of no feedback dependence on evolving SST 
patterns (i.e., α’ = 0) and thus represent an underestimate of the 
true ECS ranges that can be inferred from this line of evidence ( high 
confidence ). Historical global energy budget changes do not provide constraints on the upper bound of ECS, while the studies assessed in 
Section 7.5.2.3 based on climate variability provide low confidence  
in its value owing to limited evidence .
Global energy budget constraints indicate a central estimate (median) 
TCR value of 1.9°C and that TCR is likely  in the range 1.5 to 2.3 °C and

very likely in the range 1.3 to 2.7 °C ( high confidence ). Studies that 
constrain TCR based on the instrumental temperature record used in 
conjunction with ESM simulations are summarized in Section 7.5.4.3.
7.5.3 Estimates of ECS Based on Paleoclimate Data
Estimates of ECS based on paleoclimate data are complementary 
to, and largely independent from, estimates based on process-based 
studies (Section  7.5.1) and the instrumental record (Section  7.5.2). 
The  strengths of using paleoclimate data to estimate ECS include: 
(i) the estimates are based on observations of a real-world Earth system 
response to a  forcing, in contrast to using estimates from process-
based modelling studies or directly from models; (ii) the forcings 
are often relatively large (similar in magnitude to a CO 2 doubling or 
more), in contrast to data from the instrumental record; (iii) the forcing 
often changes relatively slowly so the system is close to equilibrium;

as such, all individual feedback parameters, αx, are included, and 
complications associated with accounting for ocean heat uptake are 
reduced or eliminated, in contrast to the instrumental record. However, 
there can be relatively large uncertainties on estimates of both the 
paleo forcing and paleo global surface temperature response, and 
care must be taken to account for long-term feedbacks associated 
with ice sheets (Section 7.4.2.6), which often play an important role 
in the paleoclimate response to forcing, but which are not included in 
the definition of ECS. Furthermore, the state-dependence of feedbacks 
(Section 7.4.3) means that climate sensitivity during Earth’s past may 
not be the same as it is today, which should be accounted for when 
interpreting paleoclimate estimates of ECS.
AR5 stated that data and modelling of the Last Glacial Maximum 
(LGM; Cross-Chapter Box 2.1) indicated that it was very unlikely  that

ECS lay outside the range 1°C–6°C (Masson-Delmotte et al., 2013). 
Furthermore, AR5 reported that climate records of the last 65 million 
years indicated an ECS 95% confidence interval of 1.1 to 7.0 °C.
Compared with AR5, there are now improved constraints on estimates 
of ECS from paleoclimate evidence. The strengthened understanding 
and improved lines of evidence come in part from the use of 
high-resolution paleoclimate data across multiple glacial–interglacial 
cycles, taking into account state-dependence (Section  7.4.3; von 
der Heydt et  al., 2014; Köhler et  al., 2015, 2017, 2018; Friedrich 
et al., 2016; Snyder, 2019; Stap et al., 2019) and better constrained 
pre-ice-core estimates of atmospheric CO 2 concentrations (Martínez-
Botí et al., 2015; Anagnostou et al., 2016, 2020; de la Vega et al., 
2020) and surface temperature (Hollis et al., 2019; Inglis et al., 2020; 
McClymont et al., 2020).
Overall, the paleoclimate lines of evidence regarding climate sensitivity

can be broadly categorized into two types: estimates of radiative 
forcing and temperature response from paleo proxy measurements,

1000
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7and emergent constraints on paleoclimate model simulations. This 
section focuses on the first type only; the second type (emergent 
constraints) are discussed in Section 7.5.4.
In order to provide estimates of ECS, evidence from the paleoclimate 
record can be used to estimate forcing (Δ F) and global surface 
temperature response (Δ T) in Equation 7.1, Box  7.1, under the 
assumption that the system is in equilibrium (i.e., Δ N = 0). However, 
there are complicating factors when using the paleoclimate record 
in this way, and these challenges and uncertainties are somewhat 
specific to the time period being considered.
7.5.3.1 Estimates of ECS from the Last Glacial Maximum
The LGM (Cross-Chapter Box 2.1) has been used to provide estimates 
of ECS (see Table 7.11 for estimates since AR5; Sherwood et al., 2020; 
Tierney et al., 2020b). The major forcings and feedback processes that

led to the cold climate at that time (e.g., CO 2, non-CO 2 greenhouse 
gases, and ice sheets) are relatively well-known (Section  5.1), 
orbital forcing relative to pre-industrial was negligible, and there 
are relatively high spatial resolution and well-dated paleoclimate 
temperature data available for this time period (Section  2.3.1). 
Uncertainties in deriving global surface temperature from the LGM 
proxy data arise partly from uncertainties in the calibration from the 
paleoclimate data to local annual mean surface temperature, and 
partly from uncertainties in the conversion of the local temperatures to 
an annual mean global surface temperature. Overall, the global mean 
LGM cooling relative to pre-industrial is assessed to be very likely  
from 5 to 7 °C (Section 2.3.1). The LGM climate is often assumed to 
be in full equilibrium with the forcing, such that Δ N in Equation 7.1, 
Box 7.1, is zero. A calculation of sensitivity using solely CO 2 forcing,

and assuming that the LGM ice sheets were in equilibrium with that 
forcing, would give an Earth System Sensitivity (ESS) rather than an 
ECS (see Box 7.1). In order to calculate an ECS, which is defined here 
to include all feedback processes except ice sheets, the approach 
of Rohling et  al. (2012) can be used. This approach introduces an 
additional forcing term in Equation 7.1, Box 7.1, that quantifies the 
resulting forcing associated with the ice-sheet feedback (primarily 
an estimate of the radiative forcing associated with the change in 
surface albedo). However, differences between studies as to which 
processes are considered as forcings (for example, some studies 
also include vegetation and/or aerosols, such as dust, as forcings), 
means that published estimates are not always directly comparable. 
Additional uncertainty arises from the magnitude of the ice-sheet 
forcing itself (Stap et  al., 2019; Zhu and Poulsen, 2021), which is

often estimated using ESMs. Furthermore, the ECS at the LGM may 
differ from that of today due to state-dependence (Section  7.4.3). 
Here, only studies that report values of ECS that have accounted for 
the long-term feedbacks associated with ice sheets, and therefore 
most closely estimate ECS as defined in this chapter, are assessed 
here (Table 7.11).
7.5.3.2 Estimates of ECS from Glacial–Interglacial Cycles
Since AR5, several studies have extended the Rohling et  al. 
(2012) approach (described above for the LGM) to the glacial–
interglacial cycles of the last approximately 1 to 2 million years (von  der  Heydt  et  al., 2014; Köhler et  al., 2015, 2017, 2018; 
Friedrich et al., 2016; Royer, 2016; Snyder, 2019; Stap et al., 2019; 
Friedrich and Timmermann, 2020; see Table  7.11). Compared to 
the LGM, uncertainties in the derived ECS from these periods are 
in general greater, due to greater uncertainty in global surface

temperature (due to fewer individual sites with proxy temperature 
records), ice-sheet forcing (due to a  lack of detailed ice-sheet 
reconstructions), and CO 2 forcing (for those studies that include 
the pre-ice-core period, where CO 2 reconstructions are substantially 
more uncertain). Furthermore, accounting for varying orbital forcing 
in the traditional global mean forcing and response energy budget 
framework (Box 7.1) is challenging (Schmidt et al., 2017b), due to 
seasonal and latitudinal components of the forcing that, despite 
a close-to-zero orbital forcing in the global annual mean, can directly 
result in responses in annual mean global surface temperature 
(Liu et al., 2014), ice volume (Abe-Ouchi et al., 2013), and feedback 
processes such as those associated with methane (Singarayer et al., 
2011). In addition, for time periods in which the forcing relative to 
the modern era is small (interglacials), the inferred ECS has relatively

large uncertainties because the forcing and temperature response 
(ΔF and Δ T in Equation 7.1, Box 7.1) are both close to zero.
7.5.3.3 Estimates of ECS from Warm Periods 
of the Pre-Quaternary
In the pre-Quaternary (prior to about 2.5 million years ago), 
the forcings and response are generally of the same sign and 
similar magnitude as future projections of climate change (Burke 
et  al., 2018; Tierney et  al., 2020a). Similar uncertainties as for 
the LGM apply, but in this case a major uncertainty relates to the 
forcing, because prior to the ice-core record there are only indirect 
estimates of CO 2 concentration. However, advances in pre-ice-core 
CO 2 reconstruction (e.g.,  Foster and Rae, 2016; Super et  al., 2018; 
Witkowski et al., 2018) mean that the estimates of pre-Quaternary 
CO 2 have less uncertainty than at the time of AR5, and these time 
periods can now contribute to an assessment of climate sensitivity

(Table 7.11). The mid-Pliocene Warm Period (MPWP; Cross-Chapter 
Box 2.1 and Cross-Chapter Box 2.4) has been targeted for constraints 
on ECS (Martínez-Botí et al., 2015; Sherwood et al., 2020), due to 
the fact that CO 2 concentrations were relatively high at this time 
(350–425  ppm) and because the MPWP is sufficiently recent that 
topography and continental configuration are similar to modern-day. 
As such, a comparison of the MPWP with the pre-industrial climate 
provides probably the closest natural geological analogue for the 
modern day that is useful for assessing constraints on ECS, despite 
the effects of different geographies not being negligible (global 
surface temperature patterns; ocean circulation). Furthermore, 
the global surface temperature of the MPWP was such that non-
linearities in feedbacks (Section 7.4.3) were relatively modest. Within 
the MPWP , the KM5c interglacial has been identified as a particularly

useful time period for assessing ECS (Haywood et al., 2013, 2016b) 
because Earth’s orbit during that time was very similar to that of the 
modern day.
Further back in time, in the Early Eocene (Cross-Chapter Box  2.1), 
uncertainties in forcing and temperature change become larger, but 
the signals are generally larger too (Anagnostou et al., 2016, 2020;

1001
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Shaffer et al., 2016; Inglis et al., 2020). Caution must be applied when 
estimating ECS from these time periods, due to differing continental 
position and topography/bathymetry (Farnsworth et  al., 2019), 
and due to temperature-dependence of feedbacks (Section  7.4.3). 
On even longer time scales of the last 500 million years (Royer, 2016) 
the temperature and CO 2 measurements are generally asynchronous, 
presenting challenges in using this information for assessments 
of ECS.
7.5.3.4 Synthesis of ECS Based on Paleo Radiative 
Forcing and Temperature
The lines of evidence directly constraining ECS from paleoclimates 
are summarized in Table  7.11. Although some of the estimates 
in Table  7.11 are not independent because they use similar proxy 
records to each other (e.g., von der Heydt et al., 2014; Köhler et al.,

2015, 2017; Stap et al., 2019), there are still multiple independent lines of paleoclimate evidence regarding ECS, from differing past 
time periods: LGM (Sherwood et  al., 2020; Tierney et  al., 2020b); 
glacial–interglacial (Royer, 2016; Köhler et  al., 2017; Snyder, 
2019; Friedrich and Timmermann, 2020); Pliocene (Martínez-Botí 
et  al., 2015; Sherwood et  al., 2020); and the Eocene (Anagnostou 
et  al.,  2016, 2020; Shaffer et  al., 2016; Inglis et  al., 2020), with 
differing proxies for estimating forcing (e.g.,  CO 2 from ice cores 
or boron isotopes) and response (e.g.,  global surface temperature 
from δ18O, Mg/Ca or Antarctic δD). Furthermore, although different 
studies have uncertainty estimates that account for differing sources 
of uncertainty, some studies (Snyder, 2019; Inglis et  al., 2020; 
Sherwood et al., 2020; Tierney et al., 2020b) do consider many of the 
uncertainties discussed in Sections 7.5.3.1–7.5.3.3. All the studies

based on glacial–interglacial cycles account for some aspects of the 
state-dependence of climate sensitivity (Section 7.4.3) by considering 
only the warm phases of the Pleistocene, although what constitutes 
a warm phase is defined differently across the studies.
Table 7.1 1 | Estimates of equilibrium climate sensitivity (ECS) derived from paleoclimates; from AR5 (above double lines) and from post-AR5 studies 
(below double lines).  Many studies provide an estimate of ECS that includes only CO 2 and the ice-sheet feedback as forcings, providing an estimate of S [CO2, LI]  using the 
notation of Rohling et al. (2012), which is equivalent to our definition of ECS (Box 7.1). However, some studies provide estimates of other types of sensitivity (column 4). Different 
studies (column 1) focus on different time periods (column 2) and use a variety of different paleoclimate proxies and models (column 3) to give a best estimate (column 5) and/or

a range (column 5). The published ranges given account for varying sources of uncertainty (column 6). See Cross-Chapter Box 2.1 for definition of time periods. All temperature 
values in column 5 are shown to a precision of 1 decimal place.
(1) Study (2) Time Period 
(kyr = thousand years; 
Myr = million years; 
Ma = million years ago)(3) Proxies/Models Used for 
CO 2, Temperature (T) and 
Global Scaling (GS)(4) Climate 
Sensitivity 
Classification 
According to 
Rohling et al. (2012)(5) Published Best 
Estimate of ECS  
[and/or Range] (6) Range 
Accounts For:
AR5 (Masson-Delmotte 
et al., 2013)LGM (Last Glacial Maximum)Assessment of multiple lines 
of evidenceSa = ECSa [very likely  >1.0;  
very unlikely  >6.0°C]Multiple sources 
of uncertainty
et al., 2013)Cenozoic (last 65 Myr)Assessment of multiple lines 
of evidenceS[CO2,LI] [95% range: 1.1°C to 7.0°C]Multiple sources 
Tierney et al. (2020b) LGMCO 2: ice core

T: multi-proxyS[CO2,LI,CH4, N2O]3.8°C
[68% range: 3.3°C to 4.3°C]Multiple sources 
Sherwood et al. (2020) LGMCO 2: ice core
T: multiple lines of evidenceS[CO2, LI, CH4, N2O, dust, VG]maximum likelihood 
[likelihood of 1.0]: 2.6°C 
[likely  range depends on 
chosen prior; likelihood of 
0.6: 1.6°C to 4.4°C]Multiple sources 
von der Heydt et al. 
(2014)Warm states of glacial–
interglacial cycles of last 800 kyrCO 2: ice core 
T: ice core δD, benthic δ18O
GS: Schneider von Deimling et al. (2006); 
Annan and Hargreaves (2013)S[CO2,LI]3.5°C 
[range: 3.1°C to 5.4°C]bVarying LGM global 
mean temperatures 
used for scaling
Köhler et al. (2015)Warm states of glacial–
interglacial cycles of last 2 MyrCO 2: ice core alkenones and 
boron isotopes
T: benthic δ18O
GS: PMIP LGM and PlioMIP MPWPS[CO2,LI]5.7°C
[68% range: 3.7°C to 8.1°C]bTemporal variability 
in records
Köhler et al. (2017)Warm states of glacial–
interglacial cycles of last 2 MyrCO 2: boron isotopes

GS: PMIP LGM and PlioMIP MPWP S[CO2,LI]5.6°C
[16th to 84th percentile: 
3.6°C to 8.1°C]bTemporal variability 
in records
Köhler et al. (2018)Warm states of glacial–
interglacial cycles of last 
800 kyr, excluding those for 
which CO 2 and T divergeCO 2: ice cores
T: benthic δ18O, alkenone, Mg/Ca, 
MAT, and faunal SST
GS: PMIP3 LGMS[CO2, LI] [range: 3.0°C to 5.9°C]bVarying 
reconstructions
Stap et al. (2019)States of glacial–interglacial 
cycles of last 800 kyr for which 
forcing is zero compared with 
modern, excluding those for 
GS: PMIP LGM and PlioMIP MPWPS[CO2, LI] [range: 6.1°C to 11.0°C]bVarying efficacies 
of ice-sheet forcing

1002
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7None of the post-AR5 studies in Table 7.11 have an estimated lower 
range for ECS below 1.6°C. As such, based solely on the paleoclimate 
record, it is assessed to be very likely  that ECS is greater than 1.5°C 
In general, it is the studies based on the warm periods of the 
glacial–interglacial cycles (Section 7.5.3.2) that give the largest values 
of ECS. Given the large uncertainties associated with estimating 
the magnitude of the ice-sheet forcing during these intervals (Stap 
et  al., 2019), and other uncertainties discussed in Section  7.5.3.2, 
in particular the direct effect of orbital forcing on estimates of ECS, 
there is only low confidence  in estimates from the studies based on 
glacial–interglacial periods. This low confidence  also results from the 
temperature-dependence of the net feedback parameter, α, resulting

from several of these studies (Figure 7.10), that is hard to reconcile 
with the other lines of evidence for α, including proxy estimates from warmer paleoclimates (Section  7.4.3.2). A  central estimate 
of  ECS, derived from the LGM (Section  7.5.3.1) and warm periods 
of the pre-Quaternary (Section  7.5.3.3), that takes into account 
some of the interdependencies between the different studies, can be 
obtained by averaging across studies within each of these two time 
periods, and then averaging across the two time periods; this results 
in a central estimate of 3.4°C. This approach of focussing on the LGM 
and warm climates was also taken by Sherwood et al. (2020) in their 
assessment of ECS from paleoclimates. An alternative method is to 
average across all studies, from all periods, that have considered 
multiple sources of uncertainty (Table  7.11); this approach leads 
to a  similar central estimate of 3.3°C. Overall, we assess medium

confidence  for a central estimate of 3.3°C to 3.4°C.
There is more variation in the upper bounds of ECS than in the lower 
bounds. Estimates of ECS from pre-Quaternary warm periods have an (1) Study (2) Time Period 
Friedrich et al. (2016)Warm states of glacial–
interglacial cycles of last 780 kyrCO 2: ice cores
T: alkenone, Mg/Ca, MAT, and 
faunal SST
GS: PMIP3 LGMS[GHG,LI,AE]4.9°C
[Likely  range: 4.3°C to 5.4°C]bVarying LGM global 
mean temperatures, 
aerosol forcing 
Friedrich and 
Timmermann (2020)Last glacial–interglacial cycle CO 2: ice cores
T: alkenone, Mg/Ca, MATS[GHG,LI,AE]4.2°C
[range: 3.4°C to 6.2°C]bVarying aerosol 
forcings
Snyder (2019)Interglacial periods and

intermediateglacial climates 
of last 800 kyrCO 2: ice cores
T: alkenone, Mg/Ca, species 
assemblages
GS: PMIP models S[GHG,LI,AE,VG]3.1°C
[67% range: 2.6°C to 3.7°C]bMultiple sources 
Royer (2016)Glacial–interglacial cycles of 
the Pliocene (3.4 to 2.9 Ma)CO 2: boron isotopes
T: benthic δ18OS[CO2,LI]10.2°C 
[68% range: 8.1°C to 12.3°C]Temporal variability 
in records
Martínez-Botí et al. 
(2015)PlioceneCO 2: boron isotopes
T: benthic δ18OS[CO2,LI]3.7°C
[68% range: 3.0°C to 4.4°C]bPliocene sea level, 
temporal variability 
in records
Sherwood et al. (2020) PlioceneCO 2: boron isotopes
T: multiple lines of evidenceS[CO2, LI,N2O,CH4,VG]maximum likelihood 
[likelihood of 1.0]: 3.2°C 
chosen prior; likelihood 
of 0.6: 1.8°C to 5.2°C]Multiple sources 
Anagnostou et al. 
(2016)Early EoceneCO 2: boron isotopes
T: various terrestrial MAT, Mg/Ca, 
TEX, δ18O SSTS[CO2,LI]3.6°C
[66% range: 2.1°C to 4.6°C]Varying calibrations

for temperature 
and CO 2
(2020)Late Eocene (41.2 to 33.9 Ma)CO 2: boron isotopes
T: one SST record
GS: CESM1S[CO2,LI]3.0°C
[68% range: 1.9°C to 4.1°C]Temporal variability 
in records
Shaffer et al. (2016)Pre-PETM (  Paleocene–Eocene 
Thermal Maximum)CO 2: mineralogical, carbon cycling, 
and isotope constraints 
TEX, δ18O SSTS[GHG,AE,VG,LI] [range: 3.3°C to 5.6°C]Varying calibration 
of temperature 
and CO 2
Inglis et al. (2020)Mean of EECO (Early Eocene 
Climatic Optimum), PETM, 
and latest PaleoceneCO 2: boron isotopes
T: multiproxy SST and SAT
GS: EoMIP models S[CO2,LI, VG,AE]3.7°C  
[likely  range: 2.2°C to 5.3°C]Multiple sources 
a Sa in this table denotes a classification of climate sensitivity following Rohling et al. (2012). 
b Although our assessed value of ERF due to CO 2 doubling is 3.93 W m–2 (Section 7.3.2.1), for these studies the best estimate and range of temperature is calculated from the

published estimate of sensitivity in units of °C (W m–2)–1 using an ERF of 3.7 W m–2, for consistency with the typical value used in the studies to estimate the paleo CO 2 forcing.

1003
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77average upper range of 4.9°C, and from the LGM of 4.4°C; taking 
into account the independence of the estimates from these two time 
periods, and accounting for state-dependence (Section  7.4.3) and 
other uncertainties discussed in Section 7.5.3, the paleoclimate record 
on its own indicates that ECS is likely  less than 4.5°C. Given the higher 
values from many glacial–interglacial studies, this value has only 
medium  confidence . Despite the large variation in individual studies 
at the extreme upper end, all except two studies (both of which are 
from glacial–interglacial time periods associated with low confidence ) 
have central estimates that are below 6°C; overall we assess that it is 
extremely likely  that ECS is below 8°C ( high confidence ).
7.5.4 Estimates of ECS and TCR Based 
on Emergent Constraints
ESMs exhibit substantial spread in ECS and TCR (Section  7.5.7).

Numerous studies have leveraged this spread in order to narrow 
estimates of Earth’s climate sensitivity by employing methods known 
as ‘emergent constraints’ (Section  1.5.4). These methods establish 
a relationship between an observable and either ECS or TCR based 
on an ensemble of models, and combine this information with 
observations to constrain the probability distribution of ECS or TCR. 
Most studies of this kind have clearly benefitted from the international 
efforts to coordinate the CMIP and other multi-model ensembles.
A number of considerations must be taken into account when 
assessing the diverse literature on ECS and TCR emergent constraints. 
For instance, it is important to have physical and theoretical bases 
for the connection between the observable and modelled ECS or 
TCR since in model ensembles thousands of relationships that pass 
statistical significance can be found simply by chance (Caldwell

et al., 2014). It is also important that the underlying model ensemble 
does not exhibit a shared bias that influences the simulation of the 
observable quantity on which the emergent constraint is based. 
Also, correctly accounting for uncertainties in both the observable 
(including measurement uncertainty and natural variability) and 
the emergent constraint statistical relationship can be challenging, 
in particular in cases where the latter is not expected to be linear 
(Annan et  al., 2020). A  number of proposed emergent constraints 
leverage variations in modelled ECS arising from tropical low-clouds, 
which was the dominant source of inter-model spread in the CMIP5 
ensemble used in most emergent constraint studies. Since ECS is 
dependent on the sum of individual feedbacks (Section 7.5.1) these 
studies implicitly assume that all other feedback processes in models 
are unbiased and should therefore rather be thought of as constraints

on tropical low-cloud feedback (Klein and Hall, 2015; Qu et al., 2018; 
Schlund et al., 2020). The following sections go through a range of 
emergent constraints and assess their strengths and limitations.
7.5.4.1 Emergent Constraints Using Global or Near-global 
Surface Temperature Change
Perhaps the simplest class of emergent constraints regress past 
equilibrium paleoclimate temperature change against modelled ECS 
to obtain a relationship that can be used to translate a past climate 
change to ECS. The advantage is that these are constraints on the sum of all feedbacks, and furthermore unlike constraints on the 
instrumental record they are based on climate states that are at, or 
close to, equilibrium. So far, these emergent constraints have been 
limited to the Last Glacial Maximum (LGM; Cross-Chapter Box 2.1) 
cooling (Hargreaves et al., 2012; Schmidt et al., 2014; Renoult et al., 
2020) and warming in the mid-Pliocene Warm Period (MPWP;

Cross-Chapter Box 2.1 and Cross-Chapter Box 2.4; Hargreaves and 
Annan, 2016; Renoult et al., 2020) due to the availability of sufficiently 
large multi-model ensembles for these two cases. The paleoclimate 
emergent constraints are limited by structural uncertainties in the 
proxy-based global surface temperature and forcing reconstructions 
(Section  7.5.3), possible differences in equilibrium sea surface 
temperature patterns between models and the real world, and 
a small number of model simulations participating, which has led to 
divergent results. For example, Hopcroft and Valdes (2015) repeated 
the study based on the LGM by Hargreaves et  al. (2012) using 
another model ensemble and found that the emergent constraint 
was not robust, whereas studies using multiple available ensembles 
retain useful constraints (Schmidt et al., 2014; Renoult et al., 2020). 
Also, the results are somewhat dependent on the applied statistical

methods (Hargreaves and Annan, 2016). However, Renoult et  al. 
(2020) explored this and found 95th percentiles of ECS below 6°C for 
LGM and Pliocene individually, regardless of statistical approach, and 
by combining the two estimates the 95th percentile dropped to 4.0°C. 
The consistency between the cold LGM and warm MPWP emergent 
constraint estimates increases confidence in these estimates, and 
further suggests that the dependence of feedback on climate mean 
state (Section 7.4.3) as represented in PMIP models used in these 
studies is reasonable.
Various emergent constraint approaches using global warming over 
the instrumental record have been proposed. These benefit from more 
accurate data compared with paleoclimates, but suffer from the fact 
that the climate is not in equilibrium, thereby assuming that ESMs 
on average accurately depict the ratio of short-term to long-term 
global warming. Global warming in climate models over 1850 to the

present day exhibits no correlation with ECS, which is partly due to 
a  substantial number of models exhibiting compensation between 
a high climate sensitivity with strong historical aerosol cooling (Kiehl, 
2007; Forster et al., 2013; Nijsse et al., 2020). However, the aerosol 
cooling increased up until the 1970s, when air quality regulations 
reduced the emissions from Europe and North America whereas 
other regions saw increases resulting in a subsequently reduced pace 
of global mean aerosol ERF increase (Section 2.2.8 and Figure 2.10). 
Energy balance considerations over the 1970–2010 period gave 
a  best estimate ECS of 2.0°C (Bengtsson and Schwartz, 2013), 
however this estimate did not account for pattern effects. To address 
this limitation an emergent constraint on 1970–2005 global warming 
was demonstrated to yield a  best estimate ECS of 2.83 [1.72 to 
4.12] °C (Jiménez-de-la-Cuesta and Mauritsen, 2019). The study

was followed up using CMIP6 models yielding a best estimate ECS 
of 2.6 [1.5 to 4.0] °C based on 1975–2019 global warming (Nijsse 
et  al., 2020), thereby confirming the emergent constraint. Internal 
variability and forced or unforced pattern effects may influence the 
results (Jiménez-de-la-Cuesta and Mauritsen, 2019; Nijsse et  al., 
2020). For instance the Atlantic Multi-decadal Oscillation changed 
from negative to positive anomaly, while the Indo-Pacific Oscillation

1004
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7changed less over the 1970–2005 period, potentially leading to 
high-biased results (Jiménez-de-la-Cuesta and Mauritsen, 2019), 
whereas during the later period 1975–2019 these anomalies roughly 
cancel (Nijsse et al., 2020). Pattern effects may have been substantial 
over these periods (Andrews et  al., 2018), however the extent to 
which TOA radiation anomalies influenced surface temperature may 
have been dampened by the deep ocean (Hedemann et  al., 2017; 
Newsom et al., 2020). It is therefore deemed more likely than not  that 
these estimates based on post-1970s global warming are biased low 
by internal variability.
A study that developed an emergent constraint based on the response 
to the Mount Pinatubo 1991 eruption yielded a  best estimate of 
2.4 [likely range 1.7 to 4.1] °C (Bender et al., 2010). When accounting 
for ENSO variations they found a somewhat higher best estimate of

2.7°C, which is in line with results of later studies that suggest ECS 
inferred from periods with substantial volcanic activity are low-biased 
due to strong pattern effects (Gregory et  al., 2020) and that the 
short-term nature of volcanic forcing could exacerbate possible 
underestimates of modelled pattern effects.
Lagged correlations present in short-term variations in the global surface 
temperature can be linked to ECS through the fluctuation–dissipation 
theorem, which is derived from a single heat-reservoir model (Einstein, 
1905; Hasselmann, 1976; Schwartz, 2007; Cox et al., 2018a). From this 
it follows that the memory carried by the heat capacity of the ocean 
results in low-frequency global temperature variability (red noise) 
arising from high-frequency (white noise) fluctuations in the radiation 
balance, for example, caused by weather. Initial attempts to apply the 
theorem to observations yielded a fairly low median ECS estimate of

1.1°C (Schwartz, 2007), a result that was disputed (Foster et al., 2008; 
Knutti et al., 2008). Recently it was proposed by Cox et al. (2018a) to use 
variations in the historical experiments of the CMIP5 climate models as 
an emergent constraint giving a median ECS estimate of 2.8 [1.6 to 
4.0] °C. A  particular challenge associated with these approaches is 
to separate short-term from long-term variability, and slightly arbitrary 
choices regarding the methodology of separating these in the global 
surface temperature from long-term signals in the historical record, 
omission of the more strongly forced period after 1962, as well as input 
data choices, can lead to median ECS estimates ranging from 2.5°C to 
3.5°C (Brown et al., 2018; Po-Chedley et al., 2018a; Rypdal et al., 2018). 
Calibrating the emergent constraint using CMIP5 modelled internal 
variability as measured in historical control simulations (Po-Chedley

et  al., 2018a) will inevitably lead to an overestimated ECS due to 
externally forced short-term variability present in the historical record 
(Cox et  al., 2018b). Contrary to constraints based on paleoclimates 
or global warming since the 1970s, when based on CMIP6 models 
a  higher, yet still well-bounded ECS estimate of 3.7 [2.6 to 4.8] °C 
is obtained (Schlund et al., 2020). A more problematic issue is raised 
by Annan et  al. (2020) who showed that the upper bound on ECS 
estimated this way is less certain when considering deep-ocean heat 
uptake. In conclusion, even if not inconsistent, these limitations prevent 
us from directly using this type of constraint in the assessment.
Short-term variations in the TOA energy budget, observable from 
satellites, arising from variations in the tropical tropospheric 
temperature have been linked to ECS through models, either as a  range of models consistent with observations (those with ECS

values between 2.0°C and 3.9°C; Dessler et al., 2018) or as a formal 
emergent constraint by deriving further model-based relationships 
to yield a median of 3.3 [2.4 to 4.5] °C (Dessler and Forster, 2018). 
There are major challenges associated with short-term variability 
in the energy budget, in particular how it relates to the long-term 
forced response of clouds (Colman and Hanson, 2017; Lutsko and 
Takahashi, 2018). Variations in the surface temperature that are not 
directly affecting the radiation balance lead to an overestimated 
ECS when using linear regression techniques where it appears as 
noise in the independent variable (Proistosescu et al., 2018; Gregory 
et  al., 2020). The latter issue is largely overcome when using the 
tropospheric mean or mid-tropospheric temperature (Trenberth et al., 
2015; Dessler et al., 2018).
7.5.4.2 Emergent Constraints Focused on Cloud 
Feedbacks and Present-day Climate
A substantial number of emergent constraint studies focus on

observables that are related to tropical low-cloud feedback processes 
(Volodin, 2008; Sherwood et al., 2014; Zhai et al., 2015; Brient and 
Schneider, 2016; Brient et al., 2016). These studies yield median ECS 
estimates of 3.5°C–4°C and in many cases indicate low likelihoods 
of values below 3°C. The approach has attracted attention since 
most of the spread in climate sensitivity seen in CMIP5, and earlier 
climate model ensembles, arises from uncertainty in low-cloud 
feedbacks (Bony and Dufresne, 2005; Wyant et  al., 2006; Randall 
et al., 2007; Vial et al., 2013). Nevertheless, this approach assumes 
that all other feedback processes are unbiased (Klein and Hall, 2015; 
Qu et al., 2018; Schlund et al., 2020), for instance the possibly missing 
negative anvil area feedback or the possibly exaggerated mixed-
phase cloud feedback (Section 7.4.2.4). Thus, the subset of emergent 
constraints that focus on low-level tropical clouds are not necessarily

inconsistent with other emergent constraints of ECS. Related 
emergent constraints that focus on aspects of the tropical circulation 
and ECS have led to conflicting results (Su et al., 2014; Tian, 2015; 
Lipat et  al., 2017; Webb and Lock, 2020), possibly because these 
processes are not the dominant factors in causing the inter-model 
spread (Caldwell et al., 2018).
The fidelity of models in reproducing aspects of temperature variability 
or the radiation budget has also been proposed as emergent constraints 
on ECS (Covey et al., 2000; Knutti et al., 2006; Huber et al., 2010; Bender 
et al., 2012; Brown and Caldeira, 2017; Siler et al., 2018a). Here indices 
based on spatial or seasonal variability are linked to modelled ECS, 
and overall the group of emergent constraints yields best estimates 
of 3.3°C–3.7°C. Nevertheless, the physical relevance of present-day 
biases to the sum of long-term climate change feedbacks is unclear

and therefore these constraints on ECS are not considered reliable.
7.5.4.3 Assessed ECS and TCR Based on Emergent Constraints
The available emergent constraint studies have been divided into 
two classes: (i) those that are based on global or near-global indices, 
such as global surface temperature and the TOA energy budget; 
and (ii) those that are more focussed on physical processes, such as 
the fidelity of phenomena related to low-level cloud feedbacks or

1005
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77present-day climate biases. The former class is arguably superior in 
representing ECS, since it is a global surface temperature or energy 
budget change, whereas the latter class is perhaps best thought of as 
providing constraints on individual climate feedbacks, for example, the 
determination that low-level cloud feedbacks are positive. The latter 
result is consistent with and confirms process-based estimates of 
low-cloud feedbacks (Section 7.4.2.4), but are potentially biased as 
a group by missing or biased feedbacks in ESMs and is accordingly 
not taken into account here. A  limiting case here is Dessler and 
Forster (2018) which is focused on monthly co-variability in the 
global TOA energy budget with mid-tropospheric temperature, at 
which time scale the surface-albedo feedback is unlikely to operate, 
thus implicitly assuming it is unbiased in the model ensemble.

In the first group of emergent constraints there is broad agreement 
on the best estimate of ECS ranging from 2.4°C–3.3°C. At the lower 
end, nearly all studies find lower bounds (5th percentiles) around 
1.5°C, whereas several studies indicate 95th percentiles as low 
as 4°C. Considering both classes of studies, none of them yield 
upper very likely bounds above 5°C. Since several of the emergent 
constraints can be considered nearly independent one could assume 
that emergent constraints provide very strong evidence on ECS by 
combining them. Nevertheless, this is not done here because there 
are sufficient cross-dependencies, as for instance models are re-used 
in many of the derived emergent constraints, and furthermore the 
methodology has not yet reached a sufficient level of maturity since 
systematic biases may not have been accounted for. Uncertainty is 
therefore conservatively added to reflect these potential issues. This

leads to the assessment that ECS inferred from emergent constraints 
is very likely  1.5 to 5 °C with medium confidence .
Emergent constraints on TCR with a  focus on the instrumental 
temperature record, though less abundant, have also been proposed. 
These can be influenced by internal variability and pattern effects, as 
discussed in Section 7.5.4.1, although the influence is smaller because 
uncertainty in forced pattern effects correlates between transient 
historical warming and TCR. In the simplest form Gillett et al. (2012) 
regressed the response of one model to individual historical forcing 
components to obtain a tight range of 1.3°C–1.8°C, but later when an 
ensemble of models was used the range was widened to 0.9°C–2.3°C (Gillett et al., 2013), and updated by Schurer et al. (2018). A related 
data-assimilation-based approach that accounted also for uncertainty 
in response patterns gave 1.33°C–2.36°C (Ribes et al., 2021), but is

dependent on the choice of prior ensemble distribution (CMIP5 or 
CMIP6). Another study used the response to the Pinatubo volcanic 
eruption to obtain a  range of 0.8°C–2.3°C (Bender et  al., 2010). 
A tighter range, notably at the lower end, was found in an emergent 
constraint focusing on the post-1970s warming exploiting the lower 
spread in aerosol forcing change over this period (Jiménez-de-la-
Cuesta and Mauritsen, 2019). Their estimate was 1.67  [1.17 to 
2.16] °C. Two studies tested this idea: Tokarska et al. (2020) estimates 
TCR was 1.60 [0.90 to 2.27] °C based on CMIP6 models, whereas 
Nijsse et al. (2020) found 1.68 [1.0 to 2.3] °C. In both cases there was 
a small sensitivity to choice of ensemble, with CMIP6 models yielding 
slightly lower values and ranges. Combining these studies gives a best 
estimate of 1.7°C and a  very likely  range of TCR of 1.1 to 2.3 °C with 
high confidence .
7.5.5 Combined Assessment of ECS and TCR

Substantial quantitative progress has been made in interpreting 
evidence of Earth’s climate sensitivity since AR5, through innovation, 
scrutiny, theoretical advances and a rapidly evolving data base from 
current, recent and paleo climates. It should be noted that, unlike AR5 
and earlier reports, our assessment of ECS is not directly informed by 
ESM simulations (Section 7.5.6). The assessments of ECS and TCR are 
focussed on the following lines of evidence: process-understanding; 
the instrumental record of warming; paleoclimate evidence; and 
emergent constraints. ESMs remain essential tools for establishing 
these lines of evidence, for instance, in estimating part of the feedback 
parameters and radiative forcings, and emergent constraints rely on 
substantial model spread in ECS and TCR (Section 7.5.6).
A key advance over the AR5 assessment is the broad agreement across 
multiple lines of evidence. These support a  central estimate of ECS

close to, or at least not inconsistent with, 3°C. This advance is foremost 
following improvements in the understanding and quantification of 
Earth’s energy imbalance, the instrumental record of global temperature 
change, and the strength of anthropogenic radiative forcing. Further 
Table 7.12  | Emergent constraint studies used in the assessment of equilibrium climate sensitivity (ECS).  These are studies that rely on global or near-global 
temperature change as the observable.
Study Emergent Constraint DescriptionPublished Best Estimate 
and Uncertainty (°C)Uncertainty 
Estimate
Bender et al. (2010)Pinatubo integrated forcing normalized by CMIP3 models’ own forcing versus 
temperature change regressed against ECS2.4 [1.7 to 4.1] 5–95%
Dessler and Forster (2018)Emergent constraint on TOA radiation variations linked to mid-tropospheric 
temperature in CMIP5 models3.3 [2.4 to 4.5] 17–83%

Hargreaves et al. (2012) Last Glacial Maximum tropical SSTs in PMIP2 models 2.5 [1.3 to 4.2] 5–95%
Hargreaves and Annan (2016) Pliocene tropical SSTs in PlioMIP models [1.9 to 3.7] 5–95%
Jiménez-de-la-Cuesta and Mauritsen (2019) Post-1970s global warming, 1995–2005 relative to 1970–1989, CMIP5 models 2.83 [1.72 to 4.12] 5–95%
Nijsse et al. (2020) Post-1970s global warming, 2009–2019 relative to 1975–1985, CMIP6 models 2.6 [1.5 to 4.0] 5–95%
Renoult et al. (2020)Combined Last Glacial Maximum and Pliocene tropical SSTs in PMIP2, PMIP3, 
PMIP4, PlioMIP and PlioMIP2 models2.5 [0.8 to 4.0] 5–95%

1006
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7advances include increased understanding of how the pattern effect 
influences ECS inferred from historical global warming (Sections 7.4.4 
and 7.5.3), improved quantification of paleo climatechange from proxy 
evidence and a deepened understanding of how feedback mechanisms 
increase ECS in warmer climate states (Sections 7.4.3, 7.4.4 and 7.5.4), 
and also an improved quantification of individual cloud feedbacks 
(Sections 7.4.2 and 7.5.4.2). The assessment findings for ECS and TCR 
are summarized in Table 7.13 and Table 7.14, respectively, and also 
visualized in Figure 7.18.
The AR5 assessed ECS to have a  likely  range from 1.5 to 4.5 °C 
(M. Collins et al., 2013) based on the majority of studies and evidence 
available at the time. The broader evidence base presented in this 
Report and the general agreement among different lines of evidence

means that they can be combined to yield a narrower range of ECS 
values. This can be done formally using Bayesian statistics, though 
such a  process is complex and involves formulating likelihoods 
and priors (Annan and Hargreaves, 2006; Stevens et  al., 2016; 
Sherwood et al., 2020). However, it can be understood that if two 
lines of independent evidence each give a  low probability of an 
outcome being true, for example, that ECS is less than 2.0°C, then 
the combined probability that ECS is less than 2.0°C is lower than 
that of either line of evidence. On the contrary, if one line of evidence 
is unable to rule out an outcome, but another is able to assign a low 
probability, then there is a low probability that the outcome is true 
(Stevens et al., 2016). This general principle applies even when there 
is some dependency between the lines of evidence (Sherwood et al., 
2020), for instance between historical energy budget constraints

(Section  7.5.2.1) and those emergent constraints that use the 
historically observed global warming (Section 7.5.4.1). Even in this 
case the combined constraint will be closer to the narrowest range 
associated with the individual lines of evidence.
In the process of providing a  combined and self-consistent ECS 
assessment across all lines of evidence, the above principles were all considered. As in earlier reports, a 0.5°C precision is used. Starting 
with the very likely  lower bound, there is broad support for a value of 
2.0°C, including process understanding and the instrumental record 
(Table 7.13). For the very likely  upper bound, emergent constraints 
give a  value of 5.0°C whereas the three other lines of evidence 
are individually less tightly constrained. Nevertheless, emergent 
constraints are a relatively recent field of research, in part taken into 
account by adding uncertainty to the upper bound (Section 7.5.4.3),

and the underlying studies use, to a varying extent, information that 
is also used in the other three lines of evidence, causing statistical 
dependencies. However, omitting emergent constraints and 
statistically combining the remaining lines of evidence likewise yields 
95th percentiles close to 5.0°C (Sherwood et al., 2020). Information 
for the likely  range is partly missing or one-sided, however it must 
necessarily reside inside the very likely  range and is therefore 
supported by evidence pertaining to both the likely  and very likely  
ranges. Hence, the upper likely  bound is assessed to be about halfway 
between the best estimate and the upper very likely  bound while 
the lower likely  bound is assessed to be about halfway between the 
best estimate and the lower very likely  bound. In summary, based on 
multiple lines of evidence the best estimate of ECS is 3°C, it is likely  
within the range 2.5 to 4 °C and very likely  within the range 2 to 5 °C.

It is virtually certain  that ECS is larger than 1.5°C. Whereas there is 
high confidence based on mounting evidence that supports the best 
estimate, likely  range and  very likely  lower end, a higher ECS than 
5°C cannot be ruled out, hence there is medium confidence in the 
upper end of the very likely  range. Note that the best estimate of ECS 
made here corresponds to a feedback parameter of –1.3 W m–2 °C–1 
which is slightly more negative than the feedback parameter from 
process-based evidence alone that is assessed in Section 7.4.2.7.
There has long been a consensus (Charney et al., 1979) supporting an 
ECS estimate of 1.5°C–4.5°C. In this regard it is worth remembering 
the many debates challenging an ECS of this magnitude. These started 
as early as Ångström (1900) criticizing the results of Arrhenius (1896) 
(a) Equilibrium climate sensitivity  estimates (ºC) (b) Transient climate response estimates (ºC) 
0 1 2 3 4 5 6 7 8 9Process understanding
Instrumental record

Paleoclimates
Emergent constraints
Combined assessment
CMIP6 ESMs
Best estimate range or value Likely  range or limit
0 1 2 3 4Process understanding
CMIP6 ESMs
Very likely  range or limit Extremely likely  limitFigure 7.18 | Summary of the equilibrium climate sensitivity (ECS panel (a)) and transient climate response (TCR panel (b)) assessments using different 
lines of evidence.  Assessed ranges are taken from Tables 7.13 and 7.14 for ECS and TCR respectively. Note that for the ECS assessment based on both the instrumental record 
and paleoclimates, limits (i.e., one-sided distributions) are given, which have twice the probability of being outside the maximum/minimum value at a given end, compared to 
ranges (i.e., two-tailed distributions) which are given for the other lines of evidence. For example, the extremely likely  limit of greater than 95% probability corresponds to one

side of the  very likely  (5–95%) range. Best estimates are given as either a single number or by a range represented by a grey box. CMIP6 model values are not directly used 
as a line of evidence but presented on the Figure for comparison. ECS values are taken from Schlund et al. (2020) and TCR values from Meehl et al. (2020); see Supplementary 
Material 7.SM.4. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

1007
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77arguing that the atmosphere was already saturated in infrared 
absorption such that adding more CO 2 would not lead to warming. 
The assertion of Ångström was understood half a century later to be 
incorrect. History has seen a multitude of studies (e.g., Svensmark, 
1998; Lindzen et al., 2001; Schwartz, 2007) mostly implying lower 
ECS than the range assessed as very likely  here. However, there are 
also examples of the opposite, such as very large ECS estimates based 
on the Pleistocene records (Snyder, 2016), which have been shown to 
be overestimated due to a lack of accounting for orbital forcing and 
long-term ice-sheet feedbacks (Schmidt et al., 2017b), or suggestions 
that global climate instabilities may occur in the future (Steffen et al., 
2018; Schneider et  al., 2019). There is, however, no evidence for 
unforced instabilities of such magnitude occurring in the paleo-record

temperatures of the past 65 million years (Westerhold et al., 2020), 
possibly short of the Paleocene–Eocene Thermal Maximum (PETM) 
excursion (Section 5.3.1.1) that occurred at more than 10°C above 
present-day levels (Anagnostou et  al., 2020). Looking back, the 
resulting debates have led to a deeper understanding, strengthened 
the consensus, and have been scientifically valuable.
In the climate sciences, there are often good reasons to consider 
representing deep uncertainty, or what are sometimes referred to as 
‘unknown unknowns’. This is natural in a field that considers a system 
that is both complex and at the same time challenging to observe. 
For instance, since emergent constraints represent a relatively new 
line of evidence, important feedback mechanisms may be biased 
in process-level understanding; pattern effects and aerosol cooling 
may be large; and paleo evidence inherently builds on indirect and 
incomplete evidence of past climate states, there certainly can be

valid reasons to add uncertainty to the ranges assessed on individual 
lines of evidence. This has indeed been addressed throughout 
Sections  7.5.1–7.5.4. Since it is neither probable that all lines of 
evidence assessed here are collectively biased nor is the assessment 
sensitive to single lines of evidence, deep uncertainty is not considered 
as necessary to frame the combined assessment of ECS.
The evidence for TCR is less abundant than for ECS, and focuses 
on the instrumental temperature record (Sections 7.5.2 and 7.5.6), 
emergent constraints (Section  7.5.4.3) and process understanding 
(Section  7.5.1). The AR5 assessed a  likely  range for TCR of 1.0  to 
2.5  °C. TCR and ECS are related, though, and in any case TCR is less than ECS (see the introduction to Section  7.5). Furthermore, 
estimates of TCR from the historical record are not as strongly 
influenced by externally forced surface temperature pattern effects

as estimates of ECS are since both historical transient warming and 
TCR are affected by this phenomenon (Section  7.4.4). A  slightly 
higher weight is given to instrumental record warming and emergent 
constraints since these are based on observed transient warming, 
whereas the process-understanding estimate relies on pattern 
effects and ocean heat uptake efficiency from ESMs to represent 
the transient dampening effects of the ocean. If these effects are 
underestimated by ESMs then the resulting TCR would be lower. 
Given the interdependencies of the other two lines of evidence, 
a  conservative approach to combining them as reflected in the 
assessment is adopted. Since uncertainty is substantially lower than 
in AR5 a 0.1°C precision is therefore used here. Otherwise the same 
methodology for combining the lines of evidence as applied to ECS 
is used for TCR. Based on process understanding, warming over the 
instrumental record and emergent constraints, the best estimate TCR

is 1.8°C, it is likely  1.4 to 2.2 °C and very likely  1.2 to 2.4 °C. The 
assessed ranges are all assigned high confidence  due to the high 
level of agreement among the lines of evidence.
7.5.6 Considerations on the ECS and TCR in Global 
Climate Models and Their Role in the Assessment
Coupled climate models, such as those participating in CMIP , have 
long played a central role in assessments of ECS and TCR. In reports 
up to and including the IPCC Third Assessment Report (TAR), climate 
sensitivities derived directly from ESMs were the primary line of 
evidence. However, since AR4, historical warming and paleoclimate 
information provided useful additional evidence and it was noted 
that assessments based on models alone were problematic (Knutti, 
2010). As new lines of evidence have evolved, in AR6 various 
numerical models are used where they are considered accurate, or 
in some cases the only available source of information, and thereby

support all four lines of evidence (Sections 7.5.1–7.5.4). However, 
AR6 differs from previous IPCC reports in excluding direct estimates 
of ECS and TCR from ESMs in the assessed ranges (Section 7.5.5), 
following several recent studies (Annan and Hargreaves, 2006; 
Stevens et  al., 2016; Sherwood et  al., 2020). The purpose of this 
section is to explain why this approach has been taken and to 
provide a perspective on the interpretation of the climate sensitivities 
exhibited in CMIP6 models.Table 7 .14 | Summary of TCR assessment.
Transient Climate 
Response (TCR)Central 
ValueLikely  RangeVery likely 
Range
Process understanding 
(Section 7.5.1)2.0°C 1.6°C to 2.7°C 1.3°C to 3.1°C
Warming over instrumental record 
(Section 7.5.2)1.9°C 1.5°C to 2.3°C 1.3°C to 2.7°C
Emergent constraints 
(Section 7.5.4)1.7°C – 1.1°C to 2.3°C
Combined assessment 1.8°C 1.4°C to 2.2°C 1.2°C to 2.4°C
Table 7.13  | Summary of equilibrium climate sensitivity (ECS) assessment.
Equilibrium Climate

Sensitivity (ECS)Central 
ValueLikely  Very 
likely Extremely 
likely
(Section 7.5.1)3.4°C2.5°C to 
5.1°C2.1°C to 
7.7°C–
Warming over instrumental 
record (Section 7.5.2)2.5°C to 
3.5°C>2.2°C >1.8°C >1.6°C
Paleoclimates 
(Section 7.5.3)3.3°C to 
3.4°C<4.5°C >1.5°C <8°C
(Section 7.5.4)2.4°C to 
3.3°C–1.5°C to 
5.0°C–
Combined assessment 3°C2.5°C to 
4.0°C2.0°C to 
5.0°C–

1008
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7The primary consideration that led to excluding ECS and TCR 
directly derived from ESMs is that information from these models 
is incorporated in the lines of evidence used in the assessment: 
ESMs are partly used to estimate historical and paleoclimate ERFs 
(Sections 7.5.2 and 7.5.3); to convert from local to global mean paleo 
temperatures (Section 7.5.3); to estimate how feedbacks change with 
SST patterns (Section 7.4.4.3); and to establish emergent constraints 
on ECS (Section 7.5.4). They are also used as important evidence in the 
process understanding estimates of the temperature, water vapour, 
albedo, biogeophysical, and non-CO 2 biogeochemical feedbacks, 
whereas other evidence is primarily used for cloud feedbacks where 
the climate model evidence is weak (Section 7.4.2). One perspective 
on this is that the process understanding line of evidence builds on 
and replaces ESM estimates.

The ECS of a model is the net result of the model’s effective radiative 
forcing from a doubling of CO 2 and the sum of the individual feedbacks 
and their interactions. It is well known that most of the model spread 
in ECS arises from cloud feedbacks, and particularly the response of 
low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since 
these clouds are small-scale and shallow, their representation in climate 
models is mostly determined by sub-grid-scale parametrizations. 
It is sometimes assumed that parametrization improvements will 
eventually lead to convergence in model response and therefore 
a decrease in the model spread of ECS. However, despite decades of 
model development, increases in model resolution and advances in 
parametrization schemes, there has been no systematic convergence 
in model estimates of ECS. In fact, the overall inter-model spread 
in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are

given for CMIP6 models in Supplementary Material 7.SM.4 based 
on Schlund et al. (2020) for ECS and Meehl et al. (2020) for TCR (see 
also Figure  7.18 and FAQ 7.3). The upward shift does not apply to 
all models traceable to specific modelling centres, but a  substantial 
subset of models have seen an increase in ECS between the two model 
generations. The increased ECS values, as discussed in Section 7.4.2.8, 
are partly due to shortwave cloud feedbacks (Flynn and Mauritsen, 
2020) and it appears that in some models extra-tropical clouds with 
mixed ice and liquid phases are central to the behaviour (Zelinka et al., 
2020), probably borne out of a recent focus on biases in these types of 
clouds (McCoy et al., 2016; Tan et al., 2016). These biases have recently 
been reduced in many ESMs, guided by process understanding from 
laboratory experiments, field measurements and satellite observations 
(Lohmann and Neubauer, 2018; Bodas-Salcedo et al., 2019; Gettelman

et  al., 2019). However, this and other known model biases are 
already factored into the process-level assessment of cloud feedback 
(Section 7.4.2.4), and furthermore the emergent constraints used here 
focus on global surface temperature change and are therefore less 
susceptible to shared model biases in individual feedback parameters 
than emergent constraints that focus on specific physical processes 
(Section 7.5.4). The high values of ECS and TCR in some CMIP6 models 
lead to higher levels of surface warming than CMIP5 simulations and 
also the AR6 projections based on the assessed ranges of ECS, TCR 
and ERF (Box 4.1 and FAQ 7.3; Forster et al., 2020).
It is generally difficult to determine which information enters the 
formulation and development of parametrizations used in ESMs. 
Climate models frequently share code components, and in some cases entire sub-model systems are shared and slightly modified. 
Therefore, models cannot be considered independent developments,

but rather families of models with interdependencies (Knutti et al., 
2013). It is therefore difficult to interpret the collection of models 
(Knutti, 2010), and it cannot be ruled out that there are common 
limitations and therefore systematic biases to model ensembles 
that are reflected in the distribution of ECS as derived from them. 
Although ESMs are typically well-documented, in ways that 
increasingly include information on critical decisions regarding tuning 
(Mauritsen et al., 2012; Hourdin et al., 2017; Schmidt et al., 2017a; 
Mauritsen  and Roeckner, 2020), the full history of development 
decisions could involve both process-understanding and sometimes 
also other information such as historical warming. As outlying or 
poorly performing models emerge from the development process, 
they can become re-tuned, reconfigured or discarded and so might 
not see publication (Hourdin et al., 2017; Mauritsen and Roeckner,

2020). In the process of addressing such issues, modelling groups 
may, whether intentionally or not, modify the modelled ECS.
It is problematic and not obviously constructive to provide weights 
for, or rule out, individual CMIP6 model ensemble members based 
solely on their ECS and TCR values. Rather these models must be 
tested in a like-with-like way against observational evidence. Based 
on the currently published CMIP6 models we provide such an analysis, 
marking models with ECS above and below the assessed very likely  
range (Figure 7.19). In the long-term historical warming (Figure 7.19a) 
both low- and high-ECS models are able to match the observed 
warming, presumably in part as a  result of compensating aerosol 
cooling (Kiehl, 2007; Forster et al., 2013; Wang et al., 2021). In several 
cases of high ECS models that apply strong aerosol cooling it is found 
to result in surface warming and ocean heat uptake evolutions that

are inconsistent with observations (Golaz et al., 2019; Andrews et al., 
2020; Winton et  al., 2020). Modelled warming since the 1970s is 
less influenced by compensation between climate sensitivity and 
aerosol cooling (Jiménez-de-la-Cuesta and Mauritsen, 2019; Nijsse 
et  al., 2020) resulting in the high-ECS models in general warming 
more than observed, whereas low-sensitivity models mostly perform 
better (Figure 7.19b); a result that may also have been influenced by 
temporary pattern effects (Sections 7.4.4 and 7.5.4). Paleoclimates 
are not influenced by such transient pattern effects, but are limited by 
structural uncertainties in the proxy-based temperature and forcing 
reconstructions as well as possible differences in equilibrium sea 
surface temperature patterns between models and the real world 
(Section 7.5.4). Across the LGM, MPWP and EECO (Figure 7.19c–e), 
the few high-ECS models that simulated these cases were outside the

observed very likely  ranges (see also Feng et al., 2020; Renoult et al., 
2020; Zhu et al., 2020). Also the low-ECS model is either outside or 
on the edge of the observed very likely  ranges.
As a  result of the above considerations, in this Report projections 
of global surface temperature are produced using climate model 
emulators that are constrained by the assessments of ECS, TCR and 
ERF . In reports up to and including AR5, ESM values of ECS did not fully 
encompass the assessed very likely  range of ECS, raising the possibility 
that past multi-model ensembles underestimated the  uncertainty in 
climate change projections that existed at the times of those reports 
(e.g., Knutti, 2010). However, due to an increase in the modelled ECS

1009
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77spread and a decrease in the assessed ECS spread based on improved 
knowledge in multiple lines of evidence, the CMIP6 ensemble 
encompasses the very likely  range of ECS [2 to 5]  °C assessed in 
Section 7.5.5. Models outside of this range are useful for establishing 
emergent constraints on ECS and TCR and provide useful examples of 
‘tail risk’ (Sutton, 2018), producing dynamically consistent realizations 
of future climate change to inform impact studies and risk assessments.
In summary, the distribution of CMIP6 models have higher average 
ECS and TCR values than the CMIP5 generation of models and 
the assessed values of ECS and TCR in Section  7.5.5. The high 
ECS and TCR values can in some CMIP6 models be traced to 
improved representation of extratropical cloud feedbacks ( medium 
confidence ). The ranges of ECS and TCR from the CMIP6 models are

not considered  robust samples of possible values and the models 
are not considered a  separate line of evidence for ECS and TCR. 
Solely based on its ECS or TCR values an individual ESM cannot be 
ruled out as implausible, though some models with high (greater 
than 5°C) and low (less than 2°C) ECS are less consistent with past 
climate change ( high confidence ). High climate sensitivity in models 
leads to generally higher projected warming in CMIP6 compared to both CMIP5 and that assessed based on multiple lines of evidence 
(Sections 4.3.1 and 4.3.4, and FAQ 7.3).
7.5.7 Processes Underlying Uncertainty in the 
Global Temperature Response to Forcing
While the magnitude of global warming by the end of the 21st century 
is dominated by future GHG emissions, the uncertainty in warming for 
a given ERF change is dominated by the uncertainty in ECS and TCR 
(Section  4.3.4). The proportion of variation explained by ECS and

TCR varies with scenario and the time period considered, but within 
CMIP5 models around 60–90% of the globally averaged projected 
surface warming range in 2100 can be explained by the model range 
of these metrics (Grose et  al., 2018). Uncertainty in the long-term 
global surface temperature change can further be understood in 
terms of the processes affecting the global TOA energy budget, 
namely the ERF , the radiative feedbacks which govern the efficiency of 
radiative energy loss to space with surface warming, and the increase 
in the global energy inventory (dominated by ocean heat uptake) 
which reduces the transient surface warming. A  variety of studies (a) Historical
0.00.20.40.60.81.01.21.4temperature anomaly (°C)
observations
ESMs:
ECS>5.0
ECS<2.02.0<ECS<5.0(b) post 1975
0.00.20.40.60.81.01.21.4(c) LGM
-12-10-8-6-4-20(d) MPWP
0123456(e) EECO
0510152025

Figure 7.19  | Global mean temperature anomaly in models and observations from five time periods. (a)  Historical (CMIP6 models); (b) post-1975 (CMIP6 
models); (c) Last Glacial Maximum (LGM; Cross-Chapter Box 2.1; PMIP4 models; Kageyama et al., 2021; Zhu et al., 2021);  (d) mid-Pliocene Warm Period (MPWP; Cross-Chapter 
Box 2.4; PlioMIP models; Haywood et al., 2020; Zhang et al., 2021); (e) Early Eocene Climatic Optimum (EECO; Cross-Chapter Box 2.1; DeepMIP models; Zhu et al., 2020; Lunt 
et al., 2021). Grey circles show models with ECS in the assessed very likely range; models in red have an ECS greater than the assessed very likely range (>5°C); models in blue 
have an ECS lower than the assessed very likely range (<2°C). Black ranges show the assessed temperature anomaly derived from observations (Section 2.3). The historical

anomaly in models and observations is calculated as the difference between 2005–2014 and 1850–1900, and the post-1975 anomaly is calculated as the difference between 
2005–2014 and 1975–1984. For the LGM, MPWP and EECO, temperature anomalies are compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’). All model 
simulations of the MPWP and LGM were carried out with atmospheric CO 2 concentrations of 400 and 190 ppm respectively. However, CO 2 during the EECO is relatively more 
uncertain, and model simulations were carried out at either 1120ppm or 1680 ppm (except for the one high-ECS EECO simulation which was carried out at 840 ppm; Zhu et al., 
2020). The one low-ECS EECO simulation was carried out at 1680 ppm. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

1010
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7evaluate the effect of each of these processes on surface changes 
within coupled ESM simulations by diagnosing so-called ‘warming 
contributions’ (Dufresne and Bony, 2008; Crook et  al., 2011; Feldl 
and Roe, 2013; Vial et al., 2013; Pithan and Mauritsen, 2014; Goosse 
et  al., 2018). By construction, the individual warming contributions 
sum to the total global surface warming (Figure 7.20b). For long-term 
warming in response to CO 2 forcing in CMIP5 models, the energy 
added to the climate system by radiative feedbacks is larger than the 
ERF of CO 2 (Figure 7.20a), implying that feedbacks more than double 
the magnitude of global warming (Figure  7.20b). Radiative kernel 
methods (see Section 7.4.1) can be used to decompose the net energy 
input from radiative feedbacks into its components. The water-vapour, 
cloud and surface-albedo feedbacks enhance global warming, while

the lapse-rate feedback reduces global warming. Ocean heat uptake 
reduces the rate of global surface warming by sequestering heat at depth away from the ocean surface.  Section  7.4.4.1 shows the 
warming contributions from these factors at the regional scale.
Differences in projected transient global warming across ESMs are 
dominated by differences in their radiative feedbacks, while differences 
in ocean heat uptake and radiative forcing play secondary roles 
(Figure 7.20b; Vial et al., 2013). The uncertainty in projected global 
surface temperature change associated with inter-model differences 
in cloud feedbacks is the largest source of uncertainty in CMIP5 and 
CMIP6 models (Figure  7.20b), just as they were for CMIP3 models 
(Dufresne and Bony, 2008). Extending this energy budget analysis to 
equilibrium surface warming suggests that about 70% of the inter-
model differences in ECS arises from uncertainty in cloud feedbacks,

with the largest contribution to that spread coming from shortwave 
low-cloud feedbacks (Vial et al., 2013; Zelinka et al., 2020).
0 2 3
Global warming(b) Global warming contributions (ºC)
Ocean heat uptake–10 0 10
Effective radiative forcing(a) Global atmospheric energy inputs (W m-2)
Planck response
Radiative feedbacksGlobal energy inputs from individual
radiative feedbacks (W m-2)
0 –4 4 8
Global warming contributions from
individual radiative feedbacks (ºC)
Net cloud40 –1 2 3 1–20Contributions to global mean warming in CMIP6 ESMs in response to CO2 quadrupling
Net cloudWater-vapour + lapse-rate
Figure 7.20  | Contributions of effective radiative forcing, ocean heat uptake and radiative feedbacks to global atmospheric energy input and near-surface

air temperature change at year 100 of abrupt4xCO2  simulations of CMIP6 models. (a) The energy flux to the global atmosphere associated with the effective CO 2 
forcing, global ocean heat uptake, Planck response, and radiative feedbacks, which together sum to zero. The inset shows energy input from individual feedbacks, summing 
to the total feedback energy input. (b) Contributions to net global warming are calculated by dividing the energy inputs by the magnitude  of the global Planck response 
(3.2 W m–2 °C–1), with the contributions from radiative forcing, ocean heat uptake, and radiative feedbacks (orange bars) summing to the value of net warming (grey bar). The 
inset shows warming contributions associated with individual feedbacks, summing to the total feedback contribution. Uncertainties show the interquartile range (25th and

75th percentiles) across models. Radiative kernel methods (see Section 7.4.1) were used to decompose the net energy input from radiative feedbacks into contributions from 
changes in atmospheric water vapour, lapse rate, clouds, and surface albedo (Zelinka et al. (2020) using the Huang et al. (2017) radiative kernel). The CMIP6 models included 
are those analysed by Zelinka et al. (2020) and the warming contribution analysis is based on that of Goosse et al. (2018). Further details on data sources and processing are 
available in the chapter data table (Table 7.SM.14).

1011
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Interactions between different feedbacks within the coupled 
climate system pose a challenge to our ability to understand global 
warming and its uncertainty based on energy budget diagnostics 
(Section 7.4.2). For example, water-vapour and lapse-rate feedbacks 
are correlated (Held and Soden, 2006) owing to their joint dependence 
on the spatial pattern of warming (Po-Chedley et  al., 2018b). 
Moreover, feedbacks are not independent of ocean heat uptake 
because the uptake and transport of heat by the ocean influences 
the SST pattern on which global feedbacks depend (Section 7.4.4.3). 
However, alternative decompositions of warming contributions that 
better account for correlations between feedbacks produce similar 
results (Caldwell et al., 2016). The key role of radiative feedbacks in 
governing the magnitude of global warming is also supported by the

high correlation between radiative feedbacks (or ECS) and transient 
21st-century warming within ESMs (Grose et al., 2018).
Another approach to evaluating the roles of forcing, feedbacks and 
ocean heat uptake in projected warming employs idealized energy 
balance models that emulate the response of ESMs, and which 
preserve the interactions between system components. One such 
emulator, used in Section  7.5.1.2, resolves the heat capacity of 
both the surface components of the climate system and the deep 
ocean (Held et  al., 2010; Geoffroy et  al., 2013a, b; Kostov et  al., 
2014; Armour, 2017). Using this emulator, Geoffroy et al. (2012) find 
that: under an idealized 1% per year increase in atmospheric CO 2, 
radiative feedbacks constitute the greatest source of uncertainty 
(about 60% of variance) in transient warming beyond several 
decades; ERF uncertainty plays a  secondary but important role in 
warming uncertainty (about 20% of variance) that diminishes beyond

several decades; and ocean heat uptake processes play a minor role 
in warming uncertainty (less than 10% of variance) at all time scales.
More computationally intensive approaches evaluate how the 
climate response depends on perturbations to key parameter  or 
structural choices within ESMs. Large ‘perturbed parameter 
ensembles’, wherein a  range of parameter settings associated with 
cloud physics are explored within atmospheric ESMs, produce a wide 
range of ECS due to changes in cloud feedbacks, but often produce 
unrealistic climate states (Joshi et al., 2010). Rowlands et al. (2012) 
generated an ESM perturbed-physics ensemble of several thousand 
members by perturbing model parameters associated with radiative 
forcing, cloud feedbacks and ocean vertical diffusivity (an important 
parameter for ocean heat uptake). After constraining the ensemble to 
have a reasonable climatology and to match the observed historical

surface warming, they found a wide range of projected warming by 
the year 2050 under the SRES A1B scenario (1.4°C–3°C relative to 
the 1961–1990 average) that is dominated by differences in cloud 
feedbacks. The finding that cloud feedbacks are the largest source 
of spread in the net radiative feedback has since been confirmed in 
perturbed parameter ensemble studies using several different ESMs 
(Gettelman et al., 2012; Tomassini et al., 2015; Kamae et al., 2016b; 
Rostron et al., 2020; Tsushima et al., 2020). By swapping out different 
versions of the atmospheric or oceanic components in a  coupled 
ESM, Winton et al. (2013) found that TCR and ECS depend on which 
atmospheric component was used (using two versions with different 
atmospheric physics), but that only TCR is sensitive to which oceanic 
component of the model was used (using two versions with different vertical coordinate systems, among other differences); TCR and ECS

changed by 0.4°C and 1.4°C, respectively, when the atmospheric 
model component was changed, while TCR and ECS changed by 
0.3°C and less than 0.05°C, respectively, when the oceanic model 
component was changed. By perturbing ocean vertical diffusivities 
over a wide range, Watanabe et al. (2020) found that TCR changed by 
0.16°C within the model MIROC5.2 while Krasting et al. (2018) found 
that ECS changed by about 0.6°C within the model GFDL-ESM2G, 
with this difference linked to different radiative feedbacks associated 
with different spatial patterns of sea surface warming (Section 7.4.4.3). 
By comparing simulations of CMIP6 models with and without the 
effects of CO 2 on vegetation, Zarakas et al. (2020) find a physiological 
contribution to TCR of 0.12°C (range 0.02°C–0.29°C across models) 
owing to physiological adjustments to the CO 2 ERF (Section 7.3.2.1).
There is robust evidence  and high agreement across a  diverse

range of modelling approaches and thus high confidence  that 
radiative feedbacks are the largest source of uncertainty in projected 
global warming out to 2100 under increasing or stable emissions 
scenarios, and that cloud feedbacks in particular are the dominant 
source of that uncertainty. Uncertainty in radiative forcing plays an 
important but generally secondary role. Uncertainty in global ocean 
heat uptake plays a lesser role in global warming uncertainty, but 
ocean circulation could play an important role through its effect on 
sea surface warming patterns which in turn project onto radiative 
feedbacks through the pattern effect (Section 7.4.4.3).
The spread in historical surface warming across CMIP5 ESMs 
shows a weak correlation with inter-model differences in radiative 
feedback or ocean heat uptake processes but a high correlation with 
inter-model differences in radiative forcing owing to large variations

in aerosol forcing across models (Forster et al., 2013). Likewise, the 
spread in projected 21st-century warming across ESMs depends 
strongly on which emissions scenario is employed (Section  4.3.1; 
Hawkins and Sutton, 2012). Strong emissions reductions would 
remove aerosol forcing (Section 6.7.2) and this could dominate the 
uncertainty in near-term warming projections (Armour and Roe, 2011; 
Mauritsen and Pincus, 2017; Schwartz, 2018; Smith et al., 2019). On 
post-2100 time scales carbon cycle uncertainty such as that related to 
permafrost thawing could become increasingly important, especially 
under high-emissions scenarios (Figure 5.30).
In summary, there is high confidence that cloud feedbacks are the 
dominant source of uncertainty for late 21st-century projections 
of transient global warming under increasing or stable emissions 
scenarios, whereas uncertainty is dominated by aerosol ERF in strong 
mitigation scenarios. Global ocean heat uptake is a smaller source of

uncertainty in long-term surface warming ( high confidence ).
7.6 Metrics to Evaluate Emissions
Emissions metrics are used to compare the relative effect of emissions 
of different gases over time in terms of radiative forcing, global surface 
temperature or other climate effects. They are introduced in Chapter 1 
(Box 1.3). Chapter 8 of AR5 (Myhre et al., 2013b) comprehensively 
discussed different emissions metrics so this section focuses on

1012
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7updates since that report. Section  7.6.1 updates the physical 
assessment. Section 7.6.2 assesses developments in the comparison 
of emissions of short- and long-lived gases. Box 7.3 assesses physical 
aspects of emissions metric use within climate policy.
7.6.1 Physical Description of Metrics
This section discusses metrics that relate emissions to physical 
changes in the climate system. Other metrics, for instance relating 
to economic costs or ‘damage’ are discussed in WGIII, Chapter  2. 
The same Chapter also assesses literature examining the extent 
to which different physical metrics are linked to cost–benefit and 
cost-effectiveness metrics. One metric, the 100-year global warming 
potentials (GWP-100), has extensively been employed in climate 
policy to report emissions of different GHGs on the same scale. Other 
physical metrics exist, and these are discussed in this section.

Emissions metrics can be quantified as the magnitude of the effect 
a unit mass of emission of a species has on a key measure of climate 
change. This section focuses on physical measures such as the radiative 
forcing, GSAT change, global average precipitation change, and global 
mean sea level rise (Myhre et al., 2013b; Sterner et al., 2014; Shine 
et al., 2015). When used to represent a climate effect, the metrics are 
referred to as absolute metrics and expressed in units of ‘effect per kg’ 
(e.g., absolute global warming potentials, AGWP or absolute global 
temperature-change potentials, AGTP). More commonly, these are 
compared with a reference species (almost always CO 2 in kg (CO 2)), to 
give a dimensionless factor (written as e.g., global warming potentials 
(GWP) or global temperature-change potential (GTP)). The unit mass 
is usually taken as a 1 kg instantaneous ‘pulse’ (Myhre et al., 2013b), 
but can also refer to a ‘step’ in emissions rate of 1 kg yr –1.

There is a cause–effect chain that links human activity to emissions, 
then from emissions to radiative forcing, climate response and climate 
impacts (Fuglestvedt et  al., 2003). Each step in the causal  chain 
requires an inference or modelling framework that maps causes to 
effects. Emissions metrics map from emissions of some compound 
to somewhere further down the cause-and-effect chain, radiative 
forcing (e.g., GWP) or temperature (e.g., GTP) or other effects (such 
as sea level rise or socio-economic impacts). While variables later 
in the chain have greater policy or societal relevance, they are also 
subject to greater uncertainty because each step in the chain includes 
more modelling systems, each of which brings its own uncertainty 
(Figure 1.15; Balcombe et al., 2018).
Since AR5, understanding of the radiative effects of emitted 
compounds has continued to evolve and these changes are assessed 
in Section 7.6.1.1. Metrics relating to precipitation and sea level have

also been quantified (Section 7.6.1.2). Understanding of how emissions 
metrics are affected by the carbon cycle response to temperature has 
improved. This allows the carbon cycle response to temperature 
to be more fully included in the emissions metrics presented here 
(Section 7.6.1.3). There have also been developments in approaches 
for comparing short-lived GHGs to CO 2 in the context of mitigation and 
global surface temperature change (Section 7.6.1.4). Emissions metrics 
for selected key compounds are presented in Section 7.6.1.5.7.6.1.1 Radiative Properties and Lifetimes
The radiative properties and lifetimes of compounds are the 
fundamental component of all emissions metrics. Since AR5, there 
have been advances in the understanding of the radiative properties 
of various compounds (see Sections 7.3.1, 7.3.2 and 7.3.3), and 
hence their effective radiative efficiencies (ERFs per unit change 
in concentration). For CO 2, CH 4 and N 2O, better accounting of the

spectral properties of these gases has led to re-evaluation of their 
stratospheric-temperature-adjusted radiative forcing (SARF) 
radiative efficiencies and their dependence on the background 
gas concentrations (Section  7.3.2). For CO 2, CH 4, N 2O, CFC-11 and 
CFC-12 the tropospheric adjustments (Sections 7.3.1 and 7.3.2) are 
assessed to make a non-zero contribution to ERF . There is insufficient 
evidence to include tropospheric adjustments for other halogenated 
compounds. The re-evaluated effective radiative efficiency for CO 2 
will affect all emissions metrics relative to CO 2.
The effective radiative efficiencies (including adjustments from 
Section 7.3.2) for 2019 background concentrations for CO 2, CH 4 and N 2O 
are assessed to be 1.33×10–5, 3.89×10–4 and 3.19×10–3 W m–2 ppb–1 
respectively (see Table  7.15 for uncertainties), compared to AR5 
assessments of 1.37×10–5, 3.63×10–4 and 3.00×10–3 W  m–2 ppb–1.

For CO 2, increases due to the adjustments do not quite balance the 
decreases due to the increasing background concentration. For CH 4, 
increases due to the re-evaluated radiative properties more than offset 
the decreases due to the increasing background concentration. For 
N2O the addition of tropospheric adjustments increases the effective 
radiative efficiency. Radiative efficiencies of halogenated species have 
been revised slightly (Section 7.3.2.4) and for CFCs include tropospheric 
adjustments.
The perturbation lifetimes of CH 4 (Section  6.3.1). and N 2O (Section  5.2.3.1) 
have been slightly revised since AR5 to be 11.8  ±  1.8  years and 
109 ± 10 years, respectively (Table 7.15). The lifetimes of halogenated 
compounds have also been slightly revised (Hodnebrog et al., 2020a).
Although there has been greater understanding since AR5 of the 
carbon cycle responses to CO 2 emissions (Sections 5.4 and 5.5), there

has been no new quantification of the response of the carbon cycle 
to an instantaneous pulse of CO 2 emission since Joos et al. (2013).
7.6.1.2 Physical Indicators
The basis of all the emissions metrics is the time profile of effective 
radiative forcing (ERF) following the emission of a  particular 
compound. The emissions metrics are then built up by relating the 
forcing to the desired physical indicators. These forcing–response 
relationships can either be generated from emulators (Cross-Chapter 
Box 7.1; Tanaka et al., 2013; Gasser et al., 2017b), or from analytical 
expressions based on parametric equations (response functions) 
derived from more complex models (Myhre et al., 2013b).
To illustrate the analytical approach, the ERF time evolution following 
a  pulse of emission can be considered an absolute global forcing 
potential (AGFP; similar to the ‘Instantaneous Climate Impact’ of 
Edwards and Trancik, 2014). This can be transformed into an absolute

1013
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77global temperature-change potential (AGTP) by combining the 
radiative forcing with a global surface temperature response function. 
This temperature response is typically derived from a two-layer energy 
balance emulator (Supplementary Material 7.SM.5; Myhre et  al., 
2013b). For further physical indicators further response functions are 
needed based on the radiative forcing or temperature, for instance. 
Sterner et  al. (2014) used an upwelling-diffusiveenergy balance 
model to derive the thermosteric component of sea level rise as 
response functions to radiative forcing or global surface temperature. 
A metric for precipitation combines both the radiative forcing (AGFP) 
and temperature (AGTP) responses to derive an absolute global 
precipitation potential (AGPP; Shine et  al., 2015). The equations 
relating these metrics are given in Supplementary Material 7.SM.5.

The physical emissions metrics described above are functions of time 
since typically the physical effects reach a peak and then decrease in 
the period after a pulse emission as the concentrations of the emitted 
compound decay. The value of the metrics can therefore be strongly 
dependent on the time horizon of interest. All relative metrics (GWP , 
GTP etc.) are also affected by the time dependence of the CO 2 metrics 
in the denominator. Instantaneous or endpoint metrics quantify the 
change (e.g., in radiative forcing, global surface temperature, global 
mean sea level) at a particular time after the emission. These can be 
appropriate when the goal is to not exceed a  fixed target such as 
a temperature or global mean sea level rise at a specific time. Emissions 
metrics can also be integrated from the time of emission. The most 
common of these is the absolute global warming potential (AGWP), 
which is the integral of the AGFP . The physical effect is then in units

of forcing-years, degree-years or metre-years for forcing, temperature, 
or sea level rise, respectively. These can be appropriate for trying to 
reduce the overall damage potential when the effect depends on 
how long the change occurs for, not just how large the change is. 
The integrated metrics still depend on the time horizon, though for 
the shorter-lived compounds this dependence is somewhat smoothed 
by the integration. The integrated version of a metric is often denoted 
as iAGxx, although the integral of the forcing-based metric (iAGFP) 
is known as the AGWP . Both the endpoint and integrated absolute 
metrics for non-CO 2 species can be divided by the equivalent for CO 2 
to give relative emissions metrics (e.g., GWP (=iGFP), GTP , iGTP).
Each step from radiative forcing to global surface temperature to 
sea level rise introduces longer time scales and therefore prolongs 
further the contributions to climate change of short-lived GHGs

(Myhre et al., 2013b). Thus, short-lived GHGs become more important 
(relative to CO 2) for sea level rise than for temperature or radiative 
forcing (Zickfeld et al., 2017). Integrated metrics include the effects 
of a pulse emission from the time of emission up to the time horizon, 
whereas endpoint metrics only include the effects that persist out 
to the time horizon. Because the largest effects of short-lived GHGs 
occur shortly after their emission and decline towards the end of 
the time period, short-lived GHGs have relatively higher integrated 
metrics than their corresponding endpoint metrics (Peters et  al., 
2011; Levasseur et al., 2016).
For species perturbations that lead to a strong regional variation in 
forcing pattern, the regional temperature response can be different 
to that for CO 2. Regional equivalents to the global metrics can be derived by replacing the global surface temperature response 
function with a  regional response matrix relating forcing changes

in one region to temperature changes in another (W.J. Collins et al., 
2013; Aamaas et al., 2017; Lund et al., 2017).
For the research discussed above, metrics for several physical 
variables can be constructed that are linear functions of radiative 
forcing. Similar metrics could be devised for other climate variables 
provided they can be related by response functions to radiative 
forcing or global surface temperature change. The radiative forcing 
does not increase linearly with emissions for any species, but the 
non-linearities (for instance changes in CO 2 radiative efficiency) are 
small compared to other uncertainties.
7.6.1.3 Carbon Cycle Responses and Other 
Indirect Contributions
The effect of a  compound on climate is not limited to its direct 
radiative forcing. Compounds can perturb the carbon cycle affecting 
atmospheric CO 2 concentrations. Chemical reactions from emitted 
compounds can produce or destroy other GHGs or aerosols.

Any agent that warms the surface perturbs the terrestrial and oceanic 
carbon fluxes (Sections 5.4.3 and 5.4.4), typically causing a net flux 
of CO 2 into the atmosphere and hence further warming. This aspect is 
already included in the carbon cycle models that are used to generate 
the radiative effects of a  pulse of CO 2 (Joos et  al., 2013), but was 
neglected for non-CO 2 compounds in the conventional metrics so 
this introduces an inconsistency and bias in the metric values (Gillett 
and Matthews, 2010; MacDougall et al., 2015; Tokarska et al., 2018). 
A  simplistic account of the carbon cycle response was tentatively 
included in AR5 based on a  single study (W.J. Collins et  al., 2013). 
Since AR5 this understanding has been revised (Gasser et al., 2017b; 
Sterner and Johansson, 2017) using simple parametrized carbon cycle 
models to derive the change in CO 2 surface flux for a unit temperature 
pulse as an impulse response function to temperature. In W.J. Collins

et al. (2013) this response function was assumed to be simply a delta 
function, whereas the newer studies include a  more complete 
functional form accounting for subsequent re-uptake of CO 2 after the 
removal of the temperature increase. Accounting for re-uptake has 
the effect of reducing the carbon-cycle responses associated with 
the metrics compared to AR5, particularly at large time horizons. The 
increase in any metric due to the carbon cycle response can be derived 
from the convolution of the global surface temperature response with 
the CO 2 flux response to temperature and the equivalent metric for 
CO 2 (Equation 7.SM.5.5 in the Supplementary Material). Including 
this response also increases the duration of the effect of short-lived 
GHGs on climate (Fu et al., 2020). An alternative way of accounting for 
the carbon cycle temperature response would be to incorporate it into 
the temperature response function (the response functions used here

and given in Supplementary Material 7.SM.5.2 do not explicitly do 
this). If this were done, the correction could be excluded from both the 
CO 2 and non-CO 2 forcing responses as, in Hodnebrog et al. (2020a).
Including the carbon cycle response for non-CO 2 treats CO 2 and 
non-CO 2 compounds consistently and therefore we assess that its 
inclusion more accurately represents the climate effects of non-CO 2

1014
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7species. There is high confidence  in the methodology of using carbon 
cycle models for calculating the carbon cycle response. The magnitude 
of the carbon cycle response contributions to the emissions metrics 
varies by a factor of two between Sterner and Johansson (2017) and 
Gasser et al. (2017b). The central values are taken from Gasser et al. 
(2017b) as the OSCAR 2.2 model used is based on parameters derived 
from CMIP5 models, and the climate–carbon feedback magnitude is 
therefore similar to the CMIP5 multi-model mean (Arora et al., 2013; 
Lade et al., 2018). As values have only been calculated in two simple 
parametrized carbon cycle models the uncertainty is assessed to be 
±100%. Due to there being few studies and a factor of two difference 
between them, there is low confidence  that the magnitude of the 
carbon cycle response is within the higher end of this uncertainty

range, but high confidence  that the sign is positive. Carbon cycle 
responses are included in all the metrics presented in Table 7.15 and 
Supplementary Table 7.SM.7. The carbon cycle contribution is lower 
than in AR5, but there is high confidence  in the need for its inclusion 
and the method by which it is quantified.
Emissions of non-CO 2 species can affect the carbon cycle in other 
ways: emissions of ozone precursors can reduce the carbon uptake 
by plants (W.J. Collins et al., 2013); emissions of reactive nitrogen 
species can fertilize plants and hence increase the carbon uptake 
(Zaehle et al., 2015); and emissions of aerosols or their precursors can 
affect the utilisation of light by plants (Cohan et al., 2002; Mercado 
et  al., 2009; Mahowald et  al., 2017; see Section  6.4.4 for further 
discussion). There is robust evidence  that these processes occur and 
are important, but insufficient evidence  to determine the magnitude

of their contributions to emissions metrics. Ideally, emissions metrics 
should include all indirect effects to be consistent, but limits to our 
knowledge restrict how much can be included in practice.
Indirect contributions from chemical production or destruction of 
other GHGs are quantified in Chapter 6 (Section 6.4). For methane 
(CH 4), AR5 (Myhre et al., 2013b) assessed that the contributions from 
effects on ozone and stratospheric water vapour add 50% ± 30% 
and 15% ± 11% to the emissions-based ERF , which were equivalent 
to 1.8 ± 0.7 ×10–4 and 0.5 ± 0.4 ×10–4 W m–2 ppb (CH 4)–1. In AR6 
the radiative efficiency formulation is preferred as it is independent 
of the assumed radiative efficiency for methane. The assessed 
contributions to the radiative efficiency for methane due to ozone 
are 1.4 ± 0.7 ×10–4 W m–2 ppb (CH 4)–1, based on 0.14 W  m–2 
forcing from a  1023 ppb (1850–2014) methane change (Thornhill

et  al., 2021b). The contribution from stratospheric water vapour is 
0.4 ± 0.4 ×10–4 W m–2 ppb (CH 4)–1, based on 0.05 W m–2 forcing from 
a 1137 ppb (1750–2019) methane change (Section 7.3.2.6). Nitrous 
oxide (N 2O) depletes upper stratospheric ozone (a positive forcing) 
and reduces the methane lifetime. In AR5 the methane lifetime effect 
was assessed to reduce methane concentrations by 0.36 ppb per ppb 
increase in N 2O, with no assessment of the effective radiative forcing 
from ozone. This is now increased to –1.7 ppb methane per ppb N 2O 
(based on a methane lifetime decrease of 4% ± 4% for a 55 ppb 
increase in N 2O (Thornhill et al., 2021b) and a radiative efficiency of 
5.5 ± 0.4 ×10–4 W m–2 ppb (N 2O)–1 through ozone (Thornhill et al., 
2021b)). In summary, GWPs and GTPs for methane and nitrous oxide 
are slightly lower than in AR5 ( medium confidence ) due to revisions

in their lifetimes and updates to their indirect chemical effects.Methane can also affect the oxidation pathways of aerosol formation 
(Shindell et  al., 2009) but the available literature is insufficient to 
make a  robust assessment of this. Hydrocarbon and molecular 
hydrogen oxidation also leads to tropospheric ozone production 
and change in methane lifetime (Collins et  al., 2002; Hodnebrog 
et al., 2018). For reactive species the emissions metrics can depend 
on where the emissions occur, and the season of emission (Aamaas 
et al., 2016; Lund et al., 2017; Persad and Caldeira, 2018). The AR5 
included a contribution to the emissions metrics for ozone-depleting 
substances (ODSs) from the loss of stratospheric ozone. The 
assessment of ERFs from ODSs in Chapter 6 (Section 6.4.2) suggests 
the quantification of these terms may be more uncertain than the 
formulation in AR5 so these are not included here.
Oxidation of methane leads ultimately to the net production of

atmospheric CO 2 (Boucher et al., 2009). This yield is less than 100% 
(on a molar basis) due to uptake by soils and some of the reaction 
products (mainly formaldehyde) being directly removed from the 
atmosphere before being completely oxidized. Estimates of the yield 
are 61% (Boucher et al., 2009) and 88% (Shindell et al., 2017), so 
the assessed range is 50–100% with a central value of 75% ( low 
confidence ). For methane and hydrocarbons from fossil sources, 
this will lead to additional fossil CO 2 in the atmosphere whereas 
for biogenic sources of methane or hydrocarbons, this replaces CO 2 
that has been recently removed from the atmosphere. Since the ratio 
of molar masses is 2.75, 1 kg of methane generates 2.1 ± 0.7 kgCO 2 
for a 75% yield. For biogenic methane the soil uptake and removal of 
partially oxidized products is equivalent to a sink of atmospheric CO 2 
of 0.7 ± 0.7 kg per kg methane. The contributions of this oxidation

effect to the methane metric values allow for the time delay in the 
oxidation of methane. Methane from fossil fuel sources has therefore 
slightly higher emissions metric values than those from biogenic 
sources ( high confidence ). The CO 2 can already be included in carbon 
emissions totals (Muñoz and Schmidt, 2016) so care needs to be 
taken when applying the fossil correction to avoid double counting.
7.6.1.4 Comparing Long-lived with Short-lived 
Greenhouse Gases
Since AR5 there have been developments in how to account for 
the different behaviours of short-lived and long-lived compounds. 
Pulse-based emissions metrics for short-lived GHGs with lifetimes 
less than 20 years are very sensitive to the choice of time horizon 
(e.g.,  Pierrehumbert, 2014). Global surface temperature changes 
following a  pulse of CO 2 emission are roughly constant in time 
(the principle behind TCRE; Section 5.5.1 and Figure 7.21b) whereas

the temperature change following a  pulse of short-lived GHG 
emission declines with time. In contrast to a  one-off pulse, a  step 
change in short-lived GHG emissions that is maintained indefinitely 
causes a  concentration increase that eventually equilibrates to 
a steady state in a way that is more comparable to a pulse of CO 2. 
Similarly the resulting change in global surface temperature from 
a step change in short-lived GHGs (Figure 7.21a) after a few decades 
increases only slowly (due to accumulation of heat in the deep ocean) 
and hence its effects are more similar to a pulse of CO 2 (Smith et al., 
2012; Lauder et  al., 2013; Allen et  al., 2016, 2018b). The different 
time dependence of short-lived and long-lived compounds can be

1015
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77accounted for exactly with the CO 2 forcing equivalent metric (Wigley, 
1998; Allen et al., 2018b; Jenkins et al., 2018) that produces a CO 2 
emissions time profile such that the radiative forcing matches the 
time evolution of that from the non-CO 2 emissions. But other metric 
approaches can approximate this exact approach.
The similarity in behaviour of step changes in short-lived GHG 
emissions and pulses of CO 2 emissions has recently been used to 
formulate new emissions metric concepts (Collins et al., 2020). For 
short-lived GHGs, these new concepts use a step change in the rate 
of emissions, in contrast to an instantaneous pulse in a given year 
that is typically used (e.g.,  Myhre et  al., 2013b). Metrics for step 
emissions changes are denoted here by a superscript ‘S’ (e.g.,  AGTPSX 
is the absolute global surface temperature-change potential from

a  unit step change in emissions of species “ X”). These can be 
derived by integrating the more standard pulse emission changes 
up to the time horizon. The response to a step emissions change is 
therefore equivalent to the integrated response to a pulse emission 
(AGTPSX  = iAGTP X); and the radiative forcing response to a  step 
emissions change AGFPSX is equivalent to the integrated forcing 
response iAGFP X which is the AGWP . The step metric for short-lived 
GHGs can then be compared with the pulse metric for CO 2 in a ratio AGTPSX/AGTP CO2 (Collins et al., 2020). This is referred to as 
a  combined GTP (CGTP) in Collins et  al.  (2020), and has units of 
years (the standard GTP is dimensionless). This CGTP shows less 
variation with time than the standard GTP (comparing Figure 7.21c 
with Figure 7.21d) and provides a scaling for comparing a change 
in emissions rate (in kg yr –1) of short-lived GHGs with a  pulse

emission or change in cumulative CO 2 emissions (in kg). Cumulative 
CO 2 equivalent emissions are given by CGTP × emissions rate of 
short-lived GHGs. The CGTP can be calculated for any species, but 
it is least dependent on the chosen time horizon for species with 
lifetimes less than half the time horizon of the metric (Collins 
et al., 2020). Pulse-step metrics can therefore be useful where time 
dependence of pulse metrics, like GWP or GTP , complicates their use 
(see Box 7.3).
For a  stable global warming from non-CO 2 climate agents (gas or 
aerosol) their effective radiative forcing needs to gradually decrease 
(Tanaka and O’Neill, 2018). Cain et al. (2019) find this decrease to 
be around 0.3% yr –1 for the climate response function in AR5 
(Myhre et  al., 2013b). To account for this, a  quantity referred to 
as GWP* has been defined that combines emissions (pulse) and 
changes in emissions levels (step) approaches (Cain et  al., 2019;

Figur e 7.21 | Emissions metrics for two short-lived greenhouse gases: HFC-32 and methane (CH 4; lifetimes of 5.4 and 11.8 years).  The temperature response 
function comes from Supplementary Material 7.SM.5.2. Values for non-CO 2 species include the carbon cycle response (Section 7.6.1.3). Results for HFC-32 have been divided 
by 100 to show on the same scale. (a) Temperature response to a step change in short-lived greenhouse gas emissions. (b) Temperature response to a pulse CO 2 emission. 
(c) Conventional GTP metrics (pulse vs pulse). (d) Combined GTP metric (step versus pulse). Further details on data sources and processing are available in the chapter data 
table (Table 7.SM.14).

1016
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Smith  et  al.,  2021).2 The emissions component accounts for the 
need for emissions to decrease to deliver a stable warming. The step 
(sometimes referred to as flow or rate) term in GWP* accounts for 
the change in global surface temperature that arises from a change 
in short-lived GHG emissions rate, as in CGTP , but here approximated 
by the change in emissions over the previous 20 years.
Cumulative CO 2 emissions and GWP*-based cumulative CO 2 
equivalent GHG emissions multiplied by TCRE closely approximate 
the global warming associated with emissions time series (of CO 2 and 
GHG, respectively) from the start of the time series (Lynch et al., 2020). 
Both the CGTP and GWP* convert short-lived GHG emissions rate 
changes into cumulative CO 2 equivalent emissions, hence scaling 
these by TCRE gives a  direct conversion from short-lived GHG

emissions to global surface temperature change. By comparison 
expressing methane emissions as CO 2 equivalent emissions using 
GWP-100 overstates the effect of constant methane emissions on 
global surface temperature by a  factor of 3–4 (Lynch et  al., 2020, 
their Figure 5), while understating the effect of any new methane 
emission source by a factor of 4–5 over the 20 years following the 
introduction of the new source (Lynch et al., 2020, their Figure 4).
Figure  7.22 explores how cumulative CO 2 equivalent emissions 
estimated for methane vary under different emissions metric choices 
and how estimates of the global surface air temperature (GSAT) 
change deduced from these cumulative emissions compare to the 
2 To calculate CO 2 equivalent emissions under GWP*, the short-lived greenhouse gas emissions are multiplied by GWP-100 × 0.28 and added to the net emissions increase or decrease over the

previous 20 years multiplied by GWP-100 × 4.24 (Smith et al., 2021).actual temperature response computed with the two-layer emulator. 
Note that GWP and GTP metrics were not designed for use under 
a cumulative carbon dioxide equivalent emissions framework (Shine 
et al., 1990, 2005), even if they sometimes are (e.g., Cui et al., 2017; 
Howard et al., 2018) and analysing them in this way can give useful 
insights into their physical properties. Using these standard metrics 
under such frameworks, the cumulative CO 2 equivalent emissions 
associated with methane emissions would continue to rise if methane 
emissions were substantially reduced but remained above zero. 
In reality, a decline in methane emissions to a smaller but still positive 
value could cause a declining warming. GSAT changes estimated with 
cumulative CO 2 equivalent emissions computed with GWP-20 matches 
the warming trend for a few decades but quickly overestimates the

response. Cumulative emissions using GWP-100 perform well when 
emissions are increasing but not when they are stable or decreasing. 
Cumulative emissions using GTP-100 consistently underestimate 
the warming. Cumulative emissions using either CGTP or GWP* 
approaches can more closely match the GSAT evolution (Allen et al., 
2018b; Cain et al., 2019; Collins et al., 2020; Lynch et al., 2020).
In summary, new emissions metric approaches such as GWP* and 
CGTP are designed to relate emissions changes in short-lived GHGs 
to emissions of CO 2 as they better account for the different physical 
behaviours of short- and long-lived gases. Through scaling the 
corresponding cumulative CO 2 equivalent emissions by the TCRE, 
the GSAT response from emissions over time of an aggregated set of Figure 7.2 2 | Explores how cumulative carbon dioxide equivalent emissions estimated for methane vary under different emissions metric choices and

how estimates of the global surface air temperature (GSAT) change deduced from these cumulative emissions compare to the actual temperature 
response computed with the two-layer emulator (solid black lines).  Panels (a) and (b) show the SSP4-6.0 and SSP1-2.6 scenarios respectively. The panels show 
annual methane emissions as the dotted lines (left axis) from 1750 to 2100. The solid lines can be read as either estimates of GSAT change or estimates of the cumulative 
carbon dioxide equivalent emissions. This is because they are related by a constant factor, the TCRE. Thus, values can be read using either of the right-hand axes. Emissions metric 
values are taken from Table 7.15. The GWP* calculation is given in Section 7.6.1.4. The two-layer emulator has been calibrated to the central values of the Report’s assessment 
(see Supplementary Material 7.SM.5.2). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).

1017
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity Chapter 77Box 7.3 | Physical Considerations in Emissions Metric Choice
Following AR5, this Report does not recommend an emissions metric because the appropriateness of the choice depends on the 
purposes for which gases or forcing agents are being compared. Emissions metrics can facilitate the comparison of effects of emissions 
in support of policy goals. They do not define policy goals or targets but can support the evaluation and implementation of choices within 
multi-component policies (e.g., they can help prioritize which emissions to abate). The choice of metric will depend on which aspects of 
climate change are most important to a particular application or stakeholder and over which time horizons. Different international and 
national climate policy goals may lead to different conclusions about what is the most suitable emissions metric (Myhre et al., 2013b).

Global warming potentials (GWP) and global temperature-change potentials (GTP) give the relative effect of pulse emissions, that is, how 
much more energy is trapped (GWP) or how much warmer (GTP) the climate would be when unit emissions of different compounds are 
compared (Section 7.6.1.2). Consequently, these metrics provide information on how much energy accumulation (GWP) or how much 
global warming (GTP) could be avoided (over a given time period, or at a given future point in time) by avoiding the emission of a unit of 
a short-lived greenhouse gas compared to avoiding a unit of CO 2. By contrast, the new metric approaches of combined GTP (CGTP) and 
GWP* closely approximate the additional effect on climate from a time series of short-lived GHG emissions, and can be used to compare

this to the effect on temperature from the emission or removal of a unit of CO 2 (Section 7.6.1.4; Allen et al., 2018b; Collins et al., 2020).gases can be estimated. Using either these new approaches, or treating 
short- and long-lived GHG emissions pathways separately, can improve 
the quantification of the contribution of emissions to global warming 
within a  cumulative emissions framework, compared to approaches 
that aggregate emissions of GHGs using standard CO 2 equivalent 
emissions metrics. As discussed in Box 7.3, there is high confidence 
that multi-gas emissions pathways with the same time-dependence 
of aggregated CO 2 equivalent emissions estimated from standard 
approaches, such as weighting emissions by their GWP-100 values, 
rarely lead to the same estimated temperature outcomes.
7.6.1.5 Emissions Metrics by Compounds
Emissions metrics for selected compounds are presented in Table 7.15, 
with further compounds presented in the Supplementary Material,

Table 7.SM.7. The evolution of the CO 2 concentrations in response to 
a pulse emission is as in AR5 (Joos et al., 2013; Myhre et al., 2013b), 
the perturbation lifetimes for CH 4 and N 2O are from Section 7.6.1.1. The lifetimes and radiative efficiencies for halogenated compounds 
are taken from Hodnebrog et al. (2020a). Combined metrics (CGTPs) 
are presented for compounds with lifetimes less than 20 years. Note 
that CGTP has units of years and is applied to a change in emissions rate 
rather than a change in emissions amount. Changes since AR5 are due 
to changes in radiative properties and lifetimes (Section 7.6.1.1), and 
indirect contributions (Section 7.6.1.3). Table 7.15 also gives overall 
emissions uncertainties in the emissions metrics due to uncertainties 
in radiative efficiencies, lifetimes and the climate response function 
(Supplementary Material, Tables 7.SM.8 to 7.SM.13).
Following their introduction in AR5 the assessed metrics now routinely

include the carbon cycle response for non-CO 2 gases (Section 7.6.1.3). 
As assessed in this earlier section, the carbon cycle contribution is 
lower than in AR5. Contributions to CO 2 formation are included for 
methane depending on whether or not the source originates from 
fossil carbon, thus methane from fossil fuel sources has slightly higher 
emissions metric values than that from non-fossil sources.
Table 7.15  | Emissions metrics for selected species: global warming potential (GWP), global temperature-change potential (GTP).  All values include carbon 
cycle responses as described in Section 7.6.1.3. Combined GTPs (CGTPs) are shown only for species with a lifetime less than 20 years (Section 7.6.1.4). Note CGTP has units of 
years and is applied to a change in emissions rate rather than a change in emissions amount. The radiative efficiencies are as described in Section 7.3.2 and include tropospheric

adjustments where assessed to be non-zero in Section 7.6.1.1. The climate response function is from Supplementary Material 7.SM.5.2. Uncertainty calculations are presented 
in Supplementary Tables 7.SM.8 to 7.SM.13. Chemical effects of CH 4 and N 2O are included (Section 7.6.1.3). Contributions from stratospheric ozone depletion to halogenated 
species metrics are not included. Supplementary Table 7.SM.7 presents the full table.
SpeciesLifetime
(Years)Radiative 
Efficiency 
(W m–2 ppb–1)GWP-20 GWP-100 GWP-500 GTP-50 GTP-100CGTP-50 
(years)CGTP-100 
(years)
CO 2 Multiple 1.33 ± 0.16 ×10–5 1. 1.000 1.000 1.000 1.000
CH 4-fossil 11.8 ± 1.8 5.7 ± 1.4 ×10–482.5 ± 25.8 29.8 ± 11 10.0 ± 3.8 13.2 ± 6.1 7.5 ± 2.9 2823 ± 1060 3531 ± 1385
CH 4-non fossil 11.8 ± 1.8 5.7 ± 1.4 ×10–479.7 ± 25.8 27.0 ± 11 7.2 ± 3.8 10.4 ± 6.1 4.7 ± 2.9 2675 ± 1057 3228 ± 1364
N2O 109 ± 10 2.8 ± 1.1 ×10–3 273 ± 118 273 ± 130 130 ± 64 290 ± 140 233 ± 110

HFC-32 5.4 ± 1.1 1.1 ± 0.2 ×10–1 2693 ± 842 771 ± 292 220 ± 87 181 ± 83 142 ± 51 78,175 ± 29,402 92,888 ± 36,534
HFC-134a 14.0 ± 2.8 1.67 ± 0.32 ×10–1 4144 ± 1160 1526 ± 577 436 ± 173 733 ± 410 306 ± 119 146,670 ± 53,318 181,408 ± 71,365
CFC-11 52.0 ± 10.4 2.91 ± 0.65 ×10–1 8321 ± 2419 6226 ± 2297 2093 ± 865 6351 ± 2342 3536 ± 1511
PFC-14 50,000 9.89 ± 0.19 ×10–2 5301 ± 1395 7380 ± 2430 10,587 ± 3692 7660 ± 2464 9055 ± 3128

1018
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity77.6.2 Applications of Emissions Metrics
One prominent use of emissions metrics is for comparison of efforts 
measured against climate change goals or targets. One of the most 
commonly discussed goals is in Article 2 of the Paris Agreement 
which aims to limit the risks and impacts of climate change by setting 
temperature goals. In addition, the Paris Agreement has important 
provisions which relate to how the goals are to be achieved, including 
making emissions reductions in a  manner that does not threaten 
food production (Article 2), an early emissions peaking target, and 
the aim to ‘achieve a balance between anthropogenic emissions by 
sources and removals by sinks of greenhouse gases in the second half 
of this century’ (Article 4). Article 4 also contains important context 
regarding international equity, sustainable development, and poverty

reduction. Furthermore, the United Nations Framework Convention 
on Climate Change (UNFCCC) sets out as its ultimate objective, the 
‘stabilization of greenhouse gas concentrations in the atmosphere 
at a level that would prevent dangerous anthropogenic interference 
with the climate system.’
How the interpretation of the Paris Agreement and the meaning of 
‘net zero’ emissions, reflects on the appropriate choice of  metric is an active area of research (Schleussner et  al., 2016, 2019; 
Fuglestvedt et al., 2018; Collins et al., 2020). Several possible scientific 
interpretations of the Article 2 and 4 goals can be devised, and these, 
along with emissions metric choice, have implications both for when 
a balance in GHG emissions, net zero CO 2 emissions or net zero GHG 
emissions are achieved, and for their meaning in terms of temperature 
outcome (Fuglestvedt et al., 2018; Rogelj et al., 2018; Wigley, 2018). 
In AR6 net zero GHG emissions is defined as the condition in which

metric-weighted anthropogenic GHG emissions are balanced by 
metric-weighted anthropogenic GHG removals over a specified period 
(see Box  1.4 and Appendix VII: Glossary). The quantification of net 
zero GHG emissions depends on the GHG emissions metric chosen 
to compare emissions and removals of different gases, as well as the 
time horizon chosen for that metric. As the choice of emissions metric 
affects the quantification of net zero GHG emissions, it therefore 
affects the resulting temperature outcome after net zero emissions 
are achieved (Lauder et  al., 2013; Rogelj et  al., 2015; Fuglestvedt 
et al., 2018; Schleussner et al., 2019). Schleussner et al. (2019) note 
that declining temperatures may be a desirable outcome of net zero. 
Rogelj and Schleussner (2019) also point out that the use of physical 
metrics raises questions of equity and fairness between developed and 
developing countries.Box 7.3 (continued)

If global surface temperature stabilization goals are considered, cumulative CO 2 equivalent emissions computed with the GWP-100 
emissions metric would continue to rise when short-lived GHG emissions are reduced but remain above zero (Figure 7.22b). Such 
a  rise would not match the expected global surface temperature stabilization or potential decline in warming that comes from 
a reduction in emissions of short-lived greenhouse gases (Pierrehumbert, 2014; Allen et al., 2018b; Cain et al., 2019; Collins et al., 
2020; Lynch et al., 2020, 2021). This is relevant to net zero GHG emissions goals (Section 7.6.2 and Box 1.4).
When individual gases are treated separately in climate model emulators (Cross-Chapter Box  7.1), or weighted and aggregated 
using an emissions metric approach (such as CGTP or GWP*) which translate the distinct behaviour from cumulative emissions of

short-lived gases, ambiguity in the future warming trajectory of a given emissions scenario can be substantially reduced (Cain et al., 
2019; Denison et al., 2019; Collins et al., 2020; Lynch et al., 2021). The degree of ambiguity varies with the emissions scenario. For 
mitigation pathways that limit warming to 2°C with an even chance, the ambiguity arising from using GWP-100 as sole constraint 
on emissions of a mix of greenhouse gases (without considering their economic implications or feasibility) could be as much as 
0.17°C, which represents about one-fifth of the remaining global warming in those pathways (Denison et al., 2019). If the evolution 
of the individual GHGs is not known, this can make it difficult to evaluate how a given global multi-gas emissions pathway specified 
only in CO 2 equivalent emissions would achieve (or not) global surface temperature goals. This is potentially an issue as Nationally

Determined Contributions frequently make commitments in terms of GWP-100-based CO 2 equivalent emissions at 2030 without 
specifying individual gases (Denison et al., 2019). Clear and transparent representation of the global warming implications of future 
emissions pathways including Nationally Determined Contributions could be achieved either by their detailing pathways for multiple 
gases or by detailing a pathway of cumulative carbon dioxide equivalent emissions approach aggregated across GHGs evaluated 
by either GWP* or CGTP metric approaches (Cain et  al., 2019; Collins et  al., 2020; Lynch et  al., 2021). It should be noted that 
although the Paris Agreement Rulebook asks countries to report emissions of individual GHGs separately for the global stocktake 
(Decision 18/CMA.1, annex, paragraph 38), which can allow the current effects of their emissions on global surface temperature to

be accurately estimated, estimates of future warming are potentially ambiguous where emissions are aggregated using GWP-100 or 
other pulse metrics.
Although there is significant history of using single-basket approaches, supported by emissions metrics such as GWP-100, in climate 
policies such as the Kyoto Protocol, multi-basket approaches also have many precedents in environmental management, including the 
Montreal Protocol (Daniel et al., 2012). Further assessment of the performance of physical and economics-based metrics in the context 
of climate change mitigation is provided in the contribution of Working Group III to AR6.

1019
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77Based on SR1.5 (Allen et al., 2018a), there is high confidence that 
forcing would halt human-induced warming. Based on (Bowerman 
et  al., 2013; Pierrehumbert, 2014; Fuglestvedt et  al., 2018; Tanaka 
and O’Neill, 2018; Schleussner et  al., 2019) there is also high 
confidence  that reaching net zero GHG emissions as quantified by 
GWP-100 typically leads to reductions from peak global surface 
temperature after net zero GHGs emissions are achieved, depending 
on the relative sequencing of mitigation of short-lived and long-lived 
species. If both short- and long-lived species are mitigated together, 
then temperatures peak and decline. If mitigation of short-lived 
species occurs much earlier than that of long-lived species, then 
temperatures stabilize very near peak values, rather than decline.

Temperature targets can be met even with positive net GHG emissions 
based on GWP-100 (Tanaka and O’Neill, 2018). As demonstrated 
by Allen et al. (2018b), Cain et al. (2019), Schleussner et al. (2019) 
and Collins et  al. (2020) reaching net zero GHG emissions when 
quantified using the new emissions metric approaches such as 
CGTP or GWP* would lead to an approximately similar temperature 
evolution as achieving net zero CO 2. Hence, net zero CO 2 and net zero 
GHG, quantified using these new approaches, would both lead to 
approximately stable contributions to temperature change after net 
zero emissions are achieved ( high confidence ).
Comparisons with emissions or global surface temperature 
stabilization goals are not the only role for emissions metrics. 
Other important roles include those in pricing approaches where 
policymakers choose to compare short-lived and long-lived climate 
forcers (e.g.,  Manne and Richels, 2001), and in life cycle analyses

(e.g., Hellweg and Milà i Canals, 2014). Several papers have reviewed 
the issue of metric choice for life cycle analyses, noting that analysts 
should be aware of the challenges and value judgements inherent in 
attempting to aggregate the effects of forcing agents with different 
time scales onto a common scale (e.g., Mallapragada and Mignone, 
2017) and recommend aligning metric choice with policy goals as 
well as testing sensitivities of results to metric choice (Cherubini et al., 
2016). Furthermore, life cycle analyses approaches which are sensitive 
to choice of emissions metric benefit from careful communication of 
the reasons for the sensitivity (Levasseur et al., 2016).

1020
Chapter 7 The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity7Frequently Asked Questions
FAQ 7.1 | What Is the Earth’s Energy Budget, and What Does It Tell Us About Climate Change?
The Earth’s energy budget describes the flow of energy within the climate system. Since at least 1970 there 
has been a  persistent imbalance in the energy flows that has led to excess energy being absorbed by the 
climate system. By measuring and understanding these energy flows and the role that human activities play in 
changing them, we are better able to understand the causes of climate change and project future climate change 
more accurately.
Our planet receives vast amounts of energy every day in the form of sunlight. Around a third of the sunlight is 
reflected back to space by clouds, by tiny particles called aerosols , and by bright surfaces such as snow and ice. 
The rest is absorbed by the ocean, land, ice and atmosphere. The planet then emits energy back out to space in

the form of thermal radiation. In a world that was not warming or cooling, these energy flows would balance. 
Human activity has caused an imbalance in these energy flows.
We measure the influence of various human and natural factors on the energy flows at the top of our atmosphere 
in terms of radiative forcings , where a positive radiative forcing has a warming effect and a negative radiative 
forcing has a cooling effect. In response to these forcings, the Earth system will either warm or cool, so as to 
restore balance through changes in the amount of outgoing thermal radiation (the warmer the Earth, the more 
radiation it emits). Changes in Earth’s temperature in turn lead to additional changes in the climate system 
(known as climate feedbacks ) that either amplify or dampen the original effect. For example, Arctic sea ice has 
been melting as the Earth warms, reducing the amount of reflected sunlight and adding to the initial warming

(an amplifying feedback). The most uncertain of those climate feedbacks are clouds, as they respond to warming in 
complex ways that affect both the emission of thermal radiation and the reflection of sunlight. However, we are 
now more confident that cloud changes, taken together, will amplify climate warming (see FAQ 7.2).
Human activities have unbalanced these energy flows in two main ways. First, increases in greenhouse gas levels 
have led to more of the emitted thermal radiation being absorbed by the atmosphere, instead of being released to 
space. Second, increases in pollutants have increased the amount of aerosols such as sulphates in the atmosphere 
(see FAQ 6.1). This has led to more incoming sunlight being reflected away, by the aerosols themselves and 
through the formation of more cloud drops, which increases the reflectivity of clouds (see FAQ 7.2).
Altogether, the global energy flow imbalance since the 1970s has been just over half a watt per square metre

of the Earth’s surface. This sounds small, but because the imbalance is persistent and because Earth’s surface is 
large, this adds up to about 25 times the total amount of primary energy consumed by human society, compared 
over 1971 to 2018. Compared to the IPCC Fifth Assessment Report (AR5), we are now better able to quantify 
and track these energy flows from multiple lines of evidence, including satellite data, direct measurements of 
ocean temperatures, and a wide variety of other Earth system observations (see FAQ 1.1). We also have a better 
understanding of the processes contributing to this imbalance, including the complex interactions between 
aerosols, clouds and radiation.
Research has shown that the excess energy since the 1970s has mainly gone into warming the ocean (91%), 
followed by the warming of land (5%) and the melting of ice sheets and glaciers (3%). The atmosphere has

warmed substantially since 1970, but because it is comprised of thin gases it has absorbed only 1% of the excess 
energy (FAQ 7.1, Figure 1). As the ocean has absorbed the vast majority of the excess energy, especially within 
its top two kilometres, the deep ocean is expected to continue to warm and expand for centuries to millennia, 
leading to long-term sea level rise – even if atmospheric greenhouse gas levels were to decline (see FAQ 5.3). 
This is in addition to the sea level rise expected from melting ice sheets and glaciers.
Understanding the Earth’s energy budget  al.o helps to narrow uncertainty in future projections of climate. 
By testing climate models against what we know about the Earth’s energy budget, we can make more confident 
projections of surface temperature changes we might expect this century and beyond.

1021
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77FAQ 7.1 (continued)
FAQ 7.1:  The Earth’ s energy budget and climate change
Since at least 1970, there has been a persistent imbalance in the energy flows that has 
led to excess energy being absorbed by different components of the climate system.
IceOceanLand
3%91%Outgoing 
energy
Excess energy accumulatingLess outgoing 
energy due to 
greenhouse gasesIncoming
solar energyIncoming
solar energyStable climate: in balance Today: imbalanced
Atmosphere  1%
5%
FAQ 7.1, F igure 1 | The Earth’s energy budget compares the flows of incoming and outgoing energy that are relevant for the climate 
system.  Since at least the 1970s, less energy is flowing out than is flowing in, which leads to excess energy being absorbed by the ocean, land, ice and 
atmosphere, with the ocean absorbing 91%.

1022
FAQ 7.2 | What Is the Role of Clouds in a Warming Climate?
One of the biggest challenges in climate science has been to predict how clouds will change in a warming world 
and whether those changes will amplify or partially offset the warming caused by increasing concentrations of 
greenhouse gases and other human activities. Scientists have made significant progress over the past decade and 
are now more confident that changes in clouds will amplify, rather than offset, global warming in the future.
Clouds cover roughly two-thirds of the Earth’s surface. They consist of small droplets and/or ice crystals, which 
form when water vapour condenses or deposits around tiny particles called aerosols  (such as salt, dust, or smoke). 
Clouds play a critical role in the Earth’s energy budget  at the top of our atmosphere and therefore influence

Earth’s surface temperature (see FAQ 7.1). The interactions between clouds and the climate are complex and 
varied. Clouds at low altitudes tend to reflect incoming solar energy back to space, creating a cooling effect by 
preventing this energy from reaching and warming the Earth. On the other hand, higher clouds tend to trap 
(i.e., absorb and then emit at a lower temperature) some of the energy leaving the Earth, leading to a warming 
effect. On average, clouds reflect back more incoming energy than the amount of outgoing energy they trap, 
resulting in an overall net cooling effect on the present climate. Human activities since the pre-industrial era 
have altered this climate effect of clouds in two different ways: by changing the abundance of the aerosol 
particles in the atmosphere and by warming the Earth’s surface, primarily as a result of increases in greenhouse 
gas emissions.

The concentration of aerosols in the atmosphere has markedly increased since the pre-industrial era, and this 
has had two important effects on clouds. First, clouds now reflect more incoming energy because cloud droplets 
have become more numerous and smaller. Second, smaller droplets may delay rain formation, thereby making 
the clouds last longer, although this effect remains uncertain. Hence, aerosols released by human activities have 
had a cooling effect, counteracting a considerable portion of the warming caused by increases in greenhouse 
gases over the last century (see FAQ 3.1). Nevertheless, this cooling effect is expected to diminish in the future, as 
air pollution policies progress worldwide, reducing the amount of aerosols released into the atmosphere.
Since the pre-industrial period, the Earth’s surface and atmosphere have warmed, altering the properties of

clouds, such as their altitude, amount and composition (water or ice), thereby affecting the Earth’s energy budget 
and, in turn, changing temperature. This cascading effect of clouds, known as the cloud feedback , could either 
amplify or offset some of the future warming and has long been the biggest source of uncertainty in climate 
projections. The problem stems from the fact that clouds can change in many ways and that their processes occur 
on much smaller scales than global climate models can explicitly represent. As a result, global climate models 
have disagreed on how clouds, particularly over the subtropical ocean, will change in the future and whether the 
change will amplify or suppress the global warming.
Since the last IPCC Report in 2013 (the Fifth Assessment Report, or AR5), understanding of cloud processes has 
advanced with better observations, new analysis approaches and explicit high-resolution numerical simulation of

clouds. Also, current global climate models simulate cloud behaviour better than previous models, due both to 
advances in computational capabilities and process understanding. Altogether, this has helped to build a more 
complete picture of how clouds will change as the climate warms (FAQ 7.2, Figure 1). For example, the amount 
of low-clouds will reduce over the subtropical ocean, leading to less reflection of incoming solar energy, and 
the altitude of high-clouds will rise, making them more prone to trapping outgoing energy; both processes 
have a warming effect. In contrast, clouds in high latitudes will be increasingly made of water droplets rather 
than ice crystals. This shift from fewer, larger ice crystals to smaller but more numerous water droplets will 
result in more of the incoming solar energy being reflected back to space and produce a cooling effect. Better 
understanding of how clouds respond to warming has led to more confidence than before that future changes

in clouds will, overall, cause additional warming (i.e., by weakening the current cooling effect of clouds). This is 
called a  positive net cloud feedback .
In summary, clouds will amplify rather than suppress the warming of the climate system in the future, as more 
greenhouse gases and fewer aerosols are released to the atmosphere by human activities.

1023
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77FAQ 7.2 (continued)
Altitude (Warming)
 Amount (Warming) Composition (Cooling)
Fewer (low level) clouds Higher clouds More water droplets
Incoming 
solar 
energyIncoming 
solar 
energy
Outgoing
energyLess incoming energy 
reflected back to spaceMore incoming energy 
reflected back to spaceFAQ 7.2: What is the role of clouds in a warming climate?
Clouds affect and are affected by climate change. Overall, scientists expect clouds to amplify future warming.
Future climate Present climate Future climate Present climate Future climate Present climate
Surface
More outgoing energy 
trapped by clouds
FAQ 7.2, Figure  1 | Interactions between clouds and the climate, today and in a warmer future.  Global warming is expected to alter the altitude 
(left)  and the amount (centre)  of clouds, which will amplify warming. On the other hand, cloud composition will change (right) , offsetting some of the

warming. Overall, clouds are expected to amplify future warming.

1024
FAQ 7.3 | What Is Equilibrium Climate Sensitivity and How Does It Relate to Future Warming?
For a given future scenario, climate models project a range of changes in global surface temperature. This range 
is closely related to equilibrium climate sensitivity, or ECS, which measures how climate models respond to 
a doubling of carbon dioxide in the atmosphere. Models with high climate sensitivity project stronger future 
warming. Some climate models of the new generation are more sensitive than the range assessed in the IPCC 
Sixth Assessment Report. This leads to end-of-century global warming in some simulations of up to 2°C–3°C 
above the current IPCC best estimate. Although these higher warming levels are not expected to occur, high-ECS 
models are useful for exploring low-likelihood, high-impact futures.

The equilibrium climate sensitivity  (ECS) is defined as the long-term global warming caused by a doubling of 
carbon dioxide above its pre-industrial concentration. For a given emissions scenario, much of the uncertainty in 
projections of future warming can be explained by the uncertainty in ECS (FAQ 7.3, Figure 1). The significance 
of equilibrium climate sensitivity has long been recognized, and the first estimate was presented by Swedish 
scientist Svante Arrhenius in 1896.
This Sixth Assessment Report concludes that there is a 90% or more chance ( very likely ) that the ECS is between 
2°C and 5°C. This represents a significant reduction in uncertainty compared to the Fifth Assessment Report, 
which gave a 66% chance ( likely ) of ECS being between 1.5°C and 4.5°C. This reduction in uncertainty has been 
possible not through a single breakthrough or discovery but instead by combining evidence from many different

sources and by better understanding their strengths and weaknesses.
There are four main lines of evidence for ECS.
• The self-reinforcing processes, called feedback loops , that amplify or dampen the warming in response to 
increasing carbon dioxide are now better understood. For example, warming in the Arctic melts sea ice, 
resulting in more open ocean area, which is darker and therefore absorbs more sunlight, further intensifying 
the initial warming. It remains challenging to represent realistically all the processes involved in these 
feedback loops, particularly those related to clouds (see FAQ 7.2). Such identified model errors are now taken 
into account, and other known, but generally weak, feedback loops that are typically not included in models 
are now included in the assessment of ECS.
• Historical warming since early industrialisation provides strong evidence that climate sensitivity is not small.

Since 1850, the concentrations of carbon dioxide and other greenhouse gases have increased, and as a result 
the Earth has warmed by about 1.1°C. However, relying on this industrial-era warming to estimate ECS is 
challenging, partly because some of the warming from greenhouse gases was offset by cooling from aerosol 
particles and partly because the ocean is still responding to past increases in carbon dioxide.
• Evidence from ancient climates that had reached equilibrium with greenhouse gas concentrations, such as the 
coldest period of the last ice age around 20,000 years ago, or warmer periods further back in time, provide 
useful data on the ECS of the climate system (see FAQ 1.3).
• Statistical approaches linking model ECS values with observed changes, such as global warming since the 
1970s, provide complementary evidence.
All four lines of evidence rely, to some extent, on climate models, and interpreting the evidence often benefits

from model diversity and spread in modelled climate sensitivity. Furthermore, high-sensitivity models can provide 
important insights into futures that have a low likelihood of occurring but that could result in large impacts. 
But, unlike in previous assessments, climate models are not considered a line of evidence in their own right in the 
IPCC Sixth Assessment Report.
The ECS of the latest climate models is, on average, higher than that of the previous generation of models 
and also higher than this Report’s best estimate of 3.0°C. Furthermore, the ECS values in some of the new 
models are both above and below the 2°C to 5°C very likely  range, and although such models cannot be ruled 
out as implausible solely based on their ECS, some simulations display climate change that is inconsistent with 
the observed changes when tested with ancient climates. A slight mismatch between models and this Report’s

assessment is only natural because this Report’s assessment is largely based on observations and an improved 
understanding of the climate system.

1025
The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity  Chapter 77FAQ 7.3 (continued)
Projected warming in 2081-2100 (ºC)Equilibrium Climate Sensitivity (ºC)
FAQ 7.3: Equilibrium climate sensitivity and future warming
Equilibrium climate sensitivity measures how climate models respond to a doubling of carbon dioxide in the atmosphere. 
Climate sensitivity of models
CMIP5 CMIP6 AR6 CMIP5 CMIP6 AR6Future projections
Climate models from the new generation  (  )
are on average more sensitive to carbon dioxide 
than those of the last generation (  )But projections in 
this assessment  
do not solely 
rely on models 
IPCC best 
estimate  
(and range)  
IPCC best 
estimate  
(and range)  12
333 2 1 More sensitive 
models  
project stronger 
warming7
6
5
4
3
27
6
5
4
3
2
FAQ 7.3, Figure 1  | Equilibrium climate sensitivity and future warming. (left)  Equilibrium climate sensitivities for the current generation (Coupled

Model Intercomparison Project Phase 6, CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. 
(right)  Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. The thick horizontal lines 
represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 
and CMIP6 and the range assessed in AR6.

1057
Water Cycle Changes Chapter 88Executive Summary
This chapter assesses multiple lines of evidence to evaluate past, 
present and future changes in the global water cycle. It complements material in Chapters 2, 3 and 4 on observed and projected changes 
in the water cycle, and Chapters 10 and 11 on regional climate 
change and extreme events. The assessment includes the physical basis for water cycle changes, observed changes in the water cycle and attribution of their causes, future projections and related key uncertainties, and the potential for abrupt change. Paleoclimate 
evidence, observations, reanalyses and global and regional model 
simulations are considered. The assessment shows widespread, non-uniform human-caused alterations of the water cycle, which have been obscured by a competition between different drivers across the 
20th century and that will be increasingly dominated by greenhouse 
gas forcing at the global scale.
Physical Basis for Water Cycle Changes

Modifications of Earth’s energy budget by anthropogenic radiative forcings drive substantial and widespread changes in the global water cycle . There is high confidence that global 
mean precipitation and evaporation increase with global warming, but the estimated rate is model-dependent ( very likely range of 
1–3% per 1°C). The global increase in precipitation is determined by a robust response to global mean surface air temperature ( very likely 
2–3% per 1°C) that is partly offset by fast atmospheric adjustments 
to atmospheric heating by greenhouse gases and aerosols. The overall 
effect of anthropogenic aerosols is to reduce global precipitation and alter large-scale atmospheric circulation patterns through their well-understood surface radiative cooling effect (high confidence). Land-
use and land-cover changes also drive regional water cycle changes 
through their influence on surface water and energy budgets ( high 
confidence). {8.2.1, 8.2.3.4, 8.2.2.2, Box 8.1}

A warmer climate increases moisture transport into weather 
systems, which, on average, makes wet seasons and events 
wetter (high confidence) . An increase in near-surface atmospheric 
water holding capacity of about 7%  per  1°C of warming explains a  similar magnitude of intensification of heavy precipitation events (from sub-daily up to seasonal time scales) that increases the severity 
of flood hazards when these extremes occur ( high confidence). 
The severity of very wet and very dry events increases in a warming 
climate (high confidence), but changes in atmospheric circulation patterns alter where and how often these extremes occur, with substantial regional differences and seasonal contrasts. A slowdown 
of tropical circulation with global warming partly offsets the warming-
induced strengthening of precipitation in monsoon regions (high confidence). {8.2.2, 8.2.3, 8.3.1.7, 8.4.1, 8.5.1}
Warming over land drives an increase in atmospheric

evaporative demand and the severity of droughts ( high 
confidence). Greater warming over land than over the ocean alters atmospheric circulation patterns and, on average, reduces continental near-surface relative humidity, which contributes to regional drying 
(high confidence). Increasing atmospheric CO
2 concentrations increase plant growth and water-use efficiency, but there is low 
confidence in how these factors drive regional water cycle changes. {8.2.2, 8.2.3}
Causes of Observed Changes
Human-caused climate change has driven detectable changes 
in the global water cycle since the mid-20th century (high confidence). Global warming has contributed to an overall increase 
in atmospheric moisture and precipitation intensity (high confidence), 
increased terrestrial evapotranspiration (medium confidence), influenced global patterns in aridity ( very likely), and enhanced

contrasts in surface salinity and precipitation minus evaporation patterns over the oceans ( high confidence). {3.4.2, 3.4.3, 3.5.2, 
8.3.1, 9.2.2}
Greenhouse gas forcing has driven increased contrasts 
in precipitation amounts between wet and dry seasons 
and weather regimes over tropical land areas ( medium 
confidence) and a  detectable precipitation increase in the 
northern high latitudes (high confidence). Greenhouse gas forcing has also contributed to drying in dry summer climates, including the Mediterranean, south-western Australia, south-western 
South America, South Africa, and western North America ( medium 
to high confidence ). Earlier onset of spring snowmelt and increased 
melting of glaciers have already contributed to seasonal changes in 
streamflow in high-latitude and low-elevation mountain catchments (high confidence). {Box 8.2, 8.2.2.1, 8.3.1, 3.3.2, 3.3.3, 3.5.2}
Anthropogenic aerosols have driven detectable large-scale

water cycle changes since at least the mid-20th century ( high 
confidence). Shifts in the tropical rain belt are associated with 
the inter-hemispheric temperature response to the time-evolving 
radiative influence of anthropogenic aerosols and the ongoing 
warming influence of greenhouse gases
 (high confidence). Cooling in 
the Northern Hemisphere by sulphate aerosols explained a southward shift in the tropical rain belt and contributed to the Sahel drought 
from the 1970s to the 1980s ( high confidence ), subsequent recovery 
from which has been linked with greenhouse gas warming ( medium 
confidence). Observed changes in regional monsoon precipitation, 
especially over South Asia, East Asia and West Africa, have been limited over much of the 20th century due to increases driven by 
warming from greenhouse gases being counteracted by decreases 
due to cooling from anthropogenic aerosols ( high confidence). 
{8.3.1.3, 8.3.2.4, Box 8.1}

Land-use change and water extraction for irrigation have 
influenced local and regional responses in the water cycle (high confidence). Large-scale deforestation has likely decreased 
evapotranspiration and precipitation and increased runoff over the deforested regions. Urbanization has increased local precipitation 
(medium confidence) and resulting runoff intensity (high confidence). 
Increased precipitation intensities have enhanced groundwater recharge, most notably in tropical regions ( medium confidence). 
There is high confidence that groundwater depletion has occurred 
since at least the start of the 21st century as a  consequence of

1058
Chapter 8  W ater Cycle Changes8groundwater withdrawals for irrigation in agricultural areas in 
drylands (e.g., the southern High Plains and California Central Valley 
of the USA, North China Plain, and north-west India). {8.2.3.4, 
8.3.1.7, Box 10.3, FAQ 8.1}
Southern Hemisphere storm tracks and associated precipitation 
have shifted polewards since the 1970s, especially in the austral 
summer and autumn ( high confidence ). It is very likely that these 
changes  are associated with a positive trend in the Southern Annular 
Mode, related to both stratospheric ozone depletion and greenhouse 
gas increases. There is medium confidence  that the recent observed 
expansion of the Hadley circulation was caused by greenhouse gas 
forcing, especially in the Southern Hemisphere, but there is only  low 
confidence in how it influences the drying of subtropical land areas. 
{8.2.2, 8.3.2, 3.3.3}
Future Water Cycle Changes
Without large-scale reduction in greenhouse gas emissions,

global warming is projected to cause substantial changes 
in the water cycle at both global and regional scales ( high 
confidence ). Global annual precipitation over land is projected to 
increase on average by 2.4 [–0.2 to +4.7] %  ( likely   range) in the 
SSP1-1.9 low-emissions scenario and by 8.3 [0.9 to 12.9] % in the 
SSP5-8.5 very high-emissions scenario by 2081–2100, relative to 
1995–2014. It is virtually certain  that evaporation will increase over 
the oceans and very likely  that evapotranspiration will increase over 
land with regional exceptions in drying areas. There is low confidence  
in the sign and magnitude of projected changes in global land runoff 
in all Shared Socio-economic Pathway scenarios. Projected increases 
in precipitation amount and intensity will be associated with 
increased runoff in the northern high latitudes ( high confidence ). 
There is high confidence  that mountain glaciers will diminish in

all regions and that seasonal snow cover duration will generally 
decrease. Runoff from small glaciers will typically decrease through 
loss of ice mass, while runoff from large glaciers is  likely  to increase 
with increasing global warming until glacier mass becomes depleted 
(high confidence ). {4.5.1, 8.4.1}
Increased evapotranspiration due to growing atmospheric 
water demand will decrease soil moisture over the 
Mediterranean, south-western North America, southern Africa, 
south-western South America, and south-western Australia 
(high confidence ). In the Mediterranean, south-western South 
America, and western North America, future aridification will far 
exceed the magnitude of change seen in the last millennium ( high 
confidence ). Some tropical regions are also projected to experience 
increased aridity, including the Amazon basin and Central America 
(high confidence ). {8.4.1}
Water cycle variability and extremes are projected to increase

faster than average changes in most regions of the world and 
under all emissions scenarios ( high confidence ). In the tropics 
and in the extratropics of both hemispheres during summer/warm 
season, interannual variability of precipitation and runoff over land 
is projected to increase at a  faster rate than changes in seasonal mean precipitation amount ( medium confidence ). It is  very likely  that 
rainfall variability related to the El Niño–Southern Oscillation will be 
amplified by the end of the 21st century. Sub-seasonal precipitation 
variability is also projected to increase, with fewer rainy days but 
increased daily mean precipitation intensity over many land regions 
(high confidence ). Precipitation extremes will increase in almost all 
regions ( high confidence ), even where seasonal mean precipitation is 
projected to decrease ( medium confidence ). There is high confidence  
that heavy precipitation events associated with both tropical and

extratropical cyclones will intensify. {4.5.1.4, 4.5.3.2, 8.2.3.2, 8.4.1, 
8.4.2, 8.5.2, 11.7.1.5}
There are contrasting projections in monsoon precipitation, 
with increases in more regions than decreases ( medium 
confidence ). Summer monsoon precipitation is projected to increase 
for the South, South East and East Asian monsoon domains, while 
North American monsoon precipitation is projected to decrease 
(medium confidence ). West African monsoon precipitation is 
projected to increase over the Central Sahel and decrease over the 
far western Sahel ( medium confidence ). There is low confidence  in 
projected precipitation changes in the South American and Australian 
monsoons (for both magnitude and sign). There is high confidence  
that the monsoon season will be delayed in North and South 
America and medium confidence  that it will be delayed in the Sahel. 
{8.2.2, 8.4.2.4}
Precipitation associated with extratropical storms and

atmospheric rivers will increase in the future in most regions 
(high confidence ). A  continued poleward shift of storm tracks in 
the Southern Hemisphere ( likely ) and the North Pacific ( medium 
confidence ) will lead to similar shifts in annual or seasonal 
precipitation. There is low confidence  in projections of blocking and 
stationary waves and therefore their influence on precipitation for 
almost all regions. {8.4.2}
The seasonality of precipitation, water availability and 
streamflow will increase with global warming over the 
Amazon ( medium confidence ) and in the subtropics, especially 
in the Mediterranean and southern Africa ( high confidence ). 
The annual contrast between the wettest and driest month of the 
year is likely  to increase by 3–5% per 1°C in most monsoon regions 
in terms of precipitation, precipitation minus evaporation, and runoff 
(medium confidence ). There is high confidence  in an earlier onset in

spring snowmelt, with higher peak flows at the expense of summer 
flows in snow-dominated regions globally, but medium confidence  
that reduced snow volume in lower-latitude regions will reduce 
runoff from snowmelt. {8.2.2, Box 8.2, 8.4.1.7, 8.4.2.4}
Confidence in Projections, Non-linear Responses and the 
Potential for Abrupt Changes
Representation of key physical processes has improved in 
global climate models, but they are still limited in their ability 
to simulate all aspects of the present-day water cycle and to 
agree on future changes ( high confidence ). Climate change 
studies benefit from sampling the full distribution of model

1059
Water Cycle Changes Chapter 88outputs when considering future projections at regional 
scales.  Increasing horizontal resolution in global climate models 
improves the representation of small-scale features and the statistics 
of daily precipitation ( high confidence ). High-resolution climate and 
hydrological models provide a better representation of land surfaces, 
including topography, vegetation and land-use change, which 
improve the accuracy of simulations of regional changes in the water 
cycle ( high confidence ). There is high confidence  in the potential 
added value of regional climate models but only medium confidence  
that this potential is currently realized. {8.5.1}
Natural climate variability will continue to be a major source of 
uncertainty in near-term (2021–2040) water cycle projections 
(high confidence ). Decadal predictions of water cycle  changes 
should be considered with low confidence  in most land areas

because the internal variability of precipitation is difficult to predict 
and can offset or amplify the forced water cycle response. Water 
cycle changes that have already emerged from natural variability 
will become more pronounced in the near term, but the occurrence 
of volcanic eruptions (either single large events or clustered smaller 
ones) can alter the water cycle for several years, decreasing global 
mean land precipitation and altering monsoon circulation ( high 
confidence ). {8.5.2, Cross-Chapter Box 4.1}
Continued global warming will further amplify greenhouse 
gas-induced changes in large-scale atmospheric circulation 
and precipitation patterns ( high confidence ), but in some 
cases regional water cycle changes are not linearly related to 
global warming. Non-linear water cycle responses are explained by 
the interaction of multiple drivers, feedbacks and time scales ( high 
confidence ). Non-linear responses of regional runoff, groundwater

recharge and water scarcity highlight the limitations of simple 
pattern-scaling techniques ( medium confidence ). Water resources fed 
by melting glaciers are particularly exposed to non-linear responses 
(high confidence ). {8.5.3}
Abrupt human-caused changes to the water cycle cannot be 
excluded. There is evidence of abrupt change in some high-emissions 
scenarios, but there is no overall consistency regarding the magnitude 
and timing of such changes. Positive land surface feedbacks, including 
vegetation and dust, can contribute to abrupt changes in aridity, 
but there is only low confidence  that such changes will occur during 
the 21st century. Continued Amazon deforestation, combined with 
a  warming climate, raises the probability that this ecosystem will 
cross a  tipping point into a  dry state during the 21st century ( low 
confidence ). The paleoclimate records show that a  collapse in the 
Atlantic Meridional Overturning Circulation (AMOC) causes abrupt

shifts in the water cycle ( high confidence ), such as a southward shift in 
the tropical rain belt, weakening of the African and Asian monsoons, 
strengthening of Southern Hemisphere monsoons, and drying in 
Europe. There is medium confidence that AMOC will not collapse 
before 2100, but should it collapse, it is very likely  that there would be 
abrupt changes in the water cycle. {8.6.1, 8.6.2}
Solar radiation modification could drive abrupt changes in 
the water cycle ( high confidence ). It is very likely  that abrupt 
water cycle changes will occur if solar radiation modification (SRM) techniques are implemented rapidly or terminated abruptly. 
The impact of SRM is spatially heterogeneous ( high confidence ), will 
not fully mitigate the greenhouse gas-forced water cycle changes 
(medium confidence ), and can affect different regions in potentially 
disruptive ways ( low confidence ). {8.6.3}

1060
Chapter 8  W ater Cycle Changes88.1 Introduction
8.1.1  Scope and Overview 
8.1.1.1  Importance of Water for Human Societies 
and Ecosystems
Water is vital to all life on Earth. Seventy-one percent of the Earth 
is covered by water, with saline ocean water accounting for around 97% of total water availability (Figure  8.1). Terrestrial freshwater 
represents less than 2% of all water on Earth, and the remainder 
(around 1
–2%) is primarily made up of saline groundwater and saline 
lakes (Durack, 2015; Abbott et al., 2019). Ice sheets, glaciers and snow pack account for approximately 96% of all freshwater, with less 
than 4% of freshwater considered easily accessible and available for 
essential ecosystem functioning and human society’s water resource needs (Durack, 2015; Abbott et al., 2019). This very small fraction of freshwater represents a total volume of about 835,000 km
3, mostly 
contained in groundwater (630,000 km3), the remaining 205,000 km3

being stored in lakes, rivers, wetlands and soils (Abbott et al., 2019). Although the natural cycling rate of this amount is theoretically enough to meet global human and ecosystem needs, there are large 
geographical and seasonal differences that influence the availability 
of freshwater to meet regional demands. 
Freshwater is the most essential natural resource on the planet 
(Mekonnen and Hoekstra, 2016; Djehdian et al., 2019) and underpins 
almost all Sustainable Development Goals (SDGs), which require 
access to adequate and safe resources for drinking and sanitation (SDG 6) and many other purposes. Freshwater supports a range of human activities from irrigation to industrial processes including 
the generation of hydro-electricity and the cooling of thermoelectric

power plants (Bates et al., 2008; Schewe et al., 2014). These activities require sufficient quantities of freshwater that can be drawn from rivers, lakes, groundwater stores, and in some cases, desalinated sea water (Schewe et al., 2014). Recent estimates of global water pools 
and fluxes suggest that half of global river discharge is redistributed 
each year by human water use (Abbott et al., 2019). This emphasizes the need to consider both anthropogenic climate change and direct human influences, such as population increase or migration, economic development, urbanization, and land use change, when 
planning water-related mitigation or adaptation strategies (Jiménez 
Cisneros et al., 2014).
Water scarcity occurs when there are insufficient freshwater resources 
to meet water demands, although water problems may also arise 
from water quality issues or from economic and institutional barriers

(AR6 WGII Chapter 4). This affects the preservation of environmental flows that ultimately influence ecosystem functioning and services (Schewe et al., 2014; Mekonnen and Hoekstra, 2016; Djehdian 
et al., 2019). As such, water availability is a  major constraint on 
human society’s ability to meet the future food and energy needs of a growing population (D’Odorico et al., 2018). Water plays a key role in the production of energy, including hydro-electricity, bioenergy, 
1 In this chapter , the term ‘evaporation’ includes all evaporative processes over land and ocean, including transpiration over land, while the term ‘evapotranspiration’ (ET) is also used interchangeably 
when the focus is only on land.and the extraction of unconventional fossil fuels (Schewe et al., 2014; D’Odorico et al., 2018; Djehdian et al., 2019). These dependencies have resulted in increasing competition for water between the food 
and energy sectors. Pressures on this ‘food-energy-water nexus’ are

further compounded by increasing globalization, which can transfer large-scale water demands to other regions of the world, raising serious concerns about local food and water security in regions that are highly dependent on agricultural exports or imports (D’Odorico 
et al., 2018).
The consequences of climate change on terrestrial ecosystems and 
human societies are primarily experienced through changes to 
the global water cycle (Jiménez Cisneros et al., 2014). Changes in 
the quantity and seasonality of water due to climate change have long been recognized by IPCC and global development agencies as heavily influencing the food security and economic prosperity of many countries, particularly in the arid and semi-arid areas of the world 
including Asia, Africa, Australia, Latin America, the Mediterranean,

and small island developing states (Bates et al., 2008; Schewe et al., 2014; Mekonnen and Hoekstra, 2016). Having too much or too little water increases the likelihood of flooding and drought, as precipitation variability increases in a  warming climate (Stocker 
et al., 2013; Hoegh-Guldberg et al., 2018). Climate change poses 
a threat to both regional water availability and global water security. Changes in precipitation and glacier runoff and snowmelt influence other hydroclimate variables like surface and subsurface runoff, and groundwater recharge, which are critical to the water, food and energy 
security of many regions (Oki and Kanae, 2006; Jiménez Cisneros 
et al., 2014; Schewe et al., 2014; Mekonnen and Hoekstra, 2016). 
Currently, around four billion people live under conditions of severe 
freshwater scarcity for at least one month of the year, with half

a  billion people in the world facing severe water scarcity all year round (Mekonnen and Hoekstra, 2016). The AR5 WGII reported that approximately 80% of the world’s population already suffers from high levels of threat to water security (Jiménez Cisneros et al., 2014). 
Given the vulnerability of the planet’s freshwater resources and the 
role of climate change in intensifying adverse impacts on human societies and ecosystems (Hoegh-Guldberg et al., 2018; IPCC, 2018), this chapter evaluates advances in the theoretical, observational and model based understanding of the global water cycle made since AR5 
(IPCC, 2013) and AR6 Special Reports.
8.1.1.2  Overview of the Global Water Cycle 
in the Climate System
As shown in Figure  8.1, the global water cycle is the continuous, 
naturally occurring movement of water through the climate system from its liquid, solid and gaseous forms among reservoirs of the 
ocean, atmosphere, cryosphere and land (Stocker et al., 2013).

In the atmosphere, water primarily occurs as a gas (water vapour), but it is also present as ice and liquid water within clouds where it substantially affects Earth’s energy balance (Sections  7.4.2.2 and 7.4.2.4). The water cycle primarily involves the evaporation
1

1061
Water Cycle Changes Chapter 88
Soil moisture 
54±90%Atmosphere  13±3%
Seasonal snow 
3±15%Oceans, inland seas
and saline lakes (97%)
Saline groundwater (<  2%)Total water on Earth
(1 380 000 thousand km3)(a) Water stores
Saline/fossil groundwater  22000±80%Fresh/young groundwater 630±70% Ice 26000 ±10%Reservoirs
11±40%Permafrost
210±100%
Rivers
2±20%Biological water 
1±20%Saline lakes 
54±90%
Wetlands
14±20%Fresh water consists 
of:
Soil moisture
Wetlands
Artificial reservoirs
Rivers
Biological water 
IceFresh groundwater
Fresh lakesUsableUsable
Ocean 1 335 000 ±1%
(b) Water fluxes
Fresh lakes  
220±20% Fresh water (<  2%)Units in thousands of km3
Units in thousands of km3  per yearUnusable
Ocean to land water
vapour transport
46±10%
Ocean evaporation 
470±10%Land evaporation
74±10%Land precipitation
120±10%Ocean precipitation
424±10%
Fresh groundwater discharge  0.25±90% Groundwater 
recharge  13±60%Inland drainage 
basin  1±30%

River discharge 
46±10%Net loss of land ice and
groundwater to oceans 
0.8±15% 
Human 
water use 
24±10%
Figure 8.1 | Depiction of the present-day water cycle based on previous assessments (Trenberth et al., 2011; Rodell et al., 2015; Abbott et al., 2019) 
with adjustments for groundwater flows (Zhou et al., 2019c; Luijendijk et al., 2020), seasonal snow (Pulliainen et al., 2020) and ocean precipitation and 
evaporation (Stephens et al., 2012; Allan et al., 2020; Gutenstein et al., 2021). The net loss of frozen and liquid water from land to ocean is estimated from Chapter 9, 
Table 9.5. In the atmosphere, which accounts for only 0.001% of all water on Earth, water primarily occurs as a gas (water vapour), but it is also present as ice and liquid water 
within clouds. The ocean is the primary water reservoir on Earth: it comprises mostly liquid water across much of the globe but also includes areas covered by ice in polar regions.

Liquid freshwater on land forms surface water (lakes, rivers) and, together with soil moisture and mostly unusable groundwater stores, accounts for less than 2% of global water 
(Stocker et al., 2013). Solid terrestrial water that occurs as ice sheets, glaciers, snow and ice on the surface, and permafrost currently represents nearly 2% of the planet’s water 
(Stocker et al., 2013). Water that falls as snow in winter provides soil moisture and streamflow after melting, which are essential for human activities and ecosystem functioning. 
Note that these best estimates do not lead to a perfectly closed global water budget and that this budget has no reason to be closed given the ongoing human influence through 
both climate change (e.g., melting of ice sheets and glaciers, see Chapter 9) and water use (e.g., groundwater depletion through pumping into fossil aquifers, see Figure 8.10).

1062
Chapter 8  W ater Cycle Changes8and precipitation of moisture at the Earth’s surface including 
transpiration associated with biological processes. Water that falls on 
land as precipitation, supplying soil moisture, groundwater recharge, 
and river flows, was once evaporated from the ocean or sublimated 
from ice-covered regions before being transported through the 
atmosphere as water vapour, or in some areas was generated over 
land through evapotranspiration (Gimeno et al., 2010; van der Ent 
and Savenije, 2013). In addition, the net flux of atmospheric and 
continental freshwater is a key driver of sea surface salinity, which in 
turn influences the density and circulation of the ocean (Chapter 9). 
Understanding the interactions between the water and energy 
cycles is one of the four core projects of the World Climate Research 
Programme (WCRP). Latent heat fluxes, released by condensation of 
atmospheric water vapour and absorbed by evaporative processes, are

critical to driving the circulation of the atmosphere on scales ranging 
from individual thunderstorm cells to the global circulation of the 
atmosphere (Stocker et al., 2013; Miralles et al., 2019). Water vapour 
is the most important gaseous absorber in the Earth’s atmosphere, 
playing a key role in the Earth’s radiative budget (Schneider et al., 2010). 
As atmospheric water vapour content increases with temperature, it 
has a  considerable influence on climate change (Section  7.4.2.2). 
Additionally, a  small fraction of the atmospheric water content is 
liquid or solid and has a  major effect on both solar and longwave 
radiative fluxes, from the Earth’s surface to the top of the atmosphere. 
The cloud response to anthropogenic radiative forcings, both in the 
tropics and in the extratropics (Zelinka et al., 2020), is therefore also 
crucial for understanding climate change (Section 7.4.2.4).
The terrestrial water and carbon cycles are also strongly coupled (Cross-

Chapter Box 5.1). As atmospheric carbon dioxide (CO 2) concentration 
increases, the physical environment in which plants grow is altered, 
including the availability of soil moisture necessary for plants’ CO 2 
uptake and, potentially, the effectiveness of CO 2 removal techniques 
to mitigate climate change (Section  5.6.2.1.2). Rising surface CO 2 
concentrations also modify stomatal (small pores at the leaf surface) 
regulation as well as the plants’ biomass, thus affecting ecosystem 
photosynthesis and transpiration rates and leading generally to 
a net increase in water use efficiency (Lemordant et al., 2018). These 
coupled changes have profound implications for the simulation of the 
carbon and water cycles (Gentine et al., 2019 ; see also Section 5.4.1), 
which can be better assessed with the new generation Earth system 
models, although both the carbon concentration and carbon-climate 
feedbacks remain highly uncertain over land (Section  5.4.5; Arora

et al., 2020). The water constraints on the terrestrial carbon sinks 
are a matter of debate regarding the feasibility or efficiency of some 
land-based CO 2 removal and sequestration techniques requested to 
comply with the Paris Agreement (Section  5.6.2.2.1;  Fuss et al., 2018; 
Belyazid and Giuliana, 2019) .
8.1.2 Summary of Water Cycle Changes From AR5 
and Special Reports
This Report is the first IPCC assessment to include a chapter specifically 
dedicated to providing an integrated assessment of the global water 
cycle changes, by building on many chapters from previous reports. This section summarizes observed and projected water cycle changes 
reported in AR5 (IPCC, 2013) and in the recent IPCC Special Reports 
on Global Warming of 1.5°C (SR1.5), the Ocean and Cryosphere in 
a Changing Climate (SROCC), and Climate Change and Land (SRCCL). 
8.1.2.1 Summary of Observed and Projected Water Cycle 
Changes from AR5

Based on long-term observational evidence (Hartmann et al., 2013), 
AR5 concluded it was likely  that anthropogenic influence has affected 
the water cycle since the 1960s (IPCC, 2018). Detectable human 
influ ence on changes to the water cycle were found in atmospheric 
moisture content ( medium confidence ), global-scale changes of 
precipitation over land ( medium confidence ), intensification of heavy 
precipitation events over land regions where sufficient data networks 
exist ( medium confidence ), and very likely  changes to ocean salinity 
through its connection with evaporation minus precipitation change 
patterns (Sections  2.5, 2.6, 3.3, 7.6, 10.3 and 10.4; Stocker et al., 
2013). The AR5 also reported that it is very likely  that global surface air 
specific humidity increased since the 1970s. There was low confidence  
in the observations of global-scale cloud variability and trends, medium 
confidence  in reductions of pan-evaporation, and medium confidence

in the non-monotonic changes of global evapotranspiration since the 
1980s. In terms of streamflow and runoff, AR5 identified that there 
is low confidence  in the observed increasing trends of global river 
discharge during the 20th century. Similarly, AR5 concluded that there 
is low confidence in any global-scale observed trend in drought or 
dryness (lack of rainfall) since the mid-20th century. Yet, the frequency 
and intensity of drought likely  increased in the Mediterranean and 
West Africa, while they likely  decreased in central North America and 
north-western Australia since 1950. 
Water cycle projections in AR5 (Collins et al., 2013) were 
considered primarily in terms of water vapour, precipitation, surface 
evaporation, runoff, and snowpack. Globally-averaged precipitation 
was projected to increase with global warming with virtual certainty 
(Chapter 12 Executive Summary and Section 12.4.1.1). Regionally,

precipitation in some areas of the tropics and polar regions could 
increase by more than 50% by the end of the 21st century under 
the RCP8.5 emissions scenario, while precipitation in large areas 
of the subtropics could decrease by 30% or more (AR5 FAQ 12.2, 
Figure  12.22). Overall, the contrast of annual mean precipitation 
between dry and wet regions and between dry and wet seasons 
(‘wet get wetter, dry get drier’) was projected to increase over 
most of the globe with high confidence (Chapter  12 Executive 
Summary and Section 12.4.5.2). Globally, the frequency of intense 
precipitation events was projected to increase while the frequency 
of all precipitation events was projected to decrease, leading to 
the contradictory-seeming projection of a  simultaneous increase 
in both droughts and floods (12.2 and Section  12.4.5.5 in AR5 
WGI). Surface evaporation change was projected to be positive 
over most of the ocean and to generally follow the pattern of

precipitation change over land (Chapter  12 Executive Summary, 
and Section  12.4.5.4). Near-surface relative humidity reductions 
over many land areas were projected to be likely , with medium 
confidence  (Section 12.4.5.1). General decreases in soil moisture in 
present-day dry regions were considered likely,  and projected with

1063
Water Cycle Changes Chapter 88medium confidence  under the RCP8.5 scenario (Section 12.4.5.3). 
Soil moisture drying in the Mediterranean, south-west USA 
and southern African regions was considered likely , with high 
confidence  by the end of this century under the RCP8.5 scenario 
(Section  12.4.5.3). Projections for annual runoff included both 
decreases and increases. Decreases in Northern Hemisphere snow 
cover were assessed as very likely  with continued global warming 
(Section  12.4.6.2). As temperatures increase, snow accumulation 
was projected to begin later in the year and melting to start earlier, 
with related changes in snowmelt-driven river flows (FAQ 12.2 and 
Section 12.4.6.2 in AR5 WGI). In terms of the potential for abrupt 
change in components of the water cycle, long-term droughts and 
monsoonal circulation were identified as potentially undergoing 
rapid changes, but the assessment was reported with low

confidence (Sections 12.5.5.8.1 and 12.5.5.8.2, and Table 12.4).
8.1.2.2 Key Findings of AR6 Special Reports
The SR1.5 assessed the impacts of global warming of 1.5°C above 
pre-industrial levels. The dominant human influence on observed 
global warming and related water cycle changes was confirmed. 
Further evidence that anthropogenic global warming has caused 
an increase in the frequency, intensity and/or amount of heavy 
precipitation events at the global scale ( medium confidence ), as 
well as in drought occurrence in the Mediterranean region ( medium 
confidence ) was also reported. Chapter 3 of SR1.5 (Hoegh-Guldberg 
et al., 2018) highlights that each half degree of additional global 
warming influences the climate response. Heavy precipitation shows 
a  global tendency to increase more at 2°C compared to 1.5°C, 
though there is low confidence  in projected regional differences 
in heavy precipitation at 1.5°C compared to 2°C global warming,

except at high latitudes or at high altitude where there is medium 
confidence . A key finding is that ‘limiting global warming to 1.5°C 
compared to 2°C would approximately halve the proportion of the 
world population expected to suffer water scarcity, although there 
is considerable variability between regions ( medium confidence )’ 
(SR1.5). This is consistent with greater adverse impacts found at 
2°C compared to 1.5°C for a number of dryness or drought indices 
(Schleussner et al., 2016; Lehner et al., 2017; Greve et al., 2018). There 
is also medium confidence  that land areas with increased runoff and 
exposure to flood hazards will increase more at 2°C compared to 
1.5°C of global warming.
The Special Report on the Ocean and Cryosphere in a  Changing 
Climate (SROCC) provides a comprehensive assessment of recent and 
projected changes, specifically in snow and ice-covered areas that 
form a key component of the water cycle in high-elevation and high-

latitude areas. High mountain regions have experienced significant 
warming since the early 20th century, resulting in reduced snowpack 
on average (Marty et al., 2017), with glaciers retreating globally 
since the mid-20th century (Marzeion et al., 2018; Zemp et al., 2019). 
Glacier shrinkage and snow cover changes have led to changes 
(both increases and decreases) in streamflow in many mountain 
regions in recent decades (Milner et al., 2017). Permafrost regions 
have undergone degradation and ground-ice loss due to recent 
warming (Lu et al., 2017). Glacier mass loss is projected to continue 
through the 21st century under all scenarios. In high mountain areas, low-elevation snow cover is also projected to decrease, regardless 
of emissions scenario. Widespread permafrost thaw is projected to 
continue through this century and beyond. River runoff in snow- or 
glacier-fed basins is projected to increase in winter and to decrease in

summer (and in the annual mean) by 2100. In the oceans, the Atlantic 
Meridional Overturning Circulation (AMOC) will  very likely  weaken 
over the 21st century under all emissions scenarios (SROCC), with 
potential effects on atmospheric circulation and the water cycle at 
the regional scale (see also Section 8.6).
The Special Report on climate change, desertification, land 
degradation, sustainable management, food security, and 
greenhouse gas (GHG) fluxes in terrestrial ecosystems (SRCCL) has 
clear connections with the water cycle. This Report indicates that since 
1850–1900, land surface temperature has risen nearly twice as much 
as global surface temperature ( high confidence ), with an increase 
in dry climates ( high confidence ). Land surface processes modulate 
the likelihood, intensity and duration of many extreme events 
including droughts ( medium confidence ) and heavy precipitation 
(medium confidence ). The direction and magnitude of hydrological

changes induced by land use change and land surface feedbacks 
vary with location and season ( high confidence ). Desertification 
exacerbates climate change through feedbacks involving vegetation 
cover, greenhouse gases and mineral dust aerosol ( high confidence ). 
Urbanization increases extreme rainfall events over or downwind of 
cities ( medium confidence ). Intensification of rainy events increase 
their consequences on land degradation. 
8.1.3 Chapter Motivations, Framing and Preview
The AR5 report was a major step forward in the assessment of the 
human influence on the Earth’s water cycle, yet regional projections 
of precipitation and water resources often remained very uncertain 
for a range of reasons including modelling uncertainty and the large 
influence of internal variability (Sections  1.4.3 and 8.5.2; Hawkins 
and Sutton, 2011; Deser et al., 2012) . Since AR5, longer and more 
homogeneous observational and reanalysis datasets have been

produced along with new ensembles of historical simulations driven 
by all or individual anthropogenic forcings. These factors, together 
with improved detection-attribution tools, has enabled a  more 
comprehensive assessment and a  better understanding of recent 
observed water cycle changes, including the competing effects of 
GHGs and aerosol emissions. New paleoclimate reconstructions 
have been also developed, particularly from the SH, that were not 
available at the time of AR5. There have also been advances in 
modelling clouds, precipitation, surface fluxes, vegetation, snow, 
floodplains, groundwater and other processes relevant to the water 
cycle. Convection permitting and cloud-resolving models have been 
implemented over increasingly large domains and can be used as 
benchmarks for the evaluation of the current-generation climate 
models. The added value of increased resolution in global or regional 
climate models can be also assessed more thoroughly based on

dedicated model intercomparison projects (Sections 10.3.3 and 8.5.1). 
Ongoing research activities on decadal predictions and observational 
constraints are aimed at narrowing the plausible range of near-term 
(2021 –2040) to long-term (2081 –2100) water cycle changes.

1064
Chapter 8  W ater Cycle Changes8This chapter assesses water cycle changes and considers climate 
change from the perspective of its effects on water availability 
(including streamflow and soil moisture, snow mass and glaciers, 
groundwater, wetlands and lakes) rather than only precipitation. 
The chapter highlights the sensitivity of the water cycle to multiple 
drivers and the complexity of its responses, depending on regions, 
seasons and time scales. Anthropogenic drivers include not only 
emissions of GHGs but also different species of aerosols, land and 
water management practices. Emphasis is placed on assessing the 
full range of projections, including ‘low likelihood, high impact’ 
climate trajectories such as the potential for abrupt changes in the 
water cycle.
The chapter starts with theoretical evidence that link small-scale 
processes and drivers, as well as global energy budget and large-
scale circulation constraints to physically-understood changes in the

global water cycle (Section 8.2). Observed and projected water cycle 
changes (Sections 8.3 and 8.4, respectively) are assessed in separate 
sections, but with a  parallel structure to facilitate comparison of 
a specific topic across sections. Projections are primarily assessed on 
the basis of contrasted emissions scenarios to emphasize the water 
cycle response to mitigation. Unless otherwise specified, projected 
anomalies are estimated relative to the 1995 –2014 baseline 
climatology and are assessed over 20-year time slices, 2021 –2040, 
2041 –2060 and 2081 –2100 for near-, mid- and long-term changes 
respectively. Beyond multi-model ensemble means, model response 
uncertainty, the influence of natural climate variability, and the 
potential non-linearities in the regional water cycle response are 
also considered (Section 8.5). Low likelihood but physically plausible high-impact scenarios are also assessed, especially the potential

for abrupt climate change (Section 8.6). Final remarks about future 
studies on water cycle changes (Section 8.7) are also provided, and 
the chapter addresses three frequently asked questions (FAQs) on the 
water cycle’s sensitivity to land use change (FAQ 8.1), the projected 
occurrence and severity of floods (FAQ 8.2) and droughts (FAQ 8.3) at 
the global scale. This chapter outline is summarized with a schematic 
(Figure  8.2) which also provides a  quick guide to the main topics 
addressed across the different sections.
Chapter  8  has multiple links across all AR6 WGI chapters, so 
necessarily includes references to other chapter subsections and 
figures. Model evaluation of large-scale circulation and precipitation 
is mostly covered by Chapter  3, while hydrological extremes are 
covered by Chapter 11. Chapter 8 focuses on key processes relevant 
to the water cycle and their resolution-dependent representation in

models. Observed and projected changes in large-scale circulation and 
precipitation are primarily assessed in Chapters 2, 3 and 4. Beyond 
global and regional mean precipitation amounts, Chapter  8  also 
focuses on other precipitation properties (e.g.,  frequency, intensity 
and seasonality) and other water cycle variables (evapotranspiration, 
runoff, soil moisture and aridity, solid and liquid freshwater reservoirs). 
Key regional phenomena (e.g.,  tropical overturning circulations, 
monsoons, extratropical stationary waves and storm tracks, modes 
of variability and related teleconnections) are also assessed given 
their major dynamical contribution to regional water cycle changes. 
Although the biosphere and the cryosphere are key components of 
the water cycle, a more comprehensive assessment of their responses 
can be found in Chapters 5 and 9, respectively. Further assessment 
Chapter 8:  Water cycle changes Chapter 8: Quick guide
Section 8.1
Section 8.2

Theoretical basis 
for changesGlobal water cycle intensity
8.2.1 | 8.3.1.1 | 8.4.1.1
Water vapour and its transport
8.2.2 | 8.3.1.2 | 8.4.1.2 Intertropical Convergence Zone and tropical rainbelts
8.2.2.2 | 8.3.2.1 | 8.4.2.1 
8.2 | 8.3.1.3 | 8.4.1.3 | 8.5.1Evapotranspiration
8.2.3.3 | 8.3.1.4 | 8.4.1.4
Monsoons
8.2.2.2 | 8.3.2.4 | 8.4.2.4
Runoff, streamflow, flooding
8.2.3.2 | 8.3.1.5 | 8.4.1.5 | FAQ 8.2
Soil moisture, aridity and drought
8.2.3.3 | 8.3.1.6 | 8.4.1.6 | 8.6.2 | FAQ 8.3Section 8.5
Internal variability and non-linearitiesSection 8.3
Observed changesSection 8.4
Projected changes
Section 8.7
Final remarksSection 8.6
Potential abrupt changes
Box 8.1
Role of aerosolsBox 8.2
Changes in seasonalityChapter 8 explores the changing water cycle and its sensitivity to multiple 
drivers.Key topics and corresponding sub-sections
BoxesFAQs
Figure 8.2 | Visual guide to Chapter 8.

1065
Water Cycle Changes Chapter 88on regional water cycle changes can be found in Chapters 10 to 12 
and in the Atlas. The reader is also referred to the interactive Atlas 
for a  more detailed assessment of the range of model biases and 
responses at the regional scale. Beyond WGI, water is also a major 
topic for both adaptation and mitigation policies so has strong 
connections with both WGII and WGIII. Assessment of hydrological 
impacts at basin and catchment scales, including a broader discussion 
on adaptation and vulnerability, potential threats to water security, 
societal responses, improving resilience in water systems and related 
case studies is provided in WGII (Chapter 4).
8.2 Why Should We Expect Water  
Cycle Changes?
It is well understood that global precipitation and evaporation 
changes are determined by Earth’s energy balance (Section 8.2.1). 
At regional scales smaller than about 4000 km, water cycle changes

become dominated by the transport of moisture (Dagan et al., 
2019a; Jakob et al., 2019; Dagan and Stier, 2020), which depend 
on both thermodynamic and dynamical processes (Section  8.2.2). 
The constraints of energy budgets at global scales and moisture 
budgets at regional scales cause key water cycle characteristics 
such as precipitation intensity, duration and intermittence to alter as the climate warms (Pendergrass and Hartmann, 2014b; Döll et al., 
2018). Future water availability is also determined by changes in 
evaporation, which is driven by a general increase in the atmospheric 
evaporative demand (Scheff and Frierson, 2014) and modulated by 
vegetation controls on evaporative losses (Milly and Dunne, 2016; 
Lemordant et al., 2018; Vicente-Serrano et al., 2020) . At regional 
scales, water cycle changes result from the interplay between 
multiple potential drivers (CO 2, aerosols, land use change and human

water use; Section 8.2.3). This section assesses advances in physical 
understanding of global to regional drivers of water cycle changes.
8.2.1 Global Water Cycle Constraints
The Clausius–Clapeyron equation determines that low-altitude 
specific humidity increases by about 7% °C–1 of warming, assuming 
that relative humidity remains constant, which is approximately 
true at a  global scale but not necessarily valid regionally. It is 
very likely  that near surface specific humidity has increased since 
the 1970s (Section  2.3.1) and total atmospheric water vapour 
content (precipitable water) is very likely to increase at close to 
a thermodynamic rate on average globally with continued warming. 
Different radiative forcing mechanisms lead to some variation in 
the global mean thermodynamic response by altering the relative 
Atmospheric energy budget
P = L - S - H
Precipitation (P)
75 - 93 Wm-2
Evaporation 
(E)Sensible 
heat (H)
16 - 24 Wm-2Longwave 
radiative 
cooling (L)

176 - 194 Wm-2Absorbed
sunlight (S)
75 - 89 Wm-2
Baseline Increase DecreaseInstant response to 
radiative forcingRapid atmospheric 
adjustmentsSemi-rapid atmospheric 
adjustments
Further adjustments 
in clouds, water 
vapour, temperature
3 Altered atmospheric wind 
patterns for all major forcing 
agents (greenhouse gases, 
anthropogenic aerosols, natural 
forcing)
Increased land 
temperature (  T)Vegetation 
response to 
increasing CO2Slow feedback responses
Increased greenhouse gases 
or absorbing aerosol reduce 
net radiative cooling
(L - S)
Minor effects of 
scattering aerosol and 
solar forcing on 
atmospheric absorptionReduced
precipitation 
  P
HE1
Rapid adjustments in 
cloud, water vapour, 
Reduced 
global
   P
H
EIncreased land
  P2
 4
Increased 
absorbed 
sunlight 
 SIncreased
global
precipitation
  P
ocean > land
Increased longwave 
radiative cooling 
   L
EH
Surface and
vegetation 
feedbacksIncreased
atmospheric

temperature (   T)Cloud 
feedback
Increased land (   T)
and ocean (  T)
Global temperaturePrecipitation Global temperaturePrecipitation Global temperaturePrecipitation
Global Land
Global temperaturePrecipitationGlobal 
Land
Days to months Years to decadesQuantifying precipitation response 1 23 4
ηηa
Hydrological 
sensitivityApparent 
hydrological 
sensitivityAltered precipitation patterns
(b) (c) (d) (a)
Responses across all timescales combine to determine transient climate changeIncreased 
water vapor
Figure 8.3 | Schematic representation of fast and slow responses of the atmospheric energy balance and global precipitation to radiative forcing.  
(‘Baseline’  panel) The atmospheric energy budget: (1) responds instantaneously to radiative forcings; (2) leading to rapid atmospheric adjustments; and (3) slower semi-rapid

adjustments involving the land surface and vegetation that further modify atmospheric circulation patterns.  (4) This slow precipitation response to global mean surface air 
temperature is quantified as (a) the hydrological sensitivity, η, and the total precipitation response, including initial rapid adjustments, is termed the apparent hydrological 
sensitivity, ηa. (b–d) The slow precipitation response over land and ocean develops over time. Large, filled arrows (in panels from ‘baseline’ to 4) depict fluxes or circulation 
change while small arrows (1–4) denote increases ( ↑) or decreases ( ↓) in variables (P is precipitation; L is atmospheric longwave radiative cooling; S is solar radiation absorption 
by the atmosphere; H is sensible heat flux; E is surface evaporative heat flux; and T is temperature). Adapted from Allan et al. (2020) with statistics taken from Figures 7.2 and 
Figure 8.1.

1066
Chapter 8  W ater Cycle Changes8humidity distribution: the rate of global precipitable water increase 
with global surface temperature ranges2 from 6.4 ± 1.5% °C–1 for 
sulphate aerosol-induced changes to 9.8 ±  3.3%  °C–1 for black 
carbon-induced changes based on idealized modelling (Hodnebrog 
et al., 2019b). Specific humidity increases at a  lower rate over 
land due to decreasing relative humidity (Collins et al., 2013) 
as corroborated by observations and simple models (Byrne and 
O’Gorman, 2018). Prevalent increases in atmospheric water vapour 
drive powerful amplifying feedbacks (Section  7.4.2.2), intensify 
atmospheric moisture transport and heavy precipitation events 
(Section  8.2.3.2), and alter the surface and atmospheric energy 
balance, thereby influencing global evaporation and precipitation 
changes (Figure 8.3).
While thermodynamics exert a  strong control on water vapour 
changes, global mean precipitation and evaporation are constrained

by the balance of energy fluxes in the atmosphere and at the surface 
(Figure 8.3). Global mean precipitation increases of 1 –3% per 1 °C of 
warming, as estimated in AR5 (Collins et al., 2013), are explained as 
a combination of rapid (or fast) atmospheric adjustments and slow 
temperature-driven responses (Figure 8.3, panels 1 –4) to radiative 
forcings (Andrews et al., 2010; Bala et al., 2010; Cao et al., 2012). Fast 
atmospheric adjustments are caused by near-instantaneous (hours 
to days) changes in the atmospheric energy budget (Figure  8.3, 
panels 1–3) and atmospheric properties (e.g.,  temperature, clouds 
and water vapour) in direct response to the radiative effects of 
a forcing agent (Sherwood et al., 2015). A further relatively fast (days 
to months) adjustment of the climate system involves interactions 
with vegetation and land surface temperature (Figure 8.3, panel 3), 
which respond more rapidly than ocean temperature to a radiative

forcing (Cao et al., 2012; Dong et al., 2014). The slower temperature-
dependent precipitation response is driven by the increased 
atmospheric radiative cooling rate of a  warming atmosphere. 
Warming drives increases in precipitation intensity while frequency is 
dominated by rapid atmospheric adjustments to the radiative forcing 
based on abrupt 4×CO 2 CMIP6 simulations (Douville and John, 2021). 
Since AR5, many new studies applying the dual rapid adjustment and 
slow response framework show that global precipitation responses 
to different forcing agents are physically well understood (Fläschner 
et al., 2016; MacIntosh et al., 2016; Samset et al., 2016; Myhre et al., 
2018a) . Further confidence in the coupled processes involved are 
provided by simple models representing the energy budget and 
thermodynamic constraints that limit global mean evaporation to 
around 1.5% °C–1 (Siler et al., 2019). This strengthens the physical

link between energy budget and thermodynamic drivers of the global 
water cycle (Section 8.2.2.1).
Hydrological sensitivity ( η) is defined as the linear change in 
global mean precipitation with global surface air temperature 
(GSAT) once rapid adjustments of the hydrological cycle to radiative 
forcings have occurred (Figure 8.3a). There is robust understanding 
and high agreement  across idealized CO 2 forcing CMIP5 and CMIP6 
experiments (Fläschner et al., 2016; Samset et al., 2018b; Pendergrass, 
2020b) that  η  = 2.1 –3.1%  °C–1 (Figure  8.4). The magnitude of 
η depends primarily on atmospheric net radiative cooling which is 
2 5–95% confidence range estimates are quoted unless otherwise stated.controlled by thermal deepening of the troposphere (Jeevanjee and 
Romps, 2018) and limited by surface evaporation and consequent 
atmospheric latent heat release and warming (Webb et al., 2018). 
Climate feedbacks (e.g., temperature lapse rate and clouds) that vary

across models (Sections 7.4 and 3.8.2) also modulate the magnitude 
of η (O’Gorman et al., 2012; Fläschner et al., 2016; T.B. Richardson 
et al., 2018a) . Uncertainty in η  across CMIP5 models relating to 
deficiencies in representing low-altitude cloud feedbacks (Watanabe 
et al., 2018) and absorption of shortwave radiation by atmospheric 
water vapour (DeAngelis et al., 2015) do not apply well to CMIP6 
simulations, the latter improvement explained by more accurate 
radiative transfer modelling (Pendergrass, 2020b).
Observed estimates of hydrological sensitivity ( η = 3.2 ± 0.8% °C–1) 
based on interannual variability (Allan et al., 2020) or responses to 
El Niño–Southern Oscillation (ENSO) of 9% °C–1 (Adler et al., 2017) are 
not suitable to assess the magnitude of η (Figure 8.4) . This is because 
these relationships depend on amplifying feedbacks associated with 
ENSO-related cloud changes (G.L. Stephens et al., 2018) that may not

be relevant for longer term climate change. However, there is robust 
evidence  and high agreement  across observations, modelling and 
supporting physics that precipitation increases at a lower % °C–1 rate 
than water vapour content in the global mean (Held and Soden, 2006; 
Collins et al., 2013; Allan et al., 2020), implying an increased residence 
time of atmospheric water vapour (Hodnebrog et al., 2019b; Dijk et al., 
2020). Increasing global precipitation, evaporation and moisture fluxes 
with warming thereby drive an intensification but not acceleration of 
the global water cycle (Sections 8.3.1.1 and 8.4.1.1). 
The overall global mean rate of precipitation change per 1 °C of 
GSAT increase, apparent hydrological sensitivity ( ηa), is reduced 
compared to hydrological sensitivity by the direct influence of 
radiative forcing agents on the atmospheric energy balance. Rapid 
atmospheric adjustments that alter precipitation are primarily

caused by GHGs and absorbing aerosols, with high agreement  and 
medium evidence  across idealized simulations (Fläschner et al., 2016; 
Samset et al., 2016). A range of rapid precipitation adjustments to 
CO 2 between models are also attributed to vegetation responses 
leading to a re-partitioning of surface latent and sensible heat fluxes 
(DeAngelis et al., 2016). Values obtained from six CMIP5 models 
simulating the Last Glacial Maximum (LGM; 21,000–19,000 years 
ago) and pre-industrial period ( ηa = 1.6–3.0% °C–1) are larger than for 
each corresponding abrupt 4×CO 2 experiment ( ηa = 1.3–2.6% °C–1) 
due to differences in the mix of forcings, vegetation and land surface 
changes and a  higher thermodynamic %  °C–1 evaporation scaling 
in the colder state (Figure  8.4, Section  8.4.1.1; G.  Li et al., 2013). 
Updated estimates across comparable experiments from 22 CMIP5/
CMIP6 models (Rehfeld et al., 2020) display a  consistent range

(ηa = 1.7 ± 0.6%  °C–1). Confirming ηa in observations (Figure 8.4) is 
difficult due to measurement uncertainty, varying rapid adjustments 
to radiative forcing and unforced variability (Dai and Bloecker, 2019; 
Allan et al., 2020).
Climate drivers that instantaneously affect the surface much more than 
the atmospheric energy budget (such as solar forcing and sulphate

1067
Water Cycle Changes Chapter 88aerosol) produce only a small rapid adjustment of the global water 
cycle and therefore larger ηa than drivers that immediately modulate 
the atmospheric energy budget such as GHGs and absorbing aerosol (Salzmann, 2016; Samset et al., 2016; Lin et al., 2018; F . Liu et al., 
2018). Thus, global precipitation appears more sensitive to radiative 
forcing from sulphate aerosols (2.8 ± 0.7% °C
–1; ηa≈η) than GHGs 
(1.4 ± 0.5% °C–1; ηa<η) while the response to black carbon aerosol 
can be negative ( –3.5 ± 5.0% °C–1; ηa<<η) due to strong atmospheric 
solar absorption (Samset et al., 2016). Therefore, artificially reducing surface-absorbed sunlight through solar radiation modification 
strategies to mitigate GHG warming will not mitigate precipitation

changes (see Sections  4.6.3.3, 6.4.7 and 8.6.3). Aerosol-induced precipitation changes depend upon the type of aerosol species and their spatial distribution. Global mean precipitation increases after complete removal of present-day anthropogenic aerosol 
emissions (see also Section 4.4.4) in four different climate models 
(η
a = 1.6 –5.5% °C–1) are mainly attributed to sulphate aerosol as 
opposed to other aerosol species (Samset et al., 2018b). Idealized modelling studies show that sulphate aerosol increases over Europe 
produce a  larger global precipitation response than an equivalent 
increase in aerosol burden or radiative forcing over Asia, explained by differences in cloud climatology and cloud-aerosol interaction (Kasoar et al., 2018; L. Liu et al., 2018). The vertical profiles of black carbon and ozone further influence the magnitude of the rapid global 
precipitation response, yet are difficult to observe and simulate (Allen

and Landuyt, 2014; MacIntosh et al., 2016; Stjern et al., 2017; Sand et al., 2020). 
Hydrological sensitivity is generally lower over land but with a large 
uncertainty range (η  = 
–0.1 to 3.0%  °C–1) relative to the oceans 
(η  =  2.3 to 3.3%  °C–1) based on multi-model 4  × CO 2 CMIP6 simulations 
(Pendergrass, 2020b), broadly consistent with comparable CMIP5 experiments (T.B.  Richardson et al., 2018a; Samset et al., 2018a) . 
Suppressed hydrological sensitivity over land (Figures 8.3d and 8.4) is associated with greater warming compared with the oceans, which alters atmospheric circulation and precipitation patterns (Saint-Lu et al., 2020). Also, since oceans supply much of the moisture to fuel precipitation over land, the slower ocean warming rate means there 
is insufficient moisture supplied to maintain continental relative

humidity levels (Byrne and O’Gorman, 2018), which can inhibit convection (J. Chen et al., 2020a). Land surface feedbacks involving soil-vegetation-atmosphere coupling further drive continental drying 
(Berg et al., 2016; Kumar et al., 2016; Chandan and Peltier, 2020). 
The suppressed hydrological sensitivity is counteracted by rapid precipitation responses in most GHG-forced simulations, explained by increases in surface downward longwave radiation due to CO
2 
increases that rapidly warm the land, destabilize the troposphere and strengthen vertical motion in the short term (Chadwick et al., 2014; 
T.B. Richardson et al., 2016, 2018a). There is medium understanding 
of how land–sea warming contrast governs rapid precipitation responses based on idealized modelling that shows similar spatial patterns of precipitation response to radiative forcing from GHGs, solar forcing and absorbing aerosols (Xie et al., 2013; Samset et al.,

2016; Kasoar et al., 2018). Rapid precipitation adjustments to CO
2 
have been counteracted by cooling from anthropogenic aerosol 
increases over land (Box 8.1) but this compensation is expected to 
diminish as aerosol forcing declines (T.B. Richardson et al., 2018a). The fast and slow precipitation responses over global land combine 
on average during transient climate change (Figure  8.3d). This explains a  consistent land and ocean mean precipitation increase in projections (Table 4.3) but this is determined by a complex and 
model-dependent evolution of continental water cycle changes over 
space and time.
Increases in global precipitation over time, as the climate warms, are 
partly offset by the overall cooling effects of anthropogenic aerosol

and by rapid atmospheric adjustments to increases in GHGs and absorbing aerosol. This explains why multi-decadal trends in global precipitation responses in the satellite era (Adler et al., 2017; Allan et al., 2020) are small and difficult to interpret given observational 
uncertainty, internal variability and volcanic forcings. The delayed 
warming effect of rising CO
2 concentration, combined with declining 
aerosol cooling, are expected to increase the importance of the slow temperature-related effects on the energy budget relative to the 
more rapid direct radiative forcing effects as transient climate change 
progresses (Shine et al., 2015; Salzmann, 2016; Myhre et al., 2018b). 
In summary, there is high confidence that global mean evaporation 
and precipitation increase with global warming, but the estimated 
rate is model-dependent (very likely
 range of 1 –3 % °C–1). The global 
increase in precipitation is determined by a robust response to global

surface temperature only ( very likely 2–3% °C–1) that is partly offset 
by fast atmospheric adjustments to the vertical profile of atmospheric heating by GHGs and aerosols. Global precipitation increases due to 
GHGs are offset by the well-understood overall surface radiative 
cooling effect by aerosols ( high confidence). Over land, the average 
warming-related increase in precipitation is expected to be smaller than over the ocean due to increasing land
–ocean thermal contrast 
and surface feedbacks, but the overall precipitation increase over land is generally reinforced by fast atmospheric responses to GHGs 
that strengthens convergence of winds ( medium confidence). Global 
mean precipitation and evaporation increase at a  lower rate than 
atmospheric moisture per 1°C of global warming ( high confidence), 
leading to longer water vapour lifetime in the atmosphere and driving changes in precipitation intensity, duration and frequency and an

overall intensification but not acceleration of the global water cycle.
8.2.2  Constr aints on the Regional Water Cycle
8.2.2.1  T hermodynamic Constraints on Atmospheric 
Moisture Fluxes
A warming climate drives increases in atmospheric moisture and 
horizontal moisture transport from the divergent to the convergent portions of the atmospheric circulation (including storm systems, 
the tropical rain belt and monsoons) that on average amplifies 
existing precipitation minus evaporation (P–E) patterns (Held and Soden, 2006). Increased latent heat transports in high latitudes also contribute to polar amplification of warming (Section 7.4.4.1). Although convergent parts of the atmospheric circulation are 
expected to become wetter (in terms of increasing P–E) and net 
evaporative regions drier (increasing E–P) these regions are not geographically and seasonally fixed and their location and timing

1068
Chapter 8  W ater Cycle Changes8
are expected to alter (Section  8.2.2.2). Atmospheric and ocean 
circulation changes overall decrease the amplification of P–E 
and salinity patterns. Paleoclimate evidence confirms that during 
the LGM zonal mean changes were roughly in agreement with 
thermodynamic expectations (G. Li et al., 2013). However regional 
changes can be dominated by dynamics, including responses to the 
large Northern Hemisphere (NH) ice sheets (DiNezio and Tierney, 
2013; T.  Bhattacharya et al., 2017; Scheff et al., 2017; D’Agostino 
et al., 2019; Lowry and Morrill, 2019) such that altered P–E patterns 
are not well described by thermodynamic drivers (Oster et al., 2015; 
Lora, 2018; Morrill et al., 2018).
There is robust evidence  and high agreement  across thermodynamics, 
detailed modelling and observations that amplification of P–E 
patterns occurs over the oceans (Figure  8.5a) with an associated

‘fresh gets fresher, salty gets saltier’ signature in ocean salinity 
(Sections  2.3.3.2 and 3.5.2). This amplification is moderated by 
proportionally larger increases in subtropical ocean evaporation 
and weakening of the tropical circulation (Section  8.2.2.2), an 
expectation supported by observations (Skliris et al., 2016) and 
process understanding (Yang and Roderick, 2019). Thermodynamics 
explain a smaller low latitude evaporation increase (1% °C–1) than in 
high latitudes (5% °C–1) with changes in surface radiation, boundary layer adjustments and ocean heat uptake playing a  secondary 
role, based on idealized modelling (Siler et al., 2019). Increased 
evaporation from warmer oceans and lakes is exacerbated by the 
loss of surface ice in some regions (Bintanja and Selten, 2014; Laîné 
et al., 2014; W. Wang et al., 2018; Sharma et al., 2019; Woolway 
et al., 2020). This can generate a  more local moisture source for

precipitation, for example in north-west Greenland during non-
summer months since the 1980s (Nusbaumer et al., 2019), though 
moisture transport changes can counteract this effect (Nygård 
et al., 2020). Ocean stratification due to heating of the upper layers 
through radiative forcing has been identified as a mechanism that 
further amplifies surface salinity patterns beyond the responses 
driven by water cycle changes alone (Zika et al., 2018) . 
Since AR5, numerous studies have confirmed that changes in P–E 
with warming over land cannot be interpreted simply as a  ‘wet 
regions get wetter, dry regions get drier’ response (Chadwick et al., 
2013; Greve et al., 2014; Roderick et al., 2014; Byrne and O’Gorman, 
2015; Scheff and Frierson, 2015). Firstly, P–E is a simplistic diagnostic 
of the water cycle that inadequately describes ‘dryness’ or aridity (Fu 
and Feng, 2014; Roderick et al., 2014; Greve and Seneviratne, 2015;

Scheff and Frierson, 2015; Greve et al., 2019; Vicente-Serrano et al., 
2020). Secondly, terrestrial P–E is generally positive and balanced 
by surface runoff and percolation into subsurface soils and 
aquifers (Figure 8.1). As a result, the simple thermodynamic scaling 
(Figure 8.5b) predicts that P–E over land will become more positive 
(wetter) with warming (Greve et al., 2014; Roderick et al., 2014; 
Byrne and O’Gorman, 2015). This is not necessarily true, however, 
in the dry seasons and regions where terrestrial water is lost to 
the atmosphere and exported (Sheffield et al., 2013; Kumar et al., 
2015; Keune and Miralles, 2019). Thirdly, regional P–E patterns over 
land are affected by changes in atmospheric circulation, oceanic 
moisture supply and land surface feedbacks. As the land warms 
more than oceans, spatial gradients in temperature and relative 
humidity influence moisture supply and reduce P–E over some land

regions, such as southern Chile and Argentina around 30°S –50°S 
as captured by an extended thermodynamic scaling (Figure 8.5b). 
Drying of soils can be amplified by vegetation responses (Berg et al., 
2016; Byrne and O’Gorman, 2016; Lambert et al., 2017) but limited 
by atmospheric circulation feedbacks (Zhou et al., 2021). Changes 
in soil moisture and rainfall intensity (Sections 8.2.3.2  and 8.2.3.3) 
can alter the partitioning of precipitation between evaporation and 
runoff, further complicating terrestrial P–E responses (Short Gianotti 
The strong physical basis for regionally and seasonally dependent 
responses of P–E and the expectation for an increasing contrast 
between wet and dry seasons and weather regimes is supported 
by high agreement  across multiple observational and CMIP5/CMIP6 
modelling studies (Liu and Allan, 2013; Kumar et al., 2015; Polson 
and Hegerl, 2017; Ficklin et al., 2019; Deng et al., 2020; Schurer

et al., 2020). Increased moisture transports into storm systems, 
monsoons and high latitudes increase the intensity of wet events 
(Section  8.2.3.2), while stronger atmospheric evaporative demand 
with warming (Scheff and Frierson, 2014; Vicente-Serrano et al., 
2018; Cook et al., 2019) is an important mechanism for intensifying 
dry events (Section  8.2.3.3) and decreasing soil moisture over Precipitation change (% °C-1)20
18
16
14
12
10
8
6
4
2
0Observed changes
GCM
GCM constrained
RCM/CRM
24hour extreme ≤1 hour  extreme Global time averageWarming 
only
Increasing
CO2 OL
Figure 8.4 | Estimate (5–95% range) of the increase in precipitation and 
its extremes with global mean surface warming. Global time-averaged 
precipitation changes (left)  are based on responses to increasing CO 2 (apparent 
hydrological sensitivity, ηa) and the temperature-dependent component (hydrological 
sensitivity, η), both of which are based on GCM experiments; the land (L) and ocean

(O) components (Fläschner et al., 2016; T.B. Richardson et al., 2018a; Samset et al., 
2018a; Pendergrass, 2020b; Rehfeld et al., 2020) and observational estimates (GPCP/
HadCRUTv4.6) use trends (1988–2014) as a proxy for ηa and interannual variability 
as a proxy for η, with 90% confidence range accounting for statistical uncertainty 
only (Adler et al., 2017; Allan et al., 2020). For extreme precipitation, assessment 
is for 24 hour, 99.9th percentile or annual maximum extremes from GCMs (Fischer 
and Knutti, 2015; Pendergrass et al., 2015; Borodina et al., 2017; Pfahl et al., 
2017; Sillmann et al., 2017), regional climate models (RCMs) (Bao et al., 2017), 
an  observationally-constrained tropical estimate (O’Gorman, 2012) and estimates 
from observed changes (Westra et al., 2013; Donat et al., 2016; Borodina et al., 
2017; Zeder and Fischer, 2020; Sun et al., 2021). For hourly and sub-hourly extremes

observed changes (Barbero et al., 2017; Guerreiro et al., 2018) and high-resolution 
models, including RCM and cloud-resolving models (CRMs) are assessed (Ban et al., 
2015; Prein et al., 2017; Haerter and Schlemmer, 2018; Hodnebrog et al., 2019a; 
Lenderink et al., 2019). Further details on data sources and processing are available 
in the chapter data table (Table 8.SM.1).

1069
many subtropical land regions. However, aridification is modulated 
regionally by poleward migration of the subtropical dry zones and 
an increasing land –ocean temperature contrast that drives declining 
relative humidity (Section 8.2.2.2).
To summarize, increased moisture transport from evaporative oceans 
to high precipitation regions of the atmospheric circulation will drive 
amplified P–E and salinity patterns over the ocean ( high confidence ) 
while more complex regional changes are expected over land. 
Greater warming over land than ocean alters atmospheric circulation 
patterns and on average reduces continental near-surface relative 
humidity which along with vegetation feedbacks can contribute to 
regional decreases in precipitation ( high confidence ). Based on an 
improved understanding of thermodynamic drivers since AR5 and 
multiple lines of evidence, there is high confidence  that very wet or

dry seasons and weather patterns will intensify in a warming climate 
such that wet spells become wetter and dry spells drier.  
8.2.2.2 Large-scale Responses in Atmospheric 
Circulation Patterns
Responses of the large-scale atmospheric circulation to a  warming 
climate are not as well understood as thermodynamic drivers 
(Shepherd, 2014). The AR5 identified robust features including 
a  weakening and broadening of tropical circulation with poleward 
movement of tropical dry zones and mid-latitude jets (Collins et al., 2013). These can dominate regional water cycle changes, affecting 
the availability of freshwater and the occurrence of climate extremes. 
Atmospheric circulation changes generally dominate the spatial 
pattern of rapid precipitation adjustments (Section 8.2.1) to different 
forcing agents in the tropics (Bony et al., 2013; He and Soden, 2015; 
T.B. Richardson et al., 2016, 2018a; Tian et al., 2017; X. Li et al., 2018).

Radiative forcing with heterogeneous spatial patterns such as ozone 
and aerosols (including cloud interactions; Section 6.4.1 and Box 8.1) 
drive substantial responses in regional atmospheric circulation 
through uneven heating and cooling effects (L. Liu et al., 2018; Dagan 
et al., 2019b; Wilcox et al., 2019). Changes in atmospheric circulation 
are also driven by slower, evolving patterns of warming and associated 
changes in temperature and moisture gradients (Bony et al., 2013; 
Samset et al., 2016, 2018a; Ceppi et al., 2018; Ma et al., 2018). There is 
strong evidence that large regional water cycle changes arise from the 
atmospheric circulation response to radiative forcings and associated 
SST pattern evolution but low agreement  in the sign and magnitude 
(Chadwick et al., 2016b). The role of prolonged weather regimes in 
determining wet and dry extremes is also better understood since AR5 
(Kingston and McMecking, 2015; Schubert et al., 2016; D. Richardson

et al., 2018; Barlow et al., 2019). Advances in knowledge of expected 
large-scale dynamical responses of the water cycle are further 
assessed in this section (see also Figure 8.21).
Long-term weakening of the tropical atmospheric overturning 
circulation is expected as climate warms in response to elevated 
CO 2 (Collins et al., 2013). A  weaker circulation is required 
to reconcile  global mean low-level water vapour increases 
(around  7%  °C–1) with the smaller global precipitation responses 
of about 1–3% °C–1 (Section 8.2.1). The slowdown can occur in both 
the Hadley and Walker circulations, but occurs preferentially in the 
Walker circulation in most climate models (Vecchi and Soden, 2007) 
but this response has been questioned on the basis of model bias 
in east Pacific SST (Seager et al., 2019a). Weakening is expected 
to drive P–E decreases over the western Pacific and increases over 
the eastern Pacific. However, the driving mechanisms for Walker

circulation weakening differ to those involved in determining ENSO 
variability, so it is too simplistic to interpret changes as an El Niño 
pattern of regional hydrological cycle extremes (Sohn et al., 2019). 
Internal variability is also capable of temporarily strengthening the 
Walker circulation (Section 2.3.1.4.1; L’Heureux et al., 2013; Chung 
et al., 2019) while regional responses depend on the pattern of 
warming (Sandeep et al., 2014). 
Model simulations show a stronger Pacific Walker circulation during 
the LGM in response to a cooler climate (consistent with an expected 
weakening in a warmer climate), but a weaker Indian Ocean east –
west circulation in response to the exposure of the Sunda and Sahul 
shelves due to lowered sea level (DiNezio et al., 2011). The latter 
effect is detectable in proxies for hydroclimate, as well as salinity and 
sea surface temperature (DiNezio and Tierney, 2013; DiNezio et al.,

2018). More relevant to future warming is the mid-Pliocene period 
(3 million years ago), the last time the Earth experienced CO 2 levels 
comparable to present (see Cross-Chapter Box  2.4). Sea surface 
temperature (SST) reconstructions show a weakening of the Pacific 
zonal gradient and a  pattern of warmth consistent with a  weaker 
Walker cycle response (Corvec and Fletcher, 2017; Tierney et al., 2019; Figure 8.5 | Zonally-averaged annual mean changes in precipitation minus 
evaporation (P–E) over (a) ocean and (b) land between the historical 
(1995–2014) and SSP2-4.5 (2081–2100) CMIP6 simulations (blue lines, 
an average of the CanESM5 and MRI-ESM2-0 models).  Dashed lines show 
estimated P–E changes using a  simple thermodynamic scaling (Held and Soden, 
2006); dotted lines show estimates using an extended scaling (Byrne and O’Gorman, 
2016). All curves have been smoothed in latitude using a three grid-point moving-

average filter. Further details on data sources and processing are available in the 
chapter data table (Table 8.SM.1).

1070
Chapter 8  W ater Cycle Changes8McClymont et al., 2020). Although the Pliocene SST pattern and wet 
subtropics contrast with present conditions (Burls and Fedorov, 
2017), the paleoclimate record strengthens evidence that a warmer 
climate is associated with a weaker Walker circulation (Cross-Chapter 
Box 2.4; Section 3.3.3).
Since AR5, weakening of the tropical circulation has been explained 
as a  rapid response to increasing CO 2 concentrations and slower 
response to warming and evolving SST patterns (He and Soden, 2017; 
Xia and Huang, 2017; Shaw and Tan, 2018; Chemke and Polvani, 2020). 
Large-scale tropical circulation weakens by 3 –4% in a rapid response 
to a quadrupling of CO 2 concentrations (Plesca et al., 2018), which 
suppresses tropospheric radiative cooling, particularly in subtropical 
ocean subsidence regions (Bony et al., 2013; Merlis, 2015; Richardson 
et al., 2016). The resulting increased atmospheric stability explains

the rapid weakening of the Walker circulation (Wills et al., 2017) 
and Northern Hemisphere Hadley Cell (Chemke and Polvani, 2020). 
Subsequent surface warming contributes up to a  12% slowing of 
circulation for a uniform 4°C SST increase, driven by thermodynamic 
decreases in temperature lapse rate (Plesca et al., 2018).
The regional Inter-tropical Convergence Zone (ITCZ) position, width 
and strength determine the location and seasonality of the tropical 
rain belt. Since AR5, multiple studies have linked cross-equatorial 
energy transport to the mean ITCZ position (Donohoe et al., 2013; 
Frierson et al., 2013; Bischoff and Schneider, 2014; Boos and Korty, 
2016; Loeb et al., 2016; Adam et al., 2018; Biasutti and Voigt, 2019). 
Multi-model studies agree that aerosol cooling in the NH led to 
a southward shift in the ITCZ and tropical precipitation after the 1950s 
up to the 1980s that is linked with the 1980s Sahel drought (Box 8.1;

Section 8.3.2.4 and 10.4.2.1). In particular, aerosol-cloud interaction 
was identified as a potentially important driver of this shift (Chung 
and Soden, 2017) but this is uncertain since observations suggest that 
models may overestimate (Malavelle et al., 2017; Toll et al., 2017)  or 
underestimate (Rosenfeld et al., 2019)  the aerosol cloud-mediated 
cooling effects. In addition, greenhouse gas forcing has been invoked 
in explaining much of the increase in Sahel precipitation since the 
1980s through enhanced meridional temperature gradient, with only 
a secondary role for aerosol (Dong and Sutton, 2015).
Understanding of how ITCZ width and strength respond to a warming 
climate has improved since AR5 (Byrne and Schneider, 2016; Harrop and 
Hartmann, 2016; Popp and Silvers, 2017; Dixit et al., 2018; Zhou et al., 
2020). Studies suggest that convection gets stronger and more focused 
within the core of the ITCZ (Lau and Kim, 2015; Byrne et al., 2018). This

leads to drying on the equatorward edges of the ITCZ and a moistening 
tendency in the ITCZ core (Byrne and Schneider, 2016). Feedbacks 
involving clouds have been identified as an important mechanism 
leading to tightening and strengthening of the ITCZ (Popp and Silvers, 
2017; Su et al., 2017, 2019, 2020; Talib et al., 2018). Stronger ascent 
in the core amplifies the ‘wet get wetter’ response while reduced 
moisture inflow near the ITCZ edges reduces this response below the 
7%  °C–1 thermodynamic increase in moisture transport. Thus, there 
is a  range of evidence and medium agreement  for strengthening 
and contraction of the ITCZ with warming that sharpens contrasts 
between wet and dry regimes. However, understanding of how the 
regional ITCZ location responds in a  warming climate is not robust (Section 8.4.2.1) with limited evidence  of distinct regional responses 
to GHG forcing including a northward shift over eastern Africa and the

Indian Ocean and a southward shift in the eastern Pacific and Atlantic 
oceans (Mamalakis et al., 2021). Paleoclimate evidence highlights the 
distinct regional ITCZ responses to hemispheric asymmetry in volcanic 
and orbital forcing (McGee et al., 2014; Boos and Korty, 2016; Colose 
et al., 2016; Denniston et al., 2016; PAGES Hydro2K Consortium, 2017; 
Singarayer et al., 2017; Atwood et al., 2020) and rapid (>1° latitude 
over decades) shifts in the ITCZ and regional monsoons in response to 
AMOC collapse cannot be ruled out (Sections 8.6.1.1 and 5.1.3).
Monsoons are key components of the tropical overturning circulation 
that can be understood as a  balance between net energy input 
(e.g., radiative and turbulent fluxes) and the export of moist static energy. 
This is determined by contrasting surface heat capacity between ocean 
and land and modified through changes in atmospheric dynamics, 
tropical tropospheric stability and land surface properties (Jalihal et al.,

2019). Thermodynamic increases in moisture transport are expected to 
increase monsoon strength and area (Christensen et al., 2013). Since 
AR5, evidence continues to demonstrate that monsoon circulation is 
sensitive to spatially varying radiative forcing by anthropogenic aerosols 
(Hwang et al., 2013; R.J. Allen et al., 2015; Z. Li et al., 2016b)  and GHGs 
(Dong and Sutton, 2015). Changes in SST patterns also play a role (Guo 
et al., 2016; W. Zhou et al., 2019; Cao et al., 2020) by altering cross-
equatorial energy transports and land–ocean temperature contrasts. 
This evidence continues to support a thermodynamic strengthening of 
monsoon precipitation that is partly offset by slowing of the tropical 
circulation but with weak evidence  and low agreement  for regional 
aspects of circulation changes.  Disagreement between paleoclimate 
and modern observations, physical theory and numerical simulations 
of global monsoons have been partly reconciled (Section  3.3.3.2)

through improved understanding of regional processes ( Harrison et al., 
2015; R. Bhattacharya et al., 2017; Bhattacharya et al., 2018; Biasutti 
et al., 2018; D’Agostino et al., 2019; Jalihal et al., 2019; Seth et al., 
2019 ), although interpreting past changes in the context of future 
projections requires careful account of differing forcings and feedbacks 
(D’Agostino et al., 2019). Assessment of past changes and future 
projections in regional monsoons are provided in Sections 2.3.1.4.2, 
8.3.2.4 and 8.4.2.4. 
Since AR5, understanding of poleward expansion of the Hadley Cells 
has improved (Section 2.3.1.4.1) but its role in subtropical drying is 
limited to the zonal mean and dominated by ocean regions (Byrne 
and O’Gorman, 2015; Grise and Polvani, 2016; He and Soden, 2017; 
Schmidt and Grise, 2017; Siler et al., 2018; Chemke and Polvani, 2019; 
Grise and Davis, 2020). Over subtropical land, evolving SST patterns

and land–ocean warming contrasts, that are partly explained by rapid 
responses to CO 2 increases, can dominate aspects of the atmospheric 
circulation response (Byrne and O’Gorman, 2015; He and Soden, 2015; 
Chadwick et al., 2017; H. Yang et al., 2020) and resultant regional 
water cycle changes, particularly for projected drying in semi-arid, 
winter-rainfall dominated subtropical climates (Deitch et al., 2017; 
Brogli et al., 2019; Seager et al., 2019b; Zappa et al., 2020) . Poleward 
expansion of the tropical belt is expected to drive a corresponding 
shift in mid-latitude storm tracks, but the controlling mechanisms 
differ between hemispheres. Southern Hemisphere expansion is driven 
by GHG forcing and amplified by stratospheric ozone depletion, while

1071
Water Cycle Changes Chapter 88weaker Northern Hemisphere expansion in response to GHG forcing 
is modulated by tropospheric ozone and aerosol forcing, particularly 
black carbon (Davis et al., 2016; Grise et al., 2019; Watt-Meyer 
et al., 2019; Zhao et al., 2020) . However, internal variability is found 
to dominate observed responses in the NH, precluding attribution to 
radiative forcing (D’Agostino et al., 2020a). Paleoclimate evidence 
of poleward expansion and weakening of westerly winds in both 
hemispheres in the warmer Pliocene is linked to reduced equator-to-
pole thermal gradients and ice volume  (Abell et al., 2021).
The influence of amplified Arctic warming on mid-latitude regional 
water cycles is not well understood based on simple physical 
grounds due to the large number of competing physical processes 
(Cross-Chapter Box 10.1). The thermal gradient between polar and 
lower latitude regions decreases at low levels due to Arctic warming

amplification. However, at higher altitudes, the corresponding 
thermal gradient increases with warming due to cooling of the Arctic 
stratosphere and this is consistent with a strengthening of the winter 
jet stream in both hemispheres, yet there is low agreement  on the 
precise mechanisms (Vallis et al., 2015; Vihma et al., 2016).  Changes 
in the strength of the polar stratospheric vortex can also alter the 
mid-latitude circulation in winter, but responses are not consistent 
across models (Oudar et al., 2020a). Nevertheless, thermodynamic 
strengthening of moisture convergence into weather systems and 
polar regions is robust (Section 8.2.2.1) and remains valid despite 
weak understanding of atmospheric circulation change.
In summary, there is high confidence that altered atmospheric 
wind patterns in response to radiative forcing and evolving 
surface temperature patterns will affect the regional water cycle 
in most regions. Mean tropical circulation is expected to slow with

global warming ( high confidence ) but temporary multi-decadal 
strengthening is possible due to internal variability ( medium 
confidence ). Slowing of the tropical circulation reduces the meridional 
P–E gradient over the Pacific and can partly offset thermodynamic 
amplification of P–E patterns and strengthening of monsoons ( high 
confidence ) but regional characteristics of tropical rain belt changes 
are not well understood. There is medium confidence  in processes 
driving strengthening and tightening of the ITCZ that increase 
the contrasts between wet and dry tropical weather regimes and 
seasons. There is high confidence  in understanding of how radiative 
forcing and global warming drive a  poleward expansion of the 
subtropics and mid-latitude storm tracks but only low confidence  in 
how poleward expansion influences drying of subtropical and mid-
latitude climates. There is low confidence  in understanding how Arctic

warming amplification affects mid-latitude regional water cycles but 
high confidence  that thermodynamic strengthening of precipitation 
within weather systems and in monsoons and polar regions is robust 
to large-scale circulation changes.
8.2.3 Local-scale Physical Processes Affecting 
the Water Cycle
Processes operating at local scales are capable of substantially 
modifying the regional water cycle. This section assesses the 
development in understanding of processes affecting the atmosphere, surface and subsurface, including cryosphere and biosphere 
interactions and the direct impacts of human activities.
8.2.3.1 Hydrological Processes Related to Ice and Snow
Declining ice-sheet mass, glacier extent and Northern Hemisphere 
(NH) sea ice, snow cover and permafrost (Collins et al., 2013; 
Vaughan et al., 2013) is an expected consequence of a  warming 
climate (Sections  2.3.2, 3.4, 4.3.2.1 and 9.3 –9.5). A  decline in 
mountain snow cover and increased snow and glacier melt will

alter the amount and timing of seasonal runoff in mountain regions 
(Sections  3.4.2, 3.4.3 and 9.5). Earlier and more extensive winter 
and spring snowmelt (X. Zeng et al., 2018) can reduce summer and 
autumn runoff in snow-dominated river basins of mid–high latitudes 
of the NH (Rhoades et al., 2018; Blöschl et al., 2019). Since AR5, 
an earlier but less rapid snowmelt has been explained by reduced 
winter snowfall and less intense solar radiation earlier in the season 
(Musselman et al., 2017; Wu et al., 2018; Grogan et al., 2020). 
Reduced snow cover also increases energy available for evaporation, 
which can dominate declining river discharge based on modelling 
of the Colorado River (Milly and Dunne, 2020). An increase in the 
fraction of precipitation falling as rain compared with snow can lead 
to declines in both streamflow and groundwater storage in regions 
where snowmelt is the primary source of recharge (Earman and

Dettinger, 2011; Berghuijs et al., 2014). Such regions include western 
South America and western North America, semi-arid regions which 
rely on snowmelt from high mountain chains (Ragettli et al., 2016; 
Milly and Dunne, 2020). Rain-on-snow melt events reduce at lower 
altitudes due to declining snow cover but increase at higher altitudes 
where snow tends to be replaced by rain based on observations and 
modelling (Musselman et al., 2018; Pall et al., 2019), thereby altering 
seasonal and regional characteristics of flooding (Section 11.5).
Seasonal melt water from high mountain glaciers in Asia (see Cross-
Chapter Box 10.4) supply the basic needs of 221 ± 97 million people 
(Pritchard, 2019; Immerzeel et al., 2020). Glacier-melt in response to 
warming can initially lead to increased runoff volumes, especially in 
peak summer flows, but they will eventually decline as most glaciers 
continue to shrink. SROCC concluded there is high confidence  that

the peak runoff has already been passed for some smaller glaciers 
(Hock et al., 2019a). Increased precipitation and glacier-melt can also 
contribute to rising lake levels and flood hazards in regions such as 
the inner Tibetan Plateau, Patagonia, Peru, Alaska and Greenland 
(Lei et al., 2017; Shugar et al., 2020; Stuart-Smith et al., 2020) . Since 
AR5, evidence from multiple locations (New Zealand, Greenland, 
Antarctica) shows that intrusions of warm, moist air are important in 
controlling glacier mass balance, the likelihood of extreme ablation 
or snowfall events depending on air temperature (Gorodetskaya  
et al., 2014; Mackintosh et al., 2017; Mattingly et al., 2018; 
Little et al., 2019; Oltmanns et al., 2019; Wille et al., 2019; Adusumilli 
et al., 2021) . Sensible heating from warm air and increased longwave 
radiation from atmospheric moisture and low clouds drive melt 
events (Stuecker et al., 2018). 
Reductions in snow, freshwater ice and permafrost affect terrestrial

hydrology. Permafrost degradation reduces soil ice and alters the 
extent of thermokarst lake coverage (Section  9.5.2; M.  Meredith

1072
Chapter 8  W ater Cycle Changes8et al., 2019). A lag between current climate change and permafrost 
degradation is expected, given the slow response rates in frozen 
ground and the fact that snow cover insulates soil from sensible heat 
exchanges with the air above (Hoegh-Guldberg et al., 2018; García-
García et al., 2019; Soong et al., 2020) . Post-wildfire areas are also 
linked with permafrost degradation in the Arctic based on satellite 
observations (Yanagiya and Furuya, 2020). An increase in spring 
rainfall can increase heat advection by infiltration, exacerbating 
permafrost thaw and leading to increased methane emissions 
(Section  5.4.7; Neumann et al., 2019). Increased heat transport 
by Arctic rivers can also contribute to earlier sea ice melt (Park 
In summary, it is virtually certain  that warming will cause a loss of 
frozen water stores, except in areas where temperatures remain 
below 0°C for most of the year. There is high confidence that warming

and reduced snow volume drives an earlier snowmelt, leading to 
seasonally dependent changes in streamflow. There is medium 
confidence  that weaker sunlight earlier in the season can reduce 
the rate of snowmelt. Melting of snowpack or glaciers can increase 
streamflow in high-latitude and high-altitude catchments until 
frozen water reserves are depleted ( high confidence ). There is high 
confidence  that warm, moist airflows and associated precipitation 
dominate glacier mass balance in some regions (New Zealand, 
Greenland, Antarctica).  
8.2.3.2 Processes Determining Heavy Precipitation 
and Flooding
Evidence that heavy precipitation events (from sub-daily up to 
seasonal time scales) intensify as the planet warms has strengthened 
since AR5 (Section 11.4, Box 11.1 and Cross-Chapter Box 3.2) based 
on improved physical understanding, extensive modelling and 
increasing observational corroboration (O’Gorman, 2015; Fischer

and Knutti, 2016; Neelin et al., 2017). There is robust evidence , with 
medium agreement  across a range of modelling and observational 
studies, of thermodynamic intensification of wet seasons (Chou 
et al., 2013; Liu and Allan, 2013; Dunning et al., 2018; Lan et al., 
2019; Zhang and Fueglistaler, 2019). Extreme daily precipitation is 
expected to increase at close to the 7% °C–1 increase in the near-
surface atmospheric moisture-holding capacity determined by the 
Clausius–Clapeyron equation (Section 11.4, Figure 8.4), with limited 
evidence  that higher rates apply for shorter duration precipitation 
events (Formayer and Fritz, 2017; Lenderink et al., 2017; Ali et al., 
2018; Guerreiro et al., 2018; Burdanowitz et al., 2019; W.  Zhang 
et al., 2019a) . However, observed estimates sample multiple synoptic 
weather states, mixing thermodynamic and dynamic factors, so are 
not directly relatable to climate change responses (Bao et al., 2017;

Drobinski et al., 2018). The contrasting spatial scales sampled by the 
observations and models (from global to cloud resolving) explain 
the large range of daily and sub-daily precipitation scaling with 
temperature assessed in Figure 8.4.
Since AR5, advances in understanding the expected changes in 
intense rainfall at the sub-daily time scale (Section 11.4, Figure 8.4) 
are provided by idealized or high resolution model experiments and 
observations (Westra et al., 2014; Fowler et al., 2021). There is robust evidence  from simplified calculations, convection resolving models and 
observations that thermodynamics drives an increase in convective 
available potential energy (CAPE) with warming and therefore the 
intensity of convective storms (Singh and O’Gorman, 2013; Romps, 
2016; Barbero et al., 2019). Also, declining relative humidity over land 
(Sections 2.3.1.3.2 and 8.2.2.1) increases lifting condensation level,

thereby delaying but intensifying convective systems (Louf et al., 
2019; J. Chen et al., 2020a). Larger systems are linked with increasing 
tropopause height (Lenderink et al., 2017) that can also amplify storm 
precipitation (Prein et al., 2017). However, the heaviest rainfall is not 
necessarily associated with the most intense (deepest) storms based 
on satellite data (Hamada et al., 2015; Hamada and Takayabu, 2018). 
Precipitation intensification can exceed thermodynamic expectations 
where and when additional latent heating invigorates individual 
storms (Section  11.4.1) as implied by medium agreement  across 
modelling and observational studies (Berg et al., 2013; Molnar et al., 
2015; Scoccimarro et al., 2015; Prein et al., 2017; Zhou and Wang, 
2017; Nie et al., 2018; Kendon et al., 2019; Z.  Zhang et al., 2019) . 
This intensification depends on time of day, based on convection-
permitting simulations (E.P . Meredith et al., 2019).

Intensification of sub-daily rainfall is inhibited in regions and seasons 
where available moisture is limited (Prein et al., 2017). However, 
a fixed threshold temperature above which precipitation is limited 
by moisture availability is not supported by modelling evidence 
(Neelin et al., 2017; Prein et al., 2017). Enhanced latent heating 
within storms can also suppress convection at larger scales due to 
atmospheric stabilization as demonstrated with high resolution, 
idealized and large ensemble modelling studies (Loriaux et al., 2017; 
Chan et al., 2018; Nie et al., 2018; Tandon et al., 2018; Kendon et al., 
2019) . Stability is also increased by the direct radiative heating effect 
of higher CO 2 concentrations (Baker et al., 2018) and  influenced 
by aerosol effects on the atmospheric energy budget and cloud 
development (Box  8.1). Since AR5, modelling evidence shows 
increases in convective precipitation extremes are limited by droplet/

ice fall speeds (Singh and O’Gorman, 2014; Sandvik et al., 2018) 
but these processes are only crudely represented (Tapiador et al., 
2019a). Idealized regional and coupled global models combined 
with limited  observational evidence  shows that instantaneous 
precipitation extremes are sensitive to microphysical processes, while 
daily extremes are determined more by the degree of convective 
aggregation (Bao and Sherwood, 2019; Pendergrass, 2020a). 
Dynamical changes modify and can dominate thermodynamic drivers 
of local rainfall and flood hazard change (Box  11.1). For example, 
increased land –ocean temperature gradients (Section 8.2.2.2) explain 
more intense rain from convective systems over the Sahel based on 
satellite data since the 1980s (Taylor et al., 2017) and dynamical 
feedbacks can invigorate active to break phase transition over India 
(Karmakar et al., 2017; Roxy et al., 2017). Satellite data shows long-lived,

organized mesoscale convective systems contribute disproportionally 
to extreme tropical precipitation (Roca and Fiolleau, 2020). Since AR5, 
the spatial variability in soil moisture has been linked with the timing 
and location of convective rainfall by altering the partitioning between 
latent and sensible heating. This was demonstrated for the Sahel, 
Europe and India in observations (C.M. Taylor et al., 2013; Taylor, 2015; 
Petrova et al., 2018; Barton et al., 2020; Klein and Taylor, 2020) but

1073
Water Cycle Changes Chapter 88depends on the moisture-convergence regime (Welty et al., 2020). Only 
high-resolution convection-permitting models can capture the sub-grid 
scale mechanisms for convective initiation ( C.M. Taylor et al., 2013; 
H. Moon et al., 2019 ). There is medium evidence  that greater tropical 
cyclone rainfall totals can be caused by dynamical feedbacks (Chauvin 
et al., 2017) and slower propagation speed as tropical circulation 
weakens (Kossin, 2018). These processes amplify the thermodynamic 
intensification of rainfall (Section 11.7.1.2), yet observational support 
is weak (Chan, 2019; Lanzante, 2019; I.J. Moon et al., 2019; Knutson 
et al., 2020) . Slower decay following landfall, explained by larger stores 
of heat and moisture at higher SSTs, can also amplify rainfall amount 
based on observations and modelling (Li and Chakraborty, 2020). 
Rainfall intensity from the outer rain bands of tropical cyclones is also

increased by aerosol –cloud interactions (Box 8.1).
The amount and intensity of rainfall within extratropical storms is 
expected to increase with atmospheric moisture. This is particularly 
evident for atmospheric rivers (see Glossary) and research since AR5 
has confirmed their link with flooding and terrestrial water storage 
(Froidevaux and Martius, 2016; Paltan et al., 2017; Waliser and 
Guan, 2017; Adusumilli et al., 2019; Ionita et al., 2020; Payne et al., 
2020) . There is robust evidence  based on simple physics and detailed 
modelling that extratropical cyclone rainfall, including atmospheric 
river events, will intensify through increased atmospheric moisture 
flux (Lavers et al., 2013; Ramos et al., 2016; Yettella and Kay, 2017; 
V. Espinoza et al., 2018; Algarra et al., 2020; Xu et al., 2020; Zavadoff 
and Kirtman, 2020; Zhao, 2020), although changes in dynamical 
aspects will modify responses regionally (Section  8.4.2.8). For

example, stronger latitudinal temperature gradients in the high-
latitude upper troposphere drive increased extratropical storm 
speed around 30°N –70°N based on CMIP5 simulations (Dwyer and 
O’Gorman, 2017), causing reduced precipitation accumulation.
The response of flood hazard to changing rainfall characteristics 
depends on time and space scale and the nature of the land surface 
(Section 11.5.1 and FAQ 8.2). Sustained and heavy rainfall can lead 
to widespread flooding and landslides while intensification of short-
duration intense rainfall can increase the severity and frequency 
of flash flooding (Marengo et al., 2013; Chan et al., 2016; Gariano 
and Guzzetti, 2016; Sandvik et al., 2018). Flooding events in many 
tropical regions (e.g., north-western South America, southern Africa 
and Australasia) are associated with ENSO variability (Emerton et al., 
2017; Takahashi and Martínez, 2019; Pabón-Caicedo et al., 2020) and

amplified by thermodynamic increases in water vapour. Flood hazard 
from heavy rainfall is modulated by snowmelt (Section  8.2.3.1), 
vegetation characteristics (Page et al., 2020; Murphy et al., 2021) and 
direct human intervention (Sections  8.2.3.4 and FAQ 8.2) but also 
can be compounded by sea level rise (Sections 4.3.2.2 and 9.6.4) in 
coastal and delta regions (Bevacqua et al., 2019; Ganguli and Merz, 
2019; Eilander et al., 2020). Antecedent soil moisture conditions are 
an important modulator of flooding (Section 11.5.1) but become less 
important for smaller catchments and for more severe floods (Wasko 
and Nathan, 2019). Depleted soil moisture after more intense dry 
seasons (Section  8.2.2.1) can allow greater uptake of wet season 
rainfall before soils saturate. Since AR5, evidence confirms that more 
intense rainfall increases the proportion of runoff and reservoir

recharge relative to infiltration into the soil (Eekhout et al., 2018; Yin et al., 2018). More intense but less frequent storms (Kendon et al., 
2019) favour focused groundwater recharge through leakage from 
surface waters (R.G. Taylor et al., 2013a; Cuthbert et al., 2019a) and 
runoff and flash flooding where the percolation capacity of the soil is 
exceeded (Yin et al., 2018). 
Increased severity of flooding on larger, more slowly-responding rivers 
is expected as precipitation accumulations increase during persistent 
wet events over a season. This can occur where atmospheric blocking 
patterns repeatedly steer extratropical cyclones across large river 
catchments, as identified for NH mid-latitudes and Asia (Takahashi 
et al., 2015; Pfleiderer et al., 2018; Zhou et al., 2018; Blöschl et al., 
2019; Lenggenhager et al., 2019; Nikumbh et al., 2019; Zanardo et al., 
2019), although groundwater flooding and antecedent conditions

including soil moisture and snowmelt also play a  role (Muchan 
et al., 2015; Berghuijs et al., 2019). Increased atmospheric moisture 
amplifies the severity of these events when they occur in a warmer 
climate, yet drivers of change in the occurrence of blocking patterns, 
stationary waves and jet stream position are not well understood 
(Section 8.2.2.2 and Cross-Chapter Box 10.1). 
In summary, there is very high confidence  that heavy precipitation 
events will become more intense in a  warming climate. There is 
high confidence  that increased moisture and its convergence within 
extratropical and tropical cyclones and storms will increase rainfall 
totals during wet events at close to the 7%  °C–1 thermodynamic 
response, with low confidence  of higher rates for sub-daily intensities. 
There is medium confidence that more intense but less frequent 
rainfall increases the proportion of rainfall leading to surface runoff

and focused groundwater recharge from temporary water bodies. 
There is low confidence  in how the frequency of flooding will change 
regionally as it is strongly dependent on catchment characteristics, 
antecedent conditions and how atmospheric circulation systems 
respond to climate change, which is less certain than thermodynamic 
drivers (Section 11.5). However, there is high confidence  that increases 
in precipitation intensity and amount during very wet events (from 
sub-daily up to seasonal time scales) will intensify severe flooding 
when these extremes occur.
8.2.3.3 Drivers of Aridity and Drought
Regional changes in aridity  – broadly defined as a  deficit of 
moisture  – are expected to occur in response to anthropogenic 
forcings as a consequence of shifting precipitation patterns, warmer 
temperatures, changes in cloudiness (affecting solar radiation), 
declining snowpack, changes in winds and humidity, and vegetation

cover (Figure  8.6). Evapotranspiration (see Annex VII: Glossary) 
is a  key component of aridity, and is composed of two main 
processes: evaporation from soil, water and vegetation surfaces; 
and transpiration, the exchange of moisture between plants and 
atmosphere through plant stomata. On a  global level, warmer 
temperatures increase evaporative demand in the atmosphere, and 
thus (assuming sufficient soil moisture is available) increase moisture 
loss from evapotranspiration ( high confidence ) (Dai et al., 2018; 
Vicente-Serrano et al., 2020). On a regional level, aridity is further 
modulated by seasonal rainfall patterns, runoff, water storage, and 
interactions with vegetation.

1074
Chapter 8  W ater Cycle Changes8Vegetation is a crucial interface between subsurface water storage 
(in soil moisture and groundwater) and the atmosphere. Plants 
alter evapotranspiration and the surface energy balance, and 
thus can have a  large influence on regional aridity (Lemordant 
et al., 2018). SRCCL concluded there is high confidence  that higher 
atmospheric CO 2 increases the ratio of plant CO 2 uptake to water 
loss (water-use efficiency; WUE) through the combined enhancement 
of photosynthesis and stomatal regulation ( Section 5.4.1;  De Kauwe 
et al., 2013; C.D. Jones et al., 2013; Deryng et al., 2016; Swann et al., 2016; Cheng et al., 2017; Knauer et al., 2017; Peters et al., 2018; 
Guerrieri et al., 2019) . Modelling studies suggest that increasing 
WUE can partly counteract water losses from increased evaporative 
demand in a warmer atmosphere, potentially mitigating aridification 
(Milly and Dunne, 2016; Bonfils et al., 2017; Cook et al., 2018; Y . Yang

et al., 2018) . However, observational studies suggest that this effect 
may be counter-balanced by the increase in plant growth in response 
to elevated CO 2, which results in increased water consumption 
(De Kauwe et al., 2013; Donohue et al., 2013; Ukkola et al., 2016b; 
Climatic drivers
PrecipitationTemperature
CO 2
Evaporative demand & plant 
growth water use 
Evapo-
transpiration
Water availability
ImpactsAgricultural & 
EcologicalHydrological
SocioeconomicSnowpack
Groundwater, 
lakes, reservoirsSoil MoistureRadiation
Wind
Humidity
Drought types
Meteorological
(environmental drought, 
tree mortality, ﬁre, habitat 
loss, erosion, water quality)(crop failure, livestock 
mortality, low water supply, 
less hydropower)Environmental++ +
+
+
+
+++
Figure 8.6 | Climatic drivers of drought, effects on water availability, and impacts.  Plus and minus signs denote the direction of change that drivers have on factors

such as snowpack, evapotranspiration, soil moisture, and water storage. The three main types of drought are listed, along with some possible environmental and socio-economic 
impacts of drought (bottom).

1075
Water Cycle Changes Chapter 88Yang et al., 2016; Guerrieri et al., 2019; Mankin et al., 2019; A. Singh 
et al., 2020) . In semi-arid regions, increased plant water consumption 
can reduce streamflow and exacerbate aridification (Ukkola et al., 
2016b; Mankin et al., 2019; A.  Singh et al., 2020) . Thus, there is 
low confidence  that increased WUE in plants can counterbalance 
increased evaporative demand (Cross-Chapter Box 5.1).
A drought is a period of abnormally dry weather that persists for long 
enough to cause a serious hydrological imbalance (Glossary; Wilhite 
and Glantz, 1985; Wilhite, 2000; Cook et al., 2018). Most droughts 
begin as persistent precipitation deficits (‘meteorological drought’) 
that propagate over time into deficits in soil moisture, streamflow, 
and water storage (Figure 8.6), leading to a reduction in water supply 
(‘hydrological drought’). Increased atmospheric evaporative demand 
increases plant water stress, leading to ‘agricultural and ecological

drought’ (Williams et al., 2013; C.D. Allen et al., 2015; Anderegg et al., 
2016; McDowell et al., 2016; Grossiord et al., 2020) . Evaporative 
demand affects plants in two ways. It increases evapotranspiration, 
depleting soil moisture and stressing plants through lack of water 
(Teuling et al., 2013; Sperry et al., 2016), and also directly affects plant 
physiology, causing a decline in hydraulic conductance and carbon 
metabolism, leading to mortality (Figure 8.6; Breshears et al., 2013; 
Hartmann, 2015; McDowell and Allen, 2015; Fontes et al., 2018). 
While droughts are traditionally viewed as ‘slow moving’ disasters 
that typically take months or years to develop, rapidly evolving and 
often unpredictable flash droughts  can also occur (Otkin et al., 2016, 
2018). Flash droughts  can develop within a  few weeks, causing 
substantial disruption to agriculture and water resources (Pendergrass 
et al., 2020). Conversely, droughts that persist for a long time (usually

a decade or more) are called megadroughts . Droughts span a large 
range of spatial and temporal scales, arise through a  variety of 
climate system dynamics (e.g., internal atmospheric variability, ocean 
teleconnections), and can be amplified or alleviated by a variety of 
physical and biological processes. As such, droughts occupy a unique 
space within the framework of extreme climate and weather events, 
possessing no singular definition.
While the role of precipitation in droughts is obvious, other climatic 
drivers are also important, such as temperature, radiation, wind, 
and humidity (Figure  8.6). These factors have a  strong influence on 
atmospheric evaporative demand, which affects evapotranspiration 
and soil moisture (Figure  8.6). In snow-dominated regions, high 
temperatures increase the fraction of precipitation falling as rain 
instead of snow and advance the timing of spring snowmelt ( high 
confidence ) (Vincent et al., 2015; Mote et al., 2016, 2018; Berg and

Hall, 2017; Solander et al., 2018) . This can result in lower than normal 
snowpack levels (a ‘snow drought’), and thus reduced streamflow, even 
if total precipitation is at or above normal for the cold season (Harpold 
et al., 2017). Plants also affect the severity of droughts by modulating 
evapotranspiration (Figure  8.6). As discussed above, the  effect of 
elevated CO 2 on plants has the potential to both increase and reduce 
water loss through evapotranspiration via enhanced WUE and plant 
growth, respectively (Figure 8.6), but there is low confidence  in whether 
one process dominates over another at the global scale. 
Drought severity also depends on human activities and decision-
making (AghaKouchak et al., 2015; Van Loon et al., 2016; Pendergrass et al., 2020) . Societies have developed a  variety of strategies to 
manipulate the water cycle to increase resiliency in the face of water 
scarcity, including irrigation, creation of artificial reservoirs, and

groundwater pumping. While potentially buffering water resource 
capacity, in some cases these interventions may unexpectedly 
increase vulnerability ( medium confidence ). For example, while 
increased irrigation efficiency may ensure more water is available 
to crops, the corresponding reduction in runoff and subsurface 
recharge may exacerbate hydrologic drought (Grafton et al., 2018). 
Furthermore, while building dams and increasing surface reservoir 
capacity can boost water resources, they may actually increase 
drought vulnerability if demands rise to take advantage of the 
increased supply or if over-reliance on these surface reservoirs is 
encouraged (Di Baldassarre et al., 2018). Interactions between 
adaptation, vulnerability, and drought impacts are discussed further 
in WGII (Chapters 2 and 4).
In summary, there is high confidence  that a warming climate drives 
an increase in atmospheric evaporative demand, decreasing available

soil moisture. There is high confidence   that higher atmospheric 
CO 2 increases plant water-use efficiency, but low confidence  that 
this physiological effect can counterbalance water losses. Since 
drought can be defined in a number of ways, there are potentially 
different responses under a warming climate depending on drought 
type. Beyond a lack of precipitation, changes in evapotranspiration 
are critical components of drought, because these can lead to soil 
moisture declines ( high confidence ). Under very dry soil conditions, 
evapotranspiration becomes restricted and plants experience water 
stress in response to increased atmospheric demand ( medium 
confidence ). Human activities and decision-making have a  critical 
impact on drought severity ( high confidence ).
8.2.3.4 Direct Anthropogenic Influence  
on the Regional Water Cycle
Human activities influence the regional water cycle directly through 
modifying and exploiting stores and flows from rivers, lakes and

groundwater and by altering land cover characteristics. These 
actions alter surface energy and water balances through changes in 
permeability, surface albedo, evapotranspiration, surface roughness 
and leaf area. Direct redistribution of water by human activities for 
domestic, agricultural and industrial use of about 24,000  km3 yr–1 
(Figure 8.1) is equivalent to half the global river discharge or double 
the global groundwater recharge each year (Abbott et al., 2019). Since 
AR5, both modelling studies and observations have demonstrated 
that land use change can drive local and remote responses in 
precipitation and river flow by altering the surface energy balance, 
moisture advection and recycling, land –sea thermal contrast and 
associated wind patterns (Alter et al., 2015; Wey et al., 2015; De Vrese 
et al., 2016; Pei et al., 2016; Wang-Erlandsson et al., 2018; Vicente-
Serrano et al., 2019) . There is robust evidence  that a warming climate

combined with direct human demand for groundwater will deplete 
groundwater resources in already dry regions (Wada and Bierkens, 
2014; D’Odorico et al., 2018; Jia et al., 2019).
The SRCCL presented evidence that extraction of water from the 
ground or river systems and intensive irrigation increases evaporation

1076
Chapter 8  W ater Cycle Changes8and atmospheric water vapour locally (Jia et al., 2019; Mishra 
et al., 2020). Irrigation can explain declining groundwater storage 
in some regions, including north-western India and North America 
(Asoka et al., 2017; G. Ferguson et al., 2018). Simulations spanning 
1960–2010 indicate that approximately 30% of the present human 
water consumption is supplied from non-sustainable water resources 
(Wada and Bierkens, 2014). However, there is only limited evidence 
that groundwater extraction is lowering streamflow (Mukherjee 
et al., 2018; de Graaf et al., 2019). Model experiments show that 
irrigation can either aggravate or alleviate climate-induced changes 
of surface or subsurface water (Leng et al., 2015). Widespread 
extraction of water from rivers can reduce flows and decrease the 
level and area of inland seas and lakes (Wurtsbaugh et al., 2017; 
Torres-Batlló et al., 2020; X. Wang et al., 2020) . Between 1985 and

2015, about 139,000 km2 of inland water areas have become land, 
while creation of dams has converted about 95,000 km2 of land to 
water, particularly in the Amazon and Tibetan Plateau (Donchyts et al., 
2016). Direct management of river flow is comparable in magnitude 
to climate change effects for snow-fed rivers at a continental scale 
based on a global analysis and a study of 96 Canadian catchments 
(Tan and Gan, 2015; Arheimer et al., 2017). 
The SRCCL assessed with medium confidence  that mean and extreme 
precipitation is increased over and downwind of urban areas (Jia 
et al., 2019). There is medium confidence  that altered thermodynamic 
and aerodynamic properties of the land surface from urbanization 
affects evaporation and increases precipitation over or downwind of 
cities (Box 10.3) due to altered stability and turbulence (Han et al., 
2014; Pathirana et al., 2014; Jiang et al., 2016; D’Odorico et al., 2018;

Sarangi et al., 2018; Boyaj et al., 2020) . However, reduced biogenic 
aerosol, but increased anthropogenic aerosol emissions modify 
cloud microphysics and precipitation processes ( Box  8.1;  Schmid 
and Niyogi, 2017; D’Odorico et al., 2018; Fan et al., 2020; Zheng 
et al., 2020) . Urbanization also decreases permeability of the surface, 
leading to increased surface runoff (Chen et al., 2017; Jia et al., 2019). 
Large-scale infrastructure, such as the construction and operation of 
dikes, weirs, and hydropower plants, also alters surface energy and 
moisture fluxes, potentially influencing the regional water cycle. 
Limited  modelling evidence  suggests that large-scale solar and wind 
farms can increase precipitation locally (over the Sahel and North 
America) when dynamic vegetation responses are represented 
(Y . Li et al., 2018; Pryor et al., 2020), with remote effects also possible 
(Lu et al., 2021). Changes in land use from forest to agriculture can exert profound

regional effects on the water cycle (FAQ 8.1) by modifying the 
surface energy balance and moisture recycling (Krishnan et al., 2016; 
Paul et al., 2016; Llopart et al., 2018; Singh et al., 2019) . There is 
medium evidence  from  modelling and observations over the Amazon 
and East Africa that deforestation drives increased streamflow 
(Dos Santos et al., 2018; Guzha et al., 2018; Levy et al., 2018) but 
limited evidence  that increases in global runoff due to deforestation 
are counterbalanced by decreases resulting from irrigation (Hoegh-
Guldberg et al., 2018). Total Amazon deforestation drives reductions 
in precipitation but with a large 90% confidence range ( –38 to +5 %) 
based on 44 primarily pre-AR5 climate model simulations (Spracklen 
and Garcia-Carreras, 2015) with smaller reductions ( –2.3 to –1.3 %) 
attributed to observed Amazon deforestation up to 2010. Climate 
model development has reduced this uncertainty range but has not

altered the median change (Lejeune et al., 2015). Large-scale global 
deforestation (20 million km2) simulated by 9 CMIP6 models confirms 
a large range in precipitation amount reduction of –37 ± 54 mm yr–1 
over the deforested regions (Boysen et al., 2020). However, small-
scale deforestation can increase precipitation locally (Lawrence and 
Vandecar, 2015). A 50–60% deforestation rate corresponded to a wet 
season delay of about one week and greater chance of dry spells 
of eight days or longer based on correlation analysis of rain gauge 
and land-use data for South America (Leite-Filho et al., 2019). Forest 
and grassland fires can also modify hydrological response at the 
watershed scale (Havel et al., 2018). Afforestation or reforestation 
aimed at removing CO 2 from the atmosphere can also alter the water 
cycle at the regional scale (Section 8.4.3 and Cross-Chapter Box 5.1).
In summary, there is high confidence  that land-use change and

water extraction for irrigation drive local, regional and remote 
responses in the water cycle. Large-scale deforestation is likely  to 
decrease precipitation over the deforested regions but there is low 
confidence  in the effects of limited deforestation. There is medium 
confidence that deforestation drives increased streamflow relative to 
the responses caused by climate change. Urbanization can increase 
local precipitation ( medium confidence ) and resulting runoff intensity 
(high confidence ). A warming climate combined with direct human 
demand for water is expected to deplete groundwater resources in 
dry regions ( high confidence ).
Box 8.1 | Role of Anthropogenic Aerosols in Water Cycle Changes
Aerosols affect precipitation in two major pathways, by altering the shortwave and longwave radiation and influencing cloud 
microphysical properties.
Aerosol radiative effects on precipitation

Aerosols scatter and absorb solar radiation which reduces the energy available for surface evaporation and subsequent precipitation. 
In addition, cooling is incurred by the radiation that is reflected back to space directly by the aerosols and indirectly by the aerosol 
effect on cloud brightening. Northern Hemisphere (NH) station data indicate decreasing precipitation trends during the 1950s to the 
1980s, which have since partially recovered (Wild, 2012; Bonfils et al., 2020). These changes are attributable with high confidence  
to anthropogenic aerosol emissions from North America and Europe causing dimming through reduced surface solar radiation.

1077
This peaked during the late-1970s and partially recovered thereafter following improved air quality regulations (Section  6.2.1;  
Box 8.1, Figure 1).
Dimming over the NH causes a relative cooling, compared to the Southern Hemisphere (SH), which induces a southward shift of the 
northern edge of the tropical rain belt (Section 3.3.2.3; Allen et al., 2014; Brönnimann et al., 2015). CMIP5 simulations show that most of the 
cooling is caused by the aerosol cloud-mediated effect (Chung and Soden, 2017). Dimming also weakens monsoon flow and precipitation, 
offsetting or even overcoming the expected precipitation increase due to increased GHGs (Ayantika et al., 2021). The oceanic response to 
a weakened monsoon cross-equatorial flow can further weaken the South Asian monsoon through an amplifying feedback loop (Swapna

et al., 2012; Krishnan et al., 2016; Patil et al., 2019). These processes partially explain ( medium confidence ) the southward shift of the NH 
tropical edge of the tropical rain belt from the 1950s to the 1980s (Allen et al., 2014; Brönnimann et al., 2015) and the severe drought in 
the Sahel that peaked in the mid-1980s (Rotstayn et al., 2002; Undorf et al., 2018b). These processes also explain ( high confidence ) the 
observed decrease of South East Asian monsoon precipitation during the second half of the 20th century (Figure 8.7; Bollasina et al., 2011; 
Sanap et al., 2015; Krishnan et al., 2016; Lau and Kim, 2017; Lin et al., 2018; Undorf et al., 2018b). 
Absorption of solar radiation by anthropogenic aerosols such as black carbon warms the lower troposphere and increases moist 
static energy, but also results in larger convection inhibition that suppresses light rainfall (Box 8.1, Figure 2; Y . Wang et al., 2013).

Release of aerosol-induced instability, often triggered by topographical barriers, produces intense rainfall, flooding (Fan et al., 2015;  
Box 8.1 (continued)
ALLGEBA
ALLGHG AER
GPCC CRU(a) NH summer SW radiation anomalies
(b) NH summer precipitation anomaliesPrecipitation anomalies SW radiation anomalies
1951          1961        1971          1981           1991         2001          2011
1951          1961        1971          1981           1991         2001          20114
3
2
1
0
-1
-2
-3
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3AER
Box 8.1, Figure 1 | Northern Hemisphere surface downward radiation anomalies (W m–2; a) and precipitation anomalies (mm day–1; b) for 
1951–2014 for summer season (May–September) monsoon region (Polson et al., 2014)  from CMIP6 DAMIP experiments. Observed solar radiation 
anomalies are from GEBA global data from 1961–2014 (Wild et al., 2017) and observed precipitation anomalies are from GPCC and CRU. CMIP6 multi-model mean

anomalies are from all-forcings (ALL), greenhouse gas forcing (GHG) and anthropogenic aerosol forcing (AER) experiments. Anomalies are with respect to 1961–1990 
and smoothed with a 11-year running mean. Red shading shows the ensemble spread of ALL forcing experiment (5–95% range). Models are masked to the GPCC 
data set. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1078
Lee et al., 2016) and severe convective storms ( medium confidence ) (Saide et al., 2015). In particular, aerosols induce intense convection 
at the Himalaya foothills during the pre-monsoon season, which generates a  regional convergence there ( medium confidence ).  
This mechanism is termed the ‘elevated heat pump hypothesis’ (Lau and Kim, 2006; D’Errico et al., 2015).
Aerosol cloud microphysical effects
Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols 
add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce 
into raindrops and to freeze into ice hydrometeors at temperatures below 0°C. Adding CCN suppresses light rainfall from shallow 
and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air

invigorates them by more efficient vapour condensation on the added drop surfaces (Koren et al., 2014; Fan et al., 2018). Clouds 
forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain (Freud 
and Rosenfeld, 2012; Konwar et al., 2012; Campos Braga et al., 2017). This leads to larger amount of cloud water evaporating aloft 
while cooling and moistening the air there at the expense of the lower levels, which leads to convective invigoration (Dagan et al., 
2017; Chua and Ming, 2020), followed by convergence, air mass destabilization and added rainfall in an amplifying feedback loop  
Temperature (°C)Height (hPa)
0      10           20 0      10           20600      
700           
850
Box 8.1, Figure 2 | Schematic depiction of the atmospheric effects of light-absorbing aerosols on convection and cloud formation: (a) without

and (b) with the presence of absorbing aerosols in the planetary boundary layer.  The dashed and solid blue lines correspond to the vertical temperature 
profiles in the absence and presence of the absorbing aerosol layer, respectively, and the solid and dashed red lines denote the dry and moist adiabats, respectively. 
Absorbing aerosols result in an increasing temperature in the atmosphere but a reduced temperature at the surface. The reduced surface temperature and the 
increased temperature aloft led to a larger negative energy associated with convective inhibition (–) and a higher convection condensation level (CCL) under the 
polluted conditions. On the other hand, the absorbing aerosol layer induces a larger convective available potential energy (+) above CCL, facilitating more intensive 
vertical development of clouds, if lifting is sufficient to overcome the larger convective inhibition. Figure from Y . Wang et al. (2013).

1079
8.3 How Is the Water Cycle 
Changing and Why?
This section focuses on the evaluation and attribution of past and 
recent water cycle changes using observational datasets, theoretical 
understanding and model simulations. Paleoclimate records and 
historical observations provide evidence for past water cycle changes 
caused both by natural variability and human activities (Haug et al., 
2003; Buckley et al., 2010; Pederson et al., 2014). Key elements of 
the observed water cycle changes are assessed in this section, 
including flux and storage variations across the atmosphere, the 
continents and to a lesser extent the ocean and cryosphere, as well 
as related changes in large-scale atmospheric circulation and modes 
of variability. Particular emphasis is placed on assessing changes 
across regions and seasons (Box 8.2). Detailed regional assessments 
are presented in Chapters 10, 11, 12 and Atlas. Further information

concerning large-scale observed water cycle changes and their 
attribution is available in Sections 2.3.1.3 and 3.3.2. 
8.3.1 Observed Water Cycle Changes Based 
on Multiple Datasets
This section provides a process-based evaluation and a comprehensive 
assessment of observed water cycle changes by integrating multiple 
lines of evidence including paleoclimate data, historical datasets, 
theoretical understanding (Section 8.2) and model simulations.8.3.1.1 Global Water Cycle Intensity and P–E 
Over Land and Oceans
The human influence on the global water cycle is often summarized 
as an intensification (Huntington, 2006; DeAngelis et al., 2015; 
W. Zhang et al., 2019b) or an overall strengthening which has been 
observed since at least 1980 ( high confidence ) (see Chapter 2). There 
is, however, no unique definition of the global water cycle intensity 
(Trenberth, 2011; Ficklin et al., 2019; Sprenger et al., 2019). One

simple metric is the global and annual mean amount of precipitation. 
Although an increase in global precipitation is consistent with 
physical expectations (Section 8.2.1), it has not yet been detected and 
attributed to human activities given large observational uncertainties 
and low signal-to-noise ratio (Section  3.3.2.2). Other metrics are 
more suitable to detect and attribute changes in the global water 
cycle, including the likely  increase in global land precipitation since 
1950 (Section  2.3.1.4) which is likely  due to a  human influence 
(Section 3.3.2.3).
The flux of freshwater between the ocean and atmosphere is 
determined by the difference between precipitation and evaporation 
(P–E). Evaporation is measured in very few locations across the 
global ocean, so that directly assessing P–E over the ocean is very 
challenging and relies on indirect reanalysis estimates (Robertson 
et al., 2020). The AR5 presented robust evidence  of an amplified

oceanic pattern in P–E since the 1960s from both regional and 
global surface and subsurface salinity measurements and reanalyses.  
(Abbott and Cronin, 2021). In addition, delaying rain initiation until greater altitudes are reached transports more cloud water above 
the 0°C altitude and leads to additional release of latent heat of freezing and/or vapour deposition, which in combination with the 
added latent heat of condensation enhances the cloud updrafts (Fan et al., 2018). The stronger updrafts invigorate mixed-phase 
precipitation and the resultant hail and cloud electrification (Rosenfeld et al., 2008; Thornton et al., 2017). This includes the outer 
convective rainbands of tropical cyclones. There is medium confidence  that air pollution enhances flood hazard associated with the 
outer rain bands at the expense of the inner rain bands (Wang et al., 2014; C. Zhao et al., 2018; Souri et al., 2020).

The aerosol effect on invigoration and rainfall from deep convective clouds peaks at moderate levels (aerosol optical depth of 0.2 to 0.3), 
but reverses into suppression with more aerosols (H. Liu et al., 2019). More generally, the microphysical aerosol-related processes 
often compensate or buffer each other (Stevens and Feingold, 2009). For example, suppressed rain by slowing drop coalescence 
enhances mixed-phase precipitation. Therefore, despite the potentially large aerosol influence on the precipitation forming processes, 
the net outcome of aerosol microphysical effects on precipitation amount has generally low confidence , especially when evaluated 
with respect to the background of high natural variability in precipitation (Tao et al., 2012).
Ice nucleating particle (INP) initiate ice precipitation from persistent supercooled water clouds that have cloud droplets too small for

efficient warm rain, or expedite mixed-phase precipitation in short-lived supercooled rain clouds (Creamean et al., 2013). Most INPs 
are desert and soil dust particles, rather than air pollution aerosols (DeMott et al., 2010). Biogenic particles from terrestrial and marine 
origin are more rare, but important at temperatures above about –15°C (Murray et al., 2012; DeMott et al., 2016). Dust particles 
from long-range transport across the Pacific were found to enhance snow-forming processes over the Sierra Nevada in California 
(Creamean et al., 2013; Fan et al., 2014). The impact of INPs was demonstrated by glaciogenic cloud seeding experiments, which 
enhanced orographic supercooled clouds with medium confidence  of success (French et al., 2018; Rauber et al., 2019; Friedrich et al., 
2020). There are still major gaps in understanding the effects of INPs mainly on deep convective clouds (Kanji et al., 2017; Stanford 
et al., 2017; Korolev et al., 2020).

1080
Chapter 8  W ater Cycle Changes8This  pattern is consistent with our theoretical understanding of 
human-induced changes in the water cycle, leading to the conclusion 
that these changes are very likely  the result of anthropogenic forcings 
(Section 9.2.2.2). 
In contrast, AR5 did not provide a  conclusive assessment of 
observed changes in P–E over land. Continental P–E estimated 
from reanalyses and data-driven land surface models indicate that 
interannual variations are linked to ENSO (Robertson et al., 2014, 
2020). Increasing trends in P–E since 1979 based on land models are 
not statistically significant. Observations and models show evidence 
that P–E increases in the wet parts and decreases in the dry parts 
of tropical circulation systems, which shift in location seasonally 
and from year to year, with increases in seasonality since 1979 (see 
Box 8.2; Chou et al., 2013; Liu and Allan, 2013; Fu and Feng, 2014).

In summary, a low signal-to-noise ratio, observational uncertainties 
and current data assimilation techniques limit the assessment of 
recent global trends in P–E over both land and ocean. It is likely  
that the global land P–E variations observed since the late 1970s 
were dominated by internal variability, mostly linked to ENSO 
teleconnections ( medium confidence ). In contrast, the attribution of 
changes in sea surface salinity (Section 3.5.2.2) suggests that it is 
extremely likely  that human influence has contributed to the regional 
changes in P–E observed over the global ocean since the mid-
20th century.
8.3.1.2 Water Vapour and Its Transport
The AR5 presented evidence of increases in global near-surface and 
tropospheric specific humidity since the 1970s but with medium 
confidence  of a  slowing of near-surface moistening trends over 
land associated with reduced relative humidity since the late 1990s. 
According to AR5, radiosonde, Global Positioning System (GPS) and

satellite observations of tropospheric water vapour indicate very likely  
increases at near global scales since the 1970s occurring at a rate that 
is generally consistent with the Clausius–Clapeyron relation (about 
7%  °C–1 at low altitudes) and the observed atmospheric warming 
(Hartmann et al., 2013). 
Since AR5, it is very likely  that increases in global atmospheric water 
vapour were observed based on in situ, satellite and reanalysis data 
(with medium confidence  in the magnitude; Section 2.3.1.3). Satellite 
records show increases in upper tropospheric water vapour (constant 
relative humidity while temperatures have increased) since 1979 
(E.-S. Chung et al., 2014 ; Blunden and Arndt, 2020), to which human 
influence has likely  contributed (Section 3.3.2.2). Combined satellite 
and reanalysis estimates and CMIP6 atmosphere-only simulations 
(1988–2014) show global mean precipitable water vapour increases 
of 6.7 ±  0.3 %  °C–1, very close to the Clausius–Clapeyron rate

(Allan et al., 2020). Satellite-based products show increases close 
to the Clausius–Clapeyron rate over the ice-free oceans (about 7 to 
9 % °C–1; 1998 –2008), but reanalysis estimates outside this range 
(Schröder et al., 2019) are an expected consequence of their changing 
observing systems (Allan et al., 2014; Parracho et al., 2018). Increases 
in precipitable water vapour are found over the central and sub-
Arctic based on multiple reanalyses with some corroboration from sparse, in situ data (Vihma et al., 2016; Rinke et al., 2019; Nygård 
et al., 2020). 
Declining near-surface relative humidity over land areas (e.g.,  the 
USA, Mediterranean, South Asia, South America and southern Africa) 
is evident in surface observations (Willett et al., 2014, 2020; Dunn 
et al., 2017). This is consistent with a faster rate of warming over 
land than ocean (Sections 2.3.1.3 and 8.2.2.1; Byrne and O’Gorman, 
2018). CMIP5 simulations underestimate the observed decreases

in relative humidity over much of global land during 1979–2015 
(Douville and Plazzotta, 2017; Dunn et al., 2017)  even when observed 
SSTs are prescribed (–0.05 to –0.25% per decade compared with an 
observed rate of –0.4 to –0.8% per decade). It is not yet clear if this 
discrepancy is related to internal variability or can be explained by 
deficiencies in models (Vannière et al., 2019; Douville et al., 2020)  
or observations (Willett et al., 2014). Over the NH mid-latitude 
continents, there is  medium confidence  that human influence has 
contributed to a decrease in near-surface relative humidity in summer  
(Sections 2.3.1.3 and 3.3.2.3). 
Water vapour transport (or convergence) estimates from observations 
have substantial uncertainties even in regions of high quality 
radiosonde data. Consequently many studies use reanalyses for 
water transport estimates instead of instrumental observations. For 
example, increases in low-level (800 –1000 hPa) moisture convergence

into the tropical wet regime with a smaller outflow increase in the 
mid-troposphere (400 –800 hPa) with warming was detected in one 
reanalysis (ERA-Interim; Allan et al., 2014). Modelling evidence 
combined with statistical analysis demonstrate consistency between 
reanalysis moisture convergence and P–E over land (Robertson et al., 
2016). Advances in reanalysis representation of atmospheric moisture 
and winds in addition to new observational isotope analysis have 
improved the ability to identify the main sources of water vapour for 
key continental regions and quantify the relative contributions from 
moisture advection and recycling (Gimeno et al., 2012; van Der Ent 
et al., 2014; Joseph et al., 2016). 
Observed changes in moisture transport can also arise from changes 
in atmospheric circulation as well as thermodynamics.  For instance, 
moisture transport into the Arctic region estimated from reanalyses 
datasets is consistent with radiosonde data (Dufour et al., 2016) ,

with increases since 1979 linked to atmospheric circulation (Nygård 
et al., 2020). Moisture transport into the Eurasian Arctic was 
identified to increase by 2.6% per decade during 1948 –2008 based 
on a reanalysis estimate (X. Zhang et al., 2013). More intense moist 
intrusions associated with atmospheric rivers affecting the Arctic and 
Europe have been documented since 1979, but with a  substantial 
influence from decadal internal variability (Ummenhofer et al., 2017; 
Mattingly et al., 2018). A recent strengthening of tropical circulation 
and associated moisture convergence has been identified since 
around 2000 for the Amazon region (Arias et al., 2015; Barichivich 
et al., 2018; J.C. Espinoza et al., 2018; X.Y . Wang et al., 2018). This was 
also strenghtened by increased moisture transport from the North 
Atlantic, driving more abundant latent heat release (Segura et al., 
2020) and leading to an increased frequency of extreme floods in the

northern Amazon (Barichivich et al., 2018; Heerspink et al., 2020). 
Overall, increased moisture transport has been linked to increased

1081
Water Cycle Changes Chapter 88precipitation over wet tropical land areas (Gimeno et al., 2020) and to 
more extreme and persistent wet and dry weather events (Konapala 
et al., 2020) in many regions worldwide. 
In summary, there is high confidence  that human-caused global 
warming has led to an overall increase in water vapour and moisture 
transport throughout the troposphere, at least since the mid-1990s. 
In particular, there is high confidence  that moisture transport into the 
Arctic has increased but only medium confidence  in the attribution 
of such a trend to a human influence. There is medium confidence  
that human influence has contributed to a decrease in near-surface 
relative humidity over the Northern Hemisphere mid-latitude 
continents during summer  (see also Sections 2.3.1.3 and 3.3.2.3).
8.3.1.3 Precipitation Amount, Frequency and Intensity
This section assesses observed changes in precipitation at global

and regional scales. Note that changes in precipitation seasonality 
are assessed in Box  8.2 and that changes in regional monsoons 
are assessed in section  8.3.2.4 where observed changes in both 
circulation and rainfall are considered. Further assessment of regional 
changes in precipitation is presented in Chapters 10, 12 and Atlas, 
while extreme precipitation is presented in Chapter 11.
The AR5 concluded that it is likely  there has been an overall increase 
in annual mean precipitation amount over mid-latitude land areas in 
the NH, with low confidence  since 1901, but medium confidence  after 
1951. There is further evidence of a faster increase since the 1980s 
(medium confidence ) (Sections 2.3.1.3.4 and 3.3.2.2). Precipitation 
has increased from 1950 to 2018 over mid-high latitude Eurasia, 
most of North America, south-eastern South America, and north-
western Australia, while it has decreased over most of Africa, eastern

Australia, the Mediterranean region, the Middle East, and parts of 
East Asia, central South America, and the Pacific coasts of Canada, as 
simulated by the CMIP5 multi-ensemble mean (Dai, 2021). Since AR5, 
there have been updates of several precipitation datasets, including 
satellite estimates, reanalysis and merged products (Adler et al., 
2017; Roca, 2019). However, observational uncertainties remain 
an issue for assessing regional trends in seasonal or annual mean 
precipitation amount (Hegerl et al., 2015; Maidment et al., 2015; 
Sarojini et al., 2016; Beck et al., 2017) , as well as the convective and 
stratiform types of precipitation (e.g., Ye et al., 2017). Precipitation 
trends at regional scales are dominated by internal variability across 
much of the world (Knutson and Zeng, 2018). Regional changes in 
precipitation amounts can also be obscured by contrasting responses 
to GHG compared with aerosol forcings (Wu et al., 2013; Hegerl

et al., 2015; Xie et al., 2016; Zhao and Suzuki, 2019; Zhao et al., 
2020) and changes in precipitation intensity versus frequency (Shang 
et al., 2019). 
Global and regional changes in precipitation frequency and intensity 
have been observed over recent decades. An analysis of 1875 rain 
gauge records worldwide over the period 1961–2018 indicates that 
there has been a general increase in the probability of precipitation 
exceeding 50  mm  day–1, mostly due to an overall boost in rain 
intensity (Benestad et al., 2019). Such changes in precipitation 
intensity and frequency have not been formally attributed to human activities, but are consistent with the heating effect of increasing CO 2 
levels on the distribution of daily precipitation rates (Section 8.2.3.2) 
and with a  distinct overall intensification of heavy precipitation 
events found in both observations and CMIP5 models, though with 
an underestimated magnitude (Fischer and Knutti, 2014). Beyond

amplified precipitation extremes (Section  11.4.2), CMIP5 models 
also indicate that anthropogenic forcings have increased temporal 
variability of annual precipitation amount over land from 1950 to 
2005, which is most pronounced in annual mean daily precipitation 
intensity (Konapala et al., 2017). 
Anthropogenic aerosols can alter precipitation intensities 
both through radiative and microphysical effects (Box  8.1 and 
Section  8.5.1.1.2). Precipitation suppression through aerosol 
microphysical effects has been observed in shallow cloud regimes 
over South America and the south-eastern Atlantic, associated 
with local biomass burning (Andreae et al., 2004; Costantino and 
Bréon, 2010), and in industrial regions in Australia (Rosenfeld, 
2000; Hewson et al., 2013; Heinzeller et al., 2016). In contrast, 
precipitation intensification through aerosol microphysical effects 
in deep convective clouds is seen in many regions such as the

Amazon, southern USA, India, and Korea. This is associated with 
anthropogenic aerosols from cities (Hewson et al., 2013; Fan et al., 
2018; S.S. Lee et al., 2018; Sarangi et al., 2018) .
In the tropics, increases in precipitation amount are observed in 
convergence zones and decreases in the descending branches 
of the atmospheric circulation since 1979 (Chou et al., 2013; Liu 
and Allan, 2013; Gu et al., 2016; Polson et al., 2016; Polson and 
Hegerl, 2017), consistent with increased moisture transports with 
warming (Gimeno et al., 2020). Over tropical land areas, there is 
substantial variability in the ‘wet convergent regimes get wetter’ 
and ‘dry divergent regimes get drier’ pattern of trends observed 
since 1950 that are modulated by decadal changes in ENSO (Liu 
and Allan, 2013; Gu and Adler, 2018). CMIP6 models indicate an 
increased contrast between wet and dry regions in the tropics and 
subtropics (Figure 8.7; Schurer et al., 2020). This provides further

evidence that rainfall has increased in wet regimes, and slightly 
decreased in dry regimes over the period 1988 –2019 (Figure 3.14). 
This greater contrast is primarily attributable to greenhouse gas 
forcings, although the observed trends are statistically larger than 
the model responses (Section 3.3.2.3).
Over the African continent, there are distinct precipitation trends 
observed in multiple datasets since the 1980s (Figure 8.7; Maidment 
et al., 2015; P . Nguyen et al., 2018). Increases in intense convective 
storms affecting the Sahel have been attributed to increased 
land–ocean temperature gradients (Taylor et al., 2017), enhanced 
by intense heating of the Sahara (Dong and Sutton, 2015) rather 
than thermodynamics (Section  8.2.2). Changes in Sahel rainfall, 
with reduced precipitation amounts from the 1960s to the 1980s 
and a  subsequent recovery, are assessed in Sections  8.3.2.4.3 
and 10.4.2.1. In eastern Africa, decreasing precipitation amount

(−2 to −7 % per decade for 1983 –2010) was reported for the March 
to May ‘long rains’ season (Lyon and Dewitt, 2012; Viste et al., 2013; 
Liebmann et al., 2014; Maidment et al., 2015; Rowell et al., 2015) 
and evidence of a  recovery since, with internal variability playing

1082
Chapter 8  W ater Cycle Changes8a large role in these decadal changes (Wainwright et al., 2019). In 
contrast, the second ‘short rains’ season in eastern Africa (October to 
December) does not exhibit significant precipitation trends (Rowell 
et al., 2015).  Increases in annual southern African rainfall of 6 –7% 
per decade during 1983 –2010 are linked with the Pacific Decadal 
Oscillation (PDO; Maidment et al., 2015).
Section  8.3.1.6 assesses changes in precipitation over the 
Mediterranean region and its connection with drought and aridity.
Rainfall increases have been observed over northern Australia since 
the 1950s, with most of the increases occurring in the north-west 
(Dey et al., 2019a, b; Dai, 2021) and decreases observed in the north-
east (J. Li et al., 2012) since the 1970s. In contrast, there has been 
a  decline in rainfall over southern Australia related to changes in 
the intensification and position of the subtropical ridge (CSIRO and

BoM, 2015) and anthropogenic effects (Knutson and Zeng, 2018). 
The drying trend over south-west Australia is most pronounced 
during May to July, where rainfall has declined by 20% below the 
1900–1969 average since 1970 and by about 28% since 2000 (BoM 
and CSIRO, 2020). 
Over South America, there is observational and paleoclimate 
evidence of declining precipitation amount during the past 50 years 
over the Altiplano and central Chile, primarily explained by the PDO 
but with at least 25% of the decline attributed to anthropogenic 
influence (Morales et al., 2012; Neukom et al., 2015; Boisier et al., 
2016; Seager et al., 2019b; Garreaud et al., 2020). In contrast, 
a significant rainfall increase has been detected over the Peruvian–
Bolivian Altiplano (from observational data and satellite-based 
estimations) since the 1980s (Figure 8.7; Imfeld et al., 2020; Segura 
et al., 2020). Long-term (1902 –2005) precipitation data indicate

positive trends over south-eastern South America and negative 
trends over the southern Andes, with at least a partial contribution 
from anthropogenic forcing (Gonzalez et al., 2014; Vera and Díaz, 
2015; Díaz and Vera, 2017; Boisier et al., 2018; Knutson and Zeng, 
2018; see further assessment in Section 10.4.2.2 and Atlas.7.2.2). 
The Peruvian Amazon has exhibited significant rainfall decreases 
during the dry season since 1980 (Lavado et al., 2013; Ronchail 
et al., 2018). Increases in wet season rainfall in the northern and 
central Amazon since the 1980s and decreases during the dry 
season in the southern Amazon ( Barreiro et al., 2014; Gloor et al., 
2015; Martín-Gómez and Barreiro, 2016; J.C. Espinoza et al., 2018; 
X.Y . Wang et al., 2018; Haghtalab et al., 2020 ) are not explained 
by radiative forcing based on CMIP6 experiments (Figure 8.7) and 
trends are insignificant over longer periods since 1930 (Kumar

et al., 2013)  or more recently, since 1973 (Almeida et al., 2017). 
See Section 8.3.2.4.5 for monsoon-related changes. For the tropical 
Andes region, trends in annual precipitation show heterogenous 
patterns, ranging between –4% per decade and +4% per decade 
in the northern and southern tropical Andes for a 30-year period at 
the end of the 20th century, although increases during 1965 –1984 
and decreases since 1984 have been registered in Bolivia (Carmona 
and Poveda, 2014; Pabón-Caicedo et al., 2020). 
Over China, annual precipitation totals changed little from 1973 
to 2016, but precipitation intensity significantly increased at a rate of 0.12  mm  day–1 per decade, while the number of days with 
precipitation exceeding 0.1  mm  day–1 significantly decreased at 
a rate of 0.9 days per decade (Shang et al., 2019). There is consistency 
in trend estimates during 1998 –2015 over mainland China among 
satellite-based products and station data, which show increased

precipitation amounts in autumn and winter and decreases in summer 
(Chen and Gao, 2018), consistent with a decreased intensity of East 
Asian monsoon precipitation (Lin et al., 2014; Deng et al., 2018). 
Further assessment of precipitation changes over the South and 
South East Asian and the East Asian monsoon regions is presented in 
Section 8.3.2.4. An increasing trend in the frequency of heavy rainfall 
occurrences at the expense of low and moderate rainfall occurrences 
is found over central India (Krishnan et al., 2016; Roxy et al., 2017) 
and over eastern China with the latter due to increasing high aerosol 
levels ( Qian et al., 2009; J. Guo et al., 2017; Xu et al., 2017; Day et al., 
2018 ), consistent with the effects of absorbing aerosol on stability 
and convective inhibition (Box 8.1).
Observed precipitation records since the early 1900s show 
increases in precipitation totals over central and north-eastern

North America that are attributable to anthropogenic warming but 
larger in magnitude than found in CMIP5 simulations (Knutson and 
Zeng, 2018; Guo et al., 2019). Decreases in precipitation amount 
over the central and south-western USA and increases over the 
north-central USA during 1983 –2015 (Cui et al., 2017; P . Nguyen 
et al., 2018), are not clearly associated with forced responses in 
CMIP6 simulations (Figure  8.7; see also Section  10.4.2.3). Over 
Europe, precipitation trends since 1979 do not show coherence 
across datasets (Zolina et al., 2014; P . Nguyen et al., 2018). Longer 
records since 1910 show increases for much of Scandinavia, north-
western Russia, and parts of north-western Europe/United Kingdom 
and Iceland (Knutson and Zeng, 2018). Records since 1930 show 
increases of annual preciptation amount over western Russia (see 
also Atlas.8.2). Widespread increases in daily precipitation intensity 
appear clearly over regions with a  high density of rain gauges,

such as Europe and North America over the 1951 –2014 period 
(Alexander, 2016). Observations during 1966 –2016 over northern 
Eurasia show increases in the contribution of heavy convective 
showers to total precipitation by 1 –2% on average (with local 
trends of up to 5%) for all seasons except for winter (Chernokulsky 
et al., 2019). Increases in convective precipitation intensity have 
been identified, particularly on sub-daily time scales, using a range 
of modelling and observational data (Berg et al., 2013; Kanemaru 
et al., 2017; Pfahl et al., 2017).
Snowfall is an important component of precipitation in high-latitude 
and mountain watersheds. Reanalysis data indicate significant 
reductions in annual mean potential snowfall areas over NH land by 
0.52 million km2 per decade, with the largest decline over the Alps, 
with snow water equivalent reductions of about 20 mm per decade 
(Tamang et al., 2020). In the Tibetan Plateau, region-wide winter

snowfall has increased but summer snowfall has decreased during 
the 1960 –2014 period (Deng et al., 2017). State-of-the-art model 
simulations indicate reduced mean annual snowfall in the Arctic, 
despite the strong precipitation increase, mainly in summer and 
autum, when temperatures are close to the melting point (Bintanja 
and Andry, 2017).

1083
Water Cycle Changes Chapter 88Trend in annu al mean  Precipitation
1985– 2014
(e) Obs (G PCP)
(b) ALL
(c) GHG
(d) AER(f) ALL
(g) GHG
(h) AER1901– 1984
(a) Obs ( GPCC )
mm/day(decade)-1Non-significanttrendsSignificant trendsColor
Figure 8.7 | Linear trends in annual mean precipitation (mm day–1 per decade) for 1901–1984 (left) and 1985–2014 (right): (a, e) observational dataset, 
and the CMIP6 multi-model ensemble mean historical simulations driven by: (b, f) all radiative forcings; (c, g) GHG-only radiative forcings; (d, h) aerosol-
only radiative forcings experiment. Colour shades without grey cross correspond to the regions exceeding 10% significant level. Grey crosses correspond to the regions 
not reaching the 10% statistically significant level. Nine CMIP6-DAMIP models have been used having at least three members. The ensemble mean is weighted per each model

on the available and used members. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1084
Chapter 8  W ater Cycle Changes8In summary, regional changes in precipitation amounts can be 
obscured by the contrasting responses to GHG and aerosol forcings 
across much of the 20th century and can thus be dominated 
by internal variability at decadal to multi-decadal time scales 
(high confidence ). There is, however, a  detectable increase in 
northern high-latitude annual precipitation over land which has 
been primarily driven by human-induced global warming ( high 
confidence ) (Section  3.3.2). Human influence has strengthened 
the zonal mean precipitation contrast between the wet tropics 
and dry subtropics since the 1980s ( medium confidence ), although 
regional studies suggest a more complex precipitation response to 
evolving anthropogenic forcings. There is high confidence  that daily 
mean precipitation intensities have increased since the mid-20th 
century in a majority of land regions with available observations

and it  is likely  that such an increase is mainly due to GHG forcing 
(see Section 11.4). Section 8.3.2.4 assesses monsoon precipitation 
changes in detail.
8.3.1.4 Evapotranspiration 
The AR5 assessed that there was medium confidence  that pan 
evaporation declined in most regions over the last 50 years, yet 
medium confidence  that evapotranspiration increased from the early 
1980s to the late 1990s. Since AR5, these conflicting observations 
have been attributed to internal variability and by the fact that 
evapotranspiration is less sensitive to trends in wind speed and is partly 
controlled by vegetation greening (K.  Zhang et al., 2015; Y .  Zhang 
et al., 2016; Z.  Zeng et al., 2018b). Observation-based estimates 
show a robust positive trend in global terrestrial evapotranspiration 
between the early 1980s and the early 2010s (Miralles et al., 2014b; 
Z.  Zeng et al., 2014, 2018b; K.  Zhang et al., 2015; Y .  Zhang et al.,

2016). The rate of increase varies among datasets, with an ensemble 
mean terrestrial average rate of 7.6 ± 1.3 mm yr–1 per decade for 
1882–2011 (Z. Zeng et al., 2018a). In addition, a decreasing trend 
in pan evaporation plateaued or reversed after the mid-1990s 
(C.M. Stephens et al., 2018) has been reported as due to a shift from 
a dominant influence of wind speed to a dominant effect of water 
vapour pressure deficit, which has increased sharply since the 1990s 
(Yuan et al., 2019). The absence of a  trend in evapotranspiration 
in the decade following 1998 was shown to be at least partly an 
episodic phenomenon associated with ENSO variability (Miralles 
et al., 2014b; K. Zhang et al., 2015; Martens et al., 2018). Thus, there 
is medium confidence  that the apparent pause in the increase in 
global evapotranspiration from 1998 to 2008 is mostly due to internal 
variability. In contrast to AR5, there are now consistent trends in

pan evaporation and evapotranspiration at the global scale, given 
the recent increase in both variables since the mid-1990s ( medium 
confidence ). Given the growing number of quantitative studies, there 
is high confidence  that global terrestrial annual evapotranspiration 
has increased since the early 1980s.
Since AR5, the predominant contribution of transpiration to the 
observed trends in terrestrial evapotranspiration has been revisited 
and confirmed (Good et al., 2015; Wei et al., 2017). Using satellite 
and ecosystem models, Zhu et al. (2016) found a positive trend in 
leaf area index during 1982 –2009, indicating that greening could 
contribute to the observed positive trend of evapotranspiration, in line with similar studies that focused on the 1981–2012 (Y . Zhang 
et al., 2016) and 1982–2013 (K. Zhang et al., 2015) periods . Zeng 
et al. (2018) determined that the 8% global increase in satellite-
observed leaf area index between the 1980s and the 2010s may

explain an increase in evapotranspiration of 12.0 ±  2.4  mm yr–1 
(about 55 ±  25% of the total observed increase). Forzieri et al. 
(2020) estimated that the recent increase in leaf area index led to 
3.66 ± 0.45 W m–2 in latent heat flux (about 51 ± 6 mm yr–1) and 
that the sensitivity of energy fluxes to leaf area index increased by 
about 20% over the 1982–2016 period. Overall, there is medium 
confidence  that greening has contributed to the global increase in 
evapotranspiration since the 1980s.
Plant water use efficiency (WUE) is expected to rise with CO 2 levels 
(high confidence ) (Section 8.2.3.3 and Box 5.2), and  can in theory 
counteract rising evapotranspiration in a  warmer atmosphere 
(Section  8.2.3.3). However, observational studies suggest that this 
may not be the case in some ecosystems. For example, Frank et al. 
(2015) found that while the WUE increased in European forests across 
the 20th century, transpiration also increased due to more plant

growth, a  lengthened growing season, and increased evaporative 
demand. Likewise Guerrieri et al. (2019) observed that while WUE 
and photosynthesis increased in North American forests, stomatal 
conductance experienced only modest declines that were restricted 
to moisture-limited forests. Other studies further suggest that in 
many ecosystems increased WUE will not compensate for increased 
plant growth, amplifying declines in surface water availability 
(De Kauwe et al., 2013; Ukkola et al., 2016b; A. Singh et al., 2020) , 
while drought conditions can also offset the CO 2 fertilization effect 
and lead to a  decline in WUE (N.  Liu et al., 2020). There is low 
confidence regarding the impact of plant physiological effects on 
observed trends in evapotranspiration.
An increasing number of studies have identified signals of attribution 
in the recent observed trends in evapotranspiration. Douville 
et al. (2013) found that the post-1960 rise in evapotranspiration in

both the mid-latitudes and northern high latitudes was related to 
anthropogenic radiative forcing. An analysis of CMIP5 simulations 
suggests that anthropogenic forcing accounts for a  large fraction 
of the global mean evapotranspiration trend from 1982 to 2010 
(Dong and Dai, 2017) . Padrón et al. (2020) determined that 
increases in evapotranspiration were responsible for the majority 
of the anthropogenic pattern in dry-season water availability that 
dominates global trends since 1984. These findings are further 
supported by CMIP6 model results (Figure  8.8) that show that 
the recent summer increase in evapotranspiration in the northern 
mid- and high latitudes is due to GHG forcing and decreasing 
anthropogenic aerosol emissions over Europe. 
In summary, there is high confidence  that terrestrial evapotranspiration 
has increased since the 1980s. There is medium confidence  that 
this trend is driven by both increasing atmospheric water demand

and vegetation greening, and high confidence  that it can be partly 
attributed to anthropogenic forcing. There is low confidence  about 
the extent to which increases in plant water use efficiency have 
influenced observed changes in evapotranspiration.

1085
Water Cycle Changes Chapter 88Trend in annu al mean  Evapotr anspira tion 
(e) Obs (G LDAS) (a) LMIP
mm/day(decade)-1Non-significanttrendsSignificant trendsColor1985– 2014
(b) ALL
(c) GHG
(g) GHG
Figure 8.8 | Linear trends in annual mean evapotranspiration (mm day–1 per decade) for 1901–1984 (left) and 1985–2014 (right): (a, e) Land Model 
Intercomparison Project (LMIP) and observational dataset, and the CMIP6 multi-model ensemble mean historical simulations driven by (b, f) all radiative 
forcings, (c, g) GHG-only radiative forcings, (d, h) aerosol-only radiative forcings experiment.  Colour shade without grey cross correspond to the regions exceeding 
10% significant level. Grey crosses correspond to the regions not reaching the 10% statistically significant level. Nine CMIP6-DAMIP models have been used having at least

three members. The ensemble mean is weighted per each model on the available and used members. The Global Land Data Assimilation System (GLDAS) was not available over 
the early 20th century so was replaced by a multi-model off-line reconstruction, LMIP , which is consistent with GLDAS over the recent period but may be less reliable over the 
early 20th century given larger uncertainties in the atmospheric forcings. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1086
Chapter 8  W ater Cycle Changes88.3.1.5 Runoff, Streamflow and Flooding
The AR5 reported low confidence  in the assessment of trends in 
global river discharge during the 20th century. This is because many 
streamflow observations have been impacted by land use and dam 
construction, and the largest river basins worldwide differ in many 
characteristics, including geography and morphology. In regions 
with seasonal snow storage, AR5 WGII assessed that there is robust 
evidence  and high agreement  that warming has led to earlier spring 
discharge maxima and robust evidence  of earlier breakup of Arctic 
river ice, as well as indications that warming has led to increased 
winter flows and decreased summer flows where streamflows are 
lower and that the observed increases in extreme precipitation led 
to greater probability of flooding at regional scales with medium 
confidence . The SROCC found robust evidence  and high agreement

that discharge due to melting glaciers has already reached its 
maximum point and has begun declining with smaller glaciers, but 
only low confidence  that anthropogenic climate change has already 
affected the frequency and magnitude of floods at the global scale. 
Significant trends in streamflow and continental runoff were observed 
in 55 out of 200 large river basins during 1948 –2012, with an even 
distribution of increasing and decreasing trends (Section 2.3.1.3.6; 
Dai, 2016). A global detection and attribution study shows that the 
simulation of spatially heterogeneous historical trends in streamflow 
is consistent with observed trends only if anthropogenic forcings are 
considered (Gudmundsson et al., 2019). Section  3.3.2.4 assesses 
with medium confidence  that anthropogenic climate change has 
altered regional and local streamflows, although a significant trend 
has not been observed in the global average (Sections 2.3.1.3.6 and

3.3.2.3). Multiple human-induced and natural drivers have been 
shown to play an important but variable role in observed regional 
trends of streamflow for several different areas (Fenta et al., 2017; 
Ficklin et al., 2018; Glas et al., 2019; Vicente-Serrano et al., 2019) . 
For instance, decreasing runoff during the dry season has been 
observed over the Peruvian Amazon since the 1980s (Lavado et al., 
2013; Ronchail et al., 2018). Up to 30–50% of the recent multi-
decadal decline in streamflow across the Colorado River Basin can 
be attributed to anthropogenic warming and its impacts on snow and 
evapotranspiration (Woodhouse et al., 2016; McCabe et al., 2017; 
Udall and Overpeck, 2017; Xiao et al., 2018; Milly and Dunne, 2020). 
In the Upper Missouri River basin, Martin et al. (2020) found that 
warming temperatures have contributed to streamflow reductions 
since at least the late 20th century. Cold regions in the NH have

experienced an earlier occurrence of snowmelt floods, an overall 
increase in water availability and streamflow during winter, and 
a  decrease in water availability and streamflow during the warm 
season (Aygün et al., 2019). 
Some studies have suggested that dam construction and water 
withdrawals can be the dominant drivers in observed trends in 
streamflow amount (Wada et al., 2013). Regionally, land-use and 
land cover changes have been identified as important factors for 
streamflow (H.  Chen et al., 2020). The impact of surface dimming 
from aerosol emissions on evaporation was identified as a discernible 
influence in NH streamflows (Gedney et al., 2014). While changes 
in annual mean streamflow present a  complicated picture, recent studies of changes in the timing of streamflow in snow-influenced 
basins continue to support a  prominent influence from warming 
(Kang et al., 2016; Dudley et al., 2017; Kam et al., 2018). Global

land runoff variations correlate significantly with ENSO variability 
(Miralles et al., 2014b; Schubert et al., 2016).
Observed changes in flooding are assessed in Section  11.5.2 and 
are summarized as follows. For changes in the magnitude of peak 
flow, recent studies show strong spatial heterogeneity in the sign, 
size and significance of trends. For changes in timing of peak flows, 
recent studies further support observed changes in snowmelt-driven 
rivers. Observed changes in runoff and flood magnitude cannot be 
explained by precipitation changes alone given the possible season- 
and region-dependent decreases in antecedent soil moisture and 
snowmelt, which can partly offset the increase in precipitation 
intensity (Sharma et al., 2018), or the expected effect of urbanization 
and deforestation which can, on the contrary, amplify the runoff 
response (Chen et al., 2017; Abbott et al., 2019; Cavalcante et al., 
2019). Simulations of mean and extreme river flows are consistent

with the observations only when anthropogenic radiative forcing is 
considered (Gudmundsson et al., 2021). 
In summary, the assessment of observed trends in the magnitude 
of runoff, streamflow, and flooding remains challenging, due to the 
spatial heterogeneity of the signal and to multiple drivers. There is, 
however, high confidence  that the amount and seasonality of peak 
flows have changed in snowmelt-driven rivers due to warming. There 
is also high confidence  that land-use change, water management 
and water withdrawals have altered the amount, seasonality, 
and variability of river discharge, especially in small and human-
dominated catchments. 
8.3.1.6 Aridity and Drought
The AR5 reported low confidence  that changes in drought since the 
mid-20th century could be attributed to human influence, owing to 
observational uncertainties and difficulties in distinguishing decadal-
scale variability from long-term trends. Changes in soil moisture,

a metric of aridity, were not assessed thoroughly in AR5. Since AR5, 
new satellite products, land surface reanalyses, and land surface 
models have been used to document recent changes in soil moisture 
at the global scale. The science of detection and attribution has also 
progressed considerably (Trenberth et al., 2015; Easterling et al., 
2016; Stott et al., 2016). Attribution efforts have further benefited 
from the increased use of paleoclimate information, which provides 
an important constraint on natural variability that is insufficiently 
sampled by short observational record (Cook et al., 2018; Kageyama 
Several studies have identified a  persistent ‘fingerprint’ of 
anthropogenic forcing in global trends in aridity spanning the last 
120 years. Using a  combination of tree ring data, CMIP5 model 
simulations, and reanalysis products, Marvel et al. (2019) determined 
that the dominant trend in aridity since 1900, characterized by

drying in North and Central America and the Mediterranean, is 
detectable and attributable to external forcing from 1900 to 1949. 
This trend weakens from 1950 to 1975, possibly due to aerosol

1087
Water Cycle Changes Chapter 88forcing (Marvel et al., 2019), but then emerges again from 1981 to 
present, although it is not detectable in the GLEAM nor MERRA-2 
soil moisture reanalysis products. Likewise, Bonfils et al. (2020) 
investigated changes in precipitation, temperature and continental 
aridity in CMIP5 historical simulations and found that the dominant 
multivariate fingerprint, an amplification of wet–dry latitudinal 
patterns and progressive continental aridification, was associated 
with greenhouse gas emissions (Figure  8.9a ,d), and the second 
leading fingerprint was associated with anthropogenic aerosols 
(Figure 8.9e ,h). This study found that the anthropogenic greenhouse 
gas signal is statistically detectable in reanalyses over the 1950–2014 
period (signal-to-noise ratio above 1.96). Gu et al. (2019) found that 
a global trend in declining soil moisture is detectable in the GLDAS-2 
reanalysis product and is attributable to greenhouse gas forcing.

Padrón et al. (2020) reconstructed the global patterns of dry season 
water availability from 1902–2014, and found it extremely likely  
(99% range) that trends in the last three decades of the analysis 
period could be attributed to anthropogenic forcing, mainly due to 
increases in evapotranspiration. It is very likely  (>90% range) that 
anthropogenic forcing has affected global patterns of soil moisture 
over the 20th century.
On a regional scale, the robustness of trend attribution for drought 
and aridity varies widely. Key trends and their attributions are 
summarized here, while a complete regional assessment of observed 
trends in drought and aridity is in Chapter 11 (Sections 11.6.2, 12.3.2 
and 12.4).
Several studies have analyzed CMIP5 and land surface models and 
detected a significant summer drying trend in the NH across the late 
20th century that is attributable to anthropogenic forcings (Mueller 
and Zhang, 2016; Douville and Plazzotta, 2017). This trend is mainly

driven by dryland areas such as the western USA and west-central 
Asia, where both reanalysis products and satellite data confirm 
there has been a persistent decline in soil moisture since 1990 (Y . Liu 
et al., 2019a). In the western USA, snow deficits have very likely  
contributed to recent drying (Mote et al., 2018). Spring snow water 
equivalent across the Sierra Nevada Mountains reached a  record 
low in 2015 (Margulis et al., 2016; Mote et al., 2016), possibly the 
lowest of the last five hundred years (Belmecheri et al., 2016). Over 
the longer California drought (2011–2015) anthropogenic warming 
alone reduced snowpack levels in the Sierras by 25% (Berg and Hall, 
2017). The north-western USA also experienced snow drought in 
2015, despite near-normal levels of total cold season precipitation 
(Mote et al., 2016; Marlier et al., 2017). There is high confidence  that 
anthropogenic warming contributed to these recent snow droughts

(Belmecheri et al., 2016; Mote et al., 2016). 
In the western USA, anthropogenic warming is amplifying drought 
and aridity by increasing evaporative demand and water loss to the 
atmosphere (Weiss et al., 2009; Overpeck, 2013; Cook et al., 2014; 
Griffin and Anchukaitis, 2014; Williams et al., 2020). For the California 
drought between 2012 –2014, Griffin and Anchukaitis (2014) 
used paleoclimate reconstructions to determine that while rainfall 
deficits were not unprecedented, record-high temperatures drove an 
exceptional decline in soil moisture relative to the last millennium. 
Williams et al. (2015) concluded that anthropogenic warming accounted for 8–27% of these soil moisture deficits. Robeson (2015) 
estimated that the California drought was a 1-in-10,000 year event. 
Tree ring reconstructions indicate that prolonged megadroughts 
have occurred in the western USA throughout the last 1200 years 
(Cook et al., 2004, 2010; B.I. Cook et al., 2015 ), forced by internal

variability (Coats et al., 2016; Cook et al., 2016b). However, Williams 
et al. (2020) determined that 2000–2018 drought across the south-
western USA was the second driest 19-year period since 800 CE, and 
attributed nearly half the magnitude of this event to anthropogenic 
forcing (see also Section  10.4.2.3). Evidence for human signals in 
drought can also be found in western North American streamflow 
records, as noted above in Section 8.3.1.5. There is high confidence  
that anthropogenic forcing has contributed to recent droughts and 
drying trends in western North America.
Large areas of east-central Asia experienced drying in the early 2000s 
as a result of warmer temperatures, lower humidity, and declining 
soil moisture (Wei and Wang, 2013; Z. Li et al., 2017; Hessl et al., 
2018). Paleoclimate data from the Mongolian plateau suggest that 
this recent central Asian drought exceeds the 900-year return interval,

but is not unprecedented in the last 2060 years (Hessl et al., 2018). 
There is low confidence  due to limited evidence that recent droughts 
in central Asia can be attributed to anthropogenic forcing.
The Mediterranean region has experienced notable changes 
in drought and aridity. A number of studies have identified a decline 
in precipitation since 1960 and attributed this to anthropogenic 
forcing (Hoerling et al., 2012; Gudmundsson and Seneviratne, 2016; 
Knutson and Zeng, 2018; Seager et al., 2019b). Kelley et al. (2015) 
showed that climate change caused a  three-fold increase in the 
likelihood of the 2007–2010 meteorological drought in the eastern 
Mediterranean. However, historical trends in precipitation across 
the Mediterranean are spatially variable and contain substantial 
decadal variability, such that an anthropogenic influence may not 
be detectable in all areas (Zittis, 2018; Vicente-Serrano et al., 2021).

Records of soil moisture provide a  clearer signal, indicating that 
higher temperatures and increased atmospheric demand have played 
a strong role in driving Mediterranean aridity (Vicente-Serrano et al., 
2014). Hydrological modeling suggests that the recent decline in 
soil moisture in the Mediterranean is unprecedented in the last 
250 years (Hanel et al., 2018). Paleoclimate evidence extends this 
view, additionally indicating that dryness in the Mediterranean is 
approaching an extreme condition compared to the last millennium 
(Markonis et al., 2018) and that the 15-year drought in the Levant 
(1998–2012) has an 89% likelihood of being the driest of the last 
900 years (Cook et al., 2016a). Marvel et al. (2019) found that the 
Mediterranean region contributes strongly to the anthropogenic 
warming component of the global trend in aridity. There is high 
confidence  that anthropogenic forcings are causing increased aridity 
and drought severity in the Mediterranean region.

Both central and north-eastern Africa have experienced a  decline 
in rainfall since about 1980 ( high confidence ) (Lyon and Dewitt, 
2012; Lyon, 2014; Hua et al., 2016; Nicholson, 2017). In Central 
Africa, the decline has been attributed to atmospheric responses to 
Indo-Pacific sea surface temperature variability (Hua et al., 2018). 
In north-eastern Africa, droughts have become longer and more

1088
Chapter 8  W ater Cycle Changes8(a)Temperature /f_ingerprint of greenhouse gases
 (e)Temperature /f_ingerprint of aerosols
(b)Precipitation /f_ingerprint of greenhouse gases
mm/day(f)Precipitation /f_ingerprint of aerosols
mm/day
(c)Aridity /f_ingerprint of greenhouse gases
CMI(g)Aridity /f_ingerprint of aerosols
Time series of greenhouse gas /f_ingerprint
CMIP5 signal (unitless)
Reanalyses (unitless)
CO/two.numerator  Emissions (GtC per year)
Year0246810GtC per year(d) Time series of aerosols /f_ingerprint
SOX Emissions (MtS per year)
Year0
20
40
60
80MtS per year(h)                0.2             0.4             0.6             0.8               1 0
-0.15 -0.09 -0.03 0.03 0.09 0.15
-0.02 -0.012 -0.004 0.004 0.012 0.02
1890 1920 1950 1980 2010 1860 1860 1890 1920 1950 1980 2010-0.15 -0.09 -0.03 0.03 0.09 0.15-0.2 -0.12 -0.04 0.04 0.12 0.2
CMI

Figure  8.9 |  Spatial expressions (a–c, e–g) of the leading multivariate fingerprints of temperature (°C), precipitation (mm day–1), and aridity (CMI;  
the Climate Moisture Index) in CMIP5 historical simulations and the corresponding temporal evolution in both CMIP5 and reanalysis products (d, h).
The first leading fingerprint is associated with greenhouse gas forcing (a–d) and the second leading fingerprint is associated with aerosol forcing (e–h). CMI is a dimensionless 
aridity indicator that combines precipitation and atmospheric evaporative demand. Figure after Bonfils et al. (2020). Further details on data sources and processing are available 

1089
Water Cycle Changes Chapter 88intense in recent decades, continuing across rainy seasons (Hoell 
et al., 2017b; Nicholson, 2017), and this trend appears to be unusual 
in the context of the last 1500 years (Tierney et al., 2015). Knutson 
and Zeng (2018) attribute decreased annual precipitation over the 
Sudan to anthropogenic forcing, but other studies argue that the 
recent trend cannot yet be distinguished from natural variability, at 
least over parts of this region (Hoell et al., 2017b; Philip et al., 2018). 
There remains low confidence  due to limited evidence  that drying 
the north-eastern Africa is attributable to human influence. In the 
Western Cape region of South Africa, human influence increased 
the likelihood of the severe 2015–2017 drought by a  factor of 
3–6, depending on the analysis (Otto et al., 2018; Pascale et al., 
2020). Anthropogenic forcing also contributed to the 2018 drought, 
mainly by increasing evapotranspiration (Nangombe et al., 2020).

While some analysis of instrumental precipitation data in this region 
detect a slight long-term drying trend consistent with the simulated 
anthropogenic response (Seager et al., 2019b), there is strong 
multi-decadal variability in the data (Wolski et al., 2021). However, 
a  study of streamflow in southern Africa detected a  significant 
decline (Gudmundsson et al., 2019; see also Section 10.6.2). There 
is medium confidence in the long-term drying trend in this region 
and its attribution to anthropogenic forcing, and medium confidence  
that anthropogenic warming has contributed to recent severe 
drought events.
Several subtropical, semi-arid regions in the Southern Hemisphere 
have experienced long-term drying trends in the late 20th century. 
South-western South America (central Chile) experienced a  multi-
decadal decline in precipitation and streamflow culminating in 
a  post-2010 megadrought that has been partly attributed to

anthropogenic GHG emissions and ozone depletion (Boisier et al., 
2016, 2018; Saurral et al., 2017; Knutson and Zeng, 2018; Seager 
et al., 2019b; Garreaud et al., 2020). There is medium confidence  that 
drying in central Chile can be attributed to human influence. The tree-
ring paleoclimate record demonstrates that the mid-century increase 
in exteme drought events in southern South America is unusual in 
the context of the last 600 years, suggesting an emerging influence 
of anthropogenic forcing (Morales et al., 2020). 
There has been a 20% decrease in winter (May to July) rainfall in 
south-western Australia since 1970, with the decline increasing to 
around 28% since 2000 (Delworth and Zeng, 2014; BoM and CSIRO, 
2020). There has also been a  significant increase in the average 
intensity of seasonal droughts in the region since 1911in response 
to both lower precipitation and increased atmospheric evaporative 
demand (Gallant et al., 2013). Several studies attribute the

precipitation declines in south-western Australia to anthropogenic 
changes in GHG and ozone (Delworth and Zeng, 2014; Knutson 
and Zeng, 2018; Seager et al., 2019b). There is high confidence  that 
the observed drying in south-western Australia can be attributed to 
anthropogenic forcing. 
In south-eastern Australia, the average length of droughts have 
increased significantly, lasting between 10 and 69% longer than 
droughts during the first half of the 20th century (Gallant et al., 
2013). Paleoclimate reconstructions indicate a  97.1% probability 
that the decadal rainfall anomaly recorded during the 1997–2009 Millennium drought in south-eastern Australia was the worst 
experienced since 1783 (Gergis et al., 2012), and that the spatial 
extent and duration of cool season (April to September) rainfall 
anomalies were either very much below average or unprecedented 
over at least the last 400 years (Freund et al., 2017). Other

paleoclimate studies suggest that the Millennium drought in 
eastern Australia was not unusual in the context of natural 
variability reconstructed over the past millennium (Palmer et al., 
2015; Cook et al., 2016c; Kiem et al., 2020). While there is currently 
low confidence  that recent droughts in eastern Australia can be 
clearly attributed to human influence (Cai et al., 2014; Delworth 
and Zeng, 2014; Rauniyar and Power, 2020), there is emerging 
evidence that declines in April to October rainfall in south-eastern 
Australia since the 1990s would not have been as large without the 
influence of increasing levels of atmospheric GHGs (Rauniyar and 
Power, 2020). 
In summary, it is very likely  that anthropogenic factors have 
influenced global trends in aridity, mainly through competing 
changes in evapotranspiration and/or atmospheric evaporative 
demand due to anthropogenic emissions of GHG and aerosols. There

is high confidence  that the frequency and the severity of droughts has 
increased over the last decades in the Mediterranean, western North 
America, and south-western Australia and that this can be attributed 
to anthropogenic warming. There is medium confidence  that recent 
drying and severe droughts in southern Africa and south-western 
South America can be attributed to human influence. In some regions 
of western North America and the Mediterranean, paleoclimate 
evidence suggests that recent warming has resulted in droughts that 
are of similar or greater intensity than those reconstructed over the 
last millennium ( medium confidence ).
8.3.1.7 Freshwater Reservoirs
8.3.1.7.1 Glaciers
The AR5 and SROCC found, with very high confidence,  a  general 
decline in glaciers due to climate change in recent decades. There 
is very high confidence that during the decade 2010–2019 glaciers 
lost more mass than in any other decade since the beginning of the

observational record (Sections 2.3.2.3 and 9.5.1). Human influence 
is very likely  the main driver of the global, near-universal retreat of 
glaciers since the 1990s (Section 3.4.3.1). In Table 9.5, the contribution 
of glaciers to sea level rise for different periods is presented; in 
1971 –2018 glacier mass loss contributed 20.9 [10.0  to 31.7] mm 
or 22.2% of the sea level rise during that period. The highest mass 
loss rates are observed in the southern Andes, New Zealand, Alaska, 
Central Europe and Iceland while the largest mass loss are observed in 
Alaska, the periphery of Greenland and Arctic Canada (Section 9.5.1 
and Figure 9.20). Predominantly , runoff from small glaciers such as in 
Canada has decreased because of glacier mass loss, while runoff from 
larger glaciers such as in Alaska has typically increased (Bolch et al., 
2010; Thomson et al., 2011; Tennant et al., 2012; WGMS, 2017; Huss 
and Hock, 2018). Asia contains the largest concentration of glaciers

outside the polar regions where the total glacier mass change is 
–16.3 ± 3.5 Gt yr–1 over 2000 –2016 with considerable intra-regional 
variability (Brun et al., 2017). Mass losses of glaciers in Asia between

1090
Chapter 8  W ater Cycle Changes82000 and 2018 are –19.0 ± 2.5 Gt yr–1 (Shean et al., 2020). The most 
negative changes were found in Nyainqentanglha with −4.0 ± 1.5 
Gt yr–1, while glaciers in Kunlun, northern Tibetan Plateau, slightly 
gained mass at 1.4 ± 0.8 Gt yr–1.
There is some evidence that an increase of precipitation over high 
mountains can offset glacier ablation (melt; Farinotti et al., 2020). 
However, this process has only been described from the Karakoram 
region in the north-western Himalaya, where it is thought to be 
partly responsible to the advances of glacier changes in the last two 
decades, referred to as the ‘Karakoram Anomaly’ (Farinotti et al., 
2020). In the Himalaya, Maurer et al. (2019) observed faster ice 
loss during 2000–2016 (7.5 ± 2.3 Gt yr–1) compared to 1975–2000 
(–3.9  ±  2.2 Gt yr–1). In the Southern Hemisphere, the rate of 
glacier mass lost in South America is estimated at 19.4 ± 0.6 Gt

yr–1 based on surface elevation changes over 2000 –2011, which 
include the North and South Patagonian Icefields of South America 
(Braun et al., 2019), and at −22.9 ± 5.9 Gt yr–1 over 2000 –2018 
(Dussaillant et al., 2019).
In summary, human-induced global warming has been the primary 
driver of a  global glacier recession since the early 20th century 
(high confidence ). Most glaciers have lost mass more rapidly since 
the 1960s and in an unprecedented way over the last decade, 
thereby contributing to increased glacier runoff, especially from 
larger glaciers until a maximum is reached, which tends to occur 
later in basins with larger glaciers and higher ice-cover fractions 
8.3.1.7.2 Seasonal snow cover
The AR5 assessed that Northern Hemisphere (NH) snow cover 
extent (SCE) has decreased since the late 1960s, especially in 
spring ( very high confidence ). This is confirmed by recent studies 
(Section 2.3.2.2; Kunkel et al., 2016). AR6 assesses that NH spring

snow cover has been decreasing since 1978 ( very high confidence ) 
and  that this trend extends back to 1950 ( high confidence ) 
(Section  9.5.3). Human-caused global warming is the dominant 
driver of this observed decline (Section 3.4.2; Estilow et al., 2015). 
Model simulations suggest that surface temperature responses at 
hemispheric/regional scales explain between 40% and 85% of the 
SCE trend variability (Mudryk et al., 2017). A decreasing trend in 
snowfall has also been detected in the NH (Figure 8.1; Rupp et al., 
2013). Snowfall as a  proportion of precipitation has decreased 
significantly in recent years (Berghuijs et al., 2014). However, a late-
20th-century increase in snowfall in West Antarctica observed in ice 
cores has been linked to a  combination of factors including the 
anthropogenically forced deepening of the Amundsen Sea Low 
(Thomas et al., 2015, 2017).
Observations show a rapid recent decrease of spring SCE in NH,

mostly in Eurasia and North America, closely linked to temperature 
change, for example, March to April SCE is decreasing at 3.4% ± 1.1 
% per decade (1979–2005; Brown and Robinson, 2011; Hernández-
Henríquez et al., 2015). An overall increasing annual trend of the 
NH SCE since the late 1980s has been observed, in contrast to 
decreasing trends over 1960s to 1980s that are dominated by the autumn and winter seasons (Barry and Gan, 2020). Such recent 
positive trends in snow cover extent are however at odds with 
other surface and satellite datasets and with the negative trends 
simulated by most CMIP5 and CMIP6 models (Mudryk et al., 2017, 
2020). Hernández-Henríquez et al. (2015) also detected positive 
trends in October  to November SCE in in the NOAA SCE Climate 
Data Record (NOAA-CDR), which are not replicated in other datasets 
(Section 9.5.3). Wu et al. (2018) found slower snowmelt rates over 
the NH in 1980–2017, with higher ablation rates in locations with

deep snow water equivalent (SWE), but due to the reduction of 
SWE in deep snowpacks, moderate/high ablation rates showed 
decreasing trends. Santolaria-Otín and Zolina (2020) reported 
weak but significant decline in SCE in autumn over northern 
Eurasia and North America during 1979 –2005, and similarly for 
spring, except for northern Siberia which showed higher spring 
SCE.  Kapnick and Hall (2012) detected significant loss of spring 
mountain snowpack in western USA in 1950 –2008. For Canada, 
extensive decreasing snow depths, SCE and duration were detected 
since mid-1970s, especially in western Canada during winter and 
spring (DeBeer et al., 2016). Berghuijs et al. (2014) show that across 
the continental USA, catchments with more snowfall than rainfall 
generally have higher mean streamflow, which will probably 
decrease with smaller fractions of precipitation falling as snow 
because of climate warming.
In summary, a  decline in the spring NH snow cover extent, snow

depth and duration has been observed since the late 1960s and has 
been attributed to human influence ( high confidence ). Depending 
on the region and season, there is low-to-medium confidence  in the 
main drivers of snow cover changes, although various regions exhibit 
a  shortening of the snow cover season which is consistent with 
global warming. A more detailed assessment of observed changes in 
seasonal snow cover is provided in Section 9.5.3.
8.3.1.7.3 Wetlands and lakes
Wetlands and lakes affect the climate through their impact on carbon 
and methane budgets (Section 5.2.2; e.g., Saunois et al., 2016; Zhan 
et al., 2019) and on surface heat fluxes, with coupled weather and 
climate effects (e.g., Zhan et al., 2019). Although these features are 
also affected by human activities and by climate change, AR5 did not 
specifically report on wetlands and lakes.
Inventories of surface water bodies are not systematically produced

at national or regional levels. However, assessments are undertaken 
at the global scale (Ramsar Convention on Wetlands, 2018). Merging 
observations from multiple satellite sensors makes it possible to 
detect surface water even under vegetation and clouds over about 
25 years, but with low spatial resolution (Prigent et al., 2016). 
Most recent multi-satellite products from visible, infrared, and 
microwave measurements, estimate a surface water area of about 
12 to 14 million km2 (including permanent and transitory surfaces, 
e.g., Aires et al., 2018; Davidson et al., 2018), which is much higher 
than those provided by optical imagery (about 3  million km2). 
Inventories show a strong decrease in natural surface water of about 
0.8% yr–1 in total from 1970 to the present (Ramsar Convention 
on Wetlands, 2018) but the sites are not evenly distributed. Multi-

1091
Water Cycle Changes Chapter 88satellite estimates show a  strong interannual variability in surface 
water extent over the period 1992 –2015 with no clear long-term 
trend (Prigent et al., 2020). 
Human-made water bodies represent approximately 10% of the 
total continental water surfaces (Figure 8.1; Ramsar Convention on 
Wetlands, 2018) and consist mainly of reservoirs and rice paddies. 
High resolution optical imagery over the period 1984 –2015 
(Donchyts et al., 2016; Pekel et al., 2016) shows a net increase of 
about 0.1 million km2 in artifical water surfaces, mainly due to the 
construction of reservoirs. Surfaces of rice paddies are also increasing, 
especially in South East Asia (Davidson et al., 2018). 
In summary, there is high confidence  that the extent of human-made 
surface water has increased over the 20th and early 21st centuries. 
In contrast, due to low agreement  in the observational records at the

global scale, there is only low confidence  in the observed decline of 
the natural surface water extent in recent years (see also SRCCL).
8.3.1.7.4 Groundwater
As the world’s most widespread store of freshwater (R.G.  Taylor 
et al., 2013a), groundwater is estimated to supply between a quarter 
and a third of the world’s annual freshwater withdrawals to meet 
agricultural, industrial and domestic demands (Döll et al., 2012; Wada 
et al., 2014; Hanasaki et al., 2018).
Attribution of changes in groundwater storage, observed locally 
through piezometry (Figure  8.10; R.G.  Taylor et al., 2013a) or 
estimated from GRACE satellite measurements (Rodell et al., 2018) at 
regional scales (>100,000 km2), is often complicated by non-climate 
influences that include land-use change (Favreau et al., 2009) and 
human withdrawals (Bierkens and Wada, 2019). 
Following a  global review of groundwater and climate change 
(R.G. Taylor et al., 2013a) and AR5 WGII, evidence of an association

between heavy or extreme precipitation and groundwater recharge 
has continued to grow, especially in tropical (Asoka et al., 2018; 
Cuthbert et al., 2019a; Kotchoni et al., 2019) and subtropical regions 
(Meixner et al., 2016). Stable-isotope ratios of oxygen and hydrogen 
at 14 of 15 sites across the tropics trace groundwater recharge to 
intensive monthly rainfall, commonly exceeding the 70th intensity 
percentile, approximately (Jasechko and Taylor, 2015). Further, heavy 
rainfall recharging groundwater resources is often influenced by 
climate variability such as ENSO and PDO (R.G. Taylor et al., 2013b; 
Kuss and Gurdak, 2014; Asoka et al., 2017; Cuthbert et al., 2019b; 
Kolusu et al., 2019; Shamsudduha and Taylor, 2020). Additionally, 
increases in groundwater storage estimated from GRACE for 37 
of the world’s large-scale aquifer systems from 2002 to 2016 are 
generally found to result from episodic recharge associated with 
extreme (>90th percentile) annual precipitation.

The overall underestimation of precipitation intensities in global 
climate models (Wehner et al., 2010, 2020; Goswami and Goswami, 
2017) and of their sensitivity to warming temperatures (Borodina 
et al., 2017) may lead to underestimates of their recharging effect 
on groundwater (Mileham et al., 2009; Cuthbert et al., 2019b). The limited ability of global climate models to represent key controls 
on regional rainfall variability like ENSO (Technical Annex VI and 
Section 3.7.3; R. Chen et al., 2020) may also underestimate observed 
recharge from such events that are of particular importance in 
drylands (R.G. Taylor et al., 2013b; Cuthbert et al., 2019b) . Numerical 
representations of the impact of precipitation intensification on 
groundwater recharge in large-scale models remain constrained by 
the challenges of including key recharge pathways that consider 
preferential flowpaths in soils (Beven, 2018) and focused recharge

through leakage from surface waters (Döll et al., 2014).
Increasing global freshwater withdrawals, primarily associated 
with the expansion of irrigated agriculture in drylands, have led 
to global groundwater depletion that has an estimated range of 
about 100 and about 300 km3 yr–1 from hydrological models and 
volumetric-based calculations (Bierkens and Wada, 2019). The 
magnitude of this change is such that its estimated contribution 
to global sea level rise is in the order of 0.3 to 0.9 mm yr−1 (Wada 
et al., 2010; Konikow, 2011; Döll et al., 2014; Pokhrel et al., 2015; 
de Graaf et al., 2017; Hanasaki et al., 2018) . Groundwater depletion 
has been observed regionally in The USA High Plains, California’s 
Central Valley (Scanlon et al., 2012), north-west India (Rodell et al., 
2009; Asoka et al., 2017), Upper Ganges in India (MacDonald 
et al., 2016), North China Plain (Feng et al., 2013), north-central 
Middle East region of Tigris–Euphrates–Western Iran (Voss et al.,

2013), Central Asia (Hu et al., 2019), and North Africa (Bouchaou 
et al., 2013). The regional contribution of agricultural irrigation to 
groundwater depletion was previously highlighted by SRCCL but 
no formal assessment of observed changes in global or regional 
groundwater featured in AR5. 
Quantification of changes in groundwater storage from GRACE is 
currently constrained by uncertainty in the estimation of changes 
in other terrestrial water stores using uncalibrated, global-scale 
Land Surface Models (Döll et al., 2014; Scanlon et al., 2018) and 
the limited duration of the period of GRACE observations (2002 to 
2016). Centennial-scale piezometry in north-west India reveals that 
recent groundwater depletion traced by GRACE (Rodell et al., 2009; 
Chen et al., 2014), follows more than a  century of groundwater 
accumulation through canal leakage (MacDonald et al., 2016). 
Further, groundwater depletion is often localized occurring below the

footprint (200,000 km2) of GRACE, as has been well demonstrated by 
detailed modelling studies in the California Central Valley (Scanlon 
et al., 2012) and North China Plain (Cao et al., 2016).
Climate variability and drought affect groundwater depletion 
mainly due to amplified groundwater withdrawals. For instance, the 
depletion rate in Central Valley aquifer in the USA from 2006 to 2010 
is estimated to range from 6 to 8 km3 yr–1 using GRACE data (Scanlon 
et al., 2012). In India, Asoka et al. (2017) show contrasting trends in 
groundwater storage in the north (declining at 2 cm yr–1) and south 
(increasing at 1–2 cm yr–1) that is explained by variations in human 
withdrawals and precipitation linked to Indian Ocean sea surface 
temperature variability.
Changes in meltwater regimes from glaciers and seasonal snow 
packs tend to reduce the seasonal duration and magnitude of

1092
recharge (Tague and Grant, 2009). Aquifers in mountain valleys 
show shifts in the timing and magnitude of: (i) peak groundwater 
levels due to an earlier spring melt; and (ii) low groundwater levels 
associated with lower baseflow periods (Allen et al., 2010; Dierauer 
et al., 2018; Hayashi, 2020). The effects of receding alpine glaciers on 
groundwater systems are not well understood but long-term loss of 
glacier storage is estimated to reduce summer baseflow (Gremaud 
et al., 2009). In permafrost regions, coupling between surface water 
and groundwater systems may be particularly enhanced by warming 
(Lamontagne-Hallé et al., 2018; Lemieux et al., 2020). In areas of 
seasonal or perennial ground frost, increased recharge is expected 
despite a  decrease in absolute snow volume (Okkonen and Kløve, 
2011; Walvoord and Kurylyk, 2016) .
Coastal aquifers are the interface between the oceanic and terrestrial

hydrological systems. Global sea level rise (SLR) causes interfaces 
between freshwater and saline-water to move inland. The extent 
of seawater intrusion into coastal aquifers depends on a variety of 
factors including coastal topography, recharge, and groundwater 
abstraction from coastal aquifers (Comte et al., 2016). Modelling 
results suggest that the impact of SLR on seawater intrusion is 
negligible compared to that of groundwater abstraction (Ferguson 
and Gleeson, 2012; Yu and Michael, 2019) . Coastal aquifers under 
very low hydraulic gradients, such as the Asian mega-deltas, are 
theoretically sensitive to SLR but, according to evidence from Akter et al. (2019) in the Ganges-Brahmaputra-Megna basin, may be more 
severely and widely affected by changes in upstream river discharge. 
They argue further that saltwater inundation from storm surges will 
have the greatest localized effects.
In summary, there is medium confidence  that increased precipitation

intensities, partly due to human influence, have enhanced groundwater 
recharge, most notably in the tropics. There is high confidence  that 
groundwater depletion has occurred since at least the start of the 
21st century as a  consequence of groundwater withdrawals for 
irrigation in some of the world’s most productive agricultural areas 
in drylands (e.g., southern High Plains and California Central Valley in 
the USA, the North China Plain, north-west India).
8.3.2 Observed Variations in Large-scale Phenomena 
and Regional Variability
Observed changes in large-scale circulation indicators (Cross-Chapter 
Box 2.2) are assessed in Chapters 2 and 3 (Sections 2.3.1.4 and 3.3.3). 
In this chaper we focus on the influence of regional scale teleconnection 
variabililty on the water cycle and the attribution of these circulation 
changes. While observed changes in modes of variability are assessed 
in Chapters 2 and 4 (Sections 2.4 and 4.3.3), here focus on hydrological

teleconnections of relevance to the water cycle.
Figure 8.10 | Trends in Terrestrial Water Storage (TWS; in centimetres per year, cm yr–1) obtained on the basis of GRACE observations from April 2002 
to March 2016.  The cause of the trend in each outlined study region is briefly explained and colour-coded by category. The trend map was smoothed with a 150 km radius 
Gaussian filter for the purpose of visualization. However, all calculations were performed at the native 3° resolution of the data product. Figure from Rodell et al. (2018). Further 
details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1093
Water Cycle Changes Chapter 888.3.2.1 Inter-tropical Convergence Zone and Tropical 
Rain Belts
The AR5 concluded it is likely  that the tropical belt, as delimited by 
the Hadley circulation, has widened since the 1970s. Observations 
in the satellite era indicate precipitation increases in the core of 
the Pacific Inter-tropical Convergence Zone (ITCZ) and decreases on 
the ITCZ margins (Gu et al., 2016; Su et al., 2017). As the satellite 
period has lengthened, observations have increasingly been used to 
assess trends in the ITCZ and tropical rain belt. Since AR5, significant 
narrowing and strengthening of the Pacific ITCZ after 1979 have been 
identified in atmospheric reanalyses (Wodzicki and Rapp, 2016), but 
no change in the ITCZ location (Byrne et al., 2018). Atmospheric 
model simulations suggest that with a narrower ITCZ, the subtropical 
jet becomes baroclinically unstable at a  lower latitude and allows

mid-latitude eddies to propagate farther equatorward (Watt-Meyer 
and Frierson, 2019). Observational analyses also show that the 
ITCZ narrowing (Zhou et al., 2020) is associated with increased 
precipitation in the ITCZ core region that is strongly coupled to 
increasing Outgoing Longwave Radiation (OLR) in the expanding 
dry zones, particularly over land regions in the subtropics and mid-
latitudes (Lau and Tao, 2020). In addition, an eastward movement of 
the South Pacific Convergence Zone (SPCZ) between 1977 and 1999 
has been reported, with associated significant precipitation trends in 
the South Pacific regions (Salinger et al., 2014). 
ITCZ trends seen in satellites, precipitation measurements and 
reanalysis data are further supported by ocean surface-salinity 
observations. Long-term salinity observations show a  freshening in 
the cores of the Atlantic and Pacific ITCZs and increased salinity on 
the ITCZ margins (Durack and Wijffels, 2010; Durack et al., 2012;

Terray et al., 2012; Skliris et al., 2014). By investigating simultaneous 
changes in precipitation, temperature and continental aridity in CMIP5 
historical simulations, Bonfils et al. (2020) found a  secondary signal 
(Figure 8.9, right column) characterized by a robust inter-hemispheric 
temperature contrast (Section 3.3.1.1), a latitudinal shift in the ITCZ 
(in accordance with the theory of cross-equatorial energy transport; 
Section 8.2.2.2), and changes in aridity in the Sahel (Section 8.3.1.6). 
These forced changes are statistically detectable in reanalyses datasets 
over the 1950 –2014 period at the 95% confidence level.
Reconstructions in the Sahel (Carré et al., 2019) and Belize (Ridley 
et al., 2015) support the southward displacement of the tropical 
rain belt since 1850 and the narrowing trend of the tropical rainbelt 
detected in observations (Rotstayn et al., 2002; Hwang et al., 2013). 
Decreasing precipitation trends in the NH during the 1950s to 1980s

have been attributed to anthropogenic aerosol emissions from North 
America and Europe, which peaked during the late1970s and declined 
thereafter following improved air quality regulations, causing dimming 
(brightening) through reduced (increased) surface solar radiation 
(Box 8.1 Figure 1), in agreement with model simulations (Chiang et al., 
2013; Hwang et al., 2013). This is consistent with energetic constraints 
where tropical precipitation shifts are anti-correlated with cross-
equatorial energy transport (Section  6.3.3, Box 8.1). It also provides 
a physical mechanism for the severe drought in the Sahel that peaked 
in the mid-1980s (Sections 8.3.2.4.3 and 10.4.2.1) and the southward 
shift of the NH tropical edge from the 1950s to the 1980s (Allen et al., 2014; Brönnimann et al., 2015). However, CMIP5 and CMIP6 models still 
exhibit strong biases in representing the ITCZ, such as the simulation 
of a double ITCZ (Oueslati and Bellon, 2015; Adam et al., 2018; Tian

and Dong, 2020). The impacts of aerosols and volcanic activity on the 
position of the ITCZ have been investigated but changes are difficult to 
characterize from observations (Section 6.3.3.2; Friedman et al., 2013; 
J.M. Haywood et al., 2013; Iles and Hegerl, 2014; Colose et al., 2016; 
Chung and Soden, 2017 ). Such systematic shifts of the ITCZ can have 
important regional impacts like changes in precipitation (Figure 8.9).
In summary, there is medium confidence that the tropical rain belts 
over the oceans have been narrowing and strengthening in recent 
decades, leading to increased precipitation in the ITCZ core region 
(Section 8.2.2.2). Decreasing precipitation trends in the NH during 
the 1950s –1980s have been attributed to anthropogenic aerosol 
emissions from North America and Europe ( high confidence ).
8.3.2.2 Hadley Circulation and Subtropical Belt
The AR5 reported low confidence  in trends in the strength of the

Hadley circulation (HC) due to uncertainties in reanalyses but high 
confidence  on the widening of the tropical belt since 1979. In AR6, 
Chapter  2  (Section  2.3.1.4.1) states that the HC has very likely  
widened and strengthened since at least the 1980s, mostly in the NH 
The poleward shift of the HC is closely related to migration of the 
location of tropical cyclone trajectories in both hemispheres (Sharmila 
and Walsh, 2018; Studholme and Gulev, 2018), with a  very likely  
poleward shift over the western North Pacific Oceans since the 1940s 
(Section  11.7.1.2). Moreover, the Western North Pacific Subtropical 
High has extended westward since the 1970s, resulting in a monsoon 
rain band shift over China, with excessive rainfall along the middle 
and lower reaches of the Yangtze River valley along about 30°N over 
eastern China. At the same time, the effect of anthropogenic aerosols 
dominated the response to GHG increases over East Asia, resulting in

a weakening of the East Asian summer monsoon and causing a drying 
trend in north-eastern China (Hu, 2003; Yu and Zhou, 2007; T. Wang 
et al., 2013; Z. Li et al., 2016b; Lau and Kim, 2017) and northern parts of 
South Asia (Section 8.3.2.4.2; Preethi et al., 2017). During 1977 –2007, 
the precipitation variability over the eastern USA increased due to 
changes in the intensity and position of the western ridge of the North 
Atlantic Subtropical High (Li et al., 2011; Diem, 2013). 
In the Southern Hemisphere (SH), the HC expansion has 
been  associated with both the intensification and poleward shift 
of the subtropical high pressure belt (Nguyen et al., 2015), with 
consequences for precipitation amount over Africa, Australia, 
South  America, and subtropical Pacific islands (Cai et al., 2012; 
Grose et al., 2015; Nguyen et al., 2015; Sharmila and Walsh, 2018; 
McGree et al., 2019). The subtropical ridge in Australia has intensified

significantly since 1970, with marked declines observed in April to 
October rainfall across south-eastern and south-western Australia 
(Timbal and Drosdowsky, 2013). 
The local tropical edges of the meridional overturning cells (as 
diagnosed from the horizontally divergent wind) are more closely

1094
Chapter 8  W ater Cycle Changes8associated with hydroclimate variations than the subtropical ridge 
(Staten et al., 2019). Poleward expansion of the tropical belt strongly 
contributes to precipitation decline in the poleward edge of the 
subtropics (Cai et al., 2012; Scheff and Frierson, 2012; Timbal and 
Drosdowsky, 2013; He and Soden, 2017; H.  Nguyen et al., 2018; 
Tang et al., 2018), although recent modelling evidence suggests 
that subtropical precipitation declines are a response to direct CO 2 
radiative forcing mainly over ocean, irrespective of the HC expansion 
(He and Soden, 2017). Both reanalyses datasets and climate model 
simulations suggest that the HC expansion is not associated with 
widespread, zonally symmetric subtropical drying over land (Schmidt 
and Grise, 2017). 
Since AR5, an improved understanding of the key drivers of the 
recent HC expansion has been achieved, identifying the role of 
both internal variability and anthropogenic climate change. Part of

the recent expansion (1979 –2005) of the HC has been driven by 
a swing from warm to cold phase of the Pacific Decadal Variability 
(PDV; Meehl et al., 2016; Grise et al., 2019). The presence of large 
multi-decadal variability in 20th-century reanalyses means there 
is limited evidence  on the human influence on the recent HC 
strengthening, yet the southward shift of the southern edge and 
widening of the SH HC appeared as robust features in all reanalysis 
datasets, and their trends have accelerated during 1979 –2010 
(D’Agostino and Lionello, 2017). As assessed in Section  3.3.3.1, 
GHG increases and stratospheric ozone depletion have contributed 
to the expansion of the zonal mean HC in the SH since around 
1980, and the expansion of the NH HC has not exceeded the range 
of internal variability ( medium confidence ). Moreover, Antarctic 
ozone depletion can cause a poleward shift in the SH mid-latitude 
jet and HC (Sections 3.3.3 and 6.3.3.2). Further assessment of the

attribution of recently observed changes in the HC extent and 
intensity is found in Section 3.3.3.1.
In summary, it is very likely  that the recent HC expansion was 
associated with poleward shifts of tropical cyclone tracks over the 
western North Pacific Ocean since the 1940s, and of extratropical 
storm tracks in the SH since the 1970s. Changes to the HC in the 
NH may have contributed to subtropical drying and a  poleward 
expansion of aridity during the boreal summer, but there is 
low confidence  due to limited evidence . GHG increases and 
stratospheric ozone depletion have contributed to expansion of the 
zonal mean HC in the SH since around 1970, while the expansion 
of the NH  HC has not exceeded the range of internal variability 
8.3.2.3 Walker Circulation
The AR5 concluded that the long-term weakening of the Pacific Walker 
circulation (WC) from the late 19th century to the 1990s has been

largely offset by a  recent strengthening ( high confidence ), though 
with low confidence  in trends of the WC strength due to reanalysis 
uncertainties and large natural variability. The observed trends in the 
WC since 1980 are consistent with a  very likely WC strengthening in  
the Pacific, similar to a  La Niña pattern, with medium confidence  
in  the magnitude of these changes due to differences between 
satellite observations and reanalyses. The causes of the observed strengthening of the WC during 1980 –2014 
are not well understood due to competing influences from individual 
external forcings and since this strengthening is outside the range 
of variability simulated in coupled models ( medium confidence ), as 
assessed in Chapter 3 (Section 3.3.3.1). Recent strengthening in the WC 
has been linked with internal variability (Chung et al., 2019), although 
one study argues that it could be a  response forced by GHG that

models do not capture because of common sea surface temperature 
(SST) biases in the equatorial Pacific (Seager et al., 2019a). It could 
be also related to an interbasin thermostat mechanism whereby 
the human-induced Indian Ocean warming emerged earlier than in 
the tropical Pacific (L.  Zhang et al., 2018) and induced a  transient 
strengthening of the zonal sea level pressure gradient and easterly 
trades in the tropical Pacific (L. Zhang et al., 2019).
The weakening of the PWC observed during most of the 20th 
century is associated with reductions in land rainfall over the 
Maritime Continent during 1950 –1999 (Tokinaga et al., 2012; 
Yoden et al., 2017). In contrast, the recent strengthening of the WC 
has been associated with an intensification of extreme flooding 
(Barichivich et al., 2018) and an increased frequency of wet days 
(J.C.  Espinoza et al., 2016, 2018) over the north-western Amazon, 
increased precipitation in South America (Yim et al., 2017), reduced

precipitation over eastern Africa (Williams and Funk, 2011; Lyon and 
Dewitt, 2012), and increased rainfall in southern Africa (Maidment 
et al., 2015). Internal variability has been shown to have a dominant 
role in the recent strengthening of the WC (Chung et al., 2019).
In summary, there is high confiden ce that changes in the WC are 
associated with changes in the water cycle over regions like the 
Maritime Continent, South America and Africa. It is very likely  
that the WC has strengthened in the Pacific since the 1980s, with 
medium confidence  that this strengthening is within the range of 
internal variability. 
8.3.2.4 Monsoons
The AR5 reported low confidence  in the attribution of changes in 
monsoons to human influence, although a  detailed attribution 
assessment of the observed changes in the regional monsoons was 
not presented. 
Large human populations in the monsoon regions of the world 
heavily depend on freshwater supply for agriculture, water resources,

industry, transport and various socio-economic activities. The effects 
of GHG forcing combined with water vapour feedback (R.J.  Allen et al., 
2015; Dong and Sutton, 2015; Evan et al., 2015; Dunning et al., 2018) 
and cloud feedbacks (Stephens et al., 2015; Potter et al., 2017) are 
fundamental to monsoon precipitation changes in a warming world. 
Since AR5 there has been improved understanding of precipitation 
changes associated with regional monsoons. Sections  2.3.1.4.2 
and 3.3.3.2 provide an assessment of observed changes and 
attribution for the global monsoon. Here we provide an assessment 
of the observed changes in regional monsoons (see Annex V  and 
Figure 8.11) and underlying causes. In AR6, the definition of regional 
monsoons slightly differs from AR5 and the rationale for it is provided 
in Annex V (see Glossary). Specific examples of regional monsoons

1095
NAmerM
EqAmerWAfriM
SAfriSAsiaM
EAsiaM
AusMCM
SAmerM
NAmerM WAfriM SAsiaM EAsiaM
0.12
0.08
0.04
0
-0.04
-0.08
-0.120.12
0.08
0.04
0
-0.04
-0.08
-0.120.12
0.08
0.04
0
-0.04
-0.08
-0.120.12
0.08
0.04
0
-0.04
-0.08
-0.12
0.8
0.4
0
-0.4
-0.80.8
0.4
0
-0.4
-0.80.8
0.4
0
-0.4
-0.80.8
0.4
0
-0.4
-0.8
0.8Trend in Precip Precip a nomaly mm/da y/decade                    mm /day
EqAmer SAmerM SAfri AusMCM
0.12
0.08
0.04
0
-0.04
-0.08
-0.120.12
0.08
0.04
0
-0.04
-0.08
-0.120.12
0.08
0.04
0
-0.04
-0.08
-0.120.12
0.08
0.04
0
-0.04
-0.08
-0.12Trend in Precip0.8
0.4
0
-0.4
-0.80.8
0.4
0
-0.4
-0.80.8
0.4
0
-0.4
-0.80.8
0.4
0
-0.4
-0.8Precip a nomalyxGPCC CRU APHRO
NAT ALL GHG AER
ALL CRU APHROTrend and c hange in precipita tion (1951 –2014)  over monsoon regions
GPCCx
xx
xxxxxxx x x
xx
x
xxx
xx

mm/day                         mm/d ay/decade1951       1972        1993       2014 1951       1972        1993       2014 1951       1972        1993       2014 1951       1972        1993       2014
1951       1972        1993       2014 1951       1972        1993       2014 1951       1972        1993       2014 1951       1972        1993       2014
Figure 8.11 | Regional monsoon precipitation changes from observations and model attribution.  Precipitation changes during 1951–2014 are shown as least-
square linear trends in box-whisker plots (first and fourth rows) over the six regional monsoons, for example, North American monsoon (NAmerM, July–August–September, JAS), 
West African monsoon (WAfriM, June–July–August–September, JJAS), South and South East Asian monsoon (SAsiaM, June–July–August–September, JJAS), East Asian monsoon

(EAsiaM, June–July–August), South American monsoon (SAmerM, December–January–February, DJF), Australian and Maritime Continent monsoon (AusMCM, December–
January–February, DJF), and over the two land domains, for example, equatorial America (EqAmer, June–July–August, JJA) and South Africa (SAfri, December–January–February, 
DJF), as identified in the map shown in the middle and as described in Annex V . Precipitation changes are computed from observations and from Detection and Attribution Model 
Intercomparison Project (DAMIP) CMIP6 experiments over the historical period with all-forcing (ALL), GHG-only forcing (labelled GHG), Aerosol-only (AER) and Natural (NAT) 
forcings prescribed. Observations are based on the CRU (light green) and GPCC (light blue) datasets and the APHRODITE (light orange) dataset for SAsiaM and EAsiaM. CMIP6

simulations are taken from nine CMIP6 models contributing to DAMIP , with at least three members. Ensembles are weight-averaged for the respective model ensemble size. 
Observed trends are shown as coloured circles and the simulated trends from the CMIP6 multi-model experiments are shown as box-whisker plots. Precipitation anomaly time-
series are shown in the second and third row. The thick black line is the multi-model ensemble-mean precipitation anomaly time-series from the ALL experiment and the grey 
shading shows the spread across the multi-model ensembles. An 11-year running mean has been applied on the precipitation anomaly time-series prior to calculating the  
multi-model ensemble mean. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1096
Chapter 8  W ater Cycle Changes8are discussed further in Section  10.4.2, from the perspective of 
climate change attribution and in Section 10.6.3, from the viewpoint 
of constructing regional climate messages.
8.3.2.4.1 South and South East Asian Monsoon
The AR5 reported a  decreasing trend of global land monsoon 
precipitation over the last half-century, with primary contributions 
from the weakened summer monsoon systems in the Northern 
Hemisphere (NH). Since AR5, several studies have documented 
long-term variations and changes in the South and South East 
Asian summer monsoon (SAsiaM) rainfall. The SAsiaM strengthened 
during past periods of enhanced summer insolation in the NH, 
such as the early-to-mid Holocene warm period around 9000 to 
6000 years before the present (BP) (Masson-Delmotte et al., 2013; 
Mohtadi et al., 2016; Braconnot et al., 2019) and weakened during 
cold periods ( high confidence ), such as the Last Glacial Maximum

(LGM) and Younger Dryas (Shakun et al., 2007; Cheng et al., 2012; 
Dutt et al., 2015; Chandana et al., 2018; Hong et al., 2018; E. Zhang 
et al., 2018). These long-time scale changes in monsoon intensity are 
tightly linked to orbital forcing and changes in high-latitude climate 
(Braconnot et al., 2008; Battisti et al., 2014; Araya-Melo et al., 2015; 
Rachmayani et al., 2016; Bosmans et al., 2018; E. Zhang et al., 2018). 
A weakening trend of the SAsiaM during the last 200 years has been 
documented based on tree ring oxygen isotope chronology from the 
northern Indian subcontinent (Xu et al., 2018) and South East Asia 
(Xu et al., 2013), oxygen isotopes in speleothems from northern India 
(Sinha et al., 2015), and tree ring width chronologies from the Indian 
core monsoon region (Shi et al., 2017). Nevertheless, the detection 
of century-long decreases in regional monsoon rainfall is obscured 
by the presence of multi-decadal time scale precipitation variations

(Turner and Annamalai, 2012; Knutson and Zeng, 2018) which are 
evident in long-term rain guage records extending back to the early 
1800s (Sontakke et al., 2008) and emerge in long-term climate 
simulations (Braconnot et al., 2019).
A significant decline in summer monsoon precipitation is observed 
over India since the mid-20th century, which is accompanied by 
a weakening of the large-scale monsoon circulation (Mishra et al., 
2012; Abish et al., 2013; Krishnan et al., 2013, 2016; Saha et al., 2014; 
Roxy et al., 2015; Guhathakurta et al., 2017; Samanta et al., 2020). 
This precipitation decline is corroborated by a  decreasing trend in 
the frequency of monsoon depressions that form over Bay of Bengal 
(Prajeesh et al., 2013; Vishnu et al., 2016), an increasing trend in 
the frequency and duration of monsoon breaks or ‘dry spells’ (Singh 
et al., 2014), significant decreases in soil moisture and increases in 
drought severity across different parts of India post-1950 (Niranjan

Kumar et al., 2013; Ramarao et al., 2015, 2019; Krishnan et al., 2016; 
Ganeshi et al., 2020; Mujumdar et al., 2020). While recent studies 
have reported an apparent recovery of the Indian summer monsoon 
over a relatively short period since 2003 (Jin and Wang, 2017; Hari 
et al., 2020), long-term trends for the period 1951 –2015 indicate 
an overall decrease in the regional monsoon precipitation (Kulkarni 
et al., 2020; Ayantika et al., 2021). A case study on the Indian summer 
monsoon is provided in Section 10.6.3.Evidence from several climate modelling studies indicates that the 
observed decrease in the regional monsoon precipitation during the 
second half of the 20th century is dominated by the radiative effects 
of NH anthropogenic aerosols, with smaller contributions due to 
volcanic aerosols from the Mount Pinatubo (1991) and El Chichón 
(1982) eruptions (Bollasina et al., 2011; Polson et al., 2014; Sanap

et al., 2015; Krishnan et al., 2016; Liu et al., 2016; Lau and Kim, 2017; 
Lin et al., 2018; Takahashi et al., 2018; Undorf et al., 2018a, b; Patil 
et al., 2019; M. Singh et al., 2020; see Box 8.1, Figure 1 and Figure 8.11). 
Land-use changes over South and South East Asia and  the rapid 
warming trend of the equatorial Indian Ocean during the recent few 
decades also appear to have contributed to the observed decrease 
in monsoon precipitation (Roxy et al., 2015; Krishnan et al., 2016; 
Singh, 2016). Overall, the magnitude of the precipitation response 
to anthropogenic forcing exhibits large spread across CMIP5 models 
pointing to the strong internal variability of the regional monsoon 
(Saha et al., 2014; Salzmann et al., 2014; Sinha et al., 2015), 
including variations linked to phase changes of the Pacific Decadal 
Variability (Section AVI.2.6; X. Huang et al., 2020a), uncertainties in 
representing aerosol –cloud interactions (Takahashi et al., 2018), and

the effects of local compared with remote aerosol forcing (Bollasina 
et al., 2014; Polson et al., 2014; Undorf et al., 2018b). CMIP3 and 
CMIP5 models do not accurately reproduce the observed seasonal 
cycle of precipitation over the major river basins of South and South 
East Asia, limiting the attribution of observed regional hydroclimatic 
changes (Hasson et al., 2014, 2016; Biasutti, 2019). While warm 
rain processes and organized convection are known to dominate 
the heavy orographic monsoon rainfall over the Western Ghats 
mountains (Shige et al., 2017; Choudhury et al., 2018), in various 
parts of India (Konwar et al., 2012) and East Asia (Section 11.7.3.1), 
there are uncertainties in representing the regional physical processes 
of the monsoon environment, including cloud –aerosol interactions 
(Sarangi et al., 2017), land –atmosphere (e.g., Barton et al., 2020) and 
ocean –atmosphere coupling (Annamalai et al., 2017), in state-of-

the-art climate models (see also Section 8.5.1).
In summary, there is high confidence in observational evidence for 
a weakening of the SAsiaM in the second half of the 20th century. 
Results from climate models indicate that anthropogenic aerosol 
forcing has dominated the recent decrease in summer monsoon 
precipitation, as opposed to the expected intensification due to GHG 
forcing ( high confidence ). On paleoclimate time scales, the SAsiaM 
strengthened in response to enhanced summer warming in the NH 
during the early-to-mid Holocene, while it weakened during cold 
intervals ( high confidence ). These changes are tightly linked to orbital 
forcing and changes in high-latitude climate ( medium confidence ).
8.3.2.4.2 East Asian Monsoon
The AR5 reported low confidence  in the observed weakening of the 
East Asian monsoon (EAsiaM) since the mid-20th century. Since 
AR5, there has been improved understanding of changes in the

EAsiaM, based on paleoclimatic evidence, instrumental observations 
and climate modeling simulations. Rainfall reconstructions from 
the Loess Plateau in China indicate that the northern extent of the 
monsoon rain belts migrated at least 300 km to the north-west from 
the LGM to the mid-Holocene (Yang et al., 2015). Similarly, Pliocene

1097
Water Cycle Changes Chapter 88reconstructions indicate stronger intensity of the EAsiaM with a more 
northward penetration of the monsoon rain belt (S.  Yang et al., 
2018a). EAsiaM variability has been related to Atlantic Meridional 
Overturning Circulation (AMOC) dynamics, especially during the last 
glacial period, but whether the relationship is negative or positive 
remains uncertain (Sun et al., 2012; Cheung et al., 2018; Kang 
Long-term precipitation observations from China indicate a trend of 
drying in the north and wetting in the central-eastern China along 
the Yangtze river valley since the 1950s (Qian and Zhou, 2014; Zhou 
et al., 2017b; Day et al., 2018), with a weakened EAsiaM low-level 
circulation that penetrates less far into northern China, increased 
surface pressure over north-east China and southward shift of the 
jet stream (Song et al., 2014). The southward shift and enhancement

of the jet stream explains the increase of rainfall especially from the 
Meiyu front (Day et al., 2018) at the expense of drying over north-
east China. 
Anthropogenic factors such as GHGs and aerosols had an influence 
on the EAsiaM changes (Figure 8.11; T. Wang et al., 2013; Song et al., 
2014; Xie et al., 2016; Chen and Sun, 2017; Ma et al., 2017; L. Zhang 
et al., 2017; Day et al., 2018; Tian et al., 2018). Increased precipitation 
in the southern region has been linked to increased moisture flux 
convergence driven by GHG forcing while changes in anthropogenic 
aerosols have weakened the EAsiaM and reduced precipitation in 
the northern regions (Tian et al., 2018). Aerosol-induced cooling, 
associated atmospheric circulation changes and sea surface 
temperature (SST) feedbacks weaken the EAsiaM and favour the 
observed dry-north and wet-south pattern of rainfall anomalies 
(T. Wang et al., 2013; Song et al., 2014; L. Zhang et al., 2017; G. Chen

et al., 2018; X. Chen et al., 2018; Undorf et al., 2018b). 
Internal variability and volcanic eruptions also contributed to the 
weakened EAsiaM (Hsu et al., 2014; Qian and Zhou, 2014; Zhou et al., 
2017a; Knutson and Zeng, 2018). Since the late 1970s, the EAsiaM 
weakening has been also linked to SST changes in the Pacific Ocean 
with warm conditions in the central-eastern tropical part and cold 
ones in the north, similar to a positive phase of the Pacific Decadal 
Variability (PDV; Section AVI.2.6; Z. Li et al., 2016b; Zhou et al., 2017a ). 
In the late 1990s the transition from a positive to a negative PDV has 
been associated with the recent recovery observed in the EAsiaM 
strength (Zhou et al., 2017a). Atlantic Multi-decadal Variability (AMV) 
also has an influence on the EAsiaM via the global teleconnection 
pattern propagating from the North Atlantic through the westerly jet 
(Zuo et al., 2013; Wu et al., 2016a, b). This North Atlantic influence

has contributed to the increase of precipitation over the Huaihe-
Huanghe valley since the late 1990s (Y . Li et al., 2017). When PDV 
and AMV are in opposite phase, the former has a larger influence in 
driving the southern flooding and northern drought pattern over the 
region (Q. Yang et al., 2017).
In summary, there is strong evidence of a  stronger EAsiaM and 
northward migration of the rainbelt during warmer climates based 
on paleoclimate reconstructions. There is high confidence  that 
anthropogenic forcing has been influencing historical EAsiaM 
changes with drying in the north and wetting in the south observed since the 1950s, but there is low confidence  in the magnitude of the 
anthropogenic influence. The transition towards a positive PDV phase 
has been one of the main drivers of the EAsiaM weakening since the 
1970s ( high confidence ).
8.3.2.4.3 West African Monsoon
Since AR5, there has been improved understanding of the West

African monsoon (WAfriM) response to natural and anthropogenic 
forcing. On paleoclimate time scales, enhanced summer insolation in 
the Northern Hemisphere (NH) intensified the WAfriM precipitation 
during the early-to-mid Holocene ( high confidence ), as seen in rainfall 
proxy records and climate model simulations (Masson-Delmotte 
et al., 2013; Mohtadi et al., 2016; Braconnot et al., 2019). Despite 
improvements in model simulations of the present-day monsoons, 
CMIP5 and CMIP6 models underestimate mid-Holocene changes 
in the amount and spatial extent of the WAfriM precipitation 
(Section 3.3.3.2; Brierley et al., 2020). 
During the recent past, long-term rain gauge observations display 
substantial variability in the WAfriM precipitation over the 
20th century (Section 10.4.2.1). The WAfriM experienced the wettest 
decade of the 20th century during the 1950s and early 1960s ( high 
confidence ), over much of the western and central Sahel region,

followed abruptly by the driest years during 1970 –1989 (Ali and 
Lebel, 2009; Nicholson, 2013; Descroix et al., 2015). The percentage 
deficit in the annual rainfall during 1970–1989, relative to the long-
term mean, ranged from 60% in the north of Sahel to 25 –30% in 
the south (Le Barbé et al., 2002; Lebel et al., 2003). The long decline 
in annual rainfall is related to a decrease of rain occurrence over the 
Sahel (Le Barbé and Lebel, 1997; Frappart et al., 2009; Bodian et al., 
2016) and the Soudano-Guinean sub-region of West Africa (Le Barbé 
et al., 2002), even though the interannual variability pattern is 
more complex (Balme et al., 2006). Decrease of rainfall occurrences 
resulted from decreases in large convective events in the core of the 
rainy season (Bell et al., 2006), that modulate interannual variability 
of the WAfriM (Panthou et al., 2018).
Wetter conditions of the WAfriM prevailed later from the mid-to-late

1990s, although the positive trend in precipitation started since the 
late 1980s (see also Section 10.4.2.1) over the Sahel ( high confidence ) 
and in the Guinean coastal region ( medium confidence ), indicating 
the geographical variation in the wetting recovery (Descroix et al., 
2015; Sanogo et al., 2015; Bodian et al., 2016; Nicholson et al., 2018). 
While the interannual and decadal variability of annual rainfall is not 
homogeneous over the entire Sahel, the rainfall recovery was stronger 
in the east than in the west of the region (Section 10.4.2.1; Nicholson 
et al., 2018). A  shift in the seasonality of the Sahelian rainfall, 
including delayed cessation has also been reported (Section 10.4.2.1; 
Nicholson, 2013; Dunning et al., 2018). 
In the Sahel region, the emergence of this new rainfall regime is 
reflected in increased number of heavy and extreme events, compared 
to the 1970s –1980s, still not exceeding the values registered in the

1950s to 1960s (Descroix et al., 2013, 2015; Panthou et al., 2014, 
2018; Sanogo et al., 2015), and in higher interannual variability 
(W. Zhang et al., 2017b; Akinsanola and Zhou, 2020) associated with

1098
Chapter 8  W ater Cycle Changes8SST variations in the tropical Atlantic, Pacific and Mediterranean Sea 
(Rodríguez-Fonseca et al., 2015; Diakhaté et al., 2019). Increased 
frequency of extreme rainfall events impacts high flow occurrences 
of the large Sahelian rivers as well as small to meso-scale catchments 
(Wilcox et al., 2018). Overall, extreme intense precipitation events are 
more frequent in the Sahel since the beginning of the 21st century 
(Giannini et al., 2013; Panthou et al., 2014, 2018; Sanogo et al., 
2015; Taylor et al., 2017). Intensification of mesoscale convective 
systems associated with extreme rainfall in the WAfriM is favoured 
by enhancement of meridional temperature gradient by the warming 
of the Sahara desert (Taylor et al., 2017) at a pace that is two to four 
times greater than that of the tropical-mean temperature (K.H. Cook 
et al., 2015; Vizy et al., 2017). Periods of monsoon-breaks and the

persistence of low rainfall events are still prominent, particularly 
after the onset, thus exposing West Africa simultaneously to the 
potential impacts of dry spells (W.  Zhang et al., 2017b) and also 
extreme localized rains and floods (Engel et al., 2017; Lafore et al., 
2017). Occurrence of extreme events is compounded by land use and 
land cover changes leading to increased runoff (Bamba et al., 2015; 
Descroix et al., 2018). 
The Sahel drought from the 1970s until the early 1990s was related to 
anthropogenic emissions of sulphate aerosols in the Atlantic, which 
led to an inter-hemispheric pattern of SST anomalies and associated 
regional precipitation changes (Section 6.3.3.2 and Box 8.1). Also the 
combined effects of anthropogenic aerosols and GHG forcing appear 
to have contributed to the late twentieth century drying of the Sahel 
through their effect on SST, by cooling the North Atlantic and warming 
the tropical oceans (Giannini and Kaplan, 2019; Hirasawa et al.,

2020). Subsequent aerosol removal led to SST warming of the North 
Atlantic, shifting the ITCZ further northward and strengthening the 
WAfriM (Giannini and Kaplan, 2019). The recent recovery has been 
ascribed to prevailing positive SST anomalies in the tropical North 
Atlantic potentially associated with a positive phase of the Atlantic 
Multi-decadal Oscillation (Diatta and Fink, 2014; Rodríguez-Fonseca 
et al., 2015). The Sahel rainfall recovery has also been attributed to 
higher levels of GHG in the atmosphere and increases in atmospheric 
temperature (Dong and Sutton, 2015). 
In summary, most regions of West Africa experienced a wet period 
in the mid-20th century followed by a very dry period in the 1970s 
and 1980s that is attributed to aerosol cooling of the NH ( high 
confidence ). Recent estimates provide evidence of a WAfriM recovery 
from the mid-to-late 1990s, with more intense extreme events partly 
due to the combined effects of increasing GHG and decreasing

anthropogenic aerosols over Europe and North America ( high 
confidence ). On paleoclimate time scales, there is high confidence  
that the WAfriM strengthened during the early-to-mid Holocene 
in response to orbitally-forced enhancement of summer warming 
in the NH.
8.3.2.4.4 North American Monsoon
Since AR5, there have been updates on the observed long-term 
variations and changes in the North American monsoon (NAmerM). 
During the Last Glacial Maximum (LGM; 21,000 –19,000 years ago), 
the NAmerM was substantially weaker due to cold, dry mid-latitude air associated with the Laurentide Ice Sheet ( T. Bhattacharya et al., 
2017, 2018 ). The NAmerM strengthened until the mid-Holocene 
period, in response to ice-emsheet retreat and rising summer 
insolation, but probably did not exceed the strength of the modern 
system ( low confidence ), as indicated by model simulations (Metcalfe 
et al., 2015) and paleoclimatic reconstructions (Bhattacharya et al.,

2018). Paleoclimatic evidence from proxy datasets and mid-Pliocene 
(PlioMIP1) simulations suggest a wetter south-western USA during 
that warmer period (A.M. Haywood et al., 2013; Pound et al., 2014; 
Ibarra et al., 2018) but it is not clear whether this is due to increases 
of precipitation associated with the monsoon or occurring during the 
winter season. 
During 1948 –2010, trends of boreal summer precipitation amount 
were significantly positive over New Mexico and the core NAmerM 
region, but significantly negative over south-western Mexico (Hoell 
et al., 2016). In addition, diverse datasets like CRU, CHIRPS and GPCP 
show significant decreases of precipitation in parts of the south-
western USA and north-western Mexico, including the NAmerM 
region (Cavazos et al., 2020; Ashfaq et al., 2021). Other studies 
suggest a  strengthening of the NAmerM upper level anticyclone 
since the mid-1970s, with a more frequent northward location (Diem

et al., 2013). Between 1910 –2010, the number of precipitation 
events increased across the northern Chihuahuan desert, within 
the NAmerM domain, despite a  decrease in their magnitude, and 
the length of extreme dry and wet periods also increased (Petrie 
et al., 2014). 
An increase in intense rainfall and severe weather events has been 
observed in several locations, especially in south-western Arizona 
since 1991, resulting from increases in atmospheric moisture content 
and instability; a  change that has been confirmed by convective-
permitting model simulations (Luong et al., 2017; Pascale et al., 
2019). A dense network of 59 rain gauges located in south-eastern 
Arizona suggests an intensification of monsoon sub-daily rainfall 
since the mid-1970s (Demaria et al., 2019), as expected by a stronger 
global warming signature for sub-daily rather than daily or monthly 
precipitation accumulation (Section 11.4). Section 10.4.2.3 provides

further details on changes in precipitation in south-western North 
America. Evidence from multiple reanalyses suggests that increases 
in NAmerM rainfall have contributed to the increasing trend of global 
monsoon precipitation (Section 2.3.1.4.2; Lin et al., 2014). In addition, 
more frequent occurrence of earlier retreats of the NAmerM since 
1979 is documented (Arias et al., 2012, 2015), in association with the 
positive phase of the Atlantic Multi-decadal Variability (AMV) and 
a westward expansion of the North Atlantic Subtropical High (W. Li 
et al., 2011, 2012). 
Analyses from a 50-km resolution GCM indicate that the NAmerM 
response to CO 2 is very sensitive to SST biases, showing reductions 
in summer NAmerM precipitation with increased CO 2 when the SST 
biases are small (Pascale et al., 2017) in contrast to CMIP5 models 
(Cook and Seager, 2013; Maloney et al., 2014; Torres-Alavez et al., 
2014; Hoell et al., 2016). The NAmerM has been shown to be also

sensitive to sulphur dioxide (SO 2) emissions (García-Martínez 

1099
Water Cycle Changes Chapter 88In summary, both paleoclimate evidence and observations indicate 
an intensification of the NAmerM in a  warmer climate ( medium 
confidence). The intensification recorded since about the 1970s has 
been partly driven by GHG emissions (medium confidence). 
8.3.2.4.5  Sout h American Monsoon
Since AR5, there has been improved understanding of changes in the 
South American monsoon (SAmerM) as evidenced from paleoclimate records, instrumental observations and climate model simulations. 
However, general circulation models (GCMs) still exhibit difficulties 
in reproducing SAmerM precipitation amount (Rojas et al., 2016; D’Agostino et al., 2020b). Paleoclimate evidence suggests a relatively stronger SAmerM during the 1400–1600 period (Bird et al., 2011b; 
Vuille et al., 2012; Ledru et al., 2013; Apaéstegui et al., 2014; Novello 
et al., 2016; Wortham et al., 2017). Last millennium GCM simulations

are able to reproduce stronger SAmerM during the 1400–1600 
period in comparison with warmer epochs such as the 900–1100 period (Rojas et al., 2016) or the current warming period (Díaz and 
Vera, 2018). PMIP3/CMIP5 simulations indicate a consistent weaker 
SAmerM during the mid-Holocene (6000 years ago; see Cross-Chapter Box  2.1) in comparison to current conditions (Bird et al., 2011a; Mollier-Vogel et al., 2013; Prado et al., 2013a; D’Agostino et al., 2020b), thus favouring savannah/grassland-like vegetation 
(Smith and Mayle, 2018), in agreement with climate reconstructions 
from different proxies (Prado et al., 2013b). Signals of weak and strong SAmerM during mid-Holocene and LGM, respectively, are evident also in high-resolution long-term (i.e., more than about 22,000 years) rainfall reconstructions based on oxygen isotopes in 
speleothems from Brazil (Novello et al., 2017; Stríkis et al., 2018; 
Campos et al., 2019).
Isotope records from caves in the central Peruvian Andes show

that the late Holocene (<3000 years ago) was characterized by 
multi-decadal and centennial-scale periods of significant decline in 
intensity of the SAmerM (Bird et al., 2011a; Vuille et al., 2012). This could be partly due to a reduction in the zonal SST gradient of the Pacific Ocean, favouring El Niño-like conditions (Kanner et al., 2013). 
Other studies suggest increased SAmerM precipitation amount 
during the Late Holocene, in association with the expansion of the tropical forest (Smith and Mayle, 2018). Well-dated equilibrium lines of glaciers during the deglaciation suggest that the AMOC enhances Atlantic moisture sources and precipitation amount increase over the 
tropical and southern Andes (Beniston et al., 2018). 
Observations during 1979
–2014 suggest that poleward shifts in the 
South Atlantic Convergence Zone (SACZ) noted in recent decades 
(Talento and Barreiro, 2018; Zilli et al., 2019), are associated with

precipitation amount decrease along the equatorward margin and 
increase along the poleward margin of the convergenze zone (Zilli et al., 2019). Several observational studies identified delayed onsets of the SAmerM after 1978 related to longer dry seasons in the southern Amazon (Fu et al., 2013; Yin et al., 2014; Arias et al., 2015; Debortoli 
et al., 2015; Arvor et al., 2017; Giráldez et al., 2020; Haghtalab et al., 2020; Correa et al., 2021). In contrast, other studies indicate a trend 
toward earlier onsets of the SAmerM (Jones and Carvalho, 2013). These discrepancies are explained by the methodology used and the domain considered for the SAmerM, confirming the occurrence of delayed onsets of the SAmerM since 1978 (Correa et al., 2021). CMIP5 simulations show trends toward delayed onsets of the SAmerM in association with anthropogenic forcing, although the simulated trends underestimate the observed trends (Fu et al., 2013).

Total rainfall reductions are observed in the southern Amazon during 
September
–October –November after 1978 (Fu et al., 2013; Bonini 
et al., 2014; Debortoli et al., 2015, 2016; Espinoza et al., 2019), consistent with reductions in river discharge in the region (Molina-
Carpio et al., 2017; Espinoza et al., 2019; Heerspink et al., 2020).
Significant increases in precipitation have been observed over 
south-eastern Brazil during 1902
–2005 while non-significant 
decreases have been found over central Brazil (Vera and Díaz, 2015). In Bolivia, increases were observed during 1965
–1984, while 
reductions have occurred since then (Seiler et al., 2013). However, the Peruvian Amazon does not reveal significant changes in mean 
rainfall during 1965–2007 (Lavado et al., 2013; Ronchail et al., 2018).

Historical simulations from CMIP5 ensembles adequately capture the observed summer precipitation amount over central and south-eastern Brazil, thereby providing high confidence in interpreting the observed variability of SAmerM for the period 1960
–1999 (Gulizia 
and Camilloni, 2015; Pascale et al., 2019). Also, CMIP5 simulations indicate that the anthropogenic forcing associated with increased 
GHG emissions is necessary to explain the positive trends in upper-
troposphere zonal winds observed over the South American Altiplano (Vera et al., 2019). However, the detection of anthropogenically-induced signals for precipitation is still ambiguous in monsoon regions, like the SAmerM (Hoegh-Guldberg et al., 2018).
In summary, there is high confidence that the SAmerM onset has 
been delayed since the late 1970s. This is reproduced by CMIP5 simulations that consider anthropogenic forcing. There is also 
high confidence that precipitation during the dry-to-wet transition

season has been reduced over the southern Amazon. Paleoclimate reconstructions and simulations suggest a weaker SAmerM during warmer epochs such as the Mid-Holocene or the 900–1100 period, and stronger monsoon during colder epochs such as the LGM or the 
1400–1600 period (high confidence). 
8.3.2.4.6  Australian and Maritime Continent Monsoon
Since AR5, several studies have examined observed variability 
and changes in the Australian and Maritime Continent monsoon (AusMCM) using paleoclimate records, instrumental observations 
and modeling studies (Denniston et al., 2016; Zhang and Moise, 
2016). Paleoclimate reconstructions and modelling indicate that the Indo–Australian monsoon may vary in or out of phase with the EAsiaM, depending on whether there is a meridional displacement or expansion of the tropical rainfall belt (Ayliffe et al., 2013; Denniston 
et al., 2016). For instance, mid-Holocene simulations suggest that the

AusMCM weakens and contracts due to a decreased net energy input and a weaker dynamic component (D’Agostino et al., 2020b). 
the 1950s, with most of the increases occurring in the north-west (Dey et al., 2019a, b; Dai, 2021) and decreases observed in the north-

1100
Chapter 8  W ater Cycle Changes8east (J. Li et al., 2012) since the 1970s. There is also a trend towards 
more intense convective rainfall from thunderstorms over northern 
Australia (Dowdy, 2020). There is no consensus on the cause of the 
observed Australian monsoon rainfall trends, with some studies 
suggesting changes are due to altered circulation driving increased 
moisture transport or increased frequency of the wettest synoptic 
regimes (Catto et al., 2012; Clark et al., 2018). Other studies find 
that model simulations that include anthopogenic aerosols (Rotstayn 
et al., 2012; Dey et al., 2019a) are better able to capture observed 
Australian monsoon rainfall trends than simulations with natural or 
GHG forcing only (Knutson and Zeng, 2018). 
The Maritime Continent (MC) experiences the influence of both the 
Asian and the Australian monsoons, with rainfall peaking during 
boreal winter/austral summer (Robertson et al., 2011). Reductions in

land rainfall and marine cloudiness over the MC and weakening of 
surface moisture flux convergence have been observed in the period 
1950 –1999 (Tokinaga et al., 2012; Yoden et al., 2017). These trends 
are indicative of a slowdown of the Walker Circulation, with positive 
sea level pressure trends over the MC and negative trends over 
the central equatorial Pacific (Tokinaga et al., 2012). More recently 
(1981 –2014), a trend of increasing annual rainfall over large areas 
of the MC has been identified (Hassim and Timbal, 2019). Given the 
large variability in MC rainfall on interannual time scales, the choice 
of time period may influence the calculated rainfall trend (Hassim 
and Timbal, 2019).
During 1951 –2007 daily rainfall extremes did not increase over 
the MC, in contrast to the rest of South East Asia (Section 11.4.2; 
Villafuerte and Matsumoto, 2015). Rainfall extremes in Indonesia 
increased in austral summer, as evidenced from station weather

observations for the period 1983 –2012 (Supari et al., 2018). 
In summary, notable rainfall increases have been observed in parts of 
northern Australia since the 1970s, although there is low confidence  
in the human contribution to these changes. Rainfall changes have 
been observed over the MC region but there is low confidence  in 
the identification of trends because of large variability at interannual 
time scales.
8.3.2.5 Tropical Cyclones
The AR5 assessed  low confidence  in centennial changes in tropical 
cyclone (TC) activity globally, and in the attribution of observed 
changes in TCs to anthropogenic forcing. Since AR5, there has been 
considerable progress in understanding the observed changes of TCs 
and an overall improved knowledge of the sensitivity of TCs to both 
GHG and aerosol forcing (Knutson et al., 2019; Sobel et al., 2019). 
Although observational data limitations (Lau and Zhou, 2012) 
tend to limit detection of anthropogenic forced increases in TC

precipitation (Knutson et al., 2019), there is medium confidence  that 
anthropogenic forcing has contributed to observed heavy rainfall 
events over the USA associated with TCs (Kunkel et al., 2012) and 
other regions with sufficient data coverage (Section 11.7.1.2; Bindoff 
et al., 2013). There has been increased frequency of TC heavy rainfall 
events over several areas in the USA since the late 19th century that is greater than what would be expected solely from changes in US 
landfall frequency, suggesting the increasing role of TCs have in 
causing heavy rainfall events (Kunkel et al., 2010). For example, there 
is evidence for an anthropogenic contribution to the extreme rainfall 
of Hurricane Harvey in 2017 (Emanuel, 2017; Risser and Wehner, 
2017; van Oldenborgh et al., 2017; Trenberth et al., 2018; S.-Y .S.  Wang 
et al., 2018). 
While TCs cause extreme local rainfall and flooding, they can be also 
an important contributor to annual precipitation and regional fresh

water resources (Hristova-Veleva et al., 2020). Transport of moisture 
by TCs is an important contributor for precipitation over the coastal 
areas of East Asia mostly from July through October, with the TC 
rainfall accounting for nearly 10% to 30% of the total rainfall in 
the region (L. Guo et al., 2017). Local TC rainfall totals depend on 
rain-rate and translation speed (the speed of TC movement along 
the storm track) with slow TCs such as Hurricane Harvey (2017), 
providing a clear example of the effect of slow translation speed on 
local rainfall accumulation, with urbanization exacerbating the storm 
total rainfall and flooding (Section 11.7.1; W. Zhang et al., 2018). 
In addition to evidence that rain-rates have increased, there is 
evidence that TC translation speed has slowed globally (Kossin, 
2018) thus amplifying thermodynamic intensification of rainfall 
and may be linked to anthropogenic forcing (Gutmann et al., 2018).

This is limited evidence  however, so there is medium  confidence of 
a detectable change in TC translation speed over the US. Since the 
1900s, and there is low confidence  for a  global signal because of 
limited agreement among models and due to data heterogeneity. 
However, the slowdown is consistent with theoretical and modelling 
studies that indicate a general weakening of the tropical circulation 
with warming that reduces the speed of the TC system (Chauvin 
et al., 2017), though there is limited  observational evidence  
(Sections 8.2.3.5 and 11.7.1). 
In summary, there is medium confidence  of an observed increase in 
TC precipitation intensity in regions with sufficient data coverage 
Robust physical understanding (Section 8.2.3.2) and detailed singular 
event attribution studies provide evidence that tropical cyclone 
rainfall has increased with a  warming climate ( high confidence , 
Section 11.7.1.4).
8.3.2.6 Stationary Waves

Stationary waves are planetary-scale waves that are approximately 
stable (stationary) in terms of geographic position, as opposed to 
propogating planetary waves, and are important both as part of the 
climatological general circulation and seasonal and shorter-term 
anomalies. They are related to surface features including land –
ocean contrasts and major mountain ranges, as well as atmospheric 
features including the jet stream, storm tracks, and blocking, which 
are considered separately in the following sections. While zonal mean 
changes in P–E (precipitation minus evaporation) are dominated 
by thermodynamic effects (Section  8.2.2.1), changes in stationary 
waves are of key importance in understanding zonal asymmetries 
in the water cycle response to global warming (Wills and Schneider, 
2015; Wills et al., 2019). The AR5 did not explicitly assess stationary

1101
Water Cycle Changes Chapter 88waves, but noted changes in related circulation features such as 
a likely  poleward shift of the Northern Hemisphere (NH) storm tracks 
and an increase in frequency and eastward shift in North Atlantic 
blocking anticyclones, although there was low confidence  in the 
global assessment of blocking.
Since AR5, several studies have demonstrated a  link between 
stationary wave amplitude and wet and dry extremes in several 
different regions of the NH (Liu et al., 2012; Coumou et al., 2014; 
Screen and Simmonds, 2014; Yuan et al., 2015) with changes in 
moisture transport playing an important role (Yuan et al., 2015). 
A  ‘resonance mechanism’ has been proposed for an increasing 
amplitude of stationary waves (Petoukhov et al., 2013, 2016; Coumou 
et al., 2014; Kornhuber et al., 2017)  and several studies have linked 
increasing amplitude of stationary waves to Arctic warming (Francis

and Vavrus, 2012, 2015; Liu et al., 2012; Tang et al., 2014) as well 
as to global warming (Mann et al., 2017). However, other studies 
have not identified an increase in stationary wave amplitude (Barnes, 
2013; Screen and Simmonds, 2013a, b). 
There has been considerable work on linkages (teleconnections) 
between Arctic warming and the mid-latitude circulation (see also 
Cross-Chapter Box 10.1). The limited amount of research on Southern 
Hemisphere (SH) stationary waves suggests changes in high-latitude, 
mid-tropospheric stationary waves which influence Antarctic 
precipitation (Turner et al., 2017) and changes in stratospheric 
stationary waves that are associated with ozone depletion rather than 
increases in GHGs (L. Wang et al., 2013). The observed climatology 
of NH winter stationary waves is well-represented in the CMIP5 
multi-model mean (Wills et al., 2019) but individual models have 
important deficiencies in reproducing stationary wave variability (Lee

and Black, 2013). In the SH, the observed climatology of stationary 
waves in CMIP5 models has considerable bias in both phase and 
amplitude (Garfinkel et al., 2020). A comprehensive assessment is not 
yet available for CMIP6 models.
In summary, there is low confidence  in strengthened winter stationary 
wave activity over the North Atlantic, associated with increased 
poleward moisture fluxes east of North America There is medium 
confidence  in a recent amplification of the NH stationary waves in 
summer, but no formal attribution to anthropogenic climate change.
8.3.2.7 Atmospheric Blocking
Atmospheric blocking refers to persistent, semi-stationary weather 
patterns characterized by a high-pressure (anticyclonic) anomaly that 
interrupts the westerly flow in the mid-latitudes of both hemispheres. 
By redirecting the pathways of mid-latitude cyclones, blocking can 
affect the water cycle and lead to negative precipitation anomalies

in the region of the blocking anticyclone and positive anomalies in 
the surrounding areas (Sousa et al., 2017). In this way, blocking can 
also be associated with extreme events such as heavy precipitation 
(Lenggenhager et al., 2019), drought (Schubert et al., 2014) and 
heatwaves (Miralles et al., 2014a). The AR5 reported low confidence  in 
global-scale changes in blocking, due to methodological differences 
between studies.Currently no consensus exists on observed trends in blocking during 
1979 –2013. (Horton et al., 2015) identified increasing trends in 
anticyclonic circulation regimes based on geopotential height 
fields in the mid-troposphere, which may be partly related to the 
tropospheric warming itself and thus not represent real changes 
in the statistics of weather (Horton et al., 2015; Woollings et al., 
2018). Hanna et al. (2018) and (Davini and D’Andrea, 2020) reported 
a  significant increase in the frequency of summer blocking over

Greenland. A weakening of the zonal wind, eddy kinetic energy and 
amplitude of Rossby waves in summer in the NH (Coumou et al., 
2015, Kornhuber et al., 2019) and an increased ‘waviness’ of the jet 
stream associated with Arctic warming (Francis and Vavrus, 2015; 
Pfahl et al., 2015; Luo et al., 2019) have also been identified, which 
may be linked to increased blocking. 
In contrast, it has been shown that observed trends in blocking are 
sensitive the choice of the blocking index, and that there is a large 
internal variability that complicates the detection of forced trends 
(Barnes et al., 2014; Cattiaux et al., 2016; Woollings et al., 2018), 
compromising the attribution of any observed changes in blocking. 
Many climate models still underestimate the occurrence of blocking, 
at least in winter over north-eastern Atlantic and Europe (Dunn-
Sigouin and Son, 2013), which leads to caution in the interpretation 
of their results for these regions. However, over the Pacific Ocean

there have been large improvements in the simulation of blocking 
for the last 20 years (Davini and D’Andrea, 2016; Patterson et al., 
2019) . In the SH, increases in blocking frequency have occurred in the 
South Atlantic in austral summer (Dennison et al., 2016) and in the 
southern Indian Ocean in austral spring (Schemm, 2018). A reduced 
blocking frequency has been found over the south-western Pacific 
in austral spring (Sections 2.3.1.4.3 and 3.4.1.3.3; Schemm, 2018).
In summary, no robust trend in atmospheric blocking has been 
detected in modern reanalyses and in CMIP6 historical simulations 
(medium confidence ). The lack of trend is explained by strong 
internal variability and/or the competing effects of low-level Arctic 
amplification and upper-level tropical amplification of the equator-
to-pole temperature gradient ( medium confidence ).
8.3.2.8 Extratropical Cyclones, Storm Tracks 
and Atmospheric Rivers
8.3.2.8.1 Extratropical cyclones and storm tracks

The AR5 indicated low confidence  in long-term changes in the 
intensity of extratropical cyclones (ETC) over the 20th century derived 
from centennial reanalyses and storminess proxies based upon sea 
level pressure. This was confirmed by the SREX assessment that the 
main Northern Hemisphere (NH) and Southern Hemisphere (SH) 
extratropical storm tracks likely  experienced a poleward shift during 
the last 50 years (Seneviratne et al., 2012) with low confidence , and 
inconsistencies within reanalysis datasets remain.
Since AR5 there has been considerable progress in quantifying 
storm track activity using multiple reanalysis products and different 
methodologies (Hodges et al., 2011; Neu et al., 2013; Tilinina et al., 
2013; X.L. Wang et al., 2016). Over the NH increases in the total

1102
1980 1985 1990 1995 2000 2005 2010 2015-150-100-50050100150
anomalies of cyclone numbers
ERA 5 ERA Interim(a) N orthern Hemisphe re, annual, total
(b) Southern Hemispher e, annual, total
1980 1985 1990 1995 2000 2005 2010 2015-75-60-45-30-1501530456075
NCEP CFSR MERRA 2 JRA 55(c) Northern H emispher e, annual, d eep
(d) Southern Hemispher e, annual, deep
(e) Northern Hemisphere, JFM, boreal winte r (f) Southern Hemisphere, JFM, austral summer
Figure 8.12 | Annual anomalies (with respect to the reference period 1979–2018) of the total number of extratropical cyclones (a, b) and of the number 
of deep cyclones (<980 hPa) (c, d) over the Northern (a, c) and the Southern (b, d) Hemispheres in different reanalyses (shown in colours in the legend).

Note different vertical scales for panels (a, b) and (c, d). Thin lines indicate annual anomalies and bold lines indicate five-year running averages. (e, f) The number of reanalyses 
(out of five) simultaneously indicating statistically significant (90% level) linear trends of the same sign during 1979–2018 for JFM (January–February–March) over the Northern 
Hemisphere (e) and over the Southern Hemisphere (f). Updated from Tilinina et al. (2013). Further details on data sources and processing are available in the chapter data table 
(Table 8.SM.1).

1103
Water Cycle Changes Chapter 88number of cyclones from 1979 show a large spread of trends across 
different estimates (Section 2.3.1.4.3; Neu et al., 2013; Z. Li et al., 
2016a; Grieger et al., 2018) resulting in low confidence in any clear 
increase of in the total number of cyclones. However, starting from 
the early 1990s, most reanalyses show increases in the total cyclone 
number by about 2 –5% per decade (Figure 8.12). Increasing trends 
in the total number of cyclones are dominated by the increase in 
the number of shallow and moderate cyclones (which are more 
dependent on the datasets and identification methods used) than 
with decreasing number of deep cyclones since the early 1990s 
(Tilinina et al., 2013; Chang, 2018). In the SH the variability of the 
total number of cyclones is characterized by strong inter-decadal 
variability preventing a  clear assessment of trends. However, in 
contrast to the NH,there is a  significant increasing trend in the

number of deep cyclones (about 10% over 1979 –2018) in ERA5, 
ERA-Interim, JRA55 and MERRA, and in the CFSR dataset after 2000 
(Figure 8.12; Reboita et al., 2015; X.L. Wang et al., 2016).
Changes in the number of deep storms, which are often associated 
with heavier precipitiation over the North Atlantic and North 
Pacific, exhibit strong seasonal differences and decadal variability 
(Colle et al., 2015; Chang et al., 2016; Matthews et al., 2016; Priestley 
et al., 2020a). An increase in the number of summer cyclones over the  
Atlantic-European sector (Tilinina et al., 2013)  is consistent with 
the  increase in the strength of the strongest fronts over Europe 
(Schemm et al., 2018). Chang et al. (2016) reported a decrease in the 
number of strong summer storms in the latitudinal band 40°N –75°N 
over the last decades, however, the assessment of seasonal trends in 
the Atlantic-European sector is complicated by the choice of region,

attribution of tracks to the region selected, and thresholds used to 
identify trajectories, leading to low confidence  on regional seasonal 
trends. For the SH, Grieger et al. (2018) reported a growing number 
of cyclones over sub-Antarctic region in the austral-summer during 
1979 –2010, while statistically significant trends were absent 
during the austral winter. 
Analysis of storm track activity over longer periods suffers from 
uncertainties associated with changing data assimilation and 
observations before and during the satellite era, resulting in in 
homogeneities and discontinuities in centennial reanalyses (Krueger 
et al., 2013; X.L. Wang et al., 2013, 2016; Chang and Yau, 2016; 
Varino et al., 2019). Feser et al. (2015) reviewed multiple storm track 
records for the Atlantic-European sector and demonstrated growing 
storm activity north of 55°N from the 1970s to the mid-1990s with 
declining trend thereafter, sugesting strong inter-decadal variability

in storm track activity. This was also confirmed by Krueger et al. 
(2019) from the analysis of geostrophic winds derived from sea level 
pressure gradients. 
Poleward deflection of mostly oceanic winter storm tracks since 
1979 was reported in both the North Atlantic and North Pacific 
(Tilinina et al., 2013; J. Wang et al., 2017). This large-scale tendency 
has regional variations and may be seasonally dependent. Wise and 
Dannenberg (2017) reported a  southward shift in the east Pacific 
storm track from the 1950s to mid-1980s followed by northward 
deflection in the later decades. (King et al., 2019) reported an 
association of Atlantic storm track migrations with SSW events with Central and South European precipitation anomalies. Over centennial 
time scales, Gan and Wu (2014) reported an intensification of storm 
tracks in the poleward and downstream regions of the North Pacific 
and North Atlantic upper troposphere using the NOAA–CIRES–DOE

Twentieth Century Reanalysis. Poleward migration of the SH storm 
tracks (Grise et al., 2014; X.L. Wang et al., 2016; Dowdy et al., 2019) 
was identified during the austral summer and is closely associated 
with cyclone-associated frontal activity (Solman and Orlanski, 2014, 
2016) and cloud cover (Bender et al., 2012; Norris et al., 2016).
The representation of ETCs in both climate models and reanalyses 
is resolution-dependent, hence changes must be assessed with 
caution (Section  3.3.3.3). In particular, CMIP5 models show 
a  systematic underestimation of the intensity of ETCs (Zappa 
et al., 2014), a  feature that is partially related to their relatively 
coarse resolution or other possible deficiencies such as an excess 
of dissipation (Chang et al., 2013). The best representation of ETCs 
and their intensity in the  North Atlantic are provided by relatively 
high horizontal resolution CMIP5 models (Zappa et al., 2014). Using

a single high-resolution climate model, (Hawcroft et al., 2016) showed 
that precipitation amount associated with ETCs was generally well 
simulated, though with too much precipitation during the strongest 
ECTs compared with observed estimations.
In summary, there is low confidence  in recent changes in the total 
number of extratropical cyclones over both hemispheres. It is as likely 
as not  that the number of deep cyclones over the NH has decreased 
after 1979 and it is likely  that the number of deep extratropical 
cyclones increased over the same period in the SH. It is likely  that 
extratropical cyclone activity in the SH has intensified during austral 
summer with no significant changes in austral winter. There is 
medium confidence  that boreal-winter storm tracks during the last 
decades experienced poleward shifts over the NH and SH oceans. 
There is low confidence  of changes in extratropical cyclone activity

prior 1979 due to inhomogeneities in the intrumental records and 
modern reanalyses. 
8.3.2.8.2 Atmospheric rivers 
Atmospheric rivers (ARs) are long, narrow (up to a  few hundred 
kilometres wide), shallow (up to few kilometres deep) and transient 
corridors of strong horizontal water vapour transport that are typically 
associated with a low-level jet stream ahead of the cold front of an 
extratropical cyclone (Ralph et al., 2018). Atmospheric rivers were 
not assessed in AR5. ARs are associated with atmospheric moisture 
transport from the tropics to the mid- and high latitudes  (Zhu 
and Newell, 1998), although the drivers of moisture transport 
relative to the different airstreams within extratropical cyclones 
remains a subject of current study (Dacre et al., 2019). While much 
previous research has focused on the west coast of North America, 
ARs occur throughout extratropical and polar regions (e.g.,  Guan 
and Waliser, 2015) and are often associated with locally-heavy

precipitation, including a  substantial fraction of all mid-latitude 
extreme precipitation events (e.g., Waliser and Guan, 2017). ARs 
also affect East Asia strongly during the period from late spring to 
summer (Kamae et al., 2017). ARs can be related to warming/melt 
events trough the intrusions of warm and moist air in Antarctica,

1104
Chapter 8  W ater Cycle Changes8Greenland and New Zealand (Bozkurt et al., 2018; Mattingly et al., 
2018; Little et al., 2019), contributing about 45 –60% of total annual 
precipitation in subtropical South America (Viale et al., 2018). They 
also transport moisture from South America to the western and 
central South Atlantic, feeding the ARs that reach the west coast 
of South Africa  (Ramos et al., 2019). However, the estimation of 
precipitation rate from ARs can have large uncertainties, especially 
as ARs hit topographically complex coastal regions (Behrangi 
et al., 2016), which can cause complexities in quantifying AR-related 
precipitation.
Analysis of observed trends in the characteristics of ARs has been 
limited. Gershunov et al. (2017) and Sharma and Déry (2019) have 
shown a rising trend in land-falling AR activity over the west coast 
of North American since 1948. (Gonzales et al., 2019) have also 
documented a  seasonally-asymmetric warming of ARs affecting

the West Coast of the USA since 1980, which has hydrological 
implications for the timing and magnitude of regional runoff. Longer-
term paleoclimate analysis of ARs is even more limited, although 
Lora et al. (2017) reported that in the last glacial maximum, AR 
landfalls over the North American west coast were shifted southward 
compared to the present conditions. 
In summary, it is likely  that there was an increasing trend in the 
AR activity in the eastern North Pacific since the mid-20th century. 
However, there is low confidence  in the magnitude of this trend 
and no formal attribution, although such an increase in activity is 
consistent with the expected and observed increase in precipitable 
water associated with human-induced global warming. 
8.3.2.9 Modes of Climate Variability  
and Regional Teleconnections
Following on from the assessment in Chapters 2 and 3, this section 
considers changes in modes of variability at seasonal to interannual

time scales in terms of their implications on recent water cycle 
changes. These modes are described in details in Technical Annex IV.
8.3.2.9.1 Tropical modes
The amplitude of the El  Niño–Southern Oscillation (ENSO; Section 
AIV.2.3) variability has increased since 1950 (Section 2.4.2) but there 
is no clear evidence of human influence (Sections 2.4.2 and 3.7.3). 
ENSO influences precipitation and evaporation dynamics, river flow 
and flooding at a global scale (Figure 3.37; Ward et al., 2014, 2016; 
Martens et al., 2018). Reconstruction (1804 –2005) of Thailand’s 
Chao Praya River peak season streamflow displays a  strong 
correlation with ENSO (Xu et al., 2019). Based on water storage 
estimates from 2002 to 2015, drought conditions over the Yangtze 
River basin followed La Niña events and flood conditions followed 
El Niño events (Z. Zhang et al., 2015). Strong correlation between 
ENSO and terrestrial water storage has been identified mostly in the

subtropics but with diverse intensities and time lags depending on 
the region (Ni et al., 2018). The likelihood of increased/decreased 
flood hazard during ENSO events has a complex spatial pattern with 
large uncertainties (Emerton et al., 2017). Tropical SSTs and associated global circulation may increase rainfall 
in West Africa, as observed in some years during 1950 –2015, despite 
the presence of El  Niño (Pomposi et al., 2020). During an El  Niño 
summer, equatorial convective systems and the associated Walker 
circulation tend to shift eastward, leading to decreases in Indian 
summer monsoon rainfall (Li and Ting, 2015; Roy et al., 2019). This 
teleconnection is modulated by Indian Ocean Variability (Terray et al., 
2021), as observed during the extreme positive IOD event in 2019 
(Ratna et al., 2021). Since the end of the 19th century, synchronous 
hydroclimate changes ( medium confidence ) have been identified

over south-eastern Australia and South Africa (Gergis and Henley, 
2017) modulated by ENSO, as well as other regional fluctuations like 
the Botswana High over southern Africa (Driver and Reason, 2017). 
Over southern South America, the ENSO influence on precipitation 
(Cai et al., 2020; Poveda et al., 2020) interacts with the influence of 
SAM (Pedron et al., 2017), exhibiting large multi-decadal variations 
because of changes in the correlation between the two large-scale 
modes (Vera and Osman, 2018). Other processes underlying ENSO 
teleconnections of relevance for water cycle changes include water 
vapour and moisture transports, like over the Middle East (Sandeep 
and Ajayamohan, 2018), south-eastern China (S.  Yang et al., 
2018b), or central Asia (X. Chen et al., 2018), south-eastern South 
America (Martin-Gomez et al., 2016; Martín-Gómez and Barreiro, 
2016), Australia (Rathore et al., 2020) and southern USA (Okumura 

There is no evidence of a trend in the Indian Ocean Dipole (IOD; Section 
AIV.2.4) mode and associated anthropogenic forcing (Sections 2.4.3 
and 3.7.4). The AR5 concluded that the IOD is likely  to remain active, 
affecting climate extremes in Australia, Indonesia and East Africa. 
Since the AR5, IOD teleconnections have been identified extending 
further to the Middle East (Chandran et al., 2016), to the Yangtze 
river (Xiao et al., 2015), where in boreal summer and autumn positive 
IOD events tend to increase the precipitation in the south-eastern 
and central part of the basin, and to the southern Africa extreme 
wet seasons (Hoell and Cheng, 2018). During the last millenium, 
the combined effect of a positive IOD and El Niño conditions have 
caused severe droughts over Australia (Abram et al., 2020). In the 
satellited period, it is found more effective in inducing significant 
decrease of rainfall over Indonesia, with the opposite occurring for

negative IOD events (As-syakur et al., 2014; Nur’utami and Hidayat, 
2016; Pan et al., 2018). Similarly, over the Ganges and Brahmaputra 
river basins major droughts have been recorded during co-occurring 
El Niño and positive IOD, while floods occurred during La Niña and 
negative IOD conditions (Pervez and Henebry, 2015). Over equatorial 
East Africa the IOD affects the short rain season ( medium confidence ) 
exacerbating flooding and inundations independently of ENSO 
(Behera et al., 2005; Conway et al., 2005; Ummenhofer et al., 2009; 
Hirons and Turner, 2018). Extreme conditions, like the 2019 Australian 
bushfires and African flooding, have been associated with strong 
positive IOD conditions (Cai et al., 2021).
Intraseasonal variability, like the Madden Julian Oscillation (MJO, 
Section AIV.2.8) and the Boreal Summer Intraseasonal Oscillation 
(BSISO), are highly relevant to the water cycle (Maloney and Hartmann,

2000; Lee et al., 2013; Yoshida et al., 2014; Nakano et al., 2015). Since 
AR5, studies on MJO teleconnections within the tropics and from

1105
Water Cycle Changes Chapter 88the tropics to higher latitudes have continued (Guan et al., 2012;  
Mundhenk et al., 2018; Tseng et al., 2019; Aberson and Kaplan, 
2020; Finney et al., 2020b; Fowler and Pritchard, 2020; Fromang and 
Rivière, 2020). 
The strength and frequency of the MJO have increased over the past 
century ( medium confidence ) (Oliver and Thompson, 2012; Maloney 
et al., 2019; Cui et al., 2020) because of global warming (Arnold 
et al., 2015; Carlson and Caballero, 2016; Wolding et al., 2017; 
Maloney et al., 2019). A 20th century reconstruction suggests a 13% 
increase of the MJO amplitude (Oliver and Thompson, 2012), with 
differences in seasonal variability (Tao et al., 2015; Z. Wang et al., 
2020). However, up to half of changes recorded during the second 
half of the 20th century could be due to internal variability (Schubert 
et al., 2013). Other observed changes in MJO characteristics include

a  decrease (by three  to four days) in the residence time over the 
Indian Ocean but an increase (by five to six days) over the Indo-
Pacific and Maritime Continent sectors (Roxy et al., 2019). 
Consequences of these changes are increased rainfall over South 
East Asia, northern Australia, south-west Africa and the Amazon, 
and drying over the west coast of the USA and Equador (Roxy et al., 
2019). During the austral summer, air –sea interactions and location 
of the MJO active phase are important to modulate the strength of 
the rainfall response in the South Atlantic Convergence Zone (Shimizu 
and Ambrizzi, 2016; Alvarez et al., 2017), including its southward 
shift (Barreiro et al., 2019). In the austral winter, the intraseasonal 
variability is mostly influential over regions of the Amazonian basin 
(Mayta et al., 2019). Some MJO phases are particularly effective in 
conjuction with tropical cyclones in enhancing westerly moisture

fluxes towards East Africa (Finney et al., 2020b).
Simulated changes in MJO precipitation amplitude are extremely 
sensitive to the pattern of SST warming (Takahashi et al., 2011; 
Maloney and Xie, 2013; Arnold et al., 2015) and ocean –atmosphere 
coupling (DeMott et al., 2019; Klingaman and Demott, 2020). In 
agreement with results from previous model generations, most 
CMIP5 models still underestimate MJO amplitude, and struggle 
to generate a  coherent eastward propagation of precipitation 
and wind (Hung et al., 2013; Jiang et al., 2015; Ahn et al., 2017), 
affecting regional surface climate in the tropics and extratropics. 
In addition, most CMIP5 models simulate an MJO that propagates 
faster compared with observations, with a poorly represented intra-
seasonal precipitation variability (Ahn et al., 2017). Over the Indian 
Ocean, the propagation speed of convection in some CMIP5 models 
tends to be slower than observed due to a strong persistence of

equatorial precipitation (Hung et al., 2013; Jiang et al., 2015). Among 
other processes, improving the moisture-convection coupling, 
the  representation of moist convection, the interaction between 
lower tropospheric heating and boundary layer convergence, and the  
topography of the Maritime Continent improve simulations of 
the MJO ( Ahn et al., 2017, 2020a; Kim and Maloney, 2017; Yang 
and Wang, 2019; H. Tan et al., 2020; Y .-M. Yang et al., 2020 ). In 
fact, CMIP6 models reproduce the amplitude and propagation of 
the MJO better than CMIP5 models due to increased horizontal 
moisture advection over the Maritime Continent (Ahn et al., 2020b). 
Despite the diverse theories of MJO evolution and processes that have been developed since its discovery, a better understanding of 
its dynamics is still needed (Jiang et al., 2020; Zhang et al., 2020). 
Furthermore, metrics based on dynamical processes are needed to 
assess model simulations of these events (Stechmann and Hottovy,

2017; B.  Wang et al., 2018) as well as related teleconnections 
(J. Wang et al., 2020).
In summary, multiple water cycle changes related to ENSO and IOD 
teleconnections have been observed across the 20th century ( high 
confidence ), mostly dominated by interannual to multi-decadal 
variations. The MJO amplitude has increased in the second half of 
the 20th century partly because of anthropogenic global warming 
(medium confidence ) altering regional precipitation signals.
8.3.2.9.2 Extratropical modes
A positive trend has been observed in the Northern Annular Mode 
(NAM; Section AIV.2.1) in the second half of the 20th century, which 
partially reversed since the 1990s (Section 2.4.5.1), but the detection 
and attribution of these changes remain difficult (Section  3.7.1). 
The linkages of the NAM with weather and climate extremes in the 
northern extratropics are still unclear in models and observations 
(Vihma, 2014; Overland et al., 2016; Screen et al., 2018). However,

robust links are identified between precipitation trends and 
variability in Europe and the phases of the Atlantic component of 
the NAM, that is, the NAO (Moore et al., 2013; Comas-Bru and 
McDermott, 2014). Reduced winter precipitation is well correlated 
with the NAO over Southern Europe and Mediterranean countries 
(Kalimeris et al., 2017; Corona et al., 2018; Vazifehkhah and Kahya, 
2018; Neves et al., 2019). NAO teleconnections in those regions 
include influences on groundwater and streamflow (Zamrane et al., 
2016; Massei et al., 2017; Jemai et al., 2018). Remote teleconnections 
of the NAO have been identified over Northern China, the Yangtze 
River valley and India (Jin and Guan, 2017; Di Capua et al., 2020). The 
summer phase of the NAO is significantly correlated with variations 
in summer rainfall in East China, with the thermal forcing of the 
Tibetan Plateau providing a  link to this Eurasian teleconnection 
(Z. Wang et al., 2018).

In the Southern Hemisphere (SH), an observed positive trend is 
identified in the strength of the Southern Annular Mode (SAM, 
Section AIV.2.2) since 1950, especially in austral summer ( high 
confidence , Section  2.4.1.2). While stratospheric ozone depletion 
and GHG increases largely contributed to this change, climate 
models still have trouble simulating the SAM and its response to 
ozone and GHGs (Section 3.7.2). Shifts in the south-westerly winds 
(Fletcher et al., 2018) and the expansion of the SH Hadley cell (Kang 
and Polvani, 2011; H.  Nguyen et al., 2018) influence SAM-related 
rainfall anomalies in in southern South America and southern 
Australia during the austral spring–summer. Over New Zealand, 
large-scale SLP and zonal wind patterns associated with SAM 
phases modulate regional river flow (Li and McGregor, 2017). The 
SAM also influences precipitation and water vapour changes over 
Antarctica via moisture fluxes (Marshall et al., 2017; Oshima and

Yamazaki, 2017; Grieger et al., 2018) but CMIP5 models are limited 
in their ability to simulate these regional teleconnections (Marshall 
and Bracegirdle, 2015; Palerme et al., 2017). SAM and its interaction

1106
Chapter 8  W ater Cycle Changes8with other large-scale modes of climate variability, like ENSO (Fogt 
et al., 2011) and the Indian Ocean Dipole (Hoell et al., 2017a), are 
responsible for fluctuations in southern African rainfall (Nash, 2017) 
and southern South America (Gergis and Henley, 2017). In May, the 
SAM can trigger a southern Indian Ocean Dipole SSTA favoring more 
or less precipitation over the Indian sub-continent and adjacent 
areas (Dou et al., 2017), also affecting subsequent summer monsoon 
in the South China Sea (T.  Liu et al., 2018). Over South America, 
a positive SAM is associated with dry conditions (Holz et al., 2017) 
due to reduced frontal and orographic precipitation and weakening 
of moisture convergence. Regions particularly affected include Chile 
(Boisier et al., 2018) and the rivers of central Patagonia (Rivera 
In summary, while the attribution of 20th century variations of

the NAM/NAO is still unclear, there is a  strong relationship with 
precipitation changes over Europe and in the Mediterranean region 
(high confidence ). SAM teleconnections are associated with changes 
in moisture transport and extend to South America, Australia and 
Antarctica ( high confidence ) with documented drying occurring as 
a result of the very likely  human-induced SAM trend toward its positive 
phase observed from the 1970s until the 1990s (Section 3.7.2).
8.4 What Are the Projected Water 
We consider global and regional climate projections of the 
water  cycle, assessing projected changes in each component of 
the water cycle  (Section  8.4.1) and the global-scale and regional 
phenomena that directly impact it (Section 8.4.2). 
8.4.1 Projected Water Cycle Changes 
Most projected changes in the water cycle are not expected to be 
uniform in space or time. They are driven by both dynamical and

thermodynamical processes (Section  8.2) and have not necessarily 
emerged yet in the recent observational record (Section 8.3) as they 
are superimposed on substantial natural fluctuations in weather and 
climate. Therefore, projecting regional water cycle changes remains 
challenging. However, a number of physically understood responses 
can be evaluated using both CMIP5 and CMIP6 models, which are 
important for guiding decision making that anticipates, prepares for, 
and responds to water cycle changes. In this section, global maps of 
projected changes in water cycle variables are assessed using the 
WGI AR6 ‘simple method’ (see Cross-Chapter Box Atlas1), which uses 
hatching to highlight where less than 80% of the models agree on 
the sign of projected changes. This choice differs from Section 4.2.6 
for a  number of reasons. These include the weak signal-to-noise 
ratio of projected hydrological changes in low to medium emissions

scenarios, the sensitivity of their statistical significance to the 
baseline reference period, and the non-Gaussian distribution of many 
water cycle variables (see Cross-Chapter Box Atlas.1 for more details 
on strengths and limitations of the hatching methods implemented 
within AR6).8.4.1.1 Global Water Cycle Intensity and P–E  
Over Land and Oceans
As discussed in 8.3.1.1, the definition of global water cycle intensity 
varies from the simple metric of increases in global mean precipitation 
to broader joint considerations of water vapour and its transport, 
precipitation minus evaporation (P–E) rates and continental runoff 
(Figure 8.1). The AR5 determined that globally averaged precipitation 
is virtually certain  to increase with temperature and that there is high 
confidence  that the contrast of annual mean precipitation between 
dry and wet regions and seasons will increase over most of the globe 
as temperatures and moisture transports increase (Collins et al.,

2013). The AR5 also highlighted that continued ocean warming for 
a  few decades after GHG forcing stabilizes or begins to decrease 
will also lead to further increases in global mean precipitation 
and evaporation.
In this Report, Chapter 4 provides an updated assessment of global 
annual precipitation (Section 4.3.1), finding that it is very likely  that 
annual precipitation averaged over all land regions continuously 
increases as global surface temperatures increase in the 21st 
century ( high confidence ). CMIP6 projections for long-term changes 
in P–E (Figure 8.13) show that, for all scenarios, P–E increases over 
the tropics and high latitudes and decreases over the subtropics, 
resulting from a  thermodynamically driven amplification of P–E 
patterns (Section  8.2.2.1). Both the intensity of changes and the 
spread among the models is larger for the higher emissions scenarios. 
A less coherent latitudinal pattern and smaller magnitude of P–E

changes over land reflect the complex influence of land–ocean 
warming contrast, atmospheric circulation change and vegetation 
feedbacks (Section  8.2.2.1). However, stronger atmospheric 
moisture transport, increases in precipitation and evaporation over 
global land and ocean and larger continental runoff that is in part 
fed by melting of glaciers characterizes a more intense water cycle 
with global warming.
Global and global land mean water cycle changes from CMIP6 
projections are shown in Table 8.1. Increases in global and continental 
precipitation, P–E and runoff in both the mid-term and long-term 
illustrate the future intensification of the water cycle, with the 
magnitude of change increasing with emissions scenarios. Consistent 
with AR5, CMIP6 simulations of global mean precipitation show 
a systematic multi-model mean increase of 1.6 to 2.9 % °C–1 warming 
(apparent hydrological sensitivity; Section  8.2.1) by 2081 –2100

relative to present day across the new SSP scenarios (using global 
surface air temperature change from Table 4.1). It is well understood 
that rising concentrations of CO 2 drive a long-term increase in global 
precipitation with warming, but with the increase partly offset by 
rapid atmospheric adjustments to the direct atmospheric heating 
from radiative forcing agents (Section  8.2.1). The largest apparent 
hydrological sensitivity is found for SSP1-1.9, where the suppressing 
effects on precipitation from atmospheric heating by greenhouse 
gases (GHGs) rapidly reduce as their concentration falls. Additional 
warming due to reduced aerosol loadings under the SSP scenarios 
(Lund et al., 2019)  further increases global precipitation (Rotstayn 
et al., 2013; Wu et al., 2013; Salzmann, 2016; T.B. Richardson et al.,

1107
Water Cycle Changes Chapter 882018b; Samset et al., 2018b; Westervelt et al., 2018), with particularly 
strong contributions from increased monsoon rainfall over East and South Asia (Levy et al., 2013; Westervelt et al., 2015; Dwyer and 
O’Gorman, 2017).
Over global land there is a  small range in global mean multi-
model mean precipitation increase across scenarios in the mid-term (2.6
–4.0  %), which widens (to 2.6 –8.8 %) in the long-term 
(Table  8.1). The long-term projections are consistent with the Chapter 4 assessment that global annual precipitation over land is 
projected to increase on average by 2.4 [
–0.2 to +4.7] % (likely range) 
in the SSP1-1.9 low-emissions scenario and by 8.3 [0.9 to 12.9] % in 
the SSP5-8.5 high emissions scenario by 2081–2100 relative to 
1995–2014. Small differences in assessed model mean changes in 
Chapter  4, Table  4.2 result from a  slightly different set of models considered for Table 8.1. Over land, P–E increases by around 2
–3%

in the mid-term (apart from SSP5-8.5 where increases are almost 5%) and around 1
–12% in the long-term, determined by increased 
moisture transport from the ocean to land (Section 8.4.1.2). Runoff increases are larger and less certain due to additional inputs from 
glacier melt and changes in groundwater storage (Section 8.4.1.7). 
Overall, precipitation and runoff are very likely to increase over the global land in all scenarios in the mid- and long term. P–E is likely to 
increase over global land in the mid- and long term and very likely in 
SSP1-1.9, SSP3-7.0 and SSP5-8.5 pathways. The mid-term consistency 
in projections across scenarios is not apparent for precipitable water 
vapour, which increases over land by around 6
–15% in the mid-term 
and 5 –36% in the long-term across all scenarios. This implies that

increases in extreme precipitation (closely related to atmospheric water vapour content; Section 8.2.3.2) are dependent on mitigation pathway, even in the mid-term (Section  11.4.5).  Water vapour 
residence time (computed as the ratio of precipitable water vapour 
to precipitation from values in Table 8.1) increases from eight days in the present to nine days in mid-term and up to about ten days in the long-term over land in SSP3-7.0, indicating a longer time to moisten the atmosphere between precipitation events. The CMIP6 projections 
are therefore consistent with an intensification but not acceleration 
of the global water cycle.
In summary, it is virtually certain that global water cycle intensity, 
considered in terms of global and continental mean precipitation, 
evaporation and runoff, will increase with continued global warming. 
Global annual precipitation over land is projected to increase on average by 2.4 [–0.2 to +4.7] % ( likely range) in the SSP1-1.9 low-

emissions scenario and by 8.3 [0.9 to 12.9] % in the SSP5-8.5 high emissions scenario by 2081–2100 relative to 1995–2014.
8.4.1.2  W ater Vapour and Its Transport
Globally, AR5 assessed that by the end of the 21st century, the average quantity of water vapour in the atmosphere could increase by 5–25%, depending on emissions. The AR5 assessed that increases 
in near-surface specific humidity over land are very likely, but that it 
was also likely that near-surface relative humidity would decrease 
over many land areas, although with only medium confidence. In 
terms of moisture transport, AR5 assessed that it was likely that 
moisture transport into the high latitudes would increase and that there was high confidence that, over the ocean, atmospheric 
moisture transport from the evaporative regions to the wet regions would increase. 
Table 8.1 | Global and global land annual mean water cycle projections in the mid-term (2041–2060) and long term (2081–2100) relative to present day

(1995–2014), showing present day mean and 90% confidence range across CMIP6 models (historical experiment) and projected mean changes and the 90% confidence range across the same set of models and a range of Shared Socio-economic Pathway scenarios.  Note that the exact value of changes can 
vary slightly based on the number of models assessed, but not sufficiently to affect the assessment. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).
Mid-term: 2041–2060 Minus Reference Period Long Term: 2081–2100 Minus Reference Period
1995–2014 
reference 
periodSSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5 SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5
Global Annual
Precipitation 
(mm day–1)2.96 [2.76 to 3.17]0.06 [0.03 to 0.11]0.07 [0.03 to 0.12]0.07 [0.04 to 0.12]0.06 [0.03 to 0.11]0.08 [0.03 to 0.14]0.06 [0.02 to 0.11]0.09 [0.04 to 0.17]0.12 [0.07 to 0.21]0.15 [0.08 to 0.24]0.2 [0.1 to 0.33]
Precipitable Water (kg m

2)24.79 [23.06 to 26.82]1.42 [0.7 to 2.26]1.84 [1.03 to 2.62]2.29 [1.6 to 3.09]2.7 [1.92 to 3.92]3.15 [2.13 to 4.38]1.11 [0.28 to 2.13]2.11 [0.98 to 3.15]3.76 [2.41 to 5.08]6.2 [4.24 to 8.83]7.92 [5.21 to 10.69]
Global Land Annual
Precipitation (mm day
–1)2.27 [1.98  to 2.58]0.07 [0.02 to 0.11]0.07 [–0.0 to 0.13]0.06 [0.01 to 0.13]0.06 [0.02 to 0.12]0.09 [0.01 to 0.16]0.06 [0.01 to 0.1]0.08 [0.02 to 0.16]0.11 [0.02 to 0.19]0.14 [0.03 to 0.22]0.2 [0.07 to 0.32]
Precipitation – Evaporation 
(mm day
–1)0.87 [0.49 
to 1.26]0.02 [0.0 to 0.03]0.02 [–0.01 to +0.05]0.02 [–0.02 to +0.06]0.03 [–0.0 to +0.06]0.04 [0.0 to 0.1]0.01 [–0.0 to +0.03]0.03 [–0.01 to +0.08]0.04 [–0.01 to 0.07]0.07 [0.0 to 0.12]0.1 [0.01 to 0.22]
Runoff (mm day
–1)0.79 [0.54 
to 1.0]0.02 [0.0 to 0.05]0.04 [–0.0 to +0.1]0.04 [–0.0 to 
+0.11]0.04 [0.01 
to 0.08]0.06 [0.01 to 0.14]0.02 [–0.0 to 
+0.03]0.04 
[–0.0 to 
+0.13]0.06 [0.0 
to 0.17]0.1 [0.02 to 0.2]0.15 [0.04 to 0.27]

2)18.86 [17.12 to 21.28]1.23 [0.57 to 1.96]1.58 [0.77 to 2.42]1.96 [1.34 to 2.76]2.33 [1.63 to 3.46]2.72 [1.79 to 3.84]0.95 [0.19 to 1.95]1.78 [0.8 to 2.77]3.18 [2.04 to 4.34]5.33 [3.57 to 7.5]6.81 [4.35 to 9.32]

1108
Figure 8.13 | Zonal and annual-mean projected long-term changes in the atmospheric water budget.  Zonal and annual mean projected changes (mm day–1) 
in P (precipitation, left column), E (evaporation, middle column), and P–E (right column) over both land and ocean areas (coloured lines) and over land only (black lines) 
averaged across available CMIP6 models (number provided at the top left of each panel) in the SSP1-2.6 (top row), SSP2-4.5 (middle row) and SSP5-8.5 (bottom row) scenario, 
respectively. Shading denotes confidence intervals estimated from the CMIP6 ensemble under a normal distribution hypothesis. Colour shading denotes changes over both land 
and ocean. Grey shading represents internal variability derived from the pre-industrial control simulations. All changes are estimated for 2081–2100 relative to the 1995–2014 
base period. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

CMIP6 climate models continue to project a  steady increase in 
global mean column-integrated water vapour by around 6 –13% 
by 2041 –2060 and 5 –32% by 2081 –2100, depending on scenario 
(Table  8.1). This is consistent with projected atmospheric warming 
(Section  4.5.1.2) and the Clausius–Clapeyron relationship 
(Section 8.2.1) where every degree Celsius of warming is associated 
with an approximate 7% increase in atmospheric moisture in the lower 
atmospheric layers where most of the water vapour is concentrated. 
This increase sustains a positive feedback on anthropogenic global 
warming (Section  7.4.2.2). In contrast, the response of clouds is much more spatially heterogeneous, microphysically complex, and 
model-dependent so that the projected cloud feedbacks remain a key 
uncertainty for constraining climate sensitivity (Section 7.4.2.4). 
CMIP6 models project an overall decrease in near-surface relative 
humidity over land, although with some regional and seasonal

variations in their response (Figure 4.26). Regional changes in near-
surface humidity over land are dominated by thermodynamic processes 
and are primarily controlled by moisture transport from the warming 
ocean (Chadwick et al., 2016a). Increases in specific humidity lower

1109
Water Cycle Changes Chapter 88than the thermodynamic rate are explained by greater warming over 
land than ocean and modulated by land –atmosphere feedbacks such 
as soil moisture and plant stomatal changes (Section 8.2.2.1; Berg 
et al., 2017; Douville et al., 2020). This explains why climate models 
continue to project a  contrasting response of near-surface relative 
humidity, with a  slight and possibly overestimated increase over 
the oceans and a consistent but possibly underestimated decrease 
over land (Byrne and O’Gorman, 2016; Douville and Plazzotta, 2017; 
R. Zhang et al., 2018).
While projections of water vapour are well understood due to the 
constraints of the Clausius–Clapeyron relationship, projections 
of water vapour transport are complicated regionally by the role of  
changes in the wind field, which is influenced by a  wide variety 
of  factors. Additionally, there has been relatively little general

evaluation of moisture transport in models. In CMIP5 models, 
both the mean and variability of the vertically-integrated moisture 
transport is projected to increase, largely due to increases in water 
vapour (Lavers et al., 2015), with substantial regional differences 
(Levang and Schmitt, 2015). Single-model studies have illustrated 
projected increases in low-altitude moisture transport into 
convergence regions (Allan et al., 2014) and from ocean to land 
(Zahn and Allan, 2013) that are consistent with present day trends. 
Increases in moisture transport have been linked to increases in large 
precipitation accumulations over land (Norris et al., 2019). Based 
on robust physics and supported by modelling studies, it is well 
understood that moisture transport increases into convergent parts 
of the atmospheric circulation such as storm systems, the tropical rain 
belt and high latitudes (Section 8.2.2.1), but changes in atmospheric

circulation that are less well understood alter moisture transport 
regionally (Section 8.2.2.2). Therefore, given the limited examination 
of moisture transport in models, regional projections should be 
considered with caution. Changes in moisture transport specifically 
associated with monsoons, atmospheric rivers, and other specific 
circulation features are discussed further in the following sections.
In summary, there is high confidence  in continued increases in global 
mean column integrated water vapour and near-surface specific 
humidity over land. There is medium confidence  in region and season-
dependent decreases in near-surface relative humidity over land, due 
to the complex physical processes involved . In general, there will be 
increases in moisture transport into storm systems, monsoons and 
high latitudes ( medium confidence ).
8.4.1.3 Precipitation Amount, Frequency and Intensity
This section assesses projected changes in precipitation at regional

scales. Note that changes in precipitation seasonality are assessed 
in Box 8.2 and that changes in regional monsoons are assessed in 
Section 8.4.2.4, where both circulation and rainfall are considered. 
Further assessments of regional projections of precipitation are 
presented in Chapters 10, 12 and the Atlas, while a comprehensive 
assessment of changes in precipitation extremes is provided in 
Chapter 11.
The AR5 assessed that the contrast of mean precipitation amount 
between dry and wet regions and seasons is expected to increase over most of the globe as temperatures increase ( high confidence ), 
but with large regional variations. Precipitation over the high 
latitudes, equatorial Pacific Ocean, mid-latitude wet regions, and 
monsoon regions were assessed as likely  to increase under the 
RCP8.5 scenario, and in many mid-latitude and subtropical dry 
regions as likely  to decrease (AR5 Chapters 7, 12, and 14). Extreme

precipitation over most mid-latitude land areas and wet tropical 
regions was assessed as very likely  to become more intense and 
more frequent.
Geographical patterns of projected precipitation changes show 
substantial seasonal contrasts and regional differences, including 
over land (Figure 8.14 and Figure 4.27). Projections for 2081 –2100 
under the SSP2-4.5 scenario  suggest increased precipitation over 
the tropical oceans, north-eastern Africa, the Arabian Peninsula, 
India, south-eastern Asia and the Polar regions while decreased 
precipitation is projected mainly over the subtropical regions 
(Section  4.5.1.4). Precipitation changes contrast regionally in the 
tropics with wetter wet seasons over South Asia, central Sahel and 
eastern Africa, but less precipitation over Amazonia and coastal 
West Africa (Section  8.4.2.4). These large-scale responses are 
associated with stronger moisture transports in a warmer climate

that are modulated by the greater warming over land than ocean, 
atmospheric circulation responses and land surface feedbacks 
(Section  8.2.2). There is agreement across CMIP5 and CMIP6 
modelling studies that precipitation increases in wet parts of the 
atmospheric circulation and decreases in dry parts (Liu and Allan, 
2013; Kumar et al., 2015; Deng et al., 2020; Schurer et al., 2020) 
although these regions shift with atmospheric circulation changes. 
The overall pattern is robust across different model scenarios and 
time horizons (Tebaldi and Knutti, 2018), but some deviations 
from the mean pattern cannot be excluded due to the multiple 
time scales and non-linear atmospheric or land surface processes 
involved (Section 8.5.3). Near-term regional changes in precipitation 
are more uncertain because of a  stronger sensitivity to natural 
variability (Section  8.5.2) and non-GHG anthropogenic forcings 
(Section 4.4.1.3 and 8.4.3.1).

Projected changes in regional precipitation also arise as a response 
to changes in large-scale atmospheric circulation (Section  8.2.2.2 
and 8.4.2), both in the tropics (Chadwick et al., 2016b; Byrne et al., 
2018)  and extratropics (Shaw, 2019; Oudar et al., 2020b). Despite 
variability in simulated changes, CMIP5 climate models consistently 
project large rainfall changes (of varying sign) over considerable 
proportions of tropical land during the 21st century (Chadwick 
et al., 2016b). Since AR5, some robust responses in large-scale 
circulation patterns have been identified. For example, and as further 
assessed in Section 8.4.2, CMIP6 models project a northward shift 
in the tropical rain belt over eastern Africa and the Indian Ocean 
and a  southward shift in the eastern Pacific and Atlantic oceans 
(Mamalakis et al., 2021). A projected strengthening and tightening 
of the tropical rain belt increases the contrasts between wet and

dry tropical weather regimes and seasons. It is less clear how the 
well understood poleward expansion of the subtropics and mid-
latitude storm tracks influences precipitation over subtropical and 
mid-latitude continents (Section 8.2.2.2).

1110
An ensemble of 31 CMIP6 models under the SSP5-8.5 scenario 
projects increases precipitation by 10–30% over much of the USA 
and decreases by 10–40% over Central America and the Caribbean 
by 2080 –2099 (Almazroui et al., 2021). This CMIP6 ensemble also 
projects an increase in annual precipitation over the southern 
Arabian Peninsula and a  decrease over the northern Arabian 
Peninsula, as also projected by CMIP3 and CMIP5 models (Almazroui 
et al., 2020a). Annual mean precipitation is projected to increase over 
South Asia during the 21st century under all scenarios, although the 
rate of change varies within the region based on 27 CMIP6 models 
(Almazroui et al., 2020c). CMIP6 projections also display a reduction 
in annual mean precipitation over northern and southern Africa 
while increases are projected over Central Africa, under the SSP1-2.6, 
SSP2-4.5 and SSP5-8.5 scenarios (Almazroui et al., 2020b). The AR6

Atlas assesses that regions where annual mean rainfall is likely  to 
increase include the Ethiopian Highlands, East, South and North Asia, 
south-eastern South America, northern Europe, northern and eastern 
North America, and the Polar Regions. In contrast, regions where annual mean rainfall is likely  to decrease include southern Africa, 
coastal West Africa, Amazonia, south-western Australia, Central 
America, south-western South America, and the Mediterranean.
The AR5 identified that high-latitude precipitation increase may lead 
to an increase in snowfall in the coldest regions and a decrease of 
snowfall in warmer regions due to a decreased number of freezing 
days. The fraction of precipitation falling as snow and the duration 
of snow cover was projected to decrease. Heavy snowfall events 
globally are not expected to decrease significantly with warming 
as they occur close to the water freezing point, which will migrate

poleward and in altitude (O’Gorman, 2014; Turner et al., 2019). There 
are only a  small number of studies evaluating the implications of 
this mechanism in specific regions. A study for the north-eastern USA 
indicates smaller reductions for major snowfall events against the 
broader decline in snowfall expected from thermodynamic effects 
(Bintanja and Andry, 2017). Arctic snowfall is projected to decrease 
as rainfall makes up more of the precipitation (Zarzycki, 2018). 
Figure 8.14 | Projected long-term relative changes in seasonal mean precipitation. Global maps of projected relative changes (%) in seasonal mean of precipitation 
averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP2-4.5 scenario. All changes are estimated for 2081–2100 relative to the 
1995–2014 base period. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign

of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please 
refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1111
Figure 8.15 | Projected long-term relative changes in daily precipitation statistics.  Global maps of projected seasonal mean relative changes (%) in the number of 
dry days (i.e., days with less than 1 mm of rain) and daily precipitation intensity (in mm day–1, estimated as the mean daily precipitation amount at wet days – for example, days 
with intensity above 1 mm day–1) averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP1-2.6 (a, b) , SSP2-4.5 (c, d)  and SSP5-8.5 
(e, f)  scenario respectively. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on 
sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach,

please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).
Beyond annual or seasonal mean precipitation amounts, an implication 
of the parallel intensification of the global water cycle and of the 
increased residence time of atmospheric water vapour (Section 8.2.1) 
is that the distribution of daily and sub-daily precipitation intensities 
will experience significant changes (Pendergrass and Hartmann, 
2014b; Pendergrass et al., 2015; Bador et al., 2018; Douville and John, 
2021), with fewer but potentially stronger events ( high confidence ) (Section  4.3.3). CMIP6 projections show that in the long-term 
more drier days but more intense single events of precipitation are 
expected, regardless of scenario (Figure 8.15). Over almost all land 
regions, it is  very likely  that extreme precipitation will intensify at 
a rate close to the 7% °C–1 of global warming, but with large spatial

differences (Sections  11.4 and 8.2.3.2). The projected increase in 
precipitable water is expected to lead to an increase in the highest

1112
Figure 8.16 | Rate of change in components of water cycle mean and variability across increasing global warming levels. Relative change (%) in seasonal 
mean total precipitable water (grey line), precipitation (red solid lines), runoff (blue solid lines), as well as in standard deviation of precipitation (red dashed lines) and runoff 
(blue dashed lines) averaged over extratropical land in (c) summer and (d) winter, and tropical land in (a) June–July–August (JJA) and (b) December–January–February (DJF) 
as a function of global mean surface temperature for the CMIP6 multi-model mean across the SSP5-8.5 scenario. Extratropical winter refers to DJF for Northern Hemisphere 
and JJA for Southern Hemisphere (and the reverse for extratropical summer). Each marker indicates a 21-year period centred on consecutive decades between 2015 and 2085

relative to the 1995–2014 base period. Precipitation and runoff variability are estimated by their standard deviation after removing linear trends from each time series. Error 
bars show the 5–95% confidence interval for the warmest 5°C global warming level. Figure adapted from Pendergrass et al. (2017) and updated with CMIP6 models. Further 
possible precipitation intensities and an increase in the probability 
of occurrence of extreme precipitation events on the global scale 
(Neelin et al., 2017), regardless of how annual-mean precipitation 
changes (O’Gorman and Schneider, 2009; O’Gorman, 2015). The 
projected increase in heavy precipitation intensity is also found for daily mean precipitation intensity though at a lower rate (Pendergrass 
and Hartmann, 2014a).
An increase in the number of dry days is also projected in several

regions of the world (Polade et al., 2014; Berthou et al., 2019a),

1113
Water Cycle Changes Chapter 88which can dominate the annual precipitation change at least 
in the subtropics (Polade et al., 2014; Douville and John, 2021). 
These findings are supported by CMIP6 projections showing 
a  widespread increase in daily mean precipitation intensity over 
land (Figure 8.15b,d,f) as well as an increase in the number of dry 
days in the subtropics and over Amazonia and Central America 
(Figure 8.15a,b,c). Such changes in precipitation regimes, as well as 
the general increase in the frequency and intensity of precipitation 
extremes (Section  11.4.5), contribute to an overall increase in 
precipitation variability (Polade et al., 2014; Pendergrass et al., 
2017; Douville and John, 2021). This is also found in CMIP6 models, 
which show a stronger increase of interannual variability than in 
seasonal mean precipitation changes, apart from in the winter 
extratropics where both quantities increase at the same rate with

increasing global warming levels (Figure 8.16). In summary, it is virtually certain  that global precipitation will 
increase with warming due to increases in GHG concentrations 
and decreases in air pollution. There is high confidence  that total 
precipitation will increase in the high latitudes, with a  shift from 
snowfall to rainfall except in the coldest regions and seasons. 
There is also high confidence  that precipitation will decrease over 
the Mediterranean, southern Africa, Amazonia, Central America, 
south-western South America, south-western Australia and coastal 
West Africa and that monsoon precipitation will increase over South 
Asia, East Asia and central-eastern Sahel. See Section  8.4.2.4 for 
a more detailed assessment of changes in regional monsoons. Daily 
mean precipitation intensities, including extremes, are projected to 
increase over most regions ( high confidence ). The number of dry days

is projected to increase over the subtropics, Amazonia, and Central 
America ( medium confidence ). There is high confidence  in an overall 
increase in precipitation variability over most land areas.
Box 8.2 | Changes in Water Cycle Seasonality
Observed changes
The AR5 did not highlight observed changes in water cycle seasonality  and SRCCL mostly emphasized changes in vegetation 
seasonality. Since AR5, a  number of relevant studies have been published, but often with conflicting results. Based on three in 
situ datasets, reduced precipitation seasonality was identified over 62% of the terrestrial ecosystems analysed from 1950 –2009 
(Murray-Tortarolo et al. 2017). In contrast, both in situ  and satellite data show a general increase in the annual range of precipitation 
from 1979 to 2010, which is dominated by wetter wet seasons (Chou et al., 2013). This paradox may be partly explained by a larger

aerosol radiative forcing in the middle of the 20th century as well as by internal variability (Kumar et al., 2015; see also Box 8.1). For 
instance, the ‘long rains’ over East Africa experienced declining trends in the 1980s and 1990s (Nicholson, 2017), which was linked 
to anthropogenic aerosols and SST patterns (Rowell et al., 2015), followed by a recent recovery that was linked to internal variability 
(Wainwright et al., 2019). Two satellite datasets revealed decreased rainfall seasonality in the tropics but an increased seasonality in 
the subtropics and mid-latitudes since 1979, without clear attribution (Marvel et al., 2017).
Large differences have been found across seven global precipitation datasets, with no region showing a  consistent, statistically 
significant, positive or negative trend over the last three decades (X. Tan et al., 2020). Regional studies suggest that observed changes

in precipitation seasonality are neither uniform nor stable across the 20th century (X. Li et al., 2016; Mallakpour and Villarini, 2017; 
Sahany et al., 2018; Deng et al., 2019) . Since the 1980s, there is growing evidence that contrasts between wet and dry regimes, 
including seasonality, have increased (Liu and Allan, 2013; Polson et al., 2013; Murray-Tortarolo et al., 2016; Tapiador et al., 2016; 
Gallego et al., 2017; Polson and Hegerl, 2017; Barkhordarian et al., 2018; Lan et al., 2019; Liang et al., 2020; Schurer et al., 2020) . 
Additional changes in seasonality may manifest in the timing and duration of wet seasons. A later monsoon onset trend was reported 
throughout India from 1901 to 2013 (Sahany et al., 2018). Conversely, an earlier rainfall onset was implicated in increased springtime 
rainfall over the Tibetan Plateau in recent decades (W. Zhang et al., 2017a). Winter and early spring precipitation over the north-western

Himalaya for the period 1951 –2007 shows an increasing trend of daily precipitation extremes in association with enhanced amplitude 
variations of extratropical synoptic-scale systems known as ‘Western Disturbances’ (Madhura et al., 2014; Cannon et al., 2015; Krishnan 
et al., 2019) . In China, an earlier onset was observed during 1961-2012 (Deng et al., 2019). In the African Sahel, rainfall has been most 
concentrated in the peak of the rainy season since the end of the 20th century (Biasutti, 2019). A shift in the seasonality of Sahelian 
rainfall, including delayed cessation has also been reported (Section 10.4.2.1; Nicholson, 2013; Dunning et al., 2018). Over southern Africa, 
an observed earlier onset (1985 –2007) is in contrast to a simulated historical and projected future delay in the wet season (Maidment 
et al., 2015; Dunning et al., 2018). An increasingly early onset of the North American monsoon has been observed from 1978 to 2009

(Arias et al., 2015) . Seasonality changes in the South American monsoon indicate delayed onsets since 1978 (Fu et al., 2013; Yin et al., 
2014; Arias et al., 2015; Debortoli et al., 2015; Arvor et al., 2017; Giráldez et al., 2020; Haghtalab et al., 2020; Correa et al., 2021). 
In northern high latitudes, a shorter snow season (X. Zeng et al., 2018) is mainly due to an earlier onset of spring snowmelt (Peng et al., 
2013) which has been attributed to anthropogenic climate change (Najafi et al., 2016). Changes in snow seasonality affect streamflow 
at the regional scale, with an earlier peak in spring and a possible decrease of low-level flow in summer (Berghuijs et al., 2014; Kang 
et al., 2016; Dudley et al., 2017), while glacier shrinking can also alter the low-level flow in mountain catchments (Lutz et al., 2014; Milner

1114
Chapter 8  W ater Cycle Changes8 
Box 8.2 (continued)
et al., 2017; Huss and Hock, 2018). This can be partly ameliorated by water management in regulated catchments (Arheimer et al., 2017), 
but not in large river basins such as the Amazon which also shows an increased seasonality of discharge since 1979 (Liang et al., 2020).
Increasing aridity contrasts between wet and dry seasons over the late 20th century have been suggested (Kumar et al., 2015), with 
a human-induced decrease of water availability during the dry season over Europe, western North America, northern Asia, southern 
South America, Australia and eastern Africa (Padrón et al., 2020). Seasonal contrasts in microwave surface soil moisture measurements 
have also increased over 1979 –2016 (Pan et al., 2019). Terrestrial water storage variations derived from gravimetric measurements 
since 2003 show a strong seasonality which is underestimated by global hydrological models (Scanlon et al., 2019) and whose multi-

decadal trends are difficult to interpret given the direct effect of enhanced water use (Rodell et al., 2018; Scanlon et al., 2018).
In summary, there is medium confidence  that the annual range of precipitation has increased since the 1980s, at least in subtropical 
regions and over the Amazon. There is low confidence  that this increase is due to human influence and that GHG forcing has already 
altered the timing or duration of wet seasons . There is high confidence  that the human-induced retreat of the springtime snow cover 
and melting of glaciers have already contributed to changes in streamflow seasonality in high-latitude and low-elevation mountain 
catchments, and medium confidence  that human activities have also contributed to an increased seasonality of water availability, 
including a drier dry season, in the extratropics.
The AR5 reported with high confidence  that the contrast between wet and dry seasons will generally increase with global warming

and that monsoon onset dates will likely  become earlier or show little change, while monsoon retreat dates will likely  be delayed, 
resulting in a lengthening of the wet season in many regions. 
Since AR5, several studies have further documented a projected increase in rainfall seasonality and the understanding of the underlying 
mechanisms has been improved (Sections 8.2.1 and 8.3.2). CMIP5 models show that the seasonal concentration of annual precipitation 
will increase over many regions by the end of the 21st century, with robust model agreement in most subtropical regions where an 
increase in the mean number of dry days was also reported in the RCP8.5 scenario (Pascale et al., 2016). The semi-arid, winter rainfall 
dominated subtropical climate is projected to shift poleward and eastward, with the equatorward margins replaced by a more arid 
climate type. However, evolving SST patterns and land –ocean warming contrasts cause more complex responses (Alessandri et al.,

2015; Polade et al., 2017; Brogli et al., 2019; Zappa et al., 2020) . Projections over California show a stronger and shorter wet season 
(Polade et al., 2017; Dong et al., 2019). Decreases in future winter and spring rainfall are projected over south-western Australia (Hope 
et al., 2015). Central Asia is projected to experience wetter winters, associated with an increase in snow depth in the north-eastern 
regions (Y . Li et al., 2019). Even in a +2°C climate, both extreme precipitation and dryness will increase significantly in the extratropics, 
amplifying the seasonal precipitation range (Fujita et al., 2019). A single-model study shows that the annual range of precipitation 
increases globally by 2.6%  per 1°C of global warming in stabilized low-warming scenarios (Z. Chen et al., 2020a).
In the tropics, an amplified annual cycle (by about 3–5% °C–1) of global land monsoon hydroclimates (precipitation , precipitation minus

evaporation ( P–E), and runoff) is projected by CMIP5 models under the RCP8.5 scenario, mostly due to a more intense wet season 
(W. Zhang et al., 2019b). A longer rainy season is projected by CMIP6 models over most regional monsoon areas except in the Americas 
(Moon and Ha, 2020). A delayed onset and cessation of the wet season over West Africa and the Sahel (Dunning et al., 2018) and 
a slightly delayed onset of South Asian monsoon rainfall (Hasson et al., 2016) are projected by CMIP5 models. CMIP5 projections suggest 
a strengthening of the annual cycle and a lengthening of the dry season in Southern Amazonia (Fu et al., 2013; Reboita et al., 2014; Boisier 
et al., 2015; Pascale et al., 2016; Sena and Magnusdottir, 2020) . This is further verified by the projections from six CMIP6 models (Moon 
and Ha, 2020). A wet season shorter by 5 –10 days by the end to the 21st century is projected for southern Africa (Dunning et al., 2018).

An increase in streamflow seasonality is projected over several large rivers in the low-mitigation RCP8.5 scenario, but with only small 
changes in the seasonality timing, except in northern high latitudes due to the earlier but potentially slower snowmelt in a warmer world 
(Eisner et al., 2017; Musselman et al., 2017). At the end of the century in a high-emissions scenario, peak snowmelt timing is projected to 
occur one month earlier and peak water volume is 79% lower in the eastern USA (Rhoades et al., 2018). Earlier snowmelt is projected, for 
example, by 30 days at the end of the 21st century in RCP4.5 for the Sierra Nevada in the western USA (F . Sun et al., 2018). Sub-seasonal 
changes in water availability were found in many regions in the RCP8.5 scenario. However, these should be considered with caution given 
the magnitude of model errors (C.R. Ferguson et al., 2018). Increases in the seasonality of water availability has been found to be more

pronounced in areas with high atmospheric evaporative demand, giving rise to a pattern of seasonally variable regimes becoming even 
more variable (Konapala et al., 2020). RCP4.5 and RCP8.5 projections show a pronounced soil drying in summer and autumn over western 
Europe, and a springtime drying over northern Europe due to an earlier snowmelt (Ruosteenoja et al., 2018).

1115
Water Cycle Changes Chapter 88A simple relative seasonality metric (Walsh and Lawler, 1981) applied to global projections based on CMIP6 models and SSP scenarios 
supports previous CMIP5 findings, especially the amplified seasonality of precipitation around the Mediterranean, and across southern 
Africa, California, southern Australia and the Amazon (Box 8.2, Figure 1). While such changes are not significant in the low-emissions 
SSP1-2.6 scenario, they are consistent with the increased frequency of dry days projected over the same regions (Figure  8.16). 
In monsoon regions outside the Americas, rainfall seasonality does not show a significant increase even in high-emissions scenarios. 
This challenges previous CMIP5 findings based on the difference between maximum and minimum monthly precipitation in a year 
(W. Zhang et al., 2019b) and higher sensitivity to the projected increase in precipitation extremes (Section 11.4.5). In the northern high

latitudes, milder winters are associated with wetter conditions and a decrease in precipitation seasonality.
In summary, the annual range of precipitation, water availability and streamflow will increase with global warming over subtropical 
regions and the Amazon ( medium confidence ), especially around the Mediterranean and across southern Africa ( high confidence ). The 
contrast between the wettest and driest month of the year is likely  to increase by 3–5% °C–1 with global warming in most monsoon 
regions, in terms of precipitation, water availability (P–E) and runoff ( medium confidence ). There is medium confidence  that the 
monsoon season could be delayed in a warmer climate in the Sahel. There is high confidence of earlier snowmelt.  
Box 8.2, Figure 1 | Projected long-term changes in precipitation seasonality.  Global maps of projected changes in precipitation seasonality (simply defined

as the sum of the absolute deviations of mean monthly rainfalls from the overall monthly mean, divided by the mean annual rainfall as in Walsh and Lawler, 1981) 
averaged across available CMIP6 models (number provided at the top right of each panel) in the SSP1-2.6 (b), SSP2-4.5 (c) and SSP5-8.5 (d) scenario respectively. 
The simulated 1995–2014 climatology is shown in panel (a). All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the 
simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with 
low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. 
Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1116
Figure 8.17 | Projected long-term relative changes in seasonal mean evapotranspiration.  Global maps of projected relative changes (%) in seasonal mean of 
surface evapotranspiration for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across available CMIP6 models (number 
provided at the top right of each panel) for SSP1.2-6 (a, b)  SSP2-4.5 (c, d)  and SSP5-8.5 (e, f)  scenario respectively. All changes are estimated in 2081–2100 relative to 
1995–2014. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; 
diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the

Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).8.4.1.4 Evapotranspiration 
Since AR5, there is a  growing body of evidence suggesting that 
future projections in evapotranspiration are driven by changes in 
temperature and relative humidity (Laîné et al., 2014; Pan et al., 
2015; Ukkola et al., 2016a) , as well as precipitation patterns, as 
found in AR5 .Analysis of CMIP5 models suggests that atmospheric evaporative 
demand will increase over most areas of the world in high-emissions 
scenarios ( virtually certain ), mostly as a consequence of an increase 
in vapour pressure deficit (Scheff and Frierson, 2014, 2015; Greve 
and Seneviratne, 2015; Vicente-Serrano et al., 2020). CMIP5 models 
also project an increase in evapotranspiration over most land areas 
(medium confidence ) (Laîné et al., 2014). However, regional changes

1117
Water Cycle Changes Chapter 88in evapotranspiration can also be influenced by changes in soil 
moisture and vegetation, which modulate the moisture flux from the 
land to the atmosphere. Several studies of CMIP5 projections suggest 
that increases in plant water use efficiency will limit or counteract 
rising evapotranspiration (Milly and Dunne, 2016; Swann et al., 
2016; Lemordant et al., 2018; Y . Yang et al., 2018) . However, other 
studies have found that transpiration increases due to the impact of 
climate change on growing season length, leaf area, and evaporative 
demand (Section  8.2.3.3; Frank et al., 2015; Mankin et al., 2017, 
2018, 2019; Guerrieri et al., 2019; S.  Zhou et al., 2019; Vicente-
Serrano et al., 2020) . The parametrizations accounting for these 
complex physiological processes in global climate models may also 
be insufficient (Franks et al., 2017; Peters et al., 2018; Peano et al.,

2019). Thus, there is currently low confidence in the role of vegetation 
physiology in modulating future projections of evapotranspiration. 
CMIP6 models project a  geographical pattern of changes in 
evapotranspiration similar to previous generation models 
(Figure  8.17), although the magnitude is generally larger than 
found for CMIP5 projections (X. Liu et al., 2020). There is, however, 
a strong seasonality in many regions, with a larger relative increase 
in the winter season of the Northern Hemisphere (NH) and smaller 
relative changes in the summer (Figure  8.17). Evapotranspiration 
increases in most land regions, except in areas that are projected to 
become moisture-limited (due to reduced precipitation and increased 
evaporative demand), such as the Mediterranean, South Africa, and 
the Amazonian basin ( medium confidence ). The patterns of change 
increase in magnitude from low to high-emissions SSP scenarios 

In summary, future projections indicate that anthropogenic 
forcings will drive an increase in global mean evaporation over 
most oceanic areas ( high confidence ) (Figure  8.17), an increase 
in global atmospheric demand ( virtually certain ) and an increase 
in evapotranspiration over most land areas, with the exception of 
moisture-limited regions ( medium confidence ). However, substantial 
uncertainties in projections of evapotranspiration, especially at 
seasonal and regional scales, remain (see also Section 8.2.3.3 and 
Cross-Chapter Box 5.1).
8.4.1.5 Runoff, Streamflow and Flooding
The AR5 assessed that projected changes in runoff had low confidence  
over the period 2016–2035; however, under the RCP8.5 scenario, 
runoff increases by 2100 are likely  in high northern latitudes. This is 
consistent with projected regional precipitation increases, based on 
consistency of changes across different generations of models and

different forcing scenarios, and with runoff decreases being likely  
in southern Europe, the Middle East and southern Africa. There was 
considerable uncertainty in the magnitude and direction of change 
for some regions, largely driven by the uncertainty in projected 
precipitation changes, particularly across south Asia. For flooding, 
AR5 assessed with medium confidence  that flooding would increase 
over parts of South and South East Asia, tropical Africa, north-east 
Eurasia, and South America, and decrease for parts of Northern 
and Eastern Europe, Anatolia, Central Asia, Central North America, 
and southern South America. The SR1.5 assessed with medium confidence  that warming of 2°C would increase the fraction of global 
area affected by flood hazard relative to warming of 1.5°C. Projected 
climate-driven changes to runoff, streamflow, and flooding will occur 
in the context of potential human-caused land-use and land-cover

changes, which can have a large influence on surface water (Sterling 
et al., 2013) but which have considerable uncertainty in projections 
(Prestele et al., 2016).
Since AR5, studies confirm that global mean annual runoff increases 
with global surface temperature increase (X.  Zhang et al., 2014, 
2018; Lehner et al., 2019) , but varies regionally (Chen et al., 2017; 
H. Yang et al., 2017; Cook et al., 2020) . CMIP5 models display a large 
spread in the ratio of runoff to precipitation for the present-day 
climate, which applies also to future runoff changes under global 
warming (Lehner et al., 2019). In studies of CMIP6 projections, runoff 
increases in most parts of the northern high latitudes and Asia and 
north and eastern Africa, and decreases in the Mediterranean region, 
southern Africa, southern Australia and in parts of western Africa, 
as well as in Central and South America (Greve et al., 2018; Cook

et al., 2020). Projected changes in runoff also vary seasonally. In the 
Northern Hemisphere (NH), runoff increases during winter since more 
precipitation falls as rain than snow and decreases in the summer as 
less snow is available to contribute to runoff during the warm season 
(Cook et al., 2020). Global maps of projected changes for December–
January–February and June–July–August are shown in Figure 8.18, 
showing projected changes becoming larger and more consistent 
in the higher emissions scenarios. Runoff projections for CMIP6 are 
also shown in Figure 8.16 for tropical and extratropical averages at 
a range of global mean warming levels and in Table 8.1 for global 
land in different future scenarios. In the tropics, both the mean and 
interannual variability of runoff increase with warming. The increase 
in variability is roughly twice as large as the increase in the mean, 
and has a large spread across models. In the extratropics, changes

are small in the summer but there are large increases in the winter, 
with the mean increasing much more than the variability, in contrast 
to the tropics. 
Changes in streamflow vary regionally and increase in magnitude 
with emissions scenarios, as with runoff (although the two are not 
equivalent, as runoff includes both surface runoff and streamflow). 
Streamflow projections additionally require the use of hydrologic 
models forced by the output from climate models and have not been 
as widely explored as they are not variables directly included in climate 
models. On an annual basis, streamflows have been projected to 
increase in the northern high latitudes and tropical Asia and Africa, and 
to decrease in the Mediterranean, tropical South America, and South 
Africa (Döll et al., 2018). For a 4°C global warming, half of the global 
land area is projected to be exposed to increased high flows (average 
increase 25%), while about 60% may be exposed to decreased low

flows (average decrease 50%) (Asadieh and Krakauer, 2017). 
Changes in the seasonality of runoff and streamflow are assessed 
in Box  8.2. The seasonality of runoff and streamflow (calculated 
as the annual difference between the wettest and driest months 
of the year), is expected to increase with global warming in the 
subtropics, especially in the Mediterranean and southern Africa with 
high confidence , and in the Amazon with medium confidence . For

1118
regions where snowmelt is an important contributor to streamflow, 
there is high confidence that snowmelt occurring earlier in the 
year will result in peak flows also occurring earlier in the year, and 
medium confidence  that reduced snow volume and the weaker solar 
radiation earlier in the year will reduce the most intense flows (see 
Section 8.2.3.1). In roughly half of 56 large-scale glacierized drainage basins, projected runoff changes show an increase until a maximum 
is reached, beyond which runoff steadily declines because of limited 
ice volumes (Huss and Hock, 2018).
As future changes in flood events are assessed in Chapters 9, 11 
and 12, only a summary is presented here. There are a number of 
Figure 8.18 | Projected long-term relative changes in seasonal mean runoff. Global maps of projected relative change (%) in runoff seasonal mean for December–

January–February (DJF; left panels) and June–July –August (JJA; right panels) averaged across available CMIP6 models (number provided at the top right of each panel) SSP1.2-6 
(a, b) , SSP2-4.5 (c, d)  and SSP5-8.5 (e, f)  scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple 
approach. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change, diagonal lines indicate regions with low model agreement, 
where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources 
and processing are available in the chapter data table (Table 8.SM.1).

1119
Water Cycle Changes Chapter 88complicating factors for projecting both pluvial (overland) and fluvial 
(river) flooding that limit confidence in their assessment. In addition to 
precipitation, flooding also depends on basin and river characteristics 
such as permeability, antecedent soil moisture, and antecedent flow 
levels for river flooding, so projections of extreme precipitation and 
flooding are not always closely linked (Section  8.2.3.2). Possible 
changes in water resources management and land use add another 
layer of complexity to future changes. There is medium confidence  
in a general increase in pluvial and fluvial flooding, although there 
are large regional variations, discussed further in Sections  11.5.5, 
and 12.4. There is medium confidence  in a  substantial increase 
in the frequency of extreme sea level events for coastal regions 
(Section  9.6.4.2) and the associated coastal flooding is regionally

assessed in Section  12.4. The risk of glacier lake outburst floods 
(GLOFs) is expected to increase with glacier melting in some high 
mountain regions (Section 12.4).
In summary, there is medium confidence  that global runoff will 
increase with global warming, but with large regional and seasonal 
variations. There is high confidence  that runoff will increase in the 
northern high latitudes and decrease in the Mediterranean region and 
southern Africa. There is medium confidence  that runoff will increase 
in regions of central and eastern Africa, and decrease in Central 
America and parts of southern South America, with the magnitude 
of the change increasing with emissions. There is medium confidence  
that the seasonality of runoff and streamflow will increase with 
global warming in the subtropics. In snow-dominated regions, there 
is high confidence  that peak flows associated with spring snowmelt 
will occur earlier in the year and medium confidence  that snowmelt-

induced runoff will decrease with reduced snow, except in glacier-fed 
basins where runoff may increase in the near term. There is medium 
confidence  that flooding in general will increase, although with 
considerable variation based on geographic region and flood type. 
These projected climate-related changes will occur in the context of 
human-caused land-use and land-cover changes, which may also 
have a large influence.
8.4.1.6 Aridity and Drought
The AR5 concluded that regional to global-scale projections of 
aridity and drought remained relatively uncertain compared to other 
aspects of the water cycle. It reported that there is a  likely  increase in 
drought occurrence ( medium confidence ) by 2100 in regions that are 
currently drought-prone under the RCP8.5 scenario due to projected 
decreases in soil moisture. It stated that it is likely  that the most 
prominent projected decreases in soil moisture would occur in the

Mediterranean, south-western USA, and southern Africa, consistent 
with projected changes in the Hadley circulation and increased 
surface temperatures. These AR5 conclusions are generally supported 
by more recent analyses of CMIP5 models (Feng and Fu, 2013; Berg 
et al., 2017; Cook et al., 2018).
Results from the latest generation of models in CMIP6 are largely 
congruent with CMIP5. Consistent with the coherent nature of 
warming in future projections, increases in vapour pressure deficit 
and evaporative demand are widespread and consistent across 
regions, seasons, and models, increasing in magnitude in accordance with the emissions scenario ( high confidence ) (Figure  8.19; Scheff 
and Frierson, 2014, 2015; Vicente-Serrano et al., 2020). Even under 
a  low-emissions scenario (SSP1-2.6), projections of soil moisture 
show significant decreases in the Mediterranean, southern Africa, 
and the Amazonian basin ( high confidence ) (Figure 8.19). Under mid-

and high-emissions scenarios (SSP2-4.5 and SSP5-8.5), coherent 
declines emerge across Europe, westernmost North Africa, south-
western Australia, Central America, south-western North America, 
and south-western South America ( high confidence ) (Figure  8.19; 
Cook et al., 2020). Compared to CMIP5 results, CMIP6 models exhibit 
more consistent drying in the Amazonian basin (Parsons, 2020), 
more extensive declines in total soil moisture in Siberia (Cook et al., 
2020), and stronger declines in westernmost North Africa and south-
western Australia (Figure 8.19). 
Soil moisture in the top soil layer (10 cm) shows more widespread 
drying than total soil moisture, reflecting a  greater sensitivity of 
the upper soil layer to increasing evaporative demand (Figure 8.19; 
Berg et al., 2017). Conversely, total column soil moisture represents 
the carry-over of moisture from previous seasons deeper in the soil 
column, and potentially higher sensitivity to vegetation processes

(Berg et al., 2017; Kumar et al., 2019). Central America, the 
Amazonian basin, the Mediterranean region, southern Africa, and 
south-western Australia are projected to experience significant 
declines in total soil moisture, whereas declines in Europe (north 
of the Mediterranean), western Siberia, and north-eastern North 
America are limited to the surface (Figure 8.19). It should be noted 
that because models differ in their number of hydrologically active 
layers, there is less confidence in total soil moisture projections than 
surface soil moisture projections. Based on surface soil moisture 
projections, more than 40% of global land areas (excluding 
Antarctica and Greenland) are expected to experience robust year-
round drying, even under lower emissions scenarios (Cook et al., 
2020). The percentage of land area experiencing drying is slightly 
lower when runoff is used as an aridity metric instead (20–30%);

taking this into consideration, it is estimated that about a  third 
of global land areas will experience at least moderate drying in 
response to anthropogenic emissions, even under SSP1-2.6 ( medium 
confidence ) (Cook et al., 2020).
Although there are regions where multiple models predict consistent 
and significant changes in soil moisture, as with evapotranspiration 
(Section 8.4.1.4), there is still uncertainty in these projections related 
to the response of plants to elevated CO 2. Most models project 
increases in two variables that have opposite effects on surface 
water availability: plant water use efficiency (WUE) and leaf area 
index (LAI; see Section  8.4.1.4). As discussed in Sections  8.2.3.3, 
8.3.1.4 and 8.4.1.4, there is low confidence  in how these changes in 
plant physiology will affect future projections of evapotranspiration, 
and likewise, drought and aridity.
Changes in meteorological (precipitation-based) drought duration

and intensity in CMIP6 models are more robust than projected 
changes in mean precipitation, more than found in CMIP5 projections 
(Ukkola et al., 2020). Significant increases in drought duration are 
expected in Central America, the Amazonian basin, south-western 
South America, the Mediterranean, westernmost North Africa,

1120
Chapter 8  W ater Cycle Changes8southern Africa, and south-western Australia, on the order of 
0.5  to  1  month for a  moderate emissions scenario (SSP2-4.5) and 
two months for a high-emissions scenario (SSP5-8.5; Ukkola et al., 
2020). Drought intensity is projected to increase in the tropics, mainly 
in the Amazonian basin, Central Africa, and southern Asia, as well as 
in Central America and south-western South America (Ukkola et al., 
2020). The CORDEX South Asia multi-model ensemble projections 
indicate an increase in the frequency and severity of droughts over 
central and northern India during the 21st century, under the RCP4.5 
and RCP8.5 scenarios ( medium confidence ) (Mujumdar et al., 2020). 
Under intermediate or high-emissions scenarios, the likelihood of 
extreme droughts (events that have magnitudes equal to or less than 
the 10th percentile of the 1851–1880 baseline period) increases by 
200–300% in the Amazonian basin, south-western North America,

Central America, the Mediterranean, southern Africa, and south-
western South America (Cook et al., 2020). Even under a  low-
emissions scenario (SSP1-2.6), the likelihood of extreme droughts 
increases by 100% in south-western North America, south-western 
South America, the Amazon, the Mediterranean, and southern Africa 
(Cook et al., 2020). Thus, there is high confidence  that drought 
severity and intensity will increase in the Mediterranean, southern Africa, south-western South America, south-western North America, 
south-western Australia, Central America and the Amazonian basin.
Paleoclimate records provide context for these future expected changes 
in drought and aridity. In the Mediterranean, western North America, 
and Central Chile, there is high confidence that climate change will shift 
soil moisture (as represented by the Palmer Drought Severity Index) 
outside the range of observed and reconstructed values spanning the

last millennium (Figure  8.20; Cook et al., 2014; Otto-Bliesner et al., 
2016). Warmer temperatures, leading to increased evaporative losses, 
are clearly implicated in the projected future drying in these semi-arid 
regions (Dai et al., 2018), emphasizing the central role that warming 
plays in driving increased evaporative demand (Vicente-Serrano et al., 
2020). In contrast, future trajectories are more uncertain in regions 
like Central Asia and eastern Australia–New Zealand where projected 
changes in precipitation and soil moisture are less coherent (Figure 8.19 
and 8.20; Hessl et al., 2018). More information on projected changes 
in drought, including specific categories or drought, can be found in 
Section 11.6.5 and Section 12.4.
In summary, there is high confidence  that soil moisture will 
decline in semi-arid, winter-rainfall dominated areas including the 
Soil Moisture (top 10 cm) Soil Moisture (total column) Vapor Pressure Deficit

Figure 8.19 | Projected long-term relative changes in annual mean soil moisture and vapour pressure deficit. Global maps of projected relative changes (%) 
in annual mean vapor pressure deficit (left), surface soil moisture (top 10cm, middle) and total column soil moisture (right) from available CMIP6 models (number provided at 
the top right of each panel) for the SSP1.2-6  (a, b, c) , SSP2-4.5 (d, e, f)  and SSP5-8.5 (g, h, i)  scenarios respectively. All changes are estimated for 2081–2100 relative to 
a 1995–2014 base period. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement (‘Robust change’), where ≥80% of 
models agree on sign of change, diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple

approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1121
Mediterranean
Model mean 
Reconstruction
-4-20PDSI Past & Present
Future
Central Chile
-4-202PDSI
Western North America
Eastern Australia and New Zealand
-2-101PDSI
Central Asia
1100 900 1300 1500 1700 1900 2100
Year-202PDSI
-8 -4 048
PDSI(a)
(c)
(e)(b)
(d)
(f)
(h) (g)
(i) (j)
Figure 8.20 | Past-to-future drought variability in paleoclimate reconstructions and models for select regions.  On the left (a, c, e, g, i) , tree-ring reconstructed 
Palmer Drought Severity Index (PDSI) series (black line) for the Mediterranean (10°W–45°E, 30°–47°N; E.R. Cook et al., 2015; Cook et al., 2016a), central Chile (70°W–74°W, 
32°S–37°S; Morales et al., 2020), western North America (117°W–124°W, 32°N–38°N; Cook et al., 2010; Griffin and Anchukaitis, 2014), Eastern Australia and New Zealand

(136°E–178°E, 46°S–11°S; Palmer et al., 2015), and Central Asia (99°E–107°E, 47°N–49°N; Pederson et al., 2014; Hessl et al., 2018) plotted in comparison to the past-to-
future, fully-forced simulations from four ensemble members (thin blue lines) from the NCAR CESM Last Millennium Ensemble (thick blue line = ensemble mean) (Otto-Bliesner 
et al., 2016) for the same regions. The shaded area represents the range (10th to 90th percentile) of historical and future (RCP8.5) PDSI (Penman–Monteith) simulations from 
15 CMIP5 models and 34 ensemble members for the same regions (1900–2100; Cook et al., 2014). On the right (b, d, f, h, j) , the distribution of annual PDSI values from the 
past and present (850 to 2005 CE) (black) is compared to the future distribution (2006 to 2100 CE) (blue). The distributions show each of the four ensemble members from

the CESM LME simulations. The future component of the CESM LME follows the RCP8.5 scenario. Further details on data sources and processing are available in the chapter 
data table (Table 8.SM.1).

1122
Chapter 8  W ater Cycle Changes8Mediterranean, southern Africa, south-western North America, south-
western South America, and south-western Australia, as well as in 
Central America and the Amazonian basin. In general, these regions 
are expected to become drier both due to reduced precipitation 
(medium confidence ) and increases in evaporative demand ( high 
confidence ). These same regions are likely  to experience increases in 
drought duration and/or severity ( high confidence ). The magnitude of 
expected change scales with emissions scenarios ( high confidence ) 
but even under low-emissions trajectories, large changes in 
drought and aridity are expected to occur ( high confidence ) with 
consequences for regional water availability. In the Mediterranean, 
Central Chile, and western North America, future aridification will 
far exceed the magnitude of change seen over the last millennium 
8.4.1.7 Freshwater Reservoirs
8.4.1.7.1 Glaciers

Previous assessments have concluded that recent warming has led to 
a reduction in low-elevation snow cover ( high confidence ) (SROCC), 
permafrost ( high confidence ) (SROCC), and glacier mass ( high to 
very high confidence ) (AR5; SROCC). The SROCC noted that these 
declines are projected to continue almost everywhere over the 21st 
century ( high confidence ), with complete glacier loss expected in 
regions with only small glaciers ( very high confidence ). The SROCC 
supported the AR5 finding that glacier recession would continue 
even without further changes in climate. The SROCC concluded that 
cryosphere changes had already altered the seasonal timing and 
volume of runoff ( very high confidence ), which in turn had affected 
water resources and agriculture ( medium confidence ), and projected 
peak water runoff had already been reached before 2019 in some of 
the glacier regions considered. 
Chapter  9  provides detailed assessment of glacier observations

and projections (Figures  9.20 and 9.21, and Section  9.5.1). Here, 
a  summary of their key findings is presented. Since SROCC, the 
coordinated Glacier Model Intercomparison Project (GlacierMIP; 
Box  9.3; Marzeion et al., 2020) has advanced modelling efforts. 
Global glacier volumes will substantially decline in coming decades 
regardless of emissions scenario; under a  high-emissions scenario 
some areas will lose nearly all of their glacier mass (Section 9.5.1.3). 
The projected global glacier mass loss over 2015 –2100 is 29,000 
±  20,000 Gt for SSP1-2.6 to 58,000 ±  30,000 Gt for SSP5-8.5 
(Section 9.5.1). Because of their lagged response to warming, glaciers 
will continue to lose mass for decades even if global temperature is 
stabilized ( very high confidence ) (Section 9.5.1).
Global glacier mass loss projections show a  scenario-dependent 
geographic partitioning of when peak in runoff occurs (Marzeion

et al., 2020), consistent with previous studies (Radić et al., 2014; Huss 
and Hock, 2018; Hock et al., 2019b). Under a low-emissions scenario 
(Marzeion et al., 2020) all regions exhibit runoff in the decades 
prior to 2050. Under a high-emissions scenario however, low- and 
mid-latitude regions show peak runoff before approximately 2060, 
whereas Arctic regions peak in later decades around 2070 –2090. 
Antarctic glacier losses will not have peaked by the end of the century in the high-emissions scenario. Globally, peak runoff of 2.5 to 
3 mm yr–1 sea level equivalent occurs around 2090 (Marzeion et al., 
2020). Regional projections are presented in detail in Section 9.5.1 
and Figure 9.21, and briefly summarized below.
Himalaya and Central Asia:  Glaciers in the Himalayas feed ten of the 
world’s most important river systems and are critical water sources 
for nearly two billion people (Wester et al., 2019). However, they are

some of the most vulnerable ‘water towers’ (Immerzeel et al., 2020) 
that are projected to experience volume losses of approximately 30 
to 100% by 2100 depending on global emissions scenarios (Marzeion 
et al., 2020). Under mid-range emissions scenarios glaciers in this 
region are projected to reach peak runoff during the period 2020 to 
2040 (Marzeion et al., 2020).
Alaska, Yukon, British Columbia: Post-AR5 but pre-SROCC 
projections indicated a  potential 70 ±  10% reduced volume of 
glacier ice in western Canada relative to 2005 (Clarke et al., 2015) , 
with few glaciers remaining in the Interior and Rockies regions 
and maritime glaciers in north-western British Columbia surviving 
only in a diminished state. Recent global projections support these 
earlier findings, showing that glacier mass in western Canada and 
the USA may reduce by 50% under low-emissions scenarios and 
be completely lost under the highest emissions and most sensitive

glacier model combinations (Figure  9.21; Marzeion et al., 2020) . 
Arctic Canada and Alaskan glaciers are projected to experience more 
modest mass loss (0–60% depending on region, scenario, and model;  
Marzeion et al., 2020) .
Andes:  Huss and Hock (2018)  concluded that peak glacier mass was 
reached prior to 2019 for 82–95% of the glacier area in the tropical 
Andes. This is consistent with more recent global model simulations 
that show mass loss rates from low latitude glaciers that universally 
decline from the start of simulations in 2015, regardless of emissions 
scenario (Marzeion et al., 2020). Peak runoff in low-latitude Andean 
glacier-fed rivers has therefore already passed (Frans et al., 2015; Polk 
et al., 2017) but in the Southern Andes may occur in the latter half of 
the century under high-emissions scenarios (Marzeion et al., 2020).
In summary, glaciers are projected to continue to lose mass under all

emissions scenarios ( very high confidence ). Runoff from glaciers is 
projected to peak at different times in different places, with maximum 
rates of glacier mass loss in low latitude regions taking place in the 
next few decades in all scenarios ( high confidence ). While runoff from 
small glaciers will typically decrease because of glacier mass depletion, 
runoff from larger glaciers will increase with increasing global warming 
until glacier mass is similarly depleted, after which runoff peaks and 
then declines and which tends to occurs later in basins with larger 
glaciers and higher ice-cover fractions ( high confidence ). Glaciers in 
the Arctic and Antarctic will continue to lose mass through the latter 
half of the century and beyond ( high confidence ).

1123
8.4.1.7.2 Seasonal snow cover 
The AR5 assessed as very likely  that the amount and seasonal 
duration of Northern Hemisphere (NH) snow cover will reduce under 
global warming (AR5 Sections 11.3.4.2 and 12.4.6.2). Changes in the 
total amount of water in the snow cover (snow water equivalent) 
are less certain because of the competing influences of temperature 
and precipitation.
As snow cover is assessed in Chapter  9  (Section  9.5.3.3), only an 
overview of that assessment is provided here. Changes in seasonality 
of snow cover are assessed in Box  8.2. The continued consistency 
of reported results across all generations of model projections, 
along with improvements in process understanding, has increased 
confidence in snow cover projections since AR5. 
In summary, based on the results of Chapter  9, it is now virtually 
certain that future NH snow cover extent and duration will continue

to decrease with global warming. While most studies have focused 
on the NH, process understanding suggests with high confidence  that 
these results apply to the Southern Hemisphere (SH) as well. There is 
high confidence  in snowmelt occurring earlier in the year. Changes 
to the timing and amount of snowmelt will have a strong influence 
on all the other aspects of the water cycle in regions with seasonal 
snow, including run-off, soil moisture, and evapotranspiration.
8.4.1.7.3 Wetlands and lakes
The AR5 did not include specific projections for wetlands and lakes. 
The SRCCL and SROCC provided some discussion of wetlands 
projections. For coastal wetlands, SRCCL noted the importance of sea 
level rise for increased saltwater intrusion, although projections of 
coastal wetland area with sea level rise are inconclusive. Some studies 
project substantial decreases (Spencer et al., 2016) while others 
indicate possible increases (Schuerch et al., 2018). SRCCL also noted

the general expectation for decreases in water resources, including 
wetlands, in areas of decreased rainfall due to increased evaporation.
Local studies of inland wetlands project decreases in a  range of 
environments including mountain (Lee et al., 2015), mid- to high 
latitude (D. Zhao et al., 2018), and prairie  (Sofaer et al., 2016) regions. 
In addition to affecting wetland extent and density, changes in 
flooding can also affect the connectivity between wetlands and rivers 
(Karim et al., 2016). Despite a number of uncertainties underlying the 
general response of wetlands to climate change, there are multiple 
ways climate change may cause considerable stress on both inland 
and coastal wetlands (Junk et al., 2013; Moomaw et al., 2018).  
Widespread changes are also projected for lakes (Woolway et al., 
2020), including changes in lake temperature (Fang and Stefan, 1999; 
Sahoo et al., 2016), ice (Sharma et al., 2019), evaporation (W. Wang

et al., 2018), and stability and mixing (Woolway and Merchant, 
2019). Note that lake ice is also considered in Chapter  12 of this 
Report. To date, CO 2-induced lake acidification, analogous to ocean 
acidification, has not been the focus of many studies but may occur 
with continued emissions (Phillips et al., 2015). While glacier lakes in 
general increase with melting glaciers (Linsbauer et al., 2016; Colonia et al., 2017; Magnin et al., 2020) no clear projections are currently 
available (see discussion in Chapter 9).  Projections of lake level means 
and variability show substantial changes for individual lakes (Bucak 
et al., 2017; Li et al., 2021) but can be sensitive to methodology, due 
to the competing processes involved (Notaro et al., 2015). Projected 
changes to wetlands and lakes due to climate change will occur in 
the context of widespread and continuing human-caused conversion 
and degradation of wetlands (e.g, Davidson, 2014), and where water

withdrawals have a large impact on lake levels (e.g., Micklin, 2016).
In summary, there is medium confidence  that inland wetland extent 
will decrease in regions of projected precipitation decrease and 
evaporation increase, and high confidence  that sea level rise will 
increase saltwater intrusion into coastal wetlands. However, there 
is low agreement on the influence of sea level rise on the extent 
of coastal wetlands. Regarding lakes, there is high confidence  for 
temperature increases and ice decreases, based on both projections 
and physical expectations, and low confidence  for non-homogeneous 
decreases in mixing, given there is currently limited evidence .
8.4.1.7.4 Groundwater
Groundwater projections were not assessed in AR5. Groundwater 
processes are not explicitly included in most current CMIP6 models 
and so must be calculated separately with hydrologic models 
(e.g.,  R.G. Taylor et al., 2013; Cuthbert et al., 2019a) . A  range of

factors are important in assessing groundwater projections, including 
the mean difference between precipitation and evaporation, the 
intensity of precipitation (R.G. Taylor et al., 2013a), and in changes 
in snow (Tague and Grant, 2009), glaciers (Gremaud et al., 2009), 
and permafrost (Okkonen and Kløve, 2011). Climate impacts on 
groundwater are occurring in the context of severe and growing 
human-caused groundwater depletion (WGII; Konikow and Kendy, 
2005; Rodell et al., 2018; Bierkens and Wada, 2019), and water 
scarcity issues (Mekonnen and Hoekstra, 2016). Climate-related 
changes to the water cycle can influence water demand (for example, 
precipitation decreases in an irrigated area), and anthropogenic 
groundwater depletion can influence the water cycle through 
interactions with surface energy fluxes, surface water, and vegetation 
(Cuthbert et al., 2019a), although uncertainties in estimates of future 
groundwater depletion are large (Smerdon, 2017; Bierkens and Wada,

2019) . Some aspects of groundwater change will be irreversible, 
including the increase of saltwater intrusion into coastal aquifers 
with sea level rise (Werner and Simmons, 2009), and depletion of 
fossil aquifers and aquifers with very long recharge times (Bierkens 
and Wada, 2019). 
Globally, two modelling studies have shown substantial decreases in 
groundwater in regions including the Mediterranean, north-eastern 
Brazil and south-western Africa, with less clarity for other regions 
(Döll, 2009; Portmann et al., 2013) . Recent regional-scale analyses 
of the impact of water cycle changes on groundwater recharge 
(e.g.,  Meixner et al., 2016; Tillman et al., 2017; Shrestha et al., 2018 ) 
suggest changes in both seasonality and spatial distribution, which are 
amplified under a higher greenhouse-gas emissions scenario (i.e., RCP 
8.5 compared to RCP4.5). Seasonality changes are linked to increases 
during wet winter periods and declines during dry summer periods.

1124
Chapter 8  W ater Cycle Changes8Changes in spatial distribution are linked with increases in more humid 
regions and declines in more arid locations. Uncertainty in projections 
of groundwater were found to be substantially influenced by the 
conceptual and numerical models employed to estimate groundwater 
recharge (Meixner et al., 2016; Hartmann et al., 2017). Accordingly, 
current research on estimating water cycles change on groundwater 
includes a  focus on improving the numerical representation of 
groundwater systems (Bierkens et al., 2015; Döll et al., 2016). 
In summary, based on known limitations in current modelling, no 
confident assessment of groundwater projections is made here, 
although important climate-related changes in groundwater 
recharge are expected. In many environments, such climate-related 
impacts are expected to occur in the context of substantial human 
groundwater withdrawals depleting groundwater storage.

8.4.2 Projected Changes in Large-scale Phenomena 
A weakening of the tropical circulation represents a balance between 
thermodynamic increases in low level water vapour (about 7% °C–1) 
and smaller increases in global precipitation  (1–3% °C–1) that are 
influenced by rapid adjustments to radiative forcings as well as slow 
responses to warming (Section 8.2.2.2; Bony et al., 2013; Chadwick 
et al., 2013; Ma et al., 2018) . Since AR5, additional drivers of tropical 
circulation weakening have been identified, including mean SST 
warming and changes in spatial patterns of SST (He and Soden, 
2015), and the direct CO 2 radiative effect (Bony et al., 2013; He and 
Soden, 2015; Merlis, 2015). 
8.4.2.1 ITCZ and Tropical Rain Belts
CMIP5 projections show no consistent shift in the zonal mean 
position of the ITCZ ( Donohoe et al., 2013; Donohoe and Voigt, 2017; 
Byrne et al., 2018 ). The ITCZ position is strongly connected to cross-

equatorial energy transport ( Kang et al., 2008; Bischoff and Schneider, 
2014 ), which also shows no consistent change in future projections 
(Donohoe et al., 2013). Since AR5 it has been reported that most 
CMIP5 models project a narrowing of the ITCZ in response to surface 
warming together with intensified ascent in the core region and 
weakened ascent on the ITCZ edges (Lau and Kim, 2015; Byrne et al., 
2018), implying a narrowing of precipitation regions influenced by 
the ITCZ. Modelled changes in the width and intensity of the zonal 
mean ITCZ are strongly anti-correlated, for example, narrowing is 
associated with increased intensity while broadening with decreased 
intensity. Such changes are associated with changes in tropical high 
cloud fraction and outgoing longwave radiation (Su  et al., 2017; 
Byrne et al., 2018).
Regional shifts in tropical convergence zones are much larger than 
their zonal mean, and associated regional changes in precipitation

(Chadwick et al., 2013; Mamalakis et al., 2021) are characterized by 
considerable uncertainties across models (Kent et al., 2015; Oueslati 
et al., 2016). Over the tropical oceans, shifts in rain bands are strongly 
coupled with changes in SSTs (Xie et al., 2010; Huang et al., 2013) . 
Over tropical land, factors including remote SST increases (Giannini, 2010), the direct CO 2 effect (Biasutti, 2013) and land–atmosphere 
interactions (Chadwick et al., 2017; Kooperman et al., 2018) influence 
projections. CMIP6 models project a clear northward ITCZ shift over 
eastern Africa and the Indian Ocean as well as a  southward shift 
over the eastern Pacific and Atlantic oceans, as a result of regionally-
contrasting inter-hemispheric energy flows (Mamalakis et al., 2021). 
The northward movement of the ITCZ over Africa has been linked to an 
intensification of the Saharan heat low associated with greenhouse 
gas (GHG) warming (Dong and Sutton, 2015), causing the tropical

rain belt to seasonally migrate farther northward and reside there 
longer (Cook and Vizy, 2012; Dunning et al., 2018). In southern Africa, 
the projected delay in the wet season onset (Dunning et al., 2018)  
is also associated with a  circulation-based northward shift in the 
tropical rain band (Lazenby et al., 2018) . 
In summary, consistent with the AR5, the overall weakening of the 
tropical circulation is projected in CMIP5 and CMIP6 simulations 
with high confidence . It is likely  that the zonal mean of the ITCZ 
will narrow and strengthen in the core region with projected surface 
warming ( high confidence ). Distinct regional shifts in the ITCZ will 
be associated with regional changes in precipitation amount and 
seasonality ( medium confidence ).
8.4.2.2 Hadley Circulation and Subtropical Belt
The AR5 found that the Hadley cells are likely  to slow down and 
expand in response to radiative forcing, but with considerable

internal variability. Given the complexities in forcing mechanisms, 
AR5 assigned low confidence  to near-term changes in the structure 
of the Hadley circulation. The widening Hadley cells were expected to 
result in a poleward expansion of subtropical dry zones.
Model simulations since AR5 project a more noticeable and consistent 
weakening of the Northern Hemisphere (NH) winter Hadley cell than 
the Southern Hemisphere (SH) winter cell (Seo et al., 2014; Zhou 
et al., 2016), related to changes in meridional temperature gradient, 
static stability, and tropopause height (Seo et al., 2014; D’Agostino 
et al., 2017). Changes in SST patterns reduces the magnitude of 
Hadley cell weakening (Gastineau et al., 2009; Ma et al., 2012). There 
is considerable structure in Hadley circulation strength changes 
with longitude, associated with cloud-circulation interactions 
(Su et al., 2014). Subtropical anticyclones are projected to intensify

over the north Atlantic and south Pacific but to weaken elsewhere 
(He et al., 2017).
A consistent poleward expansion of the edges of the Hadley cells is 
projected (Nguyen et al., 2015; Grise and Davis, 2020), particularly in the 
SH, consistent with observed trends (Figure 8.21 and Section 8.3.2.2; 
Nguyen et al., 2015) . The main driver of future expansion appears to 
be greenhouse gas forcing (Grise et al., 2019), with uncertainty in 
magnitude due to internal variability (Kang et al., 2013). Proposed 
mechanisms for poleward expansion include increased dry static 
stability (Frierson et al., 2007; Lu et al., 2007) , increased tropopause 
height (Chen and Held, 2007; Chen et al., 2008) , stratospheric 
influences (Kidston et al., 2015) and radiative effects of clouds and 
water vapour (Shaw and Voigt, 2016; see also Section 4.5.1.5). Hadley 
cell expansion is thought to be associated with the precipitation

1125
declines projected in many subtropical regions (Shaw and Voigt, 
2016), but more recent work suggests that these reductions are 
mainly due to the direct radiative effect of CO 2 forcing  (He and 
Soden, 2015), land –sea contrasts in the response to forcing  
(Shaw and Voigt, 2016; Brogli et al., 2019)  and SST changes (Sniderman 
et al., 2019). In semi-arid, winter rainfall-dominated regions (such 
as the Mediterranean), thermodynamic processes associated with  
the land –sea thermal contrast and lapse rate changes dominate the  
projected precipitation decline in summer, whereas circulation 
changes are of greater importance in winter (Brogli et al., 2019) . 
The hydroclimates in these regions are projected to evolve with time 
due to changing contributions from rapid atmospheric circulation 
changes and their associated SST responses, as well as slower SST 
responses to anthropogenic forcing (Zappa et al., 2020) .

In summary, CMIP5 and CMIP6 models project a weakening of the 
Hadley cells, with  high confidence  for the NH in boreal winter and 
low confidence  for the SH in austral winter. The Hadley cells are 
projected to expand polewards with global warming, most notably 
in the SH ( high confidence ). There is currently low confidence  in the 
impacts on regional precipitation in subtropical regions. 
8.4.2.3 Walker Circulation
The AR5 determined that the Pacific Walker circulation was likely  to 
slow down over the 21st century, which would lead to decreased 
precipitation over the western tropical Pacific and increases over the 
central and eastern Pacific. Recent studies show consistency with 
AR5 conclusions but also show an eastward shift over the Pacific, 
mostly due to a shift towards more ‘El Niño-like’ conditions under 
global warming (Bayr et al., 2014). Other studies suggest that the 
weakening of the Walker circulation is related to the response

of the western North Pacific monsoon and to changing land–sea 
temperature contrasts, while a positive ocean–atmosphere feedback 
amplifies the weakening of both east–west SST gradient and trade 
winds in the tropical Pacific (Zhang and Li, 2017).
Since AR5, the paradox between the projected weakening and the 
observed strengthening of the Walker circulation since the 1990s 
(Section  8.3.2.2) has triggered debate about the drivers of these 
changes (England et al., 2014; McGregor et al., 2014; Kociuba and 
Power, 2015; Vilasa et al., 2017; Chung et al., 2019) . Projected changes 
in equatorial SST gradients are not entirely consistent with observed 
trends (Coats and Karnauskas, 2017; Seager et al., 2019a), and one 
CMIP5 model that projects a  future strengthening of the Walker 
circulation is more consistent with observations than other models 
(Kohyama et al., 2017). Other studies suggest that these differences 
arise from the dominant influence of internal climate variability to

the observed trends (Chung et al., 2019), or as a  consequence of 
a  systematic cold bias of most CMIP5 models in their Equatorial 
Pacific cold tongues (Seager et al., 2019a). However, the latter 
hypothesis is based on a simplified model of tropical Pacific dynamics 
and is not consistent with the current physical understanding of the 
tropical circulation response to increasing CO 2 levels (Section 8.2.2.2) 
or with independent paleoclimate evidence suggesting a  weaker 
Walker circulation under warmer climates (Tierney et al., 2019; 
McClymont et al., 2020). Different time scales of the tropical Pacific responses to global warming have been highlighted by numerical 
experiments with both comprehensive and simplified models. Results 
suggest a transient strengthening of the Walker circulation related to 
Indian Ocean warming (L. Zhang et al., 2018), followed by a slower 
weakening linked to a  strengthened eastern Pacific cold tongue

warming emerging after 50 –100 years (Section  7.4.4.2.1; Heede 
CMIP6 projections provide further evidence of a  significant long-
term weakening of the Walker circulation (Figure 8.21). For instance, 
a  pronounced weakening of the upper-level tropical easterly jet is 
projected both over the Indian Ocean and tropical eastern Pacific, 
where declines are projected to exceed 70% by 2100 in the high-
emissions SSP5-8.5 scenario (S. Huang et al., 2020). CMIP6 models 
agree on a  future decrease of the equatorial zonal temperature 
gradient (Fredriksen et al., 2020), which can lead to weaker trade 
winds over the tropical Pacific. However, CMIP6 models show 
a  diversity of SST warming patterns in the tropical Pacific (Freund 
et al., 2020), which contributes to uncertainties in the response of 
both Walker circulation and ENSO to continued warming. 
In summary, there is high confidence  that the Pacific Walker circulation

will weaken by the end of the 21st century, and will be associated 
with decreased precipitation over the western tropical Pacific 
and increases farther east. Discrepancies between observed and 
simulated changes in SSTs in the tropics indicate that a temporary 
strengthening of the Walker Circulation can arise from a  transient 
response to GHG radiative forcing ( low confidence ) and from internal 
variability ( medium confidence ).

1126
Effect: Thermodynamic strengthening of contrasts between wet and dry events regionally despite shifts in 
circulation patterns. Increases in convergence of moisture into mid-latitude weather systems, tropical cyclones, 
and monsoons; increases precipitation.
Regions: Global
Confidence: High Moist air
Large Scale Circulation projected changes and their effect on the water cycle
Effect: Broadening of the Hadley 
Cells and poleward expansion of 
subtropical dry zones 
Regions: Uncertain influence on 
drying of subtropical land regions 
that are also controlled by 
evolving surfac e temperature 
patterns 
Confidence: Low
Effect: Drying tendency on edges of the ITCZ; 
moistening tendency  in the core, where the wet 
gets wetter response is expected to be amplified.
Regions: Impacts on individual regions only 
beginning to be inve stigated.
Confidence: MediumEffect: Reduces thermodynamic strengthening 
of monsoons and the increasing contrasts

between wet and dry regimes
Regions: Maritime Continent
Confidence: MediumEffect: Annual-mean mid-latitude 
storm tracks shifting polewards. 
Regions: Regional storm tracks 
moving polewards at dif ferent rates in 
different basins. Southern 
hemisphere storm tracks have moved 
polewards more clearly , partly driven 
by ozone depletion. Poleward 
movement likely to weaken as ozone 
hole recovers.
The maps show the e ffect of 3°C of 
global warming on mean P-E compared 
to preindustrial levels (1850-1900)
Subtropical boundaries
Southern hemisphereSubtropical boundaries
Northern hemisphere   
Inter-tropical 
convergence zone 
(ITCZ)Annual mean
mm/day
Drier   Wetter   Drier
Moist 
air
DJF Seasonal mean
Effect: Shifts in location of wettest tropical 
regions, increases or decreases in 
precipitation amount
Regions: Southward shift 1950-1970s over 
west Africa due to aerosol cooling of 
northern hemisphere, recovery since driven 
mainly by GHGs. Signals of regional shifts 
emerging.

Confidence: LowRegional shifts in ITCZ Narrowing/strengthening of ITCZ core
 Weaker W alker circulation
Present
Future-1   -0.8  - 0.6  - 0.4  - 0.2    0    0.2   0.4   0.6   0.8    1JJA Poleward expansion of Hadley Cells 
Seasonal meanSouthern hemisphere Northern hemisphere
Confidence: Low Confidence: Medium
Future
Present
South 
Pole
Thermodynamic inc reases in moisture transport into weather systems 
ITCZ
ITCZ
PresentFuture
North 
PolePoleward migration of storm tracks
Figure 8.21 | Schematic depicting large-scale circulation changes and impacts on the regional water cycle. The central figures show precipitation minus 
evaporation (P–E) changes at 3°C or global warming relative to an 1850–1900 base period (mean of 23 CMIP6 SSP5-8.5 simulations). Annual mean changes (large map) 
include contours (ocean only) depicting control climate P–E = 0 mm day–1 lines with the solid contour enclosing the tropical rain belt region and dashed lines representing the

edges of subtropical regions. Confidence levels assess understanding of how large-scale circulation change affect the regional water.

1127
8.4.2.4 Monsoons
In AR5, monsoon precipitation over land was projected to intensify 
by the end of the 21st century, due to thermodynamic increases in 
moisture convergence despite weakening of the tropical circulation 
(see Section 8.2.1.3). Following the definition of regional monsoons 
in Annex V  and Figure  8.11, and the assessment of the observed 
changes (Section  8.3.2.4), here we provide an assessment of 
projected changes in regional monsoons. Assessment is provided 
either in terms of SSP and RCP scenarios and global warming levels 
available since AR5, or from the newly available CMIP6 projections 
(Figure  8.22 and Table  8.2). Table  8.2 provides projected changes 
across the five SSPs used in this Report for precipitation (mm day–1), 
P–E (mm day–1) and runoff (mm day–1) over the regional monsoons 
for the mid (2041 –2060) and long term (2081 –2100). 
8.4.2.4.1  South and South East Asian Monsoon

In AR5, South and South East Asian monsoon (SAsiaM) precipitation 
was projected to increase by the end of the 21st century but with 
a  weakening of the circulation, with high agreement  across the 
CMIP5 models (Kitoh et al., 2013; Menon et al., 2013; Sharmila et al., 
2015; Sooraj et al., 2015; Kitoh, 2017; Kulkarni et al., 2020) . Since 
AR5, most studies have confirmed projected increases in South Asian 
monsoon precipitation ( high confidence ), while one high-resolution 
model (35 km in latitude/longitude) projects monsoon precipitation 
decreases during the 21st century following the RCP4.5 scenario 
(Krishnan et al., 2016).
Over South Asia, the moisture-bearing monsoon low-level jet is 
projected to shift northward in CMIP3 and CMIP5 models (Sandeep and Ajayamohan, 2015). Greater warming over the Asian land region 
compared to the ocean contributes to intensification of the monsoon 
low-level south-westerly winds and precipitation (Endo et al., 2018),

even though the combined effect of upper and lower tropospheric 
warming makes the Asian monsoon circulation response rather 
complicated. A  high resolution model projection, based on the 
RCP8.5 scenario, indicates that a  northward shift of the low-level 
jet and associated weakening of the large-scale monsoon circulation 
can induce a large reduction in the genesis of monsoon low pressure 
systems by the late 21st century (Sandeep et al., 2018). Experiments 
with constant forcing indicate that at 1.5°C and 2°C global warming 
levels, mean precipitation and monsoon extremes are projected to 
intensify in summer over India and South Asia (Chevuturi et al., 2018; 
D. Lee et al., 2018) and that a 0.5°C difference would imply a 3% 
increase of precipitation (Chevuturi et al., 2018). CMIP5 models 
project an increase in short intense active days and decrease in long 
active days, with no significant change in the number of break spells 
for India (Sudeepkumar et al., 2018).

Future monsoon projections from CMIP6 models show an increase 
of SAsiaM precipitation across all the scenarios and across all the 
time frames (Figure 8.22) with the maximum increase at the end of 
the 21st century in SSP5-8.5 (Almazroui et al., 2020c; Z. Chen et al., 
2020b; Ha et al., 2020; Wang et al., 2021) . Table 8.2 confirms that 
changes in runoff and P–E over SAsiaM region are positive and largest 
in the higher emissions scenarios considered, as in precipitation. On 
the other hand, changes in the ensemble mean for all the variables 
considered in the SSP1-1.9 scenario are negative for both mid and 
long-term periods (Table  8.2). This is also consistently reflected 
in the spatial map of future precipitation changes (Figure  8.15). 
Different near-term projections of the SAsiaM may result given the 
SSP1-2.6
SSP2-4.5
SSP5-8.5NAmerM WAfriM SAsiaM EAsiaM
EqAmer SAmerM SAfri AusMCMNear ter m
Mid ter m

Long ter mProjected futu re cha nges in precipitation over  monsoon r egions
10020
2010
10020
2010
10020
2010
10020
201010020
2010
10020
2010
10020
2010
10020
2010Percenta ge cha nge in pr ecipitation
Figure 8.22 | Projected regional monsoons precipitation changes.  Percentage change in projected seasonal mean precipitation over regional monsoon domains (as 
defined in Figure 8.11, Section 8.3.2.4 and Annex V) for near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) periods based on 24 CMIP6 models and 
three SSP scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1128
Chapter 8  W ater Cycle Changes8Table 8.2 | Monsoon mean water cycle projections in the mid-term (2041–2060) and long term (2081–2100) relative to present day (1995–2014), showing 
present-day mean and 90% confidence range across CMIP6 models (historical experiment) and projected mean changes and the 90% confidence range 
across the same set of models and a range of Shared Socio-economic Pathway scenarios. All statistics are in units of mm day–1. Further details on data 
sources and processing are available in the chapter data table (Table 8.SM.1).
Mid-term: 2041–2061 Minus Reference Period Long Term: 2081–2100 Minus Reference Period
1995 –2014 
Reference 
PeriodSSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5 SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5
South and South East Asian Monsoon (June–July–August–September, JJAS)
Precipitation8.42 [6.66 
to 10.14]0.44 [0.08 
to 0.74]0.47 [0.1 
to 0.96]0.42 [0.03 
to 0.81]0.32 
[–0.08 to 
+0.94]0.54 
[0.11–
1.18]0.46 [0.16 
to 0.7]0.52 [0.13

to 1.09]0.66 [0.16 
to 1.1]0.94 [0.3 
to 1.78]1.46 [0.66 
to 2.49]
Runoff3.75 [1.8 
to 5.71]0.23 [0.1 
to 0.38]0.29 [0.02 
to 0.65]0.29 [–0.0 
to +0.66]0.24 
[–0.04 to 
+0.52]0.38 
[0.07–
0.78]0.19 
[–0.02 to 
+0.35]0.29 
[–0.04 to 
+0.65]0.42 [0.04 
to 0.83]0.7 [0.12 
to 1.2]1.14 [0.36 
to 2.05]
P–E5.19 [3.68 
to 6.5]0.28 [0.03 
to 0.52]0.36 [–0.0 
to +0.76]0.36 [0.02 
to 0.69]0.3 [–0.04 
to +0.85]0.45 
[0.06–
0.95]0.27 [0.06 
to 0.38]0.38 [0.11 
to 0.76]0.51 [0.02 
to 0.83]0.81 [0.24 
to 1.56]1.15 [0.45 
to 1.84]
East Asian Monsoon (June–July–August, JJA)
Precipitation5.59 [4.47 
to 6.86]0.37 
[–0.09 to 
+0.93]0.37 
[–0.09 to 
+0.87]0.34 [0.05 
to 0.76]0.22 
[–0.16 to 
+0.88]0.43 [0.03 
to 1.1]0.43 [0.07 
to 1.02]0.44 [–0.0 
to +1.08]0.51 [0.11 
to 1.09]0.59 [0.02 
to 1.31]0.84 [0.24 
to 1.74]
Runoff2.24 [1.28 
to 3.41]0.11 
[–0.16 to 
+0.4]0.13 
[–0.19 to 
+0.42]0.13 
[–0.15 to 
+0.4]0.15 
[–0.29 to 
+0.76]0.2 [–0.11 
to +0.72]0.16 
[–0.08 to 
+0.49]0.16 
[–0.13 to 
+0.58]0.22

[–0.13 to 
+0.64]0.36 
[–0.05 to 
+0.87]0.51 [0.06 
to 1.24]
P–E2.41 [1.51 
to 3.31]0.1 [–0.31 
to +0.51]0.13 [–0.2 
to +0.48]0.17 
[–0.04 to 
+0.53]0.17 [–0.2 
to +0.75]0.23 
[–0.09 to 
+0.86]0.16 
[–0.07 to 
+0.57]0.18 
[–0.18 to 
+0.65]0.24 [–0.1 
to +0.76]0.4 [–0.08 
to +0.93]0.5 [–0.13 
to +1.34]
North American Monsoon (July–August–September, JAS)
Precipitation3.05 [2.24 
to 3.96]0.13 
[–0.08 to 
+0.43]0.07 
[–0.27 to 
+0.32]0.02 
[–0.32 to 
+0.41]–0.03 
[–0.37 to 
+0.38]–0.03 
[–0.43 to 
+0.52]0.18 
[–0.05 to 
+0.44]0.04 
[–0.35 to 
+0.39]–0.1 
[–0.51 to 
+0.37]–0.19 
[–0.76 to 
+0.44]–0.15 
[–0.96 to 
+0.57]
Runoff0.46 [0.09 
to 0.87]0.03 
[–0.04 to 
+0.12]0.03 
[–0.07 to 
+0.16]0.02 [–0.1 
to +0.14]–0.0 [–0.1 
to +0.14]–0.0 
[–0.11 to 
+0.14]0.04 
[–0.03 to 
+0.15]–0.0 
[–0.19 to 
+0.15]–0.03 
[–0.22 to 
+0.14]–0.05 
[–0.23 to 
+0.19]–0.06 
[–0.29 to 
+0.23]
P–E0.78 [–0.1 
to +1.45]0.06 [–0.1 
to +0.2]0.02 
[–0.18 to 
+0.24]0.0 [–0.22 
to +0.23]–0.03 
[–0.24 to 
+0.2]–0.04

[–0.31 to 
+0.27]0.09 
[–0.06 to 
+0.31]0.01 
[–0.22 to 
+0.25]–0.08 
[–0.28 to 
+0.25]–0.17 
[–0.68 to 
+0.25]–0.18 
[–0.72 to 
+0.38]
South American Monsoon (December–January–February, DJF)
Precipitation8.44 [5.98  
to 10.22]0.09 [–0.2 
to +0.3]0.12 
[–0.29 to 
+0.62]0.09 
[–0.47 to 
+0.62]0.07 
[–0.55 to 
+0.62]0.07 [–0.5 
to +0.71]0.02 
[–0.32 to 
+0.36]0.09 
[–0.33 to 
+0.58]0.07 
[–0.63 to 
+0.81]0.05 
[–1.17 to 
+0.82]–0.0 
[–1.22 to 
+1.19]
Runoff2.49 [1.11  
to 4.38]–0.02 
[–0.23 to 
+0.26]–0.01 
[–0.43 to 
+0.53]0.01 
[–0.45 to 
+0.46]–0.03 
[–0.49 to 
+0.36]–0.03 
[–0.56 to 
+0.53]–0.04 
[–0.27 to 
+0.28]–0.01 
[–0.41 to 
+0.39]–0.01 
[–0.58 to 
+0.55]–0.06 
[–0.81 to 
+0.24]–0.04 
[–0.85 to 
+0.93]
P–E4.5 [2.83  
to 6.01]0.04 
[–0.23 to 
+0.25]0.08 
[–0.26 to 
+0.47]0.04 
[–0.43 to 
+0.53]0.04 [–0.5 
to +0.61]0.02 
[–0.45 to 
+0.58]–0.01 
[–0.32 to 
+0.29]0.03 
[–0.34 to 
+0.43]–0.02 
[–0.63 to 
+0.62]–0.02 
[–1.03 to 
+0.72]–0.09 
[–1.11 to 
+0.98]

Australian and Maritime Continent Monsoon (December–January–February, DJF)
Precipitation8.63 [6.79  
 to 10.7]0.26 [0.04 
to 0.49]0.22 
[–0.23 to 
+0.53]0.28 [–0.2 
to +0.79]0.25 
[–0.14 to 
+0.73]0.38 [0.0 
to 0.84]0.15 
[–0.09 to 
+0.34]0.24 
[–0.36 to 
+0.74]0.5 [–0.1 
to +1.07]0.65 
[–0.08 to 
+1.33]0.9 [0.09 
to 1.76]
Runoff3.82 [1.78  
to 7.25]0.2 [–0.01 
to +0.48]0.23 
[–0.11 to 
+0.48]0.29 
[–0.11 to 
+0.7]0.24 
[–0.13 to 
+0.56]0.35 
[–0.03 to 
+0.87]0.12 
[–0.06 to 
+0.39]0.29 
[–0.08 to 
+0.88]0.49 [0.09 
to 1.25]0.61 
[–0.09 to 
+1.05]0.92 [0.14 
to 1.83]
P–E4.8 [3.19  
to 6.63]0.22 [0.03 
to 0.47]0.13 
[–0.23 to 
+0.42]0.2 [–0.16 
to +0.7]0.2 [–0.14 
to +0.62]0.27 
[–0.09 to 
+0.61]0.12 [–0.1 
to +0.31]0.16 
[–0.31 to 
+0.54]0.38 
[–0.05 to 
+0.75]0.54 
[–0.08 to 
+1.13]0.69 [0.09 
to 1.27]

1129
diversity in the future aerosol emissions pathways and policies for 
regulating air pollution (Wilcox et al., 2020). Additionally, near-term 
projections of SAsiaM precipitation are expected to be constrained 
by internal variability associated with the PDV (X.  Huang et al., 
2020a). CMIP6 models also indicate a lengthening of the summer 
monsoon over India by the end of the 21st century, at least in 
SSP2-4.5, with considerable inter-model spread in the projected late 
retreat (Ha et al., 2020).
In summary, consistent with AR5, there is high confidence that 
SAsiaM precipitation is projected to increase during the 21st century 
in response to continued global warming across the CMIP6 higher 
emissions scenarios, mostly in the mid- and long terms. 
8.4.2.4.2 East Asian Monsoon
In AR5, the East Asian monsoon (EAsiaM) was projected to intensify 
in terms of precipitation, with an earlier onset and longer duration

of the summer season. Since AR5, there has been improved 
understanding of future projected changes in the EAsiaM. 
CMIP5 projections indicated a possible intensification of the EAsiaM 
circulation during the 21st century, in addition to precipitation 
increase, although there is a  lack of consensus on changes in the 
western North Pacific subtropical high, this is an important feature 
of the EAsiaM circulation (Kitoh, 2017). Furthermore, the EAsiaM 
precipitation enhancements in the CMIP5 projections are prominent 
over the southern part of the Baiu rainband by the late 21st century, 
with no significant changes in the Meiyu precipitation over central-
eastern China (Horinouchi et al., 2019). It was also shown that the 
Baiu precipitation response in CMIP5 projections is accompanied by 
a southward retreat of the western North Pacific subtropical high and 
a southward shift of the East Asian subtropical jet (Horinouchi et al.,

2019). According to the high-resolution MRI-AGCM global warming 
experiments, future summer precipitation could potentially increase 
on the southern side and decrease on the northern side of the present-
day Baiu location in response to downward-motion tendencies which 
can offset the ‘wet-gets-wetter’ effect, but is subject to large model 
uncertainties (Ose, 2019). Future projections of land warming over 
the Eurasian continent (Endo et al., 2018) and intensified land –sea 
thermal contrast (Z.  Wang et al., 2016; Tian et al., 2019) can potentially intensify the EAsiaM circulation during the 21st century. However, 
there are large uncertainties in projected water cycle changes over 
the region (Endo et al., 2018), mostly in the near-term because of 
uncertainties in future aerosol emissions scenarios (Wilcox et al., 
2020), as well as due to the interplay between internal variability 
and anthropogenic external forcing (Wang et al., 2021).

Inter-hemispheric mass exchange can act as a  bridge connecting 
SH circulation with EAsiaM rainfall, however this inter-hemispheric 
link is projected to weaken in a future warmer climate as seen from 
a  CCSM4 projection using the RCP8.5 scenario (Yu et al., 2018). 
A  comparison of 1.5°C and 2°C global warming levels reveals 
how a  0.5°C difference could result in precipitation enhancement 
over large areas of East Asia ( D. Lee et al., 2018; J. Liu et al., 2018; 
Chen et al., 2019 ), with substantial increases in the frequency and 
intensity of extremes (Chevuturi et al., 2018; D. Li et al., 2019). Future 
monsoon projections from the CMIP6 models show increase of 
EAsiaM precipitation across all the scenarios (Z. Chen et al., 2020b), 
though with a large model spread mostly on the long-term and in 
the higher emissions scenarios (Figure 8.22). Considering all the five 
scenarios used across the report, changes in precipitation, runoff

and P–E over the EAsiaM are positive and become larger for highest 
emissions scenarios and for the long-term mean, except for the mid-
term SSP1-1.9 scenario where the changes are close to zero or even 
negative (Table 8.2). Additionally, CMIP6 models confirm a projected 
increased length of the EAsiaM season due to early onset and late 
retreat (Ha et al., 2020).
In summary, despite the uncertainties in the monsoon circulation 
response in CMIP5 and CMIP6 models, there is high confidence  that 
summer monsoon precipitation over East Asia will increase in the 
21st century and medium confidence  that the monsoon season will 
be longer.
8.4.2.4.3 West African Monsoon
The AR5 concluded that projections of West African monsoon 
(WAfriM) rainfall are highly uncertain in CMIP3 and CMIP5 models, 
but still suggest a small delay and intensification in late wet season 
rains. Studies published since AR5 are broadly consistent with this

assessment. CMIP6 models agree on statistically significant projected Mid-term: 2041–2061 Minus Reference Period Long Term: 2081–2100 Minus Reference Period
1995 –2014 
Reference 
West African Monsoon (June–July–August–September, JJAS)
Precipitation5.14 [3.62  
to 7.18]0.16 
[–0.19 to 
+0.4]0.14 
[–0.22 to 
+0.56]0.24 
[–0.14 to 
+0.72]0.3 [–0.1 
to +0.85]0.38 
[–0.12 to 
+1.24]0.06 
[–0.25 to 
+0.52]0.1 [–0.25 
to +0.57]0.25 
[–0.32 to 
+0.91]0.38 
[–0.49 to 
+1.14]0.49 
[–0.55 to 
+1.56]
Runoff1.43 [0.34  
to 2.57]0.06 
[–0.07 to 
+0.22]0.05 
[–0.18 to 
+0.27]0.14 
[–0.13 to 
+0.54]0.2 [–0.05 
to +0.7]0.24 [–0.1 
to +0.8]–0.01 
[–0.2 to 
+0.21]0.03 
[–0.25 to 
+0.35]0.1 [–0.25 
to +0.51]0.25 
[–0.28 to 
+0.85]0.3 [–0.33 
to +0.93]
P–E2.41 [1.05  
to 4.07]0.08 [–0.2 
to +0.35]0.1 [–0.2 
to +0.4]0.2 [–0.11 
to +0.63]0.23 
[–0.11 to 
+0.74]0.36 
[–0.06 to 
+1.11]–0.01 
[–0.27 to

+0.35]0.07 [–0.2 
to +0.44]0.18 
[–0.21 to 
+0.6]0.28 
[–0.38 to 
+0.95]0.46 
[–0.44 to 
+1.4]

1130
Chapter 8  W ater Cycle Changes8increases in rainfall in eastern-central Sahel and a decrease in the 
west for the end of the 21st century (Roehrig et al., 2013; Biasutti, 
2019; Monerie et al., 2020). However, the magnitude of WAfriM 
projected precipitation depends on the convective parametrization 
used (Hill et al., 2017), and large uncertainties remain in WAfriM 
projections because of large inter-model spread, particularly over 
the western Sahel (Roehrig et al., 2013; Biasutti, 2019; Monerie 
et al., 2020). CMIP6 models show a  general increase of WAfriM 
precipitation across all future scenarios but with a substantial model 
spread for the SSP5-8.5 scenario (Figure 8.22). This sensitivity arises 
from the combined and contrasting influences of anthropogenic 
greenhouse gas and aerosol forcing that affect WAfriM precipitation 
(particularly over the Sahel) directly and also indirectly through 
subtropical North Atlantic SST changes (Giannini and Kaplan, 2019) .

The large model spread and associated uncertainties in projected 
precipitation changes is reflected also in runoff and P–E changes 
(Table 8.2). Regional climate models (RCMs) ensembles (e.g., Klutse 
et al., 2018) agree with CMIP5 projected rainfall trends but some 
individual models show rainfall declines (e.g.,  Sylla et al., 2015; 
Akinsanola et al., 2018), highlighting the existing large uncertainties 
in RCMs WAfriM rainfall projections.
Changes in seasonality (Box 8.2) are projected with a later monsoon 
onset ( high confidence ) over the Sahel and a late cessation ( medium 
confidence ), suggesting a  delayed wet season as a  regional 
response to global GHG forcing (Biasutti, 2013; Dunning et al., 2018; 
Akinsanola and Zhou, 2019). Rainfall distribution is projected to be 
highly variable with a decrease in the number of rainy days in the 
western Sahel, consistent with an increase in consecutive dry days 
and a reduction in the number of growing season days (Cook and

Vizy, 2012; Diallo et al., 2016). A decrease in the frequency but an 
increase in the intensity of very wet events is projected to be more 
pronounced over the Sahel than over Guinean coast, and also under 
higher emissions scenarios (i.e., RCP8.5; Sylla et al., 2015; Akinsanola 
In summary, post-AR5 studies and newly available CMIP6 results 
indicate projected rainfall increases in the eastern-central WAfriM 
region but decreases in the west ( high confidence ), with a delayed 
wet season ( medium confidence ). Overall, WAfriM summer 
precipitation is projected to increase during the 21st century but 
with larger uncertainty noted under high-emissions scenarios 
8.4.2.4.4 North American Monsoon
The AR5 concluded that the North American monsoon (NAmerM) 
will likely  intensify in the future, even though there is low agreement  
among models. The AR5 reported medium confidence  that 
precipitation associated with the NAmerM will arrive later in the

annual cycle and persist longer.
Since AR5, analyses of CMIP5 projections suggest little change in the 
overall amount of NAmerM precipitation in response to rising global 
surface temperature. However, significant declines are projected in 
the early monsoon season and increases in the late monsoon season, 
suggesting a shift in seasonality toward a delayed monsoon onset and demise (Cook et al., 2013). It is recognized that CMIP5 models 
are generally too coarsely-resolved to simulate the Gulf of California 
and the moisture surges associated with the NAmerM (Pascale et al., 
2017). Under different RCPs, CMIP5 models tend to project a reduction 
in NAmerM precipitation but an increase in extreme precipitation 
events (Torres-Alavez et al., 2014; Bukovsky et al., 2015; Pascale 
et al., 2019) . The almost unchanged or slight decrease in NAmerM 
total precipitation amount under global warming projections is at 
odds with paleoclimate records that suggest increased monsoon

precipitation under past warm conditions (D’Agostino et al., 2019; 
Seth et al., 2019). However, there is low agreement  on how those 
changes and the mechanisms that drive them are affected under 
different RCPs since most simulations are model-dependant (Cook 
and Seager, 2013; Geil et al., 2013; Pascale et al., 2019). Projections 
from six CMIP6 models show a  shortening of the NAmerM under 
the SSP5-8.5 scenario due to earlier demises (Moon and Ha, 
2020). In addition, CMIP6 projections show a decrease in NAmerM 
precipitation under SSP2-4.5 and SSP5-8.5 scenarios by the end of 
the 21st century with large inter-model spread (Figure  8.22). This 
result is also supported by the analysis of 31 CMIP6 models under 
the SSTP5-8.5 scenario for the 2080 –2099 period (Almazroui et al., 
2021). Non-linearities and uncertainties in the NAmerM projected 
changes are valid for many water cycle variables, like precipitation, 
runoff and P–E (Table 8.2).

In summary, there is low agreement  on a  projected decrease of 
NAmerM precipitation, however there is high confidence  in delayed 
onsets and demises of the summer monsoon.
8.4.2.4.5 South American Monsoon 
The AR5 reported medium confidence  that the South American 
monsoon (SAmerM) overall precipitation will remain unchanged, and 
medium confidence  in projections of extreme precipitation. The AR5 
also stated high confidence  in the spatial expansion of the SAmerM, 
resulting from increased temperature and humidity.
Since AR5, some studies indicate that the SAmerM would experience 
changes in its seasonal cycle, with delayed monsoon onsets under 
increasing GHG emissions associated to different RCPs (Fu et al., 
2013; Reboita et al., 2014; Boisier et al., 2015; Pascale et al., 2016; 
Seth et al., 2019; Sena and Magnusdottir, 2020) . In contrast, other 
studies indicate projected earlier onsets and delayed retreats of the

SAmerM under the RCP8.5 scenario based on six CMIP5 models 
(Jones and Carvalho, 2013). These differences have been linked to 
the methodology used to determine monsoon timing, and sensitivity 
to the monsoon domain considered (Section 8.3.2.4.5; Correa et al., 
2021). Recent studies provide further evidence for the projection 
of delayed SAmerM onsets by the late 21st century (Sena and 
Magnusdottir, 2020). An analysis of six CMIP6 models under the 
SSP5-8.5 scenario confirm the projections of delayed SAmerM onsets 
by the end of the 21st century (Moon and Ha, 2020). In addition, 
projected changes in the intensity and length of the SAmerM season 
have been found to be model-dependent (Pascale et al., 2019). The 
analysis of CMIP5 projections of total monsoon rainfall indicate mixed 
signals in the Amazon and SAmerM regions (Jones and Carvalho, 
2013; Marengo et al., 2014), with some studies suggesting increased

1131
summer precipitation in the core SAmerM region (Kitoh et al., 2013; 
Seth et al., 2013). Dynamical downscaling of CMIP5 projections 
under the RCP4.5 and RCP8.5 scenarios with the Eta RCM suggests 
reductions of austral summer precipitation over the SAmerM region 
throughout the 21st century (Chou et al., 2014). Further analysis 
using 15 different CMIP6 models for the SSP2-4.5 scenario suggest 
reductions in total SAmerM rainfall (B. Wang et al., 2020). However, 
other analyses of CMIP6 projections under different SSP scenarios 
do not report clear changes in the SAmerM precipitation throughout 
the 21st century (Figure 8.22; Z. Chen et al., 2020b; Jin et al., 2020). 
Similar uncertainties for all the SSP scenarios used across the report 
are found for other water cycle variables, including runoff and P–E 
(Table 8.2). Furthermore, there is disagreement in projected extreme 
precipitation in the region, with some CMIP5-based studies suggest

reductions (Marengo et al., 2014), while others indicate increases 
based on CMIP5 and CMIP6 models (Kitoh et al., 2013; Sena and 
Magnusdottir, 2020). 
In summary, there is high confidence  that the SAmerM will experience 
delayed onsets in association with increases in GHG.  However, 
there is low agreement  on the projected changes in terms of total 
precipitation of the South American summer monsoon season. 
8.4.2.4.6 Australian and Maritime Continent Monsoon
The AR5 concluded that projected changes in Australian and Maritime 
Continent monsoon (AusMCM) rainfall and seasonality are uncertain 
in the CMIP5 models, with some projecting increases and others 
projecting decreases for the range of emissions scenarios. Models 
that perform better at simulating present day regional climate project 
little change or an increase in Australian monsoon rainfall (Jourdain 
et al., 2013; CSIRO and BoM, 2015; Brown et al., 2016b). CMIP6

models project increased AusMCM precipitation in the 21st century 
but with a more robust signal in SSP2-4.5 and SSP5-8.5 rather than 
in lower emissions scenarios (Figure 8.22). A reduced range of CMIP6 
rainfall projections but continued disagreement on the sign of change 
is reported over Australia (Narsey et al., 2020).
The northern and eastern parts of the Maritime Continent have 
projected increases in rainfall in CMIP5 models (Siew et al., 2014), 
while there are projected decreases over Java, Sulawesi and southern 
parts of Borneo and Sumatra. Rainfall changes are correlated with 
the extent of warming in the western tropical Pacific in CMIP5 
models (Brown et al., 2016b) but inter-model differences are also 
related to modelled large-scale zonal mean precipitation response 
in both CMIP5 and CMIP6 model ensembles (Narsey et al., 2020). 
Decomposition of projected rainfall changes indicates that the  
largest source of model uncertainty is associated with shifts in

the spatial pattern of convection (Chadwick et al., 2013; Brown et al., 
2016b). Uncertainties in capturing the spatial and temporal features 
of the Maritime Continent monsoon depend also on the horizontal 
resolution of coupled climate models (e.g., Jourdain et al., 2013).
The role of anthropogenic aerosol forcing in future projections of the 
Australian monsoon has been investigated for CMIP5 models (Dey 
et al., 2019a); decreases in anthropogenic aerosol concentrations 
over the 21st century are expected to produce relatively greater warming in the NH than SH, favouring a  northward shift of the 
tropical rain belt (e.g., Rotstayn et al., 2015). 
There are some clear projected changes in the rainfall variability 
and extremes of the Australian monsoon. Rainfall variability in the 
Australian monsoon domain increases on time scales from daily to 
decadal in CMIP5 models (Brown et al., 2017), indicating either more

intense wet days or more dry days or both. There is also a projected 
increase in the intensity of extreme rainfall but a reduction in the 
frequency of heavy rainfall days for the Australian monsoon (Dey 
et al., 2019a) . This is consistent with Moise et al. (2020), who found 
an increase in Australian monsoon active phase or ‘burst’ rainfall 
intensity but a reduction in the number of burst days and events.
H.  Zhang et al. (2013) examined changes in Australian monsoon 
onset and duration in CMIP3 models and found model agreement 
on a delay in onset and shortened duration to the north of Australia, 
but less agreement over the interior of the continent. An updated 
study of CMIP5 models found similar mean changes with delayed 
onset and shortened duration, but substantial model disagreement 
(H. Zhang et al., 2016). 
In summary, CMIP6 projections show an increase of AusMCM 
precipitation across all emissions scenarios. There is strong model

agreement on an increase in monsoon precipitation over the 
Maritime Continent while there is low agreement  on the direction 
of change over northern Australia. There is a projected increase in 
rainfall variability over northern Australia, with increased intensity of 
rainfall during the active or ‘burst’ phase ( medium confidence ). 
8.4.2.5 Tropical Cyclones
Tropical cyclones (TCs) projections are primarily assessed in 
Section  11.7.1.5. Here, we extend this analysis by assessing the 
implications of projected changes in tropical cyclones on the water cycle.
The AR5 concluded that TC rainfall rate was likely  to increase 
through the 21st century. Section 11.7.1.5 assesses that the average 
tropical cyclone rain-rate is projected to increase with warming ( high 
confidence ), and peak rain rates are projected to increase at greater 
than the Clausius–Clapeyron scaling rate of 7% °C–1 warming in 
some regions due to increased low-level moisture convergence

(medium confidence ). The increase in TC rainfall rate is explained by 
increased TC intensity resulting from increasing SSTs, and increased 
environmental water vapour (Chauvin et al., 2017; M. Liu et al., 2019).
Consistent with the observed poleward migration of tropical cyclone 
activity (Kossin et al., 2014), in the SH a larger proportion of storms 
are projected to decay south of 25°S at the end of the 21st century 
but with negligible changes in genesis latitude and storm duration 
for the Australian region (CSIRO and BoM, 2015; Sharmila and Walsh, 
2018) . An analysis of projections for North Pacific islands indicate 
that the maximum intensity of storms will increase but the number 
of tropical cyclones will decrease in some places, such as Guam and 
Kwajalein Atoll in the tropical north-western Pacific, or remain the 
same in other regions like near Okinawa (Japan) or Oahu (Hawaii) 
(Widlansky et al., 2019). TC-induced storm tides affecting landfall in

1132
Chapter 8  W ater Cycle Changes8the Pearl River delta over South China are projected to increase by 
the end of the 21st century (J. Chen et al., 2020b)
In summary, there is high confidence that heavy precipitation 
associated with tropical cyclones is projected to increase, in response 
to well-understood processes related to increased low-level moisture 
convergence and environmental water vapour. 
8.4.2.6 Stationary Waves
The AR5 did not provide an assessment of stationary wave 
projections as distinct from other related aspects of circulation, such 
as blocking, modes of variability, and storm tracks. Here we provide 
a  brief assessment of stationary wave projections from the water 
cycle perspective, with the related circulation aspects considered 
separately in the following sections. 
Several studies based on CMIP5 projections show changes in 
NH winter stationary waves that increase precipitation over the 
west coast of North America and decrease it over the eastern

Mediterranean and parts of south-western North America ( Neelin 
et al., 2013; Seager et al., 2014a, b, 2019b; Simpson et al., 2016; 
Wills et al., 2019 ), although the underlying dynamics are not yet fully 
understood (Seager et al., 2019b; Wills et al., 2019). For the NH winter 
global teleconnection pattern, the majority of the models analyzed in 
(Sandler and Harnik, 2020) project the development of a preferred 
longitudinal phasing for the pattern, but with strong disagreement 
among models over the details of the phasing and therefore the 
associated regional hydrologic impacts.
While the potential role of increasing hydrologic extremes with 
quasi-resonant stationary waves during NH summer has received 
considerable attention (see Section 8.3.2.6), as yet there is no clear 
evidence in model projections that this variability will increase (Teng 
and Branstator, 2019). The influence of the Arctic on mid-latitude

circulation is assessed in Cross-Chapter Box 10.1, which reports that 
there is low confidence  in the dominant contribution of Arctic warming 
compared to other drivers in future projections. Potential changes to 
the stratospheric polar vortex in CMIP5 models have a substantial 
influence on tropospheric stationary waves and associated hydrologic 
impacts in both the NH (Zappa and Shepherd, 2017)  and SH (Mindlin 
et al., 2020). CMIP5 models have some important limitations in their 
representation of stationary waves (Lee and Black, 2013; Simpson 
et al., 2016; Garfinkel et al., 2020) and this aspect of CMIP6 models 
has not yet been comprehensively evaluated.
In summary, future changes in stationary waves may have an 
important influence on both the mean state and variability of the water 
cycle. Limitations in model representation, dynamical understanding, 
and the number of targeted studies on the topic currently constrain

the assessment of future changes in stationary waves. Based on 
current knowledge, there is low confidence that projected changes in 
stationary wave activity will contribute to decreases of cold season 
precipitation over the eastern Mediterranean and increases over the 
west coast of North America.8.4.2.7 Atmospheric Blocking
In AR5, the increased ability of models to simulate blocking and 
higher agreement on projections led to an assessment with medium 
confidence  that the frequency of NH and SH blocking will not 
increase, but future changes in blocking intensity and persistence 
were deemed uncertain (AR5 Chapter  14, ES and Box  14.2).  
Blocking influences precipitation (e.g., Trigo et al., 2004), flooding 
(e.g.,  Yamada et al., 2016) , drought (e.g.,  Dong et al., 2018b) ,  
snow (e.g.,  García-Herrera and Barriopedro, 2006) , and glacier melt 
(e.g., Hanna et al., 2013), and so is of broad importance to the water 
cycle in areas of blocking activity.

Blocking projections are assessed in this Report in 
Chapter  4  (Section  4.5.1.6), and model performance in simulating 
blocking is also discussed in Chapter  3  (Section  3.3.3.3). CMIP5 
projections suggest a complex response in blocking frequencies with 
an eastward shift in NH winter blocking, mid-latitude decreases in 
boreal summer except in eastern Europe –western Russia, and SH 
decreases in the Pacific sector during austral spring and summer. 
CMIP6 projections (Figure. 4.28) show a notable decrease in blocking 
activity over Greenland and the North Pacific for the SSP3-7.0 and 
SSP5-8.5 scenarios. However, the continued large differences among 
current models as well as the sensitivity to blocking detection methods 
limits confidence in projected regional changes in blocking (see also 
Section 10.3.3.3.1). The influence of blocking on multiple elements of 
the water cycle means that the uncertainty in blocking projections adds

a corresponding layer of uncertainty to water cycle projections. 
In summary, and despite recent improvements in the simulation of 
blocking, there is limited evidence  in model projections of future 
changes, except for boreal winter over Greenland and the North 
Pacific where there is high confidence that blocking events are not 
expected to increase in the SSP3-7.0 and SSP5-8.5 scenarios. As with 
stationary waves, this adds uncertainty to mid-latitude water cycle 
projections at the regional scale.
8.4.2.8 Extratropical Cyclones, Storm Tracks 
8.4.2.8.1 Extratropical cyclones and storm tracks
The AR5 found that extratropical storms were expected to decrease in 
the Northern Hemisphere (NH), but only by a few percent. Meanwhile, 
precipitation associated with extratropical storms was projected to 
increase due to thermodynamic increases in moisture but potentially 
also due to intensification from increased latent heat release. Latent

heating is a strong influence on extratropical storms, so it is plausible 
that changes in precipitation and associated latent heating could 
affect extratropical storm intensity and thus precipitation (Z. Zhang 
et al., 2019) .
There is increased evidence that precipitation associated with 
individual extratropical storms is projected to increase, following 
thermodynamic drivers with negligible dynamic change (Yettella and 
Kay, 2017). Comparisons with reanalyses also support the projected 
increase in thermodynamic precipitation with little dynamic response

1133
for precipitation associated with extratropical storms (Li et al., 2014). 
There is high confidence that projected increases in precipitation 
associated with extratropical storms in the NH (Marciano et al., 2015; 
Pepler et al., 2016; Michaelis et al., 2017; Yettella and Kay, 2017; 
Zhang and Colle, 2017; Hawcroft et al., 2018; Kodama et al., 2019) . 
A projected decrease in the number of extratropical cyclones over 
the NH during the boreal summer in CMIP5 models was reported 
by Chang et al. (2016) who related this decrease with a decrease in 
cloudiness and thus accentuating increased maximum temperatures. 
However, model spread was quite large, especially over North 
America, thus there is only low confidence  in this seasonal signal.
In AR5, the Southern Hemisphere (SH) storm track was deemed likely  
to shift poleward, the North Pacific storm track more likely than not to  
shift poleward, while the North Atlantic storm track was unlikely

to display any discernible changes. There was low confidence  in 
regional storm track changes and the associated surface climate 
impacts, although a  weakening of the Mediterranean storm track 
was a robust response of the models. Since AR5, the SH mid-latitude 
storm track is projected to shift poleward and the westerlies are 
projected to strengthen over Australia (CSIRO and BoM, 2015). 
Although thermodynamic effects were considered to be the most 
important factor in overall projections of increased mid-latitude 
precipitation, the general poleward shift in cyclogenesis and an 
enhanced latitudinal displacement of individual cyclones may play 
a role (Tamarin-Brodsky and Kaspi, 2017).
In AR5, several factors were identified as relevant to the uncertainties 
in projections of cyclone intensity, frequency, location of storm tracks 
and precipitation associated with ETCs. These include horizontal 
resolution, resolution of the stratosphere, and how changes in the

Atlantic meridional overturning circulation (AMOC) were simulated. 
Since AR5, projections of extratropical cyclones and storm tracks have 
been examined further, largely confirming previous assessments. In 
particular, extratropical cyclone precipitation scales with the product 
of cyclone intensity (as measured by near-surface wind speed) 
and atmospheric moisture content (Pfahl and Sprenger, 2016) . 
Booth et al. (2018) showed that the fraction of rainfall generated 
by the convection scheme in simulated extratropical cyclones is 
highly model- and resolution-dependent, which may be a source of 
uncertainty regarding their precipitation response to anthropogenic 
forcings. Also, increased moisture availability may increase the 
maximum intensity of individual storms while reducing the overall 
frequency as poleward energy transport becomes more efficient.
The role of temperature trends in influencing storm tracks has 
been  further investigated, both in terms of upper tropospheric

tropical warming (Zappa and Shepherd, 2017)  and lower 
tropospheric  Arctic  amplification (J.  Wang et al., 2017), including 
the direct role of  Arctic sea ice loss (Zappa et al., 2018), and the 
competition between their influences (Shaw et al., 2016) . Physical 
linkages between Arctic amplification and changes in the mid-latitudes 
are uncertain, as discussed in Chapter 10 (Cross-Chapter Box 10.1). The 
remote and local SST influence has been further examined by Ciasto 
et al. (2016), who confirmed sensitivity of the storm tracks to the 
SST trends generated by the models and suggested that the primary 
greenhouse gas influence on storm track changes was indirect, acting through the greenhouse gas influence on SSTs. The importance of the 
stratospheric polar vortex in storm track changes has received more 
attention (Zappa and Shepherd, 2017; Mindlin et al., 2020)  and the 
anticipated recovery of the ozone layer further complicates the role of

the stratosphere (Shaw et al., 2016; Bracegirdle et al., 2020b).
Biases remain in cyclone locations, intensities, cloud features, and 
precipitation (Catto, 2016, Chang et al., 2016). Uncertainties in 
projected precipitation changes in many mid-latitude regions can 
be explained to a large degree by uncertainties in projected storm 
track or ETC changes. Multiple studies (Chang et al., 2013; Zappa 
et al., 2015; Chang, 2018) have shown strong relationships between 
model-projected precipitation change in many regions and model-
projected change in storm track activity near that regions. While front 
frequency is well represented, frontal precipitation frequency is too 
high and the intensity is too low (Catto et al., 2015). Some of the 
bias in storm tracks appears to be related to limitations in model 
realization of blocking (Zappa et al., 2014). The CMIP6 generation 
of models has improved representation of storm tracks in both

hemispheres (Bracegirdle et al., 2020a; Harvey et al., 2020). Simulation 
of storm tracks and their associated precipitation generally improve 
with increasing resolution beyond that used in most current climate 
models ( Jung et al., 2006; Michaelis et al., 2017; Barcikowska et al., 
2018 ). In terms of projections, the decreases in cyclone occurrence 
over the Mediterranean were replicated in a higher resolution model 
(Raible et al., 2018).
The projected changes in storm tracks and the associated mechanisms 
have several important implications for water cycle projections. P–E 
changes in the Mediterranean, California and Chile are directly 
linked to storm track changes (Zappa et al., 2020) . Where the storm 
tracks are robustly projected to shift (SH, North Pacific) or weaken 
(Mediterranean), understanding the physical causes of the related 
changes in precipitation helps increase confidence in the projections. 
Understanding the competing influences provides context for why

other regions do not exhibit a consistent signal and cautions against 
regional projections based on individual models. However, model 
bias and the need for relatively high resolution to reproduce the 
relevant dynamics is an important overall limit on confidence in 
current CMIP6 projections.
In summary, there is the  high confidence  that precipitation associated 
with extratropical storms will increase with global warming in most 
regions. The SH storm track will likely  shift poleward, the North Pacific 
storm track more likely than not will shift poleward, and the North 
Atlantic storm track is unlikely  to have a  simple poleward shift/ 
display any discernible changes. There is low confidence  in regional 
storm track changes, although a  weakening of the Mediterranean 
storm track is a robust response of the models.
8.4.2.8.2 Atmospheric rivers
Atmospheric rivers were not assessed in AR5 but are important in

the water cycle as they are linked to extreme rainfall, flooding, and 
changes in terrestrial water storage including melt and ablation of 
glaciers and snowpack (Sections 8.2.3). In a warming world, there 
is high confidence that thermodynamical increases in atmospheric

1134
Chapter 8  W ater Cycle Changes8water vapour ensure that atmospheric rivers will become wetter, 
hence stronger, and longer-lasting (Payne et al., 2020). This is 
clearly observed in several regional (Ralph and Dettinger, 2011; 
Lavers et al., 2013; Gao et al., 2015; Payne and Magnusdottir, 2015; 
Warner et al., 2015; Hagos et al., 2016; Gershunov et al., 2019) and 
in one global study (V.  Espinoza et al., 2018) of atmospheric river 
activity in CMIP5 model projections. Lavers et al. (2015) indicate that 
integrated vapour transport under RCP 8.5 and 4.5 could increase, 
and consequently this thermodynamic response (O’Gorman, 2015) 
could affect mid-latitude regions where orographic precipitation is 
important (Gershunov et al., 2019). 
Under continued global warming, more intense moisture transport 
within atmospheric river events is projected to increase the magnitude 
of heavy precipitation events on the west coast of the USA (Ralph

and Dettinger, 2011; Lavers et al., 2015; Warner and Mass, 2017), 
in Western Europe (Lavers et al., 2015; Ralph et al., 2016; Ramos 
et al., 2016), and in East Asia ( very likely ) (Kamae et al., 2019). All 
CMIP5 models analysed agreed under a range of scenarios, except 
over the Iberian Peninsula (Ramos et al., 2016) where there is only 
low confidence  in projected changes. Kamae et al. (2019) reported 
a 1% increase per °C warming in the frequency of atmospheric rivers 
affecting East Asia, but this is strongly affected by SST changes. 
Emerging evidence of possible regional changes due to dynamical 
factors are uncertain (Lavers et al., 2013; Gao et al., 2015; Payne 
and Magnusdottir, 2015). The frequency, magnitude and duration of 
atmospheric rivers making landfall along the North American west 
coast are projected to increase (Gershunov et al., 2019). In contrast, 
V. Espinoza et al. (2018) suggest that the number of atmospheric river

events is projected to slightly decrease globally. 
In semi-arid regions where atmospheric rivers have historically been 
important and precipitation is mainly confined to the cold season, the 
contribution of atmospheric rivers to annual total precipitation may 
be expected to grow disproportionately. For example, in California 
decreases in precipitation frequency are projected as a  result of 
fewer non-atmospheric river storms, while the projected increase 
in heavy and extreme precipitation events are almost entirely 
a  result of increased atmospheric river activity (Gershunov et al., 
2019). Interannual variability in precipitation amounts is projected 
to increase because of the overall decrease in the frequency of 
storms but a stronger dependence on extremes (Polade et al., 2014), 
particularly due to atmospheric rivers (Gershunov et al., 2019), 
especially where interaction with topography are important ( Polade 
et al., 2014; Gershunov et al., 2019 ).

In summary, there is high confidence that the magnitude and 
duration of atmospheric rivers are projected to increase in future, 
leading to increased precipitation. This is projected to increase the 
intensity of heavy precipitation events on the west coast of the USA 
and in western Europe ( high confidence ).8.4.2.9 Modes of Climate Variability and Regional 
Teleconnections
Following on from the assessment of projected changes in 
modes of climate variability (MoVs) and regional teleconnections 
(Section  4.5.3), here we assess their consequences for projected 
water cycle changes.
8.4.2.9.1 Tropical modes
CMIP6 projections indicate that the amplitude of ENSO (Annex IV.2.3) 
variability will not substantially change during the 21st century ( high 
confidence ) (Section  4.4.3.2). However, rainfall variability related 
to ENSO is projected to increase significantly by the second half of 
the 21st century, regardless of ENSO amplitude (Section  4.5.3.2).

Regional precipitation variability associated with ENSO increases 
due to increases in atmospheric moisture, regardless of changes in 
ENSO variability itself (Pendergrass et al., 2017). In many regions, 
the magnitude of the projected changes related to ENSO is small 
compared with historical interannual variability (Bonfils et al., 
2015; Power and Delage, 2018; Perry et al., 2020). Uncertainties 
in precipitation projections related to ENSO depend on internal 
variability associated with the mode (Section 8.5.2), hence the need 
to have relatively large ensembles (about 15 members) to adequately 
estimate uncertainty (Deser et al., 2018; N. Maher et al., 2018; C. Sun 
et al., 2018; Zheng et al., 2018) .
Even over regions with statistically significant simulated rainfall 
teleconnections during the historical period, CMIP5 models do not 
project clear changes (Perry et al., 2020). Nonetheless, CMIP5 models 
that realistically reproduce Indian summer monsoon rainfall indicate

a strengthening of its relationship with ENSO in RCP8.5 projections, 
though the response is not consistent for different varieties of ENSO 
events (Roy et al., 2019). Inconsistent changes in the ENSO–Indian 
summer monsoon relationship in response to global warming 
in CMIP5 and CMIP6 models may be related to statistical issues 
rather than dynamical changes (Bódai et al., 2020; Haszpra et al., 
2020). Over East Africa during the boreal spring and summer, ENSO 
teleconnections are projected to become stronger in the future 
(Endris et al., 2019). Meteorological drought consequences of each 
strong El Niño are projected to become more severe in the region 
(Rifai et al., 2019).
Indian Ocean Dipole (IOD, Annex IV.2.4) and Indian Ocean Basin (IOB, 
Annex IV.2.4) interactions with ENSO are expected to persist in the 
future (Section 4.5.3.3) but projected changes in the frequency and 
intensity of events remain uncertain (Hui and Zheng, 2018; Endris

et al., 2019; McKenna et al., 2020). Climate extremes such as those 
associated with the extreme positive IOD event of 2019 are expected 
to occur more frequently under continued global warming (Cai et al., 
2021). Projected changes in IOD teleconnections are linked to model 
performance in representing the IOD and its remote influence in the 
present climate, apparently dominated by a positive IOD event-like 
mean state (G. Wang et al., 2017; Huang et al., 2019). Interactions 
between the IOD and the Indian Ocean mean state, via atmosphere–
ocean feedbacks, can affect the behaviour of the IOD (Ng et al., 2018). 
In the eastern Horn of Africa, OND rainfall is projected to increase

1135
because of IOD-ENSO related SST changes in the Indo-Pacific region 
and associated Walker circulation changes (Endris et al., 2019). 
Sensitivity studies generally project increases in Madden Julian 
Oscillation (MJO, Annex IV.2.8) precipitation amplitude in a warmer 
climate, with increases of up to 14% °C–1 of warming (Arnold et al., 
2013, 2015; Caballero and Huber, 2013; Liu and Allan, 2013; Maloney 
and Xie, 2013; Schubert et al., 2013; Subramanian et al., 2014; 
Carlson and Caballero, 2016; Pritchard and Yang, 2016; Adames 
et al., 2017a; Wolding et al., 2017; Haertel, 2018). However, in CMIP5 
models with realistic historical MJO behaviour, the precipitation 
amplitude over the Indo-Pacific warm pool region changes from 
–4% to +8% °C–1 in the RCP8.5 scenario relative to the end of the 
20th century (Bui and Maloney, 2018; Maloney et al., 2019). When 
simulated MJO precipitation amplitude increases with warming, the

leading factor for such change is the intensification of the lower 
tropospheric vertical moisture gradient, that supports stronger 
vertical moisture advection per unit diabatic heating ( Arnold et al., 
2015; Adames et al., 2017a, b; Wolding et al., 2017 ). In idealized 
simulations with constant CO 2 forcing with El  Niño-like patterns, 
the MJO activity penetrates farther east into the central and east 
Pacific with increased warming (Subramanian et al., 2014; Adames 
et al., 2017a). Increased MJO convective variability in a  warmer 
climate does not reflect into increased ability of the MJO to force the 
extratropics (Wolding et al., 2017).
In summary, even though there is low confidence  in how the tropical 
MoVs will change in the future (Sections 4.3.3.2 and 4.5.3.3), their 
regional hydrological consequences, in terms of precipitation, are 
projected to intensify ( medium confidence ). For example, the ENSO

influence on precipitation over the Indo–Pacific sector is projected 
to strengthen and shift eastward ( medium confidence ). The MJO is 
projected to intensify in a warmer climate, with increased associated 
precipitation ( medium confidence ).
8.4.2.9.2 Extratropical modes
CMIP6 projections indicate that the Northern Annular Mode (NAM; 
Annex IV.2.1) is expected to become more positive in winter 
throughout the 21st century in the SSP3-7.0 and SSP5-8.5 scenarios 
(Section 4.5.1). In the near term, the Southern Annular Mode (SAM, 
Annex IV.2.2) is projected to become less positive than observed 
during the end of the 20th century during the austral summer in all 
SSPs scenarios (Section 4.3.3.1). 
In the CMIP5 RCP8.5 scenario, increased amplitude and frequency 
of the North Atlantic Oscillation (NAO, Annex IV.2) during boreal 
winter (December–January–February, DJF) is associated with higher 
precipitation in northern Europe and lower precipitation in southern

Europe (Tsanis and Tapoglou, 2019). However, large-ensemble 
analyses show how the NAO leads to significant uncertainty in future 
changes of regional climate (Section 8.5.2). For example, more than 
a 85% increase in precipitation is projected over northern Europe, 
western Russia and much of eastern North America, with similar 
decreasing resulting in drying over north-western Africa and regions 
adjacent to the Mediterranean Sea (Deser et al., 2017). In the SH, the positive trend projected for the SAM in the CMIP5 
RCP8.5 scenario appears to mitigate the wetting in the mid- to high 
latitudes and the drying over the subtropics, but with strong seasonal 
dependence (Lim et al., 2016). Regional precipitation changes in 
South America, South Africa, Southern Australia and New Zealand are 
not well explained by changes in the SAM, but are related to broad-
scale changes in north –south temperature gradients associated 
with enhanced warming of the tropical upper troposphere and

strengthening of the stratospheric polar vortex (Mindlin et al., 2020).
In summary, projected changes in the intensity, frequency and 
phase of extratropical MoVs (see also Sections  4.3 and 4.5) may 
amplify regional changes in precipitation and contribute to an 
increase in their intra-seasonal and interannual variability ( medium 
confidence ). Regionally, there are potentially significant precipitation 
and atmospheric circulation changes associated with changes in 
extratropical dynamics ( low confidence ). 
8.5 What Are the Limits for Projecting Water 
Understanding the limits to projecting water cycle changes are 
fundamental for refining climate and hydrological models needed to 
develop successful climate change adaptation strategies. Regional 
water cycle projections depend on a  range of model-dependent 
responses (Section 8.5.1) and are also strongly influenced by internal 
variability, especially in the near term (Section 8.5.2; Hawkins and

Sutton, 2012; Rowell, 2012; Orlowsky and Seneviratne, 2013; Kent 
et al., 2015; Fatichi et al., 2016; Greve et al., 2018; Chegwidden 
et al., 2019). CMIP6 models show that different model responses 
to the same forcing scenario remain the main source of uncertainty 
for projected changes in regional precipitation (Figure 8.23; Lehner 
et al., 2020). Section  8.5.3 assesses the potential for non-linear 
responses when shifting from low- to high-global warming levels 
(Section  8.4.2.4; James et al., 2017). While regional uncertainties 
related to downscaling methods (Section 10.3.3) and impact models 
(WGII Chapter 4) are not covered here, the added value of regional 
climate models is briefly discussed (Section 8.5.1.2.2) with a focus on 

1136
8.5.1 Model Uncertainties of Relevance  
for the Water Cycle
Model response uncertainty is typically estimated as the inter-model 
spread (range) projected by a  set of climate models for a  given 
emissions scenario. It is best estimated at the end of a high-emissions 
scenario when internal variability has a  limited contribution to 
total uncertainty (Figure 8.23). Even for aggregated quantities, like 
decadal-mean precipitation averaged over relatively large domains, 
model response uncertainty is substantial and can exceed scenario 
uncertainty (Hawkins and Sutton, 2011; Lehner et al., 2020, 1.5.4, 
4.4.1.3). This can also be true for other water cycle variables such 
as soil moisture, runoff and streamflow at the regional scale, either derived directly from global climate models (GCMs) or produced 
by ‘offline’ using global hydrological models (GHMs) driven by 
the same GCMs (Orlowsky and Seneviratne, 2013; Giuntoli et al.,

2015, 2018; Chegwidden et al., 2019). Although some of the model 
response uncertainty is related to climatological biases (Grose 
et al., 2017; G. Li et al., 2017; Lehner et al., 2019; Samanta et al., 
2019), model biases are not the only way to assess the reliability of 
climate projections (compare with Box 4.1). Therefore, our focus here 
is on the representation of key processes that are not completely 
resolved in current-generation GCMs (Section  8.5.1.1) and on the 
model improvements associated with increased horizontal resolution 
(Section 8.5.1.2).
Internal variability Model uncertainty Scenario uncertainty2021–2040 2041–2060 2081–2100
Land+Ocean
Land41% 58%
77% 17%2%
6%
17% 78% 5%
10     20     30    40     50     60    70      80    90
Fractional uncertainty (%)
Figure 8.23 | Geographical and zonal mean distribution of the percentage of variance explained by the three sources of uncertainty in CMIP6 projections

of 20-year mean precipitation changes in 2021–2040 (top), 2041–2060 (middle) and 2081–2100 (bottom) relative to the 1995–2014 base period: Internal 
climate variability (left), model response uncertainty (middle) and scenario uncertainty (right, considering four plausible concentration scenarios: 
SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5).  Percentage numbers give the area-weighted global average value for each map. Right panels show the zonal mean fractions 
over both land and sea (solid lines) and over land only (dashed line). The figure was adapted from Figure 4a in Lehner et al. (2020), https://creativecommons.org/licenses/by/4.0/ . 
The relative contributions of internal variability, models and emissions scenarios to the total uncertainty depend on both region and time horizon. The scenario uncertainty is

relatively low in near and mid-term time horizons while it increases in the long term mostly over the high latitudes. The model response uncertainty is the most influential factor 
across all time horizons. Internal variability also plays a key role in the near term, especially in the subtropics. Further details on data sources and processing are available in 
the chapter data table (Table 8.SM.1).

1137
8.5.1.1 Fitness-for-purpose and Poorly Constrained 
Key Processes
The AR5 Chapter  7  recognized that the simulation of clouds and 
precipitation remains challenging for state-of-the-art GCMs. 
Model development and evaluation have continued since 
AR5, with a  particular emphasis on the representation of new 
model components, like interactive vegetation, aerosols and 
biogeochemical cycles. For example, the comparison of simulated 
tropical precipitation across three successive generations of CMIP 
models (including CMIP6) indicates overall little improvement for the 
summer monsoons, the double-ITCZ bias, the diurnal cycle and the 
frequency of precipitation (Fiedler et al., 2020). Some of these issues 
are related to inherent model limitations in three specific areas: 
atmospheric convection, cloud –aerosol interactions and land surface 
processes (ocean and cryosphere-related processes are addressed

in Chapter 9). These limitations do not weaken the overall progress 
made in the large-scale simulation of present-day climate (FAQ 3.3 
and Section  3.3.2.3), even though the improvement of CMIP6 
compared with CMIP5 models is limited (Figure 3.12) and is generally 
less systematic or obvious at the regional scale (e.g.,  Gusain et al., 
2020; Monerie et al., 2020; Oudar et al., 2020a) . Instead, they call for 
a careful interpretation of hydrological projections with the full range 
of plausible outcomes, rather than only considering the most likely 
scenarios (Sutton, 2018, 2019). 
8.5.1.1.1 Atmospheric convection
Moist convection is fundamental to the water cycle through its vertical 
transport of momentum, heat, and moisture across the atmosphere. 
It is particularly active in the tropics where it contributes to more 
than half of annual precipitation and to the development of severe 
weather events. Given limitations in computing resources, the current-

generation GCMs cannot yet represent small-scale cloud processes 
and consequently shallow and deep convection is determined by 
sub-grid-scale parametrizations. While such parametrizations can be 
evaluated against field observations (e.g., Abdel-Lathif et al., 2018), 
it remains challenging to estimate convective entrainment that is 
valid for both shallow and deep convection (G.J. Zhang et al., 2016). 
Comparisons between regional projections with explicit compared 
with parametrized convection also highlight the limitations of 
parametrized convection for assessing climate change (Kendon et al., 
2019; Jackson et al., 2020).
Atmospheric convection is particularly important for a  realistic 
simulation of tropical precipitation intensities (Pendergrass and 
Hartmann, 2014a; Kendon et al., 2019). Many CMIP5 models 
produce rainfall at water vapour amounts lower than in observations 
(Takahashi, 2018), as well as too light and too frequent precipitation

events (Sun et al., 2015; Trenberth et al., 2017). Such biases can 
be explained by a  lack of convective inhibition (Rochetin et al., 
2014a, b) and by too much convective and too little non-convective 
precipitation (Chen and Dai, 2019). Tropical convection controls the 
amount of precipitable water simulated over the equatorial Indian 
Ocean, which has been identified as a key metric for differentiating 
model skill in simulating South Asian monsoon precipitation (Hagos 
et al., 2019). Many models have difficulty in adequately simulating the diurnal cycle of precipitation over land (Couvreux et al., 2015), 
the rainfall intensity distribution associated with the West African 
monsoon (Roehrig et al., 2013), and the intensity of tropical cyclones 
(Sections 10.3.3.4 and 11.7.1.3), phenomena for which atmospheric 
convection also plays a key role.
Since AR5, there have been improvements in the representation of 
convective clouds and related precipitation in GCMs. For instance,

the drizzle issue (too light and too frequent rainfall events) has led 
to modifications in the deep convection triggering scheme (Rochetin 
et al., 2014b; Han et al., 2017; Xie et al., 2018; Wu et al., 2019) . 
Although high-resolution studies have highlighted these limitations, 
most GCMs still rely on a convective available potential energy (CAPE) 
closure which has been adapted to various cloud regimes (Bechtold 
et al., 2014; Han et al., 2017; Walters et al., 2019) or evaluated 
against convection-permitting models (CPMs; J. Chen et al., 2020a). 
To increase the sensitivity of convection to tropospheric humidity, 
several models now include a  representation of deep convective 
entrainment dependent on relative humidity (Bechtold et al., 2008; 
Han et al., 2017; M. Zhao et al., 2018; Walters et al., 2019) . Other 
efforts have focused on the improvement of shallow convection and 
low-level cloudiness due to their major contribution to uncertainty

in climate sensitivity (Section 7.4.2.4). A cloud-regime-based study 
however highlights an apparent disconnection between cloud 
and precipitation processes in GCMs (Tan et al., 2018), suggesting 
that a  good representation of clouds does not lead to systematic 
improvement in simulated precipitation. A global simulation in which 
the parametrized convection is switched off shows a strong influence 
of parametrized convection on daily precipitation extremes (P . Maher 
et al., 2018). Regional simulations at a 25km resolution suggest that 
an explicit deep convection can be beneficial even at such a relatively 
coarse resolution (Vergara-Temprado et al., 2020). Perturbed physics 
ensembles (PPE, Section 1.4.4) make it possible to identify parameters 
in the convection scheme that are most important in determining 
future precipitation changes (Bernstein and Neelin, 2016).
Since AR5, spatial aggregation of tropical convection has also received

growing attention in both observational (Holloway et al., 2017) and 
modelling studies (Muller and Bony, 2015; Wing et al., 2017; Tan 
et al., 2018). The changing degree of convective organization was 
highlighted as a  key mechanism for dynamic changes in extreme 
precipitation (Pendergrass, 2020a). Yet, convective parametrizations 
do not represent all aspects of mesoscale convective systems (Hourdin 
et al., 2013; Park et al., 2019). This is related to the complexity of 
mechanisms involved from synoptic to mesoscale dynamics, which 
are only partially resolved by models. Cloud-resolving models (CRMs, 
Section  8.5.1.2.2) represent a  useful benchmark for improving the 
parametrization of mesoscale convective systems. Machine learning 
can also be used to parametrize moist convection after training 
the model with a conventional or a super parametrization scheme 
(Gentine et al., 2018; O’Gorman and Dwyer, 2018), but has not yet 
been used in the CMIP framework.

While some global modelling centres have reported progress in their 
parametrization of convection and in their simulation of seasonal, 
daily and sub-daily precipitation (e.g.,  Danabasoglu et al., 2020; 
Roehrig et al., 2020), CMIP6 models as a whole only show limited

1138
Chapter 8  W ater Cycle Changes8improvements in their simulation of the tropical precipitation 
climatology compared to CMIP5 (Figure  3.10; Fiedler et al., 2020). 
For instance, the double-ITCZ syndrome is still prominent (Tian and 
Dong, 2020) despite being reduced in some models (e.g., Qin and 
Lin, 2018) . This systematic bias was shown to arise from atmospheric 
processes including cloud feedbacks (Tian, 2015; Dixit et al., 2018; 
Talib et al., 2018) and the SST threshold at which deep convection 
occurs in the tropics (Oueslati and Bellon, 2015; Xiang et al., 2017; 
Adam et al., 2018). Such biases can also arise from a  too weak 
sensitivity of seasonal tropical precipitation to local SSTs compared 
with observations (Good et al., 2021). These biases are large enough 
to alter forced precipitation changes, and consequently limit our 
confidence in projected precipitation changes (Samanta et al., 2019; 
Aadhar and Mishra, 2020). Observational constraints can be used to

narrow model response uncertainties (DeAngelis et al., 2015; G. Li 
et al., 2017; Ham et al., 2018; Watanabe et al., 2018), although there 
is still no consensus that model selection or weighting is a reliable 
alternative to the ‘one-model-one-vote’ approach used in Section 8.4 
(Box  4.1). The detrimental influence of model errors can also be 
mitigated by focusing on phenomena or events (Polson and Hegerl, 
2017; Weller et al., 2017), implementing bias adjustment techniques 
(Section 10.2.3.2), or adopting a non-probabilistic storyline approach 
(Zappa and Shepherd, 2017). 
In summary, since AR5 empirical convective parametrization schemes 
and associated precipitation biases have improved in some but not 
all global climate models. There is still low confidence  in their ability 
to accurately simulate the spatio-temporal features of present-day 
precipitation, especially in the tropics where a  double-ITCZ bias is

still apparent in many models. While such biases limit the reliability of 
precipitation projections in some cases, there is currently only medium 
confidence  that model selection or weighting is a better alternative 
to the one-model-one-vote approach (Box 4.1). Improved water cycle 
projections can be achieved by focusing on phenomena or weather 
events, such as a thermodynamic intensification of convective events 
(high confidence , Section  8.2.2.1), however accurate quantitative 
estimates are currently hampered by complex, model-dependent 
dynamical responses (Section 8.2.2.2).
8.5.1.1.2 Aerosol microphysical effects on clouds and precipitation
In AR5 Chapter 7, there was low confidence  in the representation 
of cloud–aerosol interactions in climate models. Despite progresses 
in this field since AR5, cloud–aerosol interactions remain a  major 
obstacle to understanding climate and severe weather (Varble, 2018).

High aerosol concentrations have been observed to suppress rain in 
water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, 
such aerosol effects are muted in GCMs, which tend to produce 
precipitation from shallow clouds too frequently at the expense 
of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises 
from incomplete knowledge of how clouds adjust to aerosol primary 
effects such as cloud condensation nuclei (CCN). The adjustment 
occurs mainly as a dynamic response to the impacts of CCN on cloud 
droplet size and number concentrations on precipitation-forming 
processes (Rosenfeld et al., 2008; Goren and Rosenfeld, 2014; Koren 
et al., 2014; Camponogara et al., 2018). Uncertainties are large 
for deep clouds, as their processes are much more complex and include also the impacts of aerosols on ice-precipitation processes. 
Aerosols can substantially invigorate (Rosenfeld et al., 2008; Koren

et al., 2014; Fan et al., 2018) and electrify (Thornton et al., 2017; 
Q. Wang et al., 2018) deep tropical convective clouds. High-resolution 
atmospheric simulations suggest that high aerosol concentrations 
can increase environmental humidity by producing clouds that 
mix more condensed water into the surrounding air, which in turn 
favours large-scale ascent and strong convective events (Abbott and 
Cronin, 2021). Further assessment of uncertainties in aerosol –cloud 
interactions for shallow water clouds is provided in Section 7.3.3.2.
A major challenge in representing convective clouds and related 
precipitation events in GCMs is a  lack of sophisticated cloud 
microphysics in convective parametrization schemes (e.g., Fan et al., 
2016). Most of these schemes only include simple microphysical 
treatments, such as direct partition between cloud condensation and 
precipitation, and do not include advanced treatment of conversion

among different types of hydrometeors. As such these schemes are 
unable to simulate microphysical cloud and precipitation responses 
to aerosol-related perturbations in cloud droplet concentration 
and ice crystals (see Box 8.1), or perturbations in thermodynamical 
states from global warming. Efforts have been made to include more 
advanced cloud microphysical treatment in cumulus parametrizations 
(Song and Zhang, 2011; Grell and Freitas, 2014; Berg et al., 2015) or 
to use explicit cloud microphysics schemes in climate models with 
a  ‘super parametrization’ (Wang et al., 2015), which have been 
shown to improve the performance in simulating cloud properties 
and precipitation. However, few of these improvements have been 
incorporated into CMIP6 climate models so the projected precipitation 
response to anthropogenic perturbation may still be hindered by the 
inadequate microphysical treatment in cumulus parametrization 
(Smith et al., 2020).

In summary, there is still low confidence  in the simulated influence 
of the aerosol microphysical effects on future precipitation changes.
8.5.1.1.3 Land surface processes
Land surface processes determine the partitioning of net surface 
radiation into sensible, latent and ground heat fluxes, the partitioning 
of precipitation into evapotranspiration and runoff, and the net 
terrestrial carbon flux at the Earth’s surface. They are relevant for 
simulating the terrestrial water cycle responses to climate change, as 
well as the response to land use change (FAQ 8.1). Even basic land 
surface properties such as albedo (Terray et al., 2018) or the ratio of 
transpiration to total evaporation (Chang et al., 2018) still need to 
be improved in state-of-the-art coupled GCMs. Runoff sensitivities 
are also not well constrained in these models, which display a large 
spread for the present-day climate, influencing simulated changes

under global warming (Lehner et al., 2019). Earth System Models 
(ESMs) incorporate some combined biophysical and biogeochemical 
processes to a  limited extent, and many relevant processes about 
how plants and soils interactively respond to climate changes are yet 
to be considered (e.g., Y . Liu et al., 2020). Consequently, land surface 
processes and their atmospheric coupling contribute to the range in 
water cycle projections (Jia et al., 2019).

1139
Since AR5, development of new and existing processes in land surface 
models (LSMs) have been evaluated. These include soil freezing and 
permafrost (Vergnes et al., 2014; Chadburn et al., 2015; K. Yang et al., 
2018; Gao et al., 2019) , soil and snow hydrology (Brunke et al., 2016; 
Decharme et al., 2016), glaciers (Shannon et al., 2019), surface waters 
and rivers (Decharme et al., 2012), as well as vegetation (Bartlett and 
Verseghy, 2015; Betts et al., 2015; Knauer et al., 2015; Tang et al., 
2015) and the representation of hydraulic gradients throughout the 
soil–plant–atmosphere continuum (Bonan et al., 2014). Such land 
surface model developments have led to significant improvements 
in global offline hydrological simulations driven by observed 
atmospheric forcings (e.g., C. Li et al., 2017; Decharme et al., 2019).
Progress in the representation of land surface heterogeneity has

been made, in the form of improved mapping of root zone storage 
capacity (Wang-Erlandsson et al., 2016), improved vegetation 
stand, disturbance and fire dynamics (F .  Li et al., 2013; Fisher 
et al., 2018; Haverd et al., 2018; Yue et al., 2018; Zou et al., 2019), 
better representation of urban surfaces (Box 10.3), and the explicit 
representation of inland water bodies (Gu et al., 2015; Verseghy and 
MacKay, 2017). The representation of realistic snow and vegetation 
cover significantly affects the simulation of the land surface energy 
and water budgets at multiple time scales (Loranty et al., 2014; 
Bartlett and Verseghy, 2015; Thackeray et al., 2015; Qiu et al., 
2016; Thackeray and Fletcher, 2016; L. Wang et al., 2016; Alessandri 
et al., 2017). Groundwater remains inadequately represented in 
many models, which limits our current understanding of the two-
way interactions between groundwater and the rest of the hydrologic

cycle (R.G. Taylor et al., 2013a; Leng et al., 2014; Vergnes et al., 2014; 
Pokhrel et al., 2015; Maxwell and Condon, 2016; Collins, 2017; 
Scanlon et al., 2018; Condon et al., 2020) . Land management exerts 
an increasing influence on the water cycle (Abbott et al., 2019) whose 
representation in the current-generation climate models is generally 
incomplete (Section 10.3.3.7.2).
Aside from land surface models (LSMs), global hydrological models 
(GHMs) have been further developed for off-line simulations of the 
hydrological impacts of both climate change and water management 
(Jiménez Cisneros et al., 2014; Schewe et al., 2014; Döll et al., 2016, 
2018; Pokhrel et al., 2016, 2017; Veldkamp et al., 2018) . GHMs can 
equal or outweigh the contribution of GCMs to uncertainties in 
hydrological projections at the regional scale (Giuntoli et al., 2015). 
Historical GHM simulations are currently not sufficient to improve

regional water cycle projections, due to modelling uncertainties in 
both the driving GCMs and land surface hydrology (Pechlivanidis 
et al., 2017; Samaniego et al., 2017; Hattermann et al., 2018; 
Krysanova et al., 2018) . Biophysical vegetation processes are still 
not accounted for in many GHMs, which may lead to inadequate 
projections of terrestrial runoff and water resources. However, 
hydrological models that do simulate these effects often disagree 
(Prudhomme et al., 2014), so do not necessarily provide the added 
value of a more sophisticated representation of vegetation processes 
and land surface conditions (Döll et al., 2016). 
Since AR5, there has been increasing recognition of the need to 
better understand the role of land–atmosphere coupling and related 
feedbacks (Joetzjer et al., 2014; Berg et al., 2016; Catalano et al., 2016; Berg and Sheffield, 2018a; Santanello et al., 2018) . This has 
led to the development of dedicated field campaigns (Song et al.,

2016; Phillips et al., 2017; Dirmeyer et al., 2018), remotely sensed 
observations (Ferguson and Wood, 2011; Roundy and Santanello, 
2017), and tailored diagnostics (Tawfik et al., 2015a, b; Miralles et al., 
2016, 2019; Dirmeyer and Halder, 2017). Dynamic vegetation models 
have been introduced in global ESMs but they need further evaluation 
(Medlyn et al., 2015; Prentice et al., 2015; Cantú et al., 2018; Franks 
et al., 2018) to provide valuable information on potential vegetation 
feedbacks. Plant migration and mortality, increased disturbances 
from wild fires, insects and extreme events, interactive nitrogen cycle, 
or the impact of increased levels of tropospheric ozone are often 
ignored or poorly represented in the current-generation of ESMs 
(Bonan and Doney, 2018; Fisher et al., 2018). 
The physiological response of plants to increasing atmospheric CO 2 is 
generally accounted for, but only using empirical models of stomatal

conductance that are characterized by a  single critical parameter 
of intrinsic water-use efficiency (Franks et al., 2017, 2018). This 
reflects a lack of structural diversity and caution about the consensus 
of the photosynthesis response to increasing CO 2 (Knauer et al., 
2015; Huang et al., 2016), which has implications for the ability of 
the current-generation models to account for uncertainty in future 
evapotranspiration changes. Most CMIP5 models underestimate the 
ratio of plant transpiration to total terrestrial evapotranspiration, 
which may suggest that they also underestimate the impact of plant 
physiology on the water cycle (Lian et al., 2018). Plant hydraulics 
are not explicitly considered in many land surface models, which 
may lead to an underestimation of the influence of the increasing 
atmospheric moisture stress on plant transpiration under climate 
change (Massmann et al., 2019; Grossiord et al., 2020; Y .  Liu

et al., 2020) . Most ESMs underestimate the water use efficiency 
measured at many sites and, consequently overestimate the ratio of 
evapotranspiration to precipitation (J. Li et al., 2018). 
In summary, since AR5 substantial advances have been made in 
the representation of land surface processes in current-generation 
Earth System Models (ESMs). Offline hydrological models allow 
the application of bias-adjusted atmospheric forcings, but there 
is low confidence  of an improved response compared to coupled 
climate models, given their inherent limitations (Box  10.2). While 
improvements in the representation of complex land surface 
feedbacks relevant to the water cycle are needed, there is currently 
low confidence  that they will systematically improve the reliability of 
water cycle projections. 
8.5.1.2 Added Value of Increased Horizontal Model Resolution
Coarse spatial resolution of climate models has often been considered

a key limitation in global climate projections (Di Luca et al., 2015; 
Roberts et al., 2018). Proposed and tested solutions include a uniform 
or regional increase in the resolution of GCMs, or the use of regional 
climate models (RCMs). The increase in computing resources has 
also led to the development of convection-permitting models (Prein 
et al., 2015), which have been integrated over larger domains, but 
are still unsuitable for CMIP simulations. Statistical downscaling 
tools are also widely used to generate fine-scale regional climate

1140
Chapter 8  W ater Cycle Changes8information necessary for climate impacts and adaptation studies. 
A  comprehensive assessment of the added value of increased 
spatial resolution and of the benefits and shortcomings of statistical 
downscaling tools are addressed in Chapter 10 (Section 10.3.3).
8.5.1.2.1 High-resolution global climate models
Since AR5, horizontal resolution has increased in most global climate 
models, which has led to several improvements in the simulation of 
the water cycle (see also Section 10.3.1.1), not only in areas with 
steep or complex orography, but also over the tropical oceans and 
within the North Pacific and North Atlantic storm tracks (Piazza 
et al., 2016; Roberts et al., 2018; Bui et al., 2019; Chen and Dai, 2019; 
Vannière et al., 2019) . Yet, the added value of higher resolution global 
climate models is not systematic (Johnson et al., 2016; Ogata et al., 
2017; D. Huang et al., 2018; Mahajan et al., 2018; Vannière et al.,

2019)  and needs careful assessment (Haarsma et al., 2016; Caldwell 
et al., 2019). Several AGCM studies suggest that increased spatial 
resolution leads to better simulation of the atmospheric moisture 
transport from ocean to land, the geographical distribution of 
annual mean precipitation (Demory et al., 2014), and the frequency 
distribution of daily precipitation intensities (L. Zhang et al., 2016; 
Chen and Dai, 2019) including extremes in many (Jacob et al., 2014; 
Westra et al., 2014), but not all cases (Bador et al., 2020). 
Part of the improvement in simulated precipitation accuracy is related 
to improved simulation of the frequency and/or mean intensity of 
tropical (Roberts et al., 2015; Walsh et al., 2015) and extratropical 
(Hawcroft et al., 2016) cyclones. Idealized regional experiments also 
show that the North Atlantic storm track response to global warming 
can be amplified in higher resolution models (Willison et al., 2015).

Increased atmospheric horizontal resolution can be also important 
for simulating Northern Hemisphere (NH) blockings (Davini et al., 
2017; Schiemann et al., 2017) and synoptic features of the East 
Asian summer monsoon (Yao et al., 2017; Kusunoki, 2018). Variable 
resolution based on grid stretching may be a valuable alternative for 
simulating regional phenomena like monsoons (Sabin et al., 2013; 
Krishnan et al., 2016)  or tropical cyclones (Harris et al., 2016; Chauvin 
et al., 2017), while avoiding inconsistencies in the forcings or physics 
that can be found in RCMs driven by GCMs (Boé et al., 2020; Tapiador 
Increasing horizontal model resolution in CMIP5 and CMIP6 models 
leads to a systematic increase in global mean precipitation, enhanced 
moisture advection to land in close connection with increased 
orographic precipitation, and a partial reduction of the long-standing 
double ITCZ bias (Demory et al., 2014; Caldwell et al., 2019; Vannière

et al., 2019). Recent studies based on HighResMIP simulations 
(Haarsma et al., 2016) confirm the added value of increased 
horizontal resolution (at least 50 km in the atmosphere and 25 km 
in the ocean) for the simulation of tropical (Roberts et al., 2020) and 
extratropical cyclones (Priestley et al., 2020b). CMIP6 model biases 
in annual mean precipitation are only slightly reduced at higher 
resolution (Figure 3.10). 
High resolution representation of the land surface is also important 
for simulating many features of the terrestrial water cycle, such as orographic precipitation, snow, runoff and streamflow in complex 
topography areas (Zhao and Li, 2015). However, the added value 
may be easier to assess in offline rather than online land surface 
simulations (Döll et al., 2016) given the possible use of bias-corrected 
atmospheric forcings. Offline high-resolution GHMs are routinely used 
to monitor water resources or to assess the hydrological impacts of

bias-adjusted global climate projections (Davie et al., 2013; S. Huang 
et al., 2017, 2018). Yet, the development and calibration of ‘hyper-
resolution’ hydrological models, with gridcells of typically 100 m to 
1  km, raises a  number of issues given the lack of comprehensive 
surface or subsurface information (Bierkens et al., 2015) and the lack 
of coupling with the atmosphere (Berg and Sheffield, 2018a). 
In summary, there is high confidence  that increasing horizontal 
resolution in GCMs can reduce a  number of systematic model 
errors of relevance for the water cycle, including synoptic circulation 
and the statistics of daily precipitation. High-resolution GCMs and 
GHMs provide improved representation of land surfaces, including 
topography, vegetation and land use change, which are required to 
accurately simulate changes in the terrestrial water cycle. However, 
there is low confidence  that the higher horizontal resolution

simulations currently available provide more accurate projections of 
the large-scale features of the water cycle.
8.5.1.2.2 Regional climate models and  
convective-permitting models
Regional Climate Models (RCMs) are used to dynamically downscale 
global model simulations for a particular region (usually at a spatial 
resolution of the order of 10 to 50 km; see Section 10.3.3). The AR5 
reported that RCMs are useful for regions with variable topography 
and for small-scale phenomena. However, they inherit biases from 
their driving GCMs and thus may lack physical consistency with 
them. Since AR5, the application of RCMs has largely increased due 
to international model intercomparison projects such as CLARIS-
LPB (Sánchez et al., 2015). Many studies have focused on present-
day climatological precipitation, showing with high confidence  
improvements in its monthly to seasonal accumulation and spatial 
distribution (Dosio et al., 2015; Giorgi et al., 2016; Bozkurt et al., 2019;

Falco et al., 2019; Di Virgilio et al., 2020), although the modelling of 
precipitation remains the ‘Achilles heel’ of both GCMs and RCMs and 
should be considered cautiously when informing regional climate 
change adaptation strategies (Tapiador et al., 2019b).
Regional Convective Permitting Models (CPMs), typically run at 
a resolution less than 10 km, have been implemented over increasingly 
large domains. Compared to models with parametrized convection 
(Section 8.5.1.1.1), they generally show improved simulation of key 
features of the water cycle such as orographic precipitation, sea 
breeze dynamics, the diurnal cycle in precipitation, soil-moisture–
precipitation feedbacks, daily precipitation persistence, sub-daily to 
daily precipitation intensities and related extremes (Section 8.2.3.2; 
Birch et al., 2015; Prein et al., 2015; Kendon et al., 2017; Leutwyler 
et al., 2017; Willetts et al., 2017; Hohenegger and Stevens, 2018;

Berthou et al., 2019b; Takahashi and Polcher, 2019; Fumière et al., 
2020; Scaff et al., 2020; Caillaud et al., 2021) . A growing number of 
studies have also assessed the potential added value of using CPMs

1141
for regional climate projections (Ban et al., 2015; Giorgi et al., 2016; 
Fosser et al., 2017; Kendon et al., 2017, 2019; C.  Liu et al., 2017; 
Lenderink et al., 2019; Rasmussen et al., 2020; see also Atlas 5.6.3) . 
Although projected changes in rainfall occurrence in CPMs are broadly 
and qualitatively consistent with the results of GCMs and RCMs 
(Kendon et al., 2017), there is a tendency towards stronger changes 
in both wet and dry extremes (Berthou et al., 2019a; Kendon et al., 
2019; Lenderink et al., 2019; Finney et al., 2020a) . While both GCMs 
and RCMs project an overall decrease in summer precipitation over 
the Alps, RCMs simulate an increase over the high Alpine elevations 
that is not present in the global simulations (Giorgi et al., 2016). 
Recent studies based on both GCMs and CPMs indicate that both 
CAPE and convective inhibition will increase in a  warmer climate

(Section 8.2.3.2; J. Chen et al., 2020a), consistent with a shift from 
moderate to less frequent but stronger convective events (Rasmussen 
et al., 2020). If underestimated by models with parametrized 
convection, such a mechanism could explain the underestimation of 
both projected increase in precipitation extremes (Borodina et al., 
2017; Yin et al., 2018) and land surface drying (Douville and Plazzotta, 
2017) in the extratropics. CMIP5 models with a  larger increase in 
extreme precipitation also exhibit larger declines or smaller increases 
in light to moderate events (Thackeray et al., 2018).
In summary, there is high confidence  that dynamical downscaling 
using limited area models adds value in simulating precipitation 
and related water cycle processes at the regional scale, especially 
in complex orography areas (Section  10.3.3.5.1). There is high 
confidence  that the explicit simulation of atmospheric convection can

improve the representation of weather phenomena, including the life 
cycle of convective storms and related precipitation extremes. Even 
with an improved simulation of small-scale processes, there is only 
medium confidence  that there will be an improvement in RCM-based 
water cycle projections as they rely on GCM boundary conditions.
8.5.2 Role of Internal Variability and Volcanic Forcing
Beyond modelling uncertainties, internal variability and unpredictable 
natural forcings may also lower the degree of confidence in projected 
water cycle changes, especially in the near term (2021-2040) and 
regional-scale projections (Hawkins and Sutton, 2011; Kent et al., 
2015; Thompson et al., 2015; Fatichi et al., 2016; McKinnon and 
Deser, 2018; Chen and Brissette, 2019; Lehner et al., 2020) . Although 
there is low confidence  that the main modes of climate variability 
(Annex IV) are altered in a  warmer climate (Sections  4.4.3 and

4.5.3), increasing contrast between wet and dry weather regimes 
(Section 8.2.2.1) will amplify their influence on water cycle variability 
(Section 8.4.2.9) and therefore contribute to uncertainties in near-
term precipitation changes (Figure  8.23). The role of internal 
variability as source of uncertainties in regional climate projections 
is assessed in Section 10.3.4.3. Here we assess the role of internal 
variability in influencing water cycle projections using paleoclimate 
reconstructions, pre-industrial model simulations, and large single 
model ensembles (Section 8.5.2.1). Implications for the predictability 
of near-term water cycle changes are specifically assessed, as 
they show significant but model-dependent regional hydrological fingerprints over land (Section 8.5.2.2). The role of volcanic eruptions 
is also briefly assessed in terms of consequences and uncertainties in 
water cycle projections (Section 8.5.2.3).

8.5.2.1 Quantification of Water Cycle Internal Variability
Estimating internal variability is an important challenge in the 
assessment of human-induced changes in the water cycle since its 
magnitude and range of variability can exceed the anthropogenic 
signal, at least at the regional scale and for near-term projections 
or low-emissions scenarios (Sections  4.4.1.4 and 8.4.2.9; Deser 
et al., 2012; Shepherd, 2014; Xie et al., 2015; Sarojini et al., 2016; 
Dai and Bloecker, 2019; Lehner et al., 2020) . Underestimating 
internal variability in models may result in the overestimation of 
anthropogenic climate change because the ‘noise’ in the signal-to-
noise ratio is underestimated (Knutson and Zeng, 2018). There is 
medium confidence that this underestimation affects global water 
cycle projections, for instance, in terms of drought persistence and 
severity in the south-western USA, eastern Australia, southern Africa,

the Mediterranean, the southern Amazonian basin and China (Ault 
et al., 2014; Cook et al., 2018; Gu et al., 2018). In CMIP6 models, 
the uncertainty in future projections of 20-year mean precipitation 
changes attributable to internal variability ranges from 41% in 
the near term (2021–2040) to 5% in the long term (2081–2100) 
(Figure 8.23). For decadal-mean precipitation changes, the relative 
contribution of internal variability is even larger when using large 
ensembles (Lehner et al., 2020).
Over the 20th century, CMIP5 models show a realistic magnitude of 
decadal precipitation variability, if not a slight overestimation in some 
regions (Knutson and Zeng, 2018). However, the relatively short and 
human-influenced instrumental record limits our ability to quantify the 
magnitude of internal variability in the water cycle, particularly over 
long time scales (decadal and beyond). Global extended reanalyses 
(Section 1.5.2) have been used to derive long-term variability in the

regional water cycle components (Caillouet et al., 2017), merged with 
historical meteorological and hydrological local observations (Bonnet 
et al., 2017; Devers et al., 2020). Specific assessment of these types 
of methodology and related uncertainties is provided in Chapter 10 
(Sections  10.2 and 10.3). Paleoclimate archives (tree rings, corals, 
ice core, speleothems, lake and ocean sediments) provide extended 
reconstructions of key water cycle metrics and large-scale circulation 
features. Some studies have suggested that CMIP5 models 
underestimate internal variability at decadal and longer time scales, 
and therefore may be missing important processes in the climate 
system (Ault et al., 2012, 2013; Bunde et al., 2013; Franke et al., 2013; 
Cheung et al., 2017; Hope et al., 2017; Kravtsov, 2017; Cassou et al., 
2018) . However, recent assessments using paleoclimate records have 
found that CMIP5 models are able to reproduce decadal-to-centennial

variability, including the severity, persistence and spatial extent of 
megadroughts (Coats et al., 2015; Stevenson et al., 2015; PAGES 
Hydro2K Consortium, 2017), once signal reddening (autocorrelation) 
in proxy archives is accounted for (Dee et al., 2017; PAGES Hydro2K 
Consortium, 2017). Implementation of proxy system models, that 
is, functions that transform model variables into proxy units, has 
reduced model–proxy disagreement, although some differences in 
the magnitude of internal variability remain, particularly at centennial

1142
Chapter 8  W ater Cycle Changes8time scales (Dee et al., 2017; Parsons et al., 2017). It is unclear 
whether remaining discrepancies represent limitations of the climate 
models, or limitations of the proxy system models. Therefore, there 
is medium to high confidence  (i.e.,  depending on the region) that 
climate models do not underestimate water cycle internal variability. 
The mechanisms driving internal variability in the water cycle in 
climate model simulations varies. While models indicate that cool 
SSTs in the eastern tropical Pacific (La Niña or the cool phase of 
the PDO) are associated with drought in south-western North 
America, they also show that atmospheric internal variability may 
be a  more prominent driver (Coats et al., 2015, 2016; Stevenson 
et al., 2015; Parsons et al., 2018). Simulations of the last millennium 
from CMIP5–PMIP3 reproduce the observed negative correlation between eastern Australian rainfall and the central equatorial Pacific

SSTs with varying skill, and also display periods when the ENSO 
teleconnection weakens substantially for several decades (Brown 
et al., 2016a). Differences in simulated internal variability have been 
found to be responsible for the inter-model spread in predicted shifts 
in subtropical dry zones for a given shift in the Hadley cell (Seviour 
et al., 2018). CMIP5 models show that both internal variability 
and anthropogenic forcings are responsible for the drying over 
the South Atlantic Convergence Zone region, though with large 
uncertainties (Zilli and Carvalho, 2021). Moreover, the detection of 
the anthropogenic forcing on the South Atlantic Convergence Zone 
is strongly dependent on the characterization of model internal 
variability (Talento and Barreiro, 2012). 
NAO inﬂuence on precipitation and SLP trends
Internal Forced
Forced - Internal Forced + Internal(a) (b)
(c) (d)
(mm/day/30 year)-0.75 -0.6 -0.45 -0.3 -0.15 00.15 0.30.45 0.6 0.75

Figure 8.24 | Impact of the North Atlantic Oscillation (NAO) on 2016–2045 climate trends.  (a) Regressions of winter sea level pressure (SLP) and precipitation 
trends upon the normalized leading principal component (PC) of winter SLP trends in the CESM1 Large Ensemble, multiplied by two to correspond to a two standard deviation 
anomaly of the PC (as internal climate variability component); (b) CESM1 ensemble-mean winter SLP and precipitation trends (as forced climate variability component);  
(c) b – a (forced minus internal climate variability component); (d) b + a (forced plus internal climate variability component). Precipitation in colour shading (mm day–1 per 
30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values dashed). Figure adapted from Deser et al. (2017), https://creativecommons.org/licenses/by/4.0/ ; 
further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

1143
Beyond the tropics, North Pacific decadal variability (Annex IV.2.6, 
2.4.5, 3.7.6) exerts a  strong modulation of extratropical ENSO 
teleconnections, but also influences low-frequency variability of the 
Walker circulation, which is underestimated by most CMIP5 models 
(England et al., 2014). Atlantic Multi-decadal Variability (Annex IV.2.7, 
2.4.6, 3.7.7) teleconnections show a high model spread among CMIP5 
models, both in terms of persistence and spatial coherence (Qasmi 
et al., 2017), which has potential consequences for the water cycle 
variability simulated over Europe. For example, internal variability 
will continue to play an important role in the variability of river flows 
over France in coming decades ( medium confidence ) (Giuntoli et al., 
2013; Boé and Habets, 2014; Bonnet et al., 2017).
Ensembles of atmosphere-only simulations driven by observed or 
reconstructed SST are useful for evaluating the ability of models to

capture the circulation and/or precipitation variability observed over 
the historical period (Zhou et al., 2016; Deng et al., 2018; Douville 
et al., 2019). However, limitations of such AGCM-based attribution 
methods, that is, related to the lack of air–sea interactions in the 
response, may lead to erroneous attribution conclusions in some 
regions for local circulation and mean and extreme precipitation 
(Dong et al., 2017). Other methods to measure the portion of 
precipitation variability include the partitioning into dynamical 
as opposed to thermodynamical components (Saffioti et al., 2016; 
Fereday et al., 2018; Lehner et al., 2018), the analysis of variance 
(Dong et al., 2018a) and direct characterization of stochastic weather-
noise (Short Gianotti et al., 2014). 
Single-model initial condition large ensembles (SMILEs) are a powerful 
tool for estimating the magnitude of internal variability in historical 
and future climates (Section  1.4.4). Using SMILEs, it has been

shown, for example, that internal NAO variability imparts substantial 
uncertainty to future changes in European precipitation (Figure 8.24; 
Deser et al., 2017). For the South Asian summer monsoon, internal 
variability can overshadow the forced monsoon rainfall trend, thereby 
increasing near-term projection uncertainties (X. Huang et al., 2020a). 
Specific regional applications of the use of large ensembles are further 
assessed in Sections 10.3.4.3 and 10.3.4.4. 
Since AR5, SMILEs have helped quantify the time of emergence of 
climate change signals (see Sections  1.4.2.2 and 10.4.3). Results 
from SMILEs indicate that by 2000–2009 (compared to 1950–1999), 
simulated anthropogenic shifts in mean annual precipitation 
already emerged over 36–41% of the globe including high latitudes 
(Frankcombe et al., 2018; Kumar and Ganguly, 2018), the eastern 
subtropical oceans, and the tropics (Zhang and Delworth, 2018). 
By 2050 (2100), more than 60% (85%) of the globe is projected to

show detectable anthropogenic shifts in mean annual precipitation 
(Zhang and Delworth, 2018). Other SMILE results for the 1950–2100 
period (Kay et al., 2015; Sigmond and Fyfe, 2016) indicate that 
internal variability can obscure the detection of the anthropogenic 
hydroclimatic signal until the middle to late 21st century in many 
parts of the world for both mean and extreme precipitation 
(Martel et al., 2018; Dai and Bloecker, 2019). A  common finding 
is that changes in the characteristics of wet extreme events will 
emerge earlier than changes in average conditions (Gaetani et al., 
2020; Hawkins et al., 2020; Kusunoki et al., 2020). An assessment of the methods used to estimate time of emergence is presented 
in Chapter 10 (Section 10.3.4.3). For specific regional examples of 
climate change attribution and emergence of anthropogenic signal, 
see Section 10.4.2.
In summary, there is medium confidence  that climate models

reproduce the general magnitude and character of internal variability 
that influences water cycle variables. There is high confidence  that 
internal variability will continue to be a major source of uncertainty, 
at least for near-term water cycle projections at the regional scale. 
There is low confidence  in the region-dependent time of emergence 
of water cycle changes (see also Section 10.4.3), but there is medium 
confidence  that changes in wet extreme events will emerge earlier 
than changes in average conditions.
8.5.2.2 Implications for Near-Term Water Cycle Projections
Adapting water resource management in the face of climate change 
will greatly benefit from improved prediction of land surface hydrology 
at the decadal time scale. Climate predictions (Section 1.4.4) differ 
from climate projections by constraining the initial state of the slow 
components of the climate system (i.e., the ocean, the cryosphere

and the terrestrial hydrology) as well as volcanic aerosols and ozone 
depleting substances with observations. Anthropogenic and natural 
radiative forcing and low-frequency modes of variability (e.g., AMV 
and PDV, Annex IV.2.7 and IV.2.6) suggest the possible predictability 
of climate in the first decade or so of the 21st century, in addition to 
the projected response to the anthropogenic forcing (Sections 4.2.3 
and 4.4.1.3). 
In AR5, decadal prediction of precipitation over some land areas 
showed improved skill due to specified radiative forcing, with almost 
no added value from ocean initialization. Since AR5, more studies 
have been devoted to understanding the potential or effective 
water cycle predictability related to ocean multi-decadal variability. 
Decadal hindcast experiments based on large ensembles highlight 
increasing skill scores in annual mean precipitation three to seven 
years ahead, at least over the Sahel and Europe (Yeager et al., 2018).

There is relatively high predictability of the AMV impacts over the 
Mediterranean basin, Central Asia and the Americas (from the USA to 
northern South America) during boreal summer, but in boreal winter 
the signal-to-noise ratio shows only weak predictability over land 
(Yamamoto and Palter, 2016; Ruprich-Robert et al., 2017). The link 
between South Asian summer monsoon changes and the AMOC 
and the decadal variability in the Pacific Ocean open the possibility 
of increased predictability for the near future (Kushnir et al., 2017; 
X. Huang et al., 2020b; Sandeep et al., 2020) .
The additional skill associated with the initialization of the cryosphere 
and the land surface has received limited attention. However, there 
is observational evidence that oceanic decadal variations can 
propagate into the atmosphere and, consequently accumulate into 
terrestrial land surface reservoirs (e.g.,  Bonnet et al., 2017) and

vegetation (e.g., Zeng et al., 1999). This land surface memory, like in 
soil moisture (Alessandri and Navarra, 2008; Catalano et al., 2016) 
or snow (Loranty et al., 2014), may also contribute to the decadal 
predictability of the terrestrial component of the water cycle, but

1144
Chapter 8  W ater Cycle Changes8remains difficult to assess given the limitations of observational 
records. Vegetation initialization seems to generate as much noise as 
signal and does not necessarily translate into improved skill in early 
decadal predictions based on ESMs (Weiss et al., 2014). 
Decadal hydrological predictability in an idealized setting has also 
been investigated through offline land surface hindcast experiments, 
driven by observed atmospheric forcing and/or initial conditions, 
suggesting the potential for skilful predictions for terrestrial water 
storage, deep soil moisture, and groundwater (Yuan and Zhu, 2018). 
Yet, a real-world assessment is hampered by the lack of observations 
and is only feasible when multi-decadal records of satellite estimates 
of terrestrial water storage, snow mass or soil moisture are available.
In summary, there is high confidence  that the water cycle changes

that have already emerged from internal variability will become more 
pronounced in near-term (2021–2040) projections. However, there 
is low confidence  in decadal predictions of precipitation changes, 
particularly over most land areas, because internal variability 
remains difficult to predict and can offset or amplify the forced water 
cycle response. 
8.5.2.3 Volcanic Forcing
Volcanic eruptions can affect climate projections in the near term 
(2021–2040; Section  4.4.4 and Cross-Chapter Box  4.1). In this 
chapter, they are of interest because they can trigger a  transient 
departure from the water cycle response to anthropogenic radiative 
forcing. Major volcanic eruptions temporarily reduce total global and 
wet tropical region precipitation ( high confidence ) (Iles and Hegerl, 
2014), can weaken or shift the ITCZ (Iles and Hegerl, 2014; Colose 
et al., 2016; Liu et al., 2016), and reduce summer monsoon rainfall 
(medium confidence ) (Pausata et al., 2015b; Zambri and Robock,

2016; Zambri et al., 2017; Zuo et al., 2019; M. Singh et al., 2020) . 
Monsoon precipitation in one hemisphere can be enhanced by the 
remote volcanic forcing occurring in the other hemisphere ( medium 
confidence ) (Pausata et al., 2015a; Liu et al., 2016; Zuo et al., 2019) . 
Over the Sahel, the sign of hydrological changes depend on the 
hemisphere where the volcanic eruptions occur (J.M.  Haywood 
et al., 2013). Out of phase changes in the Sahel and the Amazonian 
basin are expected from the effect of volcanic aerosols on tropical 
Atlantic SST and the ITCZ (Hua et al., 2019). Over the last millennium, 
uncertainties remain in the symmetry/asymmetry of the monsoon 
response because it is difficult to estimate the exact latitude and 
season of past volcanic eruptions further back in time (Colose et al., 
2016; Fasullo et al., 2019).
Data for six major eruptions over the last century along with 
CMIP5 historical experiments indicate that volcanic eruptions cause

a  detectable decrease in streamflow in northern South America, 
Central Africa, high-latitude Asia and in wet tropical–subtropical 
regions, and a detectable increase in south-western North America 
and southern South America (Iles and Hegerl, 2015). Attempts 
to include volcanic forcing in future projections show enhanced 
precipitation variability on annual to decadal time scales with 
small reductions in Asian monsoon rainfall (Bethke et al., 2017). 
The occurrence of volcanic eruptions in the coming century, either as single large events or clustered smaller ones, can alter the water cycle 
(see also Cross-Chapter Box 4.1), and regional drought events may 
be enhanced by co-occurring volcanic (Liu et al., 2016; Gao and Gao, 
2017; Zambri et al., 2017) and GHG (e.g., Cook et al., 2018) forcing 
(low confidence ). Volcanic eruptions may also lead to widespread 
precipitation anomalies up to several years following an eruption

through their potential influence on the El Niño Southern Oscillation 
(low confidence ) (Stevenson et al., 2016; Dee et al., 2020; McGregor 
In summary, large volcanic eruptions reduce global mean precipitation, 
as well as precipitation in tropical wet regions ( high confidence ). 
There is low confidence  in specific regional and seasonal responses, 
primarily due to the limitations of the observational record.
8.5.3 Non-linearities Across Global Warming Levels
The AR5 concluded that annual and seasonal mean precipitation 
changes can be estimated by linear pattern-scaling techniques (Santer 
and Wigley, 1990; Arnell and Gosling, 2016; Greve et al., 2018), 
which represent regional changes in precipitation as a linear function 
of global mean temperature change. However, there are a number of 
caveats when pattern-scaling is applied to low-emissions scenarios 
or to scenarios where localized forcing (e.g., anthropogenic aerosols)

are significant and vary in time (Collins et al., 2013). Here the focus is 
in on non-linear water cycle responses to increasing global warming 
levels, as estimated for instance from the difference between the first 
2°C of global warming, and the next 2°C of warming (Figure 8.25), 
and their possible underlying mechanisms.
8.5.3.1 Non-linearities in Large-scale Atmospheric Circulation 
and Precipitation
Since AR5, there is further evidence that the pattern-scaling technique 
has limitations (Lopez et al., 2014; Wartenburger et al., 2017; Tachiiri 
et al., 2019), and that alternative approaches, such as multiple 
regressions using the land–sea warming contrast as an additional 
predictor, offer added value (Joshi et al., 2013). The  simplest 
traditional pattern-scaling approach approximates future changes by 
the product of a time-evolving global surface temperature change 
and a  pattern that varies spatially but is constant across time,

scenarios, and models. This technique was shown to be more robust 
across scenarios rather than across models, with better results for 
temperature compared with precipitation (Tebaldi and Arblaster, 
2014; see also Section 4.2.4). One approach which avoids scaling is to 
consider a period in a different scenario with the same global surface 
temperature change (Herger et al., 2015). It is attractive as it provides 
patterns of any temporal resolution that are consistent across 
variables. Nonetheless, this technique is still only based on global 
surface temperature and is not necessarily suitable for precipitation 
changes projected in stabilized versus transient scenarios (at the 
same global warming level) given the fast-atmospheric adjustment 
to GHG radiative forcing (Sections 8.2.1 and 8.4.1.1).
Even in a  theoretical climate system governed by linear processes, 
pattern-scaling assumptions can fail because the different forcing time

1145
Figure 8.25 | Effect of first versus second 2°C of global warming relative to the 1850–1900 base period on seasonal mean precipitation (mm day–1). CMIP6 
multi-model ensemble mean December–January–February (left panels) and June–July–August (right panels) precipitation difference for (a, b)  SSP5-8.5 at +2°C (c, d)  SSP5-8.5 at 
+4°C minus SSP5-8.5 at +2°C (second 2°C warming); (e, f)  second minus first 2°C fast warming (c–a and d–b). Only models reaching the +4°C warming levels in SSP5-8.5 are 
considered. Differences are computed based on 21-year time windows centred on the first year reaching or exceeding the selected global warming level using a 21-year running 
mean global surface atmospheric temperature criterion. Uncertainty is represented using the simple approach. No overlay indicates regions with high model agreement, where

≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the 
simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).
response of different parts of the Earth system cause evolving spatial 
warming patterns (Good et al., 2016a). This occurs primarily because 
different feedbacks occur at different time scales (Armour et al., 2013; 
Andrews et al., 2015), which in turn implies that the atmospheric 
circulation and water cycle is dependent both on the level of warming and the rate of change (Ceppi et al., 2018). The usual distinction 
between the fast adjustment to increased GHG concentrations and 
the slower response to SST warming (Section 8.2.2.2) may, however, 
not be sufficient to explain the time evolution of the hydroclimatic

response at the regional scale, especially in subtropical land areas

1146
Chapter 8  W ater Cycle Changes8where this response critically depends on shifts in atmospheric 
circulation associated with distinct ‘fast’ (typically five to ten years, 
that is however much slower than the atmospheric adjustment 
assessed in Section  8.2.1) and slow SST warming patterns (Zappa 
et al., 2020). The changing balance between the water cycle response 
to anthropogenic GHG and aerosol forcings is another source of non-
linearity across time and global warming levels (Ishizaki et al., 2013; 
Rowell et al., 2015; Y . Liu et al., 2019b; Wilcox et al., 2020) . 
Non-linearities in the climate response are thought to arise from 
multiple factors. These include state-dependent ice-albedo feedback 
and its potential influence on Northern Hemisphere (NH) storm 
tracks (Peings and Magnusdottir, 2014; Semenov and Latif, 2015; see 
also Cross-Chapter Box 10.1 and Section 8.6.1.2); a state-dependent 
sensitivity of tropical precipitation to increased SST (Schewe and

Levermann, 2017; He et al., 2018); a complex response of the Atlantic 
meridional overturning circulation (AMOC; Sections  9.2.4.1 and 
8.6.1.1) and its model- and magnitude dependent teleconnections 
with regional temperature and precipitation (Kageyama et al., 
2013; Jackson et al., 2015; Qasmi et al., 2017, 2020); and other 
atmospheric and terrestrial (Section 8.5.3.2) processes such as cloud 
and land surface feedbacks (Ceppi and Gregory, 2017; King, 2019). 
The response of convective precipitation may exhibit non-linearities 
because it is itself modulated by both dynamics and atmospheric 
water content, each responding independently to warming (Chadwick 
and Good, 2013; Neupane and Cook, 2013). 
Based on a  simple model, it was also suggested that the Indian 
summer monsoon may exhibit a moisture-advection feedback which 
allows multiple stable states as boundary conditions change (Zickfeld 
et al., 2005). However, limitations of this theory and comprehensive

GCMs suggest a  near-linear monsoon response to a  broad range 
of radiative forcings (Boos and Storelvmo, 2016). Non-linear 
precipitation responses to global warming have been reported in 
the Indo-Pacific, where a  linear increase in SSTs can trigger non-
linear changes in precipitation and a  shift in the ITCZ depending 
on the relative amplitudes of uniform and structured SST anomalies 
(C.T.Y .  Chung et al., 2014; Toda and Watanabe, 2018).
Compared to atmospheric circulation and seasonal mean precipitation, 
extreme precipitation has been found to scale more accurately with 
local and global mean temperature (Chou et al., 2012; Pendergrass 
et al., 2015). The projected increase in the magnitude of extreme 
precipitation is generally proportional to the global warming level, 
with an increase of around 7%  per  1°C warming (Section  11.4.5) 
although this rate shows seasonal and geographical variations and 
is slightly less for five-day than for one-day precipitation maxima.

Projected changes in extreme precipitation are the result of both 
thermodynamical and more model-dependent and potentially less 
linear dynamical contributions (Pfahl et al., 2017). Projected changes 
in precipitation extremes are also potentially sensitive to a  non-
linear response of spatial convective organization (Pendergrass et al., 
2016), and can exhibit a  quadratic rather than linear response to 
global warming (Pendergrass et al., 2019).
Within CMIP6, the linearity to CO 2 forcing can be assessed through the 
comparison of the model response to abrupt doubling compared with abrupt quadrupling of atmospheric CO 2 (Webb et al., 2017). Preliminary 
analyses based on CMIP5 models showed that annual precipitation 
changes following a doubling step change in CO 2 from pre-industrial 
levels are not necessarily consistent with the response to the step 
from doubling to quadrupling despite a  similar change in radiative

forcings (Good et al., 2016a; Ceppi and Shepherd, 2017). Beyond the 
visual comparison of the climate response at various global warming 
levels (e.g., Figure 4.35), the linearity across global warming levels can 
be assessed by using the highest emissions scenario and comparing 
seasonal mean relative precipitation changes at +2°C versus +4°C 
above pre-industrial (1850–1900) temperatures (Figure  8.25). The 
results support the previous finding (Good et al., 2016b) that a second 
2°C warming does not necessarily lead to the same precipitation 
anomaly pattern as the first 2°C, especially in the tropics where regional 
differences can be large but not necessarily consistent among different 
models. They are also consistent with a  recent analysis of CMIP5 
models showing that the projected drying in the Mediterranean and 
in Chile is substantially faster than the increase in GSAT, and therefore 
does not scale linearly with global warming (Zappa et al., 2020).

In summary, there is high confidence  that continued global warming 
will further amplify GHG-induced changes in large-scale atmospheric 
circulation and precipitation. Nonetheless, there are cases where 
regional water cycle changes are not linearly related to global 
warming due to the interaction of multiple forcings, feedbacks and 
time scales ( medium confidence , see also Sections 4.2.4, 7.4.3 and 
8.2.1). Aridity in subtropical regions is highly sensitive to fast shifts 
in large-scale atmospheric circulation so are particularly susceptible 
to such non-linearities.
8.5.3.2 Non-linearities in Land Surface Processes 
and Feedbacks
Land surface responses and feedbacks represent a potential source of 
non-linearity for the water cycle response, at least at regional and local 
scales. The forced response of soil moisture and freshwater resources 
not only depends on precipitation, but also on evaporation (Laîné

et al., 2014), snowmelt (Thackeray et al., 2016), and runoff (X. Zhang 
et al., 2018) which are intrinsically non-linear processes depending 
on soil moisture or temperature thresholds. Bare ground evaporation 
is, for instance, usually estimated as a non-linear function of surface 
soil moisture (Jefferson and Maxwell, 2015). Plant transpiration 
requires more complex formulations with non-linear dependencies 
on multiple environmental factors including root-zone soil moisture 
and atmospheric CO 2 concentration (Franks et al., 2017). Globally, 
land surface evaporation is both energy and soil-moisture limited, 
but one of these limitations can become dominant depending on 
regions and seasons. Non-linearities may be particularly strong in 
transitional regimes where and when soil moisture limitation plays 
a major role (Berg and Sheffield, 2018b).
Snowmelt is a nonl-inear process and projected changes in snowfall 
are also a  non-linear combination of changes in total precipitation

and in the fraction of solid precipitation. In cold regions, snowfall may 
first increase because of the increased water capacity of a  warmer 
atmosphere and then decrease because snow falls as rain in an even 
warmer atmosphere. Such non-linearities can contribute to elevation,

1147
Figure 8.26 | Rate of change in basin-scale annual mean runoff with increasing global warming levels.  Relative changes (%) in basin-averaged annual mean 
runoff estimated as multi-model ensemble median from a variable subset of CMIP6 models for each SSP over nine major river basins: (a) Mississippi, (b) Danube, (c) Lena, 
(d) Amazon, (e) Euphrates, (f) Yangtze, (g) Niger, (h) Indus, and (i) Murray. The basin averages have been estimated after a first-order conservative remapping of the model 
outputs on the 0.5° by 0.5° river network of Decharme et al. (2019). The shaded area indicates the 5–95% confidence interval of the ensemble values across all SSPs. Note 
that the y-axis range differs across basins and is particularly large for Niger and Murray (panels g and i). The number of models considered is specified for each scenario in the

legend located inside panel b. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).latitudinal and seasonal contrasts in the observed and projected 
retreat of the Northern Hemisphere (NH) snow cover (Shi and Wang, 
2015; Thackeray et al., 2016). Mountain glaciers also represent source 
of non-linear runoff responses since the annual runoff can first increase 
due to additional melting and then decrease as the glaciers shrink 
(Kraaijenbrink et al., 2017; Shannon et al., 2019). Section  9.5.1.3 
concludes with high confidence  that the average annual runoff from glaciers will generally reach a peak at the latest by the end of the 21st 
century, and decline thereafter. This peak may have already occurred for 
small catchments with little ice cover, but tends to occur later in basins 
with large glaciers. Permafrost thawing is another mechanism which 
can trigger a non-linear hydrological response in the high latitudes of

the NH (Walvoord and Kurylyk, 2016), whose magnitude and potential 
abruptness is assessed in Section 5.4.3.3.

1148
Chapter 8  W ater Cycle Changes8Land surface runoff and groundwater recharge are highly non-
linear process, depending for instance on rainfall intensity, soil 
infiltration capacity, vertical profile of soil moisture and water table 
depth. A non-linear relationship between rainfall and groundwater 
recharge was observed in the tropics where intense seasonal 
rainfalls associated with internal climate variability contribute 
disproportionately to recharge (R.G. Taylor et al., 2013a; Cuthbert 
et al., 2019a) . Groundwater fluxes in arid regions are generally less 
responsive to climate variability than in humid regions, which can 
temporarily buffer climate change impacts on water resources or lead 
to a long, initially hidden, hydrological responses to global warming 
(Cuthbert et al., 2019a). Hydrological model simulations driven by 
individual and combined forcing show that decreased precipitation 
can cause larger deficits in soil moisture, streamflow and water table

depth than other forcings, but also that these factors are not linearly 
cumulative when applied in combination (Hein et al., 2019). Surface 
runoff was found to scale only approximately with global warming 
(Tanaka et al., 2017). Significant non-linearities were found in the 
projected annual mean runoff response to global warming in CMIP5 
projections, which could not be entirely explained by precipitation 
changes (X.  Zhang et al., 2018). Similar non-linear behaviours are 
found in CMIP6 models over the Amazon, Yangtze, Niger, Euphrates 
and Mississippi river basins (Figure 8.26), highlighting the need to 
reassess the assumption of linearity when estimating regional water 
cycle changes. 
Beyond changes in land surface water fluxes, non-linearities in the 
response of soil moisture and freshwater reservoirs have not been 
well documented in global climate projections but deserve further 
attention given the complex interactions between the water, energy

and carbon cycles (Berg and Sheffield, 2018a), the growing direct 
human influence on rivers and groundwater (Abbott et al., 2019), 
and a possible offset between the linear components of changes in 
precipitation and evapotranspiration. Significant non-linearities were 
found in water scarcity projections, as seen by the stronger sensitivity 
to the first 2°C increase in global warming (Gosling and Arnell, 2016).
In summary, there is both numerical and process-based evidence 
that terrestrial water cycle changes can be non-linear at the regional 
scale ( high confidence ). Non-linear regional responses of runoff, 
groundwater recharge and water scarcity have been documented 
based on both CMIP5 and CMIP6 models, and highlight the limitations 
of simple pattern-scaling techniques ( medium confidence ). Water 
resources fed by melting glaciers are particularly exposed to such 
non-linearities ( high confidence ).
8.6 What Is the Potential for Abrupt Change?

In this Report, abrupt change is defined as a regional-to-global scale 
change in the climate system that occurs faster than the  typical 
rate of changes in its history, implying non-linearity in the climate 
response (see Glossary). Often, abrupt change arises from positive 
feedbacks in the climate system that cause the current state to 
become unstable, and cross a ‘tipping point’ (Lenton et al., 2008); 
that is, a rapid shift from one climate state to another. The water cycle 
has several attributes with potential to produce abrupt change. Non-linear interactions between the ocean, atmosphere, and land surface 
can result in rapid shifts between wet and dry states (Sections 8.6.1 
and 8.6.2). Cessation of solar radiation modification could also result 
in abrupt changes in the water cycle (Section  8.6.3). This section 
reviews these types of abrupt shifts and assesses the likelihood that 
they will occur by 2100.
8.6.1 Abrupt Water Cycle Responses to a Collapse

of Atlantic Meridional Overturning Circulation
Multiple lines of evidence, including both paleoclimate reconstructions 
and simulations, suggest that a  severe weakening or collapse of 
Atlantic Meridional Overturning Circulation (AMOC, see Glossary) 
causes abrupt and profound changes in the global hydrological cycle 
(Chiang and Bitz, 2005; Broccoli et al., 2006; Chiang and Friedman, 
2012; Jackson et al., 2015; Renssen et al., 2018). Deep water 
formation in the North Atlantic is dependent on a delicate balance of 
heat and salt fluxes (Buckley and Marshall, 2016); disruption in either 
of these due to melting ice sheets, a  change in precipitation and 
evaporation, or ocean circulation can force AMOC to cross a tipping 
point (SROCC; Drijfhout et al., 2015). During the last deglacial 
transition, one such slowdown in AMOC – during the Younger Dryas 
event (12,800–11,700 years ago)  – caused worldwide changes in 
precipitation patterns. These included a southward migration of the

tropical ITCZ (Peterson et al., 2000; McGee et al., 2014; Schneider 
et al., 2014; Mohtadi et al., 2016; Reimi and Marcantonio, 2016; 
P .X.  Wang et al., 2017) and systematic weakening of the African 
and Asian monsoons (Tierney and DeMenocal, 2013; Otto-Bliesner 
et al., 2014; Cheng et al., 2016; Grandey et al., 2016; Wurtzel et al., 
2018). Conversely, the Southern Hemisphere (SH) monsoon systems 
intensified (Cruz et al., 2005; Ayliffe et al., 2013; Stríkis et al., 2015, 
2018; Campos et al., 2019) . Drying occurred in Meso-America 
(Lachniet et al., 2013) while the North American monsoon system 
was largely unaffected (Bhattacharya et al., 2018). The mid-latitude 
region in North America was wetter (Polyak et al., 2004; Grimm et al., 
2006; Wagner et al., 2010; Voelker et al., 2015) , while Europe was 
drier (Genty et al., 2006; Rach et al., 2017; Naughton et al., 2019). 
A transient coupled climate model simulation was able to reproduce

the large-scale precipitation response to such an event (Figure 8.27a; 
Liu et al., 2009). 
These patterns of past hydroclimatic change are relevant for future 
projections because it is very likely  that AMOC will weaken by 2100 
in response to increased greenhouse gas emissions (Section 9.2.3.1; 
Weaver et al., 2012; Drijfhout et al., 2015; Bakker et al., 2016; 
Reintges et al., 2017) . Furthermore, there is medium confidence  
that the decline in AMOC will not involve an abrupt collapse before 
2100 (Section 9.2.3.1). The response of precipitation to hypothetical 
AMOC collapse under elevated greenhouse gases bears resemblance 
to the paleoclimate response during the Younger Dryas event, with 
some important differences due to effects of increased CO 2 on global 
precipitation patterns (Figure  8.27b). As with the paleoclimate 
events, AMOC collapse results in a southward shift in the ITCZ that 
is most pronounced in the tropical Atlantic. This could cause drying

in the Sahel region (Defrance et al., 2017) as well as Meso-America 
and northern Amazonia (Parsons et al., 2014; Y . Chen et al., 2018).

1149
Water Cycle Changes Chapter 88(a)Younger Dryas Event
% Mean Annual Precipitation ChangeDrier No Change Wetter(b)AMOC collapse under doubled CO/two.numerator
0 20 40 60 -60 -40 -20
Figure  8.27 |  Comparison of reconstructed past and idealized future annual mean precipitation responses to an Atlantic Meridional Overturning 
Circulation (AMOC) collapse. (a)  Model simulation of precipitation response to the Younger Dryas event relative to the preceding warm Bølling-Allerød period (base 
colours, calculated as the difference between 12,600–11,700 years before the present (BP) and 14,500–12,900 BP from the Transient Climate Evolution (TraCE) paleoclimate 
simulation of Liu et al., 2009), with paleoclimate proxy evidence superimposed on top (dots). (b) Model simulation of precipitation response to an abrupt collapse in AMOC

under a doubling of 1990 CO 2 levels (after W. Liu et al., 2017). Regions with rainfall rates below 20 mm yr–1 are masked. Further details on data sources and processing are 
available in the chapter data table (Table 8.SM.1). 
AMOC collapse also causes the Asian monsoon systems to weaken 
(Figure 8.27b; W. Liu et al., 2017) counteracting the strengthening 
expected in response to elevated greenhouse gases (see 
Section 8.4.2). Europe is projected to experience moderate drying in 
response to AMOC collapse (Jackson et al., 2015). 
In summary, given that there is medium confidence  that the decline 
in AMOC will not involve an abrupt collapse before 2100, there is low 
confidence  that an AMOC-driven abrupt change in the water cycle 
will occur by 2100. However, if AMOC collapse does occur, it is very 
likely  that there would be large regional impacts on the water cycle.
8.6.2 Abrupt Water Cycle Responses to Changes  
in the Land Surface

Changes in the land surface, including vegetation cover and dust 
emissions, can trigger abrupt changes in the water cycle. Plants 
regulate the exchange of water and energy between the land surface 
and the atmosphere (Section  8.2.3.3), such that sudden shifts in 
plant functions, types, or biomes can trigger feedbacks that have 
the potential to cause abrupt changes in the regional water cycle. 
Dust emissions, from either climatic or land use changes, affect the 
radiation budget and can regionally exacerbate dry extremes. Below, 
we assess the likelihood of abrupt changes in the water cycle for the 
well-studied regions of the Amazon and the Sahel, and the potential 
for dust emissions to amplify drought and aridity.
8.6.2.1 Amazon Deforestation and Drying
The Amazon forest plays an active role in driving atmospheric moisture 
transport and generating precipitation in the South American region 
(SRCCL;  Drumond et al., 2014; Poveda et al., 2014; Yin et al., 2014;

Staal et al., 2018, 2020; Agudelo et al., 2019; Espinoza et al., 2019) . This close association between the land surface and the water cycle 
makes the Amazon a  potential hotspot for abrupt change (Torres 
and Marengo, 2014). Both deforestation and drying are projected to 
increase by 2100, resulting in a worst-case scenario of up to a 50% 
loss in forest cover by 2050 (Soares-Filho et al., 2006; Boisier et al., 
2015; ter Steege et al., 2015; Gomes et al., 2019) . Deforestation in 
the Amazon also raises the probability of catastrophic fires (Brando 
et al., 2014). The combination of deforestation, drier conditions, 
and increased fire can push the rainforest ecosystem past a tipping 
point, beyond which there is rapid land surface degradation, a sharp 
reduction in atmospheric moisture recycling, an increase in the 
fraction of precipitation that runs off, and a  further shift towards 
a drier climate (Staal et al., 2015; Boers et al., 2017; Zemp et al., 2017;

Ruiz-Vásquez et al., 2020) . A rapid drop in precipitation has a direct 
impact on river flows, driving basin-scale shifts from a regulated to 
unregulated state (Salazar et al., 2018). Regional climate modeling 
experiments confirm that increased deforestation leads to a  drier 
climate, although not all models show a true tipping point, at least 
under present-day climatic conditions (Lejeune et al., 2015; Spracklen 
and Garcia-Carreras, 2015).
In AR5, some simulations using a  coupled climate–carbon cycle 
model exhibited an abrupt dieback of the Amazon forest in future 
climate scenarios (Oyama and Nobre, 2003; Cox et al., 2004; Malhi 
et al., 2008). However, subsequent work demonstrated that abrupt 
Amazon dieback does not occur consistently across, or even within, 
Earth system models (Lambert et al., 2013; Boulton et al., 2017). 
The occurrence of dieback is highly dependent on both how dry the 
simulated climate is in the present day (Malhi et al., 2009) as well

as the representation of forest structure and competitive dynamics 
(Levine et al., 2016). Models with a low diversity of plant characteristics 
and types have a higher tendency for abrupt change (Sakschewski 
et al., 2016). Abrupt shifts and ecosystem disruptions can occur on 
the sub-regional level (Pires and Costa, 2013), highlighting the need

1150
Chapter 8  W ater Cycle Changes8for higher-resolution modelling studies. Since AR5, CMIP6 projections 
suggest that a tipping point in the Amazon system may be crossed on 
a local or regional scale (Staal et al., 2020) but continue to be highly 
dependent on model biases in precipitation and the simulation of the 
land surface. Consequently, the timing, and probability, of an abrupt 
shift remains difficult to ascertain. 
In summary, while there is a  strong theoretical expectation that 
Amazon drying and deforestation can cause a rapid change in the 
regional water cycle, currently there is limited  model evidence  to 
verify this response, hence there is low confidence  that such a change 
will occur by 2100.
8.6.2.2 Greening of the Sahara and the Sahel
Greening of the Sahara and Sahel regions in North Africa, in 
response to an increase in precipitation, has long been considered 
an amplifying mechanism that can lead to abrupt change. Although

the high surface albedo of the desert stabilizes the energy balance 
of the system (Charney, 1975), greening can induce strong, positive 
feedbacks between the land surface and precipitation that can shift 
the region into a ‘Green Sahara’ state. The fact that the transition 
phase between a Desert Sahara and Green Sahara is not theoretically 
stable (Brovkin et al., 1998) creates a tipping point and allows for the 
possibility of an abrupt shift between dry and wet climate regimes. 
Paleoclimate reconstructions provide evidence of past Green Sahara 
states (DeMenocal and Tierney, 2012), under which rainfall rates 
increased by an order of magnitude (Tierney et al., 2017), leading 
to a vegetated landscape (Jolly et al., 1998) with large lake basins 
(Gasse, 2000; Drake and Bristow, 2006). The underlying driver of the 
Green Sahara is the periodic increase in summer insolation associated 
with the orbital precession cycle (Kutzbach, 1981). In this sense, Green

Saharas are not direct analogues for a  response to anthropogenic 
greenhouse gas emissions (GHGs), as these past states were forced 
by natural, seasonal changes in solar radiation. However, the climate 
dynamics of Green Sahara periods (which have global impacts, 
Pausata et al., 2020), and the speed of the transitions between Desert 
Saharas and Green Saharas, are relevant for future projections.
Since AR5, paleoclimatic studies have improved our view of the 
timing, spatial extent, and speed of transitions associated with the 
early Holocene (11,000–5,000 years ago) Green Sahara. Observed 
transitions into and out of Green Sahara states are always 
faster than the underlying forcing, in agreement with theoretical 
considerations ( high confidence ) (Tierney and DeMenocal, 
2013; Shanahan et al., 2015; Tierney et al., 2017). However, 
there is low confidence  in the duration of the transition because 
sedimentary records cannot typically resolve changes on decadal

to multi-decadal time scales (Tierney and DeMenocal, 2013). Both 
paleoclimate data and modelling experiments suggest that the 
timing and speed of the transition was spatially heterogeneous 
(high confidence ), with northern Saharan locations becoming drier 
thousands of years before more equatorial locations (Shanahan 
et al., 2015; Tierney et al., 2017; Dallmeyer et al., 2020). These 
observations are consistent with theoretical studies suggesting 
that spatial heterogeneity and diversity in ecosystems can mitigate 
the probability of catastrophic change (Van Nes and Scheffer, 2005; Bathiany et al., 2013). Conversely, low ecosystem diversity can 
produce local or regional ‘hot spots’ of abrupt change such as those 
seen in some paleoclimate records (Claussen et al., 2013). 
CMIP5 and CMIP6 models , some of which include dynamic 
vegetation schemes, cannot simulate the magnitude, nor the spatial 
extent, of greening and precipitation change associated with the

last Green Sahara under standard mid-Holocene (6,000 years ago) 
boundary conditions ( high confidence ) (Figure 3.11; Harrison et al., 
2014; Tierney et al., 2017; Brierley et al., 2020). This result remains 
unchanged since AR4 (Jansen et al., 2007). This may be due to 
climatological biases in the models (Harrison et al., 2015) or could 
imply that the strength of the feedbacks between vegetation and 
the water cycle in the models is too weak (Hopcroft et al., 2017). To 
date, climate models still only produce the amount and spatial extent 
of rainfall that is needed to sustain a Green Sahara if they are given 
prescribed changes in the land surface, such as albedo, soil moisture, 
vegetation cover and/or dust emissions (Pausata et al., 2016; Skinner 
and Poulsen, 2016; Tierney et al., 2017). 
Some climate model simulations suggest that under future high-
emissions scenarios, CO 2 radiative forcing causes rapid greening in

the Sahel and Sahara regions via precipitation change (Claussen 
et al., 2003; Drijfhout et al., 2015). For example, in the BNU-ESM 
RCP8.5 simulation, the change is abrupt with the percentage of bare 
soil dropping from 45% to 15%, and percentage of tree cover rising 
from 50% to 75%, within 10 years (2050–2060; Drijfhout et al., 
2015). However, other modelling results suggest that this may be 
a short-lived response to CO 2 fertilization (Bathiany et al., 2014). 
In summary, given outstanding uncertainties in how well the current 
generation of climate models capture land surface feedbacks in the 
Sahel and Sahara, there is low confidence  that an abrupt change to 
a greener state will occur in these regions before 2100 or 2300.
8.6.2.3 Amplification of Drought by Dust
Mineral dust aerosols in the climate system originate from both semi-
permanent and transient sources (Prospero et al., 2002; Ginoux et al., 
2012). The former are typically arid regions where significant alluvial

sediments have accumulated over time, while the latter are often 
associated with natural (e.g., droughts, wildfires) and anthropogenic 
(e.g.,  land use change, desertification) disturbances. Modern-day 
dust emissions are dominated by natural sources (Ginoux et al., 
2012), although human emissions may contribute 10–60% of the 
global atmospheric dust load (Webb and Pierre, 2018). Paleo-dust 
records suggest that human factors (land use change and landscape 
disturbance) may have doubled global dust emissions between 1750 
and the last quarter of the 20th century (Section 2.2.6; Hooper and 
Marx, 2018).
Dust aerosols influence the climate system and hydrologic cycle 
through both direct impacts on radiation (absorbing and scattering 
longwave and shortwave) and via indirect effects on cloud and 
precipitation processes (Box  8.1; Choobari et al., 2014; Kok et al., 
2018; Schepanski, 2018). The capacity of dust aerosols to suppress

1151
Water Cycle Changes Chapter 88increasing stability in the atmosphere (Cook et al., 2013; Huang et al., 
2014) can drive positive feedbacks (see also Section  6.3.6). Thus 
there is strong potential for dust to contribute to abrupt changes in 
the water cycle, especially in semi-arid regions where wind erosion 
is highly sensitive to vegetation cover and drought variability (Yu 
et al., 2015). One such event occurred over the Central USA during 
the 1930s: the Dust Bowl drought, an iconic event characterized by 
widespread land degradation and historically unprecedented levels 
of dust storm activity (Hansen and Libecap, 2004; Lee and Gill, 
2015). While initialized by warm sea surface temperatures in the 
North Atlantic, modeling work indicates that land cover changes and 
resulting dust emissions contributed to the severity and spatial extent 
of the drought by further suppressing precipitation (Cook et al., 2009;

Hu et al., 2018; Cowan et al., 2020). There is also increasing evidence 
that dust aerosol feedbacks are necessary to explain the magnitude 
of rainfall increase during the mid-Holocene Green Sahara (Pausata 
et al., 2016; Tierney et al., 2017).
The importance of dust aerosol feedbacks in future abrupt climate 
events, like droughts or rapid aridification, is unclear. In part, this is 
because the response of dust aerosol emissions and loading levels 
in the atmosphere to climate change is highly uncertain (Tegen and 
Schepanski, 2018; Webb and Pierre, 2018). This difficulty in predicting 
future dust responses is rooted in the fact that emissions depend on 
both changes to the land surface (e.g., land use/land cover change, 
aridification, ecological responses to climate change) and the state 
of the atmosphere (Tegen and Schepanski, 2018). While there is some 
evidence that global dust aerosol concentrations in the future will

increase (Allen et al., 2016; Tegen and Schepanski, 2018), it is highly 
dependent on changes in precipitation patterns and atmospheric 
circulation (see the SRCCL, Section 2.4.1), and it is not clear what the 
radiative impact will be (Allen et al., 2016; Kok et al., 2018). 
In summary, due to limited evidence , there is low confidence  
regarding the role of dust in abrupt climate change events over the 
next century.
8.6.3 Abrupt Water Cycle Responses to Initiation or 
Termination of Solar Radiation Modification
Solar radiation modification (SRM) techniques seek to reduce the 
impacts of climate change by modifying the Earth’s radiation budget, 
either by reflecting incoming solar radiation or increasing the amount 
of heat lost to space. Note that, following SR1.5, the definition of 
SRM in this Report refers to changes in both solar and longwave 
radiation (Section 4.6.3.3 and Glossary). A variety of methods have

been proposed, including injection of aerosols or their precursors 
into the stratosphere, cloud brightening, and cirrus cloud thinning 
(Table 4.8). Since SRM alters the planetary energy balance, changes in 
the hydrological cycle are theoretically expected (Section 8.2). These 
changes can be abrupt if the initial magnitude of SRM is large, rather 
than increased gradually. Since AR5, a diversity of SRM techniques 
have been tested using climate model simulations, with an increasing 
focus on consequences for regional water availability. Techniques 
targeting shortwave radiation (sulfate injection, surface albedo 
modification, cloud brightening) are likely  to reduce global mean precipitation relative to future CO 2-emissions scenarios (Bala et al., 
2008; A. Jones et al., 2013; Tilmes et al., 2013; Ferraro et al., 2014; 
Crook et al., 2015) . In contrast, cirrus cloud thinning, a  longwave 
radiation technique, results in increased global precipitation as it

causes enhanced radiative cooling in the troposphere ( medium 
confidence ) (Crook et al., 2015; Kristjánsson et al., 2015; Jackson 
et al., 2016).
The magnitude of hydrological disruption for both the initiation and 
termination of SRM depends on the method used, as well as the 
strength and duration of its implementation (Ekholm and Korhonen, 
2016; Irvine et al., 2019). Under abrupt SRM implementation, 
hydrological shifts are rapid, occurring within the first decade 
(Crook et al., 2015). Artificial enhancement of albedo in Northern 
Hemisphere desert regions causes a southward shift in the Hadley 
Cell and ITCZ, and extreme drying in the northern tropics (Crook et al., 
2015). Uniform or tropical stratospheric sulfate injection weakens 
the African and Asian summer monsoons and causes drying in the 
Amazon (Robock et al., 2008; Crook et al., 2015; Dagon and Schrag, 
2016). Changes in evapotranspiration can produce large deficits or

surpluses in soil moisture and runoff in different regions and seasons 
(Dagon and Schrag, 2016). 
Rapid changes (years to decades) in the hydrological cycle are 
also expected if SRM is terminated abruptly, either purposefully or 
because of technical failure or political disagreement. We reiterate 
the AR5 conclusion that if SRM ‘were terminated for any reason, there 
is high confidence  that surface temperatures would increase rapidly 
(within a decade or two) to values consistent with the GHG forcing’. 
The additional global warming caused by SRM termination may  result 
in a rapid increase in global mean precipitation ( medium confidence ) 
(A. Jones et al., 2013). Heterogenous regional and seasonal changes 
are also expected, but are model-dependent (A. Jones et al., 2013). 
As with SRM initiation, the impact of SRM termination is expected to 
be dependent on the technique deployed. 
In summary, it is very likely  that abrupt water cycle changes will occur

if SRM is abruptly initiated or halted, especially in tropical regions. 
Further assessment of the potential side-effects of SRM is found in 
Section 4.6.3.3.
8.7 Final Remarks
Despite the advances presented in this chapter, there are still many 
opportunities to improve the understanding and quantification of 
human influence on past, present and future water cycle changes:
• Extension and development of pre-instrumental data and 
paleoclimate records, particularly from the Southern Hemisphere, 
will improve estimates of the range of natural climate variability 
and extremes, and our knowledge of how the water cycle 
responded in past high CO 2 climates.
• Development of longer observational time series that will improve 
our understanding of physical processes and the analysis and 
simulation of natural modes of weather and climate variability. 
• The use of large model ensembles will help better understand the

1152
Chapter 8  W ater Cycle Changes8interactions between climate change and internal variability and 
in the detection and attribution of observed water cycle changes.
• An improvement of the general circulation model (GCM)-
simulated precipitation, latent heating and radiative effects 
of deep convective clouds would benefit from an improved 
representation of their interactions with aerosols.
• Further research on land surface processes, including 
groundwater recharge, the role of plant physiological changes, 
land use change, dams and irrigation, will improve future 
projections of key aspects of the terrestrial water cycle such as 
aridity and drought.
• Ongoing efforts to develop higher-resolution ‘convection 
permitting’ regional or global climate models will lead to an 
improved simulation of clouds and precipitation, their coupling 
with boundary layer and surface processes, their diurnal cycle 
and high-frequency variability, and their response to climate

change, including extreme precipitation events.
• Further analysis of past and current climate variability alongside 
future climate change projections will provide physically 
understood constraints for improving the accuracy of regional 
water cycle simulations, adding value to the results obtained 
from global climate models.
• Increased understanding of internal variability and interactions 
with human-induced change will improve efforts to attribute 
changes in the water cycle and to understand and anticipate 
future non-linear change.Acknowledgements
The authors are extremely grateful to the WGI Bureau and Technical 
Support Unit for their outstanding support throughout the writing 
of the chapter. Special thanks to Sarah Connors for her exceptional 
contribution to the development of this chapter. Her help with 
chapter meetings and organizational support was invaluable. Thanks 
to Sophie Berger, Melissa Gomis, Nigel Hawtin and Tom Maycock for

their contributions to figures and tables. We must also make a special 
acknowledgment of our chapter scientists, Sabin Thazhe Purayil and 
Stéphane Sénési, without whom we could not have completed our 
assessment nor could we have produced the excellent figures that 
appear in the chapter. We would also like to thank all reviewers 
for their useful comments. Finally, we are infinitely indebted to 
our families for their extended patience and support during this 
demanding process.

1153
FAQ 8.1 | How Does Land Use Change Alter the Water Cycle?   
The ways in which humans use and change land cover, for example by converting fields to urban areas or clearing 
forests, can affect every aspect of the water cycle. Land-use changes can alter precipitation patterns and how 
water is absorbed into the ground, flows into streams and rivers, or floods the land surface, as well as how 
moisture evaporates back into the air. Changes in any of these aspects of the interconnected water cycle can 
affect the entire cycle and the availability of freshwater resources.
Land use describes the combination of activities and ground cover defining each area of the Earth’s continental 
surface. Altering land use can modify the exchange of water between the atmosphere, soil and subsurface 
(FAQ 8.1, Figure 1). 
For instance, changes in land cover can affect the ability of soils to soak up surface water (infiltration). When soil

loses its capacity to soak up water, precipitation that would normally infiltrate and contribute to groundwater 
reserves will instead overflow, increasing surface water (runoff) and the likelihood of flooding. For example, 
changing from vegetation to urban cover can cause water to flow rapidly over buildings, roads and driveways 
and into drains rather than soaking into the ground. Deforestation over wide areas can also directly reduce 
soil moisture, evaporation and rainfall locally but can also cause regional temperature changes that affect 
rainfall patterns.
Extracting water from the ground and river systems for agriculture, industry and drinking water depletes 
groundwater and can increase surface evaporation because water that was previously in the ground is now in 
direct contact with the atmosphere, being available for evaporation. 
Changing land use can also alter how wet the soil is, influencing how quickly the ground heats up and cools

down and the local water cycle. Drier soils evaporate less water into the air but heat up more in the day. This can 
lead to warmer, more buoyant plumes of air that can promote cloud development and precipitation if there is 
enough moisture in the air. 
Changes in land use can also modify the amount of tiny aerosol  particles in the air. For instance, industrial and 
domestic activities can contribute to aerosol emissions, as do natural environments such as forests or salt lakes. 
Aerosols cool down global temperature by blocking out sunlight but can also affect the formation of clouds and 
therefore the occurrence of precipitation (see FAQ 7.2). 
Vegetation plays an important role in soaking up soil moisture and evaporating water into the air ( transpiration ) 
through tiny holes ( stomata ) that allow the plants to take in carbon dioxide. Some plants are better at retaining

water than others, so changes in vegetation can affect how much water infiltrates into the ground, flows into 
streams and rivers, or is evaporated. 
More globally, land-use change is currently responsible for about 15% of the emissions of carbon dioxide 
from human activities, leading to global warming, which in turn affects precipitation, evaporation, and plant 
transpiration. In addition, higher atmospheric concentrations of carbon dioxide due to human activities can 
make plants more efficient at retaining water because the stomata do not need to open so widely. Improved 
land and water management (e.g., reforestation, sustainable irrigation) can also contribute to reducing climate 
change and adapting to some of its adverse consequences. 
In summary, there is abundant evidence that changes in land use and land cover alter the water cycle globally, 
regionally and locally, by changing precipitation, evaporation, flooding, groundwater, and the availability of

freshwater for a variety of uses. Since all the components of the water cycle are connected (and linked to the 
carbon cycle), changes in land use trickle down to many other components of the water cycle and climate system.

1154
Chapter 8  W ater Cycle Changes8FAQ 8.1 (continued) 
FAQ 8.1: How do land use changes affect the water cycle?
Land-use changes
and consequencesWater cycle 
effectsAltering land use affects the water cycle in many ways, with subsequent consequences for the whole cycle.
Urban 
cover
Soil 
moisture
Aerosols
Water 
extraction
Vegetation
Global 
warmingPrecipitation
Runoff
Infiltration
Groundwater
Surface 
evaporation
Plant 
FAQ 8.1, Figure 1 | Land-use changes and their consequences on the water cycle.  As all the components or the water cycle are tightly connected, 
changes in one aspect of the cycle affects almost all the cycle.

1155
FAQ 8.2 | Will Floods Become More Severe or More Frequent as a Result of Climate Change?
A warmer climate increases the amount and intensity of rainfall during wet events, and this is expected to 
amplify the severity of flooding. However, the link between rainfall and flooding is complex, so while the most 
severe flooding events are expected to worsen, floods could become rarer in some regions. 
Floods are a natural and important part of the water cycle but they can also threaten lives and safety, disrupt 
human activities, and damage infrastructure. Most inland floods occur when rivers overtop their banks ( fluvial 
flooding) or when intense rainfall causes water to build up and overflow locally ( pluvial flooding). Flooding is 
also caused by coastal inundation by the sea, rapid seasonal melting of snow, and the accumulation of debris, such as vegetation or ice, that stops water from draining away.

Climate change is already altering the location, frequency and severity of flooding. Close to the coasts, rising 
sea levels increasingly cause more frequent and severe coastal flooding, and the severity of these floods is 
exacerbated when combined with heavy rainfall. The heavy and sustained rainfall events responsible for most 
inland flooding are becoming more intense in many areas as the climate warms because air near Earth’s surface can carry around 7% more water in its gas phase (vapour) for each 1°C of warming. This extra moisture is drawn 
into weather systems, fueling heavier rainfall (FAQ 8.2, Figure 1). 
A warming climate also affects wind patterns, how storms form and evolve, and the pathway those storms 
usually travel. Warming also increases condensation rates, which in turn releases extra heat that can energize storm systems and further intensify rainfall. On the other hand, this energy release can also inhibit the uplift

required for cloud development, while increases in particle pollution can delay rainfall but invigorate storms. 
These changes mean that the character of precipitation events (how often, how long lasting and how heavy they are) will continue to change as the climate warms. 
In addition to climate change, the location, frequency and timing of the heaviest rainfall events and worst 
flooding depend on natural fluctuations in wind patterns that make some regions unusually wet or dry for 
months, years, or even decades. These natural variations make it difficult to determine whether heavy rainfall events are changing locally as a result of global warming. However, when natural weather patterns bring heavy 
and prolonged rainfall in a warmer climate, the intensity is increased by the larger amount of moisture in the air.
An increased intensity and frequency of record-breaking daily rainfall has been detected for much of the land

surface where good observational records exist, and this can only be explained by human-caused increases in atmospheric greenhouse gas concentrations. Heavy rainfall is also projected to become more intense in the future 
for most places. So, where unusually wet weather events or seasons occur, the rainfall amounts are expected to 
be greater in the future, contributing to more severe flooding. 
However, heavier rainfall does not always lead to greater flooding. This is because flooding also depends 
upon the type of river basin, the surface landscape, the extent and duration of the rainfall, and how wet the 
ground is before the rainfall event (FAQ 8.2, Figure 1). Some regions will experience a drying in the soil as the 
climate warms, particularly in subtropical climates, which could make floods from a rainfall event less probable because the ground can potentially soak up more of the rain. On the other hand, less frequent but more intense

downpours can lead to dry, hard ground that is less able to soak up heavy rainfall when it does occur, resulting 
in more runoff into lakes, rivers and hollows. Earlier spring snowmelt combined with more precipitation falling as rain rather than snow can trigger flood events in cold regions. Reduced winter snow cover can, in contrast, 
decrease the chance of flooding arising from the combination of rainfall and rapid snowmelt. Rapid melting of 
glaciers and snow in a warming climate is already increasing river flow in some regions, but as the volumes of ice diminish, flows will peak and then decline in the future. Flooding is also affected by changes in the management 
of the land and river systems. For example, clearing forests for agriculture or building cities can make rainwater 
flow more rapidly into rivers or low-lying areas. On the other hand, increased extraction of water from rivers can reduce water levels and the likelihood of flooding.

1156
Chapter 8  W ater Cycle Changes8FAQ 8.2 (continued) 
A mix of both increases and decreases in flooding have been observed in some regions and these changes have 
been attributed to multiple causes, including changes in snowmelt, soil moisture and rainfall. Although we 
know that a warming climate will intensify rainfall events, local and regional trends are expected to vary in 
both direction and magnitude as global warming results in multiple, and sometimes counteracting, influences. 
However, even accounting for the many factors that generate flooding, when weather patterns cause flood 
events in a warmer future, these floods will be more severe. 
++
++++
+++
+++
+–
––– – –
+– – –FAQ 8.2: Causes of more severe floods from climate change
Flooding presents a hazard but the link between rainfall and flooding is not simple.
While the largest flooding events can be expected to worsen, flood occurrence may decrease in some regions. 
Decreased glaciers 
and snow reduce

river flowGreater heat released 
by condensation 
leads to fewer but 
stronger storms 
More moisture fuels 
heavier precipitation
Heavier rainfall 
increases flood severityEarlier and stronger 
spring melt increase 
river flowLarge-scale 
deforestation decreases 
rainfall but increases 
runoff and river flowIncreased 
water use 
reduces 
river flow
Urbanisation 
increases runoff 
and flash floodingParticle 
pollution 
alters storm 
developmentCloud 
limits rainfall 
increase
Greater capacity of 
drier soils to soak up 
water from 
sustained rainfall
More runoff from heavy 
rain falling on dry, 
encrusted soilsWorsening coastal 
flooding from sea level 
rise and combined 
with heavier rainfallChanging 
location and 
persistence of 
wettest events
Less severe flooding
More severe floodingAtmosphere Human Soils Cryosphere Coastal
FAQ 8.2, Figure 1 | Schematic illustrating factors important in determining changes in heavy precipitation and flooding.

1157
FAQ 8.3 | What Causes Droughts, and Will Climate Change Make Them Worse?
Droughts usually begin as a deficit of precipitation, but then propagate to other parts of the water cycle (soils, 
rivers, snow/ice and water reservoirs). They are also influenced by factors like temperature, vegetation and 
human land and water management. In a  warmer world, evaporation increases, which can make even wet 
regions more susceptible to drought.
A drought is broadly defined as drier than normal conditions; that is, a moisture deficit relative to the average 
water availability at a given location and season. Since they are locally defined, a drought in a wet place will 
not have the same amount of water deficit as a drought in a dry region. Droughts are divided into different

categories based on where in the water cycle the moisture deficit occurs: meteorological drought (precipitation), hydrological drought (runoff, streamflow, and reservoir storage), and agricultural or ecological drought (plant 
stress from a combination of evaporation and low soil moisture). Special categories of drought also exist. For 
example, a snow drought occurs when winter snowpack levels are below average, which can cause abnormally low streamflow in subsequent seasons. And while many drought events develop slowly over months or years, 
some events, called flash droughts, can intensify over the course of days or weeks. One such event occurred 
in 2012 in the Midwestern region of North America and had a severe impact on agricultural production, with losses exceeding $30 billion US dollars. Droughts typically only become a concern when they adversely affect 
people (reducing water available for municipal, industrial, agricultural, or navigational needs) and/or ecosystems

(adverse effects on natural flora and fauna). When a drought lasts for a very long time (more than two decades) it is sometimes called a megadrought.
Most droughts begin when precipitation is below normal for an extended period of time (meteorological 
drought). This typically occurs when high pressure in the atmosphere sets up over a  region, reducing cloud 
formation and precipitation over that area and deflecting away storms. The lack of rainfall then propagates across the water cycle to create agricultural drought in soils and hydrological drought in waterways. Other 
processes act to amplify or alleviate droughts. For example, if temperatures are abnormally high, evaporation 
increases, drying out soils and streams and stressing plants beyond what would have occurred from the lack of precipitation alone. Vegetation can play a critical role because it modulates many important hydrologic processes

(soil water, evapotranspiration, runoff). Human activities can also determine how severe a  drought is. For 
example, irrigating croplands can reduce the socio-economic impact of a drought; at the same time, depletion of groundwater in aquifers can make a drought worse.
The effect of climate change on drought varies across regions. In the subtropical regions like the Mediterranean, 
southern Africa, south-western Australia and south-western South America, as well as tropical Central America, 
western Africa and the Amazon basin, precipitation is expected to decline as the world warms, increasing the possibility that drought will occur throughout the year (FAQ 8.3, Figure 1). Warming will decrease snowpack, 
amplifying drought in regions where snowmelt is an important water resource (such as in south-western South

America). Higher temperatures lead to increased evaporation, resulting in soil drying, increased plant stress, and impacts on agriculture, even in regions where large changes in precipitation are not expected (such as central 
and northern Europe). If emissions of greenhouse gases are not curtailed, about a third of global land areas 
are projected to suffer from at least moderate drought by 2100. On the other hand, some areas and seasons (such as high-latitude regions in North America and Asia, and the South Asian monsoon region) may experience 
increases in precipitation as a result of climate change, which will decrease the likelihood of droughts. FAQ 8.3, 
Figure 1 highlights the regions where climate change is expected to increase the severity of droughts.

1158
Chapter 8  W ater Cycle Changes8FAQ 8.3 (continued) 
FAQ 8.3: Climate change and droughts
In some regions, drought  is expected to increase under future warming.
FAQ 8.3, Figure 1 | Schematic map highlighting in brown the regions where droughts are expected to become worse as a result of climate 
change.  This pattern is similar regardless of the emissions scenario; however, the magnitude of change increases under higher emissions.

1214
Chapter 9 Ocean, Cryosphere and Sea Level Change9
This chapter assesses past and projected changes in the ocean, 
cryosphere and sea level using paleoreconstructions, instrumental 
observations and model simulations. In the following summary, we 
update and expand the related assessments from the IPCC Fifth 
Assessment Report (AR5), the Special Report on Global Warming of 
1.5°C (SR1.5) and the Special Report on Ocean and Cryosphere in 
a Changing Climate (SROCC). This chapter covers major advances since 
SROCC, including the synthesis of extended and new observations. 
These advances allow for improved assessment of past change, 
processes and budgets for the last century, and the use of a hierarchy 
of models and emulators, which provide improved projections and 
uncertainty estimates of future change. In addition, the systematic use 
of model emulators makes our projections of ocean heat content, land

ice loss and sea level rise fully consistent with each other and with 
the assessed equilibrium climate sensitivity and projections of global 
surface air temperature across the entire report. In this executive 
summary, uncertainty ranges are reported as  very likely  ranges and 
expressed by square brackets, unless otherwise noted.
Ocean Heat and Salinity
At the ocean surface, temperature has, on average, increased by 
0.88 [0.68 to 1.01] °C between 1850–1900 and 2011–2020, with 
0.60 [0.44 to 0.74] °C of this warming having occurred since 1980. 
The ocean surface temperature is projected to increase between 
1995 to 2014 and 2081 to 2100 on average by 0.86 [0.43 to 
1.47, likely  range] °C in SSP1-2.6 and by 2.89 [2.01  to 4.07, 
likely  range] °C in SSP5-8.5.  Since the 1950s, the fastest surface 
warming has occurred in the Indian Ocean and in western boundary 
currents, while ocean circulation has caused slow warming or surface

cooling in the Southern Ocean, equatorial Pacific, North Atlantic, and 
coastal upwelling systems ( very high  confidence ). At least 83% of the 
ocean surface will very likely  warm over the 21st century in all Shared 
Socio-economic Pathways (SSP) scenarios. {2.3.3, 9.2.1}
The heat content of the global ocean has increased since at 
least 1970, and will continue to increase over the 21st century 
(virtually certain ). The associated warming will likely  continue 
until at least 2300, even for low-emissions scenarios, because 
of the slow circulation of the deep ocean.  Ocean heat content 
has increased from 1971 to 2018 by 0.396 [0.329 to 0.463, likely  
range] yottajoules  and will  likely   increase until 2100 by two to four 
times that amount under SSP1-2.6 and four to eight times that amount 
under SSP5-8.5. The long time scale also implies that the amount of 
deep-ocean warming will only become scenario-dependent after about

2040 ( medium confidence ), and that the warming is irreversible over 
centuries to millennia ( very high confidence ). On annual to decadal 
time scales, the redistribution of heat by the ocean circulation dominates 
spatial patterns of temperature change ( high confidence ). At longer time 
scales, the spatial patterns are dominated by additional heat, primarily 
stored in water masses formed in the Southern Ocean, and by weaker 
warming in the North Atlantic where heat redistribution caused by 
changing circulation counteracts the additional heat input through the 
surface ( high confidence ). {9.2.2, 9.2.4, 9.6.1, Cross-Chapter Box 9.1}Marine heatwaves  – sustained periods of anomalously high 
near-surface temperatures that can lead to severe and persistent 
impacts on marine ecosystems – have become more frequent 
over the 20th century ( high confidence ). Since the 1980s, they 
have approximately doubled in frequency ( high confidence ) and

have become more intense and longer ( medium confidence ). 
This  trend will continue, with marine heatwaves at global scale 
becoming four times [2 to 9, likely  range] more frequent in 2081–2100 
compared to 1995–2014 under SSP1-2.6, and eight times [3 to 15, likely  
range] more frequent under SSP5-8.5. The largest changes will occur in 
the tropical ocean and the Arctic ( medium confidence ). {Box 9.2}
The upper ocean has become more stably stratified since 
at least 1970 over the vast majority of the globe ( virtually 
certain ), primarily due to surface-intensified warming and 
high-latitude surface freshening ( very high confidence ). Changes 
in ocean stability affect vertical exchanges of surface waters with 
the deep ocean and large-scale ocean circulation. Based on recent 
refined analyses of the available observations, the global 0–200 m 
stratification is now assessed to have increased about twice as much

as reported by SROCC, with a 4.9 ± 1.5% increase from 1970 to 2018 
(high confidence ) and even higher increases at the base of the surface 
mixed layer. Upper-ocean stratification will continue to increase 
throughout the 21st century ( virtually certain ). {9.2.1}
Ocean Circulation
The Atlantic Meridional Overturning Circulation (AMOC) will 
very likely  decline over the 21st century for all SSP scenarios. 
There is  medium confidence  that the decline will not involve 
an abrupt collapse before 2100.  For the 20th century, there is  low 
confidence  in reconstructed and modelled AMOC changes because of 
their  low agreement  in quantitative trends. The low confidence  also 
arises from new observations that indicate missing key processes in 
both models and measurements used for formulating proxies and 
from new evaluations of modelled AMOC variability.  This results 
in low confidence  in quantitative projections of AMOC decline in the

21st  century, despite the  high confidence   in the future decline as 
a qualitative feature based on process understanding. {9.2.3}
Southern Ocean circulation and associated temperature 
changes in Antarctic ice-shelf cavities are sensitive to changes 
in wind patterns and increased ice shelf melt ( high confidence ). 
However,  limitations in understanding feedback mechanisms 
involving the ocean, atmosphere and cryosphere, which are not fully 
represented in the current generation of climate models, generally 
limit our confidence in future projections of the Southern Ocean and 
of its forcing on Antarctic sea ice and ice shelves. {9.2.3, 9.3.2, 9.4.2}
Many ocean currents will change in the 21st  century as 
a  response to changes in wind stress associated with 
anthropogenic warming ( high confidence ). Western boundary 
currents have shifted poleward since 1993 ( medium confidence ), 
consistent with a poleward shift of the subtropical gyres. Of the four

eastern boundary upwelling systems, only the California Current 
system has experienced some large-scale upwelling-favourable

1215
Ocean, Cryosphere and Sea Level Change  Chapter 99
wind intensification since the 1980s ( medium confidence ). In the 
21st century, consistent with projected changes in the surface winds, 
the East Australian Current Extension and Agulhas Current Extension 
will intensify, while the Gulf Stream and Indonesian Throughflow 
will weaken ( medium confidence ). Eastern boundary upwelling 
systems will change, with a dipole spatial pattern within each system 
of reduction at low latitude and enhancement at high latitude 
(high confidence ). {9.2.1, 9.2.3}
Sea Ice
The Arctic Ocean will likely  become practically sea ice free1 
during the seasonal sea ice minimum for the first time before 
2050 in all considered SSP scenarios. There is no tipping 
point for this loss of Arctic summer sea ice ( high confidence ). 
The practically ice-free state is projected to occur more often with higher 
greenhouse gas concentrations, and it will become the new normal for

high-emissions scenarios by the end of this century ( high confidence ). 
Based on observational evidence, Coupled Model Intercomparison 
Project Phase 6 (CMIP6) models and conceptual understanding, the 
substantial satellite-observed decrease of Arctic sea ice area over 
the period 1979 –2019 is well described as a linear function of global 
mean surface temperature, and thus of cumulative anthropogenic 
carbon dioxide (CO 2) emissions, with superimposed internal variability 
(high confidence ). According to both process understanding and 
CMIP6 simulations, a  practically sea ice-free state will likely  be 
observed some years before additional (post-2020) cumulative 
anthropogenic CO 2 emissions reach 1000 GtCO 2. {4.3.2, 9.3.1}
For Antarctic sea ice, regionally opposing trends and large 
interannual variability result in no significant trend in satellite-
observed sea ice area from 1979 to 2020 in both winter and 
summer ( high confidence ). The regionally opposing trends result

primarily from changing regional wind forcing ( medium confidence ). 
There is low confidence  in model simulations of future Antarctic sea 
ice decrease, and lack of decrease, due to deficiencies of process 
representation, in particular at the regional level. {2.3.2, 9.2.3, 9.3.2}
Ice Sheets
The Greenland Ice Sheet has  lost 4890 [4140 to 5640] Gt 
mass over the period 1992–2020, equivalent to 13.5 [11.4 to 
15.6] mm global mean sea level rise. The mass-loss rate was 
on average 39 [–3 to +80] Gt yr –1 over the period 1992–1999, 
175 [131 to 220] Gt yr –1 over the period 2000–2009 and 
243 [197 to 290] Gt yr –1 over the period 2010–2019.  This mass 
loss is driven by both discharge and surface melt, with the latter 
increasingly becoming the dominating component of mass loss with 
high interannual variability in the last decade ( high confidence ). The 
largest mass losses occurred in the north-west and the south-east of 
Greenland ( high confidence ). {2.3.2, 9.4.1}

1 Sea ice area below 1 million km2. The Antarctic Ice Sheet has  lost 2670 [1800 to 3540] Gt 
mass over the period 1992–2020, equivalent to 7.4 [5.0  to 
9.8] mm global mean sea level rise. The mass-loss rate was, on 
average, 49 [–2 to +100] Gt yr –1 over the period 1992–1999, 
70 [22 to 119] Gt yr –1 over the period 2000–2009 and 148 [94 to 
202] Gt yr –1 over the period 2010–2019.  Mass losses from West 
Antarctic outlet glaciers outpaced mass gain from increased snow 
accumulation on the continent and dominated  the ice-sheet mass 
losses since 1992 ( very high confidence ). These mass losses from the 
West Antarctic outlet glaciers were mainly induced by ice-shelf basal 
melt ( high confidence ) and locally by ice-shelf disintegration preceded 
by strong surface melt ( high confidence ). Parts of the East Antarctic 
Ice Sheet have lost mass in the last two decades ( high confidence ). 
{2.3.2, 9.4.2, Atlas.11.1}
Both the Greenland Ice Sheet ( virtually certain ) and the

Antarctic Ice Sheet ( likely ) will continue to lose mass 
throughout this century under all considered SSP scenarios. 
The related contribution to global mean sea level rise until 
2100 from the Greenland Ice Sheet will likely  be 0.01 to 
0.10 m under SSP1-2.6, 0.04 to 0.13 m under SSP2-4.5 and 
0.09–0.18 m under SSP5-8.5, while the Antarctic Ice Sheet will 
likely  contribute 0.03 to 0.27 m under SSP1-2.6, 0.03 to 0.29 m 
under SSP2-4.5, and 0.03 to 0.34 m under SSP5-8.5.  The loss of 
ice from Greenland will become increasingly dominated by surface 
melt, as marine margins retreat and the ocean-forced dynamic 
response of ice-sheet margins diminishes ( high confidence ). In the 
Antarctic, dynamic losses driven by ocean warming and ice-shelf 
disintegration will  likely  continue to outpace increasing snowfall this 
century  ( medium confidence ). Beyond 2100, total mass loss from 
both ice sheets will be greater under high-emissions scenarios than

under low-emissions scenarios ( high confidence ). The assessed likely  
ranges consider those ice-sheet processes in whose representation 
in current models we have at least medium confidence , including 
surface mass balance and grounding-line retreat in the absence of 
instabilities.  Under high-emissions scenarios, poorly understood 
processes related to marine ice sheet instability and marine ice cliff 
instability, characterized by deep uncertainty, have the potential to 
strongly increase Antarctic mass loss on century to multi-century time 
scales. {9.4.1, 9.4.2, 9.6.3, Box 9.3, Box 9.4}
Glaciers
Glaciers lost 6200 [4600 to 7800] Gt of mass (17.1 [12.7 to 
21.5] mm global mean sea level equivalent) over the period 
1993–2019 and will continue losing mass under all SSP scenarios 
(very high confidence ). During the decade 2010–2019, glaciers 
lost more mass than in any other decade since the beginning of 
the observational record ( very high confidence ). For all regions

with long-term observations, glacier mass in the decade 2010 –2019 
is the smallest since at least the beginning of the 20th  century 
(medium confidence ). Because of their lagged response, glaciers 
will continue to lose mass at least for several decades even if global 
temperature is stabilized ( very  high  confidence ). Glaciers  will lose

1216
29,000 [9000 to 49,000] Gt and 58,000 [28,000 to 88,000] Gt over 
the period 2015–2100 for RCP2.6 and RCP8.5, respectively ( medium 
confidence ), which represents 18 [5 to 31] % and 36 [16 to 56] % 
of their early-21st-century mass, respectively. {2.3.2, 9.5.1, 9.6.1, 
9.6.3, 12.4}
Increases in permafrost temperature have been observed over 
the past three to four decades throughout the permafrost 
regions ( high confidence ), and further global warming will 
lead to near-surface permafrost volume loss ( high confidence ). 
Complete permafrost thaw in recent decades is a  common 
phenomenon in discontinuous and sporadic permafrost regions 
(medium confidence ). Permafrost warmed globally by 0.29 [0.17 to 
0.41, likely range] °C between 2007 and 2016 ( medium confidence ). 
An increase in the active layer thickness is a pan-Arctic phenomenon 
(medium confidence ), subject to strong heterogeneity in surface

conditions. The volume of perennially frozen soil within the upper 3 m 
of the ground will decrease by about 25% per 1°C of global surface 
air temperature change (up to 4°C above pre-industrial temperature) 
(medium confidence ). {9.5.2}
Snow
Northern Hemisphere spring snow cover extent has been 
decreasing since 1978 ( very high confidence ), and there is  
high confidence  that this trend extends back to 1950. Further 
decrease of Northern Hemisphere seasonal snow cover 
extent is virtually certain  under further global warming.  The 
observed sensitivity of Northern Hemisphere snow cover extent to 
Northern Hemisphere land surface air temperature for 1981–2010 
is –1.9 [–2.8 to –1.0, likely range] million km2 per 1°C throughout 
the snow season. It is virtually certain  that Northern Hemisphere 
snow cover extent will continue to decrease as global climate 
continues to warm, and process understanding strongly suggests 
that this also applies to Southern Hemisphere seasonal snow

cover ( high  confidence ). Northern Hemisphere spring snow cover 
extent will decrease by about 8% per 1°C of global surface air 
temperature change (up to 4°C above pre-industrial temperature) 
(medium confidence ). {9.5.3}
Sea Level
Global mean sea level (GMSL) rose faster in the 20th century 
than in any prior century over the last three millennia 
(high confidence ), with a  0.20 [0.15 to 0.25] m rise over 
the period 1901–2018 ( high confidence ). GMSL rise has 
accelerated since the late 1960s, with an average rate of 
2.3 [1.6 to 3.1] mm yr –1 over the period 1971–2018 increasing 
to  3.7  [3.2  to 4.2]  mm  yr –1 over the period 2006–2018 
(high confidence ). New observation-based estimates published 
since SROCC lead to an assessed sea level rise over the period 
1901–2018 that is consistent with the sum of individual components. Ocean thermal expansion (38%) and mass loss from glaciers (41%) 
dominate the total change from 1901 to 2018. The contribution of

Greenland and Antarctica to GMSL rise was four times larger during 
2010–2019 than during 1992 –1999 ( high confidence ). Because of 
the increased ice-sheet mass loss, the total loss of land ice (glaciers 
and ice sheets) was the largest contributor to global mean sea level 
rise over the period 2006–2018 ( high confidence ). {2.3.3, 9.6.1, 9.6.2, 
Cross-Chapter Box 9.1, Table 9.A.1, Box 7.2}
At the basin scale, sea levels rose fastest in the Western Pacific 
and slowest in the Eastern Pacific over the period 1993–2018 
(medium confidence ). Regional differences in sea level arise from: 
ocean dynamics; changes in Earth gravity, rotation and deformation 
due to land ice and land-water changes; and vertical land motion. 
Temporal variability in ocean dynamics dominates regional patterns 
on annual to decadal time scales ( high confidence ). The anthropogenic 
signal in regional sea level change will emerge in most regions 
by 2100 ( medium confidence ). {9.2.4, 9.6.1}

Regional sea level change has been the main driver of changes 
in extreme still water levels across the quasi-global tide gauge 
network over the 20th century ( high confidence ) and will be 
the main driver of a  substantial increase in the frequency 
of extreme still water levels over the next century ( medium 
confidence ). Observations show that high-tide flooding events that 
occurred five times per year during the period 1960–1980 occurred, 
on average, more than eight times per year during the period 
1995–2014 ( high confidence ). Under the assumption that other 
contributors to extreme sea levels remain constant (e.g., stationary 
tides, storm-surge, and wave climate), extreme sea levels that 
occurred once per century in the recent past will occur annually or 
more frequently at about 19–31% of tide gauges by 2050 and at 
about 60% (SSP1-2.6) to 82% (SSP5-8.5) of tide gauges by 2100 
(medium confidence ). In total, such extreme sea levels will occur

about 20 to 30 times more frequently by 2050 and 160 to 530 times 
more frequently by 2100 compared to the recent past, as inferred 
from the median amplification factors for SSP1-2.6, SSP2-4.5, and 
SSP5-8.5 ( medium confidence ). Over the 21st century, the majority 
of coastal locations will experience a  median projected regional 
sea level rise within ±20% of the median projected GMSL change 
(medium confidence ). {9.6.3, 9.6.4}
It is  virtually certain   that GMSL will continue to rise until 
at least 2100, because all assessed contributors to GMSL 
are likely  to virtually certain   to continue contributing 
throughout this century. Considering only processes for which 
projections can be made with at least medium confidence , 
relative to the period 1995–2014, GMSL will rise by 2050 
between 0.18  [0.15  to 0.23, likely  range] m (SSP1-1.9) and 
0.23  [0.20  to  0.29, likely  range]  m (SSP5-8.5), and by 2100 
between 0.38  [0.28 to  0.55, likely  range] m (SSP1-1.9) and

0.77 [0.63  to 1.01, likely  range]  m (SSP5-8.5).  This GMSL 
rise is primarily caused by thermal expansion and mass loss from 
glaciers and ice sheets, with minor contributions from changes in 
land-water storage. These likely range projections do not include 
those ice-sheet-related processes that are characterized by deep 
uncertainty. {9.6.3}

1217
Higher amounts of GMSL rise before 2100 could be caused 
by earlier-than-projected disintegration of marine ice shelves, 
the abrupt, widespread onset of marine ice sheet instability 
and marine ice cliff instability around Antarctica, and faster-
than-projected changes in the surface mass balance and 
discharge from Greenland.  These processes are characterized by 
deep uncertainty arising from limited process understanding, limited 
availability of evaluation data, uncertainties in their external forcing 
and high sensitivity to uncertain boundary conditions and parameters. 
In a low-likelihood, high-impact storyline, under high emissions such 
processes could in combination contribute more than one additional 
metre of sea level rise by 2100. {9.6.3, Box 9.4}
Beyond 2100, GMSL will continue to rise for centuries due 
to continuing deep-ocean heat uptake and mass loss of the

Greenland and Antarctic ice sheets, and will remain elevated for 
thousands of years ( high confidence ). Considering only processes 
for which projections can be made with at least medium confidence  
and assuming no increase in ice-mass flux after 2100, relative to the 
period 1995–2014, by 2150, GMSL will rise between 0.6 [0.4 to 0.9, 
likely  range] m (SSP1-1.9) and 1.4 [1.0 to 1.9, likely  range] m (SSP5-8.5). 
By 2300, GMSL will rise between 0.3 m and 3.1 m under SSP1-2.6, 
between 1.7 m and 6.8 m under SSP5-8.5 in the absence of marine ice 
cliff instability, and by up to 16 m under SSP5-8.5 considering marine 
ice cliff instability ( low confidence ). {9.6.3}
Cryospheric Changes and Sea Level Rise 
at Specific Levels of Global Warming
At sustained warming levels between 1.5°C and 2°C , the Arctic 
Ocean will become practically sea ice-free in September in some 
years ( medium confidence ); the ice sheets will continue to lose mass

(high confidence ), but will not fully disintegrate on time scales of 
multiple centuries ( medium confidence ); there is limited evidence  
that the Greenland and West Antarctic ice sheets will be lost almost 
completely and irreversibly over multiple millennia; about 50 to 60% 
of current glacier mass excluding the two ice sheets and the glaciers 
peripheral to the Antarctic Ice Sheet will remain, predominantly in 
the polar regions ( low confidence ); Northern Hemisphere spring 
snow cover extent will decrease by up to 20% relative to 1995–2014 
(medium confidence ); the permafrost volume in the top 3 m will 
decrease by up to 50% relative to 1995–2014 ( medium confidence ). 
Committed GMSL rise over 2000 years will be about 2 to 6 m 
with 2°C of peak warming ( medium agreement, limited evidence ). 
{9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, 9.5.3, 9.6.3}At sustained warming levels between 2°C and 3°C , the Arctic 
Ocean will be practically sea ice free throughout September in

most years ( medium confidence ); there is limited evidence  that the 
Greenland and West Antarctic ice sheets will be lost almost completely 
and irreversibly over multiple millennia; both the probability of their 
complete loss and the rate of mass loss will increase with higher 
temperatures ( high confidence ); about 50 to 60% of current glacier 
mass outside Antarctica will be lost ( low confidence ); Northern 
Hemisphere spring snow cover extent will decrease by up to 30% 
relative to 1995–2014 ( medium confidence ); permafrost volume 
in the top 3 m will decrease by up to 75% relative to 1995–2014 
(medium confidence ). Committed GMSL rise over 2000 years will be 
about 4 to 10 m with 3°C of peak warming ( medium agreement, 
limited evidence ). {9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, 9.5.3, 9.6.3}
At sustained warming levels between 3°C and 5°C , the Arctic 
Ocean will become practically sea ice free throughout several months

in most years ( high confidence ); near-complete loss of the Greenland 
Ice Sheet and complete loss of the West Antarctic Ice Sheet will occur 
irreversibly over multiple millennia ( medium confidence ); substantial 
parts or all of Wilkes Subglacial Basin in East Antarctica will be lost 
over multiple millennia ( low confidence ); 60 to 75% of current glacier 
mass outside Antarctica will disappear ( low confidence ); nearly all 
glacier mass in low latitudes, Central Europe, Caucasus, western 
Canada and the USA, North Asia, Scandinavia and New Zealand 
will  likely  disappear; Northern Hemisphere spring snow cover 
extent will decrease by up to 50% relative to 1995–2014 ( medium 
confidence ); permafrost volume in the top 3 m will decrease by up 
to 90% compared to 1995–2014 ( medium confidence ). Committed 
GMSL rise over 2000 years will be about 12 to 16 m with 4°C of 
peak warming and 19 to 22 m with 5°C of peak warming ( medium

agreement, limited evidence ). {9.3.1, 9.4.1, 9.4.2, 9.5.1, 9.5.2, 
9.5.3, 9.6.3}

1218
9.1 Introduction
This chapter provides a holistic assessment of the physical processes 
underlying global and regional changes in the ocean, cryosphere and 
sea level, as well as improved understanding of observed, attributed 
and projected future changes since the IPCC Fifth Assessment Report 
(AR5) and the Special Report on the Ocean and Cryosphere in 
a Changing Climate (SROCC; see outline in Figure 9.1). The ocean and 
cryosphere (defined as the frozen components of the Earth system 
such as sea ice, ice sheets, glaciers, permafrost and snow) exchange 
heat and freshwater with the atmosphere and each other (Figure 9.2). 
In a warming climate, the combined effects of thermal expansion of 
seawater and melting of the terrestrial cryosphere result in global 
mean sea level rise (Box 9.1).
Ocean acidification and deoxygenation are covered in Chapter  5, 
and regional changes to the ocean and cryosphere are covered in

Chapter 12 and the Atlas. Ecosystem range shifts and climate risk 
for marine biodiversity associated with ocean change are assessed 
in AR6 Working Group II (WGII). The notion of ‘climate velocity’ often 
used in impact studies, which is defined as the speed and direction 
at which a  climate variable moves across a  corresponding spatial 
field, is underpinned by the assessment of changes in the physical 
characteristics of the ocean provided in this chapter.
2 In particular, this range of tools leads to advances in the evaluation of confidence in projections. When CMIP6 models are used without additional evidence, the 5–95% confidence range 
of projections is assigned to a  likely range to acknowledge that there are uncertainty sources not reflected by model spread, consistent with Chapter 4.There are two major advances of this chapter compared with AR5 
and SROCC facilitated by community efforts. The first is the temporal

and spatial increase in observations of both the ocean and the 
cryosphere (Section  1.5.1.1). In particular, extended observations 
have allowed for improved assessment of past change and closure 
of both the energy and sea level budgets in a  consistent way 
(Cross-Chapter Box 9.1) and the sea level budget for the last century 
(Section 9.6.1.1). Higher resolution observations have revealed the 
details of the Atlantic Meridional Overturning Circulation (AMOC; 
Section 9.2.3.1) and globally resolved glacier changes for the first time 
(Section 9.5.1.1). Improved methodology has resulted in a doubling 
of the assessed level of observed increase in global ocean 0–200 m 
stratification compared to SROCC assessment (Section 9.2.1.3).
The second advance is the use of a  hierarchy of models and 
emulators to update projections of oceanic, cryospheric and sea 
level change arising from Coupled Model Intercomparison Project

Phase 6 (CMIP6) and related projects (Section  1.5.4.3, Table  1.3, 
and Annex  II).2 The CMIP6 included an ice-sheet modelling 
intercomparison for the  first time. Particular modelling advances 
relevant to this chapter are the increase in ocean resolution in the 
High Resolution Model Intercomparison Project (HighResMIP) and 
Ocean Model Intercomparison Project phase 2 (OMIP-2) experiments 
(Sections 1.5.3.1 and 9.2), projections of future glacier (GlacierMIP) 
and ice sheet (ISMIP6) and Linear Antarctic Response Model Chapter 9:  Ocean, cryosphere and sea level Chapter 9: Quick guide
Section 9.1
Section 9.2
OceanArctic
9.3.1 | 9.4.1 
Antarctic
9.2.3.2 | 9.3.2 | 9.4.2 
Marine extremes
9.6.4 | Box 9.2 
Sea level contributions
9.2.4 | 9.4 | 9.5.1 | 9.6 | Boxes 9.1, 9.3, 9.4  | CC Box 9.1 | FAQ 9.2Paleo evidence
9.2 | 9.3.1.1 | 9.5.1 | 9.6.2
Warming levels
9.3.1 | 9.4.1 | 9.4.2 | 9.5.1 | 9.5.2 | 9.5.3 | 9.6.3Atlantic Overturning Meridional Circulation
9.2.3.1 | FAQ 9.3

Commitment
9.6.3.5 | FAQ 9.1Section 9.6
Sea levelSection 9.3
Sea iceSection 9.5
Glaciers, 
permafrost, snowSection 9.4
Ice sheets
Section 9.7
Box 9.1
Drivers of sea level 
changeBox 9.2
Marine 
heatwavesBox 9.3
Model intercomparisons 
of land iceBox 9.4
High-end 
sea level riseChapter 9 assesses the physical processes underlying global and regional 
changes in the ocean, cryosphere and sea level.Key topics and corresponding sub-sections
Boxes Cross-chapter boxes
CC Box 9.1
Global energy inventory and sea level budgetFAQs
Figure  9.1 | Visual guide to Chapter 9. Sections dealing with the cryosphere are highlighted with a snowflake.

1219
Ice sheet
Snow
Sea ice
Glaciers
Ocean (Speed)(a)
(b)
Figure 9.2  | Components of ocean, cryosphere and sea level assessed in this chapter. (a)  Schematic of processes (mCDW=modified Circumpolar Deep Water, 
GIA=Glacial Isostatic Adjustment). White arrows indicate ocean circulation. Pinning points indicate where the grounding line is most stable and ice-sheet retreat will slow. 
(b) Geographic distribution of ocean and cryosphere components (numbers indicate glacierized regions (RGI Consortium, 2017)). See Figures 9.20 and 9.21 for labels. Sea ice 
shaded to indicate the annual mean concentration. Green ocean colours indicate larger surface current speed. Further details on data sources and processing are available 
in the chapter data table (Table 9.SM.9).

1220
Chapter 9 Ocean, Cryosphere and Sea Level Change9 Box 9.1 | Key Processes Driving Sea Level Change
Sea level change  arises from processes acting on a  range of spatial and temporal scales, in the ocean, cryosphere, solid Earth, 
atmosphere and on land (Figure 9.2). Relative sea level (RSL) change  is the change in local mean sea surface height relative to 
the sea floor, as measured by instruments that are fixed to the Earth’s surface (e.g., tide gauges). This reference frame is used when 
considering coastal impacts, hazards and adaptation needs. In contrast, geocentric sea level change  is the change in local mean 
sea surface height with respect to the terrestrial reference frame, and is the sea level change observed with instruments from space. 
This box provides a brief summary of sea level processes using standard terminology (Gregory et al., 2019). 
Global processes

Global mean sea level change  (Sections 9.6 and 2.3.3.3) is the change in volume of the ocean divided by the ocean surface area. 
It is the sum of changes in ocean density (‘global mean thermosteric sea level change’) and changes in the ocean mass as a result of 
changes in the cryosphere or land-water storage (‘barystatic sea level change’). 
Steric sea level change  is caused by changes in the ocean density and is composed of ‘thermosteric sea level change’ and ‘halosteric 
sea level change’.  Thermosteric sea level  change  (also referred to as ‘thermal expansion’) occurs as a result of changes in ocean 
temperature: increasing temperature reduces ocean density and increases the volume per unit of mass. Halosteric sea level change  
occurs as a result of salinity variations: higher salinity leads to higher density and decreases the volume per unit of mass. Although

both processes can be relevant on regional to local scales, thermosteric changes contribute to global mean sea level change, whereas 
global mean halosteric change is negligible (Gregory et al., 2019). There is high confidence  in the understanding of processes causing 
thermosteric sea level change (Section 9.2.4.1).
The Greenland and Antarctic ice sheet s are the largest reservoirs of frozen freshwater and therefore potentially the largest contributors 
to sea level rise. Fluctuations in ice-sheet volume arise from the imbalance between accumulation (either at the ice-sheet surface or on 
the underside of ice shelves) and loss from sublimation, surface and basal melting, and iceberg calving. Ice sheets discharge the majority 
of their mass through marine-terminating ice streams that are in some cases buttressed by floating ice shelves. Changes in the thickness

and extent of the ice shelves due to melt from below, calving, or disintegration, as a result of surface meltwater penetrating crevasses, 
can affect the flow of the inland ice streams. There is medium confidence  in ice-sheet processes but low confidence in their forcing 
(ocean changes and ice-shelf collapse) and in instability processes (Sections 9.4.1 and 9.4.2).3
Glaciers  contribute to sea level change via an imbalance between mass gain and mass loss processes, which leads to adjustments 
in the glacier geometry over an extended period of time, called the response time. The response time may range from a few years to 
a few hundred years. The glacial meltwater does not all flow immediately into the ocean: it can refreeze, feed rivers (where it may be 
extracted for domestic use), evaporate, or be stored in (proglacial) lakes or closed basins. There is medium  to high confidence  in the 
understanding of processes leading to sea level contributions from glaciers (Section 9.5.1).

Land-water storage  includes surface water, soil moisture, groundwater storage and snow, but excludes water stored in glaciers 
and ice sheets. Changes in land-water storage can be caused either by direct human intervention in the water cycle (e.g., storage of 
water in reservoirs by building dams in rivers, groundwater extraction for consumption and irrigation, or deforestation) or by climate 
variations (e.g., changes in the amount of water in internally drained lakes and wetlands, the canopy, the soil, the permafrost and 
3 The conversion of land ice mass loss to global mean sea level rise used in this Report – the sea level equivalent (SLE) – is 362.5 gigatonnes (Gt) of ice loss for 1 mm of sea level rise.Intercomparison Project (LARMIP-2) response from multi-model 
studies (Sections 9.5.1 and 9.4, and Box 9.3), and new methods to 
synthesize ocean and cryosphere models into sea level projections for 
all Shared Socieo-economic Pathway scenarios (SSPs; Sections 1.6.1,

9.4.1.3, 9.4.2.5 and 9.6.3, and Cross-Chapter Box 1.4) and warming 
levels (Sections 9.6.3 and 1.6.2, and Cross-Chapter Box  11.1). 
In  particular, sea level projections and the individual contributions 
(Section 9.6.3.3) are consistent with equilibrium climate sensitivity 
and surface temperature assessments across this Report (Box 4.1 and 
Cross-Chapter Box 7.1).There are other advances in scientific understanding. In the cryosphere, 
this chapter assesses how fast-responding elements (sea ice, permafrost 
and snow; Sections 9.3, 9.5.2 and 9.5.3) track warming levels across 
observations and projections independent of scenario, process 
understanding of uncertainty in Antarctic Ice Sheet projections 
(Section 9.4.2 and Box 9.4) and new insight into thresholds for Arctic 
sea ice (Section 9.3.1.1) and Greenland and West Antarctic ice sheets 
(Sections 9.4.1.4 and 9.4.2.6). In the ocean, process understanding 
of ocean heat uptake (Section 9.2.2.1 and Cross-Chapter Box 5.3)

and observed changes in ocean stratification (Section 9.2.1.3) have 
implications for ocean biogeochemistry are also important.

1221
Ocean, Cryosphere and Sea Level Change Chapter 99Box 9.1 (continued)
the snowpack). Land-water storage changes caused by climate variations may be indirectly affected by anthropogenic influences. 
It is difficult to assign a single confidence level to land-water storage as understanding can vary from low confidence  in groundwater 
recharge processes to high confidence  in water storage via snowpack changes (Sections 8.2.3 and 8.3.1.7).
Regional and local processes
Ocean dynamic sea level  change  refers to the change in mean sea level relative to the geoid and is associated with the circulation 
and density-driven changes in the ocean. Ocean dynamic sea level change varies regionally but by definition has a  zero global 
mean. It includes the depression of the sea surface by atmospheric pressure. There is medium confidence  in the understanding of 
ocean processes leading to dynamic sea level change (Section 9.2.4.2).

Changes in Earth gravity, Earth rotation and viscoelastic solid Earth deformation  (GRD)  – result from the redistribution of 
mass between terrestrial ice and water reservoirs and the ocean. Contemporary terrestrial mass loss leads to elastic solid Earth uplift 
and a nearby RSL fall. (For a single source of terrestrial mass loss, this is within about 2000 km; for multiple sources, the distance 
depends on the interaction of the different RSL patterns.) Farther away (around more than 7000 km for a single source of terrestrial 
mass loss), RSL rises more than the global average, due to first-order gravitational effects. Earth deformation associated with adding 
water to the ocean and a shift of the Earth’s rotation axis towards the source of terrestrial mass loss leads to second-order effects 
that increase spatial variability of the pattern globally. GRD effects due to the redistribution of ocean water within the ocean itself are

referred to as self-attraction and loading effects . There is high confidence  in the understanding of GRD processes.
Glacial isostatic adjustment  is ongoing GRD in response to past changes in the distribution of ice and water on Earth’s surface. 
On a time scale of decades to tens of millennia following mass redistribution, Earth’s mantle flows viscously as it evolves toward 
isostatic equilibrium, causing solid Earth movement and geoid changes, which can result in regional to local sea level variations. There 
is medium confidence  in the understanding of glacial isostatic adjustment processes.
Vertical land motion  is the change in height of the land surface or the sea floor and can have several causes in addition to elastic 
deformation associated with contemporary GRD and viscoelastic deformation associated with glacial isostatic adjustment. Subsidence

(sinking of the land surface or sea floor) can occur through compaction of alluvial sediments in deltaic regions, removal of fluids such 
as gas, oil, and water, or drainage of peatlands. Tectonic deformation of the Earth’s crust can occur as a result of earthquakes and 
volcanic eruptions. There is medium confidence  in the understanding of vertical land motion processes. 
Extreme sea level  is an exceptionally low or high local sea surface height arising from combined short-term phenomena (e.g., storm 
surges, tides and waves). RSL changes affect extreme sea levels directly by shifting the mean water levels, and indirectly by modulating 
the depth for propagation of tides, waves and/or surges. Extreme sea levels can be influenced by changes in the frequency, tracks, 
or strength of weather systems, or anthropogenic changes such as dredging. Extreme still water level  refers to the combined

contribution of RSL change, tides and storm surges. Wind-generated waves also contribute to coastal sea level. Extreme total water 
level  is the extreme still water level plus wave setup (time-mean sea level elevation due to wave energy dissipation). When considering 
coastal impacts, swash (vertical displacement up the shore-face induced by individual waves) is also important and included in Extreme 
coastal water level . There is low to medium confidence  in the understanding of extreme sea level processes (Sections 9.6.4 and 12.4).
9.2 Oceans
9.2.1 Ocean Surface
9.2.1.1 Sea Surface Temperature
The IPCC Fifth Assessment Report (AR5; Hartmann et  al., 2013) 
assessed that it is virtually certain  that global sea surface temperature 
(SST) has increased since the beginning of the 20th century ( very high 
confidence ). The Special Report on Ocean and Cryosphere in 
a Changing Climate (SROCC) did not assess past SST change. Since

AR5, improvements in the understanding of recent SST biases in the 
observational records, especially extending ship-based observations with buoy-based observations and improved treatment of sea ice, 
have had important consequences for key climate change indicators 
such as global mean surface temperature (GMST), global surface 
air temperature (GSAT), and SST (Cross-Chapter Box  2.3). The AR5 
assessment is confirmed, and it is now very likely  that global mean SST 
changed by 0.88 [0.68 to 1.01] °C from 1850–1900 to 2011–2020, and 
0.60 [0.44 to 0.74] °C from 1980 to 2020 (Figure 9.3 and Table 2.4).
Regions vary in the rate of SST warming, with slight cooling in some 
regions (Figure 9.3). The SROCC (Collins et al., 2019) and Section 7.4.4 
assess SST changes over specific regions, which are consistent with the 
changes reported here. The tropical ocean has been warming faster 
than other regions since 1950, with the fastest warming in regions

1222
Chapter 9 Ocean, Cryosphere and Sea Level Change9of the tropical Indian and western Pacific oceans (Figure 9.3), due to 
a combination of local atmosphere–ocean coupling, the Indonesian 
Throughflow (Section  9.2.3.4 and Figure  9.11), and trends in the 
Walker circulation (Sections 2.3.1.4.1 and 3.3.3.1, and Figure 3.16). 
The western boundary currents of the subtropical gyres have warmed 
faster than the global mean over the past century. There remains low 
agreement  in the changes of the location and the dynamical changes 
in western boundary current extensions (Sections 2.3.3.4.2 and 
9.2.3.4, and Figure 9.3). In the Arctic, the mean SST increase over the 
last two decades is similar to, or only slightly higher than, the global 
average (J.-L Chen et al., 2019). In contrast, the eastern Pacific Ocean, subpolar  North Atlantic Ocean and Southern Ocean have warmed 
more slowly than the global average or cooled (Figure 9.3). Surface

warming in the subpolar Southern Ocean has been slower than 
the global average since the 1950s, and this pattern is consistent 
with the upwelling around Antarctica renewing surface water with 
pre-industrial, deeper water masses (Section  9.2.3.2; Frölicher 
et al., 2015; J. Marshall et al., 2015; Armour et al., 2016). New evidence 
since SROCC (Meredith et  al., 2019) confirms slight cooling since 
the 1980s around the subpolar Southern Ocean, contrasting with 
marked warming directly northward of it (Section 9.2.3.2; Haumann 
et al., 2020; Rye et al., 2020; Auger et al., 2021). In eastern boundary 
Sea surface temperature (SST) anomalies and maps
Observation-based estimated and CMIP6 multi-model means, biases and projected changes
2100 2300051015
 °C     SSP5-8.53
SSP1-2.63
Extended
projections
1850 1900 1950 2000 2050 2100012345
 °C     
SSP5-8.522
SSP1-2.623SSP2-4.523SSP3-7.019
CMIP40 modelsHighResMIP
(historical9/SSP5-8.55)Observational

ReanalysesGlobal Mean SST (GMSST); modern history and model projections to 2100
Baseline PeriodCMIP6
2100 means &
(very) likely
rangesMean GMSST
CMIP6 Likely (17-83%) ranges
Low model agreement (<80%)High model agreement (≥80%) Color
SSP5-8.5
SSP5-8.5Observation-basedObservation-based-4-2024
o C   
Models (PMIP)Paleo GMSST
Mean & 95% conf.
                 interval
Figure  9.3 | Sea surface temperature (SST) and its changes with time. (a) Time series of global mean SST anomaly relative to 1950–1980 climatology. Shown 
are paleoclimate reconstructions and PMIP models, observational reanalyses (HadISST) and multi-model means from the Coupled Model Intercomparison Project (CMIP) 
historical simulations, CMIP projections, and HighResMIP experiment. (b) Map of observed SST (1995–2014 climatology HadISST). (c) Historical SST changes from observations.

(d) CMIP 2005–2100 SST change rate. (e) Bias of CMIP . (f) CMIP change rate. (g) 2005–2050 change rate for SSP5-8.5 for the CMIP ensemble. (h) Bias of HighResMIP (bottom 
left) over 1995–2014. (i) HighResMIP change rate for 1950–2014. (j) 2005–2050 change rate for SSP5-8.5 for the HighResMIP ensemble. No overlay indicates regions with 
high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of 
change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1223
Ocean, Cryosphere and Sea Level Change  Chapter 99upwelling systems, SROCC (Bindoff et  al., 2019) reported low 
agreement  between SST trends in recent decades, due to varying 
spatio-temporal resolution and interannual to multi-decadal 
variability. Satellite evidence not included in SROCC shows that 
92% of these regions warmed more slowly than neighbouring 
offshore locations between 1982 and 2015, so upwelling may buffer 
the near shore from warming (Section 9.2.3.5; Varela et al., 2018). 
Coupled ocean-atmospheric modes of variability strongly affect 
regional SST (Cross-Chapter Box  3.1 and Annex IV). In  summary, 
a positive SST trend since 1950 is evident globally, but there is very 
high confidence  that the Indian Ocean, western equatorial Pacific 
Ocean, and western boundary currents have warmed faster than the 
global average, while the Southern Ocean, the eastern equatorial 
Pacific, and the North Atlantic Ocean have warmed more slowly, or 
have slightly cooled.

In AR5 (Flato et  al., 2013), a  marginal improvement was noted in 
Coupled Model Intercomparison Project Phase 5 (CMIP5) climate 
model SST biases compared to Phase 3 (CMIP3) models in AR4, with 
a reduction in the magnitude of biases. The AR5 noted that, in several 
regions, large SST biases are symptomatic of errors in the representation 
of important processes, such as dynamics in the equatorial Pacific and 
North Atlantic, and Southern Ocean. Common regional biases in SST 
or historical SST trends are not exclusively linked to the representation 
of the ocean ( high confidence ), but can have multiple causes, 
including: errors in the representation of long-term historical trends 
in equatorial winds (Section 9.2.1.2); misrepresentation of the forced 
equatorial ocean response (Karnauskas et al., 2012; Kohyama et al., 
2017; Coats and Karnauskas, 2018); thermocline depth errors (Linz 
et  al., 2014); errors in atmospheric model cloud-related shortwave

radiation (Hyder et al., 2018); biases in ocean circulation variability (C. 
Wang et al., 2014); and deficiencies in upper ocean (Q. Li et al., 2019) 
and atmospheric (Bates et al., 2012) boundary layer parametrizations. 
In CMIP6, the mid-latitude biases in the Northern Hemisphere are 
improved in the  multi-model mean, and the inter-model standard 
deviation of the zonal mean SST error is significantly decreased 
in the northern Hemisphere south of 50°N compared to CMIP5, 
though biases in equatorial regions remain essentially unchanged 
(Section 3.5.1.1 and Figures 3.23, 3.24 and 9.3). Some long-standing 
ocean model biases have been reduced through increases in model 
resolution in CMIP6 (Bock et al., 2020) and improved parametrizations 
(Fox-Kemper et al., 2011; Q. Li et al., 2016; Qiao et al., 2016; Reichl 
and Hallberg, 2018). The High Resolution Model Intercomparison 
Project (HighResMIP) ensemble (Figure 9.3) has smaller cold biases

in the North Atlantic and the tropical Pacific, and smaller warm biases 
in the upwelling regions off the western coasts of Africa, North and 
South America  (Roberts et  al., 2018, 2019; Caldwell et  al., 2019; 
Docquier et  al., 2019). In summary, CMIP6 models show persistent 
regional biases in representing the climatological SST state ( very high 
confidence ), but higher resolution reduces some biases, particularly 
in the North Atlantic and eastern boundary upwelling systems 
(Figure 9.3; high confidence ).
The CMIP6 models represent the observed trends in SST patterns with 
greater fidelity than CMIP5, with the ocean area that is inconsistent 
with the observed trends decreasing by about three  quarters from 
CMIP5 to CMIP6 (Olonscheck et  al., 2020). In some regions, the direction of SST changes in observations are consistent with CMIP6 
only when including internal variability (Olonscheck et  al., 2020). 
This is notably the case in the equatorial Pacific, North Atlantic,

and Southern Ocean, which are regions where SST is of known 
importance in controlling heat uptake (Section 9.2.2.1) and the global 
radiative feedback parameter (Section 7.4.4.3). Overall, despite some 
persistent regional biases, CMIP6 coupled climate models reproduce 
the observed SST trends or high internal variability over the past 
century over a range of different multi-decadal periods (Figure 9.3; 
Olonscheck et al., 2020; Watanabe et al., 2021), highlighting their skill 
to inform future large-scale SST changes at regional scale. Warming 
is projected at varying rates in all regions by 2050, except the North 
Atlantic Subpolar Region, the equatorial Pacific, and the Southern 
Ocean where models disagree ( high confidence ).
It is  virtually certain   that SST will continue to increase in the 
21st  century, at a  rate depending on future emissions scenarios. 
The  future global mean SST increase projected by CMIP6 models

for the period 1995–2014 to 2081–2100 is 0.86 [5–95% range: 
0.43–1.47] °C under SSP1-2.6, 1.51 [1.02 to 2.19] °C under SSP2-4.5, 
2.19 [1.56 to 3.30] °C under SSP3-7.0, and 2.89 [2.01 to 4.07] °C under 
SSP5-8.5 (Figure  9.3). While under SSP1-2.6, the CMIP6 ensemble 
consistently projects that it is very likely  at least 83% of the world 
ocean surface will have warmed by 2100, and under SSP5-8.5, at least 
98% of the world ocean surface will have warmed. The spatial pattern 
of future change is consistent with observed SST change over the 
20th century, though with notable regional differences (Figure 9.3). 
Long-term change in SST patterns is important for regional impacts 
but also affects radiative feedbacks, and therefore long-term change 
in climate sensitivity (Section 7.4.4.3). In the Southern Ocean, CMIP6 
models project that SSTs will eventually consistently increase in the 
21st  century, at a  rate dependent on future scenarios (Figure  9.3

and Section 9.2.3.2; Bracegirdle et al., 2020). Yet, there is only low 
confidence  that this Southern Ocean warming will emerge by the 
end of the century (Section 7.4.4.1), due to the inconsistent historical 
and near-term simulations and observations over the 20th  century 
(Figure  9.3). Furthermore, the equilibrium SST pattern from proxy 
records or simulated by climate models under CO 2 forcing stand in 
contrast with the cooling trends in the Southern Ocean observed over 
the past decades (Section 7.4.4.1.2). Similarly, the SST change pattern 
observed in the tropical Pacific Ocean will transition on centennial 
time scales to a mean pattern resembling the El Niño pattern ( medium 
confidence ) (Annex IV). However, it is difficult to delineate a climate 
change trend ressembling an El Niño pattern and El Niño variability 
(Wittenberg, 2009; Collins et  al., 2010) without large ensembles 
(Kay et  al., 2015). Several Pliocene SST reconstructions indicate

enhanced warming in the centre of the eastern Pacific equatorial 
cold tongue upwelling region, consistent with reconstruction of 
enhanced subsurface warming and enhanced warming in coastal 
upwelling regions (Section  7.4.4.2.2). The North Atlantic subpolar 
gyre is projected to continue to warm more slowly than surrounding 
regions (Suo et  al., 2017), as the Gulf Stream concurrently warms 
rapidly (Figure 9.3; Cheng et al., 2013) and the Atlantic Meridional 
Overturning Circulation further declines under greenhouse gas forcing, 
although models disagree about the rate of change (Figure 9.3 and 
Section 9.2.3.1). In summary, CMIP6 models show a future pattern of 
SST change comparable to historical trends with intensity depending

1224
Chapter 9 Ocean, Cryosphere and Sea Level Change9on future emissions scenario, and some of the observed cooling trends 
over the 20th  century will eventually transition to a  warming SST 
on centennial time scales, in particular in the Southern Ocean ( high 
confidence ) and in the equatorial Pacific ( medium confidence ), while 
the North Atlantic subpolar gyre will continue to warm more slowly 
than the global average ( high confidence ).
9.2.1.2 Air–Sea Fluxes
Air–sea fluxes of energy, freshwater, and momentum (wind stresses) 
are difficult to observe directly (Cronin et al., 2019), so estimates of 
the global mean net air–sea heat flux are inferred from observed 
ocean warming (Section 2.3.3.1, Box 7.2, and Cross-Chapter Box 9.1). 
Air–sea heat fluxes resemble the warming patterns of CMIP3 
(Domingues et al., 2008; Levitus et al., 2012) and are consistent with 
the ensemble mean warming rate of CMIP5 (Cheng et  al., 2017,

2019) and CMIP6 models (Section 3.5.1.3). Regional air–sea fluxes 
in models remain a  key driver of uncertainty (Huber and Zanna, 
2017; Tsujino et  al., 2020). A  substantial part of the upper 700 m 
energy increase is very likely  attributed to anthropogenic forcing via 
increasing radiative forcing (Sections 3.5.1.3, 7.2 and 7.3).The SROCC (Abram et al., 2019) and AR5 (Rhein et al., 2013) assessed 
that observations of air–sea fluxes had not yet reached the density 
or accuracy to directly detect trends beyond the noise. New evidence 
since SROCC confirms that direct heat and freshwater flux trends 
have not emerged yet as spatial (Figure 9.4), annual (Yu, 2019), and 
decadal (Zanna et al., 2019) variability overwhelm detection. Since 
AR5, comprehensive comparisons (Bentamy et al., 2017; Valdivieso 
et al., 2017; Yu et al., 2017) have used updated and new surface 
flux products to improve surface flux uncertainty estimates, and

these comparisons note that implied global energy imbalances 
often exceed the observed ocean warming. Flux estimates using 
top of atmosphere observations and atmospheric fluxes from 
reanalysis have improved over past products (Trenberth and Fasullo, 
2018) but require consistency adjustments (Trenberth et al., 2019) 
as the energy budget is not closed. Adjustments are needed for 
all flux products, and they remain less accurate than direct ocean 
heat content change measurements (Cheng et  al., 2017). Some 
regional changes are likely  robust in both satellite observations and 
projections (Figure 9.4). Recent satellite-based surface flux products 
with improved retrieval algorithms and new satellites, for example, 
J-OFURO3 (Tomita et al., 2019) and OAFlux-HR (Yu, 2019), provide 
Figure  9.4 | Global maps of observed mean fluxes (a, d, g), the observed trends in these fluxes (b, e, h) and the projected rate of change in these fluxes

from SSP5-8.5 (c, f, i).  Shown are the freshwater flux (a–c) , net heat flux (d–f) , and momentum flux or wind stress magnitude (g–i) , with positive numbers indicating ocean 
freshening, warming, and accelerating respectively. The means and observed trends are calculated between 1995–2014 (freshwater and wind stress) or 2001–2014 (heat). 
The SSP5-8.5 projected rates are between 1995–2100 using 20-year averages at each end of the time period. Observations show objective interpolation from Clouds and the 
Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) v4 (Kato et al., 2018), Objectively Analyzed air–sea Fluxes-High Resolution (OAFlux-HR) (Yu, 2019), 
and Global Precipitation Climatology Project (GPCP) (Adler et al., 2003) of fluxes and flux trends (b, e, h). Observed trends with no overlay indicate regions where the trends

are significant at p = 0.34 level. Crosses indicate regions where trends are not significant. For (c, f, i) projections, no overlay indicates regions with high model agreement, 
where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change 
(see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1225
Ocean, Cryosphere and Sea Level Change  Chapter 99a complete suite of turbulent fluxes including heat, moisture, and 
momentum. When combined with satellite-based surface radiation 
from Clouds and the Earth’s Radiant Energy System (CERES) Energy 
Balanced and Filled (EBAF; Kato et al., 2018) and precipitation from 
Global Precipitation Climatology Project (GPCP; Adler et al., 2003), 
full ocean-surface forcing is available since 1987 (Figure 9.4). These 
products agree with sparse buoy and ship observations within 
30 W m–2 (Bentamy et al., 2017; Cronin et al., 2019) . While patterns 
agree between models and satellites in net fluxes (Figure  9.4), 
the trend magnitudes are substantially weaker in models. The 
fluxes tending to warm the North Atlantic and Southern Ocean 
are consistent with the largest changes observed in the surface 
properties and water masses (Sections 9.2.1.1, 9.2.2.1 and 9.2.2.3). 
The observed trend toward a saltier Atlantic Ocean and a fresher

Indian Ocean, as well as trends in evaporation minus precipitation 
(E-P) patterns in the equatorial Pacific (see also Section  8.3.1) 
enhance the present mean pattern of wetting and drying. Elsewhere 
patterns are less clear, with only partial, large-scale agreement 
with the ‘wet gets wetter’ simplification (Sections 3.3.2.3, 4.4.1 
and 4.5.1). In summary, globally integrated and large-scale fluxes 
are more reliably inferred from heat content and salinity change, 
while regional trends are rarely robust in observations; where they 
are robust, they tend to be underestimated or in disagreement 
in models ( very high confidence ).
There is low confidence  in long-term wind stress trends in most 
regions, but a few locations have  likely  trends over the scatterometer 
era and in projections, as shown in Figure  9.4 (Desbiolles et  al., 
2017; Young and Ribal, 2019; Yu, 2019). The AR5 (Rhein et al., 2013) 
assessed with medium confidence  that zonal wind stress over the

Southern Ocean increased from the early 1980s to the 1990s ( medium 
confidence ) (Figure  9.4). Over 1995–2014, the zonal wind  stress 
over the Southern Ocean continued to increase, westerly winds in 
the North Pacific and North Atlantic weakened, while the easterly 
equatorial Pacific winds of the Walker circulation strengthened 
(Figure  9.4). In historical simulations, CMIP5 models projected 
annular modes (Annex IV) to move poleward and strengthen in both 
hemispheres (Yang et al., 2016), while in CMIP6 models westerlies 
only strengthen over the Southern Ocean, with a weaker trend than 
recently observed (Figure 9.4 and Sections 4.5.1 and 4.5.3). In the 
tropical Pacific Ocean, a  weakening trend in easterly winds and 
Walker circulation in the 20th century has been inferred based on 
observed sea level pressure data (Vecchi et  al., 2006; Vecchi and 
Soden, 2007) and coral proxies (Carilli et al., 2014) and is projected

to continue by CMIP6 models (Figure  9.4). Yet, over 1995–2014 
observed winds have strengthened (Figure  9.4). The observed 
strengthening may have been influenced by a combination of factors 
(Section 7.4.4.2.1), but there is  low confidence  in the attribution of 
this signal to anthropogenic warming (Section 3.3.3.1) and  medium 
confidence   that it reflects internal variability (Section  8.3.2.3). 
Near-term projected changes over the Southern Ocean result from 
ozone recovery and greenhouse gases (Sections 4.3.3 and 4.4.3). 
Overall, there is only low confidence  in observed and projected wind 
stress trends in most regions because trends in oceanic wind stresses 
during the satellite era have not emerged or are inconsistent with 
historical simulated changes.Air–sea flux biases result from common causes in most models, and 
many are the same as during AR5 (Rhein et al., 2013). Important 
currents (e.g., Gulf Stream, Kuroshio, Antarctic Circum-polar Current

patterns) are often found in erroneous locations in models, affecting 
SST and flux signatures (Bates et al., 2012; Beadling et al., 2020; J.-
L.F . Li et al., 2020), but their locations are improved in high-resolution 
ocean models (Chassignet et  al., 2017, 2020; Hewitt et  al., 2020), 
and high-resolution coupled models reduce the mean air–sea flux 
biases (Delworth et  al., 2012; Sakamoto et  al., 2012; Small et  al., 
2014; Haarsma et  al., 2016; Caldwell et  al., 2019; L.C Jackson 
et al., 2020). Oceanic variability stems either from internal chaotic 
variability or atmospheric forcing (Hasselmann, 1976; Sérazin 
et  al., 2016, 2017). Large-scale variability in the ocean tends to 
follow atmospheric forcing in low-resolution models, while in high-
resolution coupled models ocean variability drives atmospheric 
variability on small scales (Bishop et al., 2017; Small et al., 2019), 
allowing these high-resolution models to mimic the coupling with

clouds, precipitation, and atmospheric and oceanic boundary layers 
apparent in observations (Chelton and Xie, 2010; Frenger et  al., 
2013). Even coarse-resolution models, such as the ocean and sea ice 
components used in CMIP6, show significant sensitivity in the mean 
and variability of SST and sea ice to modest changes in flux forcing 
(Tsujino et al., 2020). Finally, there is still considerable disagreement 
between different parametrizations of air–sea fluxes used in models 
and strong scatter in direct observations (Renault et  al., 2016; 
Brodeau et al., 2017). In summary, there is very high confidence  that 
air–sea heat flux and stress biases are reduced in coupled models 
with high ocean resolution over coarse-resolution models, although 
the effect on trends remain unclear.
9.2.1.3 Upper-ocean Stratification and Surface Mixed Layers
The density difference from surface to deep ocean is the upper-ocean 
stratification. The AR5 (Rhein et  al., 2013) assessed that it is very

likely  that the thermal contribution to stratification over the fixed 
0–200 m layer increased by about 1% per decade between 1971 and 
2010 (based on linear trend consistently across reports). The SROCC 
(Bindoff et  al., 2019) found it very likely  that density stratification 
increased by 0.46–0.51% per decade between 60°S and 60°N from 
1970 to 2017). New published estimates based on a  variety of 
different interpolated observations show that SROCC assessed rate 
is too low, even using the same data and methods (Li et al., 2020). 
The 1960–2018 stratification increase is estimated at 1.2  ±  0.1% 
per decade from the IAP dataset, 1.2 ± 0.4% per decade from the 
Ishii product, 0.7 ± 0.5% per decade from the EN4 dataset, 0.9 
± 0.5% per decade from ORAS4, and 1.2 ±0.3% per decade from the 
National Centers for Environmental Information (NCEI) dataset (G. Li 
et al., 2020). The improved methodology for computing stratification

change on individual profiles before gridding yields a global annual 
mean increase of 0–200 m stratification change of 0.8 ± 0.2% per 
decade between 1960 and 2018 (Yamaguchi and Suga, 2019) and 
a  global summer mean increase of 0–200 m stratification change 
of 1.3 ± 0.3% per decade between 1970 and 2018 (Sallée et  al., 
2021) is of a similar magnitude to the long-term trend (Yamaguchi 
and Suga, 2019; G. Li et  al., 2020). In summary, there is limited 
evidence  that focusing on changes over a fixed depth range might

1226
hide larger increases occurring at the seasonally and regionally 
variable pycnocline depth. There is also limited evidence  that summer 
stratification change within the pycnocline has occurred at a rate of 
8.9 ± 2.7% per decade from 1970 to 2018, and limited evidence  of 
a winter pycnocline stratification increase (Cummins and Ross, 2020; 
Sallée et al., 2021).
While AR5 and SROCC did not assess change in mixed-layer depth, the 
reported changes in stratification can modulate the surface mixed-
layer depth, which is set by a balance between fluxes and dynamical 
mixing (winds, tides, waves, convection) acting against the background 
stratification and restratification processes (solar and dynamical). 
Despite the large stratification increase observed at a  global scale, 
new evidence shows that summer mixed-layer depth deepened 
consistently over the globe at a rate of 2.9 ± 0.5% per decade from

1970 to 2018, with the largest deepening observed in the Southern 
Ocean, corresponding to overall deepening from 3–15 m per decade 
depending on region (Somavilla et al., 2017; Sallée et al., 2021). While 
the shorter observational record in winter (compared to summer) 
does not allow global winter mixed-layer trends to be reliably 
assessed (Sallée et al., 2021), winter mixed-layer depths deepening 
at rates of 10  m per decade have been reported at individual 
long-term mid-latitude monitoring sites (Somavilla et  al., 2017). 
Projections agree that shoaling of mixed-layer depth is expected in 
the 21st  century, but only for strong emissions scenarios, and only 
in some regions (Figure 9.5). In summary, there is limited observational  
evidence  that the mixed layer is globally deepening, while models 
show no emergence of a trend until later in the 21st century under 
strong emissions.The SROCC assessed that upper-ocean stratification will continue

to increase in the 21st century under increased radiative forcing ( high 
confidence ), due to increased surface temperature and high-latitude 
surface freshening (Bindoff et  al., 2019). New climate model 
simulations concur with SROCC assessment of a future increase of 
the 0–200 m stratification under increased radiative forcing in all 
regions of the world ocean (Kwiatkowski et al., 2020). In addition, 
CMIP6 climate models project a  shallowing of the mixed-layer 
in summer and winter by the end of the century under increased 
radiative forcing (Figure  9.5; Kwiatkowski et  al., 2020), with the 
exception of the Arctic showing deepening of the mixed layer as 
a result of sea ice retreat (Figure 9.5; Lique et al., 2018). The regions 
of largest shallowing are associated with the deepest climatological 
mixed layer, in both winter and summer, particularly affecting the 
North Atlantic and the Southern Ocean basins (Figure  9.5). While

CMIP6 models tend to project shallowing mixed layers under 
a warming climate, except at high latitudes (Figure 9.5; Lique et al., 
2018; Kwiatkowski et al., 2020), a deepening in the summer mixed-
layer depth by intensification of the surface winds and storms may 
explain inconsistency among models in many regions (Figure  9.5; 
Young and Ribal, 2019), although model mixed-layer biases are 
large in the summer in the Southern Ocean (Belcher et al., 2012; 
Sallée et al., 2013a; Q.  Li et al., 2016; Tsujino et al., 2020). Lack of 
observed ocean turbulence and climate model limitations do not allow 
for direct assessment of ocean surface turbulence change and limit 
confidence in past and future mixed-layer change. Understanding 
of turbulent processes, their representation in ocean and climate 
models, and their effect on mixed-layer biases have been an active 
and rapidly evolving topic of research since AR5 (Buckingham et al.,

2019; Q.  Li  et  al., 2019). Small-scale mixed-layer processes are 
SSP1-2.6 SSP5-8.5Low model agreement (<80%)High model agreement (≥80%) Color
m m
m m mm
Figure  9.5 | Mixed-layer depth in (a–d) winter and (e–h) summer. (a, e) Observed climatological mean mixed-layer depth (based on density threshold) from 
the Argo Mixed Layer Depth Climatology (Holte et al., 2017) using  observations for 2000–2019. (b, f)  Bias between the observation-based estimate (2000–2019) and 
the 1995–2014 Coupled Model Intercomparison Project Phase 6 (CMIP6) climatological mean mixed-layer depth. (c, d, g, h)  Projected mixed-layer depth (MLD) change from 
1995–2014 to 2081–2100 under (c, g)  SSP1-2.6 and (d, h)  SSP5-8.5 scenarios. The (a–d)  winter row shows December–January–February (DJF) in the Northern Hemisphere 
and June–July–August (JJA) in the Southern Hemisphere; the (e–h)  summer row shows JJA in the Northern Hemisphere and DJF in the Southern Hemisphere. The mixed-layer

depth is the depth where the potential density is 0.03 kg m–3 denser than at 10 m. No overlay indicates regions with high model agreement, where ≥80% of models agree on 
the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more 
information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1227
Ocean, Cryosphere and Sea Level Change Chapter 99Box 9.2 | Marine Heatwaves
Marine heatwaves (MHW) are periods of extreme high sea temperature relative to the long-term mean seasonal cycle (Hobday et al., 
2016). Studies since the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; Collins et al., 2019) confirm the 
assessment that MHW can lead to severe and persistent impacts on marine ecosystems – from mass mortality of benthic communities, 
including coral bleaching, changes in phytoplankton blooms, shifts in species composition and geographical distribution, and toxic algal 
blooms, to decline in fisheries catch and mariculture (Smale et al., 2019; Cheung and Frölicher, 2020; Hayashida et al., 2020; Piatt et al., 
2020). Unlike synoptic atmospheric heatwaves (Section 11.3), MHWs can extend for millions of square kilometres, persist for weeks to

months, and occur at subsurface (Bond et al., 2015; Schaeffer and Roughan, 2017; Perkins-Kirkpatrick et al., 2019; Laufkötter et al., 2020).
The SROCC established that MHWs have occurred in all basins over the last decades. Additional evidence documenting widespread 
occurrence of marine heat waves in all basins and marginal seas continues to accumulate (Y .  Li et al., 2019; Yao et al., 2020). The SROCC 
highlighted the role of large-scale climate modes of variability in amplifying or suppressing MHW occurrences, which has since 
been further corroborated, increasing confidence in climate modes as important drivers of MHWs (Holbrook et al., 2019; Sen Gupta 
et al., 2020). More generally, understanding of processes leading to MHWs has increased since SROCC, including air–sea heat flux 
(Section 9.2.1.2), increased horizontal heat advection, shoaling of the mixed-layer and suppressed mixing processes (Section 9.2.1.3),

reduced coastal upwelling and Ekman pumping (Section 9.2.3.5), changes in eddy activities and planetary waves, and the re-emergence 
of warm subsurface anomalies (Holbrook et al., 2020; Sen Gupta et al., 2020).
The SROCC reported with high confidence  that MHWs – defined as days exceeding the 99th percentile in sea surface temperature 
(SST) from 1982 to 2016 – have very likely  doubled in frequency between 1982 and 2016. Additional observation-based evidence 
and acquisition of longer observation time series since SROCC have confirmed and expanded on this assessment: since the 1980s 
MHWs have also become more intense and longer (Frölicher and Laufkötter, 2018; Smale et al., 2019; Laufkötter et al., 2020). Satellite 
observations and reanalyses of SST show an increase in intensity of 0.04°C per decade from 1982 to 2016, an increase in spatial 
extent of 19% per decade from 1982 to 2016, and an increase in annual MHW days of 54% between the 1987–2016 period compared

to 1925–1954 (Frölicher et al., 2018; Oliver, 2019). The SROCC assessed that 84–90% of all MHWs that occurred between 2006 and 
2015 are very likely  caused by anthropogenic warming. There is new evidence since SROCC that the frequency of the most impactful 
marine heatwaves over the last few decades has increased more than 20-fold because of anthropogenic global warming (Laufkötter 
et al., 2020). In summary, there is high confidence  that MHWs have increased in frequency over the 20th century, with an approximate 
doubling from 1982 to 2016, and medium confidence  that they have become more intense and longer since the 1980s.
Consistent with SROCC, future MHWs are defined with reference to the historical climate conditions. The SROCC assessed that MHWs will 
very likely  further increase in frequency, duration, spatial extent and intensity under future global warming in the 21st century. The CMIP6

projections allow us to confirm this assessment and quantify future change based on global mean probability ratio change (Box 9.2, 
Figure 1): they project MHWs will  become four times (5–95% range: 2–9 times] more frequent in 2081–2100 compared to 1995–2014 
under SSP1-2.6, or eight times (3–15 times) more frequent under SSP5-8.5. The SROCC highlighted that future change of MHWs will 
not be globally uniform, with the largest changes in the frequency of marine heatwaves being projected to occur in the western tropical 
Pacific and the Arctic Ocean ( medium confidence ). New evidence from the latest generation of climate models confirms and complements 
SROCC assessment (Box 9.2, Figure 1). Moderate increases are projected for mid-latitudes, and only small increases are projected for 
the Southern Ocean ( medium confidence ) (Hayashida et al., 2020). While under the SSP5-8.5 scenario, permanent MHWs (more than

360 days per year) are projected to occur in the 21st century in parts of the tropical ocean, the Arctic Ocean and around 45°S, the 
occurrence of such permanent MHWs can largely be avoided under the SSP1-2.6 scenario (Frölicher et al., 2018; Oliver et al., 2019; Plecha 
and Soares, 2020). The resolution of current climate models (CMIP5 and CMIP6) capture the broad features of MHWs, but they may have 
a bias towards weaker and longer MHWs in the historical period ( medium confidence ) (Frölicher et al., 2018; Pilo et al., 2019; Plecha and 
Soares, 2020) and greater intensification in western boundary current regions (Hayashida et al., 2020).not resolved in climate models (D’Asaro, 2014; Buckingham et al., 
2019; McWilliams, 2019) and despite significant improvements 
in their parametrization over the last decade (Fox-Kemper et  al., 
2011; Jochum et al., 2013; Q. Li et al., 2016, 2019; Qiao et al., 2016) 
and significant improvement in some models (Li and Fox-Kemper,

2017; Dunne et  al., 2020), biases in mixed-layer representation 
generally persist (Heuzé, 2017; Williams et al., 2018; Cherchi et al., 
2019; Golaz et al., 2019; Voldoire et al., 2019; Yukimoto et al., 2019; Boucher et al., 2020; Danabasoglu et al., 2020; Dunne et al., 2020; 
Kelley et al., 2020). In summary, the representation of upper-ocean 
stratification and mixed layers has improved in CMIP6 compared 
to CMIP5. While it is virtually certain  that the global mean upper 
ocean will continue to stratify in the 21st  century, there is only 
low confidence  in the future evolution of mixed-layer depth, 
which is projected to mostly shoal under high emissions, except in 
high-latitude regions where sea ice retreats.

1228
9.2.2 Changes in Heat and Salinity
9.2.2.1 Ocean Heat Content and Heat Transport
Ocean warming – that is, increasing ocean heat content (OHC) – is 
an important aspect of energy on Earth: SROCC (Bindoff et al., 2019) 
reported that there is high confidence  that ocean warming during 
1971–2010 dominated the increase in the Earth’s energy inventory, 
which is confirmed by the Box 7.2 assessment that the ocean has 
stored 91% of the total energy gained from 1971 to 2018. As reported 
in Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2, Box 7.2 and Cross-Chapter 
Box 9.1, confidence in the assessment of global OHC change since 
1971 is strengthened compared to previous reports, and extended 
backward to include likely  warming since 1871. Table  7.1 updates 
the estimates of total ocean heat gains from 1971 to 2018, 1993 to 
2018 and 2006 to 2018. Section 3.5.1.3 assesses that it is extremely

likely that anthropogenic forcing was the main driver of the OHC 
increase over the historical period. Section  2.3.3.1 reports that 
current multi-decadal to centennial rates of OHC gain are greater 
than at any point since the last deglaciation ( medium confidence ).Ocean warming is not uniform with depth. The AR5 (Rhein  et  al., 
2013) assessed that, since 1971, ocean warming was virtually 
certain for the upper 700 m and likely for the 700–2000  m layer. 
Both AR5 and SROCC (Bindoff et al., 2019) assessed that the deep 
ocean below 2000  m had likely warmed since 1992, especially 
in the Southern Ocean. Section  2.3.3.1 provides an updated 
assessment of ocean temperature change for different depth layers, 
time periods and observation-based reconstructions (Table  2.7). 
Section 2.3.3.1 confirms the previous assessment that it is virtually 
certain that the upper ocean (0–700  m) has warmed since 1971, 
that ocean warming at intermediate depths (700–2000 m) is very

likely since 2006, and that it is likely  that ocean warming has 
occurred below 2000 m since 1992. Section 3.5.1.3 assessed that it 
is extremely likely that human influence was the main driver of the 
ocean heat content increase observed since the 1970s, which extends 
into the deeper ocean ( very high confidence ), and shows that biases 
in potential temperature have a complex pattern (Figure 3.25). In the 
present section, we assess the regional patterns of this warming and 
associated processes driving regional ocean warming.Box 9.2 (continued)
Box 9.2, Figure 1 | Observed and simulated regional probability ratio of marine heatwaves (MHWs) for the 1985–2014 period and for the end 
of the 21st century under two different greenhouse gas emissions scenarios.  The probability ratio is the proportion by which the number of MHW days

per year has increased relative to pre-industrial times. An MHW is defined as a deviation beyond the daily 99th percentile (11-day window) in the deseasonalized sea 
surface temperature. (a) The MHW probability ratio from satellite observations (NOAA OISST V2.1; Huang et al. 2020) during 1985–2014. The mean warming pattern 
(difference in ERSST5 (Huang et al. 2017) sea surface temperature between the 1985–2014 and 1854–1900 periods) has been added to the satellite observations to 
calculate the probability ratio. (b–d)  Coupled Model Intercomparison Project Phase 6 (CMIP6) simulated multi-model mean probability ratio of the (b) 1985–2014 
period, and 2081–2100 period in the (c) SSP1 2.6 and (d) SSP5 8.5 scenarios. The areas with grey diagonal lines in (d) indicate permanent MHWs (>360 heatwave 
days per year). These 14 CMIP6 models are included in the analysis: ACCESS-CM2, CESM2, CESM2-WACCM, CMCCCM2-SR5, CNRM-CM6-1, CNRM-ESM2-1,

CanESM5, EC-Earth3, IPSL-CM6A-LR, MIROC6, MRI-ESM2-0, NESM3, NorESM2-LM, NorESM2-MM. Further details on data sources and processing are available in 
the chapter data table (Table 9.SM.9).

1229
Ocean, Cryosphere and Sea Level Change  Chapter 99The rate of ocean warming varies regionally, with some regions having 
experienced slight cooling (Figure  9.6). The SROCC (Bindoff  et  al., 
2019) assessed that ocean warming in the 0–700 m depth is 
globally widespread, with slower than global average warming in 
the subpolar North Atlantic. The SROCC (Meredith et al., 2019) also 
estimated that the Southern Ocean accounted for around 75% of 
global ocean heat uptake during 1870–1995 and that 35–43% of the 
upper 2000 m global ocean warming occurred in the Southern Ocean 
over 1970–2017 (45–62% for 2005–2017). The SROCC noted that this 
interhemispheric asymmetry might (at least partially) be explained by 
high concentrations of aerosols in the Northern Hemisphere. Here, 
we confirm these assessments, bring new evidence attributing these 
regional trends, and discuss the role of decadal ocean circulation variability in redistributing heat, driving interhemispheric asymmetry

of the recent rate of ocean warming (Rathore et al., 2020; L. Wang 
et al., 2021). Since SROCC, one new study shows that the subpolar 
North Atlantic ‘warming hole’ observed since the 1980s has emerged 
from internal climate variability and can be attributed to greenhouse 
gas emissions (Chemke et  al., 2020). A  new analysis of a  suite of 
climate models (Hobbs et  al., 2021) confirms SROCC assessment, 
based on one paper (Swart et  al., 2018), attributing the observed 
Southern Ocean warming to anthropogenic forcing. Given the large 
fraction of global ocean warming in the Southern Ocean and the 
sparse observations there before 2005, there is limited evidence  that 
global OHC increase since 1971 might have been underestimated 
(Cheng and Zhu, 2014; Durack et al., 2014). Cross-Chapter Box 9.1 
Low model agreement (<80%)High model agreement (≥80%) Color1850 1900 1950 2000 2050 2100-10123
1024 J            
SSP5-8.518
SSP1-2.617SSP2-4.517SSP3-7.018
CMIP30 modelsObservations

& HybridGlobal OHC (0-2000m depth); Modern history and model projections to 2100
Baseline
PeriodCMIP6
rangesGlobal Mean OHC
CMIP6 17-83% ranges
Observations (Ishii)
Hybrid (Zanna)
Hybrid (Cheng)
± 1σ (Obs. & Hybrid)
Low model agreement (<80%)High model agreement (≥80%) Color-20-1001020
Observed means and
(very) likely rangesPaleo OHC(a)
Figure 9. 6 | Ocean heat content (OHC) and its changes with time. (a)  Time series of global OHC anomaly relative to a 2005–2014 climatology in the upper 2000 m 
of the ocean. Shown are observations (Ishii et al., 2017; Baggenstos et al., 2019; Shackleton et al., 2020), model-observation hybrids (Cheng et al., 2019; Zanna et al., 2019), 
and multi-model means from the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical (29 models) and Shared Socio-economic Pathway (SSP) scenarios (label

subscripts indicate number of models per SSP). (b–g)  Maps of OHC across different time periods, in different layers, and from different datasets/experiments. Maps show the 
CMIP6 ensemble bias and observed (Ishii et al., 2017) trends of OHC for (b, c)  0–700 m for the period 1971–2014, and (e, f)  0–2000 m for the period 2005–2017. CMIP6 
ensemble mean maps show projected rate of change 2015–2100 for (d) SSP5-8.5 and (g) SSP1-2.6 scenarios. Also shown are the projected change in 0–700 m OHC for 
(d) SSP1-2.6 and (g) SSP5-8.5 in the CMIP6 ensembles, for the period 2091–2100 versus 2005–2014. No overlay indicates regions with high model agreement, where ≥80% of 
models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter 
Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1230
Chapter 9 Ocean, Cryosphere and Sea Level Change9accounts for an increased error before 2005 in global OHC change. 
In summary, in the upper 2000 m since the 1970s, the subpolar 
North Atlantic has been slowly warming, and the Southern Ocean 
has stored a disproportionally large amount of anthropogenic heat 
Below 2000 m, direct observations of full-depth ocean temperature 
change are limited to ship-based, high-quality deep-ocean 
temperature measurements. Such high-quality full-depth ship-based 
sampling has improved from 1990 to the present due to the World 
Ocean Circulation Experiment (WOCE) and the Global Ocean 
Ship-based Hydrographic Investigations Program (GO-SHIP; Sloyan 
et al., 2019). The SROCC (Bindoff et al., 2019) assessed that the likely  
warming of the ocean since the 1990s below 2000 m is associated 
with a marked regional pattern, with larger warming in the Southern 
Ocean. In the deep North Atlantic, warming has reversed to cooling

over the past decade, possibly due to internal variability fed by North 
Atlantic Deep Water (Section  9.2.2.3). Over the past decade, the 
warming rate of Antarctic Bottom Water (AABW; Section 9.2.2.3) has 
been dependent on origin: slower from the Weddell Sea and faster 
from the Ross Sea and Adélie Land. One new study (Purkey et al., 
2019) strengthens confidence in AABW warming: below 4000  m 
a  monotonic, basin-wide, and multi-decadal temperature change 
is found in the southern Pacific basin, with larger warming rates 
near the bottom water formation sites than further downstream. 
New analysis of one model provides limited evidence  that the sparse observational record may underestimate the rate of deep-ocean 
warming from 1990 to 2010 by about 20% (Garry et  al., 2019) 
which is included in the assessed OHC error (Cross-Chapter Box 9.1). 
There is still low agreement  in deep-ocean changes from ocean data 
assimilation reanalyses (Palmer et  al., 2017) and low confidence

in such inferences. In summary, while observational coverage 
below 2000 m is sparser than in the upper 2000 m, there is high 
confidence  that deep-ocean warming below 2000 m has been larger 
in the Southern Ocean than in other ocean basins due to widespread 
AABW warming.
Different processes drive OHC patterns over a range of time scales. 
Recent literature has highlighted the role of ocean circulation 
variability in driving OHC patterns by decomposing the global pattern 
of OHC change into a  combination of added heat due to climate 
change taken up under fixed ocean circulation (‘added heat’), and 
redistribution of heat associated with changing ocean currents 
(‘redistributed heat’; Gregory et al., 2016; Bronselaer and Zanna, 2020; 
Couldrey et al., 2021). Redistributed heat alters regional patterns of 
heat storage and carbon storage (Cross-Chapter Box 5.3; Bronselaer 
and Zanna, 2020; Todd et al., 2020; Couldrey et al., 2021) but does

not affect the global OHC. There is medium confidence  that decadal 
variability of the ocean circulation strengthened the rate of ocean 
warming in the Southern Hemisphere compared to the Northern 
Hemisphere in the decade from 2005 (Rathore et al., 2020; L. Wang 
et al., 2021; Zika et al., 2021). More generally, since 2005, the OHC 
Figure  9.7 | Meridional-depth profiles of zonal-mean potential temperature in the ocean and its rate of change in the upper 2000 m of the Global, 
Pacific, Atlantic and Indian oceans.  Shown are (a, e, i, m)  observed temperature (Argo climatology 2005–2014), (b, f, j, n)  bias of the Coupled Model Intercomparison 
Project Phase 6 (CMIP6) ensemble over this period, and future changes under (c, g, k, o)  SSP1-2.6 and (d, h, l, p)  SSP5-8.5. No overlay indicates regions with high model 
agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of

1231
pattern observed is predominantly due to heat redistribution with 
regions of both warming and cooling (Figure 9.6; Zika et al., 2021); 
however, extending analysis back to 1972 shows the importance of 
added heat setting a large-scale warming pattern with mid-latitude 
maxima consistent with subduction of water masses, particularly in 
Southern Hemisphere Mode Waters (Section 9.2.2.3, and Figures 9.6 
and 9.8; Bronselaer and Zanna, 2020). The longer the analysis 
window, the more added heat dominates over redistributed heat. 
This  translates into more ocean area with statistically significant 
warming trends and less area with statistically significant cooling 
trends (Johnson and Lyman, 2020). The region where added heat 
is most compensated for by redistributed cooling is in the northern 
North Atlantic basin, where changes in the subpolar gyre circulation 
and Atlantic Meridional Overturning Circulation (AMOC) result in

cooling (Section 9.2.3.1; Williams et al., 2015; Piecuch et al., 2017; 
Zanna et al., 2019; Bronselaer and Zanna, 2020). In summary, and 
strengthening SROCC assessment, ocean warming is not globally 
uniform due to patterns of uptake predominantly along known water 
mass pathways, and due to changing ocean circulation redistributing 
heat within the ocean ( high confidence ).
While heat redistribution reflects changes in ocean circulation and is 
a useful concept to understand the underlying processes driving OHC 
patterns, change in ocean heat transport (OHT) arises due to changes 
in ocean circulation and ocean temperature and affects regional 
OHC  change. The AR5 did not assess change in OHT  and SROCC 
(Meredith et al., 2019) only assessed projected OHT increases into 
the Nordic Seas and the Arctic Ocean. New evidence of increasing 
northward OHT into the Arctic has been observed in recent decades (Muilwijk et al., 2018; Q. Wang et al., 2019; Tsubouchi et al., 2021),

similar to SROCC assessment, and consistent with observed increase 
in OHC in the ice-free Arctic ocean (Mayer et al., 2019). It is estimated 
that an increase of 0.021 PW of OHT occurred after 2001 into the 
Arctic, which is sufficient to account for the recent OHC change in the 
northern seas (Tsubouchi et al., 2021). However, these trends cannot 
yet be attributed to anthropogenic forcing due to potential internal 
variability (Muilwijk et al., 2018; Wang et al., 2019). New evidence 
strengthens the case that El Niño–Southern Oscillation (ENSO) and the 
Northern Annular Mode affect interannual OHT variability (Trenberth 
et  al., 2019) and shows that a  slowing AMOC reduces northward 
OHT in the Atlantic at 26.5°N (Section 9.2.3.1 and Figure 9.8; Bryden 
et al., 2020). Despite a decrease of AMOC northward heat (0.17 PW) 
and mass (2.5 Sverdrup (Sv); 1 Sv = 109 kg s–1) transport, OHT has 
increased toward the Arctic through increased upper northern North

Atlantic temperatures and stronger wind-driven gyres ( medium 
confidence ) (Section  9.2.3.4 and Figure  9.11; Singh et  al., 2017; 
Oldenburg et al., 2018). In summary, OHT has increased toward the 
Arctic in recent decades, which at least partially explains the recent 
OHC change in the Arctic ( medium confidence ).
Major volcanic eruptions have caused interannual to decadal cooling 
phases within the marked long-term increase in global OHC  – 
Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo 
in 1991 (Cross-Chapter Box 4.1; Church et al., 2005; Fasullo et al., 
2016; Stevenson et  al., 2016; Fasullo and Nerem, 2018). In  the 
first few years following an eruption, heat exchange with the 
subsurface ocean allows atmospheric cooling to be sequestered 
into the seasonal thermocline, therefore reducing the magnitude of

Figure  9.8 | Decomposition of simulated ocean heat content and northward ocean heat transport. (a, c, e)  Total ocean heat content (0–2000 m) warming rate as 
observed and simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) models over the historical period (1972–2011) and under the RCP8.5 future (2021–2060) 
versus the associated decomposed (b, d, f)  added heat contribution (neglecting changes in ocean circulation) to the total  (Bronselaer and Zanna, 2020). (g) Relationship 
between northward heat transport and Atlantic Meridional Overturning Circulation (AMOC) in HighResMIP models (1950–2050) and observations during the RAPID period 
(2004–2018). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1232
Chapter 9 Ocean, Cryosphere and Sea Level Change9the  peak atmospheric temperature anomaly (Gupta and Marshall, 
2018). However, while explosive volcanic eruptions only disturb 
the Earth’s radiative budget and surface fluxes for a  few years, 
the ocean preserves an anomaly in OHC in the upper 500 m (also 
affecting thermosteric sea level) many years after the eruption 
(Gupta and Marshall, 2018; Bilbao et al., 2019). The anomaly affects 
the atmosphere through air–sea heat fluxes with surface conditions 
returning to normal only after several decades (Gupta and Marshall, 
2018; Bilbao et al., 2019), or on centennial time scales in the case 
of repeated eruptions (G.H. Miller et al., 2012; Atwood et al., 2016; 
Gupta and Marshall, 2018). In summary, there is medium confidence  
that oceanic mechanisms buffer the atmospheric response to 
volcanic eruptions on annual time scales by storing volcanic cooling 
in the subsurface ocean, affecting OHC and thermosteric sea level on

decadal to centennial time scales.
CMIP5 and CMIP6 models simulate OHC changes that are consistent 
with the updated observational and improved estimates of OHC 
over the period 1960 to 2018 (Figures 9.6, 9.7 and 9.8), and they 
replicate the vertical partitioning of OHC change for the industrial 
era, although with a  tendency to underestimate OHC gain 
shallower than 2000 m and overestimate it deeper than 2000 m 
(Section 3.5.1.3). The AR5 (Flato et al., 2013) assessed that climate 
models transport heat downward more than the real ocean. Since 
AR5, studies have shown that increasing the horizontal resolution of 
ocean models tends to increase agreement of vertical heat transport 
with observations as the dependency on ad-hoc choices of eddy 
parametrizations is relaxed (Griffies et al., 2015; Chassignet et al., 
2020). The magnitude of the AMOC and Indonesian Throughflow 
affect future OHC change  – for example, through overestimated

modelled downward heat pumping (Kostov et al., 2014) – and there 
are indications of greater model consistency in these transports at 
higher resolution (Figure 9.10; Chassignet et al., 2020; L.C. Jackson 
et al., 2020). Climate models tend to reproduce the observed added 
heat, but redistributed heat is less well represented (Figure  9.8; 
Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey et al., 2021). 
Since redistributed heat dominates historical OHC change, historical 
simulations poorly reproduce regional patterns, but as future OHC 
change will become dominated by added heat, more skill in future 
modelled OHC patterns is expected (Bronselaer and Zanna, 2020). 
In summary, climate models have more skill in representing OHC 
change from added heat than from ocean circulation change ( high 
confidence ). Since added heat dominates over redistributed heat 
on a  centennial scale (especially under high-emissions scenarios)

confidence in future modelled OHC patterns at the end of the 
21st century is greater than at decadal scale.
The SROCC (Bindoff et al., 2019) assessed that the ocean will continue 
to take up heat in the coming decades for all plausible scenarios, 
and here this assessment is confirmed with very high confidence . 
The SROCC reported that, compared with the observed changes since 
the 1970s, the warming of the ocean by 2100 would very likely  double 
to quadruple for low-emissions scenarios (RCP2.6) and increase five 
to seven times for high-emissions scenarios (RCP8.5). The SROCC also 
concluded with high confidence  that the overall warming of the ocean 
would continue this century, even after radiative forcing and mean 
surface temperatures stabilize. The SROCC projected that OHC in the 0–2000 m layer will increase from 2017 to 2100 by 0.900 ± 0.345 YJ 
(1 YJ = 1024 Joules) under RCP2.6 and 2.150 ± 0.540  YJ under 
RCP8.5. Updating SROCC estimates with CMIP6 projections gives

heat content increases and 17–83% ranges in the 0–2000 m layer 
between 1995–2014 and 2081–2100 of 1.06 (0.80–1.31) YJ, 
1.35 (1.08–1.67) YJ, 1.62 (1.37–1.91) YJ, 1.89 (1.60–2.29) YJ under 
scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively 
(Figure 9.6 and Table 9.1). The two-layer model used here to calculate 
thermosteric sea level rise (9.SM.4) and tuned for AR6-assessed 
equilibrium climate sensitivity (ECS; Section  7.SM.2), provides 
consistent 17–83% ranges of 1.18 (0.99–1.42) YJ, 1.56 (1.33–1.86) YJ, 
1.90 (1.63–2.21) YJ, 2.23  (1.92–2.64) YJ under scenarios SSP1-2.6, 
SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively (Table  9.1). Based 
on CMIP6 models and the two-layer model, it is likely  that, between 
1995–2014 and 2081–2100, OHC will increase two to four times the 
amount of the 1971–2018 OHC increase under SSP1-2.6, and four 
to eight times that amount under SSP5-8.5. The CMIP6 models show 
that OHC dependence on scenarios begins only after about 2040

(Figure 9.6).
The OHC patterns projected by CMIP6 models (Figures 9.6 and 9.7) 
are similar to the CMIP5 projections assessed in SROCC (Bindoff et al., 
2019): faster warming in all water mass subduction regions 
(e.g., subtropical cells and mode waters); deeper penetration in the 
centre of subtropical gyres; slower northern North Atlantic warming 
due to slowing AMOC; and slower subpolar Southern Ocean 
warming  due upwelled pre-industrial water masses. Decreased 
aerosol forcing will allow Northern Hemisphere ocean warming 
to be faster and less dominated by Southern Hemisphere change 
(Shi  et  al., 2018; Irving et  al., 2019). Since SROCC, distinguishing 
between added and redistributed heat has aided in understanding 
projections (Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey 
et  al., 2021). The near-term decades will feature patterns strongly 
influenced by heat redistribution and internal variability (Rathore

et  al., 2020). Strengthening Southern Hemisphere westerlies are 
projected, except for stringent mitigation scenarios (Bracegirdle et al., 
2020), and will cause a northward and downward OHT. There is low 
agreement  in future Southern Ocean warming across model results 
due to uncertainties in the magnitude of westerly wind changes 
(Figure 9.4; Liu et al., 2018; He et al., 2019; Dias et al., 2020; Lyu et al., 
2020b) and the degree of eddy compensation of overturning across 
different parametrizations and resolutions (Section 9.2.3.2; Beal and 
Elipot, 2016; Mak et al., 2017; Roberts et al., 2020). By 2100, however, 
the OHC change will be dominated by the added heat response, 
particularly for strong warming scenarios (Garuba and Klinger, 2018; 
Bronselaer and Zanna, 2020) with added heat following unperturbed 
water mass pathways in the North Atlantic and Southern Ocean 
(Figure  9.8; Dias et  al., 2020; Couldrey et  al., 2021). There is high

confidence  that projected weakening of the AMOC (Section 9.2.3.1) 
will cause a decrease in northward OHT in the Northern Hemisphere 
mid-latitudes (Figure 9.8 and Sections 9.2.3.1 and 4.3.2.3; Weijer et al., 
2020) associated with a dipole pattern of Atlantic OHC redistributed 
from northern to low latitudes that may override added heating in 
the northern North Atlantic (Figures 9.6, 9.7 and 9.8). Variations 
in  the degree of AMOC redistributed heat (Menary  and Wood, 
2018) causes large intermodel spread in SST (Figure  9.3) and  OHC 
change (Figure 9.6; Kostov et al., 2014; Bronselaer and Zanna, 2020;

1233
Ocean, Cryosphere and Sea Level Change  Chapter 99Todd et al., 2020; Couldrey et al., 2021). In the 700–2000 m depth 
range, CMIP5 and CMIP6 models project the largest warming to be 
in the North Atlantic Deep Water and Antarctic Intermediate Water 
(Figure 9.7) while below 2000 m, the North Atlantic cools in many 
models, and Antarctic Bottom Waters warm (Sallée et  al., 2013b; 
Heuzé et al., 2015). In summary, on decadal time scales, redistribution 
will dominate regional patterns of OHC change without affecting the 
globally integrated OHC; however, by 2100, particularly under strong 
warming scenarios, there is high confidence  that regional patterns 
of OHC change will be dominated by added heat entering the sea 
surface, primarily in water mass formation regions in the subtropics; 
and reduced aerosols will increase the relative rate of Northern 
Hemisphere heat uptake ( medium confidence ).
The SROCC assessed that the warming of the deep ocean is slow

to manifest, with multi-century or longer response times, so global 
OHC (and global mean thermosteric sea level) will continue to 
rise for centuries (Figures 9.9 and 9.30). New studies show that 
this continuation persists, even after cessation of greenhouse gas 
emissions (Ehlert and Zickfeld, 2018). Ocean warming will continue, 
even after emissions reach zero because of slow ocean circulation 
(Larson et al., 2020). OHC will increase until at least 2300, even for 
low-emissions scenarios, but with a scenario-dependent rate (Nauels 
et  al., 2017; Palmer et  al., 2018) and depends on cumulative CO 2 
emissions, as well as the time profile of emissions (Bouttes et  al., 
2013). Past long-term changes in total OHC illustrate adjustment 
relevant to expected future changes (Figure 9.9). Observational data from ice core rare gas elemental and isotopic ratios document a rise 
in global OHC relative to the Last Glacial Maximum of >17,000 ZJ

(change in mean ocean temperature >3.1°C; 1 ZJ = 1021 Joules) 
(Figure 9.9; Bereiter et al., 2018; Baggenstos et al., 2019; Shackleton 
et al., 2019, 2020). This temperature increase is significantly larger 
than the modelled OHC changes associated with collapse of AMOC 
alone, and tracks rising Southern Ocean SST (Uemura et al., 2018), 
strengthening of the deep abyssal overturning cell (Du et al., 2020) 
and increased North Atlantic water in the Southern Ocean (Wilson 
et  al., 2020). This underscores the importance of Antarctic abyssal 
ventilation on long-term oceanic heat budgets (Section  9.2.3.2). 
An  ensemble of four intermediate-complexity models project 
10,000-year future responses to CO 2 emissions (Clark et al., 2016) with 
SST change peaking around 2300 and a varying scenario-dependent 
magnitude approaching the scale of glacial-to-interglacial changes 
in paleodata (Figure 9.9). Long-term OHC commitments relative to

1850–1900 conditions are 2.6, 9.7, 15.2, 21.6, and 28.0 YJ (with 
mean ocean temperature change as much as 5.1°C) for emissions 
of 0, 1280, 2560, and 3840 and 5120 Gt after 2000 CE respectively, 
with OHC peaking near 4000 CE, reflecting whole-ocean warming 
lagging SST by thousands of years. The exact timing is uncertain, 
subject to rates of high-latitude meltwater input (Van Breedam et al., 
2020) and circulation time (Gebbie and Huybers, 2019). In summary, 
there is very high confidence  that there is a long-term commitment 
to increased OHC in response to anthropogenic CO 2 emissions, which 
is essentially irreversible on human time scales.
Long-term trends of ocean heat content and surface temperature
Observed and modelled historical data, and model projections under diﬀerent emissions scenarios
Model projections under different emissions
OHC and atmospheric surface temperature:
1280 Gt , 2560 Gt , 3840 Gt , 5120 Gt
1500 CE
1600
1700
1800
1900
2000
-0.06
-0.03
0
0.03
0.06
10
25

J
-6
-3
0
3
6
o
C
Modern Historical Data
OHC: 
Levitus (observed), 
HadCM3 (modelled)
SST (Southern Ocean): 
Uemera
Deglacial/Holocene Observations
OHC: 
Baggenstos
, 
Shackleton
Uemera, 
Proxies
-2
-1
0
1
2
3
OHC anomaly (10
25
 J = 10
4
 ZJ)
Last Interglacial Observations
OHC: 
Uemera, 
Proxies
Ocean Heat Content (OHC)
Surface Temperature
Figure 9.9 | Long-term trends of ocean heat content (OHC) and surface temperature. (a, b)  Ice-core rare gas estimates of past mean OHC (ZJ), scaled to global 
mean ocean temperature (°C), and to steric global mean sea level (GMSL) (m) per CCB-2 (red dashed line), compared to surface temperatures (black solid line, gold solid 
line; °C rightmost axis). Southern Ocean sea surface temperature (SST) from multiple proxies in 11 sediment cores and from ice core deuterium excess (Uemura et  al.,

2018). (a) Penultimate glacial interval to last interglacial, 150,000–100,000 yr B2K (before 2000) (Shackleton et al., 2020). (b) Last glacial interval to modern interglacial, 
40,000–0 yr B2K (Baggenstos et al., 2019; Shackleton et al., 2019). Changes in OHC (dashed lines) track changes in Southern Ocean SST (solid lines). (c) Long-term projected 
(2000 to 12000 CE) changes of OHC (dashed lines) in response to four greenhouse gas emissions scenarios (Clark et al., 2016) scale similarly to large-scale paleo changes but 
lag projected global mean SST (solid lines). (d) model simulated 1500–1999 OHC (Gregory et al., 2006) and 1955–2019 observations (Levitus et al., 2012) updated by NOAA 
NODC. All data expressed as anomalies relative to pre-industrial time. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1234
Chapter 9 Ocean, Cryosphere and Sea Level Change99.2.2.2 Ocean Salinity
The AR5 (Rhein et  al., 2013) assessed that it was very likely that 
subsurface salinity changes reflect surface salinity change, and 
that basin-scale regions of high salinity and evaporation had trended 
more saline, while regions of low salinity and more precipitation had 
trended fresher since the 1950s. The SROCC (Bindoff et  al., 2019) 
assessment was consistent with AR5. Section  2.3.3.2 strengthens 
evidence that subsurface salinity trends are connected to surface trends 
(very likely ), which are, in turn, linked to an intensifying hydrological 
cycle ( medium confidence ). Increasing evidence from updated 
observational records indicates that it is now virtually certain  that 
surface salinity contrasts are increasing. At basin scale, Section 2.3.3.2 
and AR5 concur that it is very likely  that the Pacific and Southern Ocean 
have freshened, and the Atlantic has become more saline. Figures 3.25

and 3.27 compare CMIP6 models to salinity observations.
Globally the mean salinity contrast at near-surface between high- and 
low-salinity regions increased 0.14 [0.07 to 0.20] from 1950 to 
2019 (Section  2.3.3.2). At regional scale, SROCC (Meredith et  al., 
2019) assessed an Arctic liquid freshwater trend of 600 ± 300 km3 yr –1 
(600 ± 200 Gt yr –1) between 1992 and 2012, reflecting changes 
associated with continental freshwater imports that affect ocean 
mass (land ice, rivers) as well as changes in sea ice volume. Since AR5, 
regional observation-based analyses not assessed in SROCC further 
confirm the long-term, large-scale and regional patterns of salinity 
change, both at the ocean surface and in the subsurface ocean, 
including almost 120 years of changes in the North Atlantic (Friedman 
et al., 2017) and 60 years of monitoring in the subpolar North Pacific 
(Cummins and Ross, 2020). These longer time series also provide

context to detect large multi-annual change from 2012 to 2016 in 
the subpolar North Atlantic, unprecedented over the centennial 
record (Holliday et al., 2020). In summary, there is high confidence  
that salinity trends have extended for more than 60 to 100 years in 
the regions with long historical observation records, such as the North 
Pacific and the North Atlantic basin.
While there is low confidence  in direct estimates of trends in 
surface freshwater fluxes (Sections 2.3.1.3.5, 8.3.1.1 and 9.2.1.2), 
as discussed in SROCC (Meredith et al., 2019), observational studies 
coupled with modelling studies suggest that surface flux changes 
drive many observed near-surface salinity changes, on top of 
changes specific to polar regions. Advances in salinity observations – 
for example, the Argo program (Riser et al., 2016); Soil Moisture and 
Ocean Salinity (SMOS), Aquarius and Soil Moisture Active Passive 
(SMAP; Supply et  al., 2018; Vinogradova et  al., 2019)  – combined

with process studies (SPURS-1/2; Lindstrom et  al., 2015; SPURS-2 
Planning Group 2015) and methodological and numerical advances, 
have increased understanding of how subsurface salinity anomalies 
link to surface fluxes, and thus increase confidence that near-surface 
and subsurface salinity pattern changes since the 1950s are linked 
to changing surface freshwater fluxes (Zika et al., 2018; Cheng et al., 
2020) with an additional contribution from changes in sea ice and 
land ice discharge at high latitudes (Haumann et  al., 2016; Purich 
et  al., 2018; Dukhovskoy et  al., 2019; Rye et  al., 2020). There is 
therefore medium confidence  in the processes linking surface fluxes 
to surface and subsurface salinity change.Ocean circulation changes also affect salinity, largely on annual 
to decadal time scales (Du et  al., 2019; Liu et  al., 2019; Holliday 
et al., 2020). For instance, in the subpolar North Atlantic, increasing

northward transport of Atlantic waters entering the subpolar gyre 
from the South have compensated the salinity decrease expected 
from increased Greenland meltwater flux since the early 1990s 
(Dukhovskoy et  al., 2016, 2019; Stendardo et  al., 2020). After 
the mid-2010s the trend reversed towards a  broad freshening, 
the  largest in 120 years, in the North Atlantic (Holliday et  al., 
2020). The long-term freshening in the Pacific Ocean has also been 
subject to decadal variability, such as a marked salinification since 
2005 associated with increased surface fluxes (G. Li et al., 2019). 
Local salinity anomalies forced by water cycle intensification can 
be weakened by rapid exchange between basins with opposing 
trends, such as by water mass exchange in shallow wind-driven cells 
between the tropics and the subtropics (Levang and Schmitt, 2020). 
Similarly, eddy exchanges between neighbouring gyres can partly 
counterbalance decadal time scale long-term subpolar freshening

and affect deep convection (Levang and Schmitt, 2020). There is high 
confidence  that, at annual to decadal time scales, regional salinity 
changes are driven by ocean circulation change superimposed on 
longer-term trends.
The CMIP5 historical simulations have patterns similar to, but 
with greater spatial variability than, observed estimates and 
correspondingly smaller amplitudes in the multi-model mean (Durack, 
2015; Cheng et al., 2020; Silvy et al., 2020). Section 3.5.2.1  reports, 
however, that the fidelity of ocean salinity simulation has improved 
in CMIP6, and near-surface and subsurface biases have been reduced 
(medium confidence ), though the structure of the biases strongly 
reflects those of CMIP5. At regional scale, salinity biases are at least 
partially a result of inaccurate ocean dynamics (Levang and Schmitt, 
2020). Despite the regional limitations, Section 3.5.2.2  assesses that, 
at the global scale, it is extremely likely  that human influence has

contributed to observed surface and subsurface salinity changes 
since the mid-20th  century (strengthened from the very likely 
AR5 assessment).
The SROCC (Bindoff et al., 2019) assessed that projected salinity 
changes in the subsurface ocean reflect changes in the rates of 
formation of water masses or their newly formed properties. 
Additional consistent newer evidence based on CMIP5 and regional 
climate models confirms that 21st  century projections adhere 
to the ‘fresh gets fresher, salty gets saltier’ paradigm, through 
subduction of freshening high-latitude waters into the ventilated 
water masses in both hemispheres in the Pacific, Indian and 
Southern Ocean – especially the Arctic and upper Southern Ocean, 
and saltier subtropical and Mediterranean surface waters – lead to 
saltier pycnoclines and North Atlantic mode water (Metzner et al., 
2020; Parras-Berrocal et al., 2020; Silvy et al., 2020; Soto-Navarro 
et  al., 2020). Overall, projections confirm SROCC assessment

that fresh ocean regions will continue to get fresher and salty 
ocean regions will continue to get saltier in the 21st  century 

1235
Ocean, Cryosphere and Sea Level Change  Chapter 999.2.2.3 Water Masses
Water masses refer to connected bodies of ocean water, formed at the 
ocean surface with identifiable properties (temperature, salinity, density, 
chemical tracers) resulting from the unique formation conditions of the 
overlying atmosphere and/or ice, before being transferred (subducted) 
to the deeper ocean below the surface  turbulent layer. As water 
masses subduct, they ventilate the subsurface ocean, transferring 
characteristics acquired at the ocean surface to the subsurface. 
By  integrating surface flux changes, water masses provide higher 
signal-to-noise ratios for detecting and monitoring climate change 
than surface fluxes (Bindoff and McDougall, 2000; Durack and Wijffels, 
2010; Silvy et al., 2020).
Subtropical mode waters (STMW) ventilate the main thermocline of 
the ocean at mid- to low-latitudes and have circulation time scales

away from the surface of the order of years to decades. The SROCC 
(Bindoff et al., 2019) reported that warming in the subtropical gyres 
penetrates deeper than in other gyres, following the density surfaces 
in these gyres. Consistently, we assess that STMW have deepened 
worldwide, with greatest deepening in the Southern Hemisphere 
(high confidence ) (Häkkinen et al., 2016; Desbruyères et al., 2017). 
Subsurface warming in the Northern Hemisphere STMW is larger 
than at the surface (Sugimoto et al., 2017) because they are formed in 
winter western boundary current extensions, where surface warming 
is larger than the global average (Section  9.2.1.1). Variability 
in STMW thickness or temperature has a  large imprint on OHC 
(Section 9.2.2.1; Kolodziejczyk et al., 2019). STMW are observed to be 
freshening in the North Pacific and associated with increased salinity 
in the North Atlantic (Oka et al., 2017; Silvy et al., 2020), with large

decadal variability (Oka et al., 2019; Wu et al., 2020). Anthropogenic 
temperature and salinity changes in the STMW layer are projected 
to intensify in the future, with emergence from natural variability 
around 2020 to 2040 (Silvy et al., 2020).
Subantarctic mode water (SAMW) and Antarctic intermediate water 
(AAIW) form at the Southern Ocean surface directly north of the 
Antarctic Circumpolar Current and ventilate the upper 1000 m of 
the Southern Hemisphere subtropics. The SROCC (Meredith et  al., 
2019) reported a freshening of these water masses between 1950 
and 2018, and they are projected to have the largest subsurface 
temperature increase of the Southern Hemisphere oceans, along with 
a continued freshening, in the 21st century. The SROCC connected 
SAMW and AAIW to Southern Ocean temperature changes as the 
large Southern Ocean surface heat uptake is circulated and mixed 
along with these water masses ( high confidence ). Close to its

formation region, SAMW is predominantly affected by air–sea flux 
changes, while further northward it is influenced by wind-forced 
changes (Meredith et al., 2019). New evidence shows that a change 
in SAMW heat content over the last decade is primarily attributable 
to its thickening (Kolodziejczyk et al., 2019). Over the past decade, the 
SAMW and AAIW volumes have changed by thickening of the lighter 
and thinning of the denser parts of SAMW and AAIW, leading to 
lightening of these ventilated ocean layers overall (Hong et al., 2020; 
Portela et al., 2020). Over the last decade, there is limited evidence  
of increased subduction of SAMW due to deepening mixed layers 
in the SAMW formation region (Section  9.2.1.3; Qu et  al.,  2020). Climate models from CMIP3 to CMIP5 generally simulated shallower 
and lighter SAMW and AAIW than is observed (Flato et al., 2013). 
New analysis of CMIP5 models suggests that the freshening of

these water masses is one of the most prominent projected salinity 
changes in the world ocean, and that this freshening emerged from 
internal variability as early as the 1980s to 1990s (Silvy et al., 2020).
Trends in North Atlantic Deep Water (NADW) are obscured by decadal 
variability (Rhein et al., 2013; Bindoff et al., 2019). The AR5 (Rhein 
et al., 2013) assessed that it is very likely that the temperature, salinity, 
and formation rate of the Upper NADW (formed by deep convection 
in the Labrador and Irminger Seas) is dominated by strong decadal 
variability related to the North Atlantic Oscillation (NAO) and it is 
likely that Lower NADW (formed in the Nordic Seas and supplied to 
the North Atlantic by deep overflows over the sills between Scotland 
and Greenland) cooled from 1955 to 2005. New insights from 
observations have emphasized the stability of the deep overflows 
associated with Lower NADW (Hansen et al., 2016; Jochumsen et al.,

2017; Østerhus et al., 2019) and even slight warming in the Faroe 
Bank Channel (Hansen et al., 2016). As a result, the AR5 assessment 
that Lower NADW likely  cooled between 1955 and 2005 is revised 
to: it is likely that any observed changes in temperature, salinity, 
and formation rate of the Lower NADW are dominated by decadal 
variability. For CMIP5 models, it was shown that AMOC variability is 
linked to variability in NADW formation (Heuzé, 2017) and projected 
AMOC decline to decreased NADW formation (both Lower NADW 
and Upper NADW; Heuzé et al., 2015). For CMIP6 models, projected 
AMOC decline is also associated with a decline in NADW formation 
(Reintges et al., 2017; Weijer et al., 2020). The link between AMOC 
and NADW formation appears insensitive to the large range in 
model bias in NADW water mass characteristics (Heuzé, 2017). Many 
models may overestimate deep water formation in the Labrador Sea, 
but at least one new model is consistent with recent Overturning in

the Subpolar North Atlantic Program (OSNAP) observations showing 
very weak overturning in the western subpolar gyre, where Labrador 
Sea water is formed (Menary et al., 2020a). The CMIP6 models show 
a reduced bias in NADW properties compared to CMIP5 models, but 
still feature varying locations of deep convection in the subpolar 
gyre: some convect only in the Labrador Sea (6/35 models), most in 
both the Labrador and Irminger Seas (26/35 models; as is observed), 
and some only in the Irminger Sea (3/35 models), but in general, the 
area where deep convection takes place has expanded relative to 
CMIP5, which appears unrealistic (Heuzé, 2021). Models with most 
deep convection in the subpolar gyre feature the smallest bias in 
NADW characteristics, partly associated with NADW formed in the 
Nordic Seas (as observed) being largely unable to leave the area 
(Heuzé, 2021) due to inaccurate overflows (Danabasoglu et al., 2010;

Deshayes et al., 2014; Wang et al., 2015). Despite the wide range in 
model bias, it remains very likely  that any long-term (multi-decadal 
or longer) decrease in AMOC is accompanied by a decline in NADW 
formation, associated with lighter densities in the northern North 
Atlantic and Arctic basins.
The SROCC (Meredith et al., 2019) assessed that the global volume 
of Antarctic Bottom Water (AABW) had decreased and warmed 
since the 1980s, most noticeably near Antarctica. The SROCC also 
noted freshening in the Indian and Pacific sectors of the Southern

1236
Chapter 9 Ocean, Cryosphere and Sea Level Change9Ocean and a higher rate of freshening in the Indian Sector from the 
2000s to 2010s than from the 1990s to 2000s ( low confidence ). 
Since SROCC, freshening of Indian Ocean AABW from 1974 to 2016 
has been revealed (Aoki et  al., 2020). Additionally, interannual to 
decadal variability in AABW has been quantified to be larger than 
previously thought in terms of temperature, salinity and thickness, 
and in volume transport (Abrahamsen et  al., 2019; Purkey et  al., 
2019; Gordon et  al., 2020; Silvano et  al., 2020). Multi-decadal to 
centennial modes of variability could have driven the observed trends 
of the lower cell over the past decades via the opening of a Weddell 
Sea Polynya (L. Zhang et  al., 2019), although other studies find it 
contributed minimally to the observed abyssal warming (Zanowski 
et al., 2015; Zanowski and Hallberg, 2017). Therefore, there is limited

evidence  and low agreement  in the role of open ocean polynyas in 
driving past decadal observed trends of AABW. Beyond variability, all 
observational, theoretical, and numerical evidence supports SROCC 
assessment that formation and export of AABW will continue to 
decrease due to warming and freshening of surface source waters 
near the Antarctic continent. Consistent with Section  9.2.3.2, 
confidence in this assessment is increased to medium confidence  
compared to SROCC.
Circumpolar Deep Water (CDW) lies in the Southern Ocean and forms 
by the mixing of NADW and AABW (Talley, 2013). The SROCC (Meredith 
et  al., 2019) assessed with low confidence  that mean southward 
and upward CDW transport is linked to decadal wind variability 
(Section 9.2.3.2), and that CDW has warmed south of the Antarctic 
Circumpolar Current (ACC) in the past decades. New  evidence 
reinforces SROCC assessment: changes in Southern Ocean wind stress

have been confirmed to drive variability and increase the large-scale 
southward CDW transport (Waugh et al., 2019). In addition, growing 
evidence suggests that the upper-ocean stratification increase in 
the subpolar Southern Ocean since the 1970s (Section 9.2.1.3) has 
reduced the volume of CDW that is mixed to the surface, causing 
subsurface CDW warming (Bronselaer et al., 2020; Haumann et al., 
2020; Jeong et al., 2020; Moorman et al., 2020). Large regions of the 
Antarctic shelves are currently isolated from warm CDW (Thompson 
et al., 2018; Jourdain et al., 2020). The SROCC (Meredith et al., 2019) 
assessed that subsurface warming extends close to Antarctica and has 
co-occurred with shoaling of the CDW since the 1980s, influencing 
the continental shelf most in the Amundsen-Bellingshausen Seas, 
Wilkes Land, and the Antarctic Peninsula. New evidence since SROCC 
reinforces confidence in the importance of the role of winds in

transporting heat associated with CDW to continental shelves and 
ice cavities in the Amundsen-Bellingshausen Seas (Dotto et al., 2019) 
and via variable small-scale undercurrents to the Shirase Glacier 
Tongue in East Antarctica (Hirano et al., 2020; Kusahara et al., 2021). 
There is limited evidence  that increased greenhouse gas forcing has 
caused a slight mean change of the local winds from 1920 to 2018, 
facilitating CDW heat intrusion onto the Amundsen-Bellingshausen 
continental shelf and ice shelf melt (Holland et al., 2019). Multiple 
lines of observational, numerical, theoretical, and paleo evidence 
provide high confidence  that changes in wind pattern (Spence et al., 
2014; Dotto et  al., 2019;  Holland et  al., 2019), increased ice-shelf 
melt (Golledge et al., 2019; Moorman et al., 2020), reduction in sea 
ice production (Timmermann and Hellmer, 2013; Obase et al., 2017),

and eddies (Stewart and Thompson, 2015; Thompson et al., 2018) can facilitate access of CDW to the sub-ice-shelf cavities (Section 9.4.2.1). 
However, there is low confidence  in the quantitification, importance 
and the ability of present models, especially at coarse resolution, to 
project changes in each of these processes (Section 9.4.2.2). Some 
studies have projected a possible shift from cold to warm sub-ice-shelf 
cavities causing a  sudden flush of warm water underneath ice 
shelves, but there is low confidence  in the driving processes and the 
threshold to trigger the shift (Box  9.4; Hellmer et  al., 2012, 2017; 
Silvano et al., 2018; Hazel and Stewart, 2020).
9.2.3 Regional Ocean Circulation
9.2.3.1 Atlantic Meridional Overturning Circulation
Atlantic Meridional Overturning Circulation (AMOC) is the main 
overturning current system in the South and North Atlantic 
oceans. It transports warm upper-ocean water northwards,

and cold, deep water southwards, as part of the global ocean 
circulation system (Section  2.3.3.4.1). Changes in AMOC influence 
global  ocean heat content (OHC) and transport (Section  9.2.2.1); 
global ocean  anthropogenic carbon uptake changes and climate 
sensitivity (Cross-Chapter Box  5.3); and dynamical sea level 
change (Section  9.2.4). Since AR5/SROCC, confidence in modelled 
and reconstructed AMOC has decreased due to new observations 
and model disagreement. Confidence levels have been revisited in 
modelled AMOC evolution during the 20th century, the magnitude of 
21st-century AMOC decline, and the possibility of an abrupt collapse 
before 2100.
The AR5 (Flato et  al., 2013) found that the mean AMOC strength 
in CMIP5 models ranges from 15 to 30 Sv for the historical period. 
The multi-model mean overturning at 26°N in CMIP5 and CMIP6 is 
comparable to the RAPID array measurements (Reintges et al., 2017), 
but the inter-model spread in CMIP6 is as large (10–31 Sv) as in

CMIP5 (Section 3.5.4; Weijer et al., 2020). Biases in simulations of the 
present-day AMOC and associated deep convection in the subpolar 
gyre and Nordic Seas were large in CMIP5 models, with many models 
exhibiting ocean convection that is too deep, over too large an area, 
too far south, and occurring too frequently (Section  9.2.1.3 and 
Figure 9.5; Heuzé, 2017) related to biases in sea ice extent, overflows, 
and freshwater forcing (Deshayes et al., 2014; H. Wang et al., 2015). 
As a result, the AMOC in CMIP5 was nearly always too shallow, with too 
weak a temperature contrast between the northward and southward 
flowing branches. Deep convection errors are still large in CMIP6, and 
the shallow bias in AMOC persists (Weijer et al., 2020; Heuzé, 2021). 
Since AR5, there is emerging evidence that enhancing horizontal 
resolution can reduce long-standing climate model biases in AMOC 
strength, where the magnitude and profile of northward heat transport

at 26°N become more comparable to observations (Chassignet et al., 
2020; Roberts et  al., 2020). The sensitivity of the AMOC to ocean 
resolution, however, is model-dependent and can be positive as well as 
negative (Roberts et al., 2020). An increase in AMOC strength at 26°N, 
with higher resolution in the ocean component, has been associated 
with too strong (deep) convection in the subpolar gyre and too deep 
winter mixed layers (L.C. Jackson et al., 2020), which occurs in most 
CMIP6 models that are unable to overflow deep water formed in the

1237
Ocean, Cryosphere and Sea Level Change  Chapter 99Nordic Seas across the Greenland–Iceland–Scotland Ridge. Models 
with a correct AMOC strength may do so by compensating a lack of 
deep-water outflow from the Nordic Seas through too much deep 
convection and deep-water formation in the Labrador and Irminger 
Seas (Heuzé, 2021).
Models and paleoreconstructions have often assumed a close relation 
between the AMOC and deep convection in the Labrador Sea; the 
Labrador Sea convection variability has been interpreted as connecting 
to AMOC variability. Observational studies have been inconclusive on 
whether this relation exists (Buckley and Marshall, 2016). New insight 
from observed overturning in the eastern and western subpolar 
gyre in the North Atlantic in OSNAP (Lozier et al., 2019; Petit et al., 
2020) reveals that 15.6 ± 3.1Sv takes place north of the OSNAP array 
between Greenland and Scotland, with only 2.1 ± 0.9 Sv of overturning

occurring across the Labrador Sea, as found with the OSNAP 53°N 
array spanning the mouth, calling into question the validity of the 
Labrador Sea convection–AMOC link (Lozier et al., 2019). Although 
these results are derived from only the first 21 months of data from 
monitoring since 2014, hydrographic observations during 1990–1997 
previously found small overturning (1–2 Sv) in the Labrador Sea 
(Pickart and Spall, 2007). However, previous estimates of Labrador 
Sea Water formation (obtained with different techniques) suggest 
larger overturning (Haine et al., 2008). Part of this controversy could 
be explained if a large fraction of newly formed Labrador Sea Water 
is not exported from the Labrador Sea. The OSNAP observations are 
supported by previous hydrographic measurements in showing strong 
east–west symmetry in isopycnal slope in the Labrador Sea in periods 
of both strong and weak convection; this implies compensating

northward and southward transport above and below the potential 
density surface that separates the upper and lower overturning 
limbs (Lozier et al., 2019), despite large deep convection variability 
(Yashayaev, 2007; Yashayaev and Loder, 2016). New observations 
of deep winter mixing in the Irminger Basin (de  Jong et  al., 2018; 
Josey et  al., 2019) support the assertion that the Irminger Sea, in 
addition to the Nordic Seas (Chafik and Rossby, 2019), are the main 
sources of overturning in the eastern subpolar gyre, consistent with 
OSNAP (Petit et al., 2020). It is unclear to what extent models are in 
disagreement with this view of overturning in the subpolar gyre, as 
a direct comparison with OSNAP of model analyses partitioning the 
overturning into a western and eastern part is mostly lacking, with 
a notable exception (Menary et al., 2020a). Other results give rise to 
considerable uncertainty over veracity of the models in simulating

the overturning partitioning between east and west and the role of 
various drivers of AMOC variability, including: the analysis of water 
mass formation in CMIP6 models (Heuzé, 2021); the analysis between 
Labrador Sea Water formation and AMOC in a  suite of ocean-only 
models (Danabasoglu et al., 2014); and the fact that when the OSNAP 
observing system design was tested in an eddy-permitting ocean 
model comparable amounts of overturning in the western and eastern 
subpolar gyre were found (Susan Lozier et al., 2017). Disagreement 
between models and OSNAP observations may decrease in higher-
resolution models (Menary et  al., 2020a). In summary, multiple 
lines of evidence provide medium agreement  between models and 
observations on drivers of change and variability in the AMOC and, in 
particular, the role of Labrador Sea deep convection in constituting 
AMOC variability.The AMOC is a potential driver of Atlantic Multi-decadal Variability

(AMV), but there is new evidence that anthropogenic aerosol 
changes have contributed to observed AMV changes, and that 
underestimation of the magnitude and duration of AMV changes 
in CMIP5 is tempered in CMIP6 (Section  3.7.7 and Annex IV.2.7). 
Comparison of observed AMOC variability at the RAPID section with 
modelled variability reveals that CMIP5 models appear to largely 
underestimate the interannual and decadal time scale variability 
(Roberts et al., 2014; Yan et al., 2018), and similar results are found 
when analysing CMIP6 models (Section 3.5.4.1). By underestimating 
the multi-decadal AMOC–AMV link and other low-frequency AMOC 
variability, climate models also underestimate internal variability 
in subpolar SSTs that feed back on the North Atlantic Oscillation 
(NAO). This causes the NAO to lack variability on multi-decadal time 
scales (Kim et al., 2018). Despite the role of the AMOC in generating 
AMV through subsurface temperatures in antiphase with SST and

downward heat fluxes into the ocean that anticorrelate with SSTs 
(R. Zhang et al., 2019), it is generally accepted that AMOC forcing 
of SST variability exists alongside stochastic wind forcing and 
external  forcing by aerosols (Bellomo et  al., 2018; Haustein et  al., 
2019; O’Reilly et al., 2019; Wills et al., 2019).
The SROCC (Collins et al., 2019) assessed that in situ observations 
(2004–2017) and sea surface temperature reconstructions indicate 
that AMOC has weakened relative to 1850–1900 ( medium 
confidence ). However, SROCC also assessed that there is insufficient 
data to quantify the magnitude of the weakening, or to properly 
attribute it to anthropogenic forcing, due to the limited length of 
the observational record. Here, this assessment is adjusted to low 
confidence in the weakening (as also discussed in Sections 2.3.3.4.1 
and 3.5.4.1). The CMIP5 multi-model mean showed no 20th century 
trend in AMOC (Cheng et al., 2013). The CMIP6 multi-model mean

slightly opposes the reconstructed decline due to a strong increase 
in the 1940–1985 period (Menary et al., 2020b; Weijer et al., 2020), 
thought to be in response to aerosol forcing (Section  3.5.4.1), 
followed by a  smaller decline since the 1990s. Also, agreement 
between different proxy-based reconstructions is weak in many 
details (Moffa-Sánchez et  al., 2019) and questions can be raised 
regarding various proxies used in reconstructions (Section 2.3.3.4.1). 
For instance, SST-based proxies can be influenced by atmospheric 
and other processes acting on different time scales (Moffa-Sánchez 
et  al., 2019; Jackson and Wood, 2020). In addition, many proxies 
are indirect and based on AMOC-related processes assumed to be 
similar to those found in models, such as the link between AMOC 
and Labrador Sea convection, which has been questioned recently 
(see above). In addition, the subpolar gyre from which many AMOC 
proxies are taken may vary independently of AMOC, with similar

patterns in SST and OHC driven by wind variability (Williams et al., 
2014; Piecuch et  al., 2017). Finally, a  new dynamic reconstruction 
of the Atlantic inflow to the Nordic Seas suggests no slowdown 
over the past 70 to 100 years (Rossby et al., 2020), in contrast to 
a  new compilation of proxy reconstructions which suggests that 
AMOC is presently in its weakest state in the last millennium (Caesar 
et  al., 2021), reinforcing the evidence that motivated the previous 
SROCC assessment. Section  3.5.4.1 also questions the veracity of 
the models’ forced AMOC response during the 20th century. Given 
the  large discrepancy between modelled and reconstructed AMOC

1238
Chapter 9 Ocean, Cryosphere and Sea Level Change9in the 20th  century, and the uncertainty over the realism of the 
20th  century modelled AMOC response (Section  3.5.4.1), we have 
low confidence  in both.
The strength of AMOC has been measured directly since 2004 using 
the RAPID Array (Section 2.3.3.4.1; Smeed et al., 2018). RAPID-based 
estimates show a  large amount of variability compared to CMIP 
models (Roberts et  al., 2014). Observed changes since 2004 are 
too short for the evaluation of a long-term trend given the decadal 
scale internal variability (Section  2.3.3.4.1). Nevertheless, Smeed 
et al. (2018) argue that, between 2007 and 2011, AMOC shifted to 
a state of reduced overturning – decreasing from 18.8 Sv between 
2004 and 2008 to 16.1 Sv after 2008. A shift in AMOC strength of 
this magnitude is not captured by CMIP5 and CMIP6 models, which 
generally underestimate interannual to decadal AMOC variability

(Section  3.5.4.1). Additional evidence since SROCC also raises the 
inconsistency between the RAPID weakening in the 3000–5000 m 
depth range and the relative constancy of deep overflows from the 
Arctic (Østerhus et al., 2019), implying that the recent decrease in 
AMOC at 26.5°N (Smeed et  al., 2018) is not caused by overflow 
weakening or reduced overturning in the Nordic Seas, although the 
weakening occurred almost exclusively in the 3000–5000 m depth 
range associated with a reduction of Lower NADW (Section 9.2.2.3). It is unclear what causes a weakening of the deepest limb of AMOC 
at 26.5°N, if the main sources for this flow farther north remain 
constant. Various estimates of AMOC and associated heat transport 
suggest an increase since the 1940s with a  subsequent decrease 
since the 1990s (Section 2.3.3.4.1), supported by ocean reanalysis 
(Jackson et al., 2019), forced ocean model simulations (Robson et al., 
2012; Danabasoglu et  al., 2016) and CMIP6 simulations (Menary

et al., 2020a). This suggests that the observed AMOC-shift between 
2007 and 2011 may be part of a  longer-term decrease ( medium 
confidence ), which has been attributed to be part of multiannual 
variability (Rhein et al., 2019).
The SROCC (Collins et  al., 2019) found that AMOC will very likely 
weaken over the 21st century. In CMIP6 projections, the modelled 
decline starting in the 1990s continues in all future projections, almost 
independent of the forcing scenario until about 2060, after which 
low-emissions scenarios show stabilization, while high-emissions 
scenarios continue to exhibit AMOC decline (Figure  9.10; Menary 
et al., 2020b; Weijer et al., 2020). Despite differences in overall AMOC 
strength, location and latitude of deep convection, sea ice and SST 
bias and representation of deep overflows, the model projections are 
qualitatively similar. This agreement suggests that AMOC decline may 
be governed by large-scale constraints independent of the details of

Figure 9 .10 | Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing.  (Top left)  AMOC 
magnitude (units: Sverdrup (Sv) = 109 kg s–1) in Paleoclimate Modelling Intercomparison Project (PMIP) experiments. (Top right)  Time series of AMOC from Coupled Model 
Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) based on (Menary et al., 2020b). (Bottom left) Percent change in AMOC strength per year at different resolutions 
over the 1950–2050 period with colours for model families (Roberts et al., 2020). (Bottom right)  A compilation of percentage changes in the simulated AMOC after applying 
an additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson,

2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019). Symbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards 
arrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. Further details on data sources and processing are available 
in the chapter data table (Table 9.SM.9).

1239
Ocean, Cryosphere and Sea Level Change  Chapter 99the models. In theoretical models of the thermohaline circulation, the 
circulation strength is proportional to a density or pressure difference 
between the subpolar North Atlantic and subtropical South Atlantic 
(Kuhlbrodt et al., 2007; Weijer et al., 2019). In all models, the north-
south pressure gradient decreases in the 21st century, as subpolar 
waters warm faster than subtropical waters, and an enhanced 
hydrological cycle drives freshening at subpolar latitudes, while 
subtropical latitudes feature more evaporation and salinification 
(Section  9.2.1). As a  result, surface waters at subpolar latitudes 
become more buoyant and more stable, so that deep water formation 
driving the AMOC declines (Section 9.2.1.3). Projected AMOC decline 
by 2100 ranges from 24 [4 to 46] % in SSP1-2.6 to 39 [17–55] % 
in SSP5-8.5 ( medium  confidence ) (Section  4.3.2.3). Note that

these ranges are based on ensemble means of individual models, 
largely smoothing out internal variability. If single realizations are 
considered, the ranges become wider, especially by lowering the low 
end of the range (Section 4.3.2.3). In summary, it is very likely  that 
AMOC will decline in the 21st century, but there is low confidence  in 
the model’s projected timing and magnitude. In addition, freshwater 
from the melting of the Greenland Ice Sheet (Sections 9.4.1.3 and 
9.4.1.4) could further enhance the future weakening of AMOC in the 
21st century (Collins et al., 2019; Golledge et al., 2019).
Both AR5  (Collins et  al., 2013)  and SROCC (Collins et  al., 2019) 
assessed that an abrupt collapse of AMOC before 2100 was very 
unlikely , but SROCC added that, by 2300, an AMOC collapse was 
as likely as not  for high-emissions scenarios. The SROCC also 
assessed that model bias may considerably affect the sensitivity of

the modelled AMOC to freshwater forcing. Tuning towards stability 
and model biases (Valdes, 2011; Liu et  al., 2017; Mecking et  al., 
2017; Weijer et al., 2019) provides CMIP models a tendency toward 
unrealistic stability ( medium confidence ). By correcting for existing 
salinity biases, Liu et al. (2017) demonstrated that AMOC behaviour 
may change dramatically on centennial to millennial time scales, 
and that the probability of a collapsed state increases. None of the 
CMIP6 models features an abrupt AMOC collapse in the 21st century, 
but they neglect meltwater release from the Greenland Ice Sheet. 
Also, a  recent process study reveals that a  collapse of AMOC 
can be induced, even by small-amplitude changes in freshwater 
forcing (Lohmann and Ditlevsen, 2021). As a result, we change the 
assessment of an abrupt collapse before 2100 to medium confidence 
that it will not occur .
9.2.3.2 Southern Ocean
The changing Southern Ocean circulation system exerts a  strong

influence on the global climate by modulating: (i) global OHC 
(Section  9.2.2.1); (ii) global ocean anthropogenic carbon uptake 
(Cross-chapter Box  5.3); global ocean overturning circulation 
(Section  9.2.3.1); (iii) climate sensitivity (Section  7.4.4 and 
Cross-chapter Box 5.3); (iv) sea level through basal melt of ice shelves 
(9.4.2); and (v) Southern Hemisphere sea ice cover (Section 9.3.2).
The SROCC (Meredith et  al., 2019) had low  confidence  in all 
CMIP5-based model projections due to their inability to explicitly 
resolve eddy processes, and their inability to properly consider future 
meltwater change from the Antarctic Ice Sheet. These limitations of climate models to represent the Southern Ocean persist due to most 
CMIP6 models still using parameterized mesoscale eddy processes, 
which are limited in projecting the future response of the horizontal 
and vertical circulation under climate warming, and also because

of the continued absence of active ice-shelf and ice-sheet coupling 
in the CMIP6 model suite, therefore ignoring basal meltwater 
and calving feedback on the circulation (Meredith et al., 2019). In 
addition, two important limitations of CMIP6 models of the Southern 
Ocean involve processes that were not assessed in SROCC. First, 
the poor representation of dense overflows causes most of the 
Antarctic Bottom Water (AABW) to be formed by spurious open 
ocean convection rather than by dense overflows from the Antarctic 
continental shelves that feed the lower overturning cell (Snow et al., 
2015; Dufour et al., 2017; Heuzé, 2021). Second, Antarctic continental 
shelf waters are poorly simulated because potentially important 
controlling mechanisms tend to be too small and transient to 
observe and resolve in CMIP ocean models. These small processes 
include: the heterogeneity of observed sub-ice-shelf melt with 
warm water driving narrow basal channels that cut underneath

the ice (Drews, 2015; Alley et al., 2016; Marsh et al., 2016; Milillo 
et al., 2019); eddies and tides (Stewart et al., 2018; Jourdain et al., 
2019; Hausmann et  al., 2020), which can drive Circumpolar Deep 
Water (CDW) onto the continental shelves or dynamically increase 
melting (Section  9.2.3.6); and feedback mechanisms between 
ocean, atmosphere and cryosphere that can weaken or amplify 
initial perturbations (Donat-Magnin et al., 2017; Spence et al., 2017; 
Turner et al., 2017; Silvano et al., 2018; Webber et al., 2019; Hazel 
and Stewart, 2020). In addition, the Southern Ocean in CMIP5 and 
CMIP6 models exhibit surface temperature biases (Section 9.2.1.1), 
which have been linked in CMIP5 models to errors in atmospheric 
model cloud-related shortwave radiation (Hyder et  al., 2018) and 
are somewhat improved in High Resolution Model Intercomparison 
Project (HighResMIP) models (Figure 9.3). In summary, there is high

confidence  that future change in the subpolar Southern Ocean region, 
including sea ice cover and ocean temperature change on Antarctic 
continental shelves, depends on feedback mechanisms involving the 
ocean, atmosphere and cryosphere that are poorly understood and 
not represented in the current generation of climate models. This 
results in large uncertainty and low confidence  in the future sea 
ice cover (Section 9.3.2) and in ocean temperature change on the 
Antarctic continental shelf (Section 9.4.2.3).
Despite these challenges, the CMIP6 ensemble does represent the 
main Southern Ocean circulation characteristics: the simulated 
Antarctic Circumpolar Current (ACC) transport is generally lower than 
observation-based values but consistent when considering ensemble 
spread, and the inter-model spread in ACC transport has greatly 
reduced from previous generations of climate models from CMIP3 
to CMIP6 (Beadling et  al., 2019, 2020). The structure (but not the

magnitude) of the two-cell zonally averaged overturning is captured 
by most CMIP6 models (Russell et al., 2018; Beadling et al., 2019). 
In addition, while issues remain, CMIP6 climate models show clear 
improvements in their representation of AABW compared to CMIP5: 
several models correctly represent or parameterize Antarctic shelf 
processes, fewer models exhibit Southern Ocean deep convection, 
bottom density biases are reduced, and abyssal overturning is more 
realistic (Heuzé, 2021). In terms of atmospheric wind forcing, CMIP6

1240
Chapter 9 Ocean, Cryosphere and Sea Level Change9models show an improvement compared to CMIP5 models, with 
an overall reduction in the equatorward bias of the annual mean 
westerly jet from 1.9° in CMIP5 to 0.4° in CMIP6, but in contrast, 
they show no such overall improvements for their representation of 
the Amundsen Sea Low (Bracegirdle et al., 2020; Lyu et al., 2020a), 
which can be critical in driving variability of water masses on the 
Antarctic continental shelf in west Antarctica, the Weddell Sea or 
the Ross Sea (Holland et al., 2019; Silvano et al., 2020).
The SROCC (Meredith et  al., 2019) established that, while trends in 
the atmospheric forcing of the Southern Ocean have been dominated 
by a  strengthening of the Southern Hemisphere westerly winds in 
recent decades, there is medium confidence  that ACC transport is 
weakly sensitive to changes in winds. It also reported that, instead of 
increasing the mean ACC transport, additional energy input associated

with increased wind stress cascades into the eddy field ( medium 
confidence ). In contrast with the AR5 assessment (Rhein et al., 2013), 
SROCC evaluated that it was unlikely  that there has been a  net 
southward migration of the mean ACC position over the past 20 years. 
There is no additional evidence to revisit SROCC assessment on wind 
sensitivity. However, new evidence does suggest that air–sea buoyancy 
forcing associated with idealized 4×CO 2 forcing leads to an increase in 
ACC transport ( limited evidence ) (Shi et al., 2020). The SROCC noted 
that, if the general strengthening in westerly winds is sustained, then 
it is very likely  that the eddy field will continue to increase in intensity, 
and it is likely  that  the mean position and strength of the ACC will 
remain only weakly sensitive to winds. In the future, the strength of 
the Southern Hemisphere westerly wind jet results from a competition 
between decrease due to ozone hole recovery and increase due to

increased radiative forcing (Section 4.3.3.1). This competition results 
in an increased atmospheric jet by 2100 compared to present day 
under SSP2-4.5, SSP3-7.0, and SSP5-8.5, but a decreased jet by 2100 
under SSP1-2.6 (Bracegirdle et  al., 2020). There is little inter-model 
spread in the CMIP6 future response of the atmospheric westerly 
jet, providing high confidence  in this assessment (in contrast, CMIP6 
models show no consistency in their future projection of easterly wind 
change along the Antarctic continental shelf break; Bracegirdle et al., 
2020). Paleo-oceanographic evidence suggests that ACC flow through 
Drake Passage was consistently stronger during warm intervals of the 
past (both during interstadials and interglacials), but with relatively 
little change and no consensus on the sign of change in other regions 
(Lamy et al., 2015; Toyos et al., 2020). In summary, additional evidence 
since SROCC confirms that there is medium confidence  that the ACC

has been weakly sensitive to Southern Hemisphere atmospheric jet 
increase in the past decades. New evidence since SROCC suggests that 
there is high confidence  that the Southern Hemisphere atmospheric jet 
will increase in the 21st century for all scenarios (except for SSP1-1.9 
and SSP1-2.6; Section  4.3.3.1) with a  greater increase for larger 
radiative forcing. An increase in westerly winds will very likely  force 
an increase of the eddy field in the ACC, and while there is medium 
confidence  that the ACC is weakly sensitive to wind change, new 
advances since SROCC provide limited evidence  that the ACC transport 
will nevertheless increase in response to wind and buoyancy fluxes.
For the upper cell overturning circulation, SROCC concluded that: 
its transport has experienced significant inter-decadal variability in 
response to wind forcing since the 1990s; and there is low confidence  in the assessments of a long-term increase in upper-ocean overturning.

Consistent with SROCC, the importance of eddy processes and winds 
in driving long-term change and variability have been reinforced, with 
a potential fast wind response partially counteracted by a slower eddy 
response (Doddridge et al., 2019; Waugh et al., 2019; Stewart et al., 
2020). Eddy parametrizations affect the strength of overturning, its 
sensitivity to winds and the ACC transport (Mak et al., 2017). Even 
in eddy-resolving simulations, sub-gridscale dissipation affects the 
overturning and ACC (Pearson et  al., 2017). In addition, there has 
been progress in understanding the importance of Antarctic Ice Shelf 
meltwater and sea ice, in driving the observed changes in the near 
surface and in the upper overturning cell over the past decades, on 
top of changes induced by winds and eddies (Bronselaer et al., 2020; 
Haumann et al., 2020; Jeong et al., 2020; Rye et al., 2020). In particular, 
increased stratification caused by increased freshwater flux to the

surface ocean (Section 9.2.1.3) can cause a shoaling and warming of 
the CDW layer, and create a positive feedback, enhancing basal melt 
of the Antarctic Ice Sheet (Section 9.4.2.1; Bronselaer et al., 2018; 
Golledge et al., 2019; Schloesser et al., 2019; Sadai et al., 2020). There 
is medium confidence  in the existence of this feedback mechanism 
but low agreement  on the magnitude of the feedback. The SROCC 
reported that CMIP5 models project that the  overall transport 
of upper-ocean overturning cell will increase by up to 20% in the 
21st century, and no new studies alter that assessment.
For the lower cell overturning circulation, SROCC assessed that 
a slowdown of its transport is consistent with the observed decrease 
in volume ( medium confidence ) of AABW in the global ocean 
(Section  9.2.2.3). Additional evidence since SROCC strengthens 
confidence that increased glacial meltwater flux will reduce the 
density of bottom waters during the 21st century. It will eventually

reach a  point where deep convection will be curtailed, and shelf 
water will become too buoyant to sink to the ocean interior, thereby 
slowing the lower cell overturning circulation (Bronselaer et al., 2018; 
Golledge et al., 2019; Lago and England, 2019; Moorman et al., 2020). 
While such changes are consistent with the observed freshening 
and decreased volume of the AABW layer reported in SROCC 
(as discussed in Section 9.2.2.3), new observation-based studies have 
highlighted how the lower cell overturning can episodically increase 
as a  response to climate anomalies, temporally counteracting the 
tendency for melt to reduce AABW formation (Abrahamsen et  al., 
2019; Castagno et al., 2019; Gordon et al., 2020; Silvano et al., 2020). 
In addition, while the opening of open ocean polynyas can affect 
the lower cell on decadal to centennial time scales, there is limited 
evidence  and low agreement  in the role of open ocean polynyas

in driving observed trends of the lower cell in the last decade 
(Section 9.2.2.3). Based on CMIP5 models, SROCC reported with low 
confidence  that formation and export of AABW associated with the 
lower overturning cell will decrease in the 21st century, and there 
is no new evidence to revisit that assessment from climate models. 
However, additional paleo evidence from marine sediments suggests 
that AABW formation/ventilation was vulnerable to freshwater 
fluxes during past interglacials (Hayes et  al., 2014; Huang et  al., 
2020; Turney et  al., 2020) and that AABW formation was strongly 
reduced (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard et al., 
2016) or possibly totally curtailed (Huang et  al., 2020) during the 
Last Glacial Maximum (LGM) and transient cold intervals of marine

1241
Ocean, Cryosphere and Sea Level Change  Chapter 99isotope stages 2 and 3 (MIS2 and MIS3). Specifically, sedimentary 
reconstructions show a  transient reduction in AABW ventilation 
in the Atlantic sector of the Southern Ocean during MIS5e, which 
is assessed to have been warmer than modern climate (Thomas 
et  al., 2020). However, long multi-centennial or millennial model 
runs under higher-than-pre-industrial CO 2 concentrations show that, 
after 500–1000  years, ventilation in the Southern Ocean resumes, 
and possibly overshoots with enhanced convection in the Weddell 
and Ross seas, leading to enhanced bottom water ventilation globally 
(Yamamoto et  al., 2015; Frölicher et  al., 2020). AABW ventilation 
increased at the onset of the last deglacial transition, promoting 
the release of previously sequestered CO 2 to the atmosphere on 
centennial to millennial time scales (Bauska et al., 2016; Jaccard et al., 
2016; Rae et al., 2018), concomitant with a southward shift of the

Southern Hemisphere westerly wind belt (Denton et al., 2010; Jaccard 
et al., 2016) and reduced sea ice cover (Ferrari et al., 2014; Stein et al., 
2020). In summary, the combination of observational, numerical and 
paleoclimate evidence provides us with medium confidence  that the 
lower cell will continue decreasing in the 21st century as a result of 
increased basal melt from the Antarctic Ice Sheet.
9.2.3.3 Tropical Oceans
The tropics are a  tightly coupled ocean-atmosphere system with 
tightly interconnected basins (Cai et al., 2019). The zonal atmospheric 
Walker Circulation and the Indonesian Throughflow (Figure 9.11) are 
key connections between the Pacific and Indian oceans, and variations 
in the Walker and Hadley Circulations are tightly linked to the tropical 
Pacific SST and currents. The tropics have a profound influence on the 
climate system through the multiple modes of variability they host, 
which have widespread global influence at seasonal to annual time

scale (Annex IV).
The effect of tropical modes of variability on climate and their 
long-term changes are reviewed in detail in Annex IV, while changes 
to the tropical ocean are assessed throughout the report and 
briefly summarized here. Section  2.4 concludes that a  sustained 
shift beyond multi-centennial variability has not been observed 
for El  Niño–Southern Oscillation (ENSO) ( medium confidence ) and 
that there is limited evidence  and limited agreement  about the 
long-term behaviour of other tropical modes. Section  3.7 assesses 
with high confidence  that human influence has not affected the 
principal tropical modes of interannual climate variability and their 
associated regional teleconnections beyond the range of internal 
variability. Section  4.3.3.2 assesses with medium confidence  
that there is no consensus from models for a  systematic change 
in the amplitude of ENSO sea surface temperature variability over

the 21st century. The related change in tropical SSTs is covered in 
Section 9.2.1.1. The projected changes in SST have implications for 
marine heat wave characteristics, which are assessed in Box  9.2. 
SST changes in the tropics are related to changes in the atmospheric 
circulation, including surface equatorial easterly trade winds and 
Walker Circulation (Section 4.5.3.2), and the weakening Indonesian 
Throughflow and strengthening Agulhas Extension and leakage 
(Section  9.2.3.4). Weakening trade winds under climate change 
(Vecchi and Soden, 2007) will tend to decrease upwelling, along 
isopycnals in the eastern Pacific and diapycnal upwelling in  the central Pacific, and thus the meridional temperature gradients that 
drive tropical instability waves (Terada et  al., 2020), along with 
a weakening, flattening and shoaling of the tropical thermocline and 
equatorial undercurrent (Luo and Rothstein, 2011). A weak or absent

equatorial undercurrent (Kuntz and Schrag, 2020) and a too-diffuse 
and incorrectly sloped tropical thermocline (Zhu et al., 2020) remain 
issues in most CMIP6 models. In summary, while future changes in 
tropical modes of variability remain unclear, change in atmospheric 
and ocean circulation will drive continued change in tropical ocean 
temperature in the 21st  century ( medium confidence ), with part 
of the region experiencing drastic marine heat wave conditions 
9.2.3.4 Gyres, Western Boundary Currents  
and Inter-basin Exchanges
The AR5 (Rhein et al., 2013) assessed with medium  to high confidence  
that the North Pacific subpolar gyre, the South Pacific subtropical gyre, 
and the subtropical cells have intensified. They also reported that the 
North Pacific subtropical gyre had expanded since the 1990s, and 
that, overall, the changes in gyre systems were likely  predominantly 
due to interannual-to-decadal variability. The SROCC (Meredith et al.,

2019) complemented the AR5 assessment by reporting that the polar 
Beaufort Gyre in the Arctic expanded to the north-west between 
2003 and 2014, contemporaneous with changes in its freshwater 
accumulation and alterations in wind forcing. Consistent with the 
reported change over the gyres, both AR5 and SROCC (Bindoff et al., 
2019; Collins et al., 2019) reported that western boundary currents 
(WBCs) have intensified (Figure  9.11), and expanded poleward, 
except for the Gulf Stream and the Kuroshio. Section  2.3.3.4 
provides an overall assessment of gyres and WBCs, including an 
assessment of change from paleoclimate archives. Section  2.3.3.4 
assesses that, while WBC strength is highly variable at multi-decadal 
scale ( high confidence ), WBCs and subtropical gyres have shifted 
poleward since 1993 ( medium confidence ), at a rate on the order of 
0.04–0.1 degree per decade during 1993–2018. Figure 9.11 shows 
that CMIP5 and CMIP6 models agree in projecting a  weaker Gulf

Stream and Gulf Stream Extension, while the Kuroshio changes less 
(Sen Gupta et al., 2016).
Although the observed wind stress curl shows systematic 
poleward shift in each basin as a result of anthropogenic warming 
(Section 2.3.1.4; Chen and Wu, 2012; Wu et al., 2012; Zhai et al., 2014), 
which has caused a  systematic shift of the WBCs and subtropical 
gyres since 1993 (Wu et  al., 2012; Yang et  al., 2016, 2020), the 
response of current strength is more complex and inconsistent across 
regions (Sloyan and O’Kane, 2015; Y .-L. Wang et al., 2016; Elipot and 
Beal, 2018; McCarthy et al., 2018; Wang and Wu, 2018; Dong et al., 
2019). The strength of WBCs and gyres exhibit inconsistent responses 
because they are dependent on wind stress forcing and because 
multi-scale interaction and air–sea interaction have an important 
role in their long-term trends and variability (Zhang et  al., 2020). 
Observed changes in gyre circulation are dominated by interannual

and decadal modes of variability globally (Qiu and Chen, 2012; Melzer 
and Subrahmanyam, 2017; McCarthy et al., 2018; Hu et al., 2020). 
The North Atlantic subpolar gyre is strongly modulated by variability 
associated with the NAO and AMV (Annex IV; Robson et al., 2016).

1242
Chapter 9 Ocean, Cryosphere and Sea Level Change9Subpolar gyre systems can change abruptly due to a  positive 
feedback between convective mixing and salinity transport (Born 
et  al., 2013, 2016) and air–sea interaction (Moffa-Sánchez et  al., 
2014; Moreno-Chamarro et al., 2017) within the gyre. In the Arctic, 
both the Beaufort gyre and mesoscale eddies strengthened between 
2003 and 2014 (Armitage et al., 2017), which might be partly due 
to increased wind stress (Oldenburg et al., 2018) or reduced sea ice 
thickness and changes in sea ice pack morphology (van der Linden 
et  al., 2019). Presently, there is limited evidence  in attributing 
causality to these changes for any of the proposed mechanisms. 
In the North Pacific, there has been an increasing trend in the Alaska 
Gyre from 1993 to 2017 (Cummins and Masson, 2018), which might 
be attributed to Pacific Decadal Oscillation ( low confidence ) (Hristova

et al., 2019). In the Southern Ocean, limited evidence  indicates that 
the subpolar gyres respond to Southern Hemisphere atmospheric 
modes of variability at interannual time scale (Armitage et al., 2018; 
Dotto et al., 2018).
All climate models reproduce WBCs and gyres, but eddy-
present or eddy-rich models (roughly 10–25 km and about 
10 km resolution, respectively) represent these currents more 
realistically than eddy-parameterized models ( very high confidence ) 
(Small  et  al.,  2014; Griffies et  al., 2015; Chassignet et  al., 2017, 
2020; Hewitt et  al., 2017, 2020; Roberts et  al., 2018). Compared 
to observations or to eddy-present and eddy-rich models, the eddy-
parameterized models from CMIP5 and CMIP6 simulate weaker and 
wider WBCs, as well as less realistic locations of subtropical and 
subpolar gyre boundaries (Figure 9.11). Increased resolution admits mesoscale eddies, and also improves simulation of the strength and

position of WBCs such as the Kuroshio Current, Gulf Stream, and 
East Australian Current ( very high confidence ) (Sasaki et al., 2004; 
Chassignet and Marshall, 2008; Delworth et al., 2012; Yu et al., 2012; 
Small et  al., 2014; Haarsma et  al., 2016; Chassignet et  al., 2017, 
2020; Hewitt et  al., 2020). Improved boundary current location 
relates to improved recirculation regions (Jayne et al., 2009), mean 
path and variability, and existence of multiple stable paths (Qiu 
et al., 2005; Delman et al., 2015), air–sea fluxes (Small et al., 2014), 
and related coastal weather patterns (Kaspi and Schneider, 2011). 
The wind-current feedback, implemented by considering relative 
velocity of currents and wind, realistically dampens mesoscale 
eddies and WBCs, through mesoscale air–sea interaction (Ma et al., 
2016; Renault et al., 2016, 2019), even though sub-mesoscale wind-
current damping feedback is missing in these models ( medium

confidence ) (Z. Zhang et al., 2016). As eddies potentially play a role 
in determining the strength of gyre circulations and their low-
frequency variability (Fox-Kemper and Pedlosky, 2004; Berloff et al., 
2007), it is expected that eddy-present and eddy-rich models will 
differ in their decadal variability and sensitivity to changes in the 
wind stress of gyres from eddy-parameterized models ( medium 
confidence ). Nonetheless, important aspects of gyre strength 
depend primarily on forcing and not resolution, allowing long-term 
changes in gyre strength to be investigated with low-resolution 
climate models (Hughes and de Cuevas, 2001; Yeager, 2015).
Under future scenarios RCP4.5 and RCP8.5, AR5 (Collins et  al., 
2013) assessed an intensification and poleward extension of 
SSP5-8.5

Figure 9 .11 | Simulated barotropic streamfunction, surface speed and major current transport in Coupled Model Intercomparison Project Phase 5 and 6 
(CMIP5 and CMIP6).  (a) Mean barotropic streamfunction (unit: 109 kg s–1; 1995–2014) and projected barotropic streamfunction change (109 kg s–1; 2018–2100 vs 1995–2014) 
under (b) SSP5-8.5. (d) Mean surface (0–100 m) speed (m s–1) and projected surface speed change (m s–1, 2081–2100) versus 1995–2014 under (e) SSP5-8.5. (c, f)  Median 
and likely range of 1995–2014 and 2081–2100 transport of three currents with the largest transport change and four with the largest fractional change (Sen Gupta et al., 
2016). (c) Deep currents: Agulhas Extension (ACx), Gulf Stream (GS), Gulf Stream Extension (GSx), Tasman Leakage (TASL), East Australia Current Extension (EACx), Indonesian

Throughflow (ITF), and Brazil Current (BC). (f) Shallow currents: as for deep but with New Guinea Current (NGC), and without ACx. No overlay indicates regions with high model 
agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change 
(see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1243
Ocean, Cryosphere and Sea Level Change  Chapter 99the southern Hemisphere subtropical gyres in the 21st  century. 
New evidence since AR5 further reinforces their conclusions, which 
are now extended to all subtropical gyre systems in the Northern 
and Southern hemispheres (Yang et al., 2016, 2020). CMIP6 models 
project changes in WBCs that are consistent with projected changes 
in the surface winds. Under strong radiative forcing, in scenario 
SSP5-8.5, CMIP6 models project that the East Australian Current 
Extension, Agulhas Current Extension and Brazil Current will intensify 
in the 21st century, while the Gulf Stream will weaken (Figure 9.11). 
Although CMIP5/CMIP6 are limited in resolution, medium confidence  
is given to changes in WBCs due to consistency across generations of 
climate models, including CMIP6, despite changes in model structure, 
resolution and parametrizations.
The SROCC (Collins et al., 2019) concluded with high confidence that

Indonesian Throughflow (ITF) transport from the Pacific Ocean to 
the Indian Ocean has increased in the past two decades as a result 
(medium confidence ) of an unprecedented intensification of the 
equatorial Pacific trade wind system. Section 2.3.3.4 assesses that 
there is high confidence  that the increase in the ITF over the past 
two decades is linked to multi-decadal scale variability rather than 
a  longer-term trend. Consistently, in the future, as winds change 
under increased radiative forcing, most models project a decline of 
the ITF on the centennial time scale (Figure 9.11). One of the clearest 
changes of ocean current transport simulated by climate models is 
a  weakening of the Indonesian Throughflow, projected in CMIP5 
simulations under RCP4.5 and RCP8.5 scenarios (Sen Gupta et al., 
2016; Stellema et  al., 2019), and in CMIP6 simulations under the 
SSP5-8.5 scenario ( high confidence , Figure 9.11).
The SROCC reports with high confidence  that the Agulhas leakage

from the Indian to the Atlantic Ocean has increased in the past two 
decades (Collins et  al., 2019), and there is no additional evidence 
since then allowing this assessment to be revisited (Biastoch et al., 
2015; Loveday et  al., 2015; Lübbecke et  al., 2015). There is low 
confidence  in future projections of Agulhas leakage because most 
CMIP models cannot directly simulate it, due to coarse resolution. 
However, there is medium evidence  that the strength of the Southern 
Hemisphere westerlies controls Agulhas leakage  (Durgadoo et  al., 
2013; Biastoch et al., 2015; Loveday et al., 2015), and high confidence  
that the strength of the Southern Hemisphere westerlies will increase 
under increased radiative forcing, except in lower warming scenarios 
(SSP1-1.9, SSP1.2-6; Section 4.3.3.1; Bracegirdle et al., 2020). There 
is also evidence that increasing Agulhas leakage is consistent with 
observed change of the temperature and salinity structure in the

Atlantic ocean, and with variability of the AMOC (Section  9.2.3.1; 
Biastoch et al., 2015). This range of indirect evidence provides medium 
confidence  that the Agulhas leakage will increase in the 21st century, 
except for the strongest mitigation scenario (Figure 9.11).
The SROCC assessed that the annual Bering Strait volume transport 
from the Pacific to the Arctic Ocean increased from 2001–2014, 
consistent with an estimated increased northward heat transport of 
about 60% from 2001–2014, and an increased freshwater transport 
of 30 ± 20 km3 yr –1 from 1991 to 2015 (Meredith et  al., 2019). 
Section  2.3.3.4 assesses that volume transport from the Pacific to 
the Arctic has increased since the 1990s from 0.8 Sv to 1.0 Sv over 1990–2015. Realistic representation of the Bering Strait transport 
in the current generation of climate models is challenging because 
the strait is narrow compared to the resolution of climate models

(Clement Kinney et al., 2014; Aksenov et al., 2016). For the Atlantic 
to Arctic transport, Section 2.3.3.4 reports that the major branches 
of Atlantic Water inflow across the Greenland–Scotland Ridge have 
remained stable, with only the smaller pathway of Atlantic Water 
north of Iceland showing a strengthening trend during 1993–2018. 
Section 2.3.3.4 also assesses that the Arctic outflow remained stable 
from the mid-1990s to the mid-2010s. Future changes in these 
currents have not yet been studied in CMIP6 models.
9.2.3.5 Eastern Boundary Upwelling Systems
Eastern boundary upwelling systems (EBUS) exist where trade winds 
draw cold and generally low-pH/low-oxygen waters upward. Coastal 
upwelling plays a key role in supplying the food chain with nutrients, 
hence the richness and productivity of EBUS (Bindoff et al., 2019). 
The SROCC (Bindoff et al., 2019) assessed with high confidence  that 
three out of the four major EBUS have experienced large-scale wind

intensification in the past 60 years (only the trend for the Canary 
Current is considered uncertain). However, it also emphasized that 
various processes can also modulate, or even reverse, wind trends 
locally (Bindoff et  al., 2019). Here we revisit SROCC assessment 
(Bindoff et  al., 2019) based on evidence showing low agreement  
between studies that have investigated trends over past decadess of 
upwelling-favourable winds (Varela et al., 2015). This low agreement  
has been related to differences in wind products, season of interest, 
and length of the considered time series (Varela et al., 2015). Based 
on this, we assess that only the California Current system has 
experienced large-scale upwelling-favorable wind intensification over 
the period 1982–2010, albeit with regional differences (García-Reyes 
and Largier, 2010; Seo et  al., 2012). In the Benguela, Canary, and 
Humboldt systems, large-scale, upwelling-favourable wind trends

are ambiguous, owing to low confidence  in long-term in situ 
marine wind data (Cardone et al., 1990; Bakun et al., 2010) and low 
agreement  among available studies (Narayan et al., 2010; Sydeman 
et  al., 2014; Varela et  al., 2015). Our assessment confirms SROCC 
assessment (Bindoff et al., 2019) in that high natural variability of 
EBUS and their inadequate representation by most climate models 
gives low confidence  in attribution of observed changes, while 
anthropogenic changes are projected to emerge primarily in the 
second half of the 21st century ( limited evidence : one model and one 
study) (Brady et al., 2017).
Under increased radiative forcing, SROCC (Bindoff et  al., 2019) 
assessed that climate models project, in the 21st  century, 
a reduction of wind and upwelling intensity in EBUS at low latitudes, 
and enhancement at high latitudes, under scenario RCP8.5, with an 
overall reduction in either upwelling intensity or extension. It also

highlighted that coastal warming and wind intensification may lead 
to variable countervailing responses to upwelling intensification 
at local scales. Despite differences among EBUS (D. Wang et  al., 
2015), there is growing evidence since SROCC in this pattern of 
change. While it has long been hypothesized that, for upwelling 
winds, change is linked to air temperature contrast between ocean 
and land (Bakun, 1990), this hypothesis has increasingly been

1244
Chapter 9 Ocean, Cryosphere and Sea Level Change9challenged. Changes in sea level pressure and wind fields in EBUS 
appear to be primarily tied  to those affecting subtropical highs 
(García-Reyes et al., 2013). Poleward expansion of the Hadley cell 
(Section  2.3.1.4.1;  Staten et  al., 2018) and the related poleward 
migration of subtropical highs (He et al., 2017; Cherchi et al., 2018), 
produce robust patterns of changes of reduced upwelling at low 
latitude and enhanced upwelling at high latitude (Echevin et  al., 
2012; Belmadani et al., 2014; Bettencourt et al., 2015; Rykaczewski 
et al., 2015; Sousa et al., 2017; Lamont et al., 2018; Sylla et al., 2019). 
These patterns are most apparent in summer in both hemispheres. 
Synoptic variability of upwelling winds, important to the functioning 
of upwelling ecosystems (García-Reyes et  al., 2014), may also be 
affected by climate change (Aguirre et al., 2019). However, coarse

resolution model projections of winds in upwelling regions may be 
more consistent than higher-resolution projections, as these regions 
are highly sensitive to resolution (Small et al., 2015).
Projected future annual cumulative upwelling wind changes at most 
locations, and seasons remain within  ±10–20% of present-day 
values in the 21st  century, even in the context of high-end 
emissions scenarios (4×CO 2 or RCP8.5)  ( medium confidence ). 
Changes due to wind stress curl and alongshore pressure gradients 
tend to agree with alongshore wind changes (Oerder et al., 2015; 
Sylla et  al., 2019). Direct  estimation of oceanic upward transport 
(Oyarzún and Brierley, 2019; Sylla et  al., 2019) and nutrient flux 
into the euphotic layer (Jacox et  al., 2018) provide a  meaningful 
estimator of upwelling, integrating all relevant processes, including 
changes in wind stress curl. However,  there is limited evidence  
from vertical velocity of climate models and missing processes in

coarse-resolution climate models that presently limit this approach. 
Change in upper-ocean stratification (Section 9.2.1.3) is projected 
to increase confinement of upwelling vertical velocities to near the 
ocean surface ( high confidence ) (Oerder et al., 2015; Oyarzún and 
Brierley, 2019).
In summary, SROCC and this Report conclude that the California 
Current system has experienced some upwelling-favourable wind 
intensification since the 1980s ( high confidence ), while low agreement  
among reported wind changes in the Benguela, Canary, and Humboldt 
systems prevent a  similar assessment. As in SROCC, there is low 
confidence  in attribution of observed changes to anthropogenic or 
natural causes. New evidence reinforces our confidence in SROCC 
assessment that, under increased radiative forcing, EBUS winds will 
change with a dipole spatial pattern within each EBUS of reduction 
(weaker and/or shorter) at low latitude, and enhancement (stronger

and/or longer) at high latitude ( high confidence ). There is medium 
confidence that, across all scenarios, upwelling wind changes in 
EBUS will remain moderate in the 21st  century, within ±10–20% 
from present-day values.
9.2.3.6 Coastal Systems and Marginal Seas
Beyond the world’s coastlines lie the shoreline, shallow estuaries, 
continental shelves, and deeper fjords and slopes, where depths 
increase rapidly from the shelves to the deep-ocean floor. It is more 
difficult to transport fluid across (rather than along) the shelf-break 
or slope (Brink, 2016), and estuaries and shelves have complex circulations and mixing, leading to indirect connections between 
the inner shelves and coastlines and offshore conditions. Coastal 
processes link to large-scale metrics of climate and regional effects, 
from changing rivers and estuaries, melt and runoff to deep water, 
to how changes offshore affect regional and coastal conditions.

Shelf-deep ocean exchanges involve eddying, tidal, or turbulent 
motions and small-scale topography such as submarine canyons; 
high-resolution observations and models are needed to capture 
these effects (Greenberg et al., 2007; Capet et al., 2008; Allen and 
Durrieu de Madron, 2009; Colas et  al., 2012; Trotta et  al., 2017). 
Example coastal processes that introduce uncertainty into large-scale 
projections are exchange of CDW across the Antarctic shelf-break, 
which affects AABW formation and Antarctic ice-shelf–ocean 
interaction (Sections  9.2.2.3 and 9.2.3.2; Stewart and Thompson, 
2013, 2015), river and estuarine plumes and their responses to water 
level and hydrology change (Banas et al., 2009; Sun et al., 2017), 
fjord dynamics linked to glacial outflows (Straneo and Cenedese, 
2015; Torsvik et al., 2019), and changing formation of water masses 
in marginal seas (Kim et al., 2001; Greene and Pershing, 2007; Giorgi

and Lionello, 2008; Renner et  al., 2009). Downscaling projections 
to the local level allows process detail (Foreman et  al., 2014; 
Mathis and Pohlmann, 2014; Meier, 2015; Tinker et al., 2016). Some 
processes can only be simulated when coastal models are forced by 
larger-scale models of the atmosphere, cryosphere, or hydrosphere 
(Seo et al., 2007, 2008; Somot et al., 2008; Oerder et al., 2015; Renault 
et al., 2016; Y . Zhang et al., 2016; Wåhlin et al., 2020), including the 
addition of tides (Janeković and Powell, 2012; Timko et  al., 2013; 
Tinker et al., 2015; Pickering et al., 2017; Hausmann et al., 2020). 
Due to coastal process complexity and small scale, linking the 
effects of coastal ocean changes to global ocean changes requires 
high-resolution modelling (Holt et al., 2017, 2018), two-way nesting, 
or local mesh refinement (Fringer et al., 2006; Zhang and Baptista, 
2008; Mason et al., 2010; Dietrich et al., 2012; Hellmer et al., 2012;

Ringler et al., 2013; Q. Wang et al., 2014; Zängl et al., 2015; Y .J. Zhang 
et al., 2016; Soto-Navarro et al., 2020). Coarse climate models and 
HighResMIP models do not represent some coastal phenomena such 
as cross-shelf exchanges and sub-mesoscale eddies, which require 
1 km or finer resolution. Thus, there is low confidence  in projecting 
centennial scale coastal climate change where regional downscaling 
or refinement is lacking. There is high confidence  in the ability of 
regional coupled models to improve coastal climate change process 
understanding and provide regional information (Section 12.4), but 
many sites globally await such projections.
9.2.4 Steric and Dynamic Sea Level Change
9.2.4.1 Global Mean Thermosteric Sea Level Change
Changes in globally averaged ocean heat content (OHC) cause 
global mean thermosteric sea level (GMTSL) change (Box 9.1). The 
observed increased OHC for 1971–2018 of 325 to 546 ZJ ( very likely

range) (Section 7.2, Box 7.2) has led to a GMTSL rise of 0.03 to 0.06 
m out of a total global mean sea level (GMSL) of 0.07 to 0.15 m 
(very likely  range) (Section 2.3.3.3, Tables 2.7 and 9.5, and Cross-
Chapter Box 9.1).

1245
Ocean, Cryosphere and Sea Level Change  Chapter 99Projections of GMTSL rise in AR5 (Church et al., 2013b) and SROCC 
(Oppenheimer et al., 2019) were derived from the CMIP5 ensemble, 
after removing drift estimated based on pre-industrial control 
simulations. Differences between removing a  linear and a  quadratic 
drift are small (Hobbs et al., 2016a; Hermans et al., 2021). These prior 
assessments filled in projections for models that did not provide GMTSL 
rise for all scenarios, by calculating the heat content of the climate 
system from global surface air temperature and net radiative flux, then 
converting this to GMTSL rise using each model’s diagnosed expansion 
efficiency coefficient. In AR5, the associated uncertainties were derived 
by assuming a normal distribution, with the 5th–95th percentile CMIP5 
ensemble range taken as the likely  range (±1 standard deviation).
In this Report, global surface air temperature projections are not

derived directly from the CMIP6 ensemble (Box  4.1). Therefore, to 
produce projections of OHC and GMTSL rise consistent with the 
Report’s assessment of equilibrium climate sensitivity and transient 
climate response (Section 7.5.2.2), this chapter employs a two-layer 
energy budget emulator (Supplementary Materials 7.SM.2, 9.SM.4.3). 
Since AR5, climate model emulators have been increasingly used to 
predict GMTSL (Cross-Chapter Box 7.1; Kostov et al., 2014; Palmer 
et  al., 2018, 2020; Nauels et  al., 2019). The expansion efficiency 
coefficient that relates GMTSL and OHC for the two-layer emulator 
has a  mean and standard deviation of 0.113  ±  0.013  m  YJ–1 
(Supplementary Material 9.SM.4.3). This approach yields 
a likely  thermosteric contribution between 1995–2014 and 
2100 that represents a  minimal change from AR5 and SROCC 
(Table  9.8). The two-layer emulator GMTSL projected median and 
17th–83rd  percentile, or likely , range is 0.12  (0.09  to 0.15) m for

SSP1-1.9, 0.14 (0.11 to 0.18) m for SSP1-2.6, 0.20 (0.16 to 0.24) m 
for SSP2-4.5, 0.25 (0.21 to 0.30) m for SSP3-7.0, and 0.30 (0.24 to 
0.36)  m for SSP5-8.5 by 2100 (Section  9.6.3.2 and Tables 9.1, 9.8 
and 9.9). The two-layer model heat content increases slightly faster 
than that of the total depth CMIP6 ensemble, which is related to 
its role in the assessed energy balance (Section  7.SM.2), but with 
a similar ensemble spread (Table 9.1). Projecting the likely  factor by 
which 1995–2014 to 2081–2100 OHC change exceeds change over 
1971 to 2018 in CMIP6 yields 3 to 5 for SSP1-2.6, 4 to 6 for SSP2-4.5, 
5 to 7 for SSP3-7.0, and 5 to 8 for SSP5-8.5. The two-layer model 
likely  equivalents are 2 to 3 for SSP1-2.6, 3 to 4 for SSP2-4.5, 4 to 5 
for SSP3-7.0, and 4 to 6 for SSP5-8.5.
For reconstructions, the expansion efficiency coefficient is required for 
the conversion between ocean temperature and steric sea level over

a specific time scale. Combining the assessed sea level and energy 
data over 1995 to 2014 (drawn from the analysis in Cross-Chapter 
Box  9.1) results in a  coefficient of 0.1210  ±  0.0014  m  YJ–1, or 
0.6607  ±  0.0076  m °C–1 in terms of mean ocean temperature. 
The  two-layer emulator assessment used in AR6 results in 
0.113 ± 0.013 m YJ–1, or 0.617 ± 0.071 m °C–1 (Appendices 7.SM.2, 
9.SM.4). Both of these estimates are in line with an independent 
estimate of 0.70 m/°C (Hieronymus, 2019) and other estimates, for 
example, 0.116 ± 0.011 m YJ–1 (Kuhlbrodt and Gregory, 2012), but 
are significantly larger than the temperature to sea level conversion 
used in AR5 (0.42 m °C–1 based on SST and the estimated range from 
Levermann et al., 2013). The expansion coefficient is not fixed across 
models, nor in time, as it varies depending on which water masses are storing the added heat, and the commitment time scale (Hallberg

et al., 2013). For paleoclimate, a scaling for sea surface temperature 
(0.6 m °C–1) or global surface air temperature (GSAT; see Cross-Chapter 
Box 2.3) can be estimated, but mean ocean temperature is in phase 
with steric sea level change, while sea surface temperatures are not 
(Figure  9.9; Shakun et  al., 2012; Tierney et  al., 2020). Thus, while 
conversions between OHC, mean ocean temperature and GMTSL 
across applications are within uncertainty ranges ( medium confidence ) 
(Table 9.1), little consistency is found when correlating these variables 
to SST or GSAT, which may vary independently.
Short-lived climate forcers (Sections 6.3 and 6.6.3) are associated 
with a  sea level commitment, due to an OHC and mean ocean 
temperature response that lasts substantially longer than their 
atmospheric forcing and SST response, although not as long as the sea 
level commitment associated with CO 2 emissions (Sections 9.2.1.1

and 4.4.4). For example, Zickfeld et al. (2017) find that about 70% of 
the thermosteric sea level rise associated with methane  forcing 
would persist 100 years after the elimination of methane emissions, 
and 40% would persist for more than 500 years.
In summary, consistent relationships between OHC (Section 9.2.2.1), 
mean ocean temperature and GMTSL are found using two-layer 
emulators, CMIP6 models, and modern and paleo observations 
to provide medium confidence  in the 0.113 ± 0.013 m YJ–1, 
or 0.617 ± 0.071 m °C–1 likely  ranges of assessed conversion values. 
It is possible to estimate relationships between SST or GSAT change 
and GMTSL rise, but conversions are not generally applicable and 
depend on time scale and application.
Table 9 .1 | Projected contributions to median and 17–83% (parentheses) 
and 5–95% [square brackets] ranges of thermosteric sea level from AR5 
(Church et al., 2013b), CMIP6 (Jevrejeva et al., 2020; Hermans et al., 2021)

and the two-layer energy balance model (described in Sections 7.SM.2, 
9.SM.4 and Box 4.1) averaged over 2081–2100, with respect to a baseline 
of 1995–2014.  Note that AR5 and SROCC interpret 5–95% range as the likely 
range, while in this table square brackets are used for consistency.
StudyRCP2.6/
SSP1-2.6RCP4.5/
SSP2-4.5RCP8.5/
SSP5-8.5
IPCC AR5 and SROCC GMTSL
(Church et al., 2013b; 
Oppenheimer et al., 2019 )0.13  
[0.09 to 0.17] m0.18  
[0.13 to 0.22] m0.26  
[0.20 to 0.32] m
CMIP6 5–95% GMTSL
(Hermans et al., 2021)0.14  
[0.08 to 0.17] m0.18  
[0.11 to 0.23] m0.26  
[0.17 to 0.33] m
(Jevrejeva et al., 2020)–0.19  
[0.13 to 0.24] m0.27  
[0.19 to 0.35] m
Assessed GMTSL based 
on two-layer model  
17–83% and 5–95% 
(Sections 7.SM.2, 9.SM.4)0.13  
(0.11 to 0.16)  
[0.09 to 0.19] m0.17  
(0.14 to 0.21) 
[0.12 to 0.25] m0.25  
(0.20 to 0.30) 
[0.18 to 0.35] m
Total OHC 17–83% and 
5–95% from assessed 
two-layer model 
(Sections 7.SM.2, 9.SM.4)1.18

(0.99 to 1.42) 
[0.86 to 1.65] YJ 1.56  
(1.33 to 1.86) 
[1.19 to 2.12] YJ2.23  
(1.92 to 2.64) 
[1.71 to 3.00] YJ
0–2000 m OHC 17–83% 
and 5–95% from CMIP6 
(Figure 9.6)1.06  
(0.80 to 1.31) 
[0.66 to 1.64] YJ 1.35  
(1.08 to 1.67) 
[0.90 to 1.84] YJ1.89  
(1.60 to 2.29) 
[1.28 to 2.58] YJ

1246
Chapter 9 Ocean, Cryosphere and Sea Level Change99.2.4.2 Ocean Dynamic Sea Level Change
Projections of ocean dynamic sea level change (Box  9.1) on 
multi-annual time scales resemble the patterns of steric sea level 
change in the open ocean (Figures 9.11 and 9.12; Lowe and Gregory, 
2006; Pardaens et al., 2011; Couldrey et al., 2021). On shorter time 
scales, especially in extratropical coastal areas, there may be an 
important barotropic component (also called bottom pressure change) 
due mostly to changes in wind-driven circulation and eddies apparent 
in the variance of ocean dynamic sea level (Figure 9.12; Roberts et al., 
2016; Hughes et al., 2018). This component is highly sensitive to ocean 
model resolution (Chassignet et al., 2020). Steric sea level change is 
associated with local changes in temperature and salinity, which 
come about through changes in surface fluxes of heat and freshwater 
(Section 9.2.1.2) and through redistribution of existing water masses

by changed ocean circulation and mixing processes (Figure  9.12  
and Sections 9.2.2.1 and 9.2.3). Redistribution of water masses 
often involves anticorrelated thermosteric and halosteric changes 
(Figure 9.12), especially in the Atlantic (Pardaens et al., 2011; Bouttes 
et al., 2014; Durack et al., 2014; Griffies et al., 2014; Han et al., 2017).Ocean dynamic sea level change is strongly affected by internal 
variability (Section 9.6.1.4), partly from interannual to decadal coupled 
atmosphere–ocean modes of variability via wind-driven redistribution 
(Annex IV; Griffies et  al., 2014; Han et  al., 2017) and partly from 
intrinsic ocean variability, particularly in higher-resolution simulations 
(such as HighResMIP), which statistically resemble observations, even 
on short time scales (Figure 9.12; Griffies et al., 2014; Sérazin et al., 
2016; Llovel et  al., 2018; Chassignet et  al., 2020). High-resolution 
simulations are not used in relative sea level projections (Section 9.6.3)

due to the limited range of forcing scenarios. The most marked feature 
of long-term regional sea level change in the continuous satellite 
altimetry record, beginning in 1992, is the east–west dipole in the 
Pacific Ocean (rising more rapidly in the east, see also Section 9.6.1.3), 
which persisted until 2015, and can be explained by anomalously 
strong trade winds (Merrifield et al., 2012; England et al., 2014; Griffies 
et al., 2014; Takahashi and Watanabe, 2016; Han et al., 2017) together 
with associated changes in surface heat flux (Piecuch et al., 2019). 
The most notable features of sub-annual variability in altimetry are 
eddies and tides, which are directly simulated only in high-resolution 
models (Haigh et al., 2019; Chassignet et al., 2020).
SSP5-8.5
(17 models)
SSP1-2.6
Figure 9. 12 | (a–f) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean projected change contributions to relative sea level change

in (a, d) steric sea level anomaly, (b, e) thermosteric sea level anomaly, and (c, f) halosteric sea level anomaly between 1995–2014 and 2081–2100 using 
a method that does not require a reference level (Landerer et al., 2007).  Global mean change has been removed from these figures, consistent with the methods 
in Sections 9.6.3 and 9.SM.4 and the definitions of Gregory et al. (2019). (Gregory et al., 2019). See Figure 9.27 for global mean sea level (GMSL). (g–i)  Standard deviation 
of ocean dynamic sea level change from (g) Aviso observations (10-day high-pass filter); (h) five-day mean of high-resolution Ocean Model Intercomparison Project phase 2 
(OMIP-2) models forced with observed fluxes; and (i) five-day mean of low-resolution OMIP-2 models which are comparable in resolution to the models in (a–f). No overlay 
indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80%

of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data 
table (Table 9.SM.9).

1247
Ocean, Cryosphere and Sea Level Change  Chapter 99Projections of the pattern and amplitude of regional ocean dynamic 
sea level change in CMIP6 and previous model generations show 
a large model spread, of a similar size to the geographical spread 
(Figure  9.12). The model spread derives from model dependence 
of changes both in surface fluxes (Section  9.2.1.2) and in the 
ocean response (Section 9.2.2). The spread is similar in CMIP6 and 
CMIP5, and is largest in regions with large projected variations in 
ensemble-mean ocean dynamic sea level change (Lyu et al., 2020a), 
such as the Southern Ocean Dipole with an ocean dynamic sea level 
rise north of the ACC and a  fall to the south, the Atlantic Dipole 
with a  sea level rise north of 40°N and a  fall in 20°N–40°N, the 
Northwest Pacific Dipole, and the large sea level rise in the Arctic 
(Church et  al.,  2013b; Slangen et  al., 2014a, 2015; Bilbao et  al.,

2015; Gregory et al., 2016; C. Chen et al., 2019; Lyu et al., 2020a; 
Couldrey et  al., 2021). Patterns of change are consistent between 
model simulations and observations ( medium confidence ). The major 
model ensemble-mean features resemble thermosteric sea level 
change, as expected from altered input of heat to the ocean 
without changing circulation, while model spread results from the 
diversity in redistribution of the heat content of the unperturbed 
ocean (Section 9.2.2.1; Bouttes and Gregory, 2014; Gregory et  al., 
2016; Huber and Zanna, 2017; Lyu et al., 2020b; Todd et al., 2020; 
Couldrey et al., 2021).
The Southern Ocean Meridional Dipole is driven by a  northward 
advection of excess heat (from changes in surface fluxes) by the 
wind-driven circulation followed by subduction or diffusive uptake 
in mid-latitudes, northward redistribution of existing heat by the 
strengthening of that circulation, and the meridional contrast in

thermal expansivity due to its temperature-dependence (Armour et al., 
2016; Gregory et  al., 2016; Lyu et  al., 2020b; Todd  et  al.,  2020; 
The positive Arctic ocean dynamic sea level change is driven by 
increased freshwater input (Couldrey et  al., 2021). The Northwest 
Pacific Dipole is driven by the intensification of the Kuroshio Current 
in response to reduced heat loss and in some models to wind stress 
change (C. Chen et al., 2019; Couldrey et al., 2021).
The North Atlantic sea level change dipole is forced by a reduction 
in heat loss from the ocean north of 40°N (i.e., net heat uptake), 
which in all Earth system models leads to a weakening of the AMOC, 
although the magnitude has a large model spread (Section 9.2.3.1; 
Gregory et al., 2016; Huber and Zanna, 2017). The reduced northward 
transport of warm, salty water (Section 9.2.2) causes further ocean 
dynamic sea level change, whose details are model-dependent. North

of 40°N, this redistribution leads to a sea level rise, predominantly 
halosteric, reinforcing the thermosteric effect of heat uptake (Couldrey 
et al., 2021). Comparison of observed Atlantic OHC for 1955–2017 
with a  reconstruction assuming no change in circulation indicates 
that the thermosteric sea level change resulting from southward 
redistribution of heat may be detectable (Zanna et  al., 2019). This 
redistribution causes a tendency for SST cooling north of 40°N and 
anomalous heat input from the atmosphere, and thus a  positive 
feedback on AMOC weakening (Winton et al., 2013; Gregory et al., 
2016; Todd et  al., 2020; Couldrey et  al., 2021). Many climate and 
ocean models agree that the AMOC weakening is associated with pronounced thermosteric sea level rise along the American coast 
around 40°N (Figures 9.12 and 9.26), leading to a relatively large 
ocean dynamic sea level rise in this region (Yin, 2012; Bouttes et al.,

2014; Slangen et al., 2014b; Little et al., 2019; Lyu et al., 2020a).
In summary, ocean dynamic sea level change involves changes to 
temperature and salinity and responses of currents to changing 
forcing, with significant variability driven by unforced oceanic 
variability. Projections of dynamic sea level variability require fully 
three-dimensional ocean models, and only high-resolution ocean 
models are statistically consistent on short time scales with satellite 
altimeter observations ( very high confidence ).
9.3 Sea Ice
9.3.1 Arctic Sea Ice
9.3.1.1 Arctic Sea Ice Coverage
The observed decrease of Arctic sea ice area is a  key indicator of 
large-scale climate change (Section 2.3.2.1.1, Cross-Chapter Box 2.2). 
The SROCC (Meredith et al., 2019) assesses that sea ice extent, which 
is the total area of all grid cells with at least 15% sea ice concentration, 
has declined since 1979 in each month of the year ( very high

confidence ). In contrast to SROCC, we assess changes in sea ice area 
(the actual area of the ocean covered by sea ice) rather than sea ice 
extent, because sea ice area is geophysically more relevant and not 
grid-dependent (Notz, 2014; Ivanova et al., 2016; Notz et al., 2016; 
Notz and SIMIP Community, 2020). Arctic sea ice area is calculated 
based on measurements by passive microwave satellite sensors that 
provide near-continuous measurements of gridded, pan-Arctic sea 
ice concentration from 1979 onwards. Irreducible uncertainties in the 
conversion of thermal microwave brightness temperature to sea ice 
concentration, and choices in algorithm design, cause uncertainties in 
observed Arctic sea ice area, which are far smaller than the observed 
sea ice loss (e.g., Comiso et al., 2017a; Niederdrenk and Notz, 2018; 
Alekseeva et al., 2019; Kern et al., 2019; Meier and Stewart, 2019). 
Sea ice area has decreased in every month of the year from 1979 to

the present ( very high confidence ) (Figure  9.13). The absolute and 
the relative ice losses are highest in late summer-early autumn ( high 
confidence ) (Figure 9.13). Averaged over the decade 2010–2019, the 
monthly Arctic sea ice area from August to October has been around 
2 million km² (or about 25%) smaller than during 1979–1988 ( high 
confidence ) (Figure 9.13).
The SROCC discussed the regional distribution of Arctic sea ice loss, 
and the findings remain valid for the updated time series covering 
2019 (Figure 9.13). Sea ice loss in winter is strongest in the Barents 
Sea, while summer losses occur primarily at the summer sea ice 
region margins, in particular in the East Siberian, Chukchi, Kara and 
Beaufort Seas (Frey et al., 2015; Chen et al., 2016; Onarheim et al., 
2018; Peng and Meier, 2018; Maksym, 2019). In the Bering Sea, 
expanding winter sea ice cover was observed until 2017 (Frey et al., 
2015; Onarheim et al., 2018; Peng and Meier, 2018), but a marked

reduction in sea ice concentration has occurred since then ( high 
confidence ) (Stabeno and Bell, 2019).

1248
With respect to seasonal changes in the sea ice cover, the winter sea 
ice loss causes a decrease in the average sea ice age and fraction of 
multi-year ice, as assessed by SROCC ( very high confidence ), and also 
of the ocean area covered intermittently by sea ice (Bliss et al., 2019). 
In contrast, the seasonal ice zone (covered by sea ice in winter but not 
in summer) has expanded regionally (Bliss et al., 2019) and over the 
whole Arctic (Steele and Ermold, 2015), because the loss of summer 
sea ice area is larger than the loss of winter sea ice area. Arctic sea 
ice retreat includes an earlier onset of surface melt in spring and 
a later freeze up in autumn, lengthening the open water season in 
the seasonal sea ice zone (Stroeve and Notz, 2018). However, there is 
low agreement  in quantification of regional trends of melt and freeze 
onset between different observational products (Bliss et  al., 2017;

Smith and Jahn, 2019).
Reconstructions of Arctic sea ice coverage put the satellite period 
changes into centennial context. Direct observational data coverage 
(Walsh et al., 2017) and model reconstructions (Brennan et al., 2020) 
warrant high confidence  that the low Arctic sea ice area of summer 
2012 is unprecedented since 1850, and that the summer sea ice loss is 
significant in all Arctic regions except for the Central Arctic (Cai et al., 
2021). Direct winter observational data coverage before 1953 is too 
sparse to reliably assess Arctic sea ice area. Since 1953, the years 
2015 to 2018 had the four lowest values of maximum Arctic sea ice 
area, which usually occurs in March ( high confidence ) (Figure 2.20). 
Reconstructions of Arctic sea ice area before 1850 remain sparse, 
and as in SROCC, there remains medium confidence that the current 
sea ice levels in late summer are unique during the past 1 kyr

(Section 2.3.2.1.1; Kinnard et al., 2011; De Vernal et al., 2013b).The observed fluctuations and trends of the Arctic sea ice cover 
arise from a combination of changes in natural external forcing and 
anthropogenic forcing, internal variability and internal feedbacks 
(e.g., Notz and Stroeve, 2018; Halloran et al., 2020). New paleo-proxy 
techniques indicate regional sea ice changes over epochs and 
millennia and allow possible drivers to be assessed. Biomarker IP25 
(Belt et  al., 2007) together with other sedimentary biomarkers 
(Belt, 2018) provide local temporal information on seasonal sea 
ice coverage, permanent sea ice coverage and ice-free waters, with 
occasional ambiguous contrasting results (Belt, 2019). These records 
and other proposed paleo proxies, including bromine in ice cores 
(Spolaor et al., 2016), dinocyst assemblages (e.g., De Vernal et al., 
2013b) and driftwood (e.g., Funder et al., 2011), provide evidence of

sea ice fluctuations that exceed internal variability ( high confidence ).
The inferred sea ice fluctuations over millennia can be related 
to Northern Hemisphere temperature evolution and give rise to 
Arctic-wide fluctuations in sea ice coverage in the paleorecord 
(Section 2.3.2.1.1). On a regional scale, fluctuations include decreased 
sea ice cover during the Allerød warm period (14.7–12.9 ka) in the 
Laptev (Hörner et al., 2016) and Bering Seas (Méheust et al., 2018); 
an extensive sea ice cover during the Younger Dryas (around 12 ka) 
in the Bering (Méheust et  al., 2018), Kara (Hörner et  al., 2018), 
Laptev (Hörner et al., 2016) and Barents (Belt et al., 2015) Seas, and 
at the Yermak Plateau (Kremer et al., 2018); little sea ice during the 
early Holocene, when Northern Hemisphere summer insolation was 
higher than today (8000 to 9000 years before present), in the North 
Icelandic Shelf area (Cabedo-Sanz et al., 2016; Xiao et al., 2017), Sea

of Okhotsk (Lo et al., 2018), Canadian Arctic (Spolaor et al., 2016), 
Arctic sea-ice historical records and CMIP6 projections
Anomaly time series, maps of seasonal sea-ice concentration and changes, and projected sea-ice metrics in SSP2-4.5
Figure  9.13 | Arctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (Left)  Absolute anomaly of 
monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. (Right)  Sea ice 
concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. First column: Satellite-retrieved 
mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010–2019. Third column:

Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth column: Number of available CMIP6 models 
that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. The average observational record of sea ice area is derived from the UHH sea ice area 
product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA 
Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice 
concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1249
Ocean, Cryosphere and Sea Level Change  Chapter 99Barents (Berben et  al., 2017), Bering (Méheust et  al., 2018), and 
Chukchi (Stein et  al., 2017) Seas, at the Yermak Plateau (Kremer 
et al., 2018) and north of Greenland (Funder et al., 2011); increasing 
sea ice cover throughout much of the middle and late Holocene 
around Svalbard (Knies et  al., 2017), in the North Icelandic Shelf 
area (Cabedo-Sanz et al., 2016; Harning et al., 2019; Halloran et al., 
2020), north of Greenland (Funder et al., 2011), and in the Western 
Greenland (Kolling et  al., 2018), Barents (Belt et  al., 2015; Berben 
et al., 2017), Chukchi (De Vernal et al., 2013a; Stein et al., 2017) and 
Laptev (Hörner et al., 2016) Seas. The consistent, Arctic-wide changes 
give high confidence  in millennial-scale co-variability of the sea ice 
cover with temperature fluctuation.
The SROCC assessed that approximately half of the satellite-observed 
Arctic summer sea ice loss is driven by increased concentrations

of atmospheric greenhouse gases ( medium confidence ). Recent 
attribution studies now allow the strengthened assessment that it 
is very likely  that more than half of the observed Arctic sea ice loss 
in summer is anthropogenic (Section  3.4.1.1). This assessment is 
confirmed by process-based analyses of Arctic sea ice loss not assessed 
by SROCC. Similar to the paleorecord, the satellite record of Arctic 
sea ice area from 1979 onwards is strongly and linearly correlated 
with global mean temperature on decadal and longer time scales 
(Figures 9.14a,e) (e.g., Gregory et al., 2002; Rosenblum and Eisenman, 
2017). The correlation holds across all months with R2 ranging from 
0.61 to 0.81 (Niederdrenk and Notz, 2018). However, in contrast 
to paleorecords, sea ice fluctuations during the satellite period are 
only weakly correlated with Northern Hemisphere insolation (Notz 
and Marotzke, 2012); modern Northern Hemisphere sea ice area

is more strongly correlated with atmospheric carbon dioxide (CO 2) 
concentration (Johannessen, 2008; Notz and Marotzke, 2012) and 
cumulative anthropogenic CO 2 emissions (Figures 9.14b,f; Zickfeld 
et al., 2012; Herrington and Zickfeld, 2014; Notz and Stroeve, 2016). 
The R2 values of the correlation between sea ice area and cumulative 
CO 2 emissions range across all months from 0.76 to 0.92 (Stroeve 
and Notz, 2018). In summary, there is high confidence  that satellite-
observed Arctic sea ice area is strongly correlated with global mean 
temperature, CO 2 concentration and cumulative anthropogenic 
CO 2 emissions.
In addition to changes in the external forcing, internal variability 
substantially affects Arctic sea ice, evidenced from both paleorecords 
(e.g., Chan et al., 2017; Hörner et al., 2017; Kolling et al., 2018) and 
satellites after 1979 (e.g.,  Notz and Stroeve, 2018; Roberts et  al., 
2020) ( high confidence ). Most of the internal variability on annual

time scales is related to atmospheric temperature fluctuations, 
for example linked to cyclone activities (Wernli and Papritz, 2018; 
Olonscheck et  al., 2019), while multi-decadal internal variability 
is primarily related to changes in oceanic heat transport (Zhang, 
2015; Halloran et  al., 2020). These mechanisms are represented 
in current climate models (Olonscheck et al., 2019; Halloran et al., 
2020), but the resulting internal variability of September sea 
ice area in CMIP5 and CMIP6 models, as given by the ensemble 
mean standard deviation ΣSIA,Sep = 0.5 million km² (Olonscheck 
and Notz, 2017; Notz and SIMIP Community, 2020), exceeds the 
estimated internal variability for the period 1850 to 1979 from 
both reanalyses ( ΣSIA,Sep = 0.3 million km2) and direct observational reconstructions  ( ΣSIA,Sep = 0.2 million km2) (medium confidence  
because of limited reliability of longer-term sea ice reconstructions)

(Brennan et  al., 2020). Internal variability has been estimated to 
have contributed 30 to 50% of the observed Arctic summer sea ice 
loss since 1979 (Kay et al., 2011; Stroeve et al., 2012; Ding et al., 
2017, 2019; England et  al., 2019). However, this estimate from 
models might be biased towards internal over forced variability 
because of the models’ high internal variability and because the 
CMIP5 simulated September sea ice sensitivity to forcing is lower 
than observed, even if internal variability is taken into account (Notz 
and Stroeve, 2016; Rosenblum and Eisenman, 2017). Most CMIP6 
models fail to simulate the observed sensitivity of sea ice loss to CO 2 
emissions (as a proxy for time) and to temperature simultaneously. 
However, they better capture the observed sensitivity of sea ice loss 
to CO 2 emissions than CMIP5 models (Section 3.4.1; Figure 9.14h; 
Notz and SIMIP Community, 2020). 
The SROCC examined the different atmospheric and oceanic processes

that caused the observed sea ice loss, with recent studies providing 
new evidence for the importance of variations in air temperature 
(Olonscheck et al., 2019; Dahlke et al., 2020), wind patterns (Graham 
et al., 2019), oceanic heat flux (Docquier et al., 2021) and riverine 
heat influx (Park et al., 2020). As in SROCC, the relative contribution of 
each physical cause to the sea ice loss cannot be robustly quantified 
because of disagreement among models (Burgard and Notz, 2017), 
sparse observations, and limited understanding of the variation of 
each factor with global mean temperature. This is addressed by new 
diagnostics available from CMIP6 simulations, which now allow for 
more detailed analyses of the drivers of sea ice loss at a process level 
(Keen et al., 2021).
In examining temperature thresholds for the loss of Arctic summer 
sea ice, the Special Report on Global Warming of 1.5°C (SR1.5; 
Hoegh-Guldberg et  al., 2018) and SROCC assess that a  reduction

of September mean sea ice area to below 1 million km2, practically 
a  sea ice-free Arctic Ocean, is more probable for a  global mean 
warming of 2°C compared to global mean warming of 1.5°C 
(high confidence ). Analyses of CMIP6 simulations (Notz and SIMIP 
Community, 2020) confirm this result, as they show that, on decadal 
and longer time scales, Arctic summer sea ice area will remain highly 
correlated with global mean temperature until the summer sea 
ice has vanished (Figure  9.14a,e). Quantitatively, existing studies 
(Screen and Williamson, 2017; Jahn, 2018; Ridley and Blockley, 2018; 
Sigmond et al., 2018; Notz and SIMIP Community, 2020) also show 
that, for a  warming between 1.5 and 2°C, the Arctic will only be 
practically sea ice free in September in some years, while at 3°C 
warming, the Arctic is practically sea ice free in September in most 
years, with longer practically sea ice-free periods at higher warming

levels ( medium confidence ). However, because of the CMIP5 and 
CMIP6 models’ generally too low sensitivity of sea ice loss to global 
warming, there is only low confidence  regarding the specific warming 
level at which the Arctic Ocean first becomes practically sea ice free 
(Section 4.3.2.1; Notz and SIMIP Community, 2020).
In contrast, CMIP6 models capture the observed sensitivity of 
Arctic sea ice area to cumulative anthropogenic CO 2 emissions well, 
providing high confidence  that the Arctic Ocean will likely  become

1250
practically sea ice free in the September mean for the first time for 
future CO 2 emissions of less than 1000 Gt and before the year 2050 
in all SSP scenarios (Notz and SIMIP Community, 2020). This new 
assessment is consistent with an observation-based projection of 
a  practically sea ice-free Arctic Ocean in September for additional 
anthropogenic CO 2 emissions of 800 ± 330 GtCO 2 beyond the 
year 2018 (Notz and Stroeve, 2018; Stroeve and Notz, 2018). This 
estimate may, however, be too high due to neglecting possible 
future reduction in atmospheric aerosol load that would cause 
additional warming (Gagné et  al., 2015a; Wang et  al., 2018), and 
is subject to the same constraints as the carbon budget analysis 
for global mean temperature (see section 5.5 for details). Based on 
CMIP6 simulations, it is very likely  that the Arctic Ocean will remain 
sea ice covered in winter in all scenarios throughout this century

(Sections 4.3.2 and 4.4.2).There is an indication that CMIP6 simulations of Arctic sea ice 
have improved relative to CMIP5 (Section  3.4.1.1), but detailed 
evaluation studies exist mainly for CMIP5 models. These studies 
found that CMIP5 model projections and reanalyses show a  large 
spread of simulated regional Arctic sea ice concentration (Laliberté 
et al., 2016; Chevallier et al., 2017), which remains true for CMIP6 
models (Shu et al., 2020; Wei et al., 2020). In addition, both CMIP5 
and CMIP6 models show a large spread in the simulated seasonal 
cycle of Arctic sea ice area, with too high a sea ice area in March in 
the ensemble mean (Notz and SIMIP Community, 2020). The CMIP5 
models have also had difficulty simulating realistic landfast sea ice 
(Laliberté et  al., 2018). These findings imply that both CMIP5 and 
CMIP6 models do not realistically capture the regional and seasonal 
processes governing observed Arctic sea ice evolution, causing low

confidence  in the models’ projections of future regional sea ice 
evolution, including updated projections for shipping routes across 
the Northern Sea Route and Northwest Passage (Wei et al., 2020).
Figure 9 .14 | Monthly mean March (a–d) and September (e–h) sea ice area as a function of global surface air temperature (GSAT) anomaly (a, e); 
cumulative anthropogenic CO 2 emissions (b, f); year (c, g) in Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations (shading, 
ensemble mean as bold line) and in observations (black dots). Panels (d) and (h) show the sensitivity of sea ice loss to anthropogenic CO 2 emissions as a function 
of the modelled sensitivity of GSAT to anthropogenic CO 2 emissions. In panels (d) and (h), the black dot denotes the observed sensitivity, while the shading around it denotes

internal variability as inferred from CMIP6 simulations (after Notz and SIMIP Community, 2020). Further details on data sources and processing are available in the chapter 
data table (Table 9.SM.9).

1251
Ocean, Cryosphere and Sea Level Change  Chapter 99The CMIP5 models also have issues with capturing the seasonal 
cycle of observed changes in Arctic sea ice drift speed, which affects 
their simulation of regional sea ice concentration patterns. Direct 
measurements of Arctic sea ice from drift buoys and satellites show 
that drift speed of Arctic sea ice has increased over the satellite 
period in all seasons (e.g., Rampal et al., 2009; Docquier et al., 2017). 
In summer, CMIP5 models show a slowdown of Arctic sea ice drift 
rather than the observed acceleration (Tandon et al., 2018). In winter, 
CMIP5 models generally capture the observed acceleration of Arctic 
drift speed. The drift acceleration is primarily caused by the decrease 
in concentration and thickness in the observational record (Rampal 
et al., 2009; Spreen et al., 2011; Olason and Notz, 2014; Docquier 
et al., 2017) and, for winter, in CMIP5 models (Tandon et al., 2018).

Changes in wind speed are less important for the observed large-scale 
changes (Spreen et al., 2011; Vihma et al., 2012; Olason and Notz, 
2014; Docquier et al., 2017; Tandon et al., 2018). In summary, there 
is high confidence  that Arctic sea ice drift has accelerated because of 
the decrease in sea ice concentration and thickness.
The SR1.5 assessed with high confidence  that there is no hysteresis 
in the loss of Arctic summer sea ice. In addition, there is no tipping 
point or critical threshold in global mean temperature beyond which 
the loss of summer sea ice becomes self-accelerating and irreversible 
(high confidence ). This is because stabilizing feedbacks during winter 
related to increased heat loss through thin ice and thin snow, and 
increased emission of longwave radiation from open water, dominate 
over the amplifying ice albedo feedback (see Section 7.4.2 for details 
on the  individual feedbacks; e.g.,  Eisenman, 2012; Wagner and

Eisenman, 2015; Notz and Stroeve, 2018). Observed and modelled 
Arctic summer sea ice and global mean temperature are linked 
with little temporal delay, and the summer sea ice loss is reversible 
on decadal time scales (Armour et  al., 2011; Ridley et  al., 2012; 
Li et al., 2013; Jahn, 2018). The loss of winter sea ice is reversible 
as well, but the loss of winter sea ice area per degree of warming 
in CMIP5 and CMIP6 projections increases as the ice retreats from 
the continental shore lines, because these limit the possible areal 
fluctuations ( high confidence ) (Section 4.3.2.1; Bathiany et al., 2016, 
2020; Meccia et al., 2020).
9.3.1.2 Arctic Sea Ice Volume and Thickness
The SROCC assessed with very high confidence  that Arctic sea ice 
has become thinner over the satellite period from 1979 onwards, 
and this assessment is confirmed for the updated time series 
(Section 2.3.2.1.1). Sea ice area has also decreased substantially

over this period (Section 9.3.1.1), leading to the assessment that 
Arctic sea ice volume has also decreased with very high confidence 
over the satellite period since 1979. There is, however, only low 
confidence in quantitative estimates of the sea ice volume 
loss over this period because of a  lack of reliable, long-term, 
pan-Arctic observations and substantial spread in available 
reanalyses (Chevallier et  al., 2017). Current best estimates from 
reanalyses suggest a reduction of September Arctic sea ice volume 
of 55 to 65% over the period 1979–2010, and of about 72% over 
the period 1979–2016, with the latter deemed a  conservative 
estimate (Schweiger et al., 2019).For the more recent past, ice thickness can be directly estimated from 
satellite retrievals of sea ice freeboard (Kwok and Cunningham, 2015; 
Kwok, 2018). Based on these retrievals, there is medium confidence  
that Arctic sea ice volume has decreased since 2003. There is low

confidence  in the amount of decrease over this period and over 
the CryoSat-2 period from 2011 onwards, primarily because of 
snow-induced uncertainties in the retrieval algorithms, the shortness 
of the record, and the small identified trend (e.g., Bunzel et al., 2018; 
Petty et al., 2018, 2020).
Observations of regional changes in sea ice thickness vary in quality. 
Analysis of submarine data in the central Arctic Ocean suggests that 
its sea ice has thinned by about 75 cm compared to the mid-1970s 
(Section 2.3.2.1.1). For smaller regions, data are too sparse to allow 
for quantitative estimates of long-term trends (King et  al., 2017; 
Rösel et al., 2018), but a clear thinning signal over 10 to 20 years has 
been found for sea ice in the Fram Strait (Spreen et al., 2020), north of 
Canada (Haas et al., 2017) and for landfast ice in the Kongsfjorden/
Svalbard Arctic border (Pavlova et al., 2019). The CMIP5 models and

reanalyses fail to capture the observed distribution (Stroeve et al., 
2014; Shu et al., 2015) and evolution (Chevallier et al., 2017) of Arctic 
sea ice thickness. Most CMIP6 models do not capture the observed 
spatial distribution of sea ice thickness realistically (Wei et al., 2020). 
This leads to low confidence  in estimates of thickness from reanalyses 
and from CMIP5 and CMIP6 models and in their projections of sea 
ice volume.
9.3.2 Antarctic Sea Ice
9.3.2.1 Antarctic Sea Ice Coverage
The SROCC (Meredith et  al., 2019) assessed that there was no 
significant trend in annual mean Antarctic sea ice area over the period 
of reliable satellite retrievals starting in 1979 ( high confidence ). The 
updated time series is consistent with this assessment. It includes 
a  maximum sea ice area in 2014, then a  substantial decline until 
the minimum sea ice area in 2017, and an increase in sea ice area 
since 2017 (Figures 2.20 and 9.15; Schlosser et al., 2018; Maksym,

2019; Parkinson, 2019). As assessed in Section 2.3.2.1.2, the possible 
significance of the increase in mean Antarctic sea ice area over 
the shorter period 1979 to 2014 (Figure  2.20; Simmonds, 2015; 
Comiso et  al., 2017b) is unclear. This is because of observational 
uncertainty (see Section  9.3.1.1), large year-to-year fluctuations in 
all months (Figure 9.15), and limited understanding of the processes 
and reliability of year-to-year correlation of Antarctic sea ice area 
(Yuan et al., 2017).
As assessed by SROCC, the evolution of mean Antarctic sea ice area 
is the result of opposing regional trends ( high confidence ), with 
slightly decreasing sea ice cover during the period 1979 to 2019 in 
the Amundsen and Bellingshausen Seas, particularly during summer, 
and slightly increasing sea ice cover in the eastern parts of the 
Weddell and Ross Seas (Figure 9.15). With the exception of the Ross 
Sea, these trends are not significant, considering the large variability

1252
Chapter 9 Ocean, Cryosphere and Sea Level Change9The SROCC assessed that the regional trends are closely related 
to meridional wind trends ( high confidence ). This is the case as the 
regional trends in the maximum northward extent of the ice cover 
(Figure 9.15) are determined by the balance between the northward 
advection of the ice that is formed in polynyas near the continental 
margin, and the lateral and subsurface melting through oceanic heat 
fluxes. The advection of the sea ice is strongly correlated with winds 
and cyclones (Schemm, 2018; Vichi et al., 2019; Alberello et al., 2020). 
Accordingly, the increasing sea ice area in the Ross Sea can be linked 
to a  strengthening of the Amundsen Sea low (e.g.,  Holland et  al., 
2017b, 2018), while other regional sea ice trends in the austral autumn 
can be linked to changes in westerly winds, cyclone activity and the 
Southern Annular Mode (SAM) in summer and spring (Doddridge and

Marshall, 2017; Holland et al., 2017a; Schemm, 2018). In addition to 
the wind-driven changes, increased near-surface ocean stratification 
(Section 9.2.1.3) has contributed to the observed increase in sea ice 
coverage (e.g., Purich et al., 2018; L. Zhang et al., 2019) as it tends 
to cool the surface ocean (Sections 9.2.1.1 and 9.2.3.2). The changes 
in stratification result partly from surface freshening (De  Lavergne 
et al., 2014), associated with increased northward sea ice advection 
(Haumann et al., 2020) and/or melting of the Antarctic ice sheet ( medium 
confidence ) (e.g.,  Haumann et  al., 2020; Jeong et  al., 2020; Mackie 
et al., 2020), and amplified by local ice–ocean feedbacks (Goosse and 
Zunz, 2014; Lecomte et al., 2017; Goosse et al., 2018). In the Amundsen 
Sea, strong ice shelf melting can cause local sea ice melt next to the 
ice shelf front by entraining warm circumpolar deep water to the ice

shelf cavity and surface ocean ( medium confidence ) (Sections 9.2.3.2 
and 9.4.2.2; Jourdain et al., 2017; Merino et al., 2018). It has also been 
suggested that the observed regional increase in sea ice coverage since 1979 results from a long-term Southern Ocean surface cooling trend 
(e.g., Kusahara et al., 2019; Jeong et al., 2020) but the importance of 
this mechanism for the observed sea ice evolution is unclear owing 
to intricate feedbacks between sea ice  change and surface cooling 
(Haumann et  al., 2020). The importance of changing wave activity 
(Section 9.6.4.2; Kohout et al., 2014; Bennetts et al., 2017; Roach et al., 
2018b) on sea ice is unclear due to limited process understanding. In 
summary, there is  high confidence  that regional Antarctic trends are 
primarily caused by changes in sea ice drift and decay, with medium 
confidence  in a dominating role of changing wind pattern. The precise 
relative contribution of individual drivers remains uncertain because

of limited observations, disagreement between models, unresolved 
processes, and temporal and spatial remote linkages caused by sea ice 
drift (Section 9.2.3.2; Pope et al., 2017).
Recent research has confirmed SROCC assessment of atmospheric and 
oceanic drivers of the sea ice decline from 2014 to 2017, which can be 
linked to changes in both subsurface ocean heat flux (Meehl et al., 2019; 
Purich and England, 2019) and atmospheric circulation, with the latter 
partly related to teleconnections with the tropics (Meehl et al., 2019; 
Purich and England, 2019; G. Wang et al., 2019). In the Weddell Sea, 
these changes caused in 2017 the re-emergence of the largest polynya 
over the Maud Rise since the 1970s (Section 9.2.3.2; Campbell et al., 
2019; Jena et al., 2019; Turner et al., 2020).
The AR5 (Collins et al., 2013) and SROCC found low confidence  in future 
projections of Antarctic sea ice. This includes the projected mitigation

of the sea ice loss by stratospheric ozone recovery (Smith et al., 2012) 
and by an increased freshwater input from melting of the Antarctic 
Antarctic sea-ice historical records and CMIP6 projections
Anomaly time series, maps of seasonal sea-ice concenration and changes, and projected sea-ice metrics in SSP2-4.5
Figur e 9.15 | Antarctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections.  (Left)  Absolute anomaly of 
observed monthly mean Antarctic sea ice area during the period 1979–2019 relative to the average monthly mean Antarctic sea ice area during the period 1979–2008. 
(Right)  Sea ice coverage in the Antarctic as given by the average of the three most widely used satellite-based estimates for September and February, which usually are the 
months of maximum and minimum sea ice coverage, respectively. First column: Mean sea ice coverage during the decade 1979–1988. Second column: Mean sea ice coverage

during the decade 2010–2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth 
column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15% for the decade 2045–2054. The average observational record of sea ice area 
is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) 
(Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels 
showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1253
Ocean, Cryosphere and Sea Level Change  Chapter 99Ice Sheet (Bronselaer et  al., 2018). Compared to the interannual 
variability during the satellite record from 1979 onwards, models 
simulate too much variability in both CMIP5 (Zunz et al., 2013) and 
CMIP6 (Roach et al., 2020). The seasonal cycle in sea ice coverage is 
misrepresented in most CMIP5 (e.g., Holmes et al., 2019) and CMIP6 
models (Roach et al., 2020), but the multi-model mean seasonal cycle 
in CMIP5 and CMIP6 agrees well with observations (Shu et al., 2015; 
Roach et al., 2020). Most CMIP5 models do not realistically simulate 
the evolution of Antarctic sea ice volume (Shu et  al., 2015) and 
consistently overestimate the amount of low concentration sea ice, 
and underestimate the amount of high concentration sea ice (Roach 
et al., 2018a). In contrast, CMIP6 models simulate a more realistic 
distribution of regional sea ice coverage (Roach et al., 2020). Most

CMIP5 models poorly represent Antarctic sea ice drift (e.g., Schroeter 
et  al., 2018; Holmes et  al., 2019), affecting simulated historical 
trends, with models that simulate a strong sea ice motion showing 
more variability in sea ice coverage than models with weaker sea ice 
motion (Schroeter et al., 2018). Owing to limited agreement between 
model simulations and observations, limited reliable observations on 
a process level, and a lack of process understanding of the substantial 
spread in CMIP5 and CMIP6 model simulations, there remains low 
confidence in existing future projections of Antarctic sea ice decrease 
and lack of decrease.
The discrepancy between the modelled and observed evolution 
of Antarctic sea ice has been related by SROCC to deficiencies in  
modelled stratification, freshening by ice-shelf meltwater, clouds, and 
other wind- and ocean-driven processes. Recent studies highlight the 
possible mis-representation of freshwater fluxes from ice shelves

(Jeong et al., 2020), and the possible effect of the low resolution of 
most models (Sidorenko et al., 2019), even though lower-resolution 
models are, in principle, capable of a  realistic simulation of the 
seasonal sea ice budgets in the Southern Ocean (Holmes et al., 2019). 
The relative importance of these possible reasons for the models’ 
shortcomings remains unclear (see Section 3.4.1.2 for details).
The analysis and understanding of the long-term evolution of the 
Antarctic sea ice cover is hindered by the scarcity of observational 
records before the satellite period, and the scarcity of paleorecords 
(see Section  2.3.2.1.2 for further details). Such long records are 
particularly relevant given that the Southern Ocean response 
to external forcing takes longer than the length of the available 
direct observational record (Goosse and Renssen, 2001; Armour 
et al., 2016). There is only  limited evidence for large-scale decadal

fluctuations in sea ice coverage caused by large-scale temperature 
and wind forcing. Sparse direct pre-satellite observations suggest 
a decrease in sea ice coverage from the 1950s to the 1970s (Fan 
et al., 2014). Paleo-proxy data indicate that, on multi-decadal to 
multi-centennial time scales, sea ice coverage of the Southern 
Ocean follows large-scale temperature trends (e.g., Crosta et al., 
2018; Chadwick et  al., 2020; Lamping et  al., 2020), for example 
linked to fluctuations in the El  Niño–Southern Oscillation and 
Southern Annular Mode (Crosta et al., 2021), and that during the 
Last Glacial Maximum, Antarctic sea ice extended to about the 
polar front latitude in most regions during winter, whereas the 
extent during summer is less well understood (e.g.,  Benz et  al., 
2016; Xiao et al., 2016; Nair et al., 2019).Regionally, proxy data from ice cores consistently indicate that the 
increase of sea ice area in the Ross Sea and the decrease of sea ice

area in the Bellingshausen Sea are part of longer centennial trends 
and exceed internal variability on multi-decadal time scales ( medium 
confidence ) (e.g.,  Thomas et  al., 2019; Tesi et  al., 2020). These 
centennial trends are consistent with simulations from CMIP5 models 
(Hobbs et al., 2016b; J.M. Jones et al., 2016; Kimura et al., 2017).
There is low confidence  in the attribution of the observed changes 
in Antarctic sea ice area (Section  3.4.1.2). Based on the available 
evidence, the lack of a  negative trend of Antarctic sea ice area, 
despite substantial global warming in recent decades, has been 
attributed to internal variability in analyses of the observational 
record (Meier  et  al., 2013; Gallaher et  al., 2014; Gagné et  al., 
2015b), reconstructions from early observations (Fan et  al., 2014; 
Edinburgh and Day, 2016) and proxy data (Hobbs et al., 2016b) in 
model simulations (Turner et al., 2013; Zunz et al., 2013; L. Zhang

et al., 2019). Nonetheless, without accurate simulations of observed 
changes, the possible contribution of anthropogenic forcing to the 
regional changes in sea ice area remains unclear (Hosking et  al., 
2013; Turner et al., 2013; Haumann et al., 2014; L. Zhang et al., 2019).
The attribution of the observed trends in atmospheric and oceanic 
forcing is also uncertain because of limited observational records and 
discrepancies between modelled and observed evolution of the sea 
ice cover. More specifically, there is contrasting evidence for a direct 
role of stratospheric ozone depletion on the observed changes in 
atmospheric circulation (Haumann et al., 2014; England et al., 2016; 
Landrum et  al., 2017). In contrast, there is high confidence  that 
multi-decadal variations in the tropical Pacific and in the Atlantic 
affect the Amundsen Sea low (Li et al., 2014; Kwok et al., 2016; Meehl 
et al., 2016; Purich et al., 2016; Simpkins et al., 2016), while other

modes of climate variability (Annex IV) affect, for example, Southern 
Ocean cyclone activity (Simpkins et al., 2012; Cerrone et al., 2017; 
Schemm, 2018) .
9.3.2.2 Antarctic Sea Ice Thickness
The SROCC assessed that observations are too sparse to reliably 
estimate long-term trends in Antarctic sea ice thickness. This remains 
true, and only qualitative statements on prevailing thicknesses are 
possible. Data from ICESat-1 laser altimetry (Kurtz and Markus, 2012), 
from Operation IceBridge (Kwok and Kacimi, 2018), and long-term 
shipboard observations collected in the Antarctic Sea Ice Processes 
and Climate (ASPeCt) dataset (Worby et  al., 2008) suggest that 
sea ice thicker than 1 m prevails in regions of multi-year ice along 
the eastern coast of the Antarctic Peninsula in the Weddell Sea, in the 
high-latitude embayment of the Weddell Sea, and along the coast 
of the Amundsen Sea, with remaining regions dominated by thinner

first-year sea ice ( high confidence ). Regional patterns in ice thickness 
are affected by areas of high snow deposition and resulting snow-ice 
formation (Massom et  al., 2001; Maksym and Markus, 2008), and 
deformation, ridging, and rafting that regionally cause formation of 
very thick sea ice (Massom et  al., 2006; G. Williams et  al., 2015). 
In addition, near ice shelves a sub-ice platelet layer from supercooled 
water can significantly increase sea ice thickness (Hoppmann et al., 
2020; Haas et  al., 2021). Regarding snow thickness, observations

1254
Chapter 9 Ocean, Cryosphere and Sea Level Change9are too sparse in space and time to reliably estimate changes across 
Southern Ocean sea ice (Webster et al., 2018).
There is low confidence  in the long-term trend of Antarctic sea ice 
thickness. Both ASPeCt and ICESat-1 measurements are biased low 
in regions with thick ice (Kern and Spreen, 2015), compared to results 
from reanalyses (Massonnet et al., 2013; Haumann et al., 2016) and 
observations with autonomous vehicles under sea ice (G. Williams 
et al., 2015). Estimates of sea ice thickness from CryoSat-2 do not 
substantially reduce uncertainty, primarily because of the unknown 
snow thickness and radar scattering above the snow–ice interface 
(Bunzel et  al., 2018; Kwok and Kacimi, 2018; Kacimi and Kwok, 
2020). Isolated in situ time series show no clear long-term trend 
in landfast ice thickness in the Weddell Sea (Arndt et  al.,  2020). 
Reanalyses suggest overall increasing sea ice thickness and volume

between 1980 and 2010 (Holland et  al., 2014; Zhang, 2014; 
Massonnet et  al., 2015), while CMIP5 (Shu et  al., 2015; Schroeter 
et al., 2018) and CMIP6 models simulate a decrease in Antarctic sea 
ice volume over the historical period. Because of this discrepancy, 
and the unclear reliability of the reanalyses (Uotila et al., 2019), there 
is low confidence  in CMIP5 and CMIP6 simulated future Antarctic 
sea ice thickness.
9.4 Ice Sheets
9.4.1 Greenland Ice Sheet
9.4.1.1 Recent Observed Changes
In this section we present regional mass change time series for the 
Greenland Ice Sheet and assess the different processes that are 
causing the increase in mass loss. The vast increase in observational 
products from various platforms (e.g, GRACE, PROMICE, ESA-
CCI, NASA MEaSUREs) provide a  consistent and clear picture of 
a shrinking Greenland Ice Sheet (Colgan et al., 2019; Mottram et al., 
2019; Mouginot et al., 2019; King et al., 2020; Mankoff et al., 2020;

Moon et al., 2020; Sasgen et al., 2020; Velicogna et al., 2020; The 
IMBIE Team, 2020). Section 2.3.2.4.1 provides an updated estimate 
of the total Greenland Ice Sheet mass change in a  global context 
(Figure  2.24). The estimated ice-sheet extent at different times is 
shown in Figure 9.17, and the paleo perspective on Greenland Ice 
Sheet evolution is presented in Section 9.6.2.
For the 20th  century, SROCC (Meredith et  al., 2019) presented 
one reconstruction for 1900–1983 and estimated mass change for 
the Greenland Ice Sheet and its peripheral glaciers for the period 
1901–1990. Since SROCC, a comprehensive new study has extended 
the satellite record back to 1972 (Figure  9.16; Mouginot et  al., 
2019). The rate of ice-sheet mass change was positive (i.e., it gained 
mass) in 1972–1980 (47 ± 21 Gt yr –1) and then negative (i.e., it lost 
mass; –51 ± 17 Gt yr –1 and –41 ± 17 Gt yr –1) in 1980–1990 and 
1990–2000, respectively. Other ice discharge time series starting in

1985 (King et al., 2018, 2020; Mankoff et al., 2019, 2020) agree with 
Mouginot et al. (2019) (see also Figure 9.16). There is limited evidence  
of temporally and spatially heterogeneous Greenland outlet glacier 
evolution during the 20th century (Lea et al., 2014; Lüthi et al., 2016; Andresen et  al., 2017; Khan et  al., 2020; Vermassen et  al., 2020). 
Historical photographs (Khan et al., 2020) show large mass losses 
of Jakobshavn and Kangerlussuaq Glaciers in West Greenland 
from 1880 until the 1940s, exceeding their 21st-century mass loss, 
whereas the Helheim Glacier in East Greenland remained stable, 
gained mass in the 1990s, then rapidly lost mass after 2000. Together, 
these three large outlet glaciers, draining about 12% of the ice sheet 
surface area, have lost 22 ± 3 Gt yr –1 in the period 1880–2012 (Khan 
et al., 2020). Overall, these studies provide a variable picture of the 
Greenland Ice Sheet mass change in the 20th century. The updated

mass loss  of Greenland Ice Sheet, including peripheral glaciers for 
the period 1901–1990, is 120 [70–170] Gt yr –1 (see Table 9.5 and 
Figures 9.16 and 9.17).
Post-1992, SROCC stated that it is extremely likely that the rate 
of mass change of Greenland Ice Sheet was more negative during 
2012–2016 than during 1992–2001, with very high confidence  
that summer melting has increased since the 1990s to a  level 
unprecedented over at least the last 350 years. Since SROCC, the 
updated synthesis of satellite observations by the Ice Sheet Mass 
Balance Intercomparison Exercise (The IMBIE Team, 2020) and the 
GRACE Follow-On (GRACE-FO) Mission (Abich et al., 2019; Kornfeld 
et al., 2019), have confirmed the mass change record, and the record 
has been extended to 2020 (The IMBIE Team, 2021) as presented 
in 2.3.2.4. The Greenland Ice Sheet lost 4890 [4140–5640] Gt of ice 
between 1992 and 2020, causing sea level to rise by 13.5 [11.4 to

15.6]  mm (The IMBIE Team, 2021; see also Section  2.3.2.4.1, 
Figure 9.16 and Table 9.5). The IMBIE Team’s (2020) estimates are 
consistent with other post-AR5 reviews (Figure 9.17, Table 9.SM.1; 
Bamber et al., 2018a; Cazenave et al., 2018; Mouginot et al., 2019; 
Slater et  al.,  2021). Recent GRACE-FO data (Sasgen et  al., 2020; 
Velicogna et al., 2020) show that, after two cold summers in 2017 
and 2018, with relatively moderate mass change of about –100 
Gt yr –1, the 2019 mass change (–532 ± 58 Gt yr –1) was the largest 
annual mass loss in the record. The high agreement  across a variety 
of methods confirms SROCC and Chapter 2 assessments. The mass-
loss rate was, on average, 39 [–3 to 80] Gt yr –1 over the period 
1992–1999, 175 [131 to 220] Gt yr –1 over the period 2000–2009 
and 243 [197 to 290] Gt yr –1 over the period 2010–2019 (see Table 9.
SM.1).
The SROCC assessed with high confidence  that surface mass balance

(SMB), rather than discharge, has started to dominate the mass loss 
of the Greenland Ice Sheet (due to increased surface melting and 
runoff), increasing from 42% of the total mass loss for 2000–2005 
to 68% for 2009–2012. While these estimates have been confirmed 
since SROCC (Mouginot et  al., 2019), the new longer record, as 
well as further comprehensive studies  (Khan et  al., 2015; Colgan 
et  al., 2019; Mottram et  al., 2019; The IMBIE Team, 2020)  and 
detailed discharge records  (King et al., 2020; Mankoff et al., 2020)  
reveal a  more complex picture than the continuous trajectory this 
statement may have implied. Discharge was relatively constant from 
1972–1999, varying by around 6% for the whole ice sheet, while SMB 
varied by a factor of over two interannually, leading to either mass 
gain or loss in a given year (Figure 9.16). During 2000–2005, the rate 
of discharge increased by 18%, then remained fairly constant again

1255
Figure 9.1 6 | Mass changes and mass change rates for Greenland and Antarctic ice sheet regions. (a)  Time series of mass changes in Greenland for each of the 
major drainage basins shown in the inset figure (Bamber et al., 2018b; Mouginot et al., 2019; The IMBIE Team, 2021) for the periods 1972–2016, 1992–2018, and 1992–2020. 
(b) Time series of mass changes for three portions of Antarctica (Bamber et al., 2018b; The IMBIE Team, 2021) for the period 1992–2016 and 1992–2020. Estimates of mass 
change rates of surface mass balance, discharge and mass balance in (g) all of Greenland and  (c –f, h–j)  in seven Greenland regions (Bamber et al., 2018b; Mankoff et al., 
2019; Mouginot et al., 2019; King et al., 2020). Estimates of mass change rates of surface mass balance, discharge and mass balance for (k) all of Antarctica and (l–n)  for

three regions of Antarctica (Bamber et al., 2018b; The IMBIE Team, 2018; Rignot et al., 2019). Further details on data sources and processing are available in the chapter data 

1256
Chapter 9 Ocean, Cryosphere and Sea Level Change9more rapidly than discharge increased. In summary, the consistent 
temporal pattern in these longer datasets leads to high confidence  
that the Greenland Ice Sheet mass losses are increasingly dominated 
by SMB, but there is high confidence that mass loss varies strongly, 
due to large interannual variability in SMB.
On a regional scale, the surface elevation is lowering in all regions, and 
widespread terminus and calving front retreats have been observed 
(with no glaciers advancing; Mottram et al., 2019; Moon et al., 2020). 
The largest mass losses have occurred along the west coast and in 
south-east Greenland (Figure  9.16), concentrated at a  few major 
outlet glaciers (Mouginot et al., 2019; Khan et al., 2020). This regional 
pattern is consistent with independent Global Navigation Satellite 
System (GNSS) observations from the Greenland Global Positioning 
System (GPS) network which show elastic bedrock uplift of tens of

centimetres between 2007–2019 as a result of ongoing ice mass loss 
(Bevis et al., 2019). The regional time series (Figures 9.16; Atlas.30) show that SMB has been gradually decreasing in all regions, while 
the increase in discharge in the south-east, central east, north-west 
and central west has been linked to retreating tidewater glaciers 
(Figure  9.16). In summary, the detailed regional records show an 
increase in mass loss in all regions after the 1980s, caused by both 
increases in discharge and decreases in SMB ( high confidence ), 
although the timing and patterns vary between regions. The largest 
mass loss occurred in the north-west and the south-east of Greenland 
The SROCC stated with high confidence  that variability in large-scale 
atmospheric circulation is an important driver of short-term SMB 
changes for the Greenland Ice Sheet. This effect of atmospheric 
circulation variability on both precipitation and melt rates (and SROCC

assessment) is confirmed by more recent publications (Välisuo et al., 
2018; B. Zhang et al., 2019; Velicogna et al., 2020). The strong mass 
loss in 2019 (Cullather et  al., 2020; Hanna et  al., 2020; Tedesco 
Greenland ice sheet cumulative mass change and equivalent sea level contribution
1980
2000
2020
2040
2060
2080
2100
-8
-4
0
4
10
4
 Gt    
-0.1
0
0.1
0.2
Box
Box Range
1840-1972
Mouginot
Bamber
IMBIE
SSP1-2.6
SSP5-8.5
Modern & Projected Changes
m
2100 medians,
66% and 90% ranges
ISMIP6
Emulator
Emulator median (SSPs)
Emulator
 90%
 range
Emulator
 66%
 range
ISMIP6 models (RCPs/SSPs)
Figure 9.17 | Greenland Ice Sheet cumulative mass change and equivalent sea level contribution. (a)  A p-box (Section 9.6.3.2) based estimate of the range of values 
of paleo Greenland Ice Sheet mass and sea level equivalents relative to present day and the median over all central estimates (Simpson et al., 2009; Argus and Peltier, 2010; Colville

et al., 2011; Dolan et al., 2011; Fyke et al., 2011; Robinson et al., 2011; Born and Nisancioglu, 2012; K.G. Miller et al., 2012; Dahl-Jensen et al., 2013; Helsen et al., 2013; Nick 
et al., 2013; Quiquet et al., 2013; Stone et al., 2013; Colleoni et al., 2014; Lecavalier et al., 2014; Robinson and Goelzer, 2014; Calov et al., 2015, 2018; Dutton et al., 2015; Koenig 
et al., 2015; Peltier et al., 2015; Stuhne and Peltier, 2015; Vizcaino et al., 2015; Goelzer et al., 2016; Khan et al., 2016; Yau et al., 2016; de Boer et al., 2017; Simms et al., 2019);  
(b, left)  cumulative mass loss (and sea level equivalent) since 2015 from 1972 (Mouginot et al., 2019) and 1992 (Bamber et al., 2018b; The IMBIE Team, 2020), the estimated 
mass loss from 1840 (Box and Colgan, 2013; Kjeldsen et al., 2015) indicated with a shaded box, and projections from Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6)

to 2100 under RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6 scenarios (thin lines from Goelzer et al. (2020); Edwards et al. (2021); Payne et al. (2021)) and ISMIP6 emulator under 
SSP5-8.5 and SSP1-2.6 to 2100 (shades and bold line; Edwards et al., 2021); (b, right)  17th–83rd and 5th–95th percentile ranges for ISMIP6 and ISMIP6 emulator at 2100. 
Schematic interpretations of individual reconstructions (Lecavalier et al., 2014; Goelzer et al., 2016; Berends et al., 2019) of the spatial extent of the Greenland Ice Sheet are shown 
for the: (c) mid-Pliocene Warm Period; (d) the Last Interglacial; and (e) the Last Glacial Maximum: grey shading shows extent of grounded ice. Maps of mean elevation changes 
(f) 2010–2017 derived from CryoSat 2 radar altimetry (Bamber et al., 2018b) and (g) ISMIP6 model mean (2093–2100) projected changes for the MIROC5 climate model under

the RCP8.5 scenario (Goelzer et al., 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1257
Ocean, Cryosphere and Sea Level Change  Chapter 99and Fettweis, 2020) was driven by highly anomalous atmospheric 
circulation patterns, both on daily (Cullather et al., 2020) and seasonal 
time scales (Tedesco and Fettweis, 2020). Although surface melt 
is anticorrelated with the summer North Atlantic Oscillation Index 
(Välisuo et al., 2018; Ruan et al., 2019; Sherman et al., 2020), especially 
in West Greenland (Bevis et al., 2019), Greenland Ice Sheet melt is 
more strongly correlated with the Greenland Blocking Index (Hanna 
et al., 2016, 2018) than with the summer North Atlantic Oscillation 
index (Huai et al., 2020).
The SROCC did not assess the role of cloud changes in detail. Studies 
since AR5 have shown that higher incident shortwave radiation in 
conjunction with reduced cloud cover leads to increased melt rates, 
particularly over the low-albedo ablation zone in the southern 
part of the Greenland Ice Sheet (Hofer et al., 2017; Niwano et al.,

2019; Ruan et  al., 2019). Conversely, an increase in cloud cover 
over the high-albedo central parts of the ice sheet, leading to 
higher downwelling longwave radiation, was shown to lead either 
to increased melt (Bennartz et  al., 2013) or reduced refreezing of 
meltwater (van Tricht et al., 2016). The elevation dependence of the 
cloud radiative effect and its control on surface meltwater generation 
and refreezing (W. Wang et al., 2019; Hahn et al., 2020) can induce 
a spatially consistent response of the integrated Greenland Ice Sheet 
melt to dominant patterns of cloud and atmospheric variability. 
The shortwave and longwave radiation effects on surface melt by 
clouds have been shown to compensate for each other during strong 
atmospheric river events, and the increase in melt is caused by 
increased sensible heat fluxes during such events (Mattingly et al., 
2020). In summary, there is medium confidence  that cloud cover

changes are an important driver of the increasing melt rates in the 
southern and western part of the Greenland Ice Sheet.
The SROCC stated with high confidence  that positive albedo feedbacks 
contributed substantially to the post-1990s Greenland Ice Sheet melt 
increase. Several (mostly positive) feedbacks involving surface albedo 
operate on ice sheets (e.g.,  Fyke et  al., 2018). Melt amplification 
by the observed increase of bare ice exposure through snowline 
migration to higher parts of the ice sheet since 2000 (Shimada et al., 
2016; Ryan et al., 2019) was five times stronger than the effect of 
hydrological and biological processes that lead to reduced bare 
ice albedo (Ryan et al., 2019). Impurities, in part biologically active 
(Ryan et al., 2018), have been observed to lead to albedo reduction 
(Stibal et al., 2017) and are estimated to have increased runoff from 
bare ice in the southwestern sector of the Greenland Ice Sheet by

about 10% (Cook et al., 2020). In summary, new studies confirm that 
there is high confidence  that the Greenland Ice Sheet melt increase 
since about 2000 has been amplified by positive albedo feedbacks, 
with the expansion of bare ice extent being the dominant factor, and 
albedo in the bare ice zone being primarily controlled by distributed 
biologically active impurities (see also Section 7.3.4.3).
The SROCC reported with medium confidence  that around half of 
the 1960–2014 Greenland Ice Sheet surface meltwater ran off, while 
most of the remainder infiltrated firn and snow, where it either 
refroze or accumulated in firn aquifers. Studies since SROCC show 
a decrease of firn air content between 1998–2008 and 2010–2017 
(Vandecrux et  al., 2019) in the low-accumulation percolation area of western Greenland, reducing meltwater retention capacity. 
Moreover, meltwater infiltration into firn can be strongly limited 
by low-permeability ice slabs created by refreezing of infiltrated

meltwater (Machguth et al., 2016). Recent observations and modelling 
efforts indicate that rapidly expanding low-permeability layers have 
led to an increase in runoff area since 2001 (MacFerrin et al., 2019). 
In summary, there is medium confidence  that meltwater storage and 
refreezing can temporarily buffer a  large-scale melt increase, but 
limiting factors have been identified.
The SROCC reported that there was medium confidence  that ocean 
temperatures near the grounding zone of tidewater glaciers are 
critically important to their calving rate, but there was low confidence 
in understanding their response to ocean forcing. The increase in ice 
discharge in the late 1990s and early 2000s (Mouginot et al., 2019; 
King et  al., 2020; Mankoff et  al., 2020) has been associated with 
a period of widespread tidewater glacier retreat (Murray et al., 2015; 
Wood et al., 2021) and speed up (Moon et al., 2020). Since SROCC,

new studies provide strong evidence for rapid submarine melting 
at tidewater glaciers (Sutherland et al., 2019; Wagner et al., 2019; 
Bunce et al., 2020; R.H. Jackson et al., 2020). Changes in submarine 
melting and subglacial meltwater discharge can trigger increased 
ice discharge by reducing the buttressing to ice flow and promoting 
calving (Benn et al., 2017; Todd et al., 2018; Ma and Bassis, 2019; 
Mercenier et  al., 2020); through undercutting (Rignot et  al., 2015; 
D.A.  Slater et  al., 2017; Wood et  al., 2018; Fried et  al., 2019) and 
frontal incision (Cowton et al., 2019). Warming ocean waters have 
been implicated in the recent thinning and breakup of floating ice 
tongues in north-eastern and north-western Greenland (Mouginot 
et al., 2015; Wilson et al., 2017; Mayer et al., 2018; Washam et al., 
2018; An et al., 2021; Wood et al., 2021). On decadal time scales, 
tidewater glacier terminus position correlates with submarine melting

(Slater et al., 2019). Over shorter time scales, individual glaciers or 
clusters of glaciers can behave differently and asynchronously (Bunce 
et  al., 2018; Vijay et  al., 2019; An et  al., 2021), and there are not 
always clear associations between water temperature and glacier 
calving rates (Motyka et al., 2017), retreat or speed-up (Joughin et al., 
2020; Solgaard et al., 2020). Variations in ice mélange at the front 
of a glacier, associated with changes in ocean and air temperature, 
have also emerged as a plausible control on calving (Burton et al., 
2018; Xie et  al., 2019; Joughin et  al., 2020). In summary, there is 
high confidence  that warmer ocean waters and increased subglacial 
discharge of surface melt at the margins of marine-terminating 
glaciers increase submarine melt, which leads to increased ice 
discharge. There is medium confidence  that this contributed to the 
increased rate of mass loss from Greenland, particularly in the period

2000–2010 when increased discharge was observed in the south-east 
and north-west.
The SROCC reported that accurate bedrock topography is required for 
understanding and projecting the glacier response to ocean forcing. 
Accurate bathymetry is essential for establishing which water masses 
enter glacial fjords, and for reliable estimates of the submarine melt 
rates experienced by tidewater glaciers (Schaffer et al., 2020; T. Slater 
et al., 2020; Wood et al., 2021). Subglacial and lateral topography 
is known to strongly modulate tidewater glacier dynamics and the 
sensitivity of tidewater glaciers to climatic forcing (Enderlin et  al.,

1258
Chapter 9 Ocean, Cryosphere and Sea Level Change92013; Catania et  al., 2018). Bathymetric mapping around the ice 
sheet has greatly improved with direct and gravimetric surveys 
(Millan et al., 2018; An et al., 2019a, b; Jakobsson et al., 2020) leading 
to the improvement of Greenland-wide bathymetric and topographic 
mapping (e.g., Morlighem et al., 2017). However, large uncertainties 
in ice thickness remain for around half of the outlet glaciers 
(Mouginot et al., 2019; Wood et al., 2021) and sea ice covered and 
iceberg-packed regions remain poorly sampled near glacier termini 
(Morlighem et al., 2017). There is high confidence  that bathymetry 
(governing the water masses that flow into fjord cavities) and 
fjord geometry and bedrock topography (controlling ice dynamics) 
modulate the response of individual glaciers to climate forcing.
The AR5 assessed that it is likely  that anthropogenic forcing has 
contributed to the surface melting of Greenland since 1993 (Bindoff

et al., 2013). Section 3.4.3.2 assesses that it is very likely  that human 
influence has contributed to the observed surface melting of the 
Greenland Ice Sheet over the past two decades. There is medium 
confidence  of an anthropogenic contribution to recent mass loss 
from Greenland.
9.4.1.2 Model Evaluation
The SROCC (Oppenheimer et  al., 2019) stated that substantial 
challenges remained for modelling of the Greenland SMB and the 
dynamical ice sheet. Since SROCC, further insights into modelling of 
the Greenland ice sheet has come from model intercomparison studies 
of the SMB (Fettweis et al., 2020) and dynamical ice sheets (Goelzer 
et al., 2020; Payne et al., 2021). Further aspects relevant to the forcing 
of the ice sheet from large scale global climate models and regional 
climate models are discussed in Box 9.3 and Section Atlas.11.2.
The SROCC stated that climate model simulations of Greenland SMB 
had improved since AR5, giving medium confidence in the ability of

climate models to simulate changes in Greenland SMB. Since SROCC, 
a multi-model intercomparison study (Fettweis et al., 2020) of regional 
and global climate models has shown that the greatest inter-model 
spread occurs in the ablation zone, due to deficiencies in an accurate 
model representation of the ablation zone extent and processes 
related to surface melt and runoff, confirming SROCC statement that 
there is large uncertainty in the bare ice model (Ryan et al., 2019). 
This intercomparison showed that simple, well-tuned SMB models 
using positive degree day melt schemes can perform as well as more 
complex physically based models (Figure Atlas 30). Furthermore, the 
ensemble mean of the models produced the best estimate of the 
present-day SMB relative to observations (particularly in the ablation 
zone). Further assessment of Greenland Ice Sheet regional SMB can 
be found in Section Atlas.11.2.3. Recent progress confirms SROCC

assessment that there is medium confidence  in the ability of climate 
models to simulate changes in Greenland SMB.
The SROCC noted increased use of coupled climate–ice sheet 
models for simulating the Greenland ice sheet, but it also noted 
that remaining deficiencies in coupling between models of climate 
and ice sheets (e.g.,  low spatial resolution) limited the adequate 
representation of the feedbacks between them. Some Earth system 
models (ESMs) now incorporate multi-layer snow models and full energy balance models (Punge et  al., 2012; Cullather et  al., 2014; 
van  Kampenhout  et  al.,  2017,  2020; Alexander et  al., 2019) or use 
elevation classes to compensate for their coarser resolution (Lipscomb 
et  al., 2013; Sellevold et  al., 2019; Gregory et  al., 2020; Muntjewerf 
et  al., 2020a, b). Resulting SMB simulations compare better with 
regional climate models and observations (Alexander et  al., 2019; 
van Kampenhout et  al., 2020), but the remaining shortcomings

lead to problems reproducing a  present-day ice-sheet state close to 
observations. In summary, there is medium confidence  in quantitative 
simulations of the present-day state of the Greenland Ice Sheet in ESMs.
The SROCC (Meredith et al., 2019) stated that there is low confidence  
in understanding coastal glacier response to ocean forcing because 
submarine melt rates, calving rates, bed and fjord geometry and 
the roles of ice mélange and subglacial discharge are poorly 
understood. Ice–ocean interactions remain poorly understood and 
difficult to model, with parametrizations often used for calving of 
marine-terminating glaciers (Mercenier et al., 2018) and submarine 
and plume-driven melt (Beckmann et  al., 2019). Due to the 
difficulties of modelling the large number of marine-terminating 
glaciers and limited availability of high-resolution bedrock data, the 
majority of recent modelling work on Greenland outlet glaciers is

focused on individual or a  limited number of glaciers (Krug et  al., 
2014; Bondzio et al., 2016, 2017; Morlighem et al., 2016b; Muresan 
et al., 2016; Choi et al., 2017; Beckmann et al., 2019), or a specific 
region (Morlighem et al., 2019). Since SROCC, using a flowline model 
that includes calving and submarine melting, Beckmann et al. (2019) 
concluded that the AR5 upscaling of contributions from four of the 
largest glaciers (Nick et  al., 2013) overestimated the total glacier 
contribution from the Greenland Ice Sheet, due to differences in 
response between large and small glaciers. The regional study of 
Morlighem et al. (2019) confirms that ice–ocean interactions have 
the potential to trigger extensive glacier retreat over decadal time 
scales, as indicated by observations (Section  9.4.1.1). One focus 
of  continental ice-sheet models has been the improved treatment 
of marine-terminating glaciers via the inclusion of calving processes

and freely moving calving fronts (Aschwanden et  al., 2019; Choi 
et al., 2021). An improved bedrock topographic dataset (Morlighem 
et al., 2017) allows for ice discharge to be better captured for outlet 
glaciers in continental ice-sheet models, and simulations indicate 
that bedrock topography controls the magnitude and rate of retreat 
(Aschwanden et al., 2019; Rückamp et al., 2020). Overall, although 
there is high confidence  that the dynamic response of Greenland 
outlet glaciers is controlled by bedrock topography, there is low 
confidence  in quantification of future mass loss from Greenland 
triggered by warming ocean conditions, due to limitations in the 
current understanding of ice–ocean interactions, its implementation 
in ice-sheet models, and knowledge of bedrock topography.
The SROCC (Oppenheimer et  al., 2019) noted the progress made 
in Greenland Ice Sheet models since AR5. New since SROCC is 
a focus on improved representation of the present-day state of the

ice sheet (Box  9.3; Goelzer et  al., 2018, 2020). Improvements are 
closely linked to the growing number and quality of observations 
(Section 9.4.1.1), new techniques to generate internally consistent 
input datasets (Morlighem et  al., 2014, 2016a), wider use of data 
assimilation techniques (Larour et  al., 2014, 2016; Perego et  al.,

1259
Ocean, Cryosphere and Sea Level Change  Chapter 992014; Goldberg et al., 2015; Lee et al., 2015; Schlegel et al., 2015; 
Mosbeux et al., 2016), increased model resolution (Aschwanden et al., 
2016) and tuning of key processes such as calving (Choi et al., 2021). 
A  remaining challenge is low confidence  in reproducing historical 
mass changes of the Greenland Ice Sheet (Box 9.3). However, there 
is medium confidence  in ice-sheet models reproducing the present 
state of the Greenland Ice Sheet, leading to medium confidence  in 
the current ability to accurately project its future evolution.
9.4.1.3 Projections to 2100
The AR5 and SROCC projected that changes in Greenland SMB will 
contribute to sea level in 2100 by 0.03 (0.01 to 0.07) m sea level 
equivalent (SLE) under RCP2.6, and 0.07 (0.03 to 0.16) m SLE under 
RCP8.5. New since SROCC are the projections of SMB obtained by 
an ESM, two regional climate models, and reconstructions based on

temperature from the CMIP5 and CMIP6 ensembles (Hofer et al., 2020; 
Noël et al., 2021). The range of sea level contribution from Greenland 
SMB in Noël et al. (2021) is comparable to the AR5 assessment when 
either CMIP5 or CMIP6 models are used, while Hofer et al. (2020) 
find a greater mass loss across all CMIP6 emissions scenarios when 
compared to CMIP5 scenarios. Using SSP5-8.5 instead of RCP8.5 
increases the mean projected sea level from 2005–2100 by up to 
0.06 m in the regional climate model simulations of Hofer et al. (2020) 
who attribute the difference mainly to a greater Arctic amplification 
and associated cloud and sea ice feedbacks in the CMIP6 SSP5-8.5 
simulations. In summary, these new projections with fixed ice-sheet 
topography do not provide sufficient evidence to change the AR5 and 
SROCC assessments.
Reviewing modelling studies since AR5 (Church et al., 2013b), SROCC 
(Oppenheimer et  al., 2019) assessed Greenland’s contribution to

future sea level to be relatively similar to AR5 (Table 9.2). The baseline 
for projections has shifted from 1986–2005 in SROCC, to 1995–2014 
in this Report. Adjusted to the new 1995–2014 baseline by subtracting 
0.01 m, SROCC projected a  likely  contribution of 0.07 (0.0–0.11) m 
SLE under RCP2.6, and 0.14 (0.08–0.27) m SLE under RCP8.5 by 2100. 
Since SROCC, new projections for the 21st  century have included 
dynamic ice sheets coupled to ESMs (Muntjewerf et  al., 2020a; 
Van Breedam et al., 2020) or regional atmospheric models (Table 9.2; 
Le  clec’h et  al., 2019). The coupled ESM–ice-sheet model CESM2–
CISM2 (Community Earth System Model Version 2 and Community Ice 
Sheet Model 2) projects a sea level rise of 0.109 m in 2100 relative 
to 2015 under SSP5-8.5 (Muntjewerf et  al., 2020a) and a  similar 
contribution under the idealized 1% yr –1 increase in CO 2 scenario 
(Muntjewerf et  al., 2020b). The CESM2–CISM2 simulations include

ice-sheet–atmosphere interactions and ice-sheet surface meltwater 
routed to the ocean. The coupled regional atmospheric model and 
ice-sheet model MAR-GRISLI (Modèle Atmosphérique Régional and 
Grenoble ice sheet and land ice model) projects a sea level rise of 
0.079 m in 2100 relative to 2000 under RCP8.5 (Le Clec’h et al., 2019). 
An  ESM of lower complexity coupled to an ice-sheet model gives 
a sea level contribution of 0.025 to 0.064 m under RCP2.6 and 0.056 
to 0.12 m under RCP8.5 (the range is due to four simulations with 
different parameter sets for the atmosphere model) (Van  Breedam 
et  al., 2020). Van Breedam et  al. (2020) identify a  simulation with 
a  preferred parameter set that projects 0.034 m for RCP2.6 and 0.073 m for RCP8.5. Although the ocean does not directly force the 
ice-sheet models in these simulations, the new coupled models allow 
for interactions between ice-sheet dynamics, SMB and local climate.

The coupled projections fall within the lower bounds of AR5 and 
SROCC and, as these studies do not prescribe ocean forcing directly, it 
is possible that the dynamic response is underestimated.
Since SROCC, projections of the Greenland Ice Sheet are also 
available from The Ice Sheet Model Intercomparison Project for 
CMIP6 (ISMIP6) (Box  9.3; Annex II; Figure  9.17; Nowicki et  al., 
2016, 2020a). ISMIP6 multi-model projections are corrected with an 
assessment of the historical dynamical response to pre-2015 climate 
forcing (Box 9.3). For the period 2015–2100, the ISMIP6 uncorrected 
multi-model ensemble projects sea level contributions ranging from 
0.01 to 0.05 m under RCP2.6, 0.04 to 0.14 m under RCP8.5 (Goelzer 
et  al., 2020), 0.02 to 0.06 m under SSP1-2.6, and 0.08 to 0.25 m 
under SSP5-8.5 (Table 9.2; Payne et al., 2021). The higher mass loss 
in the SSPs is attributed to a larger decrease in SMB due to the high

climate sensitivity of the models used (Payne et al., 2021). This finding 
is confirmed by Choi et al. (2021), where CMIP6 SSP5-8.5 SMB leads 
to larger ice loss than CMIP5 RCP8.5, while ice discharge is similar. 
As the ISMIP6 framework considers a  subset of the RCPs/SSPs 
and CMIP models, SSP-based projections have been inferred from 
multiple approaches. First, the ISMIP6 CMIP5-forced (Goelzer et al., 
2020) and CMIP6-forced (Payne et  al., 2021) combined ensemble 
projections were corrected with the historical trend (Box 9.3) using 
bootstrapping. Second, an emulator of the ISMIP6 projections 
(Box 9.3; Edwards et al., 2021) is forced by distributions of global 
surface air temperature for each SSP from a two-layer energy budget 
emulator (Supplementary Material 7.SM.2) and then corrected with 
the historical trend in the same way. These two approaches result 
in projections that are similar in their median values to AR5 and

SROCC projections (Table 9.2), but differ in their range. Similar results 
are obtained when the AR5 parametric fit is applied to the ISMIP6 
models (Table 9.2, Supplementary Material 9.SM.4.4), which is used 
to estimate rates of change and post-2100 projections (Sections 
9.4.1.4 and 9.6.3.2).
The SROCC noted that the study by Aschwanden et al. (2019) projects 
a significantly higher Greenland contribution to sea level than the 
assessed likely  range in AR5 and SROCC. Under RCP8.5, Aschwanden 
et al. (2019) found that Greenland could contribute up to 0.33 m to 
sea level by 2100 relative to 2000 (the ensemble member that best 
reproduces the 2000–2015 mean SMB from a regional climate model 
projects Greenland mass losses of 0.08 m SLE under RCP2.6 and 
0.18 m SLE under RCP8.5). The SROCC noted that the potentially high 
sea level contribution in this study could be due to the assumption 
of spatially uniform warming, which can overestimate surface melt

rates. However, it also reflects the deep uncertainty  surrounding 
atmospheric forcing, surface processes, submarine melt, calving 
and ice dynamics. Goelzer et al. (2020) ascribe 40% of the ISMIP6 
multi-model ensemble spread to ice-sheet model uncertainty, 40% to 
climate model uncertainty and 20% to ocean forcing uncertainty. 
We note that this finding reflects the current challenges associated 
with the representation of ice–ocean interactions in models, and 
the uncertainty in basal conditions (Section 9.4.1.2). However, this 
finding is consistent with the work of Aschwanden et  al. (2019)

1260
and thus, there is  medium confidence  that uncertainty in mass loss 
from the Greenland Ice Sheet is dominated by uncertainty in climate 
scenario and surface processes, whereas uncertainty in calving and 
frontal melt play a minor role.
The SROCC stated that surface processes, rather than ice 
discharged into the ocean, will dominate Greenland ice loss 
over the 21st  century, regardless of the emissions scenario ( high 
confidence ). This is confirmed by the ISMIP6 projections (Goelzer 
et al., 2020; Payne et al., 2021). The projected mass loss of Greenland 
is predominantly due to increased surface meltwater and loss in 
refreezing capacity resulting in decreasing SMB ( high confidence ), 
concurrent with rising temperatures and darkening of the ice-sheet 
surface (Fettweis et al., 2013; Vizcaino et al., 2015; Le Clec’h et al., 
2019; Muntjewerf et  al., 2020a, b; Sellevold and Vizcaíno, 2020).

Mass changes due to SMB and outlet glacier dynamics are linked 
(Goelzer et  al., 2013; Fürst et  al., 2015; Rückamp et  al., 2020), as 
mass loss by one process decreases mass loss by the other  – for 
example, SMB removes ice before it can reach the marine glacier 
terminus. There is medium confidence  that the mass loss through ice 
discharge will decrease in the future (Fürst et al., 2015; Aschwanden 
et al., 2019; Golledge et al., 2019), because an increase in mass loss 
(via increased discharge or surface runoff) leads, in most areas, to 
a retreat of the glacier margin onto land above sea level, isolating the 
ice sheet from marine influence.In summary, it is virtually certain  that the Greenland Ice Sheet will 
continue to lose mass this century under all emissions scenarios, 
and high confidence  that total mass loss by 2100 will increase with 
cumulative emissions. The sea level assessment (Section  9.6.3.3) is

based on the emulated ISMIP6 projections, allowing a more consistent 
approach to a wider range of climate and ocean forcings. The Greenland 
Ice Sheet is likely  to contribute 0.06 (0.01 to 0.10) m under SSP1-2.6 and 
0.13 (0.09 to 0.18) m under SSP5-8.5 by 2100 relative to 1995–2014. 
These projections (as well as those of AR5 and SROCC) are lower 
than the study of Aschwanden et al. (2019) or the range of possible 
sea level changes resulting from Structured Expert Judgement (SEJ; 
Section 9.6.3.2; Bamber et al., 2019), contributing to the deep uncertainty  
in projected sea level (Box 9.4). There is, however, high confidence that 
the loss from Greenland will become increasingly dominated by SMB 
and surface melt, as the ocean-forced dynamic response of glaciers will 
diminish as marine margins retreat to higher grounds.
9.4.1.4 Projections Beyond 2100
The AR5 (Church et  al., 2013b) assessed the contribution from 
Greenland to sea level projections in 2300 as 0.15 m SLE in

low-emissions scenarios (about RCP2.6) and 0.31–1.19 m in high 
scenarios (approximately RCP6.0/RCP8.5). The SROCC (Oppenheimer 
et  al., 2019) did not update AR5 estimates, given  limited 
evidence  and  low agreement  from three new studies (Vizcaino et al., Table  9.2 | Projected sea level contributions in metres from the Greenland Ice Sheet by 2100 relative to 1995–2014, unless otherwise stated, for 
selected Representative Concentration Pathway (RCP) and Shared Socio-economic Pathways (SSP) scenarios.  Italics denote partial contributions. Historical 
dynamic response omitted from ISMIP6 simulations is estimated to be 0.19 ± 0.10 mm yr –1 (0.02 m ± 0.01 m in 2100 relative to 2015). The climate forcing is described in 
Appendix 7.SM.2.
Representative Concentration Pathways (RCPs)
Study RCP2.6 RCP4.5 RCP8.5 Notes
IPCC AR5 and SROCC 
(Oppenheimer et al., 2019) 0.07  
(0.03 to 0.11) 0.08  
(0.04 to 0.15) 0.14

(0.08 to 0.27) Median and likely  (66%  range) contributions in 2100 relative 
to 1995–2014. Median of multiple studies
ISMIP6 CMIP5-forced (Goelzer et al., 2020);  
excludes historical dynamic response0.01 to 0.05 n/a 0.04 to 0.14 Range of multi-model contributions in 2100 relative to 2015 
from 1 ESM for RCP2.6 and 6 ESMs for RCP8.5 (see caption)
Coupled regional atmosphere–ice sheet 
model (Le clec’h et al., 2019)n/a n/a 0.079 Contribution in 2100 relative to 2000 from AR-GRISLI model
Coupled Earth system model (ESM) 
of lower complexity-ice-sheet model 
(Van Breedam et al., 2020)0.034  
(0.025 to 0.064) n/a0.073  
(0.056 to 0.12) Contribution in 2100 relative to 2000 from LOVECLIM-AGISM 
model; preferred parameter set and range from four simulations 
with different parameters for atmosphereodel
Shared Socio-economic Pathways (SSPs)
Study SSP1-2.6 SSP2-4.5 SSP5-8.5 Notes
Coupled ESM–ice sheet model

(Muntjewerf et al., 2020a)n/a n/a 0.109 Contribution in 2100 relative to 2015 from coupled 
CESM2–CISM2
ISMIP6 CMIP6-forced (Payne et al., 2021) ; 
excludes historical dynamic response0.02 to 0.06 n/a 0.08 to 0.25 Range of multi-model contributions in 2100 relative to 2015 
from one ESM for SSP1-2.6 and four ESMs for SSP5-8.5 
ISMIP6 CMIP5 and CMIP6 forced ensemble 
including historical dynamic response0.06 (0.05 to 0.07)
[0.04 to 0.08] n/a 0.11 (0.09 to 0.14)
[0.07 to 0.17] Median (66% range) [90% range] contribution from ISMIP6 
CMIP5- and CMIP6-forced multi-model ensembles
ISMIP6 with AR5 parametric fit: used to 
estimate rates (Supplementary Material 
9.SM.4.4) including historical dynamic response0.08 (0.06 to 0.10)
[0.05 to 0.12] 0.10 (0.08 to 0.13) 
[0.07 to 0.15] 0.14 (0.11 to 0.18)
[0.10 to 0.22] Median (66% range) [90% range] contribution from AR5 
parametric fit to ISMIP6 ensemble, relative to 1995–2014  
Emulated ISMIP6; excludes historical dynamic

response (Edwards et al., 2021)0.03 (–0.01 to 0.08) 
[–0.04 to 0.12] 0.06 (0.01 to 0.10) 
[–0.02 to 0.15] 0.11 (0.06 to 0.16)
[0.03 to 0.21] Median (66% range) [90% range] contribution in 
2100 relative to 2015 from emulator of ISMIP6 used 
with Chapter 7: Climate Forcing
This assessment: emulated ISMIP6 total0.06 (0.01 to 0.10)
[–0.02 to 0.15] 0.08 (0.04 to 0.13)
[0.01 to 0.18] 0.13 (0.09 to 0.18)
[0.05 to 0.23] As above, but relative to 1995–2014 and including 
historical dynamic response

1261
Ocean, Cryosphere and Sea Level Change Chapter 99
Box 9.3 | Insights into Land Ice Evolution From Model Intercomparison Projects
Projections of ice sheets and glaciers in AR5 (Church et al., 2013b) and SROCC (Oppenheimer et al., 2019) were assessed by collecting 
single model studies – with the exception of glaciers in SROCC (Hock et al., 2019b). Community benchmark experiments (ISMIP-HOM; 
Pattyn et al., 2008) or Marine Ice Sheet Model Intercomparison Projects (MISMIP; Pattyn et al., 2012); MISMIP3d, (Pattyn and Durand, 
2013); MISMIP+ (Asay-Davis et al., 2016; Cornford et al., 2020) have substantially advanced ice-sheet modelling since AR5. Model 
Intercomparison Projects (MIPs) now inform projections of both ice sheets and glaciers: the Ice Sheet MIP for CMIP6 (ISMIP6; 
Sections 9.4.1.3 and 9.4.2.5), the Linear Antarctic Response MIP (LARMIP-2; Section 9.4.2.5) and GlacierMIP (Section 9.5.1.3).
Regional forcing for land ice intercomparison projects

Simulations of ice sheets and glaciers are dependent on forcing provided by atmosphere and ocean models.  Despite progress in 
representing processes, reducing biases and increasing resolution, regional and global models still have difficulties reproducing observed 
regional air temperature, surface mass balance (SMB) and ocean changes (Sections 9.4.1.2 and 9.4.2.2, and Atlas.11). An assessment of 
CMIP5 and CMIP6 climate models, as forcing for land ice models, has been undertaken (Walsh et al., 2018; Barthel et al., 2020; Marzeion 
et al., 2020; Nowicki et al., 2020b) with the aim of selecting the best available historical forcings and sampling potential regional future 
climate changes. Despite improvement in simulation of atmospheric forcing, persistent biases remain in CMIP5 and CMIP6, which reduces 
the fidelity of historical and future simulations of land ice.2015; Calov et  al., 2018; Aschwanden et  al., 2019). Since SROCC,

a new study gives a sea level contribution of 0.11 to 0.20 m in low-
emissions scenarios and 0.61 to 1.29 m in high-emissions scenarios 
(Van Breedam et  al., 2020). The low-emissions projections by Van 
Breedam et al. (2020) encompass AR5’s assessed contribution, while 
the high emissions projections are higher than that from AR5. The 
‘optimal’ ensemble member of Aschwanden et  al. (2019) (see also 
Section  9.4.1.3) indicates that Greenland could contribute 0.25  m 
under RCP2.6 and 1.74 m under RCP8.5. Structured expert judgement 
(Bamber et al., 2019) projects Greenland losses of 0.54 (0.28–1.28) m 
under 2°C warming and 0.97 (0.4–2.23) m under 5°C warming. These 
studies therefore agree that the AR5 and SROCC assessments are 
at the low end of the range of projections. In addition, observations 
suggest that Greenland Ice Sheet losses are tracking the upper range 
of AR5 projections (T. Slater et al., 2020). Therefore, we update the likely

range for the contribution of the Greenland Ice Sheet to global mean 
sea level (GMSL) by 2300 to 0.11–0.25 m under RCP2.6/SSP1-2.6 and 
0.31–1.74 m under RCP8.5/SSP5-8.5. However, given the uncertainty in 
climatic drivers used to project ice-sheet change over the 21st century 
(Goelzer et  al., 2020; Hofer et  al., 2020; Noël et  al., 2021) and the 
large range in simulations since AR5 extending beyond 2100, we only 
have  low confidence  in the contribution to GMSL by 2300 and beyond. 
The role of the elevation–mass feedback for future projections of 
Greenland can be assessed from paleo simulations. Ice-sheet model 
simulations of the Laurentide (Gomez et al., 2015; Gregoire et al., 
2016) and Eurasian (Alvarez-Solas et al., 2019) ice sheets invoke at 
least some contribution to last glacial termination mass loss from SMB 
reduction, as a consequence of an elevation–mass balance feedback 
(Levermann and Winkelmann, 2016). In a model spanning Meltwater

Pulse 1A, this mechanism increased mass loss by approximately 66% 
(Gregoire et al., 2016) but in Last Interglacial simulations, the effect 
of this feedback is shown to depend on the surface scheme of the 
climate model employed (Plach et al., 2019). Given the agreement 
between theoretical analyses and paleo-ice-sheet model experiments, 
there is high confidence  that the elevation–mass balance feedback 
is most relevant at multi-centennial and millennial time scales, consistent with future-focused studies (Aschwanden et al. 2019, Le 
Clec’h et al., 2019, Gregory et al., 2020).
The SROCC adopted the AR5 assessment that complete loss of 
Greenland ice, contributing about 7 m to sea level, over a millennium 
or more would occur for a sustained global mean surface temperature 
(GMST) between 1°C ( low confidence ) and 4°C ( medium confidence ) 
above pre-industrial levels. New studies since SROCC (Gregory et al., 
2020; Van Breedam et al., 2020) confirm this assessment (see also

Figure 9.30). Clark et al. (2016) estimate a complete loss to take about 
8000 years at 5.5°C and about 3000 years at 8.6°C. Based on the 
agreement between new and previous studies, there is therefore high 
confidence  that the rate at which Greenland Ice Sheet commitment is 
realized depends on the amount of warming.
Accounting for more detailed feedbacks between the atmosphere 
and the ice sheet (Gregory et al., 2020) found a gradual relationship 
between sustained global mean warming and the corresponding near-
equilibrium ice-sheet volume, in contrast to a sharp threshold as found 
by Robinson et al. (2012). Rather than a climatically controlled tipping 
point for irreversible loss of the Greenland Ice Sheet, Gregory et  al. 
(2020) found a threshold of irreversibility linked to ice-sheet size, similar 
to previous work (Ridley et al., 2010). The results of Gregory et al. (2020) 
show that, if the ice sheet loses mass equivalent to about 3–3.5 m of

sea level rise, it would not regrow to its present state, and 2 m of the sea 
level rise would be irreversible. The point in time at which the current 
ice sheet might reach this critical volume depends on oceanic and 
atmospheric conditions, ice dynamics, and climate–ice sheet feedbacks 
(Gregory et al., 2020; Van Breedam et al., 2020). Therefore, projections 
differ in the magnitude and rate of temperature change to cross the 
threshold for irreversible loss. Projections from a  large ensemble 
indicate that the mass threshold may be reached in as early as 400 years 
under extended RCP8.5 if warming reaches 10°C or more above present 
levels (Aschwanden et al., 2019). In summary, there is high confidence  
in the existence of threshold behaviour of the Greenland Ice Sheet in 
a warmer climate; however, there is low agreement  on the nature of the 
thresholds and the associated tipping points.

1262
Chapter 9 Ocean, Cryosphere and Sea Level Change9Box 9.3 (continued)
ISMIP6 initial state intercomparison projects 
The ISMIP6 initial state intercomparison projects (initMIP) for the Greenland (Goelzer et al., 2018) and Antarctic (Seroussi et al., 
2019) ice sheets were designed to understand the uncertainty in sea level projections resulting from the choice of initialization 
procedures used for projections of sea level (Nowicki et al., 2016). Participating modelling groups (Annex II) were free to decide 
on the initialization method used to bring ice-sheet models to a  present-day state, with the effect of these choices captured in 
a control simulation (starting from the present-day state, with no further climate forcing applied), which measures intrinsic model drift. 
Compared to the earlier SeaRISE intercomparison project (Bindschadler et al., 2013; Nowicki et al., 2013), the modelled present-day

ice sheets are in closer agreement with observations, and the model drift has been reduced (Goelzer et al., 2018; Seroussi et al., 
2019). Nonetheless, historical simulations remain challenging for ice-sheet models, due to limited ice-sheet observations prior to the 
satellite era and biases in the historical atmospheric and oceanic forcings from climate models (Nowicki and Seroussi, 2018). ISMIP6 
and LARMIP-2 therefore did not provide a protocol for the historical runs used to bring the ice sheets to present day, nor criteria for 
sub-selecting models from the multi-model ensemble based on the ability to reproduce historical changes (Levermann et al., 2020; 
Nowicki et al., 2020a).
ISMIP6 projections for the Greenland and Antarctic ice sheets
The ISMIP6 projection protocol (Nowicki et al., 2016, 2020a) was designed to sample the uncertainty in future sea level due to climate

scenarios (via the use of high- and low-emissions scenarios and multiple climate models), ice–ocean interactions and inland response 
to ice-shelf collapse, and ice-sheet model diversity. The participating ice-sheet models are listed in Annex II. For each ice sheet, forcing 
was selected (Barthel et al., 2020) from the CMIP5 (Taylor et al., 2012) and CMIP6 (Eyring et al., 2016) models. Atmospheric forcing fields 
consisted of anomalies in SMB and surface air temperatures; these were generated directly from the CMIP models for the Antarctic 
Ice Sheet and downscaled using the regional climate model (MAR) for the Greenland Ice Sheet (Hofer et  al., 2020). To sample the 
uncertainty due to ocean forcings, models used either a model-specific scheme with the ISMIP6-provided oceanic dataset or a standard 
ISMIP6 approach. For the Greenland Ice Sheet, the oceanic dataset consists of thermal forcing (temperature minus freezing temperature)

extrapolated into fjords and subglacial runoff. The standard approach uses timelines of tidewater glacier retreat (D.A. Slater et al., 2019, 
2020). For the Antarctic Ice Sheet, the oceanic dataset consists of salinity, thermal forcing and temperature added to an observationally 
derived climatology and extrapolated under ice shelves. The standard approach is a basal melt rate that depends quadratically on thermal 
forcing, adapted from Favier et al. (2019), with two different calibrations (Figure 9.19, Jourdain et al., 2020) that reproduce observed 
basal melt rates across Antarctica or Pine Island Glacier, respectively (Sections 9.4.2.2, 9.4.2.3). Antarctic ice-shelf disintegration datasets 
(Nowicki et al., 2020a) assume that ice shelves disintegrate when annual surface melt reaches a threshold (Trusel et al., 2015).
The ISMIP6 projections (Goelzer et al., 2020; Seroussi et al., 2020; Payne et al., 2021) are reported as experiment minus control and

represent the sea level resulting from future climate change only. The control simulation, which has constant climate conditions 
starting in 2015 from the historical run, captures drift associated with the choices made for the initialization method and historical 
run. Subtraction of this control removes any long-term dynamic response of the ice sheet to pre-2015 climate change. This response 
has been assessed using dynamic discharge derived from observations over the last 40 years (Mouginot et al., 2019; Rignot et al., 
2019), under an assumption that it persists at the past rate until 2100, rather than diminishing. The dynamic response to historical 
forcing is estimated as 0.19 ± 0.10 mm yr –1 for the Greenland Ice Sheet (Section 9.4.1.3) and 0.33 ± 0.16 mm yr –1 for the Antarctic 
Ice Sheet (Section 9.4.2.5). Over the period 2015–2100, this leads to an additional sea level contribution of 1.7 cm for Greenland and 
2.8 cm for Antarctica.

LARMIP-2 projections for the Antarctic Ice Sheet
LARMIP-2 is focused on the uncertainty in the ocean forcing and associated ice-shelf melting (Levermann et al., 2014, 2020) with 
the majority of the models also participating in ISMIP6 (Annex II). The experiments start from present day and impose an additional 
basal ice-shelf melting of 8 m yr –1 at the beginning of the 100-year simulation. A control run is used to remove drift resulting from 
initialization. The time derivative of the ice-sheet response yields a linear response function, which is then convoluted with a forcing of 
basal shelf melt time series for five Antarctic regions. The forcing time series for RCP2.6, 4.5, 6.0 and 8.5 were obtained from a random 
combination of global mean temperature for each Representative Concentration Pathway (RCP) from MAGICC-6.0 (Meinshausen 
et  al., 2011), a  scaling factor and time delay for the relationship between global surface air temperature and subsurface ocean

warming in a given sector of the Southern Ocean from one of 19 CMIP5 models (Taylor et al., 2012) and a basal melting sensitivity 
from the interval [7–16] m yr –1 °C–1 to convert the regional subsurface warming into basal ice-shelf melting. This process is repeated 
20,000 times to obtain a probability distribution of the sea level contribution for five Antarctic sectors. The linear response framework 
captures complex temporal responses of the ice sheets resulting from an increase in basal ice-shelf melting, but neglects the response 
to SMB and any self-dampening or self-amplifying processes, such as marine ice shelf instability (MISI). The LARMIP-2 method is

1263
9.4.2 Antarctic Ice Sheet
9.4.2.1 Recent Observed Changes
As stated in Section 2.3.2.4, satellite observations by Ice Sheet Mass 
Balance Intercomparison Exercise (IMBIE) combining multi-team 
estimates based on altimetry, gravity anomalies (GRACE) and the 
input-output method, already presented in SROCC (Meredith et al., 
2019), are updated and extended to 2020 (The IMBIE Team, 2021). 
The Antarctic Ice Sheet (AIS) lost 2670 [1800 to 3540]  Gt mass over 
the period 1992–2020, equivalent to 7.4 [5.0 to 9.8]  mm GMSL rise 
(for contribution to sea level budget, see Figures 9.16 and 9.18, and 
Table 9.5). Within uncertainties, this estimate agrees with a review of 
post-AR5 studies up to 2016 (Bamber et al., 2018b) and is consistent 
with recent single studies based on satellite laser altimetry (Smith 
et  al., 2020), the input-output method (Rignot et  al., 2019) and

gravimetry (Velicogna et al., 2020). The mass-loss rate was on average 
49 [–2 to 100] Gt yr –1 over the period 1992–1999, 70 [22 to 119] 
Gt yr –1 over the period 2000–2009, and 148 [94 to 202] Gt yr –1 over 
the period 2010–2016 (see Figures 9.16 and 9.18, and Table 9.SM.1). 
However, recent work suggests that the mass loss has not further 
increased since 2016 because of regional mass gains in Dronning 
Maud Land (Velicogna et al., 2020). Mass loss of the West Antarctic 
and Antarctic Peninsula ice sheets has increased since about 2000 
(very high confidence ), essentially due to increased ice discharge 
(Harig and Simons, 2015; Paolo et al., 2015; Forsberg et al., 2017; Bamber et al., 2018b; Gardner et al., 2018; The IMBIE Team, 2018; 
Rignot et al., 2019).
The SROCC reported with very high confidence  that the acceleration, 
retreat and thinning of the principal West Antarctic outlet glaciers 
has dominated the observed Antarctic mass loss over the last

decades, and stated with high confidence  that these losses were 
driven by melting of ice shelves by warm ocean waters. The average 
West Antarctic Ice Sheet (WAIS) mass loss of 82 ± 9 Gt yr –1 between 
1992 and 2017 (The IMBIE Team, 2021) leads to substantial observed 
surface lowering (e.g., Schröder et al., 2019; Shepherd et al., 2019), 
particularly in coastal regions (Figure  9.18). Recent studies using 
satellite altimetry (Schröder et al., 2019) and the input-output method 
(Rignot et  al., 2019) consistently show mass loss in these coastal 
regions since the late 1970s (Figure  9.16). Because of consistent 
multiple lines of evidence, there is high confidence  in mass loss of the 
Totten Glacier in East Antarctica (Miles et al., 2013; X. Li et al., 2016; 
Mohajerani et al., 2018; Rignot et al., 2019; Schröder et al., 2019; 
Shepherd et al., 2019) since about 2000, dominated by changes in 
coastal ice dynamics (X. Li et al., 2016). It is currently unclear whether

mass loss of the EAIS over the last three decades has been significant 
(Rignot et al., 2019) or, at 5 ± 46 Gt yr –1 between 1992 and 2017, 
essentially zero within uncertainties (The IMBIE Team, 2018). 
In summary, WAIS losses, through acceleration, retreat and thinning 
of the principal outlet glaciers, dominated the AIS mass losses over 
the last decades ( very high confidence ) and there is high confidence  Box 9.3 (continued)
applied to temperature projections for the Shared Socio-economic Pathways (SSPs; Supplementary Material 7.SM.2) and an estimate 
of SMB change from the AR5 parametric Antarctic Ice Sheet SMB model (Church et al., 2013b) is added to the results (Sections 9.4.2.4, 
9.4.2.5 and 9.6.3.2). It is not necessary to add a long-term dynamic response to the LARMIP-2 projections, as this is incorporated in 
the basal melt time series.
GlacierMIP projections

GlacierMIP (Marzeion et al., 2020) was designed to estimate the glacier contribution to sea level rise, including from peripheral 
glaciers in Greenland and Antarctica that can be considered to be dynamically decoupled, or entirely separate, from the ice sheets. 
Glacier models are described in Annex II. Initial conditions were based on Randolph Glacier Inventory Version 6 (RGI Consortium, 2017) 
and initial ice thickness and volume were provided from an update of Huss and Farinotti (2012), although some glacier models used 
their own estimates. Forcings were taken from 10 different CMIP5 general circulation models, selected based on availability of multiple 
RCPs, the choice in a previous model intercomparison (Hock et al., 2019a), and performance in glacier-covered regions according to 
Walsh et al. (2018). In addition, two global glacier models performed the same experiment with 13 CMIP6 models (Section 9.5.1.3).
Use of an emulator with ISMIP6 and GlacierMIP projections

The ISMIP6 and GlacierMIP projections are primarily based on a limited number of CMIP5 RCPs and CMIP6 SSPs, and a limited sampling 
of ice–ocean interaction parameters and ice-shelf collapse simulations. Emulators provide a method for expanding these projections 
to a range of SSPs with more comprehensive sampling of climate, ice-sheet and glacier modelling uncertainties. Sections 9.4.1.3, 
9.4.2.5 and 9.5.1.3 show estimates from the emulator of Edwards et al. (2021). This is a Gaussian Process, rather than a physically 
based (Cross-Chapter Box 7.1) model derived from the ISMIP6 and GlacierMIP simulations; projections use distributions of global 
surface air temperature (GSAT) from the two-layer emulator (Supplementary Material 7.SM.2) and ice-sheet parameters as inputs, 
and include estimates of the emulator uncertainty. Therefore, probability intervals are not inflated by a further factor, as is often the

case for multi-model ensemble projections, to account for missing uncertainties (Section 9.6.3.2). The emulator is used in Section 9.6.3 
to provide projections of the land ice contribution to sea level that are fully consistent with each other, ocean heat content, and the 
assessed equilibrium climate sensitivity and projections of GSAT across the entire report.

1264
Chapter 9 Ocean, Cryosphere and Sea Level Change9that this is the case since the late 1970s. Furthermore, parts of the 
EAIS have lost mass in the last two decades ( high confidence ).
As stated in SROCC, snowfall and glacier flow are the largest 
components determining AIS mass changes, with glacier flow 
acceleration (dynamic thinning) on the WAIS and the Antarctic 
Peninsula driving total loss trends in recent decades ( very high 
confidence ), and a partial offset of the dominating dynamic-thinning 
losses by increased snowfall ( high confidence ). The SROCC attributed 
medium confidence  to estimates of 20th-century snowfall increases 
equivalent to a sea level change of –7.7 ± 4.0 mm on the EAIS, and 
–2.8 ± 1.7 mm on the WAIS, respectively (Medley and Thomas, 2019). 
Loss of buttressing, which can be caused by ice-shelf thinning, gradual 
ice-shelf front retreat or ice-shelf disintegration, has been linked to

instantaneous ice velocity increases, and thus dynamic thinning, since 
the early 1990s. This link is clearly evident in the Amundsen and, to 
a lesser degree, Bellingshausen sectors (Gudmundsson et al., 2019), 
where passive shelf ice (ice that can be removed without major 
effects on the ice-shelf dynamics) is very limited or absent (Fürst 
et  al., 2016). Surface mass balance (SMB) changes, dominated by 
snowfall, exhibit strong regional and temporal variability, for example with multi-decadal increases in the Antarctic Peninsula inferred since 
the 1930s (Medley and Thomas, 2019), and dominate the interannual 
to decadal variability of the AIS mass balance (Rignot et al., 2019). 
However, no significant continent-wide SMB trend is inferred since 
1979 (The IMBIE Team, 2018; Medley and Thomas, 2019; regional 
changes of Antarctic SMB are assessed further in Atlas Section 11.1). 
In summary, there is very high confidence  that the observed AIS mass

loss since the early 1990s is primarily linked to ice-shelf changes.
The SROCC stated with high confidence  that melting of ice shelves by 
warm ocean waters, leading to reduction of ice-shelf buttressing, has 
driven the observed ongoing thinning of major WAIS outlet glaciers. 
Since SROCC, digitized radar measurements have shown that the 
eastern ice shelf of Thwaites Glacier in the Amundsen Sea Embayment 
thinned between 10 and 33% during the three decades after 1978 
(Schroeder et al., 2019), and the role of basal ice-shelf melting has 
been emphasized (Smith et  al., 2020). Strong surface meltwater 
production has been noted as a precursor of ice-shelf disintegration 
in and since SROCC (Bell et al., 2018), and recent work placed strong 
meltwater production events (Lenaerts et  al., 2017; Nicolas et  al., 
2017; Wille et  al., 2019) and seasons (Robel  and  Banwell,  2019) 
Antarctic ice sheet cumulative mass change & equivalent sea level contribution
1980
2000
2020
2040
2060

2080
2100
-16
-12
-8
-4
0
4
4
 Gt    
-0.1
0
0.1
0.2
0.3
0.4
Rignot
IMBIE
Bamber
SSP1-2.6
SSP5-8.5
m
ISMIP6
Emulator
LARMIP-2
Emulator
 90%
 range
Emulator
 66%
 range
(a) (b)
Figure 9. 18 | Antarctic Ice Sheet cumulative mass change and equivalent sea level contribution. (a)  A p-box (Section 9.6.3.2) based estimate of the range of values of 
paleo Antarctic ice sheet mass and sea level equivalents relative to present day and the median over all central estimates (Bamber et al., 2009; Argus and Peltier, 2010; Dolan et al., 
2011; Mackintosh et al., 2011; Golledge et al., 2012, 2013, 2014, 2015, 2017b; K.G. Miller et al., 2012; Whitehouse et al., 2012; Ivins et al., 2013; Argus et al., 2014; Briggs et al.,

2014; Maris et al., 2014; de Boer et al., 2015, 2017; Dutton et al., 2015; Pollard et al., 2015; DeConto and Pollard, 2016; Gasson et al., 2016; Goelzer et al., 2016; Yan et al., 2016; 
Kopp et al., 2017; Simms et al., 2019); (b left) cumulative mass loss (and sea level equivalent) since 2015, with satellite observations shown from 1993 (Bamber et al., 2018a; The 
IMBIE Team, 2018; WCRP Global Sea Level Budget Group, 2018) and observations from 1979 (Rignot et al., 2019), and projections from Ice Sheet Model Intercomparison Project 
for CMIP6 (ISMIP6) to 2100 under RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6 scenarios (thin lines from Seroussi et al., 2020; Edwards et al., 2021; Payne et al., 2021) and ISMIP6 
emulator under SSP5-8.5 and SSP1-2.6 to 2100 (shades and bold line; Edwards et al., 2021); (b, right)  17th–83rd, 5th–95th percentile ranges for ISMIP6, ISMIP6 emulator, and

LARMIP-2 including surface mass balance (SMB) at 2100. (c–e)  Schematic interpretations of individual reconstructions (Anderson et al., 2002; Bentley et al., 2014; de Boer et al., 
2015; Goelzer et al., 2016) of the spatial extent of the Antarctic Ice Sheet are shown for the: (c) mid-Pliocene Warm Period, (d) Last Interglacial; and (e) Last Glacial Maximum 
(Fretwell et al., 2013): grey shading shows extent of grounded ice. (f–g)  Maps of mean elevation changes (f) 1978–2017 derived from multi-mission satellite altimetry (Schröder 
et al., 2019) and (g) ISMIP6: 2061–2100 projected changes for an ensemble using the Norwegian Climate Center’s Earth System Model (NorESM1-M) climate model under the 
RCP8.5 scenario (Seroussi et al., 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1265
Ocean, Cryosphere and Sea Level Change  Chapter 99in  this context. Antarctic ice-shelf basal meltwater flux varied 
between about 1100  ± 150 Gt yr –1 in the mid-1990s and about 
1570  ± 140  Gt  yr –1 in the late 2000s before decreasing to 
1160 ± 150 Gt yr –1 in 2018, and basal melt rates strongly vary with 
geographical position and depth, as a function of the surrounding 
water temperature (Adusumilli et al., 2020). Section 9.2.2.3 assesses 
that the intrusion of warm Circumpolar Deep Water (CDW), which 
has warmed and shoaled since the 1980s, has been at least partially 
controlled by forcing with significant decadal variability . Limited 
evidence suggests that, beyond strong internal decadal wind 
variability, increased greenhouse gas forcing has slightly modified the 
mean local winds between 1920 and 2018, facilitating the intrusion 
of CDW heat on the Amundsen-Bellingshausen continental shelf, 
and increased ice shelf melt (Section 9.2.2.3). However, theoretical

understanding is still incomplete and in situ measurements within 
the ice–ocean boundary layer are sparse (Wåhlin et  al., 2020). 
Modelling, and therefore attribution of ice shelf basal melt, remains 
challenging because of insufficient process understanding, required 
spatial resolution, the paucity of in situ observations (Dinniman et al., 
2016; Asay-Davis et al., 2017; Turner et al., 2017), and uncertainties 
of bathymetric datasets under ice-shelf cavities (Goldberg et  al., 
2019, 2020; Morlighem et al., 2020). In summary, ice-shelf thinning, 
mainly driven by basal melt, is widespread around the Antarctic coast 
and particularly strong around the WAIS ( high confidence ), although 
basal melt rates show substantial spatio-temporal variability.
Satellite observations suggest that changes in sea ice coverage and 
thickness can modulate iceberg calving, ice shelf flow and glacier 
terminus position around Antarctica (Miles et al., 2013, 2016, 2017;

Massom et al., 2015; Greene et al., 2018; Bevan et al., 2019), either 
through mechanical coupling or via changes to ocean stratification, 
influencing basal melting. A combined observational and modelling 
study (Massom et al., 2018) showed that regional loss of a protective 
sea ice buffer played a role in the rapid disintegration events of the 
Larsen A  and B and Wilkins ice shelves in the Antarctic Peninsula 
between 1995 and 2009, by exposing damaged (rifted) outer ice shelf 
margins to enhanced flexure by storm-generated ocean swells. One 
observational study (Sun et al., 2019) suggests that the absence of sea 
ice in front of ice shelves, which leads to strengthened topographic 
waves, favours higher ice-shelf basal melt rates by increasing the 
baroclinic (depth varying) ocean heat flux which can enter the cavity 
(Wåhlin et al., 2020). Paleo evidence for sea ice control on ice sheets 
is lacking, but geologic evidence shows a  concordance between

periods of ice-sheet growth and the expansion of sea ice (Patterson 
et al., 2014; Levy et al., 2019), both being favoured by reduced sea 
surface temperatures. Modelling confirms that sea ice controls the 
strength of ice mélange (Robel, 2017; Schlemm and Levermann, 2021) 
and thus influences ice-shelf flexure and calving rates and stability of 
floating ice margins, but one model shows this had negligible effect 
on AIS retreat rates during past warm periods (Pollard et al., 2018). 
Loss of ice-shelf-proximal sea ice is also associated with increased 
solar heating of surface waters and increased sub-shelf melting 
(Bendtsen et al., 2017; Stewart et al., 2019). In summary, although 
in some cases sea ice decrease and glacier and ice-shelf flow and 
terminus position changes can have the same common cause, there 
is medium confidence  that sea ice decrease ultimately favours the

mass loss of nearby ice shelves through a variety of processes.The SROCC stated with high confidence  that ice-shelf disintegration 
has driven dynamic thinning in the northern Antarctic Peninsula over 
recent decades, and expressed high confidence  in current ongoing 
mass loss from glaciers that fed now-disintegrated ice shelves. 
However, the mass loss rate has decreased in the 20 years since 
the immediate speed-up following ice-shelf disintegration in 1995 
and 2002. Observed flow speed of these tributary glaciers is still 
26% higher than before the ice shelf disintegration (Seehaus et al., 
2018). Conversely, one study interpreted the increased flow speed 
of the Scar Inlet Ice Shelf’s tributary glaciers as a sign of evolving 
instability of the currently intact ice shelf (Qiao et al., 2020).
Ongoing grounding line retreat, indicating dynamic thinning, 
is observed with high confidence  in many areas of Antarctica,

and particularly on the WAIS, with the highest rates being in the 
Amundsen and Bellingshausen Sea areas, and around Totten Glacier 
in East Antarctica, as stated in SROCC. Research published since 
SROCC has evidenced grounding line retreat of the West Antarctic 
Berry Glacier on the Getz Coast (Millan et al., 2020) and on the East 
Antarctic Denman Glacier (Brancato et al., 2020), both since 1996. 
Furthermore observed grounding line retreat in excess of 1.5 km 
between 2003 and 2015 has been reported for parts of Marie Byrd 
Land (Christie et  al., 2018). In summary, there is high confidence  
that grounding lines of marine-terminating glaciers are currently 
retreating in many areas around Antarctica, particularly around 
the WAIS, and additional areas of grounding line retreat have been 
evidenced since SROCC.
The SROCC stated with medium confidence  that sustained mass 
losses of several major glaciers in the Amundsen Sea Embayment

(ASE) are compatible with the onset of marine ice sheet instability 
(MISI). However, whether unstable WAIS retreat had begun, or was 
imminent, remained a  critical uncertainty. New publications since 
SROCC have not substantially clarified this question. One study 
that combined satellite measurements with a numerical model and 
prescribed ice-shelf thinning (Gudmundsson et  al., 2019) suggests 
that MISI is not required to explain the observed current mass 
loss rates of the WAIS, because they are consistent with external 
climate drivers. Furthermore, the fast grounding line retreat of the 
Pine Island Glacier in the ASE, which was triggered in the 1940s 
(Smith et al., 2017), observed after 1992 (Rignot et al., 2014) and 
previously interpreted as a sign of MISI (Favier et al., 2014), seems to 
have stabilized recently (Milillo et al., 2017; Konrad et al., 2018), and 
its current flow patterns do not suggest ongoing or imminent MISI

(Bamber and Dawson, 2020). However, sustained fast grounding line 
retreat has been observed for the Smith Glacier in the ASE (Scheuchl 
et  al., 2016), and an analysis of flow patterns and grounding line 
retreat of the ASE Thwaites Glacier between 1992 and 2017 (Milillo 
et  al., 2019) showed sustained, albeit spatially heterogeneous, 
grounding line retreat, highlighting ice–ocean interactions that 
lead to increased basal melt. In addition, Denman Glacier in East 
Antarctica was shown to hold potential for unstable retreat (Brancato 
et al., 2020). In summary, the observed evolution of the ASE glaciers 
is compatible with, but not unequivocally indicating an ongoing MISI 

1266
Chapter 9 Ocean, Cryosphere and Sea Level Change9The SROCC reported limited evidence  and medium agreement  for 
anthropogenic forcing of the observed AIS mass balance changes. 
As  stated in Section  3.4.3.2, there remains low confidence  in 
attributing the causes of the observed mass of loss from the AIS since 
1993, in spite of some additional process-based evidence to support 
attribution to anthropogenic forcing.
9.4.2.2 Model Evaluation
The AR5 (Church et al., 2013b; Flato et al., 2013) stated that regional 
climate models and global models with bias-corrected SST and sea 
ice concentration tended to produce more accurate simulations of 
Antarctic SMB than coupled climate models. It also noted strong 
climate model temperature biases over the Antarctic, though the 
latter may reflect known biases in the reanalysis used (Fréville et al., 
2014). Section Atlas.11.1 assesses that there is medium confidence  
in the capacity of climate models to simulate Antarctic climatology

and SMB changes.
Section 9.2.3.2 assesses that there is low confidence  in simulations 
of Southern Ocean temperature. Few ocean models resolve ice-shelf 
cavities, and biases in present-day melt rates can be substantial in 
some sectors, including the key region of the Amundsen Sea (e.g., an 
exception is the FESOM simulation in Figure  9.19 includes ice-
shelf cavities and simulates ice-shelf basal melting and refreezing) 
(Naughten et  al., 2018). An  increasing number of observational 
studies from which basal melt rates are calculated (Huhn et al., 2018; 
Adusumilli et al., 2020; Das et al., 2020; Hirano et al., 2020; Stevens 
et al., 2020), combined with improved understanding of influences 
specific to water-masses and modes of melting or dissolving (Silvano 
et al., 2018; Adusumilli et al., 2020; Malyarenko et al., 2020; Wåhlin 
et al., 2020), may help to refine these models in the future. However, 
given the limited number of available models and their biases, there

is currently low confidence  in the sub-shelf melt rates simulated by 
Improvements in the representation of grounding line evolution in 
ice-sheet models since AR5 (such as sub-grid schemes for basal friction 
and ice-shelf melt, and local grid refinement) means that most of the 
model simulations presented in SROCC were dominated by physical 
processes. Since then, these advances have been applied in several 
model intercomparison projects  – such as ISMIP6 and LARMIP-2 
(see Box 9.3); MISMIP+ (Cornford et al. 2020); and ABUMIP (Sun et al. 
2020). All models participating in ISMIP6 and LARMIP-2 simulate 
ice-shelf and grounding-line evolution, and include sub-shelf melt 
parametrization, which was not the case in the Sea-level Response to 
Ice Sheet Evolution (SeaRISE) project intercomparison (Bindschadler 
et  al., 2013; Nowicki et  al., 2013). Simulations of grounding line 
evolution (Seroussi et al., 2017, 2020) have benefitted from improved

bedrock topography (Morlighem et al., 2020). Treatment of sub-shelf 
melting, however, remains one of the causes of large differences in 
AIS models, particularly for partially floating grid cells in models with 
coarse resolution (Levermann et al., 2020; Edwards et al., 2021). Due 
to the limitations in resolving cavities in ocean models, as described 
above, basal melt rates are generally parameterized at the ice shelf 
base, based on ocean model simulations of temperatures and salinity 
instead (Nowicki et al., 2020b; Seroussi et al., 2020). While this has the advantage of connecting melt rates to emissions scenarios, 
a large variety of melt parametrizations exist (DeConto and Pollard, 
2016; Lazeroms et al., 2018; Reese et al., 2018; Hoffman et al., 2019; 
Pelle et al., 2019; Jourdain et al., 2020), and there is low agreement  
due to limited observational constraints (ocean temperature, salinity, 
velocity, and ice shelf draft)(Jourdain et al., 2020), uncertainty in the

physics of parametrized processes, missing processes (e.g.,  tides), 
and uncertainty in the treatment of ice-sheet–climate feedbacks 
(Donat-Magnin et al., 2017; Bronselaer et al., 2018; Golledge et al., 
2019). Parametrizations are usually calibrated to present-day melt 
rates, but can respond differently to projected ocean warming (Favier 
et  al., 2019; Jourdain et  al., 2020). Two different calibrations were 
used in ISMIP6 (Box 9.3; Jourdain et al., 2020; Nowicki et al., 2020b): 
one reproducing melt rates averaged around the whole continent 
(MeanAnt: Figure 9.19), and the other reproducing melt rates near the 
grounding line of Pine Island Glacier (PIGL; see Figure 9.19), leading to 
large differences in melt rates. Evaluation with observations and two 
cavity-resolving models suggests that the MeanAnt parametrization 
better reproduces observed melt rates and projected increases in both 
the warm Amundsen Sea Embayment and cold Ronne-Filchner shelf

cavity, as well as total Antarctic melting (Jourdain et al., 2020). The 
PIGL calibration represents the upper end for increased basal melt 
sensitivity that would be caused by continent-wide changes to ocean 
water properties and circulation under strong future forcing (Jourdain 
et  al., 2020). The basal sliding law also has a  strong influence 
on grounding line retreat and glacier acceleration in response to 
perturbations, and varies spatially (Sun et  al., 2020). Sliding laws 
(Joughin et al., 2019) can only be constrained with observations in 
regions experiencing significant change, and with sufficiently long 
observational records.
The SROCC noted that AIS simulations are increasingly evaluated 
or formally calibrated with modern observations and/or paleodata – 
to obtain more realistic initial conditions (ice-sheet geometry, 
velocity and forcing) and to constrain uncertainty in probabilistic 
projections. This trend continues (Nias et  al., 2019; Gilford et  al.,

2020; Hamlington et  al., 2020b; Wernecke et  al., 2020). However, 
while the large-scale characteristics of the initial ice-sheet state 
have improved significantly (Box  9.3), capturing the smaller-scale 
rates of change, including mass trends, remains challenging for 
many models (Goldberg et  al., 2015; Reese et  al., 2020; Seroussi 
et  al., 2020; Siegert et  al., 2020). This  increases uncertainty in 
projections, especially for the 21st  century (Section  9.4.2.5). 
However, uncertainties in ice-sheet model simulations have been 
much better quantified since AR5, through model intercomparison 
projects (in  particular, ISMIP6 and LARMIP-2; see Box  9.3), 
perturbed parameter ensembles, and increasing use of statistical 
emulation (Gilford et al., 2020; Levermann et al., 2020; Wernecke 
et al., 2020; DeConto et al., 2021; Edwards et al., 2021) to better 
sample the parameter space. By exploring uncertainties more fully,

these methods have the potential to identify better simulations of 
the historical period.
An important difficulty is how to evaluate simulations of processes 
that are: not currently observed; or rare; or indirectly deduced – in 
particular, the ice-shelf disintegrations and cliff failures that would 
drive the proposed marine ice cliff instability (MICI; Section 9.4.2.4

1267
and Box 9.4; DeConto and Pollard, 2016; DeConto et al., 2021). Models 
of ice-cliff failure can only be indirectly and partially evaluated, using 
existing (i.e., static) cliffs and laboratory experiments (Clerc et  al., 
2019). The SROCC stated that there was low agreement on the exact 
MICI mechanism and limited evidence of its occurrence in the present 
or the past, and that the validity of MICI remains unproven. Only one 
ice-sheet model represents MICI (Pollard et al., 2015; DeConto and 
Pollard, 2016; DeConto et al., 2021). The mechanism has not been 
found to be essential for reproducing Mid Pliocene Warm Period and 
Last Interglacial reconstructions or satellite observations, though 
Last Interglacial data slightly favours it in this model (Edwards et al., 
2019; Gilford et al., 2020; DeConto et al., 2021).
In summary, there is now medium confidence in many ice-sheet

processes in ice-sheet models, including grounding line evolution. 
However, there remains low confidence in the ocean forcing affecting 
the basal melt rates, and  low confidence  in simulating mechanisms 
that have the potential to cause widespread, sustained and very 
rapid ice loss from Antarctica through MICI.9.4.2.3 Drivers of Future Antarctic Ice Sheet Change
9.4.2.3.1 Surface mass balance
The AR5 projected a  negative contribution from Antarctic surface 
mass balance (SMB) changes to sea level over the 21st  century 
(i.e., mitigating sea level rise), due to increased snowfall associated 
with warmer air temperatures. Sensitivity of SMB to Antarctic 
surface air temperature change varied from 3.7 to 7% °C–1, and 
the sea level projections assumed a sensitivity of 5.1 ± 1.5% °C–1 
from CMIP3 era models (Gregory and Huybrechts, 2006) to estimate 
SMB changes from Antarctic temperatures in the CMIP5 ensemble. 
Since the AR5, analyses of CMIP5 and CMIP6 models have found

Antarctic temperature sensitivity for accumulation (precipitation 
minus sublimation) of 3.5 to 8.7% °C–1 (Frieler et al., 2015), for SMB 
of 6.0 to 9.9% °C–1 (Previdi and Polvani, 2016) and for precipitation of 
around 4 to 9% °C–1 (±1 standard deviation ranges; Bracegirdle et al., 
2020). An accumulation sensitivity estimate derived from ice core 
data lies in the middle of the range, around 6% °C–1 (Frieler et al., 
2015). These are consistent, within uncertainties, with each other and 
AR5, under the approximation that SMB is dominated by snowfall.
Figure 9. 19 | Ice-shelf basal melt rates for present-day (upper panels) and changes from present-day to the end of the 21st century under the RCP8.5 
scenario (lower panels).  Present-day melt rates were estimated through: the input-output method constrained by satellite observations and atmosphere/snow simulations

(Rignot et al., 2013) and representative of 2003–2008 (upper left); the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) non-local-PIGL parametrization constrained 
by observation-based ocean properties (Jourdain et al., 2020) and representative of 1995–2014 (upper centre); the Finite Element Sea ice/Ice Shelf Ocean Model (FESOM) 
simulation over 2006–2015, forced by atmospheric conditions from a Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model mean (MMM) under the RCP8.5 
scenario (Naughten et al., 2018) (upper right). Future anomalies are calculated as 2081–2100 minus present-day using the ISMIP6 non-local-MeanAnt and non-local-PIGL 
parametrizations (Jourdain et al., 2020) (lower left and centre, respectively) based on projections from the Norwegian Climate Center’s Earth System Model (NorESM1-M)

CMIP5 model, and the FESOM-MMM projection (lower right). Note the symmetric-log colour bar (linear around zero, logarithmic for stronger negative and positive values). Inset 
highlights the Amundsen Sea Region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1268
Chapter 9 Ocean, Cryosphere and Sea Level Change9The AR5 found that the median and likely  sea level contributions 
due to SMB from 1986–2005 to 2100 were –0.05 (–0.09 to 
–0.02) m under RCP8.5 and –0.02 (–0.05 to 0.00) m under RCP2.6. 
The SROCC did not present a  separate SMB contribution, instead 
showing total Antarctic projections derived from ice-sheet models 
(Section 9.4.2.5). Projections of the SMB contribution to sea level 
tend to be slightly more negative since AR5, due at least in part to 
the higher range in equilibrium climate sensitivity values in CMIP6 
(Payne et  al., 2021). Mean and ±1 standard deviation ranges for 
grounded Antarctic Ice Sheet SMB changes from 2000 to 2100 
computed from CMIP5 models are –0.08 (–0.13 to –0.04) m sea 
level equivalent (SLE) for RCP8.5 and, similarly for CMIP6 models, 
are –0.07 (–0.11 to –0.03) m for SSP5-8.5 (Gorte et al., 2020). The 
general circulation models (GCMs) used to drive ice-sheet models

in ISMIP6 (Box 9.3) project mean grounded AIS SMB changes from 
2005 to 2100 of –0.06 (range –0.08 to –0.03) m SLE under RCP8.5 for 
the six CMIP5 models (Seroussi et al., 2020) and –0.09 (range –0.10 
to –0.07) m SLE under SSP5-8.5 for the four CMIP6 models, which 
have climate sensitivity values of 4.8°C –5.3°C (Payne et al., 2021). 
We apply the AR5 parametric AIS SMB model (Section 9.6.3.2) to 
updated projections of global mean temperature from a two-layer 
energy budget emulator (Supplementary Material  7.SM.2), which 
gives a  median –0.05 (5–95% range –0.07 to –0.02) m SLE for 
SSP5-8.5 (Section  9.4.2.5, Table  9.3), that is, similar to the AR5 
assessment and slightly smaller than the CMIP6 estimate. This 
estimate is used to augment the LARMIP-2 dynamic projections 
(Box 9.3) in Sections 9.4.2.5 and 9.4.2.6. Overall, CMIP5 and CMIP6 
GCM simulations of sea level fall by 2100 due to Antarctic SMB 
increases are around 2–4 cm greater than estimates derived with

the statistical method used in AR5. Further details about projections 
of Antarctic temperature, precipitation and SMB are provided in 
Section Atlas.11.1.4, which assesses that, due to the challenges of 
model evaluation (Section 9.4.2.2) and the possibility of increased 
meltwater runoff (Kittel et  al., 2021), there is only medium 
confidence  that the future contribution of Antarctic SMB to sea level 
this century will be negative under all greenhouse gas emissions 
scenarios. Longer time scales are discussed in 9.4.2.6.
9.4.2.3.2 Sub-shelf melting
The SROCC highlighted that an important ongoing deficiency in 
projections of Antarctic sub-shelf melting is the lack of ice–ocean 
coupling in most continental-scale studies. Increased basal melting is 
mainly caused by warmer CDW (Section 9.2.2.3) on the continental 
shelves, and warming surface waters intruding under ice shelves 
(Naughten et al., 2018). Predicting whether or not open ocean water

masses will freely penetrate ice shelf cavities, or will be partially 
blocked by ocean density gradients, is complex (Wåhlin et al., 2020); 
while melting related to CDW inflow is currently dominant in the 
Amundsen Sea Embayment, melt in other embayments is limited 
by deep inflows of high-salinity shelf water or seasonally warmed 
shallow incursions of Antarctic Surface Water (Stewart et al., 2019; 
Adusumilli et  al., 2020). There is little consensus regarding future 
change in CDW (Section 9.2.2.3), and more generally low confidence  
in future change in the temperature of Antarctic ice-shelf cavities 
(Section 9.2.3.2).The response of sub-shelf melting to ocean warming is also poorly 
constrained. A  key unknown is whether, and when, cold ice-shelf 
cavities might become more similar to the Amundsen Sea Embayment, 
not only in ocean temperature but also ice–ocean  heat exchange, 
which depends on the cavity geometry and ocean circulation

(Little et al., 2009). Only two ocean models with ice-shelf cavities have 
been used to make sub-shelf basal melting projections for Special 
Report on Emissions Scenarios and Representative Concentration 
Pathway (RCP) scenarios (Hellmer et  al., 2012; Timmermann and 
Hellmer, 2013; Timmermann and Goeller, 2017; Naughten et  al., 
2018). The FESOM simulation, forced by a CMIP5 multi-model mean 
under RCP8.5, projects a  90% increase in melting (Figure  9.19), 
although this could be overestimated due to an underestimation 
of present-day melt  rates (Section  9.4.2.2; Naughten et  al., 2018). 
The temperature–melt relationship was parameterized by ISMIP6 in 
terms of heat exchange velocity in m a–1, and by LARMIP-2 as basal 
melt sensitivity in m a–1 °C–1 (Box 9.3; Jourdain et al., 2020; Levermann 
et  al., 2020; Reese et  al., 2020), and both vary widely around the 
continent, depending on cavity type. Median values of ISMIP6 heat

exchange velocity vary by a  factor of 5–10 when calibrating to 
either mean Antarctic or high Pine Island Glacier observed melt rates 
(Section 9.4.2.2; Box 9.3; Jourdain et al., 2020). Basal melt sensitivities 
near the grounding line estimated by Reese et al. (2020)  with a box 
model of ocean overturning range from 3.9 m a–1 °C–1 for the Weddell 
Sea to 10.5 m a–1 °C–1 for the Amundsen Sea region, with a continental 
mean of 5.3 m a–1 °C–1. Similarly high Amundsen Sea sensitivities 
are estimated in coupled ice–ocean simulations of Thwaites Glacier 
(mean 9.4 m a–1 °C–1; range 6–16 m a–1 °C–1) (Seroussi et al., 2017). 
These large variations lead to large differences in basal melt rates and 
projected sea level contributions when applied to the whole ice sheet 
in ISMIP6 and LARMIP-2 (Box 9.3). Projections of melt rates from the 
two ISMIP6 calibrations are higher than those from FESOM, driven 
by a  CMIP5 multi-model mean (Figure  9.19; Jourdain et  al., 2020).

The ISMIP6 ensemble mostly uses the mean Antarctic calibration, but 
includes some simulations with the Pine Island Glacier calibration, 
and the ISMIP6 emulator samples more of these higher values; 
LARMIP-2 uses basal melt sensitivities (7–16 m a–1 °C–1) consistent 
with estimates for the Amundsen Sea Embayment. Due to the limited 
availability of cavity-resolving ocean models, and the wide regional 
variation in estimates of basal melt sensitivity to ocean temperature, 
there is only low confidence in projected future sub-ice-shelf melt 
rates. The impact of this uncertainty on AIS model projections to 2100 
is discussed in Section 9.4.2.5.
9.4.2.3.3 Ice-shelf disintegration
Antarctic ice shelves modulate grounded ice flow through buttressing, 
so their weakening or disintegration is crucial for the timing and 
magnitude of ice loss and onset of instabilities (Section  9.4.2.4; 
Box 9.4). Projections of ice-shelf disintegration are uncertain in terms

of atmospheric warming and the response of the shelf surface  – 
that is, surface melting, and whether shelves then disintegrate due 
to hydrofracturing and flexing, or are resilient through refreezing or 
drainage (Bell et al., 2018). The SROCC stated it is not expected that 
widespread ice-shelf loss will occur before the end of the 21st century, 
but this was based on only one study, using a regional climate model 
forced by five GCMs (Trusel et al., 2015), so there was low confidence

1269
Ocean, Cryosphere and Sea Level Change  Chapter 99in this assessment. The study of DeConto and Pollard (2016) projected 
the appearance of extensive surface meltwater several decades 
earlier than Trusel et al. (2015) and was therefore assessed to be too 
uncertain to include in SROCC projections of the AIS.
Since SROCC, further studies have highlighted the modelling 
uncertainties in this area. Coastal surface air temperature projections 
in CMIP6 models show large inter-model differences driven by 
sea ice retreat and exhibit more warming relative to global mean 
temperature under low emissions than high, due to delayed response 
of the Southern Ocean to stabilized emissions and stratospheric ozone 
recovery (Bracegirdle et  al., 2020). The updated study of DeConto 
et  al. (2021) includes improvements to the climate simulations 
relative to those in DeConto and Pollard (2016), and the resulting 
surface meltwater projections are now consistent with Trusel et al.

(2015). However, the net effect of meltwater feedbacks on ice shelves 
is uncertain. Ice discharge is expected to lead to surface ocean and 
atmosphere cooling: this increases ocean stratification and sub-shelf 
melting, but also reduces ice-shelf surface melting and delays 
hydrofracturing (Golledge et al., 2019; Sadai et al., 2020; DeConto 
et al., 2021). The new studies are insufficient to change SROCC’s low 
confidence  assessment on ice-shelf loss. The consequence of this 
uncertainty on projections is discussed in Section 9.4.2.5 and Box 9.4.
9.4.2.4 Ice-sheet Instabilities
A major uncertainty in future Antarctic mass losses is the possibility 
of rapid and/or irreversible ice losses through instability of marine 
parts of the ice sheet, via the proposed mechanisms of marine ice 
sheet instability (MISI) and marine ice cliff instability (MICI), and 
whether these processes will lead to a collapse of the West Antarctic 
Ice Sheet (WAIS).

MISI is a  proposed self-reinforcing mechanism within marine ice 
sheets that lie on a  bed that slopes down towards the interior of 
the ice sheet, whereby, in the absence of ice-shelf buttressing, 
the position of the grounding line is inherently unstable until 
reaching an upward sloping bed. The SROCC (Meredith et al., 2019) 
noted advances in modelling MISI since AR5, but that ‘significant 
discrepancies’ remained in projections due to poor understanding 
of mechanisms, and lack of observational data to constrain the 
models. Since SROCC, modelling uncertainties have been more 
thoroughly explored, rather than constrained (compatibility of 
current observations in the Amundsen Sea Embayment with MISI is 
assessed in Section 9.4.2.1). Internal climate variability might either 
slow (Hoffman et al., 2019) or amplify (Robel et al., 2019) MISI, and 
stable grounding line positions can be reached on downward sloping 
beds if ice shelves provide buttressing (Sergienko and Wingham,

2019; Cornford et al., 2020). Ice-sheet model simulations that remove 
all Antarctic ice shelves (and prevent them from reforming) show 
2–10 m SLE Antarctic mass loss after 500 years due to MISI, of which 
WAIS collapse contributes 2–5 m (Sun et al., 2020), with the majority 
of the mass loss in the first one to two centuries. Much of the multi-
model variation is due to the sliding law (Section 9.4.2.2). However, 
it is not expected that widespread ice-shelf loss will occur before the 
end of the 21st century (Section 9.4.2.3; Box 9.4). A recent update 
of bed topography that unveiled large and overdeepened subglacial troughs in East Antarctica potentially vulnerable to MISI (Morlighem 
et al., 2020) has only been used by a few models (Seroussi et al., 
2020; Sun et al., 2020), so current projections could underestimate 
vulnerability in these regions. The sea level rise contribution of the 
AIS therefore crucially depends on the behaviour of individual ice

shelves and outlet glacier systems and whether they enter MISI for 
a  given level of warming (Box  9.4; Pattyn and Morlighem, 2020). 
As for Antarctic simulations generally (Sections 9.4.2.2 and 9.4.2.3), 
there is medium confidence in simulating MISI but low confidence 
in projecting the sub-shelf melting and ice-shelf disintegration that 
drive it.
The SROCC noted  limited evidence from geological records and 
ice-sheet modelling, suggesting that parts of the AIS experienced 
rapid (centennial) retreat likely due to MISI between 20,000 and 
9,000 years ago, and also described more uncertain evidence for the 
Last Interglacial (LIG) and mid-Pliocene Warm Period (MPWP). Recent 
support for past MISI is provided by model simulations of the WAIS 
during the LIG (Clark et al., 2020), the British Ice Sheet during the last 
termination (Gandy et al., 2018) and the Laurentide Ice Sheet during 
the Younger Dryas (Pico et al., 2019), which show progressive retreat

despite declining temperatures, indicative of a  true (ice  dynamic) 
instability. Direct observational evidence of rapid paleo ice-sheet 
grounding line retreat is rare but, on the Larsen continental shelf, 
retreat rates of >10 km yr –1 during the deglaciation have been 
estimated (Dowdeswell et al., 2020). MISI has also been inferred from 
sedimentological evidence of ice loss from Wilkes Subglacial Basin, 
East Antarctica (Bertram et al., 2018; Wilson et al., 2018; Blackburn 
et al., 2020) but these reconstructions cannot unambiguously identify 
unstable from progressive retreat. Therefore, there is limited evidence  
to identify the operation of instability mechanisms such as MISI 
in paleo ice-sheet retreat.
The SROCC assessed that ice-sheet interactions with the solid Earth 
are not expected to substantially slow sea level rise from marine-based 
ice in Antarctica over the 21st  century ( medium confidence ), but

that these processes could become important on multi-century and 
longer time scales. More recent modelling of deglaciation of the Ross 
Embayment by Lowry et al. (2020) is consistent with this assessment. 
However, new projections for Pine Island Glacier (Kachuck et  al., 
2020) support previous work (Barletta et al., 2018) suggesting that 
lower mantle viscosity in this region leads to a negative feedback on 
decadal time scales. Grounding line stabilization by the solid Earth 
response may therefore occur over the 21st century in the Amundsen 
Sea Embayment, where most mass loss is occurring (Section 9.4.2.1), 
but more generally occurs over multi-centennial to millennial time 
scales ( medium confidence ).
The MICI hypothesis describes rapid, unmitigated calving triggered 
by ice-shelf collapse (Pollard et  al., 2015). The SROCC noted that 
the MICI mechanism led one model (DeConto and Pollard, 2016) 
to lose mass far more rapidly, but excluded the mechanism from

its projections due to uncertainty in the timing of the ice-shelf 
disintegration (Section  9.4.2.3). They stated that MICI could lead 
to sea level contributions beyond 2100 considerably higher than 
the likely  range projected by other models. However, given the low 
agreement  on the exact MICI mechanism and limited evidence  of its

1270
Chapter 9 Ocean, Cryosphere and Sea Level Change9occurrence in the present or the past (Section 9.4.2.2), its potential 
to affect future sea level rise was very uncertain. Since SROCC, new 
simulations show later ice-shelf disintegration, in agreement with 
other models (Section 9.4.2.3; DeConto et al., 2021), and therefore 
lower projections at 2100 (Section  9.4.2.5). New theoretical 
evidence suggests that ice-cliff collapse may only occur after very 
rapid ice shelf disintegration caused by unusually high meltwater 
production (Clerc et al., 2019; Robel and Banwell, 2019), and that 
the subsequent rate of retreat depends on the terminus geometry 
(Bassis and Ultee, 2019). As SROCC noted, only Crane Glacier on 
the Peninsula has shown retreat consistent with MICI, after the 
Larsen B ice shelf collapsed, and MICI-style behaviour at Jakobshavn 
and Helheim Glaciers in Greenland might not be representative of 
wider Antarctic glaciers. Observations from Greenland show that

steep cliffs commonly evolve into short floating extensions, rather 
than collapsing catastrophically (Joughin et al., 2020). As assessed 
in Section  9.4.2.2 and 9.4.2.3, there is therefore low confidence  
in simulating mechanisms that have the potential to cause 
widespread, sustained and very rapid ice loss from Antarctica this 
century through MICI, and low confidence  in projecting the driver of 
ice-shelf disintegration.
In summary, poorly understood processes of instabilities, characterized 
by deep uncertainty , have the potential to strongly increase Antarctic 
mass loss under high greenhouse gas emissions on century-to-
multicentury time scales (Box  9.4). These instabilities are therefore 
considered separately in assessments of the future contribution 
to global mean sea level (GMSL; Sections 9.4.2.5, 9.4.2.6, 9.6.3.2 
and 9.6.3.5).
9.4.2.5 Projections to 2100
The AR5 assessed the median and likely (66–100% probability) sea

level contributions of the AIS in 2100 relative to 1986–2005 to be 
0.06 (–0.04 to +0.16) m SLE under RCP2.6 and 0.04 (–0.08 to +0.14) m 
SLE under RCP8.5 (Table 9.3; no change when using the AR6 baseline). 
The AR5 stated that only the collapse of the marine-based sectors of 
the AIS, if initiated, could cause GMSL to rise substantially above the 
likely  range during the 21st century, with medium confidence  that 
this would not exceed several tenths of a metre during this period. 
The assessment of the dynamical contribution had no dependence on 
emissions scenarios, due to the lack of literature, so the decrease in 
sea level contribution in the higher-emissions scenario was solely due 
to increased SMB (Section 9.4.2.3). The SROCC (Oppenheimer et al., 
2019) assessed the total contribution based on five new ice-sheet 
modelling studies that incorporated marine ice-sheet dynamics, 
combining their estimates and interpreting the 5–95th percentile

range of the resulting distribution as the likely  range (17–83% 
probability interval, i.e., not open-ended as in the AR5). The median 
and likely  range  contributions by 2100 were 0.04 (0.01–0.11) m 
under RCP2.6 and 0.12 (0.03–0.28)  m under RCP8.5 (Table  9.3). 
The positive scenario-dependence in SROCC  – where increases in 
dynamic losses driven by ocean warming and ice-shelf disintegration 
under higher emissions (Section 9.4.2.3) dominate over increases in 
SMB – arose from a combination of physical processes and model 
limitations. Modelling improvements in these studies included 
improved representations of grounding line response to drivers, more extensive exploration of uncertainties, and inclusion of a  positive 
feedback of meltwater on climate (Golledge et al., 2019). However, 
two of the projections did not include SMB changes that would 
offset dynamic losses (Levermann et  al., 2014; Ritz et  al., 2015),

and the scenario dependence may have been further amplified by 
highly sensitive sub-shelf melt parametrizations and use of simplified 
SMB schemes (Golledge et  al., 2015, 2019; Bulthuis et  al., 2019; 
Oppenheimer et al., 2019).
Since SROCC, new projections have arisen from multi-model 
intercomparison projects ISMIP6 and LARMIP-2 (Box 9.3) and one 
model that includes MICI (Section 9.4.2.4; Table 9.3; DeConto et al., 
2021). Corrections are added to allow comparison: all ISMIP6-derived 
projections have an estimate of the historical dynamical response 
to pre-2015 climate forcing added, which increases contributions 
(Box  9.3; Figure  9.18); the LARMIP-2 dynamic projections are 
combined with an estimate of SMB, which decreases contributions 
(Sections 9.4.2.3 and 9.6.3.2); and the ISMIP6 emulated and LARMIP-2 
projections were re-estimate using the global surface air temperature 
distributions from the two-layer energy budget emulator described in

Supplementary Material 7.SM.2. The majority of the new projections 
indicate that, under all emissions scenarios, the AIS will lose mass 
overall and contribute to sea level rise. Most thinning occurs in the 
Amundsen Sea sector in WAIS and Totten Glacier in EAIS (Figure 9.18). 
The most negative contribution is –0.02 m (5th percentile of ISMIP6 
combined RCP8.5 and SSP5-8.5 projections after correction) and 
the largest contribution is 0.57 m SLE (95th percentile; Levermann 
et al., 2020), or 0.63 m SLE with MICI (95th percentile; DeConto et al., 
2021). ISMIP6 ensemble ranges are wider for the high scenarios 
(RCP8.5/SSP5-8.5) than the low (RCP2.6/SSP1-2.6), in part because 
more simulations were available. The ISMIP6 simulations that apply 
an ice-shelf collapse scenario based on exceedance of a  surface 
meltwater threshold (Trusel et al., 2015), driven by CMIP5 models, 
show only a small increase in mass loss (around 0–0.04 m), mostly

from the Peninsula, due in part to the small number of ice shelves 
predicted to collapse this century (Seroussi et al., 2020). Simulations 
driven by the CMIP5 model HadGEM2-ES, which has unusually 
extreme warming in the Ross Sea (Barthel et al., 2020), show a larger 
mass loss (up to about 0.05 m) in East Antarctica under ice-shelf 
collapse (Edwards et al., 2021). The ISMIP6 projections do not include 
the efficient meltwater drainage or atmospheric feedbacks that could 
reduce mass loss further (Seroussi et al., 2020).
The relationship between emissions scenario and AIS response 
varies across the studies, with emulated ISMIP6 projections showing 
a slight negative scenario dependence in the median (–0.01 m) from 
SSP1-2.6 to SSP5-8.5, and LARMIP-2-based projections showing 
a  slight positive scenario-dependence in the median (0.02 m; 
Table 9.3). A lack of clear scenario dependence in the median masks 
large individual variations across climate and ice-sheet models,

whereby the net AIS contribution response to emissions scenario 
depends on the relative magnitudes of the atmosphere, ocean 
and ice-sheet responses (Barthel et al., 2020; Seroussi et al., 2020; 
Edwards et al., 2021). Climate and ice-sheet models do not project 
that the AIS response will be the same under high or low greenhouse 
gas emissions in 2100; rather, there is no consensus on the sign of 
the change. In contrast, strong scenario dependence is seen from

1271
RCP4.5 to RCP8.5 in projections that allow MICI (Section  9.4.2.4; 
DeConto et  al., 2021), though less so than earlier projections 
(DeConto and Pollard, 2016) due to later ice-shelf disintegrations. 
A negative or positive scenario dependence of the AIS response this 
century cannot be deduced from recent observations, because there 
is still low confidence in attributing the causes of observed mass 
loss (Section 9.4.2.1), and neither regional mass increases by SMB 
nor regional mass losses by ice flow have a linear relationship with 
global mean temperature (Sections 9.4.2.1, 9.4.2.2, 9.4.2.3). There 
is therefore low agreement on the relationship between emissions 
scenario and AIS response. However, in the longer term, mass loss is 
expected to dominate (Section 9.4.2.6).The LARMIP-2 median projections are higher than those of the ISMIP6 
emulator (by 0.04–0.07 m), and the 95th percentiles are two to three

times higher. Two possible reasons for the differences between the 
emulated ISMIP6 and LARMIP-2 projections are assessed: the set of 
ice-sheet models (Annex II) and the parameter values determining sub-
shelf melt sensitivity to ocean temperature (Section 9.4.2.3; Box 9.3). 
Using only the 13 ice-sheet models common to ISMIP6 and LARMIP-2 
reduces the LARMIP-2 median projections by 0.02–0.03 m SLE and the 
95th percentiles by 0.04–0.08 m SLE (Table 9.3). This approximately 
halves the difference in medians, but has a relatively small effect on 
the upper end. Sub-shelf melt sensitivity has a larger effect, due to the 
wide variation of estimates from different regions and methods. Using 
only the Pine Island Glacier sub-shelf melt distribution (Sections 9.4.2.2 Table 9 .3 | Projected sea level contributions in metres from the Antarctic Ice Sheet in 2100 relative to 1995–2014, unless otherwise stated, for selected

Representative Concentration Pathway (RCP) and Shared Socio-economic Pathways (SSP) scenarios.  Italics denote partial contributions. The historical dynamic 
response omitted from ISMIP6 simulations is estimated to be 0.33 ± 0.16 mm yr –1 (0.03 m ± 0.01 m in 2100 relative to 2015; Box 9.3). The climate forcing is described in 
Supplementary Material 7.SM.2.
IPCC AR5 (Church et al., 2013b) 0.06 (–0.04 to +0.16) 0.05 (–0.05 to +0.15) 0.04 (–0.08 to +0.14) Median and likely  (≥ 66%  range) contribution
IPCC SROCC (Oppenheimer et al., 2019) 0.04 (0.01 to 0.11) 0.06 (0.01 to 0.15) 0.12 (0.03 to 0.28)Median and likely  (66%  range) contribution. 
Combination of five studies
ISMIP6 CMIP5-forced (Seroussi et al., 2020) ; 
excludes historical dynamic response –0.01 to +0.16 – –0.08 to +0.30Range of ISMIP6 multi-model contributions in 2100 relative 
to 2015 from 2 ESMs for RCP2.6 and 6 ESMs for RCP8.5

LARMIP-2; excludes surface mass balance 
(SMB) (Levermann et al., 2020)0.13 (0.07 to 0.24) 
[0.04 to 0.37]0.14 (0.07 to 0.28)
[0.05 to 0.44]0.17 (0.09 to 0.36)
[0.06 to 0.58] Median (67% range) [90% range] LARMIP-2 multi-model 
dynamic contribution in 2100 relative to 1900
MICI (DeConto et al., 2021)0.08 (0.06 to 0.12)
[0.06 to 0.15]0.09 (0.07 to 0.11)
[0.07 to 0.15]0.34 (0.19 to 0.53)
[0.11 to 0.63] Median (66% range) [90% range] 
Multi-model ensemble projections
ISMIP6 CMIP6-forced  (Payne et al., 2021) ; 
excludes historical dynamic response–0.05 to +0.01 – –0.09 to +0.11Range of ISMIP6 multi-model contributions in 2100 relative 
to 2015 from 1 ESM for SSP1-2.6 and 4 ESMs for SSP5-8.5 
ISMIP6 all (CMIP5 and CMIP6-forced) 
including historical dynamic response –0.05 (0.04 to 0.08)
[0.03 to 0.11]–0.04 (0.00 to 0.12) 
[–0.02 to +0.23]Median (66% range) [90% range] contribution from

ISMIP6 CMIP5 and CMIP5-forced multi-model ensembles, 
(see caption) 
Emulated ISMIP6; excludes historical 
dynamic response  (Edwards et al., 2021)0.04 (–0.01 to +0.10)
[–0.05 to +0.14]0.04 (–0.02 to +0.10)
[–0.06 to +0.14]0.04 (–0.01 to +0.09)
[–0.05 to +0.14]Median (66% range) [90% range] contribution in 2100 
relative to 2015 from emulator of ISMIP6 used with 
Chapter 7 climate forcing
Emulated ISMIP6 total 0.09 (0.03 to 0.14)
[–0.01 to +0.19]0.09 (0.03 to 0.14)
[–0.01 to +0.18]0.08 (0.03 to 0.14)
[0.00 to 0.18]Emulated ISMIP6, but relative to 1995–2014 and 
including historical dynamic response (see caption)
SMB–0.02 (–0.03 to –0.01)
[–0.04 to –0.01]–0.03 (–0.04 to –0.02) 
[–0.06 to –0.01]–0.05 (–0.07 to –0.03)
[–0.09 to –0.02]Median (66% range) [90% range] SMB estimated for the 
AR5, used to correct LARMIP-2 below
LARMIP-2; excludes SMB0.15 (0.08 to 0.29)
[0.05 to 0.44]0.17 (0.09 to 0.33) 
[0.06 to 0.49]0.20 (0.10 to 0.39)

[0.07 to 0.61]Median (66% range) [90% range] dynamic contribution 
from LARMIP-2 multi-model method used with Chapter 7 
climate forcing
LARMIP-2 subset of models; excludes SMB 0.14 (0.08 to 0.26) 
[0.05 to 0.39]0.15 (0.08 to 0.29) 
[0.05 to 0.45]0.17 (0.10 to 0.35) 
[0.06 to 0.54]As above, but using only the 13 of 16 ice-sheet models 
common to both ISMIP6 and LARMIP-2
LARMIP-2 subset of models; includes SMB 0.11 (0.05 to 0.24) 
[0.03 to 0.37]0.12 (0.05 to 0.26) 
[0.02 to 0.42]0.12 (0.05 to 0.30) 
[0.01 to 0.49]As above, but including the SMB estimate
LARMIP-2 total0.13 (0.06 to 0.27) 
[0.03 to 0.41]0.14 (0.06 to 0.29)
[0.02 to 0.46]0.15 (0.05 to 0.34)
[0.01 to 0.57]Median (66% range) [90% range] dynamic contribution 
from LARMIP-2 multi-model method used with 
Chapter 7 climate forcing, including the SMB estimate
This assessment: combination of 
emulated ISMIP6 and LARMIP-2 0.11 (0.03 to 0.27)
[–0.01 to +0.41]0.11 (0.03 to 0.29) 
[–0.01 to +0.46]0.12 (0.03 to 0.34)

[0.00 to 0.57]Median (66% range) [90% range] assessment 
combining emulated ISMIP6 and LARMIP-2

1272
Chapter 9 Ocean, Cryosphere and Sea Level Change9and 9.4.2.3) in the ISMIP6 emulator gives a median Antarctic projection 
of about 0.08 m in 2100 in all scenarios before historical correction, 
compared with around 0 m using only the mean Antarctic distribution; 
the published projections use a  joint distribution (Edwards et  al., 
2021). Reese et al. (2020) find that using the basal melt sensitivities 
of LARMIP-2 yields an order of magnitude greater mass loss under 
RCP8.5 than with the ISMIP6 mean Antarctic values. Halving the basal 
melt sensitivity parameter range (i.e., in line with a continental mean 
estimate: Section  9.4.2.3) would lead to a  halving of the LARMIP-2 
dynamic contribution. This would reconcile the LARMIP-2 and ISMIP6 
emulator median and 95th percentile projections using the common 
subset of models within about 0.02–0.05 m. There is therefore limited 
evidence  that the ISMIP6 and LARMIP-2 projections could be reconciled

by using common ice-sheet models and basal melt sensitivity values.
It is not possible to distinguish which of ISMIP6 and LARMIP-2 is 
more realistic, due to limitations in historical simulations (Box 9.3) 
and understanding of basal melting (Section  9.4.2.3.2), so the 
projections are combined using a ‘p-box’ approach (Section 9.6.3.2). 
The mean of the ISMIP6 emulated and LARMIP-2 medians gives the 
assessed median projections, and the outer edges of the 17–83% 
ranges give the outer edges of the assessed likely (17–83%) ranges – 
that is, encompassing the structural and parametric uncertainties 
of both methods, giving medium confidence  in their combined 
projections. The main difference between this assessment and SROCC 
is to increase the medians of the lower scenarios by 0.05–0.07 m, 
so that all SSPs are similar to SROCC assessment of RCP8.5, and 
to substantially increase the upper ends of the likely  ranges: by

0.14–0.16 m for RCP2.6/SSP1-2.6 and RCP4.5/SSP2-4.5, and 0.06 m 
for RCP8.5/SSP5-8.5. The increase relative to SROCC is partly due to 
the increase in LARMIP-2 projections relative to the original LARMIP 
study (Levermann et  al., 2014), arising from the larger number of 
participating ice-sheet models (Levermann et al., 2020). The historical 
dynamic response to pre-2015 climate forcing applied to the ISMIP6 
emulator could be overestimated, due to the assumption of a constant 
future rate (Box 9.3). This assessment encompasses SROCC and all 
projections since, except the 83rd percentiles of projections that 
allow MICI under RCP8.5 (DeConto et al., 2021) and the Structured 
Expert Judgement (SEJ) under 5°C shown in SROCC (Bamber et al., 
2019). Both are used in further p-box estimates to give the outer 
limits of low confidence assessments (Section 9.6.3.2).
In summary, it is likely  that the AIS will continue to lose mass

throughout this century under all emissions scenarios  – that is, 
will likely continue to outpace increasing snowfall ( medium 
confidence ). The upper end of projections is not well constrained, 
due to different assumptions about the future sensitivity of sub-shelf 
basal melting to ocean warming and the proposed marine ice cliff 
instability triggered by ice-shelf disintegration (Sections 9.4.2.3 
and 9.4.2.4; Box 9.4).
9.4.2.6 Projections Beyond 2100
The SROCC assessed the median and likely  range of Antarctic SLE 
contributions at 2300 as 0.16 (0.07–0.37) m under RCP2.6 and 
1.46  (0.60–2.89) m under RCP8.5, based on three studies. It was noted that deep uncertainty  remained beyond 2100: while solid 
Earth feedbacks could reduce ice loss over multi-century time scales, 
MICI (Section 9.4.2.4) might give contributions higher than the likely  
ranges. The SROCC also presented structured expert judgement

(SEJ) projections for comparison (Bamber et al., 2019), which give 
higher values. Since SROCC, three studies have made projections 
to 2300: (i) Rodehacke et  al. (2020) assessed two methods for 
implementing precipitation changes (based on repeating 2071–2100 
forcings beyond 2100), which both gave negative projections at 
2300 because the dynamic response was very small (–0.11 to 
–0.01 m SLE for RCP2.6; –0.25 to –0.07 m for RCP8.5 forcing); (ii) In 
contrast, simulations forced by 2081–2100 ocean-only projections 
under RCP8.5/SSP5-8.5 beyond 2100, using two implementations 
of the ISMIP6 ‘non-local’ basal melt parametrizations (Box 9.3 and 
Section 9.4.2.2) and two sliding laws, are all positive (0.08 m to 0.96 m 
SLE by 2300), though these do not include the negative contribution 
from SMB changes (Lipscomb et al., 2021); (iii) Finally, DeConto et al. 
(2021) update projections for the MICI hypothesis (Section 9.4.2.4)

using the extensions of the RCPs to 2300, and obtain far higher 
contributions: median (17–83%) ranges of 1.09 (0.71–1.35) m SLE 
under RCP2.6 and 9.60 (6.87–13.54) m SLE under RCP8.5. These 
are larger than previous estimates (DeConto and Pollard, 2016), 
particularly at the upper end: 0.68 (0.29–1.13) m SLE for RCP2.6 
and 8.40 (7.47–9.76) m for RCP8.5 (Edwards et al., 2019), which can 
largely be explained by the higher maximum ice cliff calving rate. 
LARMIP-2 dynamic projections (Box  9.3) are also estimated under 
the extended SSPs and corrected with SMB (as in Section 9.4.2.5), 
giving median (17–83%) ranges of 0.40 (0.18–0.78)  m SLE at 
2300 under SSP1-2.6 and 1.57 (0.68–3.14) m under SSP5-8.5. The 
longer time scale may invalidate the linear response assumption of 
LARMIP-2, which neglects any self-dampening or self-amplifying 
processes. The ranges of projections for 2300 without MICI (Golledge 
et al., 2015; Bulthuis et al., 2019; Levermann et al., 2020; Rodehacke

et al., 2020; Lipscomb et al., 2021; ‘assessed ice-sheet contributions’ 
in Section 9.6.3.5 are –0.14 to +0.78 m SLE under RCP2.6/SSP1-2.6, 
and –0.27 to 3.14 m SLE under RCP8.5/SSP5-8.5). The lower bounds 
are the 5th percentile of Bulthuis et al. (2019) and the lowest mean/
median from Rodehacke et al. (2020), respectively; the upper bounds 
are the 83% percentiles of the LARMIP-2 estimates. These ranges 
are wider than SROCC  likely ranges, and more consistent with the 
SEJ (Bamber et al., 2019). However, projections in which Antarctica 
contributes much more than the assessed ranges under sustained 
very high greenhouse gas emissions – that is, around 7–14 m to GMSL 
by 2300 (DeConto et al., 2021), cannot be ruled out, and are taken 
as a sensitivity case (Section 9.6.3.5; Table 9.11). In summary, there 
is high confidence  that Antarctic mass loss will be greater beyond 
2100 under high greenhouse gas emissions, but the large range

of projections mean we have only low confidence  in the likely AIS 
contribution to GMSL by 2300 for a given scenario. Deep uncertainty  
remains in the role of AIS instabilities under very high emissions.
The West and East Antarctic ice sheets are considered to be tipping 
elements  – that is, susceptible to critical thresholds.  The SR1.5 
(Hoegh-Guldberg et  al., 2018) assessed that a  threshold for WAIS 
instability may be close to 1.5°C–2°C ( medium confidence ), as only 
RCP2.6 led to long-term projections of less than 1 m (Golledge 
et al., 2015; DeConto and Pollard, 2016). Based on the agreement

1273
Ocean, Cryosphere and Sea Level Change  Chapter 99of a  further study (Bulthuis et  al., 2019), SROCC confirmed that 
low emissions would limit Antarctic ice loss over multi-century 
time scales ( high confidence ), but it was not possible to determine 
whether this was sufficient to prevent substantial ice loss ( medium 
confidence ). Since SROCC, new studies have revisited this topic 
(Garbe et  al., 2020; Rodehacke et  al., 2020; Van Breedam et  al., 
2020; DeConto et al., 2021; Lipscomb et al., 2021), allowing a more 
complete assessment along with other studies (Feldmann and 
Levermann, 2015; Clark et al., 2016; Golledge et al., 2017a; Edwards 
et  al., 2019) and the extension to LARMIP-2 above. The majority 
project 0–1.3 m SLE on multi-century time scales under scenarios of 
1°C–2°C warming. Projections can increase up to 2 m SLE under high 
basal melt sensitivity to ocean warming (Section 9.4.2.3; Lipscomb

et al., 2021) or MICI (Section 9.4.2.4). On multi-millennial time scales 
(≥2,000 years), many projections remain below 1.6 m SLE under 
1°C–2°C warming – that is, less than about half of the WAIS in SLE 
(see also Section  9.6.3.5 and Figure  9.30). Other studies project 
majority or total loss of WAIS under 1°C–2°C warming, exceeding 
2 m SLE, under the higher end of the warming range (≥1.5°C), or high 
ocean warming (≥0.5°C) and/or high basal melting around WAIS, or 
MICI. All but two of these multi-millennial studies use variants of 
the same ice-sheet model, though different modelling choices mean 
they can be considered quasi-independent. Simulations of previous 
interglacial periods often show near or total WAIS disintegration, with 
mass loss exceeding 3 m SLE (e.g. Figure 9.18), although limitations 
of these studies or inferences that can be drawn under different 
forcings limit confidence in the robustness of these as quantitative

analogues (Sections 9.4.2.4 and 9.6.2). Overall, increased evidence 
and agreement on the time scales and drivers of mass loss confirm 
the SR1.5 assessment that a threshold for WAIS instability may be 
close to 1.5°C–2°C ( medium confidence ), and that the probability 
of passing a  threshold is larger for 2°C warming than for 1.5°C 
(medium confidence ), particularly under strong ocean warming. New 
projections agree with previous studies that only part of WAIS would 
be lost on multi-century time scales if warming remains less than 
2°C ( medium confidence ). There is limited agreement  about whether 
complete disintegration would eventually occur at this level of 
warming, but medium confidence  this would take millennia.
Under around 2°C–3°C peak warming, complete or near-complete 
loss of the WAIS is projected in most studies after multiple millennia 
(low confidence ), with continent-wide mass losses of around 2–5 m

SLE or more; this could occur on multi-century time scales under very 
high basal melting (Lipscomb et al., 2021) or widespread ice-shelf 
loss and/or MICI ( low confidence ) (Sun et al., 2020; DeConto et al., 
2021). Mass losses under around 2°C–3°C warming could be less 
than 2 m SLE, particularly for multi-century time scales, low basal 
melting, or less responsive sliding laws. If warming exceeds around 
3°C above pre-industrial, part of the EAIS (typically the Wilkes 
Subglacial Basin) is projected to be lost on multi-millennial time 
scales ( low confidence ), with total AIS mass loss equivalent to around 
6–12 m or more sea level rise; mass loss could be much smaller if the 
dynamic response is small (Bulthuis et al., 2019; Rodehacke et al., 
2020), or much faster under widespread ice-shelf loss and/or MICI 
(Sun et al., 2020; DeConto et al., 2021). A study by Garbe et al. (2020) 
suggests that 6°C sustained warming and associated mass loss of

about 12 m SLE may be a  critical threshold beyond which the ice sheet reorganizes to a new state, leading to large losses from East 
Antarctica (including the Aurora Subglacial Basin) and leading to 
a further 10 m sea level contribution per degree of warming; other 
studies also show much higher mass loss per °C at higher levels of 
warming (Section 9.6.3.5 and Figure 9.30; Van Breedam et al., 2020; 
DeConto et al., 2021).
The SROCC (Meredith et al., 2019; Oppenheimer et al., 2019) assessed 
that Antarctic mass losses could be irreversible over decades to 
millennia ( low confidence ). Garbe et  al. (2020) show that the AIS 
is always volumetrically smaller when regrowing under a  given 
warming level than when it retreats under the same forcing. Even if 
retreat followed by regrowth results in a net zero change in volume, 
the spatial distribution of mass may be altered, especially in parts of 
West Antarctica vulnerable to MISI. Projections that start reducing

CO 2 concentrations from 2030 onwards, reaching pre-industrial 
levels around 2300, show sea level contributions exceeding 1 m 
by 2500 when including MICI (DeConto et al., 2021). New research 
therefore confirms SROCC assessment that mass loss from the AIS is 
irreversible on decadal to millennial time scales ( low confidence ) (FAQ 
9.1), and suggests that reducing atmospheric CO 2 concentrations or 
temperatures to pre-industrial levels may not be sufficient to prevent 
or reverse substantial Antarctic mass losses ( low confidence ).
9.5 Glaciers, Permafrost and Seasonal 
Snow Cover
9.5.1 Glaciers
9.5.1.1 Observed and Reconstructed Glacier Extent 
and Mass Changes
9.5.1.1.1 Global glacier contribution
The IPCC’s Fifth Assessment Report (AR5; Vaughan et  al., 2013) 
assessed glacier changes from studies based on the regions defined 
in the Randolph Glacier Inventory (RGI; RGI version 2.0): a satellite 
observation-based, global inventory of glacier outlines for the

year 2000. Following Special Report on the Ocean and Cryosphere 
in a  Changing Climate (SROCC; Hock et  al., 2019b; Meredith 
et  al., 2019), we report on studies based on RGI version 6.0 (RGI 
Consortium, 2017). Increased volume of satellite observations and 
the inclusion of detailed regional glacier inventories has resulted in 
an improved inventory (RGI Consortium, 2017). A  new consensus 
estimate for the ice thickness distribution of all glaciers in RGI 6.0 
was obtained from an ensemble of five numerical models. However, 
only one out of five models covered all regions (Farinotti et al., 2019), 
and was, where possible, calibrated and validated with the worldwide 
Glacier Thickness Database (GlaThiDa 3.0: GlaThiDa Consortium, 
2019; Welty et  al., 2020). The updated inventory shows decreases 
in estimated glacier volume in the Arctic, High Mountain Asia and 
Southern Andes, partially compensated by increases in Antarctica.

15% of the total glacier volume is estimated to be below sea level 
and would not contribute to sea level rise if melted (Farinotti et al., 
2019). Supplementary Material Table  9.SM.2 shows the inventory 
glacier area and mass for each region in the year 2000.

1274
Global (all)
−1000−5000500
Global (excep 5&19)
Alaska (1)
−3000−2000−1000010002000
West Canada and U.S. (2)
Arctic Canada (N) (3)
Arctic Canada (S) (4)
Greenland periphery (5)
Iceland (6)
Mass change rate (kg m-2 yr-1) Svalbard (7)
Scandinavia (8)
Russian Arctic (9)
North Asia (10)
Central Europe (1 1)
Caucasus (12)
High Mountain Asia (13−15)
YearsLow Latitudes (16)
1960 1980 2000 2020−3000−2000−1000010002000
Southern Andes (17)
New Zealand (18)
Antarctic periphery . (19)
Zemp et al. (2019/20)

Ciraci et al. (2020)
SROCC
Hugonnet et al. (2021)
Regional estimates()∗
Figure 9.2 0 | Global and regional glacier mass change rate between 1960 and 2019.  The time series of annual and decadal mean mass change are based on 
glaciological and geodetic balances (Zemp et al., 2019, 2020). Superimposed are the 2002–2019 average rates by (Ciracì et al., 2020) based on the Gravity Recovery and 
Climate Experiment (GRACE), 2006–2015 estimated rates as assessed in Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and the new decadal 
averages (2000–2009 and 2010–2019) by Hugonnet et al. (2021). * New regional estimates for the Andes (Dussaillant et al., 2019), High Mountain Asia (Shean et al., 2020), 
Iceland (Aðalgeirsdóttir et al., 2020), Central Europe (Sommer et al., 2020) and Svalbard (Schuler et al., 2020) are also shown. The uncertainty reported in each study is shown.

See Figure 9.2 for the location of each region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1275
Ocean, Cryosphere and Sea Level Change  Chapter 99The SROCC found a  globally coherent trend of glacier decline in 
the last decades, despite large annual variability and regional 
differences ( very high confidence ). Section  2.3.2.3 assesses the 
global glacier mass changes for the whole 20th  century (see 
Table  9.5 for contribution to the sea level budget. Note that the 
peripheral glaciers in Greenland and Antarctica are added to the ice 
sheets for the budget). The AR6 assessment is based on Marzeion 
et  al. (2015), using glacier-length reconstructions (Leclercq et  al., 
2011) and a glacier model forced by gridded climate observations 
(Marzeion et  al., 2012), and not considering the estimated mass 
loss of uncharted glaciers (100 ± 50 Gt yr –1; Parkes and Marzeion, 
2018). The time series are assumed independent, resulting in larger 
uncertainty than presented in SROCC (see also Section 9.6.1). The rate

of global glacier mass loss (excluding the periphery of ice sheets) for 
the period 1901–1990 is estimated to be very likely  210 ± 90 Gt yr –1, 
representing 16 [28 to 7] % of the glacier mass in 1901, in agreement 
with SROCC within uncertainty estimates.
Since SROCC, new regional estimates for the Andes (Dussaillant 
et  al., 2019), High Mountain Asia (Shean et  al., 2020), Iceland 
(Aðalgeirsdóttir et al., 2020), the European Alps (Davaze et al., 2020; 
Sommer et al., 2020) and Svalbard (Schuler et al., 2020), two new 
global (Ciracì et  al., 2020; Hugonnet et  al., 2021) and an ad  hoc 
estimate for the latest glaciological observations (Zemp et al., 2020) 
have extended the glacier mass change time series up to 2018–2019 
(Figure 9.21 and Supplementary Material Table 9.SM.3). A reconciled 
global estimate for the period 1962–2019 has been compiled by Slater 
et al. (2021). However, in contrast to Slater et al. (2021), after 2000

this assessment is based on the first globally complete and consistent 
estimate of 21st-century glacier mass change from differencing 
of digital elevation models (Hugonnet et  al., 2021) covering 
94.7% of glacier area with glacier mass change for each glacier in 
the inventory produced with unprecedented accuracy. The estimates 
from Hugonnet et al. (2021) agree within uncertainties with new and 
previous estimates at global (Hock et al., 2019b; Wouters et al., 2019; 
Zemp et al., 2019; Ciracì et al., 2020; Slater et al., 2021) and regional 
scale (Dussaillant et  al., 2019; Aðalgeirsdóttir et  al., 2020; Schuler 
et al., 2020; Shean et al., 2020). Excluding peripheral glaciers of ice 
sheets (RGI regions 5 and 19), glacier mass loss rate was very likely  
170 ± 80 Gt yr –1 for the period 1971 to 2019 (8 [4 to 14] % of 1971 
glacier mass) , 210 ± 50 Gt yr –1 over the period 1993–2019 (6 [4 to 
8] % of 1993 glacier mass) and 240 ± 40 Gt yr –1 over the period

2006–2019 (3 [2 to 4] % of 2006 glacier mass; Sections  2.3.2.3 
and 9.6.1, Table  9.5,4 and Cross-Chapter Box  9.1). Including the 
peripheral glaciers of the ice sheets, the global glacier mass loss rate 
in the period 2000–2019 is very likely  266 ± 16 Gt yr –1 (4 [3 to 6] % 
of glacier mass in 2000) with an increase in the mass loss rate from 
240 ± 9 Gt yr –1 in 2000–2009 to 290 ± 10 Gt yr –1 in 2010–2019 ( high 
confidence ). These estimates are in agreement with SROCC estimate 
and extend the period to 2018–2019. In summary, new evidence 
published since SROCC shows that, during the decade 2010–2019, 
glaciers lost more mass than in any other decade since the beginning 
of the observational record ( very high confidence ) (Section 8.3.1.7.1 
and Figure 9.20).
4 The periods in Table 9.5 end in 2018, leading to a slight difference in the values.9.5.1.1.2 Regional glacier changes
A major advance since SROCC is the availability of high-accuracy

mass loss estimates for individual glaciers (Hugonnet et al., 2021). 
These results show that, during the last 20 years, the highest regional 
mass loss rates (>720 kg m–2 yr –1) were observed in the Southern 
Andes, New Zealand, Alaska, Central Europe, and Iceland. Meanwhile, 
the lowest regional mass loss rates (<250 kg m–2 yr –1) were observed 
in High Mountain Asia, the Russian Arctic, and the periphery of 
Antarctica. Glacier mass loss in Alaska (25% of 2000–2019 total 
mass loss), the periphery of Greenland (13%), Arctic Canada North 
(11%), Arctic Canada South (10%), the periphery of Antarctica (8%), 
the Southern Andes (8%) and High Mountain Asia (8%), represent 
the majority (83%) of the total glacier mass loss during the last 
20 years (2000–2019).
The glacier mass loss rate from geodetic mass balance 
assessments in the Southern Andes during 2006–2015 was smaller 
(720  ±  70  kg  m–2  yr –1; Braun et  al., 2019; Dussaillant et  al.,

2019; Hugonnet et  al., 2021) than previously assessed in SROCC 
(860 ± 160 kg m–2 yr –1), though within uncertainties. In the Central 
and Desert regions of the Southern Andes, an increase in mass loss 
from 2000–2009 to 2010–2018, and a high loss rate in Patagonia for 
the whole period, are observed (Dussaillant et al., 2019). Records of 
glacier mass loss in Peru (Seehaus et al., 2019a) and Bolivia (Seehaus 
et al., 2019b) in the period 2000–2016 show an increase in mass 
loss towards the end of the observation period. In western North 
America, outside of Alaska and western Yukon, there was a fourfold 
increase in mass loss for 2009–2018 (860 ± 320 kg  m–2  yr –1) 
compared to 2000–2009 (203 ± 214 kg m–2 yr –1; Menounos et al., 
2019), and in the Canadian Arctic there was a  doubling of mass 
loss in the last two decades compared with pre-1996 (Noël et al., 
2018; Cook et al., 2019). The peripheral glaciers in NE Greenland

experienced a 23% increase in mass loss in 1980–2014 compared 
to the period 1910 to 1978–1987 (Carrivick et al., 2019). In Iceland, 
16 ± 4% of the around 1890 glacier mass has been lost; about half 
of that loss occurred in the period 1994–2019 (Aðalgeirsdóttir et al., 
2020). Glacier records starting in 1960 in Norway show that half of 
the observed glaciers advanced in the 1990s but all have retreated 
since 2000 (Andreassen et  al., 2020). In Svalbard, glaciers have 
been losing mass since the 1960s, with a tendency towards more 
negative mass balance since 2000 (Deschamps-Berger et al., 2019; 
Van Pelt et al., 2019; Morris et al., 2020; Noël et al., 2020; Schuler 
et al., 2020). A similar increase in mass loss has been observed for 
Franz Josef Land in the Russian Arctic (Zheng et al., 2018). Rapid 
retreat and downwasting throughout the European Alps in the 
early 21st century is reported (Sommer et al., 2020) and long-term

records, although limited, indicate sustained glacier mass loss in 
High Mountain Asia since around 1850, with increased mass loss 
in recent decades (Shean et  al., 2020). In summary, although 
interannual variability is high in many regions, glacier mass records 
throughout the world show with very high confidence  that the 
loss rate has been increasing in the last two decades (see also 
Section 8.3.1.7.1 and 12.4 for regional glacier assessment).

1276
Chapter 9 Ocean, Cryosphere and Sea Level Change9Section 2.3.2.3 assesses that the rate and global character of glacier 
retreat in the latter part of 20th century, and finds that the first decades 
of the 21st century appear to be unusual in the context of the Holocene 
(medium confidence ) and the global glacier recession in the beginning 
of the 21st  century to be unprecedented in the last 2000 years 
(medium confidence ). These assessments are supported by regional 
evidence. New reconstructions of the Patagonian Ice Sheet suggest 
that 20th-century glacial recession occurred faster than at any time 
during the Holocene (Davies et al., 2020). The reconstructions of glacier 
variations show that the glaciers in some regions are now smaller 
than previously recorded: since the mid-16th  century in the Mont 
Blanc and Grindelwald regions of the European Alps (Nussbaumer and 
Zumbühl, 2012), since the 9th century in Norway (Nesje et al., 2012),

and for the past 1800 years in north-west Iceland (Harning et  al., 
2016, 2018). In Arctic Canada and Svalbard, many glaciers are now 
smaller than they have been in at least 4000 years (Lowell et al., 2013; 
Miller et al., 2013, 2017; Schweinsberg et al., 2017, 2018) and more 
than 40,000 years in Baffin Island (Pendleton et al., 2019). Although 
the millennial glacier length variation records are incomplete and 
discontinuous, and glacier fluctuations depend on multiple factors 
(e.g., temperature, precipitation, topography, internal glacial dynamics), 
there is a coherent relationship between rising temperatures, negative 
mass balance and glacier retreat on centennial time scales across most 
of the world. Glaciological and geodetic observations show that the 
rates of early 21st-century mass loss are the highest since 1850 (Zemp 
et al., 2015). For all regions with long-term observations, glacier mass 
in the decade 2010–2019 was the smallest since at least the beginning

of the 20th century ( medium confidence ).
In contrast to the global glacier mass decline (Figure 9.21, Table 9.5, and 
Supplementary Material 9.SM.2), a few glaciers have gained mass or 
advanced due to internal glacier dynamics or locally restricted climatic 
causes. The SROCC discusses the ‘Karakoram anomaly’ (centred on the 
western Kunlun range (at about 80°E, 35°N), but also covering part 
of the Pamir and Karakoram ranges), where glaciers have been close 
to balance since at least the 1970s, and had a slightly positive mass 
balance since the 2000s. Since SROCC, new evidence suggests that this 
anomaly is related to a combination of low-temperature sensitivity of 
debris-covered glaciers, a decrease of summer air temperatures (Cross-
Chapter Box 10.3), and an increase in snowfall, possibly caused by 
increases in evapotranspiration from irrigated agriculture (Bonekamp 
et al., 2019; de Kok et al., 2020; Farinotti et al., 2020; Shean et al.,

2020). However, a recent geodetic mass balance estimate suggests 
substantially increased thinning rates of High Mountain Asian glaciers 
after about 2010 (Hugonnet et al., 2021). There is limited evidence  to 
assess whether the Karakoram anomaly will persist in coming decades 
but, due to the projected increase in air temperature throughout the 
region, its long-term persistence is unlikely  (high confidence ) (Cross-
Chapter Box  10.3; Kraaijenbrink et  al., 2017; de Kok et  al., 2020; 
Farinotti et al., 2020).
9.5.1.1.3 Drivers of glacier change
The AR5 (Masson-Delmotte et  al., 2013) noted that early-to-mid-
Holocene glacier minima could be attributed to high summer insolation 
(high confidence ), unlike the current situation. Since AR5, new and 
improved chronologies of glacier size variations from the end of the last glacial period and the Holocene (e.g., Solomina et al., 2015, 2016; 
Eaves et al., 2019; Hall et al., 2019; Marcott et al., 2019; Bohleber et al.,

2020; Davies et al., 2020; Palacios et al., 2020) confirm the dominant 
role of orbital forcing for millennial-scale glacier fluctuations, but 
emphasize the role of other forcings – solar and volcanic activity, ocean 
circulation, sea ice and internal climate variability – in explaining the 
regional variability of glacier fluctuations at shorter time scales. Shakun 
et  al. (2015) demonstrated that, during the last deglacial transition 
(18–11 ka), the mid-to-low-latitude glacier retreat was driven by an 
increase in atmospheric CO 2 and global temperature.
In the Northern Hemisphere, where summer insolation decreased 
during the Holocene (Section 2.2.1), glaciers generally waxed (Briner 
et al., 2016; Kaufman et al., 2016; Lecavalier et al., 2017; Zhang et al., 
2017; Axford et al., 2019; Geirsdóttir et al., 2019; Larsen et al., 2019; 
Luckman et al., 2020). Conversely, in the Southern Hemisphere, where 
summer insolation increased during the Holocene, glaciers generally

waned (Solomina et al., 2015; Kaplan et al., 2016; Reynhout et al., 
2019). However, these general global trends were modulated by 
regional climate variations in temperature and precipitation (Murari 
et al., 2014; Kaplan et al., 2016; Batbaatar et al., 2018; Saha et al., 
2018) and there are a number of examples of this. A precipitation 
increase led to a local early Holocene (7–8 ka) glacier maximum in arid 
Mongolia (Gichginii Range). Glacier advances at about 9 ka in south-
west Greenland have been suggested to be a consequence of the 
freshwater pulse from the Laurentide Ice Sheet, which led to cooling 
in the Baffin Bay area (Schweinsberg et al., 2018). Lake sediments 
indicate that the glaciers in the region were smaller than today, or 
absent between 8.6 and 1.4 ka (Larocca et  al., 2020). Glaciers on 
the Antarctic Peninsula and in Patagonia during the Holocene were 
strongly affected by the southern westerly winds, sea ice extent,

and ocean circulation (García et al., 2020). Recent studies indicate 
that explosive volcanism can drive glacier advances (Solomina et al., 
2015, 2016; Schweinsberg et  al., 2018; Brönnimann et  al., 2019). 
In summary, on millennial time scales over the Holocene, there is 
high confidence  that orbital forcing drove hemispheric-scale glacier 
variations, but new studies provide a nuanced picture of responses to 
a variety of regional-scale forcings.
Section  3.4.3.1 assesses new attribution studies for glaciers and 
finds that human influence is very likely  the main driver of the global, 
near-universal retreat of glaciers since the 1990s. The SROCC assessed 
that it is very likely that atmospheric warming is the primary driver 
for the global glacier recession. Since SROCC, a  study of glaciers 
in New Zealand used event attribution to confirm a  connection 
between extreme glacier mass loss years and anthropogenic warming 
(Vargo et al., 2020).

The SROCC stated with high confidence  that, besides temperature, 
other factors, such as precipitation changes or internal glacier 
dynamics, have modified the temperature-induced glacier response 
in some regions. Deposition of a thin layer (<2 cm) of light-absorbing 
particles (e.g.,  black carbon, brown carbon, algae, mineral dust 
or volcanic ash) can exert an important control on glacier mass 
balance, by decreasing surface albedo and thus increasing absorbed 
shortwave radiation and melt (see also Section 7.3.4.3). The SROCC 
found limited evidence  and low agreement  that this process has

1277
050100150200
 Alaska (1)
 West Canada and U.S. (2)
 Greenland periphery (5)
 Iceland (6)
Glacier mass relative to year 2015 (%)Svalbard (7)
 Russian Arctic (9)
 North Asia (10)
Central Europe (11)
 High Mountain Asia (13−15)
 Low Latitudes (16)
1950 2000 2050 2100
1950 2000 2050 2100050100150200
Antarctic periphery (19)
1950 2000 2050 2100Reconstruction(1)
Observation(2)
RCP 2.6(3)
RCP 4.5(3)
RCP 8.5(3)
Figure 9. 21 | Global and regional glacier mass evolution between 1901 and 2100 relative to glacier mass in 2015.

1278
Chapter 9 Ocean, Cryosphere and Sea Level Change9had a  significant effect on observed long-term glacier changes. 
Several studies have shown melt increases due to the deposition 
of light-absorbing particles (Schmale et  al., 2017; Wittmann et  al., 
2017; Sigl et al., 2018; Di Mauro et al., 2019, 2020; Magalhães et al., 
2019; Constantin et al., 2020). Conversely, increasingly thick debris 
cover (>2–5 cm) on retreating glaciers can slow down glacier melt 
(Pratap et al., 2015; Brun et al., 2016). Although debris covers only 
about 4–7% of the total glacier area globally (Scherler et al., 2018; 
Herreid and Pellicciotti, 2020), many glaciers are heavily debris-
covered in their lower reaches, especially in High Mountain Asia, the 
Caucasus, the European Alps, Southern Andes and Alaska, resulting 
in different responses to warming than similar clean-ice glaciers.  A 
shift in regional meteorological conditions, driven by the location and

strength of the upper level zonal wind, has been found to have forced 
recent high mass loss rates in Western North America (Menounos 
et al., 2019). High geothermal heat flux areas underneath glaciers and 
high energy dissipation in the flow of water and ice causes additional 
mass loss of the glaciers in Iceland (Jóhannesson et  al., 2020), 
accounting for 20% of the mass loss since 1994 (Aðalgeirsdóttir 
et al. 2020). Glacier lake volume in front of retreating glaciers, has 
increased globally by around 48% between 1990 and 2018 (Shugar 
et al., 2020), which can increase both subaqueous melt and calving. 
In summary, there is high confidence  that non-climatic drivers have 
and will continue to modulate the first-order temperature response 
of glaciers in some regions.
9.5.1.2 Model Evaluation
Since AR5, glacier mass projections have been coordinated by the 
Glacier Model Intercomparison Project (GlacierMIP; Hock et  al.,

2019a; Marzeion et al., 2020). The SROCC (Hock et al., 2019b) relied 
on six global-scale glacier models based on previously published 
glacier model projections (Hock et  al., 2019a). It found with high 
confidence  that glaciers will lose substantial mass by the end of 
the century, but assigned medium confidence  to the magnitude and 
timing of the projected glacier mass loss, because of the simplicity 
of the models, the limited observations in some regions to calibrate 
them, and the diverging initial glacier volumes.
Since SROCC, Marzeion et al. (2020) projected 21st century global-
scale glacier mass changes based on seven global-scale and four 
regional-scale glacier models (Annex II). All models used the same 
initial and boundary conditions, forming a more coherent ensemble 
of projections compared to SROCC. Nevertheless, challenges remain 
because of scarcity of glacier thickness, surface mass balance (SMB) 
and frontal ablation data for model calibration, but also due to

uncertainties in glacier outlines, surface elevations and ice velocities. 
The global SMB models are of varying complexity, including mass 
balance sensitivity approaches (van de Wal and Wild, 2001), 
temperature-index methods (Anderson and Mackintosh, 2012; 
Marzeion et  al., 2012; Radić et  al., 2014; Huss and Hock, 2015; Kraaijenbrink et al., 2017; Maussion et al., 2019; Zekollari et al., 2019; 
Rounce et al., 2020) and simplified energy balance calculations (Sakai 
and Fujita, 2017; Shannon et al., 2019). Compared to simpler, empirical 
parametrizations, full energy-balance models are not necessarily the 
most appropriate choice for simulating future glacier response to 
climate change, even at the local scale (Réveillet et al., 2017, 2018), 
because of parameter and forcing uncertainties. All models account 
for glacier retreat and advance, but only two models (Anderson and 
Mackintosh, 2012; Huss and Hock, 2015) include frontal ablation.

Secondary processes such as debris-cover thickening (e.g.,  Herreid 
and Pellicciotti, 2020), albedo changes due to light-absorbing 
particles (e.g., Magalhães et al., 2019; Williamson et al., 2019), trends 
of refreezing and water storage in firn (e.g., Ochwat et al., 2021), 
dynamic instabilities such as surges (e.g., Thøgersen et  al., 2019) 
or glacier collapse (e.g.,  Kääb et  al., 2018), are not represented 
in global glacier models, resulting in both underestimated and 
overestimated sensitivity to warming that is currently not possible to 
quantify. Furthermore, challenges for future projections are caused 
by the low-resolution and high-spatial variability at sub-grid scale 
of the precipitation amount provided by general circulation models 
(GCMs), which requires downscaling to the spatial scale of a glacier 
(Maussion et al., 2019; Zekollari et al., 2019; Marzeion et al., 2020). 
In summary, in agreement with SROCC, progress in global scale

glacier modelling efforts allows medium confidence  in the capability 
of current-generation glacier models to simulate the magnitude and 
timing of glacier mass changes as a response to climatic forcing.
9.5.1.3 Projections
The AR5 (Vaughan et  al., 2013) and SROCC (Hock et  al., 2019b) 
stated with  high confidence  that the world’s glaciers are presently 
in imbalance due to the warming of recent decades. The observed 
retreat of glaciers is only a partial response to the already realized 
warming (Christian et al., 2018), and they are committed to losing 
considerable mass in the future, even without further change in 
air temperature (Mernild et al., 2013; Trüssel et al., 2013; Zekollari 
and Huybrechts, 2015; Huss and Fischer, 2016; Marzeion et  al., 
2018; Jouvet and Huss, 2019). One model estimates that 36 ± 8 % 
of global glacier mass is already committed to be lost due to 
past greenhouse gas emissions (Marzeion et  al., 2018). Although

accumulation and ablation instantly determine the SMB, the glacier 
geometries adjust to changed atmospheric conditions over a longer 
time (Zekollari et  al., 2020). The adjustment time, often referred 
to as the response time, is variable from one glacier to another, 
depending on the glacier geometry (thickness and steepness), SMB 
and gradient (e.g., Jóhannesson et al., 1989; Harrison et al., 2001; 
Lüthi, 2009; Zekollari et al., 2020). Response time is variable: years 
for smaller and steeper glaciers (Beedle et  al., 2009; Lüthi and 
Bauder, 2010; Rabatel et al., 2013), up to tens or hundreds of years 
for larger and gentle-sloped glaciers (e.g., Burgess and Sharp, 2004; Figure 9.21 (continued):  Reconstructed glacier mass change through the 20th century (Marzeion et al., 2015) and observed during 1961–2016 (Zemp et al., 2019). Projected

(2015–2100) glacier mass evolution is based on the median of three RCP emissions scenarios (Marzeion et al., 2020). In all cases, uncertainties are the 90% confidence interval. 
For a better comparison between regions, the maximum relative mass change was set to 200%, although for three regions, the volume changes between 1901 and 2015 
exceeded that value. For the Low Latitude, New Zealand, and High Mountain Asia glaciers, the changes were larger than 1000%, 350%, and 250%, respectively. See Figure 9.2 
for the location of each region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1279
Ocean, Cryosphere and Sea Level Change  Chapter 99Lüthi et  al., 2010; Zekollari et  al., 2020). The models indicate that 
the disequilibrium between the glaciers and present atmospheric 
conditions (1995 to 2014) reduces and then disappears at around 
year 2070 (Marzeion et  al., 2020). There is therefore very high 
confidence  that the disequilibrium of glaciers will persist as warming 
continues, and that glaciers will continue to lose mass for at least 
several decades because of their lagged response, even if global 
temperature is stabilized.
The SROCC assessed that global glacier mass loss by 2100, relative 
to 2015 will be 18 [ likely range 11 to 25] % for scenario RCP2.6 
and 36 [ likely range 26 to 47] % for RCP8.5, and that many glaciers 
will disappear regardless of the emissions scenario ( very high 
confidence ). Since SROCC, new results from Marzeion et al. (2020) 
have been published (Box 9.3, Figure 9.21 and Table 9.4, including

peripheral glaciers in Greenland and Antarctica). Glaciers will lose 
29,000 [9000 to 49,000] Gt and 58,000 [28,000 to 88,000] Gt 
over the period 2015–2100 for RCP2.6 and RCP8.5, respectively 
(medium confidence ), which represents 18 [5 to 31] % and 36 [16 to 
56] % of their early 21st  century mass, respectively (Table  9.4). 
Within  uncertainties, these agree with SROCC estimates, although 
with a slightly smaller mass loss due to the inclusion of models with 
lower sensitivity to changing climate conditions (Marzeion et  al., 
2020). The greatest source of uncertainty in glacier mass loss until 
the middle of the 21st century is the disagreement between glacier 
models, with emissions scenario becoming the dominant cause of 
uncertainty by the end of the 21st century (Marzeion et al., 2020).
Although the GlacierMIP projections (Hock et al., 2019a; Marzeion 
et al., 2020) were forced by RCP scenarios, two global glacier models

(Huss and Hock, 2015; Maussion et al., 2019) were also run with 
13 GCMs and SSP scenarios (Table 9.4). These results show increased mass loss compared to the RCP forced simulations, although with 
fewer global glacier models. To enable the glacier contribution to 
future sea level rise to be estimated under the full range of SSP 
scenarios (Section  9.6.3.3), the GlacierMIP results are emulated 
using a Gaussian process model (Box 9.3 and Table 9.4; Edwards 
et al., 2021). The emulated projections show a narrower range than 
the roughly equivalent RCP projections, which may be explained 
by not accounting for covariance in the regional uncertainties 
(Marzeion et al., 2020) and by the fact that the emulator caps sea 
level contribution for each region at the volume above floatation 
estimated by Farinotti et al. (2019) (Table 9.SM.2). Comparison of 
simulated and emulated regional sea level contributions support this 
explanation. Rates of change and post-2100 sea level projections

are estimated with the AR5 parametric fit (Supplementary 
Material 9.SM.4.5; Church et al., 2013b) applied to the GlacierMIP 
results (Marzeion et al., 2020), and these are also shown in Table 9.4 
for comparison.
The mass loss rates vary between regions and there are distinctively 
different patterns between scenarios (Marzeion et  al., 2020). 
The  global models agree that regions characterized by relatively 
little glacier-covered area (Low Latitude, Central Europe, Caucasus, 
Western Canada and USA, North Asia, Scandinavia and New Zealand) 
will lose nearly all (>80%) glacier mass by 2100 in the RCP8.5 
scenario, but their corresponding contribution to sea level rise will 
be small. A study using detailed ice dynamics for the largest glacier 
of the European Alps, Great Aletsch Glacier, projects 60% of present 
ice volume will be lost by 2100 in RCP2.6 and an almost complete 
wastage of the ice in RCP8.5 (Jouvet and Huss, 2019). Due to their

larger mass, the largest contribution to sea level rise comes from 
glaciers in the Arctic and Antarctic regions (Antarctic, Arctic Canada, 
Alaska, Greenland, Svalbard and Russian Arctic), in spite of having 
Table  9.4 | Projected sea level contributions from global glaciers (including peripheral glaciers in Greenland and Antarctica) by 2100 relative to 2015, 
for selected Representative Concentration Pathway (RCP) and Shared Socio-economic Pathway (SSP) scenarios.
IPCC AR5 and SROCC  
(Church et al., 2013b; Oppenheimer et al., 2019)  0.10 
(0.04–0.16) m0.12 
(0.06–0.19) m0.17 
(0.09–0.25) mMedian and  likely (66% range) contributions in 
2100 relative to 1995–2014  
GlacierMIP
Hock et al. (2019a)0.094 
(0.069–0.119) m0.142  
(107–177) m0.200 
(0.156–0.240) mMean (±1 standard deviation range) contributions
Marzeion et al. (2020)0.079 
[0.023–0.135] m0.119 
[0.053–0.185] m0.159

[0.073–0.245] mMedian [90% range]
GlacierMIP experimental protocol 
(Marzeion et al., 2020) with CMIP6 forcing0.111
(0.077–0.145)
[0.05–0.167] m0.136 
(0.096–0.176) 
[0.07–0.201] m0.190 
(0.133–0.247) 
[0.09–0.283] mMean (66% range) [90% range] using 13 GCMs 
and 2 glacier modelsa 
GlacierMIP (Marzeion et al., 2020) with AR5 
parametric fit: used for rates and post-2100 
projections (Supplementary Material 9.SM.4.5)0.102 
(0.076 –0.134) 
[0.059 –0.154] m0.128 
(0.095 –0.167) 
[0.076 –0.192] m0.171 
(0.124 –0.224) 
[0.098 –0.259] mMedian (66% range) [90% range] contribution from 
AR5 parametric fit to GlacierMIP ensemble, relative 
to 1995–2014  
Emulated  
(Marzeion et al., 2020; Edwards et al., 2021)0.080 
(0.059 –0.101) 
[0.046 –0.116]  m 0.115 
(0.093 –0.137) 
[0.077 –0.155] m0.170
(0.144 –0.196) 
[0.124 –0.218]  mMedian (66% range) [90% range] contribution in

2100 relative to 2015 from emulator of GlacierMIP6 
used with Chapter 7: Climate Forcing
a OGGM (Maussion et al., 2019) and GloGEM (Huss and Hock, 2015).

1280
Chapter 9 Ocean, Cryosphere and Sea Level Change9the smallest relative mass loss, and it is expected that they will 
continue to contribute to sea level rise beyond 2100. The regions 
with intermediate glacier mass (Southern Andes, High Mountain 
Asia and Iceland) show decreasing mass loss rates for RCP2.6 
throughout the 21st  century, and increasing rates for  RCP8.5 that 
peak in the mid-to-late 21st century (Figure 9.21). The peak in mass 
loss rate followed by reduction is due to decreasing glacier volume 
and stabilizing mass balance (Marzeion et  al., 2020). Vatnajökull, 
the largest glacier in Iceland, is projected to lose about 50% of 
its mass by 2300 in extended RCP4.5 and 80–100% in extended 
RCP8.5 scenarios (Schmidt et  al., 2019). In summary, both global 
and regional studies agree that glacier mass loss will continue in 
all regions, with larger mass loss for high-emissions scenarios ( high 
confidence ) (see also Section 8.4.1.7.1).

In AR5 and SROCC, glacier mass loss beyond 2100 was calculated 
using a parametric fit to available model simulations. In section 9.6.3.5, 
that same parametric fit is applied to Marzeion et al. (2020) projections, 
resulting in complete glacier mass loss at year 2300 under SSP5-8.5 
and 40–100% mass loss under SSP1-2.6. Clark et al. (2016) simulate 
glacier mass evolution, not including glaciers peripheral to the Antarctic 
Ice Sheet (AIS), for different warming levels for the next 10,000 years. 
There is limited evidence  and low confidence  that, at sustained 
warming levels between 1.5 and 2°C, about 50–60% of glacier mass 
will remain, predominantly in the polar regions. At sustained warming 
levels between 2 and 3°C, about 50–60% of glacier mass outside 
Antarctica will be lost and, at sustained warming levels, between 3 and 
5°C, 60–75% of glacier mass outside Antarctica will disappear. Based 
on Marzeion et al. (2020), there is medium confidence  that nearly all

glacier mass in low latitudes, Central Europe, the Caucasus, western 
Canada and the USA, North Asia, Scandinavia and New Zealand will 
disappear at this high warming level.
9.5.2 Permafrost
This section focuses on the physical aspects of permafrost (perennially 
frozen ground) as an element of the climate system, drawing on 
the assessment of observed global permafrost changes provided 
in Section  2.3.2.5, and more specifically model evaluation and 
projections. The permafrost carbon feedback is assessed in Box 5.1. 
Section 12.4 of this Report provides permafrost information relevant 
to impacts and risk on regional scales.
9.5.2.1 Observed and Reconstructed Changes
The current extent of the global permafrost region is about 
22 ± 3×106 km2 (Gruber, 2012). Permafrost underlies about 15% of 
Northern Hemisphere land and more than 50% of the unglacierized 
land north of 60°N (Zhang et al., 1999; Gruber, 2012; Obu et al.,

2019). It is also found in high-altitude areas of mountain ranges 
in both hemispheres – estimated in SROCC (Hock et al., 2019b) as 
representing about 27–29% of the global permafrost area ( medium 
confidence ) and most unglacierized areas in Antarctica (Vieira et al., 
2010; Obu et al., 2020). Ground ice volume in permafrost is variable, 
reaching up to 90% in syngenetic permafrost deposits (Kanevskiy 
et al., 2013; Gilbert et al., 2016). The SROCC (Meredith et al., 2019) reported medium confidence  in the estimation that Earth’s total 
perennial ground ice volume is equivalent to 2–10  cm of global 
sea level (Zhang et  al., 2000). There is no evidence suggesting 
that a  large part of this volume, if melted, would run off and 
contribute to global sea level. Therefore, and because of the modest 
total volume of mobilizable water, the contribution of permafrost 
thaw to past and future sea level budgets is usually neglected 
(see Section 9.6.3.2).

Permafrost changes mostly refer to changes in extent, temperature 
and active layer thickness (ALT). The SROCC (Hock et  al., 2019b; 
Meredith et  al., 2019) reported with very high confidence  that 
record high permafrost temperatures at the depth of the zero 
annual amplitude (the depth about 10–20 m below the surface 
where the seasonal soil temperature cycle vanishes) were attained 
in recent decades in the Northern circumpolar permafrost region, 
high confidence  that permafrost has warmed over recent decades in 
many mountain ranges, and overall very high confidence  that global 
warming over the last decades has led to widespread permafrost 
warming. As reported in SROCC, the global (polar and mountain) 
permafrost temperature has increased at 0.29°C  ±  0.12°C near 
the depth of zero annual amplitude between 2007 and 2016 
(Biskaborn et al., 2019). Stronger warming has been observed in 
the continuous permafrost zone (0.39°C ± 0.15°C) compared to

the discontinuous zone (0.20°C ± 0.10°C), consistent with the fact 
that, near the melting point, a large amount of energy is required 
for melting the ice (Figure 9.22), and because of the reduced effect 
of Arctic amplification in more southerly locations (Romanovsky 
et  al., 2017). This is consistent with longer-term Arctic trends 
from deep boreholes shown in Figure 2.22. Mountain permafrost 
temperature trends are heterogeneous, reflecting variations in local 
conditions such as topography, surface type, soil texture and snow 
cover, but again, generally weaker warming rates are observed 
in warmer permafrost at temperatures close to 0°C, particularly 
when ice content is high (e.g., Mollaret et al., 2019; Noetzli et al., 
2019; PERMOS, 2019). In summary, strong variability in recent 
permafrost temperature trends is linked to local conditions, 
regionally varying temperature trends, and the thermal state of 
permafrost itself. However, as discussed in Section  2.3.2.5, there

is overall high confidence in the observed increases in permafrost 
temperature over the past three to four decades throughout the 
permafrost regions.
Closer to the surface, the active layer undergoes annual cycles of 
freeze and thaw. The SROCC reported medium confidence in ALT 
increase as a  pan-Arctic phenomenon. Recent evidence presented 
in Section  2.3.2.5 shows pervasive ALT increase in the European 
and Russian Arctic in the 21st century, and in high elevation areas 
in Europe and Asia since the mid-1990s. Emergence of a  clearer 
global picture is hampered by: (i) uneven distribution of observing 
sites; (ii)  substantial variability among the existing sites, strongly 
influenced by local conditions (soil constituents and moisture, 
snow cover, vegetation); (iii) interannual variability; and (iv) thaw 
settlement in ice-rich terrain (Streletskiy et al., 2017; O’Neill et al., 
2019). In summary, in agreement with SROCC and recent evidence

presented in Section  2.3.2.5, there is medium confidence  that ALT 
increase is a pan-Arctic phenomenon.

1281
Ocean, Cryosphere and Sea Level Change  Chapter 99There is medium confidence  that the observed acceleration and 
destabilization of rock glaciers is related to warming temperatures 
and increase in water content at the permafrost table in recent 
decades (Deline et al., 2015; Cicoira et al., 2019; Marcer et al., 2019; 
PERMOS, 2019; Kenner et al., 2020). There is also medium confidence  
that observed increases in size and frequency of rock avalanches 
are linked to permafrost degradation in rock walls (Ravanel et al., 
2017; Patton et al., 2019; Tapia Baldis and Trombotto Liaudat, 2019). 
In summary, there is medium confidence  that mountain permafrost 
degradation at high altitude has increased the instability of mountain 
slopes in the past decade.
The SROCC assessed with high confidence  that the extent of subsea 
permafrost, formed before submersion on Arctic continental shelves 
during the last deglaciation, is much reduced compared to older

studies that had estimated the entire formerly exposed Arctic shelf 
area to be underlain by permafrost. This is supported by observations 
(Shakhova et al., 2017) that show rapid thaw of recently submerged 
permafrost on the East Siberian Shelf. A modelling study (Overduin 
et al., 2019) estimates that 97% of permafrost under Arctic shelves 
is currently thinning.
Based on multiple studies, there is medium confidence that 
widespread retreat of coastal permafrost is accelerating in the 
Arctic (Günther et al., 2015; Cunliffe et al., 2019; Isaev et al., 2019). 
There is also consistent evidence of complete permafrost thaw in 
areas of discontinuous and sporadic permafrost since about 1980, 
but this evidence is geographically scattered (Camill, 2005; Kirpotin 
et al., 2011; James et al., 2013; B.M. Jones et al., 2016; Borge et al., 
2017; Chasmer and Hopkinson, 2017; Gibson et al., 2018). In spite 
of increasing evidence of landscape changes from site studies and

remote sensing, quantifying permafrost extent change remains 
challenging because it is a subsurface phenomenon that cannot be 
observed directly (Jorgenson and Grosse, 2016; Trofaier et al., 2017). 
A modelling study for the Qinghai-Tibet Plateau between the 1960s 
and the 2000s (Ran et al., 2018) suggests transition from permafrost 
to seasonally frozen ground over an area of more than 400,000 km2. 
In summary, there is medium confidence  that complete permafrost 
thaw in recent decades is a common phenomenon in discontinuous 
and sporadic permafrost regions. In addition, paleoclimatic evidence 
presented in Section  2.3.2.5 confirms a  long-term sensitivity of 
permafrost extent to climatic variations, although an analysis 
of North American speleothem records over the last two glacial cycles 
indicates that this apparent high sensitivity could be a consequence 
of regional-scale variability (Batchelor et al., 2019).

There is a  lack of formal studies attributing observed permafrost 
changes (thaw depth, thermal state) or associated landscape changes 
to anthropogenic forcing. However, the observed Arctic warming has 
been attributed to anthropogenic forcing (e.g., Najafi et al., 2015) and 
an obvious physical link exists between ground temperatures (and thus 
permafrost) and surface air temperatures. Therefore, physically 
consistent and convergent lines of evidence lead to medium confidence  
in anthropogenic forcing being the dominant cause of the observed 
pan-Arctic permafrost changes. Added to this, local permafrost change 
by soil and ecosystem disturbance is induced by increasing human 
industrial activities in the Arctic (e.g., Raynolds et al., 2014).9.5.2.2 Evaluation of Permafrost in Climate Models
As stated in AR5 (Flato et al., 2013), coupled models contributing to 
CMIP5 showed large inter-model variability of permafrost extent due

to deficiencies in reproducing surface characteristics and processes 
(Koven et  al., 2013), particularly thermal properties of the ground 
and snow. These deficiencies led SROCC (Meredith et al., 2019) to 
express only medium confidence  in the models’ capacity to correctly 
project the magnitude of future permafrost changes, in spite of high 
confidence  in the models’ projection of a general thaw depth increase 
and a substantial loss of shallow permafrost. The SROCC further noted 
that several types of physical ‘pulse’ disturbances, in particular fire 
and thermokarst formation, are usually not represented in coupled 
climate models. This has been discussed in detail in SROCC, which 
assessed that there is high confidence  that permafrost degradation 
through fire (Jones et  al., 2015; Gibson et  al., 2018) is currently 
occurring faster in some well-studied regions than during the first 
half of the 20th century, and medium confidence  that thermokarst

formation, to which about 20% of the northern permafrost region 
is vulnerable (Olefeldt et  al., 2016), can lead to faster large-scale 
permafrost degradation in response to climate change.
Since SROCC, dedicated modelling of the evolution of ice- and 
organic-rich permafrost in the north-east Siberian lowlands (Nitzbon 
et al., 2020) has shown that not representing thermokarst-inducing 
processes in ice-rich terrain leads to a  systematic underestimation 
of the rapidity and magnitude of permafrost thaw. Simplified 
inventory-based modelling (Turetsky et  al., 2020) points towards 
similar conclusions. Although these pulse disturbances still need to 
be represented in CMIP-type models, there have been many new 
developments to that type of model since CMIP5 and AR5. Soil 
freezing and its thermal and hydrological effects are now included in 
a large number of land-surface modules that are part of the CMIP6 
ensemble (S. Chadburn et al., 2015; Hagemann et al., 2016; Cuntz

and Haverd, 2018; Guimberteau et al., 2018; Yokohata et al., 2020), 
sometimes allowing for the effects of excess ice (Lee et al., 2014). 
Improved representation of snow insulation in models has led 
to more realistic simulated permafrost extents (e.g.,  Paquin and 
Sushama, 2015). In a post-CMIP5 ensemble of land-surface models 
driven by observed meteorological conditions (McGuire et al., 2016), 
inter-model spread was substantially reduced when the ensemble 
was restricted to models that appropriately represented the effect of 
snow insulation on the underlying soil (W. Wang et al., 2016). More 
detailed descriptions of high-latitude vegetation characteristics, 
vegetation dynamics, and snow-vegetation interactions have been 
included in several models since AR5 (S.E. Chadburn et  al., 2015; 
Porada et al., 2016; Druel et al., 2017).
A total soil column depth of at least about 10 m is required to 
adequately represent the dampening effect of seasonal-scale heat

exchanges between shallow and deeper ground, and thus to correctly 
simulate ALT (Lawrence et al., 2008; Ekici et al., 2015). However, many 
CMIP6 models still have shallower total soil columns (Burke et al., 
2020) and the proportion of models with deeper total soil columns 
has not increased since CMIP5 (Koven et al., 2013). Another recently 
identified process, usually not represented in the current (CMIP6) 
generation of climate models (Zhu et al., 2019), is warming-driven

1282
Chapter 9 Ocean, Cryosphere and Sea Level Change9decomposition and burning of organic material that provides strong 
thermal insulation of underlying ground. Decay of the insulating 
organic material can lead to increased permafrost thaw, creating 
a positive feedback loop.
In spite of the aforementioned structural improvements to many 
models, the simulated current permafrost extent from available 
CMIP6 models shows no substantial improvement with respect to 
CMIP5 (see Figure 9.22a). The extent of the region where permafrost 
is simulated within the top 15 m in the Northern Hemisphere for the 
1979–1998 period is characterized by very large scatter in the coupled 
CMIP5 and CMIP6 historical simulations compared to estimates 
of the present permafrost extent based on multiple observational 
lines of evidence (Zhang et al., 1999) and models based on satellite 
observations and reanalyses (Gruber, 2012; Obu et al., 2019). Outliers

with very low simulated permafrost extent are models that have only 
a very shallow soil column (leading to an underestimate of thermal 
inertia at depth) and do not take into account soil water phase 
changes. These inadequacies lead to an overestimate of seasonal 
thaw depth, exceeding the total thickness of the models’ soil 
columns (Burke et al., 2020). Excessive simulated permafrost extent 
can in several cases be traced to insufficient thermal insulation by the 
winter snow cover (Burke et al., 2020).
Figure  9.22a also shows that the corresponding land-atmosphere 
simulations with prescribed observed sea surface temperatures and 
sea ice concentrations, and the land-only simulations with prescribed 
reanalysis-based meteorological forcing, do not provide an improved 
simulation of the current permafrost extent, although, by construction,

they can be expected to exhibit lower land surface climate biases. This further points to deficiencies in the land modules as the main reason 
for biases, consistent with conclusions drawn from the analysis of CMIP5 
output (Koven et al., 2013), as reported in SROCC and AR5.
In spite of more realistic description of permafrost-related processes in 
many coupled climate models, the CMIP6 models still produce a very 
scattered ensemble of estimates of current permafrost extent, and 
there is high confidence  that this is strongly linked to deficiencies of 
the representation of soil processes. Furthermore, current-generation 
climate models tend to neglect several physical disturbances that 
can lead to faster permafrost thaw. Because of large uncertainties 
in the future evolution of these drivers (see SROCC), there is limited 
evidence that these shortcomings lead to an underestimate of 
permafrost degradation rates in response to climate change in the

CMIP6 ensemble. In summary, there is high confidence  that coupled 
models correctly simulate the sign of future permafrost changes 
linked to surface climate changes, but only medium confidence  in the 
amplitude and timing of the transient response.
9.5.2.3 Projected Permafrost Changes
The AR5 (Collins et  al., 2013) and SROCC (Meredith et  al., 2019) 
(based on available CMIP5 output) both expressed high confidence  
that future pan-Arctic thaw depth will increase and near-surface 
permafrost extent will decrease under future global warming, and 
medium confidence  in the magnitude of the simulated changes 
because of model deficiencies and the large spread of the results.
The equilibrium sensitivity of permafrost extent to stabilized global 
mean warming has been inferred (by constraining CMIP5 output 
Figure 9.22 | Simulated versus observed permafrost extent and volume change by warming level. (a)  Diagnosed Northern Hemisphere permafrost extent (area

with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available Coupled Model Intercomparison Project 
Phase 5 and 6 (CMIP5 and CMIP6) models, from the first ensemble member of the historical coupled run, and for CMIP6 Atmospheric Model Intercomparison Project (AMIP) 
(atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. Estimates of current permafrost extents based on physical evidence 
and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). (b) Simulated 
global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble

members of a selection of scenarios, for available CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1283
Ocean, Cryosphere and Sea Level Change  Chapter 99with diagnosed relationships between the observed present-day 
spatial distribution of permafrost and air temperature) to be about 
4.0×106  km2 °C–1 (Chadburn et  al., 2017) for global surface air 
temperature (GSAT) changes with respect to the present below 
about +3°C. This equilibrium permafrost sensitivity, relevant for 
assessing long-term permafrost changes at a  stabilized warming 
level, is about 20% higher than the transient centennial-scale 
near-surface permafrost extent sensitivity (diagnosed from seasonal 
thaw down to 3 m depth) suggested by direct analysis of CMIP5 
output (Slater and Lawrence, 2013). Compared to these and other 
studies reported in AR5 and SROCC (Koven et al., 2013), the recently 
suggested equilibrium extent sensitivity to GSAT changes of about 
1.5×106 km2 °C–1 based on idealized ground temperature modelling 
(Liu et al., 2021) appears unrealistically low.

A strong transient temperature sensitivity of the volume of 
perennially frozen soil in the top 3 m below the surface is consistently 
suggested by the available CMIP6 models (Figure  9.22b). Relative 
to the current volume, the transient sensitivity of the modelled 
permafrost volume in the top 3 m to GSAT changes (with respect 
to the 1995–2014 average and up to +3°C change, that is, about up 
to +4°C with respect to pre-industrial levels) is about 25 ± 5 % °C–1 
(Burke et  al., 2020), but there is only medium confidence  in this 
value and 1 standard deviation uncertainty range because of the 
model deficiencies discussed in 9.5.2.2. It is important to note that 
permafrost loss will not be limited to the top 3 m, with delayed 
response of deeper permafrost. The simulated transient temperature 
sensitivity of permafrost volume is slightly stronger in the SSP1-2.6 
scenario than in other SSPs because subsurface temperature lag

increases with higher atmospheric warming rates, particularly when 
ground ice melting induces additional delays.
Due to the role of air temperature as a major driver of permafrost 
change, SROCC (Hock et al., 2019b) expressed very high confidence  
that permafrost in high mountain regions is expected to undergo 
increasing thaw and degradation during the 21st  century, with 
stronger consequences expected for higher greenhouse gas emissions 
scenarios. Recently published studies (e.g., Zhao et al., 2019) support 
this SROCC assessment.
In summary, based on high agreement  across CMIP6 and older model 
projections, fundamental process understanding, and paleoclimate 
evidence, it is virtually certain  that permafrost extent and volume 
will shrink as global climate warms.
9.5.3 Seasonal Snow Cover
Mean snow cover extent in January and February, the usual months 
of maximum extent, covers about 45% of the Northern Hemisphere

(NH) land surface – more than 45 million km2 over the 1967–2014 
period (Estilow et al., 2015). In contrast, maximum seasonal snow 
cover in South America, the dominant ice-free land mass in the 
Southern Hemisphere in terms of seasonal snow cover extent, 
remains well below 1 million km2 (Foster et al., 2009) or less than 2% 
of the Southern Hemisphere land surface.Terrestrial snow cover is characterized via three variables: (i) areal snow 
cover extent (SCE); (ii) the time period of continuous snow cover  – 
snow cover duration (SCD) that reflects snow-on and snow-off dates 
(i.e., the first and last days of observed snow cover); and (iii)  snow 
accumulation – expressed either as snow depth (SD) or snow water 
equivalent (SWE), the depth of water stored by the snowpack.
Observed large-scale snow cover changes, their attribution to 
human activity, and their effects on the hydrological cycle are also 
discussed in Chapter 2 (Section 2.3.2.2), Chapter 3 (Section 3.4.2)

and Chapter 8 (Section 8.2.3.1) of this Report. The role of snow in 
the global surface albedo feedback is assessed in Section  7.4.2.3. 
The  effect of aerosol deposition on snow albedo and associated 
climate forcing is assessed in Section 7.3.4.3.
9.5.3.1 Observed Changes of Seasonal Snow Cover
The AR5 (Vaughan et  al., 2013) reported that NH SCE in June 
very likely  decreased by 11.7 [8.8 to 14.6] % per decade over the 
1967–2012 period, exceeding the absolute and relative reductions 
observed in March and April. The AR5 further reported very high 
confidence  that NH March and April SCE decreased over the 90 years 
after 1922. The SROCC only assessed snow cover changes for the 
Arctic and mountain areas. For the Arctic (north of 60°N), SROCC 
(Meredith et al., 2019) expressed high confidence  in SCE decreases 
of –3.5 ± 1.9% per decade in May and –13.4 ± 5.4% per decade in 
June, based on a combination of multiple datasets (Mudryk et al.,

2017). Concerning mountain snow cover, SROCC (Hock et al., 2019b) 
reported with high confidence  that mountain snow cover (both in 
terms of SCE and maximum SWE) has generally declined since the 
middle of the 20th century at lower elevations. At higher elevations, 
SROCC reported medium confidence  in generally insignificant 
snow cover trends (where these were available). The large-scale 
assessment provided in Section 2.3.2.2 of this Report reports very 
high confidence  in substantial reductions of NH SCE (particularly in 
spring) since 1978, and states that there is limited evidence  that this 
decline extends back to the early 20th century.
Since SROCC, progress has been made in characterizing seasonal 
NH snow cover changes through the combined analysis of datasets 
from multiple sources (surface observations, remote sensing, land 
surface models and reanalysis products). A recent combined dataset 
(Mudryk et al., 2020) identified negative NH SCE trends in all months

between 1981 and 2018, exceeding –50 × 103 km2 yr –1 in November, 
December, March and May (Figure 9.23a,b). The loss of spring SCE 
is also reflected in earlier spring snow melt, derived from surface 
observations (Bulygina et  al., 2011; Brown et  al., 2017), satellite 
observations (Wang et al., 2013; Estilow et al., 2015; Anttila et al., 
2018), and model-based analyses (Liston and Hiemstra, 2011). 
There is considerable inter-dataset and regional variability, but the 
continental-scale trends of snow-off dates from these datasets are 
consistently negative (Brown et al., 2017; Kouki et al., 2019).
Satellite-derived estimates of NH SCE compiled within the National 
Oceanic and Atmospheric Administration Climate Data Record 
(NOAA CDR) snow chart extend back to 1967, providing one of the 
longest environmental data records from spaceborne measurements

1284
Chapter 9 Ocean, Cryosphere and Sea Level Change9(Estilow et al., 2015). Continental trends from these coarse resolution 
estimates (about 200 km) show declining snow cover during 
the spring period, consistent with surface warming (Hernández-
Henríquez et al., 2015; Mudryk et al., 2017). Therefore, as assessed in 
Section 2.3.2.2, there is very high confidence  that the NH spring SCE 
has been decreasing since 1978.
Hemispheric reconstructions with simple snow models and in situ 
observations have extended a  pre-satellite record to precede the 
satellite record and extend back to 1922 (Brown and Robinson, 
2011), putting the satellite era in historical context. This study, also 
assessed in AR5, suggests an increase in North American spring 
(March–April) SCE from 1915 to about 1950, followed by a decrease 
of the same total magnitude afterwards. In Eurasia, a negative trend 
in April is visible over the entire 1922–2010 period of record, while

in March, a step decrease at about 1985 separates two periods with 
insignificant trends. Overall, combining March and April, consistency 
between the continental trends since 1950, and agreement in sign 
with the NOAA satellite record since 1967, provides high confidence  
in Northern Hemisphere spring snow cover decrease since about 
1950. Analysis of paleoclimate records (Pederson et  al., 2011; 
Belmecheri et al., 2016) suggests that recent snowpack reductions 
in western North America are exceptional on a millennial time scale 
Recent remote sensing global-scale studies (Hammond et al., 2018; 
Notarnicola, 2020) report that, since 2000, snow cover area and/or 
duration decreased in 78% of global mountain areas (Notarnicola, 
2020). Due to the shortness of these records and high spatial 
variability, they only provide limited evidence  in medium agreement  
that snow cover area and duration changes over that recent period

are more consistently negative at higher (>4000 m) than at lower 
elevations, and do not alter the high confidence  in longer-term 
mountain snow cover decrease at lower elevations since the middle 
of the 20th century that was already reported in SROCC.
As assessed in detail in Section 3.4.2, it is very likely  that anthropogenic 
influence contributed to the observed reductions in Northern 
Hemisphere spring snow cover since the mid-20th century. The reasons 
for this assessment are: (i) physical consistency of the observed 
spring snowpack and surface temperature changes in observations 
and models; (ii) the strong observed hemispheric and regional spring 
SCE and SWE trends; and (iii) the general attribution of hemispheric 
temperature changes to human influence. Consistent between multiple 
observational products and historical climate model simulations, the 
observed NH SCE sensitivity to NH land (>30°N) warming (Mudryk

et  al., 2017) is approximately –1.9×106  km2  °C–1 (95% confidence 
range of ±0.9×106 km2 °C–1) throughout the snow season.
Compared to numerous studies on spring SCE changes, less attention 
has been paid to changes in NH snow cover during the onset period 
in the autumn, a  challenging period to retrieve snow information 
from optical satellite imagery due to persistent clouds and decreased 
solar illumination at higher latitudes. Positive trends in October and 
November SCE in the NOAA CDR (Hernández-Henríquez et al., 2015) 
are not replicated in other surface, satellite, and model datasets 
(Brown and Derksen, 2013; Peng et  al., 2013; Hori et  al., 2017; Mudryk et al., 2017). The positive trends from the NOAA CDR are also 
inconsistent with later autumn snow-on dates since 1980 (–0.6 to 
–1.4 days per decade), based on historical surface observations, 
model-derived analyses and independent satellite datasets (updated

from Derksen et  al., 2017). The SCE trend sensitivity to surface 
temperature forcing in the NOAA CDR is anomalous compared to 
other datasets during October and November (Mudryk et al., 2017). 
There is therefore medium confidence  that the NH SCE trend for 
the 1981–2016 period was also negative during these two months 
(Mudryk et al., 2020).
In the low-to-mid latitude (18°S–40°S) South American Andes, 
a  dry-season snow cover decrease of about 12% per decade has 
been reported for the 1986–2018 period (Cordero et al., 2019), linked 
to El Niño–Southern Oscillation (ENSO) changes dominant in the 
northern part, and an additional influence of poleward migration of 
the westerly wind zone in the southern part of the study area. Further 
south, long-term warming has been identified as the dominant cause 
of observed winter snow cover reduction over the 1972–2016 period 
at about 53°S in Brunswick Peninsula (Aguirre et al., 2018).

The AR5 (Hock et  al., 2019b) reported on SWE and SD in situ 
observations mostly from mountain areas, the majority of which 
showed negative trends over their respective observational periods. 
However, AR5 did not provide an assessment of large-scale snow 
mass changes across the Northern Hemisphere. The SROCC attributed 
medium confidence  to reports of negative SWE trends in the Russian 
Arctic between 1966 and 2014, and stated that seasonal maximum 
SD trends in the North American Arctic were mostly insignificant 
and inconsistently positive or negative. It further attributed medium 
confidence  to gridded products that suggest negative pan-Arctic SWE 
trends between 1981 and 2016, and high confidence  in a general 
decline of mountain snow mass at lower elevations, albeit with 
regional variations.
Since AR5, the number of global or hemispheric-scale gridded 
SWE products has substantially increased. A  validation and

intercomparison (Mortimer et al., 2020) of datasets – derived from: 
(i) reanalysis-based products; (ii) a combined surface observation – 
passive  microwave remote sensing product; and (iii) stand-alone 
passive microwave products – has led to better understanding of the 
strengths and limitations of each. These gridded products consistently 
identify negative trends in maximum pre-melt SWE across the 
1981–2016 period over Eurasia and North America (Figure 9.23c,d; 
Mudryk et al., 2020). To further constrain SWE uncertainty, Pulliainen 
et  al. (2020) implemented a  bias correction based on snow course 
observations which yielded a current best estimate for the average 
1980–2018 March SWE over NH non-alpine land north of 40°N of 2938 
[likely  range 2846–3062] Gt. Using this method, the bias-corrected 
GlobSnow v3.0 dataset suggests a 4.6 Gt yr –1 decrease of March SWE 
over this 39-year period across North America, and a negligible trend

across Eurasia. These SWE trends are consistent with the continental 
SCE trends over this period, as assessed above, but strong regional 
and temporal variability only allows medium confidence  in the signs 
and magnitudes of these trends. However, there is high confidence  in 
a general decline of NH spring SWE since 1981 (Section 2.3.2.2). In the 
longer term (see also Section  2.3.2.2), annual maximum SD trends

1285
Ocean, Cryosphere and Sea Level Change  Chapter 99from site measurements confirm mostly negative trends in North 
America (Kunkel et al., 2016) between 1960–1961 and 2014–2015, 
and strong spatial variability in Eurasia (Zhong et al., 2018) between 
1966 and 2012, with spatial patterns bearing some resemblance to 
the shorter satellite-based trends reported by Pulliainen et al. (2020). 
However, over this longer period, the Eurasian measurements (Zhong 
et al., 2018) exhibit, on average, a positive trend. On the Qinghai-Tibet 
Plateau, site measurements between 1961 and 2010 (Xu et al., 2017) 
suggest a shift from an initial increase of spring SD until about 1980 
to a decreasing trend afterwards.
Concerning the assessment of SWE trends in mountainous regions, 
SROCC noted a  need for observations spanning several decades 
because of very strong temporal variability. Moreover, determining 
SWE trends in mountain regions is challenging because the coarse

resolution (typically 25 to 50 km) of gridded SWE products is 
inadequate in areas of mountainous terrain (Snauffer et al., 2016). 
Based on a compilation of a large number of studies of SWE trends 
in mountain regions, SROCC noted strong regional variations, but 
a general consistency in greater reductions in SWE at lower elevations 
associated with shifts from solid to liquid precipitation. A  recent 
synthesis of snow observations in the European Alps (Matiu et al., 
2021) shows a 1971–2019 seasonal (November to May) SD trend of 
–8.4% per decade, along with negative maximum SD and seasonal 
snow cover duration trends. The trends are stronger and  more significant during transitional seasons and at transitional (from 
no snow to snow) altitudes, and exhibit strong regional variations, 
consistent with earlier reports for the Swiss and Austrian Alps 
(Schöner et al., 2019) and the Pyrenees (López-Moreno et al., 2020).

In summary, since AR5, intercomparison, dataset blending of gridded 
products, and bias correction using snow course measurements 
contributed to an improved estimate of the average 1980–2018 
March SWE over NH non-alpine land north of 40°N of 2938 [ likely  
range 2846–3062] Gt, with medium confidence  in the magnitudes 
of continental-scale trends over that period. However, there is 
high confidence  in a general decline of NH spring SWE since 1981 
(Section  2.3.2.2). In mountain areas, in situ observations tend to 
suggest that annual maximum SWE reductions are generally stronger 
at elevation bands where shifts from solid to liquid precipitation 
affected the snow mass.
9.5.3.2 Evaluation of Seasonal Snow in Climate Models
Building on AR5 (Flato et al., 2013) and subsequent published work, 
SROCC (Meredith et  al., 2019) stated that CMIP5 models tended 
to underestimate the observed decrease of Northern Hemisphere

spring SCE due to inappropriate parametrization of snow processes, 
misrepresentation of the snow-albedo feedback, underestimated 
temperature sensitivity, and biased climatological spring snow cover. 
Figure 9.2 3 | Observed monthly Northern Hemisphere snow cover (a) trends and (b) anomalies, and snow mass (c) trends and (d) anomalies. From the 
observation-based ensemble discussed in the text (Mudryk et al., 2020). Trends and anomalies are calculated over the 1981–2018 period. Further details on data sources and 
processing are available in the chapter data table (Table 9.SM.9).

1286
Chapter 9 Ocean, Cryosphere and Sea Level Change9Since AR5, progress in the observation, description and understanding 
of snow microstructure (Kinar and Pomeroy, 2015; Calonne et  al., 
2017) and its links to physical (thermal and radiative) properties (Löwe 
et al., 2013; Calonne et al., 2014) has prompted efforts to represent 
physical properties as a function of the evolving snow microstructure in 
models (Carmagnola et al., 2014; Calonne et al., 2015). However, even 
state-of-the-art snow models intended for meteorological and climate 
applications still struggle to correctly represent the time evolution of 
the snow thermal properties, particularly of cold and dry tundra snow 
(Domine et al., 2016). Moreover, most, if not all, CMIP6 climate models 
do not explicitly represent the darkening of snow by deposition of black 
carbon and other light-absorbing aerosol species known to influence 
snow melt rates (Section 7.3.4.3). Regardless of these shortcomings,

snow modules of climate models continue to be improved. Recent 
progress includes the incorporation of multiple energy balances 
within the canopy and between sub-grid tiles with different snow 
heights (Aas et al., 2017; Boone et al., 2017) and inclusion of advanced 
specific snow models in coupled climate models (Niwano et al., 2018; 
Voldoire et  al., 2019), opening the prospect of future progress in 
quantifying snow-related feedbacks in a  changing climate. Recently 
developed multi-physics snow models (Essery, 2015; Lafaysse et  al., 
2017), which are able to emulate the behaviour of a large number of 
models in a broad range of climates, allow model shortcomings and 
key parameter uncertainties, for example, concerning snow masking by 
vegetation or snow thermal conductivity, to be identified. Guidance for 
future model improvement can be provided by improved diagnostics, 
such as a concise metric of snow insulation (A.G. Slater et al., 2017),

which builds on an observed relation between effective seasonal mean 
SD and the dampening of winter season temperature decrease within 
the soil, and allows an efficient quantification of inaccuracies in the 
simulated snow insulation effect.There is high confidence  that large inter-model variations in the 
snow-cover sensitivity to temperature can largely be explained by 
inaccuracies in the simulated snow-albedo feedback (Qu and Hall, 
2014); a multi-model sub-ensemble of CMIP5 models that simulate 
a  correct magnitude of this feedback presents a  40%  reduced 
spread in the projected 21st  century Northern Hemisphere land 
warming trend (Thackeray and Fletcher, 2016). Errors of the 
simulated feedback strength were linked to: (i) systematic positive 
albedo biases over the boreal forest belt, mostly due to unrealistic 
treatment of vegetation masking (Thackeray and Fletcher, 2016); 
(ii) inaccurate prescribed tree cover fraction and inappropriate

parametrization of leaf area index in some models (Loranty et al., 
2014; L. Wang et al., 2016); and (iii) low spatial resolution leading 
to inaccuracies in the strength of the simulated snow albedo 
feedback in mountainous regions (Letcher and Minder, 2015). 
Although the representation of snow-albedo feedback improved 
in many CMIP5 models over CMIP3, some models deteriorated 
(Thackeray et al., 2018).
Analysis of the available CMIP6 historical simulations for the 
1981–2014 period shows that, on average, CMIP6 models simulate 
well the observed SCE (Mudryk et al., 2020), except for outliers and 
a median low bias during the winter months (Figure 9.24a). This is 
an improvement over CMIP5 (Mudryk et  al., 2020), where many 
snow-related biases were linked to inadequacies of the vegetation 
masking of snow cover over the boreal forests (Thackeray et  al., 
2015). A  comparison between CMIP5 and CMIP6 results (Mudryk

et al., 2020) shows that there is no notable progress in the quality 
of the representation of the observed 1981–2014 monthly snow 
cover trends.
Figure 9 .24 | Simulated Coupled Model Intercomparison Project Phase 6 (CMIP6) and observed snow cover extent (SCE). (a)  Simulated CMIP6 and observed 
(Mudryk et al., 2020) SCE (in millions of km2) for 1981–2014. Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 
1981–2014, with the red bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed interannual distribution over the period is represented in 
green, with the yellow bar indicating the median. (b) Spring (March to May) Northern Hemisphere SCE against global surface air temperature (GSAT) (relative to the 1995–2014 
average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. Each data point is the mean for one CMIP6 simulation (first

ensemble member for each available model) in the corresponding temperature bin. Further details on data sources and processing are available in the chapter data table 
(Table 9.SM.9).

1287
Ocean, Cryosphere and Sea Level Change  Chapter 999.5.3.3 Projected Snow Cover Changes
The AR5 (Collins et al., 2013) stated that substantial NH spring snow 
cover reductions at the end of the 21st century were very likely  under 
strong emissions scenarios, and expressed medium confidence  in the 
projected geographic patterns of annual maximum SWE changes. 
Based on studies using downscaled CMIP5 or regional climate model 
output, either directly or via snowpack models driven by such output, 
SROCC (Hock et al., 2019b) reported likely  SD or mass decreases at 
lower elevations in many mountain ranges over the 21st century and 
high confidence  in smaller future changes at higher elevations.
Since AR5, one study (Brown et  al., 2017), applying a  method 
developed by de Elía et  al. (2013) to a  CMIP5 sub-ensemble, 
suggested that over most of the Northern Hemisphere, the projected 
decrease of SCD will exceed natural variability before this will be the

case for annual maximum SWE. The same study reports that, over 
large parts of Eastern and Western North America and Europe, forced 
SCD changes are projected to exceed natural variability in the 2020s 
in spring and autumn, while the signals tend to emerge later in the 
Arctic regions and particularly late, after 2060, in Eastern Siberia 
under the RCP8.5 scenario. Thackeray and Fletcher (2016) have 
shown that inter-model spread in projected spring SCE trends could 
be reduced through improved simulation of spring season warming 
because of the tight coupling between temperature and SCE linked 
to the snow-albedo feedback (Qu and Hall, 2014; Thackeray and 
Fletcher, 2016).
Across all emissions scenarios, and with negligible scenario 
dependence (Figure 9.24b), CMIP6 models consistently (all models 
and all months) simulate Northern Hemisphere snow cover decrease 
in response to future GSAT change over the 21st century (Mudryk

et al., 2020). The simulated SCE decrease is close to a linear function 
of global temperature change for all months (shown in Figure 9.24b 
for spring, with medium confidence  in an average sensitivity of about 
–8% per °C of GSAT increase), except when snow cover vanishes. 
This occurs at about +2°C of GSAT change above the 1995–2014 
level (that is, about +3°C above the pre-industrial level) for the 
months of July and August, and at about +3°C above the 1995–2014 
level for June and September. Possible effects of such changes on 
the hydrological cycle are assessed in Section 8.2.3.1.
In summary, consistent projections from all generations of global 
climate models, elementary process understanding and strong 
covariance between snow cover and temperature on several time 
scales make it virtually certain  that future Northern Hemisphere snow 
cover extent and duration will continue to decrease as global climate 
continues to warm, and process understanding strongly suggests

that this also applies to Southern Hemisphere seasonal snow cover 
Seasonal snow cover, by definition, has a  clear annual cycle 
with usually complete disappearance in spring and summer and 
re-formation in autumn or winter. Therefore, there is very high 
confidence  that the current and projected changes to seasonal snow 
cover are reversible (Verfaillie et  al., 2018). In the case of global 
or regional cooling, abrupt large-scale snow-cover changes, with a transition from seasonal to persistent snow cover due to a strong 
snow-albedo feedback, are a  typical feature of glacial inceptions 
(e.g., Baum and Crowley, 2003; Calov et al., 2005), and these can 
be irreversible on centennial or longer time scales because of this 
feedback. In summary, based on physical understanding and the 
absence of occurrence of such events in climate model projections, 
abrupt future changes of seasonal snow cover on large scales in

the absence of concomitant abrupt atmospheric change as a driver 
appear very unlikely  in the context of current and projected warming.
9.6 Sea Level Change
9.6.1 Global and Regional Sea Level Change 
in the Instrumental Era
9.6.1.1 Global Mean Sea Level Change Budget 
in the Pre-satellite Era
The SROCC (Oppenheimer et al., 2019) discussed the development 
and application of new statistical methodologies for reconstructing 
global mean sea level (GMSL) from tide gauge data over the 
20th  century (Box  9.1). Based on an ensemble of tide gauge 
reconstructions, SROCC assessed an average rate of GMSL rise 
of 1.38  [0.81 to 1.95, very likely  range]  mm yr –1 for the period 
1901–1990. Since SROCC, two new GMSL reconstructions have 
been published (Dangendorf et  al., 2019; Frederikse et  al., 2020b) 
and are included in an updated ensemble estimate of GMSL change 
(Section 2.3.3.3; Palmer et al., 2021). Based on these updated data and

methods, the GMSL change over the (pre-satellite) period 1901–1990 
is assessed to be 0.12 [0.07 to 0.17, very likely range] m with an 
average rate of 1.35 [0.78 to 1.92, very likely  range] mm yr –1 (high 
confidence ) (Table  9.5; Section  2.3.3.3) in agreement with SROCC 
assessment. Both this assessment and SROCC have substantially 
larger uncertainties than the AR5 assessment, which was based on 
a single tide gauge reconstruction and did not account for structural 
uncertainty (see Palmer et al., 2021 for a discussion).
The SROCC found that four of the five available tide gauge 
reconstructions that extend back to at least 1902 showed a robust 
acceleration ( high confidence ) of GMSL rise over the 20th century, 
with estimates for the period 1902–2010 (–0.002 to +0.019 mm yr –2) 
that were consistent with AR5. New tide gauge reconstructions 
published since SROCC (Dangendorf et  al., 2019; Frederikse et  al., 
2020b) support this assessment and suggest that increased ocean

heat uptake related to changes in Southern Hemisphere winds 
and increased mass loss from Greenland are the primary physical 
mechanisms for the acceleration (Section  2.3.3.3). Therefore, 
the  SROCC assessment on the acceleration of GMSL rise over the 
20th century is maintained.
The evaluation of the sea level budget presented here, and in 
Section 9.6.1.2, draws on assessments of the individual components 
(Sections 2.3.3.1 and 9.2.4.1 for global-mean thermosteric and 
Sections 9.5.1.1, 9.4.1.1 and 9.4.2.1 for ice mass loss contributions 
to GMSL change from glaciers and ice sheets). Following SROCC 
approach, the mass loss from ice sheet peripheral glaciers is

1288
Chapter 9 Ocean, Cryosphere and Sea Level Change9included in the ice-sheet contributions to GMSL change (glacier mass 
loss from regions 5 and 19 of the Randolph Glacier Inventory  6.0 
(RGI Consortium, 2017) are added to ice-sheet mass loss where 
applicable, with uncertainties added in quadrature). The total change 
in GMSL for each component, and their sum, is summarized in 
Table 9.5 (uncertainties added in quadrature). For consistency across 
the report, and to simplify the treatment of uncertainties, all budget 
calculations are based on the difference between the first and last 
year in each period (Palmer et al., 2021), rather than a linear fit to the 
underlying time series as used in SROCC and AR5.
The sea level budget in SROCC included the anthropogenic 
contribution of land-water storage (LWS; Box  9.1) change from 
a  single estimate (Wada, 2016). Since SROCC, two studies have 
combined estimates of natural LWS change with anthropogenic LWS

changes from reservoir impoundment and groundwater depletion 
(Cáceres et  al., 2020; Frederikse et  al., 2020b). For Cáceres et  al. 
(2020), zero change is assumed for the period 1901–1948, since 
their LWS change estimates are not available before 1948. Given the 
large year-to-year changes associated with hydrological variability, 
the assessed changes in LWS (Table 9.5) are based on linear trends 
for each period, following Palmer et al. (2021). Structural uncertainty 
is estimated from the standard deviation of the trends across the 
two studies, and parametric uncertainty is estimated based on the 
Monte Carlo simulations of Frederikse et  al. (2020b). These two 
sources of uncertainty are combined in quadrature, and the assessed 
central estimate is taken as the average of the ensemble mean 
trends. Compared to SROCC-assessed LWS trend of -0.12  mm yr –1 
for the period 1901–1990, the updated assessment leads to a more

negative trend of –0.16 [–0.35 to 0.04] mm yr –1, although the two 
are consistent within the estimated uncertainties. Previous studies 
and SROCC have highlighted the large uncertainty in estimates 
of LWS change over the 20th  century (Gregory et  al., 2013), and 
therefore SROCC assessment of low confidence  in the estimated LWS 
contribution to GMSL change is maintained.
Since SROCC, a  new ocean heat content reconstruction 
(Section 2.3.3.1; Zanna et al., 2019) has allowed global thermosteric 
sea level change to be estimated over the 20th century. As a result, 
the sea level budget for the 20th century can now be assessed for the 
first time. For the periods 1901–1990 and 1901–2018, the assessed 
very likely  range for the sum of components is found to be consistent 
with the assessed very likely  range of observed GMSL change 
(medium confidence ), in agreement with Frederikse et  al. (2020b; 
Table 9.5). This represents a major step forward in the understanding

of observed GMSL change over the 20th century, which is dominated 
by glacier (52%) and Greenland Ice Sheet mass loss (29%) and 
the effect of ocean thermal expansion (32%), with a  negative 
contribution from the LWS change (–14%). While the combined mass 
loss for Greenland and glaciers is consistent with SROCC, updates 
in the underlying datasets lead to differences in partitioning of the 
mass loss.9.6.1.2 Global Mean Sea Level Change Budget  
in the Satellite Era
The SROCC (Oppenheimer et  al., 2019) concluded that GMSL 
increased at a rate of 3.16 [2.79 to 3.53, very likely  range] mm yr –1 
in the period 1993–2015 (the satellite altimetry era),  and a  rate 
of 3.58  [3.10 to 4.06, very likely  range]  mm yr –1 in the period 
2006–2015  – the Gravity Recovery and Climate Experiment 
(GRACE)/Argo data era  (high confidence ). An updated assessment 
for the periods 1993–2018 and 2006–2018 yields values of 
3.25  [2.88 to 3.61] and 3.69 [3.21 to 4.17]  mm yr –1 (high

confidence ) (Table  9.5), with the slightly larger central estimates 
consistent with the observed acceleration in GMSL rise since the 
late 1960s (Dangendorf et al., 2019), given the longer assessment 
periods. Based on the GMSL assessed time series presented in 
Section 2.3.3.3, GMSL acceleration is estimated as 0.075 [0.066 to 
0.080] mm yr –2 for 1971–2018 and 0.094 [0.082–0.115] mm yr –2 
for 1993–2018 ( high confidence ). For the common period of 
1993–2010, the assessed rate of GMSL rise based on tide gauge 
reconstructions (3.19 [1.18 to 5.20]  mm  yr –1) is consistent 
with the assessment based on satellite altimetry (2.77  [2.26 to 
3.28] mm yr –1), within the estimated uncertainties.
Since SROCC, two new estimates of the LWS contribution have 
been published (Section  9.6.1.1; Cáceres et  al., 2020; Frederikse 
et  al., 2020b). For the early 21st  century (the periods 1993–2018 
and 2006–2018) both publications find a positive LWS contribution

(Table  9.5), based on the most recent GRACE-derived estimates. 
This contrasts with the negative LWS contribution presented for the 
same periods in SROCC based on World Climate Research Programme 
(WCRP) Global Sea Level Budget Group (2018), and reinforces the 
low confidence  assessment of the LWS contribution.
For both periods in the satellite era  – that is, 1993–2018 and 
2006–2018 – the sum of contributions is consistent with the total 
observed GMSL change ( high confidence ) (Table 9.5). However, the 
latter period, which is characterized by improved data quality and 
coverage associated with satellite and Argo observations, shows 
much closer agreement in the central estimates. The marginal 
sea level budget closure for the period 1993–2018 may indicate 
underestimated uncertainty, which may be structural as well as 
parametric. The sea level budget assessments across the various 
periods in Table 9.5 demonstrate that the acceleration in GMSL rise

(Section 2.3.3.3) since the late 1960s is mostly the result of increased 
ice-sheet mass loss. However, all contributions to GMSL rise show 
their largest rate during 2006–2018, with the ice sheets accounting 
for 27% of the total change during this period. Because of the 
increased ice-sheet mass loss, the total loss of land ice (glaciers and 
ice sheets) was the largest contributor to GMSL rise over the period 
2006–2018 ( high confidence ).

1289
9.6.1.3 Regional Sea Level Change in the Satellite Era 
Regional sea level changes are resolved by both tide gauge and 
satellite altimetry observations (Hamlington et al., 2020a). Altimeters 
have the advantage of  quasi-global coverage but are limited to 
a period (1993–present) in which the forced trend response is just 
emerging on regional scales  (Section  9.6.1.4).  An analysis of the 
local altimetry error budget to estimate 90% confidence intervals 
on regional sea level trends and accelerations reports that 98% of 
the ocean surface has experienced significant sea level rise over 
the satellite era (Prandi et al., 2021). The same study finds that sea 
level accelerations display a less uniform pattern, with an east–west 
dipole in the Pacific, a north–south dipole in the Southern Ocean and in the North Atlantic, and 85% of the ocean surface experiencing

significant sea level acceleration or deceleration, above instrumental 
and post-processing noise. Longer records are available from tide 
gauges, albeit with variable coverage by basin. Regional departures 
from GMSL rise are primarily driven by ocean transport divergences 
that result from wind stress anomalies and spatial variability 
in atmospheric heat and freshwater fluxes (Section 9.2.4).
The SROCC (Oppenheimer et  al., 2019) noted the occurrence of 
large multiannual sea level variations in the Pacific, associated with 
the  Pacific Decadal Oscillation (PDO) in particular, and involving the 
El  Niño Southern Oscillation (ENSO), North Pacific Gyre Oscillation 
(NPGO) and Indian Ocean Dipole (IOD; Annex IV; Royston et al., 2018; Table  9.5 | Observed contributions to global mean sea level (GMSL) change for five different periods.  Values are expressed as the total change (Δ) in the annual

mean or year mid-point value over each period (mm) along with the equivalent rate (mm yr –1). The very likely  ranges appear in brackets based on the various section assessments 
as indicated. Uncertainties for the sum of contributions are added in quadrature, assuming independence. Percentages are based on central estimate contributions compared 
to the central estimate of the sum of contributions.
Observed contribution 
to GMSL change1901–1990
{9.6.1.1}1971–2018
{CCBox 9.1}1993–2018
{9.6.1.2}2006–2018
{9.6.1.2}1901–2018
{9.6.1.1}
Thermal expansion 
(Section 2.3.3.1; Table 2.7)Δ (mm)31.6 
[14.7 to 48.5]
(31.9%)47.5 
[34.3 to 60.7]
(50.4%)32.7 
[23.8 to 41.6]
(45.9%)16.7 
[8.9 to 24.6]
(38.6%)63.2 
[47.0 to 79.4]
(38.4%)
mm yr –10.36 
[0.17 to 0.54]1.01 
[0.73 to 1.29]1.31 
[0.95 to 1.66]1.39 
[0.74 to 2.05]0.54 
[0.40 to 0.68]
Glaciers (excluding peripheral glaciers) 
(Sections 2.3.2.3, 9.5.1.1)Δ (mm)51.8 
[30.4 to 73.2]
(52.3%)20.9 
[10.0 to 31.7]
(22.2%)13.8 
[10.0 to 17.6]

(19.4%)7.5 
[6.8 to 8.2]
(17.3%)67.2 
[41.8 to 92.6]
(40.8%)
mm yr –10.58 
[0.34 to 0.82]0.44 
[0.21 to 0.67]0.55 
[0.40 to 0.70]0.62 
[0.57 to 0.68]0.57 
[0.36 to 0.79]
Greenland Ice Sheet (including peripheral glaciers)
(Sections 2.3.2.4.1, 9.4.1.1)Δ (mm)29.0 
[16.3 to 41.7]
(29.3%)11.9 
[7.7 to 16.1]
(12.6%)10.8
[8.9 to 12.7]
(15.2%)7.5
[6.2 to 8.9]
(17.3%)40.4 
[27.2 to 53.5]
(24.5%)
mm yr –10.33 
[0.18 to 0.47]0.25
[0.16 to 0.34]0.43
[0.36 to 0.51]0.63
[0.51 to 0.74]0.35 
[0.23 to 0.46]
Antarctic Ice Sheet (including peripheral glaciers)
(Sections 2.3.2.4.2, 9.4.2.1)Δ (mm)0.4 
[–8.8 to 9.6]
(0.4%)6.7
[–4.0 to 17.3]
(7.1%)6.1 
[4.0 to 8.3]
(8.6%)4.4 
[2.9 to 6.0]
(10.2%)6.7 
[–4.0 to 17.4]
(4.1%)
mm yr –10.00 
[–0.10 to 0.11]0.14 
[–0.09 to 0.37]0.25 
[0.16 to 0.33]0.37
[0.24 to 0.50]0.06 
[–0.03 to 0.15]
Land-water storagea
(Section 9.6.1.1) Δ (mm)–13.8 
[–31.4 to 3.8]
(-13.9%)7.3 
[–2.4 to 16.9]
(7.7%)7.8 
[3.3 to 12.2]
(10.9%)7.2 
[3.8 to 10.6]
(16.6%)–12.9 
[–45.8 to 20.0]

(–7.8%)
mm yr –1–0.15 
[–0.35 to 0.04]0.15
[–0.05 to 0.36]0.31
[0.13 to 0.49]0.60
[0.32 to 0.88]–0.11 
[–0.39 to 0.17]
Sum of observed contributionsΔ (mm)99.0 
[63.0 to 135.1]94.2
[71.5 to 117.0]71.2 
[60.2 to 82.3]43.4
[34.5 to 52.2]164.6 
[116.9 to 212.4]
mm yr –11.11 
[0.71 to 1.52]2.00 
[1.52 to 2.49]2.85 
[2.41 to 3.29]3.61
[2.88 to 4.35]1.41
[1.00 to 1.82]
Observed GMSL change 
(Section 2.3.3.3)Δ (mm)120.1T 
[69.3 to 170.8]109.6T&A
[72.8 to 146.4]81.2A 
[72.1 to 90.2]44.3A
[38.6 to 50.0]201.9T&A 
[150.3 to 253.5]
mm yr –11.35T 
[0.78 to 1.92]2.33T&A 
[1.55 to 3.12]3.25A 
[2.88 to 3.61]3.69A 
[3.21 to 4.17]1.73T&A 
[1.28 to 2.17]
T, A and T&A indicate assessments based on tide gauge reconstructions (T), satellite altimetry (A), or a combination of both (T&A). The assessment uses tide gauge reconstructions 
before 1993 and satellite altimetry after 1993.

a For the periods 1971–2018, 1993–2018, 2006–2018 and 1901–2018 the Cáceres et al. (2020) linear trends are based on the period up to 2016.

1290
Chapter 9 Ocean, Cryosphere and Sea Level Change9Hamlington et al., 2020b). There was intensified sea level rise during 
the 1990s and 2000s, with 10-year trends exceeding 20 mm yr –1 in the 
western tropical Pacific Ocean, while sea level trends were negative 
on the North American west coast. During the 2010s, the situation 
reversed, with western Pacific sea level falling at more than 10 mm yr –1 
(Hamlington et al., 2020b). For the Atlantic Ocean, SROCC described 
regional sea level variability as being driven primarily by wind and heat 
flux variations associated with the North Atlantic Oscillation (NAO) and 
heat transport changes associated with Atlantic Meridional Overturning 
Circulation (AMOC) variability . During periods of subpolar North 
Atlantic warming, winds along the European coast are predominantly 
from the south and may communicate steric anomalies onto the 
continental shelf, driving regional sea level rise, with the reverse during

periods of cooling (Chafik et al., 2019). High rates of sea level rise in the 
North Indian Ocean are accompanied by a weakening summer South 
Asian monsoon circulation (Swapna et al., 2017).
The Arctic ocean is typically excluded from global sea level studies, 
owing to the uncertainties associated with resolving sea level in 
ice-covered regions, strong variations in gravitational, rotational, 
and deformational (GRD) effects, and uncertain glacial isostatic 
adjustment (GIA) estimates (Box 9.1). Spanning 1991–2018, a  very 
likely  sea level rise of 1.16–1.81 mm yr –1 is observed (Rose et al., 
2019). Since SROCC, the forced response in regional sea level 
varies in time with the relative influence of different forcing agents 
(Fasullo et al., 2020).
The SROCC estimated regional sea level changes from combinations 
of the various contributions to sea level change from CMIP5 climate 
model outputs, allowing comparison with satellite altimeter and

tide gauge observations. Closure of the regional sea level budget is 
complicated by the fact that regional sea level variability is larger 
than GMSL variability. Also, there are more processes that need to 
be considered, such as vertical land movement and ocean dynamical 
changes (Box  9.1). A  number of observation-based studies have 
focused on specific areas, such as the Mediterranean (García et al., 
2006), the South China Sea (Feng et al., 2012), the east coast of the 
USA (Frederikse et al., 2017; Piecuch et al., 2018), the North Atlantic 
basin (Kleinherenbrink et al., 2016) and the north-western European 
continental shelf seas (Frederikse et  al., 2016). Studies using tide 
gauge data and observation-based estimates of the contributions 
find that, while local agreement is not yet possible, the observational 
sea level budget can be closed on a  basin scale (Slangen et  al., 
2014b; Frederikse et al., 2016, 2018, 2020b). A budget analysis for

the GRACE era found that the budget closes in some, but not all, 
coastal regions: substantial parts of the sea level change signal in 
the North Atlantic could not be explained by steric or barystatic 
changes (Rietbroek et  al., 2016). This is in agreement with other 
work comparing climate model estimates to 20th-century tide 
gauge observations (Meyssignac et  al., 2017), where the majority 
of local spatial variability is determined by the ocean dynamic 
component. Vertical land movement is another major cause of local 
spatial variability in sea level change and, for instance, relevant for 
oceanic islands (Forbes et al., 2013; Martínez-Asensio et al., 2019). 
In summary, the regional sea level budget, using either observations 
or models, can currently only be closed on basin scales ( medium 
confidence ), with large uncertainties remaining on smaller scales .9.6.1.4 Attribution and Time of Emergence  
of Regional Sea Level Change

The SROCC (Oppenheimer et  al., 2019) attributed anthropogenic 
forcing to be the dominant cause of GMSL rise since 1970 (see also 
Section  3.5.3.2), but detection and attribution (Cross-Working 
Group Box: Attribution in Chapter  1) of 20th  century externally 
forced regional sea level changes is more challenging, as regional 
variability is larger (Section  9.6.1.3), and therefore the signal-to-
noise ratio is smaller (Richter and Marzeion, 2014; Monselesan 
et al., 2015; Palanisamy et al., 2015). Whereas SROCC assessed with 
high confidence  that GMSL rise is attributable to anthropogenic 
greenhouse gas emissions, they assessed with medium confidence  
that the regional anomalies in ocean basins are a  combination of 
the response to anthropogenic greenhouse gas emissions and 
internal variability.
The simulated ocean dynamic and thermosteric response to external 
forcings during 1861–2005 is only larger than simulated internal

variability in the Southern Ocean and North Pacific on a  1° grid 
(Slangen et al., 2015). However, on spatial scales exceeding 2000 km, 
a  detectable signal is revealed in the last 45 years in 63% of the 
global ocean area (Richter et al., 2017). The thermosteric change in 
the upper 700 m in the period 1970–2005 shows similar observed 
and simulated forced geographical patterns, and anthropogenic 
forcing accounts for part (North Atlantic, 65%) or all (tropical Pacific, 
Southern Ocean) of the observed regional mean (Marcos and Amores, 
2014). The influences of greenhouse gases and anthropogenic 
aerosols can be partially distinguished by considering geographical 
or vertical ocean temperature variations (Slangen et al., 2015; Bilbao 
et al., 2019; Fasullo et al., 2020). Zonal-mean forced ocean dynamic 
sea level change alone is not detectable but, using spatial correlation, 
the global geographical pattern during the altimeter period is

detectable in sea level trends (Fasullo and Nerem, 2018). This pattern 
may already or will soon be detectable in individual years, based 
on an analysis of CMIP5 climate model simulations (Bilbao et  al., 
2015). Anthropogenic forcing, dominated by greenhouse gases, 
has strengthened the meridional sea level gradient in the Southern 
Ocean since the 1960s (Slangen et  al., 2015; Bilbao et  al., 2019; 
Fasullo et al., 2020). New evidence finds that observed zonal-mean 
total sea level trends during 1993–2018 in all basins are inconsistent 
with unforced variability alone, but are consistent with the modelled 
response to external forcing (Richter et al., 2020).
A region that has been studied intensely in the context of sea 
level detection and attribution is the tropical Pacific. Observed 
sea level trends in the tropical Pacific show a PDO-like (Annex IV) 
east–west dipole (with a  greater rate of rise in the west, see

Section  9.6.1.3). This dipole does not occur in CMIP5 simulations 
with the magnitude and duration that was observed in the 1990s 
and 2000s, neither in response to historical forcing, nor as internal 
variability after removing the variability associated with the PDO 
(Bilbao et al., 2015). Hamlington et al. (2014) did obtain a residual 
trend pattern for 1993–2010 in the tropical Pacific that may link to 
anthropogenic warming of the tropical Indian Ocean. Allowing for 
PDO and ENSO variations, (Royston et  al., 2018) describe patches 
of the Pacific Ocean where the sea level trend for 1993–2015 is

1291
Cross-Chapter Box 9.1 | Global Energy Inventory and Sea Level Budget
Coordinators: Matthew D. Palmer (United Kingdom), Aimée B.A. Slangen (The Netherlands)
Contributors:  Guðfinna Aðalgeirsdóttir (Iceland), Fábio Boeira Dias (Finland/Brazil), Catia M. Domingues (Australia, United Kingdom/
Brazil), Gerhard Krinner (France/Germany, France), Johannes Quaas (Germany), Lucas Ruiz (Argentina)
Increased atmospheric greenhouse gas emissions since the 19th century have led to a net positive radiative forcing of Earth’s climate 
(Sections 2.2 and 7.3) and a corresponding accumulation of energy in the Earth system. Quantification of this energy gain is essential 
to our understanding of observed climate change, and for estimates of climate sensitivity (Section 7.5). The global energy inventory is 
closely linked to our understanding of observed global sea level change, through the energy associated with loss of land-based ice and

the effect of thermal expansion associated with ocean warming (Box 9.1, Sections 2.3.3.1 and 9.6.1; Table 9.5). 
Cross-Chapter 9.1, Figure 1 | Global Energy Inventory and Sea Level Budget. (a)  Observed changes in the global energy inventory for 1971–2018 (shaded 
time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the 
right of the plot (red bar = central estimate; shading = very likely  range); (b) Observed changes in components of global mean sea level for 1971–2018 (shaded time 
series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements 
(1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole

period is indicated to the right of the plot (red bar = component sum central estimate; red shading = very likely  range; black bar = total sea level central estimate; grey 
shading = very likely  range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported 
in Table 7.1 and Table 9.5.
distinguishable from temporally correlated noise. The acceleration in 
eastern Pacific sea level rise is largely accounted for by variations 
resembling PDO and ENSO (Hamlington et al., 2020a).
In the future, the anthropogenic signal in regional sea level change 
from ocean density and dynamics is projected to emerge first in 
regions with relatively small internal variability, such as the tropical 
Atlantic Ocean and the tropical Indian Ocean (Jordà, 2014; Lyu et al., 
2014; Richter and Marzeion, 2014; Bilbao et  al., 2015). The  signal 
is projected to emerge over 50% of the ocean area by the 2040s

(Lyu  et  al., 2014), but in regions where variability is large and 
projected changes are small, such as the Southern Ocean, the signal will not emerge before late in the century. Adding the projected sea 
level change from land ice mass loss and groundwater extraction 
strengthens and modifies the forced signal, leading to times of 
emergence 10 to 20 years earlier in most parts of the ocean, except 
in regions close to sources of mass loss, with emergence over 50% of 
the ocean area by 2020, and nearly everywhere by 2100 ( medium 
confidence ) (Lyu et al., 2014; Richter et al., 2017).
In summary, detection of forced regional changes for some ocean 
areas in recent decades is possible ( medium confidence ), but 
attribution of regional sea level change to forcings over longer 
periods (20th century) and for all ocean basins is not yet possible.

1292
9.6.2 Paleo Context of Global and Regional  
Sea Level Change
As SROCC (Oppenheimer et al., 2019) noted, paleo sea level records 
provide information on past ice-sheet changes, and process-based 
ice-sheet models of past warm periods inform equilibrium responses. 
However, given uncertainties in paleo sea level and polar paleoclimate, 
and limited temporal resolution of paleo sea level records, there is low 
confidence in the utility of paleo sea level records for quantitatively 
informing near-term GMSL change. Nonetheless, the paleorecord does 
contextualize sea level and can test projection models (see also FAQ 1.3). Proxy constraints on GMSL and global ice volume are assessed in 
Sections 2.3.2.4. and 2.3.3.3 (see also FAQ 9.1). This section updates 
prior assessments of drivers of past GMSL changes and climatically 
coherent areas of relative sea level (RSL) variability. GMSL changes are

framed in terms of global mean surface temperature (GMST) but noting 
that amplified high-latitude warming is a robust equilibrium response 
to elevated CO 2 (Masson-Delmotte et al., 2013): polar air temperatures 
during past warm periods were up to twice the GMST changes shown 
in Table  9.6. The SROCC assessment that past multi-metre sea level 
changes have resulted from significant ice-sheet changes beyond those 
presently observed is confirmed ( very high confidence ).Cross-Chapter Box 9.1 (continued)
The Earth system gained substantial energy over the period 1971–2018 ( high confidence ), with an assessed very likely  range of 
325–546 ZJ or 0.43–0.72 W m–2 expressed per unit area of the Earth’s surface (Cross-Chapter Box 9.1, Figure 1a; Section 7.2, Box 7.2). 
Ocean warming dominates the energy inventory change ( high confidence ), accounting for 91% of the observed energy increase for the

period 1971–2018, with upper-ocean warming (0–700 m) accounting for 56% (Section 7.2). Much smaller amounts went into melting 
of ice (3%) and heating of the land (5%) and atmosphere (1%). Overall, the percentage contributions are similar to those reported in 
IPCC’s Fifth Assessment Report (AR5) for the period 1971–2010 (Rhein et al., 2013).
The observed global mean sea level (GMSL) budget is assessed through comparison of the sum of individual components of GMSL 
change with independent observations of total GMSL change from tide gauge and satellite altimeter observations (Cross-Chapter 
Box 9.1, Figure 1b; Sections 2.3.3 and 9.6.1 and Table 9.5). The assessed sum of the observed components indicates that GMSL very 
likely  increased by 72 mm to 117 mm over the period 1971–2018 (Table 9.5), with the largest contributions from ocean thermal 
expansion (50%) and melting of ice sheets and glaciers (42%). The assessed total GMSL change (Section  2.3.3) for the period

1971–2018 has a  very likely  range of 73–146 mm and, as a result, the sea level budget is closed for this period (Cross-Chapter Box 9.1, 
Figure 1b; Section 9.6.1, Table 9.5).
The sea level budget closure demonstrates improved quantification of the processes of observed GMSL change for this period relative to 
previous IPCC assessments (Church et al., 2013b; Oppenheimer et al., 2019). A related assessment presented in Chapter 7 demonstrates 
closure of the global energy budget ( high confidence ) (Box 7.2) and strengthens the confidence in scientific understanding of both of 
these key aspects of climate change.
Table 9 .6 | Reference ranges of age, global mean surface temperature, atmospheric carbon dioxide (CO 2) concentration, and global mean sea level 
(GMSL) for the paleo periods discussed in this chapter.
Paleo PeriodYears
Cross-Chapter Box 2.1GMST relative to 
1850–1900
Section 2.3.1.1CO 2
Sections 2.2.3.1 
and 2.2.3.2Global Mean Sea 
Level (GMSL)
Section 2.3.3.3

Early Eocene Climatic Optimum (EECO) 53–49 Ma +10°C to +18°C 1150 to 2500 ppm +70 to +76 m
Mid-Pliocene Warm Period (MPWP) 3.3–3.0 Ma +2.5°C to +4°C 360 to 420 ppm +5 to +25 m
Marine Isotope Stage (MIS) 11 about 424–395 ka 0.5°C ± 1.6°Ca265 to 286 ppm +6 to +13 m
Last Interglacial (LIG) about 129–116 ka +0.5°C to +1.5°C 266 to 282 ppm +5 to +10 m 
Last Glacial Maximum (LGM) 21–19 ka –5°C to –7°C 188 to 194 ppm –125 to –134 m
Last Deglacial Transition 18–11 ka n/a 193 to 271 ppm –120 to –50 m
Early Holocene 11.65–6.5 ka n/a 250 to 268 ppm –50 to –3.5 m
Mid-Holocene 6.5–5.5 ka +0.2°C to +1.0°C 260 to 268 ppm –3.5 to +0.5 m
Last Millennium 850–1850 CE –0.14°C to +0.24°C 278 to 285 ppm –0.05 to +0.03 m
a Based on one study (Irvalı et al., 2020) relative to SST values around year 2000.

1293
Ocean, Cryosphere and Sea Level Change  Chapter 999.6.2.1 Mid-Pliocene Warm Period 
During the mid-Pliocene Warm Period (MPWP), GMST was 
2.5°C–4°C warmer than 1850–1900 ( medium confidence ) and GMSL 
was between 5 and 25 m higher than today ( medium confidence ) 
(Table  9.6 and Section  2.3.3.3). The AR5 (Masson-Delmotte et  al., 
2013) concluded that ice-sheet models consistently produce 
near-complete deglaciation of the Greenland and West Antarctic 
ice sheets, and multi-meter loss of the East Antarctic Ice Sheet 
(EAIS) in response to MPWP climate conditions. Studies since AR5 
have yielded a consistent but broader range, due in part to larger 
ensembles exploring more parameters (DeConto and Pollard, 2016; 
Yan et al., 2016; DeConto et al., 2021). Partly on the basis of these 
studies, SROCC proposed a ‘plausible’ upper bound on GMSL of 25 m 
(low confidence ) with evidence suggesting an Antarctic contribution 
of anywhere between 5.4 and 17.8 m.

The MPWP climate had substantial polar amplification, up to 8°C 
above pre-industrial levels in Arctic Russia (Section 7.4.4.1; Fischer 
et  al., 2018). Ice-sheet model simulations indicate that Northern 
Hemisphere glaciation was limited to high-elevation regions in 
eastern and southern Greenland ( medium confidence ) (Figure 9.17; 
De Schepper et  al., 2014; Yan et  al., 2014; Koenig et  al., 2015; 
Dowsett et al., 2016; Berends et al., 2019) with Northern Hemisphere 
glaciation only becoming more widespread from the (cooler) late 
Pliocene (Bachem et al., 2017; Blake-Mizen et al., 2019; Knutz et al., 
2019; Sánchez-Montes et al., 2020). Southern Hemisphere glaciation 
was characterized by an Antarctic Ice Sheet (AIS) reduced in volume 
from the present ( medium confidence ) (Figure 9.18; Dowsett et al., 
2016; Berends et al., 2019; Grant et al., 2019; Miller et al., 2020) with 
mountain ice fields in the Andes of South America (De Schepper et al.,

2014). Ice-sheet models are inconsistent in the magnitude of the sea 
level contribution from Antarctica (DeConto and Pollard, 2016; Yan 
et al., 2016; Golledge et al., 2017b; Berends et al., 2019; DeConto 
et al., 2021) but near-field sedimentological reconstructions support 
precessionally modulated and eccentricity-paced multi-metre sea 
level contributions from the Wilkes Subglacial Basin over 3–5 kyr 
(Patterson et al., 2014; Bertram et al., 2018). In summary, under a past 
warming level of around 2.5°C–4°C, ice sheets in both hemispheres 
were reduced in extent compared to present ( high confidence ). 
Proxy-based evidence (Section  2.3.3.3) combined with numerical 
modelling indicates that, on millennial time scales, the GMSL 
contribution arising from ice sheets was >5 m ( high confidence ) 
or >10 m ( medium confidence ) (Figures 9.17 and 9.18; Moucha and 
Ruetenik, 2017; Berends et al., 2019; Dumitru et al., 2019).
9.6.2.2 Marine Isotope Stage 11

The SROCC (Meredith et  al., 2019) noted that Greenland may 
have been ice-free for extensive periods during Pleistocene 
interglaciations, implying a  high sensitivity of the Greenland Ice 
Sheet to warming levels close to present day. The AR5 (Church et al., 
2013b) assigned medium confidence  to a  Marine Isotope Stage 
11 (MIS 11) GMSL of 6–15 m above present, requiring a  loss of 
much of the Greenland and West Antarctic ice sheets, and a possible 
contribution from East Antarctica. High-resolution multi-proxy sea 
surface temperature reconstructions and climate model simulations concur that MIS 11 was an extremely long interglacial that exhibited 
positive annual at 0.5°C ± 1.6 °C (Irvalı et al., 2020) and summer 
at 2.1°C–3.4  °C (Robinson et  al., 2017) temperature anomalies 
(de Wet et al., 2016). The GMSL was 6–13 m above present ( medium 
confidence ) (Section 2.3.3.3). The Greenland Ice Sheet lost 4.5–6 m 
(Reyes et al., 2014) or about 6.1 m (3.9–7 m, 95% confidence) sea

level equivalent (SLE) by about 7  kyr after peak summer warmth 
(Robinson et al., 2017), with marine-based ice from AIS (Blackburn 
et al., 2020) contributing 6.4–8.8 m SLE at this time (Mas e Braga 
et al., 2021). Agreement between GMSL and ice-sheet reconstructions 
gives high confidence  in identifying a  high sensitivity of both ice 
sheets to the protracted duration of thermal forcing, even at low 
warming levels (Reyes et al., 2014; Robinson et al., 2017; Irvalı et al., 
2020; Mas e Braga et al., 2021). Modelled mean mass loss rates for 
the Greenland Ice Sheet of 0.4 m kyr –1 during MIS  11 (Robinson 
et  al., 2017) are indistinguishable from recent mass loss rates 
averaged over 1992–2018 (Section 9.4.1.1). In summary, geological 
reconstructions and numerical simulations consistently show that 
past warming levels of <2°C (GMST) are sufficient to trigger multi-
metre mass loss from both the Greenland and Antarctic ice sheets

if maintained for millennia ( high confidence ), in agreement with 
SROCC findings for comparable warming levels during MIS 5e, the 
Last Interglacial.
9.6.2.3 Last Interglacial 
The AR5 found that the Last Interglacial (LIG) GMSL was >5 m 
(very high confidence ) but <10 m ( high confidence ). Their best 
estimate of 6 m was based on two studies (Kopp et  al., 2009; 
Dutton and Lambeck, 2012). The SROCC concluded that, during the 
LIG, Greenland’s contribution to the GMSL highstand (the highest 
sea levels during the LIG) of 6–9 m increased gradually, whereas 
the Antarctic contribution occurred early, from about 129 ka. Due 
to widely varying reconstructions from model studies (Greenland) 
and the paucity of direct evidence of ice-sheet change (Antarctic), 
the magnitude of sea level contributions from both ice sheets was 
assigned low confidence.
Since AR5, information has improved about the LIG, when GMST 
was about 0.5°C–1.5°C above 1850–1900 ( medium confidence )

(Section 2.3.1.1). The LIG had higher summer insolation than present 
and polar amplified sea surface and surface air temperatures that 
reached >1°C–4°C and >3°C–11 °C in the Arctic respectively (Landais 
et al., 2016; Capron et al., 2017; Fischer et al., 2018). Mean annual and 
maximum summer ocean temperatures peaked early (129–125 ka) 
in the interglacial period, reaching 1.1 ± 0.3 °C above the modern 
global mean (Shackleton et  al., 2020) with summer anomalies of 
2.5°C–3.5 °C in the Southern Ocean (Bianchi and Gersonde, 2002) 
and spatially variable timing (Chadwick et al., 2020). It is virtually 
certain  that GMSL was higher than today, likely  by 5–10 m ( medium 
confidence ) (Section  2.3.3.3). Global mean thermal expansion 
peaked at about 0.9 ± 0.3 m early in the LIG (about 129 ka), declining 
to modern levels by about 127 ka (Shackleton et al., 2020). With no 
more than 0.3 ± 0.1 m of GMSL rise from glaciers (Section 9.5.1), at

most 1.0 ± 0.3 m of the GMSL rise originated from sources other than 
the polar ice sheets.

1294
Chapter 9 Ocean, Cryosphere and Sea Level Change9Recent LIG ice-sheet simulations agree that peak loss from the 
Greenland Ice Sheet occurred late (125–120 ka; Goelzer et al., 2016; 
Tabone et al., 2018; Plach et al., 2019) when Northern Hemisphere 
insolation was greater than at present ( medium confidence ) (Capron 
et al., 2017), consistent with inferences from marine sediment records 
(Hatfield et al., 2016; Irvalı et al., 2020) and far-field GMSL indicators 
(Rohling et al., 2019). Best estimates of the GMSL contribution from 
Greenland (Figure 9.17) differ between models: ≤1 m (Albrecht et al., 
2020; Clark et  al., 2020), 1–2 m (Calov et  al., 2015; Goelzer 
et al., 2016; Bradley et al., 2018), up to 3 m (Tabone et al., 2018; Plach 
et al., 2019), and >5 m (Yau et al., 2016). There is high confidence  
that the response time of the Greenland Ice Sheet to LIG warming 
was multi-millennial, and high confidence  that it contributed to LIG

GMSL change, but low agreement  in the contribution magnitude.
Far-field GMSL records suggest that the AIS contributed to LIG sea 
level from 129.5–125 ka (Figure 9.18) but direct evidence is sparse. 
Thinning of part of the WAIS is interpreted from a 130–80 ka hiatus in 
the Patriot Hills horizontal ice core record (Turney et al., 2020). Marine 
sediment records suggest a dynamic response of the Wilkes Subglacial 
Basin (WSB) of the EAIS during this period, indicating a response time 
scale of 1000–2500 yr (Wilson et al., 2018), consistent with modelling 
studies (Mengel and Levermann, 2014; Golledge et  al., 2017b; 
Sutter et al., 2020). Isotopic changes in the Talos Dome ice core are 
inconsistent with local surface lowering, limiting retreat to 0.4–0.8 m 
SLE from this sector (Sutter et al., 2020). Ice-sheet models forced with 
unmodified atmosphere–ocean models (Goelzer et  al., 2016; Clark 
et al., 2020) simulate 3–4.4 m SLE mass loss, primarily from the WAIS,

with no retreat in WSB (e.g., Figure 9.18). Models forced with proxy-
based or ad hoc LIG ocean temperature anomalies (DeConto and 
Pollard, 2016; Sutter et al., 2016) indicate collapse of West Antarctica 
under 2°C–3°C ocean forcing yielding 3–7.5 m sea level contribution, 
but modest or no retreat in the WSB. Based on limited evidence  and 
limited agreement  between models, there is low confidence  in both 
the magnitude and timing of LIG mass loss from the AIS.
In summary, paleo-environmental and modelling studies indicate 
that, under past warming of the level achieved during the LIG 
(ca. 0.5°C–1.5°C), it is likely  that both the Greenland and Antarctic ice 
sheets responded dynamically over multiple millennia ( high confidence ).
9.6.2.4 Last Glacial Maximum 
At the Last Glacial Maximum (LGM) geological proxies and GIA models 
indicate that GMSL was 125–134 m below present (Section 2.3.3.3 
and Figures 9.17 and 9.18). New studies have not changed AR5’s

conclusions regarding the size or timing of the LGM and last glacial 
termination, but have further examined the LGM sea level budget. 
Based on a synthesis of multiple prior studies, (Simms et al., 2019) 
estimated central 67% probability  contributions to the LGM lowstand 
(i.e., lowest levels during the LGM) of 76 ±  7  m from the North 
American Laurentide Ice Sheet, 18 ± 5 m from the Eurasian Ice Sheet, 
10 ± 2 m from Antarctica, 4 ± 1 m from Greenland, 5.5 ± 0.5 m from 
glaciers, and 2.4 ± 0.3 m due to an increase in ocean density. Of the 
residual, up to about 1.4 m may be ascribed to groundwater, leaving 
a shortfall of 16 ± 10 m yet to be allocated among land ice reservoirs 
or lakes.9.6.2.5 Last Deglacial Transition: Meltwater pulse 1A 
During Meltwater pulse 1A (MWP-1A), GMSL very likely  (medium 
confidence ) rose by 8–15 m (Liu et al., 2016). Consistent with AR5, the 
drivers of this rapid rise remain ambiguous. The spatial patterns of RSL

change over this interval are inadequately observed to constrain the 
relative contributions of the North American and Antarctic ice sheets (Liu 
et al., 2016). Modelling studies of the North American Ice Sheet permit 
a 3–6 m (Gregoire et al., 2016) or 6–9 m contribution over the duration 
of MWP-1A (Tarasov et al., 2012). Sedimentological evidence (Weber 
et al., 2014; Bart et al., 2018) provides near-field evidence for an Antarctic 
contribution, consistent with modelling studies (Golledge et al., 2014; 
Stuhne and Peltier, 2015), but does not constrain the magnitude of the 
contribution. A recent statistical analysis of Norwegian Sea and Arctic 
Ocean sediments suggests a 3–7 m contribution from the Eurasian Ice 
Sheet (Brendryen et al., 2020), a possibility not considered in AR5 or 
the meta-analysis of Liu et al. (2016). In summary, MWP-1A appears 
to have been driven by a combination of melt in North America ( high

confidence ), Eurasia ( low confidence ), and Antarctica ( low confidence ), 
but the budget is not closed.
9.6.2.6 Holocene
Around half (50–60 m) of the GMSL rise since the LGM occurred 
during the early Holocene at a sustained rate of about 15 m kyr –1 from 
around 11.4–8.2 ka (Lambeck et  al., 2014), possibly punctuated by 
abrupt meltwater pulses (Smith et al., 2011; Carlson and Clark, 2012; 
Törnqvist and Hijma, 2012; Harrison et  al., 2019). An abrupt (about 
1.1 m) sea level rise around 8.2 ka was associated with drainage of the 
pro-glacial Agassiz and Ojibway lakes, attributed to accelerated melt 
from collapsing Laurentide Ice Sheet ice saddles (Matero et al., 2017). 
The Laurentide Ice Sheet provided the greatest contribution (27 m) to 
early Holocene GMSL (Peltier et al., 2015; Roy and Peltier, 2017), the 
Scandinavian Ice Sheet contributed about 2 m from the beginning of 
the Holocene until its demise by around 10.5 ka, (Cuzzone et al., 2016),

while the Barents Sea Ice Sheet contributed a  small but unknown 
amount (Patton et al., 2015, 2017; Auriac et al., 2016). The Greenland 
Ice Sheet contributed about 4 m, consistent with ice thinning rates 
inferred from the Camp Century ice core (Lecavalier et  al., 2017; 
McFarlin et  al., 2018). Recent estimates of Antarctic contributions 
during the early Holocene vary considerably from about 1.2 m to 
8.5 m (Whitehouse et al., 2012; Ivins et al., 2013; Argus et al., 2014; 
Briggs et al., 2014; Golledge et al., 2014; Pollard et al., 2016; Roy and 
Peltier, 2017; Albrecht et al., 2020). In summary, the early Holocene was 
characterized by steadily rising GMSL as global ice sheets continued 
to retreat from their LGM extents. This steady rise was punctuated by 
abrupt pulses during episodes of rapid meltwater discharge.
In the middle Holocene, GMST peaked at 0.2°C–1.0°C higher than 
1850–1900 temperature between 7 and 6 ka (Section  2.3.1.1.2).

GMSL rise slowed coincidently with final melting of the Laurentide ice 
sheet by 6.7 ± 0.4 ka (Ullman et al., 2016), after which only Greenland 
and Antarctic ice sheets could have contributed significantly. At 6 ka, 
GMSL was –3.5 to +0.5 m ( medium confidence ) (Section  2.3.3.3). 
Simulations of the Holocene Thermal Maximum give a Greenland Ice 
Sheet broadly consistent with geological reconstructions so, despite 
uncertainties regarding the timing of minimum ice-sheet volume

1295
Ocean, Cryosphere and Sea Level Change  Chapter 99and extent, there is medium confidence  that minima were reached 
at different times in different areas during the period 8–3  ka  BP 
(Larsen et  al., 2015; Young and Briner, 2015; Briner et  al., 2016). 
Geochronological and numerical modelling studies indicate that it 
is likely  (medium confidence ) that the period of smaller-than-present 
ice extent in all sectors of Greenland persisted for at least 2000 
to 3000 years (Larsen et  al., 2015; Young and Briner, 2015; Briner 
et al., 2016; Nielsen et al., 2018). Based on ice-sheet modelling and 
carbon-14 (14C) dating (Kingslake et al., 2018) suggested that West 
Antarctic grounding lines retreated prior to around 10 ka BP , followed 
by a  readvance. Other studies from the same region conclude 
that retreat was fastest from 9–8 ka BP (Spector et  al., 2017), or 
from 7.5–4.8  ka  BP (Venturelli et  al., 2020). Marine geological

evidence indicates open marine conditions east of Ross Island by 
8.6 ± 0.2 ka BP (McKay et al., 2016). In the western Weddell Sea, 
Johnson et al. (2019) reported rapid glacier thinning from 7.5–6 ka BP . 
Hein et al. (2016) concluded that the fastest thinning further south 
took place from 6.5–3.5 ka BP , potentially contributing 1.4–2 m to 
GMSL. Geophysical data indicate stabilization or readvance in this 
area around 6 ± 2 ka BP (Wearing and Kingslake, 2019). In coastal 
Dronning Maud Land (East Antarctica) rapid thinning occurred 
9–5 ka BP (Kawamata et al., 2020), whereas glaciers in the Northern 
Antarctic Peninsula receded during the period 11–8 ka BP and 
readvanced to their maximal extents by 7–4 ka BP (Kaplan et  al., 
2020). In summary, higher-than-pre-industrial GMST during the 
mid-Holocene coincided with recession of the Greenland Ice Sheet 
to a  smaller-than-present extent ( high confidence ). Multiple lines

of evidence give high confidence  that thinning or retreat in parts 
of Antarctica during  the Holocene  took place at different times in 
different places. However, limited data means there is only low 
confidence  in whether or not the ice sheet as a whole was smaller 
than present during the mid-Holocene.
In summary, both proxies and model simulations indicate that GMSL 
changes during the early to mid-Holocene were the result of episodic 
pulses, due to drainage of meltwater lakes, superimposed on a trend 
of steady rise due to continued ice-sheet retreat ( high confidence ).
The combination of tide gauge observations and geological 
reconstructions indicates that a sustained increase of GMSL began 
between 1820–1860 and led to a 20th-century GMSL rise that was 
very likely  (high confidence ) faster than in any preceding century in 
the last 3000 years (Section 2.3.3.3). At a regional level, tide gauge 
and geological data from the North Atlantic and Australasia show

inflections in RSL trends between 1895–1935, with an increase of 
0.8 to 2.5  mm yr –1 across the inflection (Gehrels and Woodworth, 
2013). A statistical meta-analysis of globally distributed geological 
and tide gauge data (Kopp et al., 2016) found that, in all 20 examined 
regions with geological records stretching back at least 2000 years, 
the rate of RSL rise in the 20th century was greater than the local 
average over 0–1700 CE. In four of the 20 regions, all in the North 
Atlantic (Connecticut, New Jersey, North Carolina, and Iceland), the 
19th  century rate was also greater than the 0–1700 CE average 
(90% confidence interval). In summary, rates of RSL rise exceeding 
the pre-industrial background rate of rise are apparent in parts of the 
North Atlantic in the 19th century ( medium confidence ), and in most 
of the world in the 20th century ( high confidence ).9.6.3 Future Sea Level Changes
This section first assesses sea level projections since AR5 (Church

et al., 2013b) and including SROCC (Oppenheimer et al., 2019) based 
on Representative Concentration Pathways (RCPs; Section 9.6.3.1). 
Process-level assessments in sections 9.2.4, 9.4.1.3, 9.4.1.4, 9.4.2.5, 
9.4.2.6 and 9.5.1.3 are synthesized (Section 9.6.3.2) to produce new 
global mean and regional sea level projections based on the Shared 
Socio-economic Pathways up to 2150 (Section 9.6.3.3) and on global 
warming levels up to 2100 (Section 9.6.3.4). Long-term global mean 
sea level (GMSL) projections, both at 2300 and on multimillennial 
time scales, are also assessed (Section 9.6.3.5).
Sections 9.6.3.3 and 9.6.3.4 present likely  ranges of the new global 
mean sea levels, incorporating only processes in whose projections 
there is at least medium confidence , consistent with headline 
projections in AR5 and SROCC. As emphasized by SROCC, there is 
a  substantial likelihood that sea level rise will be outside the likely

range. As described in Box 1.1, since the definition of ‘ likely ’ refers to 
at least 66% probability, there may be as much as a 34% probability 
that the processes in which there is at least medium confidence  will 
generate outcomes outside the likely  range. Furthermore, additional 
processes in which there is low confidence  (Section 9.4.2.4; Box 9.4) 
may also contribute to sea level change. The presentation of likely  sea 
level change (Tables 9.8–9.9 and in Figures 9.27, 9.29) is therefore 
accompanied by a  low confidence  range intended to reflect potential 
contributions from additional processes under high-emissions 
scenarios. The low confidence  range incorporates ice-sheet projections 
based on Structured Expert Judgement (SEJ)  – that is, a  formal, 
calibrated method of combining quantified expert assessments that 
incorporates all potential processes  – and projections from an AIS 
model that includes the marine ice cliff instability (a specific uncertain

process not generally included in ice-sheet models; Section 9.4.2.4).
9.6.3.1 Global Mean Sea Level Projections Based on 
the Representative Concentration Pathways
The AR5 (Church et al., 2013b) generated GMSL projections for the RCPs 
by combining information from CMIP5 climate models with glacier 
and ice-sheet surface mass balance (SMB) models and assessments 
of projected ice-sheet dynamic and land-water storage contributions 
(Section 9.6.3.2). The SROCC (Oppenheimer et al., 2019) updated AR5 
projections based on a revised assessment of the AIS contribution to 
GMSL rise. The AR5 and SROCC employ a  baseline period of 1986 
to 2005, which is updated in this Report to a baseline period of 1995 
to 2014 (Section  1.4.1). Between these two periods, GMSL rose by 
3 cm, and this correction is applied to projections from previous reports 
to allow comparison (Table  9.8). Accounting for this shift, SROCC 
concludes that, with medium confidence,  GMSL will rise between

0.40 (0.26–0.56, likely  range) m (RCP2.6) and 0.81 (0.58–1.07 m, likely  
range) m (RCP8.5) by 2100 relative to 1995–2014. The AR5 and SROCC 
GMSL projections for the 2007–2018 period have been shown to be 
consistent with observed trends in GMSL and regional weighted mean 
tide gauges (J. Wang et al., 2021). 
Since AR5, a number of projections of GMSL rise have been published 
based on the RCPs (Kopp et al., 2014, 2017; Slangen et al., 2014b;

1296
Chapter 9 Ocean, Cryosphere and Sea Level Change9Grinsted et al., 2015; Jackson et al., 2016; Mengel et al., 2016; Bakker 
et  al., 2017; Bittermann et  al., 2017; Le Bars et  al., 2017; Nauels 
et al., 2017; Wong et al., 2017; Goodwin et al., 2018; Nicholls et al., 
2018; Le  Cozannet et  al., 2019; Palmer et  al., 2020). See Garner 
et  al. (2018)  or a  database (Tables 9.SM.5, 9.SM.6). Some studies 
also produced associated global sets of regional projections (Kopp 
et al., 2014, 2017; Slangen et al., 2014b; Le Cozannet et al., 2019; 
Palmer et al., 2020). Since SROCC (Le Cozannet et al., 2019) focused 
on the low end of the probability distribution of GMSL rise, Palmer 
et  al. (2020) extended projections beyond 2100 using a  climate 
model emulator (Cross-Chapter Box  7.1), and Horton et  al. (2020) 
conducted a  survey of 106 sea level experts, providing additional 
context for interpreting sea level rise projections for 2100 and 2300.

As noted by SROCC, the largest differences between projections of 
GMSL in 2100 are due to the ice-sheet projection method, which 
generally fall into one of three categories: (i) projections from 
ice-sheet models that represent processes where there is at least 
medium confidence (Sections 9.4.1.2 and 9.4.2.2); (ii) projections 
from an Antarctic ice-sheet model that incorporates the marine 
ice cliff instability (MICI; Section  9.4.2.4; DeConto and Pollard, 
2016); or (iii) projections based on SEJ (Sections 9.4.1.3, 9.4.1.4, 
9.4.2.5 and 9.4.2.6; Bamber and Aspinall, 2013; Bamber et  al., 
2019). Low  confidence  is ascribed to projections incorporating MICI because there is low confidence  in the current ability to quantify 
MICI (Section 9.4.2.4). Low confidence  is also ascribed to projections 
based on SEJ, because individual experts participating in the SEJ study 
may have incorporated processes in whose quantification there is

low confidence , and the experts’ reasoning has not been examined in 
detail. In general, the range of GMSL projections based on ice-sheet 
models not incorporating MICI overlaps with, but is lower than, 
projections incorporating MICI or employing SEJ (Figure 9.25).
There is high agreement  across published GMSL projections for 
2050, and there is little sensitivity to emissions scenario (Figure 9.25, 
left panel). Up to 2050, projections are broadly consistent 
with extrapolation of the observed acceleration of GMSL rise 
(Sections 2.3.3.3, 9.6.1.1 and 9.6.1.2). Considering only projections 
incorporating ice-sheet processes in whose quantification there is at 
least medium confidence , the GMSL projections for 2050, across all 
emissions scenarios, fall between 0.1 and 0.4 m (5th–95th percentile 
range). Projections incorporating MICI or SEJ  do not extend this range 
under RCP2.6 or RCP4.5 but do extend the upper part of the range to

0.6 m under RCP8.5. On the basis of these studies, we therefore 
have high confidence  that GMSL in 2050 will be between 0.1 and 
0.4 m higher than in 1995–2014 under low- and moderate-emissions 
scenarios, and between 0.1 and 0.6 m under high-emissions scenarios.
0
0.1
0.2
0.3
0.4
0.5
0.6
m
MED
MICI
Survey
AR5
SROCC
AR6
MED
MICI
AR5
SROCC
AR6
MED
MICI
SEJ
Survey
AR5
SROCC
AR6
2050 GMSL Projections
RCP 8.5
RCP 4.5
SSP2-4.5
SSP5-8.5
RCP 2.6
SSP1-2.6
0
0.5
1
1.5
2
2.5
m
2100 GMSL Projections
17
th
-83
rd
 percentile
5
th
-95
th
17
th
-83
rd
 (low confidence; see caption)
5
th
-95
th
 (low confidence)
Figure 9. 25 | Literature global mean sea level (GMSL) projections (m) for 2050 (left) and 2100 (right) since 1995–2014, for RCP8.5/SSP5-8.5 (top set), 
RCP4.5/SSP2-4.5 (middle set), and RCP2.6/SSP1-2.6 (bottom set).  Projections are standardized to account for minor differences in time periods. Thick bars span from

the 17th–83rd percentile projections, and thin bars span the 5th–95th percentile projections. The different assessments of ice-sheet contributions are indicated by ‘MED’ 
(ice-sheet projections include only processes in whose quantification there is medium confidence ), ‘MICI’ (ice-sheet projections which incorporate marine ice cliff instability), 
and ‘SEJ’ (structured expert judgement) to assess the central range of the ice-sheet projection distributions. ‘Survey’ indicates the results of a 2020 survey of sea level experts 
on global mean sea level (GMSL) rise from all sources (Horton et al., 2020). Projection categories incorporating processes in which there is low confidence  (MICI and SEJ) are 
lightly shaded. Dispersion among the different projections represents deep uncertainty , which arises as a result of low agreement  regarding appropriate conceptual models

describing ice-sheet behaviour and low agreement  regarding probability distributions used to represent key uncertainties. Individual studies are shown in Tables 9.SM.5 and 
9.SM.6. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1297
Ocean, Cryosphere and Sea Level Change  Chapter 99Conversely, there is low agreement  across published GMSL projections 
for 2100, particularly for higher-emissions scenarios, as well as a higher 
degree of sensitivity to the choice of emissions scenario (Figure 9.25, 
right panel). Considering only projections representing processes in 
whose quantification there is at least medium  confidence , the GMSL 
projections for 2100 fall between 0.2 and 1.0 m (5th–95th percentile 
range) under RCP2.6 and RCP4.5, and between 0.3 and 1.6 m under 
RCP8.5. Considering also projections incorporating MICI or SEJ 
(low confidence ), the projections for 2100 fall between 0.2 and 1.0 m 
(5th–95th percentile  range) under RCP2.6,  0.2, and 1.6 m under RCP4.5, and 0.4 and 2.4 m under RCP8.5. In summary, RCP-based 
projections published since AR5 show high agreement  for 2050, 
but exhibit broad ranges and low agreement  for 2100, particularly 
under RCP8.5.

9.6.3.2 Drivers of Projected Sea Level Change
This section describes the choices made for the contributions 
to the updated global mean and regional sea level projections 
(Section 9.6.3.3) based on assessments in this Report and compares 
Table  9.7 | Methods used to project the drivers of global mean sea level (GMSL) and relative sea level (RSL) change in the Shared Socio-economic Pathway 
(SSP) and warming-level-based projections of GMSL, RSL and extreme sea level (ESL) change.  Section numbers indicate location of primary assessment text.
Driver of Global Mean or 
Regional Sea Level changeSROCC Projection Method AR6 Projection method
Thermal expansion  
(Section 9.2.4.1)CMIP5 ensemble drift-corrected zostoga ,  
with surrogates derived from climate system heat 
content where not availableTwo-layer emulator with climate sensitivity calibrated to AR6 assessment 
(Supplementary Material 7.SM.2) and expansion coefficients calibrated to emulate

CMIP6 models (Supplementary Material 9.SM.4.2 and 9.SM.4.3) 
Greenland Ice Sheet 
(excluding peripheral glaciers)  
(Sections 9.4.1.3 and 9.4.1.4)Surface mass balance:  
scaled cubic polynomial fit to global mean surface 
temperature (GMST)
Dynamics:  
Quadratic function of time, calibrated based on multi-
model assessment Medium confidence  processes up to 2100: Emulated Ice Sheet Model Intercomparison 
Project for CMIP6 (ISMIP6) simulations (Box 9.3; Edwards et al., 2021)
Medium confidence  processes after 2100: Parametric model fit to ISMIP6 simulations 
up to 2100, extrapolated based on either constant post-2100 rates or a quadratic 
interpolation to the multi-model assessed 2300 range (Supplementary Material 9.SM.4.4)
Low confidence  processes: Structured expert judgement (Bamber et al., 2019)
Antarctic Ice Sheet 
(excluding peripheral glaciersa) 
(Sections 9.4.2.5 and 9.4.2.6)Multi-model assessmentMedium confidence  processes up to 2100: p-box including: (i) Emulated ISMIP6

simulations (Edwards et al., 2021); and (ii) Linear Antarctic Response Model 
Intercomparison Project (LARMIP-2) simulations (Levermann et al., 2020) 
augmented by AR5 surface mass balance model (Box 9.3)
Medium confidence  processes after 2100: p-box including: (i) AR5 parametric AIS 
model; and (ii) LARMIP-2 simulations augmented by AR5 surface mass balance model 
applied to CMIP6 models, with both methods extrapolated based on either constant 
post-2100 rates or a quadratic interpolation to the multi-model assessed 2300 range 
(Section 9.6.3.2)
Low confidence  processes: (i) Single-ice-sheet-model ensemble simulations 
incorporating marine ice cliff instability (DeConto et al., 2021); and (ii) structured 
expert judgement (Bamber et al., 2019)
Glaciers (including 
peripheral glaciers)  
(Section 9.5.1.3)Power law function of integrated GMST fit to 
glacier modelsUp to 2100: Emulated GlacierMIP (Marzeion et al., 2020; Edwards et al., 2021) 
simulations (Box 9.3)

Beyond 2100: AR5 parametric model re-fit to GlacierMIP (Supplementary Material 
9.SM.4.5; Marzeion et al., 2020)
Land-water storage  
(Section 9.6.3.2)Groundwater depletion:  
combination of: (i) continuation of early 21st-century 
trends; and (ii) land-surface hydrology models 
(Wada et al., 2012)
Water impoundment: 
combination of: (i) continuation of historical rate; and 
(ii) assumption of no net impoundment after 2010 Groundwater depletion: 
Population/groundwater depletion relationship calibrated based on Konikow (2011) 
and Wada et al. (2012, 2016)
Population/dam impoundment relationship calibrated based on Chao et al. (2008), 
adjusted for new construction following Hawley et al. (2020) for 2020 to 2040
Ocean dynamic sea level  
(Section 9.2.4.2)CMIP5 ensemble zos field after polynomial drift removalDistribution derived from CMIP6 ensemble zos field after linear drift removal 
(Supplementary Material 9.SM.4.2 and 9.SM.4.3)
Gravitational, rotational,

and deformational effects  
(Section 9.6.3.2)Sea level equation solver (Slangen et al., 2014b) driven by projections of ice-sheet, glacier, and land-water storage changes
Glacial isostatic adjustment 
and other drivers of vertical 
land motion  (Section 9.6.3.2)Glacial Isostatic Adjustment model, with ice history from 
mean of the Australian National University (ANU) and 
ICE-5G reconstructionsSpatio-temporal statistical model of tide gauge data (updated from Kopp et al., 2014) 
(Supplementary Material 9.SM.4.6)
a Ice-sheet models include some of the larger islands in the Antarctic periphery, so there is some overlap in the projected glacier contribution and the projected Antarctic 
contribution, but the effect is estimated to be on the order of 0.5–1 cm or less (Edwards et al., 2021).

1298
Chapter 9 Ocean, Cryosphere and Sea Level Change9the updated projections to AR5 (Church et al., 2013b) and SROCC 
(Oppenheimer et  al., 2019) (Tables 9.7 and 9.8). Since there is no 
single model that can directly compute all of the contributions 
to sea level change (Box  9.1), the contributions to sea level are 
computed separately and then combined (Tables 9.8 and 9.9). For 
consistency with global surface air temperature (GSAT) projections 
(Section 4.3.1.1), and assessment of equilibrium climate sensitivity 
(ECS) and transient climate response (TCR; Section 7.5), temperature-
dependent projections (thermal expansion, ice sheets, glaciers) are 
forced by GSAT projections from a two-layer energy budget emulator 
(Smith et  al., 2018) that is calibrated to be consistent with the 
assessment of ECS and TCR (Box 7.1, Supplementary Material 7.SM.2). 
Throughout, likely ranges are assessed based on the combination 
of uncertainty in the GSAT distribution and uncertainty in the

relationships between GSAT and changes to individual components. 
In general, 17th–83rd percentile results, incorporating both GSAT 
and sea level process uncertainty, are interpreted as likely  ranges. 
This is distinct from the approach used by AR5, which interpreted 
the 5th–95th percentile range of CMIP5 projections, and therefore 
of GMSL projections driven by them, as likely  ranges. The shift in 
interpretation is consistent with the use of the emulator for GSAT 
(Box 4.1, Cross-Chapter Box 7.1). Very likely  ranges are not assessed 
because of the potential for processes in whose projections there 
is currently low confidence  to substantially augment total projected 
GMSL change.
9.6.3.2.1 Global mean thermosteric sea level rise 
In AR5 and SROCC, global mean thermosteric sea level rise 
was derived from the 21 members of the CMIP5 ensemble that 
provided the required variables (Section  9.2.4.1). The AR5 and 
SROCC removed drift estimated based on a  pointwise polynomial

fit to pre-industrial control simulations. They extended projections 
to scenarios not provided by the models by calculating the heat 
content of the climate system from GMST and net radiative flux, 
and converting this to global mean thermosteric sea level rise 
using each model’s diagnosed expansion efficiency coefficient. The 
AR5 and SROCC derived the associated uncertainties by assuming 
a normal distribution, with the 5th–95th percentile CMIP5 ensemble 
interpreted as the likely  range. In this Report, global mean thermosteric 
sea level rise is derived from a  two-layer energy budget emulator 
consistent with the assessment of ECS and TCR (Section  9.2.4.1; 
Supplementary Material 9.SM.4.2 and 9.SM.4.3). Despite the change 
in methodology, this leads to a  likely  global mean thermosteric 
contribution (17th–83rd percentile) between 1995–2014 and 2100 
that represents a minimal change from AR5 and SROCC (Table 9.8).
9.6.3.2.2 Greenland Ice Sheet

The AR5 and SROCC projected the Greenland surface-mass balance 
using a cubic polynomial fit to a regional climate model as a function 
of global mean surface temperature (with a log-normal scaling factor 
reflecting uncertainty in surface-mass balance models, and another 
scaling factor reflecting the positive feedback of ice-sheet elevation 
5 Note that the use of this approach implies that the likely ranges are likely in the use of the term to mean 66–100% probable; this is distinct from usage in SROCC, where the likely range was 
defined to have a precise 66% probability. changes on mass loss), and the dynamic contribution was estimated 
based on a  multi-model assessment interpolated as a  quadratic 
function of time.
For processes whose projections we have at least medium 
confidence  in, the updated projections use emulated Ice Sheet 
Model Intercomparison Project for CMIP6 (ISMIP6) projections of the 
Greenland Ice Sheet (Section 9.4.1.3; Figure 9.17; Tables 9.2 and 9.7;

Box 9.3). Since the ISMIP6 emulator does not account for temporal 
correlation, a parametric fit to the ISMIP6 results is used to calculate 
rates of change (Supplementary Material 9.SM.4.4). For projections 
beyond 2100 (when the ISMIP6 simulations end), the polynomial fit 
is extrapolated based on two alternate approaches: (i) an assumption 
of constant rates of mass change after 2100; and (ii) for SSP1-2.6 
and SSP5-8.5, a quadratic function of time extending to 2300 based 
on the multi-model assessment of contributions under RCP2.6 and 
RCP8.5 at 2300 (Section  9.4.1.4). Differences between the two 
approaches are small up to 2150, and since the latter approach is not 
available for all scenarios, only the former (constant rates) is used 
for time series projections up to 2150. Both approaches are used for 
examining uncertainty in the timing of different levels of GMSL rise 
and to inform projections for the year 2300 (Section  9.4.1.4). For

2100, the ISMIP6 emulator yields the likely  contribution from the 
Greenland Ice Sheet shown in Table 9.2 and Figure 9.17, representing 
a slight narrowing from AR5 projections.
9.6.3.2.3 Antarctic Ice Sheet
For the Antarctic Ice Sheet (AIS), AR5 applied a temperature-based 
scaling approach for SMB and a quadratic function of time, calibrated 
to a multi-model assessment, for dynamic contributions. The SROCC 
used a new assessment based on the results of five process-based 
studies (Section  9.4.2.5). For processes in whose projections we 
have at least medium confidence , the likely range  projections 
for the AIS are based on: (i) the emulated ISMIP6 ensemble; and 
(ii) the  LARMIP-2 ensemble, augmented with AR5 parametric 
Antarctic SMB model. The GMSL projections are produced with both 
distributions and combined in a  ‘p-box’ (Kriegler and Held, 2005; 
Le Cozannet et  al., 2017), which represents the upper and lower

bounds of the distribution (Section 9.4.2.5, Box 9.3 and Table 9.3). 
A likely range is then identified, spanning the lower of the two 
17th percentile projections and the higher of the two 83rd percentile 
projections,5 with the median taken as the mean of the medians of 
the two projections. Since the ISMIP6 emulator does not account 
for temporal correlation, the AR5 parametric AIS model is substituted 
for the emulator in the p-box for rates of change. As AR5 projections 
are modestly lower than those from the ISMIP6 emulator, this 
substitution modestly broadens the likely  range at the low end for 
projections of rate and changes beyond 2100. For projections beyond 
2100 (when the ISMIP6 and LARMIP-2 simulations end), the AIS 
simulations are extrapolated using the same two approaches as the 
Greenland Ice Sheet (GrIS) projections (Section  9.4.1.4). The likely  
ranges to 2100 are consistent with SROCC (Table 9.8).

1299
Ocean, Cryosphere and Sea Level Change  Chapter 999.6.3.2.4 Low confidence ice-sheet projections
To test the possible effect of additional ice-sheet processes for which 
there is low confidence  (Sections 9.4.1.3, 9.4.1.4, 9.4.2.5, 9.4.2.6 and 
9.6.3.1, and Box 9.4), two additional approaches are considered. For 
both the Greenland and Antarctic ice sheets, we produce sensitivity 
cases employing the SEJ projections of Bamber et al. (2019), mapping 
2°C and 5°C stabilization scenarios to SSP1-2.6 and SSP5-8.5, 
respectively. For the AIS, we produce an additional sensitivity case 
using projections, which incorporate MICI (DeConto et  al., 2021), 
mapping projections for RCP2.6 and RCP8.5 to SSP1-2.6 and SSP5-8.5. 
For the Greenland Ice Sheet, the SEJ projections indicate the potential 
for outcomes outside the corresponding likely  ranges (Table  9.8). 
For the AIS, there is no evidence from these studies to suggest an

important role under lower-emissions scenarios for processes in 
whose projections we have low confidence . By contrast, for SSP5-8.5, 
the SEJ and MICI projections exhibit 17th–83rd percentile ranges of 
0.02–0.56 m and 0.19–0.53  m by 2100, consistent with one another 
but considerably broader than the likely  contribution for medium 
confidence  processes of 0.03–0.34 m. This lower level of agreement 
for higher-emissions scenarios reflects the deep uncertainty  in the 
AIS contribution to GMSL change under higher-emissions scenarios 
(Box  9.4). This deep uncertainty  grows after 2100: by 2150, under 
SSP5-8.5, medium confidence  processes likely lead to a  –0.1–0.7 
m AIS contribution, while SEJ- and MICI-based projections indicate 
0.0–1.1 m and 1.4–3.7 m, respectively.9.6.3.2.5 Glaciers 
In AR5 and SROCC, global glacier mass changes were derived from 
a power law of integrated global mean surface temperature change fit

to results from four different glacier models. The updated projections 
use emulated GlacierMIP projections (Section 9.5.1.3; Box 9.3). Since 
the GlacierMIP emulator does not account for temporal correlation 
and terminates, along with the GlacierMIP  simulations, in 2100, 
we employ a  parametric fit to the GlacierMIP simulations,  with 
a  functional form similar to that employed by AR5, to calculate 
rates of change and extrapolate changes beyond 2100 (up to 
a  maximum potential contribution of 0.32 m; see Supplementary 
Material  9.SM.4.5). This approach leads to a  median glacier 
contribution that is a  minimal change (Table  9.8) from AR5 and 
SROCC and a modest narrowing of likely  ranges (Section 9.5.1.3). For 
RCP2.6, AR5 projected 0.10 (0.04 to 0.16, likely  range) m, compared 
to 0.09 (0.07 to 0.11) m projected for SSP1-2.6. For RCP8.5, AR5 
projected a  likely  contribution of 0.17 (0.09 to 0.25) m, compared to 
0.18 (0.15 to 0.21) m projected here.

9.6.3.2.6 Land-water storage
In AR5 and SROCC, the groundwater depletion contribution to 
GMSL rise was based on combining results from two approaches: 
one assuming a  continuation of early 21st-century trends 
(Konikow, 2011); and the other using land-surface hydrology 
Table 9.8  | Global mean sea level projections between 1995–2014 and 2100 for total change and individual contributions, median values, (likely) ranges 
of the process-based model ensemble  for RCP 2.6 (from AR5 (Church et al., 2013a) and SROCC (Oppenheimer et al., 2019)) and SSP1-2.6 (this Report), and for RCP8.5 
(from AR5 (Church et al., 2013a) and SROCC (Oppenheimer et al., 2019)) and SSP5-8.5 (this Report). Values for AR5 (Church et al., 2013a) and SROCC (Oppenheimer et al., 
2019) are adjusted from the 1986–2005 baseline used in past reports. Only the Antarctic contribution changed between AR5 (Church et al., 2013a) and SROCC (Oppenheimer

et al., 2019). Unshaded cells represent processes in which there is medium confidence ; shading indicates the inclusion of processes in which there is low confidence . For the 
MICI- and SEJ-based projections, parenthetical numbers represent the 17th–83rd percentile of the associated probability distributions, not assessed likely ranges.
  RCP2.6 SSP1-2.6
m relative to 1995–2014 AR5 SROCCMedium confidence  
processesMICI SEJ
Thermal expansion (Section 9.2.4.1) 0.14 (0.10–0.19) m 0.14 (0.11–0.18) m
Greenland (Section 9.4.1.3) 0.07 (0.03–0.11) m 0.06 (0.01–0.10) m 0.13 (0.07–0.30) m
Antarctica (Section 9.4.2.5) 0.06 (–0.04 to +0.16) m 0.04 (0.01–0.11) m 0.11 (0.03–0.27) m 0.08 (0.06–0.12) m 0.09 (–0.01 to +0.25) m
Glaciers (Section 9.5.1.3) 0.10 (0.04–0.16) m 0.09 (0.07–0.11) m
Land-water storage (Section 9.6.3.2) 0.05 (–0.01 to +0.11) m 0.03 (0.01–0.04) m
Total (2100) 0.41 (0.25–0.58) m 0.40 (0.26–0.56) m 0.44 (0.33–0.62) m 0.41 (0.35–0.48) m 0.53 (0.38–0.79) m

Total (2150) 0.29–0.63 m 0.56 (0.40–0.73) m 0.68 (0.46–0.99) m 0.74 (0.62–0.91) m 0.84 (0.56–1.34) m
GMSL rate, 2080–2100 (mm  yr –1) 4.4 (2.0–6.8) mm yr –14 (2–6) mm yr –15.2 (3.2–8.0) mm yr –15.1 (4.3–6.2) mm yr –15.9 (2.8–11.0) mm yr –1
  RCP8.5 SSP5-8.5
Thermal expansion (Section 9.2.4.1) 0.31 (0.24–0.38) m 0.30 (0.24–0.36) m
Greenland (Section 9.4.1.3) 0.14 (0.08–0.27) m 0.13 (0.09–0.18) m 0.23 (0.10–0.59) m
Antarctica (Section 9.4.2.5) 0.04 (–0.08 to +0.14) m 0.12 (0.03–0.28) m 0.12 (0.03–0.34) m 0.34 (0.19–0.53) m 0.21 (0.02–0.56) m
Glaciers (Section 9.5.1.3) 0.17 (0.09–0.25) m 0.18 (0.15–0.20) m
Total (2100) 0.71 (0.49–0.95) m 0.81 (0.58–1.07) m 0.77 (0.63–1.01) m 0.99 (0.82–1.19) m 1.00 (0.70–1.60) m
Total (2150) 0.34–1.35 m 1.27 (0.80–1.79) m 1.32 (0.98–1.88) m 3.48 (2.57–4.82) m 1.79 (1.22–2.94) m

GMSL rate, 2080–2100 (mm  yr –1) 11.2 (7.5–15.7) mm yr –115 (10–20) mm yr –112.1 (8.6–17.6) mm yr –123.1 (17.5–30.1) mm yr –116.0 (9.8–28.9) mm yr –1

1300
Chapter 9 Ocean, Cryosphere and Sea Level Change9models (Wada  et  al.,  2012). Together, these yielded a  range of 
about 0.02–0.09 m of GMSL rise by 2080–2099. The rate of water 
impoundment in reservoirs was likewise based on two approaches: 
one assuming the continuation of the average rate over 1971–2010 
(and thus –0.01 to –0.03 m by 2080–2099; Chao et al., 2008); and 
the other assuming no net impoundment after 2010 (Lettenmaier 
and Milly, 2009). Together, these yield a  GMSL contribution from 
groundwater impoundment of –0.03 to 0 m. Combining groundwater 
depletion and water impoundment led AR5 and SROCC to infer 
a projected range of –0.01 to +0.11 m by 2100.
In the updated projections, a statistical relationship is applied, linking 
historical and future SSP global population to dam impoundment and 
groundwater extraction (Rahmstorf et al., 2012; Kopp et al., 2014). 
The population/groundwater depletion relationship is calibrated

based on the same studies used in AR5 (Konikow, 2011; Wada et al., 
2012), reduced by about 20% to account for water retained on land 
(Wada et al., 2016). The population/dam impoundment relationship 
is calibrated based on Chao et al. (2008). However, while historically 
dam impoundment has been declining with population, recent 
literature shows that planned dam construction considerably 
exceeds the historical trend (Zarfl et al., 2015; Hawley et al., 2020). 
Over 2020–2040, the impoundment contribution to GMSL rise 
based on past trends would be about –0.1 mm yr –1, compared to 
about –0.5 mm yr –1 if all currently planned dams are built (Hawley 
et al., 2020) and the statistical projection is therefore augmented 
by an additional –0.4 to 0.0 mm yr –1 over 2020–2040 to account 
for the possible effects of planned dam construction. As in AR5 and 
SROCC, climatically driven changes to land-water storage (LWS) 
have not been included in published sea level projections, as they

are not well quantified (e.g., Jensen et al., 2019) or are considered 
negligible (e.g.,  permafrost, Section  9.5.2). This approach yields 
a likely  global-mean land-water storage contribution (Figure 9.27, 
Table  9.8) that is slightly lower and narrower than the AR5 and 
SROCC likely  ranges. Since the projections are explicitly population 
driven, these projections also exhibit a weak scenario dependence, 
with a contribution around 0.01 m higher under SSP3 than under 
other scenarios.
9.6.3.2.7 Ocean dynamic sea level
In AR5 and SROCC, the ocean dynamic sea level contribution to RSL 
projections was derived from the CMIP5 ensemble, after removing the 
drift estimate based on pre-industrial control simulations. This Report 
uses updated simulations from the CMIP6 ensemble (Section 9.2.4.2; 
Supplementary Material 9.SM.4.2) to project the ocean dynamic 
sea level contribution to RSL change (Section 9.2.4.2; Figure 9.26).

To produce ocean dynamic sea level projections consistent with the 
global mean thermosteric projections from the two-layer energy 
budget emulator, we follow the approach of Kopp et  al. (2014), 
employing a  correlation between global-mean thermosteric sea 
level change and ocean dynamic sea level derived from the CMIP6 
ensemble (Supplementary Material 9.SM.4.3). Since CMIP6 models 
are of fairly coarse resolution (typically about 100 km), and even 
the models participating in HighResMIP (near 10 km resolution) 
do not capture all the phenomena that contribute to coastal ocean 
dynamic sea level change, there is low confidence  in the details of ocean dynamic sea level change along the coast (Section 9.2.3.6) and 
in semi-enclosed basins, such as the Mediterranean, where coarse 
models can misrepresent key dynamic processes. Regional high-
resolution models can improve projections of coastal ocean dynamic 
sea level change (Section 12.4; Hermans et al., 2020), but have not

been implemented at a global scale.
9.6.3.2.8 Gravitational, rotational and deformational effects
Gravitational, rotational, and deformational (GRD) effects (Box 9.1) 
lead to distinct variations in the RSL change pattern, which are similar 
across a range of benchmarked GRD solvers (Martinec et al., 2018; 
Palmer et al., 2020). There is high confidence  in the understanding 
of GRD processes. RSL rise associated with GRD is very likely  to be 
largest in the Pacific, due to the combined effects of projected GrIS, 
AIS and glacier mass loss ( high confidence ) (e.g., Kopp et al., 2014; 
Slangen et  al., 2014b; Larour et  al., 2017; Mitrovica et  al., 2018). 
The GRD effect associated with mass loss from an ice sheet is sensitive 
to the spatial distribution of that mass loss. For example, the GRD 
contribution to RSL rise in Australia will be larger for Antarctic mass 
loss sourced fromthe Antarctic Peninsula than for Antarctic mass loss

sourced fromThwaites Glacier. In parts of north-eastern North 
America and north-western Europe, GRD effects associated with 
mass loss from southern Greenland will lead to an RSL fall, whereas 
mass loss from northern Greenland will lead to an RSL rise ( high 
confidence ) (Figure 9.26; Larour et al., 2017; Mitrovica et al., 2018). 
The AR5 and SROCC computed RSL patterns using a gravitationally 
self-consistent GRD solver given the amounts, locations and timing 
of the projected barystatic sea level changes driven by glaciers, ice 
sheets and LWS (Church et al., 2013b). A similar GRD solver is used 
in the updated projections (following Slangen et  al., 2014b). The 
Earth model used is based on the Preliminary reference Earth model 
(PREM: Dziewonski and Anderson, 1981), and is elastic, compressible 
and radially stratified.
9.6.3.2.9 Glacial isostatic adjustment and other drivers 
of vertical land motion 
Glacial Isostatic Adjustment (GIA) leads to vertical land motion

(VLM; see Box 9.1) and changes in sea surface height, both of which 
contribute to RSL change. GIA uncertainty is caused by uncertainty 
in the rheological structure of the solid Earth, which drives the 
longer-term viscous Earth deformation, as well as uncertainty in 
the modelled global ice history (e.g., Whitehouse, 2018). In AR5 and 
SROCC, GIA contributions to RSL change were calculated using a sea 
level equation solver with an ice-sheet history taken as the mean 
of the ICE5G (Peltier et al., 2015) and ANU (Lambeck et al., 2014) 
ice-sheet models. Since AR5, new global models are emerging that 
more rigorously treat ice and Earth structure uncertainty (Caron 
et al., 2018). However, there is also a growing recognition that lateral 
variations in Earth structure limit the utility of global models that treat 
the solid Earth as though it were laterally uniform (Love et al., 2016; 
Huang et al., 2019; T. Li et al., 2020).

As noted by SROCC, VLM from sources other than GIA – including 
tectonics and mantle dynamic topography, volcanism, compaction, 
and anthropogenic subsidence – can be locally important, producing

1301
Ocean, Cryosphere and Sea Level Change  Chapter 99VLM rates comparable to or greater than rates of GMSL change. 
Complete global projections of these processes are not available 
because of the small spatial scales, the sensitivity of subsidence to 
local human activities, and the stochasticity of tectonics (Wöppelmann 
and Marcos, 2016; Oppenheimer et al., 2019). Therefore, integrated 
RSL projections to date have either included only the component of 
VLM associated with GIA (as in AR5 and SROCC), or used a constant 
long-term background rate of change (including both GIA and other 
long-term drivers of VLM) estimated from historical tide gauge trends 
(e.g.,  Kopp et  al., 2014). The updated projections use the second 
approach and extrapolate the field of long-term background rates 
of RSL change, including long-term VLM derived from tide gauges, 
to global coverage using a  spatio-temporal statistical approach

(Supplementary Material 9.SM.4.6; Kopp et al., 2014). The combined 
GIA and long-term VLM is assumed to be scenario independent and constant over the projected period. In areas where rapid subsidence 
occurs in a cluster of tide gauges (e.g., the western Gulf of Mexico), 
the associated rates are interpolated between the tide gauges. 
In areas where the available tide gauges exhibit large, tectonically 
driven VLM that changes considerably in rate over short distances 
(e.g., Alaska and the Bering Strait), a sizable uncertainty propagates 
into the RSL projections (Figure 9.26). Rates of RSL rise are likely to 
be underestimated due to subsidence in shallow strata that are not 
recorded by tide gauges (Keogh and Törnqvist, 2019) and in some 
locations may therefore be minimum values, especially if anomalously 
high subsidence rates associated with fluid extraction are also 
considered (e.g.,  Minderhoud et  al., 2017). Therefore, depending

on location, there is  low to medium confidence  in the GIA and VLM 
projections employed in this Report. In many regions, higher-fidelity 
projections would require more detailed regional analysis.
SSP1-2.6 SSP5-8.5
Figure 9.2 6 | Median global mean and regional relative sea level projections (m) by contribution for the SSP1-2.6 and SSP5-8.5 scenarios. Upper time 
series: Global mean contributions to sea level change as a function of time, relative to 1995–2014. Lower maps:  Regional projections of the sea level contributions in 
2100 relative to 1995–2014 for SSP5-8.5 and SSP1-2.6. Vertical land motion is common to both Shared Socio-economic Pathways (SSPs). Further details on data sources and 

1302
Chapter 9 Ocean, Cryosphere and Sea Level Change99.6.3.3 Sea Level Projections to 2150 Based on Shared 
Socio-economic Pathway Scenarios
Up to 2050, consistent with AR5 and SROCC, GMSL projections 
exhibit little scenario dependence ( high confidence ) (Figure 9.27 and 
Table 9.9) with likely (medium confidence ) sea level rise between the 
baseline period (1995–2014) and 2050 of 0.19 (0.16–0.25) m under 
SSP1-2.6 and 0.23 (0.20–0.30) m under SSP5-8.5. These projections 
fall centrally within the range of published projections for RCP2.6 
and RCP8.5 (Section 9.6.3.1).
Beyond 2050, the scenarios increasingly diverge. Between the baseline 
period (1995–2014) and 2100, processes in whose projection there 
is medium confidence  drive likely GMSL rise of 0.44 (0.32–0.62) m 
and 0.77 (0.63–1.01) m under SSP1-2.6 and SSP5-8.5, respectively 
(Tables 9.8, 9.9). While derived using substantially updated methods, 
these projections are broadly consistent with SROCC, which projected

likely GMSL rise of 0.41 (0.26–0.56) m and 0.81 (0.58–1.07) m 
under RCP2.6 and RCP8.5, respectively, over this period. They are 
modestly higher than those of AR5, which projected likely  GMSL rise 
of 0.41 (0.25–0.58) m under RCP2.6 and 0.71 (0.49–0.95) m under 
RCP8.5 (Figure 9.25, Table 9.8). They are also broadly consistent with 
projections produced by driving AR5 methods with CMIP6 temperature 
and thermal expansion projections, which leads to 0.44 (0.27–0.61) m 
under SSP1-2.6 and 0.73 (0.49–1.02) m under SSP5-8.5 (Hermans 
et  al., 2021). The SSP1-2.6 and SSP5-8.5 projections are consistent 
with the ranges of published projections for RCP2.6 and RCP8.5 that 
do not incorporate MICI or SEJ (Section 9.6.3.1).
The likely GMSL projections for SSP3-7.0 and SSP5-8.5 are 
consistent with a  continuation of the GMSL satellite-observed 
rate ( very likely 3.25 [2.88–3.61]  mm yr –1) and acceleration ( very

likely 0.094 [0.082–0.115] mm yr –2) of GMSL rise over 1993–2018 (Table  9.5 and Section  2.3.3.3), which would imply a  likely  GMSL 
rise of 0.24 m (0.23–0.25 m) by 2050 and 0.73 m (0.69–0.77 m) by 
2100. This extrapolation would also imply a  likely rate of GMSL rise of 
7.5 (7.4–7.6) mm yr –1over 2040–2060 and 11.2 (10.6–11.8) mm yr –1 
over 2080–2100. Over the satellite period, the observed acceleration 
has been driven primarily by ice-sheet contributions (Section 9.6.1.2 
and Table 9.5); in the median projections for SSP3-7.0 and SSP5-8.5, 
these accelerations are projected to continue at a  slightly lower 
level, while the GMSL acceleration is augmented by an acceleration 
of thermal expansion and glacier loss associated with rising 
global temperature. Overall, these extrapolations imply that, under 
SSP1-1.9, SSP1-2.6, and SSP2-4.5, the GMSL acceleration is projected 
to decrease from its current level.
While ice-sheet processes in whose projection there is low confidence

have little influence up to 2100 on projections under SSP1-1.9 and 
SSP1-2.6 (Table  9.9), this is not the case under higher emissions 
scenarios, where they could lead to GMSL rise well above the likely 
range. In particular, under SSP5-8.5, low-confidence  processes 
could lead to a total GMSL rise of 0.6–1.6 m over this time period 
(17th–83rd percentile range of p-box, including SEJ- and MICI-based 
projections), with 5th–95th percentile projections extending to 
0.5–2.3 m ( low confidence ). The assessed low confidence  range is 
slightly narrower than, but broadly consistent with, the full 0.4–2.4 m 
range of published 5th–95th percentile projections for RCP8.5 since 
AR5 (Section 9.6.3.1) – including those based on SEJ or incorporating 
MICI – and highlights the deep uncertainty  in GMSL rise under the 
highest emissions scenarios (Box 9.4). The assessment of the potential 
contribution of processes in which there is low confidence  to GMSL

rise by 2100 is broadly consistent with the AR5’s assessment (Church 
et al., 2013b), which concluded that collapse of marine-based sectors 
of the AIS could cause several tenths of a metre of GMSL rise above 
the likely  range.
Table 9 .9 | Global mean sea level projections for five Shared Socio-economic Pathway (SSP) scenarios, relative to a baseline of 1995–2014, in metres.  
Individual contributions are shown for the year 2100. Median values ( likely ranges) are shown. Average rates for total sea level change are shown in mm yr –1. Unshaded cells 
represent processes in whose projections there is medium confidence . Shaded cells incorporate a representation of processes in which there is low confidence ; in particular, the 
SSP5-8.5 low confidence  column shows the 17th–83rd percentile range from a p-box including SEJ- and MICI-based projections rather than an assessed likely range. Methods 
are described in 9.6.3.2.
  SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5SSP5-8.5

Low Confidence
Thermal expansion 0.12 (0.09–0.15) 0.14 (0.11–0.18) 0.20 (0.16–0.24) 0.25 (0.21–0.30) 0.30 (0.24–0.36) 0.30 (0.24–0.36)
Greenland 0.05 (0.00–0.09) 0.06 (0.01–0.10) 0.08 (0.04–0.13) 0.11 (0.07–0.16) 0.13 (0.09–0.18) 0.18 (0.09–0.59)
Antarctica 0.10 (0.03–0.25) 0.11 (0.03–0.27) 0.11 (0.03–0.29) 0.11 (0.03–0.31) 0.12 (0.03–0.34) 0.19 (0.02–0.56)
Glaciers 0.08 (0.06–0.10) 0.09 (0.07–0.11) 0.12 (0.10–0.15) 0.16 (0.13–0.18) 0.18 (0.15–0.21) 0.17 (0.11–0.21)
Land-water Storage 0.03 (0.01–0.04) 0.03 (0.01–0.04) 0.03 (0.01–0.04) 0.03 (0.02–0.04) 0.03 (0.01–0.04) 0.03 (0.01–0.04)
Total (2030) 0.09 (0.08–0.12) 0.09 (0.08–0.12) 0.09 (0.08–0.12) 0.10 (0.08–0.12) 0.10 (0.09–0.12) 0.10 (0.09–0.15)
Total (2050) 0.18 (0.15–0.23) 0.19 (0.16–0.25) 0.20 (0.17–0.26) 0.22 (0.18–0.27) 0.23 (0.20–0.29) 0.24 (0.20–0.40)
Total (2090) 0.35 (0.26–0.49) 0.39 (0.30–0.54) 0.48 (0.38–0.65) 0.56 (0.46–0.74) 0.63 (0.52–0.83) 0.71 (0.52–1.30)

Total (2100) 0.38 (0.28–0.55) 0.44 (0.32–0.62) 0.56 (0.44–0.76) 0.68 (0.55–0.90) 0.77 (0.63–1.01) 0.88 (0.63–1.60)
Total (2150) 0.57 (0.37–0.86) 0.68 (0.46–0.99) 0.92 (0.66–1.33) 1.19 (0.89–1.65) 1.32 (0.98–1.88) 1.98 (0.98–4.82)
Rate (2040–2060) 4.1 (2.8–6.0) 4.8 (3.5–6.8) 5.8 (4.4–8.0) 6.4 (5.0–8.7) 7.2 (5.6–9.7) 7.9 (5.6–16.1)
Rate (2080–2100) 4.2 (2.4–6.6) 5.2 (3.2–8.0) 7.7 (5.2–11.6) 10.4 (7.4–14.8) 12.1 (8.6–17.6) 15.8 (8.6–30.1)

1303
While prior assessment reports, starting with the First Assessment 
Report (Warrick et al., 1990), have focused on projecting GMSL up 
to the year 2100, time has progressed, and the year 2100 is now 
within the time frame of some long-term infrastructure decisions. 
For this reason, projections up to the year 2150 are also highlighted 
(Table  9.9). Over this time period, assuming no acceleration in 
ice-sheet mass fluxes after 2100, processes in which there is medium 
confidence  lead to GMSL rise of 0.5–1.0 m under SSP1-2.6 and 1.0–1.9 m under SSP5-8.5. Processes in which there is low confidence  
could drive GMSL rise under SSP5-8.5 to 1.0–4.8 m (17th–83rd 
percentile) or even 0.9–5.4 m (5th–95th percentile).
Median projected RSL changes are shown in Figure 9.28, with driving 
factors highlighted in Figure  9.26. Approximately 60% (SSP1-1.9) 
to 70% (SSP5-8.5) of the global coastline has a projected median

21st  century regional RSL rise within ±20% of the global mean m
1950 2000 2050 2100 2150         00.511.522.5
SSP5-8.5
SSP3-7.0
SSP2-4.5
SSP1-2.6 SSP1-1.9 HistoricalMedian (medium confidence)
Likely range (medium confidence)
Satellite extrapolation (see caption)
Likely range of extrapolation
SSP5-8.5 Low confidence 83rd percentile
SSP5-8.5 Low confidence 95th percentile
2150 medium
& low confidence
(see caption)Projected global mean sea level rise under different SSP scenarios
Figure 9. 27 | Projected global mean sea level rise under different Shared Socio-economic Pathway (SSP) scenarios.  Likely  global mean sea level (GMSL) change 
for SSP scenarios resulting from processes in whose projection there is medium confidence . Projections and likely ranges at 2150 are shown on right. Lightly shaded ranges and

thinner lightly shaded ranges on the right show the 17th–83rd and 5th–95th percentile ranges for projections including low confidence  processes for SSP1-2.6 and SSP5-8.5 
only, derived from a p-box including structured expert judgement and marine ice-cliff instability projections. Black lines show historical GMSL change, and thick solid and 
dash-dotted black lines show the mean and likely range extrapolating the 1993–2018 satellite altimeter trend and acceleration. Further details on data sources and processing 
are available in the chapter data table (Table 9.SM.9).
SSP5-8.5 SSP2-4.5
SSP1-2.6SSP1-1.9
SSP3-7.0SSP3-7.0
Figure 9.2 8 | Regional sea level change at 2100 for different scenarios (with respect to 1995–2014).  Median regional relative sea level change from 1995–2014 
up to 2100 for: (a) SSP1-1.9;  (b) SSP1-2.6; (c) SSP2-4.5; (d) SSP3-7.0;  (e) SSP5-8.5; and (f) width of the likely range for SSP3-7.0. The high uncertainty in projections around

Alaska and the Aleutian Islands arises from the tectonic contribution to vertical land motion, which varies greatly over short distances in this region. Further details on data 
sources and processing are available in the chapter data table (Table 9.SM.9).

1304
Chapter 9 Ocean, Cryosphere and Sea Level Change9increase ( medium confidence ). Consistent with AR5, loss of land 
ice mass will be an important contributor to spatial patterns in 
RSL change ( high confidence ), with ocean dynamic sea level being 
particularly important as a  dipolar contributor in the north-west 
Atlantic, a positive contributor in the Arctic Ocean, and a negative 
contributor in the Southern Ocean south of the Antarctic Circumpolar 
Current ( medium confidence ) (Section  9.2.4.2). As today, VLM 
will remain a  major driver of RSL change ( high confidence ). 
Uncertainty in RSL projections is greatest in tectonically active 
areas in which VLM varies over short distances (e.g., Alaska) and in 
areas potentially subject to large ocean dynamic sea level change 
(e.g., the north-western Atlantic) ( high confidence ).
An alternative perspective on uncertainty in future sea level rise is 
provided by looking at uncertainty in time rather than elevation;

that is, looking at the range of dates when specific thresholds of 
sea level rise are projected to be crossed (Figure 9.29). Considering 
only medium confidence  processes, GMSL rise is likely to exceed 
0.5 m between about 2080 and 2170 under SSP1-2.6 and between 
about 2070 and 2090 under SSP5-8.5. It is likely  to exceed 1.0 m 
between about 2150 and some point after 2300 under SSP1-2.6, 
and between  about 2100 and 2150 under SSP5-8.5. It is unlikely 
to exceed 2.0 m until after 2300 under SSP1-2.6, while it is likely  to do so between about 2160 and 2300 under SSP5-8.5. However, 
processes in whose projections there is low confidence  could lead to 
substantially earlier exceedances under higher emissions scenarios: 
under SSP5-8.5, 1.0 m could be exceeded by about 2080 and 
2.0 m could be exceeded by about 2110 (17th percentile of p-box, 
incorporating projections based on SEJ and MICI), with 5th percentile 
projections as early as about 2070 for 1.0 m and 2090 for 2.0 m.

9.6.3.4 Sea Level Projections up to 2100 Based 
on Global Warming Levels
Global warming levels represent a  new dimension of integration 
in the AR6 cycle (Section 1.6.2, Cross-Chapter Box 11.1). The SR1.5 
(Hoegh-Guldberg et al., 2018) concluded that, based on an assessment 
of GMSL projections published for 1.5°C and 2.0°C scenarios, there is 
medium agreement that GMSL in 2100 would be 0.04–0.16 m higher 
in a 2°C warmer world, compared to a 1.5°C warmer world based 
on 17–84% confidence interval projections (0.00–0.24  m based 
on 5–95% confidence interval projections) with a  central value of 
around 0.1 m. The SR1.5 did not attempt to standardize the definition 
of warming-level scenarios, or to examine additional warming levels. 
No new integrated GMSL projections for 1.5°C or 2.0°C scenarios 
have been published since SR1.5.
2000 2100 2200 2300+SSP1−2.6
0.5 m1.0 m1.5 m2.0 meter s since 1995-2014
2000 2100 2200 2300+SSP5−8.5
No acceler ationAssessed ice sheetsMICISEJ

0.5 m1.0 m1.5 m2.0 mProjected timing of sea−le vel rise milestones
Under diff erent f orcing scena rios and workflow assumptions
Figure  9.29 | Timing of when global mean sea level (GMSL) thresholds of 0.5, 1.0, 1.5 and 2.0 m are exceeded, based on four different ice-sheet projection 
methods informing post-2100 projections.  Methods are labelled based on their treatment of ice sheets. ‘No acceleration’ assumes constant rates of mass change after 2100. 
‘Assessed ice sheet’ models post-2100 ice-sheet losses using a parametric fit (Supplementary Material 9.SM.4) extending to 2300 based on a multi-model assessment of contributions 
under RCP2.6 and RCP8.5 at 2300. Structured expert judgement (SEJ) employs ice-sheet projections from Bamber et al. (2019). Marine ice-cliff instability (MICI) combines the parametric

fit (Supplementary Material 9.SM3.4) for Greenland with Antarctic projections based on DeConto et al. (2021). Circles, thick bars and thin bars represent the 50th, 17th–83rd and 
5th–95th percentiles of the exceedance timing for the indicated projection method. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

1305
Most of the contributors to GMSL are more closely tied to time-
integrated GSAT than instantaneous GSAT (Hermans et  al., 2021), 
which means that sea level projections by warming level can only 
be interpreted if the warming levels are linked to a  specific time 
frame. Here, the warming level projections are defined based on 
the 2081–2100 GSAT anomaly (Supplementary Material 9.SM.4.7). 
Different pathways in GSAT can be followed to reach a  certain 
temperature level, which affects the temporal evolution of the 
different contributors to sea level change. For instance, there will 
be different ice-sheet and glacier responses to a  fast increase to 
a peak warming of 2°C in 2050, followed by a plateau or a decrease, 
compared to a  gradual increase to the same level of warming in 
2100. The sea level projections presented might include different 
pathways to the same warming level in 2100, which is reflected

in  the uncertainty ranges, and should therefore be interpreted as 
illustrative of sea level scenarios under a certain warming level.
Projections of likely 21st-century GMSL rise along climate trajectories 
leading to different increases in GSAT between 1850–1900 and 
2081–2100 are shown in Table  9.10, along with the SSPs for 
which the temperature-level projections are most closely aligned. 
For example, considering only processes in which there is medium 
confidence , from the baseline period (1995–2014) up to 2100, GMSL 
in a 2°C scenario is likely  to rise by 0.40–0.69, which is intermediate 
between the projections for SSP1-2.6 and SSP2-4.5. GMSL in a 4°C 
scenario is likely  to rise by 0.58–0.92 m, similar to the projection 
for SSP3-7.0. Consistent with the discussion in Section 9.6.3.3, there 
is deep uncertainty  in the projections for temperature levels above 
3°C, and alternative approaches to projecting ice-sheet changes may

yield substantially different projections in 4°C and 5°C futures. For 
example, employing SEJ ice-sheet projections (Bamber et al., 2019) 
instead of the projections for medium confidence  processes only 
leads to a  17th–83rd percentile rise between the baseline period 
(1995–2014) and 2100 of 0.7–1.6 m, rather than 0.7–1.1 m in 
a 5°C scenario.9.6.3.5 Multi-century and Multi-millennial Sea Level Rise
Neither AR5 nor SROCC discussed the sea level commitment 
associated with historical emissions. Since AR5, new evidence has 
suggested that historical emissions up to 2016 will lead to a  likely 
committed sea level rise (i.e., the rise that would occur in the absence 
of additional emissions) of 0.7–1.1 m up to 2300, while pledged 
emissions through 2030 increase the committed rise to 0.8–1.4 m 
(Nauels et al., 2019).
Between the baseline period (1995–2014) and 2300, AR5 projected 
a  GMSL rise of 0.38–0.82 m under a  non-specific low-emissions

scenario and 0.9–3.6 m under a non-specific high-emissions scenario 
(Table  9.11). The SROCC projected 0.6–1.0 m under RCP2.6 and 
2.3–5.3 m under RCP8.5 ( low confidence ). RCP-based projections 
for 2300 published since AR5 span a broader range, even excluding 
studies employing SEJ or MICI, with 17th–83rd percentile projections 
ranging from 0.3–2.9 m for RCP2.6 and 1.7–6.8 m for RCP8.5 
(Table  9.SM.8; Kopp et  al., 2014, 2017; Nauels et  al., 2017, 2019; 
Bamber et al., 2019; Palmer et al., 2020). Conservatively extending the 
ISMIP6- and LARMIP-2-based projections beyond 2100 by assuming 
no subsequent change in ice-sheet mass flux rates (an approach 
similar to that adopted by Palmer et  al. (2020) for the Greenland 
Ice Sheet and for the Antarctic Ice Sheet dynamics) leads to a GMSL 
change up to 2300 of 0.8–2.0 m under SSP1-2.6 and 1.9–4.1 m under 
SSP5-8.5 (17th–83rd percentile), while incorporating the ice-sheet

contributions for 2300 assessed in Section 9.4.1.4 and Section 9.4.2.6 
leads to 0.6–1.5 m and 2.2–5.9 m, respectively. Incorporating Antarctic 
results from a model with MICI (Section 9.4.2.4), using RCP forcing 
to inform SSP-based projections, leads to 1.4–2.1 m for SSP1-2.6 
and 9.5–16.2 m for SSP5-8.5 (DeConto et al., 2021). Incorporating 
the SEJ-based ice-sheet projections of Bamber et al. (2019) for 2°C 
and 5°C stabilization scenarios yields 1.0–3.1 m for SSP1-2.6, and 
2.4–6.3 m for SSP5-8.5, although because of the differences in 
scenarios, the SSP1-2.6 estimates may be overestimated and the 
SSP5-8.5 may be underestimated. The eightfold uncertainty range 
across projection methods under SSP5-8.5 reflects deep uncertainty  
in the multi-century response of ice sheets to strong climate forcing.Table 9.1 0 | Global mean sea level (GMSL) projections and commitments for exceedance of five global warming levels, defined by sorting GSAT change in

2081–2100 with respect to 1850–1900.  Median values and ( likely) ranges are in metres relative to a 1995–2014 baseline. Rates are in mm yr –1. Unshaded cells represent 
processes in whose projections there is medium confidence . Shaded cells incorporate a representation of processes in which there is low confidence ; in particular, the SSP5-8.5 
low confidence  column shows the 17th–83rd percentile range from a p-box, including projections based on structured expert judgement (SEJ) and marine ice cliff instability 
(MICI) rather than an assessed likely range. Methods are described in 9.6.3.2.
  1.5°C 2.0°C 3.0°C 4.0°C 5.0°CSSP5-8.5 
Closest SSPs SSP1-2.6 SSP1-2.6/SSP2-4.5 SSP2-4.5/SSP3-7.0 SSP3-7.0 SSP5-8.5  
Total (2050) 0.18 (0.16–0.24) m 0.20 (0.17–0.26) m 0.21 (0.18–0.27) m 0.22 (0.19–0.28) m 0.25 (0.22–0.31) m 0.24 (0.20–0.40) m
Total (2100) 0.44 (0.34–0.59) m 0.51 (0.40–0.69) m 0.61 (0.50–0.81) m 0.70 (0.58–0.92) m 0.81 (0.69–1.05) m 0.88 (0.63–1.60) m

Rate (2040–2060) 4.1 (2.9–5.7) mm yr –15.0 (3.7–7.0) mm yr –16.0 (4.6–8.1) mm yr –16.4 (5.0–8.6) mm yr –17.2 (5.7–9.8) mm yr –17.9 (5.6–16.1) mm yr –1
Rate (2080–2100) 4.3 (2.6–6.4) mm yr –15.5 (3.4–8.4) mm yr –17.8 (5.3-–11.6) mm yr –19.9 (7.1–14.3) mm yr –111.7 (8.5–17.0) mm yr –115.8 (8.6–30.1) mm yr –1
2000-yr 
commitment2 to 3 m 2 to 6 m 4 to 10 m 12 to 16 m 19 to 22 m  
10,000-yr 
commitment6 to 7 m 8 to 13 m 10 to 24 m 19 to 33 m 28 to 37 m

1306
Chapter 9 Ocean, Cryosphere and Sea Level Change9Taking into account all these approaches, including published 
projections for RCP2.6, under SSP1-2.6 GMSL will rise between 
0.3  and 3.1 m by 2300 ( low confidence ). This projection range 
indicates that, while SROCC projections under low emissions to 2300 
are consistent with no ice-sheet acceleration after 2100, there is the 
possibility of a  much broader range of outcomes at the high end, 
reflected in the range of published GMSL projections. Under SSP5-8.5, 
GMSL will rise between 1.7 and 6.8 m by 2300 in the absence of 
MICI and by up to 16 m considering MICI, a wider range than AR5 
or SROCC assessments, but consistent with published projections 
On still longer time scales, AR5 concluded with low confidence  that 
the multi-millennial GMSL commitment sensitivity to warming was 
about 1–3 m °C–1 GSAT increase. Two process-model studies since

AR5 (Clark et al., 2016; Van Breedam et al., 2020) indicate higher 
commitments (Figure 9.30). Ice sheets dominate the multi-millennial 
sea level commitment (Sections 9.4.1.4 and 9.4.2.6), but the two 
studies disagree on the relative contribution of the Greenland and 
Antarctic ice sheets. Notably, processes such as MICI (Section 9.4.2.4) 
that are a major factor behind the deep uncertainty  in century-scale 
AIS response do not appear to have a  substantial effect on the 
multi-millennial magnitude (DeConto and Pollard, 2016). Only one of 
the studies of multimillennial GMSL commitments includes scenarios 
consistent with 1.5°C of peak warming (Clark et al., 2016); this study suggests a  2000-year commitment at 1.5°C of about 2.3–3.1 m, 
with approximately an additional 1.4–2.3 m commitment between 
1.5°C and 2.0°C (i.e.,  about 3 to 5 m °C–1). Taken together, both 
studies show a 2000-year GMSL commitment of about 2–6 m for

peak warming of about 2°C, 4–10 m for 3°C, 12–16 m for 4°C, and 
19–22 m for 5°C ( medium agreement , limited evidence ) (Table 9.10). 
GMSL rise continues after 2000 years, leading to a  10,000-year 
commitment of about 6–7 m for 1.5°C of peak warming (based on 
Clark et al., 2016), and based on both studies of about 8–13 m for 
2.0°C, 10–24 m for 3.0°C, 19–33 m for 4.0°C, and 28–37 m for 5°C 
(medium agreement , limited evidence ) (Table 9.10).
An indicative metric for the equilibrium sea level response can 
be provided by comparing paleo GSAT and GMSL during past 
multimillennial warm periods (Sections 2.3.1.1, 2.3.3.3 and 9.6.2; 
Figure 9.9). However, caution is needed as the present and past warm 
periods differ in astronomical and other forcings (Cross-chapter 
Box 2.1) and in terms of polar amplification. The Last Interglacial 
(likely  5–10 m higher GMSL than today and 0.5°C–1.5°C warmer 
than 1850–1900; Section  9.6.2; Table  9.6) is consistent with the

Clark et  al. (2016) projections for the 10,000-year commitment 
associated with 1.5°C of warming. Similarly, the Mid-Pliocene Warm 
Period ( very likely  5–25 m higher GMSL than today and very likely 
2.5°C–4°C warmer) (Section  9.6.2; Table  9.6) is consistent with 
the range of 10,000-year commitments associated with 2.5–4°C 
Table 9. 11 | Global mean sea level (GMSL) projections between 1995–2014 and 2300 for total change and individual contributions. Low emissions 
projections from: AR5 (Church et al., 2013b); RCP2.6 from SROCC (Oppenheimer et al., 2019) and published projections (Table 9.SM.8); and SSP1-2.6 
(from this Report). High emissions projections from: AR5 (Church et al., 2013b); RCP8.5 from SROCC (Oppenheimer et al., 2019) and published projections 
(Table 9.SM.8); and SSP5-8.5 (this Report).  Values for AR5 (Church et al., 2013b) and SROCC (Oppenheimer et al., 2019) are adjusted from the 1986–2005 baseline used

in past reports. Only total values are shown for published ranges. Only the Antarctic contribution changed between AR5 (Church et al., 2013b) and SROCC (Oppenheimer et al., 
2019). If a range is given, it is the 17th–83rd percentile range.
  Low RCP2.6 SSP1-2.6
m relative to 
1995–2014AR5 SROCCPost-AR5 
Published RangeNo Ice-sheet 
Acceleration 
After 2100Assessed 
Ice-sheet 
ContributionMICI SEJ
Thermal expansion 0.07–0.46 m 0.19–0.35 m
Greenland 0.14 m   0.22–0.39 m 0.11–0.25 m 0.28–1.28 m
Antarctica 0.21–0.25 m   –0.05 to +1.14 m –0.14 to +0.78 m 0.71–1.35 m –0.11 to +1.56 m
Glaciers n/a   0.12–0.29 m
Land-water storage –0.03 m 0.07–0.37 m   0.05–0.10 m
Total (2300) 0.38–0.82 m 0.57–1.04 m 0.3–2.9 m 0.8–2.0 m 0.6–1.5 m 1.4–2.1 m 1.0–3.1 m
           
  High RCP8.5 SSP5-8.5
Published Range 
Without (with) 
MICINo Ice-Sheet 
after 2100Assessed 
Ice-sheet 
Thermal expansion 0.28–1.80 m   0.92–1.51 m

Greenland 0.30–1.18 m   0.53–0.88 m 0.32–1.75 m 0.40–2.23 m
Antarctica 0.02–0.19 m 0.60–2.89 m   –0.39 to +1.55 m –0.28 to +3.13 m 6.87–13.54 m 0.03–3.05 m
Glaciers 0.29–0.39 m   0.32 m
Land-water storage n/a   0.05–0.10 m
Total (2300) 0.89–3.56 m 2.25–5.34 m 1.7–6.8 (up to 14.1) m 1.7–4.0 m 2.2–5.9 m 9.5–16.2 m 2.4–6.3 m

1307
of warming, but GMSL reconstructions provide only a  weak, 
broad constraint on model-based projections. An additional paleo 
constraint comes from the Early Eocene Climatic Optimum, which 
indicates that 10–18°C of warming is associated with ice-free 
conditions and a  likely GMSL rise of 70–76 m (Sections 2.3.3 and 
9.6.2). Together with model-based projections (Clark et  al., 2016; 
Van Breedam et al., 2020), this period suggests that commitment 
to ice-free conditions would occur for peak warming of about 
7°C–13°C ( medium agreement,  limited evidence ).
On the basis of modelling studies, paleo constraints, single-ice-sheet 
studies finding multimillennial nonlinear responses from both the 
Greenland and Antarctic ice sheets (Sections 9.4.1.4 and 9.4.2.6), 
and the underlying physics, we conclude that GMSL commitment 
is nonlinear in peak warming on time scales of both 2,000 and

10,000 years ( medium confidence) and exceeds the AR5 assessment 
of 1–3 m °C–1 (medium agreement , limited evidence ) (Table  9.9). 
Although thermosteric sea level will start to decline slowly about 
2,000 years after emissions cease, the slower responses from the 
Greenland and Antarctic ice sheets mean that GMSL will continue 
to rise for 10,000 years under most scenarios ( medium confidence ). Since AR5, a small number of modelling studies have examined the 
reversibility of the multimillennial sea level commitment under carbon 
dioxide (CO 2) removal, solar radiation modification or local ice shelf 
engineering. The slow response of the deep ocean to forcing leads 
to global-mean thermosteric sea level fall occurring long afterward, 
even if CO 2 levels are restored after a transient increase: global mean 
thermosteric sea level rise takes more than a millennium to reverse 
(Ehlert and Zickfeld, 2018). Rapid reversion to pre-industrial CO 2

concentrations has been found to be ineffective at fostering regrowth 
of the AIS (DeConto et al., 2021) but may reduce the multimillennial 
sea level commitment (DeConto and Pollard, 2016). Altering 
sub-ice-shelf bathymetry (Wolovick and Moore, 2018) or triggering 
ice shelf advance through massive snow deposition (Feldmann et al., 
2019) might interrupt marine ice sheet instability (Section 9.4.2.4) 
and thus reduce sea level commitment. A reversion to pre-industrial 
Greenland Ice Sheet temperatures with solar radiation modification 
is projected to stop mass loss in Greenland but leads to minimal 
regrowth (Applegate and Keller, 2015). Based on limited evidence , 
carbon dioxide removal, solar radiation modification, and local 
ice-shelf engineering may be effective at reducing the yet-to-be-
realized sea level commitment, but ineffective at reversing GMSL rise 

Figure 9. 30 | Global mean sea level (GMSL) commitment as a function of peak global surface air temperature.  From models (Clark et al., 2016; DeConto and 
Pollard, 2016; Garbe et al., 2020; Van Breedam et al., 2020) and paleo data on 2000-year (lower row)  and 10,000 year (upper row)  time scales. Columns indicate different 
contributors to GMSL rise (from left to right: total GMSL change, Antarctic Ice Sheet, Greenland Ice Sheet, global mean thermosteric sea level rise, and glaciers). Further details 
on data sources and processing are available in the chapter data table (Table 9.SM.9).

1308
Chapter 9 Ocean, Cryosphere and Sea Level Change9Box 9.4 | High-end Storyline of 21st-century Sea Level Rise
In this box, we outline a storyline (Glossary, Box 10.2; Shepherd et al., 2018) for high-end sea level projections for 2100. This storyline 
considers processes whose quantification is highly uncertain regarding the timing of their possible onset and/or their potential to 
accelerate sea level rise. These processes are therefore not considered for the assessed upper bound of likely  sea level rise by 2100 in 
section 9.6.3.3, as the  likely  range includes only processes that can be projected skilfully with at least  medium confidence  (based on 
agreement and evidence ).
As noted by SROCC, stakeholders with a low risk tolerance (e.g., those planning for coastal safety in cities and long-term investment in 
critical infrastructure) may wish to consider global-mean sea level rise above the assessed likely  range by the year 2100, because ‘ likely ’

implies an assessed likelihood of up to 16% that sea level rise by 2100 will be higher (see also Siegert et al., 2020). Because of our 
limited understanding of the rate at which some of the governing processes contribute to long-term sea level rise, we cannot currently 
robustly quantify the likelihood with which they can cause higher sea level rise before 2100 (Stammer et al., 2019).
In light of such deep uncertainty , we employ a  storyline approach in examining the potential for, and early warning signals of 
a high-end sea level scenario unfolding within this century. In doing so, we note upfront that the main uncertainty related to high-end 
sea level rise is ‘when’ rather than ‘if’ it arises: the upper limit of 1.01 m of likely  sea level range by 2100 for the SSP5-8.5 scenario will 
be exceeded in any future warming scenario on time scales of centuries to millennia ( high confidence ), but it is uncertain how quickly

the long-term committed sea level will be reached (Section 9.6.3.5). Hence, global mean sea level might rise well above the likely  
range before 2100, which is reflected by assessments of ice-sheet contributions based on structured expert judgement (Bamber et al., 
2019) leading to a 95th percentile of projected future sea level rise as high as 2.3 m in 2100 (Section 9.6.3.3).
A plausible storyline for such high-end sea level rise in 2100 assumes a strong warming scenario (Section 4.8). The storyline considers 
faster-than-projected disintegration of marine ice shelves and the abrupt, widespread onset of marine ice cliff instability (MICI) and 
marine ice sheet instability (MISI) in Antarctica (Section 9.4.2.4), and faster-than-projected changes in both the surface mass balance 
and dynamical ice loss in Greenland. While conceptual studies provide medium evidence  of these processes, substantial uncertainties

and low agreement in quantifying their future evolution arise from limited process understanding, limited availability of evaluation 
data, missing or crude representation in model simulations, their high sensitivity to uncertain boundary conditions and parameters, 
and/or uncertain atmosphere and ocean forcing (Sections 9.4.1.2; 9.4.2.2).
In Antarctica, high warming might lead to floating ice shelves starting to break up earlier than expected due to processes not yet 
accounted for in ice-sheet models or in current climate models used to force ice-sheet projections. Such processes include hydrofracturing 
driven by surface meltwater, and increase in ocean thermal forcing driven by ocean circulation changes (Sections 9.2.2.3, 9.2.3.2 and 
9.4.2.3; Hellmer et al., 2012, 2017; Silvano et al., 2018; Hazel and Stewart, 2020). In particular, the Thwaites and Pine Island Glacier

ice shelves could potentially disintegrate this century, which might trigger MICI before 2100 (DeConto and Pollard, 2016; DeConto 
et al., 2021). MISI could potentially develop earlier and faster than simulated by the majority of models if fast flowing ice streams 
follow plastic, instead of currently assumed more viscous, sliding laws (Sun et al., 2020). Oceanic feedbacks could drive high-end sea 
level rise by changes in the meltwater-driven overturning circulation in ice cavities that cause additional melting (Jeong et al., 2020); 
by a warming of the ocean water in contact with the ice shelves due to increased stratification and thus reduced vertical mixing 
(Sections 9.2.2.3 and 9.2.3.2; Golledge et al., 2019; Moorman et al., 2020; Sadai et al., 2020); or by an increase in sea ice cover due 
to increased ocean stratification (Section 9.3.2.1), which could reduce the amount of warm, moist air that reaches the continent,

and limit the mass gain from snowfall over the ice sheet (Sadai et al., 2020).
In Greenland, stronger mass loss than currently projected might also occur (Aschwanden et al., 2019; Khan et al., 2020; T. Slater 
et  al., 2020). For example, warming-induced dynamical changes in atmospheric circulation could enhance summer blocking and 
produce more frequent extreme melt events over Greenland similar to the record mass loss of more than 500 Gt in summer 2019 
(Section 9.4.1.1; Delhasse et al., 2018; Sasgen et al., 2020). Cloud processes in polar areas that are not well represented in models 
could further enhance surface melt (Hofer et al., 2019), as could feedbacks between surface melt and the increasing albedo from 
meltwater, detritus and pigmented algae (Section 9.4.1.1; Cook et al., 2020). The same ice dynamical processes associated with basal 
melt and MISI discussed for Antarctica could also occur in Greenland, as long as the ice sheet is in contact with the ocean.

The strength of all these processes is currently understood to depend strongly on global mean temperature and polar amplification, 
with additional linkages through feedback from global mean sea level (Gomez et al., 2020). These dependencies on a joint forcing 
imply that processes are strongly correlated. Hence, both their uncertainties and their possible cascading contribution to high-end sea 
level rise are expected to combine. Therefore, high-end sea level rise can occur if one or two processes related to ice-sheet collapse

1309
9.6.4 Extreme Sea Levels: Tides, Surges and Waves
An extreme sea level (ESL) refers to an occurrence of exceptionally 
high or low local sea surface height (Box 9.1). This section focuses on 
oceanographic-driven changes in ESL (Box 9.1).
9.6.4.1 Past Changes
The AR5 (Church et al., 2013b) concluded that changes in extreme still 
water levels (ESWL), combining RSL, tide and surge as observed by tide 
gauges (Box 9.1) are very likely  to be caused by observed increases in 
RSL, but noted low confidence  in region-specific results owing to the 
limited number of studies considering localized contributions from 
storm surge, tide or wave effects. Influences from dominant modes 
of climate variability, particularly ENSO and NAO (Annex IV), were 
also noted. Climate modes affect sea level extremes in many regions, 
as a result of both sea level anomalies (Sections 9.2.4.2 and 9.6.1.3)

and changes in storminess (Section 11.7). The SROCC (Oppenheimer 
et al., 2019) concluded with high confidence that inclusion of local 
processes (wave effects, storm surges, tides plus other regional 
morphology changes due to erosion, sedimentation and compaction) 
is essential for estimation of changes in ESL events.
As in AR5 and SROCC, tide gauge observations show that RSL rise 
(Section 9.6.1.3) is the primary driver of changes in ESWL at most 
locations and, across tide gauges, has led to a median 165% increase 
in high-tide flooding over 1995–2014 relative to those over 
1960–1980 ( high confidence ) (Figure 9.31). Some locations exhibit 
substantial differences between long-term RSL trends and ESWL 
(high confidence ), particularly given decadal to multi-decadal 
variations of other ESWL contributors (Rashid and Wahl, 2020). Since 
SROCC, RSL rise has been shown to be the dominant contributor 
to ESWL rise at most gauge sites along the Chinese coast, but, at

some locations, the surge contribution dominates (Feng et  al., 
2019). Trends in the difference between ESWL and mean RSL rise can result from changes (either positive or negative) in the surge or 
tidal components, and can include non-linear interactions between 
tide, surge, and RSL (Arns et al., 2015; Schindelegger et al., 2018). 
The positive phase of the 18.6-year nodal cycle of the astronomical 
tide  is a  further consideration, contributing to an increased flood 
hazard relative to the long-term average (Talke et al., 2018; Peng 
et al., 2019; Baranes et al., 2020). Failing to consider the non-linear 
interactions between tide, surge and RSL may overestimate trends in 
ESWL ( low confidence ) (Arns et al., 2020). In some regions, changes 
in ESWL depend more on changes in surge or tide than on sea 
level trends.
Ongoing development of the Global Extreme Sea Level Analysis 
(GESLA) tide gauge database (Woodworth et al., 2016) along with

data archaeology (Talke and Jay, 2013) extends availability of tide 
gauge records back to the mid 19th century (or earlier). Dynamical 
datasets used to assess trends in ESL at global or regional scales – 
for example, tide and surge contributions from the Global Tide and 
Surge Reanalysis (GTSR; Muis et  al., 2016, 2020), or wave setup/
swash contributions from available wave hindcasts/reanalyses 
(Melet et al., 2018) – have model biases introduced with resolution 
and parametrization limitations, incomplete atmospheric data, 
and currently span only a few decades, so they are not yet long or 
accurate enough to assess long-term trends in ESLs. Therefore, there 
is medium confidence  in observed trends in ESWL, but only low 
confidence  in modelled ESL trends.
The AR5 indicated that the amplitude and phase of major tidal 
constituents have exhibited long-term change, but that their effects 
on ESL were not well understood. The SROCC (Bindoff et al., 2019)

reported changes in tides (amplification and dampening) at some 
locations to be of comparable importance to changes in mean sea 
level for explaining changes in high water levels, with the sign of 
change being dependent on stability of shoreline position. RSL rise 
causes water depth-based alterations to the resonant characteristics Box 9.4 (continued)
in Antarctica result in an additional sea level rise at the maximum of their plausible ranges (Sections 9.4.2.5 and 9.6.3.3; Table 9.7) or 
if several of the processes described in this box result in individual contributions to additional sea level rise at moderate levels. In both 
cases, global-mean sea level rise by 2100 would be substantially higher than the assessed likely  range, as indicated by the projections 
including low confidence  processes reaching in 2100 as high as 1.6 m at the 83rd percentile and 2.3 m at the 95th  percentile 
(Section 9.6.3.3).

Identifying the potential drivers of a high-end sea level rise allows identification of sites and observables that can provide early 
warnings of a much faster sea level rise than the likely  range of this and previous reports. One potential site for such monitoring is 
Thwaites Glacier, which is melting faster in some places and slower in others than models simulate. At this glacier, the effect of tides 
and channelling of warm water flows on the melting is evident (Milillo et al., 2019), making the floating ice shelf potentially vulnerable 
to breakup from hydrofracturing, driven by surface meltwater, much earlier than expected. In addition, the glacier is retreating towards 
a zone with deeper bedrock, which at its present rate of retreat would be reached in 30 years (Yu et al., 2019). Thwaites Glacier is 
therefore a strong candidate to experience large-scale MISI and/or MICI (Golledge et al., 2019; DeConto et al., 2021), making it the

ideal site for monitoring early warning signals of accelerated sea level rise from Antarctica. Such signals could possibly be observed 
within the next few decades (Scambos et al., 2017).

1310
Chapter 9 Ocean, Cryosphere and Sea Level Change9of the basin, changes the bottom friction and increases the wave 
speed (Pickering et  al., 2012) and remains the primary hypothesis 
for observed tidal changes. Other contributing processes include 
strong localized anthropogenic drivers (e.g.,  port development, 
dredging, flood defences, land reclamation), changes in stratification 
associated with ocean warming (Section  9.2.1.3), and changes in 
seabed roughness associated with ecological change (e.g.,  Haigh 
et  al., 2019). Tide gauge data show that, although principal tidal 
components have varied in amplitude on the order of 2% to 
10% per century (Jay, 2009; Ray, 2009), identifying direct causality 
remains challenging (Haigh et  al., 2019). Combined, observations 
and models indicate RSL rise and direct anthropogenic factors are 
the primary drivers of observed tidal changes at tide gauge stations 

The SROCC (Oppenheimer et al., 2019) reported variations in storm 
surge not related to changes in RSL, and concluded with high 
confidence  that consideration of localized storm surge processes was 
essential to monitor trends in ESL. SL events driven by storm surge 
are a response to tropical and extratropical cyclones. While historical 
trends in extra-tropical cyclones are less clear (Section  11.7.2.1), 
there is mounting evidence for an increasing proportion of stronger 
tropical cyclones globally, with an associated poleward migration 
(Section 11.7.1.2). These changes are captured in the ESL record, for 
example, via increasing intensity and poleward shift in the location of 
typhoon-driven storm surges reported across 64 years (1950–2013) 
in the western North Pacific (Oey and Chou, 2016). Along the 
east coast of the USA, there has been an increase in frequency of 
ESL events due to tropical cyclone changes since 1923 that can

be statistically linked to changes in global average temperature 
(Grinsted et al., 2013), and the signal is projected to emerge around 
2030 (Lee et al., 2017). At century and longer time scales, geological 
proxies such as overwash deposits in coastal lagoons or sinkholes can 
be used to reconstruct past changes in storm activity (e.g., Brandon 
et  al., 2013; Lin et  al., 2014) and put recent events into historical 
perspective (e.g.,  Brandon et  al., 2015). However, there is  low 
confidence  in the current ability to quantitatively compare geological 
proxies with gauge data. Historical storm surge activity is being 
increasingly assessed with use of hydrodynamic model simulations 
and data-driven global reconstructions to supplement tide gauge 
observations to investigate historical changes at centennial to 
millennial time scales (e.g., Ji et al., 2020; Muis et al., 2020; Tadesse 
et al., 2020). Large regional variations and limited observational data

lead to low confidence  in observed trends in the surge contribution 
to increasing ESL.
Waves contribute to ESL via wave setup, infra-gravity waves and 
swash processes (Dodet et  al., 2019), with Extreme Total Water 
Level (ETWL; Box  9.1) used to represent ESWL with addition of 
wave setup, and Extreme Coastal Water Level (ECWL; Box 9.1) also 
including contributions from swash. The SROCC (Oppenheimer et al., 
2019) reported the dependency of these processes on nearshore 
geomorphology and deep-water wave climate, and thus sensitivity 
to internal climate variability and climate change. Few long-term deployments of in situ measurements in the very dynamic surf 
zone means that long-term records of ETWL or ECWL are limited to 
a few sites; tidal gauges are typically located in sheltered locations 
(e.g.,  harbours) where wave contributions are absent (Lambert 
et al., 2020). Consequently, trends in wave contributions to ESL are

typically derived from trends in wave conditions observed offshore. 
On the basis of satellite altimeter observations, SROCC reported 
increasing extreme wave heights in the Southern and North Atlantic 
oceans of around 1.0 and 0.8 cm yr –1, respectively, over the period 
1985–2018 ( medium confidence ). The SROCC (Collins et al., 2019) 
also identified sea ice loss in the Arctic as leading to increased wave 
heights over the period 1992–2014 ( medium confidence ). Since 
SROCC, the satellite wave record has been shown to be sensitive 
to alternate processing techniques, leading to important differences 
in reported trends (Timmermans et  al., 2020). The most common 
observation platforms for surface waves over the past 30 years are 
in situ buoys. However, evolving biases associated with changing 
instrument type, configuration and sampling methodology introduce 
artificial trends (e.g.,  Gemmrich et  al., 2011; Timmermans et  al.,

2020). Accurate metadata is required to address these issues, and, 
while available locally, are only beginning to be globally coordinated 
(Centurioni et  al., 2019). Wave reanalysis and hindcast products 
have also been used to investigate total water level at global scale 
(Melet et  al., 2018; Reguero et  al., 2019). Their applicability for 
trend analysis is limited by inhomogeneous data for assimilation 
(Stopa et al., 2019), but they inform relationships between seasonal, 
interannual to inter-decadal variability of climate indices and wind-
wave characteristics (A.G. Marshall et al., 2015, 2018; Kumar et al., 
2016; Stopa et al., 2016). To summarize, satellite era trends in wave 
heights of order 0.5 cm yr –1 have been reported, most pronounced in 
the Southern Ocean. However, sensitivity of processing techniques, 
inadequate spatial distribution of observations, and homogeneity 
issues in available records limit confidence in reported trends 

Only a  few studies have attempted to quantify the role of 
anthropogenic climate change in ESL events (e.g., Mori et al., 2014; 
Takayabu et al., 2015; Turki et al., 2019). Detection and attribution of 
the human influence on climatic changes in surges, and waves remains 
a  challenge (Ceres et  al., 2017), with limited evidence  to suggest 
in some instances  – for example, poleward migration of tropical 
cyclones in the Western North Pacific (Section  11.7.1.2), changes 
in surges and waves can be attributed to anthropogenic climate 
change ( low confidence ). With RSL change being considered the 
primary driver of observed tidal changes, there is medium confidence  
that these changes can be attributed to human influence. The close 
relationship between local ESL and long-term RSL change, combined 
with the robust attribution of GMSL change (Section 9.6.1.4), implies 
that observed global changes in ESL can be attributed, at least in

part, to human-caused climate change ( medium confidence ), but 
reconciling regional variation in these changes is not yet possible 
(Section 9.6.1.4).

1311
Figure 9.3 1 | Historical occurrences of minor extreme still water levels. Defined as the 99th percentile of daily observed water levels over 1995–2014.  (a) Percent 
change in occurrences over 1995–2014 relative to those over 1960–1980. (b–g)  Annual mean sea level (blue) and annual occurrences of extreme still water levels over the 
1995–2014 99th percentile daily maximum (yellow) at six selected tide gauge locations. Further details on data sources and processing are available in the chapter data table 

1312
Chapter 9 Ocean, Cryosphere and Sea Level Change99.6.4.2 Future Changes
There are two distinct methods used to project future ESL changes: 
(i) The static, or mean sea level offset, approach employs historical 
distributions of tidal, surge and wave components and adjusts future 
ESL distributions for mean RSL rise; (ii) The dynamic approach employs 
hydrodynamic and/or wave models forced with atmospheric fields 
derived from general circulation models (GCMs) to project changes 
in tidal, storm surge and wave distributions, which are then combined 
with RSL projections to project future ESLs; and (iii) The  dynamic 
approach is computationally expensive. Use of the dynamic approach 
on large spatial or global scales has only recently been successful 
to project 21st-century changes in ETWL (Vousdoukas et al., 2017, 
2018) and ECWL (Melet et  al., 2020). Kirezci et  al. (2020) assume 
stationarity in global wave and storm surge simulations to assess

projected 21st-century changes in episodic coastal ETWL-driven 
flooding under global sea level rise scenarios.
The SROCC (Oppenheimer et al., 2019) presents projections of ESL 
derived using a  static approach. Such projections often quantify 
changes in ESL event frequency, expressed as ‘frequency amplification 
factors’ (Hunter, 2010, 2012). Like RSL projections, frequency 
amplification factors increase under higher-emissions scenarios, 
and differences between scenarios increase over time. The  SROCC 
concludes that even small to moderate changes in mean RSL can 
lead to hundred- to thousand-fold increases in the frequencies 
with which certain thresholds are exceeded  – for example, what 
is currently a  1-in-100-year ESL height (1% annual probability or 
0.01 expected annual events) will be expected once or even multiple 
times per year in future at many locations (Figure 9.32). The SROCC 
showed that currently rare ESL events (e.g., with an average return

period of 100 years) will occur annually or more frequently at most 
available locations for RCP4.5 by the end of the century ( high 
confidence ). Results from these assessments are sensitive to the type 
of ESL probability distribution assumed (Buchanan et al., 2016; Wahl 
et al., 2017), as well as the magnitude and uncertainty of projected 
RSL change (Slangen et al., 2017; Wahl et al., 2017; Frederikse et al., 
2020a). Frequency amplification factors tend to be largest in tropical 
regions due in part to higher RSL rise projections, but primarily to 
the relative rarity of high ESLs in areas with little historical exposure 
to tropical or extratropical cyclones. Alternative representation of 
changes in ESL, such as presenting changes in exceedances per year 
(Sweet and Park, 2014), are subject to similar sensitivities, and lead 
to medium confidence  in projected changes of event frequency using 
these methods.
Employing a similar static approach – fitting a Gumbel distribution

between Mean Higher High Water (average of higher high water 
height of each tidal day) and a  threshold following Buchanan 
et al. (2016) – this Report updates SROCC projections of ESL with 
the RSL projections from Section  9.6.3.3 (see also Supplementary 
Material  9.SM.4). By 2050, the median increase in frequency 
amplification factor at 634 tide gauge stations is 19 for SSP1-2.6, 
22 for SSP2-4.5, and 30 for SSP5-8.5 (Figure 9.32). This means that, 
by 2050, a  historical (1995–2014) 1% annual probability ESL will 
have increased to an 19–30% annual probability. The 1% historical 
annual probability event is expected to become an annual event at 19–31% of the 634 stations by 2050, consistent with SROCC. 
By 2100, the median frequency amplification factor is projected to 
be 163 for SSP1-2.6, 325 for SSP2-4.5, and 532 for SSP5-8.5, with 
respectively 60%, 71%, and 82% of the stations experiencing 
a  currently 1% annual probability event at least yearly ( medium

confidence ) (Figure 9.32).
In the dynamic approach, the low resolution of the forcing fields 
arising from GCMs limits the ability to resolve historical and future 
changes in tropical and extra-tropical storm frequency and intensity, 
and resolution of local geography and morphology limit ability to 
represent ECWL (Box 9.1). Not all relevant processes – such as river 
discharge – are included in the dynamic models, and ESL events are 
typically a  combination of multiple contributing processes, which 
are often not independent (Jevrejeva et  al., 2019). In both static 
and dynamical approaches, global assessment of the performance 
of modelled storm surge and wave contributions to ESL is limited 
by poor coverage of observations (limited to tide gauges for 
ESWL, Muis et al., 2020), and unavailable for the wave dependent 
ETWL and ECWL estimates (Vitousek et al., 2017; Vousdoukas et al., 
2018; Kirezci et al., 2020; Lambert et al., 2020; Melet et al., 2020).

In studies to date, individual models are used to simulate different 
contributions to ESL, non-linear interactions are not well captured, 
and uncertainties associated with downscaling methodology are 
poorly resolved, leading to low confidence in available ESL projections 
that include these modelled wave and surge contributions.
Assessment of dynamic ETWL changes for regions is presented in 
Chapter  12, following the methods of Vousdoukas et  al. (2018) 
and Kirezci et  al. (2020). Consistent with studies using the static 
approach, Vousdoukas et al. (2018) finds that by 2050 the historical 
1% average annual probability ETWL will have increased to a 2–50% 
average annual probability for most high latitude regions, and more 
often (up to multiple times a  year, >100% annual probability) in 
the tropics, under both RCP4.5 and RCP8.5. For 2100, present-day 
1% average annual probability extreme sea levels will be exceeded

multiple times each year almost everywhere. In summary, despite 
waves and surges being non-negligible contributors to projected 
ETWL and ECWL changes (Vousdoukas et  al., 2018; Melet et  al., 
2020), RSL change is expected to be the main driver in changes in 
future ESL return periods in most areas ( medium confidence ).
The SROCC (Bindoff et  al., 2019) concluded that the majority of 
coastal regions will experience statistically significant changes 
in tidal amplitudes through the 21st  century. Comprehensive 
high-resolution (of the order 10 km) numerical modelling studies 
provide evidence for spatially coherent changes in tidal amplitudes in 
shelf seas as a result of RSL rise (Haigh et al., 2019, and references 
therein). There is high confidence  that GMSL rise will be the primary 
driver of global tidal amplitude increases and decreases over the next 
100–200 years, changing the baseline tide that ESLs are imposed

on. At local and regional scales, anthropogenic factors such as major 
land reclamation efforts, as in the East China Sea (Song et al., 2013) 
or differing national coastal management strategies (maintaining the 
present coastline position or managed retreat) will locally modulate 
the influence of GMSL rise on tidal amplitude ( medium confidence ).

1313
Ocean, Cryosphere and Sea Level Change  Chapter 99The SROCC (Oppenheimer et al., 2019) concluded that the intensity 
of severe tropical cyclones will increase in a  warmer climate 
(Section 11.7.1), but low confidence  remains in the future frequency 
of tropical cyclones. Changes in tropical cyclone climatology will 
contribute to variations in frequency and magnitude of future ESL 
surge events, although estimates of this contribution range widely 
(Lin et al., 2012; McInnes et al., 2014, 2016; Little et al., 2015; Garner 
et al., 2017; Mori et al., 2019; Muis et al., 2020). In the Gulf of Mexico, 
changes in ESL due to tropical cyclone activity may be as important 
as SLR in enhancing future flood hazards (Marsooli et  al., 2019). 
For  the Korean Peninsula, a  maximum change in 100-year return 
height associated with typhoon-induced storm surges of 10% under 
4°C warming is found (Yang et al., 2018). The effects of projected

changes in tropical cyclone intensity may be enhanced or offset in different locations by effects of changes in tracks (Section  11.7.1; 
Garner et al., 2017). There is low confidence  in projected changes in 
ESL driven by changes in tropical cyclone climatology.
Changes in surface wave conditions occur in response to changes in 
frequency; intensity and position of forcing winds and storms (Morim 
et  al., 2018, 2019); reduction in sea ice and associated changes 
in fetch conditions (Thomson and Rogers, 2014; Casas-Prat and 
Wang, 2020); and changes in coastal morphology associated with 
RSL rise (Wandres et al., 2017; Storlazzi et al., 2018). A few studies 
considering the contribution of a  non-stationary wave climate on 
future changes in ESL infer a small but non-negligible contribution 
(Vousdoukas et al., 2018; Melet et al., 2020). The SROCC presented 
qualitative assessments of projected changes in wave conditions. 
SSP5-8.5
SSP1-2.6SSP2-4.5

Figure 9.32  | Projected median frequency amplification factors for the 1% average annual probability extreme still water level in 2050 (a, c, e) and 2100 
(b, d, f).  Based on a peak-over-threshold (99.7%) method applied to the historical extreme still water levels of Global Extreme Sea Level Analysis version 2 (GESLA2) following 
Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and additionally fitting a Gumbel distribution between Mean Higher High Water (MHHW) and the 
threshold following Buchanan et al. (2016), using the regional sea level projections of Section 9.6.3.3 for (a, b) SSP5-8.5, (c, d) SSP2-4.5 and (e, f) SSP1-2.6. Further details on 
data sources and processing are available in the chapter data table (Table 9.SM.9).

1314
Chapter 9 Ocean, Cryosphere and Sea Level Change9Since SROCC, a quantitative assessment of a community ensemble 
of global wind-wave projections (Morim et al., 2019) found robust 
projected changes of around 5–10% (positive or negative, depending 
on region) in annual mean significant wave height, mean wave 
period, and/or mean wave directions along about 52% of the world’s 
coastline that exceed internal climate variability under RCP8.5 by 
2100. Continued retreat of sea ice cover in the Arctic will lead to 
more energetic wind-wave conditions (Casas-Prat and Wang, 2020). 
Wave climate modelling methods introduce up to around 50% of 
the ensemble variance in mean wave climate projections (Morim 
et  al., 2019). GCMs do not typically resolve the higher-resolution 
tropical and extratropical storm features required to accurately 
determine the contribution of extreme waves to ESLs and individual 
studies have sought to improve resolution to address these issues

(e.g., Timmermans et al., 2017). To date, projections of wave height 
extremes have been constrained to single wave model configurations 
(e.g., Timmermans et  al., 2017; Meucci et  al., 2020). In summary, 
there is medium confidence  in projections of changes in mean wave 
climate but low confidence  in the projected changes in extreme 
wave conditions due to limited evidence .
Correlations between changes in sea level-forced (mean sea level 
and tidal) and atmospherically-forced drivers (ocean surface waves 
and surges) of ESLs have only been considered in a  few studies, 
although high surge and high waves co-occur along a  majority of 
the world’s coastlines (Marcos et  al., 2019). Along the east coast 
fo the USA, ocean dynamic sea level change and change in power 
dissipation index (a proxy for North Atlantic tropical cyclone activity) 
are correlated across CMIP5 GCMs, resulting in an increase in ESLs 
relative to analyses assuming independence of these changes

(Little et al., 2015). In the Irish Sea, dynamically coupled wave-tide 
modelling results in high water wave heights up to 20% higher than 
in an uncoupled analysis (Lewis et al., 2019). In the German Bight, 
RSL rise relaxes the breaking criterion of nearshore waves (assuming 
no geomorphological response), allowing larger waves to propagate 
closer to shore, leading to increased wave runup (Arns et al., 2017). 
In south-western Australia, the influence of projected SLR was found 
to exceed the influence of projected changes in forcing winds on wave 
characteristics at the coast (Wandres et al., 2017). Thus, projections of 
ESL that do not consider correlations between and among sea level 
forced and atmospherically forced drivers can differ strongly from 
coupled projections ( medium confidence ). 
The SROCC (Collins et  al., 2019)  highlighted compound events, or 
coincident occurrence of multiple hazards, as an example of deep

uncertainty , and noted that failing to account for multiple factors 
contributing to extreme events will lead to underestimation of the 
probabilities of occurrence ( high confidence ). Statistical studies 
have shown that high rain or streamflow often co-occurs with storm 
surge as examples of ‘compound’ surge-rain or surge-discharge 
events (Sections 11.8.1 and 12.4.5.6; Wahl and Chambers, 2015; 
Moftakhari et al., 2017; Ward et al., 2018; Wu et al., 2018; Couasnon 
et al., 2020). Dynamical modelling studies show that co-occurrence 
of flood drivers raises ESLs at some locations in estuaries, such 
as the Rhine Delta (Zhong et al., 2013), the Netherlands (van den 
Hurk et  al., 2015), Taiwan, China (Chen and Liu, 2016), and the 
Hudson River, USA (Orton et al., 2020), particularly when hydrologic catchments are steep and cause high rainfall near the coast, such as 
in south-west UK (Svensson and Jones, 2004). The compound effect

of storm surge and rainfall contributes greater projected flood risk 
than climate-induced amplification (Hsiao et al., 2021). However, at 
other locations, co-occurrence was unimportant because streamflow 
timing did not coincide with the coastal peak storm surge (Hudson 
River, Orton et al., 2012; Rhine delta, Klerk et al., 2015). The SROCC 
(Oppenheimer et  al., 2019) detailed the complexity of interactions 
in deltaic environments. Direct increases in flooding driven by 
increasing RSL and storm surge, rain, or correlations between these 
flood-drivers (e.g.,  Moftakhari et  al., 2017; Orton et  al., 2020) are 
expected to be further accompanied by increases in flooding due to 
subsidence (vertical land movement) and sedimentation (RSL-driven 
blockage of river flows). The probability of concurrent surge, wave 
and precipitation events has been projected to increase by more 
than 25% by 2100 compared to present, with high northern latitudes

displaying compound flooding becoming more than 2.5 times as 
frequent, and weakening in the subtropics (Bevacqua et al., 2020). 
However, the number of studies on compound events is still limited 
and so there is low confidence  in understanding the extent by which 
compound events of surge with rain will change in response to RSL 
rise and climate change.
9.7 Final Remarks
The process-based assessment of observed and projected change in 
the ocean, cryosphere and sea level undertaken here reveals advances 
and gaps in reconstructions, observations, models and process 
understanding. Revisiting the updated assessments since AR5 and 
SROCC helps to gauge the robustness of understanding and quantitative 
assessments. The CMIP6 family of models builds on the experience 
of the CMIP5 models, and the projections of ISMIP6, LARMIP-2 and 
GlacierMIP strengthen understanding. Taken together with emulators 
of these simulations (Box 9.3) and transparent statistical approaches

(Section 9.6.3), this chapter provides projections that are consistent 
with the assessment of equilibrium climate sensitivity in this Report 
and that have improved estimates of uncertainty.
The largest uncertainties in future sea level and cryosphere change are 
related to the Greenland and Antarctic ice sheets (Sections 9.4.1.3, 
9.4.1.4, 9.4.2.5 and 9.4.2.6). While the ISMIP6 and LARMIP-2 
protocols provide simulations permitting uncertainty estimation 
and probabilistic inferences, remaining deep uncertainty   relates to 
ice-sheet processes and the atmospheric and oceanic conditions 
simulated by CMIP models in polar regions (Sections 9.4.2.3 and 
9.4.2.4). ISMIP6 and LARMIP-2 have not been simulated beyond 
2100, which greatly reduces the amount and variety of state-of-the-
art projections available to make ice-sheet and sea level projections 
beyond 2150. After 2150, limited agreement  causes us to consider all

projections as low confidence . Critically, the uncertainty in ice-sheet 
projections is the leading uncertainty in projections of future global 
sea level for the second half of this century and beyond (Section 9.6.3).
Glacier inventory and projection uncertainty has been a significant 
source of past sea level budget uncertainty and remains a dominant 
uncertainty until mid-century. Emissions scenario becomes the largest

1315
Ocean, Cryosphere and Sea Level Change  Chapter 99source of glacier change uncertainty by 2100, just as the relative 
importance of glacier loss is projected to decrease (Section 9.5.1). 
New high-resolution climate models show that sea surface 
temperature, overturning circulation, ocean heat content change 
and sea ice cover are considerably improved in most models when 
compared to the coarser resolution models. Change in the Southern 
Ocean and adjacent shelves (Section 9.2.3.2) is intimately linked to 
the future of the Antarctic Ice Sheet (Section 9.4.2.3), and projection 
of the Southern Ocean depends on oceanic and atmospheric drivers 
affecting heat (and carbon) uptake and sea ice. However, resolution 
remains a factor, as most CMIP6 models are far from resolutions that 
directly represent coastal and regional shallow-water processes, such 
as those beneath Antarctic ice shelves, in Greenland fjords and the

eddying convection found by the Overturning in the Subpolar North 
Atlantic Program.
Processes that change on long time scales  – particularly Atlantic 
Meridional Overturning Circulation, ocean heat content, and ice 
sheets – require additional projections beyond the CMIP scenarios to 
explore longer-term commitment, post-forcing recovery measured in 
centuries rather than years or decades, and potential tipping points 
and thresholds. Only a few new studies focused on longer time scales, 
and none based on CMIP6 models.Acknowledgements
We acknowledge the contribution of invited expert reviewers and 
the Ice Sheet Mass Balance Intercomparison Exercise (IMBIE) Team. 
Their  valuable input and advice have significantly improved the 
chapter. We thank colleagues, institutions and, in particular, our 
families for their support. Thanks to the Technical Support Unit and 
especially Sophie Berger for her support.

1316
Chapter 9 Ocean, Cryosphere and Sea Level Change9Frequently Asked Questions
FAQ 9.1 | Can Continued Melting of the Greenland and Antarctic Ice Sheets Be Reversed? How Long 
Would It Take for Them to Grow Back?
Evidence from the distant past shows that some parts of the Earth system might take hundreds to thousands of 
years to fully adjust to changes in climate. This means that some of the consequences of human-induced climate 
change will continue for a very long time, even if atmospheric heat-trapping gas levels and global temperatures 
are stabilized or reduced in the future. This is especially true for the Greenland and Antarctic ice sheets, which 
grow much more slowly than they retreat. If the current melting of these ice sheets continues for long enough, 
it becomes effectively irreversible on human time scales, as does the sea level rise caused by that melting.
Humans are changing the climate and there are mechanisms that amplify the warming in the polar regions

(Arctic and Antarctic). The Arctic is already warming faster than anywhere else (see FAQ 4.3). This is significant 
because these colder high latitudes are home to our two remaining ice sheets: Antarctica and Greenland. Ice 
sheets are huge reservoirs of frozen freshwater, built up by tens of thousands of years of snowfall. If they were 
to completely melt, the water released would raise global sea level by about 65 m. Understanding how these ice 
sheets are affected by warming of nearby ocean and atmosphere is therefore critically important. The Greenland 
and Antarctic ice sheets are already slowly responding to recent changes in climate, but it takes a long time 
for these huge masses of ice to adjust to changes in global temperature. That means that the full effects of 
a warming climate may take hundreds or thousands of years to play out. An important question is whether these

changes can eventually be reversed, once levels of greenhouse gases in the atmosphere are stabilized or reduced 
by humans and natural processes. Records from the past can help us answer this question.
For at least the last 800,000 years, the Earth has followed cycles of gradual cooling followed by rapid warming 
caused by natural processes. During cooling phases, more and more ocean water is gradually deposited as 
snowfall, causing ice sheets to grow and sea level to slowly decrease. During warming phases, the ice sheets 
melt more quickly, resulting in more rapid rises in sea level (FAQ 9.1, Figure 1). Ice sheets build up very slowly 
because growth relies on the steady accumulation of falling snow that eventually compacts into ice. As the 
climate cools, areas that can accumulate snow expand, reflecting back more sunlight that otherwise would keep 
the Earth warmer. This means that, once started, glacial climates develop rapidly. However, as the climate cools,

the amount of moisture that the air can hold tends to decrease. As a result, even though glaciations begin quite 
quickly, it takes tens of thousands of years for ice sheets to grow to a point where they are in balance with the 
colder climate.
Ice sheets retreat more quickly than they grow because of processes that, once triggered, drive self-reinforcing 
ice loss. For ice sheets that are mostly resting on bedrock above  sea level – like the Greenland Ice Sheet – the main 
self-reinforcing loop that affects them is the ‘elevation–mass balance feedback’ (FAQ 9.1, Figure 1, right). In this 
situation, the altitude of the ice-sheet surface decreases as it melts, exposing the sheet to warmer air. The lowered 
surface then melts even more, lowering it faster still, until eventually the whole ice sheet disappears. In places 
where the ice sheet rests instead on bedrock that is below  sea level, and which also deepens inland, including

many parts of the Antarctic Ice Sheet, an important process called ‘marine ice sheet instability’ is thought to drive 
rapid retreat (FAQ 9.1, Figure 1, left). This happens when the part of the ice sheet that is surrounded by sea water 
melts. That leads to additional thinning, which in turn accelerates the motion of the glaciers that feed into these 
areas. As the ice sheet flows more quickly into the ocean, more melting takes place, leading to more thinning 
and even faster flow that brings ever-more glacier ice into the ocean, ultimately driving rapid deglaciation of 
whole ice-sheet drainage basins.
These (and other) self-reinforcing processes explain why relatively small increases in temperature in the past led 
to very substantial sea level rise over centuries to millennia, compared to the many tens of thousands of years it 
takes to grow the ice sheets that lowered the sea level in the first place. These insights from the past imply that,

if human-induced changes to the Greenland and Antarctic ice sheets continue for the rest of this century, it will 
take thousands of years to reverse that melting, even if global air temperatures decrease within this or the next 
century. In this sense, these changes are therefore irreversible, since the ice sheets would take much longer to 
regrow than the decades or centuries for which modern society is able to plan.

1317
Ocean, Cryosphere and Sea Level Change  Chapter 99FAQ 9.1 (continued)
FAQ 9.1, Figure  1 | Ice sheets growth and decay. (Top)  Changes in ice-sheet volume modulate sea level variations. The grey line depicts data from a range 
of physical environmental sea level recorders such as coral reefs while the blue line is a smoothed version of it. (Bottom left)  Example of destabilization 
mechanism in Antarctica. (Bottom right)  Example of destabilization mechanism in Greenland.
Ice sheet
OceanFAQ 9.1: Can melting of the ice sheets be reversed?  
Once ice sheets are destabilised, it takes them tens of thousands of years to re-grow. 
These changes strongly affect sea level.
Global mean sea level (m)
Ice sheet Bedrock
Ice sheetIce sheet
Ocean
BedrockYears before present
Melting driven by ocean temperature Melting driven by air temperature10
0
-20
-40
-60
-80
-100
-120
-140
140,000 120,000 100,000 80,000 60,000 40,000 20,000 Today
The ice sheet is very thick therefore its surface is

very high and the air at high altitude is very cold
As the ice sheet melts, its surface goes down 
until it reaches a threshold, where the surrounding 
air is warmer and melts the ice even more quicklyWhen bedrock dips seaward or is flat, the retreat 
stops when warming stops. When ice sheet retreats, 
less ice is released into ocean
When bedrock dips landward the retreat is quick and 
self-sustained.  When ice sheet retreats, more ice 
is released into ocean – ice sheet retreats furtherSlow ice sheet growth
Rapid ice sheet retreat
Bedrock Bedrock

1318
FAQ 9.2 | How Much Will Sea Level Rise in the Next Few Decades?
As of 2018, global average sea level was about 15–25 cm higher than in 1900, and 7–15 cm higher than in 1971. 
Sea level will continue to rise by an additional 10–25 cm by 2050. The major reasons for this ongoing rise in sea 
level are the thermal expansion of seawater as its temperature increases, and the melting of glaciers and ice 
sheets. Local sea level changes can be larger or smaller than the global average, with the smallest changes in 
formerly glaciated areas, and the largest changes in low-lying river delta regions.
Across the globe, sea level is rising, and the rate of increase has accelerated. Sea level increased by about 4 mm 
per year from 2006 to 2018, which was more than double the average rate over the 20th century. Rise during

the early 1900s was due to natural factors, such as glaciers catching up to warming that occurred in the Northern 
Hemisphere during the 1800s. However, since at least 1970, human activities have been the dominant cause of 
global average sea level rise, and they will continue to be for centuries into the future.
Sea level rises either through warming of ocean waters or the addition of water from melting ice and bodies of 
water on land. Expansion due to warming caused about 50% of the rise observed from 1971 to 2018. Melting 
glaciers contributed about 22% over the same period. Melting of the two large ice sheets in Greenland and 
Antarctica has contributed about 13% and 7%, respectively, during 1971 to 2018, but melting has accelerated 
in the recent decades, increasing their contribution to 22% and 14% since 2016. Another source is changes in 
land-water storage: reservoirs and aquifers on land have reduced, which contributed about an 8% increase 
in sea level.

By 2050, sea level is expected to rise an additional 10–25 cm whether or not greenhouse gas emissions are reduced 
(FAQ 9.2, Figure 1). Beyond 2050, the amount by which sea level will rise is more uncertain. The accumulated 
total emissions of greenhouse gases over the upcoming decades will play a big role beyond 2050, especially in 
determining where sea level rise and ice-sheet changes eventually level off.
Even if net zero emissions are reached, sea level rise will continue because the deep ocean will continue to warm 
and ice sheets will take time to catch up to the warming caused by past and present emissions: ocean and ice 
sheets are slow to respond to environmental changes (see FAQ 5.3). Some projections under low emissions show 
sea level rise continuing as net zero is approached at a rate comparable to today (3–8 mm per year by 2100 versus 
3–4 mm per year in 2015), while others show substantial acceleration to more than five times the present rate by

2100, especially if emissions continue to be high and processes that accelerate retreat of the Antarctic Ice Sheet 
occur widely (FAQ 9.1).
Sea level rise will increase the frequency and severity of extreme sea level events at coasts (see FAQ 8.2), such as 
storm surges, wave inundation and tidal floods: risk can be increased by even small changes in global average 
sea level. Scientists project that, in some regions, extreme sea level events that were recently expected once in 
100 years will occur annually at 20–25% of locations by 2050 regardless of emissions, but by 2100 emissions choice 
will matter: annually at 60% of locations for low emissions, and at 80% of locations under strong emissions.
In many places, local sea level change will be larger or smaller than the global average. From year to year and 
place to place, changes in ocean circulation and wind can lead to local sea level change. In regions where large

ice sheets, such as the Fennoscandian in Eurasia and the Laurentide and Cordilleran in North America, covered 
the land during the last ice age, the land is still slowly rising up now that the extra weight of the ice sheets is 
gone. This local recovery is compensating for global sea level rise in these regions and can even lead to local 
decrease in sea level. In regions just beyond where the former ice sheets reached and the Earth bulged upwards, 
the land is now falling and, as a result, local sea level rise is faster than the global rate. In many regions within 
low-lying delta regions (such as New Orleans and the Ganges–Brahmaputra delta), the land is rapidly subsiding 
(sinking) because of human activities such as building dams or groundwater and fossil fuel extraction. Further, 
when an ice sheet melts, it has less gravitational pull on the ocean water nearby. This reduction in gravitational

attraction causes sea level to fall close to the (now less-massive) ice sheet while causing sea level to rise farther 
away. Melt from a polar ice sheet therefore raises sea level most in the opposite hemisphere or in low latitudes – 
amounting to tens of centimetres difference in rise between regions by 2100.

1319
Ocean, Cryosphere and Sea Level Change  Chapter 99FAQ 9.2 (continued)
FAQ 9.2, Figu re 1 | Observed and projected global mean sea level rise and the contributions from its major constituents.
FAQ 9.2: How much will sea level rise in the next few decades?
Emissions scenarios influence little sea level rise of the coming decades but has a huge ef fect 
on sea level at the end of the centur y. 
Global sea level rise (cm)Sea level has risen 
faster in the recent years Antarctic ice sheet
Greenland ice sheet
Glaciers
Ocean thermal expansioncm of sea level rise
(since 1971)
Water stored on landContributionsIn 2018 In 2050 In 2100
ObservationsVery high-emission scenario – SSP5-8.5
Low-emission scenario – SSP1-2.6
The scenario
matters a lot The scenario does
not matter too much 
SSP1-2.6SSP5-8.5
1980 2000 2020 2040 2060100
50
0
2080 2100

1320
FAQ 9.3 | Will the Gulf Stream Shut Down?
The Gulf Stream is part of two circulation patterns in the North Atlantic: the Atlantic Meridional Overturning 
Circulation (AMOC) and the North Atlantic subtropical gyre. Based on models and theory, scientific studies 
indicate that, while the AMOC is expected to slow in a warming climate, the Gulf Stream will not change much 
and would not shut down totally, even if the AMOC did. Most climate models project that the AMOC slows in the 
later 21st century under most emissions scenarios, with some models showing it slowing even sooner. The Gulf 
Stream affects the weather and sea level, so if it slows, North America will see higher sea levels and Europe’s 
weather and rate of relative warming will be affected.
The Gulf Stream is the biggest current in the North Atlantic Ocean. It transports about 30 billion kilograms

of water per second northward past points on the east coast of North America. It is a  warm current, with 
temperatures 5°C to 15°C warmer than surrounding waters, so it carries warmer water (thermal energy) from its 
southern origins and releases warmth to the atmosphere and surrounding water.
The Gulf Stream is part of two major circulation patterns, the Atlantic Meridional Overturning Circulation (AMOC) 
and the North Atlantic Subtropical Gyre (FAQ 9.3, Figure 1). The rotation of the Earth causes the big currents in 
both circulations to stay on the western side of their basin, which in the Atlantic means the circulations combine 
to form the Gulf Stream. Other large currents contribute to gyres, such as the Kuroshio in the North Pacific 
and the East Australian Current in the South Pacific, but the Gulf Stream is special in its dual role. There is no 
comparable deep overturning circulation in the North Pacific to the AMOC, so the Kuroshio plays only one role 
as part of a gyre.

The gyres circulate surface waters and result primarily from winds driving the circulation. These winds are not 
expected to change much and so neither will the gyres, which means the gyre portion of the Gulf Stream and the 
Kuroshio will continue to transport thermal energy poleward from the equator much as they do now. The gyre 
contribution to the Gulf Stream is 2 to 10 times larger than the AMOC contribution.
The Gulf Stream’s role in the AMOC is supplying surface source water that cools, becomes denser and sinks to 
form cold, deep waters that travel back equatorward, spilling over features on the ocean floor and mixing with 
other deep Atlantic waters to form a southward current at a depth of about 1500 metres beneath the Gulf 
Stream. This overturning flow is the AMOC, with the Gulf Stream in the upper kilometre flowing northward, 
and the colder deep water flowing southward.

The AMOC is expected to slow over the coming centuries. One reason why is freshening of the ocean waters: 
by meltwater from Greenland, changing Arctic sea ice, and increased precipitation over warmer northern seas. 
An array of moorings across the Atlantic has been monitoring the AMOC since 2004, with recently expanded 
capabilities. The monitoring of the AMOC has not been long enough for a trend to emerge from variability 
and detect long-term changes that may be underway (see FAQ 1.2). Other indirect signs may indicate slowing 
overturning – for example, slower warming where the Gulf Stream’s surface waters sink. Climate models show 
that this ‘cold spot’ of slower-than-average warming occurs as the AMOC weakens, and they project that this 
will continue. Paleoclimate evidence indicates that the AMOC changed significantly in the past, especially during 
transitions from colder climates to warmer ones, but that it has been stable for 8000 years.

What happens if the AMOC slows in a warming world? The atmosphere adjusts somewhat by carrying more heat, 
compensating partly for the decreases in heat carried by AMOC. But the ‘cold spot’ makes parts of Europe warm 
more slowly. Models indicate that weather patterns in Greenland and around the Atlantic will be affected, with 
reduced precipitation in the mid-latitudes, changing strong precipitation patterns in the tropics and Europe, and 
stronger storms in the North Atlantic storm track. The slowing of this current combined with the rotation of the 
Earth means that sea level along North America rises as the AMOC contribution to the Gulf Stream slows.
The North Atlantic is not the only site of sensitive meridional overturning. Around Antarctica, the world’s densest 
seawater is formed by freezing into sea ice, leaving behind salty, cold water that sinks to the bottom and spreads

northward. Recent studies show that melting of the Antarctic Ice Sheet and changing winds over the Southern 
Ocean can affect this southern meridional overturning, affecting regional weather.

1321
Ocean, Cryosphere and Sea Level Change  Chapter 99FAQ 9.3 (continued)
FAQ 9.3, Figure  1 | Horizontal (gyre) and vertical (Atlantic Meridional Overturning Circulation, AMOC) circulations in the Atlantic today 
(left) and in a warmer world (right).  The Gulf Stream is a warm current composed of both circulations.
The Gulf Stream, a warm current, is expected to weaken but not cease. This slowdown will affect regional weather 
and sea level.  
Warm surface current 
driven by winds and replenishing sinking   1
Water has become fresher and 
lighter and therefore sinks less 
 2   Much less 
heat and water 
are transferred
  3
The Gulf Stream 
weakens but the portion pushed by winds remainsThe Gulf Stream is part of both the horizontal, subtropical gyre and the vertical, Atlantic Meridional Overturning Circulation (AMOC)Climate change weakens the AMOC, which slows the Gulf Stream down
2
W
ater and 
heat transferred 
   from the tropics to    
northern latitudesClose to the poles,

water cools, becomes heavier 
and sinks to the bottom  
4
The cold deep water 
is exported southwardToday In a warmer worldFAQ 9.3: Will the Gulf Stream shut down?
GULF 
STREAM
GULF 
STREAM
Weakened
AMOC AMOC
GYRE
GYRE3
1

1366
Chapter 10 Linking Global to Regional Climate Change10Executive Summary
Although climate change is a global phenomenon, its manifestations 
and consequences are different in different regions, and therefore 
climate information on spatial scales ranging from sub-continental to 
local is used for impact and risk assessments. Chapter  10 assesses 
the foundations of how regional climate information is distilled 
from multiple, sometimes contrasting, lines of evidence. Starting 
from the assessment of global-scale observations in Chapter  2, 
Chapter 10 assesses the challenges and requirements associated with 
observations relevant at the regional scale. Chapter 10 also assesses 
the fitness of modelling tools available for attributing and projecting 
anthropogenic climate change in a regional context starting from the 
methodologies assessed in Chapters 3 and 4. Regional climate change 
is the result of the interplay between regional responses to both

natural forcings and human influence (considered in Chapters 2, 5, 6 
and 7), responses to large-scale climate phenomena characterizing 
internal variability (considered in Chapters 1–9), and processes and 
feedbacks of a regional nature.
Chapter  10 is the first of four chapters that assess regional-scale 
information in this Report. The region-by-region assessment 
of past and future changes in extremes (Chapter  11), climatic 
impact-drivers (Chapter  12) and mean climate (Atlas) relies on 
the sources and methodologies used for constructing regional 
climate change information assessed in Chapter  10. Building on 
the assessment of observations and modelling tools of Chapter 10, 
Chapter  11  assesses  the observation and modelling of extremes. 
Chapter  10 assesses methodologies to attribute multi-decadal 
regional trends to the interplay between external forcing and internal 
variability, while Chapter  11 assesses the attribution of extreme

events. The assessment of climate services in Chapter 12 builds on 
the assessment of distillation of regional climate information from 
multiple lines of evidence in Chapter 10.
Distilling regional climate information from multiple lines of 
evidence and taking the user context into account will increase 
the fitness, usefulness and relevance for decision-making 
and enhances the trust users will have in applying it 
(high confidence ). This distillation process can draw upon multiple 
observational datasets, ensembles of different model types, process 
understanding, expert judgement and indigenous knowledge. 
Important elements of distillation include attribution studies, the 
characterization of possible outcomes associated with internal 
variability and a comprehensive assessment of observational, model 
and forcing uncertainties and possible contradictions using different 
analysis methods. Taking the values of the relevant actors into

account when co-producing climate information, and translating this 
information into the broader user context, improves the usefulness 
and uptake of this information ( high confidence ). {10.5}  Observations and Models as Sources of Regional Information
The use of multiple sources of observations and tailored 
diagnostics to evaluate climate model performance increases 
trust in future projections of regional climate ( high confidence ). 
The availability of multiple observational records, including reanalyses, 
that are fit for evaluating the phenomena of interest and account for 
observational uncertainty, are fundamental for both understanding 
past regional climate change and assessing climate model 
performance at regional scales ( high confidence ). Employing tailored, 
process-oriented and potentially multivariate diagnostics to evaluate 
whether a  climate model realistically simulates relevant aspects of 
present-day regional climate increases trust in future projections

of these aspects ( high confidence ). {10.2.2, 10.3.3}
Currently, scarcity and reduced availability of adequate 
observations increase the uncertainty of long-term 
temperature and precipitation estimates ( virtually certain ). 
Precipitation measurements in mountainous areas, especially of solid 
precipitation, are strongly affected by gauge location and setup ( very 
high confidence ). Over data-scarce regions or over complex orography, 
gridded temperature and precipitation products are strongly affected 
by interpolation methods. Lack of access to the raw station data used 
to create gridded products compromises the trustworthiness of these 
products since the influence of the gridding process on the product 
cannot be assessed. The use of statistical homogenization methods 
reduces uncertainties related to long-term warming estimates at regional 
scales ( virtually certain ). {10.2.2, 10.6.2, 10.6.3, 10.6.4, Box 10.3} 
Regional reanalyses provide surrogates of observed climate

variables that are highly relevant in areas with scarce surface 
observations.  Regional reanalyses represent the distributions of 
precipitation, surface air temperature, and surface wind, including the 
frequency of extremes, better than global reanalyses ( high confidence ). 
However, their usefulness is limited by their short length, the typical 
regional model errors, and the relatively simple data assimilation 
algorithms. {Section 10.2.1} 
Global and regional climate models are important sources 
of climate information at the regional scale.  Global models by 
themselves provide a useful line of evidence for the construction of 
regional climate information through the attribution or projection 
of forced changes or the quantification of the role of the internal 
variability ( high confidence ). Dynamical downscaling using regional 
climate models adds value in representing many regional weather and 
climate phenomena, especially over regions of complex orography or

with heterogeneous surface characteristics ( very high confidence ). 
Increasing climate model resolution improves some aspects of 
model performance ( high confidence ). Some local-scale phenomena 
such as land–sea breezes and mountain wind systems can only be 
realistically represented by simulations at a  resolution of the order 
of 10 km or finer  ( high confidence ). Simulations at kilometre-scale 
resolution add value in particular to the representation of convection, 
sub-daily precipitation extremes ( high confidence ) and soil-moisture–
precipitation feedbacks ( medium confidence ). Sensitivity experiments 
aid the understanding of regional processes and can provide additional 
user-relevant information. {10.3.3, 10.4, 10.5, 10.6}

1367
Linking Global to Regional Climate Change Chapter 1010The performance of global and regional climate models 
and their fitness for future projections depend on their 
representation of relevant processes, forcings and drivers 
and on the specific context.  Improving global model performance 
for regional scales is fundamental for increasing their usefulness 
as regional information sources. It is also key for improving the 
boundary conditions for dynamical downscaling and the input for 
statistical approaches, in particular when regional climate change 
is strongly influenced by large-scale circulation changes. Increasing 
resolution per se does not solve all performance limitations. 
Including the relevant forcings (e.g.,  aerosols, land-use change 
and stratospheric ozone concentrations) and representing the 
relevant feedbacks (e.g.,  snow–albedo, soil-moisture–temperature, 
soil-moisture–precipitation) in global and regional models is

a prerequisite for reproducing historical regional trends and ensuring 
fitness for future projections ( high confidence ). The sign of projected 
regional changes of variables such as precipitation and wind speed 
is in some cases only simulated in a trustworthy manner if relevant 
regional processes are represented ( medium confidence ). {10.3.3, 
10.4.1, 10.4.2, 10.6.2, Cross-Chapter Box 10.2} 
Statistical downscaling, bias adjustment and weather 
generators are useful approaches for improving the 
representation of regional climate from dynamical climate 
models.  Statistical downscaling methods with carefully chosen 
predictors and an appropriate model structure for a given application 
realistically represent many statistical aspects of present-day daily 
temperature and precipitation ( high confidence ). Bias adjustment 
has proven beneficial as an interface between climate model 
projections and impact modelling in many different contexts

(high confidence ). Weather generators realistically simulate many 
statistical characteristics of present-day daily temperature and 
precipitation, such as extreme temperatures and wet- and dry-day 
transition probabilities ( high confidence ). {10.3.3} 
The performance of statistical downscaling, bias adjustment 
and weather generators in climate change applications 
depends on the specific model and on the dynamical climate 
model driving it.  Knowledge is still limited about suitable predictors 
for statistical downscaling of regional climate change, particularly for 
precipitation. Bias adjustment cannot overcome all consequences 
of unresolved or strongly misrepresented physical processes, 
such as large-scale circulation biases or local feedbacks, and may 
instead introduce other biases and implausible climate change 
signals ( medium confidence ). Using bias adjustment as a  method 
for statistical downscaling, particularly for coarse-resolution global

models, may lead to substantial misrepresentations of regional 
climate and climate change ( medium confidence ). Instead, dynamical 
downscaling may resolve relevant local processes prior to bias 
adjustment, thereby improving the representation of regional 
changes. The performance of statistical approaches and their fitness 
for future projections depends on predictors and change factors 
taken from the driving dynamical models ( high confidence ). {10.3.3, 
Cross-Chapter Box 10.2}Different types of climate model ensembles allow for the 
assessment of regional climate projection uncertainties, 
although ensemble spread is not a  full measure of the 
uncertainty ( very high confidence ). Multi-model ensembles 
enable the assessment of regional climate response uncertainty 
(very  high confidence ). Discarding models that fundamentally 
misrepresent processes relevant for a  given purpose improves the 
fitness of multi-model ensembles for generating regional climate

information ( high confidence ). At the regional scale, multi-model 
mean and ensemble spread are not sufficient to characterize 
low-likelihood, high-impact changes or situations where different 
models simulate substantially different or even opposing changes 
(high confidence ). In such cases, storylines aid the interpretation 
of projection uncertainties. Since AR5, the availability of multiple 
single-model initial-condition large ensembles (SMILEs) allows for 
a more robust separation of model uncertainty and internal variability 
in regional-scale projections and provides a  more comprehensive 
spectrum of possible changes associated with internal variability 
(high confidence ). {10.3.4} 
Interplay Between Human Influence and 
Internal Variability at Regional Scales
Human influence has been a  major driver of regional mean 
temperature change since 1950 in many sub-continental 
regions of the world ( virtually certain ). Regional-scale detection

and attribution studies as well as observed emergence analysis 
provide robust evidence  supporting the dominant contribution of 
human influence to regional temperature changes over multi-decadal 
periods. {10.4.1, 10.4.3}
While human influence has contributed to multi-decadal mean 
precipitation changes in several regions, internal variability 
can delay emergence of the anthropogenic signal in long-term 
precipitation changes in many land regions ( high confidence ). 
Multiple attribution approaches, including optimal fingerprinting, 
grid-point detection, pattern recognition and dynamical adjustment 
methods, as well as multi-model, single-forcing large ensembles and 
multi-centennial paleoclimate records, support the contribution of 
human influence to several regional multi-decadal mean precipitation 
changes  ( high confidence ). At regional scale, internal variability 
is stronger and uncertainties in observations, models and human

influence are all larger than at the global scale, precluding a robust 
assessment of the relative contributions of greenhouse gases, 
stratospheric ozone, different aerosol species and land-use/land-cover 
changes. Multiple lines of evidence, combining multi-model ensemble 
global projections with those coming from SMILEs, show that internal 
variability is largely contributing to the delayed or absent emergence 
of the anthropogenic signal in long-term regional mean precipitation 
changes ( high confidence ). {10.4.1, 10.4.2, 10.4.3, 10.6.3, 10.6.4}

1368
Chapter 10 Linking Global to Regional Climate Change10Various mechanisms operating at different time scales can 
modify the amplitude of the regional-scale response of 
temperature, and both the amplitude and sign of the response 
of precipitation, to human influence ( high confidence ).  These 
mechanisms include non-linear temperature, precipitation and soil 
moisture feedbacks, slow and fast responses of sea surface temperature 
patterns and atmospheric circulation changes to increasing greenhouse 
gases. {10.4.3}
Urban Climate
Many types of urban parametrizations simulate radiation and 
energy exchanges in a  realistic way ( very high confidence ). 
For urban climate studies focusing on the interplay between the 
urban heat island and regional climate change, a simple single-layer 
parametrization is fit for purpose ( medium confidenc e). New networks 
of monitoring stations in urban areas provide key information to

enhance the understanding of urban microclimates and improve 
urban parametrizations. {Box 10.3}
The difference in observed warming trends between cities and 
their surroundings can partly be attributed to urbanization 
(very high confidence ). Annual mean daily minimum temperature 
is more affected by urbanization than annual mean daily maximum 
temperature ( very high confidence ). The global annual mean surface 
air temperature response to urbanization is, however, negligible ( very 
high confidence ). {Box 10.3}
Future urbanization will amplify the projected air temperature 
change in cities regardless of the characteristics of the 
background climate, resulting in a warming signal on minimum 
temperatures that could be as large as the global warming 
signal ( very high confidence ). A  large effect is expected from 
the combination of future urban development and more frequent 
occurrence of extreme climatic events, such as heatwaves ( very high 
confidence ). {Box 10.3}

Distillation of Regional Climate Information
The process of distilling regional climate information from 
multiple lines of evidence can vary substantially from one 
case to another.  Although methodologies for distillation have been 
established, in practice the process is conditioned by the sources 
available, the actors involved and the context, which depend heavily 
on the regions considered, and is framed by the question being 
addressed. To make the most appropriate decisions and responses to 
changing climate, it is necessary to consider all physically plausible 
outcomes from multiple lines of evidence, especially in the case 
when they are contrasting. {10.5, 10.6, Cross-Chapter Box  10.1, 
Cross-Chapter Box 10.3} Confidence in the distilled regional climate information is 
enhanced when there is agreement across multiple lines of 
evidence.  For example, the apparent contradiction between the 
observed decrease in Indian monsoon rainfall over the second half of

the 20th century and the projected long-term increase is explained by 
attribution of the trends to different forcings, with aerosols dominating 
recently and greenhouse gases in the future ( high confidence ). For 
the Mediterranean region, the agreement between different lines of 
evidence, such as observations, projections by regional and global 
models, and understanding of the underlying mechanisms, provides 
high confidence  in summer warming that exceeds the global average. 
{10.5.3, 10.6, 10.6.3, 10.6.4, Cross-Chapter Box 10.3}
The outcome of distilling regional climate information can 
be limited by inconsistent or contradictory information.  Initial 
observational analyses of the Cape Town drying showed a  strong, 
post-1979 association between increasing greenhouse gases, changes 
in a key mode of variability (the Southern Annular Mode) and drought 
in the Cape Town region. However, not all global models show this

association, and subsequent analysis extending farther back in time, 
when human influence was weaker, showed no strong association in 
observations between the Southern Annular Mode and Cape Town 
drought. Thus, despite the consistency among global-model future 
projections, there is medium confidence  in a projected future drier 
climate for Cape Town. Likewise, the distillation process results in 
low confidence  in the influence of Arctic warming on mid-latitude 
climate because of contrasting lines of evidence. {10.5.3, 10.6.2, 
Cross-Chapter Box 10.1, Cross-Chapter Box 10.3}

1369
Linking Global to Regional Climate Change Chapter 101010.1 Foundations for Regional 
Climate Change Information
10.1.1 Introduction
This chapter assesses the foundations for the distillation of regional 
climate change information from multiple lines of evidence. The 
AR5, SR1.5 and SRCCL reports underlined the relevance of assessing regional climate information that is useful and relevant to the decision 
scale (Box 10.1). To respond to this need, the AR6 WGI Report includes 
four regional chapters of which this is the first. Chapter 10 assesses the 
sources and methodologies used by the Chapters 11, 12 and Atlas to 
construct regional information. Chapter 10 builds on the assessment 
of methodologies considered to construct global climate change 
information in Chapters 2 to 4 and on the processes assessed in 
Chapters 5 to 9. Additionally, this chapter assesses the methodologies 
OutcomeData 
with uncertainty estimatesObservational 
dataModel
dataProcess

understandingLiterature Climate
experts
Global models
Regional models
Statistical 
downscaling
Bias-adjusted 
model dataMultiple lines of evidence
Storylines
of plausible futuresGraphicsIdealised scenario 
experiments
Sensitivity and attribution 
Theoretical knowledge of 
drivers and forcings of 
regional climateIn situ and remote 
Paleo-observations
Climate 
distilled from the 
climate sources and 
provided in the form 
of:Climate information construction
User-relevant 
climate 
The output format 
is flexible, 
determined by the 
user needsUser elements User elements
Co-producing climate 
information User-oriented climate information constructionDistillation process (framed by context and values)Input
Process
Output
ProcessSection 10.2
Chapter 2
Sections 10.3/10.4
Chapters 3/4/8/9/11
AtlasSection 10.4/10.6
Chapters 2-9/11
Atlas
Section 10.5/10.6 
Chapters 11/12
Section 10.6
Section 10.5
Chapters 1/11/12
WGII

Sources of climate information
Synthesis
with uncertainty assessment
for user action following a co-exploration 
approach and introducing user elements
Figure 10. 1 | Diagram of the processes leading to the construction of regional climate information (blue) and user-relevant regional climate information 
(brown).  The chapter sections and the other chapters of the Report involved in each step are indicated in rectangles. WGII stands for Working Group II. Literature refers to 
scientific and technical literature, and climate experts refers to climate scientists, practitioners and local communities, as defined in Section 10.5.

1370
Chapter 10 Linking Global to Regional Climate Change10for the co-production of regional climate information, the role of the 
different actors involved in the process and the relevance of the user 
context and values.
Regional climate change refers to a change in climate in a given region 
(Section 10.1.2.1) identified by changes in the mean or higher moments 
of the probability distribution of a climate variable and persisting for 
a  few decades or longer. It can also refer to a  change in temporal 
properties such as persistence and frequency of occurrence of weather 
and climate extreme events. Regional climate change may be caused by 
natural internal processes such as atmospheric internal variability and 
local climate response to low-frequency modes of climate variability 
(Technical Annex IV), as well as by changes in external forcings such as 
modulations of the solar cycle, orbital forcing, volcanic eruptions, and

persistent anthropogenic changes in the composition of the atmosphere 
or in land use and land cover (Cross-Chapter Box 3.2; IPCC, 2018a), 
in addition to the interactions and feedbacks between them. Process 
interaction in space is pervasive, which means that small spatial scales 
often have an influence on the larger scales (Palmer, 2013; Sandu 
et al., 2016). Depending on the context, a region may refer to a large 
area such as a monsoon region, but may also be confined to smaller 
areas such as coastlines, mountain ranges or human settlements like 
cities. Users (understood as anyone incorporating climate information 
into their activity) often request climate information for these range of 
scales since their operating and adaptation decision scales range from 
the local to the sub-continental level.
Given the many types of regional climates, the broad range of spatial

and temporal scales (Section 10.1.2), and the diversity of user needs, a variety of methodologies and approaches have been developed to 
construct regional climate change information. The sources include 
global and regional climate model simulations, statistical downscaling 
and bias adjustment methods. A commonly used source is long-term 
(end-of-century) model projections of regional climate change, as well 
as near-term (next 10 years) climate predictions (Kushnir et al., 2019; 
Rössler et al., 2019a). Regional observations, with their associated 
challenges, are a  key source for the regional climate information 
construction process (Q. Li et  al., 2020). High-quality observations 
that enable monitoring of the regional aspects of climate are used to 
adjust inherent model biases and are the basis for assessing model 
performance. Process understanding and attribution of observed 
changes to large- and regional-scale anthropogenic and natural

drivers and forcings are also important sources.
All these sources are used, when available, to distil regional climate 
information from multiple lines of evidence (Figure 10.1). The resulting 
climate information can then be integrated, following a co-production 
process involving both the user and the producer, into a user context 
that often is already taken into account when constructing the regional 
climate information. In fact, the distillation process leading to the 
climate information can consider the specific context of the question at 
stake, the values of both the user and the producer, and the challenge 
of communicating across different communities (Section 10.5).
The chapter (Figure 10.2) starts with an introduction of the concepts 
used in the distillation of regional climate information (Section 10.1). 
Section  10.2 addresses the aspects associated with the access to 
and use of observations, while different modelling approaches 
Values and context

10.5.2Chapter 10: Linking global to regional climate change Chapter 10: Quick guide
Cross-chapter boxesSection 10.1
Foundations
Section 10.2
ObservationsSection 10.3
ModelsSection 10.4
Attribution 
and emergenceDistillation
10.1.4 | 10.5.3
Emergence
10.4.3
Regional attribution
10.4.1 | 10.4.2Internal variability
10.3.4 | 10.4
Observational uncertainty
10.2.2Model evaluation and fitness
10.3.1Section 10.6
Comprehensive examplesSection 10.5
Context and distillation
Section 10.7
Box 10.1
Regional climate in AR5 
and recent special reportsBox 10.2
StorylinesBox 10.3
Urban climateCC Box 10.1
Arctic influence 
on mid-latitudesCC Box 10.2
Bias 
adjustmentCC Box 10.3
Assessment of 
climate informationCC Box 10.4
Climate change Hindu 
Kush HimalayaChapter 10 assesses the foundations of how regional climate information is 
distilled from multiple, sometimes contrasting, lines of evidence.Key topics and corresponding sub-sections
BoxesFAQsDownscaling methods
10.3

1371
Linking Global to Regional Climate Change Chapter 1010are introduced and assessed in Section  10.3. Section  10.3 also 
addresses the performance of models in simulating relevant climate 
characteristics as needed to estimate the credibility of future 
projections. Section 10.4 assesses the interplay between anthropogenic 
causes and internal variability at regional scales , and its relevance for 
the attribution of regional climate changes and the emergence of 
regional climate change signals. Section 10.5 tackles the issue of how 
regional climate information is distilled from different sources taking 
into account the context and the  values of both the producer and 
the user. Section 10.6 illustrates the distillation approach using three 
comprehensive examples. Finally, Section 10.7 lists some limitations to 
the assessment of regional climate information.
10.1.2 Regional Climate Change and the 
Relevant Spatial and Temporal Scales

The global coupled atmosphere–ocean–land–cryosphere system, 
including its feedbacks, shows variability over a  wide spectrum 
of spatial and temporal scales (Hurrell et  al., 2009). This section discusses concepts and definitions of what can be considered 
a region, the relevant temporal scales and region-specific aspects of 
the baselines used.
10.1.2.1 Spatial Scales and Definition of Regions
Large-scale climate and the associated phenomena have been defined 
in Chapter 2 (e.g., Cross-Chapter Box 2.2) as ranging from global and 
hemispheric, to ocean basin and continental scales. The definition of 
the regional scale is case specific in the AR6 WGI Report. Section 1.4.5  
provides definitions of the different regional types adopted by the 
different chapters. In this chapter, regional scales are defined as 
ranging from the size of sub-continental areas (e.g., the Mediterranean 
basin) to local scales (e.g.,  coastlines, mountain ranges and cities)

without prescribing any formal regional boundaries. These spatial 
length scales range from a few thousand down to a few kilometres 
and the relevant driving modes and processes at regional scales are 
summarized in Figure 10.3. In contrast to Chapters 11, 12 and Atlas, 
which make a  region-by-region assessment of climate change, this 
chapter does not necessarily restrict itself to the use of the AR6 WGI 
Global mode ls
100 m 1 km 1000 km 100 km 10 km 10000 km 10 mHigh-re s. globa l mode ls
Regi onal mode ls
CPMscenturies
UC mode ls
Circumfere nce of the Earth PDV, AMV
ENSOQBO
Monsoon
Tropical
wavesMJO
Blockings
Fronts
Polar LowsTC
MCS
Flines
öhnSquall 
ThermalsBaroclinic
waves
Land-Sea
breeze
Thunde r 
Storms
CATconvectionTornadoes
Whirlwin d
ABLPlumes
turbulenceCumulusPlane tary
Down BurstsUrban
effect
Reference region
Continen tal region
Typolog ical regionwaves
secondsminu tesdaysweeksmon ths
hoursdecades
yearsABL: atmospheric boundary layer

AMV: Atlantic multidecadal variability
CAT: clear air turbulence
CPMs: convection permitting models
ENSO: El Niño-Southern oscillation
MCS: mesoscale convective system
MJO: Madden-Julian oscillation
PDV: Paciﬁc decadal variability
QBO: quasi-biennial oscillation
TC: tropical cyclone
UC: urban canopy
CH1–CH9
CH10–CH12, Atlas
Not covered in AR6 WGI report
Figure  10.3 | Schematic diagram to display interacting spatial and temporal scales relevant to regional climate change 
information.  Figure adapted from Orlanski (1975). The processes included in the different models and model components considered in 
Chapter  10 are indicated as a  function of these scales. The various types of models (including global and regional climate models) for 
constructing regional climate information are assessed in Section 10.3.1 and Box 10.3.

1372
Chapter 10 Linking Global to Regional Climate Change10Reference Regions (Section 1.4.5 and Atlas.1.3). Different regional 
definitions have been used in sections 10.4 and 10.6, selected for their 
adequacy to illustrate methodological aspects (e.g., for the attribution 
of long-term regional trends, regions that display such trends have 
been selected). Typological regions (Section 1.4.5 and Atlas.1.3) are 
used in Box 10.3 and Cross-Chapter Box 10.4.
10.1.2.2 Temporal Scales, Baselines and 
Dimensions of Integration
The concept of a unified and seamless framework for weather and 
climate prediction (A. Brown et  al., 2012; Hoskins, 2013) provides 
the context for understanding and simulating regional climate across 
multiple spatial and temporal scales. This concept is embodied in the 
subseasonal-to-seasonal (Vitart et  al., 2017) and the seasonal-to-
multi-annual (Smith et  al., 2020) prediction activities that generate

regional climate information across temporal scales. The seamless 
framework benefits from the convergence of methods traditionally 
used in weather forecasting and climate projections, in particular 
the role of the initialization in climate models and the strategies for the 
evaluation of physical processes relevant at different temporal scales.
The relatively short observational record (Section 10.2) is a primary 
challenge to estimate the forced signal and to isolate low-frequency, 
multi-decadal and longer-term internal variability (Frankcombe 
et al., 2015; Overland et al., 2016; Bathiany et al., 2018). Because 
only one realization of the actual climate exists, it is non-trivial to 
extract estimates of internal and forced variability from the available 
data (Frankcombe et al., 2015). As an alternative, approaches that 
use large observational ensembles can be applied (Section  10.4; 
McKinnon and Deser, 2018).

There is a  close relationship between spatial and temporal scales 
(Figure 10.3). For example, an individual convective storm may exhibit 
scales of variability ranging from metres and seconds to kilometres 
and hours, while for El Niño–Southern Oscillation (ENSO) the scales 
of variability are regional to hemispheric in extent and multi-year 
in length. These scales interact and the interactions are represented 
in climate models, although the ability of current models to simulate 
regional phenomena and even large-scale climate drivers still leaves 
room for improvement (Section 10.3) and limits their capability to 
represent the interactions across spatial and temporal scales.
It is important to note that in this chapter and subsequent regional 
chapters, including the Interactive Atlas, the baselines and reference 
periods used for climate change estimates from regional models may 
vary from those used in Chapters 1 to 9. In these chapters three main

time baselines are defined for the past, for example, pre-industrial 
(before 1750), early industrial (1850–1900) and recent (1995–2014), 
while the future reference periods are 2021–2040 (near term), 
2041–2060 (mid-term) and 2081–2100 (long term) (Section  1.4.1 
and Cross-Chapter Box  1.2). Regional climate simulations used in 
the recent literature have been performed with different baselines. 
The differences are often due to the availability of the boundary 
conditions from global simulations, leading to periods chosen 
for those simulations like 1950–2005, in line with the CMIP5 
historical simulations followed by projections from 2005 onwards (Vaittinada Ayar et al., 2016; Zhang et al., 2017; L. Cai et al., 2018). 
For simulations that use CMIP3 boundary conditions other periods 
have been used. As a consequence, these regional simulations mix 
for the recent period historical simulations with projections. The 
mismatch needs to be considered when assessing results obtained

from both global and regional models in the context of the climate 
information distillation process, or when linking the regional chapters 
to the assessments performed in previous chapters. The choice 
of baseline provides a  source of uncertainty for the assessment 
of climate impacts (e.g., for the response of bird species in Africa; 
Baker et al., 2016). Besides, a range of different baselines may need 
to be considered to satisfy a variety of users, since this choice affects 
the perceived result (Dobor and Hlásny, 2019). The influence of the 
different baseline periods can be explored using the Interactive Atlas 
where different baselines are available, for example, 1986–2005 
(according to AR5), 1995–2014 (this Report), and both 1961–1990 
and 1981–2010 (WMO).
One way of overcoming the baseline uncertainty is to define the 
results for a given model based on specific global mean temperature 
changes from the pre-industrial period (e.g.,  Sylla et  al., 2018 for

West Africa; Kjellström et al., 2018 for Europe; Taylor et al., 2018 for 
the Caribbean; Montroull et al., 2018 for South America). The specific 
global mean temperature is known as global warming level (GWL; 
Sections 1.6.2 and 10.6.4, and Cross-Chapter Box  11.1). The GWL 
is a useful dimension of integration because important changes in 
regional climate, including many types of extremes, scale quasi-
linearly with the GWLs, often independently of the underlying 
emissions scenarios (e.g., Hoegh-Guldberg et al., 2018; Beusch et al., 
2020; Seneviratne and Hauser, 2020), always taking into account 
caveats described in Cross-Chapter Box 11.1. In addition, GWLs allow 
a  separated analysis of the global and regional climate responses 
associated with a  warming level (Section  10.6.4; Seneviratne and 
Hauser, 2020). The choice of global temperature goal in the context 
of the 2015 Paris Agreement means that there is an increasing desire

for the regional climate information to be expressed as a function 
of GWLs.
10.1.3 Sources of Regional Climate 
Variability and Change
Variability in regional climate arises from natural and anthropogenic 
forcings, internal variability including the local expression of large-scale 
remote drivers (also known as teleconnections), and the feedbacks 
between them. Due to the many possible drivers of variability and 
change (Figure  10.3), quantifying the interplay between internal 
modes of decadal variability and any externally forced component 
is crucial in attempts to attribute causes of regional climate changes 
(e.g., Hoell et al., 2017; Nath et al., 2018). A regional climate signal 
could arise purely due to some anthropogenic influence or conversely, 
entirely due to internal variability, but it is most likely the result of 
a combination of both (Section 10.4). This section briefly introduces 
these sources of regional variability and should be read along with

corresponding sections in Chapters 3, 6 and 7. Section 10.3 assesses 
their representation in climate models, Section 10.4 discusses their 
relevance for the attribution of multi-decadal trends and Section 10.6

1373
Linking Global to Regional Climate Change Chapter 1010refers to them as sources in specific examples where regional climate 
information is built. Section  8.2 offers a  companion discussion 
focussing on changes in the water cycle. An example of how changes 
in one region could act as a source for changes in a neighbouring one 
is assessed in the Cross-Chapter Box 10.1 for the linkages between 
polar and mid-latitude regions, an interaction that has led to 
substantial recent research. This section also introduces the sources 
of uncertainty in model-derived regional climate information and 
how the quantification of the uncertainties influences the confidence 
of the regional climate information.
10.1.3.1 Forcings Controlling Regional Climate
There are important differences in the processes affected by 
greenhouse gases (GHGs) over land and ocean. Notably, this leads 
to preferential warming of the land regions, which are themselves 
skewed towards the Northern Hemisphere (NH).

Variations in solar forcing (Section  2.2.1) could influence regional 
climate through its modulation of circulation patterns, although this 
research field is still hampered by large observational and modelling 
uncertainties. The 11-year solar cycle has been suggested to affect the 
leading atmospheric circulation modes of the North Atlantic region 
in model-based studies (Gray et al., 2013; Thiéblemont et al., 2015; 
Sjolte et al., 2018). In particular the solar cycle has been suggested as 
an important source of near-term predictability of the North Atlantic 
Oscillation (NAO; Kushnir et  al., 2019), while other studies have 
not found evidence for links between the solar cycle and NAO in 
observational records (Ortega et al., 2015; Sjolte et al., 2018; Chiodo 
et al., 2019). On centennial time scales, solar fluctuations were found 
to be correlated with the Eastern Atlantic Pattern (Sjolte et al., 2018). 
Possible influences on winter circulation and temperature over

Eurasia (Chen et al., 2015) and North America (Liu et al., 2014; Li and 
Xiao, 2018) have also been identified.
An updated assessment of past changes in stratospheric ozone can 
be found in Section 2.2.5.2. The AR6 assesses that both GHG and 
stratospheric ozone depletion have contributed to the expansion of 
the zonal mean Hadley cell in the Southern Hemisphere (SH) for the 
period 1981–2000 with medium confidence  (Section 3.3.3; Garfinkel 
et al., 2015; Waugh et al., 2015; Grise et al., 2019). There is medium 
confidence  that stratospheric ozone depletion contributed to the 
strengthening trend of the summer Southern Annular Mode (SAM) 
for the period 1970–1990, but this influence has been weaker since 
2000 (Section  3.7.2). The poleward shift of the SH westerlies has 
also been explained by stratospheric ozone depletion (Solman and 
Orlanski, 2016). Section 10.4 assesses its role in the multi-decadal 
increase of rainfall in south-eastern South America and Section 10.6.2

does so for the occurrence of the Cape Town drought.
Both natural and anthropogenic aerosols are often emitted at 
a regional scale, have a short atmospheric lifetime (from a few hours 
to several days; Section  6.1), are dispersed regionally and affect 
climate at a regional scale through radiative cooling/heating and cloud 
microphysical effects (Chapter 8; Rotstayn et al., 2015; Sherwood et al., 
2015). The majority of aerosols scatter solar radiation, but with strong 
regional variations (Shindell and Faluvegi, 2009) that lead to regional radiative effects of up to two orders of magnitude larger than the global 
average (B. Li et al., 2016; K. Li et al., 2016; Mallet et al., 2016). Black 
carbon, instead, is known to absorb solar radiation, leading to regional 
atmospheric warming patterns due to its inhomogeneous spatial 
distribution (Gustafsson and Ramanathan, 2016). Patterns of forcing 
generally follow those of aerosol burden. However, temperature and

precipitation responses are both local and remote (Z. Li et al., 2016; 
Kasoar et al., 2018; L. Liu et al., 2018; Samset et al., 2018; Thornhill 
et al., 2018; Westervelt et al., 2018). For instance, changes in aerosol 
concentrations in the NH have been reported to modulate monsoon 
precipitation in West Africa and the Sahel (Undorf et  al., 2018; 
Section 10.4.2.1) and in Asia (H. Zhang et al., 2018; Section 10.6.3).
Natural aerosols include mineral dust, volcanic aerosol and sea salt. 
The feedback processes between climate and mineral dust as well 
as sea salt are assessed in Section 6.4, while the volcanic aerosol 
is dealt with in Cross-Chapter Box  4.1. Mineral dust created by 
wind erosion of arid and semi-arid surfaces dominates the aerosol 
load over some areas. The major sources of contemporary dust are 
located in the arid topographic basins of northern Africa, Middle 
East, Central and south-west Asia, the Indian subcontinent, and East

Asia (Prospero et al., 2002; Ginoux et al., 2012) and emissions are 
controlled by changes in surface winds, precipitation, and vegetation 
(Ridley et al., 2014; W. Wang et al., 2015; DeFlorio et al., 2016; Evan 
et al., 2016; Pu and Ginoux, 2018). Dust both scatters and absorbs 
radiation and serves as a nuclei of warm and cold clouds (Chapter 6). 
The surface direct radiative effect is likely negative over land and 
ocean, especially when the assumed solar absorption by dust is 
large (Miller et al., 2014; Strong et al., 2015). Surface temperature 
and precipitation adjust to the direct radiative effect with both sign 
and magnitude depending on the dust absorptive properties. Dust 
often cools the surface,  but in regions such as the Sahara surface 
air temperature increases as the shortwave absorption by dust is 
increased, leading to increases of surface temperature over the major 
reflective dust sources (Miller et al., 2014; Solmon et al., 2015; Strong

et al., 2015; Jin et al., 2016; Sharma and Miller, 2017).
Volcanic eruptions load the atmosphere with large amounts of sulphur, 
which is transformed through chemical reactions and micro-physics 
processes into sulphate aerosols (Cross-Chapter Box 4.1; Stoffel et al., 
2015; LeGrande et al., 2016). If the plume reaches the stratosphere, 
sulphate aerosols can remain there for months or years (about two to 
three for large eruptions) and can then be transported to other areas by 
the Brewer-Dobson circulation. If the eruption occurs in the tropics, its 
plume is dispersed across the Earth in a few years, while if the eruption 
occurs in the high latitudes, aerosols mainly remain in the same 
hemisphere (Pausata et  al., 2015). The global temperature response 
observed after the last five major eruptions of the last two centuries 
is estimated to be around –0.2°C (Swingedouw et  al., 2017), in 
association with a general decrease of precipitation (Iles and Hegerl,

2017). Nevertheless, the statistical significance of the regional response 
remains difficult to evaluate over the historical era (Bittner et al., 2016; 
Swingedouw et al., 2017) due to the small sampling of large volcanic 
eruptions over this period and the fact that the signal is superimposed 
upon relatively large internal variability (Gao and Gao, 2018; Dogar 
and Sato, 2019). Evidence from paleoclimate observations is therefore 
crucial to obtain a  sufficient signal-to-noise ratio (Sigl  et  al.,  2015).

1374
Chapter 10 Linking Global to Regional Climate Change10Reconstructed modes of climate variability based on proxy records 
allow evaluation of the influence on those modes (Zanchettin et al., 
2013; Ortega et al., 2015; Sjolte et al., 2018; Michel et al., 2020).
Anthropogenic aerosols play a key role in climate change (Chapter 6). 
Although the global mean optical depth caused by anthropogenic 
aerosols did not change from 1975 to 2005 (Chapter 6), the regional 
pattern changed dramatically between Europe and eastern Asia 
(Fiedler et  al., 2017, 2019; Stevens et  al., 2017). Large regional 
differences in present-day aerosol forcing exist with consequences 
for regional temperature, hydrological cycle and modes of variability 
(Chapter 8, Section 10.6). Examples of regions with a notable role 
for anthropogenic aerosol forcing are the Indian monsoon region 
(Section  10.6.3) and the Mediterranean basin (Section  10.6.4).

Anthropogenic aerosols are also very relevant in many urban areas 
(Box 10.3; Gao et al., 2016; Kajino et al., 2017).
The SRCCL assessed that nearly three-quarters of the land surface is 
under some form of land use, particularly in agriculture and forest 
management (Jia et  al., 2019). The effects of land management 
on climate are much less studied than land cover effects although 
net cropland has changed little over the past 50 years, while 
land management has continuously changed (Jia et  al., 2019). 
Section 7.3.4.1 assesses the global influence of both land use and 
irrigation on the effective radiative forcings. Land cover changes 
and land management can influence climate locally, such as the 
urban heat island and non-locally as in the case of increased rainfall 
downwind of a  city (Jia et  al., 2019; Box  10.3) or the monsoon 
circulation affected by irrigation (Section  10.6.3). The influence 
of land cover changes and land management on regional climate

extremes is assessed in Section 11.1.6.
It is very likely  that the global land surface air temperature 
response to urbanization is negligible (Section  2.3.1.1.3). However, 
there is evidence that urbanization may regionally amplify the air 
temperature response to climate change in different climatic zones 
(Mahmood et  al., 2014), either under present (Doan et  al., 2016; 
Kaplan et  al.,  2017;  X.  Li et  al., 2018) or future climate conditions 
(Argüeso et al., 2014; Kim et al., 2016; Kusaka et al., 2016; Grossman-
Clarke et al., 2017; Krayenhoff et al., 2018). For instance, in northern 
Belgium, Berckmans et al. (2019) found that including urbanization 
scenarios for the near future (up to 2035) have a  comparable 
influence on minimum temperature (increasing it by 0.6°C) to that of 
the GHG-induced warming under RCP8.5.
10.1.3.2 Internal Drivers of Regional Climate Variability
Internal climate variability on seasonal to multi-decadal temporal

scales is substantial at regional scales. This variability arises from 
internal modes of atmospheric and oceanic variability, intrinsically 
coupled climate modes, and may additionally be driven by processes 
other than those originating the modes. It also interacts with the 
response of the climate system to external forcing. The teleconnections 
associated with the modes are useful to understand the relationship 
between large and regional scales (Annex IV: Modes of Variability). 
A description of various large-scale modes of variability can be found 
in Chapters 2, 3 and 8, and in Annex IV, while their future projections are assessed in Chapter 4. The specificities of their regional influence 
are briefly discussed here. More details of their typical temporal 
scales and regional influences can be found in Annex IV.
Atmospheric modes of variability may have seasonally-dependent 
regional effects like the North Atlantic Oscillation (NAO) in European

winter (Tsanis and Tapoglou, 2019) and summer (Bladé et al., 2012; 
Dong et al., 2013). Even though these modes are internal to the climate 
system, their variability can be affected by anthropogenic forcings. 
For instance, the SAM (Hendon et al., 2014) is both internally driven 
(Smith and Polvani, 2017), but also affected by recent stratospheric 
ozone changes (Bandoro et al., 2014). The teleconnections between 
these modes of variability and surface weather often exhibit 
considerable non-stationarity (Hertig et al., 2015).
Due to the large ocean heat capacity and their long temporal scales, 
multi-annual to multi-decadal modes of ocean variability such as 
the Pacific Decadal Variability (PDV; Newman et al., 2016) and the 
Atlantic Multi-decadal Variability (AMV; Buckley and Marshall, 2016) 
are key drivers of regional climate change. In the case of the AMV 
both natural (volcanic) and anthropogenic (aerosol) external forcings

are thought to be involved in its timing and intensity (Section 3.7.7). 
These modes not only affect nearby regions but also remote parts 
of the globe through atmospheric teleconnections (Meehl et  al., 
2013; Dong and Dai, 2015) and can act to modulate the influence of 
natural and anthropogenic forcings (Davini et al., 2015; Ghosh et al., 
2017; Ménégoz et  al., 2018b). The dynamics of the ocean modes 
is simultaneously affected by other modes of variability spanning 
the full range of spatial and temporal scales due to non-linear 
interactions (Figure 10.3; Kucharski et al., 2010; Dong et al., 2018).  
This mutual interdependence can result in changing characteristics of 
the connection over time (Gallant et al., 2013; Brands, 2017; Dong and 
McPhaden, 2017), and of their regional climate impact (Martín-Gómez 
and Barreiro, 2016, 2017). As with atmospheric modes of variability, 
the regional influence of ocean modes of variability on regional

climates can be seasonally dependent (Haarsma et al., 2015).
10.1.3.3 Uncertainty and Confidence
Uncertainty and confidence are treated in the same way in regional 
climate change information as in larger-scale (continental and 
global) climate problems (Chapter 1 and Section 10.3.4). The degree 
of confidence in climate simulations and in the resulting climate 
information typically depends on the identification of the role of the 
uncertainties (Section 10.3.4). Since the direct verification of simulations 
of future climate changes is not possible, model performance and 
reliable (i.e., trustworthy) uncertainty estimates need to be assessed 
indirectly through process understanding and a systematic comparison 
with observations of past and current climate (Section 10.3.3; Knutti 
et al., 2010; Eyring et al., 2019). The observational uncertainty, which is 
particularly large at regional scales, also has to be taken into account

(Section 10.2). These uncertainty estimates are then propagated in the 
distillation process to generate climate information.
Uncertainties in model-based future regional climate information arise 
from different sources and are introduced at various stages in the 
process (Lehner et al., 2020): (i) forcing uncertainties associated with

1375
Linking Global to Regional Climate Change Chapter 1010the future scenario or pathway that is assumed; (ii) internal variability; 
and (iii) uncertainties related to imperfections in climate models, also 
referred to as structural or model uncertainty. However, the relative 
role of each of these sources of uncertainty differs between the global 
and the regional scales as well as between variables and also between 
different regions (Lehner et al., 2020). One way to address the internal 
variability and model uncertainties is to consider results from both 
multiple models and multiple realizations of the same model (Eyring 
et al., 2016a; Lehner et al., 2020; Díaz et al., 2021). These models are 
at times also combined with different weights that are a function of 
their performance and independence to increase the confidence of the 
multi-model ensemble (Abramowitz et al., 2019; Brunner et al., 2019).
Other elements that play a  role are the inconsistency between

the global and regional models in dynamical downscaling or the 
observational and methodological uncertainty in bias-adjustment 
methods (Sørland et al., 2018). These elements, in addition to those 
typical of the uncertainty in global and large-scale phenomena 
(Chapters 1–9), affect the overall confidence of regional climate 
information. This complex scene with different sources of uncertainty 
makes the collection of results available from multi-model, 
multi-member simulations most useful when synthesized through 
a distillation process (Section 10.5.3).
10.1.4 Distillation of Regional Climate Information
Regional climate information is synthesized from different lines of 
evidence from a  number of sources (Sections 10.2–10.4) taking 
into account the context of a  user vulnerable to climate variability 
and change at regional scales (Baztan et  al., 2017) and the values 
of all relevant actors (Corner et  al., 2014; Bessette et  al., 2017) in

a process called distillation (Section 10.5). Distillation, understood as 
the process  of synthesizing information about climate change from 
different lines of evidence obtained from a  variety of sources and 
taking into account the user context and the values of all relevant 
actors, allows the connection of global climate change to the local 
and regional scales, where adaptation responses and policy decisions 
take place. Climate information is translated into the user context in 
a co-production process that introduces further user-relevant elements 
leading to user-relevant climate information (Figure  10.1; Pettenger, 
2016; Verrax, 2017) for a  specific demand like, for instance, guiding 
climate-resilient development (Kruk et al., 2017; Parker and Lusk, 2019).
The approaches adopted in the distillation of regional climate 
information are diverse and range from the simple delivery of data 
as information to co-production with the user using as many lines

of evidence as possible (Lourenço et  al., 2016). The availability 
and selection of the sources and the approach followed has 
implications for the usefulness of the information. For instance, it is 
well-established that it is invalid to take a time series from a gridcell 
of a  model simulation as equivalent to an observational estimate 
of a  point within the cell, due to the lack of representativeness 
(Section  10.3), and consequently the information building solely 
on this type of data source is of limited use. Relevant decisions are 
made during the distillation process, such as what method is most 
suitable to a specific user context and the question being addressed. The information may be provided in the form of summarized raw 
data, a set of user-oriented indicators, a set of figures and maps with 
either a brief description, in the form of a storyline, or formulated as 
rich and complex climate adaptation plans. The information typically

includes a description of the sources and assumptions, estimates of 
the associated uncertainty and its sources, and guidance to prevent 
possible misunderstandings in its communication.
The choices made for the distillation have typically been part of 
a linear supply chain, starting from the access to climate data that 
are transformed into maps or derived climate data products, and 
finally formulating statements that are communicated and delivered 
to a broad range of users (Hewitt et al., 2012; Hewitson et al., 2017). 
This methodology has proven to be valuable in many cases, but it 
is equally fraught with dangers of not communicating important 
assumptions, not estimating the impact of relevant uncertainties, 
and possibly causing misunderstandings in the handover to the 
user community. This has led to the emergence of new pathways 
to generate user-oriented climate information, many in the context 
of emerging climate services (Buontempo et al., 2018; Hewitt et al.,

2020), which are assessed in Section 10.5 and in Chapter 12.
10.1.5 Regional Climate Information  
in the AR6 WGI Report
This chapter is part of a  cluster devoted to regional climate 
(Chapters 10, 11, 12 and Atlas). It introduces many of the aspects 
relevant to the generation of regional climate information that are 
dealt with in detail elsewhere. Figure  10.4 summarizes how these 
chapters relate to one another and to the rest of the report.
Chapter 11 assesses observed, attributed and projected changes in 
weather and climate extremes, provides a mechanistic understanding 
on how changes in extremes are related to human-induced climate 
change and provides regional, continental and global-scale 
assessments on changes in extremes, including compound events. 
Chapter  12 identifies elements of the climate system relevant for 
sectoral impacts referred to as climatic impact-drivers (CIDs), assesses 
past and future evolutions of sector-relevant CIDs for each AR6

region, synthesizes such evolutions for different time periods and by 
GWL, and assesses how CIDs are used in climate services. The Atlas 
assesses observed, attributed and projected changes in mean climate, 
performs a comparison of CMIP5, CMIP6 and CORDEX simulations, 
evaluates downscaling performance and assesses approaches to 
communicate climate information. The Interactive Atlas facilitates 
the exploration of datasets assessed in all chapters through a wide 
range of maps, graphs and tables generated in an interactive manner. 
This allows for the comparison of changes at warming levels and 
scenario/time-period combinations, display of  indices for extremes 
and CIDs, and serves all chapters in the report to facilitate synthesis 
information and support the Technical Summary and the Summary 
for Policymakers.
Other chapters also include a strong regional component and provide 
context for the assessment of regional climate. Chapter 1 introduces

the different types of climatic regions used in the AR6 WGI Report

1376
Chapter 10 Linking Global to Regional Climate Change10
WGII and WGIII
Handshake and consistency with the WGII and WGIII 
Regional climate information relevant to mitigation and adaptation effortsRegional climate information in WGI AR6
Regional assessment Atlas
Observations, attribution, projections
Synthesis of regional and continental 
climate changes
Interactive Atlas guidance
Box: Water resources in small islandsClimatic impact-drivers (CIDs)12 Chapter
Observations, emergence, projections
Regional, Continental and global
12.4: Climate hazards in small islands, 
open ocean, polar terrestrial regions
Climate servicesMethods10 Chapter
Distillation of regional information
10.4: Southeastern South America, 
Sahel, North America
10.6: Cape Town, South Asian monsoon, 
Mediterranean region
Boxes: Hindu-Kush Himalaya and urban 
change, Arctic linkages to mid-latitudesExtremes11 Chapter
Regional, continental and global

Compound events
Box: 2015 El Niño and 2018 boreal 
spring/summer extremes 
Box: Global warming levels, thermodynamic 
versus dynamic changes and low-likelihood 
high impact changesRegional climate information
Exploration of datasets assessed in all 
chapters through a wide range of maps, graphs 
and tables
Comparison of changes at warming levels and 
scenario/time-period combinations
Indices include extremes and CIDs
Serves all chapters to facilitate synthesis 
information and support TS and SPMInteractive Atlas
Regional precipitation
Monsoons
Other circulations8 Chapter Water and circulation 9 Chapter Ocean / cryosphere
Regional sea level
Regional seas
Coastal processes6 Chapter Short-lived climate forcers
Forcings and radiative effect
Air quality
Impact on regional climate5 Chapter
Regional fluxes and emissions
Regional responsesBiogeochemical cycles Introduction1 Chapter
Region definition
Typological regions
Baselines2 Chapters 34
Observations and attribution

Near and long-term projectionsGlobal, current, future climateLarge-scale climate informationWGI
Technical Summary/Summary for Policy Makers
Synthesis of available lines of evidence for regional climate
Climate information on a region-by-region basisCCBox 10.3
Figure 10.4  | Schematic diagram that illustrates the treatment of regional climate change in the different parts of the WGI Report and how the chapters 
relate to each other.

1377
Linking Global to Regional Climate Change Chapter 1010Box 10.1 | Regional Climate in AR5 and the Special Reports SRCCL, SROCC and SR1.5
This box summarizes the information on linking global and regional climate change information in the Fifth Assessment Report (AR5) 
and the three Special Reports of the IPCC Sixth Assessment Cycle. This information frames the treatment of the production of regional 
climate information in previous reports and identifies some of the gaps that the AR6 WGI Report needs to address.
Fifth Assessment Report, AR5
In WGI Chapter 9 (Flato et al., 2014), regional downscaling methods were addressed as tools to provide climate information at the 
scales needed for many climate impact studies. The assessment found high confidence  that downscaling adds value both in regions with 
highly variable topography and for various small-scale phenomena. Regional models necessarily inherit biases from the global models

used to provide boundary conditions. Furthermore, the ability of AR5 to systematically evaluate regional climate models (RCMs), and 
statistical downscaling schemes, were hampered because coordinated intercomparison studies were still emerging. However, several 
studies demonstrated that added value arises from higher resolution in regions where stationary small-scale features like topography 
and complex coastlines are present, and from improved representation of small-scale processes like convective precipitation.
WGI Chapter 14 (Christensen et al., 2013) stressed that credibility in regional climate change projections increases when key drivers 
of the change are known to be well-simulated and well-projected by climate models.
Working Group II (WGII) Chapter 21 (Hewitson et al., 2014b) addressed the regional climate change context from the perspective of

impacts, vulnerability and adaptation. This chapter emphasized that a good understanding of decision-making contexts is essential 
to define the type and scale of information required from physical climate. Further, the chapter identified that the regional climate 
information was limited by the paucity of comprehensive observations and their analysis along with the different levels of confidence 
in projections ( high confidence ). Notably, at the time of AR5, many studies still relied on global datasets, models, and assessment 
methods to inform regional decisions, which were not considered as effective as tailored regional approaches. The regional scale 
was not defined but instead it was emphasized that climate change responses play out on a range of scales, and the relevance and 
limitations of information differ strongly from global to local scales, and from one region to another.

Chapter 21 noted that the production of downscaled datasets (by both dynamical and statistical methods) remains weakly coordinated, 
and that results indicate that high-resolution downscaled reconstructions of the current climate can have significant errors. Key in this 
was that the increase in downscaled datasets has not narrowed the uncertainty range, and that integrating these data with historical 
change and process-based understanding remains an important challenge.
The chapter identified the common perception that higher resolution (i.e., more spatial detail) equates to more usable and robust 
information, which is not necessarily true. Instead, it is through the integration of multiple sources of information that robust understanding 
of change is developed.
WGII Chapter 21 highlighted that the different contexts of an impact study are defining features for how climate risk is perceived.

Perspectives were characterized as top-down (physical vulnerability) and bottom-up perspectives (social vulnerability). The top-down 
perspective uses climate change impacts as the starting point of how people and/or ecosystems are vulnerable to climate change, and 
commonly applies global-scale scenario information or refines this to the region of interest through downscaling procedures. Conversely, 
in the ‘bottom-up’ approach the development context is the starting point, focusing on local scales, and layers climate change on 
top of this. An impact focus tends to look to the future to see how to adjust to expected changes, whereas a vulnerability-focused 
approach is centred on addressing the drivers of current vulnerability.and the main types of climatic models. Chapter  2 describes the 
recent and current state of the climate from observations, most of 
which are key for the production of regional information. Chapter 3

assesses  human influence on the climate system and Chapter  4 
assesses climate change projections, with a  global focus. These 
three chapters include phenomena that are important for shaping 
regional climate such as general circulation, jets, storm tracks, 
blocking and modes of variability. At the same time, the visualization 
of information in global maps in these chapters provides valuable information for the sub-continental scale. Chapter  5 assesses the 
knowledge about the carbon and biogoechemical cycles, whose 
fluxes and responses show variability that is strongly regional in 
nature. Chapter  6 assesses the regional evolution of short-lived 
climate forcers as well as their influence on regional climate and air 
quality. Chapter 8 assesses observed and projected changes in the 
variability of the regional water cycle, including monsoons, while 
changes of the regional oceans, changes in cryosphere and regional 
sea level change are assessed in Chapter 9.

1378
Chapter 10 Linking Global to Regional Climate Change10Box 10.1 (continued)
Special Report on Climate Change and Land (SRCCL; IPCC, 2019a)
The SRCCL (Jia et al., 2019) assessed that there is robust evidence  and high agreement  that land cover and land use or management 
exert significant influence on atmospheric states (e.g., temperature, rainfall, wind intensity) and phenomena (e.g., monsoons), at various 
spatial and temporal scales, through their biophysical influences on climate. There is robust evidence  that dry soil moisture anomalies 
favour summer heatwaves. Part of the projected increase in heatwaves and droughts can be attributed to soil moisture feedbacks 
in regions where evapotranspiration is limited by moisture availability ( medium confidence ). Vegetation changes can also amplify 
or dampen extreme events through changes in albedo and evapotranspiration, which will influence future trends in extreme events 

The influence of different changes in land use (e.g., afforestation, urbanization), on the local climate depends on the background 
climate ( robust evidence , high agreement ). There is high confidence  that regional climate change can be dampened or enhanced by 
changes in local land cover and land use, with sign and magnitude depending on region and season.
Water management and irrigation were generally not accounted for by CMIP5 global models available at the time of SRCCL. 
Additional water can modify regional energy and moisture balance particularly in areas with highly productive agricultural crops 
with high rate of evapotranspiration. Urbanization increases the risks associated with extreme events ( high confidence ). Urbanization 
suppresses evaporative cooling and amplifies heatwave intensity ( high confidence ) with a strong influence on minimum temperatures 
Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b)

The SROCC (IPCC, 2019b) stated that observations and models for assessing changes in the ocean and the cryosphere have 
been developed considerably during the past century but observations in some key regions remain under-sampled and were very 
short relative to the time scales of natural variability and anthropogenic changes. Retreat of mountain glaciers and thawing of 
mountain permafrost continues and will continue due to significant warming in those regions, where it is likely  to exceed global 
temperature increase.
The SROCC assessed that it is virtually certain  that Antarctica and Greenland have lost mass over the past decade and observed glacier 
mass loss over the last decades is attributable to anthropogenic climate change ( high confidence ). It is virtually certain  that projected 
warming will result in continued loss in Arctic sea ice in summer, but there is low confidence  in climate model projections of Antarctic

sea ice change because of model biases and disagreement with observed trends. Knowledge and observations of the polar regions 
were sparse compared to many other regions, due to remoteness and challenges of operating in them.
The sensitivity of small islands and coastal areas to increased sea levels differs between emissions scenarios and regionally, 
and a consideration of local processes is critical for projections of sea level influences at local scales.
Special Report on Global Warming of 1.5°C (SR1.5; IPCC, 2018b)
The SR1.5 (Hoegh-Guldberg et al., 2018) assessed that most land regions were experiencing greater warming than the global average, 
with annual average warming already exceeding 1.5°C in many regions. Over one quarter of the global population live in regions that 
have already experienced more than 1.5°C of warming in at least one season. Land regions will warm more than ocean regions over 
the coming decades (transient climate conditions).

Transient climate projections reveal observable differences between 1.5°C and 2°C global warming in terms of mean temperature and 
extremes, both at a global scale and for most land regions. Such studies also reveal detectable differences between 1.5°C and 2°C 
precipitation extremes in many land regions. For mean precipitation and various drought measures there is substantially lower risk for 
human systems and ecosystems in the Mediterranean region at 1.5°C compared to 2°C.
The different pathways to a 1.5°C warmer world may involve a transition through 1.5°C, with both short- and long-term stabilization 
(without overshoot), or a temporary rise and fall over decades and centuries (overshoot). The influence of these pathways is small for 
some climate variables at the regional scale (e.g., regional temperature and precipitation extremes) but can be very large for others 
(e.g., sea level).

1379
Linking Global to Regional Climate Change Chapter 1010Cross-Chapter Box 10.1 | Influence of the Arctic on Mid-latitude Climate
Coordinator:  Rein Haarsma (The Netherlands)
Contributors:  Francisco J. Doblas-Reyes (Spain), Hervé Douville (France), Nathan P . Gillett (Canada), Gerhard Krinner (France/Germany, 
France), Dirk Notz (Germany), Krishnan Raghavan (India), Alex C. Ruane (United States of America), Sonia I. Seneviratne (Switzerland), 
Laurent Terray (France), Cunde Xiao (China)
The Arctic has very likely  warmed more than twice the global rate over the past 50 years with the greatest increase during the cold 
season (Atlas.11.2). Several mechanisms are responsible for the enhanced lower troposphere warming of the Arctic, including ice 
albedo, lapse rate, Planck and cloud feedbacks (Section 7.4.4.1). The rapid Arctic warming strongly affects the ocean, atmosphere,

and cryosphere in that region (Section 2.3.2.1 and Atlas.11.2). Averaged over the decade 2010–2019, monthly average sea ice area 
in August, September and October has been about 25% smaller than during 1979–1988 ( high confidence ) (Section 9.3.1.1). It is very 
likely  that anthropogenic forcings mainly due to greenhouse gas increases have contributed substantially to Arctic sea ice loss since 
1979, explaining at least half of the observed long-term decrease in summer sea ice extent (Section 3.4.1.1).
Warming of the Arctic 
 Coldwaves    FloodsPotential impacts of a warming Arctic on Northern mid-latitude climate
Weakening of storm tracksSummerWinter
Impacts on mid-latitudesWeakening of polar vortex
 Shifting position of jet stream 
66°
23°66°
23°
66°
23°66°
23°Amplified and more stationary 
planetary waves 
Impacts on mid-latitudes
Impact of
Global Clima te Chang eImpact of
Natural Variability
    
Arctic amplification
Arctic Sea Ice Loss
66°          Polar vortex
Jet stream

Low confidence
Floods Droughts Heatwaves
Cross-Chapter Box  10.1, Figure  1 | Mechanisms of potential influences of recent and future Arctic warming on mid-latitude climate and 
variability.  Mechanisms are different for winter and summer with different associated influences on mid-latitudes. The mechanisms involve changes in the polar 
vortex, storm tracks, planetary waves and jet stream.

1380
Chapter 10 Linking Global to Regional Climate Change10Cross-Chapter Box 10.1 (continued)
In this box, the possible influences of the Arctic warming on the lower latitudes are assessed. This linkage was also the topic of Box 3.2 
of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b). It is a topic that has been strongly 
debated (Ogawa et al., 2018; K. Wang et al., 2018). Separate hypotheses have emerged for winter and summer that describe possible 
mechanisms of how the Arctic can influence the weather and climate at lower latitudes. They involve changes in the polar vortex, 
storm tracks, jet stream, planetary waves, stratosphere-troposphere coupling, and eddy-mean flow interactions, thereby affecting the 
mid-latitude atmospheric circulation, and the frequency, intensity, duration, seasonality and spatial extent of extremes and climatic

impact-drivers like cold spells, heatwaves, and floods (Cross-Chapter Box 10.1, Figure 1). However, we note that a decrease in the 
intensity of cold extremes has been observed in the Northern Hemisphere mid-latitudes in winter since 1950 (Section 11.3.2; van 
Oldenborgh et al., 2019). Since SROCC, new literature has appeared, and the mechanisms and their criticisms are assessed here as an 
update and extension to the SROCC box.
Mechanisms for a potential influence in winter
It has been proposed that Arctic amplification, by reducing the equator–pole temperature contrast, could result in a weaker and 
more meandering jet with Rossby waves of larger amplitude (Francis et al., 2017; Zhang and Luo, 2020). This may cause weather 
systems to travel eastward more slowly and thus, all other things being equal, Arctic amplification could lead to more persistent

weather patterns over the mid-latitudes (Francis and Vavrus, 2012). The persistent large meandering flow may increase the likelihood 
of connected patterns of temperature and precipitation climatic impact-drivers because they frequently occur when atmospheric 
circulation patterns are persistent, which tends to occur with a strong meridional wind component. Another possible consequence of 
Arctic warming is on the NAO/AO that shows a negative trend over the 1990s and early 2000s (Robson et al., 2016; Iles and Hegerl, 
2017), and has been linked to the reduction of sea ice in the Barents and Kara seas, and the increase in Eurasian snow cover (Cohen 
et al., 2012; Nakamura et al., 2015; Yang et al., 2016). During negative NAO/AO the storm tracks shift equatorward and winters are 
predominantly more severe across northern Eurasia and the eastern United States, but relatively mild in the Arctic. This temperature

pattern is sometimes referred to as the ‘warm Arctic–cold continents (WACC)’ pattern (Chen et al., 2018). However, L. Sun et al. (2016) 
noticed that the WACC is a manifestation of natural variability. Enhanced sea ice loss in the Barents-Kara Sea has also been related 
to a weakening of the stratospheric polar vortex (Kretschmer et al., 2020) and its increased variability (Kretschmer et al., 2016) that 
would induce a negative NAO/AO (Kim et al., 2014), the WACC pattern (Kim et al., 2014), and an increase in cold air outbreaks (CAO) 
in mid-latitudes (Kretschmer et al., 2018). Arctic warming might also increase Eurasian snow cover in autumn caused by the moister 
air that is advected into Eurasia from the Arctic with reduced sea ice cover (Cohen et al., 2014; Jaiser et al., 2016), although Peings 
(2019) suggests a possible influence of Ural blockings on both the autumn snow cover and the early winter polar stratosphere. The

circulation changes over the Ural-Siberian region are also suggested to provide a link between Barents-Kara sea ice and the NAO 
(Santolaria-Otín et al., 2021).
Mechanisms for a potential influence in summer
As in winter, Arctic summer warming may result in a weakening of the westerly jet and mid-latitude storm tracks, as suggested for 
the recent period of Arctic warming (Coumou et al., 2015; Petrie et al., 2015; Chang et al., 2016). Additional proposed consequences 
are a southward shift of the jet (Butler et al., 2010) and a double jet structure associated with an increase of the land–ocean thermal 
gradient at the coastal boundary (Coumou et al., 2018). It is hypothesized that weaker jets, diminished meridional temperature 
contrast, and reduced baroclinicity might induce a larger amplitude in stationary wave response to stationary forcings (Zappa et al.,

2011; Petoukhov et al., 2013; Hoskins and Woollings, 2015; Coumou et al., 2018; Mann et al., 2018; R. Zhang et al., 2020), and 
also that a double jet structure would favour wave resonance (Kornhuber et al., 2017; Mann et al., 2017). Some studies suggest 
that this is corroborated by an observed increase of quasi-stationary waves (Di Capua and Coumou, 2016; Vavrus et al., 2017; 
Coumou et al., 2018).
The above proposed hypotheses are based on concepts of geophysical fluid dynamics and surface coupling and can, in principle, 
help explain the existence of a link between the Arctic changes and the mid-latitudes with the potential to affect many impact 
sectors (Barnes and Screen, 2015). However, the validity of some dynamical underlying mechanisms, such as a reduced meridional 
temperature contrast inducing enhanced wave amplitude, has been questioned (Hassanzadeh et al., 2014; Hoskins and Woollings,

2015). On the contrary, the reduced meridional temperature contrast has been related to reduced meridional temperature advection 
and thereby reduced winter temperature variability (Collow et al., 2019).
Studies that support the Arctic influence are mostly based on observational relationships between the Arctic temperature or sea ice 
extent and mid-latitude anomalies or extremes (Cohen et al., 2012; Francis and Vavrus, 2012, 2015; Budikova et al., 2017). They are

1381
Linking Global to Regional Climate Change Chapter 1010Cross-Chapter Box 10.1 (continued)
often criticized for the lack of statistical significance and the inability to disentangle cause and effect (Barnes, 2013; Barnes and 
Polvani, 2013; Screen and Simmonds, 2013; Barnes et al., 2014; Hassanzadeh et al., 2014; Barnes and Screen, 2015; Sorokina et al., 
2016; Douville et al., 2017; Gastineau et al., 2017; Blackport and Screen, 2020a; Oudar et al., 2020; Riboldi et al., 2020). The role of the 
Barents-Kara sea ice loss is challenged by Blackport et al. (2019) who find a minimal influence of reduced sea ice on severe mid-latitude 
winters, and by Warner et al. (2020) who suggest that the apparent winter NAO response to the Barents-Kara sea ice variability is 
mainly an artefact of the Aleutian Low internal variability and of the co-variability between sea ice and the Aleutian Low originating

from tropical-extratropical teleconnections. Also Gong et  al. (2020) do not find a  link between Rossby wave propagation into the 
mid-latitudes and Arctic sea ice loss. Mori et al. (2019) argue that models underestimate the influence of the Barents-Kara Sea ice 
loss on the atmosphere, which is disputed by Screen and Blackport (2019). Other studies have stressed the importance of atmospheric 
variability as a driver of Arctic variability (Lee, 2014; Woods and Caballero, 2016; Praetorius et al., 2018; Olonscheck et al., 2019). 
Analysing observed key variables of mid-latitude climate for 1980–2020, Blackport and Screen (2020b) and Riboldi et al. (2020) argue 
that the Arctic influence on mid-latitudes is small compared to other aspects of climate variability, and that observed periods of strong 
correlation are an artefact of internal variability or intermittency (Kolstad and Screen, 2019; Siew et al., 2020; Warner et al., 2020).

An additional argument in the criticism is the inability of climate models to simulate a significant response to Arctic sea ice loss, larger 
than the natural variability (Screen et al., 2014; Walsh, 2014; H.W. Chen et al., 2016; Peings et al., 2017; Dai and Song, 2020), or that 
a very large multi-model ensemble is needed (Liang et al., 2020), although some studies find a significant response in summer, because 
then the internal variability is weaker (Petrie et al., 2015).
Finally, a  warmer Arctic climate can, without any additional changes in atmospheric dynamics, reduce cold extremes in winter 
due to advection of increasingly warmer air from the Arctic into the mid-latitudes (Screen, 2014; Ayarzagüena and Screen, 2016; 
Ayarzagüena et al., 2018).
Summarizing, different hypotheses have been developed about the influence of recent Arctic warming on the mid-latitudes in both

winter and summer. Although some of the proposed mechanisms seem to be supported by various studies, the underlying mechanisms 
and relative strength compared to internal climate variability have been questioned. A recent review (Cohen et al., 2020) states that 
divergent conclusions between model and observational studies, and also between different model studies, continue to obfuscate 
a clear understanding of how Arctic warming is influencing mid-latitude weather. In this context, Shepherd (2016b) stresses the need 
for collaboration between scientists with different viewpoints for further understanding that could be achieved by carefully designed, 
multi-investigator, coordinated, multi-model simulations, data analyses and diagnostics (Overland et al., 2016). In agreement with 
Box 3.2 of SROCC, there is low to medium confidence  in the exact role and quantitative effect of historical Arctic warming and sea ice 
loss on mid-latitude atmospheric variability.

Regarding future climate, it is important to note that mid-latitude variability is also affected by many drivers other than the Arctic 
changes and that those drivers as well as the linkages to mid-latitude variability might change in a warmer world. The AMV, PDV, 
ENSO (see Annex IV), upper tropospheric tropical heating, polar stratospheric vortex, and land surface processes associated with soil 
moisture (Miralles et al., 2014; Hauser et al., 2016) and snow cover (Nakamura et al., 2019; Sato and Nakamura, 2019) are a few 
examples. A considerable body of literature has shown that changes to the NAO/AO on seasonal and climate change time scales 
can be driven by variations in the wavelength and amplitude of Rossby waves, mainly of tropical origin (Fletcher and Kushner, 2011; 
Cattiaux and Cassou, 2013; Ding et al., 2014; Goss et al., 2016). The influence of future Arctic warming on mid-latitude circulation

is difficult to disentangle from the effect of such a plethora of drivers (Blackport and Kushner, 2017; F . Li et al., 2018). One of the 
consequences of climate change is a poleward shift of the jet induced by the tropical warming (Barnes and Polvani, 2013), which is 
less obvious in winter especially over the North Atlantic (Peings et al., 2018; Oudar et al., 2020), and the increase of the meridional 
temperature gradient in the upper troposphere, which increases storm track activity (Barnes and Screen, 2015; Parding et al., 2019). 
Although climate models indicate that future Arctic warming and the associated equator–pole temperature gradient decrease could 
affect mid-latitude climate and variability (Haarsma et al., 2013a; McCusker et al., 2017; Zappa et al., 2018), and even the tropics and 
subtropics (Deser et al., 2015; Cvijanovic et al., 2017; K. Wang et al., 2018; England et al., 2020; Kennel and Yulaeva, 2020), they do

not reveal a strong influence on extreme weather (Woollings et al., 2014).
In conclusion, there is low confidence in the relative contribution of Arctic warming to mid-latitude atmospheric changes compared to 
other drivers. Future climate change could affect mid-latitude variability in a number of ways that are still to be clarified, and which 
may also include the influence of Arctic warming. The linkages between the Arctic warming and the mid-latitude circulation are an 
example of contrasting lines of evidence that cannot yet be reconciled (Section 10.5).

1382
Chapter 10 Linking Global to Regional Climate Change1010.2 Using Observations for Constructing 
Regional Climate Information
Considerable challenges (and opportunities) remain in using 
observations for climate monitoring, for evaluating and improving 
climate models (Section  10.3.1), for constructing reanalyses and 
post-processing model outputs, and therefore, ultimately, for increasing 
our confidence in the attribution of past climate changes and in future 
climate projections at the regional scale. While an assessment of 
large-scale observations can be found in Chapter  2 (Cross-Chapter 
Box 2.2 and Section 2.3), this section discusses the specific aspects 
of the observations at regional scale and over the typological 
regions considered in the regional chapters (Section  10.1.5). This 
section focuses on land regions and does not consider the specific 
requirements of ocean observations (see Chapter 9 and SROCC (IPCC, 
2019b) for more information on this aspect).

10.2.1 Observation Types and Their 
Use at Regional Scale
10.2.1.1 In Situ and Remote-sensing Data
Surface or in situ observations can come from a variety of networks: 
climate reference networks, mesoscale weather and supersite 
observation networks, citizen science networks, among others, all 
with their strengths and weaknesses (McPherson, 2013; Thorne et al., 
2018). Supersite observatories are surface and atmospheric boundary 
layer observing networks that measure a large number of atmospheric 
and soil variables at least hourly over a  decade or more, ideally 
located in rural areas (Ackerman and Stokes, 2003; Haeffelin et al., 
2005; Xie et al., 2010; Chiriaco et al., 2018). Adequate calibration of 
instruments, quality control and homogenization are essential in these 
sites. They produce valuable data needed to diagnose processes and 
changes in regional and local climate. Many climate datasets have 
been developed from in situ station observations, at different spatial

scales and temporal frequencies (Annex I: Observational Products). 
These include sub-daily (Dumitrescu et al., 2016; Blenkinsop et al., 
2017), daily (Chen et  al., 2008; Camera et  al., 2014; Journée 
et al., 2015; Funk et al., 2015; Aalto et al., 2016; Beck et al., 2017a, b; 
Schneider et al., 2017) or monthly time scales (Cuervo-Robayo et al., 
2014; Aryee et al., 2018). Sub-daily data is useful for estimating storm 
surge (Mori et al., 2014) or river discharge (Shrestha et al., 2015), 
daily data for carbon-stock dynamics (Haga et al., 2020) or tourism 
(Watanabe et  al., 2018), and monthly data for beach morphology 
(Bennett et al., 2019).
Satellite products provide a  valuable complement to in situ 
measurements, particularly over regions where in situ measurements 
are unavailable. They have been discussed in earlier chapters 
(e.g.,  Chapters 2 and 8) for large-scale assessment. Currently 
54 essential climate variables (ECVs; Bojinski et al., 2014) are defined

by the Global Climate Observing System (GCOS) program, and passed 
on, for example, to NASA programmes through the Decadal Survey, to 
the Copernicus Climate Change Service of the European Union, to the 
ESA Climate Change Initiative ESA-CCI, as well as to the international 
collaborations with geostationary Earth orbit  (GEO) satellites. Their observations are valuable ( high confidence ) for regional applications 
since they provide multi-channel images at very high spatiotemporal 
resolutions, typically 16 channels, 1–2 km, every 10 to 15 minutes. 
The advanced geostationary satellites are: Himawari-8 and 9 (Kurihara 
et  al., 2016), GOES-East and GOES-17 (Goodman et  al., 2018), 
Meteosat-10 and 11 (Schmetz et al., 2002) and FY-4 (Cao et al., 2014). 
Geostationary satellite networks or constellations form an essential 
component of the Global Observation System ( https://www.wmo.int/
pages/prog/www/OSY/GOS.html ), providing measurements not only

for various cloud properties and moisture but also for air quality, land 
and ocean surface conditions, and lightning.
Low Earth orbit (LEO) satellites, with orbits typically at 400–700 km, 
provide advanced measurements of the Earth’s surface. 
Sun-synchronous polar orbiters can also cover the polar regions, which 
cannot be observed with GEO satellites. Examples of LEO observations 
for land surface monitoring are NASA’s Landsat (Wulder et al., 2016), 
ESA’s Soil Moisture Ocean Salinity Earth Explorer (SMOS) mission (Kerr 
et al., 2012), the Sentinel missions of the Copernicus programme, and 
JAXA’s ALOS-2 (Ohki et al., 2019), providing high spatial resolution 
land surface images. Many kinds of data are accumulated for land use 
and land cover studies, targeting aspects like urban footprint (Florczyk 
et al., 2019), land-cover data (Global Land 30; CCI-LC: ESA, 2021; Chen 
and Chen, 2018), land surface temperature data (Landsat, Parastatidis

et al., 2017), and surface albedo (Chrysoulakis et al., 2019).
Availability of active sensors on LEO satellites enables measurement 
of microphysical properties of aerosol, cloud and precipitation, which 
can advance regional climate studies and process evaluation studies 
to improve regional climate models ( high confidence ). An example is 
the polar-orbiting ‘afternoon-train’ satellite constellation (known as 
the A-train), incorporating Aqua, CALIPSO, Cloudsat, PARASOL, Glory 
and Aura satellites. Vertical profiling observations from Cloudsat 
(with a W-band cloud radar) and CALIPSO (with a cloud lidar) led 
to considerable advances in measurements of cloud microphysics 
(Stephens et al., 2018). Precipitation and its extremes are essential 
concerns of regional climate studies. The GPM (65°N–65°S, 
2014–present) and the preceding TRMM (36.5°N–36.5°S, 
1997–2015) with Ku-/Ka-band precipitation radars have provided 
three-dimensional measurements of precipitation with about 5 km

resolution and sub-daily sampling (Skofronick-Jackson et al., 2017). 
Their non-sun-synchronous observation works to cross-calibrate the 
constellation satellites to produce global high-resolution mapped 
products of precipitation, such as Integrated Multi-satellitE Retrievals 
for GPM (IMERG; Huffman et  al., 2007) and the Global Satellite 
Mapping of Precipitation (GSMaP; Kubota et al., 2007), with hourly 
sampling at about 11 km resolution. The CPC MORPHing technique 
(CMORPH) has provided 30 min interval global precipitation with 
about 8 km coverage since 2002 (Joyce et al., 2004). Precipitation 
estimations from Remotely Sensed Information using Artificial Neural 
Networks (PERSIANN) is a  sub-daily to daily rainfall product that 
covers 50°S to 50°N globally with 25 km resolution from 2000 to 
the present (Nguyen et al., 2019), and is used for semi-global-scale 
precipitation coverage (Benestad, 2018). TRMM/GPM observations

have enabled estimates to be obtained for global four-dimensional 
convective heating (Shige et al., 2009; Tao et al., 2016; Takayabu and 
Tao, 2020).

1383
Linking Global to Regional Climate Change Chapter 1010The use of these data has enhanced our understanding of precipitation 
processes at regional scale ( high confidence ), such as diurnal cycles 
in a large river valley (H. Chen et al., 2012), and in coastal (Hassim 
et al., 2016; Yokoi et al., 2017) and mountainous regions (Hirose et al., 
2017). Three-dimensional observations revealed the contrasts in 
regional characteristics of rainfall extremes in monsoon regions and 
continental dry regions (Sohn et al., 2013; Hamada and Takayabu, 
2018). Satellite measurements are also used to evaluate climate 
model performance, as well as to develop new parametrizations. As 
a demonstration of the utility of these products in studying model bias, 
a subtropical cumulus congestus regime has been identified that may 
be implicated in the unrealistic double Inter-tropical Convergence 
Zone (ITCZ) found in some climate models (Takayabu et al., 2010;

Hirota et al., 2011, 2014). Another example is a parametrization of 
a land surface model that was developed specifically for a certain soil 
type. By assimilating satellite brightness temperature observations 
with their LDAS-UT scheme, Yang et al. (2007) successfully optimized 
a land surface model for the Tibetan Plateau.
For application at a  regional scale, it is important to consider 
variations in the spatiotemporal resolution of the satellite products. 
A simple concatenation of data in time can show artificial jumps that 
are artefacts of changes in calibration and processing algorithms, or 
related to satellite orbital stability or changing performance of the 
instruments (Wielicki et  al., 2013; Barrett et  al., 2014). Recalibration and 
cross-calibration are then prerequisites for obtaining homogeneous 
time series of measurements across different or successive satellites 
that can then be used to produce long series that are valid as climate

data records (Kanemaru et al., 2017; Merchant et al., 2017). Scale 
representativeness is also an issue in utilizing soil observations 
(Taylor et  al., 2012, 2013). Although a  variety of technologies to 
measure soil moisture at the point scale exist (Dobriyal et al., 2012), 
its spatial representativeness is less than 1 m2 (Ochsner et al., 2013; 
L. Liu et al., 2016). Therefore, to be able to use in situ soil moisture for 
validating coarser-scale data from satellites or models, networks of 
point-scale measurements are used (Crow et al., 2015; Polcher et al., 
2016). Smaller networks are typically of the size of a single climate 
model gridcell or a  satellite pixel and are suitable for monitoring 
watersheds, while small numbers of those representing larger areas 
(>100 km2) are emerging (Ochsner et al., 2013).
10.2.1.2 Derived Products
Derived observational products are created from raw datasets 
collected from surface stations, remote-sensing instruments, or

research vessels, which are converted into meaningful physical 
quantities by applying a suitable measurement theory, using either 
statistical interpolation techniques (Section  10.2.2.4) or numerical 
atmospheric and land surface models (Bosilovich et al., 2015).
Most global observational datasets are available at coarse temporal 
and spatial resolution, and do not include all available station data 
from a particular region, due to data availability problems. Therefore, 
efforts have been made to develop regional or country-scale datasets 
(Annex I). Radar and satellite remote sensing are resources that can 
provide a valuable complement to direct measurements at regional 
scale. Examples for precipitation have been described already, some of which have been released to the community (Dinku et al., 2014; 
Oyler et  al., 2015; Manz et  al., 2016; Dietzsch et  al., 2017; Yang 
et al., 2017; Bližňák et al., 2018; Krähenmann et al., 2018; Panziera

et al., 2018; Shen et al., 2018). However, some of these datasets are 
limited by their short record, varying between one (Shen et al., 2018) 
and 64 years (Oyler et al., 2015).
Reanalysis products are numerical climate simulations that use 
data assimilation to incorporate as many irregular observations as 
possible. These products encompass many physical and dynamical 
processes. They generate a  coherent estimate of the state of the 
climate system on uniform grids either at global (Chaudhuri et al., 
2013; Balsamo et al., 2015), regional (Chaney et al., 2014; Maidment 
et al., 2014; Dahlgren et al., 2016; Langodan et al., 2017; Attada et al., 
2018; Mahmood et  al., 2018) or country scales (Rostkier-Edelstein 
et al., 2014; Krähenmann et al., 2018; Mahmood et al., 2018).
Reanalyses incorporate an increasing volume of observations from 
a growing number of sources over time, which sometimes presents 
a  difficulty for trend analysis. However, regional reanalyses are

valuable for regional climate assessments, since they can employ 
high-resolution model simulations due to their limited spatial domain. 
Their accuracy is also better than global reanalyses since they are 
often developed over regions with a high density of observational 
data (sometimes not freely available for all regions) to be assimilated 
into the model (e.g., Yamada et al., 2012). Regional reanalyses can 
assimilate locally dense and high-frequency observations, such as 
from local observation networks (Mahmood et al., 2018; Su et al., 
2019) and radar precipitation (Wahl et al., 2017) in addition to the 
observations assimilated by global reanalyses. In some regional 
reanalyses, satellite-derived high-resolution sea ice (Bromwich 
et al., 2016, 2018) and sea surface temperature (Su et al., 2019) are 
also applied as lower boundary conditions. The periods of regional 
reanalyses are limited by the availability of the observations for

assimilation and by the global reanalyses needed as lateral boundary 
conditions. Most regional reanalyses cover the past 10 to 30 years. 
There are also regional reanalysis activities that use conventional 
observations only, which produce consistent datasets over 60 years 
to capture precipitation trends, extremes and changes (Fukui et al., 
2018). Existing regional reanalyses cover North America (Mesinger 
et  al., 2006), Europe (Dahlgren et  al., 2016; Jermey and Renshaw, 
2016; Kaspar et al., 2020), the Arctic (Bromwich et al., 2016, 2018), 
South Asia (Mahmood et al., 2018), and Australia (Su et al., 2019). 
A project for regional reanalysis covering Japan has also started (Fukui 
et al., 2018), where grid spacing is between 5 and 32 km, although 
cumulus parametrizations are still needed to compute sub-grid 
scale cumulus convection. Recently, reanalyses using convection-
permitting regional models have been published (e.g., Wahl et  al., 
2017, for central Europe).

The data assimilation schemes used in regional reanalyses are often 
relatively simple methods, specifically nudging (Kaspar et al., 2020) 
and 3DVAR (Mesinger et al., 2006; Bromwich et al., 2016; Dahlgren 
et al., 2016), rather than the more complex schemes implemented 
in state-of-the-art global reanalysis systems. This is partly due 
to limitations of computational resources. Recently, a  number of 
regional reanalyses using more sophisticated methods, such as

1384
Chapter 10 Linking Global to Regional Climate Change104DVAR and Ensemble Kalman filter, have been published (Jermey and 
Renshaw, 2016; Fukui et al., 2018; Mahmood et al., 2018; Su et al., 
2019). The regional reanalyses also incorporate uncertainties 
due to deficiencies of the models, data assimilation schemes and 
observations. To estimate uncertainties, some regional reanalyses 
apply data assimilation using ensemble forecasts (Bach et al., 2016). 
Another approach compares multiple regional reanalyses produced 
with different systems covering the same domain, which represents 
the uncertainties better than single reanalysis systems with ensemble 
data assimilation schemes (Kaiser-Weiss et al., 2019).
The regional reanalyses represent the frequencies of extremes and 
the distributions of precipitation, surface air temperature, and surface 
wind better than global reanalyses ( high confidence ). This is due to the

use of high-resolution regional climate models (RCMs), as indicated 
by different regional climate modelling studies (Mesinger et al., 2006; 
Bollmeyer et al., 2015; Bromwich et al., 2016, 2018; Dahlgren et al., 
2016; Jermey and Renshaw, 2016; Fukui et al., 2018; Su et al., 2019). 
Regional reanalyses, however, retain uncertainties due to deficiencies 
in the physical parametrization used in RCMs and by the use of 
relatively simple data assimilation algorithms (Bromwich et al., 2016; 
Jermey and Renshaw, 2016; Su et  al., 2019). Regional reanalyses 
can provide estimates that are more consistent with observations 
than dynamical downscaling approaches, due to the assimilation 
of additional local observations ( high confidence ) (Bollmeyer et al., 
2015; Fukui et al., 2018).
10.2.2 Challenges for Regional Climate 
Change Assessment
10.2.2.1 Quality Control
The usefulness of any observational dataset is conditioned by 
the availability and outcome of a  quality control (QC) process.

The objective of the QC is to verify that data are representative 
of the measured variable and to what degree the value could be 
contaminated by unrelated or conflicting factors (WMO, 2017a). 
Data quality assessment is key for ensuring that the data are credible 
and to establish trusted relationships between the data provider and 
the users (Nightingale et al., 2019). QC is performed for all relevant 
global climate datasets (e.g., Menne et al., 2018). For instance, QC 
informs users that old reanalysis datasets can be inconsistent in the 
long term because they assimilated inhomogeneous observations 
over the reanalyses period (Kobayashi et al., 2015). As a consequence, 
the evaluation against independent observations suggests that 
reanalyses should not be automatically regarded as climate-
quality products for monitoring long-term trends at the regional 
level (Manzanas et al., 2014; Torralba et al., 2017). QC needs to be

systematically carried out by the institutions responsible for handling 
the data (e.g., Cao et al., 2016b).
The QC procedure depends strongly on the specific nature of the 
dataset. It focuses on aspects such as the correct identification of 
sensor, time and location, detection of unfeasible or inconsistent 
data, error estimation, assessment of the adequacy of the 
uncertainty information and the adequacy of the documentation (e.g., Heaney et al., 2016). QC principles also apply to model data 
(Tapiador et al., 2017). An important piece of information provided 
is the representativeness error (Section  10.2.1.1; Gervais et  al., 
2014). When problems in the data representativeness are identified, 
observational datasets are provided with a quality mask (Contractor 
et al., 2020), or the problematic data are either removed or corrected 
(Ashcroft et al., 2018). These are factors often taken into account in 
constructing regional climate information (Kotlarski et al., 2019).

Quality-controlled data are now produced widely at the regional level, 
as in the case of sub-daily precipitation records in the United Kingdom 
(Blenkinsop et al., 2017) and the USA (Nelson et al., 2016). However, 
many more datasets and variables lack the same level of scrutiny 
(Alexander, 2016). Quality-controlled, high-resolution observational 
datasets are especially needed at regional and local scales to assess 
models as their resolution increases (Di Luca et al., 2016; Zittis and 
Hadjinicolaou, 2017), although the awareness and appropriate use 
of the QC information is challenging (Tapiador et  al., 2017) when 
generating regional climate information ( high confidence ).
10.2.2.2 Homogenization
Homogenization aims to make data spatially and temporally 
‘homogeneous’. Changes in a  homogeneous time series are solely 
due to large-scale climatic changes (whether forced or due to internal 
variability). Station data are influenced by factors that act at regional

scales, from the mesoscale and local scale down to the microscale 
(WMO, 2019). Station time series contain inhomogeneities such as 
artificial jumps or trends, which hamper assessments of regional long-
term trends. Typical reasons for this are the urbanization of a station’s 
surroundings, which can lead to warming (Hamdi, 2010; Hansen et al., 
2010; Adachi et al., 2012; Jones, 2016; Y . Sun et al., 2016), or relocations 
outside of the urban area, which could lead to cooling (Tuomenvirta, 
2001; Yan et  al., 2010; Xu et  al., 2013; Dienst et  al., 2017, 2019). 
Another potential source of inhomogeneity is a change in measurement 
methods that affect most instruments of an observational network 
over a limited time span, such as the transition to Stevenson screens 
(Parker, 1994; Böhm et al., 2010; Brunet et al., 2011; Auchmann and 
Brönnimann, 2012) or to automatic weather stations (WMO, 2017b).
The above examples have been selected as they are present in

many stations and without going through homogenization they 
could potentially have influenced global land warming estimates 
(Section 1.5.1). Single-break inhomogeneities tend to have a magnitude 
comparable to global climate change (Tuomenvirta, 2001; Venema 
et al., 2012) and are thus important for analyses of small regions. Also 
station records in national networks often have similar changes, making 
them important for national climate change estimates, but many of 
these influences are averaged out at the global scale (Jones, 2016).
The main approach to reduce the influence of inhomogeneities in 
station observations is statistical homogenization by comparing the 
data from a candidate station with those of neighbouring reference 
stations in conjunction with the use of metadata (Trewin, 2010). 
This is a  challenging task because both reference and candidate 
records normally have multiple inhomogeneities. Three challenges

should be considered. First, most of our understanding of statistical

1385
Linking Global to Regional Climate Change Chapter 1010homogenization stems from the homogenization of temperature 
observations from dense networks. Recent studies suggest that 
our ability to remove biases quickly diminishes for sparse networks 
(Gubler et al., 2017; Lindau and Venema, 2018a). This affects early 
instrumental data and observations that are not strongly correlated 
between stations, such as wind and humidity (Chimani et al., 2018).
Second, in addition to systematic errors, homogenized data also 
suffer from random errors, introduced by the homogenization process. 
These errors are largest at the station level but are also present in 
network-averaged signals (Lindau and Venema, 2018b). These errors 
are determined by the break time series, as well as the noise series 
and the performance of the homogenization method, are spatially 
correlated, and have an impact on activities such as interpolation and

statistical post-processing of climate simulations (Section 10.2.3.1). 
Third, the above discussion pertains to the homogenization of 
monthly and annual means. Homogenization of daily variability 
around the mean is more difficult. For daily data, specific correction 
methods are used (Della-Marta and Wanner, 2006; Mestre et  al., 
2011; Trewin, 2013; C. Zhou et al., 2021) that are able to improve the 
homogeneity of test cases, although recent independent validation 
efforts were not able to show much improvement (Chimani et al., 
2018). The difference with homogenization methods of monthly 
and annual means may stem from assumptions on the nature of 
inhomogeneities for daily data, which are not yet well understood 
(Chimani et al., 2018).
It is virtually certain  that statistical homogenization methods reduce the 
uncertainties of long-term estimates. Considering a decomposition of 
the long-term warming error into a bias and a noise uncertainty around

the bias, the (trend) bias especially will be reduced, but also most of 
the noise uncertainty. This conclusion is based on our understanding 
of the causes of inhomogeneities and their statistical nature combined 
with the design principles of statistical homogenization methods, as 
well as on analytical (Lindau and Venema, 2018b), numerical (Venema 
et  al., 2012; Williams et  al., 2012) and empirical validation studies 
(Hausfather et al., 2016; Gubler et al., 2017; Killick et al., 2020).
The above section is about the homogenization of land stations. 
Satellite data has its own issues and methods for homogenization 
(Brinckmann et al., 2013; Huang et al., 2015; Brogniez et al., 2016). 
The homogenization of radiosonde data and land station data use 
similar methods (Haimberger et al., 2012; Jovanovic et al., 2017).
10.2.2.3 Data Scarcity
Data scarcity arises largely due to the lack of maintenance of 
observing stations, inaccessibility of the data held in national

networks, and uneven spatial distribution of stations that lead to 
a low density in many regions. This is particularly problematic when 
trying to assess regional climate change, for which a  high density 
of observational data is desirable. Although in several regions 
numerous stations provide (monthly) data covering more than 
100 years for both temperature and precipitation (GCOS, 2015), large 
areas of the world remain sparsely covered. The post-1990 decline in 
the total number of stations contributing to the Global Precipitation 
Climatology Centre (GPCC) monthly product may be related to delays in data acquisition and not paucity of data (GCOS, 2015). This is 
because GPCC is the result of a  single time scale, single Essential 
Climate Variable (ECV) and single data collection centre. There is 
no similar drop-off of the rainfall reports in the Global Historical 
Climatology Network Daily database (GHCNd, Menne et al., 2012)

or the Integrated Surface Database (ISD) at the sub-daily time scale.
Kidd et  al. (2017) made some assumptions about GPCC-available 
gauges and indicated that only 1.6% of Earth’s surface lies within 
10  km of a  rain gauge, and many areas of the world are beyond 
100  km from the nearest rain gauge. Data scarcity is especially 
critical over Africa (Nikulin et  al., 2012; Dike et  al., 2018) but the 
apparent data scarcity could be due to reasons other than actual 
paucity of data, as stated earlier. For instance, over South Africa, 
the number of weather stations collecting daily temperature used 
in the fourth version of the Climatic Research Unit Temperature 
dataset (CRUTEM4, Osborn and Jones, 2014) has significantly 
declined since 1980 (Archer et  al., 2018). Although CRUTEM4 has 
now been replaced by CRUTEM5 (Osborn et  al., 2021) it has yet 
to take advantage of the significant international efforts to curate

and make available improved global holdings (Rennie et al., 2014) 
which increased the global available station count for monthly mean 
temperatures. This includes additional stations from many African 
countries. The apparent decline in stations since the 1980s could also 
be due to countries not contributing their data to the SYNOP/CLIMAT 
networks for reasons other than having non-operational stations.
Even in Europe, precipitation station density in the widely used E-OBS 
gridded dataset varies largely in space and time across regions (Prein 
and Gobiet, 2017). This variability is partly due to the reluctance of 
some data owners to share their data with an international effort. 
Regardless of the reason, low station density is a major source of 
uncertainty (Isotta et al., 2015). Kirchengast et al. (2014) and O and 
Foelsche (2019) found that at least 2 to 5 (12) stations are required for 
capturing the area-averaged precipitation amount of heavy summer

precipitation events on a daily (hourly) basis with a normalized root-
mean-square error of less than 20%. Like the E-OBS dataset, gridded 
daily temperature and precipitation datasets are being developed 
for other regions of the world. Examples include south-east Asia 
(SA-OBS, Van den Besselaar et al., 2017), and Latin America and West 
Africa (ICA&D, Van den Besselaar et al., 2015). Despite the uneven 
distribution of stations in space and time, the value in these initiatives 
is illustrated by the large number of studies in which the data product 
is used. This is the case, for instance, in the work of Condom et al. 
(2020) over the Andes, a region with prominent data scarcity, and 
the African Monsoon Multidisciplinary Analyses project over West 
Africa (AMMA; e.g.,  Lebel and Ali, 2009). There have been efforts 
to reduce data scarcity through initiatives such as the International 
Surface Temperature Initiative (ISTI, Thorne et al., 2011), GHCND, and

the Expanding Met Office Hadley Centre ISD with quality-controlled, 
sub-daily station data from 1931 (HadISD, Dunn et al., 2016).
Data scarcity arising from changing coverage in observation station 
networks results in substantial problems for climate monitoring 
(e.g.,  trend analysis of extreme events requires high temporal and 
spatial resolutions) or model evaluation (Section 10.3.3.1). It is virtually 
certain  that the scarcity and decline of observational availability in

1386
Chapter 10 Linking Global to Regional Climate Change10some regions (but not necessarily globally), increase the uncertainty of 
the long-term global temperature and precipitation estimates. As an 
example, Lin and Huybers (2019) found that changes in the number 
of rain gauges after 1975 resulted in spurious trends in extremes of 
Indian rainfall in a 0.25° gridded dataset spanning the 20th century. 
In fact, the number of stations used to construct the gridded dataset 
dropped by half after 1990, leading to inhomogeneity and spurious 
trends (Section 10.6.3). Over the southern part of the Mediterranean, 
which is an area sparsely covered by meteorological stations, data 
scarcity can lead to large uncertainties in the different gridded datasets 
and strongly affect model evaluation (Section  10.6.4). Satellite 
observations can compensate the ground-based precipitation radar 
data sparsity to prevent an oversight of significant climate change 
signals (Yokoyama et al., 2019).

There are techniques for estimating and reconstructing missing 
data. The methods depend on the variable of interest, the temporal 
resolution (e.g., daily or monthly), and the type of climate (wet or dry), 
among others. There has been very little evaluation of the performance 
of classical and data mining methods (e.g., Sattari et al., 2017). The 
classical methods include the arithmetic mean, inverse distance 
weighting method, multiple regression analysis, multiple imputation, 
and single best estimator, while the data-mining methods include 
multilayer perceptron artificial neural network, support vector machine, 
adaptive neuro-fuzzy inference system, gene expression programming 
method, and K-nearest neighbour. Crowd-sourced data (individuals 
contribute their own data points to create a dataset for others to use) 
could play a role in minimizing data scarcity (Section 10.2.4).
10.2.2.4 Gridding
Derived gridded datasets require merging data from different sources of

observations and/or reanalysis data on a regular grid (Section 10.2.1.2; 
e.g., Xie and Arkin, 1997). However, in situ observations are distributed 
irregularly, especially over sparsely populated areas. This leads to 
an interpolation challenge. Gridded products of climate variables, 
including temperature and precipitation, are strongly affected ( high 
confidence ) by the interpolation method over complex orography and 
data scarce regions (Hofstra et al., 2008; Herrera et al., 2016).
There are two main approaches to produce gridded datasets: (i) based 
on in situ observations only, and (ii) combining in situ observations 
with remote-sensing data and/or reanalysis data. The first approach 
has been widely employed in regions with high station density using 
interpolation techniques, such as inverse-distance weighting, optimal 
interpolation, and kriging (Chen et al., 2008; Haylock et al., 2008; Frei, 
2014; Isotta et al., 2014; Masson and Frei, 2014; Hiebl and Frei, 2016;

Nguyen-Xuan et  al., 2016). The second approach has been mainly 
applied in data-sparse regions with low station density, using simple 
bias adjustment, quantile mapping, and kriging techniques with in 
situ observations, remote-sensing and reanalysis data (Cheema and 
Bastiaanssen, 2012; Erdin et al., 2012; Dinku et al., 2014; Abera et al., 
2016; Krähenmann et al., 2018).
Gridding of station data is affected by uncertainties stemming 
from measurement errors, inhomogeneities, the distribution of the 
underlying stations and the interpolation error, with station density being the dominant factor (Herrera et al., 2019). Uncertainty due to 
interpolation is typically small for temperature but substantial for 
precipitation and its derivatives, such as drought indices (Chubb et al., 
2015; Hellwig et al., 2018). The largest uncertainties typically occur 
in sparsely sampled mountain areas (Section 10.2.2.5). Interpolation

generally give rise to smoothing effects, such as low variability of 
the derived dataset with respect to the in situ observations (Chen 
et al., 2019). As a result, the effective resolution of gridded data is 
typically much lower than its nominal resolution. For instance, a 5 km 
gridded precipitation dataset for the European Alps has an effective 
resolution of about 10 to 25 km (Isotta et al., 2014). In an example 
for precipitation in Spain, the effective resolution converged to the 
nominal resolution only when at least 6 to 7 stations were inside 
the gridcell (Herrera et al., 2019). To account for the smoothing errors, 
new stochastic ensemble observation datasets have been introduced 
(Von Clarmann, 2014).
10.2.2.5 Observations in Mountain Areas
Spatiotemporal variability of meteorological parameters observed over 
mountainous areas is often large, indicating strong control exerted by 
local topography on meteorological parameters (Gultepe et al., 2014).

Difficult access, harsh climatic conditions as well as instrumental 
issues make meteorological measurements extremely challenging 
at higher elevations (Azam et  al., 2018; Beniston et  al., 2018). 
Measurements of wind speed, temperature, relative humidity and 
radiative fluxes are critical for climate model evaluation, but difficult 
to handle due to their point-scale representativeness and small-scale 
spatiotemporal variability over mountainous terrain, and often need 
adjustment (Gultepe, 2015). High-altitude (>3000 metres) permanent 
meteorological stations are limited and current knowledge is mainly 
based on valley-bottom or low-elevation meteorological stations (Qin 
et  al., 2009; Lawrimore et  al., 2011; Gultepe, 2015; Condom et  al., 
2020), which, generally do not represent the higher elevation climate 
(Immerzeel et al., 2015; Shea et al., 2015).
Measuring precipitation amounts, especially of solid precipitation, in

mountainous areas is particularly challenging due to the presence 
of orographic barriers, strong vertical and horizontal precipitation 
rate variability, and the difficulty in finding representative sites for 
precipitation measurements (Barry, 2012). However, the precipitation 
amounts can be indirectly estimated by the observed point mass 
balances at glacier accumulation areas representing net snow 
accumulation (Haimberger et  al., 2012; Immerzeel et  al., 2015; 
Sakai et al., 2015; Azam et al., 2018). There is very high confidence  
that precipitation measurements, especially solid precipitation, in 
mountainous areas are strongly affected by the gauge location and 
setup. Precipitation measurements are also affected by the type of 
measurement method, presence/absence of shielding, presence/
absence of a heating system and operating meteorological conditions 
(Nitu et al., 2018). Solid precipitation measurements may have errors

ranging from 20% to 50%, largely due to under-catch in windy, icing 
and riming conditions (Rasmussen et al., 2012), and therefore require 
corrections by applying transfer functions developed mainly from 
collected wind speed and temperature data (Kochendorfer et  al., 
2017). The latest Solid Precipitation Intercomparison Experiment 
(SPICE) report recommends measurements of wind speed, wind

1387
Linking Global to Regional Climate Change Chapter 1010direction and temperature as the minimum standard ancillary data 
for solid precipitation monitoring (Nitu et al., 2018).
Recent advances in remote-sensing methods provide an alternative, 
but they also have limitations over mountainous areas. Different 
versions of the Tropical Rainfall Measuring Mission (TRMM) 
products were found to perform differently over mountainous 
areas (Zulkafli et  al., 2014). Orographic heavy rainfall associated 
with Typhoon Morakot in 2009 was severely underestimated in all 
microwave products including TRMM 3B42 (Shige et al., 2013). The 
underestimation has been mitigated in the Global Satellite Mapping 
of Precipitation (GSMaP) product by considering the orographic 
effects (Shige et al., 2013). Studies have suggested a high accuracy of 
passive optical satellite (e.g., MODIS, Landsat) snow products under 
clear skies when compared with the field observations. However,

cloud masking and sub-pixel cloud heterogeneity in these snow-cover 
products considerably restrict their applications (Kahn et al., 2011; 
Brun et al., 2015; Tang et al., 2017; Stillinger et al., 2019). Gridded 
datasets (e.g.,  CRU, GPCC Full Data Product, GPCC Monitoring 
Product, ERA-Interim, ERA5, ERA5-land, MERRA-2, MERRA-2 bias 
adjusted, PERSIANN-CDR) are of paramount importance, yet they 
often lack enough in situ observations to improve the temporal 
and spatial distribution of meteorological parameters over complex 
mountain terrain (Zandler et al., 2019).
10.2.2.6 Structural Uncertainty
Beyond climate monitoring, the quality and availability of multiple 
observational reference datasets play a  central role in model 
evaluation. In fact, when using observations for model evaluation, 
there are multiple examples where inter-observational uncertainty is 
as large as the inter-model variability. This has been shown for various

aspects of the Indian monsoon (Section 10.6.3; Collins et al., 2013a) 
and for precipitation uncertainties over Africa (Section 10.6.4; Nikulin 
et al., 2012; Sylla et al., 2013; Dosio et al., 2015; Bador et al., 2020) 
and Europe (Prein and Gobiet, 2017). Kotlarski et al. (2019) compared 
three high-resolution observational temperature and precipitation 
datasets (E-OBS, a compilation of national/regional high-resolution 
gridded datasets, and the EURO4M-MESAN 0.22° reanalysis based on 
a high-resolution limited-area model) with five EURO-CORDEX RCMs 
driven by ERA-Interim. Generally, the differences between RCMs are 
larger than those between observation datasets, but for individual 
regions and performance metrics, observational uncertainty can 
dominate. They also showed that the choice of reference dataset 
can have an influence on the RCM performance score. Over the high 
mountain Asia region and East Asia, differences among gridded

precipitation datasets can generate significant uncertainties in 
deriving precipitation characteristics (J. Kim et al., 2015; Kim and Park, 
2016; Guo et al., 2017). Over western North America, observational 
uncertainty induces differences in multi-decadal precipitation 
trends (Lehner et al., 2018). Taking a very different perspective, the 
agreement between model simulations may be used to estimate 
the uncertainty and quality of observations (Massonnet et al., 2016). 
There is high confidence  that an ensemble of multiple observational 
references at a regional scale is fundamental for model performance 
assessment. The uncertainties vary according to region, season, and 
statistical properties (Cross-Chapter Box 10.2).10.2.3 Other Uses of Observations at Regional Scale
10.2.3.1 Observations for Calibrating Statistical Methods
Statistical downscaling, bias adjustment and weather generators are 
post-processing methods used to derive climate information from

climate simulations. They all require observational data for calibration 
as well as evaluation (Section  10.3.3.1). Typically, the so-called 
perfect prognosis methods use quasi-observations for the predictors 
(i.e.,  reanalyses) and actual observations for the predictands (the 
surface variables of interest). By contrast, bias adjustment methods 
use observations only for the predictands. Weather generators typically 
require only observed predictands, although some are conditioned 
on observed predictors as well. Very often these methods are based 
on daily data, because of user needs, but also because of the limited 
availability of sub-daily observations and the limited ability of climate 
models to realistically simulate sub-daily weather (Iizumi et  al., 
2012). Some methods are calibrated on the monthly scale, but some 
of the generated time series are then further disaggregated to the 
daily scale (e.g., Thober et al., 2014). A few methods, mainly weather

generators, represent sub-daily weather (Mezghani and Hingray, 
2009; Kaczmarska et al., 2014). Many methods simulate temperature 
and precipitation only, although some also represent wind, radiation 
and other variables. The limited availability of high quality and long 
observational records typically restricts these applications to a  few 
cases (Verfaillie et al., 2017; Pryor and Hahmann, 2019). Overall, there 
is high confidence  that limited availability of station observations, 
including variables beyond temperature and precipitation as well as 
sub-daily data, limit the use of statistical modelling of regional climate.
All the limitations and challenges of observational data discussed 
in Section 10.2.2 also apply to its use for post-processing of climate 
model data. High quality and long observational data series are 
particularly relevant to quantify uncertainties. Different reanalyses 
present significant discrepancies when used as key predictor variables

at the daily scale and may even affect the downscaled climate change 
signal (Brands et  al., 2012; Dayon et  al., 2015; Manzanas et  al., 
2015; Horton and Brönnimann, 2019). There is high confidence  that 
reanalysis uncertainties limit the quality of statistical downscaling in 
some regions, although no assessment has been made for the most 
recent reanalysis products.
An important issue for bias adjustment is the correct representation 
of the required spatial scale. Ideally, bias adjustment is calibrated 
against area-averaged data of the same spatial scale as the climate 
model output. Hence, high-quality observed gridded datasets with an 
effective resolution close to the nominal model resolution are required. 
Driven by the need to also generate regional-scale information in 
station-sparse regions, researchers have considered derived datasets 
that blend in situ and remote-sensing data to produce high-resolution

observations to be used as predictands (Sections 10.2.1.2 and 10.2.2.4; 
Haiden et al., 2011; Wilby and Yu, 2013).
10.2.3.2 Observation for Paleoclimate Data Assimilation
Following some early concept studies, the first practical applications of 
paleoclimate data assimilation over past centuries used only selected

1388
Chapter 10 Linking Global to Regional Climate Change10data to reconstruct past climate changes for analysis of a  specific 
process or case (Widmann et  al., 2010). Recently, assimilation of 
multiple series from various data sources, including tree rings, ice 
cores, lake cores, corals, and bivalves, has allowed production of 
reconstructions that can be widely shared and applied to multiple 
purposes, as with modern reanalyses (Hakim et  al., 2016; Franke 
et al., 2017; Steiger et al., 2018; Tardif et al., 2019). Most of these 
paleo-reanalyses are global but there are products using regional 
models or targeted at specific regions such as Europe, East Africa and 
the Indian Ocean (Fallah et al., 2018; Klein and Goosse, 2018).
Paleo-reanalyses are enabling a new range of applications and have 
already provided useful information on seasonal-to-multi-decadal 
climate variability over past millennia. They are useful tools to study

the co-variance between variables at interannual-to-centennial time 
scales and at regional to global spatial scales. In particular, they have 
highlighted the processes that can be responsible for changes in 
continental hydrology at multi-decadal time scales (Franke et al., 2017; 
Klein and Goosse, 2018; Steiger et al., 2018). Paleo-reanalyses have 
confirmed a large contribution of internal variability in past changes 
at regional scale during the pre-industrial period, superimposed on 
a weak common signal due to forcing changes (Goosse et al., 2012) 
and the absence of a globally coherent warm period in the common 
era before the recent warming (Neukom et al., 2019). Reconstructions 
of the atmospheric state obtained in the reanalysis also provide robust 
evidence  of a local enhancement of warming or cooling conditions 
due to changes in atmospheric circulation, such as for the warm 
conditions in some European regions around 950–1250 CE, the cooling

observed in 1809/1810, or the cold and rainy 1816 summer in Europe 
(Cross-Chapter Box 4.1; Goosse et al., 2012; Hakim et al., 2016; Franke 
et al., 2017; Schurer et al., 2019).
10.2.4 Outlook for Improving Observational 
Data for Regional Climates
An encouraging development for understanding climate variations 
over the past 250 years or so at the global and regional scale lies 
in the field of data rescue, in which hitherto hidden archives of 
meteorological data are brought to the forefront (Sections 1.5.1.1 
and 2.5). Surface observations from data rescue projects may then 
be assimilated to derive long-term high-resolution gridded surface 
regional reanalysis (Devers et al., 2020). Global extended reanalyses 
such as 20CR (Compo et al., 2011), ERA-20C (Poli et al., 2016a, b) 
or CERA-20C (Laloyaux et  al., 2018) may be further downscaled 
to quantify the variability of past climate at the regional scale 
(Caillouet et al., 2016, 2019).

One of the main scientific challenges related to high-resolution 
regional climate modelling is dealing with the representation of 
fine-scale processes (e.g., Yano et al., 2018) in observational datasets. 
Additionally, reliable observation networks following WMO standards 
have a  very sparse geographical representation. Hence, regional 
climate models have started to use high-resolution data combined 
with crowdsourced observations (Zheng et al., 2018). Recent efforts 
have led to the production of homogeneously processed long-term 
datasets for regional climate model evaluation (Goudenhoofdt and Delobbe, 2016; Humphrey et al., 2017; Yang and Ng, 2019). While 
they are far less reliable and accurate than professional observations, 
crowdsourced data are abundantly available and can give spatial 
representations at very high resolution. This technological trend 
could prove very useful ( high confidence ), and the regional climate

community is making efforts to understand the extent to which these 
data sources can be exploited, at least as a complement to traditional 
datasets (Overeem et al., 2013; Meier et al., 2017; Uijlenhoet et al., 
2018; de Vos et al., 2019; Langendijk et al., 2019b).
10.3 Using Models for Constructing 
Regional Climate Information
Much of the information available on future regional climate arises 
from studies based on climate model simulations (Chapters 3, 4 
and 8). In this section, different types of models (Section  10.3.1) 
and model experiments (Section  10.3.2) for generating regional 
climate information are discussed, followed by an assessment of 
the performance, added value, and fitness-for-purpose of different 
model types (Section 10.3.3). The focus is on the representation of 
large- to local-scale phenomena and processes relevant for regional 
climate. Finally, uncertainties of regional climate projections and

methodologies to manage these are assessed (Section 10.3.4).
10.3.1 Model Types
Regional climate change information may be derived from a hierarchy 
of different model types covering a  wide range of spatial scales 
and processes (Figure  10.5). The application of any model relies 
on assumptions, depending on the specific model as well as the 
application. Table 10.1 gives an overview of the generic assumptions 
of the different model types discussed here for generating regional 
climate information. The violation of these assumptions will affect 
the model performance, which is discussed in Section 10.3.3.
Global models (ESMs, AOGCMs, AGCMs)
Standard resolution High resolution
Dynamical downscaling
High resolution
RCMs
Convection
permitting RCMs
Statistical approaches
Bias adjustment Perfect prognosis Weather generators
Target variable at target resolution
Figure 10. 5 | Typical model types and chains used in modelling regional

climate.  The dashed lines indicate model chains that might prove useful but have 
not or only rarely been used. Hybrid approaches combining the model types shown 
have been developed.

1389
Linking Global to Regional Climate Change Chapter 1010
10.3.1.1 Global Models, Including High-resolution 
and Variable Resolution Models
Model-based regional climate projections are all based upon 
some type of global model, including state-of-the-art Earth system 
models (ESMs), coupled atmosphere–ocean general circulation models 
(GCMs) or atmosphere-only general circulation models (AGCMs) 
(see Section 1.5.3.1). They are collectively referred to as global models.State-of-the-art global models are generally used to derive climate 
information at continental to global scales both for past and future 
climates (e.g., Chapters 3 and 4). The nominal horizontal resolution 
in CMIP5 global models is typically 100–200 km. The effective 
resolution, for which the shape of the kinetic energy spectrum is 
simulated correctly, is about three to five times larger (Klaver et al., 
2020), and a similar relationship also applies to RCMs (Skamarock,

2004). This strongly limits their ability to resolve local details. Since Table  10.1 | Assumptions underlying different model types in simulating regional climate and climate change. Violating these assumptions will affect model 
performance (see links to different subsections for details). All assumptions regarding future climate are in addition to those regarding present climate and predicated on 
the driving global model simulating a plausible global climate sensitivity (Section 1.3.5, Chapters 4 and 7). The assumptions listed for future climate applications of perfect prognosis 
statistical downscaling and bias adjustment are often called the ‘stationarity assumption’. Numbers in curly brackets refer to chapters and sections assessing these assumptions.
Model TypeScale at Which 
the Assumption 
AppliesAssumptions to Realistically Simulate 
Present Regional ClimateAdditional Assumptions to Be Fit for 
Simulating Future Regional Climate
Global model i.e., atmosphere-

only general circulation model, 
global climate model, Earth 
system model (AGCM, GCM 
or ESM; not bias adjusted) 
(Section 10.3.1.1)Large (>1000 km)Global model includes all relevant large-scale forcings 
and realistically simulates relevant large-scale circulation 
(Sections 3.3.3, 8.5.1 and 10.3.3.3).Global model realistically simulates processes 
controlling large-scale changes. Parametrizations are 
valid in future climate (Chapter 3, and Sections 4.2, 4.5, 
8.5.1 and 10.3.3.9).
Regional (<1000 km)Global model includes all relevant regional forcings and 
realistically simulates all relevant regional-scale processes 
and feedbacks and their dependence on large-scale 
climate (Sections 8.5.1, 10.3.3.4–10.3.3.6 and 10.3.3.8).Global model realistically simulates processes controlling 
regional changes. Parametrizations are valid in future 
climate (Sections 8.5.1 and 10.3.3.9).
Dynamical downscaling of global 
model with regional climate 
model (RCM; not bias adjusted)

(Section 10.3.1.2)LargeDriving global model includes all relevant large-scale 
forcings and realistically simulates relevant large-scale 
circulation, RCM does not deteriorate global simulations. 
Feedbacks from regional into large-scale processes are 
negligible (Sections 3.3.3, 8.5.1 and 10.3.3.3).Driving global model realistically simulates processes 
controlling large-scale changes, RCM does not deteriorate 
global model changes. Parametrizations are valid in 
future climate (Chapter 3 and Sections 4.2, 4.5. 8.5.1 
and 10.3.3.9).
RegionalRCM includes all relevant regional forcings and realistically 
simulates all relevant regional-scale processes and 
feedbacks and their dependence on large-scale climate 
(Sections 10.3.3.4–10.3.3.6 and 10.3.3.8).RCM realistically simulates processes controlling regional 
changes. Parametrizations are valid in future climate 
(Section 10.3.3.9).
Perfect prognosis statistical 
downscaling of GCM

(Section 10.3.1.3)LargeGlobal model realistically simulates all relevant large-scale 
predictors. The predictors are bias free and represent 
the regional variability at all desired time scales 
(Sections 3.3.3, 8.5.1 and 10.3.3.3). Global model realistically simulates processes controlling 
changes in the predictors. The predictors represent the 
response to external forcing (Chapter 3 and Sections 4.2, 
4.5. 8.5.1 and 10.3.3.9).
RegionalThe statistical model structure is adequate to represent 
the predictor influence on regional-scale variability. 
There is no relevant feedback involving the predictands 
(Section 10.3.3.7).The statistical model structure is adequate under the 
required extrapolation (Section 10.3.3.9).
Bias adjustment of dynamical 
model (GCM or RCM) 
(Section 10.3.1.3)Large As per driving model. As per driving model.
RegionalAs per driving model, apart from adjustable biases. 
The gap between driving model resolution and target

resolution is minor (Sections 10.3.3.4–10.3.3.6 and 
10.3.3.8, and Cross-Chapter Box 10.2).As per driving model, apart from adjustable biases. 
The chosen bias adjustment is applicable in a future 
climate (Section 10.3.3.9 and Cross-Chapter Box 10.2).
Delta change approach 
applied to dynamical model 
(Section 10.3.1.3)Large Not applicableAs per driving model. There are no changes altering 
the non-changed statistics (e.g., no circulation changes 
that alter temporal structure) (Chapter 3 and Sections 4.2, 
4.5, 8.5.1 and 10.3.3.9).
Regional Not applicableAs per driving model. There are no changes altering the 
non-changed statistics. The gap between driving model 
resolution and target resolution is minor (Section 10.3.3.9).
Change factor weather generator 
(Section 10.3.1.3)Large Not applicable As per driving model.
RegionalThe weather generator structure is adequate 
(Section 10.3.3.7).As per driving model. The weather generator structure

is adequate in a future climate. Change factors are 
adequately incorporated for all changing weather aspects. 
The gap between driving model resolution and target 
resolution is minor (Section 10.3.3.9).

1390
Chapter 10 Linking Global to Regional Climate Change10AR5 the progress in reducing biases and providing more credible 
regional projections by global models has been moderate in spite 
of the more realistic representation of a number of processes and 
the increase in resolution of some models. For AR6, several of the 
new CMIP6 (Eyring et  al., 2016a) model intercomparison projects 
(MIPs) address some of these limitations. The list of MIPs is provided 
in Chapter 1 (Table 1.3). High-Resolution MIP (HighResMIP; Haarsma 
et al., 2016) and Global Monsoons MIP (GMMIP; Zhou et al., 2016) 
specifically address the regional climate challenge using global 
models. HighResMIP focuses on producing global climate projections 
at a horizontal resolution of around 50 km grid spacing or finer while 
GMMIP aims at better understanding and predicting the monsoons.
An alternative to increasing resolution everywhere is offered by 
variable resolution global models, that is, with regionally finer

resolution. They have been developed since the 1970s (Li, 1999), 
resulting in a first coordinated effort (SGMIP) by Fox-Rabinovitz et al. 
(2006, 2008). They are expected to offer the finest resolution possible 
in the region of interest, while still resolving the climate processes at 
the global scale (although at lower resolution). An overview of recent 
developments is in McGregor (2015). This is a rapidly developing field 
(Krinner et al., 2014; Ferguson et al., 2016; Huang et al., 2016) that 
will possibly contribute to improved future regional projections.
10.3.1.2 Regional Climate Models
Regional climate models (RCMs) are dynamical models similar 
to global models that are applied over a  limited area, but with 
a  horizontal resolution higher than that of standard global 
models. They are the basis for dynamical downscaling to produce 
sub-continental climate information (e.g., Chapters 11, 12 and Atlas) 
but are also often used for process understanding. At lateral and, if

applicable, lower boundaries, RCMs take their values from a driving 
dataset, which could be a global model or a reanalysis. RCMs are 
typically one-way nested: they do not feed back into the driving 
model, although two-way nested global model-RCM simulations 
have been performed that examine regional influence on large-scale 
climate, potentially improving it (Lorenz and Jacob, 2005; Harris 
and Lin, 2013; Junquas et al., 2016). Spectral nudging (Kida et al., 
1991; Waldron et al., 1996; von Storch et al., 2000; Kanamaru and 
Kanamitsu, 2007) can increase consistency with the driving model, 
whereby selected variables, such as the wind field, are forced to 
closely follow a prescribed large-scale field over a specified range 
of spatial scales. RCMs can inherit biases from the driving global 
model in addition to producing biases themselves (Hall, 2014; 
Hong and Kanamitsu, 2014; Dosio et  al., 2015; Takayabu et  al.,

2016). The consistency between the circulation features simulated 
by the RCM and those inherited through the boundary conditions 
depends on (i) the relative importance of the large-scale forcing 
compared to local-scale phenomena, and (ii) the size of the RCM 
domain (e.g.,  Diaconescu and Laprise, 2013). Large domains also 
allow the RCM to generate much of its own internally generated 
unforced variability (Nikiema et  al., 2017, and references therein; 
Sanchez-Gomez and Somot, 2018).
The Coordinated Regional Climate Downscaling Experiment 
(CORDEX) initiative (Giorgi et al., 2009; Giorgi and Gutowski, 2015; Gutowski  Jr.  et  al., 2016) provides ensembles of high-resolution 
historical (starting as early as 1950) and future climate projections for 
various regions. RCMs in CORDEX typically have a horizontal resolution 
between 10 and 50 km. But much finer spatial resolution is required 
to fully resolve deep convection, an important cause of precipitation

in much of the world. Therefore, an emerging strand in dynamical 
downscaling employs simulations at convection permitting scales, 
at horizontal resolutions of a few kilometres, where deep-convection 
parametrizations can be switched off, approximately simulating deep 
convection (Prein et  al., 2015; Stratton et  al., 2018; Coppola et  al., 
2020). A recent study indicates that switching off the deep-convection 
parametrization may be beneficial also in simulations performed at 
coarser resolutions (Vergara-Temprado et  al., 2020). Alternatively, 
some RCMs make use of scale-aware parametrizations that are able 
to adapt to increasing resolution without switching off the convection 
scheme (Hamdi et  al., 2012; De Troch et  al., 2013; Plant and Yano, 
2015; Giot et al., 2016; Termonia et al., 2018; Yano et al., 2018).
RCMs have often consisted of atmospheric and land components 
that do not include all possible Earth system processes and therefore

neglect important processes such as air-sea coupling (in standard 
RCMs sea surface temperatures, SSTs, are prescribed from global 
model simulations or reanalyses) or the chemistry of aerosol–cloud 
interaction (aerosols prescribed with a  climatology), which may 
influence regional climate projections. Therefore, some RCMs have 
been extended by coupling to additional components like interactive 
oceans, sometimes with sea ice (Kjellström et al., 2005; Somot et al., 
2008; Van Pham et al., 2014; Sein et al., 2015; Ruti et al., 2016; Zou 
and Zhou, 2016a; Zou et  al., 2017; Samanta et  al., 2018), rivers 
(Sevault et al., 2014; Lee et al., 2015; Di Sante et al., 2019), glaciers 
(Kotlarski et al., 2010), and aerosols (Zakey et al., 2006; Zubler et al., 
2011; Nabat et al., 2015). The coupling of these components allows 
for the investigation of additional climate processes such as regional 
sea level change (Adloff et al., 2018), ocean–land interactions (Lima

et al., 2019; Soares et al., 2019a), or the impact of high-frequency 
ocean–atmosphere coupling on the climatology of Mediterranean 
cyclones (Flaounas et al., 2018).
10.3.1.3 Statistical Approaches to Generate 
Regional Climate Projections
An alternative or addition to dynamical downscaling is the use of 
statistical approaches to generate regional projections. In AR5 these 
methods were collectively referred to as statistical downscaling, 
but their performance assessment has received little attention. 
A major conclusion was that a wide range of different methods exist 
and a  general assessment of their performance is difficult (Flato 
et  al., 2014). Since AR5, several initiatives have been launched to 
improve the understanding of statistical approaches such as VALUE 
(Validating and Integrating Downscaling Methods for Climate 
Change Research, now merged into the EURO-CORDEX activities; 
Maraun et  al., 2015), STaRMIP (Statistical Regionalization Models

Intercomparisons and Hydrological Impacts Project; Vaittinada Ayar 
et  al., 2016) and BADJAM (Bias ADJustment of climate scenarios 
for Agricultural Model applications; Galmarini et  al., 2019). The 
performance of different implementations of these approaches will 
be assessed in Section 10.3.3.7.

1391
Linking Global to Regional Climate Change Chapter 101010.3.1.3.1 Perfect prognosis
Perfect-prognosis models are statistical models calibrated between 
observation-based large-scale predictors (e.g., from reanalysis) and 
observed local-scale predictands (Maraun and Widmann, 2018b). 
Regional climate projections are then generated by replacing the 
quasi-observed predictors by those from climate model (typically 
global model) projections. Predictor patterns that are common to 
observations and climate model data can be defined by common 
empirical orthogonal functions (Benestad, 2011). The perfect prognosis 
approach can either be used to generate daily (or even sub-daily) 
time series, or local weather statistics (e.g., Benestad et al., 2018).
Regression-like models (Maraun and Widmann, 2018b) rely on a transfer 
function linking an observed local statistic (such as the temperature at 
a given day) to some set of large-scale predictors. Recent developments

include stochastic regression models to explicitly  simulate local 
variability (San-Martín et al., 2017; those explicitly modelling temporal 
dependence are assessed in Section 10.3.1.3.4). The use of machine 
learning techniques has been reinvigorated, including genetic 
programming to construct a  data-driven model structure (Zerenner 
et al., 2016) and deep and convolutional neural networks (Reichstein 
et al., 2019; Baño-Medina et al., 2020).
Analogue methods (Martin et  al., 1996; Maraun and Widmann, 
2018b) compare a simulated large-scale atmospheric field with an 
archive of observations and select, using some distance metric, the 
closest observed field in the archive. The downscaled atmospheric 
field is then chosen as the local atmospheric field observed on the 
instant the analogue occurred. New analogue methods have been 
developed to simulate unobserved values including a  rescaling of 
the analogue (Pierce et  al., 2014) or by combining analogues and

regression models (Chardon et al., 2018).
10.3.1.3.2 Bias adjustment
Bias adjustment is a  statistical post-processing technique used to 
pragmatically reduce the mismatch between the statistics of climate 
model output and observations. The approach estimates the bias or 
relative error between a chosen simulated statistical property (such 
as the long-term mean or specific quantiles of the climatological 
distribution) and that observed over a  calibration period; the 
simulated statistic is then adjusted taking into account the simulated 
deviation. Bias adjustment methods are regularly applied on a spatial 
scale similar to that of the simulation being adjusted, but they are 
often used as a simple statistical downscaling method by calibrating 
them between coarse resolution (e.g., global) model output and finer 
observations (Maraun and Widmann, 2018b).
Typical implementations of bias adjustment are (i) additive

adjustments, where the model data is adjusted by adding a constant, 
(ii) rescaling, where the model data is adjusted by a  factor, and 
(iii) more flexible quantile mapping approaches that adjust different 
ranges of a  distribution individually. Hempel et  al. (2013), Pierce 
et  al. (2015), Switanek et  al. (2017), and Lange (2019) developed 
variants of quantile mapping that preserve trends in the mean or 
even further distributional statistics. Multivariate bias adjustment extends univariate methods, which adjust statistics of individual 
variables separately, to joint adjustment of multiple variables 
simultaneously. Implementations remove biases in (i) specific 
measures of multivariate dependence, like correlation structure, via 
linear transformations (Bárdossy and Pegram, 2012; Cannon, 2016), 
or, more flexibly, (ii) the full multivariate distribution via non-linear 
transformations (Vrac and Friederichs, 2015; Dekens et  al., 2017;

Cannon, 2018; Vrac, 2018; Robin et al., 2019). Other research strands 
focus on the explicit separation of bias adjustment and downscaling 
(Section  10.3.1.3.5), or the integration of process understanding 
(Maraun et  al., 2017), such as by conditioning the adjustment on 
the occurrence of relevant phenomena (Addor et al., 2016; Verfaillie 
et al., 2017; Manzanas and Gutiérrez, 2019). Some authors suggest 
to mitigate the influence of large-scale temperature or circulation 
biases by performing a bias adjustment of the driving fields prior to 
dynamical downscaling (Colette et al., 2012; Hernández-Díaz et al., 
2013, 2019). Issues that may arise when using bias adjustment are 
discussed in Cross-Chapter Box 10.2.
10.3.1.3.3 Delta-change approaches
In the delta change approach, selected observations are modified 
according to corresponding changes derived from dynamical model 
simulations. Traditionally, only long-term means have been adjusted,

but recently approaches to modify temporal dependence (Webber 
et  al., 2018) have been developed, as well as quantile mapping 
approaches that individually adjust quantiles of the observed 
distribution (Willems and Vrac, 2011). By construction, the approach 
cannot modify the spatial and temporal dependence structure of the 
input observations (Maraun, 2016).
10.3.1.3.4 Weather generators
Weather generators are statistical models that simulate weather time 
series of arbitrary length. They are calibrated to represent observed 
weather statistics, in particular daily or even sub-daily variability. One 
variant of these models are advanced stochastic perfect-prognosis 
methods, conditioned on large-scale atmospheric predictors on a daily 
basis, for instance multisite generalized linear models (Chandler, 
2020). Another widely used variant is change-factor weather 
generators: the weather generator parameters are calibrated against

present and future climate model simulations, and the climate change 
signals are then applied to the parameters calibrated to observations. 
Recent research has mainly focussed on multi-site Richardson type 
(Markov-chain) weather generators (Keller et  al., 2015; Dubrovsky 
et  al., 2019), some explicitly modelling extremes and their spatial 
dependence (Evin et al., 2018).
10.3.1.3.5 Hybrid approaches and emulators
A wide variety of approaches has been proposed to combine the 
advantages of different statistical approaches. For instance, to 
overcome the scale mismatch between climate model output and 
observations, bias adjustment has been combined with stochastic 
downscaling (Volosciuk et  al., 2017; Lange, 2019) or rescaled 
analogues (Pierce et al., 2014). Other approaches known as emulators 
have been developed to emulate an RCM using a statistical model

1392
Chapter 10 Linking Global to Regional Climate Change10and also applied to a range of driving global models (Déqué et al., 
2012; Haas and Pinto, 2012; Walton et  al., 2015, 2017; Beusch 
et al., 2020; Erlandsen et al., 2020).
10.3.2 Types of Model Experiments
The most commonly used model experiments to generate regional 
climate information are transient simulations. Alternative experiment 
types serve specific purposes. The role of these experiment types for 
generating regional climate information is assessed in this subsection.
10.3.2.1 Transient Simulations and Time-slice Experiments
Transient simulations intend to represent the evolving climate 
state of the Earth system (Chapter  4). They are typically based on 
coupled global model simulations, such as those in the Diagnostic, 
Evaluation and Characterization of Klima (DECK) and ScenarioMIP 
part of CMIP6 covering the period 1850–2100 (Eyring et al., 2016a),

and HighResMIP (1950–2050; Haarsma et al., 2016). Global transient 
climate simulations may be further downscaled by either dynamical or 
statistical downscaling. Currently available CORDEX RCM simulations 
(1950–2100) are based on CMIP5 (Gutowski Jr. et al., 2016).
In contrast, time-slice experiments are designed to represent only 
a  specific period of time (typically 30 years). They are often run 
using global and regional models in atmosphere-only mode, forced 
by SSTs derived either from observations, as AMIP experiments, or 
from historical simulations and future projections of coupled global 
models. Compared to transient simulations, they offer advantages in 
being computationally cheaper (due to the lack of coupled ocean and 
short duration), which allows for the number of ensemble members 
(T. Zhang et  al., 2016), and/or the resolution (Haarsma et  al., 
2013b; Davini et  al., 2017) to be increased. Convection-permitting

simulations, both covering the globe or particular regions, are 
currently conducted for short time slices only (Kendon et al., 2017; 
Hewitt and Lowe, 2018; Coppola et al., 2020; Pichelli et al., 2021). 
Another high-resolution time-slice data base is d4PDF (Mizuta et al., 
2017; Ishii and Mori, 2020). Experiments covering a limited integration 
period have been carried out for coupled ocean–atmosphere RCMs 
(Sein et al., 2015; Zou and Zhou, 2016b, 2017). However, long spin-up 
periods are required to reach a stable stationary state in the deep 
ocean that otherwise might lead to invalid projections (Planton et al., 
2012; Soto-Navarro et al., 2020).
10.3.2.2 Pseudo-global Warming Experiments
Results from downscaling experiments often suffer from large-scale 
circulation biases in the driving global models such as misplaced storm 
tracks (Section 10.3.3.4), while changes in atmospheric circulation 
are often uncertain owing to both climate response uncertainty

(Section 10.3.4.2) and internal variability (Section 10.3.4.3). In a given 
application, if one can assume that changes in the regional climate 
are dominated by thermodynamic rather than by circulation changes, 
so-called pseudo-global warming (PGW) experiments (Schär et  al., 
1996) may be helpful in mitigating the effects of circulation biases, 
and to fix the large-scale circulation to present climate. In classical PGW experiments, boundary conditions for the downscaling are taken 
from reanalysis data, but modified according to the thermodynamic 
signals of climate change. The boundary conditions thus represent 
the sequence of observed weather, but with adjusted temperatures, 
humidity and atmospheric stability. Recent applications of PGW 
experiments include assessments of climate change in Japan (Adachi 
et  al., 2012; Kawase et  al., 2012, 2013), the Los Angeles area 
(Walton et al., 2015), Hawaii (C. Zhang et al., 2016), and the Alps

(Keller et  al., 2018). Recently, PGW studies have been generalized 
to modify global model simulations with the objective of separating 
the drivers of regional climate change, such as the Mediterranean 
amplification (e.g., Brogli et al., 2019b; Section 10.3.2.3).
Equivalent simulations can be conducted for individual events, 
thereby allowing for very high resolution. With counterfactual past 
climate conditions, such simulations can be used for conditional event 
attribution (Trenberth et  al., 2015; Chapter  11), using hypothetical 
future conditions to generate physical climate storylines of how 
specific events may manifest in a  warmer climate. The approach 
has been employed to study extreme events that require very high 
resolution simulations such as tropical cyclones (Lackmann, 2015; 
Takayabu et al., 2015; Lau et al., 2016; Kanada et al., 2017a; Gutmann 
et  al., 2018; Patricola and Wehner, 2018; J. Chen et  al., 2020) or

convective precipitation events (Pall et al., 2017; Hibino et al., 2018). 
The range of possible events is broader and has included Korean 
heatwaves (Kim et al., 2018) and monsoon onset in West Africa (Lawal 
et  al., 2016). However, if only individual events are simulated, no 
immediate conclusions can be derived for changes to the occurrence 
probability of these events (F .E.L. Otto et al., 2016; Shepherd, 2016a).
10.3.2.3 Sensitivity Studies With Selected Drivers
Sensitivity studies are used to identify the impact of a specific forcing, 
driver or process on regional climate phenomena and changes and 
improve the process understanding. The influence of a single external 
forcing can be assessed with transient historical simulations within 
two different frameworks (Bindoff et al., 2013; Gillett et al., 2016). The 
first entails simulations taking prescribed (often observed) changes 
only in the external forcing of interest, the others being fixed at

a constant value (often pre-industrial). The second framework is based 
on simulations in which all external forcings are applied other than 
the one of interest. Both approaches may not give the same results 
since the climate response to a  range of forcings is not necessarily 
equal to the sum of climate responses to individual forcings (Ming and 
Ramaswamy, 2011; Jones et al., 2013; Schaller et al., 2013; Shiogama 
et al., 2013; Marvel et al., 2015; Deng et al., 2020).
To study the influence of internal variability, new approaches such 
as partial coupling simulations are now routinely used since AR5. 
These are coupled ocean–atmosphere simulations in which the 
interaction between atmosphere and ocean is only one-way over 
a specified ocean basin or sub-basin and two-way everywhere else. 
Different implementations have been used such as SST anomaly 
Newtonian relaxation at the air–sea interface or prescription of

wind-stress anomalies from reanalysis (Kosaka and Xie, 2013, 2016; 
England et  al., 2014; McGregor et  al., 2014; Douville et  al., 2015; 
Deser et al., 2017a). Such simulations have been applied to identify

1393
Linking Global to Regional Climate Change Chapter 1010the regional impacts of the Pacific Decadal Variability (PDV) and 
Atlantic Multi-decadal Variability (AMV) (Kosaka and Xie, 2013; 
Watanabe et  al., 2014; Delworth et  al., 2015; Boer et  al., 2016; 
Ruprich-Robert et al., 2017, 2018).
Nudging experiments have been used to identify the relative roles of 
dynamic and thermodynamic processes in climate model biases and 
specific extreme events (Wehrli et al., 2018, 2019). Another related 
framework is used to evaluate the impact land conditions have on 
a climate phenomenon in a pair of experiments with one simulation 
serving as control run, and a perturbed simulation with prescribed 
land conditions (i.e., soil moisture, leaf area index, or surface albedo) 
characterizing a specific state of the land surface (i.e., afforestation 
or deforestation). The difference between the perturbed and control 
simulations enables a robust assessment of the possible impact of

land conditions on events like droughts and heatwaves (Seneviratne 
et al., 2013; Stegehuis et al., 2015; Hauser et al., 2016, 2017; van den 
Hurk et al., 2016; Vogel et al., 2017; Rasmijn et al., 2018; Strandberg 
and Kjellström, 2019).
RCM sensitivity simulations have been used in a  similar way to 
assess the contribution of external forcings and large-scale drivers 
to projected regional climate change (Nabat et  al., 2014; Brogli 
et al., 2019a, b) and the influence of selected drivers on observed 
extreme events (Meredith et  al., 2015b; J. Wang et  al., 2017; 
Ardilouze et al., 2019).
In summary, there is robust evidence that sensitivity experiments are 
key to assessing the influence of different forcings and drivers on 
regional climate change.
10.3.2.4 Control Simulations
In recent years, the role of internal variability in the interpretation of 
climate projections has become clearer, particularly at the regional

scale (Section 10.3.4.3). A considerable fraction of CMIP5 and CMIP6 
resources has been invested in generating an ensemble of centennial 
or multi-centennial control simulations with constant external 
forcings (Pedro et  al., 2016; Rackow et  al., 2018). As part of the 
CMIP6 DECK (Eyring et al., 2016a) pre-industrial control (piControl) 
simulations have been conducted (Menary et  al., 2018). Similarly, 
control simulations with present-day conditions (pdControl) have 
been performed to represent internal variability under more recent 
forcing conditions (Pedro et al., 2016; Williams et al., 2018). Control 
simulations have been used to study the role of internal variability, 
teleconnections and many other fundamental aspects of climate 
models (Z. Wang et  al., 2015; Krishnamurthy and Krishnamurthy, 
2016). Control simulations are also used along with large ensembles 
of historical or scenario simulations to assess the characteristics of

the regional internal climate variability (Olonscheck and Notz, 2017).
10.3.2.5 Simulations for Evaluating Downscaling Methods
Experiments driven by quasi-perfect boundary conditions or predictors 
(observations or reanalysis) can be useful to evaluate downscaling 
performance (Frei et  al., 2003; Laprise et  al., 2013), including 
the simulation of observed past trends (Lorenz and Jacob, 2010; Zubler et al., 2011; Nabat et al., 2014; Gutiérrez et al., 2018; Drugé et al., 
2019; Bozkurt et al., 2020) and the added value of downscaling compared 
to the reanalysis fields (Section 10.3.3.2). Although the reanalysis model 
itself can introduce biases especially for non-assimilated variables (such 
as precipitation) it is assumed that in such a  setting, discrepancies 
between the modelled and observed climate arise mostly from errors 
in the downscaling method (Laprise et  al., 2013) or internal climate 
variability generated by the downscaling method (Böhnisch et  al.,

2020; Ehmele et  al., 2020). Since AR5, reanalysis-driven RCMs have 
been extensively evaluated for many regions, especially in the CORDEX 
framework (see region specific examples in the Atlas).
Over Europe, the VALUE initiative assessed statistical downscaling 
for marginal, temporal, and spatial aspects of temperature and 
precipitation including extremes, and performed a  process-based 
evaluation of specific climatic phenomena (Gutiérrez et  al., 2019; 
Maraun et al., 2019a). Alternatively, statistical downscaling can be 
evaluated in so-called perfect model or pseudo-reality simulations 
(Charles et  al., 1999), where a  high-resolution climate model 
simulation is used as a proxy for a hypothetical present and future 
realities. A statistical downscaling model is first calibrated with this 
pseudo present-day climate and, subsequently, assessed whether it 
correctly reproduces the pseudo-future conditions (Dixon et al., 2016).
10.3.3 Model Performance and Added Value in

Simulating and Projecting Regional Climate
Assessing model performance is a prerequisite for building confidence in 
regional climate projections. This subsection assesses the performance 
of different model types at simulating regional climate and climate 
change. The subsection builds on the assessment of global model 
performance in Chapter 3, and complements the model assessment in 
Chapter 8, which focuses on the water cycle, and the Atlas.
While the ability of global models to simulate large-scale indicators 
of climate change has improved since AR5 (Chapter 3), the simulation 
of regional climate and climate change poses an additional challenge. 
Users demand regional climate projections for decision-making and 
have high expectations regarding accuracy and resolution (Rössler 
et  al., 2019a), but some scientists consider such projections still 
a  matter of basic research (Hewitson et al., 2014a). For instance,

large-scale circulation biases or the misrepresentation of regional 
topography as well as regional phenomena and feedbacks are very 
relevant (Hall, 2014; Maraun and Widmann, 2018b).  New global 
model ensembles such as CMIP6 (Eyring et al., 2016a), HighResMIP 
(Haarsma et  al., 2016) or, at the regional scale, the convection 
permitting simulations from the CORDEX Flagship Pilot Study (FPS) 
on convective phenomena (Coppola et al., 2020) have the potential 
to substantially improve the basis for generating regional climate 
information, yet uncertainties and (often unresolved) contradictions 
between model projections at the regional scale can be substantial 
(Fernández et al., 2019).
Figure 10.6 shows the mean summer temperature and precipitation 
biases of several state-of-the-art climate model ensembles for 
the western Mediterranean. It additionally illustrates the role of

1394
Chapter 10 Linking Global to Regional Climate Change10(a) Western Mediterranean June to August mean surface air temperature (1986–2005)
(b) Western Mediterranean June to August mean precipitation (1986–2005)
Figure  10.6 | Illustration of some model biases in simulations performed with dynamical models.

1395
Linking Global to Regional Climate Change Chapter 1010observational uncertainty for model evaluation (Section 10.2), where 
observations display differences that can be substantial. Model 
performance varies strongly from model to model, but also between 
ensembles. These biases are an expression of model error that leads 
to misrepresented phenomena and processes, and thus limit the 
confidence in future projections of regional climate. The focus of this 
subsection is therefore to evaluate the representation of relevant 
regional-scale phenomena for representing regional climate.
10.3.3.1 Evaluation Diagnostics
Since AR5, model evaluation has made use of a broad combination 
of diagnostics (Colette et  al., 2012; Kotlarski et  al., 2014; Eyring 
et al., 2016b; Gleckler et al., 2016; Ivanov et al., 2017, 2018; Vautard 
et al., 2021), ranging from long-term means to indices of extreme 
events (Zhang et al., 2011; Sillmann et al., 2013) or a combination

of these (Dittus et al., 2016). This evaluation has shown that global 
models have pervasive biases in some aspects of their large-scale 
behaviour (Section 1.5.3.1, Chapter 3). More complex diagnostics are 
used to characterize specific meteorological phenomena (Sprenger 
et al., 2017), such as feedbacks in the El Niño–Southern Oscillation 
(ENSO; Bellenger et  al., 2014), Madden-Julian Oscillation (MJO) 
characteristics (Benedict et  al., 2014; Jiang et  al., 2015; D.  Kim 
et  al., 2015; Ahn et  al., 2017), extratropical modes of variability 
(Lee et al., 2019), cyclone tracking (Neu et al., 2013; Flaounas et al., 
2018), front detection (Hope et  al., 2014; Schemm et  al., 2015), 
thunderstorm environment parameters (Bukovsky et  al., 2017), 
African easterly waves (McCrary et al., 2014; Martin and Thorncroft, 
2015), land–atmosphere coupling (Spennemann and Saulo, 2015; 
Santanello et  al., 2018), and sea–atmosphere coupling (Bellenger 
et al., 2014; Mayer et al., 2017).

New diagnostics for multivariate dependencies are needed to 
characterize compound events (Section  11.8; Hobaek Haff et  al., 
2015; Wahl et al., 2015; Sippel et al., 2016, 2017; Tencer et al., 2016; 
Bevacqua et al., 2017; Careto et al., 2018; Zscheischler et al., 2018). 
However, their success depends on the availability of adequate 
observational data (Section  10.2.2). Multivariate dependencies 
discovered in compound events can also be used for designing and 
evaluating multivariate bias adjustment and statistical downscaling. 
Process-based diagnostics are useful for identifying the cause of model 
errors, although it is not always possible to associate a  systematic 
error with a  specific cause (Eyring et  al., 2019). AR5 discussed 
two approaches of process-based evaluation: (i) the isolation of 
physical components or parametrizations by dedicated experiments

(Section 10.3.2.4) and (ii) diagnostics conditioned on relevant regimes, usually synoptic-scale weather patterns. The regime-based approach 
has been used with both global models (e.g.,  Barton et  al., 2012; 
Catto et al., 2015; Taylor et al., 2019) and RCMs (Endris et al., 2016; 
Bukovsky et al., 2017; Whan and Zwiers, 2017; Pinto et al., 2018), but 
also with perfect prognosis and bias adjustment methods (Marteau 
et al., 2015; Addor et al., 2016; Beranová and Kyselý, 2016; Soares and 
Cardoso, 2018; Soares et al., 2019b).
Recent studies highlight the importance of user-defined or user-relevant 
diagnostics for model evaluation (Maraun et al., 2015; Rhoades et al., 
2018; Rössler et  al., 2019b; Nissan et  al., 2020). Diagnostics have 
been used to assess the performance of climate models to produce 
useful input data for impact models as in the comparison between 
RCMs and convection-permitting models to capture flood-generating

precipitation events in the Alps (Reszler et al., 2018). Alternatively, 
the observed impact can be compared to that simulated by an impact 
model that uses input from both observations and climate models. 
This approach has been used to evaluate the influence of statistical 
downscaling and bias adjustment on hydrological (Rojas et al., 2011; 
H. Chen et  al., 2012; Gutiérrez et  al., 2019; Rössler et  al., 2019b), 
agricultural (Ruiz-Ramos et al., 2016; Galmarini et al., 2019), forest 
and wildfire (Abatzoglou and Brown, 2012; Migliavacca et al., 2013) 
(Bedia et al., 2013), snow depth (Verfaillie et al., 2017), and regional 
ocean modelling (e.g., Macias et al., 2018).
There is high confidence  that to assess whether a  climate model 
realistically simulates required aspects of present-day regional 
climate, and to increase confidence of future projections of these 
aspects, evaluation needs to be based on diagnostics taking into

account multiple variables and process understanding.
10.3.3.2 Model Improvement and Added Value
Obtaining regional information from global simulations may involve 
a  range of different methods (Section  10.3.1). An approach with 
higher complexity or resolution is useful if it adds further, useful 
information to that of a reference model. Section 10.5 discusses the 
set of considerations that determine if the information is useful. This 
further useful information is often referred to as added value and 
is a function of variables, processes, and the temporal and spatial 
scales targeted taking into account the needs of specific users 
(Di Luca et al., 2012; Ekström et al., 2015; Giorgi and Gutowski, 2015; 
Torma et  al., 2015; Rummukainen, 2016; Falco et  al., 2019). There 
is no common definition of added value, but here it is considered 
a  characteristic that arises when one methodology gives further

value to what another methodology yields.Figure 10.6 (continued): (a)  Top row: Mean summer (June to August) near-surface air temperature (in °C) over the Mediterranean area in Berkeley Earth and respective mean 
bias for five multi-model historical experiments with global models (CMIP5, CMIP6 and HighResMIP) and regional climate models (CORDEX EUR-44 and EUR-11) averaged 
between 1986–2005. Bottom row: Box-and-whisker plot shows spread of the 20 annual mean summer surface air temperature averaged over land areas in the western 
Mediterranean region (33°N–45°N, 10°W–10°E, black quadrilateral in the first panel of the top row) for a set of references and single model runs of the five multi-model 
experiments (one simulation per model) between 1986–2005. Additional observation and reanalysis data included in the bottom row are CRU TS, HadCRUT4, HadCRUT5,

E-OBS, WFDE5, ERA5, ERA-Interim, CERA-20C, JRA-25, JRA-55, CFSR, MERRA2, MERRA. Berkeley Earth is shown in the first box to the left. (b) As (a) but for precipitation 
rate (mm day–1) and showing CRU TS in the first panel of the top row. Biases of the five multi-model experiments are shown with respect to CRU TS. Additional observation 
and reanalysis data included in the bottom row are GPCC, REGEN, E-OBS, GHCN, WFDE5, CFSR, ERA-Interim, ERA5, JRA-55, MERRA2, MERRA. CRU TS is shown in the first 
box to the left. All box-and-whisker plots show the median (line), and the interquartile range (IQR = Q3–Q1, box), with top whiskers extending to the last data less than 
Q3 + 1.5 × IQR and analogously for bottom whiskers. Data outside the whiskers range appear as flyers (circles). Further details on data sources and processing are available 
in the chapter data table (Table 10.SM.11).

1396
Chapter 10 Linking Global to Regional Climate Change10Downscaling is expected to improve the representation of a region’s 
climate compared to the driving global model (Di Luca et al., 2015). 
Arguably, there should be a clear physical reason for the improvement, 
which is applicable to the evaluation of added value in downscaled 
projections (Giorgi et  al., 2016). The added value depends on the 
region, season, and governing physical processes (Lenz et al., 2017; 
Schaaf and Feser, 2018). Thus, added value of downscaling global 
model simulations is most likely where regional- and local-scale 
processes play an important role in a region’s climate, for example 
in complex or heterogeneous terrain such as mountains (Lee and 
Hong, 2014; Prein et al., 2016b), urban areas (Argüeso et al., 2014), 
along coastlines (Feser et al., 2011; Herrmann et al., 2011; Bozkurt 
et  al., 2019), or where convective processes are important (Prein

et al., 2015). Examples of model improvements and added value are 
given in the following subsections and the Atlas.
A first step in determining added value in downscaling is to analyse 
whether the downscaling procedure gives detail on spatial or 
temporal scales not well-resolved by a global model, thus potentially 
representing climatic features missing in the GCM. This added detail, 
referred to as potential added value (PAV; Di Luca et al., 2012), is 
insufficient for demonstrating added value in downscaling (Takayabu 
et al., 2016), but lack of PAV indicates that the downscaling method 
lacks usefulness. Added value is not guaranteed simply by producing 
model output at finer resolution. It depends on several factors, such 
as the simulation setup and the specific climatic variables analysed 
(Di Luca et al., 2012; Hong and Kanamitsu, 2014; Xue et al., 2014). 
A variety of performance measures are needed to assess added value

(Section  10.3.3.1; Di Luca et  al., 2016; Wilks, 2016; Ivanov et  al., 
2017, 2018; Soares and Cardoso, 2018).
A further challenge, especially at increasingly higher resolutions, is 
that adequate observational data may not be available to assess 
added value (Section 10.2, e.g., Di Luca et al., 2016; Zittis et al., 2017; 
Bozkurt et  al., 2019). This implies a  need for additional efforts to 
obtain, catalogue and quality-control higher resolution observational 
(or observation-based) datasets (Thorne et al., 2017; Section 10.2). 
Univariate demonstration of added value is necessary, but may be 
insufficient, as better agreement with observations in the downscaled 
variable may be a consequence of compensating errors that are not 
guaranteed to compensate similarly as climate changes. Multivariate 
analysis of added value is better able to demonstrate physical 
consistency between observed and simulated behaviour (Prein et al., 
2013a; Meredith et al., 2015a; Reboita et al., 2018).

10.3.3.3 Performance at Simulating Large-scale Phenomena 
and Teleconnections Relevant for Regional Climate
Regional climate is often controlled by large-scale weather phenomena, 
modes of variability and teleconnections (e.g., Sections 2.3 and 2.4, 
Annex IV). In particular, extreme events are often caused by specific, 
in some cases persistent, circulation patterns (Sections 11.3–11.7). 
It is therefore important for climate models to reasonably represent 
not only continental, but also regional climate and its variability for 
such extremes. As explained in Section  3.3.3, standard resolution 
global models can suffer biases in the location, occurrence frequency 
or intensity of large-scale phenomena, such that statements about a specific regional climate and its change can be highly uncertain 
(Hall, 2014). RCMs have difficulties improving especially large-scale 
circulation biases, although some successful examples exist. But

due to their enhanced representation of complex topography and 
coastlines, RCMs may add value to simulating the regional expression 
of teleconnections. Bias adjustment cannot mitigate fundamental 
misrepresentations of the large-scale atmospheric circulation 
(Maraun et  al., 2017, Cross-Chapter Box  10.2). This subsection 
illustrates the relevance of large-scale circulation biases for regional 
climate assessments with selected examples from the mid- to high 
latitudes and tropics.
10.3.3.3.1 Mid- to high-latitude atmospheric variability 
phenomena: Blocking and extratropical cyclones
Major large-scale meteorological phenomena for mid- to high 
latitude mean and extreme climate include atmospheric blocking 
and extratropical cyclones (Section 2.3.1.4). Atmospheric blocking is 
characterized by a  quasi-stationary, long-lasting, high-pressure 
system that blocks and diverts the movement of synoptic cyclones 
(Woollings et al., 2018). In regions where blocking occurs, it is known

to lead to cold conditions in winter and warmth and drought during 
summer, defining the seasonal regional climate in certain years 
(Sousa et al., 2017, 2018b). Extratropical cyclones are storm systems 
that propagate preferentially in confined storm-track regions, 
characterized by large eddy kinetic energy, heat and momentum 
transports that shape regional weather at mid- to high latitudes 
(Shaw et  al., 2016). Given their importance in shaping mean and 
extreme regional climate (Sections 3.3.3.3, 11.3 and 11.4), an 
accurate representation of blocking and extratropical cyclones in 
global and regional climate models is needed to better understand 
regional climate variability and extremes as well as to project future 
changes (Section 11.7.2; Grotjahn et al., 2016; Mitchell et al., 2017; 
Rohrer et al., 2018; Huguenin et al., 2020). An overview of CMIP5 and 
CMIP6 model performance in simulating blocking and extratropical

cyclones is given in Section 3.3.3.3. CMIP6 models still suffer from 
long-standing blocking biases identified in previous generations 
of models. However, blocking location has improved compared to 
CMIP5, while comparable performance is seen for blocking frequency 
and persistence (Figure 10.7). Increasing horizontal model resolution 
to about 20 km in the HighResMIP experiments improves the 
representation of blocking frequency and its spatial pattern in most 
models, but no clear effect could be shown for blocking persistence. 
Biases associated with these two phenomena are highly region- and 
season-dependent and their amplitudes vary among CMIP models 
(Drouard and Woollings, 2018; Schaller et al., 2018; Woollings et al., 
2018; Harvey et al., 2020; Schiemann et al., 2020).
RCMs have a  very limited ability to reduce large-scale circulation 
errors of the driving GCM (Hall, 2014). In a study of five ERA-Interim-

driven RCMs, Jury et al. (2018) showed that RCMs typically simulate 
fewer blocking events over Europe than are present in the driving 
data, irrespective of the RCM horizontal resolution. Based on a simple 
blocking bias-decomposition method, they suggest that blocking 
frequency biases can contribute to the RCM mean surface biases. 
Over some large domains, reanalysis-driven RCMs can significantly 
improve the representation of storm characteristics compared to the

1397
driving reanalysis near regions with complex orography and/or large 
water masses (Poan et al., 2018). However, this is not necessarily true 
if the domain is large enough because the RCM and its biases will then 
control the circulation leading to a biased performance with regard 
to storm characteristics (Pontoppidan et al., 2019). An ensemble of 
12 RCMs with and without air-sea coupling reasonably reproduced 
the climatology of Mediterranean cyclones, and air-sea coupling had 
a rather weak impact (Flaounas et al., 2018). Over the Gulf Stream, 
however, air-sea coupling played an important role in representing 
cyclone development (Vries et al., 2019). Sanchez-Gomez and Somot 
(2018) showed that the effect of RCM internal variability on density 
of cyclone tracks is very significant and larger than for other variables 
such as precipitation. It is larger in summer than in winter, in

particular over the Iberian Peninsula, northern Africa and the eastern 
Mediterranean, which are regions of enhanced cyclogenesis during 
the warm season.
Biases in the representation of large-scale atmospheric circulation can 
result in biased representation of regional climate. While, in principle, the connection between large-scale and regional biases is obvious, 
given the strong control of regional climate by large-scale phenomena, 
research on this connection is still limited. Munday and Washington 
(2018) relate CMIP5 model rainfall biases over South Africa to 
anomalous low-level moisture transport across high topography 
due to upstream wind biases and inaccurate representation of 
unresolved orographic drag effects. Addor et  al. (2016) show that 
the overestimated frequency of westerly synoptic situations was 
a significant contributor to the wet bias in several RCMs in winter over 
Switzerland. Pepler et al. (2014, 2016) suggest that better capturing

westerly-driven synoptic systems such as cold fronts and cut-off lows 
in climate models could be key in simulating the observed pattern 
correlation between rainfall and zonal wind in southern south-east 
Australia. Cannon (2020) shows global improvement in performance 
going from CMIP5 to CMIP6 for both frequency and persistence of 
circulation types.
The robust quantification of the influence of atmospheric circulation 
errors on regional climate remains a challenge as many parametrized 
processes such as cloud radiative effects and soil moisture or snow 
feedbacks can also contribute and interact with the circulation errors. 
Atmospheric nudging experiments where the simulated circulation is 
constrained to be close to that observed have been used to separate 
the circulation effect from other contributions to regional climate 
biases (Wehrli et al., 2018). The nudging approach requires detailed 
and careful implementation in order to limit detrimental effects due

to the added tendency term in the model equations (Zhang et al., 
2014; Lin et al., 2016). Based on single-model experiments, Wehrli 
et  al. (2018) show that the circulation induced biases are often 
not the main contributors to mean and extreme temperature and 
precipitation biases for many regions and seasons.
There is high confidence  that atmospheric circulation biases can 
deteriorate the model representation of regional land surface climate. 
Assessing the relative contributions of atmospheric circulation and 
other sources of bias remains a challenge due to the strong coupling 
between the atmosphere and other components of the climate 
system, including the land surface.
10.3.3.3.2 Tropical phenomena: ENSO teleconnections
Model performance in simulating ENSO characteristics, including 
ENSO spatial pattern, frequency, asymmetry between warm and cold 
events, and diversity, is assessed in Chapter  3 (Section  3.7.3). The

ability of the recent generation of GCMs and RCMs to adequately 
simulate ENSO-related teleconnections is reviewed here along with 
relevant methodological issues (see also Annex IV2.3.2, Figure 3.38 
and Section 3.7.3).
Langenbrunner and Neelin (2013) show that there is little 
improvement in CMIP5 relative to CMIP3 in amplitude and spatial 
patterns of the ENSO influence on boreal winter precipitation 
(spatial  pattern correlations against observations are typically less 
than 0.5). However, the CMIP5 ensemble accurately represents 
the amplitude of the precipitation response in regions where 
observed teleconnections are strong. Garcia-Villada et  al. (2020) 
found a decline in performance of the representation of simulated 
root-mea n-squ are error
0.000.010.02
AWI CMCC CNRM EC-Earth ECMWF MOHC MPI-MCMIP5
CMIP6
LC
HC
ERA/IV681090th persistence 
perce ntile ( days)
CMIP5
CMIP6
LC
HC
ERA/IV
CMIP5
CMIP6
LC
HC
ERA/IV
CMIP5
CMIP6
LC
HC
ERA/IVATL
DJF JJAPAC
DJF JJA

0.000.010.020.03frequency of blocke d daysFigure  10. 7 | Northern Hemisphere blocking performance in historical 
coupled simulations for different multi-model ensembles.  Coupled Model 
Intercomparison Project Phases 5 and 6 (CMIP5/6): CMIP5 and CMIP6 Diagnostic, 
Evaluation and Characterization of Klima (DECK) historical simulations, 1950–2005, 
LC/HC: Low- (LC)/high- (HC) resolution coupled simulations from the PRIMAVERA 
project, 1950–2014 following the hist-1950 experiment of the CMIP6 HighResMIP 
Protocol (Haarsma et al., 2016). (Top) blocking frequency, for example, fraction of 
blocked days; (middle) root-mean-squared error in blocking frequency; (bottom) 
90th percentile of blocking persistence, aggregated over an Atlantic domain (left, ATL: 
90°W–90°E, 50°–75°N) and a Pacific domain (right, PAC: 90°E–270°E, 50°–75°N). 
Results are for boreal winter (December–January–February, DJF) and summer (June–

July–August, JJA). Box-and-whisker plots for CMIP5/6 follow the methodology used 
in Figure 10.6 and show median (line), mean (triangle), and interquartile range (box) 
across 29 models for each ensemble. The reference estimate (ERA, asterisk) is from 
a  50-year reanalysis dataset that merged ERA-40 (1962–1978) and ERA-Interim 
(1979–2011) reanalyses. An estimate of internal variability for each metric (IV) is 
shown as a box-and-whisker plot over the asterisk and is obtained from a single-model 
ensemble (ECMWF-IFS high-resolution hist-1950 experiment, 6 ×  65  years). For 
details on the methodology see (Schiemann et  al., 2020). Further details on data 
sources and processing are available in the chapter data table (Table 10.SM.11).

1398
Chapter 10 Linking Global to Regional Climate Change10ENSO teleconnection patterns for model experiments with fewer 
observational constraints. They also show that ENSO warm phase 
(El Niño) teleconnections are better represented than those for 
the cold phase (La Niña). Individual CMIP5 and CMIP6 models 
show a  good ability to represent the observed teleconnections at 
aggregated spatial scales (Power and Delage, 2018; Section  3.7.3 
and Figure  3.38). The evaluation of the atmospheric dynamical 
linkages is also an important part of the assessment. Hurwitz et al. 
(2014) showed that CMIP5 models broadly simulate the expected 
(as seen in the MERRA reanalysis) upper-tropospheric responses to 
central equatorial Pacific or eastern equatorial Pacific ENSO events in 
boreal autumn and winter. CMIP5 models also simulate the correct 
sign of the Arctic stratospheric response, consisting of polar vortex 
weakening during eastern and central Pacific Niño events and

vortex strengthening during both types of La Niña events. In contrast, 
most CMIP5 models do not capture the observed weakening of the 
Southern Hemisphere polar vortex in response to central Pacific 
ENSO events (Brown et al., 2013).
In RCMs, the effects of tropical large-scale modes and 
teleconnections are inherited through the boundary conditions 
and influenced by the size of the numerical domain. Done et  al. 
(2015) and Erfanian and Wang (2018) claim that large domains that 
include source oceanic regions are required to capture the remote 
influence of teleconnections, although, without spectral nudging, 
this can lead to biased synoptic-scale patterns (Prein et al., 2019). 
RCMs generally reproduce the regional precipitation responses 
to ENSO, and can sometimes even improve the representation of 
these teleconnections compared to the driving reanalysis (Endris 
et  al., 2013; Fita et  al., 2017), but the overall performance may

depend both on the driving reanalysis or GCM (Endris et al., 2016; 
Chandrasa and Montenegro, 2020) and on the chosen RCMs (Whan 
and Zwiers, 2017).
New studies since AR5 have shown that model performance 
assessment regarding ENSO teleconnections remains a  difficult 
challenge due to the different types of ENSO and model errors in 
ENSO spatial patterns, as well as the strong influence of atmospheric 
internal variability at mid- to high latitudes (Coats et al., 2013; Polade 
et  al., 2013; Capotondi et  al., 2015; Deser et  al., 2017c; Tedeschi 
and Collins, 2017; Garcia-Villada et  al., 2020). Another difficulty 
comes from the non-stationary aspects of teleconnections in both 
observations and models, raising methodological questions on how 
best to compare a given model with another model or observations 
(Herein et al., 2017; Perry et al., 2017; O’Reilly, 2018; O’Reilly et al., 
2019; Abram et al., 2020).
There is robust evidence  that an accurate representation of both

atmospheric circulation and sea surface temperature (SST) variability 
are key factors for the realistic representation of ENSO teleconnections 
in climate models. A  robust and thorough evaluation of model 
performance regarding ENSO teleconnections is a challenging task 
with many methodological issues related to asymmetry between the 
warm and cold phases, non-stationarity and time-varying interaction 
between the Pacific and other ocean basins, signal-to-noise issues 
in the mid-latitudes and observational uncertainties, particularly for 
precipitation (Section 10.2.2.3).10.3.3.4 Performance at Simulating Regional 
Phenomena and Processes
Regional climate is shaped by a wide range of weather phenomena 
occurring at scales from about 2000 km to 2 km (Figure 10.3). These 
modulate the influence of large-scale atmospheric phenomena and 
create the characteristic and potentially severe weather conditions. 
The climate in different regions will be affected by different mesoscale

phenomena, of which several may be relevant. A skilful representation 
of these phenomena is a  necessary condition for providing credible 
and relevant climate information for a given region and application. 
Therefore, it is important to understand the strengths and weaknesses 
of different model types in simulating these phenomena. The 
performance of different dynamical climate model types to simulate 
a selection of relevant mesoscale weather phenomena is assessed here.
10.3.3.4.1 Convection including tropical cyclones
Convection is the process of vertical mixing due to atmospheric 
instability. Deep moist convection is associated with thunderstorms 
and severe weather such as heavy precipitation and strong wind 
gusts. Convection may occur in single locations, in spatially extended 
severe events such as supercells, and organized into larger mesoscale 
convective systems such as squall lines or tropical cyclones, and

embedded in fronts (see below). Shallow and deep convection are 
not explicitly simulated but parametrized in standard global and 
regional models. In consequence, these models suffer from several 
biases. AR5 has stated that many CMIP3 and CMIP5 models simulate 
the peak in the diurnal cycle of precipitation too early, but increasing 
resolution and better parametrizations help to mitigate this problem 
(Flato et al., 2014). Similar issues arise for RCMs with parametrized 
deep convection (Prein et al., 2015), which also tend to overestimate 
high cloud cover (Langhans et al., 2013; Keller et al., 2016).
Non-hydrostatic RCMs at convection-permitting resolution (4 km 
and finer) improve features such as the initiation and diurnal cycle 
of convection (Zhu et al., 2012; Prein et al., 2013a, b; Fosser et al., 
2015; Stratton et al., 2018; Sugimoto et al., 2018; Finney et al., 2019; 
Berthou et  al., 2020; Ban et  al., 2021; Pichelli et  al., 2021), the

triggering of convection by orographic lifting (Langhans et al., 2013; 
Fosser et al., 2015), and maximum vertical wind speeds in convective 
cells (Meredith et  al., 2015a). Also spatial patterns of precipitation 
(Prein et al., 2013a, b; Stratton et al., 2018), precipitation intensities 
(Prein et al., 2015; Fumière et al., 2020; Ban et al., 2021; Pichelli et al., 
2021), the scaling of precipitation with temperature (Ban et al., 2014), 
cloud cover (Böhme et al., 2011; Langhans et al., 2013) and its resultant 
radiative effects (Stratton et al., 2018), as well as the annual cycle 
of tropical convection (Hart et al., 2018) are improved. Phenomena 
such as supercells, mesoscale convective systems, or the local weather 
associated with squall lines are not captured by global models and 
standard RCMs. Convection-permitting RCM simulations, however, 
have been shown to realistically simulate supercells (Trapp et  al.,

2011), mesoscale convective systems, their life cycle and motion (Prein 
et al., 2017; Crook et al., 2019), and heavy precipitation associated 
with a squall line (Kendon et al., 2014). There is high confidence  that 
simulations at convection-permitting resolution add value to the 
representation of deep convection and related phenomena.

1399
Linking Global to Regional Climate Change Chapter 1010Convection is the key ingredient of tropical cyclones. An 
intercomparison of high-resolution AGCM simulations (Shaevitz 
et al., 2014) showed that tropical cyclone intensities appeared to be 
better represented with increasing model resolution. Takayabu et al. 
(2015) have compared simulations of typhoon Haiyan at different 
resolutions ranging from 20  km to 1 km (Figure  10.8). While the 
eyewall structure in the precipitation pattern was strongly smoothed 
in the coarse resolution simulations, it was well-resolved at the highest 
resolution. Gentry and Lackmann (2010) found similar improvements 
in simulating hurricane Ivan for horizontal resolutions between 8 km 
and 1 km. High-resolution coupled ocean–atmosphere simulations 
improve the representation of the radial structure of core convection 
and thereby the rapid intensification of the cyclone (Kanada

et  al., 2017b). There is high confidence  that convection-permitting 
resolution is required to realistically simulate the three-dimensional 
structure of tropical cyclones.
Initial studies with convection-permitting global models suggests 
that improvements in representing convection, as described for 
RCMs above, have a positive impact on the tropical and extratropical 
atmospheric circulation and, thus, regional climate (Satoh et  al., 
2019; Stevens et al., 2019; see also Section 8.5.1.2 and Chapter 7). 
Computational constraints currently limit these simulations to 
a length of few months only, such that they cannot yet be used for 
routine climate change studies.
10.3.3.4.2 Mountain wind systems
Mountain slope and valley winds are localized thermally generated 
diurnal circulations that have a strong influence on temperature and 
precipitation patterns in mountain regions. During the day, heating 
of mountain slopes induces upslope winds; during the night this

circulation reverses. This phenomenon is not realistically represented 
by global models and coarse-resolution RCMs. RCM simulations at 4 km resolution showed good skill in simulating the diurnal cycle 
of temperature and wind on days of weak synoptic forcing in the 
Rocky Mountains (Letcher and Minder, 2017) as well as in simulating 
the mountain-plain wind circulation over the Tianshan mountains in 
central Asia (Cai et al., 2019), while in the Alps, a 1 km resolution has 
been required (Zängl, 2004).
Föhn winds are synoptically-driven winds across a mountain range 
that are warm and dry due to adiabatic warming in the downwind 
side. In an RCM study for the Japanese Alps, Ishizaki and Takayabu 
(2009) found that at least 10 km resolution was required to 
realistically simulate the basic characteristics of Föhn events.
Synoptically-forced winds may be channelled and accelerated in long 
valleys. For instance, the Tramontana, Mistral and Bora are northerly

winds blowing down-valley from central France and the Balkans into 
the Mediterranean (Flaounas et  al., 2013). In winter, these winds 
may cause severe cold air outbreaks along the coast. Flaounas 
et al. (2013) have shown that a GCM with a horizontal resolution of 
roughly 3.75° longitude/1.875° latitude (roughly 400 km × 200 km 
depending on latitude) is unable to reproduce these winds because 
of the coarse representation of orography. Fifty-kilometre RCM 
simulations did not realistically represent the Mistral (Obermann 
et al., 2018) and Bora winds (Belušić et al., 2018), but simulations 
at 12 km added substantial value. Similarly, Cholette et  al. (2015) 
found that a 30 km RCM resolution was not sufficient to adequately 
simulate the channelling of winds in the St Lawrence River Valley in 
eastern Canada, whereas a 10 km resolution was.
There is  high confidence  that climate models with resolutions 
of around 10 km or finer are necessary for realistically simulating

mountain wind systems such as slope and valley winds and the 
channelling of winds in valleys.
10.3.3.4.3 Coastal winds and lake effects
Simulating coastal climates and the influence of big lakes are 
a modelling challenge, due to the complex coastlines, the different 
heat capacities of land and water, the resulting wind system, and 
differential evaporation. The AR5 concluded that RCMs can add value 
to the simulation of coastal climates.
Summer coastal low-level jets off the mid-latitude western continental 
coasts are forced by the semi-permanent subtropical anticyclones, 
inland thermal lows, strong across-shore temperature contrasts in 
upwelling regions, and high coastal topography. They are important 
factors in shaping regional climate by, for instance, preventing 
onshore advection of humidity and thereby causing aridity in the 
Iberian Peninsula (Soares et al., 2014), or by transporting moisture 
towards precipitating regions as in the North American monsoon

(Bukovsky et al., 2013).
Reanalyses and most global models do not well resolve the details 
of coastal low-level jets (Bukovsky et al., 2013; Soares et al., 2014), 
but they are still able to represent annual and diurnal cycles and 
interannual variability (Cardoso et  al., 2016; Lima et  al., 2019). 
Bukovsky et al. (2013) found RCM simulations at a 50 km resolution (a) G SMaP (b) Ra dar
12°N
11°N
10°N
9°N
(d) 20k (f) 1km
125°E12°N
11°N
10°N
9°N
1
mm h-1 2 4 20126°E127°E128°E125°E126°E127°E128°E125°E126°E127°E128°E(c) 60 km
(e) 5km
50
Figure 10 .8 | Hourly accumulated precipitation profiles (mm hour –1) around 
the eye of Typhoon Haiyan.  Represented by (a) Global Satellite Mapping of 
Precipitation (GSMaP) data (multi-satellite observation), (b) Guiuan radar (PAGASA), 
(c) Weekly Ensemble Prediction System (WEPS) data (JMA; 60 km), (d)  NHRCM 
(20 km), (e) NHRCM (5 km), and (f) WRF (1 km) models. Panels (b), (d–f) are adapted

from Takayabu et  al. (2015), CCBY3.0 https://creativecommons.org/licenses/by/3.0 . 
(Table 10.SM.11).

1400
Chapter 10 Linking Global to Regional Climate Change10to improve the representation of the coastal low-level jet in the Gulf 
of California and the associated precipitation pattern compared to 
the driving global models. Lucas-Picher et  al. (2017) find indirect 
evidence via precipitation patterns that 12 km simulations further 
improve the representation. Soares et al. (2014) demonstrated that 
an 8 km resolution RCM simulated a  realistic three-dimensional 
structure of the Iberian coastal low-level jet, and the surface winds 
compare well with observations. Lucas-Picher et al. (2017) showed 
that a  0.44° resolution RCM underestimated winds along the 
Canadian east coast, whereas a 0.11° resolution version simulated 
more realistic 10 metre wind speed. Also, the Etesian winds in the 
Aegean Sea were realistically simulated by 12 km resolution RCMs 
(Dafka et al., 2018).
A particularly relevant coastal phenomenon is the sea breeze, which

is caused by the differential heating of water and land during the 
diurnal cycle and typically reaches several tens of kilometres inland. 
Reanalyses and global models have too coarse a  resolution to 
realistically represent this phenomenon, such that they typically 
underestimate precipitation over islands and misrepresent its diurnal 
cycle (Lucas-Picher et al., 2017). RCMs improve the representation of 
sea breezes and thereby precipitation in coastal areas and islands. 
Over Cuba and Florida only a  12 km-resolution RCM is able to 
realistically simulate the inland propagation of precipitation during 
the course of the day (Lucas-Picher et al., 2017). RCM simulations 
at 20 km horizontal resolution realistically represented the sea 
breeze circulation in the Mediterranean Gulf of Lions including the 
intensity, direction and inward propagation (Drobinski et al., 2018). 
Even though a coupled ocean–atmosphere simulation improved the

representation of diurnal SST variations, the sea breeze representation 
itself was not improved.
Big lakes modify the downwind climate. In particular during winter 
they are relatively warm compared to the surrounding land, provide 
moisture, destabilize the passing air column and produce convective 
systems. The increase in friction when moving air reaches land causes 
convergence and uplift, and may trigger precipitation. Gula and 
Peltier (2012) found that a state-of-the-art GCM does not realistically 
simulate these effects over the North American Great Lakes, but 
a 10 km RCM better represents them and thereby simulates realistic 
downwind precipitation patterns, in particular enhanced snowfall 
during the winter season. Similar results were found by Wright 
et  al. (2013), Notaro et  al. (2015) and Lucas-Picher et  al. (2017). 
In a  convection permitting simulation of the Lake Victoria region, 
a  too strong nocturnal land breeze resulted in unrealistically high

precipitation (Finney et al., 2019).
There is high confidence  that climate models with sufficiently high 
resolution are necessary for realistically simulating lake and coastal 
weather including coastal low-level jets, lake and sea breezes, as well 
as lake effects on rainfall and snow.
In regions like Fenno-Scandinavia or central-eastern Canada, very large 
fractions of land are covered by small and medium sized lakes. Other 
regions have fewer but larger lakes, such as central-eastern Africa, 
the eastern border between the USA and Canada, and central Asia. In 
these regions it has been considered essential to include a lake model in an RCM to realistically represent regional temperatures (Samuelsson 
et al., 2010; Deng et al., 2013; Mallard et al., 2014; Thiery et al., 2015; 
Pietikäinen et al., 2018), as well as remote effects (Spero et al., 2016). 
The most common approach in RCMs is the two-layer lake model, 
including a  lake-ice model, with parametrized vertical temperature

profiles (Mironov et al., 2010; Golosov et al., 2018). For the Caspian 
Sea, it is found that a three-dimensional ocean model simulated the 
SST fields better than a one-dimensional lake model when coupled to 
the same RCM (Turuncoglu et al., 2013).
There is medium evidence  and high agreement  that it is important to 
include interactive lake models in RCMs to improve the simulation 
of regional temperature, in particular in seasonally ice-covered areas 
with large fractions of lakes. There is medium evidence  of the local 
influence of lakes on snow and rainfall as well as the importance of 
including lakes in regional climate simulations.
10.3.3.4.4 Fronts
Weather fronts are two-dimensional surfaces separating air masses 
of different characteristics and are a  key element of mid-latitude 
cyclones. In particular cold fronts are regions of relatively strong 
uplift and hence often associated with severe weather (e.g., Schemm

et al., 2016). Stationary or slowly moving fronts may cause extended 
heavy precipitation. The evaluation of how climate models represent 
fronts, however, remains limited. Catto et  al. (2014) found in both 
ERA-Interim and CMIP5 models that frontal frequency and strength 
were realistically simulated, albeit with some biases in the location. 
Follow-up investigations, for boreal and austral winter (Catto et al., 
2015) found frontal precipitation frequency to be too high and the 
intensity too low, but these compensating biases resulted in only 
a small total precipitation bias. Blázquez and Solman (2018) found 
similar results for Southern Hemisphere (SH) winter, and also showed 
that CMIP5 models typically overestimate the fraction of frontal 
precipitation compared to total precipitation. As for the reference, the 
ERA-Interim reanalysis misrepresents conditional symmetric instability 
associated with fronts, and the corresponding precipitation (Glinton

et  al., 2017). Only a  few studies evaluating fronts in RCMs have 
been conducted. Kawazoe and Gutowski (2013) diagnosed strong 
temperature gradients associated with extreme winter precipitation 
in the North American Regional Climate Change Assessment Program 
(NARCCAP) RCM ensemble (Mearns et  al., 2012) and found the 
models agreed well with gradients in a  reanalysis. De Jesus et  al. 
(2016) diagnosed the representations of cold fronts over southern 
Brazil in two RCMs, finding that they were only underestimated 
by about 5% across the year, but in one RCM, summer cold fronts 
were underestimated by 17%. An RCM-based reanalysis suggests 
that high-resolution RCM simulations improve the representation of 
orographic influences on fronts (Jenkner et al., 2009).
10.3.3.5 Performance at Simulating Regional Feedbacks
Both SRCCL (Jia et al., 2019) and SROCC (Hock et al., 2019) highlight 
the weaknesses of climate models at simulating atmosphere–surface

feedbacks. The performance at simulating some of these feedbacks 
is assessed below (climate feedbacks in urban areas are discussed in 
Box 10.3).

1401
Linking Global to Regional Climate Change Chapter 1010The snow-albedo feedback contributes to enhanced warming at 
high elevations (Section  8.5; Pepin et  al., 2015). Global models 
often do not simulate it realistically due to their misrepresentation 
of orography in complex terrain (Hall, 2014; Walton et  al., 2015). 
The elevation dependence of historical warming, which is partly 
caused by the snow-albedo effect, is realistically represented across 
Europe by the ENSEMBLES RCMs (Kotlarski et  al., 2015). Some 
EURO-CORDEX RCMs simulate a  spring snow–albedo feedback 
close to that observed, whereas others considerably overestimate it 
(Winter et al., 2017). In a multi-physics ensemble RCM experiment, 
the cold bias in north-eastern Europe is amplified by the albedo 
feedback (García-Díez et al., 2015). For the Rocky Mountains, RCM 
simulations generally reproduce the observed spatial and seasonal 
variability in snow cover, but strongly overestimate the snow albedo

(Minder et al., 2016). There is high confidence (medium evidence  and 
high agreement ) that RCMs considerably improve the representation 
of the snow-albedo effect in complex terrain.
Soil-moisture feedbacks influence changes in both temperature 
and precipitation. More than 30% of CMIP5 models overestimate 
the influence of preceding precipitation (a proxy for soil moisture) 
on temperature extremes in Europe and the USA (Donat et  al., 
2018), and many CMIP5 models simulate an unrealistic influence 
of evaporation on temperature extremes for wet regions in Europe 
and the US (Ukkola et al., 2018). RCMs were found to realistically 
simulate the correlation between latent and sensible heat fluxes and 
temperature (coupling strength) over Africa (Knist et al., 2017; Careto 
et al., 2018) and in northern and southern Europe, but to overestimate 
it in central Europe (Knist et al., 2017). Land surface models driven 
by global reanalysis agreed relatively well with observations.

However, the coupling strength varied strongly across models at the 
regional scale, and a realistic partitioning of the incoming radiation 
into latent and sensible heat fluxes did not necessarily result in 
a realistic soil moisture-temperature coupling (Gevaert et al., 2018; 
Boé et al., 2020a).
Evaluating the representation of soil-moisture–precipitation feedbacks 
in climate models is challenging as different processes may induce 
feedbacks including moisture recycling, boundary-layer dynamics 
and mesoscale circulation. Moreover, the effects of soil moisture 
on precipitation may be region and scale dependent and may even 
change sign depending on the strength of the background flow (Taylor 
et al., 2013; Froidevaux et al., 2014; Guillod et al., 2015; Larsen et al., 
2016; Tuttle and Salvucci, 2016). On seasonal-to-interannual time 
scales, CMIP5 models showed a stronger soil-moisture–precipitation 
feedback than estimated by satellite data (Levine et al., 2016). Taylor

et  al. (2013) found that convection-permitting RCMs perform well 
at simulating surface-induced mesoscale circulations in daytime 
convection and the observed negative soil moisture feedback, 
whereas an RCM with parametrized convection, even when run at 
the same resolution, simulated an unrealistic positive feedback. 
There is medium evidence and high agreement  that simulations at 
convection-permitting resolution are required to realistically represent 
soil-moisture–precipitation feedbacks.
Ocean–atmosphere RCMs have successfully been used to 
understand and simulate phenomena involving strong regional feedbacks like tropical cyclones in the Indian Ocean (Samson 
et al., 2014), Indian summer monsoon (Samanta et al., 2018), East 
Asian summer monsoon (Zou et al., 2016), near coastline intense 
precipitation in the Mediterranean (Berthou et  al., 2015, 2018), 
air-sea fluxes influencing heat and humidity advection over land

(Sevault et al., 2014; Lebeaupin Brossier et al., 2015; Akhtar et al., 
2018) or snow bands in the Baltic region (Pham et al., 2017). The 
positive impact of ocean-coupling on the simulation of strongly 
convective phenomena such as Medicanes, a  class of severe 
cyclones in the Mediterranean, can only be diagnosed when using 
relatively fine atmospheric resolution of about 10 km (Akhtar et al., 
2014; Flaounas et al., 2018; Gaertner et al., 2018). A positive impact 
of ocean coupling has been quantified in marginal sea regions with 
reduced large-scale influence (e.g.,  in the Baltic Sea area during 
weak phases of the NAO and thus weak influence of Atlantic 
westerlies (Kjellström et al., 2005; Pham et al., 2018). There is some 
evidence that coupled ocean components also positively impact 
RCM simulations of inland climates such as precipitation extremes 
in central Europe (Ho-Hagemann et al., 2017; Akhtar et al., 2019).

There is high confidence  that coupled ocean–atmosphere RCMs 
improve the representation of ocean–atmosphere feedbacks and 
related phenomena.
The influence of ice-sheet mass balance on regional climate, explored 
with global and regional models by (Noël et al., 2018; Fettweis et al., 
2020), is discussed in Section 9.4.
10.3.3.6 Performance at Simulating Regional Drivers 
of Climate and Climate Change
Dust, with its regional character in both emissions and climatic 
influences, has traditionally been specified in climate simulations 
with a  climatological estimate. In CMIP5 models, the influence of 
vegetation changes on mineral dust is largely underestimated while 
the influence of surface wind and precipitation are overestimated, 
resulting in a low bias of dust load (Pu and Ginoux, 2018). Interactive 
dust emission modules that simulate the dust optical depth in most of 
the key emission regions have only been recently introduced (Pu and

Ginoux, 2018). However, coarse dust is underestimated in global 
models (Adebiyi and Kok, 2020). Simulations of future changes in 
dust are hindered by the uncertainties in future regional wind and 
precipitation as the climate warms (Evan et al., 2016), in the effect 
of CO 2 fertilization on source extent (Huang et al., 2017), in the dust 
feedbacks (Evans et al., 2019), and in the effect of human activities 
that change land use and disturb the soil, including cropping and 
livestock grazing, recreation and urbanization, and water diversion 
for irrigation (Ginoux et al., 2012).
Volcanoes also provide forcings with a  marked regional impact 
(Cross-Chapter Box  4.1). This implies that models are expected to 
capture these effects (Bethke et al., 2017). Both proxy analyses and 
simulations have demonstrated reduced Asian monsoon precipitation 
after tropical and Northern Hemisphere (NH) volcanic eruptions due 
to reduced humidity and divergent circulation (Man and Zhou, 2014;

Zhuo et al., 2014; F . Liu et al., 2016; Stevenson et al., 2016). Global 
model experiments (Zanchettin et al., 2013; Ortega et al., 2015; Sjolte 
et al., 2018; Michel et al., 2020) have suggested that tropical volcanic

1402
Chapter 10 Linking Global to Regional Climate Change10eruptions (larger than the one from Mount Pinatubo in 1991) may 
lead to a positive phase of the winter NAO in the following few years 
(with an uncertainty on the exact years affected), but this influence 
is not well-reproduced in climate models and requires very large 
ensembles (Driscoll et  al., 2012; Toohey et  al., 2014; Swingedouw 
et  al., 2017; Ménégoz et  al., 2018b). The ability to simulate the 
effect of volcanic aerosol in global models is evaluated in VolMIP 
(Zanchettin et  al., 2016). Given the relevance of volcanic aerosol, 
a  good knowledge of the initial conditions is important because 
the response has proven to be sensitive to them (Ménégoz et  al., 
2018a; Zanchettin et  al., 2019). A  few decadal prediction systems 
have illustrated that current systems can predict some aspects of 
regional climate a few years in advance (Swingedouw et al., 2017;

Illing et al., 2018; Ménégoz et al., 2018a; Hermanson et al., 2020). 
However, a better performance requires information about volcanic 
location (Haywood et al., 2013; Pausata et al., 2015; Stevenson et al., 
2016; F . Liu et al., 2018a), strength (Emile-Geay et al., 2008; H.-G. Lim 
et al., 2016; F . Liu et al., 2018b), and seasonality (Stevenson et al., 
2017; Sun et al., 2019a, b).
Some recent regional climate changes can only be simulated by 
climate models if anthropogenic aerosols are correctly included 
(Sections 10.4.2.1, 10.6.3 and 10.6.4; Chapters 6 and 8). Examples 
of the importance of correctly representing anthropogenic aerosols 
are the recent enhanced warming over Europe (Nabat et al., 2014; 
Dong et al., 2017), the cooling over the East Asian monsoon region, 
leading to a weakening of the monsoon (Section 8.3.2.4; Song et al., 
2014; Q. Wang et  al., 2017), as well as changes in the monsoons 
of West Africa (Sections 8.3.2.4 and 10.4.2.1) and South Asia

(Sections 8.3.2.4 and 10.6.3; Undorf et al., 2018). The relevance of 
appropriately representing anthropogenic aerosols has been widely 
studied in regional models (Boé et al., 2020a; Gutiérrez et al., 2020), 
with an advantage for models with interactive aerosol schemes 
(Drugé et  al., 2019; Nabat et  al., 2020). Without a  fully coupled 
chemistry module, radiative forcing can be simulated by including 
simple models of sulphate chemistry or specifying the optical 
properties from observations and prescribing the effect of aerosols 
on the cloud-droplet number (Fiedler et  al., 2017, 2019; Stevens 
et al., 2017). In all cases, the specification of the aerosol load limits 
the trustworthiness of the simulations at the regional scale when 
enough detail is not provided (Samset et al., 2019; Shonk et al., 2020; 
Z. Wang et al., 2021).
The inclusion of irrigation in global and regional models over the 
South Asian monsoon region (Section  10.6.3) has been found to

be important to represent the monsoon circulation and rainfall 
correctly (Lucas-Picher et al., 2011; Guimberteau et al., 2012; Shukla 
et al., 2014; Tuinenburg et al., 2014; Cook et al., 2015a; Devanand 
et al., 2019). Similarly, the inclusion of irrigation over northern India 
and western Pakistan could be important for the correct simulation 
of precipitation over the Upper Indus Basin in northern Pakistan 
(Saeed et  al., 2013). Irrigation in the East African Sahel inhibits 
rainfall over the irrigated region and instead enhances rainfall to the 
east, coherent with both observations and theoretical understanding 
of the local circulation anomalies induced by the lower surface 
air temperatures over the irrigated region (Alter et  al., 2015). 
Although several studies show how modelled irrigation reduces daytime temperature extremes, few compare modelled results with 
observations. Global model studies have found improvements in

simulated surface temperature when including irrigation (Thiery 
et  al., 2017), in particular in areas where the model used has 
a  strong land-atmosphere coupling (Chen and Dirmeyer, 2019). 
An RCM study over the North China Plain showed that the inclusion 
of irrigation led to a better representation of the observed nighttime 
warming (Chen and Jeong, 2018).
There is medium confidence  that representing irrigation is important 
for a realistic simulation of South Asian monsoon precipitation. There 
is limited evidence  that including irrigation in climate models improves 
the simulation of maximum and minimum daily temperatures as well 
as precipitation for other regions.
Regional land-radiation management, including modifying the 
albedo through, for instance, no-tillage practices, has been suggested 
as a  measure to decrease regional maximum daily temperatures 
(see  review in Seneviratne et  al., 2018), but although modelled

results and theoretical understanding are coherent, few studies 
have verified the results with observations. Hirsch et al. (2018) is an 
exception, showing that implementing minimal tillage, crop residue 
management and crop rotation in a global model over regions where 
it is practiced, improves the simulation of surface heat fluxes.
10.3.3.7 Statistical Downscaling, Bias Adjustment 
and Weather Generators
The performance of statistical downscaling models, bias adjustment 
and weather generators is determined by the chosen model structure 
(e.g.,  to represent variability and extremes or spatial dependence) 
and, if applicable, the predictors selected (Maraun et  al., 2019a). 
The VALUE initiative has assessed a  range of such methods in 
a perfect-predictor experiment where the predictors are taken from 
reanalysis data (Maraun et al., 2015, 2019a; Gutiérrez et al., 2019). 
Table 10.2 shows an overview comprising performance results from

VALUE and other studies. These results isolate the performance of the 
statistical method in the present climate. The overall performance in 
a climate change application also depends on the performance of the 
driving climate model (Sections 10.3.3.3–10.3.3.6) and the fitness of 
both the driving model and the statistical method for projecting the 
climatic aspects of interest (Section 10.3.3.9).
10.3.3.7.1 Performance of perfect prognosis methods
Perfect prognosis methods can perform well when the synoptic 
forcing (i.e., the explanatory power of large-scale predictors) is strong 
(Schoof, 2013). Using this approach, downscaling of precipitation is 
particularly skilful in the presence of strong orographic forcing. The 
representation of daily variability and extremes requires analogue 
methods or stochastic regression models, although the former 
typically do not extrapolate to unobserved values (Gutiérrez et al.,

2019; Hertig et al., 2019). Temporal precipitation variability is well-
represented by analogue methods and stochastic regression, but 
analogue methods typically underestimate temporal dependence 
of temperature (Maraun et al., 2019b). Spatial dependence of both 
temperature and precipitation is only well-represented by analogue

1403
methods, for which analogues are defined jointly across locations, 
and by stochastic regression methods explicitly representing spatial 
dependence (Widmann et al., 2019). Overall, there is high confidence  
that analogue methods and stochastic regression are able to 
represent many aspects of daily temperature and variability, but 
the analogue method is inherently limited in representing climate 
change (Gutiérrez et al., 2013).10.3.3.7.2 Performance of bias adjustment methods
This subsection assesses the performance of bias adjustment in 
a perfect predictor context. In practice, climate model imperfections 
may cause substantial additional issues in the application of 
bias adjustment. These are assessed separately in Cross-Chapter 
Box 10.2.Table 10.2  | Performance of different statistical method types in representing local weather at daily resolution.  Individual state-of-the-art implementations

may perform better. ‘+’: should work reasonably well based on empirical evidence and/or expert judgement; ‘ o’: problems may arise depending on the specific context; ‘–’: weak 
performance either by construction or inferred from empirical evidence; ‘?’: not studied. The categorisation assumes that predictors are provided by a well-performing dynamical 
model. Statements about extremes refer to moderate events occurring at least once every 20 years. Adopted and extended from Maraun and Widmann (2018b).
Aspect Perfect Prognosis Bias Adjustment Weather Generators ReferencesDeterministic regression
Inflated regression
White noise regression
Analogue method  
(single/multi-site) SS/MS
Additive/scaling
Empirical quantile mapping
Parametric quantile mapping
Quantile mapping with 
model for extremes
Richardson  
(single/multi-site)
Richardson with predictors 
Poisson clustering  
Hidden Markov  
(without/with predictors)

Mean + + + o + + + + + + + + 6
Variance – o + o o + + + + + + + 6
Extremes – o + + o + + + + + + + 8, 10
Temperature, temporal variability
Autocorrelation + + – – + + + + + + + + 2, 10, 12
Mean spells o o – – + + + + + + + + 2, 10, 12
Extreme spells + + – o + + + + + + + + 2, 8
Interannual variance – o – – + o o o – o – –/o 12
Climate change + – + – + o o o + + + + 1, 5, 10, 12
Temperature, spatial variability
Means o o – –/+ + + + + –/? –/? –/? ? 2, 14
Extremes – – – –/+ + + + + –/? –/? –/? ? 8, 14
Precipitation, marginal
Wet-day probabilities – – + + + + + + + + + + 3, 6, 7, 11
Mean intensity – – + + + + + + + + + + 3, 6, 7, 9, 11
Extremes – – + + o + o + o o o o 3, 7, 8, 9, 11
Precipitation, temporal variability
Transition probabilities – – + + o + + + + + + + 3, 11, 12
Mean spells – – + + o + + + o + o o/+ 3, 4, 7, 11, 12
Extreme spells – – + + + + + + – o – –/o 3, 4, 8, 9, 11
Interannual variance – o o o + o o o – o – –/o 4, 7, 12
Climate change + – + o + o o o + + + + 1, 12, 13

Precipitation, spatial variability
Means – – – –/+ o + + + –/o –/o –/o o 3, 4, 11, 14
Extremes – – – –/+ o o o o –/? –/? –/? ? 3, 14
References: (1) Casanueva et al. (2020); (2) Dubrovsky et al. (2019); (3) Evin et al. (2018); (4) Frost et al. (2011); (5) Gutiérrez et al. (2013); (6) Gutiérrez et al. (2019); 
(7) Gutmann et al. (2014); (8) Hertig et al. (2019); (9) Hu et al. (2013a); (10) Huth et al. (2015); (11) Keller et al. (2015); (12) Maraun et al. (2019b); (13) San-Martín et al. (2017); 
(14) Widmann et al. (2019).

1404
Chapter 10 Linking Global to Regional Climate Change10Bias adjustment methods, if driven by reanalysis predictors, in 
principle adjust well all the aspects that they intend to address 
(Maraun and Widmann, 2018b). For temperature, all univariate 
methods are good for adjusting means, variance, and high quantiles 
(Gutiérrez et  al., 2019; Hertig et  al., 2019). For precipitation, 
means, intensities, wet-day frequencies, and wet–dry and dry–wet 
transitions are well-adjusted (Gutiérrez et al., 2019; Maraun et al., 
2019b). The representation of high quantiles depends on the 
chosen method, although flexible quantile mapping performs best 
(Hertig et  al., 2019). Empirical (non-parametric) methods perform 
better than parametric methods over the observed range, but it is 
unclear how this translates into extrapolation to unobserved values 
(IPCC, 2015; Hertig et al., 2019). Many quantile mapping methods 
overestimate interannual variability (Maraun et al., 2019b). Temporal

and spatial dependence are usually not adjusted and thus inherited 
from the driving model (Maraun et al., 2019b; Widmann et al., 2019). 
Spatial fields are thus typically too smooth in space, even after bias 
adjustment (Widmann et al., 2019).
Several studies show improved simulations of present-day impacts, 
when the impact model is fed with bias-adjusted climate model 
output, including the assessment of river discharge (Rojas et al., 2011; 
Muerth et al., 2013; Montroull et al., 2018), forest fires (Migliavacca 
et al., 2013), crop production (Ruiz-Ramos et al., 2016), and regional 
ocean modelling (Macias et al., 2018).
There is high confidence  that bias adjustment can improve the 
marginal distribution of simulated climate variables, if applied to 
a climate model that adequately represents the processes relevant 
for a given application (Cross-Chapter Box 10.2).
10.3.3.7.3 Performance of weather generators
Weather generators represent well most aspects that are explicitly

calibrated. This typically includes mean, variance, high quantiles 
(for precipitation, if explicitly modelled), and short-term temporal 
variability for both temperature and precipitation, whereas 
interannual variability is strongly underestimated (Frost et al., 2011; 
Hu et al., 2013a; Keller et al., 2015; Dubrovsky et al., 2019; Gutiérrez 
et  al., 2019; Hertig et  al., 2019; Maraun et  al., 2019b; Widmann 
et al., 2019). There is growing evidence that some spatial weather 
generators fairly realistically capture the spatial dependence of 
temperature and precipitation (Frost et al., 2011; Hu et al., 2013a; 
Keller et al., 2015; Evin et al., 2018; Dubrovsky et al., 2019). There is 
high confidence  that weather generators can realistically simulate 
a  wide range of local weather characteristics at single locations, 
but there is limited evidence  and low agreement  of the ability of 
weather generators to realistically simulate the spatial dependence

of atmospheric variables across multiple sites.
10.3.3.8 Performance at Simulating Historical 
Regional Climate Changes
This section assesses how well climate models perform at realistically 
simulating historical regional climatic trends. Current global model 
ensembles reproduce global to continental-scale surface temperature 
trends at multi-decadal to centennial time scales (CMIP5, CMIP6), but underestimate precipitation trends (CMIP5) (Sections 3.3.1 and 3.3.2). 
For regional trends, AR5 concluded that the CMIP5 ensemble cannot 
be taken as a  reliable representation of reality and that the true 
uncertainty can be larger than the simulated model spread (Kirtman 
et  al., 2014). Case studies of regional trend simulations by global 
models can be found in Sections 10.4.1 and 10.6, and region-by-
region assessments in the Atlas. A  key limitation for assessing the 
representation of regional observed trends by single transient

simulations of global models (or downscaled versions thereof) is the 
strong amplitude of internal variability compared to the forced signal 
at the regional scale (Section 10.3.4.3). Even on multi-decadal time 
scales, an agreement between observed and individual simulated 
trends would be expected to occur only by chance (Laprise, 2014).
In the context of downscaling, the ability of downscaling methods to 
reproduce observed trends when driven with boundary conditions or 
predictors taken from reanalysis data (which reproduce the observed 
internal variability on long time scales) can be assessed. For temperature 
in the continental USA, reanalysis-driven RCMs skilfully simulated recent 
spring and winter trends, but did not reproduce summer and autumn 
trends, (Bukovsky, 2012). Over Central America, observed warming 
trends were reproduced (Cavazos et al., 2020). In contrast, a reanalysis-
driven coupled atmosphere–ocean RCM covering the Mediterranean

could not reproduce the observed SST trend (Sevault et al., 2014).
Similar studies have been carried out for statistical downscaling 
and bias adjustment using predictors from reanalyses (or in case of 
bias adjustment, dynamically downscaled reanalyses). For a  range 
of different perfect prognosis methods, Huth et  al. (2015) found 
that simulated temperature trends were too strong for winter and 
too weak for summer. The performance was similar for the different 
methods, indicating the importance of choosing informative predictors. 
Similarly, Maraun et al. (2019b) found that the performance of perfect 
prognosis methods depends mostly on the predictor and domain 
choice (for instance, temperature trends were only captured by those 
methods including surface temperature as predictor). Bias adjustment 
methods reproduced the trends of the driving reanalysis, apart from 
quantile mapping methods, which deteriorated these trends.

RCM experiments are often set up such that changes in forcing agents 
are included only via the boundary conditions, but not explicitly 
included inside the domain. Jerez et al. (2018) demonstrated that not 
including time-varying GHG concentrations within the RCM domain 
may misrepresent temperature trends by 1–2°C per century. Including 
the past trend in anthropogenic sulphate aerosols in reanalysis-driven 
RCM simulations substantially improved the representation of recent 
brightening and warming trends in Europe (Nabat et al., 2014; see 
Sections 10.3.3.6 and 10.6.4, and Atlas.8.4). Similarly, Bukovsky (2012) 
argued that RCMs may not capture observed summer temperature 
trends in the USA because changes in land cover are not taken 
into account. Barlage et al. (2015) have revealed that including the 
behaviour of groundwater in land schemes increases the performance 
of an RCM model to represent climate variability in the central USA.

Hamdi et al. (2014) found that an RCM that did not incorporate the 
historical urbanization in the land-use, land-cover scheme is not able 
to reproduce the warming trend observed in urban stations, with 
a larger bias for the minimum temperature trend.

1405
Linking Global to Regional Climate Change Chapter 1010Overall, there is high confidence  that including all relevant forcings is 
a prerequisite for reproducing historical trends.
10.3.3.9 Fitness of Climate Models for 
Projecting Regional Climate
AR5 stated that confidence in climate model projections is based 
on the physical understanding of the climate system and its 
representation in climate models. A  climate model’s credibility for 
future projections may be increased if the model is able to simulate 
past variations in climate (Sections 10.3.3.8, 10.4.1 and 10.6; Flato 
et al., 2014). In particular, the credibility of downscaled information 
depends on the quality of both the downscaling method and of the 
global model providing the large-scale boundary conditions (Flato 
et al., 2014). Credibility is closely linked to the concept of adequacy 
or fitness-for-purpose (Section 1.5.4.1; Parker, 2009). From a regional

perspective, one may ask about the fitness of a climate model for 
simulating future changes of specific aspects of a specific regional 
climate. The required level of model fitness may depend on the user 
context (Section  10.5). A  key challenge is to link performance at 
representing present and past climate (Sections 10.3.3.3–10.3.3.8) 
to the confidence in future projections (Section 1.3.5; Baumberger 
et al., 2017) and it is addressed in this subsection.
A general idea of model fitness for a  given application may be 
obtained by checking whether relevant large- (Section 10.3.3.4) and 
regional-scale (Sections 10.3.3.5 and 10.3.3.6) processes are explicitly 
resolved (Figure 10.3). The basis for confidence in climate projection 
is a solid process understanding (Flato et al., 2014; Baumberger et al., 
2017). Thus, the key to assessing the fitness-for-purpose of a model is 
the evaluation of how relevant processes controlling regional climate

are represented (Collins et al., 2018). A process-based evaluation may 
be more appropriate than an evaluation of the variables of interest 
(e.g.,  temperature, precipitation), because biases in the latter may 
in principle be reduced if the underlying processes are realistically 
simulated (Cross-Chapter Box 10.2), while individual variables may 
appear as well-represented because of compensating errors (Flato 
et al., 2014; Baumberger et al., 2017). Combining a process-based 
evaluation with a  mechanistic explanation of projected changes 
further increases confidence in projections (Bukovsky et al., 2017). 
Fitness-for-purpose can also be assessed by comparing the simulated 
response of a  model with simulations of higher resolution models 
that better represent relevant processes (Baumberger et al., 2017). 
For instance, Giorgi et al. (2016) have corroborated their findings on 
precipitation changes comparing standard RCM simulations with 
convection-permitting simulations.

The evaluation of model performance at historical variability and long-
term changes provides further relevant information (Flato et al., 2014). 
Trend evaluation may provide very useful insight, but has limitations, 
in particular at the regional scale, mainly due to multi-decadal internal 
climate variability (Section  10.3.3.8), observational uncertainty 
(in  both driving reanalysis and local trends; Section  10.2), and the 
fact that often not all regional forcings are known, and that past 
trends  may be driven by forcings other than those driving future 
trends (Sections 10.4.1 and 10.6.3).Increasing resolution (Haarsma et  al., 2016) or performing downscaling 
may be particularly important when it modifies the climate change 
signal of a  lower resolution model in a  physically plausible way 
(Hall, 2014). Improvements may result from a better representation 
of regional processes, upscale effects, as well as the possibility of

a  region-specific model tuning (Sørland et  al., 2018). For instance, 
Gula and Peltier (2012) showed that a higher resolution allows for 
a more realistic simulation of lake-induced precipitation, resulting in 
a more credible projection of changes in the snow belts of the North 
American Great Lakes. Similarly, Giorgi et al. (2016) demonstrated 
that an ensemble of RCMs better represents high-elevation surface 
heating and in turn increased convective instability. As a result, the 
summer convective precipitation response was opposite to that 
simulated by the driving global models (Figure  10.9). Similarly, 
Walton et  al. (2015) showed that a  kilometre-scale RCM enables 
a  more realistic representation of the snow-albedo feedback in 
mountainous terrain compared to standard resolution global models, 
leading to a  more plausible simulation of elevation-dependent 
warming. Bukovsky et al. (2017) argue that strong seasonal changes

in warm-season precipitation in the Southern Great Plains of the USA, 
projected by RCMs, are more credible than the weaker global model 
changes because precipitation is better simulated in the RCMs.
Including additional components, feedbacks and drivers can 
substantially modify the simulated future climate. For example, 
Kjellström et  al. (2005) and Somot et  al. (2008) have shown that 
a  regional ESM can significantly modify the SST response to 
climate change of its driving global model with implications for 
the climate change signal over both the sea and land. In particular, 
coupled ocean–atmosphere RCMs may increase the credibility of 
projections in regions of strong air-sea coupling such as the East 
Asia–western North Pacific domain (Zou and Zhou, 2016b, 2017). 
Recent studies demonstrate the importance of including regional 
patterns of evolving aerosols in RCMs for simulating regional climate 
change (Boé et al., 2020a; Gutiérrez et al., 2020). RCMs not including

the plant physiological response to increasing CO 2 concentrations 
have been shown to substantially underestimate projected increases 
30 20 10        0          10        20         30
%(a) GCMs
6°E 8°E 10°E 12°E 14°E 16°E44°N46°N48°N(b) RCMs
6°E 8°E 10°E 12°E 14°E 16°E
incomp lete o r 
missing data
Figure 10. 9 | Projected changes in summer (June to August) precipitation 
(in percent with respect to the mean precipitation) over the Alps between 
the periods 2070–2099 and 1975–2004. (a)  Mean of four global climate 
models (GCMs) regridded to a common 1.32° × 1.32° grid resolution; (b) mean of 
six regional climate models (RCMs) driven with these GCMs. The grey isolines show 
elevation at 200 m intervals of the underlying model data. Further details on data 
sources and processing are available in the chapter data table (Table  10.SM.11). 
Figure adapted from Giorgi et al. (2016).

1406
Chapter 10 Linking Global to Regional Climate Change10in extreme temperatures across Europe compared to global models 
that explicitly model this effect (Schwingshackl et al., 2019).
A difference between the climate changes simulated by two models 
does not automatically imply the more complex or higher resolution 
model is superior (e.g.,  Dosio et  al., 2019). Studies comparing 
convection-permitting RCM simulations to simulations of climate 
models with parametrized convection find, depending on the 
considered models, regions and seasons, either similar or qualitatively 
different projected changes in short duration extreme precipitation 
(Chan et  al., 2014a, b, 2020; Ban et  al., 2015; Tabari et  al., 2016; 
Fosser et al., 2017; Kendon et al., 2017, 2019; Vanden Broucke et al., 
2018). Process studies provide evidence that convection-permitting 
simulations better represent crucial local and mesoscale features of

convective storms and thus simulate more plausible changes (Meredith 
et al., 2015a; Prein et al., 2017; Fitzpatrick et al., 2020), but further 
research is required to confirm and reconcile the different findings.
Studies assessing the fitness of statistical approaches for regional 
climate projections are still very limited in number. For statistical 
downscaling, a key issue is to include predictors that control long-term 
changes in regional climate. Models differing only in the choice 
of predictors may perform similarly in the present climate, but may 
project opposite precipitation changes (Fu et  al., 2018; Manzanas 
et al., 2020). In addition to trend-evaluation studies (Section 10.3.3.8), 
perfect-model experiments (Section 10.3.2.5) have been used to assess 
whether a  given model structure with a  chosen set of predictors is 
capable of reproducing the simulated future climates (Gutiérrez et al., 
2013; Räty et al., 2014; Dayon et al., 2015; Dixon et al., 2016; San-Martín

et al., 2017). Importantly, it is found that standard analogue methods 
inherently underestimate future warming trends because of missing 
analogues for a warmer climate (Gutiérrez et al., 2013). 
Bias adjustment assumes that model biases are time invariant 
(or more precisely, independent of the climate state), such that the 
adjustment made to present climate simulations is still applicable 
to future climate simulations. Many findings challenge the validity 
of this assumption, as already assessed in AR5 (Flato et al., 2014). 
Further research has addressed this issue by means of perfect 
model experiments (Section  10.3.2.5) and process understanding. 
Perfect-model studies with GCMs found that circulation, energy, 
and water-cycle biases are roughly state-independent (Krinner 
and Flanner, 2018), whereas temperature biases depend linearly 
on temperature (Kerkhoff et  al., 2014). Others show that regional 
temperature biases may depend on soil moisture and albedo,

and may thus be state-dependent (Maraun, 2012; Bellprat et  al., 
2013; Maraun et al., 2017; see Cross-Chapter Box 10.2 for further 
limitations of bias adjustment). The fitness of weather generators for 
future projections depends on whether they account for all relevant 
changes in their parameters, either by predictors or change factors 
(Maraun and Widmann, 2018b).
In any case, the fitness of regional climate projections based on 
dynamical downscaling or statistical approaches depends on the fitness 
of the driving models in projecting boundary conditions, predictors and 
change factors (Hall, 2014; Maraun and Widmann, 2018b).Overall, there is high confidence  that an assessment of model fitness 
for projections applying process-based evaluation, process-based 
plausibility checks of projections and a comparison of different model 
types, increases the confidence in climate projections. There is high 
confidence  that increasing model resolution, dynamical downscaling,

statistical downscaling with well-simulated predictors controlling 
regional climate change, and adding relevant model components can 
increase the fitness for projecting some aspects of regional climate 
when accompanied by a process-understanding analysis.
10.3.3.10 Synthesis of Model Performance at Simulating 
Regional Climate and Climate Change
Global models reproduce many of the features of observed climate 
and its variability at regional scales. However, global models 
can show a  variety of biases in, for instance, precipitation and 
temperature at scales ranging from continental (Prasanna, 2016) to 
sub-continental scales (Lovino et al., 2018), both in the mean and 
in higher order moments of the climatological distribution of the 
variable (Figure  10.6; Ren et  al., 2019; Xin et  al., 2020). Regional 
biases could occur even if all the relevant large-scale processes are 
correctly represented, but not their interaction with regional features

such as orography or land–sea contrasts (Section  10.3.3.4). These 
biases have been considered an important limiting factor in model 
usability, especially at the regional scale (Palmer, 2016). In  spite 
of this, global model simulations have been extensively used to 
create regional estimates of climate change (Chapters 11, 12 and 
Atlas), taking into account the result of a performance assessment 
(Chapter  11, Sections 10.3.3.3–10.3.3.8, and Atlas; Jiang et  al., 
2020). However, their application is limited in part by the effective 
resolution of these models (Klaver et al., 2020).
Global model performance at the regional scale is assessed in 
terms of the time or spatial averages of key variables (see Atlas; 
Brunner et  al., 2019), the ability to reproduce their seasonal 
cycle (Hasson et  al.,  2013) or a  set of extreme climate indicators 
(Chapter  11; Luo et  al., 2020) and the representation of regional 
processes and phenomena, feedbacks, drivers and forcing impacts

(Sections  10.3.3.4–10.3.3.6). In many cases, the performance 
estimates have been used to select models for either an application 
or a more in-depth study (Lovino et al., 2018), to select the models 
that provide boundary conditions to perform RCM simulations 
(McSweeney et al., 2015) or to weight the results of the global model 
simulations (Sanderson et al., 2015; Brunner et al., 2020). While some 
large-scale metrics are improved between the CMIP5 and CMIP6 
experiments (Chapter 3; Cannon, 2020), there is not yet concluding 
evidence of a systematic improvement for surface variables at the 
regional scale.
The special class of high-resolution global models (Sections 1.5.3.1 
and 10.3.3.1, Chapter  3; Haarsma et  al., 2016; Prodhomme et  al., 
2016) is expected to improve some of the regional processes 
that are not appropriately represented in standard global models 
(Roberts et  al., 2018). There is general consensus that increasing

global model resolution improves some long-standing biases 
(Chapter  3, Section  10.3.3.3, and Figures 10.6 and 10.7; Demory 
et al., 2014, 2020; Schiemann et al., 2014; Dawson and Palmer, 2015;

1407
Linking Global to Regional Climate Change Chapter 1010van Haren et al., 2015; Feng et al., 2017; Fabiano et al., 2020), although 
the resolution increase is not a  guarantee of overall improvement 
(Section 8.5.1; Fabiano et al., 2020; Hertwig et al., 2021). For instance, 
increasing resolution in global models has been shown to improve 
Asian monsoon rainfall anchored to orography and the monsoon 
circulation (Johnson et al., 2016), but fails to solve the major dry bias. 
It is also difficult to disentangle the role of resolution increase and 
model tuning on the performance of the GCM (Anand et al., 2018). 
Some efforts have been undertaken to complement the performance 
improvements of resolution by using stochastic parametrizations 
(Palmer, 2019), which explicitly acknowledge the multi-scale nature 
of the climate system, in standard resolution global models with some 
success (Dawson and Palmer, 2015; MacLeod et al., 2016; Zanna et al.,

2017, 2019). The expectation is to achieve a similar performance to 
the increase in resolution at a reduced computational cost.
Despite their known errors that affect model performance, there 
is high confidence  that global models provide useful information 
for the production of regional climate information. There is robust 
evidence and high agreement  that the increase of global model 
resolution helps in reducing the biases limiting performance at the 
regional scale, although resolution per se does not automatically 
solve all performance limitations shown by global models. There is 
robust evidence  that stochastic parametrizations can help to improve 
some aspects of the global model performance that are relevant to 
regional climate information.
Global models tend to have difficulties in simulating climate over 
regions where unresolved local scale processes, feedbacks and 
non-linear scale interactions result in a  degradation of the model

performance compared to models with higher resolution. In this case, 
RCMs and variable resolution global models can resolve part of these 
processes in the regions of interest at an acceptable computational 
cost (Rummukainen, 2016; Giorgi, 2019; Gutowski Jr. et al., 2020).
The assessment of RCM performance needs to focus not only on 
mean climatology (Atlas), but also trends (Section  10.3.3.8) and 
extremes (Chapter 11), and the RCM’s ability at correctly reproducing 
relevant processes, forcings and feedbacks including aerosols, plant 
responses to increasing CO 2, and so on, (Schwingshackl et al., 2019; 
Boé et al., 2020a; Sections 11.2. and 10.3.3.3 to 10.3.3.8) to be fit for 
future projections (Section 10.3.3.9).
When RCMs are driven by global models, part of the uncertainty 
in the RCM simulation is introduced by the global model biases 
(Kjellström et  al., 2018; Sørland et  al., 2018; Christensen and 
Kjellström, 2020). As RCMs are typically not able to mitigate global

model biases in large-scale dynamical processes, if such biases 
are substantial, and if the corresponding large-scale processes are 
important drivers of regional climate, downscaling is questionable 
(Section  10.3.3.3). However, when global models have weak 
circulation biases and regional climate change is controlled mainly 
by regional-scale processes and feedbacks, dynamical downscaling 
has the potential to add substantial value to global model 
simulations (Section 10.3.3.4 and Atlas; Hall, 2014; Rummukainen, 
2016; Giorgi, 2019; Schwingshackl et al., 2019; Boé et al., 2020a; 
Lloyd et al., 2021).There is very high confidence  (robust evidence  and high agreement ) 
that RCMs add value to global simulations in representing many 
regional weather and climate phenomena, especially over regions 
of complex orography or with heterogeneous surface characteristics 
and for local-scale phenomena. Realistically representing local-scale

phenomena such as land–sea breezes requires simulations at 
a  resolution of the order of 10 km ( high confidence ). Simulations 
at  kilometre-scale resolution add value in particular to the 
representation of convection, sub-daily summer precipitation extremes 
(high confidence ) and soil-moisture–precipitation feedbacks ( medium 
confidence ). Resolving regional processes may be required to correctly 
represent the sign of regional climate change ( medium confidence ). 
However, the performance of RCMs and their fitness for future 
projections depend on their representation of relevant processes, 
forcings and drivers in the specific context (Sections 10.3.3.4–10.3.3.8).
Statistical downscaling, bias adjustment and weather generators 
outperform uncorrected output of global and regional models 
for a  range of statistical aspects at single locations due to their 
calibration (Casanueva et  al., 2016), but RCMs are superior when

spatial fields are relevant (Mehrotra et  al., 2014; Vaittinada Ayar 
et al., 2016; Maraun et al., 2019a). Similarly, there is some evidence 
that bias adjustment is comparable in performance when applied to 
global models and dynamically downscaled global models only for 
single locations, but dynamical downscaling prior to bias adjustment 
clearly adds value once spatial dependence is relevant (Maraun 
et al., 2019a). These results may explain why dynamical downscaling 
does not add value to global model simulations for (single-site) 
agricultural modelling, when both global and regional models are 
bias adjusted (Glotter et al., 2014), but dynamical downscaling adds 
value compared to bias-adjusted global model output for spatially 
distributed hydrological models (Qiao et al., 2014).
Overall, statistical downscaling methods with carefully chosen 
predictors and an appropriate model structure for a given application

temperature and precipitation ( high confidence , Section  10.3.3.7). 
Bias adjustment has proven beneficial as an interface between 
climate model projections and impact modelling in many different 
contexts ( high confidence ) (Section  10.3.3.7). Weather generators 
realistically simulate many statistical aspects of present-day daily 
temperature and precipitation ( high confidence ) (Section 10.3.3.7). 
The performance of these approaches and their fitness for future 
projections also depends on predictors and change factors taken from 
the driving dynamical models ( high confidence ) (Section 10.3.3.9).
10.3.4 Managing Uncertainties in 
Regional climate projections are affected by three main sources 
of uncertainty (Sections 10.2.2, 1.4.3 and 4.2.5): unknown future 
external forcings, imperfect knowledge and implementation of the

response of the climate system to external forcings, and internal 
variability (Lehner et al., 2020). In a regional downscaling context, 
uncertainties arise in every step of the modelling chain. Here the 
propagation of uncertainties (Section 10.3.4.1), the management of

1408
Chapter 10 Linking Global to Regional Climate Change10uncertainties (Section  10.3.4.2), the role of the internal variability 
for regional projections (Section 10.3.4.3), and the design and use 
of ensembles to account for uncertainties (Section 10.3.4.4) will be 
assessed. Observational uncertainty, in particular for the calibration of 
statistical downscaling methods (Section 10.2.3.1), also contributes 
to projection uncertainty.
10.3.4.1 Propagation of Uncertainties
Modelling chains for generating regional climate information range 
from the definition of forcing scenarios to the global modelling, 
and potentially to dynamical or statistical downscaling and bias 
adjustment (Section  10.3.1). The propagation and potential 
accumulation of uncertainties along the chain has been termed the 
cascade of uncertainty (Wilby and Dessai, 2010). Even within one 
model, like a global model, uncertainty propagates across scales. From

a process point of view, these uncertainties are related to forcings 
and global climate sensitivity, and errors in the representation of the 
large-scale circulation (Section  10.3.3.3; McNeall et  al., 2016) and 
regional processes (Section  10.3.3.4), feedbacks (Section  10.3.3.5) 
and drivers (Section 10.3.3.6). From a modelling point of view, these 
uncertainties are related to the choice of dynamical and statistical 
models (Section  10.3.1) and experimental design (Section  10.3.2). 
The overall uncertainty can be statistically decomposed into the 
individual sources (Evin et al., 2019; Christensen and Kjellström, 2020), 
although there might be non-linear dependencies between them.
Uncertainty propagation often increases the spread in regional climate 
projections when comparing global model and downscaled results, 
which has been used as an argument against top-down approaches 
to climate information (Prudhomme et al., 2010). Increased spread

in the modelling chain may also arise from a more comprehensive 
representation of previously unknown or underrepresented 
uncertainties (Maraun and Widmann, 2018b). The increased spread 
in this case goes together with a better representation of processes 
and thus an increased model fitness-for-purpose (Section 10.3.3.9).
10.3.4.2 Representing and Reducing Uncertainties
Climate response uncertainties (Chapter  1) can be represented by 
multi-model ensembles, although the sampled uncertainty typically 
underestimates the full range of uncertainty (Collins et al., 2013b; 
Shepherd et al., 2018; Almazroui et al., 2021). Traditionally, climate 
response uncertainty has been characterized by the ensemble spread 
around the multi-model mean change. The change has then further 
been qualified in terms of the agreement across models and compared 
to estimates of internal climate variability (Collins et  al., 2013b). 
Since AR5, several limitations of this approach have been identified

(Madsen et  al., 2017) such as the failure to address physically 
plausible, but low-likelihood, high-impact scenarios (Chapters 1, 4, 8 
and 9; Sutton, 2018) or that qualitatively different or even opposite 
changes may be equally plausible at the regional scale (Shepherd, 
2014). In a  multi-model mean these different responses would be 
lumped together, strongly dampened, and qualified as non-robust, 
whereas in fact high impacts might occur. Further, the multi-model 
mean itself is often implausible because it is a statistical construct 
(Zappa and Shepherd, 2017). Overall, there is high confidence  that some regional future climate changes are not well-characterized by 
multi-model mean and spread.
Since AR5, physical climate storyline approaches (see also 
Chapter  1, Section  10.5.3, Box  10.2, and Atlas.2.5.2) have been 
developed to better characterize and communicate uncertainties 
in regional climate projections (Shepherd, 2019). A  special class

of such storylines attempts to attribute regional uncertainties 
to uncertainties in remote drivers. For instance, the Dutch 
Meteorological Service has presented climate projections for the 
Netherlands for different plausible changes of the mid-latitude 
atmospheric circulation and different levels of European warming 
(van den Hurk et al., 2014). Manzini et al. (2014) have quantified 
the impact of uncertainties in tropical upper troposphere warming, 
polar amplification, and stratospheric wind change on Northern 
Hemisphere winter climate change. Based on these results, Zappa 
and Shepherd (2017) separated the multi-model ensemble into 
physically consistent sub-groups or storylines of qualitatively 
different projections in relevant remote drivers of the atmospheric 
circulation. In a similar vein, (Ose et al., 2020) trace uncertainties in 
projections of the East Asian summer monsoon and Mindlin et al. 
(2020) conditioned the response of Southern Hemisphere mid-

latitude circulation and precipitation to greenhouse gas forcing on 
large-scale climate indicators (Section 8.4.2.9.2).
These physical climate storylines help to physically explain contradicting 
regional projections and thus make the conveyed information a better 
representation of the true uncertainty (Hewitson et al., 2014a). 
Additionally, the attribution of regional uncertainties to drivers may in 
principle help reduce uncertainty in the case where some storylines can 
be ruled out because the projected changes in the driving processes 
appear to be physically implausible (Zappa and Shepherd, 2017). There 
is thus high confidence  that storylines attributing uncertainties in 
regional projections to uncertainties in changes of remote drivers aid 
the interpretation of uncertainties in climate projections.
Another approach that has continued to develop for characterising 
and reducing projection uncertainties is the use of emergent

constraints (Chapters 1, 4, 5 and 7; Hall et  al., 2019). The idea is 
to link the spread in climate model projections via regression to 
the spread in present climate model biases for relevant driving 
processes. Models with lower biases are assigned higher weight in 
the projections, which in turn reduces the spread of the projections 
in a physical way and may additionally reduce projection uncertainty. 
For instance, Simpson et  al. (2016) have reduced the spread in 
projections of North American winter hydroclimate by linking this 
spread to model biases in the representation of relevant stationary 
wave patterns. Other examples of using emergent constraints in 
a regional context are Brown et al. (2016), G. Li et al. (2017), Giannini 
and Kaplan (2019), Ose (2019) and Zhou et al. (2019).
10.3.4.3 Role of Internal Variability
A regional climate projection based on a  single simulation from 
a single global model or driving a single RCM alone will inevitably

be affected by not considering the internal variability (Figure 10.10). 
This is mainly due to the dominant influence of the chaotic atmospheric

1409
Linking Global to Regional Climate Change Chapter 1010circulation on regional climate variability, in particular at mid- to high 
latitudes. Internal variability is an irreducible source of uncertainty 
for mid- to long-term projections with an amplitude that typically 
decreases with increasing spatial scale and lead time (Sections 1.4.3 
and 4.2.1). However, regional-scale studies show that both large- 
and local-scale internal variability together can still represent 
a substantial fraction of the total uncertainty related to hydrological 
cycle variables, even at the end of the 21st century (Lafaysse et al., 
2014; Vidal et al., 2016; Aalbers et al., 2018; Gu et al., 2018).
Analysis of multi-model archives such as CMIP or CORDEX simulation 
results cannot easily disentangle model uncertainty and uncertainty 
related to internal variability. Since AR5, the development of 
single-model (global model and/or RCM) initial-condition large

ensembles (SMILEs) has emerged as a  promising way to robustly 
assess the regional-scale forced response to external forcings and the 
respective contribution of internal variability and model uncertainty 
to future regional climate changes (Section 4.2.5; Deser et al., 2014, 
2020; Kay et al., 2015; Sigmond and Fyfe, 2016; Aalbers et al., 2018; 
Bengtsson and Hodges, 2019; Dai and Bloecker, 2019; Leduc et al., 
2019; Maher et al., 2019; von Trentini et al., 2019; Lehner et al., 2020). 
The recent development of a multi-model archive of SMILE simulations 
facilitates the quantification and comparison of  the influence of 
internal variability on global model-based regional climate projections 
between different models (Deser et  al., 2020; Lehner et  al., 2020). 
Another related development is the more frequent use of observation-
based statistical models to assess the influence of internal variability 
on regional-scale global and regional model projections (Thompson

et  al., 2015; Salazar et  al., 2016). However, these methods often 
implicitly assume that regional-scale internal variability does not 
change under anthropogenic forcing, which is a strong assumption 
that does not seem to hold at regional and local scales (LaJoie and 
DelSole, 2016; Pendergrass et  al., 2017; W. Cai et  al., 2018; Dai 
and Bloecker, 2019; Mankin et al., 2020; Milinski et al., 2020).
The appropriate ensemble size for a robust use of SMILEs depends on 
the model and physical variable being investigated, the spatial and 
time aggregation being performed, the magnitude of the acceptable 
error and the type of questions one seeks to answer (Deser et al., 2012, 
2017b; Kang et al., 2013; Wettstein and Deser, 2014; Dai and Bloecker, 
2019; Maher et al., 2019). It is noteworthy that the recent development 
of ensembles with a very large ensemble size (greater than 100) have 
led to new insights and methodologies to robustly assess the required

ensemble size for questions such as the estimation of the forced response 
to external forcing or a forced change in modes of internal variability, 
such as ENSO, and its associated teleconnections (Herein et al., 2017; 
Maher et al., 2018; Haszpra et al., 2020; Milinski et al., 2020).
The use of SMILEs assumes that they have a realistic representation 
of internal variability and its evolution under anthropogenic climate 
change (Eade et  al., 2014; McKinnon et  al., 2017; McKinnon and 
Deser, 2018; Chen and Brissette, 2019). Assessing the realism of 
simulated internal variability for past and current climates remains 
an active research field with a  number of issues such as the 
shortness and uncertainties of the observed record, in particular in 
data-scarce regions (Section  10.2.2.3), the signal-to-noise paradox 
(Section 4.4.3.1; Scaife and Smith, 2018), uncertainty in past observed external forcing estimates (Chapters 2, 6 and 7) and the limitations

of assumptions underlying the statistical methods used to derive 
observational large ensembles (McKinnon et al., 2017; McKinnon and 
Deser, 2018; Castruccio et al., 2019). Calibration methods inspired by 
Wettest(a) MPI-ESM tr ends (20 15-2070)
Driest
(b) P recipita tion a noma lies a nd trend
Trend (% d ecade1)
1086420246420202
20246%
1960 1980 2000 2020 2040 2060 2080 21007550250255075
Base period Trend period20020
200204060010globa l
SES - S.E.S outh-America
Sao Pa ulo
Buen os Aires
MPI-ESM
MPI-ESM Driest
MPI-ESM WettestCRU TSGPCCsea-m aske d 
data10               -5                 0                 5                10
% decade-1
Figure  10.10 | Observed and projected changes in austral summer 
(December to February) mean precipitation in Global Precipitation 
Climatoloy Centre (GPCC), Climatic Research Unit Time Series (CRU TS) and 
100 members of the Max Planck Institute for Meteorology Earth System 
Model (MPI-ESM. (a)  55-year trends (2015–2070) from the ensemble members

with the lowest (left) and highest (right) trend (% per decade, baseline 1995–2014). 
(b) Time series (%, baseline 1995–2014) for different spatial scales (from top to 
bottom: global averages; South-Eastern South America; grid boxes close to São Paulo 
and Buenos Aires) with a five-point weighted running mean applied (a variant on 
the binomial filter with weights [1-3-4-3-1]). The brown (green) lines correspond 
to the ensemble member with weakest (strongest) 55-year trend and the grey lines 
to all remaining ensemble members. Box-and-whisker plots show the distribution of 
55-year linear trends across all ensemble members, and follow the methodology used 
in Figure 10.6. Trends are estimated using ordinary least squares. Further details on 
data sources and processing are available in the chapter data table (Table 10.SM.11).

1410
Chapter 10 Linking Global to Regional Climate Change10weather and seasonal forecasts can be used to improve the reliability 
of regional-scale climate projections from large ensembles (Brunner 
et al., 2019; O’Reilly et al., 2020). Interestingly, reliability is improved 
when the calibration is performed separately for the dynamical and 
residual components of the ensemble resulting from dynamical 
adjustment (Section 10.4.1; O’Reilly et al., 2020).
Importantly, accurately partitioning uncertainty in regional climate 
projections can provide an incentive for immediate action, accepting 
a  large range of possible outcomes due to internal variability, 
while confounding model uncertainty with internal variability may 
be understood as a lack of knowledge and lead to delayed action 
in adaptation decision-making (Section  10.5.3; Maraun, 2013b; 
Mankin et al., 2020).
There is high confidence  that the availability of SMILEs allows a robust

assessment of the relative contributions of model uncertainty and 
internal variability in regional-scale projection uncertainty. There is 
high confidence that the use of SMILEs with appropriate ensemble 
size leads to an improved estimate of regional-scale forced response 
to an external forcing as well as of the full spectrum of possible 
changes associated with internal variability. There is high confidence 
that these improved estimates are beneficial for characterizing the 
full distribution of outcomes that is a  key ingredient of climate 
information for robust decision-making and risk-analysis frameworks.
10.3.4.4 Designing and Using Ensembles for Regional Climate 
Change Assessments to Take Uncertainty Into Account
Ensembles of climate simulations play an important role in quantifying 
uncertainties in the simulation output (Sections 10.3.4.2 and 
10.3.4.3). In addition to providing information on internal variability,

ensembles of simulations can estimate scenario uncertainty and 
model (structural) uncertainty. Chapter  4, especially Box  4.1, 
discusses issues involved with evaluating ensembles of global 
model simulations and their uncertainties. In a downscaling context, 
further considerations are necessary, such as the selection of global 
model–RCM combinations when performing dynamical downscaling. 
This is a  relevant issue when resources are limited. The structural 
uncertainty of both the global model and the downscaling method 
can be important (e.g., Mearns et al., 2012; Dosio, 2017), as well as 
further potential uncertainty created by inconsistencies between the 
global model and the downscaling method (e.g., Dosio et al., 2019), 
which could include, for example, differences in topography or the 
way to model precipitation processes (Mearns et al., 2013).
An important consideration is which set of global models should be

used for global model–RCM combinations. If adequate resources 
exist, then large numbers of global model–RCM combinations are 
possible (Déqué et  al., 2012; Coppola et  al., 2021; Vautard et  al., 
2021). However, coordinated downscaling programmes can be limited 
by the human and computational resources available, for producing 
ensembles of downscaled output, which limits the number of feasible 
global model–RCM combinations. With this limitation in mind, a small 
set of GCMs may be chosen that span the range of equilibrium climate 
sensitivity in available global models (e.g., Mearns et al., 2012, 2013; 
Inatsu et al., 2015), though this range may be inconsistent with the likely range (Chapter 4), or some other relevant measure of sensitivity, 
such as the projected range of tropical SSTs (Suzuki-Parker et al., 2018). 
A further choice is to emphasize models that do not have the same 
origins or that do not use similar parametrizations and thus might be

viewed as independent, a criterion that could be applied to both global 
models (Chapter 4) and RCMs (Evans et al., 2014). Global models and 
RCMs could also be discarded that unrealistically represent processes 
controlling the regional climate of interest (McSweeney et al., 2015; 
Maraun et  al., 2017; Bukovsky et  al., 2019; Eyring et  al., 2019). 
Box  4.1 offers a  more detailed discussion of the issues surrounding 
these approaches. Finally, global models may be selected to represent 
different physically self-consistent changes in regional climate (Zappa 
and Shepherd, 2017). Statistical methods can provide estimates of 
outcomes from missing global model–RCM combinations in a  large 
matrix (Déqué et al., 2012; Heinrich et al., 2014; Evin et al., 2019).
However, even using a relatively small set of global models can still 
involve substantial computation that strains available resources, 
both for performing the simulations and for using all simulations

in the ensemble for further impacts assessment. The NARCCAP 
programme (Mearns et al., 2012) used only a subset of its possible 
global model–RCM combinations that balanced comprehensiveness 
of sampling the matrix with economy of computation demand, while 
still allowing discrimination, via ANOVA methods, of global model 
and RCM influences on regional climate change (Mearns et  al., 
2013). An advantage of the sparse, but balanced matrix for those 
using the downscaling output for further studies, is that they have 
a smaller, yet comprehensive set of global model–RCM combinations 
to work with. Alternatively, data-clustering methods can clump 
together downscaling simulations featuring similar climate-change 
characteristics, so that only one representative simulation from 
each cluster may be needed for further impacts analysis, again 
systematically reducing the necessary number of simulations to work 
with (Mendlik and Gobiet, 2016; Wilcke and Bärring, 2016).

Independently of the resources, participation of multiple models in 
a  simulation programme such as CORDEX for RCMs or CMIP for 
global models creates ensembles of opportunity, which are ensembles 
populated by models that participants chose to use without there 
necessarily being an overarching guiding principle for an optimum 
choice. As discussed in Chapter 4, these ensembles are likely suboptimal 
for assessing sources of uncertainty. An important contributor to the 
suboptimal character of such an ensemble is that the models are 
not independent. Some may also have larger biases than others. Yet 
often, the output from models in these ensembles has received equal 
weight when viewed collectively, as was the case in much of the AR5 
assessment (e.g., Collins et al., 2013b; Knutti et al., 2013; Flato et al., 
2014; Kirtman et al., 2014). A number of emerging methodologies aim 
at optimizing the ensembles available by weighting the simulation

results according to a  number of criteria relevant at the regional 
scale that aim at obtaining more realistic estimates of the uncertainty 
(Sanderson et al., 2015; Brunner et al., 2020).
There is high confidence  that ensembles for regional climate projections 
should be selected such that models unrealistically simulating processes 
relevant for a given application are discarded, but at the same time, the 
chosen ensemble spans an appropriate range of projection uncertainties.

1411
Linking Global to Regional Climate Change Chapter 1010Cross-Chapter Box 10.2 | Relevance and Limitations of Bias Adjustment
Coordinators:  Alessandro Dosio (Italy), Douglas Maraun (Austria/Germany)
Contributors:  Ana Casanueva (Spain), José Manuel Gutiérrez (Spain), Stefan Lange (Germany), Jana Sillmann (Norway/Germany)
Bias adjustment is an approach to post-process climate model output and has become widely used in climate hazard and impact studies 
(Gangopadhyay et  al., 2011; Hagemann et  al., 2013; Warszawski et  al., 2014) and national assessment reports (Cayan et  al., 2013; 
Georgakakos et al., 2014). Despite its wide use, bias adjustment was not assessed in AR5 (Flato et al., 2014). Several problems have been 
identified that may arise from an uncritical use of bias adjustment, and that may result in misleading impact assessments. The rationale of

this Cross-Chapter Box is to provide an overview of the use of bias adjustment in this Report, and to assess key limitations of the approach.
Bias-adjusted climate model output is used extensively throughout this Report. Several results from Chapter 8, and many of the 
climatic impact-drivers in Chapter 12 (Section 12.2) are based on bias adjustment. The Atlas presents many results both as raw and 
bias-adjusted data (Atlas.1.4.5). The application of bias adjustment in the WGI report was informed by the assessment in Chapter 10 
and this Cross-Chapter Box. Finally, bias adjustment is crucial for many studies assessed in the WGII report. An overview of bias 
adjustment can be found in Section 10.3.1.3, a general performance assessment of individual method classes in Section 10.3.3.7. 
The fitness of bias adjustment for climate change applications is assessed in Section 10.3.3.9.
Relevance of bias adjustment

An argument made for the use of bias adjustment is the fact that impact models are commonly very sensitive, often non-linearly, to 
the input climatic variables and their biases, in particular when threshold-based climate indices are required (Dosio, 2016). There are, 
however, cases where bias adjustment may not be necessary or useful, such as: when only qualitative statements are required; when 
only changes in mean climate are considered (instead of absolute values); when percentile-based indices are used.
Modification of the climate change signal
Bias adjustment methods like quantile mapping can modify simulated climate trends, with impacts on changes to climate indices, 
in particular, extremes (Haerter et al., 2011; Dosio et al., 2012; Ahmed et al., 2013; Hempel et al., 2013; Maurer and Pierce, 2014; 
Cannon et al., 2015; Dosio, 2016; Casanueva et al., 2020). Some argue that these trend modifications are implicit corrections of

state-dependent biases (Boberg and Christensen, 2012; Gobiet et al., 2015). However, others argue that the modification is generally 
invalid because the modification is linked to the representation of day-to-day rather than long-term variability (Pierce et al., 2015; 
Maraun et al., 2017); a given temperature value does not necessarily belong to the same weather state in present and future climate 
(Maraun et al., 2017); the modification affects the models climate sensitivity (Hempel et al., 2013); and is affected by random internal 
climate variability (Switanek et al., 2017). Thus, trend preserving quantile mapping methods have been developed (Section 10.3.1.3.2), 
although some authors found no clear advantage of these methods (Maurer and Pierce, 2014). Further research is required to fully 
understand the validity of trend modifications by quantile-mapping.
Bias adjustment in the presence of large-scale circulation errors

The large-scale circulation has a strong impact on regional climate, thus circulation errors will cause regional climate biases (Section 10.3.3.3). 
As bias adjustment in general does not account for circulation errors, it is therefore important to understand the impact of these errors on the 
outcome of the bias adjustment (Addor et al., 2016; Photiadou et al., 2016; Maraun et al., 2017). If the frequency of precipitation-relevant 
weather types is biased, a standard bias adjustment (not accounting for this frequency bias) would remove the overall climatological bias, 
but the precipitation falling in a given weather type could still be substantially biased (Addor et al., 2016). Adjusting the number of wet 
days can artificially deteriorate the spell-length distribution (Maraun et al., 2017). In the presence of location biases of circulation patterns,

bias adjustment may introduce physically implausible solutions (Maraun et al., 2017). Bias adjusting the location of circulation features 
(Levy et al., 2013) may introduce inconsistencies with the model orography, land–sea contrasts, and SSTs (Maraun et al., 2017).
There is medium confidence  that the selection of climate models with low biases in the frequency, persistence and location of 
large-scale atmospheric circulation can reduce negative impacts of bias adjustment.
Using bias adjustment for statistical downscaling
Bias adjustment is often used to downscale climate model results from grid box data to finer resolution or point scale. It is sometimes 
even directly applied to coarse-resolution global model output to avoid an intermediate dynamical downscaling step (Johnson and 
Sharma, 2012; Stoner et al., 2013). But bias adjustment does not add any information about the processes acting on unresolved scales

and is therefore by construction not capable of bridging substantial scale gaps (Maraun, 2013a; Maraun et al., 2017). Using bias

1412
Chapter 10 Linking Global to Regional Climate Change10Cross-Chapter Box 10.2 (continued)
adjustment for downscaling has been shown to artificially modify long-term trends, misrepresent the spatial characteristics of extreme 
events, and misrepresent local weather phenomena such as temperature inversions (Maraun, 2013a; Gutmann et al., 2014; Maraun 
et al., 2017). Crucially, sub-grid influences on the local climate change signal are not represented. For instance, if a mountain chain is 
not resolved in the driving model, the snow–albedo feedback is not represented by the bias adjustment such that local temperature 
trends in high altitudes are under-represented (Cross-Chapter Box 10.2, Figure 1; Maraun et al., 2017). It has therefore been suggested 
to account for local random variability by combining bias adjustment with stochastic downscaling (Volosciuk et al., 2017; Lange,

2019), although this approach still does not account for local modifications of the climate change signal. Two approaches have been 
proposed to represent these local changes: dynamical downscaling with high-resolution RCMs (Maraun et al., 2017) or statistical 
emulators of such (Walton et al., 2015). Sections 10.3.3.4–10.3.3.6 and 10.3.3.9 discuss other examples where RCMs improve the 
representation of regional phenomena and regional climate change.
36°N37°N38°N39°N40°N36°N37°N38°N39°N40°N(a) M AM tem perature 1981-2000
RCM (WRF)
(b) M AM tem perature change 2081-2100 vs. 19 81-2000−2 0 2 4 6 8 10 12 14 16 18
°C
4 4.5 5 5.566.5 7 7.5
°C121°W 120°W 119°W 118°W 122°W 121°W 120°W 119°W 118°W 121°W 120°W 119°W 118°W121°W 120°W 119°W 118°W 122°W 121°W 120°W 119°W 118°W 121°W 120°W 119°W 118°Wbias a djuste d GCM GCM (GF DL-CM3)
RCM (WRF) bias a djuste d GCM GCM (GF DL-CM3)

Cross-Chapter Box 10.2, Figure 1 | Boreal spring (March to May) daily mean surface air temperature in the Sierra Nevada region in California. 
(a) Present climate (1981–2000 average, in °C) in the GFDL-CM3 GCM, interpolated to 8 km (left), GCM bias adjusted (using quantile mapping) to observations at 
8 km resolution (middle) and WRF RCM at 3 km horizontal resolution (right). (b) Climate change signal (2081–2100 average minus 1981–2000 average according 
to RCP8.5, in °C) in the GCM (left), the bias adjusted GCM (middle) and the RCM (right). Further details on data sources and processing are available in the chapter 
data table (Table 10.SM.11). Figure adapted from Maraun et al. (2017).

1413
10.4 Interplay Between Anthropogenic 
Change and Internal Variability 
at Regional Scales
This section focuses on the assessment of the methodologies used to 
identify the physical causes of past and future regional climate change 
in the context of the ongoing anthropogenic influence on the global 
climate. The main foci are the attribution of past regional-scale changes 
(Sections 10.4.1–2) and the robustness and future emergence of the 
regional-scale response to anthropogenic forcing (Section 10.4.3).In this chapter, regional-scale attribution is defined as the process 
of evaluating the relative contributions of multiple causal factors 
(or drivers) to regional climate change (Cross-Working Group Box: 
Attribution in Chapter 1; Rosenzweig and Neofotis, 2013; Shepherd, 
2019). Attribution at regional scale builds upon the usual definition of 
attribution used in the AR5 (Cross-Working Group Box: Attribution in

Chapter 1; Hegerl et al., 2010). However, in contrast with global-scale 
attribution methods where internal variability might be considered as 
a noise problem (Section 3.2), the preliminary detection step is not 
always required to perform regional-scale attribution since causal Cross-Chapter Box 10.2 (continued)
Overall, there is high confidence  that the use of bias adjustment for statistical downscaling, in particular to downscale coarse resolution 
global models, has severe limitations.
Bias adjustment of multiple variables
Impact models, as well as indices of climatic impact-drivers, often require input of several meteorological variables (Chapter 12). In several 
situations, for example, if the dependence between the variables is not well-simulated, univariate bias adjustment of the individual 
variables may increase biases in the resulting indicator (Zscheischler et al., 2019). A simple alternative would be a bias adjustment of

the indicator, but such a procedure may substantially alter the climate change signal, in particular for extreme events (Casanueva et al., 
2018). In principle, multivariate bias adjustment methods are good to adjust all statistical aspects of the multivariate distribution that they 
intend to adjust. Depending on the method, this includes the correlation structure or even broader aspects of the dependence (Cannon, 
2016, 2018; Vrac, 2018; François et al., 2020). If multivariate adjustment includes a spatial dimension, then spatial dependence is adjusted 
well (Vrac, 2018), but care is needed when applied across large areas (François et al., 2020). Adjustment of multivariate dependence 
necessarily modifies the temporal sequencing of the driving model (Cannon, 2016; Maraun, 2016). The extent of the modification depends 
on the chosen method and the number of variables to adjust (Vrac and Friederichs, 2015; Cannon, 2016; Vrac, 2018; François et al., 2020).

Bias adjustment in the presence of observational uncertainty and internal variability
Observational uncertainties and internal variability introduce uncertainty in the estimation of biases and thus in the calibration of 
bias-adjustment methods. Dobor and Hlásny (2019) found a considerable influence of the choice of the observational dataset and 
calibration period on the adjustment for some regions. RCM biases are typically larger than observational uncertainties, but in some 
regions, and in particular for wet-day frequencies, spatial patterns and the intensity distribution of daily precipitation, the situation 
may reverse (Kotlarski et al., 2019). Switanek et al. (2017) found a strong influence of internal variability and thus of the choice of 
calibration period on the calibration of quantile mapping and on the modification of the climate change signal.

Bias adjustment is typically evaluated using cross-validation, that is, by calibrating the adjustment function to one period of the observational 
record, and by evaluating it on a different one. Maraun et al. (2017) and Maraun and Widmann (2018a) demonstrated that, in the presence 
of multi-decadal internal variability, cross-validation may lead to a rejection of a valid bias adjustment or even lead to a positive evaluation 
of an invalid adjustment. The authors therefore argued that, in the presence of substantial internal variability, the evaluation of bias 
adjustment requires to consider aspects that have not been adjusted, such as temporal, spatial, or multivariable dependence.
There is high confidence  that observational uncertainty and internal variability adversely affect bias adjustment and introduce 
uncertainties in bias-adjusted future projections.
Overall assessment and new avenues

In the light of these issues, several authors dismiss the use of bias adjustment for climate change studies (Vannitsem, 2011; Ehret et al., 2012). 
Ehret et al. (2012) and IPCC (2015) propose to at least provide the raw model output alongside the adjusted data. Maraun et al. (2017) 
argue that the target resolution should be similar to the model resolution to avoid downscaling issues. IPCC (2015) and Maraun et al. 
(2017) highlighted the relevance of understanding model biases and the misrepresentations of the underlying physical processes prior to 
any adjustment. Together with Galmarini et al. (2019), they point out the need for collaboration between bias adjustment users, experts in 
climate modelling and experts in the considered regional climate. As new research avenues, development of process-oriented bias adjustment 
methods (Addor et al., 2016; Verfaillie et al., 2017; Manzanas and Gutiérrez, 2019) or run-time bias adjustment integrated into the climate

simulation, for example, to reduce circulation errors (Guldberg et al., 2005; Kharin et al., 2012; Krinner et al., 2019, 2020) are proposed.

1414
Chapter 10 Linking Global to Regional Climate Change10factors of regional climate change may also include internal modes of 
variability in addition to external natural and anthropogenic forcing. 
Importantly, regional-scale (or process-based) attribution also seeks 
to determine the physical processes and uncertainties involved in the 
causal factor’s influence (Cross-Working Group Box: Attribution in 
Chapter 1).
Section  10.4.1 describes regional-scale attribution methodologies 
and assesses their application to regional changes of temperature 
and precipitation. Section 10.4.2 presents three illustrative attribution 
examples that illustrate a number of specific regional-scale challenges 
and methodological aspects. Section 10.4.3 focuses on methodologies 
used to assess the robustness and emergence of the regional climate 
response to anthropogenic forcing. A  basic description of future 
regional climate change for all regions considered in the report

(as defined in Section 1.4.5) appears in the Atlas.
10.4.1 Methodologies for Regional 
Climate Change Attribution
Attribution at sub-continental and regional scales is usually more 
complicated than at the global scale due to various factors: a larger 
contribution from internal variability, an increased similarity among 
the responses to different external forcings leading to a more difficult 
discrimination of their effects, the importance at regional scale of 
some omitted forcings in global model simulations, and model 
biases related to the representation of small-scale phenomena 
(Zhai et  al., 2018). Since AR5 and in addition to standard optimal 
fingerprint regression-based approaches (Section  3.2.1 and Zhai 
et al. 2018), several emerging methodologies have been increasingly 
used for regional-scale climate change attribution. These include 
several statistical approaches that differ in their use or omission of

spatiotemporal co-variance information. Dynamical adjustment and 
pattern recognition techniques fall into the category of spatiotemporal 
methods while univariate detection and attribution methods rely on 
single grid-point analysis. Finally, the development, evaluation and use 
of all these methodologies rely upon the availability of multiple and 
high-quality observational datasets (Section 10.2) as well as multi-
model simulations of the historical period constrained by different 
external forcing combinations, including single-forcing experiments 
and single-model initial-condition large ensembles (SMILEs).
10.4.1.1 Optimal Fingerprinting Methods
Optimal fingerprint regression-based methods have been applied to 
detection and attribution of mean temperature anthropogenic signal 
in several regions of the world such as Canada, India, central Asia, 
northern and western China, Australia, and North Africa (Xu et al.,

2015; C. Li et al., 2017; Dileepkumar et al., 2018; Y . Wang et al., 2018; 
Peng et al., 2019; Wan et al., 2019). The influence of anthropogenic 
forcing, and in particular that of greenhouse gases (GHGs), is 
robustly detected in annual and seasonal mean temperatures for 
all considered regions. Most of the observed regional temperature 
changes since the mid-twentieth century can only be explained by 
external forcings, with anthropogenic influence being the dominant 
factor. GHG increase is found to be the primary factor of the anthropogenic-induced warming while the aerosol forcing leads to 
a cooling offsetting a fraction of the GHG change (C. Li et al., 2016, 
2017). While the influence of external natural forcing can often be 
detected as well, its contribution to observed changes is usually much 
smaller (C. Li et al., 2017; Wan et al., 2019). Temperature detection 
results are found to be robust to the use of different observational

datasets and detection methodologies (Dileepkumar et al., 2018).
Detection of mean precipitation changes caused by human influence is 
much more difficult, due to a larger role of internal variability at regional 
to local scales, as well as substantial modelling and observational 
uncertainty (Wan et al., 2015; Sarojini et al., 2016; C. Li et al., 2017). 
However, multi-decadal precipitation changes due to anthropogenic 
forcing have been detected for several regions. Ma et al. (2017b) show 
that anthropogenic forcing has strongly contributed to the observed 
shift of China daily precipitation towards heavy precipitation. The 
observed weakening of the East Asia summer monsoon, also known 
as the southern flooding and northern drought pattern has been 
partially linked to anthropogenic forcing (Section  8.3.2.4.2; Song 
et  al., 2014; Zhou et  al., 2017; Tian et  al., 2018). Changes in GHGs 
lead to increasing precipitation over southern China, while changes

in anthropogenic aerosols over East Asia are the dominant factors 
determining drought conditions over northern China (Song et al., 2014; 
Tian et al., 2018). Based on all-forcing and single-forcing simulation 
ensembles with a  high-resolution model, Delworth and Zeng (2014) 
found that the observed long-term regional austral autumn and winter 
rainfall decline over southern and particularly south-west Australia is 
partially reproduced in response to anthropogenic changes in GHGs 
and ozone in the atmosphere, whereas anthropogenic aerosols do 
not contribute to the simulated precipitation decline. In contrast, the 
observed increase of north-west Australian summer rainfall since 1950 
has been partially attributed to anthropogenic aerosol based on CMIP5 
detection and attribution single-forcing simulations (Section 8.3.2.4.6; 
Dey et al., 2019a, b).
It is noteworthy that these methods require a very significant reduction

of spatial and temporal dimensions in order to reliably estimate the 
co-variance matrix of internal variability (an entire region is thus often 
considered as being only one or a few spatial points that represent 
the spatial average of the whole region or a few sub-regions; time 
samples are often 5- or 10-year averages). Finally, model bias is rarely 
considered in statistical models used in detection and attribution 
regional studies, while it has been shown to have a strong impact 
on the stability of detection results and their associated confidence 
intervals when increasing the spatial dimension (Ribes and Terray, 
2013). New statistical methods are emerging to provide some 
alternative to standard optimal fingerprinting but they have not yet 
been evaluated and applied at regional scales (Section 3.2.2).
10.4.1.2 Other Spatiotemporal Statistical Methods for Isolating 
Regional Climate Responses to External Forcing

The primary objective of any attribution method is to optimally 
separate the influences of external forcing and internal variability 
on a global or regional climate record. In a multi-model ensemble 
context, the estimation of the externally-forced climate response has 
been typically performed by ensemble averaging of linear trends or

1415
Linking Global to Regional Climate Change Chapter 1010regional domain spatial average, thus not taking into account the 
available and complete space and time co-variance information. 
Since AR5, methods using spatiotemporal information have been 
further developed and used to improve the separation between 
external and internal drivers in multiple or single historical climate 
realizations performed by a given global model.
The typical ensemble size of CMIP historical climate simulations for 
a  given model traditionally range between one and ten members, 
with three often being the default choice. At the regional scale, 
a simple ensemble average with such sample sizes does not provide 
robust estimates of the response patterns to external forcing (Maher 
et al., 2019; Deser et al., 2020). Since AR5, pattern filtering methods 
such as signal-to-noise maximizing empirical orthogonal functions 
(Ting et  al., 2009) have been shown to improve the identification

of forced response patterns when few model members are available 
(Wills et al., 2020). Using SMILEs as a test bed, it has been shown that 
pattern filtering strongly reduces the number of ensemble members 
needed to estimate the forced response pattern compared to simple 
ensemble averaging. Pattern filtering allows the identification of low 
signal-to-noise signals such as the El Niño-like response to volcanic 
eruptions (Khodri et al., 2017; Wills et al., 2020).
Methods to extract the response to external forcing in an observed or 
simulated single realization include dynamical adjustment (Smoliak 
et al., 2015; Deser et al., 2016; Sippel et al., 2019) and time scale 
separation methods (DelSole et al., 2011; Wills et al., 2018, 2020). 
Dynamical adjustment seeks to isolate changes in surface air 
temperature or precipitation that are due purely to atmospheric 
circulation changes. The residual can then be analysed and attributed

to internal changes in both land or ocean surface conditions and 
the thermodynamical response to external forcing. Smoliak et  al. 
(2015) performed their dynamical adjustment using partial least 
squares regression of temperature to remove variations arising from 
sea level pressure changes. Deser et  al. (2016) used constructed 
atmospheric circulation analogues and resampling to estimate the 
dynamical contribution to changes in temperature. Sippel et  al. 
(2019) used machine learning techniques known as regularized linear 
regression to provide estimates of circulation-induced components 
of precipitation and temperature variability from global to local 
scales. It is noteworthy that the dynamical adjustment method by 
itself cannot account for the component of the forced response 
associated with circulation changes that project onto atmospheric 
internal variability. However, this component can be estimated within

a model framework by averaging the dynamical contribution across 
multiple members of a SMILE (Deser et al., 2016).
Dynamical adjustment methods have been used by, for instance, 
Deser et al. (2016), Saffioti et al. (2016), O’Reilly et al. (2017), Gong 
et al. (2019), and R. Guo et al. (2019). Deser et al. (2016) focused on 
the causes of observed and simulated multi-decadal trends in North 
American temperature. They demonstrated that the main advantage 
of this technique is to narrow the spread of temperature trends 
found by the model ensemble and to bring the dynamically-adjusted 
observational trend much closer to the forced response estimated 
by the model ensemble mean. Similar results were obtained by 
Saffioti et al. (2016) regarding recent observed winter temperature and precipitation trends over Europe. Similarly, O’Reilly et al. (2017) 
applied dynamical adjustment techniques to more carefully 
determine the influence of the Atlantic Multi-decadal Variability

(AMV; Annex IV.2.7) on continental climates. Over Europe, summer 
temperature anomalies induced thermodynamically by the warm 
phase of the AMV are further reinforced by circulation anomalies; 
meanwhile, precipitation signals are largely controlled by dynamical 
responses to the AMV. Based on a  partial least-squares approach, 
Gong et al. (2019) showed that recent winter temperature 30-year 
trends over northern East Asia are strongly influenced by internal 
variability linked to decadal changes of the Arctic Oscillation. Using 
dynamical adjustment purely on precipitation observations, R. Guo 
et  al. (2019) showed that human influence has led to increased 
winter precipitation across north-eastern North America, as well as 
a small region of north-western North America, and to an increase in 
precipitation across much of north-western and north central Eurasia. 
The latter results confirm previous findings obtained by standard 
optimal fingerprinting methods (Wan et al., 2015).

Time scale separation methods such as the low-frequency 
component analysis and ensemble empirical mode decomposition 
methods take advantage of the longer time scale associated with 
anthropogenic external forcing compared to that of most internal 
modes of variability. The low-frequency component analysis method 
tries to find low-frequency variability patterns by searching for linear 
combinations of a moderate number of empirical orthogonal functions 
that maximize the ratio of low-frequency to total variance. It has first 
been used to separate internal modes of interannual and decadal 
variability from slowly varying and externally-forced variability 
in the Pacific and Atlantic oceans (Wills et  al., 2018, 2019). The 
methodology has also been applied to patterns of observed surface 
air temperature to isolate the slow components of observed changes 
that are consistent with the expected response to anthropogenic 
greenhouse gas and aerosol forcing (Wills et al., 2020).

The ensemble empirical mode decomposition method (Wu and 
Huang, 2009; Wilcox et  al., 2013; Ji et  al., 2014; Qian and Zhou, 
2014) decomposes data, such as time series of historical temperature 
and precipitation, into independent oscillatory modes of decreasing 
frequency. The last step of the method leaves behind a  smooth 
and low-frequency residual time series. Typically, the non-linear 
anthropogenic trend (e.g.,  of 20th-century temperature) can be 
reconstructed by summing the long-term mean, the residual, and 
eventually the lowest-frequency mode to account for a multi-decadal 
forced signal, for instance associated with anthropogenic aerosol 
forcing. The ensemble empirical mode decomposition method is an 
example of a data-driven, non-parametric approach that can be used 
to directly provide an estimate of the forced response without the 
need for model data (Qian, 2016).
10.4.1.3 Other Regional-scale Attribution Approaches

The univariate detection method does not use spatial pattern 
information, but compares observed trends in gridded datasets 
with distributions of trends from ensembles of simulations during 
the historical period (Knutson et al., 2013; Knutson and Zeng, 2018). 
The trends arising from simulations constrained by natural forcing-only

1416
Chapter 10 Linking Global to Regional Climate Change10and all-forcing are compared with distributions of trends purely due 
to internal variability and derived from long simulations with constant 
pre-industrial external forcing. Consistency between observed and 
simulated historical trends is also assessed with statistical tests that 
can be applied independently over a large number of grid points. The 
fraction of area over a given region where the change is classified 
as detectable, attributable, or consistent/inconsistent, is then finally 
estimated. The method can be viewed as a  simple consistency 
test for both amplitude and pattern of observed versus simulated 
trends. Its application to CMIP3 and CMIP5 models suggests that 
80% of the Earth’s surface has a detectable anthropogenic warming 
signal (Knutson et al., 2013). Regarding regional land precipitation 
changes over the 1901–2010 and 1951–2010 periods, application of

the univariate detection method based on CMIP5 models suggests 
attributable anthropogenic changes at several locations such as 
increases over regions of the north-central USA, southern Canada, 
Europe, and southern South America and decreases over parts of the 
Mediterranean region, northern tropical Africa and south-western 
Australia (Delworth and Zeng, 2014; Knutson and Zeng, 2018).
Another regional attribution technique is based on the similarity of 
past changes between observations and one or several simulations 
of a large ensemble that share the same time evolution for a suggested 
driver of these changes. Huang et al. (2020b) used a perturbed physics 
ensemble to attribute the drying trend of the Indian monsoon over 
the latter half of the 20th century to decadal forcing from the Pacific 
Decadal Variability (PDV; Annex IV.2.6). The ensemble members 
predicted different trends in PDV behaviour across the 20th century

and the negative precipitation trend was only replicated in those 
members with a  strong negative-to-positive PDV transition across 
the 1970s, consistent with the observed PDV behaviour (see also the 
detailed case study in Section 10.6.3). In a similar manner, Cvijanovic 
et al. (2017) addressed the possible influence of Arctic sea ice loss on 
the North Pacific pressure ridge and, consequently, on south-western 
USA precipitation. They sampled the uncertainties in selected sea 
ice physics parameters to achieve a ‘low Arctic sea ice’ state in their 
perturbed simulations. They then compared the latter with control 
simulations representative of sea ice conditions at the end of the 
20th century to assess changes purely due to sea ice loss.
New methods aiming to remove underlying model biases before 
performing detection and attribution, for instance related to 
precipitation changes, are emerging based on image transformation

techniques such as warping (Levy et al., 2014a). By correcting location 
and seasonal precipitation biases in CMIP5 models, Levy et al. (2014b) 
showed that the agreement between observed and fingerprint 
patterns can be improved, further enhancing the ability to attribute 
observed precipitation changes to external forcings. The improvement 
mainly relies on the assumption that precipitation changes are tied to 
the underlying climatology, which has been shown to be a reasonable 
assumption in regions of the world where intensification of the 
hydrological cycle is expected (Held and Soden, 2006).
Importantly, evidence that the models employed in regional-scale 
attribution are fit for purpose is essential in order to estimate the 
degree of confidence in the attribution results (Section 10.3.3). For 
example, models need to be evaluated and assessed in their ability to simulate internal variability modes that are known to be important

drivers of regional climate change (Sections 3.7 and 10.3.3.3 
and Annexes IV.2 and IV.3). Models are likely to have different 
performance in different regions and therefore their evaluation 
needs to be performed in terms of key physical processes and 
mechanisms relevant to the climate of the region under consideration 
(Section 10.3.3).
To conclude, there is very high confidence (robust evidence  and high 
agreement ) that the use of diverse and independent attribution 
methods, multiple model ensemble types and observed datasets 
strengthens the robustness of results of regional-scale attribution 
studies. Since AR5, multiple SMILEs have provided an adequate 
testbed for new attribution methodologies aimed at separating 
forced signals from internal variability in observational records as 
well as small-size single-model ensembles.
10.4.2 Regional Climate Change Attribution Examples
This section focuses on three illustrative examples that span

different regions, time scales, and attribution methods, without 
aiming at being comprehensive. These examples illustrate attribution 
statements that are based upon multiple lines of evidence, combining 
multiple observational datasets, different generations and types of 
models, process understanding and assessment of various sources 
of uncertainty. Detection and attribution assessments for all AR6 
regions and specific variables can be found in the Atlas.
10.4.2.1 The Sahel and West African Monsoon 
Drought and Recovery
The Sahel, fed by the West African monsoon, has experienced severe 
decadal rainfall variations (Figure 10.11a). Abundant rainfall in the 
1950s–1960s was followed by a large negative trend (Figure 10.11b) 
until at least the 1980s, over which annual rainfall fell by 20–30% 
(Hulme, 2001). The subsequent partial recovery (B. Wang et al., 2021) 
is more uncertain: rain-gauge studies suggest a return to long-term

positive anomalies in the western Sahel in the early 2000s (Panthou 
et  al., 2018), while CHIRPS merged satellite/gauge data show 
a wetter western Sahel since 1981 (Bichet and Diedhiou, 2018a, b). 
The recovery has been more significant over the central rather than 
the western Sahel (Lebel and Ali, 2009; Maidment et  al., 2015; 
Sanogo et al., 2015) and a multiple-gauge record supports a greater 
recovery to the eastern side (Nicholson et al., 2018). In this attribution 
example, drivers of the long-term drought and subsequent partial 
recovery are discussed, including anthropogenic GHG and aerosol 
emissions, and sea surface temperature (SST) variations that, in part, 
relate to internal variability. The reader is also referred to assessment 
in Section 8.3.2.4. We define the Sahel within 10°N–20°N across to 
30°E, consistent with the eastern boundary used in Chapter 8, and 
the rainy season as spanning June to September.

The role of SST forcing in the rainfall decline is assessed first. Competing 
mechanisms from equatorial Atlantic SSTs and inter-hemispheric 
SST gradients regulate decadal variability in the Sahel (Nicholson, 
2013), alternatively explained by tropical warming leading to

1417
Sahel drought, while North Atlantic warming promotes increased 
rainfall (Rodríguez-Fonseca et al., 2015). The SST influence has been 
formalized in an AMV framework (Giannini et al., 2013; Martin and 
Thorncroft, 2014; Martin et al., 2014; Park et al., 2015), suggesting 
that relative North Atlantic SST warming increases the Northern 
Hemisphere differential warming, enhancing Sahel rainfall. The AMV 
influence is supported by CMIP5 initialized decadal hindcasts (Gaetani 
and Mohino, 2013; Mohino et al., 2016; Sheen et al., 2017), which 
outperform empirical predictions based on persistence. Some caution 
is needed since the full magnitude of internal variability is not captured 
in most CMIP5 models, as poor resolution prevents reproduction 
of AMV teleconnection responses (Vellinga et  al., 2016), and the 
magnitude of AMV-related SST variation may be underestimated

in CMIP5 (Section 3.7.7, which also assesses that the AMV may be 
partially forced). The influence of PDV has been studied to a lesser 
extent, with the PDV positive phase having a  negative impact on 
Sahel rainfall in combined observational/CMIP5 analysis (Villamayor 
and Mohino, 2015). The closer match between the observed rainfall 
declining trend and those in an atmosphere-only SMILE, in which SSTs 
are matched to observations, compared to three coupled SMILEs in which they are not, suggests that the underlying ocean surface might 
be essential in driving the decline (Figure 10.11e).
In terms of anthropogenic emissions, regional aerosol emissions 
from Europe, and to a  lesser extent from Asia, have been shown 
in a  global model to weaken Sahel precipitation either through 
a weakened Saharan heat low or via the Walker circulation (Dong 
et al., 2014). Greenhouse gases (GHGs) and anthropogenic aerosol 
can be considered together to control ITCZ position based on

temperature asymmetry at the hemispheric scale. GHGs increase 
Sahel precipitation, while aerosol reduces it (in coupled slab-ocean 
model experiments by Ackerley et al. (2011) following Biasutti and 
Giannini (2006)). This effect is stronger when models account for 
aerosol–cloud interactions (Allen et al., 2015). Perturbed physics GCM 
ensembles suggests that aerosol emissions were the main driver of 
observed drying over 1950–1980 (Ackerley et al., 2011), supported 
by CMIP5 single-forcing experiments (Polson et al., 2014). A coherent 
drying signal in CMIP5 over the extended 1901–2010 period has 
also been found, although smaller than the observed trend (Knutson 
and Zeng, 2018). By applying aerosol scaling factors to the historical 2 1 0 1 2
mm day11.0 0.5 0.0 0.5 1.0
mm day1
(e) Sahel precipiation trend distribution (Baseline  period is 1955-1984)
Decline period (1955-1984) Recovery period (1985-2014)(b) Precipitation change over Africa

CRU T S (1980-199 0 – 1950-1 960)(c) 1.5x - 0.2x aerosol scaling (1955-1984)
HadGEM3-GC3.1 hist-aer scalings
(d) Sahel precipitation anomalies (CMIP6)
Baseline period is 1955-1984(a) S ahel p recipita tion anomalies (CRU TS)
Baseline period is 1955-1984
1920 1940 1960 1980 2000 20201.51.00.50.00.51.0mm day1CRU TS
Wet Dry
1920 1940 1960 1980 2000 20200.20.10.00.10.20.3mm day1
Decline RecoveryCMIP6 h istorical ( N=49)
CMIP6 historical* (N=13)
CMIP6 hist-aer (N=13)
CMIP6 hist-GHG (N=13)
15 10 5 0 5 10 15 20
Trend (% d ecade1)15 10 5 0 5 10 15 20
Trend (% d ecade1)CMIP6 hist-aerCMIP6 hist-GHGCMIP6 historical*CMIP6 historicalCMIP5 historical-RCP8.5Ensemble meansOBS
d4PDFMPI-ESMCSIRO-Mk3-6-0MIROC6incomp lete a nd 
sea-m asked  data
Figure 10. 11 | Attribution of historic precipitation change in the Sahelian West African monsoon during June to September. (a)  Time series of CRU TS

precipitation anomalies (mm day–1, baseline 1955–1984) in the Sahel box (10°N–20°N, 20°W–30°E) indicated in panel (b) applying the same low-pass filter as that used in 
Figure 10.10. The two periods used for difference diagnostics are shown in grey columns. (b) Precipitation change (mm day–1) in CRU TS data for 1980–1990 minus 1950–1960 
periods. (c) Precipitation difference (mm day–1) between 1.5× and 0.2× historical aerosol emissions scaling factors averaged over 1955–1984 and five ensemble members 
of HadGEM3 experiments after Shonk et al. (2020). (d) Sahel precipitation anomaly time series (mm day–1, baseline 1955–1984) in Coupled Model Intercomparison Project 
Phase 6 (CMIP6) for 49 historical simulations with all forcings (red), and thirteen for each of greenhouse gas-only forcing (light blue) and aerosol-only forcing (grey), with

a thirteen-point weighted running mean applied (a variant on the binomial filter with weights [1-6-19-42-71-96-106-96-71-42-19-6-1]). The CMIP6 subsample of all forcings 
matching the individual forcing simulations is also shown (pink). (e) Precipitation linear trend (% per decade) for (left) decline (1955–1984) and (right) recovery periods 
(1985–2014) for ensemble means and individual CMIP6 historical experiments (including single-forcing) as in panel (d) plus 34 CMIP5 models (dark blue). Box-and-whisker 
plots show the trend distribution of the three coupled and the d4PDF atmosphere-only single-model initial-condition large ensembles (SMILEs) used throughout Chapter 10 and 
follow the methodology used in Figure 10.6. The two black crosses represent observational estimates from GPCC and CRU TS. Trends are estimated using ordinary least-squares 
regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1418
Chapter 10 Linking Global to Regional Climate Change10period in order to sample the uncertainty in CMIP5 aerosol radiative 
forcing, Shonk et al. (2020) found differences of 0.5 mm day–1 for Gulf 
of Guinea rainfall between strong and weak aerosol experiments 
as illustrated in Figure 10.11c, although the drying appears further 
south than observed due to model bias.
For the partial recovery in West African monsoon and Sahel rainfall 
since the late 1980s, a  detection study using three reanalyses 
(Cook and Vizy, 2015) shows a  connection to increasing Saharan 
temperatures at a rate two to four times greater than the tropical 
mean, also confirmed by multiple observational and satellite-based 
data (Zhou and Wang, 2016; Vizy and Cook, 2017) and the review 
of Cook and Vizy (2019). Reanalyses are also noted to significantly 
underestimate the Saharan warming (Zhou and Wang, 2016). 
Saharan warming causes a stronger thermal low and more intense

monsoon flow, providing more moisture to the central and eastern 
Sahel, supported by CMIP5 models (Lavaysse et al., 2016), although 
not all models capture the observed rainfall–heat–low relationship. 
Sahel rainfall is also incorrectly located in prototype versions of 
a  few CMIP6 models, related to tropospheric temperature biases 
(Martin et  al., 2017). Amplified Saharan warming has increased 
the wind shear, leading to a tripling of extreme storms since 1982, 
which may partially explain the recovery (Taylor et al., 2017). Instead, 
observations, multiple models and SST-sensitivity experiments with 
AGCMs have suggested that stronger Mediterranean Sea evaporation 
enhances low-level moisture convergence to the Sahel, increasing 
rainfall (Park et  al., 2016). Meanwhile, an AGCM study suggested 
that GHGs alone (in the absence of SST warming) could cause 
Sahel rainfall recovery, with an additional role for anthropogenic

aerosol (Dong and Sutton, 2015); recent changes in North Atlantic 
SSTs, although substantial, did not exert a  significant impact on 
the recovery. Large spread in the recovery in a five-member AGCM 
ensemble suggests that atmospheric internal variability cannot be 
discounted (Roehrig et al., 2013).
Consistent timing of the southward ITCZ shift during the decline 
period in CMIP3 and CMIP5 historical simulations supports the role 
of external forcing, chiefly anthropogenic aerosol (Hwang et  al., 
2013). The evolution of the observed decline and recovery is largely 
followed by the CMIP5 multi-model mean, further supporting the 
role of external drivers (Giannini and Kaplan, 2019). Updated results 
from CMIP6 for historical simulations with all and single forcings 
are represented in Figure  10.11d,e showing smaller trends than 
those observed. Giannini and Kaplan (2019) attempted to unify the 
driving mechanisms for decline and recovery based on singular-value

decomposition of observed and modelled SSTs. Since the 1950s, 
tropical warming arising from GHGs and North Atlantic cooling 
from aerosol led to regional stabilization, suppressing Sahel rainfall. 
The subsequent reduction in aerosol emissions then led to North 
Atlantic warming and recovery of Sahel rainfall. Such mechanisms 
continue into the near-term future in idealized and modified RCP 
experiments, with scenarios featuring more aggressive reductions in 
aerosol emissions, or including aerosol–cloud interactions, favouring 
a greater northward shift of rainfall (Allen, 2015; Westervelt et al., 
2017, 2018; Scannell et al., 2019). There is paleoclimate evidence of 
changes to Sahel rainfall in the past, in particular with enhancement 
of the West African monsoon during the mid-Holocene. However, the mechanisms governing such a change have been shown to be largely 
dynamical in nature (D’Agostino et  al., 2019), suggesting that the

mid-Holocene cannot be used to inform the credibility of changes 
due to greenhouse warming.
There is very high confidence  (robust evidence  and high agreement ) 
that patterns of 20th-century ocean and land surface temperature 
variability have caused the Sahel drought and subsequent recovery 
by adjusting meridional gradients. There is high confidence  (robust 
evidence  and medium agreement ) that the changing temperature 
gradients that perturb the West African monsoon and Sahel rainfall 
are themselves driven by anthropogenic emissions: warming by 
GHG emissions was initially restricted to the tropics but suppressed 
in the North Atlantic due to nearby emissions of sulphate aerosols, 
leading to a  reduction in rainfall. The North Atlantic subsequently 
warmed following the reduction of aerosol emissions, leading to 
rainfall recovery.
10.4.2.2 The South-Eastern South America Summer Wetting
A positive trend in summer (December to February) precipitation has

been detected in multiple observational sources in south-eastern 
South America since the beginning of the 20th  century (Gonzalez 
et al., 2013; Vera and Díaz, 2015; Wu et al., 2016; H. Zhang et al., 
2016; Díaz and Vera, 2017; Saurral et  al., 2017). Sedimentary 
records from the Mar Chiquita lake indicate that the last quarter 
of the 20th  century was wetter than any period during the last 
200  years (Piovano et  al., 2004). In this attribution example the 
drivers contributing to the positive trend for the period 1951–2014 
are discussed (Figure  10.12a). Precipitation anomalies of Climatic 
Research Unit Time Series (CRU TS) as well as for the two members 
of a SMILE with the most negative and positive trends for 1951–2014 
are displayed in Figure 10.12b. The trend for 1951–2014 using CRU TS 
and GPCC is illustrated in Figure 10.12c, and for the region defined by 
the black quadrilateral, it amounts to 2.8 (CRU TS) – 3.5 (GPCC) mm

per month and decade (see black crosses in Figure  10.12d) while 
the mean summer monthly precipitation for the same period is 
104 (CRU TS) –109 (GPCC) mm. The trend is also detectable in daily 
and monthly extremes (Re and Barros, 2009; Marengo et al., 2010; 
Penalba and Robledo, 2010; Doyle et al., 2012; Donat et al., 2013; 
Lorenz et al., 2016).
The influence of SST anomalies on south-eastern South America 
precipitation have been studied extensively on interannual to 
multi-decadal time scales (Paegle and Mo, 2002). The positive phase 
of El Niño–Southern Oscillation (ENSO; Annex IV.2.3) is related 
to stronger mean and extreme rainfall over south-eastern South 
America (Ropelewski and Halpert, 1987; Grimm and Tedeschi, 2009; 
Robledo et al., 2016). The ENSO influence may be modulated by the 
PDV (Kayano and Andreoli, 2007; Fernandes and Rodrigues, 2018) 
and the AMV (Kayano and Capistrano, 2014). PDV and AMV also 
influence the south-eastern South American climate independently

of ENSO (Barreiro et al., 2014; Grimm and Saboia, 2015; Robledo 
et al., 2020). While Pacific SSTs dominate the overall influence of 
oceanic variability in the region, the Atlantic variability seems 
to dominate on multi-decadal time scales and has been proposed 
as a  driver for the long-term positive trend (Seager et  al., 2010;

1419
(b) So utheaste rn So uth America  precipitatio n anomalies
Baseline period is 1995-2014
1950 1960 1970 1980 1990 2000 201040200204060%CRU TS MPI-ESM Driest MPI-ESM Wettest
(c) Ob served  precipitati on trends over south ern South  America
CRU TS GPCC(a) Mechanisms contributing to the southe astern South America summer wetting (1951-2014)
GHG fo rcing
Cold a noma lies in th e 
tropical Atlant ic
Expan sion o f the 
Hadley cell
Zonally
non-unifo rm wa rming 
of tropical o cean s
Vorticity a dvectio n
 from  tropical Atlantic
Rossby wa ve
pattern anomalies
Descen ding branch 
moves polewa rd
Moistur e 
conve rgence
Ascend ing mo tion
Incre ased  summe r 
rainfa ll
Strato sphe ric
ozon e depletio n
Delay o f stra tosph eric 
polar  vorte x
breakdown
South ward  shift o f
the jet stre am
Intensification  of 
cyclonic act ivity
AMV cold phase
(multi-d ecad al 
variab ility)
6 4 2 0246

mm month1decade1incomp lete a nd 
sea-m asked  data2 0 2 4
Trend (mm  month1decade1)(d) So utheaste rn So uth America  precipitation  trend
     distributio n
Trend period (1951-2014)
Ensemble meansOBS
CMIP6
d4PDFMPI-ESMCSIRO-Mk3-6-0MIROC6
Figure 10.1 2 | South-Eastern South America positive mean precipitation trend and its drivers during 1951–2014. (a)  Mechanisms that have been suggested to 
contribute to South-Eastern South America summer wetting. (b) Time series of austral summer (December to February) precipitation anomalies (%, baseline 1995–2014) over 
the South-Eastern South American region (26.25°S–38.75°S, 56.25°W–66.25°W), black quadrilateral in the first map of panel (c). Black, brown and green lines show low-pass 
filtered time series for CRU TS), and the members with driest and wettest trends of the MPI-ESM single-model initial-condition large ensemble (SMILE; between 1951–2014),

respectively. The filter is the same as the one used in Figure 10.10. (c) Mean austral summer precipitation spatial linear 1951–2014 trends (mm per month and decade) 
from CRU TS and GPCC. Trends are estimated using ordinary least squares regression. (d) Distribution of precipitation 1951–2014 trends over South-Eastern South America 
from GPCC and CRU TS (black crosses), CMIP6 all-forcing historical (red circles) and MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF SMILEs (grey box-and-whisker plots). Grey 
squares refer to ensemble mean trends of their respective SMILE and the red circle refers to the CMIP6 multi-model mean. Box-and-whisker plots follow the methodology used 
in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1420
Chapter 10 Linking Global to Regional Climate Change10Barreiro et al., 2014). Based on experiments designed to test how 
south-eastern South America precipitation is modulated by tropical 
Atlantic SSTs, Seager et  al. (2010) showed that cold anomalies 
in the tropical Atlantic favour wetter conditions by inducing an 
upper-tropospheric flow towards the equator, which, via advection 
of vorticity, leads to ascending motion over south-eastern South 
America (Figure  10.12a). Monerie et  al. (2019) supported this 
argument showing a negative relationship between south-eastern 
South America precipitation and the AMV index (Huang et  al., 
2015) using an AGCM coupled to an ocean mixed-layer model with 
nudged SSTs.
The positive trend of precipitation has also been attributed to 
anthropogenic GHG emissions and stratospheric ozone depletion. 
CMIP5 models only show a  positive trend when including anthropogenic 
forcings (Vera and Díaz, 2015). These results were supported by

Knutson and Zeng (2018) based on univariate detection/attribution 
analysis of annual mean trends for the 1901–2010 and 1951–2010 
periods. However, the main features of summer mean precipitation 
and variability of South America are still not well-represented in all 
CMIP5 and CMIP6 models (Gulizia and Camilloni, 2015; Díaz and Vera, 
2017; Díaz et al., 2021). This motivates the construction of ensembles 
that exclude the worst performing models (Section  10.3.3.4). The 
construction of ensembles of CMIP5 historical simulations with 
realistic representation of precipitation anomalies with opposite sign 
over south-eastern South America and eastern Brazil showed that the 
trend since the 1950s could be related to changes in precipitation 
characteristics only when simulations included anthropogenic 
forcings (Díaz and Vera, 2017). GHG emissions have been related 
to increased precipitation in south-eastern South America through

three different mechanisms (Figure  10.12a). First, GHG warming 
induces a non-zonally uniform pattern of SST warming that includes 
a  warming pattern over the Indian and Pacific oceans that excites 
wave responses over South America (Junquas et  al., 2013). Zonally 
uniform SST patterns of warming alone lead to precipitation signals 
opposite to those observed in an AGCM (Junquas et al., 2013). Second, 
GHG radiative forcing drives an expansion of the Hadley cell so that 
its descending branch moves poleward from the region, generating 
anomalous ascending motion and precipitation (H. Zhang et al., 2016; 
Saurral et al., 2019). The third mechanism by which increased GHG 
can contribute to increased precipitation in the region is through 
a  delay of the stratospheric polar vortex breakdown. As depicted 
in Figure 10.12a, both stratospheric ozone depletion and increased 
GHGs have contributed to the later breakdown of the polar vortex

in recent decades (McLandress et  al., 2010; Wu and Polvani, 2017; 
Ceppi and Shepherd, 2019). Mindlin et  al. (2020) developed future 
atmospheric circulation storylines (Section  10.3.4.2, Box  10.2) for 
Southern Hemisphere mid-latitudes with the CMIP5 models and found 
that for south-eastern South America summer precipitation, increases 
are related to the late-spring breakdown of the stratospheric polar 
vortex. The connecting mechanism is through a  lagged southward 
shift of the jet stream (Saggioro and Shepherd, 2019), which enhances 
cyclonic activity over the region (Wu and Polvani, 2017).
A common feature among the above discussed studies is that even 
if global models simulate positive trends when forced with GHG 
and/or stratospheric ozone, these trends are in general smaller than those observed (e.g.,  CMIP6 trends in red open circles in 
Figure  10.12d). Díaz  et  al. (2021) showed that to capture the 
observed trend a multi-model ensemble of SMILEs is needed. Out of

the 12 large ensembles examined (with ensemble size varying in the 
16–100  range), only seven simulated the observed trend within 
their range. This could partly be explained by model biases in mean 
precipitation and its interannual variability. In the sub-ensemble of six 
models that reproduce reasonably well the observed spatial patterns 
of mean precipitation and interannual variability, the ensemble mean 
spread is lower, and the forced response, taken as the multi-model 
ensemble mean, is slightly more positive than that of the six poorly 
performing models. The signal-to-noise ratio, estimated as the ratio 
of the forced response to the spread due to internal variability, is 
also slightly higher for the best-performing models, suggesting that 
selecting the best-performing models may have an influence on 
both attribution of the observed trend and emergence of the forced 
response in future (Section 10.4.3).
There is high confidence  that South-Eastern South America summer

precipitation has increased since the beginning of the 20th century. 
Since AR5, science has advanced in the identification of the drivers of 
the precipitation increase in South-Eastern South America since 1950, 
including GHG through various mechanisms, stratospheric ozone 
depletion and Pacific and Atlantic variability. There is high confidence  
that anthropogenic forcing has contributed to the South-Eastern 
South America summer precipitation increase since 1950, but very 
low confidence  on the relative contribution of each driver to the 
precipitation increase.
10.4.2.3 The South-western North America Drought
Persistent hydroclimatic drought in south-western North America 
remains a much-studied event. Drought is a regular feature of the 
south-western North America’s climate regime, as can be seen in 
both the modern record, and through paleoclimate reconstructions 
(Cook et al., 2010; Woodhouse et al., 2010; Williams et al., 2020), as

well as in future climate model projections (Cook et al., 2015a). Since 
the early 1980s, which were relatively wet in terms of precipitation 
and streamflow, the region has experienced major multi-year 
droughts such as the turn-of-the-century drought that lasted 
from 1999 to 2005, and the most recent and extreme 2012–2014 
drought that in certain locations is perhaps unprecedented in the 
last millennium (Section  8.3.1.6; Griffin and Anchukaitis, 2014; 
Robeson, 2015). Shorter dry spells also happened between these 
multi-year droughts making 1980 to present a  period with an 
exceptionally steep trend from wet to dry (Figure 10.13a), leading 
to strong declines in Rio Grande and Colorado river flows (Lehner 
et al., 2017b; Udall and Overpeck, 2017). While robust attribution 
of this trend is complicated by the large natural variability in this 
region, the 20th century warming has been suggested to increase 
the chances for hydrological drought periods by lowering runoff

efficiency (Woodhouse et al., 2016; Lehner et al., 2017b; Woodhouse 
and Pederson, 2018) and affecting evapotranspiration (Williams 
et al., 2020). There is some evidence suggesting that the Last Glacial 
Maximum, a period of low atmospheric CO 2, about 21 ka ago, has 
a  thermodynamically-driven zonal mean precipitation response 
similar to that of the current state with relatively high CO 2 levels

1421
Linking Global to Regional Climate Change Chapter 1010when compared with the pre-industrial period. Pluvial conditions 
at that time and a reduction in precipitation from the Last Glacial 
Maximum to the pre-industrial period are consistent with drying 
trends for the region in models with GHG concentrations exceeding 
pre-industrial levels. However, the dominant large-scale drivers 
responsible for the precipitation changes observed during these 
two transitions are markedly different: mainly ice-sheet retreat and 
increasing insolation on one hand, increasing GHGs on the other 
hand. This suggests that the Last Glacial Maximum correspondence 
is fortuitous which strongly limits its use to capture future 
hydrological cycle changes (Section 8.3.2.4.4; Morrill et al., 2018; 
Lowry and Morrill, 2019). Furthermore, the conclusion of the Last 
Glacial Maximum drying versus wetting seems to strongly depend 
on the physical property of interest, hydrologic or vegetation

indicators (Scheff et al., 2017). Droughts are characterized by deficits 
in total soil moisture content that can be caused by a combination 
of decreasing precipitation and warming temperature, which 
promotes greater evapotranspiration. Regional-scale attribution 
of the prevalence of south-western North America drought since 
1980 then mostly focuses on the attribution of change in these 
two variables.
The observed south-western North America drying fits the narrative 
of what might happen in response to increasing GHG concentrations 
due to a  poleward expansion of the subtropics, that is conducive 
to drying trends over subtropical to mid-latitude regions (Hu et al., 
2013b; Birner et  al., 2014; Lucas et  al., 2014). However, several 
studies based on modern reanalyses and CMIP5 models have 
recently shown that the current contribution of GHGs to Northern 
Hemisphere tropical expansion is much smaller than in the Southern

Hemisphere and will remain difficult to detect due to large internal 
variability, even by the end of the 21st  century (Section  3.3.3.1; 
Garfinkel et al., 2015; Allen and Kovilakam, 2017; Grise et al., 2018, 
2019). In addition, the widening of the Northern Hemisphere tropical 
belt exhibits strong seasonality and zonal asymmetry, particularly 
in autumn and the North Atlantic (Amaya et al., 2018; Grise et al., 
2018). Therefore, it seems that the recent Northern Hemisphere 
tropical expansion results from the interplay of internal and forced 
modes of tropical width variations and that the forced response 
has not robustly emerged from internal variability (Sections 3.3.3.1 
and 10.4.3).
A second possible causal factor is the role for ocean-forced or 
internal atmospheric circulation change. Analysis of observed 
and CMIP5-simulated precipitation indicates that the drought 
prevalence since 1980 is linked to natural, internal variability in the

climate system (Knutson and Zeng, 2018). Based on observations 
and ensembles of SST-driven atmospheric simulations, Seager 
and Hoerling (2014) suggested that robust tropical Pacific and 
tropical North Atlantic forcing drove an important fraction of 
annual mean precipitation and soil moisture changes and that 
early 21st  century multi-year droughts could be attributed to 
natural decadal swings in tropical Pacific and North Atlantic SSTs. 
A cold state of the tropical Pacific would lead by well-established 
atmospheric teleconnections to anomalous high pressure across 
the North Pacific and southern North America, favouring a weaker 
jet stream and a  diversion of the Pacific storm track away from the south-west (Delworth et al., 2015; Seager and Ting, 2017). The 
multi-year drought of 2012–2016 has been linked to the multi-year 
persistence of anomalously high atmospheric pressure over the 
north-eastern Pacific Ocean, which deflected the Pacific storm track

northward and suppressed regional precipitation during California’s 
rainy season (Swain et al., 2017). Going into more detail, Prein et al. 
(2016a) used an assessment of changing occurrence of weather 
regimes to judge that changes in the frequency of certain regimes 
during 1979–2014 have led to a decline in precipitation by about 
25%, chiefly related to the prevalence of anticyclonic circulation 
patterns in  the north-east Pacific. Finally, the moderate model 
performance in representing Pacific SST decadal variability and 
its remote influence (Section  3.7.6) as well as its change under 
warming may affect attribution results of observed and future 
precipitation changes (Seager et al., 2019).
It has also been suggested that the ocean-controlled influence 
is limited and internal atmospheric variability has to be invoked 
to fully explain the observed history of drought on decadal time 
scales (Seager and Hoerling, 2014; Seager and Ting, 2017). From

roughly 1980 to the present, the regional climate signals show an 
interesting mix between forced and internal variability. Lehner et al. 
(2018) used a dynamical adjustment method and large ensembles 
of coupled and SST-forced atmospheric experiments to suggest 
that the observed south-western North America rainfall decline 
mainly results from the effects of atmospheric internal variability, 
which is in part driven by a PDV-related phase shift in Pacific SST 
around 2000 (Figure 10.13b,c). Based upon four SMILEs (three using 
a  GCM and another one an AGCM constrained by observed SSTs) 
and a  CMIP6 multi-model suite constrained by observed external 
forcings, Figure 10.13 shows, in agreement with Lehner et al. (2018), 
that observed SSTs with their associated atmospheric response are 
the main drivers of the south-western North America precipitation 
decrease during the 1983–2014 period. Once aspects of the internal 
variability are removed by dynamical adjustment, the observed

precipitation change signal and simulated anthropogenically-forced 
components look more similar (Lehner et al., 2018).
Importantly, as the AR6 assessment views the PDV as being mostly 
driven by internal variability (Section 3.7.6), the lines of evidence cited 
above suggest that the contribution of natural and anthropogenic 
forcings to the precipitation decline has a  small amplitude. Unlike 
the precipitation deficit, the accompanying south-western North 
America warming is driven primarily by anthropogenic forcing from 
GHGs rather than atmospheric circulation variability and may help to 
enhance the drought through increased evapotranspiration (Knutson 
et  al., 2013; Diffenbaugh et  al., 2015; Williams et  al., 2015, 2020; 
Lehner et al., 2018, 2020).
To conclude, there is high confidence  (robust evidence  and medium 
agreement ) that most (>50%) of the anomalous atmospheric 
circulation that caused the south-western North America negative

precipitation trend can be attributed to teleconnections arising from 
tropical Pacific SST variations related to PDV. There is high confidence  
(robust evidence  and medium agreement ) that anthropogenic 
forcing has made a  substantial contribution (about 50%) to the 
south-western North America warming since 1980.

1422
10.4.2.4 Assessment Summary
The robustness of regional-scale attribution differs strongly between 
temperature and precipitation changes. While the influence of 
anthropogenic forcing on regional temperature long-term change 
has been detected and attributed in almost all land regions, a robust 
detection and attribution of human influence on regional precipitation change has not yet fully occurred for many land regions 
(Section 10.4.3). Although the contribution of anthropogenic forcing 
to long-term regional precipitation change has been detected in 
some regions, a robust quantification of the contributions of different 
drivers remains elusive. The delayed emergence of the anthropogenic 
precipitation fingerprint with respect to temperature is likely due 
to the opposing sign of the fast and slow land precipitation forced 20 15 10 5 0 5 10 15 20

% decade1(a) Annua l mea n precipita tion tr end over  North  America ( 1983-2014)
CRU TS GPCC GPCP REGEN
Wettest DriestObse rvations
Histo rical simul ations
MPI-GE ( 100 members )
with all (n atural an d 
anthropogenic) 
exter nal fo rcing
d4PDF (100 m embers)
with all e xtern al for cing 
and obse rved sea 
surfa ce te mper atureMean
(c) Annua l mea n precipita tion tr end distribu tion o ver so uthwester n 
     re gion
Baseline period is 1983-2014
20 15 10 5 0 5 10 15 20
Trend (% d ecade1)CMIP6 historicalEnsemble meansOBS(b) Annua l mea n precipita tion a noma lies in s outhwe stern  region
Baseline period is 1971-2000
1950 1960 1970 1980 1990 2000 2010402002040%
d4PDF Driest
d4PDF WettestCRU TS
Trend periodincomp lete a nd 
Figure 10.13  | Attribution of the south-western North America precipitation decline during the 1983–2014 period. (a)  Water year (October to September)

precipitation spatial linear trend (in percent per decade) over North America from 1983 to 2014. Trends are estimated using ordinary least squares. Top row: observed 
trends from CRU TS, REGEN, GPCC, and the Global Precipitation Climatology Project (GPCP). Middle row: driest, mean and wettest trends (relative to the region enclosed in 
the black quadrilateral, bottom row) from the 100 members of the MPI-ESM coupled SMILE. Bottom row: driest, mean and wettest trends relative to the above region from 
the 100 members of the d4PDF atmosphere-only SMILE. (b) Time series of water year precipitation anomalies (%, baseline 1971–2000) over the above south-western North 
America region for CRU TS (grey bar charts). Black, brown and green lines show low-pass filtered time series for CRU TS, driest and wettest members of the d4PDF SMILE,

respectively. The filter is the same as the one used in Figure 10.10. (c) Distribution of south-western region-averaged water-year precipitation 1983–2014 trends (in percent 
per decade) for observations (CRU TS, REGEN, GPCC and GPCP , black crosses), CMIP6 all-forcing historical simulations (red circles), the MIROC6, CSIRO-Mk3-6-0, MPI-ESM and 
d4PDF SMILEs (grey box-and-whisker plots). Grey squares refer to ensemble mean trends of their respective SMILE and the red circle refers to the CMIP6 multi-model mean. 
Box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1423
Linking Global to Regional Climate Change Chapter 1010responses and time-dependent SST change patterns (Sections 8.2.1 
and Section 10.4.3), stronger internal variability (Section 10.3.4.3) as 
well as larger observational uncertainty (Section 10.2) and impact of 
model biases. The contribution of internal variability to the observed 
changes can also be very sensitive to the period length and level 
of spatial aggregation for the region under scrutiny (Section 4.4.1 
and Cross-Chapter Box 3.1; Kumar et al., 2016). Finally, even in the 
case of temperature changes at multi-decadal time scale, internal 
variability can still be a substantial driver of regional changes due to 
cancellation between different external forcings (Nath et al., 2018).
To conclude, it is virtually certain  (robust evidence  and high agreement ) 
that anthropogenic forcing has been a major driver of temperature 
change since 1950 in many sub-continental regions of the world.

There is high confidence  (robust evidence  and medium agreement ) 
that anthropogenic forcing has contributed to multi-decadal mean 
precipitation changes in several regions, for example western Africa, 
south-east South America, south-western Australia, northern central 
Eurasia, and South and East Asia. However, at regional scale, the role 
of internal variability is stronger while uncertainties in observations, 
models and external forcing are all larger than at the global scale, 
precluding a  robust assessment of the magnitude of the relative 
contributions of greenhouse gases, including stratospheric ozone, 
and different aerosol species.
10.4.3 Future Regional Changes: Robustness and 
Emergence of the Anthropogenic Signal
Regional climate projections are one key element of the multiple lines 
of evidence that are used for climate risk assessments as well as for 
adaptation and policy decisions at regional scales (Sections 10.3.3.9

and 10.5). Regional climate projections can be separated into 
two components: the regional-scale forced response or regional-scale 
climate sensitivity when normalized by the global mean temperature 
change (Seneviratne and Hauser, 2020) and the climate internal 
variability characterizing the future period or global warming level 
under scrutiny. This section assesses a few methodological aspects 
related to robustness and emergence properties of the regional-
scale forced response as well as the possible influence of internal 
variability on the emergence of the anthropogenic signal.
10.4.3.1 Robustness of the Anthropogenic 
Signal at Regional Scale
Standard methodologies to derive the regional forced response include 
pattern-scaling and the time-shift or epoch approach (Section 4.2.4; 
Tebaldi and Arblaster, 2014; Vautard et al., 2014; Herger et al., 2015; 
Tebaldi and Knutti, 2018; Christensen et  al., 2019). Pattern-scaling

assumes that the spatial patterns of regional change, often based on 
a time-averaged 20- or 30-year period at the end of the 21st century, 
are roughly constant in time, and simply scale linearly with global 
mean warming. The time-shift approach defines a target in terms of 
global warming level (GWL) and locates the time segment, usually 
20 or 30 years, in historical or scenario simulations in which global 
mean warming matches the required GWL (Section  10.1.2 and 
Cross-Chapter Box  11.1). Physical consistency between multiple variables and space-time co-variance are fully preserved in the 
time-shift approach, which is not the case for pattern-scaling (Herger 
et  al., 2015). Importantly, pattern scaling cannot account for the 
non-linearity arising from either interacting quasi-linear processes 
(Chadwick and Good, 2013) and purely non-linear mechanisms, 
which have been shown to be present in CMIP5 models for high

GWL (4°C) and affect precipitation more than temperature at the 
regional-scale (Section 8.5.3.1; Good et al., 2015, 2016). The time-shift 
approach can also be used to test whether regional climate change 
patterns depend on the rate of global mean warming and external 
forcing pathways, in addition to global warming magnitude. A global 
evaluation of both approaches in projecting the forced temperature 
and precipitation response for a highly mitigated scenario based on 
a moderately mitigated one has been performed using a perfect-model 
framework (Tebaldi and Knutti, 2018). The amplitude of errors for 
both approaches appears to be substantially smaller than model 
uncertainty approximated by the CMIP5 multi-model spread.
Based on large and coordinated modelling exercises such as CMIP5 
and CORDEX, the time-shift approach has been largely used to 
assess differences in regional climate impacts for different GWLs, 
with a  strong focus on 1.5°C versus 2°C (Karmalkar and Bradley,

2017; Dosio and Fischer, 2018; Karnauskas et al., 2018; W. Liu et al., 
2018; Taylor et  al., 2018; Weber et  al., 2018; Chapter  3, SR1.5, 
Hoegh-Guldberg et al., 2018). Comparisons between pattern-scaling 
and time-shift approaches allow assessment of the scalability 
of the regional climate change signal and the extent to which 
pattern-scaling assumptions still hold at regional scale for a  wide 
range of GWL. This was the approach followed by Matte et al. (2019) 
in their assessment of the scalability of European regional climate 
projections. Based on EURO-CORDEX projections, they performed 
a  detailed comparison between the pattern scaling and the GWL 
spatial patterns (GWL range: 1°C, 2°C and 3°C) for different seasons, 
regional model resolutions, and both temperature and precipitation. 
High pattern correlation values (greater than 0.9) are found between 
the scaled pattern and all GWL patterns for temperature. In the case

of precipitation, the correspondence is slightly lower, especially in 
summer, for high GWLs (2°C and 3°C) and much lower for 1°C.
Figure  10.14 illustrates a  similar comparison based on the CMIP6 
multi-model ensemble forced with the scenario SSP5-8.5 and 
applied to two large-scale continental areas. The forced response 
to anthropogenic forcing is simply taken as the CMIP6 multi-model 
mean of future regional climate change relative to the 1850–1900 
reference period. Robustness of the forced response is based on 
both significance of the change and model agreement about the 
sign (direction) of change (Cross-Chapter Box Atlas.1; Figure 10.14). 
Caution has to be exercised against a  too literal interpretation of 
lack of robust change given that significance and sign agreement 
can be sensitive to spatial and temporal aggregation (Cross-Chapter 
Box Atlas.1, Figure  2) and lack of a  robust change does not 
necessarily translate to lack of regional-scale climate change impacts

(McSweeney and Jones, 2013; Hibino and Takayabu, 2016).
If projected regional mean temperature (Figure  10.14a) and 
precipitation (Figure 10.14b) changes were to scale linearly with global 
mean warming, the adjusted spatial patterns would be congruent

1424
with each other at different GWLs. While pattern scaling seems to 
be a reasonable first-order approximation for both temperature and 
precipitation changes in tropical and high latitude regions (high pattern 
correlation values), there are a number of regions exhibiting substantial 
amplitude differences at different GWLs (northern Africa and Middle 
East, southern and eastern Europe for temperature; south-western 
North America, Chile and north-eastern Brazil for precipitation). These 
differences hint at the possible influence of non-linear mechanisms 
(Good et  al., 2015), including soil-moisture feedbacks (Seneviratne 
et al., 2010; Vogel et al., 2017), a time-dependent balance between the 
different contributions of fast and slow response to greenhouse gas 
forcing as well as changing SST response patterns (Long et al., 2014;

Good et al., 2016; Ceppi et al., 2018; Zappa et al., 2020). Decreasing spatial pattern amplitude with increasing GWL suggests that the 
initial transient regional response overshoots the long-term change 
in regions such as northern Africa for summer temperature and south-
western South America for precipitation (Zappa et al., 2020). In the 
latter region, long simulations with stabilized GHG concentrations 
even suggest a  change of sign when near-equilibrium is reached 
(Sniderman et  al., 2019). The reverse behaviour, increasing pattern 
amplitude with increasing GWL, is seen for summer temperature in 
southern and eastern Europe and for precipitation in south-western 
North America (Sniderman et al., 2019; Zappa et al., 2020), suggesting 
that, in these regions, the initial transient response is lagging global 
mean warming and final regional climate change will be reached once 
GHG concentrations are stabilized.

Spatial patte rns of change at increasing global warming levels since pre-indust rial period (1850 1900)
(All patte rns are CMIP6 multi-model  mean changes  and have been scaled to a 1C global warming level)
(a) Summer  land tempe rature 
(b) Annua l mean l and precipitation
Robust ness and scalability of anthropogen ic signals at regional scale
Correlation, RMSD45 45 45 45 31 45Number of models
45 45 45 45 31 45
Figure 10.14  | Robustness and scalability of anthropogenic signals at regional scale. (a) Spatial patterns of European and African summer (June to August) surface 
air temperature change (in °C °C–1) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean (45 models, one member per model, historical 
simulations and scenario SSP5-8.5) at different global warming levels (GWLs) and the end-21st century scaling pattern estimated from the multi-model mean difference

between 2081–2100 and the pre-industrial period (1850–1900) divided by the corresponding global mean warming. The scale of all GWL patterns has been adjusted to 
a global mean warming of 1°C (for example, the resulting 3°C spatial pattern has been divided by three). The scales of the GWL patterns have to be multiplied by their threshold 
values to obtain the actual simulated warming. The metrics shown in the bottom left corner of the GWL pattern plots indicate the spatial pattern correlation and the root-mean-
square difference between the GWL patterns and the scaling pattern. The number in bold just above the metrics gives the number of used CMIP6 models (out of 45) that have 
reached the GWL threshold. Areas with robust change (at least 66% of the models have a signal-to-noise ratio greater than one and 80% or more of the models agree on

the sign of the change) are coloured with no pattern overlaid (Cross-Chapter Box Atlas.1). Areas with a significant change (at least 66% of the models have a signal-to-noise 
ratio greater than one) and lack of model agreement (meaning that less than 80% of the models agree on the sign of the change) are marked by cross-hatching. Areas with 
no change or no robust change (less than 66% of the models have a signal-to-noise ratio greater than one) are marked by negatively sloped hatching. (b) Same as (a) but for 
North, Central and South America annual mean precipitation relative change (percent °C–1). The baseline for precipitation climatology is 1850–1900. Further details on data 

1425
Linking Global to Regional Climate Change Chapter 1010There is high confidence  that the time-evolving contribution of 
different mechanisms operating at different time scales can modify 
the amplitude of the regional-scale response of temperature, and 
both the amplitude and sign of the regional-scale response of 
precipitation, to anthropogenic forcing. These mechanisms include 
non-linear temperature, precipitation and soil-moisture feedbacks, 
and slow and fast response of SST patterns and atmospheric 
circulation changes to increasing GHGs.
10.4.3.2 Emergence of the Anthropogenic 
This section provides an assessment of the different approaches 
used in emergence studies as well as sensitivities to methodological 
choices. The section then focuses on the possible influence of 
internal variability on future emergence of the simulated mean 
precipitation anthropogenic signal at regional scales with some 
illustrative examples.

In climate science, emergence or distinguishability of a signal refers to 
the appearance of a persistent change in the probability distribution 
and/or temporal properties of a  climate variable compared with 
that of a  reference period (Section  1.4.2; Giorgi and Bi, 2009; 
Mahlstein et al., 2011, 2012; Hawkins and Sutton, 2012). Similar to 
anthropogenic climate change detection (Cross-Working Group Box: 
Attribution in Chapter 1), signal emergence can be detected, at least 
initially, without identifying the physical causes of the emergence 
(Section  1.4.2). In the context of human influence on climate, the 
objective of emergence studies is the search for the appearance of 
a signal characterizing an anthropogenically-forced change relatively 
to the climate variability of a reference period, defined as the noise.
Precise definitions of signal and noise as well as a  metric to 
measure the relative importance of the signal are key ingredients

of the emergence framework and depend on the framing question. 
In particular, emergence study results can depend on the specific 
definitions of signal and noise such as the level of spatial and 
temporal aggregation (McSweeney and Jones, 2013). For instance, 
grid-point scale emergence will likely be delayed compared with 
region-average emergence (Section  11.2.4 and Cross-Chapter 
Box Atlas.1, Figure 2; Fischer et al., 2013; Maraun, 2013b; Lehner et al., 
2017a). The signal is often estimated by a running mean multi-decadal 
average or probability distribution function of the physical variable 
under scrutiny in order to avoid false emergence due to manifestation 
of multi-decadal internal variability (King et al., 2015). In the case of 
extremes such as climate records, a notion of multi-year persistence 
or recurrence can also be used to fully characterize the anthropogenic 
signal and its emergence (Christiansen, 2013; Bador et al., 2016).

Emergence is also sensitive to the noise characteristics: assuming 
a  common signal definition, larger signal-to-noise values and 
earlier emergence will arise if the noise is based on decadal mean 
variability rather than interannual variability (Kusunoki et al., 2020). 
Depending on the framing question, the noise can include or omit 
external natural forcing such as volcanic and solar forcing (Zhang 
and Delworth, 2018; Silvy et  al., 2020). Furthermore, emergence 
results are very sensitive to the choice and length of the reference period (Section 1.4.1). The reference period can be the pre-industrial, 
the very recent past or even a  time-evolving baseline, depending 
on both the framing and assumption that adaptation to the current 
climate has already occurred (King et al., 2015; Zhang and Delworth, 
2018; Brouillet and Joussaume, 2020). These choices will then 
determine the type of simulations and periods that will be used to

construct the noise distribution. Finally, the permanence of future 
emergence cannot be taken for granted when emergence occurs in 
the late-21st century based on simulations ending in 2100 (Hawkins 
et al., 2014; King et al., 2015; Lehner et al., 2017a).
Robust assessments and comparisons of past emergence between 
observations and models are strengthened by the use of consistent 
definitions of signal and noise (Lehner et al., 2017a; Hawkins et al., 
2020). In the case of future emergence under increasing greenhouse 
gas emissions, two main approaches have been followed to assess 
emergence. The first is based on estimating the signal and noise (and 
sometimes the signal-to-noise ratio as well) in individual models 
before using the resulting distribution median or mean to construct 
the final emergence metric (Hawkins and Sutton, 2012; Maraun, 
2013b; Sui et  al., 2014; Barrow and Sauchyn, 2019). The second 
method first estimates the signal as a  multi-model mean change

and the noise variance as a combination of internal variability and 
model structural differences (Giorgi and Bi, 2009; Mariotti et  al., 
2015; Nguyen et al., 2018). The first approach allows the definition 
of emergence of the signal relative to internal variability only and 
treats model error as source of uncertainty (Maraun, 2013b; Lehner 
et al., 2017a). The second assumes that the multi-model mean is the 
optimal estimate of the signal and confounds internal variability and 
model structural differences in the noise estimate. It is noteworthy 
that most emergence studies implicitly assume model independence 
(Annan and Hargreaves, 2017; Boé, 2018; Box  4.1) and therefore 
sensitivity of emergence results to model selection or weighting is 
rarely performed (Akhter et al., 2018).
Metrics can vary from a  simple signal-to-noise ratio to statistical 
distributional tests (King et al., 2015; Gaetani et al., 2020) and give

median estimates and uncertainty bounds for the date (or time of 
emergence) corresponding to the exceedance of specific thresholds by 
the emergence metric. Reconciling future emergence results among 
different studies is challenging due to their many methodological 
differences including the choice of the reference period, the selected 
climate models and scenario, the precise definition of signal and noise 
and the choice of different signal-to-noise thresholds to characterize 
robust emergence. Contrasting with binary yes/no statements, 
emergence can also be viewed as a continuous process characterized 
by an amplitude or level, for example the value of the signal-to-noise 
ratio, that is a function of time or global warming level.
Since AR5, the development and production of SMILEs (Sections 4.2.5 
and 10.3.4.3) has allowed the assessment of the influence of internal 
variability on anthropogenic signal emergence. The influence of

internal variability, and specifically of the unforced atmospheric 
circulation, on temperature signal emergence can delay or advance 
the time of emergence by a decade or two in mid- to high-latitude 
regions (Lehner et al., 2017a; Koenigk et al., 2020). Internal variability 
can also result in small or decreasing decadal to multi-decadal

1426
Chapter 10 Linking Global to Regional Climate Change10heatwave frequency trends under the historical anthropogenic forcing 
over most regions, thereby delaying emergence of unprecedented 
heatwave frequency trends relative to the pre-industrial trend 
distribution (Sections 11.2–11.3; Perkins-Kirkpatrick et al., 2017).
Regional precipitation future changes are much more impacted by 
internal variability than their temperature counterpart (Monerie 
et al., 2017b; Dai and Bloecker, 2019; Singh and AchutaRao, 2019; 
von Trentini et  al., 2019; Koenigk et  al., 2020). Relative to mean 
temperature changes, this larger influence of internal variability 
on mean precipitation changes contributes, among other factors (Sarojini et al., 2016), to a much delayed emergence of the forced 
precipitation response in observations (Hawkins et al., 2020). Based on 
the CMIP6 multi-model ensemble forced with the scenario SSP5-8.5, 
we assess the future emergence of mean precipitation forced change

as a  function of GWLs for all AR6 land regions (Figure  10.15a). 
The methodology is a  straightforward adaptation of the standard 
approach (Hawkins and Sutton, 2012). While the standard method 
is only based on the signal-to-noise ratio exceedance of a specified 
threshold (taken as one), the approach used here assumes that 
grid-point emergence occurs when the forced change is considered 
robust following the AR6 WGI definition of robustness for projected 
Percentage area of AR6 land regions with robust annual  mean precipitation change
(a) Func tion of Global Warming Level s 
Future emergence of anthropogeni c signal at regional scale
2°C
(b) Func tion of time 
AR6 Multi-model Single-model initial condition large ensemblesWetter
Drier1.5°C 1°C
End of 21st century 4°C 3°C
Figure 10. 15 | Future emergence of anthropogenic signal at regional scale. (a)  Percentage area of land regions with robust annual mean precipitation change as

a function of increasing global warming levels (GWLs). Robustness of the precipitation change is first estimated at each grid-point followed by the estimation of the AR6 region area 
with robust changes. For each Coupled Model Intercomparison Project Phase 6 (CMIP6) model considered (45 models, one member per model, historical simulations and scenario 
SSP5-8.5), the annual mean precipitation change is based on the difference between a 20-year average centred on the GWL crossing year and the mean precipitation during the 
pre-industrial period (1850–1900) taken as a reference. The change is considered to be robust when at least 66% of the models (30 out of 45) have a signal-to-noise ratio greater 
than one and at least 80% of them (36 out of 45) agree on the sign of change. The signal-to-noise ratio is estimated for each model from the ratio between the change and the

standard deviation of non-overlapping 20-year means of the corresponding pre-industrial simulation (scaled by square root of 2 times 1.645). (b) Time evolution of the percentage 
area of land region with robust annual mean precipitation change for five AR6 land regions. Thick solid lines represent precipitation changes based on the same CMIP6 ensemble 
as in (a). Thin solid, dotted and dashed lines represent changes based on the three coupled single-model initial-condition large ensembles (SMILEs) used in Chapter 10, illustrating 
the influence of internal variability on the emergence of robust change. The change is estimated from the difference between all consecutive 20-year periods from 1900–1919 up 
to 2081–2100 and the pre-industrial period. The line colour indicates the sign of the robust change given by the multi-model mean (CMIP6) or ensemble mean (SMILE) change:

brown (decreasing precipitation) and dark green (increasing precipitation). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1427
Linking Global to Regional Climate Change Chapter 1010changes (Cross-Chapter Box Atlas.1). At a GWL of 1°C, emergence 
only occurs in high-latitude regions (Wan et al., 2015; R. Guo et al., 
2019), albeit with only small (less than 30%) area fraction with 
robust change. Robust changes in tropical and subtropical regions 
only appear from GWLs of 1.5°C, for example in south-western South 
America (Boisier et al., 2016), western Africa (Hawkins et al., 2020; 
Section 10.4.2.1) and southern Australia (Delworth and Zeng, 2014). 
Substantial (taken here simply as area fraction greater than 50%) 
emergence only occurs in some tropical, subtropical and mid-latitude 
regions when high GWLs (3°C–4°C) are reached. Importantly, even 
at these high GWL values, there are still a  large number of these 
regions with robust changes covering less than 50% of their area. 
In contrast, most high-latitude regions have an area fraction with 
robust changes greater than 80% at GWLs of 3°C and above.

We now illustrate the potential influence of internal variability on 
late or lack of emergence for a few AR6 land regions (Figure 10.15b). 
For each of these AR6 regions, the time evolution of the percentage 
area with robust annual mean precipitation change is estimated 
for both the CMIP6 multi-model ensemble and the three coupled 
SMILEs used throughout Chapter 10. Similarity in percentage area 
time evolution between CMIP6 and the three coupled SMILEs 
suggests that internal variability can substantially influence the 
timing of emergence. For example, internal variability could explain 
the mid-21st  century emergence (percentage area greater than 
50%) of the drying and wetting signal over the Mediterranean and 
South Asia (see also Section  10.6.3) regions, respectively. Internal 
variability can also contribute to the late and moderate emergence 
over South-Eastern South America (see also Section 10.4.2) and West

South Africa (see also Section 10.6.2). In contrast, it cannot explain 
the lack of robust changes (percentage area less than 30%) over 
Western Africa at the end of the 21st century, suggesting that model 
differences are also contributing to the lack of emergence (Monerie 
et al., 2017a, b). In addition to different forced signals, the differences 
of time evolution between the three SMILEs, in particular for African 
regions, point to the issue of global model performance in accurately 
representing internal variability and its future changes. While 
overestimation and underestimation of internal variability in current 
models have been reported (Eade et al., 2014; Laepple and Huybers, 
2014), methodological challenges to assess the magnitude and 
spatial pattern of model biases in simulating internal variability, still 
remain (Section 10.3.4.3). Therefore, the existence of model biases 
and the limited knowledge of their characteristics lead to limitations

about a  precise quantification of internal variability influence on 
delayed regional-scale emergence.
There is high confidence that consistency in definitions of signal and 
noise, choice of the reference period and signal-to-noise threshold, is 
important to robustly assess the future emergence of anthropogenic 
signals across different types or generations of models, as well 
as comparing past emergence results between observations and 
models. There is high confidence  that internal variability can delay the 
emergence of the regional-scale mean precipitation anthropogenic 
signal in many regions, mainly located in the tropics, subtropics and 
mid-latitudes. An accurate estimation of the delay in regional-scale 
emergence caused by internal variability remains challenging due to 
global model biases in their representation of internal variability as well as methodological difficulties to precisely estimate these biases 

10.5 Combining Approaches to Constructing 
This section assesses approaches and challenges for producing 
climate information for climate risk assessments as well as for 
adaptation and policy decisions at regional scales (Section 10.1.2.1). 
An overview of the different sources used for developing regional 
climate information is given in Section 10.5.1. The role of the user 
context in the construction of climate information is assessed in 
Section 10.5.2. The distillation to combine multiple lines of evidence 
is assessed in Section 10.5.3. Finally, climate services in the context 
of regional climate information are assessed in Section 10.5.4. The 
role of storylines in constructing climate information is assessed in 
Box  10.2. The assessment of how regional climate information is 
distilled in the report is treated in Cross-Chapter Box 10.3, whereas 
the assessment of information on regional, physical climate processes

that impact society or ecosystems, termed climatic impact-drivers 
(Section 10.1), appears in Chapter 12, as well as more information 
on climate services in Cross-Chapter Box 12.2.
The rise in demand for relevant regional climate information 
(Hewitt et  al., 2012, 2020; Lourenço et  al., 2016) has resulted in 
diverse approaches to produce it. Historically, the construction of 
climate information has been embedded in a  linear supply chain: 
extracting the source data, processing into maps or derived data 
products, preparing the material for communication, and delivering 
to users (Section  10.1.4). Typical products are open-access, 
web-portal delivery services of data (Hewitson et al., 2017), which 
may also be implemented as commercialized climate services 
(Webber and  Donner, 2017). Such a  chain, although it is intended 
to meet a demand for regional climate information, contains many 
assumptions that are not obvious to the recipients and that may

introduce possible misunderstandings in the handover from one 
community to the next (Meinke et al., 2006; Lemos et al., 2012). In 
recognition that data is not necessarily relevant information, a new 
pathway towards a  tailored distillation of climate information has 
emerged. The construction of information assessed in this section 
draws on multiple sources (Figure  10.16), whereby the context 
framing for an application is addressed through co-design with 
users. The constructed information is then translated into the context 
of the user taking into account the values of all actors involved 
(Sections 10.5.2 and 10.5.3, and Figure 10.1).
10.5.1 Sources of Regional Climate Information
Regional climate information may be constructed from a  diverse 
range of sources, each depending on different assumptions and 
affected by different methodological limitations (Sections 10.2, 10.3 
and 10.4). The construction of information may lead to products for

direct adoption by users, or intermediate products for further analysis 
by users and climate services agencies in collaboration with climate 
scientists. Widely used sources include:

1428
• Extrapolation of observed historical trends into the future 
(e.g., Livezey et al., 2007; Laaha et al., 2016). Given that internal 
variability can affect regional trends significantly on decadal to 
multi-decadal time scales (Section  10.4), this approach could 
be potentially misleading without other supporting evidence 
(Westra et al., 2010), or finding congruence with other changes 
(e.g., Langodan et al., 2020).
• The output from global models (Section 10.3.1), including high-
resolution GCMs and ESMs, for which performance has been 
assessed and documented (Section  10.3.3). Model data can be 
used in its raw form or may be bias adjusted (Section 10.3.1 and 
Cross-Chapter Box 10.2) or weighted (Section 10.3.4 and Box 4.1).
• The output from dynamically (Section  10.3.1.2) or statistically 
(Section  10.3.1.3) downscaled global model simulations for 
which performance has been assessed and documented as

trustworthy (Section 10.3.3). Model data can be used in its raw 
form or may be bias adjusted, in the case of regional climate 
models (RCMs, Section 10.3.1).• Process understanding about climate and the drivers of regional 
climate variability and change, grounded in theory about 
dynamics, thermodynamics and other physics of the climate 
system as a  basis for process-based evaluation. For instance, 
teleconnections are useful to understand the links between 
large and regional scales at both near and long-term depending 
on the application. (Sections 10.1.3, 10.3.3, 10.4.1, 10.4.3 and 
Annex IV).
• Idealized scenarios of possible future climates as narratives to 
explore the implications and consequences of such scenarios in 
the presence of uncertainty (Jack et al., 2021). This approach has 
been used to explore the response to geoengineering (Cao et al., 
2016a), as well as alternative scenarios where model projections

are highly uncertain (Brown et al., 2016; Jack et al., 2021).
• Information directly from research reported in the peer-reviewed 
scientific literature (e.g., Sanderson et al., 2017) or related research 
reports such as communications to the UN Framework Convention 
on Climate Change (UNFCCC) about national adaptation.Daily pre cipitatio n change (2071-2100) RCP8.5
GCM1 -  RCM1
20°N
15°N
10°N
5°N
JFMAM JJASOND(b)
40°N
30°N
20°N
10°N
0°
10°S
20°W 10°W 0° 10°E 20°E 30°EMulti-mo del me an, April-Sep temb er (a) GCM1 -  RCM2
20°N
15°N
10°N
5°N
JFMAM JJASOND(c)
GCM2 -  RCM1
20°N
15°N
10°N
5°N
JFMAM JJASOND(d) GCM2 -  RCM2
20°N
15°N
10°N
5°N
JFMAM JJASOND(e)
-3 -2 -1.5 -1 -0.5 -0.25 0.25 0.5 1 1.5 2 3
mm
Figure  1 0.16 | Illustration of how using different sources can result in different and potentially conflicting information.  Change in daily precipitation

(2071–2100 RCP8.5 relative to 1981–2010) over Western Africa as simulated by an ensemble of regional climate models (RCMs) driven by global climate models (GCMs). 
(a) Change in daily precipitation (mm) for April to September, as mean of 17 CORDEX models (Dosio et al., 2020) (b–e)  Time-latitude diagram of daily precipitation change for 
four selected RCM-GCM combinations. For each month and latitude, model results are zonally averaged between 10°W–10°E (blue box in a). Different GCM–RCM combinations 
can produce substantially different and contrasting results, when the same RCM is used to downscale different GCMs (b, d), or the same GCM is downscaled by different 
RCMs (d, e). GCM1=IPSL-IPSL-CM5A, GCM2=ICHEC-EC-EARTH, RCM1=RCA4, RCM2=REMO2009. Adapted from Dosio et al. (2020), CCBY4.0  https://creativecommons.org/
licenses/by/4.0/ .  Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1429
Linking Global to Regional Climate Change Chapter 1010• Engaging with climate scientists and local communities who may 
provide indigenous information (Rosenzweig and Neofotis, 2013; 
Makondo and Thomas, 2018).
• Relevant information may also be drawn from paleoclimate 
studies (e.g., McGregor, 2018; Armstrong et al., 2020; Kiem et al., 
2020) to support and contextualize other sources about more 
recent and projected changes.
Different sources of information may be more appropriate for 
some purposes than others, as they may provide information better 
aligned to the spatial and temporal scales of interest, in different 
formats, and tailored to different types of application. In some cases, 
a  purpose may be best served using several types of information 
together. For example, when model data is the primary source, it can 
be advantageous to employ data from multiple models or even from 
a range of different experiment types (Section 10.3.2) supported by

assessing how the models reflect changes in driving processes. In this 
manner a purpose may be best served by seeking the congruence of 
several types of information together, though one needs to recognize 
how well the attributes of each source align with the specific need 
for information. Depending on resources, one may even design model 
experiments specifically for a given use, such as constructing physical 
climate storylines of individual events (Section 10.3.2 and Box 10.2). 
Such analyses may be complemented by event attribution studies 
(Section 11.1.4).
Users of climate information may face the so-called practitioner’s 
dilemma: a  plethora of different and potentially contrasting 
sources (Figure  10.16) may be available without a  comprehensive 
and user-relevant evaluation, and these datasets may also lack 
a transparent and easily understandable explanation of underlying 
assumptions, strengths and limitations (Barsugli et  al., 2013;

Hewitson et  al., 2017). Often, the choice of information source is 
therefore not determined by what is most relevant and informative 
for the question at hand, but rather by practical constraints such as 
accessibility and ease of use and may be limited to the availability of 
just one source in extreme cases (Rössler et al., 2019a).
10.5.2 Framing Elements for Constructing 
User-Relevant Information
10.5.2.1 Consideration of Different Contexts
Without considering the specific context, the distillation of climate 
information relevant to users may poorly serve the goal of informing 
adaptation and policy (Cash et al., 2003; Lemos et al., 2012; Baztan 
et al., 2017). Section 10.1.4 identifies three implicit framing issues 
of constructing and delivering user-relevant climate information: 
practical issues arising from the climate information sources, issues 
with including the context in constructing the information, and

difficulties presented by complex networks of practitioners. The social 
context strongly influences decisions about constructing information 
and requires a  nuanced and holistic approach to recognize the 
complexity of a  coupled social and physical system (Daron et  al., 
2014). For example, urban water managers must recognize the 
dependency of the city on different water resources and the interplay of both local and national government legislation that can involve 
a range of different constituencies and decision makers (Scott et al., 
2018; Savelli et al., 2021).
Context plays a role in determining the risks that may affect human 
systems and ecosystems and consequently the climate information 
needs. The context may also limit access to such information. Hence, 
the context imposes inherent constraints on how climate information 
can be constructed and optimally aligned with its intended 
application. Although contexts are unlimited in variety, some key

contextual elements include:
• Whether the problem formulation needs to be constructed 
through consultative activities that, for instance, help identify 
thresholds of vulnerability in complex urban or rural systems 
(Baztan et al., 2017; Willyard et al., 2018) or is more a matter of 
addressing a generic vulnerability already identified, such as the 
frequency of flood events or recurrence intervals of multi-year 
droughts (Hallegatte et al., 2013).
• Societal capacity, such as cultural or institutional flexibility 
and willingness to respond to different scientific information 
(e.g., Hart and Nisbet, 2012; Kahan, 2012, 2013).
• The technical capability and expertise of the different actors, 
including users, producers, and communicators (e.g.,  Sarewitz, 
2004; Gorddard et al., 2016).
• Potential contrasts in value systems such as the different views of 
the Global North compared to those of economies in transition or

under development (Henrich et al., 2010a, b; Sapiains et al., 2021).
• The relative importance of climate change in relation to 
non-climate stressors on the temporal and spatial scales of 
interest to the user, which at times are not the ones initially 
assumed by the producers (Otto et al., 2015).
• Availability, timing and accessibility of the required climate 
information, including the availability of sources such as 
observations, model simulations, literature and experts of the 
relevant regional climate (Mulwa et  al., 2017). In developing 
countries, the availability of all or some of these sources may be 
limited (Dinku et al., 2014).
These and other contextual elements can frame subsequent 
decisions about the construction of regional climate information for 
applications. For example, an engineer typically seeks quantitative 
information, while the policy community may be more responsive to 
storylines and how information is positioned within a causal network

describing regional climate risk (Section 1.4.4 and Box 10.2). Multiple 
contexts can coexist and potentially result in competing approaches 
(for example, when urban governance contends with regional 
water-resource management in the same area).
10.5.2.2 Developing Climate Information Conditioned 
by Values of Different Actors and Communities
Developing climate information relevant to user needs can be 
influenced by the explicit and implicit values of all parties: those 
constructing the information, those communicating the information, 
those receiving the information, and, critically, those who construct 
the problem statement being addressed. A  discussion of how

1430
Chapter 10 Linking Global to Regional Climate Change10values in the scientific community shape climate research appears 
in Section 1.2.3.2. The influence of values need not be a source of 
bias or distortion; it is sometimes appropriate and beneficial: critical 
scrutiny from a diverse range of value-governing perspectives may 
uncover and challenge biases and omissions in the information that 
might otherwise go unrecognized (Longino, 2004). Dialogue among 
all parties in a culturally, socially, and economically heterogeneous 
society is therefore important for recognizing and reconciling value 
differences to best yield information that is salient, relevant and 
avoids ambiguity, most notably when informing the complexity of 
risks and resilience for human systems and ecosystems in developing 
nations (e.g., Baztan et al., 2017).
Thus, a  challenge with constructing climate information for users, 
especially about impactful change, is that producing the information

may need to involve people with a  variety of backgrounds, who 
have different sets of experiences, capabilities, and values. The 
information thus would need to accommodate and be relevant to 
a range of different ways of viewing the problem (Sarewitz, 2004; 
Rosenzweig and Neofotis, 2013; Gorddard et  al., 2016). Failure to 
recognize the variety of people using the climate information can 
make it ineffective, even if the source data on which it is based is of 
the highest quality, and may create a danger of maladaptation.A substantial body of evidence shows that the receptivity of 
individuals to climate information is strongly conditioned by 
motivated reasoning (Hart and Nisbet, 2012; Kahan, 2012, 2013), 
wherein a  person’s reception of climate information is influenced 
by the values of the community with which the person identifies. 
Adherence to a  community’s values forms part of an individual’s 
social identity (Hart and Nisbet, 2012). Individuals thus frame their

analysis and understanding of climate information in the context 
of cultural values espoused by their community (Hart and Nisbet, 
2012; Kahan, 2012, 2013; Campbell and Kay, 2014; Bessette et al., 
2017; Tschakert et al., 2017; Vezér et al., 2018). Successful framing of 
climate information products thus seeks to identify common ground 
with users, taking account of their values and interests.
Given the relevance of both context and values, the effectiveness of 
climate information can increase if developed in partnership with the 
target communities (Figure  10.17; Tschakert et  al., 2016). Such an 
approach can inspire trust among all parties and at the same time 
promote a  co-production process (Cash et  al., 2003). Recipients 
of information have the greatest trust when the communicator is 
perceived as understanding their context and sharing their values 
and identity (Corner et  al., 2014). As a  consequence, developing

mental models informed by user values can help with understanding 
complex climate models and their outcomes (Bessette et al., 2017).
The importance of a  co-production process does not preclude the 
climate-research community from taking steps to develop and convey 
relevant information on its own. Indeed, communicating expert 
consensus about contested scientific issues is beneficial (Goldberg 
et  al., 2019). Climate services (Section  10.5.4), in particular, can 
become an effective means for using sources from the climate 
community and crafting these to be consistent with the needs, 
interests and values of stakeholder communities. However, simply 
presenting more information without recognizing user values and 
the contextual elements listed in Section 10.5.2.1 may be ineffective 
(Kahan, 2013). An aversion to climate information discordant with 
one’s pre-existing beliefs can actually become stronger for people

who are more scientifically literate: they feel more confident sifting 
through all sources of information to find support for their positions 
(Kahan, 2012). A  challenge is that if climate information is not 
framed carefully, recognizing context and user values, it may make 
the sceptical person less receptive to further information about 
climate change (Corner et al., 2012; Hart and Nisbet, 2012; Shalev, 
2015). A  further complication is that audiences may view climate 
change as a problem distant in time and space (Spence et al., 2012), 
too threatening to acknowledge (Brügger et  al., 2015; McDonald 
et  al.,  2015), or too economically challenging to accept (Bessette 
et al., 2017). Identifying positive outcomes that align with user values, 
instead of adaptation and mitigation efforts, appears to promote the 
interest in and the success of climate information (Bain et al., 2012).
10.5.2.3 The Roles of Spatial and Temporal 
Resolution in Relation to Decision Scale

Climate processes occur on a range of spatial and temporal scales, from 
global to local, from centuries and longer to days or less (Section 10.1.2 
and Figure 10.3). Similarly, decisions by stakeholders cover a range of 
Regio nal clima te 
information
● Met. services
● Climate se rvices
● Oper ation al 
    private sector
● ...
Providing  Con text a nd
Sharin g Climate -Releva nt
Informatio nCo-pr oducing  
Region al Climate
Informatio n
              
● Gover nmen ts / IGOs
● Civic orga nization s / NGO s
● Conce rned indu stries
● ...
● Resea rch 
    inst itutes
● Universities
● Resea rch
    co nsulta ncies
● ...Scien tistsProduc ersUsersFigure  1 0.17 | Effective regional climate information requires shared 
development of actionable information that engages all parties involved 
and the values that guide their engagement.  Participants in the development of 
climate information come from varying perspectives, based in part on their professions

and  communities. Each of the three broad categories shown in the Venn diagram 
(Users, Producers, Scientists) is not a homogenous group, and often has a diversity 
of perspectives, values and interests among its members. The subheadings in each 
category are illustrative and not all-inclusive. The arrows connecting those categories 
represent the distillation process of providing context and sharing climate relevant 
information. The arrows that point toward the centre represent the distillation of 
climate information that involves all three categories.

1431
Linking Global to Regional Climate Change Chapter 1010spatial and temporal scales that can vary with the size of their region 
of interest and scope of activity. However, the link between decision 
scales and the spatial and temporal resolution of climate and related 
non-climatic, natural-system information is not straightforward, and 
failure to recognize mismatches between the two can undermine the 
effectiveness and relevance of the information (Cumming et al., 2006; 
Sayles, 2018).
Nevertheless, the scale of regional climate information does not 
have to be the same as the decision scale. Physical-climate storylines 
(Box 10.2) valid at large scales can be used to develop understanding 
that is relevant to local decisions. For example, global climate change 
affecting Antarctic ice-mass loss is relevant to formulating Dutch 
responses to sea level rise (Haasnoot et  al., 2020). On the other 
hand, extreme precipitation processes can occur on scales of tens

of kilometres and smaller and thus require high resolution climate 
information when projecting future changes (e.g., Xie et al., 2015). 
An important factor for developing effective climate information 
using the distillation process is aligning the vulnerabilities of the 
social and economic systems under consideration ranging from, for 
example, those important to a farmer to those important to a national 
agricultural ministry (Andreassen et al., 2018; O’Higgins et al., 2019). 
Thus, more sophisticated matching of spatial and temporal resolution 
of climate information with decision scales requires engagement 
across a hierarchy of governance structures at national, regional and 
local level (e.g., Lagabrielle et al., 2018).
10.5.3 Distillation of Climate Information
The preceding sections laid out the diversity of sources of climate 
information (Section  10.5.1) and important elements for its use 
in a  decision context (Section  10.5.2). Here, it is assessed how

context-relevant climate information can be distilled from these 
sources of information. Although the term distillation lacks a clear 
definition in the literature, it has, in principle, two aspects: the 
construction of (potentially user-targeted) information that is 
defensible and evidence-based (Giorgi, 2020), and the translation 
of this information into a  specific context, targeting a  specific 
purpose and set of values. The former typically involves data from 
multiple sources, including expert knowledge, and comprehensively 
considers relevant uncertainties to give physically plausible climate 
information. The latter translates the information explicitly into the 
user context, such as by linking it to experience, by formulating 
a  narrative, by highlighting the relevance for the user context, or 
by putting the climate information into the context of the relevant 
non-climatic stressors.
Distilling climate information for a  specific purpose benefits from

a co-production process that includes non-climate-scientists in the 
research design, analysis and the exploration and interpretation of 
the  results to best place it in context of the intended application 
(Collins and Ison, 2009; Berkhout et  al., 2013; Wildschut, 2017; 
Bhave et al., 2018; Dessai et al., 2018). Consideration of the specific 
contexts of information requirements by the provider as well as 
including the user values in connecting the science with users is 
increasingly recognized as paramount to construct information relevant for decisions at the regional scale (Section  10.5.2; Kruk 
et  al., 2017; Vizy and Cook, 2017; Djenontin and Meadow, 2018; 
Parker and Lusk, 2019; Norström et al., 2020; Turnhout et al., 2020). 
As a response, regional climate change information is increasingly 
being developed through participatory and context-specific dialogues 
that bring together producers and users across disciplines and define

climate impacts as one of the many stressors shaping user decisions 
(Brown and Wilby, 2012; Lemos et  al., 2012). Although there are 
multiple practical issues involving communication (Rössler et  al., 
2019a), such as providing data in a format that users can interpret, 
being mindful of the contextual issues raised in Section  10.5.2 
allows non-scientists to be involved in decisions about approaches 
and assumptions for the distillation and thus to take ownership of 
the resultant information and to make informed decisions based on 
the distilled information (Pettenger, 2016; Verrax, 2017). Importantly, 
the application of transdisciplinary engagement processes that 
emphasize the role of non-scientists in the learning and knowledge 
production process builds relationships and trust between information 
users and producers, which is arguably as important for the uptake 
of climate science into decision-making as the nature of the climate 
information itself (Section 10.5.2).

10.5.3.1 Information Construction
Data, either from observations or models, is in general not inherently 
information, but may contain relevant information if interpreted 
appropriately (Hewitson et  al., 2017). The same applies to other 
sources of climate information. Relevance is controlled by the given 
user context (Section 10.5.2.1) and relates to the required temporal 
and spatial scales (Section 10.5.2.3), the characteristics of required 
variables (often referred to as indicators), and the meteorological 
and climatic phenomena driving these variables (Section 10.1.3). For 
example, if climate information for driving impact models is sought 
(e.g., McSweeney et al., 2015), the impact modelling analysis in the 
target region is the specific user context.
Climate risk assessment considers all plausible outcomes (Weaver 
et al., 2017; Marchau et al., 2019; Sutton, 2019). Thus, a key element 
of information construction is the exploration and reconciliation of

different sources of information (Barsugli et al., 2013; Hewitson et 
al., 2014b; Maraun and Widmann, 2018b) and involves mainly two 
issues: first, assessing the fitness of different sources in the given 
context and thereby potentially omitting (or down-weighting) 
selected sources (Sections 10.3.3), and, second, integrating different 
sources into a broader picture within a context (Sections 10.3.4). 
A non-comprehensive selection of approaches that may contribute 
to the construction of information includes:
• Overall assessment and intercomparison of different sources 
of information, including hierarchies of models and identification 
of potentially conflicting results (Figure  10.16), where 
observational availability plays a critical role (Section 10.2.3).
• Assessing the emergence of forced trends from internal 
variability (Section  10.4.3), and testing whether differences in 
simulations can be explained by internal variability, ideally using

initial-condition large ensembles (Sections 10.3.4.3 and 10.4.3).

1432
Chapter 10 Linking Global to Regional Climate Change10• Assessing the interdependence of chosen models to identify the 
amount of independent information (Section 10.3.4.4).
• Process-based evaluation with focus on those processes that are 
relevant for the specific application (Sections 10.3.3.4–10.3.3.10).
• Weighting or sub-selecting ensembles based on a  priori 
knowledge or the outcome of a process-based evaluation, while 
sampling as much uncertainty as possible (Section 10.3.4.4).
• Tracing back differences in projections to the representation of 
fundamental processes, for example, by using physical climate 
storylines (Sections 10.3.4.2 and Box  10.2) or sensitivity 
simulations (Section 10.3.2.3).
• Producing physical-climate storylines (Box  10.2) to explore 
uncertainties not sampled by available model ensembles (Shepherd 
et al., 2018), for example in pseudo-global warming experiments 
(Section 10.3.2.2), or to simulate events that have never happened

before but are nevertheless plausible (Lin and Emanuel, 2016).
• Attributing observed changes to different external forcings and 
internal drivers (Section 10.4.1).
• Comparing observed trends with past simulated trends in 
order to constrain projections with, for instance, the Allen–
Stott–Kettleborough method (Allen et  al., 2000; Stott and 
Kettleborough, 2002; Stott et al., 2013) to explain drivers of past 
observed trends (Section 10.4.2) for understanding future trends.
• Integrating present-day performance via emergent constraints to 
reduce projection uncertainty (Section 10.3.2).
• Complementing the observational and model-based sources 
with expert judgement (e.g., integrating knowledge from theory 
or experience that is available from experts or the literature; 
Section 10.5.1).
These approaches often can be used in combination to increase 
confidence in conclusions drawn (Hewitson et al., 2017).
10.5.3.2 Translating Climate Information Into the User Context

Awareness and understanding of the users’ decision-making context 
is a central and key aspect of developing tailored, context-appropriate 
information (Briley et al., 2015), as clearly evidenced by the climate 
services’ experiences (e.g.,  Vincent et  al., 2018). Understanding 
the context, however, is not trivial and requires understanding of 
both the user and provider (Guido et  al., 2020) if the information 
is to be robust, reliable and relevant (Giorgi, 2020). Translating the 
information into context requires consideration of terminology and 
expectations (Briley et al., 2015), issues of user interpretation (Daron 
et al., 2015), and hence necessitating engagement in co-production 
with all attendant challenges (Vincent et  al., 2021). The actual 
provision of climate information may be conducted at different levels 
of sophistication, ranging from generic data provision via web portals 
(Hewitson et al., 2017), potentially including impact-relevant climate

indicators, region-specific factsheets and stakeholder reports, social 
media (Pearce et  al., 2019), to a  close engagement with specific 
stakeholders in co-exploring the research (Steynor et al., 2016).
Climate information products may often lack explanations of their 
potential use and misuse (Street, 2016; Lamb, 2017; Chimani et  al., 
2020). This is particularly important if the information is provided 
as a  generic, publicly accessible product without a  specific context (Hewitson et  al., 2017). Context-specific collaboration, especially if 
organized in workshop, enables a close transdisciplinary co-exploration 
of the results as in the form of climate risk narratives (Jack et al., 2020, 
Box  10.2). Such approaches explicitly account for the user context, 
values and non-climatic stressors (Steynor and Pasquini, 2019).
10.5.3.3 Transdisciplinary Approaches 
to Stakeholder Interaction
The transdisciplinary interaction with stakeholders has been

categorized into top-down, bottom-up and interactive approaches 
(Berkhout et al., 2013). Traditional top-down approaches frame the 
research from the perspective of global climate change as a driver 
of regional climate risk. Bottom-up approaches, also referred to as 
scenario-neutral impact studies (Prudhomme et al., 2010; A. Brown 
et al., 2012; C. Brown et al., 2012; Culley et al., 2016) begin with 
the user’s articulation of vulnerability in the context of climatic 
and non-climatic stressors, follow with the definition of key system 
thresholds of climatic variables, and only incorporate climate data 
to assess the likelihood of threshold exceedances. Bottom-up 
approaches are special cases of robust decision-making (Lempert 
et al., 2006; Lempert and Collins, 2007; Walker et al., 2013; Weaver 
et  al., 2013), which are designed to account for uncertainties not 
represented by climate models as well as non-climatic stressors.

Interactive approaches combine aspects of top-down and bottom-up 
approaches. The choice of approach depends on the context. While 
bottom-up approaches might be optimal in a local context, where 
case-specific risks are addressed, top-down approaches provide 
generic information that may serve a  range of different purposes, 
for example, at the national scale (Berkhout et al., 2013). All these 
approaches benefit from the integration of fully distilled climate 
information (Berkhout et al., 2013; Maraun and Widmann, 2018b).
10.5.3.4 Barriers to the Distillation of Climate Information
As implied by Section  10.5.2, meeting the needs of users can be 
a substantial challenge for climate scientists if they misunderstand 
or have limited understanding of user needs and context (Porter 
and Dessai, 2017). Several barriers in user communities can trigger 
and sustain this challenge. This can include an institutional aversion to

incorporating new tools into decision-making (Callahan et al., 1999). 
Coincident with this factor, there may be limited staff capacity, lack 
of management support and lack of a mandate to plan for climate 
change (Lee and Whitely Binder, 2010).
Following from those challenges, constructing and communicating 
regional climate information often occurs under the overarching 
assumption that uncertainty is a  problem and reducing uncertainty 
is the priority (Eisenack et al., 2014; J. Otto et al., 2016). This is both 
a psychological (Morton et al., 2011) as well as a pragmatic barrier in 
cases where uncertainty appears to limit the ability to make decisions 
(Mukheibir and Ziervogel, 2007). However, where in-depth engagements 
with decision contexts are undertaken, these initial barriers are often 
dismantled to reveal a more complex, nuanced and potentially more 
productive intersection with climate information producers that can

efficiently handle uncertainty (e.g., Rice et al., 2009; Lemos et al., 2012; 
Moss, 2016). Specifically, disclosure of all uncertainties in the climate

1433
Box 10.2 | Storylines for Constructing and Communicating Regional Climate Information
Communicating the full extent of available information on future climate for a region, including an uncertainty quantification, can 
act as a barrier to the uptake and use of such information (Lemos et al., 2012; Daron et al., 2018). To address the need to simplify 
and increase the relevance of information for specific contexts, recent studies have adopted storyline and narrative approaches 
(Section 1.4.4.2; Hazeleger et al., 2015; Shepherd et al., 2018). As such, these approaches are an important tool for the climate 
information distillation (Section  10.5.3). Here we assess these in a  regional climate information context, namely for exploring 
uncertainties, embedding climate information into a given user context, and communicating climate change information.

Physical climate storylines are self-consistent and plausible unfolding of a physical trajectory of the climate system, or a weather or 
climate event, on time scales from hours to multiple decades (Section 1.4.4.2). Storylines that condition climatic features and processes 
on a set of plausible but distinct large-scale climatic changes enables the exploration of uncertainties in regional climate projections 
(Box 10.2, Figure 1 and Section 10.3.4.2). For instance, Zappa and Shepherd (2017) condition projected changes in European surface 
wind speeds on different plausible projections of tropical upper tropospheric warming and the polar vortex strength in the CMIP5 
multi-model ensemble. Storylines of specific events are generated to explore the unfolding and impacts of comparable events in 
counterfactual climates (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Hegdahl et al., 2020; Sillmann et al., 2021). Those

event storylines can be based on pseudo-global warming studies (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; see 
Section 10.3.2.2), selected and possibly downscaled events from long-term climate projections (Hegdahl et al., 2020; Huang et al., 2020a),information, transparency about the sources of these uncertainties, 
and tailoring the uncertainty information to specific decision 
frameworks have the potential for reducing problems of distilling and 
communicating uncertain climate information (J. Otto et al., 2016).
10.5.3.5 Synthesis Assessment of Climate 
Information Distillation
There is high confidence  that distilling climate information for 
a  specific purpose benefits from a  co-production process that 
involves users of the information, considers the specific user context 
and the values of relevant actors such as users and scientists, and 
translates the resultant information into the broader user context.

This process allows users to take ownership of the information, builds 
relationships and trust between information users and producers 
and helps to overcome barriers in the information construction. This 
process enhances trust in the information as well its usefulness, 
relevance, and uptake, especially when the communication involves 
complex, contextual details ( high confidence ). The optimal approach 
for the transdisciplinary collaboration with users depends on the 
specific context conditioned by the sources available and the actors 
involved, which together are dependent on the regions considered 
and the framing by the question being addressed.
Drawing upon multiple lines of evidence in the construction of 
climate information increases the fitness of this information and 
creates a  stronger foundation ( high confidence ). The lines of 
evidence can include multiple observational datasets, ensembles 
of different model types, process understanding, expert judgement,

and indigenous knowledge, among others. Attribution studies, the 
analysis methods are important elements of distillation. To make the 
most appropriate decisions and responses to changing climate it is necessary to consider all physically plausible outcomes from multiple 
lines of evidence, especially in the case when they are contrasting 
such as in the examples of Cross-Chapter Box 10.1 and Section 10.6.2.
10.5.4 Climate Services and the Construction 
of Regional Climate Information
Climate services have been defined as the provision of climate 
information to assist decision-making (Sections 1.2.3, and 12.6, 
and Cross-Chapter Box 12.2). Services are expected to be based on 
scientifically credible information and expertise, have appropriate

engagement from users and providers, have an effective access 
mechanism and aim at meeting the users’ needs (Hewitt et  al., 
2020). To achieve this, climate services synthesize context-relevant 
climate information addressing questions for a wide range of climate 
time scales. From this point of view, climate services are instruments 
for the production, translation and transfer of climate information 
and knowledge for their use in climate-informed decision-making 
and climate-smart policy and planning (Hewitt et  al., 2012). The 
appropriate provision of climate services considers the diagnosis of 
climate information needs, the service itself and a number of good 
practices still under development (Vaughan et al., 2018).
The preceding subsections assess research on the distillation of 
climate information, which is directly relevant for the development 
of climate services. Distillation, when implemented appropriately and

interpreted with all due caveats, leads to credible climate information 
with a broader foundation of evidence to be used in climate services 
practice according to the recommendations of the Global Framework 
for Climate Services (Hewitt et al., 2012). As stated in Chapter 12, 
climate services set new scientific challenges to research. Examples 
of some of the challenges have been given in Chapters 1 and 12, 
which are complemented by the barriers to the distillation assessed 
in Section 10.5.3.3.

1434
Chapter 10 Linking Global to Regional Climate Change10Box 10.2 (continued)
or based on expert judgment of plausible changes to observed events (Pisaric et al., 2011; Dessai et al., 2018). They can be used for 
attributing events to different causal factors (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Trenberth et al., 2015; 
Shepherd, 2016a; Section 11.2.4) as well as for exploring the unfolding of events in future climates.
Physical climate storylines are complementary to probabilistic or unconditional risk-based approaches, and are particularly suitable to 
explore low-likelihood changes or events, which are often associated with the highest impacts (Shepherd et al., 2018; Sillmann et al., 
2020; Section 4.8). They also facilitate providing local context to large-scale trends and changes, by conditioning the projections on 
locally relevant circumstances (Hazeleger et al., 2015). Storylines are also developed based on expert elicitation and include plausible

changes beyond those simulated by existing model projections in order to explore deep uncertainties (Dessai et al., 2018).
Storylines can be combined with impact modelling (Strasser et al., 2019; Hegdahl et al., 2020) and can be embedded in a user’s 
risk landscape (Shepherd, 2019; Box 10.2, Figure 1). In particular, this holds for event storylines, where confounding factors such as 
regional characteristics like land-use changes and non-climatic drivers of the event are an element of the storyline (Pisaric et al., 2011; 
Dessai et al., 2018; Lloyd and Shepherd, 2020; Sillmann et al., 2021). In a co-production process, multidisciplinary expert knowledge 
as well as the values and interests of the intended audiences and stakeholders can be explicitly considered (Kok et al., 2014; Bhave 
et al., 2018; Dessai et al., 2018; Scott et al., 2018; Hegdahl et al., 2020).

Storylines can also be used to communicate climate information by narrative elements describing the main climatological features and 
the relevant consequences in the user context (Fløttum and Gjerstad, 2017; Moezzi et al., 2017; Dessai et al., 2018; Scott et al., 2018; 
Jack et al., 2020). Co-produced narratives have been demonstrated to enhance knowledge integration in decision-making contexts 
(e.g., de Bruijn et al., 2016). Narrative elements have also been employed to convey information from climate models (Corballis, 2019). 
Jack et al. (2020) introduced the concept of climate risk narratives and developed a set of principles, such as using present tense in 
their presentation to avoid the effects of future discounting and writing individual narratives without uncertainty language to assume 
an imagined observer perspective. From this point of view, event storylines are particularly useful for communication purposes as they

link to the experience and episodic memory of stakeholders (Schacter et al., 2007; Steynor et al., 2016; Shepherd et al., 2018).
Box 10.2, Figure 1 | Schematic of two types of physical climate storylines with a particular climate impact of concern (red). The storylines are 
defined by specified elements (dark blue). Variable elements (light blue) are simulated conditional on the specified elements. The white elements are ‘blocked’ since 
their state does not need to be known to determine the light blue elements. Other types of storylines could be defined by specifying other elements (e.g., storylines 
of different climate sensitivities or different representative concentration pathways). (a) Event storyline, where the particular dynamical conditions during the event 
as well as the regional warming are specified and control the hazard arising from the event. (b) Dynamical storyline, where the global warming level and remote

drivers are specified and control the long-term changes in atmospheric dynamics and regional warming. In both storylines, the impact is also conditioned on specified 
exposure and vulnerability. Figure adapted from Shepherd (2019).

1435
Linking Global to Regional Climate Change Chapter 1010Cross-Chapter Box 10.3 | Assessment of Climate Change Information at the Regional Scale
Coordinators:  Erika Coppola (Italy), Alessandro Dosio (Italy), Friederike Otto (United Kingdom/Germany)
Contributors:  Claudine Dereczynski (Brazil), Melissa I. Gomis (France/Switzerland), Richard G. Jones (United Kingdom), Roshanka  Ranasinghe 
(The Netherlands/Sri Lanka, Australia), Alex C. Ruane (The United States of America), Sonia I. Seneviratne (Switzerland), Anna A. Sörensson 
(Argentina), Bart van den Hurk (The Netherlands), Robert Vautard (France), Sergio M. Vicente-Serrano (Spain)
This Cross-Chapter Box illustrates how assessments of past, present and future regional climate changes (e.g., change in an extreme 
event index or climatic impact-driver, CID) are derived in the WGI report. Robust assessments can be derived when changes are 
supported by multiple lines of evidence.

Multiple, sometimes contrasting, lines of evidence are derived from the various data sources, methodologies and approaches 
that can be used to construct climate information (Section 10.5 and Figure 10.1). Such data sources and methodologies include 
theoretical understanding of relevant processes, drivers and feedbacks of climate at regional scale, observed data from multiple 
datasets (e.g., ground station networks, satellite products, reanalysis, etc.), simulations from different model types (including general 
circulation models (GCMs), regional climate models (RCMs), statistical downscaling methods, etc.) and experiments (e.g., Coupled 
Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6), Coordinated Regional Climate Downscaling Experiment (CORDEX), and 
single-model initial-condition large ensembles), methodologies to attribute observed changes or events to large- and regional-scale

anthropogenic and natural drivers and forcings as well as other relevant local knowledge (e.g., indigenous knowledge).
Extreme heat
CIDs are first 
defined by one or 
a set of indicesIndex 1
Index 2
Relevant climate 
information is then 
collected for each 
region and indicator 
and evaluated
1   Collect information for each line of evidence
Identifying relevant processes responsible for 
changes
Interplay with other drivers (SLCFS, land use...)
Connection between human drivers and 
physical change2   Assess each line of evidence
The confidence is obtained from 
expert judgment, derived from 
specific questions
Do observed trends agree? 
Are they significant? 
Do they cover the same period and region?O
A
PAre past trends attributable to human activities? 
Are attributed trends and observed events 
consistent?Observations Attribution Projections
UUnderstanding processes
The matrix acts as a roadmap 
to the sections of the report

containing the assessment 
information (step       and       ) 
for each line of evidence, for 
each CID in each region. 1 2  limatic        mpact-      rivers C I D
Do model projections agree? 
Are model changes significant?
How does change depend on scenario?  
CID CID CID CID CID
Region 1
Region 2
Region 3Do we have a sound physical explanation of the processes
responsible for past and future changes?
In the Technical Summary
(Table TS.5)In the Summary for 
PolicymakersAssessment of climate change information at the regional scale
Synthesis  Document each line of evidence in a Traceback Matrix3a Perform synthesis assessment3b
O
A
P
O
A
P
O
A
Cross-Chapter Box 10.3, Figure 1 | Schematic illustration of the process to derive the assessment of regional climate change information based 
on a distillation process of multiple lines of evidence taken from observed trends, attribution of trends or events, climate model projections, 
and physical understanding.

1436
Chapter 10 Linking Global to Regional Climate Change10Cross-Chapter Box 10.3 (continued)
The assessment is derived following the IPCC uncertainty guidance through a distillation process of multiple lines of evidence on 
observed trends, attribution of trends or events, climate model projections and physical understanding, covered in several chapters 
of the WGI Report.
In particular, this Cross-Chapter Box explains the methodology used to derive the regional assessments summarized in the Technical 
Summary (TS) table that are, in turn, used as a basis for the synthesis assessment in the Summary for Policymakers (SPM).
The process consists of three discrete steps, listed below and schematically illustrated in Cross-Chapter Box 10.3, Figure 1:
1. Collection and assessment of the fitness-for-purpose of available information
Any specific climate change that is regionally relevant is assessed looking at lines of evidence, potentially across multiple indices. For

example, several definitions of ‘drought’ exist that refer to a variety of the underlying processes, temporal and spatial scales, as well as 
sectoral applications and associated impacts (Sections 11.6 and 12.3). Such diverse definitions need to be gathered from the relevant 
literature, compared, and individually assessed if appropriate.
Once the indices of change are properly defined, the relevant climate information is collated from the available sources.
The information is then evaluated against its fitness-for-purpose, for example, whether it is adequate to provide robust evidence  to 
derive an assessment. In the case of observed data, issues to be considered include (but are not limited to): spatial and temporal 
resolution, accuracy, gaps in the recorded data, homogeneity in the station network, uncertainty treatment, etc. (Sections 10.2, 11.2, 
11.9, 12.4; Atlas.1.4). In the case of modelled data, an assessment of the fitness-for-purpose typically includes an evaluation of

numerical or statistical methods adopted, adequate representation of the physical processes, forcings and feedbacks relevant for the 
region and the change under consideration, the availability of adequate ensembles to assess the interplay between forced response 
and internal variability and the uncertainty in future projections (Sections 10.3, 10.4, 11.2, 11.9, 12.4 and Chapter Atlas). Attribution 
assessments are usually based on models and observations for which the fitness-for-purpose is assessed with similar criteria as 
those described above (Cross-Working Group Box: Attribution in Chapter 1). The assessment is made either directly or indirectly by 
scrutinizing the data and methods of the relevant literature against the criteria listed above.
2. Assessment of confidence of the multiple lines of evidence
Once the relevant information has been collated for a given regional change, an assessment of the confidence is first made for each

line of evidence separately. The assessment of confidence is the result of expert judgment drawing around a set of questions such as:
• Do we have a physical explanation of the processes responsible for past and future changes in the region? 
• Do observed trends agree amongst different observational products/datasets? Are they statistically significant? Do the observations 
cover the same temporal period and/or spatial area? Are the observations homogeneous in time?
• Can past trends be attributed to human activities (greenhouse gases, short-lived climate forcers or land-use/management 
changes)? Are attributed trends and events consistent? What is the interplay between internal variability and forced response? 
• Do model projections agree on the magnitude and sign of the projected signal? Are we able to understand the reasons underlying 
any discrepancies? Can we quantify the uncertainty in the projected signal? Are the projections based on similar SSP-RCP/time

horizon or global warming level (GWL; Cross-Chapter Box 11.1)? If not, are they comparable? 
• Has the signal already emerged? Are there studies indicating the time of emergence of the signal?
The assessment is then tested for overall coherence across the available lines of evidence, for example:
• Are observed historical changes consistent with future projections?
• Are attributed events similar to the types of changes projected for the future?
• Is there a physical explanation for changes that are projected but have not yet been clearly observed or attributed?
• Are assessments of confidence and likelihood performed in a similar way across regions?
3. Distillation of regional information and synthesis of the independent assessments
To ensure transparency, a traceback matrix is constructed (refer to 10.SM) that, for each region and index, identifies where in the

chapters the relevant information can be found, together with a summary of the relevant information in the Technical Summary.

1437
Linking Global to Regional Climate Change Chapter 1010Cross-Chapter Box 10.3 (continued)
Based on assessments mainly in Chapters 8, 9 11, 12 and Atlas, the table in Technical Summary (TS.4.3.1) collates, by means of colours 
and symbols, the assessment of the confidence in past trend, attribution and direction of future change. This distillation process is 
illustrated below with two examples: (i) a relatively simple case for the assessment of extreme heat over South-Eastern South America, 
where most of the lines of evidence agree, and (ii) ecological, agricultural and hydrological drought in the Mediterranean, which is 
more complex due to the different definitions of ‘drought’ and the sometimes conflicting information arising from different lines 
of evidence and the example shown here is preceded by the decision to focus on these types of drought rather than, for example, 
meteorological drought.
(a) Extreme heat in South-Eastern South America (SES)
Observed past trends

Mean temperature and extreme maximum and minimum temperatures have shown an increasing trend ( high confidence ). An increase 
in the intensity and in the frequency of heatwave events between 1961 and 2014 is also observed. However, there is medium 
confidence  that warm extremes have decreased in the last decades over the central region of SES during austral summer (Section 11.9 
and Atlas.7.2.2).
There is evidence of increasing heat stress during summer in much of SES for the period 1973–2012 (Section 12.4.4.1).
Attribution
Based on trend detection and attribution studies of maximum and minimum temperatures and event attribution of heatwaves in 
the region, there is high confidence  in a human contribution to the observed increase in the intensity and frequency of hot extremes 
(Section 11.9).
The increasing heat stress over summer in much of SES has been attributed to human influence on the climate system (Section 12.4.4.1).
Projections

There is high confidence  that by the end of century most regions in South America will undergo extreme heat stress conditions much 
more often than in the recent past, with about 50–100 more days per year under SSP1-2.6 and more than 200 additional days per year 
under SSP5-8.5 ( high confidence ) (Section 12.4.4.1).
Based on different lines of evidence (GCMs, RCMs) an increase in the intensity and frequency of hot extremes is extremely likely  for 
SES at all assessed warming levels (compared with pre-industrial) (Section 11.9).
Synthesized assessment in the Technical Summary from multiple lines of evidence
There is high confidence  that extreme temperatures have increased over SES over the last decades and that human influence likely  
contributed to the observed changes in extreme temperatures. An increase in the frequency and intensity of heatwave events has been

observed. Most land regions will frequently undergo extreme heat stress conditions by the end of the 21st century, with an increase 
in the frequency of heatwaves and heat stress conditions (Technical Summary TS.4.3.2).
(b) Mediterranean ecological, agricultural and hydrological droughts
Hydrological modelling suggests that the recent decline in soil moisture in the Mediterranean is unprecedented in the last 250 years. 
Paleoclimate evidence extends this view, additionally indicating that dryness in the Mediterranean is approaching an extreme 
condition compared to the last millennium (Section 8.3.1.6).
There is an increase in probability and intensity of agricultural and ecological droughts ( medium confidence ) and there is an increase 
in frequency and severity of hydrological droughts ( high confidence ) (Section 11.9).
Global warming has contributed to drying in dry summer climates including the Mediterranean ( high confidence ). Records of soil

moisture indicate that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean 
aridity. Multiple lines of evidence suggest that anthropogenic forcings are causing increased aridity and drought severity in the 
Mediterranean region ( high confidence ) (Section 8.3.1.6).

1438
10.6 Comprehensive Examples of Steps 
Toward Constructing Regional 
Climate Information
10.6.1 Introduction
This section presents three comprehensive examples of steps for 
distilling regional climate information from the multiple sources 
of regional climate information presented in this chapter. These 
examples build on the general framework presented in Section 10.5, 
examining in particular the strengths and challenges in linking the 
different sources, while also exposing the assumptions behind and 
consequences of decisions made in the process. The examples are 
framed taking into account societal perspectives that provide context 
for their regional climate statements. Although the nature of an 
IPCC Working Group I assessment precludes engaging with users of 
climate information (Section 10.5), we do cite relevant national and 
regional reports that give user perspectives to set a foundation from

which one could distil climate information for users. We have chosen 
the recent Cape Town drought, Indian summer-monsoon trends 
and the Mediterranean summer warming because they provide 
a geographically diverse set of locations and relevant processes and because most of the components for constructing regional climate 
information outlined in Chapter 10 are directly relevant to each case.
The three comprehensive examples follow a similar structure:
1. Motivation and regional context.
2. The region’s climate. 
3. Observational issues.
4. Relevant anthropogenic and natural drivers.
5. Model simulation and attribution over the historical period. 
6. Future climate information from global simulations.
7. Future climate information from regional downscaling.
8. Storylines.
9. Climate information distilled from multiple lines of evidence.
Following this structure, construction of the regional climate 
information presented in these examples depends on an assessment

of observational uncertainty relative to the magnitude of a climate 
change signal (Section 10.2), the evaluations of model performance 
to judge the fitness-for-purpose of a  given model (Section  10.3), 
and expert judgement.  These factors contribute to attribution of 
historical climate change signals (Section  10.4), recognizing that 
attribution must account for the interplay between externally forced Cross-Chapter Box 10.3 (continued)
An increasing trend towards agricultural and ecological droughts has been attributed to human-induced climate change in the 
Mediterranean ( medium confidence ). Model-based assessment shows with medium confidence  a human fingerprint on increased 
hydrological drought, related to rising temperature and atmospheric demand, and frequency and intensity of recent drought events. 
There is medium confidence  that change in land-use and terrestrial water management contribute to trends in hydrological drought 

There is high confidence  that drought severity and intensity will increase in the Mediterranean. Increased evapotranspiration due 
to growing atmospheric water demand will decrease soil moisture ( high confidence ). The seasonality of runoff and streamflow (the 
annual difference between the wettest and driest months of the year) is expected to increase with global warming ( high confidence ). 
Annual runoff is very likely to decrease. Under middle or high-emissions scenarios, the likelihood of extreme droughts increases by 
200–300% in the Mediterranean. The paleoclimate record provides context for these future expected changes: climate change will shift 
soil moisture outside the range of observed and reconstructed values spanning the last millennium ( high confidence ) (Sections 8.4.1.5 
and 8.4.1.6).
There is medium confidence  in the increase of agricultural and ecological drought at +1.5°C, high confidence  at +2°C and very likely

at +4°C, with large decreases in soil water availability during drought events and increase in drought magnitude. There is medium 
confidence  in the increase in hydrological drought at +1.5°C, high confidence  at +2°C and very likely  at +4°C with very strong decrease 
(40–60%) of total runoff in the spring-summer half-year and a 50–60% increase in frequency of days under low flow (Section 11.9).
There is high confidence  that agricultural, ecological and hydrological droughts will increase in the Mediterranean region by mid- and 
end-of-century under all RCPs (except RCP2.6/SSP1-2.6), or for GWLs equal to or higher than 2°C (Section 12.4.5.2).
There is high confidence  that hydrological droughts have increased in the Mediterranean since the 1960s related to rising temperature

and atmospheric demand, and medium confidence  of a human fingerprint on this increase. There is medium confidence  in the increase 
of ecological and agricultural droughts and in their attribution to human-induced climate change. There is high confidence  of an 
increase in ecological, agricultural and hydrological droughts for warming levels exceeding 2°C, and medium confidence  of an increase 
for lower warming levels (Technical Summary TS4.3.2).

1439
Linking Global to Regional Climate Change Chapter 1010signals and unforced internal variability. This interplay is explored 
using multiple model ensembles, including, when appropriate and 
feasible, single-model initial-condition large ensembles (SMILEs). The 
multiple lines of evidence for the climate information may conflict, 
thus requiring distillation of the evidence (Section  10.5) to arrive 
at climate-change statements. When moving from global climate 
information to climate information at the regional scale, following the 
structure above provides a basis for arriving at relevant and credible 
climate information. The comprehensive examples of distilling climate 
information thus show the value of working with multiple lines of 
evidence to develop robust climate change information for a region.
In addition to the three comprehensive examples, this section 
contains two additional examples analysing multiple sources of

regional climate information. Box  10.3 on urban climate assesses 
information that provides a  foundation for understanding climatic 
behaviour in urban areas and its projected change. Cross-Chapter 
Box 10.4 on climate change over the Hindu Kush Himalaya assembles 
information rooted in several chapters and previous assessment 
reports to assess understanding of several climate elements 
(temperature, precipitation, snow and glaciers, and extreme events) 
for the region and their projected changes.
As these examples will show, the distillation process of regional climate 
information from multiple lines of evidence can vary substantially 
from one case to another. Confidence in the distilled regional climate 
information is enhanced when there is agreement across multiple lines 
of evidence, but the outcome of distilling regional climate information 
can be limited by inconsistent or contradictory sources.
10.6.2 Cape Town Drought
10.6.2.1 Motivation and Regional Context

Cape Town’s ‘Day Zero’ water crisis in 2018 threatened a shut-down 
of water supply to 3.4 million inhabitants of the city and resulted in 
domestic water use restriction of 50 litres per person per day lasting for 
nine months (pre-drought unconstrained water use was about 170 litres 
per person per day, DWA, 2013), punitive water tariffs, and temporary 
closure of irrigation systems. Problems with water supply in many 
large cities in developing countries are endemic and rarely reported 
internationally. The water crisis in Cape Town attracted considerable 
international attention to a city with functional government structures, 
well-developed services (compared to other urban centres in Africa), 
a centre of international tourism, and an economic hub with GDP of 
22 billion USD (about 7,500 USD per capita, Gallie et al., 2018). Economic 
and social impacts of the crisis were significant. Loss of revenue for 
companies of all sizes resulted not only from the scaling down of water-

dependent activities, but also from the need to invest in water-efficient 
technologies and processes. Tourism was affected through reduced 
arrivals and  bookings, although only temporarily (CTT, 2018). In the 
agricultural sector, 30,000 people were laid-off and production dropped 
by 20% (Piennaar and Boonzaaier, 2018). The crisis initially polarized 
society, with conflict emerging between various water users and erosion 
of trust in the government, but eventually social cohesion and an acute 
awareness of limited water resources emerged (Robins, 2019).Cape Town’s crisis resulted from a  combination of a  strong, rare 
multi-year meteorological drought (Figure  10.18), estimated at 1 in 
300 years (Wolski, 2018), and factors related to the nature of the water 
supply system, operational water management and water resource 
policies. Cape Town was very successful in implementing water-saving 
actions after the previous drought of 2000–2003, reducing water losses

from over 22% to 15% (Frame and Killick, 2007; DWA, 2013), breaking 
the previous coupling of growth in water demand with growth in 
population. As a  consequence, Cape Town won a Water Smart City 
award from the C40 Cities program only three years prior to the crisis. 
However, the water-saving actions, together with changing priorities 
in water resource provision from infrastructure-oriented towards 
resource and demand management, may well have led to delays 
in implementation of the expansion of water supply infrastructure 
(Muller, 2018). The expansion plan, formulated a decade prior to the 
crisis, included an expectation of long-term climate-change drying in 
the region (DWAF , 2007). The crisis also exposed structural deficiencies 
of water management and inadequacy of a  policy process in which 
decisions about local water resources are taken at a  national level, 
particularly in a situation of political tension (Visser, 2018). The crisis

was widely seen as a harbinger of future problems to be faced by the 
city, and a highlight of vulnerability of many cities in the world resulting 
from the interplay of three factors: (i) the fast urban-population growth, 
(ii) the economic, policy, infrastructural and water resource paradigms 
and constraints, and (iii) anthropogenic climate change.
10.6.2.2 The Region’s Climate
An evaluation of the relative role of rainfall and temperature signal 
in the 2015–2017 hydrological drought gives a  strong indication 
that lack of rainfall was the primary driver (Otto et al., 2018) leading 
to the 2018 water crisis. Thus, the remainder of this section focuses 
on rainfall. Section 11.6 offers a discussion of African drought over 
broader areas, including mechanisms relevant to them.
Cape Town is located at the south-western tip of Africa, within an 
approximately 100 km × 300 km region that receives 80% of its 
rainfall during the austral winter (March to October), with the largest

portion in June to August. In the vicinity of Cape Town, rainfall is 
strongly heterogeneous, ranging from about 300 mm/year on coastal 
plains to >2,000 mm/year in mountain ranges. The Cape Town water 
supply relies on surface water reservoirs located in a  few small 
mountain catchments (about 800 km2 in total). The Cape Town region 
receives 85% of its rainfall from a series of cold fronts forming within 
mid-latitude cyclones. The remainder is brought in by infrequent 
cut-off lows that occur throughout the year (Favre et al., 2013). This 
creates a very strong water resource dependency on a single rainfall 
delivery mechanism that may be strongly affected by anthropogenic 
climate change (Chapter 4 and Section 10.6.2.6).
The 2015–2017 drought had strong low-rainfall anomalies in shoulder 
seasons (March to May and September to November, though weaker 
in the latter), and average rainfall in June and July (Sousa et al., 2018a;

Mahlalela et al., 2019). The anomaly resulted from fewer rainfall events 
and lower average intensity of events. The anomaly was strongest in 
the mountainous region where the water supply system’s catchments 
are located (Wolski et al., 2021).

1440
Figure 10.1 8 | Historical and projected rainfall and Southern Annular Mode (SAM) over the Cape Town region. (a)  Yearly accumulation of rainfall (in mm) 
obtained by summing monthly totals between January and December, with the drought years 2015 (orange), 2016 (red), and 2017 (purple) highlighted in colour. (b) Monthly 
rainfall for the drought years (in colour) compared with the 1981–2014 climatology (grey line). Rainfall in (a) and (b) is the average of 20 quality controlled and gap-filled 
series from stations within the Cape Town region (31°S–35°S, 18°W–20.5°W). (c) Time series of the SAM index and of historical and projected rainfall anomalies (%, baseline 
1980–2010) over the Cape Town region. Observed data presented as 30-year running means of relative total annual rainfall over the Cape Town region for station-based

data (black line, average of 20 stations as in (a) and (b), and gridded data (average of all gridcells falling within 31°S–35°S, 18°W–20.5°W), GPCC (green line) and CRU TS 
(olive line). Model ensemble results presented as the 90th-percentile range of relative 30-year running means of rainfall and the SAM index from 35 CMIP5 (blue shading) and 
35 CMIP6 (red shading) simulations, 6 CORDEX simulations driven by 1 to 10 GCMs (cyan shading), 6 CCAM (purple shading) simulations from individual ensemble members, 
and 50 members from the MIROC6 SMILE simulations (orange shading). The light blue, dark red and yellow lines correspond to NCEP/NCAR, ERA20C and 20CR, respectively. 
The SAM index is calculated from sea level pressure reanalysis and GCM data as per Gong and Wang (1999) and averaged over the aforementioned bounding box. CMIP5,

CORDEX and CCAM projections use RCP8.5, and CMIP6 and MIROC6 SMILE projections use SSP5-8.5. (d) Historical and projected trends in rainfall over the Cape Town region 
and in the SAM index. Observations and gridded data processed as in (c). Trends calculated as Theil-Sen trend with block-bootstrap confidence interval estimate. Markers show 
median trend, bars 95% confidence interval. Global models in each CMIP group were ordered according to the magnitude of trend in rainfall, and the same order is maintained 
in panels showing trends in the SAM. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1441
Linking Global to Regional Climate Change Chapter 1010Although the 2015–2017 drought was unprecedented in the historical 
record, the Cape Town region has experienced other droughts of 
substantial magnitude, notably in the 1930s, 1970s and more recently 
in 2000–2003. Long-term (>90 years) rainfall trends are mixed in sign, 
location-dependent, and weak (Kruger and Nxumalo, 2017; Wolski 
et al., 2021); mid-term (about 50 years) trends are similarly mixed in 
sign (MacKellar et al., 2014). In the south-western part of the region, 
rainfall is mostly decreasing in the post 1981 period, particularly in 
December–January–February and March–April–May, although there 
is no trend or a weak wetting in June–July–August (Sousa et al., 2018a; 
Wolski et al., 2021). Rainfall trends of similar magnitude and duration 
to the post-1981 trend accompanied previous strong droughts in the 
region (Wolski et al., 2021).
10.6.2.3 Observational Issues

South Africa and the Cape Town region have good instrumental 
weather data. Records start in the late 1800s, with in excess of 
10 gauges reporting since the 1920s, expanding to about 80 gauges 
in the 1980s, but the number of stations has declined since. The 
mountains have only a  few stations, which receive more than 
1000 mm per year. In view of the strong heterogeneity of rainfall, 
changes in the number of stations contributing to datasets such as 
Climatic Research Unit (CRU) and Global Precipitation Climatology 
Project results in a lack of consistency between them, which limits 
their reliability in the region (Section 10.2; Wolski et al., 2021).
10.6.2.4 Relevant Anthropogenic and Natural Drivers
Because the primary rainfall mechanism is frontal rain, the most 
relevant large-scale drivers are those that affect cyclogenesis, 
frontogenesis and the mid-latitude westerlies’ latitudinal position 
and moisture supply. These drivers and, thus, the region’s rainfall

are linked to the Antarctic Oscillation (AAO; Reason and Rouault, 
2005) or Southern Annular Mode (SAM), the dominant monthly and 
interannual mode of Southern Hemisphere atmospheric variability, 
and a  measure of the pressure gradient between mid- and high 
latitudes. (See Sections 3.3, 3.7, 4.3 and Annex IV.2.2 for more general 
discussion of the SAM.) While in the post-1930 period, the SAM 
displays a long-term positive trend, the Cape Town region’s rainfall 
does not, and only the post-1979 trends of rainfall and SAM  are 
conceptually consistent. For example, a  positive trend in the SAM 
is associated with a negative trend in rainfall (Section 10.6.2.5 and 
Figure 10.18). There is also good agreement between the seasonality 
of the SAM and rainfall trends in the post-1979 period: a drying trend 
appears strongly in December to February and March to May, but not 
in June to August and September to November (Wolski et al., 2021),

and trends in the SAM have similar seasonal dependence (E.-P . Lim 
et al., 2016; Section 3.7.2). Additionally, there is a similar seasonal 
pattern in the post-1979 trends in indices capturing the southern 
edge of the Hadley circulation (Grise et al., 2018).
In the longer-term, Cape Town regional rainfall is characterized by 
a multi-decadal scale quasi-periodicity (Figure 10.18; Dieppois et al., 
2019; Wolski et al., 2021), with the 2015–2017 drought and previous 
strong droughts (1930s and 1970s) occurring during the rainfall’s 
periodic low phases. However, the studies linking the Cape Town 2015–2017 drought to the hemispheric processes expressed by the 
SAM (Sousa et al., 2018a; Burls et al., 2019; Mahlalela et al., 2019) 
focused almost exclusively on the post-1979 period, when global 
reanalyses are available. Detailed understanding of the drivers of 
previous (1930s and 1970s) Cape Town region droughts and the

role of hemispheric processes expressed by the SAM in the pre-1979 
period is missing.
The Cape Town regional rainfall is also potentially linked to other 
hemispheric phenomena, such as the expansion of the tropics and, 
specifically, the South Atlantic high-pressure system and the position 
of the subtropical jet, which share some variability with the SAM. 
The relationships between these phenomena and Cape Town rainfall 
have not been thoroughly investigated outside of the context of 
the 2015–2017 drought, but the drought itself was associated 
with poleward expansion of the subtropical anticyclones in the 
South Atlantic and South Indian oceans and (a resulting) poleward 
displacement of the moisture corridor across the South Atlantic 
(Sousa et al., 2018a), as well as a weaker subtropical jet (Mahlalela 
et al., 2019). Burls et al. (2019) also link the decline in the number of 
rainy days to the increase in sea level pressure along the poleward

flank of the South Atlantic high-pressure system and the intensity 
of the post-frontal ridging high. Additionally, there is a  possible 
linkage between Cape Town rainfall and near-shore cold sea surface 
temperature (SST) anomalies arising from Ekman upwelling due to 
reduced westerly and increased south-easterly winds. These might 
lead to suppression of convection and reduction of rainfall over 
land (Rouault et  al., 2010). All these phenomena are conceptually 
consistent with the poleward migration of the westerlies and 
expansion of the tropics.
Rainfall in the Cape Town region also responds to SST anomalies in 
the south-east Atlantic, including the Agulhas Current retroflection 
region, which may drive intensification of low-pressure systems, 
leading to the trailing front strengthening as it makes landfall over 
the Cape Town region (Reason and Jagadheesha, 2005). There are 
also linkages at the seasonal time scale between the Cape Town

regional rainfall and Antarctic sea ice (Blamey and Reason, 2007).
In addition to mid-latitude controls, subtropical processes also play 
a role in the Cape Town region’s rainfall variability. The 10°S–30°S 
region of the subtropical Atlantic, parts of the South American 
continent and even parts of the African continent north of Cape Town 
are sources of moisture for atmospheric river events contributing 
to frontal rainfall (Blamey et  al., 2018; Ramos et  al., 2019), with 
implications for the 2015–2017 drought (Sousa et  al., 2018a). 
Also, the second major rainfall contributing system, cut-off-lows, is 
conditional on moisture supply from the subtropics (Abba Omar and 
Abiodun, 2020).
Although El Niño–Southern Oscillation (ENSO) influences climate in 
southern Africa, any relationship between ENSO and Cape Town’s 
rainfall is weak and inconsistent, showing the strongest impact in 
May to June (Philippon et al., 2012). ENSO, however, does influence

large-scale processes and phenomena relevant to the drought, 
though the relationship between ENSO and the SAM is complex, with 
each ENSO event influencing the SAM differently in different seasons

1442
Chapter 10 Linking Global to Regional Climate Change10(Ding et al., 2012). Similarly, ENSO affects meridional circulation and 
thus the subtropical anticyclone as well as the polar and subtropical 
jets (Seager et al., 2019), but only modifying, not controlling, their 
role in Cape Town’s rainfall.
Paleoclimate studies reveal that long-term variability in the winter 
rainfall region of South Africa (including Cape Town) is consistent with 
a general framework of warming/cooling-induced latitudinal migration 
of the westerlies and transformation of the subtropical high-pressure 
belt and associated hemispherical processes (see  section  10.2.3.2 
for assessment of paleoclimate analysis). The synchronicity of winter 
rainfall with Antarctic ice-core-derived polar temperature anomalies is 
consistently revealed in studies using different paleoclimate proxies and 
time scales of 1400 years (Stager et al., 2012), about 3000 years (Hahn

et al., 2016) and 12,000 years (Weldeab et al., 2013). Changes in rainfall 
regimes at shorter (decadal) time scales appear to reflect influence 
of local processes such as the Agulhas current’s interaction with the 
Atlantic, resulting in changes in SST and coastal upwelling, as well as 
modification of the wind tracks by topography (Stager et al., 2012).
10.6.2.5 Model Simulation and Attribution 
Over the Historical Period
Due to the small scale of the Cape Town region, robust comparison 
of CMIP simulations to observations is difficult. However, in general, 
CMIP5 models capture the seasonality well, such as the dominance 
of austral winter rains, although they overestimate the peak and 
underestimate the shoulder season rainfall (Mahlalela et  al., 
2019). Trends in rainfall are particularly difficult to assess as they 
are generally weak and depend strongly on the time period and 
dataset adopted for the analyses (Section 10.6.2.3). A multi-method

attribution study (Otto et al., 2018) estimates the probability of the 
2015–2017 drought to have increased by a  factor of three since 
pre-industrial times (with a wide 95% confidence interval of 1.5 to 6). 
However, throughout the 20th century, a substantial portion of the 
global models (about 36% of CMIP5 and 44% of CMIP6 models, as 
well as many of the MIROC SMILE members) simulate a statistically 
significant (95% level) decline in total annual rainfall, while there is 
no robust long-term trend in observations (Figure 10.18). Section 10.4 
offers a more detailed assessment of attribution challenges.
Global models capture the overall behaviour of the observed main 
hemispherical processes, such as the expansion of the tropics, 
a positive trend in SAM and the poleward shift of the westerly jet. 
However, they fail to capture details of their observed climatology 
and variability (Simpson and Polvani, 2016), and the magnitudes of

simulated trends vary, though the models typically underestimate 
observed trends in these processes (Purich et al., 2013; Staten et al., 
2018). In general, CMIP5 models do capture the SAM-regional 
rainfall association, although not consistently across all seasons 
(Purich et al., 2013; E.-P . Lim et al., 2016).
10.6.2.6 Future Climate Information from Global Simulations
Global models show strong consistency in a drying signal for the Cape 
Town region, with the reduction in total annual rainfall of up to 20% 
by the end of the 21st century in CMIP5 RCP8.5 and CMIP6 SSP5-8.5 simulations (Figure  10.18; Almazroui et  al., 2020c). The consistency 
across the models is a robust signal compared to the rest of southern 
Africa, where the climate change signal varies spatially: stronger drying 
in the west and moderate drying or weak wetting in the east (DEA, 
2013, 2018; see Atlas.4.4 for further discussion of southern Africa

precipitation projections). Rainfall changes projected for the Cape 
Town region are consistent with projected changes in hemispheric-
scale processes and regional-scale dynamics that point toward reduced 
frequency of frontal systems affecting that  region. These changes 
include robust signals in CMIP5 models for the Southern Hemisphere 
for a poleward expansion of the tropics (Hu et al., 2013b), poleward 
displacement of mid-latitude storm tracks (Chang et al., 2012), increased 
strength and poleward shift of the westerly winds (Bracegirdle et al., 
2018) and subtropical jet-streams (Chenoli et  al., 2017), and a  shift 
toward a  more positive phase of the SAM (E.-P . Lim et  al., 2016). 
However, despite the consistency in circulation changes, the emergence 
of anthropogenic rainfall change above unforced variability in West 
Southern Africa remains uncertain for annual rainfall throughout most 
of the 21st century, even under SSP5-8.5 (Figure 10.15).

There is also a  substantial increase in the frequency of conditions 
supporting atmospheric rivers and water vapour transport towards 
the south-west coast of southern Africa in the projected climate 
(Espinoza et  al., 2018). This behaviour has strong implications for 
the region, as most topographically high locations receive rainfall 
from persistent atmospheric rivers (Blamey et al., 2018). A thorough 
understanding of the role of atmospheric rivers in the Cape Town 
region under a changing climate is missing.
10.6.2.7 Future Climate Information from 
Regional Downscaling
Dynamical downscaling studies implemented with a stretched-grid 
model (Engelbrecht et al., 2009) revealed a signal compatible with 
the driving CMIP5 ensemble, that is, consistent drying throughout 
the region, amplifying in time, irrespective of the considered 
emissions scenario and the generation of global models (DEA, 2013, 
2018). A  multi-model CORDEX ensemble indicates a  robust signal

of reduction of total annual rainfall in the future, although there is 
less agreement on how changes in rainfall occurrence may evolve 
in the region, such as through fewer consecutive rain days or longer 
dry spells (Abiodun et al., 2017; Maúre et al., 2018). For the end of 
the century under RCP8.5, Dosio et  al. (2019) also found drying. 
Moreover, in their analysis, the drying is associated with an increase 
in the number of consecutive dry days and a reduction in number of 
rainy days. Their results are consistent with the driving global models 
for all the precipitation indices, and they are robust independent of 
the choice of the regional climate model (RCM) or global model. 
However, collectively, these analyses indicate that uncertainty 
remains in the characteristics of the precipitation decrease.
10.6.2.8 Storyline Approaches
There is a  consistency in rainfall projections with the projections of 
rainfall drivers and with the general understanding of the influence

of global warming on the circulation dynamics and rainfall patterns 
in the region. Thus, the expansion of the South Atlantic high-pressure

1443
Linking Global to Regional Climate Change Chapter 1010system, related to widespread warming of the tropics and poleward 
shift of the subsiding limb of the Hadley cell, is associated with the 
southward displacement of the subtropical jet, and southward 
migration of mid-latitude westerlies and storm tracks, in addition to 
changes in the SAM (Section 10.6.2.4). These effects are also relatively 
consistent with recent (post-1980s) declines in rainfall in the Cape Town 
region. The storyline of an extended drought is thus a set of events that 
can yield reduced rainfall in the Cape Town region: a poleward shift 
of the downward branch of the Hadley cell that produces a sustained 
southward shift in mid-latitude westerlies and storm tracks. The 
behaviour is potentially reinforced by changes in the SAM.
10.6.2.9 Climate Information Distilled From 
Multiple Lines of Evidence
There is high agreement  among observational data and reanalyses

that the recent (post-1979) downward trend in the Cape Town 
region’s rainfall leading to the 2015–2017 drought is related to 
the hemispheric processes of poleward shift in the westerlies and 
expansion of the Hadley circulation. However, there is less support 
for the precipitation–circulation relationship in historical CMIP5 
and CMIP6 simulations. As a  consequence, there is only medium 
confidence  that these process changes produced the 2015–2017 
drought leading to the 2018 water crisis.
For the water-resource planner who has to deal with potential 
drought like the 2015–2017 event, several lines of evidence 
indicate future drying: the projected precipitation by global models 
and RCMs of different spatial resolutions, and the observed and 
projected changes of circulation patterns consistent with drier 
conditions, the paleoclimatic evidence confirming a millennial-scale 
circulation–rainfall link. However, the distillation is limited by a lack

of information about whether or not a relationship between Cape 
Town precipitation and large-scale circulation processes adequately 
explains droughts in the twentieth century prior to 1979.
Thus, although a clear association appears in observations from 1979 
onward between increasing GHG concentrations, drying in the Cape 
Town region and behaviour of a key circulation process, the SAM, 
further analysis suggests caution. Not all global models show 
the historical post-1979 association among these factors, and when 
the observational record is extended back further to times when the 
anthropogenic greenhouse forcing was weaker, there is no strong 
association between the SAM and Cape Town drought. Thus, there is 
only medium confidence  in the expectation of a future drier climate 
for Cape Town.
10.6.3 Indian Summer Monsoon
10.6.3.1 Motivation and Regional Context
The Indian summer monsoon provides 80% of the country’s annual

rainfall from June to September, supplying the majority of water for 
agriculture, industry, drinking and sanitation to over a billion people. 
Any variations in the monsoon on time scales from days to decades can 
have large impacts (Challinor et al., 2006; Gadgil and Gadgil, 2006). Evidence from paleoclimate records (Sections 8.3.2.4.1) shows high 
confidence in a  weakened Indian monsoon during cold epochs of the past 
such as the Younger Dryas (12,800–11,600 years ago) as measured by 
speleothem oxygen isotopes (Kathayat et al., 2016). There is a pressing 
need to understand if the monsoon will change in the future under 
anthropogenic forcing and to quantify such changes. Multiple datasets 
have shown robust negative trends since the 1950s until the turn of the 
century (Bollasina et al., 2011) followed by a recovery (Jin and Wang, 
2017), yet repeated assessments project the monsoon to increase in 
strength under enhanced GHG forcing (Christensen et al., 2007, 2013;

Sections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between 
future projections and observed historical trends makes the region an 
ideal choice for an in-depth assessment. The reader is also referred to 
the South Asia (SAS) regional assessment of precipitation extremes 
(Section 11.9), which is not discussed here for brevity.
10.6.3.2 The Regional Climate of India
Local geography gives rise to distinct differences in societal experience 
of the summer monsoon. The south-westerly monsoon winds are 
incident upon the Western Ghats mountains on the west coast, leading 
to orographic enhancement and heavy rains (Shige et al., 2017), which 
supply rivers with water for much of the southern peninsula, often the 
subject of inter-regional water disputes. The northern plains contain 
the Ganges river and also India’s most intensive agriculture, both rainfed 
and irrigated. Synoptic systems known as monsoon depressions cross

the northern east coast, supplying much of the rain in central India 
(Hunt and Fletcher, 2019). Further north, the eastern Himalayas  are 
dominated by the summer monsoon, while the western Himalayas 
receive most rainfall from western disturbances during winter (Palazzi 
et al., 2013). Meanwhile, south-eastern India sits under a rain shadow 
(the only region to receive more rainfall during the winter monsoon).
10.6.3.3 Observational Issues for India
India has one of the oldest rain-gauge networks in the world, leading 
to the production of numerous observational products (reviewed 
in Khouider et  al., 2020). Gridded gauge-based products dating 
back to the 19th  century reveal pronounced decadal variability 
(Sontakke et al., 2008). Trends for India over the whole 20th century 
are inconclusive (Knutson and Zeng, 2018), although declining over 
central and northern areas (Roxy et al., 2015). Assessment of multiple 
observational datasets covering the Indian summer monsoon reveals

significant declining rainfall over the second half of the 20th century 
(Section 8.3.2.4.1 and Figure 10.19c,d). A subsequent recovery has 
been noted since the early 2000s (Jin and Wang, 2017).
Observational products containing critical inhomogeneities in gauge 
distribution and reporting over time are acknowledged as suitable for 
mesoscale analysis (Rajeevan and Bhate, 2009), while use for climate 
trends requires consistent reporting over time from quality-controlled 
gauges (e.g., about 2000 gauges since the 1950s in Rajeevan et al., 
2006). A  newer 0.25°-gridded product covering 1901 onwards 
(Pai et  al., 2014, 2015), based on Shepard’s interpolation method 
for irregularly-spaced stations (Shepard, 1968), shows increased 
intensity of daily rainfall and extremes over some regions, especially 
in the late-20th century. However, changes to the inputted gauges

1444
Chapter 10 Linking Global to Regional Climate Change10may have introduced an artificial jump in extreme rainfall since 1975 
over central India (Section  10.2.2.3; Lin and Huybers, 2019). They 
suggest that this method may have masked declines in mean rainfall 
and highlight the need for availability of raw gauge data to allow 
transparent assessments. Khouider et  al. (2020) have successfully 
tested a  probabilistic interpolation method for India to overcome 
problems inherent in algorithms based on inverse-distance weighting 
when applied to data-sparse regions. An example snapshot of the 
uneven distribution of rain gauges in a  common observational 
product is shown in Figure 10.19a.
The uncertainty among local and international observational products 
for India can pose challenges when evaluating climate models 
(as in Section 10.2.2.6; Prakash et al., 2015). For the seasonal mean 
summer monsoon rainfall, Collins et al. (2013a) found large biases

separating many CMIP5 models from the available observational 
products. However, for seasonal mean variability, the spread across 
observational products was larger than across the CMIP5 ensemble.
10.6.3.4 Relevant Anthropogenic and Natural 
Drivers for Long-term Change
The relevant drivers for long-term change in the mean Indian summer 
monsoon are summarized briefly:
• Increased greenhouse gas (GHG) concentrations (chiefly CO 2) 
are a  strong contributor to changes in the monsoon, with 
repercussions for the meridional temperature contrast driving 
the monsoon circulation (Ueda et al., 2006; Roxy et al., 2015), 
for the monsoon winds in the lower troposphere (Cherchi et al., 
2011; Krishnan et al., 2013), or for the availability of moisture 
from the Indian Ocean (May, 2011).
• Industrial emissions of sulphate aerosol predominantly in the 
Northern Hemisphere could change inter-hemispheric energy 
transports and weaken the monsoon (Polson et al., 2014; Undorf

et al., 2018). The effect of local anthropogenic emissions of black 
carbon (chiefly from cooking fires) is uncertain (Lau and Kim, 
2006; Nigam and Bollasina, 2010).
• India’s green revolution over the late-20th  century led to 
considerable land-use change, with massive expansion of 
agriculture at the expense of forest and shrublands. As a result, 
India’s northern plains feature widespread irrigation, suggested 
to be a cause of drying (Mathur and AchutaRao, 2020).
• Decadal modes of variability such as the Pacific Decadal Variability 
(PDV, Annex IV) and Atlantic Multi-decadal Variability (AMV, 
Annex IV), which may be partly forced (Section 3.7.7), are known 
to cause decadal modulation of the monsoon (Krishnamurthy 
and Krishnamurthy, 2014; Naidu et al., 2020). 
The interplay of these external and internal drivers is key to 
understanding past and future monsoon change.
10.6.3.5 Model Simulation and Attribution of Drying 
Over the Historical Period

The robust decline of Indian summer monsoon rainfall averaged 
over India in the second half of the 20th century (Section 10.6.3.3) is not in line with expectations arising from thermodynamic 
constraints on the water cycle in a  warming world (Section  8.2.2) 
and has been regarded as a puzzle (Goswami et al., 2006). Assessing 
the attribution of 20th-century changes to Indian rainfall is the 
subject of coordinated modelling under the Global Monsoon MIP 
(GMMIP; Zhou et al., 2016), but is complicated by long-standing dry 
biases in coupled CMIP3, CMIP5 (Sperber et al., 2013) and CMIP6 
(Figure  10.19b) global models. These dry biases are connected to 
a lower tropospheric circulation that is too weak (Sperber et al., 2013) 
and wet biases in the equatorial Indian Ocean (Bollasina and Ming, 
2013). Section  8.3.2.4.1 finds high confidence  that anthropogenic 
aerosol emissions have dominated the observed declining trends

of countrywide Indian summer monsoon rainfall, consistent with 
findings at the global-monsoon scale (Section 3.3.3.2).
Stronger Northern Hemisphere aerosol emissions cool it relative to 
the Southern Hemisphere, increasing northward energy transport 
at the expense of moisture transport towards India (Bollasina et al., 
2011). The attribution to anthropogenic aerosols is supported in CMIP5 
single-forcing experiments, including some testing the sensitivity to 
local and remote emissions (Guo et al., 2015, 2016; Shawki et al., 2018), 
comparing CMIP5 GCMs forced by both aerosol and GHG to GHG 
only (Salzmann et al., 2014) and reducing emissions to pre-industrial 
levels (Takahashi et al., 2018). The large spread between individual 
model realisations of comparable magnitude to the aerosol-induced 
signal suggested to Salzmann et  al. (2014) that internal variability 
may also play a  role over regions such as northern-central India.

Further uncertainty surrounds the level of radiative forcing. Dittus 
et al. (2020) forced a GCM with historical aerosol emissions scaled 
between 0.2  and 1.5 times their observed values, representing the 
spread in CMIP5 effective radiative forcing. The strongest forcing led 
to around 0.5 mm day–1 less late-20th century Indian monsoon rainfall 
than the weakest (Shonk et  al., 2020).  Meanwhile,  the uncertainty 
surrounding aerosol–cloud interactions could change the sign of 
long-term precipitation trends (Takahashi et al., 2018).
There is some evidence that declining Indian monsoon rainfall 
is due to regional SST warming patterns, themselves arising due 
to radiative forcing from GHG (e.g.,  in the Indian Ocean, Guemas 
et al., 2013). Roxy et al. (2015) artificially raised SST in a GCM in 
the equatorial Indian Ocean (the region of strongest observed SST 
warming), leading to a weakened monsoon. Annamalai et al. (2013)

used a  GCM to suggest instead that preferential warming of the 
western North Pacific may force a Rossby-wave response to its west 
that weakens the monsoon through dry advection and subsidence. 
These hypotheses are not borne out in GHG-forced future projections 
(Section 10.6.3.6).
A small anthropogenic contribution may be expected from local 
land-use/land-cover changes and land management. India is the 
world’s most irrigated region with around 0.5  mm/day in places, 
although peaking higher in summer (Cook et al., 2015b; McDermid 
et  al., 2017). Including irrigation in GCMs and RCMs slows the 
monsoon circulation and diminishes rainfall (Lucas-Picher et al., 2011; 
Guimberteau et al., 2012; Shukla et al., 2014; Tuinenburg et al., 2014; 
Cook et al., 2015b) due to reduced surface temperature (Thiery et al., 
2017), reducing the monsoon wind and moisture fluxes towards

1445
Linking Global to Regional Climate Change Chapter 1010India (Mathur and AchutaRao, 2020). However, implementation 
methodologies for irrigation in climate models are simplified and 
often do not account for spatial heterogeneity while overestimating 
demand and supply (Section  10.3.3.6; Nazemi and Wheater, 2015; 
Pokhrel et al., 2016). Changing forest cover to agricultural land in 
an RCM (Paul et al., 2016) finds weakened summer monsoon rainfall 
especially in central and eastern India, due to decreased local 
evapotranspiration. Decreased evapotranspiration from a  warmer 
surface since the 1950s in the CMIP5 ensemble may also feedback 
on the supply of moisture (Ramarao et al., 2015). Based on an AGCM 
study and literature review, Krishnan et al. (2016) support the role 
of land-use/land-cover change in adding to the effects of aerosol 
in weakening the monsoon, in addition to dynamic effects on the 
circulation caused by rapid warming of the Indian Ocean.

In addition to anthropogenic forcing, there is evidence that internal 
variability in the Pacific is a significant driver. Huang et al. (2020b) 
compared a  large perturbed-physics ensemble (HadCM3C) with 
a SMILE for the historical period. Ensemble members replicating the 
negative Indian rainfall trend were accompanied by a strong phase 
change in the PDV from negative to positive, consistent with SST 
observations. Jin and Wang (2017) have demonstrated increasing 
Indian monsoon rainfall since 2002 in a variety of observed datasets, 
suggesting the increase is due either to a  change in dominance 
of a  particular forcing (for example from aerosol to GHG) or to 
a change in phase of internal variability such as the PDV. Huang et al. 
(2020b) partially attribute the rainfall recovery to a  phase change 
in the PDV, supported by a SMILE study combined with reanalyses 
(Ha et al., 2020).
The drying trend of Indian summer monsoon rainfall since the

mid-20th century can be attributed with high confidence  to aerosol 
as the dominant anthropogenic forcing with a further contribution 
from internal variability, supported by the review of B. Wang et al. 
(2021) including CMIP6 results. Understanding the interplay 
between anthropogenic and internal drivers will be important for 
understanding future change.
10.6.3.6 Future Climate Projections from Global Simulations
The AR5 (Christensen et  al., 2013) concluded that Indian summer 
monsoon rainfall will strengthen under all RCP future climate 
scenarios, while the circulation will weaken ( medium confidence ). 
SR1.5 (Hoegh-Guldberg et al., 2018) found only low confidence  in 
projections of monsoon change at 1.5°C and 2°C, or any difference 
between them. The AR6 assessment of Chapter 8 (Section 8.4.2.4.1) 
finds more precipitation in future projections (also depicted in 
Figure  10.19c,d,e), supported by reviews of CMIP3, CMIP5 and

CMIP6 models (Turner and Annamalai, 2012; Kitoh, 2017; Z. Chen 
et al., 2020; B. Wang et al., 2021).
Given the assessment for a  future wetter monsoon dominated by 
GHG emissions and attribution of the late-20th  century decline to 
aerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between 
dominant forcings will lead, at some point, to a positive trend. For 
example, RCP4.5 experiments in an AGCM forced by coupled model-
derived future SSTs showed continuation of 20th-century drying, before a rainfall recovery (Krishnan et al., 2016). By holding aerosol 
emissions at 2005 levels, lower monsoon rainfall is found throughout 
the 21st  century than in a  standard RCP8.5 scenario (Zhao et  al., 
2019), suggesting that the timing of the recovery will be partially 
controlled by the rate at which aerosol emissions decline. The spread 
in spatial distribution of aerosol emissions in SSPs may also play 
a role in near-term projections (Samset et al., 2019). Under divergent

air-quality policies, SSP3 features a  dipole of declining sulphate 
emissions for China but increases over India, leading to suppression 
of GHG-related precipitation increases there (Wilcox et  al., 2020). 
For the near-term future around the mid-21st century, the interplay 
between internal variability and external forcing must be considered 
(Singh and AchutaRao, 2019). Huang et al. (2020a) used two SMILEs 
to show that internal variability related to PDV could potentially 
overcome the GHG-forced upward trend in Indian monsoon rainfall, 
consistent with assessments of the global monsoon for the near 
term (Section  4.4.1.4). Emergence of the anthropogenic signal for 
South Asian precipitation is shown from the 2050s onwards in CMIP6 
(Figure 10.15b).
In long-term projections, robust signals consist of a  weakened 
upper-tropospheric meridional temperature gradient, either due to 
upper-level heating over the tropical Pacific (Sooraj et al., 2015) or

Indian oceans (Sabeerali and Ajayamohan, 2018) in CMIP5, and 
increased seasonal mean rainfall, including in CMIP6 (Almazroui 
et  al., 2020b; B. Wang et  al., 2021). The weakened temperature 
gradient combines with increased atmospheric stability to weaken 
the monsoon overturning circulation, with some findings showing 
northward movement of the lower-tropospheric monsoon winds 
in response to a stronger land–sea temperature contrast in CMIP3 
and CMIP5 (Sandeep and Ajayamohan, 2015; Endo et al., 2018). The 
northward shift was also found in the genesis of synoptic systems 
(monsoon depressions) in a  single high-resolution AGCM forced 
by an ensemble of SSTs derived from four GCMs under the RCP8.5 
scenario (Sandeep et al., 2018).
Projections can also be expressed in terms of global-mean warming 
levels (GWLs) rather than time horizons (Cross-Chapter Box  11.1). 
Advancing on SR1.5, amplification of mean and extreme monsoon

rainfall at 2.0°C compared to 1.5°C has been found both by an 
AGCM forced by future SST patterns (Chevuturi et al., 2018) and by 
using time slices in CMIP5 GCMs (Yaduvanshi et al., 2019; J. Zhang 
et al., 2020). These findings are consistent with the general scaling 
of Indian monsoon precipitation per degree of warming in CMIP5 
(Zhang et  al., 2019) and CMIP6 (B. Wang et  al., 2021). Increasing 
GWLs also lead to emergence of the anthropogenic signal over larger 
proportions of the South Asian region (Figure 10.15a).
Decomposition of the increased rainfall signal showed that while the 
dynamic component led to a drying tendency, this was overcome by 
the thermodynamic contribution (Sooraj et al., 2015; Z. Chen et al., 
2020). Alternative decomposition experiments using AGCMs and 
their coupled counterparts found increases in the lower-tropospheric 
temperature gradient and monsoon rainfall to be dominated by the 
fast radiative response to GHG increase rather than SST changes

(Li and Ting, 2017; Endo et al., 2018). The response to SST forcing 
featured a large model spread, particularly arising from the dynamic

1446
Chapter 10 Linking Global to Regional Climate Change100.3 0.2 0.1 0.0 0.1 0.2 0.3
mm day1decade1
(e) Precipiation trend distribution over central In dia(a) Rain-gauge density
APHRO-MA  V1101 (JJAS 1 956)(b) CMIP6 MME mean bias
1985-2010
6 4 2 0 2 4 6
mm day134
(f) Sp read of precip itation  trends (3  min - 3 ma x)
MPI-ESM (2016-2045)
1.2 0.8 0.4 0.0 0.4 0.8 1.2
mm day1decade1(c) Precipitation trends
(d) Precipitation anomalies over central India
Baseline period is 1995-20140.0 2.5 5.0 7.5 10.0 12.5
%sea-m asked  
data
1960 1980 2000 2020 2040 2060 2080 210020 2010 100 010 1020 2030 3040 4050 5060 60%
Past period Future periodCMIP6 historical* (N=13)
CMIP6 hist-GHG (N=13)CMIP6 ( N=34)
CMIP5 ( N=41)CRU TS
GPCC
REGEN
APHRO-MA
IITMCMIP6 SSP5 -8.5 (N=34 )
CMIP5 RCP8 .5 (N=41)long term 
precip itation  chan ge34CMIP6 SSP5 -8.5 (2015-2100)
13CMIP6 h ist-ae r (1950-2000)
13CMIP6 h ist-GHG ( 1950-2000)CRU  TS (1 950-2000)

Future period (2015-2100) Past period (1950-2000)
10 5 0 5 10 15 20 25 30
Trend (% d ecade1)20 15 10 5 0 5 10 15 20
Trend (% d ecade1)CMIP5 historical-RCP8.5
CMIP6 hist-aerCMIP6 hist-GHGCMIP6 historical*CMIP6 historical-SSP5-8.5Ensemble meansOBS
Figure 10. 19 | Changes in the Indian summer monsoon in the historical and future periods.

1447
Linking Global to Regional Climate Change Chapter 1010component (Li and Ting, 2017). Chen and Zhou (2015) found that 
the Indo-Pacific SST warming pattern dominated the uncertainty in 
Indian monsoon rainfall change. Finally, in assessing the relative 
impact of CO 2 radiative forcing and plant physiological changes in 
quadrupled CO 2 experiments in four Earth system models, Cui et al. 
(2020) showed little impact of plant physiology on annual rainfall for 
the Indian region.
While several of the above studies selected model subsets to 
constrain future projections based on standard performance metrics 
of the historical period, such as pattern correlation and root-mean-
square error, Latif et al. (2018) included a performance measure based 
on agreement with historical rainfall trends. This is an unproven 
constraint for regional projections (Section  10.3.3.9), since the 
20th-century rainfall trend over India is assessed to have been driven

chiefly by aerosol and other factors such as PDV (Sections 8.3.2.4.1 
and 10.6.3.5), while the dominant late-21st century forcing is GHG 
emissions. Modern emergent-constraint techniques (Section 10.3.4.2) 
are being applied to the Indian monsoon such as G. Li et al. (2017), 
who found that models with excessive tropical western Pacific rainfall 
tend to project a greater Indian monsoon rainfall change in future, 
due to an exaggerated cloud-radiation feedback. Correcting for this 
bias reduces the future change.
In summary, long-term future scenarios dominated by GHG increases 
(such as the RCPs) suggest increases in Indian summer monsoon 
rainfall ( high confidence ), dominated by thermodynamic mechanisms 
leading to increases in the available moisture. In the near-term, 
there is high confidence  (medium agreement , robust evidence ) that 
increased rainfall trends due to GHGs could be overcome by aerosol 
forcing or internal variability.

10.6.3.7 Future Climate Projections from Regional Downscaling
Coordinated monsoon-relevant dynamical downscaling efforts 
such as CORDEX South Asia (Gutowski Jr. et  al., 2016; Choudhary 
et al., 2018) are relevant to the Indian summer monsoon, first with 
assessment of their added value (Section 10.3.3.2 and Atlas.5.3.3). 
Singh et  al. (2017) compared nine CORDEX-South Asia RCMs 
against their driving CMIP5 GCMs, for present-day rainfall patterns 
and processes related to intra-seasonal variability. They found no consistent improvement other than for spatial patterns (e.g., rainfall 
close to better-resolved orography); some characteristics were made 
worse. Both the rainfall pattern and its bias were worsened in CORDEX 
compared to CMIP5 in Mishra et al. (2018). In contrast, Varikoden 
et  al. (2018) found improved representation of historical rainfall 
patterns, such as over the Western Ghats mountains (consistent with

Singh et  al., 2017), reducing the dry bias; improvements were not 
found over the northern plains, which are dominated by synoptic 
variability known as monsoon depressions. Similarly, Sabin et  al. 
(2013) compared a  uniform 1° resolution model ensemble with 
another zoomed to about 35 km over South Asia. Local zooming 
improved simulation of orographic precipitation and the monsoon 
trough. For the future, a  surrogate approach (like pseudo-global 
warming, see Section  10.3.2.2) was used in an RCM to test the 
impacts of warming or moistening on monsoon depressions (Sørland 
and Sorteberg, 2016; Sørland et  al., 2016). The depressions  are 
found to give more rainfall in future, dominated by strengthened 
synoptic circulation from the warming perturbation. By forcing an 
RCM with a  perturbed parameter ensemble of a  GCM, Bal et  al. 
(2016) made projections under SRES A1B for the 2020s, 2050s and 
2080s. They noted increases in rainfall of 15–24% for India. Finally,

evidence from a single CORDEX South Asia RCM showed a mixed 
signal for changes in peak season rainfall under RCP2.6 and RCP8.5 
(Ashfaq et al., 2021).
Statistical downscaling and other post-processing require calibration 
in historical conditions (e.g., Akhter et  al., 2019) and assessment 
of fitness-for-purpose (Section  10.3.3.9) before use for future 
projections. Given the noted biases in GCM monsoon simulation 
(Section  10.6.3.5), Vigaud et  al. (2013) used a  variant of quantile 
mapping to bias adjust (Section  10.3.1.3.2 and Cross-Chapter 
Box 10.2) GCM outputs. For the historical period, the pattern, mean 
and seasonal cycle of rainfall versus the input GCMs were improved. 
Increased future monsoon rain, albeit in older SRES A2 projections, 
was found for southern India. Salvi et  al. (2013) used regression-
based perfect prognosis (Section 10.3.1.3.1) for the whole country at 
0.5° resolution based on five ensemble members of a GCM in SRES

scenarios. They noted increases over rainy regions of west coast and 
north-east India, but decreases in the north, west and south-east. 
Madhusoodhanan et  al. (2018) statistically downscaled 20 CMIP5 
models to 0.05° resolution. While the global models projected Figure 10.19 (continued): (a)  Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05° subgrid boxes containing at least one gauge) 
in the APHRO-MA 0.5° daily precipitation dataset for June to September 1956. (b) Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September 
precipitation (mm day–1) compared to CRU TS observations for the 1985–2010 period. (c) Maps of rainfall trends (mm day–1 per decade) in CRU TS observations (1950–2000), 
the CMIP6 MME-mean of SSP5-8.5 future projections for 2015–2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. (d) Low-pass

filtered time series of June to September precipitation anomalies (%, relative to 1995–2014 baseline) averaged over the central India box shown in panel (b). The averaging 
region (20°N–28°N, 76°E–87°E) follows other works (Bollasina et al., 2011; Jin and Wang, 2017; Huang et al., 2020b). Time series are shown for CRU TS (brown), GPCC (dark 
blue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models 
for the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean 
change in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that

used in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081–2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some 
models exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G). (e) Precipitation linear trend 
(% per decade) over Central India for historical 1950–2000 (left) and future 2015–2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the 
CMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue 
triangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots

show the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout Chapter 10 and follow the methodology used 
in Figure 10.6. (f) Example spread of trends (mm day–1 per decade) for the period 2016–2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference 
between the three driest and three wettest trends among ensemble members over central India. All trends are estimated using ordinary least-squares regression. Further details 
on data sources and processing are available in the chapter data table (Table 10.SM.11).

1448
Chapter 10 Linking Global to Regional Climate Change10increased rainfall, the downscaled ensemble depicted both increasing 
and decreasing trends, indicating uncertainty. However, key physical 
processes operating at below-GCM scale cannot be resolved nor 
calibrated for, such as aspects of the flow around topography. 
This is notably an issue given the resolution disparity between the 
driving global models and output, and the regional challenges in 
observational data used for calibration (Section 10.6.3.3).
There are mixed messages as to whether downscaling adds value to 
climate projections of the Indian summer monsoon; however, there is 
high confidence  in projections of precipitation changes in orographic 
regions given the consistent improved representation in these regions 
among several dynamical downscaling studies.
10.6.3.8 Storyline Approaches for India
Formal storyline approaches (see Box  10.2) have been used

infrequently for the Indian summer monsoon, representing 
a  knowledge gap. In an expert-elicitation approach (Dessai et  al., 
2018), physically plausible futures substantiated by climate processes 
were constructed, focusing on a river basin in southern India. Possible 
outcomes were framed based on changes in two drivers: availability 
of moisture from the Arabian Sea and strength of the low-level flow. 
The narratives identified were able to explain 70% of the variance in 
monsoon rainfall over 1979–2013, the implication being that climate 
uncertainties could be easily communicated to stakeholders in the 
context of present-day variability.
The storylines terminology could be used to loosely describe 
the interplay between internal variability and forced change 
(see Section 10.6.3.6), such as considering the difference between 
groups of wettest and driest ensemble members of a SMILE for the 
near-term future in Figure  10.19f. However, given the interest in

low-likelihood high-impact scenarios (Sutton, 2018), we can also 
consider possible storylines for the Indian monsoon constructed 
from evidence in paleoclimate records and modelling. For example, 
a  future AMOC collapse could cause reduced monsoon rainfall 
(Section  8.6.1; Liu et  al., 2017), offsetting increases expected 
due to GHG. Large tropical volcanic eruptions are also known to 
weaken the Asian summer monsoon, in observations and model 
simulations over the last millennium (Section 8.5.2.3; Zambri et al., 
2017), although a hemispheric dependence is found, with Southern 
Hemisphere eruptions even strengthening the monsoon around 
India (Zuo et al., 2019). Typically, future climate projections do not 
consider plausible eruption scenarios and their mitigating effects 
on greenhouse warming (see also Cross-Chapter Box 4.1). A single-
model ensemble (Bethke et al., 2017) demonstrates a future drier 
Indian monsoon relative to conditions in which volcanic eruptions

are not considered, although the effects of GHG warming dominate 
beyond the mid-term.
The few studies on low-likelihood high-impact scenarios, often in 
single models, together with findings in SR1.5 (Hoegh-Guldberg et al., 
2018), noting the small radiative forcing in 1.5°C or 2°C scenarios, or 
the absence of large aerosol emissions at the end of the 21st century 
in RCPs, give us low confidence  in abrupt changes to the monsoon 
on this time scale.10.6.3.9 Regional Climate Information Distilled 
from Multiple Lines of Evidence
Above, we presented assessments from observational and model 
attribution studies of the historical period, followed by future climate 
projections in global and regional models, and storylines approaches 
including low-likelihood high impact events. Miscellaneous lines of 
evidence are considered here.
Our assessment could also be informed by attempting to constrain 
future projections of the Indian summer monsoon using paleoclimate

evidence. In modelling work of the mid-Holocene (D’Agostino et al., 
2019), the increased obliquity (axial tilt) and altered orbital precession 
lead to an enhanced monsoon with a stronger dynamic component 
(strengthening the mean monsoon overturning) controlling the 
increase in monsoon rainfall. In future climates however, the dynamic 
contribution decreases (Section  10.6.3.6), yet the increased 
thermodynamic component (greater moisture availability) overcomes 
this to cause a wetter monsoon. Monsoon changes under different 
epochs may not be governed by the same mechanisms (D’Agostino 
et  al., 2019; Hill, 2019), making the mid-Holocene, in particular, 
unsuitable as a period to compare with.
Finally, the recent national climate-change assessment for India 
(Krishnan et  al., 2020) has distilled multiple lines of evidence to 
show declining summer monsoon rainfall over the second half 
of the 20th  century, attributable to emissions of anthropogenic

aerosols, while future projections informed by CMIP5 modelling and 
dominated by GHG forcing show increased mean rainfall by the end 
There is very high confidence  (robust evidence , high agreement ) of 
a negative trend of summer monsoon rainfall over the second half 
of the 20th  century averaged over all of India. There is medium 
agreement over trends at the regional level owing to uncertainty 
among observational products, which hinders model evaluation, 
downscaling and assessment of changes to extremes. There is  high 
confidence  (robust evidence , medium agreement ) that anthropogenic 
aerosol emissions over the Northern Hemisphere and internal 
variability have contributed to the negative trend, while there is 
high confidence  (robust evidence , medium agreement ) that Indian 
summer monsoon rainfall will increase at the end of the 21st century 
in response to increased GHG forcing, due to the dominance of

thermodynamic mechanisms. No  contradictory evidence is found from 
downscaling methods. The contrast between declining rainfall in the 
observational record and long-term future increases can be explained 
using multiple lines of evidence. They are not contradictory since they 
are attributable to different mechanisms (primarily aerosols and 
greenhouse gases, respectively). The long-term future changes are 
generally consistent across global (including at high resolution) and 
regional climate models, and supported by theoretical arguments. 
Furthermore, while there are subtle differences found in past periods 
with a  climate similar to the future climate (the mid-Holocene), 
different physical mechanisms at play suggest that paleoclimate 
evidence does not reduce confidence in the future projections. 
In the near term, there is high confidence  that internal variability 
will dominate.

1449
Linking Global to Regional Climate Change Chapter 101010.6.4 Mediterranean Summer Warming
10.6.4.1 Motivation and Regional Context
The Mediterranean region is loosely denoted as the region that 
surrounds the Mediterranean Sea, and it is characterized by complex 
orography and strong land–sea contrasts. The region contains a dense 
and growing human population, with large regional differences: 
whereas the population of the European Mediterranean countries has 
been relatively stable or even declining during the past decades, the 
population of countries in Mediterranean areas of the Middle East and 
North Africa has quadrupled between 1960 and 2015, and the degree 
of urbanization has risen from 35 to 64% during the same period 
(Cramer et al., 2018) and during the more recent period 2000–2020 
the urban expansion rate has exceeded 5% (Kuang et al., 2021).
The Mediterranean region has experienced significant climate variability

over recent decades and has been affected in particular by severe 
heatwaves and droughts (Sections 8.3, 11.3, 11.6 and 12.4; Hoegh-
Guldberg et al., 2018). Increasing summer temperatures will enhance 
the frequency and intensity of such extreme events and will cause 
additional environmental and socio-economic pressure on the region.
10.6.4.2 The Region’s Climate
The Mediterranean has a heterogeneous climate that is partly semi-arid, 
especially along the southern coast of the Mediterranean Sea (Lionello 
et al., 2012). It is characterized by mild humid winters and dry warm or 
hot summers, which are associated with large scale subsidence that is 
partly related to the downward branch of the Hadley circulation. Other 
factors affecting the Mediterranean circulation include the monsoon 
heating over Asia (Rodwell and Hoskins, 1996; Cherchi et al., 2014; Ossó 
et al., 2019) and circulation anomalies induced by topography (Simpson

et al., 2015). Seasonal and interannual variability is strongly linked to 
natural modes of variability (Section  10.6.4.4). The Mediterranean 
Sea acts as an evaporation source that dominates the  regional 
hydrological cycle, which is characterized by local cyclogenesis and 
a separate branch of the mid-latitude storm track (Lionello et al., 2016). 
It also affects remote locations such as the Sahel (Park et al., 2016; 
Section 10.4.2.1). Strong storms can develop over the Mediterranean. 
Among these, Medicanes are particularly destructive and exhibit 
several similarities with tropical cyclones (Cavicchia et  al., 2014; 
Kouroutzoglou et al., 2015; Gaertner et al., 2018). The Mediterranean 
region is also characterized by strong land-atmosphere coupling and 
feedbacks (Seneviratne et  al., 2006) generating prolonged droughts 
and intense heatwaves, which can also affect continental Europe 
(Zampieri et al., 2009). Other aspects of Mediterranean climate include

regional winds, which can be very strong due to the channelling effect 
(Obermann et al., 2018) and extreme rainfall during autumn (Ducrocq 
et al., 2014; Ribes et al., 2019).
10.6.4.3 Observational Issues
The Mediterranean region spans a  wide variety of countries 
and economies. This has led to large differences in the existence and 
availability of observational records, with the southern part of the area 
being sparsely covered by meteorological stations (Figure 10.20b). Consequently, basin-wide, homogeneous, quality controlled 
observational datasets are lacking, especially before the advent 
of substantial satellite observations in the 1970s. Observational 
uncertainties exist also for those regions that are covered by high 
quality networks such as European Climate Assessment & Dataset 
(ECA&D; Flaounas et al., 2012).
Large differences of up to 7°C between the CRU and UDEL datasets 
have been found especially over mountainous areas, such as the Atlas

in Morocco (Zittis and Hadjinicolaou, 2017; Strobach and Bel, 2019). 
Bucchignani et al. (2016a, b) compared three different datasets (CRU, 
UDEL, and MERRA) with the available ground observations and found 
that although the geographical distribution of the bias is qualitatively 
similar for the three datasets, differences exist, with the absolute 
bias being generally lower in Modern-Era Retrospective Analysis 
for Research and Applications (MERRA) especially over North Africa 
during the summer and winter season. There is  high confidence that 
the sparse monitoring network in parts of the Mediterranean region 
strongly increases the uncertainty across different gridded datasets 
(Section 10.2.2.3, Figure 10.20b,c).
10.6.4.4 Relevant Anthropogenic and Natural Drivers
The Mediterranean summer climate is affected by large-scale modes 
of natural variability, the most dominant being the NAO (Annex IV) 
in winter and the summer NAO in summer (Folland et al., 2009; Bladé

et al., 2012), although regional differences exist. The influence of those 
modes of variability over the eastern Mediterranean is recognized by 
some studies (Chronis et  al., 2011; Kahya, 2011; Black, 2012; Bladé 
et al., 2012), but disputed by others (Ben-Gai et al., 2001; Ziv et al., 2006; 
Donat et al., 2014; Turki et al., 2016; Zamrane et al., 2016; Han et al., 
2019). During positive summer NAO phase, associated with an upper-
level trough over the Balkans, the Mediterranean is anomalously wet 
(Bladé et al., 2012). Drivers of Mediterranean climate variability include 
modes of variability such as the AMV (Sutton and Dong, 2012) and the 
Asian monsoon (Rodwell and Hoskins, 1996; Logothetis et al., 2020). 
In addition, the increase of GHGs (e.g., Zittis et al., 2019), the decrease 
of anthropogenic aerosols over Europe and the Mediterranean since 
the 1980s resulting from air pollution policies (Turnock et al., 2016),

and anthropogenic land-use change (Millán, 2014; MedECC 2020) 
have been shown to be linked to the regional warming. The role of the 
zonal averaged circulation as a driver for the Mediterranean climate 
has been stressed by (Garfinkel et al., 2020).
The attribution of observed Mediterranean summer warming to 
above drivers and implications for future projections will be discussed 
in Sections 10.6.4.5 and 10.6.4.6.
10.6.4.5 Model Simulation and Attribution 
Observational datasets show large agreement on the historical 
(1960–2014) temperature evolution at basin-wide scale (Figure 10.20e), 
with an enhanced warming since the 1990s, and the early decades 
of the 21st century being on average approximately more than 1°C 
warmer than late 19th  century levels (van der Schrier et  al., 2013; 
Cramer et  al., 2018; Lionello and Scarascia, 2018; Figure  10.20e).

1450
Chapter 10 Linking Global to Regional Climate Change10Over recent decades, the surface air temperature of the Mediterranean 
including the Mediterranean Sea has warmed by around 0.4°C per 
decade (Macias et al., 2013). Observed trends over land show large 
geographical heterogeneity (Figure  10.20d) and notable differences 
exist amongst different datasets at grid point scale (Figure  10.20c; 
Qasmi et al., 2021).
Several mechanisms have been proposed for the enhanced 
Mediterranean warming, although their relative importance and the 
possible interplay between them are not fully understood. Circulation 
changes might have contributed to this enhanced warming 
(Figure  10.20a). Sutton and Dong (2012) argued that the AMV 
induced a shift around the 1990s towards warmer southern European 
summers. This mechanism is associated with a  linear baroclinic 
atmospheric response to the AMV-related surface heat flux. Also 
O’Reilly et al. (2017) related warm summer decades to the AMV, but

the connection was shown to be mainly thermodynamic. Qasmi et al. 
(2021) estimate an increase in Mediterranean summer temperature of 
0.2°C–0.8°C during a positive AMV.
Increased warming over land compared to the sea is expected due to 
the lapse-rate changes associated with tropospheric moisture contrasts 
(Kröner et al., 2017; Byrne and O’Gorman, 2018; Brogli et al., 2019b; 
Figure  10.20a). Enhanced land–sea temperature contrast leads to 
relative humidity and soil moisture feedbacks (Rowell and Jones, 2006), 
the latter also depending on weather regimes (Quesada et al., 2012). 
The globally enhanced land–sea contrast in near surface temperature 
is also a robust result in CMIP5 and CMIP6 models (Section 4.5.1.1).
Due to its semi-arid climate, strong atmosphere–land coupling has 
contributed to the larger increase of mean summer temperature 
compared to the increase of annual mean temperature (Seneviratne 
et al., 2006). In particular, during drought spells, limits to evaporation

due to low soil moisture provide a positive feedback and enhances the 
intensity of heatwaves (Lorenz et al., 2016; Box 11.1). By comparing 
reanalysis-driven RCM simulations with observations, Knist et  al. 
(2017) found that RCMs are able to reproduce soil moisture interannual 
variability, spatial patterns, and annual cycles of surface fluxes over 
the period 1990–2008, revealing a strong land–atmosphere coupling 
especially in southern Europe in summer. In addition cloud feedbacks 
can modulate the Mediterranean summer temperature (Mariotti and 
Dell’Aquila, 2012).
The observed trends over 1901–2010 are outside the range of internal 
variability shown in CMIP5 pre-industrial control experiments and 
consistent with, or greater than those simulated by experiments 
including both anthropogenic and natural forcings (Knutson et al., 
2013) and therefore partly attributable to anthropogenic forcing. 
The decrease of anthropogenic aerosols over Europe including the

Mediterranean resulting from European de-industrialisation and 
air pollution policies (Turnock et  al., 2016) has been highlighted 
as an important contributor to the observed warming (Ruckstuhl 
et al., 2008; Philipona et al., 2009; de Laat and Crok, 2013; Nabat 
et al., 2014; Besselaar et al., 2015; Dong et al., 2017; Boé et al., 2020a). 
Pfeifroth et al. (2018) argue that this brightening is mainly due to 
cloud changes caused by the indirect aerosol effect with a minor role 
for the direct aerosol effect, in contrast to Nabat et al. (2014) and Boers et al. (2017) who attribute it to the direct aerosol effect. Using 
model sensitivity experiments, Nabat et al. (2014) also associated the 
increase in Mediterranean SST since 1980–2012 with the decrease in 
aerosol concentrations (Atlas.8.2, Atlas.8.3 and Atlas.8.5).
Over the period 1960–2014, observed trends over land are consistent 
with those of most of the multi-model or SMILEs ensembles

(Figure 10.20f), although large differences exist for individual models 
and ensemble members. The modelled ensemble-mean trends show 
large geographical variations. Generally, both global and regional 
models often underestimate the observed trend especially over parts 
of North Africa, Italy, the Balkans and Turkey. The cold bias in global 
models is related to simulated SLP trends that are anti-correlated 
to the observed trend, which is probably due to systematic model 
errors (Boé et al., 2020b). Biases in the simulation of soil-moisture 
and cloud-cover might also have contributed to the underestimation 
of the warming trend in GCMs (van Oldenborgh et  al., 2009). 
The CORDEX results (at both 0.44° and 0.11° resolution) show 
consistently smaller values than those in global models and the 
available datasets (Figure 10.20g; Vautard et al., 2021). This is partly 
due to the overestimation in the temperature evolution before 1990

(Figure 10.20e), possibly because of differences in the aerosol forcing 
(Boé et  al., 2020a), although the driving global models also have 
a cold bias (Vautard et al., 2021). Cold biases for recent decades are 
also found in Med-CORDEX simulations (Dell’Aquila et al., 2018) and 
by RCM simulations over the southern part of the Mediterranean, 
Middle East and North Africa region (Almazroui, 2016; Almazroui 
et al., 2016a, b; Zittis and Hadjinicolaou, 2017; Ozturk et al., 2018), 
although higher resolution, new bare soil albedo and modified aerosol 
parametrization significantly improve the results (Bucchignani et al., 
2016a, b, 2018). Despite large differences in the multi-model mean 
trend (Figure  10.20g), in most of the land points the observed 
trend lies within the model range in all ensembles. For the SST bias 
exhibited by coupled RCMs the choice of driving global model has 
the largest impact (Darmaraki et al., 2019; Soto-Navarro et al., 2020).

10.6.4.6 Future Climate Information From Global Simulations
The Mediterranean is expected to be one of the most prominent 
and vulnerable climate change hotspots (Diffenbaugh and Giorgi, 
2012). CMIP5, CMIP6, HighResMIP and CORDEX (Section 10.6.4.7) 
simulations all project a  future warming for the 21st  century that 
ranges between 3.5°C and 8.75°C for RCP8.5 at the end of this 
century for those ending at 2100 (Figure 10.21a, b). CMIP6 results 
project more pronounced warming than CMIP5 for a given emissions 
scenario and time period (Figure  10.21c; Coppola et  al., 2020). 
However, when analysing the Mediterranean warming in terms 
of mean global warming levels, the two ensembles largely agree, 
showing that summer warming is projected to reach values up to 
40–50% larger than the global annual warming, largely independent 
of models and emissions scenarios (Figure 10.21d). Large regional 
differences exist, with enhanced warming projected over Turkey,

the Balkans, the Iberian Peninsula and North African regions 
(Figures 10.14a, 10.21c; Almazroui et al., 2020a) and reaching, locally, 
values of up to double the global mean (Lionello and Scarascia, 
2018). The enhanced summer warming also increases the amplitude 
of the seasonal cycle (Yettella and England, 2018).

1451
Linking Global to Regional Climate Change Chapter 1010As noted in Section 10.6.4.4, the Mediterranean summer climate is 
affected by large-scale circulation patterns, of which the summer 
NAO is the most important (Folland et al., 2009; Bladé et al., 2012). 
Barcikowska et  al. (2020) highlight the importance of correctly 
simulating the summer NAO impact on the Mediterranean climate, 
as it partly offsets the anthropogenic warming signal in the western 
and central Mediterranean.
Climate models project a reduction in precipitation in all seasons, and 
a northward and eastward expansion of the Mediterranean climate, 
with the affected areas becoming more arid with an increased 
summer drying (Atlas.8.5; Alessandri et al., 2015; Mariotti et al., 2015; 
Rajczak and Schär, 2017; Waha et  al., 2017; Barredo et  al., 2018; 
Lionello and Scarascia, 2018; Spinoni et al., 2018, 2020). The drying can contribute to the enhanced warming by land surface feedbacks

(Whan et al., 2015; Lorenz et al., 2016; Russo et al., 2019). A negative 
feedback to this dryness-induced warming might be provided by an 
enhanced moisture transport into the dry area associated with the 
dynamical response of the atmosphere (Zhou et al., 2021). Due to 
the arid climate, no positive soil moisture-temperature feedback is 
found over the North African regions of the Mediterranean, where 
the surface energy budget is mostly governed by radiative cooling 
(Lelieveld et al., 2016), implying that soil moisture feedbacks are not 
contributing to enhanced warming over those regions.
Over the Mediterranean region, daily maximum temperature is 
projected to increase more than the daily minimum. Consequently, 
the difference between daytime maxima and nighttime minima is 
(d) Observed trend (BerkeleyEarth)
Past period (1960-2014)
0.0 0.1 0.2 0.3 0.4 0.5
°C decade1(e) Mediterranean temperature anomalies
Berkeley Earth
CRU TS
HadCRUT5HighResMIP

CORDEX EUR-44
CORDEX EUR-11CMIP6
CMIP5
(b) Station locations
Dona t et al. 20 14 E-OBS
0.20 0.15 0.10 0.05 0.00 0.05 0.10 0.15 0.20
°C decade1(g) Ensemble mean trend differences with respect to the observed trend ((d), 1960-2014)
41CMIP5
36CMIP6
37CORDEX EUR-11
20CORDEX EUR-44
4HighResMIP
missing a nd se a-
masked  dataObs. o utside  
mode l range(c)Observational grid point difference
0.2 0.0 0.2
Trend difference to E-OBS
(°C decade1)050.1° ×0.1° grid po ints (%)(a) Mechanisms of enhanced Mediterranean warming
Less d imming and
increa se of  solar radia tionRedu ction o f
aerosols
Redu ction o f late nt heat flux a nd
increa se of  sensib le he at flu xEnha nced
sensible
heat flux
Dynamic a nd
thermodyn amic e ffectsWarm Atlantic O cean
Land Sea
TemperatureAltitudeLapse ra te difference Surfa ce dr ying
Redu ced
laten t
heat fluxOver land
sea warming  than over
(f) Temperature trend distribution
Past period (1960-2014)stron ger
1960 1970 1980 1990 2000 20102101(°C)

CORDEX EUR-11CORDEX EUR-44HighResMIPCMIP6CMIP5Ensemble meansOBS
0.2 0.4 0.6
Trend (°C decade1)GISTEMPNOAA  Globa l Temp
Berkele y EarthCRUTEM4
Figure  10.20 | Aspects of Mediterranean summer warming. (a)  Mechanisms and feedbacks involved in enhanced Mediterranean summer warming. (b) Locations of 
observing stations in E-OBS and Donat et al. (2014). (c) Differences in temperature observational datasets (NOAA Global Temp, Berkeley Earth, CRUTEM4 and GISTEMP) with respect 
to E-OBS for the land points between the Mediterranean Sea and 46°N and west of 30°E. (d) Observed summer (June to August) surface air temperature linear trends (°C decade–1) 
over the 1960–2014 period from Berkeley Earth. (e) Time series of area averaged Mediterranean (25°N–50°N, 10°W–40°E) land point summer temperature anomalies (°C, baseline

1995–2014). Dark blue, brown and turquoise lines show low-pass filtered temperature of Berkeley Earth, CRU TS and HadCRUT5, respectively. Orange, light blue and green lines 
show low-pass filtered ensemble means of HighResMIP (4 members), CORDEX EUR-44 (20 members) and CORDEX EUR-11 (37 members). Blue and red lines and shadings show 
low-pass filtered ensemble means and standard deviations of CMIP5 (41 members) and CMIP6 (36 members). The filter is the same as the one used in Figure 10.10. (f) Distribution 
of 1960–2014 Mediterranean summer temperature linear trends (°C decade–1) for observations (black crosses), CORDEX EUR-11 (green circles), CORDEX EUR-44 (light blue circles), 
HighResMIP (orange circles), CMIP6 (red circles), CMIP5 (blue circles) and selected SMILEs (grey box-and-whisker plots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM and d4PDF). Ensemble

means are also shown. CMIP6 models showing a very high ECS (Box. 4.1) have been marked with a black cross. All trends are estimated using ordinary least-squares and box-and-
whisker plots follow the methodology used in Figure 10.6. (g) Ensemble mean differences with respect to the Berkeley Earth linear trend for 1960–2014 (°C decade–1) of CMIP5, 
CMIP6, HighResMIP , CORDEX EUR-44 and CORDEX EUR-11. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1452
expected to increase, particularly in summer (Lionello and Scarascia, 
2018). Temperature extremes will be affected as well, with a dramatic 
increase in the number of warm days and reduction of cold nights 
(Section  11.9; Lionello and Scarascia, 2020). The Mediterranean 
summer warming will also increase the frequency and intensity of 
heatwaves (Section 11.9).
10.6.4.7 Future Climate Information 
From Regional Downscaling
To unravel the complex interactions and feedbacks over the region on 
a range of spatial and temporal scales, regional downscaling projects 
are being developed to provide more comprehensive and detailed 
information on the future of the Mediterranean. The importance 
of regional downscaling for investigating the subregional details 
caused by the complex morphology of the Mediterranean region

is a well-known issue in the literature (Planton et al., 2012), which has been addressed in many studies since AR5. Recent examples 
of dynamical downscaling are EURO-CORDEX (Jacob et  al., 2014) 
and Med-CORDEX (Ruti et al., 2016; Somot et al., 2018), but earlier 
activities have included ENSEMBLES (Déqué et al., 2012; Fernández 
et  al., 2019), PRUDENCE (Christensen et  al., 2002), CIRCE (Gualdi 
et al., 2013) and ESCENA (Jiménez-Guerrero et al., 2013).
From an analysis of CORDEX results, studies showed that southern 
Europe is projected to face a  robust non-linear increase in 
temperature larger than the global mean (Zittis et al., 2019), EURO-
CORDEX projections, that are driven by CMIP5 global models, 
project a less pronounced warming than that of CMIP6 (Coppola 
et al., 2021; see Figure 10.21c). The non-linear increase is especially 
evident for both hot and cold extremes (Section 11.9; Maule et al., 
2017; Jacob et al., 2018; Kjellström et al., 2018). In particular, Dosio

and Fischer (2018) showed that in many places in southern Europe 
and the Mediterranean, the increase in the number of nights 
(a) Projected Mediterranean temperature anomalies
2020 2040 2060 2080 21000 02 24 46 68 8(°C)
Future period Long termCMIP6 lo ng term 
temp erature chan ge
SSP5-8. 5SSP1-2. 6
SSP2-4. 5
SSP3-7. 0HighResMIP
CORDEX EUR-11CMIP6CMIP5
(c) Ensemble mean trends future perio d(2015-2050)
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85
°C decade1CMIP5
41
CMIP6
36CORDEX EUR-11
37CORDEX EUR-44
20
HighResMIP
4
sea-m asked  
data(d) Mediterranean summer vs global warming
Baseline period is 1861-1900
0 1 2 3 4
global temperature (°C)012345678Mediterranean summer temperature (°C)
RCP8.5RCP6.0RCP4.5RCP2.6CMIP5 ( N=21)
SSP5-8. 5SSP3-7. 0SSP2-4. 5SSP1-2. 6CMIP6 ( N=31)(b) Temperature trend distribution
Future period (2015-2050)
0.2 0.4 0.6 0.8 1.0
Trend (°C decade1)CORDEX EUR-11CORDEX EUR-44HighResMIPCMIP6CMIP5Ensemble means

MPI-ESMCSIRO-Mk3-6-0MIROC6Figure 10. 21 | Projected Mediterranean summer warming. (a)  Time series of area averaged Mediterranean (25°N–50°N, 10°W–40°E) land point summer surface 
air temperature anomalies (°C, baseline period is 1995–2014). Orange, light blue and green lines show low-pass filtered ensemble means of HighResMIP (highres-future, 
four members), CORDEX EUR-44 (RCP8.5, 20 members) and CORDEX EUR-11 (RCP8.5, 37 members). Blue and dark red lines and shadings show low-pass filtered ensemble 
means and standard deviations of CMIP5 (RCP8.5, 41 members) and CMIP6 (SSP5-8.5, 36 members). The filter is the same as the one used in Figure 10.10. The box-and-
whisker plots show long-term (until 2081–2100) temperature changes of different CMIP6 scenarios with respect to the baseline period (SSP1-2.6 in dark blue, SSP2-4.5 in

yellow, SSP3-7.0 in red, SSP5-8.5 in dark red). (b) Distribution of 2015–2050 Mediterranean summer temperature linear trends (°C per decade) for CORDEX EUR-11 (RCP8.5, 
green circles), CORDEX EUR-44 (RCP8.5, light blue circles), HighResMIP (highres-future, orange circles), CMIP6 (SSP5-8.5, dark red circles), CMIP5 (RCP8.5, blue circles) and 
selected SMILEs (grey box-and-whisker plots, MIROC6, CSIRO-Mk3-6-0 and MPI-ESM). Ensemble means are also shown. CMIP6 models showing a very high ECS (Box 4.1) have 
been marked with a black cross. All trends are estimated using ordinary least-squares and box-and-whisker plots follow the methodology used in Figure 10.6. (c) Projections 
of ensemble mean 2015–2050 linear trends (°C per decade) of CMIP5 (RCP8.5), CORDEX EUR-44 (RCP8.5), CORDEX EUR-11 (RCP8.5), CMIP6 (SSP5-8.5) and HighResMIP

(highres-future). All trends are estimated using ordinary least-squares. (d) Projected Mediterranean summer warming in comparison to global annual mean warming of CMIP5 
(dashed lines, RCP2.6 in dark blue, RCP4.5 in light blue, RCP6.0 in orange and RCP8.5 in red) and CMIP6 (solid lines, SSP1-2.6 in dark blue, SSP2-4.5 in yellow, SSP3-7.0 in red 
and SSP5-8.5 in dark red) ensemble means. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1453
Linking Global to Regional Climate Change Chapter 1010with temperature above 20°C is more than 60% larger under 
2°C warming compared to 1.5°C. Over the region, the projected 
temperature increase, including a  higher probability of severe 
heatwaves (Russo et al., 2015), is accompanied by a reduction in 
precipitation (Jacob et al., 2014; Dosio, 2016; Rajczak and Schär, 
2017), resulting in projected increases of drought frequency and 
severity (Spinoni et al., 2018, 2020; Raymond et al., 2019). Also, the 
frequency and severity of marine heatwaves of the Mediterranean 
Sea are projected to increase (Darmaraki et  al., 2019; see 
Section 12.4 and Atlas.8.4).
Only a  limited number of RCM simulations for the MENA domain 
are currently available. For the southern and eastern Mediterranean, 
they project a mean warming ranging from 3°C for RCP4.5 to 9°C 
for RCP8.5 at the end of this century compared to its beginning

(Bucchignani et  al., 2018; Ozturk et  al., 2018). The frequency and 
duration of heatwaves and annual number of extremely hot days 
(i.e., those with maximum temperature >50°C) in the southern 
Mediterranean will increase substantially. For 2070–2099 with 
respect to 1971–2000 the latter might even reach 70 days for 
RCP8.5 (Lelieveld et al., 2016; Almazroui, 2019; Driouech et al., 2020; 
Varela et al., 2020).
Despite the large efforts of these regional downscaling projects, the 
global model–RCM matrix is still sparse and lacking a systematic 
design to explore the uncertainty sources (e.g.,  global model, 
RCM, scenario, resolution) (Section 10.3). Focusing on the Iberian 
peninsula, Fernández et  al. (2019) argued that the driving global 
model is the main contributor to uncertainty in the ensemble. 
Physically consistent but implausible temperature changes in 
RCMs can occur. An example is a strong temperature increase over

the Pyrenees due to excessive snow cover in the present climate 
(Fernández et al., 2019). Based on an older set of RCM simulations 
(ENSEMBLES), Déqué et al. (2012) also argued that the largest source 
of uncertainty in the temperature response over southern Europe 
is the choice of the driving global model (whereas for summer 
precipitation the choice of the RCM dominates the uncertainty). 
Finally, Boé et al. (2020a) found that over a large area of Europe, 
including parts of the Mediterranean, RCMs project a  summer 
warming 1.5°C–2°C colder than in their driving global models for 
the end of the 21st century. This is caused by differences in solar 
radiation related to the absence of time-varying anthropogenic 
aerosols in RCMs (Boé et al., 2020a; Gutiérrez et al., 2020), which 
also affects the noted differences in cloud cover between global 
models and RCMs (Bartók et al., 2017).
Statistical downscaling studies for the Mediterranean confirm the

results from global model and RCM studies, with large agreement 
among future projections showing lower rates of warming in winter 
and spring, and, in most cases, higher ones in summer and autumn 
(Jacobeit et al., 2014).
10.6.4.8 Storyline Approaches
The atmospheric circulation is influenced by large-scale, often 
slowly varying components of the climate system, such as ocean, 
sea ice and soil moisture. Historical and future changes of the atmospheric circulation depend, among other factors, on how these 
drivers have changed and will change. Zappa and Shepherd (2017) 
have analysed this for the Mediterranean region and developed 
a set of storylines based on different plausible evolutions of those 
drivers and their impact on the Mediterranean winter climate. 
Important identified drivers during winter are tropical and polar 
amplification of global warming and the polar stratospheric vortex 
(Manzini et  al., 2014; Simpson et  al., 2018), with implications

for precipitation. Zappa (2019) discusses the relative amplitude 
of tropical and Arctic warming, response of the AMOC, patterns of 
Pacific SST change, and changes in stratospheric vortex strength as 
possible drivers of the Mediterranean summer climate and stresses 
that given the present state of knowledge, alternative storylines 
based on these drivers should be considered as equally plausible 
future manifestations of regional climate change. Brogli et  al. 
(2019a, b) and Kröner et al. (2017) have revealed thermodynamic 
processes, lapse rate, and circulation as important drivers for 
Mediterranean summer climate.
Low-likelihood high-impact events might affect future Mediterranean 
climate. An example of such an event is the collapse of the AMOC 
(Weijer et al., 2019), that would bring widespread cooling over the 
Northern Hemisphere. For the Mediterranean this is estimated to be 
a few degrees Celsius during summer in the case of a total collapse 
(Jackson et al., 2015).

10.6.4.9 Climate Information Distilled From 
There is very high confidence (high agreement , robust evidence ) that 
the Mediterranean region has experienced a  summer temperature 
increase in recent decades that is faster than the increase for 
the Northern Hemisphere summer mean. There is also very high 
confidence (high agreement , robust evidence ) that the projected 
Mediterranean summer temperature increase will be larger than the 
global warming level, with an increase in the frequency and intensity 
of heatwaves.
Traditionally, the distillation process to produce contextualized, 
policy relevant information has taken place at regional or national 
level. For example, the potential effects of climate change on 
public health are discussed in several national climate change 
and adaptation reports (Bruci et  al., 2016; MoARE, 2016; MoE, 
2016; MoEP , 2018; MoEU, 2018). Although these reports are

extremely helpful and widely used for the development of national 
adaptation policies, they are often based on non-comprehensive 
and heterogeneous sources of climate information (e.g.,  MEEN, 
2018; MoE/UNDP/GEF , 2019). For instance, future climate change 
projections are based on a  limited number of socio-economic 
scenarios and climate model simulations, which are also often 
not evaluated comprehensively (e.g.,  Bruci et  al., 2016; MoARE, 
2016; MoEU, 2018). In addition, these reports are often not peer-
reviewed, not available in English, and mainly limited to the country 
level, thus making it difficult to compare the details of the climate 
information across them.

1454
Chapter 10 Linking Global to Regional Climate Change10Box 10.3 | Urban Climate: Processes and Trends
Urban areas have special interactions with the climate system that produce heat islands. This box presents information about these 
processes, how they are parametrized in climate modules, and on the role of urban monitoring networks. A discussion on the observed 
climate trends and climate change projections for urban areas follows.
Urban heat island
During nighttime, urban centres are often several degrees warmer than the surrounding rural area, a phenomenon known as the 
nighttime canopy urban heat island effect (Bader et al., 2018; Kuang, 2019; Li et al., 2019; Y . Li et al., 2020a). While green and blue 
infrastructures can mitigate the urban heat island effect, three main factors contribute to its development (Hamdi et al., 2020; Masson 
et al., 2020): (i) three-dimensional urban geometry including building density and plan area, street aspect ratio and building height;

(ii) thermal characteristics of impervious surfaces; and (iii) anthropogenic heat release, either from building energy consumption, 
especially waste heat from air conditioning systems, or as direct emissions from industry, traffic, or human metabolism (Ichinose et al., 
1999; Sailor, 2011; de Munck et al., 2013; Bohnenstengel et al., 2014; Chow et al., 2014; Salamanca et al., 2014; Dou and Miao, 2017; 
Ma et al., 2017a; Chrysoulakis et al., 2018; Takane et al., 2019). Urban heat island magnitude is also affected by aerosols due to air 
pollution in urban areas (Cheng et al., 2020; Han et al., 2020) and by local background climate (Zhao et al., 2014; Ward et al., 2016).
Monitoring network
Long-term climate datasets (a year or more) at the small spatial scales required to resolve processes of interest for cities (<1 km) 
are scarce (Bader et al., 2018; Caluwaerts et al., 2020). Moreover, urban observation sites often represent only parts of the urban

environment and are suboptimal for detecting urban effects (e.g., sites in city parks). Recently, city-scale climate monitoring networks 
as well as satellite and ground-based remote sensing are being used (though still missing in Global South cities; Technical Annex I), 
enhancing our understanding of the urban microclimate and its interaction with climate change, and providing key information 
for users (F . Chen et al., 2012; Barlow et al., 2017; Bader et al., 2018). It has been found that harmonization of collection practices, 
instrumentation, station locations, and quality control methodologies across urban environments needs improvement to facilitate 
collaborative research (Muller et al., 2013; Barlow et al., 2017). Real time crowdsourcing data is becoming available (Section 10.2.4). 
The urban climate community is making efforts to understand how these methods can complement traditional datasets (Meier et al.,

2017; Zheng et al., 2018; Langendijk et al., 2019b; Venter et al., 2020).
Urban modules in climate models
Exchanges of heat, water and momentum between the urban surface and its overlying atmosphere are calculated using specific surface-
atmosphere exchange schemes. Three different schemes, here in order of increasing complexity, can be distinguished (Masson, 2006; 
Grimmond et al., 2010, 2011; Chen et al., 2011; Best and Grimmond, 2015): (i) in the slab or bulk approach, the three-dimensional city 
structure is not resolved but cities are represented by modifying soil and vegetation parameters within land surface models, increasing 
roughness length and displacement height (e.g., Seaman et al., 1989; Dandou et al., 2005; Best et al., 2006; Liu et al., 2006). The energy 
balance is often modified to account for the radiation trapped by the urban canopy, heat storage, evaporation and anthropogenic

heat fluxes. (ii) Single-layer urban canopy modules use a simplified geometry (urban canyon, with three surface types: roof, road and 
wall) that approximately capture the three-dimensional dynamical and thermal physical processes influencing radiative and energy 
fluxes (Masson, 2000; Kusaka et al., 2001). (iii) Multi-layer urban canopy modules compute urban effects vertically, allowing a direct 
interaction with the planetary boundary layer (Brown, 2000; Martilli et al., 2002; Hagishima et al., 2005; Dupont and Mestayer, 2006; 
Hamdi and Masson, 2008; Schubert et al., 2012). Building-energy models that estimate anthropogenic heat from a building for given 
atmospheric conditions can be incorporated. Recent model development has focused on improving the representation of urban 
vegetation (Lee et al., 2016; Redon et al., 2017; Mussetti et al., 2020).

Global (McCarthy et al., 2010; Oleson et al., 2011; Zhang et al., 2013; H. Chen et al., 2016; Katzfey et al., 2020; Sharma et al., 2020; 
Hertwig et al., 2021) and regional modelling groups (Oleson et al., 2011; Kusaka et al., 2012a; McCarthy et al., 2012; Hamdi et al., 
2014; Trusilova et al., 2016; Daniel et al., 2019; Halenka et al., 2019; Langendijk et al., 2019a) are beginning to implement these 
urban parametrizations within the land surface component of their models. There is very high confidence  (robust evidence  and 
high agreement ) that while all types of urban parametrizations generally simulate radiation exchanges in a realistic way, they have 
strong biases when simulating latent heat fluxes, though recent research incorporating in-canyon vegetation processes improved their 
performance. There is medium confidence  (medium evidence , high agreement ) (Kusaka et al., 2012b; McCarthy et al., 2012; Hamdi

et al., 2014; Trusilova et al., 2016; Jänicke et al., 2017; Daniel et al., 2019) that a simple single-layer parametrization, is sufficient for 
the correct simulation of the urban heat island magnitude and its interplay with regional climate change.

1455
Linking Global to Regional Climate Change Chapter 1010Box 10.3 (continued)
Observed trends
There is medium evidence  but high agreement  (Parker, 2010; Zhang et al., 2013; H. Chen et al., 2016) that the global annual mean 
surface air temperature response to urbanization is negligible. There is very high confidence that the different observed warming trend 
in cities as compared to their surroundings can partly be attributed to urbanization (Box 10.3, Figure 1; Park et al., 2017).
Box 10.3, Figure 1 | Urban warming compared to global GHG-induced warming. (a)  Change in the annual mean surface air temperature over the period 
1950–2018 based on the local linear trend retrieved from CRU TS (°C per 68 years). This background warming is compared to the local warming that has been 
reported during 1950–2018 in the literature from historical urbanization. The relative share of the total warming as percentage between the urban warming and

the surrounding warming is plotted in a circle for each city. This map has been compiled from a review study (Hamdi et al., 2020). (b) Low-pass filtered time series 
of the annual mean temperature (°C) observed in the urban station of Tokyo (red line) and the rural reference station in Choshi (blue line) in Japan. The filter is the 
same as the one used in Figure 10.10. (c) Uncertainties in the relative share of urban warming with respect to the total warming (%) related to the use of different 
global observational datasets: CRU TS (brown circles), Berkeley Earth (dark blue downward triangle), HadCRUT5 (cyan upward triangle), Cowtan Way (orange plus) 
and GISTEMP (purple squares). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).
There is very high confidence  (robust evidence  and high agreement ) that the annual mean minimum temperature is more affected by

urbanization than the maximum temperature (Ezber et al., 2007; Fujibe, 2009; Hamdi, 2010; Elagib, 2011; Camilloni and Barrucand, 
2012; Hausfather et al., 2013; Robaa, 2013; Argüeso et al., 2014; Alghamdi and Moore, 2015; Alizadeh-Choobari et al., 2016; Sachindra 
et al., 2016; Liao et al., 2017; Lokoshchenko, 2017; J. Wang et al., 2017; Arsiso et al., 2018). Beside temperature, urbanization can 
induce an urban dryness island, which refers to lower relative humidity in cities than in nearby rural locations (Lokoshchenko, 2017; 
Bian et al., 2020) and the urban wind island, where slower wind speeds are observed in cities (Wu et al., 2017; Bader et al., 2018; Peng 
et al., 2018). There is medium confidence  (medium evidence  and medium agreement ) (Schlünzen et al., 2010; Ganeshan et al., 2013;

1456
Cross-Chapter Box 10.4 | Climate Change over the Hindu Kush Himalaya
Coordinators: Izuru Takayabu (Japan), Andrew Turner (United Kingdom), Zhiyan Zuo (China)
Contributors:  Bhupesh Adhikary (Nepal), Muhammad Adnan (Pakistan), Muhammad Amjad (Pakistan), Subimal Ghosh (India), 
Rafiq Hamdi (Belgium), Akm Saiful Islam (Bangladesh), Richard G. Jones (United Kingdom), Martin Jury (Austria), Asif Khan (Pakistan), 
Akio Kitoh (Japan), Krishnan Raghavan (India), Lucas Ruiz (Argentina), Laurent Terray (France)
The Hindu Kush Himalaya (HKH) constitutes the largest glacierized region outside the poles and provides the headwaters for several 
major rivers (Sharma et al., 2019). Since the 1960s, the HKH has experienced significant trends in the mean and extremes of temperature 
and precipitation, accompanied by glacier mass loss and retreat, snowmelt and permafrost degradation (Yao et al., 2012a, b; Azam

et al., 2018; Bolch et al., 2019; Krishnan et al., 2019a, b; Chug et al., 2020; Sabin et al., 2020). Observational uncertainty and lack 
of consistent, high-quality datasets hamper reliable assessments of climate change and model evaluation over several mountain 
areas, including the HKH (Section 10.2.2). This box assesses observed and projected climate change in the extended HKH (outline in 
Cross-Chapter Box 10.4, Figure 1a), in which we include the Tibetan Plateau (TP) and Pamir mountains.
Temperature trends
Little evidence was presented in the AR5 (IPCC, 2014) other than increased minimum and maximum temperature trends in the western 
Himalaya (Hartmann et al., 2013). The SROCC assessed that HKH (named High Mountain Asia) surface-air temperature has warmed 
more rapidly than the global mean over recent decades ( high confidence ). Annual mean HKH surface air temperature increased

significantly (about 0.1°C per decade) over 1901–2014 (Ren et al., 2017), although Cross-Chapter Box 10.4, Figure 1d shows an 
observational range of 0.20°C–0.25°C per decade over 1961–2014. There is a rising trend of extreme warm events and fewer extreme 
cold events over 1961–2015 (Krishnan et al., 2019b; Wester et al., 2019). However, summer cooling over the Karakoram (western 
HKH) was reported for 1960–2010 (Forsythe et al., 2017). A key relevant process is elevation-dependent warming (EDW; reviewed inBox 10.3 (continued)
Ganeshan and Murtugudde, 2015; Haberlie et al., 2015; Daniels et al., 2016; Liang and Ding, 2017; McLeod et al., 2017; Y . Li et al., 2020b) 
that cities induce increases in mean and extreme precipitation over and downwind of the city especially in the afternoon and early evening.
Climate projections
Estimates of the urban heat island under further climate change are very uncertain  because studies using different methods report

contrasting results. However, there is very high confidence (robust evidence  and high agreement ) that  the projected change of the urban 
heat island under climate change conditions is one order of magnitude less than the projected warming in both urban and rural areas under 
simulation constraints of no urban growth  (McCarthy et al., 2010, 2012; Oleson et al., 2011; Früh et al., 2011; Adachi et al., 2012; Kusaka et 
al., 2012a; Oleson, 2012; Hamdi et al., 2014; Sachindra et al., 2016; Hatchett et al., 2016; Arsiso et al., 2018; Hoffmann et al., 2018).
Combining climate change conditions together with urban growth scenarios, there is very high confidence  (robust evidence  and 
high agreement ) that future urbanization will amplify the projected air temperature warming irrespective of the background climate 
(Georgescu et al., 2013; Argüeso et al., 2014; Mahmood et al., 2014; Doan et al., 2016; Kim et al., 2016; Kusaka et al., 2016; Grossman-

Clarke et al., 2017; Kaplan et al., 2017; X. Li et al., 2018). Urbanization will have a strong influence on minimum temperatures that 
could be locally comparable in magnitude to the global GHG-induced warming (Berckmans et al., 2019) . There  is very high confidence  
(robust evidence  and high agreement ) for the combination of future urban development and more frequent occurrence of extreme 
climatic events, such as heatwaves (Hamdi et al., 2016; Bader et al., 2018; He et al., 2021).
The choice of urban planning scenarios and RCM projections shows a large sensitivity during nighttime, up to 0.6°C (Kusaka et al., 2016). 
The sensitivity is significantly less than the uncertainties arising from global emissions scenarios or global model projections. However, 
there is a large difference between RCM simulations with and without urban land use, indicating that this impact is comparable to

the uncertainties related to the use of different global model projections (Hamdi et al., 2014; Kusaka et al., 2016; Daniel et al., 2019). 
Therefore, impact assessments and adaptation plans for urban areas require high spatial resolution climate projections along with 
models that represent urban processes, ensemble dynamical and statistical downscaling, and local-impact models (Masson et al., 
2014; Baklanov et al., 2018, 2020; Duchêne et al., 2020; Schoetter et al., 2020; Le Roy et al., 2021; Zhao et al., 2021).

1457
Linking Global to Regional Climate Change Chapter 1010Cross-Chapter Box 10.4 (continued)
Pepin et al., 2015), leading to warming of 2°C–2.5°C at 5000 m over 1961–2006, but only 0.5°C at sea level (Xu et al., 2016). However, 
EDW behaviour appears to depend on region, time period and elevation (D. Guo et al., 2019; b. Li et al., 2020) and understanding is limited 
by the sparse observational network (You et al., 2020). Observational and model analyses have attributed EDW to GHG and black carbon 
emissions, accelerating warming by snow-albedo feedback (Ming et al., 2012; Gautam et al., 2013; Xu et al., 2016; Yan et al., 2016; Lau 
and Kim, 2018; Y . Zhang et al., 2018), or the more pronounced cooling effect of scattering aerosols at low elevations and stratospheric 
ozone depletion (Guo and Wang, 2012; Zeng et al., 2015). There is high confidence that the eastern and central HKH has exhibited rising

temperatures (Cross-Chapter Box 10.4, Figure 1), with warming dependent on season and elevation. There is  high confidence  that much of 
the warming can be attributed to GHGs, but the effect of albedo has only medium confidence . There is high confidence in more frequent 
extreme warm events and fewer extreme cold events over the eastern Himalayas in the last five decades.
0.6 0.4 0.2 0.0 0.2 0.4 0.6
°C decade1
(d) Temperature trend distribution over HKH
Trend period is 1961-2014APHRO-MA(a) O bserve d trend over  sout hern Asia and  the Hindu  Kush  Himalaya  (HKH, 1 961-2014)
BerkeleyEarth JRA-55
Median Warmest(b) Modelled trend over southern Asia and HKH (1961-2014)
Coldest
CMIP6 historical
(c) Temperature anomalies over HKH
Baseline period is 1961-1980
1960 1970 1980 1990 2000 20100.500.250.000.250.500.751.001.251.50°CCMIP6 hist-aer (N=12)
CMIP6 hist-GHG (N=12)CMIP6 historical* (N=12)CMIP6 historical (N=50) Berkeley Earth
CRU TS
APHRO-MA
JRA-55
0.2 0.0 0.2 0.4 0.6

Trend (°C decade-1)CMIP6 hist-aerCMIP6 hist-GHGCMIP6 historical*CMIP6 historicalEnsemble meansOBSsea-m asked  
dataCRU TS
Cross-chapter Box 10.4, Figure 1 | Historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the 
Hindu Kush Himalaya (HKH) region. (a)  Observed trends from Berkeley Earth (also showing the HKH outline), CRU TS (also showing the AR6 Tibetan Plateau (TIB) 
outline, for ease of comparison to the Interactive Atlas), APHRO-MA and JRA-55 datasets over 1961–2014.  (b) Models showing the coldest, median and warmest 
HKH temperature linear trends among the CMIP6 historical ensemble over 1961–2014. (c) Low-pass-filtered time series of annual-mean surface air temperature 
anomalies (°C, baseline 1961–1980) over the HKH region as outlined in panel (a), showing means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings

sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue). Observed datasets are Berkeley Earth (dark blue), CRU (brown), 
APHRO-MA (light green) and JRA-55 (dark green). The filter is the same as that used in Figure 10.10. (d) Distribution of annual mean surface air temperature trends 
(°C per decade) over the HKH region from 1961 to 2014 for ensemble means, the aforementioned observed and reanalysis data (black crosses), individual members 
of CMIP6 hist all-forcings (red circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs used throughout 
Chapter 10 (grey shading). Ensemble means are also shown. All trends are estimated using ordinary least-squares regression and box-and-whisker plots follow the 
methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

1458
Chapter 10 Linking Global to Regional Climate Change10Cross-Chapter Box 10.4 (continued)
Precipitation trends
Annual and summer precipitation over the central-eastern HKH show decreasing trends over 1979–2010 in multiple observed datasets, 
attributable to a weakening South Asian monsoon (Yao et al., 2012a; Palazzi et al., 2013; Roxy et al., 2015). There are contradictory 
trends in the western HKH (Azmat et al., 2017; Yadav et al., 2017; H. Li et al., 2018; Meher et al., 2018), where most precipitation is 
associated with western disturbances on the subtropical westerly jet, but trends in western disturbance activity are unclear (Kumar 
et al., 2015; Hunt et al., 2019; Krishnan et al., 2019a). There has been an increased frequency and intensity of extreme precipitation 
over the central-western HKH but contrasting evidence in the east (Sheikh et al., 2015; Talchabhadel et al., 2018). The number of

consecutive wet days has increased over 1961–2012, but with no uniform trend in consecutive dry days (Zhan et al., 2017). There is 
medium confidence  that the eastern-central HKH has experienced decreased summer precipitation (Section 10.6.3). There is medium 
confidence  in the increase of summer extreme precipitation over the western HKH.
Glacier trends
The SROCC assessed that snow cover has declined in duration, depth and accumulated mass at lower elevations in mountain regions, 
including the HKH ( high confidence ). Glaciers are losing mass ( very high confidence ) and permafrost is warming ( high confidence ) 
over high mountains in recent decades, and it is very likely  that atmospheric warming is the main driver. A significant reduction in 
HKH glacier area has been observed since the 1970s, with smaller glaciers generally shrinking faster (e.g., Bolch et al., 2019). HKH

glacier mass loss took place at the lowest rate among high mountain areas in the last 20 years, although with one of the largest 
total losses (Section 9.5.1.1 and Figure 9.20; Shean et al., 2020). The highest mass-loss rates occurred in the eastern and northern 
HKH, while gains occurred in the west (e.g., Shean et al., 2020). Glacier mass gain has been coined as the ‘Karakoram anomaly’ 
(Sections 8.3.1.7.1 and 9.5.1), explained by a combination of low temperature sensitivity of debris-covered glaciers, a decrease in 
summer air temperatures, and increased snowfall possibly linked to evapotranspiration from irrigated agriculture (You et al., 2017; 
Bolch et al., 2019; de Kok et al., 2020a; Farinotti et al., 2020). Meanwhile, increased air temperature and decreased snowfall explain 
the glacier mass decrease elsewhere (Bonekamp et al., 2019; de Kok et al., 2020b; Farinotti et al., 2020; Shean et al., 2020). There is

high confidence  that glaciers in most HKH regions have thinned, retreated and lost mass since the 1970s.
In AR5, the HKH was projected to continue warming over the 21st century, faster than the likely  ranges for the global mean and 
South Asia. New CMIP5 results show temperature increases across mountainous HKH by about 1°C–2°C (in some places in summer 
4°C–5°C) during 2021–2050 compared to 1961–1990 (Shrestha et al., 2015). Projected warming differs by up to 1°C between east and 
west, with higher values in winter (Sanjay et al., 2017; see Interactive Atlas). Statistically significant mean warming (0.30°C–0.90°C 
per decade until the end of the 21st century) across all RCPs has been projected by CORDEX South Asia (Dimri et al., 2018). CMIP6 
models report that north-western South Asia, including the western Himalayas, is projected to experience temperature increases

exceeding 6°C by the end of the 21st century under SSP5-8.5 relative to 1995–2014 (Almazroui et al., 2020b). Results from CMIP5, 
CMIP6 and CORDEX ensembles for different warming levels are shown in the Interactive Atlas and summarized in Figure Atlas.20. 
The HKH will likely  continue warming in the coming decades.
The SR1.5 (IPCC, 2018b) stated that heavy precipitation risk in high-elevation regions is projected to be higher at 2°C compared to 
1.5°C of global warming ( medium confidence ). CMIP5 models project increased annual or summer monsoon precipitation over the 
HKH in the 21st century (Palazzi et al., 2015; Kitoh and Arakawa, 2016), intensifying by about 22% in the hilly south-eastern Himalaya 
and TP for the long term in RCP8.5, but with no trends in the western HKH (Rajbhandari et al., 2015; Krishnan et al., 2019a). CMIP6 
projects an increase of winter precipitation over the western Himalayas, with a corresponding decrease in the east (Almazroui et al.,

2020b). HKH projections are subject to large uncertainties in CMIP5 and CORDEX (Hasson et al., 2013, 2017; Mishra, 2015; Sanjay 
et al., 2017). CORDEX, in particular, has inherent limitations at reproducing the characteristics of summer monsoon rainfall variability 
(Singh et al., 2017). There is medium confidence that HKH precipitation will increase in the coming decades.
The SROCC assessed that glaciers will lose substantial mass ( high confidence ) and permafrost will undergo increasing thaw and 
degradation ( very high confidence ) over high mountain regions (including the HKH), with stronger changes for higher emissions 
scenarios. Regional differences in warming and precipitation projections and glacier properties cause considerable differences in 
glacier response within High Mountain Asia (Kraaijenbrink et al., 2017). Glacier mass loss will accelerate through the 21st century,

increasing with RCP after 2030 (Section 9.5.1.3; Marzeion et al., 2014). Loss of between 40 ± 25% to 69 ± 21 % of 2015 glacier 
volume is expected by 2100 in RCP 2.6 and RCP 8.5, respectively (Section 9.5.1.3 and Figure 9.21). Glacier mass loss is expected due to 
decreased snowfall, increased snowline elevations and longer melt seasons. However, due to projection uncertainties, simplicity of the 
models, and limited observations, there is medium confidence  in the magnitude and timing of glacier mass changes (Section 9.5.1.3). 
Glacier mass in HKH will decline through the 21st century ( high confidence ), more so under high-emissions scenarios.

1459
Linking Global to Regional Climate Change Chapter 101010.7 Final remarks
The assessments in this chapter are based on a  rapidly growing 
body of evidence from the peer-reviewed literature, most of which 
was not previously considered by IPCC reports. Several challenges 
in the construction of regional climate change information have 
been identified:
• Limited climate monitoring in some regions impedes the full 
understanding of the relevant climate processes, an appropriate 
validation of model simulations, and the formulation of 
trustworthy regional climate information. Beyond temperature 
and precipitation, there is a  shortage of observed variables 
needed for regional process understanding, attribution, and 
model development and validation, among others. Examples 
include surface evapotranspiration, soil moisture, radiation, wind 
and relative humidity, among many others identified by sectors 
sensitive to climate (Sections 10.2, 10.3 and 10.6).

• Compared to the increasing number of large-scale evaluations, 
there is a  shortage of process-based model evaluations at 
regional scales to assess the fitness of the chosen models for 
specific purposes (Sections 10.3 and 10.4).
• There is a  general lack of studies of the simulation of large-
scale, downscaling-relevant processes in global models to 
support the design of global/regional model matrices that both 
span a  sufficiently large range of projection uncertainty and 
realistically represent the regional climate of interest. The fitness 
of statistical methods for regional climate change studies has 
received limited attention by the scientific community, while 
as in the case of global models, process-based evaluation has 
proven useful (Soares et  al., 2019b). Studies of past changes 
and pseudo-reality studies to assess the predictors and model 
structures required for downscaling in a  future climate are 
promising avenues (Section 10.3).

• Internal variability is a  large contributor to climate uncertainty 
at regional scales, especially for extreme events. Further study of 
the processes governing regional internal variability, such as the 
modes of variability and the teleconnections that connect them 
to the regional variability, but also of the local processes and 
drivers involved, will help improve its understanding. The same 
applies to the validation of the simulated internal variability that 
underpins the trustworthiness of model-based climate information 
(Sections 10.3, 10.4 and 10.6, and Cross-Chapter Box 10.1).
• Methodologies on how to propagate climate uncertainties from 
global and regional scales down to the human settlement scale 
are still under development. In some cases, bias-adjustment 
methods are used with substantial neglect of the physical 
processes involved (Section 10.3 and Cross-Chapter Box 10.2).
• The production of regional climate information relies mainly

on global and regional models that often do not incorporate 
human-controlled surface processes (urban parametrizations is 
one example) in their land surface components. This limits the 
representation of uncertainties for climate information at the urban 
scale (Section 10.3, Box 10.2, and Cross-Chapter Box 10.2).• Literature plays a central role as a source for constructing regional 
climate change information. The amount of climate change 
literature available is unevenly distributed across the world, 
and large bodies of literature (e.g.,  local and regional reports) 
are often overlooked in the construction of climate information. 
Furthermore, research tends to focus on regions that attract the 
attention of the Global North so that climate aspects relevant to 
other regions may not receive sufficient attention for generating 
appropriate regional climate information (Sections 10.2, 10.3, 
10.5 and 10.6).
• Governmental institutions producing regional and local climate

information often use diverging approaches that are not necessarily 
coherent with each other. Coherency could be improved by 
implementing a quality control system and a traceability solution 
for the sources of the information. Collective work with the 
social sciences and humanities will improve the communication, 
perception and response to regional climate information and help 
translate user requirements (Sections 10.5 and 10.6).
• There is a shortage of regional climate change studies distilling 
multiple lines of evidence. Most studies rely on either global 
models or downscaled global models, with an increasing number 
of studies focusing on the use of emulators and the selection 
and combination of models. However, there are limited studies 
distilling this information with a wider range of lines of evidence 
that includes observations, process understanding, attribution, 
and hierarchies of models (Sections 10.3, 10.5 and 10.6).

Addressing these challenges could facilitate the assessment of both 
sources and methodologies that lead to an increased fitness and 
usefulness of regional climate information for a wide range of purposes.
We acknowledge the E-OBS dataset and the data providers in the 
ECA&D project ( https://www.ecad.eu ) for their help and the Japan 
Aerospace Exploration Agency (JAXA) for delivering the  GSMaP 
(Global Satellite Mapping of Precipitation) data to us. The invaluable 
contributions from Lisa van Aardenne (South Africa), Peng Cai 
(China), Joseph Ching (China), Huili He (China), Kenshi  Hibino 
(Japan), Yukiko  Imada (Japan), Nazrul Islam (Saudi Arabia), 
Isadora Christel Jiménez (Spain) and Misako Kachi (Japan) are also 
greatly acknowledged. We acknowledge the World Climate Research 
Programme for coordinating the modelling intercomparison projects 
CMIP and CORDEX and thank the climate modelling groups for 
producing and making available their model output.

1460
Chapter 10 Linking Global to Regional Climate Change10Frequently Asked Questions
FAQ 10.1 | How Can We Provide Useful Climate Information for Regional Stakeholders?
The world is physically and culturally diverse, and the challenges posed by climate change vary by region and 
location. Because climate change affects so many aspects of people’s daily work and living, climate change 
information can help with decision-making, but only when the information is relevant for the people involved in 
making those decisions. Users of climate information may be highly diverse, ranging from professionals in areas 
such as human health, agriculture or water management to a broader community that experiences the impacts 
of changing climate. Providing information that supports response actions thus requires engaging all relevant 
stakeholders, their knowledge and their experiences, formulating appropriate information, and developing

a mutual understanding of the usefulness and limitations of the information.
The development, delivery, and use of climate change information requires engaging all parties involved: 
those producing the climate data and related knowledge, those communicating it, and those who combine 
that information with their knowledge of the community, region or activity that climate change may impact. 
To be successful, these parties need to work together to explore the climate data and thus co-develop the 
climate information needed to make decisions or solve problems, distilling output from the various sources 
of climate knowledge into relevant climate information. Effective partnerships recognize and respond to the 
diversity of all parties involved (including their values, beliefs and interests), especially when they involve 
culturally diverse communities and their indigenous and local knowledge of weather, climate and their society.

This is particularly true for climate change – a global issue posing challenges that vary by region. By recognizing 
this diversity, climate information can be relevant and credible, most notably when conveying the complexity of 
risks for human systems and ecosystems and for building resilience.
Constructing useful climate information requires considering all available sources in order to capture the fullest 
possible representation of projected changes and distilling the information in a way that meets the needs of the 
stakeholders and communities impacted by the changes. For example, climate scientists can provide information 
on future changes by using simulations of global and/or regional climate and inferring changes in the weather 
behaviour influencing a region. An effective distillation process (FAQ 10.1, Figure 1) engages with the intended 
recipients of the information, especially stakeholders whose work involves non-climatic factors, such as human

health, agriculture or water resources. The distillation evaluates the accuracy of all information sources 
(observations, simulations, expert judgement), weighs the credibility of possible conflicting information, and 
arrives at climate information that includes estimating the confidence a user should have in it. Producers of 
climate data should further recognize that the geographic regions and time periods governing stakeholders’ 
interest (for example, the growing season of an agricultural zone) may not align well with the time and space 
resolution of available climate data; thus additional model development or data processing may be required to 
extract useful climate information.
One way to distil complex information for stakeholder applications is to connect this information to experiences 
stakeholders have already had through storylines as plausible unfoldings of weather and climate events related

to stakeholders’ experiences. Dialogue between stakeholders and climate scientists can determine the most 
relevant experiences to evaluate for possible future behaviour. The development of storylines uses the experience 
and expertise of stakeholders, such as water-resource managers and health professionals, who seek to develop 
appropriate response measures. Storylines are thus a pathway through the distillation process that can make 
climate information more accessible and physically comprehensible. For example, a storyline may take a common 
experience like an extended drought, with depleted water availability and damaged crops, and show how 
droughts may change in the future, perhaps with even greater precipitation deficits or longer duration. With 
appropriate choices, storylines can engage nuances of the climate information in a meaningful way by building 
on common experiences, thus enhancing the information’s usefulness.

Forging partnerships among all involved with producing, exploring and distilling climate data into climate 
information is at the centre of creating stakeholder-relevant information. These partnerships can occur through 
direct interaction between climate scientists and stakeholders as well as through organizations that have 
emerged to facilitate this process, such as climate services, national and regional climate forums, and consulting 
firms providing specialized climate information. These so-called ‘boundary organizations’ can serve the varied 
needs of all who would fold climate information into their decision processes. All of these partnerships are vital

1461
Linking Global to Regional Climate Change Chapter 1010FAQ 10.1 (continued)
for arriving at climate information that responds to physical and cultural diversity and to challenges posed by 
climate change that can vary region-by-region around the world.
Climate 
simulations
Human health Agriculture Water resources Transportation Distillation
involving 
scientists and 
stakeholders
Fisheries 
managementNatural
ecosystemsHuman
habitationsEnergy 
resourcesHistorical 
trends and 
observationsStorylines of 
complex, high 
impact eventsDominant weather 
& climate 
processesFAQ 10.1: How can scientists provide useful regional climate information?     
In decision-making, climate information is more useful if the physical and cultural diversity across the world is considered.
FAQ 10.1, F igure 1 | Climate information for decision makers is more useful if the physical and cultural diversity across the world is

considered.  The figure illustrates schematically the broad range of knowledge that must be blended with the diversity of users to distil information that will 
have relevance and credibility. This blending or distillation should engage the values and knowledge of both the stakeholders and the scientists. The bottom 
row contains examples of stakeholders’ interests and is not all-inclusive. As part of the distillation, the outcomes can advance the United Nations’ Sustainable 
Development Goals, covered in part by these examples.

1462
FAQ 10.2 | Why Are Cities Hotspots of Global Warming?
Urban areas experience air temperatures that can be several degrees Celsius warmer than surrounding areas, 
especially during the night. This ‘urban heat island’ effect results from several factors, including reduced 
ventilation and heat trapping due to the close proximity of tall buildings, heat generated directly from human 
activities, the heat-absorbing properties of concrete and other urban building materials, and the limited amount 
of vegetation. Continuing urbanization and increasingly severe heatwaves under climate change will further 
amplify this effect in the future.
Today, cities are home to 55% of the world’s population. This number is increasing, and every year cities welcome 
67 million new residents, 90% of whom are moving to cities in developing countries. By 2030, almost 60% of

the world’s population is expected to live in urban areas. Cities and their inhabitants are highly vulnerable to 
weather and climate extremes, particularly heatwaves, because urban areas already are local hotspots. Cities are 
generally warmer – up to several degrees Celsius at night – than their surroundings. This warming effect, called 
the urban heat island, occurs because cities both receive and retain more heat than the surrounding countryside 
areas and because natural cooling processes are weakened in cities compared to rural areas.
Three main factors contribute to amplify the warming of urban areas (orange bars in FAQ 10.2, Figure 1). The 
strongest contribution comes from urban geometry, which depends on the number of buildings, their size and 
their proximity. Tall buildings close to each other absorb and store heat and also reduce natural ventilation. 
Human activities, which are very concentrated in cities, also directly warm the atmosphere locally, due to heat

released from domestic and industrial heating or cooling systems, running engines, and other sources. Finally, 
urban warming also results directly from the heat-retaining properties of the materials that make up cities, 
including concrete buildings, asphalt roadways, and dark rooftops. These materials are very good at absorbing 
and retaining heat, and then re-emitting that heat at night.
The urban heat island effect is further amplified in cities that lack vegetation and water bodies, both of which can 
strongly contribute to local cooling (green bars in FAQ 10.2, Figure 1). This means that when enough vegetation 
and water are included in the urban fabric, they can counterbalance the urban heat island effect, to the point of 
even cancelling out the urban heat island effect in some neighbourhoods.
The urban heat island phenomenon is well-known and understood. For instance, temperature measurements

from thermometers located in cities are corrected for this effect when global warming trends are calculated. 
Nevertheless, observations, including long-term measurements of the urban heat island effect are currently too 
limited to allow a full understanding of how the urban heat island varies across the world and across different 
types of cities and climatic zones, or how this effect will evolve in the future.
As a result, it is hard to assess how climate change will affect the urban heat island effect, and various studies 
disagree. Two things are, however, very clear. First, future urbanization will expand the urban heat island areas, 
thereby amplifying future warming in many places all over the world. In some places, the nighttime warming 
from the urban heat island effect could even be on the same order of magnitude as the warming expected from 
human-induced climate change. Second, more intense, longer and more frequent heatwaves caused by climate

change will more strongly impact cities and their inhabitants, because the extra warming from the urban heat 
island effect will exacerbate the impacts of climate change.
In summary, cities are currently local hotspots because their structure, material and activities trap and release 
heat and reduce natural cooling processes. In the future, climate change will, on average, have a limited effect 
on the magnitude of the urban heat island itself, but ongoing urbanization together with more frequent, longer 
and warmer heatwaves will make cities more exposed to global warming.

1463
Linking Global to Regional Climate Change Chapter 1010FAQ 10.2 (continued)
Local effect on temperature (ºC) COOLING WARMING
Local effect on temperature (ºC) COOLING WARMINGVegetationWaterHeat-retaining 
propertiesHeat from 
human activitiesCity 
geometry-3 -2 -1 +1 +2 0
-3 -2 -1 +1 +2 0Parks, forests, 
gardensSea, river, 
lakes, irrigationBuildings and 
road materialsDomestic/
industrial heatingBuilding density, 
city layout, height 
and sizeFAQ 10.2: Why are cities the hotspots of global warming?
Cities are usually warmer than their surrounding areas due to factors that trap and release heat 
and a lack of natural cooling influences , such as water and vegetation.
Cities often lack 
vegetation and waterVariations across 
different climates
FAQ 10.2, Fig ure 1 | Efficiency of the various factors at warming up or cooling down neighbourhoods of urban areas.  Overall, cities tend to be

warmer than their surroundings. This is called the ‘urban heat island’ effect. The hatched areas on the bars show how the strength of the warming or cooling 
effects of each factor varies depending on the local climate. For example, vegetation has a stronger cooling effect in temperate and warm climates. Further 
details on data sources are available in the chapter data table (Table  10.SM .11).

1517
Weather and Climate Extreme Events in a Changing Climate  Chapter 1111
Executive Summary 
This chapter assesses changes in weather and climate extremes 
on regional and global scales, including observed changes and 
their attribution, as well as projected changes.  The extremes 
considered include temperature extremes, heavy precipitation 
and pluvial floods, river floods, droughts, storms (including 
tropical cyclones), as well as compound events (multivariate and 
concurrent extremes). The assessment focuses on land regions 
excluding Antarctica. Changes in marine extremes are addressed 
in Chapter  9  and Cross-Chapter Box  9.1. Assessments of past 
changes and their drivers are from 1950 onward, unless indicated 
otherwise. Projections for changes in extremes are presented for 
different levels of global warming, supplemented with information 
for the conversion to emissions scenario-based projections (Cross-
Chapter Box  11.1 and Table  4.2). Since the IPCC Fifth Assessment

Report (AR5), there have been important new developments and 
knowledge advances on changes in weather and climate extremes, in 
particular regarding human influence on individual extreme events, 
on changes in droughts, tropical cyclones, and compound events, and 
on projections at different global warming levels (1.5°C–4°C). These, 
together with new evidence at regional scales, provide a  stronger 
basis and more regional information for the AR6 assessment on 
weather and climate extremes.
It is an established fact that human-induced greenhouse gas 
emissions have led to an increased frequency and/or intensity 
of some weather and climate extremes since pre-industrial 
time, in particular for temperature extremes.  Evidence of 
observed changes in extremes and their attribution to human 
influence (including greenhouse gas and aerosol emissions and land-
use changes) has strengthened since AR5, in particular for extreme

precipitation, droughts, tropical cyclones and compound extremes 
(including dry/hot events and fire weather). Some recent hot extreme 
events would have been  extremely unlikely  to occur without human 
influence on the climate system. {11.2, 11.3, 11.4, 11.6, 11.7, 11.8}
Regional changes in the intensity and frequency of climate 
extremes generally scale with global warming. New evidence 
strengthens the conclusion from the IPCC Special Report on 
Global Warming of 1.5°C (SR1.5) that even relatively small 
incremental increases in global warming (+0.5°C) cause 
statistically significant changes in extremes on the global scale 
and for large regions ( high confidence ). In particular, this is the 
case for temperature extremes ( very likely ), the intensification 
of heavy precipitation ( high confidence ) including that 
associated with tropical cyclones ( medium confidence ), and 
the worsening of droughts in some regions ( high confidence ).

The occurrence of extreme events unprecedented in the observed 
record will rise with increasing global warming, even at 1.5°C of 
1 See Figure 1.18 for definition of AR6 regions. Acronyms for inhabited regions: ARP: Arabian Peninsula; CAF: Central Africa; CAR: Caribbean; CAU: Central Australia; CNA: Central North America; 
EAS: East Asia; EAU: Eastern Australia; ECA: East Central Asia; EEU: Eastern Europe; ENA: Eastern North America; ESAF: East Southern Africa; ESB: East Siberia; GIC: Greenland/Iceland; MDG: 
Madagascar; MED: Mediterranean; NAU: Northern Australia; NCA: Northern Central America; NEAF: North Eastern Africa; NEN: North-Eastern North America; NES: North-Eastern South America; 
NEU: Northern Europe; NSA: Northern South America; NWN: North-Western North America; NWS: North-Western South America; NZ: New Zealand; RAR: Russian Arctic; RFE: Russian Far East;

SAH: Sahara; SAM: South American Monsoon; SAS: South Asia; SAU: Southern Australia; SCA: Southern Central America; SEA: Southeast Asia; SEAF: South Eastern Africa; SES: South-Eastern South 
America; SSA: Southern South America; SWS: South-Western South America; TIB: Tibetan Plateau; WAF: Western Africa; WCA: West Central Asia; WCE: Western and Central Europe; WNA: Western 
North America; WSAF: West Southern Africa; WSB: West Siberia.            global warming. Projected percentage changes in frequency are 
higher for the rarer extreme events ( high confidence ). {11.1, 11.2, 
11.3, 11.4, 11.6, 11.9, Cross-Chapter Box 11.1}
Methods and Data for Extremes
Since AR5, the confidence about past and future changes 
in weather and climate extremes has increased due to 
better physical understanding of processes, an increasing 
proportion of the scientific literature combining different 
lines of evidence, and improved accessibility to different

types of climate models ( high confidence ). There have 
been improvements in some observation-based datasets, 
including reanalysis data ( high confidence ). Climate models 
can reproduce the sign (direction) of changes in temperature 
extremes observed globally and in most regions, although the 
magnitude of the trends may differ ( high confidence ). Models 
are able to capture the large-scale spatial distribution of precipitation 
extremes over land ( high confidence ). The intensity and frequency of 
extreme precipitation simulated by Coupled Model Intercomparison 
Project Phase 6 (CMIP6) models are similar to those simulated by 
CMIP5 models ( high confidence ). Higher horizontal model resolution 
improves the spatial representation of some extreme events 
(e.g., heavy precipitation events), in particular in regions with highly 
varying topography ( high confidence ). {11.2, 11.3, 11.4}
Temperature Extremes
The frequency and intensity of hot extremes (including

heatwaves) have increased, and those of cold extremes have 
decreased on the global scale since 1950 ( virtually certain ). 
This also applies at regional scale, with more than 80% of AR6 
regions1 showing similar changes assessed to be at least  likely . 
In a few regions, limited evidence  (data or literature) prevents the 
reliable estimation of trends. {11.3, 11.9}
Human-induced greenhouse gas forcing is the main driver 
of the observed changes in hot and cold extremes on the 
global scale ( virtually certain ) and on most continents ( very 
likely ). The effect of enhanced greenhouse gas concentrations on 
extreme temperatures is moderated or amplified at the regional 
scale by regional processes such as soil moisture or snow/ice-albedo 
feedbacks, by regional forcing from land-use and land-cover changes, 
or aerosol concentrations, and decadal and multi-decadal natural 
variability. Changes in anthropogenic aerosol concentrations have

likely  affected trends in hot extremes in some regions. Irrigation and 
crop expansion have attenuated increases in summer hot extremes 
in some regions, such as the Midwestern USA ( medium confidence ).

1518
Chapter 11  W eather and Climate Extreme Events in a Changing Climate11
Urbanization has likely exacerbated changes in temperature extremes 
in cities, in particular for nighttime extremes. {11.1, 11.2, 11.3}
The frequency and intensity of hot extremes will continue to 
increase and those of cold extremes will continue to decrease, 
at global and continental scales and in nearly all inhabited 
regions1 with increasing global warming levels. This will be the 
case even if global warming is stabilized at 1.5°C. Relative to present-
day conditions, changes in the intensity of extremes would be at least 
double at 2°C, and quadruple at 3°C of global warming, compared 
to changes at 1.5°C of global warming. The number of hot days and 
hot nights and the length, frequency, and/or intensity of warm spells 
or heatwaves will increase over most land areas ( virtually certain ). In 
most regions, future changes in the intensity of temperature extremes

will very likely  be proportional to changes in global warming, and up 
to two to three times larger ( high confidence ). The highest increase 
of temperature of hottest days is projected in some mid-latitude and 
semi-arid regions and in the South American Monsoon region, at 
about 1.5 times to twice the rate of global warming ( high confidence ). 
The highest increase of temperature of coldest days is projected 
in Arctic regions, at about three times the rate of global warming 
(high confidence ). The frequency of hot temperature extreme events 
will very likely  increase nonlinearly with increasing global warming, 
with larger percentage increases for rarer events. {11.2, 11.3, 11.9; 
Table 11.1; Figure 11.3}
Heavy Precipitation and Pluvial Floods 
The frequency and intensity of heavy precipitation events have 
likely  increased at the global scale over a  majority of land 
regions with good observational coverage.  Heavy precipitation

has likely  increased on the continental scale over three 
continents: North America, Europe, and Asia. Regional increases 
in the frequency and/or intensity of heavy precipitation have been 
observed with at least medium confidence for nearly half of AR6 
regions, including WSAF , ESAF , WSB, SAS, ESB, RFE, WCA, ECA, TIB, 
EAS, SEA, NAU, NEU, EEU, GIC, WCE, SES, CNA, and ENA. {11.4, 11.9}
Human influence, in particular greenhouse gas emissions, 
is likely  the main driver of the observed global-scale 
intensification of heavy precipitation over land regions. It is 
likely  that human-induced climate change has contributed to the 
observed intensification of heavy precipitation at the continental 
scale in North America, Europe and Asia.  Evidence of a  human 
influence on heavy precipitation has emerged in some regions ( high 
confidence ). {11.4, 11.9, Table 11.1}
Heavy precipitation will generally become more frequent and 
more intense with additional global warming. At a global

warming level of 4°C relative to the pre-industrial level, very 
rare (e.g., one in 10 or more years) heavy precipitation events 
would become more frequent and more intense than in the 
recent past, on the global scale ( virtually certain ) and in all 
continents and AR6 regions. The increase in frequency and 
intensity is extremely likely  for most continents and very likely  
for most AR6 regions.  At the global scale, the intensification of heavy precipitation will follow the rate of increase in the maximum 
amount of moisture that the atmosphere can hold as it warms ( high 
confidence ), of about 7% per 1°C of global warming. The increase in 
the frequency of heavy precipitation events will be non-linear with 
more warming and will be higher for rarer events ( high confidence ), 
with a  likely doubling and tripling in the frequency of 10-year and 
50-year events, respectively, compared to the recent past at 4°C of

global warming. Increases in the intensity of extreme precipitation 
at regional scales will vary, depending on the amount of regional 
warming, changes in atmospheric circulation and storm dynamics 
(high confidence ). {11.4, Box 11.1}
The projected increase in the intensity of extreme precipitation 
translates to an increase in the frequency and magnitude 
of pluvial floods  – surface water and flash floods  – ( high 
confidence ), as pluvial flooding results from precipitation 
intensity exceeding the capacity of natural and artificial 
drainage systems.  {11.4}
River Floods
Significant trends in peak streamflow have been observed 
in some regions over the past decades (high confidence ). 
The  seasonality of river floods has changed in cold regions where 
snow-melt is involved, with an earlier occurrence of peak streamflow 
(high confidence ). {11.5}
Global hydrological models project a larger fraction of land 
areas to be affected by an increase in river floods than by

a  decrease in river floods ( medium confidence ). Regional 
changes in river floods are more uncertain than changes in pluvial 
floods because complex hydrological processes and forcings, 
including land cover change and human water management, are 
involved. {11.5}
Droughts 
Different drought types exist, and they are associated with 
different impacts and respond differently to increasing 
greenhouse gas concentrations.  Precipitation deficits and changes 
in evapotranspiration govern net water availability. A lack of sufficient 
soil moisture, sometimes amplified by increased atmospheric 
evaporative demand, results in agricultural and ecological drought. 
Lack of runoff and surface water result in hydrological drought. {11.6}
Human-induced climate change has contributed to increases 
in agricultural and ecological droughts in some regions due to 
evapotranspiration increases ( medium confidence ). Increases 
in evapotranspiration have been driven by increases in atmospheric

evaporative demand induced by increased temperature, decreased 
relative humidity and increased net radiation ( high confidence ). 
Trends in precipitation are not a main driver in affecting global-scale 
trends in drought ( medium confidence ), but have induced increases in 
meteorological droughts in a few AR6 regions (NES: high confidence ; 
WAF , CAF , ESAF , SAM, SWS, SSA, SAS: medium confidence ). Increasing

1519
trends in agricultural and ecological droughts have been observed 
on all continents (WAF , CAF , WSAF , ESAF , WCA, ECA, EAS, SAU, MED, 
WCE, WNA, NES: medium confidence ), but decreases only in one AR6 
region (NAU: medium confidence ). Increasing trends in hydrological 
droughts have been observed in a  few AR6 regions (MED: high 
confidence ; WAF , EAS, SAU: medium confidence ). Regional-
scale attribution shows that human-induced climate change has 
contributed to increased agricultural and ecological droughts (MED, 
WNA), and increased hydrological drought (MED) in some regions 
(medium confidence ). {11.6, 11.9}
More regions are affected by increases in agricultural and 
ecological droughts with increasing global warming ( high 
confidence ). Several regions will be affected by more severe 
agricultural and ecological droughts even if global warming

is stabilised at 2°C, including MED, WSAF , SAM and SSA ( high 
confidence ), and ESAF , MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, 
WCE, NCA, WNA and CNA ( medium confidence ). Some regions 
are also projected to be affected by more severe agricultural and 
ecological droughts at 1.5°C (MED, WSAF , ESAF , SAU, NSA, SAM, 
SSA, CNA, medium confidence ). At 4°C of global warming, about 
50% of all inhabited AR6 regions would be affected by increases 
in agricultural and ecological droughts (WCE, MED, CAU, EAU, SAU, 
WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CNA, ENA, 
WNA, WSAF , ESAF , MDG: medium confidence or higher), and only two 
regions (NEAF , SAS) would experience decreases in agricultural and 
ecological drought ( medium confidence ). There is high confidence  
that the projected increases in agricultural and ecological droughts 
are strongly affected by evapotranspiration increases associated with 
enhanced atmospheric evaporative demand.  Several regions are

projected to be more strongly affected by hydrological droughts with 
increasing global warming (at 4°C of global warming: NEU, WCE, 
EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF , ESAF , 
MDG: medium confidence or higher).  There is  low confidence  that 
effects of enhanced atmospheric carbon dioxide (CO 2) concentrations 
on plant water-use efficiency alleviate extreme  agricultural and 
ecological droughts in conditions  characterized by  limited soil 
moisture and enhanced atmospheric evaporative demand. There is 
also low confidence  that these effects will substantially reduce global 
plant transpiration and the severity of hydrological droughts. There is 
high confidence  that the land carbon sink will become less efficient 
due to soil moisture limitations and associated drought conditions 
in some regions in higher-emissions scenarios, in particular under 
global warming levels above 4°C. {11.6, 11.9, Cross-Chapter Box 5.1}

Extreme Storms, Including Tropical Cyclones
The average and maximum rain rates associated with tropical 
cyclones (TCs), extratropical cyclones and atmospheric rivers 
across the globe, and severe convective storms in some 
regions,  increase  in a  warming world ( high confidence ). 
Available event attribution studies of observed strong TCs provide 
medium confidence  for a human contribution to extreme TC rainfall. 
Peak TC rain rates increase with local warming at least at the rate 
2 Six-hourly intensity estimates during the lifetime of each TC.of mean water vapour increase over oceans (about 7% per 1°C of 
warming)  and in some cases exceeding this rate  due to increased 
low-level moisture convergence caused by increases in TC wind 
intensity ( medium confidence ).  {11.7, 11.4, Box 11.1}
It is likely that the global proportion of Category 3–5 tropical 
cyclone instances2 has increased over the past four decades. 
The average location where TCs reach their peak wind intensity

has very likely  migrated poleward in the western North Pacific 
Ocean since the 1940s, and TC translation speed has likely  slowed 
over the conterminous USA since 1900. Evidence of similar trends 
in other regions is not robust. The global frequency of TC rapid 
intensification events has likely  increased over the past four decades. 
None of these changes can be explained by natural variability alone 
The proportion of intense TCs, average peak TC wind speeds, 
and peak wind speeds of the most intense TCs will increase 
on the global scale with increasing global warming ( high 
confidence ). The total global frequency of TC formation will 
decrease or remain unchanged with increasing global warming 
(medium confidence ). {11.7.1} 
There is low confidence  in past changes of maximum 
wind speeds and other measures of dynamical intensity of 
extratropical cyclones. Future wind speed changes are expected

to be small, although poleward shifts in the storm tracks 
could lead to substantial changes in extreme wind speeds in 
some regions ( medium confidence ). There is low confidence  in 
past trends in characteristics of severe convective storms, such as 
hail and severe winds, beyond an increase in precipitation rates. The 
frequency of spring severe convective storms is projected to increase 
in the USA, leading to a lengthening of the severe convective storm 
season ( medium confidence ); evidence in other regions is limited. 
{11.7.2, 11.7.3}.
Compound Events, Including Dry/Hot Events, Fire Weather, 
Compound Flooding, and Concurrent Extremes
The probability of compound events has likely increased in 
the past due to human-induced climate change and will likely 
continue to increase with further global warming.  Concurrent 
heatwaves and droughts have become more frequent, and this 
trend will continue with higher global warming ( high confidence ).

Fire weather conditions (compound hot, dry and windy events) 
have become more probable in some regions ( medium confidence ) 
and there is high confidence  that they will become more frequent 
in some regions at higher levels of global warming. The probability 
of compound flooding (storm surge, extreme rainfall and/or river 
flow) has increased in some locations ( medium confidence ), and will 
continue to increase due to sea level rise and increases in heavy 
precipitation, including changes in precipitation intensity associated 
with tropical cyclones ( high confidence ). The land area affected by 
concurrent extremes has increased ( high confidence ). Concurrent

1520
extreme events at different locations, but possibly affecting similar 
sectors (e.g.,  critical crop-producing areas for global food supply) 
in different regions, will become more frequent with increasing 
global warming, in particular above 2°C of global warming ( high 
confidence ). {11.8, Box 11.2, Box 11.4}. 
Low-likelihood, High-impact Events Associated 
With Climate Extremes
The future occurrence of low-likelihood, high-impact 
events linked to climate extremes is generally associated 
with low confidence , but cannot be excluded, especially at 
global warming levels above 4°C. Compound events, including 
concurrent extremes, are a factor increasing the probability of low-
likelihood, high-impact events ( high confidence ). With increasing 
global warming, some compound events with low likelihood in 
past and current climates will become more frequent, and there is

a higher chance of occurrence of historically unprecedented events 
and surprises ( high confidence ). However, even extreme events that 
do not have a particularly low probability in the present climate (at 
more than 1°C of global warming) can be perceived as surprises 
because of the pace of global warming ( high confidence ). {Box 11.2}

1521
11.1 Introduction
11.1.1 Scope of the Chapter
This chapter provides assessments of changes in weather and climate 
extremes (collectively referred to as extremes) framed in terms of 
the relevance to the Working Group II (WGII) assessment. It assesses 
observed changes in extremes, their attribution to causes, and future 
projections, at three global warming levels: 1.5°C, 2°C, and 4°C. This 
chapter is also one of the four ‘regional chapters’ of the WGI Report 
(along with Chapters 10 and 12 and the Atlas). Consequently, while 
it encompasses assessments of changes in extremes at global and 
continental scales to provide a large-scale context, it also addresses 
changes in extremes at regional scales. 
Extremes are climatic impact-drivers (Annex VII: Glossary, see 
Chapter  12 for a  comprehensive assessment). The IPCC risk 
framework (Chapter  1) articulates clearly that the exposure and

vulnerability to climatic impact-drivers, such as extremes, modulate 
the risk of adverse impacts of these drivers, and that adaptation 
which reduces exposure and vulnerability will increase resilience, 
resulting in a reduction in impacts. Nonetheless, changes in extremes 
lead to changes in impacts as a direct consequence of changes in 
their magnitude and frequency, and also through their influence on 
exposure and resilience. 
The Special Report on Managing the Risks of Extreme Events and 
Disasters to Advance Climate Change Adaptation (referred as the SREX report, IPCC, 2012) provided a comprehensive assessment on 
changes in extremes and how exposure and vulnerability to extremes 
determine the impacts and likelihood of disasters. Chapter  3  of 
that report (Seneviratne et al., 2012, hereafter also referred to as 
SREX Chapter  3) assessed physical aspects of extremes, and laid 
a foundation for the follow-up IPCC assessments. Several chapters

of the IPCC Fifth Assessment Report (AR5) (IPCC, 2013) addressed 
climate extremes with respect to observed changes (Hartmann et al., 
2013), model evaluation (Flato et al., 2013), attribution (Bindoff 
et al., 2013), and projected long-term changes (Collins et al., 2013). 
Assessments were also provided in the IPCC Special Report on Global 
Warming of 1.5°C (SR1.5) (IPCC, 2018; Hoegh-Guldberg et al., 2018), 
on climate change and land (SRCCL; (IPCC, 2019a), and on oceans 
and the cryosphere (SROCC; IPCC, 2019b). These assessments are the 
starting point for the present assessment. 
This chapter is structured as follows (Figure  11.1): This section 
(11.1) provides the general framing and introduction to the chapter, 
highlighting key aspects that underlie the confidence and uncertainty 
in the assessment of changes in extremes, and introducing some main 
elements of the chapter. To provide readers with a quick overview

of past and future changes in extremes, a synthesis of global-scale 
assessments for different types of extremes is included at the end 
of this section (Tables 11.1 and 11.2). Section  11.2 introduces 
methodological aspects of research on climate extremes. Sections 
11.3 to 11.7 assess past changes and their attribution to causes, 
and projected future changes in extremes, for different types of 
extremes, including temperature extremes, heavy precipitation and 
FAQs
Chapter 11: Weather and climate extremes Chapter 11: Quick guide
Cross-chapter boxesSection 11.1
FramingAfrica
Table 11.4 | Table 11.5 | Table 11.6 
Asia
Table 11.7 | Table 11.8 | Table 11.9 | Box 11.4  
Table 11.10 | Table 11.11 | Table 11.12 
Central and South America
Table 11.13 | Table 11.14 | Table 11.15 
Europe
Table 11.16 | Table 11.17 | Table 11.18 | Box 11.4  
Table 11.19 | Table 11.20 | Table 11.21 Section 11.3
extremesSection 11.5
Floods
Section 11.7

Extreme stormsSection 11.2
Data and methods
Section 11.8
Compound eventsSection 11.6
DroughtsSection 11.4
Heavy 
Section 11.9
Regional information
Box 11.1
Thermodynamic and 
dynamic changesBox 11.2
Low-likelihood, 
high-impact extremesBox 11.4
Examples of concurrent 
climate extremesBox 11.3
Paleo 
extremesCC Box 11.1
Translation between global warming levels and scenariosChapter 11 assesses observed changes in weather and climate extremes, their 
attribution to human influence, and future projections at +1.5ºC, +2ºC and +4ºC.Key topics and corresponding sub-sections
Regional information distributed within Sections 11.3-11.8
BoxesSyntheses for all regions
11.9 | Table 11.A.2 
Figure 11.1 | Visual guide to Chapter 11.

1522
pluvial floods, river floods, droughts, and storms, in separate sections. 
Section 11.8 addresses compound events. Section 11.9 summarizes 
regional assessments of changes in temperature extremes, in 
precipitation extremes and in droughts by continents in tables. The 
chapter also includes several boxes and FAQs on more specific topics.
11.1.2 What Are Extreme Events and How are Their 
Changes Studied?
Building on the SREX report and AR5, this Report defines an extreme 
weather event as ‘an event that is rare at a  particular place and 
time of year’, and an extreme climate event as ‘a pattern of extreme 
weather that persists for some time, such as a season’ (see Glossary). 
The definitions of ‘rare’ are wide ranging, depending on applications. 
Some studies consider an event as an extreme if it is unprecedented; 
other studies consider events that occur several times a  year as

moderate extreme events. Rarity of an event with a fixed magnitude 
also changes under human-induced climate change, making events 
that are unprecedented so far rather probable under present 
conditions, but unique in the observational record – and thus often 
considered as ‘surprises’ (see Box 11.2). 
Various approaches are used to define extremes. These are generally 
based on the determination of relative (e.g.,  90th percentile) or 
absolute (e.g., 35°C for a hot day) thresholds above which conditions 
are considered extremes. Changes in extremes can be examined from 
two perspectives, either focusing on changes in frequency of given 
extremes, or on changes in their intensity. These considerations in the 
definition of extremes are further addressed in Section 11.2.1.
11.1.3 Types of Extremes Assessed in this Chapter
The types of extremes assessed in this chapter include temperature 
extremes, heavy precipitation and pluvial floods, river floods, droughts,

and storms. The drought assessment addresses meteorological 
droughts, agricultural and ecological droughts, and hydrological 
droughts (see Glossary). The storms assessment addresses tropical 
cyclones, extratropical cyclones, and severe convective storms. 
This chapter also assesses changes in compound events  – that 
is, multivariate or concurrent extreme events  – because of their 
relevance to impacts as well as the emergence of new literature on 
the subject. Most of the considered extremes were also assessed in 
SREX and AR5. Compound events were not assessed in depth in past 
IPCC reports (SREX Chapter 3; Section 11.8 of this Report). Marine-
related extremes such as marine heatwaves and extreme sea level, 
are assessed in Section 9.6.4 and Box 9.2 of this Report. 
Extremes and related phenomena are of various spatial and 
temporal scales. Tornadoes have a  spatial scale as small as less 
than 100 metres and a temporal scale as short as a few minutes.

In contrast, a  drought can last for multiple years, affecting vast 
regions. The level of complexity of the involved processes differs from 
one type of extreme to another, affecting our capability to detect, 
attribute and project changes in weather and climate extremes. 
Temperature and precipitation extremes studied in the literature are often based on extremes derived from daily values. Studies of events 
on longer time scales for temperature or precipitation, or on sub-
daily extremes, are scarcer, which generally limits the assessment for 
such events. Nevertheless, extremes on time scales different from 
daily are assessed for temperature extremes and heavy precipitation, 
when possible (Sections 11.3 and 11.4). Droughts and tropical 
cyclones are treated as phenomena in general in the assessment, 
not limited by their extreme forms, because these phenomena are 
relevant to impacts (Sections 11.6 and 11.7). Both precipitation and

wind extremes associated with storms are considered. 
Multiple concomitant extremes can lead to stronger impacts than 
those resulting from the same extremes had they happened in 
isolation. For this reason, the occurrence of multiple extremes that 
are multivariate and/or concurrent and/or happening in succession, 
also called ‘compound events’ (SREX Chapter 3), are assessed in this 
chapter based on emerging literature on this topic (Section  11.8). 
Box 11.2 also provides an assessment on low-likelihood, high-impact 
scenarios associated with extremes.
The assessment of projected future changes in extremes is presented 
as function of different global warming levels (Section  11.2.4 and 
Cross-Chapter Box  11.1). This provides traceability and comparison 
to the SR1.5 assessment (Hoegh-Guldberg et al., 2018, hereafter 
referred to as SR1.5 Chapter 3). Also, this is useful for decision makers 
as actionable information, as much of the mitigation policy discussion

and adaptation planning can be tied to the level of global warming. 
For example, regional changes in extremes, and thus their impacts, can 
be linked to global mitigation efforts. There is also the advantage of 
separating uncertainty in future projections due to regional responses 
as a  function of global warming levels from other factors such as 
differences in global climate sensitivity and emissions scenarios (Cross-
Chapter Box  11.1). Information is also provided on the translation 
between information provided at global warming levels and for single 
emissions scenarios (Cross-Chapter Box  11.1). This facilitates easier 
comparison with the AR5 assessment and with some analyses provided 
in other chapters as function of emissions scenarios.
A global-scale synthesis of this chapter’s assessments is provided in 
Section 11.1.7. In particular, Tables 11.1 and 11.2 provide a synthesis 
for observed and attributed changes, and projected changes in

extremes, respectively, at different global warming levels (1.5°C, 
2°C, and 4°C). Tables on regional-scale assessments for changes 
in temperature extremes, heavy precipitation and droughts, are 
provided in Section 11.9.
11.1.4 Effects of Greenhouse Gas and Other External 
Forcings on Extremes
The SREX, AR5, and SR1.5 assessed that there is evidence from 
observations that some extremes have changed since the mid-
20th century, that some of the changes are a result of anthropogenic 
influences, and that some observed changes are projected to continue 
into the future. Additionally, other changes are projected to emerge 
from natural climate variability under enhanced global warming 
(SREX Chapter 3; AR5 Chapter 10).

1523
At the global scale, and also at the regional scale to some extent, 
many of the changes in extremes are a  direct consequence of 
enhanced radiative forcing, and the associated global warming and/
or resultant increase in the water-holding capacity of the atmosphere, 
as well as changes in vertical stability and meridional temperature 
gradients that affect climate dynamics (see Box 11.1). Widespread 
observed and projected increases in the intensity and frequency of 
hot extremes, together with decreases in the intensity and frequency 
of cold extremes, are consistent with global and regional warming 
(Section 11.3 and Figure 11.2). Extreme temperatures on land tend 
to increase more than the global mean temperature (Figure 11.2), 
due in large part to the land–sea warming contrast, and additionally 
to regional feedbacks in some regions (Section 11.1.6). Increases in

the intensity of temperature extremes scale robustly, and in general 
linearly, with global warming across different geographical regions 
in projections up to 2100, with minimal dependence on emissions 
scenarios (Section 11.2.4, Figure 11.3, and Cross-Chapter Box 11.1; 
Seneviratne et al., 2016; Wartenburger et al., 2017; Kharin et al., 
2018). The frequency of hot temperature extremes (see Figure 11.6), 
the number of heatwave days and the length of heatwave seasons 
in various regions also scale well, but nonlinearly (because of 
threshold effects, Section  11.2.1), with global mean temperatures 
(Wartenburger et al., 2017; Y . Sun et al., 2018a). 
Changes in annual maximum one-day precipitation (Rx1day) are 
proportional to mean global surface temperature changes, at about 
7% increase per 1°C of warming, that is, following the Clausius–
Clapeyron relation (Box  11.1), both in observations (Westra et al., 
2013) and in future projections (Kharin et al., 2013) at the global

scale. Extreme short-duration precipitation in North America also 
scales with global surface temperature (Prein et al., 2016b; C. Li et al., 
2019a). At the local and regional scales, changes in extremes are also 
strongly modulated and controlled by regional forcings and feedback 
mechanisms (Section  11.1.6), whereby some regional forcings, for example, associated with changes in land cover and land use or 
aerosol emissions, can have non-local or some (non-homogeneous) 
global-scale effects. In general, there is high confidence in changes in 
extremes due to global-scale thermodynamic processes (i.e., global 
warming, mean moistening of the air) as the processes are well 
understood, while the confidence in those related to dynamic 
processes or regional and local forcing, including regional and local 
thermodynamic processes, is much lower due to multiple factors (see 
the following subsection and Box 11.1). 
Since AR5, the attribution of extreme weather events, or the

investigation of changes in the frequency and/or magnitude of 
individual and local- and regional-scale extreme weather events due 
to various drivers (Section 11.2.3 and Cross-Working Group Box 1.1) 
has provided evidence that greenhouse gases and other external 
forcings have affected individual extreme weather events. The events 
that have been studied are geographically uneven. For example, 
extreme rainfall events in the UK (Schaller et al., 2016; Vautard et al., 
2016; Otto et al., 2018b) or heatwaves in Australia (King et al., 2014; 
Perkins-Kirkpatrick et al., 2016; Lewis et al., 2017b) have spurred more 
studies than other events. Many highly impactful extreme weather 
events have not been studied in the event attribution framework. 
Studies in the developing world are also generally lacking. This is due 
to various reasons (Section 11.2) including lack of observational data, 
lack of reliable climate models and other problems (Otto et al., 2020).

While the events that have been studied are not representative of all 
extreme events that occurred, and results from these studies may also 
be subject to selection bias, the large number of event attribution 
studies provide evidence that changes in the properties of these 
local and individual events are in line with expected consequences 
of human influence on the climate and can be attributed to external 
drivers (Section  11.9). Figure  11.4 summarizes assessments of 
observed changes in temperature extremes, in heavy precipitation 
and in droughts, and their attribution in a map form. 
Figure 11.2 | Time series of observed temperature anomalies for global average annual mean temperature (black), land average annual mean temperature 
(green), land average annual hottest daily maximum temperature (TXx, purple), and land average annual coldest daily minimum temperature (TNn, blue).

Global and land mean temperature anomalies are relative to their 1850–1900 means and are based on the multi-product mean annual time series assessed in Section 2.3.1.1.3 
(see text for references). TXx and TNn anomalies are relative to their respective 1961–1990 means and are based on the HadEX3 dataset (Dunn et al., 2020) using values for 
grid boxes with at least 90% temporal completeness over 1961–2018. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1524
Figure 11.3 | Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except 
Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) 
models under different Shared Socio-economic Pathway (SSP) forcing scenarios, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7. 0, and SSP5-8.5.  Changes in TXx and 
GSAT are relative to the 1850–1900 baseline, and changes in GSAT are expressed as global warming level. (a) Individual models from the CMIP6 ensemble (grey), the multi-
model median under three selected SSPs (colours), and the multi-model median (black); (b) to (l) Multi-model median for the pooled data for individual AR6 regions. Numbers

in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the 
definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2.

1525
NENGIC
CAR
NWS
SAM
SSASWSNSA
NESWAFSAH
SEAF
WSAFCAFRAR
ARPEEU
CNA MEDNEU
WCE
NAUNEAFENA
SEA
CAU
SAU
NZRFE WSB ESB
EASTIBWCA
SASNWN
WNA
NCA
SCA
SESESAFMDGECA
EAU
Confidence level
Attribution*Observed change
Increase
Decrease
Mixed signalNo signalOverview of assessed events
Likely
High
Medium
LowLack of evidenceExtreme heat
Extreme precipitationType of extreme events 
Extreme cold
Observed trends 
 
* human influence 
has contributed to 
the observed 
change in extremeFigure 11.4 | Overview of observed changes for cold, hot, and wet extremes and their potential human contribution.  Shown are the direction of change and the confidence in: 1) the observed changes in cold and hot as well

as wet extremes across the world; and 2) whether human-induced climate change contributed to causing these changes (attribution). In each region changes in extremes are indicated by colour (orange – increase in the type of extreme; blue – 
decrease; both colours – changes of opposing direction within the region, with the signal depending on the exact event definition; grey – there are  no changes observed; and no fill – the data/evidence is too sparse to make an assessment). 
The squares and dots next to the symbol indicate the level of confidence for observing the trend and the human contribution, respectively. The more black dots/squares, the higher the level of confidence. The information on this figure is based 
on regional assessment of the literature on observed trends, detection and attribution and event attribution in Section 11.9.

1526
Box 11.1 | Thermodynamic and Dynamic Changes in Extremes Across Scales
Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other related quantities 
(thermodynamic changes) and those associated with atmospheric and oceanic motions (dynamic changes). While thermodynamic and 
dynamic processes are interconnected, considering them separately helps to disentangle the roles of different processes contributing 
to changes in climate extremes (e.g., Shepherd, 2014). 
Temperature extremes
An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth’s 
surface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity

of warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other 
thermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour 
content of the atmosphere (water vapour feedback, see Section 7.4.2.2) and a change in the vertical profile of temperature (lapse rate 
feedback, see Section 7.4.2.2). While the water vapour feedback always amplifies the initial temperature increases (positive feedback), 
the lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces 
temperature increases (negative feedback) in tropical regions (Pithan and Mauritsen, 2014). 
Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations

show that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due 
to decreases in surface albedo (see Section 11.3.1). In some mid-latitude areas, temperature increases are amplified by the higher 
atmospheric evaporative demand (Fu and Feng, 2014; Vicente-Serrano et al., 2020a) that results in a drying of soils in some regions 
(Section 11.6), leading to increased sensible heat fluxes (soil-moisture –temperature feedback, see Sections 11.1.6 and 11.3.1 for 
more background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced 
atmospheric carbon dioxide (CO 2) concentrations that can reduce the overall transpiration, and thus also enhance temperature in 
projections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6).
Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather

patterns (e.g.,  Suarez-Gutierrez et al., 2020a). For example, a  robust thermodynamic effect of polar amplification is a  weakened 
north-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes 
because of the reduction of cold air advection (Holmes et al., 2015; Schneider et al., 2015; Gross et al., 2020). Much less robust is the 
dynamic effect of polar amplification (Section 7.4.4.1) and the reduced low-altitude meridional temperature gradient that has been 
linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes 
(Cross-Chapter Box 10.1; Francis and Vavrus, 2012; Coumou et al., 2015, 2018; Mann et al., 2017). 
Precipitation extremes
Changes in temperature also control changes in water vapour through increases in evaporation and in the water-holding capacity of

the atmosphere (Section 8.2.1). At the global scale, column-integrated water vapour content increases roughly following the Clausius–
Clapeyron (C-C) relation, with an increase of approximately 7% per 1°C of global-mean surface warming (Section 8.2.1). Nonetheless, 
at regional scales, water vapour increases differ from this C-C rate due to several reasons (Section 8.2.2), including a change in 
weather regimes and limitations in moisture transport from the ocean, which warms more slowly than land (Byrne and O’Gorman, 
2018). Observational studies (Fischer and Knutti, 2016; Sun et al., 2021) have shown that the observed rate of increased precipitation 
extremes is similar to the C-C rate at the global scale. Climate model projections show that the increase in water vapour leads to 
robust increases in precipitation extremes everywhere, with a magnitude that varies between 4% and 8% per 1°C of surface warming

(thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models show that the dynamic contribution (Box 11.1, 
Figure 1c) can be substantial and strongly modify the projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large 
regions in the subtropics showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1, 
Figure 1c). However, the dynamic contributions show large differences across models and are more uncertain than thermodynamic 
contributions (Box 11.1, Figure 1c; Shepherd, 2014; Trenberth et al., 2015; Pfahl et al., 2017). 
Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of temperature (thermodynamics) 
and can affect the frequency and intensity of synoptic and subsynoptic phenomena, including tropical cyclones, extratropical cyclones,

fronts, mesoscale-convective systems and thunderstorms. For example, the poleward shift and strengthening of the Southern 
Hemisphere mid-latitude storm tracks (Section 4.5.1) can modify the frequency or intensity of extreme precipitation. However, the 
precise way in which dynamic changes will affect precipitation extremes is unclear due to several competing effects (Shaw et al., 2016; 

1527
 
Box 11.1 (continued)
Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra 
latent heat released from the thermodynamic increases in moisture (Lackmann, 2013; Willison et al., 2013; Marciano et al., 2015; 
Nie et al., 2018; Mizuta and Endo, 2020). The extra latent heat released within storms has been shown to increase precipitation 
extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., Molnar et al., 2015; Nie et al., 2018), 
although weakening effects have also been found in mid-latitude cyclones (e.g., Kirshbaum et al., 2017). Additionally, the increase 
in latent heat can also suppress convection at larger scales due to atmospheric stabilization (Nie et al., 2018; Tandon et al., 2018;

Kendon et al., 2019). As these dynamic effects result from feedback processes within storms where convective processes are crucial, 
their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, 
in current climate models (i.e.,  Kendon et al., 2014; Westra et al., 2014; Ban et al., 2015; Meredith et al., 2015; Prein et al., 2015; 
Nie et al., 2018).
Droughts
Droughts are also affected by thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6). Thermodynamic processes affect 
droughts by increasing atmospheric evaporative demand (Martin, 2018; Gebremeskel Haile et al., 2020; Vicente-Serrano et al., 
2020a) through changes in air temperature, radiation, wind speed, and relative humidity. Dynamic processes affect droughts through 
changes in the occurrence, duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight

(Section 11.6). While atmospheric evaporative demand increases with warming, regional changes in aridity are affected by increasing 
land–ocean warming contrast, vegetation feedbacks and responses to rising CO 2 concentrations, and dynamic shifts in the location of 
the wet and dry parts of the atmospheric circulation in response to climate change, as well as internal variability (Byrne and O’Gorman, 
2015; Kumar et al., 2015; Allan et al., 2020).
In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. 
Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall 
increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify 
heatwaves, droughts and heavy precipitation events when they occur ( high confidence ). Dynamic processes are often indirect responses

to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low 
confidence  in how dynamic changes affect the location and magnitude of extreme events in a warming climate.
Change in annual maximum daily precipitation
(a) Total change (b) Thermodynamic contribution (c) Dynamic contribution
-15  -12    -9   -6     -3    -1     1     3     6      9    12   15
Change per °C global warming (% °C-1)Colour
Low model agreementHigh model agreement
Box 11.1, Figure 1: Multi-model Coupled Model Intercomparison Project Phase 5 (CMIP5) mean fractional changes (in % per degree of warming). 
(a) changes in annual maximum precipitation (Rx1day); (b) changes in Rx1day due to the thermodynamic contribution; and (c) changes in Rx1day due to the dynamic 
contribution estimated as the difference between the total changes and the thermodynamic contribution. Changes were derived from a linear regression for the period

1950–2100. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models (n=22) agree 
on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the 
simple approach, please refer to the Cross-Chapter Box Atlas 1. A detailed description of the estimation of dynamic and thermodynamic contributions is given in Pfahl 
et al. (2017). Figure adapted from Pfahl et al. (2017), originally published in Nature Climate Change/Springer Nature.  Further details on data sources and processing 
are available in the chapter data table (Table 11.SM.9).

1528
11.1.5 Effects of Large-scale Circulation 
on Changes in Extremes
Atmospheric large-scale circulation patterns and associated 
atmospheric dynamics are important determinants of the regional 
climate (Chapter 10). As a result, they are also important to the magnitude, 
frequency, and duration of extremes (Box 11.4). Aspects of changes in 
large-scale circulation patterns are assessed in Chapters 2, 3, 4 and 8, 
and representative atmospheric and oceanic modes are described in 
Annex IV. This subsection provides some general concepts, through 
a couple of examples, on why the uncertainty in the response of large-
scale circulation patterns to external forcing can cascade to uncertainty 
in the response of extremes to external forcings. Details for specific 
types of extremes are covered in the relevant subsections. For example, 
the occurrence of the El Niño–Southern Oscillation (ENSO) influences

precipitation regimes in many areas, favouring droughts in some regions 
and heavy rains in others (Box 11.4). The extent and strength of the Hadley 
circulation influences regions where tropical and extratropical cyclones 
occur, with important consequences for the characteristics of extreme 
precipitation, drought, and winds (Section 11.7). Changes in circulation 
patterns associated with land–ocean heat contrast, which affect the 
monsoon circulations (Section 8.4.2.4), lead to heavy precipitation along 
the coastal regions in East Asia (Freychet et al., 2015). As a result, changes 
in the spatial and/or temporal variability of the atmospheric circulation in  
response to warming affect characteristics of weather systems such 
as tropical cyclones (Sharmila and Walsh, 2018), storm tracks (Shaw 
et al., 2016), and atmospheric rivers (Section  11.7; Waliser and Guan, 
2017). Changes in weather systems come with changes in the frequency

and intensity of extreme winds, extreme temperatures, and extreme 
precipitation, on the backdrop of thermodynamic responses of extremes 
to warming (Box 11.1). Floods are also affected by large-scale circulation 
modes, including ENSO, the North Atlantic Oscillation (NAO), the Atlantic 
Multi-decadal Variability (AMV), and the Pacific Decadal Variability (PDV) 
(Kundzewicz et al., 2018; Annex IV). Aerosol forcing, through changes 
in patterns of sea surface temperatures (SSTs), also affects circulation 
patterns and tropical cyclone activities (Takahashi et al., 2017).
In general, changes in atmospheric large-scale circulation due to 
external forcing are uncertain, but there are some robust changes 
(Sections 2.3.1.4 and 8.2.2.2). Among them, there has been a  very likely 
widening of the Hadley circulation since the 1980s and the extratropical 
jets and cyclone tracks have likely  been shifting poleward since the 
1980s (Section  2.3.1.4). The poleward expansion affects drought

occurrence in some regions (Section  11.6), and results in poleward 
shifts of tropical cyclones and storm tracks (Sections 11.7.1 and 
11.7.2). Although it is very likely  that the amplitude of ENSO variability 
will not robustly change over the 21st century (Section 4.3.3.2), the 
frequency of extreme ENSO events (Box 11.4), defined by precipitation 
threshold, is projected to increase with global warming (Section 6.5 
of SROCC). This would have implications for projected changes in 
extreme events affected by ENSO, including droughts over wide areas 
(Section 11.6; Box 11.4) and tropical cyclones (Section 11.7.1). A case 
study is provided for extreme ENSO events in 2015–2016 in Box 11.4 
to highlight the influence of ENSO on extremes. 
In summary, large-scale atmospheric circulation patterns are 
important drivers for local and regional extremes. There is overall low

confidence about future changes in the magnitude, frequency, and spatial distribution of these patterns, which contributes to uncertainty 
in projected responses of extremes, especially in the near term. 
11.1.6 Effects of Regional-scale Processes and Forcings 
and Feedbacks on Changes in Extremes
At the local and regional scales, changes in extremes are strongly 
modulated by local and regional feedbacks (SRCCL, Jia et al., 2019; 
Seneviratne et al., 2013; Miralles et al., 2014a; Lorenz et al., 2016; 
Vogel et al., 2017), changes in large-scale circulation patterns 
(Section 11.1.5), and regional forcings such as changes in land use 
or aerosol concentrations (Chapters 3  and 7; Findell et al., 2017; 
Hirsch et al., 2017, 2018; Thiery et al., 2017; Z. Wang et al., 2017b). 
In some cases, such responses may also include non-local effects 
(e.g., de Vrese et al., 2016; Persad and Caldeira, 2018; Miralles et al., 
2019; Schumacher et al., 2019). Regional-scale forcing and feedbacks

often affect temperature distributions asymmetrically, with generally 
higher effects for the hottest percentiles (Section 11.3). 
Land use can affect regional extremes, in particular hot extremes, 
in several ways ( high confidence ). This includes effects of land 
management (e.g.,  cropland intensification, irrigation, double 
cropping) as well as of land cover changes (deforestation; Sections 
11.3.2 and 11.6). Some of these processes are not well represented 
(e.g.,  effects of forest cover on diurnal temperature cycle) or not 
integrated (e.g.,  irrigation) in climate models (Sections 11.3.2 and 
11.3.3). Overall, the effects of land-use forcing may be particularly 
relevant in the context of low-emissions scenarios, which include 
large land-use modifications, for instance those associated with the 
expansion of biofuels, bioenergy with carbon capture and storage, 
or re-/afforestation to ensure negative emissions, as well as with the

expansion of food production (e.g., SR1.5, Chapter 3; Cross-Chapter 
Box 5.1 in this Report; van Vuuren et al., 2011; Hirsch et al., 2018). 
There are also effects on the water cycle through freshwater use 
(Section 11.6 and Cross-Chapter Box 5.1).
Aerosol forcing also has a strong regional footprint associated with 
regional emissions, which affects temperature and precipitation 
extremes ( high confidence ) (Sections 11.3 and 11.4). From around the 
1950s to 1980s, enhanced aerosol loadings led to regional cooling 
due to decreased global solar radiation (‘global dimming’) which 
was followed by a phase of ‘global brightening’ due to a reduction 
in aerosol loadings (Chapters 3 and 7; Wild et al., 2005). King et al. 
(2016b) show that aerosol-induced cooling delayed the timing of 
a significant human contribution to record-breaking heat extremes in 
some regions. However, the decreased aerosol loading since the 1990s

has led to an accelerated warming of hot extremes in some regions. 
Based on Earth system model (ESM) simulations, Dong et al. (2017) 
suggest that a  substantial fraction of the warming of the annual 
hottest days in Western Europe since the mid-1990s has been due to 
decreases in aerosol concentrations in the region. Dong et al. (2016b) 
also identify non-local effects of decreases in aerosol concentrations 
in Western Europe, which they estimate played a dominant role in 
the warming of the hottest daytime temperatures in north-east Asia 
since the mid-1990s, via induced coupled atmosphere–land surface 
and cloud feedbacks, rather than a direct impact of anthropogenic 
aerosol changes on cloud condensation nuclei.

1529
In addition to regional forcings, regional feedback mechanisms can 
also substantially affect extremes ( high confidence ) (Sections 11.3, 
11.4 and 11.6). In particular, soil moisture feedbacks play an important 
role for extremes in several mid-latitude regions, leading to a marked 
additional warming of hot extremes compared to mean global warming 
(Seneviratne et al., 2016; Bathiany et al., 2018; Miralles et al., 2019), 
which is superimposed on the known land–sea contrast in mean 
warming (Vogel et al., 2017). Soil moisture–atmosphere feedbacks 
also affect drought development (Section 11.6). Additionally, effects of 
land surface conditions on circulation patterns have also been reported 
(Koster et al., 2016; Sato and Nakamura, 2019). These regional feedbacks 
are also associated with substantial spread in models (Section 11.3), 
and contribute to the identified higher spread of regional projections of

temperature extremes as a function of global warming, compared with 
the spread resulting from the differences in projected global warming 
(global transient climate responses) in climate models (Seneviratne 
and Hauser, 2020). In addition, there are also feedbacks between soil 
moisture content and precipitation occurrence, generally characterized 
by negative spatial feedbacks and positive local feedbacks (Taylor 
et al., 2012; Guillod et al., 2015). Climate model projections suggest 
that these feedbacks are relevant for projected changes in heavy 
precipitation (Seneviratne et al., 2013). However, there is evidence that 
climate models do not capture the correct sign of the soil moisture–
precipitation feedbacks in several regions, in particular spatially, and/
or in some cases also temporally (Taylor et al., 2012; Moon et al., 2019). 
In the Northern Hemisphere high latitudes, the snow- and ice-albedo 
feedback, along with other factors, is projected to largely amplify

temperature increases (e.g., Pithan and Mauritsen, 2014), although the 
effect on temperature extremes is still unclear. It also remains unclear 
whether snow-albedo feedbacks in mountainous regions might have 
an effect on temperature and precipitation extremes  (e.g., Gobiet et al., 2014). However, these feedbacks play an important role in projected 
changes in high-latitude warming (Hall and Qu, 2006), and, in 
particular, in changes in cold extremes in these regions (Section 11.3).
Finally, extreme events may also regionally amplify one another. 
For example, this is the case for heatwaves and droughts, with high 
temperatures and stronger radiative forcing leading to drying tendencies 
on land due to increased evapotranspiration (Section 11.6), and drier 
soils then inducing decreased evapotranspiration and higher sensible 
heat flux and hot temperatures (Box 11.1, Section 11.8; Seneviratne 
et al., 2013; Miralles et al., 2014a; Vogel et al., 2017; Zscheischler and

Seneviratne, 2017; S. Zhou et al., 2019; Kong et al., 2020). 
In summary, regional forcings and feedbacks  – in particular those 
associated with land use and aerosol forcings – and soil-moisture–
temperature, soil moisture–precipitation, and snow/ice–albedo–
temperature feedbacks, play an important role in modulating 
regional changes in extremes. These can also lead to a  higher 
warming of extreme temperatures compared to mean temperature 
(high confidence ), and possibly cooling in some regions ( medium 
confidence ). However, there is only medium confidence  in the 
representation of the associated processes in state-of-the-art ESMs.
11.1.7 Global-scale Synthesis
Tables 11.1 and 11.2 provide a synthesis for observed and attributed 
changes in extremes, and projected changes in extremes, respectively, 
at different levels of global warming. This synthesis assessment 
focuses on the assessed range of observed and projected changes.

In this chapter, the assessed likely  range in a  projection typically 
Heatdaysand nights,heat waves
(intensit y↑,frequenc y↑)
Cold daysand nights
(temperat ure↑,frequenc y↓)
Heavyprecipit ationeventsinmost land areas
(intensit yorfrequency↑)
Tropical cyclones
(precipita tion↑)
(intensit y↑)
Agricultural andecologica ldroughts
(dryingindryseasonoverpredom inant
fractionofland area↑)
Compounddry&hotevents
(frequenc y↑)
Confidenceinattribution oftrends tohuman-induce dclimatechan geConfidenceinproject edchangesforglobalwarmin glevelof+2°C
low medium highlow medium high
L VL EL VCLVLELVC
L:likely
VL: verylikely
EL:extremelylikely
VC:virtuallycertain
Global-sc aletrends
Figure 11.5 | Confidence and likelihood of past changes and projected future changes at 2°C of global warming on the global scale.  The information in this 
figure is based on Tables 11.1 and 11.2.

1530
0153045N.W. Nor th−Amer ica
0153045N.E. Nor th−Amer ica
0153045Greenland/Iceland
0153045W. Nor th−Amer ica
0153045C. Nor th−Amer ica
0153045E. Nor th−Amer ica
0153045N. Centr al−Amer ica
0153045S. Centr al−Amer ica
0153045Caribbean
0153045N.W. South−Amer ica
0153045N. South−Amer ica
0153045South−Amer ica−monsoon
0153045N.E. South−Amer ica
0153045S.W. South−Amer ica
0153045S.E. South−Amer ica
0153045S. South−Amer ica
0153045W. Antarctica
0153045E. Antarctica
0153045N. Europe
0153045Wester n&Centr al−Europe
0153045E. Europe
0153045Mediterr anean
0153045Sahar a
0153045Wester n−Afr ica
0153045Centr al−Afr ica
0153045N. Easter n−Afr ica
0153045S. Easter n−Afr ica
0153045W. Souther n−Afr ica
0153045E. Souther n−Afr ica
0153045Madagascar
0153045Russian−Arctic
0153045W. Siber ia
0153045E. Siber ia
0153045Russian−F ar−East
0153045W. C. Asia
0153045E. C. Asia
0153045Tibetan−Plateau
0153045E. Asia

0153045Arabian−P eninsula
0153045S. Asia
0153045S. E. Asia
0153045N. Australia
0153045C. Australia
0153045E. Australia
0153045S. Australia
0153045New−Zealand
1.0 1.5 2.0 3.0 4.0
°C0153045Number of times as frequentGlobal land
10−y ear e vent
50−y ear e vent
Figure 11.6 | Projected changes in the frequency of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 18 50–1900 baseline. Extreme temperatures are defined as 
the maximum daily temperatures that were exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results are shown for the 
global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble, and the ‘whiskers’

extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model Intercomparison Project 
Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from Li et al. (2021). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1531
02468N.W. Nor th−Amer ica
02468N.E. Nor th−Amer ica
9
02468Greenland/Iceland
02468W. Nor th−Amer ica
02468C. Nor th−Amer ica
02468E. Nor th−Amer ica
02468N. Centr al−Amer ica
02468S. Centr al−Amer ica
02468Caribbean
11
02468N.W. South−Amer ica
14
02468N. South−Amer ica
02468South−Amer ica−monsoon
02468N.E. South−Amer ica
02468S.W. South−Amer ica
02468S.E. South−Amer ica
02468S. South−Amer ica
02468W. Antarctica
02468E. Antarctica
02468N. Europe
02468Wester n&Centr al−Europe
02468E. Europe
02468Mediterr anean
9
02468Sahar a
34
34
02468Wester n−Afr ica
17
24
02468Centr al−Afr ica
9
18
02468N. Easter n−Afr ica
9
11
02468S. Easter n−Afr ica
02468W. Souther n−Afr ica
02468E. Souther n−Afr ica
02468Madagascar
02468Russian−Arctic
02468W. Siber ia
02468E. Siber ia
9
02468Russian−F ar−East
02468W. C. Asia
02468E. C. Asia
9
02468Tibetan−Plateau
02468E. Asia
02468Arabian−P eninsula
02468S. Asia
9
11
02468S. E. Asia

02468N. Australia
02468C. Australia
02468E. Australia
02468S. Australia
02468New−Zealand
°C02468Number of times as frequentGlobal land
Figure 11.7 | Projected changes in the frequency of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative to the 1850 –1900 baseline.  Extreme precipitation is defined 
as the annual maximum daily precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-year event, blue) and once during a 50-year period (50-year event, orange) during the 1850–1900 base period. Results 
are shown for the global land area and the AR6 regions. For each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency changes across the multi-model ensemble,

and the ‘whiskers’ extend to the 90% uncertainty range. The dotted line indicates no change in frequency. The results are based on the multi-model ensemble from simulations of global climate models contributing to the Coupled Model 
Intercomparison Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Adapted from Li et al. (2021). Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1532
corresponds to the 90% range of the multi-model ensemble spread 
to take into account other sources of uncertainty, unless stated 
otherwise. Some low-likelihood, high-impact scenarios that can be of 
high relevance are addressed in Box 11.2.Building on the assessments from Tables 11.1 and 11.2, Figure 11.5 
provides a synthesis on the level of confidence in the attribution and 
projection of changes in extremes. In the case where the signal in 
the observations is still relatively weak but the physical processes 
underlying the changes in extremes in response to human forcing 
are well understood, confidence in the projections would be higher 
than in the attribution because of strengthening in the signal with 
warming. But, when the observed signal is already strong and when 
observational evidence is consistent with model simulated responses,

Table 11.1 | Synthesis table on observed changes in extremes and contribution by human influence.  Note that observed changes in marine extremes are assessed 
in Cross-Chapter Box 9.1. 
Phenomenon and Direction 
of TrendObserved/Detected Trends Since 1950  
(for +0.5°C global warming or higher)Human Contribution to the Observed Trends Since 
1950 (for +0.5°C global warming or higher) 
Warmer and/or more frequent hot days 
and nights over most land areas
Warmer and/or fewer cold days and nights 
over most land areas
Warm spells/heatwaves: increases in 
frequency or intensity over most land areas
Cold spells/cold waves: decreases in 
frequency or intensity over most land areasVirtually certain on global scale {11.3}
Continental-scale evidence:
Asia, Australasia, Europe, North America: Very likely 
Central and South America:  High confidence
Africa: Medium confidence
{11.3, 11.9}Extremely likely main contributor  on global scale {11.3}

North America, Europe, Australasia, Asia:  Very likely  
Africa:  Medium confidence 
{11.3, 11.9}
Heavy precipitation events: increase in 
the frequency, intensity, and/or amount 
of heavy precipitationLikely  on global scale, over majority of land regions with good 
observational coverage {11.3}
Asia, Europe, North America: Likely
Africa, Australasia, Central and South America: Low confidence
{11.3, 11.9}Likely main contributor to the observed intensification of heavy 
precipitation in land regions on global scale.
{11.3}
Asia, Europe, North America: Likely 
Increases in agricultural and ecological 
drought eventsMedium confidence  some regions {11.6, 11.9}
Increasing trends in agricultural and ecological droughts 
have been observed in AR6 regions on all continents

(medium confidence ) {11.6, 11.9}Medium confidence  some regions
{11.6, 11.9}
Increase in precipitation associated 
with tropical cyclones (TCs)Medium confidence
{11.7}High confidence
{11.7}
Increase in likelihood that a TC will 
be at major TC intensity (Cat. 3–5)Likely
{11.7}Medium confidence
{11.7}
Changes in frequency of rapidly 
intensifying tropical cyclonesLikely
{11.7}
Poleward migration of tropical cyclones 
in the western PacificMedium confidence  
{11.7}Medium confidence  
{11.7}
Decrease in TC forward motion 
over the USAIt is likely  that TC translation speed has slowed over  
the USA since 1900. 
{11.7}It is more likely than not  that the slowdown of TC translation 
speed over the USA has contributions from anthropogenic forcing.
{11.7}
Severe convective storms (tornadoes, hail, 
rainfall, wind, lightning)Low confidence  in past trends in hail and winds and tornado 
activity due to short length of high-quality data records. {11.7}Low confidence

{11.7}
Increase in compound eventsLikely increase in the probability of compound events.
High confidence  that concurrent heatwaves and droughts are 
becoming more frequent under enhanced greenhouse gas forcing 
at global scale. 
Medium confidence  that fire weather, i.e. compound hot, dry 
and windy events, have become more frequent in some regions.  
Medium confidence  that compound flooding risk has increased 
in some locations.
{11.8}Likely that human-induced climate change has increased the 
probability of compound events.
High confidence that human influence has increased the frequency 
of concurrent heatwaves and droughts.  
Medium confidence that human influence has increased fire 
weather occurrence in some regions.  
Low confidence that human influence has contributed to changes 
in compound events leading to flooding.
{11.8}

1533
confidence in the projection may be lower than that in attribution if 
certain physical processes could be expected to behave differently 
in a  much warmer world and under much higher greenhouse gas 
forcing, and in particular if such a behaviour is poorly understood.Further synthesis for regional assessments are provided in 
Figure  11.4 (event attribution), Figure  11.6 (projected change 
in hot temperature extremes) and Figure 11.7 (projected changes in  
precipitation extremes). A  synthesis on regional assessments for 
observed, attributed and projected changes in extremes is provided 
in Section 11.9 for all AR6 reference regions (see Section 1.4.5 and 
Figures 1.18 and Atlas.2 for definitions of AR6 regions).
Table 11.2 | Synthesis table on projected changes in extremes.  Note that projected changes in marine extremes are assessed in Chapter 9 and Cross-Chapter Box 9.1

(marine heatwaves). Assessments are provided compared to pre-industrial conditions.
Phenomenon and 
Direction of TrendProjected Changes at 
+1.5ºC Global WarmingProjected Changes at  
+2°C Global WarmingProjected Changes at  
+4°C Global Warming
Warmer and/or more frequent 
hot days and nights over most 
land areas
Warmer and/or fewer cold days 
and nights over most land areas
Warm spells/heatwaves; increases 
in frequency or intensity over 
most land areas
Cold spells/cold waves: decreases 
most land areasVirtually certain on global scale
Extremely likely  on all continents  
Highest increase of temperature of hottest 
days is projected in some mid-latitude and 
semi-arid regions, and the South American 
Monsoon region, at about 1.5 times to twice 
the rate of global warming ( high confidence ) 
{11.3, Figure 11.3}
Highest increase of temperature of coldest 
days is projected in Arctic regions, at about 
three times the rate of global warming

(high confidence ) 
{11.3}
Continental-scale projections:
Extremely likely : Africa, Asia, Australasia, 
Central and South America, Europe, 
North America
{11.3, 11.9}Virtually certain on global scale
Virtually certain  on all continents
the rate of global warming ( high confidence )  
three times the rate of global warming 
{11.3}
Virtually certain:  Africa, Asia, Australasia, 
semi-arid regions, and the South American

{11.3}
Virtually certain: Africa, Asia, Australasia, 
Heavy precipitation events: 
increase in the frequency, 
intensity, and/or amount 
of heavy precipitation High confidence  that increases take place 
in most land regions  
{11.4}
Very likely: Asia, North America
Likely: Africa, Europe 
High confidence: Central and South America
Medium confidence: Australasia
{11.4, 11.9}Likely  that increases take place in most 
land regions  
{11.4}
Extremely likely: Asia, North America
Very likely: Africa, Europe
Likely: Australasia, Central and South America
{11.4, 11.9}Very likely  that increases take place in most 
land regions  
{11.4}

Virtually certain: Africa, Asia, North America 
Extremely likely: Central and South America, 
Europe 
Very likely  Australasia
{11.4, 11.9}
Agricultural and ecological 
droughts: increases in intensity 
and/or duration of drought events More regions affected by increases in 
agricultural and ecological droughts 
compared to observed changes 
(high confidence ). {11.6, 11.9}
Decreased precipitation is going to increase 
the severity of drought in some regions; 
atmospheric evaporative demand will 
continue to increase compared to pre-
industrial conditions and lead to further 
increases in agricultural and ecological 
droughts due to increased evapotranspiration 
in some regions. ( high confidence )
{11.6, 11.9}More regions affected by increases in 
agricultural and ecological droughts than at 
1.5°C of global warming ( high confidence ). 
atmospheric evaporative demand will

agricultural and ecological droughts than 
at 2°C of global warming ( very likely ). 
{11.6, 11.9}
the severity of drought in several regions; 
in several regions. ( high confidence )
Increase in precipitation 
associated with tropical 
cyclones (TCs)High confidence  in a projected increase of TC 
rain rates at the global scale with a  median 
projected increase due to human emissions 
of about 11%. {11.7}
Medium confidence  that rain rates will

increase in every basin. {11.7}High confidence  in a projected increase of TC 
rain rates at the global scale with a median 
of about 14%. {11.7}
Medium confidence  that rain rates will 
of about 28%. {11.7}
increase in every basin. {11.7}
Increase in mean TC lifetime-
maximum wind speed (intensity)Medium confidence 
{11.7}High confidence 
{11.7}

1534
Increase in likelihood that a TC 
will reach major TC intensity 
(Category 4–5)High confidence  for an increase in the 
proportion of TCs that reach the strongest 
(Category 4–5) levels. The median projected 
increase in this proportion is about 10%.  
{11.7}High confidence  for an increase in the 
increase in this proportion is about 13%.
{11.7}High confidence for an increase in the 
increase in this proportion is about 20%.
{11.7}

Severe convective stormsHigh confidence that the average and maximum rain rates associated with severe convective storms increase in some regions, including the 
USA. High confidence that convective available potential energy (CAPE) increases in response to global warming in the tropics and subtropics, 
suggesting more favourable environments for severe convective storms. Medium confidence that the frequency of spring severe convective storms 
is projected to increase in the USA, leading to a lengthening of the severe convective storm season. {11.7}
Increase in compound events 
(frequency, intensity)Likely that probability of compound events will continue to increase with global warming.
High confidence  that concurrent heatwaves and droughts will continue to increase under higher levels of global warming, with higher frequency/
intensity with every additional 0.5°C of global warming.

High confidence  that fire weather, (i.e. compound hot, dry and windy events), will become more frequent in some regions at higher levels 
of global warming.  
High confidence that compound flooding at the coastal zone will increase under higher levels of global warming. {11.8}
Box 11.2 | Changes in Low-likelihood, High-impact Extremes
The SREX (Chapter 3) assigned low confidence  to changes in low-likelihood, high-impact (LLHI) events (termed ‘low-probability high-
impact scenarios‘). Such events are often not anticipated and thus sometimes referred to as ‘surprises’. There are several types of LLHI 
events. Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result from tipping points 
in the climate system (Section 1.4.4.3), such as the shutdown of the Atlantic thermohaline circulation (SROCC Chapter 6; Collins

et al., 2019) or the drydown of the Amazonian rainforest (SR1.5 Chapter 3, Hoegh-Guldberg et al., 2018; Drijfhout et al., 2015), or 
from uncertainties in climate processes, including climate feedbacks, that may enhance or damp extremes either related to global or 
regional climate responses (Seneviratne et al., 2018a; Sutton, 2018). The low confidence  does not by itself exclude the possibility of 
such events occuring, rather it indicates a poor state of knowledge. Such outcomes, while improbable, could be associated with very 
high impacts, and are thus highly relevant from a risk perspective (see Section 1.4.3 and Box 11.4; Sutton, 2018, 2019). Alternatively, 
high impacts can occur when different extremes occur at the same time, or in short succession at the same location, or in several 
regions with shared vulnerability (e.g., food-basket regions Gaupp et al., 2019). These ‘compound events’ are assessed in Section 11.8, 
and Box 11.4 provides a case study example.

Difficulties persist in determining the likelihood of occurrence and time frame of potential tipping points and LLHI events. However, 
new literature has emerged on unanticipated and LLHI events. There are some events that are sufficiently rare that they have not been 
observed in meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate system – 
see examples below and in McCollum et al. (2020). The rare nature of such events and the limited availability of relevant data makes 
it difficult to estimate their occurrence probability and thus gives little evidence on whether to include such hypothetical events in 
planning decisions and risk assessments. The estimation of such potential surprises is often limited to events that have historical 
analogues (including before the instrumental records began, Wetter et al., 2014), albeit the magnitude of the event may differ.

Additionally, there is also a limitation of available resources to exhaust all plausible trajectories of the climate system. As a result, there 
will still be events that cannot be anticipated. These events can be surprises to many in that the events have not been experienced, 
although their occurrence could be inferred by statistical means or physical modelling approaches (Chen et al., 2017; van Oldenborgh 
et al., 2017; Harrington and Otto, 2018a). Another approach focusing on the estimation of low-probability events and of events 
whose likelihood of occurrence is unknown consists in using physical climate models to create a physically self-consistent storyline of 
plausible extreme events and assessing their impacts and driving factors in past (Section 11.2.3) or future conditions (Section 11.2.4) 
(Hazeleger et al., 2015; Shepherd, 2016; Zappa and Shepherd, 2017; Cheng et al., 2018; Shepherd et al., 2018; Sutton, 2018; Schaller 
et al., 2020; Wehrli et al., 2020) .

In many parts of the world, observational data are limited to 50–60 years. This means that the chance to observe an extreme event 
at a particular location that occurs once in several hundred or more years is small. Thus, when a very extreme event occurs, it becomes 
a  surprise to many (Bao et al., 2017; McCollum et al., 2020), and very rare events are often associated with high impacts (van 
Oldenborgh et al., 2017; Philip et al., 2018b; Tozer et al., 2020). Attributing and projecting very rare events in a particular location by 
assessing their likelihood of occurrence within the same larger region and climate thus provides another way to make quantitative 
assessments regarding events that are extremely rare locally. Some examples of such events include:

1535
• Hurricane Harvey, that made landfall in Houston, TX in August 2017 (Section 11.7.1.4.)
• The 2010–2011 extreme floods in Queensland, Australia (Christidis et al., 2013a)
• The 2018 concurrent heatwaves across the Northern Hemisphere (Box 11.4)
• Tropical Cyclone Idai in Mozambique (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)
• The California fires in 2018 and 2019
• The 2019–2020 Australia fires (Cross-Chapter Box: Disaster in WGII AR6 Chapter 4)
One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and that the framework of 
reference for adaptation is continuously moving. As an example, the concurrent heatwaves that occurred across the Northern Hemisphere 
in the summer of 2018 were considered very unusual and were unprecedented given the total area that was concurrently affected  (Drouard

et al., 2019; Kornhuber et al., 2019; Toreti et al., 2019; Vogel et al., 2019) ; however, the probability of this event under 1°C global warming 
was found to be about 16% (Vogel et al., 2019), which is not particularly low. Similarly, the 2013 summer temperature over eastern 
China was the hottest on record at the time, but it had an estimated recurrence interval of about four years in the climate of 2013 
(Sun et al., 2014). Furthermore, when other aspects of the risk, vulnerability, and exposure are historically high or have recently increased 
(see WGII, Chapter 16, Section 16.4), relatively moderate extremes can have very high impacts (Otto et al., 2015b; Philip et al., 2018b). 
As warming continues, the climate moves further away from its historical state we are familiar with, resulting in an increased likelihood of 
unprecedented events and surprises. This is particularly the case under high global warming levels – for example, the climate of the late

21st century under high-emissions scenarios, above 4°C of global warming (Cross-Chapter Box 11.1).
Another factor highlighted in Section 11.8 and Box 11.4 making events high-impact and difficult to anticipate is that several locations 
under moderate warming levels could be affected simultaneously, or very repeatedly by different types of extremes (Mora et al., 2018; 
Gaupp et al., 2019; Vogel et al., 2019). Box 11.4 shows that concurrent events at different locations, which can lead to major impacts 
across the world, can also result from the combination of anomalous circulation or natural variability (e.g., El Niño–Southern Oscillation)  
Box 11.2 (continued)
Box 11.2, Table 1 | Examples of changes in low-likelihood, high-impact extreme conditions (single extremes, compound events) at different 
global warming levels.
+1°C (Present-day) +1.5°C +2°C +3°C and Higher
Risk ratio for annual hottest daytime temperature (TXx) 
with 1% of probability under present-day warming

(+1°C) (Kharin et al., 2018): Global land13.3 (i.e., 230% 
higher probability)8.2 (i.e., 720% 
higher probability)Not assessed
Risk ratio for heavy precipitation events (Rx1day) with 
1% of probability under present-day warming (+1°C) 
(Kharin et al., 2018): Global land1 1.2 (i.e., 20% 
higher probability)1.5 (i.e., 50% 
Number of 1–5 day duration extreme floods with 1% 
of probability under present-day warming (+1°C) (H. Ali 
et al., 2019) Indian subcontinentUp to 3 in 
individual locationsUp to 5 in 
individual locations2–6 in most locationsUp to 12 in individual 
locations (4°C)
Probability of ‘extreme extremes’ hot days with 1/1000 
probability at the end of the 20th century (Vogel et al., 
2020a): Global landAbout 20 days 
over 20 years 
in most locationsAbout 50 days 
in 20 years in 
most locationsAbout 150 days 
most locationsAbout 500 days 
in 20 years in most 
locations (3°C)
Probability of co-occurrence in the same week of hot

days with 1/1000 probability and dry days with 1/1000 
2020a): Amazon0% probabilityAbout one week 
in 20 yearsAbout 4 to 5 weeks 
in 20 yearsMore than 9 weeks 
in 20 years (3°C)
Projected soil moisture drought duration per year 
(Samaniego et al., 2018): Mediterranean region41 days (+46% 
compared to the late 
20th century)58 days (+107% 
20th century)71 days (+154% 
20th century)125 days (+346% 
20th century) (3°C)
Increase in days exposed to dangerous extreme heat – 
measured in Health Heat Index (HHI) (Q. Sun et al., 2019) 
global landNot assessed, baseline 
is 1981–20001.6 times higher risk of 
experiencing heat >40.62.3 times higher risk of 
experiencing heat >40.6Around 80% of 
land area exposed to 
dangerous heat, tropical 
regions 1/3 of the year 
(4°C)
Increase in regional mean fire season length

(Q. Sun et al., 2019; Xu et al., 2020) global landNot assessed, baseline 
is 1981–20006.2 days 9.5 days About 50 days (4°C)

1536
patterns with amplification of resulting responses to human-induced global warming. Also multivariate extremes at single locations 
pose specific challenges to anticipation (Section 11.8), with low likelihoods in the current climate but the probability of occurrence of 
such compound events strongly increasing with increasing global warming levels (Vogel et al., 2020a). Therefore, in order to estimate 
whether, and at what level of global warming, very high impacts arising from extremes would occur, the spatial extent of extremes 
and the potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight increasing evidence that 
temperature extremes, higher intensity precipitation accompanying tropical cyclones, and compound events such as dry/hot conditions

conducive to wildfire or storm surges resulting from sea level rise and heavy precipitation events, pose widespread threats to societies 
already at relatively low warming levels. Studies have already shown that the probability for some recent extreme events is so small in 
the undisturbed world that these events were extremely unlikely  to occur without human influence (Section 11.2.4). Box 11.2, Table 1, 
provides examples of projected changes in LLHI extremes (single extremes, compound events) of potential relevance for impact and 
adaptation assessments showing that today’s very rare events can become commonplace in a warmer future.
In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with low confidence , but cannot 
be excluded, especially at global warming levels above 4°C. Compound events, including concurrent extremes, are a factor increasing

the probability of LLHI events ( high confidence ). With increasing global warming, some compound events with low likelihood in past 
and current climate will become more frequent, and there is a higher chance of historically unprecedented events and surprises ( high 
confidence ). However, even extreme events that do not have a particularly low probability in the present climate (at more than 1°C of 
global warming) can be perceived as surprises because of the pace of global warming ( high confidence ). 
11.2 Data and Methods 
This section provides an assessment of observational data and 
methods used in the analysis and attribution of climate change 
specific to weather and climate extremes. It also introduces some 
concepts used in presenting future projections of extremes. Later 
sections (Sections 11.3–11.8) also provide additional assessments 
on relevant observational datasets and model validation specific to

the type of extremes to be assessed. General background on climate 
modelling is provided in Chapters 4 and 10.
11.2.1 Definition of Extremes
In the literature, an event is generally considered extreme if the value 
of a variable exceeds (or lies below) a threshold. The thresholds have 
been defined in different ways, leading to differences in the meaning 
of extremes that may share the same name. For example, two sets 
of metrics for the frequency of hot/warm days have been used in 
the literature. One set counts the number of days when maximum 
daily temperature is above a relative threshold defined as the 90th or 
higher percentile of maximum daily temperature for the calendar day 
over a base period. An event based on such a definition can occur at 
any time of the year, and the impact of such an event would differ 
depending on the season. The other set counts the number of days 
in which maximum daily temperature is above an absolute threshold

such as 35°C, because exceeding this temperature can sometimes 
cause health impacts (however, these impacts may depend on 
location and whether ecosystems and the population are adapted 
to such temperatures). While both types of hot extreme indices 
have been used to analyse changes in the frequency of hot/warm 
events, they represent different events that occur at different times of the year, possibly affected by different types of processes and 
mechanisms, and possibly also associated with different impacts. 
Changes in extremes have also been examined from two 
perspectives:  changes in the frequency for a  given magnitude of 
extremes; or changes in the magnitude for a particular return period 
(frequency). Changes in the probability of extremes (e.g., temperature 
extremes) depend on the rarity of the extreme event that is assessed, 
with a  larger change in probability associated with a  rarer event 
(e.g.,  Kharin et al., 2018). However, changes in the magnitude

represented by the return levels of the extreme events may not be 
as sensitive to the rarity of the event. While the answers to the two 
different questions are related, their relevance may differ for distinct 
audiences. Conclusions regarding the respective contribution of 
greenhouse gas forcing to changes in magnitude versus frequency 
of extremes may also differ (Otto et al., 2012). Correspondingly, the 
sensitivity of changes in extremes to increasing global warming is 
also dependent on the definition of the considered extremes. In the 
case of temperature extremes, changes in magnitude have been 
shown to often depend linearly on global surface temperature 
(Seneviratne et al., 2016; Wartenburger et al., 2017), while changes in 
frequency tend to be nonlinear and can, for example, be exponential 
for increasing global warming levels (Fischer and Knutti, 2015; Kharin 
et al., 2018). When similar damage occurs once a  fixed threshold

is exceeded, it is more important to ask a  question regarding 
changes in the frequency. But when the exceedance of this fixed 
threshold becomes a normal occurrence in the future, this can lead 
to a  saturation in the change of probability (Harrington and Otto, 
2018a). Also, if the impact of an event increases with the intensity 
of the event, it would be more relevant to examine changes in the 
magnitude. Finally, adaptation to climate change might change 
the  relevant thresholds over time, although such aspects are still

1537
rarely integrated in the assessment of projected changes in extremes. 
Framing is considered when forming the assessments of this Chapter, 
including how extremes are defined and how the questions are asked 
in the literature .
11.2.2 Data
Studies of past and future changes in weather and climate extremes, 
and in the mean state of the climate, use the same original sources 
of weather and climate observations, including in situ observations, 
remotely sensed data, and derived data products such as reanalyses. 
Sections 2.3 and 10.2 assess various aspects of these data sources 
and data products from the perspective of their general use, and in 
the analysis of changes in the mean state of the climate in particular. 
Building on these previous chapters, this subsection highlights 
particular aspects that are related to extremes and are most relevant

to the assessment of this Chapter. The SREX (Chapter 3, Seneviratne 
et al., 2012) and AR5 (Chapter 2, Hartmann et al., 2013) addressed 
critical issues regarding the quality and availability of observed data 
and their relevance for the assessment of changes in extremes. 
Extreme weather and climate events occur on time scales of 
hours (e.g.,  convective storms that produce heavy precipitation) 
to days (e.g.,  tropical cyclones, heatwaves), to seasons and years 
(e.g., droughts). A robust determination of long-term changes in these 
events can have different requirements for the spatial and temporal 
scales and sample size of the data. In general, it is more difficult 
to determine long-term changes for events of fairly large temporal 
duration, such as ‘megadroughts’ that last several years or longer 
(e.g., Ault et al., 2014), because of the limitations of the observational 
sample size. Literature that studies changes in extreme precipitation

and temperature often uses indices representing specifics of extremes 
that are derived from daily precipitation and temperature values. 
Station-based indices would have the same issues as those for the 
mean climate regarding the quality, availability, and homogeneity of 
the data. For the purpose of constructing regional information and/
or for comparison with model outputs, such as model evaluation, 
and detection and attribution, these station-based indices are often 
interpolated onto regular grids. Two different approaches, involving 
two different orders of operation, have been used in producing such 
gridded datasets. 
In some cases, such as for the HadEX3 dataset (Dunn et al., 2020), 
indices of extremes are computed using time series directly derived 
from stations first, and are then gridded over the space. As the indices 
are computed at the station level, the gridded data products represent 
point estimates of the indices averaged over the spatial scale of the

grid box. In other instances, daily values of station observations are 
first gridded (e.g.,  Contractor et al., 2020a), and the interpolated 
values can then be used to compute various indices. Depending on the 
station density, values for extremes computed from data gridded this 
way represent extremes of spatial scales anywhere from the size of 
the grid box to a point. In regions with high station density (e.g., North 
America, Europe), the gridded values are closer to extremes of area 
means and are thus more appropriate for comparisons with extremes 
estimated from climate model output, which is often considered to represent areal means (Chen and Knutson, 2008; Gervais et al., 
2014; Avila et al., 2015; Di Luca et al., 2020b). In regions with very 
limited station density (e.g., Africa), the gridded values are closer to 
point estimates of extremes. The difference in spatial scales among 
observational data products and model simulations needs to be

carefully accounted for when interpreting the comparison among 
different data products. For example, the average annual maximum 
daily maximum temperature (TXx) over land computed from the 
original ERA-Interim reanalysis (at 0.75° resolution) is about 0.4°C 
warmer than that computed when the ERA-Interim dataset is 
upscaled to the resolution of 2.5° × 3.75° (Di Luca et al., 2020).
Extreme indices computed from various reanalysis data products have 
been used in some studies, but reanalysis extreme statistics have not 
been rigorously compared to observations (Donat et al., 2016a). 
In general, changes in temperature extremes from various reanalyses 
were most consistent with gridded observations after about 1980, 
but larger differences were found during the pre-satellite era (Donat 
et al., 2014b). Overall, lower agreement across reanalysis datasets 
was found for extreme precipitation changes, although temporal

and spatial correlations against observations were found to be still 
significant. In regions with sparse observations (e.g., Africa and parts 
of South America), there is generally less agreement for extreme 
precipitation between different reanalysis products, indicating 
a  consequence of the lack of an observational constraint in these 
regions (Donat et al., 2014b, 2016a). More recent reanalyses, such as 
ERA5 (Hersbach et al., 2020), seem to have improved over previous 
products, at least over some regions (e.g., Mahto and Mishra, 2019; 
Gleixner et al., 2020; Sheridan et al., 2020). Caution is needed when 
reanalysis data products are used to provide additional information 
about past changes in these extremes in regions where observations 
are generally lacking. 
Satellite remote sensing data have been used to provide information 
about precipitation extremes because several products provide 
data at sub-daily resolution for precipitation, for example, Tropical

Rainfall Measuring Mission (TRMM; Maggioni et al., 2016) and 
clouds, for example, Himawari (Bessho et al., 2016; Chen et al., 
2019). However, satellites do not observe the primary atmospheric 
state variables directly and polar orbiting satellites do not observe 
any given place at all times. Hence, their utility as a substitute for 
high-frequency (i.e., daily) ground-based observations is limited. For 
instance, Timmermans et al. (2019) found little relationship between 
the timing of extreme daily and five-day precipitation in satellite and 
gridded station data products over the USA.

1538
Box 11.3 | Extremes in Paleoclimate Archives Compared to Instrumental Records
Examining extremes in pre-instrumental information can help to put events occurring in the instrumental record (referred to as 
‘observed’) in a longer-term context. This box focuses on extremes in the Common Era (CE, the last 2000 years), because there is 
generally higher confidence in pre-instrumental information gathered from the more recent archives from the Common Era than from 
earlier evidence. It addresses evidence of extreme events in paleoreconstructions, documentary evidence (such as grape harvest data, 
religious documents, newspapers, and logbooks) and model-based analyses, and whether observed extremes have or have not been 
exceeded in the Common Era. This box provides overviews of: (i) AR5 assessments; (ii) types of evidence assessed here; evidence of:

(iii) droughts; (iv) temperature extremes; (v) paleofloods; and (vi) paleotempests; and (vii) a summary. 
Chapter 5 of AR5 (Masson-Delmotte et al., 2013) concluded with high confidence  that droughts of greater magnitude and of longer 
duration than those observed in the instrumental period occurred in many regions during the preceding millennium. There was high 
confidence  in evidence that floods during the past five centuries in northern and Central Europe, the western Mediterranean region, 
and eastern Asia were of a greater magnitude than those observed instrumentally, and medium confidence  in evidence that floods 
in the Near East, India and Central North America were comparable to modern observed floods. While AR5 assessed 20th century 
summer temperatures compared to those reconstructed in the Common Era, it did not assess shorter duration temperature extremes.

Many factors affect confidence in information on pre-instrumental extremes. First, the geographical coverage of paleoclimate 
reconstructions of extremes is not spatially uniform (Smerdon and Pollack, 2016) and depends on both the availability of archives and 
records, which are environmentally dependent, and also the differing attention and focus from the scientific community. In Australia, 
for example, the paleoclimate network is sparser than for other regions, such as Asia, Europe and North America, and synthesized 
products rely on remote proxies and assumptions about the spatial coherence of precipitation between remote climates (Cook et al., 
2016c; Freund et al., 2017). Second, pre-instrumental evidence of extremes may be focused on understanding archetypal extreme 
events, such as the climatic consequences of the 1815 eruption of Mount Tambora, Indonesia (Veale and Endfield, 2016). These studies

provide narrow evidence of extremes in response to specific forcings (M. Li et al., 2017) for specific epochs. Third, natural archives may 
provide information about extremes in one season only and may not represent all extremes of the same types. 
Evidence of shorter duration extreme event types, such as floods and tropical storms, is further restricted by the comparatively low 
chronological controls and temporal resolution (e.g., monthly, seasonal, yearly, multiple years) of most archives compared to the 
events (e.g., minutes to days). Natural archives may be sensitive only to intense environmental disturbances, and so only sporadically 
record short-duration or small spatial-scale extremes. Interpreting sedimentary records as evidence of past short-duration extremes is 
also complex and requires a clear understanding of natural processes (Wilhelm et al., 2019) . For example, paleoflood reconstructions

of flood recurrence and intensity produced from geological evidence (e.g., river and lake sediments), speleothems (Denniston and 
Luetscher, 2017), botanical evidence (e.g.,  flood damage to trees, or tree ring reconstructions), and floral and faunal evidence 
(e.g.,  diatom fossil assemblages) require understanding of sediment sources and flood mechanisms. Pre-instrumental records of 
tropical storm intensity and frequency (also called paleotempest records) derived from overwash deposits of coastal lake and marsh 
sediments are difficult to interpret. Many factors have an impact on whether disturbances are deposited in archives (Muller et al., 
2017) and deposits may provide sporadic and incomplete preservation histories (e.g., Tamura et al., 2018). 
Overall, the most complete pre-instrumental evidence of extremes occurs for long-duration, large spatial-scale extremes, such as for

multi-year meteorological droughts or seasonal- and regional-scale temperature extremes. Additionally, more precise insights into 
recent extremes emerge where multiple studies have been undertaken, compared to the confidence in extremes reported at single sites 
or in single studies, which may not necessarily be representative of large-scale changes, or for reconstructions that synthesize multiple 
proxies over large areas (e.g., drought atlases). Multiproxy synthesis products combine paleoclimate temperature reconstructions 
and cover sub-continental- to hemispheric-scale regions to provide continuous records of the Common Era (e.g., Ahmed et al., 2013; 
Neukom et al., 2014 for temperature).
There is high confidence  in the occurrence of long-duration and severe drought events during the Common Era for many locations, 
although their severity compared to recent drought events differs between locations and the lengths of reconstruction provided.

Recent observed drought extremes in some regions – such as the eastern Mediterranean Levant (Cook et al., 2016a), California in the 
USA (Cook et al., 2014b; Griffin and Anchukaitis, 2014), and in the Andes (Garreaud et al., 2017; Domínguez-Castro et al., 2018) – do 
not have precedents within the multi-century periods reconstructed in these studies, in terms of duration and/or severity. In some 
regions (in south-western North America (Asmerom et al., 2013; Cook et al., 2015), the Great Plains region (Cook et al., 2004), the 
Middle East (Kaniewski et al., 2012), and China (Gou et al., 2015)), recent drought extremes may have been exceeded in the Common 
Era. In further locations, there is conflicting evidence for the severity of pre-instrumental droughts compared to observed extremes,

1539
depending on the length of the reconstruction and the seasonal perspective provided (see Cook et al., 2016c; Freund et al., 2017 for 
Australia). There can also be differing conclusions for the severity, or even the occurrence, of specific individual pre-instrumental 
droughts when different evidence is compared (e.g., Wetter et al., 2014; Büntgen et al., 2015). 
There is medium confidence  that the magnitudes of large-scale, seasonal-scale extreme high temperatures in observed records exceed 
those reconstructed over the Common Era in some locations, such as Central Europe. In one example, multiple studies have examined 
the unusualness of present-day European summer temperature records in a long-term context, particularly in comparison to the 
exceptionally warm year of 1540 CE in Central Europe. Several studies indicate that recent extreme summers (2003 and 2010) in

Europe have been unusually warm in the context of the last 500 years (Barriopedro et al., 2011; Wetter and Pfister, 2013; Wetter et al., 
2014; Orth et al., 2016b), or longer (Luterbacher et al., 2016). Others studies show that summer temperatures in Central Europe in 
1540 were warmer than the present-day (1966–2015) mean, but note that it is difficult to assess whether or not the 1540 summer 
was warmer than observed record extreme temperatures (Orth et al., 2016b). 
There is high confidence  that the magnitude of floods over the Common Era exceeded observed records in some locations, including 
Central Europe and eastern Asia. Recent literature supports the AR5 assessments of floods (Masson-Delmotte et al., 2013). For example, 
high temporally resolved records provide evidence of Common Era floods exceeding the probable maximum flood levels in the Upper

Colorado River, USA (Greenbaum et al., 2014) and peak discharges that are double gauge levels along the middle Yellow River, China 
(Liu et al., 2014). Further studies demonstrate pre-instrumental or early instrumental differences in flood frequency compared to 
the instrumental period, including reconstructions of high and low flood frequency in the European Alps (e.g., Swierczynski et al., 
2013; Amann et al., 2015) and Himalayas (Ballesteros Cánovas et al., 2017). The combination of extreme historical flood episodes 
determined from documentary evidence also increases confidence in the determination of flood frequency and magnitude, compared 
to using geomorphological archives alone (Kjeldsen et al., 2014). In regions, such as Europe and China, that have rich historical flood 
documents, there is strong evidence of high-magnitude flood events over pre-instrumental periods (Kjeldsen et al., 2014; Benito et al.,

2015; Macdonald and Sangster, 2017). A key feature of paleoflood records is variability in flood recurrence at centennial timescales 
(Wilhelm et al., 2019), although constraining climate-flood relationships remains challenging. Pre-instrumental floods often occurred 
in considerably different contexts in terms of land use, irrigation, and infrastructure, and may not provide direct insight into modern 
river systems, which further prevents long-term assessments of flood changes being made based on these sources. 
There is medium confidence  that periods of both more and less tropical cyclone activity (frequency or intensity) than observed 
occurred over the Common Era in many regions. Paleotempest studies cover a limited number of locations that are predominantly 
coastal, and hence provide information on specific locations that cannot be extrapolated basin-wide (see Muller et al., 2017). In

some locations, such as the Gulf of Mexico and the New England, USA, coast, similarly intense storms to those observed recently 
have occurred multiple times over centennial timescales (Donnelly et al., 2001; Bregy et al., 2018). Further research focused on the 
frequency of tropical storm activity. Extreme storms occurred considerably more frequently in particular periods of the Common Era, 
compared to the instrumental period in north-east Queensland, Australia (Nott et al., 2009; Haig et al., 2014), and the Gulf Coast 
(e.g., Brandon et al., 2013; Lin et al., 2014). 
The probability of finding an unprecedented extreme event increases with a longer length of past record-keeping, in the absence 
of longer-term trends. Thus, as a record is extended to the past based on paleoreconstruction, there is a higher chance of very rare 
extreme events having occurred at some time prior to instrumental records. Such an occurrence is not, in itself, evidence of a change,

or lack of a change, in the magnitude or the likelihood of extremes in the past or in the instrumental period at regional and local scales. 
Yet, the systematic collection of paleoclimate records over wide areas may provide evidence of changes in extremes. In one study, 
extended evidence of the last millennium from observational data and paleoclimate reconstructions using tree rings indicates that 
human activities affected the worldwide occurrence of droughts as early as the beginning of the 20th century (Marvel et al., 2019). 
In summary, there is  low confidence in overall changes in extremes derived from paleo-archives. There is  high confidence  that long-
duration and severe drought events occurred at many locations during the last 2000 years. There is also  high confidence  that high-
magnitude flood events occurred at some locations during the last 2000 years, but overall changes in infrastructure and human water

management make the comparison with present-day records difficult. But these isolated paleo-drought and paleo-flood events are 
not evidence of a change, or lack of a change, in the magnitude or the likelihood of relevant extremes.  
Box 11.3 (continued)

1540
11.2.3 Attribution of Extremes
Attribution science concerns the identification of causes for changes 
in characteristics of the climate system (e.g., trends, single extreme 
events). A general overview and summary of methods of attribution 
science is provided in  the Cross-Working Group Box  1.1. Trend 
detection using optimal fingerprinting methods is a well-established 
field, and has been assessed in AR5 (Chapter 10, Bindoff et al., 2013), 
and Section 3.2.1 of this Report. There are specific challenges when 
applying optimal fingerprinting to the detection and attribution of 
trends in extremes and on regional scales where the lower signal-
to-noise ratio is a  challenge. In particular, the method generally 
requires the data to follow a Normal (Gaussian) distribution, which 
is often not the case for extremes. However, recent studies showed

that extremes can be transformed to a  Gaussian distribution, for 
example, by averaging over space, so that optimal fingerprinting 
techniques can still be used (Wen et al., 2013; Zhang et al., 2013; 
Wan et al., 2019). Non-stationary extreme value distributions, which 
allow for the detailed detection and attribution of regional trends in 
temperature extremes, have also been used (Z. Wang et al., 2017a). 
Apart from the detection and attribution of trends in extremes, new 
approaches have been developed to answer the question of whether, 
and to what extent, external drivers have altered the probability and 
intensity of an individual extreme event (NASEM, 2016). In AR5, there 
was an emerging consensus that the role of external drivers of climate 
change in specific extreme weather events could be estimated and 
quantified in principle, but related assessments were still confined 
to particular case studies, often using a single model, and typically

focusing on high-impact events with a clear attributable signal. 
However, since AR5, the attribution of extreme weather events has 
emerged as a growing field of climate research with an increasing 
body of literature (see series of supplements to the annual State 
of the Climate report (Peterson et al., 2012, 2013a; Herring et al., 
2014, 2015, 2016, 2018), including the number of approaches to 
examining extreme events (described in Easterling et al., 2016; 
Otto, 2017; Stott et al., 2016)). A commonly used approach – often 
called the risk-based approach in the literature, and referred to 
here as the ‘probability-based approach’  – produces statements 
such as ‘anthropogenic climate change made this event type twice 
as likely’ or ‘anthropogenic climate change made this event 15% 
more intense’. This is done by estimating probability distributions of 
the index characterizing the event in today’s climate, as well as in

a counterfactual climate, and either comparing intensities for a given 
occurrence probability (e.g.,  1-in-100-year event) or probabilities 
for a  given magnitude (see FAQ 11.3). There are a  number of 
different analytical methods encompassed in the probability-based 
approach, building on observations and statistical analyses (e.g., van 
Oldenborgh et al., 2012), optimal fingerprint methods (Sun et al., 
2014), regional climate and weather forecast models (e.g., Schaller 
et al., 2016), global climate models (GCMs) (e.g., Lewis and Karoly, 
2013), and large ensembles of atmosphere-only GCMs (e.g.,  Lott 
et al., 2013). A key component in any event attribution analysis is 
the level of conditioning on the state of the climate system. In the 
least conditional approach, the combined effect of the overall 
warming and changes in the large-scale atmospheric circulation are considered and often utilize fully coupled climate models (Sun

et al., 2014). Other more conditional approaches involve prescribing 
certain aspects of the climate system. These range from prescribing 
the pattern of the surface ocean change at the time of the event 
(e.g.,  Hoerling et al., 2013, 2014), often using Atmospheric Model 
Intercomparison Project (AMIP) style global models, where the 
choice of sea surface temperature and ice patterns influences the 
attribution results (Sparrow et al., 2018), to prescribing the large-
scale circulation of the atmosphere and using weather forecasting 
models or methods (e.g., Pall et al., 2017; Patricola and Wehner, 2018; 
Wehner et al., 2018a). These highly conditional approaches have also 
been called ‘storylines’ (Cross-Working Group Box  1.1; Shepherd, 
2016) and can be useful when applied to extreme events that are 
too rare to otherwise analyse, or where the specific atmospheric 
conditions were central to the impact. These methods are also used

to enable the use of very high-resolution simulations in cases were 
lower-resolution models do not simulate the regional atmospheric 
dynamics well (Shepherd, 2016; Shepherd et al., 2018). However, the 
imposed conditions limit an overall assessment of the anthropogenic 
influence on an event, as the fixed aspects of the analysis may also 
have been affected by climate change. For instance, the specified 
initial conditions in the highly conditional hindcast attribution 
approach often applied to tropical cyclones (e.g.,  Takayabu 
et al., 2015; Patricola and Wehner, 2018) permit only a conditional 
statement about the magnitude of the storm if similar large-scale 
meteorological patterns could have occurred in a  world without 
climate change, thus precluding any attribution statement about 
the change in frequency if used in isolation. Combining conditional 
assessments of changes in the intensity with a multi-model approach 
does allow for the latter as well (Shepherd, 2016).

The outcome of event attribution is dependent on the definition of 
the event (Leach et al., 2020), as well as the framing (Otto et al., 
2016; Christidis et al., 2018; Jézéquel et al., 2018) and uncertainties 
in observations and modelling. Observational uncertainties arise in 
estimating the magnitude of an event as well as its rarity (Angélil 
et al., 2017). Results of attribution studies can also be very sensitive 
to the choice of climate variables (Sippel and Otto, 2014; Wehner 
et al., 2016). Attribution statements are also dependent on the spatial 
(Uhe et al., 2016; Cattiaux and Ribes, 2018; Kirchmeier -Young et al., 
2019) and temporal (Harrington, 2017; Leach et al., 2020) extent 
of event definitions, as events of different scales involve different 
processes (W. Zhang et al., 2020) and large-scale averages generally 
yield higher attributable changes in magnitude or probability due 
to the smoothing out of noise. In general, confidence in attribution

statements for large-scale heat and lengthy extreme precipitation 
events have higher confidence than shorter and more localized events, 
such as extreme storms, an aspect also relevant for determining the 
emergence of signals in extremes or the confidence in projections 
(see also Cross-Chapter Box Atlas.1). 
The reliability of the representation of the event in question in the 
climate models used in a  study is essential (Angélil et al., 2016; 
Herger et al., 2018). Extreme events characterized by atmospheric 
dynamics that stretch the capabilities of current-generation models 
(Section 10.3.3.4; Shepherd, 2014; Woollings et al., 2018) limit the 
applicability of the probability-based approach of event attribution.

1541
The lack of model evaluation, in particular in early event attribution 
studies, has led to criticism of the emerging field of attribution science 
as a whole (Trenberth et al., 2015) and of individual studies (Angélil 
et al., 2017). In this regard, the storyline approach (Shepherd, 2016) 
provides an alternative option that does not depend on the model’s 
ability to represent the circulation reliably. In addition, several ways 
of quantifying statistical uncertainty (Paciorek et al., 2018) and model 
evaluation (Lott and Stott, 2016; Philip et al., 2018b, 2020) have been 
employed to evaluate the robustness of event attribution results. 
For the unconditional probability-based approach, multi-model and 
multi-approach (e.g.,  combining observational analyses and model 
experiments) methods have been used to improve the robustness of 
event attribution (Hauser et al., 2017; Otto et al., 2018a; Philip et al.,

2018b, 2019, 2020; van Oldenborgh et al., 2018; Kew et al., 2019). 
In the regional tables provided in Section  11.9, the different lines 
of evidence from event attribution studies and trend attributions 
are assessed alongside one another to provide an assessment 
of the human contribution to observed changes in extremes in 
all AR6 regions. 
11.2.4 Projecting Changes in Extremes as a Function 
of Global Warming Levels 
The most important quantity used to characterize past and future 
climate change is global warming relative to its pre-industrial 
level. Changes in global warming are linked quasi-linearly to 
global cumulative carbon dioxide (CO 2) emissions (IPCC, 2013), 
and for their part, changes in regional climate, including many 
types of extremes, scale quasi-linearly with changes in global 
warming, often independently of the underlying emissions scenarios 
(SR1.5 Chapter  3; Seneviratne et al., 2016; Matthews et al., 2017;

Wartenburger et al., 2017; Kharin et al., 2018; Y . Sun et al., 2018a; 
Tebaldi and Knutti, 2018; Beusch et al., 2020; Li et al., 2021). 
In addition, the use of global warming levels in the context of global 
policy documents – in particular the 2015 Paris Agreement (UNFCCC, 
2016) implies that information on changes in the climate system, 
and specifically extremes, as a  function of global warming are of 
particular policy relevance. Cross-Chapter Box  11.1 provides an 
overview on the translation between information at global warming 
levels (GWLs) and scenarios.
The assessment of projections of future changes in extremes as 
function of GWL has an advantage in separating uncertainty 
associated with the global warming response (see Chapter 4) from 
the uncertainty resulting from the regional climate response as 
a function of GWLs (Seneviratne and Hauser, 2020). If the interest is 
in the projection of regional changes at certain GWLs, such as those

defined by the Paris Agreement, projections based on time periods 
and emissions scenarios have unnecessarily larger uncertainty due 
to differences in model global transient climate responses. To take 
advantage of this feature and to provide easy comparison with 
SR1.5, assessments of projected changes in this chapter are largely 
provided in relation to future GWLs, with a  focus on changes at 
+1.5°C, +2°C, and +4°C of global warming above pre-industrial 
levels (e.g., Tables 11.1 and 11.2 and regional tables in Section 11.9). These encompass a scenario compatible with the lowest limit of the 
Paris Agreement (+1.5°C), a scenario slightly overshooting the aims 
of the Paris Agreement (+2°C), and a ‘worst-case’ scenario with no 
mitigation (+4°C). Cross-Chapter Box  11.1 provides a  background 
on the GWL sampling approach used in AR6, for the computation 
of GWL projections from climate models contributing to Phase 6 of

the Coupled Model Intercomparison Project (CMIP6) as well as for 
the mapping of existing scenario-based literature for CMIP6 and the 
CMIP Phase 5 (CMIP5) to assessments as function of GWLs (see also 
Section 11.9. and Table 11.3 for an example).
While regional changes in many types of extremes do scale robustly 
with global surface temperature, generally irrespective of emissions 
scenarios (Section  11.1.4, Figures 11.3, 11.6 and 11.7 and Cross-
Chapter Box 11.1), effects of local forcing can distort this relation. 
For example, emissions scenarios with the same radiative forcing 
can have different regional extreme precipitation responses resulting 
from different aerosol forcing (Z. Wang et al., 2017b). Another 
example is related to forcing from land-use and land cover changes 
(Section  11.1.6). Climate models often either overestimate or 
underestimate observed changes in annual maximum daily maximum 
temperature, depending on the region and considered models (Donat

et al., 2017; Vautard et al., 2020). Part of the discrepancies may be 
due to the lack of representation of some land forcings, in particular 
crop intensification and irrigation (N.D. Mueller et al., 2016; Findell 
et al., 2017; Thiery et al., 2017, 2020). Since these local forcings are 
not represented, and their future changes are difficult to project, 
these can be important caveats when using GWL scaling to project 
future changes for these regions. However, these caveats also apply 
to the use of scenario-based projections.
The SR1.5 (Chapter  3) assessed different climate responses at 
+1.5°C of global warming, including transient climate responses, 
short-term stabilization responses, and long-term equilibrium 
stabilization responses, and their implications for future projections 
of different extremes. Indeed, the temporal dimension  – that 
is, when the given GWL occurs – also matters for projections, in 
particular beyond the 21st century, and for some climate variables

related to components of the climate system associated with large 
inertia (e.g., sea level rise and associated extremes). Nonetheless, 
for assessments focused on conditions within the next decades, 
and for the main extremes considered in this chapter, derived 
projections are relatively insensitive to details of climate scenarios 
and can be well-estimated based on transient simulations (Cross-
Chapter Box 11.1; see also SR1.5).
An important question is the identification of the GWL at which 
a  given change in a  climate extreme can begin to emerge from 
climate noise. Figure 11.8 displays analyses of the GWLs at which 
emergence in hot extremes – annual maximum daily temperature 
represented by TXx and heavy precipitation represented by Rx1day 
is identified in AR6 regions for the whole CMIP5 and CMIP6 
ensembles. Overall, signals for extremes emerge very early for 
TXx, already below 0.2°C in many regions (Figure 11.8a,b), and at

around 0.5°C in most regions. This is consistent with conclusions 
from the SR1.5 Chapter 3 for less-rare temperature extremes (TXx 
on the yearly time scale), which shows that a difference as small as

1542
0.5°C of global warming – for example, between +1.5°C and +2°C 
of global warming – leads to detectable differences in temperature 
extremes in TXx in most WGI AR6 regions in CMIP5 projections 
(e.g., Wartenburger et al., 2017; Seneviratne et al., 2018b). The GWL 
emergence for Rx1day is also largely consistent with analyses for 
less-extreme heavy precipitation events (Rx5day on the yearly time 
scale) in SR1.5 (see Chapter 3). To some extent, analyses as functions of GWLs replace the time axis 
with a global surface temperature axis. Nonetheless, information on 
the timing of given changes in extremes is obviously also relevant. 
(For information on the time frame at which given GWLs are reached, 
see Cross-Chapter Box  11.1 and Section  4.6). Figure  11.5 provides 
a synthesis of attributed and projected changes in extremes as function

of GWLs (see also Figures. 11.3, 11.6 and 11.7 for regional analyses).glob. oc.land GIC NECCNA ENANWNWNANCA SCACAR NWSSAM SSA SWS SES NSANES NEU CEUEEU MEDCMIP5CMIP6 0.1 0.1 0.2 0.5 0.7 0.8 0.7 0.7 0.7 0.6 0.5 0.3 0.3 0.4 0.6 0.2 0.4 0.3 0.3 0.8 0.9 0.9 0.4
0.1 0.1 0.2 0.5 0.5 0.5 0.4 0.7 0.4 0.4 0.4 0.2 0.3 0.4 0.9 0.3 0.4 0.3 0.3 1.0 0.7 0.7 0.4(a)Annual hotte sttemperat ure(TXx)
WAF SAH NEAF CEAF SWAF SEAF CAF RAR RFE ESB WSB WCA TIB EAS ARP SAS SEA NAU CAU SAU NZ EAN WANCMIP5CMIP6 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.7 0.7 0.8 0.8 0.4 0.5 0.6 0.2 0.6 0.4 0.4 0.4 0.6 0.7 0.8 1.1
0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.6 0.7 0.8 0.8 0.3 0.5 0.6 0.2 0.3 0.2 0.4 0.5 0.7 0.7 0.6 1.0
glob. oc.land GIC NECCNA ENANWNWNANCA SCACAR NWSSAM SSA SWS SES NSANES NEU CEUEEU MEDCMIP5CMIP6 0.3 0.4 0.4 0.8 0.7 1.4 1.0 0.8 1.7 2.0 2.2 2.8 0.9 1.3 2.1 2.7 0.8 1.2 1.2 0.8 1.0 1.2 2.0
0.3 0.4 0.4 0.7 0.7 1.4 1.0 0.8 1.5 1.9 2.0 2.7 0.8 1.1 2.2 2.2 0.9 0.9 1.3 0.9 1.0 1.2 2.1(b)Annual maximum daily precipitation (Rx1day)

WAF SAH NEAF CEAF SWAF SEAF CAF RAR RFE ESB WSB WCA TIB EAS ARP SAS SEA NAU CAU SAU NZ EAN WANCMIP5CMIP6 0.9 1.5 1.1 0.8 1.9 1.2 0.6 0.9 0.9 1.0 0.9 1.3 1.0 1.1 2.6 0.9 0.8 2.1 2.2 2.6 1.7 0.6 0.9
1.3 2.7 1.3 0.8 1.7 1.3 0.7 0.8 1.0 0.9 1.1 1.8 1.0 0.9 2.6 1.1 0.8 1.9 1.9 2.5 1.9 0.7 1.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
Global warming levelofemergenc e(°C)
Figure 11.8 | Global and regional-scale emergence of changes in temperature (a) and precipitation (b) extremes for the globe (glob.), global oceans (oc.), 
global lands (land), and the AR6 regions. Colours indicate the multi-model mean global warming level at which the difference in 20-year means of the annual maximum 
daily maximum temperature (TXx) and the annual maximum daily precipitation (Rx1day) become significantly different from their respective mean values during the 1850–1900

base period. Results are based on simulations from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) multi-model ensembles. See Atlas.1.3.2 for 
the definition of regions. Adapted from Seneviratne and Hauser (2020) under the terms of the Creative Commons Attribution licence. 
Cross-Chapter Box 11.1 | Translating Between Regional Information at Global Warming Levels 
Versus Scenarios for End Users
Contributors:  Erich Fischer (Switzerland), Mathias Hauser (Switzerland), Sonia I. Seneviratne (Switzerland), Richard Betts (United Kingdom), 
José M. Gutiérrez (Spain), Richard G. Jones (United Kingdom), June-Yi Lee (Republic of Korea), Malte Meinshausen (Australia/Germany), 
Friederike Otto (United Kingdom/Germany), Izidine Pinto (Mozambique), Roshanka Ranasinghe (The Netherlands/Sri Lanka/Australia), 
Joeri Rogelj (Germany/Belgium), Bjørn Samset (Norway), Claudia Tebaldi (United States of America), Laurent Terray (France)
Background

Traditionally, projections of climate variables are summarized and communicated as function of time and emissions scenarios. Recently, 
quantifying global and regional climate at specific global warming levels (GWLs) has become widespread, motivated by the inclusion of 
explicit GWLs in the long-term temperature goal of the Paris Agreement (Section 1.6.2). GWLs, expressed as changes in global surface

1543
temperature relative to the 1850–1900 period (see Cross-Chapter 
Box 2.3), are used in SR1.5 and in the assessment of Reasons for 
Concerns in the WGII reports (see also Cross-Chapter Box 12.1). 
Cross-Chapter Box 11.1, Figure 1 illustrates how the assessment 
of the climate response at GWLs relates to the uncertainty in 
scenarios regarding the timing of the respective GWLs, as well as 
to the uncertainty in the associated regional climate responses, 
including extremes and other climatic impact-drivers (CIDs). 
For many (but not all) climate variables and CIDs, the response 
pattern for a given GWL is consistent across different scenarios 
(Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-term 
means (e.g.,  20-year averages) compared to the pre-industrial 
period, are commonly used in the literature, and were also 
underlying main assessments of SR1.5 (Chapter 3).

Numerous studies have compared the regional response to 
anthropogenic forcing at GWLs in annual and seasonal mean 
values and extremes of different climate and impact variables 
across different multi-model ensembles and/or different 
scenarios (e.g.,  Frieler et al., 2012; Schewe et al., 2014; Herger 
et al., 2015; Schleussner et al., 2016; Seneviratne et al., 2016; 
Wartenburger et al., 2017; Betts et al., 2018; Dosio and Fischer, 2018; Samset et al., 2019; Tebaldi et al., 2020 ; see Sections 4.6.1, 8.5.3, 
9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent 
across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2;  Pendergrass et al., 2015; Seneviratne et al., 
2016; Wartenburger et al., 2017; Seneviratne and Hauser, 2020) . The consistency tends to be higher for temperature-related variables

than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions 
scenarios than for low-emissions scenarios (e.g.,  for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 
scenario: Pendergrass et al., 2015; Wartenburger et al., 2017). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for 
mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at 
a given GWL are smaller than model uncertainty and internal variability in many regions (Herger et al., 2015). The response pattern is 
further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given 
Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h).

In contrast to linear pattern scaling (Mitchell, 2003; Collins et al., 2013), the use of GWLs as a dimension of integration does not 
require linearity in the response of a climate variable. It is therefore useful even for metrics that do not show a linear response, such 
as the frequency of heat extremes over land and oceans (Fischer and Knutti, 2015; Frölicher et al., 2018; Kharin et al., 2018; Perkins-
Kirkpatrick and Gibson, 2017) if the relationship of the variable of interest to the GWL is scenario independent. The latter means that 
the response is independent of the pathway and relative contribution of various radiative forcings. For some more complex indices like 
warm-spell duration, or for regions with strong aerosol changes, discrepancies can be larger (Z. Wang et al., 2017b; King et al., 2018; 
Tebaldi et al., 2020). (See also the subsection below on GWLs vs scenarios for further caveats.)

The limited scenario dependence of the GWL-based response for many variables implies that the regional response to emissions 
scenarios can be split in almost independent contributions of: (i) the transient global warming response to scenarios (see Chapter 4); 
and (ii) the regional response as function of a given GWL, which has also been referred to as ‘regional climate sensitivity’ (Seneviratne 
and Hauser, 2020). This property has also been used to develop regionally resolved emulators for global climate models, using global 
surface temperature as input (Beusch et al., 2020; Tebaldi et al., 2020). Analyses of the CMIP6 and CMIP5 multi-model ensembles 
shows that the GWL-based responses are very similar for temperature and precipitation extremes across the ensembles ( Seneviratne 
and Hauser, 2020; Wehner, 2020; Li et al., 2021 ). This is despite their difference in global warming response (Chapter 4), confirming

a substantial decoupling between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the 
GWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty induced by scenario, 
global mean model response and internal variability (Cross-Chapter Box, Figure 1). 
Cross-Chapter Box 11.1 (continued)
emissionsclimate
forcingglobal mean
responseregional
climate
responsephysicaland
societal
impacts
emissions
questionimpacts
question regional climate
3.0°C
2.0°CGlobal warming
levels (GWLs)
1.5°C
Cross-Chapter Box  11.1, Figure  1 | Schematic representation of 
relationship between emissions scenarios, global warming levels 
(GWLs), regional climate responses, and impacts.  The illustration shows 
the implied uncertainty problem associated with differentiating between 1.5°C, 
2°C, and other GWLs. Focusing on GWLs raises questions associated with

emissions pathways to get to these temperatures (scenarios), as well as regional 
climate responses and the associated impacts at the corresponding GWL (the 
impacts question). Adapted from James et al. (2017) and Rogelj (2013) under 
the terms of the Creative Commons Attribution licence.

1544
Mapping between GWL- and scenario-based responses in model analyses 
To map scenario-based climate projections into changes at specific GWLs, first, all individual Earth system model (ESM) simulations 
that reach a certain GWL are identified. Second, the climate response patterns at the respective GWL are calculated using an approach 
termed here ‘GWL-sampling’ – sometimes also referred to as epoch analysis, time shift, or time sampling approach – taking into 
account all models and scenarios (Cross-Chapter Box, Figure 3). Note that the range of years when a given GWL is reached in the  
CMIP6 ensemble is different from the AR6 assessed range of projected global surface temperature (Section 4.3.4; Table 4.5). The latter 
further takes into account different lines of evidence, including the assessed observed warming between pre-industrial and present  

SSP1-2.6Annual mean precipit ation response at2°C
SSP2-4.5 SSP5-8.5
SSP5-8.5 SSP2-4.5Annual mean tempe ratureresponse at2°C
SSP1-2.6
High warming rate Medium tolowwarming rate(a) (b) (c)
(d) (e) (f)
(g) (h)
Cross-Chapter Box 11.1, Figure 2 | (a–c) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean precipitation change at 2°C 
global warming level (GWL) (20-year mean) in three different Shared Socio-economic Pathway (SSP) scenarios relative to 1850–1900. All models 
reaching the corresponding GWL in the corresponding scenario are averaged. The number of models averaged across is shown at the top right of the panel. The maps 
for the other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown) are consistent. (d–f)  Same as (a–c) but for annual mean temperature. (g) 
Annual mean temperature change at 2°C in CMIP6 models with high warming rate reaching the GWL in the corresponding scenario before the earliest year of the

assessed very likely range (Section 4.3.4). (h) Climate response at 2°C GWL across all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not 
shown in (g). The close agreement of (g) and (h) demonstrates that the mean temperature response at 2°C is not sensitive to the rate of warming, and thereby the 
global mean surface air temperature (GSAT) warming of the respective models in 2081–2100. Uncertainty is represented using the advanced approach: No overlay 
indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on the sign of change; 
diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate 
regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For

more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1.

1545
day, information from observational constraints on CMIP6, and emulators using the assessed transient climate response (TCR) 
and equilibrium climate sensitivity (ECS) ranges (Section 4.3.4). Hence the Chapter 4 assessed range (Table 4.5) is the reference to 
determine when a given GWL is likely  reached under given scenarios, while the mapping between scenarios/time frames and GWLs is 
used to assess the respective regional responses happening at these time frames (which also allows accounting for the global surface 
temperature assessment, rather than using scenarios analyses directly from CMIP6 output). 
In the model-based asssessment of Chapters 4, 8, 10, 11, 12 and the Atlas, the estimation of changes at GWLs are generally defined as 
the 20-year time period in which the mean global surface air temperature (GSAT; Cross-Chapter Box 2.3) first exceeds a certain anomaly

relative to 1850–1900 – for simulations that start after 1850, relative to all years up to 1900 (Cross-Chapter Box Figure 3). The years 
when each individual model reaches a given GWL for CMIP6 and CMIP5 can be found in Hauser et al. (2021). The changes at given GWLs 
are identified for each ensemble member (for all scenarios) individually. Thereby, a given GWL is potentially reached a few years earlier 
or later in different realizations of the same model due to internal variability, but the temperature averaged across the 20-year period 
analysed in any simulation is consistent with the GWL. Instead of blending the information from the different scenarios, the Interactive 
Atlas can be used to compare the GWL spatial patterns and timings across the different scenarios (see Section Atlas 1.3.1).  
Response atglobal warming level (GWL)
Timing ofGWL
Timing2°C Best estimate Very likely range
SSP1-1.9 NANA
SSP1-2.6 NA (2031–50 toNA)

SSP2-4.5 2043-62 (2028–47 to2075–94)
SSP3-7.0 2037–56 (2026–45 to2053–72)
SSP5-8.5 2032–51 (2023–42 to2044–63)2°CinSSP1-2.62°CinSSP3-7.0Assesse dGSATrange(table 4.5andsectio n4.3.4)
2000 2020 2040 2060 2080 21002000 2020 2040 2060 2080 21002°CinSSP1-2.62°CinSSP3-7.0CMIP6 range(table 4.2andHauser etal.2021)
Thebestestimateandlikelyrange when a
GWLiscrossed isnotbased on CMIP6 only
butontheGSATassessmen t insection 4.3.4.Theclima te
response atagiven
GWLisestimated
based onan
averageacross all
models andallSSP
scenario sinCMIP6
inwhich the
correspondin gGWL
isreached.
2000 2020 2040 2060 2080 210012345CMIP6
SSP1-2.6 (20-yrGSATmeans)SSP3-7.0 (20-yrGSATmeans)Temper aturechang eat4°C
Precipit ationchang eat2°C
Precipit ationchang eat1.5°CPrecipit ationchang eat4°C
Temper aturechang eat2°C
Temper aturechang eat1.5°C
°C
-5-4-3-2.5-2-1.5 01.522.5 345%°C
-40 -30 -20 -10010 20 30 40

Cross-Chapter Box11.1, Figure 3 | Illustration of the AR6 global warming level (GWL) sampling approach to derive the timing and the response 
at a given GWL for the case of Coupled Model Intercomparison Project Phase 6 (CMIP6) data.  For the mapping of scenarios/time slices into GWLs for 
CMIP6, please refer to Table 4.2. Respective numbers for the CMIP5 multi-model experiment are provided in Chapter 11 Supplementary Material (11.SM.1). Note that 
the time frames used to derive the GWL time slices can also include a different number of years (e.g., 30 years for some analyses).

1546
 
Mapping between GWL- and scenario-based responses for literature
A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based information is required 
instead, an approximated mapping of the multi-model mean can be derived based on the known GWL in the given experiments for 
a particular time period. As a rough approximation, CMIP6 multi-model mean projections for the near-term (2021–2040) correspond 
to changes at about 1.5°C, and projections for the high-end scenario (SSP5-8.5) for the long-term (2081–2100) correspond to about 
4°C–5°C of global warming  (see Table 4.2 for changes in the CMIP6 ensemble and the Chapter 11 Supplementary Material (11.SM.1) 
and Hauser (2021) for details on other time periods and CMIP5).  These approximated changes are used for some of the GWL-based

assessments provided in the Chapter 11 regional tables (Section 11.9 and Table 11.3) when literature based on scenario projections is 
used to assess estimated changes at given GWLs.
GWLs versus scenarios 
The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways 
are consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached (Section 4.3.4), and 
to assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for 
variables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land 
management changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium

states. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need 
to account for the uncertainty in GSAT changes or climate-carbon feedbacks.
Forcing dependence of the GWL response is found for global mean precipitation (Section 8.4.3), but less for regional patterns of mean 
precipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the 
cryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence 
(Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with 
a substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent

for sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach 
a given GWL (Section 9.6.3.4). 
In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example 
in the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in 
the response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state 
when the land–sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context 
of projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables 
are less dependent on the pathway than for projections beyond 2100 (Section 9.6.3.4).
Key conclusions on assessments based on GWLs

GWL-based projections can inform society and policymakers on how climate would change under GWLs consistent with the aims 
of the Paris Agreement (stabilization at 1.5°C/well below 2°C), as well as on the consequences of missing these aims and reaching 
GWLs of 3°C or 4°C by the end of the century. The AR6 assessment shows that every bit of global warming matters and that changes 
in global warming of 0.5°C lead to statistically significant changes in mean climate and climate extremes on global scale and for 
large regions (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6 and 11.9, Figures 11.8 and 11.9, Atlas and Interactive Atlas), as also assessed 
in IPCC SR1.5. 
11.3 Temperature Extremes
This section assesses changes in temperature extremes at global, 
continental and regional scales. The main focus is on the changes in the 
magnitude and frequency of moderate extreme temperatures (those 
that occur several times a  year) to very extreme temperatures

(those that occur once in 10 or more years) of time scales from a day 
to a season, though there is a strong emphasis on the daily scale 
where literature is most concentrated. 11.3.1 Mechanisms and Drivers
The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that 
greenhouse gas forcing is the dominant factor for the increases in the 
intensity, frequency, and duration of warm extremes and the decrease 
in those of cold extremes. This general global-scale warming is 
modulated by large-scale atmospheric circulation patterns, as well as 
by feedbacks such as soil moisture-evapotranspiration–temperature 
and snow/ice-albedo–temperature feedbacks, and local forcings 
such as land-use change or changes in aerosol concentrations at the

1547
regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). 
Therefore, changes in temperature extremes at regional and local 
scales can have heterogeneous spatial distributions. Changes in 
the magnitudes (or intensities) of extreme temperatures are often 
larger than changes in global surface temperature, because of larger 
warming on land than on the ocean surface (Section 2.3.1.1), and 
because of feedbacks, though they are of similar magnitude to 
changes in the local mean temperature (Figure 11.2). 
Extreme temperature events are associated with large-scale 
meteorological patterns (Grotjahn et al., 2016). Quasi-stationary 
anticyclonic circulation anomalies or atmospheric blocking events are 
linked to temperature extremes in many regions, such as in Australia 
(Parker et al., 2014; Perkins-Kirkpatrick et al., 2016), Europe (Brunner

et al., 2017, 2018; Schaller et al., 2018), Eurasia (Yao et al., 2017), 
Asia (Chen et al., 2016; Ratnam et al., 2016; Rohini et al., 2016), 
and North America (Yu et al., 2018, 2019; Zhang and Luo, 2019). 
Mid-latitude planetary wave modulations affect short-duration 
temperature extremes such as heatwaves (Perkins, 2015; Kornhuber 
et al., 2020). The large-scale modes of variability (Annex IV) affect 
the strength, frequency and persistence of these meteorological 
patterns and, hence, temperature extremes. For example, cold 
and warm extremes in the mid-latitudes are associated with 
atmospheric circulation patterns such as the Pacific-North American 
(PNA) pattern, as well as atmosphere–ocean coupled modes such 
as Pacific Decadal Variability (PDV), the North Atlantic Oscillation 
(NAO), and Atlantic Multi-decadal Variability (AMV) (Section 11.1.5; 
Kamae et al., 2014; Johnson et al., 2018; Ruprich-Robert et al., 2018;

Yu et al., 2018, 2020; Müller et al., 2020; Qasmi et al., 2021). Changes 
in the modes of variability in response to warming would therefore 
affect temperature extremes (Clark and Brown, 2013; Horton 
et al., 2015). The level of confidence in those changes varies, both 
in the observations and in future projections, affecting the level of 
confidence in changes in temperature extremes in different regions. 
As highlighted in Chapters 2  to 4  of this Report, it is likely  that 
there have been observational changes in the extratropical jets and 
mid-latitude jet meandering (Section  2.3.1.4.3 and Cross-Chapter 
Box  10.1). There is low confidence  in possible effects of Arctic 
warming on mid-latitude temperature extremes (Cross-Chapter 
Box 10.1). A large portion of the multi-decadal changes in extreme 
temperature remains after the removal of the effect of these modes 
of variability, and can be attributed to human influence (Kamae et al.,

2017b; Wan et al., 2019). Thus, global warming dominates changes in 
temperature extremes at the regional scale and it is very unlikely  that 
dynamic responses to greenhouse-gas induced warming would alter 
the direction of these changes. 
Land–atmosphere feedbacks strongly modulate regional- and 
local-scale changes in temperature extremes ( high confidence ) 
(Section 11.1.6; Seneviratne et al., 2013; Lemordant et al., 2016; Donat 
et al., 2017; Sillmann et al., 2017b; Hirsch et al., 2019). This effect is 
particularly notable in mid-latitude regions where the drying of soil 
moisture amplifies high temperatures, especially through increases 
in sensible heat flux (Whan et al., 2015; Douville et al., 2016; Vogel 
et al., 2017). Land–atmosphere feedbacks amplifying temperature 
extremes also include boundary-layer feedbacks and effects on 
atmospheric circulation (Miralles et al., 2014a; Schumacher et al., 2019). Soil-moisture–temperature feedbacks affect past and present-

day heatwaves in observations and model simulations, both locally 
(Miralles et al., 2014a; Cowan et al., 2016, 2020; Hauser et al., 2016; 
Meehl et al., 2016; Wehrli et al., 2019) and beyond the regions of 
feedback occurrence through changes in regional circulation patterns 
(Stéfanon et al., 2014; Koster et al., 2016; Sato and Nakamura, 2019). 
The uncertainty due to the representation of land–atmosphere 
feedbacks in ESMs is a cause of discrepancy between observations 
and simulations (Clark et al., 2006; Mueller and Seneviratne, 2014; 
Meehl et al., 2016). The decrease of plant transpiration or the 
increase of stomata resistance under enhanced CO 2 concentrations 
is a direct CO 2 forcing of land temperatures (warming due to reduced 
evaporative cooling), which contributes to higher warming on land 
(Lemordant et al., 2016; Vicente-Serrano et al., 2020b). The snow/ice-
albedo feedback plays an important role in amplifying temperature

variability in the high latitudes (Diro et al., 2018) and can be the 
largest contributor to the rapid warming of cold extremes in the mid- 
and high latitudes of the Northern Hemisphere (Gross et al., 2020). 
Regional external forcings, including land-use changes and 
emissions of anthropogenic aerosols, play an important role in the 
changes of temperature extremes in some regions ( high confidence ) 
(Section  11.1.6). Deforestation may have contributed to about 
one third of the warming of hot extremes in some mid-latitude 
regions since the pre-industrial time (Lejeune et al., 2018). Aspects 
of agricultural practice, including no-till farming, irrigation, and 
overall cropland intensification, may cool hot temperature extremes 
(Davin et al., 2014; N.D. Mueller et al., 2016). For instance, cropland 
intensification has been suggested to be responsible for a cooling of 
the highest temperature percentiles in Midwest USA (N.D. Mueller

et al., 2016). Irrigation has been shown to be responsible for 
a cooling of hot temperature extremes of up to 1°C–2°C in many 
mid-latitude regions in the present climate (Thiery et al., 2017, 2020), 
a process not represented in most of state-of-the-art ESMs (CMIP5, 
CMIP6). Double cropping may have led to increased hot extremes 
in the inter-cropping season in part of China (Jeong et al., 2014). 
Rapid increases in summer warming in western Europe and north-
east Asia since the 1980s are linked to a reduction in anthropogenic 
aerosol precursor emissions over Europe (Nabat et al., 2014; Dong 
et al., 2016b, 2017), in addition to the effect of increased greenhouse 
gas forcing (see also Section  10.1.3.1). This effect of aerosols on 
temperature-related extremes is also noted for declines in short-lived 
anthropogenic aerosol emissions over North America (Mascioli et al., 
2016). On the local scale, the urban heat island (UHI) effect results

in higher temperatures in urban areas than in their surrounding 
regions, and contributes to warming in regions of rapid urbanization, 
in particular for nighttime temperature extremes (Box 10.3; Phelan 
et al., 2015; Chapman et al., 2017; Y . Sun et al., 2019). But these local 
and regional forcings are generally not or not well represented in the 
CMIP5 and CMIP6 simulations (see also Section 11.3.3), contributing 
to uncertainty in model simulated changes. 
In summary, greenhouse gas forcing is the dominant driver leading 
to the warming of temperature extremes. At regional scales, changes 
in temperature extremes are modulated by changes in large-scale 
patterns and modes of variability, feedbacks including soil-moisture–
evapotranspiration–temperature or snow/ice–albedo–temperature

1548
feedbacks, and local and regional forcings such as land-use and land-
cover changes, or aerosol concentrations, and decadal and multi-
decadal natural variability. This leads to heterogeneity in regional 
changes and their associated uncertainties ( high confidence ). 
Changes in anthropogenic aerosol concentrations have likely affected 
trends in hot extremes in some regions. Irrigation and crop expansion 
have attenuated increases in summer hot extremes in some regions, 
such as the Midwestern USA ( medium confidence ). Urbanization has 
likely exacerbated the effects of global warming in cities, in particular 
for nighttime temperature extremes.
11.3.2 Observed Trends 
The SREX (IPCC, 2012) reported a  very likely  decrease in the number 
of cold days and nights and increase in the number of warm days 
and nights at the global scale. Confidence in trends was assessed as

regionally variable ( low to medium confidence ) due to either a lack 
of observations or varying signals in sub-regions. 
Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional-
scale studies have examined trends in temperature extremes using 
different metrics that are based on daily temperatures, such as the 
Commission for Climatology/World Climate Research Program/
Commission for Oceanography and Marine Meteorology joint Expert 
Team on Climate Change Detection and Indices (ETCCDI) indices 
(Dunn et al., 2020). The additional observational records, along with 
a stronger warming signal, show very clearly that changes observed 
at the time of AR5 (IPCC, 2014) continued, providing strengthened 
evidence of an increase in the intensity and frequency of hot extremes 
and decrease in the intensity and frequency of cold extremes. While 
the magnitude of the observed trends in temperature-related 
extremes varies depending on the region, spatial and temporal

scales, and metric assessed, evidence of a  warming effect is 
overwhelming, robust, and consistent. In particular, an increase 
in the intensity and frequency of hot extremes is almost always 
associated with an increase in the hottest temperatures and in the 
number of heatwave days. It is also the case for changes (decreases) 
in cold extremes. For this reason, and to simplify the presentation, 
the phrase ‘increase in the intensity and frequency of hot extremes’ 
is used to represent, collectively, an increase in the magnitude of 
extreme day and/or night temperatures, in the number of warm days 
and/or nights, and in the number of heatwave days. Changes in cold 
extremes are assessed similarly. 
On the global scale, evidence of an increase in the number of 
warm days and nights and a decrease in the number of cold days 
and nights, and an increase in the coldest and hottest extreme 
temperatures is very robust and consistent among all variables.

Figure 11.2 displays time series of globally averaged TXx and TNn on 
land. Warming of land mean TXx is similar to the mean temperature 
warming on land, which is about 45% higher than global warming 
(Section 2.3.1). Warming of land mean TNn is even higher, with about 
3°C of warming since 1960 (Figure 11.2). Figure 11.9 shows maps 
of linear trends over 1960–2018 in TXx, TNn, and frequency of warm 
days (TX90p). The maps for TXx and TNn show trends consistent 
with overall warming in most regions, with a  particularly high warming of TXx in Europe and north-western South America, and 
a particularly high warming of TNn in the Arctic. Consistent with the 
observed warming in global surface temperature (Section 2.3.1.2) 
and the observed trends in TXx and TNn, the frequency of TX90p 
has increased, while that of cold nights (TN10p) has decreased since 
the 1950s: Nearly all land regions showed statistically significant 
decreases in TN10p (Alexander, 2016; Dunn et al., 2020), though

trends in TX90p are variable with some decreases in Southern South 
America, mainly during austral summer (Rusticucci et al., 2017). 
A decrease in the number of cold spell days is also observed over 
nearly all land surface areas (Easterling et al., 2016) and in the 
northern mid-latitudes in particular (van Oldenborgh et al., 2019). 
These observed changes are also consistent when a  new global 
land surface daily air temperature dataset is analysed (P . Zhang 
et al., 2019). Warming trends in temperature extremes globally, 
and in most land areas, over the path  century are also found to 
be consistent in a  range of observation-based datasets (Fischer 
and Knutti, 2014; Donat et al., 2016a; Dunn et al., 2020), with the 
extremes related to daily minimum temperatures changing faster 
than those related to daily maximum temperatures (Dunn et al., 
2020; see Figure 11.2). Seasonal variations in trends in temperature-
related extremes have been demonstrated. A  warming in warm-

season temperature extremes is detected, even during the ‘slower 
surface global warming’ period from the late 1990s to early 2010s 
(Cross-Chapter Box 3.1; Kamae et al., 2014; Seneviratne et al., 2014; 
Imada et al., 2017). Many studies of past changes in temperature 
extremes for particular regions or countries show trends consistent 
with this global picture, as summarized below and in Tables 11.4, 
11.7, 11.10, 11.13, 11.16 and 11.19. 
In Africa (Table  11.4), while it is difficult to assess changes in 
temperature extremes in parts of the continent because of a  lack 
of data, evidence of an increase in the intensity and frequency of 
hot extremes and decrease in the intensity and frequency of cold 
extremes is clear and robust in regions where data are available. 
These include an increase in the frequency of warm days and nights 
and a decrease in the frequency of cold days and nights with high 
confidence  (Donat et al., 2013a, 2014b; Kruger and Sekele, 2013;

Chaney et al., 2014; Filahi et al., 2016; Moron et al., 2016; Ringard 
et al., 2016; Barry et al., 2018; Gebrechorkos et al., 2018) and an 
increase in heatwaves (Russo et al., 2016; Ceccherini et al., 2017). The 
increase in TNn is more notable than in TXx (Figure 11.9). Cold spells 
occasionally strike subtropical areas, but are likely  to have decreased 
in frequency (Barry et al., 2018). The frequency of cold events has likely  
decreased in South Africa (Song et al., 2014; Kruger and Nxumalo, 
2017), North Africa (Filahi et al., 2016; Driouech et al., 2021), and 
the Sahara (Donat et al., 2016a). Over the whole continent, there 
is medium confidence in an increase in the intensity and frequency 
of hot extremes and decrease in the intensity and frequency of cold 
extremes; it is likely that similar changes have also occurred in areas 
with poor data coverage, as warming is widespread and as projected 
future changes are similar over all regions (Section 11.3.5).

In Asia (Table 11.7), there is very robust evidence  for a  very likely  
increase in the intensity and frequency of hot extremes and decrease 
in the intensity and frequency of cold extremes in recent decades. 
This is clear in global studies (e.g.,  Alexander, 2016; Dunn et al.,

1549
2020), as well as in numerous regional studies (Table 11.7). The area 
fraction with extreme warmth in Asia increased during 1951–2016 
(Imada et al., 2018). The frequency of warm extremes increased and 
the frequency of cold extremes decreased in East Asia (B. Zhou et al., 
2016; Chen and Zhai, 2017; Yin et al., 2017; W. Lee et al., 2018; Qian 
et al., 2019) and west Asia (Acar Deniz and Gönençgil, 2015; Erlat 
and Türkeş, 2016; Rahimi and Hejabi, 2018; Rahimi et al., 2018) with 
high confidence . The duration of heat extremes has also lengthened 
in some regions, for example, in southern China (Luo and Lau, 
2016), but there is medium confidence  of heat extremes increasing 
in frequency in South Asia (AlSarmi and Washington, 2014; Sheikh 
et al., 2015; Mazdiyasni et al., 2017; Zahid et al., 2017; Nasim et al., 
2018; Khan et al., 2019; Sen Roy, 2019). Warming trends in daily

temperature extremes indices have also been observed in central 
Asia (Hu et al., 2016; Feng et al., 2018), the Hindu Kush Himalaya (Sun 
et al., 2017), and South East Asia (Supari et al., 2017; Cheong et al., 
2018). The intensity and frequency of cold spells in all Asian regions 
have been decreasing since the beginning of the 20th century ( high 
confidence ) (Sheikh et al., 2015; Donat et al., 2016a; Dong et al., 
2018; van Oldenborgh et al., 2019).
In Australasia (Table 11.10), there is very robust evidence  for very 
likely increases in the number of warm days and warm nights and 
decreases in the number of cold days and cold nights since 1950 
(Lewis and King, 2015; Jakob and Walland, 2016; Alexander and 
Arblaster, 2017). The increase in extreme minimum temperatures 
occurs in all seasons over most of Australia and typically exceeds the 
increase in extreme maximum temperatures (X.L. Wang et al., 2013b; 
Jakob and Walland, 2016). However, some parts of Southern Australia

have shown stable or increased numbers of frost days since the 
1980s (Dittus et al., 2014) (see also Section 11.3.4). Similar positive 
trends in extreme minimum and maximum temperatures have been 
observed in New Zealand, in particular in the autumn and winter 
seasons, although they generally show higher spatial variability 
(Caloiero, 2017). In the tropical Western Pacific region, spatially 
coherent warming trends in maximum and minimum temperature extremes have been reported for the period 1951–2011 (Whan et al., 
2014; McGree et al., 2019). 
In Central and South America (Table 11.13), there is high confidence  
that observed hot extremes (TN90p, TX90p) have increased, and 
cold extremes (TN10p, TX10p) have decreased over recent decades, 
though trends vary among different extremes types, datasets, and 
regions (Skansi et al., 2013; Dittus et al., 2016; Rusticucci et al., 2017; 
Meseguer-Ruiz et al., 2018; Salvador and de Brito, 2018; Dereczynski

et al., 2020; Dunn et al., 2020; Olmo et al., 2020). An increase in 
the intensity and frequency of heatwave events was also observed 
between 1961 and 2014 in an area covering most of South America 
(Ceccherini et al., 2016; Geirinhas et al., 2018). However, there 
is medium confidence  that warm extremes (TXx and TX90p) have 
decreased in the last decades over the central region of South-Eastern 
South America (SES) during austral summer (Tencer and Rusticucci, 
2012; Skansi et al., 2013; Rusticucci et al., 2017; Wu and  Polvani, 
2017). There is medium confidence  that TNn extremes are warming 
faster than TXx extremes, with the largest warming rates observed 
over North-East Brazil (NEB) and Northern South America (NSA) for 
cold nights (Skansi et al., 2013). 
In Europe (Table  11.16), there is very robust evidence  for a  very 
likely increase in maximum temperatures and the frequency of 
heatwaves. The increase in the magnitude and frequency of high

maximum temperatures has been observed consistently across 
regions, including in central Europe (Twardosz and Kossowska-Cezak, 
2013; Christidis et al., 2015; Lorenz et al., 2019) and southern Europe 
(Croitoru and Piticar, 2013; El Kenawy et al., 2013; Christidis et al., 
2015; Nastos and Kapsomenakis, 2015; Fioravanti et al., 2016; Ruml 
et al., 2017). In Northern Europe, a strong increase in extreme winter 
warming events has been observed (Matthes et al., 2015; Vikhamar-
Schuler et al., 2016). Temperature observations for winter cold spells 
show a  long-term decreasing frequency in Europe (Brunner et al., 
2018; van Oldenborgh et al., 2019), and typical cold spells, such 
as that observed during the 2009–2010 winter, had an occurrence 
Non-significant trendsSignificant trends Colour
No data
Figure 11.9 | Linear trends over 1960–2018 for three temperature extreme indices: (a) the annual maximum daily maximum temperature (TXx), (b) the annual

minimum daily minimum temperature (TNn), and  (c) the annual number of days when daily maximum temperature exceeds its 90th percentile from a base period of 1961–1990 
(TX90p); based on the HadEX3 dataset (Dunn et al. , 2020). Linear trends are calculated only for grid points with at least 66% of the annual values over the period and which 
extend to at least 2009. Areas without sufficient data are shown in grey. No overlay indicates regions where the trends are significant at the p = 0.1 level. Crosses indicate 
regions where trends are not significant. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1550
probability two times smaller currently than if climate change had 
not occurred (Christiansen et al., 2018).
In North America (Table  11.19), there is very robust evidence  for 
a very likely increase in the intensity and frequency of hot extremes 
and decrease in the intensity and frequency of cold extremes for the 
whole continent, though there are substantial spatial and seasonal 
variations in the trends. Minimum temperatures display warming 
consistently across the continent, while there are more contrasting 
trends in the annual maximum daily temperatures in parts of the 
USA (Figure 11.9; Lee et al., 2014; van Oldenborgh et al., 2019; Dunn 
et al., 2020). In Canada, there is a  clear increase in the intensity 
and frequency of hot extremes and decrease in the intensity and 
frequency of cold extremes (Vincent et al., 2018). In Mexico, a clear

warming trend in TNn was found, particularly in the northern arid 
region (Montero-Martínez et al., 2018). The number of warm days has 
increased and the number of cold days has decreased (García-Cueto 
et al., 2019). Cold spells have undergone a reduction in magnitude 
and intensity in all regions of North America (Bennett and Walsh, 
2015; Donat et al., 2016a; Grotjahn et al., 2016; Vose et al., 2017; 
García-Cueto et al., 2019; van Oldenborgh et al., 2019). 
Extreme heat events have increased around the Arctic since 1979, 
particularly over Arctic North America and Greenland (Matthes 
et al., 2015; Dobricic et al., 2020), which is consistent with summer 
melt (Section 9.4.1). Observations north of 60˚N show increases in 
winter warm days and nights over 1979–2015, while cold days and 
nights declined (Sui et al., 2017). Extreme heat days are particularly 
strong in winter, with observations showing the warmest mid-winter

temperatures at the North Pole rising at twice the rate of mean 
temperature (Moore, 2016), as well as increases in Arctic winter 
warm days (Vikhamar-Schuler et al., 2016; Graham et al., 2017). 
Arctic annual minimum temperatures have increased at about three 
times the rate of global surface temperature since the 1960s (Figures 
11.2 and 11.9), consistent with the observed mean cold season 
(October–May) warming of 3.1°C in the region (Atlas 11.2).
Trends in some measures of heatwaves are also observed at the 
global scale. Globally averaged heatwave intensity, heatwave 
duration, and the number of heatwave days have significantly 
increased from 1950–2011 (Perkins, 2015). There are some regional 
differences in trends in characteristics of heatwaves, with significant 
increases reported in Europe (Russo et al., 2015; Forzieri et al., 2016;   
Sánchez-Benítez et al., 2020) and Australia (CSIRO and BOM, 
2016;  Alexander and Arblaster, 2017). In Africa, there is medium

confidence  that heatwaves, regardless of the definition, have been 
becoming more frequent, longer-lasting, and hotter over more 
than three decades (Fontaine et al., 2013; Mouhamed et al., 2013; 
Ceccherini et al., 2016, 2017; Forzieri et al., 2016; Moron et al., 2016; 
Russo et al., 2016). The majority of heatwave characteristics examined 
in China between 1961 and 2014 show increases in heatwave days, 
consistent with warming (You et al., 2017; Xie et al., 2020). Increases 
in the frequency and duration of heatwaves are also observed in 
Mongolia (Erdenebat and Sato, 2016) and India (Ratnam et al., 2016; 
Rohini et al., 2016). In the UK, the lengths of short heatwaves have 
increased since the 1970s, while the lengths of long heatwaves (more 
than 10 days) have decreased over some stations in the south-east of England (M. Sanderson et al., 2017). In Central and South America, 
there are increases in the frequency of heatwaves (Barros et al.,

2015; Bitencourt et al., 2016; Ceccherini et al., 2016; Piticar, 2018), 
although decreases in Excess Heat Factor (EHF), which is a metric for 
heatwave intensity, are observed in South America in data derived 
from HadGHCND (Cavanaugh and Shen, 2015). 
In summary, it is virtually certain  that there has been an increase in 
the number of warm days and nights and a decrease in the number of 
cold days and nights on the global scale since 1950. Both the coldest 
extremes and hottest extremes display increasing temperatures. It is 
very likely  that these changes have also occurred at the regional 
scale in Europe, Australasia, Asia, and North America. It is  virtually 
certain  that there has been increases in the intensity and duration 
of heatwaves and in the number of heatwave days at the global 
scale. These trends likely occur in Europe, Asia, and Australia. There 
is medium confidence  in similar changes in temperature extremes in

Africa and high confidence in South America; the lower confidence is 
due to reduced data availability and fewer studies. Annual minimum 
temperatures on land have increased about three times more than 
global surface temperature since the 1960s, with particularly strong 
warming in the Arctic ( high confidence ).
11.3.3 Model Evaluation 
The AR5 assessed that CMIP3 and CMIP5 models generally captured 
the observed spatial distributions of the mean state and that the 
inter-model range of simulated temperature extremes was similar 
to the spread estimated from different observational datasets; 
the models generally captured trends in the second half of the 
20th  century for indices of extreme temperature, although they 
tended to overestimate trends in hot extremes and underestimate 
trends in cold extremes (Flato et al., 2013). Post-AR5 studies on 
the CMIP5 models’ performance in simulating mean and changes 
in temperature extremes continue to support the AR5 assessment

(Fischer and Knutti, 2014; Sillmann et al., 2014; Ringard et al., 2016; 
Borodina et al., 2017b; Donat et al., 2017; Di Luca et al., 2020b). Over 
Africa, the observed warming in temperature extremes is captured by 
CMIP5 models, although it is underestimated in Western and Central 
Africa (Sherwood et al., 2014; Diedhiou et al., 2018). Over East Asia, 
the CMIP5 ensemble performs well in reproducing the observed 
trend in temperature extremes averaged over China (Dong et al., 
2015). Over Australia, the multi-model mean performs better than 
individual models in capturing observed trends in gridded station-
based ETCCDI temperature indices (Alexander and Arblaster, 2017). 
Initial analyses of CMIP6 simulations (H. Chen et al., 2020; Di Luca 
et al., 2020a; Kim et al., 2020; Thorarinsdottir et al., 2020; Wehner 
et al., 2020; Li et al., 2021) indicate that the CMIP6 models perform 
similarly to the CMIP5 models regarding biases in hot and cold

extremes. In general, CMIP5 and CMIP6 historical simulations are 
similar in their performance in simulating the observed climatology 
of extreme temperatures ( high confidence ). The general warm bias 
in hot extremes and cold bias in cold extremes reported for CMIP5 
models (Kharin et al., 2013; Sillmann et al., 2013a) remain in CMIP6 
models (Di Luca et al., 2020a). However, there is some evidence that

1551
CMIP6 models better represent some of the underlying processes 
leading to extreme temperatures, such as seasonal and diurnal 
variability and synoptic-scale variability (Di Luca et al., 2020a). Whether 
these improvements are sufficient to enhance our understanding of 
past changes, or to reduce uncertainties in future projections, remains 
unclear. The relative error estimates in the simulation of various indices 
of temperature extremes in the available CMIP6 models show that 
no single model performs the best on all indices, and the multi-model 
ensemble seems to outperform any individual model due to its reduction 
in systematic bias (Kim et al., 2020). Figure 11.10 show errors in the 
1979–2014 average annual TXx and annual TNn simulated by available 
CMIP6 models in comparison with HadEX3 and ERA5 (Kim et al., 2020; 
Wehner et al., 2020; Li et al., 2021). While the magnitude of the model

error depends on the reference dataset, the model evaluations drawn 
from different reference datasets are quite similar. In general, models 
reproduce the spatial patterns and magnitudes of both cold and hot 
temperature extremes quite well. There are also systematic biases. 
Hot extremes tend to be too cool in mountainous and high-latitude 
regions, but too warm in the eastern USA and South America. For cold 
extremes, CMIP6 models are too cool, except in north-eastern Eurasia 
and the southern mid-latitudes. Errors in seasonal mean temperatures 
are uncorrelated with errors in extreme temperatures and are often of 
opposite sign (Wehner et al., 2020). 
Atmospheric Model Intercomparison Project (AMIP) simulations 
are often used in event attribution studies to assess the influence 
of global warming on observed temperature-related extremes. These 
simulations typically capture the observed trends in temperature 
extremes, though some regional features, such as the lack of warming

in daytime warm temperature extremes over South America and 
parts of North America, are not reproduced in the model simulations 
(Dittus et al., 2018), possibly due to internal variability, deficiencies 
in local surface processes, or forcings that are not represented in 
the sea surface temperatures (SSTs). Additionally, the AMIP models 
assessed tend to produce overly persistent heatwave events. This bias in the duration of the events does not impact on the reliability of the 
models’ positive trends (Freychet et al., 2018). 
Several regional climate models (RCMs) have also been evaluated in 
terms of their performance in simulating the climatology of extremes 
in various regions of the Coordinated Regional Downscaling 
Experiment (CORDEX) (Giorgi et al., 2009), especially in East Asia 
(Ji and Kang, 2015; Yu et al., 2015; Park et al., 2016; Bucchignani 
et al., 2017; Gao et al., 2017a; Niu et al., 2018; Y . Sun et al., 2018b;

Wang et al., 2019), Europe (Vautard et al., 2013, 2021; Smiatek 
et al., 2016; Gaertner et al., 2018; Cardoso et al., 2019; Lorenz et al., 
2019; Jacob et al., 2020; Kim et al., 2020), and Africa (J. Kim et al., 
2014; Diallo et al., 2015; Dosio, 2017; Samouly et al., 2018; Mostafa 
et al., 2019). Compared to GCMs, RCM simulations show an added 
value in simulating temperature-related extremes, though this 
depends on topographical complexity and the parameters employed 
(see  Section  10.3.3). The improvement with resolution is noted in 
East Asia (Park et al., 2016; W. Zhou et al., 2016; Shi et al., 2017; Hui 
et al., 2018). However, in the European CORDEX ensemble, different 
aerosol climatologies with various degrees of complexity were used 
in projections (Bartók et al., 2017; Lorenz et al., 2019) and the land 
surface models used in the RCMs do not account for physiological CO 2 
effects on photosynthesis leading to enhanced water-use efficiency

and decreased evapotranspiration (Schwingshackl et al., 2019), 
which could lead to biases in the representation of temperature 
extremes in these projections (Boé et al., 2020). In addition, there 
are key cold biases in temperature extremes over areas with complex 
topography (Niu et al., 2018). Over North America, 12 RCMs were 
evaluated over the ARCTIC-CORDEX region (Diaconescu et al., 2018). 
Models performed well at simulating climate indices related to mean 
air temperature and hot extremes over most of the Canadian Arctic, 
with the exception of the Yukon region where models displayed 
the largest biases related to topographic effects. Two RCMs were 
evaluated against observed extremes indices over North America 
over the period 1989–2009, with a cool bias in minimum temperature (a) Bias in annual hottest temperature (TXx) (b) Bias in annual coldest temperature (TNn)
-10     -8     -6      -4     -2       0       2       4       6      8      10

too cold too warmTemperature bias (°C)No dataMulti model mean bias in temperature extremes
Figure 11.10 | Multi-model mean bias in temperature extremes (°C) for the period 1979–2014, calculated as the difference between the Coupled Model 
Intercomparison Project Phase 6 (CMIP6) multi-model mean and the average of observations from the values available in HadEX3.  (a) The annual hottest 
temperature  (TXx); and (b) the annual coldest temperature (TNn). Areas without sufficient data are shown in grey. Adapted from Wehner et al. (2020) under the terms of the 
Creative Commons Attribution licence. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1552
extremes shown in both RCMs (Whan and Zwiers, 2016). The most 
significant biases are found in TXx and TNn, with fewer differences 
in the simulation of annual minimum daily maximum temperature 
(TXn) and annual maximum daily minimum temperature (TNx) 
in Central and Western North America. Over Central and South 
America, maximum temperatures from the Eta RCM are generally 
underestimated, although hot days, warm nights, and heatwaves are 
increasing in the period 1961–1990, in agreement with observations 
(Chou et al., 2014b; Tencer et al., 2016; Bozkurt et al., 2019).
Some land forcings are not well represented in climate models. 
As  highlighted in the Special Report on Climate Change and Land 
(SRCCL) Chapter 2, there is high agreement  that temperate deforestation 
leads to summer warming and winter cooling (Anderson et al., 2011; 
Gálos et al., 2011, 2013; Anderson-Teixeira et al., 2012; Chen et al.,

2012; Wickham et al., 2013; Zhao and Jackson, 2014; Ahlswede and 
Thomas, 2017; Bright et al., 2017; Strandberg and Kjellström, 2019), 
which has substantially contributed to the warming of hot extremes in 
the northern mid-latitudes over the course of the 20th century (Lejeune 
et al., 2018) and in recent years (Strandberg and Kjellström, 2019). 
However, observed forest effects on the seasonal and diurnal cycle of 
temperature are not well-captured in several ESMs: while observations 
show a cooling effect of forest cover compared to non-forest vegetation 
during daytime (Li et al., 2015), in particular in arid, temperate, and 
tropical regions (Alkama and Cescatti, 2016), several ESMs simulate 
a warming of daytime temperatures for regions with forest versus non-
forest cover (Lejeune et al., 2017). Also irrigation effects, which can 
lead to regional cooling of temperature extremes, are generally not 
integrated in current generations of ESMs (Section 11.3.1).

In summary, there is high confidence  that climate models can 
reproduce the mean state and overall warming of temperature 
magnitude of the trends may differ. The ability of models to capture 
observed trends in temperature-related extremes depends on the 
metric evaluated, the way indices are calculated, and the time 
periods and spatial scales considered. Regional climate models add 
value in simulating temperature-related extremes over GCMs in some 
regions. Some land forcings on temperature extremes are not well-
captured (effects of deforestation) or generally not representated 
(irrigation) in ESMs.
11.3.4 Detection and Attribution, Event Attribution
The SREX (IPCC, 2012) assessed that it is likely  anthropogenic 
influences have led to the warming of extreme daily minimum and 
maximum temperatures at the global scale. The AR5 concluded that 
human influence has very likely contributed to the observed changes

in the intensity and frequency of daily temperature extremes on the 
global scale in the second half of the 20th  century (IPCC, 2014). 
With regard to individual, or regionally or locally specific events, AR5 
concluded that it is likely  human influence has substantially increased 
the probability of occurrence of heatwaves in some locations.
Studies since AR5 continue to attribute the observed increase in the 
frequency or intensity of hot extremes and the observed decrease in the frequency or intensity of cold extremes to human influence, 
dominated by anthropogenic greenhouse gas emissions, on global 
and continental scales, and for many AR6 regions. These include 
attribution of changes in the magnitude of annual TXx, TNx, TXn, and 
TNn, based on different observational datasets including, HadEX2 
and HadEX3, CMIP5 and CMIP6 simulations, and different statistical 
methods (Kim et al., 2016; Z. Wang et al., 2017a; Seong et al., 2021).

As is the case for an increase in mean temperature (Section 3.3.1), 
an increase in extreme temperature is mostly due to greenhouse gas 
forcing, offset by aerosol forcing. The aerosols’ cooling effect is clearly 
detectable over Europe and Asia (Seong et al., 2021). As much as 75% 
of the moderate daily hot extremes (above 99.9th percentile) over 
land are due to anthropogenic warming (Fischer and Knutti, 2015). 
New results are found to be more robust due to the extended period 
that improves the signal-to-noise ratio. The effect of anthropogenic 
forcing is clearly detectable and attributable in the observed changes 
in these indicators of temperature extremes, even at country and 
sub-country scales, such as in Canada (Wan et al., 2019). Changes in 
the number of warm nights, warm days, cold nights, and cold days, 
and other indicators such as the Warm Spell Duration Index (WSDI), 
are also attributed to anthropogenic influence (Christidis and Stott, 
2016; Hu et al., 2020).

Regional studies, including for Asia (Dong et al., 2018; Lu et al., 2018), 
Australia (Alexander and Arblaster, 2017), and Europe (Christidis and 
Stott, 2016), found similar results. A  clear anthropogenic signal is 
also found in the trends in the Combined Extreme Index (CEI) for 
North America, Asia, Australia, and Europe (Dittus et al., 2016). While 
various studies have described increasing trends in several heatwave 
metrics (heatwave duration, the number of heatwave days, etc.) in 
different regions (e.g.,  Cowan et al., 2014; Bandyopadhyay et al., 
2016; M. Sanderson et al., 2017), few recent studies have explicitly 
attributed these changes to causes; most of them stated that observed 
trends are consistent with anthropogenic warming. The detected 
anthropogenic signals are clearly separable from the response to 
natural forcing, and the results are generally insensitive to the use 
of different model samples, as well as different data availability,

indicating robust attribution. Studies of monthly, seasonal, and 
annual records in various regions (Kendon, 2014; Lewis and King, 
2015; Bador et al., 2016; Meehl et al., 2016; C. Zhou et al., 2019) and 
globally (King, 2017) show an increase in the breaking of hot records 
and a decrease in the breaking of cold records (King, 2017). Changes 
in anthropogenically attributable record-breaking rates are noted to 
be largest over the Northern Hemisphere land areas (Shiogama et al., 
2016). Yin and Sun (2018) found clear evidence of an anthropogenic 
signal in the changes in the number of frost and ice days, when 
multiple model simulations were used. In some key wheat-producing 
regions of Southern Australia, increases in frost days or frost season 
length have been reported (Dittus et al., 2014; Crimp et al., 2016); 
these changes are linked to decreases in rainfall, cloud-cover, and 
subtropical ridge strength, despite an overall increase in regional

mean temperatures (Dittus et al., 2014; Pepler et al., 2018). 
A significant advance since AR5 has been a large number of studies 
focusing on extreme temperature events at monthly and seasonal 
scales, using various extreme event attribution methods. Diffenbaugh 
et al. (2017) found that anthropogenic warming has increased the

1553
severity and probability of the hottest month by more than 80% of 
the available observational area on the global scale. Christidis and 
Stott (2014) provide clear evidence that warm events have become 
more probable because of anthropogenic forcings. Sun et al. (2014) 
found that human influence has caused a more than 60-fold increase 
in the probability of the extreme warm 2013 summer in eastern 
China since the 1950s. Human influence is found to have increased 
the probability of the historically hottest summers in many regions of 
the world, both in terms of mean temperature (B. Mueller et al., 2016) 
and wet bulb globe temperature (WBGT; C. Li et al., 2017). In most 
regions of the Northern Hemisphere, changes in the probability of 
extreme summer average WBGT were found to be about an order 
of magnitude larger than changes in the probability of extreme hot

summers estimated by surface air temperature (C. Li et al., 2017). 
In addition to these generalized, global-scale approaches, extreme 
event studies have found an attributable increase in the probability 
of hot annual and seasonal temperatures in many locations, 
including Australia (Knutson et al., 2014b; Lewis and Karoly, 2014), 
China (Sun et al., 2014; Sparrow et al., 2018; Zhou et al., 2020), Korea 
(Y .-H. Kim et al., 2018) and Europe (King et al., 2015b). 
There have also been many extreme event attribution studies that 
examined short-duration temperature extremes, including daily 
temperatures, temperature indices, and heatwave metrics. Examples 
of these events from different regions are summarized in various 
annual Explaining Extreme Events supplements of the Bulletin of 
the American Meteorological Society  (Peterson et al., 2012, 2013a; 
Herring et al., 2014, 2015, 2016, 2018, 2019, 2020), including 
a  number of approaches to examine extreme events (described in

Easterling et al., 2016; Stott et al., 2016; Otto, 2017). Several studies 
of recent events from 2016 onwards have determined an infinite 
risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that 
the occurrence probability for such events is close to zero in model 
simulations without anthropogenic influences (see Herring et al., 
2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019). Though it 
is difficult to accurately estimate the lower bound of the uncertainty 
range of the FAR in these cases (Paciorek et al., 2018), the fact that 
those events are so far outside the envelop of the models with only 
natural forcing indicates that it is extremely unlikely  for those events 
to occur without human influence. 
Studies that focused on the attributable signal in observed cold extreme 
events show human influence reducing the probability of those events. 
Individual attribution studies on the extremely cold winter of 2011 in

Europe (Peterson et al., 2012), in the eastern USA during 2014 and 
2015 (Trenary et al., 2015, 2016; Wolter et al., 2015; Bellprat et al., 
2016), in the cold spring of 2013 in the United Kingdom (Christidis 
et al., 2014), and of 2016 in eastern China (Qian et al., 2018; Y . Sun 
et al., 2018b) all showed a reduced probability due to human influence 
on the climate. An exception is the study of Grose et al. (2018), which 
found an increase in the probability of the severe western Australian 
frost of 2016 due to anthropogenically-driven changes in circulation 
patterns that drive cold outbreaks and frost probability. 
Different event attribution studies can produce a  wide range of 
changes in the probability of event occurrence because of different 
framing. The temperature event definition itself plays a crucial role in the attributable signal (Fischer and Knutti, 2015; Kirchmeier -Young 
et al., 2019). Large-scale, longer-duration events tend to have notably

larger attributable risk ratios (Angélil et al., 2014, 2018; Uhe et al., 
2016; Harrington, 2017; Kirchmeier -Young et al., 2019), as natural 
variability is smaller. While uncertainty in the best estimates of the 
risk ratios may be large, their lower bounds can be quite insensitive to 
uncertainties in observations or model descriptions, thus increasing 
confidence in conservative attribution statements (Jeon et al., 2016).
The relative strength of anthropogenic influences on temperature 
extremes is regionally variable, in part due to differences in changes 
in atmospheric circulation, land–surface feedbacks, and other 
external drivers such as aerosols. For example, in the Mediterranean 
and over western Europe, risk ratios on the order of 100 have been 
found (Kew et al., 2019; Vautard et al., 2020), whereas in the USA, 
changes are much less pronounced. This is probably a reflection of 
the land–surface feedback enhanced extreme 1930s temperatures

that reduce the rarity of recent extremes, in addition to the definition 
of the events and framing of attribution analyses (e.g., spatial and 
temporal scales considered). Local forcing may mask or enhance 
the warming effect of greenhouse gases. In India, short-lived 
aerosols or an increase in irrigation may be masking the warming 
effect of greenhouse gases (Wehner et al., 2018c). Irrigation and 
crop intensification have been shown to lead to a cooling in some 
regions, in particular in North America, Europe, and India ( high 
confidence ) (N.D. Mueller et al., 2016; Thiery et al., 2017, 2020; Chen 
and Dirmeyer, 2019). Deforestation has contributed about one third 
of the total warming of hot extremes in some mid-latitude regions 
since pre-industrial times (Lejeune et al., 2018). Despite all of these 
differences, and larger uncertainties at the regional scale, nearly 
all studies demonstrated that human influence has contributed

to an increase in the frequency or intensity of hot extremes and to 
a decrease in the frequency or intensity of cold extremes. 
In summary, long-term changes in various aspects of long- and 
short-duration extreme temperatures, including intensity, frequency, 
and duration have been detected in observations and attributed to 
human influence at global and continental scales. It is extremely 
likely that human influence is the main contributor to the observed 
increase in the intensity and frequency of hot extremes and the 
observed decrease in the intensity and frequency of cold extremes 
on the global scale. It is very likely  that this applies on continental 
scales as well. Some specific recent hot extreme events would have 
been extremely unlikely  to occur without human influence on the 
climate system. Changes in aerosol concentrations have affected 
trends in hot extremes in some regions, with the presence of aerosols

leading to attenuated warming, in particular from 1950 to 1980. 
Crop intensification, irrigation and no-till farming have attenuated 
increases in summer hot extremes in some regions, such as Central 
North America ( medium confidence ).
11.3.5 Projections
The AR5 (Chapter12, Collins et al., 2013) concluded that it is virtually 
certain  there will be more frequent hot extremes and fewer cold 
extremes at the global scale and over most land areas in a future

1554
warmer climate, and it is very likely  that heatwaves will occur 
with a higher frequency and longer duration.The SR1.5 (Chapter 3, 
Hoegh-Guldberg et al., 2018) assessment on projected changes in 
hot extremes at 1.5°C and 2°C global warming is consistent with the 
AR5 assessment, concluding that it is very likely  a global warming 
of 2°C, when compared with a 1.5°C warming, would lead to more 
frequent and more intense hot extremes on land, as well as to longer 
warm spells, affecting many densely inhabited regions. The SR1.5 
also assessed it is very likely  that the strongest increases in the 
frequency of hot extremes are projected for the rarest events, while 
cold extremes will become less intense and less frequent, and cold 
spells will be shorter. 
New studies since AR5 and SR1.5 confirm these assessments. New 
literature since AR5 includes projections of temperature-related

extremes in relation to changes in mean temperatures, projections 
based on CMIP6 simulations, projections based on stabilized global 
warming levels, and the use of new metrics. Constraints for the 
projected changes in hot extremes were also provided (Borodina 
et al., 2017b; Sippel et al., 2017b; Vogel et al., 2017). Overall, 
projected changes in the magnitude of extreme temperatures over 
land are larger than changes in global mean temperature, over mid-
latitude land regions in particular (Figures 11.3, 11.11; Fischer et al., 
2014; Seneviratne et al., 2016; B.M. Sanderson et al., 2017; Wehner 
et al., 2018b; Di Luca et al., 2020b). Large warming in hot and cold extremes will occur, even at the 1.5°C GWL (Figure 11.11). At this 
level, widespread significant changes at the grid-box level occur for 
different temperature indices (Aerenson et al., 2018). In agreement 
with CMIP5 projections, CMIP6 simulations show that a  0.5°C

increment in global warming will significantly increase the intensity 
and frequency of hot extremes, and decrease the intensity and 
frequency of cold extremes on the global scale (Figures 11.6, 11.8 
and 11.12). It takes less than half of a degree for the changes in TXx 
to emerge above the level of natural variability (Figure 11.8) and the 
66% ranges of the land medians of the 10-year or 50-year TXx events 
do not overlap between 1.0°C and 1.5°C in the CMIP6 multi-model 
ensemble simulations (Figure 11.6, Li et al., 2021). 
Projected warming is larger for TNn and exhibits strong equator-to-
pole amplification, similar to the warming of boreal winter mean 
temperatures. The warming of TXx is more uniform over land and 
does not exhibit this behaviour (Figure  11.11). The warming of 
temperature extremes on global and regional scales tends to scale 
linearly with global warming (Section 11.1.4; Fischer et al., 2014;

Seneviratne et al., 2016; Wartenburger et al., 2017; Li et al., 2021; 
see also SR1.5, Chapter 3). In the mid-latitudes, the rate of warming  
of hot extremes can be as large as twice the rate of global 
warming (Figure 11.11). In the Arctic winter, the rate of warming of 
the temperature of the coldest nights is about three times the rate 
of global warming (Appendix, Figure  11.A.1). Projected changes Colour
Figure 11.11 | Projected changes in (a–c) annual maximum temperature (TXx) and (d–f) annual minimum temperature (TNn) at 1.5°C, 2°C, and 4°C of 
global warming compared to the 1850–1900 baseline.  Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model 
ensemble under the Shared Socio-economic Pathways (SSPs) SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number

of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the 
sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, 
please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in TXx and TNn are also displayed in the Interactive 
Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1555
in temperature extremes can deviate from projected changes in 
annual mean warming in the same regions (Figures 11.3, 11.A.1 and 
11.A.2; Di Luca et al., 2020b; Wehner, 2020) due to the additional 
processes that control the response of regional extremes, including, 
in particular, soil moisture–evapotranspiration–temperature 
feedbacks for hot extremes in the mid-latitudes and subtropical 
regions, and snow/ice–albedo–temperature feedbacks in high-
latitude regions. 
The probability of exceeding a  certain hot extreme threshold will 
increase, while those for cold extreme will decrease with global 
warming (B. Mueller et al., 2016; Lewis et al., 2017b; Suarez-
Gutierrez et al., 2020b). The changes tend to scale nonlinearly with 
the level of global warming, with larger changes for more rare 
events (Section 11.2.4; Cross-Chapter Box 11.11; Figures 11.6 and

11.12; Fischer and Knutti, 2015; Kharin et al., 2018; Li et al., 2021). 
For  example, the CMIP5 ensemble projects the frequency of the 
present-day climate 20-year hottest daily temperature to increase 
by 80% at the 1.5°C GWL and by 180% at the 2.0°C GWL, and 
the frequency of the present-day climate 100-year hottest daily 
temperature to increase by 200% and more than 700% at the 1.5°C 
and 2.0°C warming levels, respectively (Kharin et al., 2018). CMIP6 
simulations project similar changes (Li et al., 2021).
Tebaldi and Wehner (2018) showed that, at the middle of 
the  21st  century, 66% of the land surface area would experience the present-day 20-year return values of TXx and the running three-
day average of the daily maximum temperature every other year, on 
average, under the Representative Concentration Pathway 8.5 (RCP8.5) 
scenario, as opposed to only 34% under RCP4.5. By the end of the 
century, these area fractions increase to 92% and 62%, respectively.

Such nonlinearities in the characteristics of future regional extremes are 
shown, for instance, for Europe (Dosio and Fischer, 2018; Spinoni et al., 
2018b; Lionello and Scarascia, 2020), Asia (Guo et al., 2017; Harrington 
and Otto, 2018b; King et al., 2018), and Australia (Lewis et al., 2017a) 
under various global warming thresholds. The nonlinear increase in 
fixed-threshold indices (e.g., based on a percentile for a given reference 
period, or on an absolute threshold) as a function of global warming 
is consistent with a  linear warming of the absolute temperature of 
the temperature extremes (e.g., Whan et al., 2015). Compared to the 
historical climate, warming will result in strong increases in heatwave 
area, duration and magnitude (Vogel et al., 2020b). These changes are 
mostly due to the increase in mean seasonal temperature, rather than 
changes in temperature variability, though the latter can have an effect

in some regions (Brown, 2020; Di Luca et al., 2020b; Suarez-Gutierrez 
et al., 2020a).
Projections of temperature-related extremes in RCMs in the CORDEX 
regions demonstrate robust increases under future scenarios and can 
provide information on finer spatial scales than GCMs (e.g., Coppola 
et al., 2021b). Five RCMs in the CORDEX–East Asia region project 
increases in the 20-year return values of temperature extremes 
(summer maxima), with models that exhibit warm biases projecting 
stronger warming (Park and Min, 2019). Similarly, in the African 
domain, future increases in TX90p and TN90p are projected (Dosio, 
2017; Mostafa et al., 2019). This regional-scale analysis provides 
fine-scale information, such as distinguishing the increase in TX90p 
over sub-equatorial Africa (Democratic Republic of the Congo, 
Angola, and Zambia) with values over the Gulf of Guinea, Central 
African Republic, South Sudan, and Ethiopia. Empirical statistical

downscaling has also been used to produce more robust estimates 
for future heatwaves compared to RCMs based on large multi-model 
ensembles (Furrer et al., 2010; Keellings and Waylen, 2014; Wang 
et al., 2015; Benestad et al., 2018).
In all continental regions, including Africa (Table  11.4), Asia 
(Table  11.7), Australasia (Table  11.10), Central and South America 
(Table 11.13), Europe (Table 11.16), North America (Table 11.19) and 
at the continental scale, it is very likely  that the intensity and frequency 
of hot extremes will increase and the intensity and frequency of cold 
extremes will decrease compared with the 1995–2014 baseline, even 
under 1.5°C global warming. Those changes are virtually certain  
to occur under 4°C global warming. At the regional scale, and for 
almost all AR6 regions, it is likely  that the intensity and frequency of 
hot extremes will increase and the intensity and frequency of cold 
extremes will decrease compared with the 1995–2014 baseline,

even under 1.5°C global warming. Those changes are virtually 
certain  to occur under 4°C global warming. Exceptions include lower 
confidence in the projected decrease in the intensity and frequency of 
cold extremes compared with the 1995–2014 baseline under 1.5°C 
of global warming ( medium confidence ) and 4°C of global warming 
(very likely ) in Northern Central America, Central North America, and 
Western North America.
Figure 11.12 | Projected changes in the intensity of extreme temperature 
events under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative 
to the 185 0–1900 baseline. Extreme temperature events are defined as the 
daily maximum temperatures (TXx) that were exceeded on average once during 
a 10-year period (10-year event, blue) and once during a 50-year period (50-year 
event, orange) during the 1850–1900 base period. Results are shown for the global 
land. For each box plot, the horizontal line and the box represent the median and

central 66% uncertainty range, respectively, of the intensity changes across the 
multi-model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. 
The results are based on the multi-model ensemble from simulations of global climate 
models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) 
under different Shared Socio-economic Pathway forcing scenarios. Adapted from 
Li et al. (2021). Further details on data sources and processing are available in the 
chapter data table (Table 11.SM.9).

1556
In Africa (Table  11.4), evidence includes increases in the intensity 
and frequency of hot extremes, such as warm days, warm nights, 
and heatwaves, and decreases in the intensity and frequency of cold 
extremes, such as cold days and cold nights over the continent, as 
projected by CMIP5, CMIP6, and CORDEX simulations (Giorgi et al., 
2014; Engelbrecht et al., 2015; Lelieveld et al., 2016; Russo et al., 
2016; Dosio, 2017; Bathiany et al., 2018; Mba et al., 2018; Nangombe 
et al., 2018; Weber et al., 2018; Kruger et al., 2019; Coppola et al., 
2021b; Li et al., 2021). Cold spells are projected to decrease under 
all RCPs, and even at low warming levels in Western and Central 
Africa (Diedhiou et al., 2018). The number of cold days is projected to 
decrease in East Africa (Ongoma et al., 2018b).
In Asia (Table  11.7), evidence includes increases in the intensity

and frequency of hot extremes, such as warm days, warm nights, and  
heatwaves, and decreases in the intensity and frequency of cold 
projected by CMIP5, CMIP6, and CORDEX simulations (Sillmann 
et al., 2013b; Zhou et al., 2014; R. Zhang et al., 2015; Zhao et al., 
2015; Pal and Eltahir, 2016; Singh and Goyal, 2016; Xu et al., 2017; 
Gao et al., 2018; Han et al., 2018; Shin et al., 2018; Sui et al., 2018; 
L. Li et al., 2019; Zhu et al., 2020). More intense heatwaves of longer 
durations and occurring at a  higher frequency are projected over 
India (Murari et al., 2015; Mishra et al., 2017) and Pakistan (Nasim 
et al., 2018). Future mid-latitude warm extremes, similar to those 
experienced during the 2010 event, are projected to become more 
extreme, with temperature extremes increasing potentially by 8.4°C 
(RCP8.5) over north-west Asia (van der Schrier et al., 2018). Over

West and East Siberia, and Russian Far East, an increase in extreme 
heat durations is expected in all scenarios (Sillmann et al., 2013b; 
Kattsov et al., 2017; Reyer et al., 2017). In the MENA regions (Arabian 
Peninsula and Western Central Asia), extreme temperatures could 
increase by almost 7°C by 2100 under RCP8.5 (Lelieveld et al., 2016).
In Australasia (Table  11.10), evidence includes increases in the 
intensity and frequency of hot extremes, such as warm days, warm 
nights, and heatwaves, and decreases in the intensity and frequency 
of cold extremes, such as cold days and cold nights over the 
continent, as projected by CMIP5, CMIP6, and CORDEX simulations 
(CSIRO and BOM, 2015; Alexander and Arblaster, 2017; Lewis et al., 
2017a; Herold et al., 2018; Coppola et al., 2021b; Evans et al., 2021). 
Over most of Australia, increases in the intensity and frequency of 
hot extremes are projected to be predominantly driven by the long-

term increase in mean temperatures (Di Luca et al., 2020b). Future 
projections indicate a decrease in the number of frost days regardless 
of the region and season considered (Alexander and Arblaster, 2017; 
Herold et al., 2018).
In Central and South America (Table  11.13), evidence includes 
increases in the intensity and frequency of hot extremes, such as warm 
days, warm nights, and heatwaves, and decreases in the intensity and 
frequency of cold extremes, such as cold days and cold nights over the 
(Chou et al., 2014a; Cabré et al., 2016; López-Franca et al., 2016; 
Stennett-Brown et al., 2017; Coppola et al., 2021b; Li et al., 2021; 
Vichot-Llano et al., 2021). Over South-Eastern South America during 
the austral summer, the increase in the frequency of TN90p is larger than that projected for TX90p, consistent with observed past changes 
(López-Franca et al., 2016). Under RCP8.5, the number of heatwave

days are projected to increase for the intra-Americas region for the 
end of the 21st century (Angeles-Malaspina et al., 2018). A general 
decrease in the frequency of cold spells and frost days is projected, as 
indicated by several indices based on minimum temperature (López-
Franca et al., 2016).
In Europe (Table 11.16), evidence includes increases in the intensity 
projected by CMIP5, CMIP6, and CORDEX simulations (Lau and Nath, 
2014; Ozturk et al., 2015; Russo et al., 2015; Schoetter et al., 2015; 
Vogel et al., 2017; Winter et al., 2017; Jacob et al., 2018; Lhotka et al., 
2018; Rasmijn et al., 2018; Suarez-Gutierrez et al., 2018; Cardoso 
et al., 2019; Lionello and Scarascia, 2020; Molina et al., 2020; Coppola

et al., 2021b; Li et al., 2021). Increases in heatwaves are greater over 
the southern Mediterranean and Scandinavia (Forzieri et al., 2016; 
Abaurrea et al., 2018; Dosio and Fischer, 2018; Rohat et al., 2019). 
The biggest increases in the number of heatwave days are expected 
for southern European cities (Guerreiro et al., 2018a; Junk et al., 
2019), and Central European cities will see the biggest increases in 
maximum heatwave temperatures (Guerreiro et al., 2018a).
In North America (Table 11.19), evidence includes increases in the 
(Grotjahn et al., 2016; Vose et al., 2017; Alexandru, 2018; C. Li et al., 
2018, 2021; C. Yang et al., 2018; X. Zhang et al., 2019; Coppola

et al., 2021b). Projections of temperature extremes for the end of 
the 21st century show that warm days and nights are very likely  to 
increase, and cold days and nights are very likely  to decrease in all 
regions. There is medium confidence  in large increases in warm days 
and warm nights in summer, particularly over the USA, and in large 
decreases in cold days in Canada in autumn and winter (Grotjahn 
et al., 2016; Vose et al., 2017; Alexandru, 2018; C. Li et al., 2018, 2021; 
C. Yang et al., 2018; X. Zhang et al., 2019; Coppola et al., 2021b). 
Minimum winter temperatures are projected to rise faster than mean 
winter temperatures (Underwood et al., 2017). Projections for the 
end of the century under RCP8.5 showed the four-day cold spell that 
happens on average once every five years is projected to warm by 
more than 10°C. CMIP5 models do not project current 1-in-20-year 
annual minimum temperature extremes to recur over much of the 
continent (Wuebbles et al., 2014).

In summary, it is virtually certain  that further increases in the intensity 
and frequency of hot extremes, and decreases in the intensity and 
frequency of cold extremes, will occur throughout the 21st century 
and around the world. It is virtually certain  that the number of hot 
days and hot nights and the length, frequency, and/or intensity of 
warm spells or heatwaves compared to 1995–2014 will increase 
over most land areas. In most regions, changes in the magnitude 
of temperature extremes are proportional to global warming levels 
(high confidence ). The highest increase of temperature of hottest

1557
days is projected in some mid-latitude and semi-arid regions, 
at about 1.5 times to twice the rate of global warming ( high 
confidence ). The highest increase of temperature of coldest days is 
projected in Arctic regions, at about three times the rate of global 
warming ( high confidence ). The probability of temperature extremes 
generally increases nonlinearly with increasing global warming 
levels ( high confidence ). Confidence in assessments depends on the 
spatial and temporal scales of the extreme in question, with high 
confidence in projections of temperature-related extremes at global 
and continental scales for daily to seasonal scales. There is high 
confidence  that, on land, the magnitude of temperature extremes 
increases more strongly than global mean temperature.
11.4 Heavy Precipitation
This section assesses changes in heavy precipitation at global and

regional scales. The main focus is on extreme precipitation at a daily 
scale where literature is most concentrated, though extremes of 
shorter (sub-daily) and longer (five-day or more) durations are also 
assessed to the extent the literature allows.
11.4.1 Mechanisms and Drivers
The SREX (Chapter 3, Seneviratne et al., 2012) assessed changes in 
heavy precipitation in the context of the effects of thermodynamic 
and dynamic changes. Box  11.1 assesses thermodynamic and 
dynamic changes in a  warming world to aid the understanding 
of changes in observations and projections in some extremes and 
the sources of uncertainties (see also Section  8.2.3.2). In general, 
warming increases the atmospheric water-holding capacity following 
the Clausius–Clapeyron (C-C) relation. This thermodynamic effect 
results in an increase in extreme precipitation at a similar rate at the 
global scale. On a regional scale, changes in extreme precipitation

are further modulated by dynamic changes (Box 11.1). 
Large-scale modes of variability, such as the North Atlantic Oscillation 
(NAO), El Niño–Southern Oscillation (ENSO), Atlantic Multi-decadal 
Variability (AMV), and Pacific Decadal Variability (PDV) (Annex IV), 
modulate precipitation extremes through changes in environmental 
conditions or embedded storms (Section 8.3.2). Latent heating can 
invigorate these storms (Nie et al., 2018; Z. Zhang et al., 2019a); 
changes in dynamics can increase precipitation intensity above 
that expected from the C-C scaling rate (Sections 8.2.3.2 and 11.7; 
Box  11.1). Additionally, the efficiency of converting atmospheric 
moisture into precipitation can change as a  result of cloud 
microphysical adjustment to warming, resulting in changes in the 
characteristics of extreme precipitation; but changes in precipitation 
efficiency in a warming world are highly uncertain (Sui et al., 2020).

It is difficult to separate the effect of global warming from internal 
variability in the observed changes in the modes of variability 
(Section  2.4). Future projections of modes of variability are highly 
uncertain (Section  4.3.3), resulting in uncertainty in regional 
projections of extreme precipitation. Future warming may amplify 
monsoonal extreme precipitation. Changes in extreme storms, including tropical/extratropical cyclones and severe convective 
storms, result in changes in extreme precipitation (Section  11.7). 
Also,  changes in sea surface temperatures (SSTs) alter land–sea 
contrast, leading to changes in precipitation extremes near coastal 
regions. For example, the projected larger SST increase near the 
coasts of East Asia and India can result in heavier rainfall near these 
coastal areas from tropical cyclones (Mei and Xie, 2016) or torrential 
rains (Manda et al., 2014). The warming in the western Indian Ocean

is associated with increases in moisture surges on the low-level 
monsoon westerlies towards the Indian subcontinent, which may 
lead to an increase in the occurrence of precipitation extremes over 
central India (Krishnan et al., 2016; Roxy et al., 2017). 
Decreases in atmospheric aerosols results in warming and thus an 
increase in extreme precipitation (Samset et al., 2018; Sillmann 
et al., 2019). Changes in atmospheric aerosols also result in dynamic 
changes such as in tropical cyclones (Takahashi et al., 2017; Strong 
et al., 2018). Uncertainty in the projections of future aerosol 
emissions results in additional uncertainty in the heavy precipitation 
projections of the 21st century (Lin et al., 2016). 
There has been new evidence of the effect of local land-use and 
land-cover change on heavy precipitation. There is a growing set of 
literature linking increases in heavy precipitation in urban centres 
to urbanization (Argüeso et al., 2016; Y . Zhang et al., 2019b).

Urbanization intensifies extreme precipitation, especially in the 
afternoon and early evening, over the urban area and its downwind 
region ( medium confidence ) (Box  10.3). There are four possible 
mechanisms: (i) increases in atmospheric moisture due to horizontal 
convergence of air associated with the urban heat island effect (Shastri 
et al., 2015; Argüeso et al., 2016); (ii) increases in condensation due 
to urban aerosol emissions (Han et al., 2011; Sarangi et al., 2017); 
(iii)  aerosol pollution that impacts cloud microphysics (Box  8.1; 
Schmid and Niyogi, 2017); and (iv) urban structures that impede 
atmospheric motion (Shepherd, 2013; Ganeshan and Murtugudde, 
2015; Paul et al., 2018). Other local forcing, including reservoirs 
(Woldemichael et al., 2012), irrigation (Devanand et al., 2019), or 
large-scale land-use and land-cover change (Odoulami et al., 2019), 
can also affect local extreme precipitation.
In summary, precipitation extremes are controlled by both

thermodynamic and dynamic processes. Warming-induced 
thermodynamic change results in an increase in extreme precipitation, 
at a  rate that closely follows the C-C relationship at the global 
scale ( high confidence ). The effects of warming-induced changes 
in dynamic drivers on extreme precipitation are more complicated, 
difficult to quantify, and are an uncertain aspect of projections. 
Precipitation extremes are also affected by forcings other than 
changes in greenhouse gases, including changes in aerosols, land-
use and land-cover change, and urbanization ( medium confidence ). 
11.4.2 Observed Trends
Both SREX (Chapter  3, Seneviratne et al., 2012) and AR5 (IPCC, 
2014 Chapter 2) concluded it was likely that the number of heavy 
precipitation events over land had increased in more regions than

1558
it had decreased, though there were wide regional and seasonal 
variations, and trends in many locations were not statistically 
significant. This assessment has been strengthened with multiple 
studies finding robust evidence  of the intensification of extreme 
precipitation at global and continental scales, regardless of spatial 
and temporal coverage of observations and the methods of data 
processing and analysis.
The average annual maximum precipitation amount in a  day 
(Rx1day) has significantly increased since the mid-20th century over 
land (Du et al., 2019; Dunn et al., 2020) and in the humid and dry 
regions of the globe (Dunn et al., 2020). The percentage of observing 
stations with statistically significant increases in Rx1day is larger 
than expected by chance, while the percentage of stations with 
statistically significant decreases is smaller than expected by chance,

over the global land as a  whole and over North America, Europe, 
and Asia (Figure 11.13; Sun et al., 2021) and over global monsoon 
regions (Zhang and Zhou, 2019) where data coverage is relatively 
good. The addition of the past decade of observational data shows 
a more robust increase in Rx1day over the global land region (Sun 
et al., 2021). Light, moderate, and heavy daily precipitation has all 
intensified in a gridded daily precipitation dataset (Contractor et al., 
2020a). Daily mean precipitation intensities have increased since 
the mid-20th century in a majority of land regions ( high confidence ) 
(Section 8.3.1.3). The probability of precipitation exceeding 50 mm/
day increased during 1961–2018 (Benestad et al., 2019). The globally 
averaged annual fraction of precipitation from days in the top 5% 
(R95pTOT) has also significantly increased (Dunn et al., 2020). The 
increase in the magnitude of Rx1day in the 20th century is estimated

to be at a  rate consistent with C-C scaling with respect to global 
mean temperature (Fischer and Knutti, 2016; Sun et al., 2021). Studies 
on past changes in extreme precipitation of durations longer than 
a  day are more limited, though there are some studies examining 
long-term trends in annual maximum five-day precipitation (Rx5day). 
On global and continental scales, long-term changes in Rx5day are 
similar to those of Rx1day in many aspects (Zhang and Zhou 2019; 
Sun et al., 2021). As discussed below, at the regional scale, changes in 
Rx5day are also similar to those of Rx1day where there are analyses 
of changes in both Rx1day and Rx5day. 
Overall, there is a lack of systematic analysis of long-term trends in 
sub-daily extreme precipitation at the global scale. Often, sub-daily 
precipitation data have only sporadic spatial coverage and are of 
limited length. Additionally, the available data records are far shorter

than needed for a robust quantification of past changes in sub-daily 
extreme precipitation (C. Li et al., 2019a). Despite these limitations, 
there are studies in regions of almost all continents that generally 
indicate intensification of sub-daily extreme precipitation, although 
there remains low confidence  in an overall increase at the global 
scale. Studies include an increase in extreme sub-daily rainfall in   
summer over South Africa (Sen Roy and Rouault, 2013), annually  
in Australia (Guerreiro et al., 2018b), over 23 urban locations in 
India (Ali and Mishra, 2018), in Peninsular Malaysia (Syafrina et al., 
2015), and in eastern China in the summer season during 1971–2013 
(Xiao  et al., 2016). In some regions in Italy (Arnone et al., 2013; 
Libertino et al., 2019) and in the USA during 1950–2011 (Barbero 
et al., 2017), there is also an increase. In general, an increase in sub-daily heavy precipitation results in an increase in pluvial floods over

smaller watersheds (Ghausi and Ghosh, 2020). 
There is a  considerable body of literature examining scaling of 
sub-daily precipitation extremes, conditional on day-to-day air or 
dew-point temperatures (Westra et al., 2014; Fowler et al., 2021). 
This scaling, also termed ‘apparent scaling’ (Fowler et al., 2021), is 
robust when different methodologies are used in different regions, 
ranging between the C-C and two-times the C-C rate (e.g., Formayer 
and Fritz, 2017; Lenderink et al., 2017; Burdanowitz et al., 2019). 
This is confirmed when sub-daily precipitation data collected from 
multiple continents (Lewis et al., 2019) are analysed in a consistent 
manner using different methods (Ali et al., 2021). It has been 
hoped that apparent scaling might be used to help understand past 
and future changes in extreme sub-daily precipitation. However, 
apparent scaling samples multiple synoptic weather states, mixing 
thermodynamic and dynamic factors that are not directly relevant for

climate change responses (Section 8.2.3.2; Prein et al., 2016b; Bao 
et al., 2017; X. Zhang et al., 2017; Drobinski et al., 2018; Sun et al., 
2020). The spatial pattern of apparent scaling is different from those 
of projected changes over Australia (Bao et al., 2017) and North 
America (Sun et al., 2020) in regional climate model simulations. 
It thus remains difficult to use the knowledge about apparent scaling 
to infer past and future changes in extreme sub-daily precipitation 
according to observed and projected changes in local temperature. 
In Africa (Table 11.5), evidence shows an increase in extreme daily 
precipitation for the late half of the 20th century over the continent 
where data are available; there is a  larger percentage of stations 
showing significant increases in extreme daily precipitation than 
decreases (Sun et al., 2021). There are increases in different metrics 
relevant to extreme precipitation in various regions of the continent

(Chaney et al., 2014; Harrison et al., 2019; Dunn et al., 2020; Sun 
et al., 2021). There is an increase in extreme precipitation events in 
Southern Africa (Weldon and Reason, 2014; Kruger et al., 2019) and 
a general increase in heavy precipitation over East Africa, the Greater 
Horn of Africa (Omondi et al., 2014). Over sub-Saharan Africa, 
increases in the frequency and intensity of extreme precipitation 
have been observed over the well-gauged areas during 1950–2013; 
however, this covers only 15% of the total area of sub-Saharan Africa 
(Harrison et al., 2019). There is medium  confidence about the increase 
in extreme precipitation for some regions where observations are 
more abundant , but for Africa as whole, there is low confidence 
because of a general lack of continent-wide systematic analysis, the 
sporadic nature of available precipitation data over the continent, 
and spatially non-homogenous trends in places where data are

available (Donat et al., 2014a; Mathbout et al., 2018b; Alexander 
et al., 2019; Funk et al., 2020).
In Asia (Table 11.8), there is robust evidence  that extreme precipitation 
has increased since the 1950s ( high confidence ), however, this 
is dominated by high spatial variability. Increases in Rx1day and 
Rx5day during 1950–2018 are found over two-thirds of stations. 
The  percentage of stations with statistically significant trends is 
larger than can be expected by chance (Figure  11.13; Sun et al., 
2021). An increase in extreme precipitation has also been observed 
in various regional studies based on different metrics of extreme

1559
precipitation and spatial and temporal coverage of the data. These 
include an increase in daily precipitation extremes over central Asia 
(Hu et al., 2016), most of South Asia (Zahid and Rasul, 2012; Pai et al., 
2015; Sheikh et al., 2015; Adnan et al., 2016; Malik et al., 2016; Dimri 
et al., 2017; Priya et al., 2017; Roxy et al., 2017; Hunt et al., 2018; 
Kim et al., 2019; Wester et al., 2019), the Arabian Peninsula (Rahimi 
and Fatemi, 2019; Almazroui and Saeed, 2020; Atif et al., 2020), 
South East Asia (Siswanto et al., 2015; Supari et al., 2017; Cheong 
et al., 2018); the north-west Himalaya (Malik et al., 2016), parts of 
East Asia (Baek et al., 2017; Nayak et al., 2017; Ye and Li, 2017), the 
western Himalayas since the 1950s (Ridley et al., 2013; Dimri et al., 
2015; Madhura et al., 2015), West and East Siberia, and Russian Far 
East (Donat et al., 2016a). A decrease was found over the eastern

Himalayas (Sheikh et al., 2015; Talchabhadel et al., 2018). Increases 
have been observed over Jakarta (Siswanto et al., 2015), but Rx1day 
over most parts of the Maritime Continent has decreased (Villafuerte 
and Matsumoto, 2015). Trends in extreme precipitation over China 
are mixed with increases and decreases (G. Fu et al., 2013; Jiang 
et al., 2013; Ma et al., 2015; Yin et al., 2015; Xiao et al., 2016) and 
are not significant over China as whole (Jiang et al., 2013; Hu et al., 
2016; Ge et al., 2017; Deng et al., 2018; He and Zhai, 2018; W. Li et al., 
2018a; Tao et al., 2018; M. Liu et al., 2019b; Chen et al., 2021). With 
few exceptions, most South East Asian countries have experienced an 
increase in rainfall intensity, but with a reduced number of wet days 
(Donat et al., 2016a; Cheong et al., 2018; Naveendrakumar et al., 
2019), though large differences in trends exists if the trends are 
estimated from different datasets, including gauge-based, remotely

sensed, and reanalysis data, over a relatively short period (Kim et al., 
2019). There is a significant increase in heavy rainfall (>100 mm day–
1) and a significant decrease in moderate rainfall (5–100 mm day–1) 
in central India during the South Asian monsoon season (Deshpande 
et al., 2016; Roxy et al., 2017). 
In Australasia (Table  11.11), available evidence has not shown 
an increase or a  decrease in heavy precipitation over Australasia 
as a  whole ( medium confidence ), but heavy precipitation tends 
to increase over Northern Australia (particularly the north-west) 
and decrease over the eastern and southern regions (e.g., Jakob and  
Walland, 2016; Guerreiro et al., 2018b; Dey et al., 2019b; Dunn 
et al., 2020; Sun et al., 2021). Available studies that used long-
term observations since the mid-20th  century showed nearly as 
many stations with an increase as those with a decrease in heavy 
precipitation (Jakob and Walland, 2016) or slightly more stations

with a decrease than with an increase in Rx1day and Rx5day (Sun 
et al., 2021), or strong differences in Rx1day trends with increases 
over Northern Australia and Central Australia in general, but mostly 
decreases over Southern Australia and Eastern Australia (Dunn et al., 
2020). Over New Zealand, decreases are observed for moderate–
heavy precipitation events, but there are no significant trends for very 
heavy events (more than 64 mm in a day) for the period 1951–2012. 
The number of stations with an increase in very wet days is similar 
to that with a decrease during 1960–2019 (MfE and Stats NZ, 2020). 
Overall, there is low confidence  in trends in the frequency of heavy 
rain days, with mostly decreases over New Zealand (Harrington and 
Renwick, 2014; Caloiero, 2015). In Central and South America (Table  11.14), evidence shows an 
increase in extreme precipitation, but in general there is low 
confidence; while continent-wide analyses produced wetting trends

are not robust. Rx1day increased at more stations than it decreased 
in South America between 1950 and 2018 (Sun et al., 2021). Over 
the period 1950–2010, both Rx5day and R99p increased over large 
regions of South America, including North-Western South America, 
Northern South America, and South-Eastern South America (Skansi 
et al., 2013). There are large regional differences. A decrease in daily 
extreme precipitation is observed in north-eastern Brazil (Skansi 
et al., 2013; Bezerra et al., 2018; Dereczynski et al., 2020). Trends 
in extreme precipitation indices were not statistically significant 
over the period 1947–2012 within the São Francisco River basin in 
the Brazilian semi-arid region (Bezerra et al., 2018). An increase in 
extreme rainfall is observed in the Amazon with medium confidence  
(Skansi et al., 2013) and in South-Eastern South America with 
high confidence (Skansi et al., 2013; Valverde and Marengo, 2014;

Barros et al., 2015; Ávila et al., 2016; Wu and Polvani, 2017; Lovino 
et al., 2018; Dereczynski et al., 2020). Among all sub-regions, South-
Eastern South America shows the highest rate of increase for rainfall 
extremes, followed by the Amazon (Skansi et al., 2013). Increases 
in the intensity of heavy daily rainfall events have been observed in  
the southern Pacific and in the Titicaca basin (Skansi et al., 2013; 
Huerta and Lavado-Casimiro, 2021). In Southern Central America, 
trends in annual precipitation are generally not significant, although 
small (but significant) increases are found in Guatemala, El Salvador, 
and Panama (Hidalgo et al., 2017). Small positive trends were found 
in multiple extreme precipitation indices over the Caribbean region 
over a  short time period (1986–2010) (Stephenson et al., 2014; 
McLean et al., 2015).
In Europe (Table 11.17), there is robust evidence  that the magnitude 
and intensity of extreme precipitation has very likely  increased since

the 1950s. There is a  significant increase in Rx1day and Rx5day 
during 1950–2018 in Europe as a  whole (Sun et al., 2021, also 
Figure  11.13). The number of stations with increases far exceeds 
those with decreases in the frequency of daily rainfall exceeding its 
90th or 95th percentile in century-long series (Cioffi et al., 2015). The 
five-, 10-, and 20-year events of one-day and five-day precipitation 
during 1951–1960 became more common since the 1950s (van 
den Besselaar et al., 2013). There can be large discrepancies among 
studies and regions and seasons (Croitoru et al., 2013; Willems, 
2013; Casanueva et al., 2014; Roth et al., 2014; Fischer et al., 2015); 
evidence for increasing extreme precipitation is more frequently 
observed for summer and winter, but not in other seasons (Madsen 
et al., 2014; Helama et al., 2018) . An increase is observed in central 
Europe (Volosciuk et al., 2016; Zeder and Fischer, 2020), and in

Romania (Croitoru et al., 2016). Trends in the Mediterranean region 
are in general not spatially consistent (Reale and Lionello, 2013), with 
decreases in the western Mediterranean and some increases in the 
eastern Mediterranean (Rajczak et al., 2013; Casanueva et al., 2014; 
de Lima et al., 2015; Gajić-Čapka et al., 2015; Sunyer et al., 2015; 
Pedron et al., 2017; Serrano-Notivoli et al., 2018; Ribes et al., 2019). 
In the Netherlands, the total precipitation contributed from extremes 
higher than the 99th percentile doubles per 1°C increase in warming 
(Myhre et al., 2019), though extreme rainfall trends in Northern 
Europe may differ in different seasons (Irannezhad et al., 2017).

1560
In North America (Table  11.20), there is robust evidence  that the 
magnitude and intensity of extreme precipitation has very likely  
increased since the 1950s. Both Rx1day and Rx5day have significantly 
increased in North America during 1950–2018 (Sun et al., 2021, also 
Figure 11.13). There is, however, regional diversity. In Canada, there 
is a  lack of detectable trends in observed annual maximum daily 
(or shorter duration) precipitation (Shephard et al., 2014; Mekis et al., 
2015; Vincent et al., 2018). In the USA, there is an overall increase in 
one-day heavy precipitation, both in terms of intensity and frequency 
(Villarini et al., 2012; Donat et al., 2013b; Wu, 2015; Easterling et al., 
2017; H. Huang et al., 2017; Howarth et al., 2019; Sun et al., 2021), 
except for the southern USA (Hoerling et al., 2016) where internal 
variability may have played a substantial role in the lack of observed

increases. In Mexico, increases are observed in R10mm and R95p 
(Donat et al., 2016a), very wet days over the cities (García-Cueto 
et al., 2019) and in total precipitation (PRCPTOT) and Rx1day (Donat 
et al., 2016b). In Small Islands, there is a lack of evidence showing changes in heavy 
precipitation overall. There were increases in extreme precipitation 
in Tobago from 1985–2015 (Stephenson et al., 2014; Dookie et al., 
2019) and decreases in south-western French Polynesia and the 
southern subtropics ( low confidence ) (Table 11.5; Atlas.10). Extreme 
precipitation leading to flooding in the Small Islands has been 
attributed in part to tropical cyclones, as well as being influenced by 
ENSO (Box 11.5; Khouakhi et al., 2016; Hoegh-Guldberg et al., 2018).
In summary, the frequency and intensity of heavy precipitation have 
likely  increased at the global scale over a majority of land regions 
with good observational coverage. Since 1950, the annual maximum

amount of precipitation falling in a day, or over five consecutive days, 
has likely increased over land regions with sufficient observational 
coverage for assessment, with increases in more regions than 
there are decreases. Heavy precipitation has likely  increased on the 
continental scale over three continents (North America, Europe, and 
Asia) where observational data are more abundant. There is very low 051015Percentage of stations (%)Global land Asia Africa Europe North America South America Australasia
 Significant positive
 Expected percentage of significant stations by chance 
5th25th95th
75th
Median  Significant negative (a) Percentage of stations with significant positive or negative trendsObserved trends in annual maximum daily precipitation (Rx1day)
Figure 11.13 | Signs and significance of the observed trends in annual maximum daily precipitation (Rx1day) during 1950–2018 at 8345 stations with

sufficient data.  (a) Percentage of stations with statistically significant trends in Rx1day; green dots show positive trends and brown dots negative trends. Box and ‘whisker’ 
plots indicate the expected percentage of stations with significant trends due to chance estimated from 1000 bootstrap realizations under a no-trend null hypothesis. The boxes 
mark the median, 25th percentile, and 75th percentile. The upper and lower whiskers show the 97.5th and the 2.5th percentiles, respectively. Maps of stations with positive 
(b) and negative (c) trends. The light colour indicates stations with non-significant trends, and the dark colour stations with significant trends. Significance is determined by 
a two-tailed test conducted at the 5% level. Adapted from Sun et al. (2021). Figure copyright © American Meteorological Society (used with permission). Further details on data 
sources and processing are available in the chapter data table (Table 11.SM.9).

1561
confidence  about changes in sub-daily extreme precipitation due to 
the limited number of studies and available data. 
11.4.3 Model Evaluation
Evaluating climate model competence in simulating heavy 
precipitation extremes is challenging due to a  number of factors, 
including the lack of reliable observations and the spatial scale 
mismatch between simulated and observed data (Avila et al., 2015; 
Alexander et al., 2019). Simulated precipitation represents areal 
means, but station-based observations are conducted at point 
locations and are often sparse. The areal-reduction factor, the ratio 
between pointwise station estimates of extreme precipitation and 
extremes of the areal mean, can be as large as 130% at CMIP6 
resolutions (about 100 km) (Gervais et al., 2014). Hence, the order in 
which gridded station based extreme values are constructed (i.e., if

the extreme values are extracted at the station first and then gridded, 
or if the daily station values are gridded and then the extreme values 
are extracted) represents different spatial scales of extreme  
precipitation and needs to be taken into account in model evaluation 
(Wehner et al. 2020). This aspect has been considered in some studies. 
Reanalysis products are used in place of station observations for their 
spatial completeness as well as spatial-scale comparability (Sillmann 
et al., 2013a; Kim et al., 2020; Li et al., 2021). However, reanalyses 
share similar parametrizations to the models themselves, reducing 
the objectivity of the comparison. 
Different generations of CMIP models have improved over time, 
though quite modestly (Flato et al., 2013; Watterson et al., 2014). 
Improvements in the representation of the magnitude of the Expert 
Team on Climate Change Detection and Indices (ETCCDI) in CMIP5 
over CMIP3 (Sillmann et al., 2013a; Chen and Sun, 2015a) have

been attributed to higher resolution, as higher-resolution models 
represent smaller areas at individual grid boxes. Additionally, the 
spatial distribution of extreme rainfall simulated by high-resolution models is generally more comparable to observations (Sillmann 
et al., 2013b; Kusunoki, 2017, 2018b; Scher et al., 2017) as these 
models tend to produce more realistic storms compared to coarser 
models (Section 11.7.2). Higher horizontal resolution alone improves 
simulation of extreme precipitation in some models (Wehner et al., 
2014; Kusunoki, 2017, 2018b), but this is insufficient in other models 
(Bador et al., 2020) as parametrization also plays a significant role 
(M. Wu et al., 2020). A simple comparison of climatology may not 
fully reflect the improvements of the new models that have more 
comprehensive process formulations (Di Luca et al., 2015). Dittus et al. 
(2016) found that many of the eight CMIP5 models they evaluated

reproduced the observed increase in the difference between areas 
experiencing an extreme high (90%) and an extreme low (10%) 
proportion of the annual total precipitation from heavy precipitation 
(R95p/PRCPTOT) for Northern Hemisphere regions. Additionally, 
CMIP5 models reproduced the relation between changes in extreme 
and non-extreme precipitation: an increase in extreme precipitation 
is at the cost of a decrease in non-extreme precipitation (Thackeray 
et al., 2018), a  characteristic found in the observational record 
(Gu and Adler, 2018).
The CMIP6 models perform reasonably well in capturing large-scale 
features of precipitation extremes, including intense precipitation 
extremes in the intertropical convergence zone (ITCZ), and weak 
precipitation extremes in dry areas in the tropical regions (Li et al., 
2021) but a double-ITCZ bias over the equatorial central and eastern 
Pacific that appeared in CMIP5 models remains (Section 3.3.2.3). There

are also regional biases in the magnitude of precipitation extremes 
(Kim et al., 2020). The models also have difficulties in reproducing 
detailed regional patterns of extreme precipitation, such as over the 
north-east USA (Agel and Barlow, 2020), though they performed 
better for summer extremes over the USA (Akinsanola et al., 2020). The 
comparison between climatologies in the observations and in model 
simulations shows that the CMIP6 and CMIP5 models that have similar 
horizontal resolutions also have similar model evaluation scores, 
and their error patterns are highly correlated (Wehner et al., 2020). 
Figure 11.14 | Multi-model mean bias in annual maximum daily precipitation (Rx1day, %) for the period 1979–2014.  Calculated as the difference between the 
Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean and the average of available observational or reanalysis products including (a) ERA5, (b) HadEX3,

and (c) REGEN. Bias is expressed as the percent error relative to the long-term mean of the respective observational data products. Brown indicates that models are too dry, 
while green indicates that they are too wet. Areas without sufficient observational data are shown in grey. Adapted from Wehner et al. (2020) under the terms of the Creative 
Commons Attribution licence. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1562
In general, extreme precipitation in CMIP6 models tends to be 
somewhat larger than in CMIP5 models (Li et al., 2021), reflecting 
smaller spatial scales of extreme precipitation represented by slightly 
higher-resolution models (Gervais et al., 2014). This is confirmed by 
Kim et al. (2020), who showed that Rx1day and Rx5day simulated 
by CMIP6 models tend to be closer to point estimates of HadEX3 
data (Dunn et al., 2020) than those simulated by CMIP5. Figure 11.14 
shows the multi-model ensemble bias in mean Rx1day over the 
period 1979–2014 from 21 available CMIP6 models when compared 
with observations and reanalyses. Measured by global land root-
mean-square error, the model performance is generally consistent 
across different observed/reanalysis data products for the extreme 
precipitation metric (Figure 11.14). The magnitude of extreme area

mean precipitation simulated by the CMIP6 models is consistently 
smaller than the point estimates of HadEX3, but the model values 
are more comparable to those of areal-mean values (Figure 11.14) 
of the ERA5 reanalysis or REGEN (Contractor et al., 2020b). Taylor-
plot-based performance metrics reveal strong similarities in the 
patterns of extreme precipitation errors over land regions between 
CMIP5 and CMIP6 (Srivastava et al., 2020; Wehner et al., 2020) and 
between annual mean precipitation errors and Rx1day errors for both 
generations of models (Wehner et al., 2020).
In general, there is high confidence that historical simulations 
by CMIP5 and CMIP6 models of similar horizontal resolutions are 
interchangeable in their performance in simulating the observed 
climatology of extreme precipitation .
Studies using regional climate models (RCMs), for example, CORDEX 
(Giorgi et al., 2009) over Africa (Dosio et al., 2015; Klutse et al., 2016;

Pinto et al., 2016; Gibba et al., 2019), Australia, East Asia (Park et al., 
2016), Europe (Prein et al., 2016a; Fantini et al., 2018), and parts 
of North America (Diaconescu et al., 2018) suggest that extreme 
rainfall events are better captured in RCMs compared to their host 
GCMs due to their ability to address regional characteristics, for 
example, topography and coastlines. However, CORDEX simulations 
do not show good skill over South Asia for heavy precipitation, and 
do not add value with respect to their GCM source of boundary 
conditions (Mishra et al., 2014b; S. Singh et al., 2017). The evaluation 
of models in simulating regional processes is discussed in detail in 
Section  10.3.3.4. The high-resolution simulation of mid-latitude 
winter extreme precipitation over land is of similar magnitude to 
point observations. Simulation of summer extreme precipitation has 
a large bias when compared with observations at the same spatial

scale. Simulated extreme precipitation in the tropics also appears to 
be too large, indicating possible deficiencies in the parametrization 
of cumulus convection at this resolution. Indeed, precipitation 
distributions at both daily and sub-daily time scales are much 
improved with a convection-permitting model (Belušić et al., 2020) 
over Western Africa (Berthou et al., 2019b), East Africa (Finney et al., 
2019), North America and Canada (Cannon and Innocenti, 2019; 
Innocenti et al., 2019) and over Belgium in Europe (Vanden Broucke 
In summary, there is  high confidence   in the ability of models to 
capture the large-scale spatial distribution of precipitation extremes 
over land. The magnitude and frequency of extreme precipitation simulated by CMIP6 models are similar to those simulated by CMIP5 
models ( high confidence ). 
11.4.4 Detection and Attribution, Event Attribution
Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10,

IPCC, 2014) concluded with medium confidence  that anthropogenic 
forcing has contributed to a  global-scale intensification of heavy 
precipitation over the second half of the 20th  century. These 
assessments were based on the evidence of anthropogenic influence 
on aspects of the global hydrological cycle, in particular, the human 
contribution to the warming-induced observed increase in atmospheric 
moisture that leads to an increase in heavy precipitation, and limited 
evidence  of anthropogenic influence on extreme precipitation of 
durations of one and five days.
Since AR5 there has been new and robust evidence  and improved 
understanding of human influence on extreme precipitation. In 
particular, detection and attribution analyses have provided consistent 
and robust evidence  of human influence on extreme precipitation 
of one- and five-day durations at global to continental scales. 
The observed increases in Rx1day and Rx5day over the Northern

Hemisphere land area during 1951–2005 can be attributed to the 
effect of combined anthropogenic forcing, including greenhouse 
gases and anthropogenic aerosols, as simulated by CMIP5 models 
and the rate of intensification with regard to warming is consistent 
with C-C scaling (Zhang et al., 2013). This is confirmed to be robust 
when an additional nine years of observational data and the CMIP6 
model simulations were used (Cross-Chapter Box 3.2, Figure 1; Paik 
et al., 2020). The influence of greenhouse gases is attributed as the 
dominant contributor to the observed intensification. The global 
average of Rx1day in the observations is consistent with simulations 
by both CMIP5 and CMIP6 models under anthropogenic forcing, 
but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The 
observed increase in the fraction of annual total precipitation falling 
into the top fifth or top first percentiles of daily precipitation can

also be attributed to human influence at the global scale (Dong 
et al., 2021). The CMIP5 models were able to capture the fraction 
of land experiencing a strong intensification of heavy precipitation 
during 1960–2010 under anthropogenic forcing, but not in unforced 
simulations (Fischer et al., 2014). But the models underestimated 
the observed trends (Borodina et al., 2017a). Human influence also 
significantly contributed to the historical changes in record-breaking 
one-day precipitation (Shiogama et al., 2016). There is also limited 
evidence  of the influences of natural forcing. Substantial reductions in 
Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation 
intensity over the global summer monsoon regions occurred during 
1957–2000 after explosive volcanic eruptions (Paik and Min, 2018). 
The reduction in post-volcanic eruption extreme precipitation in the 
simulations is closely linked to the decrease in mean precipitation, for

which both thermodynamic effects (moisture reduction due to surface 
cooling) and dynamic effects (monsoon circulation weakening) play 
important roles. 
There has been new evidence of human influence on extreme 
precipitation at continental scales, including the detection of

1563
the combined effect of greenhouse gases and aerosol forcing on 
Rx1day and Rx5day over North America, Eurasia, and mid-latitude 
land regions (Zhang et al., 2013) and of greenhouse gas forcing 
in Rx1day and Rx5day in the mid-to-high latitudes, western and 
eastern Eurasia, and the global dry regions (Paik et al., 2020). These 
findings are corroborated by the detection of human influence in the 
fraction of extreme precipitation in the total precipitation over Asia, 
Europe, and North America (Dong et al., 2021). Human influence 
was found to have contributed to the increase in frequency and 
intensity of regional precipitation extremes in North America during 
1961–2010, based on optimal fingerprinting and event attribution 
approaches (Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) 
found the observed latitudinal increase in extreme precipitation

over Europe to be consistent with model-simulated responses to 
Evidence of human influence on extreme precipitation at regional 
scales is more limited and less robust. In north-west Australia, the 
increase in extreme rainfall since 1950 can be related to increased 
monsoonal flow due to increased aerosol emissions, but cannot be 
attributed to an increase in greenhouse gases (Dey et al., 2019a). 
Anthropogenic influence on extreme precipitation in China was 
detected in one study (H. Li et al., 2017), but not in another using 
different detection and data-processing procedures (W. Li et al., 
2018a), indicating the lack of robustness in the detection results. 
A still weak signal-to-noise ratio seems to be the main cause for the 
lack of robustness, as detection would become robust 20 years in 
the future (W. Li et al., 2018a). Krishnan et al. (2016) attributed the 
observed increase in heavy rain events (intensity >100 mm day–1)

in the post-1950s over central India to the combined effects of 
greenhouse gases, aerosols, land-use and land-cover changes, and 
rapid warming of the equatorial Indian Ocean SSTs. Roxy et al. (2017) 
and Devanand et al. (2019) showed that the increase in widespread 
extremes over the South Asian Monsoon during 1950–2015 is due 
to the combined impacts of the warming of the Western Indian 
Ocean (Arabian Sea) and the intensification of irrigation water 
management over India. 
Anthropogenic influence may have affected the large-scale 
meteorological processes necessary for extreme precipitation 
and the localized thermodynamic and dynamic processes, both 
contributing to changes in extreme precipitation events. Several new 
methods have been proposed to disentangle these effects by either 
conditioning on the circulation state or attributing analogues. In 
particular, the extremely wet winter of 2013–2014 in the UK can be

attributed, approximately to the same degree, to both temperature-
induced increases in saturation vapour pressure and changes in the 
large-scale circulation (Vautard et al., 2016; Yiou et al., 2017). There 
are multiple cases indicating that very extreme precipitation may 
increase at a rate more than the C-C rate (7% per 1°C of warming) 
(Pall et al., 2017; Risser and Wehner, 2017; van der Wiel et al., 2017; 
van Oldenborgh et al., 2017; S.-Y .S. Wang et al., 2018). 
Event attribution studies found an influence of anthropogenic 
activities on the probability or magnitude of observed extreme 
precipitation events, including European winters (Schaller et al., 2016; 
Otto et al., 2018b), extreme 2014 precipitation over the northern Mediterranean (Vautard et al., 2015), parts of the USA for individual 
events (Knutson et al., 2014a; Szeto et al., 2015; Eden et al., 2016; 
van Oldenborgh et al., 2017), extreme rainfall in 2014 over Northland,

New Zealand (Rosier et al., 2015) or China (Burke et al., 2016; Sun 
and Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018). However, 
for other heavy rainfall events, studies identified a lack of evidence 
about anthropogenic influences (Imada et al., 2013; Schaller et al., 
2014; Otto et al., 2015c; Siswanto et al., 2015). There are also studies 
where results are inconclusive because of limited reliable simulations 
(Christidis et al., 2013b; Angélil et al., 2016). Overall, both the spatial 
and temporal scales on which extreme precipitation events are 
defined are important for attribution; events defined on larger scales 
have larger signal-to-noise ratios and thus the signal is more readily 
detectable. At the current level of global warming, there is a strong 
enough signal to be detectable for large-scale extreme precipitation 
events, but the chance of detecting such signals for smaller-scale 
events decreases (Kirchmeier-Young et al., 2019).

In summary, most of the observed intensification of heavy precipitation 
over land regions is likely due to anthropogenic influence, for which 
greenhouse gases emissions are the main contributor. New and 
robust evidence  since AR5 includes attribution to human influence 
of the observed increases in annual maximum one-day and five-
day precipitation and in the fraction of annual precipitation falling 
in heavy events. The evidence since AR5 also includes a  larger 
fraction of land showing enhanced extreme precipitation and 
a  larger probability of record-breaking one-day precipitation than 
expected by chance, both of which can only be explained when 
anthropogenic greenhouse gas forcing is considered. Human 
influence has contributed to the intensification of heavy precipitation 
in three continents where observational data are more abundant 
(high confidence ) (North America, Europe and Asia). On the spatial

scale of AR6 regions, there is limited evidence  of human influence on 
extreme precipitation, but new evidence is emerging; in particular, 
studies attributing individual heavy precipitation events  found 
that human influence was a  significant driver of the events,  
particularly in the winter season. 
11.4.5 Projections
The AR5 concluded it is very likely that extreme precipitation events 
will be more frequent and more intense over most of the mid-
latitude land masses and wet tropics in a  warmer world (Collins 
et al., 2013). Post-AR5 studies provide more and robust evidence  
to support the previous assessments. These include an observed 
increase in extreme precipitation (Section 11.4.3) and human causes 
of past changes (Section  11.4.4), as well as projections based on 
either GCM and/or RCM simulations. The CMIP5 models project 
that the rate of increase in Rx1day with warming is independent 
of the forcing scenario (Section  8.5.3.1; Pendergrass et al., 2015)

or forcing mechanism (Sillmann et al., 2017a). This is confirmed 
in CMIP6 simulations (Sillmann et al., 2019; Li et al., 2021). In 
particular, for extreme precipitation that occurs once a year or less 
frequently, the magnitudes of the rates of change per 1°C change 
in global mean temperature are similar, regardless of whether the 
temperature change is caused by increases in carbon dioxide (CO 2),

1564
methane (CH 4), solar forcing, or sulphate (SO 4) (Sillmann et al., 2019). 
In some models – CESM1 in particular – the extreme precipitation 
response to warming may follow a quadratic relation (Pendergrass 
et al., 2019). Figure  11.15 shows changes in the 10- and 50-year 
return values of Rx1day at different warming levels as simulated by 
the CMIP6 models. The median value of the scaling over land, across 
all Shared Socio-economic Pathway (SSP) scenarios and all models, 
is close to 7% per 1°C of warming for the 50-year return value of 
Rx1day. It is just slightly smaller for the 10- and 50-year return 
values of Rx5day (Li et al., 2021). The 90% ranges of the multimodel 
ensemble changes across all land grid boxes in the 50-year return 
values for Rx1day and Rx5day do not overlap between 1.5°C and 
2°C warming levels (Li et al., 2021), indicating that a small increment

such as 0.5°C in global warming can result in a significant increase 
in extreme precipitation. Projected long-period Rx1day return value 
changes are larger than changes in mean Rx1day and with larger 
relative changes for more rare events (Pendergrass, 2018; Mizuta and 
Endo, 2020; Wehner, 2020). The rate of change of moderate extreme 
precipitation may depend more on the forcing agent, similar to the 
mean precipitation response to warming (Lin et al., 2016, 2018). 
Thus, there is high confidence  that extreme precipitation that occurs 
once a year or less frequently increases proportionally to the amount 
of surface warming, and the rate of change in precipitation is not 
dependent on the underlying forcing agents of warming.
The spatial patterns of the projected changes across different 
warming levels are quite similar, as shown in Figure  11.16, and confirmed by near-linear scaling between extreme precipitation and

global warming levels at regional scales (Seneviratne and Hauser, 
2020). Internal variability modulates changes in heavy rainfall (Wood 
and Ludwig, 2020), resulting in different changes in different regions 
(Seneviratne and Hauser, 2020). Extreme precipitation nearly always 
increases across land areas with larger increases at higher global 
warming levels, except in very few regions, such as Southern Europe 
around the Mediterranean Basin at low warming levels (Table 11.17). 
The very likely  ranges of the multi-model ensemble changes across 
all land grid boxes in the 50-year return values for Rx1day and 
Rx5day between 1.5°C and 1°C warming levels are above zero for 
all continents except Europe, with the lower bound of the likely 
range above zero over Europe (Li et al., 2021). Decreases in extreme 
precipitation are confined mostly to subtropical ocean areas and are 
highly correlated to decreases in mean precipitation due to storm

track shifts. These subtropical decreases can extend to nearby land 
areas in individual realizations. 
Projected increases in the probability of extreme precipitation of 
fixed magnitudes are nonlinear and show larger increases for more 
rare events (Figures 11.7 and 11.15; Fischer and Knutti, 2015; Kharin 
et al., 2018; Li et al., 2021). The CMIP5 model projected increases 
in the probability of high (99th and 99.9th) percentile precipitation 
between 1.5°C and 2°C warming scenarios are consistent with what 
can be expected based on observed changes (Fischer and Knutti, 
2015), providing confidence in the projections. The CMIP5 model 
simulations show that the frequency for present-day climate 20-year 
extreme precipitation is projected to increase by 10% at the 1.5°C 
global warming level, and by 22% at the 2.0°C global warming 
level, while the increase in the frequency for present-day climate 
100-year extreme precipitation is projected to increase by 20% and

more than 45% at the 1.5°C and 2.0°C warming levels, respectively 
(Kharin et al., 2018). CMIP6 simulations with SSP scenarios show that 
the frequency of 10-year and 50-year events will be approximately 
doubled and tripled, respectively, at a very high warming level of 4°C 
(Figure 11.7; Li et al., 2021). 
There is a limited number of studies on the projections of extreme 
hourly precipitation. The ability of GCMs to simulate hourly 
precipitation extremes is limited (Morrison et al., 2019) and very 
few modelling centres archive sub-daily and hourly precipitation 
prior to CMIP6 experiments. RCM simulations project an increase in 
extreme sub-daily precipitation in North America (C. Li et al., 2019b) 
and Sweden (Olsson and Foster, 2013), but these models still do not 
explicitly resolve convective processes that are important for properly 
simulating extreme sub-daily precipitation. Simulations by RCMs 
that explicitly resolve convective processes (convection-permitting

models) are limited in length and only available in a  few regions 
because of high computing costs. Yet, a  majority of the available 
convection-permitting simulations project increases in the intensities 
of extreme sub-daily precipitation events, with the amount similar to 
or higher than the C-C scaling rate (Kendon et al., 2014, 2019; Ban 
et al., 2015; Prein et al., 2016b; Helsen et al., 2020; Fowler et al., 2021). 
An increase is projected in extreme sub-daily precipitation over Africa 
(Kendon et al., 2019); East Africa (Finney et al., 2020) and Western 
Africa (Berthou et al., 2019a; Fitzpatrick et al., 2020), even for areas 
where parametrized RCMs project a decrease; in Europe (Hodnebrog Figure 11.15 | Projected changes in the intensity of extreme precipitation 
to the 1850–1900 baseline.  Extreme precipitation events are defined as the annual

maximum daily maximum precipitation (Rx1day) that was exceeded on average once 
during a  10-year period (10-year event, blue) and once during a  50-year period 
(50-year event, orange) during the 1850–1900 base period. Results are shown for the 
global land. For each box plot, the horizontal line and the box represent the median 
and central 66% uncertainty range, respectively, of the intensity changes across 
the multi-model median, and the ‘whiskers’ extend to the 90% uncertainty range. The 
results are based on the multi-model ensemble estimated from simulations of global 
climate models contributing to the Coupled Model Intercomparison Project Phase 
6 (CMIP6) under different Shared Socio-economic Pathway forcing scenarios. Based 
on Li et al. (2021). Further details on data sources and processing are available in the 

1565
et al., 2019; Chan et al., 2020); as well as in the continental USA 
(Prein et al., 2016b). Overall, while limited, the available evidence 
points to an increase in extreme sub-daily precipitation in the future. 
Studies on future changes in extreme precipitation for a month or 
longer are limited. One study projects an increase in extreme monthly 
precipitation in Japan under 4°C global warming for around 80% of 
stations in the summer (Hatsuzuka and Sato, 2019). 
In Africa (Table 11.5), extreme precipitation will likely  increase under 
warming levels of 2°C or below (compared to pre-industrial values) 
and very likely  increase at higher warming levels. Simulations by 
CMIP5, CMIP6 and CORDEX regional models project an increase 
in daily extreme precipitation between 1.5°C and 2.0°C warming 
levels. The pattern of change in heavy precipitation under different

scenarios or warming levels is similar with larger increases for 
higher warming levels (e.g., Nikulin et al., 2018; Li et al., 2021). With 
increases in warming, extreme precipitation is projected to increase 
in the majority of land regions in Africa (Mtongori et al., 2016; Pfahl 
et al., 2017; Diedhiou et al., 2018; Dunning et al., 2018; Akinyemi 
and Abiodun, 2019; Giorgi et al., 2019). Over Southern Africa, heavy 
precipitation will likely  increase by the end of the 21st century under 
RCP 8.5 (Dosio, 2016; Pinto et al., 2016; Abiodun et al., 2017; Dosio 
et al., 2019). However, heavy rainfall amounts are projected to 
decrease over western South Africa (Pinto et al., 2018) as a result 
of a projected decrease in the frequency of the prevailing westerly 
winds south of the continent that translates into fewer cold fronts 
and closed mid-latitudes cyclones (Engelbrecht et al., 2009; Pinto 
et al., 2018). Heavy precipitation will likely  increase by the end of the

century under RCP8.5 in West Africa (Diallo et al., 2016; Dosio, 2016; 
Sylla et al., 2016; Abiodun et al., 2017; Akinsanola and Zhou, 2019; 
Dosio et al., 2019) and is projected to increase ( high confidence ) 
in Central Africa (Fotso-Nguemo et al., 2018, 2019; Sonkoué et al., 
2019) and eastern Africa (Thiery et al., 2016; Ongoma et al., 2018a). 
In north-east and central east Africa, extreme precipitation intensity 
is projected to increase across CMIP5, CMIP6 and CORDEX-CORE 
(high confidence ) in most areas annually (Coppola et al., 2021a), 
but the trends differ from season to season in all future scenarios 
(Dosio et al., 2019). In northern Africa, there is low confidence  in 
the projected changes in heavy precipitation, either due to a lack of 
agreement among studies on the sign of changes (Sillmann et al., 
2013a; Giorgi et al., 2014) or due to insufficient evidence. 
In Asia (Table 11.8), extreme precipitation will likely increase at global

warming levels of 2°C and below, but very likely increase at higher 
warming levels for the region as whole. The CMIP6 multi-model 
median projects an increase in the 10- and 50-year return values 
of Rx1day and Rx5day over more than 95% of regions, even at the 
2°C warming level, with larger increases at higher warming levels, 
independent of emissions scenarios (Li et al., 2021, also Figure 11.7). 
The CMIP5 models produced similar projections. Both heavy rainfall 
and rainfall intensity are projected to increase (Zhou et al., 2014; Guo 
et al., 2016, 2018; Y . Xu et al., 2016; Endo et al., 2017; Han et al., 2018; 
G. Kim et al., 2018). A half-degree difference in warming between the 
1.5°C and 2.0°C warming levels can result in a detectable increase 
in extreme precipitation over the region (Li et al., 2021), in the 
Asian–Australian monsoon region (Chevuturi et al., 2018), and over

South Asia and China (D. Lee et al., 2018; W. Li et al., 2018b). While there are regional differences, extreme precipitation is projected 
to increase in almost all sub-regions, though there can be spatial 
heterogeneity within sub-regions, such as in India (Shashikanth et al., 
2018) and South East Asia (Ohba and Sugimoto, 2019). In East and 
South East Asia, there is high confidence that extreme precipitation 
is projected to intensify (Seo et al., 2014; Zhou et al., 2014; Y . Xu 
et al., 2016; Nayak et al., 2017; X. Wang et al., 2017; Y . Wang et al., 
2017; Guo et al., 2018; D. Li et al., 2018; Sui et al., 2018). Extreme 
daily precipitation is also projected to increase in South Asia (Xu 
et al., 2017; Han et al., 2018; Shashikanth et al., 2018). The extreme 
precipitation indices, including Rx5day, R95p, and days of heavy 
precipitation (i.e., R10mm), are all projected to increase under the 
RCP4.5 and RCP8.5 scenarios in central and northern Asia (Xu et al.,

2017; Han et al., 2018). A general wetting across the whole Tibetan 
Plateau and the Himalayas is projected, with increases in heavy 
precipitation in the 21st  century (Palazzi et al., 2013; Zhou et al., 
2014; Rajbhandari et al., 2015; R. Zhang et al., 2015; Wu et al., 2017; 
Gao et al., 2018; Paltan et al., 2018). Agreement in projected changes 
by different models is low in regions of complex topography such 
as Hindu-Kush Himalayas (Roy et al., 2019), but CMIP5, CMIP6 and 
CORDEX-CORE simulations consistently project an increase in heavy 
precipitation in higher latitude areas, such as West and East Siberia, 
and Russian Far East ( high confidence ) (Coppola et al., 2021a). 
In Australasia (Table  11.11), most CMIP5 models project an 
increase in Rx1day under RCP4.5 and RCP8.5 scenarios for the late 
21st century (CSIRO and BOM, 2015; Alexander and Arblaster, 2017; 
Grose et al., 2020) and the CMIP6 multi-model median projects an

increase in the 10- and 50-year return values of Rx1day and Rx5day 
at a rate between 5% and 6% per 1°C of near-surface global mean 
warming (Figure 11.7; Li et al., 2021). Yet, there is large uncertainty 
in the increase because projected changes in dynamic processes 
lead to a  decrease in Rx1day that can offset the thermodynamic 
increase over a large portion of the region (Box 11.1, Figure 1; Pfahl 
et al., 2017). Projected changes in moderate extreme precipitation 
(the 99th percentile of daily precipitation) by RCMs under RCP8.5 
for 2070–2099 are mixed, with more regions showing decreases 
than increases (Evans et al., 2021). It is likely  that daily rainfall 
extremes such as Rx1day will increase at the continental scale for 
global warming levels at or above 3°C. Daily rainfall extremes are 
projected to increase at the 2.0°C global warming level ( medium 
confidence ), and there is low confidence in changes at the 1.5°C .

Projected changes show important regional differences with very 
likely  increases over Northern Australia (Alexander and Arblaster, 
2017; Herold et al., 2018; Grose et al., 2020) and New Zealand (MfE, 
2018) where projected dynamic contributions are small (Box  11.1 
Figure 1; Pfahl et al., 2017) and medium confidence  on increases over 
central, eastern, and Southern Australia where dynamic contributions 
are substantial and can affect local phenomena (CSIRO and BOM, 
2015; Pepler et al., 2016; Bell et al., 2019; Dowdy et al., 2019). 
In Central and South America (Table  11.14), extreme precipitation 
will likely  increase at global warming levels of 2°C and below, but 
very likely increase at higher warming levels for the region as whole. 
A  larger increase in global surface temperature leads to a  larger 
increase in extreme precipitation, independent of emissions scenarios 
(Li et al., 2021). But there are regional differences in the projection,

1566
and projected changes for more moderate extreme precipitation 
are also more uncertain. Extreme precipitation, represented by 
the number of days with daily precipitation exceeding 50 mm and the 
annual fraction of precipitation falling during days with the top 10% 
daily precipitation amount, is projected to increase on the eastern 
coast of Southern Central America, but to decrease along the Pacific 
coasts of El Salvador and Guatemala (Imbach et al., 2018). Chou et al. 
(2014b) and Giorgi et al. (2014) projected an increase in extreme 
precipitation over South-Eastern South America and the Amazon. 
Projected changes in moderate extreme precipitation represented 
by the 99th percentile of daily precipitation by different models 
under different emissions scenarios, even at high warming levels, 
are mixed: increases are projected for all regions by the CORDEX-

CORE and CMIP5 simulations, while increases for some regions and 
decreases for other regions are projected by CMIP6 simulations 
(Coppola et al., 2021a). Extreme precipitation is projected to increase 
in the La Plata basin (Cavalcanti et al., 2015; Carril et al., 2016). Taylor 
et al. (2018) projected a decrease in days with intense rainfall in the 
Caribbean under 2°C global warming by the 2050s under RCP4.5 
relative to 1971–2000. 
In Europe (Table 11.17), extreme precipitation will likely increase at 
global warming levels of 2°C and below, but very likely increase for 
higher warming levels for the region as whole. The CMIP6 multi-
model median projects an increase in the 10- and 50-year return 
values of Rx1day and Rx5day over a majority of the region at the 2°C 
global warming level, with more than 95% of the region showing 
an increase at higher warming levels (Figure 11.7; C. Li et al., 2021). 
The most intense precipitation events observed today in Europe are

projected to almost double in occurrence for each 1°C of further global 
warming (Myhre et al., 2019). Extreme precipitation is projected to 
increase in both boreal winter and summer over Europe (Madsen 
et al., 2014; Christensen et al., 2015; Nissen and Ulbrich, 2017). There 
are regional differences, with decreases or no change for the southern part of Europe, such as the southern Mediterranean (Tramblay and 
Somot, 2018; Lionello and Scarascia, 2020; Coppola et al., 2021a), 
uncertain changes over central Europe (Argüeso et al., 2012; Croitoru 
et al., 2013; Rajczak et al., 2013; Casanueva et al., 2014; Patarčić 
et al., 2014; Paxian et al., 2014; Roth et al., 2014; Fischer and Knutti, 
2015; Monjo et al., 2016) and a  strong increase in the remaining 
parts, including the Alps region (Gobiet et al., 2014; Donnelly et al., 
2017), particularly in winter (Fischer et al., 2015), and in northern 
Europe. In a 3°C warmer world, there will be a robust increase in

extreme rainfall over 80% of land areas in northern Europe (Madsen 
et al., 2014; Donnelly et al., 2017; Cardell et al., 2020). 
In North America (Table 11.20), the intensity and frequency of extreme 
precipitation will likely increase at the global warming levels of 
2°C and below, and very likely increase at higher warming  levels. 
An increase is projected by CMIP6 model simulations (Li et al., 
2021) and by previous model generations (Wu, 2015; Easterling 
et al., 2017; Innocenti et al., 2019), as well as by RCMs (Coppola 
et al., 2021a). Projections of extreme precipitation over the southern 
portion of the continent and over Mexico are more uncertain, with 
decreases possible (Sillmann et al., 2013b; Alexandru, 2018; Coppola 
et al., 2021a). 
In summary, heavy precipitation will generally become more frequent 
and more intense with additional global warming. At global warming 
levels of 4°C relative to the pre-industrial, very rare (e.g.,  one in

10 or more years) heavy precipitation events would become more 
frequent and more intense than in the recent past, on the global 
scale ( virtually certain ), and in all continents and AR6 regions: The 
increase in frequency and intensity is extremely likely  for most 
continents and very likely  for most AR6 regions. The likelihood 
is lower at lower global warming levels and for less-rare heavy 
precipitation events.  At the global scale, the intensification of 
heavy precipitation will follow the rate of increase in the maximum Annual maximum daily precipitation change (Rx1day) - median
(a) At 1.5°C global warming (b) At 2.0°C global warming (c) At 4.0°C global warming
127 112 37
Colour
 -40     -30      -20       -10        0          1         2          3         4
Change (%)
Figure 11.16 | Projected changes in annual maximum daily precipitation at (a) 1.5°C, (b) 2°C, and (c) 4°C of global warming compared to the 1850–1900

baseline.  Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic 
Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers on the top right indicate the number of simulations included. Uncertainty is 
represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change; diagonal lines indicate 
regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box 
Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in Rx1day are also displayed in the Interactive Atlas. Further details on data sources and 
processing are available in the chapter data table (Table 11.SM.9).

1567
with 10- and 50-year events to be approximately double and triple, 
respectively, at the 4°C warming level. Increases in the intensity of 
extreme precipitation events at regional scales will depend on the 
amount of regional warming as well as changes in atmospheric 
circulation and storm dynamics leading to regional differences 
in the rate of heavy precipitation changes ( high confidence ). 
11.5 Floods
Floods are the inundation of normally dry land, and are classified 
into types (e.g., pluvial floods, flash floods, river floods, groundwater 
floods, surge floods, coastal floods) depending on the space and time

scales and the major factors and processes involved (Section 8.2.3.2; 
Nied et al., 2014; Aerts et al., 2018). Flooded area is difficult to 
measure or quantify and, for this reason, many of the existing studies 
on changes in floods focus on streamflow. Thus, this section assesses 
changes in flow as a proxy for river floods, in addition to some types 
of flash floods. Pluvial and urban floods  – types of flash floods 
resulting from the precipitation intensity exceeding the capacity of 
natural and artificial drainage systems – are directly linked to extreme 
precipitation. Because of this link, changes in extreme precipitation 
are the main proxy for inferring changes in pluvial and urban floods 
(see also Section  12.4), assuming there is no additional change 
in the surface condition. Changes in these types of floods are not 
assessed in this section, but can be inferred from the assessment of 
changes in heavy precipitation in Section 11.4. Coastal floods due to

extreme sea levels and flood changes at regional scales are assessed 
in Section 12.4. 
11.5.1 Mechanisms and Drivers
Since AR5, the number of studies on understanding how floods 
may have changed, and will change in the future, has substantially 
increased. Floods are a complex interplay of hydrology, climate, and 
human management, and the relative importance of these factors 
varies for different flood types and regions. 
In addition to the amount and intensity of precipitation, the main 
factors for river floods include antecedent soil moisture (Paschalis 
et al., 2014; Berghuijs et al., 2016; Grillakis et al., 2016; Woldemeskel 
and Sharma, 2016) and snow water-equivalent in cold regions 
(Sikorska et al., 2015; Berghuijs et al., 2016). Other factors are also 
important, including stream morphology (Borga et al., 2014; Slater 
et al., 2015), river and catchment engineering (Pisaniello et al., 2012; 
Nakayama and Shankman, 2013; Kim and Sanders, 2016), land-use

and land-cover characteristics (Aich et al., 2016; Rogger et al., 2017) 
and changes (Knighton et al., 2019), and feedbacks between climate, 
soil, snow, vegetation, etc. (Hall et al., 2014; Ortega et al., 2014; 
Berghuijs et al., 2016; Buttle et al., 2016; Teufel et al., 2019). Water 
regulation and management have, in general, increased resilience to flooding (Formetta and Feyen, 2019), masking effects of an increase 
in extreme precipitation on flood probability in some regions, even 
though they do not eliminate very extreme floods (Vicente-Serrano 
et al., 2017). This means that an increase in precipitation extremes 
may not always result in an increase in river floods (Sharma et al., 
2018; Do et al., 2020). Yet, as very extreme precipitation can become 
a dominant factor for river floods, there can be some correspondence 
in the changes in very extreme precipitation and river floods (Ivancic 
and Shaw, 2015; Wasko and Sharma, 2017; Wasko and Nathan, 2019).

This has been observed in the western Mediterranean (Llasat et al., 
2016), in China (Q. Zhang et al., 2015a) and in the USA (Peterson 
et al., 2013b; Berghuijs et al., 2016; Slater and Villarini, 2016).
In regions with a seasonal snow cover, snowmelt is the main cause 
of extreme river flooding over large areas (Pall et al., 2019). Extensive 
snowmelt combined with heavy and/or long-duration precipitation 
can cause significant floods (D. Li et al., 2019; Krug et al., 2020). 
Changes in floods in these regions can be uncertain because of 
the compounding and competing effects of the responses of snow 
and rain to warming that affect snowpack size: warming results 
in an increase in precipitation, but also a  reduction in the time 
period of snowfall accumulation (Teufel et al., 2019). An increase in 
atmospheric CO 2 enhances water-use efficiency by plants (Roderick 
et al., 2015; Milly and Dunne, 2016; Swann et al., 2016; Swann,

2018); this could reduce evapotranspiration and contribute to the 
maintenance of soil moisture and streamflow levels under enhanced 
atmospheric CO 2 concentrations (Yang et al., 2019). This mechanism 
would suggest an increase in the magnitude of some floods in the 
future (Kooperman et al., 2018). But this effect is uncertain as an 
increase in leaf area index, and vegetation coverage could also result 
in overall larger water consumption (Mátyás and Sun, 2014; Mankin 
et al., 2019; Teuling et al., 2019), and there are also other CO 2-related 
mechanisms that come into play (Cross-Chapter Box 5.1). 
Various factors, such as extreme precipitation (Cho et al., 2016; Archer 
and Fowler, 2018), glacier lake outbursts (Schneider et al., 2014; 
Schwanghart et al., 2016), or dam breaks (Biscarini et al., 2016) can 
cause flash floods. Very intense rainfall, along with a high fraction 
of impervious surfaces can result in flash floods in urban areas

(Hettiarachchi et al., 2018). Because of this direct connection, changes 
in very intense precipitation can translate to changes in urban flood 
potential (Rosenzweig et al., 2018), though there can be a spectrum 
of urban flood responses to this flood potential (Smith et al., 2013), 
as many factors, such as the overland flow rate and the design of 
urban (Falconer et al., 2009) and storm water drainage systems 
(Maksimović et al., 2009), can play an important role. Nevertheless, 
changes in extreme precipitation are the main proxy for inferring 
changes in some types of flash floods, (which are addressed in 
Section 12.4), given the relation between extreme precipitation and 
pluvial floods, the very limited literature on urban and pluvial floods 
(e.g., Skougaard Kaspersen et al., 2017), and limitations of existing 
methodologies for assessing changes in floods (Archer et al., 2016). 
In summary, there is not always a  one-to-one correspondence

between an extreme precipitation event and a flood event, or between 
changes in extreme precipitation and changes in floods, because 
floods are affected by many factors in addition to heavy precipitation

1568
(high confidence ). Changes in extreme precipitation may be used as 
a proxy to infer changes in some types of flash floods that are more 
directly related to extreme precipitation ( high confidence ). 
11.5.2 Observed Trends
The SREX (Seneviratne et al., 2012) assessed low confidence  for 
observed changes in the magnitude or frequency of floods at the 
global scale. This assessment was confirmed by AR5 (Hartmann et al., 
2013). The SR1.5 (Hoegh-Guldberg et al., 2018) found increases 
in flood frequency and extreme streamflow in some regions, but 
decreases in other regions. While the number of studies on flood 
trends has increased since AR5, and there were also new analyses 
after the release of SR1.5 (Berghuijs et al., 2017; Blöschl et al., 2019; 
Gudmundsson et al., 2019), hydrological literature on observed 
flood changes is heterogeneous, focusing at regional and sub-

regional basin scales, making it difficult to synthesize at the global 
and sometimes regional scales. The vast majority of studies focus on 
river floods using streamflow as a proxy, with limited attention to 
urban floods. Streamflow measurements are not evenly distributed 
over space, with gaps in spatial coverage, and their coverage in many 
regions of Africa, South America, and parts of Asia is poor (e.g., Do 
et al., 2017), leading to difficulties in detecting long-term changes in 
floods (Slater and Villarini, 2017). See also Section 8.3.1.5.
Peak flow trends are characterized by high regional variability and 
lack overall statistical significance of a decrease or an increase over 
the globe as a  whole. Of more than 3500 streamflow stations in 
the USA, central and Northern Europe, Africa, Brazil, and Australia, 
7.1% stations showed a  significant increase, and 11.9% stations 
showed a significant decrease in annual maximum peak flow during

1961–2005 (Do et al., 2017). This is in direct contrast to the global 
and continental scale intensification of short-duration extreme 
precipitation (Section 11.4.2). There may be some consistency over 
large regions (see Gudmundsson et al., 2019), in high streamflows 
(>90th percentile), including a decrease in some regions (e.g., in the 
Mediterranean) and an increase in others (e.g., northern Asia), but 
gauge coverage is often limited. On a continental scale, a decrease 
seems to dominate in Africa (Tramblay et al., 2020) and Australia 
(Ishak et al., 2013; Wasko and Nathan, 2019), an increase in the 
Amazon (Barichivich et al., 2018), and trends are spatially variable 
in other continents (Q. Zhang et al., 2015b; Bai et al., 2016; Do et al., 
2017; Hodgkins et al., 2017). In Europe, flow trends have large spatial 
differences (Hall et al., 2014; Mediero et al., 2015; Kundzewicz et al., 
2018; Mangini et al., 2018), but there appears to be a  pattern of

increase in north-western Europe, and a decrease in southern and 
eastern Europe in annual peak flow during 1960–2000 (Blöschl et al., 
2019). In North America, peak flow has increased in north-east USA 
and decreased in south-west USA (Peterson et al., 2013b; Armstrong 
et al., 2014; Mallakpour and Villarini, 2015; Archfield et al., 2016; Burn 
and Whitfield, 2016; Wehner et al., 2017; Neri et al., 2019). There are 
important changes in the seasonality of peak flows in regions where 
snowmelt dominates, such as northern North America (Burn and 
Whitfield, 2016; Dudley et al., 2017) and Northern Europe (Blöschl 
et al., 2017), corresponding to strong winter and spring warming. In summary, the seasonality of floods has changed in cold regions 
where snowmelt dominates the flow regime in response to warming 
(high confidence ). There is low confidence about peak flow trends over 
past decades on the global scale , but there are regions experiencing

increases, including parts of Asia, Southern South America, north-
east USA, north-western Europe, and the Amazon, and regions 
experiencing decreases, including parts of the Mediterranean, 
Australia, Africa, and south-western USA. 
11.5.3 Model Evaluation
Hydrological models used to simulate floods are structurally diverse 
(Dankers et al., 2014; Mateo et al., 2017; Şen, 2018), often requiring 
extensive calibration since sub-grid processes and land-surface 
properties need to be parametrized, irrespective of the spatial 
resolutions (Döll et al., 2016; Krysanova et al., 2017). The data used 
to drive and calibrate the models are usually of coarse resolution, 
necessitating the use of a wide variety of downscaling techniques 
(Muerth et al., 2013). This adds uncertainty not only to the models 
but also to the reliability of the calibrations. The quality of the flood 
simulations also depends on the spatial scale, as flood processes

are different for catchments of different sizes. It is more difficult to 
replicate flood processes for large basins, as water management and 
water use are often more complex for these basins. 
Studies that use different regional hydrological models show a large 
spread in flood simulations (Dankers et al., 2014; Roudier et al., 
2016; Trigg et al., 2016; Krysanova et al., 2017). Regional models 
reproduce moderate and high flows reasonably well (0.02–0.1 flow 
annual exceedance probabilities), but there are large biases for the 
most extreme flows (0–0.02 annual flow exceedance probability), 
independent of the climatic and physiographic characteristics of the 
basins (S. Huang et al., 2017a). Global-scale hydrological models have 
even more challenges, as they struggle to reproduce the magnitude 
of the flood hazard (Trigg et al., 2016). Also, the ensemble mean of 
multiple models does not perform better than individual models 
(Zaherpour et al., 2018).

The use of hydrological models for assessing changes in floods, 
especially for future projections, adds another dimension of 
uncertainty on top of uncertainty in the driving climate projections, 
including emissions scenarios, and in the driving climate models 
(both RCMs and GCMs) (Arnell and Gosling, 2016; Hundecha 
et al., 2016; Krysanova et al., 2017). The differences in hydrological 
models (Roudier et al., 2016; Thober et al., 2018), as well as post-
processing of climate model output for the hydrological models 
(Muerth et al., 2013; Maier et al., 2018), add to uncertainty for 
flood projections. 
In summary, there is medium confidence that simulations for the most 
extreme flows by regional hydrological models can have large biases. 
Global-scale hydrological models still struggle with reproducing the 
magnitude of floods. Projections of future floods are hampered by 
these difficulties and cascading uncertainties, including uncertainties

in emissions scenarios and the climate models that generate inputs.

1569
11.5.4 Detection and Attribution, Event Attribution
There are very few studies focused on the attribution of long-term 
changes in floods, but there are studies on changes in flood events. 
Most of the studies focus on flash floods and urban floods, which 
are closely related to intense precipitation events (Hannaford, 2015). 
In other cases, event attribution focused on runoff using hydrological 
models, and examples include river basins in the UK (Section 11.4.4; 
Schaller et al., 2016; Kay et al., 2018), the Okavango River in Africa 
(Wolski et al., 2014), and the Brahmaputra River in Bangladesh (Philip 
et al., 2019). Findings about anthropogenic influences vary between 
different regions and basins. For some flood events, the probability of 
high floods in the current climate is lower than in a climate without 
an anthropogenic influence (Wolski et al., 2014), while in other

cases anthropogenic influence leads to more intense floods (Cho 
et al., 2016; Pall et al., 2017; van der Wiel et al., 2017; Philip et al., 
2018a; Teufel et al., 2019). Factors such as land-cover change and 
river management can also increase the probability of high floods 
(Ji et al., 2020). These, along with model uncertainties and the lack 
of studies overall, suggest a  low confidence  in general statements to 
attribute changes in flood events to anthropogenic climate change. 
A few individual regions have been well studied, which allows for 
high confidence  in the attribution of increased flooding in these 
cases. For example, flooding in the UK following increased winter 
precipitation (Schaller et al., 2016; Kay et al., 2018) can be attributed 
to anthropogenic climate change (Schaller et al., 2016; Vautard et al., 
2016; Yiou et al., 2017; Otto et al., 2018b). 
Attributing changes in heavy precipitation to anthropogenic activities

(Section 11.4.4) cannot be readily translated to attributing changes 
in floods to human activities, because precipitation is only one of 
the multiple factors, albeit an important one, that affect floods. For 
example, Teufel et al. (2017) showed that, while human influence 
increased the odds of the flood-producing rainfall for the 2013 
Alberta flood in Canada, it was not detected to have influenced 
the probability of the flood itself. Schaller et al. (2016) showed 
that human influence on the increase in the probability of heavy 
precipitation translated linearly into an increase in the resulting river 
flow of the Thames in the UK in winter 2014, but its contribution to 
the inundation was inconclusive. 
Gudmundsson et al. (2021) compared the spatial pattern of the observed 
regional trends in high river flows (>90th percentile) over 1971–2010 
with that simulated by global hydrological models. The hydrological 
models were driven by outputs of climate model simulations under

all historical forcing and pre-industrial forcing conditions. They found 
complex spatial patterns of extreme river flow trends. They also found 
the observed spatial patterns of trends can be reproduced only if 
anthropogenic climate change is considered, and that simulated effects 
of water and land management cannot reproduce the observed spatial 
pattern of trends. As there is only one study and multiple caveats 
associated with the study, including relatively poor observational 
data coverage, there is low confidence  about human influence on the 
changes in high river flows on the global scale. 
In summary there is low confidence  in the human influence on 
the changes in high river flows on the global scale. In general, there is low confidence in attributing changes in the probability or 
magnitude of flood events to human influence because of a limited 
number of studies, differences in the results of these studies and 
large modelling uncertainties.

11.5.5 Future Projections 
The SREX (Chapter  3, Seneviratne et al., 2012) stressed the low 
availability of studies on flood projections under different emissions 
scenarios, and concluded that there was low confidence  in 
projections of flood events given the complexity of the mechanisms 
driving floods at the regional scale. The AR5 WGII report (Chapter 3, 
Jimenez Cisneros et al., 2014) assessed with medium confidence  the 
pattern of future flood changes, including flood hazards increasing 
over about half of the globe (parts of southern and South East Asia, 
tropical Africa, north-east Eurasia, and South America) and flood 
hazards decreasing in other parts of the world, despite uncertainties 
in GCMs and their coupling to hydrological models. The SR1.5 
(Chapter  3, Hoegh-Guldberg et al., 2018) assessed with medium 
confidence  that global warming of 2°C would lead to an expansion 
of the fraction of global area affected by flood hazards, compared to

conditions at 1.5°C of global warming, as a consequence of changes 
in heavy precipitation. 
The majority of new studies that produce future flood projections 
based on hydrological models do not typically consider aspects 
that are also important to actual flood severity or damages, such 
as flood prevention measures (Neumann et al., 2015; Şen, 2018), 
flood control policies (Barraqué, 2017), and future changes in land 
cover (see also Section  8.4.1.5). At the global scale, Alfieri et al. 
(2017) used downscaled projections from seven GCMs as input to 
drive a hydrodynamic model. They found successive increases in the 
frequency of high floods in all continents except Europe, associated 
with increasing levels of global warming (1.5°C, 2°C, 4°C). These 
results are supported by Paltan et al. (2018), who applied a simplified 
runoff aggregation model forced by outputs from four GCMs. S. Huang 
et al. (2018) used three hydrological models forced with bias-adjusted

outputs from four GCMs to produce projections for four river basins 
including the Rhine, Upper Mississippi, Upper Yellow, and Upper 
Niger under 1.5°C, 2°C, and 3°C global warming. This study found 
diverse projections for different basins, including a  shift towards 
earlier flooding for the Rhine and the Upper Mississippi, a substantial 
increase in flood frequency in the Rhine only under the 1.5°C and 2°C 
scenarios, and a decrease in flood frequency in the Upper Mississippi 
under all scenarios. 
At the continental and regional scales, the projected changes in 
floods are uneven in different parts of the world, but there is a larger 
fraction of regions with an increase than with a decrease over the 
21st  century (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell 
and Gosling, 2016; Döll et al., 2018). These results suggest medium 
confidence  in flood trends at the global scale, but low confidence  in 
projected regional changes. Increases in flood frequency or magnitude

are identified for south-eastern and northern Asia and India ( high 
agreement  across studies), eastern and tropical Africa, and the high 
latitudes of North America ( medium agreement ), while decreasing

1570
frequency or magnitude is found for central and eastern Europe and 
the Mediterranean ( high confidence ), and parts of South America, 
southern and central North America, and south-west Africa ( low 
confidence ) (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell and 
Gosling, 2016; Döll et al., 2018). Over South America, most studies 
based on global and regional hydrological models show an increase 
in the magnitude and frequency of high flows in the western Amazon 
(Guimberteau et al., 2013; Langerwisch et al., 2013; Sorribas et al., 
2016; Zulkafli et al., 2016) and the Andes (Hirabayashi et al., 2013; 
Bozkurt et al., 2018). Section 12.4 provides a detailed assessment of 
regional flood projections. 
In summary, global hydrological models project a  larger fraction 
of land areas to be affected by an increase in river floods than by

a  decrease in river floods ( medium confidence ). There is medium 
confidence that river floods will increase in the western Amazon, 
the Andes, and south-eastern and northern Asia. Regional changes 
in river floods are more uncertain than changes in pluvial floods 
because complex hydrological processes and forcings are involved, 
including land cover change and human water management.
11.6 Droughts
Droughts refer to periods of time with substantially below-
average moisture conditions, usually covering large areas, during 
which limitations in water availability result in negative impacts 
for various components of natural systems and economic sectors 
(Wilhite and Pulwarty, 2017; Ault, 2020). Depending on the 
variables used to characterize it and the systems or sectors being 
impacted, drought may be classified in different types (Figure 8.6 
and Appendix Table 11.A.1) such as meteorological (precipitation 
deficits), agricultural  (e.g.,  crop yield reductions or failure, often

related to soil moisture deficits), ecological (related to plant water 
stress that causes e.g.,  tree mortality), or hydrological  droughts 
(e.g.,  water shortage in streams or storages such as reservoirs, 
lakes, lagoons, and groundwater; see Glossary). The distinction of 
drought types is not absolute, as drought can affect different sub-
domains of the Earth system concomitantly, but sometimes also 
asynchronously, including propagation from one drought type to 
another (Brunner and Tallaksen, 2019). Because of this, drought 
cannot be characterized using a single universal definition (Lloyd-
Hughes, 2014) or directly measured based on a  single variable 
(SREX Chapter  3; Wilhite and Pulwarty, 2017). Drought can 
happen on a wide range of timescales – from ‘flash droughts’ on 
a scale of weeks, and characterized by a sudden onset and rapid 
intensification of drought conditions (Hunt et al., 2014; Otkin et al., 
2018; Pendergrass et al., 2020) to multi-year or decadal rainfall

deficits  – sometimes termed ‘megadroughts’ (see Glossary; Ault 
et al., 2014; Cook et al., 2016b; Garreaud et al., 2017). Droughts 
are often analysed using indices that are measures of drought 
severity, duration and frequency (Sections 8.3.1.6, 8.4.1.6, 12.3.2.6 
and 12.3.2.7, and Table  11.A.1). There are many drought indices 
published in the scientific literature, as also highlighted in SREX 
(SREX Chapter 3). These can range from anomalies in single variables 
(e.g.,  precipitation, soil moisture, runoff, evapotranspiration) to 
indices combining different atmospheric variables. This assessment is focused on changes in physical conditions and 
metrics of direct relevance to droughts: (i) precipitation deficits; 
(ii)  excess of atmospheric evaporative demand (AED); (iii) soil 
moisture deficits; (iv) hydrological deficits; and e) atmospheric-
based indices combining precipitation and AED (Table 11.A.1). In the

regional tables (Section 11.9), the assessment is structured by drought 
types, addressing: (i) meteorological, (ii) agricultural and ecological, 
and (iii) hydrological droughts. Note that the latter two assessments 
directly inform the Chapter  12 assessment on projected regional 
changes in these climatic impact-drivers (Section  12.4). The text 
refers to AR6 region acronyms (Section 11.9, and see Section 1.4.5).
11.6.1 Mechanisms and Drivers
Similar to many other extreme events, droughts occur as 
a combination of thermodynamic and dynamic processes (Box 11.1). 
Thermodynamic processes contributing to drought, which are 
modified by greenhouse gas forcing both at global and regional 
scales, are mostly related to heat and moisture exchanges, and are 
also partly modulated by plant coverage and physiology. They affect, 
for instance, atmospheric humidity, temperature, and radiation, 
which in turn affect precipitation and/or evapotranspiration in some

regions and time frames. However, dynamic processes are particularly 
important to explain drought variability on different time scales, 
from a few weeks (flash droughts) to multiannual (megadroughts). 
There is low confidence  in the effects of greenhouse gas forcing 
on changes in atmospheric dynamic (Section  2.4; Section  4.3.3), 
and on associated changes in drought occurrence.  Thermodynamic 
processes are thus the main driver of drought changes in a warming 
climate ( high confidence ). 
11.6.1.1  Precipitation Deficits
Lack of precipitation is generally the main factor controlling drought 
onset. There is high confidence  that atmospheric dynamics, which 
vary on interannual, decadal and longer time scales, is the dominant 
contributor to variations in precipitation deficits in the majority of 
world regions (Dai, 2013; Miralles et al., 2014b; Seager and Hoerling, 
2014; Burgman and Jang, 2015; Dong and Dai, 2015; Schubert et al.,

2016; Raymond et al., 2018; Baek et al., 2019; Drumond et al., 2019; 
Herrera-Estrada et al., 2019; Gimeno et al., 2020; Mishra, 2020). 
Precipitation deficits are driven by dynamic mechanisms taking 
place on different spatial scales, including synoptic processes  – 
atmospheric rivers and extratropical cyclones, blocking and ridges 
(Section 11.7; Sousa et al., 2017), dominant large-scale circulation 
patterns (Kingston et al., 2015), and global ocean–atmosphere 
coupled patterns such as inter-decadal Pacific Oscillation (IPO), 
Atlantic Multi-decadal Oscillation (AMO) and El Niño–Southern 
Oscillation (ENSO; Dai and Zhao, 2017). These various mechanisms 
occur on different scales, are not independent, and substantially 
interact with one another. Also regional moisture recycling and land–
atmosphere feedbacks play an important role for some precipitation 
anomalies (see below). 
There is high confidence  that land–atmosphere feedbacks play

a substantial or dominant role in affecting precipitation deficits in

1571
some regions (SREX, Chapter 3; Koster et al., 2011; Gimeno et al., 
2012; Taylor et al., 2012; Guillod et al., 2015; Tuttle and Salvucci, 
2016; Santanello Jr. et al., 2018; Haslinger et al., 2019; Herrera-
Estrada et al., 2019). The sign of the feedbacks can be either positive 
or negative, as well as local or non-local (Taylor et al., 2012; Guillod 
et al., 2015; Tuttle and Salvucci, 2016). Earth system models (ESMs) 
tend to underestimate non-local negative soil-moisture–precipitation 
feedbacks (Taylor et al., 2012) and also show high variations in their 
representation in some regions (Berg et al., 2017b). Soil-moisture–
precipitation feedbacks contribute to changes in precipitation 
in climate model projections in some regions, but ESMs display 
substantial uncertainties in their representation, and there is thus 
only low confidence  in these contributions (Berg et al., 2017b; Vogel

et al., 2017, 2018). 
11.6.1.2  Atmospheric Evaporative Demand 
Atmospheric evaporative demand (AED) quantifies the maximum 
amount of actual evapotranspiration (ET) that can happen from land 
surfaces if they are not limited by water availability (Table 11.A.1). 
AED is affected by radiative and aerodynamic components. For this 
reason, the atmospheric dryness, often quantified with the relative 
humidity or the vapour pressure deficit (VPD), is not equivalent to 
the AED, as other variables are also highly relevant, including solar 
radiation and wind speed (Hobbins et al., 2012; McVicar et al., 2012a; 
Sheffield et al., 2012). AED can be estimated using different methods 
(McMahon et al., 2013), and those solely based on air temperature 
(e.g., Hargreaves, Thornthwaite) usually overestimate it in terms of 
magnitude and temporal trends (Sheffield et al., 2012), in particular, 
in the context of substantial background warming. Physically-based

combination methods such as the Penman-Monteith equation are 
more adequate and recommended since 1998 by the United Nations 
Food and Agriculture Oganization (Pereira et al., 2015). For this reason, 
the assessment of this Chapter, when considering atmospheric-
based drought indices, only includes AED estimates using the latter 
(see also Section 11.9). AED is generally higher than ET, since AED 
represents an upper bound for ET. Hence, an AED increase does not 
necessarily lead to increased ET (Milly and Dunne, 2016), in particular 
under drought conditions given soil moisture limitation (Bonan et al., 
2014; Berg et al., 2016; Konings et al., 2017; Stocker et al., 2018). 
In general, AED is highest in regions where ET is lowest (e.g., desert 
areas), further illustrating the decoupling between the two variables 
under limited soil moisture. 
The influence of AED on drought depends on the drought type, 
background climate, the environmental conditions and the moisture

availability (Hobbins et al., 2016, 2017; Vicente-Serrano et al., 
2020a). This influence also includes effects not related to increased 
ET. Under low soil moisture conditions, increased AED increases 
plant stress, enhancing the severity of agricultural and ecological 
droughts (Williams et al., 2013; Allen et al., 2015; McDowell et al., 
2016; Grossiord et al., 2020). Moreover, high VPD impacts overall 
plant physiology; it affects the leaf and xylem safety margins, and 
decreases the sap velocity and plant hydraulic conductance (Fontes 
et al., 2018). VPD also affects the plant metabolism of carbon and, 
if prolonged, it may cause plant mortality via carbon starvation 
(Breshears et al., 2013; Hartmann, 2015). Drought projections based exclusively on AED metrics overestimate changes in soil moisture and 
runoff deficits. Nevertheless, AED also directly impacts hydrological 
drought, as ET from surface waters is not limited (Wurbs and Ayala,

2014; Friedrich et al., 2018; Hogeboom et al., 2018; K. Xiao et al., 2018), 
and this effect increases under climate change projections (W. Wang 
et al., 2018; Althoff et al., 2020). In addition, high AED increases crop 
water consumptions in irrigated lands (García-Garizábal et al., 2014), 
contributing to intensifying hydrological droughts downstream (Fazel 
et al., 2017; Vicente-Serrano et al., 2017).
On subseasonal to decadal scales, temporal variations in AED are 
strongly controlled by circulation variability (Williams et al., 2014; 
Chai et al., 2018; Martens et al., 2018), but thermodynamic processes 
also play a  fundamental role and, under human-induced climate 
change, dominate the changes in AED. Atmospheric warming due 
to increased atmospheric CO 2 concentrations increases AED by 
means of enhanced VPD in the absence of other influences (Scheff 
and Frierson, 2015). Because of the greater warming over land than

over oceans (Sections 2.3.1.1 and 11.3), the saturation pressure of 
water vapour increases more over land than over oceans; oceanic 
air masses advected over land thus contain insufficient water vapour 
to keep pace with the greater increase in saturation vapour pressure 
over land (Sherwood and Fu, 2014; Byrne and O’Gorman, 2018; 
Findell et al., 2019). Land–atmosphere feedbacks are also important 
in affecting atmospheric moisture content and temperature, with 
resulting effects on relative humidity and VPD (Box 11.1; Berg et al., 
2016; Haslinger et al., 2019; S. Zhou et al., 2019).
11.6.1.3  Soil Moisture Deficits
Soil moisture shows an important correlation with precipitation 
variability (Khong et al., 2015; Seager et al., 2019), but ET also plays 
a substantial role in further depleting moisture from soils, in particular 
in humid regions during periods of precipitation deficits (Teuling 
et al., 2013; Padrón et al., 2020). In addition, soil moisture plays

a role in drought self-intensification under dry conditions in which ET 
is decreased and leads to higher AED (Miralles et al., 2019), an effect 
that can also contribute to triggering flash droughts (Otkin et al., 
2016, 2018; DeAngelis et al., 2020; Pendergrass et al., 2020). If soil 
moisture becomes limited, ET is reduced, which may decrease the 
rate of soil drying, but can also lead to further atmospheric dryness 
through various feedback loops (Seneviratne et al., 2010; Miralles 
et al., 2014a, 2019; Teuling, 2018; Vogel et al., 2018; S. Zhou et al., 
2019; Liu et al., 2020). The process is complex since vegetation cover 
plays a role in modulating albedo and in providing access to deeper 
stores of water (both in the soil and groundwater). Also, changes in 
land cover and in plant phenology may alter ET (Sterling et al., 2013; 
Woodward et al., 2014; Frank et al., 2015; Döll et al., 2016; Ukkola 
et al., 2016; Trancoso et al., 2017; Hao et al., 2019; Lian et al., 2020).

Snow depth has strong and direct impacts on soil moisture in many 
systems (Gergel et al., 2017; Williams et al., 2020). 
Soil moisture directly affects plant water stress and ET. Soil moisture 
is the primary factor that controls xylem hydraulic conductance  – 
that is, water uptake in plants (Sperry et al., 2016; Hayat et al., 2019; 
X. Chen et al., 2020). For this reason, soil moisture deficits are the 
main driver of xylem embolism, the primary cause of plant mortality

1572
(Anderegg et al., 2012, 2016; Rowland et al., 2015). Also carbon 
assimilation by plants strongly depends on soil moisture (Hartzell 
et al., 2017), with implications for carbon starvation and plant 
dying if soil moisture deficits are prolonged (Sevanto et al., 2014). 
These mechanisms explain that soil moisture deficits are usually 
more relevant than AED excess to explain gross primary production 
anomalies and vegetation stress, mostly in sub-humid and semi-arid 
regions (Stocker et al., 2018; Liu et al., 2020). High CO 2 concentrations 
are shown to potentially decrease plant ET and increase plant water-
use efficiency, affecting soil moisture levels, but this effect interacts 
with other CO 2 physiological and radiative effects (Section 11.6.5.2 
and Cross-Chapter Box 5.1), and has less relevance under low soil 
moisture (Morgan et al., 2011; Z. Xu et al., 2016; Nackley et al., 2018;

Dikšaitytė et al., 2019). ESMs represent both surface (around 10cm) 
and total column soil moisture, whereby total soil moisture is of more 
direct relevance for root water uptake, in particular by trees. There is 
evidence that surface soil moisture projections are substantially drier 
than total soil moisture projections, and may overestimate drying of 
relevance for most vegetation (Berg et al., 2017a).
11.6.1.4  Hydrological Deficits
Drivers of streamflow and surface water deficits are complex 
and strongly depend on the hydrological system analysed 
(e.g., streamflows in the headwaters, medium course of the rivers, 
groundwater, highly regulated hydrological basins). Soil hydrological 
processes, which control the propagation of meteorological droughts 
throughout different parts of the hydrological cycle (Van Loon and 
Van Lanen, 2012), are spatially and temporally complex (Herrera-
Estrada et al., 2017; S. Huang et al., 2017b) and difficult to quantify

(Van Lanen et al., 2016; Apurv et al., 2017; Caillouet et al., 2017; 
Konapala and Mishra, 2017; Hasan et al., 2019). The physiographic 
characteristics of the basins also affect how droughts propagate 
throughout the hydrological cycle (Van Loon and Van Lanen, 2012; 
Van Lanen et al., 2013; Van Loon, 2015; Konapala and Mishra, 2020; 
Veettil and Mishra, 2020). In addition, the assessment of groundwater 
deficits is very difficult given the complexity of processes that 
involve natural and human-driven feedbacks and interactions with 
the climate system (Taylor et al., 2013). Streamflow and surface 
water deficits are affected by land cover, groundwater and soil 
characteristics (Van Lanen et al., 2013; Van Loon and Laaha, 2015; 
Barker et al., 2016; Tijdeman et al., 2018), as well as human activities 
(water management and demand, damming) and land-use changes 
(Section 11.6.4.3; Van Loon et al., 2016; He et al., 2017; Veldkamp

et al., 2017; J. Wu et al., 2018; Y . Xu et al., 2019; Jehanzaib et al., 
2020). Finally, snow and glaciers are relevant for water resources 
in some regions. For instance, warming affects snowpack levels 
(Dierauer et al., 2019; Huning and AghaKouchak, 2020), as well as 
the timing of snow melt, thus potentially affecting the seasonality 
and magnitude of low flows (Barnhart et al., 2016).
11.6.1.5  Atmospheric-based Drought Indices
Given the difficulties of drought quantification and data constraints, 
atmospheric-based drought indices combining both precipitation 
and AED have been developed, as they can be derived from 
meteorological data that is available in most regions (with few exceptions). These demand/supply indices are not intended to be 
metrics of soil moisture, streamflow or vegetation water stress. 
Because of their reliance on precipitation and AED, they are mostly 
related to the actual water balance in humid regions, in which ET is

not limited by soil moisture and tends towards AED. In water-limited 
regions and in dry  periods everywhere, they constitute an upper 
bound for overall  water-balance  deficits  (e.g.,  of surface waters) 
but are also related to  conditions  conducive  to vegetation stress, 
particularly under soil moisture limitation (Section 11.6.1.2). 
Although there are many atmospheric-based drought indices, two are 
assessed in this chapter: the Palmer Drought Severity Index (PDSI) and 
the Standardized Precipitation Evapotranspiration Index (SPEI). The 
PDSI has been widely used to monitor and quantify drought severity 
(Dai et al., 2018), but is affected by some constraints (SREX Chapter 3; 
Mukherjee et al., 2018a). Although the calculation of the PDSI is based 
on a soil water budget, the PDSI is essentially a climate drought index 
that mostly responds to the precipitation and the AED (van der Schrier 
et al., 2013; Vicente-Serrano et al., 2015; Dai et al., 2018). The SPEI also

combines precipitation and AED, being equally sensitive to these two 
variables (Vicente-Serrano et al., 2015). The SPEI is more sensitive to 
AED than the PDSI (Cook et al., 2014a; Vicente-Serrano et al., 2015), 
although under humid and normal precipitation conditions, the effects 
of AED on the SPEI are small (Tomas-Burguera et al., 2020). Given 
the limitations associated with temperature-based AED estimates 
(Section  11.6.1.2), only studies using the Penman-Monteith-based 
SPEI and PDSI (hereafter SPEI-PM and PDSI-PM) are considered in this 
assessment and in the regional tables in Section 11.9.
11.6.1.6 Relation of Assessed Variables and Metrics 
for Changes in Different Drought Types
This Chapter assesses changes in meteorological drought, 
agricultural and ecological droughts, and hydrological droughts. 
Precipitation-based indices are used for the estimation of changes 
in meteorological droughts, such as the Standardized Precipitation

Index (SPI) and the number of consecutive dry days (CDD). Changes 
in total soil moisture and soil moisture-based drought events are 
used for the estimation of changes in agricultural and ecological 
droughts, complemented by changes in surface soil moisture, water-
balance estimates (precipitation minus ET), and SPEI-PM and PDSI-
PM. For hydrological droughts, changes in low flows are assessed, 
sometimes complemented by changes in mean streamflow.
In summary, different drought types exist and they are associated 
with different impacts and respond differently to increasing 
greenhouse gas concentrations. Precipitation deficits and changes in 
evapotranspiration govern net water availability. A lack of sufficient 
evaporative demand, result in agricultural and ecological drought. Lack 
of runoff and surface water result in hydrological drought. Drought 
events are the result of dynamic and/or thermodynamic processes,

with thermodynamic processes being the main driver of drought 
changes under human-induced climate change ( high confidence ).

1573
11.6.2 Observed Trends
Evidence on observed drought trends was limited at the time of SREX 
(Chapter 3) and AR5 (Chapter 2). The SREX concluded: ‘There is medium 
confidence  that since the 1950s some regions of the world have 
experienced a trend to more intense and longer droughts, in particular 
in southern Europe and west Africa, but in some regions droughts have 
become less frequent, less intense, or shorter, for example, in Central 
North America and north-western Australia.’ The assessment at the 
time did not distinguish between different drought types. This Chapter 
includes numerous updates on observed drought trends, associated 
with extensive new literature and longer datasets since AR5. 
11.6.2.1  Precipitation Deficits
Strong precipitation deficits have been recorded in recent decades 
in the Amazon (2005, 2010), south-western China (2009–2010),

south-western North America (2011–2014), Australia (1997–2009), 
California (2014), the middle East (2012–2016), Chile (2010–2015), 
the Great Horn of Africa (2011), among others (van Dijk et al., 2013; 
Mann and Gleick, 2015; Rowell et al., 2015; Marengo and Espinoza, 
2016; Dai and Zhao, 2017; Garreaud et al., 2017, 2020; Marengo 
et al., 2017; Brito et al., 2018; Cook et al., 2018). Global studies 
generally show no significant trends in SPI time series (Orlowsky 
and Seneviratne, 2013; Spinoni et al., 2014), and in derived drought 
frequency and severity data (Spinoni et al., 2019), with very few 
regional exceptions (Section  11.9 and Figure  11.17). Long-term 
decreases in precipitation are found in some AR6 regions in Africa 
(Central Africa and East Southern Africa), and several regions in South 
America (North-Eastern South America, South American Monsoon, 
South-Western South America, and Southern South America)

(Section 11.9). Evidence of precipitation-based drying trends is also 
found in Western Africa, consistent with studies based on CDD trends 
(Figure 11.17; Chaney et al., 2014; Donat et al., 2014b; Barry et al., 
2018; Dunn et al., 2020), however, there is a partial recovery of the 
rainfall trends since the 1980s in this region (Section  10.4.2.1). 
Some AR6 regions show a  decrease in meteorological drought, 
including Northern Australia, Central Australia, Northern Europe 
and Central North America (Section 11.9). Other regions either do 
not show substantial trends in long-term meteorological drought, or 
they display mixed signals depending on the considered time frame 
and sub-regions, such as in Southern Australia (Gallant et al., 2013; 
Delworth and Zeng, 2014; Alexander and Arblaster, 2017; Spinoni 
et al., 2019; Dunn et al., 2020; Rauniyar and Power, 2020) and the 
Mediterranean (Camuffo et al., 2013; Gudmundsson and Seneviratne,

2016; Spinoni et al., 2017; Stagge et al., 2017; Caloiero et al., 2018; 
Peña-Angulo et al., 2020b; see also Section 11.9 and Atlas.8.2).
11.6.2.2 Atmospheric Evaporative Demand
In several regions, AED increases have intensified recent drought 
events (Williams et al., 2014, 2020; Seager et al., 2015b; Basara 
et al., 2019; García-Herrera et al., 2019), enhanced vegetation stress 
(Allen et al., 2015; Sanginés de Cárcer et al., 2018; Yuan et al., 2019), 
or contributed to the depletion of soil moisture or runoff through 
enhanced ET ( high confidence ) (Teuling et al., 2013; Padrón et al., 2020). 
Trends in pan evaporation measurements and Penman-Monteith AED estimates provide an indication of possible trends in the influence of 
AED on drought. Given the observed global temperature increases 
(Sections 2.3.1.1 and 11.3) and dominant decrease in relative 
humidity over land areas (Simmons et al., 2010; Willett et al., 2014),

VPD has increased globally (Barkhordarian et al., 2019; Yuan et al., 
2019). Pan evaporation has increased as a  consequence of VPD 
changes in several AR6 regions, such as East Asia (Li et al., 2013; 
Z.  Sun et al., 2018; M.-Z. Yang et al., 2018), Western and Central 
Europe (Mozny et al., 2020), the Mediterranean, (Azorin-Molina et al., 
2015) and Central and Southern Australia (Stephens et al., 2018). 
Nevertheless, there is an important regional variability in observed 
trends, and in other AR6 regions pan evaporation has decreased – 
for example, in North Central America (Breña-Naranjo et al., 2017) 
and in the Tibetan Plateau (C. Zhang et al., 2018)). Physical models 
also show an important regional diversity, with an increase in New 
Zealand (Salinger and Porteous, 2014) and the Mediterranean (Gocic 
and Trajkovic, 2014; Azorin-Molina et al., 2015; Piticar et al., 2016), 
a decrease in South Asia (Jhajharia et al., 2015), and strong spatial

variability in North America (Seager et al., 2015b). This variability is 
driven by the role of other meteorological variables affecting AED. 
Changes in solar radiation as a consequence of solar dimming and 
brightening may affect trends (Section  7.2.2.2; Kambezidis et al., 
2012; Wang and Yang, 2014; Sanchez-Lorenzo et al., 2015). Wind 
speed is also relevant (McVicar et al., 2012b), and studies suggest 
a reduction of the wind speed in some regions (Z. Zhang et al., 2019b) 
that could compensate the role of the VPD increase. Nevertheless, the 
VPD trend seems to dominate the overall AED trends, compared to 
the effects of trends in wind speed and solar radiation (Wang et al., 
2012; Park Williams et al., 2017; Vicente-Serrano et al., 2020a).   
11.6.2.3  Soil Moisture Deficits
There are limited long-term measurements of soil moisture from 
ground observations (Dorigo et al., 2011; Qiu et al., 2016; Quiring 
et al., 2016), which impedes their use in the analysis of trends. Among

the few existing observational studies covering at least two decades, 
several studies have investigated trends in ground soil moisture in 
East Asia (Section 11.9; Chen and Sun, 2015b; Liu et al., 2015; Qiu 
et al., 2016). Alternatively, microwave-based satellite measurements 
of surface soil moisture have also been used to analyse trends (Dorigo 
et al., 2012; Jia et al., 2018). Although there is regional evidence that 
microwave-based soil moisture estimates can capture well drying 
trends in comparison with ground soil moisture observations (Jia 
et al., 2018), there is only medium confidence  in the derived trends, 
since satellite soil moisture data are affected by inhomogeneities 
(Dorigo et al., 2015; Rodell et al., 2018; Preimesberger et al., 2021). 
Furthermore, microwave-based satellites only sense surface soil 
moisture, which differs from root-zone soil moisture (Berg et al., 
2017a), although relationships can be derived between the two

(Brocca et al., 2011). Several studies have also analysed long-term 
soil moisture time series from observation-driven land-surface 
or hydrological models, including land-based reanalysis products 
(Albergel et al., 2013; Jia et al., 2018; Gu et al., 2019b; Markonis 
et al., 2021). Such models have also been used to assess changes in 
land water availability, estimated as precipitation minus ET, which 
is equal to the sum of soil moisture and runoff (Greve et al., 2014; 
Padrón et al., 2020).

1574
Overall, evidence from global studies suggests that several land 
regions have been affected by increased soil moisture drying or water 
balance drying in past decades, despite some spread among products 
(Albergel et al., 2013; Greve et al., 2014; Gu et al., 2019b; Padrón 
et al., 2020). Drying has not only occurred in dry regions but also in 
humid regions (Greve et al., 2014). Some studies have specifically 
addressed changes in soil moisture at regional scale (Section 11.9). 
For AR6 regions, several studies suggest an increase in the frequency 
and areal extent of soil moisture deficits, with examples in East Asia 
(Cheng et al., 2015; Y . Qin et al., 2015; Jia et al., 2018), Western and 
Central Europe (Trnka et al., 2015b), and the Mediterranean (Hanel 
et al., 2018; Moravec et al., 2019; Markonis et al., 2021). Nonetheless, 
some analyses also show no long-term trends in soil drying in some

AR6 regions – for example, in Eastern North America (Park Williams 
et al., 2017) and Central North America (Seager et al., 2019), as well 
as in North Eastern Africa (Kew et al., 2021). The soil moisture drying 
trends identified in both global and regional studies are generally 
related to increases in ET (associated with higher AED) rather than 
decreases in precipitation, as identified on global land for trends in 
water balance in the dry season (Padrón et al., 2020), as well as for 
some regions (Teuling et al., 2013; Cheng et al., 2015; Trnka et al., 
2015a; van Der Linden et al., 2019; X. Li et al., 2020). 
Evidence from observed or observations-derived trends in soil 
moisture and precipitation minus ET, are combined with evidence 
from SPEI and PDSI-PM studies to derive regional assessments of 
changes in agricultural and ecological droughts (Section 11.9). This 
assessment is summarized in Section 11.6.2.6.
11.6.2.4  Hydrological Deficits

There is evidence based on streamflow records of increased 
hydrological droughts in East Asia (D. Zhang et al., 2018) and 
southern Africa (Gudmundsson et al., 2019). In areas of Western 
and Central Europe and Northern Europe, there is no evidence of 
changes in the severity of hydrological droughts since 1950 based on flow reconstructions (Caillouet et al., 2017; Barker et al., 2019) and 
observations (Vicente-Serrano et al., 2019). In the Mediterranean 
region, there is high confidence  in hydrological drought intensification 
(Section  11.9; Giuntoli et al., 2013; Lorenzo-Lacruz et al., 2013; 
Gudmundsson et al., 2019). In south-eastern South America there 
is a  decrease in the severity of hydrological droughts (Rivera and 
Penalba, 2018). In North America, depending on the methods, datasets 
and study periods, there are differences between studies that suggest 
an increase (Shukla et al., 2015; Udall and Overpeck, 2017) versus

a  decrease in hydrological drought frequency (Mo and Lettenmaier, 
2018), but in general there is strong spatial variability (Poshtiri and 
Pal, 2016). Streamflow observation reference networks of near-natural 
catchments have also been used to isolate the effect of climate trends 
on hydrological drought trends in a few regions, but these show limited 
trends in Northern Europe and Western and Central Europe (Stahl et al., 
2010; Bard et al., 2015; Harrigan et al., 2018), North America (Dudley 
et al., 2020) and most of Australia, with the exception of Eastern and 
Southern Australia (X.S. Zhang et al., 2016). Given the low availability 
of observations, there are few studies analysing trends of drought 
severity in the groundwater. Nevertheless, some studies suggest 
a noticeable response of groundwater droughts to climate variability 
(Lorenzo-Lacruz et al., 2017) and increased drought frequency and 
severity associated with warming, probably as a  consequence of

enhanced ET induced by higher AED (Maxwell and Condon, 2016). This 
is supported by studies in Northern Europe (Bloomfield et al., 2019) 
and North America (Condon et al., 2020).
11.6.2.5 Atmospheric-based Drought Indices
Globally, trends in SPEI-PM and PDSI-PM suggest slightly higher 
increases of drought frequency and severity in regions affected by 
drying over the last decades in comparison to the SPI (Dai and Zhao, 
2017; Spinoni et al., 2019; Song et al., 2020), mainly in regions of 
Western and Southern Africa, the Mediterranean and East Asia 
(Figure 11.17), which is consistent with observed soil moisture trends 
(Section 11.6.2.3). These indices suggest that AED has contributed to 
No data
Figure 11.17 | Observed linear trend for (a) consecutive dry days (CDD) during 1960–2018, (b) standardized precipitation index (SPI) and (c) standardized

precipitation-evapotranspiration index (SPEI) during 1951–2016.  CDD data are from the HadEx3 dataset (Dunn et al., 2020), trend calculation of CDD as in Figure 11.9. 
Drought severity is estimated using 12-month SPI (SPI-12) and 12-month SPEI (SPEI-12). SPI and SPEI datasets are from Spinoni et al. (2019). The threshold to identify drought 
episodes was set at -1 SPI/SPEI units. Areas without sufficient data are shown in grey. No overlay indicates regions where the trends are significant at the p = 0.1 level. Crosses 
indicate regions where trends are not significant. For details on the methods see Supplementary Material 11.SM.2. Further details on data sources and processing are available 
in the chapter data table (Table 11.SM.9).

1575
increase the severity of agricultural and ecological droughts compared 
to meteorological droughts (García-Herrera et al., 2019; Williams et al., 
2020), reduce soil moisture during the dry season (Padrón et al., 2020), 
increase plant water stress (Allen et al., 2015; Grossiord et al., 2020; 
Solander et al., 2020) and trigger more severe forest fires (Abatzoglou 
and Williams, 2016; Turco et al., 2019; Nolan et al., 2020). A number 
of regional studies based on these drought indices have also shown 
stronger drying trends in comparison to trends in precipitation-based 
indices in the following AR6 regions (see also Section  11.9): NSA 
(R. Fu et al., 2013; Marengo and Espinoza, 2016), SCA (Hidalgo et al., 
2017), WCA (Tabari and Aghajanloo, 2013; Sharafati et al., 2020), SAS 
(Niranjan Kumar et al., 2013), NEAF (Zeleke et al., 2017), WSAF (Edossa

et al., 2016), NWN and NEN (Bonsal et al., 2013), EAS (Yu et al., 2014; 
Chen and Sun, 2015b; L. Li et al., 2020; Liang et al., 2020; Z. Wu et al., 
2020) and MED (Kelley et al., 2015; Stagge et al., 2017; González-
Hidalgo et al., 2018; Mathbout et al., 2018a). 
11.6.2.6  Synthesis for Different Drought Types
Few AR6 regions show observed increases in meteorological drought 
(Section  11.9), mostly in Africa and South America (NES: high 
confidence ; WAF , CAF , ESAF , SAM, SWS, SSA, SAS: medium confidence ); 
a  few others show a  decrease (WSB, ESB, NAU, CAU, NEU, CNA: 
medium confidence ). There are stronger signals indicating observed 
increases in agricultural and ecological drought (Section 11.9), which 
highlights the role of increased ET, driven by increased AED, for these 
trends (Sections 11.6.2.3 and11.6.2.5). Past increases in agricultural 
and ecological droughts are found on all continents and several 
regions (WAF , CAF , WSAF , ESAF , WCA, ECA, EAS, SAU, MED, WCE,

NES: medium confidence ), while decreases are found only in one 
AR6 region (NAU: medium confidence ). The more limited availability 
of datasets makes it more difficult to assess historical trends in 
hydrological drought at regional scale (Section  11.9). Increasing 
(MED: high confidence ; WAF , EAS, SAU: medium confidence ) and 
decreasing (NEU, SES: medium confidence ) trends in hydrological 
droughts have only been observed in a few regions.
In summary, there is high confidence  that AED has increased on 
average on continents, contributing to increased ET and resulting 
water stress during periods with precipitation deficits, in particular 
during dry seasons. There is medium confidence  in increases in 
precipitation deficits in a few regions of Africa and South America. 
Based on multiple evidence, there is medium confidence  that 
agricultural and ecological droughts have increased in several 
regions on all continents (WAF , CAF , WSAF , ESAF , WCA, ECA, EAS,

SAU, MED, WCE, NES: medium confidence ), while there is only 
medium confidence  in decreases in one AR6 region (NAU). More 
severe hydrological droughts are found in fewer regions (MED: high 
confidence ; WAF , EAS, SAU: medium confidence ). 
11.6.3 Model Evaluation
11.6.3.1  Precipitation Deficits
ESMs generally show limited performance and large spread 
in identifying precipitation deficits and associated long-term trends in comparison with observations (Nasrollahi et al., 2015). 
Meteorological drought trends in the CMIP5 ensemble showed 
substantial disagreements compared with observations (Orlowsky 
and Seneviratne, 2013; Knutson and Zeng, 2018) including 
a  tendency to overestimate drying, in particular in mid- to high 
latitudes (Knutson and Zeng, 2018). The CMIP6 models display 
a better performance in reproducing long-term precipitation trends 
or seasonal dynamics in some studies in Southern South America 
(Rivera and Arnould, 2020), East Asia (Xin et al., 2020), southern

Asia (Gusain et al., 2020), and south-western Europe (Peña-Angulo 
et al., 2020b), but there is still too limited evidence to allow for 
an assessment of possible differences in performance between 
CMIP5 and CMIP6. Furthermore, ESMs are generally found to 
underestimate the severity of precipitation deficits and the dry day 
frequencies in comparison to observations (Fantini et al., 2018; 
Ukkola et al., 2018). This is probably related to shortcomings in 
the simulation of persistent weather events in the mid-latitudes 
(Section  10.3.3.3). ESMs also show a  tendency to underestimate 
precipitation-based drought persistence at monthly to decadal 
time scales (Ault et al., 2014; Moon et al., 2018). The overall inter-
model spread in the projected frequency of precipitation deficits is 
also substantial (Touma et al., 2015; Zhao et al., 2016; Engström 
and Keellings, 2018). Moreover, there are spatial differences in the 
spread, which is higher in the regions where enhanced drought

conditions are projected and under high-emissions scenarios 
(Orlowsky and Seneviratne, 2013). Nonetheless, some event 
attribution studies have concluded that droughts at regional scales 
can be adequately simulated by some climate models (Schaller 
et al., 2016; Otto et al., 2018c).
11.6.3.2  Atmospheric Evaporative Demand
There is only limited evidence  on the evaluation of AED in state-
of-the-art ESMs, which is performed on externally computed AED, 
based on model output (Scheff and Frierson, 2015; Liu and Sun, 
2016, 2017). An evaluation of average AED in 17 CMIP5 ESMs for 
1981–1999 based on potential evaporation show that the models’ 
spatial patterns resemble the observations, but the magnitude of 
potential evaporation displays strong divergence among models 
globally and regionally (Scheff and Frierson, 2015). The evaluation 
of AED in 12  CMIP5 ESMs with pan evaporation observations in 
East Asia for 1961–2000 (Liu and Sun, 2016, 2017) show that

the ESMs capture seasonal cycles well, but that regional AED 
averages are underestimated due to biases in the meteorological 
variables controlling the aerodynamic and radiative components 
of AED. The CMIP5 ESMs also show a  strong underestimation of 
atmospheric drying trends compared to reanalysis data (Douville and 
Plazzotta, 2017).
11.6.3.3  Soil Moisture Deficits
The performance of climate models for representing soil moisture 
deficits shows more uncertainty than for precipitation deficits since, 
in addition to the uncertainties related to cloud and precipitation 
processes, there is uncertainty related to the representation of 
complex soil hydrological and boundary-layer processes (van den 
Hurk et al., 2011; Lu et al., 2019; Quintana-Seguí et al., 2020).

1576
Another limitation is the lack of observations, particularly for soil 
moisture, in most regions (Section  11.6.2.3) and the paucity of 
land surface property data to parametrize land surface models, in 
particular soil types, soil properties and depth (Xia et al., 2015). The 
spatial resolution of models is an additional limitation since the 
representation of some land–atmosphere feedbacks and topographic 
effects requires detailed resolution (Nicolai-Shaw et al., 2015; Van 
Der Linden et al., 2019). In addition to climate models, land surface 
and hydrological models are also used to derive historical and 
projected trends in soil moisture and related land water variables 
(Albergel et al., 2013; Cheng et al., 2015; Gu et al., 2019b; Padrón 
et al., 2020; Markonis et al., 2021; Pokhrel et al., 2021). 
Overall, there are contrasting results on the performance of land

surface models and climate models in representing soil moisture. 
Some studies suggest that soil moisture anomalies are well captured 
by land surface models driven with observation-based forcing 
(Dirmeyer et al., 2006; Albergel et al., 2013; Xia et al., 2014; Balsamo 
et al., 2015; Reichle et al., 2017; Spennemann et al., 2020), but other 
studies report limited agreement in the representation of interannual 
soil moisture variability (Stillman et al., 2016; Yuan and Quiring, 2017; 
Ford and Quiring, 2019) and noticeable seasonal differences in model 
skill in some regions (Xia et al., 2014, 2015). Models with good skill 
can nonetheless display biases in absolute soil moisture (Xia et al., 
2014; Gu et al., 2019a), but these are not necessarily of relevance 
for the simulation of surface water fluxes and drought anomalies 
(Koster et al., 2009). There is also substantial inter-model spread 
(Albergel et al., 2013), particularly for the root-zone soil moisture

(Berg et al., 2017a). 
Regarding the performance of regional and global climate models, an 
evaluation of an ensemble of RCM simulations for Europe (Stegehuis 
et al., 2013) shows that these models display overly strong drying 
in early summer, resulting in an excessive decrease of latent heat 
fluxes, with potential implications for more severe droughts in dry 
environments (Teuling, 2018; van Der Linden et al., 2019). Compared 
with a range of observational ET estimates, CMIP5 models show an 
overestimation of ET on annual scale, but an ET underestimation in 
boreal summer in many Northern Hemisphere mid-latitude regions, 
also suggesting a  tendency towards excessive soil drying (Mueller 
and Seneviratne, 2014), consistent with identified biases in soil-
moisture–temperature coupling (Donat et al., 2018; Vogel et al., 
2018; Selten et al., 2020). Land surface models used in ESMs display 
a bias in their representation of the sensitivity of interannual land

carbon uptake to soil moisture conditions, which appears related to 
a limited range of soil moisture variations compared to observations 
(Humphrey et al., 2018).
For future projections, the spread of soil moisture outputs among 
different ESMs is more important than internal variability and 
scenario uncertainty, and the bias is strongly related to the sign of 
the projected change (Ukkola et al., 2018; Lu et al., 2019; Selten et al., 
2020). The CMIP5 ESMs that project more drying and warming in mid-
latitude regions show a substantial bias in soil-moisture–temperature 
coupling (Donat et al., 2018; Vogel et al., 2018). Although CMIP6 and 
CMIP5 simulations for soil moisture changes are similar overall, some 
differences are found in projections in a few regions (Section 11.9; Cook et al., 2020). There is still limited evidence  to assess whether 
there are substantial differences in model performance in the two 
ensembles, but improvements in modelling aspects relevant for soil

moisture have been reported for precipitation (Section 11.6.3.2), and 
a better performance has been found in CMIP6 for the representation 
of long-term trends in soil moisture in continental USA (Yuan et al., 
2021). Despite the mentioned model limitations, the representation 
of soil moisture processes in ESMs uses physical and biological 
understanding of the underlying processes, which can well represent 
the temporal anomalies associated with temporal variability and 
trends in climate. In summary, there is medium confidence  in the 
representation of soil moisture deficits in ESMs and related land 
surface and hydrological models. 
11.6.3.4  Hydrological Deficits
Streamflow and groundwater are not directly simulated by ESMs, 
which only simulate runoff, but they are generally represented in 
hydrological models (Prudhomme et al., 2014; Giuntoli et al., 2015), 
which are typically driven in a stand-alone manner by observed or

simulated climate forcing. The simulation of hydrological deficits is 
much more problematic than the simulation of mean streamflow or 
peak flows (Fundel et al., 2013; Stoelzle et al., 2013; Velázquez et al., 
2013; Staudinger et al., 2015), since models tend to be too responsive 
to the climate forcing and do not satisfactorily capture low flows 
(Tallaksen and Stahl, 2014). Simulations of hydrological drought 
metrics show uncertainties related to the contribution of both GCMs 
and hydrological models (Bosshard et al., 2013; Giuntoli et al., 2015; 
Samaniego et al., 2017; Vetter et al., 2017), but hydrological models 
forced by the same climate input data also show a  large spread 
(van Huijgevoort et al., 2013; Ukkola et al., 2018). At the catchment 
scale, the hydrological model uncertainty is higher than both GCM 
and downscaling uncertainty (Vidal et al., 2016), and the hydrological 
models show issues in representing drought propagation throughout

the hydrological cycle (Barella-Ortiz and Quintana Seguí, 2019). 
A study on the evaluation of streamflow droughts in seven global 
(hydrological and land surface) models compared with observations 
in near-natural catchments of Europe showed a substantial spread 
among models, an overestimation of the number of drought events, 
and an underestimation of drought duration and drought-affected 
area (Tallaksen and Stahl, 2014). 
11.6.3.5  Atmospheric-based Drought Indices
A number of studies have analysed the ability of models to capture 
drought severity and trends based on climatic drought indices. Given 
the limitations of ESMs in reproducing the dynamic of precipitation 
deficits and AED (11.6.3.1, 11.6.3.2), atmospheric-based drought 
indices derived from ESM data for these two variables are also 
affected by uncertainties and biases. A  comparison of historical 
trends in PDSI-PM for 1950–2014 derived from CMIP3 and CMIP5,

with respective estimates derived from observations (Dai and 
Zhao, 2017) show a  similar behaviour at global scale (long-term 
decrease), but low spatial agreement in the trends except in a few 
regions (Mediterranean, South Asia, north-western USA). In future 
projections, there is an important spread in PDSI-PM and SPEI-PM 
among different models (Cook et al., 2014a).

1577
11.6.3.6  Synthesis for Different Drought Types
The performance of ESMs used to assessed changes in variables 
related to meteorological droughts, agricultural and ecological 
droughts, and hydrological droughts, shows the presence of biases 
and uncertainties compared to observations, but there is medium 
confidence  in their overall performance for assessing drought 
projections given process understanding. Given the substantial inter-
model spread documented for all related variables, the consideration 
of multi-model projections increases the confidence of model-based 
assessments, with only low confidence  in assessments based on 
single models. 
In summary, the evaluation of ESMs, land surface and hydrological 
models for the simulation of droughts is complex, due to the regional 
scale of drought trends, their overall low signal-to-noise ratio, and the

lack of observations in several regions, in particular for soil moisture 
and streamflow. There is medium confidence  in the ability of ESMs 
to simulate trends and anomalies in precipitation deficits and AED, 
and also medium confidence  in the ability of ESMs and hydrological 
models to simulate trends and anomalies in soil moisture and 
streamflow deficits, on global and regional scales. 
11.6.4 Detection and Attribution, Event Attribution
11.6.4.1 Precipitation Deficits
There are only two AR6 regions where there is at least medium 
confidence  that human-induced climate change has contributed 
to changes in meteorological droughts (Section  11.9). In South-
Western South America, there is medium confidence  that human-
induced climate change has contributed to an increase in 
meteorological droughts (Boisier et al., 2016; Garreaud et al., 2020), 
while in Northern Europe, there is medium confidence  that it has 
contributed to a decrease in meteorological droughts (Section 11.9;

Gudmundsson and Seneviratne, 2016). In other AR6 regions, there 
is inconclusive evidence in the attribution of long-term trends, but 
a human contribution to single meteorological events or sub-regional 
trends has been identified in some instances (Section 11.9; see also 
below). In the Mediterranean region, some studies have identified 
a  precipitation decline or increase in meteorological drought 
probability for time frames since the early or mid 20th century, and 
a possible human contribution to these trends (Hoerling et al., 2012; 
Gudmundsson and Seneviratne, 2016; Knutson and Zeng, 2018), also 
on sub-regional scale in Syria from 1930 to 2010 (Kelley et al., 2015). 
On the contrary, other studies have not identified precipitation and 
meteorological drought trends in the region for the long term (Camuffo 
et al., 2013; Paulo et al., 2016; Vicente-Serrano et al., 2021) and also 
from the mid 20th century (Norrant and Douguédroit, 2006; Stagge

et al., 2017). There is evidence of substantial internal variability in 
long-term precipitation trends in the region (Section 11.6.2.1), which 
limits the attribution of human influence on variability and trends 
of meteorological droughts from observational records (Kelley et al., 
2012; Peña-Angulo et al., 2020b). In addition, there are important 
sub-regional trends showing mixed signals (Section 11.9; MedECC, 
2020). The evidence thus leads to an assessment of low confidence  in the attribution of observed short-term changes in meteorological 
droughts in the region (Section 11.9). In North America, the human 
influence on precipitation deficits is complex (Wehner et al., 2017), 
with low confidence in the attribution of long-term changes in 
meteorological drought in AR6 regions (Section 11.9; Lehner et al., 
2018). In Africa there is low confidence  that human influence has 
contributed to the observed long-term meteorological drought

increase in Western Africa (Sections 11.9 and 10.6.2). There is low 
confidence  in the attribution of the observed increasing trends in 
meteorological drought in East Southern Africa, but evidence that 
human-induced climate change has affected recent meteorological 
drought events in the region (Section 11.9). 
Attribution studies for recent meteorological drought events are 
available for various regions. In Western and Central Europe, a multi-
method and multi-model attribution study on the 2015 Central 
European drought did not find conclusive evidence for whether 
human-induced climate change was a driver of the rainfall deficit, 
as the results depended on model and method used (Hauser et al., 
2017). In the Mediterranean region, a human contribution was found 
in the case of the 2014 meteorological drought in the southern Levant 
based on a single-model study (Bergaoui et al., 2015). In Africa, there 
is some evidence of a  contribution of human emissions to single

meteorological drought events, such as the 2015–2017 southern 
African drought (Funk et al., 2018a; Yuan et al., 2018a; Pascale et al., 
2020), and the three-year (2015–2017) drought in the western Cape 
Town region of South Africa (Otto et al., 2018c). An attributable 
signal was not found in droughts that occurred in different years 
with different spatial extents in the last decade in North and South 
Eastern Africa (Marthews et al., 2015; Uhe et al., 2017; Otto et al., 
2018a; Philip et al., 2018b; Kew et al., 2021). However, an attributable 
increase in 2011 long rain failure was identified (Lott et al., 2013). 
Further studies have attributed some African meteorological drought 
events to large-scale modes of variability, such as the strong 2015 
El Niño (Box 11.4; Philip et al., 2018b) and increased SSTs overall 
(Funk et al., 2015a, 2018b). Natural variability was dominant in 
the California droughts of 2011–2012 to 2013–2014 (Seager et al.,

2015a). In Asia, no climate change signal was found in the record dry 
spell over Singapore and Malaysia in 2014 (Mcbride et al., 2015) or 
the drought in central south-west Asia in 2013–2014 (Barlow and 
Hoell, 2015). Nevertheless, the South East Asia drought of 2015 has 
been attributed to anthropogenic warming effects (Shiogama et al., 
2020). Recent droughts occurring in South America, specifically in 
the southern Amazon region in 2010 (Shiogama et al., 2013) and 
in north-east South America in 2014 (Otto et al., 2015b) and 2016 
(Martins et al., 2018) were not attributed to anthropogenic climate 
change. Nevertheless, the central Chile drought between 2010 
and 2018 has been suggested to be partly associated to global 
warming (Boisier et al., 2016; Garreaud et al., 2020). The 2013 New 
Zealand meteorological drought was attributed to human influence 
by Harrington et al. (2014, 2016) based on fully coupled CMIP5 
models, but no corresponding change in the dry end of simulated

precipitation from a stand-alone atmospheric model was found by 
Angélil et al. (2017).   
Event attribution studies also highlight a  complex interplay of 
anthropogenic and non-anthropogenic climatological factors for

1578
some events. For example, anthropogenic warming contributed to 
the 2014 drought in North Eastern Africa by increasing east African 
and west Pacific temperatures, and increasing the gradient between 
standardized western and central Pacific SSTs, causing reduced 
rainfall (Funk et al., 2015a). As different methodologies, models 
and data sources have been used for the attribution of precipitation 
deficits, Angélil et al. (2017) re-examined several events using 
a  single analytical approach and climate model and observational 
datasets. Their results showed a  disagreement in the original 
anthropogenic attribution in a number of precipitation deficit events, 
which increased uncertainty in the attribution of meteorological 
droughts events. 
11.6.4.2  Soil Moisture Deficits
There is a  growing number of studies on the detection and 
attribution of long-term changes in soil moisture deficits. Mueller

and Zhang (2016) concluded that anthropogenic forcing contributed 
significantly to soil moisture drying in the warm season in the 
Northern Hemisphere from 1951 to 2005 and also led to an increase 
in the land surface area affected by soil moisture deficits, which can 
be reproduced by CMIP5 models only if anthropogenic forcings are 
involved. Gu et al. (2019b) similarly identified a  global-scale soil 
moisture drying tendency in land surface model data from the Global 
Land Data Assimilation System 2 over the time frame 1948–2005, 
which was attributed to anthropogenic forcing based on evaluation 
with CMIP5 models using optimal fingerprinting. Padrón et al. (2019) 
analysed long-term reconstructed and CMIP5 simulated dry season 
water availability, defined as precipitation minus ET (i.e., equivalent 
to soil moisture and runoff availability), also related to agricultural 
and ecological droughts. They found an intensification of dry-season

precipitation minus evapotranspiration deficits over a predominant 
fraction of the land area in the last three decades, which can only be 
explained by anthropogenic forcing and is mostly related to increases 
in ET. Similarly, Williams et al. (2020) concluded that human-
induced climate change contributed to the strong soil moisture 
deficits recorded in the last two decades in Western North America 
through VPD increases associated with higher air temperatures and 
lower air humidity. There are few studies analysing the attribution 
of particular episodes of soil moisture deficits to anthropogenic 
influence. Nevertheless, the available modelling studies coincide 
in supporting an anthropogenic attribution associated with more 
extreme temperatures, exacerbating AED and increasing ET, and 
thus depleting soil moisture, as observed in southern Europe in 2017 
(García-Herrera et al., 2019) and in Australia in 2018 (Lewis et al.,

2020) and 2019 (van Oldenborgh et al., 2021), the latter event having 
strong implications in the propagation of widespread megafires 
(Nolan et al., 2020).  
11.6.4.3 Hydrological Deficits
It is often difficult to separate the role of climate trends from 
changes in land use, water management and demand for changes 
in hydrological deficits, especially on a  regional scale. However, 
a global study based on a recent multi-model experiment with global 
hydrological models and covering several AR6 regions suggests 
a  dominant role of anthropogenic radiative forcing for trends in low, mean and high flows, while simulated effects of water and land 
management do not suffice to reproduce the observed spatial pattern 
of trends (Gudmundsson et al., 2021). Regional studies also suggest 
that climate trends have been dominant compared to land use and 
human water management for explaining trends in hydrological 
droughts in some regions, for instance in Ethiopia (Fenta et al., 2017),

China (Xie et al., 2015), and North America for the Missouri and 
Colorado basins, as well as in California (Shukla et al., 2015; Udall 
and Overpeck, 2017; Ficklin et al., 2018; K. Xiao et al., 2018; Glas 
et al., 2019; Martin et al., 2020; Milly and Dunne, 2020).
In other regions, the influence of human water uses can be more 
important to explain hydrological drought trends (Y . Liu et al., 2016; 
Mohammed and Scholz, 2016). There is medium confidence  that 
human-induced climate change has contributed to an increase of 
hydrological droughts in the Mediterranean (Giuntoli et al., 2013; 
Vicente-Serrano et al., 2014; Gudmundsson et al., 2017), but also 
medium confidence  that changes in land use and terrestrial water 
management contributed to these trends (Section 11.9; Teuling et al., 
2019; Vicente-Serrano et al., 2019). A  global study with a  single 
hydrological model estimated that human water consumption has 
intensified the magnitude of hydrological droughts by 20–40%

over the last 50 years, and that the human water use contribution 
to hydrological droughts was more important than climatic factors 
in the Mediterranean, and central USA, as well as in parts of Brazil 
(Wada et al., 2013). However, Gudmundsson et al. (2021) concluded 
that the contribution of human water use is smaller than that of 
anthropogenic climate change to explain spatial differences in the 
trends of low flows based on a multi-model analysis. There is still 
limited evidence  and thus  low confidence  in assessing these trends at 
the scale of single regions, with few exceptions (Section 11.9).
11.6.4.4  Atmospheric-based Drought Indices
Different studies using atmospheric-based drought indices suggest 
an attributable anthropogenic signal, characterized by the increased 
frequency and severity of droughts (Cook et al., 2018), associated to 
increased AED (Section 11.6.4.2). The majority of studies are based on

the PDSI-PM. Williams et al. (2015) and Griffin and Anchukaitis (2014) 
concluded that increased AED has had an increased contribution to 
drought severity over the last decades, and played a dominant role in 
the intensification of the 2012–2014 drought in California. The same 
temporal pattern and physical mechanism was stressed by Z. Li et al. 
(2017) in central Asia. Marvel et al. (2019) compared tree ring-based 
reconstructions of the PDSI-PM over the past millennium with PDSI-
PM estimates based on output from CMIP5 models. The comparisons 
suggested a contribution of greenhouse gas forcing to the changes 
since the beginning of the 20th century, although characterized with 
temporal differences that could be driven by temporal variations 
in the aerosol forcing. This was in agreement with the dominant 
external forcings of aridification at global scale between 1950 and 
2014 (Bonfils et al., 2020). In the Mediterranean region, there is

medium confidence  of drying attributable to antropogenic forcing 
as a consequence of the strong AED increase (Gocic and Trajkovic, 
2014; Azorin-Molina et al., 2015; Liuzzo et al., 2016; Maček et al., 
2018), which has enhanced the severity of drought events (Vicente-
Serrano et al., 2014; Stagge et al., 2017; González-Hidalgo et al.,

1579
2018). In particular, this effect was identified to be the main driver of 
the intensification of the 2017 drought that affected south-western 
Europe, and was attributed to the human forcing (García-Herrera 
et al., 2019). Nangombe et al. (2020) and L. Zhang et al. (2020) 
concluded from differences between precipitation and AED that 
anthropogenic forcing contributed to the 2018 droughts that affected 
southern Africa and south-eastern China, respectively, principally as 
consequence of the high AED that characterized these two events.
11.6.4.5  Synthesis for Different Drought Types
The regional evidence on attribution for single AR6 regions generally 
shows low confidence  for a human contribution to observed trends 
in meteorological droughts at regional scale, with few exceptions 
(Section 11.9). There is medium confidence that human influence has

contributed to increases in agricultural and ecological droughts in 
the dry season in some regions and has led to an overall increase in the 
affected land area. At regional scales, there is medium confidence  
in a  contribution of human-induced climate change to increases 
in agricultural and ecological droughts in the Mediterranean and 
Western North America (Section 11.9). There is medium confidence  
that human-induced climate change has contributed to an increase 
in hydrological droughts in the Mediterranean region, but also 
medium confidence  in contributions from other human influences, 
including water management and land use (Section  11.9). Several 
meteorological and agricultural and ecological drought events have 
been attributed to human-induced climate change, even in regions 
where no long-term changes are detected ( medium confidence ). 
However, a lack of attribution to human-induced climate change has 
also been shown for some events ( medium confidence ).

In summary, human influence has contributed to increases in 
agricultural and ecological droughts in the dry season in some 
regions due to increases in evapotranspiration ( medium confidence ). 
The increases in evapotranspiration have been driven by increases in 
atmospheric evaporative demand induced by increased temperature, 
decreased relative humidity and increased net radiation over affected 
land areas ( high confidence ). There is low confidence  that human 
influence has affected trends in meteorological droughts in most 
regions, but medium confidence  that they have contributed to the 
severity of some single events. There is medium confidence  that 
human-induced climate change has contributed to increasing trends 
in the probability or intensity of recent agricultural and ecological 
droughts, leading to an increase of the affected land area. Human-
induced climate change has contributed to global-scale change in

low flow, but human water management and land-use changes are 
also important drivers ( medium confidence ).
11.6.5 Projections
The SREX (Chapter 3) asssessed with medium confidence  projections 
of increased drought severity in some regions, including southern 
Europe and the Mediterranean, central Europe, central America and 
Mexico, north-east Brazil, and southern Africa, and low confidence  
elsewhere given large inter-model spread. The AR5 (Chapters 11 and 
12) also assessed large uncertainties in drought projections at the regional and global scales. The assessment of drought mechanisms 
under future climate change scenarios depends on the model used 
(Section  11.6.3). Moreover, uncertainties in drought projections are 
affected by the consideration of plant physiological responses to 
increasing atmospheric CO 2 (Cross-Chapter Box 5.1; Milly and Dunne, 
2016; Greve et al., 2019; Mankin et al., 2019; Yang et al., 2020), the

role of soil-moisture–atmosphere feedbacks for changes in water 
balance and aridity (Berg et al., 2016; Zhou et al., 2021), and statistical 
issues related to considered drought time scales (Vicente-Serrano 
et al., 2020c). Nonetheless, the extensive literature available since AR5 
allows a substantially more robust assessment of projected changes in 
droughts, also subdivided in different drought types (meteorological 
drought, agricultural and ecological drought, and hydrological drought). 
This includes assessments of projected changes in droughts, including 
changes at 1.5°C, 2°C and 4°C of global warming, for all AR6 regions 
(Section 11.9). Projected changes show increases in drought frequency 
and intensity in several regions as function of global warming ( high 
confidence ). There are also substantial increases in drought hazard 
probability from 1.5°C to 2°C global warming and for further additional 
increments of global warming ( high confidence ) (Figures 11.18 and

11.19). These findings are based on both CMIP5 and CMIP6 analyses 
(Section 11.9; Wartenburger et al., 2017; Greve et al., 2018; L. Xu et al., 
2019), and strengthen the conclusions of SR1.5 Chapter 3. 
11.6.5.1 Precipitation Deficits
Studies based on CMIP5, CMIP6 and Coordinated Regional 
Climate Downscaling Experiment (CORDEX) projections show 
a consistent signal in the sign and spatial pattern of projections of 
precipitation deficits. Global studies based on these multi-model 
ensemble projections (Orlowsky and Seneviratne, 2013; Martin, 
2018; Spinoni et al., 2020; Ukkola et al., 2020; Coppola et al., 
2021b) show particularly strong signal-to-noise ratios for increasing 
meteorological droughts in the following AR6 regions: MED, ESAF , 
WSAF , SAU, CAU, NCA, SCA, NSA and NES (Section 11.9). There is also 
substantial evidence of changes in meteorological droughts at 1.5°C 
versus 2°C of global warming from global studies (Wartenburger

et al., 2017; L. Xu et al., 2019). The patterns of projected changes 
in mean precipitation are consistent with the changes in the 
drought duration, but they are not consistent with the changes in 
drought intensity (Ukkola et al., 2020). In general, CMIP6 projections 
suggest a stronger increase of the probability of precipitation deficits 
than CMIP5 projections (Cook et al., 2020; Ukkola et al., 2020). 
Projections for the number of CDDs in CMIP6 (Figure  11.19) for 
different levels of global warming relative to 1850–1900 show similar 
spatial patterns as projected precipitation deficits. The robustness of 
the patterns in projected precipitation deficits identified in the global 
studies is also consistent with results from regional studies (Giorgi 
et al., 2014; Marengo and Espinoza, 2016; Pinto et al., 2016; J. Huang 
et al., 2018; Maúre et al., 2018; Nangombe et al., 2018; Tabari and 
Willems, 2018; Abiodun et al., 2019; Dosio et al., 2019).

In Africa, a  strong increase in the length of dry spells (CDD) is 
projected for 4°C of global warming over most of the continent, with 
the exception of central and eastern Africa (Section 11.9; Sillmann 
et al., 2013a; Giorgi et al., 2014; Han et al., 2019). In West Africa, 
a strong reduction of precipitation is projected (Sillmann et al., 2013a;

1580
Diallo et al., 2016; Akinsanola and Zhou, 2019; Han et al., 2019; Todzo 
et al., 2020) at 4°C of global warming, and CDD would increase with 
stronger global warming levels (Klutse et al., 2018). The regions most 
strongly affected are southern Africa (ESAF , WSAF) (Nangombe et al., 
2018; Abiodun et al., 2019) and northern Africa (part of the MED 
region), with increases in meteorological droughts already at 1.5°C 
of global warming, and further increases with increasing global 
warming (Section  11.9). CDD is projected to increase more in the 
southern Mediterranean (northern Africa) than in the northern part of 
the Mediterranean region (Lionello and Scarascia, 2020).
In Asia, most AR6 regions show low confidence  in projected changes 
in meteorological droughts at 1.5°C and 2°C of global warming, with 
a few regions displaying a decrease in meteorological droughts at

4°C of global warming (RAR, ESB, RFE, ECA; medium confidence ), 
although there is a  projected increase in meteorological droughts 
in South East Asia at 4°C ( medium confidence ) (Section  11.9). In 
South East Asia, an increasing frequency of precipitation deficits is 
projected as a consequence of an increasing frequency of extreme 
El Niño (Cai et al., 2014b, 2015, 2018). 
In Central America, projections suggest an increase in mid-summer 
meteorological drought (Imbach et al., 2018) and increased CDD 
(Chou et al., 2014a; Giorgi et al., 2014; Nakaegawa et al., 2014). In 
the Amazon, there is also a projected increase in dryness (Marengo 
and Espinoza, 2016), which is the combination of a projected increase 
in the frequency and geographic extent of meteorological drought in 
the eastern Amazon, and an opposite trend in the west (Duffy et al., 
2015). In South-Western South America, there is a projected increase

of CDD (Chou et al., 2014a; Giorgi et al., 2014) and in Chile, drying 
is projected to prevail (Boisier et al., 2018). In the South America 
monsoon region, an increase in CDD is projected (Chou et al., 2014a; 
Giorgi et al., 2014), but a decrease is projected in South-Eastern and 
Southern South America (Giorgi et al., 2014). In Central America, 
mid-summer meteorological drought is projected to intensify during 
2071–2095 for the RCP8.5 scenario (Corrales-Suastegui et al., 2020). 
An increase in the frequency, duration and intensity of meteorological 
droughts is projected in south-west, south and east Australia (Kirono 
et al., 2020; Shi et al., 2020). In Canada and most of the USA, based on 
the SPI, Swain and Hayhoe (2015) identified drier summer conditions 
in projections over most of the region, and there is a consistent signal 
toward an increase in duration and intensity of droughts in southern 
North America (Pascale et al., 2016; Escalante-Sandoval and Nuñez-

Garcia, 2017). In California, more precipitation variability is projected, 
characterized by increased frequency of consecutive drought and 
humid periods (Swain et al., 2018). 
Substantial increases in meteorological drought are projected in 
Europe, in particular in the Mediterranean region, already at 1.5°C of 
global warming (Section 11.9). In southern Europe, model projections 
display a consistent drying among models (Russo et al., 2013; Hertig 
and Tramblay, 2017; Guerreiro et al., 2018a; Raymond et al., 2019). In 
Western and Central Europe there is some spread in CMIP5 projections, 
with some models projecting very strong drying, and others close to 
no trend (Vogel et al., 2018), although CDD is projected to increase 
in CMIP5 projections under the RCP 8.5 scenario (Hari et al., 2020). The overall evidence suggests an increase in meteorological drought 
at 4°C in the WCE region ( medium confidence ) (Section 11.9).

Overall, based on global and regional studies, several hot spot 
regions are identified, displaying more frequent and severe 
meteorological droughts with increasing global warming, including 
several AR6 regions at 1.5°C (WSAF , ESAF , SAU, MED, NES) and 
2°C of global warming (WSAF , ESAF , EAU, SAU, MED, NCA, SCA, 
NSA, NES) (Section  11.9). At 4°C of global warming, there is also 
confidence  in increases in meteorological droughts in further regions 
(WAF , WCE, ENA, CAR, NWS, SAM, SWS, SSA; Section 11.9), showing 
a geographical expansion of meteorological drought with increasing 
global warming. Only few regions are projected to have less intense 
or frequent meteorological droughts (Section 11.9).
11.6.5.2 Atmospheric Evaporative Demand
Effects of AED on droughts in future projections is under debate. 
The CMIP5 models project an increase in AED over the majority of 
the world with increasing global warming, mostly as a consequence

of strong VPD increases (Scheff and Frierson, 2015; Vicente-Serrano 
et al., 2020a). However, ET is projected to increase less than AED in 
many regions due to plant physiological responses related to: i) CO 2 
effects on plant photosynthesis; and ii) soil moisture control on ET. 
Several studies suggest that increasing atmospheric CO 2 could 
lead to reduced leaf stomatal conductance, which would increase 
water-use efficiency and reduce plant water needs, thus limiting ET 
(Cross-Chapter Box 5.1; Roderick et al., 2015; Milly and Dunne, 2016; 
Swann et al., 2016; Greve et al., 2017; Scheff et al., 2017; Lemordant 
et al., 2018; Swann, 2018). The implemention of a  CO 2-dependent 
land resistance parameter has been suggested for the estimation 
of AED (Yang et al., 2019). Nevertheless, there are other relevant 
mechanisms, as soil moisture deficits and VPD also play an important 
role in the control of the leaf stomatal conductance (Z. Xu et al., 2016;

Menezes-Silva et al., 2019; Grossiord et al., 2020), and a  number 
of ecophysiological and anatomical processes affect the response 
of plant physiology under higher atmospheric CO 2 concentrations 
(Cross-Chapter Box  5.1; Mankin et al., 2019; Menezes-Silva et al., 
2019). The benefits of the atmospheric CO 2 for plant stress and 
agricultural and ecological droughts would be minimal precisely 
during dry periods given stomatal closure in response to limited 
soil moisture (Allen et al., 2015; Z. Xu et al., 2016). In addition, CO 2 
effects on plant stomatal conductance could not entirely compensate 
for the increased demand associated with warming (Liu and Sun, 
2017); in large tropical and subtropical regions (e.g., southern Africa, 
the Amazon, the Mediterranean and southern North America), AED 
is projected to increase, even considering the possible CO 2 effects 
on land resistance (Vicente-Serrano et al., 2020a). Moreover, these

CO 2 effects would not affect the direct evaporation from soil and 
water bodies, which is very relevant in the reservoirs of warm areas 
(Friedrich et al., 2018). Because of these uncertainties, there is low 
confidence  whether increased CO 2-induced water-use efficiency 
in vegetation will substantially reduce global plant transpiration 
and will diminish the frequency and severity of soil moisture and 
streamflow deficits associated with the radiative effect of higher CO 2 
concentrations (Cross-Chapter Box 5.1).

1581
Another mechanism reducing the ET response to increased AED in 
projections is the control of soil moisture limitations on ET, which 
leads to reduced stomatal conductance under water stress (Berg and 
Sheffield, 2018; Stocker et al., 2018; Zhou et al., 2021). This response 
may be further amplified through VPD-induced decreases in stomatal 
conductance (Anderegg et al., 2020). However, the decreased 
stomatal conductance in response to soil moisture limitation and 
enhanced CO 2 would further enhance AED (Sherwood and Fu, 2014; 
Berg et al., 2016; Teuling, 2018; Miralles et al., 2019), whereby the 
overall effects on AED in ESMs are found to be of similar magnitude 
for soil moisture limitation and CO 2 physiological effects on stomatal 
conductance (Berg et al., 2016). Increased AED is thus both a driver 
and a  feedback with respect to changes in ET, complicating the

interpretation of its role on drought changes with increasing CO 2 
concentrations and global warming. 
11.6.5.3 Soil Moisture Deficits
Areas with projected soil moisture decreases do not fully coincide 
with areas that have projected precipitation decreases, although 
there is substantial consistency in the respective patterns (Dirmeyer 
et al., 2013; Berg and Sheffield, 2018). However, there are more 
regions affected by increased soil moisture deficits (Figure 11.19) than 
precipitation deficits (Figures 2a,b,c and Cross-Chapter Box  11.1) 
as  a  consequence of enhanced AED and the associated increased 
ET, as highlighted by some studies (Orlowsky and Seneviratne, 2013; 
Dai et al., 2018; Section  8.2.2.1). Moisture in the top soil layer is 
projected to decrease more than precipitation at all warming levels (Lu et al., 2019), extending the regions affected by severe soil 
moisture deficits over most of south and central Europe (Lehner

et al., 2017; Ruosteenoja et al., 2018; Samaniego et al., 2018; van Der 
Linden et al., 2019), southern North America (Cook et al., 2019), South 
America (Orlowsky and Seneviratne, 2013), southern Africa (Lu et al., 
2019), East Africa (Rowell et al., 2015), Southern Australia (Kirono 
et al., 2020), India (Mishra et al., 2014a) and East Asia (Figure 11.19; 
Cheng et al., 2015). Projected changes in total soil moisture display 
less widespread drying than those for surface soil moisture (Berg 
et al., 2017a), but still more than for precipitation (Cross-Chapter 
Box 11.1, Figures 2a,b,c). The severity of droughts based on surface 
soil moisture in future projections is stronger than projections 
based on precipitation and runoff (Dai et al., 2018; Vicente-Serrano 
et al., 2020c). Nevertheless, in many parts of the world where soil 
moisture is projected to decrease, the signal-to-noise ratio among 
models is low; only the projections in the Mediterranean, Europe,

the south-western USA, and southern Africa show a  high signal-
to-noise ratio in soil moisture projections (Figure  11.19; Lu et al., 
2019). Increases in soil moisture deficits are found to be statistically 
signicant at regional scale in the Mediterranean region, southern 
Africa and western South America for changes as small as 0.5°C in 
global warming, based on differences between +1.5°C and +2°C of 
global warming (Wartenburger et al., 2017). Several other regions 
are affected when considering changes in droughts for higher 
changes in global warming (Section 11.9 and Figure 11.19). Seasonal 
projections of drought frequency for boreal winter (December–
January–February) and summer (June–July–August), from CMIP6 
multi-model ensemble for 1.5°C, 2°C and 4°C global warming 
Global warming level (°C)0.00.51.01.52.02.5Increase indrought intensit y()Drought intensity (a)

Global warming level (°C)02468Number oftimes asfrequent (-)Drought frequency (b)Changes in10-year soilmoisture drought indrying regions
(c) Drying regions
1.0          1.5        2.0         3.0         4.0 1.0          1.5        2.0         3.0         4.0(140) (132) (119) (78) (39) (140) (132) (119) (78) (39)
Figure 11.18 | Projected changes in (a) the intensity and (b) the frequency of drought under 1°C, 1.5°C, 2°C, 3°C, and 4°C global warming levels relative 
to the 1850–1900 baseline. (c) Summaries are computed for the AR6 regions in which there is at least medium confidence in an increase in agriculture/ecological drought 
at the 2°C global warming level (‘drying regions’), including Western North America, Central North America, North Central America, Southern Central America, Northern South 
America, North-Eastern South America, South American Monsoon, South-Western South America, Southern South America, West and Central Europe, Mediterranean, West

Southern Africa, East Southern Africa, Madagascar, Eastern Australia, Southern Australia. Caribbean is not included in the calculation because the number of land grid points 
was too small. A drought event is defined as a 10-year drought event whose annual mean soil moisture was below its 10th percentile from the 1850–1900 base period. For 
each box plot, the horizontal line and the box represent the median and central 66% uncertainty range, respectively, of the frequency or the intensity changes across the multi-
model ensemble, and the ‘whiskers’ extend to the 90% uncertainty range. The line of zero in (a) indicates no change in intensity, while the line of one in (b) indicates no change 
in frequency. The results are based on the multi-model ensemble estimated from simulations of global climate models contributing to the Coupled Model Intercomparison

Project Phase 6 (CMIP6) under different Shared Socio-economic Pathway (SSP) forcing scenarios. Intensity changes in (a) are expressed as standard deviations of the interannual 
variability in the period 1850–1900 of the corresponding model. For details on the methods see Supplementary Material 11.SM.2. Further details on data sources and processing 

1582
levels, show contrasting trends (Figure 11.19). In the boreal winter 
in the Northern Hemisphere, the areas affected by drying show high 
agreement  with those characterized by an increase in meteorological 
drought projections (Figures 8.14 and 12.4). On the contrary, in the 
boreal summer, the drought frequency increases worldwide in 
comparison to meteorological drought projections, with large areas 
of the Northern Hemisphere displaying a high signal-to-noise ratio 
(low spead between models). This stresses the dominant influence 
of ET (as a result of increased AED) in intensifying agricultural and 
ecological droughts in the warm season in many locations, including 
mid- to high latitudes.
Increased soil moisture limitation and associated changes in droughts 
are projected to lead to increased vegetation stress affecting the 
global land carbon sink in ESM projections (Green et al., 2019), with

implications for projected global warming (Cross-Chapter Box  5). 
There is high confidence  that the global land sink will become less 
efficient due to soil moisture limitations and associated agricultural 
and ecological drought conditions in some regions in higher-emissions 
scenarios, specially under global warming levels above 4°C; however, 
there is low confidence  in how these water cycle feedbacks will play 
out in lower-emissions scenarios (at 2°C global warming or lower; 
11.6.5.4 Hydrological Deficits
Some studies support wetting tendencies as a response to a warmer 
climate when considering globally averaged changes in runoff over 
land (Roderick et al., 2015; Greve et al., 2017; Y . Yang et al., 2018), 
and streamflow projections respond to enhanced CO 2 concentrations 
in CMIP5 models (Yang et al., 2019). Nevertheless, when focusing 
regionally on low-runoff periods, model projections also show an

increase of hydrological droughts in large world regions (Wanders 
and Van Lanen, 2015; Dai et al., 2018; Vicente-Serrano et al., 2020c). 
In general, the frequency of hydrological deficits is projected to 
increase over most of the continents, although with regionally 
and seasonally differentiated effects (Section  11.9), with medium 
confidence  of increase in the following AR6 regions: WCE, MED, 
SAU, WCA, WNA, SCA, NSA, SAM, SWS, SSA, WSAF , ESAF and MDG 
(Section 11.9; Forzieri et al., 2014; Prudhomme et al., 2014; Giuntoli 
et al., 2015; Wanders and Van Lanen, 2015; Roudier et al., 2016; Marx 
et al., 2018; Cook et al., 2019; Zhao et al., 2020). However, there 
are large uncertainties related to the hydrological/impact model used 
(Prudhomme et al., 2014; Schewe et al., 2014; Gosling et al., 2017), 
limited signal-to-noise ratio (due to model spread) in several regions 
(Giuntoli et al., 2015), and also uncertainties in the projection of future

human activities, including water demand and land cover changes, 
which may represent more than 50% of the projected changes in 
hydrological droughts in some regions (Wanders and Wada, 2015). 
Regions dependent on mountainous snowpack as a  temporary 
reservoir may be affected by severe hydrological droughts in 
a  warmer world. In the southern European Alps, both winter and 
summer low flows are projected to be more severe, with a  25% 
decrease in the 2050s (Vidal et al., 2016). In western USA, a 22% 
reduction in winter snow water equivalent is projected at around 2°C 
of global warming, with a further decrease of a 70% reduction at 4°C global warming (Rhoades et al., 2018). This decline would cause 
less predictable hydrological droughts in snowmelt-dominated areas 
of North America (Livneh and Badger, 2020). The exact magnitude 
of the influence of higher temperatures on snow-related droughts 
is, however, difficult to estimate (Mote et al., 2016), since the

streamflow changes could affect the timing of peak streamflows but 
not necessarily their magnitude. In addition, projected changes in 
hydrological droughts downstream of declining glaciers can be very 
complex to assess (Chapter 9, see also SROCC).
11.6.5.5 Atmospheric-based Drought Indices
Studies show a stronger drying in projections based on atmospheric-
based drought indices compared to ESM projections of changes in 
soil moisture (Berg and Sheffield, 2018) and runoff (Yang et al., 2019). 
It has been suggested that this difference is due to physiological CO 2 
effects (Section  11.6.5.2; Roderick et al., 2015; Milly and Dunne, 
2016; Swann et al., 2016; Lemordant et al., 2018; Scheff, 2018; 
Swann, 2018; Greve et al., 2019; Yang et al., 2020). Nonetheless, there 
is evidence that differences in projections between atmospheric-
based drought indices and water-balance metrics from ESMs are not 
alone due to CO 2-plant effects (Berg et al., 2016; Scheff et al., 2021).

Differences can also be related to the fact that AED is an upper bound 
for ET in dry regions and conditions (Section 11.6.1.2) and that soil 
moisture stress limits increases in ET in projections (Section 11.6.5.2; 
Berg et al., 2016; Zhou et al., 2021). In general, atmospheric-based 
indices show more drying than total column soil moisture (Berg and 
Sheffield, 2018; Cook et al., 2020; Scheff et al., 2021), but are more 
consistent with projected increases in surface soil moisture deficits 
(Dirmeyer et al., 2013; Dai et al., 2018; Lu et al., 2019; Cook et al., 
2020; Vicente-Serrano et al., 2020c). 
Atmospheric-based drought indices are not metrics of soil moisture 
or runoff (Section 11.6.1.5) so their projections may not necessarily 
reflect the same trend of online simulated soil moisture and runoff. 
Independently of effects on the land water balance, atmospheric-
based drought indices will reflect the potential vegetation stress

resulting from deficits between available water and enhanced 
AED, even in conditions with no or low ET. Under dry conditions, 
the enhanced AED associated with human forcing would increase 
plant water stress (Brodribb et al., 2020), with effects on widespread 
forest dieback and mortality (Anderegg et al., 2013; Williams et al., 
2013; Allen et al., 2015; McDowell and Allen, 2015; McDowell et al., 
2016, 2020), and stronger risk of megafires (Flannigan et al., 2016; 
Podschwit et al., 2018; Clarke and Evans, 2019; Varela et al., 2019). 
For these reasons, there is high confidence  that the future projections 
of enhanced drought severity showed by the PDSI-PM and the SPEI-
PM are representative of more frequent and severe plant stress 
episodes and more severe agricultural and ecological drought 
impacts in some regions. 
Global tendencies towards more severe and frequent agricultural and 
ecological drought conditions are identified in future projections when

focusing on atmospheric-based drought indices such as the PDSI-PM 
or the SPEI-PM. They expand the spatial extent of drought conditions 
compared to meteorological drought to most of North America, 
Europe, Africa, Central and East Asia and Southern Australia (Cook

1583
et al., 2014a; Chen and Sun, 2017a, b; Gao et al., 2017b; Lehner et al., 
2017; Zhao and Dai, 2017; Dai et al., 2018; Naumann et al., 2018; 
Potopová et al., 2018; Gu et al., 2020; Vicente-Serrano et al.,  
2020c; Dai, 2021). Projections in PDSI-PM and SPEI-PM are used to 
complement total soil moisture projections in assessing projected 
changes in agricultural and ecological drought (Section 11.9).
11.6.5.6 Synthesis for Different Drought Types
The tables in Section  11.9 provide assessed projected changes in 
metorological drought, agricultural and ecological drought, and 
hydrological droughts. The assessment shows that several regions will 
be affected by more severe agricultural and ecological droughts even 
if global warming is stabilized at 2°C, including MED, WSAF , SAM and 
SSA ( high confidence ), and ESAF , MDG, EAU, SAU, SCA, CAR, NSA, 
NES, SWS, WCE, NCA, WNA and CNA ( medium confidence ). Some

regions are also projected to be affected by more severe agricultural 
and ecological droughts at 1.5°C (MED, WSAF , ESAF , SAU, NSA, SAM, 
SSA, can; medium confidence ) At 4°C of global warming, even more 
regions would be affected by agricultural and ecological droughts 
(WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, 
SWS, SSA, NCA, CNA, ENA, WNA, WSAF , ESAF and MDG). NEAF , SAS 
are also projected to experience less agricultural and ecological 
drought with global warming ( medium confidence ). Projected 
changes in meteorological droughts are, overall, less extended but 
also affect several AR6 regions, at 1.5°C and 2°C (MED, EAU, SAU, 
SCA, NSA, NCA, WSAF , ESAF , MDG) and 4°C of global warming 
(WCE, MED, EAU, SAU, SEA, SCA, CAR, NWS, NSA, NES, SAM, SWS, 
SSA, NCA, ENA, WAF , WSAF , ESAF , MDG). Several regions are also 
projected to be affected by more hydrological droughts at 1.5°C and 
2°C (WCE, MED, WNA, WSAF , ESAF) and 4°C of global warming (NEU,

WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF , 
ESAF , MDG). To illustrate the changes in both intensity and frequency 
of drought in the regions where strongest changes are projected, 
Figure  11.18 displays changes in the intensity and frequency of 
soil moisture drought under different global warming levels (1.5°C, 
2°C, 4°C) relative to the 1851-1900 baseline based on CMIP6 
simulations under different SSP forcing scenarios averaged over 
“drying regions”, i.e. AR6 regions for which there is at least medium 
confidence  in increase in agricultural and ecological drought at 2°C 
of global warming. The 90% uncertainty ranges for the projected 
changes in both intensity and frequency are above zero, indicating 
significant increase in both intensity and frequency of drought in 
these regions as whole.
In summary, more regions are affected by increases in agricultural 
and ecological droughts with increasing global warming ( high

confidence ). New evidence strengthens the SR1.5 conclusion that 
even relatively small incremental increases in global warming 
(+0.5°C) cause a  worsening of droughts in some regions ( high 
confidence ). Some regions are projected to be affected by more 
severe agricultural and ecological droughts at 1.5°C of global 
warming (MED, WSAF , ESAF , SAU, NSA, SAM, SSA, can; medium 
confidence ). A larger number of regions are projected to be affected 
by more severe agricultural and ecological droughts at 2°C of global 
warming, including MED, WSAF , SAM and SSA ( high confidence ), 
and ESAF , MDG, EAU, SAU, SCA, CAR, NSA, NES, SWS, WCE, NCA, WNA and CNA ( medium confidence ). At 4°C of global warming, 
even more regions would be affected by agricultural and ecological 
droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, 
NES, SAM, SWS, SSA, NCA, CNA, ENA, WNA, WSAF , ESAF and MDG). 
Some regions are also projected to experience less agricultural

and ecological drought with global warming ( medium confidence; 
NEAF , SAS). There is high confidence  that the projected increases in 
agricultural and ecological droughts are strongly affected by AED 
increases in a warming climate, although ET increases are projected 
to be smaller than those in AED due to soil moisture limitations and 
CO 2 effects on leaf stomatal conductance. Enhanced atmospheric 
CO 2 concentrations lead to enhanced water-use efficiency in plants 
(medium confidence ), but there is low confidence  that it can alleviate 
agricultural and ecological droughts, or hydrological droughts, at 
higher global warming levels characterized by limited soil moisture 
and enhanced AED.
Projected changes in meteorological droughts are overall less 
extended than for agricultural and ecological droughts, but also 
affect several AR6 regions, even at 1.5°C and 2°C of global warming. 
Several regions are also projected to be more strongly affected by

hydrological droughts with increasing global warming (NEU, WCE, 
MDG). Increased soil moisture limitation and associated changes in 
droughts are projected to lead to increased vegetation stress in many 
regions, with implications for the global land carbon sink (Cross-
Chapter Box 5). There is high confidence  that the global land carbon 
sink will become less efficient due to soil moisture limitations and 
associated drought conditions in some regions in higher-emissions 
scenarios, especially under global warming levels above 4°C; 
however, there is low confidence  on how these water cycle feedbacks 
will play out in lower-emissions scenarios (at 2°C global warming or 
lower; Cross-Chapter Box5.1).
11.7 Extreme Storms
Extreme storms, such as tropical cyclones (TCs), extratropical cyclones 
(ETCs), and severe convective storms often have substantial societal

impacts. Quantifying the effect of climate change on extreme storms 
is challenging, partly because extreme storms are rare, short-lived, 
and local, and individual events are largely influenced by stochastic 
variability. The high degree of random variability makes detection and 
attribution of extreme storm trends more uncertain than detection 
and attribution of trends in other aspects of the environment in 
which the storms evolve (e.g.,  larger-scale temperature trends). 
Projecting changes in extreme storms is also challenging because of 
constraints in the models’ ability to accurately represent the small-
scale physical processes that can drive these changes. Despite the 
challenges, progress has been made since AR5.
The SREX (Chapter 3) concluded that there is low confidence  in observed 
long-term (40 years or more) trends in TC intensity, frequency, and 
duration, and any observed trends in phenomena such as tornadoes

and hail; it is likely  that extratropical storm tracks have shifted 
poleward in both the Northern and Southern Hemispheres, and that 
heavy rainfalls and mean maximum wind speeds associated with TCs

1584
Colour
Low model agreementHigh model agreementColour
Figure 11.19 | Projected changes in (a–c) the number of consecutive dry days (CDD), (d–f) annual mean soil moisture over the total column, and (g–l) the 
frequency and intensity of 1-in-10-year soil moisture drought for the June-to-August and December-to-February seasons at 1.5°C, 2°C, and 4°C of global 
warming compared to the 18 50–1900 baseline. The unit for soil moisture change is the standard deviation of interannual variability in soil moisture during 1850–1900. 
Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture

conditions typical of about 1-in-6-year droughts during 1850–1900 becoming the norm in the future. Results are based on simulations from the Coupled Model Intercomparison 
Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers 
in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, 
where ≥80% of models agree on the sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more 
information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in CDDs are also 
displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).

1585
will increase with continued greenhouse gas warming; it is likely  that 
the global frequency of TCs will either decrease or remain essentially 
unchanged, while it is more likely than not  that the frequency of the 
most intense storms will increase substantially in some ocean basins; 
there is low confidence  in projections of small-scale phenomena such 
as tornadoes and hail storms; and there is medium confidence  that 
there will be a reduced frequency and a poleward shift of mid-latitude 
cyclones due to future anthropogenic climate change. 
Since SREX, several IPCC Reports also assessed storms. The AR5 
(Chapter  2, Hartmann et al., 2013) assessment observed with 
low confidence  long-term trends in TC metrics, but revised the 
statement from SREX to state that it is virtually certain  that there 
are increasing trends in North Atlantic TC activity since the 1970s,

with medium confidence  that anthropogenic aerosol forcing has 
contributed to these trends. The AR5 concluded that it is likely  that 
TC precipitation and mean intensity will increase and more likely 
than not  that the frequency of the strongest storms will increase 
with continued greenhouse gas warming. Confidence  in projected 
trends in overall TC frequency remained low. Confidence  in observed 
and projected trends in hail storm and tornado events also remained 
low. The SROCC (Chapter 6, Collins et al., 2019) assessed past and 
projected TCs and ETCs, supporting the AR5 conclusions with some 
additional detail. Literature subsequent to AR5 adds support to the 
likelihood of increasing trends in TC intensity, precipitation, and 
frequency of the most intense storms, while some newer studies 
have added uncertainty to projected trends in overall frequency. 
A growing body of literature since AR5 on the poleward migration

of TCs led to a  new assessment in SROCC of low confidence  that 
the migration in the western North Pacific represents a detectable 
climate change contribution from anthropogenic forcing.  The SR1.5 
(Chapter 3, Hoegh-Guldberg et al., 2018) essentially confirmed the 
AR5 assessment of TCs and ETCs, adding that heavy precipitation 
associated with TCs is projected to be higher at 2°C compared to 
1.5°C global warming ( medium confidence ).
The SREX, AR5, SROCC, and SR1.5, do not provide assessments of 
the atmospheric rivers, and SROCC and SR1.5 do not assess severe 
convective storms and extreme winds. This section assesses the state 
of knowledge on the four phenomena of TCs, ETCs, severe convective 
storms, and extreme winds. Atmospheric rivers are addressed 
in Chapter  8. In this respect, this assessment closely mirrors the 
SROCC assessment of TCs and ETCs, while updating SREX and AR5 
assessments of severe convective storms and extreme winds.
11.7.1 Tropical Cyclones

11.7.1.1 Mechanisms and Drivers
The genesis, development, and tracks of TCs depend on conditions 
of the larger-scale circulations of the atmosphere and ocean 
(Christensen et al., 2013). Large-scale atmospheric circulations, such 
as the Hadley and Walker circulations and the monsoon circulations 
can significantly affect TCs, as can internal variability acting on various 
time scales (Annex IV), from intra-seasonal (e.g., the Madden–Julian 
and Boreal Summer Intraseasonal oscillations and equatorial waves) and interannual (e.g., the El Niño–Southern Oscillation and Pacific 
and Atlantic Meridional Modes), to inter-decadal (e.g.,  Atlantic 
Multidecadal Variability and Pacific Decadal Variability). This broad 
range of natural variability makes detection of anthropogenic 
effects difficult, and uncertainties in the projected changes of these 
modes of variability increase uncertainty in the projected changes 
in TC activity. Aerosol forcing also affects sea surface temperature

(SST) patterns and cloud microphysics, and it is likely  that observed 
changes in TC activity are partly caused by changes in aerosol forcing 
(Evan et al., 2011; Ting et al., 2015; Sobel et al., 2016, 2019; Takahashi 
et al., 2017; Zhao et al., 2018; Reed et al., 2019). Among possible 
changes from these drivers, there is medium confidence  that the 
Hadley cell has widened and will continue to widen in the future 
(Sections 2.3, 3.3 and 4.5). This likely  causes latitudinal shifts of TC 
tracks (Sharmila and Walsh, 2018). Regional TC activity changes are 
also strongly affected by projected changes in SST warming patterns 
(Yoshida et al., 2017), which are highly uncertain (Chapters 4 and 9).
11.7.1.2 Observed Trends
Identifying past trends in TC metrics remains a challenge due to the 
heterogeneous character of the historical instrumental data, which 
are known as ‘best-track’ data (Schreck et al., 2014). There is low

confidence  in most reported long-term (multi-decadal to centennial) 
trends in TC frequency- or intensity-based metrics due to changes in 
the technology used to collect the best-track data. This should not be 
interpreted as implying that no physical (real) trends exist, but rather 
as indicating that either the quality or the temporal length of the 
data is not adequate to provide robust trend detection statements, 
particularly in the presence of multi-decadal variability. 
There are previous and ongoing efforts to homogenize the best-track 
data (Elsner et al., 2008; Kossin et al., 2013, 2020; Choy et al., 2015; 
Landsea, 2015; Emanuel et al., 2018) and there is substantial literature 
that finds positive trends in intensity-related metrics in the best-track 
during the ‘satellite period’, which is generally limited to around the 
past 40 years (Kang and Elsner, 2012; Kishtawal et al., 2012; Kossin 
et al., 2013, 2020; Mei and Xie, 2016; Zhao et al., 2018; Tauvale and

Tsuboki, 2019). When best-track trends are tested using homogenized 
data, the intensity trends generally remain positive, but are smaller in 
amplitude (Kossin et al., 2013; Holland and Bruyère, 2014). Kossin et al. 
(2020) extended the homogenized TC intensity record to the period 
1979–2017 and identified significant global increases in major TC 
exceedance probability of about 6% per decade. In addition to trends 
in TC intensity, there is evidence that TC intensification rates and the 
frequency of rapid intensification events have increased within the 
satellite era (Kishtawal et al., 2012; Balaguru et al., 2018; Bhatia et al., 
2018). The increase in intensification rates is found in the best-track 
and the homogenized intensity data.
A subset of the best-track data corresponding to hurricanes that have 
directly impacted the USA since 1900 is considered to be reliable, and 
shows no trend in the frequency of USA landfall events (Knutson et al.,

2019). However, an increasing trend in normalized USA hurricane 
damage, which accounts for temporal changes in exposed wealth 
(Grinsted et al., 2019), and a  decreasing trend in TC translation 
speed over the USA (Kossin, 2019) have also been identified in this

1586
period. A similarly reliable subset of the data representing TC landfall 
frequency over Australia shows a decreasing trend in Eastern Australia 
since the 1800s (Callaghan and Power, 2011), as well as in other parts 
of Australia since 1982 (Chand et al., 2019; Knutson et al., 2019). 
A  paleoclimate proxy reconstruction shows that recent levels of TC 
interactions along parts of the Australian coastline are the lowest 
in the past 550–1500 years (Haig et al., 2014). Existing TC datasets 
show substantial inter-decadal variations in basin-wide TC frequency 
and intensity in the western North Pacific, but a statistically significant 
north-westward shift in the western North Pacific TC tracks since the 
1980s (T.-C. Lee et al., 2020). In the case of the North Indian Ocean, 
analyses of trends are highly dependent on the details of each analysis

(e.g., pre- and/or post-monsoon season period, or Bay of Bengal and/
or Arabian Sea region). The most consistent trends are an increase in 
the occurrence of the most intense TCs, and a decrease in the overall 
TC frequency, in particular in the Bay of Bengal (Sahoo and Bhaskaran, 
2016; Balaji et al., 2018; Singh et al., 2019; Baburaj et al., 2020). In 
the South Indian Ocean (SIO), an increase in the occurrence of the 
most intense TCs has been noted; however, there are well-known 
data quality issues there (Kuleshov et al., 2010; Fitchett, 2018). When 
the SIO data are homogenized, a significant increase is found in the 
fractional proportion of global Category 3–5 TC instances (6-hourly 
intensity estimates during the lifetime of each TC) to all Category 1–5 
instances (Kossin et al., 2020). 
As with all confined regional analyses of TC frequency, it is generally

unclear whether any identified changes are due to a  basin-wide change in TC frequency, or to systematic track shifts (or both). 
From an impacts perspective, however, these changes over land are 
highly relevant and emphasize that large-scale modifications in TC 
behaviour can have a broad spectrum of impacts on a regional scale.
Subsequent to AR5, two metrics have been analysed that are argued 
to be comparatively less sensitive to data issues than frequency- and 
intensity-based metrics. Trends in these metrics have been identified 
over the past 70 years or more (Knutson et al., 2019). The first metric – 
the mean latitude where TCs reach their peak intensity  – exhibits 
a global and regional poleward migration during the satellite period 
(Kossin et al., 2014). The poleward migration can influence TC hazard 
exposure and risk (Kossin et al., 2016a) and is consistent with the 
independently observed expansion of the tropics (Lucas et al., 2014).

The migration has been linked to changes in the Hadley circulation 
(Altman et al., 2018; Sharmila and Walsh, 2018; Studholme and 
Gulev, 2018). The migration is also apparent in the mean locations 
where TCs exhibit eyes (Knapp et al., 2018), which is when TCs are 
most intense. Part of the Northern Hemisphere poleward migration 
is due to basin-wide changes in TC frequency (Kossin et al., 2014, 
2016b; Moon et al., 2015, 2016) and the trends, as expected, can 
be sensitive to the time period chosen (Tennille and Ellis, 2017; 
Kossin, 2018; Song and Klotzbach, 2018) and to subsetting of the 
data by intensity (Zhan and Wang, 2017). The poleward migration is 
particularly pronounced and well-documented in the western North 
Pacific basin (Kossin et al., 2016a; Oey and Chou, 2016; Liang et al., 
2017; Nakamura et al., 2017; Altman et al., 2018; Daloz and Camargo, 
Changes instorms with increasing global warming
Increase instrength

Decreased orunchanged
genesis frequencyTropical cyclones
Extratropical cyclones
Atmospheric river
Global
Changes (increase or
decrease) inwind speed
following storm tracks
poleward shift insome regionsRegionalRegional
Increased
exposureDecreased
exposureSevere convective storms
Slower
motionLonger
season
60°N
30°N
60°S30°SequatorAverage andmaximum
precipitation rates increase
with warming
Figure 11.20 | Summary schematic of past and projected changes in tropical cyclone (TC), extratropical cyclone (ETC), atmospheric river (AR), and severe 
convective storm (SCS) behaviour.  Global changes (blue shading) from top to bottom: (i) Increased mean and maximum rain rates in TCs, ETCs, and ARs [past ( low 
confidence  due to lack of reliable data) and projected ( high confidence )]; (ii) Increased proportion of stronger TCs [past ( medium confidence ) and projected ( high confidence )];

(iii) Decrease or no change in global frequency of TC genesis [past ( low confidence  due to lack of reliable data) and projected ( medium confidence )]; and (iv) Increased and 
decreased ETC wind speed, depending on the region, as storm tracks change [past ( low confidence  due to lack of reliable data) and projected ( medium confidence )]. Regional 
changes, from left to right: (i) Poleward TC migration in the western North Pacific and subsequent changes in TC exposure [past ( medium confidence ) and projected ( medium  
confidence )]; (ii) Slowdown of TC forward translation speed over the contiguous USA and subsequent increase in TC rainfall [past ( medium confidence ) and projected ( low 
confidence  due to lack of directed studies)]; and (iii) Increase in mean and maximum SCS rain rate and increase in spring SCS frequency and season length over the contiguous 
USA [past ( low confidence  due to lack of reliable data) and projected ( medium confidence )].

1587
2018; J. Sun et al., 2019; T.-C. Lee et al., 2020; Yamaguchi and Maeda, 
2020a; Kubota et al., 2021). 
A second metric that is argued to be comparatively less sensitive 
to data issues than frequency- and intensity-based metrics is TC 
translation speed (Kossin, 2018), which exhibits a global slowdown 
in the best-track data over the period 1949–2016. TC translation 
speed is a  measure of the speed at which TCs move across the 
Earth’s surface, and is very closely related to local rainfall amounts 
(i.e.,  a slower translation speed causes greater local rainfall). TC 
translation speed also affects structural wind damage and coastal 
storm surge by changing the hazard event duration. The slowdown is 
observed in the best-track data from all basins except the Northern 
Indian Ocean, and is also found in a number of regions where TCs 
interact directly with land. The slowing trends identified in the best-

track data by Kossin (2018) have been argued to be largely due to 
data heterogeneity. Moon et al. (2019) and Lanzante (2019) provide 
evidence that meridional TC track shifts project onto the slowing 
trends, and argue that these shifts are due to the introduction of 
satellite data. Kossin (2019) provides evidence that the slowing trend 
is real by focusing on Atlantic TC track data over the contiguous USA 
in the 118-year period 1900–2017, which are generally considered 
reliable. In this period, mean TC translation speed has decreased by 
17%. The slowing TC translation speed is expected to increase local 
rainfall amounts, which would increase coastal and inland flooding. 
In combination with slowing translation speed, abrupt TC track 
direction changes  – that can be associated with track ‘meanders’ 
or ‘stalls’  – have become increasingly common along the North 
American coast since the mid-20th century, leading to more rainfall 
in the region (Hall and Kossin, 2019).

In summary, there is mounting evidence that a  variety of TC 
characteristics have changed over various time periods. It is likely  that 
the global proportion of Category 3–5 tropical cyclone instances and 
the frequency of rapid intensification events have increased globally 
over the past 40 years. It is very likely  that the average location 
where TCs reach their peak wind intensity has migrated poleward in 
the western North Pacific Ocean since the 1940s. It is likely  that TC 
translation speed has slowed over the USA since 1900. 
11.7.1.3 Model Evaluation
Accurate projections of future TC activity have two principal 
requirements: accurate representation of changes in the relevant 
environmental factors (e.g.,  SSTs) that can affect TC activity, and 
accurate representation of actual TC activity in given environmental 
conditions. In particular, models’ capacity to reproduce historical trends 
or interannual variabilities of TC activity is relevant to the confidence in

future projections. One test of the models is to evaluate their ability to 
reproduce the dependency of the TC statistics in the different basins in 
the real world, in addition to their capability of reproducing atmospheric 
and ocean environmental conditions. For the evaluation of projections 
of TC-relevant environmental variables, AR5 confidence statements 
were based on global surface temperature and moisture, but not on 
the detailed regional structure of SST and atmospheric circulation 
changes such as steering flows and vertical shear, which affect 
characteristics of TCs (genesis, intensity, tracks, etc.). Various aspects of TC metrics are used to evaluate how capable models are of simulating 
present-day TC climatologies and variability (e.g., TC frequency, wind 
intensity, precipitation, size, tracks, and their seasonal and interannual 
changes) (Walsh et al., 2015; Camargo and Wing, 2016; Knutson et al.,

2019, 2020). Other examples of TC climatology/variability metrics are 
spatial distributions of TC occurrence and genesis (Walsh et al., 2015), 
seasonal cycles and interannual variability of basin-wide activity (Zhao 
et al., 2009; Shaevitz et al., 2014; Kodama et al., 2015; Murakami et al., 
2015; Yamada et al., 2017) or landfalling activity (Lok and Chan, 2018), 
as well as newly developed process-diagnostics designed specifically 
for TCs in climate models (D. Kim  et al., 2018; Wing et al., 2019; 
Moon et al., 2020). 
Confidence in the projection of intense TCs, such as those of 
Category 4–5, generally becomes higher as the resolution of the 
models becomes higher. The Coupled Model Intercomparison Project 
Phases 5 and 6 (CMIP5/6) class climate models (around 100–200 km 
grid spacing) cannot simulate TCs of Category 4–5 intensity. They do 
simulate storms of relatively high vorticity that are at best described

as ‘TC-like’, but metrics such as storm counts are highly dependent 
on tracking algorithms (Camargo, 2013; Wehner et al., 2015; Zarzycki 
and Ullrich, 2017; Roberts et al., 2020a). High-resolution GCMs 
(around 10–60 km grid spacing), as used in HighResMIP (Haarsma 
et al., 2016; Roberts et al., 2020a), begin to capture some structures 
of TCs more realistically, as well as produce intense TCs of Category 
4–5 despite the effects of parametrized deep cumulus convection 
processes (Murakami et al., 2015; Wehner et al., 2015; Yamada 
et al., 2017; Roberts et al., 2018; Moon et al., 2020). Convection-
permitting models (around 1–10 km grid-spacing), such as used 
in some dynamical downscaling studies, provide further realism 
with capturing TC eye-wall structures (Tsuboki et al., 2015). Model 
characteristics besides resolution, especially details of convective 
parametrization, can influence a model’s ability to simulate intense

TCs (Reed and Jablonowski, 2011; Zhao et al., 2012; He and Posselt, 
2015; D. Kim et al., 2018; Zhang and Wang, 2018; Camargo et al., 
2020). However, models’ dynamical cores and other physics also 
affect simulated TC properties (Reed et al., 2015; Vidale et al., 2021). 
Both wide-area regional and global convection-permitting models 
without the need for parameterized convection are becoming more 
useful for TC regional model projection studies (Tsuboki et al., 2015; 
Kanada et al., 2017a; Gutmann et al., 2018) and global model 
projection studies (Satoh et al., 2015, 2017; Yamada et al., 2017), 
as they capture more realistic TC eye wall structures (Kinter III et al., 
2013) and are becoming more useful for investigating changes in TC 
structures (Kanada et al., 2013; Yamada et al., 2017). Large ensemble 
simulations of GCMs with 60 km grid spacing provide TC statistics 
that allow more reliable detection of changes in the projections,

which are not well captured in any single experiment (Yoshida et al., 
2017; Yamaguchi et al., 2020). Variable resolution global models offer 
an alternative to regional models for individual TC or basin-wide 
simulations (Yanase et al., 2012; Zarzycki et al., 2014; Harris et al., 
2016; Reed et al., 2020; Stansfield et al., 2020). Computationally less 
intense than equivalent uniform resolution global models, they also 
do not require lateral boundary conditions, thus reducing this source 
of error (Hashimoto et al., 2016). Confidence in the projection of TC 
statistics and properties is increased by the use of higher-resolution 
models with more realistic simulations.

1588
Operational forecasting models also reproduce TCs, and their use for 
climate projection studies shows promise. However, there is limited 
application for future projections as they are specifically developed 
for operational purposes, and TC climatology is not necessarily well 
evaluated. Intercomparison of operational models indicates that 
enhancement of horizontal resolution can provide more credible 
projections of TCs (Nakano et al., 2017). Likewise, high-resolution 
climate models show promise as TC forecast tools (Zarzycki and 
Jablonowski, 2015; Reed et al., 2020), further narrowing the 
continuum of weather and climate models, and increasing confidence 
in projections of future TC behaviour. However, higher horizontal 
resolution does not necessarily lead to an improved TC climatology 
(Camargo et al., 2020). 
Atmosphere–ocean interaction is an important process in TC

evolution. Atmosphere–ocean coupled models are generally better 
than atmosphere-only models at capturing realistic processes related 
to TCs (Murakami et al., 2015; Ogata et al., 2015, 2016; Zarzycki, 2016; 
Kanada et al., 2017b; Scoccimarro et al., 2017). However, the basin-
scale SST biases commonly found in atmosphere–ocean models can 
introduce substantial errors in the simulated TC number (Hsu et al., 
2019). Higher-resolution ocean models improve the simulation of TCs 
by reducing the SST climatology bias (Li and Sriver, 2018; Roberts 
et al., 2020a). Coarse resolution atmospheric models may degrade 
coupled model performance as well. For example, in a  case study 
of Hurricane Harvey, Trenberth et al. (2018) suggested that the lack 
of realistic hurricane frequency and intensity within coupled climate 
models hampers the models’ ability to simulate SST and ocean heat 
content and their changes. 
Even with higher-resolution atmosphere–ocean coupled models, TC

projection studies still rely on assumptions in experimental design 
that introduce uncertainties. Computational constraints often limit 
the number of simulations, resulting in relatively small ensemble 
sizes and incomplete analyses of possible future SST magnitude 
and pattern changes (Zhao and Held, 2011; Knutson et al., 2013). 
Uncertainties in aerosol forcing also are reflected in TC projection 
uncertainty (Wang et al., 2014).
Regional climate models (RCM) with grid spacing around 15–50 km  
can be used to study the projection of TCs. RCMs are run with 
lateral and surface boundary conditions, which are specified by the 
atmospheric state and SSTs simulated by GCMs. Various combinations 
of the lateral and surface boundary conditions can be chosen for RCM 
studies, and uncertainties in the projection can be further examined 
in general. They are used for studying changes in TC characteristics 
in a specific area, such as Vietnam (Redmond et al., 2015) and the

Philippines (Gallo et al., 2019).
Less computationally expensive downscaling approaches that allow 
larger ensembles and long-term studies are also used in the projection 
of TCs (Emanuel et al., 2006; C.Y . Lee et al., 2018). A  statistical–
dynamical TC downscaling method requires assumptions of the 
rate of seeding of random initial disturbances, which are generally 
assumed to not change with climate change (Emanuel et al., 2008; 
Emanuel, 2013). The results with the downscaling approach might depend on the assumptions, which are required for the simplification 
of the methods. 
In summary, various types of models are useful to study how TCs 
change in response to climate changes, and there is no unique 
solution for choosing a model type. However, higher-resolution models 
generally capture TC properties more realistically ( high confidence ). 
In particular, models with horizontal resolutions of 10–60 km are 
capable of reproducing strong TCs with Category 4–5 and those of

1–10 km are capable of the eye wall structure of TCs. Uncertainties in 
TC simulations come from details of the model configuration of both 
dynamical and physical processes. Models with realistic atmosphere–
ocean interactions are generally better than atmosphere-only models 
at reproducing realistic TC evolutions ( high confidence ).
11.7.1.4 Detection and Attribution, Event Attribution
There is general agreement in the literature that anthropogenic 
greenhouse gases and aerosols have measurably affected observed 
oceanic and atmospheric variability in TC-prone regions (see 
Chapter  3). This underpinned the SROCC assessment of medium 
confidence  that humans have contributed to the observed increase in 
Atlantic hurricane activity since the 1970s (Chapter 5, Bindoff et al., 
2013). Literature subsequent to AR5 lends further support to this 
statement (Knutson et al., 2019). However, there is still no consensus 
on the relative magnitude of human and natural influences on past

changes in Atlantic hurricane activity, and particularly on which 
factor has dominated the observed increase (Ting et al., 2015) and 
it remains uncertain whether past changes in Atlantic TC activity 
are outside the range of natural variability. A  recent result using 
high-resolution dynamical model experiments suggested that the 
observed spatial contrast in TC trends cannot be explained only by 
multi-decadal natural variability, and that external forcing plays an 
important role (Murakami et al., 2020). Observational evidence for 
significant global increases in the proportion of major TC intensities 
(Kossin et al., 2020) is consistent with both theory and numerical 
modelling simulations, which generally indicate an increase in mean 
TC peak intensity and the proportion of very intense TCs in a warming 
world (Knutson et al., 2015, 2020; Walsh et al., 2015, 2016). In 
addition, high-resolution coupled model simulations provide support

that natural variability alone is unlikely  to explain the magnitude 
of the observed increase in TC intensification rates and upward TC 
intensity trend in the Atlantic basin since the early 1980s (Bhatia 
et al., 2019; Murakami et al., 2020).
The cause of the observed slowdown in TC translation speed is not 
yet clear. Yamaguchi et al. (2020) used large ensemble simulations 
to argue that part of the slowdown is due to actual latitudinal shifts 
of TC tracks, rather than data artefacts, in addition to atmospheric 
circulation changes. G. Zhang et al. (2020) used large ensemble 
simulations to show that anthropogenic forcing can lead to a robust 
slowdown, particularly outside of the tropics at higher latitudes. 
Yamaguchi and Maeda (2020b) found a  significant slowdown in 
the western North Pacific over the past 40 years and attributed the 
slowdown to a combination of natural variability and global warming. 
The slowing trend since 1900 over the USA is robust and significant

1589
(Kossin, 2019). Among the hypotheses discussed is the physical 
linkage between warming and slowing circulation (Held and 
Soden, 2006; see also Section 8.2.2.2), with expectations of Arctic 
amplification and weakening circulation patterns through weakening 
meridional temperature gradients (Coumou et al., 2018; see also 
Cross-Chapter Box  10.1), or through changes in planetary wave 
dynamics (Mann et al., 2017). The tropics expansion and the poleward 
shift of the mid-latitude westerlies associated with warming is also 
suggested as the reason of the slowdown (G. Zhang et al., 2020). 
However, the connection of these mechanisms to the slowdown has 
not been robustly shown. Furthermore, slowing trends have not been 
unambiguously observed in circulation patterns that steer TCs, such 
as the Walker and Hadley circulations (Section  2.3.1.4), although

these circulations generally slow down in numerical simulations 
under global warming (Sections 4.5.1.6 and 8.4.2.2). 
The observed poleward trend in western North Pacific TCs remains 
significant after accounting for the known modes of dominant 
interannual to decadal variability in the region (Kossin et al., 2016a), 
and is also found in CMIP5 model-simulated TCs (in the recent 
historical period 1980–2005), although it is weaker than observed 
and is not statistically significant (Kossin et al., 2016a). However, 
the trend is significant in 21st-century CMIP5 projections under the 
RCP8.5 scenario, with a similar spatial pattern and magnitude to the 
past observed changes in that basin over the period 1945–2016, 
supporting a possible anthropogenic greenhouse gas contribution to 
the observed trends (Kossin et al., 2016a; Knutson et al., 2019). 
The recent active TC seasons in some basins have been studied to 
determine whether there is anthropogenic influence. For 2015,

Murakami et al. (2017b) explored the unusually high TC frequency 
near Hawaii and in the eastern Pacific basin. W. Zhang et al. (2016b) 
considered unusually high Accumulated Cyclone Energy (ACE) in the 
western North Pacific; and S.-H. Yang et al. (2018) and Yamada et al. 
(2019) looked at TC intensification in the western North Pacific. These 
studies suggest that the anomalous TC activity in 2015 was not solely 
explained by the effect of an extreme El Niño (see Box  11.4) and 
that there was also an anthropogenic contribution, mainly through 
the effects of SSTs in subtropical regions. In the post-monsoon 
seasons of 2014 and 2015, tropical storms with lifetime maximum 
winds greater than 46 m s−1 were first observed over the Arabian 
Sea, and Murakami et al. (2017a) showed that the probability of late-
season severe tropical storms is increased by anthropogenic forcing 
compared to the preindustrial era. Murakami et al. (2018) concluded

that the active 2017 Atlantic hurricane season was mainly caused 
by pronounced SSTs in the tropical North Atlantic and that these 
types of seasonal events will intensify with projected anthropogenic 
forcing. The trans-basin SST change, which might be driven by 
anthropogenic aerosol forcing, also affects TC activity. Takahashi 
et al. (2017) suggested that a decrease in sulphate aerosol emissions 
caused about half of the observed decreasing trends in TC genesis 
frequency in the south-eastern region of the western North Pacific 
during 1992–2011.
Event attribution is used in TC case studies to test whether the 
severities of recent intense TCs are explained without anthropogenic 
effects. In a case study of Hurricane Sandy (2012), Lackmann (2015) found no statistically significant impact of anthropogenic climate 
change on storm intensity, while projections in a  warmer world 
showed significant strengthening. However, Magnusson et al. (2014)

found that, in European Centre for Medium-Range Weather Forecast 
(ECMWF) simulations, the simulated cyclone depth and intensity, 
as well as precipitation, were larger when the model was driven 
by the warmer actual SSTs than the climatological average SSTs. In 
Super Typhoon Haiyan, which struck the Philippines on 8 November 
2013, Takayabu et al. (2015) took an event attribution approach 
with cloud system-resolving (around 1  km) downscaling ensemble 
experiments to evaluate the anthropogenic effect on typhoons, and 
showed that the intensity of the simulated worst-case storm in the 
actual conditions was stronger than that in a hypothetical condition 
without historical anthropogenic forcing in the model. However, 
in a  similar approach with two coarser parametrized convection 
models, Wehner et al. (2019) found conflicting human influences 
on Haiyan’s intensity. Patricola and Wehner (2018) found little 
evidence of an attributable change in intensity of hurricanes Katrina

(2005), Irma (2017), and Maria (2017) using a  regional climate 
model configured between 3  km and 4.5 km resolution. They did, 
however, find attributable increases in heavy precipitation totals. 
These results imply that higher resolution, such as in a convective 
permitting 5 km or less mesh model, is required to obtain a robust 
anthropogenic intensification of a strong TC by simulating realistic 
rapid intensification (Kanada and Wada, 2016; Kanada et al., 2017a), 
and that whether the TC intensification can be attributed to the 
recent warming depends on the case.
The dominant factor in the extreme rainfall amounts during Hurricane 
Harvey’s passage onto the USA in 2017 was its slow translation speed. 
But studies published after the event have argued that anthropogenic 
climate change contributed to an increase in rain rate, which 
compounded the extreme local rainfall caused by the slow translation. 
Emanuel (2017) used a large set of synthetically-generated storms

and concluded that the occurrence of extreme rainfall as observed 
in Harvey was substantially enhanced by anthropogenic changes 
to the larger-scale ocean and atmosphere characteristics; Trenberth 
et al. (2018) linked Harvey’s rainfall totals to the anomalously large 
ocean heat content from the Gulf of Mexico; and van Oldenborgh 
et al. (2017) and Risser and Wehner (2017) applied extreme value 
analysis to extreme rainfall records in the Houston, Texas region, both 
attributing large increases to climate change. Large precipitation 
increases during Harvey due to global warming were also found 
using climate models (van Oldenborgh et al., 2017; S.-Y .S. Wang et al., 
2018). Harvey precipitation totals were estimated in these papers to 
be three to 10 times more probable due to climate change. A best 
estimate from a regional climate and flood model is that urbanization 
increased the risk of the Harvey flooding by a factor of 21 (W. Zhang

et al., 2018), using a regional climate and flood model, found that 
surface roughness from urbanization increased the risk of the Harvey 
flooding by a  factor of 21. Anthropogenic effects on precipitation 
increases were also predicted in advance from a forecast model for 
Hurricane Florence in 2018 (Reed et al., 2020). 
In summary, it is very likely  that the recent active TC seasons in 
the North Atlantic, the North Pacific, and Arabian basins cannot be 
explained without an anthropogenic influence. The anthropogenic

1590
influence on these changes is principally associated to aerosol forcing, 
with stronger contributions to the response in the North Atlantic. It 
is more likely than not  that the slowdown of TC translation speed 
over the USA has contributions from anthropogenic forcing. It is 
likely  that the poleward migration of TCs in the western North Pacific 
and the global increase in TC intensity rates cannot be explained 
entirely by natural variability. Event attribution studies of specific 
strong TCs provide limited evidence  for anthropogenic effects on TC 
intensifications so far, but high confidence  for increases in TC heavy 
precipitation. There is high confidence  that anthropogenic climate 
change contributed to extreme rainfall amounts during Hurricane 
Harvey (2017) and other intense TCs.
11.7.1.5    Projections
A summary of studies on TC projections for the late 21st  century,

particularly studies since AR5, is given by Knutson et al. (2020), which 
is an assessment report mandated by the World Meteorological 
Organization (WMO). Studies subsequent to Knutson et al. (2020) 
are generally consistent, and the confidence assessments here 
closely follow theirs (Cha et al., 2020), although there are some 
differences due to the varying confidence calibrations between the 
IPCC and WMO reports. 
There is not an established theory for the drivers of future changes 
in the frequency of TCs. Most, but not all, high-resolution global 
simulations project significant reductions in the total number of TCs, 
with the bulk of the reduction at the weaker end of the intensity 
spectrum as the climate warms (Knutson et al., 2020). Recent 
exceptions based on high-resolution coupled model results are 
noted in Bhatia et al. (2018) and Vecchi et al. (2019). Vecchi et al. 
(2019) showed that the representation of synoptic-scale seeds for TC

genesis in their high-resolution model causes different projections of 
global TC frequency, and there is evidence for a decrease in cyclone 
seeds in some projected TC simulations (Sugi et al., 2020; Yamada 
et al., 2011). However, other research indicates that TC seeds are not 
an independent control on climatological TC frequency, rather the 
seeds covary with the large-scale controls on TCs (Patricola et al., 
2018). While empirical genesis indices derived from observations and 
reanalysis describe well the observed subseasonal and interannual 
variability of current TC frequency (Camargo et al., 2007, 2009; Tippett 
et al., 2011; Menkes et al., 2012), they fail to predict the decreased TC 
frequency found in most high-resolution model simulations (Zhang 
et al., 2010; Camargo, 2013; Wehner et al., 2015), as they generally 
project an increase as the climate warms. This suggests a limitation 
of the use of the empirical genesis indices for projections of TC

genesis, in particular due to their sensitivity to the humidity variable 
considered in the genesis index for these projections (Camargo et al., 
2014). In a different approach, a statistical–dynamical downscaling 
framework assuming a constant seeding rate with warming (Emanuel, 
2013, 2021) exhibits increases in TC frequency consistent with 
genesis indices-based projections, while downscaling with a different 
model leads to two different scenarios depending on the humidity 
variable considered (C.-Y . Lee et al., 2020). This disparity in the sign 
of the projected change in global TC frequency, and the difficulty in 
explaining the mechanisms behind the different signed responses, 
further emphasize the lack of process understanding of future changes in tropical cyclogenesis (Walsh et al., 2015; Hoogewind 
et al., 2020). Even within a single model, uncertainty in the pattern of 
future SST changes leads to large uncertainties (including the sign)

in the projected change in TC frequency in individual ocean basins, 
although global TCs would appear to be less sensitive (Yoshida et al., 
2017; Bacmeister et al., 2018).
Changes in SST and atmospheric temperature and moisture play 
a  role in tropical cyclogenesis (Walsh et al., 2015). Reductions in 
vertical convective mass flux due to increased tropical stability have 
been associated with a  reduction in cyclogenesis (Held and Zhao, 
2011; Sugi et al., 2012). Satoh et al. (2015) further posit that the 
robust simulated increase in the number of intense TCs, and hence 
increased vertical mass flux associated with intense TCs, must lead 
to a decrease in overall TC frequency because of this association. The 
Genesis Potential Index can be modified to mimic the TC frequency 
decreases of a model by altering the treatment of humidity (Camargo 
et al., 2014). This supports the idea that increased mid-tropospheric 
saturation deficit (Emanuel et al., 2008) controls TC frequency,

but the approach remains empirical. Other possible controlling 
factors, such as a  decline in the number of seeds (held constant 
in Emanuel’s downscaling approach, or dependent on the genesis 
index formulation in the approach proposed by C.-Y . Lee et al., 2020) 
caused by increased atmospheric stability have been proposed, 
but questioned as an important factor (Patricola et al., 2018). The 
resolution of atmospheric models affects the number of seeds, hence 
TC genesis frequency (Vecchi et al., 2019; Sugi et al., 2020; Yamada 
et al., 2021). The diverse and sometimes inconsistent projected 
changes in global TC frequency by high-resolution models indicate 
that better process understanding and improvement of the models 
are needed to raise confidence in these changes. 
Most TC-permitting model simulations (10–60 km or finer grid 
spacing) are consistent in their projection of increases in the 
proportion of intense TCs (Category 4–5), as well as an increase in

the intensity of the strongest TCs defined by maximum wind speed 
or central pressure fall (Murakami et al., 2012; Tsuboki et al., 2015; 
Wehner et al., 2018a; Knutson et al., 2020). The general reduction 
in the total number of TCs, which is concentrated in storms weaker 
than or equal to Category 1, contributes to this increase. The models 
are somewhat less consistent in projecting an increase in the 
frequency of Category 4–5 TCs (Wehner et al., 2018a; Knutson et al., 
2020). The projected increase in the intensity of the strongest TCs is 
consistent with theoretical understanding (e.g., Emanuel, 1987) and 
observations (e.g.,  Kossin et al., 2020). For a  2°C global warming, 
the median proportion of Category 4–5 TCs increases by 13%, 
while the median global TC frequency decreases by 14%, which 
implies that the median of the global Category 4–5 TC frequency is 
slightly reduced by 1% or almost unchanged (Knutson et al., 2020).

Murakami et al. (2020) projected a decrease in TC frequency over the 
coming century in the North Atlantic due to greenhouse warming, 
as consistent with Dunstone et al. (2013), and a  reduction in TC 
frequency almost everywhere in the tropics in response to +1% 
CO 2 forcing. Exceptions include the central North Pacific (Hawaii 
region), east of the Philippines in the North Pacific, and two relatively 
small regions in the northern Arabian Sea and Bay of Bengal. These 
projections can vary substantially between ocean basins, possibly

1591
due to differences in regional SST warming and warming patterns 
(Sugi et al., 2017; Yoshida et al., 2017; Bacmeister et al., 2018). 
A  summary of projections of TC characteristics is schematically 
shown by Figure 11.20.
The increase in global TC maximum surface wind speeds is about 5% 
for a 2°C global warming across a number of high-resolution multi-
decadal studies (Knutson et al., 2020). This indicates the deepening 
in global TC minimum surface pressure under the global warming 
conditions. A regional cloud-permitting model study shows that the 
strongest TC in the western North Pacific can be as strong as 857 hPa 
in minimum surface pressure with a wind speed of 88 m s–1 under 
warming conditions in 2074–2087 (Tsuboki et al., 2015). TCs are 
also measured by quantities such as ACE and the power dissipation 
index (PDI), which conflate TC intensity, frequency, and duration

(Murakami et al., 2014). Several TC modelling studies (Yamada et al., 
2010; H.S. Kim et al., 2014; Knutson et al., 2015) project little change 
or decreases in the globally accumulated value of PDI or ACE, which 
is due to the decrease in the total number of TCs. 
A projected increase in global average TC rain rates of about 12% 
for a 2°C global warming is consistent with the Clausius–Clapeyron 
scaling of saturation-specific humidity (Knutson et al., 2020). 
Increases substantially greater than Clausius–Clapeyron scaling are 
projected in some regions, which is caused by increased low-level 
moisture convergence due to projected TC intensity increases in those 
regions (Knutson et al., 2015; Phibbs and Toumi, 2016; Patricola and 
Wehner, 2018; M. Liu et al., 2019a). Projections of TC precipitation 
using large-ensemble experiments (Kitoh and Endo, 2019) show 
that the annual maximum one-day precipitation total is projected

to increase, except for the western North Pacific where only a small 
change (or even a reduction) is projected, mainly due to a projected 
decrease of TC frequency. They also show that the 10-year return 
value of extreme Rx1day associated with TCs will greatly increase 
in a region extending from Hawaii to the south of Japan. TC tracks 
and the location of topography relative to TCs significantly affect 
precipitation, thus, in general, areas on the eastern and southern 
faces of mountains have more impacts of TC precipitation changes 
(Hatsuzuka et al., 2020). Projection studies using variable-resolution 
models in the North Atlantic (Stansfield et al., 2020) indicate that TC-
related precipitation rates within North Atlantic TCs and the amount 
of hourly precipitation due to TC are projected to increase by the end 
of the century compared to a  historical simulation. However, the 
annual average TC-related Rx5day over the eastern USA is projected

to decrease because of a reduction in landfalling TCs. RCM studies 
with around 25–50 km grid spacing are used to study projected 
changes in TCs. The projected changes of TCs in South East Asia 
simulated by RCMs are consistent with those of most GCMs, showing 
a  decrease in TC frequency and an increase in the amount of TC-
associated precipitation or an increase in the frequency of intense 
TCs (Redmond et al., 2015; Gallo et al., 2019). 
Projected changes in TC tracks or TC areas of occurrence in the 
late 21st  century vary considerably among available studies, 
although there is better agreement in the western North Pacific. 
Several studies project either poleward or eastward expansion 
of TC occurrence over the western North Pacific region, and more TC occurrence in the central North Pacific (Yamada et al., 2017; 
Yoshida et al., 2017; Wehner et al., 2018a; Roberts et al., 2020b). 
The observed poleward expansion of the latitude of maximum TC

intensity in the western North Pacific is consistently reproduced 
by the CMIP5 models and downscaled models, and these models 
show further poleward expansion in the future; the projected mean 
migration rate of the mean latitude where TCs reach their lifetime-
maximum intensity is 0.2±0.1° from CMIP5 model results, while it 
is 0.13±0.04° from downscaled models in the western North Pacific 
(Kossin et al., 2014, 2016a). In the North Atlantic, while the location 
of TC maximum intensity does not show clear poleward migration 
observationally (Kossin et al., 2014), it tends to migrate poleward 
in projections (Garner et al., 2017). The poleward migration is less 
robust among models and observations in the Indian Ocean, eastern 
North Pacific, and South Pacific (e.g.,  Tauvale and Tsuboki, 2019; 
Ramsay et al. 2018; Cattiaux et al. 2020). There is presently no clear 
consensus in projected changes in TC translation speed (Knutson

et al., 2020), although recent studies suggest a slowdown outside 
of the tropics (Kossin, 2019; Yamaguchi et al., 2020; G. Zhang et al., 
2020), but regionally there can even be an acceleration of the storms 
(Hassanzadeh et al., 2020).
The spatial extent, or ‘size’, of the TC wind field is an important 
determinant of storm surge and damage. No detectable anthropogenic 
influences on TC size have been identified to date, because TCs in 
observations vary in size substantially (Chan and Chan, 2015) and 
there is no definite theory on what controls TC size, although this 
is an area of active research (Chavas and Emanuel, 2014; Chan 
and Chan, 2018). However, projections by high-resolution models 
indicate future broadening of TC wind fields when compared to 
TCs of the same categories (Yamada et al., 2017), while Knutson 
et al. (2015) simulate a reasonable interbasin distribution of TC size 
climatology, but project no statistically significant change in global

average TC size. A plausible mechanism is that, as the tropopause 
height becomes higher with global warming, the eye wall areas 
become wider because the eye walls are inclined outward with 
height to the tropopause. This effect is only reproduced in high-
resolution convection-permitting models capturing eye walls, and 
such modelling studies are not common. Moreover, the projected TC 
size changes are generally on the order of 10% or less, and these size 
changes are still highly variable between basins and studies. Thus, the 
projected change in both magnitude and sign of TC size is uncertain. 
The coastal effects of TCs depend on TC intensity, size, track, and 
translation speed. Projected increases in sea level, average TC 
intensity, and TC rainfall rates each generally act to further elevate 
future storm surge and fresh-water flooding (see Section  9.6.4.2). 
Changes in TC frequency could contribute toward increasing or

decreasing future storm surge risk, depending on the net effects of 
changes in weaker vs stronger storms. Several studies (McInnes et al., 
2014, 2016; Little et al., 2015; Garner et al., 2017; Timmermans et al., 
2017, 2018) have explored future projections of storm surge in the 
context of anthropogenic climate change with the influence of both 
sea level rise and future TC changes. Garner et al. (2017) investigated 
the near-future changes in the New York City coastal flood hazard, 
and suggested a small change in storm-surge height because effects 
of TC intensification are compensated by the offshore shifts in TC

1592
tracks, but concluded that the overall effect due to the rising sea 
levels would increase the flood hazard. Future projection studies 
of storm surge in East Asia, including China, Japan and Korea, also 
indicate that storm surges due to TCs become more severe (J.A. Yang 
et al., 2018; Mori et al., 2019, 2021; J. Chen et al., 2020b). For the 
Pacific Islands, McInnes et al. (2014) found that the future projected 
increase in storm surge in Fiji is dominated by sea level rise, and 
projected TC changes make only a minor contribution. Among various 
storm surge factors, there is high confidence  that sea level rise will 
lead to a higher possibility of extreme coastal water levels in most 
regions, with all other factors assumed equal. 
In the North Atlantic, vertical wind shear, which inhibits TC genesis 
and intensification, varies in a quasi-dipole pattern, with one centre

of action in the tropics and another along the south-east USA 
coast (Vimont and Kossin, 2007). This pattern of variability creates 
a protective barrier of high shear along the USA coast during periods 
of heightened TC activity in the tropics (Kossin, 2017), and appears 
to be a  natural part of the Atlantic ocean–atmosphere climate 
system (Ting et al., 2019). Greenhouse gas forcing in CMIP5 and the 
Community Earth System Model Large Ensemble (Kay et al., 2015) 
simulations, however, erodes the pattern and degrades the natural 
shear barrier along the USA coast. Following the RCP8.5 emissions 
scenario, the magnitude of the erosion of the barrier equals the 
amplitude of past natural variability (time of emergence) by the 
mid-21st century (Ting et al., 2019). The projected reduction of shear 
along the USA East Coast with warming is consistent among studies 
(e.g., Vecchi and Soden, 2007).
In summary, average peak TC wind speeds and the proportion of

Category 4–5 TCs will very likely  increase globally with warming. 
It is likely  that the frequency of Category 4–5 TCs will increase in 
limited regions over the western North Pacific. It is very likely  
that average TC rain rates will increase with warming, and likely  that 
the peak rain rates will increase at rate greater than the Clausius–
Clapeyron scaling rate of 7% per 1°C of warming in some regions 
due to increased low-level moisture convergence caused by regional 
increases in TC wind intensity. It is likely  that the average location 
where TCs reach their peak wind intensity will migrate poleward in 
the western North Pacific Ocean as the tropics expand with warming, 
and that the global frequency of TCs over all categories will decrease 
or remain unchanged. 
11.7.2 Extratropical Storms
This section focuses on extratropical cyclones (ETCs) that are either 
classified as strong or extreme by using some measure of their

intensity, or by being associated with the occurrence of extremes 
in variables such as precipitation or near-surface wind speed 
(Seneviratne et al., 2012). Since AR5, the high relevance of ETCs for 
extreme precipitation events has been well established (Pfahl and 
Wernli, 2012; Catto and Pfahl, 2013; Utsumi et al., 2017), with 80% 
or more of hourly and daily precipitation extremes being associated 
with either ETCs or fronts over oceanic mid-latitude regions, and 
somewhat smaller, but still very large, proportions of events over 
mid-latitude land regions (Utsumi et al., 2017). The emphasis in this section is on individual ETCs that have been identified using some 
detection and tracking algorithms. Mid-latitude atmospheric rivers 
are assessed in Section 8.3.2.8.
11.7.2.1 Observed Trends
Section 2.3.1.4.3 concluded that there is overall low confidence  in 
recent changes in the total number of ETCs over both hemispheres,

and that there is medium confidence  in a poleward shift of the storm 
tracks over both hemispheres since the 1980s. Overall, there is also 
low confidence  in past-century trends in the number and intensity 
of the strongest ETCs due to the large interannual and decadal 
variability (Feser et al., 2015; Reboita et al., 2015; Wang et al., 2016; 
Varino et al., 2019) and due to temporal and spatial heterogeneities 
in the number and type of assimilated data in reanalyses, particularly 
before the satellite era (Krueger et al., 2013; Tilinina et al., 2013; 
Befort et al., 2016; Chang and Yau, 2016; Wang et al., 2016). There 
is medium confidence  that the agreement among reanalyses and 
detection and tracking algorithms is higher when considering stronger 
cyclones (Neu et al., 2013; Pepler et al., 2015; Wang et al., 2016). 
Over the Southern Hemisphere, there is high confidence  that the total 
number of ETCs with low central pressures (<980 hPa) has increased

between 1979 and 2009, with all eight reanalyses considered by 
Wang et al. (2016) showing positive trends, and five of them showing 
statistically significant trends. Similar results were found by Reboita 
et al. (2015) using a different detection and tracking algorithm and 
a  single reanalysis product. Over the Northern Hemisphere, there 
is high agreement  among reanalyses that the number of cyclones 
with low central pressures (<970 hPa) has decreased in summer and 
winter during the period 1979–2010 (Tilinina et al., 2013; Chang 
et al., 2016). However, changes exhibit substantial decadal variability 
and do not show monotonic trends since the 1980s. For example, 
over the Arctic and North Atlantic, Tilinina et al. (2013) showed that 
the number of cyclones with very low central pressure (<960 hPa) 
increased from 1979 to 1990 and then declined until 2010 in all five 
reanalyses considered. Over the North Pacific, the number of cyclones

with very low central pressure reached a peak around 2000 and then 
decreased until 2010 in the five reanalyses considered (Tilinina et al., 
2013). Overall, however, it should be noted that characterising trends 
in the dynamical intensity of ETCs (e.g.,  wind speeds) using the 
absolute central pressure is problematic because the central pressure 
depends on the background mean sea level pressure, which varies 
seasonally and regionally (e.g., Befort et al., 2016). 
11.7.2.2 Model Evaluation
There is high confidence  that coarse-resolution climate models 
(e.g.,  CMIP5 and CMIP6) underestimate the dynamical intensity 
of ETCs, including the strongest ETCs, as measured using a variety of 
metrics, including mean pressure gradient, mean vorticity and near-
surface wind speeds, over most regions (Colle et al., 2013; Zappa 
et al., 2013a; Govekar et al., 2014; Di Luca et al., 2016; Trzeciak 
et al., 2016; Seiler et al., 2018; Priestley et al., 2020). There is also

high confidence  that most current climate models underestimate 
the number of explosive systems (i.e., systems showing a decrease 
in mean sea level pressure of at least 24 hPa in 24 hours) over 
both hemispheres (Seiler and Zwiers, 2016a; Gao et al., 2020;

1593
Priestley et al., 2020). There is high confidence  that 
the underestimation of the intensity of ETCs is associated with the 
coarse horizontal resolution of climate models, with higher 
horizontal resolution models, including HighResMIP and CORDEX, 
usually showing better performance (Colle et al., 2013; Zappa 
et al., 2013a; Di Luca et al., 2016; Trzeciak et al., 2016; Seiler et al., 
2018; Gao et al., 2020; Priestley et al., 2020). The improvement by 
higher-resolution models is found, even when comparing models 
and reanalyses after post-processing data to a common resolution 
(Zappa et al., 2013a; Di Luca et al., 2016; Priestley et al., 2020). 
The systematic bias in the intensity of ETCs has also been linked to 
the inability of current climate models to resolve diabatic processes, 
particularly those related to the release of latent heat (Willison et al.,

2013; Trzeciak et al., 2016) and the formation of clouds (Govekar 
et al., 2014). There is medium confidence  that climate models 
simulate well the spatial distribution of precipitation associated with 
ETCs over the Northern Hemisphere, together with some of the main 
features of the ETC life cycle, including the maximum in precipitation 
occurring just before the peak in dynamical intensity (e.g., vorticity) 
as observed in a reanalysis and observations (Hawcroft et al., 2018). 
There is, however, large observational uncertainty in ETC-associated 
precipitation (Hawcroft et al., 2018) and limitations in the simulation 
of frontal precipitation, including overly low rainfall intensity over 
mid-latitude oceanic areas in both hemispheres (Catto et al., 2015).
11.7.2.3 Detection and Attribution, Event Attribution
Section  3.3.3.3 concluded that there is low confidence  in the 
attribution of observed changes in the number of ETCs in the Northern

Hemisphere and high confidence  that the poleward shift of storm 
tracks in the Southern Hemisphere is linked to human activity, mostly 
due to emissions of ozone-depleting substances. Specific studies 
attributing changes in the most extreme ETCs are not available. 
The human influence on individual extreme ETC events has been 
considered only a few times and there is overall low confidence  in 
the attribution of these changes (NASEM, 2016; Vautard et al., 2019).
11.7.2.4 Projections
The frequency of ETCs is expected to change, primarily following 
a poleward shift of the storm tracks as discussed in Section 4.5.1.6 
(see also Figure  4.31) and Section  8.4.2.8. There is medium 
confidence  that changes in the dynamical intensity (e.g.,  wind 
speeds) of ETCs will be small, although changes in the location of 
storm tracks can lead to substantial changes in local extreme wind 
speeds (Zappa et al., 2013b; Chang, 2014; Li et al., 2014; Seiler and

Zwiers, 2016b; Yettella and Kay, 2017; Barcikowska et al., 2018; Kar-
Man Chang, 2018). Yettella and Kay (2017) detected and tracked 
ETCs over both hemispheres in an ensemble of 30 Community Earth 
System Model Large Ensemble simulations, differing only in their 
initial conditions, and found that changes in mean wind speeds 
around ETC centres are often negligible between present (1986–
2005) and future (2081–2100) periods. Using 19 CMIP5 models, 
Zappa et al. (2013b) found an overall reduction in the number of 
cyclones associated with low-troposphere (850-hPa) wind speeds 
larger than 25 m s–1 over the North Atlantic and Europe with the 
number of the 10% strongest cyclones decreasing by about 8% and 6% in December–January–February and June–July–August 
according to the RCP4.5 scenario (2070–2099 vs. 1976–2005). Over 
the North Pacific, Chang (2014) showed that CMIP5 models project 
a decrease in the frequency of ETCs, with the largest central pressure

perturbation (i.e.,  the depth, strongly related with low-level wind 
speeds) by the end of the century according to simulations using 
the RCP8.5 scenario. Using projections from CMIP5 GCMs under 
the RCP8.5 scenario (1981–2000 to 2081–2100), Seiler and Zwiers 
(2016b) projected a  northward shift in the number of explosive 
ETCs in the northern Pacific, with fewer and weaker events south, 
and more frequent and stronger events north of 45°N. Using 19 
CMIP5 GCMs under the RCP8.5 scenario, Kar-Man Chang (2018) 
found a significant decrease in the number of ETCs associated with 
extreme wind speeds (2081–2100 vs. 1980–99) over the Northern 
Hemisphere (average decrease of 17%) and over some smaller 
regions, including the Pacific and Atlantic regions.
Over the Southern Hemisphere, future changes (RCP8.5 scenario; 
1980–1999 to 2081–2100) in extreme ETCs were studied by Chang 
(2017) using 26 CMIP5 models, and a  variety of intensity metrics

(850-hPa vorticity, 850-hPa wind speed, mean sea level pressure and 
near-surface wind speed). They found that the number of extreme 
cyclones is projected to increase by at least 20% and as much as 
50%, depending on the specific metric used to define extreme ETCs. 
Increases in the number of strong cyclones appear to be robust across 
models and for most seasons, although they show strong regional 
variations, with increases occurring mostly over the southern flank of 
the storm track, consistent with a shift and intensification of the storm 
track. Overall, there is medium confidence that projected changes 
in the dynamical intensity of ETCs depend on the resolution and 
formulation (e.g.,  explicit or implicit representation of convection) 
of climate models (Booth et al., 2013; Michaelis et al., 2017; Zhang 
and Colle, 2017). 
As reported in AR5 and in Section 8.4.2.8, despite small changes in 
the dynamical intensity of ETCs, there is high confidence  that the

precipitation associated with ETCs will increase in the future (Zappa 
et al., 2013b; Marciano et al., 2015; Pepler et al., 2016; Michaelis et al., 
2017; Yettella and Kay, 2017; Zhang and Colle, 2017; Barcikowska 
et al., 2018; Hawcroft et al., 2018; Zarzycki, 2018; Kodama et al., 
2019; Bevacqua et al., 2020a; Reboita et al., 2021). There is high 
confidence  that increases in precipitation will follow increases in 
low-level water vapour (i.e., about 7% per 1°C of surface warming; 
see Box 11.1) and will be larger for higher warming levels (Zhang 
and Colle, 2017). There is medium confidence  that precipitation 
changes will show regional and seasonal differences due to distinct 
changes in atmospheric humidity and dynamical conditions (Zappa 
et al., 2015; Hawcroft et al., 2018), with decreases in some specific 
regions such as the Mediterranean (Zappa et al., 2015; Barcikowska 
et al., 2018). There is high confidence  that snowfall associated

with winter ETCs will decrease in the future, because increases in 
tropospheric temperatures lead to a lower proportion of precipitation 
falling as snow (O’Gorman, 2014; Rhoades et al., 2018; Zarzycki, 
2018). However, there is medium confidence  that extreme snowfall 
events associated with winter ETCs will change little in regions 
where snowfall will be supported in the future (O’Gorman, 2014; 
Zarzycki, 2018).

1594
In summary, there is  low confidence  in past changes in the dynamical 
intensity (e.g., maximum wind speeds) of ETCs and medium confidence  
that, in the future, these changes will be small, although changes in 
the location of storm tracks could lead to substantial changes in local 
extreme wind speeds. There is high confidence  that average and 
maximum ETC precipitation-rates will increase with warming, with the 
magnitude of the increases associated with increases in atmospheric 
water vapour. There is medium confidence  that projected changes in 
the intensity of ETCs, including wind speeds and precipitation, depend 
on the resolution and formulation of climate models.
11.7.3 Severe Convective Storms
Severe convective storms are convective systems that are associated 
with extreme phenomena such as tornadoes, hail, heavy precipitation

(rain or snow), strong winds, and lightning. The assessment of changes 
in severe convective storms in SREX (Chapter 3, Seneviratne et al., 
2012) and AR5 (Chapter 12, Collins et al., 2013) is limited and focused 
mainly on tornadoes and hail storms. The SREX assessed that there 
is low confidence  in observed trends in tornadoes and hail because 
of data inhomogeneities and inadequacies in monitoring systems. 
Subsequent literature assessed in the Climate Science Special Report 
(Kossin et al., 2017) led to the assessment of the observed tornado 
activity over the 2000s in the USA, with a decrease in the number 
of days per year with tornadoes and an increase in the number of 
tornadoes on these days ( medium confidence ). However, there 
is low confidence  in past trends for hail and severe thunderstorm 
winds. Climate models consistently project environmental changes 
that would support an increase in the frequency and intensity of

severe thunderstorms that combine tornadoes, hail, and winds 
(high confidence ), but there is low confidence  in the details of the 
projected increase. Regional aspects of severe convective storms and 
details of the assessment of tornadoes and hail are also assessed in 
Section 12.3.3.2 (tornadoes), Section 12.3.4.5 (hail), Section 12.4.5.3 
(Europe), Section  12.4.6.3 (North America), and Section  12.7.2 
(regional gaps and uncertainties).
11.7.3.1 Mechanisms and Drivers
Severe convective storms are sometimes embedded in synoptic-
scale weather systems, such as TCs, ETCs, and fronts (Kunkel et al., 
2013). They are also generated as individual events as mesoscale 
convective systems (MCSs) and mesoscale convective complexes 
(MCCs, a  special type of a  large, organized and long-lived MCS), 
without being clearly embedded within larger-scale weather systems. 
In addition to the general vigorousness of precipitation, hail, and

winds associated with MCSs, characteristics of MCSs are viewed 
in new perspectives in recent years, probably because of both the 
development of dense mesoscale observing networks and advances in 
high-resolution mesoscale modelling (Sections 11.7.3.2 and 11.7.3.3). 
The horizontal scale of MCSs is discussed with their organization 
of the convective structure, and  it is examined with a  concept of 
‘convective aggregation’  in recent years (Holloway et al., 2017). 
MCSs sometimes take a linear shape and stay almost stationary with 
successive production of cumulonimbus on the upstream side (back-
building type convection), and cause heavy rainfall (Schumacher and Johnson, 2005). Many of the recent severe rainfall events in Japan 
are associated with band-shaped precipitation systems (Kunii et al., 
2016; Oizumi et al., 2018; Tsuguti et al., 2018; Kato, 2020), suggesting 
common characteristics of severe precipitation, at least in East Asia.

The convective modes of severe storms in the USA can be classified 
into rotating or linear modes and preferable environmental conditions 
for these modes, such as vertical shear, have been identified (Trapp 
et al., 2005; Smith et al., 2013; Allen, 2018). Cloud microphysics 
characteristics of MCSs were examined and the roles of warm rain 
processes on extreme precipitation were emphasized recently (Sohn 
et al., 2013; Hamada et al., 2015; Hamada and Takayabu, 2018). 
Idealized studies also suggest the importance of ice and mixed-phase 
processes of cloud microphysics on extreme precipitation (Sandvik 
et al., 2018; Bao and Sherwood, 2019). However, it is unknown 
whether the types of MCS are changing in recent periods or observed 
ubiquitously all over the world.
Severe convective storms occur under conditions preferable for deep 
convection, that is, conditionally unstable stratification, sufficient 
moisture, both in lower and middle levels of the atmosphere, and

a  strong vertical shear. These large-scale environmental conditions 
are viewed as necessary conditions for the occurrence of severe 
convective systems, or the resulting tornadoes and lightning, and 
the relevance of these factors strongly depends on the region 
(e.g.,  Antonescu et al., 2016a; Allen, 2018; Tochimoto and Niino, 
2018). Frequently used metrics are atmospheric static stability, 
moisture content, convective available potential energy (CAPE) and 
convective inhibition, wind shear or helicity, including storm-relative 
environmental helicity (Tochimoto and Niino, 2018; Elsner et al., 
2019). These metrics, largely controlled by large-scale atmospheric 
circulations or synoptic weather systems, such as TCs and ETCs, 
are then generally used to examine severe convective systems. In 
particular, there is high confidence  that CAPE in the tropics and the 
subtropics increases in response to global warming (M.S. Singh et al.,

2017), as supported by theoretical studies (Singh and O’Gorman, 
2013; Seeley and Romps, 2015; Romps, 2016; Agard and Emanuel, 
2017). The uncertainty, however, arises from the balance between 
factors affecting severe storm occurrence. For example, the warming 
of mid-tropospheric temperatures leads to an increase in the freezing 
level, which leads to increased melting of smaller hailstones, while 
there may be some offset by stronger updrafts driven by increasing 
CAPE, which would favour the growth of larger hailstones, leading to 
less melting when falling (Allen, 2018; Mahoney, 2020). 
There are few studies on relations between changes in severe 
convective storms and those of the large-scale circulation patterns. 
Tornado outbreaks in the USA are usually associated with ETCs with 
their frontal systems and TCs (Fuhrmann et al., 2014; Tochimoto and 
Niino, 2016). In early June to late July in East Asia, associated with

the Baiu/Changma/Mei-yu, severe precipitation events are frequently 
caused by MCSs. Severe precipitation events are also caused by 
remote effects of TCs, known as predecessor rain events (Galarneau 
et al., 2010). Atmospheric rivers and other coherent types of 
enhanced water vapour flux also have the potential to induce severe 
convective systems (Kamae et al., 2017a; Waliser and Guan, 2017; 
Ralph et al., 2018; see Section 8.3.2.8.2). Combined with the above 
drivers, topographic effects also enhance the intensity and duration

1595
of severe convective systems and the associated precipitation 
(Ducrocq et al., 2008; Piaget et al., 2015). However, the changes in 
these drivers are not generally significant, so their relations to severe 
convective storms are unclear.
In summary, severe convective storms are sometimes embedded 
in synoptic-scale weather systems, such as TCs, ETCs and fronts, 
and modulated by large-scale atmospheric circulation patterns. 
The occurrence of severe convective storms and the associated 
severe events, including tornadoes, hail, and lightning, is affected by 
environmental conditions of the atmosphere, such as CAPE and vertical 
shear. The uncertainty, however, arises from the balance between these 
environmental factors affecting severe storm occurrence.
11.7.3.2 Observed Trends
Observed trends in severe convective storms or MCSs are not well 
documented, but the climatology of MCSs has been analysed in

specific regions (North America, South America, Europe, Asia; regional 
aspects of convective storms are separately assessed in Chapter 12). 
As the definition of severe convective storms varies depending on 
the literature, it is not straightforward to make a synthesizing view 
of observed trends in severe convective storms in different regions. 
However, analysis using satellite observations provides a global view 
of MCSs (Kossin et al., 2017). The global distribution of thunderstorms 
is captured (Zipser et al., 2006; Liu and Zipser, 2015) by using 
the satellite precipitation measurements by the Tropical Rainfall 
Measuring Mission (TRMM) and Global Precipitation Mission (GPM) 
(Hou et al., 2014). The climatological characteristics of MCSs are 
provided by satellite analyses in South America (Durkee and Mote, 
2010; Rasmussen and Houze, 2011; Rehbein et al., 2018) and those of 
MCCs in the Maritime Continent by Trismidianto and Satyawardhana

(2018). Analysis of the environmental conditions favourable for 
severe convective events indirectly indicates the climatology and 
trends of severe convective events (Allen et al., 2018; Taszarek et al., 
2018, 2019), though favourable conditions depend on the location, 
such as the difference for tornadoes associated with ETCs between 
the USA and Japan (Tochimoto and Niino, 2018).
Observed trends in severe convective storms are highly regionally 
dependent. In the USA, it is indicated that there is no significant 
increase in convective storms, and hail and severe thunderstorms 
(Kunkel et al., 2013; Kossin et al., 2017). There is an upward trend 
in the frequency and intensity of extreme precipitation events in the 
USA ( high confidence ) (Kunkel et al., 2013; Easterling et al., 2017), and 
MCSs have increased in occurrence and precipitation amounts since 
1979 ( limited evidence ) (Feng et al., 2016). Significant interannual

variability of hailstone occurrences is found in the Southern Great 
Plains of the USA (Jeong et al., 2020). The mean annual number 
of tornadoes has remained relatively constant, but their variability 
of occurrence has increased since the 1970s, particularly over the 
2000s, with a  decrease in the number of days per year, but an 
increase in the number of tornadoes on these days (Brooks et al., 
2014; Elsner et al., 2015, 2019; Kossin et al., 2017; Allen, 2018). There 
has been a shift in the distribution of tornadoes, with increases in the 
mid-south of the USA and decreases over the High Plains (Gensini 
and Brooks, 2018). Trends in MCSs are relatively more visible for particular aspects of MCSs, such as lengthening of active seasons 
and dependency on duration. MCSs have increased in occurrence 
and precipitation amounts since 1979 (Easterling et al., 2017). Feng 
et al. (2016) analysed that the observed increases in spring total and

extreme rainfall in the central USA are dominated by MCSs, with 
increased frequency and intensity of long-lasting MCSs. 
Studies on trends in severe convective storms and their ingredients 
outside of the USA are limited. Westra et al. (2014) found that there 
is an increase in the intensity of short-duration convective events 
(minutes to hours) over many regions of the world, except eastern 
China. In Europe, a climatology of tornadoes shows an increase in 
detected tornadoes between 1800 and 2014, but this trend might be 
affected by the density of observations (Antonescu et al., 2016a, b). 
An increase in the trend in extreme daily rainfall is found in south-
eastern France, where MCSs play a  key role in this type of event 
(Blanchet et al., 2018; Ribes et al., 2019). Trend analysis of the mean 
annual number of days with thunderstorms since 1979 in Europe 
indicates an increase over the Alps and central, south-eastern, and

eastern Europe, with a decrease over the south-west (Taszarek et al., 
2019). In the Sahelian region, Taylor et al. (2017) analysed MCSs 
using satellite observations since 1982 and showed an increase in the 
frequency of extreme storms. In Bangladesh, the annual number of 
propagating MCSs decreased significantly during 1998–2015 based 
on TRMM precipitation data (Habib et al., 2019). Prein and Holland 
(2018) estimated the hail hazard from large-scale environmental 
conditions using a statistical approach and showed increasing trends 
in the USA, Europe, and Australia. However, trends in hail on regional 
scales are difficult to validate because of an insufficient length of 
observations and inhomogeneous records (Allen, 2018). The high 
spatial variability of hail suggests it is reasonable that there would be 
local signals of both positive and negative trends, and the trends that 
are occurring in hail globally are uncertain. In China, the total number

of days that have either a thunderstorm or hail have decreased by 
about 50% from 1961 to 2010, and the reduction in these severe 
weather occurrences correlates strongly with the weakening of the 
East Asian summer monsoon (Q. Zhang et al., 2017). More regional 
aspects of severe convective storms are detailed in Chapter 12.
In summary, because the definition of severe convective storms varies 
depending on the literature and the region, it is not straightforward 
to make a synthesizing view of observed trends in severe convective 
storms in different regions. In particular, observational trends in 
tornadoes, hail, and lightning associated with severe convective 
storms are not robustly detected due to insufficient coverage of the 
long-term observations. There is medium confidence  that the mean 
annual number of tornadoes in the USA has remained relatively 
constant, but their variability of occurrence has increased since the

1970s, particularly over the 2000s, with a decrease in the number of 
days per year, and an increase in the number of tornadoes on these 
days ( high confidence ). Detected tornadoes have also increased in 
Europe, but the trend depends on the density of observations. 
11.7.3.3 Model Evaluation
The explicit representation of severe convective storms requires non-
hydrostatic models with horizontal grid spacings finer than 4  km,

1596
denoted as convection-permitting models or storm-resolving models 
(Section  10.3.1). Convection-permitting models are becoming 
available to run over a wide domain, such as a continental scale or even 
over the global area, and show realistic climatological characteristics 
of MCSs (Prein et al., 2015; Guichard and Couvreux, 2017; Satoh et al., 
2019). Such high-resolution simulations are computationally too 
expensive to perform at the larger domain and for long periods, and 
alternative methods by using an RCM with dynamical downscaling 
are generally used (Section  10.3.1). Convection-permitting models 
are used as the flagship project of CORDEX to particularly study 
projections of thunderstorms (Section 10.3.3). Simulations of North 
American MCSs by a  convection-permitting model conducted by 
Prein et al. (2020) were able to capture the main characteristics of

the observed MCSs, such as their size, precipitation rate, propagation 
speed, and lifetime. Cloud-permitting model simulations in Europe 
also showed sub-daily precipitation realistically (Ban et al., 2014; 
Kendon et al., 2014). Evaluation of precipitation conducted using 
convection-permitting simulations around Japan showed that finer 
resolution improves intense precipitation (Murata et al., 2017). MCSs 
over Africa simulated using convection-permitting models showed 
better extreme rainfall (Kendon et al., 2019) and diurnal cycles and 
convective rainfall over land than the coarser-resolution RCMs or 
GCMs (Stratton et al., 2018; Crook et al., 2019).
The other modelling approach is the analysis of the environmental 
conditions that control characteristics of severe convective storms 
using the typical climate model results in CMIP5/6 (Allen, 2018). 
Severe convective storms are generally formed in environments 
with large CAPE and tornadic storms are, in particular, formed with

a  combination of large CAPE and strong vertical wind shear. As 
the processes associated with severe convective storms occur over 
a  wide range of spatial and temporal scales, some of which are 
poorly understood and are inadequately sampled by observational 
networks, the model calibration approaches are generally difficult 
and insufficiently validated. Therefore, model simulations and their 
interpretations should be done with much caution. 
In summary, there are typically two kinds of modelling approaches 
for studying changes in severe convective storms. One is to use 
convection-permitting models in wider regions or the global domain 
in time-sliced downscaling methods to directly simulate severe 
convective storms. The other is the analysis of the environmental 
conditions that control characteristics of severe convective 
storms by using coarse-resolution GCMs. Even in finer-resolution 
convection-permitting models, it is difficult to directly simulate

tornadoes, hail storms, and lightning, so modelling studies of these 
changes are limited.
11.7.3.4 Detection and Attribution, Event Attribution
It is extremely difficult to detect differences in time and space of 
severe convective storms (Kunkel et al., 2013). Although some 
ingredients that are favourable for severe thunderstorms have 
increased over the years, others have not; thus, overall, changes in 
the frequency of environments favourable for severe thunderstorms 
have not been statistically significant. Event attribution studies on 
severe convective events have now been undertaken for some cases. For the case of the heavy rain event of July 2018 in Japan (Box 11.4), 
Kawase et al. (2020) took a  storyline approach to show that the 
rainfall during this event in Japan was increased by approximately 
7% due to recent rapid warming around Japan. For the case of the 
December 2015 extreme rainfall event in Chennai, India, the extremity

of the event was equally caused by the warming trend in the Bay of 
Bengal SSTs and the strong El Niño conditions (van Oldenborgh et al., 
2016; Boyaj et al., 2018). For hailstorms, such as those that caused 
disasters in the USA in 2018, detection of the role of climate change 
in changing hail storms is more difficult, because hail storms are 
not, in general, directly simulated by convection-permitting models 
and not adequately represented by the environmental parameters of 
coarse-resolution GCMs (Mahoney, 2020). 
In summary, it is extremely difficult to detect and attribute changes 
in severe convective storms. There is limited evidence  that extreme 
precipitation associated with severe convective storms has 
increased in some cases.
11.7.3.5 Projections
Future projections of severe convective storms are usually studied 
either by analysing the environmental conditions simulated by 
climate models, or by a time-slice approach with higher-resolution

convection-permitting models by comparing simulations downscaled 
with climate model results under historical conditions and those 
under hypothesized future conditions (Kendon et al., 2017; Allen, 
2018). Up to now, individual studies using convection-permitting 
models gave projections of extreme events associated with severe 
convective storms in local regions, and it is not generally possible 
to obtain global or general views of projected changes of severe 
convective storms. Prein et al. (2017) investigated future projections 
of North American MCS simulations and showed an increase in 
MCS frequency and an increase in total MCS precipitation volume 
by the combined effect of increases in maximum precipitation 
rates associated with MCSs and increases in their size. Rasmussen 
et al. (2020) investigated future changes in the diurnal cycle of 
precipitation by capturing organized and propagating convection 
and showed that weak-to-moderate convection will decrease, and

strong convection will increase in frequency in the future. Ban et al. 
(2015) found that the day-long and hour-long precipitation events 
in summer intensify in the European region covering the Alps. 
Kendon et al. (2019) showed future increases in extreme three-
hourly precipitation in Africa. Murata et al. (2015) investigated 
future projections of precipitation around Japan and showed 
a decrease in monthly mean precipitation in the eastern Japan Sea 
region in December, suggesting that convective clouds become 
shallower in the future in the winter over the Japan Sea.
The other approach is the projection of the environmental conditions 
that control characteristics of severe convective storms by analysing 
climate model results. There is high confidence  that CAPE, particularly 
summer mean CAPE and high percentiles of the CAPE in the tropics 
and subtropics, increases in response to global warming in an 
ensemble of climate models including those of CMIP5, mainly from

increased low-level specific humidity (Sobel and Camargo, 2011; 
M.S. Singh et al., 2017; J. Chen et al., 2020a). Convective inhibition

1597
becomes stronger over most land areas under global warming, 
resulting mainly from reduced low-level relative humidity over land 
(J. Chen et al., 2020a). However, there are large differences within 
the CMIP5 ensemble for environmental conditions, which contribute 
to some degree of uncertainty (Allen, 2018). Because the relation 
between simulated environments in models and the occurrence of 
severe convective storms are, in general, insufficiently validated, there 
is generally low confidence  in the projection of severe convective 
storms with the approach of the environmental conditions. 
In the USA, projected changes in the environmental conditions show 
an increase in CAPE and no changes or decreases in the vertical 
wind shear, suggesting favourable conditions for an increase in 
severe convective storms in the future, but the interpretation of how

tornadoes or hail will change is an open question because  of the 
strong dependence on shear (Brooks, 2013). Diffenbaugh et al. 
(2013) showed robust increases in the occurrence of the favourable 
environments for severe convective storms with increased CAPE 
and stronger low-level wind shear in response to future global 
warming. A downscaling approach showed that the variability of the 
occurrence of severe convective storms increases in spring in late 
21st-century simulations (Gensini and Mote, 2015). Future changes in 
hail occurrence in the USA examined through convection-permitting 
dynamical downscaling suggested that the hail season may begin 
earlier in the year and exhibit more interannual variability, with 
increases in the frequency of large hail in broad areas over the USA 
(Trapp et al., 2019). There is medium confidence  that the frequency 
and variability of the favourable environments for severe convective

storms will increase in spring, and low confidence  for summer 
and autumn (Diffenbaugh et al., 2013; Gensini and Mote, 2015; 
Hoogewind et al., 2017). The occurrence of hail events in Colorado in 
the USA was examined by comparing both present-day and projected 
future climates using high-resolution model simulations capable of 
resolving hailstorms (Mahoney et al., 2012), which showed that 
hail is almost eliminated at the surface in the future in most of the 
simulations, despite more intense future storms and significantly 
larger amounts of hail generated in-cloud.
Future changes in severe convection environments show 
enhancement of instability with less robust changes in the frequency 
of strong vertical wind shear in Europe (Púčik et al., 2017) and 
in Japan (Muramatsu et al., 2016). In Japan, the frequency of 
conditions favourable for strong tornadoes increases in spring, and 
partly in summer. 
In summary, the average and maximum rain rates associated

with severe convective storms increase in a  warming world in 
some regions, including the USA ( high confidence ). There is high 
confidence  from climate models that CAPE increases in response 
to global warming in the tropics and subtropics, suggesting more 
favourable environments for severe convective storms. The frequency 
of severe convective storms in spring is projected to increase in 
the USA, leading to a  lengthening of the severe convective storm 
There is significant uncertainty about projected regional changes in 
tornadoes, hail, and lightning due to limited analysis of simulations 
using convection-permitting models ( high confidence ).11.7.4 Extreme Winds
Extreme winds are defined here in terms of the strongest near-
surface wind speeds that are generally associated with extreme 
storms, such as TCs, ETCs, and severe convective storms. In previous

IPCC reports, near-surface wind speed (including extremes), has not 
been assessed as a variable in its own right, but rather in the context 
of other extreme atmospheric or oceanic phenomena. The exception 
was the SREX report (Seneviratne et al., 2012), which specifically 
examined past changes and projections of mean and extreme near-
surface wind speeds. A strong decline in extreme winds compared to 
mean winds was reported for the continental northern mid-latitudes. 
Due to the small number of studies and uncertainties in terrestrial-
based surface wind measurements, the findings were assigned low 
confidence  in SREX. The AR5 reported a  weakening of mean and 
maximum winds from the 1960s or 1970s to the early 2000s in the 
tropics and mid-latitudes, and increases in high latitudes, but with 
low confidence  in changes in the observed surface winds over land 
(Hartmann et al., 2013). Observed trends in mean wind speed over

land and the ocean are assessed in Section  2.3.1.4.4. Aspects of 
climate impact-drivers for winds are addressed in Sections 12.3.3 
and 12.5.2, and their regional changes are assessed in Section 12.4.
Observationally, although not specifically addressing extreme 
wind speed changes, negative surface wind speed trends (stilling) 
were found in the tropics and mid-latitudes of both hemispheres 
of –0.014 m s–1 yr–1, while positive trends were reported at high 
latitudes poleward of 70 degrees, based on a review of 148 studies 
(McVicar et al., 2012b). An earlier study attributed the stilling to 
both changes in atmospheric circulation and an increase in surface 
roughness due to an overall increase in vegetation cover (Vautard 
et al., 2010). Since then, a number of studies have mostly confirmed 
these general negative mean-wind trends based on anemometer 
data for Spain (Azorin-Molina et al., 2017), Turkey, (Dadaser-Celik

and Cengiz, 2014), the Netherlands, (Wever, 2012), Saudi Arabia, 
(Rehman, 2013), Romania, (Marin et al., 2014), and China (Chen 
et al., 2013). Lin et al. (2013) note that wind speed variability over 
China is greater at high-elevation locations compared to those 
closer to mean sea level. Hande et al. (2012), using radiosonde 
data, found an increase in surface wind speed on Macquarie 
Island of Australia. 
A number of new studies have examined surface wind speeds over 
the ocean using ship-based measurements, satellite altimeters, and 
Special Sensor Microwave/Imagers (Tokinaga and Xie, 2011; Zieger 
et al., 2014). It has been noted that wind speed trends tend to be 
stronger in altimeter measurements, although the spatial patterns 
of change are qualitatively similar in both instruments (Zieger et al., 
2014). Q. Liu et al. (2016) found positive trends in surface wind 
speeds over the Arctic Ocean in 20 years of satellite observations.

Small positive trends in mean wind speed were found in 33 years 
of satellite data, together with larger trends in the 90th percentile 
values over global oceans (Ribal and Young, 2019). These results 
were consistent with an earlier study that found a positive trend in 
1-in-100-year wind speeds (Young et al., 2012). A positive change 
in mean wind speeds was found for the Arabian Sea and the Bay of 
Bengal (Shanas and Kumar, 2015) and Zheng et al. (2017) found that

1598
positive wind speed trends over the ocean were larger during winter 
seasons than summer seasons.
Changes in extreme winds are associated with changes in the 
characteristics (locations, frequencies, and intensities) of extreme 
storms, including TCs, ETCs, and severe convective storms. For TCs, 
as assessed in Section  11.7.1.5, it is projected that the average 
peak TC wind speeds will increase globally with warming, while the 
global frequency of TCs over all categories will decrease or remain 
unchanged; the average location where TCs reach their peak wind 
intensity will migrate poleward in the western North Pacific Ocean 
as the tropics expand with warming. Frequency, intensities, and 
geographical distributions of extreme wind events associated with 
TCs will change according to these TC changes. For ETCs, by the 
end of the century, CMIP5 models show that the number of ETCs

associated with extreme winds will significantly decrease in the mid- 
and high latitudes of the Northern Hemisphere in winter, with the 
projected decrease being larger over the Atlantic (Kar-Man Chang, 
2018), while it will significantly increase irrespective of the season 
in the Southern Hemisphere (Section 11.7.2.4; Chang, 2017). Over 
the ocean in the subtropics, a large ensemble of 60-km global model 
simulations indicated that extreme winds associated with storm 
surges will intensify over 15–35°N in the Northern Hemisphere 
(Mori et al., 2019). However, extreme surface wind speeds will 
mostly decrease due to decreases in the number and intensity of TCs 
over most tropical areas of the Southern Hemisphere (Mori et al., 
2019). The projected changes in the frequency of extreme winds are 
associated with the future changes in TCs and ETCs.
Extreme cyclonic windstorms that share some characteristics with 
both TCs and ETCs occur regularly over the Mediterranean Sea and

are often referred to as ‘medicanes’ (Ragone et al., 2018; Miglietta 
and Rotunno, 2019; Zhang et al., 2021). Medicanes pose substantial 
threats to regional islands and coastal zones. A  growing body 
of literature consistently found that the frequency of medicanes 
decreases under warming, while the strongest medicanes become 
stronger (Gaertner et al., 2007; Romero and Emanuel, 2013, 2017; 
Cavicchia et al., 2014; Tous et al., 2016; Romera et al., 2017; 
González-Alemán et al., 2019). This is also consistent with expected 
global changes in TCs under warming (Section 11.7.1). Based on the 
consistency of these studies, it is likely  that medicanes will decrease 
in frequency, while the strongest medicanes become stronger under 
warming scenario projections ( medium confidence ).
In summary, the observed intensity of extreme winds is becoming 
less severe in the low to mid-latitudes, while becoming more 
severe in high latitudes poleward of 60 degrees ( low confidence ).

Projected changes in the frequency and intensity of extreme winds 
are associated with projected changes in the frequency and intensity 
of TCs and ETCs ( medium confidence ).
11.8 Compound Events 
The SREX (SREX Chapter 3) first defined compound events as: (i) two 
or more extreme events occurring simultaneously or successively, 
(ii) combinations of extreme events with underlying conditions that amplify the impact of the events, or (iii) combinations of events that 
are not themselves extremes but lead to an extreme event or impact 
when combined. 
Further definitions of compound events have emerged since SREX. 
Zscheischler et al. (2018) defined compound events broadly as ‘the 
combination of multiple drivers and/or hazards that contributes to 
societal or environmental risk’. This definition is used in the present 
assessment, because of its clear focus on the risk framework 
established by the IPCC, and also highlighting that compound events

may not necessarily result from dependent drivers. Compound events 
have been classified into: preconditioned events, where a  weather-
driven or climate-driven precondition aggravates the impacts 
of a  climatic impact-driver; multivariate events, where multiple 
drivers and/or climatic impact-drivers lead to an impact; temporally 
compounding events, where a  succession of hazards leads to an 
impact; and spatially compounding events, where hazards in multiple 
connected locations cause an aggregated impact (Zscheischler et al., 
2020). Drivers include processes, variables, and phenomena in the 
climate and weather domain that may span over multiple spatial 
and temporal scales. Hazards (such as floods, heatwaves, wildfires; 
also termed ´climatic impact-drivers´ in this report, see Chapter 12) 
are usually the immediate physical precursors to negative impacts, 
but can occasionally have positive outcomes (Flach et al., 2018). The

present assessment focuses on the physical dimension of changes in 
compound events, as it is part of the IPCC AR6 Working Group I Report.
11.8.1 Overview
The combination of two or more – not necessarily extreme – weather 
or climate events that occur: i) at the same time; ii) in close succession; 
or iii) concurrently in different regions, can lead to extreme impacts 
that are much larger than the sum of the impacts due to the occurrence 
of individual extremes alone. This is because multiple stressors can 
exceed the coping capacity of a system more quickly. The contributing 
events can be of similar types (clustered multiple events) or of 
different types (Zscheischler et al., 2020). Many major weather- and 
climate-related catastrophes are inherently of a  compound nature 
(Zscheischler et al., 2019). This has been highlighted for a  broad 
range of hazards, such as droughts, heatwaves, wildfires, coastal 
extremes, and floods (Westra et al., 2016; AghaKouchak et al., 2020;

Ridder et al., 2020). Co-occurring extreme precipitation and extreme 
winds can result in infrastructural damage (Martius et al., 2016); the 
compounding of storm surge and precipitation extremes can cause 
coastal floods (Wahl et al., 2015); the combination of drought and 
heat can lead to tree mortality (Section 11.6; Allen et al., 2015); and 
wildfires increase occurrences of hailstorms and lightning (Y . Zhang 
et al., 2019a). Compound storm types consisting of co-located 
cyclone, front and thunderstorm systems have a  higher chance of 
causing extreme rainfall and extreme winds than individual storm 
types (Dowdy and Catto, 2017). Extremes may occur at similar times 
at different locations (De Luca et al., 2020a, b) but affect the same 
system, for instance, spatially concurrent climate extremes affecting 
crop yields and food prices (Singh et al., 2018; Anderson et al., 2019). 
Studies also show an increasing likelihood for breadbasket regions to

be concurrently affected by climate extremes with increasing global

1599
warming, even between 1.5°C and 2°C of global warming (Box 11.2; 
Gaupp et al., 2019). Concomitant extreme conditions at different 
locations become more probable as changes in climate extremes 
are emerging over an increasing fraction of the land area (Sections 
11.2.3, 11.2.4, 11.8.2 and 11.8.3, and Box 11.4). 
Finally, impacts may occur because of large multivariate anomalies in 
the climate drivers, if systems are adapted to historical multivariate 
climate variability (Flach et al., 2017). For instance, ecosystems 
are typically adapted to the local covariability of temperature and 
precipitation such that a bivariate anomaly may have a large impact, 
even though neither temperature nor precipitation may be extreme 
based on a  univariate assessment (Mahony and Cannon, 2018). 
Given that almost all systems are affected by weather and climate

phenomena at multiple space-time scales (Raymond et al., 2020), it  
is natural to consider extremes in a  compound event framework. 
It should be noted, however, that multi-hazard dependencies can also 
decrease risk, for instance when hazards are negatively correlated 
(Hillier et al., 2020). Despite this recognition, the literature on past 
and future changes in compound events has been limited, but is 
growing. This section assesses examples of types of compound 
events in available literature. 
In summary, compound events include the combination of two or 
more  – not necessarily extreme  – weather or climate events that 
occur (i) at the same time, (ii) in close succession, or (iii) concurrently 
in different regions. The land area affected by concurrent extremes has 
increased ( high confidence ). Concurrent extreme events at different 
locations, but possibly affecting similar sectors (e.g., breadbaskets) 
in different regions, will become more frequent with increasing

global warming, in particular above +2°C of global warming 
(high confidence ). 
11.8.2 Concurrent Extremes in Coastal 
and Estuarine Regions
Coastal and estuarine zones are prone to a number of meteorological 
extreme events and also to concurrent extremes (see also 
Section 6.8.2 in SROCC). Floods are a major climatic impact-driver in 
coastal regions around the world (Chapter 12), and flood occurrence 
may be influenced by the dependence between storm surge, extreme 
rainfall, and river flow, but also by sea level rise, waves and tides, as 
well as groundwater for estuaries. Floods with multiple drivers are 
often referred to as ‘compound floods’ (Wahl et al., 2015; Moftakhari 
et al., 2017; Bevacqua et al., 2020c). 
At USA coasts, the probability of co-occurring storm surge and heavy 
precipitation is higher for the Atlantic/Gulf coast relative to the Pacific 
coast (Wahl et al., 2015). Furthermore, six studied locations on the

USA coast with long overlapping time series show an increase in the 
dependence between heavy precipitation and storm surge over the 
last century, leading to more frequent co-occurring storm surge and 
heavy precipitation events at the present day (Wahl et al., 2015). Storm 
surge and extreme rainfall are also dependent in most locations on the 
Australian coasts (Zheng et al., 2013) and in Europe along the Dutch 
coasts (Ridder et al., 2018), along the Mediterranean Sea, the Atlantic coast and the North Sea (Bevacqua et al., 2019). The probability of 
flood occurrence can be assessed via the dependence between storm 
surge and river flow (Bevacqua et al., 2020b, c). For instance, the 
occurrence of a North Sea storm surge in close succession with an 
extreme Rhine or Meuse river discharge is much more probable due 
to their dependence, compared to if both events were independent 
(Kew et al., 2013; Klerk et al., 2015). Significant dependence between

high sea levels and high river discharge are found for more than 
half of the available station observations, which are mostly located 
around the coasts of North America, Europe, Australia, and Japan 
(Ward et al., 2018). Combining global river discharge with a global 
storm surge model, hotspots of compound flooding have been 
discovered that are not well covered by observations in some regions, 
including Madagascar, Northern Morocco, Vietnam, and Taiwan of 
China (Couasnon et al., 2020).  In  the Dutch Noorderzijlvest area, 
there is more than a two-fold increase in the frequency of exceeding 
the highest warning level compared to the case if storm surge and 
heavy precipitation were independent (van den Hurk et al., 2015). In 
other regions and seasons, the dependence can be insignificant (W. 
Wu et al., 2018) and there can be significant seasonal and regional 
differences in the storm surge–heavy precipitation relationship.

Assessments of flood probabilities are often not based on actual flood 
measurements; instead, they are estimated from its main drivers, 
including astronomical tides, storm surge, heavy precipitation, and 
high streamflow. Such single driver analyses might underestimate 
flood probabilities if multiple correlated drivers contribute to flood 
occurrence (e.g., van den Hurk et al., 2015). 
Many coastal areas are also prone to the occurrence of compound 
precipitation and wind extremes, which can cause damage, including 
to infrastructure and natural environments. A  high percentage of 
co-occurring wind and precipitation extremes are found in coastal 
regions and in areas with frequent tropical cyclones. Finally, the 
combination of extreme wave height and duration is also shown to 
influence coastal erosion processes (Corbella and Stretch, 2012). 
Aspects of concurrent extremes in coastal and estuarine environments

have increased in frequency and/or magnitude over the last century in 
some regions. These include an increase in the dependence between 
heavy precipitation and storm surge over the last century, leading 
to more frequent co-occurring storm surge and heavy precipitation 
events in the present day along USA coastlines (Wahl et al., 2015). In 
Europe, the probability of compound flooding occurrence increases 
most strongly along the Atlantic coast and the North Sea under 
strong warming. This increase is mostly driven by an intensification 
of precipitation extremes and aggravated flooding probability due 
to sea level rise (Bevacqua et al., 2019). At the global scale and 
under a  high-emissions scenario, the concurrence probability of 
meteorological conditions driving compound flooding would increase 
by more than 25%, on average, along coastlines worldwide by 2100, 
compared to the present (Bevacqua et al., 2020c). Sea level extremes

and their physical impacts in the coastal zone arise from a complex 
set of atmospheric, oceanic, and terrestrial processes that interact 
on a range of spatial and temporal scales and will be modified by 
a changing climate, including sea level rise (McInnes et al., 2016). 
Interactions between sea level rise and storm surges (Little et al., 
2015), and sea level and fluvial flooding (Moftakhari et al., 2017) are

1600
projected to lead to more frequent and intense compound coastal 
flooding events as sea levels continue to rise. 
In summary, there is medium confidence  that, over the last century, 
the probability of compound flooding has increased in some locations, 
including along the USA coastline. There is high confidence  that the 
occurrence and magnitude of compound flooding in coastal regions 
will increase in the future due to both sea level rise and increases in 
heavy precipitation.
11.8.3 Concurrent Droughts and Heatwaves
Concurrent droughts and heatwaves have a  number of negative 
impacts on human society and natural ecosystems. Studies since 
SREX and AR5 show several occurrences of observed combinations 
of drought and heatwaves in various regions.
Over most land regions, temperature and precipitation are strongly 
negatively correlated during summer (Zscheischler and Seneviratne,

2017), mostly due to land–atmosphere feedbacks (Sections 11.1.6 and 
11.3.2), but also because synoptic-scale weather systems favourable 
for extreme heat are also unfavourable for rain (Berg et al., 2015). 
This leads to a strong correlation between droughts and heatwaves 
(Zscheischler and Seneviratne, 2017). Drought events characterized 
by low precipitation and extreme high temperatures have occurred, 
for example, in California (AghaKouchak et al., 2014), inland Eastern 
Australia (King et al., 2014), and large parts of Europe (Orth et al., 
2016a). The 2018 growing season was both record-breaking dry and 
hot in Germany (Zscheischler and Fischer, 2020).
The probability of co-occurring meteorological droughts and 
heatwaves has increased in the observational period in many regions 
and will continue to do so under unabated warming (Herrera-Estrada 
and Sheffield, 2017; Zscheischler and Seneviratne, 2017; Hao et al.,

2018; Sarhadi et al., 2018; Alizadeh et al., 2020; Wu et al., 2021). Overall, 
projections of increases in co-occurring drought and heatwaves are 
reported in northern Eurasia (Schubert et al., 2014), Europe (Orth 
et al., 2016a; Sedlmeier et al., 2018), south-east Australia (Kirono 
et al., 2017), multiple regions of the USA (Diffenbaugh et al., 2015; 
Herrera-Estrada and Sheffield, 2017), north-west China (X. Li et al., 
2019; Kong et al., 2020) and India (Sharma and Mujumdar, 2017). The 
dominant signal is related to the increase in heatwave occurrence, 
which has been attributed to anthropogenic forcing (Section 11.3.4). 
This means that, even if drought occurrence is unaffected, compound 
hot and dry events will be more frequent (Sarhadi et al., 2018; Yu 
and Zhai, 2020). 
Drought and heatwaves are also associated with fire weather, related 
through high temperatures, low soil moisture, and low humidity. 
Fire weather refers to weather conditions conducive to triggering

and sustaining wildfires, which generally include temperature, soil 
moisture, humidity, and wind (Chapter 12). Concurrent hot and dry 
conditions amplify conditions that promote wildfires (Schubert et al., 
2014; Littell et al., 2016; Dowdy, 2018; Hope et al., 2019). Burnt area 
extent in western USA forests (Abatzoglou and Williams, 2016) and 
particularly in California (Williams et al., 2019) has been linked to anthropogenic climate change via a  significant increase in vapour 
pressure deficit, a primary driver of wildfires. A study of the western 
USA examined the correlation between historical water-balance 
deficits and annual area burned, across a range of vegetation types, 
from temperate rainforest to desert (McKenzie and Littell, 2017). 
The relationship between temperature and dryness, and wildfire, 
varied with ecosystem type, and the fire–climate relationship was 
nonstationary and vegetation-dependent. In many fire-prone regions,

such as the Mediterranean and China’s Daxing’anling region, 
projections for increased severity of future drought and heatwaves 
may lead to an increased frequency of wildfires relative to observed 
climatology (Tian et al., 2017; Ruffault et al., 2018). Observations 
show a long-term trend towards more dangerous weather conditions 
for bushfires in many regions of Australia, which is attributable (at 
least in part) to anthropogenic climate change (Dowdy, 2018). There 
is emerging evidence that recent regional surges in wildland fires are 
being driven by changing weather extremes (Cross-Chapter Box 3; Jia 
et al., 2019; SRCCL Chapter 2). Between 1979 and 2013, the global 
burnable area affected by long fire weather seasons doubled, and 
the mean length of the fire weather season increased by 19% (Jolly 
et al., 2015). However, at the global scale, the total burned area has 
been decreasing between 1998 and 2015 due to human activities

mostly related to changes in land use (Andela et al., 2017). Given 
the projected high confidence  increase in compound hot and dry 
conditions, there is high confidence  that fire weather conditions 
will become more frequent at higher levels of global warming in 
some regions. This assessment is also consistent with Chapter 12’s 
examination of regional projected changes in fire weather. The 
SRCCL (Chapter 2) assessed with high confidence  that future climate 
variability is expected to enhance the risk and severity of wildfires in 
many biomes such as tropical rainforests.
In summary, there is high confidence  that concurrent heatwaves and 
droughts have increased in frequency over the last  century at the 
global scale due to human influence. There is medium confidence  
that weather conditions that promote wildfires (fire weather) have 
become more probable in southern Europe, northern Eurasia, the 
USA, and Australia over the last century. There is high confidence  that

compound hot and dry conditions become more probable in nearly 
all land regions as global mean temperature increases. There is high 
confidence  that fire weather conditions will become more frequent at 
higher levels of global warming in some regions.

1601
Box 11.4 | Case Study: Global-scale Concurrent Climate Anomalies – the 2015–2016 Extreme 
El Niño and 2018 Boreal Spring–Summer
Occurrence of concurrent or near-concurrent extremes in 
different parts of a region, or in different locations around the 
world, challenges adaptation and risk management capacity. This 
can occur as a result of natural climate variability, as climates 
in different parts of the world are interconnected through large-
scale atmospheric–oceanic teleconnections. In addition, in 
a  warming climate, the probability of having several locations 
being affected simultaneously by, for example, hot extremes and 
heatwaves increases strongly as a function of global warming, 
with detectable changes even for changes as small as +0.5°C 
of additional global warming (Sections 11.2.4 and 11.3, Cross-
chapter Box  11.1). Recent articles have highlighted the risks

associated with concurrent extremes over large spatial scales 
(e.g.,  Lehner and Stocker, 2015; Boers et al., 2019; Gaupp 
et al., 2019). There is evidence that such global-scale extremes 
associated with hot temperature extremes are increasing in 
occurrence (Sippel et al., 2015; Vogel et al., 2019). Hereafter, the 
focus is on two case studies of recent global-scale events that 
featured concurrent extremes in several regions across the world. 
The first focuses on concurrent extremes driven by variability in 
tropical Pacific sea surface temperatures (SSTs) associated with the 2015–2016 extreme El Niño, while the second addresses the 
impacts of global warming combined with abnormal atmospheric circulation patterns in the 2018 boreal spring/summer. 
The extreme El Niño in 2015–2016
El Niño–Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different

parts of the world, especially in extreme El Niño (Annex IV.2.3) cases. Additionally, the background climate warming associated with 
greenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Niño conditions. The 2015–
2016 extreme El Niño event was one of the three extreme El Niño events since the 1980s and the availability of satellite rainfall 
observations. According to some measures, it was the strongest El Niño in the past 145 years (Barnard et al., 2017). The 2015–2016 
warmth was unprecedented at the central equatorial Pacific (Niño4: 5°N–5°S, 150°E–150°W), and this exceptional warmth was 
unlikely  to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend (Newman et al., 2018). In particular, 
its signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing

to the highest record of GMST in 2016 (Section 2.3.1.1). Both the ENSO amplitude and the frequency of high-magnitude events since 
1950 is higher than over the pre-industrial period ( medium confidence ) (Section 2.4.2), suggesting that global extremes similar to 
those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. A brief 
summary of extreme events that happened in 2015–2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean 
and Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of 
the world during the 2015–2016 extreme El Niño in Box 11.4, Table 1, as well as in the short summary that follows.
Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa,

and Europe. As a result, global measurements of land water anomalies were particularly low in that year (Humphrey et al., 2018). In 2015, 
Indonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze 
crisis (Field et al., 2016; Huijnen et al., 2016; Patra et al., 2017; Hartmann et al., 2018). The northern part of Australia experienced high 
temperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the 
Gulf of Carpentaria in Northern Australia (Duke et al., 2017). The Amazon region experienced the most intense droughts of this century 
in 2015–2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 (Lewis 
et al., 2011; Erfanian et al., 2017; Panisset et al., 2018). The 2015–2016 Amazon drought impacted the entirety of South America north

of 20°S during the austral spring and summer (Erfanian et al., 2017). It also increased forest fire incidence by 36% compared to the 
preceding 12 years (Aragão et al., 2018) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) 
concentration in the area, affecting air quality (Ribeiro et al., 2018). This out-of-season drought affected the water availability for human 
consumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation 
of food, medicines, and fuels (INMET, 2017). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the 
worst in several decades and was associated with the 2015–2016 extreme El Niño (Blunden and Arndt, 2016; Philip et al., 2018b). It was Box 11.4, Figure 1 | Analysis of the percentage of land area affected 
by temperature extremes larger than two (blue) or three (orange)

standard deviations in June–July–August (JJA) between 30°N and 
80°N using a normalization.  This figure shows a substantial increase in the 
overall land area affected by very strong hot extremes since 1990. Adapted from 
Sippel et al. (2015).

1602
suggested that anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs and local 
air temperatures (Funk et al., 2016, 2018b; Yuan et al., 2018a). It has also been suggested that the 2015–2016 extreme El Niño affected 
circulation patterns in Europe during the 2015–2016 winter (Geng et al., 2017; Scaife et al., 2017). 
The atmospheric CO 2 growth rate was particularly high in 2015, possibly related to some of the mentioned droughts, in particular in 
Indonesia and the Amazon region, leading to higher CO 2 release in combination with less CO 2 uptake from land areas (Humphrey et al., 
2018). The impact of the 2015–2016 extreme El Niño on vegetation systems via drought was also shown from satellite data (Kogan 
and Guo, 2017). Overall, tropical forests were a carbon source to the atmosphere during the 2015–2016 El Niño-related drought, with

some estimates suggesting that up to 2.3 PgC were released (Brando et al., 2019).
The 2015–2016 extreme El Niño has induced extreme precipitation in some regions. Severe rainfall events were observed in Chennai 
city in India in Devember 2015, and the Yangtze river region in China in June–July 2016, and it was shown that these rainfall events 
are partly attributed to the 2015–2016 extreme El Niño (van Oldenborgh et al., 2016; Boyaj et al., 2018; Sun and Miao, 2018; Yuan 
et al., 2018b; Zhou et al., 2018). 
In 2015, tropical cyclone activity was notably high in the North Pacific (Blunden and Arndt, 2016). Over the western North Pacific, there 
were 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area’s typical annual value of 6.3 (W. Zhang et al., 2016b). 
Similarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain

(Collins et al., 2016; Murakami et al., 2017b). These extraordinary TC activities were related to the average SST anomaly during that 
year, which were associated with the 2015–2016 extreme El Niño and the positive phase of the Pacific Meridional Mode (Murakami 
et al., 2017b; Hong et al., 2018; Yamada et al., 2019). However, it has been suggested that the intense TC activities in both the western 
and the eastern North Pacific in 2015 were not only due to the El Niño, but also to a contribution of anthropogenic forcing (Murakami 
et al., 2017b; S.-H. Yang et al., 2018). The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity 
in the western North Pacific in 2015 (Zhan et al., 2018). In contrast, in Australia, it was the least active TC season since satellite records 
began in 1969–1970 (Blunden and Arndt, 2017).Box 11.4, Table 1 | List of events related to the 2015–2016 Extreme El Niño in the literature.
Region Period Events References

Indonesia July 2015 to June 2016 Droughts, forest fireField et al. (2016); Huijnen et al. (2016); 
Patra et al. (2017); Hartmann et al. (2018)
Northern AustraliaBetween late 2015 
and early 2016High temperature and drought Duke et al. (2017)
Amazon September 2015 to May 2016 Droughts, forest fireJiménez-Muñoz et al. (2016); Erfanian et al. 
(2017); Aragão et al. (2018); Panisset et al. (2018); 
Ribeiro et al. (2018)
The entirety of South America 
north of 20°SAustral spring and  
2015–2016 summer Droughts Erfanian et al. (2017)
Ethiopia February-September 2015 Droughts Blunden and Arndt (2016); Philip et al. (2018b)
Southern Africa November 2015–April 2016 DroughtsFunk et al. (2016, 2018a); Blamey et al. (2018); 
Yuan et al. (2018a)
Europe Boreal 2015–2016 winter Effects on circulation patterns Geng et al. (2017); Scaife et al. (2017)
India May 2016 High temperature van Oldenborgh et al. (2018)
India December 2015 Extreme rainfall van Oldenborgh et al. (2016); Boyaj et al. (2018)

China June–July 2016 Extreme rainfallSun and Miao (2018); Yuan et al. (2018b); 
Zhou et al. (2018)
Western North Pacific Boreal summer 2015The large number (13) of Category 4 and 
5 tropical cyclonesBlunden and Arndt (2016); B. Mueller et al. 
(2016); W. Zhang et al. (2016a); Hong et al., 
(2018); Yamada et al. (2019)
Eastern North Pacific Boreal summer 2015 A record-breaking number of tropical cyclones Collins et al. (2016); Murakami et al. (2017b)
Global 2015–2016 El NiñoHigh CO 2 release to the atmosphere 
associated with droughts and fires 
in several affected regionsHumphrey et al. (2018); Brando et al. (2019) 
Box 11.4 (continued)

1603
Global-scale temperature extremes and concurrent precipitation extremes in boreal 2018 spring and summer
In the 2018 boreal spring–summer season (May–August), wide areas of the mid-latitudes in the Northern Hemisphere experienced 
heat extremes and (in part) enhanced drought (Box 11.4, Figure 2; Kornhuber et al., 2019; Vogel et al., 2019). The reported impacts 
included (Vogel et al., 2019): 90 deaths from heat strokes in Quebec (Canada); 1469 deaths from heat strokes in Japan (Shimpo et al., 
2019); 48 heat-related deaths in the Republic of Korea (Min et al., 2020); heat warnings affecting 90,000 students in the USA; fires 
in numerous countries (Canada (British Columbia), USA (California), Finland (Lapland), Latvia); crop losses in the UK, Germany and 
Switzerland (Vogel et al., 2019) and overall in central and Northern Europe (leading to yield reductions of up to 50% for the main

crops (Toreti et al., 2019); fish deaths in Switzerland; and melting of roads in the Netherlands and the UK, among others. In addition 
to the numerous hot and dry extremes, an extremely heavy rainfall event occurred over wide areas of Japan from 28 June to 8 July 
2018 (Tsuguti et al., 2018), which was followed by a heatwave (Shimpo et al., 2019). The heavy precipitation event caused more than 
230 deaths in Japan, and was named ‘the Heavy Rain Event of July 2018’. 
The heavy precipitation event was characterized by unusually widespread and persistent rainfall and locally anomalous total 
precipitation led by band-shaped precipitation systems, which are frequently associated with heavy precipitation events in East 
Asia (Section 11.7.3; Kato, 2020). The extreme rainfall in Japan was caused by anomalous moisture transport with a combination 
of abnormal jet condition (Takemi and Unuma, 2019; Takemura et al., 2019; Tsuji et al., 2020; Yokoyama et al., 2020), which can be

viewed as an atmospheric river (Sections 8.2.2.8 and 11.7.2; Yatagai et al., 2019) caused by intensified inflow velocity and high SST 
around Japan (Sekizawa et al., 2019; Kawase et al., 2020). 
This precipitation event and the subsequent heatwave are related to abnormal condition of the jet stream and North Pacific Subtropical 
High in this month (Shimpo et al., 2019; Ren et al., 2020), which caused extreme conditions from Europe, Eurasia, and North America 
(Box 11.4, Figure 2; Kornhuber et al., 2019). A combination of the positive anomaly of the North Atlantic Oscillation (NAO, Annex IV.2.1) 
and the meandering jets is necessary to explain the pattern of the observed anomalies (Drouard et al., 2019) . A role of Atlantic SST 
anomaly on the meandering jets and the subtropical high have been suggested (B. Liu et al., 2019). These dynamic and thermodynamic

components generally have substantial influence on extreme rainfall in East Asia (Oh et al., 2018), but it is under investigation whether 
these factors were due to anthropogenic forcing.
Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May–July period, Vogel et al. (2019) 
found that the event was unprecedented in terms of the total area affected by hot extremes (on average, about 22% of populated 
and agricultural areas in the Northern Hemisphere) for that period, but was consistent with a +1°C climate which was the estimated  
Box 11.4, Figure 2 | Meteorological conditions in July 2018.  The colour shading shows the monthly mean near-surface air temperature anomaly with respect 
to 1981–2010. Contour lines indicate the geopotential height in m, highlighted are the isolines on 12,000 m and 12,300 m, which indicate the approximate positions

of the polar-front jet and subtropical jet, respectively. The light blue-green ellipse shows the approximate extent of the strong precipitation event that occurred at the 
beginning of July in the region of Japan and Korea. All data is from the global European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5, 
Hersbach et al., 2020).

1604
global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May–July 
temperature extremes would approximately occur two out of three years under +1.5°C of global warming, and every year under +2°C of global warming. Imada et al. (2019) also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to 
increase by 1.8 times under a global warming level of 2°C above pre-industrial levels. Kawase et al. (2020) showed that the extreme 
rainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) 
showed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic warming. Hence, it is virtually certain that these 2018 concurrent events would not have occurred without human-induced global

warming. Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, 
there is currently low confidence in projected changes in the frequency or strength of the anomalous circulation patterns leading to 
concurrent extremes (e.g., Cross-Chapter Box 10.1). 
The case studies presented in this Box illustrate the current state of knowledge regarding the contribution of human-induced climate 
change to recent concurrent extremes in the global domain. Recent years have seen a more frequent occurrence of such events.

The heatwave in Europe in the 2019 boreal summer and its coverage in the global domain is an additional example (Vautard et al., 2020). However, very few studies investigate which types of concurrent extreme events could occur under increasing global warming. It has been noted that such events could also be of particular risk for concurrent impacts in the world’s breadbaskets (Zampieri et al., 2017; Kornhuber et al., 2020; see also Section 11.8.1).
In summary, the 2015–2016 extreme El Niño and the 2018 boreal spring/summer extremes were two examples of recent concurrent 
extremes. The El Niño event in 2015–2016 was one of the three extreme El Niño events since the 1980s, and there are many extreme events concurrently observed in this period including droughts, heavy precipitation, and more frequent intense tropical cyclones. Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period ( medium

confidence), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. The 2018 boreal spring/summer extremes were characterized by heat extremes and enhanced droughts in wide areas of the mid-latitudes in the Northern Hemisphere and extremely heavy rainfall in East Asia. These concurrent events were generally related to the abnormal condition of the jet and North Pacific Subtropical High, but also amplified by background global warming. It is virtually certain that these 2018 concurrent extreme events would not have occurred without 
human-induced global warming. Recent years have seen a more frequent occurrence of such concurrent events. However, it is still unknown which types of concurrent extreme events could occur under increasing global warming.Box 11.4 (continued)
11.9 Regional Information on Extr emes

This section complements the assessments of changes in temperature extremes (Section  11.3), heavy precipitation (Section  11.4), and droughts (Section  11.6), by providing additional regional details. 
Regional changes in floods are assessed in Chapter  12. Owing to 
the large number of regions and space limitations, the regional assessment for each of the AR6 reference regions (see Section 1.5.2.2 for a  description) is presented here in a  set of tables. The tables are organized according to types of extremes (temperature, 
heavy precipitation, droughts) for Africa (Tables 11.4–11.6), Asia 
(Tables  11.7–11.9), Australasia (Tables 11.10–11.12), Central and South America (Tables 11.13–11.15), Europe (Tables 11.16–11.18), and North America (Tables 11.19–11.21). Each table contains 
regional assessments for observed changes, the human contribution

to the observed changes, and projections of changes in these extremes at 1.5°C, 2°C and 4°C of global warming. A synthesis of regional changes in hot extremes, heavy precipitation, agricultural and ecological droughts, and hydrological droughts can be found in 
the Chapter 11 Appendix in Table 11.A.2.11.9.1  Overview
Sections 11.9.2, 11.9.3 and 11.9.4 provide brief summaries of the 
underlying evidence used to derive the regional assessments for temperature extremes, heavy precipitation events, and droughts, 
respectively. The assessments take into account evidence from 
studies based on global datasets (global studies), as well as regional studies. Global studies include analyses for all continents and AR6 regions with sufficient data coverage, and provide an important basis for cross-region consistency, as the same data and methods 
are used for all regions. However, individual regional studies may

include additional information that is missed in global studies, and thus provide an important regional calibration for the assessment.
The assessments are presented using the calibrated confidence and 
likelihood language (Box  1.1). Low confidence is assessed when 
there is limited evidence, either because of a lack of available data 
in the region and/or a  lack of relevant studies. Low confidence is also assessed when there is a  lack of agreement on the evidence 
of a change, which may be due to large variability or inconsistent 
changes depending on the considered sub-regions, time frame, models, assessed metrics, or studies. In cases when the evidence is

1605
strongly contradictory, for example, with substantial regional changes 
of opposite sign, ‘mixed signal’ is indicated. With an assessment of 
low confidence , the direction of change is not indicated in the tables. 
A  direction of change (increase or decrease) is provided with an 
assessment of medium confidence , high confidence , likely , or higher 
likelihood levels. Likelihood assessments are only provided in the 
case of high confidence . In some cases, there may be confidence in 
a small or no change.
For projections, changes are assessed at three global warming levels 
(GWLs; Cross-Chapter Box 11.1): 1.5°C, 2°C and 4°C. The assessments 
use literature based both on GWL projections and scenario-based 
projections. In the case of literature on scenario-based projections, 
a mapping between scenarios/time frames and GWLs was performed, 
as documented in Cross-Chapter Box 11.1. Projections of changes in

temperature and precipitation extremes are assessed relative to two 
different baselines: the recent past (1995–2014) and pre-industrial 
(1850–1900). With smaller changes relative to the variability, in 
particular because droughts happen on longer timescales compared 
to extremes of daily temperature and precipitation, it is more difficult 
to distinguish changes in drought relative to the recent past. As such, 
changes in droughts are assessed relative to the pre-industrial 
baseline, unless indicated otherwise. 
11.9.2 Temperature Extremes
Tables 11.4, 11.7, 11.10, 11.13, 11.16, and 11.19 include assessments 
for past temperature extremes and their attribution, as well as future 
projections. The evidence is mostly drawn from changes in metrics based 
on daily maximum and minimum temperatures, similar to those used 
in Section 11.3. The regional assessments start from global studies that 
used consistent analyses for all regions globally with sufficient data.

This includes Dunn et al. (2020) for observed changes, and Li et al. (2021) 
and the Chapter 11 Supplementary Material (11.SM) for projections 
with the CMIP6 multi-model ensemble. Evidence from regional studies, 
and those based on the CMIP5 multi-model ensemble or CORDEX 
simulations, are then used to refine the confidence assessments. 
For attribution, Seong et al. (2020) provide a  consistent analysis for 
AR6 regions, and Z. Wang et al. (2017a) for SREX regions. Additional 
regional studies, including event attribution analyses (Section  11.2), 
are used when available. In some regions that were not analysed 
in Seong et al. (2020), and those with no known event attribution 
studies, medium confidence  of a  human contribution is assessed: 
when there is strong evidence of changes from observations that are 
in the direction of model-projected changes for the future; when the 
magnitude of projected changes increases with global warming; and

where there is no other evidence to the contrary. This assessment is 
further supported by an understanding of how temperature extremes 
change with the mean temperature and overwhelming evidence of 
a  human contribution to the observed larger-scale changes in the 
mean temperature and temperature extremes. 11.9.3 Heavy Precipitation
Tables 11.5, 11.8, 11.11, 11.14, 11.17, and 11.20 include assessments 
for past changes in heavy precipitation events and their attribution, as 
well as future projections. The evidence is mostly drawn from changes 
in metrics based on one-day or five-day precipitation amounts, as 
addressed in Section  11.4. Similar to temperature extremes, the 
assessment of changes in heavy precipitation uses global studies, 
including Dunn et al. (2020) and Sun et al. (2021) for observed 
changes, and Li et al. (2021) and the Chapter  11 Supplementary 
Material (11.SM) for projected changes using the CMIP6 multi-model

ensemble. For attribution, Paik et al. (2020) provided continental 
analyses where data coverage was sufficient, but no attribution 
studies based on global data are available for the regional scale. 
For each region, regional studies, and studies based on the CMIP5 
multi-model ensemble or CORDEX simulations, are also considered in 
the assessments for past changes, attribution, and projections.
11.9.4 Droughts
Tables 11.6, 11.9, 11.12, 11.15, 11.18, and 11.21 provide regional 
assessments on past, attributed and projected changes in 
droughts. The assessment is subdivided in three drought categories 
corresponding to four drought types: i) meteorological droughts, 
ii)  agricultural and ecological droughts, and iii) hydrological 
droughts  (see Section  11.6). A  list of metrics and global studies 
used for the assessments is provided below. The evidence from 
global studies is complemented in each continent with evidence

from regional studies. An overview of studies considered for the 
assessments in projections is provided in Table 11.3.
Meteorological droughts are assessed based on observed and 
projected changes in precipitation-only metrics such as the 
Standardized Precipitation Index (SPI) and Consecutive Dry Days 
(CDD). Observed changes are assessed based on two global 
studies, Dunn et al. (2020) for CDD, and Spinoni et al. (2019) for SPI. 
For  projections, evidence for changes at 1.5°C and 2°C of global 
warming is drawn from L. Xu et al. (2019) and Touma et al. (2015) 
(based on RCP8.5 for 2010–2054 compared to 1961–2005) for SPI 
(CMIP5) and the Chapter  11 Supplementary Material (11.SM) for 
CDD (CMIP6). For projections at 4°C of global warming, evidence is 
drawn from several sources, including Touma et al. (2015) and Spinoni 
et al. (2020) for SPI (from CMIP5 and CORDEX, respectively), and 
11.SM for CDD (CMIP6). No global-scale studies are available for the

attribution of meteorological drought, so this assessment is based on 
regional detection and attribution or event attribution studies.
Agricultural and ecological droughts are primarily assessed based 
on observed and projected changes in total column soil moisture, 
complemented by evidence on changes in surface soil moisture, 
water-balance (precipitation minus evapotranspiration (ET)) and 
metrics driven by precipitation and atmospheric evaporative demand 
(AED) such as the SPEI and PDSI (Section 11.6). In the latter, only 
studies including estimates based on the Penman–Monteith equation 
(SPEI-PM and PDSI-PM) are considered because of biases associated 
with temperature-only approaches (Section  11.6). Medium  to

1606
high confidence  in drying was assigned in the assessment for arid 
regions if a  signal was also identifiable in total soil moisture in 
addition to surface soil moisture or metrics that combine AED and 
precipitation, which tend to dry more in these regions. For observed 
changes, evidence is drawn from several sources: Padrón et al. 
(2020) for changes in precipitation minus ET, as well as soil moisture 
from the multi-model Land Surface Snow and Soil Moisture Model 
Intercomparison Project within CMIP6 (11.SM; van Den Hurk et al., 
2016); Greve et al. (2014) for changes in precipitation minus ET, and 
precipitation minus AED; Spinoni et al. (2019) for changes in SPEI-PM; 
and Dai and Zhao (2017) for changes in PDSI-PM. 
For projections at 1.5°C of global warming, evidence is drawn 
from: L. Xu et al. (2019), based on CMIP5; 11.SM based on CMIP6

for changes in total column and surface soil moisture; and from 
Naumann et al. (2018) for changes in SPEI-PM, based on EC-Earth 
simulations driven with SSTs from seven CMIP5 Earth system models. 
For projections at 2°C of global warming, evidence is drawn from 
L. Xu et al. (2019) based on CMIP5, and Cook et al. (2020) (SSP1-2.6, 
2071–2100 compared to pre-industrial) and the Chapter  11 
Supplementary Material (11.SM) based on CMIP6, for changes in 
total column and surface soil moisture; evidence is also drawn from 
Naumann et al. (2018) for changes in SPEI-PM. For projections at 4°C of global warming, evidence is mostly drawn from: Cook et al. (2020) 
(SSP3-7.0, 2071–2100) and the Chapter 11 Supplementary Material 
(11.SM) based on CMIP6 for changes in total column and surface 
soil moisture; and from Vicente-Serrano et al. (2020c) for changes 
in SPEI-PM based on CMIP5. No global-scale studies with regional-
scale information are available for the attribution of agricultural

and ecological droughts, so this assessment is based on regional 
detection and attribution or event attribution studies.
Hydrological droughts are assessed based on observed and projected 
changes in low flows, complemented by information on changes in 
mean runoff. For observed changes, evidence is drawn from three 
studies (Dai and Zhao, 2017; Gudmundsson et al., 2019, 2021). For 
projected changes at 1.5°C of global warming, evidence is drawn from 
Touma et al. (2015) based on analyses of the Standardized Runoff 
Index (SRI) (CMIP5, based on 2010–2054 compared to 1961–2005), 
complemented with regional studies when available. For projected 
changes at 2°C of global warming, evidence is also drawn from Cook 
et al. (2020) for changes in runoff in CMIP6 (Scenario SSP1-2.6, 2071–
2100), and from J. Zhai et al. (2020) for changes in low flows based 
on simulations with a single model. For projected changes at 4°C of

global warming, evidence is drawn from: Touma et al. (2015) based on 
CMIP5 analyses of SRI; Cook et al. (2020) for changes in surface and 
Table 11.3 | Global analyses considered for the assessments of drought projections.  MET refers to meteorological droughts, AGR/ECOL to agricultural and ecological 
droughts, and HYDR to hydrological droughts.
Reference Model DataaIndexaDrought Type Projection Horizons Baseline
11.SM CMIP6CDD, Soil moisture 
(total, surface)MET 1.5°C, 2°C, 4°C 1850–1900
Cook et al. (2020) CMIP6Soil moisture 
(total, surface), 
runoff (total, surface)AGR/ECOL, 
HYDR2071–2011, SSP1-2.6 (about 2°C,  
Cross-Chapter Box 11.1; Table 4.2)
2071–2011, SSP3-7-3 (about 4°C,  
Cross-Chapter Box 11.1; Table 4.2)1850–1900
L. Xu et al. (2019) CMIP5SPI, soil moisture (total, 
surface)MET, AGR/ECOL 1.5°C, 2°C 1971–2000
Touma et al. (2015) CMIP5 SPI, SRI MET, HYDR2010–2054, RCP8.5 (about 1.5°C;  
Cross-Chapter Box 11.1 and 11.SM.1)
2055–2099, RCP8.5 (about 3.5°C,

Cross-Chapter Box 11.1 and 11.SM.1)1961–2005
Spinoni et al. (2020)CORDEX (CMIP5 driving GCMs, 
RCMs)SPI MET2071–2100, RCP4.5 (about 2.5°C,  
2071–2100, RCP8.5 (about 4.5°C,  
Cross-Chapter Box 11.1 and 11.SM.1)1981–2010
Naumann et al. (2018)One GCM (EC-EARTH3-HR v3.1) 
driven with SST fields from seven 
CMIP5 GCMsSPEI-PM AGR/ECOL 1.5°C, 2°C, (3°C) 0.6°C
Vicente-Serrano et al. 
(2020c)CMIP5 SPEI-PM AGR/ECOL2070–2100, RCP8.5 (about 4.5°C,  
Cross-Chapter Box 11.1 and 11.SM.1)1970–2000
Giuntoli et al. (2015)ISI-MIP (six GHMs and five 
CMIP5 GCMs)Low-flows days HYDR2066–2099, RCP8-5 (about 4°C,  
Cross-Chapter Box 11.1 and 11.SM.1)1972–2005
J. Zhai et al. (2020)One GHM (VIC) driven by four 
CMIP5 GCMs Extreme low runoff HYDR 1.5°C, 2°C 2006–2015
a CMIP5 and CMIP6: Coupled Model Intercomparison Project Phases 5/6; CORDEX: Coordinated Regional Downscaling Experiment; GCMs: global climate models; RCMs:

regional climate models; SST: sea surface temperatures; ISI-MIP: Inter-Sectoral Impact Model Intercomparison Project; GHMs: Global Hydrological Models; CDD: consecutive 
dry days index; SPI: Standardized Precipitation Index; SRI: Standardized Runoff Index; SPEI-PM: Penman–Monteith-based Standardized Precipitation Evapotranspiration Index.

1607
total runoff based on CMIP6; and Giuntoli et al. (2015) for changes in 
low flows based on the Inter-Sectoral Impact Model Intercomparison 
Project (ISI-MIP) based on six Global Hydrological Models (GHMs) and 
five GCMs, including an analysis of inter-model signal-to-noise ratio. 
One global-scale study with regional-scale information is available for 
the attribution of hydrological droughts (Gudmundsson et al., 2021), 
but only in a  few AR6 regions. This information was complemented 
with evidence from regional detection and attribution, and event 
attribution studies when available.
Alexis Berg, Tim Brodribb, Jamie Hannaford, Nate McDowell, 
Jack Scheff, Peter Stott, Lena Tallaksen, Peter Thorne, Francis Zwiers .

1608
FAQ 11.1 | How Do Changes In Climate Extremes Compare With Changes In 
Climate Averages?
Human-caused climate change alters the frequency and intensity of climate variables (e.g., surface temperature) 
and phenomena (e.g., tropical cyclones) in a variety of ways. We now know that the ways in which average and 
extreme conditions have changed (and will continue to change) depend on the variable and the phenomenon 
being considered. Changes in local surface temperature extremes closely follow the corresponding changes in 
local average surface temperatures. On the contrary, changes in precipitation extremes (heavy precipitation) 
generally do not follow those in average precipitation, and can even move in the opposite direction (e.g., with 
average precipitation decreasing but extreme precipitation increasing).

Climate change will manifest very differently depending on which region, season and variable we are interested 
in. For example, over some parts of the Arctic, temperatures will warm at rates about three to four times higher 
during winter compared to summer months. And in summer, most of northern Europe will experience larger 
temperatures increases than most places in south-east South America and Australasia, with differences that can 
be larger than 1°C, depending on the level of global warming. In general, differences across regions and seasons 
arise because the underlying physical processes differ drastically across regions and seasons.
Climate change will also manifest differently for different weather regimes and can lead to contrasting changes 
in average and extreme conditions. Observations of the recent past and climate model projections show that, 
in most places, changes in daily temperatures are dominated by a general warming where the climatological

average and extreme values are shifted towards higher temperatures, making warm extremes more frequent and 
cold extremes less frequent. The top panels in FAQ 11.1, Figure 1 show projected changes in surface temperature 
for long-term average conditions (left) and for extreme hot days (right) during the warm season (summer in mid- 
to high latitudes). Projected increases in long-term average temperature differ substantially between different 
places, varying from less than 3°C in some places in central South Asia and southern South America to over 7°C 
in some places in North America, North Africa and the Middle East. Changes in extreme hot days follow changes 
in average conditions quite closely, although, in some places, the warming rates for extremes can be intensified 
(e.g., southern Europe and the Amazon basin) or weakened (e.g., northern Asia and Greenland) compared to 
average values.

Recent observations and global and regional climate model projections point to changes in precipitation 
extremes (including both rainfall and snowfall extremes) differing drastically from those in average precipitation. 
The bottom panels in FAQ 11.1, Figure 1 show projected changes in the long-term average precipitation (left) 
and in heavy precipitation (right). Averaged precipitation changes show striking regional differences, with 
substantial drying in places such as southern Europe and northern South America and wetting in places such as 
the Middle East and southern South America. Changes in extreme precipitation are much more uniform, with 
systematic increases over nearly all land regions. The physical reasons behind the different responses of averaged 
and extreme precipitation are now well understood. The intensification of extreme precipitation is driven by 
the increase in atmospheric water vapour (about 7% per 1°C of warming near the surface), although this is

modulated by various dynamical changes. In contrast, changes in average precipitation are driven not only by 
moisture increases but also by slower processes that constrain future changes over the globe to only 2–3% per 
1°C of warming near the surface.
In summary, the specific relationship between changes in average and extreme conditions strongly depends on 
the variable or phenomenon being considered. At the local scale, average and extreme surface temperature 
changes are strongly related, while average and extreme precipitation changes are often weakly related. For 
both variables, the changes in average and extreme conditions vary strongly across different places due to the 
effect of local and regional processes.

1609
FAQ 11.1 (continued) 
FAQ 11.1: How will changes in climate extremes compare with changes in climate averages?
Climate extreme Climate average
Climate extreme Climate averageThe direction and magnitude of future changes in climate extremes and averages depend on the variable considered.
-
Warmer
Wetter Drier
Future changes
in temperature 
averages and 
extremes will be  
similar
in precipitation  
extremes can be
very different0 8 6 4 °C °C 2
-40% 40% 0% -20 20
FAQ 11.1, Figure 1 |  Global maps of future changes in surface temperature (top panels) and precipitation (bottom panels) for long-term 
average (left) and extreme conditions (right).  All changes were estimated using the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble

median for a scenario with a global warming of 4°C relative to 1850–1900 temperatures. Average surface temperatures refer to the warmest three-month season 
(summer in mid- to high latitudes) and extreme temperatures refer to the hottest day in a year. Precipitation changes, which can include both rainfall and snowfall 
changes, are normalized by 1850–1900 values and shown as a percentage; extreme precipitation refers to the largest daily precipitation in a year.

1610
FAQ 11.2 | Will Unprecedented Extremes Occur As a Result Of Human-Induced Climate Change?
Climate change has already increased the magnitude and frequency of extreme hot events and decreased 
the magnitude and frequency of extreme cold events, and, in some regions, intensified extreme precipitation 
events. As the climate moves away from its past and current states, we will experience extreme events that 
are unprecedented, either in magnitude, frequency, timing or location. The frequency of these unprecedented 
extreme events will rise with increasing global warming. Additionally, the combined occurrence of multiple 
unprecedented extremes may result in large and unprecedented impacts.
Human-induced climate change has already affected many aspects of the climate system. In addition to the increase

in global surface temperature, many types of weather and climate extremes have changed. In most regions, the 
frequency and intensity of hot extremes have increased and those of cold extremes have decreased. The frequency 
and intensity of heavy precipitation events have increased at the global scale and over a majority of land regions. 
Although extreme events such as land and marine heatwaves, heavy precipitation, drought, tropical cyclones, and 
associated wildfires and coastal flooding have occurred in the past and will continue to occur in the future, they 
often come with different magnitudes or frequencies in a warmer world. For example, future heatwaves will last 
longer and have higher temperatures, and future extreme precipitation events will be more intense in several 
regions. Certain extremes, such as extreme cold, will be less intense and less frequent with increasing warming.

Unprecedented extremes – that is, events not experienced in the past – will occur in the future in five different ways 
(FAQ 11.2, Figure 1). First, events that are considered to be extreme in the current climate will occur in the future 
with unprecedented magnitudes. Second, future extreme events will also occur with unprecedented frequency. 
Third, certain types of extremes may occur in regions that have not previously encountered those types of events. 
For example, as the sea level rises, coastal flooding may occur in new locations, and wildfires are already occurring 
in areas, such as parts of the Arctic, where the probability of such events was previously low. Fourth, extreme events 
may also be unprecedented in their timing. For example, extremely hot temperatures may occur either earlier or 
later in the year than they have in the past.
Finally, compound events  – where multiple extreme 
events of either different or similar types occur

simultaneously and/or in succession  – may be more 
probable or severe in the future. These compound 
events can often impact ecosystems and societies more 
strongly than when such events occur in isolation. 
For example, a drought along with extreme heat will 
increase the risk of wildfires and agriculture damages 
or losses. As individual extreme events become more 
severe as a  result of climate change, the combined 
occurrence of these events will create unprecedented 
compound events. This could exacerbate the intensity 
and associated impacts of these extreme events.
Unprecedented extremes have already occurred in 
recent years, relative to the 20th century climate. Some 
recent extreme hot events would have had very little 
chance of occurring without human influence on the 
climate (see FAQ 11.3). In the future, unprecedented 
extremes will occur as the climate continues to warm. 
Those extremes will happen with larger magnitudes

and at higher frequencies than previously experienced. 
Extreme events may also appear in new locations, at 
new times of the year, or as unprecedented compound 
events. Moreover, unprecedented events will become 
more frequent with higher levels of warming, for 
example at 3°C of global warming compared to 2°C of 
FAQ 11.2: Will climate change cause 
unprecedented extremes? 
Yes, in a changing climate, 
extreme events may be 
unprecedented when they occur with...
Larger magnitude
Increased frequency
New locations
Different timing
New combinations (compound)FAQ 11.2, Figure 1 | New types of unprecedented extremes that will 
occur as a result of climate change.

1611
FAQ 11.3 | Did Climate Change Cause That Recent Extreme Event In My Country?
While it is difficult to identify the exact causes of a particular extreme event, the relatively new science of event 
attribution is able to quantify the role of climate change in altering the probability and magnitude of some 
types of weather and climate extremes. There is strong evidence that characteristics of many individual extreme 
events have already changed because of human-driven changes to the climate system. Some types of highly impactful extreme weather events have occurred more often and have become more severe due to these human 
influences. As the climate continues to warm, the observed changes in the probability and/or magnitude of some 
extreme weather events will continue as the human influences on these events increase.

It is common to question whether human-caused climate change caused a major weather- and climate-related 
disaster. When extreme weather and climate events do occur, both exposure and vulnerability play an important 
role in determining the magnitude and impacts of the resulting disaster. As such, it is difficult to attribute a specific 
disaster directly to climate change. However, the relatively new science of event attribution enables scientists to attribute aspects of specific extreme weather and climate events to certain causes. Scientists cannot answer directly 
whether a particular event was caused by climate change, as extremes do occur naturally, and any specific weather 
and climate event is the result of a complex mix of human and natural factors. Instead, scientists quantify the relative importance of human and natural influences on the magnitude and/or probability of specific extreme weather

events. Such information is important for disaster risk reduction planning, because improved knowledge about 
changes in the probability and magnitude of relevant extreme events enables better quantification of disaster risks.
On a case-by-case basis, scientists can now quantify the contribution of human influences to the magnitude and 
probability of many extreme events. This is done by estimating and comparing the probability or magnitude of 
the same type of event between the current climate – including the increases in greenhouse gas concentrations 
and other human influences – and an alternate world 
where the atmospheric greenhouse gases remained 
at pre-industrial levels. FAQ 11.3 Figure  1  illustrates 
this approach using differences in temperature and probability between the two scenarios as an example. 
Both the pre-industrial (blue) and current (red) 
climates experience hot extremes, but with different probabilities and magnitudes. Hot extremes of a given

temperature have a  higher probability of occurrence 
in the warmer current climate than in the cooler pre-industrial climate. Additionally, an extreme hot event 
of a particular probability will be warmer in the current 
climate than in the pre-industrial climate. Climate model simulations are often used to estimate the occurrence 
of a specific event in both climates. The change in the 
magnitude and/or probability of the extreme event in the current climate compared to the pre-industrial 
climate is attributed to the difference between the two
scenarios, which is the human influence.
Attributable increases in probability and magnitude 
have been identified consistently for many hot 
extremes. Attributable increases have also been found 
for some extreme precipitation events, including hurricane rainfall events, but these results can vary 
among events. In some cases, large natural variations

in the climate system prevent attributing changes in the probability or magnitude of a specific extreme to 
human influence. Additionally, attribution of certain 
classes of extreme weather (e.g., tornadoes) is beyond current modelling and theoretical capabilities. As 
the climate continues to warm, larger changes in 
FAQ 11.3: Climate change and extreme events
Extreme events have become more probable and more intense. 
Many of these changes can be attributed to human influence 
on the climate.
Same temperature 
but more probableCurrent climate
Same probability 
but hotterPre-industrial 
climate
Cooler Warmer TemperatureAnnual 
chance 
of eventMore 
probable
Probabilities are lower 
for hotter (more extreme) eventsLess 
probableFAQ 11.3, Figure  1 | Changes in climate result in changes in the 
magnitude and probability of extremes.  Example of how temperature 
extremes differ between a climate with pre-industrial greenhouse gases (shown

in blue) and the current climate (shown in orange) for a representative region. The horizontal axis shows the range of extreme temperatures, while the vertical axis shows the annual chance of each temperature event’s occurrence. Moving towards the right indicates increasingly hotter extremes that are more rare (less probable). For hot extremes, an extreme event of a particular temperature in the pre-industrial climate would be more probable (vertical arrow) in the current climate. An event of a certain probability in the pre-industrial climate would be warmer (horizontal arrow) in the current climate. While the climate under greenhouse gases at the pre-industrial level experiences a  range of hot extremes, such events are hotter and more frequent in the current climate.

1612
probability and magnitude are expected and, as a result, it will be possible to attribute future temperature and 
precipitation extremes in many locations to human influences. Attributable changes may emerge for other types 
of extremes as the warming signal increases.
In conclusion, human-caused global warming has resulted in changes in a wide variety of recent extreme weather 
events. Strong increases in probability and magnitude, attributable to human influence, have been found for 
many heatwaves and hot extremes around the world.FAQ 11.3 (continued)

1770
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Executive Summary
Climate change information is increasingly available and robust 
at regional scale for impacts and risk assessment.  Climate 
services and vulnerability, impacts and adaptation studies require 
regional scale multi-decadal climate observations and projections. 
Since the IPCC Fifth Assessment Report (AR5), the increased availability 
of coordinated ensemble regional climate model projections and 
improvements in the level of sophistication and resolution of global 
and regional climate models, completed by attribution and sectoral 
vulnerability studies, have enabled the investigation of past and 
future evolution of a range of climatic quantities that are relevant to 
socio-economic sectors and natural systems. Chapter 12 consolidates 
core physical knowledge from preceding AR6 Working Group I (WGI) 
chapters and post-AR5 climate impact assessment literature to

assess the spatio-temporal evolution of the climatic conditions that 
may lead to regional scale impacts and risks (following the sectoral 
classes adopted by AR6 WGII) in the world’s regions (presented in 
Chapter 1). {12.1}
The climatic impact-driver (CID) framework adopted in 
Chapter  12 allows for assessment of changing climate conditions 
that are relevant for regional impacts and risk assessment.  CIDs 
are physical climate system conditions (e.g., means, events, extremes) 
that affect an element of society or ecosystems and are thus a priority 
for climate information provision. Depending on system tolerance, 
CIDs and their changes can be detrimental, beneficial, neutral or 
a  mixture of each across interacting system elements, regions and 
society sectors. Each sector is affected by multiple CIDs and each CID 
affects multiple sectors. A CID can be measured by indices to represent 
related tolerance thresholds. {12.1–12.3}

The current climate in most regions is already different from 
the climate of the early or mid-20th century with respect to 
several CIDs. Climate change has already altered CID profiles 
and resulted in shifts in the magnitude, frequency, duration, 
seasonality and spatial extent of associated indices ( high 
confidence ). Changes in temperature-related CIDs such as mean 
temperatures, growing season length, extreme heat and frost have 
already occurred and many of these changes have been attributed to 
human activities ( medium confidence ). Mean temperatures and heat 
extremes have emerged above natural variability in all land regions 
(high confidence ). In tropical regions, recent past temperature 
distributions have already shifted to a  range different to that of 
the early 20th  century ( high confidence ). Ocean acidification and 
deoxygenation have already emerged over most of the global open 
ocean, as has reduction in Arctic sea ice ( high confidence ). Using CID

index distributions and event probabilities accurately in both current 
and future risk assessments requires accounting for the climate 
change-induced shifts in distributions that have already occurred. 
{12.4, 12.5}
Several impact-relevant changes have not yet emerged 
from the natural variability but will emerge sooner or later 
in this century depending on the emissions scenario ( high 
confidence ). Increasing precipitation is projected to emerge before 
the middle of the century in the high latitudes of the Northern Hemisphere ( high confidence ). Decreasing precipitation will emerge 
in a very few regions (Mediterranean, Southern Africa, south-western 
Australia) ( medium confidence ) by mid-century ( medium confidence ). 
The anthropogenic forced signal in near-coast relative sea level rise 
will emerge by mid-century RCP8.5 in all regions with coasts, except 
in the West Antarctic region where emergence is projected to occur

before 2100 ( medium confidence ). The signal of ocean acidification in 
the surface ocean is projected to emerge before 2050 in every ocean 
basin ( high confidence ). However, there is limited evidence  of drought 
trends emerging above natural variability in the 21st century. {12.5}
Every region of the world will experience concurrent changes 
in multiple CIDs by mid-century ( high confidence ), challenging 
the resilience and adaptation capacity of the region.  Changes in 
heat, cold, snow and ice, coastal, oceanic, and CO 2 at surface CIDs are 
projected with high confidence  in most regions, indicating worldwide 
challenges, while additional region-specific changes are projected in 
other CIDs that may lead to more regional challenges. High confidence  
increases in some of the drought, aridity and fire weather CIDs will 
challenge, for example, agriculture, forestry, water systems, health 
and ecosystems in Southern Africa, the Mediterranean, North Central

America, Western North America, the Amazon regions, South-Western 
South America, and Australia. High confidence  changes in snow, ice 
and pluvial or river flooding will pose challenges for, for example, 
energy production, river transportation, ecosystems, infrastructure 
and winter tourism in North America, Arctic regions, Andes regions, 
Europe, Siberia, Central, South and East Asia, Southern Australia and 
New Zealand. Only a  few CIDs are projected to change with high 
confidence  in the Sahara, Madagascar, Arabian Peninsula, Western 
Africa and Small Islands; however, the lower confidence  levels for 
CID changes in these regions can originate from knowledge gaps 
or model uncertainties, and does not necessarily mean that these 
regions have relatively low risk. {12.5}
Worldwide changes in heat, cold, snow and ice, coastal, oceanic 
and CO 2-related CIDs will continue over the 21st  century, 
albeit with regionally varying rates of change, regardless of

the climate scenario ( high confidence ). In all regions there is high 
confidence  that, by 2050, mean temperature and heat extremes will 
increase, and there is high confidence  that sea surface temperature 
will increase in all oceanic regions except the North Atlantic. Apart 
from a few regions with substantial land uplift, relative sea level rise 
is very likely to virtually certain  (depending on the region) to continue 
in the 21st century, contributing to increased coastal flooding in most 
low-lying coastal areas ( high confidence ) and coastal erosion along 
most sandy coasts ( high confidence ), while ocean acidification is 
virtually certain  to increase. It is virtually certain  that atmospheric 
CO 2 at the surface will increase in all emissions scenarios until 
net zero emissions are achieved. Glaciers will continue to shrink 
and permafrost to thaw in all regions where they are present 
(high confidence ). These changes will lead to climate states with no

recent analogue that are of particular importance for specific regions 
such as tropical forests or biodiversity hotspots. {12.4}
A wide range of region-specific CID changes relative to recent 
past are expected with high or medium confidence , by 2050 and 
beyond.  Most of these changes concern CIDs related to the water cycle

1771
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212and storms. Agricultural and ecological drought changes are generally 
of higher confidence  than hydrological drought changes,  with 
increases projected in North and Southern Africa, Madagascar, 
Southern and Eastern Australia, some regions of Central and South 
America, Mediterranean Europe, Western North America and North 
Central America ( medium to high confidence ). Fire weather conditions 
will increase by 2050 under RCP4.5 or above in several regions in 
Africa, Australia, several regions of South America, Mediterranean 
Europe, and North America ( medium to high confidence ). Extreme 
precipitation and pluvial flooding will increase in many regions 
around the world ( high confidence ). Increases in river flooding are 
also expected in Western and Central Europe and in polar regions 
(high confidence ), most of Asia, Australasia, North America, the South

American Monsoon region and South-Eastern South America ( medium 
confidence ). Mean winds are projected to slightly decrease by 2050 
over much of Europe, Asia and Western North America, and increase in 
many parts of South America except Patagonia, West and South Africa 
and the eastern Mediterranean ( medium confidence ). Extratropical 
storms are expected to have a decreasing frequency but increasing 
intensity over the Mediterranean, increasing intensity over most of 
North America, and an increase in both intensity and frequency over 
most of Europe ( medium confidence ). Enhanced convective conditions 
are expected in North America ( medium confidence ). Tropical cyclones 
are expected to increase in intensity despite a decrease in frequency 
in most tropical regions ( medium confidence ). Climate change will 
modify multiple CIDs over Small Islands in all ocean basins, most 
notably those related to heat, aridity and droughts, tropical cyclones 
and coastal impacts. {12.4}

The level of confidence in the projected direction of 
change in CIDs and the intensity of the signal depend on 
mitigation efforts over the 21st century, as reflected by the 
differences between end-century projections for different 
climate scenarios. Dangerous humid heat thresholds, such as 
the US National Oceanic and Atmospheric Administration Heat 
Index (NOAA HI) of 41°C, will be exceeded much more frequently 
under the SSP5-8.5 scenario than under SSP1-2.6 and will affect 
many more regions ( high confidence ). In  many tropical regions, 
the number of days per year where an HI of 41°C is exceeded will 
increase by more than 100 days relative to the recent past under 
SSP5-8.5, while this increase will be limited to less than 50 days 
under SSP1-2.6 ( high confidence ). The number of days per year 
where temperature exceeds 35°C will increase by more than 150 
days in many tropical areas, such as the Amazon basin and South

East Asia under SSP5-8.5, while it is expected to increase by less 
than two months in these areas under SSP1-2.6 (except for the 
Amazon Basin). There is high confidence  that sandy shorelines will 
retreat in most regions of the world, in the absence of additional 
sediment sources or physical barriers to shoreline retreat. The total 
length of sandy shorelines around the world that are projected to 
retreat by more than 100 m by the end of the century is about 35% 
greater under RCP8.5 (about 130,000 km) compared to that under 
RCP 4.5 (about 95,000 km). The frequency of the present-day 1-in-
100 year extreme sea level event (represented here by extreme 
total water level) is also projected to increase substantially in most 
regions ( high confidence ). In a  globally averaged sense, the 1-in-
100 year extreme sea level is projected to become an event that occurs multiple times per year under RCP8.5, while under RCP4.5

it is projected to become a one-in-five-year event, representing at 
least a five fold increase from RCP4.5 to RCP8.5. {12.4, 12.5}
There is low confidence  in past and future changes of several 
CIDs.  In nearly all regions there is low confidence  in changes in 
hail, ice storms, severe storms, dust storms, heavy snowfall and 
avalanches, although this does not indicate that these CIDs will not 
be affected by climate change. For such CIDs, observations are short 
term or lack homogeneity, and models often do not have sufficient 
resolution or accurate parametrization to adequately simulate them 
over climate change time scales. {12.4}
Many global- and regional-scale CIDs have a  direct relation 
to global warming levels (GWLs) and can thus inform the 
hazard component of ‘Representative Key Risks’ and ‘Reasons 
for Concern’ assessed by AR6 WGII.  These include both mean 
and extreme heat, cold, wet and dry hazards; cryospheric hazards

(snow cover, ice extent, permafrost); and oceanic hazards (marine 
heatwaves) ( high confidence ). For some of these, a  quantitative 
relation can be drawn ( high confidence ). For example, with each 
degree of global surface air temperature (GSAT) warming, the 
magnitude and intensity of many heat extremes show a  linear 
change, while some changes in frequency of threshold exceedances 
are exponential: Arctic temperatures warm about twice as fast as 
GSAT; global sea surface temperatures increase by about 80% of 
GSAT change; Northern Hemisphere spring snow cover decreases 
by about 8% per 1°C. For other hazards (e.g., ice-sheet behaviour, 
glacier mass loss, global mean sea level rise, coastal floods and 
coastal erosion) the time and/or scenario dimensions remain critical 
and a simple relation with GWLs cannot be drawn ( high confidence ), 
but still quantitative estimates assuming specific time frames and/
or stabilized GWLs can be derived ( medium confidence ). Model

uncertainty challenges the link between specific GWLs and tipping 
points and irreversible behaviour, but their occurrence cannot be 
excluded and their chances increase with warming levels ( medium 
confidence ). {CCB 12.1}
Since AR5, climate change information produced in climate 
service contexts has increased significantly due to scientific,  
technological advancements and growing user demand ( very 
high confidence ). Climate services involve the provision of climate 
information in such a way as to assist decision-making. These services 
include appropriate engagement from users and providers, are based 
on scientifically credible information and expertise, have an effective 
access mechanism, and respond to user needs.  Climate services are 
being developed across regions, sectors, time scales and target users. 
{12.6}
Climate services are growing rapidly and are highly diverse 
in their practices and products ( very high confidence ). The

decision-making context, level of user engagement and co-production 
between scientists, practitioners and intended users are important 
determinants of the type of climate service developed and its utility 
supporting adaptation, mitigation and risk management decisions. 
User needs and decision-making contexts are very diverse and there 
is no universal approach to climate services. {12.6}

1772
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Realization of the full potential of climate services is often 
hindered by limited resources for the co-design and co-production 
process, including sustained engagement between scientists, 
service providers and users ( high confidence ). Further challenges 
relate to climate services development, provision of climate services, 
generation of climate service products, communication with users, and 
evaluation of the quality and socio-economic value of climate services. 
The development of climate services often uncovers and presents new 
research challenges to the scientific community. {12.6}

1773
Climate Change Information for Regional Impact and for Risk Assessment Chapter 121212.1 Framing
Climate change is already resulting in significant societal and 
environmental impacts and will induce major socio-economic damages 
in the future (AR5 WGII). The society, at large, benefits from information 
related to climate change risks, which enables the development of 
options to protect lives, preserve nature, build resilience and prevent 
avoidable loss and damage. Climate change can also lead to beneficial 
conditions that can be taken into account in adaptation strategies.
This chapter assesses climate change information relevant for 
regional impact and for risk assessment. It complements other 
WGI chapters that focus on the physical processes determining 
changes in the climate system and on methods for estimating 
regional changes.
Impacts of climate change are driven not only by changes in climate 
conditions, but also by changes in exposure and vulnerability

(Cross-Chapter Box 1.3). This chapter concentrates on drivers of impacts 
that are of climatic origin (see also the IPCC Special Report on Global 
Warming of 1.5°C (SR1.5, IPCC, 2018), and Section 1.3.2 in this Report), 
referred to in WGI as ‘climatic impact-drivers’ (CIDs). CIDs are physical 
climate system conditions (e.g., means, events, extremes) that affect an 
element of society or ecosystems. Depending on system tolerance, CIDs 
and their changes can be detrimental, beneficial, neutral, or a mixture 
of each across interacting system elements and regions. However, this 
chapter largely focuses on drivers commonly connected to hazards, 
and adopts the IPCC risk framework (Cross-Chapter Box  1.3) since 
the main objective of the United Nations Framework Convention on 
Climate Change (UNFCCC) is to ‘prevent dangerous anthropogenic 
interference with the climate system’ (Article 2).
In some cases, risk assessments may require climate information

beyond the CIDs identified in this chapter, with further impacts or 
risk modelling often driven by historical climate forcing datasets 
(e.g.,  Ruane et  al., 2021) and full climate scenario time series 
(e.g., Lange, 2019) produced using methods described in Chapter 10. 
Chapter 12 focuses on the assessment of a finite number of drivers 
and how they are projected to evolve with climate change, in order 
to inform impact and risk assessments.
This chapter is new in IPCC WGI assessment reports, in that it represents 
a  contribution to the ‘IPCC risk framework’. Within this framework, 
climate-related impacts and risks are determined through an interplay 
between the occurrence of climate hazards and their consequences 
depending on the exposure of the affected human or natural system 
and its vulnerability to the hazardous conditions. In Chapter 12, we 
are assessing climatic impact-drivers that could lead to hazards or to

opportunities, from the literature and model results since AR5. This 
will particularly support the assessment of key risks related to climate 
change by WGII (Chapter 16). Despite the fact that impacts may also be 
induced by climate adaptation and mitigation policies themselves, as 
well as by socio-economic trends, changes in vulnerability or exposure, 
and external geophysical hazards such as volcanoes, the focus here is 
only on climatic impacts and risks induced by shifts in physical climate 
phenomena that directly influence human and ecological systems 
(Cross-chapter Box 1.3).This chapter follows the terminology associated with the framing 
introduced in Chapter  1 (Cross-Chapter Box  1.2) and as found in 
Annex VII: Glossary. The highlighted terms below are introduced and 
used extensively in this chapter:
• Indices for climatic impact-drivers:  numerically computable 
indices using one or a combination of climate variables designed

to measure the intensity of the climatic impact-driver, or the 
probability of exceedance of a threshold. For instance, an index 
of heat inducing human health stress is the Heat Index (HI) that 
combines temperature and relative humidity (e.g., Burkart et al., 
2011; Lin et al., 2012; Kent et al., 2014) and is used by the NOAA 
for issuing heat warnings. 
• Thresholds for climatic impact-drivers:  an identified index value 
beyond which a climatic impact-driver interacts with vulnerability 
or exposure to create, increase or reduce an impact, risk or 
opportunity. Thresholds can be used to measure various aspects 
of the climatic impact-driver (magnitude or intensity, duration, 
frequency, timing, and spatial extent of threshold exceedance). 
For instance, a  threshold of daily maximum temperature above 
35°C is considered critical for maize pollination and production 
(e.g., Schauberger et al., 2017; Tesfaye et al., 2017).

The approach adopted here is consistent with the UN Sendai Framework 
for Disaster Risk Reduction 2015–2030, which aims to face disaster 
consequences (including but not limited to climate disasters) and 
reduce risks in natural, managed and built environments (Aitsi-Selmi 
et  al., 2015; UNISDR, 2015). The classification of climatic impact-
drivers in this chapter is largely consistent with the classification of 
hazards used in the Sendai Framework. However, the UNISDR hazard 
list spans a  wider range of hazards inducing damage to society, 
including hazards that are not directly related to climate (such as 
volcanoes and earthquakes), which are excluded from the assessment 
herein. Furthermore, the UNISDR classification of hazards does not 
include mean climatic conditions, which are also discussed as climatic 
impact-drivers in this chapter. The first priority mentioned in the Sendai 
Framework is understanding disaster risk as a  necessary step for

action. Facilitating such an understanding is a clear goal of this chapter.
The chapter adopts a  regional perspective (continental regions as 
defined in Chapter 1 and used in WGII; see Figure 1.18) on climatic 
impact-drivers to support decision-making across a  wide audience 
of global and regional stakeholders in addition to governments 
(e.g., civil society organizations, public and private sectors, academia). 
While the focus here is on future changes, it also describes current 
levels and observed trends of CIDs as an important point of reference 
for informing adaptation strategies.
Figure 12.1 summarizes the rationale behind Chapter 12 as the linkage 
(also referred to as a ‘handshake’) between WGI and WGII, illustrating 
how the changing profile of risk may be informed by an assessment 
of climatic impact-drivers, aligning WGI findings on physical climate 
change with WGII needs. The implementation of mitigation policy

shifts may modulate hazard probability changes (i.e.,  by reducing 
emissions to limit global warming) as well as regional vulnerability and 
exposure. The assessment herein is organized around regional climatic 
impact-drivers, but also relates key indices and thresholds to increasing

1774
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12global drivers (such as mean surface warming) as a contribution to the 
assessment of ‘Reasons for Concern’ in WGII (O’Neill et al., 2017).
The narrative of Chapter  12 is illustrated in Figure  12.2. First, 
Section 12.2 defines a range of climatic impact-driver categories that 
are relevant for regional and sectoral impacts. Next, Section  12.3 
identifies climatic impact-drivers and their relevant indices that 
are frequently used in the context of climate impacts in the WGII 
focus sectors (AR6 WGII Chapters 2–8). The  assessment of changes 
in regional-scale climatic impact-drivers is then developed within 
Section  12.4 by continent, following the structure of the WGII 
assessment report regional chapters (AR6 WGII Chapters 9–15), and 
adding the polar regions, open/deep ocean and other specific zones 
corresponding to the WGII Cross-Chapter Papers. Section  12.5 then

presents a global perspective (both bottom-up and top-down) on the 
change of regional climatic impact-drivers, including an assessment of 
the ‘emergence’ of climatic impact-drivers. Section 12.6 discusses how 
climate information is used in ‘climate services’, which encompasses 
a range of activities bridging climate science and its use for adaptation 
and mitigation decision-making (see also AR6 WGII Chapter  17). 
The chapter concludes with final remarks in Section 12.7.
The chapter includes two Cross-Chapter Boxes. Cross-Chapter Box 12.1 
connects climatic impact-drivers to global climate drivers and levels 
of warming as an element of the ‘Reasons for Concern’ framework 
(AR6 WGII Chapter 16). An additional Cross-Chapter Box, including 
three case studies from Europe, Asia and Africa, describes how climate 
services draw upon and apply regional climate information to support 
stakeholder decisions (Cross-Chapter Box 12.2).12.2 Methodological Approach

This section details the methodological approach followed in 
Chapter 12 and discusses the underlying rationale for the assessments 
presented herein. Scientific literature on vulnerability, impacts, and 
adaptation (as typically asssessed in IPCC WGII) is examined to identify 
relevant climatic impact-drivers (CIDs) that contribute to sectoral risks 
and opportunities. Projected changes in corresponding CID indices are 
then derived from existing literature on changes in the physical climate 
system, results of other AR6 WGI chapters, and direct calculations 
based on climate projections from several model ensembles.
The classification of climatic impact-drivers, the ways that they 
change (e.g.,  their magnitude or intensity, duration, frequency, 
timing and spatial extent) is described in this section. It is 
emphasized that this chapter assesses literature relating only 
to physical climatic impact-drivers, not their impacts on human

systems or the environment. Thus, here we do not consider indicators 
including exposure or vulnerability as assessed by WGII, although 
the selection of climatic impact-drivers is informed by literature 
feeding into WGII.
Chapter  12 assesses climate change information relevant for 
regional impact and for risk assessment in the seven main sectors 
corresponding to Chapters 2–8 of the WGII Assessment Report:
• Terrestrial and freshwater ecosystems and their services (WGII 
Chapter 2);
• Ocean and coastal ecosystems and their services (WGII Chapter 3);
Changing profile of riskImpact & risk assessments Climate change information
Vulnerability Exposure
WGI Chapters 1-11 + Atlas
WGI Chapter 12
WGII Report
Risk of property damage due to coastal floodingExample of coastal flooding
Buildings in coastal 
zone below dike heightUn-elevated buildings will be 
damaged if sea water flows over 
dike and floods neighborhood    
Likelihood of extreme sea

levels exceeding dike heightClimate change alters relative sea 
level, extreme precipitation, river 
flood, storm surge and wavesCoastal flooding occurs when extreme 
sea level exceeds dike heightLarge-scale processes, radiative 
forcing, water and biogeochemical 
cycles, feebacks
Identify key climatic 
impact-driver indices 
and local thresholds  
Assess changes in key 
climatic impact-driver 
indices by region 
Identify physical 
processes 
Assess induced changes in 
trends and extremes
Figure 1 2.1 | Schematic diagram showing the use of climate change information (AR6 WGI chapters) for typical impacts or risk assessment (AR6 WGII 
chapters) and the role of Chapter 12, via an illustration of the assessment of property damage or loss in a particular region when extreme sea level 
exceeds dike height.

1775
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212• Water (WGII Chapter 4);
• Food, fibre and other ecosystem products (WGII Chapter 5);
• Cities, settlements and key infrastructure (WGII Chapter 6);
• Health, well-being and the changing structure of communities 
(WGII Chapter 7);
• Poverty, livelihoods and sustainable development (WGII Chapter 8).
Many of these sectors also include assets affected by climate change 
that are important for recreation and tourism, including elements 
of ecosystems services, health and well-being, communities, 
livelihoods and sustainable development (see also Chapter  1 on 
the Intergovernmental Science-Policy Platform on Biodiversity 
and Ecosystem Services (IPBES), and the IPCC Special Report on 
climate change, desertification, land degradation, sustainable land 
management, food security, and greenhouse gas fluxes in terrestrial 
ecosystems (SRCCL; Hurlbert et al., 2019; IPCC, 2019c)).

CIDs can be captured in seven main types: heat and cold; wet and 
dry; wind; snow and ice; coastal; oceanic and other. Table  12.1 
provides an overview of the seven CID types and the CID categories 
associated with each type. The type ‘Other’ comprises additional CIDs 
that are not encompassed within the other six CID types, including 
air pollution weather (e.g.,  meteorological conditions that favour 
high concentrations of surface ozone, particulate matter or other air 
pollutants), near-surface atmospheric CO 2 concentrations, and mean 
radiation forcing at the surface (which are, for example, relevant for 
plant growth). Icebergs, fog and lightning are also noted in this chapter 
but are not broadly assessed across all subsections. In addition, there can be changes in impacts associated with earthquakes that interact 
with climate variables and climate change, such as liquefaction 
(e.g.,  Yasuhara et  al., 2012) during earthquakes, or earthquakes

caused by snow and water changes (Amos et  al., 2014; Johnson 
et  al., 2017), which are secondary effects on geophysical hazards 
that are not further assessed in this chapter. The characteristics and 
physical description of the climate phenomena or essential climate 
variables associated with each of these CID categories are assessed 
and described in previous Chapters 2–11 or Chapter 12 directly as 
indicated in Table 12.1. The CID categories are further mapped on to 
different sectors in Section 12.3 (Table 12.2).
Potential changes in the seasonality of CIDs or the length and 
characteristics of seasons (e.g., changes in growing season length or 
pollen season) are also important as they may shift the timing of many 
CIDs with broad implications for sectors and regional stakeholders 
(Wanders and Wada, 2015; Cassou and Cattiaux, 2016; Hansen and 
Sato, 2016; Brönnimann et al., 2018; Marelle et al., 2018; Unterberger

et al., 2018; Kuriqi et al., 2020). Episodic CIDs characterize impact-
relevant conditions persisting from short to long time frames but 
eventually returning to normal conditions.
In some situations, phenomena causing severe impacts go well 
beyond a single extreme event or a single climate variable, and can 
include interaction of climatic conditions, such as sea level rise and 
storm surge (Wahl et  al., 2015), precipitation in combination with 
strong winds (Martius et al., 2016) or flooding quickly followed by 
a heatwave (S.S.-Y . Wang et al., 2019; see also Section 10.5.2.4). Such Figure 1 2.2 | Visual guide to Chapter 12.FAQs
Chapter 12: Climate information for regional risk assessment Chapter 12: Quick guide
Cross-chapter boxesSection 12.1
Framing
Types of climatic impact-drivers
 Heat and cold  
 Wet and dry
 Wind
 Snow and ice
 Coastal
 Open ocean
 OtherRegions
 Africa | 12.4.1
 Asia | 12.4.2
 Australasia | 12.4.3
 Central and South America |12.4.4
 Europe | 12.4.5

North America | 12.4.6
 Small islands | 12.4.7
 Ocean |12.4.8
 Polar regions | 12.4.9
 Biodiversity hotspots, cities, deserts, mountains, 
       tropical forests | 12.4.10Section 12.2
Methodological approach
Section 12.3
Climatic impact-drivers for sectors
Section 12.4
Regional information on changing climate
Section 12.5
Global perspective on climatic 
impact-driversSection 12.6
Context of climate 
services
Section 12.7
CC Box 12.1
Linking climatic impact-drivers to representative key risks and reasons for concernCC Box 12.2
Climate information for climate servicesChapter 12 assesses the climate change information relevant to regional impact 
and risk assessment.Key topics and corresponding sub-sections

1776
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Table 12.1  | Overview of the main climatic impact-driver (CID) types and related CID categories with a short description and their link to other chapters 
where the underlying climatic phenomenon and its associated essential climate variables are assessed and described.
CID Type CID Category Brief DescriptionPhysical Description 
of Phenomena
Heat and ColdMean air temperature Mean surface air temperature and its diurnal and seasonal cycles. Chapters 2, 3, 4 and Atlas
Extreme heat Episodic high surface air temperature events potentially exacerbated by humidity. Chapter 11
Cold spell Episodic cold surface air temperature events potentially exacerbated by wind. Chapter 11
Frost Freeze and thaw events near the land surface and their seasonality. Chapter 12
Wet and DryMean precipitation Mean precipitation and its diurnal and seasonal cycles. Chapters 2, 8 and Atlas

River floodEpisodic high water levels in streams and rivers driven by basin runoff and the expected seasonal 
cycle of flooding. Chapters 8 and 11 
Heavy precipitation 
and pluvial floodHigh rates of precipitation and resulting episodic, localized flooding of streams and flat lands. Chapter 11
LandslideGround and atmospheric conditions that lead to geological mass movements, including landslide, 
mudslide and rockfall.Chapter 12
AridityMean conditions of precipitation and evapotranspiration compared to potential atmospheric and surface 
water demand, resulting in low mean surface water, low soil moisture and/or low relative humidity.Chapters 8, 11 and Atlas
Hydrological droughtEpisodic combination of runoff deficit and evaporative demand that affects surface water or 
groundwater availability.Chapters 8 and 11
Agricultural and 
ecological drought Episodic combination of soil moisture supply deficit and atmospheric demand requirements

that challenges the vegetation’s ability to meet its water needs for transpiration and growth. 
Note: ‘agricultural’ vs. ‘ecological’ term depends on affected biome.Chapters 8 and 11
Fire weatherWeather conditions conducive to triggering and sustaining wildfires, usually based on a set of indicators 
and combinations of indicators including temperature, soil moisture, humidity and wind. Fire weather does 
not include the presence or absence of fuel load. Note: distinct from wildfire occurrence and area burned.Chapters 11 and 12
WindMean wind speed Mean wind speeds and transport patterns and their diurnal and seasonal cycles. Chapters 2 and 12
Severe wind stormEpisodic severe storms including extratropical cyclone wind storms, thunderstorms, wind gusts, 
derechos and tornadoes. Chapters 11 and 12
Tropical cycloneStrong, rotating storm originating over tropical oceans accompanied by high winds, rainfall and 
storm surges. Chapter 11

Sand and dust storm Storms causing the transport of soil and fine dust particles. Chapters 8 and 12
Snow and IceSnow, glacier 
and ice sheetSnowpack seasonality and characteristics of glaciers and ice sheets including calving events 
and meltwater.Chapters 2, 9 and Atlas
PermafrostPermanently frozen deep soil layers, their ice characteristics, and the characteristics of seasonally 
frozen soils above. Chapters 2 and 9
Lake, river and sea ice The seasonality and characteristics of ice formations on the ocean and freshwater bodies of water. Chapters 2 and 9
Heavy snowfall 
and ice stormHigh snowfall and ice storm events including freezing rain and rain-on-snow conditions. Chapters 11 and 12
Hail Storms producing solid hailstones. Chapters 11 and 12
Snow avalanche Cryospheric mass movements and the conditions of collapsing snowpack. Chapter 12
CoastalRelative sea level The local mean sea surface height relative to the local solid surface. Chapter 9

Coastal floodFlooding driven by episodic high coastal water levels that result from a combination of relative sea level 
rise, tides, storm surge and wave setup. Chapters 9 and 12
Coastal erosionLong term or episodic change in shoreline position caused by relative sea level rise, nearshore currents, 
waves and storm surge. Chapter 12
Open OceanMean ocean 
temperatureMean temperature profile of ocean through the seasons, including heat content at different depths 
and associated stratification. Chapters 2 and 9
Marine heatwave Episodic extreme ocean temperatures. Chapters 9 and 12
Ocean acidity Profile of ocean water pH levels and accompanying concentrations of carbonate and bicarbonate ions. Chapter 5
Ocean salinityProfile of ocean salinity and associated seasonal stratification. Note: distinct from salinization 
of freshwater resources.Chapters 2 and 5
Dissolved oxygen Profile of ocean water dissolved oxygen and episodic low oxygen events. Chapter 5

OtherAir pollution weatherAtmospheric conditions that increase the likelihood of high particulate matter or ozone concentrations 
or chemical processes generating air pollutants. Note: distinct from aerosol emissions or air pollution 
concentrations themselves.Chapter 6
Atmospheric CO 2 
at surfaceConcentration of atmospheric carbon dioxide (CO 2) at the surface.  Note: distinct from overall radiative 
effect of CO 2 as greenhouse gas.Chapter 5
Radiation at surfaceBalance of net shortwave, longwave and ultraviolet radiation at the Earth’s surface and their diurnal 
and seasonal patterns.Chapter 7

1777
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212compound events, particularly in the context of climate extremes, 
are assessed in Section 11.8. A combination of non-extreme climatic 
impact-drivers in time or space can also lead to severe impacts 
(Cutter, 2018).
Several climatic impact-drivers are reliant on many factors beyond 
their associated primary climatic phenomenon. For example, river 
flooding is heavily dependent on river management and engineering 
and could also be affected by tidal water levels due to sea level 
rise and/or storm surge. Coastal flooding could be affected by 
coastal protection structures, port and harbour structures, as well 
as river flows (on inlet-interrupted coasts). Coastal erosion could 
be influenced by coastal protection measures as well as fluvial 
sediment supply to the coast. Furthermore, air pollution weather 
is not the only or dominant driver, for instance, of surface ozone

pollution, but precursor emissions from anthropogenic sources can 
play a significant role (Section 6.5). Chapter 12 focuses only on the 
influence of the atmospheric, land and oceanic conditions associated 
with the climatic impact-drivers and the confidence in the direction 
of CID changes given here does not take into account existing or 
potential future adaptation measures, unless otherwise stated.
For each CID category there can be a range of indices that capture 
the sector- or application-relevant characteristics of a  climatic 
impact-driver as described in Sections 12.3 and 12.4. Indices for 
climatic impact-drivers that are based on absolute or percentile 
thresholds (e.g.,  daily maximum temperature above 35°C) can be 
affected by biases in climate model simulations, such as local or 
regional deviations of a  simulated climate variable from observed 
values (Sillmann et al., 2014; Dosio, 2016). Where sensible (i.e., where

reliable observational data are available and a climate model that fits 
for the desired purpose), the output of climate model simulations can 
be bias-adjusted, potentially involving advanced methods to account 
for multiple variables and extreme value statistics as assessed in 
detail in Cross-Chapter Box 10.2. Yet, there is no general agreement 
about which bias adjustment methods to apply, as artefacts can arise 
both from the climate model and from the bias adjustment method, 
and the number of available methods has considerably grown in 
recent years (for a  detailed discussion of available methods and 
their performance see Sections 10.3.1.3.2 and 10.3.3.7.2, and Cross-
Chapter Box 10.2). The WGI Interactive Atlas illustrates original and 
bias-adjusted CIDs (see Atlas.1.4.5).
A global perspective on climatic impact-drivers is provided in 
Section 12.5.1. Section 12.5.2 focuses on assessing evidence for the 
emergence (Section  1.4.2.2) of an anthropogenic climate change

signal on the change in CIDs beyond natural climate variability, based 
on the literature assessed in other chapters and additional literature, 
at both global and regional scales. The process of generating 
user-relevant regional climate information in the context of co-
production and climate services is assessed in Sections 10.5, 12.6, 
Box 10.2 and Cross-Chapter Boxes 10.3 and 12.2. Cross-Chapter Box 
12.1 provides a global perspective on climatic impact-drivers related 
to their evolution for different GWLs (Section 1.6).12.3 Climatic Impact-drivers for Sectors
Climate change becomes relevant for regional impact management 
and for risk assessment when changes in mean conditions or episodic 
events affect natural and societal assets (system components with 
socio-economic, cultural or intrinsic value) positively or negatively 
(Table  12.2). Decision makers, policymakers, risk managers and 
engineers therefore benefit from climate information that tracks key

trends and exceedance of thresholds that represent crucial challenges 
for natural and human systems. While useful indices can vary widely 
for a given sector and precise tolerance threshold values are often 
unknown, common metrics, categories and progressions of threshold 
levels allow experts to recognize coherent messages concerning 
altered regional impacts and risk profiles under climate change.
This section surveys the links between CIDs and affected sectors; 
not to perform specific climate change impact or risk assessments 
(see AR6 WGII), but to describe key indices (among many) that 
quantify these links as guidance for stakeholders seeking applicable 
climate information. This survey builds on the work of the World 
Meteorological Organization Expert Team on Sector-Specific Climate 
Indices (ET-SCI) and previous IPCC assessments, notably AR5 WGII 
(Birkmann et al., 2014; IPCC, 2014a) and IPCC Special Reports (IPCC,

2018, 2019b, c) that have assessed climate hazards affecting sectors 
but is organized from a CID perspective drawing also upon recent 
summaries of sectoral hazards (Mora et  al., 2018; ICOMOS, 2019; 
Yokohata et  al., 2019). Impacts, risks and opportunities are rarely 
attributable to a single CID index or threshold, but climate shifts that 
push conditions outside of expected conditions and beyond tolerance 
levels are indicative of impact, risk or benefit given vulnerability 
and exposure. Focus is on direct sectoral connections of a  CID 
(Hallegatte and Przyluski, 2010) rather than cascading or secondary 
effects (e.g., water-borne diseases following a flood, mental health 
challenges following a  severe storm, or the effects of drought on 
poverty), as these are strongly affected by exposure, vulnerability and 
response, as discussed in the WGII Report.
Table 12.2 presents a summary of Section 12.3 connections between

CIDs as defined in Table 12.1 and key sectoral assets, utilizing the 
WGII organization of sectors (corresponding to WGII Chapters 2–8). 
Colours are shown for connections with at least medium confidence  
as assessed from sectoral impacts and risk literature, with relevance 
assessed according to the prominence of that specific CID/asset 
connection in analyses of current and future impacts and risk. Within 
each sector there is a  multitude of specific sectoral systems that 
may be affected by CID increases and decreases, with consequences 
further distinguished by region, background climate and socio-
economic or ecological context of the affected asset. Our aim is 
therefore to recognize important drivers and the common attributes 
of change within each CID that scientists and practitioners monitor to 
understand current and future challenges for important asset groups, 
thereby pointing to the climate information that needs to be tailored

and analysed for impacts and for risk assessment (Section  12.6). 
Additional effects whereby CIDs affect each other (across Table 12.2 
columns) are discussed as climatic phenomena within WGI. The ways 
sectoral assets affect each other (across Table  12.2 rows) are 
described throughout WGII, for example with information about the

1778
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12
Table 12.2  | Relevance of key climatic impact-drivers (and their respective changes in intensity, frequency, duration, timing and spatial extent) for major categories of sectoral assets, as assessed with at least 
medium confidence  in Section 12.3 across many studies and applications.  ‘High relevance’ indicates climatic impact-drivers that are most prominent and widely studied for their direct connection to assets, while lower relevance 
indicates weaker linkages and less commonly-studied driving behaviours. Specific levels of risk and opportunity depend on the changing character of regional hazards, vulnerability and exposure as assessed in WGII.
Climatic Impact-driver
Heat and Cold Wet and Dry Wind Snow and Ice Coastal Open Ocean Other

Mean air temperatureExtreme heatCold spellFrostMean precipitationRiver floodHeavy precipitation and pluvial floodLandslideAridityHydrological droughtAgricultural and ecological droughtFire weatherMean wind speedSevere wind stormTropical cycloneSand and dust stormSnow, glacier and ice sheetPermafrostLake, river and sea iceHeavy snowfall and ice stormHailSnow avalancheRelative sea levelCoastal floodCoastal erosionMean ocean temperatureMarine heatwaveOcean acidityOcean salinityDissolved oxygenAir pollution weatherAtmospheric CO 2 at surfaceRadiation at surface
Sector Asset
Terrestrial and 
Freshwater 
Ecosystems
(WGII Chapter 2) Tropical forests
Temperate and boreal forests
Lakes, rivers and wetlands
Grasslands and savanna
Deserts
Mountains
Polar
Ocean and Coastal 
(WGII Chapter 3) Coastal land and intertidal zones
Coastal seas
Shelf seas and upwelling zones
Polar seas
Open ocean and deep sea
Water
(WGII Chapter 4)Cryosphere reservoir
Aquifers and groundwater

Streamflow and surface water
None/low 
confidenceLow/moderate High
Impacts and risk relevance

1779
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Climatic Impact-driver
Food, Fibre and 
Other Ecosystem 
Products
(WGII Chapter 5) Crop systems
Livestock and pasture systems
Forestry systems
Fisheries and aquaculture systems
Cities, Settlements 
and Key 
Infrastructure
(WGII Chapter 6) Cities

Land and water transportation
Energy infrastructure
Built environment
Health, Well-being 
and Communities
(WGII Chapter 7) Labour productivity
Morbidity
Mortality
Recreation and tourisma
Poverty, Livelihoods 
and Sustainable 
Development
(WGII Chapter 8) Housing stockb
Farmlandb
Livestock mortalityb
Indigenous traditions
a The Recreation and tourism asset category includes outdoor exercise and the tourism industry (including ecosystem services) assessed in many WGII chapters.
b This asset category is distinguished by the threat of a full loss of key investments and living environments rather than a recoverable damage or loss of productivity or profit.
None/low 

1780
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12suitability of future climate zones and climate velocity challenges for 
a given asset potentially drawing from multiple CIDs and associated 
system tolerance thresholds (Hamann et  al., 2015). Some broad 
connections indicated as low confidence  may be under-represented 
in the literature or could be acute under specific circumstances.
12.3.1 Heat and Cold
12.3.1.1 Mean Air Temperature
Information about increasing mean annual and seasonal air 
temperature is relevant in the determination of suitable species range 
for terrestrial, freshwater and intertidal species (Thomas et  al., 2004; 
Elith et al., 2010; Hincapie and Caicedo, 2013; Cooper, 2014; Krist et al., 
2014; Lindner et al., 2014; Saintilan et al., 2014; Lenoir and Svenning, 
2015; Myers-Smith et al., 2015; Urban, 2015; Thorne et al., 2017). Ocean 
ecosystems are affected by the ocean temperature CID (described in

Section  12.3.6.1). Species redistribution and extinction studies also 
need information about climate velocity, a comparison of the pace of 
warming to geographical temperature gradients that indicates the rate 
at which a species would have to move to maintain its climatological 
temperature (Thomas et  al., 2004; Loarie et  al., 2009; Dobrowski 
et al., 2013; Burrows et al., 2014; Dobrowski and Parks, 2016; Sittaro 
et al., 2017) with some studies incorporating additional variables beyond 
temperature (Hamann et al., 2015). Many freshwater ecosystems are 
strongly constrained by stream and lake temperatures (Scheurer et al., 
2009; Comte and Grenouillet, 2013; Contador et al., 2014; Knouft and 
Ficklin, 2017). Warmer and more stratified lake temperatures are more 
conducive to cyanobacteria blooms with implications for ecosystem 
health and water resource quality (Whitehead et al., 2009; Moss et al., 
2011; Jones and Brett, 2014; Chapra et al., 2017; Shatwell et al., 2019).

Consideration of nighttime and daytime temperature trends also 
elucidates different biophysical effects on vegetation (Peng et al., 2013). 
Changes in the seasonal timing caused by warming trends are critical 
to species ranges and ecosystem function (Pearce-Higgins et al., 2015; 
Hughes et al., 2017b), and indices that characterize the onset of spring 
shed light on plant emergence and development (Ault et al., 2015).
Mean air temperature dictates many aspects of crop cultivation, 
livestock production, agroforestry and output from freshwater 
aquaculture and fisheries, as well as the potential for food 
contamination. Mean warming alters suitable cultivation zones for crop 
species (Bragança et al., 2016; Gendron St-Marseille et al., 2019; IPCC, 
2019c) and tree species (Hanewinkel et al., 2013; Fei et al., 2017). Crop 
and ecosystem service productivity often responds directly to mean 
temperatures, although this is dependent on farming systems (Bassu

et al., 2014; Challinor et al., 2014; Lobell and Tebaldi, 2014; Rosenzweig 
et al., 2014; Asseng et al., 2015; Li et al., 2015; Fleisher et al., 2017; 
Zhao et  al., 2017; Smith and Fazil, 2019). Many studies relate plant 
development (phenology), insect generation cycles and pest outbreaks 
to growing degree days, an aggregation of daily thermal units above 
a threshold (e.g., T mean >5°C) that accelerates with warmer conditions 
(Hof and Svahlin, 2016; Ruosteenoja et al., 2016; Tripathi et al., 2016). 
Many plants respond to changes in nighttime temperatures that affect 
respiration and transpiration rates (Narayanan et  al., 2015; X.  Chen 
et  al., 2019), and warming of the soil column is also relevant to determine plant sprouting (Grotjahn, 2021). A number of indices have 
been developed to represent the length of the viable local growing 
season, including a count of days where T max >5°C (Mueller et al., 2015)

or the period between a year’s first and last set of five consecutive days 
with a  weighted T mean ≥10°C (G. Li et  al., 2018). Warmer conditions 
and altered seasonality modify the range and metabolism of some 
pollinators, pests, diseases and weeds (Wolfe et al., 2008; Bebber, 2015; 
Aljaryian and Kumar, 2016; IPBES, 2016; Ramesh et al., 2017; Deutsch 
et al., 2018; Nyangiwe et al., 2018) and may reduce the effectiveness 
of winter storage for farmers and caching species (Sutton et al., 2016).
Warming raises accumulated seasonal heat indices used in livestock 
production, especially when humidity is high (Key et al., 2014; Lallo 
et al., 2018), determines aquaculture suitability and is important for 
wild fish species migration (Tripathi et al., 2016; Brander et al., 2017). 
Agricultural planners may also calculate how overall warming trends 
alter the accumulation of vernalization units or chilling hours for 
agricultural or horticultural crops (often accumulated temperature

deficit below a given daily or hourly threshold; Dennis and Peacock, 
2009; Luedeling, 2012; Tripathi et  al., 2016; Grotjahn, 2021). 
Warming in the post-harvest is also important for the determination 
of spoilage and waste (Stathers et al., 2013) as well as food-borne 
diseases (Kovats et al., 2004; Mbow et al., 2019).
Warming affects road degradation rates (Chinowsky and Arndt, 2012; 
Espinet et al., 2016) and warming rates inform designs for long-term 
energy efficiency of buildings (Kalvelage et al., 2014). Mean temperature 
drives seasonal energy demand, often expressed using winter heating 
degree days (the accumulated deficit of daily temperatures below 
a  ‘comfortable’ indoor temperature, e.g.,  15.5°C) and summer 
cooling degree days (the accumulated excess of temperature above 
a ‘comfortable’ level, e.g., 18°C; Spinoni et al., 2015; Arnell et al., 2019). 
Energy resources may also need information on warming trends to

determine suitable zones and overall productivity for biofuels and solar 
panels, the efficiency of which decreases with higher temperatures 
(Schaeffer et al., 2012; Wild et al., 2015; Solaun and Cerdá, 2019).
Health impacts and risk studies compare seasonal temperature 
conditions to limiting thresholds to understand range shifts and 
incubation rates for pathogens, disease vectors and zoonotic hosts 
(e.g.,  mosquitoes, ticks; Caminade et  al., 2012, 2014; Eisen and 
Moore, 2013; Lima et al., 2016; Ogden, 2017; Monaghan et al., 2018) 
and warming of surface ocean and lake waters conducive to bacterial 
outbreaks (Baker-Austin et  al., 2013; Jacobs et  al., 2015; Vezzulli 
et al., 2015). Warmer conditions can also affect tourism (Kovács et al., 
2017) and impact human health by lengthening the allergy season 
and increasing pollen concentration (Hamaoui-Laguel et  al., 2015; 
Kinney et al., 2015a; Lake et al., 2017; Upperman et al., 2017; Sapkota 
et al., 2019; Ziska et al., 2019).

12.3.1.2 Extreme Heat
Impacts and risk assessments utilize a  large variety of indices and 
approaches tailored to evaluate heat impacts on human health 
(Sanderson et al., 2017; C. Gao et al., 2018; McGregor and Vanos, 2018; 
Staiger et  al., 2019; J. Zhu et  al., 2019; Schwingshackl et  al., 2021). 
A  mixture of simple and complex heat stress indices often combine

1781
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212extreme temperatures and high humidity to capture human health 
challenges (Aström et al., 2013; Chow et al., 2016; Dahl et al., 2017a; 
Im et al., 2017; Coffel et al., 2018; J. Li et al., 2018; Vanos et al., 2020). 
Different optimum temperatures and extreme heat thresholds based on 
local distributions are needed to reflect acclimation of different locations 
and populations (Hajat et al., 2014; WHO, 2014; Kinney et al., 2015b; 
Russo et al., 2015; Petitti et al., 2016; Dosio, 2017; Cheng et al., 2018; 
Lay et  al., 2018; Schwingshackl et  al., 2021). Hot and humid heat 
episodes can be deadly (Mora et al., 2017), are associated with elevated 
hospital intake (Goldie et al., 2017) and lower safety and productivity 
of outdoor labourers (Dunne et al., 2013; Graff Zivin and Neidell, 2014; 
Kjellstrom et al., 2016; Pal and Eltahir, 2016; Y . Zhao et al., 2016; Mora

et al., 2017; Watts et al., 2018; Orlov et al., 2019). Elevated nighttime 
temperatures prevent the human body from experiencing relief from 
heat stress (Zhang et  al., 2012) and can be tracked over extended 
periods of sequential day and night heat extremes (Murage et al., 2017; 
Mukherjee and Mishra, 2018). Extreme heat also exacerbates asthma, 
respiratory difficulties and response to airborne allergens such as hay 
fever (Upperman et al., 2017). Extreme heat affects outdoor exercise 
such as the use of bike-share facilities (Heaney et al., 2019; Vanos et al., 
2020). Large-scale recreational and sporting events such as marathons 
and tennis tournaments monitor heat extremes when determining the 
viability of host cities (Smith et al., 2016, 2018).
Short-term exposure of crops to temperatures beyond a  critical 
temperature threshold can lead to lower yields and above a limiting 
temperature threshold, crops may fail altogether (Schlenker and

Roberts, 2009; Lobell et al., 2012, 2013; Gourdji et al., 2013; Deryng 
et al., 2014; Schauberger et al., 2017; Tesfaye et al., 2017; Vogel et al., 
2019). The exact level of these thresholds depends on species, cultivar 
and farm management (Hatfield and Prueger, 2015; Hatfield et al., 
2015; Bisbis et al., 2018; Grotjahn, 2021). The timing of heatwaves 
is particularly important, as extreme heat is more damaging during 
critical phenological stages (Teixeira et al., 2013; Eyshi Rezaei et al., 
2015; Fontana et al., 2015; B. Wang et al., 2017; Mäkinen et al., 2018). 
Extreme canopy temperatures, rather than 2 m air temperatures, 
may be a more robust biophysical indicator of heat impacts on crop 
production (Siebert et  al., 2017). Heat stress indices based upon 
temperature and humidity determine livestock productivity as well 
as conception and mortality rates (Key et al., 2014; Dash et al., 2016; 
Pragna et al., 2016; Rojas-Downing et al., 2017).

Heat extremes factor in mortality, morbidity and the range of some 
thermally sensitive ecosystem species (Smith and Nagy, 2015; 
Ratnayake et al., 2019; Thomsen et al., 2019). Combined heat and 
drought stress can reduce forest and grassland primary productivity 
(Ciais et al., 2005; De Boeck et al., 2018) and even cause tree mortality 
at higher extremes (Teskey et al., 2015).
Extreme heat events raise temperatures in buildings and cities already 
warmed by the urban heat island effect (Gaffin et al., 2012; Oleson 
et al., 2018; Zhao et al., 2018; Mauree et al., 2019; Box 10.3) and 
can induce disruptions in critical infrastructure networks (Chapman 
et  al., 2013). Heat affects transportation infrastructure by warping 
roads and airport runways (Chinowsky and Arndt, 2012) or buckling 
railways (Dobney et al., 2010; Dépoues, 2017; Chinowsky et al., 2019),

and high temperatures reduce air density leading to aircraft take-off weight restrictions (Coffel et  al., 2017; Palko, 2017; T. Zhou et  al., 
2018). Heat extremes increase peak cooling demand and challenge 
transmission and transformer capacity (Sathaye et al., 2013; Russo 
et al., 2016; Craig et al., 2018; X. Gao et al., 2018) and may cause 
transmission lines to sag or fail (Gupta et  al., 2012). Thermal and 
nuclear electricity plants may be challenged when using warmer river 
waters for cooling or when mixing waste waters back into waterways 
without causing ecosystem impacts (Kopytko and Perkins, 2011; van 
Vliet et al., 2016; Tobin et al., 2018). Extreme temperature can also 
reduce photovoltaic panel efficiency (Jerez et al., 2015).
12.3.1.3 Cold Spells
The magnitude and timing (relative to developmental stages) of cold 
extremes (such as the typical coldest day of the year) set limits in 
the range of species habitat for ecosystems as well as for agricultural

and forest pests (Osland et al., 2013; Cavanaugh et al., 2014; Parker 
and Abatzoglou, 2016; Brunner et  al., 2018; Unterberger et  al., 
2018). Cold air outbreaks can lead to chilling injuries for crops (even 
above 0°C) and may kill outdoor livestock (particularly young animals; 
Mader et  al., 2010; Liu et  al., 2013; Grotjahn, 2021), but are often 
necessary for crop chill requirements (Dennis and Peacock, 2009).
Increases in human mortality can occur on exceptionally cold days 
(e.g., <1st percentile of temperatures in winter) although thresholds 
and human-perceived temperatures linked to wind speed (i.e., ‘wind 
chill’) vary geographically due to acclimatization (Li et  al., 2013; 
Gao et al., 2015; J. Li et al., 2018; J. Zhu et al., 2019). The timing of 
‘unseasonal’ cold spells also affect human health (Kinney et al., 2015b). 
Extreme cold can increase heat and electricity demand (Stuivenvolt-
Allen and Wang, 2019), cause water pipes to burst, and mechanically

alter roads, railroads and buildings (Underwood et al., 2017).
12.3.1.4 Frost
Frost (T min <0°C) is a  natural and fundamental aspect of many 
ecosystems, with more extreme conditions defined as ice (or icing) 
days (T max <0°C) (L.A. Vincent et  al., 2018). Agricultural systems 
planning (e.g., planting calendars, seed selection or the opportunity 
to double-crop) requires information about the start and end of the 
frost-free season (Wypych et  al., 2017; Wolfe et  al., 2018). Crops 
and wild plants can be directly damaged by frost, but hard or killing 
frosts (at a threshold several degrees below freezing) can kill crops 
or lower harvest quality depending on duration (which relates to 
soil temperature penetration) and plant developmental stage (Crimp 
et al., 2016a; Cradock-Henry, 2017; G. Li et al., 2018; Mäkinen et al., 
2018; Grotjahn, 2021). Earlier disappearance of snow cover reduces 
natural insulation that protects plants and burrowing animals from

hard frost damages (Trnka et al., 2014; Mäkinen et al., 2018). In some 
cases an early season warm spell may reduce plant hardiness or induce 
fruit tree flowering that exposes plants to devastating subsequent 
frost impacts (Hufkens et  al., 2012; Hatfield et  al., 2014; Tripathi 
et  al., 2016; Brunner et  al., 2018; DeGaetano, 2018; Unterberger 
et al., 2018; Wolfe et al., 2018). Shifts in the seasonality of frozen soils 
also affect groundwater recharge and surface streamflow for water 
resource applications, particularly when peak precipitation is shifted 
to a season that no longer has frozen soils (Jyrkama and Sykes, 2007).

1782
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Regional information about the spring and autumn seasonal periods in 
which freeze-thaw cycles are common (such as the dates of first spring 
thaw and last spring frost, or the number of days where T max >0°C and 
Tmin <0°C) are particularly useful in estimating the rate of potential road 
and building damage or determining seasonal truck weight restrictions 
(Kvande and Lisø, 2009; Chinowsky and Arndt, 2012; Palko, 2017; 
Daniel et  al., 2018). The altitude of the freezing level also identifies 
portions of mountain slopes where freeze/thaw transitions or changes 
in snowpack condition can influence landslide and snow avalanche 
hazards (Coe et al., 2018). The geographical distribution of frost is also 
a determining factor in the range of vectors for human diseases such 
as malaria (X. Zhao et al., 2016; Smith et al., 2020).
Figure  12.3 illustrates how successive heat and cold hazards can

potentially affect important natural and human systems, with climatic 
pressures reaching new sectoral assets or becoming increasingly 
severe as conditions become more extreme. While the precise value 
of any CID threshold may depend strongly on local environmental 
and system characteristics, there are common patterns and 
interdependencies in the types of thresholds encountered. Changes 
in the regional profile of CIDs can thus substantially alter threshold 
exceedance likelihoods.
12.3.2 Wet and Dry
12.3.2.1 Mean Precipitation
Changes in mean precipitation alter total water resources and 
long-term surface, snowpack and groundwater reservoirs (Schewe 
et al., 2014). Annual and seasonal wet trends can alter the suitable 
geographic range of species, with implications for biodiversity and 
vector-borne diseases (Knouft and Ficklin, 2017; Smith et al., 2020). 
The rate at which higher total streamflow increases river erosion

and changes sediment loading is relevant for fish breeding (Scheurer 
et al., 2009), the location of riverine salt fronts that affect coastal 
agriculture and ecosystems (Chun et  al., 2018; Vu et  al., 2018), 
coastal  freshwater stratification (Baker-Austin et  al., 2013; Bell 
et al., 2013), and the accretion of sediment in estuaries and beaches 
(Syvitski and Milliman, 2007). Wetter conditions may shift tourist 
appeal (Kovács et al., 2017) and alter the pace of degradation for 
paved and especially unpaved roads (Chinowsky and Arndt, 2012).
Many agricultural systems require minimum rainfall totals or rely 
upon irrigation (Mbow et al., 2019). The length of the wet season 
helps determine the potential for multiple cropping seasons, but 
inconsistency of wet season arrival times poses challenges for farm 
management (Waha et al., 2020). Wetter growing season conditions 
increase the chance of waterlogging, which can delay planting or

damage planted seeds (Rosenzweig et  al., 2002; Ben-Ari et  al., 
2018; Mäkinen et al., 2018; Wolfe et al., 2018; Kolberg et al., 2019; 
Grotjahn, 2021). Tomasek et  al. (2017) calculated ‘workable days’ 
for agricultural machinery around planting and harvest time set in 
part by limits in soil moisture saturation below which farmers can 
utilize critical machinery with less rutting or soil compaction. Wetter 
conditions may also increase canopy moisture that is conducive to 
crop pathogens (Garrett et al., 2006; Kilroy, 2015; Grotjahn, 2021).
Thotlim
Tcrit
opt
GDDmin
Tfrost
ThfrostThotlim
Tfrost
TcoldlimCDDmin
HDDmaxTdeadly
Tsevere
TdangerTchillminmaxTcrit
Health Cities and
 infrastructuresAgriculture EcosystemsTaircraft
Thotroads
blooms
permafrostT
T ice
TT T
TT
overwinterTemperature CID Thresholds
TcoldroadsThotpest
TcoldlimCold HotTtrans
Tstream
Tcoldpest
Figure  12.3  | Conceptual illustration of representative climatic impact-

driver thresholds showing how graduating thresholds affect successive 
sectoral assets and lead to potentially more acute hazards as conditions 
become more extreme (exact values are not shown as these must be 
tailored to reflect diverse vulnerabilities of regional assets).  Representative 
threshold definitions (T = instantaneous temperature; T  = mean temperature): Cities 
and Infrastructures:  Ttrans = temperature at which energy transmission lines efficiency 
reduced; T aircraft = temperature at which aircraft become weight-restricted for takeoff; 
Thotroads  = temperature above which roads begin to warp; T stream = temperature at which 
streams are not capable of adequately cooling thermal plants; CDD min = minimum 
temperature for calculating cooling degree days; HDD max  =  maximum temperature 
for calculating heating degree days; T ice = temperature at which ice threatens 
transportation; T  permafrost  = mean seasonal temperature above which permafrost thaws

at critical depths; T coldroads  = temperature below which road asphalt performance 
suffers. Health:  Tdeadly = temperature above which prolonged exposure may be deadly 
(often combined with humidity for heat indices); T severe = temperature above which 
prolonged exposure may cause elevated morbidity; T  blooms = mean temperature for 
harmful algal or cyanobacteria blooms; T danger = level of dangerous cold temperatures 
(often combined with wind for chill indices); T overwinter  = temperature below which 
disease vector species cannot survive winter. Ecosystems  (CID indices for air 
and ocean temperature): T hotlim and T coldlim = limiting hot and cold temperatures for 
a given species range; T frost = frost threshold; T  max and T min = maximum and minimum 
suitable annual mean temperatures for a  given species; T crit = critical temperature 
above which a  given species is stressed. Agriculture:  Thotlim = temperature above

which a crop or livestock species dies; T hotpest = maximum (or ‘lethal’) temperature 
above which an agricultural pest/disease/weed cannot survive; T crit = temperature at 
which productivity for a given crop is depressed; T  opt = optimal mean temperature 
for a given plant’s productivity; GDD min = threshold temperature for growing degree 
days determining plant development; T chill = temperature below which chilling units 
are accumulated; T frost = temperature below which frost occurs; T hfrost = temperature 
below which a  hard frost threatens crops or livestock; T coldpest = minimum winter 
temperature below which a given agricultural pest cannot survive; T coldlim = minimum 
temperature below which a given crop cannot survive.

1783
Climate Change Information for Regional Impact and for Risk Assessment Chapter 121212.3.2.2 River Flood
A large variety of climate indices and models are utilized to understand 
how river flooding affects both natural or built environments with 
highly variable hazard thresholds, given unique local topography 
and engineered defences such as dams and polders (Arnell and 
Gosling, 2016; Ekström et al., 2018). Key transportation routes, built 
infrastructure and agricultural lands are threatened when floods 
exceed design standards commonly based around flood magnitudes 
of a given historic return period (e.g., 1-in-100-year flood event), an 
annual exceedance probability or precipitation intensity-duration-
frequency relationships with key indices (e.g.,  10-day cumulative 
precipitation) related to catchment size and properties (Hirabayashi 
et al., 2013; Arnell and Lloyd-Hughes, 2014; Kundzewicz et al., 2014; 
Arnell and Gosling, 2016; Dikanski et al., 2016; Gosling and Arnell,

2016; Forzieri et al., 2017; Fluixá-Sanmartín et al., 2018; Koks et al., 
2019). Floods and high-flow events can scour river beds and elevate 
silt loads, reducing water quality and accelerating deposition in 
estuaries and reservoirs (Khan et al., 2018; Parasiewicz et al., 2019). 
Floods can knock down, drown or wash away crops and livestock, 
and partially submerged plants can have yield reduction depending 
on water turbidity and their development stage (Ruane et al., 2013; 
Shrestha et  al., 2019). Basin snowpack properties may also be 
important during heavy rain events, as rain-on-snow events can 
lead to rapid acceleration of flood stages that threaten wildlife and 
society (Hansen et al., 2014).
12.3.2.3 Heavy Precipitation and Pluvial Flood
Heavy downpours can lead to pluvial flooding in cities, roadways, 
farmland, subway tunnels and buildings (particularly those with 
basements; Grahn and Nyberg, 2017; Palko, 2017; Pregnolato et al.,

2017; Orr et  al., 2018). Heavy precipitation may overwhelm city 
transportation and storm water drainage systems, which are typically 
designed using intensity-duration-frequency information such as 
the return periods for 1-, 6- or 24-hour rainfall totals (Kermanshah 
et al., 2017; Depietri and McPhearson, 2018; Rosenzweig et al., 2018; 
Courty et al., 2019). Heavy rain events can directly cause leaf loss 
and damage, or knock over crops, also driving pollutant entrainment 
and erosion hazards in terrestrial ecosystems and farmland, with 
downstream ramifications for water quality (Hatfield et  al., 2014; 
Segura et  al., 2014; Li and Fang, 2016; Chhetri et  al., 2019). The 
proportion of total precipitation that falls in heavy events also 
affects the percentage that is retained in the soil column, altering 
groundwater recharge and deep soil moisture content for agricultural 
use (Fishman, 2016; Lesk et al., 2020).
12.3.2.4 Landslide

Landslides, mudslides, rockfalls and other mass movements can lead 
to fatalities, destroy infrastructure and housing stock, and block 
critical transportation routes. Climate models cannot resolve these 
complex slope failure processes (nor triggering mechanisms such as 
earthquakes), so most studies rely on proxies or conditions conducive 
to slope failure (Gariano and Guzzetti, 2016; Ho et al., 2017). Common 
indices include precipitation intensity-duration thresholds (Brunetti 
et  al., 2010; Khan et  al., 2012; Melchiorre and Frattini, 2012) and thresholds related to antecedent wet periods and extreme rainfall 
intensities (Alvioli et al., 2018; Monsieurs et al., 2019). Landslides and 
rockfalls may also be exacerbated by permafrost thaw and receding 
glaciers in polar and mountain areas (Cook et  al., 2016; Haeberli 
et al., 2017; Patton et al., 2019).
12.3.2.5 Aridity
Aridity indices may track long-term changes in precipitation,

evapotranspiration demand, surface water, groundwater or soil 
moisture (Sherwood and Fu, 2014; Herrera-Pantoja and Hiscock, 2015; 
B.I. Cook et al., 2020). Changes in soil moisture and surface water can 
shift the rate of carbon uptake by ecosystems (Humphrey et al., 2018) 
and alter suitable climate zones for wild species and agricultural 
cultivation (Feng and Fu, 2013; Garcia et  al., 2014; Huang et  al., 
2016a; Schlaepfer et al., 2017; Fatemi et al., 2018; IPCC, 2019c) as 
well as the prevalence of related pests and pathogen-carrying vectors 
(Paritsis and Veblen, 2011; Smith et al., 2020). Water table depth, in 
relation to rooting depth, is also important for farms and forests under 
dry conditions (Feng et  al., 2006). A  reduction in water availability 
(via  aridity or hydrological drought) challenges water supplies 
needed for for municipal, industrial, agriculture and hydropower use 
(Schaeffer et al., 2012; Arnell and Lloyd-Hughes, 2014; Schewe et al.,

2014; Gosling and Arnell, 2016; van Vliet et al., 2016).
12.3.2.6 Hydrological Drought
Water managers often utilize a  variety of hydrological drought 
indices and hydrological models to characterize water resources, low 
flow conditions and the potential for irrigation (Wanders and Wada, 
2015; Mukherjee et al., 2018). Low flow volume and intermittency 
thresholds can indicate reductions in dissolved oxygen, more 
concentrated pollutants, and higher stream temperatures relevant for 
ecosystems, water resource quality and thermal power plant cooling 
(Feeley et al., 2008; Döll and Schmied, 2012; Schaeffer et al., 2012; 
Prudhomme et  al., 2014; van Vliet et  al., 2016). Low water levels 
may also restrict waterway navigation for commerce and recreation 
(Forzieri et al., 2018).
12.3.2.7 Agricultural and Ecological Drought
Agricultural and ecological drought indices relate to the ability of 
plants to meet growth and transpiration needs (Table  11.3; Zargar

et al., 2011; Lobell et al., 2015; Pedro-Monzonís et al., 2015; Bachmair 
et  al.,  2016; Wehner et  al., 2017; Naumann et  al., 2018) and the 
timing and duration of droughts can lead to substantially different 
impacts (Peña-Gallardo et  al., 2019). Drought stress for agriculture 
and ecosystems is difficult to directly observe, and therefore scientists 
use a variety of drought indices (Table 11.3), proxy information about 
changes in precipitation supply and reference evapotranspiration 
demand, the ratio of actual/potential evapotranspiration or a deficit in 
available soil water content, particularly at rooting level (Park Williams 
et al., 2013; Trnka et al., 2014; C.D. Allen et al., 2015; Svoboda and 
Fuchs, 2017; Mäkinen et al., 2018; Otkin et al., 2018). Severe water 
stress can lead to crop failure, in particular when droughts persist for 
an extended period or occur during key plant developmental stages

(Hatfield et al., 2014; Jolly et al., 2015; Leng and Hall, 2019). Projections

1784
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12of high wind speed and low humidity (even for just a portion of the 
day) can also inform studies examining fruit desiccation and rice 
cracking (Grotjahn, 2021). Drought also raises disease infection rates 
for West Nile virus (Paull et al., 2017), and the alternation of dry and 
wet spells induces swelling and shrinkage of clay soils that can lead to 
sinkholes and destabilize buildings (Hadji et al., 2014).
12.3.2.8 Fire Weather
Complex fire weather indices shed light on conditions that increase 
the likelihood of wildfire and shifts in the fire season (Flannigan 
et  al., 2013; Bedia et  al., 2015; Jolly et  al., 2015; Harvey, 2016; 
Littell et al., 2016; Westerling, 2016; Abatzoglou et al., 2019), which 
pose particularly acute challenges for indigenous communities 
(Christianson and McGee, 2019). Projection of future lightning 
frequency provides information on an important natural triggering

mechanism, particularly when coupled with long-term warming 
and drying trends (Romps et al., 2014; Jin et al., 2015; Veraverbeke 
et al., 2017). Fuel aridity metrics also help determine vegetative fuel 
desiccation and therefore the ignitability, flammability and spread of 
fires when they occur (Abatzoglou and Williams, 2016). The presence 
of snow cover can influence the length of the fire season and the 
penetration of fire danger into new portions of the Arctic tundra 
(Young et al., 2017; Abatzoglou et al., 2019). Data on the changing 
characteristics of local wind circulations like the Santa Ana in 
California shed light on future intensity and spread patterns for fires 
(Jin et al., 2015). Fires also produce smoke plumes that reduce air and 
water quality (via deposition), adversely affecting health, visibility 
and water resources both near and far downwind (Dennekamp and 
Abramson, 2011; McKenzie et  al., 2014; Dreessen et  al., 2016; Liu 
et al., 2016; Martin, 2016).

12.3.3 Wind
12.3.3.1 Mean Wind Speed
Changes in the speed and direction of prevailing winds can alter 
the profile of seed dispersal, windblown pest and disease vectors, 
animal activities, and dust or pollen dispersal affecting ecosystems, 
agriculture and human health (Reid and Gamble, 2009; Bullock et al., 
2012; Hellberg and Chu, 2016; Nourani et al., 2017). Seasonal winds 
influence algal blooms, ecosystems and fisheries via lake mixing, 
ocean currents and coastal upwelling (Bakun et al., 2015; Townhill 
et  al., 2018; Woolway et  al., 2020). Changes to wind density also 
modify a  region’s wind and wave renewable energy endowment 
(Schaeffer et al., 2012; Sierra et al., 2017; Craig et al., 2018; Devis 
et al., 2018; Tobin et al., 2018; Yalew et al., 2020). D. Li et al. (2020) 
and Karnauskas et al. (2018a) evaluated wind thresholds at turbine 
height (about 80–100 m above ground) including periods outside of

cut-in (2.5–3 m s–1) and cut-out (about 25 m s–1) levels beyond which 
given turbines could not operate.
12.3.3.2 Severe Wind Storm
High winds associated with severe storms can destroy trees and 
houses, break plant stems and knock fruits, nuts and grains to the ground, with tolerance thresholds depending on crop species and 
developmental stage (Seidl et  al., 2017; Lai, 2018; Elsner et  al., 
2019; Grotjahn, 2021). Severe storms particularly threaten energy 
infrastructure, with maximum wind speed associated with treefall 
and breaking of above-ground electrical transmission lines (Ward, 
2013; Nik et al., 2020). The profile of heavy wind gusts is also required 
in the design of skyscrapers (C.-H. Wang et al., 2013) and bridges 
(Mondoro et al., 2018). Severe storms are difficult to simulate at the 
relatively coarse spatial scales of Earth system models, thus scientists 
often project changes by noting areas with increased convective

available potential energy (CAPE) and strong low-level wind shear as 
these are conducive to tornado formation (Diffenbaugh et al., 2013; 
Tippett et al., 2016; Glazer et al., 2021).
12.3.3.3 Tropical Cyclone
Tropical cyclones and severe coastal storms can deliver wind, water 
and coastal hazards with the potential for widespread mortality and 
damages to cities, housing, transportation and energy infrastructure, 
ecosystems and agricultural lands (Burkett, 2011; NASEM, 2012; Bell 
et al., 2013; Wehof et al., 2014; Ward et al., 2016; Cheal et al., 2017; 
Godoi et al., 2018; Koks et al., 2019; Pinnegar et al., 2019). Storm 
planning is often tied to the Saffir –Simpson scale related to peak 
sustained wind speed (Izaguirre et  al., 2021), with several indices 
focusing on storms’ overall power and energy, size and translation 
speed to anticipate destructive potential (Knutson et al., 2015; Wang 
and Toumi, 2016; Parker et al., 2018; Hassanzadeh et al., 2020).

12.3.3.4 Sand and Dust Storm
Sand and dust storms erode soils, damage crops and induce 
problems for health, transportation, mechanical equipment and 
built infrastructure corresponding to the magnitude and duration 
of high winds and particulate matter concentrations (Goudie, 2014; 
O’Loingsigh et al., 2014; Crooks et al., 2016; Barreau et al., 2017; 
Bhattachan et  al., 2018; Al Ameri et  al., 2019; Middleton et  al., 
2019). Dust events may be represented as the number of dust hours 
per year and by particulate matter (PM) concentrations (Leys et al., 
2011; Spickett et al., 2011; Hand et al., 2016). Photovoltaic panels 
can lose energy production efficiency with dust accumulation 
(Patt et al., 2013; Javed et al., 2017). It is also useful to track dust 
storm deposition of nutrients necessary for coral and tropical forest 
systems, but they may also feed algal blooms harming lake and 
coastal ecosystems, health and recreation (Jickells et  al., 2005;

Hallegraeff et al., 2014; Gabric et al., 2016). Dust storms also cause 
air pollution and redistribute the soil-based fungus associated with 
Valley fever (Barreau et  al., 2017; Coopersmith et  al., 2017; Tong 
et al., 2017; Gorris et al., 2018).
12.3.4 Snow and Ice
Cryospheric changes are a focus of Chapter 9 and were central to 
the recent IPCC Special Report on the Ocean and Cryosphere in 
a  Changing Climate (SROCC; IPCC, 2019b). Here we focus on the 
ways that scientists use snow and ice CIDs to understand current and 
future societal impacts and risks.

1785
Climate Change Information for Regional Impact and for Risk Assessment Chapter 121212.3.4.1 Snow, Glacier and Ice Sheet
A large number of indices have been used in water resource and 
ecosystem studies to track changes in snow under current and future 
climate conditions, including measurements of the snow water 
equivalent at key seasonal dates, the fraction of precipitation falling 
as snow, the first and last days of snow cover, and cold season 
temperatures (Mills et al., 2013; Pierce and Cayan, 2013; Berghuijs 
et al., 2014; Klos et al., 2014; Musselman et al., 2017; Rhoades et al., 
2018). Impact studies also examine shifts in seasonal streamflow 
for snow-fed river basins (Mote et al., 2005; Pederson et al., 2011; 
Beniston and Stoffel, 2014; Coppola et al., 2014b, 2018; Fyfe et al., 
2017; Islam et  al., 2017; Knouft and Ficklin, 2017) as well as the 
geographic extent of snow cover and the depth of frosts when

snow cover’s natural insulation is absent (Scheurer et  al., 2009; 
Millar and Stephenson, 2015). Studies examining the impact of 
snow changes on winter recreation and transportation have used 
thresholds of about 30 cm snow depth or snow water equivalent 
>10 cm to determine the length of the season for alpine and cross-
country skiing and snowmobiling (Damm et al., 2017; Wobus et al., 
2017b; Spandre et al., 2019; Steiger et al., 2019; Abegg et al., 2021). 
Changes in snow quality also affect recreational activities (Rutty 
et  al., 2017), and artificial snowmaking can augment recreational 
snowpack depending on the number of suitable snowmaking 
hours (e.g.,  where wet bulb globe temperature (WBGT) <–2.2°C; 
Wobus et al., 2017b). Local detail may also be provided by tracking 
the seasonal rain–snow transition line across space and elevation 
(Berghuijs et  al.,  2014) (Pierce and Cayan, 2013; Berghuijs et  al., 
2014; Klos et al., 2014; Musselman et al., 2017).

Change in ice sheet and glacier spatial extent and surface mass 
balance is relevant for polar and high mountain ecosystems and 
downstream assets that rely on glacial water resources (J.R. Lee et al., 
2017; Milner et al., 2017; Huss and Hock, 2018; Schaefli et al., 2019). 
The loss of glaciers reduces the thermal consistency of cold streams 
suitable for some freshwater species (Giersch et  al., 2017), and 
parks and recreation areas may lose appeal as glaciers and seasonal 
snow cover retreat (Gonzalez et al., 2018; Wang and Zhou, 2019). 
Rapid glacial retreat can lead to glacial lakes and outburst floods 
that endanger downstream communities (Carrivick and Tweed, 2016; 
Cook et al., 2016; Harrison et al., 2018).
12.3.4.2 Permafrost
Changes in permafrost temperature, extent and active layer thickness 
are metrics that track how permafrost thaw below, for example, roads, 
airstrips, rails and building foundations in high-latitude and mountain

regions may destabilize settlements and critical infrastructure 
(Pendakur, 2016; Derksen et al., 2018; Duvillard et al., 2019; Olsson 
et  al., 2019; Streletskiy et  al., 2019). Warmer conditions can also 
affect ecosystems, built infrastructure and water resources through 
thawing of especially ice-rich permafrost (≥20% ice content) and by 
thawing of ice wedges (Shiklomanov et al., 2017; Hjort et al., 2018), 
creation of thermokarst ponds and increased subsurface drainage 
for polar and high-mountain wetlands (Walvoord and Kurylyk, 2016; 
Farquharson et al., 2019) and the release of water pollutants such as 
mercury (Burkett, 2011; Schaeffer et al., 2012; Schuster et al., 2018).12.3.4.3 Lake, River and Sea Ice
Reductions in the duration of thick sea, lake and river ice influence 
ecosystems as well as ice fishing, hunting, dog sledding and 
snowmobiling, which are recreation activities for some but vital aspects 
of many traditional indigenous communities (Durkalec et  al.,  2015;

AMAP , 2017; Baztan et  al., 2017; Arp et  al., 2018; Rokaya et  al., 
2018; Knoll et al., 2019; Meredith et al., 2019; Sharma et al., 2019). 
The seasonal extent of thin ice and iceberg density also determines the 
viability of shipping lanes and seasonal roads (Valsson and Ulfarsson, 
2011; Pizzolato et al., 2016; AMAP , 2017; Mullan et al., 2017; Sturm 
et  al., 2017), oil and gas exploration timing (Schaeffer et  al., 2012) 
and the seasonality of phytoplankton blooms (Oziel et al., 2017). Sea 
ice is a critical aspect of some ecosystems and fisheries (Massom and 
Stammerjohn, 2010; Jenouvrier et al., 2014; Bindoff et al., 2019; Meredith 
et al., 2019). Various definitions of ‘ice free’ Arctic Ocean conditions can 
be tailored to represent transportation needs, including thresholds of 
ice coverage (<5% or <30% or <1 million km2) in September or over 
a four-month period (Laliberté et al., 2016; Jahn, 2018).
12.3.4.4 Heavy Snowfall and Ice Storm

Heavy snowfall is a substantial concern for cities, settlements and 
key transportation and energy infrastructure (Ward, 2013; Palko, 
2017; Janoski et al., 2018; Collins et al., 2019). Heavy snowfall can 
interfere with transportation (Herring et al., 2018) and cause a loss 
of both work and school days depending on local snow removal 
infrastructure. Freezing rain and ice storms can be treacherous for 
road and air travel (Tamerius et al., 2016), and can knock down power 
and telecommunication lines if ice accumulation is high (Degelia 
et  al., 2016). Rain-on-snow events can create a  solid barrier that 
hinders wildlife and livestock grazing that is important to indigenous 
communities (Forbes et al., 2016). Shifts in the frequency, seasonal 
timing and regions susceptible to ice storms alter risks for agriculture 
and infrastructure (Lambert and Hansen, 2011; Klima and Morgan, 
2015; Ning and Bradley, 2015; Groisman et al., 2016).
12.3.4.5 Hail

Information on the changing frequency and size distribution of hail can 
help stakeholders build resilience for agriculture, vehicles, transportation 
infrastructure and buildings, solar panels and wild species that see 
critical damage at particular hail size thresholds (Dessens et al., 2007; 
Webb et  al., 2009; Patt et  al., 2013; Fiss et  al., 2019). Most climate 
models do not directly resolve hail and therefore studies often examine 
proxies associated with severe mesoscale storms (Tippett et al., 2015; 
Prein and Holland, 2018), although some regional studies now utilize 
hail-resolving models (Mahoney et al., 2012; Brimelow et al., 2017).
12.3.4.6 Snow Avalanche
Information about the changing frequency and seasonal timing of 
snow avalanches is important to assess threats to transportation 
routes, infrastructure, recreational skiing and people living in alpine 
communities (Lazar and Williams, 2008; Mock et al., 2017; Ballesteros-

Cánovas et  al., 2018; Hock et  al., 2019). Like landslides and other 
mass movements, snow avalanches are not directly resolved by

1786
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12climate models and are thus tracked using proxy climate information 
describing snow avalanche susceptibility, particularly the snow water 
equivalent, and triggering mechanisms such as warm spells, high 
winds, rain-on-snow and heavy precipitation (Hock et al., 2019). The 
quality of snow also provides insight into avalanche hazards (Mock 
et al., 2017), with the seasonal altitude of wet snowpack (>0.5% liquid 
water by volume) particularly important in determining characteristics 
of potential avalanches (Castebrunet et al., 2014).
12.3.5 Coastal
The SROCC included in-depth discussions of threats facing the world’s 
coastlines (IPCC, 2019b) and Section 9.6 provides further discussion 
on coastal processes. Here we note major connections between 
coastal CIDs and ecosystem and societal assets near coastlines.
12.3.5.1 Relative Sea Level

Sea level rise hazards for coastal ecosystems, infrastructure, farmland, 
cities and settlements in a  particular region are often driven by 
regional changes in relative sea level (RSL) that account for land 
uplift or subsidence and thus represent local asset vulnerability better 
than global mean sea level (Box 9.1; Hallegatte et al., 2013; Hinkel 
et al., 2013; McInnes et al., 2016; Weatherdon et al., 2016; Brown et al., 
2018; IPCC, 2019b; Rasoulkhani et  al., 2020). Vertical land motion 
(i.e.,  land subsidence) caused by local fluid (gas or groundwater) 
extraction can also have a  large influence on relative sea levels 
(Minderhoud et  al., 2020). Several indices have been suggested to 
signify coastal inundation, including a threshold when the local land 
elevation falls below the local mean higher high water (MHHW) that 
is close to the ‘high tide’ level (Kulp and Strauss, 2019) or a threshold 
when flooding occurs about once every two weeks (Sweet and Park,

2014; Dahl et al., 2017b). RSL rise (or RSLR) can drive increased inland 
penetration of above-ground and subterranean salt water fronts 
(i.e., salinity intrusion) affecting coastal ecosystems, agriculture and 
water resources (Ferguson and Gleeson, 2012; Kirwan and Megonigal, 
2013; Rotzoll and Fletcher, 2013; Chen et al., 2016; Colombani et al., 
2016; Holding et  al., 2016; Sawyer et  al., 2016; Mohammed and 
Scholz, 2018). The rate of RSLR can determine the survival and net 
pressure on niche coastal ecosystems such as mangroves, tidal flats, 
sea grasses and coral reefs (Hubbard et al., 2008; Craft et al., 2009; 
Bell et al., 2013; Kirwan and Megonigal, 2013; Alongi, 2015; Ellison, 
2015; Lovelock et al., 2015; Ward et al., 2016; Lee et al., 2018).
12.3.5.2 Coastal Flood
Episodic coastal flooding of coastal communities, farmland, buildings, 
transportation routes, industry and other infrastructure is caused 
by extreme total water levels (ETWL), which is the combination

of RSL, tides, storm surge and high wave setup at the shoreline 
(Vitousek et al., 2017; Melet et al., 2018; Vousdoukas et al., 2018, 
2020a; Koks et al., 2019; Kirezci et al., 2020). Coastal settlement and 
infrastructure design often uses coastal flooding metrics such as the 
ETWL frequency distribution or the 100-year average return interval 
storm tide (storm surge + high tide) level (McInnes et al., 2016; Mills 
et al., 2016; Walsh et al., 2016b; Zheng et al., 2017). The duration of floods that overtop coastal protection, due to extreme coastal 
water levels (ECWL), is important for port and harbour operations 
and coastal energy infrastructure thresholds (Bilskie et  al., 2016; 
Camus et  al., 2017). Frequent inundation by salt water can also 
have significant impacts on water resources, crops, aquaculture and 
transportation systems due to corrosion and undercutting of coastal 
roads, bridges and rails (Zimmerman and Faris, 2010; N. Ahmed et al.,

2019; Gopalakrishnan et al., 2019).
12.3.5.3 Coastal Erosion
Effective management of coastal ecosystems, cities, settlements, 
beaches and infrastructure requires information about coastal 
erosion driven by storm surge, waves and sea level rise (Dawson 
et al., 2009; Hinkel et al., 2013; Harley et al., 2017; Mentaschi et al., 
2017). Coastal erosion is generally accompanied by shoreline retreat, 
which can occur as a  gradual process (e.g.,  due to sea level rise) 
or as an episodic event due to storm surge and/or extreme waves, 
especially when combined with high tide (Ranasinghe, 2016). The 
most commonly used shoreline retreat index is the magnitude of 
shoreline retreat by a  pre-determined planning horizon such as 
50 or 100 years into the future. Commonly used metrics for episodic 
coastal erosion include the beach erosion volume due to the 100-year 
recurrence storm wave height, the full exceedance probability

distribution of coastal erosion volume (Li et al., 2014a; Pender et al., 
2015; Ranasinghe and Callaghan, 2017) and the cumulative storm 
energy and storm power index (Godoi et al., 2018). The destruction 
or overtopping of barrier islands may lead to irreversible changes in 
the physical system as well as in coastal ecosystems (Carrasco et al., 
2016; Zinnert et  al., 2019). Shoreline position change rates along 
inlet-interrupted coasts may also be affected by changes in river 
flows and fluvial sediment supply (Hinkel et al., 2013; Bamunawala 
et al., 2018; Ranasinghe et al., 2019). Permafrost thaw and Arctic sea 
ice decline also reduce natural coastal protection from wave erosion 
for communities and industry (Forbes, 2011; Melvin et al., 2017).
12.3.6 Oceanic
Oceanic changes and impacts were a  substantial focus of SROCC 
(IPCC, 2019b). Chapter 9 of this Report assesses changes in ocean 
processes, and here we note major connections used by scientists to

understand how oceanic CIDs affect ecosystems and society.
12.3.6.1 Mean Ocean Temperature
Shifts in thermal zones affect the suitability of fisheries and marine 
and coastal species habitat and migration routes (Hoegh-Guldberg 
and Bruno, 2010; Doney et al., 2012; Burrows et al., 2014; Urban, 2015; 
Hixson and Arts, 2016; Tripathi et  al., 2016; N. Ahmed et  al., 2019; 
Bindoff et al., 2019). Intertidal species are particularly dependent on 
suitable conditions for both air and sea surface temperatures (Monaco 
and McQuaid, 2019). The structure of ocean warming also affects the 
intensity of upper-ocean stratification and the timing and strength of 
coastal upwelling (driven also by mean wind changes), which alters 
the vertical transport of oxygen- and nutrient-rich waters affecting 
fishery and marine ecosystem productivity (D. Wang et al., 2015).

1787
Climate Change Information for Regional Impact and for Risk Assessment Chapter 121212.3.6.2 Marine Heatwave
Marine heatwaves (MHW) push water temperatures above key 
thresholds and have been associated with coral bleaching episodes, 
species shifts and harmful algal blooms that can disrupt ecosystems, 
tourism and human health (Box  9.2; Wernberg et  al., 2016; Arias-
Ortiz et  al., 2018; Oliver et  al., 2018; Frölicher, 2019; Smale et  al., 
2019; Sully et al., 2019). The duration and return period of marine 
heatwaves provide insight into aggregate stresses on marine species, 
fisheries and ecosystems, with various indices gauging cumulative 
intensity or the number of days, weeks or months exceeding critical 
thresholds (Frieler et al., 2013; Frölicher et al., 2018; Hughes et al., 
2018b; Cheung and Frölicher, 2020). Hobday et  al. (2016) defined 
marine heatwaves as the exceedance of the 90th percentile of the sea

surface temperature (SST) distribution on a given Julian day during 
five or more consecutive days, while Box 9.2, Figure 1 shows MHW 
as an exceedance of 99th-percentile 11-day de-seasonalized SSTs. 
The return period of marine heatwaves is also critical in determining 
a  coral system’s ability to recover before the next event (Hughes 
et al., 2018a).
12.3.6.3 Ocean Acidity
Uptake of atmospheric CO 2 and subsequent increases in dissolved 
CO 2 lowers ocean pH and can reduce carbonate ion concentrations 
below critical calcium carbonate saturation thresholds for marine 
and aquatic organisms growth, reproduction and/or survival, with 
extended implications for marine ecosystems including fisheries (Bell 
et al., 2013; Kroeker et al., 2013; Barange et al., 2014; Dutkiewicz 
et al., 2015; Ekstrom et al., 2015; Gattuso et al., 2015; Mathis et al., 
2015a; Nagelkerken and Connell, 2015; Behrenfeld et  al., 2016; 
Nagelkerken and Munday, 2016; Tripathi et  al., 2016; Jiang et  al.,

2018; Weiss et al., 2018; N. Ahmed et al., 2019; Bindoff et al., 2019). 
Lower pH may provide more favourable conditions for toxic algal 
blooms (Riebesell et al., 2018) and can interact with hypoxic zones 
to impact ecosystems (Gobler and Baumann, 2016; Cai et al., 2017).
12.3.6.4 Ocean Salinity
Changes in currents, sea ice brine rejection and net freshwater flux 
in the ocean can alter salinity with effects on mixed layer structure, 
density stratification and the vertical movement of nutrients and 
marine organisms (Freeland, 2013; Haumann et al., 2016).
12.3.6.5 Dissolved Oxygen
Ocean warming and increased stratification decrease the oxygen 
content of the ocean (Griffiths et  al., 2017; Schmidtko et  al., 2017; 
Bindoff et al., 2019), lead to an expansion of oxygen minimum zones 
in the open ocean (Stramma et  al., 2012; Zhang et  al., 2013) and 
exacerbate the creation of anoxic ‘dead zones’ in the coastal oceans

(Breitburg et al., 2018). Such a decline (characterized by successive 
dissolved oxygen concentration thresholds) could affect a wide range 
of marine organisms and reduce marine habitats (Chan et al., 2008; 
Vaquer-Sunyer and Duarte, 2008; Hoegh-Guldberg and Bruno, 2010; 
Altieri and Gedan, 2015; Breitburg et al., 2018) and can also lead to 
further local acidification (Zhang and Gao, 2016; Laurent et al., 2017).12.3.7 Other Climatic Impact-drivers
12.3.7.1 Air Pollution Weather
Although future air pollution will be strongly driven by air quality 
policies, anthropogenically-driven changes to temperature, humidity, 
precipitation and synoptic patterns have the potential to affect the 
emissions, production, concentration and transport of particulate matter 
(e.g., from dust, fires, pollen) and gaseous pollutants such as sulphur 
dioxide, tropospheric ozone and nitrogen dioxide (Section  6.5) with 
resulting impacts on human health, agriculture and ecosystems (Ren

et al., 2011; Fiore et al., 2015; Kinney et al., 2015a; Tian et al., 2016; 
Orru et al., 2017; Emberson et al., 2018; Hayes et al., 2020). Information 
about conditions leading to poor air quality is also important for 
visibility in natural parks and tourist locations (Yue et  al., 2013; Val 
Martin et al., 2015), as well as the efficiency of solar photovoltaic panels 
(Sweerts et al., 2019). Relevant information about conditions favouring 
air pollution includes tracking warmer conditions that accelerate ozone 
formation (Peel et al., 2013; Schnell et al., 2016) and the frequency and 
duration of stagnant air events (Horton et al., 2014; Fann et al., 2015; 
Lelieveld et al., 2015; Vautard et al., 2018), although no regional index 
has proven sufficient to capture regional changes or acute events (Kerr 
and Waugh, 2018; Schnell et al., 2018). By contrast, precipitation and 
moister air tend to reduce pollution (Section 6.5).
12.3.7.2 Atmospheric Carbon Dioxide at Surface

Carbon dioxide (CO 2) is a  well-mixed greenhouse gas with global 
repercussions on Earth’s energy balance; however, atmospheric 
CO 2 concentration changes at the land surface also affect plant 
functions within ecosystems and agriculture (see also Chapter  5). 
High CO 2 concentration can increase photosynthesis rates and 
primary production within natural ecosystems (Norby et  al., 2010; 
Ratliff et al., 2015; Zhu et al., 2016) and agricultural crops (Hatfield 
et al., 2011; Leakey et al., 2012; Bell et al., 2013; Glenn et al., 2014; 
Nagelkerken and Connell, 2015; Behrenfeld et al., 2016; Deryng et al., 
2016; Kimball, 2016). High CO 2 concentration affects total biomass 
and plant sugar content important to bioenergy production (Schaeffer 
et al., 2012), but also helps some pests and weeds flourish (Hamilton 
et  al., 2005; Wolfe et  al., 2008; Valerio et  al., 2013; Korres et  al., 
2016; Stinson et  al., 2016; Ramesh et  al., 2017), while potentially

shifting the effectiveness of herbicides (Varanasi et al., 2016; Refatti 
et al., 2019). Higher CO 2 concentration reduces transpiration losses 
during drought conditions (Cammarano et al., 2016; Deryng et al., 
2016; Swann et al., 2016; Durand et al., 2018), which also changes 
the energy balance within the plant canopy (Webber et al., 2017). 
Higher CO 2 reduces the nutritional density of crops and forage lands 
(Loladze, 2014; Müller et al., 2014; Myers et al., 2014, 2017; X. Li 
et al., 2016; M.A. Lee et al., 2017; Smith and Myers, 2018; Zhu et al., 
2018; Beach et al., 2019) and can increase the production of toxins 
(Ziska et al., 2007) and allergenic pollen (Schmidt, 2016).
12.3.7.3 Radiation at Surface
Changes in surface solar and longwave radiation fluxes alter 
photosynthesis rates and potential evapotranspiration for natural 
ecosystems and food, fibre and energy crops (Mäkinen et al., 2018);

1788
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12changes in radiation fluxes can also shift solar energy resources 
(Schaeffer et al., 2012; Jerez et al., 2015; Wild et al., 2015; Fant et al., 
2016; Craig et  al., 2018). Plants and aquatic systems particularly 
respond to changes in photosynthetically active radiation (PAR) and the 
fraction of diffuse radiation (Proctor et al., 2018; Ren et al., 2018; Ryu 
et al., 2018). Increases in ultraviolet radiation can also detrimentally 
affect ecosystems and human health (Barnes et al., 2019).
12.3.7.4 Additional Relevant Climatic Impact-drivers
Additional CIDs may be relevant for regional studies but are not the 
focus of assessment in this Report. For example, information about 
changes in the frequency and seasonal timing of fog helps anticipate 
airport delays and cool beach days, and is also important for water 
delivery and retention in coastal ecological and agricultural systems

(Torregrosa et al., 2014).
Threats to many sectoral assets and associated systems may also be 
compounded when multiple hazards occur simultaneously in the same 
place, affect multiple regions at the same time, or occur in a sequence 
that may amplify overall impact (Section  11.8; IPCC, 2012; Clarke 
et al., 2018; Zscheischler et al., 2018; Raymond et al., 2020). There is 
emerging literature on many connected extremes and their associated 
hazards (e.g.,  climatic conditions that could drive multi-breadbasket 
failures; Trnka et al., 2019; Kornhuber et al., 2020), but a full accounting 
is not practical here especially considering the many possible CID 
combinations and the need to assess how exposed systems would be 
vulnerable to compound CIDs (assessed in WGII). Table 12.2 is once 
again instructive here in considering hazard-related storylines, as the 
multiple CIDs affecting a given sectoral asset (assessing across a row

of Table  12.2) point to potentially dangerous hazard combinations. 
Similarly, change in a single CID has the potential to affect multiple 
sectoral assets (assessing down a column of Table 12.2) in a manner 
with broader systemic implications (AR6 WGII).
Recent literature defines CID indices to represent trends 
and thresholds that influence sectoral assets, albeit with 
considerable variation owing to the unique characteristics of 
regional and sectoral assets. Indices include direct information 
about the CID’s profile (magnitude, frequency, duration, timing, 
spatial extent) or utilize atmospheric conditions as a proxy for 
CIDs that are more difficult to directly observe or simulate. 
Each sector is affected by multiple CIDs, and each CID affects 
multiple sectors. Assets within the same sector may require 
different or tailored indices even for the same CID. These indices 
may be defined to capture graduated thresholds associated

with tipping points or inflection points in a particular sectoral 
vulnerability, with commonalities in the types of processes 
these thresholds represent even as their precise magnitude 
may vary by specific sectoral system and asset.
12.4 Regional Information on Changing Climate
This section describes the historical and projected changes in commonly 
used indices and thresholds associated with the main climatic 
impact-drivers (Sections 12.2 and 12.3) at the scale of AR6 regions described in Figure  1.18a. The section is organised by continents 
(Sections 12.4.1–12.4.6) with a specific assessment for Small Islands 
(Section 12.4.7), open and deep ocean (Section 12.4.8), and polar regions 
(Section 12.4.9) as defined in Chapter 1 (Figure 1.18c). In addition, CID 
indices and thresholds relevant to ‘specific zones’ , as defined in AR6 
WGII Cross-Chapter Papers, are assessed in Section 12.4.10 except for 
the Mediterranean, which is addressed both under Africa and Europe

(Sections 12.4.1 and 12.4.5) and is a focus in Section 10.6.4.
Regional assessment method and tables:  In each section herein 
(Sections 12.4.1–12.4.10), we assess changes in sector-relevant CIDs 
following the main CID categories defined in Section  12.2 through 
commonly used indices and thresholds relevant for sectors described in 
12.3. Sections 12.4.1–12.4.9 each include a summary qualitative CID 
assessment table (Tables 12.3–12.11) showing the confidence levels 
associated with the direction of projected CID changes (i.e., increasing 
or decreasing) for the mid-century period (2041–2060) relative to 
the recent past, for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above 
(RCP6.0, RCP8.5, SSP3-7.0, SSP5-8.5, SRES A2), which approximately 
encompasses GWLs of 2.0°C to 2.4°C (as best estimate; see 
Chapter 4, Table 4.5). For scenarios RCP2.6, SSP1-2.6 or SSP1-1.9, the 
signal may have lower confidence levels in some cases due to smaller

overall changes, embedded in a similar internal variability, and to the 
availability of relatively few studies that account for these scenarios. 
Nevertheless, CID changes under these lower emissions scenarios are 
included in the text whenever information is available. For each cell in 
Tables 12.3–12.11, literature is assessed, aided by global Figure 12.4 or 
regional Figures 12.5–12.10. Confidence in projections is established 
considering evidence emerging from observations, attribution and 
projections, as explained in Cross-Chapter Box 10.3 while considering 
the amount of evidence and agreement across models and studies 
and model generations.
The confidence levels associated with the directions of projected CID 
changes are synthesized assessments based on literature that may 
utilize different indices and baseline periods or projections by GWLs. 
For extreme heat, cold spell, heavy precipitation and drought CIDs that

are assessed in Chapter 11, here we draw projections from the 2°C 
GWL tables in Section 11.9. In some cases, more details are needed in 
order to emphasize one aspect of projected CID change. For instance, 
the change in a CID may be different for intensity, duration, frequency; 
or there can be strong sub-regional or seasonal signals; or different 
CID indices may have conflicting signals. A footnote is added in such 
cases, but a  confidence level for a  direction of projected change is 
given based on the Section 12.3 assessment of aspects of regional 
CID change that are most relevant for impacts and for risks. As an 
example, tropical cyclones are increasing in intensity but decreasing 
in frequency in some regions. Here, in assessing the confidence of 
the direction of projected change in the Tropical cyclone CID (i.e., the 
colour of the table cell), we assign more weight to the ‘intensity’ 
rather than the ‘frequency’, corresponding to the higher relevance

of the intensity of major tropical cyclones for risk assessment. Low 
confidence of changes, arising from lack of evidence, strong spatial 
or seasonal heterogeneity, or lack of agreement, are represented by 
colour-less cells, and, for the sake of simplicity, only two categories 
of confidence are given: medium confidence  and  high  confidence  
(and higher). In addition, CID assessment tables also indicate observed

1789
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212
%
%
High model agreement
Low model agreementColour(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k) (l)
(m) (n) (o)
(p) (q) (r)days yr -1
days yr -1
events decade -1
m
Figure 12.4 | Median projected changes in selected climatic impact-driver indices based on CMIP6 models.

1790
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12or projected emergence of the CID change signal from the natural 
interannual variability if assessed with at least medium confidence  
in Section 12.5.2, using as a basis a criterion of S/N > 1, noise being 
defined as the interannual variability. The time of emergence (ToE) 
is given as either: (i) already emerged in the historical period, or 
(ii) emerging by 2050 at least for RCP8.5/SSP5-8.5, or (iii) emerging 
after 2050 but before 2100 at least for scenarios RCP8.5 or SSP5-8.5. 
Table cells that do not include emergence information are indicative 
of ‘low confidence  of emergence in the 21st century’, which includes 
situations where assessment indicates emergence will not occur 
before 2100 or that evidence is not available or insufficient for 
a confidence assessment of time of emergence.
Figures:  The assessment of changes in CIDs is based on literature,

physical understanding (Chapters 2–11), and global and regional 
climate projections of indices and thresholds presented in the 
Atlas, as well as in the global and regional figures in Section 12.4 
(Figures 12.4–12.10) showing the future evolution of nine key CID 
indices/thresholds used in this assessment (see also Cross-Chapter 
Box  10.3). The figure indices and impact-relevant thresholds are 
described in Annex VI: Climatic Impact-drivers and Extreme Indices.
Figure  12.4 shows changes in six CID indices. These global maps 
are derived from Coupled Model Intercomparison Project Phase 6 
(CMIP6) simulations for different time periods and scenarios (except 
for extreme total water level where CMIP Phase 5 (CMIP5) is used). 
The uncertainty due to climate models, time, scenarios and regional 
downscaling is illustrated in Supplementary Material SM.12.1 to 
SM.12.6, which show the distribution of the spatial average of the

index among models over each land region for CMIP5, CMIP6 and 
Coordinated Regional Climate Downscaling Experiment (CORDEX) 
ensembles for the recent past, mid- and end-21st century, and for 
GWLs of +1.5°C, +2°C and +4°C. The hatching in the figure covers 
areas where less than 80% of models agree on the sign of change.
Further regional detail is provided for the remaining indices in each 
continental section in the form of continental maps accompanied 
by regional box plots displaying changes calculated for AR6 region 
averages, and the associated regionally averaged uncertainty.
Climatic impact-drivers changing in a globally coherent way:  
For the sake of conciseness, assessments pertaining to ocean acidity 
and the ‘Other’ CID type in Sections 12.2 and 12.3 are not provided 
per region in Sections 12.4.1–12.4.9 but are summarized here, given 
the globally coherent way in which they change.
Ocean acidity:  Observations show increasing ocean acidification

(robust evidence , high agreement ), and it is virtually certain  that future ocean acidification will increase given future increases in 
greenhouse gases (Section  5.4). Areas below calcium carbonate 
saturation thresholds expanded from the 1990s to 2010 and Meredith 
et al. (2019) indicated that both the Southern and Arctic Oceans will 
experience year-round under-saturation conditions by 2100 under 
RCP8.5. The vertical level of the aragonite saturation horizon off 
the Pacific coast of North America has risen towards the surface by 
30–50 m since pre-industrial times (Mathis et al., 2015b; Feely et al., 
2016). In a study of US coastlines, Ekstrom et al. (2015) mapped out 
the projected year when aragonite saturation state drops below 1.5 
(a sublethal threshold for bivalve mollusc larvae), finding hazardous 
conditions before 2030 from northern Oregon to Alaska and before 
2100 for the Pacific coast and Atlantic coastline north of New Jersey.

Mathis et  al. (2015a) found that surface waters in the Beaufort 
Sea have already dropped below aragonite saturation thresholds, 
projecting further declines and the Chukchi Sea also dropping below 
saturation by about 2030.
Air pollution weather:  The effect of climate change on air quality is 
assessed in Section 6.5 with limitations for local planning explained 
in Section 6.1.3, and only a brief summary is given here. Section 6.5 
notes that climate change will have a  small burden on particulate 
matter (PM) pollution ( medium confidence ) while the main controlling 
factor in determining future concentrations will be future emissions 
policy for PM and their precursors ( high confidence ). Surface ozone is 
sensitive to temperature and water vapour changes, but future levels 
depend on precursor emissions. Although there is low confidence  in 
precise regional changes (Section 6.5), climate change will generally

introduce a surface ozone (O 3) penalty (increasing concentrations with 
increasing warming levels) over regions with high anthropogenic and/
or natural ozone precursor emissions, while in less polluted regions 
higher temperatures and humidity favour destruction of ozone (Schnell 
et al., 2016). There is low confidence  in changes to future stagnation 
events given the lack of robust projections of related atmospheric 
conditions, such as future atmospheric blocking events (Sections 3.3.3 
and 8.4.2). The response of regional air pollution to climate change 
will also be affected by other CIDs like fire weather, as well as by 
ecosystem responses such as shifts in emissions by vegetation (Fiore 
et al., 2015). Section 6.5 assessed medium confidence  that climate-
driven changes to meteorological conditions generally favour 
extreme air pollution episodes in heavily polluted environments, but 
noted strong regional and metric dependencies. Given the dominant

influence of future air quality policies, uncertainties around stagnation 
or blocking events, and the potential contrasting regional changes of 
conditions favouring ozone and PM formation, accumulation and 
destruction, cells in Tables 12.3–12.11 for air pollution weather are 
marked as low confidence , and the reader is referred to Section 6.5 
for further details.Figure 12.4 (continued): (a–c)  Mean number of days per year with maximum temperature exceeding 35°C; (d–f)  mean number of days per year with the NOAA Heat 
Index (HI) exceeding 41°C; (g–i)  number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index; (j–l) mean soil moisture 
(%) and (m–o)  mean wind speed (%). (p–r)  shows change in extreme sea level (1-in-100-year return period total water level from Vousdoukas et al. (2018)’s CMIP5 based

dataset; metres). Left-hand column is for SSP1-2.6, 2081–2100; middle column is for SSP5-8.5 2041–2060; and right-hand column SSP5-8.5, 2081–2100, all expressed as 
changes relative to 1995–2014. Exception is extreme total water level which is for (p) RCP4.5 2100, (q) RCP8.5 2050 and (r) RCP8.5 2100, each relative to 1980–2014. Bias 
correction is applied to daily maximum temperature and HI data (Atlas.1.4.5). Uncertainty is represented using the simple approach: no overlay indicates regions with high 
model agreement, where ≥80% of models agree on the sign (direction) of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on 
the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. See Annex VI for details of indices. Figures 12.SM.1–12.SM.6

show regionally averaged values of these indices for the AR6 WGI Reference Regions for various model ensembles, scenarios, time horizons and global warming levels. Further 
details on data sources and processing are available in the chapter data table (Table 12.SM.1).

1791
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Atmospheric CO 2 at surface:  Observations show rising atmospheric 
CO 2 concentrations at the surface over all Earth regions ( robust 
evidence , high agreement ) (Sections 2.2 and 5.1.1), and it is virtually 
certain  that surface atmospheric CO 2 concentrations will continue to 
increase without substantial changes to emissions (Section 5.4).
Radiation at surface:  Radiation has undergone decadal variations 
in past observations, which are mostly responding to the so-called 
dimming and brightening phenomenon driven by the increase and 
decrease of aerosols. Over the last two decades or so, brightening 
continues in Europe and North America and dimming stabilizes 
over South and East Asia and increases in some other areas 
(Section  7.2.2.3). Future regional shortwave radiation projections 
depend mostly on cloud trends, aerosol and water vapour trends,

and stratospheric ozone when considering UV radiation. Over Africa 
in 2050 and beyond, there is medium confidence that radiation will 
increase in North and South Africa and decrease over the Sahara, 
North Eastern Africa and Western Africa (Wild et  al., 2015, 2017; 
Soares et  al., 2019; C. Tang et  al., 2019; Sawadogo et  al., 2020, 
2021). Over Asia, the CMIP5 multi-model mean response shows 
that solar radiation will decrease in South Asia and increase in East 
Asia ( medium confidence ) by the mid-century RCP8.5 (Wild et  al., 
2015, 2017; Ruosteenoja et  al., 2019b). Projected solar resources 
show an increasing trend throughout the 21st century in East Asia 
under RCP2.6 and RCP8.5 scenarios in CMIP5 simulations ( medium 
confidence ) (Wild et al., 2015; F . Zhang et al., 2018; Shiogama et al., 
2020). More sunshine is projected over Australia in winter and spring 
by the end of the century ( medium confidence ) with the increases

in Southern Australia exceeding 10% (CSIRO and BOM, 2015; 
Wild et  al., 2015). In  Central and South America, there is medium 
confidence  of increasing solar radiation over the Amazon basin and 
the northern part of South America ( medium confidence ) (Wild et al., 
2015, 2017; de Jong et  al., 2019). There is low confidence  for an 
increase in surface radiation in central Europe, owing in particular to 
disagreement in cloud cover across global and regional models (Jerez 
et al., 2015; Bartók et al., 2017; Craig et al., 2018), as well as water 
vapour. The treatment of aerosol appears to be key in explaining these 
differences (Boé, 2016; Undorf et al., 2018; Boé et al., 2020; Gutiérrez 
et al., 2020). Regional and global studies, however, indicate that there 
is medium confidence  in increasing radiation over southern Europe 
and decreasing radiation over Northern Europe. Increasing radiation 
trends are also found over southern and eastern USA, and decreasing

trends over North-Western North America (Wild et al., 2015; Losada 
Carreño et al., 2020), despite large differences between responses 
from regional climate models (RCMs) and general circulation models 
(GCMs) over southern and eastern USA ( low confidence ), where, as 
for Central Europe, the role of aerosols appears important (Chen, 
2021). Over polar regions there is medium confidence  of a decrease 
in radiation due to increasing moisture in the atmosphere and clouds 
(Wild et al., 2015).
12.4.1 Africa
Previous IPCC assessments results are summarized in Atlas.4.1.1 
For the purpose of this assessment the Africa region has been divided 
in nine sub-regions of which eight – Sahara (SAH), Western Africa (WAF), Central Africa (CAF), North Eastern Africa (NEAF), South 
Eastern Africa (SEAF), West Southern Africa (WSAF), East Southern 
Africa (ESAF) and Madagascar (MDG) – are the official AR6 regions 
(Figure Atlas.2) and one – North Africa – is used in this assessment to

indicate the African portion of the Mediterranean region.
Quite a large body of new literature is now available for the African 
climate as a result of regionally downscaled CORDEX Africa outputs, 
in particular, providing projections of both the mean climate (Mariotti 
et al., 2014; Nikulin et al., 2018; Dosio et al., 2019; Teichmann et al., 
2021) and extreme climate phenomena (Giorgi et al., 2014; Nikulin 
et al., 2018; Dosio et al., 2019; Coppola et al., 2021b). CORDEX Africa 
simulations are assessed in the Atlas, which finds reasonable skill in 
mean temperature and precipitation as well as important features 
of regional climate (e.g.,  timing of monsoon onset in West Africa) 
although lower performance in Central Africa.
12.4.1.1 Heat and Cold
Mean air temperature:  The African continent has experienced 
increased warming since the beginning of the 20th century in regions 
where measurements allow a  sufficient homogeneous observation

coverage to estimate trends ( high confidence ) (Figure Atlas.11). This 
warming is very likely  attributable to human influence (Chapter  3 
and Atlas.4.2) at continental scale. Mean annual temperatures 
have increased at a high rate since the mid-20th century, reaching 
0.2°C–0.5°C per decade in some regions such as north, north-
eastern, west and south-western Africa (high confidence) (Atlas.4.2 
and Figure Atlas.11).
It is very likely  that temperatures will increase in all future emissions 
scenarios and all regions of Africa (Atlas.4.4). By the end of the 
century under RCP8.5 or SSP5-8.5, all African regions will very likely  
experience a warming larger than 3°C except Central Africa, where 
warming is very likely  expected above 2.5°C, while under RCP2.6 
or SSP1-2.6, the warming remains very likely  limited to below 2°C 
(Figure Atlas.12). A  very likely  warming with ranges between 0.5°C 
and 2.5°C is projected by the mid-century for all scenarios depending

on the region  (high confidence ). Mean temperatures for all regions 
are projected to increase with increasing global warming ( virtually 
certain ) (Figure Atlas.12).
Extreme heat:  Warm extremes have increased in most of the regions 
(high confidence ), NEAF , SEAF and MDG ( medium confidence ) and 
with low confidence  in CAF (Table 11.4). Despite the increasing mean 
temperature, there is low confidence (limited evidence ) that Africa 
has experienced increased extreme heat stress trend for agriculture 
or human health in the last two decades of the 20th century in a few 
regions such as West Africa, South Africa and North Africa considering 
the period from 1973 to 2012 (Knutson and Ploshay, 2016).
A substantial increase in heatwave magnitude and frequency over 
most of the Africa domain is projected for even 2°C global warming 
(high confidence ) (Sections 11.3 and 11.9, and Table  11.4), with 
potential effects on health and agriculture. The number of days

with maximum temperature exceeding 35°C is projected to  increase 
(Coppola et al., 2021b) in the range of 50–100 days by 2050 under

1792
(c)m
00.511.5MED
0.200.20.4SAH
00.511.5WAF
0.200.20.4CAF
00.511.5NEAF
0.200.20.4SEAF
00.511.5WSAF
0.200.20.4ESAF
GWLr.past.1.5 24 mid. long.
Time slices
00.511.522.5
0.200.20.40.60.8MDG
Time slices6004002000200MED
6004002000200SAH
6004002000200WAF
6004002000200CAF
6004002000200NEAF
6004002000200SEAF
6004002000200WSAF
2050 21006004002000200ESAF
2050 21006004002000200MDG
-0.2-0.1-0.05-0.02-0.0100.010.020.050.10.2(a) Change in 1-in-100 year river discharge per unit 
catchment area by mid-century, CORDEX RCP8.5(b) Shoreline position change by 2100
CMIP5 RCP8.5
m3 s-1 km-2
(d)1-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2)Change in Q100 r.t. recent past (m3 s-1 km-2)
Shoreline position change (m)
Shoreline position change (m)-200 -150 -100 -50 -10 10 50 100 150 200
Legend95th p
medianCMIP5 RCP8.5CMIP5 RCP4.5
5th pLegend
CMIP6+1.5°CGWL
+2°C +4°C

CMIP5CORDEXr.pastRCPs/SSPs
2.6 8.590th p
median
10th p
Figure 12 .5 | Projected changes in selected climatic impact-driver indices for Africa.

1793
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212SSP5-8.5 in WAF , ESAF and WSAF and NEAF ( high confidence ) 
(Figure  12.4b). Under SSP1-2.6, the change in the number of 
exceedance days remains limited to about 40–50 days per year at 
the end of the century in these regions, while it increases by 150 days 
or more in WAF , CAF for SSP5-8.5 (Figure 12.4a,c; Figure 12.SM.1).
Mortality-related heat stress levels and deadly temperatures are very 
likely  to become more frequent in the future in RCP8.5/SSP5-8.5 and 
RCP4.5/SSP2-4.5 and for a  2°C global warming (Mora et  al., 2017; 
Nangombe et al., 2018; Sylla et al., 2018a; Rohat et al., 2019; Q. Sun 
et al., 2019). In particular the equatorial regions where heat is combined 
with higher humidity levels, but also North Africa, the Sahel and Southern 
Africa (Figure 12.4d–f) are among the regions with the largest increases 
of heat stress (Zhao et al., 2015; Ahmadalipour and Moradkhani, 2018;

Coffel et  al., 2018). Mitigation scenarios make a  large difference in 
frequency of exceedance of high heat stress indices thresholds (e.g., HI 
> 41°C) by the end of the century (Figure 12.4d–f; Schwingshackl et al., 
2021). In West Africa and Central Africa, under SSP5-8.5, the expected 
number of days per year with HI > 41°C will increase by around 200 
days while in SSP1-2.6 such exceedances are expected to increase by 
less than 50 days per year (Figure 12.4; Figure 12.SM.2).
Cold spell and frost:  Africa experiences cold events and frost days 
that can affect agriculture, infrastructure, health and ecosystems, 
especially in Southern and North Africa, which have marked cold 
seasons and mountainous areas. Cold spells have likely  decreased in 
frequency over subtropical areas. In particular, in North and Southern 
Africa, the frequency of cold events has likely  decreased in the last few 
decades (Sections 11.3 and 11.9). There is a  high confidence  that cold

spells and low target temperatures will decrease in future climates 
under all scenarios in West, Central and East Africa. Heating degree 
days will have a substantial decrease by the end of the century for up 
to about one month under RCP8.5 in North and Southern Africa ( high 
confidence ) (Coppola et al., 2021b).
There is high confidence  that extreme heat has increased in 
frequency and intensity in most African regions. Heatwaves 
and deadly heat stress and the frequency of exceedance of hot 
temperature thresholds (e.g., 35°C) will drastically increase by 
the end of the century ( high confidence ) under SSP5-8.5, but 
limited increases are expected in SSP1-2.6. Dangerous heat 
stress thresholds (HI > 41°C) are projected to be crossed more 
than 200 days more in West and Central Africa under SSP5-8.5, 
while this increase remains limited to a few tens of days more 
for SSP1-2.6. Cold spells and frost days are projected to occur

less frequently in all scenarios.12.4.1.2 Wet and Dry
Mean precipitation:  Since the mid-20th  century, precipitation 
trends have varied in Africa but notable drying trends are found in 
eastern, central and north-eastern parts of Southern Africa, Central 
Africa and in the Horn of Africa (Atlas.4.2).
There is high confidence  in projected mean precipitation decreases 
in North Africa and West Southern Africa and medium confidence  
in East Southern Africa by the end of the 21st century (Dosio et al., 
2019; Gebrechorkos et al., 2019; Teichmann et al., 2021; Atlas.4.5). 
The Western Africa region features a gradient in which precipitation 
decreases in the west and increases in the east and increase is also 
projected over Eastern Africa ( medium confidence ) (Atlas.4.5), with 
trends in Western Africa affecting the boreal summer monsoon (Chen 
et al., 2020). Increasing precipitation for 1.5°C and 2°C GWLs are 
found in central and eastern Sahel with low confidence  and the

wet signal is getting stronger and more extended for a 3°C and 4°C 
warmer world (Atlas.4.4).
A change in monsoon seasonality is also reported in Western Africa 
and Sahel ( low confidence ) with a forward shift in time (later onset and 
end; Section 8.2; Mariotti et al., 2011; Seth et al., 2013; Ashfaq et al., 
2021). This shift has been associated with a  precipitation decrease 
during the monsoon season attributed to a decrease of African easterly 
wave activity in the 6–9-day regime (Mariotti et al., 2014) and a soil 
precipitation feedback reported in Mariotti et al. (2011).
River flood:  Generally in Africa from 1990 through 2014, annual 
flood frequencies have fluctuated and there is medium confidence  
in an upward trend in flood events occurrences (C.-J. Li et al., 2016). 
In  particular, over Western Africa, upward trends in hydrological 
extremes such as maximum peak discharge have likely  occurred 
during the last few decades (i.e.,  after 1980) and have caused

increased flood events in riparian countries of rivers such as Niger, 
Senegal and Volta ( high confidence ) (Nka et  al., 2015; Aich et  al., 
2016a; Wilcox et al., 2018; Tramblay et al., 2020). In Southern Africa, 
trends in flood occurrences were decreasing prior to 1980 and 
increasing afterwards ( medium confidence ) (Tramblay et al., 2020).
Under future climate scenarios, the extreme river discharge as 
characterized by the 30-year return period of 5-day average peak flow 
is projected to increase by the end of the century for RCP8.5 (more 
than 10% relative to the 1971–2000 period) for most of the tropical 
African river basins (Dankers et al., 2014) and a consistent increase 
of flood magnitude is projected across humid tropical Africa by 2050 Figure 12.5 (continued): (a)  Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX-Africa models for 2041–2060

relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline 
retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference 
Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in 
grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and 
‘long’ 2081–2100) and for three global warming levels (GWLs, defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The

bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by 
medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of 
shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional 
mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no 
additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are 
available in the chapter data table (Table 12.SM.1).

1794
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12for the A1B scenario ( medium confidence ) (Figure 12.5; Arnell and 
Gosling, 2013). Specifically, in Western Africa there is not a univocal 
pattern of change for future projections (Roudier et  al., 2014); 
However, under RCP8.5, there is medium confidence  of a projected 
increase of 20-year flood magnitudes by 2050 in countries within 
the Niger River basin (Aich et al., 2016b) and low confidence  (limited 
evidence ) of an increase in extreme peak flows and their duration in 
countries of the Volta River basin by 2050 and 2090 (Jin et al., 2018). 
A significant median change of flood magnitude for the Gambia River 
(–4.5%) and for the Sessandra (+14.4%) and Niger (+6.1%) are 
projected under several scenarios between mid- and end-of-century 
(Roudier et al., 2014). In East Africa, extreme flows are projected to 
increase for regions within the Blue Nile basin with low confidence

(limited evidence ) (Aich et  al., 2014). However, uncertainty due to 
the climate scenario dominates the projection of extreme flows (Aich 
et al., 2014; Krysanova et al., 2017) for the Blue Nile and Niger River 
basins. Averaged over the African continent for different levels of 
global warming, the present-day 100-year return period flood levels 
will have a return period of 40 years in 1.5°C and 2°C (Alfieri et al., 
2017) and 21 years for 4°C warmer climate (Hirabayashi et al., 2013; 
Alfieri et al., 2017).
Heavy precipitation and pluvial flood: Chapter  11 found that 
heavy precipitation intensity and frequency has likely  increased 
over West and East Southern Africa but there is no evidence due to 
a lack of studies that any significant trend is observed in any other 
region. In addition, East Africa has experienced strong precipitation 
variability and intense wet spells leading to widespread pluvial 
flooding events hitting most countries including Ethiopia, Somalia,

Kenya and Tanzania ( medium confidence ). Finally, with respect 
to Southern Africa, heavy precipitations events have increased in 
frequency ( medium confidence ).
In West Africa and Central Africa, there is high confidence  that the 
intensity of extreme precipitation will increase in a  future climate 
under both RCP4.5 and RCP8.5 scenarios and 1.5°C and 2°C GWLs 
threatening widespread flood occurrences before, during and after 
the mature monsoon season (Chapter  11). Extreme precipitation 
intensity is also increasing in several other regions, such as SAH, 
NEAF , SEAF , ESAF and MDG ( high confidence ) for 2°C GWL and 
higher  (Chapter 11).
Landslides:  There is an increase in reported landslides in WAF , CAF , 
NEAF and SEAF in the past decades but with limited evidence  of 
significant trends (Gariano and Guzzetti, 2016; Haque et al., 2019). 
There is low confidence (limited evidence ) of a  future increase in

landslides in central-eastern Africa, and literature is largely missing 
to assess this important hazard (Gariano and Guzzetti, 2016).
Aridity:  Section  11.9 assesses medium confidence  in observed 
long-term declines of soil moisture and aridity indices in several 
African regions (NAF , WAF). Trends in East Africa are not definitive 
given uncertain balances between precipitation and potential 
evaporation (Kew et  al., 2021). Projected declines in precipitation 
and soil moisture trends indicate high confidence  in increased aridity 
over the 21st century in NAF , WSAF and ESAF but low confidence  
elsewhere in Africa (Section 11.9; see also Figure 12.4j–l; Gizaw and Gan, 2017). A growing number of studies provide further regional 
context on expanding aridity in several places in East and West Africa, 
respectively (Sylla et al., 2016a; Liu et al., 2018b; Haile et al., 2020).
Hydrological drought:  Section 11.9 noted observed decreases in

hydrological drought over the Mediterranean ( high confidence ) and 
diminished summer river flows in West Africa ( medium confidence ). 
Recent regional modelling studies project substantial increases in 
hydrological drought affecting major West African river basins under 
1.5°C and 2°C GWLs and RCP4.5 and RCP8.5 scenarios (Oguntunde 
et al. 2018, 2020; Sylla et al., 2018b); however, there remains low 
confidence  in future projections given disagreement with global 
model runoff projections (e.g., B.I. Cook et al., 2020). There is high 
confidence  that a 2°C GWL would see an increase in hydrological 
droughts in the Mediterranean region, and medium confidence  in 
increasing hydrological drought conditions in the Southern Africa 
regions (Section 11.9).
Agricultural and ecological drought:  Farmers and food security 
experts in East Africa have noted spatial extensions in seasonal 
agricultural droughts in recent decades (Elagib, 2014), but it is difficult

to disentangle these trends from climate variability. In Ethiopia, past 
severe agricultural drought conditions in the northern regions are 
moderately common events in recent years (Zeleke et al., 2017). In 
Southern Africa, the number of ‘flash’ droughts (with rapid onset and 
durations from a few days to couple of months) have increased by 
220% between 1961 and 2016 as a result of anthropogenic warming 
(Yuan et al., 2018). Section 11.9 notes medium confidence  increases 
in agricultural and ecological drought trends in North, Western 
and Central Africa as well as both Southern Africa regions. The 
most striking drought is the Western Cape drought in 2015–2018, 
a prolonged drought that resulted in acute water shortages (Wolski, 
2018; Burls et  al., 2019; Section  10.6.2). Anthropogenic climate 
change caused a  threefold increase in the probability of such 
a drought to occur (Chapters 10 and 11; Botai et al., 2017; Otto et al.,

2018). Section 11.9 assesses increases in agricultural and ecological 
drought at 2°C GWL for North Africa and West Southern Africa ( high 
confidence ) and for East Southern Africa and Madagascar ( medium 
confidence ), with confidence generally rising for higher emissions 
scenarios (Sylla et al., 2016b; Zhao and Dai, 2017; Diedhiou et al., 
2018; Abiodun et al., 2019; Todzo et al., 2020; Coppola et al., 2021b). 
Liu et al. (2018b) identified the Southern Africa region as the drought 
‘hottest spot’ in Africa in 1.5°C and 2°C global warming scenarios.
Fire weather:  There is low confidence  (low agreement ) in recent 
reductions in fire activity given soil moisture increases in some 
regions and substantial land use changes (Andela et al., 2017; Forkel 
et  al., 2019; Zubkova et  al., 2019). Days prone to fire conditions 
are going to increase in all extratropical Africa until the end of the 
century and fire weather indices are projected to largely increase

in North and Southern Africa, where increasing aridity trends occur 
(high confidence ), with an emerging signal well before the middle 
of the century where drought and heat increase will combine 
(Chapter 11; Engelbrecht et al., 2015; Abatzoglou et al., 2019). There 
is low confidence (limited evidence ) of fire weather changes for other 
African regions.

1795
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Total precipitation is projected to decrease in the northernmost 
(high confidence ) and southernmost regions of Africa 
(medium confidence ), with West and East Africa regions each 
having a  west-to-east pattern of decreasing-to-increasing 
precipitation ( medium confidence ). Most African regions will 
undergo an increase in heavy precipitation that can lead 
to pluvial floods ( high confidence ), even as increasing dry 
climatic impact-drivers (aridity, hydrological, agricultural and 
ecological droughts, fire weather) are generally projected in 
the North Africa and Southern African regions ( high confidence ) 
and western portions of West Africa ( medium confidence ).
12.4.1.3 Wind
Mean wind speed:  Decreasing trends in wind speeds have 
occurred in many parts of Africa ( low confidence  due to observations 
with limited homogeneity) (McVicar et al., 2012; AR5 WGI). There

is high confidence  in climate change-induced future decreasing 
mean wind, wind energy potential and strong winds in North Africa 
and Mediterranean regions as a  consequence of the poleward 
shift of the Hadley cell (Karnauskas et al., 2018a; Kjellström et al., 
2018; Sivakumar and Lucio, 2018; Tobin et  al., 2018; Jung and 
Schindler, 2019) in the RCP4.5 and RCP8.5 scenarios by the middle 
of the century or beyond, and for a  GWL of 2°C or higher. Over 
Western Africa and Southern Africa a  future significant increase 
in wind speeds and wind energy potential is expected ( medium 
confidence ) (Figure 12.4m–o; Karnauskas et al., 2018a; Jung and 
Schindler, 2019).
Severe wind storm:  A limited number of studies allow an assessment 
of past trends in wind storms. In West Africa and specifically in the 
Sahel band, more intense storms have occurred since the 1980s 
(low confidence, limited evidence ). A persistent and large increase of

frequency of Sahelian mesoscale convective storms has been found 
in several studies (Panthou et  al., 2014; C.M. Taylor et  al., 2017), 
with consequences for extreme rainfalls, and potentially extreme 
winds ( low confidence, limited evidence ). There is low confidence  
of a  general increasing trend in extreme winds across Western, 
Central, Eastern and Southern Africa in a majority of regions by the 
middle of the century even in high-end scenarios. The frequency 
of Mediterranean wind storms reaching North Africa, including 
Medicanes, is projected to decrease, but their intensities are projected 
to increase, by the mid-century and beyond under SRES A1B, SRES A2 
and RCP8.5 ( medium confidence ) (Chapter 11; Cavicchia et al., 2014; 
Walsh et al., 2014; Tous et al., 2016; Romera et al., 2017; Romero and 
Emanuel, 2017; González-Alemán et al., 2019).
Tropical cyclone:  In the South Indian Ocean, an increase in Category 5

cyclones has been observed in recent decades (Fitchett, 2018) as in 
other basins (Section  11.7). However, there is a  projected decrease 
in the frequency of tropical cyclones making landfall over Madagascar, 
South Eastern Africa and East Southern Africa in a 1°C, 2°C and 3°C 
warmer world ( medium confidence ) (Malherbe et al., 2013; Roberts 
et al., 2015, 2020; Muthige et al., 2018; Knutson et al., 2020). There 
is medium confidence  in general increasing intensities for cyclones in 
such studies for African regions.Sand and dust storm:  North Africa and the Sahel, and to a  lesser extent 
Southern Africa, are prone to dust storms, having consequences on 
health (Querol et al., 2019), transmission of infectious diseases (Agier 
et al., 2013; Wu et al., 2016), and solar power generation and related 
maintenance costs. There is limited evidence  and low agreement  
of secular 20th  century trends in wind speeds or dust emissions

(limited length of data records, large variability). Dust variations are 
controlled by changes in surface winds, precipitation and vegetation, 
which in turn are modulated at multiple time scales by dominant 
modes of internal climate variability (Chapter  10). In North Africa, 
wind variability explains both the observed high concentrations 
between the 1970s and 1980s and lower concentrations thereafter 
(Ridley et al., 2014; Evan et al., 2016). Yet, the effect of vegetation 
changes may not be negligible (Pu and Ginoux, 2017, 2018).
Changes to the frequency and intensity of dust storms also remain largely 
uncertain due to uncertainty in future regional wind and precipitation 
as the climate warms, CO 2 fertilization effects on vegetation (Huang 
et al., 2017), and anthropogenic land use and land-cover change due 
to land management and invasive species (Ginoux et al., 2012; Webb 
and Pierre, 2018). Dust loadings and related air pollution hazards (from

fine particles that affect health) are projected to generally decrease 
in many regions of the Sahara and Sahel due to the changing winds 
(Evan et al., 2016) and slightly increase over the Guinea coast and West 
Africa ( low confidence ) (Ji et al., 2018).
In summary, there is high confidence  of a decrease in mean 
wind speed and wind energy potential in North Africa and 
medium confidence  of an increase in Southern and Western 
Africa, by the middle of the century regardless of climate 
scenario or global warming level equal or superior to 2°C, high 
confidence  of a decrease in frequency of cyclones landing in 
SEAF, ESAF and MDG, and low confidence  of a general increase 
in wind storms in most African regions located south of the 
Sahel. The evolution of dust storms remains largely uncertain.
12.4.1.4 Snow and Ice
Snow and glacier:  African glaciers are located in East Africa and more 
specifically on Mount Kenya, the Rwenzori Mountains and  Mount

Kilimanjaro, with glaciers reducing substantially in each region ( high 
confidence ) (Taylor et al., 2006; Cullen et al., 2013; Chen et al., 2018; 
Prinz et  al., 2018; Wang and Zhou, 2019). Observation and future 
projection of African glacier mass changes are assessed in Section 9.5.1 
within the low-latitude glacier region, which is one of the regions with 
the largest mass loss even under low-emissions scenarios (assessment 
of this region is dominated by glaciers in the South American Andes, 
however) ( high confidence ). Glaciers in the low-latitude region will 
lose 67 ± 42%, 86 ± 24% and 94 ± 13% of their mass by the end 
of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios respectively 
(Marzeion et  al., 2020). Cullen et  al. (2013) calculated that even 
imbalances between the Mount Kilimanjaro glaciers and present-day 
climate would be enough to eliminate the mountain’s glaciers by 2060. 
Snow water equivalent and snow cover season duration also decline in

the East African mountains, Ethiopian Highlands and Atlas Mountains 
with climate change ( high confidence ) (López-Moreno et al., 2017).

1796
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12In conclusion, there is high confidence  that African snow and 
glaciers have very significantly decreased in the last decades 
and that this trend will continue over the 21st century.
12.4.1.5 Coastal and Oceanic
Relative sea level:  Around Africa, from 1900 to 2018, a new tide 
gauge-based reconstruction finds a  regional mean RSL change of 
2.07 [1.36 to 2.77] mm yr –1 in the South Atlantic and 1.33 [0.80 to 
1.86] mm yr –1 in the Indian Ocean (Frederikse et al., 2020), compared 
to a  GMSL change of around 1.7  mm yr –1 (Section  2.3.3.3 and 
Table  9.5). For the period 1993–2018, these RSLR rates, based on 
satellite altimetry, increased to 3.45 [3.04 to 3.86]  mm yr –1 and 
3.65  [3.23 to 4.08]  mm yr –1 respectively (Frederikse et  al., 2020), 
compared to a GMSL change of 3.25 mm yr –1 (Section 2.3.3.3 and 
Relative sea level rise is virtually certain  to continue in the oceans

around Africa. Regional mean RSLR projections for the oceans around 
Africa range from 0.4–0.5 m under SSP1-2.6 to 0.8–0.9 m under 
SSP5-8.5 for 2081–2100 relative to 1995–2014 (median values), 
which is within the range of projected GMSL change (Section 9.6.3.3). 
These RSLR projections may, however, be underestimated due 
to potential partial representation of land subsidence in their 
assessment (Section 9.6.3.2).
Coastal flood:  The present-day 1-in-100-year extreme total water 
level is between 0.1 and 1.2 m around Africa, with values around 1 m 
or above along the south-west, south-east and central east coasts 
(Vousdoukas et al., 2018).
Extreme total water level (ETWL) magnitude and occurrence frequency 
are expected to increase throughout the region ( high confidence ) 
(Figure  12.4p–r and Figure  12.SM.6). Across the continent, the 
5th–95th percentile range of the 1-in-100-year ETWL is projected to 
increase (relative to 1980–2014) by 7–36 cm and by 14–42 cm by

2050 under RCP4.5 and RCP8.5 respectively. By 2100, this range is 
projected to be 28–86 cm and 43–190 cm under RCP4.5 and RCP8.5 
respectively (Vousdoukas et al., 2018; Kirezci et al., 2020). In terms 
of ETWL occurrence frequencies, the present-day 1-in-100-year ETWL 
is projected to have median return periods of around 1-in-10-years 
to 1-in-20-years by 2050 and 1-in-1-year to 1-in-5-years by 2100 
in southern and North Africa and occur more than once per year 
by 2050 and 2100 in most of East and West Africa under RCP4.5 
(Vousdoukas et  al., 2018). The present-day 1-in-50-year ETWL is 
projected to occur around three times a year by 2100 with an SLR of 
1 m in Africa (Vitousek et al., 2017).
Coastal erosion:  Shoreline retreat rates up to 1 m yr –1 have been 
observed around the continent during 1984–2015, except in ESAF , 
which has experienced a shoreline progradation rate of 0.1 m/r over 
the same period (Luijendijk et  al., 2018; Mentaschi et  al., 2018).

Mentaschi et  al. (2018) report a  coastal area losses of 160 km2 
and 460 km2 over a 30-year period (1984–2015) along the Atlantic 
and  Indian Ocean coasts of the continent. At the more regional 
level, in Ghana along the Gulf of Guinea about 79% of the shoreline was found to be retreating while 21% was found to be stable or 
prograding over the period 1974–1996 (Addo and Addo, 2016).
Projections indicate that a  vast majority of sandy coasts in the 
region will experience shoreline retreat throughout the 21st century 
(high confidence ), while parts of the ESAF and western MDG coastline 
are projected to prograde over the 21st century, if present ambient 
trends continue. Median shoreline change projections (CMIP5), 
relative to 2010, presented by Vousdoukas et al. (2020b) show that, 
under RCP4.5, shorelines in Africa will retreat by between 30 m (SAH, 
NEAF , WSAF , ESAF , MDG) and 55 m (WAF , CAF), by mid-century. By

the same period but under RCP8.5, the median shoreline retreat is 
projected to be between 35 m (SAH, NEAF , WSAF , ESAF) and 65 m 
(WAF , CAF). By 2100, more than 100 m of median retreat is projected 
in WAF , CAF and SEAF under RCP4.5, while under RCP8.5, more than 
100 m of shoreline retreat is projected in all regions except NEAF 
and WSAF . Under RCP8.5 especially, the projected retreat by 2100 
is greater than 150 m in WAF and CAF . The total length of sandy 
coasts in Africa that is projected to retreat by more than a median of 
100 m by 2100 under RCP4.5 and RCP8.5 is about 13,000 km and 
17,000 km respectively, an increase of approximately 33%.
Marine heatwave (MHW):  From 1982 to 2016, the coastal oceans 
of Africa have experienced on average 2–3 MHWs per year, with the 
coastal oceans around the southern half of the continent experiencing 
on average 2.5–3 MHWs per year. The average duration was between 
5 and 15 days (Oliver et al., 2018). Changes over the 20th century,

derived from MHW proxies, show an increase in frequency between 
0.5 and 2 MHWs per decade over the region, especially off the Horn 
of Africa; an increase in intensity per event around Southern Africa; 
and an increase in MHW duration along the North African coastlines 
(Oliver et al., 2018).
There is high confidence  that MHWs will increase around Africa. 
Mean SST, a common proxy for MHWs, is projected to increase by 
1°C (2°C) around Africa by 2100, with a  hotspot of around 2°C 
(5°C) along the coastlines of South Africa under RCP4.5 (RCP8.5; 
Interactive Atlas). Under global warming conditions, MHW intensity 
and duration will increase in the coastal zones of all sub-regions of 
Africa (Frölicher et al., 2018). Projections for SSP1-2.6 and SSP5-8.5 
both show an increase in MHWs around Australasia by 2081–2100, 
relative to 1985–2014 (Box 9.2, Figure 1).
In general, there is high confidence  that most coastal- 
and ocean-related hazards in Africa will increase over the

21st  century. Relative sea level rise is virtually certain  to 
continue around Africa, contributing to increased coastal 
flooding in low-lying areas ( high confidence ) and shoreline 
retreat along most sandy coasts ( high confidence ). Marine 
heatwaves are also expected to increase around the region 
over the 21st century ( high confidence ).
The assessed direction of change in CIDs for Africa and associated 
confidence levels are illustrated in Table 12.3. No relevant literature 
could be found for permafrost and hail, although these phenomena 
may be relevant in parts of the continent.

1797
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Table 12 .3 | Summary of confidence in direction of projected change in climatic impact-drivers in Africa, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, 
SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see Section 12.4 
for more details of the assessment method).  The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least medium confidence  
in Section 12.5.2.
RegionClimatic Impact-driver
Heat and Cold Wet and Dry Wind Snow and Ice Coastal and Oceanic Other

Mean air temperatureExtreme heatCold spellFrostMean precipitationRiver floodHeavy precipitation and pluvial floodLandslideAridityHydrological droughtAgricultural and ecological droughtFire weatherMean wind speedSevere wind stormTropical cycloneSand and dust stormSnow, glacier and ice sheetPermafrostLake, river and sea iceHeavy snowfall and ice stormHailSnow avalancheRelative sea levelCoastal floodCoastal erosionMarine heatwaveOcean acidityAir pollution weatherAtmospheric CO 2 at surfaceRadiation at surface
North Africaa  3  4  
Sahara (SAH)   4  
Western Africa (WAF)  1 1 1 1  4  
Central Africa (CAF)   4  
North Eastern Africa (NEAF)  2 1 1 1  4  
South Eastern Africa (SEAF)  1 1 1 3  4  
West Southern Africa (WSAF)    4  
East Southern Africa (ESAF)   3  4,5  
Madagascar (MDG)  3  4,5  
1. Contrasted regional signal: drying in western portions and wettening in eastern portions.
2. Likely increase over the Ethiopian Highlands.

3. Medium confidence of decrease in frequency and increase in intensity.
4. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat.
5. Substantial parts of the ESAF and MDG coasts are projected to prograde if present-day ambient shoreline change rates continue.
a North Africa is not an official region of IPCC AR6, but assessment here is based upon the African portions of the Mediterranean region.
 Already emerged in the historical period ( medium  to high confidence )
 Emerging by 2050 at least in scenarios RCP8.5/SSP5-8.5 ( medium  to high confidence )
 Emerging after 2050 and by 2100 at least in scenarios RCP8.5/SSP5-8.5 ( medium  to high confidence )
Medium confidence 
of decreaseMedium confidence 
of increaseHigh confidence  
of decreaseHigh confidence  
of increaseLow confidence in 
direction of changeNot broadly relevant

1798
(d)
00.20.40.60.81ECA
0.100.10.20.30.40.5TIB
00.20.40.60.81EAS
0.100.10.20.30.40.5ARP
00.511.522.53SAS
GWLr.past.1.524mid. long.
Time slices00.511.5SEA
Time slices6004002000200WCA
6004002000200EAS
6004002000200RFE
6004002000200ARP
mid long
term term6004002000200SAS
mid long
term term6004002000200SEA
-0.2 -0.1-0.05 -0.02 -0.01 00.01 0.02 0.05 0.1 0.2(a) Change in 1-in-100 year river discharge per unit 
m m3 s-1 km-2-200 -150 -100 -50 10-10 50 100 150 200 101-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2)Change in Q100 r.t. recent past (m3 s-1 km-2)
Shoreline position change (m)(c)-
Legend
CMIP5 RCP8.5CMIP5 RCP4.5Legend
+2°C +4°C
median
10th p95th p
median
10th p

Figure 12 .6 | Projected changes in selected climatic impact-driver indices for Asia. (a)  Mean change in 1-in-100-year river discharge per unit catchment area 
(Q100, m3 s–1 km–2) from CORDEX models for the West, South East and East Asia domains for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change 
along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas 
et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel 
(associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show

the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the 
pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values 
across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. 
(d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) 
and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note

that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex 
VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

1799
Climate Change Information for Regional Impact and for Risk Assessment Chapter 121212.4.2 Asia
According to the region definitions given in Chapter 1, Asia is divided 
into 11 regions: the Arabian Peninsula (ARP), Western Central Asia 
(WCA), West Siberia (WSB), East Siberia (ESB), the Russian Far East 
(RFE), East Asia (EAS), East Central Asia (ECA), the Tibetan Plateau 
(TIB), South Asia (SAS), South East Asia (SEA) and the Russian Arctic 
Region (RAR). CID changes in RAR are assessed in the Polar Region 
section (Section 12.4.9). As assessed in previous IPCC Reports, major 
concerns in Asia are associated particularly with droughts and floods 
in all regions, heat extremes in SAS and EAS, sand-dust storms in 
WCA, tropical cyclones in SEA and EAS, snow cover and glacier 
changes in ECA and the Hindu Kush Himalaya (HKH) region, and sea 
ice and permafrost thawing in northern Asia.
Since AR5, a large body of new literature is now available relevant

to climate change in Asia, which includes projections of both mean 
climate and extreme climate phenomena from global and regional 
ensembles of climate simulations such as CMIP6 and CORDEX 
(Chapter 10 and the Atlas). Literature has also considerably grown on 
several climate topics relevant to Asia such as the mountain climate 
(Chapter  3 of the SROCC), and the novel regional assessments 
such as the Hindu Kush Himalaya Assessment (Wester et al., 2019). 
Figure 12.6 shows the regional changes in indices related to floods, 
and coastal erosion over Asia, which are assessed on a regional basis 
along with other climatic impact-driver indices below.
12.4.2.1 Heat and Cold
Mean air temperature:  A  long-term warming trend in annual 
mean surface temperature has been observed across Asia during 
1960–2015, and the warming accelerated after the 1970s ( high 
confidence ) (Davi et al., 2015; Aich et al., 2017; Cheong et al., 2018;

S. Dong et al., 2018; IPCC, 2018; Krishnan et al., 2019; M. Zhang et al., 
2019). Records also indicate a higher rate of warming in minimum 
temperatures than maximum temperatures in Asia, leading to more 
frequent warm nights and warm days, and less frequent cold days 
and cold nights ( high confidence ) (Supari et al., 2017; Akperov et al., 
2018; Cheong et al., 2018; Rahimi et al., 2018; Khan et al., 2019a; L. Li 
et al., 2019; M. Zhang et al., 2019).
Projections show continued warming over Asia in the future with 
contrasted regional patterns across the continent ( high confidence ) 
(Figure 4.19). For RCP8.5/SSP5-8.5 at the end of the century, the mean 
estimated warming exceeds 5°C in WSB, ESB and RFE and 7°C in 
some parts ( high confidence ). In most areas of ARP and WCA, 5°C is 
exceeded (Ozturk et al., 2017), but EAS, SAS and SEA have a lower 
projected warming of less than 5°C (Basha et al., 2017; C. Lu et al.,

2019; Almazroui et al., 2020; Atlas.5). Under SSP1-2.6, the warming 
remains limited to 2°C in most areas except Arctic regions, where it 
exceeds 2°C (Figure 4.19).
Extreme heat:  There is increased evidence and high confidence  of 
more frequent heat extremes in the recent decades than in previous 
ones in most of Asia (Acar Deniz and Gönençgil, 2015; Rohini et al., 
2016; Mishra et al., 2017; You et al., 2017; Imada et al., 2018; Khan 
et al., 2019b; Krishnan et al., 2019; Rahimi et al., 2019; Yin et al., 2019; Chapter  11) due to the effects of anthropogenic global warming, 
El Niño and urbanization (Luo and Lau, 2017; Thirumalai et al., 2017; 
Imada et al., 2019; Y . Sun et al., 2019; Zhou et al., 2019). But there 
is medium confidence  of heat extremes increasing in frequency in 
many parts of India (Rohini et al., 2016; Mazdiyasni et al., 2017; van 
Oldenborgh et al., 2018; Sen Roy, 2019; Kumar et al., 2020) partly

due to the alleviation of anthropogenic warming by increased air 
pollution with aerosols and expanding irrigation (van Oldenborgh 
et al., 2018; Thiery et al., 2020).
Extreme heat events are very likely  to become more intense and/or more 
frequent in SAS, WCA, ARP , EAS, and SEA by the end of 21st century, 
especially under RCP6.0 and RCP8.5 (Figure 12.4a–c and Chapter 11; 
Lelieveld et al., 2016; Pal and Eltahir, 2016; Guo et al., 2017; Mishra 
et al., 2017; Dosio et al., 2018; Lin et al., 2018; Nasim et al., 2018; Shin 
et al., 2018; Hong et al., 2019; Su and Dong, 2019; Khan et al., 2020; 
Kumar et al., 2020). The exceedance of the dangerous heat stress 41°C 
threshold of the HI is expected to increase by about 250 days in SEA 
and by 50–150 days in SAS, WCA, ARP and EAS for SSP5-8.5 at the end 
of the century (Figure 12.4d–f and Figure 12.SM.2). Under SSP1-2.6, 
the increase would be restricted to less than 30 days in many of these

regions except SEA, where the number of exceedance days increases by 
about 100 days in some areas. Such increases are already present in the 
middle of the century (Figure 12.4d–f; Schwingshackl et al., 2021). In 
these regions, the increase in number of days with exceedance of 35°C 
of high heat stress is also expected to increase substantially for the mid-
century under SSP5-8.5 (typically by 10–50 days except in Arctic and 
Siberian regions), and by more than 60 days in areas of SEA, and a large 
difference is found between low- and high-end scenarios in the end of 
the century ( high confidence ) (Figure 12.4b). Over WSB, ESB and RFE 
also, an increase of extreme heat durations and frequency is expected in 
all scenarios ( high confidence ) (Kattsov et al., 2017; Khlebnikova et al., 
2019b).
Cold spell and frost:  Cold spells intensity and frequency, as well as 
the number of frost days, in most Asian regions have been decreasing

since the beginning of the 20th century ( high confidence ) (Chapter 11; 
Sheikh et al., 2015; Donat et al., 2016; Erlat and Türkeş, 2016; S. Dong 
et al., 2018; Liao et al., 2018, 2020; Lu et al., 2018; van Oldenborgh 
et al., 2019), except for the central Eurasian regions, where there was 
a  cooling trend during 1995–2014, which is linked to sea ice loss 
in the Barents–Kara Seas ( medium confidence ) (Atlas.5.2; Wegmann 
et al., 2018; Blackport et al., 2019; Mori et al., 2019).
It is very likely  that cold spells will have a decreasing frequency in all 
future scenarios across Asian regions (J. Guo et al., 2018; Sui et al., 
2018; L. Li et al., 2019), as well as frost days (L. Wang et al., 2017; 
Fallah-Ghalhari et al., 2019) except in tropical Asia (Chapter 11).
In Asia, temperatures have warmed during the last  century 
(high confidence ). Extreme heat episodes have become more 
frequent in most regions ( high confidence ), and are very likely

to increase in all regions of Asia under all warming scenarios 
during this century. Dangerous heat stress thresholds such 
as HI  > 41°C will be crossed much more often (typically 
50–150  days per year more than the recent past) in many 
southern Asia regions at the end of the century under SSP5-8.5

1800
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12while these numbers should remain limited to a few tens under 
SSP1-2.6 ( high confidence ). It is very likely  that cold spells and 
frost days will decrease in frequency in all future scenarios 
across Asian regions during the century.
12.4.2.2 Wet and Dry
Mean precipitation:  The most prominent features about changes in 
precipitation over Asia (1901–2010) are the increasing precipitation 
trends across higher latitudes, along with some scattered smaller 
regions of detectable increases and decreases (Knutson and Zeng, 
2018); however, spatial variability remains high (W. Wang et  al., 
2015; Limsakul and Singhruck, 2016; Supari et  al., 2017; Rahimi 
et al., 2018, 2019; Sein et al., 2018; Kumar et al., 2019; H. Wang et al., 
2019; see Atlas.5) ( medium confidence ).
Mean precipitation is likely  to increase in most areas of northern

(WSB, ESB, RFE), southern (ECA, TIB, SAS) and East Asia (EAS) in 
different scenarios ( high confidence ) (Huang et al., 2014; Xu et al., 
2017; Kusunoki, 2018; Mandapaka and Lo, 2018; Luo et  al., 2019; 
Wu et  al., 2019; X. Zhu et  al., 2019; Almazroui et  al., 2020; Jiang 
et al., 2020; Rai et al., 2020; see Atlas.5). Monsoon circulation will 
also increase seasonal contrasts, with SAS seeing wetter wet seasons 
and drier dry seasons (Atlas.5.3). Higher uncertainty between CMIP5 
and CMIP6 as well as spatial differences lend low confidence  to 
model projections in ARP and WCA (Atlas.5.5), with large seasonal 
differences (Zhu et al., 2020) and some models projecting decreases 
in precipitation in Central Asia (Ozturk et al., 2017), Pakistan (Nabeel 
and Athar, 2020) and SEA (Supari et al., 2020).
River flood: Flood risk has grown in many places in China from 
1961 to 2017 (Kundzewicz et  al., 2019) ( low confidence ). In SAS,

the numbers of flood events and human fatalities have increased 
in India during 1978–2006 (Singh and Kumar, 2013), whereas the 
average country-wide inundation depth has been decreasing during 
2002–2010 in Bangladesh, attributed to improved flood management 
(low confidence ) (Sciance and Nooner, 2018).
Given the increase of heavy precipitation in most Asian regions, the river 
flood frequency and intensities will change consequently in Asia. Over 
China floods will increase with different levels under different warming 
scenarios ( medium confidence ) (Lin et al., 2018; Kundzewicz et al., 2019; 
Liang et al., 2019; Gu et al., 2020). Monsoon floods will be more intense 
in SAS ( medium confidence ) (Nowreen et al., 2015; Babur et al., 2016; 
Mohammed et al., 2018). The total flood damage will increase greatly 
in river basins in SEA countries under the conditions of climate change 
and rapid urbanization in the near future (Dahal et al., 2018; Kefi et al.,

2020). A changing snowmelt regime in the mountains may contribute 
to a  shift of spring floods to earlier periods in Central Asia in future 
(medium confidence ) (Reyer et al., 2017b). The annual maximum river 
discharge can almost double by the mid-21st century in major Siberian 
rivers, and annual maximum flood area is projected to increase across 
Siberia mostly by 2–5% relative to the baseline period (1990–1999) 
under RCP8.5 scenario ( medium confidence ) (Shkolnik et al., 2018).
Heavy precipitation and pluvial flood: Pluvial floods are driven 
by extreme precipitation and land use. Observed changes in extreme precipitation vary considerably by region (Chapter  11). Heavy 
precipitation is very likely  to become more intense and frequent in all 
areas of Asia except in ARP ( medium confidence ) for a 2°C GWL or 
higher (Chapter 11).
Landslide:  The majority of non-seismic fatal landslide events were 
triggered by rainfall, and Asia is the dominant geographical area

of landslide distribution (Froude and Petley, 2018). Floods and 
landslides are the most frequently occurring natural hazards in the 
eastern Himalayas and hilly regions, particularly caused by torrential 
rain during the monsoon season (Gaire et al., 2015; Syed and Al Amin, 
2016). They accounted for nearly half of the events recorded in the 
countries of the HKH region (Vaidya et al., 2019). Intense monsoon 
rainfall in northern India and western Nepal in 2013, which led to 
landslides and one of the worst floods in history, has been linked 
to increased loading of GHG and aerosols (Cho et al., 2016). Due to an 
increase of heavy precipitation and permafrost thawing, an increase in 
landslides is expected in some areas of Asia, such as northern Taiwan 
(China), some South Korean mountains, Himalayan mountains, and 
permafrost territories of Siberia, and the increase is expected to be 
the greatest over areas covered by current glaciers and glacial lakes

(medium confidence , medium evidence ) (Kim et  al., 2015; Kharuk 
et al., 2016; C.-W. Chen et al., 2019; Kirschbaum et al., 2020).
Aridity:  Aridity in West Central Asia and parts of South Asia 
increased in recent decades ( medium confidence ), as documented in 
Afghanistan (Qutbudin et al., 2019), Iran (Zarei et al., 2016; Zolfaghari 
et  al., 2016; Pour et  al., 2020), most parts of Pakistan (K. Ahmed 
et al., 2018, 2019), and many parts of India (Roxy et al., 2015; Mallya 
et al., 2016; Matin and Behera, 2017; Ramarao et al., 2019). Some 
spatial and seasonal differences within these regions remain, with 
Ambika and Mishra (2020) noting significant aridity declines over 
the Indo–Gangetic Plain in India during 1979–2018 due in part to 
the effect of irrigation, and Araghi et  al. (2018) found that many 
parts of Iran show no significant trends in aridity. There was a drying 
tendency in the dry season and significant wetting in the wet season

in the Philippines during 1951–2010 (Villafuerte et  al., 2014), and 
slight wetting in Vietnam during 1980–2017 (Stojanovic et al., 2020) 
(low confidence ). In EAS there is low confidence  of broad aridity 
changes, as the frequency of droughts have increased (especially 
in spring) along a  strip extending from south-west China to the 
western part of north-east China; however, there is no evidence 
of a significant increase in drought severity over China as a whole 
and many parts in the arid north-west China got wetter during 
1961–2012 (W. Wang et al., 2015; Zhai et al., 2017; H. Wang et al., 
2019; Zhang and Shen, 2019). In Siberia, the number of dry days has 
decreased for much of the region, but increased in its southern parts 
(Khlebnikova et al., 2019a).
The counteracting factors of projected increases in precipitation and 
temperature across most of Asia (Section  11.9 and Atlas.5) leads 
to low confidence  (limited evidence , inconsistent trends) for broad,

long-term aridity changes with medium confidence  only for aridity 
increases in West Central Asia and East Asia. A  growing number 
of studies highlight the potential for more localized aridity trends, 
including projection ensembles indicating significant increase in 
aridity and more frequent and intense droughts in most parts of

1801
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212China (Y . Li et al., 2019; Yao et al., 2020) and India under RCP4.5 and 
RCP8.5 for the 2020–2100 period (Gupta and Jain, 2018; Bisht et al., 
2019; Preethi et al., 2019).
Hydrological drought:  Section 11.9 indicates that limited evidence  
and inconsistent regional trends gives low confidence  to observed 
and projected changes in hydrological drought in all Asian regions at 
a 2°C GWL (approximately mid-century), although West Central Asia 
hydrological droughts increase at the 4°C GWL (approximately end-
of-century under higher emissions scenarios) ( medium confidence ). 
Human activities such as reservoir operation and water abstraction 
have had a  profound effect on low river flow characteristics and 
drought impacts in many Asian regions (Kazemzadeh and Malekian, 
2016; Yang et al., 2020b). There was no observed overall long-term

change of both meteorological droughts and hydrological droughts 
over India during 1870–2018 (Mishra, 2020), but there were strong 
trends towards drying of soil moisture in north-central India (Ganeshi 
et  al., 2020) and intensified droughts in north-west India, parts of 
Peninsular India, and Myanmar (Malik et  al., 2016). The frequency 
of water scarcity connected with hydrological droughts has increased 
significantly in southern Russia since the beginning of the 21st century 
(Frolova et al., 2017). Higher future temperatures are expected to alter 
the seasonal profile of hydrologic droughts given reduced summer 
snowmelt ( medium confidence ) downstream of mountains such as 
the Himalayas and the Tibetan Plateau (Sorg et  al., 2014). Several 
studies project more severe future hydrological drought in the Weihe 
River basin in northern China (Yuan et al., 2016; Sun and Zhou, 2020).
Agricultural and ecological drought:  Section 11.9 assesses medium

confidence  in observed increases to agricultural and ecological 
droughts in West Central Asia, East Central Asia, and East Asia. 
Persistent droughts were the main factor for grassland degradation 
and desertification in Central Asia in the early 21st century (G. Zhang 
et al., 2018; Emadodin et al., 2019). Compound meteorological drought 
and heat events, which lead to water stress conditions for agricultural 
and ecological systems, have become more frequent, widespread 
and persistent in China especially since the late 1990s (Yu and Zhai, 
2020). There were more agricultural droughts in northern China than 
in southern China, and the intensity of agricultural drought increased 
during 1951–2018 (Zhao et al., 2021).
Studies examining a 2°C GWL give low confidence  for projected broad 
changes to agricultural and ecological drought across all Asia regions, 
although at 4°C GWL agricultural and ecological drought increases are

projected for West Central Asia and East Asia along with a decrease in 
South Asia ( medium confidence ) (Section 11.9). Summer temperature 
increase will enhance evapotranspiration, facilitating ecological and 
agricultural drought over Central Asia towards the latter half of this 
century (Chapter  11; see also Figure  12.4 for soil moisture and DF 
indices; Ozturk et al., 2017; Reyer et al., 2017b; Senatore et al., 2019). 
However, broader changes in droughts could not be determined in 
Asia due to the mixture of total precipitation signals together with 
temperature increase patterns (Section 11.9 and Atlas.5).
Fire weather:  Under the global warming scenario of 2°C, the 
magnitude of length and frequency of fire seasons are projected 
to increase with strong effects in India, China and Russia ( medium confidence ) (Q. Sun et al., 2019). Abatzoglou et al. (2019) found that 
higher fire weather conditions due to climate change emerge in the

first part of the 21st century in South China, WCA as well as in boreal 
areas of Siberia and RFE. The potential burned areas in five Central 
Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and 
Turkmenistan) will increase by 2–8% in the 2030s and 3–13% in the 
2080s compared with the baseline ( medium confidence ) (1971–2000; 
Zong et al., 2020).
In conclusion, there is medium confidence  that extreme 
precipitation, mean precipitation and river floods will increase 
across most Asian regions. There is low confidence  for projected 
changes in aridity and drought given overall increases in 
precipitation and regional inconsistencies, with medium 
increases for West Central Asia and East Asia especially beyond 
the middle of the century and global warming levels beyond 2°C. 
Fire weather seasons are projected to lengthen and intensify 
particularly in the northern regions ( medium confidence ).
12.4.2.3 Wind

Mean wind speed:  There is high confidence  of the slowdown in 
terrestrial near-surface wind speed (SWS) in Asia by approximately 
–0.1 m s–1 per decade since the 1950s based on observations and 
reanalysis data, with the significant decreases in Central Asia among 
the highest in the world followed by EAS and SAS (J. Wu et al., 2018; 
Tian et al., 2019; R. Zhang et al., 2019). But a short-term strengthening 
in SWS was observed during the winter since 2000 in eastern China 
(medium confidence ) (Zeng et al., 2019; Zha et al., 2019). 
There is medium confidence  of future declining mean SWS in Asia, 
except in SAS and SEA, as global projections indicate a decreasing 
trend in all climate scenarios for most of northern Asia, TIB and 
East Asia by the mid-century (Karnauskas et  al., 2018a; Fedotova, 
2019; Jung and Schindler, 2019; Ohba, 2019; J. Wu et  al., 2020; 
Zha et  al., 2020; Figure  12.4m–o), with negative effects on wind

energy potential. Decreases in North Asia are generally modest, not 
exceeding 10% for the mid-century and 20% for the end of century 
for the RCP8.5 and RCP4.5 scenarios (Figure 12.4m–o).
Severe wind storms:  Consistent with the general mean decreasing 
surface winds, there is medium confidence  that strong winds declined 
faster than weak winds in the past few decades in Asia in general 
(Vautard et al., 2010; Tian et al., 2019), but evidence is lacking for 
spatial patterns. There is low confidence  that extra-tropical cyclones 
will decline in number in future climate scenarios over WCA, TIB, 
WSB and ESB, and intensify over the Arctic regions as a result of the 
poleward shift of storm tracks (Basu et al., 2018; Chapter 11). There is 
limited evidence  for projection of changes in severe winds occurring 
in convective storms in Asia.
Tropical cyclone: There was an increase in the number and 
intensification rate of intense tropical cyclones (TC), such as

Category 4–5 (wind speeds >58 m s–1), in the Western North Pacific 
(WNP) and Bay of Bengal since the mid-1980s ( medium confidence ) 
(Section  11.7; Kim et  al., 2016; Mei and Xie, 2016; Walsh et  al., 
2016a; Knutson et  al., 2019). There is medium confidence  that

1802
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12there has been a significant north-westward shift in TC tracks and 
a poleward shift in the average latitude where TCs reach their peak 
intensity in the WNP since the 1980s (Knutson et al., 2019; J. Sun 
et al., 2019; Lee et al., 2020), increasing exposure to TC passage and 
more destructive landfall over eastern China, Japan, and Korea in the 
last few decades (Kossin et al., 2016; Li et al., 2017; Altman et al., 
2018; Liu and Chan, 2019), and decreasing exposure in the region 
of SAS and southern China (Cinco et al., 2016; Kossin et al., 2016; 
see Chapter 11). However, while the analysis shows fewer typhoons, 
more extreme TCs have affected the Philippines ( low confidence ) 
(Takagi and Esteban, 2016). The frequency and duration of tropical 
cyclones has significantly increased over time over the Arabian 
Sea and insignificantly decreased over the Bay of Bengal during

1977–2018 ( low confidence ) (Fan et al., 2020).
There is medium confidence  that future TC numbers will decrease but 
the maximum TC wind intensities will increase in the western Pacific as 
elsewhere (Chapter 11, see Figure 11.24; Choi et al., 2019; Cha et al., 
2020; Knutson et al., 2020). The simulations for the late 21st century 
for the RCP8.5 scenario yield considerably more TCs in the WNP that 
exceed 49.4 m s−1 (Category 3) intensity (Mclay et al., 2019). There is 
medium confidence  that the average location of the maximum wind 
will migrate poleward (see Chapter 11), and TC translation speeds 
at the higher latitudes would decrease (Yamaguchi et al., 2020). As 
a consequence, the intensity of TCs affecting the Japan Islands would 
increase in the future under the RCP8.5 scenario (Yoshida et  al., 
2017), whereas the frequency of TCs affecting the Philippine region 
and Vietnam is projected to decrease (Kieu-Thi et al., 2016; C. Wang

et al., 2017; Gallo et al., 2019) ( medium confidence ).
Sand and dust storm : The Asia-Pacific region contributes 
26.8 per cent to global dust emissions as of 2012 (UNESCAP , 2018). 
In West Asia, the frequency of dust events has increased markedly 
in some areas (east and north-east of Saudi Arabia, north-west of 
Iraq and east of Syria) from 1980 to the present (Nabavi et al., 2016; 
Alobaidi et al., 2017). This marked dust increase has been associated 
with drought conditions in the Fertile Crescent (Notaro et al., 2015; Yu 
et al., 2015), likely  amplified by anthropogenic warming (Kelley et al., 
2015; Chapter 10). Dust storm frequency in most regions of northern 
China show a decreasing trend since the 1960s due to the decrease in 
surface wind speed ( medium confidence ) (Guan et al., 2017).
While dust activity has decreased greatly over EAS, current climate 
models are unable to reproduce the trends (Guan et al., 2015, 2017;

Zha et al., 2017; C. Wu et al., 2018). Thus, there is limited evidence  for 
future trends of sand and dust storms in Asia.
In conclusion, surface wind speeds have been decreasing in 
Asia ( high confidence ), but there is a large uncertainty in future 
trends. There is medium confidence  that mean wind speeds will 
decrease in Central and northern Asia, and that tropical cyclones 
will have decreasing frequency and increasing intensity overall.
12.4.2.4 Snow and Ice
Snow:  There is no significant interannual trend of total snow cover 
from 2000 to 2016 over Eurasia (X. Wang et al., 2017a; Sun et al., 2020). Observations do show significant changes in the seasonal timing of 
Eurasian snow cover extent (especially for earlier spring snowmelt) 
since the 1970s, with seasonal changes expected to continue in the 
future ( high confidence ) (Yeo et al., 2017; Zhong et al., 2021). By 2100, 
snowline elevations are projected to rise between 400  and 900 m

(4.4  to 10.0 m yr –1) in the Indus, Ganges and Brahmaputra basins 
under the RCP8.5 scenario (Viste and Sorteberg, 2015).
Glacier:  Observation and future projection of glacier mass changes 
in Asia are assessed in Section 9.5.1 grouped in three main regions: 
northern Asia, High Mountains of Asia, and Caucuses and Middle 
East. All regions show continuing decline in glacier mass and area 
in the coming century ( high confidence ). Under RCP2.6 the pace of 
glacier loss slows, but glacier losses increase in RCP8.5 and peak 
in the mid to late 21st century. GlacierMIP projections indicate that 
glaciers in the High Mountains of Asia lose 42 ± 25%, 56 ± 24% and 
71 ± 21% of their 2015 mass by the end of the century for RCP2.6, 
RCP4.5 and RCP8.5 scenarios respectively. Under the same scenarios, 
glaciers in North Asia would lose 57 ± 40%, 72 ± 38% and 85 ± 30% 
of their mass, and glaciers in the Caucuses and the Middle East

would lose 68 ± 32%, 83 ± 19% and 94 ± 13% of their mass (see also 
Kraaijenbrink et al., 2017; Rounce et al., 2020).
Although enhanced meltwater from snow and glaciers largely offsets 
hydrological drought-like conditions (Pritchard, 2019), this effect is 
unsustainable and may reverse as these cryospheric buffers disappear 
(medium confidence ) (Gan et al., 2015; W. Dong et al., 2018; Huss and 
Hock, 2018). In the Himalayas and the TIB region higher temperatures 
will lead to higher glacier melt rates and significant glacier shrinkage 
and a  summer runoff decrease ( medium confidence ) (Sorg et  al., 
2014). Glacier runoff in the Asian high mountains will increase up 
to mid-century, and after that runoff might decrease due to the loss 
of glacier storage ( medium confidence ) (Lutz et al., 2014; Huss and 
Hock, 2018; Rounce et al., 2020).
Compared with the 1990s, the number of lakes in TIB in the 2010s 
decreased by 2%, whereas total lake area expanded by 25% (S. Wang

et al., 2020) due to the joint effect of precipitation increase and glacier 
retreat. Many new lakes are predicted to form as a  consequence 
of continued glacier retreat in the Himalaya-Karakoram region 
(Linsbauer et al., 2016). As many of these lakes will develop at the 
immediate foot of steep icy peaks with degrading permafrost and 
decreasing slope stability, the risk of glacier lake outburst floods 
and floods from landslides into moraine-dammed lakes is increasing 
in Asian high mountains ( high confidence ) (Haeberli et  al., 2017; 
Kapitsa et al., 2017; Bajracharya et al., 2018; Narama et al., 2018; 
S. Wang et al., 2020).
Permafrost : Permafrost is thawing in Asia ( high confidence ). 
Temperatures in the cold continuous permafrost of north-eastern 
East Siberia rose from the 1980s up to 2017, and the active layer 
thicknesses in Siberia and Russian Far East generally increased 
from late 1990s to 2017 (Romanovsky et  al., 2018). The change

in mean annual ground temperature for northern Siberia is about 
+0.1 to +0.3°C per decade since 2000 (Romanovsky et al., 2018). 
Ground temperature in the permafrost regions of TIB (taking 40% 
of TIB currently) increased (0.02–0.26°C per decade for different

1803
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212boreholes) during 1980 to 2018, and the active layer thickened at 
a  rate of 19.5  cm per decade (L. Zhao et  al., 2020). There is high 
confidence  that permafrost in Asian high mountains will continue to 
thaw and the active layer thickness will increase (Bolch et al., 2019). 
The permafrost area is projected to decline by 13.4–27.7% and 
60–90% in TIB (L. Zhao et al., 2020) and 32% ± 11% and 76% ± 12% 
in Russia (Guo and Wang, 2016) by the end of the 21st century under 
the RCP2.6 and RCP8.5 scenarios respectively ( high confidence ).
Lake and river ice:  Lake ice cover duration got shorter in many lakes 
in TIB (Yao et al., 2016; Cai et al., 2019; Guo et al., 2020) and some 
other areas such as north-west China (Cai et al., 2020) and north-east 
China (Yang et al., 2019) in the last two decades ( high confidence ). 
River ice cover extent decreased in TIB as well (H. Li et  al., 2020;

Yang et  al., 2020a). Climate warming also leads to a  significant 
reduction in the period with ice phenomena and the decrement 
of ice regime hazard in Russian lowland rivers (Agafonova et  al., 
2017), and the Inner Mongolia reach of the Yellow River in northern 
China (Wan et  al., 2020) ( high confidence ). Lake ice and river ice 
in Asia are expected to decline with projected increases in surface 
air temperature towards the end of this century ( high confidence ) 
(Guo et al., 2020; Yang et al., 2020a).
Heavy snowfall and ice storm:  Observed trends in heavy snowfall 
and ice storms are uncertain. Annual maximum snow depth decreased 
for the period between 1962 and 2016 on the western side of both 
eastern and western Japan, at rates of 12.3% and 14.6% per decade 
respectively (MOE et al., 2018). Observational results generally show 
a decrease in the frequency and an increase in the mean intensity 
of snowfalls in most Chinese regions ( medium confidence ) (B. Zhou

et  al., 2018). Because of the decrease in the snow frequency, the 
occurrence of large-scale snow disasters in TIB decreased ( low 
confidence ) (Qiu et al., 2018; S. Wang et al., 2019). Large parts of 
northern high-latitude continents (including Siberia and RFE) have 
experienced cold snaps and heavy snowfalls in the past few winters, 
and the reduction of Arctic sea ice would increase the chance of heavy 
snowfall events in those regions in the coming decades ( medium 
confidence ) (Song and Liu, 2017). Heavy snowfall is projected to 
occur more frequently in Japan’s Northern Alps, the inland areas of 
Honshu Island and Hokkaido Island (Kawase et al., 2016, 2020; MOE 
et al., 2018),  and the heavy wet snowfall can be enhanced over the 
mountainous regions in central Japan and northern part of Japan 
(Ohba and Sugimoto, 2020) ( medium confidence ).
Hail:  The hailstorm in the Asian region shows a decreasing trend in

several regions ( low confidence , limited evidence ). In China severe 
weather days including thunderstorms, hail and/or damaging wind 
have decreased by 50% from 1961 to 2010 (M. Li et  al., 2016; 
Zhang et al., 2017), and the hail size decreased since 1980 (Ni et al., 
2017). A  rate of decrease of 0.214 hail days per decade has also 
been reported for Mongolia between 1984–2013, where the annual 
number of hail days averaged is 0.74 (Lkhamjav et al., 2017).
Snow avalanche:  There is as yet limited evidence  for the evolution of 
avalanches in Asia. Tree-ring-based snow avalanche reconstructions in 
the Indian Himalayas show an increase in avalanche occurrence and 
runout distances in recent decades (Ballesteros-Cánovas et al., 2018).In summary, snowpack and glaciers are projected to continue 
decreasing and permafrost to continue thawing in Asia ( high 
confidence ). There is medium confidence  of increasing heavy 
snowfall in some regions, but limited evidence  on future

changes in hail and snow avalanches.
12.4.2.5 Coastal and Oceanic
Relative sea level:  Around Asia, from 1900–2018, a  new tide 
1.33  [0.80  to  1.86]  mm yr –1 in the Indian Ocean–Southern Pacific 
and 1.68 [1.27 to 2.09] mm yr –1 in the North-west Pacific (Frederikse 
et  al., 2020), compared to a  GMSL change of around 1.7  mm yr –1 
(Section  2.3.3.3 and Table  9.5). For the period 1993–2018, the 
RSLR rates around Asia,  based on satellite altimetry, increased 
to 3.65 [3.23  to 4.08]  mm  yr –1 and 3.53 [2.64 to 4.45]  mm yr –1 
respectively (Frederikse et al., 2020), compared to a GMSL change of 
3.25 mm yr –1 (Section 2.3.3.3 and Table 9.5). The rate of RSLR along 
the coastline of China ranges from –2.3 ± 1.9 to +5.7 ± 0.4 mm yr –1 
during 1980–2016; after removing the vertical land movement, the 
average rate of sea level rise is 2.9 ± 0.8 mm yr –1 over 1980–2016

and 3.2 ± 1.1  mm yr –1 since 1993 (Qu et  al., 2019). However, the 
rates of land subsidence reported by Minderhoud et  al. (2017) are 
substantially higher than those reported by Qu et  al. (2019). RSL 
change in many coastal areas in Asia, especially in EAS, is affected 
by land subsidence due to sediment compaction under building mass 
and groundwater extraction ( high confidence ) (Erban et  al., 2014; 
Nicholls, 2015; Minderhoud et  al., 2019; Qu et  al., 2019). During 
1991–2016, the Mekong Delta in Vietnam sank on average about 18 
cm as a consequence of groundwater withdrawal, and the subsidence 
related to groundwater extraction has gradually increased with 
highest sinking rates estimated to be 11 mm yr –1 in 2015 (Minderhoud 
et al., 2017).
Relative sea level rise is very likely to continue in the oceans around 
Asia. Regional mean RSLR projections for the oceans around Asia 
range from 0.3–0.5 m under SSP1-2.6 to 0.7–0.8 m under SSP5-8.5

for 2081–2100 relative to 1995–2014 (median values), which is 
within the range of projected GMSL change (Section 9.6.3.3). These 
RSLR projections may, however, be underestimated due to potential 
partial representation of land subsidence in their assessment 
(Section 9.6.3.2).
Coastal flood:  The present-day 1-in-100-year ETWL is between 
0.5–8 m around Asia, with values above 2.5 m or above common 
along the coasts of Central and north-eastern Asia (Vousdoukas et al., 
2018; Kirezci et  al., 2020). Sea level rise and land subsidence will 
jointly lead to more flooding in delta areas in Asia ( high confidence ) 
(Takagi et al., 2016; J. Wang et al., 2018).
Extreme total water level magnitude and occurrence frequency 
(Figure  12.4p–r and Figure  12.SM.6). Across the region, the 5–95th 
percentile range of the 1-in-100-year ETWL is projected to increase

(relative to 1980–2014) by 7–44 cm and by 10–52 cm by 2050 under 
RCP4.5 and RCP8.5 respectively. By 2100, this range is projected to 
be 11–91 cm and 28–187 cm under RCP4.5 and RCP8.5 respectively

1804
Table 12.4  | Summary of confidence in direction of projected change in climatic impact-drivers in Asia, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, 
SRES A1B or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see Section 12.4 

Arabian Peninsula (ARP)   1  
West Central Asia (WCA)  5  1,2  
West Siberia (WSB)     
East Siberia (ESB)     
Russian Far East (RFE)      1,2  
East Asia (EAS)   3  1,2  
East Central Asia (ECA)   
Tibetan Plateau (TIB)   
South Asia (SAS)    1  
South East Asia (SEA)  4 3  1,2  
1. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat.

2. Substantial parts of the coasts in these regions are projected to prograde if present-day ambient shoreline change rates continue.
3. Tropical cyclones decrease in number but increase in intensity.
4. High confidence of decrease in Indonesia (Atlas.5.4.5).
5. Medium confidence of decreasing in summer and increasing in winter.

1805
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212(Vousdoukas et al., 2018; Kirezci et al., 2020). Furthermore, the present-
day 1-in-100-year ETWL is projected to have median return periods 
of around 1-in-50-years by 2050 and 1-in-10-years by 2100 under 
RCP4.5 in most of Asia, except SEA and ARP , in which the present-day 
1-in-100-year ETWL is projected occur once per year or more, both by 
2050 and 2100 (Vousdoukas et al., 2018). The present-day 1-in-50-year 
ETWL is projected to occur around three times a year by 2100 with 
a SLR of 1 m across Asia (Vitousek et al., 2017). Compound impacts 
of precipitation change, land subsidence, sea level rise, upstream 
hydropower development, and local water infrastructure development 
may lead to larger flood extent and prolonged inundation in the 
Vietnamese Mekong Delta (Triet et al., 2020).
Coastal erosion:  Over the past 30 years, South, South East and East

Asia exhibit the most pronounced delta changes globally due to strong 
human-induced changes to the fluvial sediment flux (Nienhuis et al., 
2020). Satellite derived shoreline change estimates over 1984–2015 
indicate shoreline retreat rates between 0.5  m  yr –1 and 1  m yr –1 
along the coasts of WCA and ARP , increasing to 3 m yr –1 in SAS. Over 
the same period, shoreline progradation has been observed along 
the coasts of RFE (0.2 m yr –1), SEA (0.1 m yr –1) and EAS (0.5 m yr –1) 
(Luijendijk et  al., 2018; Mentaschi et  al., 2018). Meanwhile, there 
has been a gross coastal area loss of 3,590 km2 in South Asia, and 
a loss of 2,350 km2 in Pacific Asia, over a 30-year period (1984–2015) 
(Mentaschi et al., 2018).
Projections indicate that a majority of sandy coasts in the Asia region 
will experience shoreline retreat ( high confidence ) (Udo and Takeda, 
2017; Ritphring et al., 2018; Vousdoukas et al., 2020b), while parts of

the RFE, EAS, SEA and WCA coastline are projected to prograde over 
the 21st century, if present ambient shoreline change trends continue. 
Median shoreline change projections (CMIP5), relative to 2010, 
presented by Vousdoukas et al. (2020b) show that, by mid-century, 
sandy shorelines in Asia will retreat by between 10–50 m, except in 
SAS where shoreline retreat is projected to exceed 100 m, under both 
RCP4.5 and RCP8.5. By 2100, and under RCP4.5, shoreline retreats of 
around 85, 100 and 300 m are projected along the sandy coastlines 
of SEA and WCA, ARP and SAS respectively (50 m or less in other 
Asian regions), while under RCP8.5, over the same period, sandy 
shorelines along all regions with coastlines, except RFE and EAS, are 
projected to retreat by more than 100 m, with the retreat in SAS 
reaching 350 m (2100 RCP8.5 projections for RFE and EAS are about 
60 m and about 85 m respectively; Figure 12.6).
Marine heatwave: There have been frequent marine heatwaves

(MHW) in the coastal oceans of Asia, connected to the increase 
between 0.25°C and 1°C in mean SST of the coastal oceans since 
1982–1998 (Oliver et al., 2018). There is high confidence  that MHWs 
will increase around most of Asia. Mean SST is projected to increase 
by 1°C (2°C) around Asia by 2100, with a  hotspot of around 2°C 
(5°C) along the coastlines of the Sea of Japan and the RFE under 
RCP4.5 (RCP8.5; Interactive Atlas). Under global warming conditions, 
MHW intensity and duration are projected to increase in the coastal 
zones of all sub-regions of Asia, but most notably in SEA and SAS 
(Frölicher et al., 2018). Projections for SSP1-2.6 and SSP5-8.5 both 
show an increase in MHWs around Asia by 2081–2100, relative to 
1985–2014 (Box 9.2, Figure 1).In general, there is high confidence  that most coastal/ocean-
related climatic impact-drivers in Asia will increase over the 
21st century. Relative sea level rise is very likely  to continue

around Asia, contributing to increased coastal flooding in low-
lying areas ( high confidence ) and shoreline retreat along most 
sandy coasts ( high confidence ). Marine heatwaves are also 
expected to increase around the region over the 21st century 
The assessed direction of change in climatic impact-drivers for Asia 
and associated confidence levels are illustrated in Table 12.4.
12.4.3 Australasia
For the purpose of this assessment, Australasia is divided into five 
sub-regions as defined in Section  1.4.5: Northern Australia (NAU), 
Central Australia (CAU), Eastern Australia (EAU), Southern Australia 
(SAU) and New Zealand (NZ).
The Fourth and Fifth IPCC Assessment Reports (AR4 and AR5) identify 
the most damaging historical hazards in this region to be inland 
flooding, drought, wildfire and episodic coastal erosion due to storms 
(Hennessy et  al., 2007; Reisinger et  al., 2014). The SR1.5 (Hoegh-

Guldberg et  al., 2018) projects very likely increases in the intensity 
and frequency of warm days and warm nights and decreases in the 
intensity and frequency of cold days and cold nights in Australasia. 
Furthermore, a  likely increase in the frequency and duration of warm 
spells is also projected for Australia. The SROCC (IPCC, 2019b) projects 
a likely  global mean sea level rise (RCP8.5) that is up to 0.1 m higher 
than corresponding AR5 projections. The SROCC also projects an 
increase of mean significant wave height across the Southern Ocean 
(high confidence ) and an increase in the occurrence of historically rare 
(1-in-100-year) extreme sea levels to 1-in-1-year or more frequent 
events all around the Australasian region by 2100 under RCP8.5.
A detailed national scale climate change assessment of observed 
and projected climate change, based on over 40 CMIP5 models and 
high resolution downscaling (CSIRO and BOM, 2015), and biannual

short updates thereafter are available for Australia (CSIRO and BOM, 
2016, 2018, 2020). Similar national assessments for New Zealand are 
also available (MfE and Stats NZ, 2017, 2020; MfE, 2018). The severe 
extreme events such as heatwaves and river floods that have occurred 
in Australasia, especially over the last decade, have enabled a number 
of attribution studies, improving the understanding of regional climate 
change mechanisms that drive such extreme events (Chapter 11).
Figure  12.7 illustrates projected changes in two selected climatic 
impact-driver indices for Australasia.
12.4.3.1 Heat and Cold
Mean air temperature:  Across Australia mean temperatures have 
increased by 1.44°C ± 0.24°C during the period 1910–2019, with 
most of the warming occurring since 1950 (Atlas.6.2; CSIRO and 
BOM, 2020; Trewin et al., 2020). In New Zealand, an increase of 1.1°C 
has been measured from 1909–2016 (Atlas.6.2; MfE and Stats NZ,

1806
00.51NAU
0.20.100.10.20.30.4CAU
00.51EAU
0.20.100.10.20.30.4SAU
00.51
0.20.100.10.20.30.4NZ
Time slices6004002000200NAU
6004002000200CAU
6004002000200EAU
2050 21006004002000200SAU
2050 21006004002000200NZ
-0.2-0.1-0.05-0.02-0.01 00.010.020.050.1 0.2(a) Change in 1-in-100 year river discharge per unit 
m m3 s-1 km-2-200 -150 -100 -50 -10 50 100 150 200 10 -
Shoreline position change (m)(c)
Legend
+2°C +4°C
median
median
10th p

Figure 12. 7 | Projected changes in selected climatic impact-driver indices for Australasia. (a)  Mean change in 1-in-100-year river discharge per unit catchment area 
(Q100, m3 s–1 km–2) from CORDEX-Australasia models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative 
to 2010 for RCP8.5 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) 
averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent 
past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for

two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C 
(yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest 
colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based 
projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate 
regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are

no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are 

1807
Climate Change Information for Regional Impact and for Risk Assessment Chapter 12122020). In the period 1980–2014 a rate of increase of 0.1°C–0.3°C per 
decade has been observed (Atlas.11 and Atlas.20).
Mean temperature in Australasia is projected to continue to rise 
through the 21st  century ( virtually certain ) (Atlas.6.4). Projections 
for Australia indicate that the average temperature will increase 
by +1.1°C (0.84–1.52°C 10–90th percentile range) by 2041–2060 
(mid-century), and by +1.9°C (1.29–2.58°C) by 2081–2100 (end-
century), relative to the baseline period of 1995–2014, under SSP2-4.5 
(Interactive Atlas). For SSP5-8.5, the projected changes are up to 
+1.5°C (1.17–1.96°C) and +3.7°C (2.75–4.91°C) for mid- and end-
century respectively. For SSP1-2.6, mean temperature is projected 
to rise by +0.9°C (0.55–1.26°C) and +1.0°C (0.55–1.54°C) relative 
to 1995–2014 by mid- and end-century respectively (Interactive

Atlas). In New Zealand, an increase of mean temperature of +1.0°C 
(0.60–1.32°C) relative to 1995–2014 is projected by mid-century, and 
an increase of +1.6°C (1.03–2.26°C) by end-century under SSP2-4.5. 
For SSP5-8.5, the projected increase in mean temperature is +1.3°C 
(0.91–1.66°C 10–90th percentile range) and +3.1°C (2.20–4.05°C) 
relative to 1995–2014 by mid- and end-century respectively. For 
SSP1-2.6, the projected increase in mean temperature is +0.75°C 
(0.39–1.06°C) and +0.8°C (0.47–1.46°C) relative to 1995–2014 by 
mid- and end-century, respectively (Interactive Atlas).
Extreme heat:  The region has a  very likely  trend of increasing 
frequency and severity of hot extremes since the 1950s (Table 11.10). 
Extreme minimum temperatures have increased in all seasons over 
most of Australia and exceeds the increase in extreme maximum 
temperatures (X.L. Wang et  al., 2013; Jakob and Walland, 2016). 
Heatwave characteristics and hot extremes have increased across

many Australian regions since the mid-20th  century (Table  11.10; 
CSIRO and BOM, 2020). The number of days per year with maximum 
temperature greater than 35°C has increased over most parts 
of Australia from 1957–2015, with the largest increasing trends of 
0.4–1 days/year occurring in north-western, Northern, north-eastern 
Australia and parts of Central Australia (CSIRO and BOM, 2016). 
Long-term changes of hot extremes in Australia have been attributed 
to anthropogenic influence (Table  11.10). In New Zealand, the 
number of annual heatwave days increased at 18 of 30 sites during 
the period 1972–2019 (MfE and Stats NZ, 2020).
More frequent hot extremes and heatwaves are expected over 
the 21st  century in Australia ( virtually certain ) (Table  11.10). Heat 
thresholds potentially affecting agriculture and health, such as 35°C 
or 40°C, are projected to be exceeded more frequently over the 
21st  century in Australia under all RCPs ( high confidence ). By 2090

under RCP4.5, the average number of days per year with maximum 
temperatures above 35°C is highly spatially variable and is expected 
to increase by 50–100%, while the number of days per year with 
maximum temperatures above 40°C is expected to increase by 200%, 
relative to 1985–2005 (CSIRO and BOM, 2015). Under RCP8.5 the 
corresponding projected increases are even greater, with a greater than 
100% increase in most of Australia, and far greater increases in Central 
and Northern Australia (up to a 20-fold increase in Darwin). Projections 
for New Zealand indicate more frequent hot extremes ( virtually 
certain ) (Table  11.10). Figure  12.4b, c shows CMIP6 projections of 
mean number of days per year with maximum temperature exceeding 35°C under SSP5-8.5, which are consistent with the above assessed 
literature and across the two CMIP generations, and indicate a strong 
difference depending on the mitigation scenario (e.g., over 100 days

more per  year under  SSP5-8.5 in NAU, but, in general, less than 
60 days more per year under SSP1-2.6 in NAU; Figure 12.SM.1).
The projected frequency of exceeding dangerous humid heat 
thresholds is increasing in Australia, with a  strong increase in 
Northern Australia for RCP8.5 ( high confidence ) (Zhao et al., 2015; 
Mora et  al., 2017; Brouillet and Joussaume, 2019), consistently 
across CMIP5, CMIP6 and CORDEX simulations (Figure 12.4d–f and 
Figure  12.SM.2). Using the HI index, by end-century, the average 
number of days exceeding 41°C is projected to increase in NAU by 
about 100 days and by about 25 days under SSP5-8.5 and SSP1-2.6, 
respectively. The projections for New Zealand indicate no appreciable 
increase in the number of days with HI > 41°C across SSPs, time 
periods and CMIP generations (Figure 12.4d–f and Figure 12.SM.2).
Cold spell and frost:  Excepting parts of Southern Australia, the 
Australasian region has a significant trend of decreasing frequency

in cold extremes since the 1950s ( high confidence ) (Table  11.10) 
and there is high confidence  that such trends are attributable 
to anthropogenic influence (Table  11.10). The number of frost 
days per  year in Australia has on average declined at a  rate of 
0.15 days/decade in the past century (Alexander and Arblaster, 2017), 
except in some regions of Southern Australia, where an increase in 
both number and season length has been reported (Dittus et  al., 
2014; Crimp et al., 2016b). The number of frost days has decreased 
at 12 of 30 monitoring sites around New Zealand over the period 
1972–2019 (MfE and Stats NZ, 2020).
Less frequent cold extremes are virtually certain  in Australasia 
(Table 11.10) while a decrease of frost days is projected with high 
confidence  for the region. Projections, relative to 1986–2005, for 
the number of frost days per year in Australia indicate declines of 
0.9 days by mid-century and 1.1 days by end-century for RCP4.5,

while for RCP8.5, the projected declines are 1.0 days and 1.3 days 
by mid- and end-century respectively (Alexander and Arblaster, 2017; 
Herold et al., 2018). Projections for New Zealand indicate that the 
number of frost days will decrease by 30% (RCP2.6) to 50% (RCP8.5) 
by 2040, relative to 1986–2005. By 2090, the decrease ranges from 
30% (RCP2.6) to 90% (RCP8.5) (MfE and Stats NZ, 2017).
In general, there is high confidence  that most heat hazards in 
Australasia will increase and that cold hazards will decrease 
over the 21st century. The mean temperature in Australasia is 
virtually certain to continue to rise through the 21st century, 
accompanied by less frequent cold extremes ( virtually 
certain ) and frost days ( high confidence ), and more frequent 
hot extremes ( virtually certain ). Heat stress is projected to 
increase in Australia ( high confidence ).
12.4.3.2 Wet and Dry
Mean precipitation:  Here, only increases in precipitation (under

‘Wet’) are addressed, with decreases (under ‘Dry’) addressed in 
‘Aridity’ below.

1808
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12In terms of wet climatic impact-drivers, detectable anthropogenic 
increases in precipitation in Australia have been reported particularly 
for north-central Australia for the period 1901–2010 (Knutson 
and Zeng, 2018). Figure Atlas.11 indicates no significant trend in 
precipitation over the region during the baseline period 1960–2015, 
except for the Global Precipitation Climatology Project (GPCP) 
dataset, which shows an increasing trend in north-central Australia. 
In New Zealand, increases in annual rainfall have been observed 
between 1960–2019 in the south and west of the South Island and 
east of the North Island. Note however, for the most part, the above 
reported trends in New Zealand have been classified as statistically 
not significant (Figure Atlas.20).
Annual mean precipitation is projected to increase in Central and 
north-east Australia ( low confidence ) and in the south and west

of New Zealand ( medium confidence ) (Atlas.6.4). Liu et al. (2018a) 
show that under 1.5°C warming, Central and north-east Australia 
will become wetter. Projected patterns in annual precipitation exhibit 
increases in the west and south of New Zealand (Atlas.6.4; Liu et al., 
2018a) and project that the South Island will be wetter under both 
1.5°C and 2°C warming. However, there is limited model agreement 
for projected rainfall changes in Australasia as shown in the Atlas.
River flood:  Streamflow observations in Australia have shown that 
negative trends dominate in annual maximum flow and that stations 
with significant negative trends were mostly located in the south-east 
and south-west (Gu et  al., 2020). The observed peak flow trend in 
Southern Australia is attributed to the decrease of soil moisture, 
although an increase of flood magnitude is possible for very rare 
events. For the more frequent flood events, the increase of extreme

precipitation is balanced by the decrease of soil moisture. (Wasko and 
Nathan, 2019).
While median annual runoff is projected to decrease in most of 
Australia (Chiew et  al., 2017), consistent with projected decreases 
in average rainfall (CSIRO and BOM, 2015; Alexander and Arblaster, 
2017), river floods are projected to increase due to more intense 
extreme rainfall events and associated increase in runoff ( medium 
confidence ). Asadieh and Krakauer (2017) found a  decrease in the 
value of the 95% percentile of mean streamflow with RCP8.5 by 
the end of the century in all of Australia, except in a small part in centre 
of the country. In terms of relative increases, flooding is expected to 
increase more in Northern Australia (driven by convective rainfall 
systems) than in Southern Australia (where more intense extreme 
rainfall may be compensated by drier antecedent moisture conditions; 
Alexander and Arblaster, 2017; Dey et al., 2019) with flood frequency

increasing in Northern Australia and along parts of the east coast and 
decreasing in south-western Western Australia (Hirabayashi et  al., 
2013). Gu et al. (2020) project larger flood magnitude and volumes 
under both RCP2.6 and RCP8.5 in Northern Australia, and smaller flood 
magnitudes and volumes in Southern Australia under the same RCPs. 
These findings are in general agreement with the patterns in peak 
flow, corresponding to the 1-in-100-year return period streamflow, 
shown in Figure 12.7a,c for mid-21st century under RCP8.5.
There is medium confidence  that river flooding will increase in New 
Zealand. Projections for New Zealand indicate that the  1-in-50-year and 1-in-100-year flood peaks for rivers in many parts of the 
country may increase by 5 to 10% by 2050 and more by 2100 
(with large variation between models and emissions scenarios), with 
a  corresponding decrease in return periods for specific flood levels

(Gray et al., 2005; Carey-Smith et al., 2010; McMillan et al., 2010, 2012; 
Ballinger et al., 2011).
Heavy precipitation and pluvial flood:  Rainfall extremes have 
been detected to increase in Australasia, with low confidence  
(Table 11.10). There is high confidence  that R × 1 day and R × 5 day 
precipitation extremes will increase for 2°C or lower warming for the 
region as a whole, but on a sub-regional basis there is only medium 
confidence  of increases in NAU and CAU and low confidence  of 
increases on EAU, SAU and NZ. For warming levels exceeding 2°C, 
these extremes are very likely  to increase in NAU and CAU and they 
are likely  to increase elsewhere in the region (Section 11.9).
Landslide:  Based on local slope characteristics, lithology and seismic 
activity, the South Island and the eastern half of the North Island of 
New Zealand are vulnerable to landslide occurrence (Broeckx et al., 
2020). The potential for land and rockslides increases with, amongst

other factors, total precipitation rates, precipitation intensity, 
mountain permafrost thaw rates, glacier retreat and air temperature 
(Crozier, 2010; Allen and Huggel, 2013; Gariano and Guzzetti, 2016; 
IPCC, 2019a). Given the increase of the magnitude of these physical 
variables in areas that are already highly susceptible to mass 
movements (MfE, 2018), there is low confidence  that the occurrence 
of landslides will increase under future climate conditions.
Aridity:  In terms of dry climatic impact-drivers, a substantial decrease 
in precipitation has been observed across Southern Australia during 
the cool season (April–October) ( medium confidence ). The drying 
trend has been particularly strong over south-west Western Australia 
between May and July, with rainfall since 1970 being around 
20% less than the 1900–1969 average (CSIRO and BOM, 2020). 
Detectable decreases in mean precipitation, attributable at least

in part to anthropogenic forcing, have been reported for parts of 
south-west Australia (Delworth and Zeng, 2014; Knutson and Zeng, 
2018), south-east Australia, and Tasmania (Knutson and Zeng, 2018). 
In New Zealand, the north-east of the South Island and western and 
the northern parts of the North Island show decreasing precipitation 
trends during 1960–2019 (MfE and Stats NZ, 2020).
Aridity is projected to increase, especially during winter and spring, 
with medium confidence  in SAU but with high confidence  in south-west 
Western Australia (Table 11.11 and Atlas.6.4). In EAU and in the north 
and east of NZ, aridity is projected to increase with medium confidence , 
while a  decrease is projected with medium confidence  in the south 
and west of NZ (Atlas.6.4). Although there is only low confidence  in 
the projected decrease of mean annual precipitation in south-western 
and eastern Australia and the north and east of New Zealand, there is

high confidence  of reduced winter and spring precipitation in Australia 
in future, mostly in south-western and eastern Australia (Atlas.6.4). 
Liu et al. (2018b) show that under 2°C warming, most of Australia is 
projected to become drier based on the Palmer Drought Severity Index 
(PDSI), with the exception of the tropical north-east. Ferguson et al. 
(2018) project that between 1976–2005 and 2070–2099, winters will

1809
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212become drier (mainly in Southern Australia) under RCP8.5. Liu et al. 
(2018b) project that the North Island of New Zealand will be drier 
under both 1.5°C and 2°C warming.
Hydrological drought:  There is low confidence  of observed changes 
in hydrological droughts in Australasia, except in SAU where there 
is medium confidence  of an observed increase in the south-east 
and south-west. Future projections indicate medium confidence  
in further hydrological drought increases for Southern Australia for 
warming levels of 2°C or higher (Section 11.9). Mean annual runoff 
in far south-east and far south-west Australia are projected to 
decline by median values of 20 and 50% respectively, by mid-century 
under RCP8.5 (Chiew et al., 2017). Prudhomme et al. (2014) assess 
changes in the Drought Index (DI), defined as areal runoff less 
than the 10th percentile over the reference period 1976–2005, and

project DI increases for both Australia and New Zealand by 10–20% 
by 2070–2099 under RCP8.5, with the greatest effects being in the 
southern parts of the Australian continent. These projections are 
consistent with the trends shown in Figure 12.4g–i (Figure 12.SM.3). 
The SPI drought frequency is projected to increase in SAU and 
particularly in south-west Western Australia by mid-century, while by 
the end of the century SPI drought frequency is projected to increase 
all over Australia, and particularly strongly in south-west Western 
Australia as well as southern Victoria (see Figure  12.4g–i). For the 
Murray–Darling basin, Ferguson et  al. (2018) project effectively no 
change (–1%) in mean precipitation, a 27% decrease in P–E, and 30% 
increase in runoff in 2070–2099 relative to 1976–2005 with RCP8.5.
Agricultural and ecological drought:  There is medium confidence  
in observations of  agricultural and ecological droughts increasing in

SAU and decreasing in NAU, while there is low confidence  of changes 
elsewhere in the region (Section  11.9). More regional studies have 
observed an increase in agricultural and ecological drought intensity 
in south-west Australia and an increase in drought intensity in parts of 
south-east Australia, while the length of droughts therein has increased 
(Section 11.9). In New Zealand, since 1972–73, soils at 7 of 30 monitored 
sites became drier, while the 2012–13 drought was one of the most 
extreme in the previous 41 years (MfE and Stats NZ, 2017). Future 
evaporative demand is projected to lead to medium confidence  increases 
in agricultural and ecological droughts for 2°C of global warming in 
SAU and EAU and low confidence  for changes in CAU, NAU and NZ, 
although there is medium confidence  of increases in CAU with 4°C of 
global warming (Section 11.9). There is medium confidence  for more 
time in agricultural and ecological drought in SAU by mid-21st century

(Coppola et al., 2021b) as well as by the end of the 21st century (Herold 
et al., 2018). The Standardized Precipitation Evapotranspiration Index 
(SPEI) shows a  springtime intensification in SAU with moderate and 
severe droughts in the south-west and moderate droughts in the 
south-east (Herold et al., 2018). There is consensus among the different 
model ensembles (CORDEX-CORE, CMIP5 and CMIP6) that the drought 
frequency (DF), one of several proxies for agricultural and ecological 
drought, will increase in all four Australian regions for both mid-century 
(NAU 0.2–2 DF increase, CAU 0.5–2 DF increase, SAU 1–3 DF increase 
and EAU 0.8–3 DF increase) and end-century (0.8–2.7 DF increase for 
NAU, 1.2–2 DF increase for CAU, 2.2–3.8 for SAU and 0.2–3 for EAU) 
for both RCP8.5 and SSP5-8.5, with CMIP6 showing the lowest increase 
(Figure 12.4g–l and Figure 12.SM.4; Coppola et al., 2021b).Fire weather:  Dowdy and Pepler (2018) examined atmospheric

conditions conducive to pyroconvection in the period 1979–2016, 
and found an increased risk in south-east Australia during spring 
and summer, due to changes in vertical atmospheric stability and 
humidity, in combination with adverse near-surface fire weather 
conditions. CSIRO and BOM (2018) and Dowdy (2018) found that 
the annual 90th percentile daily Forest Fire Danger Index (FFDI) 
has increased from 1950–2016 in parts of Australia, especially in 
Southern Australia (1–2.5 per decade) and in spring and summer. 
These studies indicate an increase in the frequency and magnitude 
of FFDI extreme quantiles, as well as a shift of the fire season start 
towards spring, lengthening the fire season. The unprecedented large 
fires of austral spring and summer of 2019 in south-east Australia 
were a  result of extreme hot and dry weather in significantly 
drier than average conditions that had persisted since 2017, in 
combination with consistently stronger than average winds, resulting

in above average to highest on record FFDI values in much of the 
country (Abram et  al., 2021). These fires have been attributed to 
climate change through the temperature component of fire weather 
indices (van Oldenborgh et al., 2021). In New Zealand, days with very 
high and extreme fire weather increased in 12 out of 28 monitored 
sites, and decreased in 8, in the period 1997–2019 (MfE and Stats NZ, 
2020). Attribution studies indicate that there is medium confidence  of 
an anthropogenically driven past increase in fire weather conditions, 
essentially due to increase in frequency of extreme heat waves. (Hope 
et al., 2019; Lewis et al., 2020; van Oldenborgh et al., 2021).
Fire weather indices are projected to increase in most of Australia 
(high confidence ) and many parts of New Zealand ( medium 
confidence ), in particular with respect to extreme fire and induced 
pyroconvection (Dowdy et al., 2019b). Increasing mean temperature,

cool season rainfall decline, and changes in tropical climate 
variability all contribute to a future increase in extreme fire risk in 
Australia (Abram et al., 2021). Projections indicate that the annual 
cumulative FFDI will increase by 31–33% in Southern and Eastern 
Australia, and by 17–25% in Northern Australia and the Rangelands 
by 2090 (relative to 1995) under RCP8.5 (CSIRO and BOM, 2015). 
Using a  CMIP5 ensemble of 17 models, Abatzoglou et  al. (2019) 
found a  statistically significant positive trend for fire weather 
intensity and fire season length for future mid-century conditions 
under RCP8.5, including a  detectable anthropogenic influence 
on fire risk magnitude and fire season length by 2040 in Western 
Australia and along the Queensland coastline. Using the C-Haines 
and FFDI indices with A2 and RCP8.5 respectively, Di Virgilio et al. 
(2019) and Clarke et al. (2019) have shown that extreme fire weather

frequency will increase in south-eastern Australia by the end of the 
21st  century. Most of these projections indicate that the biggest 
increases in fire weather conditions will be in late spring, effectively 
resulting in longer (stronger) fire seasons in areas where spring 
is the shoulder (peak) season. In New Zealand, Watt et  al. (2019) 
projected that the number of days with very high to extreme fire risk 
will increase by 71% by 2040, and by a further 12% by 2090, for the 
A1B scenario, with fire risk increase all along the east coast. The most 
marked relative changes by 2090 were projected for Wellington and 
Dunedin, where very high to extreme fire risk is projected to increase 
by, respectively, 89% to 32 days and 207% to 18 days, compared to 
the baseline period 1970–1999.

1810
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Annual mean precipitation is projected to increase in Central 
and north-east Australia ( low confidence ) and in the south and 
west of New Zealand ( medium confidence ), while it is projected 
to decrease in Southern Australia ( medium confidence ), albeit 
with high confidence  in south-west Western Australia, in 
Eastern Australia ( medium confidence ), and in the north and 
east of New Zealand ( medium confidence ). Heavy precipitation 
and pluvial flooding are projected to increase with medium 
confidence  in Northern Australia and Central Australia. There 
is medium confidence  that river flooding will increase in New 
Zealand and Australia, with higher increases in Northern 
Australia. Aridity is projected to increase with medium 
confidence  in Southern Australia ( high confidence  in south-west 
Western Australia), Eastern Australia ( medium confidence ), and

in the north and east of New Zealand ( medium confidence ). 
Hydrological droughts are projected to increase in Southern 
Australia ( medium confidence ), while agricultural and 
ecological droughts are projected to increase with medium 
confidence  in Southern Australia and Eastern Australia. Fire 
weather is projected to increase throughout Australia ( high 
confidence ) and New Zealand ( medium confidence ).
12.4.3.3 Wind
Mean wind speed:  There is low confidence  of a mean wind speed 
trend in the last decades ( low agreement ) (McVicar et  al., 2012; 
Troccoli et al., 2012; Azorin-Molina et al., 2018; J. Wu et al., 2018), as 
long-term measurements are not homogeneous.
In future climate scenarios wind speed trends in Australia exhibit 
generally weak amplitudes with low agreement  among models 
(Figure 12.4m–o and Figure 12.SM.5) with uncertain consequences 
on wind power potential (CSIRO and BOM, 2015; Karnauskas

et al., 2018a; Jung and Schindler, 2019). However, there is medium 
confidence  that, by the end of the century, annual mean wind power 
will significantly increase in north-eastern Australia under RCP8.5, but 
there is low confidence  of an increase by end-century under RCP4.5, 
and for any scenario by mid-century (Karnauskas et al., 2018a). In 
New Zealand, mean wind patterns are projected to become more 
north-easterly in summer, and westerlies to become more intense in 
winter ( low confidence ), in agreement with the strengthening of the 
Southern Hemisphere storm tracks (Section 4.5.1).
Severe wind storm:  There is generally low confidence  in observed 
changes in extreme winds and extratropical storms in Australasia 
(Section 11.7.2). CMIP5 projections of severe winds indicate a general 
increase in north-eastern Australia, and decreases in some parts 
in Southern and Central Australia ( medium confidence ) by the end

of the century under RCP8.5 (CSIRO and BOM, 2015; Kumar et al., 
2015; Jung and Schindler, 2019). Elsewhere trends are diverse and 
vary across simulations with  low agreement . Projections of changes 
in the 1-in-25-year return period winds (based on annual maxima) 
for 2074–2100 relative to 1979–2005 for RCP8.5 show an increase in 
tropical areas of Northern Australia (Kumar et al., 2015).
In New Zealand, the frequency and magnitude of extreme winds 
have decreased (from 1980–2019) at 12 of 14 monitored sites and increased at two monitored sites (MfE and Stats NZ, 2020). Due to 
the intensification and the shift of the austral storm track by the 
end of the century (Yin, 2005), increases in extreme wind speed in 
New Zealand are projected over the South Island and the southern 
part of the North Island by mid- and end-century for all RCPs ( low 
confidence ) (MfE, 2018).
Tropical cyclone:  In Australia, the number of TCs has generally

declined since 1982, and the frequency of intense TCs that make 
landfall in north-eastern Australia has declined significantly since the 
19th century ( medium confidence ) (Kuleshov et al., 2010; Callaghan 
and Power, 2011; Holland and Bruyère, 2014; Knutson et al., 2019; 
CSIRO and BOM, 2020). There is high confidence  that cyclones making 
landfall along north-eastern and northern Australian coastlines will 
decrease in number and low confidence  of an increase in their 
intensities for 2°C of global warming as well as for the mid-century 
period with scenarios RCP4.5 and above (Roberts et al., 2015, 2020; 
Bacmeister et al., 2018; Knutson et al., 2020), with the amplitude of 
changes increasing from RCP4.5 to RCP8.5 (Bacmeister et al., 2018). 
Decreases in frequency are projected for ‘east coast lows’ (Walsh 
et al., 2016b; Dowdy et al., 2019a).
Sand and dust storm:  Australia is recognized to be the largest dust 
source in the Southern Hemisphere (Zheng et  al., 2016). Land-use

and land-cover change have increased dust emissions in Australia 
in the past 200 years (Marx et al., 2014). While projections suggest 
a decrease in severe winds in Central and Southern Australia, changes 
in vegetation due to increased aridity and hydrological drought could 
be expected to result in increased wind erosion and dust emission 
across the country ( medium confidence ) (Webb et al., 2020).
In Australasia, there is low confidence  in projected mean wind 
speeds and wind power potential, with a  medium confidence  
increase projected only in north-eastern Australia under high 
emissions scenarios and by the end of the 21st century. Tropical 
cyclones in north-eastern and North Australia are projected to 
decrease in number ( high confidence ) while their intensity is 
projected to increase ( low confidence ).
12.4.3.4 Snow and Ice
Snow: The snow season length in Australia has decreased by 5% 
during 2000–2013 relative to 1954–1999, especially in spring

(Pepler et al., 2015). A shift in the date of peak snowfall has also 
been observed with an 11-day advance over the same period (Pepler 
et al., 2015). A decreasing trend in maximum snow depth has been 
observed for Australian alpine regions since the late 1950s, with 
the largest declines during spring and at lower altitudes. Maximum 
snow depth is highly variable and is strongly influenced by rare heavy 
snowfall days, which have no observed trends in frequency (CSIRO 
and BOM, 2020). 
Projections for Southern Australia and New Zealand show a continuing 
reduction in snowfall during the 21st  century ( high confidence ). 
The magnitude of decrease varies with the altitude of the region and 
the emissions scenario. At elevations lower than 1500 m, years without 
snowfall are projected from 2030 in some models. By 2090, and under

1811
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212RCP8.5, such years are projected to become common (CSIRO and BOM, 
2015). The number of annual snow days in New Zealand is projected 
to decrease under all RCPs, by up to 30 days or more by 2090 under 
RCP8.5, relative to 1986–2005 (MfE, 2018).
Glacier:  Glacier mass and areal extent in New Zealand is projected 
to continue to decease over the 21st  century ( high confidence ) 
(Section 9.5.1.3). Glacier ice volume from 1977–2018 in New Zealand 
has decreased from 26.6 to 17.9 km3 (a loss of 33%; Salinger et al., 
2019). Relative to 2015, glaciers in New Zealand are projected to 
lose 36 ± 44%, 53 ± 33% and 77 ± 27% of their mass by the end of 
the century under RCP2.6, RCP4.5 and RCP8.5 respectively, with the 
loss rates decreasing over time under RCP2.6 and increasing under 
RCP8.5 (Marzeion et al., 2020).
In summary, snowfall is expected to decrease throughout the

region at high altitudes in both Australia ( high confidence ) 
and New Zealand ( medium confidence ). In New Zealand, 
glacier ice mass and extent are expected to decrease over the 
21st century for all scenarios ( high confidence ).
12.4.3.5 Coastal and Oceanic
Relative sea level:  Around Australasia, from 1900–2018, a  new 
tide gauge-based reconstruction finds a regional mean RSL change 
of 1.33 [0.80 to 1.86]  mm yr –1 in the Indian Ocean–South Pacific 
region (Frederikse et  al., 2020), compared to a  GMSL change of 
around 1.7 mm yr –1 (Section 2.3.3.3 and Table 9.5). For the period 
1993–2018, the RSLR rates, based on satellite altimetry, increased 
to 3.65 [3.23 to 4.08] mm yr –1 (Frederikse et al., 2020), compared to 
a GMSL change of 3.25 mm yr –1 (Section 2.3.3.3 and Table 9.5).
Relative sea level is virtually certain  to increase throughout the region 
over the 21st century (Section 9.6.3, Figure 9.28). Regional mean RSLR

projections for the oceans around Australasia range from 0.4–0.5 m 
under SSP1-2.6 to 0.7–0.9 m under SSP5-8.5 for 2081–2100 relative to 
1995–2014 (median values), which means local RSL change falls within 
the range of mean projected GMSL change (Section 9.6.3.1). However 
these RSLR projections may be underestimated due to potential partial 
representation of land subsidence (Section 9.6.3.2).
Coastal flood:  The most commonly used index for episodic coastal 
inundation in Australia is the summation of a  high end SLR and 
the 1-in-100-year storm tide level (the combined sea level due to 
storm surge and tide) (CSIRO and BOM, 2016; McInnes et al., 2016). 
However, episodic coastal flooding is caused by extreme total water 
levels (ETWL), which is the combination of SLR, tides, surge and 
wave setup (Section 12.3.5.2). The present-day 1-in-100-year ETWL 
is between 0.5–2.5 m around most of Australia, except the north-
western coast where 1-in-100-year ETWL can be as large as 6–7 m

(Vousdoukas et al., 2018; O’Grady et al., 2019; Kirezci et al., 2020).
(Figure 12.4p–r and Figure 12.SM.6). Across the region, the 5–95th 
percentile range of the 1-in-100-year ETWL is projected increase 
(relative to 1980–2014) by 5–35 cm and by 10–40 cm by 2050 under RCP4.5 and RCP8.5 respectively (Figure  12.4q). By 2100 
(Figure 12.4p,r), this range is projected to be 25–80 cm and 50–190 cm 
under RCP4.5 and RCP8.5 respectively (Vousdoukas et  al., 2018; 
Kirezci et al., 2020). Furthermore, the present-day 1-in-100-year ETWL 
is projected to have median return periods of around 1-in-20-years by 
2050 and 1-in-1-year by 2100 in SAU and NZ and return periods of 
around 1-in-50-years by 2050 and 1-in-20-years by 2100 in NAU under 
RCP4.5 (Vousdoukas et al., 2018), while the present-day 1-in-50-year

a SLR of 1 m around Australasia (Vitousek et al., 2017).
Coastal erosion:  Satellite derived shoreline retreat rates for 
the period between 1984–2015 show retreat rates between 
0.5 and 1 m yr –1 around the region, except in SAU where a shoreline 
progradation rate of 0.1 m yr –1 has been observed (Luijendijk et al., 
2018; Mentaschi et al., 2018). Mentaschi et al. (2018) report a coastal 
area loss of 350 km2 over the same period in Western Australia from 
satellite observations.
Projections indicate that a  majority of sandy coasts in the region 
will experience shoreline retreat, throughout the 21st century ( high 
confidence ) (Figure  12.7b,d). Median shoreline change projections 
(CMIP5) under both RCP4.5 and RCP8.5 presented by Vousdoukas 
et al. (2020b) show that, by mid-century, sandy shorelines will retreat 
(relative to 2010) by between 50 and 80 m all around Australasia,

except in SAU and NZ where the projected retreat (relative to 2010) is 
between 35 and 50 m. By 2100, median shoreline retreats exceeding 
100 m (relative to 2010) are projected along the sandy coasts of NAU 
(about 150 m), CAU (about 160 m), and EAU (about 110 m) under 
RCP4.5m, while projections for SAU and NZ are around 80–90  m. 
Under RCP8.5, shoreline retreat exceeding 100 m is projected all 
around the region by 2100 (relative to 2010) with retreats as high 
as 220 m in NAU and CAU (about 170 m in EAU and about 130 
m in SAU and NZ; Figure 12.7b,d). The total length of sandy coasts 
in Australasia that is projected to retreat by more than a  median 
of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,500 and 
16,000 km respectively, an increase of approximately 30%.
Distinct from long-term coastline recession, storms and storm surges 
also result in episodic coastal erosion.  In general, the historically 
measured maximum episodic coastal erosion (either eroded volume

or coastline retreat distance) or that due to a 1-in-100-year return 
period storm wave height is used as a design criterion for coastal 
zone management and planning in Australia (Wainwright et  al., 
2014; Mortlock et al., 2017).
While there is wide recognition in Australia that the combined 
effect of SLR, changing storm surge and wave climates will directly 
affect  future episodic coastal erosion (McInnes et  al., 2016; 
Ranasinghe, 2016; Harley et al., 2017) only a few projections of how 
this hazard may evolve are available for Australia. In one such study, 
Jongejan et al. (2016) provide projections of how the full exceedance 
probability curve of the maximum erosion per year may evolve over 
the 21st century (due to the combined action of SLR, storm surge 
and storm waves). Their results show that, for example, the 0.01 
exceedance probability maximum coastline retreat in 2025 will have 
an exceedance probability of 0.015 by 2050 and 0.07 by 2100.

1812
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Marine heatwave:  The mean SST of the ocean around Australia and 
east of New Zealand has warmed at a rate of about 0.22°C per decade 
between 1992 and 2016 (Wijffels et  al., 2018), which is higher than 
the global average SST increase of 0.16°C per decade (Oliver et  al., 
2018). This mean ocean surface warming is connected to longer and 
more frequent marine heatwaves in the region (Oliver et al., 2018). Over 
the period 1982–2016, the coastal ocean of Australia experienced on 
average more than 1.5 marine heatwaves (MHWs) per year, with the 
north coast of Western Australia and the Tasman Sea experiencing on 
average 2.5–3 MHWs per year. The average duration was between 10 
and 15 days, with somewhat longer and hotter MHWs in the Tasman Sea. 
In New Zealand, the south-east coast of South Island experiences the 
most MHWs (2.5–3 per year). The duration of MHW in New Zealand is

on average 10–15 days (Oliver et al., 2018). Changes around Australasia 
over the 20th century, derived from MHW proxies, show an increase 
in frequency between 0.3 and 1.5 MHW per decade, except along the 
south-east coast of New Zealand (Box 9.2); an increase in duration per 
event; and the total number of MHW days per decade, with the change 
being stronger in the Tasman Sea than elsewhere (Oliver et al., 2018).
There is high confidence  that MHWs will increase around most of 
Australasia. Under RCP4.5 and RCP8.5 respectively, mean SST is 
projected to increase by 1°C and 2°C around Australia by 2100, with 
a hotspot of around 2°C for RCP4.5 and of 4°C for RCP8.5 along the 
south-east coast between Sydney and Tasmania (Interactive Atlas). 
Under all RCPs, the mean SST around Australia is expected to increase 
in the future, with median values of around 0.4°C–1.0°C by 2030 
under RCP4.5, and 2°C–4°C by 2090 under RCP8.5 (CSIRO and BOM,

2015). Warming is expected to be largest along the north-west coast 
of Australia, southern Western Australia, and along the east coast of 
Tasmania (CSIRO and BOM, 2018). More frequent, extensive, intense 
and longer lasting MHWs are projected around Australia and New 
Zealand for GWLs of 1.5°C, 2°C and 3.5°C relative to the modelled 
reference value for 1861–1880 (Frölicher et  al., 2018). Projections 
for SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around 
Australasia by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).
In general, there is high confidence  that most coastal/ocean-
related hazards in Australasia will increase over the 21st century. 
Relative sea level rise is virtually certain  to continue in the 
oceans around Australasia, contributing to increased coastal 
heatwaves are also expected to increase around the region

The assessed direction of change in climatic impact-drivers for 
Australasia and associated confidence levels are illustrated in 
Table 12.5, together with emergence time information (Section 12.5.2). 
No assessable literature could be found for hail and snow avalanches, 
although these phenomena may be relevant in parts of the region.
12.4.4 Central and South America
For the purpose of this assessment, Central and South America is 
divided into eight sub-regions, as defined in Chapter  1: Southern Central America (SCA), North-Western South America (NWS), Northern 
South America (NSA), South American Monsoon (SAM), North-Eastern 
South America (NES), South-Western South America (SWS), South-
Eastern South America (SES) and Southern South America (SSA). 
The Caribbean is placed under the Small Islands section (12.4.7) of 
this chapter.
Previous assessments have documented ongoing and projected

changes in several CIDs. IPCC AR5 projections (IPCC, 2014b) pointed 
to increases in mean temperature between 2°C and 6°C by the end 
of the century ( high confidence ) and increases in the occurrence 
of warm days and nights under various future climate scenarios 
(medium confidence ). The AR5 also pointed to patterns of changes 
in precipitation ( medium confidence ), changes in the duration of 
dry spells ( medium confidence ) and decreases in water supply ( high 
confidence ). The SR1.5 projections indicated expected increases in 
river flooding and extreme runoff at 2°C warming in parts of South 
America, and decreases in runoff in Central America, central and 
Southern South America, and the Amazon basin. The SROCC reported 
an increased number of landslides, a  decreased volume of lahars 
from ice and snow-clad volcanoes, and increased frequency of glacier 
lake outburst floods. The SROCC also indicated that regional and

local-scale projections point to decreasing trends in glacier runoff.
New literature is now available for the regional climate as a result 
of observational research and coordinated modelling outputs of 
CORDEX South America (Solman, 2013; Sánchez et  al., 2015) and 
CORDEX-CORE (Giorgi et al., 2018; Coppola et al., 2021b; Teichmann 
et al., 2021). Of particular interest are the new projections of both 
mean climate and extremes. This new regional climate information is 
key to main sectors sensitive to climate change in Central and South 
America such as water resources, infrastructure, agriculture, livestock, 
forestry, silviculture and fisheries (Magrin, 2015; López Feldman 
and Hernández Cortés, 2016), human health (changes in morbidity 
and mortality, and emergence of diseases in previously non-endemic 
areas; Núñez et  al., 2016) and biodiversity (Uribe Botero, 2015), 
urban planning, navigation and tourism.
12.4.4.1 Heat and Cold

Mean air temperature:  New literature confirms a  continuous 
warming since the beginning of the 20th century in the majority of the 
eight sub-regions (Atlas.7). However, observational datasets in several 
areas are still short and trend estimation is hindered by year-to-year 
and interannual variability. Atlas projections point to a  virtually certain  
warming across all sub-regions, with the largest increases taking place 
in the Amazon basin (NSA and SAM; Atlas.7.2.4). A consistent increase 
in temperature-related indices linked to several climate-sensitive 
sectors (e.g., growing degree days, cooling degree days) is found across 
CMIP5, CMIP6 and CORDEX-CORE projections, with smaller increases 
for cooling degree days in mid-latitude regions than in SCA and the 
Amazon (Coppola et al., 2021b). Daily mean temperature exceedances 
of a  typical 21.5°C threshold for a  successful incubation of disease 
pathogens inside many mosquito vectors (Lambrechts et  al., 2011;

Blanford et  al., 2013; Mordecai et  al., 2013, 2017) will be crossed 
much more frequently, potentially driving increases in the incidence 
of vector-borne diseases (Laporta et al., 2015; Messina et al., 2019).

1813
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Table  12.5  | Summary of confidence in direction of projected change in climatic impact-drivers in Australasia, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, 
SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see 
Section 12.4 for more details of the assessment method).  The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least 
medium confidence  in Section 12.5.2.

Northern Australia (NAU)  5  7  
Central Australia (CAU)   7  
Eastern Australia (EAU)   7  
Southern Australia (SAU) 1 
3   7  
New Zealand (NZ)  2 4 6  7  
1. High confidence of decrease in the  south-west of the state of Western Australia .
2. Medium confidence of decrease in north and east and increase in south and west.
3. High confidence of increase in  the south-west of the state of Western Australia .

4. Medium confidence of increase in the north and east and decrease in south and west.
5. Low confidence of increasing intensity, and high confidence of decreasing occurrence.
6. High confidence of decrease in glacier volume, medium confidence of decrease in snow.
7. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat.

1814
00.511.522.5SCA
0.200.20.40.60.8NSA
00.511.522.5NWS
0.200.20.40.60.8SAM
00.511.522.5SWS
0.200.20.40.60.8NES
00.511.522.5SSA
Time slices0.200.20.40.60.8SES
Time slices6004002000200SCA
6004002000200NSA
6004002000200NWS
6004002000200NES
6004002000200SWS
2050 21006004002000200SES
2050 21006004002000200SSA
-0.2-0.1-0.05 -0.02 -0.01 00.01 0.02 0.05 0.1 0.2(a) Change in 1-in-100 year river discharge per unit 
CMIP5 RCP8.51-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2)Change in Q100 r.t. recent past (m3 s-1 km-2)
Shoreline position change (m)m m3 s-1 km-2(d) (c)-200 -150 -100 -50 -10 50 100 150 200 10 -
+2°C +4°C

median
10th p
Figure 12 .8 | Projected changes in selected climatic impact-driver indices for Central and South America.

1815
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Extreme heat:  Chapter  11 found high confidence  of increased 
heatwaves in all regions except SSA over the past decades. There is 
evidence of increasing heat stress over summer in much of SES and 
SWS using the wet bulb globe temperature (WBGT) index for the 
period 1973–2012, and this has been attributed to human influence 
on the climate system (Knutson and Ploshay, 2016). Climate change 
projections point to major increases in several heat indices across 
the region for all scenarios ( high confidence ). Largest increases in 
the frequency of hot days (maximum temperatures, Tx > 35°C) are 
projected for the Amazon basin under SSP5-8.5 with more than 200 
days per year at the end of the century under SSP5-8.5 relative to 1995-
2014, while such increases remain moderate (50–100 days) in SSP1-2.6. 
For the dangerous heat threshold of HI > 41°C, increases in frequency

are similar to that in Tx > 35°C (Figure 12.4 and Figures 12.SM.1 and 
12.SM.2; Coppola et al., 2021b; Schwingshackl et al., 2021).
Cold spell and frost:  A decreasing frequency of cold days and nights 
has been observed in many sub-regions (Table 11.13). There is medium 
confidence  (limited agreement ) of a decrease in frost days in SWS, SES 
and SSA. Projections consistently suggest a  general decrease in the 
frequency of cold spells and frost days in the region as indicated by 
several indices based on minimum temperature (Chou et  al., 2014; 
López-Franca et al., 2016; C. Li et al., 2021). Heating degree days are 
consistently projected to decrease by 5 degree days per year in the 
Amazon region, and up to 20–30 degree days per year in NWS, SWS 
and SES, under RCP8.5/SSP5-8.5 by mid century (Coppola et al., 2021b).
In conclusion, it is virtually certain  that warming will continue 
everywhere in Central and South America and there is  high

confidence  that by the end of the century most regions will 
undergo extreme heat stress conditions much more often than 
in recent past (e.g., increase of dangerous heat with HI > 41°C, 
or Tx > 35°C) with more than 200 additional days per year 
under SSP5-8.5, while such conditions will be met typically 
50–100 more days per year under SSP1-2.6 over the same 
regions. Cold spells and frost days will have a decreasing trend 
12.4.4.2 Wet and Dry
Mean precipitation:  The Atlas documents diverse historical 
precipitation trends in the region, including a  small but not 
significant increasing trend in SCA, a  decreasing trend in south-
eastern and north-eastern Brazil, and an increasing trend in SSA. 
Projections indicate a  drying signal for SCA ( medium confidence ) 
(Coppola et  al., 2014a; Nakaegawa et  al., 2014), NES and SWS (high confidence ) (Atlas.7.2.5) and the well-known dipole for 
South America, meaning increasing precipitation over subtropical

regions like the Río de La  Plata basin (SES) ( high confidence ) and 
decreasing precipitation in the Amazon (NSA) ( medium confidence ) 
(Chou et al., 2014; Llopart et al., 2014; Reboita et al., 2014; Sánchez 
et al., 2015; Teichmann et al., 2021). These features are consistent 
with observations (Sena et al., 2018) and are evident in regional and 
global model projections by mid- and end-of-century for both RCP4.5 
and RCP8.5 (Jones and Carvalho, 2013).
River flood:  Emerging literature in the region documents ongoing 
changes in river floods. Mernild et al. (2018) report decreases and 
increases in annual runoff west of the Andes Cordillera’s continental 
divide, with the greatest decreases in the number of low (<10th 
percentile) runoff conditions and the greatest increases in high 
(>90th  percentile) runoff conditions. In coastal north-east Peru, 
extreme precipitation events recently caused devastating river floods

and landslides (Son et al., 2020). In Brazil, floods are becoming more 
frequent and intense in wet regions but less frequent and intense 
in drier regions (Bartiko et al., 2019; Borges de Amorim and Chaffe, 
2019), with higher propagation of hydrological changes through 
anthropogenically modified agricultural basins (Chagas and Chaffe, 
2018). Record, catastrophic, unprecedented, and once-in-a-century 
flooding events have also been reported in recent decades in the 
tributaries of the Amazon River or along its mainstream (Sena 
et  al., 2012; Espinoza et  al., 2013; Marengo et  al., 2013; Filizola 
et  al.,  2014), in Argentinean rural and urban areas (Barros et  al., 
2015), in the lower reaches of the Atrato, Cauca and Magdalena 
rivers in Colombia (Hoyos et al., 2013; Ávila et al., 2019), in basins 
whose mainstreams flow through important metropolitan areas 
such as Concepción, Chile (Rojas et al., 2017), and even in one of

Earth’s driest regions, the Atacama Desert (Wilcox et al., 2016). In the 
Amazon basin, the significant increase in extreme flow is associated 
with the strengthening of the Walker circulation (Barichivich et al., 
2018).
Available projections for the region show increases in river floods in 
SES and SAM ( medium confidence ). Projections indicate that SES and 
the coasts of Ecuador and Peru will experience a tendency towards 
wetter conditions that can be a proxy for longer periods of flooding 
and enhanced river discharges (Zaninelli et al., 2019). CORDEX models 
project the strongest changes for the peak flow with a return period 
of 100 years in SES by mid-century and under RCP8.5 (Figure 12.8). At 
the continental scale, on the contrary, Alfieri et al. (2017) suggest that 
100-year river floods will decrease under RCP8.5. Regional projections

of river floods have high uncertainty, however, owing to differences in Figure 12.8 (continued): (a)  Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX-South and Central America models 
for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 for RCP8.5 (metres; negative values 
indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 
WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 
absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods

(‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C 
(brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, 
CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue.  (d) Bar plots for shoreline position change show CMIP5-based 
projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots 
indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume

that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and 
processing are available in the chapter data table (Table 12.SM.1).

1816
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12hydrological models ( low confidence ) (Reyer et al., 2017a). Fábrega 
et al. (2013) projected increases in surface runoff for Panama, while 
Zulkafli et al. (2016) identified increases in 100-year floods of 7.5 and 
12.0% in projections for the Peruvian Amazon wet season under RCPs 
4.5 and 8.5 respectively. Wetter conditions and ±20% variations in 
annual mean streamflow are also projected for the Río de La Plata 
under the warming levels of 1.5°C, 2°C and 3°C above pre-industrial 
conditions (Montroull et al., 2018). In central Chile, 50-year peak flows 
are expected to be greater by mid-century than 100-year peak flows 
observed over the reference period (Bozkurt et al., 2018).
Heavy precipitation and pluvial flood:  Table  11.14 indicated 
that there is low confidence  due to limited evidence  of extreme 
precipitation trends in almost all Central and South America, except

in SES where increases in the magnitude and frequency of heavy 
precipitation have been observed ( high confidence ). In general, 
data scarcity persists for a  representative continental assessment. 
Chapter 11 projections indicate low confidence  of increase, compared 
to the modern period, in the intensity and frequency of heavy 
precipitation in SCA and SWS for all GWLs, and  medium confidence  
of increase in NSA, NES, SSA, SAM and SES for GWL of 4°C. In NWS, 
a wide range of changes is projected ( low confidence ).
Landslide:  Several regions in Central America, as well as Colombia 
and south-eastern Brazil, are considered areas of high incidence of 
observed fatal landslides. In these areas, El Niño–Southern Oscillation 
(ENSO)-driven fluctuations in rainfall amounts (Sepúlveda and Petley, 
2015) and climate change (Nehren et  al., 2019) seem to be key 
factors. Rockfalls, ice- and rock-ice avalanches, lahars and landslides

have been reported frequently in the southern, extratropical Andes 
in the last decades (Gariano and Guzzetti, 2016). A large number of 
ice- and moraine-dammed lakes have consequently failed, causing 
floods that rank amongst the largest events ever recorded (Iribarren 
Anacona et al., 2015). However, published literature is largely missing 
for a reliable assessment of past and future trends of such hazards.
Aridity:  Several regional studies suggest increasing trends in the 
frequency and length of droughts in the region, such as: over NWS 
(Domínguez-Castro et al., 2018), NSA (Marengo and Espinoza, 2016; 
Cunha et al., 2019) and NES (Marengo and Bernasconi, 2015), over 
southern Amazonia (Fu et al., 2013; Boisier et al., 2015), in the São 
Francisco River basin and the capital city Distrito Federal in Brazil 
(Borges et  al., 2018; Bezerra et  al., 2019), in the southern Andes 
(Vera and Díaz, 2015), in central southern Chile (Boisier et al., 2018),

in SES (Rivera and Penalba, 2014) and, during recent years, in SSA 
(Rivera and Penalba, 2014). Chapter 8 indicated medium confidence  
of anthropogenic forcing on observed drying trends in central Chile. 
Additional discussion on droughts and aridity trends in South America 
is presented in Sections 8.3.1.6, 8.4.1.6 and 8.6.2.1.
Sections 8.3.2.4 and 8.4.1.6 point to two important drying hotspots in 
South America with long-term soil moisture decline and precipitation 
declines: the Amazon basin (SAM and NSA) and SWS ( medium 
confidence ) (Figure  12.4). End-of-century RCP8.5 projections 
show a  longer dry season in the central part of South America 
and decreased precipitation over the Amazon and central Brazil 
(Teichmann et al., 2013; Coppola et al., 2014a; Giorgi et al., 2014; Llopart et  al., 2014; Atlas.7). Seasonal changes are also projected 
by end century under RCP 8.5, with decreases in June–July–August 
(JJA) rainfall projected for NSA, the coastal region of SES, SAM and

the southern portion of SWS (Marengo et  al., 2016). Decreases in 
December –January–February (DJF) rainfall are projected for the 
central part of South America in the near term (Kitoh et al., 2011; 
Chou et al., 2014; Cabré et al., 2016). Regional projections for Central 
and South America also indicate an increase in dryness in SCA and 
NES by mid- to end-century ( medium confidence ) (Chou et al., 2014; 
Marengo and Bernasconi, 2015).
Hydrological drought:  Chapter 11 assessed mostly low confidence  
in observed changes in hydrological droughts given a lack of studies 
and clear evidence, with medium confidence  only for a streamflow 
decrease in sub-regions of SWS (Table 11.15). Some trends are 
becoming more clear, such as the ones reported for Colombia (NWS) 
by Carmona and Poveda (2014), who indicated that 62% of the 25- 
to 50-year-long monthly average streamflow time series exhibited 
significant decreasing trends. However, studies of discharge changes

indicate that uncertainty is still large, as argued by Pabón-Caicedo 
et al. (2020) for the full extent of the Andes.
A number of studies project decreases in runoff and river discharge 
for SCA, Colombia, Brazil and the southern part of South America 
by the end of this century (Nakaegawa and Vergara, 2010; Arnell 
and Gosling, 2013; van Vliet et al., 2013). Section 11.9 assessed high 
confidence  in projections of increases in hydrological droughts in 
NSA, SAM, SWS, and SSA under for 4°C GWL, medium confidence  
in SCA, and low confidence  in the rest of the sub-regions given 
insufficient evidence, lack of signal or mixed signals among the 
available studies. Signals are much more uncertain for the middle of 
the century (or for a 2°C GWL).
Agricultural and ecological drought:  Section  11.9 assessed low 
confidence  in observed changes in agricultural and ecological drought 
across Central and South America due to regional heterogeneity

and differences depending on the drought metrics used, except 
in NES, which has seen a  dominant increase in drought severity 
NSA and SAM are the two regions where the strongest signal of 
increasing number of dry days (NDD) and drought frequency (DF) 
is projected compared to other regions of the world (Coppola et al., 
2021b). By the end of this century and under RCP8.5, the NSA area 
average value for NDD reaches 43, 32 and 27, within the CORDEX-
CORE, CMIP5 and CMIP6 ensembles respectively. For the frequency 
of droughts, the NSA area average value is of about 4.6, 3.4 and 
3.8. For the same period and scenario, the SAM region shows NDD 
and DF values of 29, 20 and 21, and of 4, 3 and 3.5 respectively 
(Figure  12.4j–l). In Central America, a  significantly drier northern 
region and a wetter southern region are projected for mid-century 
by (Hidalgo et al., 2017), whilst Fuentes-Franco et al. (2015) pointed

to more pronounced dry periods during the rainy season in SCA by 
the end of this century under RCP8.5. Increases in the frequency 
of meteorological droughts that may initiate other drought types 
are projected for the eastern part of the Amazon and the opposite 
for the west under RCP8.5 (Duffy et al., 2015). In central Chile, the

1817
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212occurrence of extended droughts, such as the recently experienced 
2010–2015 megadrought (which is still driving impacts), is projected 
to increase from one to up to five events per 100 years under RCP8.5 
(Bozkurt et al., 2018). Section 11.9 highlights change in confidence 
in increases in drought severity in SCA, NSA, NES, SAM, SWS, and 
SSA from low to high under the three GWLs of 1.5°C, 2°C and 4°C. 
NES and SES change from low confidence  to medium  confidence  
increases in agricultural and ecological drought severity by 4°C GWL 
with different metrics and high agreement  between studies. Only 
SAM and SSA have projections of agricultural and ecological drought 
increasing with high confidence  for the middle of the century, or for 
a 2°C GWL, and NSA, NES and SCA are projected to increase with 
medium confidence .
Fire weather:  There is evidence of increases in forest fire activity

(number of fires, burned area and fire duration) in central and south-
central Chile, where more conducive fire weather conditions have 
been proposed as the main driver (González et  al., 2018; Urrutia-
Jalabert et al., 2018). Projections indicate that the Amazon will be one 
of the regions in the world with the highest increase in fire weather 
indices over the 21st century and under all RCPs ( high confidence ) 
(Betts et  al., 2015; Abatzoglou et  al., 2019; Q. Sun et  al., 2019). 
This is consistent with the large increase in the frequency of joint 
occurrence of extreme hot and dry days projected for a 2°C warming 
level or more (Vogel et al., 2020). Projections of fire weather indices 
also show an increased risk in SWS ( high confidence ), SSA and SCA 
(medium confidence ). However, wildfires highly depend on land use 
and appropriate management may help mitigate future increases in 
fire risk (Fonseca et al., 2019).

Mean precipitation is projected to change in a dipole pattern 
with increases in NWS and SES and decreases in NES and 
SWS ( high confidence ) with further decreases in NSA 
and SCA ( medium confidence ). There is medium confidence  
of an increase in river floods in SAM and SES. There is high 
confidence  of an increase in drought duration in NES, an in 
the number of dry days and drought frequency in NSA and 
SAM. Dry climatic impact-drivers are projected to increase 
at the regional level with higher global warming levels. 
The strongest signal of future increase in agricultural and 
ecological drought, aridity and fire weather is over the 
Amazon region ( high confidence ). 
12.4.4.3 Wind
Mean wind speed:  Due to the lack of long-term homogeneous 
records or limited observations in the region, past wind speed trends 
are difficult to establish. Global climate models project an increase 
in wind speeds, under all future scenarios, augmenting wind power

potential in most parts of Central and South America, especially in 
NES, where changes lie in the range 0–20% by 2050 under RCP8.5 
and 0–40% under RCP8.5 ( medium confidence ) (Karnauskas 
et  al., 2018a; Reboita et  al., 2018; Jung and Schindler, 2019). In 
Patagonia, wind speeds are projected to decrease. For RCP4.5 
changes remain marginal and have low confidence (low agreement ) 
(Figure  12.4m–o).Severe wind storm:  Similar observational limitations inhibit an 
assessment of long-term extreme wind trends. However, Pes et  al. 
(2017) found extreme wind increases in most of Brazil over the past 
decades. Future projections indicate a slight decrease in the number 
of extratropical cyclones in mid-latitudes ( limited evidence , low 
confidence ) (Reboita et al., 2018), and an increase of extreme winds in 
tropical areas ( limited evidence , low confidence ) (Kumar et al., 2015). 
Climate models project a shift and an intensification of southern storm

tracks, with most effects offshore over the Southern Ocean (Chapter 4), 
with low confidence (low agreement ) of significant extreme wind 
changes over land and coastal areas across the 21st century (Chang, 
2017; Augusto Sanabria and Carril, 2018; Reboita et al., 2021).
Tropical cyclone:  CMIP5 and CMIP6 simulations, including the 
new High Resolution Model Intercomparison Project (HighResMIP), 
project a decrease in the frequency of tropical cyclones in the Atlantic 
and Pacific coasts of Central America for the mid-century or under 
a  2°C GWL, accompanied with an increased frequency of intense 
cyclones ( medium confidence ) (Section 11.7.1.5; Diro et  al., 2014; 
Knutson et al., 2020; Roberts et al., 2020).
In summary, there is limited evidence  of current trends in 
observed wind speed and wind storms in Central and South 
America. Climate projections indicate a decrease in frequency 
of tropical cyclones in Central America accompanied with an

increased frequency of intense cyclones, and an increase in 
mean wind speed and wind power potential in most parts of 
Central and South America ( medium confidence ).
12.4.4.4 Snow and Ice
Snow:  Historical studies of seasonal snow cover are limited and 
restricted to the Andes Cordillera. Mernild et al. (2017) indicated that 
much of the area north of 23°S experienced a decrease in the number 
of snow cover days, while the southern half of the Andes Cordillera 
experienced the opposite. A reduction in snow cover of about 15% 
was simulated for areas with altitudes in the range of 3000–5000 
m, whereas in regions with altitude below 1000 m (Patagonia) snow 
cover extent increased. The reduced snowfall over the Chilean and 
Argentinean Andes mountains, which has resulted in unprecedented 
reductions in river flow, reservoir volumes and groundwater levels, led 
to the most severe and long-lasting hydrological drought (2010–2015)

reported in the adjacent semi-arid foothills of the central Andes 
(Garreaud et  al., 2017; Rivera et  al., 2017; Masiokas et  al., 2020). 
Projections (based on process understanding) in Section 9.5.3.3 point 
to decreases in seasonal snow cover extent and duration across South 
America as global climate continues to warm ( high confidence ).
Glacier:  Observation and future projection of Central and South 
America glacier mass changes are assessed in Section 9.5.1, grouped 
in two main regions: Low Latitude region (98% of which is glaciers in 
the Andes) and the Southern Andes region. An increase in the number 
and areal extent of glacial lakes in the Southern Andes was reported 
for the period 1986–2016 (Wilson et al., 2018). Similar changes are 
being observed in the central Andes (Colonia et al., 2017). Since 1800 
at least 15 ice-dammed lakes and 16 moraine-dammed lakes have 
failed in the extratropical Andes, causing high-magnitude glacial lake

1818
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12outburst floods (Rojas et al., 2014; Cook et al., 2016; Wilson et al., 
2018; Drenkhan et al., 2019). Partially due to glacier shrinkage and 
lake growth, the frequency of outburst floods has increased in the 
last 30–40 years (Carey et al., 2012; Iribarren Anacona et al., 2015).
Glaciers across South America are expected to continue to lose 
mass and glacier area in the coming century ( high confidence ) 
(Section  9.5). In terms of their mass, glaciers in the Low Latitude 
region are projected by GlacierMIP to lose 67 ± 42%, 86 ± 24% and 
94 ± 13%, of their 2015 baseline mass by the end of the century 
under RCP2.6, RCP4.5 and RCP8.5 respectively (Marzeion et  al., 
2020). Glaciers in the Southern Andes show decreasing mass loss 
rates for RCP2.6, and increasing rates for RCP8.5, which peak in the 
mid to late 21st century. Glaciers in the Southern Andes are projected

to lose 26 ± 27%, 33 ± 26% and 47 ± 26% of their 2015 mass by the 
end of the century under RCP2.6, RCP4.5 and RCP8.5 respectively 
(Section 9.5.1.3).
The central Andes will experience the highest disturbance to the thermal 
regime of the 21st  century. As a  consequence, in the Argentinean 
Andes up to 95% of rock glaciers in the southern desert Andes and 
in the central Andes will rest in areas above 0°C under the worst case 
scenario of warming (the freezing level might move up more than twice 
as much as during the entire Holocene; Drewes et al., 2018)
Permafrost:  There is limited information on the ongoing changes 
and projections of permafrost conditions in the region. Based on 
model projections under the IPCC A1B scenario, permafrost areas in 
the Bolivian Andes will eventually be lost, but this could take years 
to decades or longer depending on permafrost thickness (Rangecroft 
et al., 2016). 
In conclusion, glacier volume loss and permafrost thawing will

continue in the Andes Cordillera under all climate scenarios 
(high confidence ), causing important reductions in river flow 
and potentially high-magnitude glacial lake outburst floods.
12.4.4.5 Coastal and Oceanic
Relative sea level:  Around Central and South America, over 
1900–2018, a new tide gauge-based reconstruction finds a regional 
mean relative sea level (RSL) change of 2.07 [1.36 to 2.77] mm yr –1 in 
the South Atlantic, 2.49 [1.89 to 3.06] mm yr –1 in the subtropical North 
Atlantic and 1.20 [0.76 to 1.62] mm yr –1 in the East Pacific (Frederikse 
et al., 2020), compared to a global mean sea level (GMSL) change of 
around 1.7  mm yr –1 (Section  2.3.3.3 and Table  9.5). For the period 
1993–2018, these RSLR rates, based on satellite altimetry, increased 
to 3.45 [3.04 to 3.86)] mm yr –1, 4.04 [2.77 to 5.24] mm yr –1 and 2.35 
[0.70 to 4.06] mm yr –1 respectively (Frederikse et al., 2020), compared 
to a GMSL change of 3.25 mm yr –1 (Section 2.3.3.3 and Table 9.5).

Relative sea level rise is extremely likely to continue in the oceans 
around Central and South America. Regional mean RSLR projections 
for the oceans around Central and South America range from 0.3–0.5 
m under SSP1-2.6 to 0.5–0.9 m under SSP5-8.5 for 2081–2100 relative 
to 1995–2014 (median values), which is around the projected GMSL 
change (Section  9.6.3.3). These RSLR projections may, however, be underestimated due to potential partial representation of land 
subsidence in their assessment (Section 9.6.3.2).
Coastal flood:  The present-day 1-in-100-year ETWL ranges from 
0.5 to 2.5 m around most of Central and South America, except in 
SSA and SWS, where 1-in-100-year ETWLs can be as large as 5 to 
6 m (Vousdoukas et  al., 2018). ETWL magnitude and occurrence 
frequency are expected to increase throughout the region ( high 
confidence ) (Figure 12.4p–r and Figure 12.SM.6). Across the region, 
the 5–95th percentile range of the 1-in-100-year ETWL is projected

to increase (relative to 1980–2014) by 8–34 cm and by 10–43 cm 
by 2050 under RCP4.5 and RCP8.5 respectively (Vousdoukas et al., 
2018; Kirezci et  al., 2020). By 2100, this range is projected to be 
21–93 cm and 34–190 cm under RCP4.5 and RCP8.5 respectively 
(Vousdoukas et  al., 2018; Kirezci et  al., 2020). Furthermore, under 
RCP4.5, the present-day 1-in-100-year ETWL is projected to have 
median return periods of 1-in-10-years to 1-in-50-years by 2050 and 
1-in-1-year by 2100 in SES, SSA and SWS. In other regions of Central 
and South America, the present-day 1-in-100-year ETWL is projected 
to occur once per year or more by both 2050 and 2100 under RCP4.5. 
The present-day 1-in-50-year ETWL is projected to occur around three 
times a year by 2100 with a SLR of 1 m (Vitousek et al., 2017).
Coastal erosion:  According to satellite data, shoreline retreat rates 
of around 1 m yr –1 have been observed along the sandy coasts

of SCA, SES and SSA over the period 1984–2015, while shoreline 
progradation rates of around 0.25 m yr –1 has been observed in 
NWS and NSA. The sandy shorelines in NES and SWS have remained 
more or less stable over the same period (Luijendijk et  al., 2018; 
Mentaschi et  al., 2018). Using satellite observations, Mentaschi 
et al. (2018) report a coastal area loss of 250 km2 over a 30-year 
period (1984–2015) along the Pacific coast of South America, and of 
780 km2 along the Atlantic coastlines.
will experience shoreline retreat throughout the 21st  century ( high 
confidence ). Median shoreline change projections (CMIP5) for the 
mid-century period show that, relative to 2010, sandy shorelines will 
retreat by between 30 and 75 m in SCA, NES, SES and SSA under both 
RCP4.5 and RCP8.5, while the projected mid-century retreats are less 
than 30 m in NSA, NWS and SWS for both RCPs (Vousdoukas et al.,

2020b). Parts of the coastline in these latter three regions are projected 
to prograde over the 21st century, if present ambient shoreline change 
trends continue (Vousdoukas et al., 2020b). By 2100, median retreats 
of more than 100 m are projected in SCA, NES, SES and SSA under 
both RCPs, while retreats between 50–100 m are projected for NSA, 
NWS and SWS under both RCPs (Figure 12.8). Notably, the projected 
shoreline retreats in SCA and SES approach 150 m by 2100 under 
RCP4.5 and 200 m under RCP8.5. The total length of sandy coasts in 
Central and South America that is projected to retreat by more than 
a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 12,000 
and 15,000 km respectively, an increase of approximately 30%.
Marine heatwave:  The mean sea surface temperature (SST) of the 
Atlantic Ocean and the Caribbean around Central and South America 
increased from 0.25°C to 1°C over the period 1982–1998 (Oliver

et al., 2018). This mean ocean surface warming is connected to longer

1819
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Table  12. 6 | Summary of confidence in direction of projected change in climatic impact-drivers in Central and South America, representing their aggregate characteristic changes for mid-century for 
scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2 
and 2.4°C (see Section 12.4 for more details of the assessment method).  The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found 
with at least medium confidence  in Section 12.5.2.

Southern Central America (SCA)  2  3  
North-Western South America (NWS)   3,4  
Northern South America (NSA)  2 3,4 
South American Monsoon (SAM)  1 
North-Eastern South America (NES)   3,4  
South-Western South America (SWS)   3  
South-Eastern South America (SES)   3  
Southern South America (SSA)    3  
1. Increase in extreme flow in the Amazon basin.
2. Tropical cyclones decrease in number but increase in intensity.

3. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat.
4. Substantial parts of the NWS, NSA and NES coasts are projected to prograde if present-day ambient shoreline change rates continue.

1820
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12and more frequent marine heatwaves (MHW) in the region (Oliver 
et al., 2018). Over the period 1982–2016, the coastlines experienced 
on average more than 1.0 MHW per year, with the Pacific coast of 
Northern Central America and the coast of SES (Atlantic) experiencing 
10 and 15 days, with the notable exception of the equatorial Pacific 
coastline, which experiences MHWs with >30 days average duration 
related to ENSO conditions. In the south-western Atlantic shelf 
(32–38°S), more than half of the days with MHWs have occurred since 
2014, and the most intense event (1.7°C above previous maximum) 
took place in the austral summer of 2017 (Manta et al., 2018). Changes 
over the 20th century, derived from MHW proxies, show an increase in 
frequency between 0.5 and 2 MHW per decade in the South Atlantic,

the Caribbean and the Pacific coast of Northern Central America, an 
increase in intensity per event in the South Atlantic, and a decrease 
along the equatorial Pacific coastline. The total number of MHW 
days per year increases around Central and South America, with the 
exception of the equatorial Pacific coastline (Oliver et al., 2018).
There is high confidence  that MHWs will increase around Central 
and South America. Mean SST is projected to increase by 1°C (2°C) 
by 2100, with a  hotspot of about 2°C (4°C) along the coast of 
South-Eastern South America and North-Western South America 
under RCP4.5 (RCP8.5; Interactive Atlas). More frequent MHWs 
are projected around the region for GWLs of 1.5°C, 2°C and 3.5°C 
relative to the modelled reference value for 1861–1880 (Frölicher 
et  al., 2018). Projections for SSP1-2.6 and SSP5-8.5 both show an 
increase in MHWs around Central and South America by 2081–2100, 

In summary, relative sea level rise is extremely likely to continue 
in the oceans around Central and South America, contributing to 
increased coastal flooding in low-lying areas ( high confidence ) 
and shoreline retreat along most sandy coasts ( high confidence ). 
Marine heatwaves are also expected to increase around the 
region over the 21st century ( high confidence ).
The assessed direction of change in climatic impact-drivers for Central 
and South America and associated confidence levels are illustrated in 
Table 12.6. No assessable literature could be found for sand and dust 
storm, lake and sea ice, heavy snowfall and ice storms, hail and snow 
avalanches, although these phenomena may be relevant in parts of 
the region.
12.4.5 Europe
The regional European climate and main hazards have been previously 
assessed in SREX, AR5 WGII, SR1.5, SROCC and SRCCL and a summary 
of key findings can be found in the Europe section of Atlas.8.1. For the

purpose of this assessment Europe is divided into four climatic regions: 
Northern Europe (NEU), Western and Central Europe (WCE), Eastern 
Europe (EEU) and Mediterranean (MED) (Figure Atlas.24).
Since AR5 and SR1.5, a  large body of literature that uses the 
EURO-CORDEX and MED-CORDEX ensembles of high-resolution 
simulations (Jacob et  al., 2014; Ruti et  al., 2016; Kjellström et al., 2018; Vautard et al., 2020; Coppola et al., 2021a) to assess 
signals of climate change in Europe has emerged. These scenario-
based simulations have been the basis of a number of impact studies 
(e.g.,  Jacob et  al., 2014, 2018; Somot et  al., 2018; Faggian and 
Decimi, 2019) highlighting the use of the climatic impact-drivers. The 
development of the science of attribution of weather events (Stott 
et al., 2016) has provided evidence of links between climate change 
and hazard changes such as the 2017 Mediterranean heatwave (Kew 
et al., 2019) and many others (Chapter 11).

The ability of global and regional models to reproduce the observed 
changes in mean and extreme temperature and precipitation 
in Europe is assessed in the literature (Atlas.8.3). In summary, 
both GCMs and RCMs have their limitations but, in general, the 
increased resolution of RCMs is shown to clearly add value in terms 
of resolving spatial patterns and seasonal cycles of precipitation 
and precipitation extremes in many European regions, especially in 
regions of complex topography such as the Alps and for quantities 
such as snowmelt-driven runoff, regional winds and Mediterranean 
hurricanes (Medicanes).
Examples of projected climatic impact-driver thresholds are illustrated 
in Figures 12.4 and 12.9 based on the most recently updated EURO-
CORDEX RCM projections, CMIP5 and CMIP6 GCMs for comparison. 
For a more comprehensive representation of other climatic impact-
driver index trends assessed in this section the reader is referred to 
the interactive Atlas.

12.4.5.1 Heat and Cold
Mean air temperature:  Since AR5, studies have confirmed that the 
mean warming trend in Europe is increasing (Atlas.8.2). The observed 
warming trend patterns are largely consistent with those simulated by 
global and regional climate models and it is very likely  that such trends 
are, in large part, due to human influence on climate (Section 3.3.1).
All temperature trends are very likely  to continue for a  global 
warming of 1.5°C or 2°C and 3°C (Atlas.8.4). Future warming leads 
to the exceedance of different temperature thresholds relevant for 
vector-borne diseases ( medium confidence ) (Caminade et al., 2012; 
Medlock et  al., 2013), invasive allergens ( medium confidence ) 
(Storkey et al., 2014; Hamaoui-Laguel et al., 2015), SST thresholds in 
the Mediterranean ( likely  to exceed 20°C), or relevant for the Vibrio  
bacteria development (Vezzulli et al., 2015). Future warming is also 
projected to lead to the exceedance of cooling degree day index

(>22°C) thresholds, characterizing a  potential increase in energy 
demand for cooling in southern Europe with increases likely  exceeding 
40% in some areas (Spinoni et al., 2015) by 2050 under RCP8.5 ( high 
confidence ) (Section 12.3 and Atlas.8; Coppola et al., 2021a).
Extreme heat:  The frequency of heatwaves observed in Europe 
has very likely  increased in recent decades due to human-induced 
change in atmospheric composition (Section 11.3) and a detectable 
anthropogenic increase in a  summer heat stress index over all 
regions of Europe has been identified based on WBGT index trends 
for 1973–2012 ( medium confidence , limited evidence ) (Knutson and 
Ploshay, 2016).

1821
It is very likely  that the frequency of heatwaves will increase during 
the 21st  century regardless of the emissions scenario in each 
European region, and for 1.5°C and 2°C GWLs (Section  11.3.5). 
Heat stress due to both high temperature and humidity, affecting 
morbidity, mortality and labour capacity (Section 12.3) is projected to 
increase under all emissions scenarios and GWLs by the middle of the 
century (Figure 12.4a–f). Under RCP8.5, the expected number of days 
with WBGT higher than 31°C is about 25, 30 and 40 days per year, 
as projected by EURO-CORDEX, CMIP5 and CMIP6 respectively on 
average over the Mediterranean region, and around 30, 40 and 60 days 
per year in low coastal plain areas such as the Po Valley, the Italian, 
Greek and Spanish coasts, and the Mediterranean islands (Coppola et al., 2021a). An average increase of a few days per year of maximum

daily temperature exceeding 35°C, a typical critical threshold for crop 
productivity, is expected by the mid-century in central Europe, and 
an increase of 10–20 days is expected for the Mediterranean areas 
(Figure 12.4b; Coppola et al., 2021a). By contrast, under SSP1-2.6, the 
increase in this number of days remains limited to less than about 
10 days, and confined to the Mediterranean regions. Mitigation is 
expected to have a strong effect, with the dangerous heat threshold 
of HI > 41°C projected to be crossed 5–10 days more per year in the 
Mediterranean regions and a few days per year more in WCE and EEU 
under SSP5-8.5, while such increases would be virtually absent under 
SSP1-2.6 (Figure 12.4d–f).
00.20.40.60.8
0.100.1NEU
00.20.40.60.8WCE
Time slices0.100.1MED
Time slices-0.1-0.06 -0.02 -0.01 -0.005 00.005 0.01 0.02 0.06 0.1
020406080100NEU EEU
020406080100WCE
GWL1.5 24r.past mid. long.
Time slicesMED

Time slices(a) Change in 1-in-100 year river discharge per unit 
catchment area by mid-century, CORDEX RCP8.5(b) Change in # days with SWE>100mm 
by mid-century, CORDEX RCP8.5High model agreement
Low model agreementColour
days yr-1m3 s-1 km-2-40     -20   - 10     -5       -1       1       5       10      20      40Recent past Q100 (m3 s-1 km-2)
# days with SWE>100mm (days yr-1)
Change in Q100 r.t. recent past ( m3 s-1 km-2)(d) (c)
Legend
+2°C +4°C
median
10th p
Figure 12.9  | Projected changes in selected climatic impact-driver indices for Europe. (a)  Mean change in 1-in-100-year river discharge per unit catchment 
area (Q100, m3 s–1 km–2), and (b) median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from EURO-CORDEX

models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction) of change.  (c) Bar plots for 
Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand 
y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative 
to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 
1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model

ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) As for (c) but 
showing absolute values for number of days with SWE > 100mm, masked to grid cells with at least 14 such days in the recent past. See Technical Annex VI for details of indices. 
Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).

1822
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Cold spell and frost:  Temperature observations for winter cold spells 
in Europe show a  long-term decreasing frequency (Brunner et  al., 
2018), with their probability of occurrence projected to decrease 
in the future ( high confidence ) and virtually disappear by the end 
of the century (Section 11.3). The frequency of frost days will very 
likely  decrease for all scenarios and all time horizons (Lindner et al., 
2014; Coppola et al., 2021a) with consequences for agriculture and 
forests. A  simple heating degree day index, characterizing heating 
demand, shows a large observed decreasing trend for winter heating 
energy demand in Europe (Spinoni et  al., 2015). This trend is very 
likely  to continue through the 21st  century, with decreases in the 
range of 20–30% for Northern Europe, about 20% for central Europe 
and 35% for southern Europe, by mid-century under RCP8.5 (Spinoni

et al., 2018b; Coppola et al., 2021a; Interactive Atlas).
In summary, irrespective of the scenario, it is virtually certain  
that warming will continue in Europe, and there is high 
confidence  that the observed increase in heat extremes is 
due to human activities. It is very likely  that the frequency 
of heat extremes will increase over the 21st century with an 
increasing gradient toward the southern regions. Extreme 
heat will exceed critical thresholds for health, agriculture and 
other sectors more frequently ( high confidence ), with strong 
differences between mitigation scenarios. It is very likely  that 
the frequency of cold spells and frost days will keep decreasing 
over the course of this century and it is likely  that cold spells 
will virtually disappear towards the end of the century.
12.4.5.2 Wet and Dry
Mean precipitation:  Precipitation has generally increased in northern 
Europe and decreased in southern Europe, especially in  winter

(Fischer and Knutti, 2016; Knutson and Zeng, 2018) but in the latter, 
precipitation trends are strongly dependent on the examined period 
(Atlas.8). These trends in precipitation increases in the north and 
decreases in the south are also represented by global and regional 
climate simulations (Jacob et  al., 2014; Rajczak and Schär, 2017; 
Lionello and Scarascia, 2018; Coppola et  al., 2021a; Atlas.8.2) and 
have been attributed to climate change (Sections 3.3.2, 8.3.1).
Studies since AR5, together with EURO-CORDEX and MED-CORDEX 
experiments and the latest CMIP6 ensemble, have increased 
confidence in regional projections of mean and extreme precipitation 
(Prein et al., 2016) despite their wet bias, and show that it is very 
likely  that precipitation will increase in Northern Europe in DJF and 
decrease in the Mediterranean in JJA under all climate scenarios 
except RCP2.6/SSP1-2.6 and for both mid- and end-century periods 
(Coppola et al., 2021a; Atlas.8.5).

River flood:  There is high confidence  of an observed increasing 
trend of river floods in Western and Central Europe (WCE) and 
medium confidence  of a decrease in Northern (NEU) and southern 
Europe (MED).
The SR1.5 shows evidence of an increase in reported floods in the UK 
over the period 1884–2013, and increasing trends in annual maximum 
daily streamflow data over 1966–2005 in parts of Europe. Although high flow does not show uniform trends for the entire region (Hall et al., 
2014; Mediero et al., 2015) or specific regions (Mudersbach et al., 2017; 
Vicente-Serrano et al., 2017; Tramblay et al., 2019), regional patterns 
of significant flood trends do exist. Based on the most extended river 
flow database spanning the period 1960–2010, an increase in floods 
frequency in north-western Europe, decreasing in medium and large 
catchments in southern Europe and decreasing floods in Eastern Europe 
has been detected (Blöschl et al., 2019) in line with Mediero et al. (2014),

Arheimer and Lindström (2015), Gudmundsson et al. (2017), Krysanova 
et al. (2017), Kundzewicz et al. (2018) and Mangini et al. (2018).
There is high confidence  of river floods increasing in Western and 
Central Europe (WCE) and medium confidence  of a  decrease in 
Northern (NEU), Eastern (EEU) and southern Europe (MED) for mid- 
and end-century under RCP8.5 and low confidence  under RCP2.6. The 
projected increase in WCE is roughly 10% (18% by end of century) 
and the projected decrease in NEU is 5% (11% by end of century) for 
the peak flow with a return period of 100 years for mid-century, under 
RCP8.5 (Di Sante et al., 2021; Figure 12.9a for mid-century (Q100) 
projections of flood discharges per unit catchment area (Blöschl 
et al., 2019) based on EURO-CORDEX models).
Using frequency analysis of extreme peak flow events above 
a  100-year return period as a  threshold, which is the average 
protection level of the European river network (Rojas et al., 2013),

Alfieri et al. (2017) and Alfieri et al. (2015) show that Europe is one of 
the regions where the largest increases in flood risk may occur, with 
only few countries in Eastern Europe showing a decrease (Poland, 
Lithuania, Belarus) (Osuch et  al., 2017). They find a  significant 
increase of events with peak discharge above 100-year return period 
(Q100) in most of Europe in line with Rojas et al. (2012), Hirabayashi 
et al. (2013), Dankers et al. (2014), Forzieri et al. (2016), Roudier et al. 
(2016), Thober et  al. (2018), and an increase in the magnitude of 
floods in southern Europe, although Giuntoli et  al. (2015) projects 
no change. A modest but significant decrease in the 100-year return 
period river flood is projected for southern (due to reduction of 
precipitation) and north-eastern European regions, the latter because 
of the strong reduction in snowmelt induced river floods (Thober 
et al., 2018; Di Sante et al., 2021).

Heavy precipitation and pluvial flood:  Heavy precipitation 
frequency trends have been detected in Europe with high confidence  
for the NEU and Alpine regions and with medium confidence  in 
WCE, and also attributed to climate change with high confidence  in 
NEU (Section 11.9). Guerreiro et al. (2017), based on observations, 
showed that 20% of city areas in WCE and MED regions are affected 
by pluvial flooding and less than 10% of city areas in the northern 
and western coastal cities.
Projections based on multiple lines of evidence from global to 
convective permitting model scales show high confidence  in 
extreme precipitation increase in the northern, central and eastern 
European regions (NEU, WCE, EEU) and in the Alpine area. Increases 
with medium confidence  are projected for the Mediterranean basin 
(with a negative gradient towards the south) for mid- and end-century 
under RCP4.5, RCP8.5 and SSP5-8.5 and for 2°C GWL and higher 
(Section 11.9; MedECC, 2020).

1823
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Landslide:  Rainfall periods connected to landslides are projected to 
increase in central Europe by up to one more period per year in flat 
areas in low altitudes and by up to 14 more periods per year at higher 
altitudes by mid-century, becoming even more evident by the end of 
the century (Schlögl and Matulla, 2018). An increase of landslides 
by up to 45.7% and 21.2% is projected for southern Italy (Calabria 
region) by mid-century under both RCP4.5 and RCP8.5 (Gariano and 
Guzzetti, 2016) and by up to 40% in central Italy (Umbria) during 
the winter season (Ciabatta et al., 2016). A decrease of landslides is 
projected in the Peloritani mountains in southern Italy (RCP4.5 and 
8.5) by mid-century (Peres and Cancelliere, 2018). A slight increase 
for a 10-year return period landslide is projected in the eastern 
Carpathians, the Moldavian Subcarpathians and the northern part of

the Moldavian Tableland and a higher increase in the 100-year return 
period event is projected in the western hilly and plateau areas of 
Romania (Jurchescu et al., 2017).
Aridity:  The Mediterranean region shows evidence of large-scale 
decreasing precipitation trends over 1901–2010, which are at least 
partly attributable to anthropogenic forcing according to CMIP5 
models (Knutson and Zeng, 2018). Nevertheless, there is low 
agreement  among studies on observed precipitation trend in the 
Mediterranean region (Section 11.9.4 and Atlas.8.2).
Precipitation is projected to decrease by mid- and end-century for the 
RCP8.5 and SSP5-8.5 with strong agreement  among CMIP5, CMIP6 and 
CORDEX regional climate ensemble models on the direction of change. 
With both temperature increase and precipitation decrease there is high 
confidence  on increased aridity in the MED region (Sections 8.4.1.6 
and 11.9.4 and Atlas.8.2; Coppola et al., 2021a). In NEU there is high

confidence  of decrease in aridity linked to mean precipitation increase 
(Section 8.4.1.6, Atlas 8.2) and meteorological drought decrease based 
on indicators like Standardized Precipitation Index and consecutive dry 
days (Section 11.9.4, Figure 12.4, Coppola et al,, 2021a).
Hydrological drought: There is high confidence  that hydrological 
droughts have increased in the Mediterranean basin with medium 
confidence  in anthropogenic attribution of the signal, and high 
confidence  that they will continue to increase through the 21st century 
for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6. 
(Sections 8.3.1.6, 8,4.1.6, and 11.9.4). There is medium confidence  
in hydrological drought increase in WCE and low confidence  in 
direction of change for EEU and NEU from mid-century onwards and 
for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6 
(Section 11.9 and Figure 12.4g–i).
Streamflow droughts are projected to become more severe and

persistent in the Mediterranean and western Europe (current 
100-year events could occur approximately every 2–5 years by 2080; 
Forzieri et al., 2016). 
The opposite tendency is projected in Northern, Eastern and central 
Europe where higher precipitation that outweighs the effects of 
increased evapotranspiration is expected to result in a decrease in 
streamflow drought frequency (Forzieri et al., 2014). For a 2°C GWL 
droughts will become more intense in the MED and in France and 
longer mainly due to less rainfall and higher evapotranspiration. A reduction of drought length and magnitude is projected for NEU 
and EEU (Roudier et  al., 2016). In the southern Alps, both winter 
and summer low flows are projected to be more severe, with a 25% 
decrease in the 2050s (Vidal et al., 2016).
that agricultural and ecological  droughts have increased in 
Western and Central Europe and in the Mediterranean region, and

medium confidence  that anthropogenic drivers contributed to the 
Mediterranean increase (Sections 8.3.1.6 and 11.9).
Chapter  11 assesses that agricultural and ecological droughts  will 
increase in the Mediterranean regions ( high confidence ) and Western 
and Central Europe ( medium confidence ) by mid-century and with 
high confidence  by the end of the century for the MED for 2°C GWL 
and higher and all scenarios except RCP2.6/SSP1-2.6 (Section 11.9.4). 
Low confidence  in direction of change is assessed for EEU and NEU 
under all scenarios and global warming levels (Figure 12.4k).
Recent local studies provide additional risk-relevant context to 
changes in European drought. Agricultural and ecological  drought 
conditions are expected to intensify in southern Europe by end-of-
century based on the 12-month rainfall Drought Severity Index (a soil 
moisture indicator), precipitation deficit SPI and SPEI indices. There will

be regions in southern Europe where this type of drought could be 
up to 14 times worse than the worst drought in the historical period 
(Guerreiro et al., 2018). One-in-10-year drought events are projected to 
happen every second year (Mora et al., 2018; Ruosteenoja et al., 2018). 
The Mediterranean region will have 100 additional stress years (years 
with three consecutive months of precipitation deficits greater than 
25%; Giorgi et al., 2018); an increase of both drought frequency (up to 
two events per decade) and severity (Spinoni et al., 2014, 2020) and an 
increase of consecutive dry days in the southern part of the MED region 
(Lionello and Scarascia, 2020). In contrast, droughts are expected to 
decrease in winter in Northern Europe (Section  11.9; Spinoni et  al., 
2018a). These findings are confirmed by the EURO-CORDEX, CMIP5 
and CMIP6 ensemble that show a  change of frequency of drought 
events in the MED between 2–3 events per decade by mid-century for

scenario RCP8.5 (Figure 12.SM.3; Coppola et al., 2021a).
Fire weather:  Fire weather conditions have been increasing since 
about 1980 over a few regions in Europe including Mediterranean 
areas ( low confidence ) (Venäläinen et al., 2014; Urbieta et al., 2019; 
Barbero et al., 2020; Giannaros et al., 2021). However, beyond a few 
studies, evidence is largely missing on attribution of these trends to 
anthropogenic climate change (Forzieri et al., 2016). An increase in fire 
weather is projected for most of Europe, especially western, eastern 
and central regions, by 2080 (current 100-year events will occur every 
5–50 years), with a  progressive increase in confidence  and model 
agreement along the 21st  century ( medium confidence ) (Forzieri 
et al., 2016; Abatzoglou et al., 2019). With increased drying and heat 
combined, in Mediterranean areas, an increase in fire weather indices 
is projected under RCP4.5 and RCP8.5, or SRES A1B, as early as by

mid-century ( high confidence ) (Bedia et al., 2014; Abatzoglou et al., 
2019; Dupuy et al., 2020; Fargeon et al., 2020; Ruffault et al., 2020) 
and an increase in burned area of 40% and 100% for a 2°C and 3°C 
GWL, respectively (Turco et al., 2018).

1824
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12In summary, there is high confidence  that river floods will 
increase in central and Western Europe and medium confidence  
that they will decrease in Northern, Eastern and southern 
Europe, for mid- and end of century under RCP8.5 and  with 
low confidence  under RCP2.6. There is high confidence  that 
aridity will increase by mid- and end-century under the RCP8.5 
and SSP5-8.5, and high confidence  that agricultural, ecological 
and hydrological droughts will increase in the Mediterranean 
region by mid- and far end of century under all RCPs except 
RCP2.6/SSP1-2.6 and  also for 2°C and higher GWLs. There is high 
confidence  in fire weather increase in the Mediterranean region.
12.4.5.3 Wind
Mean wind speed:  Mean surface wind speeds have decreased in 
Europe as in many other areas of the Northern Hemisphere over the 
past four decades ( medium confidence ) (AR5 WGI), with a reversal

to an increasing trend in the last decade ( low confidence ) that is, 
however, not fully consistent across studies (Tian et  al., 2019; 
Zeng et  al., 2019; Z. Zhang et  al., 2019; Deng et  al., 2021; see 
Section 2.3.1.4.4). Re-analyses also show declining winds in Europe 
(Deng et al., 2021) with large interdecadal variability (Laurila et al., 
2021). The declining trend has induced a corresponding decline in 
wind power potential indices across Europe ( low confidence ) (Tian 
et al., 2019). However, there is low agreement  and limited evidence  
that climate model historical trends are consistent with observed 
trends (Tian et al., 2019; Deng et al., 2021). Several factors have been 
attributed to these trends, including forest growth, urbanization, 
local changes in wind measurement exposure and aerosols (Bichet 
et al., 2012), as well as natural variability (Zeng et al., 2019).
Due to changes in mean surface wind speed patterns (C. Li et al., 2018)

and the poleward shift of the North Atlantic jet stream exit, mean surface 
wind speeds are projected to decrease in the Mediterranean areas under 
RCP4.5 and RCP8.5 by the middle of the century and beyond, or for 
GWLs of 2°C and higher ( high confidence ), with a subsequent decrease 
in wind power potential ( medium confidence ) (Hueging et al., 2013; 
Tobin et al., 2015, 2018; Davy et al., 2018; Karnauskas et al., 2018a; 
Kjellström et al., 2018; Moemken et al., 2018; Figure 12.4). However, 
sub-regional patterns of change are shown in  regional climate models, 
such as an increase in wind speeds in the Aegean Sea and in the 
northern Adriatic Sea, where a reduction of Bora events and an increase 
of Scirocco events are projected for mid-century and beyond under 
RCP4.5 and RCP8.5 ( medium confidence ) (Tobin et  al., 2016; Davy 
et al., 2018; Belušić Vozila et al., 2019). Projections (as cited above) also 
indicate a decrease in mean wind speed in Northern Europe ( medium

confidence , medium agreement ) (Karnauskas et al., 2018a; Tobin et al., 
2018; Jung and Schindler, 2019). Daily and interannual wind variability 
is projected to increase under RCP8.5 only in Northern Europe ( low 
confidence ) (Moemken et  al., 2018), which can influence electrical 
grid management and wind energy production ( low confidence ). Wind 
speeds are projected to shift towards more frequent occurrences below 
thresholds inhibiting wind power production (Weber et  al., 2018). 
Wind stagnation events may become more frequent in future climate 
scenarios in some areas of Europe in the second half of the 21st century 
(Horton et al., 2014; Vautard et al., 2018), with potential consequences 
on air quality ( low confidence ).Severe wind storm: There are large uncertainties in past evolutions 
of windstorms and extreme winds in Europe. Extreme near-surface 
winds have been decreasing in the past decades (Smits et al., 2005;

Tian et  al., 2019; Vautard et  al., 2019) according to near-surface 
observations. Significant negative trends of cyclone frequency in spring 
and positive trends in summer have been found in the Mediterranean 
basin for the period 1979–2008 (Lionello et  al., 2016). By contrast 
increasing trends have been found in Arctic Ocean areas (Wickström 
et al., 2020). These trends are not associated with significant trends in 
extratropical cyclones (Sections 8.3.2.8 and 11.7.2).
There is medium confidence  that serial clustering of storms, inducing 
cumulated economic losses, in future climate will increase in many 
areas in Europe under climate projections over Europe (Karremann 
et al., 2014; Economou et al., 2015). Strong winds and extratropical 
storms are projected to have a  slightly increasing frequency and 
amplitude in the future in northern, western and Central Europe 
(Outten and Esau, 2013; Feser et al., 2015; Forzieri et al., 2016; Mölter

et al., 2016; Ruosteenoja et al., 2019a; Vautard et al., 2019) under 
RCP8.5 and SRES A1B by the end of the century ( medium confidence ), 
as well as off the European coasts (Martínez-Alvarado et al., 2018) 
due to the increase of intensity of extratropical storms at a 2°C GWL 
or above (Zappa et al., 2013) in these areas. The frequency of storms, 
including Medicanes, is projected to decrease in Mediterranean 
regions, and their intensities are projected to increase, by the middle 
of the century and beyond for SRES A1B, A2 and RCP8.5 ( medium 
confidence ) (Nissen et al., 2014; Feser et al., 2015; Forzieri et al., 2016; 
Mölter et al., 2016; Tous et al., 2016; Romera et al., 2017; González-
Alemán et al., 2019; MedECC, 2020; Chapter 11).
Projections of smaller-scale hazard phenomena such as tornadoes, 
wind gusts, hail storms and lightning are currently not directly 
available partly due to the inability of climate models to simulate

such phenomena. Observational networks for such phenomena 
usually lack homogeneity over long periods, hindering clear trends to 
be detected. For instance, while no robust trends have been identified 
(Hermida et al., 2015; Mohr et al., 2015a; Burcea et al., 2016; Ćurić 
and Janc, 2016), hail storm environments (favourable atmospheric 
configurations) have increased in frequency ( low confidence , limited 
evidence ) (Sanchez et al., 2017). In future climate periods it is more 
likely than not  that severe convection environments will become 
more frequent by the end of the century under RCP8.5 (Mohr et al., 
2015b; Púčik et al., 2017), and there is medium confidence  that such 
environments will become more frequent by the 2050s in RCP4.5. 
There is no evidence for changes in tornado frequencies in Europe 
in the observations (Groenemeijer and Kühne, 2014) as well as in 
future climate projections. Insufficient observational record length for

lightning numbers does not allow an assessment of trends.
There is high confidence  that mean wind speeds will decrease 
in Mediterranean areas and medium confidence  of such 
decreases in Northern Europe for global warming levels of 
2°C or more and beyond the middle of the century. A slightly 
increased frequency and amplitude of extratropical cyclones, 
strong winds and extratropical storms is projected for northern, 
central and western Europe by the middle of the century and 
beyond and for global warming levels of 2°C or higher ( medium

1825
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212confidence ). The frequency of Medicanes is projected to 
decrease ( medium confidence ), but their intensity is projected 
to increase by mid century and beyond and for global warming 
levels of 2°C or more. Proxies of intense convection indicate 
that the large-scale conditions conducive to severe convection 
will tend to increase in the future climate ( low confidence ).
12.4.5.4 Snow and Ice
Snow:  Widespread and accelerated declines in snow depth (Fontrodona 
Bach et al., 2018) and snow water equivalent (Marty et al., 2017a; see 
Figure 12.9b) have been observed in Europe. In the Pyrenees a slow 
snow cover decline has been observed starting from the industrial 
period with a sharp increase since 1955 (López-Moreno et al., 2020). 
Under the RCP2.6, RCP4.5 and RCP8.5 scenarios the reliability elevation 
for snowmaking will rise by 200–300 m in the Alps and 400–600 m

in the Pyrenees by mid-century. End of century projections of natural 
snow conditions are highly dependent on the scenario, being stationary 
for the RCP2.6 and continuously decreasing under RCP8.5 to not have 
any more natural snow conditions at any of the locations in the French 
Alps and Pyrenees (Spandre et al., 2019). Similarly Norway and Austria 
will also see a rising of the natural snow elevation with consequences 
for the ski season (Scott et al., 2020; Steiger and Scott, 2020). In the 
Alps, recent simulations project a reduction in snow water equivalent 
(SWE) at 1500 m above sea level of 80–90% by 2100 under the A1B 
scenario and a snow season that would start 2-4 weeks later and end 
5-10 weeks earlier than the 1992–2012 average (Schmucki et  al., 
2015), which is equivalent to a shift in elevation of about 700 m (Marty 
et al., 2017b). For elevations above 3000 m above sea level, a decline in 
SWE of at least 10% is expected by the end of the century even when

assuming the largest projected precipitation increase. Similar trends 
are observed for the Pyrenees and Scandinavia (López-Moreno et al., 
2009; Räisänen and Eklund, 2012). For the northern French Alps above 
1500 m and the Ötztal locations in the Austrian alps SWE has a similar 
decreasing trend altitudinally dependent for RCP2.6, RCP4.5 and 
RCP8.5 until mid-century and with significant differentiation among 
them in the second half of the century up to snow-free conditions 
under RCP8.5  (Hanzer et al., 2018; Verfaillie et al., 2018).
Glacier:  Observations and future projections of European glacier 
mass changes are assessed in Section  9.5.1 grouped in two main 
regions: Scandinavia and central Europe regions. It is virtually certain  
that glaciers will shrink in the future and there is medium confidence  
in the timing and mass change rates (Section 9.5.1). Central Europe 
is one of the regions where glaciers are projected to lose substantial

mass even under low-emissions scenarios (Section 9.5.1.3; MedECC, 
2020). GlacierMIP projections indicate that glaciers in the central 
Europe region will lose 63 ± 31%, 80 ± 22% and 93 ± 13% of their 
2015 mass by the end of the century under RCP2.6, RCP4.5 and 
RCP8.5 respectively (Marzeion et al., 2020). For the same scenarios, 
glaciers in Scandinavia are projected to lose 55 ± 33%, 66 ± 34% and 
82 ± 24% of their 2015 mass. The virtually certain shrink in glaciers 
is bolstered by RCM simulations from the EURO-CORDEX ensemble, 
with the Global Glacier Evolution Model (GloGEM) indicating 
a substantial reduction of glacier ice volumes in the European Alps 
by 2050 (47–52% with respect to 2017 for RCP2.6, RCP4.5 and 
RCP8.5). Under RCP2.6, about two-thirds (63 ± 11%) of the present-day (2017) ice volume is projected to be lost by 2100. In contrast, 
under the strong warming of RCP8.5, glaciers in the European Alps 
are projected to largely disappear by 2100 (94 ± 4% volume loss

compared to 2017; Zekollari et al., 2019).
Permafrost: In Europe, permafrost is found in high mountains and in 
Scandinavia, as well as in Arctic Islands (e.g., Iceland, Novaya Zemlia or 
Svalbard). In recent decades permafrost has been lost (Section 9.5.2) 
and accelerated warming at high altitudes and latitudes has favoured 
an increase of permafrost temperatures of the order of 0.2 ± 0.1°C 
between 2007 and 2016 (Romanovsky et  al., 2018; Noetzli et  al., 
2019). Over the 21st  century, permafrost is very likely  to undergo 
increasing thaw and degradation under all scenarios (Hock et  al., 
2019) and it is virtually certain  that permafrost extent and volume will 
decrease with increase of global warming (Section 9.5.2).
Permafrost thawing is projected to affect the frequency and 
magnitude of high-mountain mass wasting processes (Stoffel 
and Huggel, 2012). The temporal frequency of periglacial debris flows

in the Alps is unlikely  to change significantly by the mid-21st century 
but is likely  to decrease during the second part of the century under 
the A1B scenario, especially in summer (Stoffel et al., 2011, 2014). 
There is medium confidence  that most of the Northern Europe 
periglacial processes will disappear by the end of the century, even 
in the RCP2.6 scenario (Aalto et al., 2017). The magnitude of debris 
flow events might increase (Lugon and Stoffel, 2010) and the debris-
flow season may last longer under the A1B scenario (Stoffel and 
Corona, 2018) . Quantitative data for the European Alps is highly site 
dependent (Haeberli, 2013).
Heavy snowfall, ice storms and hail: There is low confidence  that 
climate change will affect ice and snow-related episodic hazards 
(limited evidence ). The change in snowpack in the Alps is expected 
to lead to a possible reduction in overall avalanche activity by end of 
the century ( low confidence ), except possibly in winter and at high

altitudes (Castebrunet et al., 2014).
For ice storms, or freezing rainstorms, there is also limited evidence  
due to a limited number of studies. Heavy snowfalls have decreased 
in frequency in the past decades and this is expected to continue in 
the future climate ( low confidence ) (Beniston et al., 2018). Freezing 
rain is projected to increase in western, central and southern Europe 
by the end of the century under RCP4.5 and RCP8.5 ( low confidence ) 
(Kämäräinen et  al., 2018). Rain-on-snow events, are decreasing in 
northern regions (Pall et al., 2019) and by 48% on average in southern 
Scandinavia (Poschlod et al., 2020) due to decreases in snowfall.
In summary, future snow cover extent and seasonal duration 
will reduce ( high confidence ) and it is virtually certain  that 
glaciers will continue to shrink.  A  reduction of glacier ice 
volume is projected in the European Alps and Scandinavia 
(high confidence ). There is high confidence  that permafrost

will undergo increasing thaw and degradation over the 
21st  century.  Most of the Northern Europe periglacial will 
disappear by the end of the century even for a lower emissions 
scenario ( medium confidence ) and the debris-flow season may 
last longer in a warming climate ( medium confidence ).

1826
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment1212.4.5.5 Coastal and Oceanic
Relative sea level:  Around Europe, over 1900–2018, a  new tide 
1.08 [0.79 to 1.38] mm yr –1 in the subpolar North Atlantic (Frederikse 
et al., 2020), compared to a GMSL change of around 1.7 mm yr –1 
(Section 2.3.3.3 and Table 9.5). For the period 1993–2018, the RSLR 
rates around Europe, based on satellite altimetry, increased to 2.17 
[1.66 to 2.66] mm yr –1 (Frederikse et al., 2020), compared to a GMSL 
change of 3.25 mm yr –1 (Section 2.3.3.3 and Table 9.5).
around Europe. Regional mean RSLR projections for the oceans around 
Europe range from 0.4–0.5 m under SSP1-2.6 to 0.7–0.8 m under 
which means that there are locally large deviations from the projected

GMSL change (Section 9.6.3.3). These RSLR projections may however 
be underestimated due to potential partial representation of land 
subsidence in their assessment (Section 9.6.3.2). The signal is strongest 
for the North Sea and Atlantic coasts, followed by the Black Sea. The 
Baltic Sea, on the contrary, shows the lowest increase due to land 
uplift (Vousdoukas et al., 2017). The model agreement is higher for the 
Mediterranean and in line with previous findings by Gualdi et al. (2013).
Coastal flood:  The present-day 1-in-100-year ETWL is between 0.5 
and 1.5 m in the MED basin and 2.5 and 5.0 m in the western Atlantic 
European coasts, around the UK and along the North Sea coast, and 
lower at 1.5–2.5 m along the Baltic Sea coast (Kirezci et al., 2020). 
Similar values are reported by Vousdoukas et al. (2018).
There is high confidence  that extreme total water level (ETWL) 
magnitude and occurrence frequency will increase throughout Europe

(see Figure  12.4p–r), except in the northern Baltic Sea. Across the 
region, the 5–95th percentile range of the 1-in-100-year ETWL is 
projected to increase (relative to 1980–2014) by 4–40 cm and by 
6–47 cm by 2050 under RCP4.5 and RCP8.5, respectively. By 2100, this 
range is projected to be 6–88 cm and 25–186 cm under RCP4.5 and 
RCP8.5, respectively (Figure 12.SM.6; Vousdoukas et al., 2018; Kirezci 
et al., 2020). Mass addition across the Gibraltar Strait may play a role, 
although the extent of this contribution is currently unclear (Lionello 
et al., 2017). Furthermore, under RCP4.5, the present day 1-in-100-year 
ETWL is projected to have median return periods of between 1-in-5 and 
1-in-20 years by 2050 and occur at least once per year by 2100 in the 
Mediterranean and Black Sea, while in the rest of Europe it is mostly 
projected to have median return periods of between 1-in-20-years and 
1-in-50-years by 2050 and between 1-in-5-years and 1-in-20-years

by 2100 (Vousdoukas et al., 2018). Under RCP8.5, occurrence of the 
present day 1-in-100-year ETWL is projected to increase further to 
median return periods of 1-in-1-year to 1-in-5-years by 2050 and occur 
more than once per year by 2100 in the Mediterranean and Black Sea, 
while in the rest of Europe it is mostly projected to have median return 
periods between 1-in-5-years and more than once per year by 2100.
Coastal erosion:  Satellite-derived shoreline change estimates over 
1984–2015 indicate shoreline retreat rates of around 0.5  m  yr –1 
along the sandy coasts of WCE and MED, around 4 m yr –1 in EEU 
(Caspian Sea region) and more or less stable shorelines in NEU (Luijendijk  et  al., 2018; Mentaschi et  al., 2018). Mentaschi et  al. 
(2018) report a coastal area loss of 270 km2 over a 30-year period 
(1984–2015) along the Atlantic coastlines of Europe.
Projections indicate that sandy coasts throughout the continent (except

those bordering the northern Baltic Sea) will experience shoreline retreat 
through the 21st century ( high confidence ). Median shoreline change 
projections (CMIP5) relative to 2010, show that, by mid-century, 
shorelines will retreat by between 25 m and 60 m along sandy coasts 
in WCE and MED under both RCP4.5 and RCP8.5 (Athanasiou et al., 
2020; Vousdoukas et al., 2020b). Mid-century median projections for 
NEU indicate virtually no shoreline retreat under RCP4.5, but a retreat 
of around 40 m under RCP8.5. By 2100, median shoreline retreats of 
around 50 m are projected in NEU and MED under RCP4.5, increasing 
to around 80 m under RCP8.5. End-century median projections for 
WCE are far higher at 100 m (RCP4.5) and 160 m (RCP8.5). The total 
length of sandy coasts in Europe that is projected to retreat by more 
than a median of 100 m by 2100 under RCP4.5 and RCP8.5 is about 
12,000 km and 18,000 km respectively, an increase of approximately 
54% (Vousdoukas et al., 2020b).

Local assessments of both long term shoreline retreat and episodic 
coastal erosion are given by Li et  al. (2014b), Toimil et  al. (2017), 
Bon de Sousa et al. (2018) and Le Cozannet et al. (2019). In terms 
of episodic coastal erosion, 31–88% of all Aegean beaches are 
projected to experience complete erosion, with a RCP4.5 sea level 
rise of 0.5 m and a surge of 0.6 m, but with substantial uncertainty 
(Monioudi et al., 2017).
Marine heatwave:  The mean SST of the Atlantic Ocean and 
the Mediterranean has increased between 0.25°C and 1°C since 
1982–1998. This mean ocean surface warming is correlated to longer 
and more frequent marine heatwaves in the region (Oliver et al., 2018). 
Over the period 1982–2016, the coastlines of Europe experienced on 
average more than 2.0 MHW yr –1, with the eastern Mediterranean 
and Scandinavia experiencing 2.5–3 MHWs yr –1. The average duration 
was between 10 and 15 days. Changes over the 20th century, derived

from MHW proxies, show an increase in frequency of between 1.0 and 
2.0 MHWs per decade in Europe, although the trend is not statistically 
significant; with an increase in intensity per event in the North Atlantic 
and the Mediterranean, and a decrease in the Atlantic off the British 
Isles. The total number of MHW days per decade has increased in the 
Mediterranean (Oliver et al., 2018).
Mean SST is projected to increase by 1°C–3°C around Europe by 
2100, with a hotspot of around 4°C–5°C along the Arctic coastline 
of Europe under RCP4.5 and RCP8.5 scenarios (see Interactive Atlas), 
leading to a continued increase in MHW frequency, magnitude and 
duration (Oliver et al., 2018; MedECC, 2020). Projections for SSP1-2.6 
and SSP5-8.5 both show an increase in MHWs around Europe by 
2081–2100, relative to 1985–2014 (Box  9.2, Figure  1). Darmaraki 
et al. (2019) project that, by the end of the 21st century and under 
RCP8.5, there will be one MHW occurring every year in the northern

Mediterranean sea, and that these MHWs would be three months 
longer, four times more intense, and 42 times more severe than 
present day MHWs in the region. Frölicher et al. (2018) show that, 
in Europe, the change in the probability for the number of days of

1827
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Table 12. 7 | Summary of confidence in direction of projected change in climatic impact-drivers in Europe, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, 

Mediterranean (MED)   5 6 7   2  
Western and Central Europe (WCE)  4    2  
Eastern Europe (EEU)   
Northern Europe (NEU)   1    8 2,3  
1. Excluding southern UK.
2. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat.
3. The Baltic Sea shoreline is projected to prograde if present-day ambient shoreline change rates continue.

4. For the Alps, conditions conducive to landslides are expected to increase.
5. Low confidence of decrease in the southernmost part of the region.
6. General decrease except in Aegean Sea.
7. Medium confidence of decrease in frequency and increase in intensities.
8. Except in the northern Baltic Sea region.

1828
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12MHWs exceeding the 99th percentile of the pre-industrial level is 
4%, 15% and 30% for global warming levels of 1°C, 2°C and 3.5°C, 
respectively. MHW increase in the Mediterranean will impact on 
many species that live in shallow waters and have reduced motility, 
with consequences for related economic activities (Galli et al., 2017).
related climatic impact-drivers in Europe will increase over the 
21st century for all scenarios and time horizons. Relative sea 
level rise is extremely likely  to continue around Europe (except 
in the northern Baltic Sea), contributing to increased coastal 
flooding in low-lying areas and shoreline retreat along most 
12.4.5.6 Other

Compound events:  One typical compound event that is observed in 
the European area is compound flooding due to the combination of 
extreme sea level events and extreme precipitation events associated 
with high levels of runoff. In the present climate, the Mediterranean 
coasts are exposed to a higher probability of this type of compound 
flooding event (Bevacqua et al., 2019). Under RCP8.5, the probability 
of these events is projected to increase along northern European 
coasts (west coast of UK, northern France, the east and south coast 
of the North Sea, and the eastern half of the Black Sea), with the 
percentage of coastline now experiencing such events at least once 
every 6 years increasing by between 3% and 11% by the end of the 
21st century (Bevacqua et al., 2019).
Under RCP8.5, regions in Russia, France and Germany are projected 
to experience an increase in the frequency and the length of wet and 
cold compound events, while Spain and Bulgaria are projected to stay

longer in the hot and dry state by mid-century (Sedlmeier et al., 2016).
Compound events of dry and hot summers have increased in Europe. 
Manning et al. (2019) found that the probability of such compound 
events has increased across much of Europe between 1950–1979 
and 1984–2013, notably in southern, eastern and western Europe. 
Compound hot and dry extremes are projected to increase in Europe by 
mid-century for the SRES A1B and RCP8.5 with a particularly strong 
signal projected in southern and eastern Germany and the Czech 
Republic (Sedlmeier et al., 2016).
The assessed direction of change in climatic impact-drivers for Europe 
and associated confidence levels are illustrated in Table 12.7, together 
with emergence time information (Section  12.5.2). No assessable 
literature could be found for sand and dust storms, although these 
phenomena may be relevant in parts of the region.
12.4.6 North America
Major changes in North American CIDs were assessed in WGII AR5

Chapter  26 (Romero-Lankao et  al., 2014), with additional detail on connections to warming levels provided by SR1.5 (IPCC, 2018), 
and climate information related to land degradation and land-use 
suitability in SRCCL (IPCC, 2019c), and ocean and coastal hazards in 
the SROCC (IPCC, 2019b). Recent national assessments in the USA 
(USGCRP , 2017, 2018) and Canada (Bush and Lemmen, 2019) enhance 
the local perspective and assessments across a number of CIDs and 
their sectoral connections. For the purpose of this assessment, North 
America is sub-divided into six sub-regions, as defined in Chapter 1: 
North Central America (NCA), Western North America (WNA), Central 
North America (CNA), Eastern North America (ENA), North-Eastern 
North America (NEN), and North-Western North America (NWN). 
Greenland and Arctic regions of North-Eastern and North-Western 
North America are further assessed in Section  12.4.9, and the 
Caribbean and Hawaiian Islands are assessed in Section 12.4.7.

12.4.6.1 Heat and Cold
Mean air temperature: Atlas.9.2 assessed very likely  mean warming 
in observations across North America, with highest increases at 
higher latitudes and in the winter season. Atlas.9.4 assessed very 
likely mean warming in future decades in all North American 
regions, with CMIP and CORDEX models showing median increases 
exceeding 2°C in much of the continental interior under RCP8.5 
(2041–2060 compared to 1995–2014) and higher increases towards 
the north. Mean temperatures at the end of century show strong 
scenario dependence, rising between 1°C and 2.5°C in RCP2.6 and 
about 4°C to 8°C in RCP8.5 (Figures Atlas.12, Atlas.26 and Atlas.27). 
Warming also raises stream temperatures across the continent 
(DOE, 2015; Trtanj et al., 2016; van Vliet et al., 2016; Chapra et al., 
2017), and Hill et al. (2014) projected US stream warming by 0.6°C 
(±0.3°C) per 1°C increase in local air temperature. Mean warming

drives shifts in the seasonal timing of temperature thresholds, 
including increasing growing degree days (Mu et al., 2017), longer 
growing seasons (Gowda et al., 2018; G. Li et al., 2018; L.A. Vincent 
et al., 2018), reduced chill hours (Luedeling, 2012; Lee and Sumner, 
2015; Xie et  al., 2015; Parker and Abatzoglou, 2019), and longer 
pollen and allergy seasons (Fann et al., 2016; Anenberg et al., 2017; 
Sapkota et al., 2019). Warmer temperatures reduce heating degree 
days and increase cooling degree days ( high confidence ) (Bartos et 
al., 2016; US EPA, 2016; Craig et al., 2018; X. Zhang et al., 2019; 
Coppola et al., 2021b)  
Extreme heat:  Section 11.9 assessed that extreme temperatures in 
North America have increased in recent decades ( medium evidence , 
medium agreement ) other than in Central and Eastern North America 
(low confidence ), and extreme heat in all regions is projected  to 
increase with climate change ( high confidence ). Observed trends in

extreme heat are more positive for heat extreme indices that include 
temperature and humidity given historical expansion of irrigation 
and intensification of agriculture (Mueller et al., 2017; Grotjahn and 
Huynh, 2018; Thiery et al., 2020). Several studies noted statistically 
significant increases in intensity and particularly the frequency, 
duration, and seasonal length of the physiologically hazardous 
extreme heat conditions across North America (Grineski et al., 2015; 
Habeeb et al., 2015; Martínez-Austria et al., 2016; Petitti et al., 2016; 
L.A. Vincent et al., 2018; García-Cueto et al., 2019).

1829
00.250.50.7511.25NWN
0.20.100.10.20.3NEN
00.250.50.7511.25WNA
0.20.100.10.20.3CNA
00.250.50.7511.25ENA
0.20.100.10.20.3NCA
10.500.51CAR
Time slices-0.2-0.1-0.05 -0.02 -0.01 00.01 0.02 0.05 0.1 0.2
020406080100NWN NEN
020406080100WNA CNA
020406080100ENA
Time slicesHigh model agreement
Low model agreementColour(a) Change in 1-in-100 year river discharge per unit 
by mid-century, CORDEX RCP8.5
days yr-1m3 s-1 km-2-60    -30   - 15     -7       -1      1       7       15     30     601-in-100 year river discharge per unit catchment area (Q100, m3 s-1 km-2)
Legend
+2°C +4°C

median
10th p
Figure  12.10  | Projected changes in selected climatic impact-driver indices for North America. (a)  Mean change in 1-in-100-year river discharge per unit 
catchment area (Q100, m3 s–1 km–2), and (b) median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from CORDEX-
North America models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction) of change.  
(c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated 
with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the

Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to 
the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble 
values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 
in blue. (d) As for (c) but showing absolute values for number of days with SWE > 100 mm, masked to grid cells with at least 14 such days in the recent past.  See Technical 
Annex VI for details of indices. A Caribbean (CAR) Q100 bar plot is included here but assessed in the Small Islands section (Section 12.4.7). Further details on data sources and 

1830
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Figure  12.4b shows over a  month of additional days at CMIP6 
SSP5-8.5 mid-century where temperatures exceed 35°C across 
much of southern Mexico and regions near the US–Mexico border, 
and these extreme temperatures occur at least once per year up to 
southern Canada. Coppola et al. (2021b) found similar patterns in 
CMIP5 and CORDEX-Core. Using locally tailored heat thresholds, 
Maxwell et  al. (2018) found that ‘very hot’ days in five US cities 
will occur a median of three to five times more often by 2036–2065 
under RCP8.5 (2 to 3.5 times more often in RCP4.5), Oleson et al. 
(2018) projected that annual heatwave duration will exceed 
one month in Houston in RCP8.5 2061–2080, and Anderson et al. 
(2018) projected 7 to 12  times more exceedances of thresholds 
associated with high-mortality by 2061–2080 under RCP8.5 (6 to 
7 times more exceedances in RCP4.5). Schwingshackl et al. (2021)

found that Central and Eastern North America are among the regions 
with the strongest trend in heat stress indicators. Studies also project 
increasingly surpassed heat extreme thresholds for North American 
crops (Gourdji et al., 2013), airplane weight restrictions (Coffel et al., 
2017), and peak load energy systems (Auffhammer et al., 2017).
The number of days crossing dangerous heat thresholds such as 
HI  >  41°C will be very sensitive to the mitigation scenario at the 
end of the century (Wuebbles et al., 2014; Zhao et al., 2015; Dahl 
et al., 2019; Schwingshackl et al., 2021). At the end of the century 
under SSP5-8.5, a CMIP6 median increase of exceedances of 75–150 
days per year is projected over much of North Central America, 
Central North America and the south-western USA while this 
increase is projected to remain limited below 60 days under SSP1-2.6 
(Figure  12.4d,f and Figure 12.SM.2). Steinberg et  al. (2018) also

projected more frequent and longer ‘heat-health’ events in California 
extending into October.
Cold spell:  Chapter  11 assessed high confidence  in decreasing 
frequency and intensity of cold spells over North America (Section 11.9). 
The number of days with extreme wind chill hours (humidex <–30) 
decreased at 76% of examined Canadian stations from 1953 to 2012 
(Mekis et  al., 2015) and cold days and coldest nights decreased in 
Mexico from 1980 to 2010 (García-Cueto et al., 2019).
Cold spells are projected to decrease over North America under climate 
change, with the largest decreases most common in the winter season 
(high confidence ) (Section 11.9). Minimum winter temperatures are 
projected to rise faster than the mean winter temperature (Underwood 
et  al., 2017) and alter cold-hardiness zones used to determine 
agricultural suitability (Parker and Abatzoglou, 2016). Wuebbles 
et  al. (2014) projections for RCP8.5 end-of-century show that the

four-day cold spell that happens on average once every five years 
is projected to warm by more than 10°C and CMIP5 models do not 
project current 1-in-20-year annual minimum temperature extremes 
to recur over much of the continent. Multiple studies have shown 
that Arctic warming can alter large-scale variability and change the 
frequency and duration of mid-latitude cold air outbreaks, potentially 
leading to increasing cold hazards in some regions ( low agreement ) 
(Barcikowska et al., 2019; Cohen et al., 2020; Zhou et al., 2021).
Frost: An expansion of the frost-free season is underway and 
projections for North America indicate a continuation of this trend in the future ( high confidence ). Significant decreases in frost days, 
consecutive frost days, and ice days were identified in 1948–2016 
station observations across Canada, along with a  resulting 
lengthening of the frost-free season by more than a month in many

regions (L.A. Vincent et al., 2018). Frost days also declined in nearly 
all Mexican cities from 1980 to 2010 (García-Cueto et al., 2019), and 
a 1917–2016 decline of about three weeks in frigid winter conditions 
challenges ecosystems in the north-east USA and south-east Canada 
(Contosta et  al., 2020). Studies connect projections of a  longer 
frost-free season in North America to a longer outdoor construction 
season, shifts in frost variance to orchard damages, and lower weight 
tolerances for runways (Daniel et al., 2018; DeGaetano, 2018; Jacobs 
et al., 2018). Frosts are projected to persist as an episodic hazard in 
many regions given natural variability and cold air outbreaks even as 
mean temperature rises ( high confidence ).
Climate change is virtually certain  to shift the balance of 
temperature towards warming trends and away from cold 
extremes, with increases in the magnitude, frequency, duration 
and seasonal and spatial extent of heat extremes driving

impacts across North America. The frequency of dangerous 
heat threshold exceedance (such as HI > 41°C) is particularly 
sensitive to scenario pathway, with 75–150 days more under 
SSP5-8.5 but less than 60 days more under SSP1-2.6 by the 
end of the century in NCA, CNA and the south-western USA.
12.4.6.2 Wet and Dry
Mean precipitation: Atlas.9.2 found that trends in annual 
precipitation over 1960–2015 are generally non-significant, though 
there are consistent positive trends over parts of ENA and CNA, 
together with significant decreases in precipitation in parts of the 
south-western USA and north-western Mexico.
Atlas.9.4 assessed very high confidence in increases in mean 
precipitation over most of northern and Eastern North America, with 
medium confidence  of decrease over Northern Central America and 
low confidence  elsewhere (see Figure Atlas.26, and Cross-Chapter 
Box Atlas.1, Figure  1). Changes are most dramatic in the spring

and winter, when wet conditions are projected to extend from the 
northern portions of the continent as far south as the central Great 
Plains, while Mexico becomes drier; in contrast, summer changes are 
uncertain across most of the continent other than wetter conditions 
in northern Canada (Easterling et al., 2017; Bukovsky and Mearns, 
2020; Almazroui et al., 2021; Teichmann et al., 2021).
River flood:  There is limited evidence and low agreement  on 
observed climate change influences for river floods in North America 
(Section 11.5). Trends in streamflow indices are mixed and difficult 
to separate from river engineering influences, with large changes 
but little spatial coherence across the USA, making it difficult to 
identify trends with confidence (Peterson et  al., 2013; Mallakpour 
and Villarini, 2015; Archfield et al., 2016; Wehner et al., 2017; Villarini 
and Slater, 2018; Hodgkins et al., 2019; Neri et al., 2019). There is

high confidence  in historical shifts in the timing of peak streamflow 
towards higher winter and earlier spring flows in snowmelt-driven 
basins in Canada (Burn and Whitfield, 2016; Bonsal et al., 2019) and

1831
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212the USA (Dudley et al., 2017; Wehner et al., 2017, Neri et al., 2020). 
Some rivers show ice-jam floods occurring a week earlier, but changes 
are mixed, given localized positive and negative changes across the 
continent (Rokaya et  al., 2018). There is medium confidence that 
climate change will increase river floods over the USA and Canada 
but low confidence  for changes in Mexico. Wobus et  al. (2017a) 
used a  regional hydrologic model for 57,000 streams to project 
more than a doubling in the frequency of current 1-in-100-year flow 
events in many portions of the USA for RCP8.5 2050 with additional 
contributions from earlier snowmelt. CMIP6 projections for SSP5-8.5 
2065–2099 show strongest peak USA runoff increases in the east 
(Villarini and Zhang, 2020); however, several studies applying global 
hydrological models disagree with regional streamflow projections,

indicating a decrease in the magnitude or frequency of floods over 
a large portion of North America (e.g., Hirabayashi et al., 2013; Arnell 
and Gosling, 2016; see Figure 12.10a,c).
Heavy precipitation and pluvial flood: Section 11.4 assessed high 
confidence  in observed increases in extreme precipitation events 
(including hourly totals) in Central and Eastern North America with 
low confidence  in broad trends elsewhere in the continent despite 
observational increases in some portions of each region (L.A. Vincent 
et al., 2018; García-Cueto et al., 2019; X. Zhang et al., 2019).
Section 11.4 found that high precipitation is projected to increase 
across North America ( high confidence ) except for portions of Western 
North America where projections are mixed ( medium confidence of 
increase). Maxwell et al. (2018) identified regional ‘heavy precipitation 
day’ thresholds for five cities across the USA and projected that

a tripling (or more) of these events is possible by RCP8.5 mid-century. 
Projections indicate changes to intensity-duration-frequency (IDF) 
curves typically used for construction design and automobile hazards, 
as well as increases in the 10-year recurrence level of 24-hour 
rainfall intensities that challenge storm water drainage systems 
(Hambly et al., 2013; Cheng and AghaKouchak, 2015; J.E. Neumann 
et  al.,  2015; Prein et  al., 2017b; Hettiarachchi et  al., 2018; Ragno 
et al., 2018). Precise levels of regional IDF characteristics may still 
depend substantially on the method and resolution of downscaling 
applied (DeGaetano and Castellano, 2017; L.M. Cook et al., 2020).
Landslide:  There is growing yet limited evidence  for unique climate-
driven changes in landslide and rockfall hazards in North America, 
even as theory suggests decreases in slope and rockface stability 
due to more intense rainfall, rain-on-snow events, mean warming,

permafrost thaw, glacier retreat, and coastal erosion (Cloutier 
et al., 2017; Coe et al., 2018; Handwerger et al., 2019; Hock et al., 
2019; Patton et al., 2019) although dry trends can decelerate mass 
movements (Bennett et al., 2016). Landslide frequency has increased 
in British Columbia (Canada; Geertsema et al., 2006) and is expected 
to increase in North-Western North America given the combination 
of these factors ( medium confidence ) (Gariano and Guzzetti, 2016). 
Cloutier et al. (2017)  projected an increase in landslides in western 
Canada due to wetter overall conditions and reduced return period 
for extreme rainfall. Robinson et  al. (2017) used scenarios based 
upon projection of 50-year recurrence of 7-day precipitation periods 
to highlight the potential for increased landslide hazards near Seattle 
(USA). Broad projections for the USA are more uncertain given increases in evapotranspiration that will counteract precipitation

changes over much of the country  (Coe, 2016) .
Aridity: Chapter 8 showed that aridity in North America generally 
moves opposite to mean precipitation change with an added 
evaporative demand from warmer temperatures ( high confidence  in 
aridity increase for Northern Central America; medium confidence  for 
an increase in Central North America; high confidence  for a decrease 
in North-Eastern North America; medium confidence  decreases in 
Eastern and North-Western North America; see also Section  11.9). 
Projected soil moisture declines (Figure 12.4j–l) are most widespread 
across North America during the summer, with the largest declines in 
Mexico and the southern Great Plains but also extending into Canada 
(Section  8.4.1.6; Swain and Hayhoe, 2015; Easterling et  al., 2017; 
Bonsal et al., 2019; J. Lu et al., 2019). Yoon et al. (2018) found net 
reduction in southern Great Plains groundwater storage in RCP8.5 
mid-century projections despite increases in mean precipitation

and both wet and dry extremes. Soil moisture drying could reach 
unprecedented levels by the CMIP6 RCP8.5 end-of-century, even 
when evaluating deeper soil columns relevant for crop rooting depth 
(B.I. Cook et al., 2020). Projected changes in the aridity index portray 
a shift the geographic range of temperate drylands northward and 
eastward in Central and Western North America (Schlaepfer et al., 
2017; Seager et  al., 2018) which also diminishes aquifer recharge 
rates in the southern Great Plains and in some western regions 
where snowpack is reduced (Meixner et al., 2016).
Hydrological drought: Section  11.9 asssessed low confidence  of 
significant observational trends and projected future changes in the 
characteristics of episodic hydrological drought in North America given 
limited evidence  and low agreement  in modeled changes. C. Zhao 
et al. (2020) found that increases in hydrological drought frequency 
(particularly the 100 yr drought) were far more prevalent than for

meteorological drought across 5797 watersheds in the USA and Canada, 
indicating a strong influence of evaporative demand. Reductions in the 
overall supply of meltwater from a declining snowpack also increase 
the potential for intermittent hydrological droughts in the western USA 
(Mote et al., 2018; Livneh and Badger, 2020).
Agricultural and ecological drought:  Section  11.9 assessed 
medium confidence  for an increase in agricultural and ecological 
drought in Western North America but otherwise found limited 
evidence  for broadly observed changes in North American agricultural 
and ecological drought, even as increasing evaporative demand 
intensified vegetation stress and soil moisture deficits in recent events 
(Sections 11.6, 11.9). Section 11.9 asssessed medium confidence  for 
more intense agricultural and ecological drought conditions over 
North Central America, Western North America and Central North 
America in a  2°C global warming level (about mid-century), with

medium confidence  extending to Eastern North America and high 
confidence  for Northern Central America and Central North America 
under a 4°C global warming level associated with higher emissions 
scenarios past 2050. Figure  12.4g–i shows that the frequency 
of meteorological droughts (which often initiate hydrological, 
agricultural and ecological droughts) is largely projected to increase 
in North American areas where total precipitation decreases (and 
vice versa; see Section 11.9 and Coppola et al., 2021b), and higher

1832
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12evaporative demand will extend the regions where more intense 
ecological and agricultural droughts develop when meteorological 
droughts occur (Wehner et al., 2017; B.I. Cook et al., 2019, 2020). 
Studies utilizing a  variety of drought indices and soil moisture 
projections consistently project increased drought extending from 
Mexico into the southern Canadian Plains during the summer (Swain 
and Hayhoe, 2015; Ahmadalipour et  al., 2017; Feng et  al., 2017; 
Bonsal et al., 2019; Tam et al., 2019; B.I. Cook et al., 2020).
Fire weather: Climatic conditions conducive to wildfire have increased 
in Mexico, Western and North-Western North America, primarily due 
to warming ( high confidence ). Abatzoglou and Williams (2016) found 
climate change led to higher values for eight fuel aridity indices over 
the western USA in recent decades, with 2000–2015 changes exposing

75% more forested area to high fuel aridity and adding nine more high 
fire-potential days each year, similar to 1979–2013 western USA and 
Mexico fire season expansion reported in Jolly et al. (2015). Increases 
in lightning-initiated fires have been distinguished from trends in man-
made fire in western Canada and the USA (Balch et al., 2017; Hanes 
et al., 2019). Jain et al. (2017) identified a 1979–2015 expansion in 
fire weather season in eastern Canada and the south-western USA 
(with a smaller reduction in the northern Mountain West) along with 
regional shifts in the 99th percentile Canadian Fire Weather Index (FWI) 
and potential fire-spread days. Girardin and Wotton (2009) noted that 
1951–2002 trends in the Monthly Drought Code fire index in eastern 
Canada could hardly be distinguished from decadal variability.
Climate change drives future increases in North American fire 
weather, particularly in the south-west ( high confidence ), although

further studies on shifts in exposure and vulnerability are needed 
to understand overall fire risks (see WGII Chapter  14). A  significant 
increase of FWI is apparent before 2050 under RCP8.5 in much of 
North America, including the frequency of 95th-percentile FWI days, 
peak seasonal FWI average, fire weather season length, and maximum 
fire weather index (Abatzoglou et  al., 2019), and fire season across 
North America expands dramatically beyond 2°C global warming 
levels (Q. Sun et al., 2019; Jain et al., 2020). X. Wang et al. (2017b) 
simulated fire-spread days across Canada and found increases across 
most of the areas studied by 2071–2100, with median changes of 
–20 to +140% (RCP2.6), –20 to +250% (RCP4.5) and 40 to 360% 
(RCP8.5) compared to 1976–2015. Prestemon et al. (2016) found more 
conducive conditions for lightning-ignited fires in  the south-eastern 
USA by mid-century, while warming conditions in Alaska increasingly

push July temperatures above 13.4°C, a  threshold for fire danger 
across Alaska’s tundra and boreal forest (Partain et al., 2016; Young 
et al., 2017). Longer and more intense fire seasons would also raise 
particulate matter and black carbon concentrations in the western 
USA, reducing visibility at many National Parks (Yue et al., 2013; Val 
Martin et al., 2015).
Changes in North American wet and dry climatic impact-
drivers are largely organized by the ‘north-east (more wet) to 
south-west (more dry)’ pattern of mean precipitation change, 
although heavy precipitation increases are widespread and 
increasing evaporative demand expands aridity, agricultural 
and ecological drought, and fire weather (particularly in 
summer) ( high confidence ).12.4.6.3 Wind
Mean wind speed: Mean wind speeds have declined in North 
America – as in other Northern Hemisphere areas – over the past 
four decades ( medium confidence ) (AR5 WGI) with a reversal in the

last decade ( low confidence ) not fully consistent across studies (Tian 
et al., 2019; Zeng et al., 2019; Z. Zhang et al., 2019). Tian et al. (2019) 
found a corresponding reduction in the wind power potential across 
the eastern parts of North America.
Mean wind speeds are expected to decline over much of North 
America (Figure 12.4m–o), but the only broad signal of consistent 
change across model types is a reduction in wind speed in Western 
North America ( high confidence ). These declines reduce wind power 
endowment by 2050 and as early as the 2020–2040 near-term period 
in the USA Mountain West, while there is disagreement between 
global- and regional-model change projections in the upper and 
lower Great Plains, Ohio River Valley, Mexico and eastern Canada 
(Karnauskas et al., 2018a; Jung and Schindler, 2019; Chen, 2020).
Severe wind storm: There is limited evidence and low agreement  
in observed changes in North American CID indices associated with

extratropical cyclones (Section  11.7), severe thunderstorms, severe 
wind bursts ( derechos ), tornadoes, or lightning strikes (Vose et al., 
2014; Easterling et  al., 2017; Kossin et  al., 2017). Observational 
studies have indicated a reduction in the number of tornado days in 
the USA, but increases in outbreaks with 30 or more tornadoes in one 
day (Brooks et al., 2014), the density of tornado clusters (Elsner et al., 
2015), and overall tornado power (Elsner et al., 2019).
There is medium confidence  of a general decrease in the number of 
extratropical cyclones producing high wind speeds in North America, 
except over northernmost parts, for a global warming level of 2°C or 
by the end of the century under RCP4.5 and RCP8.5 (Kumar et al., 
2015; Jeong and Sushama, 2018a; C. Li et al., 2018). GCMs cannot 
directly resolve tornadoes and severe thunderstorms, however 
projections of favourable environments for severe storms (based

on convective available potential energy and wind shear) indicate 
medium confidence  for more severe storms and a longer convective 
storm season in the USA, weaker increases extending north and east 
(Seeley and Romps, 2015; Glazer et al., 2021), and a corresponding 
increase in autumn and winter tornadic storms (H.E. Brooks, 2013; 
Diffenbaugh et al., 2013; Brooks et al., 2014; see also Section 11.7.2). 
Prein et al. (2017a) used a convection-permitting model to project 
a tripling of mesoscale convective systems over the USA for end-of-
century RCP8.5.
Tropical cyclone : Section  11.7.1 identified recent reductions in 
tropical cyclone translation speed and higher tropical cyclone rainfall 
totals over the North Atlantic, as well as substantial natural variability. 
Projections indicate low confidence  in change in North Atlantic tropical 
cyclone numbers, but medium confidence  in Mexico and the US Gulf and

Atlantic coasts for more intense storms with higher wind, precipitation, 
and storm surge totals when they do occur (Section 11.7.1; Diro et al., 
2014; DOE, 2015; Walsh et  al., 2016a; Kossin et  al., 2017; Marsooli 
et  al., 2019; Ting et  al., 2019; Knutson et  al., 2020). A  more rapid 
intensification of tropical cyclone winds and destructive power also

1833
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212heightens the tropical cyclone hazard (Bhatia et al., 2019). Greenhouse 
gas forcing is projected to shift tropical cyclones poleward (Kossin 
et al., 2016), while also holding the potential for higher precipitation 
totals (Risser and Wehner, 2017; Knutson et al., 2020) particularly given 
evidence that storms increasingly stall near North American coastlines 
(Hall and Kossin, 2019).
Sand and dust storm: Land-use change has increased dust 
emissions in the western USA in the past 200 years (Neff et  al., 
2008). However, there is medium confidence  for observed increases 
in Western North American sand and dust storm activity since 1980. 
In their study of Valley Fever spread, Tong et  al. (2017) identified 
a rapid intensification of dust storm activity using PM 10 and PM 2.5 
observations from 1980–2011 across 29 monitoring sites in the

south-western USA, similar to contiguous USA observations by 
Brahney et al. (2013). Hand et al. (2016) attributed the earlier onset 
of spring dusts in the south-west in large part to the Pacific Decadal 
Oscillation, however. The increasing trend in dust since the 1990s 
in the south-western USA can be explained by precipitation deficit 
and surface bareness (Pu and Ginoux, 2018). Projections of future 
sand and dust storms over North America are based on aridity as 
a  primary proxy for conducive conditions which lends medium 
confidence  of an increase over Mexico and the south-western USA. 
Pu and Ginoux (2017) project about five more dusty days in spring 
and summer in the southern Great Plains under RCP8.5 at the end 
of the century, while dusty days decrease in northern regions where 
mean precipitation tends towards wetter conditions.
Tropical cyclones, severe wind and dust storms  in North 
America are shifting towards more extreme characteristics,

with a  stronger signal towards heightened intensity than 
increased frequency, although specific regional patterns are 
more uncertain ( medium confidence ). Mean wind speed and 
wind power potential are projected to decrease in Western 
North America ( medium confidence ) with differences between 
global and regional models lending low confidence  elsewhere.
12.4.6.4 Snow and Ice
Snow:  The seasonal extent of snow cover has reduced over North 
America in recent decades ( robust evidence , high agreement ) 
(see also Sections 2.3.2.2 and 9.5.3, and Figure Atlas.25). The average 
snow-cover extent in North America decreased at a  rate of about 
8500  km2  yr –1 over the 1972–2015 period, reducing the average 
snow cover season by two weeks, primarily due to earlier spring melt 
(US EPA, 2016). Observations indicate earlier spring snowpack melting 
(Dudley et  al., 2017) and a  reduction in end-of-season snowpack 
metrics important to water resources over the Rocky Mountains

(particularly since 1980) and Pacific Northwest (Pederson et al., 2013; 
Kormos et al., 2016; Kunkel et al., 2016; Fyfe et al., 2017; Mote et al., 
2018). In situ measurements in Canada show more heterogenous 
trends in snow amount and density (Brown et al., 2019).
Climate change is expected to reduce the total snow amount and the 
length of the snow cover season over most of North America, with 
a corresponding decrease in the proportion of total precipitation falling 
as snow and a reduction in end-of-season snowpack ( high confidence ) (see Atlas.9.5). Changes include a reduction in the number of days with 
snowfall in across all of North America, with the exception of northern 
Canada (Danco et al., 2016; McCrary and Mearns, 2019), a delay of 
about a  week in first snowfall in the western USA by 2050 under 
RCP8.5 (Pierce and Cayan, 2013), and more prominent reductions in 
Canadian snow cover in the October –December period (Mudryk et al.,

2018). Reduced total snowpack and earlier snowmelt lower dry season 
streamflow (Kormos et al., 2016; Rhoades et al., 2018). Figure 12.10b 
shows a reduction in days suitable for skiing (SWE > 10 cm; Wobus 
et al., 2017b) across the USA and southern Canada, although some 
portions of northern central Canada see an increase.
Glacier:  Section  9.5.1 assessed that glaciers in Alaska, western 
Canada and the western USA are expected  to continue to lose mass 
and areal extent ( high confidence ). Compared to their 2015 state, 
glaciers in the western Canada and the USA region will lose 62 ± 30%, 
75 ± 29% and 85 ± 23%, of their mass by the end of the century for 
RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively (Marzeion et al., 
2020). Meanwhile glaciers in Alaska will lose 26 ± 21%, 31 ± 24% 
and 44 ± 27%, of their 2015 mass under the same scenarios. 
The overall loss of glacial mass can act as a meltwater supply for

freshwater resources, although this is expected to peak in the middle 
of the century and then fade as glaciers disappear (Fyfe et al., 2017; 
Derksen et al., 2018). Continued shrinkage of glaciers is projected 
to create further glacial lakes ( medium confidence ) similar to those 
that have led to outburst floods in Alaska and Canada (Carrivick and 
Tweed, 2016; Harrison et al., 2018).
Permafrost:  Warmer ground temperatures are expected to extend the 
geographical extent and depth of permafrost thaw across northern 
North America ( very high confidence ) (Section 9.5.2). Observations 
across Canada show that permafrost temperature is increasing and 
the active layer is getting thicker (Section  2.3.2.5; Derksen et  al., 
2018; Biskaborn et al., 2019; Romanovsky et al., 2020). Slater and 
Lawrence (2013) note that the RCP8.5 end-of-century period in North 
America only has shallow permafrost as the most probable condition

in the Canadian Archipelago. Melvin et  al. (2017) noted the loss 
of shallow permafrost in five RCP8.5 CMIP5 models across a wide 
swathe of southern Alaska by 2050, along with increases of active 
layer thickness. There is high confidence  in continued reductions in 
mountain near-surface permafrost area with high spatial variability 
given local snow and temperature changes (Section 9.5.2; Peng et al., 
2018; Hock et al., 2019).
Lake, river and sea ice:  Anthropogenic warming reduces the seasonal 
extent of lake and river ice over many North American freshwater 
systems, with ice-free winter conditions pushing further north with 
rising temperatures ( high confidence ). Observations in Central and 
Eastern North America show reduced average seasonal lake-ice cover 
duration (Benson et  al., 2012; Mason et  al., 2016; US EPA, 2016). 
Satellite observations show declines in lake ice (Du et al., 2017) and 
loss of more than 20% of winter river-ice length in much of Alaska

(2008–2018 compared to 1984–1994; Yang et al., 2020a). Spring lake 
and river ice in Canada is projected to break up 10–25 days earlier 
while autumn freeze-up occurs 5–15 days later by mid-century, with 
larger declines in lake-ice season closer to the coasts (Dibike et  al., 
2012) and for rivers in the Rocky Mountains and north-eastern USA

1834
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12(Yang et al., 2020a), although global models have difficulty with frozen 
freshwater system dynamics (Derksen et al., 2018). Substantial ice loss 
is projected over the Laurentian Great Lakes (Hewer and Gough, 2019; 
Matsumoto et  al., 2019). The southern extent of lakes experiencing 
intermittent winter ice cover moves northward with rising temperature, 
pushing nearly out of the continental USA at low elevations under a 
4.5°C GWL (Sharma et al., 2019). Higher spring flows and the potential 
for winter thaws are also projected to heighten the threat of ice jams 
(Rokaya et al., 2018; Bonsal et al., 2019) while reducing the seasonal 
viability of ice roads and recreational use (Pendakur, 2016; Mullan 
et al., 2017; Knoll et al., 2019).
Seasonal sea ice coverage along the majority of Canadian and  Alaskan 
coastlines is declining ( robust evidence , high agreement ) and there is

high confidence  that sea ice loss continues under climate change, as 
further assessed in Section 12.4.9.
Heavy snowfall:  There is low agreement (limited evidence ) for 
observed changes in heavy snowfall in North America. Kluver and 
Leathers (2015) noted a  1930–2008 frequency increase for all 
snow intensities in the northern Great Plains but declines in heavier 
snow  events in the Pacific Northwest and declines in the south-
eastern USA. Changnon (2018) found that most extreme 30-day 
high-snowfall periods in the 1900–2016 record over the eastern USA 
occurred in the 1959–1987 period, which lies between the 1930s 
Dust Bowl and recent warming. There is low agreement and medium 
evidence  for broad projected changes to heavy snowfall over North 
America given increased heavy precipitation and warmer winter 
temperatures. Several recent regional studies have projected that 
low-intensity events decrease more rapidly than heavy snowfall

events, resulting in an increase in the snowfall proportion from 
heavy snowfall events even as the number of such events decreases 
(O’Gorman, 2014; Lute et  al., 2015; Zarzycki, 2016; Janoski et  al., 
2018; Ashley et al., 2020).
Ice storm:  There is limited evidence  in the literature of unique 
changes in ice storms observed or projected over North America. 
Groisman et al. (2016) examined 40 years of observations and found 
weak decreases in freezing rain events over the south-eastern USA 
in the most recent decade. Ning and Bradley (2015) project that the 
average snow–rain transition line, which is associated with mixed 
precipitation, moves 2° latitude northward over Eastern North 
America by the end of the 21st  century under RCP4.5 (4° under 
RCP8.5; see also Klima and Morgan, 2015).
Hail:  There is limited evidence and low agreement  for observed 
changes in the frequency or intensity of North American hail

storms. J.T. Allen et al. (2015) and Allen (2018) found that temporal 
inconsistencies in the US and Canadian hail records made long-term 
climate analysis difficult, although B.H. Tang et al. (2019) identified 
an increasing frequency of environmental conditions conducive 
for large hail (diameter ≥ 5 cm) over the central and eastern USA. 
There is limited evidence and medium agreement  in projections of 
increased hail damage potential over North America. Some regional 
and convective-permitting model projections indicate a longer hail 
season with fewer events and larger hail sizes that result in higher 
hail damage potential (Brimelow et al., 2017; Trapp et al., 2019).Snow avalanche:  There is limited evidence  of directional changes 
in snow avalanches over North America. Mock and Birkeland 
(2000) identified a  1969–1995 decrease in snow avalanches over 
the western United States, although they note the heavy influence

of natural variability. A similar decline was observed over western 
Canada (Bellaire et al., 2016; Sinickas et al., 2016), but clear trends 
are difficult to discern given sparse observations and shifts in 
avalanche management. We concur with the SROCC assessment 
of medium confidence and high agreement  that snow avalanche 
hazards generally decrease at low elevations given lower snowpack, 
even as high elevations are increasingly susceptible to wet-snow 
avalanches (Hock et al., 2019; see also Lazar and Williams, 2008).
Observations and projections agree that snow and ice CIDs 
over North America are characterized by reduction in glaciers 
and the seasonality of snow and ice formation, loss of shallow 
permafrost, and shifts in the rain/snow transition line that 
alters the seasonal and geographic range of snow and ice 
conditions in the coming decades ( very high confidence ).
12.4.6.5 Coastal and Oceanic
Relative sea level:  Chapter  9 found that observations indicate

increasing sea levels along most North American coasts ( robust 
evidence , high agreement ), although there is substantial regional 
variation in relative sea level rise ( robust evidence , high agreement ). 
Around North America, over 1900–2018, a  new tide gauge-based 
reconstruction finds a  regional mean RSL change of 1.08  [0.79  to 
1.38] mm yr –1 in the subpolar North Atlantic, 2.49 [1.89 to 3.06] mm yr –1 
in the subtropical North Atlantic, and 1.20 [0.76 to 1.62] in the East 
Pacific (Frederikse et  al., 2020), compared to a  GMSL change of 
to 2.17 [1.66 to 2.66]  mm yr –1, 4.04  [2.77  to 5.24]  mm yr –1 and 
2.35  [0.70 to 4.06]  mm  yr –1,  respectively (Frederikse et  al., 2020), 
Table 9.5). Relative sea level (RSL) is falling in portions of southern

Alaska (Sweet et al., 2018) and much of the northern part of north-
eastern Canada and around Hudson Bay (where land is rising by 
>10 mm/year; Greenan et al., 2018).
Relative sea level rise is virtually certain  to continue in the oceans 
around North America, except in the northern part of north-eastern 
Canada and portions of southern Alaska. Regional mean RSLR 
projections for the oceans around North America range from 0.4–1.0 
m under SSP1-2.6 to 0.7–1.4 m under SSP5-8.5 for 2081–2100 relative 
to 1995–2014 (median values), which means that there are locally 
large deviations from the projected GMSL change (Section 9.6.3.3), 
including decreases in RSL in northern north-eastern Canada 
from land uplift (see also Sweet et al., 2017; Greenan et al., 2018; 
Oppenheimer et al., 2019). The RSLR projections here may however 

Coastal flood:  Observations indicate that episodic coastal flooding is 
increasing along many coastlines in North America ( robust evidence , 
high agreement ), and this episodic coastal flooding will increase in

1835
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Table 12 .8 | Summary of confidence in direction of projected change in climatic impact-drivers in North America, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, 
SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C 
(see Section 12.4 for more details of the assessment method).  The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at 
least medium confidence  in Section 12.5.2.

North Central America (NCA)   2  
Western North America (WNA)  3 5 5 4,7 6,7 6,7 8 6  1 1 5 2  
Central North America (CNA)  7 7 7 8 4   2  
Eastern North America (ENA)  5 7 8 
1 1 1 2  
North-Eastern North America (NEN)  
55 6,7 6,7 8 
1,6 1 4 4,6 2,6  
North-Western North America (NWN)  
56 5 6,7 6,7 8 
1 1,6 
92  

1. Snow may increase in some high elevations and during the cold season and decrease in other seasons and at lower elevations.
3. Increasing in northern regions and decreasing towards the south.
4. Decreasing in northern regions and increasing towards the south.
5. Higher confidence in northern regions and lower towards the south.
6. Higher confidence in southern regions and lower towards the north.
7. Higher confidence in increase for some climatic impact-driver indices during summer.
8. Increase in convective conditions but decrease in winter extratropical cyclones.
9. Relative sea level rise reduced given land uplift in southern Alaska.

1836
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12many North American regions under future climate change ( high 
confidence ) although land uplift from glacial isostatic adjustment in 
northern and Hudson Bay portions of North-Eastern North America 
leads to only medium confidence  of coastal flood increases in that 
region. Sweet et al. (2018) found 2000–2015 observed increases of 
about 125% in high-tide flooding frequencies along the southern 
Atlantic USA coastline, with 75% increases along the USA Gulf 
Coast and USA northern Atlantic coastlines. That same study noted 
that a GMSL of 0.5 m in 2100 would increase high tide (‘nuisance’) 
flooding from current rates of about once a month for most coastal 
regions to about once every other day along the USA Atlantic and Gulf 
coasts and smaller increases in frequency along the Pacific coast, and 
Dahl et al. (2017a) found similar trends on the USA East Coast prior to

mid-century. The present day 1-in-50-year ETWL is projected to occur 
around three times per year by 2100 with an SLR of 1 m all around 
North America, except in most of Eastern North America where 
it is expected to have return periods of 1-in-1-year to 1-in-2-years 
(Vitousek et al., 2017). Ghanbari et al. (2019) projected corresponding 
shifts towards higher frequencies of major flooding events for 20 US 
cities. Figure 12.4r and Figure 12.SM.6 show increases of 70 cm or 
more in the 100-year return period extreme total water level (ETWL) 
over much of the USA East Coast, British Columbia, Alaska, and the 
Hudson Bay under RCP8.5 by 2100 (relative to 1980–2014), with 
lower increases in northern Mexico, northern Canada, Labrador, and 
the Pacific and Gulf coasts of the USA (Vousdoukas et  al., 2018). 
Projected increases in coastal flooding generally follow patterns of 
RSL change, although sea ice loss in the north also increases open 
water storm surge (Greenan et al., 2018).

Coastal erosion:  There is limited evidence  of changes in North 
American episodic storm erosion caused by waves and storm surges. 
Observations show increased extreme wave energy on the Pacific 
coast, but no clear trend on other USA coasts given substantial 
natural variability (Bromirski et al., 2013; Vose et al., 2014). In terms of 
long-term coastal erosion, shoreline retreat rates of around 1 m yr –1 
have been observed during 1984–2015 along the sandy coasts of 
NWN and NCA while portions of the US Gulf Coast have seen a retreat 
rate approaching 2.5 m yr –1 (Luijendijk et al., 2018; Mentaschi et al., 
2018). Sandy shorelines along ENA and WNA have remained more or 
less stable during 1984–2014, but a shoreline progradation rate of 
around 0.5 m yr –1 has been observed in NEN. Mentaschi et al. (2018) 
report 1984–2015 coastal area land losses of 630 km2 and 1260 km2 
along the Pacific and Atlantic coasts of the USA, respectively.

Projections indicate that sandy coasts in most of the region 
will experience shoreline retreat through the 21st  century ( high 
confidence ). Median shoreline change projections presented by 
Vousdoukas et  al. (2020b) show that sandy shorelines in NWN, 
ENA, and NCA will retreat by between 40 and 80 m by mid-century 
(relative to 2010) under both RCP4.5 and RCP8.5. Projections for NEN 
and WNA are lower at 20–30 m under the same RCPs. The highest 
median mid-century projection in the region is for CNA at around 
125 m under both RCPs. RCP4.5 projections for 2100 show shoreline 
retreats of 100 m or more along the sandy coasts of NWN, CNA, 
and NCA, while retreats of between 40 and 80 m are projected in 
other regions. Under RCP8.5, retreats exceeding 100 m are projected 
in all regions except NEN and WNA (approximately 80 m) by 2100, with particularly high retreats in NWN (160 m) and CNA (330 m). The 
total length of sandy coasts in North America that are projected to

retreat by more than a median of 100 m by 2100 under RCP4.5 and 
RCP8.5 is about 15,000 km and 25,000 km respectively, an increase 
of approximately 70%.
Marine heatwave:  There is high confidence in observed increases 
in marine heatwave (MHW) frequency and future increases in marine 
heatwaves are very likely  around North America (Box 9.2). The total 
number of MHW days per decade increased in the North American 
coastal zone, albeit somewhat more in the Pacific (Oliver et al., 2018; 
Smale et  al., 2019). Projected increases in degree heating weeks 
(Heron et al., 2016) and degree heating months (Frieler et al., 2013) 
indicate increasing bleaching-level and mortality-level heating stress 
threshold events for reefs in Florida and Mexico.
Mean SST is projected to increase by 1°C (3°C) around North America by 
2100, with a  hotspot of around 4°C (5°C) off the North American 
Atlantic coastline under RCP4.5 (RCP8.5) conditions (see Interactive

Atlas). Frölicher et al. (2018) projected increasing MHW frequency and 
spatial extent at a 2°C global warming level with the largest increases 
in the Gulf of Mexico and off the southern USA East Coast (>20×) as 
well as off the coast of the Pacific Northwest (>15×). Projections for 
SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around North 
America by 2081–2100, relative to 1985–2014 (Box 9.2, Figure 1).
There is high confidence  that most coastal CIDs in North America 
will continue to increase in the future with climate change. An 
observed increase in relative sea level rise is virtually certain  to 
continue in North America (other than around the Hudson Bay 
and southern Alaska) contributing to more frequent and severe 
coastal flooding in low-lying areas ( high confidence ) and 
shoreline retreat along most sandy coasts ( high confidence ). 
Marine heatwaves are also expected to increase all around the 

The assessed direction of change in climatic impact-drivers for North 
America and associated confidence levels are illustrated in Table 12.8.
12.4.7 Small Islands
This section covers the climatic impact-drivers affecting small islands 
around the world (see definition of SIDS in the Glossary; Cross-
Chapter Box Atlas.2) with a particular focus on small islands in the 
Caribbean (CAR) Sea and the Pacific Ocean. Caribbean and Pacific 
small islands have mostly tropical climates and local conditions are 
also influenced by diverse topography ranging from low-lying islands 
and atolls to volcanic and mountainous terrain. Climate variability in 
these islands is influenced by the trade winds, easterly waves, tropical 
cyclones (TC), and the migrations of the Inter-tropical Convergence 
Zone (ITCZ), the North Atlantic Subtropical High, and the South Pacific 
Convergence Zone (SPCZ), and other modes of climate variability as

discussed in Cross-Chapter Box Atlas.2. Furthermore, changes in the 
ocean temperature and chemistry, and relative sea level have strong 
impacts on these small islands given their geographical location and 
dependence on coastal and marine ecosystem services.

1837
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212The AR5 recognized the heterogeneity in these small islands in terms 
of physical geography, socio-economic and cultural backgrounds, as 
well as their vulnerability to the impacts of climate change. Similar 
to previous reports, these regions have been assessed together 
in this section, given the similarities in the challenges they face in 
addressing climate change impacts and risk, which were thought – 
until AR4  – to be dominated by sea level rise (Nurse et  al., 2014; 
Betzold, 2015). Since then there has been a  substantial increase 
in the number and complexity of the literature on the drivers and 
impacts of climate change on small islands (BOM and CSIRO, 2011, 
2014; Nurse et al., 2014; Gould et al., 2018; Keener et al., 2018). There 
are also increasing efforts being made to produce higher resolution 
climate projections for small islands through downscaling methods

(Elison Timm et al., 2015; McLean et al., 2015; Khalyani et al., 2016; 
C. Zhang et al., 2016; Stennett-Brown et al., 2017; Bhardwaj et al., 
2018; Bowden et al., 2021).
The AR5 identified the key climate and ocean-related hazards affecting 
small islands, which occur at different time scales and have diverse 
impacts on multiple sectors (Christensen et al., 2013; Nurse et al., 2014). 
Recent findings from SR1.5 and SROCC emphasize that the multiple 
interrelated climate hazards currently faced by low-lying islands and 
coastal areas will be amplified in the future, especially at higher global 
warming levels (Hoegh-Guldberg et al., 2018; IPCC, 2019b).
12.4.7.1 Heat and Cold
Mean air temperature: Significant warming trends are clearly 
evident in the small islands, such as those in the Pacific, CAR, 
and western Indian Ocean, particularly over the latter half of the 
20th  century (see Figure Atlas.11; Atlas.10.2; Cross-Chapter Box

Atlas.2, Table  1). This observed warming signal in the tropical 
western Pacific has been attributed to anthropogenic forcing (Wang 
et al., 2016). There is high confidence  of warming over small islands 
even at 1.5°C GWL (Atlas.10.4 and Figure Atlas.28; Hoegh-Guldberg 
et al., 2018). Mean temperature is very likely  to increase by 1°C–2°C 
(2°C–4°C) by 2041–2060 (2081–2100) under RCP8.5 (BOM and 
CSIRO, 2014) and SSP3-7.0 (Atlas.10.4, Figure  4.19 and Figure 
Atlas.12; Almazroui et al., 2021).
Extreme heat:  Observational records indicate warming trends in 
the temperature extremes since the 1950s in CAR and the Pacific 
small islands ( high confidence ) (Sections 11.3.2 and 11.9, and 
Table 11.13). A detectable anthropogenic increase in summer heat 
stress has been identified over a number of island regions in CAR, 
western tropical Pacific, and tropical Indian Ocean, based on wet bulb 
globe temperature (WBGT) index trends for 1973–2012 ( medium

confidence ) (Knutson and Ploshay, 2016). An increasing trend in 
the maximum daytime heat index is also noted in CAR during the 
1980–2014 period, as well as more extreme heat events since 1991 
(Ramirez-Beltran et al., 2017).
Compared with the recent past, it is likely  that the intensity and 
frequency of hot (cold) temperature extremes will increase (decrease) 
in the small islands (Section 11.9 and Table 11.13; BOM and CSIRO, 
2014). Warm spell conditions will occur up to half the year in CAR 
at 1.5°C GWL with an additional 70 days at 2°C (Hoegh-Guldberg et al., 2018; Taylor et al., 2018), with livestock temperature–humidity 
tolerance thresholds increasingly surpassed (Lallo et  al., 2018). 
In  CAR, a  median increase of more than a month per year where 
temperatures exceed 35°C is projected by end of the 21st century 
under SSP5-8.5 (Figure  12.4a–c and Figure  12.SM.1). Heatwaves 
are projected to increase in CAR by the mid- and end-century under

RCP8.5 (Sections 11.3.5 and 11.9, and Table 11.13). Figure 12.4d–f 
and Figure 12.SM.2 also show an increase of about 30–60 days in 
which HI exceeds 41°C by 2041–2060 under SSP5-8.5 relative to 
1995–2014 in CAR, with an additional increase of about 50–100 
days by end of the 21st  century for RCP8.5/SSP5-8.5, but this 
increase remains below 50 days for RCP2.6/SSP1-2.6. The Pacific 
Islands region is also among those projected to have an increase in 
WBGT by end-century under RCP8.5, increasing the risk of heat stress 
in the region (Newth and Gunasekera, 2018).
It is very likely  that the significant recent warming trends 
observed in the small islands will continue in the 21st century, 
which will likely  further increase heat stress in these regions.
12.4.7.2 Wet and Dry
Mean precipitation: Observational datasets have generally 
revealed no significant long-term trends in rainfall in the Caribbean 
over the 20th century when analysed at seasonal and inter-decadal

timescales, except for some areas where there is evidence for 
decreasing trends for the period 1901–2010 but not for the period 
1951–2010 (Cross-Chapter Box Atlas.2, Table  1, and Atlas.10.2; 
Knutson and Zeng, 2018). Although there are spatial variations, 
annual rainfall trends in the western Indian Ocean are mostly 
decreasing, with generally non-significant trends in the western 
tropical Pacific since the 1950s ( low confidence ). Significant drying 
trends are noted in the southern Pacific subtropics and south-western 
French Polynesia during the 1951–2015 period (McGree et al., 2019), 
and in some areas of Hawaii during the 1920–2012 period ( medium 
confidence ) (Cross-Chapter Box Atlas.2, Table 1, and Atlas.10.2).
Atlas.10.4 projects precipitation reduction over the Caribbean 
(high confidence ) (Almazroui et al., 2021) and parts of the Atlantic and 
Indian oceans, particularly in June to August, by end of 21st century

under SSP5-8.5. Precipitation is generally projected to increase under 
SSP5-8.5 and for higher GWLs in the small islands in parts of the 
western and equatorial Pacific, but there is low confidence  in broad 
changes given drier conditions projected for the southern subtropical 
and eastern Pacific Ocean ( limited agreement  given spatial and 
seasonal variability) (Atlas.10.4 and Figure Atlas.28).
River flood:  There is limited evidence  on observed changes in river 
flooding in the small islands. Long-term records in Hawaii indicate 
no clear trends in peak flow, except for the significant decrease 
in peak streamflow in Hawaii Island over the period 1967–2016 
(Bassiouni and Oki, 2013; Clilverd et  al., 2019). Similarly, there is 
no significant trend in the frequency and height (after adjusting for 
average sea level rise) of river flood in Fiji over the period 1892–2013 
(McAneney et al., 2017). There is low confidence  on the direction of

future change of river flooding in the small islands due to the limited 
literature. In Oahu, Hawaii, extreme peak flow events with high

1838
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12return periods are projected to increase by end of the 21st century 
under RCP8.5, but there is also high uncertainty in these projections 
(Leta et al., 2018).
Heavy precipitation and pluvial flood:  Heavy precipitation days 
in CAR have increased in magnitude, and have been more frequent 
in the northern part during the latter part of the 20th century ( low 
confidence ) (Section 11.4.2 and Table 11.14). The direction of change 
in extreme precipitation varies across the Pacific and depends on 
the season ( low confidence ) (Section  11.4.2 and Cross-Chapter 
Box Atlas.2, Table  1). Although pluvial flooding events have been 
observed in some islands, there is limited evidence  for an assessment 
on past changes in pluvial flooding, unlike in other regions. There is 
low confidence  in the projected change in magnitude of very heavy

precipitation days in CAR across different GWLs (Table 11.14). On the 
other hand, there is high confidence  in the increase in frequency and 
intensity of extreme rainfall events (i.e., 1-in-20-year rainfall events) 
in the western tropical Pacific in the 21st century, even for RCP2.6 
scenario, based on model agreement and mechanistic understanding 
but low confidence  in the magnitude of change in extreme rainfall 
due to model bias (BOM and CSIRO, 2014).
Landslide:  Heavy rainfall, such as from tropical cyclones, can trigger 
landslides over steep terrain in the small islands (Bessette-Kirton 
et al., 2019). There is limited evidence  to determine long-term trends 
in rainfall-induced landslides in the small islands (Kirschbaum et al., 
2015; Sepúlveda and Petley, 2015; Froude and Petley, 2018; Bessette-
Kirton et  al., 2019). There is low confidence  in future changes in 
landslides in the small islands. The direction of change may depend

on future changes in precipitation, tropical cyclones, climate modes 
(e.g.,  El Niño–Southern Oscillation, ENSO), as well as human 
disturbance, but more data and understanding of the complexity of 
these relationships are needed, especially in these vulnerable areas 
(Sepúlveda and Petley, 2015; Gariano and Guzzetti, 2016; Froude and 
Petley, 2018).
Aridity:  Current estimates identify many small islands as being under 
water stress and thus particularly sensitive to variations in rainfall and 
groundwater, population growth and demand, and land-use change, 
among others (Cross-Chapter Box Atlas.2; Holding et al., 2016). From 
1950 to 2016, a heterogeneous but prevalent drying trend is found 
in CAR ( low confidence ), where drought variability is modulated 
by the tropical Pacific and North Atlantic oceans (Table 11.15 and 
Cross-Chapter Box Atlas.2, Table 1; Herrera and Ault, 2017). In the 
future, increased aridity and decreased freshwater availability are

projected in many small islands due to higher evapotranspiration 
in a warmer climate that partially offsets increases or exacerbates 
reductions in precipitation (Karnauskas et al., 2016, 2018b; Hoegh-
Guldberg et al., 2018). Increased aridity is projected for the majority 
of the small islands, such as in CAR, southern Pacific and western 
Indian Ocean, by 2041–2059 relative to 1981–1999 under RCP8.5 or 
at 1.5°C and 2°C GWLs, which will further intensify by 2081–2099 
(medium confidence ) (Karnauskas et al., 2016, 2018b). Groundwater 
recharge is projected to increase in Maui, Hawaii except on the 
leeward side of the island, which underscores the importance of 
topography and elevation on freshwater availability in different 
island microclimates (Brewington et al., 2019; Mair et al., 2019).Hydrological drought:  There is low confidence  of widespread 
changes to hydrological drought in CAR or Pacific small islands in

recent decades, although an increasing number of studies document 
local changes.  Records in Hawaii indicate downward trends in low 
streamflow and base flow from 1913 to 2008 (Bassiouni and Oki, 
2013). Decadal variability of Hawaiian streamflow coincides with 
rainfall fluctuations associated with the Pacific Decadal Variability 
although significant average declines in surface and baseflow runoff 
of about 8% and 11% per decade, respectively, have been noted 
during the 1987–2016 period (Clilverd et al., 2019).
There is low confidence  in hydrological drought change projections, 
given low signal-to-noise ratios and the challenge in representing 
island scales in global analyses. Prudhomme et al. (2014) recognized 
CAR as one of the regions with the highest increase in regional 
deficit index (RDI; a measure of the fraction of area in hydrological 
drought conditions) by the end of the 21st  century under RCP8.5. 
Daily streamflow and extreme low flows in two watersheds in Oahu,

Hawaii are projected to decline by mid- and end of the 21st century 
under RCP4.5 and RCP8.5, which would result in more frequent 
hydrological droughts in this area (Leta et al., 2018).
Agricultural and ecological drought:  Recent trends toward more 
frequent and severe droughts have been noted in the small islands but 
only with low confidence  in broad trend patterns, given high spatial 
variability including heightened drought on the leeward side of islands 
(e.g., Frazier and Giambelluca, 2017; Herrera and Ault, 2017; McGree 
et  al., 2019; see Table  11.15, Cross-Chapter Box  Atlas.2, Table  1). 
Agricultural and ecological droughts are projected to increase in 
frequency, duration, magnitude, and extent in small islands, such as 
in CAR ( medium confidence ) and parts of the Pacific ( low confidence ), 
particularly where future declines in precipitation are compounded 
by higher evapotranspiration, under increasing levels of warming

(Naumann et al., 2018; Taylor et al., 2018; Vichot-Llano et al., 2021). 
Relative to the period 1985–2014, decreases in annual surface and 
total column soil moisture become more robust in more areas in CAR 
by 2071–2100 under SSP3-7.0 and SSP5-8.5 scenarios (B.I. Cook 
et al., 2020), but reliably representing drought features in small island 
domains with global simulations is challenging (see also Section 11.9).
Fire weather: There is limited evidence  on trends in wildfire in CAR 
and the Pacific. Records of wildfire in Hawaii from 2005 to 2011 
indicate a peak in area burned during the hot and dry summer months, 
but Trauernicht et al. (2015) note the difficulty in establishing the link 
between past climate and wildfire trends due to human activities and 
vegetation changes. Availability of literature limits assessment on 
future fire weather in the small islands. Drying and warming trends 
tend to increase fire probability aside from the climate impact on fuel

loading, for example, grassland fires in Hawaii (Trauernicht, 2019), and 
wildfires in Puerto Rico (Van Beusekom et al., 2018).
Observed and projected rainfall trends vary spatially across 
the small islands. Higher evapotranspiration under a warming 
climate are projected to partially offset future increases 
or amplify future reductions in rainfall, resulting in drier 
conditions and increased water stress in the small islands 

1839
Climate Change Information for Regional Impact and for Risk Assessment Chapter 121212.4.7.3 Wind
Mean wind speed: Scarcity of observations limits assessment of 
long-term changes in winds over the small islands in the Pacific and 
CAR. Records indicate that average daily wind speeds have slowly 
declined in Hawaii, but have remained constant across western and 
southern Pacific sites since the mid-20th century (Marra and Kruk, 
2017). Recent studies of reanalyses and hindcast simulations indicate 
an intensification of the Pacific trade winds during the 1992–2011 
period, which contributed to the ocean cooling in the tropical central 
and eastern Pacific (England et al., 2014; Takahashi and Watanabe, 
2016). Projections estimate up to 0.4 m s–1 (8%) increase in annual 
winds in CAR under RCP8.5, which is associated with changes in 
the extension of the North Atlantic Subtropical High that enhances 
the Caribbean low-level jet during the wet season, and stronger local

easterlies due to enhanced land–ocean temperature differences in 
the dry season (Costoya et al., 2019) ( low confidence ).
Tropical cyclone: Tropical cyclones have devastating impacts on the 
small islands due to intense winds, storm surge and rainfall, although the 
associated rainfall can also be beneficial for freshwater resources. It is 
likely  that tropical cyclone intensity and intensification rates at a global 
scale have increased in the past 40 years but it is not clear if regional-
scale changes are basin-wide or due to shifts in tropical cyclone tracks 
(Section  11.7.1.2). Other, less data-sensitive tropical cyclone features, 
such as the poleward migration of where tropical cyclones reach 
peak intensity in the western North Pacific since the 1940s ( medium 
confidence ) and the slowdown in tropical cyclone translational speed 
over contiguous USA since 1900 ( medium confidence ), can affect rainfall

and flooding over small islands in CAR and the Pacific (Section 11.7.1.2).
Projections of global changes in tropical cyclones indicate more 
frequent Category  4–5 storms ( high confidence ) and increased rain 
rates ( high confidence ) (Knutson et al., 2020), with relative sea level rise 
exacerbating storm surge potential, but with large regional differences 
(see Section 11.7.1.5). By the late 21st century, tropical cyclones are 
projected to be less frequent in the basins of the western and eastern 
North Pacific, Bay of Bengal, Caribbean Sea and in the Southern 
Hemisphere, but will be more frequent in the subtropical central 
Pacific (Murakami et al., 2014; Yoshida et al., 2017; Bell et al., 2019; 
Knutson et al., 2020). Over CAR, tropical cyclone intensity is expected 
to increase by the end of the century under RCP8.5 due to higher sea 
surface temperatures but can be inhibited by increases in vertical wind

shear in the region ( medium confidence ) (Kossin, 2017; Ting et  al., 
2019). The poleward movement of the area in which tropical cyclones 
reach peak intensity in the western North Pacific is likely  to continue, 
which affects the tropical cyclone frequency over the small islands in 
the area (Section 11.7.1.5; Kossin et al., 2016). Projections also indicate 
an increase (decrease) in the tropical cyclone frequency during El Niño 
(La Niña) events in the Pacific at the end of the 21st century (Chand 
et al., 2017). RCP8.5 2080–2099 projections indicate a 2% increase in 
the number of tropical cyclones in the north-central Pacific relative to 
1980–1999, with tracks shifting northward towards Hawaii (N. Li et al., 
2018). Given projected reductions to the overall number of tropical 
cyclones but increases in storm intensity, total rainfall and storm surge 
potential, we assess medium confidence  of overall changes to tropical

cyclones affecting the Caribbean and Pacific small islands.Projections indicate that small islands will generally face 
fewer but more intense tropical cyclones ( medium confidence ) 
although there is substantial variability across small island 
regions given projected regional shifts in storm tracks.
12.4.7.4 Coastal and Oceanic
Relative sea level:  Relative sea level rise (RSLR) continues to be 
a major threat to small islands and atolls, since it can exacerbate the 
impacts of other climate hazards on low-lying coastal communities 
and infrastructures, ecosystems, and freshwater resources (Nurse 
et al., 2014; Hoegh-Guldberg et al., 2018). In the Indian Ocean–South 
Pacific region, a new tide gauge-based reconstruction finds a regional 
mean RSL change of 1.33 [0.80 to 1.86] mm yr –1 over 1900–2018 
(Frederikse et  al., 2020) compared to a  GMSL change of around 
1.7  mm yr –1 (Section  2.3.3.3 and Table  9.5). RSLR rates based on

satellite altimetry for the period 1993–2018 in the region increased 
to 3.65 [3.23 to 4.08] mm yr –1 (Frederikse et al., 2020), compared to 
a GMSL change of 3.25 mm yr –1 (Section 2.3.3.3 and Table 9.5). 
Relative sea-level rise is very likely to continue surrounding the 
oceans in the Small Island States. Around the small islands, regional 
mean RSLR projections vary widely, from 0.4–0.6 m under  SSP1-2.6 
to 0.7–1.6 m under SSP5-8.5 for 2081–2100 relative to 1995–2014 
(median values), but in general they are situated in areas with RSL 
changes ranging from the mean projected GMSL change to above-
average values (Section  9.6.3.3). These RSLR projections may 
however be underestimated due to potential partial representation 
of land subsidence in their assessment (Section 9.6.3.2).
Coastal flood:  Relative sea level rise, storm surges and swells 
contribute to coastal inundation in the small islands, where studies

on historical trends in coastal flooding are currently limited. For 
example, a  swell event due to distant extratropical cyclones in 
December 2008 raised extreme water levels leading to flooding 
affecting five Pacific island nations: Marshall Islands, Micronesia, 
Papua New Guinea, Kiribati and Solomon Islands (Hoeke et al., 2013; 
Merrifield et al., 2014). Over low-lying atoll islands in the north-west 
tropical Pacific, potential increases in the frequency and areal extent 
of coastal flooding, especially at higher SLR scenarios, are expected 
to have negative consequences for freshwater resources and island 
habitability (Storlazzi et al., 2015, 2018). Select tide gauges across 
the Pacific also indicate increasing trends in the frequency of minor 
flooding since the 1960s (Marra and Kruk, 2017).
As relative sea levels increase, the potential for coastal flooding 
increases in the small islands ( high confidence ). Across the Pacific and

CAR small islands, the 5–95th percentile range of the 1-in-100-year 
ETWL is projected to increase (relative to 1980–2014) by 10–35 cm 
and by 14–41 cm by 2050 under RCP4.5 and RCP8.5, respectively 
(Figure 12.4q). By 2100, this range is projected to be 27–81 cm and 
44–188 cm under RCP4.5 and RCP8.5, respectively (Figure 12.4p,r; 
Vousdoukas et al., 2018; Kirezci et al., 2020). Furthermore, by 2050, 
the present-day 1-in-100-year ETWL is projected to have median 
return periods of between 1-in-1-year and 1-in-50-year in both the 
Pacific and CAR small islands, with some Pacific islands projected 
to experience the present-day 1-in-100-year ETWL more than once

1840
Table 12.9  | Summary of confidence in direction of projected change in climatic impact-drivers in the small islands, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, 
(see Section 12.4 for more details of the assessment method). The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at 

Caribbean (CAR)  5  6  
Pacific Islands  1 2 3 4 5  6  
1. Very high confidence  in the direction of change, but low to medium confidence  in the magnitude of change due to model uncertainty.
2. Decrease in eastern Pacific and southern Pacific subtropics, but increase in parts of western and equatorial Pacific; with seasonal variation in future changes.

3. High confidence  in increase in extreme rain frequency and intensity in western tropical Pacific; low confidence  in magnitude of change due to model bias.
4. Increase in southern Pacific.
5. Increase in intensity; decrease in frequency except over central North Pacific.
6. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat.

1841
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212a year (Vousdoukas et al., 2018; Oppenheimer et al., 2019). By 2100, 
the present-day 1-in-50-year ETWL is projected to occur around 
three times a year by 2100 with an SLR of 1 m at Pacific and CAR 
small islands (Vitousek et al., 2017). In the western tropical Pacific, 
the magnitude and frequency of coastal flooding due to SLR can be 
modulated by changes in the wave climate (Shope et al., 2016).
Coastal erosion: Recent studies have indicated variable and dynamic 
changes in shorelines of reef islands ( medium confidence ), including 
both erosion and accretion, which suggest factors other than SLR 
affecting shoreline changes, such as in the central and western Pacific 
within the past 50-to-60-year timeframe (Webb and Kench, 2010; 
Le  Cozannet et  al., 2014; Ford and Kench, 2015; Duvat and Pillet, 
2017). For example, islands on atolls in the central and western Pacific

have not substantially eroded or reduced in size in the past decades 
while sea level has been rising, but their position and morphology have 
changed due to anthropogenic factors (e.g.,  seawalls, reclamation) 
and climate–ocean processes (Biribo and Woodroffe, 2013; McLean 
and  Kench, 2015). Analysis of aerial and satellite imagery revealed 
severe shoreline retreat in six islands and the disappearance of five 
vegetated reef islands in Solomon Islands in the western Pacific 
between 1947 and 2014, which may be due to the interaction between 
SLR and waves (Albert et al., 2016). In French Polynesia, changes in 
shoreline and island area have been observed since the 1960s, partly 
due to the effect of TCs on sediment changes and human activities 
(Duvat and Pillet, 2017; Duvat et al., 2017). Coastal erosion has also 
been noted over the small, low-lying, sandy islands, such as in French 
Polynesia and Solomon Islands, among others (Luijendijk et al., 2018;

Mentaschi et  al., 2018). Average shoreline retreat rates between 1 
and 2 m yr –1 are estimated for the islands in the equatorial Pacific 
and in CAR, while a retreat rate of 0.5 m yr –1 is estimated for islands 
in the South Pacific, based on satellite observations from 1984–2016 
(Luijendijk et al., 2018; Mentaschi et al., 2018). There was also a loss of 
610 km2 compared with a gain of 520 km2 in coastal area in Oceania 
during the 1984–2015 period (Mentaschi et al., 2018).
Projections indicate that shoreline retreat will occur over most of 
the small islands in the Pacific and CAR throughout the 21st century 
with spatial variability ( high confidence ). Median shoreline change 
projections (CMIP5) relative to 2010, presented by Vousdoukas et al. 
(2020b), show that, by mid-century, shorelines in the islands in the 
equatorial Pacific and South Pacific will retreat by around 40 m, 
under both RCP4.5 and RCP8.5. In CAR islands, sandy shorelines are

projected to retreat by about 80 m by mid-century under both RCPs. 
By 2100, more than 100 m of median shoreline retreat is projected 
for all small islands under both RCPs; notably in CAR where retreats 
approaching 200 m (relative to 2010) are projected under both RCPs. 
The total length of sandy coasts in CAR and Pacific small islands that 
is projected to retreat by more than a median of 100 m by 2100 under 
RCP4.5 and RCP8.5 is about 1100 km and 1200 km respectively, an 
increase of approximately 14%.
Marine heatwave:  Ocean temperatures from satellite observations 
noted a moderate increase of 1–4 annual marine heat wave (MHW) 
events between 1982–1988 and 2000–2016 over some areas in 
the Indian Ocean, subtropical parts of the North and South Atlantic, 
and central and western parts of the North and South Pacific, but a decrease in frequency (two annual events) over the eastern Pacific 
Ocean (Box 9.2; Oliver et al., 2018). The intensity of MHWs has also

increased between 0.2°C and 0.5°C over the equatorial portions of 
the North Atlantic and the South Pacific. Over the eastern tropical 
Pacific, the decrease in intensity and duration of MHW is between 
0.5°C and 1.0°C and between 30 and 75 days, respectively (Box 9.2; 
Oliver et al., 2018). There is high confidence  that MHWs will increase 
around all small island nations. Marine heatwaves are projected to 
be more intense and prolonged where the largest changes are noted 
in the equatorial region with maximum annual intensities up to 1.2°C 
(1.8°C) and annual mean duration reaching 100 days (200 days) at 
1.5°C (2.0°C) warming levels (Frölicher et al., 2018).  Projections for 
SSP1-2.6 and SSP5-8.5 both show an increase in MHWs around all 
small island nations by 2081–2100, relative to 1985–2014 (Box 9.2, 
Figure 1).
In summary, relative sea level rise is very likely in the oceans 
around small islands, and along with storm surges and waves

will exacerbate coastal inundation in small islands. Shoreline 
retreat is projected along sandy coasts of most small islands 
(high confidence ). There is high confidence  that MHWs will 
increase around all small island nations.
CAR and Pacific small islands and associated confidence levels are 
illustrated in Table 12.9. Cold, snow, and ice-related climatic impact-
drivers, and sand and dust storms are not broadly relevant in the small 
islands that were assessed.
12.4.8 Open and Deep Ocean
Oceans face challenges from anthropogenic perturbations to the global 
Earth system, which cause increasing ocean warming, carbon dioxide-
induced acidification and oxygen loss (Bindoff et al., 2019). Climate 
change will affect the major oceanic CIDs described in Section 12.2: 
mean ocean temperature, marine heatwave, ocean acidity, ocean 
salinity, and dissolved oxygen (O 2), as well as severe wind storm and

sea ice. These changes result in a shifting profile of hazards relevant 
to impact and risk assessments (Section  12.3). New evidence, the 
SROCC (IPCC 2019b) assessments and advances in the new CMIP6 
climate simulations reinforce confidence in projected changes in 
climatic impact-drivers in the global oceans. As the ocean has taken 
up about 90% of the global warming for the period 1971–2018 
(Section  7.2.2.2), the emergence of the sea surface temperature 
increase signal has already been observed in global oceans over the 
last century (Hawkins et al., 2020). The signal in sea ice extent decrease 
has already emerged in the Arctic Ocean (Landrum and Holland, 2020), 
while ocean acidification and low oxygen have also already emerged 
in many oceanic regions and will emerge in all global oceans by 2050 
under RCP8.5 (Section 12.5.2 and Table 12.10). This section assesses 
key climatic impact-drivers that can be linked with sectoral and

regional vulnerability and exposure in open and deep oceans, drawing 
from previous Chapters (Chapters 2, 3, 4, 5 and 9).
Mean ocean temperature:  It is very likely  that global mean sea 
surface temperature (SST) has increased by 0.88 [0.68 to 1.01] °C

1842
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12from 1850–1900 to 2011–2020, and 0.60 [0.44 to 0.74] °C from 
1980 to 2020 (Section  2.3.1.1.6 and Table  2.4). There is very high 
confidence that the Indian Ocean, the western equatorial Pacific 
Pacific, and the North Atlantic Ocean have warmed more slowly or 
slightly cooled (Section 9.2.1.1). It is virtually certain  that global mean 
SST will continue to increase in the 21st century at a rate depending 
on future emissions scenario, with CMIP6 projections indicating an 
increase of 0.86°C ( likely  range 0.43°C–1.47°C) under SSP1-2.6 
and 2.89°C (2.01°C–4.07°C) under SSP5-8.5, by 2081–2100, 
relative to 1995–2014 (Section  9.2.1.1). Global warming of 2°C 
above pre-industrial levels is projected to increase SST, resulting

in the exceedance of numerous hazard thresholds for pathogens, 
seagrasses, mangroves, kelp forests, rocky shores, coral reefs 
and other marine ecosystems ( medium confidence ) (Poloczanska 
et al., 2013a, b, 2016; Liu et al., 2014; Pörtner et al., 2014; Graham 
et al., 2015; Schoepf et al., 2015; Gobler et al., 2017; Henson et al., 
2017; Hoegh-Guldberg et  al., 2017; Krueger et  al., 2017; Hughes 
et al., 2018a, b; Perry et al., 2018). It is virtually certain  that upper-
ocean stratification has increased at a  rate of 4.9 ± 1.5% during 
1970–2018 and that this will continue to increase in the 21st century 
(Section 9.2.1.3), potentially leading to reduced nutrient supply and 
total productivity ( low confidence ) (Moore et al., 2018).
Marine heatwave:  Marine heatwaves (MHWs) have increased 
in frequency over the 20th century, with an approximate doubling 
since the 1980s ( high confidence ), and their intensity and duration

have also increased ( medium confidence ) (Box  9.2). Projections 
show that this increasing trend likely  continues with 2–9 times more 
frequent MHWs (at global scale) projected by 2081–2100, relative to 
1995–2014 under SSP1-2.6, and 4–18 times more frequent MWHs 
under SSP5-8.5. The largest changes in MHW frequency are likely  to 
occur in the tropical ocean and the Arctic, while there is medium 
confidence  of moderate increases in the mid-latitudes, and of small 
increases in the Southern Ocean (Box 9.2). Permanent MHWs (more 
than 360 days per year, relative to the historical climate conditions) 
are projected to occur in the 21st  century in parts of the tropical 
ocean, in the Arctic Ocean, and around latitude 45°S, under SSP5-8.5 
(Box 9.2). The occurrence of such permanent MHWs can be largely 
avoided under the SSP1-2.6 scenario (Box  9.2). MHWs can have 
devastating and long-term impacts on ecosystems (Oliver et al., 2018),

making them an emerging hazard for marine ecosystems (Frölicher 
and Laufkötter, 2018; Smale et  al., 2019). A  series of MHWs that 
occurred in 2010–2011 had consequences for seagrass in western 
Australia (Wernberg et al., 2013; Arias-Ortiz et al., 2018), and for the 
lobster fishery in the Gulf of Maine (Pershing et al., 2018). The MHWs 
that occured western Australia in 2015/2016 led to the third-highest 
mass coral bleaching globally (Le Nohaïc et al., 2017).
Ocean acidity: With the increasing CO 2 concentration, the global 
mean ocean surface pH is decreasing and is now the lowest it 
has been for at least a  thousand years ( very high confidence ) 
(Section  2.3.3.5). It is very likely that, since the 1980s, ocean 
surface pH has changed at a rate of –0.016 to –0.019 per decade in 
the subtropical open oceans, at –0.010 to –0.026 per decade in the

tropical Pacific, and at –0.003 to –0.026 per decade in open subpolar and polar zones (Sections 2.3.3.5 and 5.3.3.2). Over the period 
1870–1899 to 2080–2099, ocean surface pH is projected to decline 
by –0.16  ±  0.002 under SSP1–2.6, and by –0.44 ± 0.005 under 
SSP5-8.5 (Sections 4.3.2.5 and 5.3.4.1). Declining ocean pH will 
exacerbate negative impacts on marine species ( medium confidence ) 
(Albright et al., 2016; Kwiatkowski et al., 2016; Watson et al., 2017).
Ocean salinity:  Salinity contrasts have increased since the 1950s, 
near the ocean surface ( virtually certain ) and in the subsurface 
(very likely ), with high-salinity regions becoming more saline and 
low-salinity regions becoming fresher (Section  2.3.3.2). At the 
basin scale, it is very likely  that the Pacific and the Southern Oceans 
have freshened and that the Atlantic has become more saline 
(Section 2.3.3.2). The SROCC (IPCC 2019b) assessment that a general

pattern of fresh ocean regions getting fresher and salty ocean 
regions getting saltier will continue in the 21st century is confirmed 
in Section 9.2.2.2.
At the regional scale, by 2100 the average Arctic surface salinity 
is projected to decrease by 1.5 ± 1.1 psu (practical salinity units), 
and the liquid freshwater column in the Arctic Ocean is projected 
to increase by 5.4 ± 3.8 m under RCP8.5, (Shu et al., 2018). In the 
Indian Ocean, sea surface salinity is projected to decrease by 
between 0.49  and 0.75 psu by 2080, compared to 2015, under 
RCP2.6 and RCP2.6, respectively (Akhiljith et al., 2019). Projections 
for the North and South Atlantic oceans indicate increasing salinity 
in the upper layer (0–500 m) under both RCP4.5 and RCP8.5, due to 
the decreasing freshwater input from the equator and increasing net 
evaporation (Skliris et  al., 2020). There is medium confidence  that 
fresh ocean regions (Pacific, Southern and Indian oceans) will get

fresher and salty ocean regions (Atlantic Ocean) will get saltier over 
the 21st century (Section 9.2.2.2; IPCC, 2019b). Ocean warming and 
high-latitude surface freshening is projected to continue to increase 
upper-ocean stratification in the 21st century (Section 9.2.1.3).
Dissolved oxygen:  Since the middle of the 20th  century, oxygen 
concentrations of open and coastal waters have been declining, 
and such deoxygenation affects biological and biogeochemical 
processes in the ocean (Schmidtko et al., 2017). In recent decades, 
low-oxygen zones in ocean ecosystems have expanded, and 
projections indicate an acceleration with global warming ( medium 
confidence ) (Diaz and Rosenberg, 2008; Gilly et  al., 2013; Gobler 
et  al., 2014). A  2%  loss (4.8  ± 2.1 pmoles O 2) in total dissolved 
oxygen in the upper ocean layer (100–600 m) has been observed 
during 1970–2010 (Section 2.3.4.2), with the highest oxygen loss of

up to 30 mol  m–2 per decade in the equatorial and North Pacific, 
the Southern Ocean and the South Atlantic Ocean (Section 5.3.3.2). 
Global mean ocean oxygen concentration is projected to decrease by 
6.36 ± 2.92 mmol m–3 under SSP1-2.6 and by 13.27 ± 5.28 mmol m–3 
under SSP5-8.5 in the subsurface (100–600 m) by 2080–2099, 
compared to 1870–1899, which is respectively 71% and 40% greater 
than previous estimates based on CMIP5 models (Section 5.3.3.2). 
In the benthic ocean, projected future losses of dissolved oxygen 
concentration by 2080–2099, compared to 1870–1899, are 
−5.14 ± 2.04 mmol m−3 under SSP1-2.6 and −6.04 ± 2.19 mmol m−3 
under SSP5-8.5 (Kwiatkowski et al., 2020). Section 5.3.3.2 assessed 
very likely  global decreases in ocean oxygen concentrations although

1843
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212there is medium confidence  in specific regional declines that are 
expected to expand both anoxic and hypoxic zones, with such 
reductions of oxygen expected to persist for thousands of years 
(Yamamoto et al., 2015; Frölicher et al., 2020).
Sea ice:  The Arctic sea ice area for September has decreased from 6.23 
to 3.76 million km2 and for March from 14.52 to 13.42 million km2 
between 1979–1988 and 2010–19 (Section  2.3.2.1.1). There is high 
confidence  that sea ice in the Arctic will further decrease in the future 
under all emissions scenarios (Section 9.3.1.1). In contrast, there is no 
clear observed trend in the Antarctic sea ice area over the past few 
decades and there is low confidence of future changes (Section 9.3.1.1). 
The duration of the summer ice season in the Arctic has increased by 5 
to 20 weeks between 1979 and 2013, with a significant trend ranging

from 5 to 17 days per decade for earlier spring retreat and from 5 to 25 days per decade for later autumn advance, with consequences for 
Arctic marine mammals (AMMs) due to sea ice habitat loss (Laidre 
et al., 2015). The Arctic is projected to be ice-free more often during 
summer under 2°C global warming compared to 1.5°C global warming 
(Section 9.3.1.1; see also Sections 12.4.9 and 4.4.2.1), opening new 
shipping lanes for international commerce (Valsson and Ulfarsson, 
2011) and lengthening the season for offshore resource extraction 
(Schaeffer et al., 2012). Iceberg numbers are expected to increase as 
a result of global warming, forming an elevated hazard to shipping and 
offshore facilities (Bigg et al., 2018).
It is virtually certain  that global mean SST will continue 
to increase throughout the 21st  century, resulting in the 
exceedance of numerous climatic impact-driver thresholds 
relevant to marine ecosystems ( medium confidence ). Marine

Table 12.10  | Summary of confidence in direction of projected change in climatic impact-drivers in open and deep ocean regions, representing their 
aggregate characteristic changes for mid-century for scenarios RCP4.5, SSP2-4.5, SRES A1B, or above within each AR6 region (defined in Chapter 1), 
approximately corresponding (for CIDs that are independent of sea level rise) to global warming levels between 2°C and 2.4°C (see Section 12.4 for 
more details of the assessment method).  The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural 
interannual variability if found with at least medium confidence  in Section 12.5.2. Asterisks indicate regions that extend across both sides of the map.
Mean ocean 
temperatureMarine 
heatwaveOcean acidity SalinityDissolved 
oxygenSea ice
Arctic Ocean (ARO)     
South Pacific Ocean (SPO)    
Equatorial Pacific Ocean (EPO)   

North Pacific Ocean (NPO)    
South Atlantic Ocean (SAO)    
Equatorial Atlantic Ocean (EAO)   
North Atlantic Ocean (NAO)    
Equatorial Indian Ocean (EIO)    
South Indian Ocean (SIO)    
Arabian Sea (ARS)   
Bay of Bengal (BOB)    
Southern Ocean (SOO)    

1844
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12heatwave days are projected to increase in global oceans, with 
a larger increase in the tropical ocean and Arctic Ocean ( high 
confidence ). It is virtually certain  that upper-ocean stratification 
will continue to increase in the 21st  century. Future ocean 
warming will very likely  assist the development of both anoxic 
and hypoxic zones, with such reductions of oxygen expected to 
persist for thousands of years. Future projections also indicate 
freshening of the Pacific, Southern and Indian oceans and 
a saltier Atlantic Ocean ( medium confidence ).
open and deep ocean regions and associated confidence levels are 
illustrated in Table  12.10, following the AR6 WGI ocean reference 
regions (Figure Atlas.2b).
12.4.9 Polar Terrestrial Regions
Several recent climate assessments of polar regions describe robust

patterns of recent and future climatic changes driving impacts and 
risk for polar environmental, societal, and economic assets. These 
have included the IPCC SROCC (Meredith et al., 2019), the Report 
on Snow, Water, Ice and Permafrost in the Arctic (AMAP , 2017), and 
national assessments for the USA (Markon et al., 2018) and Canada 
(Derksen et al., 2018). This section examines Greenland and Iceland, 
the Russian Arctic, Antarctica, and the Arctic portions of Northern 
Europe and North America (Figure 1.18c).
12.4.9.1 Heat and Cold
Mean air temperature: Atlas.11.2 shows high confidence in 
warming of the Arctic in observations and projections, measuring 
among the fastest-warming places at more than twice the global 
mean, with substantially higher temperature increases in the cold 
season (see also AMAP , 2017; Meredith et  al., 2019). Atlas.11.1 
assessed very likely  warming in observations of West Antarctica from

1957 to 2016, but limited evidence of mean air temperature change 
across East Antarctica even as there is high confidence  in future 
warming across the continent (Figure Atlas.29; Meredith et al., 2019).
Extreme heat, cold spell and frost: Ecosystem and societal 
temperature thresholds in polar regions often reflect lower tolerance 
to heat and higher tolerance to cold. Extreme heat events have 
increased around the Arctic and Iceland since 1979, including 
increases in cold season warm days and nights, melt days, and Arctic 
winter warm events (T > –10°C) as well as decreases in cold days and 
nights (Mernild et al., 2014; Matthes et al., 2015; Vikhamar-Schuler 
et al., 2016; Graham et al., 2017; Sui et al., 2017; Dobricic et al., 2020; 
Peña-Angulo et  al., 2020). Heatwaves causing high temperature 
records have been recently documented in West and East Antarctica 
(Wille et al., 2019; Robinson et al., 2020). There is high confidence

that polar amplification will drive increases in Arctic heat extremes 
as well as continuing declines in the magnitude and frequency of 
cold extremes (Matthes et al., 2015; Kharin et al., 2018), although 
dynamical effects will still bring substantial cold air anomalies over 
the Arctic (Wu and Francis, 2019). There is medium confidence  for 
equivalent changes in extreme heat in Antarctica based primarily on higher mean temperatures, with J.R. Lee et al. (2017) projecting 
more than 50 additional degree days above freezing (2098 RCP8.5 
compared with 2014) over parts of the Antarctic Peninsula but 
smaller changes over mainland Antarctica.
12.4.9.2 Wet and Dry
Mean precipitation: Atlas.11.2 indicated medium confidence  in 
observed increases in Arctic precipitation, with the largest rises in the 
cold season. Antarctic precipitation showed no significant overall 
trend since the 1970s, with a positive trend over the 20th century

(Sections 9.4.2.1 and Atlas.11.1). Increases in Arctic and Antarctic 
precipitation during the 21st century are very likely , with projected 
percentage increases that are much higher than most subpolar 
regions of the world (Figure Atlas.29).
Floods and heavy precipitation:  Observations and model 
projections indicate high confidence  in increasing Arctic river runoff 
in response to increasing total precipitation (Box et  al., 2019; 
Durocher et  al., 2019; Meredith et  al., 2019) with a  shift towards 
earlier meltwater flooding (AMAP , 2017). Higher Arctic precipitable 
water totals are also connected with observed increases in heavy 
precipitation and convective activity ( high confidence ) (Ye et  al., 
2015; Kharin et al., 2018; Chernokulsky et al., 2019). Higher flood 
magnitudes are also driven by future increases in rain-on-snow event 
days, amounts, and runoff, which are more significant in the Arctic 
than in mid-latitudes (where seasonal snow cover is often further

reduced; AMAP , 2017; Jeong and Sushama, 2018b).
Landslide and snow avalanche: There is a growing number of studies 
on mass movements in polar regions. Although there is low confidence  
in widespread observational trends for landslides or snow avalanches, 
a rise in the number of future landslides is supported by strong links to 
increases in heavy precipitation, glacier retreat, and thawing of ice-rich 
permafrost that can lead to retrogressive thaw slumps in Arctic regions 
(Section 2.3.2.5; Kokelj et al., 2015; Derksen et al., 2018; Lewkowicz 
and Way, 2019; Patton et al., 2019; Ward Jones et al., 2019).
Aridity and drought: Recent decades have seen a general decrease 
in Arctic aridity, with projections indicating a  continuing trend 
towards reduced aridity ( high confidence ) as increased moisture 
transport leads to higher precipitation, humidity and streamflow 
(Meredith et  al., 2019) and a  corresponding decrease in dry days

(Khlebnikova et al., 2019a). There is low confidence  overall of recent 
or projected drought changes in polar regions (Section 11.9) even 
as increasing evidence shows that drainage from permafrost thaw, 
higher potential evapotranspiration, and changing seasonal patterns 
of melt have caused lake reduction and soil moisture deficits in 
several areas that match with projections of future drought increase 
despite overall precipitation increases (Andresen and Lougheed, 
2015; Bring et al., 2016; Spinoni et al., 2018a; Feng et al., 2019; Finger 
Higgens et al., 2019).
Fire weather: Fire season lengthened from 1979 to 2015 over Arctic 
portions of North America (Jain et al., 2017), corresponding also to 
a 1975–2015 increase in lightning-ignited fires in Arctic North-Western 
North America (Girardin et  al., 2013; Veraverbeke  et  al.,  2017).

1845
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Abatzoglou et al. (2019) climate model simulations project significant 
fire weather index increases in boreal forests of Arctic Europe, 
Arctic Russia and Arctic North-Eastern North America ( medium 
confidence ). Trends towards more frequent fires in tundra regions 
are expected to continue, driven in particular by increasing potential 
evapotranspiration and changes in vegetation ( high  confidence ) 
(Hu et al., 2015; AMAP , 2017; Young et al., 2017).
12.4.9.3 Wind
Mean wind speed and severe storm:  There is medium confidence 
of mean wind decrease over  the Russian Arctic, Greenland and 
Iceland, and Arctic North-Eastern North America (Karnauskas et al., 
2018a; Jung and Schindler, 2019), but low confidence  of changes 
in the other Arctic regions and Antarctica. Bintanja et  al. (2014) 
projected that a  strengthening of the Southern Annular Mode

would decrease easterlies along Antarctica’s coasts with only small 
changes in katabatic winds (although this effect may diminish 
with stratospheric ozone recovery). In contrast, Gorter et al. (2014) 
regional climate model projections indicated a  reduction in mean 
winds over the interior of Greenland by RCP4.5 2100 while coastal 
winds increase. Reanalysis data and climate models indicate few 
coherent regional trends of polar cyclone frequency or relationships 
with cyclone depth and size (Akperov et  al., 2018, 2019; Day and 
Hodges, 2018; Zahn et al., 2018).
12.4.9.4 Snow and Ice
Snow:  Atlas.11.1 identified likely  increases in surface mass balance 
(driven by snowfall) across Antarctica in the 20th  century ( medium 
confidence ). In the Arctic, overall snow extent and seasonal duration 
are projected to continue recent declines ( high confidence ), although 
mid-winter snowpack increases in some of the coldest and high-

elevation locations given higher precipitation totals ( medium 
confidence ) (Section 9.5.3, Atlas.9 and Atlas.11.2; Bring et al., 2016; 
Danco et  al., 2016; AMAP , 2017; Meredith et  al., 2019). Higher 
temperatures result in a  higher percentage of Arctic precipitation 
falling as rain (particularly in autumn and spring) ( high confidence ), 
with most land regions (outside of Greenland and Antarctica) becoming 
dominated by rainfall (more than 50% of total precipitation) by RCP8.5 
2100 (Bintanja and Andry, 2017; Irannezhad et al., 2017).
Glacier and ice sheet:  Section  9.5.1 and Section  2.3.2.3 found 
that glaciers have lost mass in all polar regions since 2000 ( high 
confidence ), and Section 9.4 assessed high confidence  in Greenland 
Ice Sheet mass losses since 1980 and Antarctic Ice Sheet losses 
since 1992 (dominated by West Antarctica, with losses in parts of 
East Antarctica in the past two decades). New simulations from

GlacierMIP (Marzeion et al., 2020) indicate glaciers in Iceland will 
lose 31 ± 35%, 41 ± 46% and 53 ± 45% of their mass in 2015 
by the end of the century for RCP2.6, RCP4.5 and RCP8.5 scenarios, 
respectively. Marzeion et  al. (2020) projected mass losses ( high 
confidence ) for those same scenarios in the Greenland Periphery: 
22 ± 23%, 29 ± 26%, and 42 ± 28%; Svalbard: 35 ± 34%, 50 ± 36%, 
and 66 ± 35%; Russian Arctic: 26 ± 26%, 38 ± 28%, and 52 ± 30%; 
Northern Arctic Canada: 12 ± 13%, 18 ± 12%, and 27 ± 18%; 
Southern Arctic Canada: 23 ± 27%, 33 ± 29%, and 48 ± 32%; and Antarctic Periphery: 7 ± 12%, 13 ± 10%, and 16 ± 19%. Areas 
with receding glaciers are also potentially vulnerable to glacial lake 
outburst floods (Harrison et al., 2018).
Permafrost: Observations from recent decades (assessed in 
Section  9.5.2 and Section  2.3.2.5) show increases in permafrost 
temperature ( very high confidence ) and active layer thickness ( medium

confidence ) across the Arctic (AMAP , 2017; Derksen et  al.,  2018; 
Markon et  al., 2018; Biskaborn et  al., 2019; Farquharson et  al., 
2019; Meredith et al., 2019; Romanovsky et al., 2020). Section 9.5.2 
noted that observations of active layer thickness in Antarctica are 
too limited to assess long-term trends (see also Hrbáček et  al., 
2018; Biskaborn et al., 2019). Future projections indicate continuing 
increases in permafrost temperature and active layer thickness 
with loss of permafrost across the Arctic (Section 9.5.2). Streletskiy 
et al. (2019) noted that changes to Russian permafrost temperature 
and active layer thickness are most pronounced in areas where 
permafrost is continuous (underlying >90% of landmass). CMIP5 
analyses by Slater and Lawrence (2013) projected that, by RCP8.5 
2100, shallow (<3 m) permafrost would be most probable only in 
portions of the Canadian Arctic Archipelago and the Russian Arctic 
coastal and eastern upland regions.

Sea ice:  Consistent with SROCC (Meredith et al., 2019), Section 9.3.1 
and Section 2.3.2.1.1 assess very high confidence  that Arctic sea ice 
thickness, extent, and average age have significantly decreased over 
the past four decades, with largest declines in September (when 
sea ice is at an annual minimum). Declines in landfast ice are most 
rapid in the Laptev Sea (Selyuzhenok et  al., 2015), with warming 
also breaking perennial landfast ice blocking ocean channels (‘ice 
plugs’) in the Canadian Archipelago (Pope et al., 2017), and landfast 
ice declining in the cold season by 7% per decade across the Arctic 
(1976–2007; Yu et  al., 2014). Observed trends and projections 
suggest that perennial sea ice is being replaced by thin, seasonal ice, 
although multi-year ice will persist above the Canadian Archipelago 
and drift into sea transportation lanes (Howell et al., 2016; Derksen 
et al., 2018). Trends from 1979 to 2013 show slightly earlier spring

melt for Arctic sea ice, but substantially delayed autumn freeze-up 
and a melt season lengthened by more than 3 days per decade off 
northern Alaska and Canada with the exception of portions of the 
Bering Sea (Parkinson, 2014; Stroeve et  al., 2014). Section  9.3.2 
assessed low confidence  in long-term trends in sea ice extent or 
thickness near Antarctica.
Future declines in Arctic sea ice are virtually certain , although there 
is low confidence  in declines of Antarctic sea ice given dynamical 
processes in the Southern Ocean and the recovery of stratospheric 
ozone (Section 9.3;  Meredith et al., 2019). Projections of an ‘ice-free’ 
Arctic vary, depending on definitions representing transportation 
needs, however Laliberté et  al. (2016) noted that the median of 
42 CMIP5 models projected <5% sea ice for the month of September 
by 2050, with equivalent conditions for the entirety of the August–
October period by 2090. Section 9.3.1 assessed high confidence  that

practically ice-free conditions (<1 million km2 in the September mean) 
would likely  first appear before 2050 even under strong mitigation 
scenarios (Sigmond et al., 2018; Stroeve and Notz, 2018; Notz and 
SIMIP Community, 2020).

1846
Table 12. 11 | Summary of confidence in direction of projected change in climatic impact-drivers in the polar regions, representing their aggregate characteristic changes for mid-century for scenarios RCP4.5, 
(see Section 12.4 for more details of the assessment method).  The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least 
medium confidence  in Section 12.5.2. Note that the Arctic portions of the NEU, NEN and NWN differ from the full AR6 regions assessed in the Europe and North America sections above (see also Figure 1.18c).

Greenland and Iceland (GIC)    2,3 1 5  
Arctic Northern Europe (aNEU)    2,3 16 
7  
Russian Arctic (RAR)    2,3 1,4  7  
Arctic North-Western North America (aNWN)   2,3 1  7  
Arctic North-Eastern North America (aNEN)   2,3 1,4  
West Antarctica (WAN)  1,4  
East Antarctica (EAN)    

2. Higher confidence in southern regions and lower toward north.
3. Higher confidence in increase for some climatic impact-driver indices during summer.
4. Glaciers decline even as some regional snow climatic impact-driver indices increase.
5. Decreasing in west and increasing in east.
6. Except for northern Baltic Sea coasts where relative sea levels fall.
of decreaseHigh confidence

1847
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Lake and river ice:  There is high confidence  in observations of 
significant declines in seasonal ice cover thickness and duration over 
most Arctic lakes, with many lakes projected to lose more than one 
month of ice cover by mid-century ( medium confidence ) (Meredith 
et al., 2019; Sharma et al., 2019). Some lakes that previously froze to 
the bottom (‘bedfast’) now maintain liquid bottom water year round, 
and others shift from perennial to seasonal ice cover (Surdu et al., 
2016; Engram et al., 2018). Yang et al. (2020a) identified a decline in 
Arctic cold-season river ice extent in satellite observations (particularly 
in Alaska) and projected reductions in average Northern Hemisphere 
seasonal river ice duration of 6.10 ± 0.08 days per  degree global 
surface air temperature.
Heavy snowfall and ice storm:  There is limited evidence  of changes

in heavy snowfall due to competing influences of shortened snowfall 
seasonality with more intense (and larger overall) precipitation in 
the Arctic. Episodic heavy snowfall trends in Antarctica are difficult 
to separate from large interannual variability ( limited evidence ) 
(Gorodetskaya et al., 2014, Turner et al., 2020). Limited evidence  also 
hinders clear signals in ice storms, although warming shifts the 
freezing line (around which ice storms occur) poleward and upslope 
(Bintanja and Andry, 2017). Groisman et  al. (2016) used 40 years 
of observations to identify an increase of freezing rain events in 
Norway, North America, and eastern and western Russia. Increases 
in winter rainfall have led to more frequent development of difficult 
wildlife and livestock grazing conditions as basal ice conditions coat 
the ground below snowpack (Peeters et al., 2019).
12.4.9.5 Coastal and Oceanic
Relative sea level:  Satellite altimetry and tide data show that

relative sea levels (with glacial isostatic adjustment) are rising 
in Arctic Europe and Arctic North-Western North America, declining in 
portions of southern Alaska and Arctic North-Eastern North America 
and no clear trend in Greenland and the Russian Arctic (Sweet et al., 
2018; Rose et al., 2019), which is broadly consistent with findings in 
Oppenheimer et al. (2019). Areas with low or negative change have 
substantial land uplift counteracting the global mean sea level trend 
(Greenan et al., 2018; Sweet et al., 2018; Madsen et al., 2019). SROCC 
projections indicate high confidence  in future rises in relative sea 
level for all Arctic regions other than areas of substantial land uplift 
in north-eastern Canada, the west coast of Greenland, and narrow 
portions of West Antarctica (Oppenheimer et al., 2019).
Coastal flooding and erosion:  Higher sea levels and reduced 
coastal sea ice protection will increase future extreme sea levels in

the Arctic ( high confidence  for Arctic Northern Europe, the Russian 
Arctic, and Arctic North-Western North America ( medium confidence ) 
for Greenland and Iceland and Arctic North-Eastern North America 
given glacial isostatic adjustment). Vousdoukas et al. (2018) project 
that the current 1-in-100-year extreme total water level would have 
median return periods of 1-in-20-years to 1-in-50-years by 2050, 
increasing to 1-in-5-years to 1-in-20-years by 2100 under RCP4.5 
along nearly the entire Arctic coastline by 2100 (excluding GIC for 
which projections are not available). Projections for RCP8.5 indicate 
that the present-day 1-in-100-year ETWL would have median return 
periods of 1-in-10-years to 1-in-50-years by 2050 and would occur once every five years (or more frequently) by 2100. Arctic coastal 
erosion is also expected to increase with climate change ( medium 
confidence ; high agreement  but limited evidence  of projections),

accelerated in some regions by subsurface permafrost thaw and 
increased wave energy (Gibbs and Richmond, 2015; Fritz et al., 2017; 
Oppenheimer et al., 2019; Casas-Prat and Wang, 2020). A longer ice-
free season for the RCP8.5 2080s is projected to help drive more 
than 100 m of shoreline retreat in North-Western North America 
Arctic coastal communities (Melvin et al., 2017; Greenan et al., 2018; 
Magnan et  al., 2019). Assessment of coastal flooding and erosion 
changes in Antarctica are limited by a lack of studies.
Marine heatwave: Recent years have seen marine heatwaves 
(MHWs) and increasing extreme coastal SSTs in Arctic systems (Lima 
and Wethey, 2012; Collins et al., 2019; Frölicher, 2019). Projections 
show increases in MHW intensity, frequency and duration will be 
larger over the Arctic Ocean than mid-latitude oceans due in part to 
low interannual variability under current sea ice ( high confidence ). 
Frölicher et  al. (2018) used 12 CMIP5 models to project median

MHW days increasing about 25-fold and 50-fold at the 2°C and 
3.5°C GWLs, respectively, in response to mean ocean warming 
and sea ice loss, and the smallest global changes still leading to 
increases in the Southern Ocean around Antarctica (see also Cross-
chapter Box 9.1).
Climate change has caused and will continue to induce an 
enhanced warming trend, increasing heat-related extremes 
and decreasing cold spells and frosts in the Arctic ( high 
confidence ), with similar changes in Antarctica but medium 
confidence  for extreme heat increases and West Antarctic 
frost change decreases and low confidence  for cold spell 
changes and East Antarctica frost. The water cycle is projected 
to intensify in polar regions, leading to more rainfall, 
higher river flood potential and more intense precipitation 
(high confidence ). Projections indicate reductions in glaciers 
at both poles, with sea ice loss, enhanced permafrost 
warming, decreasing permafrost extent, and decreasing

seasonal duration and extent of snow cover in the Arctic 
(high confidence ) even as some of the coldest regions will see 
higher total snowfall given increased precipitation ( medium 
confidence ). Projections indicate relative sea level rises in 
polar regions ( high confidence ), with the exception of regions 
with substantial land uplift including North-Eastern North 
America ( high confidence ), western Greenland, the northern 
Baltic Sea, and portions of West Antarctica. Higher sea levels 
also contribute to high confidence  for projected increases of 
Arctic coastal flooding and higher coastal erosion (aided by 
sea ice loss) ( medium confidence ) with lower confidence for 
those CIDs in regions with substantial land uplift.
12.4.10 Specific Zones and Hotspots
This section focuses on CIDs affecting specific zones with heightened 
vulnerability and coherent characteristics that cut across traditional 
continental regions (see also Section 12.3). It is designed to match the

structure of the Cross-Chapter Papers in the WGII Report, although

1848
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12polar regions were addressed in more extensive detail in Sections 
12.4.8 and 12.4.9 of this Report and the Mediterranean Region will 
not be handled separately given that its climatic impact-drivers are 
discussed in Sections 12.4.1 and 12.4.5 as well as in Cross-Chapter 
Box 10.3.
12.4.10.1 Hotspots of Biodiversity (Land, Coasts and Oceans)
Hotspots of biodiversity are defined by the AR6 WGII as ‘geographic 
areas with exceptionally high richness of species, including rare 
(endemic) species’ (WGII Cross-Chapter Paper 1). The AR6 assessment 
is based on 238 distinctive regions often called the ‘Global 200 
ecoregions’ (Olson and Dinerstein, 2002).
Mean temperature increase is a  major climatic impact-driver 
for biodiversity hotspots, and it is very likely  that it will affect all 
hotspot areas identified in the literature, at various rates in all

climate scenarios, except those located in the North Atlantic where 
warming is uncertain (see Chapter  4). Terrestrial ecosystems will 
experience an enhanced warming compared to ocean ecosystems, 
because land temperatures are warming faster than ocean 
temperatures (Chapter 4). Marine ecoregions will experience ocean 
acidification and temperatures that increase faster in high latitudes 
(high confidence ), but critical temperature and oxygen thresholds 
are projected to be crossed earlier (by mid-century RCP8.5) in 
tropical areas (Hughes et al., 2017a; Bruno et al., 2018). A warming 
trend is also expected for freshwater ecosystems, with different 
local magnitudes due to combined effects of groundwater system 
inertia as well as hydrology changes (Knouft and Ficklin, 2017). 
In tropical land areas, because interannual temperature variability 
is weak compared to mean changes, the temperature distribution

range is likely  to be shifted to a very different range in all projection 
scenarios, with unprecedented values relative to pre-industrial 
conditions. High climate velocities are particularly noteworthy for 
biodiversity hotspots given complex ecosystem dynamics and niche 
climates not easily replicated under shifted geographies (Burrows 
et al., 2014; Halpern et al., 2015; Dobrowski and Parks, 2016). In 
some regions (e.g., Central Africa, Amazon, South East Asia) the 
mean temperature change is already beyond the normal range of 
variations as it has reached levels higher than three (and up to 
six) times larger than the standard deviation of the interannual 
variations (Hawkins et  al., 2020). Together with global warming, 
land and marine heatwaves are very likely  to increase in the future 
climate in biodiversity hotspots (Sections 12.4.1–12.4.7).
There is low confidence  in broad patterns of future drying or wet

trends across the land and freshwater biodiversity hotspots in the 
humid tropics, although drying trends have been detected and 
predicted in parts of the Amazon (Fu et al., 2013; Boisier et al., 2015). 
There is medium confidence  (limited evidence , high agreement ) 
that in several regions the length of the dry season has already 
increased and is projected to further increase in some parts of the 
Mediterranean, Amazonia and sub-Saharan Africa (Debortoli et al., 
2015; Dunning et al., 2018; Hochman et al., 2018; Saeed et al., 2018). 
Longer dry seasons also extend the seasonal length and geographical 
extent of fire weather in all future scenarios ( medium confidence ) 
(Jolly et al., 2015; Abatzoglou et al., 2019).In conclusion, biodiversity hotspots around the world will 
each face unique challenges as climatic impact-drivers change. 
However, heat, drought and length of dry season, fire weather, 
sea surface temperature and deoxygenation are relevant

drivers to terrestrial, freshwater and marine ecosystems, and 
have marked increasing trends.
12.4.10.2 Cities and Settlements by the Sea
Cities and settlements by the sea are exposed to specific climate 
and climate change patterns and to compound coastal hazard risks 
(AR6  WGII Cross-Chapter Paper 2). The AR5 WGII found that, in 
general, ‘urban climate change-related risks are increasing (including 
rising sea levels and storm surges, heat stress, extreme precipitation, 
inland and coastal flooding, landslides, drought, increased aridity, 
water scarcity, and air pollution)’ (Revi et al., 2014). Since AR5 
a  number of studies have been carried out to understand urban 
climate and its change. Box 10.3 identified a continuing strong role of 
the urban heat island in amplifying heat extremes in cities, although 
changes in the urban heat island are an order of magnitude smaller 
than projected localized warming trends ( very high confidence ).

Coastal cities’ proximity to the sea somewhat mitigates the effect 
of urban heat islands ( high confidence ) (Salvati et  al., 2017; 
Santamouris et  al., 2017; Y . Wang et  al., 2018; Martinelli et  al., 
2020). Cities and settlements by the sea typically experience 
higher humidity levels than inland regions, combining with heat to 
enhance heat stress and induce exceedance of critical heat stress 
thresholds for outdoor activities, with potential enhanced exposure 
to heat for informal settlements (J. Wang et al., 2019). Such threshold 
exceedances are projected to increase for many coastal areas ( high 
confidence ), including the Persian Gulf where heat stress is projected 
to be extreme (Pal and Eltahir, 2016; Ahmadalipour and Moradkhani, 
2018), and some low-lying areas in Europe such as the Po Valley and 
coastal Mediterranean areas (Coppola et al., 2021a; Schwingshackl 
et al., 2021; see also the heat stress index shown in Figure 12.4d–f).

Climate change-related variations in oceanic drivers (e.g., relative sea 
level, storm surge, ocean waves), combined with tropical cyclones, 
extreme precipitation and river flooding, are expected to lead to 
more frequent and more intense coastal flooding and erosion ( very 
high confidence ) impacting cities and settlements located especially 
in low-elevation coastal zones and mega-deltas (Chan et  al., 2012, 
2018; Karymbalis et al., 2012; Hemer et al., 2013; Aerts et al., 2014; B. 
Neumann et al., 2015; Hauer et al., 2016; Ranasinghe, 2016; Hinkel et al., 
2018; Mavromatidi et al., 2018; Marcos et al., 2019; see also Sections 
12.3, 12.4.1–12.4.7 and 12.4.9). Coastal erosion and flooding also 
pose challenges to critical infrastructure such as roads, subway tunnels, 
electricity and phone networks, wastewater management plants and 
buildings (Grahn and Nyberg, 2017; Pregnolato et al., 2017). Compound 
flooding due to simultaneous storm surges and high river flows have

been found to be increasingly frequent in several cities and/or low-
lying areas in Europe and the USA ( high confidence ) (Wahl et al., 2015;  
Bevacqua et al., 2019; Ganguli and Merz, 2019). Chapter 11 found that 
the frequency of such compound flood events is projected to increase 
(high confidence ). In addition to changes induced by sea level change, 
many cities and settlements by the sea are in regions where tropical

1849
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212cyclones are projected to become more intense and severe tropical 
cyclones more frequent ( high confidence ) (Section 11.7).
The SROCC highlighted coastal settlements in the Arctic as being 
particularly exposed to several CID changes (Magnan et al., 2019). 
Enhanced waves, due to extended season of sea ice retreat, are 
projected to foster coastal flooding and erosion (Section  12.4.9; 
Gudmestad, 2018; Casas-Prat and Wang, 2020). Climate change is 
also affecting sea ice quality and season length along coasts of the 
Arctic Ocean where populations depend on sea ice for hunting or 
transportation (Section 12.4.9; Pearce et al., 2015).
In summary, coastal cities and settlements are particularly 
affected by a  number of climatic impact-drivers that have 
already changed and will continue to change whatever 
the emissions scenario. These include increases in extreme

heat, pluvial floods, coastal erosion and coastal flood ( high 
confidence ). Increasing relative sea level, compounding with 
increasing tropical cyclone storm surge and rainfall intensity, 
will increase the probability of coastal city flooding ( high 
confidence ). Arctic coastal settlements are particularly exposed 
to climate change due to sea ice retreat ( high confidence ).
12.4.10.3 Deserts and Semi-arid Areas
Drylands, which include hyper-arid, arid, semi-arid and dry sub-humid 
areas (IPCC, 2019c), lie on all continents and cover 46% of the global 
land area and host more than one-third of the current population 
(Olsson et al., 2019). Huang et al. (2016b) found that aridity changes 
have helped expand dryland area by about 4% from 1948 to 2004, 
with the largest expansion of drylands occurring in semi-arid regions 
since the early 1960s. Section  4.5.1 assessed high confidence  of 
a future poleward expansion of the Hadley cell, leading to a poleward

shift of dryland areas in all scenarios considered. There is no evidence 
of a future global trend in aridification of drylands (IPCC, 2019a), but 
high confidence  of aridification in some areas (e.g., Mediterranean, 
Central America, Southern Africa; IPCC, 2019a; see also Figure 12.4j–l). 
However, drivers of desertification largely include land-cover changes 
and land-use management, along with climate change (IPCC, 2019a).
Warming temperatures and extreme heat are major climatic impact-
drivers with multiple potential impacts on societies, health, and 
habitability in semi-arid and arid regions that are already near 
physiological limits for outdoor activities. Semi-arid regions will very 
likely  undergo a warming in all future scenarios (Chapter 4 and Atlas) 
and likely  undergo an increase in duration, magnitude and frequency 
of heatwaves (Chapter 11) (Figure 12.4a–c). It is likely  that heat stress 
will be much more intense by the end of the century in many areas

under all scenarios, such as deserts and semi-arid zones in Asia (Murari 
et al., 2015; Mishra et al., 2017), Australia and Africa (Zhao et al., 2015; 
Xia et al., 2016; Guo et al., 2017; Dosio et al., 2018; Schwingshackl 
et al., 2021), with consequences for labour productivity with respect 
to high heat-humidity conditions (Figure 12.4d–f).
Drought is another major climatic impact-driver for semi-arid 
areas, imposing major challenges on agriculture given existing 
water availability constraints (Kusunose and Lybbert, 2014; Barlow  et  al.,  2016; Otto et  al., 2018). Over the period 1961–2013, 
the annual area of drylands in drought has increased, on average 
by slightly more than 1% per year, with large interannual variability 
(Olsson et al., 2019). In general, droughts have increased in several arid 
and semi-arid areas over the last decades ( medium confidence ), and 
are likely  to increase in the future as indicated by a number of indices

calculated from climate (Liu et al., 2018b; Zkhiri et al., 2019; Coppola 
et al., 2021b; Driouech et al., 2021; see also Figure 12.4j–l).
Deserts and semi-arid areas are prone to dust storms, which can 
drive impacts on health and several other sectors (X. Zhang et al., 
2016; Tong et al., 2017). The SRCCL indicated that the evolution of 
dust under climate change is uncertain (Mirzabaev et  al., 2019), 
and there is a lack of evidence and agreement of a change in their 
frequency or intensity so far in most regions (Sections 12.4.1–12.4.9). 
Model projections of future changes in dust are hindered by the 
uncertainties in future regional wind and precipitation as the climate 
warms (Evan et al., 2016); in the effect of CO 2 fertilization on source 
extent (Huang et al., 2017); and in the impact of human activities 
upon the land surface (Ginoux et al., 2012; see Chapter 10). Projected 
trends in dust storms and dust loads in deserts and semi-arid areas

vary from region to region. Dust loadings are expected to decrease 
over most of the Sahara and Sahel ( low confidence ) (Section 12.4.1), 
increase over Mexico and the south-west USA ( medium confidence ) 
(Section 12.4.6), and there is low confidence  of a future trend due to 
climate change in other continents (Sections 12.4.2–12.4.5).
In conclusion, desert and semi-arid areas are strongly affected 
by climatic impact-drivers such as extreme heat, drought and 
dust storms. Heat hazards are very likely  increasing in all 
future climate scenarios, but uncertainty remains regarding 
any broadly consistent future changes in other climatic 
impact-drivers for deserts and semi-arid regions.
12.4.10.4 Mountains
Mountains cover about 30% of the land areas on Earth (not counting 
Antarctica) and deliver a  number of vital services to humanity 
(WGII Cross-Chapter Paper 5; IPCC, 2019b). Climate change in high 
mountains was addressed in SROCC, which emphasized changes in

several climatic impact-drivers. These included an observed general 
decline in low-elevation snow cover, glaciers and permafrost ( high 
confidence ), which induced changes in natural hazards such as 
decrease in slope stability ( high confidence ), changes to the frequency 
of glacial lake outbursts ( limited evidence ), and climate effects on 
other climatic impact-drivers (avalanche, rain-on-snow floods) with 
various degrees of confidence (Hock et al., 2019).
There is a growing body of literature indicating elevation-dependent 
warming (EDW; different rates of warming by altitude although not 
necessarily increasing with altitude) in several mountain regions but 
not globally (Hock et al., 2019; Pepin et al., 2019; Ahmed et al., 2020; 
B. Li et al., 2020; Williamson et al., 2020; You et al., 2020; Micu et al., 
2021). Statistically significant elevational enhancement to long-
term trends in maximum near-surface air temperatures and diurnal

temperature range were observed in southern central Himalaya 
and in the Swiss Alps (Rottler et  al., 2019; Thakuri et  al.,  2019).

1850
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Aguilar-Lome et al. (2019) reported that winter daytime land surface 
temperatures in the Andean region between 7°S and 20°S show 
the strongest trends at higher elevations: +1.7°C per decade above 
5000 m above sea level. Palazzi et  al. (2019) identified changes in 
albedo and downward thermal radiation as key drivers of EDW 
according to the simulation outputs of a  high-spatial-resolution 
model in three important mountainous areas: the Colorado Rocky 
Mountains, the Greater Alpine Region and the Himalayas–Tibetan 
Plateau, but mechanisms for EDW remain complex (Hock et  al., 
2019). Warming is also affecting mountain lake surface temperatures, 
increasing probabilities of ice-free winters and the frequency and 
duration of ‘lake heatwaves’ ( high confidence ) (O’Reilly et al., 2015; 
Woolway et al., 2020, 2021) with a high variability from lake to lake.

Elevation-dependent warming could speed up the observed, rapid 
upward shifts of the freezing level height (FLH) in several mountainous 
regions of the world and lead to faster changes in the snowline, the 
glacier equilibrium-line altitude and the snow/rain transition height 
(high confidence ). In the Indus, Ganges and Brahmaputra basins in 
Asia, the FLH is projected to rise at a rate of 4.4 to 10.0 m yr –1 under 
RCP8.5 (Viste and Sorteberg, 2015). In the Argentinian Andes, FLH 
is projected under RCP8.5 to move up more than twice as much by 
2070 as during the entire Holocene under the worst case scenario 
(Drewes et al., 2018). On the western slope of the subtropical Andes 
(30°S–38°S) in central Chile, the mean value of the free tropospheric 
height of the 0°C isotherm under wet conditions is projected to be 
close to or higher than the upper quartile of the distribution in the 
current climate, towards the end of the century and under RCP8.5

(Mardones and Garreaud, 2020). In the Alps and the Pyrenees, Spandre 
et al. (2019) projected a rise in the natural snow elevation of between 
200–300 m and 400–600 m by mid-century under RCP2.6 and RCP8.5, 
respectively. In the same region, the environmental equilibrium-line 
altitude is projected to exceed the maximum elevation of 69%, 81% 
and 92% of the glaciers by the end of the century under RCPs 2.6, 
4.5 and 8.5, respectively (Žebre et al., 2021).
Orographic effects enhance convection and stratiform heavy 
precipitation (due to uplift) and make mountains prone to extreme 
precipitation events. These events are projected to increase in major 
mountainous regions (Alps, parts of the Andes, British Columbia, North-
Western North America, Calabria, Carpathian, Hindu-Kush-Himalaya, 
Rocky Mountains, Umbria; medium to high confidence  depending on 
location), with potential cascading consequences of floods, landslides

and lake outbursts in mountainous areas in all scenarios ( medium 
confidence ) (Chapter  11 and Sections 12.4.1–12.4.9; Geertsema 
et al., 2006; Gaire et al., 2015; Kim et al., 2015; Ciabatta et al., 2016; 
Gariano and Guzzetti, 2016; Kharuk et al., 2016; Syed and Al Amin, 
2016; Cloutier et al., 2017; Gądek et al., 2017; Jurchescu et al., 2017; 
Rajczak and Schär, 2017; Alvioli et al., 2018; Coe et al., 2018; Schlögl 
and Matulla, 2018; C.-W. Chen et al., 2019; Handwerger et al., 2019; 
Hock et al., 2019; Patton et al., 2019; Vaidya et al., 2019; Kirschbaum 
et al., 2020; Coppola et al., 2021b).
Declines in low-elevation snow depth and seasonal extent are 
projected for all SSP-RCPs (see Sections 12.4.1–12.4.6), along with 
reductions in mountain glacier surface area, increases in permafrost 
temperature, decreases in permafrost thickness, changes in lake and river ice, changes in the amount and seasonality of streamflows and

hydrologic droughts in snow-dominated and glacier-fed river basins 
(e.g., in Central Asia; Sorg et al., 2014; Reyer et al., 2017b) ( medium 
confidence ), and decreases in the stability of mountain slopes and 
snowfields. Glacier recession could lead to the creation of new glacial 
lakes in places like the Himalaya-Karakoram region (Linsbauer et al., 
2016) and in Alaska and Canada (Carrivick and Tweed, 2016; Harrison 
et  al., 2018) ( medium confidence ). With increasing temperature 
and precipitation these can increase the occurrence of glacier lake 
outburst floods and landslides over moraine-dammed lakes ( high 
confidence ) (Carey et al., 2012; Rojas et al., 2014; Iribarren Anacona 
et al., 2015; Cook et al., 2016; Haeberli et al., 2017; Kapitsa et al., 
2017; Narama et al., 2018; Wilson et al., 2018; Drenkhan et al., 2019; 
S. Wang et al., 2020).
In conclusion, mountains face complex challenges from 
specific climatic impact-drivers drastically influenced by

climate change: regional elevation-dependent warming ( high 
confidence ), low-to-mid-altitude snow cover and snow-season 
decrease even as some high elevations see more snow 
(high  confidence ), glacier mass reduction and permafrost 
thawing ( high confidence ), and increases in extreme 
precipitation and floods in most parts of major mountain 
ranges ( medium confidence ).
12.4.10.5 Tropical Forests
Tropical forests, which are among the world’s most biologically diverse 
ecosystems, are essentially located in Central and South America, 
Africa and South East Asia (AR6 WGII Cross-Chapter Paper 7). The AR5 
and SR1.5 indicated several specific climatic impact-driver changes 
that are particularly important to tropical forests: mean temperature 
increase, long-term drying trends (including shifts in the length of the 
dry season), prolonged drought, wildfires and surface CO 2 increase for 
inland forests (IPCC, 2013, 2018). The SRCCL assessed an enhanced

risk and severity of wildfires in tropical rainforests ( high confidence ), 
but fires are not only a  natural process but are also affected by 
deforestation and other human influences (IPCC, 2019a).
Temperature is rising in all tropical regions covered with forests and 
will very likely  continue to rise, reaching levels unprecendented in 
recent decades as the temperature trends rapidly emerge from weak 
historical interannual variability (Sections 12.4.1–12.4.4 and 12.5.2; 
see also Chapter 4 and Atlas).
Regional patterns of increasing drought or unusual wet and dry 
periods are predicted with agreement over many climate models 
such as over the Amazon basin (Boisier et  al., 2015; Duffy et  al., 
2015; Zulkafli et al., 2016; Coppola et al., 2021b). There is medium 
confidence  (limited evidence , high agreement ) that in several 
tropical-forest regions (e.g., Amazonia, West Africa) the dry season 
length has increased (Fu et al., 2013; Debortoli et al., 2015; Saeed

et al., 2017; Dunning et al., 2018; Wadsworth et al., 2019), and there 
is low confidence  (limited evidence ) that deforestation influences the 
shift in the onset of the wet season in south Amazonia (Leite-Filho 
et  al., 2019). In contrast, the wet season is increasing in northern 
Australia tropical forests (Catto et al., 2012).

1851
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Tropical forests typically reach peak fire weather conditions in the dry 
season (Taufik et al., 2017), in particular during long-lived droughts 
(Brando et al., 2014; Marengo et al., 2018), with consequences for 
tree mortality, forest and carbon sink loss (Brando et al., 2019), and 
on the hydrological cycle in South America (Martinez and Dominguez, 
2014; Espinoza et al., 2020). Observations and reanalyses over the 
past three to four decades, combined into fire risk indices, show that 
the fire weather season length has been increasing by about 20% 
globally (Jolly et al., 2015), and this index exhibits particularly high 
trend values over tropical forest areas of South and Central America 
and Africa. There is generally low confidence  in future projections of 
general fire weather risk evolution in tropical forests and evolutions

depend on the region (Abatzoglou et al., 2019). Over the Amazon 
basin the fire risk increase is projected to emerge well before 2050 
while for other equatorial forests no significant evolution is found. 
In Savanna areas the risk increase is found to be more general.
In conclusion, most tropical forests are challenged by a mix 
of emerging warming trends that are particularly large in 
comparison to historical variability ( medium confidence ). Water 
cycle changes bring prolonged drought, longer dry seasons, 
and increased fire weather to many tropical forests, with 
plants also responding to CO 2 increases ( medium confidence ).
12.5 Global Perspective on 
Climatic Impact-drivers
12.5.1 A Global Synthesis
Section 12.4 assessed changes in climatic impact-drivers by region, 
primarily based on a large number of local- and regional-scale studies 
(even though global studies are also used). This section presents an

assessment of changes in CIDs at the global scale. It is based on both 
a bottom-up synthesis of the results in Section 12.4, and a top-down 
assessment from global-scale studies undertaken here. Cross-Chapter 
Box 12.1 summarizes global-scale CIDs with levels of warming.
Global-scale studies use similar indices of climatic impact-drivers 
across space, although these indices may not always be those used 
at the local or regional scale. Most published global-scale studies 
concentrate on single sectors or climatic impact-drivers, but some 
take a multi-sectoral perspective (e.g., Warszawski et al., 2014; Arnell 
et al., 2016, 2019; Schleussner et al., 2016; Mitchell et al., 2017; Betts 
et al., 2018; Byers et al., 2018; Mora et al., 2018; O’Neill et al., 2018; 
Zscheischler et al., 2018). Only a few published global-scale studies 
(e.g., Coppola et al., 2021; Schwingshackl et al., 2021) have used 
CMIP6 scenarios to date.

All regions will experience, before 2050, increased warming, an 
increase of extreme heat and a decrease in cold spells, regardless of 
the emissions trajectory ( high confidence ). Tropical regions, but also 
mid-latitude regions to a lesser extent, will experience an increasing 
number of days with heat indices crossing dangerous thresholds 
used to characterize heat stress, such as HI > 41°C (Figure 12.4). 
The increase, by the end of century, exceeds 100 days per year in 
most tropical areas under SSP5-8.5 but remains much more limited (almost half) under SSP1-2.6. Several global-scale studies have 
shown that high temperature extremes will increase everywhere 
(high confidence ) (Gourdji et  al., 2013; Perkins-Kirkpatrick and 
Gibson, 2017; Harrington et  al., 2018; Jones et  al., 2018; Lehner 
et al., 2018; Shi et al., 2018; Tebaldi and Wehner, 2018; Arnell et al., 
2019; Russo et  al., 2019; Schwingshackl et  al., 2021), although

the change depends on the indicator (see also Chapter  11). For 
example, by 2080, at least 80% of the land surface is expected to 
experience average summer temperatures greater than the historical 
(1920–2014) maximum with high (RCP8.5) emissions (Lehner et al., 
2018). The areas of rice and maize cropland with damaging extreme 
temperatures during the reproductive season will increase by 
a factor of three under RCP8.5 (Gourdji et al., 2013). Under a high 
emissions scenario, heatwaves that are currently considered rare 
are projected to become the norm almost everywhere by the end of 
the century (Russo et al., 2014). Heat stress as a combined function 
of temperature and humidity also increases at the global scale, 
especially with high emissions (e.g., Matthews et al., 2017). Growing 
degree-days and cooling degree-days also increase everywhere 
(Arnell et  al., 2019) with the absolute and proportional changes 
depending on temperature threshold. Increases in temperatures

will result in reductions in heating degree days (Arnell et al., 2019; 
Coppola et al., 2021b) and a widespread reduced frequency of cold 
extremes ( high confidence ).
Integrating the results of the regional assessments in Section  12.4 
shows that changes in CIDs linked with the water cycle or atmospheric 
dynamics (e.g., storms) vary more among regions, largely due to the 
spatial pattern of changes in atmospheric circulation and changes 
in precipitation and evaporation (Chapters 8 and 11). There is high 
confidence  that heavy precipitation and pluvial floods will be increasing 
in a  majority of land regions, primarily due to the well-understood 
Clausius–Clapeyron relationship describing the  increase in moisture 
content with air temperature (Chapters 8 and 11), but there is 
a  large spatial variability in fluvial flood hazards. Top-down global-
scale studies show that although fluvial flood hazards are projected

to decrease in regions where there are large reductions in seasonal 
rainfall totals or where warmer temperatures mean less accumulated 
snow, at the global scale, fluvial flood hazard (characterized as the 
area affected, size of peak or likelihood of an event) is projected to 
increase substantially through the century (Giuntoli et al., 2015; Arnell 
and Gosling, 2016; Winsemius et al., 2016; Alfieri et al., 2017; Dottori 
et al., 2018; Arnell et al., 2019). Projected changes in agricultural and 
hydrological drought characteristics are dependent on the indicator 
used to define drought (Sections 11.6 and Section 12.3.2), but there 
is at least  medium confidence  of an increase in the drought hazard in 
many parts of the world. This is also reflected in global-scale studies, 
with Naumann et  al. (2018), for example, showing that the global 
mean average drought duration (based on the SPEI index which is 
calculated from the difference between precipitation and potential

evaporation) increased from 7  months with the current climate to 
18.5 months for a global warming level of 3°C. The apparent global 
increase in drought occurrence is greater when evaporation is captured 
in the drought indicator (e.g., SPEI) than when the indicator is based on 
precipitation alone (as in SPI; Carrão et al., 2018). There is evidence that 
the likelihood of simultaneous events in several locations will increase: 
Trnka et al. (2019) found that the proportion of wheat-growing areas

1852
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12experiencing simultaneous severe water stress events (based on SPEI) 
in a year increased from 15% under current conditions to up to 60% at 
the end of the 21st century under high emissions.
The regional assessment in Section  12.4 shows that fire weather 
is projected to increase with medium or high confidence  in every 
continent of the world, including Arctic polar regions. Globally, fire 
weather is projected to increase in future, primarily due to higher 
temperatures and exacerbated where precipitation reduces. By 2050, 
60% of the global land area would see a  significant increase in 
fire weather under RCP8.5 (Abatzoglou et  al., 2019). There is less 
confidence in the projected distribution of change in fire weather 
across regions in global-scale studies. For example, Moritz et  al., 
(2012) projected an increase in fire weather in mid- and high latitudes

but a reduction in the tropics, whilst Yu et al. (2019) and Bedia et al. 
(2015) projected an increase in the tropics. These differences reflect 
differences in methodologies and fire weather indices adopted in 
different studies.Integration of the results of Section  12.4 shows that the total 
number of tropical cyclones is projected to decrease through the 
21st  century, particularly with high emissions, but the number of 
very intense tropical cyclones is projected to increase in most areas 
(at least medium confidence ) (e.g.,  Bacmeister et  al., 2018; see 
Section 11.7.1). Furthermore, regions with glaciers will lose glacier 
mass and regions concerned with snow cover will see a reduction in 
snow depth, the duration, or extent of cover ( medium confidence in 
polar regions , high confidence elsewhere).
Relative sea level rise (RSLR) is projected in all regions (except for 
a few Arctic polar regions) with likelihoods varying from very likely

to virtually certain  depending on the region. This will increase the 
frequency of extreme sea levels and, depending on the level of 
coastal flood protection, coastal flooding (Vousdoukas et al., 2018; 
Kirezci et al., 2020). In terms of globally averaged extreme total water 
level (ETWL) frequency changes, the present-day 1-in-100-year event 
is projected to become 1-in-30-year and 1-in-20-year events by 2050 
Heat Mean precipitation
Heavy precipitation
CoastalClimatic impact-drivers
Droughts 
and aridityFire weather
Snow, ice River floodMean windDirection of change
High confidence in increase
High confidence in decrease
Figure 12.11  | Synthesis of the climatic impact-driver (CID) changes projected by 2050 (2041–2060) with high confidence , relative to reference period 
(1995–2014), together with the sign (direction) of change. Information is taken from the CID tables in Section 12.4. Some CIDs are grouped in order to streamline the

information in order to fit in all information in the figure. Mean temperature, extreme heat, cold spells and frost are grouped under a single ‘heat’ icon, as they are projected to 
change simultaneously, albeit heat and cold are changing in opposite directions. Coastal CIDs (relative sea level, coastal flooding and coastal erosion at sandy beaches) are also 
grouped. In the figure, the ‘coastal’ icon indicates regions where at least two of the three individual coastal CIDs are projected to change with  high confidence . Cases where 
only two of the three CIDs increase with high confidence are in Arctic Northern Europe, Russian Arctic and Arctic North-Western North America. A single icon is used for aridity, 
hydrological drought, and agricultural and ecological drought, and only the number of drought types that change is indicated. For the ‘Snow, ice’ icon, information is taken from

the evolution of the ‘Snow, glacier and ice sheet’ CID; most regions also have similar changes for ‘permafrost’ and ‘lake, river and sea ice’. Exceptions are for North-Eastern 
North America, Russian Arctic and Arctic North-Western North America where snow is decreasing with medium confidence  (thus not appearing in the figure), while permafrost 
and lake, river and sea ice is decreasing with high confidence . The location of the icons within the regions is arbitrary. Icon sources:  https://www.flaticon.com/authors/freepik .

1853
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212under RCP4.5 and RCP8.5, respectively. The present day 1-in-100-
year ETWL is projected to become a 1-in-5-year event by 2100 under 
RCP4.5, while under RCP8.5, such events are projected to occur more 
than once a year (Vousdoukas et al., 2018).
There is high confidence  that most of the world’s sandy coasts will 
experience shoreline retreat, in the absence of terrestrial or offshore 
sediment sources. Median projections presented by (Vousdoukas et al., 
2020b) indicate that 13.6% (36,097 km) and 15.2% (40,511 km) of 
the world’s sandy beaches could retreat by more than 100 m by 2050 
(relative to 2010) under RCP4.5 and RCP8.5 respectively, implying 
a 12% increase in severely threatened shoreline length under RCP8.5, 
relative to RCP4.5. These median projections increase to 35.7%–49.5% 
(RCP4.5 and RCP8.5, respectively; or 95,061 km–131,745 km) by the

end of the century, implying a 38% increase in severely threatened 
shoreline length under RCP8.5, relative to RCP4.5.
Figure 12.11 highlights that each region will, with high confidence , 
experience changes in multiple CIDs, challenging the vulnerability of 
the region and its adaptation and mitigation capacity. All non-polar 
regions with a  coastline will see an increase in relative sea level, 
extreme sea level and coastal erosion, and will also see an increase in 
hot extremes, a decrease in cold extremes, and many will experience 
an increase in heavy precipitation. One cluster of regions  – East 
Southern and West Southern Africa regions, the Mediterranean, 
Northern Central America, Western North America, several regions 
in South America and Australia – will experience, in addition to the 
aforementioned globally changing CIDs, increases in either drought/
aridity or fire weather ( high confidence ). This will impact upon

agricultural resources, infrastructure and health and ecosystems. 
A second cluster of regions including mountainous areas or regions 
with seasonal snow cover will experience (in addition to increases 
in heat extremes, more intense short-duration rainfall, and increases in 
coastal hazards where coasts exist) reductions in snow and ice cover 
and/or increases in river flooding in many cases (Western, North-
Western, Central and Eastern North America, Arctic regions, Andes 
regions, Europe, Siberia, Central and East Asia, Southern Australia 
and New Zealand) ( high confidence ). These are places where energy 
production, ski tourism, river transportation, and infrastructure could 
in particular face increased risks.
In a few other regions, only a small number of CIDs are projected to 
change with high confidence  (e.g., Sahara, Central Africa, Western 
Africa, Madagascar, Arabian Peninsula, South-Eeastern South 
America, New Zealand, Small Islands). The lower confidence levels

associated with changes in CIDs in these regions can be due either 
to weaker change signals compared to natural variability, or due to 
limited evidence  and model uncertainties leading to low agreement , 
and does not mean that climate change will affect these regions any 
less than in other regions.
In summary, there is high confidence  that all regions of the 
world will experience changes in several climatic impact-
drivers by mid-century, albeit at region-specific rates of 
change and confidence levels for each CID. Consequently, 
changing CIDs have the potential to affect climate-related 
risks in all regions of the world.12.5.2 Emergence of Climatic Impact-drivers 
Across Time and Scenarios
The emergence of a climate change signal occurs when that signal 
exceeds some critical threshold (usually taken to be a  measure of 
natural variability; see for example, Hawkins and Sutton, 2012) 
or when the probability distribution of an indicator becomes

significantly different to that over a reference period (e.g., Chadwick 
et al., 2019; see also Chapter 10 and Section 1.4.2), in which case 
external anthropogenic forcings can be detected as causal factors. 
The ‘time of emergence’ (ToE) or ‘temperature of emergence’ is 
the time or global warming level thresholds associated with this 
exceedance. Emergence is particularly relevant to impacts, risk 
assessment and adaptation because human and natural systems 
are largely adapted to natural variability but may be vulnerable if 
exposed to changes that go beyond this variability range; this is not 
to say that changes within natural variability have no impact, as 
occurrence of damaging extremes proves. Emergence also informs 
the timing of adaptation measures. The emergence of a change is 
always relative to a reference period (e.g., the pre-industrial period 
or a recent past), depending on the framing question. In the former

case, the goal is to estimate the amplitude of an anthropogenically 
driven change while in the latter, it is to estimate the amplitude of 
change relative to a baseline that is familiar to stakeholders. Both 
questions are important for risk assessment, but the former may be 
more directly interpretable in a  mitigation context. The variability 
also refers to a time scale, generally interannual to inter-decadal. See 
Section 1.4.2 and Chapter 10 for more details about how emergence 
is defined and used in the literature.
Changes in climatic impact-drivers may remain within the range of 
natural variability or have a time of emergence that varies by region 
and scenario. This section assesses the evidence for the effects of 
anthropogenic climate change on the emergence of changes in 
CID index, past, present and future, as evidenced by the literature 
assessed in other chapters, as well as additional literature assessed

here, at both global and regional scales. In many cases, however, 
sufficient literature for a robust region-by-region assessment of ToE is 
lacking. The assessment herein is made by CID. Regional emergence 
assessment is reported in Tables 12.3–12.11 but is undertaken in this 
section.
Estimations of ToE must be done with caution given the many sources of 
inherent uncertainties, such as observations representing only a single 
realization of climate history, internal variability (whose frequency – 
e.g., annual or decadal – needs to be precisely defined), model biases, 
and potential low-frequency changes in variability (Chapter  10; 
Lehner et  al., 2017). In addition, a  homogeneous interpretation of 
multiple studies is hampered by heterogeneous methodologies used 
to calculate emergence. In this section, we assess emergence and its 
confidence level based on such multiple methods as provided by the 
literature, and unless specified otherwise, emergence here refers to

a signal to noise ration S/N > 1 relative to a pre-industrial baseline 
and interannual variability (the ‘noise’). Furthermore, observed trends 
and attribution are taken into account in combination with climate 
simulations (historical or projections) for assessing whether a trend 
has already emerged in the historical period.

1854
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Mean air temperature:  Warming of mean annual temperatures 
has already emerged in all land regions, as obtained from past 
observations and confirmed by historical simulations ( high 
confidence ) (Figure 1.13; King et al., 2015; Hawkins et al., 2020), with 
S/N ratios larger than two. In the current climate, the highest S/N 
ratios exceed five over Central Africa, Amazonia, East and South East 
Asia. Seasonal warming emergence depends on the season. Because 
the temperature variability in the mid-latitudes is higher in winter 
than in summer, the emergence of seasonal warming occurs for 
summer but not for winter in most of this part of the world. In Europe, 
summer warming has emerged in all regions ( medium confidence , 
medium agreement ), and in North America, it has emerged only over 
Eastern and Western regions while in winter there is low confidence

of an emergence in warming in all regions for both Europe and North 
America (Lehner et al., 2017; Hawkins et al., 2020). When considering 
the climate of the end of the 20th  century (i.e.,  recent past) as 
a baseline, the emergence of mean temperature is projected at very 
different times depending on the scenario. For instance, emergence is 
reached by 2050 under RCP8.5 in most areas of Europe, Australia or 
East Asia, but it does not occur within the 21st century under RCP2.6 
(medium confidence ) (Sui et al., 2014; Im et al., 2021). This means 
that under RCP2.6, mean temperatures stay within the recent climate 
variability range observed in the mid-latitudes. However, even under 
RCP2.6, mean temperatures in tropical regions that have not already 
emerged are projected to emerge before 2050 ( medium confidence ).
Extreme heat and cold:  An increase in heat extremes has emerged 
or will emerge in the coming three decades in most land regions ( high

confidence ) (Chapter 11; King et al., 2015; Seneviratne and Hauser, 
2020), relative to the pre-industrial period, as found by testing 
significance of differences in distributions of yearly temperature 
maxima in simulated 20-year periods. In tropical regions, wherever 
observed changes can be established with statistical significance, 
and in most mid-latitude regions, there is high confidence  that 
hot and cold extremes have emerged in the historical period, but 
only medium confidence  elsewhere. In other regions emergence 
is projected at the latest in the first half of the 21st century under 
RCP8.5 ( high confidence ) (King et al., 2015; Seneviratne and Hauser, 
2020). Relative to the end of 20th-century conditions, changes 
in humid heat stress as characterized by wet bulb temperature, 
indicates a ToE as early as in the first two decades of the 21st century 
in RCP8.5 at least in many tropical regions (most of Africa in the band

20°S–20°N, South Asia and South East Asia) ( medium confidence ) 
(Im et al., 2021). By 2050 and under RCP8.5, wet bulb temperature 
is projected to emerge in many other areas such as Southern Africa, 
North Africa, Europe, and most of Central, Southern and Eastern Asia 
and Northern and Eastern Australia, while under RCP2.6, emergence 
is either reached later in the century (Europe, Central Asia, Northern 
Australia), or never reached in the century (Im et al., 2021). Decrease 
of cold spells has already emerged above the interannual variability 
in Australasia, Africa and most of Northern South America, and they 
are projected to emerge before 2050 in the northern mid-latitudes 
and in Southern South America (King et  al., 2015) under RCP8.5 
(medium confidence, limited evidence and high agreement ).
Mean precipitation:  Mean precipitation changes only emerged

over a few regions in the historical period (increase in Northern and Eastern Europe and decrease in West Africa and Amazonia) from 
observations with an S/N ratio larger than one ( low confidence ) 
(Hawkins et  al., 2020). The emergence of increasing precipitation 
before the middle of the 21st century is found across scenarios in 
Siberian regions, Russian Far East, Northern Europe, Arctic regions 
and the northernmost parts of North America ( high confidence ) 
and later in other northern mid-latitude areas, depending on the 
scenario, albeit with different methods and emergence definitions 
used in climate projections (Chapter 8; Giorgi and Bi, 2009; Maraun, 
2013; King et  al., 2015; Akhter et  al., 2018; Kumar and Ganguly, 
2018; Nguyen et al., 2018; Barrow and Sauchyn, 2019; Rojas et al., 
2019; Kusunoki et  al., 2020; Pohl et  al., 2020; W. Li et  al., 2021). 
Decreases in mean precipitation are projected to emerge in parts of

Africa by the middle of the century, and later in the Mediterranean 
and Southern Australia, but the emergence depends on the scenario, 
and specific seasons for crop growth (Nguyen et  al., 2018; Rojas 
et  al., 2019). Mean precipitation does not emerge in any of these 
regions at any time in the 21st century under RCP2.6, but emerges 
in all under RCP8.5. ToE under RCP4.5 is projected to be around 
25 years later relative to RCP8.5 in many of the early emergence 
regions, highlighting the importance of mitigation to gain more time 
for adaptation.
Heavy precipitation and floods:  There is low confidence  in the 
emergence of heavy precipitation and pluvial and river flood frequency 
in observations, despite trends that have been found in a few regions 
(Chapters 8 and Chapter  11, and across Section  12.4). In  climate 
projections, the emergence of increase in heavy precipitation strongly 
depends on the scale of aggregation (Kirchmeier-Young et al., 2019),

with, in general, no emergence before a 1.5°C or 2°C warming level, 
and before the middle of the century ( medium confidence ), but results 
depend on the method used for the calculation of the ToE (Maraun, 
2013; King et al., 2015; Kusunoki et al., 2020). Emergent increases in 
heavy precipitation are found in several regions when aggregated 
at a regional scale in Northern Europe, Northern Asia and East Asia, 
at latest by the end of the century in SRES A1B or RCP8.5 scenarios 
or when considering the decadal variability as a reference ( medium 
confidence ) (Maraun, 2013; W. Li et al., 2018, 2021; Kusunoki et al., 
2020). There have been few emergence studies for streamflow 
and flooding, although one study showed emergence of different 
hydrological regimes at different times during the 21st  century 
across the USA (Leng et al., 2016). Variability in extreme streamflows 
from year to year can be high relative to a trend (Zhuan et al., 2018).

Given the heterogeneity of methods and results, there is only low 
confidence  in the emergence of heavy precipitation and flood signals 
in any region when considering the S/N ratio.
Droughts, aridity and fire weather:  There is low confidence  in 
the emergence of drought frequency in observations, for any type 
of drought, in all regions. Even though significant drought trends 
are observed in several regions with at least medium confidence  
(Sections  11.6 and 12.4), agricultural and ecological drought 
indices have interannual variability that dominates trends, as can 
be seen from their time series ( medium confidence ) (H. Guo et al., 
2018; Spinoni et  al., 2019; Haile et  al., 2020; M. Wu et  al., 2020). 
Studies of the emergence of drought with systematic comparisons 
between trends and variability of indices are lacking, precluding

1855
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212a comprehensive assessment of future drought emergence. Historical 
climate simulations indicate that fire weather indices have already 
emerged in several regions (the Amazon basin, Mediterranean, 
Central America, West and Southern Africa) ( low confidence , limited 
evidence ) (Abatzoglou et al., 2019), and emergence is projected with 
low confidence  by the middle of the century in several other regions 
(Southern Australia, Siberia, most of North America and Europe) 
when considering several indices together.
Wind:  Observed mean surface wind speed trends are present in many 
areas (Section  12.4), but the emergence of these trends from the 
interannual natural variability and their attribution to human-induced 
climate change remains of low confidence  due to various factors 
such as changes in the type and exposure of recording instruments,

and their relation to climate change is not established. For future 
conditions, there is limited evidence  of the emergence of trends in 
mean wind speeds due to the lack of studies quantifying wind speed 
changes and their interannual variability. The same limitation also 
holds for wind extremes (severe storms, tropical cyclones, sand and 
dust storms).
Snow and ice:  The decrease in the Northern Hemisphere snow 
cover extent in spring has already emerged from natural variability 
(Section 3.4.2). The snow cover duration period is projected to emerge 
over large parts of Eastern and Western North America and Europe 
by the mid-century both in spring and autumn, and emergence is 
expected in the second half of the 21st century in the Arctic regions in 
the high RCP8.5 scenario ( medium confidence ) (Chapter 9, SROCC). 
For snow depth or snow water equivalent, there is low confidence 
(limited evidence ) of the emergence of a  decrease before 2050

because climate change also increases the variability of the snow 
depth signal, for example in Europe (Section 3.4.2; Willibald et al., 
2020). Terrestrial permafrost is warming worldwide due to climate 
change (Sections 2.3.2.5 and 9.5.2). Due to weak interannual 
variability of permafrost temperatures, terrestrial permafrost warming 
has emerged above natural variability in almost all observed time 
series of the Northern Hemisphere ( medium confidence , limited 
evidence , high agreement ) (Biskaborn et  al., 2019), but the active 
layer thickness exhibits considerable interannual variability inhibiting 
evidence for emergence (Chapter 9).
Sea ice: Sea ice area decrease in the Arctic in all seasons has 
already emerged from the interannual variability ( high confidence ) 
(Chapter  9). By contrast, the Antarctic sea ice area shows no 
significant trend, and therefore no emergence.
For other snow and ice CIDs (heavy snowfall and ice storm, hail, snow

avalanche), there is limited evidence  of emerging signals.
Relative sea level, coastal flood and coastal erosion:  Near-
coast RSLR will emerge before 2050 for RCP4.5 along the coasts of 
all AR6 regions (with coasts) except East Asia, the Russian Far East, 
Madagascar, the southern part of Eastern North America and the 
Antarctic regions ( medium confidence ) (Section 9.6.1.4; Bilbao et al., 
2015). Under RCP8.5, emergence of near-coast RSLR is projected 
by mid-century along the coasts of all AR6 regions (with coasts), 
except WAN where emergence is projected to occur before 2100 (Section 9.6.1.4; Lyu et al., 2014) ( medium confidence ). Emergence 
studies for ETWL and coastal erosion are lacking and hence it is not 
currently possible to robustly assess emergence in these CIDs.
Mean ocean temperature and marine heatwave:  The emergence 
of the sea surface temperature increase signal has been observed in 
global oceans over the last century, and the largest S/N values are

found in the tropical Atlantic and tropical Indian oceans (Hawkins 
et al., 2020). There is high confidence  in the widespread occurrence 
of marine heatwaves in all basins and marginal seas over the last 
decades (Chapter  9), but the emergence of this signal above the 
natural variability has not yet been addressed in detail.
Ocean acidity, ocean salinity and dissolved oxygen:  The global 
ocean pH decline has very likely  emerged from natural variability 
for more than 95% of the global open ocean (SROCC, Chapter 2). 
The regional signals are more variable, but in all ocean basins, the 
signal of ocean acidification in the surface ocean is projected to 
emerge in the early 21st  century (Chapter  5). The mean ToE for 
acidity in the coastal subtropical to temperate north-east Pacific 
and north-west Atlantic is above two decades ( high agreement , 
medium evidence ) (Section 5.3.5.2). Salinity change signals have 
already emerged with 20–45% of the zonally averaged basin in the

Atlantic, 20–55% in the Pacific and 25–50% in the Indian oceans 
and will be reaching 35–55% in the Atlantic in 2050 to 55–65% in 
2080; 45–65% to 60–75% in the Pacific; and 45–65% to 60–80% 
in the Indian oceans (Chapter 9; Silvy et al., 2020). Deoxygenization 
has already emerged in many open oceans. The signal is most 
evident in the Pacific and Southern oceans but not evident in the 
North Atlantic Ocean (Andrews et al., 2013; Levin, 2018). However, 
there is medium confidence  in the emergence of the anthropogenic 
signal in many other oceanic regions by 2050 (Henson et al., 2017; 
Levin, 2018).
There is high confidence  that several CID changes have 
already emerged above historical period natural variability 
in many regions (e.g.,  mean temperature in most regions, 
heat extremes in tropical areas, sea ice, salinity). Heat and 
cold CIDs (excluding frost) that have not already emerged 
will emerge by 2050 whatever the scenario in almost

all land regions ( medium confidence ). The emergence of 
increasing precipitation before the middle of the century is 
also projected in Siberian regions, Russian Far East, Northern 
Europe and the northernmost parts of North America and 
Arctic regions across scenarios with the various methods 
and emergence definitions used ( high confidence ). Studies 
are missing to properly assess S/N emergence for droughts 
and for wind CIDs. Arctic sea ice extent declines have mostly 
emerged above noise level ( medium to high confidence ), and 
the emergence of declining snow cover is expected by the end 
of the century under RCP8.5. There is medium confidence  that, 
under RCP8.5, the anthropogenic forced signal in near-coast 
relative sea level change will emerge by mid-century in all 
regions with coasts, except in the West Antarctic region where 
emergence is projected to occur before 2100. In all ocean 
basins, the signal of ocean acidification in the surface ocean is

1856
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Table 12.12 | Emergence of CIDs in different time periods, as assessed in this section.  The colour corresponds to the confidence of the region with the highest 
confidence: white cells indicate where evidence is lacking or the signal is not present, leading to overall low confidence  of an emerging signal.
Climatic Impact-
driver TypeClimatic Impact-driver 
CategoryAlready Emerged in 
Historical PeriodEmerging by 2050 at Least 
for RCP8.5/SSP5-8.5Emerging Between 
2050 and 2100 for at Least 
RC8.5/SSP5-8.5
Heat and ColdMean air temperature 1
Extreme heat 2 3
Cold spell 4 5
Frost
Wet and DryMean precipitation 6 7
River flood
Heavy precipitation and pluvial flood 8
Landslide
Aridity
Hydrological drought
Agricultural and ecological drought
Fire weather
WindMean wind speed
Severe wind storm
Tropical cyclone
Sand and dust storm
Snow and IceSnow, glacier and ice sheet 9 10

Lake, river and sea ice 11
Heavy snowfall and ice storm
Hail
Snow avalanche
CoastalRelative sea level 12
Coastal flood
Coastal erosion
Open OceanMean ocean temperature
Marine heatwave
Ocean acidity
Ocean salinity 13
Dissolved oxygen 14
OtherAir pollution weather
Atmospheric CO 2 at surface
Radiation at surface
1. High confidence  except over a few regions (CNA and NWS) where there is low agreement  across observation datasets.
2. High confidence  in tropical regions where observations allow trend estimation and in most regions in the mid-latitudes, medium confidence  elsewhere.
3. High confidence  in all land regions.
4. Emergence in Australia, Africa and most of Northern South America where observations allow trend estimation.
5. Emergence in other regions.
6. Increase in most northern mid-latitudes, Siberia, Arctic regions by mid-century, others later in the century.
7. Decrease in the Mediterranean area, Southern Africa, South-west Australia.

8. Northern Europe, Northern Asia and East Asia under RCP8.5 and not in low-end scenarios.
9. Europe, Eastern and Western North America (snow).
10. Arctic (snow).
11. Arctic sea ice only.
12. Everywhere except WAN under RCP8.5.
13. With varying area fraction depending on basin.
14. Pacific and Southern oceans then many other regions by 2050.
direction of change

1857
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Cross-Chapter Box 12.1 | Projections by Warming Levels of Hazards Relevant to the 
Assessment of Representative Key Risks and Reasons for Concern
Contributors:  Claudia Tebaldi (United States of America), Guofinna Aoalgeirsdottir (Iceland), Sybren Drijfhout (United Kingdom), 
John  Dunne (United States of America), Tamsin Edwards (United Kingdom), Erich Fischer (Switzerland), John Fyfe (Canada), 
Richard Jones (United Kingdom), Robert Kopp (United States of America), Charles Koven (United States of America), Gerhard Krinner 
(France), Friederike Otto (United Kingdom/Germany), Alex C. Ruane (United States of America), Sonia I. Seneviratne (Switzerland), 
Jana Sillmann (Norway/Germany), Sophie Szopa (France), Prodromos Zanis (Greece)
A consistent risk framework (Reisinger et  al., 2020) has been adopted across the three Working Groups (WGs) in IPCC AR6 while

recognizing the diversity of risk concepts across disciplines. WGI is assessing changes in climatic impact-drivers (CIDs), which are physical 
climate system conditions (e.g., means, events and extremes) that affect an element of society or ecosystems. Depending on system 
tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and 
regions (Sections 12.1–12.3). In the assessment of Representative Key Risk (RKR) categories and Reasons for Concern (RFCs) in WGII 
Chapter 16, the focus lies on the adverse consequences of climate change, for which many types of CIDs (i.e., ‘hazards’ in the context 
of identified risks) play a key role. This box synthesizes the assessment of such hazards according to global warming levels (GWLs) from 
various chapters of WGI to inform understanding of their potential changes and associated risks with temperature levels in general,

and in particular to facilitate WGII integrated assessments of RKRs and RFCs. Cross-Chapter Box 11.1, connects the organization of 
regional information according to GWLs to the other common dimension along which future projections are organized: that is, scenarios. 
Section 1.6 describes all dimensions of integration adopted in this Report, adding cumulative carbon emissions to GWLs and scenarios.
Eight RKRs are identified within WGII Chapter 16: 
• RKR-A: risk to the low-lying coastal socio-ecological systems; 
• RKR-B: risk to terrestrial and ocean ecosystems; 
• RKR-C: risks associated with critical physical infrastructure, networks and services; 
• RKR-D: risk to living standards; 
• RKR-E: risk to human health; 
• RKR-F: risk to food security; 
• RKR-G: risk to water security; and
• RKR-H: risks to peace and to human mobility.
RFCs further synthesize the landscape of risks from climatic changes into five categories (from the IPCC Third Assessment Report

onward; Smith et al., 2001): 
• RFC1: risks to unique and threatened systems; 
• RFC2: risks associated with extreme weather events; 
• RFC3: risks associated with the distribution of impacts; 
• RFC4: risks associated with global aggregate impacts; and
• RFC5: risks associated with large-scale singular events. 
Importantly, the assessment of risk in WGII considers hazards as only one component of an integrated assessment that involves their 
complex interaction with exposure and vulnerability of the systems at risk (Reisinger et al., 2020).
Hazards relevant to RKRs and RFCs are identified among aspects of the climate system that have an episodic, short-term nature, like 
extreme events (particularly relevant to RFC2 but contributing to many other risk categories). Increasing GWLs translate into changing 
characteristics of frequency, duration, intensity, seasonality and spatial extent for many of these hazards that are also apparent in

scenario-based results (Chapters 11 and 12, and Sections 12.4 and 12.5.1).  Other relevant hazards coincide with long-term trends 
embodying a gradual change that may result in unfavourable environmental conditions. Also, increasing GWLs increase the likelihood 
of compound temporal or spatial occurrence of similar or different hazards (Section 11.8). Furthermore, RFC5’s focus on singular 
events includes concern surrounding potential tipping points and irreversible behaviour in the physical climate system.
Cross-Chapter Box 12.1, Table 1 organizes information by hazard and presents current state and future change assessments with 
increasing GWLs (defined by increasing global surface air temperature, GSAT; see Cross-Chapter Box 2.3). We draw on individual 
chapters across the WGI report for the assessment of how these hazards vary with GWL. Hazards for which a relation to GWLs has not 
been assessed are not reported in the table.

1858
Cross-Chapter Box 12.1 (continued)
Cross-Chapter Box 12.1, Table 1 | Summary of CIDs/hazards that are identified as driving RKRs and RFCs.  The behaviour of each (in most cases considered at the global scale, but for some types in terms of spatially 
resolved patterns) as a function of GWLs is described and when possible quantified, together with the level of confidence of the assessment, to be found in more detail in the chapter/sections indicated in the corresponding column. 
For the relation with GSAT levels, two columns detail current state, which can be associated to about 1°C of global warming, and future behaviour. Tipping Points and Irreversibility are comprehensively assessed with CMIP6 models 
up to GWL = 3°C, with fewer studies and lower confidence at higher GWLs up to 5°C.
Hazard Category
Sub-categoryRKR/RFC

RelevanceBehaviour at About 1°C (Present) Behaviour as a Function of GWL (Future)WGI Chapter 
References
Hot and Cold 
ExtremesAll RKRs; 
RFC2, RFC3Frequency and intensity of hot extremes increased and cold extremes 
decreased at the global scale and in most regions since 1950 (GSAT change 
about 0.6°C) ( virtually certain ). Number of warm days and nights increased; 
intensity and duration of heatwaves increased; number of cold days and 
nights decreased ( virtually certain ). Regional-to-continental scale trends 
generally consistent with global-scale trends ( high confidence ). Limited 
data in a few regions (especially Africa) hampers trend assessment. Strong linear relation between magnitude and intensity of heat and cold extremes and GSAT, 
detectable from warming as low as 1.5°C; changes in the extreme metrics twice as large 
(in mid-latitude regions) or more (in high-latitude regions) than GSAT warming ( very likely );

metrics related to frequency of exceedance may show stronger than linear relationships 
(exponential) ( very likely ). Compared to today, changes in extremes at +2°C at least 
two times larger than at +1.5°C, and four times larger at +3°C.Sections 11.3, 11.9, 
12.4; Figures 11.3, 
11.4, 11.6, 11.11, 
11.12; Table 11.2.
Extreme 
Precipitation EventsAll RKRs; 
RFC2, RFC3Frequency and intensity of heavy precipitation events increased at 
the global scale over a majority of land regions with good observational 
coverage ( high confidence ) and at the continental scale in North America, 
Europe and Asia. Larger percentage increases in heavy precipitation observed 
in the northern high latitudes in all seasons, and in the mid-latitudes in the 
cold season ( high confidence ). Regional increases in the frequency and/or 
intensity of heavy rainfall also observed in most parts of Asia, north-west 
Australia, northern Europe, South-Eastern South America, and most of

the USA ( high confidence ), and West and Southern Africa, Central Europe, 
the eastern Mediterranean region, Mexico, and North-Western South 
America ( medium confidence ). GHGs likely the main cause. Precipitation events – including those associated with tropical cyclones (TCs) – increase 
with GSAT. For GWLs >2°C very rare (e.g., 1-in-10 or more years) heavy precipitation events 
more frequent and more intense over all continents ( virtually certain ) and nearly all AR6 
regions ( likely ). Likelihood lower at lower GWLs and for less-rare events. At the global scale, 
intensification of heavy precipitation generally follows Clausius–Clapeyron (about 6–7% per °C 
of GSAT warming; high confidence ). Increase in frequency of heavy precipitation events accelerates 
with warming, higher for rarer events ( high confidence ), with approximately a doubling and 
tripling frequency of 10-year and 50-year events, respectively, at 4°C of global warming.Sections 11.4, 11.7,

12.4; Figures 11.4, 
11.7, 11.15, 11.16; 
Table 11.2.
DroughtAll RKRs; 
RFC2, RFC3Increased atmospheric evaporative demand in dry seasons over a majority 
of land areas due to human-induced climate change ( medium confidence ). 
Especially observed in dry summer climates in Europe, North America and 
Africa ( high confidence ).Upward trend with GSAT ( high confidence ).Sections 11.6, 12.4; 
Figure 11.18; 
Inland Floods All RKRs; 
RFC2, RFC3Upward trend with GSAT for flooded area extent, starting from 2°C compared with 1.5°C 
and higher levels. Increase in the frequency and magnitude of pluvial floods ( high confidence ). 
Increasing flood potential in urban areas where heavy precipitation projected to increase, 
especially at high GWLs ( high confidence ).Sections 11.5, 12.4; 
Tropical Cyclones 
(TCs)All RKRs; 
RFC2, RFC3Human contribution to extreme rainfall amount from specific TC events

(high confidence ). Global proportion of major TC intensities likely  increased 
over the past four decades.Increase in precipitation from TC with GSAT; average peak TC wind speeds, proportion 
of intense TCs, and peak wind speeds of most intense TCs increase globally with GSAT 
(high confidence ). Decrease or lack of change in global frequency of TCs (all categories) 
with GSAT ( medium confidence ).Sections 11.7.1, 12.4; 
Marine Heatwaves 
(MHWs)RKR A,B,F; 
RFC1,2,3High confidence that MHWs have increased in frequency over the 
20th century, with an approximate doubling from 1982 to 2016, and 
medium confidence that they have become more intense and longer 
since the 1980s.MHWs very likely become 2–9 times more frequent in 2081–2100 compared to 1985–2014 
under SSP1-2.6 corresponding to a GWL of 2.0 [1.3 to 2.8] °C (95% CI), or 3–15 times more 
frequent under SSP5-8.5 corresponding to a GWL of 4.8 [3.6 to 6.5] °C. Spatial heterogeneity

with larger changes in the tropical oceans and Arctic Ocean ( medium confidence ).Section 12.4; 
Box 9.2.

1859
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Hazard Category
Sub-categoryRKR/RFC 
Concurrent Events 
in Time and SpaceAll RKRs; 
RFC2, RFC3Higher frequency already detected: more frequent concurrent heatwaves 
and droughts. Increased compound flooding risk (storm surge, extreme 
rainfall and/or river flow) in some locations; the probability of concurrent 
events likely  increased.Higher frequency with increasing GSAT. Increasing trend in more frequent concurrent heatwaves 
and droughts with GSAT ( high confidence ). More frequent concurrent (in time) extreme events at 
different locations with increasing GSAT, for GWLs > 2°C ( high confidence ). Compound flooding 
risk (storm surge, extreme rainfall and/or river flow) increasing with GSAT ( high confidence ).Section 11.8; 
Table 11.2; 
Boxes 11.2, 11.4.
Trends
Fire Weather TrendsRKR-B, C;

RFC1,2,3Weather conditions that promote wildfire (compound hot, dry and 
windy events) more probable in some regions over the last century 
(medium confidence ).Weather conditions promoting wildfire (compound hot, dry and windy events) likely  more 
frequent with GSAT.Section 12.4; 
Air Pollution 
WeatherRKR-E; RFC3 Not discernible.Behaviour to first order controlled by emissions and policies, not by meteorology. Ozone decreases 
with GSAT in low-polluted regions (−0.2 to –2 ppbv per °C). Ozone increases with GSAT in 
regions close to sources of precursors (0.2 to 2 ppbv per °C).Sections 6.5, 12.4.
Patterns of 
Mean WarmingRKR-B, D, F , 
RFC1,3,4Spatial patterns of temperature changes associated with the 0.5°C 
difference in GMST warming between 1991–2010 and 1960–1970 
consistent with projected changes under 1.5°C and 2°C of global warming.Temperatures scale approximately linearly with GSAT, largely independently of scenario ( high

confidence ). High latitudes of Northern Hemisphere warm faster ( virtually certain ). Antarctic 
polar amplification smaller than Arctic ( high confidence ). Arctic annual mean temperatures warm 
between 2 and 2.4 times faster for GWLs between 1.5°C and 4°C. In the Southern Hemisphere 
relatively high rates of warming in subtropical continental areas of South America, Southern 
Africa and Australia ( high confidence ). Sections 4.6.1.1, 12.4; 
Atlas; Figures 4.31, 
Atlas.13 and all Atlas 
Sections’ figures for 
mean temperature 
changes.
Arctic Warming 
TrendsRKR-A,C,G,H; 
RFC1, RFC3 Emerged already from internal variability.Very likely  more pronounced (2–2.4 times faster) than the global average over the 21st century 
(high confidence ). Sections 4.6.1, 
7.4.4.1, 12.4.9 
Atlas.11;   
Figures 4.19, 4.31, 
Atlas.29; Table 4.2. 
ChangeRKR-B, D, F , 
RFC1, RFC3Regional patterns of recent trends, over at least the past three decades,

consistent with documented increase in precipitation over tropical wet 
regions and decrease over dry areas.Changes in large-scale atmospheric circulation and precipitation with each 0.5°C of warming 
(high confidence ). Stable pattern of change over time and scenarios. Some departures from 
linearity possible at regional scale ( medium confidence ). Precipitation increase on land higher 
at 3°C and 4°C compared to 1.5°C and 2°C. Precipitation increases in large parts of the monsoon 
regions, tropics and high latitudes, decreases in the Mediterranean and large parts of the 
subtropics ( high confidence ).Sections 2.3.1.3.4, 
4.5.1, 4.6.1, 12.4;
Figures 2.15, 4.32, 
Atlas.13.
Sea Surface 
Temperature (SST) 
WarmingRKR-A, B, D; 
RFC1, RFC4Increased 0.81 (0.65–0.94) per °C of GSAT (1850–1900 average compared 
with 2009–2018 average).Models and observations show globally averaged SSTs warming at a lower rate of about

80% that of GSAT. It is virtually certain that SST will continue to increase at a rate depending 
on future emissions scenario ranging from 0.4°C–1.5°C in 2081–2100 relative to 1995–2014 
under SSP1-2.6, corresponding to a GWL of 2.0 [1.3 to 2.8] °C, to 2°C–4°C under SSP5-8.5, 
corresponding to a GWL of 4.8 [3.6 to 6.5] °C. Sections 2.3.1.1.3, 
9.2.1, 12.4.
Ocean Acidification/
pHRKR-A,B; 
RFC1, RFC4Virtually certain  decline of surface pH globally over the last 40 years at 
a rate of 0.017–0.027 pH units per decade; decline also in the subsurface 
over the past 2–3 decades ( medium confidence ). Surface pH now the lowest 
of at least the last 26,000 years ( very high confidence ).Increase of net ocean carbon flux throughout the century irrespective of the emissions 
scenario considered ( high confidence ). Decrease of ocean surface pH through the 21st century, 
except for SSP1-1.9 and SSP1-2.6 where values increase slightly starting from 2070–2100

(high confidence ).Sections 2.3.3.5, 
4.3.2.4, 5.3.4, 5.4.2, 
12.4; Figure 4.8.
SPEI Index GlobalRKR-B,F ,G,H; 
RFC39.4% chance of at least three months of drought in a year at current levels 
(about 1°C).Increase at the global scale in the chance of at least three months of drought in a year to about 
20 [15 to 30] % at 1.5°C, 35 [20 to 45] % at 2°C to 60 [45 to 75] % at 4°C.Sections 12.4, 12.5.1.Cross-Chapter Box 12.1 (continued)

1860
El Niño–Southern 
Oscillation (ENSO) 
VariabilityRKR-B,D,F ,G; 
RFC2,3,5Medium confidence  that both ENSO amplitude and frequency of 
high-magnitude events since 1950 higher than over the pre-industrial 
period (before 1850) but low confidence  of this being outside the range 
of internal variability. No clear evidence shifts in ENSO or associated 
features or its teleconnections.No change in the amplitude of ENSO variability ( medium confidence ); enhanced ENSO-related 
variability of precipitation under SSP2-4.5 and higher ( high confidence ). Likely  shift eastward 
of the pattern of teleconnection over North Pacific and North America.Sections 2.4.2, 
4.3.3.2, 4.5.3.2; 
Figure 4.10.
Sea Ice LossRKR-A, B, H;

RFC1,3,5Arctic sea ice area decreased for all months since 1970s; strongest 
decrease in summer ( very high confidence ). Arctic sea ice younger, thinner 
and faster moving ( very high confidence ). Current pan-Arctic sea ice 
levels unprecedented since 1850 ( high confidence ). Low confidence  in 
all aspects of Antarctic sea ice prior to the satellite era. Antarctic sea ice 
area experienced little net change since 1979 ( high confidence ). The Arctic Ocean will likely become sea ice-free in September before 2050 in all considered 
SSP scenarios; such disappearance consistently occurring in most years at 2°C–3°C ( medium 
confidence ) and including several months in most years at 3°C–5°C ( high confidence ).Sections 2.3.2, 
4.3.2.1, 9.3.1, 
9.3.2, 12.4.9; 
Figures 4.2c, 4.5.
Permafrost ThawRKR-A,C; 
RFC3,5Increases in permafrost temperatures in the upper 30 m over the past three

to four decades throughout the permafrost regions ( high confidence ). Global permafrost volume in the top 3 m decreasing by about 25 ± 5% per °C for GWLs <4°C. 
Relative to 1995–2014: at 1.5°C and 2°C decreasing by less than 40% ( medium confidence ), 
at 2°C and 3°C by less than 75% ( medium confidence ), at 3°C and 5°C by more than 60% 
loss ( medium confidence ).Sections 2.3.2.5, 
9.5.2, 12.4.9.
Sea Level ChangeRKR-A,C,D,E,F , 
G,H; RFC1,3,4Gobal mean sea level (GMSL) is rising at an accelerated rate since the 
19th century ( high confidence ), almost doubled during past two decades 
(about 0.1 mm yr –2). GMSL increase over the 20th century faster than over 
any preceding century in at least the last three millennia ( high confidence ).Up to 2050, limited scenario/GWL dependency ( likely  sea level rise about 0.15–0.30 m). 
By 2100,  likely  GMSL rise with respect to 1995–2014 of 0.51 (0.40–0.69) m, 0.62 (0.50–0.81) m

and 0.70 (0.58–0.91) m for, respectively, GWLs of 2.0°C, 3.0°C, and 4.0°C ( medium confidence ). 
Deep uncertainty in projections for GWLs >3°C because of ice-sheet behaviour. For example, 
incorporation of low confidence  ice-sheet processes under SSP5-8.5 (approximately 5°C) 
leads to a rise of 0.6-1.6 m rather than 0.7–1.1 m.Sections 9.6.1.2, 
9.6.3.3, 9.6.3.4, 12.4.
Sea-Level Change 
Commitment 
(2,000 years after 
peak GWL)RFC5GMSL commitment (over the 2000-year-long period following peak warming) of 2–6 m for 2°C 
peak warming, 4–10 m for 3°C peak warming, 12–16 m for 4°C peak warming, and 19–22 m for 
5°C peak warming ( medium agreement, limited evidence ).Section 9.6.3.5.
Northern 
Hemisphere (NH) 
Spring Snow CoverRKR-G, RFC1,3Substantial reductions in spring snow cover extent in the NH since 1978 
(very high confidence ). Since 1981, general decline in NH spring snow water

equivalent ( high confidence ).Linear change of NH snow cover in spring of about 8% (area) per °C ofglobal warming 
(for GWLs <4°C). Relative to 1995–2014: at 1.5°C–2°C NH spring snow cover extent likely  
decreases by less than 20% ( medium confidence ); at 2°C–3°C likely  decreases by less than 30%; 
at 3°C–5°C, likely  decreases by more than 25%.Sections 2.3.2.2,
9.5.3, 12.4.
Mass Loss 
of GlaciersRKR-B,G; 
RFC1, RFC3Very high confidence  global glaciers continuing retreat since about 1850. 
Current global glacier mass loss highly unusual over at least the last 2000 
years ( medium confidence ). Increased rate of glacier mass loss over the 
last 3 to 4 decades ( high confidence ). Glaciers not in balance with respect 
to current climate conditions and will continue to lose mass for at least 
several decades. For 1.5°C–2°C about 50–60% ( low confidence ) of glacier mass outside the two ice sheets

and excluding peripheral glaciers in Antarctica remaining, predominantly in the polar regions. 
At 2°C–3°C about 40–50% ( low confidence ) of current glacier mass outside Antarctica 
remaining. At sustained 3°C–5°C 25–40% ( low confidence ) of current glacier mass outside 
Antarctica remaining. Likely nearly all glacier mass lost in low latitudes, Central Europe, 
Caucasus, western Canada and USA, North Asia, Scandinavia and New Zealand.Sections 2.3.2.3, 
9.5.1, 12.4.
Tipping Points/Irreversibility
Amazon Forest 
DiebackRFC1, RFC5 Highly dependent on human disturbance.Amazon drying and deforestation expected to cause a rapid change in the regional water cycle, 
possibly linked to the crossing of a climate threshold. Low confidence  change will occur by 2100.Sections 5.4.9, 
8.6.2.1, 12.4.10; 
Table 4.10.Cross-Chapter Box 12.1 (continued)

1861
Boreal Forest 
DiebackRFC1, RFC5 Highly dependent on human disturbance. Possible if climate threshold is exceeded, but counteracted by poleward expansion. Section 5.4.9.
Ice Sheets RFC5Greenland Ice Sheet mass-loss rate increased substantially since the 
turn of the 21st century ( high confidence ). The Antarctic Ice Sheet has lost 
mass between 1992 and 2017 ( very high confidence ), with an increasing 
mass-loss rate over this period ( medium confidence ).At sustained warming levels between 1.5°C and 2°C, the ice sheets will continue to lose 
mass ( high confidence ); on time scales of multiple centuries, the Greenland and West Antarctic 
ice sheets will partially be lost ( medium confidence ); there is limited evidence that the Greenland

and West Antarctic ice sheets will be lost almost completely and irreversibly over multiple 
millennia; at sustained warming levels between 2°C and 3°C, there is limited evidence that 
the Greenland and West Antarctic ice sheets will be lost almost completely and irreversibly 
over multiple millennia, and high confidence in increasing risk of complete loss and increasing 
rate of mass loss for higher warming; At sustained warming levels between 3°C and 5°C, near-
complete loss of the Greenland Ice Sheet and complete loss of the West Antarctic Ice Sheet will 
occur irreversibly over multiple millennia ( medium confidence ); substantial parts or all of Wilkes 
Subglacial Basin in East Antarctica will be lost over multiple millennia ( low confidence ).Sections 2.3.2.4,
9.4.1, 9.4.2; 
Table 4.10.
Glaciers RFC5 See Trends section of this table. Continuing substantial global mass loss.Sections 9.5.1, 
12.4.9.
Global Ocean

Temperature RFC5 See Trends section of this table. Centennial-scale irreversibility of ocean warming.Sections 4.7.2, 9.6.3; 
Sea Level Rise (SLR) RFC5 See Trends section of this table.Centennial-scale irreversibility of sea level rise. Tipping point linked to ice-sheet behaviour. 
Deep uncertainty on SLR above 3°C warming.Sections 4.7.2, 9.6.3; 
Atlantic Meridional 
Overturning 
Circulation (AMOC)RFC5Low agreement  on 20th century trend between models and most 
reconstructions. Observed decline since the mid-2000s cannot be 
distinguished from internal variability ( high confidence ).There is medium confidence  an abrupt collapse will not occur before 2100; for 1.5–2°C, 
2–3°C, 3–5°C warming in 2100, AMOC decline is 29, 32 and 39%, respectively, of its 
pre-industrial strength. Sections 2.3.3.4.1, 
9.2.3.1; Table 4.10.

Permafrost Carbon RFC5 See Trends section of this table.Will contribute as a feedback with warming, of approximately 18 ± 12 PgC per °C. Possibly 
non-linear but low confidence  in the value of any threshold for such behaviour. Likely irreversible 
at centennial time scales.Section 5.4.9; 
Table 4.10;
Box 5.1.
Arctic Sea Ice RFC5 Abrupt change already observed.Reversible within years to decades; no tipping point or threshold beyond which loss of ice 
becomes irreversible ( high confidence ).Sections 4.3.2, 9.3.1; 
Snow Cover 
of Northern 
HemisphereRFC5 See Trends section of the table Not anticipated to present tipping point/irreversible behaviour. Section 9.5.3.
Global Monsoon RFC5Has likely  increased over the last 40 years ( medium confidence ) and can be 
explained by a phase change in Atlantic Multi-decadal Variability.Not anticipated to present tipping point/irreversible behaviour, unless AMOC collapse occurs.Sections 4.4.1.4, 
4.5.1.5, 8.6.1; 

ENSO RFC5 See Trends section of the table. Not anticipated to present tipping point/irreversible behaviour. Section 4.5.3.2.
Methane Clathrates RFC5 Methane release from shelf clathrates is <10 TgCH 4 yr –1. Not anticipated to present tipping point/irreversible behaviour.Section 5.4.9; 

1862
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment1212.6 Climate Change Information 
in Climate Services
Climate services are a significantly evolving source of climate change 
information to support adaptation, mitigation and risk management 
decisions. As an evolving field, there are multiple definitions of 
climate services (Brasseur and Gallardo, 2016). The Global Framework 
for Climate Services defines a  climate service as the provision of 
climate information to assist decision-making. The service includes 
appropriate engagement from users and providers, is based on 
scientifically credible information and expertise, has an effective 
access mechanism, and responds to user needs (Hewitt et al., 2012).
The AR5 WGII introduced climate services as bridging the generation 
and application of climate knowledge, also describing their history 
and concepts (Jones et al., 2014). Since then, this transdisciplinary field

has been growing rapidly (Brasseur and Gallardo, 2016; Hewitt et al., 
2020a), with the social sciences in particular pointing out knowledge 
requirements for co-design and co-development of climate services 
(Larosa and Mysiak, 2019; Daniels et al., 2020; Steynor et al., 2020). 
Climate services differ from more research-driven vulnerability, 
impacts, and adaptation research in their orientation towards decision 
support (Stone and Meinke, 2005; Ruane et al., 2016; Golding et al., 
2019), but overlaps exist (Bruno Soares and Buontempo, 2019). 
Climate services are often targeted at building resilience to climate-
related hazards from near real-time to seasonal and multi-decadal 
time horizons, to inform adaptation to climate variability and change 
(Hewitt et al., 2012), widely recognized as an important challenge for 
sustainable development and risk management (Moss et al., 2010; 
Jones et al., 2014; Vaughan et al., 2018). This section focuses largely

on climate change time scales (past, present and future), which are 
the focus of AR6 WGI.
This section introduces the current climate services landscape, 
assesses climate service practices and products related to climate 
change information and associated challenges. Cross-Chapter 
Box 12.2 provides concrete examples of climate services. The section 
builds on the introduction to climate services in Section  1.2.3.3 
and the assessment of regional climate information construction – 
including storylines  – discussed in Sections 10.3.4.2, 10.5.3 and 
Cross-Chapter Box 10.3. The Atlas supports the provision of climate 
information across WGs by providing interactive maps and further 
details to the material made publicly accessible for use in climate 
services. WGII (Chapter 17) further elaborates on climate services as 
enablers for climate risk management.
12.6.1 Context of Climate Services
The idea of climate services is not new and has its roots in meteorology

and climatology (Larosa and Mysiak, 2019). It can be traced back to 
the late 1970s and the US National Climate Program Act of 1978 
(Henderson, 2016). The development of the Global Framework for 
Climate Services (GFCS) after the World Climate Conference-3 in 
Geneva brought international attention and renewed impetus to the 
climate services field (Hewitt et  al., 2012). As a  result, large investments 
have been made globally and regionally in the development of user-driven climate services. WMO has created Regional Climate 
Centres (RCCs) to facilitate climate service development by regional 
and national providers (Hewitt et al., 2020a). The European Union 
declared its ambition to stimulate ‘the creation of a  community 
of climate services application developers and users that matches 
supply and demand for climate information and prediction’, giving 
primacy to climate services that are user-driven and science-informed

(Lourenço et  al., 2016), thus embracing concepts of co-design, 
co-development and co-evaluation of climate services (Street, 2016). 
Diverse and action-driven international initiatives allowed climate 
services to progressively shift from mitigation towards adaptation 
(Larosa and Mysiak, 2019). Opportunities for the development of 
climate services have emerged through the 2015 Agendas (Paris 
Agreement, Sustainable Development Goals and Sendai Framework), 
Nationally Determined Contributions, National Adaptation Plans, 
Multilateral Development Banks and Task Force on Climate-related 
Financial Disclosure (see Section 1.2.2).
Scientific advancements in climate services related to meteorology 
and climatology are still closely linked to essential climate variables 
(Larosa and Mysiak, 2019) and benefit from consistently growing 
computational power, infrastructure and storage capacity to meet 
the demands of higher spatially and temporally resolved climate

information (Buontempo et al., 2020). Climate services also focus on 
impact chains, providing decision makers with information on climate 
change with cross-sectoral impact assessments for adaptation 
(Jacob and Solman, 2017). Today there is a  diversity of climate 
services that involve interpretation, analysis, and communication of 
different sources of climate data, ideally combining different types 
of knowledge (scientific/technical, experiential, indigenous, etc.), to 
a  targeted group of decision makers (Parris et  al., 2016; Olazabal 
et  al., 2018; Pezij et  al., 2019). Jacobs and Street (2020) argue 
that climate services should be expanded to also address societal 
challenges, such as system transformation, that include climate in the 
context of other risks and development challenges.
Climate services are undertaken in public and private sectors at 
global, regional, national, and local scales (Hewitt et  al., 2012,

2020b; Cortekar et al., 2020). Intermediaries such as private sector 
consulting companies, national climate service providers, research 
organizations, government agencies or academic institutions 
provide climate services that translate aspects of climate research 
to the specific context of decision makers (see also Section  10.5). 
The EU Roadmap for Climate Services (EC, 2015; Street, 2016) 
focuses on developing a market for climate services comprising of 
both public and private domains. The GFCS, under the leadership 
of several United Nations Agencies, emphasizes the public domain by 
supporting national and regional capacity building and development 
of climate services mainly through National Meteorological and 
Hydrological Services (Hewitt et  al., 2012; Domingos et  al., 2016; 
Sivakumar and Lucio, 2018; WMO, 2018). There are ongoing debates 
about the commercialization of climate services (M.S. Brooks, 2013;

WMO, 2015; Webber and Donner, 2017; Hoa, 2018; Troccoli et  al., 
2018; Bruno Soares and Buontempo, 2019; Hewitt et  al., 2020a). 
Some argue that the commercialization of climate services is needed 
to meet the diverse needs of specific clients and to drive innovation 
in the field (M.S. Brooks, 2013; Troccoli, 2018a). Others argue that if

1863
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212climate services shift incentives for climate science away from the 
public interest towards profit-seeking, this will result in less publicly 
accessible and transparent climate information and more private 
knowledge (Keele, 2019; Tart et al., 2020).
Some climate adaptation planning already uses climate information 
as provided by the IPCC. However, depending on the decision context, 
this information may be too coarse, too broad or too disciplinary 
to directly inform decision-making at the scale where adaptation 
measures are taken (Howarth and Painter, 2016; Nissan et al., 2019). 
Thus, while the IPCC’s role is clearly perceived as that of a reference – 
an authoritative starting point – there is a need for complementary 
information to translate the assessments at the national, local or 
sectoral level (Howarth and Painter, 2016; Kjellström et al., 2016; van

den Hurk et al., 2018; Vaughan et al., 2018). The AR6 Interactive Atlas 
(see Atlas.2) does provide a  collection of observational data and 
global and regional climate projections. It is designed as a climate 
service towards the needs of WGI and beyond, to assess the state of 
the climate by offering data, maps and a level of expert analysis by 
aggregation of results to regions, scenarios and warming levels.
12.6.2 Assessment of Climate Services Practice and 
Products Related to Climate Change Information
The climate services landscape is fast growing and very broad, as 
reflected in the vast diversity of practices and products that can be 
found in the peer-reviewed literature ( very high confidence ). However, 
a large part of climate services practices and products is published in 
‘grey’ literature (i.e., non-peer reviewed or non-academic) by private 
consultancy and non-scientific civil organizations, many of which are

not in the public domain. In addition, the respective climate service 
context of a specific stakeholder in a sector dictates what climate 
information is required and on what scales and in what format it 
is most usefully provided. The extent and type of engagement 
between scientists and users is another critical aspect of climate 
services (see Cross-Chapter Box 12.2, Figure 1, and Section 10.5). The 
assessment here can thus only provide a partial and rather general 
representation of available practices and products in the evolving 
climate services field.
User needs and decision-making contexts are very diverse and 
there is no ‘one size fits all’ solution to climate services ( very high 
confidence ) (Hewitt et al., 2017b; K. Vincent et al., 2018b). In many 
cases this requires recognizing that stakeholders make decisions 
through a  combination of scientific information and additional 
values (Vanderlinden et  al., 2017; Parker and Lusk, 2019; see also

Sections  1.2.3 and 10.5.4). The emerging climate service literature 
may clarify some features of climate information requested by users, 
for instance climatic impact-driver identification and prioritization 
through stakeholder engagement; the specification of thresholds for 
various regions/sectors; the types of metrics (magnitude/intensity, 
frequency, duration, timing, spatial extent) that are of primary interest; 
and decision support systems where informatics allow stakeholders 
to custom-make impact-relevant thresholds and then query databases 
to understand current and future characteristics (Bachmair et al., 2016; 
Buontempo et al., 2020). However, users also ask for capacity building activities related to basic knowledge in climate change sciences and 
climate-related risks (De Bruin et al., 2020; Sultan et al., 2020).
Since AR5 and SROCC (Chapter  2) there has been considerable 
progress in understanding climate information user needs (Baztan

et  al., 2017; Golding et  al., 2017a, b, 2019; Bruno Soares et  al., 
2018a; Hewitt and Golding, 2018; Singh et  al., 2018; Sivakumar 
and Lucio, 2018; Bessembinder et  al., 2019; Hewitt et  al., 2020b; 
Sultan et al., 2020; Y . Wang et al., 2020), better facilitation of user 
engagement (Buontempo et al., 2014, 2018; Buontempo and Hewitt, 
2018) and an appreciation from climate scientists of the need to 
involve communication specialists and social scientists to support 
the co-design and co-development process that is fundamental to 
a successful climate service (Buontempo et al., 2014; Gregow et al., 
2016; Damm et al., 2020).
Climate services require user engagement and can take various forms 
in which climate information and data are delivered or communicated 
to the users ( very high confidence ). Different levels of user engagement 
exist, which can range from passive engagement to interactive group 
activities, to focused relationships between climate service provider

and users. These result in different types of climate service products 
including websites, capacity building, and co-design of tailored climate 
indices (Cross-Chapter Box 12.2, Figure 1; Hewitt et al., 2017a). The 
fundamental basis for climate service development is the co-production 
process between climate service provider and user (Valiela, 2006; Briley 
et al., 2015; Golding et al., 2017a; K. Vincent et al., 2018a; Bruno Soares 
and Buontempo, 2019; Schipper et al., 2019), which can be very resource 
intensive (Buontempo et al., 2018; Falloon et al., 2018; Kolstad et al., 
2019) and varies strongly from case to case (Reinecke, 2015; Bremer 
et al., 2019; Goodess et al., 2019; Jung and Schindler, 2019). Climate 
services scholars and practitioners can better facilitate and embrace the 
knowledge co-production process if it is recognized as a multi-faceted 
phenomenon with several dimensions (e.g., constitutive, interactional,

institutional, pedagogical, empowerment) (Kruk et al., 2017; Knaggård 
et al., 2019; Weichselgartner and Arheimer, 2019).
Information moves from ‘useful’ to ‘usable’ only when users 
effectively incorporate this information into a  decision process 
(Lemos et al., 2012; Bruno Soares and Dessai, 2016; Prokopy et al., 
2017; see also WGII, Chapter 17). Climate services include a range 
of knowledge brokerage activities such as: identifying knowledge 
needs; dissemination of knowledge; coordinating and networking; 
compiling and translating; building capacity through informed 
decision-making; analysing, evaluating and developing policy; and 
personal consultation (e.g., De Bruin et al., 2020). When analysing 
four European climate services, Reinecke (2015) found that different 
climate services emphasized different knowledge brokerage activities.
There are various types of climate service providers and products related

to key sectors and regions, such as those described in Sections 12.3 
and 12.4 (Hewitt et al., 2017b). For instance, studies have described 
sectoral climate services in support of agriculture (Falloon et al., 2018; 
Hansen et al., 2019), health (Jancloes et al., 2014; Lowe et al., 2017), 
tourism (Morin et al., 2018; Damm et al., 2020; Matthews et al., 2021), 
energy (Troccoli, 2018b; Goodess  et  al., 2019; Soret et  al.,  2019), 
disaster risk reduction (Golding et al., 2019; Street et al., 2019), water

1864
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12(van den Hurk et  al., 2016; Vano et  al., 2018), ocean and coastal 
ecosystems (Weisse et  al., 2015; Le Cozannet et  al., 2017), cities 
(Rosenzweig and Solecki, 2014; Rosenzweig et  al., 2015; Gidhagen 
et al., 2020), and cultural heritage (ICOMOS, 2019). Many countries 
(including almost every country in Europe  – see Atlas.8.2) have 
established a climate service centre, which follow different practices 
of user engagement and provide different products (e.g., Kjellström 
et  al., 2016; Skelton et  al., 2017; Kolstad et  al., 2019). Climate 
services in other countries may be distributed across agencies and 
programmes, although these are often not centrally coordinated (Parris 
et al., 2016). One of the key pillars of the GFCS is the Climate Services 
Information System (CSIS), which is the principal mechanism through 
which information about past, present and future climate is archived,

analysed, modelled, exchanged and processed for users (Hewitt et al., 
2020a). Some national governments also have organized national 
climate projections to be used for official planning (e.g., EEA, 2018). 
A list of available national products (e.g., observational datasets) and 
projections can be found in the Atlas (e.g., Atlas.1.4).
Figure 12.12 maps a general categorization of practices and products 
that have emerged from reviewing climate service literature and 
user interviews (Visscher et  al., 2020). The categories range from 
very generic products or expert analysis focused particularly on 
climate information (climate-centric approaches) to more integrated 
products that include shared open-source products and capacity 
building as well as tailored products that treat climate information 
as part of a  larger decision-making context (climate-inclusive 
approaches). Three specific examples that elaborate in more detail

on specific practices and products related to those general categories 
are provided in Cross-Chapter Box 12.2.12.6.3 Challenges
Climate services set new scientific challenges to physical climate 
research ( high confidence ). Over at least the last decade, for instance, 
many questions have appeared in terms of optimal estimation of 
changes and uncertainties from projections of model ensembles, 
ensemble optimization, or adjustment of model biases while preserving 
essential information on trends and cross-variable, time and space 
consistencies, downscaling information at the local scale (Benestad 
et al., 2017; Hewitt et al., 2017b; Marotzke et al., 2017; Hewitt and 
Lowe, 2018; Knutti, 2019; see also Section  10.5). Other challenges 
related to climate services are the inter-operability of data (Giuliani 
et al., 2017), access to data (open/FAIR Guiding principles; Wilkinson 
et al., 2016; Georgeson et al., 2017), format of data (including moving

away from percentile-based probabilistic forecasts (e.g., Haines, 2019)) 
and funding mechanisms (Bruno Soares and Buontempo, 2019).
Understanding and modelling of weather and climate extremes is of 
great relevance for climate services and is continuing to set challenges 
for research, such as modelling changes in impact-relevant threshold 
exceedance and return periods for a  variety of extremes (Maraun 
et al., 2015; Sillmann et al., 2017; Hewitt et al., 2021; Schwingshackl 
et al., 2021; see also Chapter 11). Extreme event attribution has also 
been used in context of climate services (Philip et al., 2020) as it is 
of interest to some stakeholder groups (Sippel et al., 2015; Marjanac 
and Patton, 2018; Jézéquel et  al., 2019, 2020). The usefulness 
or applicability of available extreme event attribution methods 
(Section  11.2.4 and Cross-Working Group Box on Attribution in 
Chapter 1) for assessing climate-related risks remains subject to

open accesseducation•Interdisciplinary management, engineering, or policy services 
including climate data;
•Integrated into other processes or services;
•Tailored to speciﬁc decisions and decision-makers;
•Advisor y service/consultancy, including capacity build ing;
•Oﬀered commercially .
Examples: Catas trophe models, cost-beneﬁt analy sisfor a speciﬁcproject, portfolio
or corporate
Figure 12. 12 | Illustration of different types of climate services.  Products, for instance, can focus only on climate-related information or can be designed to integrate 
climate information with other decision-relevant context (vertical axis) and they can be very generic in terms of relevance to a wide range of sectors or stakeholders or 
customized to fit the needs of a specific sector or stakeholder (horizontal axis). Figure adapted from Visscher et al. (2020).

1865
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212The design of climate services involves addressing certain key challenges, 
such as a  domain challenge where users, tasks and data may be 
unknown; or an informational challenge related to the use and adoption 
of novel and complex scientific data (Christel et al., 2018). This includes 
challenges in the uptake of climate information in terms of coordinated 
delivery of data, information, expertise and training by public research 
institutes, the inclusion of climate change adaptation in public and private 
regulation, and uncertainties and confidence in climate projections 
(Cavelier et al., 2017). Quality control and quality assurance are still weak 
elements in the development of climate service products (Jacob, 2020). 
Quality criteria or standards (that go beyond good practice) will have 
to be developed and agreed (Baldissera Pacchetti et  al., 2021). These

challenges reflect the dilemma that exists at the interface between the 
climate modelling community and climate services regarding: (i) the 
purposes of the models for climate research versus service development; 
(ii) the gap between the spatial and temporal scales of the models 
versus the scales needed in applications; and (iii) tailoring climate model 
results to real-world applications (Benestad et al., 2017; Hackenbruch 
et al., 2017; van den Hurk et al., 2018).
Climate services require a sustained engagement between scientists, 
service providers and users that is often hindered by limited resources 
for the co-design and co-production process  (high confidence ). 
There are recurring challenges related to successful climate service 
applications: (i) climate services are not visible and are poorly 
understood by ‘end users’ (Weichselgartner and Arheimer, 2019); 
(ii)  data can be of unknown or poor quality, data formats can

be hard to access or process, and it can be difficult to utilize data 
disseminated from large databases (e.g.,  Section  1.5.4) without 
appropriate user guidance; (iii) users are unsure how to choose 
from available climate services to meet their needs (Rössler et  al., 
2019); (iv) building trust between climate service users and providers 
(Baztan et al., 2020); (v) the lack of understanding of users and their 
contexts by the climate science and service community (Porter and 
Dessai, 2017); (vi) the difficulty in scaling up services (Tall et al., 2014; van Huysen et al., 2018); (vii) the lack of trained scientists skilled at 
conducting societally relevant research (Rozance et al., 2020).
Challenges also arise in determining the effectiveness and added 
value of climate services, particularly in terms of providing quantitative 
estimates of economic benefits and making a business case for climate 
services (Bruno Soares, 2017). The market for climate services is still

in its infancy (Cavelier et al., 2017; Bruno Soares et al., 2018b; Tall 
et al., 2018; Damm et al., 2020). One form of value may be determined 
by a  particular user community’s willingness to pay (Acquah and 
Onumah, 2011; Ouédraogo et  al., 2018; Antwi-Agyei et  al., 2021), 
which however cannot reflect the value of climate services as a public 
good and for society as a whole (Hewitt et al., 2012). Literature is only 
recently emerging on the socio-economic benefits of weather and 
climate services (Vaughan et al., 2019). Early studies and guidelines 
from the WMO focus on cost–benefit ratios (Perrels et  al., 2013; 
WMO, 2015). Issues related to demand-driven versus supply-driven 
climate services (Lourenço et  al., 2016; Street, 2016; Daniels et  al., 
2020), public versus private climate services (Hewitt et  al., 2020a) 
and business models for climate services (Hoa, 2018) have been 
raised. A large share of climate services documented in peer-reviewed

literature is currently provided in non-market frameworks (e.g., public 
service obligations and research and development grants) (Hoa, 2018; 
Kolstad et al., 2019; Cortekar et al., 2020).
Other challenges related to governance and dealing with complex 
systems are sometimes acknowledged but less well described in the 
climate services domain (Hewitt et al., 2020a). Importantly, decision 
contexts are strongly rooted in past practice (which often does not 
even make optimal use of past climate information), stakeholder 
experience, and history. Even important emerging concepts of 
co-production, entry points, and champions do not always fall 
naturally into these realities without significant effort. The social 
sciences have an important role in helping understand and tackle 
these challenges (Bruno Soares and Buontempo, 2019).
Cross-Chapter Box 12.2 | Climate Services and Climate Change Information

Contributing Authors: Suraje Dessai (United Kingdom/Portugal), Jana Sillmann (Norway/Germany), Carlo Buontempo (United 
Kingdom/Italy), Cecilia Conde (Mexico), Aida Diongue-Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Christopher Jack (South 
Africa), Richard Jones (United Kingdom), Benjamin Lamptey (Niger/Ghana), Xianfu Lu (United Kingdom/China), Douglas Maraun 
(Austria/Germany), Ben Orlove (United States of America), Roshanka Ranasinghe (Netherlands/Sri Lanka/Australia), Alex C. Ruane 
(United States of America), Anna Steynor (South Africa), Bart van den Hurk (Netherlands), Robert Vautard (France)
Climate services involve the provision of climate information in such a way as to assist decision-making. The service needs to have 
appropriate engagement from users and providers; be based on scientifically credible information and expertise; have an effective

access mechanism; and meet the users’ needs (Hewitt et al., 2012). Predominantly, climate services are targeted at informing and 
enabling risk management in adaptation to climate variability and change (Jones et  al., 2014; Vaughan et  al., 2018). Chapter  1 
introduces climate services in a broader context of interaction between science and society, including how climate information can be 
tailored and co-produced for greatest utility in specific contexts. Chapter 10 assesses the key foundations for the generation of climate 
information about regional climate change. Chapters 11, 12 and Atlas comprehensively assess regional climate change information. 
The Interactive Atlas gives access to various repositories of quantitative climate information. In WGII, Chapter 17 assesses climate 
services in the context of climate risk management.

1866
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12Cross-Chapter Box 12.2 (continued)
Climate service contexts are diverse and complex. They can be characterized using different factors such as sectors, regions, purposes, 
time horizons, data sources, level of processing of climate data, background knowledge, type of climate service providers, as well 
as the nature of the interactions between providers, users and other stakeholders (Bessembinder et al., 2019). To illustrate the wide 
diversity of climate change information in climate services, a useful categorization is by user –provider engagement of climate services 
(Cross-Chapter Box  12.2, Figure  1). One broad category includes ‘websites and web tools’  which generally focuses on data and 
information provision (Hewitson et al., 2017). Websites are generally able to reach many users, but engagement is passive through

one-way transfer of information. The second broad category involves ‘interactive group activities’, such as workshops, meetings and 
interactive forums, which create a stronger dialogue between climate service providers and decision makers. Multi-way communication 
and regular interaction enable building of trust, co-learning and co-production of products and services. The third broad category involves 
‘focused relationships’ which are tailored, targeted and address very specific needs of the user. Effective engagement arises from an 
iterative process between the provider and user to ensure the user’s needs are being addressed appropriately (Hewitt et al., 2017a).
The diversity of climate services practices and products is illustrated here using three case studies each representing one of the 
broad categories of user –provider engagement (Cross-Chapter Box 12.2, Figure 1).

Cross-Chapter Box 12.2, Figure 1 | Schematic of three broad categories of engagement between users and providers of climate services.  Figure 
adapted from Hewitt et al. (2017a).

1867
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Cross-Chapter Box 12.2 (continued)
Case study 1: Websites and web tools
The Copernicus Climate Change Service (C3S) provides free and open access to climate data, tools and information through a website. 
It also includes demonstration projects that show how C3S data can be used in practice through case studies, training sessions and 
workshops (Thepaut et al., 2018). A large audience of the service is composed of intermediate users, loosely defined as the community 
of operators in one of the intermediate steps between the primary producers of climate data and the ultimate beneficiaries.
To address this audience, the strategy of C3S is to provide free and open access climate data and tools such as historical observations 
(both satellite and ground-based), climate data records relevant for a number of Essential Climate Variables (Bojinski et al., 2014),

global and regional reanalyses, climate monitoring bulletins, seasonal predictions, as well as both global (a selection of simulations 
from the Coupled Model Intercomparison Project, CMIP; Taylor et al., 2012; Eyring et al., 2016) and regional climate projections from 
the Coordinated Regional Downscaling Experiment (Euro- and Med-CORDEX; Jacob et al., 2014; Ruti et al., 2016). A number of indices 
for various sectors can be calculated through cloud-based tools. For instance, in order to address the specific needs of key sectoral users, 
climate impact indicators for common variables such as ‘heating degree days’ can be calculated by the users and made available to 
others (Buontempo et al., 2020). All this material is quality controlled following a standardized, transparent and traceable framework.
C3S also facilitates the tailoring process, by providing a series of working open-source, cloud-based demonstrators which show how

climate data can be transformed into actionable information to meet specific user requirements. This tailoring process covers the chain 
between the definition of key indicators all the way to the user interface. The definition and production of C3S products involve scientists 
that produced and assessed the data. A variety of potential users are involved in the definition of indicators and other products.
Through its quality assurance process and demonstrators, C3S provides a basic evaluation of all climate data; it provides access, and it 
encourages the users to develop their own case-specific analysis within the C3S infrastructure. Trustworthiness and relevance of such 
an analysis are substantially strengthened through a distillation process, co-designed by the user and data provider, and drawing upon 
multiple lines of evidence and process-based evaluation of model fitness (Section 10.5).
Case study 2: Interactive group activities

Science-application engagement is extremely challenging, especially in critically important but complex contexts such as rapidly 
growing cities in developing nations (Culwick and Patel, 2017). The publicly funded Future Resilience for African CiTies And Lands 
(FRACTAL) project was conceived and designed in response to extensive and strong evidence and experience that useful and useable 
climate services require strong mutual relationships across the science-application interface that can be built using supportive 
processes and structures (A. Taylor et al., 2017).
Informed by this understanding, FRACTAL was grounded in a very reflexive and context-guided approach with city decision-making 
at its core (A. Taylor et al., 2017). Representatives from selected southern African cities were included in the proposal design and, 
throughout the project, a core principle was to allow the city partners to lead and guide the process.

Two important elements were deployed in FRACTAL: ‘embedded researchers’ and ‘learning labs’. Embedded researchers were 
seconded into the municipality and served as the essential connection for the learning process within each city (Steynor et al., 2020). 
Learning labs (Arrighi et al., 2016) were interactive structures in which participants from academia, local city government and councils, 
state-owned enterprises, communities and community development institutions, and others could interact. Embedded researchers and 
learning labs were the backbone of ongoing learning processes within each city and resulted in more focused small-group dialogues, 
capacity development and training processes, and within-city research and engagement activities. Each learning lab focused initially 
on identifying ‘burning issues’ without a requirement that they involve strong climate linkages. However, with the overarching focus

on resilience, discussions evolved in that direction and the burning issues identified often centred around water in peri-urban areas, 
for example in Windhoek, Namibia (Scott et al., 2018).
The learning labs also introduced and developed the concept of Climate Risk Narratives (CRNs) as a process and product to generate 
and integrate climate and socio-economic information relevant to adaptation and resilience (see Cross-Chapter Box 12.2, Figure 2, and 
Box  10.2 on storylines) (Jack et  al., 2020). The first CRNs were informed primarily by climate evidence, but also included some tentative socio-
economic impact elements gleaned from literature and other studies. Their content was intentionally provocative and designed to promote 
debate and discussion, and subsequent iteration. Many participants noted that this was the first time that various important conversations

across governance structures and disciplinary areas had occurred around what climate change may actually mean. This demonstrates 
the engagement value of CRNs as a key element in an iterative co-production process to ensure important details are included correctly, 
such as the local context, terms and names as well as providing reality checks on the impacts and societal responses (Jack et al., 2020).

1868
This case study emphasizes the positive contributions of the fit, tailoring and contextualization of climate information with respect 
to the specific decision-making needs of particular users (Section 10.5; AR6 WGII Section 17.4.4.2.2), the importance of participatory 
planning for risk management in urban areas (AR6 WGII Sections 6.3.3.3 and 6.4.2) and the importance of networks and organizations 
which link researchers, policymakers and end-users to promote adaptation in African cities (AR6 WGII Box 9.4). Upscaling this type 
of interactive activity to cater for the large number of user demands remains a challenge.
Cross-Chapter Box 12.2, Figure 2 | Climate Risk Narrative infographic developed through the FRACTAL Windhoek Learning Lab process.  Figure 
adapted from Jack et al. (2020).
Case study 3: Focused relationships

This broad category involves one-to-one engagement between a provider and a user with very specific needs. One such user is the 
Asian Development Bank (ADB), which has committed to making all its investments climate resilient by implementing a climate 
risk management framework (ADB, 2014, 2018; Lu, 2019). The climate risk management framework mandates all climate-sensitive 
investment projects undertake a climate risk and adaptation assessment, to identify material risks of a changing climate to the proposed 
project and potential adaptive measures to be incorporated into project design, implementation, maintenance and/or monitoring. 
Typically, loan project processing teams procure consulting services for a bespoke climate risk and adaptation assessment (CRA)

1869
for a  specific project. The user –provider engagement is highly targeted and goal-oriented. An example of such a  focused user –
provider engagement is the CRA carried out as part of an investment project in Vietnam, the Water Efficiency Improvement in Drought-
Affected Provinces (WEIDAP) project.
In the wake of the El Niño-induced 2015–2016 severe drought, which caused major damage to agricultural land in the Central Highlands 
of Vietnam, the WEIDAP project was initiated to improve water productivity of irrigated agriculture. Proposed project interventions include 
a package of both ‘soft’ (e.g., policy, institutional and capacity building, on-farm water efficiency practices) and ‘hard’ (modernized 
irrigation schemes) activities. To ensure that the project delivers expected benefits under a changing climate, consultants were recruited

to carry out a detailed CRA, working as part of the overall project processing team. Through extensive consultations with the rest of the 
project team and review of literature including relevant climate projections, the CRA consultants chose to construct three broad climate 
scenarios for the 2050s (a time frame appropriate for the lifetime of the irrigation schemes being proposed under the project): a warm-
and-wet, a hot-and-wet, and a hotter future. Outputs from a selection of CMIP5 models were analysed under these three scenarios, to 
derive changes in temperature, rainfall and potential evapotranspiration, which in turn were used as inputs to hydrological, crop and 
agro-economics models to assess the impacts of climate change on the overall project performance. Table 1 presents the summary of 
the key parameters under the three scenarios. Recommendations from the CRA included (largely minor) refinements and additional

activities for drought planning, detailed engineering design of the relevant project components (such as access roads, river crossings 
and foundations), and support for poorer farmers who may not be able to afford access to water and climate-resilient technologies.
This case study illustrates that climate information distillation including a sustained iterative engagement between climate information 
users, producers and translators can improve the quality of the information and the decision-making (Section 10.5; WGII Section 17.4.4.2.2).
Cross-Chapter Box 12.2, Table 1 | Summary of annual province-level changes in temperature, precipitation and evapotranspiration under the 
three broad scenarios in southern Vietnam.  Scenario 1: warm-and-wet; Scenario 2: hot-and-wet; Scenario 3: hotter. Source: Table 3 in ADB (2020).
ItemProvince and Scenario Number
Bình Thuân Ðă´k Lă´k Ðă´k Nông Khánh Hòa Ninh Thu ân
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

ΔT (°C) 1.1 1.8 2.6 1.1 1.5 2.0 1.2 2.1 2.7 1.1 1.8 2.6 1.1 1.5 2.6
ΔP (%) 28 –12 4 8 17 –8 8 –8 7 3 –10 7 27 1 5
ΔPET (%) 3 6 8 4 5 7 4 7 9 3 6 8 3 5 8
ΔT = change in temperature; ΔP = change in precipitation; ΔPET = change in potential evapotranspiration.
Note: Colour scale indicates significance of changes for the water balance. 
12.7 Final Remarks
The assessment in this chapter is based on a rapidly growing body of 
new evidence from the peer-reviewed literature, direct calculations 
of  climate projections from several new model ensembles, and 
results from other AR6 WGI chapters. Although a large amount a new 
information on CID changes and their uptake in climate services 
has become available since AR5, some challenges still remain. This 
section summarizes some of these main challenges, with a view to 
facilitating improved assessments in future. The section is organized 
following the order of chapter sections and consolidated according 
to key assessment components.

• The adoption of the climatic impact-driver (CID) framework could 
benefit from stronger connections across disciplines, including 
between physical climate and impact scientists, and between the science community and practitioners/stakeholders on the ground. 
Co-development of CID index definitions with impact scientists 
or stakeholders helps ensure their salience and utility (Sections 
12.1, 12.2, 12.3 and 12.6).
• The ability to project all aspects of shifting CID profiles and 
their effects at fine, local scales is often reliant on dynamical 
downscaling and additional impact modelling steps, making 
a  robust and full quantification of the uncertainties involved 
more challenging. Availability of multiple models and ease 
of connecting physical climate models at different scales can 
facilitate assessment (Sections 12.2, 12.3, 12.4 and 12.5).
• Regional and sub-regional differences in coverage and access 
of homogeneous historical records, in the deployment of

regional model ensembles and the exploration of scenarios, and 
ultimately in peer-reviewed studies addressing the full range of 
past and current behaviour, detection and attribution, and future Medium negative 
impactMedium positive 
impactHigh negative 
impactHigh positive 
impactNo significant change

1870
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12projections challenge a uniformly robust assessment across all 
CIDs and regions of the world (Sections 12.4 and 12.5).
• Efforts to assess a  consistent global, large-scale view of 
CID changes across regions and sectors would benefit from 
additional coordinated studies adopting common CID indices, 
model protocols, time horizons and scenarios or global warming 
levels (Sections 12.3 and 12.5).
• Even though the body of peer-reviewed literature regarding 
climate services practices and products is growing, a large part is 
still documented only in grey literature arising from commercial 
consultancy, and thus is not publicly and freely accessible 
Section 12.6).

1871
Climate Change Information for Regional Impact and for Risk Assessment Chapter 1212Frequently Asked Questions
FAQ 12.1 | What Is a Climatic Impact-driver (CID)?
A climatic impact-driver is a physical climate condition that directly affects society or ecosystems. Climatic impact-
drivers may represent a long-term average condition (such as the average winter temperatures that affect indoor 
heating requirements), a common event (such as a frost that kills off warm-season plants), or an extreme event 
(such as a coastal flood that destroys homes). A single climatic impact-driver may lead to detrimental effects 
for one part of society while benefiting another, while others are not affected at all. A climatic impact-driver 
(or its change caused by climate change) is therefore not universally hazardous or beneficial, but we refer to it 
as a ‘hazard’ when experts determine it is detrimental to a specific system.

Climate change can alter many aspects of the climate system, but efforts to identify impacts and risks usually focus 
on a smaller set of changes known to affect, or potentially affect, things that society cares about. These climatic 
impact-drivers  (CIDs) are formally defined in this Report as ‘physical climate system conditions (e.g.,  means, 
events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and 
their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements 
and regions’. Because people, infrastructure and ecosystems interact directly with their immediate environment, 
climate experts assess CIDs locally and regionally. CIDs may relate to temperature, the water cycle, wind and 
storms, snow and ice, oceanic and coastal processes or the chemistry and energy balance of the climate system.

Future impacts and risk may also be directly affected by factors unrelated to the climate (such as socio-economic 
development, population growth, or a viral outbreak) that may also alter the vulnerability or exposure of systems.
CIDs capture important characteristics of the average climate and both common and extreme events that shape 
society and nature (see FAQ 12.2). Some CIDs focus on aspects of the average climate (such as the seasonal 
progression of temperature and precipitation, average winds or the chemistry of the ocean) that determine, 
for example, species distribution, farming systems, the location of tourist resorts, the availability of water 
resources and the expected heating and cooling needs for buildings in an average year. CIDs also include 
common episodic events that are particularly important to systems, such as thaw events that can trigger the 
development of plants in spring, cold spells that are important for fruit crop chill requirements, or frost events

that eliminate summer vegetation as winter sets in. Finally, CIDs include many extreme events connected to 
impacts such as hailstorms that damage vehicles, coastal floods that destroy shoreline property, tornadoes that 
damage infrastructure, droughts that increase competition for water resources, and heatwaves that can strain 
the health of outdoor labourers.
Many aspects of our daily lives, businesses and natural systems depend on weather and climate, and there is 
great interest in anticipating the impacts of climate change on the things we care about. To meet these needs, 
scientists engage with companies and authorities to provide climate services – meaningful and possibly actionable 
climate information designed to assist decision-making. Climate science and services can focus on CIDs that 
substantially disrupt systems to support broader risk management approaches. A single CID change can have

dramatically different implications for different sectors or even elements of the same sector, so engagement 
between climate scientists and stakeholders is important to contextualize the climate changes that will come. 
Climate services responding to planning and optimization of an activity can focus on more gradual changes in 
operating climate conditions.
FAQ 12.1, Figure 1 tracks example outcomes of seasonal snow cover changes that connect climate science to the 
need for mitigation, adaptation and regional risk management. The length of the season with snow on the ground 
is just one of many regional climate conditions that may change in the future, and it becomes a CID because 
there are many elements of society and ecosystems that rely on an expected seasonality of snow cover. Climate 
scientists and climate service providers examining human-driven climate change may identify different regions

where the length of the season with snow cover could increase, decrease, or stay relatively unaffected. In each 
region, change in seasonal snow cover may affect different systems in beneficial or detrimental ways (in the 
latter case, changing seasonal snow cover would be a ‘hazard’), although systems such as coastal aquaculture 
remain relatively unaffected. The changing profile of benefits and hazards connected to these changes in the 
seasonal snow cover CID affects the profile of impacts, risks and benefits that stakeholders in the region will 
grapple with in response to climate change.

1872
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12FAQ 12.1 (continued)
FAQ 12.1, Figure 1 | A single climatic impact-driver can affect ecosystems and society in different ways.  A variety of impacts from the same 
climatic impact-driver change, illustrated with the example of regional seasonal snow cover.
!
!Climatic impact-driver
Possible changesFAQ 12.1: What is a climatic impact-driver (CID)?
Impacts on societies and ecosystems
Climate sciences Impacts and risksHazardous for crop planting
Beneficial for skiing resorts
Impacts on snow-camouflaged species
Beneficial for some predatorsExamples for seasonal snow cover Regional climate changeA climatic impact-driver (CID) is a climate condition that directly affects elements of society or ecosystems.
Climatic impact-drivers and their changes can lead to positive , negative, or inconsequential outcomes (or a mixture).
No 
change
DecreaseInconsequential for coastal aquacultureIncrease
Seasonal

1873
FAQ 12.2 | What Are Climatic Thresholds and Why Are They Important?
Climatic thresholds tell us about the tolerance of society and ecosystems so that we can better scrutinize the types 
of climate changes that are expected to impact things we care about. Many systems have natural or structural 
thresholds. If conditions exceed those thresholds, the result can be sudden changes or even collapses in health, 
productivity, utility or behaviour. Adaptation and risk management efforts can change these thresholds, altering 
the profile of climate conditions that would be problematic and increasing overall system resilience.
Decision makers have long observed that certain weather and climate conditions can be problematic, or 
hazardous, for things they care about (i.e., things with socio-economic, cultural or intrinsic value). Many elements

of society and ecosystems operate in a suitable climate zone selected naturally or by stakeholders considering 
the expected climate conditions. However, as climate change moves conditions beyond expected ranges, they 
may cross a climatic ‘threshold’ – a level beyond which there are either gradual changes in system behaviour or 
abrupt, non-linear and potentially irreversible impacts.
Climatic thresholds can be associated with either natural or structural tolerance levels. Natural thresholds, for 
instance, include heat and humidity conditions above which humans cannot regulate their internal temperatures 
through sweat, drought durations that heighten competition between species, and winter temperatures that 
are lethal for pests or disease-carrying vector species. Structural thresholds include engineered limits of drainage 
systems, extreme wind speeds that limit wind turbine operation, the height of coastal protection infrastructure,

and the locations of irrigation infrastructure or tropical cyclone sheltering facilities.
Thresholds may be defined according to raw values (such as maximum temperature exceeding 35°C) or percentiles 
(such as the local 99th percentile daily rainfall total). They also often have strong seasonal dependence 
(see FAQ 12.3). For example, the amount of snowfall that a deciduous tree can withstand depends on whether 
the snowfall occurs before or after the tree sheds its leaves. Most systems respond to changes in complex ways, 
and those responses are not determined solely or precisely by specific thresholds of a single climate variable. 
Nonetheless, thresholds can be useful indicators of system behaviours, and an understanding of these thresholds 
can help inform risk management decisions.
FAQ 12.2 Figure  1 illustrates how threshold conditions can help us understand climate conditions that are

suitable for normal system operation and the thresholds beyond which impacts occur. Crops tend to grow most 
optimally within a suitable range of daily temperatures that is influenced by the varieties being cultivated and 
the way the farm is managed. As daily temperatures rise above a ‘critical’ temperature threshold, plants begin 
to experience heat stress that reduces growth and may lower resulting yields. If temperatures reach a higher 
‘limiting’ temperature threshold, crops may suffer leaf loss, pollen sterility, or tissue damage that can lead to 
crop failure. Farmers typically select a cropping system with some consideration to the probability of extreme 
temperature events that may occur within a typical season, and so identifying hot temperature thresholds helps 
farmers select their seed and field management strategies as part of their overall risk management. Climate

experts may therefore aim to assist farm planning by providing information about the climate change-induced 
shifts to the expected frequency of daily heat extremes that exceed crop tolerance thresholds.
Adaptation and other changes in societies and environment can shift climatic thresholds by modifying vulnerability 
and exposure. For example, adaptation efforts may include breeding new crops with higher heat tolerance levels 
so that corresponding dangerous thresholds occur less frequently. Likewise, increasing the height of a flood 
embankment protecting a given community can increase the level of river flow that may be tolerated without 
flooding, reducing the frequency of damaging floods. Stakeholders therefore benefit from climate services that 
are based on a co-development process, with scientists identifying system-relevant thresholds and developing 
tailored climatic impact-driver indices that represent these thresholds (FAQ 12.1). These thresholds help focus the

provision of action-relevant climate information for adaptation and risk management.

1874
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12FAQ 12.2 (continued)
FAQ 12.2, Figure  1 | Crop response to maximum temperature thresholds.  Crop growth rate responds to daily maximum temperature increases, 
leading to reduced growth and crop failure as temperatures exceed critical and limiting temperature thresholds, respectively. Note that changes in other 
environmental factors (such as carbon dioxide and water) may increase the tolerance of plants to increasing temperatures.FAQ 12.2: What are climatic thresholds and why are they important?
Many systems have thresholds that can lead to sudden changes, if climate conditions exceed them. 
Adaptation and risk management efforts can increase overall system resilience by identifying and 
changing tolerance thresholds.
Increasing 
crop productionCritical 
temperature threshold
Optimal growth Reduced growth Crop failureLimiting 
Increasing daily maximum temperature

1875
FAQ 12.3 | How Will Climate Change Affect the Regional Characteristics of a Climate Hazard?
Human-driven climate change can alter the regional characteristics of a climate hazard by changing the magnitude 
or intensity of the climate hazard, the frequency with which it occurs, the duration that hazardous conditions 
persist, the timing when the hazard occurs, or the spatial extent threatened by the hazard. By examining each 
of these aspects of a hazard’s profile change, climate services may provide climate risk information that allows 
decision makers to better tailor adaptation, mitigation and risk management strategies.
A climate hazard  is a climate condition with the potential to harm natural systems or society. Examples include 
heatwaves, droughts, heavy snowfall events and sea level rise. Climate scientists look for patterns in climatic

impact-drivers to detect the signature of changing hazards that may influence stakeholder planning (FAQ 12.1). 
Climate service providers work with stakeholders and impacts experts to identify key system responses and 
tolerance thresholds (FAQ 12.2) and then examine historical observations and future climate projections to 
identify associated changes to the characteristics of a regional hazard’s profile. Climate change can alter at least 
five different characteristics of the hazard profile of a region (FAQ 12.3, Figure 1):
Magnitude or intensity  is the raw value of a climate hazard, such as an increase in the maximum yearly temperature 
or in the height of flooding that results from a coastal storm with a 1% change of occurring each year.
Frequency is the number of times that a climate hazard reaches or surpasses a threshold over a given period. 
For example, increases to the number of heavy snowfall events, tornadoes, or floods experienced in a year or in 
a decade.

Duration  is the length of time over which hazardous conditions persist beyond a threshold, such as an increase 
in the number of consecutive days where maximum air temperature exceeds 35°C, the number of consecutive 
months of drought conditions, or the number of days that a tropical cyclone affects a location.
Timing captures the occurrence of a hazardous event in relation to the course of a day, season, year, or other 
period in which sectoral elements are evolving or co-dependent (such as the time of year when migrating animals 
expect to find a seasonal food supply). Examples include a shift towards an earlier day of the year when the last 
spring frost occurs or a delay in the typical arrival date for the first seasonal rains, the length of the winter period 
when the ground is typically covered by snow, or a reduction in the typical time needed for soil moisture to move 
from normal to drought conditions.

Spatial extent is the region in which a hazardous condition is expected, such as the area currently threatened by 
tropical cyclones, geographical areas where the coldest day of the year restricts a particular pest or pathogen, 
terrain where permafrost is present, the area that would flood following a common storm, zones where climate 
conditions are conducive to outdoor labour, or the size of a marine heatwave.
Hazard profile changes are often intertwined or stem from related physical changes to the climate system. For 
example, changes in the frequency and magnitude of extreme events are often directly related to each other 
as a result of atmospheric dynamics and chemical processes. In many cases, one aspect of hazard change is more 
apparent than others, which may provide a first emergent signal indicating a larger set of changes to come 
(FAQ 1.2).
Information about how a hazard has changed or will change helps stakeholders prioritize more robust adaptation,

mitigation and risk management strategies. For example, allocation of limited disaster relief resources may be 
designed to recognize that tropical cyclones are projected to become more intense even as the frequency of 
those storms may not change. Planning may also factor in the fact that even heatwaves that are not record-
breaking in their intensity can still be problematic for vulnerable populations when they persist over a long 
period. Likewise, firefighters recognize new logistical challenges in the lengthening of the fire weather season 
and an expansion of fire conditions into parts of the world where fires were not previously a great concern. 
Strong engagement between climate scientists and stakeholders therefore helps climate services tailor and 
communicate clear information about the types of changing climate hazards to be addressed in resilience efforts.

1876
Chapter 12 Climate Change Information for Regional Impact and for Risk Assessment12FAQ 12.3 (continued)
FAQ 12.3, Figure 1 | Types of changes to a region’s hazard profile.  The first five panels illustrate how climate changes can alter a hazard’s intensity 
(or magnitude), frequency, duration, and timing (by seasonality and speed of onset) in relation to a hazard threshold (horizontal grey line, marked ‘H’). The 
difference between the historical climate (blue) and future climate (red) shows the changing aspects of climate change that stakeholders will have to manage. 
The bottom right-hand panel shows how a given climate hazard (such as a current once-in-100-year river flood, geographic extent in blue) may reach new 
geographical areas under a future climate change (extended area in red).H
 H
H
HH
Hx5x3
Climate change can alter the intensity and magnitude, frequency, duration, timing and spatial extent of a region’s

climate hazards. FAQ 12.3: How will climate change affect climate hazards?
Intensity and Magnitude
Time Time
Time TimeTimeFrequency Duration
Spatial extent Timing
Seasonal shift Shift in speed of onsetCurrent climateClimate changeHazard threshold 
Variable starts to decline
River

4SPM
Summary for PolicymakersIntroduction
1  Decision IPCC/XL VI-2.
2  T he three Special Reports are: Global Warming of 1.5°C: An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse 
gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (SR1.5); 
Climate Change and Land: An IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in 
terrestrial ecosystems (SRCCL); IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC).
3  T he assessment covers scientific literature accepted for publication by 31 January 2021.
4  Each finding is grounded in an ev aluation of underlying evidence and agreement. A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high,

and typeset in italics, for example,  medium confidence . The following terms have been used to indicate the assessed likelihood of an outcome or result: virtually certain 99–100% 
probability; very likely 90–100%; likely 66–100%; about as likely as not 33–66%; unlikely 0–33%; very unlikely 0–10%; and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%; more likely than not >50–100%; and extremely unlikely 0–5%) are also used when appropriate. Assessed likelihood is typeset in italics, for example, 
very likely. This is consistent with AR5. In this Report, unless stated otherwise, square brackets [x to y] are used to provide the assessed very likely range, or 90% interval.
5  T he Interactive Atlas is available at https://interactive-atlas.ipcc.ch
6 Other GHG concentr ations in 2019 were: perfluorocarbons (PFCs) – 109 parts per trillion (ppt) CF 4 equivalent; sulphur hexafluoride (SF 6) – 10 ppt; nitrogen trifluoride (NF 3) – 2 ppt;

hydrofluorocarbons (HFCs) – 237 ppt HFC-134a equivalent; other Montreal Protocol gases (mainly chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs)) – 1032 ppt 
CFC-12 equivalent). Increases from 2011 are 19 ppm for CO 2, 63 ppb for CH 4 and 8 ppb for N 2O.
7 Land and ocean are not substantial sinks for other GHGs .This Summary for Policymakers (SPM) presents key findings of the Working Group I (WGI) contribution to the Intergovernmental 
Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)1 on the physical science basis of climate change. The report builds 
upon the 2013 Working Group I contribution to the IPCC’s Fifth Assessment Report (AR5) and the 2018–2019 IPCC Special Reports2 
of the AR6 cycle and incorporates subsequent new evidence from climate science.3

This SPM provides a high-level summary of the understanding of the current state of the climate, including how it is changing and the role of human influence, the state of knowledge about possible climate futures, climate information relevant to regions and sectors, and limiting human-induced climate change.
Based on scientific understanding, key findings can be formulated as statements of fact or associated with an assessed level of 
confidence indicated using the IPCC calibrated language.
4
The scientific basis for each key finding is found in chapter sections of the main Report and in the integrated synthesis presented in the Technical Summary (hereafter TS), and is indicated in curly brackets. The AR6 WGI Interactive Atlas facilitates exploration of these key synthesis findings, and supporting climate change information, across the WGI reference regions.
5
A. The Curr ent State of the Climate

Since AR5, improvements in observationally based estimates and information from paleoclimate archives provide a comprehensive view of each component of the climate system and its changes to date. New climate model simulations, new analyses, and methods combining multiple lines of evidence lead to improved understanding of human influence on a wider range of climate variables, 
including weather and climate extremes. The time periods considered throughout this section depend upon the availability of 
observational products, paleoclimate archives and peer-reviewed studies.
A.1  It is unequivocal that human influence has warmed the atmospher e, ocean and land. Widespread and rapid 
changes in the atmosphere, ocean, cryosphere and biosphere have occurred.
 {2.2,  2.3, Cross-Chapter Box 2.3, 3.3, 3.4, 3.5, 3.6, 3.8, 5.2, 5.3, 6.4, 7.3, 8.3, 9.2, 9.3, 9.5, 9.6, Cross-Chapter 
Box 9.1} (Figure SPM.1, Figure SPM.2)

A.1.1  Observed increases in well-mixed greenhouse gas (GHG) concentrations since around 1750 are unequivocally caused 
by human activities. Since 2011 (measurements reported in AR5), concentrations have continued to increase in the atmosphere, reaching annual averages of 410 parts per million (ppm) for carbon dioxide (CO
2), 1866 parts per billion 
(ppb) for methane (CH 4), and 332 ppb for nitrous oxide (N 2O) in 2019.6 Land and ocean have taken up a near-constant 
proportion (globally about 56% per year) of CO 2 emissions from human activities over the past six decades, with regional 
differences (high confidence).7
{2.2,  5.2, 7.3, TS.2.2, Box TS.5}

5SPM
Summary for PolicymakersA.1.2   Each of the last four decades has been successively warmer than any decade that preceded it since 1850. Global 
surface temperature8 in the first two decades of the 21st century (2001–2020) was 0.99 [0.84 to 1.10] °C higher than 
1850–1900.9 Global surface temperature was 1.09 [0.95 to 1.20] °C higher in 2011–2020 than 1850–1900, with larger 
increases over land (1.59 [1.34 to 1.83] °C) than over the ocean (0.88 [0.68 to 1.01] °C). The estimated increase in 
global surface temperature since AR5 is principally due to further warming since 2003–2012 (+0.19 [0.16 to 0.22] °C). 
Additionally, methodological advances and new datasets contributed approximately 0.1°C to the updated estimate of 
warming in AR6.10 
  {2.3, Cross-Chapter Box 2.3} (Figure SPM.1)
A.1.3  The likely  range of total human-caused global surface temperature increase from 1850–1900 to 2010–201911 is 0.8°C to

1.3°C, with a best estimate of 1.07°C. It is likely  that well-mixed GHGs contributed a warming of 1.0°C to 2.0°C, other 
human drivers (principally aerosols) contributed a cooling of 0.0°C to 0.8°C, natural drivers changed global surface 
temperature by –0.1°C to +0.1°C, and internal variability changed it by –0.2°C to +0.2°C. It is very likely  that well-mixed 
GHGs were the main driver12 of tropospheric warming since 1979 and extremely likely  that human-caused stratospheric 
ozone depletion was the main driver of cooling of the lower stratosphere between 1979 and the mid-1990s. 
  {3.3, 6.4, 7.3, TS.2.3, Cross-Section Box TS.1} (Figure SPM.2)
A.1.4   Globally averaged precipitation over land has likely  increased since 1950, with a faster rate of increase since the 1980s 
(medium confidence ). It is likely  that human influence contributed to the pattern of observed precipitation changes

since the mid-20th century and extremely likely  that human influence contributed to the pattern of observed changes 
in near-surface ocean salinity. Mid-latitude storm tracks have likely  shifted poleward in both hemispheres since the 
1980s, with marked seasonality in trends ( medium confidence ). For the Southern Hemisphere, human influence  very likely  
contributed to the poleward shift of the closely related extratropical jet in austral summer.
  {2.3, 3.3, 8.3, 9.2, TS.2.3, TS.2.4, Box TS.6}
A.1.5  Human influence is very likely  the main driver of the global retreat of glaciers since the 1990s and the decrease in Arctic 
sea ice area between 1979–1988 and 2010–2019 (decreases of about 40% in September and about 10% in March). There 
has been no significant trend in Antarctic sea ice area from 1979 to 2020 due to regionally opposing trends and large 
internal variability. Human influence very likely  contributed to the decrease in Northern Hemisphere spring snow cover

since 1950. It is very likely  that human influence has contributed to the observed surface melting of the Greenland Ice 
Sheet over the past two decades, but there is only limited evidence , with medium agreement , of human influence on the 
Antarctic Ice Sheet mass loss.
  {2.3, 3.4, 8.3, 9.3, 9.5, TS.2.5}
A.1.6  It is virtually certain  that the global upper ocean (0–700 m) has warmed since the 1970s and extremely likely  that human 
influence is the main driver. It is virtually certain  that human-caused CO 2 emissions are the main driver of current global 
acidification of the surface open ocean. There is high confidence  that oxygen levels have dropped in many upper ocean 
regions since the mid-20th century and medium confidence  that human influence contributed to this drop.
  {2.3, 3.5, 3.6, 5.3, 9.2, TS.2.4}
A.1.7   Global mean sea level increased by 0.20 [0.15 to 0.25] m between 1901 and 2018. The average rate of sea level rise was

1.3 [0.6 to 2.1] mm yr–1 between 1901 and 1971, increasing to 1.9 [0.8 to 2.9] mm yr–1 between 1971 and 2006, and 
further increasing to 3.7 [3.2 to 4.2] mm yr–1 between 2006 and 2018 ( high confidence ). Human influence was very likely  
the main driver of these increases since at least 1971.
  {2.3, 3.5, 9.6, Cross-Chapter Box 9.1, Box TS.4}
8 The term ‘global surface temperature’ is used in reference to both global mean surface temperature and global surface air temperature throughout this SPM. Changes in these 
quantities are assessed with high confidence to differ by at most 10% from one another, but conflicting lines of evidence lead to  low confidence in the sign (direction) of any 
difference in long-term trend. {Cross-Section Box TS.1}
9  The period 1850–1900 represents the earliest period of sufficiently globally complete observations to estimate global surface temperature and, consistent with AR5 and SR1.5, is 
used as an approximation for pre-industrial conditions.

10  Since AR5, methodological advances and new datasets have provided a more complete spatial representation of changes in surface temperature, including in the Arctic. These 
and other improvements have also increased the estimate of global surface temperature change by approximately 0.1°C, but this increase does not represent additional physical 
warming since AR5.
11  The period distinction with A.1.2 arises because the attribution studies consider this slightly earlier period. The observed warming to 2010–2019 is 1.06 [0.88 to 1.21] °C.
12  Throughout this SPM, ‘main driver’ means responsible for more than 50% of the change.

6SPM
Summary for PolicymakersA.1.8   Changes in the land biosphere since 1970 are consistent with global warming: climate zones have shifted poleward in 
both hemispheres, and the growing season has on average lengthened by up to two days per decade since the 1950s 
in the Northern Hemisphere extratropics ( high confidence ).
  {2.3, TS.2.6}  
Figure SPM.1 | History of global temperature change and causes of recent warming
Panel (a) Changes in global surface temperature reconstructed from paleoclimate archives  (solid grey line, years 1–2000) and from direct 
observations (solid black line, 1850–2020), both relative to 1850–1900 and decadally averaged. The vertical bar on the left shows the estimated temperature 
(very likely range) during the warmest multi-century period in at least the last 100,000 years, which occurred around 6500 years ago during the current interglacial

period (Holocene). The Last Interglacial, around 125,000 years ago, is the next most recent candidate for a period of higher temperature. These past warm periods 
were caused by slow (multi-millennial) orbital variations. The grey shading with white diagonal lines shows the very likely ranges for the temperature reconstructions.
Panel (b) Changes in global surface temperature over the past 170 years  (black line) relative to 1850–1900 and annually averaged, compared to 
Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations (see Box SPM.1) of the temperature response to both human and natural 
drivers (brown) and to only natural drivers (solar and volcanic activity, green). Solid coloured lines show the multi-model average, and coloured shades show the 
very likely range of simulations. (See Figure SPM.2 for the assessed contributions to warming).

{2.3.1; Cross-Chapter Box 2.3; 3.3; TS.2.2; Cross-Section Box TS.1, Figure 1a}Human inﬂuence has warmed the climate at a rate that is unprecedented
in at least the last 2000 years
Changes in global surface temperature relative to 1850–1900
1850 1900 1950 2000 2020ºC
–0.50.00.51.01.52.0
observed
simulated
human &
natural
simulated
natural only
(solar &
volcanic)(b) Change in global surface temperature (annual average) as observed and 
simulated using human & natural and only natural factors (both 1850–2020)(a) Change in global surface temperature (decadal average)
as reconstructed (1–2000) and observed (1850–2020)
Warmest multi-century 
period in more than 
100,000 yearsWarming is unprecedented 
in more than 2000 years
reconstructed
–0.5
–10.00.51.01.52.0ºC
2020 1850 1000 1500 500 1observed1.0
0.2

7SPM
Summary for Policymakers
Figure SPM.2 | Assessed contributions to observed warming in 2010–2019 relative to 1850–1900
Panel (a) Observed global warming  (increase in global surface temperature). Whiskers show the very likely range.
Panel (b) Evidence from attribution studies, which synthesize information from climate models and observations.  The panel shows temperature 
change attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use 
change (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.
Panel (c) Evidence from the assessment of radiative forcing and climate sensitivity.  The panel shows temperature changes from individual components 
of human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails.

Whiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers. For aerosols, 
both direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. 
{Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}Observed warming is driven by emissions from human activities, with 
greenhouse gas warming partly masked by aerosol cooling
ºC
–0.5
–1.00.00.51.01.52.0ºC
  –0.5
–0.5
–1.00.00.51.01.52.0
Halogenated gases Nitrogen oxides Sulphur dioxide Volatile organic compoundsand carbon monoxide Organic carbon Black carbon Ammonia Land-use reﬂectanceand irrigation Aviation contrails
Total human inﬂuence Well-mixed greenhouse gases Other human drivers Solar and volcanic drivers Internal variability Carbon dioxide Methane Nitrous oxide
Mainly contribute to
changes in
non-CO₂ greenhouse gasesMainly contribute to
changes in

anthropogenic aerosols (b) Aggregated contributions to 
2010– 2019 warming relative to 
1850– 1900, assessed from 
attribution studies (a) Observed warming
2010– 2019 relative to 
1850– 1900 Contributions to warming based on two complementary approaches Observed warming
(c) Contributions to 2010– 2019 
warming relative to 1850– 1900, 
assessed from radiative
forcing studies

8SPM
Summary for PolicymakersA.2   The scale of recent changes across the climate system as a whole – and the present state of many aspects of 
the climate system – are unprecedented over many centuries to many thousands of years. 
  {2.2, 2.3, Cross-Chapter Box 2.1, 5.1} (Figure SPM.1)
A.2.1  In 2019, atmospheric CO 2 concentrations were higher than at any time in at least 2 million years ( high confidence ), and 
concentrations of CH 4 and N 2O were higher than at any time in at least 800,000 years ( very high confidence ). Since 1750, 
increases in CO 2 (47%) and CH 4 (156%) concentrations far exceed – and increases in N 2O (23%) are similar to – the natural 
multi-millennial changes between glacial and interglacial periods over at least the past 800,000 years ( very high confidence ).
  {2.2, 5.1, TS.2.2}
A.2.2  Global surface temperature has increased faster since 1970 than in any other 50-year period over at least the last 2000

years ( high confidence ). Temperatures during the most recent decade (2011–2020) exceed those of the most recent 
multi-century warm period, around 6500 years ago13 [0.2°C to 1°C relative to 1850–1900] ( medium confidence ). Prior 
to that, the next most recent warm period was about 125,000 years ago, when the multi-century temperature [0.5°C to 
1.5°C relative to 1850–1900] overlaps the observations of the most recent decade ( medium confidence ). 
  {2.3, Cross-Chapter Box 2.1, Cross-Section Box TS.1} (Figure SPM.1)
A.2.3  In 2011–2020, annual average Arctic sea ice area reached its lowest level since at least 1850 ( high confidence ). Late 
summer Arctic sea ice area was smaller than at any time in at least the past 1000 years ( medium confidence ). The global 
nature of glacier retreat since the 1950s, with almost all of the world’s glaciers retreating synchronously, is unprecedented 
in at least the last 2000 years ( medium confidence ).
  {2.3, TS.2.5}

A.2.4  Global mean sea level has risen faster since 1900 than over any preceding century in at least the last 3000 years ( high 
confidence ). The global ocean has warmed faster over the past century than since the end of the last deglacial transition 
(around 11,000 years ago) ( medium confidence ). A long-term increase in surface open ocean pH occurred over the past 
50 million years ( high confidence ). However, surface open ocean pH as low as recent decades is unusual in the last 
2 million years ( medium confidence ).
  {2.3, TS.2.4, Box TS.4}
A.3   Human-induced climate change is already affecting many weather and climate extremes in every region 
across the globe. Evidence of observed changes in extremes such as heatwaves, heavy precipitation, droughts, 
and tropical cyclones, and, in particular, their attribution to human influence, has strengthened since AR5.

{2.3, 3.3, 8.2, 8.3, 8.4, 8.5, 8.6, Box 8.1, Box 8.2, Box 9.2, 10.6, 11.2, 11.3, 11.4, 11.6, 11.7, 11.8, 11.9, 12.3} 
(Figure SPM.3)
A.3.1  It is virtually certain  that hot extremes (including heatwaves) have become more frequent and more intense across most 
land regions since the 1950s, while cold extremes (including cold waves) have become less frequent and less severe, with 
high confidence  that human-induced climate change is the main driver14 of these changes. Some recent hot extremes 
observed over the past decade would have been extremely unlikely  to occur without human influence on the climate 
system. Marine heatwaves have approximately doubled in frequency since the 1980s ( high confidence ), and human 
influence has very likely  contributed to most of them since at least 2006.
  {Box 9.2, 11.2, 11.3, 11.9, TS.2.4, TS.2.6, Box TS.10} (Figure SPM.3)

A.3.2  The frequency and intensity of heavy precipitation events have increased since the 1950s over most land area for which 
observational data are sufficient for trend analysis ( high confidence ), and human-induced climate change is likely  the 
main driver. Human-induced climate change has contributed to increases in agricultural and ecological droughts15 in some 
regions due to increased land evapotranspiration16 (medium confidence ).
  {8.2, 8.3, 11.4, 11.6, 11.9, TS.2.6, Box TS.10} (Figure SPM.3)
13 As stated in section B.1, even under the very low emissions scenario SSP1-1.9, temperatures are assessed to remain elevated above those of the most recent decade until at least 
2100 and therefore warmer than the century-scale period 6500 years ago.
14  As indicated in footnote 12, throughout this SPM, ‘main driver’ means responsible for more than 50% of the change.

15  Agricultural and ecological drought (depending on the affected biome): a period with abnormal soil moisture deficit, which results from combined shortage of precipitation 
and excess evapotranspiration, and during the growing season impinges on crop production or ecosystem function in general (see Annex VII: Glossary). Observed changes in 
meteorological droughts (precipitation deficits) and hydrological droughts (streamflow deficits) are distinct from those in agricultural and ecological droughts and are addressed in 
the underlying AR6 material (Chapter 11).
16   The combined processes through which water is transferred to the atmosphere from open water and ice surfaces, bare soils and vegetation that make up the Earth’s surface (Glossary).

9SPM
Summary for PolicymakersA.3.3  Decreases in global land monsoon precipitation17 from the 1950s to the 1980s are partly attributed to human-caused 
Northern Hemisphere aerosol emissions, but increases since then have resulted from rising GHG concentrations and 
decadal to multi-decadal internal variability ( medium confidence ). Over South Asia, East Asia and West Africa, increases 
in monsoon precipitation due to warming from GHG emissions were counteracted by decreases in monsoon precipitation 
due to cooling from human-caused aerosol emissions over the 20th century ( high confidence ). Increases in West African 
monsoon precipitation since the 1980s are partly due to the growing influence of GHGs and reductions in the cooling 
effect of human-caused aerosol emissions over Europe and North America ( medium confidence ).
  {2.3, 3.3, 8.2, 8.3, 8.4, 8.5, 8.6, Box 8.1, Box 8.2, 10.6, Box TS.13}

A.3.4  It is likely  that the global proportion of major (Category 3–5) tropical cyclone occurrence has increased over the last four 
decades, and it is very likely  that the latitude where tropical cyclones in the western North Pacific reach their peak intensity 
has shifted northward; these changes cannot be explained by internal variability alone ( medium confidence ). There is low 
confidence  in long-term (multi-decadal to centennial) trends in the frequency of all-category tropical cyclones. Event 
attribution studies and physical understanding indicate that human-induced climate change increases heavy precipitation 
associated with tropical cyclones ( high confidence ), but data limitations inhibit clear detection of past trends on the 
global scale.
  {8.2, 11.7, Box TS.10}
A.3.5  Human influence has likely  increased the chance of compound extreme events18 since the 1950s. This includes increases in

the frequency of concurrent heatwaves and droughts on the global scale ( high confidence ), fire weather in some regions 
of all inhabited continents ( medium confidence ), and compound flooding in some locations ( medium confidence ).
  {11.6, 11.7, 11.8, 12.3, 12.4, TS.2.6, Table TS.5, Box TS.10}
17   The global monsoon is defined as the area in which the annual range (local summer minus local winter) of precipitation is greater than 2.5 mm day–1 (Glossary). Global land monsoon 
precipitation refers to the mean precipitation over land areas within the global monsoon.
18  Compound extreme events are the combination of multiple drivers and/or hazards that contribute to societal or environmental risk (Glossary). Examples are concurrent heatwaves 
and droughts, compound flooding (e.g., a storm surge in combination with extreme rainfall and/or river flow), compound fire weather conditions (i.e., a combination of hot, dry and

windy conditions), or concurrent extremes at different locations.

10SPM
Summary for PolicymakersClimate change is already aﬀecting every inhabited region across the globe, 
with human inﬂuence contributing to many observed changes in weather 
and climate extremes
Increase (41)Type of observed change
in hot extremes
Decrease (0)
Low agreement in the type of change (2)
Limited data and/or literature (2)
Type of observed change since the 1950s
Type of observed change since the 1950s(a) Synthesis of assessment of observed change in hot extremes and
con/f_idence in human contribution to the observed changes in the world’s regions 
Type of observed change
in agricultural and ecological droughtType of observed change
in heavy precipitation
Increase (19)
Low agreement in the type of change (8)
Limited data and/or literature (18)NWN NENGIC
AfricaAsia
AustralasiaNorth
America
Central
America
South
AmericaEuropeNEU RAR
WNA CNA ENA WCE EEU WSB ESB RFE
NCA MED WCA ECA TIB EAS
SCA CAR SAH ARP SAS SEA

NWS NSA WAF CAF NEAF
NAU
SAM NES WSAF SEAF
CAU EAU
SWS SES ESAF
SAUNZ
SSAMDG
(b) Synthesis of assessment of observed change in heavy precipitation and 
Increase (12)
Decrease (1)
Low agreement in the type of change (28)
Limited data and/or literature (4)NWN NENGIC
America
Central
America
South
NAU
CAU EAU
SAUNZ
SSAMDG
(c) Synthesis of assessment of observed change in agricultural and ecological drought 
and con/f_idence in human contribution to the observed changes in the world’s regions 
NWN NENGIC
America
Central
America
South
NAUPAC
PAC
PAC
CAU EAU

SAUNZ
SSAMDGSmall
Islands
Small
Islands
Small
IslandsSmall
Islands
Small
Islands
NWNEach hexagon corresponds 
to one of the IPCC AR6 
WGI reference regionsIPCC AR6 WGI reference regions: North America: NWN (North-Western North America, NEN (North-Eastern North America), WNA 
(Western North America), CNA (Central North America), ENA (Eastern North America), Central America: NCA (Northern Central America), 
SCA (Southern Central America), CAR (Caribbean), South America: NWS (North-Western South America), NSA (Northern South America), NES 
(North-Eastern South America), SAM (South American Monsoon), SWS (South-Western South America), SES (South-Eastern South America), 
SSA (Southern South America), Europe: GIC (Greenland/Iceland), NEU (Northern Europe), WCE (Western and Central Europe), EEU (Eastern 
Europe), MED (Mediterranean), Africa: MED (Mediterranean), SAH (Sahara), WAF (Western Africa), CAF (Central Africa), NEAF (North Eastern

Africa), SEAF (South Eastern Africa), WSAF (West Southern Africa), ESAF (East Southern Africa), MDG (Madagascar), Asia: RAR (Russian 
Arctic), WSB (West Siberia), ESB (East Siberia), RFE (Russian Far East), WCA (West Central Asia), ECA (East Central Asia), TIB (Tibetan Plateau), 
EAS (East Asia), ARP (Arabian Peninsula), SAS (South Asia), SEA (South East Asia), Australasia: NAU (Northern Australia), CAU (Central 
Australia), EAU (Eastern Australia), SAU (Southern Australia), NZ (New Zealand), Small Islands: CAR (Caribbean), PAC (Paciﬁc Small Islands)North-Western
North AmericaHigh
Medium
Low due to limited agreement
Low due to limited evidenceConﬁdence in human contribution 
to the observed change
High
Medium
High
Medium

11SPM
Summary for PolicymakersFigure SPM.3 | Synthesis of assessed observed and attributable regional changes 
19  Cumulative energy increase of 282 [177 to 387] ZJ over 1971–2006 (1 ZJ = 1021 joules).
20  Cumulative energy increase of 152 [100 to 205] ZJ over 2006–2018.
21  Understanding of climate processes, the instrumental record, paleoclimates and model-based emergent constraints (Glossary).The IPCC AR6 WGI inhabited regions are displayed as hexagons  with identical size in their approximate geographical location (see legend for regional acronyms). 
All assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or more local spatial scales might 
differ from what is shown in the figure. The colours  in each panel represent the four outcomes of the assessment on observed changes. Striped hexagons (white

and light-grey) are used where there is low agreement  in the type of change for the region as a whole, and grey hexagons are used when there is limited data and/
or literature that prevents an assessment of the region as a whole. Other colours indicate at least medium confidence in the observed change. The confidence 
level for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, and it is indicated 
by the number of dots: three dots for high confidence , two dots for medium confidence and one dot for low confidence (single, filled dot: limited agreement; single, 
empty dot: limited evidence).
Panel (a) For hot extremes,  the evidence is mostly drawn from changes in metrics based on daily maximum temperatures; regional studies using other indices

(heatwave duration, frequency and intensity) are used in addition. Red hexagons indicate regions where there is at least medium confidence  in an observed increase 
in hot extremes.
Panel (b) For heavy precipitation, the evidence is mostly drawn from changes in indices based on one-day or five-day precipitation amounts using global and 
regional studies. Green hexagons indicate regions where there is at least medium confidence  in an observed increase in heavy precipitation.
Panel (c) Agricultural and ecological droughts  are assessed based on observed and simulated changes in total column soil moisture, complemented 
by evidence on changes in surface soil moisture, water balance (precipitation minus evapotranspiration) and indices driven by precipitation and atmospheric 
evaporative demand. Yellow hexagons indicate regions where there is at least medium confidence  in an observed increase in this type of drought, and green

hexagons indicate regions where there is at least medium confidence  in an observed decrease in agricultural and ecological drought.
For all regions, Table TS.5 shows a broader range of observed changes besides the ones shown in this figure. Note that Southern South America (SSA) is the only 
region that does not display observed changes in the metrics shown in this figure, but is affected by observed increases in mean temperature, decreases in frost 
and increases in marine heatwaves.
{11.9, Atlas 1.3.3, Figure Atlas.2, Table TS.5; Box TS.10, Figure 1}
A.4   Improved knowledge of climate processes, paleoclimate evidence and the response of the climate system to 
increasing radiative forcing gives a best estimate of equilibrium climate sensitivity of 3°C, with a narrower 
range compared to AR5. 
  {2.2, 7.3, 7.4, 7.5, Box 7.2, 9.4, 9.5, 9.6, Cross-Chapter Box 9.1}

A.4.1  Human-caused radiative forcing of 2.72 [1.96 to 3.48] W m–2 in 2019 relative to 1750 has warmed the climate system. This 
warming is mainly due to increased GHG concentrations, partly reduced by cooling due to increased aerosol concentrations. 
The radiative forcing has increased by 0.43 W m–2 (19%) relative to AR5, of which 0.34 W m–2 is due to the increase in GHG 
concentrations since 2011. The remainder is due to improved scientific understanding and changes in the assessment of 
aerosol forcing, which include decreases in concentration and improvement in its calculation ( high confidence ).
  {2.2, 7.3, TS.2.2, TS.3.1}
A.4.2  Human-caused net positive radiative forcing causes an accumulation of additional energy (heating) in the climate system, 
partly reduced by increased energy loss to space in response to surface warming. The observed average rate of heating of

the climate system increased from 0.50 [0.32 to 0.69] W m–2 for the period 1971–200619 to 0.79 [0.52 to 1.06] W m–2 for 
the period 2006–201820 (high confidence ). Ocean warming accounted for 91% of the heating in the climate system, with 
land warming, ice loss and atmospheric warming accounting for about 5%, 3% and 1%, respectively ( high confidence ).
  {7.2, Box 7.2, TS.3.1}
A.4.3  Heating of the climate system has caused global mean sea level rise through ice loss on land and thermal expansion 
from ocean warming. Thermal expansion explained 50% of sea level rise during 1971–2018, while ice loss from glaciers 
contributed 22%, ice sheets 20% and changes in land-water storage 8%. The rate of ice-sheet loss increased by a factor 
of four between 1992–1999 and 2010–2019. Together, ice-sheet and glacier mass loss were the dominant contributors to 
global mean sea level rise during 2006–2018 ( high confidence ). 
  {9.4, 9.5, 9.6, Cross-Chapter Box 9.1}

A.4.4  The equilibrium climate sensitivity is an important quantity used to estimate how the climate responds to radiative 
forcing. Based on multiple lines of evidence,21 the very likely  range of equilibrium climate sensitivity is between 2°C ( high 
confidence ) and 5°C ( medium confidence ). The AR6 assessed best estimate is 3°C with a likely  range of 2.5°C to 4°C 
(high confidence ), compared to 1.5°C to 4.5°C in AR5, which did not provide a best estimate.
  {7.4, 7.5, TS.3.2}

12SPM
Summary for PolicymakersB. Possible Climate Futures
22  Throughout this Report, the five illustrative scenarios are referred to as SSPx-y, where ‘SSPx’ refers to the Shared Socio-economic Pathway or ‘SSP’ describing the socio-economic 
trends underlying the scenario, and ‘y’ refers to the approximate level of radiative forcing (in watts per square metre, or W m–2) resulting from the scenario in the year 2100. 
A detailed comparison to scenarios used in earlier IPCC reports is provided in Section TS.1.3, and Sections 1.6 and 4.6. The SSPs that underlie the specific forcing scenarios used to 
drive climate models are not assessed by WGI. Rather, the SSPx-y labelling ensures traceability to the underlying literature in which specific forcing pathways are used as input to the 
climate models. IPCC is neutral with regard to the assumptions underlying the SSPs, which do not cover all possible scenarios. Alternative scenarios may be considered or developed.

23  Net negative CO 2 emissions are reached when anthropogenic removals of CO 2 exceed anthropogenic emissions (Glossary).A set of five new illustrative emissions scenarios is considered consistently across this Report to explore the climate response to 
a broader range of greenhouse gas (GHG), land-use and air pollutant futures than assessed in AR5. This set of scenarios drives 
climate model projections of changes in the climate system. These projections account for solar activity and background forcing 
from volcanoes. Results over the 21st century are provided for the near term (2021–2040), mid-term (2041–2060) and long term 
(2081–2100) relative to 1850–1900, unless otherwise stated.
Box SPM.1 | Scenarios, Climate Models and Projections
Box SPM.1.1: This Report assesses the climate response to five illustrative scenarios that cover the range of possible future

development of anthropogenic drivers of climate change found in the literature. They start in 2015, and include scenarios22 
with high and very high GHG emissions (SSP3-7.0 and SSP5-8.5) and CO 2 emissions that roughly double from current 
levels by 2100 and 2050, respectively, scenarios with intermediate GHG emissions (SSP2-4.5) and CO 2 emissions remaining 
around current levels until the middle of the century, and scenarios with very low and low GHG emissions and CO 2 emissions 
declining to net zero around or after 2050, followed by varying levels of net negative CO 2 emissions23 (SSP1-1.9 and 
SSP1-2.6), as illustrated in Figure SPM.4. Emissions vary between scenarios depending on socio-economic assumptions, 
levels of climate change mitigation and, for aerosols and non-methane ozone precursors, air pollution controls. Alternative 
assumptions may result in similar emissions and climate responses, but the socio-economic assumptions and the feasibility

or likelihood of individual scenarios are not part of the assessment. 
{1.6, Cross-Chapter Box 1.4, TS.1.3} (Figure SPM.4)
Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project 
Phase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of 
physical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous 
IPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate 
change and many other aspects across the climate system. Some differences from observations remain, for example in 
regional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global 
surface temperature change within 0.2°C of the observations over most of the historical period, and observed warming is

within the very likely  range of the CMIP6 ensemble. However, some CMIP6 models simulate a warming that is either above 
or below the assessed very likely  range of observed warming. 
{1.5, Cross-Chapter Box 2.2, 3.3, 3.8, TS.1.2, Cross-Section Box TS.1} (Figure SPM.1b, Figure SPM.2)
Box SPM.1.3:  The CMIP6 models considered in this Report have a wider range of climate sensitivity than in CMIP5 models 
and the AR6 assessed very likely  range, which is based on multiple lines of evidence. These CMIP6 models also show 
a higher average climate sensitivity than CMIP5 and the AR6 assessed best estimate. The higher CMIP6 climate sensitivity 
values compared to CMIP5 can be traced to an amplifying cloud feedback that is larger in CMIP6 by about 20%. 
{Box 7.1, 7.3, 7.4, 7.5, TS.3.2}
Box SPM.1.4:  For the first time in an IPCC report, assessed future changes in global surface temperature, ocean warming

and sea level are constructed by combining multi-model projections with observational constraints based on past simulated 
warming, as well as the AR6 assessment of climate sensitivity. For other quantities, such robust methods do not yet exist 
to constrain the projections. Nevertheless, robust projected geographical patterns of many variables can be identified at 
a given level of global warming, common to all scenarios considered and independent of timing when the global warming 
level is reached. 
{1.6, 4.3, 4.6, Box 4.1, 7.5, 9.2, 9.6, Cross-Chapter Box 11.1, Cross-Section Box TS.1}

13SPM
Summary for PolicymakersBox SPM.1 (continued)
 
Figure SPM.4 | Future anthropogenic emissions of key drivers of climate change and warming contributions by groups of drivers for 
the five illustrative scenarios used in this report
The five scenarios are SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.
Panel (a) Annual anthropogenic (human-caused) emissions over the 2015–2100 period.  Shown are emissions trajectories for carbon dioxide 
(CO 2) from all sectors (GtCO 2/yr) (left graph) and for a subset of three key non-CO 2 drivers considered in the scenarios: methane (CH 4, MtCH 4/yr, top-right 
graph); nitrous oxide (N 2O, MtN 2O/yr, middle-right graph); and sulphur dioxide (SO 2, MtSO 2/yr, bottom-right graph, contributing to anthropogenic aerosols 
in panel (b).Future emissions cause future additional warming, with total warming 
dominated by past and future CO₂  emissions

(a) Future annual emissions of CO₂ (left) and of a subset of key non-CO₂ drivers (right), across ﬁve illustrative scenarios
Methane (MtCH₄/yr)
Nitrous oxide (MtN₂O/yr)
Sulphur dioxide (MtSO₂/yr)SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0
SSP5-8.5
SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0
SSP5-8.5
SSP1-1.9
SSP1-2.6SSP2-4.5SSP3-7.0
SSP5-8.5
(b) Contribution to global surface temperature increase from diﬀerent emissions, with a dominant role of CO₂ emissions
Change in global surface temperature in 2081–2100 relative to 1850–1900 (ºC)0200400600800
01020
04080120
2015 2050 21002015 2050 21002015 2050 2100
–20020406080100120140
2015 2050 2100Carbon dioxide (GtCO₂/yr)
SSP1-1.9
–10123456
Total
(observed)Aerosols
land useNon- CO₂
GHG sCO₂ºC
–10123456
Total
Land useNon- CO₂
GHG sCO₂ºC
–10123456
Total
GHG sCO₂ºC
–10123456
Total
GHG sCO₂ºC
–10123456
Total

GHG sCO₂ºCSSP1-2.6 SSP2-4.5 SSP5-8.5 SSP3-7.0
Total warming (observed warming to date in darker shade), warming from CO₂, warming from non-CO₂ GHG s and cooling from changes in aerosols and land useSelected contributors to non-CO ₂ GHG s
One air pollutant and contributor to aerosols

14SPM
Summary for PolicymakersPanel (b) Warming contributions by groups of anthropogenic drivers and by scenario are shown as the change in global surface 
temperature (°C)  in 2081–2100 relative to 1850–1900, with indication of the observed warming to date. Bars and whiskers represent median values 
and the very likely range, respectively. Within each scenario bar plot, the bars represent: total global warming (°C; ‘total’  bar) (see Table SPM.1); warming 
contributions (°C) from changes in CO 2 (‘CO 2’ bar) and from non-CO 2 greenhouse gases (GHGs; ‘non-CO 2 GHGs’  bar: comprising well-mixed greenhouse 
gases and ozone); and net cooling from other anthropogenic drivers (‘aerosols and land use’  bar: anthropogenic aerosols, changes in reflectance due to 
land-use and irrigation changes, and contrails from aviation) (see Figure SPM.2, panel c, for the warming contributions to date for individual drivers). The

best estimate for observed warming in 2010–2019 relative to 1850–1900 (see Figure SPM.2, panel a) is indicated in the darker column in the ‘total’ bar. 
Warming contributions in panel (b) are calculated as explained in Table SPM.1 for the total bar. For the other bars, the contribution by groups of drivers is 
calculated with a physical climate emulator of global surface temperature that relies on climate sensitivity and radiative forcing assessments.
{Cross-Chapter Box 1.4; 4.6; Figure 4.35; 6.7; Figures 6.18, 6.22 and 6.24; 7.3; Cross-Chapter Box 7.1; Figure 7.7; Box TS.7; Figures TS.4 and TS.15}
B.1   Global surface temperature will continue to increase until at least mid-century under all emissions scenarios 
considered. Global warming of 1.5°C and 2°C will be exceeded during the 21st century unless deep reductions 
in CO 2 and other greenhouse gas emissions occur in the coming decades.

{2.3, Cross-Chapter Box 2.3, Cross-Chapter Box 2.4, 4.3, 4.4, 4.5} (Figure SPM.1, Figure SPM.4, Figure SPM.8, 
Table SPM.1, Box SPM.1)
B.1.1  Compared to 1850–1900, global surface temperature averaged over 2081–2100 is very likely  to be higher by 1.0°C to 
1.8°C under the very low GHG emissions scenario considered (SSP1-1.9), by 2.1°C to 3.5°C in the intermediate GHG 
emissions scenario (SSP2-4.5) and by 3.3°C to 5.7°C under the very high GHG emissions scenario (SSP5-8.5).24 The last 
time global surface temperature was sustained at or above 2.5°C higher than 1850–1900 was over 3 million years ago 
  {2.3, Cross-Chapter Box 2.4, 4.3, 4.5, Box TS.2, Box TS.4, Cross-Section Box TS.1} (Table SPM.1)
24  Changes in global surface temperature are reported as running 20-year averages, unless stated otherwise.

25  SSP1-1.9 and SSP1-2.6 are scenarios that start in 2015 and have very low and low GHG emissions, respectively, and CO 2 emissions declining to net zero around or after 2050, 
followed by varying levels of net negative CO 2 emissions.
26  Crossing is defined here as having the assessed global surface temperature change, averaged over a 20-year period, exceed a particular global warming level.Table SPM.1 |  Changes in global surface temperature, which are assessed based on multiple lines of evidence, for selected 20-year time 
periods and the five illustrative emissions scenarios considered.  Temperature differences relative to the average global surface temperature of the 
period 1850–1900 are reported in °C. This includes the revised assessment of observed historical warming for the AR5 reference period 1986–2005, which 
in AR6 is higher by 0.08 [–0.01 to +0.12] °C than in AR5 (see footnote 10). Changes relative to the recent reference period 1995–2014 may be calculated

approximately by subtracting 0.85°C, the best estimate of the observed warming from 1850–1900 to 1995–2014. 
{Cross-Chapter Box 2.3, 4.3, 4.4, Cross-Section Box TS.1}
Near term, 2021–2040 Mid-term, 2041–2060 Long term, 2081–2100
Scenario Best estimate (°C)Very likely 
range (°C)Best estimate (°C)Very likely  
range (°C)Best estimate (°C)Very likely 
range (°C)
SSP1-1.9 1.5 1.2 to 1.7 1.6 1.2 to 2.0 1.4 1.0 to 1.8
SSP1-2.6 1.5 1.2 to 1.8 1.7 1.3 to 2.2 1.8 1.3 to 2.4
SSP2-4.5 1.5 1.2 to 1.8 2.0 1.6 to 2.5 2.7 2.1 to 3.5
SSP3-7.0 1.5 1.2 to 1.8 2.1 1.7 to 2.6 3.6 2.8 to 4.6
SSP5-8.5 1.6 1.3 to 1.9 2.4 1.9 to 3.0 4.4 3.3 to 5.7
B.1.2  Based on the assessment of multiple lines of evidence, global warming of 2°C, relative to 1850–1900, would be exceeded 
during the 21st century under the high and very high GHG emissions scenarios considered in this report (SSP3-7.0 and 
SSP5-8.5, respectively). Global warming of 2°C would extremely likely be exceeded in the intermediate GHG emissions

scenario (SSP2-4.5). Under the very low and low GHG emissions scenarios, global warming of 2°C is extremely unlikely  
to be exceeded (SSP1-1.9) or unlikely  to be exceeded (SSP1-2.6).25 Crossing the 2°C global warming level in the mid-
term period (2041–2060) is very likely  to occur under the very high GHG emissions scenario (SSP5-8.5), likely  to occur 
under the high GHG emissions scenario (SSP3-7.0), and more likely than not to occur in the intermediate GHG emissions 
scenario (SSP2-4.5).26
  {4.3, Cross-Section Box TS.1} (Table SPM.1, Figure SPM.4, Box SPM.1)

15SPM
Summary for PolicymakersB.1.3  Global warming of 1.5°C relative to 1850–1900 would be exceeded during the 21st century under the intermediate, high 
and very high GHG emissions scenarios considered in this report (SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively). Under 
the five illustrative scenarios, in the near term (2021–2040), the 1.5°C global warming level is very likely to be exceeded 
under the very high GHG emissions scenario (SSP5-8.5), likely  to be exceeded under the intermediate and high GHG 
emissions scenarios (SSP2-4.5 and SSP3-7.0), more likely than not  to be exceeded under the low GHG emissions scenario 
(SSP1-2.6) and more likely than not  to be reached under the very low GHG emissions scenario (SSP1-1.9).27 Furthermore, for 
the very low GHG emissions scenario (SSP1-1.9), it is more likely than not that global surface temperature would decline 
back to below 1.5°C toward the end of the 21st century, with a temporary overshoot of no more than 0.1°C above 1.5°C

global warming.
  {4.3, Cross-Section Box TS.1} (Table SPM.1, Figure SPM.4)
B.1.4  Global surface temperature in any single year can vary above or below the long-term human-induced trend, due to 
substantial natural variability.28 The occurrence of individual years with global surface temperature change above a certain 
level, for example 1.5°C or 2°C, relative to 1850–1900 does not imply that this global warming level has been reached.29 
{Cross-Chapter Box 2.3, 4.3, 4.4, Box 4.1, Cross-Section Box TS.1} (Table SPM.1, Figure SPM.1, Figure SPM.8)
B.2   Many changes in the climate system become larger in direct relation to increasing global warming. They 
include increases in the frequency and intensity of hot extremes, marine heatwaves, heavy precipitation, 
and, in some regions, agricultural and ecological droughts; an increase in the proportion of intense tropical 
cyclones; and reductions in Arctic sea ice, snow cover and permafrost.

{4.3, 4.5, 4.6, 7.4, 8.2, 8.4, Box 8.2, 9.3, 9.5, Box 9.2, 11.1, 11.2, 11.3, 11.4, 11.6, 11.7, 11.9, Cross-Chapter Box 
11.1, 12.4, 12.5, Cross-Chapter Box 12.1, Atlas.4, Atlas.5, Atlas.6, Atlas.7, Atlas.8, Atlas.9, Atlas.10, Atlas.11} 
(Figure SPM.5, Figure SPM.6, Figure SPM.8)
B.2.1  It is virtually certain  that the land surface will continue to warm more than the ocean surface ( likely  1.4 to 1.7 times more). 
It is virtually certain  that the Arctic will continue to warm more than global surface temperature, with high confidence  
above two times the rate of global warming.
   {2.3, 4.3, 4.5, 4.6, 7.4, 11.1, 11.3, 11.9, 12.4, 12.5, Cross-Chapter Box 12.1, Atlas.4, Atlas.5, Atlas.6, Atlas.7, Atlas.8, Atlas.9, 
Atlas.10, Atlas.11, Cross-Section Box TS.1, TS.2.6} (Figure SPM.5)
B.2.2  With every additional increment of global warming, changes in extremes continue to become larger. For example, every

additional 0.5°C of global warming causes clearly discernible increases in the intensity and frequency of hot extremes, 
including heatwaves ( very likely ), and heavy precipitation ( high confidence ), as well as agricultural and ecological 
droughts30 in some regions ( high confidence ). Discernible changes in intensity and frequency of meteorological droughts, 
with more regions showing increases than decreases, are seen in some regions for every additional 0.5°C of global 
warming ( medium confidence ). Increases in frequency and intensity of hydrological droughts become larger with 
increasing global warming in some regions ( medium confidence ). There will be an increasing occurrence of some extreme 
events unprecedented in the observational record with additional global warming, even at 1.5°C of global warming. 
Projected percentage changes in frequency are larger for rarer events ( high confidence ).

{8.2, 11.2, 11.3, 11.4, 11.6, 11.9, Cross-Chapter Box 11.1, Cross-Chapter Box 12.1, TS.2.6} (Figure SPM.5, Figure SPM.6)
B.2.3  Some mid-latitude and semi-arid regions, and the South American Monsoon region, are projected to see the highest 
increase in the temperature of the hottest days, at about 1.5 to 2 times the rate of global warming ( high confidence ). The 
Arctic is projected to experience the highest increase in the temperature of the coldest days, at about three times the rate 
of global warming ( high confidence ). With additional global warming, the frequency of marine heatwaves will continue 
to increase ( high confidence ), particularly in the tropical ocean and the Arctic ( medium confidence ).
  {Box 9.2, 11.1, 11.3, 11.9, Cross-Chapter Box 11.1, Cross-Chapter Box 12.1, 12.4, TS.2.4, TS.2.6} (Figure SPM.6)

27  The AR6 assessment of when a given global warming level is first exceeded benefits from the consideration of the illustrative scenarios, the multiple lines of evidence entering the 
assessment of future global surface temperature response to radiative forcing, and the improved estimate of historical warming. The AR6 assessment is thus not directly comparable to 
the SR1.5 SPM, which reported likely reaching 1.5°C global warming between 2030 and 2052, from a simple linear extrapolation of warming rates of the recent past. When considering 
scenarios similar to SSP1-1.9 instead of linear extrapolation, the SR1.5 estimate of when 1.5°C global warming is first exceeded is close to the best estimate reported here.
28  Natural variability refers to climatic fluctuations that occur without any human influence, that is, internal variability combined with the response to external natural factors such as

volcanic eruptions, changes in solar activity and, on longer time scales, orbital effects and plate tectonics (Glossary).
29  The internal variability in any single year is estimated to be about ±0.25°C (5–95% range, high confidence ).
30  Projected changes in agricultural and ecological droughts are primarily assessed based on total column soil moisture. See footnote 15 for definition and relation to precipitation 
and evapotranspiration.

16SPM
Summary for PolicymakersB.2.4  It is very likely  that heavy precipitation events will intensify and become more frequent in most regions with additional global 
warming. At the global scale, extreme daily precipitation events are projected to intensify by about 7% for each 1°C of global 
warming ( high confidence ). The proportion of intense tropical cyclones (Category 4–5) and peak wind speeds of the most 
intense tropical cyclones are projected to increase at the global scale with increasing global warming ( high confidence ).
  {8.2, 11.4, 11.7, 11.9, Cross-Chapter Box 11.1, Box TS.6, TS.4.3.1} (Figure SPM.5, Figure SPM.6)
B.2.5  Additional warming is projected to further amplify permafrost thawing and loss of seasonal snow cover, of land ice and of 
Arctic sea ice ( high confidence ). The Arctic is likely  to be practically sea ice-free in September31 at least once before 2050

under the five illustrative scenarios considered in this report, with more frequent occurrences for higher warming levels. 
There is low confidence in the projected decrease of Antarctic sea ice.
   {4.3, 4.5, 7.4, 8.2, 8.4, Box 8.2, 9.3, 9.5, 12.4, Cross-Chapter Box 12.1, Atlas.5, Atlas.6, Atlas.8, Atlas.9, Atlas.11, TS.2.5} 
(Figure SPM.8)
31  Monthly average sea ice area of less than 1 million km2, which is about 15% of the average September sea ice area observed in 1979–1988.
00.5 11.5 22.5 33.5 44.5 55.5 66.5 7With every increment of global warming, changes get larger 
in regional mean temperature, precipitation and soil moisture
(a) Annual mean temperature change (°C) 
at 1°C global warming 
(b) Annual mean temperature change (°C)
relative to 1850–1900Across warming levels, land areas warm more than ocean areas, and the 
Arctic and Antarctica warm more than the tropics.Warming at 1°C aﬀects all continents and 
is generally larger over land than over the

oceans in both observations and models. 
Across most regions, observed and 
simulated patterns are consistent.
Simulated change at 2°C  global warming Simulated change at 4°C  global warming Simulated change at 1.5°C  global warmingSimulated change at 1°C  global warming Observed change per 1°C global warming
Change (°C)
Warmer

17SPM
Summary for Policymakersl a. 
Figure SPM.5 |  Changes in annual mean surface temperature, precipitation, and soil moisture
Panel (a) Comparison of observed and simulated annual mean surface temperature change.  The left map shows the observed changes in annual 
mean surface temperature in the period 1850–2020 per °C of global warming (°C). The local (i.e., grid point) observed annual mean surface temperature changes 
are linearly regressed against the global surface temperature in the period 1850–2020. Observed temperature data are from Berkeley Earth, the dataset with 
the largest coverage and highest horizontal resolution. Linear regression is applied to all years for which data at the corresponding grid point is available. The 
regression method was used to take into account the complete observational time series and thereby reduce the role of internal variability at the grid point level.

White indicates areas where time coverage was 100 years or less and thereby too short to calculate a reliable linear regression. The right map is based on model 
simulations and shows change in annual multi-model mean simulated temperatures at a global warming level of 1°C (20-year mean global surface temperature 
change relative to 1850–1900). The triangles at each end of the colour bar indicate out-of-bound values, that is, values above or below the given limits.
Panel (b) Simulated annual mean temperature change (°C), panel (c) precipitation change (%), and panel (d) total column soil moisture change 
(standard deviation of interannual variability)  at global warming levels of 1.5°C, 2°C and 4°C (20-year mean global surface temperature change relative 
to 1850–1900). Simulated changes correspond to Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean change (median change for soil

moisture) at the corresponding global warming level, that is, the same method as for the right map in panel (a). 
In panel (c) , high positive percentage changes in dry regions may correspond to small absolute changes. In panel (d) , the unit is the standard deviation 
of interannual variability in soil moisture during 1850–1900. Standard deviation is a widely used metric in characterizing drought severity. A projected 
reduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of droughts that occurred about once every six years 
during 1850–1900. In panel (d), large changes in dry regions with little interannual variability in the baseline conditions can correspond to small absolute 
change. The triangles at each end of the colour bars indicate out-of-bound values, that is, values above or below the given limits. Results from all models

reaching the corresponding warming level in any of the five illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are averaged. 
Maps of annual mean temperature and precipitation changes at a global warming level of 3°C are available in Figure 4.31 and Figure 4.32 in Section 4.6 . 
Corresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and 
11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) 
where the aggregated signals are less affected by small-scale variability, leading to an increase in robustness.
{Figure 1.14, 4.6.1, Cross-Chapter Box 11.1, Cross-Chapter Box Atlas.1, TS.1.3.2, Figures TS.3 and TS.5}
(c) Annual mean precipitation change (%)
relative to 1850–1900 
Change (%)
(d) Annual mean total column soil

moisture change (standard deviation)
–1.0 –0.5 0.5 1.0 1.5 0
Change (standard deviation
of interannual variability)–1.5Precipitation is projected to increase over high latitudes, the equatorial 
Paciﬁc and parts of the monsoon regions, but decrease over parts of the 
subtropics and in limited areas of the tropics.
Relatively small absolute changes
may appear as large % changes in 
regions with dry baseline conditions.
Relatively small absolute changes 
may appear large when expressed
in units of standard deviation in dry 
regions with little interannual 
variability in baseline conditions.Across warming levels, changes in soil moisture largely follow changes in 
precipitation but also show some diﬀerences due to the inﬂuence of 
evapotranspiration.

Simulated change at 2°C  global warming Simulated change at 4°C  global warming Simulated change at 1.5°C  global warmingSimulated change at 2°C  global warming Simulated change at 4°C  global warming Simulated change at 1.5°C  global warming
–40
–30
–20
–10
0
10
20
30
40

18SPM
Figure SPM.6 |  Projected changes in the intensity and frequency of hot temperature extremes over land, extreme precipitation over land, 
and agricultural and ecological droughts in drying regions
Projected changes are shown at global warming levels of 1°C, 1.5°C, 2°C, and 4°C and are relative to 1850–1900,9 representing a climate without human 
influence. The figure depicts frequencies and increases in intensity of 10- or 50-year extreme events from the base period (1850–1900) under different global 
Hot temperature extremes are defined as the daily maximum temperatures over land that were exceeded on average once in a decade (10-year event) or once 
in 50 years (50-year event) during the 1850–1900 reference period. Extreme precipitation events are defined as the daily precipitation amount over land that 
Present 1°C Present 1°C
+6.7%
wetter+10.5%
wetter+14.0%
wetter+30.2%
wetterFuture global warming levels
1.5°C 2°C 4°C 1850–1900

0%+10%+20%+30%+40%
+0.3 sd
drier+0.5 sd
drier+0.6 sd
drier+1.0 sd
drierOnce now likely 
occurs
1.7 times
(0.7–4.1)will likely 
occur
2.0 times
(1.0–5.1)will likely 
occur
2.4 times
(1.3–5.8)will likely 
occur
4.1 times
(1.7–7.2)Future global warming levels
0 sd+1 sd+2 sd
Once now likely 
occurs
1.3 times
(1.2–1.4)will likely 
occur
1.5 times
(1.4–1.7)will likely 
occur
1.7 times
(1.6–2.0)will likely 
occur
2.7 times
(2.3–3.6)FREQUENCY  per 10 years INTENSITY increase
FREQUENCY per 10 years INTENSITY increaseProjected changes in extremes are larger in frequency and intensity with 
every additional increment of global warming
Frequency and increase in intensity of heavy 1-day 
precipitation event that occurred once in 10 years  on 
average in a climate without human inﬂuenceHeavy precipitation over land
Frequency and increase in intensity of an agricultural and ecological 
drought event that occurred once in 10 years on average across

drying regions in a climate without human inﬂuenceAgricultural & ecological droughts in drying regions
FREQUENCY per 50 years INTENSITY increase
Frequency and increase in intensity of extreme temperature 
event that occurred once in 50 years on average 
in a climate without human inﬂuence
Present 1°C
+1.2°C
hotter+2.0°C
hotter+2.7°C 
hotter+5.3°C
hotter50-year event
occurs
4.8 times
(2.3–6.4)will likely 
occur
8.6 times
(4.3–10.7)will likely 
occur
13.9 times
(6.9–16.6)will likely 
occur
39.2 times
(27.0–41.4)Future global warming levels
0°C+1°C+2°C+3°C+4°C+5°C+6°C
Hot temperature extremes over land
event that occurred once in 10 years on average 
+1.2°C
hotter+1.9°C
hotter+2.6°C 
hotter+5.1°C
hotter10-year event
10-year event 10-year eventFREQUENCY per 10 years INTENSITY increaseFuture global warming levels

occurs
2.8 times
(1.8–3.2)will likely 
occur
4.1 times
(2.8–4.7)will likely 
occur
5.6  times
(3.8–6.0)will likely 
occur
9.4 times
(8.3–9.6)

19SPM
Summary for Policymakerswas exceeded on average once in a decade during the 1850–1900 reference period. Agricultural and ecological drought events are defined as the annual 
average of total column soil moisture below the 10th percentile of the 1850–1900 base period. These extremes are defined on model grid box scale. For hot 
temperature extremes and extreme precipitation, results are shown for the global land. For agricultural and ecological drought, results are shown for drying regions 
only, which correspond to the AR6 regions in which there is at least medium confidence in a projected increase in agricultural and ecological droughts at the 2°C 
warming level compared to the 1850–1900 base period in the Coupled Model Intercomparison Project Phase 6 (CMIP6). These regions include Western North 
America, Central North America, Northern Central America, Southern Central America, Caribbean, Northern South America, North-Eastern South America, South

American Monsoon, South-Western South America, Southern South America, Western and Central Europe, Mediterranean, West Southern Africa, East Southern 
Africa, Madagascar, Eastern Australia, and Southern Australia (Caribbean is not included in the calculation of the figure because of the too-small number of full land 
grid cells). The non-drying regions do not show an overall increase or decrease in drought severity. Projections of changes in agricultural and ecological droughts 
in the CMIP Phase 5 (CMIP5) multi-model ensemble differ from those in CMIP6 in some regions, including in parts of Africa and Asia. Assessments of projected 
changes in meteorological and hydrological droughts are provided in Chapter 11. 
In the ‘frequency’ section , each year is represented by a dot. The dark dots indicate years in which the extreme threshold is exceeded, while light dots are years

when the threshold is not exceeded. Values correspond to the medians (in bold) and their respective  5–95% range based on the multi-model ensemble from 
simulations of CMIP6 under different Shared Socio-economic Pathway scenarios. For consistency, the number of dark dots is based on the rounded-up median. 
In the ‘intensity’ section , medians and their 5–95% range, also based on the multi-model ensemble from simulations of CMIP6, are displayed as dark and 
light bars, respectively. Changes in the intensity of hot temperature extremes and extreme precipitation are expressed as degree Celsius and percentage. As for 
agricultural and ecological drought, intensity changes are expressed as fractions of standard deviation of annual soil moisture. 
{11.1; 11.3; 11.4; 11.6; 11.9; Figures 11.12, 11.15, 11.6, 11.7, and 11.18}
B.3   Continued global warming is projected to further intensify the global water cycle, including its variability,

global monsoon precipitation and the severity of wet and dry events. 
  {4.3, 4.4, 4.5, 4.6, 8.2, 8.3, 8.4, 8.5, Box 8.2, 11.4, 11.6, 11.9, 12.4, Atlas.3} (Figure SPM.5, Figure SPM.6)
B.3.1  There is strengthened evidence since AR5 that the global water cycle will continue to intensify as global temperatures 
rise ( high confidence ), with precipitation and surface water flows projected to become more variable over most land 
regions within seasons ( high confidence ) and from year to year ( medium confidence ). The average annual global land 
precipitation is projected to increase by 0–5% under the very low GHG emissions scenario (SSP1-1.9), 1.5–8% for the 
intermediate GHG emissions scenario (SSP2-4.5) and 1–13% under the very high GHG emissions scenario (SSP5-8.5) by 
2081–2100 relative to 1995–2014 ( likely ranges). Precipitation is projected to increase over high latitudes, the equatorial

Pacific and parts of the monsoon regions, but decrease over parts of the subtropics and limited areas in the tropics 
in SSP2-4.5, SSP3-7.0 and SSP5-8.5 ( very likely ). The portion of the global land experiencing detectable increases or 
decreases in seasonal mean precipitation is projected to increase ( medium confidence ). There is high confidence  in an 
earlier onset of spring snowmelt, with higher peak flows at the expense of summer flows in snow-dominated regions 
globally.
  {4.3, 4.5, 4.6, 8.2, 8.4, Atlas.3, TS.2.6, TS.4.3, Box TS.6} (Figure SPM.5)
B.3.2  A warmer climate will intensify very wet and very dry weather and climate events and seasons, with implications for 
flooding or drought ( high confidence ), but the location and frequency of these events depend on projected changes in 
regional atmospheric circulation, including monsoons and mid-latitude storm tracks. It is very likely  that rainfall variability

related to the El Niño–Southern Oscillation is projected to be amplified by the second half of the 21st century in the 
SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios.
  {4.3, 4.5, 4.6, 8.2, 8.4, 8.5, 11.4, 11.6, 11.9, 12.4, TS.2.6, TS.4.2, Box TS.6} (Figure SPM.5, Figure SPM.6)
B.3.3  Monsoon precipitation is projected to increase in the mid- to long term at the global scale, particularly over South and 
South East Asia, East Asia and West Africa apart from the far west Sahel ( high confidence ). The monsoon season is 
projected to have a delayed onset over North and South America and West Africa ( high confidence ) and a delayed retreat 
over West Africa ( medium confidence ).
  {4.4, 4.5, 8.2, 8.3, 8.4, Box 8.2, Box TS.13}
B.3.4  A projected southward shift and intensification of Southern Hemisphere summer mid-latitude storm tracks and associated 
precipitation is likely  in the long term under high GHG emissions scenarios (SSP3-7.0, SSP5-8.5), but in the near term

the effect of stratospheric ozone recovery counteracts these changes ( high confidence ). There is medium confidence  in 
a continued poleward shift of storms and their precipitation in the North Pacific, while there is low confidence  in projected 
changes in the North Atlantic storm tracks.
  {4.4, 4.5, 8.4, TS.2.3, TS.4.2}
B.4   Under scenarios with increasing CO 2 emissions, the ocean and land carbon sinks are projected to be less 
effective at slowing the accumulation of CO 2 in the atmosphere.
  {4.3, 5.2, 5.4, 5.5, 5.6} (Figure SPM.7)

20SPM
Summary for PolicymakersB.4.1  While natural land and ocean carbon sinks are projected to take up, in absolute terms, a progressively larger amount 
of CO 2 under higher compared to lower CO 2 emissions scenarios, they become less effective, that is, the proportion of 
emissions taken up by land and ocean decrease with increasing cumulative CO 2 emissions. This is projected to result in 
a higher proportion of emitted CO 2 remaining in the atmosphere ( high confidence ).
  {5.2, 5.4, Box TS.5} (Figure SPM.7)
B.4.2  Based on model projections, under the intermediate GHG emissions scenario that stabilizes atmospheric CO 2 concentrations 
this century (SSP2-4.5), the rates of CO 2 taken up by the land and ocean are projected to decrease in the second half of 
the 21st century ( high confidence ). Under the very low and low GHG emissions scenarios (SSP1-1.9, SSP1-2.6), where CO 2

concentrations peak and decline during the 21st century, the land and ocean begin to take up less carbon in response 
to declining atmospheric CO 2 concentrations ( high confidence ) and turn into a weak net source by 2100 under SSP1-1.9 
(medium confidence ). It is very unlikely  that the combined global land and ocean sink will turn into a source by 2100 
under scenarios without net negative emissions (SSP2-4.5, SSP3-7.0, SSP5-8.5).32 
  {4.3, 5.4, 5.5, 5.6, Box TS.5, TS.3.3}
B.4.3  The magnitude of feedbacks between climate change and the carbon cycle becomes larger but also more uncertain 
in high CO 2 emissions scenarios ( very high confidence ). However, climate model projections show that the uncertainties in 
atmospheric CO 2 concentrations by 2100 are dominated by the differences between emissions scenarios ( high confidence ). 
Additional ecosystem responses to warming not yet fully included in climate models, such as CO 2 and CH 4 fluxes from

wetlands, permafrost thaw and wildfires, would further increase concentrations of these gases in the atmosphere 
  {5.4, Box TS.5, TS.3.2}
32  These projected adjustments of carbon sinks to stabilization or decline of atmospheric CO 2 are accounted for in calculations of remaining carbon budgets.Figure SPM.7 | Cumulative anthropogenic CO 2 emissions taken up by land and ocean sinks by 2100 under the five illustrative scenarios
The cumulative anthropogenic (human-caused) carbon dioxide (CO 2) emissions taken up by the land and ocean sinks under the five illustrative scenarios (SSP1-1.9, 
SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are simulated from 1850 to 2100 by Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models in the 
concentration-driven simulations. Land and ocean carbon sinks respond to past, current and future emissions; therefore, cumulative sinks from 1850 to 2100 are

presented here. During the historical period (1850–2019) the observed land and ocean sink took up 1430 GtCO 2 (59% of the emissions).The proportion of CO₂ emissions taken up by land and ocean carbon sinks 
is smaller in scenarios with higher cumulative CO₂ emissions
Total cumulative CO₂ emissions taken up by land and ocean (colours) and remaining in the atmosphere (grey) 
under the ﬁve illustrative scenarios from 1850 to 2100
…meaning that the proportion
of CO ₂ emissions taken up by
land and ocean carbon sinks
from the atmosphere
is smaller in scenarios
with higher CO₂ emissions.
SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5ATMOSPHERE
LAND         OCEAN LAND         OCEANATMOSPHERE ATMOSPHERE
LAND         OCEAN LAND         OCEAN LAND         OCEANATMOSPHERE ATMOSPHERE
70%0200040006000800010,00012,000
65% 54% 44% 38%
OCEANATMOSPHERE
LAND
For scenarios with 
higher cumulative
CO₂ emissions…
…the amount of CO₂ emissions 
taken up by land and ocean 
carbon sinks is larger,

but more of the emitted
CO₂ remains in the 
atmosphere…GtCO ₂
LANDOCEANATMOSPHERE
LAND

21SPM
Summary for PolicymakersThe bar chart illustrates the projected amount of cumulative anthropogenic CO 2 emissions (GtCO 2) between 1850 and 2100 remaining in the atmosphere (grey 
part) and taken up by the land and ocean (coloured part) in the year 2100. The doughnut chart illustrates the proportion of the cumulative anthropogenic 
CO 2 emissions taken up by the land and ocean sinks and remaining in the atmosphere in the year 2100. Values in % indicate the proportion of the cumulative 
anthropogenic CO 2 emissions taken up by the combined land and ocean sinks in the year 2100. The overall anthropogenic carbon emissions are calculated by 
adding the net global land-use emissions from the CMIP6 scenario database to the other sectoral emissions calculated from climate model runs with prescribed CO 2 
concentrations.33 Land and ocean CO 2 uptake since 1850 is calculated from the net biome productivity on land, corrected for CO 2 losses due to land-use change by

adding the land-use change emissions, and net ocean CO 2 flux.
{5.2.1; Table 5.1; 5.4.5; Figure 5.25; Box TS.5; Box TS.5, Figure 1}
B.5   Many changes due to past and future greenhouse gas emissions are irreversible for centuries to millennia, 
especially changes in the ocean, ice sheets and global sea level. 
  {2.3, Cross-Chapter Box 2.4, 4.3, 4.5, 4.7, 5.3, 9.2, 9.4, 9.5, 9.6, Box 9.4} (Figure SPM.8)
B.5.1  Past GHG emissions since 1750 have committed the global ocean to future warming ( high confidence ). Over the rest of 
the 21st century, likely  ocean warming ranges from 2–4 (SSP1-2.6) to 4–8 times (SSP5-8.5) the 1971–2018 change. Based 
on multiple lines of evidence, upper ocean stratification ( virtually certain ), ocean acidification ( virtually certain ) and ocean 
deoxygenation ( high confidence ) will continue to increase in the 21st century, at rates dependent on future emissions.

Changes are irreversible on centennial to millennial time scales in global ocean temperature ( very high confidence ), 
deep-ocean acidification ( very high confidence ) and deoxygenation ( medium confidence ).
  {4.3, 4.5, 4.7, 5.3, 9.2, TS.2.4} (Figure SPM.8)
B.5.2  Mountain and polar glaciers are committed to continue melting for decades or centuries ( very high confidence ). Loss of 
permafrost carbon following permafrost thaw is irreversible at centennial time scales ( high confidence ). Continued ice 
loss over the 21st century is virtually certain  for the Greenland Ice Sheet and likely  for the Antarctic Ice Sheet. There is 
high confidence  that total ice loss from the Greenland Ice Sheet will increase with cumulative emissions. There is limited 
evidence for low-likelihood, high-impact outcomes (resulting from ice-sheet instability processes characterized by deep

uncertainty and in some cases involving tipping points) that would strongly increase ice loss from the Antarctic Ice Sheet 
for centuries under high GHG emissions scenarios.34 
  {4.3, 4.7, 5.4, 9.4, 9.5, Box 9.4, Box TS.1, TS.2.5}
B.5.3  It is virtually certain  that global mean sea level will continue to rise over the 21st century. Relative to 1995–2014, the likely  
global mean sea level rise by 2100 is 0.28–0.55 m under the very low GHG emissions scenario (SSP1-1.9); 0.32–0.62 m 
under the low GHG emissions scenario (SSP1-2.6); 0.44–0.76 m under the intermediate GHG emissions scenario (SSP2-4.5); 
and 0.63–1.01 m under the very high GHG emissions scenario (SSP5-8.5); and by 2150 is 0.37–0.86 m under the very 
low scenario (SSP1-1.9); 0.46–0.99 m under the low scenario (SSP1-2.6); 0.66–1.33 m under the intermediate scenario 
(SSP2-4.5); and 0.98–1.88 m under the very high scenario (SSP5-8.5) ( medium confidence ).35 Global mean sea level rise

above the likely  range – approaching 2 m by 2100 and 5 m by 2150 under a very high GHG emissions scenario (SSP5-8.5) 
(low confidence ) – cannot be ruled out due to deep uncertainty in ice-sheet processes.
  {4.3, 9.6, Box 9.4, Box TS.4} (Figure SPM.8)
B.5.4  In the longer term, sea level is committed to rise for centuries to millennia due to continuing deep-ocean warming and 
ice-sheet melt and will remain elevated for thousands of years ( high confidence ). Over the next 2000 years, global mean 
sea level will rise by about 2 to 3 m if warming is limited to 1.5°C, 2 to 6 m if limited to 2°C and 19 to 22 m with 5°C of 
warming, and it will continue to rise over subsequent millennia ( low confidence ). Projections of multi-millennial global 
mean sea level rise are consistent with reconstructed levels during past warm climate periods: likely 5–10 m higher than 
today around 125,000 years ago, when global temperatures were very likely  0.5°C–1.5°C higher than 1850–1900; and very

likely  5–25 m higher roughly 3 million years ago, when global temperatures were 2.5°C–4°C higher ( medium confidence ).
  {2.3, Cross-Chapter Box 2.4, 9.6, Box TS.2, Box TS.4, Box TS.9}
33  The other sectoral emissions are calculated as the residual of the net land and ocean CO 2 uptake and the prescribed atmospheric CO 2 concentration changes in the CMIP6 
simulations. These calculated emissions are net emissions and do not separate gross anthropogenic emissions from removals, which are included implicitly.
34  Low-likelihood, high-impact outcomes are those whose probability of occurrence is low or not well known (as in the context of deep uncertainty) but whose potential impacts on 
society and ecosystems could be high. A tipping point is a critical threshold beyond which a system reorganizes, often abruptly and/or irreversibly. (Glossary) {1.4, Cross-Chapter Box 
1.3, 4.7}

35  To compare to the 1986–2005 baseline period used in AR5 and SROCC, add 0.03 m to the global mean sea level rise estimates. To compare to the 1900 baseline period used in 
Figure SPM.8, add 0.16 m.

22SPM
Figure SPM.8 | Selected indicators of global climate change under the five illustrative scenarios used in this Report
The projections for each of the five scenarios are shown in colour. Shades represent uncertainty ranges – more detail is provided for each panel below. The black 
curves represent the historical simulations (panels a, b, c) or the observations (panel d). Historical values are included in all graphs to provide context for the 
projected future changes. 
Human activities aﬀect all the major climate system components, with 
some responding over decades and others over centuries
(d) Global mean sea level change relative to 1900
Low-likelihood, high-impact storyline, 
including ice-sheet instability 
processes, under SSP5-8.5
SSP1-2.6SSP2-4.5SSP3-7.0SSP5-8.5
1950 2000 2020 2050 210011.52
0.5
0m
SSP1-1.9
(e) Global mean sea 
level change in 2300 
relative to 1900
2 m3 m4 m5 m6 m7 m8 m9 m
1 m
0 mSea level rise greater than

15 m cannot be ruled out 
with high emissions
2300
SSP1-2.6
SSP5-8.5
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5(c) Global ocean surface pH (a measure of acidity)
ocean
acidiﬁcation
1950 2000 2015 2050 21007.67.8
7.77.98.08.18.2
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5(b) September Arctic sea ice area
046810106 km2
2
1950 2000 2015 2050 2100Practically ice-free
°C
0
–1
1950 2000 2015 2050 210012345
SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0SSP5-8.5(a) Global surface temperature change relative to 1850–1900

23SPM
Summary for PolicymakersPanel (a) Global surface temperature changes in °C relative to 1850–1900. These changes were obtained by combining Coupled Model Intercomparison 
Project Phase 6 (CMIP6) model simulations with observational constraints based on past simulated warming, as well as an updated assessment of equilibrium climate 
sensitivity (see Box SPM.1). Changes relative to 1850–1900 based on 20-year averaging periods are calculated by adding 0.85°C (the observed global surface 
temperature increase from 1850–1900 to 1995–2014) to simulated changes relative to 1995–2014. Very likely  ranges are shown for SSP1-2.6 and SSP3-7.0.
Panel (b) September Arctic sea ice area in 106 km2 based on CMIP6 model simulations. Very likely  ranges are shown for SSP1-2.6 and SSP3-7.0. The Arctic is 
projected to be practically ice-free near mid-century under intermediate and high GHG emissions scenarios.

Panel (c) Global ocean surface pH  (a measure of acidity) based on CMIP6 model simulations. Very likely  ranges are shown for SSP1-2.6 and SSP3-7.0.
Panel (d) Global mean sea level change in metres, relative to 1900. The historical changes are observed (from tide gauges before 1992 and altimeters 
afterwards), and the future changes are assessed consistently with observational constraints based on emulation of CMIP , ice-sheet, and glacier models. Likely  
ranges are shown for SSP1-2.6 and SSP3-7.0. Only likely ranges are assessed for sea level changes due to difficulties in estimating the distribution of deeply 
uncertain processes. The dashed curve indicates the potential impact of these deeply uncertain processes. It shows the 83rd percentile of SSP5-8.5 projections that 
include low-likelihood, high-impact ice-sheet processes that cannot be ruled out; because of low confidence  in projections of these processes, this curve does not

constitute part of a likely range. Changes relative to 1900 are calculated by adding 0.158 m (observed global mean sea level rise from 1900 to 1995–2014) to 
simulated and observed changes relative to 1995–2014.
Panel (e) Global mean sea level change at 2300 in metres relative to 1900. Only SSP1-2.6 and SSP5-8.5 are projected at 2300, as simulations that extend 
beyond 2100 for the other scenarios are too few for robust results. The 17th–83rd percentile ranges are shaded. The dashed arrow illustrates the 83rd percentile 
of SSP5-8.5 projections that include low-likelihood, high-impact ice-sheet processes that cannot be ruled out.
Panels (b) and (c) are based on single simulations from each model, and so include a component of internal variability. Panels (a), (d) and (e) are based on long-term 
averages, and hence the contributions from internal variability are small.
{4.3; Figures 4.2, 4.8, and 4.11; 9.6; Figure 9.27; Figures TS.8 and TS.11; Box TS.4, Figure 1}

36  Climatic impact-drivers (CIDs) are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, 
CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions (Glossary). CID types include heat and cold, wet 
and dry, wind, snow and ice, coastal and open ocean.
37  The main internal variability phenomena include El Niño–Southern Oscillation, Pacific Decadal Variability and Atlantic Multi-decadal Variability through their regional influence.C. Climate Information for Risk Assessment 
and Regional  Adaptation
Physical climate information addresses how the climate system responds to the interplay between human influence, natural drivers 
and internal variability. Knowledge of the climate response and the range of possible outcomes, including low-likelihood, high

impact outcomes, informs climate services, the assessment of climate-related risks, and adaptation planning. Physical climate 
information at global, regional and local scales is developed from multiple lines of evidence, including observational products, 
climate model outputs and tailored diagnostics.
C.1   Natural drivers and internal variability will modulate human-caused changes, especially at regional scales and 
in the near term, with little effect on centennial global warming. These modulations are important to consider 
in planning for the full range of possible changes.
   {1.4, 2.2, 3.3, Cross-Chapter Box 3.1, 4.4, 4.6, Cross-Chapter Box 4.1, Box 7.2, 8.3, 8.5, 9.2, 10.3, 10.4, 10.6, 
11.3, 12.5, Atlas.4, Atlas.5, Atlas.8, Atlas.9, Atlas.10, Atlas.11, Cross-Chapter Box Atlas.2}
C.1.1  The historical global surface temperature record highlights that decadal variability has both enhanced and masked

underlying human-caused long-term changes, and this variability will continue into the future ( very high confidence ). For 
example, internal decadal variability and variations in solar and volcanic drivers partially masked human-caused surface 
global warming during 1998–2012, with pronounced regional and seasonal signatures ( high confidence ). Nonetheless, 
the heating of the climate system continued during this period, as reflected in both the continued warming of the global 
ocean ( very high confidence ) and in the continued rise of hot extremes over land ( medium confidence ).
  {1.4, 3.3, Cross-Chapter Box 3.1, 4.4, Box 7.2, 9.2, 11.3, Cross-Section Box TS.1} (Figure SPM.1)
C.1.2  Projected human-caused changes in mean climate and climatic impact-drivers (CIDs),36 including extremes, will be either 
amplified or attenuated by internal variability ( high confidence ).37 Near-term cooling at any particular location with

respect to present climate could occur and would be consistent with the global surface temperature increase due to 
human influence ( high confidence ).
  {1.4, 4.4, 4.6, 10.4, 11.3, 12.5, Atlas.5, Atlas.10, Atlas.11, TS.4.2}

24SPM
Summary for PolicymakersC.1.3  Internal variability has largely been responsible for the amplification and attenuation of the observed human-caused 
decadal-to-multi-decadal mean precipitation changes in many land regions ( high confidence ). At global and regional 
scales, near-term changes in monsoons will be dominated by the effects of internal variability ( medium confidence ). 
In addition to the influence of internal variability, near-term projected changes in precipitation at global and regional 
scales are uncertain because of model uncertainty and uncertainty in forcings from natural and anthropogenic aerosols 
   {1.4, 4.4, 8.3, 8.5, 10.3, 10.4, 10.5, 10.6, Atlas.4, Atlas.8, Atlas.9, Atlas.10, Atlas.11, Cross-Chapter Box Atlas.2, TS.4.2, 
Box TS.6, Box TS.13}
C.1.4  Based on paleoclimate and historical evidence, it is likely  that at least one large explosive volcanic eruption would occur

during the 21st century.38 Such an eruption would reduce global surface temperature and precipitation, especially over land, 
for one to three years, alter the global monsoon circulation, modify extreme precipitation and change many CIDs ( medium 
confidence ). If such an eruption occurs, this would therefore temporarily and partially mask human-caused climate change.  
{2.2, 4.4, Cross-Chapter Box 4.1, 8.5, TS.2.1}
C.2   With further global warming, every region is projected to increasingly experience concurrent and multiple 
changes in climatic impact-drivers. Changes in several climatic impact-drivers would be more widespread 
at 2°C compared to 1.5°C global warming and even more widespread and/or pronounced for higher 
warming levels. 
   {8.2, 9.3, 9.5, 9.6, Box 10.3, 11.3, 11.4, 11.5, 11.6, 11.7, 11.9, Box 11.3, Box 11.4, Cross-Chapter Box 11.1, 12.2, 
12.3, 12.4, 12.5, Cross-Chapter Box 12.1, Atlas.4, Atlas.5, Atlas.6, Atlas.7, Atlas.8, Atlas.9, Atlas.10, Atlas.11}

(Table SPM.1, Figure SPM.9)
C.2.1  All regions39 are projected to experience further increases in hot climatic impact-drivers (CIDs) and decreases in cold 
CIDs ( high confidence ). Further decreases are projected in permafrost; snow, glaciers and ice sheets; and lake and Arctic 
sea ice ( medium to high confidence ).40 These changes would be larger at 2°C global warming or above than at 1.5°C 
(high confidence ). For example, extreme heat thresholds relevant to agriculture and health are projected to be exceeded 
more frequently at higher global warming levels ( high confidence ).
   {9.3, 9.5, 11.3, 11.9, Cross-Chapter Box 11.1, 12.3, 12.4, 12.5, Cross-Chapter Box 12.1, Atlas.4, Atlas.5, Atlas.6, Atlas.7, 
Atlas.8, Atlas.9, Atlas.10, Atlas.11, TS.4.3} (Table SPM.1, Figure SPM.9)
C.2.2  At 1.5°C global warming, heavy precipitation and associated flooding are projected to intensify and be more frequent

in most regions in Africa and Asia ( high confidence ), North America ( medium to high confidence )40 and Europe ( medium 
confidence ). Also, more frequent and/or severe agricultural and ecological droughts are projected in a few regions in all 
inhabited continents except Asia compared to 1850–1900 ( medium confidence ); increases in meteorological droughts are 
also projected in a few regions ( medium confidence ). A small number of regions are projected to experience increases or 
decreases in mean precipitation ( medium confidence ).
  {11.4, 11.5, 11.6, 11.9, Atlas.4, Atlas.5, Atlas.7, Atlas.8, Atlas.9, Atlas.10, Atlas.11, TS.4.3} (Table SPM.1)
C.2.3  At 2°C global warming and above, the level of confidence in and the magnitude of the change in droughts and heavy 
and mean precipitation increase compared to those at 1.5°C. Heavy precipitation and associated flooding events

are projected to become more intense and frequent in the Pacific Islands and across many regions of North America 
and Europe ( medium to high confidence ).40 These changes are also seen in some regions in Australasia and Central and 
South America ( medium  confidence ). Several regions in Africa, South America and Europe are projected to experience an 
increase in frequency and/or severity of agricultural and ecological droughts with medium to high confidence ;40 increases 
are also projected  in Australasia, Central and North America, and the Caribbean with medium confidence . A small number 
of regions in Africa, Australasia, Europe and North America are also projected to be affected by increases in hydrological 
droughts, and several regions are projected to be affected by increases or decreases in meteorological droughts, with 
more regions displaying an increase ( medium confidence ). Mean precipitation is projected to increase in all polar, northern

European and northern North American regions, most Asian regions and two regions of South America ( high confidence ).
   {11.4, 11.6, 11.9, Cross-Chapter Box 11.1, 12.4, 12.5, Cross-Chapter Box 12.1, Atlas.5, Atlas.7, Atlas.8, Atlas.9, Atlas.11, 
TS.4.3} (Table SPM.1, Figure SPM.5, Figure SPM.6, Figure SPM.9)
38   Based on 2500 year reconstructions, eruptions more negative than –1 W m–2 occur on average twice per century.
39  Regions here refer to the AR6 WGI reference regions used in this Report to summarize information in sub-continental and oceanic regions. Changes are compared to averages over 
the last 20–40 years unless otherwise specified. {1.4, 12.4, Atlas.1}.
40  The specific level of confidence or likelihood depends on the region considered. Details can be found in the Technical Summary and the underlying Report.

25SPM
Summary for PolicymakersC.2.4  More CIDs across more regions are projected to change at 2°C and above compared to 1.5°C global warming 
(high  confidence ). Region-specific changes include intensification of tropical cyclones and/or extratropical storms 
(medium confidence ), increases in  river floods ( medium to high confidence ),40 reductions in mean precipitation and 
increases in aridity ( medium to high confidence ),40 and increases in fire weather ( medium to high confidence ).40 There 
is low confidence  in most regions in potential future changes in other CIDs, such as hail, ice storms, severe storms, dust 
storms, heavy snowfall and landslides.
   {11.7, 11.9, Cross-Chapter Box 11.1, 12.4, 12.5, Cross-Chapter Box 12.1, Atlas.4, Atlas.6, Atlas.7, Atlas.8, Atlas.10, TS.4.3.1, 
TS.4.3.2, TS.5} (Table SPM.1, Figure SPM.9)
C.2.5  It is very likely to virtually certain40 that regional mean relative sea level rise will continue throughout the 21st century,

except in a few regions with substantial geologic land uplift rates. Approximately two-thirds of the global coastline has 
a projected regional relative sea level rise within ±20% of the global mean increase ( medium confidence ). Due to relative 
sea level rise, extreme sea level events that occurred once per century in the recent past are projected to occur at least 
annually at more than half of all tide gauge locations by 2100 ( high confidence ). Relative sea level rise contributes to 
increases in the frequency and severity of coastal flooding in low-lying areas and to coastal erosion along most sandy 
coasts ( high confidence ).
  {9.6, 12.4, 12.5, Cross-Chapter Box 12.1, Box TS.4, TS.4.3} (Figure SPM.9)
C.2.6  Cities intensify human-induced warming locally, and further urbanization together with more frequent hot extremes will 
increase the severity of heatwaves ( very high confidence ). Urbanization also increases mean and heavy precipitation

over and/or downwind of cities ( medium confidence ) and resulting runoff intensity ( high confidence ). In coastal cities, 
the combination of more frequent extreme sea level events (due to sea level rise and storm surge) and extreme rainfall/
riverflow events will make flooding more probable ( high confidence ).
  {8.2, Box 10.3, 11.3, 12.4, Box TS.14}
C.2.7  Many regions are projected to experience an increase in the probability of compound events with higher global warming 
(high confidence ). In particular, concurrent heatwaves and droughts are likely to become more frequent. Concurrent 
extremes at multiple locations, including in crop-producing areas, become more frequent at 2°C and above compared to 
1.5°C global warming ( high confidence ).
  {11.8, Box 11.3, Box 11.4, 12.3, 12.4, Cross-Chapter Box 12.1, TS.4.3} (Table SPM.1)

26SPM
Figure SPM.9 | Synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change
A total of 35 climatic impact-drivers (CIDs) grouped into seven types are shown: heat and cold; wet and dry; wind; snow and ice; coastal; open ocean; and other. 
For each CID, the bar in the graph below displays the number of AR6 WGI reference regions where it is projected to change. The colours  represent the direction 
of change and the level of confidence in the change: purple indicates an increase while brown indicates a decrease; darker and lighter shades refer to high and 
medium confidence , respectively. Lighter background colours represent the maximum number of regions for which each CID is broadly relevant.
Panel (a) shows the 30 CIDs relevant to the land and coastal regions , while panel (b)  shows the five CIDs relevant to the open-ocean regions . Marine heatwaves

and ocean acidity are assessed for coastal ocean regions in panel (a) and for open-ocean regions in panel (b). Changes refer to a 20–30-year period centred around 2050 
and/or consistent with 2°C global warming compared to a similar period within 1960–2014, except for hydrological drought and agricultural and ecological drought, which 
is compared to 1850–1900. Definitions of the regions are provided in Sections 12.4 and Atlas.1 and the Interactive Atlas (see https://interactive-atlas.ipcc.ch/ ).
{11.9, 12.2, 12.4, Atlas.1, Table TS.5, Figures TS.22 and TS.25} (Table SPM.1)
Multiple climatic impact-drivers are projected to change in all regions
of the world
Number of land & coastal regions (a)  and open-ocean regions (b) where each climatic impact-driver (CID) is projected 
to increase or decrease with high conﬁdence (dark shade) or medium conﬁdence (light shade)Climatic impact-drivers (CIDs) are physical climate system conditions (e.g., means, events, extremes) that aﬀect an element

of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneﬁcial, neutral, 
or a mixture of each across interacting system elements and regions. The CIDs are grouped into seven types, which are 
summarized under the icons in the ﬁgure. All regions are projected to experience changes in at least 5 CIDs. Almost all 
(96%) are projected to experience changes in at least 10 CIDs and half in at least 15 CIDs. For many CID changes, there is 
wide geographical variation, and so each region is projected to experience a speciﬁc set of CID changes. Each bar in the 
chart represents a speciﬁc geographical set of changes that can be explored in the WGI Interactive Atlas.
Relative sea level
Coastal ﬂood
Mean ocean temperature
Ocean salinity
Dissolved oxygenMean surface temperature 
Cold spell
Frost
Mean precipitation
River ﬂood
Heavy precipitation and pluvial ﬂood

Landslide
Aridity
Mean wind speed
Snow, glacier and ice sheet
Lake, river and sea ice
Hail
Air pollution weather
Atmospheric CO2 at surface
Radiation at surfaceHeat and Cold Snow and Ice Coastal Wind Open Ocean Other Wet and Dry
5515
155515
1525
2535
3545
4555
55
NUMBER  OF LAND  & COASTAL  REGIONS
NUMBER  OF OPEN -OCEAN  REGIONS(a) (b)
Regions with high conﬁdence increase
Regions with medium conﬁdence increase
Regions with high conﬁdence decrease
Regions with medium conﬁdence decreaseChanges refer to a 20–30 
year period centred around 
2050 and/or consistent 
with 2°C global warming 
compared to a similar 
period within 1960–2014 
or 1850–1900.interactive-atlas.ipcc.ch
The height of the lighter shaded ‘envelope’ behind each bar 
represents the maximum number of regions for which each

CID is relevant. The envelope is symmetrical about the x-axis 
showing the maximum possible number of relevant regions 
for CID increase (upper part) or decrease (lower part).
BAR CHART  LEGEND LIGHTER -SHADED  ‘ENVELOPE ’ LEGEND ASSESSED  FUTURE  CHANGES

27SPM
Summary for PolicymakersC.3   Low-likelihood outcomes, such as ice-sheet collapse, abrupt ocean circulation changes, some compound 
extreme events, and warming substantially larger than the assessed very likely  range of future warming, 
cannot be ruled out and are part of risk assessment. 
   {1.4, Cross-Chapter Box 1.3, 4.3, 4.4, 4.8, Cross-Chapter Box 4.1, 8.6, 9.2, Box 9.4, 11.8, Box 11.2, Cross-Chapter 
Box 12.1} (Table SPM.1)
C.3.1  If global warming exceeds the assessed very likely range  for a given GHG emissions scenario, including low GHG emissions 
scenarios, global and regional changes in many aspects of the climate system, such as regional precipitation and other 
CIDs, would also exceed their assessed very likely  ranges ( high confidence ). Such low-likelihood, high-warming outcomes 
are associated with potentially very large impacts, such as through more intense and more frequent heatwaves and heavy

precipitation, and high risks for human and ecological systems, particularly for high GHG emissions scenarios.
  {Cross-Chapter Box 1.3, 4.3, 4.4, 4.8, Box 9.4, Box 11.2, Cross-Chapter Box 12.1, TS.1.4, Box TS.3, Box TS.4} (Table SPM.1)
C.3.2  Low-likelihood, high-impact outcomes34 could occur at global and regional scales even for global warming within the 
very likely  range for a given GHG emissions scenario. The probability of low-likelihood, high-impact outcomes increases 
with higher global warming levels ( high confidence ). Abrupt responses and tipping points of the climate system, such as 
strongly increased Antarctic ice-sheet melt and forest dieback, cannot be ruled out ( high confidence ).
  {1.4, 4.3, 4.4, 4.8, 5.4, 8.6, Box 9.4, Cross-Chapter Box 12.1, TS.1.4, TS.2.5, Box TS.3, Box TS.4, Box TS.9} (Table SPM.1)
C.3.3  If global warming increases, some compound extreme events18 with low likelihood in past and current climate will become

more frequent, and there will be a higher likelihood that events with increased intensities, durations and/or spatial extents 
unprecedented in the observational record will occur ( high confidence ).
  {11.8, Box 11.2, Cross-Chapter Box 12.1, Box TS.3, Box TS.9}
C.3.4  The Atlantic Meridional Overturning Circulation is very likely  to weaken over the 21st century for all emissions scenarios. 
While there is high confidence  in the 21st century decline, there is only low confidence  in the magnitude of the trend. 
There is medium confidence  that there will not be an abrupt collapse before 2100. If such a collapse were to occur, it 
would very likely  cause abrupt shifts in regional weather patterns and water cycle, such as a southward shift in the 
tropical rain belt, weakening of the African and Asian monsoons and strengthening of Southern Hemisphere monsoons, 
and drying in Europe.
  {4.3, 8.6, 9.2, TS2.4, Box TS.3}

C.3.5  Unpredictable and rare natural events not related to human influence on climate may lead to low-likelihood, high-impact 
outcomes. For example, a sequence of large explosive volcanic eruptions within decades has occurred in the past, causing 
substantial global and regional climate perturbations over several decades. Such events cannot be ruled out in the future, 
but due to their inherent unpredictability they are not included in the illustrative set of scenarios referred to in this Report
  {2.2, Cross-Chapter Box 4.1, Box TS.3} (Box SPM.1)
D. Limiting Future Climate Change
Since AR5, estimates of remaining carbon budgets have been improved by a new methodology first presented in SR1.5, updated 
evidence, and the integration of results from multiple lines of evidence. A comprehensive range of possible future air pollution 
controls in scenarios is used to consistently assess the effects of various assumptions on projections of climate and air pollution.

A novel development is the ability to ascertain when climate responses to emissions reductions would become discernible above 
natural climate variability, including internal variability and responses to natural drivers.
D.1   From a physical science perspective, limiting human-induced global warming to a specific level requires 
limiting cumulative CO 2 emissions, reaching at least net zero CO 2 emissions, along with strong reductions in 
other greenhouse gas emissions. Strong, rapid and sustained reductions in CH 4 emissions would also limit the 
warming effect resulting from declining aerosol pollution and would improve air quality. 
   {3.3, 4.6, 5.1, 5.2, 5.4, 5.5, 5.6, Box 5.2, Cross-Chapter Box 5.1, 6.7, 7.6, 9.6} (Figure SPM.10, Table SPM.2)

28SPM
Summary for PolicymakersD.1.1  This Report reaffirms with high confidence  the AR5 finding that there is a near-linear relationship between cumulative 
anthropogenic CO 2 emissions and the global warming they cause. Each 1000 GtCO 2 of cumulative CO 2 emissions is assessed 
to likely cause a 0.27°C to 0.63°C increase in global surface temperature with a best estimate of 0.45°C.41 This is a narrower 
range compared to AR5 and SR1.5. This quantity is referred to as the transient climate response to cumulative CO 2 emissions 
(TCRE). This relationship implies that reaching net zero anthropogenic CO 2 emissions42 is a requirement to stabilize 
human-induced global temperature increase at any level, but that limiting global temperature increase to a specific level 
would imply limiting cumulative CO 2 emissions to within a carbon budget.43 {5.4, 5.5, TS.1.3, TS.3.3, Box TS.5} (Figure SPM.10)

41  In the literature, units of °C per 1000 PgC (petagrams of carbon) are used, and the AR6 reports the TCRE likely  range as 1.0°C to 2.3°C per 1000 PgC in the underlying report, with 
a best estimate of 1.65°C.
42 The condition in which anthropogenic carbon dioxide (CO 2) emissions are balanced by anthropogenic CO 2 removals over a specified period (Glossary).
43  The term ‘carbon budget’ refers to the maximum amount of cumulative net global anthropogenic CO 2 emissions that would result in limiting global warming to a given level with 
a given probability, taking into account the effect of other anthropogenic climate forcers. This is referred to as the total carbon budget when expressed starting from the pre-industrial 
period, and as the remaining carbon budget when expressed from a recent specified date (Glossary). Historical cumulative CO 2 emissions determine to a large degree warming to

date, while future emissions cause future additional warming. The remaining carbon budget indicates how much CO 2 could still be emitted while keeping warming below a specific 
temperature level.Figure SPM.10 | Near-linear relationship between cumulative CO 2 emissions and the increase in global surface temperature 
Top panel: Historical data (thin black line) shows observed global surface temperature increase in °C since 1850–1900 as a function of historical cumulative carbon 
dioxide (CO 2) emissions in GtCO 2 from 1850 to 2019. The grey range with its central line shows a corresponding estimate of the historical human-caused surface 
warming (see Figure SPM.2). Coloured areas show the assessed very likely range of global surface temperature projections, and thick coloured central lines show the 
median estimate as a function of cumulative CO 2 emissions from 2020 until year 2050 for the set of illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and

SSP5-8.5; see Figure SPM.4). Projections use the cumulative CO 2 emissions of each respective scenario, and the projected global warming includes the contribution 
from all anthropogenic forcers. The relationship is illustrated over the domain of cumulative CO 2 emissions for which there is high confidence that the transient climate 
response to cumulative CO 2 emissions (TCRE) remains constant, and for the time period from 1850 to 2050 over which global CO 2 emissions remain net positive under 
all illustrative scenarios, as there is limited evidence supporting the quantitative application of TCRE to estimate temperature evolution under net negative CO 2 emissions.
Bottom panel:  Historical and projected cumulative CO 2 emissions in GtCO 2 for the respective scenarios.
{Section 5.5, Figure 5.31, Figure TS.18}Every tonne of CO₂ emissions adds to global warming
Future cumulative
CO₂ emissions diﬀer 
across scenarios and 
determine how much 
warming we will 
experience.SSP1-1.9
SSP1-2.6

SSP2-4.5
SSP3-7.0
SSP5-8.5
Cumulative CO₂ emissions between 1850 and 2019 Cumulative CO₂ emissions between 2020 and 2050Historical global
warmingThe near-linear relationship 
between the cumulative
CO₂ emissions and global 
warming for ﬁve illustrative 
scenarios until year 2050
1000 2000 3000 4000 4500Cumulative CO₂  emissions since 1850
–0.500.511.522.53Global surface temperature increase since 1850–1900 ( OC) as a function of cumulative CO₂ emissions ( GtCO ₂)
OC1850
time1900195020002020
2019203020402050
HISTORICAL PROJECTIONSGtCO ₂

29SPM
Summary for PolicymakersD.1.2  Over the period 1850–2019, a total of 2390 ± 240 ( likely range) GtCO 2 of anthropogenic CO 2 was emitted. Remaining 
carbon budgets have been estimated for several global temperature limits and various levels of probability, based on the 
estimated value of TCRE and its uncertainty, estimates of historical warming, variations in projected warming from non-
CO 2 emissions, climate system feedbacks such as emissions from thawing permafrost, and the global surface temperature 
change after global anthropogenic CO 2 emissions reach net zero.
  {5.1, 5.5, Box 5.2, TS.3.3} (Table SPM.2)
44  Compared to AR5, and when taking into account emissions since AR5, estimates in AR6 are about 300–350 GtCO 2 larger for the remaining carbon budget consistent with limiting 
warming to 1.5°C; for 2°C, the difference is about 400–500 GtCO 2.

45  Potential negative and positive effects of CDR for biodiversity, water and food production are methods-specific and are often highly dependent on local context, management, prior 
land use, and scale. IPCC Working Groups II and III assess the CDR potential and ecological and socio-economic effects of CDR methods in their AR6 contributions.Table SPM.2 | Estimates of historical carbon dioxide (CO2) emissions and remaining carbon budgets. Estimated remaining carbon budgets are 
calculated from the beginning of 2020 and extend until global net zero CO 2 emissions are reached. They refer to CO 2 emissions, while accounting for the global 
warming effect of non-CO 2 emissions. Global warming in this table refers to human-induced global surface temperature increase, which excludes the impact 
of natural variability on global temperatures in individual years. 
{Table 3.1, 5.5.1, 5.5.2, Box 5.2, Table 5.1, Table 5.7, Table 5.8, Table TS.3}
Global Warming Between

1850–1900 and 2010–2019  (°C)Historical Cumulative CO 2 Emissions from 1850 to 2019 (GtCO 2)
1.07 (0.8–1.3; likely range) 2390 (± 240; likely range)
Approximate global 
warming relative 
to 1850–1900 until 
temperature limit (°C)aAdditional global 
warming relative to 
2010–2019 until tem -
perature limit (°C)Estimated remaining carbon budgets  
from the beginning of 2020 (GtCO 2) 
 
Likelihood of limiting global warming
to temperature limitbVariations in reductions  
in non-CO 2 emissionsc
17% 33% 50% 67% 83%
1.5 0.43 900 650 500 400 300
Higher or lower reductions in 
accompanying non-CO 2 emissions can 
increase or decrease the values on 
the left by 220 GtCO 2 or more1.7 0.63 1450 1050 850 700 550
2.0 0.93 2300 1700 1350 1150 900
a Values at each 0.1°C increment of warming are available in Tables TS.3 and 5.8.
b This likelihood is based on the uncertainty in transient climate response to cumulative CO 2 emissions (TCRE) and additional Earth system feedbacks and provides the

probability that global warming will not exceed the temperature levels provided in the two left columns. Uncertainties related to historical warming (±550 GtCO 2) 
and non-CO 2 forcing and response (±220 GtCO 2) are partially addressed by the assessed uncertainty in TCRE, but uncertainties in recent emissions since 2015 
(±20 GtCO 2) and the climate response after net zero CO 2 emissions are reached (±420 GtCO 2) are separate.
c Remaining carbon budget estimates consider the warming from non-CO 2 drivers as implied by the scenarios assessed in SR1.5. The Working Group III Contribution 
to AR6 will assess mitigation of non-CO 2 emissions.
D.1.3  Several factors that determine estimates of the remaining carbon budget have been re-assessed, and updates to these 
factors since SR1.5 are small. When adjusted for emissions since previous reports, estimates of remaining carbon budgets

are therefore of similar magnitude compared to SR1.5 but larger compared to AR5 due to methodological improvements.44 
{5.5, Box 5.2, TS.3.3} (Table SPM.2)
D.1.4  Anthropogenic CO 2 removal (CDR) has the potential to remove CO 2 from the atmosphere and durably store it in reservoirs 
(high confidence ). CDR aims to compensate for residual emissions to reach net zero CO 2 or net zero GHG emissions or, if 
implemented at a scale where anthropogenic removals exceed anthropogenic emissions, to lower surface temperature. 
CDR methods can have potentially wide-ranging effects on biogeochemical cycles and climate, which can either weaken 
or strengthen the potential of these methods to remove CO 2 and reduce warming, and can also influence water availability 
and quality, food production and biodiversity45 (high confidence ).
  {5.6, Cross-Chapter Box 5.1, TS.3.3}
D.1.5  Anthropogenic CO 2 removal (CDR) leading to global net negative emissions would lower the atmospheric CO 2 concentration

and reverse surface ocean acidification ( high confidence ). Anthropogenic CO 2 removals and emissions are partially

30SPM
Summary for Policymakerscompensated by CO 2 release and uptake respectively, from or to land and ocean carbon pools ( very high confidence ). 
CDR would lower atmospheric CO 2 by an amount approximately equal to the increase from an anthropogenic emission of 
the same magnitude ( high confidence ). The atmospheric CO 2 decrease from anthropogenic CO 2 removals could be up to 
10% less than the atmospheric CO 2 increase from an equal amount of CO 2 emissions, depending on the total amount of 
CDR ( medium confidence ).
  {5.3, 5.6, TS.3.3}
D.1.6  If global net negative CO 2 emissions were to be achieved and be sustained, the global CO 2-induced surface temperature 
increase would be gradually reversed but other climate changes would continue in their current direction for decades to 
millennia ( high confidence ). For instance, it would take several centuries to millennia for global mean sea level to reverse 
course even under large net negative CO 2 emissions ( high confidence ).

{4.6, 9.6, TS.3.3}
D.1.7  In the five illustrative scenarios, simultaneous changes in CH 4, aerosol and ozone precursor emissions, which also 
contribute to air pollution, lead to a net global surface warming in the near and long term ( high confidence ). In the 
long term, this net warming is lower in scenarios assuming air pollution controls combined with strong and sustained 
CH 4 emissions reductions ( high confidence ). In the low and very low GHG emissions scenarios, assumed reductions in 
anthropogenic aerosol emissions lead to a net warming, while reductions in CH 4 and other ozone precursor emissions 
lead to a net cooling. Because of the short lifetime of both CH 4 and aerosols, these climate effects partially counterbalance 
each other, and reductions in CH 4 emissions also contribute to improved air quality by reducing global surface ozone 
  {6.7, Box TS.7} (Figure SPM.2, Box SPM.1)

D.1.8  Achieving global net zero CO 2 emissions, with anthropogenic CO 2 emissions balanced by anthropogenic removals of 
CO 2, is a requirement for stabilizing CO 2-induced global surface temperature increase. This is different from achieving 
net zero GHG emissions, where metric-weighted anthropogenic GHG emissions equal metric-weighted anthropogenic 
GHG removals. For a given GHG emissions pathway, the pathways of individual GHGs determine the resulting climate 
response,46 whereas the choice of emissions metric47 used to calculate aggregated emissions and removals of different 
GHGs affects what point in time the aggregated GHGs are calculated to be net zero. Emissions pathways that reach and 
sustain net zero GHG emissions defined by the 100-year global warming potential are projected to result in a decline in 
surface temperature after an earlier peak ( high confidence ).
  {4.6, 7.6, Box 7.3, TS.3.3}

D.2   Scenarios with very low or low GHG emissions (SSP1-1.9 and SSP1-2.6) lead within years to discernible effects 
on greenhouse gas and aerosol concentrations and air quality, relative to high and very high GHG emissions 
scenarios (SSP3-7.0 or SSP5-8.5). Under these contrasting scenarios, discernible differences in trends of global 
surface temperature would begin to emerge from natural variability within around 20 years, and over longer 
time periods for many other climatic impact-drivers ( high confidence ). 
   {4.6, 6.6, 6.7, Cross-Chapter Box 6.1, 9.6, 11.2, 11.4, 11.5, 11.6, Cross-Chapter Box 11.1, 12.4, 12.5} (Figure 
SPM.8, Figure SPM.10)
D.2.1  Emissions reductions in 2020 associated with measures to reduce the spread of COVID-19 led to temporary but detectable 
effects on air pollution ( high confidence ) and an associated small, temporary increase in total radiative forcing, primarily

due to reductions in cooling caused by aerosols arising from human activities ( medium confidence ). Global and regional 
climate responses to this temporary forcing are, however, undetectable above natural variability ( high confidence ). 
Atmospheric CO 2 concentrations continued to rise in 2020, with no detectable decrease in the observed CO 2 growth rate 
(medium confidence ).48 
  {Cross-Chapter Box 6.1, TS.3.3}
D.2.2  Reductions in GHG emissions also lead to air quality improvements. However, in the near term,49 even in scenarios with 
strong reduction of GHGs, as in the low and very low GHG emissions scenarios (SSP1-2.6 and SSP1-1.9), these improvements 
46  A general term for how the climate system responds to a radiative forcing (Glossary).
47  The choice of emissions metric depends on the purposes for which gases or forcing agents are being compared. This Report contains updated emissions metric values and assesses 
new approaches to aggregating gases.

48  For other GHGs, there was insufficient literature available at the time of the assessment to assess detectable changes in their atmospheric growth rate during 2020.
49  Near term: 2021–2040.

31SPM
Summary for Policymakersare not sufficient in many polluted regions to achieve air quality guidelines specified by the World Health Organization 
(high confidence ). Scenarios with targeted reductions of air pollutant emissions lead to more rapid improvements in air 
quality within years compared to reductions in GHG emissions only, but from 2040, further improvements are projected 
in scenarios that combine efforts to reduce air pollutants as well as GHG emissions, with the magnitude of the benefit 
varying between regions ( high confidence ).
  {6.6, 6.7, Box TS.7}.
D.2.3  Scenarios with very low or low GHG emissions (SSP1-1.9 and SSP1-2.6) would have rapid and sustained effects to limit 
human-caused climate change, compared with scenarios with high or very high GHG emissions (SSP3-7.0 or SSP5-8.5), 
but early responses of the climate system can be masked by natural variability. For global surface temperature, differences

in 20-year trends would likely  emerge during the near term under a very low GHG emissions scenario (SSP1-1.9), relative 
to a high or very high GHG emissions scenario (SSP3-7.0 or SSP5-8.5). The response of many other climate variables would 
emerge from natural variability at different times later in the 21st century ( high confidence ).
  {4.6, Cross-Section Box TS.1} (Figure SPM.8, Figure SPM.10)
D.2.4  Scenarios with very low and low GHG emissions (SSP1-1.9 and SSP1-2.6) would lead to substantially smaller changes 
in a range of CIDs36 beyond 2040 than under high and very high GHG emissions scenarios (SSP3-7.0 and SSP5-8.5). 
By the end of the century, scenarios with very low and low GHG emissions would strongly limit the change of several 
CIDs, such as the increases in the frequency of extreme sea level events, heavy precipitation and pluvial flooding, and 
exceedance of dangerous heat thresholds, while limiting the number of regions where such exceedances occur, relative

to higher GHG emissions scenarios ( high confidence ). Changes would also be smaller in very low compared to low GHG 
emissions scenarios, as well as for intermediate (SSP2-4.5) compared to high or very high GHG emissions scenarios ( high 
confidence ).
  {9.6, 11.2, 11.3, 11.4, 11.5, 11.6, 11.9, Cross-Chapter Box 11.1, 12.4, 12.5, TS.4.3}

38
TSIntroduction
The Working Group I (WGI) contribution to the Intergovernmental 
Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) assesses the physical science basis of climate change. As part of 
that contribution, this Technical Summary (TS) is designed to bridge 
between the comprehensive assessment of the WGI Chapters and its Summary for Policymakers (SPM). It is primarily built from the Executive Summaries of the individual chapters and Atlas and provides a synthesis of key findings based on multiple lines of 
evidence (e.g., analyses of observations, models, paleoclimate 
information and understanding of physical, chemical and biological processes and components of the climate system). All the findings and figures here are supported by and traceable to the underlying 
chapters, with relevant chapter sections indicated in curly brackets.
Throughout this Technical Summary, key assessment findings are

reported using the IPCC calibrated uncertainty language (Chapter 1, Box 1.1). Two calibrated approaches are used to communicate the 
degree of certainty in key findings, which are based on author teams’ 
evaluations of underlying scientific understanding:
1)Confidence1 is a qualitative measure of the validity of a finding,
based on the type, amount, quality and consistency of evidence(e.g., data, mechanistic understanding, theory, models, expert
judgment) and the degree of agreement.
2)Likelihood2 provides a quantified measure of confidence in
a  finding expressed probabilistically (e.g., based on statistical
analysis of observations or model results, or both, and expert
judgement by the author team or from a formal quantitative
survey of expert views, or both).
Where there is sufficient scientific confidence, findings can also be 
formulated as statements of fact without uncertainty qualifiers.

Throughout IPCC reports, the calibrated language is clearly identified by being typeset in italics.
The context and progress in climate science (Section TS.1) is followed 
by a Cross-Section Box TS.1 on global surface temperature change. 
Section TS.2 provides information about past and future large-scale changes in all components of the climate system. Section 
1 In this Technical Summary, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of 
confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different 
confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Chapter 1, Box 1.1 for more details).

2 In this Technical Summary, the following terms are used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, 
about as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, exceptionally unlikely 0–1%. Additional terms (extremely likely: 95–100%, more likely than not >50–100%, and extremely 
unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely (see Chapter 1, Box 1.1 for more details). Throughout the WGI report and unless stated 
otherwise, uncertainty is quantified using 90% uncertainty intervals. The 90% uncertainty interval, reported in square brackets [x to y], is estimated to have a 90% likelihood of covering the value 
that is being estimated. The range encompasses the median value, and there is an estimated 10% combined likelihood of the value being below the lower end of the range (x) and above its upper

end (y). Often, the distribution will be considered symmetric about the corresponding best estimate, but this is not always the case. In this Report, an assessed 90% uncertainty interval is referred 
to as a ‘very likely range’. Similarly, an assessed 66% uncertainty interval is referred to as a ‘ likely range’.
3 T he regional traceback matrices that provide the location of the assessment findings synthesized in Section TS.4 are in the Supplementary Material (SM) of Chapter 10.
4 Data archive is av ailable at https://catalogue.ceda.ac.uk/uuid/3234e9111d4f4354af00c3aaecd879b7 .
5 https://interactive-atlas.ipcc.ch/
6 T he AR6 figures use one of the following approaches. For observations, the absence of ‘x’ symbols shows areas with statistical significance, while the presence of ‘x’ indicates non-significance.

For model projections, the method offers two approaches with varying complexity. In the simple approach, high agreement (≥80%) is indicated with no overlay, and diagonal lines (///) show low 
agreement (<80%); In the advanced approach, areas with no overlay display robust signal (≥66% of models show change greater than the variability threshold and ≥80% of all models agree on 
the sign of change), reverse diagonal lines (\\\) show no robust signal, and crossed lines show conflicting signals (i.e., significant change but low agreement). Cross-Chapter Box Atlas.1 provides 
more information on the AR6 method for visualizing robustness and uncertainty on maps.TS.3 summarizes knowledge and understanding of climate forcings, 
feedbacks and responses. Infographic TS.1 uses a storyline approach to integrate findings on possible climate futures. Finally, Section TS.4 
provides a synthesis of climate information at regional scales.
3 The

list of acronyms used in the WGI Report is in Annex VIII.
Text at the beginning of a section presented in dark blue 
with a blue vertical bar at the left, as shown here, provides a summary of the findings discussed in that section.
The AR6 WGI Report promotes best practices in traceability 
and reproducibility, including through adoption of the Findable, 
Accessible, Interoperable, and Reusable (FAIR) principles for scientific data. Each chapter has a data table (in its Supplementary Material) documenting the input data and code used to generate its figures 
and tables. In addition, a collection of data and code from the report 
has been made freely-available online via long-term archives.
4
These FAIR principles are central to the WGI Interactive Atlas5, an 
online tool that complements the WGI Report by providing flexible spatial and temporal analyses of past, observed and projected 
climate change information. It comprises a regional information

component that supports many of the chapters of the Report and a regional synthesis component that supports the Technical Summary and Summary for Policymakers.
Regarding the representation of robustness and uncertainty in maps, 
the method chosen for the AR6
6 differs from the method used in the 
Fifth Assessment Report (AR5). This choice is based on new research on the visualization of uncertainty and on user surveys.

39
Technical SummaryTSBox TS.1 | Core Concepts Central to This Report
This box provides short descriptions of key concepts that are relevant to the AR6 WGI assessment, with a focus on their use in the 
Technical Summary and the Summary for Policymakers. The Glossary (Annex VII) includes more information on these concepts along 
with definitions of many other important terms and concepts used in this Report.
Characteristics of Climate Change Assessment
Global warming: Global warming refers to the change of global surface temperature relative to a baseline depending upon the 
application. Specific global warming levels, such as 1.5°C, 2°C, 3°C or 4°C, are defined as changes in global surface temperature 
relative to the years 1850–1900 as the baseline (the earliest period of reliable observations with sufficient geographic coverage). They 
are used to assess and communicate information about global and regional changes, linking to scenarios and used as a common basis

for Working Group II (WGII) and Working Group III (WGIII) assessments. (Section TS.1.3, Cross-Section Box TS.1) {1.4.1, 1.6.2, 4.6.1, 
Cross-Chapter Boxes 1.5, 2.3, 11.1, and 12.1, Atlas Sections 3–11, Glossary}
Emergence:  Emergence refers to the experience or appearance of novel conditions of a particular climate variable in a given region. 
This concept is often expressed as the ratio of the change in a climate variable relative to the amplitude of natural variations of that 
variable (often termed a ‘signal-to-noise’ ratio, with emergence occurring at a defined threshold of this ratio). Emergence can be 
expressed in terms of a time or a global warming level  at which the novel conditions appear and can be estimated using observations 
or model simulations. (Sections TS.1.2.3 and TS.4.2) {1.4.2, FAQ 1.2, 7.5.5, 10.3, 10.4, 12.5.2, Cross-Chapter Box Atlas.1, Glossary}

Cumulative carbon dioxide (CO 2) emissions:  The total net amount of CO 2 emitted into the atmosphere as a result of human 
activities. Given the nearly linear relationship between cumulative CO 2 emissions and increases in global surface temperature, 
cumulative CO 2 emissions are relevant for understanding how past and future CO 2 emissions affect global surface temperature. 
A related term – remaining carbon budget – is used to describe the total net amount of CO 2 that could be released in the future by 
human activities while keeping global warming to a specific global warming level, such as 1.5°C, taking into account the warming 
contribution from non-CO 2 forcers as well. The remaining carbon budget is expressed from a recent specified date, while the total 
carbon budget is expressed starting from the pre-industrial period. (Sections TS.1.3 and TS.3.3) {1.6.3, 5.5, Glossary}

Net zero CO 2 emissions:  A condition that occurs when the amount of CO 2 emitted into the atmosphere by human activities equals 
the amount of CO 2 removed from the atmosphere by human activities over a specified period of time. Net negative CO 2 emissions 
occur when anthropogenic removals exceed anthropogenic emissions. (Section TS.3.3) {Box 1.4, Glossary}
Human Influence on the Climate System
Earth’s energy imbalance:  In a stable climate, the amount of energy that Earth receives from the Sun is approximately in balance 
with the amount of energy that is lost to space in the form of reflected sunlight and thermal radiation. ‘Climate drivers’, such as an 
increase in greenhouse gases or aerosols, interfere with this balance, causing the system to either gain or lose energy. The strength 
of a climate driver is quantified by its effective radiative forcing (ERF), measured in W  m–2. Positive ERF  leads to warming, and

negative ERF leads to cooling. That warming or cooling in turn can change the energy imbalance through many positive (amplifying) 
or negative (dampening) climate feedbacks. (Sections TS.2.2, TS.3.1 and TS.3.2) {2.2.8, 7.2, 7.3, 7.4, Box 7.1, Box 7.2, Glossary}
Attribution:  Attribution is the process of evaluating the relative contributions of multiple causal factors to an observed change in 
climate variables (e.g., global surface temperature, global mean sea level), or to the occurrence of extreme weather or climate-related 
events. Attributed causal factors include human activities (such as increases in greenhouse gas concentration and aerosols, or land-use 
change) or natural external drivers (solar and volcanic influences), and in some cases internal variability. (Sections TS.1.2.4 and TS.2, 
Box TS.10) {Cross-Working Group Box: Attribution in Chapter 1; 3.5; 3.8; 10.4; 11.2.4; Glossary}

Committed change, long-term commitment: Changes in the climate system, resulting from past, present and future human 
activities, which will continue long into the future (centuries to millennia) even with strong reductions in greenhouse gas emissions. 
Some aspects of the climate system, including the terrestrial biosphere, the deep ocean and the cryosphere, respond much more slowly 
than surface temperatures to changes in greenhouse gas concentrations. As a result, there are already substantial committed changes 
associated with past greenhouse gas emissions. For example, global mean sea level will continue to rise for thousands of years, even 
if future CO 2 emissions are reduced to net zero and global warming halted, as excess energy due to past emissions continues to 
propagate into the deep ocean and as glaciers and ice sheets continue to melt. (Section TS.2.1, Box TS.4, Box TS.9) {1.2.1, 1.3, Box 1.2, 
Cross-Chapter Box 5.3}

40
TSBox TS.1 (continued)
Climate Information for Regional Climate Change and Risk Assessment 
Distillation:  The process of synthesizing information about climate change from multiple lines of evidence obtained from a variety 
of sources, taking into account user context and values. It leads to an increase in the usability, usefulness and relevance of climate 
information, enhances stakeholder trust, and expands the foundation of evidence used in climate services. It is particularly relevant in 
the context of co-producing regional-scale climate information to support decision-making. (Section TS.4.1, Box TS.11) {10.1, 10.5, 12.6}
(Climate change) risk:  The concept of risk is a key aspect of how the IPCC assesses and communicates to decision-makers about the 
potential for adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with

such systems. In the context of climate change, risks can arise from potential impacts of climate change as well as human responses to 
climate change. WGI contributes to the common IPCC risk framing through the assessment of relevant climate information, including 
climatic impact-drivers and low-likelihood, high-impact outcomes. (Sections TS.1.4 and TS.4.1, Box TS.4) {Cross-Chapter Boxes 1.3 and 
12.1, Glossary}
Climatic impact-drivers:  Physical climate system conditions (e.g., means, events, extremes) that can be directly connected with 
having impacts on human or ecological systems are described as ‘climatic impact-drivers’ (CIDs) without anticipating whether their 
impacts are detrimental (i.e., as for hazards in the context of climate change risks) or provide potential opportunities. A range of indices 
may capture the sector- or application-relevant characteristics of a climatic impact-driver and can reflect exceedances of identified

tolerance thresholds. (Sections TS.1.4 and TS.4.3) {12.1–12.3, FAQ 12.1, Glossary}
Storylines:  The term storyline is used both in connection to scenarios (related to a future trajectory of emissions or socio-economic 
developments) or to describe plausible trajectories of weather and climate conditions or events, especially those related to high levels 
of risk. Physical climate storylines are introduced in AR6 to explore uncertainties in climate change and natural climate variability, to 
develop and communicate integrated and context-relevant regional climate information, and to address issues with deep uncertainty7, 
including low-likelihood, high-impact outcomes . (Section TS.1.4, Box TS.3, Infographic TS.1) {1.4.4, Box 10.2, Glossary}
Low-likelihood, high impact outcomes:  Outcomes/events whose probability of occurrence is low or not well known (as in the context

of deep uncertainty) but whose potential impacts on society and ecosystems could be high. To better inform risk assessment and 
decision-making, such low-likelihood outcomes are considered if they are associated with very large consequences and may therefore 
constitute material risks, even though those consequences do not necessarily represent the most likely outcome. (Section TS.1.4, 
Box TS.3, Figure TS.6) {1.4.4, 4.8, Cross Chapter Box 1.3, Glossary}
7 Although not a core concept of the WGI Report, deep uncertainty is used in the Technical Summary in the following sense: ‘A situation of deep uncertainty exists when experts or stakeholders do 
not know or cannot agree on: (1) appropriate conceptual models that describe relationships among key driving forces in a system; (2) the probability distributions used to represent uncertainty

about key variables and parameters; and/or (3) how to weigh and value desirable alternative outcomes’ (Lempert et al., 2003). Lempert, R. J., Popper, S. W., and Bankes, S. C. (2003).  Shaping the 
next one hundred years: New methods for quantitative long-term strategy analysis (MR-1626-RPC) . Santa Monica, CA: The RAND Pardee Center.
8 The assessment covers scientific literature accepted for publication by 31 January 2021.As part of the AR6 cycle, the IPCC produced three Special Reports 
in 2018 and 2019: the Special Report on Global Warming of 
1.5°C (SR1.5), the Special Report on the Ocean and Cryosphere in 
a  Changing Climate (SROCC), and the Special Report on Climate 
Change and Land (SRCCL).
The AR6 WGI Report provides a full and comprehensive assessment 
of the physical science basis of climate change that builds on the 
previous assessments and these Special Reports and considers new 
information and knowledge from the recent scientific literature8,

including longer observational datasets and new scenarios and 
model results.
The structure of the AR6 WGI Report is designed to enhance the 
visibility of knowledge developments and to facilitate the integration 
of multiple lines of evidence, thereby improving confidence in findings. The Report has been peer-reviewed by the scientific community 
and governments (Annex X provides the Expert Reviewer list). The 
substantive introduction provided by Chapter 1 is followed by a first 
set of chapters dedicated to large-scale climate knowledge (Chapters 
2–4), which encompasses observations and paleoclimate evidence, 
causes of observed changes, and projections; these are complemented 
by Chapter 11 for large-scale changes in extremes. The second set of 
chapters (Chapters 5–9) is orientated around the understanding of 
key climate system components and processes, including the global 
cycles of carbon, energy and water; short-lived climate forcers and their

link to air quality; and the ocean, cryosphere and sea level change. 
The last set of chapters (Chapters 10–12 and the Atlas) is dedicated to 
the assessment and distillation of regional climate information from 
multiple lines of evidence at sub-continental to local scales (including 
urban climate), with a focus on recent and projected regional changes 
in mean climate, extremes, and climatic impact-drivers. The new online

41
Technical SummaryTSInteractive Atlas allows users to interact in a flexible manner through 
maps, time series and summary statistics with climate information for 
a set of updated WGI reference regions. The Report also includes 34 
Frequently Asked Questions and answers for the general public ( https://
www.ipcc.ch/report/ar6/wg1/faqs ).
Together, this Technical Summary and the underlying chapters aim at 
providing a comprehensive picture of knowledge progress since the 
WGI contribution to AR5. Multiple lines of scientific evidence confirm 
that the climate is changing due to human influence. Important 
advances in the ability to understand past, present and possible 
future changes should result in better-informed decision-making.
Some of the new results and main updates to key findings in this 
Report compared to AR5, SR1.5, SRCCL, and SROCC are summarized 
below. Relevant Technical Summary sections with further details are 
shown in parentheses after each bullet point.

Selected Updates and/or New Results since AR5
• Human influence9 on the climate system is now an 
established fact: The Fourth Assessment Report (AR4) stated 
in 2007 that ‘warming of the climate system is unequivocal’, and 
AR5 stated in 2013 that ‘human influence on the climate system 
is clear’. Combined evidence from across the climate system 
strengthens this finding. It is unequivocal that the increase of 
CO 2, methane (CH 4) and nitrous oxide (N 2O) in the atmosphere 
over the industrial era is the result of human activities and that 
human influence is the main driver10 of many changes observed 
across the atmosphere, ocean, cryosphere and biosphere. 
(Sections TS.1.2, TS.2.1 and TS.3.1)
• Observed global warming to date: A combination of 
improved observational records and a series of very warm years 
since AR5 have resulted in a substantial increase in the estimated 
level of global warming to date. The contribution of changes in

observational understanding alone between AR5 and AR6 leads 
to an increase of about 0.1°C in the estimated warming since 
1850–1900. For the decade 2011–2020, the increase in global 
surface temperature since 1850–1900 is assessed to be 1.09 
[0.95 to 1.20] °C.11 Estimates of crossing times of global warming 
levels and estimates of remaining carbon budgets are updated 
accordingly. (Section TS.1.2, Cross-Section Box TS.1)
• Paleoclimate evidence:  The AR5 assessed that many of the 
changes observed since the 1950s are unprecedented over 
decades to millennia. Updated paleoclimate evidence strengthens 
this assessment; over the past several decades, key indicators of 
the climate system are increasingly at levels unseen in centuries 
to millennia and are changing at rates unprecedented in at least 
the last 2000 years. (Box TS.2, Section TS.2)

9 Human influence on the climate system refers to human-driven activities that lead to changes in the climate system due to perturbations of Earth’s energy budget (also called anthropogenic 
forcing). Human influence results from emissions of greenhouse gases, aerosols and tropospheric ozone precursors, ozone-depleting substances, and land-use change.
10 Throughout this Technical Summary, ‘main driver’ means responsible for more than 50% of the change.
11 Throughout the WGI report and unless stated otherwise, uncertainty is quantified using 90% uncertainty intervals. The 90% uncertainty interval, reported in square brackets [x to y], is estimated 
to have a 90% likelihood of covering the value that is being estimated. The range encompasses the median value and there is an estimated 10% combined likelihood of the value being below the

lower end of the range (x) and above its upper end (y). Often the distribution will be considered symmetric about the corresponding best estimate, but this is not always the case. In this Report, 
an assessed 90% uncertainty interval is referred to as a ‘ very likely range’. Similarly, an assessed 66% uncertainty interval is referred to as a ‘ likely range’.• Updated assessment of recent warming: The AR5 reported 
a smaller rate of increase in global mean surface temperature over 
the period 1998–2012 than the rate calculated since 1951. Based 
on updated observational datasets showing a larger trend over 
1998–2012 than earlier estimates, there is now high confidence  
that the observed 1998–2012 global surface temperature trend 
is consistent with ensembles of climate model simulations, 
and there is now very high confidence that the slower rate of 
global surface temperature increase observed over this period 
was a temporary event induced by internal and naturally forced

variability that partly offset the anthropogenic surface warming 
trend over this period, while heat uptake continued to increase 
in the ocean. Since 2012, strong warming has been observed, 
with the past five years (2016–2020) being the hottest five-
year period in the instrumental record since at least 1850 ( high 
confidence ). (Section TS.1.2, Cross-Section Box TS.1)
• Magnitude of climate system response:  In this Report, it has 
been possible to reduce the long-standing uncertainty ranges 
for metrics that quantify the response of the climate system to 
radiative forcing, such as the equilibrium climate sensitivity (ECS) 
and the transient climate response (TCR), due to substantial 
advances (e.g., a 50% reduction in the uncertainty range of cloud 
feedbacks) and improved integration of multiple lines of evidence, 
including paleoclimate information. Improved quantification 
of ERF , the climate system radiative response, and the observed

energy increase in the Earth system over the past five decades 
demonstrate improved consistency between independent 
estimates of climate drivers, the combined climate feedbacks, and 
the observed energy increase relative to AR5. (Section TS.3.2)
• Improved constraints on projections of future climate 
change:  For the first time in an IPCC report, the assessed future 
change in global surface temperature is consistently constructed 
by combining scenario-based projections (which AR5 focused 
on) with observational constraints based on past simulations 
of warming as well as the updated assessment of ECS and TCR. 
In addition, initialized forecasts have been used for the period 
2019–2028. The inclusion of these lines of evidence reduces 
the assessed uncertainty for each scenario. (Section TS.1.3, 
Cross-Section Box TS.1)
• Air quality: The AR5 assessed that projections of air quality 
are driven primarily by precursor emissions, including CH 4. New

scenarios explore a diversity of future options in air pollution 
management. The AR6 reports rapid recent shifts in the 
geographical distribution of some of these precursor emissions, 
confirms the AR5 finding, and shows higher warming effects 
of short-lived climate forcers in scenarios with the highest air 
pollution. (Sections TS.1.3 and TS.2.2, Box TS.7)
• Effects of short-lived climate forcers on global warming: 
The AR5 assessed the radiative forcing for emitted compounds. 
The AR6 has extended this by assessing the emissions-based ERFs

42
TSalso accounting for aerosol–cloud interactions. The best estimates 
of ERF attributed to sulphur dioxide (SO 2) and CH 4 emissions are 
substantially greater than in AR5, while that of black carbon is 
substantially reduced. The magnitude of uncertainty in the ERF 
due to black carbon emissions has also been reduced relative to 
AR5. (Section TS.3.1)
• Global water cycle: The AR5 assessed that anthropogenic 
influences have likely  affected the global water cycle since 
1960. The dedicated chapter in AR6 (Chapter 8) concludes with 
high confidence  that human-caused climate change has driven 
detectable changes in the global water cycle since the mid-20th 
century, with a better understanding of the response to aerosol 
and greenhouse gas changes. The AR6 further projects with high 
confidence  an increase in the variability of the water cycle in most 
regions of the world and under all emissions scenarios. (Box TS.6)

• Extreme events:  The AR5 assessed that human influence 
had been detected in changes in some climate extremes. 
A  dedicated chapter in AR6 (Chapter  11) concludes that it is 
now an established fact that human-induced greenhouse gas 
of some weather and climate extremes since 1850, in particular 
for temperature extremes. Evidence of observed changes and 
attribution to human influence has strengthened for several types 
of extremes since AR5, in particular for extreme precipitation, 
droughts, tropical cyclones and compound extremes (including 
fire weather). (Sections TS.1.2 and TS.2.1, Box TS.10)
Selected Updates and/or New Results Since AR5 and SR1.5
• Timing of crossing 1.5°C global warming:  Slightly different 
approaches are used in SR1.5 and in this Report. SR1.5 assessed 
a likely  range of 2030 to 2052 for reaching a global warming 
level of 1.5°C (for a 30-year period), assuming a continued,

constant rate of warming. In AR6, combining the larger estimate 
of global warming to date and the assessed climate response to 
all considered scenarios, the central estimate of crossing 1.5°C of 
global warming (for a 20-year period) occurs in the early 2030s, in 
the early part of the likely  range assessed in SR1.5, assuming no 
major volcanic eruption. (Section TS.1.3, Cross-Section Box TS.1)
• Remaining carbon budgets:  The AR5 had assessed the 
transient climate response to cumulative emissions of CO 2 to be 
likely  in the range of 0.8°C to 2.5°C per 1000 GtC (1 Gigatonne 
of carbon, GtC, = 1 Petagram of carbon, PgC, = 3.664 Gigatonnes 
of carbon dioxide, GtCO 2), and this was also used in SR1.5. The 
assessment in AR6, based on multiple lines of evidence, leads to 
a narrower likely  range of 1.0°C–2.3°C per 1000 GtC. This has 
been incorporated in updated estimates of remaining carbon 
budgets (see Section TS.3.3.1), together with methodological

improvements and recent observations. (Sections TS.1.3 
and TS.3.3)
12 Increased stratification reduces the vertical exchange of heat, salinity, oxygen, carbon and nutrients. Stratification is an important indicator for ocean circulation.• Effect of short-lived climate forcers on global warming 
in coming decades:  The SR1.5 stated that reductions in 
emissions of cooling aerosols partially offset greenhouse gas 
mitigation effects for two to three decades in pathways limiting 
global warming to 1.5°C. The AR6 assessment updates the AR5 
assessment of the net cooling effect of aerosols and confirms 
that changes in short-lived climate forcers will very likely  cause 
further warming in the next two decades across all scenarios. 
(Section TS.1.3, Box TS.7)
• COVID-19:  Temporary emissions reductions in 2020 associated 
with COVID-19 containment led to small and positive net radiative 
effect (warming influence). However, global and regional climate

responses to this forcing are undetectable above internal climate 
variability due to the temporary nature of emissions reductions. 
(Section TS.3.3)
Selected Updates and/or New Results Since AR5, SRCCL 
and SROCC
• Atmospheric concentration of methane:  The SRCCL reported 
a resumption of atmospheric CH 4 concentration growth since 
2007. The AR6 reports a faster growth over 2014–2019 and 
assesses growth since 2007 to be largely driven by emissions 
from the fossil fuels and agriculture (dominated by livestock) 
sectors. (Section TS.2.2)
• Land and ocean carbon sinks: The SRCCL assessed that the 
persistence of the land carbon sink is uncertain due to climate 
change. The AR6 finds that land and ocean carbon sinks are 
projected to continue to grow until 2100 with increasing 
atmospheric concentrations of CO 2, but the fraction of emissions 
taken up by land and ocean is expected to decline as the CO 2 
concentration increases, with a much larger uncertainty range for

the land sink. The AR5, SR1.5 and SRCCL assessed carbon dioxide 
removal options and scenarios. The AR6 finds that the carbon 
cycle response is asymmetric for pulse emissions or removals, 
which means that CO 2 emissions would be more effective at 
raising atmospheric CO 2 than CO 2 removals are at lowering 
atmospheric CO 2. (Section TS.3.3, Box TS.5)
• Ocean stratification increase12 : Refined analyses of available 
observations in the AR6 lead to a reassessment of the rate of 
increase of the global stratification in the upper 200 m to be double 
that estimated in SROCC from 1970 to 2018. (Section TS.2.4)
• Projected ocean oxygen loss:  Future subsurface oxygen 
decline in new projections assessed in WGI AR6 is substantially 
greater in 2080–2099 than assessed in SROCC. (Section TS.2.4)
• Ice loss from glaciers and ice sheets: Since SROCC, globally 
resolved glacier changes have improved estimates of glacier mass 
loss over the past 20 years, and estimates of the Greenland and

Antarctic Ice Sheet loss have been extended to 2020. (Section  TS.2.5)
• Observed global mean sea level change: new observation-
based estimates published since SROCC lead to an assessed sea 
level rise estimate from 1901 to 2018 that is now consistent with 
the sum of individual components and consistent with closure of 
the global energy budget. (Box TS.4)

43
Technical SummaryTS• Projected global mean sea level change: The AR6 projections 
of global mean sea level are based on projections from ocean 
thermal expansion and land ice contribution estimates, which are 
consistent with the assessed ECS and assessed changes in global 
surface temperature. They are underpinned by new land ice model 
intercomparisons and consideration of processes associated with 
low confidence to characterize the deep uncertainty in future ice 
loss from Antarctica. The AR6 projections based on new models 
and methods are broadly consistent with SROCC findings. 
(Box TS.4)
13 Several baselines or reference periods are used consistently throughout this Report. Baseline refers to a period against which anomalies (i.e., differences from the average value for the baseline 
period) are calculated. Examples include the 1750 baseline (used for anthropogenic radiative forcings), the 1850–1900 baseline (an approximation for pre-industrial global surface temperature

from which global warming levels are calculated) and the 1995–2014 baseline (used for many climate model projections). A reference period indicates a time period over which various statistics 
are calculated (e.g., the near-term reference period, 2021–2040). Paleo reference periods are listed in Box TS.2. {1.4.1, Cross-Chapter Boxes 1.2 and 2.1}
14 Please refer to Section TS.1.3.1 for an overview of the climate change scenarios used in this Report.TS.1 A Changing Climate
This section introduces the assessment of the physical science basis of 
climate change in the AR6 and presents the climate context in which 
this assessment takes place, recent progress in climate science and 
the relevance of global and regional climate information for impact 
and risk assessments. The future emissions scenarios and global 
warming levels, used to integrate assessments across this Report, 
are introduced and their applications for future climate projections

are  briefly addressed. Paleoclimate science provides a long-term 
context for observed climate change of the past 150 years and the 
projected changes in the 21st century and beyond (Box TS.2). The 
assessment of past, current and future global surface temperature 
changes relative to the standard baselines and reference periods13 
used throughout this Report is summarized in Cross-Section Box TS.1.
TS1.1 Context of a Changing Climate
This Report assesses new scientific evidence relevant 
for a world whose climate system is rapidly changing, 
overwhelmingly due to human influence. The five IPCC 
assessment cycles since 1990 have comprehensively and 
consistently laid out the rapidly accumulating evidence 
of a changing climate system, with the Fourth Assessment 
Report in 2007 being the first to conclude that warming 
of the climate system is unequivocal. Sustained changes 
have been documented in all major elements of the climate

system: the atmosphere, land, cryosphere, biosphere and 
ocean (Section TS.2). Multiple lines of evidence indicate 
the recent large-scale climatic changes are unprecedented 
in a multi-millennial context and that they represent 
a  millennial-scale commitment for the slow-responding 
elements of the climate system, resulting in continued 
worldwide loss of ice, increase in ocean heat content, sea 
level rise and deep ocean acidification (Box TS.2; Section 
TS.2). {1.2.1, 1.3, Box 1.2, 2.2, 2.3, Figure 2.34, 5.1, 5.3, 9.2, 
9.4–9.6, Appendix 1.A}
Earth’s climate system has evolved over many millions of years, and 
evidence from natural archives provides a long-term perspective on 
observed changes and projected changes over the coming centuries. 
These reconstructions of past climate also show that atmospheric 
CO 2 concentrations and global surface temperature are strongly 
coupled (Figure TS.1), based on evidence from a variety of proxy

records over multiple time scales (Box TS.2, Section TS.2). Levels of 
global warming (see Core Concepts Box) that have not been seen 
in millions of years could be reached by 2300, depending on the 
emissions pathway that is followed (Section TS.1.3). For example, 
there is medium confidence  that, by 2300, an intermediate scenario14 
used in this Report leads to global surface temperatures of [2.3°C 
to 4.6°C] higher than 1850–1900, similar to the mid-Pliocene Warm

44
TSPeriod [2.5°C to 4°C], about 3.2 million years ago, whereas the high 
CO 2 emissions scenario SSP5-8.5 leads to temperatures of [6.6°C 
to 14.1°C] by 2300, which overlaps with the Early Eocene Climate 
Optimum [10°C to 18°C], about 50 million years ago. {Cross-Chapter 
Boxes 2.1 and 2.4, 2.3.1, 4.3.1.1, 4.7.1.2, 7.4.4.1}
Understanding of the climate system’s fundamental elements is 
robust and well established.  Scientists in the 19th century identified 
the major natural factors influencing the climate system. They 
also hypothesized the potential for anthropogenic climate change 
due to CO 2 emitted by combustion of fossil fuels (petroleum, 
coal, natural gas). The principal natural drivers of climate change, 
including changes in incoming solar radiation, volcanic activity, 
orbital cycles and changes in global biogeochemical cycles, have been studied systematically since the early 20th century. Other

major anthropogenic drivers, such as atmospheric aerosols (fine 
solid particles or liquid droplets), land-use change and non-CO 2 
greenhouse gases, were identified by the 1970s. Since systematic 
scientific assessments began in the 1970s, the influence of human 
activities on the warming of the climate system has evolved from 
theory to established fact (see also Section TS.2). The evidence for 
human influence on recent climate change strengthened from the 
IPCC First Assessment Report in 1990 to the IPCC Fifth Assessment 
Report in 2013/14, and is now even stronger in this assessment 
(Sections TS.1.2.4 and TS.2). Changes across a greater number of 
climate system components, including changes in regional climate 
and extremes can now be attributed to human influence (see 
Sections TS.2 and TS.4). {1.3.1–1.3.5, 3.1, 11.2, 11.9}
1850 2000 2150 2300
Year CE800 600 400 200 0
Thousands of years9 7 5 3 1 605040302010
Millions of years2002000Atmospheric CO2 concentration (ppm)

-5051015Global surface temperature change (°C)
(relative to 1850–1900) Atmospheric CO 2 concentration and global surface temperature change 
during the last 60 million years and projections for the next 300 years
1000
400
024681012141618
Temperature (°C) 
SSP5-8.5
2300
2100
SSP1-2.6
2300
2100 SSP1-2.6SSP2-4.5SSP5-8.5SSP1-2.6SSP2-4.5SSP5-8.5
early Eocene
 mid-Pliocene
 2020Figure TS.1 |  Changes in atmospheric CO 2 and global surface temperature (relative to 1850–1900) from the deep past to the next 300 years.  The intent of 
this figure is to show that CO 2 and temperature covary, both in the past and into the future, and that projected CO 2 and temperatures are similar to those only from many millions 
of years ago. CO 2 concentrations from millions of years ago are reconstructed from multiple proxy records (grey dots are data from Section 2.2.3.1, Figure 2.3 shown with cubic-

spline fit). CO 2 levels for the last 800,000 years through the mid-20th century are from air trapped in polar ice; recent values are from direct air measurements. Global surface 
temperature prior to 1850 is estimated from marine oxygen isotopes, one of multiple sources of evidence used to assess paleo temperatures in this Report. Temperature of the 
past 170 years is the AR6 assessed mean. CO 2 levels and global surface temperature change for the future are shown for three Shared Socio-economic Pathway (SSP) scenarios 
through 2300 CE, using Earth system model emulators calibrated to the assessed global surface temperatures. Their smooth trajectories do not account for inter-annual to 
inter-decadal variability, including transient response to potential volcanic eruptions. Global maps for two paleo reference periods are based on Coupled Model Intercomparison

Project Phase 6 (CMIP6) and pre-CMIP6 multi-model means, with site-level proxy data for comparison (squares and circles are marine and terrestrial, respectively). The map 
for 2020 is an estimate of the total observed warming since 1850–1900. Global maps at right show two SSP scenarios at 2100 (2081–2100) and at 2300 (2281–2300; map 
from CMIP6 models; temperature assessed in 4.7.1). A brief account of the major climate forcings associated with past global temperature changes is in Cross-Chapter Box 2.1. 
(Section TS.1.3, Figure TS.9, Cross-Section Box TS.1, Box TS.2) {1.2.1.2; Figures 1.14 and 1.5; 2.2.3; 2.3.1.1; 2.3.1.1.1; Figures 2.4 and 2.5; Cross-Chapter Box 2.1, Figure 1; 
4.5.1; 4.7.1; Cross-Chapter Box 4.1; Cross-Chapter Box 7.1; Figure 7.13}

45
Technical SummaryTSBox TS.2 | Paleoclimate
Paleoclimate evidence is integrated within multiple lines of evidence across the WGI Report to more fully understand 
the climate system. Paleo evidence extends instrument-based observations of climate variables and climate drivers 
back in time, providing the long-term context needed to gauge the extent to which recent and potential future 
changes are unusual (Section TS.2, Figure TS.1). Pre-industrial climate states complement evidence from climate model 
projections by providing real-world examples of climate characteristics for past global warming levels, with empirical 
evidence for how the slow-responding components of the climate system operate over centuries to millennia – the 
time scale for committed climate change (Core Concepts Box, Box TS.4, Box TS.9). Information about the state of 
the climate system during well-described paleoclimate reference periods helps narrow the uncertainty range in the

overall assessment of Earth’s sensitivity to climate forcing (Section TS.3.2.1). {Cross-Chapter Box 2.1, FAQ 1.3, FAQ 2.1}
Paleoclimate reference periods. Over the long evolution of Earth’s climate, several periods have received extensive research 
attention as examples of distinct climate states and rapid climate transitions (Box TS.2, Figure 1). These paleoclimate reference periods 
represent the present geological era (Cenozoic; past 65 million years) and are used across chapters to help structure the assessment 
of climate changes prior to industrialization. Cross-Chapter Box 2.1 describes the reference periods, along with a brief account of their 
climate forcings, and lists where each is discussed in other chapters. Cross-Chapter Box 2.4 summarizes information on one of the 
reference periods, the mid-Pliocene Warm Period. The Interactive Atlas includes model output from the World Climate Research

Programme Coupled Model Intercomparison Project Phase 6 (CMIP6) for four of the paleoclimate reference periods.
Box TS.2, Figure 1 |  Paleoclimate and recent reference periods, with selected key indicators. The intent of this figure is to list the paleoclimate reference 
periods used in this Report, to summarize three key global climate indicators, and compare CO 2 with global temperature over multiple periods.  (a) Three large-scale 
climate indicators (atmospheric CO 2, global surface temperature relative to 1850–1900, and global mean sea level relative to 1900), based on assessments in 
Chapter 2, with confidence levels ranging from low to very high . (b) Comparison between global surface temperature (relative to 1850–1900) and atmospheric CO 2 
concentration (shown on a log scale) for multiple reference periods (mid-points with 5–95% ranges). {2.2.3, 2.3.1.1, 2.3.3.3, Figure 2.34}

Paleoclimate models and reconstructions.  Climate models that target paleoclimate reference periods have been featured by 
the IPCC since the First Assessment Report. Under the framework of CMIP6-PMIP4 (Paleoclimate Modelling Intercomparison Project), 
new protocols for model intercomparisons have been developed for multiple paleoclimate reference periods. These modelling efforts 
have led to improved understanding of the climate response to different external forcings, including changes in Earth’s orbital and 
plate movements, solar irradiance, volcanism, ice-sheet size and atmospheric greenhouse gases. Likewise, quantitative reconstructions 
of climate variables from proxy records that are compared with paleoclimate simulations have improved as the number of study sites 
and variety of proxy types have expanded, and as records have been compiled into new regional and global datasets. {1.3.2, 1.5.1, 
Cross-Chapter Boxes 2.1 and 2.4}

Global surface temperature. Since AR5, updated climate forcings, improved models, new understanding of the strengths 
and weaknesses of a growing array of proxy records, better chronologies and more robust proxy data products have led to better 
agreement between models and reconstructions. For global surface temperature, the mid-point of the AR6-assessed range and the 
median of the model-simulated temperatures differ by an average of 0.5°C across five reference periods; they overlap within their 
lower 1850–1900Reference period 
(*See Interactive Atlas for climate model output)
Recent past
Approximate pre-industrial
Mid-Holocene*
Last Deglacial Transition
Last Glacial Maximum*
Last Interglacial*
Mid-Pliocene Warm Period*
Early Eocene
Paleocene-Eocene Thermal Maximum
X to Y: very likely  range (caveats in Figure 2.34)
X      Y: start to end of period, with no stated uncertainty
X ~ Y: lowest and highest values, with not stated uncertainty1995–2014 CE
1850–1900 CE

850–1850 CE
6.5–5.5 ka
18–11 ka
23–19 ka
129–116 ka
3.3–3.0 ka
53–49 Ma
55.9–55.7 Ma360
286
278
260
193
188
266
360
1150
900397
296
285
268
271
194
282
420
2500
2000CO 2
(ppm)Temperature
(°C)Sea level 
(m) Age
0.66
–0.15
–0.14
0.2
–5
0.5
2.5
10
10not assessed1.00
+0.11
0.24
1.0
–7
1.5
4.0
18
25to
to
to
to
to
toto
to
~
to
to
to
to
to
to0.15
–0.03
–0.05
–3.5
–120
–134
5
5
700.25 
0.00 
0.03 
+0.5 
–50 
–125 
10 
25 
76to
to
to
to
to
to
to
to
not assessed
 higherThree selected global climate indicators covary across multiple paleoclimate reference periods
(a)(b)
2000
1000
500
200
100Atmospheric CO 2 (ppm)
–10 –5 0 5 10 15 20Early Eocene
Mid-Pliocene Recent past
1850–1900Last Interglacial
Mid-Holocene
Last Glacial Maximum
Global surface temperature (°C) 

46
TSBox TS.2 (continued)
90% ranges in four of five cases, which together span from about 6 [5 to 7]°C colder during the Last Glacial Maximum to about 14 [10 
to 18] °C warmer during the Early Eocene, relative to 1850–1900 (Box TS.2, Figure 2a). Changes in temperature by latitude in response 
to multiple forcings show that polar amplification (stronger warming at high latitudes than the global average) is a prominent feature 
of the climate system across multiple climate states, and the ability of models to simulate this polar amplification in past warm 
climates has improved since AR5 ( high confidence ). Over the past millennium, and especially since about 1300 CE, simulated global 
surface temperature anomalies are well within the uncertainty of reconstructions ( medium confidence ), except for some short periods 
immediately following large volcanic eruptions, for which different forcing datasets disagree (Box TS.2, Figure 2b). {2.3.1.1, 3.3.3.1,

3.8.2.1, 7.4.4.1.2}
Box TS.2, Figure 2 |  Global surface temperature as estimated from proxy records (reconstructed) and climate models (simulated).  The intent of this 
figure is to show the agreement between observations and models of global temperatures during paleo reference periods.  (a) For individual paleoclimate reference 
periods. (b) For the last millennium, with instrumental temperature (AR6 assessed mean, 10-year smoothed). Model uncertainties in (a) and (b) are 5–95% ranges of 
multi-model ensemble means; reconstructed uncertainties are 5–95% ranges ( medium confidence ) of (a) midpoints and (b) multi-method ensemble median. {2.3.1.1, 
Figure 2.34, Figure 3.2c, Figure 3.44}
Equilibrium climate sensitivity.  Paleoclimate data provide evidence to estimate equilibrium climate sensitivity (ECS15) (Section 
TS.3.2.1). In AR6, refinements in paleo data for paleoclimate reference periods indicate that ECS is very likely  greater than 1.5°C and

likely  less than 4.5°C, which is largely consistent with other lines of evidence and helps narrow the uncertainty range of the overall 
assessment of ECS. Some of the CMIP6 climate models that have either high (>5°C) or low (<2°C) ECS also simulate past global 
surface temperature changes outside the range of proxy-based reconstructions for the coldest and warmest reference periods. Since 
AR5, independent lines of evidence, including proxy records from past warm periods and glacial–interglacial cycles, indicate that 
sensitivity to forcing increases as temperature increases (Section TS.3.2.2). {7.4.3.2, 7.5.3, 7.5.6, Table 7.11}
Water cycle.  New hydroclimate reconstructions and model-data comparisons have improved the understanding of the causes 
and effects of long-term changes in atmospheric and ocean circulation, including monsoon variability and modes of variability

(Box TS.13, Section TS.4.2). Climate models are able to reproduce decadal drought variability on large regional scales, including the 
severity, persistence and spatial extent of past megadroughts known from proxy records ( medium confidence ). Some long-standing 
discrepancies remain, however, such as the magnitude of African monsoon precipitation during the early Holocene (the past 11,700 
years), suggesting continuing knowledge gaps. Paleoclimate evidence shows that, in relatively high CO 2 climates such as the Pliocene, 
Walker circulation over the equatorial Pacific Ocean weakens, supporting the high confidence  model projections of weakened Walker 
cells by the end of the 21st century. {3.3.2, 8.3.1.6, 8.4.1.6, 8.5.2.1, 9.2}
15 In this Report, equilibrium climate sensitivity is defined as the equilibrium (steady state) change in the surface temperature following a doubling of the atmospheric carbon dioxide (CO 2) 
-mid-Pliocene

mid-HoloceneLast Interglacial
Year (CE)
Simulated temperature ( °C)
Reconstructed temperature (°C)Recent past
1850–1900Early Eocene
Global surface temperature 
relative to 1850–1900 (°C)Last Glacial Maximum(a)Proxy-based and model-simulated estimates of global surface temperature agree across multiple reference periods
 Global surface temperature relative to 
1850–1900(b)
Observed Reconstructed Simulated
1000                 1200               1400               1600               1800                20001.0
0.8
0.6
0.4
0.2
0
–0.2
–0.4
–0.620
15
10
5
0
–5
–10
–10         –5          0           5          10         15         20

47
Technical SummaryTSBox TS.2 (continued)
Sea level and ice sheets.  Although past and future global warming differ in their forcings, evidence from paleoclimate 
records and modelling show that ice-sheet mass and global mean sea level (GMSL) responded dynamically over multiple millennia 
(high confidence ). This evidence helps to constrain estimates of the committed GMSL response to global warming (Box TS.4). For 
example, under a past global warming levels of around [2.5°C to 4°C] relative to 1850–1900, like during the mid-Pliocene Warm 
Period, sea level was [5 to 25 m] higher than 1900 ( medium confidence ); under past global warming levels of [10°C to 18°C], like 
during the Early Eocene, the planet was essentially ice free ( high confidence ). Constraints from these past warm periods, combined 
with physical understanding, glaciology and modelling, indicate a committed long-term GMSL rise over 10,000 years, reaching about

8 to 13 m for sustained peak global warming of 2°C and up to 28 to 37 m for 5°C, which exceeds the AR5 estimate. {2.3.3.3, 9.4.1.4, 
9.4.2.6, 9.6.2, 9.6.3.5}
Ocean.  Since AR5, better integration of paleo-oceanographic data with modelling along with higher-resolution analyses of 
transient changes have improved understanding of long-term ocean processes. Low-latitude sea surface temperatures at the Last 
Glacial Maximum cooled more than previously inferred, resolving some inconsistencies noted in AR5. This paleo context supports 
the assessment that ongoing increase in ocean heat content (OHC) represents a long-term commitment (see Core Concepts Box), 
essentially irreversible on human time scales ( high confidence ). Estimates of past global OHC variations generally track those of 
sea surface temperatures around Antarctica, underscoring the importance of Southern Ocean processes in regulating deep-ocean

temperatures. Paleoclimate data, along with other evidence of glacial–interglacial changes, show that Antarctic Circumpolar flow 
strengthened and that ventilation of Antarctic Bottom Water accelerated during warming intervals, facilitating release of CO 2 stored in 
the deep ocean to the atmosphere. Paleo evidence suggests significant reduction of deep-ocean ventilation associated with meltwater 
input during times of peak warmth. {2.3.1.1, 2.3.3.1, 9.2.2, 9.2.3.2}
Carbon cycle. Past climate states were associated with substantial differences in the inventories of the various carbon reservoirs, 
including the atmosphere (Section TS.2.2). Since AR5, the quantification of carbon stocks has improved due to the development of 
novel sedimentary proxies and stable-isotope analyses of air trapped in polar ice. Terrestrial carbon storage decreased markedly 
during the Last Glacial Maximum by 300–600 PgC, possibly by 850 PgC when accounting for interactions with the lithosphere and

ocean sediments, a larger reduction than previously estimated, owing to a colder and drier climate. At the same time, the storage of 
remineralized carbon in the ocean interior increased by as much as 750–950 PgC, sufficient to balance the removal of carbon from the 
atmosphere (200 PgC) and terrestrial biosphere reservoirs combined ( high confidence ). {5.1.2.2}
TS.1.2 Progress in Climate Science
TS.1.2.1 Observation-based Products and their Assessments
Observational capabilities have continued to improve and 
expand overall since AR5, enabling improved consistency 
between independent estimates of climate drivers, the 
combined climate feedbacks, and the observed energy 
and sea level increase. Satellite climate records and 
improved reanalyses are used as an additional line of 
evidence for assessing changes at the global and regional 
scales. However, there have also been reductions in some 
observational data coverage or continuity and limited

access to data resulting from data policy issues. Natural 
archives of past climate, such as tropical glaciers, have also 
been subject to losses (in part due to anthropogenic climate 
change). {1.5.1, 1.5.2, 10.2.2}
Earth system observations are an essential driver of progress in our 
understanding of climate change. Overall, capabilities to observe 
the physical climate system have continued to improve and expand.  
and remote-sensing systems. Records from several recently instigated satellite measurement techniques are now long enough to be 
relevant for climate assessments. For example, globally distributed, 
high-vertical-resolution profiles of temperature and humidity in the 
upper troposphere and stratosphere can be obtained from the early 
2000s using global navigation satellite systems, leading to updated 
estimates of recent atmospheric warming. Improved measurements

of ocean heat content, warming of the land surface, ice-sheet mass 
loss and sea level changes allow a better closure of the global 
energy and sea level budgets relative to AR5. For surface and 
balloon-based networks, apparent regional data reductions result 
from a combination of data policy issues, data curation/provision 
challenges, and real cessation of observations, and are to an extent 
counter-balanced by improvements elsewhere. Limited observational 
records of extreme events and spatial data gaps currently limit the 
assessment of some observed regional climate change. {1.5.1, 2.3.2, 
7.2.2, Box 7.2, Cross-Chapter Box 9.1, 9.6.1, 10.2.2, 10.6, 11.2, 12.4}
New paleoclimate reconstructions from natural archives have enabled 
more robust reconstructions of the spatial and temporal patterns of 
past climate changes over multiple time scales (Box TS.2). However, 
paleoclimate archives, such as tropical glaciers and modern natural

archives used for calibration (e.g., corals and trees), are rapidly 
disappearing owing to a host of pressures, including increasing

48
TStemperatures ( high confidence ). Substantial quantities of past 
instrumental observations of weather and other climate variables, 
over both land and ocean, which could fill gaps in existing datasets, 
remain un-digitized or inaccessible. These include measurements of 
temperature (air and sea surface), rainfall, surface pressure, wind 
strength and direction, sunshine amount and many other variables 
dating back into the 19th century. {1.5.1}
Reanalyses combine observations and models (e.g., a numerical 
weather prediction model) using data assimilation techniques to 
provide a spatially complete, dynamically consistent estimate of 
multiple variables describing the evolving climate state. Since AR5, 
new reanalyses have been developed for the atmosphere and the 
ocean with various combinations of increased resolution, extended 
records, more consistent data assimilation and larger availability

of uncertainty estimates. Limitations remain, for example, in how 
reanalyses represent global-scale changes to the water cycle. Regional 
reanalyses use high-resolution, limited-area models constrained by 
regional observations and with boundary conditions from global 
reanalyses. There is high confidence  that regional reanalyses better 
represent the frequencies of extremes and variability in precipitation, 
surface air temperature and surface wind than global reanalyses 
and provide estimates that are more consistent with independent 
observations than dynamical downscaling approaches. {1.5.2, 
10.2.1.2, Annex I}
TS.1.2.2 Climate Model Performance
This report assesses results from climate models 
participating in the Coupled Model Intercomparison Project 
Phase 6 (CMIP6) of the World Climate Research Programme. 
These models include new and better representation of 
physical, chemical and biological processes, as well as 
higher resolution, compared to climate models considered

in previous IPCC Assessment Reports. This has improved 
the simulation of the recent mean state of most large-scale 
indicators of climate change and many other aspects across 
the climate system. Some differences from observations 
remain, for example in regional precipitation patterns. 
Projections of the increase in global surface temperature, 
the pattern of warming, and global mean sea level rise 
from previous IPCC Assessment Reports and other studies 
are broadly consistent with subsequent observations, 
especially when accounting for the difference in radiative 
forcing scenarios used for making projections and the 
radiative forcings that actually occurred. 
The CMIP6 historical simulations assessed in this report 
have an ensemble mean global surface temperature 
change within 0.2°C of the observations over most of the 
historical period, and observed warming is within the very 
likely range of the CMIP6 ensemble. However, some CMIP6

models simulate a warming that is either above or below 
the assessed very likely  range of observed warming. The 
information about how well models simulate past warming, 
as well as other insights from observations and theory, are 
used to assess projections of global warming (see Cross-Section Box TS.1). Increasing horizontal resolution in global 
climate models improves the representation of small-scale 
features and the statistics of daily precipitation ( high 
confidence ). Earth system models, which include additional 
biogeochemical feedbacks, often perform as well as their 
lower-complexity global climate model counterparts, which 
do not account for these additional feedbacks ( medium 
confidence ). {1.3.6, 1.5.3, 3.1, 3.5.1, 3.8.2, 4.3.1, 4.3.4, 7.5, 
8.5.1, 9.6.3.1}
Climate model simulations coordinated and collected as part of the 
World Climate Research Programme’s Coupled Model Intercomparison 
Project Phase 6 (CMIP6), complemented by a range of results from

the previous phase (CMIP5), constitute a key line of evidence 
supporting this Report. The latest generation of CMIP6 models have 
an improved representation of physical processes relative to previous 
generations, and a wider range of Earth system models now represent 
biogeochemical cycles. Higher-resolution models that better capture 
smaller-scale processes are also increasingly becoming available 
for climate change research (Figure TS.2, Panels a and b). Results 
from coordinated regional climate modelling initiatives, such as the 
complement and add value to the CMIP global models, particularly in 
complex topography zones, coastal areas and small islands, as well 
as for extremes.  {1.5.3, 1.5.4, 2.8.2, FAQ 3.3, 6.2.2, 6.4, 6.4.5, 8.5.1, 
10.3.3, Atlas.1.4}
Projections of the increase in global surface temperature and the 
pattern of warming from previous IPCC Assessment Reports and other

studies are broadly consistent with subsequent observations ( limited 
evidence, high agreement ), especially when accounting for the 
difference in radiative forcing scenarios used for making projections 
and the radiative forcings that actually occurred (Figure TS.3). The 
AR5 and SROCC projections of GMSL for the 2007–2018 period have 
been shown to be consistent with observed trends in GMSL and 
regional weighted mean tide gauges. {1.3.6, 9.6.3.1}
For most large-scale indicators of climate change, the simulated 
recent mean climate from CMIP6 models underpinning this 
assessment have improved compared to the CMIP5 models used in 
AR5 ( high confidence ). This is evident from the performance of 18 
simulated atmospheric and land large-scale indicators of climate 
change between the three generations of models (CMIP3, CMIP5, 
and CMIP6) when benchmarked against reanalysis and observational 
data (Figure TS.2, Panel c). Earth system models, characterized by

additional biogeochemical feedbacks, often perform at least as well 
as related, more constrained, lower-complexity models lacking these 
feedbacks ( medium confidence ). {3.8.2, 10.3.3.3}
The CMIP6 multi-model mean global surface temperature change 
from 1850–1900 to 2010–2019 is close to the best estimate of 
the observed warming. However, some CMIP6 models simulate 
a warming that is below or above the assessed  very likely  range. The 
CMIP6 models also reproduce surface temperature variations over 
the past millennium, including the cooling that follows periods of 
intense volcanism ( medium confidence ). For upper air temperature, 
an overestimation of the upper tropical troposphere warming by

49
Technical SummaryTSabout 0.1°C per decade between 1979 and 2014 persists in most 
CMIP5 and CMIP6 models ( medium confidence ), whereas the 
differences between simulated and improved satellite-derived 
estimates of change in global mean temperature through the depth 
of the stratosphere have decreased. {3.3.1}
Some CMIP6 models demonstrate an improvement in how clouds 
are represented. CMIP5 models commonly displayed a negative 
shortwave cloud radiative effect that was too weak in the present 
climate. These errors have been reduced, especially over the 
Southern Ocean, due to a more realistic simulation of supercooled 
liquid droplets with sufficient numbers and an associated increase 
in the cloud optical depth. Because a negative cloud optical depth 
feedback in response to surface warming results from ‘brightening’ 
of clouds via active phase change from ice to liquid cloud particles 
(increasing their shortwave cloud radiative effect), the extratropical

cloud shortwave feedback in CMIP6 models tends to be less negative, 
leading to a better agreement with observational estimates ( medium 
confidence ). CMIP6 models generally represent more processes 
enhancements improve their fitness-for-purpose of simulating 
radiative forcing of aerosol–cloud interactions. {6.4, 7.4.2, FAQ 7.2}
CMIP6 models still have deficiencies in simulating precipitation 
patterns, particularly in the tropical ocean. Increasing horizontal 
resolution in global climate models improves the representation of 
small-scale features and the statistics of daily precipitation ( high 
confidence ). There is high confidence  that high-resolution global, 
regional and hydrological models provide a better representation of 
land surfaces, including topography, vegetation and land-use change,

which can improve the accuracy of simulations of regional changes in 
the terrestrial water cycle. {3.3.2, 8.5.1, 10.3.3, 11.2.3}
There is high confidence  that climate models can reproduce the recent 
observed mean state and overall warming of temperature extremes 
globally and in most regions, although the magnitude of the trends 
may differ. There is high confidence  in the ability of models to capture 
the large-scale spatial distribution of precipitation extremes over land. 
The overall performance of CMIP6 models in simulating the intensity 
and frequency of extreme precipitation is similar to that of CMIP5 
models ( high confidence ). {Cross-Chapter Box 3.2, 11.3.3, 11.4.3}
The structure and magnitude of multi-model mean ocean temperature 
biases have not changed substantially between CMIP5 and CMIP6 
(medium confidence ). Since AR5, there is improved consistency 
between recent observed estimates and model simulations of changes

in upper (<700 m) ocean heat content. The mean zonal and overturning 
circulations of the Southern Ocean and the mean overturning 
circulation of the North Atlantic (AMOC) are broadly reproduced by 
CMIP5 and CMIP6 models. {3.5.1, 3.5.4, 9.2.3, 9.3.2, 9.4.2}
CMIP6 models better simulate the sensitivity of Arctic sea ice area 
to anthropogenic CO 2 emissions, and thus better capture the time 
16 In this Report, transient climate response is defined as the surface temperature response for the hypothetical scenario in which atmospheric carbon dioxide (CO 2) increases at 1% yr–1 from 
pre-industrial to the time of a doubling of atmospheric CO 2 concentration.evolution of the satellite-observed Arctic sea ice loss ( high confidence ). 
The ability to model ice-sheet processes has improved substantially 
since AR5. As a consequence, there is medium confidence in the 
representation of key processes related to surface-mass balance and

retreat of the grounding-line (the junction between a grounded ice 
sheet and an ice shelf, where the ice starts to float) in the absence 
of instabilities. However, there remains low confidence  in simulations 
of ice-sheet instabilities, ice-shelf disintegration and basal melting 
owing to their high sensitivity to both uncertain oceanic forcing and 
uncertain boundary conditions and parameters. {1.5.3, 2.3.2, 3.4.1, 
3.4.2, 3.8.2, 9.3.1, 9.3.2, 9.4.1, 9.4.2}
CMIP6 models are able to reproduce most aspects of the spatial 
structure and variance of the El Niño–Southern Oscillation (ENSO) 
and Indian Ocean Basin and Dipole modes of variability ( medium 
confidence ). However, despite a slight improvement in CMIP6, some 
underlying processes are still poorly represented. Models reproduce 
observed spatial features and variance of the Southern Annular 
Mode (SAM) and Northern Annular Mode (NAM) very well ( high 
confidence ). The summertime SAM trend is well captured, with

CMIP6 models outperforming CMIP5 models ( medium confidence ). 
By contrast, the cause of the NAM trend towards its positive phase 
is not well understood. In the Tropical Atlantic basin, which contains 
the Atlantic Zonal and Meridional modes, major biases in modelled 
mean state and variability remain. Model performance is limited in 
reproducing sea surface temperature anomalies for decadal modes 
of variability, despite improvements from CMIP5 to CMIP6 ( medium 
confidence ) (see also Section TS.1.4.2.2, Table TS.4). {3.7.3–3.7.7}
Earth system models (ESMs) simulate globally averaged land 
disagreements. There is also high confidence  that the ESMs simulate 
the weakening of the global net flux of CO 2 into the ocean during 
the 1990s, as well as the strengthening of the flux from 2000. {3.6}

Two important quantities used to estimate how the climate system 
responds to changes in greenhouse gas (GHG) concentrations are the 
equilibrium climate sensitivity (ECS) and transient climate response 
(TCR16). The CMIP6 ensemble has broader ranges of ECS and TCR 
values than CMIP5 (see Section TS.3.2 for the assessed range). These 
higher sensitivity values can, in some models, be traced to changes 
in extratropical cloud feedbacks ( medium confidence ). To combine 
evidence from CMIP6 models and independent assessments of ECS 
and TCR, various emulators are used throughout the report. Emulators 
are a broad class of simple climate models or statistical methods 
that reproduce the behaviour of complex ESMs to represent key 
characteristics of the climate system, such as global surface temperature 
and sea level projections. The main application of emulators in AR6 
is to extrapolate insights from ESMs and observational constraints to

produce projections from a larger set of emissions scenarios, which 
is achieved due to their computational efficiency. These emulated 
projections are also used for scenario classification in WGIII. {Box 4.1, 
4.3.4, 7.4.2, 7.5.6, Cross-Chapter Box 7.1, FAQ 7.2}

50
TSFigure TS.2 |  Progress in climate models.  The intent of this figure is to show present improvements in climate models in resolution, complexity and representation of key 
variables.  (a) Evolution of model horizontal resolution and vertical levels (based on Figure 1.19). (b) Evolution of inclusion of processes and resolution from Coupled Model 
Intercomparison Project Phase 3 (CMIP3), Phase 5 (CMIP5) and Phase 6 (CMIP6; Annex II). (c) Centred pattern correlations between models and observations for the annual 
mean climatology over the period 1980–1999. Results are shown for individual CMIP3 (cyan), CMIP5 (blue) and CMIP6 (red) models (one ensemble member is used) as short 
lines, along with the corresponding ensemble averages (long lines). The correlations are shown between the models and the primary reference observational data set (from left

to right: ERA5, GPCP-SG, CERES-EBAF , CERES-EBAF , CERES-EBAF , CERES-EBAF , JR-55, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5, ESACCI-Soilmoisture, LAI3g, MTE). In 
addition, the correlation between the primary reference and additional observational data sets (from left to right: NCEP , GHCN, -, -, -, -, ERA5, HadISST, NCEP , NCEP , NCEP , NCEP , 
NCEP , NCEP , ERA5, NCEP , -, -, FLUXCOM) are shown (solid grey circles) if available. To ensure a fair comparison across a range of model resolutions, the pattern correlations 
are computed after regridding all datasets to a resolution of 4º in longitude and 5º in latitude. (Expanded from Figure 3.43; produced with ESMValTool version 2). {Figure 3.43}Atm Res Oce Res Atm lev Ocn lev0
50
100
150
200
250
300
350
400km
0102030405060708090100110
Level(a) Model resolution
HighRes
CMIP6
CMIP5
CMIP3
# Mod Aerosols Atm Chem Land carbon
cycleN cycle vegetation
prognosticOcean BGC0102030405060Number of modelsCMIP3CMIP5CMIP6(b) Model complexity

(c) Pattern correlation with observational referenceCorrelation
Gross Primary
ProductivityLeaf Area
IndexSoil
MoistureSpecific
Humidity
Outgoing
Shortwave
Outgoing
Longwave
Cloud Rad
Cloud Rad
 Wind
 Wind
 Wind
 Wind
Height
500 hPa1.0
0.8
0.4
0.2
0CMIP6
CMIP5
CMIP3
TS.1.2.3 Understanding Climate Variability and 
Emerging Changes
Observed changes in climate are unequivocal at the global 
scale and are increasingly apparent on regional and local 
spatial scales. Both the rate of long-term change and 
the amplitude of year-to-year variations differ between 
regions and across climate variables, thus influencing when 
changes emerge or become apparent compared to natural 
variations (see Emergence in Core Concepts Box). The signal

of temperature change has emerged more clearly in tropical 
regions, where year-to-year variations tend to be small over 
land, than in regions with greater warming but larger year-
to-year variations ( high confidence ) (Figure TS.3). Long-
term changes in other variables have emerged in many 
regions, such as for some weather and climate extremes 
and Arctic sea ice area. {1.4.2, Cross-Chapter Box 3.1, 9.3.1, 
11.3.2, 12.5.2}Observational datasets have been extended and improved since 
AR5, providing stronger evidence that the climate is changing and 
allowing better estimates of natural climate variability on decadal 
time scales. There is very high confidence  that the slower rate of global 
surface temperature change observed over 1998–2012 compared to 
1951–2012 was temporary, and was, with high confidence , induced 
by internal variability (particularly Pacific Decadal Variability) and 
variations in solar irradiance and volcanic forcing that partly offset the

anthropogenic warming over this period. Global ocean heat content 
continued to increase throughout this period, indicating continuous 
warming of the entire climate system ( very high confidence ). Hot 
extremes also continued to increase during this period over land 
(high confidence ). Even in a continually warming climate, periods 
of reduced and increased trends in global surface temperature at 
decadal time scales will continue to occur in the 21st century ( very 
high confidence ). {Cross-Chapter Box 3.1, 3.3.1, 3.5.1, 4.6.2, 11.3.2}
Since AR5, the increased use of ‘large ensembles’, or multiple 
simulations with the same climate model but using different initial 
conditions, supports improved understanding of the relative roles

51
Technical SummaryTSof internal variability and forced change in the climate system. 
Simulations and understanding of modes of climate variability, 
including teleconnections, have improved since AR5 ( medium 
confidence ), and larger ensembles allow a better quantification of 
uncertainty in projections due to internal climate variability. {1.4.2, 
1.5.3, 1.5.4, 4.2, 4.4.1, Box 4.1, 8.5.2, 10.3.4, 10.4}
Changes in regional climate can be detected even though natural 
climate variations can temporarily increase or obscure anthropogenic 
climate change on decadal time scales. While anthropogenic forcing 
has contributed to multi-decadal mean precipitation changes in 
several regions, internal variability can delay emergence of the 
anthropogenic signal in long-term precipitation changes in many 
land regions ( high confidence ). {10.4}
Mean temperatures and heat extremes have emerged above natural 
variability in almost all land regions with high confidence . Changes

in temperature-related variables, such as regional temperatures, 
growing season length, extreme heat and frost, have already occurred, and there is medium confidence  that many of these changes 
are attributable to human activities. Several impact-relevant changes 
have not yet emerged from natural variability but will emerge sooner 
or later in this century depending on the emissions scenario ( high 
confidence ). Ocean acidification and deoxygenation have already 
emerged over most of the global open ocean, as has a reduction in 
Arctic sea ice ( high confidence ). {9.3.1, 9.6.4, 11.2, 11.3, 12.4, 12.5, 
Atlas.3–Atlas.11} 
TS.1.2.4 Understanding of Human Influence
has strengthened progressively from the IPCC Second 
Assessment Report to AR5 and is even stronger in this 
assessment, including for regional scales and for extremes. 
Human influence in the IPCC context refers to the human

activities that lead to or contribute to a climate response, 
such as the human-induced emissions of greenhouse 
gases that subsequently alter the atmosphere’s radiative 
Figure TS.3 |  Emergence of changes in temperature over the historical period.  The intent of this figure is to show how observed changes in temperature have 
emerged and that the emergence pattern agrees with model simulations.  The observed change in temperature at a global warming level of 1°C (a), and the signal-to-noise ratio 
(the change in temperature at a global warming level of 1°C, divided by the size of year-to-year variations, (b)) using data from Berkeley Earth. The right panels show the zonal 
means of the maps and include data from different observational datasets (red) and the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations (black, including 
the 5–95% range) processed in the same way as the observations. {1.4.2, 10.4.3}
0.51 2 3 4 5Emergence of changes in surface temperature

Annual mean temperature change and the change relative to year-to-year variations
Zonal mean
90°N
60°N
30°N
0
30°S
60°S
90°S(a) Change in temperature at a global warming level of 1°C
0.51 2 3 4 590°N
60°N
30°N
0
30°S
60°S
90°S
–4  –3.5    –3    –2.5    –2   –1.5   –1    –0.5     0       0.5     1     1.5     2      2.5     3     3.5     4
Signal to noise ratioZonal mean–2.4  –2.1 –1.8 –1.5  –2.1  –0.9  –0.6  –0.3      0      0.3    0.6    0.9   1.2    1.5     1.8   2.1    2.4
(b) Change in temperature at a global warming level of 1°C relative to the size of year-to-year variations°CMissing 
data
Missing 
dataObservation (Berkeley Earth)
Other observation datasets
CMIP6 multi-model mean (45)
5-95% model range

52
TSproperties, resulting in warming of the atmosphere, ocean 
and land components of the climate system. Other human 
activities influencing climate include the emission of 
aerosols and other short-lived climate forcers, and land-use 
change such as urbanization. Progress in our understanding 
of human influence is gained from longer observational 
datasets, improved paleoclimate information, a stronger 
warming signal since AR5, and improvements in climate 
models, physical understanding and attribution techniques 
(see Core Concepts Box). Since AR5, the attribution to 
human influence has become possible across a wider range 
of climate variables and climatic impact-drivers (CIDs, 
see Core Concepts Box). New techniques and analyses 
drawing on several lines of evidence have provided greater 
confidence in attributing changes in regional weather and 
climate extremes to human influence ( high confidence ).

{1.3, 1.5.1, Appendix 1.A, 3.1–3.8, 5.2, 6.4.2, 7.3.5, 7.4.4, 
8.3.1, 10.4, Cross-Chapter Box 10.3, 11.2–11.9, 12.4}
Combining the evidence from across the climate system increases 
the level of confidence in the attribution of observed climate change 
to human influence and reduces the uncertainties associated with 
assessments based on single variables. {Cross-Chapter Box 10.3}
Since AR5, the accumulation of energy in the Earth system has 
become established as a robust measure of the rate of global 
climate change on interannual-to-decadal time scales. The rate of 
accumulation of energy is equivalent to Earth’s energy imbalance 
and can be quantified by changes in the global energy inventory for 
all components of the climate system, including global ocean heat 
uptake, warming of the atmosphere, warming of the land and melting 
of ice. Compared to changes in global surface temperature, Earth’s 
energy imbalance (see Core Concepts Box) exhibits less variability,

enabling more accurate identification and estimation of trends. {Box 
7.2 and Section 7.2}
Identifying the human-induced components contributing to the 
energy budget provides an implicit estimate of the human influence 
on global climate change (Sections TS.2 and TS.3.1). {Cross-Working 
Group Box: Attribution in Chapter  1, 3.8, 7.2.2, Box  7.2, 
Cross-Chapter Box 9.1}
Regional climate changes can be moderated or amplified by regional 
forcing from land-use and land-cover changes or from aerosol 
concentrations and other short-lived climate forcers (SLCFs). For 
example, the difference in observed warming trends between cities 
and their surroundings can partly be attributed to urbanization 
(very high confidence ). While established attribution techniques 
provide confidence in our assessment of human influence on 
large-scale climate changes (as described in Section TS.2), new 
techniques developed since AR5, including attribution of individual

events, have provided greater confidence in attributing changes in 
climate extremes to climate change (Box TS.10). Multiple attribution 
approaches support the contribution of human influence to several 
regional multi-decadal mean precipitation changes ( high confidence ). 
Understanding about past and future changes in weather and climate 
extremes has increased due to better observation-based datasets, 
physical understanding of processes, an increasing proportion of scientific literature combining different lines of evidence, and 
improved accessibility to different types of climate models ( high 
confidence ) (see Sections TS.2 and TS.4). {Cross-Working Group Box: 
Attribution in Chapter 1, 1.5, 3.2, 3.5, 5.2, 6.4.3, 8.3, 9.6, 10.1, 10.2, 
10.3.3, 10.4.1, 10.4.2, 10.4.3, 10.5, 10.6, Cross-Chapter Box  10.3, 
Box 10.3, 11.1.6, 11.2–11.9, 12.4}
TS.1.3 Assessing Future Climate Change
Various frameworks can be used to assess future climatic changes and

to synthesize knowledge across climate change assessment in WGI, 
WGII and WGIII. These frameworks include: (i) scenarios, (ii) global 
warming levels and (iii) cumulative CO 2 emissions (see Core Concepts 
Box). The latter two offer scenario- and path-independent approaches 
to assess future projections. Additional choices, for instance with regard 
to common reference periods and time windows for which changes 
are assessed, can further help to facilitate integration across the WGI 
report and across the whole AR6 (see Section TS.1.1). {1.4.1, 1.6, Cross-
Chapter Box 1.4, 4.2.2, 4.2.4, Cross-Chapter Box 11.1}
TS.1.3.1 Climate Change Scenarios
A core set of five illustrative scenarios based on the Shared 
Socio-economic Pathways (SSPs) are used consistently 
across this Report: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, 
and SSP5-8.5. These scenarios cover a broader range of 
greenhouse gas and air pollutant futures than assessed in

earlier WGI reports, and they include high-CO 2 emissions 
pathways without climate change mitigation as well as new 
low-CO 2 emissions pathways (Figure TS.4). In these scenarios, 
differences in air pollution control and variations in climate 
change mitigation stringency strongly affect anthropogenic 
emissions trajectories of SLCFs. Modelling studies relying 
on the Representative Concentration Pathways (RCPs) used 
in AR5 complement the assessment based on SSP scenarios, 
for example at the regional scale. 
A comparison of simulations from CMIP5 using the RCPs 
with SSP-based simulations from CMIP6 shows that about 
half of the increase in simulated warming in CMIP6 versus 
CMIP5 arises because higher climate sensitivity is more 
prevalent in CMIP6 model versions; the other half arises 
from higher radiative forcing in nominally corresponding 
scenarios (e.g., RCP8.5 and SSP5-8.5; medium confidence ). 
The feasibility or likelihood of individual scenarios is not

part of this assessment, which focuses on the climate 
response to a large range of emissions scenarios. {1.5.4, 1.6, 
Cross-Chapter Box 1.4, 4.2, 4.3, 4.6, 6.6, 6.7, Cross-Chapter 
Box 7.1, Atlas.2.1}
Climate change projections with climate models require information 
about future emissions or concentrations of greenhouse gases, 
aerosols, ozone-depleting substances, and land use over time 
(Figure TS.4). This information can be provided by scenarios, which 
are internally consistent projections of these quantities based on 
assumptions of how socio-economic systems could evolve over the 
21st century. Emissions from natural sources, such as the ocean and

53
Technical SummaryTS
(e.g., SLCF, 
land use
 albedo)Other
Greenhouse
gasesNon-CO2CO2
NH3Land use albedo etc.
Global
WarmingRadiative
ForcingConcentrationsEmissions
CO₂CO₂
SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0SSP5-8.5HistoricalLegend:
Change in annual mean 
surface temperature (°C)GWL 2°C GWL 2°C
precipitation (%)2.5345 0 -1.5 1.52 -2-2.5-3-4-5Robust significant change
No or no robust 
significant change
Conflicting signalLegend
see left
2100 
RCP 
range
Temperature Precipitation
ObservationsProjections
01234567Change in global surface 
(°C rel. to 1850–1900)
-40 +40 +20 0 -20
1950 2000
 2050 2100-25
1950 2000 2050 21001000150020002500300035004000ppb
0255075100125 GtCO2 / yr
1950 2000 2050 21000100200300400500600700800900MtCH4 / yr
1950 2000 2050 2100-2024681012Effective radiative forcing
(W/m-2)Total anthropogenic
Natural
Regional Climate ChangeCH₄
CH₄
SO₂N₂O
N₂OHFCs etc.
HFCs etc.
Carbon-
Cycle and 
non-CO 2
biogeo-
chemical

feedbacks
1950 2000 2050 21000255075100125150175200 MtNOx / yrNOx
1950 2000 2050 21002004006008001000 ppm
1950 2000 2050 2100020406080100120140 MtSO2 / yr
Human 
activities
Climatic 
Impact-
Drivers
Figure TS.4 |  The climate change cause–effect chain:  The intent of this figure is to illustrate the process chain starting from anthropogenic emissions, to changes in 
atmospheric concentration, to changes in Earth’s energy balance (‘forcing’), to changes in global climate and ultimately regional climate and climatic impact-drivers . Shown 
is the core set of five Shared Socio-economic Pathway (SSP) scenarios as well as emissions and concentration ranges for the previous Representative Concentration Pathway 
(RCP) scenarios in year 2100; carbon dioxide (CO 2) emissions (GtCO 2 yr–1), panel top left; methane (CH 4) emissions (middle) and sulphur dioxide (SO 2), nitrogen oxide (NOx)

emissions (all in Mt yr–1), top right; concentrations of atmospheric CO 2 (ppm) and CH 4 (ppb), second row left and right; effective radiative forcing for both anthropogenic and 
natural forcings (W m–2), third row; changes in global surface air temperature (°C) relative to 1850–1900, fourth row; maps of projected temperature change (°C) (left) and 
changes in annual-mean precipitation (%) (right) at a global warming level (GWL) of 2°C relative to 1850–1900 (see also Figure TS.5), bottom row. Carbon cycle and non-CO 2 
biogeochemical feedbacks will also influence the ultimate response to anthropogenic emissions (arrows on the left). {1.6.1, Cross-Chapter Box 1.4, 4.2.2, 4.3.1, 4.6.1, 4.6.2}

54
TSthe land biosphere, are usually assumed to be constant, or to evolve 
in response to changes in anthropogenic forcings or to projected 
climate change. Natural forcings, such as past changes in solar 
irradiance and historical volcanic eruptions, are represented in model 
simulations covering the historical era. Future simulations assessed 
in this Report account for projected changes in solar irradiance and 
for the long-term mean background forcing from volcanoes, but not 
for individual volcanic eruptions. Scenarios have a long history in 
IPCC as a method for systematically examining possible futures and 
following the cause–effect chain: from anthropogenic emissions, to 
changes in atmospheric concentrations, to changes in Earth’s energy 
balance (‘forcing’), to changes in global climate and ultimately 
regional climate and climatic impact-drivers (Figure TS.4, Section 
TS.2, Infographic TS.1). {1.5.4, 1.6.1, 4.2.2, 4.4.4, Cross-Chapter 
Box 4.1, 11.1}

The uncertainty in climate change projections that results from 
assessing alternative socio-economic futures, the so-called scenario 
uncertainty, is explored through the use of scenario sets. Designed 
to span a wide range of possible future conditions, these scenarios 
do not intend to match how events actually unfold in the future, 
and they do not account for impacts of climate change on the socio-
economic pathways. Besides scenario uncertainty, climate change 
projections are also subject to climate response uncertainty (i.e., the 
uncertainty related to our understanding of the key physical processes 
and structural uncertainties in climate models) and irreducible and 
intrinsic uncertainties related to internal variability. Depending on 
the spatial and temporal scales of the projection, and on the variable 
of interest, the relative importance of these different uncertainties 
may vary substantially. {1.4.3, 1.6, 4.2.5, Box 4.1, 8.5.1}

Scenarios in AR6 cover a broader range of emissions futures than 
considered in AR5, including high CO 2 emissions scenarios without 
climate change mitigation as well as a low CO 2 emissions scenario 
reaching net zero CO 2 emissions (see Core Concepts Box) around mid-
century. In this Report, a core set of five illustrative scenarios is used 
to explore climate change over the 21st century and beyond (Section 
TS.2). They are labelled SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and 
SSP5-8.517 and span a wide range of radiative forcing levels in 2100. 
They start in 2015 and include scenarios with high and very high 
GHG emissions and CO 2 emissions that roughly double from current 
levels by 2100 and 2050, respectively (SSP3-7.0 and SSP5-8.5); 
scenarios with intermediate GHG emissions and CO 2 emissions 
remaining around current levels until the middle of the century 
(SSP2-4.5); and scenarios with very low and low GHG emissions and

CO 2 emissions declining to net zero around or after 2050, followed by 
varying levels of net negative CO 2 emissions (SSP1-1.9 and SSP1-2.6). 
These SSP scenarios offer unprecedented detail of input data for ESM 
simulations and allow for a more comprehensive assessment of 
climate drivers and responses, in particular because some aspects, 
such as the temporal evolution of pollutants, emissions or changes in 
land use and land cover, span a broader range in the SSP scenarios 
than in the RCPs used in AR5. Modelling studies utilizing the RCPs 
complement the assessment based on SSP scenarios, for example, 
17 Throughout this Report, scenarios are referred to as SSPx-y, where “SSPx” refers to the Shared Socio-economic Pathway or “SSP” describing the socio-economic trends underlying the scenario,

and “y” refers to the approximate target level of radiative forcing (in W m–-2) resulting from the scenario in the year 2100.at the regional scale (Section TS.4). Scenario extensions are based 
on assumptions about the post-2100 evolution of emissions or of 
radiative forcing that are independent from the modelling of socio-
economic dynamics, which does not extend beyond 2100. To explore 
specific dimensions, such as air pollution or temporary overshoot of 
a given warming level, scenario variants are used in addition to the 
core set. {1.6.1, Cross-Chapter Box  1.4, 4.2.2, 4.2.6, 4.7.1, Cross-
Chapter Box 7.1}
SSP1-1.9 represents the low end of future emissions pathways, 
leading to warming below 1.5°C in 2100 and limited temperature 
overshoot of 1.5°C over the course of the 21st century (see 
Figure TS.6). At the opposite end of the range, SSP5-8.5 represents 
the very high warming end of future emissions pathways from the

literature. SSP3-7.0 has overall lower GHG emissions than SSP5-8.5 
but, for example, CO 2 emissions still almost double by 2100 compared 
to today’s levels. SSP2-4.5 and SSP1-2.6 represent scenarios with 
stronger climate change mitigation and thus lower GHG emissions. 
SSP1-2.6 was designed to limit warming to below 2°C. Infographic 
TS.1 presents a narrative depiction of SSP-related climate futures. 
No likelihood is attached to the scenarios assessed in this Report, 
literature, the plausibility of some scenarios with high CO 2 emissions, 
such as RCP8.5 or SSP5-8.5, has been debated in light of recent 
developments in the energy sector. However, climate projections from 
these scenarios can still be valuable because the concentration levels 
reached in RCP8.5 or SSP5-8.5 and corresponding simulated climate

futures cannot be ruled out. That is because of uncertainty in carbon-
cycle feedbacks which, in nominally lower emissions trajectories, can 
result in projected concentrations that are higher than the central 
concentration levels typically used to drive model projections. {1.6.1; 
Cross-Chapter Box 1.4; 4.2.2, 5.4; SROCC; Chapter 3 in WGIII}
The socio-economic narratives underlying SSP-based scenarios 
differ in their assumed level of air pollution control. Together with 
variations in climate change mitigation stringency, this difference 
strongly affects anthropogenic emissions trajectories of SLCFs, 
some of which are also air pollutants. SSP1 and SSP5 assume strong 
pollution control, projecting a decline of global emissions of ozone 
precursors (except methane; CH 4) and of aerosols and most of 
their precursors in the mid- to long term. The reductions due to air 
pollution controls are further strengthened in scenarios that assume

a marked decarbonization, such as SSP1-1.9 or SSP1-2.6. SSP2-4.5 
is a medium pollution-control scenario with air pollutant emissions 
following current trends, and SSP3-7.0 is a weak pollution-control 
scenario with strong increases in emissions of air pollutants over the 
21st century. Methane emissions in SSP-based scenarios vary with 
the overall climate change mitigation stringency, declining rapidly 
in SSP1-1.9 and SSP1-2.6 but declining only after 2070 in SSP5-8.5. 
SSP trajectories span a wider range of air pollutant emissions than 
considered in the RCP scenarios (see Figure TS.4), reflecting the 
potential for large regional differences in their assumed pollution

55
Technical SummaryTSpolicies. Their effects on climate and air pollution are assessed in Box 
TS.7. {4.4.4, 6.6.1, Figure 6.4, 6.7.1, Figure 6.19}
Since the RCPs are also labelled by the level of radiative forcing 
they reach in 2100, they can in principle be related to the core set 
of AR6 scenarios (Figure TS.4). However, the RCPs and SSP-based 
scenarios are not directly comparable. First, the gas-to-gas 
compositions differ; for example, the SSP5-8.5 scenario has higher 
CO 2 but lower CH 4 concentrations compared to RCP8.5. Second, the 
projected 21st-century trajectories may differ, even if they result 
in the same radiative forcing by 2100. Third, the overall effective 
radiative forcing (see Core Concepts Box) may differ, and tends 
to be higher for the SSPs compared to RCPs that share the same 
nominal stratospheric-temperature-adjusted radiative forcing label. 
Comparing the differences between CMIP5 and CMIP6 projections

(Cross-Section Box TS.1) that were driven by RCPs and SSP-based 
scenarios, respectively, indicates that about half of the difference in 
simulated warming arises because of higher climate sensitivity being 
more prevalent in CMIP6 model versions; the remainder arises from 
higher ERF in nominally corresponding scenarios (e.g., RCP8.5 and 
SSP5-8.5; medium confidence ) (see Section TS.1.2.2). In SSP1-2.6 and 
SSP2-4.5, changes in ERF also explain about half of the changes in the 
range of warming ( medium confidence ). For SSP5-8.5, higher climate 
sensitivity is the primary reason behind the upper end of the CMIP6-
projected warming being higher than for RCP8.5 in CMIP5 ( medium 
confidence ). Note that AR6 uses multiple lines of evidence beyond 
CMIP6 results to assess global surface temperature under various 
scenarios (see Cross-Section Box TS.1 for the detailed assessment). 
{1.6, 4.2.2, 4.6.2.2, Cross-Chapter Box 7.1}
Earth system models can be driven by anthropogenic CO 2

emissions (‘emissions-driven’ runs), in which case atmospheric 
CO 2 concentration is a projected variable; or by prescribed time-
varying atmospheric concentrations (‘concentration-driven’ runs). In 
emissions-driven runs, changes in climate feed back on the carbon 
cycle and interactively modify the projected CO 2 concentration 
in each ESM, thus adding the uncertainty in the carbon cycle 
response to climate change to the projections. Concentration-
driven simulations are based on a central estimate of carbon cycle 
feedbacks, while emissions-driven simulations help quantify the role 
of feedback uncertainty. The differences in the few ESMs for which 
both emissions and concentration-driven runs were available for the 
same scenario are small and do not affect the assessment of global 
surface temperature projections discussed in Cross-Section Box TS.1 
and Section TS.2 ( high confidence ). By the end of the 21st century,

emissions-driven simulations are on average around 0.1°C cooler 
than concentration-driven runs, reflecting the generally lower CO 2 
concentrations simulated by the emissions-driven ESMs, and have a 
spread about 0.1°C greater, reflecting the range of simulated CO 2 
concentrations. However, these carbon cycle–climate feedbacks do 
affect the transient climate response to cumulative CO 2 emissions 
(TCRE18), and their quantification is crucial for the assessment of 
remaining carbon budgets consistent with global warming levels 
simulated by ESMs (see Section TS.3). {1.6.1, Cross-Chapter Box 1.4, 
4.2, 4.3.1, 5.4.5, Cross-Chapter Box 7.1}
18 The transient surface temperature change per unit of cumulative CO 2 emissions, usually 1000 GtC.TS.1.3.2 Global Warming Levels and Cumulative CO 2 Emissions
Quantifying geographical response patterns of climate 
change at various global warming levels (GWLs), such as 
1.5°C or 2°C above the 1850–1900 period, is useful for

characterizing changes in mean climate, extremes and 
climatic impact-drivers. Global warming levels are used 
in this Report as a dimension of integration independent 
of the timing when the warming level is reached and of 
the emissions scenario that led to the warming. For many 
climate variables the response pattern for a given GWL is 
consistent across different scenarios. However, this is not 
the case for slowly responding processes, such as ice-sheet 
and glacier mass loss, deep ocean warming, and the related 
sea level rise. The response of these variables depends on 
the time it takes to reach the GWL, differs if the warming is 
reached in a transient warming state or after a temporary 
overshoot of the warming level, and will continue to evolve, 
over centuries to millennia, even after global warming 
has stabilized. Different GWLs correspond closely to 
specific cumulative CO 2 emissions due to their near-linear 
relationship with global surface temperature. This Report

uses 1.0°C, 1.5°C, 2.0°C, 3.0°C and 4.0°C above 1850–1900 
conditions as a primary set of GWLs. {1.6.2, 4.2.4, 4.6.1, 5.5, 
Cross-Chapter Box 11.1, Cross-chapter Box 12.1}
For many indicators of climate change, such as seasonal and annual 
mean and extreme surface air temperatures and precipitation, the 
geographical patterns of changes are well estimated by the level of 
global surface warming, independently of the details of the emissions 
pathways that caused the warming, or the time at which the level of 
warming is attained. GWLs, defined as a global surface temperature 
increase of, for example, 1.5°C or 2°C relative to the mean of 1850–
1900, are therefore a useful way to integrate climate information 
independently of specific scenarios or time periods. {1.6.2, 4.2.4, 
4.6.1, 11.2.4, Cross-Chapter Box 11.1}
The use of GWLs allows disentangling the contribution of changes 
in global warming from regional aspects of the climate response,

as scenario differences in response patterns at a given GWL are 
often smaller than model uncertainty and internal variability. The 
relationship between the GWL and response patterns is often linear, 
but integration of information can also be done for non-linear 
changes, like the frequency of heat extremes. The requirement is that 
the relationship to the GWL is broadly independent of the scenario 
and relative contribution of radiative forcing agents. {1.6, 11.2.4, 
Cross-Chapter Box 11.1}
The GWL approach to integration of climate information also has 
some limitations. Variables that are quick to respond to warming, 
like temperature and precipitation, including extremes, sea ice area, 
permafrost and snow cover, show little scenario dependence for a 
given GWL, whereas slow-responding variables such as glacier and 
ice-sheet mass, warming of the deep ocean and their contributions 
to sea level rise, have substantial dependency on the trajectory of

56
TSwarming taken to reach the GWL. A given GWL can also be reached 
for different balances between anthropogenic forcing agents, such 
as long-lived greenhouse gas and SLCF emissions, and the response 
patterns may depend on this balance. Finally, there is a difference 
in the response even for temperature-related variables if a GWL is 
reached in a rapidly warming transient state or in an equilibrium 
state when the land–sea warming contrast is less pronounced. In this 
Report, the climate responses at different GWLs are calculated based 
on climate model projections for the 21st century (see Figure TS.5), 
which are mostly not in equilibrium. The SSP1-1.9 scenario allows 
assessing the response to a GWL of about 1.5°C after a (relatively) 
short-term stabilization by the end of the 21st century. {4.6.2, 9.3.1.1, 
9.5.2.3, 9.5.3.3, 11.2.4, Cross-Chapter Box  11.1, Cross-Chapter 
Box 12.1}
Global warming levels are highly relevant as a dimension of

integration across scientific disciplines and socio-economic actors 
and are motivated by the long-term goal in the Paris Agreement 
of ‘holding the increase in the global average temperature to well 
below 2°C above pre-industrial levels and to pursue efforts to limit 
the temperature increase to 1.5°C above pre-industrial levels’. 
The evolution of aggregated impacts with temperature levels has 
also been widely used and embedded in the WGII assessment. 
This includes the ‘Reasons for Concern’ (RFC) and other ‘burning 
ember’ diagrams in IPCC WGII. The RFC framework has been further 
expanded in SR1.5, SROCC and SRCCL by explicitly looking at the 
differential impacts between half-degree GWLs and the evolution of 
risk for different socio-economic assumptions. {1.4.4, 1.6.2, 11.2.4, 
12.5.2, Cross-Chapter Box 11.1, Cross-Chapter Box 12.1}SR1.5 concluded that ‘climate models project robust differences in 
regional climate characteristics between present-day and global

warming of 1.5°C, and between 1.5°C and 2°C’. This Report adopts 
a set of common GWLs across which climate projections, impacts, 
adaptation challenges and climate change mitigation challenges can 
be integrated, within and across the three Working Groups, relative 
to 1850–1900. The core set of GWLs in this Report are 1.0°C (close 
to present day conditions), 1.5°C, 2.0°C, 3.0°C and 4.0°C. {1.4, 1.6.2, 
Cross-Chapter Box 1.2, Table 1.5, Cross-Chapter Box 11.1}
Connecting Scenarios and Global Warming Levels
In this Report, scenario-based climate projections are translated 
into GWLs by aggregating the ESM model response at specific 
GWLs across scenarios (see Figure TS.5 and Figure TS.6). The climate 
response pattern for the 20-year period around when individual 
simulations reach a given GWL are averaged across all models and 
scenarios that reach that GWL. The best estimate and likely  range 
of the timing of when a certain GWL is reached under a particular

scenario (or ‘GWL-crossing time’), however, is based not only on 
CMIP6 output, but on a combined assessment taking into account 
the observed warming to date, CMIP6 output and additional lines 
of evidence (see Cross-Section Box TS.1). {4.3.4, Cross-Chapter 
Box 11.1, Atlas.2, Interactive Atlas} 
Global warming levels are closely related to cumulative CO 2 (and 
in some cases CO 2-equivalent) emissions. This Report confirms the 
assessment of the WGI contribution to AR5 and SR1.5 that a near-
linear relationship exists between cumulative CO 2 emissions and the 
Figure TS.5 |  Scenarios, global warming levels, and patterns of change.  The intent of this figure is to show how scenarios are linked to global warming levels (GWLs) 
and to provide examples of the evolution of patterns of change with global warming levels.  (a) Illustrative example of GWLs defined as global surface temperature response

to anthropogenic emissions in unconstrained Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, for two illustrative scenarios (SSP1-2.6 and SSP3-7.0). The 
time when a given simulation reaches a GWL, for example, +2°C, relative to 1850–1900 is taken as the time when the central year of a 20-year running mean first reaches 
that level of warming. See the dots for +2°C, and how not all simulations reach all levels of warming. The assessment of the timing when a GWL is reached takes into account 
additional lines of evidence and is discussed in Cross-Section Box TS.1. (b) Multi-model, multi-simulation average response patterns of change in near-surface air temperature, 
precipitation (expressed as percentage change) and soil moisture (expressed in standard deviations of interannual variability) for three GWLs. The number to the top right of

the panels shows the number of model simulations averaged across including all models that reach the corresponding GWL in any of the five Shared Socio-economic Pathways 
(SSPs). See Section TS.2 for discussion. {Cross-Chapter Box 11.1}Soil moisture change
2000 2020 2040 2060 2080 210012345
SSP1-2.6 (20-yr GSAT means)SSP3-7.0 (20-yr GSAT means)°CTemperature change Precipitation change 
°C-5-4-3-2.5-2-1.501.522.5345
%-40-30-20-10 010203040132
σ-2-1.5-1-0.500.511.522.5 -2.5 +2°C  +4°C 
 +1.5°C (a) Global mean temperature in CMIP6
 +1.5°C  +2°C  +4°C (b) Patterns of change in near-surface air temperature, precipitation and soil moisture
48 47
137 13243
119
147 153

57
Technical SummaryTSresulting increase in global surface temperature (Section TS.3.2). This 
implies that continued CO 2 emissions will cause further warming 
and associated changes in all components of the climate system. For 
declining cumulative CO 2 emissions (i.e., if negative net emissions 
are achieved), the relationship is less strong for some components, 
such as the hydrological cycle. The WGI report uses cumulative CO 2 
emissions to compare climate response across scenarios and provides 
a link to the emissions pathways assessment in WGIII. The advantage 
of using cumulative CO 2 emissions is that it is an inherent emissions 
scenario characteristic rather than an outcome of the scenario-based 
projections, where uncertainties in the cause–effect chain from 
emissions to temperature change are important (Figure TS.4), for 
example, the uncertainty in ERF and TCR. Cumulative CO 2 emissions 
can also provide a link to the assessments of mitigation options.

Cumulative CO 2 emissions do not carry information about non-CO 2 
emissions, although these can be included with specific emissions 
metrics to estimate CO 2-equivalent emissions. (Section TS.3.3) {1.3.2, 
1.6, 4.6.2, 5.5, 7.6}
TS.1.4 From Global to Regional Climate Information for 
Impact and Risk Assessment
The AR6 WGI Report has an expanded focus on regional 
information supported by the increased availability of 
coordinated regional climate model ensemble projections 
and improvements in the sophistication and resolution 
of global and regional climate models ( high confidence ). 
Multiple lines of evidence can be used to construct climate 
information on a global to regional scale and can be further 
distilled in a co-production process to meet user needs ( high 
confidence ). To better support risk assessment, a common 
risk framework across all three Working Groups has been 
implemented in AR6, and low-likelihood but high-impact

outcomes are explicitly addressed in WGI by using physical 
climate storylines (see Core Concepts Box). 
Climatic impact-drivers are physical climate system 
conditions (e.g., means, events, extremes) that affect 
an element of society or ecosystems. They are the WGI 
contribution to the risk framing without anticipating 
whether their impact provides potential opportunities 
or is detrimental (i.e., as for hazards). Many global and 
regional climatic impact-drivers have a direct relation to 
global warming levels ( high confidence ). {1.4.4, 1.5.2–1.5.4, 
Cross-Chapter Box  1.3, 4.8, 10.1, 10.5.1, Box  10.2, Cross-
Chapter Box  10.3, 11.2.4, 11.9, Box  11.2, Cross-Chapter 
Box  11.1, 12.1–12.3, 12.6, Cross-Chapter Boxes 12.1 and 
12.2, Atlas.1.3.3–1.3.4, Atlas.1.4, Atlas.1.4.4}
Climate change is a global phenomenon, but manifests differently 
in different regions. The impacts of climate change are generally 
experienced at local, national and regional scales, and these are

also the scales at which decisions are typically made. Robust climate 
change information is increasingly available at regional scales for 
impact and risk assessments. Depending on the climate information 
context, geographical regions in AR6 may refer to larger areas, such as sub-continents and oceanic regions, or to typological regions, such 
as monsoon regions, coastlines, mountain ranges or cities, as used in 
Section TS.4. A new set of standard AR6 WGI reference regions has 
also been included in this Report (Figure TS.6, bottom panels). {1.4.5, 
10.1, 11.9, 12.1–12.4, Atlas.1.3.3–1.3.4}
Global and regional climate models are important sources of climate 
information at the regional scale. Since AR5, a more comprehensive 
assessment of past and future evolution of a range of climate variables 
on a regional scale has been enabled by the increased availability 
of coordinated ensemble regional climate model projections and

improvements in the level of sophistication and resolution of 
global and regional climate models. This has been complemented 
by observational, attribution and sectoral-vulnerability studies 
informing, for instance, about impact-relevant tolerance thresholds. 
{10.3.3, 11.9, 12.1, 12.3, 12.6, Atlas.3–Atlas.11}
Multiple lines of evidence derived from observations, model simulations 
and other approaches can be used to construct climate information 
on a regional scale as described in detail in Sections TS.4.1.1 and 
TS.4.1.2. Depending on the phenomena and specific context, these 
sources and methodologies include theoretical understanding of the 
relevant processes, drivers and feedbacks of climate at regional scale; 
trends in observed data from multiple datasets; and the attribution 
of these trends to specific drivers. Furthermore, simulations from 
different model types (including global and regional climate models,

emulators, statistical downscaling methods, etc.) and experiments 
(e.g., CMIP , CORDEX, and large ensembles of single-model simulations 
with different initial conditions), attribution methodologies and other 
relevant local knowledge (e.g., indigenous knowledge) are utilized 
(see Box TS.11). {1.5.3, 1.5.4, Cross-Chapter Box 7.1, 10.2–10.6, 11.2, 
Atlas.1.4, Cross-Chapter Box 10.3}
From the multiple lines of evidence, climate information can be 
distilled in a co-production process that involves users, related 
stakeholders and producers of climate information, considering the 
specific context of the question at stake, the underlying values and 
the challenge of communicating across different communities. The 
co-production process is an essential part of climate services, which 
are discussed in Section TS.4.1.2. {10.5, 12.6, Cross-Chapter Box 12.2}
With the aim of informing decision-making at local or regional 
scales, a common risk framework has been implemented in AR6.

Methodologies have been developed to construct more impact- and 
risk-relevant climate change information tailored to regions and 
stakeholders. Physical storyline approaches are used in order to 
build climate information based on multiple lines of evidence, and 
which can explicitly address physically plausible, but low-likelihood, 
high-impact outcomes and uncertainties related to climate variability 
for consideration in risk assessments (Figure TS.6). {Cross-Chapter 
Box 1.3, 4.8, Box 9.4, 10.5, Box 10.2, Box 11.2, 12.1–12.3, 12.6, 
Glossary}
The climatic impact-driver framework developed in AR6 supports 
an assessment of changing climate conditions that are relevant 
for sectoral impacts and risk assessment.  Climatic impact-drivers 
(CIDs) are physical climate system conditions (e.g., means, extremes,

58
TSFigure TS.6 |  A graphical abstract for key aspects of the Technical Summary.  The intent of this figure is to summarize many different aspects of the Technical Summary 
related to observed and projected changes in global temperature and associated regional changes in climatic impact-drivers relevant for impact and risk assessment.  Top left:  
a schematic representation of the likelihood for equilibrium climate sensitivity (ECS), consistent with the AR6 assessment (see Chapter 7 and Section TS.3). ECS values above 
5°C and below 2°C are termed low-likelihood, high warming (LLHW) and low-likelihood, low warming, respectively (Box TS.3). Top right:  Observed (see Cross-Section Box 
TS.1) and projected global surface temperature changes, shown as global warming levels (GWLs) relative to 1850–1900, using the assessed 95% (top), 50% (middle) and 5%

(bottom) likelihood time series (see Chapter 4 and Section TS.2). Bottom panels  show maps of Coupled Model Intercomparison Project Phase 6 (CMIP6) median projections of 
two climatic impact-drivers (CIDs, see Section TS.1.4) at three different GWLs (columns for 1.5, 2 and 4°C) for the AR6 land regions (see Chapters 1, 10, and Atlas and Section 
TS.4). The heat warning index is the number of days per year averaged across each region at which a heat warning for human health at level ‘danger’ would be issued according 
to the U.S. National Oceanic and Atmospheric Administration (NOAA) (NOAA HI41, see Chapter 12 and Annex VI). The maps of extreme rainfall changes show the percentage 
change in the amount of rain falling on the wettest day of a year (Rx1day, relative to 1995–2014, see Chapter 11) averaged across each region when the respective GWL is 
reached. Additional CIDs are discussed in Section TS.4. {1.4.4, Box 4.1, 7.5, 11.4.3, 12.4}Schematic ECS likelihood

Relative likelihood0123456Equilibrium climate sensitivity (ECS, °c)Observations                                        Projections
1980 2000 2020 2040 2060 2080High ECS
1980 2000 2020 2040 2060 2080Mid-range ECS
SSP1-1.9SSP1-2.6SSP2-4.5SSP3-7.0SSP5-8.5Low ECS
0.5 1 1.5 2 3 4Global Warming Level (GWL, °C)
GWL 4.0°C GWL 2.0°C GWL 1.5°C
1 day/year 3 days/year 2 weeks/year 1 month/year 3 months/year 6 months/year
0 5 10 15 20 25 30
% more rain on wettest day of the yearAssessed changes in global surface temperature
Heat
warning
index
Changes in
extreme
rainfall

59
Technical SummaryTSevents) that affect an element of society or ecosystems and are thus 
a potential priority for providing climate information. For instance, 
the heat index used by the U.S. National Oceanic and Atmospheric 
Administration (NOAA HI) for issuing heat warnings is a CID index 
that can be associated with adverse human health impacts due to 
heat stress (see Figure TS.6). Depending on system tolerance, CIDs and 
their changes can be detrimental (i.e., hazards in the risk framing), 
beneficial, neutral, or a mixture of each across interacting system 
elements, regions and sectors (aligning with WGII Sectoral Chapters 
2–8). Each sector is affected by multiple CIDs, and each CID affects 
multiple sectors. Climate change has already altered CID profiles and 
resulted in shifting magnitude, frequency, duration, seasonality and 
spatial extent of associated indices ( high confidence ) (see regional

details in Section TS.4.3). {12.1–12.4, Table 12.1, Table 12.2, Annex VI}Many global- and regional-scale CIDs, including extremes, have 
a direct relation to global warming levels (GWLs) and can thus inform 
the hazard component of ‘Representative Key Risks’ and ‘Reasons for 
Concern’ assessed by AR6 WGII. These include heat, cold, wet and dry 
hazards, both mean and extremes; cryospheric hazards (snow cover, 
ice extent, permafrost) and oceanic hazards (marine heatwaves) 
(high confidence ) (Figure TS.6). Establishing links between specific 
GWLs with tipping points and irreversible behaviour is challenging 
due to model uncertainties and lack of observations, but their 
occurrence cannot be excluded, and their likelihood of occurrence 
generally increases at greater warming levels  (Box TS.1, Section TS.9).  
{11.2.4, Box 11.2, Cross-Chapter Boxes 11.1 and 12.1}
Cross-Section Box TS.1: Global Surface Temperature Change

This box synthesizes the outcomes of the assessment of past, current and future global surface temperature. Global mean surface 
temperature (GMST) and global surface air temperature (GSAT) are the two primary metrics of global surface temperature used to 
estimate global warming in IPCC reports. GMST merges sea surface temperature (SST) over the ocean and 2 m air temperature over 
land and sea ice areas and is used in most paleo, historical and present-day observational estimates. The GSAT metric is 2 m air 
temperature over all surfaces and is the diagnostic generally used from climate models. Changes in GMST and GSAT over time differ 
by at most 10% in either direction ( high confidence ), but conflicting lines of evidence from models and direct observations, combined 
with limitations in theoretical understanding, lead to low confidence  in the sign of any difference in long-term trend. Therefore,

long-term changes in GMST/GSAT are presently assessed to be identical, with expanded uncertainty in GSAT estimates. Hence the term 
global surface temperature is used in reference to both quantities in the text of the TS and SPM. {Cross-Chapter Box 2.3}
Global surface temperature has increased by 0.99 [0.84 to 1.10] °C from 1850–1900 to the first two decades of the 
21st century (2001–2020) and by 1.09 [0.95 to 1.20] °C from 1850–1900 to 2011–2020. Temperatures as high as 
during the most recent decade (2011–2020) exceed the warmest centennial-scale range reconstructed for the present 
interglacial, around 6500 years ago [0.2°C to 1°C] ( medium confidence ). The next most recent warm period was 
about 125,000 years ago during the last interglacial when the multi-centennial temperature range [0.5°C to 1.5°C] 
encompasses the 2011–2020 values ( medium confidence ). The likely  range of human-induced change in global surface

temperature in 2010–2019 relative to 1850–1900 is 0.8°C to 1.3°C, with a central estimate of 1.07°C, encompassing 
the best estimate of observed warming for that period, which is 1.06°C with a very likely  range of [0.88°C to 1.21°C], 
while the likely  range of the change attributable to natural forcing is only –0.1°C to +0.1°C. 
Compared to 1850–1900, average global surface temperature over the period 2081–2100 is very likely to be higher by 
[1.0°C to 1.8°C] in the low CO 2 emissions scenario SSP1-1.9 and by [3.3°C to 5.7°C] in the high CO 2 emissions scenario 
SSP5-8.5. In all scenarios assessed here except SSP5-8.5, the central estimate of 20-year averaged global surface 
warming crossing the 1.5°C level lies in the early 2030s, which is in the early part of the likely  range (2030–2052) 
assessed in SR1.5. It is more likely than not  that under SSP1-1.9, global surface temperature relative to 1850–1900 will

remain below 1.6°C throughout the 21st century, implying a potential temporary overshoot of 1.5°C global warming 
of no more than 0.1°C. Global surface temperature in any individual year could exceed 1.5°C relative to 1850–1900 
by 2030 with a likelihood between 40% and 60% across the scenarios considered here ( medium confidence ). A 2°C 
increase in global surface temperature relative to 1850–1900 will be crossed under SSP5-8.5 but is extremely unlikely 
to be crossed under SSP1-1.9. Periods of reduced and increased global surface temperature trends at decadal time 
scales will continue to occur in the 21st century ( very high confidence ). The effect of strong mitigation on 20-year 
global surface temperature trends would be likely  to emerge during the near term (2021–2040), assuming no major 
volcanic eruptions occur. (Figure TS.8, Cross-Section Box TS.1, Figure 1) {2.3, 3.3, 4.3, 4.4, 4.5, 4.6, 7.3}
Surface Temperature History

Dataset innovations, particularly more comprehensive representation of polar regions, and the availability of new datasets have led 
to an assessment of increased global surface temperature change relative to the directly equivalent estimates reported in AR5. The 
contribution of changes in observational understanding alone between AR5 and AR6 in assessing temperature changes from 1850–1900

60
TSCross-Section Box TS.1 (continued)
to 1986–2005 is estimated at 0.08 [–0.01 to 0.12] °C. Global surface temperature increased from 1850–1900 to 1995–2014 by 0.85 
[0.69 to 0.95] °C, between 1850–1900 and the first two decades of the 21st century (2001–2020) by 0.99 [0.84 to 1.20] °C, and to 
the most recent decade (2011–2020) by 1.09 [0.95 to 1.20] °C. Each of the last four decades has in turn been warmer than any decade 
that preceded it since 1850. Temperatures have increased faster over land than over the ocean since 1850–1900, with warming to 
2011–2020 of 1.59 [1.34 to 1.83] °C over land and 0.88 [0.68 to 1.01] °C over the ocean. {2.3.1, Cross-Chapter Box 2.3}
Global surface temperature during the period 1850–1900 is used as an approximation for pre-industrial conditions for consistency 
with AR5 and AR6 Special Reports, whilst recognizing that radiative forcings have a baseline of 1750 for the start of anthropogenic

influences. It is likely  that there was a net anthropogenic forcing of 0.0–0.3 Wm–2 in 1850–1900 relative to 1750 ( medium confidence ), 
and from the period around 1750 to 1850–1900, there was a change in global surface temperature of around 0.1°C ( likely  range 
–0.1 to +0.3°C, medium confidence ), with an anthropogenic component of 0.0°C to 0.2°C ( likely range , medium confidence ). {Cross-
Chapter Box 1.2, 7.3.5}
Global surface temperature has evolved over geological time (Figure TS.1, Box TS.2). Beginning approximately 6500 years ago, global 
surface temperature generally decreased, culminating in the coldest multi-century interval of the post-glacial period (since roughly 
7000 years ago), which occurred between around 1450 and 1850 ( high confidence ). Over the last 50 years, global surface temperature 
has increased at an observed rate unprecedented in at least the last two thousand years ( high confidence ). Temperatures as high as

during the most recent decade (2011–2020) exceed the warmest centennial-scale range reconstructed for the present interglacial, 
around 6500 years ago [0.2°C to 1°C] ( medium confidence ). The next most recent warm period was about 125,000 years ago during 
the Last Interglacial when the multi-centennial temperature range [0.5°C to 1.5°C] encompasses the 2011–2020 values ( medium 
confidence ) (Cross-Section Box TS.1, Figure 1). During the mid-Pliocene Warm Period, around 3.3–3.0 million years ago, global surface 
temperature was 2.5°C to 4°C warmer ( medium confidence ). {2.3.1, Cross-Chapter Box 2.1 and 2.4}
Current Warming
There is very high confidence  that the CMIP6 model ensemble reproduces observed global surface temperature trends and variability 
since 1850 with errors small enough to allow for detection and attribution of human-induced warming. The CMIP6 multi-model mean

global surface warming between 1850–1900 and 2010–2019 is close to the best estimate of observed warming, though some CMIP6 
models simulate a warming that is outside the assessed very likely  observed range. {3.3.1}
The likely  range of human-induced change in global surface temperature in 2010–2019 relative to 1850–1900 is 0.8°C to 1.3°C, 
with a central estimate of 1.07°C (Figure Cross-Section Box TS.1, Figure 1), encompassing the best estimate of observed warming 
for that period, which is 1.06°C with a very likely  range of [0.88°C to 1.21°C], while the likely  range of the change attributable 
to natural forcing is only –0.1°C to +0.1°C. This assessment is consistent with an estimate of the human-induced global surface 
temperature rise based on assessed ranges of perturbations to the top of the atmosphere (effective radiative forcing) and with metrics

of feedbacks of the climate response (equilibrium climate sensitivity and the transient climate response). Over the same period, well-
mixed greenhouse gas forcing likely  warmed global surface temperature by 1.0°C to 2.0°C, while aerosols and other anthropogenic 
forcings likely  cooled global surface temperature by 0.0°C to 0.8°C. {2.3.1, 3.3.1, 7.3.5, Cross-Chapter Box 7.1}
The observed slower increase in global surface temperature (relative to preceding and following periods) in the 1998–2012 period, 
sometimes referred to as ‘the hiatus’, was temporary ( very high confidence ). The increase in global surface temperature during the 
1998–2012 period is also greater in the data sets used in the AR6 assessment than in those available at the time of AR5. Using these 
updated observational data sets and a like-for-like consistent comparison of simulated and observed global surface temperature, all

observed estimates of the 1998–2012 trend lie within the very likely  range of CMIP6 trends. Furthermore, the heating of the climate 
system continued during this period, as reflected in the continued warming of the global ocean ( very high confidence ) and in the 
continued rise of hot extremes over land ( medium confidence ). Since 2012, global surface temperature has risen strongly, with the 
past five years (2016–2020) being the hottest five-year period between 1850 and 2020 ( high confidence ). {2.3.1, 3.3.1, 3.5.1, Cross-
Chapter Box 3.1}
Future Changes in Global Surface Temperature
The AR6 assessment of future change in global surface temperature is, for the first time in an IPCC report, explicitly constructed by 
combining new projections for the SSP scenarios with observational constraints based on past simulated warming as well as the AR6-
updated assessment of equilibrium climate sensitivity and transient climate response. In addition, climate forecasts initialized from

the observed climate state have been used for the period 2019–2028. The inclusion of additional lines of evidence has reduced the 
assessed uncertainty ranges for each scenario (Cross-Section Box TS.1, Figure 1). {4.3.1, 4.3.4, Box 4.1, 7.5}

61
−0.6−0.4−0.2−0.1 0.0 0.6 0.4 0.2 0.11981–2020
Trend (°C per decade)(b) Observed and projected warming are stronger over
     land than oceans, and strongest in the Arctic
SSP3-7.0 (2081–2100)
−6−4−3−2−1−0.5 00.5 1 2 3 4 6−0.51.0
0.5
0.0
1850 1900 1950 2000HadCRUT .5.0
(d) Internal variability will influence near-term warming rates
2000–2019 2020–2039 2040–2059 2060–2079 2081–2100012345
1.5 °C
12345GSAT 2081–2100 relative to 1995–2014 (°C)Relative to 1850–1900 (°C)
Total change (°C) (c) Global surface temperature has risen more than 1°C
     from 1850–1900
(e) Warming to 2100 depends on the scenarioCMIP6 historical
HadCRUT5
assessed SSP1-2.6
assessed SSP2-4.5
assessed SSP3-7.0
models 1-4Global surface temperature relative to 1850–1900 (°C) 
SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5Non significantSignificant Colour
−0.50.00.51.01.5
Last InterglacialLatest decade

relative to 1850–1900 (°C)
Year (CE)
  after the Last Interglacial, around 125,000 years ago
Pages 2k
AR6 mean
1.01.5
0.5
0.0
1980 1990 2000 2010 2020 2030 2040 20502.02.5
Cross-Section Box TS.1, Figure 1 |  Earth’s surface temperature history and future with key findings annotated within each panel.

62
Cross-Section Box TS.1, Figure 1 (continued): The intent of this figure is to show global surface temperature observed changes from the Holocene to now, 
and projected changes.  (a) Global surface temperature over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) 
in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE (mean of four datasets in panel c). Bold lines show the median of the 
multi-method reconstruction, with 5% and 95% percentiles of the ensemble members (thin lines). Vertical bars are 5–95th percentile ranges of estimated global 
surface temperature for the Last Interglacial and mid-Holocene ( medium confidence ) (Section 2.3.1.1). All temperatures are relative to 1850–1900. (b) Spatially

resolved trends (°C per decade) for (upper map) HadCRUTv5 over 1981–2020, and (lower map, total change) multi-model mean projected changes from 1995–2014 
to 2081–2010 in the SST3-7.0 scenario. Observed trends have been calculated where data are present in both the first and last decade and for at least 70% of all 
years within the period using ordinary least squares. Significance is assessed with autoregressive AR(1) model correction and denoted by stippling. Hatched areas 
in the lower map show areas of conflicting model evidence on significance of changes. (c) Temperature from instrumental data for 1850–2020, including annually 
resolved averages for the four global surface temperature datasets assessed in Section 2.3.1.1.3 (see text for references). The grey shading shows the uncertainty 
associated with the HadCRUTv5 estimate. All temperatures are relative to the 1850–1900 reference period. (d) Recent past and 2015–2050 evolution of annual mean

global surface temperature change relative to 1850–1900, from HadCRUTv5 (black), Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations 
(up to 2014, in grey, ensemble mean solid, 5% and 95% percentiles dashed, individual models thin), and CMIP6 projections under scenario SSP2-4.5, from four 
models that have an equilibrium climate sensitivity near the assessed central value (thick yellow). Solid thin coloured lines show the assessed central estimate of 
20-year change in global surface temperature for 2015–2050 under three scenarios, and dashed thin coloured lines the corresponding 5% and 95% quantiles. (e) 
Assessed projected change in 20-year running mean global surface temperature for five scenarios (central estimate solid, very likely range shaded for SSP1-2.6 and 
SSP3-7.0), relative to 1995–2014 (left y-axis) and 1850–1900 (right y-axis). The y-axis on the right-hand side is shifted upward by 0.85°C, the central estimate of the

observed warming for 1995–2014, relative to 1850–1900. The right y-axis in (e) is the same as the y-axis in (d). {2.3, 4.3, 4.4}
During the near term (2021–2040), a 1.5°C increase in global surface temperature, relative to 1850–1900, is very likely  to occur 
in scenario SSP5-8.5, likely to occur in scenarios SSP2-4.5 and SSP3-7.0, and more likely than not  to occur in scenarios SSP1-1.9 and 
SSP1-2.6. The time of crossing a warming level is defined here as the midpoint of the first 20-year period during which the average 
global surface temperature exceeds the level. In all scenarios assessed here except SSP5-8.5, the central estimate of crossing the 1.5°C 
level lies in the early 2030s. This is in the early part of the likely range (2030 –2052) assessed in SR1.5, which assumed continuation of 
the then-current warming rate; this rate has been confirmed in the AR6. Roughly half of this difference arises from a larger historical

warming diagnosed in AR6. The other half arises because for central estimates of climate sensitivity, most scenarios show stronger 
warming over the near term than was estimated as ‘current’ in SR1.5 ( medium confidence ). When considering scenarios similar to SSP1-
1.9 instead of linear extrapolation, the SR1.5 estimate of when 1.5°C global warming is crossed is close to the central estimate reported 
here. (Cross-Section Box TS.1, Table 1) {2.3.1, Cross-Chapter Box 2.3, 3.3.1, 4.3.4, Box 4.1}
It is more likely than not  that under SSP1-1.9, global surface temperature relative to 1850–1900 will remain below 1.6°C throughout 
the 21st century, implying a potential temporary overshoot of 1.5°C global warming of no more than 0.1°C. If climate sensitivity lies 
near the lower end of the assessed very likely  range, crossing the 1.5°C warming level is avoided in scenarios SSP1-1.9 and SSP1-2.6

(medium confidence ). Global surface temperature in any individual year, in contrast to the 20-year average, could by 2030 exceed 
1.5°C relative to 1850–1900 with a likelihood between 40% and 60%, across the scenarios considered here ( medium confidence ). 
(Cross-Section Box TS.1, Table 1) {4.3.4, 4.4.1, Box 4.1, 7.5}
During the 21st century, a 2°C increase in global surface temperature relative to 1850–1900 will be crossed under SSP5-8.5 and 
SSP3-7.0, is extremely likely  to be crossed under SSP2-4.5, but is unlikely  to be crossed under SSP1-2.6 and extremely unlikely  to be 
crossed under SSP1-1.9. For the mid-term period 2041–2060, this 2°C global warming level is very likely  to be crossed under SSP5-8.5, 
likely  to be crossed under SSP3-7.0, and more likely than not  to be crossed under SSP2-4.5. (Cross-Section Box TS.1, Table 1) {4.3.4}
Events of reduced and increased global surface temperature trends at decadal time scales will continue to occur in the 21st century

but will not affect the centennial-scale warming ( very high confidence ). If strong mitigation is applied from 2020 onward as reflected 
in SSP1-1.9, its effect on 20-year trends in global surface temperature would likely  emerge during the near term (2021–2040), 
measured against an assumed non-mitigation scenario such as SSP3-7.0 or SSP5-8.5. All statements about crossing the 1.5°C level 
assume that no major volcanic eruption occurs during the near term (Cross-Section Box TS.1, Table 1). {2.3.1, Cross-Chapter Box 2.3, 
4.3.4, 4.4.1, 4.6.3, Box 4.1}
Compared to 1850–1900, average global surface temperature over the period 2081–2100 is very likely  to be higher by [1.0°C to 
1.8°C] in the low CO 2 emissions scenario SSP1-1.9 and by [3.3°C to 5.7°C] in the high CO 2 emissions scenario SSP5-8.5. For the 
scenarios SSP1-2.6, SSP2-4.5, and SSP3-7.0, the corresponding very likely  ranges are [1.3°C to 2.4°C], [2.1°C to 3.5°C], and [2.8°C

to 4.6°C], respectively. The uncertainty ranges for the period 2081–2100 continue to be dominated by the uncertainty in equilibrium 
climate sensitivity and transient climate response ( very high confidence ) (Cross-Section Box TS.1, Table 1). {4.3.1, 4.3.4, 4.4.1, 7.5}
The CMIP6 models project a wider range of global surface temperature change than the assessed range ( high confidence ); furthermore, 
the CMIP6 global surface temperature increase tends to be larger than that in CMIP5 ( very high confidence ). {4.3.1, 4.3.4, 4.6.2, 7.5.6}

63
Technical SummaryTSCross-Section Box TS.1 (continued)
Cross-Section Box TS.1, Table 1 |  Assessment results for 20-year averaged change in global surface temperature based on multiple lines of 
evidence.  The change is displayed in °C relative to the 1850–1900 reference period for selected time periods (first three rows), and as the first 20-year period during 
which the average global surface temperature change exceeds the specified level relative to the period 1850–1900 (last four rows). The entries give both the central 
estimate and, in parentheses, the very likely  (5–95%) range. An entry n.c. means that the global warming level is not crossed during the period 2021–2100.
SSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5
Near term, 
2021 –20401.5 [1.2 to 1.7] 1.5 [1.2 to 1.8] 1.5 [1.2 to 1.8] 1.5 [1.2 to 1.8] 1.6 [1.3 to 1.9]
Mid-term,  
2041–20 601.6 [1.2 to 2.0] 1.7 [1.3 to 2.2] 2.0 [1.6 to 2.5] 2.1 [1.7 to 2.6] 2.4 [1.9 to 3.0]
Long term,

2081–21 001.4 [1.0 to 1.8] 1.8 [1.3 to 2.4] 2.7 [2.1 to 3.5] 3.6 [2.8 to 4.6] 4.4 [3.3 to 5.7]
1.5°C2025–2044
[2012–2031 to 2037–2056]2021–2040
[2013–2032 to 2033–2052]2018–2037
[2011–2030 to 2029–2048]
2°Cn.c.
[2028–2047 to 2075–2094]2037–2056
[2026–2045 to 2053–2072]2032–2051
[2023–2042 to 2044–2063]
3°Cn.c.
[2042–2061 to 2074–2093]
4°Cn.c.
TS.2 Large-scale Climate Change: Mean 
Climate, Variability and Extremes
This section summarizes knowledge about observed and projected 
large-scale climate change (including variability and extremes), 
drivers and attribution of observed changes to human activities. It 
describes observed and projected large-scale changes associated

with major components of the climate system: atmosphere, ocean 
(including sea level change), land, biosphere and cryosphere, and the 
carbon, energy and water cycles. In each subsection, reconstructed 
past changes, observed and attributed recent changes, and projected 
near- and long-term changes to mean climate, variability and 
extremes are presented, where possible, in an integrated way. See 
Section TS.1.3.1 for information on the scenarios used for projections.
TS.2.1 Changes Across the Global Climate System
In addition to global surface temperature (Cross-Section Box 
TS.1), a wide range of indicators across all components of 
the climate system are changing rapidly (Figure TS.7), with 
many at levels unseen in millennia. The observed changes 
provide a coherent picture of a warming world, many 
aspects of which have now been formally attributed to 
human influence, and human influence on the atmosphere, 
ocean, and land components of the climate system, taken

together, is assessed as unequivocal for the first time in an 
IPCC assessment report (Table TS.1, Figure TS.7). 
It is virtually certain  that global surface temperature rise 
and associated changes can be limited through rapid and substantial reductions in global GHG emissions. Continued 
GHG emissions greatly increase the likelihood of potentially 
irreversible changes in the global climate system (Box TS.9), 
in particular with respect to the contribution of ice sheets 
to global sea level change ( high confidence ). {2.3, 3.8, 4.3, 
4.6, 4.7, 7.2–7.4, Cross-Chapter Box 7.1, 9.2–9.6}
Earth system model simulations of the historical period since 1850 
are only able to reproduce the observed changes in key climate 
indicators when anthropogenic forcings are included (Figure TS.7). 
Taken together with numerous formal attribution studies across an 
even broader range of indicators and theoretical understanding, 
this underpins the unequivocal attribution of observed warming of

the atmosphere, ocean, and land to human influence (Table TS.1). 
{2.3, 3.8}

64
TSFigure TS.7 |  Simulated and observed changes compared to the 1995–2014 average in key large-scale indicators of climate change across the climate 
system, for continents, ocean basins and globally up to 2014.  The intent of this figure is to compare the observed and simulated changes over the historical period 
for a range of variables and regions, with and without anthropogenic forcings, for attribution.  Black lines show observations, orange lines and shading show the multi-model 
mean and 5–95th percentile ranges for Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations including anthropogenic and natural forcing, and green 
lines and shading show corresponding ensemble means and 5–95th percentile ranges for CMIP6 natural-only simulations. Observations after 2014 (including, for example, a

strong subsequent decrease of Antarctic sea ice area that leads to no significant overall trend since 1979) are not shown because the CMIP6 historical simulations end in 2014. 
A 3-year running mean smoothing has been applied to all observational time series. {3.8, Figure 3.41}
Global
Near-surface air temperature
over land Near-surface air temperature Ocean heat content
North America Asia ArcticSea ice
Central and South America Australasia Antarctic
Europe and North Africa AntarcticPrecipitation
Global
Africa Arctic
60°N–90°N
anthropogenic + natural      natural       observations

65
Technical SummaryTSTable TS.1 |  Assessment of observed changes in large-scale indicators of mean climate across climate system components and their attribution to human 
influence. The colour coding indicates the assessed confidence in/likelihood of the human contribution as a driver or main driver19 (main driver is specified in that case) where 
available (see colour key). Otherwise, explanatory text is provided in cells with white background. The relevant chapter section with more detailed information is listed in each 
table cell.
19 Throughout this Technical Summary, ‘main driver’ means responsible for more than 50% of the change.Change in Indicator Observed Change Assessment Human Contribution Assessment
Atmosphere and Water Cycle
Warming of global mean surface air temperature since 
1850–1900{2.3.1, Cross-Chapter Box 2.3}Likely  range of human contribution (0.8°C–1.3°C) encom -
passes observed warming (0.9°C–1.2°C) {3.3.1}

Warming of the troposphere since 1979 {2.3.1} Main driver {3.3.1}
Cooling of the lower stratosphere Since mid-20th century {2.3.1}  Main driver 1979–mid-1990s {3.3.1}
Large-scale precipitation and upper troposphere humidity 
changes since 1979{2.3.1} {3.3.2, 3.3.3}
Expansion of the zonal mean Hadley Circulation since 
the 1980s{2.3.1} Southern Hemisphere {3.3.3}
Ocean
Ocean heat content increase since the 1970s {2.3.3, 2.3.4, 9.2.1, Cross-Chapter Box 9.1} Main driver {3.5.1}
Salinity changes since the mid-20th century {2.3.3, 2.3.4, 9.2.2}  {3.5.2}
Global mean sea level rise since 1971 {2.3.3, 9.6.1} Main driver {3.5.3}
Arctic sea ice loss since 1979 {2.3.2, 9.3.1} Main driver {3.4.1}
Reduction in Northern Hemisphere spring snow cover since 
1950{2.3.2, 9.5.3} {3.4.2}
Greenland Ice Sheet mass loss since 1990s {2.3.2, 9.4.1} {3.4.3}
Antarctic Ice Sheet mass loss since 1990s {2.3.2, 9.4.2} Limited evidence  and medium agreement  {3.4.3}

Retreat of glaciers {2.3.2, 9.5.1} Main driver {3.4.3}
Carbon Cycle
Increased amplitude of the seasonal cycle of atmospheric 
CO 2 since the early 1960s{2.3.4} Main driver {3.6.1}
Acidification of the global surface ocean {SROCC, 5.3.2, Cross-Chapter Box 5.3} Main driver {3.6.2}
Land Climate (Extremes, see Table TS.12)
Mean 2 m land warming since 1850–1900 (about 40% 
larger than global mean warming){2.3.1} Main driver {3.3.1}
Synthesis
Warming of the global climate system since 
pre-industrial times{2.3.5} {3.8.1}
Future climate change across a range of atmospheric, cryospheric, 
oceanic and biospheric indicators depends upon future emissions 
pathways. Outcomes for a broad range of indicators increasingly 
diverge through the 21st century across the different SSPs (Section 
TS.1.3.1, Figure TS.8). Due to the slow response of the deep ocean 
and ice sheets, this divergence continues long after 2100, and 21st

century emissions choices will have implications for GMSL rise for centuries to millennia. Furthermore, it is likely  that at least one large 
volcanic eruption will occur during the 21st century. Such an eruption 
would reduce global surface temperature for several years, decrease 
land precipitation, alter monsoon circulation and modify extreme 
precipitation, at both global and regional scales. {4.3, 4.7, 9.4, 9.6, 
Cross-Chapter Box 4.1}See text description medium confidence likely/high confidence very likely extremely likely virtually certain fact

66
TSFigure TS.8 |  Observed, simulated and projected changes compared to the 1995–2014 average in four key indicators of the climate system through to 
2100 differentiated by Shared Socio-economic Pathway (SSP) scenario.  The intent of this figure is to show how future emissions choices impact key, iconic large-scale 
indicators and to highlight that our collective choices matter.  Past simulations are based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble. 
Future projections are based on the assessed ranges based upon multiple lines of evidence for (a) global surface temperature (Cross-Section Box TS.1) and (b) global ocean heat 
content and the associated thermosteric sea level contribution to global mean sea level change (right-hand axis) using a climate model emulator (Cross-Chapter Box 7.1), and

CMIP6 simulations for (c) Arctic September sea ice and (d) global land precipitation. Projections for SSP1-1.9 and SSP1-2.6 show that reduced greenhouse gas emissions lead 
to a stabilization of global surface temperature, Arctic sea ice area and global land precipitation over the 21st century. Projections for SSP1-2.6 show that emissions reductions 
have the potential to substantially reduce the increase in ocean heat content and thermosteric sea level rise over the 21st century but that some increase is unavoidable. The 
brackets in the x axis in panel (a) indicate assessed 20-year-mean periods. {4.3, Figure 4.2, 9.3, 9.6, Figure 9.6}Recent and future change of four key indicators of the climate system
Atmospheric temperature, ocean heat content, Arctic summer sea ice, and land precipitation 
(b) Global ocean heat content and thermosteric sea level
Future (SSP1-1.9) mean 
 Past (simulated); 5–95%  range

Past (observed) (c) Arctic September sea ice area (d) Global land precipitation
1950 2000 2050 2100(a) Global surface air temperature
–1001020 %
0246810million km2
Practically sea ice free
 
Future (SSP5-8.5) mean Future (SSP3-7.0) mean; 5–95% range Future (SSP2-4.5) mean Future (SSP1-2.6) mean; 5–95%  range 
Past (simulated)
Past (observed)
Future (CMIP6)Future (CMIP6)
Past (simulated)Past (observed)
–1034°C2
1
1950 2000 [2000/
2019][2040/
 2059][2080/
 2100]Past (observed)Future (assessed)
–10123YJ
-0.100.10.20.3m
Past (simulated)Past (observed)Future (assessed)CMIP6 Emulator
1950 2000 2050 2100Change 
in 2100
Change 
in 2100Change 
in 2100
Observational records show changes in a wide range of climate 
extremes that have been linked to human influence on the climate 
system (Table TS.2). In many cases, the frequency and intensity of 
future changes in extremes can be directly linked to the magnitude

of future projected warming. Changes in extremes have been 
widespread over land since the 1950s, including a virtually certain  
global increase in extreme air temperatures and a likely  intensification 
in global-scale extreme precipitation. It is extremely likely  that human influence is the main contributor to the observed increase 
(decrease) in the likelihood and severity of hot (cold) extremes (Table 
TS.2). The frequency of extreme temperature and precipitation events 
in the current climate will change with warming, with warm extremes 
becoming more frequent ( virtually certain ), cold extremes becoming 
less frequent ( extremely likely ) and precipitation extremes becoming 
more frequent in most locations ( very likely ). {9.6.4, 11.2, 11.3, 11.4, 
11.6, 11.7, 11.8, 11.9, Box 9.2}

67
Technical SummaryTSTable TS.2 |  Summary table on observed changes in extremes, their attribution since 1950 (except where stated otherwise), and projected changes at 
+1.5°C, +2°C and +4°C of global warming, on global and continental scales.  An increase in warm/hot extremes refers to warmer and/or more frequent hot days and 
nights and warm spells/heatwaves, over most land areas. A decrease in cold extremes refers to warmer and/or fewer cold days and nights and cold spells/cold waves, over most 
land areas. Drought events are relative to a predominant fraction of land area. For tropical cyclones, observed changes and attribution refer to Categories 3–5, while projected 
changes refer to Categories 4–5. Tables 11.1 and 11.2 are more detailed versions of this table, containing, in particular, information on regional scales. In general, higher

warming levels also imply stronger projected changes for indicators where the confidence level does not depend on the warming level and the table does not explicitly quantify 
the global sensitivity. See also Box TS.10. {9.6, Box 9.2, 11.3, 11.7}
Change in IndicatorObserved 
(since 1950)Attributed 
(since 1950)Projected at GWL (°C)
+1.5 +2 +4
Warm/hot extremes: Frequency 
or intensity ↑Main driver↑ ↑ ↑
Cold extremes: Frequency or intensity ↓Main driver↓ ↓ ↓
Heavy precipitation events: Frequency, 
intensity and/or amount↑ 
Over majority of land 
regions with good 
observational coverage 
Main driver of the 
observed intensification 
of heavy precipitation in 
land regions↑ ↑↑
in most land regions in most land regions
Agricultural and ecological droughts: 
Intensity and/or frequency↑ 
in some regions 
in some regions↑ 
in more regions 
compared to observed 
changes↑ 
compared to 1.5°C 
of global warming↑ 
compared to 2°C of 
global warming

Precipitation associated with 
tropical cyclones ↑↑
Rate +11%↑
Rate +14%↑
Rate +28%
Tropical cyclones: Proportion 
of intense cyclones ↑↑
+10%↑
+13%↑
+20%
Compound events: Co-occurrent 
heatwaves and droughts↑
(Frequency) (Frequency)↑
(Frequency and intensity increases with warming)
Marine heatwaves: Intensity & frequency ↑
(since 1900) (since 2006)↑
Strongest in tropical and Arctic Ocean
Extreme sea levels: Frequency ↑
(since 1960)↑
(Scenario-based assessment for 21st century)
TS.2.2 Changes in the Drivers of the Climate System
Since 1750, changes in the drivers of the climate system 
are dominated by the warming influence of increases in 
atmospheric GHG concentrations and a cooling influence 
from aerosols, both resulting from human activities. In 
comparison there has been negligible long-term influence 
from solar activity and volcanoes. Concentrations of CO 2, 
methane (CH 4), and nitrous oxide (N 2O) have increased to 
levels unprecedented in at least 800,000 years, and there

is high confidence that current CO 2 concentrations have not 
been experienced for at least 2 million years. Global mean 
concentrations of anthropogenic aerosols peaked in the 
late 20th century and have slowly declined since in northern 
mid-latitudes, although they continue to increase in South 
Asia and East Africa ( high confidence ). The total anthropogenic effective radiative forcing (ERF) 
in 2019, relative to 1750, was 2.72 [1.96 to 3.48] W m–2 
(medium confidence ) and has likely  been growing at an 
increasing rate since the 1970s. {2.2, 6.4, 7.2, 7.3}
Solar activity since 1900 was high but not exceptional compared to 
the past 9000 years ( high confidence ). The average magnitude and 
variability of volcanic aerosols since 1900 has not been unusual 
compared to at least the past 2500 years ( medium confidence ). 
However, sporadic strong volcanic eruptions can lead to temporary 
drops in global surface temperature lasting 2–5 years. {2.2.1, 2.2.2,

2.2.8, Cross-Chapter Box 4.1}
Atmospheric CO 2 concentrations have changed substantially 
over millions of years (Figure TS.1). Current levels of atmospheric 
CO 2 have not been experienced for at least 2 million years ( high medium confidence likely/high confidence very likely extremely likely virtually certain

68
TS
420
400
380
360
340
320
300
280(b) Information from multiple ice cores depicts a strong 
increase of CO 2, CH 4, and N 2O since the 19th century(a) Last time CO 2levels were as high as present was at least 2 million years ago
(c) Since 1960–1980 several high-accuracy global networks measure 
surface concentrations of CO 2, CH 4, and N 2O. Current concentrations  
are higher than measured in ice cores during the last 800,000 year s
(d) The increase in ef fective radiative forcing (ERF) since the late 19th century is driven predominantly by  
warming GHGs and cooling aerosol. ERF is changing at a faster rate since the 1970s1800
1600
1400
1200
1000
800
600 340
320
300
280
2601960–2019 1960–2019
CH 4 (ppb)
CO 2 (ppm)CO 2 (ppm)
N2O (ppb)N2O (ppb)409.9409.9
1866.31866.3
332.1150250350450CO 2  (ppm)
3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Age (millions of years ago)δ11B-foraminifera
Ant. ice core
Law Dome
EDMLWAIS Divide
GISP2

Law DomeWAIS Divide
NEEM
Law Dome Styx EUROGISP2
H1516001800
800100012001400
600340
280320
300
260
Year (CE)2000400420
360
320340
280380
300409.9
332.1W m−2
Rate of change anthropogenic ERF
1750 1800 1850 1900 1950 20000.5
0.4
0.3
0.2
0.1
0.0(W m−2  per decade−1)−4−202CH 4 (ppb)NOAA
CSIRO
SIO
NOAA
CSIRO
AGAGENOAA
CSIRO
AGAGE
UCI
Tropospheric Aerosol
Other anthropogenicVolcanic
Solar
TotalCarbon dioxide (CO 2)
Methane (CH 4)
Nitrous oxide (N 2O)
Ozone (O 3)
Figure TS.9 |  Changes in well-mixed greenhouse gas (WMGHG) concentrations and effective radiative forcing (EFR).  The intent of this figure is to show that the 
changes of the main drivers of climate system over the industrial period are exceptional in a long-term context.  (a) Changes in carbon dioxide (CO 2) from proxy records over the

past 3.5 million years. (b) Changes in all three WMGHGs from ice core records over the Common Era. (c) Directly observed WMGHG changes since the mid-20th century. (d) 
Evolution of ERF and components since 1750. Further details on data sources and processing are available in the associated FAIR data table. {2.2, Figures 2.3, 2.4 and 2.10}

69
Technical SummaryTSconfidence , Figure TS.9a). Over 1750–2019, CO 2 increased by 131.6 
± 2.9 ppm (47.3%). The centennial rate of change of CO 2 since 1850 
has no precedent in at least the past 800,000 years (Figure TS.9), 
and the fastest rates of change over the last 56 million years were 
at least a factor of four lower ( low confidence ) than over 1900–
2019. Several networks of high-accuracy surface observations 
show that concentrations of CO 2 have exceeded 400 ppm, reaching 
409.9 (± 0.3) ppm in 2019 (Figure TS.9c). The ERF from CO 2 in 2019 
(relative to 1750) was 2.16 Wm–2. {2.2.3, 5.1.2, 5.2.1, 7.3}
By 2019, concentrations of CH 4 reached 1866.3 (± 3.3) ppb 
(Figure TS.9c). The increase since 1750 of 1137 ± 10 ppb (157.8%) far 
exceeds the range over multiple glacial–interglacial transitions of the 
past 800,000 years ( high confidence ). In the 1990s, CH 4 concentrations 
plateaued, but started to increase again around 2007 at an average

rate of 7.6 ± 2.7 ppb yr–1 (2010–2019; high confidence ). There is high 
confidence  that this recent growth is largely driven by emissions 
from fossil fuel exploitation, livestock, and waste, with ENSO driving 
multi-annual variability of wetland and biomass burning emissions. 
In 2019, ERF from CH 4 was 0.54 Wm–2. {2.2.3, 5.2.2, 7.3}
Since 1750, N 2O increased by 62.0 ± 6.0 ppb, reaching a level of 
332.1 (± 0.4) ppb in 2019. The increase since 1750 is of comparable 
magnitude to glacial–interglacial fluctuations of the past 
800,000  years (Figure TS.9c). N 2O concentration trends since 1980 
are largely driven by a 30% increase in emissions from the expansion 
and intensification of global agriculture ( high confidence ). By 2019 
its ERF was 0.21 W m–2. {2.2.3, 5.2.3}
Halogenated gases consist of chlorofluorocarbons (CFCs), 
other gases, many of which can deplete stratospheric ozone and

warm the atmosphere. In response to controls on production and 
consumption mandated by the Montreal Protocol on Substances 
that Deplete the Ozone Layer and its amendments, the atmospheric 
abundances of most CFCs have continued to decline since AR5. 
Abundances of HFCs, which are replacements for CFCs and HCFCs, 
are increasing ( high confidence ), though increases of the major HCFCs 
have slowed in recent years. The ERF from halogenated components 
in 2019 was 0.41 Wm–2. {2.2.4, 6.3.4, 7.3.2}
Tropospheric aerosols mainly act to cool the climate system, 
directly by reflecting solar radiation, and indirectly by enhancing 
cloud reflectance. Ice cores show increases in aerosols across the 
Northern Hemisphere mid-latitudes since 1700 and reductions 
since the late 20th century ( high confidence ). Aerosol optical depth 
(AOD), derived from satellite- and ground-based radiometers, 
has decreased since 2000 over the mid-latitude continents of

both hemispheres, but increased over South Asia and East Africa 
(high confidence ). Trends in AOD are more pronounced from sub-
micrometre aerosols for which the anthropogenic contribution is 
particularly large. Global carbonaceous aerosol budgets and trends 
remain poorly characterized due to limited observations, but black 
carbon (BC), a warming aerosol component, is declining in several 
regions of the Northern Hemisphere ( low confidence ). Total aerosol 
ERF in 2019, relative to 1750, is −1.1 [−1.7 to −0.4] W m−2 (medium 
confidence ) and more likely than not  became less negative since the late 20th century, with low confidence  in the magnitude of 
post-2014 changes due to conflicting evidence (Section TS.3.1). 
{2.2.6, 6.2.1, 6.3.5, 6.4.1, 7.3.3}
There is high confidence that tropospheric ozone has been increasing 
from 1750 in response to anthropogenic changes in ozone precursor 
emissions (nitrogen oxides, carbon monoxide, non-methane volatile

organic compounds, and methane), but with medium confidenc e in 
the magnitude of this change, due to limited observational evidence 
and knowledge gaps. Since the mid-20th century, tropospheric 
ozone surface concentrations have increased by 30–70% across the 
Northern Hemisphere ( medium confidence ); since the mid-1990s, 
free tropospheric ozone has increased by 2–7% per decade in most 
northern mid-latitude regions and 2–12% per decade in sampled 
tropical regions. Future changes in surface ozone concentrations will 
be primarily driven by changes in precursor emissions rather than 
climate change ( high confidence ). Stratospheric ozone has declined 
between 60°S–60°N by 2.2% from 1964–1980 to 2014–2017 
(high confidence ), with the largest declines during 1980–1995. 
The strongest loss of stratospheric ozone continues to occur in 
austral spring over Antarctica (ozone hole), with emergent signs of 
recovery after 2000. The 1750–2019 ERF for total (stratospheric and

tropospheric) ozone is 0.47 [0.24 to 0.71] W m−2, which is dominated 
by tropospheric ozone changes. {2.2.5, 6.3.2, 7.3.2, 7.3.5}
The global mean abundance of hydroxyl (OH) radical, or ‘oxidizing 
capacity’, chemically regulates the lifetimes of many SLCFs, and 
therefore the radiative forcing of CH 4, ozone, secondary aerosols 
and many halogenated species. Model estimates suggest no 
significant change in oxidizing capacity from 1850 to 1980 ( low 
confidence ). Increases of about 9% over 1980–2014 computed by 
ESMs and carbon cycle models are not confirmed by observationally 
constrained inverse models, rendering an overall  medium confidence  
in stable OH or positive trends since the 1980s, and implying that OH 
is not the primary driver of recent observed growth in CH 4. {6.3.6, 
Cross-Chapter Box 5.2}
Land use and land-cover change exert biophysical and biogeochemical 
effects. There is medium confidence  that the biophysical effects

of land-use change since 1750, most notably the increase in 
global albedo, have had an overall cooling on climate, whereas 
biogeochemical effects (i.e., changes in GHG and volatile organic 
compound emissions or sinks) led to net warming. Overall land-use 
and land-cover ERF is estimated at –0.2 [–0.3 to –0.1] W m−2. {2.2.7, 
7.3.4, SRCCL Section 2.5}
The total anthropogenic ERF in 2019 relative to 1750 was 2.72 [1.96 
to 3.48] W m−2 (Figure TS.9), dominated by GHGs (positive ERF) and 
partially offset by aerosols (negative ERF). The rate of change of ERF 
likely  has increased since the 1970s, mainly due to growing CO 2 
concentrations and less negative aerosol ERF (Section TS.3.1). {2.2.8, 
7.3}

70
TSTS.2.3  Upper Air Temperatures and Atmospheric 
The effects of human-induced climate change have 
been clearly identified in observations of atmospheric 
temperature and some aspects of atmospheric circulation, 
and these effects are likely  to intensify in the future. 
Tropospheric warming and stratospheric cooling are 
virtually certain to continue with continued net emissions 
of greenhouse gases. Several aspects of the atmospheric 
circulation have likely  changed since the mid-20th century, 
and human influence has likely  contributed to the observed 
poleward expansion of the Southern Hemisphere Hadley Cell 
and very likely contributed to the observed poleward shift 
of the Southern Hemisphere extratropical jet in summer. It 
is likely  that the mid-latitude jet will shift poleward and 
strengthen, accompanied by a strengthening of the storm track in the Southern Hemisphere by 2100 under the high

CO 2 emissions scenarios. It is likely  that the proportion of 
intense tropical cyclones has increased over the last four 
decades and that this cannot be explained entirely by 
natural variability. There is low confidence in observed 
recent changes in the total number of extratropical cyclones 
over both hemispheres. The proportion of tropical cyclones 
that are intense is expected to increase ( high confidence ), 
but the total global number of tropical cyclones is expected 
to decrease or remain unchanged ( medium confidence ). 
{2.3, 3.3, 4.3, 4.4, 4.5, 8.3, 8.4, 11.7}
The troposphere has warmed since at least the 1950s, and it is virtually 
certain  that the stratosphere has cooled. It is very likely  that human-
induced increases in GHGs were the main driver of tropospheric 
warming since 1979. It is extremely likely  that anthropogenic 
forcing, both from increases in GHG concentrations and depletion of

Figure TS.10 |  Observed and projected upper air temperature and circulation changes. The intent of this figure is to visualize upper air temperature and circulation 
changes and the similarity between observed and projected changes. Upper panels: (Left)  Zonal cross-section of temperature trends for 2002–2019 in the upper troposphere 
region for the ROM SAF radio-occultation dataset. (Middle)  Change in the annual and zonal mean atmospheric temperature (°C) in 2081–2100 in SSP1-2.6 relative to 
1995–2014 for 36 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. (Right)  the same in SSP3-7.0 for 32 models. Lower panels: (Left)  Long-term mean (thin 
black colour) and linear trend (colour) of zonal mean December–January–February (DJF) zonal winds for ERA5. (Middle)  multi-model mean change in annual and zonal mean

wind (m s–1) in 2081–2100 in SSP1-2.6 relative to 1995–2014 based on 34 CMIP6 models. The 1995–2014 climatology is shown in contours with spacing of 10 m s–1. (Right) 
the same for SSP3-7.0 for 31 models. {2.3.1; Figures 2.12 and 2.18; 4.5.1; Figure 4.2.6}
–5–4–3–2–1012345Pressure (hPa)GNSS-RO 2002–2019 Multi-model projections (SSP1-2.6) Multi-model projections (SSP3-7.0)
ERA5 1979–2019 (DJF) Multi-model projections (DJF, SSP1-2.6) Multi-model projections (DJF, SSP3-7.0)–0.5–0.4–0.3–0.2–0.1 00.10.20.30.40.5
–5–4–3–2–1012345 –6 6 –1 -0.5 0 0.5 1 –1.5 1.5
m/s m/s per decade°C °C per decadeObserved trends Projected long-term change (2081–2100)
Non-signficant trend
Colour
Low model agreementHigh model agreement36 32
34 31Pressure (hPa)Pressure (hPa)
Contour from –50 to 40 by 10Pressure (hPa)
Contour from –50 to 40 by 10

71
Technical SummaryTSstratospheric ozone due to ozone-depleting substances, was the main 
driver of upper stratospheric cooling since 1979. It is very likely  that 
global mean stratospheric cooling will be larger for scenarios with 
higher atmospheric CO 2 concentrations. In the tropics, since at least 
2001 (when new techniques permit more robust quantification), the 
upper troposphere has warmed faster than the near-surface ( medium 
confidence ) (Figure TS.10). There is medium confidence  that most 
CMIP5 and CMIP6 models overestimate the observed warming in 
the upper tropical troposphere over the period 1979–2014, in part 
because they overestimate tropical SST warming. It is likely that 
future tropical upper tropospheric warming will be larger than at the 
tropical surface. {2.3.1, 3.3.1, 4.5.1}
The Hadley Circulation has  likely widened since at least the 1980s, 
predominantly in the Northern Hemisphere, although there is only

medium confidence  in the extent of the changes. This has been 
accompanied by a strengthening of the Hadley Circulation in the 
Northern Hemisphere ( medium confidence ). It is likely  that human 
influence has contributed to the poleward expansion of the zonal 
mean Hadley cell in the Southern Hemisphere since the 1980s, which 
is projected to further expand with global warming ( high confidence ). 
There is medium confidence  that the observed poleward expansion 
in the Northern Hemisphere is within the range of internal variability. 
{2.3.1, 3.3.3, 8.4.3}
Since the 1970s, near-surface average winds have  likely  weakened 
over land. Over the ocean, near-surface average winds likely  
strengthened over 1980–2000, but divergent estimates lead to 
low confidence thereafter. Extratropical storm tracks have likely  
shifted poleward since the 1980s. There is low confidence  in 
projected poleward shifts of the Northern Hemisphere mid-latitude

jet and storm tracks due to large internal variability and structural 
uncertainty in model simulations. There is medium confidence  in 
a projected decrease in the frequency of atmospheric blocking over 
Greenland and the North Pacific in boreal winter in 2081–2100 under 
the SSP3-7.0 and SSP5-8.5 scenarios. There is high confidence  that 
Southern Hemisphere storm tracks and associated precipitation have 
migrated polewards over recent decades, especially in the austral 
summer and autumn, associated with a trend towards more positive 
phases of the Southern Annular Mode (SAM) (Section TS.4.2.2) and 
the strengthening and southward shift of the Southern Hemisphere 
extratropical jet in austral summer. In the long term (2081–2100), the Southern Hemisphere mid-latitude jet is likely to shift poleward 
and strengthen under the SSP5-8.5 scenario relative to 1995–2014, 
accompanied by an increase in the SAM (Section TS.4.2.2). It is

likely  that wind speeds associated with extratropical cyclones will 
strengthen in the Southern Hemisphere storm track for SSP5-8.5. 
There is low confidence  in the potential role of Arctic warming and 
sea ice loss on historical or projected mid-latitude atmospheric 
variability. {2.3.1, 3.3.3, 3.7.2, 4.3.3, 4.4.3, 4.5.1, 4.5.3, 8.2.2, 8.3.2, 
Cross-Chapter Box 10.1}
It is likely  that the proportion of major (Category 3–5) tropical 
cyclones (TCs) and the frequency of rapid TC intensification events 
have increased over the past four decades. The average location 
of peak TC wind-intensity has very likely  migrated poleward in 
the western North Pacific Ocean since the 1940s, and TC forward 
translation speed has likely  slowed over the contiguous USA since 
1900. It is likely  that the poleward migration of TCs in the western 
North Pacific and the global increase in TC intensity rates cannot be 
explained entirely by natural variability . There is high confidence

that average peak TC wind speeds and the proportion of Category 
4–5 TCs will increase with warming and that peak winds of the 
most intense TCs will increase. There is medium confidence  that the 
average location where TCs reach their maximum wind-intensity will 
migrate poleward in the western North Pacific Ocean, while the total 
global frequency of TC formation will decrease or remain unchanged 
with increasing global warming. {11.7.1}
There is  low confidence  in observed recent changes in the total 
number of extratropical cyclones over both hemispheres. There is also 
low confidence  in past-century trends in the number and intensity of 
the strongest extratropical cyclones over the Northern Hemisphere 
due to the large interannual-to-decadal variability and temporal and 
spatial heterogeneities in the volume and type of assimilated data 
in atmospheric reanalyses, particularly before the satellite era. Over

the Southern Hemisphere, it is likely that the number of extratropical 
cyclones with low central pressures (<980 hPa) has increased since 
1979. The frequency of intense extratropical cyclones is projected to 
decrease ( medium confidence ). Projected changes in the intensity 
depend on the resolution of climate models ( medium confidence ). 
There is medium confidence  that wind speeds associated with 
extratropical cyclones will change following changes in the storm 
tracks. {2.3.1, 3.3.3, 4.5.1, 4.5.3, 8.3.2, 8.4.2, 11.7.2}

72
TSBox TS.3 | Low-likelihood, High-warming Storylines
Future global warming exceeding the assessed very likely  range cannot be ruled out and is potentially associated 
with the highest risks for society and ecosystems. Such low-likelihood, high-warming storylines tend to exhibit 
substantially greater changes in the intensity of regional drying and wetting than the multi-model mean. Even at 
levels of warming within the very likely range, global and regional low-likelihood outcomes might occur, such as large 
precipitation changes, additional sea level rise associated with collapsing ice sheets (see Box TS.4), or abrupt ocean 
circulation changes. While there is medium confidence that the Atlantic Meridional Overturning Circulation (AMOC) 
will not experience an abrupt collapse before 2100, if it were to occur, it would very likely cause abrupt shifts in

regional weather patterns and water cycle. The probability of these low-likelihood outcomes increases with higher 
global warming levels. If the real-world climate sensitivity lies at the high end of the assessed range, then global 
and regional changes substantially outside the very likely range projections occur for a given emissions scenario. 
With increasing global warming, some very rare extremes and some compound events (multivariate or concurrent 
extremes) with low likelihood in past and current climate will become more frequent, and there is a higher chance 
that events unprecedented in the observational record occur ( high confidence ). Finally, low-likelihood, high-impact 
outcomes may also arise from a series of very large volcanic eruptions that could substantially alter the 21st century 
climate trajectory compared to SSP-based Earth system model (ESM) projections. {Cross-Chapter Box 4.1, 4.3, 4.4, 4.8,

7.3, 7.4, 7.5, 8.6, 9.2, 9.6, Box 9.4, Box 11.2, Cross-Chapter Box 12.1}
Previous IPCC reports largely focused their assessment on the projected very likely  range of future surface warming and associated 
climate change. However, a comprehensive risk assessment also requires considering the potentially larger changes in the physical 
climate system that are unlikely or very unlikely but possible and potentially associated with the highest risks for society and 
ecosystems (Figure TS.6). Since AR5, the development of physical climate storylines of high warming has emerged as a useful approach 
for exploring the future risk space that lies outside of the IPCC very likely  range projections. {4.8}
Uncertainty in the true values of equilibrium climate sensitivity (ECS) and transient climate response (TCR) dominate uncertainty 
in projections of future warming under moderate to strong emissions scenarios (Section TS.3.2). A real-world ECS higher than the

assessed very likely  range (2°C–5°C) would require a strong historical aerosol cooling and/or a trend towards stronger warming from 
positive feedbacks linked to changes in SST patterns (pattern effects), combined with a strong positive cloud feedback and substantial 
biases in paleoclimate reconstructions – each of which is assessed as either unlikely  or very unlikely , but not ruled out. Since CMIP6 
contains several ESMs that exceed the upper bound of the assessed very likely  range in future surface warming, these models can 
be used to develop low-likelihood, high warming storylines to explore risks and vulnerabilities, even in the absence of a quantitative 
assessment of likelihood. {4.3.4, 4.8, 7.3.2, 7.4.4, 7.5.2, 7.5.5, 7.5.7}
CMIP6 models with surface warming outside, or close to, the upper bound of the very likely  range exhibit patterns of large widespread

temperature and precipitation changes that differ substantially from the multi-model mean in all scenarios. For SSP5-8.5, the 
high-warming models exhibit widespread warming of more than 6°C over most extratropical land regions and parts of the Amazon. 
In the Arctic, annual mean temperatures increase by more than 10°C relative to present-day, corresponding to about 30% more than 
the best estimate of warming. Even for SSP1-2.6, high-warming models show on average 2°C–3°C warming relative to present-day 
conditions over much of Eurasia and North America (about 40% more than the best estimate of warming) and more than 4°C warming 
relative to the present over the Arctic in 2081–2100 (Box TS.3, Figure 1). Such a high global warming storyline would imply that the 
remaining carbon budget consistent with a 2°C warming is smaller than the assessed very likely  range. Put another way, even if

a carbon budget that likely  limits warming to 2°C is met, a low-likelihood, high-warming storyline would result in warming of 2.5°C 
or more. {4.8}
CMIP6 models with global warming close to the upper bound of the assessed very likely  warming range tend to exhibit greater 
changes in the intensity of regional drying and wetting than the multi-model mean. Furthermore, these model projections show 
a larger area of drying and tend to show a larger fraction of strong precipitation increases than the multi-model mean. However, 
regional precipitation changes arise from both thermodynamic and dynamic processes so that the most pronounced global warming 
levels are not necessarily associated with the strongest precipitation response. Abrupt human-caused changes to the water cycle 
cannot be ruled out. Positive land surface feedbacks, involving vegetation and dust, can contribute to abrupt changes in aridity,

but there is only low confidence  that such changes will occur during the 21st century. Continued Amazon deforestation, combined 
with a warming climate, raises the probability that this ecosystem will cross a tipping point into a dry state during the 21st century 
(low confidence ). (See also Box TS.9). {4.8, 8.6.2}

73
Technical SummaryTSBox TS.3 (continued)
While there is medium confidence  that the projected decline in the AMOC (Section TS.2.4) will not involve an abrupt collapse 
before 2100, such a collapse might be triggered by an unexpected meltwater influx from the Greenland Ice Sheet. If an AMOC collapse 
were to occur, it would very likely  cause abrupt shifts in the regional weather patterns and water cycle, such as a southward shift in the 
tropical rain belt, and could result in weakening of the African and Asian monsoons, strengthening of Southern Hemisphere monsoons, 
and drying in Europe. (See also Boxes TS.9 and TS.13). {4.7.2, 8.6.1, 9.2.3}
Very rare extremes and compound or concurrent events, such as the 2018 concurrent heatwaves across the Northern Hemisphere, are 
often associated with large impacts. The changing climate state is already altering the likelihood of extreme events, such as decadal

droughts and extreme sea levels, and will continue to do so under future warming. Compound events and concurrent extremes 
contribute to increasing probability of low-likelihood, high-impact outcomes and will become more frequent with increasing global 
warming ( high confidence ). Higher warming levels increase the likelihood of events unprecedented in the observational record. {9.6.4, 
Box 11.2}
Finally, low likelihood storylines need not necessarily relate solely to the human-induced changes in climate. A low-likelihood, high-
impact outcome, consistent with historical precedent in the past 2500 years, would be to see several large volcanic eruptions that 
could greatly alter the 21st century climate trajectory compared to SSP-based Earth system model projections. {Cross-Chapter Box 4.1}
Box TS.3, Figure 1 | High-warming storylines.  The intent of this figure is to illustrate high warming storylines compared to the CMIP6 multi-model-mean.

(a) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean linearly scaled to the assessed best global surface temperature estimate for SSP1-
2.6 in 2081–2100 relative to 1995–2014,  (b) mean across five high-warming models with global surface temperature changes nearest to the upper bound of the 
assessed very likely range, and (c) mean across five very high-warming models with global surface temperature changes higher than the assessed very likely . (d–f)  
Same as (a–c) but for SSP5-8.5. Note the different colour bars in (a–c) and (d–f). {4.7, Figure 4.41}(a) Best estimate (scaled) (b) High-warming models (c) Very-high-warming models
4 3 2.5 2 –2 –2.5 –3 –4 1.5 1 0 –1 –1.5SSP1-2.6 (2081–2100)
(d) Best estimate (scaled) (e) High-warming models (f) Very-high-warming modelsSSP5-8.5 (2081–2100)
8 6 5 4 –4 –5 –6 –8 3 2 0 –2 –3°C
°C

74
TSTS.2.4 The Ocean
Observations, models and paleo-evidence indicate that 
recently observed changes in the ocean are unprecedented 
for centuries to millennia ( high confidence ). Over the past 
four to six decades, it is virtually certain that the global 
ocean has warmed, with human influence extremely 
likely the main driver since the 1970s, making climate 
change irreversible over centuries to millennia ( medium 
confidence ). It is virtually certain that upper ocean salinity 
contrasts have increased since the 1950s and extremely 
likely that human influence has contributed. It is virtually 
certain that upper ocean stratification has increased since 
1970 and that sea water pH has declined globally over the 
last 40 years, with human influence being the main driver 
of the observed surface open ocean acidification ( virtually 
certain ). A long-term increase in surface open ocean pH 
occurred over the past 50 million years ( high confidence ),

and surface ocean pH as low as recent times is uncommon 
in the last 2 million years ( medium confidence ). There is 
high confidence that marine heatwaves have become more 
frequent in the 20th century, and most of those since 2006 
have been attributed to anthropogenic warming ( very 
likely ). There is high confidence that oxygen levels have 
dropped in many regions since the mid 20th century and 
that the geographic range of many marine organisms has 
changed over the last two decades. 
The amount of ocean warming observed since 1971 
will likely  at least double by 2100 under a low warming 
scenario (SSP1-2.6) and will increase by 4–8 times under 
a high warming scenario (SSP5-8.5). Stratification ( virtually 
certain ), acidification ( virtually certain ), deoxygenation 
(high confidence ) and marine heatwave frequency ( high 
confidence ) will continue to increase in the 21st century. 
While there is low confidence in 20th century AMOC change,

it is very likely that AMOC will decline over the 21st century 
(Figure TS.11). {2.3, 3.5, 3.6, 4.3.2, 5.3, 7.2, 9.2, Box 9.2, 12.4}
It is virtually certain  that the global ocean has warmed since at least 
1971, representing about 90% of the increase in the global energy 
inventory (Section TS.3.1). The ocean is currently warming faster than 
at any other time since at least the last deglacial transition ( medium 
confidence ), with warming extending to depths well below 2000 m 
(very high confidence ). It is extremely likely  that human influence 
was the main driver of this recent ocean warming. Ocean warming 
will continue over the 21st century ( virtually certain ), and will likely  
continue until at least to 2300 even for low CO 2 emissions scenarios. 
Ocean warming is irreversible over centuries to millennia ( medium 
confidence ), but the magnitude of warming is scenario-dependent from 
about the mid-21st century ( medium confidence ). The warming will

not be globally uniform, with heat primarily stored in Southern Ocean 
water-masses and weaker warming in the subpolar North Atlantic ( high 
confidence ). Limitations in the understanding of feedback mechanisms 
limit our confidence in future ocean warming close to Antarctica and 
how this will affect sea ice and ice shelves. {2.3.3, 3.5.1, 4.7.2, 7.2.2, 
9.2.2, 9.2.3, 9.2.4, 9.3.2, 9.6.1, Cross-Chapter Box 9.1}Global mean SST has increased since the beginning of the 20th 
century by 0.88 [0.68 to 1.01] °C, and it is virtually certain  it will 
continue to increase throughout the 21st century, with increasing 
hazards to marine ecosystems ( medium confidence ). Marine 
heatwaves have become more frequent over the 20th century ( high 
confidence ), approximately doubling in frequency ( high confidence ) 
and becoming more intense and longer since the 1980s ( medium 
confidence ). Most of the marine heatwaves over 2006–2015 have

been attributed to anthropogenic warming ( very likely ). Marine 
heatwaves will continue to increase in frequency, with a likely global 
increase of 2–9 times in 2081–2100 compared to 1995–2014 under 
SSP1-2.6, and 3–15 times under SSP5-8.5 (Figure TS.11a), with 
the largest changes in the tropical and Arctic ocean. {2.3.1, Cross-
Chapter Box 2.3, 9.2.1, Box 9.2, 12.4.8}
Observed upper-ocean stratification (0–200 m) has increased 
globally since at least 1970  (virtually certain ). Based on recent refined 
analyses of the available observations, there is high confidence  
that it increased by 4.9 ± 1.5% from 1970–2018, which is about 
twice as much as assessed in SROCC, and will continue to increase 
throughout the 21st century at a rate depending on the emissions 
scenario ( virtually certain ). {2.3.3, 9.2.1}
It is virtually certain that since 1950 near-surface high-salinity 
regions have become more saline, while low-salinity regions have

become fresher, with medium confidence  that this is linked to an 
intensification of the hydrological cycle (Box TS.6). It is extremely 
likely  that human influence has contributed to this salinity change 
and that the large-scale pattern will grow in amplitude over the 21st 
century ( medium confidence ). {2.3.3, 3.5.2, 9.2.2, 12.4.8}
The AMOC was relatively stable during the past 8000 years ( medium 
confidence ). There is low confidence  in the quantification of AMOC 
changes in the 20th century because of  low agreement  in quantitative 
reconstructed and simulated trends, missing key processes in both 
models and measurements used for formulating proxies, and new 
model evaluations. Direct observational records since the mid-2000s 
are too short to determine the relative contributions of internal 
variability, natural forcing and anthropogenic forcing to AMOC 
change ( high confidence ). An AMOC decline over the 21st century

is very likely  for all SSP scenarios (Figure TS.11b); a possible abrupt 
decline is assessed further in Box TS.3. {2.3.3, 3.5.4, 4.3.2, 8.6.1, 
9.2.3, Cross-Chapter Box 12.3}
There is high confidence  that many ocean currents will change in 
the 21st century in response to changes in wind stress.  There is low 
confidence  in 21st century change of Southern Ocean circulation, 
despite high confidence  that it is sensitive to changes in wind 
patterns and increased ice-shelf melt. Western boundary currents 
and subtropical gyres have shifted poleward since 1993 ( medium 
confidence ). Subtropical gyres, the East Australian Current Extension, 
the Agulhas Current, and the Brazil Current are projected to intensify 
in the 21st century in response to changes in wind stress, while the 
Gulf Stream and the Indonesian Throughflow are projected to weaken 
(medium confidence ). All of the four main eastern boundary upwelling

systems are projected to weaken at low latitudes and intensify at high 
latitudes in the 21st century ( high confidence ). {2.3.3, 9.2.3}

75
Recent and Future change in the ocean 
Marine heatwaves, Atlantic Meridional Overturning Circulation (AMOC), Dissolved oxygen, and pH 
(c) Dissolved oxygen (100 –600 m) 
Recent and future change in ice sheets 
Greenland and Antarctic ice sheets
(e) Greenland ice sheet  
Future (SSP5-8.5) mean; 5 –95% range Future (SSP3-7.0) mean; 5 –95% range 
Future (SSP1-2.6) mean; 5 –95% range 
Future (SSP2-4.5) mean 
-15-10-505% changePast (observed)
Past (observed) 
 Emulator median (SSP5-8.5); 17-83% & 5 –95% ranges Emulator median (SSP1-2.6); 17-83% & 5–95% ranges 
ISMIP6 models  (SSP5-8.5/RCP8.5)ISMIP6 models  (SSP1-2.6/RCP2.6)
Future (CMIP6)
 51015multiplication factor(a) Marine heatwaves 
Past (simulated)Past (observed)Future (CMIP6)
(b) Atlantic Meridional Overturning Circulation (AMOC) 
1900 1950 2000 2050 2100-10-505Sv
1850(d) Ocean acidification
low acidity
high acidity Future (CMIP6)Past (observed)

1850 1900 1950 2000 2050 21007.67.888.2pH
(f) Antarctic ice sheet 
Mouginot/Rignot
IMBIEBamberFuture (RCP/SSP)
ISMIP6
EmulatorLARMIP104 Gt
-16-12-8-404m-0.1
0
0.1
0.2
0.3
0.4
1980 2000 2020 2040 2060 2080 2100
ISMIP6
(2061–2100)Observations 
(1978–2017)
Elevation change 
(m/yr)1
-1-0.50.5
0
Box
ISMIP6 EmulatorFuture (RCP/SSP)
-8-404104 Gt-0.1
0
0.1
0.2
 m
Observations 
(2010–2017)
ISMIP6
(2093–2100)
(m/yr)1
-1-0.50.5
0Past (simulated)Past (observed)
Historical 
(observation-based) Historical 
(observation-based)
Observation-based:Change 
in 2100
Change 
in 2100
Change 
in 2100
Figure TS.11 |  Past and future ocean and ice-sheet changes. The intent of this figure is to show that observed and projected time series of many ocean and cryosphere

indicators are consistent.  Observed and simulated historical changes and projected future changes under varying greenhouse gas emissions scenarios. Simulated and projected 
ocean changes are shown as Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble mean, and 5–95% range (shading) is provided for scenarios SSP1-2.6 and 
SSP3-7.0 (except in panel a where the range is provided for scenario SSP1-2.6 and SSP5-8.5). Mean and 5–95%  range in 2100 are shown as vertical bars on the right-hand side 
of each panel. (a) Change in multiplication factor in surface ocean marine heatwave days relative to 1995–2014 (defined as days exceeding the 99th percentile in sea surface 
temperature (SST) from 1995–2014 distribution). Assessed observational change span 1982–2019 from AVHRR satellite SST. (b) Atlantic Meridional Overturning Circulation

(AMOC) transport relative to 1995–2014 (defined as maximum transport at 26°N). Assessed observational change spans 2004–2018 from the RAPID array smoothed with 
a 12-month running mean (shading around the mean shows the 12-month running standard deviation around the mean). (c) Global mean percent change in ocean oxygen 
(100–600 m depth), relative to 1995–2014. Assessed observational trends and very likely  range are from the SROCC assessment, and span 1970–2010 centred on 2005. (d) 
Global mean surface pH. Assessed observational change spans 1985–2019, from the CMEMS SOCAT-based reconstruction (shading around the global mean shows the 90% 
confidence interval). (e), (f) : Ice sheet mass changes. Projected ice-sheet changes are shown as median, 5–95% range (light shading), and 17–83% range (dark shading) of 
cumulative mass loss and sea level equivalent from ISMIP6 emulation under SSP1-2.6 and SSP5-8.5 (shading and bold line), with individual emulated projections as thin lines.

Median (dot), 17–83% range (thick vertical bar), and 5–95% range (thin vertical bar) in 2100 are shown as vertical bars on the right-hand side of each panel, from ISMIP6, 
ISMIP6 emulation, and LARMIP-2. Observation-based estimates: For Greenland (e), for 1972–2018 (Mouginot), for 1992–2016 (Bamber), for 1992–2020 (IMBIE) and total 
estimated mass loss range for 1840–1972 (Box). For Antarctica (f), estimates based on satellite data combined with simulated surface mass balance and glacial isostatic 
adjustment for 1992–2020 (IMBIE), 1992–2016 (Bamber), and 1979–2017 (Rignot). Left inset maps: mean Greenland elevation changes 2010–2017 derived from CryoSat-2 
radar altimetry (e) and mean Antarctica elevation changes 1978–2017 derived from restored analogue radar records (f). Right inset maps: ISMIP6 model mean (2093–2100)

projected changes under the MIROC5 climate model for the RCP8.5 scenario. {2.3.3; 2.3.4; 3.5.4; 4.3.2; 5.3.2; 5.3.3; 5.6.3; 9.2.3; 9.4.1; 9.4.2; Box 9.2; Box 9.2, Figure 1; 
Figures 9.10, 9.17 and 9.18}

76
TSIt is virtually certain  that surface pH has declined globally over the 
last 40 years and that the main driver is uptake of anthropogenic 
CO 2. Ocean acidification and associated reductions in the saturation 
state of calcium carbonate – a constituent of skeletons or shells of 
a variety of marine organisms – is expected to increase in the 21st 
century under all emissions scenarios ( high confidence ). A long-term 
increase in surface open ocean pH occurred over the past 50 million 
years ( high confidence ), and surface ocean pH as low as recent 
times is uncommon in the last 2 million years ( medium confidence ). 
There is very high confidence  that present-day surface pH values 
are unprecedented for at least 26,000 years and current rates of pH 
change are unprecedented since at least that time. Over the past 2–3 
decades, a pH decline in the ocean interior has been observed in all 
ocean basins ( high confidence ) (Figure TS.11d). {2.3.3, 2.3.4, 3.6.2,

4.3.2, 5.3.2, 5.3.3, 5.6.3, 12.4.8}
Open-ocean deoxygenation and expansion of oxygen minimum zones 
have been observed in many areas of the global ocean since the 
mid 20th century ( high confidence ), in part due to human influence 
(medium confidence ). Deoxygenation is projected to continue to 
increase with ocean warming ( high confidence ) (Figure TS.11c).  
Higher climate sensitivity and reduced ocean ventilation in CMIP6 
compared to CMIP5 results in substantially greater  projections of  
subsurface (100–600 m) oxygen decline than reported in SROCC for 
the period 2080–2099. {2.3.3, 2.3.4, Cross-Chapter Box 2.4, 3.6.2, 
5.3.3, 12.4.8}
Over at least the last two decades, the geographic range of many 
marine organisms has shifted towards the poles and towards greater 
depths ( high confidence ), indicative of shifts towards cooler waters. 
The range of a smaller subset of organisms has shifted equatorward 
and to shallower depths ( high confidence ). Phenological metrics

associated with the life cycles of many organisms have also 
changed over the last two decades or longer ( high confidence ). 
Since the changes in the geographical range of organisms and their 
phenological metrics have been observed to differ with species 
and location, there is the possibility of disruption to major marine 
ecosystems. {2.3.4}
TS.2.5 The Cryosphere
Over recent decades, widespread loss of snow and ice has 
been observed, and several elements of the cryosphere 
are now in states unseen in centuries ( high confidence ). 
Human influence was very likely the main driver of 
observed reductions in Arctic sea ice since the late 1970s 
(with late-summer sea ice loss likely  unprecedented for at 
least 1000 years) and the widespread retreat of glaciers 
(unprecedented in at least the last 2,000 years, medium 
confidence ). Furthermore, human influence very likely 
contributed to the observed Northern Hemisphere spring 
snow cover decrease since 1950.

By contrast, Antarctic sea ice area experienced no significant 
net change since 1979, and there is only low confidence 
in its projected changes. The Arctic Ocean is projected to become practically sea ice-free in late summer under high 
CO 2 emissions scenarios by the end of the 21st century ( high 
confidence ). It is virtually certain that further warming will 
lead to further reductions of Northern Hemisphere snow 
cover, and there is high confidence that this is also the case 
for near-surface permafrost volume. 
Glaciers will continue to lose mass at least for several 
decades even if global temperature is stabilized ( very 
high confidence ), and mass loss over the 21st century 
is virtually certain for the Greenland Ice Sheet and likely  
for the Antarctic Ice Sheet. Deep uncertainty persists with 
respect to the possible evolution of the Antarctic Ice Sheet 
within the 21st century and beyond, in particular due to the

potential instability of the West Antarctic Ice Sheet. {2.3, 
3.4, 4.3, 8.3, 9.3–9.6, Box 9.4, 12.4}
Current Arctic sea ice coverage levels (both annual and late summer) 
are at their lowest since at least 1850 ( high confidence ), and for 
late summer for the past 1000 years ( medium confidence ). Since the 
late 1970s, Arctic sea ice area and thickness have decreased in both 
summer and winter, with sea ice becoming younger, thinner and more 
dynamic ( very high confidence ). It is very likely  that anthropogenic 
forcing, mainly due to greenhouse gas increases, was the main driver 
of this loss, although new evidence suggests that anthropogenic 
aerosol forcing has offset part of the greenhouse gas-induced losses 
since the 1950s ( medium confidence ). The annual Arctic sea ice area 
minimum will likely  fall below 1 million km2 at least once before 
2050 under all assessed SSP scenarios. This practically sea ice-free

state will become the norm for late summer by the end of the 21st 
century in high CO 2 emissions scenarios ( high confidence ). Arctic 
summer sea ice varies approximately linearly with global surface 
temperature, implying that there is no tipping point and observed/
projected losses are potentially reversible ( high confidence ). {2.3.2, 
3.4.1, 4.3.2, 9.3.1, 12.4.9}
For Antarctic sea ice, there is no significant trend in satellite-observed 
sea ice area from 1979 to 2020 in both winter and summer, due to 
regionally opposing trends and large internal variability. Due to 
mismatches between model simulations and observations, combined 
with a lack of understanding of reasons for substantial inter-model 
spread, there is low confidence  in model projections of future Antarctic 
sea ice changes, particularly at the regional level. {2.3.2, 3.4.1, 9.3.2}
In permafrost regions, increases in ground temperatures in the upper

30 m over the past three to four decades have been widespread ( high 
confidence ). For each additional 1°C of warming (up to 4°C above the 
1850–1900 level), the global volume of perennially frozen ground to 
3 m below the surface is projected to decrease by about 25% relative 
to the present volume ( medium confidence ). However, these decreases 
may be underestimated due to an incomplete representation of 
relevant physical processes in ESMs ( low confidence ). Seasonal snow 
cover is treated in Section TS.2.6. {2.3.2, 9.5.2, 12.4.9}
There is very high confidence that, with few exceptions, glaciers 
have retreated since the second half of the 19th century; this 
behaviour is unprecedented in at least the last 2000 years ( medium

77
Technical SummaryTSconfidence ). Mountain glaciers very likely  contributed 67.2 [41.8 to 
92.6] mm to the observed GMSL change between 1901 and 2018. 
This retreat has occurred at increased rates since the 1990s, with 
human influence very likely being the main driver. Under RCP2.6 and 
RCP8.5, respectively, glaciers are projected to lose 18% ± 13% and 
36% ± 20% of their current mass over the 21st century ( medium 
confidence ). {2.3.2, 3.4.3, 9.5.1, 9.6.1}
The Greenland Ice Sheet was smaller than at present during the 
Last Interglacial period (roughly 125,000 years ago) and the mid-
Holocene (roughly 6,000 years ago) ( high confidence ). After reaching 
a recent maximum ice mass at some point between 1450 and 1850, 
the ice sheet retreated overall, with some decades likely  close to 
equilibrium (i.e., mass loss approximately equalling mass gained). It 
is virtually certain that the Greenland Ice Sheet has lost mass since

the 1990s, with human influence a contributing factor ( medium 
confidence ). There is high confidence  that annual mass changes have 
been consistently negative since the early 2000s. Over the period 
1992–2020, Greenland likely  lost 4890 ± 460 Gt of ice, contributing 
13.5 ± 1.3 mm to GMSL rise. There is high confidence  that Greenland 
ice mass losses are increasingly dominated by surface melting and 
runoff, with large interannual variability arising from changes in 
surface mass balance. Projections of future Greenland ice-mass loss 
(Box TS.4, Table 1; Figure TS.11e) are dominated by increased surface 
melt under all emissions scenarios ( high confidence ). Potential 
irreversible long-term loss of the Greenland Ice Sheet, and of parts of the Antarctic Ice Sheet, is assessed in Box TS.9. {2.3.2, 3.4.3, 9.4.1, 
9.4.2, 9.6.3, Atlas.11.2}
It is likely  that the Antarctic Ice Sheet has lost 2670 ± 530 Gt, 
contributing 7.4 ± 1.5 mm to GMSL rise over 1992–2020. The total

Antarctic ice mass losses were dominated by the West Antarctic Ice 
Sheet, with combined West Antarctic and Peninsula annual loss rates 
increasing since about 2000 ( very high confidence ). Furthermore, it 
is very likely that parts of the East Antarctic Ice Sheet have lost mass 
since 1979. Since the 1970s, snowfall has likely  increased over the 
western Antarctic Peninsula and eastern West Antarctica, with large 
spatial and interannual variability over the rest of Antarctica. Mass 
losses from West Antarctic outlet glaciers, mainly induced by ice shelf 
basal melt ( high confidence ), outpace mass gain from increased 
snow accumulation on the continent ( very high confidence ). 
However, there is only  limited evidence , with medium agreement , 
of anthropogenic forcing of the observed Antarctic mass loss since 
1992 (with low confidence in process attribution). Increasing mass 
loss from ice shelves and inland discharge will likely  continue to

outpace increasing snowfall over the 21st century (Figure TS.11f). 
Deep uncertainty persists with respect to the possible evolution of 
the Antarctic Ice Sheet along high-end mass-loss storylines within 
the 21st century and beyond, primarily related to the abrupt and 
widespread onset of marine ice sheet instability and marine ice cliff 
instability. (See also Boxes TS.3 and TS.4). {2.3.2, 3.4.3, 9.4.2, 9.6.3, 
Box 9.4, Atlas.11.1}
Box TS.4 | Sea Level
Global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018, with a rate of rise 
that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm yr–1 for the period 2006–2018 (high confidence ). Human 
activities were very likely  the main driver of observed GMSL rise since 1971, and new observational evidence leads 
to an assessed sea level rise over the period 1901 to 2018 that is consistent with the sum of individual components

contributing to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets ( high 
confidence ). It is virtually certain  that GMSL will continue to rise over the 21st century in response to continued 
warming of the climate system (Box TS.4, Figure 1). Sea level responds to greenhouse gas (GHG) emissions more 
slowly than global surface temperature, leading to weaker scenario dependence over the 21st century than for global 
surface temperature ( high confidence ). This slow response also leads to long-term committed sea level rise, associated 
with ongoing ocean heat uptake and the slow adjustment of the ice sheets, that will continue over the centuries and 
millennia following cessation of emissions ( high confidence ) (Box TS.9). By 2100, GMSL is projected to rise by 0.28–
0.55 m ( likely  range) under SSP1-1.9 and 0.63–1.01 m ( likely  range) under SSP5-8.5 relative to the 1995–2014 average

(medium confidence ). Under the higher CO 2 emissions scenarios, there is deep uncertainty in sea level projections for 
2100 and beyond associated with the ice-sheet responses to warming. In a low-likelihood, high-impact storyline and a 
high CO 2 emissions scenario, ice-sheet processes characterized by deep uncertainty could drive GMSL rise up to about 
5 m by 2150. Given the long-term commitment, uncertainty in the timing of reaching different GMSL rise levels is an 
important consideration for adaptation planning. {2.3, 3.4, 3.5, 9.6, Box 9.4, Cross-Chapter Box 9.1, Table 9.5}
GMSL change is driven by warming or cooling of the ocean (and the associated expansion/contraction) and changes in the amount 
of ice and water stored on land. Paleo-evidence shows that GMSL has been about 70 m higher and 130 m lower than present within 
the past 55 million years and was likely 5 to 10 m higher during the Last Interglacial (Box TS.2, Figure 1). Sea level observations show

that GMSL rose by 0.20 [0.15 to 0.25] m over the period 1901–2018 at an average rate of 1.7 [1.3 to 2.2] mm yr–1. New analyses 
and paleo-evidence since AR5 show this rate is very likely faster than during any century over at least the last three millennia ( high 
confidence ). Since AR5, there is strengthened evidence for an increase in the rate of GMSL rise since the mid-20th century, with an 
average rate of 2.3 [1.6–3.1] mm yr–1 over the period 1971–2018 increasing to 3.7 [3.2–4.2] mm yr–1 for the period 2006–2018 ( high 
confidence ). {2.3.3, 9.6.1, 9.6.2}

78
TSBox TS.4 (continued)
Peak global surface temperature (°C)05101520253035GMSL rise (m)(b) Committed sea level rise by warming level and time scale
Mid-Pliocene
Warm Period
Last
Interglacial10,000-yr
2,000-yr
100-yrPaleo ranges
1 1.5 2 3 4 52000 2100 2200 2300+SSP1−1.9SSP1−2.6SSP2−4.5SSP3−7.0SSP5−8.5Low−confidence
processes included1900 1950 2000 2050 2100 2150         00.511.522.5GMSL rise (m)(a) Global mean sea level rise from 1900 –2150
SSP3-7.0
SSP1-2.6 ObservationsMedian (medium confidence)
SSP5-8.5 Low confidence 95th percentileSSP1-1.9 SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5
Year by which a rise of 2.0 m 
above 1995–2014 is expected
1.5 m
1.0 m
0.5 m        (c) Projected timing of sea level rise milestones2150 medium & low 
confidence projections 
(see caption)

Box TS.4, Figure 1 | Global mean sea level (GMSL) change on different time scales and under different scenarios.  The intent of this figure is to 
(i) show the century-scale GMSL projections in the context of the 20th century observations, (ii) illustrate ‘deep uncertainty’ in projections by considering the timing 
of GMSL rise milestones, and (iii) show the long-term commitment associated with different warming levels, including the paleo evidence to support this.  (a) GMSL 
change from 1900 to 2150, observed (1900–2018) and projected under the SSP scenarios (2000–2150), relative to a 1995–2014 baseline. Solid lines show median 
projections. Shaded regions show  likely ranges for SSP1-2.6 and SSP3-7.0. Dotted and dashed lines show respectively the 83rd and 95th percentile low confidence  
projections for SSP5-8.5. Bars at right show  likely  ranges for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 in 2150. Lightly shaded thick/thin bars show

17th–83rd/5th–95th percentile  low-confidence  ranges in 2150 for SSP1-2.6 and SSP5-8.5, based upon projection methods incorporating structured expert judgement 
and marine ice cliff instability.  Low confidence  range for SSP5-8.5 in 2150 extends to 4.8/5.4 m at the 83rd/95th percentile. (b) GMSL change on 100- (blue), 2000- 
(green) and 10,000-year (magenta) time scales as a function of global surface temperature, relative to 1850–1900. For 100-year projections, GMSL is projected for the 
year 2100, relative to a 1995–2014 baseline, and temperature anomalies are average values over 2081–2100. For longer-term commitments, warming is indexed by 
peak warming above 1850–1900 reached after cessation of emissions. Shaded regions show paleo-constraints on global surface temperature and GMSL for the Last 
Interglacial and mid-Pliocene Warm Period. Lightly shaded thick/thin blue bars show 17th–83rd/5th–95th percentile  low confidence  ranges for SSP1-2.6 and SSP5-8.5

in 2100, plotted at 2°C and 5°C. (c) Timing of exceedance of GMSL thresholds of 0.5, 1.0, 1.5 and 2.0 m, under different SSPs. Lightly shaded thick/thin bars show 
17th–83rd/5th–95th percentile  low-confidence  ranges for SSP1-2.6 and SSP5-8.5.  {4.3.2, 9.6.1, 9.6.2, 9.6.3, Box 9.4}

79
Technical SummaryTSBox TS.4 (continued)
GMSL will continue to rise throughout the 21st century (Box TS.4, Figure 1a). Considering only those processes in whose projections 
we have at least medium confidence , relative to the period 1995–2014, GMSL is projected to rise between 0.18 m (0.15–0.23 m, 
likely range ; SSP1-1.9) and 0.23 m (0.20–0.30 m, likely range ; SSP5-8.5) by 2050. By 2100, the projected rise is between 0.38 m 
(0.28–0.55 m, likely range ; SSP1-1.9) and 0.77 m (0.63–1.01 m, likely range ; SSP5-8.5) {Table 9.9}. The methods, models and scenarios 
used for sea level projections in the AR6 are updated from those employed by SROCC, with contributions informed by the latest 
model projections described in the ocean and cryosphere Sections (Sections TS.2.4 and TS.2.5). Despite these differences, the sea level 
projections are broadly consistent with those of SROCC. {4.3.2, 9.6.3}

Importantly, likely range projections do not include those ice-sheet-related processes whose quantification is highly uncertain 
or that are characterized by deep uncertainty.  Higher amounts of GMSL rise before 2100 could be caused by earlier-than-projected 
disintegration of marine ice shelves, the abrupt, widespread onset of marine ice sheet instability (MISI) and marine ice cliff instability 
(MICI) around Antarctica, and faster-than-projected changes in the surface mass balance and dynamical ice loss from Greenland 
(Box TS.4, Figure 1).  In a low-likelihood, high-impact storyline and a high CO 2 emissions scenario, such processes could in combination 
contribute more than one additional meter of sea level rise by 2100 (Box TS.3). {4.3.2, 9.6.3, Box 9.4}
Beyond 2100, GMSL will continue to rise for centuries to millennia due to continuing deep ocean heat uptake and mass loss from

ice sheets, and will remain elevated for thousands of years ( high confidence ). By 2150, considering only those processes in whose 
projections we have at least medium confidence  and assuming no acceleration in ice-mass flux after 2100, GMSL is projected to rise 
between 0.6 m (0.4–0.9 m, likely  range, SSP1-1.9) and 1.3 m (1.0–1.9 m, likely  range) (SSP5-8.5), relative to the period 1995–2014 
based on the SSP scenario extensions. Under high CO 2 emissions, processes in which there is low confidence , such as MICI, could drive 
GMSL rise up to about 5 m by 2150 (Box TS.4, Figure 1a). By 2300, GMSL will rise 0.3–3.1 m under low CO 2 emissions (SSP1-2.6) ( low 
confidence ). Under high CO 2 emissions (SSP5-8.5), projected GMSL rise is between 1.7 and 6.8 m by 2300 in the absence of MICI and 
by up to 16 m considering MICI ( low confidence ). Over 2000 years, there is medium agreemen t and limited evidence  that committed

GMSL rise is projected to be about 2–3 m with 1.5°C peak warming, 2–6 m with 2°C of peak warming, 4–10 m with 3°C of peak 
warming, 12–16 m with 4°C of peak warming, and 19–22 m with 5°C of peak warming. {9.6.3}
Looking at uncertainty in time provides an alternative perspective on uncertainty in future sea level rise (Box TS.4, Figure 1c). For 
example, considering only medium confidence  processes, GMSL rise is likely to exceed 0.5 m between about 2080 and 2170 under 
SSP1-2.6 and between about 2070 and 2090 under SSP5-8.5. Given the long-term commitment, uncertainty in the timing of reaching 
different levels of GMSL rise is an important consideration for adaptation planning. {9.6.3}
At regional scales, additional processes come into play that modify the local sea level change relative to GMSL, including vertical land 
motion, ocean circulation and density changes, and gravitational, rotational, and deformational effects arising from the redistribution

of water and ice mass between land and the ocean. These processes give rise to a spatial pattern that tends to increase sea level 
rise at the low latitudes and reduce sea level rise at high latitudes. However, over the 21st century, the majority of coastal locations 
have a median projected regional sea level rise within ±20% of the projected GMSL change ( medium confidence ). Further details on 
regional sea level change and extremes are provided in Section TS.4. {9.6.3}
Box TS.5 | The Carbon Cycle
The continued growth of atmospheric CO 2 concentrations over the industrial era is unequivocally due to emissions from 
human activities. Ocean and land carbon sinks slow the rise of CO 2 in the atmosphere. Projections show that while 
land and ocean sinks absorb more CO 2 under high emissions scenarios than low emissions scenarios, the fraction of 
emissions removed from the atmosphere by natural sinks decreases with higher concentrations ( high confidence ).

Projected ocean and land sinks show similar responses for a given scenario, but the land sink has a much higher 
interannual variability and wider model spread. The slowed growth rates of the carbon sinks projected for the second 
half of this century are linked to strengthening carbon–climate feedbacks and stabilization of atmospheric CO 2 under 
medium-to-no-mitigation and high-mitigation scenarios, respectively (see FAQ 5.1). {5.2, 5.4}

80
TSBox TS.5 (continued)
Carbon sinks for anthropogenic CO 2 are associated with mainly physical ocean and biospheric land processes that drive the exchange 
of carbon between multiple land, ocean and atmospheric reservoirs. These exchanges are driven by increasing atmospheric CO 2, but 
are modulated by changes in climate (Box TS.5, Figure 1c,d). The Northern and Southern Hemispheres dominate the land and ocean 
sinks, respectively (Box TS.5, Figure 1). Ocean circulation and thermodynamic processes also play a critical role in coupling the global 
carbon and energy (heat) cycles. There is high confidence  that this ocean carbon–heat nexus is an important basis for one of the 
most important carbon–climate metrics, the transient climate response to cumulative CO 2 emissions (TCRE; Section TS.3.2.1) used to 
determine the remaining carbon budget. {5.1, 5.2, 5.5, 9.2, Cross-Chapter Box 5.3}

Based on multiple lines of evidence using interhemispheric gradients of CO 2 concentrations, isotopes, and inventory data, it is 
unequivocal that the growth in CO 2 in the atmosphere since 1750 (see Section TS.2.2) is due to the direct emissions from human 
activities. The combustion of fossil fuels and land-use change for the period 1750–2019 resulted in the release of 700 ± 75 PgC ( likely  
range, 1 PgC = 1015 g of carbon) to the atmosphere, of which about 41% ± 11% remains in the atmosphere today ( high confidence ). 
Of the total anthropogenic CO 2 emissions, the combustion of fossil fuels was responsible for about 64% ± 15%, growing to an 86% ± 
14% contribution over the past 10 years. The remainder resulted from land-use change. During the last decade (2010–2019), average 
annual anthropogenic CO 2 emissions reached the highest levels in human history at 10.9 ± 0.9 PgC yr–1 (high confidence ). Of these

emissions, 46% accumulated in the atmosphere (5.1 ± 0.02 PgC yr–1), 23% (2.5 ± 0.6 PgC yr–1) was taken up by the ocean and 31% 
(3.4 ± 0.9 PgC yr–1) was removed by terrestrial ecosystems ( high confidence ). {5.2.1, 5.2.2, 5.2.3}
The ocean ( high confidence ) and land ( medium confidence ) sinks of CO 2 have increased with anthropogenic emissions over the past 
six decades (Box TS.5, Figure 1). This coherence between emissions and the growth in ocean and land sinks has resulted in the airborne 
fraction of anthropogenic CO 2 remaining at 44 ± 10% over the past 60 years ( high confidence ). Interannual and decadal variability of 
the ocean and land sinks indicate that they are sensitive to changes in the growth rate of emissions as well as climate variability and 
are therefore also sensitive to climate change ( high confidence ). {5.2.1}
The land CO 2 sink is driven by carbon uptake by vegetation, with large interannual variability, for example, linked to the El Niño–

Southern Oscillation (ENSO). Since the 1980s, carbon fertilization from rising atmospheric CO 2 has increased the strength of the net 
land CO 2 sink ( medium confidence ). During the historical period, the growth of the ocean sink has been primarily determined by 
the growth rate of atmospheric CO 2. However, there is medium confidence  that changes to physical and chemical processes in the 
ocean and in the land biosphere, which govern carbon feedbacks, are already modifying the characteristics of variability, particularly 
the seasonal cycle of CO 2, in both the ocean and land. However, changes to the multi-decadal trends in the sinks have not yet been 
observed. {2.3.4, 3.6.1, 5.2.1}
In AR6, ESM projections are assessed with CO 2 concentrations by 2100 from about 400 ppm (SSP1-1.9) to above 1100 ppm (SSP5-8.5). 
Most simulations are performed with prescribed atmospheric CO 2 concentrations, which already account for a central estimate of

climate–carbon feedback effects. Carbon dioxide emissions-driven simulations account for uncertainty in these feedbacks, but do 
not significantly change the projected global surface temperature changes ( high confidence ). Although land and ocean sinks absorb 
more CO 2 under high emissions than low emissions scenarios, the fraction of emissions removed from the atmosphere decreases 
(high confidence ). This means that the more CO 2 that is emitted, the less efficient the ocean and land sinks become ( high confidence ), 
an effect which compensates for the logarithmic relationship between CO 2 and its radiative forcing, which means that for each unit 
increase in additional atmospheric CO 2 the effect on global temperature decreases. (Box TS.5, Figure 1f,g). {4.3.1, 5.4.5, 5.5.1.2}
Ocean and land sinks show similar responses for a given scenario, but the land sink has a much higher interannual variability

and wider model spread. Under SSP3-7.0 and SSP5-8.5, the initial growth of both sinks in response to increasing atmospheric 
concentrations of CO 2 is subsequently limited by emerging carbon–climate feedbacks ( high confidence ) (Box TS.5, Figure  1f). 
Projections show that the ocean and land sinks will stop growing from the second part of the 21st century under all emissions 
scenarios, but with different drivers for different emissions scenarios. Under SSP3-7.0 and SSP5-8.5, the weakening growth rate 
of the ocean CO 2 sink in the second half of the century is primarily linked to the strengthening positive feedback from reduced 
carbonate buffering capacity, ocean warming and altered ocean circulation (e.g., AMOC changes). In contrast, for SSP1-1.9, SSP1-2.6 
and SSP2-4.5, the weakening growth rate of the ocean carbon sink is a response to the stabilizing or declining atmospheric

CO 2 concentrations. Under SSP1-1.9, models project that combined land and ocean sinks will turn into a weak source by 2100 
(medium confidence ). Under high CO 2 emissions scenarios, it is very likely  that the land carbon sink will grow more slowly due to 
warming and drying from the mid-21st century, but it is very unlikely  that it will switch from being a sink to a source before 2100.

81
Technical SummaryTSBox TS.5 (continued)
Box TS.5, Figure 1 |  Carbon cycle processes and projections.  
number of model simulations
shades: uncertainty rangeColour Low model agreement High model agreementLatitute/gid00009/gid00066/gid00013/gid00001/gid00067/gid00010/gid00001/gid00036/gid00064/gid00081/gid00065/gid00078/gid00077/gid00001/gid00084/gid00079/gid00083/gid00064/gid00074/gid00068/gid00001/gid00081/gid00068/gid00082/gid00079/gid00078/gid00077/gid00082/gid00068/gid00001/gid00083/gid00078/gid00001/gid00066/gid00075/gid00072/gid00076/gid00064/gid00083/gid00068/gid00001/gid00066/gid00071/gid00064/gid00077/gid00070/gid00068 /gid00009/gid00064/gid00013/gid00001/gid00065/gid00010/gid00001/gid00036/gid00064/gid00081/gid00065/gid00078/gid00077/gid00001/gid00084/gid00079/gid00083/gid00064/gid00074/gid00068/gid00001/gid00081/gid00068/gid00082/gid00079/gid00078/gid00077/gid00082/gid00068/gid00001/gid00083/gid00078/gid00001/gid00036/gid00048/gid00019

82
Box TS.5, Figure 1 (continued): The intent of this figure is to show the response of the carbon cycle to carbon dioxide (CO 2) emissions and climate and its 
role in determining future CO 2 levels through projected changes to sinks and sink fractions.  The figure shows changes in carbon storage in response to elevated 
CO 2 (a, b)  and the response to climate warming (c, d) . Maps show spatial patterns of changes in carbon uptake during simulations with 1% per year increase in CO 2 
(Section 5.4.5.5), and zonal mean plots show distribution of carbon changes is dominated by the land (green lines) in the tropics and Northern Hemisphere and ocean 
(blue lines) in the Southern Hemisphere. Hatching indicates regions where fewer than 80% of models agree on the sign of response. (e) Future CO 2 projections:

projected CO 2 concentrations in the Shared Socio-economic Pathway (SSP) scenarios in response to anthropogenic emissions, results from coupled Earth system 
models for SSP5-8.5 and from the MAGICC7 emulator for other scenarios (Section 4.3.1). (f) Future carbon fluxes: projected combined land and ocean fluxes (positive 
downward) up to 2100 for the SSP scenarios, and extended to 2300 for available scenarios, 5–95% uncertainty plumes shown for SSP1-2.6 and SSP3-7.0 (Sections 
4.3.2.4, 5.4.5.4 and 5.4.10). The numbers near the top show the number of model simulations used. (g) Sink fraction: the fraction of cumulative emissions of CO 2 
removed by land and ocean sinks. The sink fraction is smaller under conditions of higher emissions. {Figure 4.3; 5.4.5; Figures 5.25, 5.27 and 5.30}
Climate change alone is expected to increase land carbon accumulation in the high latitudes (not including permafrost, which is

assessed in Sections TS.2.5 and TS.3.2.2), but also to lead to a counteracting loss of land carbon in the tropics ( medium confidence ). 
Earth system model projections show that the overall uncertainty of atmospheric CO 2 by 2100 is still dominated by the emissions 
pathway, but carbon–climate feedbacks (see Section TS.3.3.2) are important, with increasing uncertainties in high emissions pathways 
(Box TS.5, Figure 1e). {4.3.2, 5.4.1, 5.4.2, 5.4.4, 5.4.5, 11.6, 11.9, Cross-Chapter Box 5.1, Cross-Chapter Box 5.3}
Under three SSP scenarios with long-term extensions until 2300 (SSP5-8.5, SSP5-3.4-OS, SSP1-2.6), ESMs project a change of the 
land from a sink to a source ( medium confidence ). The scenarios make simplified assumptions about emissions reductions, with 
SSP1-2.6 and SSP5-3.4-OS reaching about 400 ppm by 2300, while SSP5-8.5 exceeds 2000 ppm. Under high emissions, the transition

is warming-driven, whereas it is linked to the decline in atmospheric CO 2 under net negative CO 2 emissions. The ocean remains a sink 
throughout the period to 2300 except under very large net negative emissions. The response of the natural aspects of the carbon cycle 
to carbon dioxide removal is further developed in Section TS.3.3.2. {5.4.9}
TS.2.6 Land Climate, Including Biosphere and Extremes
Land surface air temperatures have risen faster than the 
global surface temperature since the 1850s, and it is  virtually 
certain  that this differential warming will persist into the 
future. It is virtually certain that the frequency and intensity 
of hot extremes and the intensity and duration of heatwaves 
have increased since 1950 and will further increase in the 
future even if global warming is stabilized at 1.5°C. The 
frequency and intensity of heavy precipitation events have 
increased over a majority of those land regions with good

observational coverage ( high confidence ) and will extremely 
likely increase over most land regions with additional global 
warming. 
Over the past half century, key aspects of the biosphere 
have changed in ways that are consistent with large-
scale warming: climate zones have shifted poleward, and 
the growing season length in the Northern Hemisphere 
extratropics has increased ( high confidence ). The amplitude 
of the seasonal cycle of atmospheric CO 2 poleward of 45°N 
has increased since the 1960s ( very high confidence ), with 
increasing productivity of the land biosphere due to the 
increasing atmospheric CO 2 concentration as the main driver 
(medium confidence ). Global-scale vegetation greenness has 
increased since the 1980s ( high confidence ). {2.3, 3.6, 4.3, 
4.5, 5.2, 11.3, 11.4, 11.9, 12.4}
Observed temperatures over land have increased by 1.59 [1.34–
1.83] °C between the period 1850–1900 and 2011–2020. Warming

of the land is about 45% larger than for global surface temperature and about 80% larger than warming of the ocean surface. Warming of 
the land surface during the period 1971–2018 contributed about 5% 
of the increase in the global energy inventory (Section TS.3.1), nearly 
twice the estimate in AR5 ( high confidence ). It is virtually certain  that 
the average surface warming over land will continue to be higher than 
over the ocean throughout the 21st century. The warming pattern 
will likely  vary seasonally, with northern high latitudes warming more 
during winter than summer ( medium confidence ). {2.3.1, 4.3.1, 4.5.1, 
7.2.2, Box 7.2, Cross-Chapter Box 9.1, 11.3, Atlas 11.2}
The frequency and intensity of hot extremes (warm days and nights) 
and the intensity and duration of heatwaves have increased globally 
and in most regions since 1950, while the frequency and intensity 
of cold extremes have decreased ( virtually certain ). There is high

confidence  that the increases in frequency and severity of hot 
extremes are due to human-induced climate change. Some recent 
extreme events would have been extremely unlikely  to occur without 
human influence on the climate system. It is virtually certain  that 
further changes in hot and cold extremes will occur throughout the 
21st century in nearly all inhabited regions, even if global warming 
is stabilized at 1.5°C (Table TS.2, Figure TS.12a). {1.3, Cross-Chapter 
Box 3.2, 11.1.4, 11.3.2, 11.3.4, 11.3.5, 11.9, 12.4}
Greater warming over land alters key water cycle characteristics 
(Box TS.6). The rates of change in mean precipitation and runoff, 
and their variability, increase with global warming (Figure TS.12e,f). 
Human-induced climate change has contributed to increases in 
agricultural and ecological droughts in some regions due to increases 
in evapotranspiration ( medium confidence ). More regions are 
affected by increases in agricultural and ecological droughts with

increasing global warming ( high confidence ; see also Figure TS.12c).

83
10-year event, frequency 
50-year event, frequency
10-year event, intensity 
50-year event, intensity
Global surface temperature change since 1850–1900 (°C) 2.0 3.0 4.0 5.0
(f) Hydrological change over extratropical land
Precipitable water annual mean
        annual mean
        interannual variability 
Runoff 
        annual mean 
        interannual variability Precipitable water annual mean
Runoff 
(e) Hydrological change over tropical land
Global surface temperature change since 1850–1900  (°C) 2.0 3.0 4.0 5.0% change - 15 models mean - SSP5-8.5% change - 15 models mean - SSP5-8.550
4060
30
20
10
0
-1050
40
30
20
10

0 17–83% range 17–83% range Global surface temperature change since 1850–1900 (°C) Global surface temperature change since 1850–1900 (°C)Global surface temperature change since 1850–1900 (°C) Global surface temperature change since 1850–1900 (°C)
2.0 3.0 4.0 1.0 1.52.0 3.0 4.0 1.0 1.5 2.0 3.0 4.0 1.0 1.5Relative frequency (pre-industrial = 1) Relative frequency (pre-industrial = 1)
Relative frequency (pre-industrial = 1)
Intensity change (°C)Intensity change (%)
Amplitude of annual soil moisture decrease  (-)(c) Droughts in drought-prone regions (d) Northern Hemisphere March–May snow cover(b) Heavy precipitation events (a) Hot extreme events
2
1
0.51.5
040
30
20
1035
25
15
5
040
30
20
1035
25
15
5
6
5
4
3
2
16
5
4
3
2
16
5
4
3
2
1
6 5 4 3 2 1 0 7- 4020
- 60
- 800
- 20Snow cover extent change (%)10-year event, frequency 
10-year event, intensitySSP5-8.5
SSP3-7.0
SSP2-4.5
SSP1-2.6
SSP1-1.9
78

Figure TS.12 |  Land-related changes relative to the 1850-1900 as a function of global warming levels.  The intent of this figure is to show that extremes and 
mean land variables change consistently with warming levels and to show the changes with global warming levels of water cycle indicators (i.e., precipitation and runoff) over 
tropical and extratropical land in terms of mean and interannual variability (interannual variability increases at a faster rate than the mean) . (a) Changes in the frequency (left 
scale) and intensity (in °C, right scale) of daily hot extremes occurring every 10 and 50 years. (b) as (a), but for daily heavy precipitation extremes, with intensity change in %. 
(c) Changes in 10-year droughts aggregated over drought-prone regions (WNA, CNA, NCA, SCA, NSA, NES, SAM, SWS, SSA, WCE, MED, WSAF , ESAF , MDG, SAU, and EAU;

for definitions of these regions, see Figure Atlas.2), with drought intensity (right scale) represented by the change of annual mean soil moisture, normalized with respect to 
interannual variability. Limits of the 5%−95% confidence interval are shown in panels (a–c). (d) Changes in Northern Hemisphere spring (March–April–May) snow cover extent 
relative to 1850–1900; (e,f) Relative change (%) in annual mean of total precipitable water (grey line), precipitation (red solid lines), runoff (blue solid lines) and in standard 
deviation (i.e., variability) of precipitation (red dashed lines) and runoff (blue dashed lines) averaged over (e) tropical and (f) extratropical land as function of global warming 
levels. Coupled Model Intercomparison Project Phase 6 (CMIP6) models that reached a 5°C warming level above the 1850–1900 average in the 21st century in SSP5-8.5 have

been used. Precipitation and runoff variability are estimated by respective standard deviation after removing linear trends. Error bars show the 17–83% confidence interval for 
the warmest +5°C global warming level. {Figures 8.16, 9.24, 11.6, 11.7, 11.12, 11.15, 11.18 and Atlas.2}

84
TSThere is low confidence  that the increase of plant water-use efficiency 
due to higher atmospheric CO 2 concentration alleviates extreme 
agricultural and ecological droughts in conditions characterized 
by limited soil moisture and increased atmospheric evaporative 
demand. {2.3.1, Cross-Chapter Box  5.1, 8.2.3, 8.4.1, 11.2.4, 11.4, 
11.6, Box 11.1}
Northern Hemisphere spring snow cover has decreased since at 
least 1978 ( very high confidence ), and there is high confidence 
that trends in snow cover loss extend back to 1950. It is very likely  
that human influence contributed to these reductions. Earlier onset 
of snowmelt has contributed to seasonally dependent changes 
in streamflow ( high confidence ). A further decrease of Northern 
Hemisphere seasonal snow cover extent is virtually certain  under 
further global warming (Figure TS.12d). {2.3.2, 3.4.2, 8.3.2. 9.5.3, 
12.4, 9.2, 11.2, Atlas 8.2}

The frequency and intensity of heavy precipitation events have 
increased over a majority of land regions with good observational 
coverage since 1950 ( high confidence, Box TS.6, Table TS.2). Human 
influence is likely  the main driver of this change (Table TS.2). It is 
extremely likely  that on most land regions heavy precipitation will 
become more frequent and more intense with additional global 
warming (Table TS.2, Figure TS.12b). The projected increase in heavy 
precipitation extremes translates to an increase in the frequency and 
magnitude of pluvial floods ( high confidence ) (Table TS.2). {Cross-
Chapter Box 3.2, 8.4.1, 11.4.2, 11.4.4, 11.5.5, 12.4}
The probability of compound extreme events has likely  increased 
due to human-induced climate change. Concurrent heatwaves and 
droughts have become more frequent over the last century, and this 
trend will continue with higher global warming ( high confidence ). 
The probability of compound flooding (storm surge, extreme

rainfall and/or river flow) has increased in some locations and will 
continue to increase due to both sea level rise and increases in heavy 
with tropical cyclones ( high confidence ). {11.8.1, 11.8.2, 11.8.3}
Changes in key aspects of the terrestrial biosphere, such as an 
increase of the growing season length in much of the Northern Hemisphere extratropics since the mid-20th century ( high 
confidence ), are consistent with large-scale warming. At the 
same time an increase in the amplitude of the seasonal cycle of 
atmospheric CO 2 poleward of 45°N since the early 1960s ( high 
confidence ) and a global-scale increase in vegetation greenness 
of the terrestrial surface since the early 1980s ( high confidence ) 
have been observed. Increasing atmospheric CO 2, warming at high 
latitudes, and land management interventions have contributed 
to the observed greening trend, but there is low confidence  in

their relative roles. There is medium confidence that increased 
plant growth associated with CO 2 fertilization is the main driver 
of the observed increase in amplitude of the seasonal cycle of 
atmospheric CO 2 in the Northern Hemisphere. Reactive nitrogen, 
ozone and aerosols affect terrestrial vegetation and carbon cycle 
through deposition and effects on large-scale radiation ( high 
confidence ), but the magnitude of these effects on the land carbon 
sink, ecosystem productivity and indirect CO 2 forcing remains 
uncertain. {2.3.4, 3.6.1, 5.2.1, 6.4.5, 12.3.7, 12.4}
Over the last century, there has been a poleward and upslope shift 
in the distribution of many land species ( very high confidence ) 
as well as increases in species turnover within many ecosystems 
(high confidence ). There is high confidence that the geographical 
distribution of climate zones has shifted in many parts of the 
world in the last half century. The SRCCL concluded that continued

warming will exacerbate desertification processes ( medium 
confidence ) and that ecosystems will become increasingly exposed 
to climates beyond those that they are currently adapted to ( high 
confidence ). There is medium confidence  that climate change will 
increase disturbance by, for example, fire and tree mortality, across 
several ecosystems. Increases are projected in drought, aridity and 
fire weather in some regions (Section TS.4.3; high confidence ). 
There is low confidence  in the magnitude of these changes, but 
the probability of crossing uncertain regional thresholds (e.g., fires, 
forest dieback) increases with further warming ( high confidence ). 
perturbation can be abrupt at regional scales, and irreversible on 
decadal to century time scales ( high confidence ). {2.3.4, 5.4.3, 
5.4.9, 11.6, 11.8, 12.5, SRCCL 2.2, SRCCL 2.5, SR1.5 3.4}

85
Technical SummaryTSBox TS.6 | Water Cycle
Human-caused climate change has driven detectable changes in the global water cycle since the mid-20th century ( high 
confidence ), and it is projected to cause substantial further changes at both global and regional scales ( high confidence ). 
Global land precipitation has  likely  increased since 1950, with a faster increase since the 1980s ( medium confidence ). 
Atmospheric water vapour has increased throughout the troposphere since at least the 1980s ( likely ). Annual global 
land precipitation will increase over the 21st century as global surface temperature increases ( high confidence ). 
Human influence has been detected in amplified surface salinity and precipitation minus evaporation (P–E) patterns 
over the ocean ( high confidence ). 
The severity of very wet and very dry events increase in a warming climate ( high confidence ), but changes in

atmospheric circulation patterns affect where and how often these extremes occur. Water cycle variability and related 
extremes are projected to increase faster than mean changes in most regions of the world and under all emissions 
scenarios ( high confidence ). 
Over the 21st century, the total land area subject to drought will increase and droughts will become more frequent 
and severe ( high confidence ). Near-term projected changes in precipitation are uncertain mainly because of internal 
variability, model uncertainty and uncertainty in forcings from natural and anthropogenic aerosols ( medium 
Over the 21st century and beyond, abrupt human-caused changes to the water cycle cannot be excluded ( medium 
confidence ). {2.3, 3.3, 4.3, 4.4, 4.5, 4.6, 8.2, 8.3, 8.4, 8.5, 8.6, 11.4, 11.6, 11.9}
There is high confidence  that the global water cycle has intensified since at least 1980 expressed by, for example, increased atmospheric

moisture fluxes and amplified precipitation minus evaporation patterns. Global land precipitation has likely  increased since 1950, 
with a faster increase since the 1980s ( medium confidence ), and a likely  human contribution to patterns of change, particularly for 
increases in high-latitude precipitation over the Northern Hemisphere. Increases in global mean precipitation are determined by a 
robust response to global surface temperature ( very likely  2–3% per °C) that is partly offset by fast atmospheric adjustments to 
atmospheric heating by greenhouse gases (GHGs) and aerosols (Section TS.3.2.2). The overall effect of anthropogenic aerosols is 
to reduce global precipitation through surface radiative cooling effects ( high confidence ). Over much of the 20th century, opposing 
effects of GHGs and aerosols on precipitation have been observed for some regional monsoons ( high confidence ) (Box TS.13). Global

annual precipitation over land is projected to increase on average by 2.4% (–0.2% to +4.7% likely  range) under SSP1-1.9, 4.6% (1.5% 
to 8.3% likely  range) under SSP2-4.5, and 8.3% (0.9% to 12.9% likely  range) under SSP5-8.5 by 2081–2100 relative to 1995–2014 
(Box TS.6, Figure 1). Inter-model differences and internal variability contribute to a substantial range in projections of large-scale and 
regional water cycle changes ( high confidence ). The occurrence of volcanic eruptions can alter the water cycle for several years ( high 
confidence ). Projected patterns of precipitation change exhibit substantial regional differences and seasonal contrast as global surface 
temperature increases over the 21st century (Box TS.6, Figure 1). {2.3.1, 3.3.2, 3.3.3, 3.5.2, 4.3.1, 4.4.1, 4.5.1, 4.6.1, Cross-Chapter 
Box 4.1, 8.2.1, 8.2.2, 8.2.3, Box 8.1, 8.3.2.4, 8.4.1, 8.5.2, 10.4.2}

Global total column water vapour content has very likely increased since the 1980s, and it is  likely  that human influence has contributed 
to tropical upper tropospheric moistening. Near-surface specific humidity has  increased over the ocean ( likely ) and land ( very likely ) 
since at least the 1970s, with a detectable human influence ( medium confidence ). Human influence has been detected in amplified 
surface salinity and precipitation minus evaporation (P–E) patterns over the ocean ( high confidence ). It is virtually certain  that 
evaporation will increase over the ocean and very likely  that evapotranspiration will increase over land, with regional variations under 
future surface warming (Box TS.6, Figure 1). There is high confidence  that projected increases in precipitation amount and intensity will 
be associated with increased runoff in northern high latitudes (Box TS.6, Figure 1). In response to cryosphere changes (Section TS.2.5),

there have been changes in streamflow seasonality, including an earlier occurrence of peak streamflow in high-latitude and mountain 
catchments ( high confidence ). Projected runoff (Box TS.6, Figure 1c) is typically decreased by contributions from small glaciers because 
of glacier mass loss, while runoff from larger glaciers will generally increase with increasing global warming levels until their mass 
becomes depleted ( high confidence ). {2.3.1, 3.3.2, 3.3.3, 3.5.2, 8.2.3, 8.4.1, 11.5}

86
TSBox TS.6 (continued)
Warming over land drives an increase in atmospheric evaporative demand and in the severity of drought events ( high confidence ). Greater 
warming over land than over the ocean alters atmospheric circulation patterns and reduces continental near-surface relative humidity, 
which contributes to regional drying ( high confidence ). A very likely  decrease in relative humidity has occurred over much of the global 
land area since 2000. Projected increases in evapotranspiration due to growing atmospheric water demand will decrease soil moisture 
over the Mediterranean region, south-western North America, South Africa, South-Western South America and south-western Australia 
(high confidence ) (Box TS.6, Figure 1). Some tropical regions are also projected to experience enhanced aridity, including the Amazon 
basin and Central America ( high confidence ). The total land area subject to increasing drought frequency and severity will expand ( high

confidence ), and in the Mediterranean, South-Western South America, and Western North America, future aridification will far exceed the 
magnitude of change seen in the last millennium ( high confidence ). {4.5.1, 8.2.2, 8.2.3, 8.4.1, Box 8.2, 11.6, 11.9}
Land-use change and water extraction for irrigation have influenced local and regional responses in the water cycle ( high confidence ). 
Large-scale deforestation likely  decreases evapotranspiration and precipitation and increases runoff over the deforested regions 
relative to the regional effects of climate change ( medium confidence ). Urbanization increases local precipitation ( medium confidence ) 
and runoff intensity ( high confidence ) (Box TS.14 ). Increased precipitation intensities have enhanced groundwater recharge, most 
notably in tropical regions ( medium confidence ). There is high confidence  that groundwater depletion has occurred since at least the

start of the 21st century, as a consequence of groundwater withdrawals for irrigation in agricultural areas in drylands. {8.2.3, 8.3.1, 
11.1.6, 11.4, 11.6, FAQ 8.1}
Water cycle variability and related extremes are projected to increase faster than mean changes in most regions of the world 
and under all emissions scenarios ( high confidence ). A warmer climate increases moisture transport into weather systems, which 
intensifies wet seasons and events ( high confidence ). The magnitudes of projected precipitation increases and related extreme 
events depend on model resolution and the representation of convective processes ( high confidence ). Increases in near-surface 
atmospheric moisture capacity of about 7% per 1ºC of warming lead to a similar response in the intensification of heavy precipitation 
from sub-daily up to seasonal time scales, increasing the severity of flood hazards ( high confidence ). The average and maximum

rain-rates associated with tropical and extratropical cyclones, atmospheric rivers and severe convective storms will therefore also 
increase with future warming ( high confidence ). For some regions, there is medium confidence  that peak tropical cyclone rain-
rates will increase by more than 7% per 1°C of warming due to increased low-level moisture convergence caused by increases 
in wind intensity. In the tropics year-round and in the summer season elsewhere, interannual variability of precipitation and 
runoff over land is projected to increase at a faster rate than changes in seasonal mean precipitation (Figure TS.12e,f) ( medium 
confidence ). Sub-seasonal precipitation variability is also projected to increase, with fewer rainy days but increased daily mean 
precipitation intensity over many land regions ( high confidence ). {4.5.3, 8.2.3, 8.4.1, 8.4.2, 8.5.1, 8.5.2, 11.4, 11.5, 11.7, 11.9}

87
Technical SummaryTSBox TS.6 (continued)
(a) Precipitation (b) Evapotranspiration
(d) Surface soil moisture (c) RunoffLong-term water cycle variables changes for SSP2-4.5 (2081–2100 vs 1995–2014)
Box TS.6, Figure 1 |  Projected water cycle changes. The intent of this figure is to give a geographical overview of changes in multiple components of the global 
water cycle using an intermediate emissions scenario. Important key message: without drastic reductions in greenhouse gas emissions, human-induced global warming 
will be associated with widespread changes in all components of the water cycle.  Long-term (2081–2100) projected annual mean changes (%) relative to present-day 
(1995–2014) in the SSP2-4.5 emissions scenario for (a) precipitation, (b) surface evapotranspiration, (c) total runoff and (d) surface soil moisture. Numbers in top

right of each panel indicate indicate the number of Coupled Model Intercomparison Project Phase 6 (CMIP6) models used for estimating the ensemble mean. For other 
scenarios, please refer to relevant figures in Chapter 8. Uncertainty is represented using the simple approach: No overlay indicates regions with high model agreement, 
where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For 
more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. {8.4.1; Figures 8.14, 8.17, 8.18, and 8.19}

88
TS Infographic TS.1 | Climate Futures
Infographic TS.1 | Climate Futures. The intent of this figure is to show possible climate futures: The climate change that people will experience this century and beyond 
depends on our greenhouse gas emissions, how much global warming this will cause and the response of the climate system to this warming.  
(top left) Annual emissions of CO 2 for the five core Shared Socio-economic Pathway (SSP) scenarios (very low: SSP1-1.9, low: SSP1-2.6, intermediate: SSP2-4.5, high: SSP3-7.0, 
very high: SSP5-8.5). (bottom left)  Projected warming for each of these emissions scenarios.
Short-term effect: Natural variabilityThe climate change that people will experience this century and beyond depends on our 
greenhouse gases emissions, how much global warming this will cause and the
response of the climate system to this warming.
Different social and economic developments can lead to substantially dif ferent future emissions of

carbon dioxide (CO 2), other greenhouse gases and air pollutants for the rest of the century .Emissions pathways
For temperature to stabilize, CO 2 emissions need to reach net zero.
Over short time scales (typically a decade), natural variability can temporarily dampen or accentuate global warming trends 
resulting from emissions.Effect on surface temperatureClimate futures
Today
Net CO2 removal
TodayCO2 peak 
CO2 doubled
CO2 peak
CO2 peak
CO2 peakCO2 halvedCO2 doubled
1980 2000 2020 2040 2060 2080Very lowLowMediumHighVery high140
120
100
80
60
40
20
0
-20
2000CO2 emissions (billion tonnes CO2 per year)
Very lowLowMediumHighVery highGlobal warming since 1850–1900 (°C) 
0.50 11.52 3 4

89
(top right)  Response of some selected climate variables to four levels of global warming (°C). Changes in the ‘Today’ column are based on a global warming level of 1°C.
(bottom right)  The long-term effect of each global warming level on sea level. See Section TS.1.3.1 for more detail on the SSP climate change scenarios. 
This infographic builds from several figures in the Technical Summary: Figure TS.4 (for top left panel), Figure TS.6 (bottom left), Figure TS.12 (top right) and Box TS.4, Figure 1b 
(bottom right).
Many aspects of the climate system react quickly to temperature changes. 
At progressively higher levels of global warming there are greater consequences (min/max range shown).Response of the climate system relative to 1850–1900
The future...Today , sea level has already  
increased by 20 cm and 
will increase an additional 
30 cm to 1 m or more by 2100, 
depending on future emissions. 
Sea level reacts very slowly to

global warming so, once started, 
the rise continues for thousands 
of years.Long-term consequences: Sea level riseTemperature
Drought
A drought that used to occur 
once in a decade now 
happens x times moreHottest day in 
a decade (+°C)
Proportion of intense tropical 
cyclones (%)Snow cover extent
change (%)What used to be a wettest 
day in a decade now 
happens x times more
Tropical cyclonesSnowPrecipitationClimate futures
+1.1°C
Today
(+0.7 to 1.5°C)
(x0.7 to 4.1)x1.7
(x1.2 to1.4)x1.3
(-3 to 1)-1%
(+1.8 to 3.1°C)
(x1.6 to 2.0)
(-13 to 2)(x1.3 to 5.8)+2.6°C
x2.4
+13%-9%x1.7+2°C
(+4.3 to 5.8°C)
(x2.3 to 3.6)
(-35 to -15)(x1.7 to 7.2)+5.1°C
x4.1
+30%-26%x2.7+4°C
2m7m
6m 3m
2000-year 
commitment10,000-year 
commitmentMetre rise+1.5°C
13m
8m
2m6m
2000-year 
commitmentMetre rise+2°C
33m
16m
12m19m
2000-year 
commitmentMetre rise+4°C
The climate we and the young generations will experience depends on future emissions.

Reducing emissions rapidly will limit further changes, but continued emissions will trigger larger , 
faster changes that will increasingly a ffect all regions. Some changes will persist for hundreds or 
thousands of years, so today ’s choices will have long-lasting consequences.+1.2°C
+1.5°C
+1.9°C
(+1.3 to 2.3°C)
(x1.4 to 1.7)
(-7 to 2)(x1.0 to 5.1)x2.0
+10%-5%x1.5

90
TSTS.3 Understanding the Climate System 
Response and Implications for Limiting 
Global Warming
This section summarizes advances in our knowledge of Earth’s 
energy budget, including the time evolution of forcings and climate 
feedbacks that lead to the climate system responses summarized 
in Section TS.2. It assesses advances since AR5 and SR1.5 in the 
estimation of remaining carbon budgets, the Earth system response to 
carbon dioxide removal, and the quantification of metrics that allow 
comparisons of the relative effects of different forcing agents. The 
section also highlights: future climate and air pollution responses due 
to projected changes in short-lived climate forcers (SLCFs); the state 
of understanding of the climate response to potential interventions 
related to solar radiation modification (SRM); and irreversibility, 
tipping points and abrupt changes in the climate system.TS.3.1 Radiative Forcing and Energy Budget

Since AR5, the accumulation of energy in the Earth system, 
quantified by observations of warming of the ocean, 
atmosphere, and land and melting of ice, has become 
established as a robust measure of the rate of global climate 
change on interannual-to-decadal time scales. Compared to 
changes in global surface temperature, the increase in the 
global energy inventory exhibits less variability, and thus 
better indicates underlying climate trends. 
The global energy inventory increased by 282 [177 to 
387] zettajoules (ZJ, equal to 1021 Joules) for the period 
1971–2006 and 152 [100 to 205] ZJ for the period 2006–
2018 (Figure TS.13), with more than 90% accounted for 
Figure TS.13 |  Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with (a) observations of changes in the global

energy inventory, (b) integrated radiative forcing, and (c) integrated radiative response. The intent is to show assessed changes in energy budget and effective radiative forcings 
(ERFs).  Black dotted lines indicate the central estimate with likely and very likely  ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated 
1850–1900 Earth energy imbalance of 0.2 W m–2 (panel a) and an illustration of an assumed pattern effect of –0.5 W m–2 °C–1 (panel c). Background grey lines indicate equivalent heating 
rates in W m–2 per unit area of Earth’s surface. Panels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative 
forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to

1850–1900 and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the figure legend, 
including assumptions of correlated and uncorrelated uncertainties in forcing plus response. Shading represents the very likely  range for observed energy change relative to 1850–1900 and 
likely range for all other quantities. Forcing and response time series are expressed relative to a baseline period of 1850–1900. {Box 7.2, Figure 1}
(a) Global energy inventory (b) Integrated radiative forcing (c) Integrated radiative response
(d) Energy inventory components (e) Radiative forcing components (f) Energy budget 1971–20182018 2018 2018
2018 2018–
–

91
Technical SummaryTSby ocean warming. To  put these numbers in context, the 
2006–2018 average Earth energy imbalance is equivalent 
to approximately 20 times the annual rate of global energy 
consumption in 2018. The accumulation of energy is driven 
by a positive total anthropogenic effective radiative forcing 
(ERF) relative to 1750. 
The best estimate ERF of 2.72 W m−2 has increased by 0.43 
W m–2 relative to that given in AR5 (for 1750–2014) due 
to an increase in the greenhouse gas ERF that is partly 
compensated by a more negative aerosol ERF compared 
to AR5. The greenhouse gas ERF has been revised due to 
changes in atmospheric concentrations and updates to 
forcing efficiencies, while the revision to aerosol ERF is due 
to increased understanding of aerosol–cloud interactions 
and is supported by improved agreement between 
different lines of evidence. Improved quantifications 
of ERF, the climate system radiative response, and the

observed energy increase in the Earth system for the 
period 1971–2018 demonstrate improved closure of the 
global energy budget (i.e., the extent to which the sum 
of the integrated forcing and the integrated radiative 
response equals the energy gain of the Earth system) 
compared to AR5 ( high confidence ). (See FAQ 7.1). {7.2.2, 
7.3.5, 7.5.2, Box 7.2, Table 7.1}
The global energy inventory change for the period 1971–2006 
corresponds to an Earth energy imbalance (Box TS.1) of 0.50 [0.32 
to 0.69] W m–2, increasing to 0.79 [0.52 to 1.06] W m–2 for the period 
2006–2018. Ocean heat uptake is by far the largest contribution and 
accounts for 91% of the total energy change. Land warming, melting 
of ice and warming of the atmosphere account for about 5%, 3% and 
1% of the total change, respectively. More comprehensive analysis of 
inventory components, cross-validation of satellite and in situ-based estimates of the global energy imbalance, and closure of the global

sea level budget have led to a strengthened assessment relative 
to AR5. ( high confidence ) {7.2.2, 7.5.2.3, Box 7.2, Table 7.1, 9.6.1, 
Cross-Chapter Box 9.1, Table 9.5}
As in AR5, the perturbations to Earth’s top-of-atmosphere energy 
budget are quantified using ERFs (see also Section TS.2.2). These 
include any consequent adjustments to the climate system  (e.g., 
from changes in atmospheric temperatures, clouds and water vapour 
as shown in Figure TS.14), but exclude any surface temperature 
response. Since AR5, ERFs have been estimated for a larger number 
of forcing agents and shown to be more closely related to the 
temperature response than the stratospheric-temperature-adjusted 
radiative forcing. ( high confidence ) {7.3.1}
Improved quantifications of ERF , the climate system radiative 
response, and the observed energy increase in the Earth system 
for the period 1971–2018 demonstrate improved closure of the 
global energy budget relative to AR5 (Figure TS.13).  Combining

the likely  range of ERF over this period with the central estimate 
of radiative response gives an expected energy gain of 340 [47 to 
662] ZJ. Both estimates are consistent with an independent 
observation-based assessment of the global energy increase of 
284 [96 to 471] ZJ ( very likely  range ), expressed relative to the 
estimated 1850–1900 Earth energy imbalance. ( high confidence ) 
{7.2.2, 7.3.5, Box 7.2}
The assessed greenhouse gas ERF over the 1750–2019 period 
(Section TS.2.2) has increased by +0.59 W  m−2 over AR5 estimates 
for 1750–2011. This increase includes +0.34 W m–2 from increases in 
atmospheric concentrations of well-mixed greenhouse gases (including 
halogenated species) since 2011, +0.15 W m–2 from upwards revisions 
of their radiative efficiencies and +0.10 W m–2 from re-evaluation of 
the ozone and stratospheric water vapour ERF . {7.3.2, 7.3.4, 7.3.5}
1
 Baseline
Cloud 
feedback
fewer low cloudsHigher cloudsAtmospheric energy budget Instantaneous changes

to radiative balanceAdjustments
to meteorology and compositionFeedback responses
Longwave 
radiative 
emission Surface and
responseSurface albedo and 
biogeochemical
feedbackBiogeochemical 
feedback
Absorbed
sunlight O
O OAdjustments in 
chemical composition
Adjustments 
vapour, temperatureO
O O
Increased water vapourIncreased greenhouse 
gases or aerosolsIncreased greenhouse 
gases or aerosolsperturbation
No change in meteorology or surface 
temperatureNo change in surface temperature Increased surface temperature2 3
Figure TS.14 |  Schematic representation of changes in the top-of-atmosphere (TOA) radiation budget following a perturbation.  The intent of this figure is to 
illustrate the concept of adjustments in the climate system following a perturbation in the radiation budget.  The baseline TOA energy budget (a) responds instantaneously to

perturbations (b), leading to adjustments in the atmospheric meteorology and composition and land surface that are independent of changes in surface temperature (c). Surface 
temperature changes (here using an increase as an example) lead to physical, biogeophysical and biogeochemical feedback processes (d). Long-term feedback processes, such 
as those involving ice sheets, are not shown here. {adapted from Figure 7.2; FAQ 7.2, Figure 1; and Figure 8.3}

92
TSFor CO 2, CH 4, N 2O, and chlorofluorocarbons, there is now evidence to 
quantify the effect on ERF of tropospheric adjustments. The assessed 
ERF for a doubling of CO 2 compared to 1750 levels (3.9 ± 0.5 Wm–2) is 
larger than in AR5. For CO 2, the adjustments include the physiological 
effects on vegetation. The reactive well-mixed greenhouse gases 
(CH 4, N 2O, and halocarbons) cause additional chemical adjustments 
to the atmosphere through changes in ozone and aerosols (Figure 
TS.15a). The ERF due to CH 4 emissions is 1.19 [0.81 to 1.58] W m–2, of 
which 0.35 [0.16 to 0.54] W m–2 is attributed to chemical adjustments 
mainly via ozone. These chemical adjustments also affect the 
emissions metrics (Section TS.3.3.3). Changes in sulphur dioxide (SO 2) emissions make the dominant contribution to the ERF from aerosol–
cloud interactions ( high confidence ). Over the 1750–2019 period, 
the contributions from the emitted compounds to global surface

temperature changes broadly match their contributions to the ERF 
(high confidence ) (Figure TS.15b). Since a peak in emissions-induced 
SO 2 ERF has already occurred recently (Section TS.2.2) and since there 
is a delay in the full global surface temperature response owing to 
the thermal inertia in the climate system, changes in SO 2 emissions 
have a slightly larger contribution to global surface temperature 
change compared with changes in CO 2 emissions, relative to their 
respective contributions to ERF . {6.4.2, 7.3.2}
(a) E ffective radiative forcing
      1750 to 2019(b) Chang e in global surface temperatu re
     1750 to 2019
Emitted Compone nts
(c) Aerosol effective radiative forcingClimate effect through:
Figure TS.15 |  Contribution to (a) effective radiative forcing (ERF) and (b) global surface temperature change from component emissions for 1750–2019

based on Coupled Model Intercomparison Project Phase 6 (CMIP6) models and (c) net aerosol ERF for 1750–2014 from different lines of evidence. The 
intent of this figure is to show advances since AR5 in the understanding of (a) emissions-based ERF , (b) global surface temperature response for short-lived climate forcers as 
estimated in Chapter 6, and (c) aerosol ERF from different lines of evidence as assessed in Chapter 7.  In panel (a),  ERFs for well-mixed greenhouse gases (WMGHGs) are 
from the analytical formulae. ERFs for other components are multi-model means based on Earth system model simulations that quantify the effect of individual components. 
The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative

efficiencies and multi-model error in the means. In panel (b),  the global mean temperature response is calculated from the ERF time series using an impulse response function. 
In panel (c),  the AR6 assessment is based on energy balance constraints, observational evidence from satellite retrievals, and climate model-based evidence. For each line of 
evidence, the assessed best-estimate contributions from ERF due to aerosol–radiation interactions (ERFari) and aerosol–cloud interactions (ERFaci) are shown with darker and 
paler shading, respectively. Estimates from individual CMIP Phase 5 (CMIP5) and CMIP6 models are depicted by blue and red crosses, respectively. The observational assessment 
for ERFari is taken from the instantaneous forcing due to aerosol–radiation interactions (IRFari). Uncertainty ranges are given in black bars for the total aerosol ERF and depict 
very likely ranges. {6.4.2, Figure 6.12, 7.3.3, Cross-Chapter Box 7.1, Table 7.8, Figure 7.5}

93
Technical SummaryTSAerosols contributed an ERF of –1.3 [–2.0 to –0.6] W m–2 over the 
period 1750 to 2014 ( medium confidence ). The ERF due to aerosol–
cloud interactions (ERFaci) contributes most to the magnitude of 
the total aerosol ERF ( high confidence ) and is assessed to be –1.0 
[–1.7 to –0.3] W m–2 (medium confidence ), with the remainder due 
to aerosol–radiation interactions (ERFari), assessed to be –0.3 [–0.6 
to 0.0] W m–2 (medium confidence ). There has been an increase in 
the estimated magnitude – but a reduction in the uncertainty – of 
the total aerosol ERF relative to AR5, supported by a combination 
of increased process-understanding and progress in modelling and 
observational analyses (Figure TS.15c). Effective radiative forcing 
estimates from these separate lines of evidence are now consistent 
with each other, in contrast to AR5, and support the assessment that 
it is virtually certain  that the total aerosol ERF is negative. Compared

to AR5, the assessed magnitude of ERFaci has increased, while that 
of ERFari has decreased . {7.3.3, 7.3.5}
TS.3.2 Climate Sensitivity and Earth System Feedbacks
TS.3.2.1 Equilibrium Climate Sensitivity, Transient Climate 
Response, and Transient Climate Response to 
Cumulative Carbon-dioxide Emissions
Since AR5, substantial quantitative progress has been 
made in combining new evidence of Earth’s climate 
sensitivity with improvements in the understanding and 
quantification of Earth’s energy imbalance, the instrumental 
record of global surface temperature change, paleoclimate 
change from proxy records, climate feedbacks and their 
dependence on time scale and climate state. A key advance 
is the broad agreement across these multiple lines of 
evidence, supporting a best estimate of equilibrium climate 
sensitivity of 3°C, with a very likely  range of 2°C to 5°C. The 
likely range of 2.5°C to 4°C is narrower than the AR5 likely 
range of 1.5°C to 4.5°C. {7.4, 7.5}

Constraints on equilibrium climate sensitivity (ECS) and transient 
climate response (TCR) (see Glossary) are based on four main lines 
of evidence: feedback process understanding, climate change and 
variability seen within the instrumental record, paleoclimate evidence, 
and so-called ‘emergent constraints’, whereby a relationship 
between an observable quantity and either ECS or TCR established 
within an ensemble of models is combined with observations to 
derive a constraint on ECS or TCR. In reports up to and including 
the IPCC Third Assessment Report, ECS and TCR derived directly 
from ESMs were the primary line of evidence. However, since AR4, 
historical warming and paleoclimates provided useful additional 
evidence (Figure TS.16a). This Report differs from previous reports in 
not directly using climate model estimates of ECS and TCR in the 
assessed ranges of climate sensitivity. {1.5, 7.5}
It is now clear that when estimating ECS and TCR, the dependence

of feedbacks on time scales and the climate state must be accounted 
for. Feedback processes are expected to become more positive 
overall (more amplifying of global surface temperature changes) on 
multi-decadal time scales as the spatial pattern of surface warming evolves and global surface temperature increases, leading to an ECS 
that is higher than was inferred in AR5 based on warming over the 
instrumental record ( high confidence ). Historical surface temperature 
change since 1870 has shown relatively little warming in several key 
regions of positive feedbacks, including the eastern equatorial Pacific 
Ocean and the Southern Ocean, while showing greater warming 
in key regions of negative feedbacks, including the western Pacific 
warm pool. Based on process understanding, climate modelling, and 
paleoclimate reconstructions of past warm periods, it is expected 
that future warming will become enhanced over the eastern Pacific

Ocean ( medium confidence ) and Southern Ocean ( high confidence ) 
on centennial time scales. This new understanding, along with 
updated estimates of historical temperature change, ERF , and energy 
imbalance, reconciles previously disparate ECS estimates. {7.4.4, 
7.5.2, 7.5.3}
The AR6 best estimate of ECS is 3°C, the likely  range is 2.5°C to 
4°C and the very likely  range is 2°C to 5°C. There is a high level 
of agreement among the four main lines of evidence listed above 
(Figure TS.16b), and altogether it is virtually certain  that ECS is larger 
than 1.5°C, but currently it is not possible to rule out ECS values 
above 5°C. Therefore, the 5°C upper end of the very likely  range is 
assessed with medium confidence  and the other bounds with high 
confidence . {7.5.5}
record, and emergent constraints, the best estimate of TCR is 1.8°C, 
the likely  range is 1.4°C to 2.2°C and the very likely  range is 1.2°C to

2.4°C. There is a high level of agreement among the different lines of 
evidence (Figure TS.16c) ( high confidence ). {7.5.5}
On average, CMIP6 models have higher mean ECS and TCR values 
than the CMIP5 generation of models and also have higher mean 
values and wider spreads than the assessed best estimates and very 
likely  ranges within this Report. These higher mean ECS and TCR 
values can be traced to a positive net cloud feedback that is larger in 
CMIP6 by about 20%. The broader ECS and TCR ranges from CMIP6 
also lead the models to project a range of future warming that 
is wider than the assessed future warming range, which is based 
on multiple lines of evidence (Cross-Section Box TS.1). However, 
some of the high-sensitivity CMIP6 models (Section TS.1.2.2) are 
less consistent with observed recent changes in global warming 
and with paleoclimate proxy records than models with ECS within 
the very likely  range. Similarly, some of the low-sensitivity models

are less consistent with the paleoclimate data. The CMIP6 models 
with the highest ECS and TCRs values provide insights into low-
likelihood, high-impact futures, which cannot be excluded based on 
currently available evidence (Cross-Section Box TS.1). {4.3.1, 4.3.4, 
7.4.2, 7.5.6}
Uncertainties regarding the true value of  ECS and TCR are the dominant 
source of uncertainty in global temperature projections over the 
21st century under moderate to high GHG concentrations scenarios. 
For scenarios that reach net zero CO 2 emissions (Section TS.3.3), the 
uncertainty in the ERF values of  aerosol and other SLCFs contribute 
substantial uncertainty in projected temperature. Global ocean heat 
uptake is a smaller source of uncertainty in centennial warming. {7.5.7}

94
TSThe transient climate response to cumulative CO 2 emissions (TCRE) 
is the ratio between globally averaged surface temperature increase 
and cumulative CO 2 emissions (see Glossary). This Report reaffirms 
with high confidence the finding of AR5 that there is a near-linear 
relationship between cumulative CO 2 emissions and the increase in 
global average temperature caused by CO 2 over the course of this 
century for global warming levels up to at least 2°C relative to 1850–
1900. The TCRE falls likely  in the 1.0°C–2.3°C per 1000 PgC range, 
with a best estimate of 1.65°C per 1000 PgC. This is equivalent to a 
0.27°C–0.63°C range with a best estimate of 0.45°C when expressed 
in units per 1000 GtCO 2. This range is about 15% narrower than the 
0.8°–2.5°C per 1000 PgC assessment of AR5 because of a better 
integration of evidence across chapters, in particular the assessment of TCR. Beyond this century, there is low confidence  that the TCRE

alone remains an accurate predictor of temperature changes in 
scenarios of very low or net negative CO 2 emissions because of 
uncertain Earth system feedbacks that can result in further changes 
in temperature or a path dependency of warming as a function of 
cumulative CO 2 emissions. {4.6.2, 5.4, 5.5.1}
TS.3.2.2 Earth System Feedbacks
The combined effect of all climate feedback processes is to amplify 
the climate response to forcing ( virtually certain ). While major 
advances in the understanding of cloud processes have increased 
the level of confidence and decreased the uncertainty range for the 1980 1990 2000 2010 2020 20301 12 23 34 45 56 6Equilib rium climat esensiti vity(°C)
Charney
FAR
SAR
TAR
AR4
Primarily model evidence p< 5%p <10%AR5
Likely :p >66%
AR6
AR6 comb inesevidencefrom:
·Process underst anding·Instrumentalrecord·Paleoclimates·Emerg entconstraintsBest estimate: 3°CLikely:2.5–4°CVerylikely:2–5°C 
YearofassessmentAlso considersinstrumental recordand paleoclimates

(b) Equilibrium climate sensitivity (ºC) assessed in AR6 and 
simulated by CMIP6 ESMs(c) Transient climate response (ºC) assessed in AR6 and 
simulated by CMIP6 ESMs(a) Evolution of equilibrium climate sensitivity assessments from Charney to AR6
CMIP6 ESMs
CMIP6 ESMs
Very likely  range or limit Extremely likely  limit
Figure TS.16 |  (a) Evolution of equilibrium climate sensitivity (ECS) assessments from the Charney Report through a succession of IPCC Assessment 
Reports to AR6, and lines of evidence and combined assessment for (b) ECS and (c) transient climate response (TCR) in AR6.  The intent of this figure is to

show the progression in estimates of ECS, including uncertainty and the lines of evidence used for assessment, and to show the lines of assessment used to assess ECS and 
TCR in AR6.  In panel (a),  the lines of evidence considered are listed below each assessment. Best estimates are marked by horizontal bars, likely ranges by vertical bars, and 
very likely ranges by dotted vertical bars. In panel (b) and (c),  assessed ranges are taken from Tables 7.13 and 7.14 for ECS and TCR respectively. Note that for the ECS 
assessment based on both the instrumental record and paleoclimates, limits (i.e., one-sided distributions) are given, which have twice the probability of being outside the 
maximum/minimum value at a given end, compared to ranges (i.e., two tailed distributions) which are given for the other lines of evidence. For example, the extremely likely limit

of greater than 95% probability corresponds to one side of the very likely (5% to 95%) range. Best estimates are given as either a single number or by a range represented by 
grey box. Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system model (ESM) values are not directly used as a line of evidence but are presented on the figure 
for comparison. {1.5, 7.5; Tables 7.13 and 7.14; Figure 7.18}

95
Technical SummaryTScloud feedback by about 50% compared to AR5, clouds remain the 
largest contribution to overall uncertainty in climate feedbacks ( high 
confidence ). Uncertainties in the ECS and other climate sensitivity 
metrics, such as the TCR and TCRE, are the dominant source of 
uncertainty in global temperature projections over the 21st century 
under moderate to high GHG emissions scenarios. CMIP6 models 
have higher mean values and wider spreads in ECS and TCR than the 
assessed best estimates and very likely  ranges within this Report, 
leading the models to project a range of future warming that is 
wider than the assessed future warming range (Section TS.2.2). {7.1, 
7.4.2, 7.5}
Earth system feedbacks can be categorized into three broad groups: 
physical feedbacks, biogeophysical and biogeochemical feedbacks, 
and feedbacks associated with ice sheets. In previous assessments, 
the ECS has been associated with a distinct set of physical feedbacks

(Planck response, water vapour, lapse rate, surface albedo, and cloud 
feedbacks). In this assessment, a more general definition of ECS is 
adopted whereby all biogeophysical and biogeochemical feedbacks 
that do not affect the atmospheric concentration of CO 2 are included. 
These include changes in natural CH 4 emissions, natural aerosol 
emissions, N 2O, ozone, and vegetation, which all act on time scales of 
years to decades and are therefore relevant for temperature change 
over the 21st century. Because the total biogeophysical and non-CO 2 
biogeochemical feedback is assessed to have a central value that is 
near zero ( low confidence ), including it does not affect the assessed 
ECS but does contribute to the net feedback uncertainty.  The 
biogeochemical feedbacks that affect the atmospheric concentration 
of CO 2 are not included because ECS is defined as the response to 
a sustained doubling of CO 2. Moreover, the long-term feedbacks

associated with ice sheets are not included in the ECS owing to their 
long time scales of adjustment. {5.4, 6.4, 7.4, 7.5, Box 7.1}
The net effect of changes in clouds in response to global warming is 
to amplify human-induced warming, that is, the net cloud feedback 
is positive ( high confidence ). Compared to AR5, major advances 
in the understanding of cloud processes have increased the level 
of confidence and decreased the uncertainty range in the cloud 
feedback by about 50% (Figure TS.17a). An assessment of the low-
altitude cloud feedback over the subtropical ocean, which was 
previously the major source of uncertainty in the net cloud feedback, 
is improved owing to a combined use of climate model simulations, 
satellite observations, and explicit simulations of clouds, altogether 
leading to strong evidence that this type of cloud amplifies global 
warming. The net cloud feedback is assessed to be +0.42 [–0.10

to 0.94] W m–2 °C–1. A net negative cloud feedback is very unlikely . 
The CMIP5 and CMIP6 ranges of cloud feedback are similar to this 
assessed range, with CMIP6 having a slightly more positive median 
cloud feedback ( high confidence ). The surface albedo feedback and 
combined water vapour-lapse rate feedback are positive (Figure 
TS.17a), with high confidence  in the estimated value of each based 
on multiple lines of evidence, including observations, models and 
theory (Box TS.6). {7.4.2, Figure 7.14, Table 7.10}
20 For reference, the Planck temperature response for a doubling of atmospheric CO 2 is approximately 1.2°C at equilibrium.Natural sources and sinks of non-CO 2 greenhouse gases such as 
methane (CH 4) and nitrous oxide (N 2O) respond both directly and 
indirectly to atmospheric CO 2 concentration and climate change, 
and thereby give rise to additional biogeochemical feedbacks 
in the climate system. Many of these feedbacks are only partially

understood and are not yet fully included in ESMs. There is medium 
confidence that the net response of natural ocean and land CH 4 and 
N2O sources to future warming will be increased emissions, but the 
magnitude and timing of the responses of each individual process is 
known with low confidence . {5.4.7}
Non-CO 2 biogeochemical feedbacks induced from changes in 
emissions, abundances or lifetimes of SLCFs mediated by natural 
processes or atmospheric chemistry are assessed to decrease 
ECS (Figure TS.17b). These non-CO 2 biogeochemical feedbacks 
are estimated from ESMs, which since AR5 have advanced to 
include a consistent representation of biogeochemical cycles and 
atmospheric chemistry. However, process-level understanding 
of many biogeochemical feedbacks involving SLCFs, particularly 
natural emissions, is still emerging, resulting in low confidence  in 
the magnitude and sign of the feedbacks. The central estimate of

the total biogeophysical and non-CO 2 biogeochemical feedback is 
assessed to be −0.01 [–0.27 to +0.25] W m–2 °C–1 (Figure TS.17a). 
{5.4.7, 5.4.8, 6.2.2, 6.4.5, 7.4, Table 7.10}
base climate response (in the absence of feedbacks), also known 
as the Planck temperature response20 (virtually certain ). Combining 
these feedbacks with the Planck response, the net climate feedback 
parameter is assessed to be –1.16 [–1.81 to –0.51] W  m–2 °C–1, 
assessment. The combined water vapour and lapse rate feedback 
from paleoclimate observations and from models, the net feedback

forcing. ( high confidence ) {7.4.2, 7.4.3, Figure 7.14, Table 7.10}
The carbon cycle provides for additional feedbacks on climate owing 
to the sensitivity of land–atmosphere and ocean–atmosphere carbon 
fluxes and storage to changes in climate and in atmospheric CO 2 
(Figure TS.17c). Because of the time scales associated with land and 
ocean carbon uptake, these feedbacks are known to be scenario 
dependent. Feedback estimates deviate from linearity in scenarios 
of stabilizing or reducing concentrations. With high confidence , 
increased atmospheric CO 2 will lead to increased land and ocean 
carbon uptake, acting as a negative feedback on climate change. It

is likely that a warmer climate will lead to reduced land and ocean 
carbon uptake, acting as a positive feedback (Box TS.5). {4.3.2, 
5.4.1–5}

96
TSOcean carbon response to climateLand carbon response to clim ateOcean carbon response to CO2Land carbon response to CO 2
−3.5 −3.0 −2.5 −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5(c) Carbon-cycle climate feedbacks
Permafrost feedbackMean [5−95% range]
0.08 [0.04 to 0.12]−0.68 [−0.98 to −0.39]−0.78 [−1.28 to −0.28]
0.25 [−0.03 to 0.54]
Climate feedback parameter (Wm-2 °C-1) BiogeophysicalOther non− CO2 biogeochemicalN2O source response to climateAtm. CH4 lifetime response to climateCH4 source response to climateTotal (without permafrost feedback)
−0.4 −0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 0.4(b) Biogeophysical and non-CO2 biogeochemical climate feedbacks
Mean [5−95% range]
 0.15 [ 0.00 to 0.30]−0.17 [−0.36 to 0.02] 0.01 [−0.01 to 0.02]−0.03 [−0.12 to 0.06] 0.03 [ 0.01 to 0.05]−0.01 [−0.27 to 0.25]

Climate feedback parameter (Wm-2 °C-1) Permafrost feedbackClimate feedback parameter (Wm-2 °C-1) −3.5 −3.0 −2.5 −2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5(a) Feedbacks in the climate system
CloudsSurface albedoWater vapour and lapse ratePlanckTotal 
Biogeophysical and
non−CO 2 biogeochemical
(Total from panel (b))
Mean [very likely  range]
−0.01 [−0.27 to 0.25] 0.42 [−0.10 to 0.94] 0.35 [ 0.10 to 0.60] 1.30 [ 1.13 to 1.47]−3.22 [−3.39 to −3.05]−1.16 [−1.81 to −0.51]
Negative feedbacks
 diminish the
initial climate response to radiative forcing
Positive feedbacks
 amplify the
Figure TS.17 |  An overview of physical and biogeochemical feedbacks in the climate system.  The intent of this figure is to summarize assessed estimates of physical, 
biogeophysical and biogeochemical feedbacks on global temperature based on Chapters 5, 6 and 7.  (a) Synthesis of physical, biogeophysical and non-carbon dioxide (CO 2)

biogeochemical feedbacks that are included in the definition of equilibrium climate sensitivity (ECS) assessed in this Technical Summary. These feedbacks have been assessed 
using multiple lines of evidence including observations, models and theory. The net feedback is the sum of the Planck response, water vapour and lapse rate, surface albedo, 
cloud, and biogeophysical and non-CO 2 biogeochemical feedbacks. Bars denote the mean feedback values, and uncertainties represent very likely  ranges; (b) Estimated values of 
individual biogeophysical and non-CO 2 biogeochemical feedbacks. The atmospheric methane (CH 4) lifetime and other non-CO 2 biogeochemical feedbacks have been calculated 
using global Earth system model simulations from AerChemMIP , while the CH 4 and nitrous oxide (N 2O) source responses to climate have been assessed for the year 2100 using

a range of modelling approaches using simplified radiative forcing equations. The estimates represent the mean and 5–95% range. The level of confidence in these estimates 
is low owing to the large model spread. (c) Carbon-cycle feedbacks as simulated by models participating in the C4MIP of the Coupled Model Intercomparison Project Phase 6 
(CMIP6). An independent estimate of the additional positive carbon-cycle climate feedbacks from permafrost thaw, which is not considered in most C4MIP models, is added. 
The estimates represent the mean and 5–95% range. Note that these feedbacks act through modifying the atmospheric concentration of CO 2 and thus are not included in the 
definition of ECS, which assumes a doubling of CO 2, but are included in the definition and assessed range of the transient climate response to cumulative CO 2 emissions (TCRE). 
{5.4.7, 5.4.8, Box 5.1, Figure 5.29, 6.4.5, Table 6.9, 7.4.2, Table 7.10}

97
Technical SummaryTSThawing terrestrial permafrost will lead to carbon release ( high 
confidence ), but there is low confidence  in the timing, magnitude 
and the relative roles of CO 2 versus CH 4 as feedback processes. An 
ensemble of models projects CO 2 release from permafrost to be 
3–41 PgC per 1ºC of global warming by 2100, leading to warming 
strong enough that it must be included in estimates of the remaining 
carbon budget but weaker than the warming from fossil fuel burning. 
However, the incomplete representation of important processes, 
such as abrupt thaw, combined with weak observational constraints, 
only allow low confidence  in both the magnitude of these estimates 
global warming. There is emerging evidence that permafrost thaw 
and thermokarst give rise to increased CH 4 and N 2O emissions, which 
leads to the combined radiative forcing from permafrost thaw being

larger than from CO 2 emissions only. However, the quantitative 
understanding of these additional feedbacks is low, particularly for 
N2O. These feedbacks, as well as potential additional carbon losses 
due to climate-induced fire feedback are not routinely included in 
Earth system models. {Box 5.1, 5.4.3, 5.4.7, 5.4.8}TS.3.3 Temperature Stabilization, Net Zero Emissions 
and Mitigation
TS.3.3.1  Remaining Carbon Budgets and Temperature 
Stabilization
The near-linear relationship between cumulative CO 2 
emissions and maximum global surface temperature increase 
caused by CO 2 implies that stabilizing human-induced global 
temperature increase at any level requires net anthropogenic 
CO 2 emissions to become zero. This near-linear relationship 
further implies that mitigation requirements for limiting 
warming to specific levels can be quantified in terms of a 
carbon budget ( high confidence ). Remaining carbon budget 
estimates have been updated since AR5 with methodological

improvements, resulting in larger estimates that are 
consistent with SR1.5. Several factors, including estimates 
of historical warming, future emissions from thawing 
permafrost, variations in projected non-CO 2 warming, and 
the global surface temperature change after cessation of 
CO 2 emissions, affect the exact value of carbon budgets ( high 
confidence ). {1.3.5, Box 1.2, 4.7.1, 5.5}
Figure TS.18 |  Illustration of (a) relationship between cumulative emissions of carbon dioxide (CO 2) and global mean surface air temperature increase 
and (b) the assessment of the remaining carbon budget from its constituting components based on multiple lines of evidence.  The intent of this figure is to 
show (i) the proportionality between cumulative CO 2 emissions and global surface air temperature in observations and models as well as the assessed range of the transient

climate response to cumulative CO 2 emissions (TCRE), and (ii) how information is combined to derive remaining carbon budgets consistent with limiting warming to a specific 
level.  Carbon budgets consistent with various levels of additional warming are provided in Table 5.8 and should not be read from the illustrations in either panel. In panel 
(a) thin black line shows historical CO 2 emissions together with the assessed global surface temperature increase from 1850–1900 as assessed in Chapter 2 (Box 2.3). The 
orange-brown range with its central line shows the estimated human-induced share of historical warming. The vertical orange-brown line shows the assessed range of historical 
human-induced warming for the 2010–2019 period relative to 1850–1900 (Chapter 3). The grey cone shows the assessed likely range for the TCRE (Section 5.5.1.4), starting

from 2015. Thin coloured lines show Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for the five scenarios of the WGI core set (SSP1-1.9, green; SSP1-2.6, 
blue; SSP2-4.5, yellow; SSP3-7.0, red; SSP5-8.5, maroon), starting from 2015 and until 2100. Diagnosed carbon emissions are complemented with estimated land-use change 
emissions for each respective scenario. Coloured areas show the Chapter 4 assessed very likely  range of global surface temperature projections and thick coloured central lines 
show the median estimate, for each respective scenario. These temperature projections are expressed relative to cumulative CO 2 emissions that are available for emissions-
driven CMIP6 ScenarioMIP experiments for each respective scenario. For panel (b),  the remaining allowable warming is estimated by combining the global warming limit

of interest with the assessed historical human-induced warming (Section 5.5.2.2.2), the assessed future potential non-CO 2 warming contribution (Section 5.5.2.2.3) and the 
zero emissions commitment (ZEC; Section 5.5.2.2.4). The remaining allowable warming (vertical blue bar) is subsequently combined with the assessed TCRE (Sections 5.5.1.4 
and 5.5.2.2.1) and contribution of unrepresented Earth system feedbacks (Section 5.5.2.2.5) to provide an assessed estimate of the remaining carbon budget (horizontal blue 
bar, Table 5.8). Note that contributions in panel (b) are illustrative and are not to scale. For example, the central ZEC estimate was assessed to be zero. {Box 2.3, 5.2.1, 5.2.2, 
Figure 5.31}0 1000 2000 3000 4000 5000 6000 7000
Cumulative carbon dioxide emissions since 1850 ( GtCO2)-101234567Global mean temperature increase since 1850 –1900 (°C)0 200 400 600 800 1000 1200 1400 1600 1800 2000Cumulative carbon emissions since 1850 (PgC)

SSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
warming
(Section 5.5.1.4)Temperature increase since pre-industrial levels (°C)
remaining
remaining
0(a) (b)

98
TSLimiting further climate change would require substantial and 
sustained reductions of GHG emissions. Without net zero CO 2 
emissions, and a decrease in the net non-CO 2 forcing (or sufficient 
net negative CO 2 emissions to offset any further warming from 
net non-CO 2 forcing), the climate system will continue to warm. 
There is high confidence  that mitigation requirements for limiting 
warming to specific levels over this century can be estimated using 
a carbon budget that relates cumulative CO 2 emissions to global 
mean temperature increase (Figure TS.18, Table TS.3). For the period 
1850–2019, a total of 2390 ± 240 GtCO 2 of anthropogenic CO 2 has 
been emitted. Remaining carbon budgets (starting from 1  January 
2020) for limiting warming to 1.5°C, 1.7°C and 2.0°C are estimated 
at 500 GtCO 2, 850 GtCO 2 and 1350 GtCO 2, respectively, based on 
the 50th percentile of TCRE. For the 67th percentile, the respective

values are 400 GtCO 2, 700 GtCO 2 and 1150 GtCO 2. The remaining 
carbon budget estimates for different temperature limits assume 
that non-CO 2 emissions are mitigated consistent with the median 
reductions found in scenarios in the literature as assessed in SR1.5, but they may vary by an estimated ±220 GtCO 2 depending on 
how deeply future non-CO 2 emissions are assumed to be reduced 
(Table TS.3). {5.5.2, 5.6, Box 5.2, 7.6}
There is high confidence that several factors, including estimates of 
historical warming, future emissions from thawing permafrost, and 
variations in projected non-CO 2 warming, affect the value of carbon 
budgets but do not change the conclusion that global CO 2 emissions 
would need to decline to net zero to halt global warming. Estimates 
may vary by ±220 GtCO 2 depending on the level of non-CO 2 emissions 
at the time global anthropogenic CO 2 emissions reach net zero levels. 
This variation is referred to as non-CO 2 scenario uncertainty and

will be further assessed in the AR6 Working Group III Contribution. 
Geophysical uncertainties surrounding the climate response to these 
non-CO 2 emissions result in an additional uncertainty of at least 
±220  GtCO 2, and uncertainties in the level of historical warming 
result in a ±550 GtCO 2 uncertainty. {5.4, 5.5.2}
Table TS.3 |  Estimates of remaining carbon budgets and their uncertainties.  Assessed estimates are provided for additional human-induced warming, expressed as 
global surface temperature, since the recent past (2010–2019), likely amounted to 0.8° to 1.3°C with a best estimate of 1.07°C relative to 1850–1900. Historical CO 2 emissions 
between 1850 and 2014 have been estimated at about 2180 ± 240 GtCO 2 (1-sigma range), while since 1 January 2015, an additional 210 GtCO 2 has been emitted until the 
end of 2019. GtCO 2 values to the nearest 50. {Table 3.1, 5.5.1, 5.5.2, Box 5.2, Table 5.1, Table 5.7, Table 5.8}
Global 
surface 
change since

2010–201 9Global 
surface 
change since  
1850–1900aEstimated remaining carbon 
budgets starting from 1 January 
2020 and subject to variations 
and uncertainties quantified in the 
columns on the rightScenario 
variationGeophysical uncertaintiesd
°C °CPercentiles of TCREb 
GtCO 2Non-CO 2 
scenario 
variationcNon-CO 2 
response 
uncertaintyHistorical 
uncertaintyaZero CO 2 
emissions 
uncertaintyRecent 
emissions 
uncertaintye
17th 33rd 50th 67th 83rd GtCO 2 GtCO 2 GtCO 2 GtCO 2 GtCO 2
0.43 1.5 900 650 500 400 300
Values can 
vary by at least 
±220 due to 
choices related 
to non-CO 2 
emissions 
mitigationValues can 
±220 due to 
uncertainty in 
the warming 
future non-CO 2 
emissions±550 ±420 ±200.53 1.6 1200 850 650 550 400
0.63 1.7 1450 1050 850 700 550
0.73 1.8 1750 1250 1000 850 650
0.83 1.9 2000 1450 1200 1000 800
0.93 2 2300 1700 1350 1150 900

a Human-induced global surface temperature increase between 1850–1900 and 2010–2019 is assessed at 0.8–1.3°C ( likely range; Cross-Section Box TS.1) with a best estimate 
of 1.07°C. Combined with a central estimate of TCRE (1.65°C per 1000 PgC) this uncertainty in isolation results in a potential variation of remaining carbon budgets of ±550 
GtCO 2, which, however, is not independent of the assessed uncertainty of TCRE and thus not fully additional.
b TCRE: transient climate response to cumulative emissions of carbon dioxide, assessed to fall likely between 1.0–2.3°C per 1000 PgC with a normal distribution, from which 
the percentiles are taken. Additional Earth system feedbacks are included in the remaining carbon budget estimates as discussed in Section 5.5.2.2.5.
c Estimates assume that non-CO 2 emissions are mitigated consistent with the median reductions found in scenarios in the literature as assessed in SR1.5. Non-CO 2 scenario

variations indicate how much remaining carbon budget estimates vary due to different scenario assumptions related to the future evolution of non-CO 2 emissions in mitigation 
scenarios from SR1.5 that reach net zero CO 2 emissions. This variation is additional to the uncertainty in TCRE. The Working Group III Contribution to AR6 will reassess the 
potential for non-CO 2 mitigation based on literature since SR1.5.
d Geophysical uncertainties reported in these columns and TCRE uncertainty are not statistically independent, as uncertainty in TCRE depends on uncertainty in the assessment 
of historical temperature, non-CO 2 versus CO 2 forcing, and uncertainty in emissions estimates. These estimates cannot be formally combined, and these uncertainty variations 
are not directly additional to the spread of remaining carbon budgets due to TCRE uncertainty reported in columns three to seven.

e Recent emissions uncertainty reflects the ±10% uncertainty in the historical CO 2 emissions estimate since 1 January 2015.

99
Technical SummaryTSMethodological improvements and new evidence result in updated 
remaining carbon budget estimates. The assessment in AR6 applies 
the same methodological improvements as in SR1.5, which uses 
a recent observed baseline for historic temperature change and 
cumulative emissions. Changes compared to SR1.5 are therefore 
small: the assessment of new evidence results in updated median 
remaining carbon budget estimates for limiting warming to 1.5°C 
and 2°C being the same and about 60 GtCO 2 smaller, respectively, 
after accounting for emissions since SR1.5. Meanwhile, remaining 
carbon budgets for limiting warming to 1.5°C would be about 
300–350 GtCO 2 larger if evidence and methods available at the 
time of AR5 would be used. If a specific remaining carbon budget 
is exceeded, this results in a lower probability of keeping warming 
below a specified temperature level and higher irreversible global 
warming over decades to centuries, or alternatively a need for net

negative CO 2 emissions or further reductions in non-CO 2 greenhouse 
gases after net zero CO 2 is achieved to return warming to lower levels 
in the long term. {5.5.2, 5.6, Box 5.2}
Based on idealized model simulations that explore the climate 
response once CO 2 emissions have been brought to zero, the 
magnitude of the zero CO 2 emissions commitment (ZEC, see Glossary) 
is assessed to be likely  smaller than 0.3°C for time scales of about 
half a century and cumulative CO 2 emissions broadly consistent with 
global warming of 2°C. However, there is low confidence  about its 
sign on time scales of about half a century. For lower cumulative CO 2 
emissions, the range would be smaller yet with equal uncertainty 
about the sign. If the ZEC is positive on decadal time scales, additional 
warming leads to a reduction in the estimates of remaining carbon 
budgets, and vice versa if it is negative. {4.7.1, 5.5.2}
Permafrost thaw is included in estimates together with other

feedbacks that are often not captured by models. Limitations in 
modelling studies combined with weak observational constraints 
only allow low confidence  in the magnitude of these estimates 
(Section TS.3.2.2). Despite the large uncertainties surrounding the 
quantification of the effect of additional Earth system feedback 
processes, such as emissions from wetlands and permafrost thaw, 
these feedbacks represent identified additional risk factors that 
scale with additional warming and mostly increase the challenge of 
limiting warming to specific temperature levels. These uncertainties 
do not change the basic conclusion that global CO 2 emissions would 
need to decline to net zero to halt global warming. {5.4.8, 5.5.2, 
Box 5.1}
TS.3.3.2 Carbon Dioxide Removal
Deliberate carbon dioxide removal (CDR) from the 
atmosphere has the potential to compensate for residual 
CO 2 emissions to reach net zero CO 2 emissions or to 
generate net negative CO 2 emissions. In the same way

that part of current anthropogenic net CO 2 emissions 
are taken up by land and ocean carbon stores, net CO 2 
removal will be partially counteracted by CO 2 release from 
these stores ( very high confidence ). Asymmetry in the 
carbon cycle response to simultaneous CO 2 emissions and 
removals implies that a larger amount of CO 2 would need to be removed to compensate for an emission of a given 
magnitude to attain the same change in atmospheric CO 2 
(medium confidence ). CDR methods have wide-ranging side-
effects that can either weaken or strengthen the carbon 
(high confidence ). {4.6.3, 5.6}
Carbon dioxide removal (CDR) refers to anthropogenic activities 
that deliberately remove CO 2 from the atmosphere and durably 
store it in geological, terrestrial or ocean reservoirs, or in products. 
Carbon dioxide is removed from the atmosphere by enhancing

biological or geochemical carbon sinks or by direct capture of CO 2 
from air. Emissions pathways that limit global warming to 1.5°C or 
2°C typically assume the use of CDR approaches in combination 
with GHG emissions reductions. CDR approaches could be used to 
compensate for residual emissions from sectors that are difficult or 
costly to decarbonize. CDR could also be implemented at a large scale 
to generate global net negative CO 2 emissions (i.e., anthropogenic 
CO 2 removals exceeding anthropogenic emissions), which could 
compensate for earlier emissions as a way to meet long-term climate 
stabilization goals after a temperature overshoot. This Report assesses 
the effects of CDR on the carbon cycle and climate. Co-benefits and 
trade-offs for biodiversity, water and food production are briefly 
discussed for completeness, but a comprehensive assessment of the 
ecological and socio-economic dimensions of CDR options is left to 
the WGII and WGIII reports. {4.6.3, 5.6}

CDR methods have the potential to sequester CO 2 from the 
atmosphere ( high confidence ). In the same way part of current 
anthropogenic net CO 2 emissions are taken up by land and ocean 
carbon stores, net CO 2 removal will be partially counteracted by CO 2 
release from these stores, such that the amount of CO 2 sequestered 
by CDR will not result in an equivalent drop in atmospheric CO 2 (very 
high confidence ). The fraction of CO 2 removed from the atmosphere 
that is not replaced by CO 2 released from carbon stores – a measure 
of CDR effectiveness – decreases slightly with increasing amounts of 
removal ( medium confidence ) and decreases strongly if CDR is applied 
at lower atmospheric CO 2 concentrations ( medium confidence ). The 
reduction in global surface temperature is approximately linearly 
related to cumulative CO 2 removal ( high confidence ). Because of this 
near-linear relationship, the amount of cooling per unit CO 2 removed

is approximately independent of the rate and amount of removal 
(medium confidence ). {4.6.3, 5.6.2.1, Figure 5.32, Figure 5.34}
Due to non-linearities in the climate system, the century-scale 
climate–carbon cycle response to a CO 2 removal from the atmosphere 
is not always equal and opposite to its response to a simultaneous 
CO 2 emission ( medium  confidence). For CO 2 emissions of 100 PgC 
released from a state in equilibrium with pre-industrial atmospheric 
CO 2 levels, CMIP6 models simulate that 27± 6% (mean ± 1 standard 
deviation) of emissions remain in the atmosphere 80–100 years 
after the emissions, whereas for removals of 100 PgC only 23 
± 6% of removals remain out of the atmosphere. This asymmetry 
implies that an extra amount of CDR is required to compensate for 
a positive emission of a given magnitude to attain the same change 
in atmospheric CO 2. Due to low agreement  between models, there

100
TSis low confidence  in the sign of the asymmetry of the temperature 
response to CO 2 emissions and removals. {4.6.3, 5.6.2.1, Figure 5.35}
Simulations with ESMs indicate that under scenarios where CO 2 
emissions gradually decline, reach net zero and become net negative 
during the 21st century (e.g., SSP1-2.6), land and ocean carbon sinks 
begin to weaken in response to declining atmospheric CO 2 
concentrations, and the land sink eventually turns into a source 
(Figure TS.19). This sink-to-source transition occurs decades to a few 
centuries after CO 2 emissions become net negative. The ocean 
remains a sink of CO 2 for centuries after emissions become net 
negative. Under scenarios with large net negative CO 2 emissions 
(e.g., SSP5-3.4-OS) and rapidly declining CO 2 concentrations, the land 
source is larger than for SSP1-2.6 and the ocean also switches to a 
source. While the general response is robust across models, there is

low confidence  in the timing of the sink-to-source transition and the 
magnitude of the CO 2 source in scenarios with net negative CO 2 
emissions. Carbon dioxide removal could reverse some aspects 
climate change if CO 2 emissions become net negative, but some 
changes would continue in their current direction for decades to 
millennia. For instance, sea level rise due to ocean thermal expansion 
would not reverse for several centuries to millennia ( high confidence ) 
(Box TS.4). {4.6.3, 5.4.10, 5.6.2.1, Figure 5.30, Figure 5.33}Carbon dioxide removal methods have a range of side effects that 
can either weaken or strengthen the carbon sequestration and 
cooling potential of these methods and affect the achievement 
of sustainable development goals ( high confidence ). Biophysical 
and biogeochemical side-effects of CDR methods are associated 
with changes in surface albedo, the water cycle, emissions of CH 4 
and N 2O, ocean acidification and marine ecosystem productivity

(high confidence ). These side-effects and associated Earth system 
feedbacks can decrease carbon uptake and/or change local and 
regional climate and in turn limit the CO 2 sequestration and cooling 
potential of specific CDR methods ( medium confidence ). Deployment 
of CDR, particularly on land, can also affect water quality and quantity, 
food production and biodiversity ( high confidence ). These effects are 
often highly dependent on local context, management regime, prior 
land use, and scale ( high confidence ). The largest co-benefits are 
obtained with methods that seek to restore natural ecosystems or 
improve soil carbon sequestration ( medium confidence ). The climate 
and biogeochemical effects of terminating CDR are expected to be 
small for most CDR methods ( medium confidence ). {4.6.3, 5.6.2.2, 
Figure 5.36, 8.4.3, 8.6.3}

Figure TS.19 |  Carbon sink response in a scenario with net carbon dioxide (CO 2) removal from the atmosphere. The intent of this figure is to show how 
atmospheric CO 2 evolves under negative emissions and its dependence on the negative emissions technologies. It also shows the evolution of the ocean and land sinks.  Shown 
are CO 2 flux components from concentration-driven Earth system model (ESM) simulations during different emissions stages of SSP1–2.6 and its long-term extension. (a) Large 
net positive CO 2 emissions, (b) small net positive CO 2 emissions, (c–d)  net negative CO 2 emissions, and (e) net zero CO 2 emissions. Positive flux components act to raise the 
atmospheric CO 2 concentration, whereas negative components act to lower the CO 2 concentration. Net CO 2 emissions and land and ocean CO 2 fluxes represent the multi-model

mean and standard deviation (error bar) of four ESMs (CanESM5, UKESM1, CESM2-WACCM, IPSL-CM6a-LR) and one Earth system model of intermediate complexity (Uvic 
ESCM). Net CO 2 emissions are calculated from concentration-driven ESM simulations as the residual from the rate of increase in atmospheric CO 2 and land and ocean CO 2 fluxes. 
Fluxes are accumulated over each 50-year period and converted to concentration units (parts per million, or ppm). {5.6.2.1, Figure 5.33}
Net CO 2 emissions 
Net ocean fluxCO 2 in atmosphereDifferent emission stages in SSP1-2.6 scenario, characterized by:
    CO 2 emissions
     CO 2 emissions

change
within
500
450
400
350
300Carbon dioxide concentration (ppm)–41
–61 –23
–33
–3033203
368469
446
411 403 400 396–7–7–7–542
3 0
+101 ppm –23 ppm –35 ppm –8 ppm –4 ppm

101
Technical SummaryTSTS.3.3.3 Relating Different Forcing Agents
When including other GHGs, the choice of emissions metric 
affects the quantification of net zero GHG emissions and 
their resulting temperature outcome ( high confidence ). 
Reaching and sustaining net zero GHG emissions typically 
leads to a peak and decline in temperatures when 
quantified with the global warming potential over a 100-
year period (GWP-100). Carbon-cycle responses are more 
robustly accounted for in emissions metrics compared to 
AR5 ( high confidence ). New emissions metric approaches 
can be used to generate equivalent cumulative emissions 
of CO 2 for short-lived greenhouse gases based on their rate 
of emissions. {7.6.2}
Over 10- to 20-year time scales, the temperature response 
to a single year’s worth of current emissions of short-
lived climate forcers (SLCFs) is at least as large as that of 
CO 2, but because the effects of SLCFs decay rapidly over

the first few decades after emission, the net long-term 
temperature response to a single year’s worth of emissions 
is predominantly determined by cumulative CO 2 emissions. 
Emissions reductions in 2020 associated with COVID-19 
containment led to small and positive global ERF; however, 
global and regional climate responses to the forcing are 
undetectable above internal variability due to the temporary 
nature of emissions reductions. {6.6, Cross-Chapter Box 6.1}
The relative climate effects of different forcing agents are typically 
quantified using emissions metrics that compare the effects of 
an idealised pulse of 1 kg of some climate forcing agent against 
a reference climate forcing agent, almost always CO 2. The two most 
prominent pulse emissions metrics are the global warming potential 
(GWP) and global temperature change potential (GTP) (see Glossary). 
The climate responses to CO 2 emissions by convention include the

effects of warming on the carbon cycle, so for consistency these also 
need to be determined for non-CO 2 emissions. The methodology 
for doing this has been placed on a more robust scientific footing 
compared to AR5 ( high confidence ). Methane from fossil fuel sources 
has slightly higher emissions metric values than those from biogenic 
sources since it leads to additional fossil CO 2 in the atmosphere 
(high confidence ). Updates to the chemical adjustments for CH 4 and 
N2O emissions (Section TS.3.1) and revisions in their lifetimes result 
in emissions metrics for GWP and GTP that are slightly lower than 
in AR5 ( medium confidence ). Emissions metrics for the entire suite 
of GHGs assessed in the AR6 have been calculated for various time 
horizons. {7.6.1, Table 7.15, Table 7.SM.7}
New emissions metric approaches, such as GWP* and Combined-
GTP (CGTP), relate changes in the emissions rate of short-lived 
greenhouse gases to equivalent cumulative emissions of CO 2

(CO 2-e). Global surface temperature response from aggregated 
emissions of short-lived greenhouse gases over time is determined 
by multiplying these cumulative CO 2-e by TCRE (see Section TS.3.2.1). 
When GHGs are aggregated using standard metrics such as GWP or 
GTP , cumulative CO 2-e emissions are not necessarily proportional to future global surface temperature outcomes ( high confidence ) {7.6.1, 
Box 7.3}
Emissions metrics are needed to aggregate baskets of gases to 
determine net zero GHG emissions. Generally, achieving net zero CO 2 
emissions and declining non-CO 2 radiative forcing would halt human-
induced warming. Reaching net zero GHG emissions quantified by 
GWP-100 typically leads to declining temperatures after net zero 
GHGs emissions are achieved if the basket includes short-lived gases, 
such as CH 4. Net zero GHG emissions defined by CGTP or GWP* 
imply net zero CO 2 and other long-lived GHG emissions and constant

(CGTP) or gradually declining (GWP*) emissions of short-lived gases. 
The warming evolution resulting from net zero GHG emissions 
defined in this way corresponds approximately to reaching net zero 
CO 2 emissions, and would thus not lead to declining temperatures 
after net zero GHG emissions are achieved but to an approximate 
temperature stabilization ( high confidence ). The choice of emissions 
metric hence affects the quantification of net zero GHG emissions, 
and therefore the resulting temperature outcome of reaching and 
sustaining net zero GHG emissions levels ( high confidence ). {7.6.1.4, 
7.6.2, 7.6.3}
As pointed out in AR5, ultimately, it is a matter for policymakers 
to decide which emissions metric is most applicable to their needs. 
This Report does not recommend the use of any specific emissions 
metric, as the most appropriate metric depends on the policy goal 
and context (see Chapter 7, Section 7.6). A detailed assessment of

GHG metrics to support climate change mitigation and associated 
policy contexts is provided in the WGIII contribution to the AR6.
The global surface temperature response following a climate change 
mitigation measure that affects emissions of both short- and long-
lived climate forcers depends on their lifetimes, their ERFs, how 
fast and for how long the emissions are reduced, and the thermal 
inertia in the climate system. Mitigation, relying on emissions 
reductions and implemented through new legislation or technology 
standards, implies that emissions reductions occur year after year. 
Global temperature response to a year’s worth of current emissions 
from different sectors informs about the mitigation potential (Figure 
TS.20). Over 10- to 20-year time scales, the influence of SLCFs is 
at least as large as that of CO 2, with sectors producing the largest 
warming being fossil fuel production and distribution, agriculture,

and waste management. Because the effects of the SLCFs decay 
rapidly over the first few decades after emission, the net long-term 
temperature effect from a single year’s worth of current emissions is 
predominantly determined by CO 2. Fossil fuel combustion for energy, 
industry and land transportation are the largest contributing sectors 
on a 100-year time scale ( high confidence ). Current emissions of CO 2, 
N2O and SLCFs from East Asia and North America are the largest 
regional contributors to additional net future warming on both 
short ( medium confidence ) and long time scales (10 and 100 years, 
respectively) ( high confidence ). {6.6.1, 6.6.2, Figure 6.16}
COVID-19 restrictions led to detectable reductions in global 
anthropogenic emissions of nitrogen oxides (NO x) (about 35% in 
April 2020) and fossil CO 2 (7%, with estimates ranging from 5.8% to 
13.0%), driven largely by reduced emissions from the transportation

102
TSsector ( medium confidence ). There is high confidence  that, with 
the exception of surface ozone, reductions in pollutant precursors 
contributed to temporarily improved air quality in most regions of 
the world. However, these reductions were lower than what would 
be expected from sustained implementation of policies addressing 
air quality and climate change ( medium confidence ). Overall, the net 
global ERF from COVID-19 containment was likely small and positive 
for 2020 (with a temporary peak value less than 0.2 W m–2), thus temporarily adding to the total anthropogenic climate influence, 
with positive forcing (warming influence) from aerosol changes 
dominating over negative forcings (cooling influence) from CO 2, NO x 
and contrail cirrus changes. Consistent with this small net radiative 
forcing, and against a large component of internal variability, Earth 
system models show no detectable effect on global or regional

surface temperature or precipitation ( high confidence ). {Cross 
Chapter Box 6.1}Figure TS.20 |  Global surface temperature change 10 and 100 years after a one-year pulse of present-day emissions.  The intent of this figure is to show the 
sectoral contribution to present-day climate change by specific climate forcers, including carbon dioxide (CO 2) as well as short-lived climate forcers (SLCFs).  The temperature 
response is broken down by individual species and shown for total anthropogenic emissions (top) , and sectoral emissions on 10-year (left)  and 100-year time scales (right) . 
Sectors are sorted by (high-to-low) net temperature effect on the 10-year time scale. Error bars in the top panel show the 5–95% range in net temperature effect due to 
uncertainty in radiative forcing only (calculated using a Monte Carlo approach and best estimate uncertainties from the literature). Emissions for 2014 are from the Coupled

Model Intercomparison Project Phase 6 (CMIP6) emissions dataset, except for hydrofluorocarbons (HFCs) and aviation H 2O, which rely on other datasets (see Section 6.6.2 for 
more details). CO 2 emissions are excluded from open biomass burning and residential biofuel use. {6.6.2, Figure 6.16}Effect of a one year pulse of present-day emissions on global surface temperature
Change in global surface temperature due to total anthropogenic emissions (°C)Response after 10 years  
Response after 100 years Net effect, 5% to 95% range
−0.010 −0.005 0 0.005 0.010 0.015
Change in global surface temperature (°C)By sector, response after 10 years
Fossil fuel production
Residential and commercial
for energy
(biofuel use only)
Industry
Shipping
Aviation

−0.010 −0.005 0 0.005 0.010 0.015By sector, response after 100 years
CO2
CH4
N2O
BC
OC
SO2
NOx
CO
VOC
NH3
Avia−stratH 2O
HFCs
Change in global surface temperature (°C)

103
Technical SummaryTSBox TS.7 | Climate and Air Quality Responses to Short-lived Climate Forcers in Shared 
Socio-economic Pathways
Future changes in emissions of short-lived climate forcers (SLCFs) are expected to cause an additional global mean 
warming, with a large diversity in the end-of-century response across the WGI core set of Shared Socio-economic 
Pathways (SSPs), depending upon the level of climate change and air pollution mitigation (Box TS.7, Figure 1). This 
additional warming is either due to reductions in cooling aerosols for air pollution regulation or due to increases in 
methane (CH 4), ozone and hydrofluorocarbons (HFCs). This additional warming is stable after 2040 in SSPs associated 
with lower global air pollution as long as CH 4 emissions are also mitigated, but the overall warming induced by SLCF 
changes is higher in scenarios in which air quality continues to deteriorate (induced by growing fossil fuel use and

limited air pollution control) ( high confidence ). 
Sustained CH 4 mitigation reduces global surface ozone, contributing to air quality improvements, and also reduces 
surface temperature in the longer term, but only sustained CO 2 emissions reductions allow long-term climate 
stabilization ( high confidence ). Future changes in air quality (near-surface ozone and particulate matter, or PM) at 
global and local scales are predominantly driven by changes in ozone and aerosol precursor emissions rather than 
climate ( high confidence ). Air quality improvements driven by rapid decarbonization strategies, as in SSP1-1.9 and 
SSP1-2.6, are not sufficient in the near term to achieve air quality guidelines set by the World Health Organization in 
some highly polluted regions ( high confidence ). Additional policies (e.g., access to clean energy, waste management) 
envisaged to attain United Nations Sustainable Development Goals bring complementary SLCF reduction. {4.4.4, 6.6.3,

6.7.3, Box 6.2}
The net effect of SLCF emissions changes on temperature will depend on how emissions of warming and cooling SLCFs will evolve 
in the future. The magnitude of the cooling effect of aerosols remains the largest uncertainty in the effect of SLCFs in future climate 
projections. Since the SLCFs have undergone large changes over the past two decades, the temperature and air pollution responses 
are estimated relative to the year 2019 instead of 1995–2014.
Temperature Response
In the next two decades, it is very likely  that SLCF emissions changes will cause a warming relative to 2019, across the WGI core set of 
SSPs (see Section TS.1.3.1), in addition to the warming  from long-lived GHGs. The net effect of SLCF and HFC changes in global surface 
temperature across the SSPs is a likely  warming of 0.06°C–0.35°C in 2040 relative to 2019. This near-term global mean warming

linked to SLCFs is quite similar in magnitude across the SSPs due to competing effects of warming (CH 4, ozone) and cooling (aerosols) 
forcers (Box TS.7, Figure 1). There is greater diversity in the end-of-century response among the scenarios. SLCF changes in scenarios 
with no climate change mitigation (SSP3-7.0 and SSP5-8.5) will cause a warming in the likely  range of 0.4°C–0.9°C in 2100 relative to 
2019 due to increases in CH 4, tropospheric ozone and HFC levels. For the stringent climate change and pollution mitigation scenarios 
(SSP1-1.9 and SSP1-2.6), the cooling from reductions in CH 4, ozone and HFCs partially balances the warming from reduced aerosols, 
primarily sulphate, and the overall SLCF effect is a likely  increase in global surface temperature of 0.0°C–0.3°C in 2100, relative to 
2019. With intermediate climate change and air pollution mitigations, SLCFs in SSP2-4.5 add a likely warming of 0.2°C–0.5°C to global

surface temperature change in 2100, with the largest warming resulting from reductions in aerosols. {4.4.4, 6.7.3}
Assuming implementation and efficient enforcement of both the Kigali Amendment to the Montreal Protocol on Substances that 
Deplete the Ozone Layer and current national plans result in limiting emissions (as in SSP1-2.6), the effects of HFCs on global 
surface temperature, relative to 2019, would remain below +0. 02°C from 2050 onwards versus about +0.04°C–0.08°C in 2050 and 
+0.1°C–0.3°C in 2100 considering only national HFC regulations decided prior to the Kigali Amendment (as in SSP5-8.5) ( medium 
confidence ). {6.6.3, 6.7.3}
Air Quality Responses
Air pollution projections range from strong reductions in global surface ozone and PM (e.g., SSP1-2.6, with stringent mitigation of both 
air pollution and climate change) to no improvement and even degradation (e.g., SSP3-7.0 without climate change mitigation and

with only weak air pollution control) ( high confidence ). Under the SSP3-7.0 scenario, PM levels are projected to increase until 2050 
over large parts of Asia, and surface ozone pollution is projected to worsen over all continental areas through 2100 ( high confidence ). 
In SSP5-8.5, a scenario without climate change mitigation but with stringent air pollution control, PM levels decline through 2100, but 
high CH 4 levels hamper the decline in global surface ozone at least until 2080 ( high confidence ). {6.7.1}

104
TSBox TS.7 (continued)
Box TS.7, Figure 1 |  Effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution across the WGI core set of 
Shared Socio-economic Pathways (SSPs).  The intent of this figure is to show the climate and air quality (surface ozone and particulate matter smaller than 
2.5 microns in diameter, or PM2.5 ) response to SLCFs in the SSP scenarios for the near and long-term.  Effects of net aerosols, tropospheric ozone, hydrofluorocarbons 
(HFCs; with lifetimes less than 50 years), and methane (CH 4) are compared with those of total anthropogenic forcing for 2040 and 2100 relative to year 2019. The 
global surface temperature changes are based on historical and future evolution of effective radiative forcing (ERF) as assessed in Chapter 7 of this Report. The

temperature responses to the ERFs are calculated with a common impulse response function (RT) for the climate response, consistent with the metric calculations in 
Chapter 7 (Box 7.1). The RT has an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO 2 concentration (feedback parameter of –1.31 W m–2 °C–1). 
The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes). Uncertainties are 5–95% ranges. The 
global changes in air pollutant concentrations (ozone and PM2.5) are based on multimodel Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and 
represent changes in five-year mean surface continental concentrations for 2040 and 2098 relative to 2019. Uncertainty bars represent inter-model ±1 standard 
deviation. {6.7.2, 6.7.3, Figure 6.24}
Box TS.8 | Earth System Response to Solar Radiation Modification

Since AR5, further modelling work has been conducted on aerosol-based solar radiation modification (SRM) options 
such as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning21 and their climate and 
biogeochemical effects. These investigations have consistently shown that SRM could offset some of the effects of 
increasing greenhouse gases on global and regional climate, including the carbon and water cycles ( high confidence ). 
However, there would be substantial residual or overcompensating climate change at the regional scales and seasonal 
time scales ( high confidence ), and large uncertainties associated with aerosol–cloud–radiation interactions persist. 
The cooling caused by SRM would increase the global land and ocean CO 2 sinks ( medium confidence ), but this 
would not stop CO 2 from increasing in the atmosphere or affect the resulting ocean acidification under continued

anthropogenic emissions ( high confidence ). It is likely  that abrupt water cycle changes will occur if SRM techniques 
are implemented rapidly. A sudden and sustained termination of SRM in a high CO 2 emissions scenario would cause 
rapid climate change ( high confidence ). However, a gradual phase-out of SRM combined with emissions reduction and 
carbon dioxide removal (CDR) would avoid these termination effects ( medium confidence ). {4.6.3, 5.6.3. 6.4.6, 8.6.3}.
21 Although cirrus cloud thinning aims to cool the planet by increasing longwave emissions to space, it is included in the portfolio of SRM options for consistency with AR5 and SR1.5. {4.6.3.3}

105
Technical SummaryTSBox TS.8 (continued)
Solar radiation modification (SRM) refers to deliberate, large-scale climate intervention options that are studied as potential 
supplements to deep mitigation, for example, in scenarios that overshoot climate stabilization goals. SRM options aim to offset some 
of the warming effects of GHG emissions by modification of Earth’s shortwave radiation budget. Following SR1.5, the SRM assessed 
in this Report also includes some options, such as cirrus cloud thinning, that alter the longwave radiation budget. 
SRM contrasts with climate change mitigation activities, such as emissions reductions and CDR, as it introduces a ‘mask’ to the 
climate change problem by altering Earth’s radiation budget, rather than attempting to address the root cause of the problem, which 
is the increase in GHGs in the atmosphere. By masking only the climate effects of GHG emissions, SRM does not address other issues

related to atmospheric CO 2 increase, such as ocean acidification. This Report assesses physical understanding of the Earth system 
response to proposed SRM, and the assessment is based primarily on idealized climate model simulations. There are other important 
considerations, such as risk to human and natural systems, perceptions, ethics, cost, governance, and trans-boundary issues and their 
relationship to the United Nations Sustainable Development Goals – issues that the WGII (Chapter 16) and WGIII (Chapter 14) Reports 
address. {4.6.3}
SRM options include those that increase surface albedo, brighten marine clouds by increasing the amount of cloud condensation 
nuclei, or reduce the optical depth of cirrus clouds by seeding them with ice nucleating particles. However, the most commonly studied 
approaches attempt to mimic the cooling effects of major volcanic eruptions by injecting reflective aerosols (e.g., sulphate aerosols)

or their precursors (e.g., sulphur dioxide) into the stratosphere. {4.6.3, 5.6.3, 6.4.6}
SRM could offset some effects of greenhouse gas-induced warming on global and regional climate, but there would be substantial 
residual and overcompensating climate change at the regional scale and seasonal time scales ( high confidence ). Since AR5, more 
modelling work has been conducted with more sophisticated treatment of aerosol-based SRM approaches, but the uncertainties in 
cloud–aerosol–radiation interactions are still large ( high confidence ). Modelling studies suggest that it is possible to stabilize multiple 
large-scale temperature indicators simultaneously by tailoring the deployment strategy of SRM options ( medium confidence ) but with 
large residual or overcompensating regional and seasonal climate changes. {4.6.3}
SRM approaches targeting shortwave radiation are likely  to reduce global mean precipitation, relative to future CO 2 emissions

scenarios, if all global mean warming is offset. In contrast, cirrus cloud thinning, targeting longwave radiation, is expected to cause 
an increase in global mean precipitation ( medium confidence ). If shortwave approaches are used to offset global mean warming, 
the magnitude of reduction in regional precipitation minus evapotranspiration (P–E) (Box TS.5), which is more relevant to freshwater 
availability, is smaller than precipitation decrease because of simultaneous reductions in both precipitation and evapotranspiration 
(medium confidence ). {4.6.3, 8.2.1, 8.6.3}.
If SRM is used to cool the planet, it would cause a reduction in plant and soil respiration and slow the reduction of ocean carbon 
uptake due to warming ( medium confidence ). The result would be an enhancement of the global land and ocean CO 2 sinks ( medium 
confidence ) and a slight reduction in atmospheric CO 2 concentration relative to unmitigated climate change. However, SRM would

not stop CO 2 from increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions 
(high confidence ). {5.6.3}
The effect of stratospheric aerosol injection on global temperature and precipitation is projected by models to be detectable after one 
to two decades, which is similar to the time scale for the emergence of the benefits of emissions reductions. A sudden and sustained 
termination of SRM in a high GHG emissions scenario would cause rapid climate change and a reversal of the SRM effects on the 
carbon sinks ( high confidence ). It is also likely that a termination of strong SRM would drive abrupt changes in the water cycle globally 
and regionally, especially in the tropical regions by shifting the Inter-tropical Convergence Zone and Hadley cells. At the regional scale, 
non-linear responses cannot be excluded, due to changes in evapotranspiration. However, a gradual phase-out of SRM combined with

emissions reductions and CDR would avoid larger rates of changes ( medium confidence ). {4.6.3, 5.6.3, 8.6.3}.

106
TSBox TS.9 | Irreversibility, Tipping Points and Abrupt Changes
The present rates of response of many aspects of the climate system are proportionate to the rate of recent temperature 
change, but some aspects may respond disproportionately. Some climate system components are slow to respond, 
such as the deep ocean overturning circulation and the ice sheets (Box TS.4). It is virtually certain that irreversible, 
committed change is already underway for the slow-to-respond processes as they come into adjustment for past and 
present emissions. 
The paleoclimate record indicates that tipping elements exist in the climate system where processes undergo sudden 
shifts toward a different sensitivity to forcing, such as during a major deglaciation, where 1°C degree of temperature 
change might correspond to a large or small ice-sheet mass loss during different stages (Box TS.2). For global climate

indicators, evidence for abrupt change is limited, but deep ocean warming, acidification and sea level rise are committed 
to ongoing change for millennia after global surface temperatures initially stabilize and are irreversible on human time 
scales ( very high confidence ). At the regional scale, abrupt responses, tipping points and even reversals in the direction of 
change cannot be excluded ( high confidence ). Some regional abrupt changes and tipping points could have severe local 
impacts, such as unprecedented weather, extreme temperatures and increased frequency of droughts and forest fires. 
Models that exhibit such tipping points are characterized by abrupt changes once the threshold is crossed, and even 
a return to pre-threshold surface temperatures or to atmospheric carbon dioxide concentrations does not guarantee 
that the tipping elements return to their pre-threshold state. Monitoring and early warning systems are being put into

place to observe tipping elements in the climate system. {1.3, 1.4.4, 1.5, 4.3.2, Table 4.10, 5.3.4, 5.4.9, 7.5.3, 9.2.2, 
9.2.4, 9.4.1, 9.4.2, 9.6.3, Cross-chapter Box 12.1}
Understanding of multi-decadal reversibility (i.e., the system returns to the previous climate state within multiple decades after 
the radiative forcing is removed) has improved since AR5 for many atmospheric, land surface and sea ice climate metrics following 
sea surface temperature recovery. Some processes suspected of having tipping points, such as the Atlantic Meridional Overturning 
Circulation (AMOC), have been found to often undergo recovery after temperature stabilization with a time delay ( low confidence ). 
However, substantial irreversibility is further substantiated for some cryosphere changes, ocean warming, sea level rise, and ocean 
acidification. {4.7.2, 5.3.3, 5.4.9, 9.2.2, 9.2.4, 9.4.1, 9.4.2, 9.6.3}

Some climate system components are slow to respond, such as the deep ocean overturning circulation and the ice sheets. It is likely  that 
under stabilization of global warming at 1.5°C, 2.0°C or 3.0°C relative to 1850–1900, the AMOC will continue to weaken for several 
decades by about 15%, 20% and 30% of its strength and then recover to pre-decline values over several centuries ( medium confidence ). 
At sustained warming levels between 2°C and 3°C, there is limited evidence  that the Greenland and West Antarctic ice sheets will be lost 
almost completely and irreversibly over multiple millennia; both the probability of their complete loss and the rate of mass loss increases 
with higher surface temperatures ( high confidence ). At sustained warming levels between 3°C and 5°C, near-complete loss of the 
Greenland Ice Sheet and complete loss of the West Antarctic Ice Sheet is projected to occur irreversibly over multiple millennia ( medium

confidence ); with substantial parts or all of Wilkes Subglacial Basin in East Antarctica lost over multiple millennia ( low confidence ). Early-
warning signals of accelerated sea level rise from Antarctica could possibly be observed within the next few decades. For other hazards 
(e.g., ice-sheet behaviour, glacier mass loss and global mean sea level change, coastal floods, coastal erosion, air pollution, and ocean 
acidification) the time and/or scenario dimensions remain critical, and a simple and robust relationship with global warming level cannot 
be established ( high confidence ). {4.3.2, 4.7.2, 5.4.3, 5.4.5, 5.4.8, 8.6, 9.2, 9.4, Box 9.3, Cross-Chapter Box 12.1}
For global climate indicators, evidence for abrupt change is limited. For global warming up to 2°C above 1850–1900 levels, paleoclimate 
records do not indicate abrupt changes in the carbon cycle ( low confidence ). Despite the wide range of model responses, uncertainty

in atmospheric CO 2 by 2100 is dominated by future anthropogenic emissions rather than uncertainties related to carbon–climate 
feedbacks ( high confidence ). There is no evidence of abrupt change in climate projections of global temperature for the next century: 
there is a near-linear relationship between cumulative CO 2 emissions and maximum global mean surface air temperature increase 
caused by CO 2 over the course of this century for global warming levels up to at least 2°C relative to 1850–1900. The increase in global 
ocean heat content (Section TS.2.4) will likely continue until at least 2300 even for low emissions scenarios, and global mean sea level 
will continue to rise for centuries to millennia following cessation of emissions (Box TS.4) due to continuing deep ocean heat uptake 
and mass loss of the Greenland and Antarctic ice sheets ( high confidence ). {2.2.3; Cross-Chapter Box 2.1; 5.1.1; 5.4; Cross-Chapter 
Box 5.1; Figures 5.3, 5.4, 5.25, and 5.26; 9.2.2; 9.2.4}

The response of biogeochemical cycles to anthropogenic perturbations can be abrupt at regional scales and irreversible on decadal to 
century time scales ( high confidence ). The probability of crossing uncertain regional thresholds increases with climate change ( high

107
Technical SummaryTSBox TS.9 (continued)
confidence ). It is very unlikely  that gas clathrates (mostly methane) in deeper terrestrial permafrost and subsea clathrates will lead to a 
detectable departure from the emissions trajectory during this century. Possible abrupt changes and tipping points in biogeochemical 
cycles lead to additional uncertainty in 21st century atmospheric GHG concentrations, but future anthropogenic emissions remain the 
dominant uncertainty ( high confidence ). There is potential for abrupt water cycle changes in some high emissions scenarios, but there 
is no overall consistency regarding the magnitude and timing of such changes. Positive land surface feedbacks, including vegetation, 
dust, and snow, can contribute to abrupt changes in aridity, but there is only low confidence  that such changes will occur during 
the 21st century. Continued Amazon deforestation, combined with a warming climate, raises the probability that this ecosystem will

cross a tipping point into a dry state during the 21st century ( low confidence ). (Section TS.3.2.2) {5.4.3, 5.4.5, 5.4.8, 5.4.9, 8.6.2, 8.6.3, 
Cross-Chapter Box 12.1}
TS.4 Regional Climate Change
This section focuses on how to generate regional climate change 
information and its relevance for climate services; the drivers of regional 
climate variability and change and how they are being affected by 
anthropogenic factors; and observed, attributed and projected changes 
in climate, including extreme events and climatic impact-drivers (CIDs), 
across all regions of the world. There is a  small set of CID changes 
common to all land or ocean regions and a specific set of changes from 
a broader range of CIDs seen in each region. This regional diversity 
results from regional climate being determined by a complex interplay 
between the seasonal-to-multi-decadal variation of large-scale modes 
of climate variability, external natural and anthropogenic forcings, local

climate processes and related feedbacks.
TS.4.1 Generation and Communication of Regional 
Climate Change Information
Climate change information at regional scale is generated 
using a range of data sources and methodologies. Multi-
model ensembles and models with a range of resolutions 
are important data sources, and discarding models that 
fundamentally misrepresent relevant processes improves 
the credibility of ensemble information related to these 
processes. A key methodology is distillation – combining 
lines of evidence and accounting for stakeholder context 
and values – which helps ensure the information is relevant, 
useful and trusted for decision-making (see Core Concepts 
Box) ( high confidence ). 
Since AR5, physical climate storylines have emerged as 
a complementary approach to ensemble projections for 
generating more accessible climate information and 
promoting a more comprehensive treatment of risk. They 
have been used as part of the distillation process within

climate services to generate the required context-relevant, 
credible and trusted climate information. 
Since AR5, climate change information produced for climate 
services has increased significantly due to scientific and 
technological advancements and growing user awareness, requirements, and demand ( very high confidence ). The 
decision-making context, level of user engagement, 
and co-production between scientists, practitioners and 
users are important determinants of the type of climate 
service developed and its utility in supporting adaptation, 
mitigation and risk management decisions. {10.3, 10.6, 
Cross-Chapter Box 10.3, 12.6, Cross-Chapter Box 12.2}
TS.4.1.1 Sources and Methodologies for Generating Regional 
Climate Information
Climate change information at regional scale is generated using a range 
of data sources and methodologies (Section TS.1.4). Understanding 
of observed regional climate change and variability is based on the

availability and analysis of multiple observational datasets that are 
suitable for evaluating the phenomena of interest (e.g., extreme 
events), including accounting for observational uncertainty (Section 
TS.1.2.1). These datasets are combined with climate model simulations 
of observed changes and events to attribute causes of those changes 
and events to large- and regional-scale anthropogenic and natural 
drivers and to assess the performance of the models. Future simulations 
with many climate models (multi-model ensembles) are then used to 
generate and quantify ranges of projected regional climate responses 
(Section TS.4.2). Discarding models that fundamentally misrepresent 
relevant processes improves the credibility of regional climate 
information generated from these ensembles ( high confidence ). 
However, multi-model mean and ensemble spread are not a full 
measure of the range of projection uncertainty and are not sufficient

to characterize low-likelihood, high-impact changes (Box TS.3) or 
situations where different models simulate substantially different 
or even opposite changes ( high confidence ). Large single-model 
ensembles are now available and provide a more comprehensive 
spectrum of possible changes associated with internal variability ( high 
confidence ) (Section TS.1.2.3). {1.5.1, 1.5.4, 10.2, 10.3.3, 10.3.4, 10.4.1, 
10.6.2, 11.2, Box 11.2, Cross-Chapter Box 11.1, 12.4, Atlas.1.4.1}
Depending on the region of interest, representing regionally important 
forcings (e.g., aerosols, land-use change and ozone concentrations) 
and feedbacks (e.g., between snow and albedo, soil moisture and 
temperature, or soil moisture and precipitation) in climate models is a 
prerequisite for them to reproduce past regional trends to underpin the

108
TSBox TS.10 | Event Attribution
The attribution of observed changes in extremes to human influence (including greenhouse gas and aerosol emissions 
and land-use changes) has substantially advanced since AR5, in particular for extreme precipitation, droughts, 
tropical cyclones, and compound extremes ( high confidence ). There is limited evidence  for windstorms and convective 
storms. Some recent hot extreme events would have been extremely unlikely to occur without human influence on the 
climate system. (Section TS.1) {Cross-Working Group Box: Attribution in Chapter 1, 11.2, 11.3, 11.4, 11.6, 11.7, 11.8}
Since AR5, the attribution of extreme weather events has emerged as a growing field of climate research with an increasing body of 
literature. It provides evidence that greenhouse gases and other external forcings have affected individual extreme weather events

by disentangling anthropogenic drivers from natural variability. Event attribution is now an important line of evidence for assessing 
changes in extremes on regional scales. (Section TS.1) {Cross-Working Group Box: Attribution, 11.1.4}
The regional extremes and events that have been studied are geographically uneven (Section TS.4.1). A few events, for example, 
extreme rainfall events in the United Kingdom, heatwaves in Australia, or Hurricane Harvey that hit Texas in 2017, have been heavily 
studied. Many highly impactful extreme weather events have not been studied in the event attribution framework, particularly in 
the developing world where studies are generally lacking. This is due to various reasons, including lack of observational data, lack of 
reliable climate models, and lack of scientific capacity. While the events that have been studied are not representative of all extreme

events that have occurred, and results from these studies may also be subject to selection bias, the large number of event attribution 
studies provide evidence that changes in the properties of these local and individual events are in line with expected consequences 
of human influence on the climate and can be attributed to external drivers. {Cross-Working Group Box: Attribution, 11.1.4, 11.2.2}
It is very likely that human influence is the main contributor to the observed increase in the intensity and frequency of hot extremes and 
the observed decrease in the intensity and frequency of cold extremes on continental scales. Some specific recent hot extreme events 
would have been extremely unlikely  to occur without human influence on the climate system. Changes in aerosol concentrations 
have likely  slowed the increase in hot extremes in some regions, in particular from 1950–1980. No-till farming, irrigation and crop

expansion have similarly attenuated increases in summer hot extremes in some regions, such as central North America ( medium 
confidence ). {11.3.4}
Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are most 
abundant: North America, Europe and Asia ( high confidence ). On regional scales, evidence of human influence on extreme precipitation 
is limited, but new evidence from attributing individual heavy precipitation events found that human influence was a significant driver 
of the events. {11.4.4}
There is low confidence that human influence has affected trends in meteorological droughts in most regions, but medium confidence  
that they have contributed to the severity of some specific events. There is medium confidence  that human-induced climate change 
has contributed to increasing trends in the probability or intensity of recent agricultural and ecological droughts, leading to an

increase of the affected land area. {11.6.4}
Event attribution studies of specific strong tropical cyclones provide limited evidence  for anthropogenic effects on tropical cyclone 
intensifications so far, but high confidence  for increases in precipitation. There is high confidence  that anthropogenic climate change 
contributed to extreme rainfall amounts during Hurricane Harvey (in 2017) and other intense tropical cyclones. {11.7.3}reliability of future projections ( medium confidence ) (Section TS.1.2.2). 
In some cases, even the sign of a projected change in regional climate 
cannot be trusted if relevant regional processes are not represented, for 
example, for variables such as precipitation and wind speed ( medium 
confidence ). In some regions, either geographical (e.g., Central Africa, 
Antarctica) or typological (e.g., mountainous areas, Small Islands and 
cities), and for certain phenomena, fewer observational records are

available or accessible, which limits the assessment of regional climate 
change in these cases. {1.5.1, 1.5.3, 1.5.4, 8.5.1, 10.2, 10.3.3, 10.4.1, 
11.1.6, 11.2, 12.4, Atlas.8.3, Atlas.11.1.5, Cross-Chapter Box Atlas.2}Methodologies such as statistical downscaling, bias adjustment and 
weather generators are beneficial as an interface between climate 
model projections and impact modelling and for deriving user-
relevant indicators ( high confidence ). However, the performance of 
these techniques depends on that of the driving climate model: in 
particular, bias adjustment cannot overcome all consequences of 
unresolved or strongly misrepresented physical processes, such as 
large-scale circulation biases or local feedbacks ( medium confidence ). 
{10.3.3, Cross-Chapter Box 10.2, 12.2, Atlas.2.2}

109
Technical SummaryTSBox TS.10 (continued)
The number of evident attribution studies on compound events is limited. There is medium confidence  that weather conditions that 
promote wildfires have become more probable in southern Europe, northern Eurasia, the USA, and Australia over the last century. In 
Australia a number of event attribution studies show that there is medium confidence  of increase in fire weather conditions due to 
human influence. {11.8.3, 12.4.3.2}
Box TS.10, Figure 1 |  Synthesis of assessed observed and attributable regional changes.  Climate change is already aﬀecting every inhabited region across the globe, 

America
Central
America
South
NAU
CAU EAU
SAUNZ
SSAMDG
America
Central
America
South

NAU
CAU EAU
SAUNZ
SSAMDG
NWN NENGIC
America
Central
America
South
NAUPAC
PAC
PAC
CAU EAU
SAUNZ
Islands
Small
Islands
Small
Islands
Small
Islands
(Western North America), CNA (Central North America), ENA (Eastern North America), Central America: NCA (Northern Central America),

Europe), MED (Mediterranean), Africa: MED (Mediterranean), SAH (Sahara), WAF (Western Africa), CAF (Central Africa), NEAF (North Eastern 
EAS (East Asia), ARP (Arabian Peninsula), SAS (South Asia), SEA (South East Asia), Australasia: NAU (Northern Australia), CAU (Central

Medium
High
Medium
High
Medium

110
TSBox TS.10 (continued)
Box TS.10, Figure 1 (continued):  The IPCC AR6 WGI inhabited regions are displayed as hexagons  of identical sizes in their approximate geographical location (see 
legend for regional acronyms). All assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or 
more local spatial scales might differ from what is shown in the figure. The colours  in each panel represent the four outcomes of the assessment on observed changes. 
Striped hexagons (white and light-grey) are used where there is low agreement in the type of change for the region as a whole, and grey hexagons are used when 
there is limited data and/or literature that prevents an assessment of the region as a whole. Other colours indicate at least medium confidence in the observed change.

The confidence level for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, 
and it is indicated by the number of dots: three dots for high confidence , two dots for medium confidence  and one dot for low confidence (single, filled dot: limited 
agreement; single, empty dot: limited evidence ).
Panel (a) For hot extremes, the evidence is mostly drawn from changes in metrics based on daily maximum temperatures; regional studies using other indices 
(heatwave duration, frequency and intensity) are used in addition. Red hexagons indicate regions where there is at least medium confidence in an observed increase 
Panel (b) For heavy precipitation, the evidence is mostly drawn from changes in indices based on one-day or five-day precipitation amounts using global and

regional studies. Green hexagons indicate regions where there is at least medium confidence in an observed increase in heavy precipitation. 
Panel (c) Agricultural and ecological droughts  are assessed based on observed and simulated changes in total column soil moisture, complemented by evidence 
on changes in surface soil moisture, water balance (precipitation minus evapotranspiration) and indices driven by precipitation and atmospheric evaporative demand. 
Yellow hexagons indicate regions where there is at least medium confidence in an observed increase in this type of drought and green hexagons indicate regions 
where there is at least medium confidence  in an observed decrease in agricultural and ecological drought.
For all regions, Table TS.5 shows a broader range of observed changes besides the ones shown in this figure. Note that Southern South America (SSA) is the only

region that does not display observed changes in the metrics shown in this figure, but is affected by observed increases in mean temperature, decreases in frost and 
increases in marine heatwaves.
(Table TS.5) {11.9, Atlas 1.3.3, Figure Atlas.2}
TS.4.1.2 Regional Climate Information Distillation and 
Climate Services
The construction of regional climate information involves people with 
a variety of backgrounds, from various disciplines, who have different 
sets of experiences, capabilities and values. The process of synthesizing 
climate information from different lines of evidence from a number 
of sources, taking into account the context of a user vulnerable to 
climate variability and change and the values of all relevant actors, is 
called distillation. Distillation is conditioned by the sources available, 
the actors involved, and the context, which all depend heavily on the 
regions considered, and is framed by the question being addressed.

Distilling regional climate information from multiple lines of evidence 
and taking the user context into account increases fitness, usefulness, 
relevance and trust in that information for use in climate services 
(Box TS.11) and decision-making ( high confidence ). {1.2.3, 10.1.4, 
10.5, Cross-Chapter Box 10.3, 12.6}
The distillation process can vary substantially, as it needs to consider 
multiple lines of evidence on all physically plausible outcomes 
(especially when they are contrasting) relevant to a specific decision 
required in response to a changing climate. Confidence in the 
distilled regional climate information is enhanced when there is 
agreement across multiple lines of evidence, so the outcome can 
be limited if these are inconsistent or contradictory. For example, in 
the Mediterranean region the agreement between different lines of 
models, and understanding of the underlying mechanisms, provides

high confidence  in summer warming that exceeds the global average 
(see Box TS.12). In a less clear-cut case for Cape Town, South Africa, 
despite consistency among global model future projections, there 
is medium confidence  in a projected future drier climate due to the 
lack of consistency in links between increasing greenhouse gases, changes in a key mode of variability (the Southern Annular Mode) 
and drought in Cape Town among different observation periods and 
in model simulations. {10.5.3, 10.6, 10.6.2, 10.6.4, Cross-Chapter 
Box 10.3, 12.4}
Since AR5, physical climate storyline approaches have emerged as 
a complementary instrument to provide a different perspective, or 
additional climate information, to facilitate communication of the 
information or provide a more flexible consideration of risk. Storylines 
that condition climatic events and processes on a set of plausible 
but distinct large-scale climatic changes enable the exploration of

uncertainties in regional climate projections. For example, they can 
explicitly address low-likelihood, high-impact outcomes, which would 
be less emphasized in a probabilistic approach, and can be embedded 
in a user’s risk landscape, taking account of socio-economic factors 
as well as physical climate changes. Storylines can also be used to 
communicate climate information by narrative elements describing 
and contextualizing the main climatological features and the relevant 
consequences in the user context and, as such, can be used as part 
of a climate information distillation process. {1.4.4., Box 10.2, 11.2, 
Box 11.2, Cross-Chapter Box 12.2}

111
Technical SummaryTSBox TS.11 | Climate Services
Climate services involve providing climate information to assist decision-making, for example, about how extreme 
rainfall will change to inform improvements in urban drainage. Since AR5, there has been a significant increase in the 
range and diversity of climate service activities ( very high confidence ). The level of user-engagement, co-design and 
co-production are factors determining the utility of climate services, while resource limitations for these activities 
constrain their full potential. {12.6, Cross-Chapter Box 12.2}
Climate services include engagement from users and providers and an effective access mechanism; they are responsive to user needs 
and based on integrating scientifically credible information and relevant expertise. Climate services are being developed across 
regions, sectors, time scales and user-groups and include a range of knowledge brokerage and integration activities. These involve

identifying knowledge needs; compiling, translating and disseminating knowledge; coordinating networks and building capacity 
through informed decision-making; analysis, evaluation and development of policy; and personal consultation.
Since AR5, climate change information produced in climate service contexts has increased significantly due to scientific and 
technological advancements and growing user awareness, requirements and demand ( very high confidence ). Climate services are 
growing rapidly and are highly diverse in their practices and products.  The decision-making context, level of user engagement and co-
production between scientists, practitioners and intended users are important determinants of the type of climate service developed 
and their utility for supporting adaptation, mitigation and risk management decisions. They require different types of user–producer

engagement depending on what the service aims to deliver ( high confidence ), and these fall into three broad categories: website-
based services, interactive group activities and focused relationships .
Realization of the full potential of climate services is often hindered by limited resources for the co-design and co-production process, 
including sustained engagement between scientists, service providers and users ( high confidence ). Further challenges relate to the 
development and provision of climate services, generation of climate service products, communication with users, and evaluation of 
their quality and socio-economic benefit. (Section TS.4.1) {1.2.3, 10.5.4, 12.6, Cross-Chapter Box 12.2, Glossary}
Box TS.12 | Multiple Lines of Evidence for Assessing Regional Climate Change and the 
Interactive Atlas
A key novel element in the AR6 is the Working Group I Atlas, which includes the Interactive Atlas ( https://interactive-

atlas.ipcc.ch/ ). The Interactive Atlas provides the ability to explore much of the observational and climate model data 
used as lines of evidence in this assessment to generate regional climate information. {Atlas.2}
A significant innovation in the AR6 WGI Report is the Atlas. Part of its remit is to provide region-by-region assessment on changes in 
mean climate and to link with other WGI chapters to generate climate change information for the regions. An important component 
is the new online interactive tool, the Interactive Atlas, with flexible spatial and temporal analyses of much of the observed, simulated 
past and projected future climate change data underpinning the WGI assessment. This includes the ability to generate global maps 
and a number of regionally aggregated products (time series, scatter plots, tables, etc.) for a range of observations and ensemble

climate change projections of variables (such as changes in the climatic impact-drivers summarized in Table TS.5) from the Coupled 
Model Intercomparison Project Phases 5 and 6 (CMIP5, CMIP6) and the Coordinated Regional Climate Downscaling Experiment 
(CORDEX). The data can be displayed and summarized under a range of SSP-RCP scenarios and future time slices and also for different 
global warming levels, relative to several different baseline periods. The maps and various statistics can be generated for annual mean 
trends and changes or for any user-specified season. A new set of WGI reference regions is used for the regional summary statistics 
and applied widely throughout the report (with the regions, along with aggregated datasets and the code to generate these, available 
at the ATLAS GitHub: https://github.com/IPCC-WG1/Atlas ).
Box TS.12, Figure 1 shows how the Interactive Atlas products, together with other lines of evidence, can be used to generate climate

information for an illustrative example of the Mediterranean summer warming. The lines of evidence include the understanding of 
relevant mechanisms, dynamic and thermodynamic processes and the effect of aerosols in this case (Box TS.12, Figure 1a); trends 
in observational datasets (which can have different spatial and temporal coverage; Box TS.12, Figure 1b, c); and attribution of these 
trends and temperature projections from global and regional climate models at different resolutions, including single-model initial-
condition large ensembles (SMILEs; Box TS.12, Figure 1d, e). Taken together, this evidence shows there is high confidence  that the

112
TSBox TS.12 (continued)
projected Mediterranean summer temperature increase will be larger than the global mean, with consistent results from CMIP5 and 
CMIP6 (Box TS.12, Figure 1e). However, CMIP6 results project both more pronounced warming than CMIP5 for a given emissions 
scenario and time period and a greater range of changes (Box TS.12, Figure 1d). {10.6.4, Atlas.2, Atlas.8.4}
Box TS.12, Figure  1 |  Example of generating regional climate information from multiple lines of evidence for the case of Mediterranean 
summer warming. 
(a) Mechanisms of enhanced Med iterranean warming
(e) Mediterranean summer vs global warming
Baseline period is 1861–1900
0 1 2 3 4
Global temperature (°C)012345678Mediterranean summer temperature (°C)
RCP8.5
RCP6.0
RCP4.5
RCP2.6CMIP5 (N=21)
SSP5-8.5
SSP3-7.0
SSP2-4.5
SSP1-2.6CMIP6 (N=31)(b) Station locations
Donat et al. 2013 E-OBS(c) Temperature trend distribution
Past period (1960–2014)
Trend (° C/decade)

(d) Mediterranean temperature anomalies
Baseline period is 1995–2014
2000 2020 2040 2060 2080 2100–20 02 24 46 68 810 10(°C)
Long termSSP5-8.5SSP1-2.6
SSP2-4.5
SSP3-7.0CMIP6 long ter m
temperat ure change
Berkeley EarthCRU TS
HadCRUT5
CMIP5
CORDEX EUR-11
CMIP6The Interactive Atlas allows for flexible 
spatial and temporal analyses of essential 
climate variables, extreme indices and 
climatic impact-drivers, including 
multiple lines of evidence to support the 
assessment of regional climate change:
- Observations
- CMIP5
- CMIP6 
- CORDEX
Regional (aggregated) information for 
reference and typological regions:
      Time series
      Stripes
      Annual cycle plots
      Summary tabular information.
      Scatter plots (e.g., precip. vs temp.)CORDEX is available for 12
continent-wide domains.
Dimensions of analysis include time 
periods across scenarios and global warming 
levels (1ºC, 2ºC, 3ºC and 4ºC).
Available in the Interactive Atlas

Not available from the Interactive Atlas
0.2 0.4 0.6CSIRO-Mk3-6-0
d4PDFMPI-ESMMIROC6CORDEX EUR-11CORDEX EUR-44HighResMIPCMIP6CMIP5Ensemble meansOBS
aerosols
sensible
heat flux
Land Sea
Redu ced
laten t
sea warming  than overstron ger
is displayed when 
clicking on one or 
several subregions

113
Technical SummaryTSTS.4.2 Drivers of Regional Climate Variability 
and Change
Anthropogenic forcing, including GHGs and aerosols, but 
also regional land use and irrigation have all affected 
observed regional climate changes ( high confidence ) and 
will continue to do so in the future ( high confidence ), with 
various degrees of influence and response times, depending 
on warming levels, the nature of the forcing and the relative 
importance of internal variability. 
Since the late 19th century, major modes of variability 
(MoVs) exhibited fluctuations in frequency and magnitude 
at multi-decadal time scales, but no sustained trends 
outside the range of internal variability (Table TS.4). An 
exception is the Southern Annular Mode (SAM), which has 
become systematically more positive ( high confidence ) and 
is projected to be more positive in all seasons, except for 
December–January–February (DJF), in high CO 2 emissions

scenarios ( high confidence ). The influence of stratospheric 
ozone forcing on the SAM trend has been reduced since the 
early 2000s compared to earlier decades, contributing to 
the weakening of its positive trend as observed over 2000–
2019 ( medium confidence ). 
In the near term, projected changes in most of the MoVs 
and related teleconnections will likely be dominated by 
internal variability. In the long term, it is very likely that 
the precipitation variance related to El Niño–Southern 
Oscillation will increase. Physical climate storylines, 
including the complex interplay between climate drivers, 
MoVs, and local and remote forcing, increase confidence 
in the understanding and use of observed and projected 
regional changes. {2.4, 3.7, 4.3, 4.4, 4.5, 6.4, 8.3, 8.4, 10.3, 
10.4, 11.3}
TS.4.2.1 Regional Fingerprints of Anthropogenic 
and Natural Forcing
While anthropogenic forcing has contributed to multi-decadal

mean precipitation changes in several regions, internal variability 
precipitation changes in many land regions ( high confidence ). At the 
regional scale, the effect of human-induced GHG forcing on extreme temperature is moderated or amplified by soil moisture feedback, 
snow/ice-albedo feedback, regional forcing from land-use/land-
cover changes, forcing from aerosol concentrations, or decadal/
multi-decadal natural variability. Changes in local and remote 
aerosol forcings lead to south–north gradients of the effective 
radiative forcing (hemispherical asymmetry). Along latitudes, 
it is more uniform, with strong amplification of the temperature 
response towards the Arctic ( medium confidence ). The decrease 
of SO 2 emissions since the 1980s reduces the damping effect of 
aerosols, leading to a faster increase in surface air temperature 
that is most pronounced at mid- and high latitudes of the Northern

Hemisphere, where the largest emissions reductions have taken 
place ( medium confidence ). {1.3, 3.4.1, 6.3.4, 6.4.1, 6.4.3, 8.3.1, 
8.3.2, Box 8.1, 10.4.2, 10.6, 11.1.6, 11.3}
Multi-decadal dimming and brightening trends in incoming solar 
radiation at Earth’s surface occurred at widespread locations ( high 
confidence ). Multi-decadal variation in anthropogenic aerosol 
emissions are thought to be a major contributor ( medium confidence ), 
but multi-decadal variability in cloudiness may also have played a 
role. Volcanic eruptions affect regional climate through their spatially 
heterogeneous effect on the radiative budget as well as through 
triggering dynamical responses by favouring a given phase from 
some MoVs, for instance. {1.4.1, Cross-Chapter Box 1.2, 2.2.1, 2.2.2, 
3.7.1, 3.7.3, 4.3.1, 4.4.1, 4.4.4, Cross-Chapter Box 4.1, 7.2.2, 8.5.2, 
10.1.4, 11.1.6, 11.3.1}
Historical urbanization affects the observed warming trends in cities

and their surroundings ( very high confidence ). Future urbanization 
will amplify the projected air temperature under different background 
climates, with a strong effect on minimum temperatures that could 
be as large as the global warming signal ( very high confidence ) 
(Box TS.14). Irrigation and crop expansion have attenuated increases 
in summer hot extremes in some regions, such as central North 
America ( medium confidence ) (Box TS.6). {Box 10.3, 11.1.6, 11.3}
TS.4.2.2 Modes of Variability and Regional Teleconnections
Modes of variability (Annex IV, Table TS.4) have existed for millennia 
or longer ( high confidence ), but there is low confidence  in detailed 
reconstructions of most of them prior to direct instrumental records. 
MoVs are treated as a main source of uncertainties associated with 
internal dynamics, as they can either accentuate or dampen, even 
mask, the anthropogenically forced responses. {2.4, 8.5.2, 10.4, 10.6, 
11.1.5, Atlas.3.1}Box TS.12 (continued)

Box TS.12, Figure 1 (continued):  The intent of this figure is to provide an example of using different lines of evidence to assess the confidence in or likelihood 
of a projected change in regional climate and which of these lines of evidence are available to view and explore in the Interactive Atlas.  (a) Mechanisms and 
feedbacks involved in enhanced Mediterranean summer warming. (b) Locations of observing stations from different datasets. (c) Distribution of 1960–2014 summer 
temperature trends (°C per decade) for observations (black crosses), CMIP5 (blue circles), CMIP6 (red circles), HighResMIP (orange circles), CORDEX EUR-44 (light 
blue circles), CORDEX EUR-11 (green circles), and selected single model initial-condition large ensembles (SMILEs; grey boxplots, MIROC6, CSIRO-Mk3-6-0, MPI-ESM 
and d4PDF). (d) Time series of area averaged (25°N–50°N, 10°W–40°E) land point summer temperature anomalies (°C, baseline period is 1995–2014): the boxplot

shows long term (2081–2100) temperature changes of different CMIP6 scenarios in respect to the baseline period. (e) Projected Mediterranean summer warming 
in comparison to global annual mean warming of CMIP5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) and CMIP6 (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) ensemble 
means (lines) and spread (shading). {Figure 10.20, Figure 10.21, Figure Atlas.8}

114
TSSince the late 19th century, major MoVs (Table TS.4) show 
no sustained trends, exhibiting fluctuations in frequency and 
magnitude at multi-decadal time scales, except for the Southern 
Annular Mode (SAM), which has become systematically more 
positive ( high confidence ) (Table TS.4). It is very likely  that human 
influence has  contributed to this trend from the 1970s to the 
1990s, and to the associated strengthening and southward shift 
of the Southern Hemispheric extratropical jet in austral summer. 
The influence of stratospheric ozone forcing on the SAM trend has 
been reduced since the early 2000s compared to earlier decades, 
contributing to the weakening of its positive trend observed over 
2000–2019 ( medium confidence ). By contrast, the cause of the 
Northern Annular Mode (NAM) trend toward its positive phase 
since the 1960s and associated northward shifts of Northern 
Hemispheric extratropical jet and storm track in boreal winter

is not well understood. The evaluation of model performance on 
simulating MoVs is assessed in Section TS.1.2.2. {2.3.3, 2.4, 3.3.3, 
3.7.1, 3.7.2}
In the near term, the forced change in SAM in austral summer is likely  
to be weaker than observed during the late 20th century under all 
five SSPs assessed. This is because of the opposing influence in the 
near to mid-term from stratospheric ozone recovery and increases in 
other greenhouse gases on the Southern Hemisphere summertime 
mid-latitude circulation ( high confidence ). In the near term, forced 
changes in the SAM in austral summer are therefore likely  to be 
smaller than changes due to natural internal variability. In the long 
term (2081–2100) under the SSP5-8.5 scenario, the SAM index is 
likely  to increase in all seasons relative to 1995–2014. The CMIP6 
multi-model ensemble projects a long-term (2081–2100) increase 
in the boreal wintertime NAM index under SSP3-7.0 and SSP5-8.5,

but regional associated changes may deviate from a simple shift 
in the mid-latitude circulation due to a modified teleconnection 
resulting from interaction with a modified mean background state. 
{4.3.3, 4.4.3, 4.5.1, 4.5.3, 8.4.2}
Human influence has not affected the principal tropical modes of 
interannual climate variability (Table TS.4) and their associated 
regional teleconnections beyond the range of internal variability 
(high confidence ). It is virtually certain  that the El Niño–Southern 
variability in a warmer world.  There is no consensus from models for 
a systematic change in amplitude of ENSO sea surface temperature 
(SST) variability over the 21st century in any of the SSP scenarios 
assessed ( medium confidence ). However, it is very likely  that 
rainfall variability related to ENSO will be enhanced significantly 
by the latter half of the 21st century in the SSP2-4.5, SSP3-7.0

and SSP5-8.5 scenarios, regardless of the amplitude changes in 
SST variability related to the mode. It is very likely  that rainfall 
variability related to changes in the strength and spatial extent of 
ENSO teleconnections will lead to significant changes at regional 
scale. {3.7.3, 3.7.4, 3.7.5, 4.3.3, 4.5.3, 8.4.2, 10.3.3}
Modes of decadal and multi-decadal variability over the Pacific 
and Atlantic Ocean exhibit no significant changes in variance 
over the period of observational records ( high confidence ). There 
is medium confidence  that anthropogenic and volcanic aerosols contributed to observed temporal evolution in the Atlantic Multi-
decadal Variability (AMV) and associated regional teleconnections, 
especially since the 1960s, but there is low confidence  in the 
magnitude of this influence and the relative contributions of 
natural and anthropogenic forcings. Internal variability is the 
main driver of Pacific Decadal Variability (PDV) observed since

the start of the instrumental records ( high confidence ), despite 
some modelling evidence for potential external influence.  There is 
medium confidence  that the AMV will undergo a shift towards a 
negative phase in the near term. {2.4, 3.7.6, 3.7.7, 8.5.2, 4.4.3}

115
Technical SummaryTSTable TS.4 |  Summary of the assessments on modes of variability (MoVs) and associated teleconnections.  (a) Assessments on observed changes since the start 
of instrumental records, Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) model performance, human influence on the observed changes, and 
near-term (2021–2040) and mid- to long-term (2041–2100) changes. Curves schematically illustrate the assessed overall changes, with the horizontal axis indicating time, 
and are not intended to precisely represent the time evolution. (b) Fraction of surface air temperature (SAT) and precipitation (pr) variance explained at interannual time scale 
by each MoV for each AR6 region (numbers in each cell; in percent). Values correspond to the average of significant explained variance fractions based on HadCRUT, GISTEMP ,

BerkeleyEarth and CRU-TS (for SAT) and GPCC and CRU-TS (for precipitation). Significance is tested based on F-statistics at the 95% level confidence, and a slash indicates that 
the value is not significant in more than half of the available data sets. The colour scale corresponds to the sign and values of the explained variance as shown at the bottom. The 
corresponding anomaly maps are shown in Annex IV . DJF: December–January–February. MAM: March–April–May. JJA: June–July–August. SON: September–October–November. 
In (b), Northern Annular Mode (NAM) and El Niño–Southern Oscillation (ENSO) teleconnections are evaluated for 1959–2019, Southern Annular Mode (SAM) for 1979–2019, 
Indian Ocean Basin (IOB), Indian Ocean Dipole (IOD), Atlantic Zonal Mode (AZM) and Atlantic Meridional Mode (AMM) for 1958–2019, and Pacific Decadal Variability (PDV)

and Atlantic Multi-decadal Variability (AMV) for 1900–2019. All data are linearly detrended prior to computation. (Section TS.1.2.2) {2.4, 3.7, 4.3.3, 4.4.3, 4.5.3, Table Atlas.1, 
Annex IV}
(a) Assessments on MoV.
NAM SAM ENSO IOB IOD AZM AMM PDV AMV
Past changes since the 
start of observations1960s1990s
2010s
Boreal  
winter1950s2000s
Austral  
summerWithin proxy-
inferred 
rangeWithin proxy-
inferred 
rangeLimited 
evidenceLimited 
evidenceDominated 
by multi-
decadal 
fluctuationsDominated 
by multi-
decadal 
fluctuations1400–
1850Since 
1950s
{2.4.1.1} {2.4.1.2} {2.4.2} {2.4.3} {2.4.3} {2.4.4} {2.4.4} {2.4.5} {2.4.6}
CMIP5 and CMIP6 
model performanceHigh 
performanceHigh 
performanceMedium 
performanceLow 
{3.7.1} {3.7.2} {3.7.3} {3.7.4} {3.7.4} {3.7.5} {3.7.5} {3.7.6} {3.7.7}
Human influence on 
the observed changesNo robust 
evidenceContributed

Low 
agreementNo robust 
evidenceNot detectedNo robust 
evidenceNo robust 
evidenceNot detectedContributed  
through 
aerosolsthrough GHG 
(all seasons) 
& ozone 
(DJF)
Near-term future 
changes (2021–2040)Internal 
dominates All seasons 
except DJFInternal 
dominatesNo robust 
evidence+
–
Phase shift 
from + to –
{4.4.3.1} {4.4.3.1} {4.4.3.2} {4.4.3.3} {4.4.3.3} {4.4.3.4} {4.4.3.4} {4.4.3.5} {4.4.3.6}
Mid-to-long-term 
future changes 
(2041–2100)DJFSSP5-8.5
SSP3-7.0
SSP1-2.6
SSP1-1.9AllseasonsSSP5-8.5
SSP3-7.0
SSP1-1.9
DJFIncrease in 
varianceNo robust 
evidenceincrease 
in extreme 
positive 
eventsNo robust 
evidence
Decrease in 
varianceNo changes
{4.3.3.1; 
4.5.3.1}{4.3.3.1; 
4.5.3.1}{4.3.3.2; 
4.5.3.2}{4.5.3.3} {4.5.3.3} {4.5.3.4} {4.5.3.4} {4.5.3.5} {4.5.3.6}

low confidence medium confidence high confidence
very likely more likely than not likely

116
TSTable TS.4 (continued): (b) Regional climate anomalies associated with MoV.
Mode NAM SAM ENSO IOB IOD AZM AMM PDV AMV
Season DJF DJF DJF MAM SON JJA JJA annual annual
Variable SAT pr SAT pr SAT pr SAT pr SAT pr SAT pr SAT pr SAT pr SAT prAfricaMediterranean 28 58 7 19
Sahara 58 14 10 19 12 9 12 25
Western Africa 25 15 45 21 10 6 6 23
Central Africa 19 8 10 14 50 13 10 14 11
North Eastern Africa 19 7 14 36 32 7 7
South Eastern Africa 14 22 36 57 10 4 9
West Southern Africa 49 26 27 16 8 4 12 5
East Southern Africa 13 75 34 35 7 4 6
Madagascar 24 24 7 11 10 9 5AsiaWest Siberia 45 7 9 11
East Siberia 52 3 11
Russian Far East 8 10 11 6 5 5
West Central Asia 15 21 4
East Central Asia 38
Tibetan Plateau 15 15 7 11 6 5 9
East Asia 7 20 23 9 9 13
South Asia 9 12 8 8 5
South East Asia 39 31 73 6 48 5 12 7
Arabian Peninsula 32 10 24 20 5 13 7AustralasiaNorthern Australia 21 13 38 19 7 7 7
Central Australia 14 21 12 18 22 20 7 7 6 5
East Australia 22 20 11 18 9 8 7 7 8

Southern Australia 11 23 40 8 3
New Zealand 16Central & South AmericaSouthern Central America 21 16 33 10 11 17 6 6 7
North-Western South America 7 14 16 82 17 54 18 13 16 7 8
Northern South America 7 56 58 61 22 17 24 9 12 7
North-Eastern South America 25 58 19 9 12 8
South American Monsoon 54 31 22 7 6 7
South-Western South America 10 16 14 17 10 16 8
South-Eastern South America 21 13 21 10 12 5 6
Southern South America 23 13 7 9EuropeMediterranean 28 58 7 19
Western and Central Europe 28 18 13 10 4 8
Eastern Europe 35 7
Northern Europe 53 32 6North AmericaNorthern Central America 10 26 13 27 18 7 12 15 12 6 19
Western North America 4 6 5
Central North America 17 12 17 8 3 9 6
Eastern North America 12 11 9 4 9 4
North-Eastern North America 18 26 8 10 9 4
North-Western North America 14 10 8 17 8 4Small 
IslandsCaribbean 10 15 18 26 8 10 17 12 7 5
PacificPolar Terrestrial 
RegionsGreenland/Iceland 42 8 7 44
Russian Arctic 25 10 6 11 8
West Antarctica 8 21
East Antarctica 38

colder warmer
Temperature anomalies and 
explained variance (%)Precipitation anomalies and 
explained variance (%)drier wetter
Not significant in >50% of available data sets
Data unavailable in >50% of data sets0 0 20 20 20 20 30 30 30 30 40 40 40 40

117
Technical SummaryTSTS.4.2.3 Interplay Between Drivers of Climate Variability 
and Change at Regional Scales
Anthropogenic forcing has been a major driver of regional mean 
temperature change since 1950 in many sub-continental regions of 
the world ( virtually certain ). At regional scales, internal variability 
is stronger, and uncertainties in observations, models and external 
forcing are all larger than at the global scale, hindering a robust 
stratospheric ozone, and different aerosol species in most of the cases. 
Multiple lines of evidence, combining multi-model ensemble global 
projections with those coming from single-model initial-condition 
large ensembles, show that internal variability is largely contributing 
to the delayed or absent emergence of the anthropogenic signal in 
long-term regional mean precipitation changes ( high confidence ).

Internal variability in ocean dynamics dominates regional patterns on annual to decadal time scales ( high confidence ). The anthropogenic 
signal in regional sea level change will emerge in most regions by 
2100 ( medium confidence ). {9.2.4, 9.6.1, 10.4.1, 10.4.2, 10.4.3}
Regional climate change is subject to the complex interplay between 
multiple external forcings and internal variability. Time evolution 
of mechanisms operating at different time scales can modify the 
amplitude of the regional-scale response of temperature, and 
both the amplitude and sign of the response of precipitation, to 
anthropogenic forcing ( high confidence ). These mechanisms include 
non-linear temperature, precipitation and soil moisture feedbacks; 
slow and fast responses of SST patterns; and atmospheric circulation 
changes to increasing GHGs. Land-use and aerosol forcings and 
land–atmosphere feedback play important roles in modulating

regional changes, for instance in weather and climate extremes ( high 
confidence ). These can also lead to a higher warming of extreme 
SES
Rossby
Waves
Polar
VortexHadley Cell
ENSO
PDVAMV
GHG
+ O3 depletionAerosol
83.6%
14.9%1.4%41.7%53.3%
5.0%Pathway to understanding past and assessing future climate changes at regional scale
The South-Eastern South America (SES) case study
(a) Identiﬁcation of climate drivers  and phenomena  for interpreting
SES observed precipitation trend and variability in austral summer (DJF)(b) Models simulations/evaluation of SES DJF precipitation over
the historical period and 21st century based on 7 large ensembles
Precipitation trend (GPCC/1950-2014)
(mm/month/decade)GPCC observationsAnomalies in mm/month w.r.t 1995-2014Near
TermMid
TermLong 
Term
uncertainty
Model uncertainty
Scenario uncertainty
Near term Mid term Long term14.7%6.3%
78.9%

Figure TS.21 |  Example of the interplay between drivers of climate variability and change at regional scale to understand past and projected changes. The 
figure intent is to show an illustrative pathway for understanding past, and anticipating future, climate change at regional scale in the presence of uncertainties. (a) Identification 
of the climate drivers and their influences on climate phenomena contributing through teleconnection to South-Eastern South America (SES) summer (December–January–
February; DJF) precipitation variability and trends observed over 1950–2014. Drivers (red squares) include modes of variability as well as external forcing. Observed precipitation 
linear trend from GPCC is shown on continents (green-brown colour bar in mm month–1 per decade) and the SES AR6 WGI reference region is outlined with the thick black

contour. Climate phenomena leading to local effects on SES are schematically presented (blue ovals). (b) Time series of decadal precipitation anomalies for DJF SES simulated 
from seven large ensembles of historical plus RCP8.5 simulations over 1950–2100. Shading corresponds to the 5–95th range of climate outcomes given from each large 
ensemble for precipitation (in mm month–1) and thick coloured lines stand for their respective ensemble mean. The thick time series in white corresponds to the multi-model 
multi-member ensemble mean, with model contribution being weighted according to their ensemble size. GPCC observation is shown in the light black line with squares over 
1950–2014, and the 1995–2014 baseline period has been retained for calculation of anomalies in all datasets. (c) Quantification of the respective weight (in percent) between

the individual sources of uncertainties (internal in grey, model in magenta and scenario in green) at near-term, mid-term and long-term temporal windows defined in AR6 and 
highlighted in (b) for SES DJF precipitation. All computations are done with respect to 1995–2014, taken as the reference period, and the scenario uncertainty is estimated 
from Coupled Model Intercomparison Project Phase 5 (CMIP5) using the same set of models as for the large ensembles that have run different Representative Concentration 
Pathway (RCP) scenarios. {Figure 10.12a}

118
TStemperatures compared to mean temperature ( high confidence ), 
and possibly cooling in some regions ( medium confidence ). The 
soil moisture–temperature feedback was shown to be relevant for 
past and present-day heatwaves based on observations and model 
simulations. {10.4.3, 11.1.6, 11.3.1}
South-Eastern South America (SES) is one of the AR6 WGI reference 
regions (outlined with black thick contour in Figure TS.21a), and 
it is used here as an illustrative example of the interplay between 
drivers of climate variability and change at regional scale. Austral 
summer (DJF) precipitation positive trends have been observed 
over the region during 1950–2014. Drivers of this change include 
MoVs, such as AMV, ENSO, and PDV, as well as external forcing, 
like GHG increases and ozone depletion together with aerosols (as 
illustrated in Figure TS.21a). Modes of variability and external forcing 
collectively affect climate phenomena, such as the Hadley cell width

and strength, Rossby waves activity emerging from the large-scale 
tropical SST anomalies, and the Southern Hemisphere polar vortex, which are relevant for the region. In fact, local changes over SES 
in terms of moisture convergence, ascending motion and storm-
track locations depend on these climate phenomena, and they are 
overall responsible for the observed precipitation trends.  Projections 
suggest continuing positive trends in rainfall over SES in the near-
term in response to GHG emissions scenarios. Multi-model mean and 
ensemble spread are not sufficient to characterize situations where 
different models simulate substantially different or even opposite 
changes ( high confidence ). In such cases, physical climate storylines 
addressing possible outcomes for climate phenomena shown to 
play a role in the variability of the region of interest can aid the 
interpretation of projection uncertainties. In addition, single-model

initial-condition large ensembles of many realizations of internal 
variability are required to separate internal variability from forced 
changes ( high confidence ) and to partition the different sources of 
uncertainties as a function of future assessed periods. {10.3.4, 10.4.2, 
Figure 10.12a}
Box TS.13 | Monsoons
Global land monsoon precipitation decreased from the 1950s to the 1980s, partly due to anthropogenic aerosols, but 
has increased since then in response to GHG forcing and large-scale multi-decadal variability ( medium confidence ). 
Northern Hemispheric anthropogenic aerosols weakened the regional monsoon circulations in South Asia, East Asia 
and West Africa during the second half of the 20th century, thereby offsetting the expected strengthening of monsoon 
precipitation in response to GHG-induced warming ( high confidence ). 
During the 21st century, global land monsoon precipitation is projected to increase in response to GHG warming in

all time horizons and scenarios ( high confidence ). Over South and South East Asia, East Asia and the central Sahel, 
monsoon precipitation is projected to increase, whereas over North America and the far western Sahel it is projected 
to decrease ( medium confidence ). There is low confidence in projected precipitation changes in the South American 
and Australian-Maritime Continent monsoons. At global and regional scales, near-term monsoon changes will be 
dominated by the effects of internal variability ( medium confidence ). {2.3, Cross-Chapter Box 2.4, 3.3, 4.4, 4.5, 8.2, 
8.3, 8.4, 8.5, Box 8.1, Box 8.2, 10.6}
Global Monsoon
Paleoclimate records indicate that during warm climates, like the mid-Pliocene Warm Period, monsoon systems were stronger 
(medium confidence ). In the instrumental records, global summer monsoon precipitation intensity has likely  increased since the

1980s, dominated by Northern Hemisphere summer trends and large multi-decadal variability. Contrary to the expected increase of 
precipitation under global warming, the Northern Hemisphere monsoon regions experienced declining precipitation from the 1950s 
to 1980s, which is partly attributable to the influence of anthropogenic aerosols ( medium confidence ) (Box TS.13, Figure 1). {2.3.1, 
Cross-Chapter Box 2.4, 3.3.2, 3.3.3}
With continued global warming, it is likely  that global land monsoon precipitation will increase during this century (Box TS.13, Figure 1), 
particularly in the Northern Hemisphere, although the monsoon circulation is projected to weaken. A slowdown of the tropical circulation 
with global warming can partly offset the warming-induced strengthening of precipitation in monsoon regions ( high confidence ). In the 
near term, global monsoon changes are likely to be dominated by the effects of internal variability and model uncertainties ( medium

confidence ). In the long term, global monsoon rainfall change will feature a robust north–south asymmetry characterized by a greater 
increase in the Northern Hemisphere than in the Southern Hemisphere and an east–west asymmetry characterized by enhanced Asian–
African monsoons and a weakened North American monsoon ( medium confidence ). {4.4.1, 4.5.1, 8.4.1}
Regional Monsoons
Paleoclimate reconstructions indicate stronger monsoons in the Northern Hemisphere but weaker ones in the Southern Hemisphere 
during warm periods, particularly for the South and South East Asian, East Asian, and North and South American monsoons, with 
the opposite occurring during cold periods ( medium confidence ). It is very likely that Northern Hemispheric anthropogenic aerosols 
weakened the regional monsoon circulations in South Asia, East Asia and West Africa during the second half of the 20th century,

119
Technical SummaryTSBox TS.13 (continued)
Box TS.13, Figure 1 |  Global and regional monsoons: past trends and projected changes.  The intent of this figure is to show changes in precipitation over 
regional monsoon domains in terms of observed past trends, how greenhouse gases and aerosols relate to these changes, and in terms of future projections in one 
intermediate emissions scenario in the near, medium and long term.  (a) Global (black contour) and regional monsoons (colour shaded) domains. The global monsoon 
(GM) is defined as the area with local summer-minus-winter precipitation rate exceeding 2.5 mm day–1 (see Annex V). The regional monsoon domains are defined 
based on published literature and expert judgement (see Annex V) and accounting for the fact that the climatological summer monsoon rainy season varies across

the individual regions. Assessed regional monsoons are South and South East Asia ( SAsiaM, Jun–July–August–September ), East Asia ( EAsiaM, June–July–August ), 
West Africa ( WAfriM, June–July–August–September ), North America ( NAmerM, July–August–-September ), South America ( SAmerM, December–January–February ), 
Australia and Maritime Continent Monsoon ( AusMCM, December–January–February ). Equatorial South America ( EqSAmer ) and South Africa ( SAfri) regions are also 
shown, as they receive unimodal summer seasonal rainfall although their qualification as monsoons is subject to discussion. (b) Global and regional monsoons 
precipitation trends based on DAMIP CMIP6 simulations with both natural and anthropogenic (ALL), greenhouse gas only (GHG), aerosols only (AER) and natural 
only (NAT) radiative forcing. Weighted ensemble means are based on nine Coupled model Intercomparison Project Phase 6 (CMIP6) models contributing to the MIP

(with at least three members). Observed trends computed from CRU, GPCP and APHRO (only for SAsiaM and EAsiaM ) datasets are shown as well. (c) Percentage 
change in projected seasonal mean precipitation over global and regional monsoons domain in the near term (2021–2040), mid-term (2041–2060), and long term 
(2081–2100) under SSP2-4.5 based on 24 CMIP6 models. {Figures 8.11 and 8.22}
x
xxx
xxxx x x
xxxxx
x
xxx
xx
 ALL GHG AER
00.040.080.12
–0.12–0.08–0.04APHRO CRU GPCP Observations:
Near term
Mid term
Long term1020
0
–10
–20(b) Historical trend in monsoon precipitation
(c) Projected future change in monsoon precipitation (SSP2-4.5)Trend in precipitation (mm/d ay/decade) Change in precipitation (%)
AusMCM SAfri SAmerM EqAmer NAmerM WAfriM SAsiaM EAsiaM GMAusMCM SAfri SAmerM EqAmer NAmerM WAfriM SAsiaM EAsiaM GMSimulations: NATNAmerM
EAsiaM
AusMCM SAmerM2014(a) Global and regional monsoon domains

120
TSBox TS.13 (continued)
thereby offsetting the expected strengthening of monsoon precipitation in response to GHG-induced warming (Box TS.13, Figure 1). 
Multiple lines of evidence explain this contrast over South Asia, with the observed trends dominated by the effects of aerosols, while 
future projections are mostly driven by GHG increases. The recent partial recovery and enhanced intensity of monsoon precipitation 
over West Africa is related to the growing influence of GHGs with an additional contribution due to the reduced cooling effect of 
anthropogenic aerosols, emitted largely from North America and Europe ( medium confidence ). For other regional monsoons, that 
is, North and South America and Australia, there is low confidence  in the attribution of recent changes in precipitation (Box TS.13, 
Figure 1) and winds. {2.3.1, 8.3.1, 8.3.2, Box 8.1, 10.6.3}

Projections of regional monsoons during the 21st century indicate contrasting (region-dependent) and uncertain precipitation and 
circulation changes. The annual contrast between the wettest and driest month of the year is likely  to increase by 3–5% per degree 
Celsius in most monsoon regions in terms of precipitation, precipitation minus evaporation, and runoff ( medium confidence ). For the 
North American monsoon, projections indicate a decrease in precipitation, whereas increased monsoon rainfall is projected over South 
and South East Asia and over East Asia ( medium confidence ) (Box TS.13, Figure 1). West African monsoon precipitation is projected 
to increase over the central Sahel and decrease over the far western Sahel ( medium confidence ). There is low confidence  in projected 
precipitation changes in the South American and Australian-Maritime Continent regional monsoons (for both magnitude and sign)

(Box TS.13, Figure 1). There is medium confidence  that the monsoon season will be delayed in the Sahel and high confidence  that it 
will be delayed in North and South America. {8.2.2, 8.4.2.4, Box 8.2}
Building the Assessment from Multiple Lines of Evidence
Large natural variability of monsoon precipitation across different time scales, found in both paleoclimate reconstructions and 
instrumental measurements, poses an inherent challenge for robust quantification of future changes in precipitation at regional and 
smaller spatial scales. At both global and regional scales, there is medium confidence  that internal variability contributes the largest 
uncertainty related to projected changes, at least in the near term (2021–2040). A collapse of the Atlantic Meridional Overturning 
Circulation could weaken the African and Asian monsoons but strengthen the Southern Hemisphere monsoons ( high confidence ). 
{4.4.4, 4.5.1, Cross-Chapter Box 4.1, 8.5.2, 8.6.1, 9.2.3, 10.6.3}

Overall, long-term (2081–2100) future changes in regional monsoons like the South and South East Asian monsoon are generally 
consistent across global (including high-resolution) and regional climate models and are supported by theoretical arguments. 
Uncertainties in simulating the observed characteristics of regional monsoon precipitation are related to varying complexities of 
regional monsoon processes and their responses to external forcing, internal variability, and deficiencies in representing monsoon warm 
rain processes, organized tropical convection, heavy orographic rainfall and cloud–aerosol interactions. {8.3.2, 8.5.1, 10.3.3, 10.6.3}
TS.4.3 Regional Climate Change and Implications for 
Climate Extremes and Climatic Impact-Drivers
Current climate in all regions is already distinct from the 
climate of the early or mid-20th century with respect to 
several climatic impact-drivers (CIDs), resulting in shifting 
magnitude, frequency, duration, seasonality and spatial

extent of associated climate indices ( high confidence ). 
It is very likely that mean temperatures have increased 
in all land regions and will continue to increase at rates 
greater than the global average ( high confidence ). The 
frequency of heat and cold extremes have increased and 
decreased, respectively. These changes are attributed to 
human influence in almost all regions ( medium to high 
confidence ) and will continue through the 21st century 
(high confidence ). In particular, extreme heat would exceed 
critical thresholds for health, agriculture and other sectors 
more frequently by the mid 21st century with 2°C of global 
warming ( high confidence ). Relative sea level rise is very likely  to virtually certain  
(depending on the region) to continue during the 21st 
century, contributing to increased coastal flooding in low-
lying areas ( high confidence ) and coastal erosion along 
most sandy coasts ( high confidence ). Sea level will continue

to rise beyond 2100 ( high confidence ) (Box TS.4). 
Every region of the world will experience concurrent 
changes in multiple CIDs by mid-century or at 2°C global 
warming and above ( high confidence ). Even for the current 
climate, climate change-induced shifts in CID distributions 
and event probabilities, some of which have occurred over 
recent decades, are relevant for risk assessments. {11.9, 
12.1, 12.2, 12.4, 12.5, Atlas.3–Atlas.11}
An overview of changes in regional CIDs (introduced in Section TS.1) 
is given in Table TS.5, which summarizes multiple lines of evidence 
on regional climate change derived from observed trends, attribution 
of these trends and future projections. The level of confidence and

121
Technical SummaryTSthe amplitude in the projected direction of change in CIDs at a given 
time horizon depends on climate change mitigation efforts over the 
21st century. It is evident from Table TS.5 that many heat, cold, snow 
and ice, coastal, and oceanic CID changes are projected with high 
confidence  in most regions starting from a global warming level 
(GWL) of 2°C, indicating worldwide challenges. Changes in many 
other regional CIDs have higher confidence later in the 21st century 
or at higher GWLs ( high confidence ), and another small subset are 
projected with high confidence  for the 1.5°C GWL. This section 
focuses on the 2°C GWL and mid-century time period because the 
signal emerges from natural variability for a wider range of CIDs 
at this higher warming level. Figure TS.22 shows the geographical 
location of regions belonging to one of five groups characterized 
by a specific combination of changing CIDs. The Regional Synthesis

component of the Interactive Atlas provides comprehensive synthesis 
information about changes in all of the individual CIDs across all of 
the AR6 WGI reference regions. {10.5, Cross-Chapter Box 10.3, 11.1, 
11.9, Box 11.1, 12.1, 12.2, 12.4, 12.5}

122
TSTable TS.5 |  Summary of confidence for climatic impact-driver changes in each AR6 WGI reference region (illustrated in Figure TS.25) across multiple lines of evidence for observed, attributed and projected 
directional changes. The colours represent their projected aggregate characteristic changes for the mid-21st century, considering scenarios RCP4.5, SSP2-4.5, SRES A1B, or above (RCP6.0, RCP8.5, SSP3-7.0, SSP5-8.5, SRES A2), which 
approximately encompasses global warming levels of 2.0°C to 2.4°C. Arrows indicate medium  to high confidence trends derived from observations, and asterisks indicate  medium and high confidence in attribution of observed changes. (North 
Africa is not an AR6 WGI reference region, but assessment here is based upon the African portion of the Mediterranean reference region). {Tables 12.3–12.11 and Tables 11.4–11.21}

Mean air temperatureExtreme heatCold spellFrostMean precipitationRiver floodHeavy precipitation and  
pluvial floodLandslideAridityHydrological droughtAgricultural and ecological droughtFire weatherMean wind speedSevere wind stormTropical cycloneSand and dust stormSnow, glacier and ice sheetPermafrostLake, river and sea iceHeavy snowfall and ice stormHailSnow avalancheRelative sea levelCoastal floodCoastal erosionMarine heatwaveOcean and lake acidityAir pollution weatherAtmospheric CO 2 at surfaceRadiation at surface
Africa
North Africa ↗ ↗***↘***          ↗  ↗  ↗       3                ↗    4↗ ↗   ↗   
Sahara ↗ ↗**↘**                                      ↗    4↗ ↗   ↗   
Western Africa ↗ ↗**↘**   1↗     ↗1↗1↗1                      ↗    4↗ ↗   ↗   
Central Africa ↗        ↘1,2           ↗                       ↗    4↗ ↗   ↗   
North Eastern 
Africa↗ ↗**↘**  ↘         1 1 1          ↘           ↗    4↗ ↗   ↗   
South Eastern

Africa↗ ↗**             1 1 1       3  ↘           ↗    4↗ ↗   ↗   
West Southern 
Africa↗ ↗***↘***  ↘ ↗ ↗       ↗                       ↗    4↗ ↗   ↗   
East Southern 
Africa↗ ↗***↘***  ↘ ↗ ↗       ↗        3              ↗    4,5↗ ↗   ↗   
Madagascar ↗ ↗ ↘                        3              ↗    4,5↗ ↗   ↗   
Note: There are several region-specific qualifiers/exceptions attached to some of the directions of change/confidence levels indicated above. {12.4}
Key for observational trend evidence ↗ Past upward trend ( medium  or higher confidence )     ↘ Past downward trend ( medium or higher confidence )
Key for attribution evidence *** High confidence (or more)  ** Medium confidence
Key for level of confidence in future changes High confidence  
of increase (or more)Medium confidence 
of decreaseHigh confidence 
of decreaseNot broadly  
relevantLow confidence 
in direction of change

123
Technical SummaryTSClimatic Impact-driver
Asia
Arabian Peninsula ↗ ↗***↘**↘                                      ↗    1↗     ↗   
West Central Asia ↗ ↗***↘***↘  5   ↗   ↗    ↗   ↘       ↘           ↗    1,2↗     ↗   
West Siberia ↗ ↗***↘***↘ ↗    ↗            ↘       ↗ ↘                      ↗   
East Siberia ↗ ↗***↘***↘ ↗    ↗            ↘       ↗ ↘                      ↗

Russian Far East ↗ ↗***↘***↘ ↗    ↗                    ↗ ↘         ↗    1,2↗ ↗   ↗   
East Asia ↗ ↗***↘***↘      ↗     ↗ ↗   ↘   ↗3      ↘       ↗    1,2↗     ↗   
East Central Asia ↗ ↗***↘***        ↗        ↗   ↘         ↘ ↘                         
Tibetan Plateau ↗ ↗***↘***↘      ↗            ↘         ↘                      ↗   
South Asia ↗ ↗***↘***↘ ↘    ↗            ↘       ↘ ↘         ↗    1↗          
South East Asia ↗ ↗***↘***   4   ↗               ↗3              ↗    1,2↗     ↗   Table TS.5 (continued)
Key for level of confidence in future changes High confidence

124
TS
Northern Australia   ↗***↘***↘     ↗       ↘       ↘5              ↗    7↗ ↗   ↗   
Central Australia ↗ ↗***↘***↘                                      ↗    7↗ ↗   ↗   
Eastern Australia ↗ ↗***↘***↘                                      ↗    7↗ ↗   ↗   
Southern Australia ↗ ↗***↘***↘  1       ↗3↗ ↗ ↗** 7      ↘           ↗    7↗ ↗   ↗

New Zealand ↗ ↗ ↘**    2        4       8      ↘6          ↗    7↗ ↗   ↗   Table TS.5 (continued)

125
Southern Central 
America↗ ↗**↘**                         2              ↗    3↗ ↗   ↗   
North-Western 
South America↗ ↗***↘***                                        ↗    3,4↗ ↗   ↗   
Northern South 
America↗ ↗**↘**                         2              ↗    3,4↗ ↗   ↗   
South American

Monsoon↗ ↗**↘**     ↗1                                                ↗   
North-Eastern 
South America↗ ↗**↘**   ↘             ↗                       ↗    3,4↗ ↗   ↗   
South-Western 
South America↗ ↗**↘**↘            ↗                           ↗    3↗ ↗   ↗   
South-Eastern 
South America↗ ↗***↘***↘ ↗    ↗     ↘                         ↗    3↗ ↗   ↗   
Southern South 
America↗       ↘                                       ↗    3↗ ↗   ↗   Table TS.5 (continued)
of increase (or more)Medium confidence

126
TS
Europe
Mediterranean ↗ ↗***↘***      ↘  5    ↗**↗**  ↘6 7    ↘           ↗    2↗ ↗   ↗   
Western and Central 
Europe↗ ↗***↘***   ↗ ↗ ↗  4    ↗   ↘       ↘ ↘         ↗    2↗ ↗   ↗   
Eastern Europe ↗ ↗***↘***   ↗    ↗           ↘       ↘                           ↗   
Northern Europe ↗ ↗***↘***   ↗ ↘1↗***    ↘     ↘       ↘ ↘         ↗  8 2,3↗ ↗   ↗

North Central 
America↗ ↗**↘**              ↗     ↗                     ↗ ↗  2↗ ↗   ↗   
Western North 
America↗ ↗**↘**     3     5 5 4,7  ↗6,7 
**↗6.7↘  8  ↗6↘1  ↘  1   1↗ ↗5 2↗ ↗   ↗   
Central North 
America↗          ↗    ↗**   7   7 7↘  8   4↘   ↘       ↗ ↗  2↗ ↗   ↗   
Eastern North 
America↗          ↗5   ↗          7↘  8    ↘1  ↘  1   1↗ ↗  2↗ ↗   ↗   
North America↗ ↗***↘***↘   5        5   6,7 6,7   8    ↘1,6↘ ↘      1  4↗4,6  2,6↗ ↗   ↗   
North America↗ ↗***↘***↘  5      6 5   6,7↗6,7   8    ↘1↘ ↘      1,6↗9↗  2↗ ↗   ↗   Table TS.5 (continued)

127
Small Islands
Caribbean ↗ ↗**                            5              ↗    6↗ ↗   ↗   
Pacific ↗ ↗**1       2    3   4           5              ↗    6↗ ↗   ↗   
Polar Terrestrial Regions
Greenland and 
Iceland↗ ↗**↘**     ↗ ↗   ↘3     2,3        ↘1↘ ↘       ↗5      ↗   ↗   
Arctic North

Europe↗ ↗         ↗      ↘3     2,3        ↘1↘ ↘       ↗6   7  ↗   ↗   
Russian Arctic ↗ ↗**↘**     ↗      ↘3     2,3        ↘1,4   ↘             7  ↗   ↗   
Arctic North-Western 
North America↗            ↗      ↘3     2,3        ↘1,4↘ ↘             7  ↗   ↗   
Arctic North-East 
North America↗ ↗         ↗      ↘3     2,3        ↘1,4↘ ↘       ↘       ↗   ↗   
West Antarctica                                    ↘1,4                  ↗   ↗   
East Antarctica                                                        ↗   ↗   Table TS.5 (continued)
Key for observational trend evidence  ↗ Past upward trend ( medium  or higher confidence )     ↘ Past downward trend ( medium or higher confidence )
Key for attribution evidence   *** High confidence (or more)  ** Medium confidence

128
TSTable TS.5 (continued)
Mean ocean temperatureMarine heatwaveOcean acidityOcean salinityDissolved oxygenSea ice
Oceans
Arctic Ocean ↗    ↗  ↘ ***
South Pacific Ocean ↗   ↗ ↗
Equatorial Pacific Ocean ↗   ↗ ↗
North Pacific Ocean ↗   ↗ ↗
South Atlantic Ocean ↗   ↗ ↗
Equatorial Atlantic Ocean ↗   ↗ ↗
North Atlantic Ocean ↗   ↗ ↗
Equatorial Indian Ocean ↗   ↗ ↗
South Indian Ocean ↗   ↗ ↗
Arabian Sea ↗   ↗ ↗
Bay of Bengal ↗   ↗ ↗
Southern Ocean      

129
Technical SummaryTSNotes:
Africa (projections)
1. Contrasted regional signal: drying in western portions and wetting in eastern portions
2. Likely  increase over the Ethiopian Highlands
3. Medium confidence  of decrease in frequency and increase in intensity
4. Along sandy coasts and in the absence of sufficient sediment supply from terrestrial or offshore sources
5. Substantial parts of the East Southern Africa and Madagascar coast are projected to prograde if present-day ambient shoreline change rates continue
Asia (projections)
2. Substantial parts of the coasts in these regions are projected to prograde if present-day ambient shoreline change rates continue
3. Tropical cyclones decrease in number but increase in intensity
4. High confidence  of decrease in Indonesia (Atlas.5.4.5)
5. Medium confidence  of decreasing in summer and increasing in winter

Australasia (projections)
1. High confidence  of decrease in the south-west of the state of Western Australia
2. Medium confidence  of decrease in north and east and increase in south and west
3. High confidence  of increase in the south-west of the state of Western Australia
4. Medium confidence  of increase in the north and east and decrease in south and west
5. Low confidence  of increasing intensity, and high confidence  of decreasing occurrence
6. High confidence  of decrease in glacier volume, medium confidence  of decrease in snow
7. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat
Central and South America (projections)
1. Increase in extreme flow in the Amazon basin
2. Tropical cyclones decrease in number but increase in intensity

4. Substantial parts of the North-Western South America, Northern South America and North-Eastern South America coasts are projected to prograde if present-day 
ambient shoreline change rates continue
Europe (projections)
1. Excluding southern United Kingdom
2. Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat
3. The Baltic Sea shoreline is projected prograde if present-day ambient shoreline change rates continue.
4. For the Alps, conditions conducive to landslides are expected to increase
5. Low confidence  of decrease in the southernmost part of the region
6. General decrease except in Aegean Sea
7. Medium confidence  of decrease in frequency and increase in intensities
8. Except in the Northern Baltic Sea region
North America (projections)
1. Snow may increase in some high elevations and during the cold season and decrease in other seasons and at lower elevations

3. Increasing in northern regions and decreasing toward the south
4. Decreasing in northern regions and increasing toward the south
5. Higher confidence in northern regions and lower toward the south
6. Higher confidence in southern regions and lower toward the north
7. Higher confidence in increase for some climatic impact-driver indices during summertime
8. Increase in convective conditions but decrease in winter extratropical cyclones
9. Relative sea level rise reduced given land uplift in Southern Alaska
Small Islands (projections)
1. Very high confidence  in the direction of change, but low to medium confidence  in the magnitude of change due to model uncertainty
2. Decrease in eastern Pacific and southern Pacific subtropics, but increase in parts of western and equatorial Pacific; with seasonal variation in future changes

3. High confidence  in increase in extreme rain frequency and intensity in western tropical Pacific; low confidence  in magnitude of change due to model bias
4. Increase in southern Pacific
Polar Terrestrial Regions (projections)
2. Higher confidence in southern regions and lower toward the north
3. Higher confidence in increase for some climatic impact-driver indices during summertime
4. Glaciers decline even as some regional snow climatic impact-driver indices increase
5. Decreasing in west and increasing in east
6. Except for Northern Baltic Sea coasts where relative sea levels fall

130
TS
While changes in climatic impact-drivers are projected everywhere, there is a 
specific combination of changes each region would experience
GIC
NWN NEN
WNA
CNAENA
NCA
SCACAR
NWSNSA
NES
SAM
SWSSES
SSANEU
WCEEEU
MED
SAH
WAF
CAFNEAF
SEAF
WSAFESAF
MDGRAR
WSB ESB RFE
WCAECA
TIB EAS
ARP SAS
SEA
PAC
PACPAC
NAU
CAUEAU
SAUNZNCANEU
MED
SAH SAS
(a) World regions grouped into five clusters, each one based on a combination of changes in climatic impact-drivers 
Assessed future changes: Changes refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period 
within 1960–2014 or 1850–1900.
Combinations of future changes in climatic impact-drivers (CIDs)
(5)
Hotter
and in some regions more  
pluvial flooding or mean 
precipitation or both(1)
Hotter
and in some regions more 
fire weather or drought or 
bothAll coastal regions except North-East North America (NEN) and

Greenland/Iceland (GIC) will be exposed to at least two among 
increases in relative sea level, coastal flood and coastal erosion
(2)
Hotter and more fire weather
and in some regions more  pluvial 
flooding or drought or less mean 
precipitation or snow and ice or 
combinations of these
(4)
Hotter and less snow/ice
pluvial flooding or river 
flooding or mean precipita -
tion or both (3)
Hotter, less snow and ice and 
more pluvial flooding
precipitation or fire weather or 
bothincrease in mean 
temperature, extreme heat
decrease in cold spell, frost
increase in aridity, hydrological 
and
agricultural drought 
decrease in snow, glacier
and ice sheet
Pluvial
flooding
increaseMean
decreaseMean
increase
River
flooding
increaseHeat
Cold
Snow and IceDrought
Fire
weather
increase
CIDs changing in all the 
regions of the cluster with 
high confidence
CIDs changing in some 
regions of the cluster with

high and sometimes with 
medium confidenceLEGEND1) Hotter and drier
2) Hotter and drier and in 
some regions wetter 
extremes
3) Hotter and wetter 
extremes and in some 
regions more precipitation or 
fire weather
4) Hotter and wetter and in 
some regions more flooding
5) Hotter and in some 
regions wetter extremes or 
more precipitation
6) Increase in Tropical 
cyclones intensity or Severe 
winds

131
Figure TS.22 |  Synthesis of the geographical distribution of climatic impact-drivers changes and the number of AR6 WGI reference regions where they 
are projected to change. 
(b) Number of land & coastal regions (i) and open-ocean regions (ii) where each climatic impact-driver (CID) is projected to 
increase or decrease with high confidence (dark shade) or medium confidence (light shade)
Cold spell
Frost
Heavy precipitation and pluvial flood
Landslide
Aridity
Hail

Radiation at surfaceHeat & Cold Snow & Ice Coastal Wind Open Ocean Other Wet & Dry
5515
155515
1525
2535
3545
4555
55
NUMBER  OF LAND & COASTAL  REGIONS
NUMBER  OF OPEN-OCEAN  REGIONS(i) (ii)
Regions with high confidence  increase
Regions with medium confidence  increase
Regions with high confidence  decrease
Regions with medium confidence decreaseThe height of the lighter shaded ‘envelope’ behind each bar represents the 
maximum number of regions for which each CID is relevant. The envelope 
is symmetrical about the x-axis showing the maximum possible number of 
relevant regions for CID increase (upper part) or decrease (lower part).
BAR CHART  LEGEND LIGHTER -SHADED  ‘ENVELOPE ’ LEGEND
Climatic impact-drivers (CIDs)  are physical climate system conditions (e.g., means, events, extremes) that affect an element of 
society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a

mixture of each across interacting system elements and regions. The CIDs are grouped into seven types, which are summarized 
under the icons in sub-panels (i) and (ii). All regions are projected to experience changes in at least 5 CIDs. Almost all (96%) are 
projected to experience changes in at least 10 CIDs and half in at least 15 CIDs. For many CID changes, there is wide 
geographical variation, and so each region is projected to experience a specific set of CID changes. Each bar in the chart 
represents a specific geographical set of changes that can be explored in the WGI Interactive Atlas.interactive-atlas.ipcc.ch

132
TSFigure TS.22 (continued): Panel (a)  shows the geographical location of regions belonging to one of five groups characterized by a specific combination of changing climatic 
impact-drivers (CIDs). The five groups are represented by the five different colours, and the CID combinations associated with each group are represented in the corresponding 
‘fingerprint’ and text below the map. Each fingerprint comprises a set of CIDs projected to change with high confidence  in every region in the group and a second set of CIDs, 
one or more of which are projected to change in each region with high or medium confidence . The CID combinations follow a progression from those becoming hotter and drier 
(group 1) to those becoming hotter and wetter (group 5). In between (groups 2–4), the CIDs that change include some becoming drier and some wetter and always include

a set of CIDs which are getting hotter. Tropical cyclones and severe wind CID changes are represented on the map with black dots in the regions affected. Regions affected 
by coastal CID changes are described by text on the map. The five groups are chosen to provide a reasonable level of detail for each region while not overwhelming the map 
with a full summary of all aspects of the assessment, which is available in Table TS.5 and can be visualized in the Regional Synthesis component of the Interactive Atlas. The 
CID changes summarized in the figure represent high and medium confidence  changes for the mid-21st century, considering scenarios SSP2-4.5, RCP4.5, SRES A1B, or above 
(SSP3-7.0, SSP5-8.5, RCP6.0, RCP8.5, SRES A2), which approximately encompasses global warming levels of 2.0°C to 2.4°C.
The bar chart in panel  (b) shows the numbers of regions where each CID is increasing or decreasing with medium  or high confidence  for all land regions and ocean regions

listed in Table TS.5. The colours represent the direction of change and the level of confidence in the change: purple indicates an increase while brown indicates a decrease; darker 
and lighter shades refer to high and medium confidence , respectively. Lighter background colours represent the maximum number of regions for which each CID is broadly 
relevant. Sub-panel (i) shows the 30 CIDs relevant to the land and coastal regions while sub-panel (ii) shows the 5 CIDs relevant to the open ocean regions. Marine heatwaves 
and ocean acidity are assessed for coastal ocean regions in panel (i) and for open ocean regions in panel (ii). Changes refer to a 20- to 30-year period centred around 2050 
and/or consistent with 2°C global warming compared to a similar period within 1960–2014, except for hydrological drought and agricultural and ecological drought, which

is compared to 1850–1900. Definitions of the regions are provided in Atlas.1, the Interactive Atlas (https://interactive-atlas.ipcc.ch/) and Chapter 12. (Table TS.5, Figure TS.24) 
{11.9, 12.2, 12.4, Atlas.1}
TS.4.3.1 Common Regional Changes in Climatic Impact-Drivers
Heat and cold: Changes in temperature-related CIDs such as mean 
temperatures, growing season length, and extreme heat and frost have 
already occurred ( high confidence ), and many of these changes have 
been attributed to human activities ( medium confidence ). Over all 
land regions with sufficient data (i.e., all except Antarctica), observed 
changes in temperature have already clearly emerged outside the 
range of internal variability, relative to 1850–1900 (Figure TS.23). In 
tropical regions, recent past temperature distributions have already 
shifted to a range different to that of the early 20th century ( high 
confidence ) (Section TS.1.2.4). Most land areas have very likely

warmed by at least 0.1°C per decade since 1960, and faster in recent 
decades. On regional-to-continental scales, trends of increased 
frequency of hot extremes and decreased frequency of cold extremes 
are generally consistent with the global-scale trends in mean 
temperature ( high confidence ). In a few regions, trends are difficult 
to assess due to limited data availability. {2.3.1.1, 11.3, 11.9, 12.4, 
Atlas.3.1}
Warming trends observed in recent decades are projected to continue 
over the 21st century and over most land regions at a rate higher 
than the global average ( high confidence ). For given global warming 
levels, model projections from CMIP6 show future regional warming 
changes that are similar to those projected by CMIP5. However, 
projected regional warming in CMIP6 for given time periods and 
emissions scenarios has a wider range with a higher upper limit 
compared to CMIP5 because of the higher climate sensitivity in some

CMIP6 models and differences in the forcings. {Atlas.3–Atlas.11}
Under RCP8.5/SSP5-8.5, it is likely  that most land areas will experience 
further warming of at least 4°C compared to a 1995–2014 baseline 
by the end of the 21st century, and in some areas significantly more. 
At increasing warming levels, extreme heat will exceed critical 
thresholds for health, agriculture and other sectors more frequently 
(high confidence ), and it is likely  that cold spells will become less 
frequent towards the end of the century. For example, by the end 
of the 21st century, dangerous humid heat thresholds, such as the 
National Oceanic and Atmospheric Administration (NOAA) heat index 
(HI) threshold of 41°C, will be exceeded much more frequently under 
the SSP5-8.5 scenario than under SSP1-2.6 and will affect many regions ( high confidence ). In many tropical regions, the number of 
days per year where a heat index of 41°C is exceeded would increase

by more than 100 days relative to the recent past under SSP5-8.5, 
while this increase will be limited to less than 50 days under SSP1-2.6 
(high confidence ) (Figure TS.6). The number of days per year where 
temperature exceeds 35°C would increase by more than 150 days in 
many tropical areas, such as the Amazon basin and South East Asia, 
by the end of century for the SSP5-8.5 scenario, while it is expected to 
increase by less than 60 days in these areas under SSP1-2.6 (except 
for the Amazon Basin) ( high confidence ) (Figure TS.24). {4.6.1, 11.3, 
11.9, 12.4, 12.5.2, Atlas}
Wet and dry: Compared to the global scale, precipitation internal 
variability is stronger at the regional scale while uncertainties in 
observations, models and external forcing are all larger. However, 
GHG forcing has driven increased contrasts in precipitation amounts 
between wet and dry seasons and weather regimes over tropical land 
areas ( medium confidence ), with a detectable precipitation increase

in the northern high latitudes ( high confidence ) (Box TS.6). The 
frequency and intensity of heavy precipitation events have increased 
over a majority of land regions with good observational coverage 
(high confidence ). A majority of land areas have experienced 
decreases in available water in dry seasons due to human-induced 
climate change associated with changes in evapotranspiration 
(medium confidence ). Global hydrological models project a larger 
fraction of land areas to be affected by an increase rather than by 
a decrease in river floods ( medium confidence ). Extreme precipitation 
and pluvial flooding will increase in many regions around the world 
on almost all continents  (high confidence ), but regional changes 
because complex hydrological processes, including land cover and 
human water management, are involved. {8.2.2.1, 8.3.1, Box  8.2, 
10.4.1, 11.5, 11.6, 11.9, 12.4, 12.5.1, Atlas.3.1}

Wind: Mean wind speed has decreased over most land areas with 
good observational coverage ( medium confidence ). It is likely  that 
the global proportion of major tropical cyclone (TC) intensities 
(Categories 3–5) over the past four decades has increased. The 
proportion of intense TCs, average peak TC wind speeds, and peak 
wind speeds of the most intense TCs will increase on the global scale 
with increasing global warming ( high confidence ). {11.7.1}

133
Figure TS.23 |  Time period during which the signals of temperature change in observed data aggregated over the reference regions emerged from the 
noise of annual variability in the respective aggregated data, using a signal-to-noise ratio of two as the threshold for emergence.  The intent of this figure 
is to show, for the AR6 WGI reference regions, when a signal of annual mean surface temperature change emerged from the noise of annual variability in two global datasets 
and thus also provide some information on observational uncertainty.  Emergence time is calculated for two global observational datasets: (a) Berkeley Earth and (b) CRUTEM5. 
Regions in the CRUTEM5 map are shaded grey when data are available over less than 50% of the area of the region. (Section TS.1.2.4) {Figure Atlas.11}

Before 1981 1981–1988 1989–1996 1997–2004 2005–2012 2013–2020Dataset: Berkeley Earth. Temperature changes relative to 1850–1900.Year of signiﬁcant emergence of changes in temperature over land regions (S/N>2)
Before 1981 1981–1988 1989–1996 1997–2004 2005–2012 2013–2020Dataset: CRUTEM5. Temperature changes relative to 1850–1900. Grey: not enough data.Year of signiﬁcant emergence of changes in temperature over land regions (S/N>2)

134
TS
Colour
Low model agreementHigh model agreement No data(a) TX35 for 2041−2060 (RCP2.6) rel. to 1995−2014CMIP5 CMIP6 CORDEX
(e) TX35 for 2041−2060 (SSP1-2.6) rel. to 1995−2014
(b) TX35 for 2081−2100 (RCP2.6) rel. to 1995−2014 (f) TX35 for 2081−2100 (SSP1-2.6) rel. to 1995−2014
(c) TX35 for 2041−2060 (RCP8.5) rel. to 1995−2014 (g) TX35 for 2041−2060 (SSP5-8.5) rel. to 1995−2014
(d) TX35 for 2081−2100 (RCP8.5) rel. to 1995−2014 (h) TX35 for 2081−2100 (SSP5-8.5) rel. to 1995−2014
Change (days)
0 250 200 150 100 50 -25(i) TX35 for 2041−2060 (SSP1-2.6) rel. to 1995−2014
(j) TX35 for 2081−2100 (SSP1-2.6) rel. to 1995−2014
(k) TX35 for 2041−2060 (SSP5-8.5) rel. to 1995−2014
(l) TX35 for 2081−2100 (SSP5-8.5) rel. to 1995−2014Figure TS.24 |  Projected change in the mean number of days per year with maximum temperature exceeding 35°C for Coupled Model Intercomparison

Project Phase 5 (CMIP5; first column), Phase 6 (CMIP6; second column) and Coordinated Regional Climate Downscaling Experiment (CORDEX; third 
column) ensembles.  The intent of this figure is to show that there is a consistent message about the patterns of projected change in extreme daily temperatures from the 
CMIP5, CMIP6 and CORDEX ensembles.  The map shows the median change in the number of days per year between the mid-century (2041–2060) or end-century (2081–2100) 
and historical (1995–2014) periods for the CMIP5 and CORDEX RCP8.5 and RCP2.6 scenario ensembles and the CMIP6 SSP5-8.5 and SSP1-2.6 scenario ensembles. Hatching 
indicates areas where less than 80% of the models agree on the sign of change. {Interactive Atlas}
Snow and ice: Many aspects of the cryosphere either have seen 
significant changes in the recent past or will see them during the 
21st century ( high confidence ). Glaciers will continue to shrink

and permafrost to thaw in all regions where they are present ( high 
confidence ). Also, it is virtually certain  that snow cover will experience 
a decline over most land regions during the 21st century, in terms 
of water equivalent, extent and annual duration. There is high 
confidence  that the global warming-induced earlier onset of spring 
snowmelt and increased melting of glaciers have already contributed 
to seasonal changes in streamflow in high-latitude and low-elevation 
mountain catchments. Nevertheless, it is very likely  that some high-
latitude regions will experience an increase in winter snow water 
equivalent due to the effect of increased snowfall prevailing over 
warming-induced increased snowmelt. (Section TS.2.5) {8.2.2.1, 
8.3.1, Box 8.2, 9.4, 9.5.1, 9.5.2, 12.4, Atlas.4–Atlas.9, Atlas.11}Coastal and oceanic: There is high confidence  that SST will 
increase in all oceanic regions except the North Atlantic. Regional

sea level change has been the main driver of changes in extreme 
sea levels across the quasi-global tide gauge network over the 
20th century ( high confidence ). With the exception of a few regions 
with substantial land uplift, relative sea level rise is very likely to 
virtually certain  (depending on the region) to continue during the 
21st century, contributing to increased coastal flooding in low-lying 
areas ( high confidence ) and coastal erosion along most sandy 
coasts ( high confidence ) over the 21st century. In the open ocean, 
acidification, changes in sea ice, and deoxygenation have already 
emerged in many areas ( high confidence ). Marine heatwaves are 
also expected to increase around the globe over the 21st century 
(high confidence ). (Section TS.2.4) {Box  9.2, 9.2.1.1, 9.6, 9.6.4, 
9.6.4.2, 12.4}

135
Technical SummaryTSOther variables and concurrent CID changes:  It is virtually certain  
that atmospheric CO 2 and oceanic pH will increase in all climate 
scenarios, until net zero CO 2 emissions are achieved (Section TS.2.2). 
In nearly all regions, there is low confidence  in changes in hail, ice 
storms, severe storms, dust storms, heavy snowfall, and avalanches, 
although this does not indicate that these CIDs will not be affected 
by climate change. For such CIDs, observations are often short-
resolution or accurate parametrizations to adequately simulate them 
over climate change time scales. The probability of compound events 
has increased in the past due to human-induced climate change and 
will likely continue to increase with further global warming, including 
for concurrent heatwaves and droughts, compound flooding, and the 
possibility of connected sectors experiencing multiple regional extreme

events at the same time (for example, in multiple breadbaskets) ( high 
confidence ). {5.3.4.2, 11.8, Box 11.3, Box 11.4, 12.4}
TS.4.3.2 Region-by-Region Changes in Climatic Impact-Drivers
This section provides a continental synthesis of changes in CIDs, 
some examples of which are presented in Figure TS.25.
With 2°C global warming, and as early as the mid-21st 
century, a wide range of CIDs, particularly related to the 
water cycle and storms, are expected to show simultaneous 
region-specific changes relative to the recent past with high 
or medium confidence . In a number of regions (Southern 
Africa, the Mediterranean, North Central America, Western 
North America, the Amazon regions, South-Western South 
America, and Australia), increases in one or more of drought, 
aridity and fire weather ( high confidence ) will affect a wide 
range of sectors, including agriculture, forestry, health and 
ecosystems. In another group of regions (North-Western,

Central and Eastern North America, Arctic regions, North-
Western South America, Northern, Western and Central 
and Eastern Europe, Siberia, Central, South and East Asia, 
Southern Australia and New Zealand), decreases in snow 
and/or ice or increases in pluvial/river flooding ( high 
confidence ) will affect sectors such as winter tourism, 
energy production, river transportation and infrastructure. 
{11.9, 12.3, 12.4, 12.5, Table 12.2}
TS.4.3.2.1 Africa
Additional regional changes in Africa, besides those 
described in Section TS.4.3.1, include a projected decrease 
in total precipitation in the northernmost and southernmost 
regions ( high confidence ), with Western Africa having a west-
to-east pattern of decreasing-to-increasing precipitation 
(medium confidence ). Increases in heavy precipitation that 
can lead to pluvial floods ( high confidence ) are projected 
for most African regions, even as increasing dry CIDs 
(aridity; hydrological, agricultural and ecological droughts;

fire weather) are projected in the western part of Western 
Africa, Southern Africa and Northern Africa and the 
Mediterranean regions ( medium  to high confidence ). {8.4, 
11.3, 11.6, 11.9, 12.4, Atlas.4}In addition to the main changes summarized above and in 
Section TS.4.3.1, additional details per CID are given below.
Heat and cold: Observed and projected increases in mean 
temperature and a shift toward heat extreme characteristics are 
broadly similar to the generic pattern described in Section TS.4.3.1. 
{2.3.1.1.2, 11.3, 11.9, 12.4.1.1, Atlas.4.2, Atlas.4.4}
Wet and dry:  Mean precipitation changes have been observed 
over Africa, but the historical trends are not spatially coherent ( high 
confidence ). North Eastern Africa, East Southern Africa and Central 
Africa have experienced a decline in rainfall since about 1980 and 
parts of West Africa an increase ( high confidence ). Increases in the 
frequency and/or the intensity of heavy rainfall have been observed in

East and West Southern Africa, and the eastern Mediterranean region 
(medium confidence ). Increasing trends in river flood occurrence can 
be identified beyond 1980 in East and West Southern Africa ( medium 
confidence ) and Western Africa ( high confidence ). However, Northern 
Africa and West Southern Africa are likely to have a reduction in 
precipitation. Over West Africa, rainfall is projected to decrease 
in the western Sahel subregion and increase along the Guinea Coast 
subregion ( medium confidence ). Rainfall is projected to increase over 
Eastern Africa ( medium confidence ). {8.3.1.6, 11.4, 11.9, 12.4.1.2, 
Atlas.4.2, Atlas.4.4, Atlas.4.5}
Precipitation declines and aridity trends in Western Africa, Central 
Africa, Southern Africa and the Mediterranean co-occur with 
trends towards increased agricultural and ecological droughts in 
the same regions ( medium confidence ). Trends towards increased 
hydrological droughts have been observed in the Mediterranean

(high confidence ) and Western Africa ( medium confidence ). These 
trends correspond with projected regional increases in aridity and 
fire weather conditions ( high confidence ). {8.3.1.6, 8.4.1.6, 11.6, 
11.9, 12.4.1.2}
Wind: Mean wind, extreme winds and the wind energy potential in 
North Africa and the Mediterranean are projected to decrease across 
all scenarios ( high confidence ). Over Western Africa and Southern 
Africa, a future significant increase in wind speed and wind energy 
potential is projected ( medium confidence ). There is a projected 
decrease in the frequency of tropical cyclones making landfall 
over Madagascar, East Southern Africa and East Africa ( medium 
confidence ). {12.4.1.3}
Snow and ice: There is high confidence  that African glaciers and 
snow have very significantly decreased in the last decades and that 
this trend will continue in the 21st century. {12.4.1.4}
Coastal and oceanic:  Relative sea level has increased at a higher

rate than GMSL around Africa over the last 3 decades. The present 
day 1-in-100-year extreme total water level (ETWL) is between 
0.1 m and 1.2 m around Africa, with values around 1 m or above 
along the East and West Southern and Central Eastern Africa coasts. 
Satellite-derived shoreline retreat rates up to 1 m yr–1 have been 
observed around the continent from 1984 to 2015, except in South 
Eastern Africa, which has experienced a shoreline progradation 
(growth) rate of 0.1 m yr–1 over the same period. {12.4.1.5}

136
TS
NEN
CNA ENANWN
WNA
NCA
SCACARNWS
SAM
SSASWSSESNSA
NES
NOAA Heat Index exceeding 41°C Change in number of days with SWE>100 mm
100-yr return period stream flow020406080100
020406080100
NWS NSA NESNCANWN NEN NWA
ENACNA
SCA CAR
SWS SAM SES SSA
Legend
CMIP6+1.5 °CGWL
+2°C +4°C
CMIP5
CORDEXr.pastRCPs/SSPs
2.6 8.5RCPs/SSPs90thp
10thpmedian00.511.522.5
00.511.522.50.200.20.40.60.8
0.200.20.40.60.80100200300
Time slices GWLr.past.1.524mid. long.
Time slicesGWL
r.past.1.524 mid. long.
GWL1.5
2
4
r.past
mid.
long.
Time slices GWL1.5
2
4
r.past
mid.
long.
2
4
r.past
mid.
long.
Time slices(a)
Days with SWE>100 mm (days yr-1)
 Recent past value (m3 s-1  km-2) days yr-1Change from recent past (m3 s-1  km-2)

137
Figure TS.25 |  Distribution of projected changes in selected climatic impact-driver (CID) indices for selected regions for Coupled Model Intercomparison 
Project Phases 5 and 6 (CMIP6, CMIP5) and Coordinated Regional Downscaling Experiment (CORDEX) model ensembles.   The intent of this figure is to show 
that many CID projections for multiple global warming levels and scenarios time slices are available for all the AR6 WGI reference regions and are based on both global (CMIP5, 
CMIP6) and regional (CORDEX) model ensembles.  Different indices are shown for different region: for Eastern Europe and North Asia, the mean number of days per year with 
maximum temperature exceeding 35°C; for Central America, the Caribbean, South West Asia, South Asia and South East Asia, the mean number of days per year with the

National Oceanic and Atmospheric Administration (NOAA) Heat Index exceeding 41°C; for Australasia, East Asia and Russian Far East, the average shoreline position change; 
for South America, Europe and Africa, the mean change in 1-in-100-year river discharge per unit catchment area (m3 s–1 km–2); and for North America, the median change in 
the number of days with snow water equivalent (SWE) over 100 mm. For each box plot, the changes or the climatological values are reported with respect to, or compared to, 
the recent past (1995–2014) period for 1.5°C, 2°C and 4°C global warming levels and for mid-century (2041–2060) or end-century (2081–2100) periods for the CMIP5 and 
CORDEX RCP8.5 and RCP2.6 and CMIP6 SSP5-8.5 and SSP1-2.6 scenarios ensembles. {Figures 12.5, 12.6, 12.9, 12.SM.1, 12.SM.2, and 12.SM.6}
Maximum temperature exceeding 35°C 100-yr return period stream flow
NEU
WCEEEU
MED
WAFSAH
NEAF
SEAFWSAF
ESAFMDGCAFRAR
RFE ESB WSB
WCAECA
TIB EAS
ARP SAS
SEA
NAU
CAU EAU

SAU NZ00.20.40.60.8
0.100.1
Time slices0100200300
01002003000100200300
0100200300RAR EEU WSB
ESB ECA TIB
GWL1.5
2
4
r.past
mid.
long.
2
4
r.past
mid.
long.
2
4
r.past
mid.
long.
Time slicesNEU WCE MED(b)
days yr -1days yr -1
NOAA Heat Index exceeding 41°C 
WCA ARP
300EAS RFE
mid- long-
term termmid- long-
term termShoreline position change
Shoreline position change
Legend
+2°C +4°C
CMIP5
2.6 8.590thp
10thpmedian00.511.5
00.511.5
GWL00.20.4r.past.1.524mid. long.
Time slices00.511.522.5
–0.200.20.40.60.8
GWL1.5
2
4
r.past
mid.
long.
2
4
r.past
mid.
long.
Time slicesSAS
01002003000100200
SEA
95thp
median
5thpCMIP5 RCP8.5
CMIP5 RCP4.5–600–400–2000200NAU CAU EAU
mid- long-

term termSAU NZWSAF ESAF MDF
–0.200.511.5SAH WAF
CAF NEAF SEAF00.20.4
–0.2
Change from recent past (m3 s-1  km-2)00.20.4
–0.2Recent past value (m3 s-1  km-2)–600–400–2000200
300Average shoreline 
position change (m)
Average shoreline 
Change from past 
(m3 s-1  km-2)Recent past value 
(m3 s-1  km-2)

138
TSTS.4.3.2.2 Asia
Due to the high climatological and geographical heterogeneity of 
Asia, some assessment findings below are summarized over five 
sub-continental areas comprising one or more of the AR6 WGI 
reference regions (Box TS.12): East Asia (EAS+ECA), North Asia 
(WSB+ESB+RFE), South Asia (SAS), South East Asia (SEA) and South 
West Asia (ARP+WCA).
Additional regional changes in Asia, besides those features 
described in Section TS.4.3.1, include historical trends 
of annual precipitation that show considerable regional 
differences ( high confidence ). East Asian Monsoon 
precipitation has changed, with drying in the north and 
wetting in the south since the 1950s, and annual mean 
precipitation totals very likely  have increased over 
most territories of North Asia since the mid-1970s ( high 
confidence ). South Asian summer monsoon precipitation 
decreased over several areas since the mid-20th century

(high confidence ) but is likely  to increase during the 21st 
century, with enhanced interannual variability. (Box TS.13)
Increases in precipitation and river floods are projected 
over much of Asia: in the annual mean precipitation in 
East, North, South and South East Asia ( high confidence ); 
for extremes in East, South, West Central, North and South 
East Asia ( high confidence ) and Arabian Peninsula ( medium 
confidence ); and for river floods in East, South and South 
East Asia and East Siberia ( medium confidence ). Aridity 
in East and West Central Asia is projected to increase, 
especially beyond the middle of the 21st century and global 
warming levels beyond 2°C ( medium confidence ). Fire 
weather seasons are projected to lengthen and intensify 
everywhere except South East Asia, Tibetan Plateau and 
Arabian Peninsula ( medium confidence ).
Surface wind speeds have been decreasing in Asia ( high 
confidence ), but there is a large uncertainty in future

trends, with medium confidence  that mean wind speeds 
will decrease in North Asia, East Asia and Tibetan Plateau 
and that tropical cyclones will have decreasing frequency 
and increasing intensity overall in South East and East 
Asia.
Over North Asia, increases in permafrost temperature and 
its thawing have been observed over recent decades ( high 
confidence ). Future projections indicate continuing decline 
in seasonal snow duration, glacial mass, and permafrost area 
by mid-century ( high confidence ). Snow-covered areas and 
snow volumes will decrease in most regions of the Hindu 
Kush Himalaya (HKH) during the 21st century, and snowline 
elevations will rise ( high confidence ) and glacier volumes 
are likely  to decline with greater mass loss in higher CO 2 
emissions scenarios. Heavy snowfall is increasing in East 
Asia and North Asia ( medium confidence ) but with limited

evidence  on future changes in hail and snow avalanches.{2.3, 8.3, 8.4, 9.5, 9.6, 10.6, Box 10.4, 11.4, 11.5, 11.7, 11.9, 
12.4.2, Atlas.3.1, Atlas.5, Atlas.5.2, Atlas.5.3, Atlas.5.4, 
Atlas.5.5}
In addition to the main changes summarized above and in Section 
TS.4.3.1, further details are given below.
Heat and cold:  Over all regions of Asia, observed and projected 
increases in mean temperature and a shift toward heat extreme 
characteristics are broadly similar to the generic pattern described 
in Section TS.4.3.1. Over South East Asia, annual mean surface 
temperature will likely increase by a slightly smaller amount than the 
global average. {Atlas.5.4.4}
Wet and dry:  Over East Asia, historical trends of annual precipitation 
show considerable regional differences but with increases over north-
west China and South Korea ( high confidence ). Daily precipitation 
extremes have increased over part of the region ( high confidence ).

Extreme hydrological drought frequency has increased in a region 
extending from south-west to north-east China, with projected 
increases of agricultural and ecological drought for 4°C GWL and 
fire weather for 2°C and above ( medium confidence ). {8.3.2, 8.4.2, 
11.4.4, 11.4.5, 11.9, 12.4.2.2, Atlas.5.1.2}
Over North Asia, annual mean precipitation totals have very likely 
increased, causing more intense flooding events, and there is 
medium confidence  that the number of dry days has decreased. 
Concurrently, total soil moisture is projected to decline extensively 
(medium confidence ). {8.3.1.3, 8.4.1.6, 11.4.5, 11.5.2, 11.5.5, 
12.4.2.2, Atlas.5.2.2}
Over South Asia, the summer monsoon precipitation decreased 
over several areas since the mid-20th century ( high confidence ), 
while it increased in parts of the western HKH and decreased over 
eastern-central HKH ( medium confidence ). The frequency of heavy 
precipitation and flood events has increased over several areas

during the last few decades ( medium confidence ). {8.3.1.3, 8.3.2.4.1, 
8.4.1.5, 8.4.2.4.1, 10.6.3.3, 10.6.3.5, 10.6.3.6, 10.6.3.8, Cross-
Chapter Box 10.4, 11.4.1, 11.4.2, 11.4.5, 11.5.5, 12.4.2.2, Box 10.4, 
Atlas 5.3.2}
Over South East Asia, mean precipitation trends are not spatially 
coherent or consistent across datasets and seasons ( high confidence ). 
Most of the region has experienced an increase in rainfall intensity 
but with a reduced number of wet days ( medium confidence ). Rainfall 
is projected to increase in the northern parts of South East Asia and 
decrease in areas in the Maritime Continent ( medium confidence ). 
{8.4.1, 11.4.2, 11.5.5, 11.9, 12.4.2.2, Atlas.3.1, Atlas.5.4.2, Atlas.5.4.4}
Over South West Asia, an observed annual precipitation decline 
over the Arabian Peninsula since the 1980s of 6.3 mm per decade 
is contrasted with observed increases between 1.3 mm and 4.8 mm 
per decade during 1960–2013 over the elevated part of eastern

West Central Asia ( very high confidence ), along with an increase of 
the frequency and intensity of extreme precipitation. {Figure  8.19, 
Figure 8.20, 8.3.1.6, 8.4.1.6, 11.9, Table 11.2A, 12.4.2.2, Atlas.5.5}

139
Technical SummaryTSWind:  Over East Asia, the terrestrial near-surface wind speed has 
decreased and is projected to decrease further in the future ( medium 
confidence ). Since the mid 1980’s, there has been an increase in the 
number and intensification rate of intense TCs ( medium confidence ), 
with a significant north-westward shift in tracks and a northward 
shift in their average latitude, increasing exposure over East China, 
the Korean Peninsula and the Japanese Archipelago ( medium 
confidence ). {11.7.1, 12.4.2.3}
Over North Asia, there is medium confidence  for a decreasing trend 
in wind speed during 1979–2018 and for projected continuing 
decreases of terrestrial near-surface wind speed. {2.3.1.4.4, 12.4.2.3}
Over South East Asia, although there is no significant long-term trend 
in the number of TCs, fewer but more extreme TCs have affected the 
Philippines during 1951–2013. {11.7.4, 12.4.2.3}
Snow and ice: Over East Asia, decreases have been observed in the

frequency, and increases in the mean intensity, of snowfall in north-
western, north-eastern and south-eastern China and the eastern 
Tibetan Plateau since the 1960s. Heavy snowfall is projected to 
occur more frequently in some parts of Japan ( medium confidence ). 
{12.4.2.4, Atlas.5.1.2}
Over North Asia, seasonal snow duration and extent have decreased 
in recent decades ( high confidence ), and maximum snow depth likely  
has increased since the mid-1970s, particularly over the south of the 
Russian Far East. {2.3.2.5, 8.3.1.7.2, 9.5, 12.4.2.4, Atlas.5.2, Atlas.5.4}
Over South Asia, snow cover has reduced over most of the HKH since 
the early 21st century, and glaciers have thinned, retreated, and lost 
mass since the 1970s ( high confidence ), although the Karakoram 
glaciers have either slightly gained mass or are in an approximately 
balanced state ( medium confidence ). {8.3.1.7.1, Cross-Chapter 
Box 10.4}
Over South West Asia, mountain permafrost degradation at high

altitudes has increased the instability of mountain slopes in the past 
decade  (medium confidence ). More than 60% of glacier mass in the 
Caucasus is projected to disappear under RCP8.5 emissions by the 
end of the 21st century ( medium confidence ). {9.5.1, 9.5.3, 12.4.2.4}
Coastal and oceanic: Over the last three decades, relative sea 
level has increased at a rate higher than GMSL around Asia ( high 
confidence ). Gross coastal area loss and shoreline retreat has been 
observed over 1984–2015, but with localized shoreline progradation 
in the Russian Far East, East and South East Asia. {12.4.2.5}
Projections show that regional mean sea level continues to rise ( high 
confidence ), ranging from 0.4–0.5 m under SSP1-2.6 to 0.8–1.0 m 
under SSP5-8.5 for 2081–2100 relative to 1995–2014 (median 
values). This will contribute to more frequent coastal flooding and 
higher ETWL in low-lying areas and coastal erosion along sandy

beaches ( high confidence ). There is high confidence  that compound 
effects of climate change, land subsidence, and human factors will 
lead to higher flood levels and prolonged inundation in the Mekong 
Delta and other Asian coasts. {9.6.1, 9.6.3, 12.4.2.5}TS.4.3.2.3 Australasia
Additional regional changes in Australasia, besides those 
features described in Section TS.4.3.1, include a significant 
decrease in April to October rainfall in the south-west of 
the state of Western Australia, observed from 1910 to 2019 
and attributable to human influence ( high confidence ), 
which is very likely  to continue in future. Agricultural 
and ecological droughts and hydrological droughts have 
increased over Southern Australia ( medium confidence ), 
and meteorological droughts have decreased over Northern 
and Central Australia ( medium confidence ). Relative sea 
level has increased over the period 1993–2018 at a rate 
higher than GMSL around Australasia ( high confidence ).

Sandy shorelines have retreated around the region, except 
in Southern Australia, where a shoreline progradation rate 
of 0.1 m yr–1 has been observed. 
In the future, heavy precipitation and pluvial flooding are 
very likely  to increase over Northern Australia and Central 
Australia, and they are likely  to increase elsewhere in 
Australasia for global warming levels (GWLs) exceeding 2°C 
and with medium confidence  for a 2°C GWL. Agricultural and 
ecological droughts are projected to increase in Southern 
and Eastern Australia ( medium confidence ) for a 2°C GWL. 
Fire weather is projected to increase throughout Australia 
(high confidence ) and New Zealand ( medium confidence ). 
Snowfall is expected to decrease throughout the region 
at high altitudes in both Australia ( high confidence ) and 
New Zealand ( medium confidence ), with glaciers receding 
in New Zealand ( high confidence ). {11.4, Table 11.6, 12.3, 
12.4.3, Atlas.6.4, Atlas.6.5}

Heat and cold:  Observed and projected increases in mean 
{11.9, 12.4.3.1, Atlas.6}
Wet and dry:  There is medium confidence  that heavy precipitation has 
increased in Northern Australia since 1950. Annual mean precipitation 
is projected to increase in the south and west of New Zealand ( medium 
confidence ) and is projected to decrease in south-west Southern 
Australia ( high confidence ), Eastern Australia ( medium confidence ), and 
in the north and east of New Zealand ( medium confidence ) for a GWL 
of 2°C. There is medium confidence  that river flooding will increase 
in New Zealand and Australia, with higher increases in Northern  
Australia. Aridity is projected to increase with medium confidence in

Southern Australia ( high confidence  in south-west Southern Australia), 
Eastern Australia ( medium confidence ) and in the north and east of 
New Zealand ( medium confidence ) for GWLs around 2°C. {11.4, 11.9, 
Table 11.6, 12.4.3.2, Atlas.6.2}
Wind: Mean wind speeds are projected to increase in parts of north-
eastern Australia ( medium confidence ) by the end of the 21st century

140
TSunder high CO 2 emissions scenarios. TCs in north-eastern and north 
Australia are projected to decrease in number ( high confidence ) but 
increase in intensity except for ‘east coast lows’ ( low confidence ). 
{12.4.3.3}
Snow and ice: Observations in Australia show that the snow season 
length has decreased by 5% in the last five decades. Furthermore, the 
date of peak snowfall in Australia has advanced by 11 days over the 
last 5 decades. Glacier ice volume in New Zealand has decreased by 
33% from 1977 to 2018. {12.4.3.4, Atlas.6.2}
Coastal and oceanic: Observed changes in marine heatwaves 
(MHWs) over the 20th century in the region show an increase in their 
occurrence frequency, except along the south-east coast of New 
Zealand, an increase in duration per event, and the total number of 
MHW days per decade, with the change being stronger in the Tasman 
Sea than elsewhere. The present day 1-in-100-year ETWL is between

0.5–2.5 m around most of Australia, except the north-western 
coast where 1-in-100-year ETWL can be as high as 6–7 m. {Box 9.1, 
12.3.1.5, 12.4.3.5}
TS.4.3.2.4 Central and South America
Additional regional changes in Central and South America, 
besides those features described in Section TS.4.3.1, include 
increases in mean and extreme precipitation in South-
Eastern South America since the 1960s ( high confidence ) 
(Section TS.4.2.3). Decreasing trends in mean precipitation 
and increasing trends in agricultural and ecological drought 
are observed over North-Eastern South America ( medium 
confidence ). The intensity and frequency of extreme 
precipitation and pluvial floods is projected to increase 
over South-Eastern South America, Southern South America, 
Northern South America, South American Monsoon and 
North-Eastern South America ( medium confidence ) for a 2°C 
GWL and above. Increases of agricultural and ecological 
drought are projected in South America Monsoon and

Southern South America, and fire weather is projected to 
increase over several regions (Northern South America, the 
South American Monsoon, North-Eastern South America 
and South-Western South America) ( high confidence ). {8.3, 
8.4, 11.3, 11.4, 11.9, Table 11.13, Table 11.14, Table 11.15, 
12.4.4.2, Atlas.7.1, Atlas.7.2}
{11.3.2, 11.3.5, Table  11.13, 12.4.4.1, Atlas.7.1.2, Atlas.7.2.2, 
Atlas.7.2.4}
Wet and dry:  Mean precipitation is projected to change in a dipole 
pattern with increases in North-Western and South-Eastern South 
America and decreases in North-Eastern and South-Western South

America ( high confidence ) and with further decreases in Northern South America and Southern Central America ( medium confidence ). 
In Northern South America and Southern Central America, aridity 
and agricultural and ecological droughts are increasing with 
medium confidence . Fire weather is projected to increase over 
Southern Central America and Southern South America with medium 
confidence . {8.3.1.3, 8.4.2.4.5, 11.4.2, 11.9, Table 11.14, Table 11.15, 
12.4.4.2, Atlas.7.2.2, Atlas.7.2.4}
Wind: Climate projections indicate an increase in mean wind speed 
and in wind power potential over the Amazonian region (Northern 
South America, South American Monsoon, North-Eastern South 
America) ( medium confidence ). {12.4.4.3}
Snow and ice:  Glacier volume loss and permafrost thawing will 
likely  continue in the Andes Cordillera under all climate scenarios, 
causing important reductions in river flow and potentially high-
magnitude glacial lake outburst floods. {9.5.1.1, 12.4.4.4}

Coastal and oceanic: Around Central and South America, relative 
sea level has increased at a higher rate than GMSL in the South 
Atlantic and the subtropical North Atlantic, and at a rate lower than 
GMSL in the East Pacific over the last 3 decades. The present day 
1-in-100-year ETWL is highest in Southern and South-Western South 
America subregions, where it can be as large as 5 to 6 m. Satellite 
observations for 1984–2015 show shoreline retreat rates along the 
sandy coasts of Southern Central America, South-Eastern South 
America and Southern South America, while shoreline progradation 
rates have been observed in North-Western South America and 
Northern South America. Over the period 1982–2016, the coastlines 
experienced at least one MHW per year, and more along the Pacific 
coast of North Central America and the Atlantic coast of South-
Eastern South America. {12.4.4.5}
TS.4.3.2.5 Europe
Additional regional changes in Europe, besides those features

described in Section TS.4.3.1, include observed increases 
in pluvial flooding in Northern Europe and hydrological 
and agricultural/ecological droughts in the Mediterranean 
(high confidence ), which have been attributed to human 
influence with high and medium confidence , respectively. 
Increased mean precipitation amounts at high latitudes 
in boreal winter and reduced summer precipitation in 
southern Europe are projected starting from a 2°C GWL 
(high confidence ). Aridity, agricultural and hydrological 
droughts and fire weather conditions will increase in 
the Mediterranean region starting from 2°C GWL ( high 
confidence ). Pluvial flooding will increase everywhere 
with high confidence except for medium confidence in the 
Mediterranean; in Western and Central Europe this also 
applies to river flooding starting from a 2°C GWL ( high 
confidence ). Most periglacial processes in Northern Europe 
are projected to disappear by the end of the 21st century,

even for a low warming scenario ( medium confidence ). {8.3, 
11.3, 11.9, 12.4.5, 12.5.2, Atlas.8.2, Atlas.8.4}

141
Technical SummaryTSIn addition to the main changes summarized above and in Section 
{11.3, 11.9, 12.4.5.1, 12.5.2, Atlas.8.2, Atlas.8.4}
Wet and dry:  There is medium confidence that annual mean 
precipitation has increased in Northern Europe, West and Central 
Europe, and Eastern Europe since the early 20th century and 
high confidence  for increases in extreme precipitation. In the 
European Mediterranean, the magnitude and sign of observed land 
precipitation trends depend on time period and exact study region 
(medium confidence ). There is medium confidence  that river floods 
will decrease in Northern, Eastern and southern Europe for high 
warming levels. {8.3.1.3, 11.3, 11.9, 12.4.5.2, Atlas.8.2, Atlas.8.4}

Wind:  Mean wind speed over land has decreased ( medium 
confidence ), but the role of human-induced climate change has not 
been established. There is high confidence  that mean wind speeds 
will decrease in Mediterranean areas and medium confidence  for 
such decreases in Northern Europe for GWLs exceeding 2°C. The 
frequency of Medicanes (tropical-like cyclones in the Mediterranean) 
is projected to decrease ( medium confidence ). {11.9, 12.4.5.3}
Snow and ice:  In the Alps, snow cover will decrease below elevations 
of 1500–2000 m throughout the 21st century ( high confidence ). 
A reduction of glacier ice volume is projected in the European Alps 
and Scandinavia with high confidence  and with medium confidence  
for the timing and mass change rates. {9.5.2, 12.4.5.4}
Coastal and oceanic:  Over the last three decades, relative sea level 
has increased at a lower rate than GMSL in the sub-polar North 
Atlantic coasts of Europe. The present-day 1-in-100-year ETWL is

between 0.5–1.5 m in the Mediterranean basin and 2.5–5.0 m in 
the western Atlantic European coasts, around the United Kingdom 
and along the North Sea coast, and lower at 1.5–2.5 m along the 
Baltic Sea coast. Satellite-derived shoreline change estimates over 
1984–2015  indicate  shoreline retreat rates of  around 0.5 m yr–1 
along the sandy coasts of Central Europe and the Mediterranean 
and more or less stable shorelines in Northern Europe. Over the 
period 1982–2016, the coastlines of Europe experienced on average 
more than 2.0 MHW per year, with the eastern Mediterranean and 
Scandinavia experiencing 2.5–3 MHWs per year. {12.4.5.5}
TS.4.3.2.6 North America
Additional regional changes in North America, besides those 
features described in Section TS.4.3.1, include changes 
in North American wet and dry CIDs, which are largely 
organized by the north-east (more wet) to south-west (more 
dry) pattern of mean precipitation change, although heavy

precipitation increases are widespread ( high confidence ). 
Increasing evaporative demand will expand agricultural 
and ecological drought and fire weather (particularly in 
summertime) in Central North America, Western North America and Northern Central America (from medium to 
high confidence ). Severe wind storms, tropical cyclones 
and dust storms in North America are shifting toward more 
extreme characteristics ( medium confidence ), and both 
observations and projections point to strong changes in the 
seasonal and geographic range of snow and ice conditions 
in the coming decades ( very high confidence ). General 
findings for relative sea level, coastal flooding and erosion 
will not apply for areas with substantial land uplift around 
the Hudson Bay and Southern Alaska. {8.4, 11.4, 11.5, 11.7, 
11.9, 12.4, Atlas.9.4}

{11.3, 11.9, 12.4.6.1, Atlas.9.2, Atlas.9.4}
Wet and dry:  Annual precipitation increased over parts of Eastern 
and Central North America during 1960–2015 ( high confidence ) and 
has decreased in parts of south-western United States and north-
western Mexico ( medium confidence ). River floods are projected to 
increase for all North American regions other than Northern Central 
America (med ium confidence ). {8.4.2.4, 11.4, 11.5, 11.9, 12.4.6.2, 
Atlas.9.2, Atlas.9.4}
Agricultural and ecological drought increases have been observed in 
Western North America ( medium confidence ), and aridity is projected 
to increase in the south-western United States and Northern Central 
America, with lower summer soil moisture across much of the

continental interior ( medium confidence ). {8.4.1, 11.6.2, 12.4.6.2}
Wind:  Projections indicate a greater number of the most intense 
TCs, with slower translation speeds and higher rainfall potential for 
Mexico’s Pacific Coast, the Gulf Coast and the United States East 
Coast ( medium confidence ). Mean wind speed and wind power 
potential are projected to decrease in Western North America ( high 
confidence ), with differences between global and regional models 
lending low confidence  elsewhere. {11.4, 11.7, 12.4.6.3}
Snow and ice:  It is likely  that some high-latitude regions will 
experience an increase in winter snow water equivalent due to the 
snowfall increase prevailing over the warming trend. At sustained 
GWLs between 3°C and 5°C, nearly all glacial mass in Western 
Canada and Western North America will disappear ( medium 
confidence ). {9.5.1, 9.5.3, 12.4.6.4, Atlas.9.4}
Coastal and oceanic: Around North America, relative sea level has

increased over the last three decades at a rate lower than GMSL 
in the subpolar North Atlantic and in the East Pacific, while it has 
increased at a rate higher than GMSL in the subtropical North Atlantic. 
Observations indicate that episodic coastal flooding is increasing 
along many coastlines in North America. Shoreline retreat rates of 
around 1 m yr–1 have been observed during 1984–2015 along the 
sandy coasts of North-Western North America and Northern Central

142
TSAmerica, while portions of the United States Gulf Coast have seen 
a retreat rate approaching 2.5 m yr–1. Sandy shorelines along Eastern 
North America and Western North America have remained more or 
less stable during 1984–2014, but a shoreline progradation rate of 
around 0.5 m yr–1 has been observed in North-Eastern North America. 
{12.4.6.5}
TS.4.3.2.7 Small Islands
Additional regional changes in Small Islands, besides those 
features described in Section TS.4.3.1, include a likely  
decrease in rainfall during boreal summer in the Caribbean 
and in some parts of the Pacific islands poleward of 20° 
latitude in both the Northern and Southern Hemispheres. 
These drying trends will likely  continue in coming decades. 
Fewer but more intense tropical cyclones are projected 
starting from a 2°C GWL ( medium confidence ). {9.6, 11.3, 
11.4, 11.7, 11.9, 12.4.7, Atlas.10.2, Atlas.10.4, Cross-Chapter 
Box Atlas.2}

Heat and cold:  It is very likely  that most Small Islands have warmed 
over the period of instrumental records, and continued temperature 
increases in the 21st century will further increase heat stress in these 
regions. {11.3.2, 11.9, 12.4.7.1, Atlas.10.2, Atlas.10.4, Cross-Chapter 
Wet and dry: Observed and projected rainfall trends vary spatially 
across the Small Islands. Higher evapotranspiration under a warming 
climate can partially offset future increases or amplify future 
reductions in rainfall, resulting in increased aridity as well as more 
severe agricultural and ecological drought in the Caribbean ( medium 
confidence ). {11.4.2, 11.9, 12.4.7.2, Atlas.10.2, Atlas.10.4, Cross-
Chapter Box Atlas.2}
Wind:  Global changes indicate that Small Islands will face fewer 
but more intense TCs, with spatial inconsistency in projections given

poleward shifts in TC tracks ( medium confidence ). {11.7.1.2, 11.7.1.5, 
12.4.7.3}
Coastal and oceanic: Continued relative sea level rise is very likely 
in the ocean around Small Islands and, along with storm surges 
and waves, will exacerbate coastal inundation with the potential to 
increase saltwater intrusion into aquifers in small islands. Shoreline 
retreat is projected along sandy coasts of most small islands ( high 
confidence ). {9.6.3.3, 12.4.7.4, Cross-Chapter Box Atlas.2}
TS.4.3.2.8 Polar
It is virtually certain that surface warming in the Arctic 
will continue to be more pronounced than the global 
average warming over the 21st century. An intensification 
of the polar water cycle will increase mean precipitation, 
with precipitation intensity becoming stronger and more  
likely to be rainfall rather than snowfall ( high confidence ). Permafrost warming, loss of seasonal snow cover, and 
glacier melt will be widespread ( high confidence ). There is

high confidence  that both the Greenland and Antarctic ice 
sheets have lost mass since 1992 and will continue to lose 
mass throughout this century under all emissions scenarios. 
Relative sea level and coastal flooding are projected to 
increase in areas other than regions with substantial land 
uplift ( medium confidence ). {2.3, 3.4, 4.3, 4.5, 7.4, 8.2, 8.4, 
Box 8.2, 9.5, 12.4.9, Atlas.11.1, Atlas.11.2}
Heat and cold: Changes in Antarctica showed larger spatial 
variability, with very likely  warming in the Antarctic Peninsula since 
the 1950s and no overall trend in East Antarctica. Less warming 
and weaker polar amplification are projected as very likely  over 
the Antarctic than in the Arctic, with a weak polar amplification 
projected as very likely  by the end of the 21st century. {4.3.1, 4.5.1, 
7.4.4, 12.4.9.1, Atlas.11.1, Atlas.11.2}

Wet and dry: Recent decades have seen a general decrease in 
Arctic aridity ( high confidence ), with increased moisture transport 
leading to higher precipitation, humidity and streamflow and a 
corresponding decrease in dry days. Antarctic precipitation showed 
a positive trend during the 20th century. The water cycle is projected 
to intensify in both polar regions, leading to higher precipitation 
totals (and a shift to more heavy precipitation) and higher fraction 
of precipitation falling as rain. In the Arctic, this will result in higher 
river flood potential and earlier meltwater flooding, altering seasonal 
characteristics of flooding ( high confidence ). A lengthening of the fire 
season ( medium confidence ) and encroachment of fire regimes into 
tundra regions ( high confidence ) are projected. {8.2.3, 8.4.1, Box 8.2, 
9.4.1, 9.4.2, 12.4.9.2, Atlas.11.1, Atlas.11.2}
Wind: There is medium confidence in mean wind decrease over

the Russian Arctic and Arctic North-East North America, but low 
confidence of changes in other Arctic regions and Antarctica. 
{12.4.9.3}
Snow and ice: Reductions in spring snow cover extent have 
occurred across the Northern Hemisphere since at least 1978 ( very 
high confidence ). Permafrost warming and thawing have been 
widespread in the Arctic since the 1980s ( high confidence ), causing 
strong heterogeneity in surface conditions. There is  high confidence  
in future glacier- and ice-sheet loss, permafrost warming, decreasing 
permafrost extent and decreasing seasonal duration and extent of 
snow cover in the Arctic. Decline in seasonal sea ice coverage along 
the majority of the Arctic coastline in recent decades is projected to 
continue, contributing to an increase in coastal hazards (including 
open water storm surge, coastal erosion and flooding). {2.3.2, 3.4.2, 
3.4.3, 9.4.1, 9.4.2, 9.5, 12.4.6, 12.4.9, Atlas.11.2}

Coastal and oceanic: Higher sea levels contribute to high 
confidence  for projected increases of Arctic coastal flooding and 
higher coastal erosion (aided by sea ice loss) ( medium confidence ),

143
Technical SummaryTSwith lower confidence for those regions with substantial land uplift 
(Arctic North-East North America and Greenland). {12.4.9.5}
TS.4.3.2.9 Ocean
The Indian Ocean, western equatorial Pacific Ocean and 
western boundary currents have warmed faster than the 
global average ( very high confidence ), with the largest 
changes in the frequency of marine heatwaves (MHWs) 
projected in the western tropical Pacific and the Arctic 
Ocean ( medium confidence ). The Pacific and Southern Ocean 
are projected to freshen and the Atlantic to become more 
saline ( medium confidence ). Anthropogenic warming is very 
likely  to further decrease ocean oxygen concentrations, and 
this deoxygenation is expected to persist for thousands 
of years ( medium confidence ). Arctic sea ice losses are 
projected to continue, leading to a practically ice-free Arctic 
in September by the end of the 21st century under high 
CO 2 emissions scenarios ( high confidence ). {2.3, 5.3, 9.2, 9.3,

Box 9.2, 12.3.6, 12.4.8}
Ocean surface temperature:  The Southern Ocean, the eastern 
equatorial Pacific, and the North Atlantic Ocean have warmed more 
slowly than the global average or slightly cooled. Global warming 
of 2°C above 1850–1900 levels would result in the exceedance of 
numerous hazard thresholds for pathogens, seagrasses, mangroves, 
kelp forests, rocky shores, coral reefs and other marine ecosystems 
(medium confidence ). {9.2.13, 12.4.8}
Marine heatwaves:  Moderate increases in MHW frequency are 
projected for mid-latitudes, and only small increases are projected 
for the Southern Ocean ( medium confidence ). Under the SSP5-8.5 
scenario, permanent MHWs (more than 360 days per year) are 
projected to occur in the 21st century in parts of the tropical ocean, 
the Arctic Ocean, and around 45°S; however, the occurrence of

such permanent MHWs can be largely avoided under the SSP1-2.6 
scenario. {Box 9.2, 12.4.8}
Ocean acidity: With the rising CO 2 concentration, the ocean surface 
pH has declined globally over the past four decades ( virtually certain ). 
{2.3.3.5, 5.3.3.2, 12.4.8}
Ocean salinity: At the basin scale, it is very likely  that the Pacific and 
the Southern Ocean have freshened while the Atlantic has become 
more saline. {2.3.3.2, 9.2.2.2, 12.4.8}
Dissolved oxygen:  In recent decades, low oxygen zones in ocean 
ecosystems have expanded. {2.3.4.2, 5.3.3.2, 12.4.8}
Sea ice:  Arctic perennial sea ice is being replaced by thin, seasonal 
ice, with earlier spring melt and delayed fall freeze up. There is no 
clear trend in the Antarctic sea ice area over the past few decades 
and low confidence  in its future change. {2.3.2.1.1, 9.3.1.1, 12.4.8, 
12.4.9}TS.4.3.2.10 Other Typological Domains
Some types of regions found in different continents face

common climate challenges regardless of their location. 
These include biodiversity hot spots that will very likely  
see even more extreme heat and droughts, mountain areas 
where a projected raising in the freezing level height will 
alter snow and ice conditions ( high confidence ), and tropical 
forests that are increasingly prone to fire weather ( medium 
confidence ). {8.4, Box 8.2, 9.5, 12.3, 12.4}
Biodiversity hotspots located around the world will each face unique 
challenges in CID changes. Heat, drought and length of dry season, 
wildfire weather, sea surface temperature and deoxygenation are 
relevant drivers to terrestrial and freshwater ecosystems and have 
marked increasing trends. {12.3, 12.4.10.1}
Desert and semi-arid areas are strongly affected by CIDs such as 
extreme heat, drought and dust storms, with large-scale aridity 
trends contributing to expanding drylands in some regions ( high 
confidence ). {12.3, 12.4.10.3}

Average warming in mountain areas varies with elevation, but 
the pattern is not globally uniform ( medium confidence ). Extreme 
precipitation is projected to increase in major mountainous regions 
(medium to high confidence  depending on location), with potential 
cascading consequences of floods, landslides and lake outbursts 
in all scenarios ( medium confidence ). {8.4.1.5, Box  8.2, 9.5.1.3, 
9.5.3.3, 9.5.2.3, Cross-Chapter Box 10.4, 11.5.5, 12.3, 12.4.1–12.4.6, 
12.4.10.4}
Most tropical forests are challenged by a mix of emerging warming 
trends that are particularly large in comparison to historical variability 
(medium confidence ). Water cycle changes bring prolonged drought, 
longer dry seasons and increased fire weather to many tropical 
forests ( medium confidence ). {10.5, 12.3, 12.4}

144
TSBox TS.14 | Urban Areas
With global warming, urban areas and cities will be affected by more frequent occurrences of extreme climate events, 
such as heatwaves, with more hot days and warm nights as well as sea level rise and increases in tropical cyclone 
storm surge and rainfall intensity that will increase the probability of coastal city flooding ( high confidence ). {Box 10.3, 
11.3, 11.5, 12.3, 12.4}
Urban areas have special interactions with the climate system, for instance in terms of heat islands and altering the water cycle, and 
thereby will be more affected by extreme climate events such as extreme heat ( high confidence ). With global warming, increasing 
relative sea level compounded by increasing tropical cyclone storm surge and rainfall intensity will increase the probability of coastal 
city flooding ( high confidence ). Arctic coastal settlements are particularly exposed to climate change due to sea ice retreat ( high

confidence ). Improvements in urban climate modelling and climate monitoring networks have contributed to understanding the 
mutual interaction between regional and urban climate ( high confidence ). {Box 10.3, 11.3, 11.5, 12.3, 12.4}
Despite having a negligible effect on global surface temperature ( high confidence ), urbanization has exacerbated the effects of global 
warming through its contribution to the observed warming trend in and near cities, particularly in annual mean minimum temperature 
(very high confidence ) and increases in mean and extreme precipitation over and downwind of the city, especially in the afternoon 
and early evening ( medium confidence ). {2.3, Box 10.3, 11.3, 11.4, 12.3, 12.4}
Combining climate change projections with urban growth scenarios, future urbanization will amplify ( very high confidence ) the 
projected local air temperature increase, particularly by strong influence on minimum temperatures, which is approximately comparable

in magnitude to global warming ( high confidence ). Compared to present day, large implications are expected from the combination 
of future urban development and more frequent occurrence of extreme climate events, such as heatwaves, with more hot days and 
warm nights adding to heat stress in cities ( very high confidence ). {Box 10.2, 11.3, 12.4}
Both sea levels and air temperatures are projected to rise in most coastal settlements ( high confidence ). There is high confidence  in 
an increase in pluvial flood potential in urban areas where extreme precipitation is projected to increase, especially at high global 
warming levels. {11.4, 11.5, 12.4}

AII
2898Annex II Glossary
(Internal) Displacement (of humans)
The involuntary movement, individually or collectively, of persons from their country or community, notably for reasons of armed conflict, civil unrest, or natural or human-made disasters (adapted from IOM, 2011).
2030 Agenda for Sustainable Development
A UN resolution in September 2015 adopting a plan of action for people, planet and prosperity in a new global development framework anchored in 17 Sustainable Development Goals (UN, 2015).
Abrupt change
A change in the system that is substantially faster than the typical rate of the changes in its history. See also Abrupt climate change and Tipping point.
Abrupt climate change
A large-scale abrupt change in the climate system that takes place over a few decades or less, persists (or is anticipated to persist) for at least a few decades and causes substantial impacts in human and/or 
natural systems. See also Tipping point and Abrupt change.
Access to food

See Access under Food security.
Acclimatisation
A change in functional or morphological traits occurring once or repeatedly (e.g., seasonally) during the lifetime of an individual organism in its natural environment. Through acclimatisation, the individual maintains performance across a range of environmental conditions. For a clear differentiation between findings in laboratory and field studies, the term ‘acclimation’ is used in ecophysiology for the respective phenomena when observed in well-defined experimental settings. The term ‘(adaptive) plasticity’ characterises the generally 
limited scope of changes in phenotype that an individual can reach through the process of acclimatisation.
Accumulation (of glaciers, ice sheets or snow cover)
All processes that add to the mass of a glacier, an ice sheet, or snow cover. The main process of accumulation is snowfall. Accumulation

also includes deposition of hoar, freezing rain, other types of solid precipitation, gain of wind-blown snow, avalanching and basal accumulation (often beneath floating ice).
Acute food insecurity
Acute food insecurity is a situation which can occur at any time with a severity that threatens lives, livelihoods or both, regardless of the causes, context or duration, as a result of shocks risking determinants of food security and nutrition, and used to assess the need for humanitarian action (IPC Global Partners, 2019).AdaptationIn human systems, the process of adjustment to actual or expected 
climate and its effects, in order to moderate harm or exploit beneficial opportunities. In natural systems, the process of adjustment to actual climate and its effects; human intervention may facilitate adjustment to expected climate and its effects. See also Adaptation options, Adaptive capacity and Maladaptive actions (Maladaptation).

Adaptation deficitThe gap between the current state of a system and a state that minimises adverse impacts from existing climate conditions and variability.
Adaptation gap
The difference between actually implemented adaptation and a societally set goal, determined largely by preferences related to tolerated climate change impacts and reflecting resource limitations and competing priorities (UNEP , 2014; UNEP , 2018).
Adaptation limits
The point at which an actor’s objectives (or system needs) cannot be secured from intolerable risks through adaptive actions.•
Hard adaptation limit – No adaptive actions are possible toavoid intolerable risks
.
•Soft adaptation limit – Options may exist but are currently notav
ailable to avoid intolerable risks through adaptive action.
Adaptation needsThe circumstances requiring action to ensure the safety of populations and the security of assets in response to climate impacts.
Adaptation options

The array of strategies and measures that are available and appropriate for addressing adaptation. They include a wide range of actions that can be categorised as structural, institutional, ecological or behavioural.
Autonomous adaptation
Adaptation in response to experienced climate and its effects, without planning explicitly or consciously focused on addressing climate change. Also referred to as spontaneous adaptation.
Community-based adaptation
Local, community-driven adaptation. Community-based adaptation 
focuses attention on empowering and promoting the adaptive capacity of communities. It is an approach that takes context, culture, knowledge, agency and preferences of communities as strengths.
Ecosystem-based adaptation (EBA)
The use of ecosystem management activities to increase the resilience and reduce the vulnerability of people and ecosystems to climate change (Campbell et al., 2009). See also Nature-based

solution (NBS).Note: This glossary defines some specific terms as the Lead Authors intend them to be interpreted in the context of this report. Italicised words in definitions indicate that the term is defined in the Glossary. Subterms appear in italics beneath main terms.

AII
2899Glossary  Annex II
Evolutionary adaptation
The process whereby a species or population becomes better able 
to live in a changing environment through the selection of heritable 
traits. Biologists usually distinguish evolutionary adaptation from 
acclimatisation, with the latter occurring within an organism’s 
lifetime.
Incremental adaptation
Adaptation that maintains the essence and integrity of a system or 
process at a given scale (Park et al., 2012). In some cases, incremental 
adaptation can accrue to result in transformational adaptation  
(Tàbara et al., 2018; Termeer et al., 2017). Incremental adaptations  
to change in climate are understood as extensions of actions and 
behaviours that already reduce the losses or enhance the benefits of 
natural variations in extreme weather/climate events.
Transformational adaptation
Adaptation that changes the fundamental attributes of a 
social-ecological system in anticipation of climate change and its 
impacts.
Adaptation Fund

A Fund established under the Kyoto Protocol in 2001 and officially 
launched in 2007. The Fund finances adaptation projects and 
programmes in developing countries that are Parties to the Kyoto 
Protocol. Financing comes mainly from sales of Certified Emissions 
Reductions (CERs) and a share of proceeds amounting to 2% of the 
value of CERs issued each year for Clean Development Mechanism 
(CDM) projects. The Adaptation Fund can also receive funds from 
governments, the private sector and individuals.
See Adaptation.
Adaptation needs
Adaptation pathways
See Pathways.
Adaptive capacity
The ability of systems, institutions, humans and other organisms to 
adjust to potential damage, to take advantage of opportunities or to 
respond to consequences (MA, 2005).
Adaptive governance
See Governance.
Adaptive management
A process of iteratively planning, implementing and modifying

strategies for managing resources in the face of uncertainty and 
change. Adaptive management involves adjusting approaches in 
response to observations of their effect on, and changes in, the system 
brought on by resulting feedback effects and other variables.Adverse side-effect
A negative effect that a policy or measure aimed at one objective has 
on another objective, thereby potentially reducing the net benefit to 
society or the environment. See also Trade-off and Co-benefit.
Aerosol
A suspension of airborne solid or liquid particles, with typical particle 
size in the range of a few nanometres to several tens of micrometres 
and atmospheric lifetimes of up to several days in the troposphere 
and up to years in the stratosphere. The term aerosol, which includes 
both the particles and the suspending gas, is often used in this report 
in its plural form to mean ‘aerosol particles’. Aerosols may be of either 
natural or anthropogenic origin in the troposphere; stratospheric

aerosols mostly stem from volcanic eruptions. Aerosols can cause an 
effective radiative forcing directly through scattering and absorbing 
radiation (aerosol–radiation interaction), and indirectly by acting as 
cloud condensation nuclei or ice nucleating particles that affect the 
properties of clouds (aerosol–cloud interaction), and upon deposition 
on snow- or ice-covered surfaces. Atmospheric aerosols may be either 
emitted as primary particulate matter or formed within the atmosphere 
from gaseous precursors (secondary production). Aerosols may be 
composed of sea salt, organic carbon, black carbon (BC), mineral 
species (mainly desert dust), sulphate, nitrate and ammonium or their 
mixtures. See also Particulate matter (PM) and Short-lived climate 
forcers (SLCFs).
Afforestation
Conversion to forest of land that historically has not contained forests. 
See also Deforestation.
[Note: For a discussion of the term forest and related terms such

as afforestation, reforestation and deforestation, see the 2006 
IPCC Guidelines for National Greenhouse Gas Inventories and their 
2019 Refinement, and information provided by the United Nations 
Framework Convention on Climate Change (IPCC 2006, 2019; UNFCCC 
2021a, 2021b)]
Agreement
In this report, the degree of agreement within the scientific body of 
knowledge on a particular finding is assessed based on multiple lines 
of evidence (e.g., mechanistic understanding, theory, data, models, 
expert judgement) and expressed qualitatively (Mastrandrea et  al., 
2010). See also Confidence, Evidence, Likelihood and Uncertainty.
See Drought.
Agroecology
The science and practice of applying ecological concepts, principles 
and knowledge (i.e., the interactions of, and explanations for, the 
diversity, abundance and activities of organisms) to the study, design 
and management of sustainable agroecosystems. It includes the roles

of human beings as a central organism in agroecology by way of social 
and economic processes in farming systems. Agroecology examines 
the roles and interactions among all relevant biophysical, technical and 
socioeconomic components of farming systems and their surrounding 
landscapes (IPBES, 2019).

AII
2900Annex II Glossary
Agroforestry
Collective name for land-use systems and technologies where woody 
perennials (trees, shrubs, palms, bamboos, etc.) are deliberately used 
on the same land-management units as agricultural crops and/or 
animals, in some form of spatial arrangement or temporal sequence. 
In agroforestry systems, there are both ecological and economical 
interactions between the different components. Agroforestry can 
also be defined as a dynamic, ecologically based, natural resource 
management system that, through the integration of trees on farms 
and in the agricultural landscape, diversifies and sustains production 
for increased social, economic and environmental benefits for land 
users at all levels (FAO, 2015a).
Air pollution
Degradation of air quality with negative effects on human health or 
the natural or built environment due to the introduction, by natural 
processes or human activity, into the atmosphere of substances

(gases, aerosols) which have a direct (primary pollutants) or indirect 
(secondary pollutants) harmful effect.
Albedo
The proportion of sunlight (solar radiation) reflected by a surface or 
object, often expressed as a percentage. Clouds, snow and ice usually 
have high albedo; soil surfaces cover the albedo range from high to 
low; vegetation in the dry season and/or in arid zones can have high 
albedo, whereas photosynthetically active vegetation and the ocean 
have low albedo. The Earth’s planetary albedo changes mainly through 
changes in cloudiness, snow, ice, leaf area and land cover.
Anomaly
The deviation of a variable from its value averaged over a reference 
period.
Anthropocene
A proposed new geological epoch resulting from significant 
human-driven changes to the structure and functioning of the 
Earth system, including the climate system. Originally proposed 
in the Earth System science community in 2000, the proposed new

epoch is undergoing a formalisation process within the geological 
community based on the stratigraphic evidence that human activities 
have changed the Earth System to the extent of forming geological 
deposits with a signature that is distinct from those of the Holocene, 
the mid-20th century to be the most appropriate starting date (Steffen 
et  al., 2016), although others have been proposed and continue to 
be discussed. The Anthropocene concept has already been informally 
adopted by diverse disciplines and the public to denote the substantive 
influence of humans on the Earth system.
Resulting from or produced by human activities.
Anthropogenic emissions
See Emissions.Anthropogenic subsidence
Downward motion of the land surface induced by anthropogenic 
drivers (e.g., loading, extraction of hydrocarbons and/or groundwater,

drainage and mining activities) causing sediment compaction or 
subsidence/deformation of the sedimentary sequence, or oxidation of 
organic material, thereby leading to relative sea level rise.
Arid zone
Areas where vegetation growth is severely constrained due to limited 
water availability. For the most part, the native vegetation of arid 
zones is sparse. There is high rainfall variability, with annual averages 
below 300 mm. Crop farming in arid zones requires irrigation.
Aridity
The state of a long-term climatic feature characterised by low average 
precipitation or available water in a region. Aridity generally arises from 
widespread persistent atmospheric subsidence or anticyclonic conditions, 
and from more localised subsidence in the lee side of mountains (adapted 
from Ogallo and Gbeckor-Kove, 1989). See also Drought.
The gaseous envelope surrounding the Earth, divided into five layers: 
the troposphere, which contains half of the Earth’s atmosphere, the

stratosphere, the mesosphere, the thermosphere and the exosphere, 
which is the outer limit of the atmosphere. The dry atmosphere consists 
almost entirely of nitrogen (78.1% volume mixing ratio) and oxygen 
(20.9% volume mixing ratio), together with a number of trace gases 
such as argon (0.93% volume mixing ratio), helium and radiatively 
active greenhouse gases (GHGs) such as carbon dioxide (CO 2) (0.04% 
volume mixing ratio) methane (CH4), nitrous oxide (N2O) and ozone 
(O3). In addition, the atmosphere contains the GHG water vapour 
(H2O), whose concentrations are highly variable (0–5% volume mixing 
ratio) as the sources (evapotranspiration) and sinks (precipitation) of 
water vapour show large spatio-temporal variations, and atmospheric 
temperature exerts a strong constraint on the amount of water vapour 
an air parcel can hold.. The atmosphere also contains clouds and 
aerosols. See also Hydrological cycle.

Attribution is defined as the process of evaluating the relative 
contributions of multiple causal factors to a change or event with an 
assessment of confidence.
Avalanche
A mass of snow, ice, earth or rocks, or a mixture of these, falling down 
a mountainside.
Baseline scenario
see Reference scenario.
Behavioural change
In this report, behavioural change refers to alteration of human decisions 
and actions in ways that mitigate climate change and/or reduce negative 
consequences of climate change impacts.

AII
2901Glossary  Annex II
Benthic
Occurring at the bottom of a body of water; related to benthos (NOAA, 
2018). See also Benthos.
Benthos
The community of organisms living on the bottom or in sediments of a 
body of water (such as an ocean, a river or a lake). The ecological zone 
at the bottom of a body of water, including the sediment surface and 
some subsurface layers, is known as the benthic zone.
Beta diversity
The change in species composition between different areas (spatial 
turnover) or times (temporal turnover) due to habitat and environmental 
heterogeneity
Biodiversity
Biodiversity or biological diversity means the variability among living 
organisms from all sources including, among other things, terrestrial, 
marine and other aquatic ecosystems, and the ecological complexes 
of which they are part; this includes diversity within species, between 
species and of ecosystems (UN, 1992). See also Ecosystem and 
Ecosystem services.
Biodiversity hotspots

Biodiversity hotspots are geographic areas exceptionally rich in species, 
ecologically distinct, and often contain geographically rare endemic 
species. They are thus priorities for nature conservation action.
Bioenergy
Energy derived from any form of biomass or its metabolic by-products. 
See also Biofuel and Biomass.
Biofuel
A fuel, generally in liquid form, produced from biomass. Biofuels 
include bioethanol from sugarcane, sugar beet or maize and biodiesel 
from canola or soybeans. See also Bioenergy.
Biomass
Organic material excluding the material that is fossilised or embedded 
in geological formations. Biomass may refer to the mass of organic 
matter in a specific area (ISO, 2014). See also Bioenergy and Biofuel.
Biomes
Global-scale zones, generally defined by the type of plant life that they 
support in response to average rainfall and temperature patterns. For 
example, tundra, coral reefs or savannas (IPBES, 2019).
Biosphere (terrestrial and marine)

The part of the Earth system comprising all ecosystems and living 
organisms, in the atmosphere, on land (terrestrial biosphere) or in the 
oceans (marine biosphere), including derived dead organic matter, 
such as litter, soil organic matter and oceanic detritus.
Blue carbon
Biologically driven carbon fluxes and storage in marine systems that 
are amenable to management. Coastal blue carbon focuses on rooted 
vegetation in the coastal zone, such as tidal marshes, mangroves and seagrasses. These ecosystems have high carbon burial rates on a per 
unit area basis and accumulate carbon in their soils and sediments. They 
provide many non-climatic benefits and can contribute to ecosystem-
based adaptation. If degraded or lost, coastal blue carbon ecosystems  
are likely to release most of their carbon back to the atmosphere. There 
is current debate regarding the application of the blue carbon concept 
to other coastal and non-coastal processes and ecosystems, including

the open ocean. See also Ecosystem services and Sequestration.
Blue infrastructure
See Infrastructure.
Burden
The total mass of a gaseous substance of concern in the atmosphere.
Business-As-Usual (BAU)
The term Business-As-Usual scenario has been used to describe a 
scenario that assumes no additional policies beyond those currently in 
place and that patterns of socio-economic development are consistent 
with recent trends. The term is now used less frequently than in the 
past. See also Reference scenario.
Calcification
The process of biologically precipitating calcium carbonate minerals 
to create organism shells, skeletons, otoliths or other body structures. 
The chemical equation describing calcification is Ca2+(aq) + 2HCO3−
(aq) → CaCO 3(s) + CO 2 + H 2O. Aragonite and calcite are two common 
crystalline forms of biologically precipitated calcium carbonate 
minerals that have different solubilities.
Capacity building

The practice of enhancing the strengths and attributes of, and resources 
available to, an individual, community, society or organisation to 
respond to change.
Carbon dioxide (CO 2)
A naturally occurring gas, CO 2 is also a by-product of burning fossil fuels 
(such as oil, gas and coal), of burning biomass, of land-use changes 
(LUC) and of industrial processes (e.g., cement production). It is the 
principal anthropogenic greenhouse gas (GHG) that affects the Earth’s 
radiative balance. It is the reference gas against which other GHGs are 
measured and therefore has a global warming potential (GWP) of 1.
Carbon dioxide (CO 2) fertilisation
The increase of plant photosynthesis and water-use efficiency in 
response to increased atmospheric carbon dioxide (CO 2) concentration. 
Whether this increased photosynthesis translates into increased plant 
growth and carbon storage on land depends on the interacting effects 
of temperature, moisture and nutrient availability.

Carbon dioxide removal (CDR)
Anthropogenic activities removing carbon dioxide (CO 2) from the 
atmosphere and durably storing it in geological, terrestrial or ocean 
reservoirs, or in products. It includes existing and potential anthropogenic  
enhancement of biological or geochemical CO 2 sinks and direct air 
carbon dioxide capture and storage (DACCS) but excludes natural CO 2 
uptake not directly caused by human activities. See also Afforestation.

AII
2902Annex II Glossary
Carbon footprint
Measure of the exclusive total amount of emissions of carbon dioxide 
(CO 2) that is directly and indirectly caused by an activity or accumulated 
over the life stages of a product (Wiedmann and Minx, 2008).
Carbon stock
The quantity of carbon in a carbon pool.
Cascading impacts
Cascading impacts from extreme weather/climate events occur when 
an extreme hazard generates a sequence of secondary events in natural 
and human systems that result in physical, natural, social or economic 
disruption, whereby the resulting impact is significantly larger than the 
initial impact. Cascading impacts are complex and multi-dimensional, 
and are associated more with the magnitude of vulnerability than with 
that of the hazard (modified from Pescaroli & Alexander, 2015).
Catchment
An area that collects and drains precipitation.
Cities
Cities are open systems, continually exchanging resources, products

and services, waste, people, ideas and finances with the hinterlands 
and broader world. Cities are complex, self-organising, adaptive 
and constantly evolving. Cities also encompass multiple actors 
with varying responsibilities, capabilities and priorities, as well as 
processes that transcend the institutional sector-based approach 
to city administration. Cities are embedded in broader ecological, 
economic, technical, institutional, legal and governance structures 
that enable or constrain their systemic function, which cannot be 
separated from wider power relations. Urban processes of a physical, 
social and economic nature are causally interlinked, with interactions 
and feedbacks that result in both intended and unintended impacts on 
emissions. See also City region, Peri-urban areas and Urban.
City region
The areal extent of an individual city’s material associations and 
economic or political influence. The city region concept accepts

that rural livelihoods and land uses can be incorporated within the 
functional activities of a city. This will include dormitory settlements, 
sources for critical inputs of water, some food, and waste disposal. See 
also Region, Cities, Urban and Urban systems.
Climate
In a narrow sense, climate is usually defined as the average weather 
-or more rigorously, as the statistical description in terms of the mean 
and variability of relevant quantities- over a period of time ranging 
from months to thousands or millions of years. The classical period 
for averaging these variables is 30  years, as defined by the World 
Meteorological Organization (WMO). The relevant quantities are most 
often surface variables such as temperature, precipitation and wind. 
Climate in a wider sense is the state, including a statistical description, 
of the climate system.
Climate change
A change in the state of the climate that can be identified (e.g., by using

statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades 
or longer. Climate change may be due to natural internal processes 
or external forcings such as modulations of the solar cycles, volcanic 
eruptions and persistent anthropogenic changes in the composition 
of the atmosphere or in land use. Note that the United Nations 
Framework Convention on Climate Change (UNFCCC), in its Article 1, 
defines climate change as: ‘a change of climate which is attributed 
directly or indirectly to human activity that alters the composition of 
the global atmosphere and which is in addition to natural climate 
variability observed over comparable time periods’. The UNFCCC thus 
makes a distinction between climate change attributable to human 
activities altering the atmospheric composition and climate variability 
attributable to natural causes. See also Climate variability, Detection,

Attribution and Ocean acidification (OA).
Climate extreme (extreme weather or climate event)
The occurrence of a value of a weather or climate variable above (or 
below) a threshold value near the upper (or lower) ends of the range 
of observed values of the variable.
By definition, the characteristics of what is called extreme weather 
may vary from place to place in an absolute sense. When a pattern of 
extreme weather persists for some time, such as a season, it may be 
classified as an extreme climate event, especially if it yields an average 
or total that is itself extreme (e.g., high temperature, drought or heavy 
rainfall over a season). For simplicity, both extreme weather events and 
extreme climate events are referred to collectively as climate extremes.
Climate feedback
An interaction in which a perturbation in one climate quantity causes 
a change in a second and the change in the second quantity ultimately

leads to an additional change in the first. A negative feedback is one in 
which the initial perturbation is weakened by the changes it causes; a 
positive feedback is one in which the initial perturbation is enhanced. 
The initial perturbation can either be externally forced or arise as part 
of internal variability.
Climate finance
There is no agreed definition of climate finance. The term climate 
finance is applied to the financial resources devoted to addressing 
climate change by all public and private actors from global to local 
scales, including international financial flows to developing countries 
to assist them in addressing climate change. Climate finance aims to 
reduce net greenhouse gas emissions and/or to enhance adaptation 
and increase resilience to the impacts of current and projected climate 
change. Finance can come from private and public sources, channelled 
by various intermediaries, and is delivered by a range of instruments,

including grants, concessional and non-concessional debt, and internal 
budget reallocations.
Climate governance
Climate information
Information about the past, current or future state of the climate system 
that is relevant for mitigation, adaptation and risk management. It may 
be tailored or “co-produced” for specific contexts, taking into account 
users’ needs and values.

AII
2903Glossary Anne x II
Climate justice
See Justice.
Climate literacy
Climate literacy encompasses being aware of climate change, its anthropogenic causes and implications.
Climate model
A qualitative or quantitative representation of the climate system based on the physical, chemical and biological properties of its components, their interactions and feedback processes and accounting for some of its known properties. The climate system can be represented by models 
of varying complexity; that is, for any one component or combination of components, a spectrum or hierarchy of models can be identified, differing in such aspects as the number of spatial dimensions, the extent to which physical, chemical or biological processes are explicitly represented, or the level at which empirical parametrisations are involved. There is an evolution towards more complex models with

interactive chemistry and biology. Climate models are applied as a research tool to study and simulate the climate and for operational 
purposes, including monthly, seasonal and interannual climate predictions. See also Earth system model (ESM).
Climate prediction
A climate prediction or climate forecast is the result of an attempt 
to produce (starting from a particular state of the climate system) an estimate of the actual evolution of the climate in the future, for example, at seasonal, interannual or decadal time scales. Because the 
future evolution of the climate system may be highly sensitive to initial conditions, such predictions are usually probabilistic in nature.
Climate projection

Simulated response of the climate system to a scenario of future emissions or concentrations of greenhouse gases (GHGs) and aerosols and changes in land use, generally derived using climate models. Climate projections depend on an emission/concentration/radiative forcing scenario, which is in turn based on assumptions concerning, for example, future socioeconomic and technological developments that may or may not be realised.
Climate refugium
A climate refugium is a geographic area that has had a stable climate 
on evolutionary time scales, or that is projected to have a stable climate into the future. See also Refugium.
Climate services
Climate services involve the provision of climate information in such 
a way as to assist decision-making. The service includes appropriate engagement from users and providers, is based on scientifically credible information and expertise, has an effective access mechanism and responds to user needs (Hewitt et al. 2012).
Climate simulation ensemble

A group of parallel model simulations characterising historical climate conditions, climate predictions or climate projections. Variation of 
the results across the ensemble members may give an estimate of modelling-based uncertainty. Ensembles made with the same model but different initial conditions characterise the uncertainty associated 
with internal climate variability, whereas multi-model ensembles including simulations by several models also include the effect of model differences. Perturbed parameter ensembles, in which model parameters are varied in a systematic manner, aim to assess the uncertainty resulting from internal model specifications within a single model. Remaining sources of uncertainty unaddressed with model ensembles are related to systematic model errors or biases, which may be assessed from systematic comparisons of model simulations with observations wherever available.
Climate system

The global system consisting of five major components: the atmosphere, the hydrosphere, the cryosphere, the lithosphere and the biosphere and the interactions between them. The climate system changes in time under the influence of its own internal dynamics and because of external forcings such as volcanic eruptions, solar variations, orbital forcing, and anthropogenic forcings such as the changing composition of the atmosphere and land-use change.
Climate variability
Deviations of some climate variables from a given mean state (including the occurrence of extremes, etc.) at all spatial and temporal scales beyond that of individual weather events. Variability may be intrinsic, due to fluctuations of processes internal to the climate system (internal variability), or extrinsic, due to variations in natural or anthropogenic 
external forcing (forced variability).
Climate velocity

The speed at which isolines of a specified climate variable travel across landscapes or seascapes due to changing climate. For example, climate velocity for temperature is the speed at which isotherms move due to changing climate (km yr
−1) and is calculated as the temporal change 
in temperature (°C yr−1) divided by the current spatial gradient in 
temperature (°C km−1). It can be calculated using additional climate 
variables such as precipitation or can be based on the climatic niche of organisms.
Climate resilient development
In the WGII report, climate resilient development refers to the process of implementing greenhouse gas mitigation and adaptation measures 
to support sustainable development for all.
Climate resilient development pathways (CRDPs)
Climate-resilient pathways
Climate-smart agriculture (CSA)

An approach to agriculture that aims to transform and reorient agricultural systems to effectively support development and ensure food security in a changing climate by sustainably increasing agricultural productivity and incomes, adapting and building resilience 
to climate change, and reducing and/or removing greenhouse gas 
emissions, where possible (FAO, 2018).

AII
2904Annex II Glossary
Climatic driver (Climate driver)
A changing aspect of the climate system that influences a component 
of a human or natural system.
Climatic impact-drivers (CIDs)
Climatic impact-drivers (CIDs) are physical climate system conditions 
(e.g., means, events, extremes) that affect an element of society or 
ecosystems. Depending on system tolerance, CIDs and their changes 
can be detrimental, beneficial, neutral, or a mixture of each across 
interacting system elements and regions.
CMIP3, CMIP5 and CMIP6
See Coupled Model Intercomparison Project (CMIP).
Co-benefit
A positive effect that a policy or measure aimed at one objective has 
on another objective, thereby increasing the total benefit to society or 
the environment. Co-benefits are also referred to as ancillary benefits. 
See also Trade-off and Adverse side-effect.
Coast
The land near to the sea. The term ‘coastal’ can refer to that land (e.g.,

as in ‘coastal communities’), or to that part of the marine environment 
that is strongly influenced by land-based processes. Thus, coastal seas 
are generally shallow and near-shore. The landward and seaward limits 
of the coastal zone are not consistently defined, neither scientifically 
nor legally. Thus, coastal waters can either be considered as equivalent 
to territorial waters (extending 12 nautical miles/22.2 km from mean 
low water), or to the full exclusive economic zone, or to shelf seas, with 
less than 200 m water depth.
Coastal erosion, sometimes referred to as shoreline retreat, occurs 
when a net loss of sediment or bedrock from the shoreline results in 
landward movement of the high-tide mark.
Communicable disease
Illness due to a specific infectious agent or its toxic products that arises 
through transmission of that agent or its products from an infected 
person, animal or reservoir to a susceptible host, either directly or

indirectly through an intermediate plant or animal host, vector or the 
inanimate environment. Communicable disease pathogens include 
bacteria, viruses, fungi, parasites and prions.
Compound risks
See Risk.
Compound weather/climate events
The terms ‘compound events’, ‘compound extremes’ and ‘compound 
extreme events’ are used interchangeably in the literature and this 
report and refer to the combination of multiple drivers and/or hazards 
that contributes to societal and/or environmental risk (Zscheischler 
et al., 2018).Concentrations scenario
See Scenarios.
The robustness of a finding based on the type, amount, quality and 
consistency of evidence (e.g., mechanistic understanding, theory, data, 
models, expert judgment) and on the degree of agreement across 
multiple lines of evidence. In this report, confidence is expressed 
qualitatively (Mastrandrea et al., 2010).
Conservation agriculture

A farming system that promotes minimum soil disturbance (e.g., by 
using no-till practices), maintenance of a permanent soil cover and 
diversification of plant species. It aims to prevent land degradation 
and regenerate degraded lands by enhancing biodiversity and natural 
biological processes above and below the ground surface that 
contribute to increased water and nutrient use efficiency and improved 
and sustained crop production (FAO, 2016).
Coping
The use of available skills, resources and opportunities to address, 
manage and overcome adverse conditions, with the aim of achieving 
basic functioning of people, institutions, organisations and systems in 
the short to medium term (UNISDR, 2009; IPCC, 2012a).
Coping capacity
The ability of people, institutions, organisations and systems, using 
available skills, values, beliefs, resources and opportunities, to address, 
manage and overcome adverse conditions in the short to medium term

(UNISDR, 2009; IPCC, 2012a). See also Resilience.
Coral bleaching
Loss of coral pigmentation through the loss of intracellular symbiotic 
algae (known as zooxanthellae) and/or loss of their pigments.
Coral reef
An underwater ecosystem characterised by structure-building stony 
corals. Warm-water coral reefs occur in shallow seas, mostly in the 
tropics, with the corals (animals) containing algae (plants) that depend 
on light and relatively stable temperature conditions. Cold-water coral 
reefs occur throughout the world, mostly at water depths of 50–500 m. 
In both kinds of reef, living corals frequently grow on older, dead 
material, predominantly made of calcium carbonate (CaCO 3). Both 
warm- and cold-water coral reefs support high biodiversity of fish and 
other groups, and are considered to be especially vulnerable to climate 
change.
Cost–benefit analysis
Monetary assessment of all negative and positive impacts associated

with a given action. Cost–benefit analysis enables comparison of 
different interventions, investments or strategies and reveals how 
a given investment or policy effort pays off for a particular person, 
company or country. Cost–benefit analyses representing society’s 
point of view are important for climate change decision-making, but 
there are difficulties in aggregating costs and benefits across different 
actors and across timescales. See also Discounting.

AII
2905Glossary  Annex II
Coupled Model Intercomparison Project (CMIP)
A climate modelling activity from the World Climate Research 
Programme (WCRP) which coordinates and archives climate model 
simulations based on shared model inputs by modelling groups from 
around the world. The CMIP3 multi-model data set includes projections 
using Special Report on Emissions Scenarios (SRES) scenarios. 
The CMIP5 data set includes projections using the Representative 
Concentration Pathways (RCP). The CMIP6 phase involves a suite of 
common model experiments as well as an ensemble of CMIP-endorsed 
Model Intercomparison Projects (MIPs).
The components of the Earth system at and below the land and ocean 
surface that are frozen, including snow cover, glaciers, ice sheets, ice 
shelves, icebergs, sea ice, lake ice, river ice, permafrost and seasonally 
frozen ground.
Cultural impacts
Impacts on material and ecological aspects of culture and the lived

experience of culture, including dimensions such as identity, community 
cohesion and belonging, sense of place, worldview, values, perceptions 
and tradition. Cultural impacts are closely related to ecological impacts, 
especially for iconic and representational dimensions of species and 
landscapes. Culture and cultural practices frame the importance and 
value of the impacts of change, shape the feasibility and acceptability 
of adaptation options, and provide the skills and practices that enable 
adaptation.
Decarbonisation
Human actions to reduce carbon dioxide emissions from human 
activities.
Deep uncertainty
See Uncertainty.
Deforestation
Conversion of forest to non-forest. See also Afforestation and Refor -
estation.
[Note: For a discussion of the term forest and related terms such as 
afforestation, reforestation and deforestation, see the 2006 IPCC 
Guidelines for National Greenhouse Gas Inventories and their 2019

Refinement, and information provided by the United Nations Frame-
work Convention on Climate Change (IPCC 2006, 2019; UNFCCC 
Desertification
Land degradation in arid, semi-arid, and dry sub-humid areas resulting 
from many factors, including climatic variations and human activities 
(UNCCD, 1994).
Detection
Detection of change is defined as the process of demonstrating that 
climate or a system affected by climate has changed in some defined 
statistical sense, without providing a reason for that change. An identified 
change is detected in observations if its likelihood of occurrence by 
chance due to internal variability alone is determined to be small, for 
example, <10%.Detection and attribution
See Attribution and Detection.
Developed/developing countries (Industrialised/developed/
developing countries)
There is a diversity of approaches for categorising countries on the 
basis of their level of development, and for defining terms such as

‘industrialised’, ‘developed’ or ‘developing’. Several categorisations 
are used in this Special Report. (1) In the United Nations (UN) system, 
there is no established convention for the designation of developed 
and developing countries or areas. (2) The UN Statistics Division 
specifies developed and developing regions based on common practice. 
In addition, specific countries are designated as Least Developed 
Countries, landlocked developing countries, Small Island Developing 
States (SIDS) and transition economies. Many countries appear in more 
than one of these categories. (3) The World Bank uses income as the 
main criterion for classifying countries as low, lower middle, upper 
middle and high income. (4) The UN Development Programme (UNDP) 
aggregates indicators for life expectancy, educational attainment 
and income into a single composite Human Development Index 
(HDI) to classify countries as low, medium, high or very high human 
development.
Development pathways

Diatoms
Microscopic (2–200 µm) unicellular photosynthetic algae that live in 
surface waters of lakes, rivers and oceans and form shells of opal. In 
the global ocean, marine diatom species distribution is primarily driven 
by nutrient availability. On regional scales, their species distribution in 
ocean sediment cores can be related to past sea surface temperatures.
Diet
The kinds of food that follow a particular pattern that a person or 
community eats (FAO, 2014).
Disaster
A ‘serious disruption of the functioning of a community or a society at any 
scale due to hazardous events interacting with conditions of exposure, 
vulnerability and capacity, leading to one or more of the following: 
human, material, economic and environmental losses and impacts’ 
(UNGA, 2016). See also Exposure, Hazard, Risk and Vulnerability.
Disaster management
Social processes for designing, implementing and evaluating strategies,

policies and measures that promote and improve disaster preparedness, 
response and recovery practices at different organisational and societal 
levels.
Disaster risk
The likelihood over a specified time period of severe alterations in 
the normal functioning of a community or a society due to hazardous 
physical events interacting with vulnerable social conditions, leading 
to widespread adverse human, material, economic or environmental 
effects that require immediate emergency response to satisfy critical 
human needs and that may require external support for recovery.

AII
2906Annex II Glossary
Disaster risk management (DRM)
Processes for designing, implementing and evaluating strategies, 
policies and measures to improve the understanding of current and 
future disaster risk, foster disaster risk reduction and transfer, and 
promote continuous improvement in disaster preparedness, prevention 
and protection, response and recovery practices, with the explicit 
purpose of increasing human security, well-being, quality of life and 
sustainable development (SD).
Disaster risk reduction (DRR)
Denotes both a policy goal or objective, and the strategic and 
instrumental measures employed for anticipating future disaster risk; 
reducing existing exposure, hazard or vulnerability; and improving 
resilience.
Discount rate
See Discounting.
Discounting
A mathematical operation that aims to make monetary (or other) 
amounts received or expended at different times (years) comparable 
across time. If the discount rate is positive, future values are given less

weight than those today. The choice of discount rate(s) is debated as it 
is a judgement based on hidden and/or explicit values.
Downscaling
A method that derives local- to regional-scale information from 
larger-scale models or data analyses. Two main methods exist: dynamical 
downscaling and empirical/statistical downscaling. The dynamical 
method uses the output of regional climate models, global models  
with variable spatial resolution or high-resolution global models. The 
empirical/statistical methods are based on observations and develop 
statistical relationships that link the large-scale atmospheric variables 
with local/regional climate variables. In all cases, the quality of the driving 
model remains an important limitation on the quality of the downscaled 
information. The two methods can be combined, for example, applying 
empirical/statistical downscaling to the output of a regional climate 
model, consisting of a dynamical downscaling of a global climate model.

Drainage
Artificial lowering of the soil water table (IPCC, 2013).
Driver
Any natural or human-induced factor that directly or indirectly causes a 
change in a system (adapted from MA, 2005). See also Climatic driver.
Drought
An exceptional period of water shortage for existing ecosystems and the 
human population (due to low rainfall, high temperature and/or wind).
Megadrought
A very lengthy and pervasive drought, lasting much longer than 
normal, usually a decade or more.
A period with large runoff and water deficits in rivers, lakes and 
reservoirs.Agricultural and ecological drought
Agricultural and ecological drought (depending on the affected 
biome): a period with abnormal soil moisture deficit, which 
results from combined shortage of precipitation and excess 
evapotranspiration, and during the growing season impinges on 
crop production or ecosystem function in general.
Meteorological drought

Early warning systems (EWS)
The set of technical and institutional capacities to forecast, predict and 
communicate timely and meaningful warning information to enable 
individuals, communities, managed ecosystems and organisations 
threatened by a hazard to prepare to act promptly and appropriately 
to reduce the possibility of harm or loss. Dependent upon context, EWS 
may draw upon scientific and/or indigenous knowledge, and other 
knowledge types. EWS are also considered for ecological applications, 
for example, conservation, where the organisation itself is not 
threatened by hazard but the ecosystem under conservation is (e.g., 
coral bleaching alerts), in agriculture (e.g., warnings of heavy rainfall, 
drought, ground frost and hailstorms) and in fisheries (e.g., warnings 
of storms, storm surges and tsunamis) (UNISDR 2009; IPCC, 2012a).
Earth system model (ESM)
A coupled Atmosphere–Ocean General Circulation Model (AOGCM)

in which a representation of the carbon cycle is included, allowing 
for interactive calculation of atmospheric carbon dioxide (CO 2) or 
compatible emissions. Additional components (e.g., atmospheric 
chemistry, ice sheets, dynamic vegetation, nitrogen cycle, but also 
urban or crop models) may be included.
Eastern boundary upwelling system (EBUS)
Eastern boundary upwelling systems (EBUS) are located at the 
eastern (landward) edges of major ocean basins in both hemispheres, 
where equatorward winds drive upwelling currents that bring cool, 
nutrient-rich (and often oxygen-poor) waters from the deep ocean to 
the surface near the coast.
Ecosystem
A functional unit consisting of living organisms, their non-living 
environment and the interactions within and between them. The 
components included in a given ecosystem and its spatial boundaries 
depend on the purpose for which the ecosystem is defined: in some 
cases, they are relatively sharp, while in others they are diffuse.

Ecosystem boundaries can change over time. Ecosystems are nested 
within other ecosystems, and their scale can range from very small 
to the entire biosphere. In the current era, most ecosystems either 
contain people as key organisms or are influenced by the effects of 
human activities in their environment. See also Ecosystem services and 
Ecosystem health.
Ecosystem health
Ecosystem health is a metaphor used to describe the condition of 
an ecosystem, by analogy with human health. Note that there is no 
universally accepted benchmark for a healthy ecosystem. Rather, the 
apparent health status of an ecosystem is judged on the ecosystem’s

AII
2907Glossary  Annex II
resilience to change, with details depending upon which metrics are 
employed in judging it and which societal aspirations are driving the 
assessment (following IPBES 2019).
Ecosystem services
Ecological processes or functions having monetary or non-monetary 
value to individuals or society at large. These are frequently classified 
as (1) supporting services such as productivity or biodiversity 
maintenance, (2) provisioning services such as food or fibre, (3) 
regulating services such as climate regulation or carbon sequestration 
and (4) cultural services such as tourism or spiritual and aesthetic 
appreciation. See also Ecosystem and Ecosystem health.
El Niño-Southern Oscillation (ENSO)
The term ‘El Niño’ was initially used to describe a warm-water current 
that periodically flows along the coast of Ecuador and Peru, disrupting 
the local fishery. It has since become identified with warming of

the tropical Pacific Ocean east of the dateline. This oceanic event is 
associated with a fluctuation of a global-scale tropical and subtropical 
surface pressure pattern called the Southern Oscillation. This coupled 
atmosphere–ocean phenomenon, with preferred time scales of 2 to 
about 7 years, is known as the El Niño-Southern Oscillation (ENSO). 
The warm and cold phases of the ENSO are called El Niño and La 
Niña, respectively. ENSO is often measured by the surface pressure 
anomaly difference between Tahiti and Darwin and/or the sea surface 
temperatures in the central and eastern equatorial Pacific. This 
phenomenon has a great impact on the wind, sea surface temperature 
and precipitation patterns in the tropical Pacific. It has climatic effects 
throughout the Pacific region and in many other parts of the world 
through global teleconnections. See WGI AR6 Annex AIV.2.3 (IPCC 
2021a).
Emergence (of the climate signal)

Emergence of a climate change signal or trend refers to when a 
change in climate (the ‘signal’) becomes larger than the amplitude 
of natural or internal variations (defining the ‘noise’), This concept is 
often expressed as a signal-to-noise ratio, and emergence occurs at 
a defined threshold of this ratio (e.g., S/N > 1 or 2). Emergence can 
refer to changes relative to a historical or modern baseline (usually at 
least 20 years long) and can also be expressed in terms of time (time 
of emergence) or in terms of a global warming level. Emergence is 
also used to refer to a time when we can expect to see a response of 
reducing greenhouse gas (GHG) emissions (emergence with respect 
to mitigation). Emergence can be estimated using observations and/
or model simulations.
Emission pathways
Emission scenario
See Scenario.Emissions
Emissions of greenhouse gases (GHGs), precursors of GHGs and

aerosols caused by human activities. These activities include 
the burning of fossil fuels, deforestation, land use and land-use 
changes (LULUC), livestock production, fertilisation, waste 
management and industrial processes.
Fossil-fuel emissions
Emissions of greenhouse gases (in particular, carbon dioxide), 
other trace gases and aerosols resulting from the combustion of 
fuels from fossil carbon deposits such as oil, gas and coal.
Non-CO 2 emissions and radiative forcing
Non-CO 2 emissions included in this report are all anthropogenic 
emissions other than CO 2 that result in radiative forcing. These 
include short-lived climate forcers, such as methane (CH 4), some 
fluorinated gases, ozone (O 3) precursors, aerosols or aerosol 
precursors, such as black carbon and sulphur dioxide, respectively, 
as well as long-lived greenhouse gases, such as nitrous oxide (N 2O) 
or other fluorinated gases. The radiative forcing associated with

non-CO 2 emissions and changes in surface albedo is referred to as 
non-CO 2 radiative forcing.
Enabling conditions (for adaptation and mitigation options)
Conditions that enhance the feasibility of adaptation and mitigation 
options. Enabling conditions include finance, technological innovation, 
strengthening policy instruments, institutional capacity, multi-level 
governance and changes in human behaviour and lifestyles.
Endemic species
Plants and animals that are only found in one geographic region.
Energy access
Access to clean, reliable and affordable energy services for cooking 
and heating, lighting, communications and productive uses (with 
special reference to Sustainable Development Goal 7) (AGECC, 2010).
Energy efficiency
The ratio of output or useful energy or energy services or other 
useful physical outputs obtained from a system, conversion process, 
transmission or storage activity to the input of energy (measured as

kWh kWh−1, tonnes kWh−1 or any other physical measure of useful 
output like tonne-km transported). Energy efficiency is often described 
by energy intensity.
Energy security
The goal of a given country, or the global community as a whole, 
to maintain an adequate, stable and predictable energy supply. 
Measures encompass safeguarding the sufficiency of energy resources 
to meet national energy demand at competitive and stable prices 
and the resilience of the energy supply; enabling the development 
and deployment of technologies; building sufficient infrastructure to 
generate, store and transmit energy supplies and ensuring enforceable 
contracts of delivery.

AII
2908Annex II Glossary
Energy system
The energy system comprises all components related to the production, 
conversion, delivery and use of energy.
Equality
A principle that ascribes equal worth to all human beings, including 
equal opportunities, rights and obligations, irrespective of origins. See 
also Equity and Fairness.
Inequality
Uneven opportunities and social positions, and processes of dis-
crimination within a group or society, based on gender, class, eth-
nicity, age and (dis)ability, often produced by uneven development. 
Income inequality refers to gaps between the highest and lowest 
income earners within a country and between countries.
Equity
The principle of being fair and impartial, and a basis for understanding 
how the impacts and responses to climate change, including costs 
and benefits, are distributed in and by society in more or less equal 
ways. Often aligned with ideas of equality, fairness and justice and

applied with respect to equity in the responsibility for, and distribution 
of, climate impacts and policies across society, generations and gender, 
and in the sense of who participates and controls the processes of 
decision-making.
Ethics
Ethics involves questions of justice and value. Justice is concerned with 
right and wrong, equity and fairness, and, in general, with the rights to 
which people and living beings are entitled. Value is a matter of worth, 
benefit or good.
Eutrophication
Over-enrichment of water by nutrients such as nitrogen and phosphorus. 
It is one of the leading causes of water quality impairment. The two 
most acute symptoms of eutrophication are hypoxia (or oxygen 
depletion) and harmful algal blooms.
Evaporation
The physical process by which a liquid (e.g., water) becomes a gas 
(e.g., water vapour).
Evapotranspiration
The combined processes through which water is transferred to the 
atmosphere from open water and ice surfaces, bare soil and vegetation

that make up the Earth’s surface.
Evidence
Data and information used in the scientific process to establish findings. 
In this report, the degree of evidence reflects the amount, quality and 
consistency of scientific/technical information on which the Lead 
Authors are basing their findings. See also Agreement, Confidence, 
Likelihood and Uncertainty.
See Adaptation.Exposure
The presence of people; livelihoods; species or ecosystems; environ-
mental functions, services, and resources; infrastructure; or economic, 
social, or cultural assets in places and settings that could be adversely 
affected.
Externality/external cost/external benefit
Externalities arise from a human activity, when agents responsible for 
the activity do not take full account of the activity’s impact on others’ 
production and consumption possibilities, and no compensation exists 
for such impacts. When the impact is negative, they are external

costs. When positive they are referred to as external benefits. See also 
Co-benefits.
Extinction
A population, species or more inclusive taxonomic group has gone 
extinct when all its individuals have died. A species may go extinct 
locally (population extinction), regionally (e.g., extinction of all 
populations in a country, continent or ocean) or globally (IPBES, 2019). 
See also Extirpation.
Extirpation
The disappearance of a species from an area, sometimes also referred 
to as local extinction. Its use implies that the species still occurs 
elsewhere. See also Extinction.
Extreme sea level (ESL)
The occurrence of an exceptionally low or high local sea surface 
height, arising from (a combination of) short-term phenomena (e.g., 
storm surges, tides and waves). Relative sea level changes affect 
extreme sea levels directly by shifting the mean water levels and 
indirectly by modulating the propagation of tides, waves and/or surges

due to increased water depth. In addition, extreme sea levels can be 
influenced by changes in the frequency, tracks or strength of weather 
systems and storms, or due to anthropogenically induced changes such 
as the modification of coastlines or dredging. In turn, changes in any 
or all of the contributions to extreme sea levels may lead to long-term 
of relative sea level change, tides and storm surges. Wind-waves also 
contribute to coastal sea level via three processes: infragravity waves 
(lower-frequency gravity waves generated by wind waves), wave setup 
(time-mean sea level elevation due to wave energy dissipation) and 
swash (vertical displacement up the shore-face induced by individual 
waves). Extreme total water level (ETWL) is the ESWL plus wave setup.

When considering coastal impacts, swash is also important, and 
extreme coastal water level (ECWL) is used. See also Sea level change 
(sea level rise/sea level fall)
Extreme weather event
An event that is rare at a particular place and time of year. Definitions 
of ‘rare’ vary, but an extreme weather event would normally be as rare 
as or rarer than the 10th or 90th percentile of a probability density 
function estimated from observations. By definition, the characteristics 
of what is called extreme weather may vary from place to place in an 
absolute sense. See also Heatwave and Climate extreme.

AII
2909Glossary  Annex II
Extreme/heavy precipitation event
An extreme/heavy precipitation event is an event that is of very high 
magnitude with a very rare occurrence at a particular place. Types of 
extreme precipitation may vary depending on its duration, hourly, daily 
or multi-days (e.g., 5 days), though all of them qualitatively represent 
high magnitude. The intensity of such events may be defined with 
block maxima approach such as annual maxima or with peak over 
threshold approach, such as rainfall above 95th or 99th percentile at 
a particular space.
Fairness
Impartial and just treatment without favouritism or discrimination in 
which each person is considered of equal worth with equal opportunity. 
See also Equality and Equity.
Feasibility
In this report, feasibility refers to the potential for a mitigation or 
adaptation option to be implemented. Factors influencing feasibility 
are context dependent, temporally dynamic and may vary between

different groups and actors. Feasibility depends on geophysical, 
environmental-ecological, technological, economic, socio-cultural and 
institutional factors that enable or constrain the implementation of an 
option. The feasibility of options may change when different options are 
combined, and increase when enabling conditions are strengthened. 
See also Enabling conditions (for adaptation and mitigation options).
Weather conditions conducive to triggering and sustaining wildfires, 
usually based on a set of indicators and combinations of indicators 
including temperature, soil moisture, humidity and wind. Fire weather 
does not include the presence or absence of fuel load.
Flood
The overflowing of the normal confines of a stream or other water 
body, or the accumulation of water over areas that are not normally 
submerged. Floods can be caused by unusually heavy rain, for example 
during storms and cyclones. Floods include river (fluvial) floods, flash

floods, urban floods, rain (pluvial) floods, sewer floods, coastal floods, 
and glacial lake outburst floods (GLOF).
Flux
A movement (flow) of matter (e.g., water vapour, particles), heat or 
energy from one place to another, or from one medium (e.g., land 
surface) to another (e.g., atmosphere).
Food security
A situation that exists when all people, at all times, have physical, social 
and economic access to sufficient, safe and nutritious food that meets 
their dietary needs and food preferences for an active and healthy life. 
The four pillars of food security are availability, access, utilisation and 
stability. The nutritional dimension is integral to the concept of food 
security (FAO, 2018/ 2009).
Availability
Physical availability of food. Food availability addresses the supply 
side of food security and is determined by the levels of food 
production, stocks and net trade.Access
Economic and/or physical access to food. Economic access is

determined by disposable income, food prices and the provision of 
and access to social support. Physical access is determined by the 
availability and quality of land and other infrastructure, property 
rights or the functioning of markets.
Utilisation
The way in which the body uses the various nutrients in food. 
Individuals achieve sufficient energy and nutrient intake through 
good care and feeding practices, food preparation, diet diversity 
and intrahousehold distribution of food. Combined with biological 
utilisation of the food consumed, energy and nutrient intake 
determine the nutrition status of individuals.
Stability
The stability of the other three dimensions over time. Even if 
individuals’ food intake is adequate today, they are still considered 
food-insecure if periodically they have inadequate access to food, 
risking deterioration of their nutrition status. Adverse weather 
conditions, political instability or economic factors (unemployment,

rising food prices) may have an impact on individuals’ food security 
status.
Food system
All the elements (environment, people, inputs, processes, infrastructures, 
institutions, etc.) and activities that relate to the production, processing, 
distribution, preparation and consumption of food, and the output of 
these activities, including socio-economic and environmental outcomes 
(HLPE, 2017). [Note: While there is a global food system (encompassing 
the totality of global production and consumption), each location’s food 
system is unique, being defined by that place’s mix of food produced 
locally, nationally, regionally or globally.]
Food-borne diseases
Illnesses transmitted through the consumption of unsafe or 
contaminated food. That contamination can come from a variety of 
sources, including contaminated water (adapted from UNEP , 2018).
Forest
A vegetation type dominated by trees. Many definitions of the term

forest are in use throughout the world, reflecting wide differences in 
bio-geophysical conditions, social structure and economics. See also 
Afforestation, Deforestation and Reforestation
[Note: For a discussion of the term forest in the context of national 
GHG inventories, see the 2006 IPCC Guidelines for National GHG 
Inventories and their 2019 Refinement, and information provided by 
the United Nations Framework Convention on Climate Change (IPCC 
2006, 2019; UNFCCC, 2021a, 2021b).]
Forest degradation
A reduction in the capacity of a forest to produce ecosystem services 
such as carbon storage and wood products as a result of anthropogenic 
and environmental changes.
Forest dieback
See Forest degradation.

AII
2910Annex II Glossary
Forest line
The upper limit of the closed upper montane forest or forest at high 
latitudes. It is less elevated or less poleward than the tree line.
Forest management
See Sustainable forest management.
Fossil fuels
Carbon-based fuels from fossil hydrocarbon deposits, including coal, 
oil and natural gas.
Glacial lake outburst flood (GLOF)/Glacier lake outburst
A sudden release of water from a glacier lake, including any of the 
following types: a glacier-dammed lake, a pro-glacial moraine-dammed 
lake or water that was stored within, under or on the glacier.
Glacier
A perennial mass of ice, and possibly firn and snow, originating on the 
land surface by accumulation and compaction of snow and showing 
evidence of past or present flow. A glacier typically gains mass by 
accumulation of snow and loses mass by ablation. Land ice masses 
of continental size (>50,000 km2) are referred to as ice sheets (Cogley 
et al., 2011).
Global change

A generic term to describe global-scale changes in systems, including 
the climate system, ecosystems and social-ecological systems.
Global mean sea level change
Global mean sea level (GMSL) change is the increase or decrease in the 
volume of the ocean divided by the ocean surface area. It is the sum of 
changes in ocean density through temperature changes (global mean 
thermosteric sea level change) and changes in the ocean mass as a 
result of changes in the cryosphere or land water storage (barystatic 
sea level change).
Global mean surface air temperature (GSAT)
The global average of near-surface air temperatures over land, oceans 
and sea ice. Changes in GSAT are often used as a measure of global 
temperature change in climate models. See also Global mean surface 
temperature (GMST).
Global mean surface temperature (GMST)
The estimated global average of near-surface air temperatures over 
land and sea ice, and sea surface temperature (SST) over ice-free ocean

regions, with changes normally expressed as departures from a value 
over a specified reference period. See also Global mean surface air 
temperature (GSAT).
Global monsoon
The global monsoon (GM) is a global-scale solstitial mode that domi-
nates the annual variation of tropical and sub-tropical precipitation and 
circulation. The GM domain is defined as the area where the annual 
range of precipitation (local summer minus winter mean precipitation 
rate) is greater than 2.5 mm/day, following on from the definition as 
in Kitoh et al. (2013). Further details on how the GM is defined, used and related to regional monsoons throughout the report are provided 
by WGI AR6 Annex V (IPCC 2021b).
Global warming
Global warming refers to the increase in global surface temperature 
relative to a baseline reference period, averaging over a period 
sufficient to remove interannual variations (e.g., 20 or 30  years). A 
common choice for the baseline is 1850–1900 (the earliest period of

reliable observations with sufficient geographic coverage), with more 
modern baselines used depending upon the application. See also 
Climate change and Climate variability.
Governance
The structures, processes and actions through which private and 
public actors interact to address societal goals. This includes formal 
and informal institutions and the associated norms, rules, laws and 
procedures for deciding, managing, implementing and monitoring 
policies and measures at any geographic or political scale, from global 
to local.
Adjusting to changing conditions, such as climate change, through 
governance interactions that seek to maintain a desired state in a 
social-ecological system.
public actors seek to mitigate and adapt to climate change.
Multi-level governance
The dispersion of governance across multiple levels of jurisdiction

and decision-making, including, global, regional, national and local 
as well as trans-regional and trans-national levels.
Polycentric governance
Polycentric governance involves multiple centres of decision-making 
with overlapping jurisdictions. While the centres have some degree 
of autonomy, they also take each other into account, coordinating 
their actions and seeking to resolve conflicts (Carlisle and Gruby, 
2017; Jordan et al., 2018; McGinnis and Ostrom, 2012).
Governance capacity
The ability of governance institutions, leaders and non-state and civil 
society to plan, coordinate, fund, implement, evaluate and adjust 
policies and measures over the short, medium and long term, adjusting 
for uncertainty, rapid change and wide-ranging impacts and multiple 
actors and demands.
Green Climate Fund (GCF)
The Green Climate Fund was established by the 16th Session of the 
Conference of the Parties (COP) in 2010 as an operating entity of the

financial mechanism of the United Nations Framework Convention 
on Climate Change (UNFCCC), in accordance with Article 11 of the 
Convention, to support projects, programmes and policies and other 
activities in developing country Parties. The Fund is governed by a 
board and will receive guidance from the COP .

AII
2911Glossary  Annex II
Green infrastructure
Greenhouse gases (GHG)
Gaseous constituents of the atmosphere, both natural and 
anthropogenic, that absorb and emit radiation at specific wavelengths 
within the spectrum of radiation emitted by the Earth’s ocean and land 
surface, by the atmosphere itself and by clouds. This property causes the 
greenhouse effect. Water vapour (H 2O), carbon dioxide (CO 2), nitrous 
oxide (N 2O), methane (CH 4) and ozone (O 3) are the primary GHGs in the 
Earth’s atmosphere. Human-made GHGs include sulphur hexafluoride 
(SF 6), hydrofluorocarbons (HFCs), chlorofluorocarbons (CFCs) and 
perfluorocarbons (PFCs); several of these are also O 3-depleting (and 
are regulated under the Montreal Protocol).
Grey infrastructure
Gross domestic product (GDP)
The sum of gross value added, at purchasers’ prices, by all resident 
and non-resident producers in the economy, plus any taxes and minus

any subsidies not included in the value of the products in a country 
or a geographic region for a given period, normally one year. GDP is 
calculated without deducting for depreciation of fabricated assets or 
depletion and degradation of natural resources.
Groundwater recharge
The process by which external water is added to the zone of saturation 
of an aquifer, either directly into a geologic formation that traps the 
water or indirectly by way of another formation.
Habitability (human)
The ability of a place to support human life by providing protection 
from hazards which challenge human survival, and by assuring 
adequate space, food and freshwater.
Hazard
The potential occurrence of a natural or human-induced physical event 
or trend that may cause loss of life, injury or other health impacts, as 
well as damage and loss to property, infrastructure, livelihoods, service 
provision, ecosystems and environmental resources. See also Impacts 
and Risk.
Health

Health is a state of complete physical, mental and social well-being 
and not merely the absence of disease or infirmity (WHO).
Heat index
A measure of how hot the air feels to the human body. The index is 
mainly based on surface air temperature and relative humidity and 
thus reflects the combined effect of high temperature and humidity 
on human physiology and provides a relative indication of potential 
health risks. See also Heatwave.
Heat stress
A range of conditions in, for example, terrestrial or aquatic organisms 
when the body absorbs excess heat during overexposure to high air or water temperatures or thermal radiation. In aquatic water-breathing 
animals, hypoxia and acidification can exacerbate vulnerability to 
heat. Heat stress in mammals (including humans) and birds, both in 
air, is exacerbated by a detrimental combination of ambient heat, 
high humidity and low wind speed, causing the regulation of body 
temperature to fail.
Heatwave

A period of abnormally hot weather, often defined with reference to 
a relative temperature threshold, lasting from two days to months. 
Heatwaves and warm spells have various and, in some cases, 
overlapping definitions. See also Heat index, Heat stress and Marine 
heatwave.
Heavy precipitation event
See Extreme/heavy precipitation event.
Human mobility
The permanent or semi-permanent move by a person for at least 
1 year and involving crossing an administrative, but not necessarily a 
national, border.
Human rights
Rights that are inherent to all human beings, universal, inalienable and 
indivisible, typically expressed and guaranteed by law. They include 
the right to life, economic, social and cultural rights, and the right to 
development and self-determination (UNOHCHR, 2018).
Human security
A condition that is met when the vital core of human lives is protected, 
and when people have the freedom and capacity to live with dignity.

In the context of climate change, the vital core of human lives includes 
the universal and culturally specific, material and non-material 
elements necessary for people to act on behalf of their interests and 
to live with dignity.
Human system
Any system in which human organisations and institutions play a 
major role. Often, but not always, the term is synonymous with society 
or social system. Systems such as agricultural systems, urban systems, 
political systems, technological systems and economic systems are all 
human systems in the sense applied in this report.
The cycle in which water evaporates from the ocean and the land 
surface, is carried over the Earth in atmospheric circulation as water 
vapour, condenses to form clouds, precipitates over the ocean and 
land as rain or snow, which on land can be intercepted by trees and 
vegetation, potentially accumulating as snow or ice, provides runoff

on the land surface, infiltrates into soils, recharges groundwater, 
discharges into streams and, ultimately, flows into the oceans as rivers, 
polar glaciers and ice sheets, from which it will eventually evaporate 
again. The various systems involved in the hydrological cycle are 
usually referred to as hydrological systems.

AII
2912Annex II Glossary
Hydropower
Power harnessed from the flow of water.
Hyperthermal events
Geologically abrupt global warming events of the past associated with 
disturbances of the carbon cycle and impacts on the biosphere.
Hypoxic
Conditions of low dissolved oxygen in shallow-water ocean and 
freshwater environments. There is no universal threshold for hypoxia. 
A value around 60 μmol kg−1 has commonly been used for some 
estuarine systems, although this does not necessarily directly translate 
into biological impacts. Anoxic conditions occur where there is no 
oxygen present at all. See also Eutrophication.
Hypoxic events
Events that lead to deficiencies of oxygen in water bodies.
Ice sheet
An ice body originating on land that covers an area of continental size, 
generally defined as covering >50,000 km2, and that has formed over 
thousands of years through accumulation and compaction of snow. 
An ice sheet flows outward from a high central ice plateau with a

small average surface slope. The margins usually slope more steeply, 
and most ice is discharged through fast-flowing ice streams or outlet 
glaciers, often into the sea or into ice shelves floating on the sea. There 
are only two ice sheets in the modern world, one on Greenland and 
one on Antarctica. The latter is divided into the East Antarctic Ice Sheet 
(EAIS), the West Antarctic Ice Sheet (WAIS) and the Antarctic Peninsula 
Ice Sheet. During glacial periods, there were other ice sheets.
Impacts
The consequences of realised risks on natural and human systems, where 
risks result from the interactions of climate-related hazards (including 
extreme weather/climate events), exposure, and vulnerability. Impacts 
generally refer to effects on lives, livelihoods, health and well-being, 
ecosystems and species, economic, social and cultural assets, services 
(including ecosystem services) and infrastructure. Impacts may be 
referred to as consequences or outcomes, and can be adverse or

beneficial. See also Adaptation, Exposure, Loss and Damage, and 
losses and damages, Vulnerability and Risk.
Income
The maximum amount that a household, or other unit, can consume 
without reducing its real net worth. Total income is the broadest 
measure of income and refers to regular receipts such as wages and 
salaries, income from self-employment, interest and dividends from 
invested funds, pensions or other benefits from social insurance, and 
other current transfers receivable. OECD (2003).
Indigenous knowledge (IK)
The understandings, skills and philosophies developed by societies 
with long histories of interaction with their natural surroundings. 
For many indigenous peoples, IK informs decision-making about fundamental aspects of life, from day-to-day activities to longer-term 
actions. This knowledge is integral to cultural complexes, which also 
encompass language, systems of classification, resource use practices,

social interactions, values, ritual and spirituality. These distinctive 
ways of knowing are important facets of the world’s cultural diversity 
(UNESCO, 2018). See also Local knowledge.
Indigenous Peoples
Indigenous Peoples and Nations are those that, having a historical 
continuity with pre-invasion and pre-colonial societies that developed 
on their territories, consider themselves distinct from other sectors 
of the societies now prevailing on those territories, or parts of them. 
They form at present principally non-dominant sectors of society and 
are often determined to preserve, develop and transmit to future 
generations their ancestral territories, and their ethnic identity, as the 
basis of their continued existence as Peoples, in accordance with their 
own cultural patterns, social institutions and common law system. 
Cobo (1987)
Indirect land-use change (iLUC)
See Land-use change.
See Equality.
Informal settlement

A term given to settlements or residential areas that, by at least one 
criterion, fall outside official rules and regulations. Most informal 
settlements have poor housing (with widespread use of temporary 
materials) and are developed on land that is occupied illegally with 
high levels of overcrowding. In most such settlements, provision 
for safe water, sanitation, drainage, paved roads and basic services 
is inadequate or lacking. The term ‘slum’ is often used for informal 
settlements, although it is misleading as many informal settlements 
develop into good-quality residential areas, especially where 
governments support such development.
The designed and built set of physical systems and corresponding 
institutional arrangements that mediate between people, their 
communities and the broader environment to provide services that 
support economic growth, health, quality of life and safety (Chester,

2019; Dawson et al., 2018) There are four categories of infrastructure:
Blue infrastructure includes bodies of water, watercourses, ponds, 
lakes and storm drainage, that provide ecological and hydrological 
functions including evaporation, transpiration, drainage, infiltration 
and temporarily storage of runoff and discharge.
The strategically planned interconnected set of natural and 
constructed ecological systems, green spaces and other landscape 
features that can provide functions and services including air 
and water purification, temperature management, floodwater 
management and coastal defence often with co-benefits for human 
and ecological well-being. Green infrastructure includes planted and

AII
2913Glossary  Annex II
remnant native vegetation, soils, wetlands, parks and green open 
spaces, as well as building and street-level design interventions that 
incorporate vegetation (after Culwick and Bobbins, 2016).
Engineered physical components and networks of pipes, wires, 
roads and tracks that underpin energy, transport, communications 
(including digital), built form, water and sanitation, and solid-waste 
management systems.
Social infrastructure
The social, cultural and financial activities and institutions as well 
as associated property, buildings and artefacts and policy domains 
such as social protection, health and education that support 
well-being and public life (Frolova et al., 2016; Latham and Layton, 
2019).
Institutional capacity
Building and strengthening individual organisations and providing 
technical and management training to support integrated planning 
and decision-making processes between organisations and people, as

well as empowerment, social capital and an enabling environment, 
including culture, values and power relations (Willems and Baumert, 
2003).
Institutions
Rules, norms and conventions that guide, constrain or enable human 
behaviours and practices. Institutions can be formally established, 
for instance through laws and regulations, or informally established, 
for instance by traditions or customs. Institutions may spur, hinder, 
strengthen, weaken or distort the emergence, adoption and 
implementation of climate action and climate governance.
[Note: Institutions can also refer to a large organisation]
Insurance/reinsurance
A family of financial instruments for sharing and transferring risk 
among a pool of at-risk households, businesses and/or governments.
Integrated assessment
A method of analysis that combines results and models from the 
physical, biological, economic and social sciences and the interactions 
among these components in a consistent framework to evaluate the

status and consequences of environmental change and the policy 
responses to it.
Integrated assessment model (IAM)
Models that integrate knowledge from two or more domains into 
a single framework. They are one of the main tools for undertaking 
integrated assessments. One class of IAM used in respect of climate 
change mitigation may include representations of: multiple sectors of 
the economy, such as energy, land use and land use change; interactions 
between sectors; the economy as a whole; associated greenhouse gas 
(GHG) emissions and sinks; and reduced representations of the climate 
system. This class of model is used to assess linkages between economic, 
social and technological development and the evolution of the climate 
the costs associated with climate change impacts, but includes less detailed representations of economic systems. These can be used to

Invasive species
A species that is not native to a specific location or nearby, lacking 
natural controls, and that has a tendency to rapidly increase in 
abundance, displacing native species. Invasive species may also 
damage the human economy or human health.
Justice
Justice is concerned with, setting out the moral or legal principles of 
fairness and equity in the way people are treated, often based on the 
ethics and values of society.
Justice that links development and human rights to achieve a 
human-centred approach to addressing climate change, safe-
guarding the rights of the most vulnerable people and sharing the 
burdens and benefits of climate change and its impacts equitably 
and fairly (MRFJC, 2018).
Procedural justice
Justice in the way outcomes are brought about, including who

participates and is heard in the processes of decision-making.
Social justice
Just or fair relations within society that seek to address the dis-
tribution of wealth, access to resources, opportunity and support 
according to principles of justice and fairness.
Key risk
Key risks have potentially severe adverse consequences for humans 
and social-ecological systems resulting from the interaction of climate 
related hazards with vulnerabilities of societies and systems exposed.
Representative Key Risks (RKRs)
are representative, thematic clusters of key risks.
Land
The terrestrial portion of the biosphere that comprises the natural 
resources (soil, near-surface air, vegetation and other biota, and water), 
the ecological processes, topography, and human settlements and 
infrastructure that operate within that system (FAO, 2007; UNCCD, 
1994).
Land cover
The biophysical coverage of land (e.g., bare soil, rocks, forests,

buildings and roads, or lakes). Land cover is often categorised in broad 
land-cover classes (e.g., deciduous forest, coniferous forest, mixed 
forest, grassland and bare ground). Note: In some literature assessed 
in this report, land cover and land use are used interchangeably, but 
the two represent distinct classification systems. For example, the 
land cover class of woodland can be under various land uses such as 
livestock grazing, recreation, conservation or wood harvest.

AII
2914Annex II Glossary
Land cover change
Change from one land cover class to another, due to change in land 
use or change in natural conditions (Pongratz et al., 2018). See also 
Land cover and Land-use change.
Land degradation
A negative trend in land condition, caused by direct or indirect 
human-induced processes including anthropogenic climate change, 
expressed as a long-term reduction or loss of at least one of the 
following: biological productivity, ecological integrity or value to 
humans. [Note: This definition applies to forest and non-forest land. 
Changes in land condition resulting solely from natural processes 
(such as volcanic eruptions) are not considered to be land degradation. 
Reduction of biological productivity or ecological integrity or value to 
humans can constitute degradation, but any one of these changes 
need not necessarily be considered degradation.]
Land management
The sum of land-use practices (e.g., sowing, fertilising, weeding,

harvesting, thinning and clear-cutting) that take place within broader 
land-use categories (Pongratz et al., 2018).
Land use
The total of arrangements, activities and inputs applied to a parcel 
of land. The term land use is also used in the sense of the social and 
economic purposes for which land is managed (e.g., grazing, timber 
extraction, conservation and city dwelling). In national greenhouse 
gas (GHG) inventories, land use is classified according to the IPCC 
land-use categories of forest land, cropland, grassland, wetlands, 
settlements and other lands (see the 2006 IPCC Guidelines for 
National GHG Inventories and their 2019 Refinement for details 
(IPCC, 2006, 2019)).
Land-use change
The change from one land-use category to another. Note that, in 
some scientific literature, land-use change encompasses changes in 
land-use categories as well as changes in land management. See also 
Afforestation, Deforestation and Reforestation.

Land-use change outside the area of focus that occurs as a 
consequence of change in use or management of land within the 
area of focus, such as through market or policy drivers. For example, 
if agricultural land is diverted to biofuel production, forest clearance 
may occur elsewhere to replace the former agricultural production. 
See Land-use change (LUC).
Least Developed Countries (LDCs)
A list of countries designated by the Economic and Social Council of the 
United Nations (ECOSOC) as meeting three criteria: (1) a low income 
criterion below a certain threshold of gross national income per capita 
of between USD 750 and USD 900, (2) a human resource weakness 
based on indicators of health, education and adult literacy, and (3) 
an economic vulnerability weakness based on indicators on instability 
of agricultural production, instability of export of goods and services, 
economic importance of non-traditional activities, merchandise export

concentration and the handicap of economic smallness. Countries in this category are eligible for a number of programmes focused on assisting 
countries most in need. These privileges include certain benefits under 
the articles of the United Nations Framework Convention on Climate 
Change (UNFCCC).
Likelihood
The chance of a specific outcome occurring, where this might be 
estimated probabilistically. Likelihood is expressed in this Special 
Report using a standard terminology (Mastrandrea et al., 2010). See 
also Agreement, Confidence, Evidence and Uncertainty.
Livelihood
The resources used and the activities undertaken in order for people 
to live. Livelihoods are usually determined by the entitlements and 
assets to which people have access. Such assets can be categorised as 
human, social, natural, physical or financial.
Local extinction
See Extirpation.
Local knowledge (LK)
The understandings and skills developed by individuals and populations,

specific to the places where they live. Local knowledge informs 
decision-making about fundamental aspects of life, from day-to-day 
activities to longer-term actions. This knowledge is a key element of 
the social and cultural systems which influence observations of and 
responses to climate change; it also informs governance decisions 
(UNESCO, 2018). See also Indigenous knowledge.
Lock-in
A situation in which the future development of a system, including 
infrastructure, technologies, investments, institutions and behavioural 
norms, is determined or constrained (‘locked in’) by historical 
developments. See also Path dependence.
Loss and Damage, and losses and damages
Research has taken Loss and Damage (capitalised letters) to refer to 
political debate under the United Nations Framework Convention on 
Climate Change (UNFCCC) following the establishment of the Warsaw 
Mechanism on Loss and Damage in 2013, which is to ‘address loss

and damage associated with impacts of climate change, including 
extreme events and slow onset events, in developing countries that 
are particularly vulnerable to the adverse effects of climate change.’ 
Lowercase letters (losses and damages) have been taken to refer 
broadly to harm from (observed) impacts and (projected) risks and can 
be economic or non-economic (Mechler et al., 2018).
Low Elevation Coastal Zones (LECZ)
Coastal areas below 10 m of elevation above sea level that are 13 
hydrologically connected to the sea.
Low-likelihood, high-impact outcomes
Outcomes/events whose probability of occurrence is low or not well 
known (as in the context of deep uncertainty) but whose potential 
impacts on society and ecosystems could be high. To better inform risk  
assessment and decision-making, such low-likelihood outcomes are 
considered if they are associated with very large consequences and may

AII
2915Glossary  Annex II
therefore constitute material risks, even though those consequences do 
not necessarily represent the most likely outcome. See also Impacts.
Maladaptive actions (Maladaptation)
Actions that may lead to increased risk of adverse climate-related 
outcomes, including via increased greenhouse gas (GHG) emissions, 
increased or shifted vulnerability to climate change, more inequitable 
outcomes, or diminished welfare, now or in the future. Most often, 
maladaptation is an unintended consequence.
Malnutrition
Deficiencies, excesses or imbalances in a person’s intake of energy 
and/or nutrients. The term malnutrition addresses three broad 
groups of conditions: undernutrition, which includes wasting (low 
weight-for-height), stunting (low height-for-age) and underweight 
(low weight-for-age); micronutrient-related malnutrition, which 
includes micronutrient deficiencies (a lack of important vitamins

and minerals) or micronutrient excess; and overweight, obesity and 
diet-related non-communicable diseases (such as heart disease, stroke, 
diabetes and some cancers) (WHO, 2018). Micronutrient deficiencies 
are sometimes termed ‘hidden hunger’ to emphasise that people can 
be malnourished in the sense of deficient without being deficient in 
calories. Hidden hunger can apply even where people are obese.
A period during which water temperature is abnormally warm for the 
time of the year relative to historical temperatures, with that extreme 
warmth persisting for days to months. The phenomenon can manifest 
in any place in the ocean and at scales of up to thousands of kilometres. 
See also Heatwave.
Mean sea level
The surface level of the ocean at a particular point averaged over an 
extended period of time such as a month or year. Mean sea level is 
often used as a national datum to which heights on land are referred.
Measurement

Processes of data collection over time, providing basic data sets, 
including associated accuracy and precision, for the range of 
relevant variables. Possible data sources are field measurements, 
field observations, detection through remote sensing and interviews 
(UN-REDD, 2009).
Megacity
An urban agglomeration with 10 million inhabitants or more (United 
Nations, Department of Economic and Social Affairs, Population 
Division (2019).
See Drought.Mental health
The state of well-being in which an individual realises his or her own 
abilities, can cope with the normal stresses of life, can work productively 
and is able to contribute to his or her community.
One of the six greenhouse gases (GHGs) to be mitigated under the 
Kyoto Protocol. Methane is the major component of natural gas and 
associated with all hydrocarbon fuels. Significant anthropogenic 
emissions also occur as a result of animal husbandry and paddy rice

production. Methane is also produced naturally where organic matter 
decays under anaerobic conditions, such as in wetlands. Under future 
global warming, there is risk of increased methane emissions from 
thawing permafrost, coastal wetlands and sub-sea gas hydrates.
Metric
A consistent measurement of a characteristic of an object or activity 
that is otherwise difficult to quantify. Within the context of the eval-
uation of climate models, this is a quantitative measure of agreement 
between a simulated and an observed quantity which can be used to 
assess the performance of individual models.
Metropolitan region
See City region.
Microclimate
Local climate at or near the Earth’s surface.
Migrant
Any person who is moving or has moved across an international 
border or within a state away from his/her habitual place of residence, 
regardless of (1) the person’s legal status, (2) whether the movement 
is voluntary or involuntary, (3) what the causes for the movement are

and (4) what the length of the stay is (IOM, 2018).
Migration (of humans)
Movement of a person or a group of persons, either across an 
international border, or within a state. It is a population movement, 
encompassing any kind of movement of people, whatever its length, 
composition and causes; it includes migration of refugees, displaced 
persons, economic migrants and persons moving for other purposes, 
including family reunification (IOM, 2018).
Mitigation (of climate change)
A human intervention to reduce emissions or enhance the sinks of 
greenhouse gases.
Mitigation measures
In climate policy, mitigation measures are technologies, processes or 
practices that contribute to mitigation, for example renewable energy 
technologies, waste minimisation processes and public transport 
commuting practices.
Mitigation option
A technology or practice that reduces greenhouse gas (GHG) emissions 
or enhances sinks.

AII
2916Annex II Glossary
Mitigation scenario
system responds to the implementation of mitigation policies and 
measures.
Model ensemble
See Climate simulation ensemble.
Models
Structured imitations of a system’s attributes and mechanisms to mimic 
the appearance or functioning of systems, for example, the climate, 
the economy of a country, or a crop. Mathematical models assemble 
(many) variables and relations (often in a computer code) to simulate 
system functioning and performance for variations in parameters and 
inputs.
Monitoring and evaluation (M&E)
Mechanisms put in place to respectively monitor and evaluate 
efforts to reduce greenhouse gas emissions and/or adapt to the 
impacts of climate change with the aim of systematically identifying, 
characterising and assessing progress over time.
Monsoon
See Global monsoon.
Mountains
A mountain is a landform formed through plate tectonics that rises

above its surrounding area, characterised by verticality and ruggedness 
such as gentle or steep sloping sides, sharp or rounded ridges and a high 
point called a peak or a summit. Mountain regions consist of mountains 
and mountain ranges as defined by ruggedness, intermontane valleys, 
plateaus and tablelands, and hills and hilly forelands, together forming 
a complex terrain.
To delineate mountain regions, a combination of terrain characteristics 
is used, such as elevation above sea level, steepness of slope and 
relative relief or local elevational range.
Three mountain characterisations using different combinations of the 
above criteria applied to digital elevation models have been developed 
to arrive at mountain area statistics, described and analysed in detail 
by Sayre et al. (2018), namely K1 (Kapos et al., 2000), K2 (Körner et al., 
2011) and K3 (Karagulle et al., 2017).
Narrative
See Storyline. See also Pathways.
Native species

Indigenous species of animals or plants that naturally occur in a given 
region or ecosystem. Under climate change, many species colonise 
new areas where they may become native over time (following IPBES, 
2019). See also Invasive species.
Natural systems
The dynamic physical, physicochemical and biological components of 
the Earth system that would operate independently of human activities.Nature-based solution (NBS)
Actions to protect, sustainably manage and restore natural or modified 
ecosystems that address societal challenges effectively and adaptively, 
simultaneously providing human well-being and biodiversity benefits. 
(IUCN, 2016). See also Biodiversity and Ecosystem.
Net primary production (NPP)
See Primary production.
Net zero CO 2 emissions
Condition in which anthropogenic carbon dioxide (CO 2) emissions are 
balanced by anthropogenic CO 2 removals over a specified period. See 
also Land use.
[Note: Carbon neutrality and net zero CO 2 emissions are overlapping

terms ‘carbon neutrality’ and net zero CO 2 emissions are equivalent. 
At sub-global scales, net zero CO 2 emissions is generally applied to 
emissions and removals under direct control or territorial responsibility 
of the reporting entity, while carbon neutrality generally includes 
emissions and removals within and beyond the direct control or territorial 
responsibility of the reporting entity. Accounting rules specified by 
quantification of relevant CO 2 emissions and removals.]
New Urban Agenda
The New Urban Agenda was adopted at the United Nations Conference 
on Housing and Sustainable Urban Development (Habitat III) in Quito, 
Ecuador, on 20 October 2016. It was endorsed by the United Nations 
General Assembly at its 68th plenary meeting of the 71st session on

23 December 2016.
Non-climatic driver (Non-climate driver)
An agent or process outside the climate system that influences a 
human or natural system.
Non-communicable diseases
Non-communicable diseases (NCDs), also known as chronic diseases, 
tend to be of long duration and are the result of a combination of 
genetic, physiological, environmental and behavioural factors. The 
main types of NCDs are cardiovascular diseases (such as heart attacks 
and stroke), cancers, chronic respiratory diseases (such as chronic 
obstructive pulmonary disease and asthma) and diabetes (WHO).
Ocean
The interconnected body of saline water that covers 71% of the Earth’s 
surface, contains 97% of the Earth’s water and provides 99% of the 
Earth’s biologically habitable space. It includes the Arctic, Atlantic, 
Indian, Pacific and Southern Oceans, as well as their marginal seas and 
coastal waters.
Ocean acidification (OA)
A reduction in the pH of the ocean, accompanied by other chemical

changes (primarily in the levels of carbonate and bicarbonate ions), 
over an extended period, typically decades or longer, which is caused 
primarily by uptake of carbon dioxide (CO 2) from the atmosphere, but 
can also be caused by other chemical additions or subtractions from

AII
2917Glossary  Annex II
the ocean. Anthropogenic OA refers to the component of pH reduction 
that is caused by human activity (IPCC, 2011, p. 37).
Ocean deoxygenation
The loss of oxygen in the ocean. It results from ocean warming, which 
reduces oxygen solubility and increases oxygen consumption and 
stratification, thereby reducing the mixing of oxygen into the ocean 
interior.  Deoxygenation can also be exacerbated by the addition of 
excess nutrients in the coastal zone.
Ocean stratification
See Stratification.
Outbreak
Often used synonymously with ‘epidemic’, usually to indicate localised 
as opposed to generalised epidemics (WHO, 2020).
Overshoot pathways
Oxygen minimum zone (OMZ)
The midwater layer (200–1000 m) in the open ocean in which oxygen 
saturation is the lowest in the ocean. The degree of oxygen depletion 
depends on the largely bacterial consumption of organic matter, 
and the distribution of the OMZs is influenced by large-scale ocean

circulation. In coastal oceans, OMZs extend to the shelves and may 
also affect benthic ecosystems.
Ozone (O 3)
The triatomic form of oxygen, and a gaseous atmospheric constituent. 
In the troposphere, O 3 is created both naturally and by photochemical 
reactions involving gases resulting from human activities (e.g., smog). 
Tropospheric O 3 acts as a greenhouse gas (GHG). In the stratosphere, 
O3 is created by the interaction between solar ultraviolet radiation 
and molecular oxygen (O 2). Stratospheric O 3 plays a dominant role in 
the stratospheric radiative balance. Its concentration is highest in the 
ozone layer.
Pandemic
A worldwide outbreak of a disease in humans in numbers clearly in 
excess of normal (WHO, 2020).
Particulate matter (PM)
Atmospheric aerosol involved in air pollution issues. Of greatest 
concern for health are particles of aerodynamic diameter less than or 
equal to 10 micrometers, usually designated as PM10 and particles of

diameter less than or equal to 2.5 micrometers, usually designated as 
PM2.5.
Pasture
Area covered with grass or other plants used or suitable for grazing of 
livestock; grassland.
Path dependence
The generic situation where decisions, events or outcomes at one point 
in time constrain adaptation, mitigation or other actions or options at 
a later point in time. See also Lock-in.Pathways
The temporal evolution of natural and/or human systems towards a future 
state. Pathway concepts range from sets of quantitative and qualitative 
scenarios or narratives of potential futures to solution-oriented 
decision-making processes to achieve desirable societal goals. Pathway  
approaches typically focus on biophysical, techno-economic and/or 
socio-behavioural trajectories and involve various dynamics, goals and 
actors across different scales. See also Scenario.
A series of adaptation choices involving trade-offs between

short-term and long-term goals and values. These are processes of 
deliberation to identify solutions that are meaningful to people in 
the context of their daily lives and to avoid potential maladaptation.
Trajectories that strengthen sustainable development and efforts 
to eradicate poverty and reduce inequalities while promoting 
fair and cross-scalar adaptation to and resilience in a changing 
climate. They raise the ethics, equity and feasibility aspects of 
the deep societal transformation needed to drastically reduce 
emissions to limit global warming (e.g., to well below 2°C) and 
achieve desirable and liveable futures and well-being for all.
Iterative processes for managing change within complex systems in 
order to reduce disruptions and enhance opportunities associated 
with climate change. See also Development pathways and Pathways.

Development pathways evolve as the result of the countless 
decisions being made and actions being taken at all levels of 
societal structure, as well due to the emergent dynamics within 
and between institutions, cultural norms, technological systems 
and other drivers of behavioural change.
Modelled trajectories of global anthropogenic emissions over the 
21st century are termed emission pathways.
Pathways that first exceed a specified concentration, forcing or 
global warming level, and then return to or below that level again 
before the end of a specified period of time (e.g., before 2100). 
Sometimes the magnitude and likelihood of the overshoot are also 
characterised. The overshoot duration can vary from one pathway 
to the next, but in most overshoot pathways in the literature and 
referred to as overshoot pathways in the AR6, the overshoot occurs 
over a period of at least one decade and up to several decades.

Scenarios that include time series of emissions and concentrations of 
the full suite of greenhouse gases (GHGs) and aerosols and chemically 
active gases, as well as land use/ land cover (Moss et al., 2008; van 
Vuuren et  al., 2011). The word ‘representative’ signifies that each 
RCP provides only one of many possible scenarios that would lead 
to the specific radiative forcing characteristics. The term pathway

AII
2918Annex II Glossary
emphasises the fact that not only the long-term concentration levels, 
but also the trajectory taken over time to reach that outcome are of 
interest (Moss et al., 2010; van Vuuren et al., 2011).
RCPs usually refer to the portion of the concentration pathway  
produced corresponding emission scenarios. Extended concentration 
that were calculated using simple rules generated by stakeholder 
consultations, and do not represent fully consistent scenarios. 
Four RCPs produced from integrated assessment models were 
selected from the published literature and are used in the Fifth IPCC 
Assessment and are also used in this Assessment for comparison, 
spanning the range from approximately below 2°C warming to 
high (>4°C) warming best-estimates by the end of the 21st century: 
RCP2.6, RCP4.5 and RCP6.0, and RCP8.5.

approximately 3 W m−2 and then declines to be limited at 
2.6 W m−2 in 2100 (the corresponding Extended Concentration 
Pathway, or ECP , has constant emissions after 2100).
• RCP4.5 and RCP6.0: Two intermediate stabilisation pathways 
in which radiative forcing is limited at approximately 4.5 W m−2 
and 6.0 W m−2 in 2100 (the corresponding ECPs have constant 
• RCP8.5: One high pathway which leads to >8.5 W m−2 in 2100 
(the corresponding ECP has constant emissions after 2100 
until 2150 and constant concentrations after 2250).
Shared socio-economic pathways (SSPs)
Shared socio-economic pathways (SSPs) have been developed 
(RCPs). By design, the RCP emission and concentration pathways 
development. Different levels of emissions and climate change

framework is now widely used in the climate impact and policy 
analysis literature (see, e.g., http://iconics-ssp.org), where climate 
projections obtained under the RCP scenarios are analysed against 
the backdrop of various SSPs.
As several emission updates were due, a new set of emission 
SSP1, SSP2, …, SSP5 is used to denote the five socio-economic 
emission scenarios that are the result of an SSP implementation 
within an integrated assessment model. Those SSP scenarios 
are bare of climate policy assumption, but in combination with

so-called shared policy assumptions (SPAs), various approximate 
radiative forcing levels of 1.9, 2.6, …, or 8.5 W m−2 are reached by 
Sustainable development pathways (SDPs)
Trajectories aimed at attaining the Sustainable Development Goals 
(SDGs) in the short term and the goals of sustainable development 
in the long term. In the context of climate change, such pathways denote trajectories that address social, environmental and eco-
nomic dimensions of sustainable development, adaptation and 
mitigation, and transformation, in a generic sense or from a par -
ticular methodological perspective such as integrated assessment 
models and scenario simulations.
Peat
Soft, porous or compressed, sedentary deposit of which a substantial 
portion is partly decomposed plant material with high water content in 
the natural state (up to about 90%) (IPCC, 2013).
Peatlands
Peatlands are wetland ecosystems where soils are dominated by

peat. In peatlands, net primary production exceeds organic matter 
decomposition as a result of waterlogged conditions, which leads to 
the accumulation of peat.
Pelagic
The pelagic zone consists of the entire water column of the open ocean. 
It is subdivided into the epipelagic zone (<200 m, the uppermost part 
of the ocean that receives enough sunlight to allow photosynthesis), 
the mesopelagic zone (200–1000 m depth) and the bathypelagic zone 
(>1000 m depth). The term pelagic can also refer to organisms that live 
in the pelagic zone.
Pelagos
Organisms large and small living in the pelagic zones. Includes plankton 
(small) and nekton (free swimming, large). See Benthos.
Percentiles
A partition value in a population distribution that a given percentage of 
the data values are below or equal to. The 50th percentile corresponds 
to the median of the population. Percentiles are often used to estimate 
the extremes of a distribution. For example, the 90th (10th) percentile

may be used to refer to the threshold for the upper (lower) extremes.
Peri-urban areas
Dynamic transition zones that have intense interaction between rural 
and urban economies, activities, households and lifestyles. Neither fully 
rural or urban (following Seto et al., 2010).
Ground (soil or rock, and included ice and organic material) that 
remains at or below 0°C for at least two consecutive years (Harris 
et al., 1988). Note that permafrost is defined via temperature rather 
than ice content and, in some instances, may be ice-free.
Permafrost degradation
Permafrost thaw
During thaw, temperature fluctuations are subdued because energy is

transferred by phase change between ice and water. After the transition 
from permafrost to non-permafrost, ground can be described as thawed.

AII
2919Glossary  Annex II
pH
A dimensionless measure of the acidity of a solution given by its con-
centration of hydrogen ions (H+). pH is measured on a logarithmic scale 
where pH = −log 10(H+). Thus, a pH decrease of 1 unit corresponds to a 
10-fold increase in the concentration of H+, or acidity.
Phenology
The relationship between biological phenomena that recur periodically 
(e.g., development stages, migration) especially related to climate and 
seasonal changes.
Photosynthesis
The production of carbohydrates in plants, algae and some bacteria 
using the energy of light. Carbon dioxide (CO 2) is used as the carbon 
source.
Planetary health
a concept based on the understanding that human health and human 
civilisation depend on ecosystem health and the wise stewardship of 
ecosystems.
Plankton
Free-floating organisms living in the upper layers of aquatic systems. 
Their distribution and migration are primarily determined by water

currents. A distinction is made between phytoplankton, which depend 
on photosynthesis for their energy supply, and zooplankton, which 
feed on phytoplankton, other zooplankton and bacterioplankton.
Planned relocation (of humans)
A form of human mobility response in the face of sea level rise and 
related impacts. Planned relocation is typically initiated, supervised 
and implemented from national to local level and involves small 
communities and individual assets but may also involve large 
populations. Also termed resettlement, managed retreat or managed 
realignment.
Plasticity (biology)
Change in organismal trait values in response to an environmental cue 
and which does not require change in underlying DNA sequence.
Policies (for climate change mitigation and adaptation)
Strategies that enable actions to be undertaken to accelerate 
adaptation and mitigation. Policies include those developed by 
national and subnational public agencies, and with the private

sector. Policies for adaptation and mitigation often take the form of 
economic incentives, regulatory instruments, and decision-making and 
engagement processes.
Political economy
The set of interlinked relationships between people, the State, society 
and markets as defined by law, politics, economics, customs and 
power that determine the outcome of trade and transactions and the 
distribution of wealth in a country or economy.
See Governance.Potential evapotranspiration
The potential rate of water loss without any limits imposed by the 
Poverty
A complex concept with several definitions stemming from different 
schools of thought. It can refer to material circumstances (such as 
need, pattern of deprivation or limited resources), economic conditions 
(such as standard of living, inequality or economic position) and/or 
social relationships (such as social class, dependency, exclusion, lack of

basic security or lack of entitlement). See also Poverty trap.
Poverty trap
Poverty trap is understood differently across disciplines. In the social 
sciences, the concept, primarily employed at the individual, household 
or community level, describes a situation in which escaping poverty 
becomes impossible due to unproductive or inflexible resources. A 
poverty trap can also be seen as a critical minimum asset threshold, 
below which families are unable to successfully educate their children, 
build up their productive assets and get out of poverty. Extreme 
poverty is itself a poverty trap since poor persons lack the means to 
participate meaningfully in society. In economics, the term poverty 
trap is often used at national scales, referring to a self-perpetuating 
condition where an economy, caught in a vicious cycle, suffers from 
persistent underdevelopment (Matsuyama, 2008). Many proposed 
models of poverty traps are found in the literature.
Pre-industrial (period)

The multi-century period prior to the onset of large-scale industrial 
activity around 1750. The reference period 1850–1900 is used to 
approximate pre-industrial global mean surface temperature (GMST).
Precursors
Atmospheric compounds that are not greenhouse gases (GHGs) or 
aerosols, but that have an effect on GHG or aerosol concentrations 
by taking part in physical or chemical processes regulating their 
production or destruction rates.
Predictability
The extent to which future states of a system may be predicted based on 
knowledge of current and past states of the system. Because knowledge 
of the climate system’s past and current states is generally imperfect, 
as are the models that utilise this knowledge to produce a climate 
prediction, and because the climate system is inherently nonlinear and 
chaotic, the predictability of the climate system is inherently limited. 
Even with arbitrarily accurate models and observations, there may still

be limits to the predictability of such a nonlinear system (AMS, 2000).
Primary production
The synthesis of organic compounds by plants and microbes, on land 
or in the ocean, primarily by photosynthesis using light and carbon 
dioxide (CO 2) as sources of energy and carbon, respectively. It can also 
occur through chemosynthesis, using chemical energy, for example, in 
deep sea vents.

AII
2920Annex II Glossary
The difference between how much CO 2 vegetation takes in during 
photosynthesis (gross primary production) minus how much CO 2 the 
plants release during respiration (IPBES, 2019, Global Assessment).
Projection
A potential future evolution of a quantity or set of quantities, often 
computed with the aid of a model. Unlike predictions, projections 
are conditional on assumptions concerning, for example, future 
socio-economic and technological developments that may or may not 
be realised. See also Pathways and Scenario.
Proxy
A proxy climate indicator is a record that is interpreted, using 
physical and biophysical principles, to represent some combination of 
climate-related variations back in time. Climate-related data derived 
in this way are referred to as proxy data. Examples of proxies include 
pollen analysis, tree ring records, speleothems, characteristics of corals

and various data derived from marine sediments and ice cores. Proxy 
data can be calibrated to provide quantitative climate information.
Radiative forcing
The change in the net, downward minus upward, radiative flux 
(expressed in W m−2) at the tropopause or top of atmosphere due to 
a change in an (external)  driver of climate change, such as a change 
in the concentration of carbon dioxide (CO 2), the concentration of 
volcanic aerosols or the output of the Sun. The traditional radiative 
forcing is computed with all tropospheric properties held fixed 
at their unperturbed values, and after allowing for stratospheric 
equilibrium. Radiative forcing is called instantaneous if no change in 
stratospheric temperature is accounted for. The radiative forcing once 
rapid adjustments are accounted for is termed the effective radiative 
forcing. Radiative forcing is not to be confused with cloud radiative

forcing, which describes an unrelated measure of the impact of clouds 
on the radiative flux at the top of the atmosphere.
Reasons for Concern (RFCs)
Elements of a classification framework, first developed in the IPCC 
Third Assessment Report, which aims to facilitate judgements about 
what level of climate change may be dangerous (in the language of 
Article 2 of the UNFCCC) by aggregating risks from various sectors, 
considering hazards, exposures, vulnerabilities, capacities to adapt and 
the resulting impacts.
Reference period
A time period of interest, or a period over which some relevant statistics 
are calculated. A reference period can be used as a baseline period or 
as a comparison to a baseline period.
Reforestation
Conversion to forest of land that has previously contained forests but 
that has been converted to some other use. See also Afforestation and 
Forest.[Note: For a discussion of the term forest and related terms such as

Guidelines for National Greenhouse Gas Inventories and their 
2021a, 2021b)].
Refugium
A refugium is a geographic area where a population found safety from 
some threat to its existence, for example, climate refugia or glacial 
refugia (refuge from glaciations). See also Climate refugium.
Region
A land and/or ocean area characterised by specific geographical and/
or climatological features. The climate of a region emerges from a 
multi-scale combination of its own features, remote influences from 
other regions and global climate conditions.
Regulation
A rule or order issued by governmental executive authorities or 
regulatory agencies and having the force of law. Regulations 
implement policies and are mostly specific for particular groups of

people, legal entities or targeted activities. Regulation is also the act 
of designing and imposing rules or orders. Informational, transactional, 
administrative and political constraints in practice limit the regulator’s 
capability for implementing preferred policies.
Relative humidity
The relative humidity specifies the ratio of actual water vapour pressure 
to that at saturation with respect to liquid water or ice at the same 
temperature.
Reporting
The process of formal reporting of assessment results to the UNFCCC, 
according to predetermined formats and according to established 
standards, especially the Intergovernmental Panel on Climate Change 
(IPCC) Guidelines and Good Practice Guidance (GPG) (UN REDD, 2009).
Reservoir
A component or components of the climate system where a greenhouse 
gas (GHG) or a precursor of a greenhouse gas is stored (UNFCCC 
Article 1.7).
Residual risk

The risk related to climate change impacts that remains following 
adaptation and mitigation efforts. Adaptation actions can redistribute 
risk and impacts, with increased risk and impacts in some areas or 
populations, and decreased risk and impacts in others. See also Loss 
and Damage, and losses and damages.
Resilience
The capacity of interconnected social, economic and ecological systems 
to cope with a hazardous event, trend or disturbance, responding or 
reorganising in ways that maintain their essential function, identity

AII
2921Glossary  Annex II
and structure. Resilience is a positive attribute when it maintains 
capacity for adaptation, learning and/or transformation (Arctic Council, 
2016). See also Hazard, Risk and Vulnerability.
Resolution
In climate models, this term refers to the physical distance (metres 
or degrees) between each point on the grid used to compute the 
equations. Temporal resolution refers to the time step or time elapsed 
between each model computation of the equations.
Respiration
The process whereby living organisms convert organic matter to carbon 
dioxide (CO 2), releasing energy and consuming molecular oxygen.
Restoration
In environmental context, restoration involves human interventions to 
assist the recovery of an ecosystem that has been previously degraded, 
damaged or destroyed.
Return period
An estimate of the average time interval between occurrences of an 
event (e.g., flood or extreme rainfall) of (or below/above) a defined 
size or intensity.
Risk

The potential for adverse consequences for human or ecological 
systems, recognising the diversity of values and objectives associated 
with such systems. In the context of climate change, risks can arise 
from potential impacts of climate change as well as human responses 
to climate change. Relevant adverse consequences include those on 
lives, livelihoods, health and well-being, economic, social and cultural 
assets and investments, infrastructure, services (including ecosystem 
services), ecosystems and species.
interactions between climate-related hazards with the exposure 
and vulnerability of the affected human or ecological system to the 
hazards. Hazards, exposure and vulnerability may each be subject to 
uncertainty in terms of magnitude and likelihood of occurrence, and 
each may change over time and space due to socio-economic changes 
and human decision-making.

societal objectives, such as the Sustainable Development Goals (SDGs). 
Risks can arise for example from uncertainty in the implementation, 
effectiveness or outcomes of climate policy, climate-related investments, 
technology development or adoption, and system transitions. See also 
Hazard and Impacts.
Compound risks {↑  Risk}
arise from the interaction of hazards, which may be characterised 
by single extreme events or multiple coincident or sequential 
events that interact with exposed systems or sectors.
Risk assessment
The qualitative and/or quantitative scientific estimation of risks. See 
also Risk management and Risk perception.Risk management
Plans, actions, strategies or policies to reduce the likelihood and/or

magnitude of adverse potential consequences, based on assessed or 
perceived risks.
Risk perception
The subjective judgement that people make about the characteristics 
and severity of a risk. See also Risk assessment and Risk management.
Risk transfer
The process of formally or informally shifting the financial consequences 
of particular risks from one party to another whereby a household, 
community, enterprise or state authority will obtain resources from 
the other party after a disaster occurs, in exchange for ongoing or 
compensatory social or financial benefits provided to that other party.
Runoff
The flow of water over the surface or through the subsurface, which 
typically originates from the part of liquid precipitation and/or snow/
ice melt that does not evaporate, transpire or refreeze, and returns to 
water bodies.
Salt-water intrusion/encroachment
Displacement of fresh surface water or groundwater by the advance

of salt water due to its greater density. This usually occurs in coastal 
and estuarine areas due to decreasing land-based influence (e.g., 
from reduced runoff or groundwater recharge, or from excessive 
water withdrawals from aquifers) or increasing marine influence (e.g., 
relative sea level rise).
Scenario
A plausible description of how the future may develop based on a 
coherent and internally consistent set of assumptions about key driving 
forces (e.g., rate of technological change (TC), prices) and relationships. 
Note that scenarios are neither predictions nor forecasts, but are used 
to provide a view of the implications of developments and actions. See 
also Pathways.
See Reference scenario.
Concentration scenario
concentrations of substances that are radiatively active (e.g., green-
house gases (GHGs), aerosols, tropospheric ozone), plus human-

induced land-cover changes that can be radiatively active via albedo  
changes, and often used as input to a climate model to compute 
climate projections.
A plausible representation of the future development of emissions 
of substances that are radiatively active (e.g., greenhouse gases 
(GHGs) or aerosols) based on a coherent and internally consistent 
set of assumptions about driving forces (such as demographic and 
socio-economic development, technological change, energy and 
land use) and their key relationships. Concentration scenarios, 
derived from emission scenarios, are often used as input to a 
climate model to compute climate projections.

AII
2922Annex II Glossary
Reference scenario
The scenario used as starting or reference point for a comparison 
between two or more scenarios.
[Note 1: In many types of climate change research, reference scenarios  
reflect specific assumptions about patterns of socio-economic 
development and may represent futures that assume no climate 
policies or specified climate policies, for example those in place 
or planned at the time a study is carried out. Reference scenarios  
may also represent futures with limited or no climate impacts  
or adaptation, to serve as a point of comparison for futures with 
impacts and adaptation. These are also referred to as baseline 
technological options, policy design and strategy or to explore

Note 3: The term Business-As-Usual scenario has been used to 
describe a scenario that assumes no additional policies beyond 
those currently in place and that patterns of socio-economic 
development are consistent with recent trends. The term is now 
used less frequently than in the past.
Note 4: In climate change, attribution or impact attribution research 
reference scenarios may refer to counterfactual historical scenarios 
assuming no anthropogenic greenhouse gas emissions (climate 
change attribution) or no climate change (impact attribution)]
Socio-economic scenario
A scenario that describes a possible future in terms of population, 
relevant to understanding the implications of climate change.
Sea ice
Ice found at the sea surface that has originated from the freezing of

seawater. Sea ice may be discontinuous pieces (ice floes) moved on 
the ocean surface by wind and currents (pack ice), or a motionless 
sheet attached to the coast (land-fast ice). Sea ice concentration is 
the fraction of the ocean covered by ice. Sea ice less than 1 year old is 
called first-year ice. Perennial ice is sea ice that survives at least one 
summer. It may be subdivided into second-year ice and multi-year ice, 
where multi-year ice has survived at least two summers.
Sea level change (sea level rise/sea level fall)
Change to the height of sea level, both globally and locally (relative 
sea level change) (at seasonal, annual or longer time scales) due to (1) 
a change in ocean volume as a result of a change in the mass of water 
in the ocean (e.g., due to melt of glaciers and ice sheets), (2) changes 
in ocean volume as a result of changes in ocean water density (e.g., 
expansion under warmer conditions), (3) changes in the shape of the

fields and (4) local subsidence or uplift of the land. Global mean 
sea level change resulting from change in the mass of the ocean is 
called barystatic. The amount of barystatic sea level change due to the 
addition or removal of a mass of water is called its sea level equivalent 
(SLE). Sea level changes, both globally and locally, resulting from 
changes in water density are called steric. Density changes induced by temperature changes only are called thermosteric, while density 
changes induced by salinity changes are called halosteric. Barystatic 
and steric sea level changes do not include the effect of changes in the 
shape of ocean basins induced by the change in the ocean mass and 
its distribution. See also Extreme sea level (ESL).
Sea surface temperature (SST)
The subsurface bulk temperature in the top few metres of the ocean, 
measured by ships, buoys and drifters. From ships, measurements of

water samples in buckets were mostly switched in the 1940s to samples 
from engine intake water. Satellite measurements of skin temperature 
Semi-arid zone
Areas where vegetation growth is constrained by limited water 
availability, often with short growing seasons and high interannual 
variation in primary production. Annual precipitation ranges from 300 
to 800 mm, depending on the occurrence of summer and winter rains.
Sendai Framework for Disaster Risk Reduction
The Sendai Framework for Disaster Risk Reduction 2015–2030 outlines 
seven clear targets and four priorities for action to prevent new, and to 
reduce existing, disaster risks. The voluntary, non-binding agreement 
recognises that the State has the primary role to reduce disaster risk

but that responsibility should be shared with other stakeholders, 
including local government and the private sector. Its aim is to achieve 
‘substantial reduction of disaster risk and losses in lives, livelihoods and 
health and in the economic, physical, social, cultural and environmental 
assets of persons, businesses, communities and countries’.
Sensitivity
The degree to which a system or species is affected, either adversely or 
beneficially, by climate variability or change. The effect may be direct 
(e.g., a change in crop yield in response to a change in the mean, range, 
or variability of temperature) or indirect (e.g., damages caused by an 
increase in the frequency of coastal flooding due to sea level rise).
Sequestration
The process of storing carbon in a carbon pool. See also Sink.
Settlements
Places of concentrated human habitation. Settlements can range from 
isolated rural villages to urban regions with significant global influence.

They can include formally planned and informal or illegal habitation 
and related infrastructure. See also Cities, Urban and Urbanisation.
Shelf seas
Relatively shallow water covering the shelf of continents or around is-
lands. The limit of shelf seas is conventionally considered as 200 m water 
depth at the continental shelf edge, where there is usually a steep slope 
to the deep ocean floor. During glacial periods, most shelf seas are lost

AII
2923Glossary  Annex II
since they become land as the build-up of ice sheets caused a decrease 
of global sea level.
Sink
Any process, activity or mechanism which removes a greenhouse gas, 
an aerosol or a precursor of a greenhouse gas from the atmosphere 
(UNFCCC Article 1.8 (UNFCCC, 1992)). See also Sequestration.
Small Island Developing States (SIDS)
Small Island Developing States (SIDS), as recognised by the United 
Nations Office of the High Representative for the Least Developed 
Countries, Landlocked Developing Countries and Small Island 
Developing States (OHRLLS), are a distinct group of developing 
countries facing specific social, economic and environmental 
vulnerabilities (UN-OHRLLS, 2011). They were recognised as a special 
case for both their environment and their development at the Rio 
Earth Summit in Brazil in 1992. Fifty-eight countries and territories 
are presently classified as SIDS by the UN OHRLLS, with 38 being UN

member states and 20 being Non-UN Members or Associate Members 
of the Regional Commissions (UN-OHRLLS, 2018).
Snow cover extent
The areal extent of snow-covered ground.
Snow water equivalent (SWE)
Social inclusion
A process of improving the terms of participation in society, particularly 
for people who are disadvantaged, through enhancing opportunities, 
access to resources and respect for rights (UN, DESA 2016).
Social learning
A process of social interaction through which people learn new 
behaviours, capacities, values and attitudes.
Social protection
In the context of development aid and climate policy, social protection  
usually describes public and private initiatives that provide income 
or consumption transfers to the poor, protect the vulnerable against

livelihood risks and enhance the social status and rights of the margin-
alised, with the overall objective of reducing the economic and social 
vulnerability of poor, vulnerable and marginalised groups (Devereux 
and Sabates-Wheeler, 2004). In other contexts, social protection may be 
used synonymously with social policy and can be described as all public 
and private initiatives that provide access to services, such as health, 
education or housing, or income and consumption transfers to people. 
Social protection policies protect the poor and vulnerable against liveli-
hood risks and enhance the social status and rights of the marginalised, 
as well as prevent vulnerable people from falling into poverty.Social-ecological system
An integrated system that includes human societies and ecosystems, in 
which humans are part of nature. The functions of such a system arise 
from the interactions and interdependence of the social and ecological

subsystems. The system’s structure is characterised by reciprocal 
feedbacks, emphasising that humans must be seen as a part of, not 
apart from, nature (Arctic Council, 2016; Berkes and Folke, 1998).
Societal (social) transformation
See Transformation.
Socio-technical transitions
Where technological change is associated with social systems and the 
two are inextricably linked.
Soil erosion
The displacement of the soil by the action of water or wind. Soil erosion 
is a major process of land degradation.
Water stored in the soil in liquid or frozen form. Root-zone soil moisture 
is of most relevance for plant activity.
Soil organic carbon
Carbon contained in soil organic matter.
Soil organic matter
The organic component of soil, comprising plant and animal residue at 
various stages of decomposition, and soil organisms.
Solar radiation modification (SRM)
Refers to a range of radiation modification measures not related to

greenhouse gas (GHG) mitigation that seek to limit global warming. 
Most methods involve reducing the amount of incoming solar radiation 
reaching the surface, but others also act on the longwave radiation 
budget by reducing optical thickness and cloud lifetime.
Solution space
The set of biophysical, cultural, socio-economic and political-institutional 
dimensions within which opportunities and constraints determine 
why, how, when and who acts to reduce climate risks. Within 
these dimensions, there are ‘hard’ (unsurpassable) limits and ‘soft’ 
(surpassable) limits. The boundaries of the solution space are path 
dependent, contested and in constant flux (Haasnoot et. al. 2020).
Source
Any process or activity which releases a greenhouse gas, an aerosol or 
a precursor of a greenhouse gas into the atmosphere (UNFCCC Article 
1.9). See also Sink and Sequestration.
Southern Ocean
The ocean region encircling Antarctica that connects the Atlantic, Indian

and Pacific Oceans together, allowing inter-ocean exchange. This region  
is the main source of much of the deep water of the world’s ocean and

AII
2924Annex II Glossary
also provides the primary return pathway for this deep water to the 
surface (Marshall and Speer, 2012; Toggweiler and Samuels, 1995). The 
drawing up of deep waters and the subsequent transport into the ocean  
interior has major consequences for the global heat, nutrient and carbon 
balances, as well as the Antarctic cryosphere and marine ecosystems.
Spatial and temporal scales
Climate may vary on a large range of spatial and temporal scales. 
Spatial scales may range from local (less than 100,000 km2), through 
regional (100,000 to 10 million km2) to continental (10–100 million 
km2). Temporal scales may range from seasonal to geological (up to 
hundreds of millions of years).
Standard
Set of rules or codes mandating or defining product performance (e.g., 
grades, dimensions, characteristics, test methods and rules for use). 
Product, technology or performance standards establish minimum 
requirements for affected products or technologies. Standards impose

reductions in greenhouse gas (GHG) emissions associated with 
the manufacture or use of the products and/or application of the 
technology.
Storm surge
The temporary increase, at a particular locality, in the height of the sea 
due to extreme meteorological conditions (low atmospheric pressure 
and/or strong winds). The storm surge is defined as being the excess 
above the level expected from the tidal variation alone at that time 
and place. See also Extreme sea level and Sea level change (sea level 
rise/sea level fall).
Storyline
A way of making sense of a situation or a series of events through the 
construction of a set of explanatory elements. Usually, it is built on 
logical or causal reasoning. In climate research, the term storyline is 
used both in connection to scenarios as related to a future trajectory 
of the climate and human systems and to a weather or climate event. 
In this context, storylines can be used to describe plural, conditional

possible futures or explanations of a current situation, in contrast to 
single, definitive futures or explanations.
Stranded assets
Assets exposed to devaluations or conversion to ‘liabilities’ because 
of unanticipated changes in their initially expected revenues due to 
innovations and/or evolutions of the business context, including 
changes in public regulations at the domestic and international levels.
Stratification
Process of forming of layers of (ocean) water with different properties 
such as salinity, density and temperature that act as barriers to water 
mixing. The strengthening of near-surface stratification generally 
results in warmer surface waters, decreased oxygen levels in deeper 
water and intensification of ocean acidification (OA) in the upper 
ocean.
Streamflow
Water flow within a river channel, for example, expressed in m3 s−1. A 
synonym for river discharge.Stressors
Events and trends, often not climate-related, that have an important

effect on the system exposed and can increase vulnerability to 
climate-related risk.
Involves ensuring the persistence of natural and human systems, 
implying the continuous functioning of ecosystems, the conservation of 
high biodiversity, the recycling of natural resources and, in the human 
sector, successful application of justice and equity.
Sustainable development (SD)
Development that meets the needs of the present without compromising 
the ability of future generations to meet their own needs (WCED, 1987) 
and balances social, economic and environmental concerns. See also 
Development pathways and Sustainable Development Goals (SDGs).
Sustainable Development Goals (SDGs)
The 17 global goals for development for all countries established by 
the United Nations through a participatory process and elaborated 
in the 2030 Agenda for Sustainable Development, including ending 
poverty and hunger; ensuring health and well-being, education,

gender equality, clean water and energy, and decent work; building 
and ensuring resilient and sustainable infrastructure, cities and 
consumption; reducing inequalities; protecting land and water 
ecosystems; promoting peace, justice and partnerships; and taking 
urgent action on climate change. See also Development pathways and 
Sustainable development.
Sustainable forest management
The stewardship and use of forests and forest lands in a way, and at 
a rate, that maintains their biodiversity, productivity, regeneration 
capacity, vitality and potential to fulfil, now and in the future, relevant 
ecological, economic and social functions, at local, national and global 
levels, and that does not cause damage to other ecosystems (Forest 
Europe, 1993).
Sustainable land management
The stewardship and use of land resources, including soils, water, animals 
and plants, to meet changing human needs, while simultaneously

ensuring the long-term productive potential of these resources and the 
maintenance of their environmental functions (adapted from WOCAT, 
undated).
Sympagic
Organisms and habitats related to the sea ice, analogous to pelagic 
(water column) or benthic (sea floor).
Teleconnection
Association between climate variables at widely separated, 
geographically fixed locations related to each other through physical 
processes and oceanic and/or atmospheric dynamical pathways. 
Teleconnections can be caused by several climate phenomena, such 
as Rossby wave-trains, mid-latitude jet and storm track displacements,

AII
2925Glossary  Annex II
fluctuations of the Atlantic Meridional Overturning Circulation, 
fluctuations of the Walker circulation, etc. They can be initiated by 
modes of climate variability, thus providing the development of remote 
climate anomalies at various temporal lags.
Temperature overshoot
Exceedance of a specified global warming level, followed by a decline 
to or below that level during a specified period of time (e.g., before 
2100). Sometimes the magnitude and likelihood of the overshoot is 
also characterized. The overshoot duration can vary from one pathway 
to the next but in most overshoot pathways in the literature and 
over a period of at least one and up to several decades. See also 
Pathways
Tier
In the context of the IPCC Guidelines for National Greenhouse Gas In-
ventories, a tier represents a level of methodological complexity. Usually

three tiers are provided. Tier 1 is the basic method, Tier 2 intermediate 
and Tier 3 most demanding in terms of complexity and data require-
ments. Tiers 2 and 3 are sometimes referred to as higher-tier methods 
and are generally considered to be more accurate (IPCC, 2019).
Tipping element
A component of the Earth system that is susceptible to a tipping point. 
See also Abrupt climate change and Tipping point.
Tipping point
A critical threshold beyond which a system reorganises, often abruptly 
and/or irreversibly. See also Abrupt climate change.
Trade-off
A competition between different objectives within a decision situation, 
where pursuing one objective will diminish achievement of other 
objective(s). A trade-off exists when a policy or measure aimed at one 
objective (e.g., reducing GHG emissions) reduces outcomes for other 
objective(s) (e.g., biodiversity conservation, energy security) due to 
adverse side effects, thereby potentially reducing the net benefit to

society or the environment. See also Adverse side-effect and Co-benefit.
Transformation
A change in the fundamental attributes of natural and human systems.
Deliberate transformations
A profound shift towards sustainability, envisioned and intended 
by at least some societal actors, facilitated by changes in individual 
and collective values and behaviours, and a fairer balance of 
political, cultural and institutional power in society.
Societal (social) transformations
A change in the fundamental attributes of human systems 
advanced by societal actors
See Adaptation.Transformative change
A system-wide change that requires more than technological change 
through consideration of social and economic factors that, with 
technology, can bring about rapid change at scale.
The process of changing from one state or condition to another in a 
given period of time. Transition can occur in individuals, firms, cities,

regions and nations, and can be based on incremental or transformative 
change.
Just transitions
A set of principles, processes and practices that aim to ensure that no 
people, workers, places, sectors, countries or regions are left behind 
in the transition from a high-carbon to a low-carbon economy. 
It stresses the need for targeted and proactive measures from 
governments, agencies and authorities to ensure that any negative 
social, environmental or economic impacts of economy-wide 
transitions are minimised, while benefits are maximised for those 
disproportionally affected. Key principles of just transitions include: 
respect and dignity for vulnerable groups; fairness in energy access 
and use, social dialogue and democratic consultation with relevant 
stakeholders; the creation of decent jobs; social protection; and 
rights at work. Just transitions could include fairness in energy, land 
use and climate planning and decision-making processes; economic

diversification based on low-carbon investments; realistic training/
retraining programs that lead to decent work; gender-specific 
policies that promote equitable outcomes; the fostering of 
international cooperation and coordinated multilateral actions; 
and the eradication of poverty. Lastly, just transitions may embody 
the redressing of past harms and perceived injustices (ILO 2015; 
UNFCCC 2016).
Tree line
The upper limit of tree growth in mountains or at high latitudes. It is 
more elevated or more poleward than the forest line.
The general term for a strong, cyclonic-scale disturbance that originates 
over tropical oceans. Distinguished from weaker systems (often named 
tropical disturbances or depressions) by exceeding a threshold wind 
speed. A tropical storm is a tropical cyclone with one-minute average 
surface winds between 18 and 32 m s−1. Beyond 32 m s−1, a tropical 
cyclone is called a hurricane, typhoon or cyclone, depending on 
geographic location.

Tsunami
A wave, or train of waves, produced by a disturbance such as a 
submarine earthquake displacing the sea floor, a landslide, a volcanic 
eruption or an asteroid impact.
Tundra
A treeless biome characteristic of polar and alpine regions.
Uncertainty
A state of incomplete knowledge that can result from a lack of information 
or from disagreement about what is known or even knowable. It may

AII
2926Annex II Glossary
have many types of sources, from imprecision in the data to ambiguously 
defined concepts or terminology, incomplete understanding of critical 
processes or uncertain projections of human behaviour. Uncertainty can 
therefore be represented by quantitative measures (e.g., a probability 
density function) or by qualitative statements (e.g., reflecting the 
judgement of a team of experts) (Moss and Schneider, 2000; IPCC, 
2004; Mastrandrea et al., 2010). See also Agreement,  Confidence and 
Likelihood.
A situation of deep uncertainty exists when experts or stakeholders 
do not know or cannot agree on: (1) appropriate conceptual models 
that describe relationships among key driving forces in a system, 
(2) the probability distributions used to represent uncertainty 
about key variables and parameters and/or (3) how to weigh and 
value desirable alternative outcomes (Lempert et al., 2003).
United Nations Framework Convention on Climate Change

(UNFCCC)
The UNFCCC was adopted in May 1992 and opened for signature 
at the 1992 Earth Summit in Rio de Janeiro. It entered into force in 
March 1994 and as of May 2018 had 197 Parties (196 States and 
the European Union). The Convention’s ultimate objective is the 
‘stabilisation of greenhouse gas concentrations in the atmosphere 
at a level that would prevent dangerous anthropogenic interference 
with the climate system’. The provisions of the Convention are pursued 
and implemented by two treaties: the Kyoto Protocol and the Paris 
Agreement.
Uptake
The transfer of substances (such as carbon) or energy (e.g., heat) from 
one compartment of a system to another; for example, in the Earth 
system from the atmosphere to the ocean or to the land. See also 
Sequestration, Sink and Source.
Upwelling region
A region of an ocean where cold, typically nutrient-rich waters well up 
from the deep ocean.
Urban
The categorisation of areas as “urban” by government statistical

departments is generally based either on population size, population 
density, economic base, provision of services, or some combination 
of the above. Urban systems are networks and nodes of intensive 
interaction and exchange including capital, culture, and material 
objects. Urban areas exist on a continuum with rural areas and tend to 
exhibit higher levels of complexity, higher populations and population 
density, intensity of capital investment, and a preponderance of 
secondary (processing) and tertiary (service) sector industries. The 
extent and intensity of these features varies significantly within and 
between urban areas. Urban places and systems are open, with much 
movement and exchange between more rural areas as well as other 
urban regions. Urban areas can be globally interconnected, facilitating 
rapid flows between them, of capital investment, of ideas and culture, 
human migration, and disease. See also City, City region, Urbanisation

and Urban systems.Urban and peri-urban agriculture
The cultivation of crops and rearing of animals for food and other uses 
within and surrounding the boundaries of cities, including fisheries and 
forestry (EPRS, 2014).
Urban heat island (UHI)
The relative warmth of a city compared with surrounding rural areas, 
associated with heat trapping due to land use, the configuration and 
design of the built environment, including street layout and building 
size, the heat-absorbing properties of urban building materials, reduced 
ventilation, reduced greenery and water features, and domestic and 
industrial heat emissions generated directly from human activities.
Urbanisation
Urbanisation is a multi-dimensional process that involves at least 
three simultaneous changes: (1) land-use change: transformation of 
formerly rural settlements or natural land into urban settlements, (2) 
demographic change: a shift in the spatial distribution of a population

from rural to urban areas and (3) infrastructure change: an increase in 
provision of infrastructure services including electricity, sanitation, etc. 
Urbanisation often includes changes in lifestyle, culture and behaviour, 
and thus alters the demographic, economic and social structure of both 
urban and rural areas (based on World Urbanization Prospects 2018; 
IPCC 2014; Stokes and Seto, 2019). See also Settlements, Urban and 
Urban systems.
Urban systems
Urban systems refer to two interconnected systems-- first, the 
comprehensive collections of city elements with multiple dimensions and 
characteristics: a) encompass physical, built, socio-economic-technical, 
political, and ecological subsystems; b) integrate social agent/
constituency/processes with physical structure and processes; and 
c) exist within broader spatial and temporal scales and governance 
and institutional contexts; and second, the global system of cities and 
towns. See also City region, and Urban

Values and beliefs
Fundamental attitudes about what is important, good and right; 
strongly held principles or qualities intrinsically valuable or desirable, 
often enshrined in laws, traditions and religions. Examples include 
human rights, subsistence and equitable distribution of costs and 
benefits of climate policies (Hulme, 2009, 2018; Nakashima et al., 2012; 
UNFCCC, 1992; UN Universal Declaration of Human Rights, 1948).
Vector-borne disease
Illnesses caused by parasites, viruses and bacteria that are transmitted 
by various vectors (e.g., mosquitoes, sandflies, triatomine bugs, 
blackflies, ticks, tsetse flies, mites, snails and lice) (UNEP 2018).
Ventilation (ocean)
The exchange of ocean properties with the atmospheric surface layer 
such that property concentrations are brought closer to equilibrium 
values with the atmosphere (AMS, 2000), and the processes that 
propagate these properties into the ocean interior.

AII
2927Glossary Anne x II
Vulnerability
The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements, including sensitivity or susceptibility to harm and lack of capacity to cope and adapt. See also Exposure, Hazard and Risk.
Vulnerability index
A metric characterising the vulnerability of a system. A climate 
vulnerability index is typically derived by combining, with or without weighting, several indicators assumed to represent vulnerability.
Water-borne diseases
Illnesses transmitted through contact with, or consumption of, unsafe or contaminated water (UNEP 2018).
Water security

The capacity of a population to safeguard sustainable access to adequate quantities of acceptable-quality water for sustaining livelihoods, human well-being and socio-economic development, for ensuring protection against water-borne pollution and water-related disasters and for preserving ecosystems in a climate of peace and political stability (UN-Water, 2013).
Water-use efficiency
Carbon gain by photosynthesis per unit of water lost by evapotranspiration. It can be expressed on a short-term basis as the ratio of photosynthetic carbon gain per unit transpirational water loss, or on a seasonal basis as the ratio of net primary production or agricultural yield to the amount of water used.
Weathering
The gradual removal of atmospheric CO
2 through dissolution of silicate 
and carbonate rocks. Weathering may involve physical processes (mechanical weathering) or chemical activity (chemical weathering).
Well-being

A state of existence that fulfils various human needs, including material living conditions and quality of life, as well as the ability to pursue one’s goals, to thrive and to feel satisfied with one’s life. Ecosystem well-being refers to the ability of ecosystems to maintain their diversity and quality.
Wetland
Land that is covered or saturated by water for all or part of the year (e.g., peatland).

CCP1
2125Biodiversity Hotspots Cr oss-Chapter Paper 1
Geographic areas that are exceptionally rich in species, ecologically 
distinct and/or show high endemism (species occurring in that defined geographic area and nowhere else) are broadly recognised as biodiversity hotspots and prioritised for conservation. Here, we assess the impacts and vulnerability to climate change of terrestrial, freshwater and marine regions considered to be biodiversity hotspots. {CCP1.1}
Species in biodiversity hotspots already show changes in 
response to climate change (high confidence
1). The geographic

ranges of the animal and plant species assessed have shifted from low to high latitudes in response to climate warming on land and in the ocean (very high confidence). On land, climate change-induced shifts towards higher elevations are also common in biodiversity hotspots (high confidence); while, in the ocean, climate-induced shifts to greater water depths are little studied. In the ocean, abrupt mortality of habitat-forming species on coral reefs and kelp forests, especially following heatwaves, are increasing in frequency in biodiversity hotspots (high confidence). {CCP1.2.1; 1.2.2; 1.2.4}
All biodiversity hotspots are impacted, to differing degrees, by 
human activities (very high confidence). Climate change impacts

are compounded by other anthropogenic impacts. These include habitat loss and fragmentation, hunting, fishing and its bycatch, over-exploitation, water abstraction, nutrient enrichment, pollution, human introduction of invasive species, pests and diseases. All of these reduce climate resilience (very high confidence), complicating the attribution 
of observed changes to climate change. {CCP1.2.1}
Observed climate velocities are approximately 20% lower 
inside than outside of terrestrial and freshwater biodiversity hotspots, but 69% higher inside than outside marine hotspots (high confidence). In spite of the lower climate velocities inside

terrestrial hotspots, these areas are not projected to serve as effective climate refugia from the effects of global warming, especially for endemic species (unique to a hotspot) (medium confidence). The greater climate velocities inside marine hotspots exposes their species to greater climate-induced pressures inside than outside hotspots (high confidence). The differences between temperatures inside and outside of hotspots narrow with increasing warming (medium confidence). {CCP1.2.2}
The risk of species extinction increases with warming in 
all climate change projections for native species studied in hotspots (high confidence), being about 10-times greater for endemic species from 1.5°C to 3°C above pre-industrial levels (medium confidence). Of the 6116 projections for more than 2700

species assessed in biodiversity hotspots, ~44% were found to be at high extinction risk, and ~24% at very high extinction risk due to climate change (medium confidence). Very high extinction risk in biodiversity hotspots due to climate change is more common for 
1 In this report, the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium, or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, for example, medium confidence. For a given evidence and agreement statement, different confidence 
levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.endemic species than other native species (high confidence). For these

endemic species, considering all scenarios and time periods evaluated, ~100% on islands, ~84% on mountains, ~12% on continents (medium confidence) and ~54% in the ocean (notably the Mediterranean) (low confidence) are projected to be threatened with extinction due to climate change. With further warming, increasingly high risks of local and global extinctions are projected in biodiversity hotspots from climate-related stressors (high confidence). {CCP1.2.1; Figure CCP1.7; Figure CCP1.6}
Adaptation options can enhance the persistence of biodiversity in

hotspots (high confidence). Noting that over 3 billion people live within biodiversity hotspots, reduction of existing (non-climatic) pressures due to human activities is critical for building resilience within hotspots. Adaptation options for biodiversity (e.g., expanding fully protected areas, restoration and sustainable use practices) are as applicable inside biodiversity hotspots as outside (high confidence). Nevertheless, the protection of biodiversity hotspots is key to preventing a substantial global biodiversity decline from climate change. {CCP1.3; 2.6; 3.6; Table CCP1.2; Cross-Chapter Box NATURAL in Chapter 2}.

CCP1
2126Cross-Chapter Paper 1 Biodiversity Hotspots
CCP1.1 Point of Departure
Biodiversity hotspots are geographic areas with an exceptionally 
high richness of species, including rare and endemic species. Such 
hotspots have deep evolutionary roots and are concentrated in areas 
where past climatic variability was moderate (Enquist et  al., 2019; 
Brown et al., 2020; Trew and Maclean, 2021). An important limitation 
of the biodiversity hotspot concept is that there may be species 
highly threatened with extinction that do not occur within what has 
traditionally been classified as a hotspot (Grenyer et al., 2006). Thus, 
biodiversity hotspot assessments need to be paralleled by assessments 
of highly endangered species, and the threats they face.
Many studies have proposed biodiversity hotspots based on different 
criteria, taxa and geographic contexts (e.g., Myers et  al., 2000; 
Mittermeier et  al., 2004; Mittermeier et  al., 2011; Williams et  al.,

2011; Noss et al., 2015; Asaad et al., 2017). A coherent comparative 
assessment of all such schemes is beyond the scope of this chapter 
but is provided in recent reviews (Asaad et  al., 2017; Jefferson and 
Costello, 2019). We base this assessment on the ‘WWF Global 200’ 
areas of conservation importance (Olson and Dinerstein, 2002). These 
238 ecoregions have been used in a previous climate risk assessment 
(Warren et  al., 2018b) and cover terrestrial, freshwater and marine 
environments (Table CCP1.1; Figures CCP1.1; CCP1.2). In addition, we 
included terrestrial ‘biodiversity hotspots’ as defined by Myers et al. 
(2000) and extended later (Mittermeier et  al., 2011; Williams et  al., 
2011; Noss et  al., 2015). This assessment thus covers the ‘Global 
200’ (hereafter G200) and ‘Myers’ biodiversity hotspots, rather than 
particular species or ecological systems, such as rainforests, coral 
reefs or the deep sea. Such systems are assessed in Chapters 2 and 3.

Chapters 2 and 3 also cover observed and projected impacts, changes 
in ecosystem functioning, and species extinction risks at a global level.
Biodiversity hotspots were not explicitly covered in the Working Group 
II (WGII) Fifth Assessment Report (IPCC, 2014) (hereafter AR5). Thus, 
the point of departure is WGII AR4 (IPCC, 2007), which assessed that 
climate change exacerbates biodiversity risks in hotspots and that 15–
40% of endemic species (species only occurring in one region) were 
projected to become extinct at 3.5°C global warming (Fischlin et al., 
2007). Risks of extinction are assessed using the guidelines in Chapter 
2, with species projected to lose 80% of their range or abundance 
being classified as at very high risk of extinction and those projected 
to lose 50% being at high risk of extinction (Figure 2.8 for definitions 
and a global overview).CCP1.2 Assessment
Specific hotspot numbers (H) are indicated in this chapter text to aid

their identification in Table CCP1.1 and Figures CCP1.1 and CCP1.2.
CCP1.2.1 Global Perspective
CCP1.2.1.1 Observed Impacts
CCP1.2.1.1.1 Observed climatic hazards
Terrestrial and freshwater hotspots have been warming less over the 
last 50 years than non-hotspot areas, whereas marine hotspots have 
been warming more (Kocsis et al., 2021). The warming inside terrestrial 
hotspots is 0.91°C (Myers) and 1.04°C (G200), respectively, while for 
freshwater hotspots it is 0.89°C, compared to 1.08°C warming outside 
(Kocsis et al., 2021). In contrast, mean annual sea surface temperatures 
in the G200 marine biodiversity hotspots have warmed 41% more than 
the regions outside (0.53°C compared with 0.38°C) (Kocsis et al., 2021). 
Thus, terrestrial biodiversity hotspots have been warming slightly less, 
and marine hotspots considerably more than non-hotspots (medium 
confidence).
Climate velocity, the direction and pace of movement in climate

variables (typically temperature) in space, is key to understanding the 
origin and fate of biodiversity hotspots under climate change (Loarie 
et al., 2009; Burrows et al., 2011). Climate trajectories generally predict 
the direction and pace of past and future species range shifts (Pinsky 
et al., 2013; Brito-Morales et al., 2018), although there are exceptions 
(Fuchs et  al., 2020). Spatial patterns of climate trajectories show 
regions where species are expected to leave, pass through, and/or 
arrive under climate change (Burrows et al., 2014). Regions of high 
climate velocities are those with low topographic relief on land, 
particularly flooded grasslands and deserts (Loarie et al., 2009), and 
tropical as well as offshore and polar sea regions (Burrows et al., 2011; 
Burrows et al., 2014; García Molinos et al., 2016; Brito-Morales et al., 
2018; Brito-Morales et al., 2020).
On millennial time scales, some areas of low climate velocity have

more endemic species and can be considered climate refugia, at least 
on land (Sandel et al., 2011) and, for marine species, around Antarctica 
(H213) (Costello et  al., 2010). This suggests that, if these areas are 
subject to increased velocities, they will lose species that are not 
able to disperse fast enough to cope with the pace of climate change 
(medium confidence) (Sandel et al., 2011; Brito-Morales et al., 2018).
Climate velocities are 47% (Myers), 29% (G200, terrestrial) and 10% 
(G200, freshwater) lower inside biodiversity hotspots than outside, 
respectively (Kocsis et  al., 2021), but are 69% higher inside marine 
hotspots than outside (medium confidence). Climate velocities 
from 1970 to 2019 ranged from 3–4 km per decade (terrestrial and 
freshwater) to ~11 km per decade in marine (Kocsis et al., 2021).
For terrestrial and freshwater hotspots, the highest climate velocities 
are in central South America, including the Amazon (H153, 154)

(Figure  CCP1.3). Terrestrial hotspots also have high velocities in the

CCP1
2127Biodiversity Hotspots Cr oss-Chapter Paper 1
Recent human 
impacts on the terrestrialbiodiversityhotspots
(a)
(b)(d)(c)
(g)(e)
(f)
(a)
(b)(d)
(c)
(g)(e)
(f)Impact
Very high
High
MediumLowVery low
Figure CCP1.1 |  Recent human impacts on the terrestrial biodiversity hotspots (coloured, grey is non hotspot) (Table CCP1.SM.1). Impacts are scaled in five 
equal 20% categories. (a)
 North and Centr
al America; (b) South America; (c) Southeast Asia; (d) Europe and North Africa; (e) Africa and Arabia; (f) North Asia; (g) Southeast Asian 
archipelagos, Australia and New Zealand. See Table CCP1.1 for key to hotspot numbers.

CCP1
2128Cross-Chapter Paper 1 Biodiversity Hotspots
impacts on
freshwater
and marine
biodiversity
hotspots
Impact
Very high
High
Medium
Low
Very low
(a)
Freshwater biodiversity hotspots
(b)
Marine biodiversity hotspots
Figure CCP1.2 |  Recent human impacts from multiple factors on (a) freshwater hotspots since 2000, based on Janse et al. (2015) and (b) marine hotspots 
based on Halpern et al. (2015). Human impacts in freshwater areas refer to the remaining wilderness. Marine impacts represent land-based, fishing, climate change and 
ocean-based stressors. Impacts are scaled into five equal 20% categories. See Table CCP1.1 for the key to hotspot numbers.

CCP1
2129Biodiversity Hotspots  Cross-Chapter Paper 1
Climate velocities
in terrestrial, freshwater
and marine
biodiversity hotspots
between 1970–2019
Km per decade
(a)
Terrestrial 
(b)
Freshwater
(c)
Marine
biodiversity hotspots60
40
20
0
-20
-40
-60
Figure CCP1.3 |  Climate velocities in terrestrial (a), freshwater (b) and marine (c) hotspots between 1970–2019. Values are presented in kilometres per decade and 
derived using the analytical package VoCC (García Molinos et al., 2019) from gridded temperature data, sea surface temperatures for marine (Rayner et al., 2003) and near-surface 
air temperatures on land and freshwater (Harris et al., 2020). Positive and negative velocities indicate warming and cooling, respectively.

CCP1
2130Cross-Chapter Paper 1 Biodiversity Hotspots
Table CCP1.1 |  List of biodiversity hotspots names from (Olson and Dinerstein, 2002) as mapped in Figures CCP1.1 for terrestrial (numbered 1 to 142) and CCP1.2 for freshwater 
(143 to 195) and marine (196 to 238). Hotspots containing islands (>100 km2) are indicated with an asterisk.
1 Hawaii Moist Forest* 81  Drakensberg Montane Woodlands and Grasslands 161 Danube River Delta
2 Hawaii Dry Forest* 82 Ural Mountains Taiga and Tundra 162 Balkan Rivers and Streams*
3 Alaskan North Slope Coastal Tundra* 83 Taimyr and Russian Coastal Tundra* 163 Anatolian Freshwater
4 Canadian Boreal Taiga* 84 Altai-Sayan Montane Forests 164 Upper Guinea Rivers and Streams*
5 Canadian Low Arctic Tundra* 85 Central and Eastern Siberian Taiga 165 Niger River Delta*
6 Muskwa/Slave Lake Boreal Forests 86 Chukhote Coastal Tundra* 166 Cameroon Crater Lakes
7 Pacific Temperate Rainforests* 87 Central Asian Deserts 167 Gulf of Guinea Rivers and Streams*

8 Northern Prairies 88 Middle Asian Montane Woodlands and Steppe 168 Congo River and Flooded Forests
9 Klamath-Siskiyou Coniferous Forests 89 Kamchatka Taiga and Grasslands* 169 Congo Basin Piedmont Rivers and Streams
10 Sierra Nevada Coniferous Forests 90 Russian Far East Broadleaf and Mixed Forests* 170 Cape Rivers and Streams
11 California Chaparral and Woodlands* 91 Daurian/Mongolian Steppe 171 Rift Valley Lakes
12 Sonoran-Baja Deserts* 92 Tibetan Plateau Steppe 172 Madagascar Freshwater Ecosystem*
13 Chihuahuan-Tehuacan Deserts 93 Hengduan Shan Conifer Forests 173 Lena River Delta
14 Sierra Madre Oriental and Occidental Pine-Oak 94 Southwest China Temperate Forests 174 Lake Baikal
15 Southern Mexican Dry Forests* 95 Western Himalayan Temperate Forests 175 Russian Far East Rivers and Wetlands*
16 Mesoamerican Pine-Oak Forests 96 Rann of Kutch Flooded Grasslands 176 Lake Biwa*
17 Appalachian and Mixed Mesophytic Forests 97 Terai-Duar Savannas and Grasslands 177 Indus River Delta*

18 Southeastern Conifer and Broadleaf Forests* 98 Eastern Himalayan Alpine Meadows 178 Western Ghats Rivers and Streams
19 Everglades Flooded Grasslands 99  Eastern Himalayan Broadleaf and Conifer Forests 179 Southwestern Sri Lanka Rivers*
20 Greater Antillean Moist Forests* 100 Eastern Deccan Plateau Moist Forests* 180 Salween River
21 Greater Antillean Pine Forests* 101 Chhota-Nagpur Dry Forests 181 Lake Inle
22 Talamancan-Isthmian Pacific Forests 102 Southwestern Ghats Moist Forest 182 Sundaland Rivers and Swamps*
23 Choco-Darien Moist Forests* 103 Sri Lankan Moist Forest* 183 Yangtze River and Lakes
24 South American Pacific Mangroves* 104 Sundarbans Mangroves 184 Xi Jiang Rivers and Streams
25 Galapagos Islands Scrub* 105 Naga-Manapuri-Chin Hills Moist Forests* 185 Yunnan Lakes and Streams
26 Northern Andean Páramo 106 Kayah-Karen/Tenasserim Moist Forests 186 Mekong River*
27 Northern Andean Montane Forests 107 Indochina Dry Forests* 187 Philippines Freshwater*

28 Tumbesian-Andean Valleys Dry Forests* 108 Cardamom Mountains Moist Forests* 188 Central Sulawesi Lakes*
29 Napo Moist Forests 109  Peninsular Malaysia Lowland and Montane Forests* 189 New Guinea Rivers and Streams*
30 Southwestern Amazonian Moist Forests 110  Sumatran Islands Lowland and Montane Forests* 190 Lakes Kutubu and Sentani*
31 Atacama-Sechura Deserts 111 Western Java Montane Forests* 191 Kimberley Rivers and Streams
32 Central Andean Dry Puna 112 Nusu Tenggara Dry Forests* 192 Southwest Australia Rivers and Streams
33 Central Andean Yungas 113 Southeast China-Hainan Moist Forests 193 New Caledonia Rivers and Streams*
34 Chilean Matorral 114 North Indochina Subtropical Moist Forests 194 Central Australian Freshwater*
35 Valdivian Temp. Rain Forests Juan Fernandez* 115 Annamite Range Moist Forests 195 Eastern Australia Rivers and Streams
36 Patagonian Steppe* 116 Kinabalu Montane Shrublands* 196 Bering Sea*

37  Amazon-Orinoco-Southern Caribbean Mangroves* 117 Borneo Lowland and Montane Forests* 197 California Current*
38 Coastal Venezuela Montane Forests 118 Greater Sundas Mangroves* 198 Hawaiian Marine*
39 Llanos Savannas 119 Nansei Shoto Archipelago Forests* 199 Gulf of California*
40 Guianan Highlands Moist Forests 120 Taiwan Montane Forests* 200 Mesoamerican Reef*
41 Rio Negro-Jurua Moist Forests 121 Philippines Moist Forests* 201 Panama Bight*
42 Guianan Moist Forests 122 Palawan Moist Forests* 202 Galapagos Marine*
43 Atlantic Dry Forests 123 Sulawesi Moist Forests* 203 Fiji Barrier Reef*
44 Cerrado Woodlands and Savannas 124 Moluccas Moist Forests* 204 Tahitian Marine*
45 Pantanal Flooded Savannas 125 New Guinea Mangroves* 205 Rapa Nui*
46 Chiquitano Dry Forests 126 Southern New Guinea Lowland Forests* 206 Humboldt Current*

CCP1
2131Biodiversity Hotspots  Cross-Chapter Paper 1
Arctic (H196, 214) and east of the Caspian Sea, while freshwater 
hotspots have low velocities in the eastern European Mediterranean 
and eastern Australia.
Marine hotspots have a wider range of climate velocities than 
terrestrial and freshwater environments (Figure CCP1.3), being faster 
in equatorial, Mediterranean (H216), Baltic (H215), North and Okhotsk 
(H229), and Arctic hotspots (H196, 214), and slow in the Antarctic 
hotspot (H213). Marine species tend to follow climate velocities more 
closely than terrestrial species (high confidence) (Sunday et al., 2012; 
Pinsky et al., 2019; Lenoir et al., 2020). The reasons may be smaller 
thermal safety margins in the seas or greater human impacts on land 
impeding species range shifts. Climate velocities are particularly fast 
in equatorial seas (Figure  CCP1.3; (Burrows et  al., 2011), which are

therefore expected to be source areas for species shifting their ranges towards the subtropics (Burrows et al., 2014). The subtropics are then 
source areas of species that shift to temperate latitudes and so forth, 
such that observed impacts in marine biodiversity hotspots are largely 
attributable to species range shifts (high confidence) (Pecl et al., 2017). 
Because marine climate velocities are significantly greater within than 
outside hotspots, marine hotspots are especially prone to species 
redistributions (medium confidence) (Figure  CCP1.3; (Kocsis et  al., 
2021).
While species from lower latitudes may shift their geographic ranges 
to higher latitudes to adapt to changing climate, there are no species 
to replace low latitude species. Thus, as already observed in the oceans 
around the equator, the loss of species in low latitudes will continue 
with future climate warming (high confidence) (Yasuhara et al., 2020;

Chaudhary et al., 2021). The issue also extends to altitudinal ranges 47 Atlantic Forests* 127 Central Range Subalpine Grasslands* 207 Grand Banks*
48 Fenno-Scandia Alpine Tundra and Taiga* 128 New Guinea Montane Forests* 208 Chesapeake Bay
49 Caucasus-Anatolian-Hyrcanian Temp. Forests 129 Solomons-Vanuatu-Bismarck Moist Forests* 209 Greater Antillean Marine*
50 European Mediterranean Montane Forests 130 Northern Australia and Trans-Fly Savannas* 210 Southern Caribbean Sea*
51 Mediterranean Forests, Woodlands, Scrub* 131 Great Sandy-Tanami-Central Ranges Desert 211 Northeast Brazil Shelf Marine*
52 Sudd-Sahelian Flooded Grasslands and Savannas 132 Carnavon Xeric Shrubs* 212 Patagonian Southwest Atlantic*
53 Guinean Moist Forests 133 Southwestern Australia Forests and Scrub* 213 Antarctic Peninsula and Weddell Sea*
54 Gulf of Guinea Mangroves* 134 Southern Australia Mallee and Woodlands* 214 Barents-Kara Seas*

55 Cameroon Highlands Forests* 135 Queensland Tropical Forests* 215 Northeast Atlantic Shelf Marine*
56 Congolian Coastal Forests* 136 New Caledonia Moist Forests* 216 Mediterranean Sea*
57 Sudanian Savannas 137 New Caledonia Dry Forests 217 Canary Current*
58 Western Congo Basin Moist Forests 138 Lord Howe and Norfolk Island Forests* 218 Benguela Current
59 Northeastern Congo Basin Moist Forests 139 New Zealand Temperate Forests* 219 Arabian Sea*
60 Central Congo Basin Moist Forests 140 Eastern Australia Temperate Forests* 220 Red Sea*
61 Albertine Rift Montane Forests 141 Tasmanian Temperate Rainforests* 221 West Madagascar Marine*
62 Central and Eastern Miombo Woodlands 142 Southern Pacific Islands Forests* 222 East African Marine*
63 Zambezian Flooded Savannas 143 Gulf of Alaska Coastal Rivers* 223 Agulhas Current
64 Namib-Karoo-Kaokoveld Deserts and Shrublands* 144 Pacific Northwest Coastal Rivers 224 Maldives-Chagos-Lakshadweep Atolls*

65 Fynbos 145 Colorado River 225 Andaman Sea*
66 Arabian Highlands Woodlands and Shrublands* 146 Chihuahuan Freshwater 226 Banda-Flores Sea*
67 Socotra Island Desert* 147 Mexican Highland Lakes 227 Western Australia Marine*
68 Ethiopian Highlands 148 Mississippi Piedmont Rivers and Streams 228 Southern Australian Marine
69 Horn of Africa Acacia Savannas 149 Lower Mississippi River 229 Okhotsk Sea*
70 East African Coastal Forests* 150 Southeastern Rivers and Stream s* 230 Yellow Sea*
71 East African Moorlands 151 Greater Antillean Freshwater* 231 Nansei Shoto*
72 Eastern Arc Montane Forests 152 Orinoco River and Flooded Forests 232 Sulu-Sulawesi Seas
73 East African Mangroves* 153 Upper Amazon Rivers and Streams 233 Palau Marine*
74 East African Acacia Savannas 154 Brazilian Shield Amazonian Rivers and Streams 234 Bismarck-Solomon Seas
75 Southern Rift Montane Woodlands 155 High Andean Lakes 235 New Caledonia Barrier Reef*

76 Madagascar Mangroves* 156 Guianan Freshwater 236 Great Barrier Reef*
77 Madagascar Dry Forests* 157 Amazon River and Flooded Forests* 237 Lord Howe-Norfolk Islands Marine
78 Seychelles and Mascarenes Moist Forests* 158 Upper Paraná Rivers and Streams 238 New Zealand Marine*
79 Madagascar Forests and Shrublands* 159 Volga River Delta
80 Madagascar Spiny Thicket* 160 Mesopotamian Delta and Marshes*

CCP1
2132Cross-Chapter Paper 1 Biodiversity Hotspots
in terrestrial environments, with species moving to higher elevations 
where surface area generally declines with increasing elevation; 
mountaintop species may have nowhere to go (Flousek et al., 2015; 
Freeman et al., 2018; Kidane et al., 2019).
CCP1.2.1.1.2 Observed impacts on biodiversity
Although conservation status has only been assessed globally for about 
6% of all species (Costello, 2019) and most confirmed extinctions and 
threatened species are terrestrial, a higher proportion of freshwater 
species are threatened. This is reflected in the higher proportion 
of freshwater hotspots impacted by humans (Collen et  al., 2014; 
Costello, 2015; Harrison et al., 2018). The rate of species endemicity 
is exceptionally high in freshwater biogeographic realms (i.e., large 
regions of distinct species composition and endemicity), at 89–96% for 
fish in all but one realm, compared to 11–98% for terrestrial vertebrate

groups (Leroy et al., 2019) and 17–84% for marine realms (Costello 
et al., 2017). Already, one-third of wetlands have been lost and 9000 
freshwater species are threatened with extinction without considering 
the effects of climate change (Darwall et al., 2018), and only 13% of 
world rivers were recently classified as least impacted (Su et al., 2021).
Globally, observed climate-driven changes in biodiversity are typically 
of species distributions shifting to higher latitudes (virtually certain) 
(Lenoir et  al., 2020, Ch.2, Ch. 3.4). Since the 1950s, marine species 
richness has shifted poleward in the Northern Hemisphere, increased 
in mid-latitudes and declined at the equator in concert with ocean 
warming (medium confidence) (Chaudhary et  al., 2021). Climate-
driven altitudinal shifts are common on land (high confidence) (Lenoir 
and Svenning, 2015; Steinbauer et al., 2018), and depth shifts in the 
ocean may occur but are little studied (low confidence) (Burrows et al.,

2019; Jorda et al., 2020). While climate-induced range expansions can 
be viewed as opportunities for increasing regional biodiversity, range 
contractions adversely affect biodiversity through regional extirpations 
(high confidence) (Cahill et al., 2013; Chaudhary et al., 2021).
Both of the two climate change associated global species extinctions 
to date support the predictions that endemic species on mountains 
and islands are at the greatest risk of extinction (Manes et al., 2021). 
The golden toad (Bufo periglenes) became extinct after some years of 
decline associated with changes in climate warming and precipitation 
in the Talamancan-Isthmian Pacific Forests biodiversity hotspot (H22) 
(Pounds et al., 1999; Cahill et al., 2013, WGII Ch2.4.2.2). The Bramble 
Cay melomys (Melomys rubicola), a rodent endemic to an island 
between Australia and Papua New Guinea and closely related to a 
mainland Australian species, became extinct due to habitat loss arising

from climate change-related sea level rise and cyclone activity (Fulton, 
2017; Roycroft et al., 2021, WGII Ch.11).
CCP1.2.1.2 Projected Impacts
CCP1.2.1.2.1 Projected climatic hazards
Comparison of climate warming projected for air and sea temperature 
shows biodiversity hotspots will continue to experience the greatest 
net increases in temperature at higher Northern Hemisphere latitudes, 
particularly in tundra regions (Figures CCP1.4; CCP1.5; Table CCP1.1). Generally, terrestrial and freshwater hotspots are projected to continue 
to warm more than marine (Figure CCP1.3). Modelled temperatures 
are projected to continue to be the highest in the tropics, indicating 
where there are more thermally stressful conditions for more species 
(high confidence) (Stuart-Smith et al., 2015; Stuart-Smith et al., 2017; 
Foster et al., 2018; Waldock et al., 2019). By the end of this century, all 
terrestrial biodiversity hotspots in Central and South America, Africa,

India and southern and eastern Asia (including the Indo–West Pacific 
islands) are projected to experience climates unprecedented in their 
species’ evolutionary history (medium confidence) (Williams et  al., 
2007).
Based on WGI Interactive Atlas data (Gutiérrez et  al., 2021), global 
warming is projected to affect terrestrial hotspots less than non-
hotspot areas: 80% less for Myers and 95–96% less for G200 terrestrial 
and freshwater hotspots at global warming of 1.5°C–3°C (medium 
confidence) (Kocsis et  al., 2021). In contrast, warming is projected 
to be 12–13% greater inside than outside marine hotspots (medium 
confidence) (Kocsis et  al., 2021). Precipitation is generally projected 
to increase more in terrestrial and freshwater biodiversity hotspots 
compared to outside them (low confidence) (Kocsis et al., 2021). The 
exception is Myers hotspots, which are projected to have, on average, 
~28% less precipitation at 1.5°C warming, but ~33% more at 2°C and

~65% more at 3°C (low confidence). However, precipitation changes 
are often difficult to assess as many hotspots cover large areas, with 
some areas projected to be wetter and some drier with wide differences 
between different climate models.
CCP1.2.1.2.2 Projected impacts on biodiversity
Biodiversity hotspots are expected to be especially vulnerable to climate 
change because their endemic species have smaller geographic ranges 
(high confidence) (Sandel et  al., 2011; Brown et  al., 2020; Manes 
et al., 2021). Manes et al. (2021) reviewed over 8000 projections of 
climate change impacts on biodiversity in 232 studies, including 6116 
projections on endemic, native and introduced species in terrestrial 
(200  studies), freshwater (14  studies) and marine (34  studies) 
environments in biodiversity hotspots. Only half of the hotspots 
had studies on climate change impacts. All measures of biodiversity 
were found to be negatively impacted by projected climate change,

namely, species abundance, diversity, area, physiology and fisheries 
catch potential (medium confidence). However, introduced species’ 
responses were neutral to positive (medium confidence). Land areas 
were projected to be more negatively affected by climate warming 
than marine. Land plants, insects, birds, reptiles and mammals were 
all projected to be negatively affected (medium confidence), as well as 
fish, coral reef, benthic, planktonic and other marine species (medium 
Of the 6116 projections for more than 2,700 species assessed in 
biodiversity hotspots, ~44% were found to be at high extinction risk, 
and ~24% at very high extinction risk due to climate change (Manes 
et al., 2021) (medium confidence). Risks of extinction were estimated 
based on the projections for all warming levels combined, showing 
that endemic species were about 2.7 times more at very high risk of 
extinction compared to non-endemic native species (Manes et  al.,

2021). Extinction risks were highest for endemic species of both land

CCP1
2133Biodiversity Hotspots  Cross-Chapter Paper 1
and ocean (medium confidence), and were higher for those living on 
islands (~100%, medium confidence) and mountains (~84%, medium 
confidence) than in the ocean (~54%, low evidence, medium agreement; 
low confidence) and on continents (~12%, robust evidence, medium 
agreement, medium confidence) (Figure  CCP1.6). Extinction risks 
for non-endemic natives were ~20% for both terrestrial and marine 
species, with introduced species projected to become more rather than 
less invasive. At 1.5°C warming, ~2% of both terrestrial and marine 
species and at 3°C, ~20% and ~32% respectively, were projected to 
be at very high risk of extinction in the hotspots (Figure CCP1.6). Thus, 
a doubling of warming results in a roughly 10-fold increase in species 
at very high extinction risk.Manes et al. (2021) found that any benefits to species (e.g., range 
or abundance increase) were projected to be localised and transient

(e.g., Arctic, H196, 214). This and previous assessments indicate 
that, while climate change varies spatially and taxa may respond 
differently, a loss of biodiversity is projected across all terrestrial 
hotspots (high confidence) (Foden et al., 2013; Warren et al., 2018a; 
Manes et al., 2021). Abrupt changes across species assemblages may 
occur under all scenarios: in 9% of assemblages at 1.75°C and 35% 
at 4.4°C on both land and sea (Trisos et al., 2020). However, species 
losses may be reduced if species have thermal microclimate refugia 
and behavioural thermoregulation, or greater due to extreme events, 
such as heatwaves.Projected loss of climatically suitable area in terrestrial biodiversity hotspots
Projected human impact
Very high High Medium Low Very lowChanges of impact categories
compared to present day-impact
+1.5°C
+2.0°C
+3.0°C(a)
(c)
(e)(b)
(d)
(f)
0 -1 -2 -3 -4 +4 +3 +2 +1

Figure CCP1.4 |  Projected loss of climatically suitable area in terrestrial biodiversity hotspots for a global average of 1.5°C (upper row, a–b), 2°C (middle, 
c–d) and 3°C (lower, e–f). Left-hand column displays the projected human impact using the five equal 20% categories of present-day impact (Figure CCP1.1). The right-hand 
column indicates the changes of impact categories compared to present-day impact. See Table SMCCP1.1 for more details.

CCP1
2134Cross-Chapter Paper 1 Biodiversity Hotspots
Projected future warming for freshwater and marine biodiversity hotspots
+1.5°C
+2.0°C
+3.0°C(a)
(c)
(e)(b)
(d)
(f)Projected warming
4°C 5°C 3°C 2°C 1°C 0°C
Increase from pre-industrial:
3.93°C
Figure CCP1.5 |  Projected future warming in degrees Celsius for freshwater (left column, near-surface air temperature, panels a, c and e) and marine (right 
column as sea surface temperature, panels b, d and f) hotspots for a global average warming of +1.5°C (a, b), +2°C (c, d) and +3°C (e, f) compared to 
pre-industrial conditions. Values in text boxes in the figures indicate temperature increase from present-day (2005–2014) settings. Projected temperatures were calculated with 
averages of multi-model, yearly means across Shared Socioeconomic Pathways (SSP) 1.26 (only for +1.5°C), SSP2-45, SSP3-70 and SSP5-85.
CCP1.2.1.3 Compounding and Cascading Effects
All biodiversity hotspots are already impacted, to differing degrees,

by human activities (high confidence) (Table CCP1.1, Figures CCP1.1, 
CCP1.2, Myers et al., 2000; Le Roux et al., 2019). At present, over three 
billion people live within terrestrial and (catchments of) freshwater 
biodiversity hotspots, many of which border marine hotspots (Figures 
CCP1.1; CCP1.2; Table  SMCCP1.1; Gutiérrez et  al., 2021). Thus, 
climate change impacts on biodiversity hotspots are compounded 
by other anthropogenic impacts, increasing the vulnerability and 
reducing the resilience of biodiversity to climate change (very high 
confidence). Projections of changing climate alone may overestimate or underestimate the impacts on biodiversity (medium evidence, high 
agreement). The additional risk of the combined effects of climate 
change and other impacts (e.g., land use change, overhunting, 
pollution and invasive species) on species has been raised since the 
Third Assessment Report. The terrestrial hotspots projected to be

most affected by global warming are, in general, those already being 
impacted by loss of habitat due to land use change (Figure CCP1.4; 
Table SMCCP1.1) (Warren et al., 2018a). This remains a trend in the 
recent literature, although most studies still address only one stressor 
(Titeux et al., 2016). For example, Mantyka-Pringle et al. (2015) show 
that when the interaction between projected climate change and 
habitat loss is taken into account, the extinction risk of birds and

CCP1
2135Biodiversity Hotspots  Cross-Chapter Paper 1
mammals in 15–32% of terrestrial biodiversity hotspots changes. 
Similarly, Bellard et al. (2014b) found different results when examining 
the impact of climate change, invasive species and land use change 
independently, as opposed to synergistically. When combining those 
three impacts they identified the Atlantic Forest (H47), Cape Floristic 
Region (H65) and Polynesia-Micronesia (H1, 2, 138, 139, 142) as 
particularly vulnerable.
In a global assessment of the threat of climate change to river fish 
biodiversity, Tedesco et  al. (2013) projected that current extinction 
rates of species may be 7% greater due to climate change. The main 
threat is due to the effects of drought and reduced river flows, which 
would be 18 times greater than without climate change. However, just 20 of the 110 river basins studied would experience sufficient climate-
driven water loss to cause fish extinctions by 2090. Moreover, the

present rates of species loss due to human activities are 130  times 
greater than those projected under future climate change (medium 
confidence) (Tedesco et al., 2013).
Marine systems are also vulnerable to cumulative human impacts, 
which can be direct (e.g., pollution, overfishing) and indirect (altered 
food webs) (very high confidence) (Halpern et al., 2008; Halpern et al., 
2015). The marine hotspots most currently threatened by non-climate-
related human impacts are all situated in the Northern Hemisphere, 
specifically along the northern European, Mediterranean and Asian 
coasts, where the overlap of overfishing and pollution is especially large 
Endemics species are only found in one place in the world
Mitigation is paramount to safeguard biodiversity and
increase resilience against climate change
2% 4%20%
2%13%32%

*Proportion of species facing extinction risks: >80% losses of abundance or range<1.5°C 1.5-2°C >3°C <1.5°C 1.5-2°C >3°CTerrestrial* Marine*Endemics’  are more at risk than: Endemics’  are at risk of extinction in:
54%
Oceans2.7
times10
times100%
Islands84%
Mountains
    Species introduced
by human activityWidespread speciesBiodiversity Hotspots
Areas with many rare and unique species
Figure CCP1.6 |  A summary of the projected risks of species extinction at global warming levels of <1.5°C, 1.5–2.0°C and >3°C in terrestrial and marine 
biodiversity hotspots. Data from Manes et al. (2021).

CCP1
2136Cross-Chapter Paper 1 Biodiversity Hotspots
(Figure CCP1.2b; Halpern et al., 2008; Halpern et al., 2015; Ramírez et al., 
2018). Although there is a strong overlap of non-climatic and climatic 
impacts in marine ecosystems (Blowes et al., 2019; Bowler et al., 2020), 
the effects suggest that climate change impacts are most severe in 
tropical and northern high-latitude seas (high confidence) (Doney 
et al., 2012; Gattuso et al., 2015; Cheung et al., 2018; IPCC, 2019b). 
Temperature-driven range shifts and range expansions are projected to 
also lead to cascading effects on marine biodiversity through ecological 
interactions (high confidence) (Pecl et  al., 2017; Vergés et  al., 2019). 
Cascading effects may be especially pronounced in temperate reefs, 
where tropicalisation could lead to the arrival of herbivorous fish and 
predators previously absent (Vergés et al., 2019). However, how these 
indirect effects of climate change on species may change food webs

and ecosystem function, including carbon sequestration, is unknown. 
Direct and indirect human impacts due to fisheries and pollution can 
also lead to cascading effects that may be additive to climate impacts 
on biodiversity. Destruction of marine biogenic habitats due to trawling 
and dredging and loss of large proportions of marine megafauna, 
particularly fish, mammals, birds and reptiles, alter food webs and 
reduce resilience to additional disturbances, such as those caused by 
climate change (medium evidence, high agreement) (Brander, 2007; 
Wernberg et al., 2011; Ramírez et al., 2017; Cheung et al., 2018; Bates 
et al., 2019; Costello, 2021).
The following sections report observed and projected climate change 
impacts on terrestrial, freshwater and marine environments.
CCP1.2.2 Terrestrial
The 177 terrestrial hotspots assessed here (including 142 G200) cover 
about 61,000,000 km2 (41% of global land area), with a 37% overlap

with freshwater hotspots (Table CCP1.1; Figure CCP1.2). They include 
wet and dry forests, woodland and scrub, highlands, mangroves, 
deserts, steppe, savanna, grasslands, moorlands and tundra (Figures 
CCP1.8-1.11). Over 77% of publications on climate change impacts 
on hotspots since AR5 have been on terrestrial ecosystems, most on 
projected (as opposed to observed) impacts (Manes et al., 2021).
CCP1.2.2.1 Observed Impacts
There is high confidence that climate change has already had impacts 
in North American hotspots. Phenological and range shifts have 
been reported for bird and mammal species within the boreal forest 
hotspot (Davidson et  al., 2020), and earlier egg laying in birds in 
tundra hotspots (H3, 5) owing to changes in snowmelt (Grabowski 
et al., 2013). Woody vegetation is already shifting north into the tundra 
(Larsen et al., 2014).
In Central and South America, observed impacts within Mesoamerica 
(H15, 16) and the Tropical Andes hotspots (H26, 27, 28, 32, 33)

comprise upward altitudinal range shifts of birds, frogs, beetles and 
butterflies (Narins and Meenderink, 2014; Molina-Martínez et  al., 
2016; Moret et al., 2016; Freeman et al., 2018) (medium confidence). 
A shift of the Guianan-Amazon mangroves (H37) to higher grounds 
inland was attributed to the effects of observed sea level rise (low 
confidence) (Cohen et al., 2018).In Europe, the Mediterranean hotspot (H216) has seen increases in 
wildfires and droughts attributed to anthropogenic climate change 
(Gudmundsson et al., 2017; Barbero et al., 2020). Range shifts in birds 
have been observed at higher elevations (medium confidence) (Tellería, 
2020).
In Africa, multiple lines of evidence suggest woody plants are increasing 
in area, density and cover in previously lightly wooded savanna and 
grassland hotspots (H65, 82) (Poulsen and Hoffman, 2015; Stevens et al., 
2017). Significant vulture and cheetah range reductions in these hotspots

are at least partially attributable to bush encroachment (Nghikembua 
et al., 2016; Wolter et al., 2016; Santangeli et al., 2018). Thus, climate-
driven bush encroachment has adversely affected unique mammal and 
bird diversity (robust evidence, medium agreement, medium confidence). 
Warming and drying trends have historically been shown to reduce the 
range of the Ethiopian wolf (Canis simensis), and they interact with land 
use pressures in the Ethiopian hotspot (H68) (Sintayehu, 2018) and plant 
species richness in the Cape Fynbos (H65) of southern Africa to reduce 
post-wildfire recruitment (low confidence) (Slingsby et al., 2017).
Observed impacts in Asia were mostly restricted to the Himalaya 
(H95, 98, 99), Sundaland (H109, 110, 111, 112, 117, 118) and Indo-
Burma (H105, 106, 107, 114, 115) hotspots, showing negative impacts 
through increased invasion by exotic plants, decreased suitable area 
for endemic species and significant changes in phenology (medium

confidence) (Telwala et  al., 2013; Braby et  al., 2014; Padalia et  al., 
2015; Lamsal et al., 2017). In the Central Asian mountain landscape 
(H87), studies have shown increased aridity induced by climate change 
impacts on several shrub species (Seim et  al., 2016). Some positive 
effects were observed for native species in terms of an increase of 
suitable habitat (limited evidence, low agreement) (Priti et al., 2016; 
Tang et al., 2017; Rathore et al., 2019).
In Australia, climate change has been implicated in: drought-induced 
canopy dieback across a range of forest and woodland types due to 
decades of declining rainfall in the southwestern hotspot (H133); fires 
in the palaeo-endemic pencil pine forests (Tasmania H142); declines in 
vertebrates in the Australian Wet Tropics World Heritage Area, which 
overlaps with the eastern part of the northern Australia hotspot 
(H131), related to warming and increased length of the dry season; and

declines in grass and increases in shrubs in the Bogong High Plains 
(high confidence) (Hoffmann et al., 2019). The Australian Alps have seen 
increased species diversity following retreat of the snow line (Slatyer, 
2010), replacement of long-lived trees by short-lived shrubs following 
multiple wildfires (Zylstra, 2018), and changing ecological interactions 
due to climate-related snow loss, drought and fires (high confidence) 
(Hoffmann et  al., 2019). While warming is allowing mangroves to 
expand their range in coastal hotspots of Asia and Australia (Ward et al., 
2016; Hughes et al., 2019a), drought and associated salinity stress has 
killed mangroves in northern Australia hotspots (Babcock et al., 2019).
Approximately 76% of biodiversity hotspots within this assessment 
either contain, or are comprised of islands >100 km2 (Table CCP1.1). 
However, just 0.08% of these hotspots were represented in post-
AR5 literature examining climate change impacts on terrestrial

biodiversity. Most observed impacts were assessed with low evidence, 
but high agreement, and focused on plants and insects. Impacts

CCP1
2137Biodiversity Hotspots  Cross-Chapter Paper 1
described included abundance changes and extirpations (Jenouvrier 
et  al., 2014), altitudinal range shifts (Koide et  al., 2017), increased 
invasive alien species’ abundance and extent in Madagascar (H76, 
77), Balearic (H51) and Pacific islands (Ghulam, 2014; Silva-Rocha 
et al., 2015; Goulding et al., 2016; Dawson et al., 2017), increased 
temperature affecting physiology, body size and behaviour of frogs in 
the Caribbean (H20) (Narins and Meenderink, 2014) and phenological 
alterations (Fontúrbel et al., 2018). One positive observation was the 
high resilience to recovery of intact forest ecosystems to tropical 
cyclones within Caribbean (H20) and Pacific islands (medium 
confidence) (Keppel et al., 2014; Marler, 2014; Shiels et al., 2014).
CCP1.2.2.2 Projected Impacts
Most terrestrial species in biodiversity hotspots in North America 
have been projected to be negatively impacted by climate change

(medium evidence, medium agreement, medium confidence). About 
~80% of projections for assessed species showed a negative impact 
of climate change, with ~25% at very high risk of extinction (Figure 
CCP1.7; Manes et al., 2021). Alterations to vegetation that would have 
ecosystem-wide impacts, such as a shift from oak-dominated forests to 
predominantly hickory and maple species in the Appalachian Forests 
(H17) (Ma et al., 2016) or the continued shrinking of tundra ecosystems, 
have also been projected. Range shifts have been projected for a variety 
of plants (Beltrán et al., 2014; Riordan and Rundel, 2014) and vertebrate 
taxa (Warren et al., 2014; Stralberg et al., 2015; McKelvy and Burbrink, 
2017). Sizeable range loss, which particularly affects endemic species, 
is projected with higher levels of climate change. Adaptation in the 
agricultural sector poses an additional risk to remaining wildlife habitat 
(e.g., wine in California: Roehrdanz and Hannah, 2016).

In Central and South America, risks have been assessed in at least 
24 terrestrial hotspots, especially within the Atlantic Forest, Cerrado, 
Mesoamerica and the Caribbean, the most studied hotspots in 
the world in terms of climate change impacts (H47, 44, 15, 16, 20, 
respectively) (Manes et  al., 2021). About 85% of projections for 
assessed species showed a negative impact of climate change (high 
confidence), with ~26% projecting species extinctions (Figure CCP1.7; 
Manes et al., 2021). Projected impacts include contraction or loss of 
species’ geographic range, loss of diversity and high species turnover 
(high confidence). Most studies had focused on vertebrates and plants 
in the Atlantic Forest (H47) and Cerrado (H44) (Loyola et al., 2014; de 
Oliveira et al., 2015; Vale et al., 2018; Vasconcelos et al., 2018; Hidasi-
Neto et al., 2019; Lima et al., 2019; Lourenço-de-Moraes et al., 2019; 
Vasconcelos and Prado, 2019; Velazco et  al., 2019). Several insect

species are projected to lose suitable climatic conditions, including 
moths in Cerrado (H44) (Khormi and Kumar, 2014). There were 
projected negative impacts on vegetation such as rupestrian grasslands 
in Cerrado (H44) (Fernandes et al., 2018) and tropical and temperate 
forests in Mesoamerica (H15, H16) (Mendoza-Ponce et  al., 2018; 
Mendoza-Ponce et al., 2019). Endemic species face consistent risks of 
decrease in suitable habitat in the Atlantic Forest (H47) (Vale et al., 
2018), Cerrado (H44) (Vasconcelos, 2014), Tumbes-Chocó-Magdalena 
(H28, H23) (Hermes et al., 2018), and Mesoamerica (H15, H16) (Garcia 
et al., 2014; Ramírez-Amezcua et al., 2016). Climate change may also 
benefit invasive plant species in terms of range expansion (Wang et al., 
2017) and physiology (de Faria et al., 2018) in the region.In European biodiversity hotspots, about 75% of projections for assessed 
species showed a negative impact of climate change, with ~30% at very

high risk of extinction (medium confidence) (Figure CCP1.7; Manes et al., 
2021). These threats are projected to be worse under higher levels of 
warming. Increased wildfire size and frequency is projected to have a 
strong effect on the Mediterranean basin (H216) ecosystems (medium 
confidence) (Lozano et al., 2017). Range reductions have been projected 
for endemic plants (Pérez-García et  al., 2013; Casazza et  al., 2014), 
reptiles (Ahmadi et al., 2019), birds (Abolafya et al., 2013) and insects 
(Sánchez-Guillén et al., 2013) (medium confidence).
In African biodiversity hotspots, about 80% of projections for assessed 
species showed a negative impact of climate change, with ~10% at 
very high risk of extinction, especially of endemic species including 
birds, plants, bees across several taxa and hotspots if warming exceeds 
2°C (high confidence) (Figure CCP1.7; Huntley and Barnard, 2012; 
Kuhlmann et  al., 2012; Baker et  al., 2015; Lee and Barnard, 2016;

Young et al., 2016; Hannah et al., 2020; Manes et al., 2021).
In Asia, there is a bias in studies towards Indo-Burma (H105, 106, 107, 114, 
115), followed by Himalaya (H95, 98, 99) and Southeast Asian montane 
tropical and temperate forests. About ~70% of projections for assessed 
species showed a negative impact of climate change, with ~30% at 
very high risk of extinction (medium confidence) (Figure CCP1.7; Manes 
et al., 2021). Impacts include species’ range changes, habitat loss for 
endemic plants, expansion of invasive species, decreased connectivity 
and overall species richness decline (high confidence) (DasGupta and 
Shaw, 2013; Telwala et al., 2013; Sridhar et al., 2014; Zomer et al., 2014; 
Ali and Begum, 2015; Aryal et al., 2016). A projected decrease in habitat 
suitability for large species like the Asiatic black bear (Ursus thibetanus) 
is of concern as alternative habitats are outside protected areas, and

may lead to human–wildlife conflicts (Farashi and Erfani, 2018). The 
few positive impacts of climate change were projected as increases in 
suitable habitat and distribution range for a few endangered plants 
and mammals (medium confidence) (Banag et al., 2015; Shrestha et al., 
2018). Animals benefiting from increased fruit and seed production in 
Southeast Asian forests during warm El Niño cycles were also projected 
to increase with climate warming (Corlett, 2011).
All projections for assessed species in Australia and New Zealand 
terrestrial biodiversity hotspots showed a negative impact of climate 
change, with half at very high risk of extinction (low confidence) 
(Manes et  al., 2021). Observed impacts in the Australian Alps were 
projected to continue under future climate change (Zylstra, 2018). The 
northern Australia savanna (H131) may experience increased rainfall 
and carbon dioxide due to climate change (Scheiter et al., 2015), and

the range of exotic grasses was projected to be reduced under climate 
warming (Gallagher et  al., 2009). In Australian tropical wet forests, 
ground-living vertebrates may be more sensitive than arboreal species 
to unstable climates (Scheffers et  al., 2017). Bellard et  al. (2016) 
projected losses of land due to sea level rise in the East Australian 
Forest hotspot (H140), and González-Orozco et al. (2016) projected the 
contraction of eucalyptus species towards the coast of the Southwest 
Australia hotspot (H134), exposing them to sea level rise. In New 
Zealand forests (H139), native plants may be replaced by more fire-
resistant introduced species following climate change-related fires 
(Perry et  al., 2014). While forest growth is projected to potentially

CCP1
2138Cross-Chapter Paper 1 Biodiversity Hotspots
increase due to carbon dioxide fertilization, this may be compromised 
by drought (low confidence) (Ausseil et  al., 2013). Seed production 
in native New Zealand beech forests is projected to increase due to 
climate warming, fuelling the abundance of invasive rats and stoats, 
which then predate native species and lead to loss of endemic fauna 
and flora (medium confidence) (Tompkins et al., 2013, Ch. 11).
About 80% of projections for assessed terrestrial species within insular 
biodiversity hotspots showed a negative impact of climate change, 
with ~50% at very high risk of extinction, including 100% of endemic 
species (medium confidence) (Figure CCP1.7; Manes et  al., 2021). 
In addition to habitat loss and species range reductions, changes in 
precipitation are projected to be a major driver impacting tropical 
and subtropical island species (medium confidence) (Maharaj and

New, 2013; Harter et al., 2015; Struebig et al., 2015; Vogiatzakis et al., 
2016; Maharaj et al., 2018). Compared to continents, island species 
are projected to undergo greater impacts from changing climate, especially birds and amphibians (high confidence) (Fortini et al., 2015; 
Holmes et al., 2015; Manes et al., 2021, Box CCP1.1). Of all biodiversity 
hotspots, island species face the highest proportion of extirpation risk 
at high elevations due to decreasing habitat area (e.g., Brown et al., 
2015) and at low elevations from sea level rise, habitat loss and 
introduced species (medium confidence) (Bellard et al., 2014a).
CCP1.2.3 Freshwater
The 53 hotspots in freshwater ecosystems assessed here cover about 
32,830,000 km2 (17% of global freshwater habitats and 22% of the 
global land area), with a 68% overlap with terrestrial ecosystems 
(Table CCP1.1; Figure CCP1.2). They include lakes, rivers and streams

(Figure CCP1.12). The projected impacts of climate change on species in 232 studies
(a) Terrestrial biodiversity hotspots
(b) Marine biodiversity hotspots
0 100% 75% 50% 25% 0 3,000 2,500 2,000 1,500 1,000 500North America
Europe
Africa
Asia
Oceania
0 100% 75% 50% 25% 0 600 500 400 300 200 100Mediterranean
Europe
PolarOceania
Central America (Caribbean)Number of species Proportion of species
Number of species Proportion of species
Figure CCP1.7 |  The projected impacts of climate change on species in 232 studies (a) terrestrial and (b) marine hotspots (adapted from Manes et al., 
2021), illustrating the number and percentage of species showing positive (blue) and negative (orange) responses to climate change, and threatened with 
extinction (red). Note Oceania includes Australia, New Zealand, Wallacea, New Guinea, New Caledonia, Polynesia and Micronesia and overlaps the global Small Islands category,

which excludes Australia. The Small Islands category represents oceanic and continent-associated small islands, and thus overlaps with Oceania and continental data.

CCP1
2139Biodiversity Hotspots  Cross-Chapter Paper 1
Terrestrial biodiversity hotspots in the Americas, Asia and New Zealand
(h)
New
Zealand
temperate
rainforest(e)
Nusu
Tennggara
forests,
Komodo
islands(b)
Arabian
highlands,
woodlands
and
shrublands(a)
Patagonian
steppe
(c)
glacier(d)
Borneo
rainforest
(f)
Atlantic
forest of
Brazil
(g)
New
Zealand
temperate
Figure CCP1.8 |  Terrestrial biodiversity hotspots in the Americas, Asia and New Zealand. Photos by Denis Costello (top four), Mariana M. Vale (Brazil), and Mark 
Costello (other three).

CCP1
2140Cross-Chapter Paper 1 Biodiversity Hotspots
Polar and boreal biodiversity hotspots in the Arctic (Norway) taiga
Figure CCP1.9 |  Polar and boreal biodiversity hotspots in the Arctic (Norway) taiga. Photos by Galice Hoarau (top three) and Mark Costello (bottom two).

CCP1
2141Biodiversity Hotspots  Cross-Chapter Paper 1
African biodiversity hotspots.African biodiversity hotspots
(d)
Fynboss,
South Africa(a)
Drakensberg
montane
woodlands
and
grasslands
(b)
East African
savannas(c)
Namibia
deserts
(e)
East Africa n
savannas
Figure CCP1.10 |  African biodiversity hotspots. Photos by Denis Costello (top row and left second row) and Mark Costello (with elephant) for Drakensberg region, and Frank 
Zachos (lower four).

CCP1
2142Cross-Chapter Paper 1 Biodiversity Hotspots
Island biodiversity hotspots
(a)
Marquesas
(c)
New
Caledonia
(b)
Reunio n
Island
(d)
Galapagos,
volcanic
crater
(e)
coastal
Figure CCP1.11 |  Island biodiversity hotspots. Photos by Galice Hoarau (top two) and Mark Costello (other four).

CCP1
2143Biodiversity Hotspots  Cross-Chapter Paper 1
CCP1.2.3.1 Observed Impacts
An analysis of trends in 190 river basins in Australia found that stream-
flows have been declining, including in the Central Australian (H194) 
and Kimberley (H191) hotspots, due to greater terrestrial plant uptake 
of water in response to climate-related increases in carbon dioxide (low 
confidence) (Ukkola et al., 2016). We did not find any other publications 
providing evidence of impacts of climate change on freshwater 
biodiversity within the hotspots. Whether this is because freshwater 
temperatures tend to be cooler due to inputs from groundwater and/or 
mountain streams (Knouft and Ficklin, 2017), resilience of freshwater 
species or lack of research is unclear.
CCP1.2.3.2 Projected Impacts
Cold-water species are projected to lose habitat in Canada and this may 
apply in the Alaskan river (H143) and Russian Far East Lake Inle (H181)

hotspots (medium confidence) (Comte et al., 2013). Water abstraction 
is significant in the Colorado river hotspot (H145) and reduces its 
resilience to climate change effects on flow rates (Grafton et al., 2013).
In South America, in the Brazilian Amazon hotspot (H153, 154, 
157), half the assessed fish species were considered sensitive to 
increased temperatures and reduced oxygen due to climate change 
(low confidence) (Frederico et al., 2016). The use of protected areas 
was recommended to reduce the impacts of deforestation and water 
pollution (Jézéquel et  al., 2020). El Niño-related floods have led to 
declines in numbers of caiman, a top predator in the Brazilian Paraná 
river hotspot (H158), which indicates that increased floods due to 
climate change may reduce its population and alter food webs (Herrera 
et al., 2015).
In Europe, including the Mediterranean freshwater hotspots, climate 
change is projected to result in reduced river flow, low oxygen in

summer, salinity incursions, further eutrophication and spread of 
invasive species, compromising the survival of native biodiversity 
(medium confidence) (Moss et al., 2009). The longer growth season 
in the boreal and Arctic latitudes is projected to aid the invasion of 
exotic species, and increase lake stratification resulting in lower 
oxygen below the hypolimnion (medium confidence). In addition, strict 
cold-water species are projected to lose suitable habitat (Moss et al., 
2009). An analysis of 1648 species of freshwater fish, amphibians, 
turtles, plants, molluscs, crayfish and dragonflies, projected ~6% of 
common and ~77% of rare species to lose 90% of their geographic 
range (low confidence) (Markovic et al., 2014). Even if some species 
can spread to other areas and follow the climate, Markovic et  al. 
(2014) projected a loss of species, especially molluscs, from the 
southeastern Mediterranean, including the Balkan biodiversity hotspot

(H162) (medium confidence). Similarly, within Europe, Mediterranean 
fish (Jarić et al., 2019) and insects (Conti et al., 2014) are the most 
threatened by climate warming, droughts and floods. The fish species 
of the Danube river delta hotspot (H161) are less susceptible to climate 
change than in the Balkans (H162) and Anatolian (H163) hotspots. 
The rest of Europe, from the Iberian Peninsula to Scandinavia is not 
classified as a biodiversity hotspot. Thus, the areas where freshwater 
biodiversity is most threatened by climate change in Europe are in two 
of the three hotspots (high confidence).The African Rift Valley Lakes (H171), including Lakes Tanganyika and 
Turkana, are suffering from climate change influenced drought, po-
tentially impacting freshwater biodiversity (medium confidence) (Dud-
geon et al., 2006). Africa and Madagascar (H172) are projected to see 
a climate-driven 10% reduction in freshwater flow that is projected to

threaten the survival of ~9% of freshwater-dependent fish and birds 
(low confidence) (Thieme et  al., 2010). Climate change is projected 
to increase the extinction vulnerability of most freshwater fish in the 
western South Africa Cape hotspot (H170) (low confidence) (Shelton 
et al., 2018).
In Asia, although climate change impacts on the Yangtze (H183) and 
Mekong river (H186) biodiversity hotspots have not been reported, 
they are subject to the range of human impacts of over-exploitation, 
pollution, water abstraction, altered flow regimes, habitat loss and 
spread of invasive species, which makes them more vulnerable to 
climate effects (medium confidence) (Dudgeon et  al., 2006). The 
release of water from shrinking glaciers in Asia to some extent protects 
downstream freshwaters against drought, but half of these glaciers are 
projected to disappear by 2100 (medium confidence) (Pritchard, 2019).
In Australia, the Murray-Darling river basin occupies much of the

Eastern Rivers hotspot (H195) and climate-related drought exacerbated 
by water abstraction is projected to drive declines in freshwater 
birds, fish and invertebrates (high confidence) (Grafton et al., 2013). 
However, a national scale analysis projected climate change to cause 
freshwater species range shifts, but no losses of species in this hotspot 
(low confidence) (James et al., 2017, WG2 Ch. 11).
CCP1.2.4 Marine
The 43 hotspots in marine ecosystems cover 46,600,000 km2, repre-
senting 9% of the ocean area (Table CCP1.1; Figure CCP1.2). They in-
clude coral reef ecosystems, kelp forests, seagrass meadows, polar and 
upwelling zones (Figures CCP1.13; CCP1.14).
CCP1.2.4.1 Observed Impacts
Observed impacts attributable to climate change are strongly 
biased geographically, with most data from the temperate Northern 
Hemisphere, followed by subtropical to temperate Australia and few 
long-term data in the tropics (Poloczanska et  al., 2013; Poloczanska

et al., 2016). Marine heatwaves have increased over the past century, 
causing mass mortalities in the hotspots of the Mediterranean (H216), 
Great Barrier Reef (H236), western and southern Australia (H227, 228), 
northwest Atlantic (H207) and northeast Pacific (H197) (high confidence) 
(Hobday et al., 2018; Oliver et al., 2018). The shift of thousands of species 
from equatorial latitudes since the 1950s has been attributed to climate 
warming (medium confidence) (Chaudhary et al., 2021).
Climate change-related hazards, particularly marine heat events, have 
caused widespread coral bleaching and mass mortalities as the time 
between consecutive bleaching events decreases (high confidence) 
(IPCC, 2018; Bindoff et al., 2019; IPCC, 2019b). Coral reefs in some 
Indian Ocean hotspots (H230, 234) already exhibit net loss of coral 
reefs (low confidence) (Perry et al., 2018). While coral bleaching is a

CCP1
2144Cross-Chapter Paper 1 Biodiversity Hotspots
(b)
Bua River(a)
Headwaters
of the
Amazon
Basin,
Bolivia
(c)
Lake
Malawi
(d)
Lake
Titicaca ,
high
Andes
(e)
Riverbank
in Borneo,
Sundaland
(f)
Mangroves
in Papua
New Guinea
Figure CCP1.12 |  Photographs of freshwater biodiversity hotspots. Photos by Will Darwall (b, c) , Pablo Tedesco (a, d), and Mark Costello (e, f) .

CCP1
2145Biodiversity Hotspots  Cross-Chapter Paper 1
visible symptom of heat stress, warming has also induced restructuring 
of associated fish and invertebrate communities in the Great Barrier 
Reef (H236) (medium confidence) (Stuart-Smith et al., 2018).
Although the number of coral species that are both exposed and 
vulnerable to climate hazards is greatest in the central Indo-Pacific, the 
proportion of corals at risk is greater in the lower diversity Caribbean 
hotspots (H209) (medium confidence) (Foden et  al., 2013). Some reef 
corals are able to acclimate to heatwaves (low confidence) (DeCarlo 
et al., 2019), and some have expanded their latitudinal ranges polewards 
(high confidence), up to 14 km yr–1 in the northwest Pacific (Yamano 
et al., 2011). Although future latitudinal expansions may be limited by 
winter light availability (Muir et al., 2015), new coral reefs are already 
emerging in Japan (Kumagai et al., 2018).

The Mediterranean Sea hotspot (H216) is negatively affected by 
climate change (high confidence) (Cross-Chapter Paper 4). Species 
entering via the Suez Canal from the Red Sea (H220) are facilitated by 
warming and lead to profound community changes (high confidence) 
(Yeruham et al., 2015; Rilov, 2016; Vasilakopoulos et al., 2017; Givan 
et al., 2018; Bianchi et al., 2019). In contrast, the more open coastal 
seas of the Atlantic and Pacific coasts of North America have had 
increasing species richness since the 1970s (Batt et al., 2017).
Kelp forests are in decline in mid-latitudes due to warming and 
associated increased herbivory (medium confidence) (Section 3.4.2.3, 
Chapter 11). South and southeastern (H228), and southwestern (H227) 
Australia have experienced a climate-related decline of kelp forests 
(Wernberg et  al., 2011; Vergés et  al., 2016; Wernberg et  al., 2016). 
West Australia (H227) has been affected by extreme climate events

characterised by the replacement of kelp and sessile invertebrates 
by algal turfs and warm-water fish species (Wernberg et  al., 2013; 
Wernberg et  al., 2016). Australia’s Great Barrier Reef (H236), kelp 
forests, seagrass meadows and mangroves (due to drought), have 
suffered mortalities due to climate change (medium confidence) 
(Babcock et al., 2019). Climate warming driven changes in seaweed 
assemblages have been reported not only in Australia, but in the marine 
biodiversity hotspots of Atlantic Canada, Japan, Mediterranean, New 
Zealand (Laffoley and Baxter, 2016; Thomsen et  al., 2019; Thomsen 
and South, 2019) and California (H207, 231, 216, 238, 199) (Arafeh-
Dalmau et al., 2019; McPherson et al., 2021). However, while climate 
change is having measurable effects on kelp, the dominant effects on 
kelp projected to 2025 are fishing, through its effects on herbivores 
and predators (medium confidence) (Steneck et al., 2002). Although

fishing affected Atlantic cod in the Barents Sea (H214) and Gulf of 
Maine (H207) biodiversity hotspots, it was also affected by climate 
change, but negatively and positively, respectively (Kjesbu et al., 2014; 
Pershing et al., 2015).
Range expansions out of the Nansei Shoto (H231) hotspot south of 
Japan has led to the replacement of temperate kelp forests by tropical 
coral and herbivorous fishes on Japanese coasts (Kumagai et  al., 
2018). The Yellow Sea (H230) is one of the most exploited marine 
hotspots, with decreasing ecosystem services compounded by climate 
change but there is low confidence for climate change contributing 
substantially to ecological degradation (Wang et al., 2016; Song and 
Duan, 2019).Upwelling systems are best known for bringing nutrients to the 
surface. These stimulate phytoplankton blooms, which in turn support 
important fisheries (Section 3.4.2.11). However, this deep water also

tends to be low in oxygen, which can be further depleted by respiration 
and surface warming. Prolonged marine heatwaves in the Californian 
Current hotspot (H197) drove major shifts in the geographic range 
of birds, mammals, fish, crustaceans, molluscs and other species, and 
toxic algal blooms (Sanford et al., 2019).
In both the Antarctic (H213) and Arctic (H196, 214), the loss of ice 
impacts on the behaviour and foraging ability of marine mammals 
and birds (Doney et al., 2012). The retreat of sea ice in the Bering Sea 
(H196) hotspot has been followed by a reorganisation of the seabed 
and fish communities, a northward shift in species, and greater species’ 
biomass and richness (Mueter and Litzow, 2008; Grebmeier et  al., 
2018). In the Eurasian Arctic (H214), species richness has similarly 
been increasing (Węsławski et al., 2011; Kortsch et al., 2012; Certain 
and Planque, 2015; Fossheim et al., 2015; Węsławski et al., 2018), as

has phytoplankton productivity (Arrigo et al., 2008). The distribution of 
krill has already contracted with ocean warming in the Southern Ocean 
(medium confidence) (Cox et al., 2018; Atkinson et al., 2019).
CCP1.2.4.2 Projected Impacts
Tropical extirpations, already underway (Section 1.2.4.1), are projected 
to reduce hotspot diversity especially in the Coral Triangle (H226, 232, 
234), Maldives (H224) and, to a lesser extent, in the Caribbean (H200, 
H210) (Jones and Cheung, 2015; García Molinos et  al., 2016) and 
Persian Gulf (H219) (Wabnitz et  al., 2018). Paleo evidence supports 
projections of tropical biodiversity loss under high global warming 
(high confidence) (Kiessling et al., 2012; Yasuhara et al., 2020).
Warm-water coral reefs are expected to decline with 1.5°C warming 
(very high confidence) (King et al., 2017; Bindoff et al., 2019) leading 
to systems with reduced biodiversity and structural complexity (high

confidence) (Chapters 3; 11; Box 11.2). In the Coral Triangle, marine 
heatwaves are projected to have the same effect as an added mean 
annual 0.5°C sea surface temperature increase (McManus et al., 2020). 
While some corals are expected to survive in deep ‘mesophotic’ reefs 
(Laverick and Rogers, 2019), the shallow coral reefs of today will not 
last the century if climate warming continues without mitigation (high 
confidence) (Hughes et al., 2018a; Hughes et al., 2018b; IPCC, 2018; 
Bindoff et al., 2019; Hughes et al., 2019b).
In the Mediterranean, ocean acidification has been projected to lead 
to increases of fleshy algae at the expense of calcifying algae (Zunino 
et al., 2017). However, seagrass has been projected to decline (Chefaoui 
et al., 2018) and increase (Zunino et al., 2017) in the Mediterranean 
Sea hotspot (H216). Kelp forests are expected to decline in the 
northwest Atlantic (Grand Banks, H207), whereas gains and losses are

projected to be approximately balanced in the Northeast Atlantic Shelf 
(H215) under Representative Concentration Pathway (RCP) 8.5 (Assis 
et al., 2018; Wilson et al., 2019), but may lead to impoverished benthic 
assemblages (Teagle and Smale, 2018).

CCP1
2146Cross-Chapter Paper 1 Biodiversity Hotspots
High latitude marine biodiversity hotspots
(c)(a) (b)
(d)
(f)(e)
(g)
Figure CCP1.13 |  High-latitude marine biodiversity hotspots. Northeast Atlantic temperate seagrass beds, soft-corals, and kelp forests in Norway (photos by Galice Hoarau). 
South African fynbos and Agulhas current and Antarctic Peninsula and Weddell Sea (photos by Denis Costello). In the Americas, the Humboldt Current Chile and Chesapeake Bay 
(photos by Mark Costello).

CCP1
2147Biodiversity Hotspots  Cross-Chapter Paper 1
Species in island coral and rocky reef biodiversity richspots
(a) Sulawesi
(b) Phillippines
(c) New Zealand
(d) New Caledonia
(e) Galapagos
(f) Okinawa, Nansi Shoto Islands
(g) Raja Ampat, Bismarck-Solomon Sea
Figure CCP1.14 |  Species in island coral and rocky reef biodiversity hotspots. Photos by Galice Hoarau (top four Sulawesi), and Mark Costello (other nine).

CCP1
2148Cross-Chapter Paper 1 Biodiversity Hotspots
Projected climate caused changes in biodiversity in coastal upwelling 
regions are uncertain. While productivity in the California Current 
(H197) system is projected to increase with future climate change, 
nonlinear plankton responses and uncertain interactions with food 
web dynamics hinder predictions of ecosystem responses (Xiu et al., 
2018). In addition, this hotspot is projected to suffer from ocean 
acidification by 2050 (Gruber et al., 2012).
Around Antarctica (H213), almost half of all species are endemic 
(Costello et al., 2010), and warming during this century is projected 
to cause a reduction in suitable thermal environment for 79% of its 
species (RCP8.5) (low confidence) (Basher and Costello, 2016; Griffiths 
et  al., 2017). The previously mentioned declines in Southern Ocean 
krill due to climate change contribute to projected declines in baleen 
whales there (Tulloch et al., 2019).

Species richness in the northern polar hotspots is expected to increase 
substantially (high confidence) (Cheung et  al., 2015). However, population sizes of presently occurring native species are expected 
to decline, especially in the Barents Sea (H214) (Koenigstein et  al., 
2018). Ocean acidification is projected to continue globally, and while 
its impact is uncertain and projected to be less than the effect of 
warming, it may lead to changes in marine food webs due to varying 
effects on marine species (Terhaar et al., 2020). Hotspots in temperate 
latitudes are projected to have assemblages modified by immigration 
from the tropics and emigration to polar waters. Where land barriers 
and other geographical limits to range shifts occur, limited dispersal 
and habitat fragmentation may also limit the capacity of some species 
to track climate velocities, such as in the Baltic Sea (H215) (Jonsson 
et al., 2018), Mediterranean Sea (H216) (Burrows et al., 2014; Arafeh-

Dalmau et  al., 2021) and Antarctica (H213) (medium confidence) 
(Cristofari et al., 2018).Table CCP1.2 |  Examples of adaptation actions that benefit the conservation of biodiversity and climate change mitigation.
Actions Terrestrial Freshwater Marine
Protect biodiversity hotspotsProtect native forests, bush and grasslandsStop pollution and sedimentation into 
streams, rivers, ponds and lakesBan seabed trawling and dredging
Control introduction and spread of invasive species and pests
Increase connectivityUse riverbank and hedgerow corridors to connect protected native habitats Already connected
Reduce habitat and species loss outside protected areas to add species dispersal (corridors)
Outside biodiversity hotspots Environmentally sustainable agriculture, tourism and other land and freshwater usesEnvironmentally sustainable aquaculture, 
fisheries and tourism
Restoration and recoveryActively rehabilitate old mines, quarries and 
industrial landsStabilise riverbanks Remove weirs and

artificial barriers to fish migrationBan removal of marine life and habitat and 
fishing in selected areas to allow passive 
recovery of habitats, natural population 
structure, and food websReintroduce extirpated native species
Reduce erosion, soil loss and floodingPreserve, reduce degradation and restore habitats to enable uplands to absorb rainfall and reduce flash floods
Protect sand-dune systems from erosion due to human and farm animal trampling
Set aside land for salt marshes and mangroves to buffer against river and seawater flooding
Link estuarine and upriver protected areas to provide more wildlife habitat and absorb storm surges and floods
Urban developmentConcentrate development to more cost 
efficiently manage transport and waste 
management infrastructureLimit upland development where it may 
affect freshwater qualityAvoid construction in areas at risk of sea 
level rise and associated storm surges
Greenhouse gas mitigationPrevent deforestation

Reforestation (especially mangroves)
Revegetation
Fewer farm mammals
Minimise release of greenhouse gases 
from soilsExpand wetlands to capture and deposit 
carbon in soilsLimit seabed disturbance by trawling and 
dredging that releases CO 2 and CH 4
Eliminate fishery subsidies and remove tax 
breaks on fuel for fishing boats
Carbon sequestration and preservationAllow plants and other organisms to flourish and capture CO 2 from the air and water, and sequester it in biomass, soils and sediments
Manage forestry to maximise in situ food 
web biomassManage fisheries to maximise in situ food web biomass
Social Communicate information on the benefits of adaptation measures to the public
Political and economicProvide leadership and governance of mitigation and adaptation measures, including through regulations and economic incentives that guide 
the transition to a low carbon emissions economy

ScientificAddress data gaps and make monitoring data and its meaning rapidly available to society so that the public and policy makers are informed 
of trends in biodiversity and related factors, including climate variables, extreme weather related events, threatened and invasive species, 
natural habitats, and their relationships
Conduct research to improve understanding of cause-effect relationships regarding environmental factors and biodiversity trends, including 
in nature conservation, forestry, agriculture, fisheries and food production sectors, and improve projections of consequences of management 
action and inaction

CCP1
2149Biodiversity Hotspots Cr oss-Chapter Paper 1
CCP1.3  Adaptation and Solutions
Terrestrial, freshwater and coastal marine ecosystems are impacted 
by the 3 billion people that currently live in in biodiversity hotspots (Gutiérrez et  al., 2021). At the same time, biodiversity in hotspots supports the livelihoods of the local communities. The suite of adaptation options for biodiversity are as applicable inside as outside hotspots (Table CCP1.2). Many of these hotspots are now faced with widespread fragmentation and habitat degradation (high confidence) (Table SMCCP1.1).
Because projected changes in biodiversity increase disproportionately

with warming, climate change mitigation is the primary action to conserve biodiversity within hotspots. If global warming is kept within the 1.5°C limit of the Paris Agreement, just ~4% of endemic species in biodiversity hotspots would be threatened with extinction from climate change. However, at the current commitments there is projected to be ~3°C warming by 2100 and ~20% and ~32% for terrestrial and marine species, respectively, fall into the category of very high extinction risk (Figure CCP1.6; Manes et al., 2021).
Although mitigation can sharply reduce extinction risk associated with

climate change (high confidence), it cannot reduce all of the risk, nor the risk associated with other drivers that can have a compound effect with climate change. Thus, in addition to mitigation, the literature consistently calls for reducing current non-climate impacts (e.g., habitat conversion, over-exploitation, hunting, fishing, wildfire, pollution, human-introduced invasive species) in order to increase biodiversity resilience to climate change (very high confidence) (Table CCP1.2; e.g., Mantyka-Pringle et  al., 2015; Warren et  al., 2018a; Costello, 2021). The main strategies to increase resilience rely on the combination of well-planned protected areas, restoration of degraded areas and the sustainable use of biodiversity (high confidence) (IPCC, 2019a; Pörtner et al., 2021) . On land, creating corridors for species is key for facilitating species movements (high confidence) (McGuire et al., 2016; Heikkinen et  al., 2020; Pörtner et  al., 2021). Habitat protection has numerous

co-benefits, including potential climate mitigation through carbon storage and sequestration, in addition to climatic regulation (Alkama and Cescatti, 2016; Mackey et  al., 2020) and pandemic prevention (Allen et al., 2017; Dobson et al., 2020, Cross-Chapter Box COVID-19 in Chapter 7).

Active relocation of endangered species to areas where they may 
be safer from predation and human impacts, as already practised for a few charismatic fauna, is expensive and fraught with complex regulations and concerns over impacts on native species (Brodie et al., 2021). Therefore, managed relocation of species threatened by climate change is questionable for most species.
Healthier marine ecosystems are more resilient to additional stressors,

such as storms and climate change (high confidence) (Isbell et al., 2015; Duffy et al., 2016; Roberts et al., 2017; Bates et al., 2019; Mariani et al., 2020; Costello, 2021; Donovan et al., 2021). Extinction risk is lower when populations are larger and more genetically diverse, individuals are larger and older, and seabed habitats (e.g., coral, kelp, seagrass) are flourishing, as occurs in marine reserves (high confidence) (Costello, 2014; Roberts et al., 2017; Bates et al., 2019; Costello, 2021). Similarly, global fish biomass may be less affected by climate change if biodiversity is greater (Duffy et al., 2016). Thus, a network of reserves representative of global biodiversity, helps attenuate the effects of climate change (medium confidence), for example, by having more abundant fish and top predator populations (Roberts et al., 2017; Beyer et al., 2018; Carter et al., 2020; Sala et al., 2021). However, the impacts of marine heatwaves on corals across marine reserves illustrates

that enhanced resilience is not enough to protect against extreme and future climate change conditions (high confidence) (Bruno et al., 2018; Hughes et al., 2018a; Kleypas et al., 2021).

Mangroves occupy the interface of terrestrial, freshwater and marine

environments, dominate in eight hotspots (Table  CCP1.1) and are connected to or integral habitats within one-third of all terrestrial and freshwater, and two-thirds of marine, hotspots (Figures CCP1.1; CCP1.2). A global analysis of sediment cores from mangroves indicated that mangroves can accrete sediment at levels of sea level rise projected under low emission scenarios, but may decline at their seaward edge under high emission scenarios (Saintilan et al., 2020, Chapter 3.4.2, Cross-Chapter Box Sea-level rise). However, even if this seaward erosion occurs, the expansion of mangroves inland due to sea level rise will increase carbon sequestration, because they capture carbon from seawater and freshwater runoff, in addition to photosynthesis, into their underlying sediments. If coastal management permits the expansion of mangroves inland with rising sea level, this will increase carbon sequestration because mangroves capture and preserve more carbon in their sediments than other

terrestrial and marine forests and biomes (high confidence) (Table CCP1.2; Alongi, 2020; Goldstein et al., 2020; Lovelock and Reef, 2020; Saintilan et al., 2020).

pervasive, imposing hard limits to adaptation of terrestrial and freshwater ecosystems (Ibisch et al., 2016; Lenoir et al., 2020; Mechler et al., 2020). Thus, the protection of existing natural habitats coupled with the restoration of the surrounding non-protected habitat can increase the effectiveness of adaptation strategies in terrestrial and freshwater hotspots (very high confidence) (Table CCP1.2; IPCC, 2019a; Jung et  al., 2021). Additionally, strategic allocation of new protected areas within gaps across elevational and climatic gradients could enhance biodiversity conservation across hotspots. This would align with Target 3 of the Convention on Biological Diversity’s post-2020 draft Global Biodiversity Framework, and could include underrepresented climate and elevation spaces as well as potential climate refugia currently not under protection (Pörtner et al., 2021). In terrestrial ecosystems, restoration initiatives can help sustain biodiversity, improve resilience in a

changing climate, and avoid maladaptation by selecting appropriate native species to be planted (Cross-Chapter Box  Bioeconomy in Chapter 5; (Gann et  al., 2019). In freshwater ecosystems, conservation needs catchment level management of human activities (Saunders et  al., 2002; Dudgeon et al., 2006), especially as 37% of the terrestrial biodiversity hotspots overlap with freshwater (Figure  CCP1.2) and 23% border marine hotspots (Olson and Dinerstein, 2002).

Protecting biodiversity hotspots is a pragmatic way to conserve 
biodiversity that is representative of a substantive fraction of genetic and species diversity on Earth (Mittermeier et al., 2011) while achieving

CCP1
2150Cross-Chapter Paper 1 Biodiversity Hotspots
co-benefits (Bonan, 2016; Sala et al., 2021). Protecting hotspots also 
helps protect important ecosystem services. In a global ranking of 
areas that combine biodiversity conservation while maximising carbon 
retention and water quality regulation, for example, the terrestrial 
and freshwater hotspots assessed here ranked high (41st and 34th 
on average, respectively, on a scale of 100) (Jung et  al., 2021). The 
solutions needed to reverse biodiversity decline are well known and 
articulated in numerous international agreements and goals, such as 
the Convention of Biological Diversity the United Nations’ Sustainable 
Development Goals, the Nationally Determined Contributions under 
the Paris Agreement, the International Union for Nature Conservation’s 
Bonn Restoration Challenge and the Ramsar Convention on Wetland Conservation. Thus, expanding and enhancing protection

of a worldwide network of fully protected areas and protection and 
restoration of non-protected areas representative of the biodiversity 
hotspots, including marine, freshwater and terrestrial environments, is 
a highly recommended adaptation strategy to increase resilience of 
biodiversity to climate change (Brito-Morales et al., 2018). However, 
adaptation strategies alone cannot protect biodiversity from climate 
impacts without complementary and concomitant reduction of 
greenhouse gas emissions.
Box CCP1.1 | Climate change and terrestrial biodiversity hotspots on small islands
Despite covering approximately 2% of the Earth’s land area, islands harbour more than 20% of extant terrestrial species (Wetzel et al., 
2013). Islands have disproportionately higher rates of endemism and threat when compared to continents, with 80% of historical 
extinctions (since 1500 CE) having occurred on islands (high confidence) (Taylor and Kumar, 2016; Spatz et al., 2017; Dueñas et al., 2021).

Current climate change projections suggest that insular species are particularly sensitive and, even at mild warming levels, substantial 
losses are expected (high confidence) (Pouteau and Birnbaum, 2016; Taylor and Kumar, 2016; Dawson et al., 2017; Manes et al., 2021). 
Given islands’ characteristic high endemicity, current high threat levels and the fact that islands host almost half of all species currently 
considered to be at risk of extinction, especially at higher warming levels (high confidence) (Taylor and Kumar, 2016; Spatz et al., 2017), 
further losses could contribute disproportionately to global biodiversity decline (medium evidence, high agreement) (Harter et al., 2015; 
Pouteau and Birnbaum, 2016; Manes et al., 2021).
The high vulnerability of terrestrial biodiversity on islands to global change can be explained by a number of limitations, characteristic of

both islands and insular species. Older, isolated islands tend to have fewer species and lower functional redundancy but a higher proportion 
of endemism (Pouteau and Birnbaum, 2016; Médail, 2017). Many of these islands contain species with inherently high sensitivity to 
environmental change (narrow habitat ranges, small population sizes, low genetic diversity and poor adaptive, dispersal and defensive 
capabilities) (Harter et  al., 2015). Unlike continental environments, insular species often have limited opportunities for autonomous 
adaptation from not having enough geographic space to shift their ranges to track suitable climatic conditions (high confidence) (Fortini 
et al., 2015; Manes et al., 2021). Local extinction risks are amplified by even small losses of habitat due to global change including human-
induced disturbances, extreme events, sea level rise (Chapter 15; Cross-Chapter Box SLR in Chapter 3) and invasive species.

However, some insular species have shown resilience to climate change. Intact island forests, for example, have shown rapid recovery 
rates after tropical cyclones, despite high levels of initial damage, especially in the Caribbean (medium confidence) (Luke et al., 2016; 
Richardson et al., 2018). Additionally, many Mediterranean islands are ‘disturbance adapted’, with continued persistence of some single-
island endemic plants, despite exposure to multiple threats (Vogiatzakis et al., 2016). This continued persistence has been attributed, at 
least partially, to climate refugia, oceanic buffering and high habitat heterogeneity within topographically complex mountainous regions 
(Pouteau and Birnbaum, 2016; Médail, 2017, Chapter 15, Table 15.1). However, this climate resilience will not be sustained under climate 
change, especially when coupled with habitat degradation (high confidence) (Wiens, 2016).

Adaptation strategies depend on the ability to project future impacts from climate change, but this is hampered by lack of fine-scale 
climate data, especially for developing small island nations. There is a paucity of robust impacts-based modelling output for terrestrial 
biodiversity from these islands due to the wide, chronic unavailability of Regional Climate Model (RCM) data premised on the most 
recent suite of scenarios (RCPs and especially SSPs) (medium evidence, high agreement) (Gutiérrez et al., 2021, Ch.15.8; Pörtner et al., 
2021; WMO, 2021). Additionally, realistic assessments of changing climate on such small ecosystems require further RCM downscaling 
and verification to sub-island resolutions of <5 km. Furthermore, widely used statistically (bias-corrected) downscaled data at sub-5 km 
resolutions, such as WorldClim are often unsuitable due to limited spatial and temporal resolutions of observation station data from small

islands (Maharaj and New, 2013; Gutiérrez et al., 2021) and higher errors associated with statistical downscaling and locations with 
complex topography and coastlines (Fick and Hijmans, 2017; Lanzante et al., 2018). Widespread unavailability of such data constrains 
accurate simulations of climatic variation within the small-scale mountainous and coastal regions of islands, associated with climate 
refugia and high habitat heterogeneity (high confidence) (Balzan et al., 2018). This is a key element contributing to the continued delay 
in development of robust adaptation strategies towards not only biodiversity conservation but other important cross-sectoral issues 
(medium confidence) (Robinson, 2020b).

CCP1
2151Biodiversity Hotspots  Cross-Chapter Paper 1
Due to islands’ limited size and isolation, conventional conservation measures focused on expanding protected areas, dispersal corridors 
and buffer zones are of limited effectiveness on islands (high confidence) (Vogiatzakis et al., 2016). Instead, multifaceted, locally driven 
holistic climate-smart strategies across mosaics of human-impacted, often heavily degraded and fragmented, landscapes are required. 
These should ideally be long-term, flexible and sustainable solutions that incorporate social and biocultural knowledge as well as 
economic co-benefits to island communities in order to ‘buy time’ (Betzold, 2015; Robinson, 2020a). Examples include ecosystem-based 
approaches such as ridge-to-reef management (Struebig et  al., 2015; Ferreira et  al., 2019, Figure  CCP1.15.4), which incorporates 
conservation partnerships among lands inside and outside protected areas to increase connectivity and reduce land use impacts, while

building on the interconnections among terrestrial, freshwater, coastal and marine ecosystems. Such strategies require raising awareness 
of biodiversity values among local communities, and cross-sectoral planning and policy at both island, regional and trans-boundary 
scales. These lend to private–public partnerships, increasing the potential of solutions reaching beyond protected areas boundaries and 
affecting socio-political change (high confidence) (Scobie, 2016).
Limited terrain, natural, economic and data resources across small developing nation islands mean that unconstrained habitat destruction 
and degradation cannot be sustained, as this harms both people and the biodiversity upon which they depend. This limitation of resources 
compromises climate adaptation, which is often further complicated by varying governance and states of economic development (Petzold

and Magnan, 2019). With changing climate conditions, there is an increased urgency to re-think how progress can be measured, and 
to create opportunities building on synergies between disaster risk reduction, food security and social justice, so that islands can most 
benefit from their natural resources and biodiversity in a sustained manner (Box 15.2; Section 15.3.4.4 ).Box CCP1.1 (continued)
FAQ CCP1.1 | Why are biodiversity hotspots important?
Biodiversity hotspots are regions that are exceptionally rich in species, ecologically unique and which may contain geographically restricted 
species. They are thus priority targets for nature conservation.
Recognising that the Convention on Biological Diversity definition of biodiversity includes the variation within and 
between species and of ecosystems, different schemes have been applied to define hotspots, leading to hundreds

of different areas being proposed as hotspots. However, all identify a set of priority areas that cover a small portion 
of the Earth, but house an exceptionally high proportion of its biodiversity. Because biodiversity underpins all life 
on Earth, these hotspots have significant global value as they contain species and habitats that are found nowhere 
else. Their loss would mean loss of species and habitats that provide wild and farmed food, medicine and other 
materials, and services such as climate regulation, pollination and water purification, all of which maintain the 
health of the ecosystems we depend upon.
Healthy ecosystems, with flourishing biodiversity in natural conditions, are more resilient to disturbances, whether 
natural or human in origin. Environmentally sustainable development inside and outside hotspots could help reverse 
human impacts on biodiversity. The hotspots also capture and store carbon, thereby helping to mitigate climate

change. Prioritisation of protecting biodiversity in hotspots thus benefits nature conservation and helps mitigate 
climate change. A global network of protected areas and restoration initiatives inside biodiversity hotspots can also 
help increase resilience to the effects of climate change on biodiversity.

CCP1
2152Cross-Chapter Paper 1 Biodiversity Hotspots
FAQ CCP1.2 | How can society ensure conservation of biodiversity in climate policies?
To reduce the effects of climate change on biodiversity, it is first essential to address direct human impacts that are already leading to a loss 
of biodiversity. This can be achieved by protecting biodiversity in conservation areas, restoring biodiversity everywhere possible and promoting 
sustainable development. Climate policies should thus integrate with policies to protect and restore nature.
Avoiding further loss of biodiversity is implicit in sustainable development. This needs to happen on land, rivers, 
lakes and in the oceans. It is especially important in ‘biodiversity hotspots’ (FAQ 1.1) and protected areas to minimise 
species losses. Hence calls by the International Union for the Conservation of Nature, Convention on Biological

Diversity, United Nations Sustainable Development Goals (SDGs) to increase the size and connectivity of fully 
protected areas (which aim to have biodiversity in a near natural condition) and include in them the biodiversity 
hotspots, need to be immediately implemented.
Five of the SDGs are life on land, life below water, good health and well-being, food security and climate action. They 
underpin and interact with many other SDGs. Healthy ecosystems play a role in mitigating greenhouse gas emissions, 
not only protecting areas to prevent the release of carbon through land conversion activities but also restoring 
otherwise degraded land. The United Nations has declared 2021–2030 as the Decade on Ecosystem Restoration and 
the Decade of Ocean Science for Sustainable Development. Restoration means actively or passively allowing habitat 
to return to its natural state (e.g., grassland, forest, peatland, oyster beds), including replanting native vegetation.

This can benefit the recovery of biodiversity, help remove carbon dioxide from the atmosphere and improve the 
delivery of nature’s contributions to people, such as climate regulation, water purification, pollination, and pest 
and disease control. Thus, protecting biodiversity helps to meet two SDGs directly, and three indirectly.
On land, the loss of natural forests and grasslands not only means a loss of carbon and many of their associated 
species, but exposes soils to erosion, affecting food production, and can affect the climate by altering the water 
cycle. Sustainable development, even within hotspots, involves active restoration of natural biodiversity, reducing 
poaching and trafficking of wildlife (UN SDG 15), and needs to include agriculture. This includes working to ensure 
biodiverse soils and supporting healthy pollinator populations. Biodiversity includes not only wild species but also

genetic diversity, including crops and wild crop relatives. These wild relatives may contain important genes that 
could help farmed crops survive better in a changed climate. At least some of these wild relatives come from 
areas designated as hotspots. In the ocean, sustainable development means reducing pollution, carefully managed 
aquaculture development, increased protected areas (from the present 2.5% of the ocean area), enforcement of 
fisheries regulations, and removal of fishery subsidies that perpetuate overfishing within Exclusive Economic Zones 
and on the High Seas (UN SDG 14). Generally, the use of freshwaters, rivers, lakes and groundwaters, has not been 
sustainable and there is a need to restore biodiversity and water quality by eliminating pollution and to better 
manage abstraction, river flows, fishing and invasive species. Thus, as is the case with land and oceans, climate

policies must prioritise the restoration of freshwater biodiversity, and reduction of the current negative impacts of 
human activities.

CCP2
2165Cities and Settlements by the Sea Cr oss-Chapter Paper 2
Cities and settlements (C&S) by the sea are on the frontline 
of climate change—they face climate-compounded risks that are amongst the highest, but are a key source of innovation in climate resilient development (high confidence
1) {Sections 6.1, 
6.2; Chapter 7; Box 15.2; Cross-Chapter Box COVID in Chapter 7; Cross-Chapter Box SLR in Chapter 3; CCP2.2; SMCCP2.1}.
Much of the world’s population, economic activities and critical

infrastructure are concentrated near the sea (high confidence), with nearly 11% of the global population, or 896 million people, already living on low-lying coasts directly exposed to interacting climatic and non-climatic coastal hazards (very high confidence). Low-lying cities and settlements (C&S) by the sea are experiencing adverse climate impacts that are superimposed on extensive and accelerating anthropogenic coastal change (very high confidence). Depending on coastal C&S characteristics, continuing existing patterns of coastal development will worsen exposure and vulnerability (high confidence).

With accelerating sea level rise (SLR) and worsening climate-driven risks in a warming world, prospects for achieving the Sustainable Development Goals (SDGs) and charting climate resilient development (CRD) pathways are dismal (high confidence). However, coastal C&S are also the source of SDG and CRD solutions, because they are centres of innovation with long histories of place-based livelihoods, many of which are globally connected through maritime trade and exchange (medium confidence) {CCP2.1, CCP2.2 CCP2.3, CCP2.4; SMCCP2.1; Chapters 16, 18}.
Regardless of climate and socioeconomic scenarios, many C&S 
face severe disruption to coastal ecosystems and livelihoods by 2050—extending to all C&S by 2100 and beyond—caused by compound and cascading risks, including submergence of some low-lying island states (very high confidence) {CCP2.1; CCP2.2; SROCC SPM, Chapter 4; 6.2}.
There is high confidence that projected climate risks will increase with

(i) exposure to climate- and ocean-driven hazards manifest at the coast, such as heat waves, droughts, pluvial floods and impacts due to SLR,tropical cyclones, marine and land heatwaves, and ocean acidification; (ii) with increasing vulnerability driven by inequity and (iii) increasingexposure driven by urban growth in at-risk locations. Compoundedand cascading climate risks, such as to coastal C&S infrastructureand supply chain networks, are also expected to increase. Theserisks are acute for C&S on subsiding and/or low-lying small islands,the Arctic, and open, estuarine and deltaic coasts (high confidence). By 2050, more than a billion people located in low-lying C&S willbe at risk from coast-specific climate hazards, influenced by coastalgeomorphology, geographical location and adaptation action (highconfidence). Between USD 7 and 14 trillion of coastal infrastructureassets will be exposed by 2100, depending on warming levels andsocioeconomic development trajectories (medium

confidence). Historically rare extreme sea level events will occur annually by 2100,

1 In this report,  the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high and very high. The terms expressing evidence, agreement and confidence are typeset in italics (e.g., medium confidence, also medium to high 
confidence, robust evidence, low agreement). For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement

are correlated with increasing confidence.with some atolls being uninhabitable by 2050. The coastal flood risk will rapidly increase during coming decades, possibly by 2–3 orders of magnitude by 2100 in the absence of effective adaptation and mitigation, with severe impacts on coast-dependent livelihoods and socioecological systems (high confidence). Impacts reach far beyond C&S; for example damage to ports severely compromising global supply chains and maritime trade, with local–global geopolitical and economic ramifications. Global investment costs to accommodate port growth and adapt to SLR will amount to USD 223–768 billion before 2050, presenting opportunities for C&S by the sea to build climate resilience (medium evidence, high agreement). Severely accelerated

SLR resulting from rapid continental ice mass loss would bring impacts forward by decades, and adaptation would need to occur much faster and on a much greater scale than ever performed in the past (medium confidence) {Table SMCCP2.1; CCP2.1; CCP2.2; Cross-Chapter Box SLR in Chapter 3; Chapter 4; Sections 6.2.7}.
A mix of interventions is necessary to manage coastal risks 
and build resilience over time. An adaptation-pathways approach sets out near-term ‘low-regret’ actions that align with societal goals, facilitates implementation of a locally appropriate sequence of interventions in the face of uncertain climate and development futures, and enables necessary transformation (high confidence) {CCP2.3; Cross-Chapter Box DEEP in Chapter 17, Cross-Chapter Box SLR in Chapter 3}.
A mix of infrastructural, nature-based, institutional and sociocultural

interventions are needed to reduce the multifaceted risk facing C&S, including vulnerability-reducing measures, avoidance (i.e., disincentivising developments in high-risk areas), hard and soft protection, accommodation, advance (i.e., building up and out to sea) and retreat (i.e., landward movement of people and development) (very high confidence). Depending on the C&S archetype, technical limits for hard protection may be reached beyond 2100 under high-emission scenarios, with socioeconomic and governance barriers reached before then (medium confidence). However, hard protection can set up lock-in of assets and people to risks and, in some cases, may reach limits—due to technical and financial constraints—by 2100 or sooner depending on the scenario, local SLR effects and community tolerance thresholds (medium confidence). Where sufficient space and adequate habitats are available, nature-based solutions can help to reduce coastal hazard risks and provide other benefits, but

biophysical limits may be reached before end-century (medium confidence). Accommodation is easier, faster and cheaper to implement than hard protection, but limits may be reached by 2100, or sooner in some settings. An adaptation-pathways planning approach demonstrates how the solution space can expand or shrink depending on the type and timing of adaptation interventions. As SLR is relentless on human timescales, the solution space will shrink without adoption of an adaptation-pathways planning approach (high confidence). Due to long implementation lead times and the need to avoid maladaptive lock-in, particularly in localities facing rapid SLR and climate-compounded risk, adaptation will be more successful if timely action is taken accounting for long-term (committed) SLR, and

CCP2
2166Cross-Chapter Paper 2 Cities and Settlements by the Sea
if this is underpinned by sustained and ambitious mitigation to slow 
greenhouse gas emission rates (high confidence) { CCP1.3.1.2; CCP2.3; 
CCP2.4; Cross-Chapter Box SLR in Chapter 3}.
Individual and collective choices founded on public-centred 
values and norms, as well as pro-social behaviour, help to 
foster climate-resilient coastal development in C&S (high 
confidence) {CCP2.4.1}.
The effectiveness of different approaches (e.g., awareness and 
education, market-based and legal strategies) is mediated by how 
well they address contextual and psychosocial factors influencing 
adaptation choices in coastal C&S (medium confidence). Adaptation 
options accounting for risk perceptions and aligning with public values 
tend to be more socioculturally acceptable, and consequently facilitate 
pro-social behavioural change {CCP2.4.1}.
Locally appropriate institutional capabilities, including

regulatory provisions and finances dedicated to maintaining 
healthy coastal socioecological systems, build adaptive 
capacity in C&S by the sea (high confidence) {CCP2.4}.
Implementing integrated multi-level coastal zone governance, pre-
emptive planning, enabling behavioural change and alignment of 
financial resources with a wide set of values will provide C&S with 
greater flexibility to open up the solution space to adapt to climate 
change (high confidence). Insufficient financial resources are a key 
constraint for coastal adaptation, particularly in the Global South 
(high confidence). Engaging the private sector in coastal adaptation 
action with a range of financial tools is crucial to address the coastal 
adaptation funding gap (high confidence). Considering the full range 
of economic and non-economic values will improve adaptation 
effectiveness and equity across C&S archetypes (high confidence). 
Aligning adaptation in C&S with socioeconomic development,

infrastructure maintenance and COVID-19 recovery investments will 
provide additional co-benefits. Urgency is also driven by the need to 
avoid lock-in to new and additional risks, for example to avoid C&S 
sprawl into fragile ecosystems and the most exposed coastal localities 
{CCP2.3; CCP2.4.2; CCP2.4.4}.
Realising global aspirations for CRD depends on the extent 
to which coastal C&S institutionalise key enabling conditions 
and chart place-based adaptation pathways to close the 
coastal adaptation gap, and on the extent to which they take 
urgent action to mitigate greenhouse gas emissions (medium 
confidence) {CCP2.4; Table CCP2.1}.
Extensive adaptation planning has been undertaken since the IPCC Fifth 
Assessment Report (AR5), but there has not been widespread effective 
implementation, thus giving rise to a ‘coastal adaptation gap’ (high 
confidence). To date, most interventions have been reactive and often 
rely on protective works alone (high confidence). The effectiveness

of alternative interventions differs among C&S archetypes, while 
their feasibility is influenced by geomorphology and socioeconomic 
conditions as well as cultural, political and institutional considerations 
(very high confidence). Mismatches between adaptation needs and 
patterns of physical development are commonplace in many coastal C&S, with particularly adverse impacts on poor and marginalised 
communities in the Global North and Global South (high confidence). 
Overcoming this gap is key to transitioning towards CRD (medium 
confidence). Under higher warming levels and higher SLR, increasingly 
dichotomous coastal futures will become more entrenched (medium 
confidence), with stark differences between more urbanised, resource-
rich coastal C&S dependent on hard protection, and more rural, 
resource-poor C&S facing displacement and migration {CCP2.3; 
CCP2.4, Chapter 18}.
Coastal adaptation innovators adopt more flexible, anticipatory

and integrative strategies, combining technical and non-technical 
interventions that account for uncertainties and facilitate effective 
resolution of conflicting interests and worldviews (limited evidence, 
high agreement). Moreover, a core set of critical enablers is 
foundational for C&S to chart CRD pathways. These include building 
and strengthening governance capabilities to tackle complex problems; 
taking a long-term perspective in making short-term decisions; 
enabling more effective coordination across scales, sectors and 
policy domains; reducing injustice, inequity, and social vulnerability; 
and unlocking the productive potential of coastal conflict while 
strengthening local democracy (medium evidence, high agreement) 
{Table CCP2.1, Table CCP2.2; CCP2.3; CCP2.4; Chapters 17, 18; Cross-
Chapter Box DEEP in Chapter 17}.
C&S play a pivotal role in global aspirations to implement the Paris 
Agreement, advance the SDGs and foster CRD. Progress towards

these ends depends on the extent to which C&S mobilise urgent and 
transformational changes to institutionalise enabling conditions, close 
the coastal adaptation gap by addressing the drivers and root causes 
of exposure and vulnerability to climate-compounded coastal hazard 
risks, and drastically reduce greenhouse gas emissions (medium 
confidence) {CCP2.4; Chapter 18}

CCP2
2167Cities and Settlements by the Sea  Cross-Chapter Paper 2
CCP2.1 Context of Cities and Settlements by 
the Sea
CCP2.1.1 Introduction and Context
This cross-chapter paper examines the distinctive roles played by 
C&S by the sea in vulnerability and coastal hazard risk reduction, 
adaptation, resilience and sustainability in a changing climate. The 
paper builds upon evidence from AR5 (Wong et al., 2014), the Special 
Report on the Ocean and Cryosphere in a Changing Climate (SROCC; 
Magnan et al., 2019; Oppenheimer et al., 2019) and draws material 
from across WGII AR6 (especially Chapters 3, 6, 9–15). It differs 
from the SLR-focused analysis of urban areas in SROCC (Section 4.3) 
through a more integrated assessment that distinguishes between 
archetypal coastal C&S (Section CCP2.1.2), sectoral risks to C&S by 
the sea (Section CCP2.2), responses to address these risks (Section 
CCP2.3) and enabling conditions and lessons learned (Section CCP2.4).

We define C&S as concentrated human habitation centres, whether 
small or large, rural or urban (Section 6.1.3). We highlight the unique 
exposure and vulnerability of coastal C&S resulting from rapid 
urbanisation at the narrow land–sea interface, and a high concentration 
of economic activity and at-risk people, many with long-standing 
cultural ties to the coast and dependence on coastal ecosystems that 
are prone to climate change impacts (high confidence; He and Silliman, 
2019; Lau et al., 2019; Oppenheimer et al., 2019; Sterzel et al., 2020).
Presently, the coastal C&S population exposure to ocean-driven 
impacts from SLR and other climate-driven impacts is considerable 
by any measure (Buddemeier et  al., 2008; Barragán and de Andrés, 
2015; Kay and Alder, 2017; Haasnoot et al., 2019; McMichael et al., 
2020; Sterzel et  al., 2020). In 2020, almost 11% of the global 
population—896  million people—resided in C&S within the low-

elevation coastal zone (LECZ; coastal areas below 10 m of elevation 
above sea level that are hydrologically connected to the sea; Haasnoot 
et al., 2021b), a figure which will potentially increase beyond 1 billion 
by 2050 (Oppenheimer et  al., 2019). Infrastructural and economic 
assets worth USD 6,500–11,000 billion are also exposed in the 1-in-
100-year floodplain for C&S of all sizes (Neumann et al., 2015; Muis 
et al., 2016; Brown et al., 2018; Andrew et al., 2019; Kulp and Strauss, 
2019; Kirezci et al., 2020; Thomas et al., 2020; Haasnoot et al., 2021b; 
Hooijer and Vernimmen, 2021).
Further, coastal cities located at higher elevations (e.g., São Paulo, 
Brazil) or distantly located inland along tidally influenced rivers (e.g., 
the Recife Metropolitan Region, Brazil) also have populations and 
infrastructure exposed to climate impacts. As such, the inclusion of 
C&S beyond the LECZ is warranted when assessing climate impacts

and associated exposure, vulnerabilities and risks. The coastal 
zone includes some of the world’s largest, most densely populated 
megacities, as well as the fastest-growing urban areas. However, vast 
coastal areas are sparsely populated, with populations in these regions 
concentrated in smaller C&S, including along subsiding shorelines and 
in deltas (Nicholls and Small, 2002; McGranahan et al., 2007; Merkens 
et al., 2018; Edmonds et al., 2020; Nicholls et al., 2021). From this wider 
perspective, climate change impacts on the coast directly or indirectly 
affect a large portion of the global population, economic activity and associated critical infrastructure. Some estimates suggest that 
23–37% of the global population lives within 100 km of the shoreline 
(Nicholls and Small, 2002; Shi and Singh, 2003; Christopher Small and 
Joel E. Cohen, 2004; McMichael et al., 2020).
C&S by the sea are thus on the frontline of action to adapt to

climate change, mitigate greenhouse gas emissions and chart CRD 
pathways for several distinct reasons. First, home to a concentrated 
(and growing) portion of the world’s population, many coastal C&S 
are simultaneously exposed and vulnerable to climate-compounded 
hazards as well as being centres of creativity and innovation (Glavovic, 
2013; Crescenzi and Rodríguez-Pose, 2017; Druzhinin et  al., 2021; 
Mariano et al., 2021; Storbjörk and Hjerpe, 2021). Second, people in 
C&S by the sea rely on coastal ecosystems, many of which are highly 
sensitive to climate change impacts that compound non-climatic risks 
and increase the precarity of coastal livelihoods (Lu et al., 2018; He 
and Silliman, 2019; Thrush et al., 2021). Third, coastal C&S are linked 
together through a network of ports and harbours that underpin global 
trade and exchange, but which are prone to climate change impacts, 
especially SLR, with significant implications for global CRD prospects

(Becker et  al., 2018; Christodoulou et  al., 2019; Walsh et  al., 2019; 
Hanson and Nicholls, 2020). For these reasons, this paper assesses 
responses, enabling conditions and lessons learned for addressing 
climate change in C&S by the sea.
CCP2.1.2 Urbanisation in Coastal Systems: Coastal City 
and Settlement Archetypes
This assessment uses an archetype framework categorizing coastal 
C&S according to geomorphological characteristics, urban growth, 
economic resources and inequalities (Figure  CCP2.1). Three broadly 
defined coastal settlement geomorphologies are used in each row: 
open coasts (a coast with sediment without river mouths) and two 
transitional coastal zones with river mouths, distinguishing between 
estuaries (a wetland receiving sediment from both fluvial and marine 
sources, which is affected by tide, wave and river processes) and deltas 
(a wetland where fluvial sediment is supplied and deposited more

rapidly than it can be redistributed by basin processes such as waves 
and tides; Bhattacharya, 1978; Barragán and de Andrés, 2015; Kay and 
Alder, 2017; Haasnoot et al., 2019; Sterzel et al., 2020). Small island 
C&S are not singled out in this typology, because their coastlines often 
include the geomorphic features listed above, or require a different 
adaptation approach at larger spatial scales (Haasnoot et al., 2019). 
Several coastal C&S have a combination of two typologies, for example, 
Maputo-Matola, Mozambique and Mumbai, India, having both open 
and transitional riverine coasts, and can be classed as mixed. We 
also acknowledge that several coastal C&S may have areas sited in 
mountainous topography that abruptly rises from the coast (e.g., along 
the Mediterranean), but generally these cities have narrow, densely 
populated coastlines exhibiting these three archetypal categories 
(Blackburn et al., 2019). Arctic settlements are addressed separately in

this cross-chapter paper.
Coastal C&S within these geomorphological categories are further 
distinguished according to higher or lower rates of urban growth 
and inequality, which can be estimated through population growth

CCP2
2168Cross-Chapter Paper 2 Cities and Settlements by the Sea
Open coast
Coast with deposited sediment without river mouthsACCR AS INGAPORE
DeltaicROTTERDAM DHAK A
CAN TH OALEXANDRI AJ AKARTA
Estuary
Wetland area receiving sediment from river and marine sources  Coast with deposited sediment without river mouth s
MONKEY RIVE R
VILLAGEESMERALDA S NEW YORK CITY
SHANGHAI
PERTHOCEAN 
IMPACTS
Sea level rise ,
Warmer Sea Surface
Temperatures ,
Ocean Acidification,
Sea Ice Melt
Erosion,
Land Subsidence,
Salinization,
Permafrost MeltATMOSPHERIC 
IMPACTS
Severe storms,
Tropical Cyclones,
Extratropical Cyclone s
HYDROLOGICAL 
IMPACTS
Pluvial, Fluvial and
Marine flooding
CAPE TOWN
Lower inequality
Lower growthHigher inequality
Lower growthLower inequality
Higher growth Higher growthHigher inequality
PHYSICAL GEOMORPHOLOGY
NASSAU
BAHAMASNEW YORK CITY
USA
ST. GEORGES
GRENADA
UTQIAGVIK
USA
ACCRA
GHANASINGAPORE
SINGAPOREALEXANDRI A
EGYPT
JAKARTA
INDONESI AMONKEY RIVER VILLAGE
BELIZESHANGHAI
CHINA

CAPE TOWN
SOUTH AFRICAPOPULATION SIZE
27,000,000
200
 S
URBAN ECONOMIC RESOURCES
NASSAU
UTQIAGVIK
CAN TH O
VIETNA M
DHAKA
BANGLADESH
Urban Annual Gross Domestic Product per capita (USD, 2020 )
Low
<USD 10,000Medium
USD 10,000 – 20,000High
>USD 20,000
ROTTERDAM
NETHERLANDSInformalHigh density
developed
TYPE OF SETTLEMEN T
Low density
developed
BELÉM
BELEM
BRAZIL
PERTH
AUSTRALI AESMERALDAS
ECUADOR
Archetypal C&S affected by ocean, terrestrial, geological, atmospheric and hydrological hazards
driven by climate change
Open coast
Deltaic
EstuaryTERRESTRIAL
IMPACTS

CCP2
2169Cities and Settlements by the Sea  Cross-Chapter Paper 2
from national census data or areal extent of urban development 
(CEIC, 2021), as well as by relative urban inequalities estimated by 
Gini coefficient data and urban–rural poverty rates (OECD, 2018; 
OECD, 2020). Combining geomorphological and socioeconomic data 
accounts for urban–rural interconnections and differences, with levels 
of capital generation, diversity of economic functions and human 
development indices having previously been used to discern cultural, 
economic, administrative and political differences between cities and 
their hinterland (Blackburn et al., 2019; Rocle et al., 2020). For instance, 
the ecological, cultural and economic footprint of tertiary sectors, for 
example, coastal tourism associated with the Australian Great Barrier 
Reef, stretches far beyond the nearest onshore settlement of Cairns 
(Bohnet and Pert, 2010; Brodie and Pearson, 2016).

Some caveats are warranted. First, locating a specific city or settlement 
in a particular archetype does not account for future reclassification 
due to growth or shifts in development trajectories. Second, significant 
socioeconomic, political and governance variations exist within many 
C&S, such as impoverished informal settlements alongside wealthy 
neighbourhoods in cities like Cape Town and São Paulo (also see 
Table SMCCP2.1). Third, this archetype framework does not explicitly 
reveal important interconnections between coastal C&S and their 
hinterlands, or between particular C&S through maritime trade or 
other economic, sociocultural and geopolitical interdependencies. 
Notwithstanding these caveats, these archetypes reveal differentiated 
physical impacts and socioeconomic conditions, as well as the variable 
challenges and opportunities arising when addressing climate change 
impacts and projected risk, which, depending on coastal type, C&S

size and resource availability, help to inform efforts to adapt and chart 
CRD for each archetype (Sánchez-Arcilla et al., 2016; Rocle et al., 2020; 
Sterzel et al., 2020).
CCP2.2 Climate Change Risks to Cities and 
Settlements by the Sea
Coastal C&S are at the forefront of climate risk (FAQ CCP2.1). The 
dynamic interaction between ocean and climate drivers and varied 
coastal geographies influences the character of coastal risks, including 
many that are unique to C&S by the sea. The interaction of coastal 
hazards with exposure and vulnerability is differentiated by coastal 
archetypes, leading to distinct climate change-compounded risks and 
associated responses (Figure CCP2.2; Section 1.3.1.2; Simpson et al., 
2021).
Overall, interactions between climatic and non-climatic drivers of 
coastal change are increasing the frequency and intensity of many

2 In this report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about 
as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100% and extremely unlikely 
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics (e.g., very likely). This report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome 
lies within the 17–83% probability range.coastal hazards, with settlement archetypes and the wider coastal 
zone subject to escalating risk (high confidence; Figure  CCP2.2; 
Table  SMCCP2.1 for examples of selected coastal C&S). Risks can 
vary markedly between different archetypes: C&S sited on deltaic and 
estuarine coasts face additional risks of pluvial flooding compared to

open coasts, while greater vulnerabilities arise in coastal settlements 
with higher inequalities.
Risks to C&S by the sea were extensively covered in SROCC 
(Oppenheimer et  al., 2019) and also in Chapters 3, 6 and regional 
chapters; in this paper, specific risks to livelihoods, activities, the built 
environment and ecosystems are assessed in detail in Supplementary 
Material SMCCP2.1. The ocean and climate impact drivers influencing 
these risks are assessed in WGI (Ranasinghe et  al., 2021 ), which 
include extreme heat, pluvial floods from increasing rainfall intensity, 
coastal erosion and coastal flood driven by increasing SLR, and 
tropical cyclone storm surges (high confidence). Further, Arctic coastal 
settlements are particularly exposed to climate change due to sea ice 
retreat as well as from permafrost melt (high confidence).
Without adaptation, risks to land and people in coastal C&S from 
pluvial and coastal flooding will very likely2 increase substantially by

2100 and likely beyond as a result of SLR, with significant impacts even 
under RCP2.6 (Neumann et al., 2015; Muis et al., 2016; Brown et al., 
2018; Nicholls et al., 2018; Kulp and Strauss, 2019; Oppenheimer et al., 
2019; Kirezci et al., 2020; Haasnoot et al., 2021b). Across these studies, 
by 2100, 158–510  million people and USD  7,919–12,739  billion 
assets under RCP4.5, and 176–880  million people and USD  8,813–
14,178  billion assets under RCP8.5 will be within the 1-in-100-year 
floodplain (very high confidence). There is medium confidence that 
accelerated SLR will increase shoreline erosion globally, although 
biophysical feedbacks will allow many coastlines to maintain relatively 
stable morphology if room exists to accommodate mangroves in 
estuarine and deltaic coasts and beach movement along open coasts 
(Kench et  al., 2015; McLean and Kench, 2015; Perkins et  al., 2015; 
Richards and Friess, 2016; CCC, 2017; Duncan et al., 2018; Luijendijk

et al., 2018; Mentaschi et al., 2018; Schuerch et al., 2018; Ghosh et al., 
2019; Masselink et  al., 2020; Toimil et  al., 2020; Vousdoukas et  al., 
2020b). Limiting emissions to RCP2.6 (corresponding to a mean post-
industrial global temperature increase of 1.5–2°C) significantly reduces 
future SLR risks (Hinkel et al., 2014; Brown et al., 2018; Nicholls et al., 
2018; Schinko et al., 2020). For example, by 2100, the population at risk 
of permanent submergence increases by 26% under RCP2.6 compared 
with 53% under RCP8.5 (median values from Kulp and Strauss, 2019).
There is high confidence regarding regionally differentiated but 
considerable global sectoral impacts in coastal C&S arising from Figure CCP2.1 |  Archetypal cities and settlements (C&S) affected by ocean, terrestrial, geological, atmospheric and hydrological hazards driven by climate

change. Coastal C&S are grouped by physical geomorphology along estuarine, deltaic or open coasts (Barragán and de Andrés, 2015; Kay and Alder, 2017; Haasnoot et al., 2019). 
C&S are also classified according to relative inequality (e.g., urban Gini coefficient or poverty rates) and growth rates (e.g., recent population growth and increasing density of urban 
form or built-up areas over the past decade; OECD, 2018; CEIC, 2021; OECD, 2020). Settlement types (e.g., informal, low-density or high-density developments) and economic resources 
(e.g., urban per capita gross domestic product) are also reflected in their respective categories. The bottom map shows location, 2020 population size and geomorphological types.

CCP2
2170Cross-Chapter Paper 2 Cities and Settlements by the Sea
exposure to hazards. Tangible impacts include damage, loss of life 
and loss of livelihoods, especially fisheries and tourism (Tessler et al., 
2015; Avelino et  al., 2018; Hoegh-Guldberg et  al., 2018; Seekamp 
et  al., 2019; Arabadzhyan et  al., 2020); negative impacts on health 
and wellbeing, especially under extreme events (McIver et al., 2016; 
Bakkensen and Mendelsohn, 2019; Bindoff et al., 2019; Pugatch, 2019); 
and involuntary displacement and migration (Hauer, 2017; Davis et al., 
2018; Neef et  al., 2018; Boas et  al., 2019; McLeman et  al., 2021). 
Intangible impacts include psychological impacts due to extreme 
events such as heatwaves, flooding, droughts and tropical cyclones; 
heightened inequality in coastal archetypes with systematic gender/
ethnicity/structural vulnerabilities; and loss of things of personal or 
cultural value and sense of place or connection, including an existential

risk of the demise of nations due to submergence (Allison and Bassett, 
2015; Barnett, 2017; Schmutter et al., 2017; Weir et al., 2017; Farbotko 
et al., 2020; Hauer et al., 2020; Hoffmann et al., 2020; Bell et al., 2021). 
Impacts extend beyond the coastal zone, for example disruption to ports 
and supply chains, with major geopolitical and economic ramifications 
from the C&S to the global scale (very high confidence; Becker et al., 2018; Camus et al., 2019; Christodoulou et al., 2019; Walsh et al., 2019; 
Hanson and Nicholls, 2020; Yang and Ge, 2020; Izaguirre et al., 2021; 
León-Mateos et al., 2021; Ribeiro et al., 2021).
Many coastal C&S have densely built physical infrastructure and assets 
that are exposed and vulnerable to climate change-compounded 
coastal hazards. There is high confidence that SLR, land subsidence, 
poorly regulated coastal development and the rise of asset values 
are major drivers of future risk in all coastal archetypes and, without

adaptation, built environment risks—especially in archetypes with 
high exposure due to rapid growth—are expected to rise considerably 
in this century across all RCPs (Koks et al., 2019; Magnan et al., 2019; 
Oppenheimer et al., 2019; Abadie et al., 2020; Nicholls et al., 2021). 
Archetypes with more informal settlements are often disproportionally 
exposed to coastal risks (Roy et  al., 2016; Hallegatte et  al., 2017; 
Bangalore et al., 2019).
There is high confidence that loss of coastal ecosystem services will 
increase risks to all coastal C&S archetypes that include reduced Coastal urban archetype relates to geomorphology (deltaic, estuary, open coast) ,
resource inequality and growth; Response space can be enhanced with enablers  
Urban feedback to climate drivers & physical 
impacts e.g., compound and cascading risksGeomorphology Inequalit
y
GrowthCoastal
hazardsVulnerability
Exposure Responses
EnablersRISKS TO CITIES
AND SETTLEMENTS
BY THE SEAClimate &
Ocean drivers

Sea level rise
Severe storms 
Changing 
precipitation  
intensity & 
frequency
Wave climat e
Warmer 
temperatures & 
extreme heat
Altered runoffPhysical impacts on
coastal cities & settlements
Submergence
Extreme water levels & wave height s
Altered geomorphology (erosion,  
accretion, beach platform )
Water quality & salinity
Rising water tables
High winds
Heat wave & drought exposure
Fluvial sediment supply
Nutrient supply
Reduced sea ice at high latitudes
Poleward species migration
Changed circulation & stratificatio n
Reduced pH (ocean acidification )Schematic of how climate- and ocean-drivers (from WGI Chapter 12.4.10.2) and consequential physical  
impacts on coastal C&S influence risks assessed in (CCP2.2; Figure based on Simpson et al. (2021) an d
Section 1.3.1.2).
Figure CCP2.2 |  Schematic of how climate and ocean drivers (from WGI Chapter 12.4.10.2 (Ranasinghe et al., 2021)) and consequential physical impacts

on coastal cities and settlements (C&S) influence risks assessed in (Section CCP2.2; figure based on Simpson et al., 2021, and Section 1.3.1.2). These risks to 
C&S by the sea are shaped and mediated by adaptation interventions aimed at reducing vulnerability and exposure to coastal hazards given settlement archetypes, as well as by 
expanding the space for responses to risk via the enabling conditions assessed in Section CCP2.4. Note that exposure to coastal hazards is controlled chiefly by underlying coastal 
C&S geomorphology and changes in coastal hazards and urban growth, including population and infrastructure growth. Vulnerability is controlled, for example, by socioeconomic 
development and inequality, and responses that shape the risks assessed in Section CCP2.3 can be enhanced by enabling conditions, including behavioural change, conducive 
finance and prudent governance.

CCP2
2171Cities and Settlements by the Sea  Cross-Chapter Paper 2
provisioning of materials and food (e.g., wood, fishery habitat; Kok 
et  al., 2021), amelioration of coastal hazards (e.g., attenuation of 
storm surges, waves and containing erosion) (Section 2.3.2.3; Godfroy 
et al., 2019; Schoutens et al., 2019; Zhu et al., 2020b), climate change 
mitigation (through carbon sequestration; Macreadie et  al., 2017; 
Rovai et  al., 2018; Ward, 2020), water quality regulation (nutrient, 
pollutant and sediment retention and cycling; Wilson et al., 2018; Zhao 
et al., 2018) and recreation and tourism (Pueyo-Ros et al., 2018).
Most studies of coastal C&S focus on adaptation to a single or limited 
set of risks, but there is high confidence that compound and cascading 
risks significantly alter C&S risk profiles (Nicholls et al., 2015; Estrada 
et  al., 2017; Edmonds et  al., 2020; Eilander et  al., 2020; Yin et  al., 
2020; Ghanbari et  al., 2021). Extreme events can lead to cascading

infrastructure failures that cause damage and economic losses well 
beyond the coastal zone (Haraguchi and Kim, 2016; Kishore et  al., 
2018; Rey et al., 2019; So et al., 2019), and have forced evacuation 
of C&S and small islands (Look et  al., 2019; Thomas and Benjamin, 
2020). These risks are exacerbated by non-climatic drivers, for example 
compound and cascading impacts arising from exposure to tropical 
cyclones and COVID-19 that threaten population health and hamper 
pandemic responses (Salas et al., 2020; Shultz et al., 2020a; Shultz et al., 
2020b). There is emerging evidence (low confidence) from individual 
coastal C&S and regional case studies (e.g., in Europe, Australia and 
the US) to illustrate the increasing influence of compound risks on vulnerability due to accelerating climate change (Wahl et al., 2015; Xu 
et al., 2019; Kirezci et al., 2020).
Figure CCP2.3 shows that ocean-driven coastal risks to people, land

and infrastructure in East and Southeast Asia are highest compared 
to other regions, even for low levels of projected SLR. However, risks 
facing coastal C&S are high across the globe, especially under higher 
SLR projections (high confidence). Without adaptation, the population 
at risk of a 100-year coastal flood increases by ~20% if the global 
mean sea level rises by 0.15 m relative to current levels; this at-risk 
population doubles at a rise of 0.75 m in mean sea level and triples 
at 1.4 m. Simultaneously, coastal C&S are projected to experience 
shoreline retreat, with coastlines having more than 100 m of retreat 
increasing by ~165% if current mean sea levels rise between 0.23 
and 0.53 m. Ocean-driven flooding in coastal C&S is also projected 
to disrupt flights by up to three orders of magnitude per year in 
selected coastal C&S as mean sea level increases. Typically, larger 
risks correspond to archetypes associated with higher inequality and

high growth rates, especially in deltas, leading to larger vulnerability 
and exposure, respectively, under higher warming levels.
10 million peopl e
100 flights
1000 km coastlin e
1000 km2 wetlands50 million peopl e
500 flights
5,000 km coastlin e
5,000 km2 wetlands100 million peopl e
1,000 flights
10,000 km coastlin e
10,000 km2 wetlandsPeople Flights
Wetlands (+/-) Coastline02.0
1.5
1.0
0.5Global mean
sea level
rise
Figure CCP2.3 |  Map of coastal cities and settlements’ risks according to IPCC regions, showing risks to people (number of people at risk from a 100-year coastal flood 
event; Haasnoot et al., 2021b), risks of loss of coastal land (length of coast with more than 100 m retreat; Vousdoukas et al., 2020b), risks to the built environment (airports at 
risk indicated by expected annual number of flights disrupted by coastal flooding (Yesudian and Dawson, 2021) and risk to wetlands (± indicates positive or negative area change;

Schuerch et al., 2018). Risks are reported against global mean sea level rise relative to 2020, depending on data availability.

CCP2
2172Cross-Chapter Paper 2 Cities and Settlements by the Sea
CCP2.3 Adaptation in Cities and Settlements by 
the Sea
CCP2.3.1 Introduction
This section extends SROCC Chapter 4 (Oppenheimer et  al., 2019), 
which focused on SLR, and draws from Chapters 6 and 9–15 to cover 
all C&S archetypes. Adaptation interventions span psychosocial (e.g., 
awareness raising), economic (e.g., insurance), physical (e.g., retreat), 
technical (e.g., sea walls) and natural dimensions (e.g., wetland 
restoration; Nicholls et  al., 2015). Adaptation strategies for coastal 
C&S are typically classified in terms of protect, accommodate, advance 
and retreat, which are used below.
Some coastal cities have adapted to meters of SLR in the past, 
indicating that adaptation is feasible (Esteban et  al., 2020a), but 
future adaptation options are influenced by variations in projected 
socioeconomic conditions and rates of SLR (Cross-Chapter Box SLR in

Chapter 3). To date, interventions are typically implemented reactively 
in response to extreme events (high confidence), but leading adaptors 
are increasingly proactive (medium confidence; Araos et  al., 2016; 
Dulal, 2019; Dedekorkut-Howes et  al., 2020) and those that move 
from previously rigid to more adaptive and flexible solutions, using an 
adaptation-pathways approach that keeps options open in the face of 
uncertainty, have improved climate risk management (high confidence; 
Sections  9.9.4; 10.5; 11.7; 12.5.5; 13.2; 14.7; 15.5; Cross-Chapter 
Box DEEP in Chapter 17; Walker et al., 2013; Marchau et al., 2019).
The effectiveness of different strategies and interventions is mediated 
by physical coastal features for hard adaptation measures and by 
the scope and depth of soft adaptation measures, for example by 
the coverage and extent of social safety nets for the urban poor 
(Section 6.3). Their feasibility is also shaped by socioeconomic, cultural,

political and institutional factors, for example social acceptance 
of measures (Section CCP2.2, SMCCP2.2.4). Together, response 
effectiveness and feasibility shape the solution space for mediating 
risks (Section 1.3.1.2; Figure CCP2.3; Simpson et al., 2021), which is 
achieved chiefly through governance interventions, for example laws 
and regulations (Haasnoot et al., 2020). Access to financial resources 
expands the solution space, most notably for some resource-rich 
coastal archetypes (Section CCP2.4.2; Table SMCCP2.1; Sections 3.6; 
14.7), but rapid population growth and unfolding climate-driven 
impacts can increase risks (Haasnoot et al., 2021a), especially for small 
island and poorer C&S (high confidence; Section  15.3; Magnan and 
Duvat, 2020).
CCP2.3.2 Protection of Coastal Cities and Settlements
CCP2.3.2.1 Hard Engineering Measures
Hard engineering protective measures are commonly used to reduce 
coastal flooding and to drain or store excess water from intense

precipitation. Many coastal cities, in particular densely populated and 
high-resource archetypes, have planned and are planning to continue 
a protection-based strategy, comprising, for example, breakwaters, sea 
walls and/or dikes, which could be raised or complemented with large barriers, or with ‘super-levees’ enabling construction on top of them 
(high confidence; Table SMCCP2.1; Takagi et al., 2016; Haasnoot et al., 
2019; Hall et al., 2019; Esteban et al., 2020b).
Protection is effective in the short- to medium-term for many coastal 
cities, and can be cost effective in the 21st century (Section CCP2.4.2), 
but residual risk remains because protection can fail. Even under 
RCP8.5, technical limits to hard protection may only be reached after 
2100 in many regions, but socioeconomic and institutional barriers 
may be reached before then (Hinkel et al., 2018). With progressive SLR, 
protection eventually becomes unaffordable and impractical (Strauss

et al., 2021). Combining hard engineering measures with nature-based 
solutions, spatial planning and early warning systems can help to 
contain residual risk (Du et al., 2020). Protective works do not prevent 
salinisation and higher groundwater levels (Alves et al., 2020), and can 
lead to loss of coastal habitat (Cross-Chapter Box SLR in Chapter 3; 
Achete et al., 2017; Cooper et al., 2020). Hard protective measures also 
create long-term path dependency as they last for decades and attract 
new development, locking in impact and exposure as C&S grow, with 
the expectation of ongoing protection (Chapter 3; Di Baldassarre et al., 
2015; Gibbs, 2016; Griggs and Patsch, 2019; Siders, 2019a).
CCP2.3.2.2 Soft Engineering and Sediment-Based Measures
Sediment-based interventions, for example beach nourishment, aim 
to limit coastal erosion and flood risk and have become a widely 
applied strategy, especially in open-coast archetypal C&S. This is in

part because there is less impact on adjacent beaches and coastal 
ecology and also lower construction and maintenance costs compared 
to hard protection (high confidence; Parkinson and Ogurcak, 2018). 
In addition, it is considered a flexible strategy under more rapid SLR 
conditions (Kabat et al., 2009; Stive et al., 2013) and can be applied 
in the form of a mega-nourishment strategy, wherein natural currents 
distribute sand along the coast (Stive et al., 2013; de Schipper et al., 
2021). However, there are limits to this strategy due to environmental 
impacts, costs and the availability of potential and permitted sand 
reserves, which may be unable to keep up with higher rates of SLR 
(Parkinson and Ogurcak, 2018; Haasnoot et  al., 2019; Harris et  al., 
2021; Staudt et al., 2021). Simultaneously, other socioeconomic needs 
(e.g., damming rivers or for building and transport infrastructure) may 
compete for sand as a limited resource (Torres et al., 2017; Bendixen

et al., 2019). Regional and global governance provisions (e.g., spatial 
reservations for sand mining, international frameworks for distribution) 
could improve long-term feasibility (Torres et al., 2017; Parkinson and 
Ogurcak, 2018; Bendixen et al., 2019; Haasnoot et al., 2019).
CCP2.3.2.3 Nature-Based Measures
Nature-based measures, such as retaining mangroves and marshes, 
have been successful in reducing deaths and damage due to storm 
surges (medium evidence, high agreement; Das and Vincent, 2009; 
Saleh and Weinstein, 2016; Narayan et al., 2017; Triyanti et al., 2017; 
Hochard et  al., 2019; del Valle et  al., 2020), and reportedly provide 
USD 23.2 billion yr–1 in storm protection services across the USA (Saleh 
and Weinstein, 2016). They are also a cost-effective strategy (medium 
confidence) that provides C&S with additional co-benefits through 
ecosystem services (high confidence; Cross-Chapter Box  NATURAL

CCP2
2173Cities and Settlements by the Sea  Cross-Chapter Paper 2
in Chapter 2; Section  2.2.4; Narayan et  al., 2016; Depietri and 
McPhearson, 2017; Morris et al., 2018; Reguero et al., 2018; Chausson 
et al., 2020; Du et al., 2020; NIES and ISME, 2020; Reguero et al., 2020; 
Sudmeier-Rieux et al., 2021).
Nature-based measures can reduce inland propagation of extreme 
sea levels (high tides, storm surges; high agreement; Godfroy et al., 
2019; James et al., 2020; Zhu et al., 2020b), with vertical reduction in 
water levels ranging from 5 to 50 cm/km behind large mangroves and 
marshes (Stark et al., 2015; Van Coppenolle and Temmerman, 2020). 
They also attenuate wind-driven waves and reduce shoreline erosion 
(high agreement), and this can be by as much as 90% over stretches 
of 10–100 m for dense mangrove and marsh vegetation (medium 
evidence; Li et  al., 2014; Möller et  al., 2014; Vuik et  al., 2016; Vuik 
et al., 2018; Godfroy et al., 2019; Zhu et al., 2020a) and up to 40%

for dunes (Feagin et al., 2019). Coral reefs on average reduce wave 
energy by 97% (Ferrario et al., 2014). Seagrass meadows attenuate 
wind waves to a lesser extent, and are only effective in water <0.2 m 
deep (Ondiviela et al., 2014; Narayan et al., 2016; Morris et al., 2019).
Within limits, coastal ecosystems can respond to RSL through sediment 
accretion and lateral inland movement (Kirwan et al., 2016; Schuerch 
et  al., 2018). Nature-based measures have the greatest potential in 
coastal deltas and estuaries, where human populations are exposed, 
but large ecosystems, like mangroves and marshes, can be conserved 
and restored (Menéndez et al., 2020; Van Coppenolle and Temmerman, 
2020). Their feasibility depends on physical, ecological, institutional 
and socioeconomic conditions that are typically locality dependent 
(Temmerman and Kirwan, 2015; Arkema et  al., 2017); space may 
not be available in certain places (e.g., intensive urbanization on

the shoreline), or these measures may conflict with other human 
demands for scarce land (Tian et al., 2016). Successful nature-based 
measures require site-specific knowledge and science-based design, 
pilot monitoring and adaptive upscaling (Evans et al., 2017; Nesshöver 
et al., 2017), as well as a more rigorous understanding of long-term 
performance, maintenance and costs (Kumar et al., 2021).
Nature-based measures are increasingly implemented in combination 
with hard protection measures (Hu et al., 2019; Schoonees et al., 2019; 
Morris et al., 2020; Oanh et al., 2020). They can reduce dike failure 
and increase design life where sediment accretion allows wetlands to 
respond to SLR (Jongman, 2018; Vuik et al., 2019; Zhu et al., 2020a). 
There is high agreement that a hybrid strategy combining hard and 
soft protection strategies is more effective and less costly under many 
circumstances, and there is limited evidence that technical limits will

be encountered with such a strategy for low-lying C&S built on soft or 
permeable soil or with high exposure to monsoons and river discharges 
(Spalding et al., 2014; Sutton-Grier et al., 2015; Pontee et al., 2016; 
Morris et al., 2018; Reguero et al., 2018; Du et al., 2020; Morris et al., 
2020; Seddon et al., 2020; Waryszak et al., 2021).
CCP2.3.3 Accommodation of the Built Environment
The most effective solution for limiting the growth of climate risks 
in C&S by the sea is to avoid new development in coastal locations 
prone to major flooding and/or SLR impacts (very high confidence; Cross-Chapter Box  SLR in Chapter 3; Oppenheimer et  al., 2019; 
Doberstein et  al., 2019). For existing C&S, accommodation includes 
biophysical and institutional responses to reduce exposure and/or 
vulnerability of coastal residents, human activities, ecosystems and 
the built environment, enabling continued habitation of coastal C&S

(Oppenheimer et al., 2019). Next to hard protection, accommodation is 
the most widely used adaptation strategy across all archetypes to date 
(high confidence; Sayers et al., 2015; Olazabal et al., 2019; Le, 2020). 
Measures include elevation or flood proofing of houses and other 
infrastructure (Garschagen, 2015; Aerts et  al., 2018; Buchori et  al., 
2018; Jamero et al., 2018; Tamura et al., 2019), spatial planning (e.g., 
Duy et al., 2018), amphibious building designs (Nilubon et al., 2016), 
increasing water storage and/or drainage capacity within C&S (Chan 
et  al., 2018), early warning systems and disaster responses (Hissel 
et al., 2014) and slum upgrading (Jain et al., 2017; Olthuis et al., 2020).
Raising land, or individual buildings, can avert flooding and be 
accomplished artificially or by nature-based interventions through river 
diversion and control in estuarine and deltaic archetypes (Nittrouer et al.,

2012; Auerbach et al., 2015; Day et al., 2016; Sánchez-Arcilla et al., 2016; 
Hiatt et al., 2019; Cornwall, 2021). Nature-based land elevation is limited 
by sediment supply and can address SLR rates of up to 10 mm  yr–1 
(Kleinhans et al., 2010; Kirwan et al., 2016; IPCC, 2019). It also assumes 
that existing land-use patterns permit land raising (e.g., in rural or 
newly developed areas; Scussolini et al., 2017). Artificial land raising can 
achieve significant elevations and be implemented over a large spatial 
scale (Esteban et al., 2015; Esteban et al., 2019). Raising land can be cost 
beneficial for small areas or where lower safety levels are satisfactory, 
but protection is usually more economical for larger areas, although both 
strategies are often combined (Lendering et al., 2020).
Accommodation measures can be very effective for current conditions 
and small changes in SLR (Laurice Jamero et  al., 2017; Scussolini

et al., 2017; Oppenheimer et al., 2019; Du et al., 2020; Haasnoot et al., 
2021a), and buy time to prepare for more significant changes in sea 
level and other climate-compounded coastal hazards. However, limits 
to this strategy occur comparatively soon in some locations, possibly 
requiring protection in the medium term and retreat in the long run and 
beyond 2100, particularly in scenarios of dramatic SLR (Oppenheimer 
et al., 2019). For the foreseeable future, accommodation can play an 
important role in combination with protective measures to form hybrid 
interventions, with higher effectiveness than either approach in isolation 
(Du et al., 2020). Accommodation can play an increasingly important 
role where hard protection is neither technically nor financially viable, 
but detailed studies about expected trends of accommodation are 
lacking (Oppenheimer et al., 2019).
CCP2.3.4 Advance
An advance strategy creates new land by building seaward, which can

reduce risk for the hinterland and the newly elevated land, either by 
land reclamation through landfilling or polderisation through planting 
of vegetation to support natural land accretion (Wang et  al., 2014; 
Sengupta et  al., 2018). Advance has occurred in all archetypes (high 
confidence), from open coasts (e.g., Singapore) and small atolls (e.g., 
Hulhumalé in the Maldives; Hinkel et al., 2018; Brown et al., 2020), to

CCP2
2174Cross-Chapter Paper 2 Cities and Settlements by the Sea
cities on estuaries (e.g., Rotterdam) and deltas (e.g., Shanghai Sengupta 
et  al., 2020), and mountainous coasts (e.g., Hong Kong SAR, China). 
Earth observations show that between 14,000 and 33,700 km2 of land 
has been gained in coastal areas over the past 30 years, the dominant 
drivers being urban development and activities like fish farming 
(Donchyts et  al., 2016; Zhang et  al., 2017; Mentaschi et  al., 2018). 
Advancing seawards through large floating structures may be a viable 
option in the future (Wang et al., 2019; Setiadi et al., 2020; Wang and 
Wang, 2020) but is at an experimental stage, and, so far, only applied in 
calm water within a city as part of an accommodate strategy (Scussolini 
et al., 2017; Penning-Rowsell, 2020; Storbjörk and Hjerpe, 2021).
Advance is seen as an attractive option to adapt to SLR in growing cities 
that are already densely populated and have limited available land

for safe development, with a moderate to high adaptive capacity. But advance can have significant negative impacts on coastal ecosystems 
and livelihoods, requires substantial financial and material resources 
and time to build, and may be subject to land subsidence (Jeuken et al., 
2014; Garschagen et  al., 2018; Brown et  al., 2019; NYCEDC, 2019; 
Oppenheimer et al., 2019; Sengupta et al., 2020; Bendixen et al., 2021).
CCP2.3.5 Retreat
Retreat is a strategy to reduce exposure and eventually risks facing 
coastal C&S by moving people, assets and activities out of coastal 
hazard zones (Oppenheimer et  al., 2019). This includes adaptive 
migration, involuntary displacement and planned relocation of 
population and assets from the coast (Section  7.2.6; Cross-Chapter 
Box CB-MIGRATE in Chapter 7).
Urban Densely
populated fringesMegacity
scaleRetreatAdvanceGeneric
strategiesExamples of
specific measures
New seawards land
claim above sea level
Sea walls, levees, 
storm barriers

Wet and dry proofing
built environment
Floating structures
(experimental, not shown)
No build zones
Planned relocationSolution space for coastal cities and settlements by the sea
(a) Generic adaptation pathways for coastal cities and settlements to sea level rise
(b) Illustrative pathways for some coastal archetypesAccommodateProtectWetlands, mangroves,
coral reefs
Islands (open coast) Rural deltas Megacities (high growth)Increasing risk to sea level rise (mean and extremes) 
1. Successful pilot, lack of development space triggers advance, or protect due to lack of 
support, time or finance.
2. Preference for nature-based solutions.
3. Unaffordable, salinisation, pumping limit, lack of support.
4. Unaffordable, pumping limit, lack of time, support, knowledge, material.
5. Warming, limited space, human pressures, frequent flooding require additional measures.6. Hybrid strategy.
7. Frequent flooding, flooding creates access problems.

8. Warming, limited space, human pressures, frequent flooding.
9. Unaffordable, salinisation, pumping limit, lack of support.
10. Long lead time to align with social goals and ensure just outcomes.
11. Lack of acceptance and equity triggers shift.
Rural Entire delta
scaleSmall scales
(e.g. neighbourhood)1.
2.
5.
7.
10.11.8. 9.4.
3.
7.6.
Figure CCP2.4 |  Generic adaptation pathways for coastal cities and settlements (a) and the typical solution space, with illustrative pathways for three 
coastal archetypes (b). As risk increases under rising sea levels, solutions need to be combined or sequenced in order to contain risk. Pathways involve different trade-offs. 
Based on Tables SMCCP2.1–2.3; Chapters 11 and 13; Magnan and Duvat (2020); Lawrence et al. (2020); Haasnoot et al. (2019). Depending on local conditions, archetype and

risk tolerance, alternative pathways are needed and possible to contain risk. Dashed lines indicate uncertainty in the pathway (a); dashed and plain borders are used for illustrating 
various local situations within each archetype (b).

CCP2
2175Cities and Settlements by the Sea Cr oss-Chapter Paper 2
Planned relocation in coastal C&S with high hazard exposure and 
climate impacts is already occurring and has been increasing in frequency (medium confidence; Hino et  al., 2017; Mortreux et  al., 2018), with some small islands purchasing land in other countries to facilitate movement (Klepp, 2018). In the Arctic, the pressure to relocate away from the coast is expected to rise given the interacting effects of permafrost thaw and coastal erosion. Native villages in Alaska are already relocating (Ristroph, 2017; Ristroph, 2019). Involuntary resettlement may be a secondary effect of large-scale hard coastal protection projects, or inner-city river and canal regulation. In Jakarta, for example, a new giant seawall project involves resettling coastal households along large parts of the coastline (Garschagen et al., 2018).
Increased migration is to be expected across different climate scenarios,

but there is limited evidence and medium agreement about the scale of climate-induced migration at the coast (Oppenheimer et al., 2019; Chapter 16, RKR on peace). Planned relocation is expected to rise in C&S in response to SLR and other coastal hazards (high agreement, medium evidence; Siders et al., 2019). Relocation has predominantly been reactive to date, but increased attention is being given to pre-emptive resettlement and the potential pathways and necessary governance, finance and institutional arrangements to support this strategy (Ramm et al., 2018; Lawrence et al., 2020; Haasnoot et al., 2021a). There is limited evidence about the costs of planned relocation 
and retreat more generally (Oppenheimer et al., 2019).
Retreat can effectively reduce exposure of urban residents to coastal

hazards and provide opportunity for re-establishment of ecosystem services (very high confidence; Song et  al., 2018; Carey, 2020; Hindsley and Yoskowitz, 2020; Lincke et al., 2020; Lincke and Hinkel, 2021). But there is high confidence that it can sever cultural ties to the coast (Reimann et al., 2018) and lead to negative and inequitable socioeconomic effects for resettled communities if not planned and implemented in ways that are inclusive and just and address cultural, place-attachment and livelihood considerations (Ajibade, 2019; Adger et al., 2020; Carey, 2020; Jain et al., 2021; Johnson et al., 2021), as well as the rights and practices of Indigenous People (Nakashima et  al., 2018; Ristroph, 2019; Mohamed Shaffril et al., 2020). If planned well ahead and aligned with social goals, pathways to managed retreat can achieve positive outcomes and provide opportunities for transformation of coastal C&S (Haasnoot et al., 2021a; Mach and Siders, 2021). There is medium confidence that

land as well as appropriate financing is a major bottleneck for planned relocation (Alexander et al., 2012; Ong et al., 2016; Hino et al., 2017; Fisher and Goodliffe, 2019; Hanna et al., 2019; Buser, 2020; Doberstein et  al., 2020), particularly in very dense mega-urban areas (Ajibade, 2019) and crowded small islands (Neise and Revilla Diez, 2019; Weber et al., 2019; Kool et al., 2020; Lincke et al., 2020).
CCP2.3.6  Adaptation P athways

No single adaptation intervention comprehensively addresses coastal risks and enables CRD. An adaptation-pathways approach can facilitate long-term thinking, foresee maladaptive consequences and lock-ins, and address dynamic risk in the face of relentless and potentially high SLR; it can also frame adaptation as a series of manageable steps over time (Cross-Chapter Box DEEP in Chapter 17; Figure CCP2.4; Haasnoot et al., 2019). A portfolio of hard, soft and nature-based interventions can be used to implement strategies to protect, accommodate, retreat and advance, individually or in combination.
The strategy and the portfolio of interventions can be adjusted in

response to new information about SLR and other climate risks according to economic, environmental, social, institutional, technical or other objectives. In cases of rapid SLR, it may be necessary to implement a short-term protection strategy to buy time to implement more transformative and enduring strategies (high confidence; Du et al., 2020; Lawrence et al., 2020; Morris et al., 2020; Haasnoot et al., 2021a). There is high agreement that combining and sequencing 
adaptation interventions can reduce risk over time (Du et al., 2020; Morris et  al., 2020). Phasing interventions can help to spread costs and minimise regret (de Ruig et al., 2019), provided options are kept open to adjust to changing conditions (Buurman and Babovic, 2016; Haasnoot et al., 2019; Hall et al., 2019).
Many megacities plan to continue a protection strategy (Table SMCCP2.1).

This becomes increasingly costly, institutionally challenging and requires space, possibly facilitated through local relocation. There is high agree-ment that many C&S are locked-in to a self-reinforcing pathway: coastal defences have a long lifetime and attract people and assets that require further protection (Gralepois et  al., 2016; Bubeck et  al., 2017; Welch et al., 2017; Di Baldassarre et al., 2018; Jongman, 2018). Transitioning to alternative pathways may involve major transfer and sunk costs (e.g., Gralepois et al., 2016), but these may prove to be less costly in the long term. Because of considerable inertia in the built form of cities, such transitions have a greater chance of success and alignment with societal goals if embedded early into C&S planning and development process-es that enable transformational change and CRD (Sections 6.4.8; 11.7; 13.11; Box 18.1; Ürge-Vorsatz et al., 2018; Siders 2019b).
In islands, hybrid options of nature-based (where space and

environmental conditions allow) and protection measures (on wealthy, already densely populated islands) could reduce risk for low SLR in the next few decades (Section 15.5). Where feasible, retreat is a compelling option to reduce risk (Figure CCP2.4). With higher rates and levels of SLR in the medium to long term, financial, governance and material barriers may differentiate resource-rich and more rural islands, leading to a dichotomy between which islands retreat or can rely on protection for a period of time.
CCP2.4  Enabling Conditions and Lessons 
Learned
Here we distil enabling conditions and lessons learned from C&S 
archetypes adapting to coastal risk (Table SMCCP2.1; Table SMCCP2.2; Sections 6.4; 9.9.4; 10.5; 10.6; 11.7; 11.8; 12.5.5; 13.6.2; 14.7.2; 15.6).
CCP2.4.1  Enabling Behaviour al Change

Changing behaviours and practices are a critical enabler of adaptation in coastal C&S. Behavioural enablers include using economic, informational, sociocultural and psychological incentives to motivate adaptation actions (van Valkengoed and Steg, 2019; Gibbs, 2020),

CCP2
2176Cross-Chapter Paper 2 Cities and Settlements by the Sea
for example leveraging Indigenous knowledge and local knowledge 
(IKLK) and religious beliefs to incentivise adaptation (Hiwasaki et al., 
2014; Ford et  al., 2015), implementing subsidies/bans to incentivise 
sustainable aquaculture (Condie et  al., 2014; Krause et  al., 2020), 
providing localised flood warnings and forecasts to inform individual 
risk perceptions and risk management (Bruine de Bruin et al., 2014; 
Gibbs, 2020) or incentivise risk insurance (Bradt, 2019).
There is high evidence with medium agreement that public attitudes 
and perceptions of climate risks significantly influence individual 
adaptation behaviour across all coastal archetypes (Bradt, 2019; 
Buchanan et al., 2019; Javeline et al., 2019). Information on climate 
risks and impacts (e.g., flood warnings, SLR projections) strongly shapes 
public perceptions of climate risks. It is most effective at incentivising

and enabling adaptation behaviour if provided on meaningful spatial 
and temporal scales, with guidance about how to interpret the 
information (medium evidence, high agreement; Gibbs, 2020; Cools 
et al., 2016). Further, there is medium evidence, high agreement that 
integrating climate information with existing knowledge systems, 
such as local norms and beliefs and IKLK, is critical to improve public 
acceptability and develop context-specific solutions (Ford et al., 2015).
A second key enabler of coastal adaptation behaviour is self-efficacy 
or belief in one’s capacity to undertake adaptation. There is medium 
evidence, high agreement that high risk perception is in itself insufficient 
to motivate people to undertake adaptation (Fox-Rogers et al., 2016; 
Roder et al., 2019; Gibbs, 2020) and needs to be supplemented with 
supportive policy and financial provisions to enable adaptation (Fox-
Rogers et al., 2016).
Third, there is medium evidence on how trust in state-led, planned

adaptation measures can hinder or enable individual adaptation (van 
Valkengoed and Steg, 2019; Schneider et  al., 2020). As an enabler, 
trust in early warnings can mitigate flood risk by incentivising 
evacuation (Binh et  al., 2020) and high trust can help overcome 
uncertainty attached to projected climate impacts and/or adaptation 
decisions (Frederiksen, 2014). As a barrier, low trust can disincentivise 
adaptation, for example willingness to pay for flood insurance (Roder 
et al., 2019) or public support for managed retreat (Hanna et al., 2020). 
Paradoxically, high trust in existing adaptation measures can reduce 
people’s perceived need for ongoing adaptation (e.g., levees potentially 
reducing individual flood-proofing actions). Adaptation decisions also 
manifest ‘single-action bias’, with modest cost-adaptation actions in 
the present disincentivising further adaptation (Buchanan et al., 2019).
Several tools to incentivise adaptation behaviour are being tested

around the world. For example, nudges and boosts3 are being 
experimented with to shape individual risk beliefs and the demand 
for flood insurance (Bradt, 2019); ordinances are being used to ban, 
authorise or limit certain activities (Herrick, 2018); subsidies and 
financial support are being used to incentivise adaptation such as 
subsidised beach nourishment (McNamara et  al., 2015); and zoning 
restrictions and building codes restrict or guide climate-resilient 
infrastructural development (Schneider et  al., 2020). Overall, the 
3 Nudges are interventions or conditions that steer people towards better choices while retaining freedom of choice. Boosts improve decision-making by creating new competencies or improving existing 
ones.literature affirms that behavioural interventions are more readily taken 
up if they are aligned with cultural practices, norms and beliefs; are on 
temporal scales within peoples’ planning horizons; and if they build

upon relationships of trust and legitimacy (Donner and Webber, 2014; 
Herrick, 2018; Schneider et al., 2020).
CCP2.4.2 Finance
Lack of financial resources is a key constraint affecting all coastal 
archetypes (high confidence; Table SMCCP2.2). Adaptation to coastal 
hazards is costly: the global costs of protecting coastal areas with 
levees (annual investment and maintenance costs) are estimated at 
USD 12–71 billion for 2100 with SLR up to 1.2 m (Hinkel et al., 2014). 
Broadly speaking, it is cost effective to contain coastal hazard risk 
in the short to medium term in densely populated wealthy localities 
by using protective works, but such measures are unaffordable in 
dispersed poorer coastal C&S (Lincke and Hinkel, 2018).
Archetypes with high adaptive capacity may currently have financial 
resources to meet adaptation needs, but such funding may be 
unsustainable in the long term. In Catalonia, while public funds are

currently used to finance beach nourishment, these costs will increase 
with SLR and it is unclear whether public finance will remain a feasible 
source (Hinkel et  al., 2018). Even in relatively richer municipalities, 
financing adaptation is constrained by other urban priorities (Bisaro 
and Hinkel, 2018). In Europe, shifting responsibilities from national 
governments to transnational and local actors has resulted in reduced 
national budgets for coastal adaptation investment and increased 
pressure on local authorities to raise public funds for adaptation 
without alienating electoral bases (Bisaro and Hinkel, 2018).
Locations in the Global South have limited public budgets allocated to 
coastal adaptation and may rely on international donor aid (Donner 
et  al., 2016; Araos et  al., 2017). Such aid is often inconsistent and 
short term, which limits long-term maintenance of the knowledge, 
equipment and infrastructure needed to sustain adaptation measures

beyond initial funding periods (Weiler et al., 2018; Thomas et al., 2020), 
with resultant negative consequences in places as different as Kiribati 
(Donner and Webber, 2014) and Bangladesh (Hinkel et  al., 2018). 
Donor-funded adaptation programs aimed at promoting behavioural 
change, for example through coastal planning or new decision-making 
systems, require enduring training and institutional capacity, which is 
difficult to upkeep after aid is depleted. Donor funding is often project 
based and there are few avenues available to fund the additional 
permanent and long-term staff needed to bolster climate change 
institutions. Without funding to support additional staff, existing 
institutions often lack the human capacity and resources needed for 
coastal adaptation (Ziervogel and Parnell, 2014).
C&S in the Global South also face financial challenges in addressing 
loss and damage due to climate-induced slow-onset and extreme

events. Financial support to address both quantifiable damages and 
non-economic losses through measures such as climate-resilient

CCP2
2177Cities and Settlements by the Sea  Cross-Chapter Paper 2
reconstruction after extreme weather events as well as national and 
local-level emergency contingency funds are lacking, which has been 
an issue of contention in international policy arenas (Bahinipati et al., 
2017; Wewerinke-Singh and Salili, 2020; Martyr-Koller et al., 2021).
While coastal adaptation has largely been viewed as the responsibility 
of governments, private finance is increasingly recognised as necessary 
to help close the coastal adaptation funding gap (Ware and Banhalmi-
Zakar, 2020). Financial arrangements for coastal adaptation measures 
that align public-actor and private-investor interests are suitable for a 
range of budgets, from USD 10,000 to 100 million (Bisaro and Hinkel, 
2018). Private equity instruments that involve real estate development 
companies have already been successfully implemented and are most 
effective in urban areas with high-value real estate development

(Chiang and Ling, 2017). Public–private partnership equity instruments 
that engage construction and real estate developers have been 
successful for small- to medium-scale infrastructural projects. While 
public–private partnership bonds and public bonds have the potential 
to align public actors and private investors, such instruments require 
de-risking of coastal adaptation through enabling economic policy 
instruments, such as concessional loans (Bisaro and Hinkel, 2018).
Explicitly identifying the benefits, or goods and services, that are 
provided by coastal adaptation is critical to supplement limited 
government funds and engage a broader set of financial tools and 
actors (Woodruff et al., 2020). Matching goods and services provided 
by particular adaptation strategies to specific beneficiaries helps 
to identify the range of fair and equitable financial tools. In the 
Netherlands, public fundings through state, regional and local entities

have independent tax revenue systems to provide the funding needed 
to maintain flooding infrastructure (Hinkel et al., 2018).
Given the high costs of coastal adaptation, benefit-to-cost ratios (BCR) 
are often used to determine the value of investing in adaptation. 
BCR are high for urbanised coastal areas with high concentrations 
of assets (13% of the world’s coastline), covering 90% of the global 
coastal floodplain population and 96% of assets in the global coastal 
floodplain (Lincke and Hinkel, 2018). A global assessment shows 
a BCR for investing in flood protection of up to ~120 (Tiggeloven et al., 
2020). For Europe, at least 83% of flood damages could be avoided by 
elevating dikes along ~23–32% of Europe’s coastline and BCR vary 
from 8.3 to 14.9, with higher ratios for higher concentration pathways 
(Section 13.2; Vousdoukas et al., 2020a). Globally, 40% of damages 
can be reduced with levees of 1 m and costs lower than avoided

damage (Tamura et  al., 2019). For a mix of expensive storm surge 
barriers, nature-based solutions and flood-proofing measures for New 
York City, Aerts et al. (2014) found BCRs <1 for the current situation, 
but >2 for a SLR scenario of +1 m.
However, BCR values may be low and adaptation investment may 
not be financially viable for small coastal settlements, less densely 
populated poorer coasts or isolated communities (medium confidence). 
Considering BCRs of protection and coastal migration across a range 
of SLR and Shared Socioeconomic Pathways (SSP) scenarios for the 
21st century, a higher BCR was found for protection of only 3% of the 
global coastline protecting 78% of the coastal population and 92% of 
global coastal floodplain assets, while for the remaining coasts, coastal migration was estimated to be optimal in terms of economic costs 
(Lincke and Hinkel, 2021). Considering coastal migration as part of the

solution space could lower global costs in investment and maintenance 
for SLR protection by a factor of 2–4 in the 21st century, but would 
result in large land losses and high levels of migration for South and 
Southeast Asia in particular, and in relative terms, small island nations 
would suffer most. The need to consider place attachment, community 
relationships, livelihoods and the spiritual and cultural significance 
of settlements limits the application of BCR as a tool for coastal 
adaptation decisions in these contexts (Thomas and Benjamin, 2020). 
Moreover, there is limited knowledge on trade-offs, including BCR, of 
alternative adaptation options and pathways at a global to regional 
scale, in particular over the long term (beyond 2100).
Even where BCR is high, finance may be inaccessible, as it is 
challenging to convert the long-term benefits of adaptation into the 
revenue streams that may be needed to initially finance adaptation

investments (Hinkel et al., 2018). For example, in Ho Chi Minh City, 
Vietnam, despite high BCR, high costs of flood protection (USD 1.4–
2.6  billion) have prevented such adaptation measures from being 
implemented (Hinkel et al., 2018; Cao et al., 2021). Moreover, drawing 
from places as distinct as small communities in Fiji (Neef et al., 2018) 
and Belize (Karlsson and Hovelsrud, 2015), and megacities like New 
York City and Shanghai (Oppenheimer et al., 2019), BCR provides only 
a limited view and consideration of feasibility, effectiveness, efficiency, 
equity, culture, politics and power, and attachment to place has a 
greater chance of fostering CRD (high confidence).
CCP2.4.3 Governance
An array of climatic and non-climatic perils (Le Cozannet et al., 2017) 
present coastal communities and their governing authorities with 
immense governance and institutional challenges that will become 
progressively more difficult as sea level rises (high confidence;

Wallace, 2017; Leal Filho et  al., 2018; Oppenheimer et  al., 2019). 
Yet a study of public provisions for coastal adaptation in 136 of the 
largest coastal port–urban agglomerations across 68 countries found 
no policy implementation in 50% of the cases; in 85% of cases, 
adaptation actions are not framed by current impacts or future risks, 
and formal efforts are recent and concentrated in more developed 
settings (Olazabal et al., 2019; Olazabal and Ruiz De Gopegui, 2021), 
thus underscoring a persistent coastal adaptation gap. Translating 
these challenges into enabling governance conditions is difficult, 
but instructive lessons are being learned and are summarised (from 
Table SMCCP2.4) for archetypal C&S in Tables CCP2.1 and 2.2.
We start with a synopsis of governance settings within which coastal 
adaptation and CRD choices are made, and spotlight factors hindering 
and enabling translation of adaptation into practice. Then, building

upon and extending the SROCC analysis of enablers and lessons 
learned in responding to SLR (Oppenheimer et al., 2019), we assess key 
governance challenges, related enablers and lessons learned (Tables 
CCP2.1, Section CCP2.2).
Governance arrangements and practices are embedded in the 
sociopolitical and institutional fabric of coastal C&S. Consequently,

CCP2
2178Cross-Chapter Paper 2 Cities and Settlements by the Sea
barriers and enablers for adapting to climate change at the coast 
and charting pathways for CRD reflect more general constraints and 
opportunities (high confidence; Meerow, 2017; Rocle and Salles, 2018; 
Rosendo et al., 2018; Di Giulio et al., 2019; Hölscher et al., 2019; Van 
Assche et al., 2020; Williams et al., 2020). Local-level action is often 
constrained: 231  cities in the USA report weak leadership, lack of 
funding and staffing, and low political will (Fu, 2020). A meta-analysis 
of coastal municipal planning documents in Australia shows that few 
localities have moved beyond risk assessment (Bradley et al., 2015). 
Coastal C&S tend to prefer strategies that protect and accommodate 
existing coastline assets in the sense of a ‘fix and forget’ approach 
(Gibbs, 2015), rather than enduring proactive adaptation (Cooper and 
Pile, 2014).
Many C&S, especially in the Global South, already face high exposure

to coastal risks and development constraints associated with poverty 
and socioeconomic inequality, lack of transparent resource allocation 
mechanisms and low political will (high confidence; Di Giulio et al., 
2019; Nagy et  al., 2019; Pasquini, 2020; Lehmann et  al., 2021). 
Research from across South America notes inadequate regulatory 
frameworks, missing data and information, widespread coastal 
ecosystem degradation and complex interactions between natural 
disasters and civil conflict (Villamizar et al., 2017; Nagy et al., 2019). 
Coastal climate risks in the Global South are often compounded by 
ongoing land-use management conflicts and other pressures including 
informal land uses, unregulated and/or inadequate infrastructure/
building development, public health priorities such as combating 
Dengue fever, inadequate income diversification, low education 
levels and political marginalization of communities historically not

represented in the urban development process (Barbi and Ferreira, 
2014; Salik et al., 2015; Cabral et al., 2017; Goh, 2019). There are also 
entrenched socioeconomic inequalities leading to the maldistribution of adaptation actions and benefits in the Global North (Gould and 
Lewis, 2018; Keenan et  al., 2018; Ranganathan and Bratman, 2019; 
Yumagulova, 2020; Long et al., 2021).
To address the myriad governance challenges attributed to low 
awareness, low skills, scalar mismatches, and high socioeconomic 
inequality and coastal vulnerability, post-AR5 research highlights 
enablers of more innovative approaches to bridge capacity, policy and 
financial deficits (Reiblich et al., 2019), and facilitate more proactive 
implementation of coastal adaptation actions (Table  SMCCP2.2; Fu, 
2020). A survey of NGOs, state and local government across Alaska, 
Florida and Maryland in the USA found that perceived risk, uncertainty

and trust in support for climate adaptation varied across two stages of 
adaptation, that is between support for the development of plans and 
willingness to allocate human and financial resources to implement 
plans (Kettle and Dow, 2016). To bridge this gap, Cinner et al. (2018) 
suggest the need to build capacity across five domains: the assets 
that people can draw upon in times of need; the flexibility to change 
strategies and interventions; the ability to organise and act collectively; 
learning to recognise and respond to change (especially as important 
thresholds are approached); and the agency to determine whether to 
change or not, and to then take prudent action.
Effective and accountable local leadership can help to mobilise 
capacities, resources and climate awareness within coastal C&S. 
Strong leadership is associated with agenda-setting authorities and 
the ability to navigate complex institutional interests towards more

strategic planning efforts (high confidence; Ferguson et  al., 2013; 
Anguelovski et al., 2014; Chu et al., 2017; Valdivieso and Andersson, 
2018; Fink, 2019; Ndebele-Murisa et al., 2020). Policy leadership can 
positively influence the motivation and initiative of municipal officers 
(Lassa and Nugraha, 2014; Wijaya et al., 2020), whilst local leadership Table CCP2.1 |  Governance challenges and critical enablers for addressing coastal hazard risk in C&S
Key governance challenges Critical enablers for C&S to address coastal hazard risk
Complexity: climate change compounds non-climatic hazard risks facing coastal C&S in 
interconnected, dynamic and emergent ways for which there are no simple solutions.Draw on multiple knowledge systems to co-design and co-produce more acceptable, 
effective and enduring responses.
Build governance capacity to tackle complex problems.
Time horizon and uncertainty: The future is uncertain, but climate change will continue

for generations and cannot be addressed by short-term (e.g., 1–10 years) responses alone.Adopt a long-term view but take action now. Keep options open to adjust responses as 
climate risk escalates and circumstances change.
Avoid new development commitments in exposed locations. Enable managed retreat in 
most at-risk locations by anticipatory actions, e.g., secure funds, legal provisions for 
buy-outs, resettlement, etc.
Cross-scale and cross-domain coordination: Decisions bound by jurisdictional and 
sectoral boundaries fail to address linkages within and between coastal ecosystems and C&S 
facing interconnected climate change-compounded impacts and risk.Develop networks and linkages within and between different governance scales and levels 
and across policy domains and sectors, to improve coordination, build trust and legitimise 
decisions.
Build shared understanding and enable locally appropriate responses through 
experimentation, innovation and social learning.

Equity and social vulnerability: Climate change compounds everyday inequity and 
vulnerability in coastal C&S, making it difficult to disentangle and address social drivers and 
root causes of risk.Recognise political realities and prioritise vulnerability, justice and equity concerns to 
enable just, impactful and enduring outcomes.
Strengthen community capabilities to respond to coastal hazard risk, using external 
assistance and government support if necessary.
Social conflict: Coastal C&S will be the focal point of contending views about appropriate 
climate responses, and face the challenge of avoiding destructive conflict and realising its 
productive potential.Design and facilitate tailor-made participation processes, involving stakeholders early 
and consistently from negotiating responses to implementation.
Create safe arenas of engagement for inclusive, informed and meaningful deliberation 
and collaborative problem-solving.

CCP2
2179Cities and Settlements by the Sea  Cross-Chapter Paper 2
Table CCP2.2 |  Lessons learned from efforts to address coastal hazard risk
Lessons to address governance challenges and unlock enablers Archetypal C&S initiatives, constraints aside
Complexity: multiple knowledge systems
 –Reveal dynamic complexity drawing on multiple sources of locally relevant evidence.
 –Use and integrate local, Indigenous and scientific knowledge.
 –Include marginalised voices and knowledge of vulnerable groups, women, young people, 
etc.
 –Build shared understanding through storytelling.
 –Bridge gaps between science, policy and practice by experimenting with novel approaches 
and working across organisational, sectoral and institutional boundaries.Seychelles (0.1 million; open coast): Science–policy–local knowledge partnerships to 
co-produce usable information for decision-making.
Dhaka, Bangladesh (21 million; delta): Climate change is national priority. Partnering

with the Netherlands to develop long-term data plans.
Jakarta, Indonesia (10.8 million; delta): Community-based efforts to foster mutual 
assistance and self-organisation.
Utqiagvik (formerly Barrow) Alaska, USA (0.04 million; Arctic, open coast): Using 
local knowledge and historical precedent of transformative change to integrate local and 
scientific knowledge.
Complexity: governance capacity
 –Joined-up visionary leadership is key, e.g., cabinet- and C&S-level commitments to 
long-term implementation.
 –Translate political will into substantial dedicated budgets to build government capacity to 
tackle complex problems.
 –Use flexible approaches to build resilience, e.g., independent agency alongside traditional 
administrative bodies.
 –Counter deadlocks due to short-term priorities and vested interests with long-term 
perspectives, considering plausible scenarios and incentivising novel solutions.

–Translate national requirements into local action with enabling provisions for tailored local 
policy and practice.
 –Tackle emergent problems by setting up enduring monitoring and lesson-learning 
processes.
 –Governance arrangements reconcile competing interests in an inclusive, timely and 
legitimate manner.
 –Make visible and reflect on underlying reasons for policy actions/inaction, including values, 
attitudes and taken-for-granted habits influencing problem-solving capability.Singapore (5.6 million; open coast): Integrated approach across ministries committing to 
long-term adaptation (and mitigation goals) by 2030.
Rotterdam, the Netherlands (0.65 million; delta): Delta Programme, supported by law, 
administrative arrangements and a €1 bill. pa budget to 2029.
Florianopolis, Santa Catarina Island, Brazil (1.2 million; mixed): Building knowledge 
hub via public–private–civil society partnerships.

Nassau, Bahamas (0.275 million; open coast, small island): Identifying responsibilities, 
accessing funding and preparing adaptation plans drawing on evidence-based studies.
Shanghai (27 million; estuary), China: Contain risk by combining long-term planning, 
political will, national and municipal provisions, and technical capability.
Can Tho City, Vietnam (0.4 million; delta): Engage international donors and research 
community.
Time horizon and uncertainty: long-term view
 –Establish national policies and guidance with a long-term view (e.g., 100 years) that 
enable action now.
 –Develop shared medium- (10–50 years) to long-term vision (100+ years).
 –Use an adaptation-pathways approach to make short-term decisions consistent with 
long-term goals.
 –Meaningfully involve stakeholders, e.g., involve representatives in decision-making.
 –Address power imbalances and human development needs, e.g., in goal setting and 
process design.

–Reconcile divergent perspectives through tailored responses.Napier (0.07 million), Hawkes Bay (0.18 million; open coast), New Zealand: National 
law compels local authorities to take a 100-year perspective; 2100 Strategy accounts for 
dynamic complexity and uncertain future through adaptation pathways.
Shanghai, China (27 million; estuary): Plans up to 2100, strong national and municipal 
focus on climate change, and access to technical expertise.
Dhaka, Bangladesh (21 million; delta): Long-term adaptation plans through to 2100.
Time horizon and uncertainty: avoidance and anticipatory action
 –Avoid development in exposed localities using spatial plans.
 –Use window of opportunity created by extreme events.
 –Prepare pre-event plans and tailor risk reduction and resilience building post disaster.
 –Reveal political pressures and opposition that hamper efforts to address intolerable risk

and unacceptable impacts.Rotterdam, the Netherlands (0.65 million; delta): Delta Programme promotes ‘living 
with water’, allowing and managing urban flooding.
Napier (0.07 million), Hawkes Bay, New Zealand (0.18 million, open coast): 
Regulatory provisions discourage new development in high-risk locations; strategy 
sequences adaptation interventions.
Florianopolis, Santa Catarina Island, Brazil (1.2 million; mixed): Research reveals 
unregulated ad hoc development in at-risk locations preventing effective adaptation.
Cross-scale and cross-domain coordination: coordination
 –Collaborative projects involve state and non-state actors.
 –Multi-lateral agreements, e.g., between neighbouring countries, coastal regions and C&S.
 –Connect people, organisations and communities through boundary-spanning 
organizations.
 –Leadership by central actors with capable teams is key.
 –Mobilise the capabilities of communities and non-state actors.

–Address policy inconsistencies and clarify roles and responsibilities.
 –Secure national and regional resources to support local efforts.
 –Use measures to promote interaction, deliberation and coordination to manage spill-over 
effects.
 –Strengthen linkages between formal (e.g., regulatory) and informal (e.g., traditions and 
rituals) institutions, e.g., through information sharing.
 –Use spatial coordination mechanisms, e.g., land-use planning, to translate national and 
regional provisions into local competencies.Seychelles (0.1 million; open coast, small island): Cross-sectoral and institutional 
collaboration to improve use of limited financial resources; community-based and 
ecosystem-based adaptation to bridge adaptation and mitigation and improve coordination.
Florianopolis, Santa Catarina Island, Brazil (1.2 million; mixed): Effective local climate 
action hampered by governance constraints and weak federal leadership.

Cape Town, South Africa (4.6 million; mixed): Multi-level climate governance advanced 
at local-provincial level, but political turf battles hamper national–provincial–local progress.

CCP2
2180Cross-Chapter Paper 2 Cities and Settlements by the Sea
is needed integrate coastal management, disaster management and 
climate adaptation mandates (Rosendo et al., 2018).
Inclusive decision-making arrangements can enable participation, local 
ownership, and further equity in crafting coastal adaptation plans and 
policies (Chu et al., 2016). Inclusion of diverse stakeholders can help 
improve awareness of adaptation needs; help to bridge existing social inequalities in decision-making about adaption needs, options and 
outcomes; close the gap between formal and informal institutions and 
engage indigenous forms of decision-making, which often associate 
climate risks with livelihood, housing and employment stressors 
(Ziervogel et  al., 2016; Fayombo, 2020). For example, research from 
Pacific island states (Nunn et al., 2017) and coastal Arctic zones (Romero

Manrique et al., 2018) highlights the need to engage with indigenous Lessons to address governance challenges and unlock enablers Archetypal C&S initiatives, constraints aside
Cross-scale and cross-domain coordination: shared understanding
 –Prioritise social learning and shared understanding, e.g., information accessible to all, 
irrespective of education, language, etc.
 –Account for local history, culture and politics through engagement, experimentation and 
innovation.
 –Generate socioeconomic, livelihood and climate development co-benefits.
 –Leverage national and trans-national community and local authority networks.Cape Town, South Africa (4.6 million; mixed): Capable local leaders collaborate with 
researchers in municipality-initiated community-based adaptation. Translating plans into 
action challenging given ‘everyday’ vulnerability exacerbated by climate change impacts.
New York City, USA (23.5 million; mixed): State and city government work with

communities to build adaptive capacity and resilience; drawing on technical capabilities but 
many challenges.
Equity and social vulnerability: address vulnerability
 –Expose drivers and root causes of injustice, structural inequity and vulnerability.
 –Link human development concerns, risk reduction, resilience and adaptation.
 –Raise awareness and public support for actions that are just and equitable.
 –Understand discriminatory drivers (e.g., on racial grounds) of coastal land-use patterns 
and risk.
 –Address barriers facing marginalised groups.
 –Use inclusive planning, decision-making and implementation processes that give voice to 
vulnerable people.Cape Town, South Africa (4.6 million; mixed): Adaptation framed by apartheid legacy; 
focus on reducing vulnerability, public safety and securing critical infrastructure and 
community assets.
Maputo-Matola, Mozambique (3 million; mixed): Livelihood opportunities compromised

by ecological degradation compelling community DIY coping in face of severe poverty and 
vulnerability, weak governance and institutional capacity, and reliance on donors.
New York City, USA (23.5 million; estuary): Hurricane Sandy (2012) focused attention on 
climate risk and the plight of exposed and vulnerable people, and sparked adaptation action.
Equity and social vulnerability: community capabilities
 –Raise vulnerability and risk awareness and understanding; build community capability 
and leverage external support by working with professionals, academics, local NGOs, 
journalists and activists.
 –Secure rights of vulnerable groups through court action where necessary.
 –Integrate traditional community responses with local government efforts.
 –Ensure gender equity, e.g., representation on planning and decision-making bodies.Monkey River Village, Belize (200 people; estuary): Remote indigenous community;

capacity to tackle erosion enabled by interventions by researchers, journalists and local 
NGOs to secure media and political attention after hurricane damage.
Accra, Ghana (2.5 million; delta): Household adaptation mediated by local government 
flood-mitigation efforts; need better early warning and maintenance of local stormwater to 
prevent flooding.
Lagos, Nigeria (14 million; open coast): Building adaptive capacity to overcome 
‘everyday’ vulnerability and poverty severely challenging.
Social conflict: tailor-made participation
 –Create opportunities for integrative and inclusive solutions.
 –Use conflict-resolution mechanisms.
 –Appoint independent facilitators/mediators and involve officials as ‘bureaucratic activists’ 
to improve inclusivity and iterative and reflexive engagement.
 –Align informal participatory processes with statutory processes and government practices.
 –Sustain engagement by securing resources for local use and aligning activities with

political and bureaucratic cycles.
 –Involve historically disadvantaged and socially vulnerable groups, e.g., accessible meeting 
locations/venues, local languages and culturally appropriate meeting protocols.
 –Involve local leaders who will champion adaptation and help mainstream findings to be 
integrated into C&S decision-making.
 –Inclusive processes help address conflict and drivers of vulnerability and promote just 
adaptationNapier (0.07 million), Hawkes Bay, New Zealand (0.18 million, open coast): 
Collaboration between local authorities and Indigenous People (Māori) involving 
stakeholders led to co-designed long-term strategy with implementation commitment.
Manila, Philippines (14 million; open coast): Metro-wide planning and infrastructure 
provisions that foster climate justice and resilience explored, with community-based actions.
Social conflict: safe arenas of engagement
 –Use flexible and enabling processes based in local institutions that are robust and fair,

supported by governing authorities.
 –Attend to local social dynamics and reduce elite domination.
 –Use local and Indigenous knowledge and science.
 –Use institutional improvisation to address local concerns.
 –Use trusted independent facilitators.
 –Incentivise participation by disadvantaged groups.
 –Focus on improving risk literacy, optimism and capacity for joint problem-solving.
 –Use joint, collaborative activities to facilitate public dialogue, and secure institutional 
support for action.
 –Enable ongoing deliberation and social learning.
 –Make continual adjustments as circumstances change, e.g., build shared understanding 
about locally relevant thresholds beyond which alternative courses of action need to be 
actioned.Napier (0.07 million), Hawkes Bay, New Zealand (0.18 million, open coast): Active 
involvement of local communities, Indigenous People (Māori) and research community to 
co-produce fit-for-purpose long-term coastal hazard risk strategy.

Rotterdam, the Netherlands (0.65 mill.; delta): Delta Programme institutionalised 
multi-level adaptation governance with strong accountability mechanisms.
Greater London, UK (8.9 mill.; estuary): Long-term provisions for at-risk Thames Estuary 
including major protective works, embedded in Greater London Spatial Development Plan 
and London Climate Change Partnership, championed by strategic leadership and supported 
by the public and strong technical capability.

CCP2
2181Cities and Settlements by the Sea  Cross-Chapter Paper 2
environmental knowledge. Case studies from Indonesia, the Philippines 
and Timor-Leste show that Indigenous knowledge and local knowledge 
and customary laws can support environmental awareness, strengthen 
social cohesion and help communities to better respond to climate 
impacts (Hiwasaki et al., 2015). Research from coastal Cambodia shows 
that inclusive governance arrangements can target empowerment of 
the most vulnerable groups to facilitate better adaptation behaviour 
and mainstream adaption knowledge through both formal and informal 
education at the community level (Ung et al., 2016).
The law is key to governing climate risks in C&S, including regulating 
exposure to coastal hazards; facilitating accountable decision-making; 
funding arrangements, liabilities and resolving disputes; and also for 
securing human rights (high confidence; Setzer and Vanhala, 2019;

Averill, 2020). However, it has limits and can be both an adaptation 
enabler and barrier (Green et  al., 2015; Cosens et  al., 2017; Craig 
et al., 2017; DeCaro et al., 2017). Contemporary legal practice has not 
enabled effective adaptation in part because SLR affects compensable 
property rights that are secured by the law, and which generally trump 
concerns about public safety, resilience and sustainability (Reiblich 
et  al., 2019). Private property rights can be used as both a sword 
and a shield to privilege dominant interests by undermining land 
use policies, plans and implementation efforts intended to promote 
integrated coastal management and risk reduction (O’Donnell et al., 
2019; Reiblich et al., 2019). Climate change litigation has proliferated 
over the past decade (Setzer and Vanhala, 2019), addressing, among 
other things, failures to prepare for or adapt to climate change, and to 
secure human rights (Peel and Osofsky, 2018). Reflexive and adaptive

law that accounts for the distinctive features of coastal hazard risk 
and associated governance imperatives builds coastal C&S adaptive 
capacity and resilience (high confidence; Garmestani and Benson, 
2013; Cosens et  al., 2017; DeCaro et  al., 2017). Procedural justice, 
due process and use of substantive standards instead of rules provide 
legal stability and enable adaptation (Craig et  al., 2017). Coastal 
adaptation efforts are ultimately implemented through C&S actions 
that are enabled or constrained by prevailing legislative, executive 
and judicial provisions and practices, which differ significantly across 
jurisdictions (He, 2018). In practice, the ‘coastal lawscape’ is made up 
of interconnected cultural normative, political and legal systems that 
need to be understood holistically to enable coastal adaptation in C&S 
(O’Donnell, 2021).
Tables CCP2.1 and CCP2.2 summarise key insights about key 
governance challenges facing archetypal coastal C&S around the

world as well as associated critical enablers and lessons learned to 
address climate change-compounded coastal hazard risk (based on 
synthesis of Table SMCCP2.3).
In sum, prospects for addressing climate risk in archetypal coastal C&S 
around the world depend on the extent to which societal choices—
and associated governance processes and practices—address the 
drivers and root causes of exposure and social vulnerability (very high 
confidence). Coastal C&S are more able to address these challenges 
when authorities work with local communities and vulnerable groups 
in particular, and with stakeholders from the local to national levels 
and beyond, to chart adaptation pathways that enable sustained 
reduction in the exposure and vulnerability of those most at risk (very high confidence; Cross-Chapter Box SLR in Chapter 3; Magnan et al., 
2019; Oppenheimer et  al., 2019). Unlocking potential enablers for 
locally appropriate and effective adaptation is difficult because many

drivers and root causes of coastal risk are historically and institutionally 
embedded (high confidence; Thomas et al., 2019). Charting credible, 
salient and legitimate adaptation pathways is consequently a struggle 
in reconciling divergent worldviews, values and interests (Sovacool, 
2018; Mendenhall et al., 2020; Bowden et al., 2021a; Bowden et al., 
2021b). Unlocking the productive potential of conflict is foundational 
for transitioning towards pathways that foster CRD (high confidence; 
Abrahams and Carr, 2017; Harris et al., 2018; Sharifi, 2020). But this 
can be especially challenging for low-lying coastal C&S characterised 
by degraded coastal ecosystems susceptible to climate change impacts 
as well as pronounced inequity and governance constraints (high 
confidence; Esteban et al., 2017; Jones et al., 2020).
CCP2.4.4 Enabling Climate Resilient Development for 
Cities and Settlements by the Sea
The above critical enablers and lessons learned from around the

world establish a strong foundation for charting pathways for CRD 
in coastal C&S. These pathways will necessarily vary in different C&S, 
and synergies and commonalities within different coastal archetypes 
can be leveraged. Pivotal is recognition of the narrow window of time 
remaining to translate embryonic risk assessment and adaptation 
planning into concerted implementation efforts. C&S by the sea could 
be the centres of innovation that lead the way to advancing SDGs 
through to 2030 and CRD beyond this decade (see Section 2.1.1).
This cross-chapter paper shows that a range of adaptation solutions, 
hard and soft protection, nature-based measures, accommodate, 
advance, retreat and behavioural change will need to be implemented 
as an integrated and sequenced portfolio of responses if coastal C&S 
are to contain the adverse risks of climate change (high confidence). 
The effectiveness and feasibility of any intervention—at any given

moment—to reduce a particular climate-compounded coastal 
hazard risk or combination of risks depend upon the settlement 
archetype, including its geomorphological, cultural, economic, 
technical, institutional and political features, as well as on its historical 
development trajectory. Coastal C&S will benefit from developing 
flexible adaptation pathways—sequences of adaptation strategies 
and intervention options—to navigate a dynamic solution space that 
changes in response to climate and other drivers of change, and is 
also shaped by human development choices and socioeconomic, 
technological and institutional change.
There is no silver bullet or panacea. But developing locally appropriate 
yet flexible pathways for CRD will help coastal communities to address 
escalating risks and uncertainty (Cross-Chapter Box DEEP in Chapter 
17). Effective pathways are based on robust integrated information 
about dynamic coastal hazard risk and plausible interventions.

However, their successful implementation requires multi-scale 
governance arrangements and practices able to bridge different 
administrative and sectoral capacities in the coastal zone; effective and 
accountable leadership; and inclusive decision-making arrangements 
to enable participation, manage conflicts and trade-offs, engender

CCP2
2182Cross-Chapter Paper 2 Cities and Settlements by the Sea
local ownership, and promote equity and justice in coastal adaptation 
plans and policies. Further, the feasibility of adaptation strategies 
and interventions, especially those entailing changing behaviours 
and practices, is increased by recognising and incorporating peoples’ 
values and beliefs and Indigenous and local knowledge systems, as 
well as the voices of women and vulnerable groups.
Coastal C&S are on the frontline of observed climate change impacts 
and future risk (high confidence). Difficult choices will be made as 
climate- and ocean-driven extremes become more frequent. In the next 
few decades, many coastal regions and C&S will have the opportunity 
to take actions to avoid and reduce risk, through incremental as well as more transformative interventions. Under higher levels of global 
warming, decisions will need to be made faster or respond to higher

levels of SLR (high confidence; Cross-Chapter Box SLR in Chapter 3). This 
is particularly challenging in coastal C&S characterised by the inertia 
and path dependency of development choices, with long lead times 
for adaptation planning and implementation, and the long design life 
and societal impact of many interventions. Given the risks assessed in 
coastal C&S, the scale of climate impacts globally will depend to a large 
extent on whether coastal settlements develop and implement pre-
emptive and flexible adaptation pathways, and whether a significant 
and timely reduction in greenhouse gas emissions is achieved in C&S 
and globally (high confidence).
FAQ CCP2.1 | Why are coastal cities and settlements by the sea especially at risk in a changing climate, and which 
cities are most at risk?
Coastal cities and settlements (C&S) by the sea face a much greater risk than comparable inland C&S because they concentrate a large

proportion of the global population and economic activity, whilst being exposed and vulnerable to a range of climate- and ocean-compounded 
hazard risks driven by climate change. Coastal C&S range from small settlements along waterways and estuaries, to small island states with 
maritime populations and/or beaches and atolls that are major tourist attractions, large cities that are major transport and financial hubs in 
coastal deltas, to megacities and even megaregions with several coastal megacities.
The concentration of people, economic activity and infrastructure dynamically interacts with coast-specific hazards to 
magnify the exposure of these C&S to climate risks. While large inland cities and coastal settlements can be exposed 
to climate-driven hazards, such as urban heat islands and air pollution, the latter are also subject to distinctive 
ocean-driven hazards, such as sea level rise (SLR), exposure to tropical cyclones and storm surges, flooding from

extreme tides and land subsidence from decreased sediment deposition along coastal deltas and estuaries. With 
climate change increasing, the intensity and frequency of hazards under all future warming levels and thus the risks 
to lives, livelihoods and property are especially acute in C&S by the sea.
Coastal cities are diverse in shape, size, growth patterns and trajectories, and in terms of access to cultural, financial 
and ecosystem resources and services. Along deltaic and estuarine archetypes, cities most vulnerable to a changing 
climate have relatively high levels of poverty and inequality in terms of access to resources and ecosystem services, 
with large populations and dense built environments translating into higher exposure to coastal climate risks.
These climate risks at the coast can also be magnified by compounding and cascading effects due to non-climate

drivers directly affecting vulnerable peri- and ex-urban areas inland. These risks include disruption to transport 
supply chains and energy infrastructure from airports and power plants sited along the coastline, as occurred in 
New York City, USA, during Hurricane Sandy in 2012. The impacts can be felt around the world through globalised 
economic and geopolitical linkages, for example through maritime trade and port linkages.
For open coasts, settlements on low-lying small island states and the Arctic are especially vulnerable to climate 
change, and SLR impacts in particular, well before 2100. While the economic risks may not compare to the scale 
of those faced in coastal megacities with high per capita GDP, the existential risks to some nations and an array of 
distinctive livelihoods, cultural heritage and ways of life in these settlements are great, even with modest SLR.

CCP2
2183Cities and Settlements by the Sea  Cross-Chapter Paper 2
FAQ CCP2.2 | What actions can be taken by coastal cities and settlements to reduce climate change risk?
Sea level rise (SLR) responds to climate change over long timeframes and will continue even after successful mitigation. However, rapid global 
mitigation of greenhouse gases significantly reduces risks to coastal cities and settlements (C&S), and, crucially, buys time for adaptation.
Appropriate actions to reduce climate change risks in coastal C&S depend on the scale and speed of coastal change 
interacting with unfolding local circumstances, reflecting the hazards, exposure, vulnerability and response to risks.
‘Hard’ protection, like dikes and seawalls, can reduce the risk of flooding for several metres of SLR in some coastal 
C&S. These are most cost effective for densely populated cities and some islands, but may be unaffordable for

poorer regions. Although these measures reduce the likelihood of coastal flooding, residual risk remains, and hard 
protection typically has negative consequences for natural systems. In low-lying protected coastal zones, draining 
river and excess water will increasingly be hampered, eventually requiring pumping or transferring to alternative 
strategies.
Whereas structures can disrupt natural beach morphology processes, sediment-based protection replenishes 
beaches. These have lower impact on adjacent beaches and coastal ecology and lower costs for construction and 
maintenance compared to hard structures. Another form of ‘soft’ protection involves establishing, rehabilitating 
and preserving coastal ecosystems, like marshes, mangroves, seagrass, coral reefs and dunes, providing ‘soft’ 
protection against storm surges, reducing coastal erosion and offering additional benefits including food, materials

and carbon sequestration. However, these are less effective where there is limited space in the coastal zone, limited 
sediment supply and under higher rates of SLR.
Coastal settlements can ‘avoid’ new flood and erosion risks by preventing development in areas exposed to 
current and future coastal hazards. Where development already exists, settlements can ‘accommodate’ climate 
change impacts through, among other things, land-use zoning, raising ground or buildings above storm surge 
levels, installing flood-proofing measures within and outside properties, and early warning systems. Improving the 
capacity of urban drainage, incorporating nature-based solutions within urban areas and managing land upstream 
of settlements to reduce runoff from the hinterland reduces the risk of compound flood events. More radically, 
land can also be reclaimed from the sea, which offers opportunities for further development but has impacts on the

natural system and wider implications for the trajectory of development.
Coastal risks and impacts such as floods, loss of fisheries or tourism, or salinization of groundwater require people 
to change behaviour to adapt, such as diversifying livelihoods or moving away from low-lying areas. Currently, 
most of these practices are reactive and help people adjust to/cope with current impacts. While a critical part of 
coastal adaptation, changing behaviour can be enabled by supportive policies and financial structures aligned with 
sociocultural values and worldviews.
Where risks are very high or resources are insufficient to manage risks, submergence or erosion of coastal C&S will 
be inevitable, requiring ‘retreat’ from the coastline. This is the outlook for millions of people in the coming decades, 
including those living in river deltas, Arctic communities, small islands and low-lying small settlements in poor and

wealthy nations. Whilst the impacts of retreat on communities can be devastating, the prospect of many C&S and 
even whole nations being permanently inundated in the coming centuries underscores the imperative for urgent 
action.
Crucial to making choices about how to mitigate greenhouse gas emissions and adapt to climate change in 
coastal C&S is to establish institutions and governance practices supporting climate resilient development—a mix 
and sequence of mitigation and adaptation actions—that are fair, just and inclusive as well as technically and 
economically effective across successive generations.

CCP2
2184Cross-Chapter Paper 2 Cities and Settlements by the Sea
FAQ CCP2.3 | Considering the wide-ranging and interconnected climate and development challenges coastal cities 
and settlements face, how can more climate resilient development pathways be enabled?
Coastal cities and settlements (C&S) are on the frontline of the climate change challenge. They are the interface of three interconnected 
realities. First, they are critical nodes of global trade, economic activity and coast-dependent livelihoods, all of which are highly and increasingly 
exposed to climate- and ocean-driven hazards (FAQ CCP2.1). Second, coastal C&S are also sites where some of the most pressing development 
challenges are at play (e.g., trade-offs between expanding critical built infrastructure while protecting coastal ecosystems, high economic

growth coupled with high inequality in some coastal megacities). Third, coastal C&S are also centres of innovation and creativity, thus presenting 
a tremendous opportunity for climate action through a range of infrastructural, nature-based, institutional and behavioural solutions (FAQ 
CCP2.2). Given these three realities of high climate change risks, rapid but contested and unequal development trajectories, and high potential 
for innovative climate action, C&S are key to charting pathways for climate resilient development (CRD).
Three key levers can enable pathways that are climate resilient and meet goals of inclusive, sustainable development. 
One key enabler involves flexible, proactive, and transparent governance systems, built on a bedrock of accountable 
local leadership, evidence-based decision-making—even under uncertainty—and inclusive institutions that consider 
different stakeholder voices and knowledge systems. Another key enabler is acknowledging the sociocultural and

psychological barriers to climate action and incentivising people to change to lifestyles and behaviours that are 
pro-climate and aligned with community-oriented values and norms. In practice, coastal C&S are experimenting 
with different strategies to change practices and behaviours, such as using subsidies and zoning policies, tax rebates 
and public awareness campaigns to promote individual and collective action. Finally, enabling CRD needs dedicated 
short- and long-term financing to reorient current trajectories of unsustainable and unequal development towards 
climate mitigation and adaptation action that reduces current and predicted losses and damages, especially in 
highly vulnerable coasts such as the small island states, the Arctic and low-lying C&S. Currently, adaptation finance 
is concentrated in coastal megacities and tends to be deployed for risk-proofing high-value waterfront properties

or key infrastructures. Addressing these financial imbalances (globally, regionally and sub-nationally) remains a 
critical barrier to inclusive climate resilient coastal development.
Notwithstanding the many interconnected challenges faced, from more frequent and intense extreme events to 
the COVID-19 pandemic, many coastal C&S are experimenting with ways to pivot towards CRD. Critical enablers 
have been identified and lesson learned, which, if translated into practice, will enhance the prospects for advancing 
the SDGs and charting pathways for CRD that are appropriate to local contexts and foster human well-being and 
planetary health.

CCP3
2197Deserts, Semiarid Areas and Desertification Cr oss-Chapter Paper 3
This cross-chapter paper on ‘Deserts, semiarid areas and desertification’ 
updates and extends Chapter 3 on ‘Desertification’ in the IPCC Special Report on Climate Change and Land (SRCCL) (Mirzabaev et al., 2019). It assesses new information and links it to the findings across the chapters of Working Group II’s contribution as well as relevant chapters of Working Group I’s contribution to the IPCC Sixth Assessment Report (AR6), with an added focus on deserts which were outside the scope of the SRCCL.
Where are we now: Observed impacts and adaptation responsesDeserts and semiarid areas have already been affected by

climate change, with some areas experiencing increases in aridity. Mixed trends of decreases and increases in vegetation productivity have been observed, depending on the time period, geographic region, detection methods used and vegetation type under consideration (high confidence
1). These changes have

had varying and location-specific impacts on biodiversity, and have altered ecosystem carbon balance, water availability and the provision of ecosystem services (high confidence). There is no evidence, however, of a global trend in dryland expansion based on analyses of vegetation patterns, precipitation and soil moisture, with overall, more greening than drying in drylands since the 1980s (medium confidence). Deserts and semiarid areas host unique biodiversity, rich cultural heritage and provide globally valuable ecosystem services. They are also highly vulnerable to climate change. The vitality of natural ecosystems in arid and semiarid regions greatly depends on water availability, as they are highly sensitive to changes in precipitation and potential evapotranspiration, as well as to land management practices. Multiple lines of evidence from 1920–2015 indicate that surface warming of 1.2
°C–1.3°C over global 
drylands (Section 1.1.1) exceeded the 0.8°C–1.0°C warming over humid

lands. From 1982 to 2015, unsustainable land use and climate change combined caused desertification of 6% of the global dryland area, while 41% showed significant increases in vegetation productivity (greening) and 53% of the area had no notable change, although greening rates are slowing or declining in some locations. Greening may cause biodiversity loss and ecosystem service degradation in relation to livelihood systems. Observed trends in deserts and semiarid areas have led to varying impacts on flora, fauna, soil, nutrient cycling, the carbon cycle and water resources. Ecological changes in dryland ecosystems detected and attributed primarily to climate change include tree mortality and losses of mesic tree species at specific sites in the African Sahel, particularly during the droughts of the 1970s and 1980s, and in North Africa from 1970 to 2007; and losses of bird species in the Mojave Desert of North America from 1908 to 2016. In contrast, growth in herbaceous vegetation

1 In this Report,  the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels 
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.
2
 In this Report,
 the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as

likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, and exceptionally unlikely 0–1%. Additional terms (extremely likely: 95–100%, more likely than not >50–100%, and extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome in some drylands since the 1980s. Widespread woody encroachment 
has occurred in many shrublands and savannas in Africa, Australia, North America and South America, due to a combination of land use change, changes in rainfall, fire suppression and CO
2 fertilization 
which, together with unsustainable management, alters biodiversity and reduces ecosystem services, such as water availability and grazing potential {3.2.1, 3.2.2}.
The impacts of climate change have affected the ecosystem

services that humans can harness from drylands, with largely negative implications for livelihoods, human health and well-being, particularly in deserts and semiarid areas with lower adaptive capacities (high confidence). Ecosystem degradation (Section  16.5.2.3.2) and desertification threaten the abilities of both natural and human systems to adapt to climate change (high confidence). Changes in desert and semiarid ecosystem services most acutely affect people who are directly dependent on natural resources for their livelihoods and survival. These groups also often have lower capacities to adapt, particularly given structural limitations of some drylands where healthcare, sanitation, infrastructure and efficient markets are lacking, reinforcing existing inequalities (high confidence). In rural drylands in tropical and Mediterranean areas, human populations are steadily expanding with mixed implications for ecosystem services under climate change, while rapid urbanisation in new and

existing dryland megacities puts additional pressure on water ecosystem services (high confidence). Impacts resulting from consumption of dryland ecosystem services elsewhere, alongside other teleconnections associated with health, trade, conflict and migration, mean that dryland adaptive capacities have far reaching implications for other locations, while other locations affect dryland adaptation options. {3.1.1, 3.2.1, 3.2.2, 3.4}.

Where are we going? Risks and adaptation under warming pathwaysSome drylands will expand by 2100, while others will shrink 
(high confidence). Climate change affects drylands through increased temperatures and more irregular rainfall, with important differences between areas with different rainfall distributions linked to the dominant climate systems in each location. Projections are nevertheless uncertain and not well supported by observed trends, while different methodological approaches and indices exhibit different strengths and weaknesses (medium confidence). A fundamental methodological challenge is how to attribute projected impacts to climate change when background climate variability in drylands is so high. Some projections show aridity (as measured by the Aridity Index, AI) to expand substantially on all continents, except Antarctica. Expansion of arid regions is probable in southwest North America, the northern

fringe of Africa, southern Africa and Australia. The main areas of semiarid expansion are likely
2 to occur on the north side of the 
Mediterranean, southern Africa and North and South America. India,

CCP3
2198Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
parts of northern China, eastern equatorial Africa and the southern 
Saharan regions are projected to have shrinking drylands. Under 
Representative Concentration Pathway (RCP) 8.5, aridity zones could 
expand by one-quarter of the 1990 area by 2100, increasing to over 
half the global terrestrial area. Lower greenhouse gas emissions, 
under RCP4.5, could limit expansion to one-tenth of the 1990 area by 
2100. Nevertheless, the utility of the AI in delineating dryland biomes 
is limited under an increasing CO 2 environment (medium confidence) 
and how well the index fits observed trends has been questioned 
in recent research. The impacts of climate change on sand and dust 
storm activity are projected to be substantial, however, there is large 
regional variability in terms of rainfall seasonality, land management 
practices and differences in rates of change and the scales at which

the projections are undertaken. The characteristics and speed of 
human responses and adaptations also affect future risks and impacts 
(high confidence). Increased temperature and rainfall variability will 
significantly change the interannual variability in the global carbon 
cycle, which is strongly influenced by the world’s drylands and the 
ways they are managed (medium confidence). Increased variability of 
precipitation would generally contribute to increased vulnerability for 
people in drylands, intensifying the challenges that people living in 
deserts and semiarid areas will face for their sustainable development 
(medium confidence). {3.3.1, 3.3.2}
Contributions of adaptation measures to climate resilient 
Drivers of desert expansion and greening are numerous, are 
attributed to environmental and human processes and differ 
across dryland types, yet a suite of adaptations can help to 
address human drivers of change, support resilience and build

the adaptive capacity of dryland people (medium confidence). 
Deserts and semiarid areas have a rich cultural heritage, and Indigenous 
knowledge and local knowledge (IKLK) which enrich and influence 
sustainability and land use globally. Growing research evidence and 
experience highlight the necessary features of an enabling environment 
for dryland adaptation (Section 8.5.2). Key enablers include supportive 
policies, institutions and governance approaches that strengthen the 
adaptive capacities of dryland farmers, pastoralists and other dryland 
resource users (high confidence), addressing drivers (proximate and 
underlying) as well as symptoms of desertification. For instance, the 
skills and capacities held by the mobile and adaptive approach of 
pastoralists may provide lessons for society at large in adapting to 
climate change and dealing with increased uncertainty. Such a policy 
would stand in contrast to previous attempts at settling pastoralists.

There is a persistent gap in terms of scaling-up already known good 
practices, combining nature-based, land-based and ecosystem-
based approaches that facilitate sustainable land management, with 
contextually appropriate and responsible governance systems (e.g., 
including those supporting communal land tenure arrangements 
and IKLK; medium confidence). Land-based adaptations can help 
manage dryland changes, including sand and dust storms and 
desertification (high confidence), while technological options linked 
to water management draw from both traditional practices and 
new innovations. Adequate financing and investment is required to 
lies within the 17–83% probability range.harness multiple benefits for managing the impacts of climate change 
and desertification while accelerating progress towards sustainable 
development in deserts and semiarid areas. {3.4}

CCP3
2199Deserts, Semiarid Areas and Desertification Cr oss-Chapter Paper 3
CCP3.1  Introduction
CCP3.1.1  Concepts , Definitions and Scope
Deserts and semiarid areas are in ‘drylands’, which comprise hyper-

arid, arid, semiarid and dry sub-humid areas (Figure CCP3.1). Drylands cover about 45–47% of the global land area (Prăvălie, 2016; Koutroulis, 2019) and are home to about 3  billion people residing primarily in semiarid and dry sub-humid areas (van der Esch et al., 2017). Drylands host unique, rich biodiversity (Maestre et  al., 2015) and provide important ecosystem services (Bidak et  al., 2015; Lu et  al., 2018), while dryland people have a rich cultural and historical heritage. Rural human populations are growing in some Mediterranean and tropical drylands, while many are rapidly urbanising (Guengant Jean-Pierre, 2003; Tabutin and Schoumaker, 2004; Denis and Moriconi-Ebrard, 2009), with varying impacts on ecosystem services and adaptive capacities. In recent decades, 6% of global megacities have been established in arid areas and 2% in hyper-arid desert areas (Cherlet et  al., 2018), with many of these areas suffering from severe water security challenges (Stringer et al., 2021).

Dryland inhabitants in many developing countries are also experiencing poverty (Section 16.1.4.3), hunger, poor health, land degradation, and economic and political marginalisation (Mbow et  al., 2019; Mirzabaev et  al., 2019), which sometimes limits their access to common pool resources. These challenges, together with a weak enabling environment, threaten opportunities to adapt to climate change.

interpretations due to the diverse components, processes and states they denote. Recognising ‘land degradation’ as a contested and perceptual term (Blaikie and Brookfield, 1987; Behnke and Mortimore, 2016; Robbins, 2020), this cross-chapter paper (CCP), defines land degradation as ‘a negative trend in land condition, caused by direct or indirect human-induced processes including climate change, expressed as long-term reduction or loss of at least one of the following: biological productivity, ecological integrity or value to humans’ (Olsson et al., 2019). Desertification is land degradation in arid, semiarid and dry sub-humid areas (UNCCD, 1994). Following the above definitions, desertification is more common in arid and semiarid climates than in hyper-arid climates. When desertification does occur in arid and hyper-arid ecosystems it is often in oases and irrigated cultivated lands (Ezcurra, 2006; Dilshat et al., 2015). Hyper-arid areas, except wetlands such as oases, wadis and

riverbanks, are excluded in the United Nations Convention to Combat Desertification (UNCCD) definition of desertification used here, yet many of the world’s deserts are in hyper-arid areas. Hyper-arid areas are therefore included when discussing deserts but not when discussing desertification. Deserts are not the end point in a desertification process (Ezcurra, 2006). There is robust evidence of desertification in deserts, mostly driven by human activities and climate variability, expressed as loss of biological productivity, ecological integrity or value to humans to below their natural levels (Moridnejad et al., 2015).

Interactions between climate change and desertification in drylands 
create challenges for both ecosystem and human resilience, affecting ecosystem services, biodiversity, food security, human health and well-being (Reed and Stringer, 2016). Dryland livelihoods that heavily rely on natural ecosystems face pressures, including high population growth rates, weak or poor governance, low investment, unemployment and poverty, market distortions and underestimates of the value of drylands (Stringer et al., 2017; Bawden, 2018). These pressures intersect with broader societal challenges such as conflict and civil unrest (Okpara et  al., 2015; Almer et  al., 2017), which together, can contribute to human displacement (Section  16.2.3.10) in some drylands (Warner, 2010; Abel et al., 2019). Nevertheless, evidence linking conflict with climate change and desertification is weak (Benjaminsen et al., 2012) and data are insufficient to draw robust conclusions.

Drylands yield important opportunities for adapting to and mitigating 
climate change. They offer abundant solar energy, which could support mitigation efforts, opportunities for cultural and nature-based tourism, rich plant biodiversity in some areas (e.g. Namibia), and extensive Indigenous knowledge and experience of adapting to dynamic climates (Christie et al., 2014; Stringer et al., 2017); for example, across West Asia and North Africa (Louhaichi and Tastad, 2010; Hussein, 2011). Improved understanding of challenges and opportunities in drylands can be achieved by transdisciplinary, multi-scale and inter-sectoral approaches encompassing links between physical, biological, socioeconomic and institutional systems (Reynolds et  al., 2007; Stringer et al., 2017).
Chapter 3 of the IPCC Special Report on Climate Change and Land

(SRCCL) focused on desertification (Mirzabaev et al., 2019), but links between climate change and deserts, desertification and semiarid areas have not been extensively considered in recent IPCC assessment cycles. Working Group II Assessment Report 5 noted that desertification contributes to atmospheric dust production, identifying desertification as needing consideration within climate change mitigation and adaptation governance and decision making (Boucher et  al., 2013; Myhre et al., 2013). This cross-chapter paper focuses on environmental and human aspects, finding that climate change impacts will intensify the challenges faced by dryland populations in advancing sustainable development. However, viable options exist for adapting to climate change, reducing desertification and supporting progress towards the Sustainable Development Goals (SDGs), particularly by combining modern science, IKLK, and livelihood and land management strategies that enable land-based adaptation,

CCP3.1.2  K ey Measurement Challenges and Observed 
Dryland Dynamics
Maps of dryland extent commonly employ a climate-based approach measured using the Aridity Index (AI), or consider the extent of dryland vegetation. The two approaches sometimes do not demarcate the same geographical areas as being drylands, particularly when projecting future changes (Stringer et  al., 2021). Dryland dynamics therefore need to be viewed specifically in relation to the definitions and measurements being used. From 1982 to 2015, unsustainable land use and climate change combined caused desertification of 6% of the global dryland area, while 41% showed significant greening (i.e., increased vegetation productivity), and 53% of the area had no notable change (Figure CCP3.1; Burrell et al., 2020). In contrast Yuan

CCP3
2200Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
et  al. (2019) conclude that during 1999–2015, trends of vegetation 
production reversed globally, and in drylands, showing extensive 
declines. Thus, while overall greening has occurred, this trend now 
appears to be declining. Analyses of vegetation, soil, and physical 
characteristics of over 50,000 sample points in drylands around the 
world indicate that aridification causes ecological degradation at three 
successive thresholds: vegetation decline at AI = 0.56, soil disruption 
at AI = 0.3 and loss of plant cover at AI = 0.2 (Berdugo et al., 2020). 
Drylands nevertheless show different dynamics depending on the 
index used and the variables assessed.
Based on the AI, some drylands are projected to expand and others 
to contract due to climate change. However, there is no evidence of 
a global trend in dryland expansion based on vegetation patterns,

precipitation and soil moisture, based on the satellite record from 
the 1980s to the present (medium confidence). The AI will also be of 
limited use under a changing CO 2 environment due to higher water use 
efficiency by some plants (Mirzabaev et al., 2019), and it overvalues 
the role of potential evapotranspiration (PET) relative to rainfall. 
It also does not account for CO 2 impacts on evapotranspiration, 
and seasonality in rainfall and evapotranspiration. Higher annual PET because of increased temperatures will have little impact if 
temperature and actual evapotranspiration are not rising during the 
period of vegetation growth (Stringer et al., 2021).
CCP3.2 Observed Impacts of Climate Change 
Across Sectors and Regions
CCP3.2.1 Observed Impacts on Natural Systems in Arid 
and Semiarid Areas
CCP3.2.1.1 Temperature and Rainfall
Significant warming has occurred across drylands globally (IPCC, 
2021). Surface warming (1920–2015) of 1.2°C–1.3°C in global drylands

has exceeded the 0.8°C–1.0°C warming over humid lands (Huang et al., 
2017). As measured by the AI, this has expanded the area of drylands by 
~4% from 1948–2004 (Ji et al., 2015; Spinoni et al., 2015; Huang et al., 
2016). However, as mentioned in Figure  CCP3.1, the AI has various 
limitations in assessing drylands expansion. Increases in potential 
evapotranspiration have exceeded increases in precipitation in the last 
Dry sub-humid
(0.5–0.65)Semi-arid
(0.2–0.5)Hyper-arid
(0–0.05)Arid
(0.05–0.2)Aridity zone extent and observed changes in dryland areas as defined by the Aridity Index
Aridity Index 1988–2017
Area expansio n
Area contractionObserved change in dryland area
between 1901–1930 and 1988–2017
Figure CCP3.1 |  Aridity zone extent and observed changes in dryland areas as defined by the Aridity Index (AI). Aridity zones, according to UNESCO (1979) and

UNEP (1992) classifications, defined by the AI, consider the ratio of average annual precipitation to potential evapotranspiration: (i) dry sub-humid (0.5 ≤ AI < 0.65), (ii) semiarid 
(0.2 ≤ AI < 0.5), (iii) arid (0.05 ≤ AI < 0.2) and (iv) hyper-arid (AI <0.05). Drylands include land with AI <0.65, humid lands are those with AI >0.65 (UNEP , 1992). Deserts represent 
a major part of the hyper-arid and arid zones. The aridity zones are shown for climate in the period 1988–2017 and changes in dryland area (combined area of four aridity zones) 
are shown between the periods 1901–1930 and 1988–2017, based on climate time series at 50 km spatial resolution (Harris et al., 2020). The AI has various limitations in assessing 
dryland expansion and different indices highlight different areas and different changes. This is known as the aridity paradox (Greve et al., 2019). See SRCCL Section 3.2.1 (Mirzabaev

et al., 2019) for an in-depth analysis of limitations, and Stringer et al. (2021) for a summary of different measures and indices used in the literature.

CCP3
2201Deserts, Semiarid Areas and Desertification Cr oss-Chapter Paper 3
half of the period 1901–2017 (Pan et  al., 2021). Observations from 
the Sahel demonstrated that temperature seasonality changes differ from rainfall seasonality changes (Guichard et al., 2015), and there has been an increase in surface water and groundwater recharge in the Sahel since the 1980s, referred to as ‘the Sahel paradox’ (Favreau et al., 2009; Gardelle et al., 2010; Descroix et al., 2013; Wendling et al., 2019). Research from the USA suggests that historical soil moisture levels can contribute to such variability (Heisler-White et al., 2009). Studies from the Middle East show rising temperatures and declining rainfall trends (ESCWA, 2017), with most decreasing aridity trends in north Sudan and most increasing aridity trends in eastern Arabia over the period 1948–2018 (Sahour et al., 2020).
CCP3.2.1.2  Ecosystem Processes

Semiarid ecosystems have a disproportionately large role in the global carbon cycle, driving trends and interannual variability of the global carbon sink (Alstrom et al., 2015). These systems are highly sensitive to annual precipitation and temperature variations (high confidence) (Alstrom et al., 2015, Poulter et al., 2014). The positive trend in semiarid regions is consistent with widespread woody encroachment and increased vegetation greenness (Andela et al., 2013; Piao et al., 2019; Piao et al., 2020) driven by CO
2 fertilization and rainfall increases (Sitch

et al., 2015; Piao et al., 2020), although some trends are complicated by irrigation practices (He et al., 2019). Increases in temperature and drought diminish this trend through reduced vegetation productivity and increased vegetation mortality (Brandt et al., 2016; Ma et al., 2016; Fernández-Martínez et al., 2019; Maurer et al., 2020) with indications that this trend is declining or reversing in some locations (Yuan et al., 2019; Wang et al., 2020).
Changed climates have increased water constraints of vegetation 
growth most notably in the Mediterranean (Sections CCP1.2.3.2; CCP4.2.1) and West and Central Asia (Jiao et al., 2021). Climate change 
and elevated CO
2 have both increased and decreased vegetation

sensitivity to rainfall throughout drylands, with the degree of variation shaped by region, land use and vegetation traits (Haverd et al., 2017; Abel et al., 2021). Mineral nitrogen production in drylands may become increasingly decoupled from consumption by plants over prolonged dry periods, and more extreme hydrological events can drive multiple changes to nutrient cycling (Manzoni et  al., 2019). Soil biocrusts (composed of lichens, bryophytes and soil microorganisms), which contribute to dryland ecosystem function, including carbon uptake and soil stabilisation (Reed et al., 2019), are sensitive to warming and altered rainfall in a shift in biocrust communities of mosses and lichens in favour of early successional cyanobacteria-dominated biocrusts (Escolar et  al., 2012; Reed et  al., 2012), which can increase surface albedo (Rutherford et al., 2017).
CCP3.2.1.3  V egetation Changes
CCP3.2.1.4
 W
oody Cover Increase

Dryland ecosystems have shown mixed trends of decreases and increases in vegetation and biodiversity, depending on the time period, geographic region and vegetation type assessed (see Table CCP3.1 for examples of observed environmental changes and impacts in drylands and the role of climate change and non-climatic factors in causing these changes).
Increases in shrub cover in arid deserts and shrublands have been

recorded in the North American drylands (Caracciolo et  al., 2016; Archer et al., 2017; Chambers et al., 2019), the Namib desert (Rohde et al., 2019), the Karoo (Ward et al., 2014; Masubelele et al., 2015b), north and central Mexico (Pérez-Sánchez et al., 2011; Báez et al., 2013; Castillón et al., 2015; Sosa et al., 2019), large parts of the West African Sahel with some local exceptions (Brandt et al., 2016) and Central Asia (Jia et al., 2015; Li et al., 2015; Deng et al., 2016; Jiao et al., 2016; Wang et al., 2016). Increasing woodiness in the Namib is consistent with an increase in rainfall extremes and westward expansion of convective rainfall (Haensler et al., 2010; Rohde et al., 2019). Increasing rainfall and rising CO
2 concentrations (which improves water use efficiency)

benefits some shrubs (Polley et al., 1997; Morgan et al., 2004; Donohue et al., 2013). Together with changes in land use (Hoffman et al., 2018), improved land management (Reij et al., 2005) and improved irrigation (He et al., 2019), this contributes to woody cover increases. Extensive woody encroachment has been recorded in savannas (measured between 1920–2015, over the past century) in Africa (2.4% woody cover increase per decade), Australia (1% increase per decade), and South America (8% increase per decade) (O’Connor et al., 2014; Stevens et  al., 2016; Skowno et  al., 2017; Venter et  al., 2018; Zhang et  al., 2019). Following drought in the Sahel (1968–1973 and 1982–1984), a rainfall increase since the mid-1990s has been linked to increases of woody cover between 1992–2011/2012 (Brandt et al., 2016; Brandt et  al., 2017; Brandt et  al., 2019). See SRCCL Section  3.2.1.1 for an evaluation of the normalized difference vegetation index (NDVI) and remote sensing approaches used in

these studies. Tree regeneration by farmers has also increased woody cover, particularly next to villages (high confidence) (Reij et  al., 2005; Reij and Garrity, 2016; Brandt et al., 2018). Otherwise, savanna encroachment has been attributed to combinations of increased rainfall (Venter et al., 2018; Zhang et al., 2019), warming (Venter et al., 2018) and CO

2 fertilization (Kgope et al., 
2010; Bond and Midgley, 2012; Buitenwerf et al., 2012; Stevens et al., 2016; Quirk et al., 2019) interacting with changing land use (Archer et al., 2017; Venter et al., 2018), where herbivory and fire regimes are altered (O’Connor et al., 2014; Archer et al., 2017; see also discussion on fire and herbivory in Section 2.4.3.1). In some cases, woody increase has been balanced locally by changes in runoff (Trichon et al., 2018) or by land clearing and fuel wood harvesting, as seen in western Niger, northern Nigeria, and at the periphery of major towns (Montagné et al., 2016).
CCP3.2.1.5  T ree Death and Woody Cover Decline

Field measurements have also detected tree mortality and loss of mesic tree species at some Sahel sites during drought periods (Gonzalez et al., 2012; Kusserow, 2017; Brandt et al., 2018; Ibrahim et al., 2018; Trichon et al., 2018; Zwarts et al., 2018; Bernardino et al., 2020; Zida et al., 2020) and a reduction of mesic species in favour of drought-tolerant species (high confidence) (Hänke et  al., 2016; Kusserow, 2017; Ibrahim et al., 2018; Trichon et al., 2018; Dendoncker et al., 2020; Zida et al., 2020b), with attribution to climate change (Gonzalez et  al., 2012). Furthermore, vegetation productivity per unit of rainfall showed a net decline of 4% in the period 2000–2015

CCP3
2202Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
Table CCP3.1 |  Observed ecological changes in drylands.
Region Observed changeClimate change 
factorsAttribution to 
climate changeNon-climate 
change factorsConfidence in 
observed changeReferences
Hyper-arid
Asian hyper-arid 
regions (Gobi)Loss of shallow rooted desert 
plantsIncrease in extreme 
warm temperaturesmedium Li et al. (2015)
North America—
Mojave DesertLoss of mesic bird species Decreased rainfallYes. Analyses of 
causal factors find 
decreased rainfall 
more important than 
non-climate factors.Livestock, 
human-ignited firesmediumIknayan and Beissinger 
(2018); Riddell et al. 
(2019)
Decline of desert tortoise 
(Gopherus agassizii) 
population by 90% from 1993 
to 2012 at one site in the 
MojaveDecreased rainfall Lovich et al. (2014)
Reduced perennial vegetation 
cover, including trees and cacti, 
in the Mojave and Sonoran 
deserts of the southwestern 
USAIncreased temperature,

decreased rainfall, 
wildfireLand use change, 
invasive plant specieshighDefalco et al. (2010); 
Munson et al. (2016b); 
Conver et al. (2017)
Arid
African SahelWoody cover increase in parts 
of the SahelIncrease in rainfall 
since the mid-1990s 
(compared to 
1968–1993)and 
increased CO 2Restoration planting, 
agroforestryhigh
Increase in grass production 
across the SahelIncreases in rainfall 
1968–1993) and 
increased CO 2mediumHiernaux et al. (2009a; 
2009b); Dardel et al. 
(2014); Venter et al. 
(2018); Zhang et al. 
(2018); Brandt et al. 
(2019); Bernardino 
et al. (2020)
Decline of mesic tree species at 
field sites across the SahelDecreased rainfall 
from 1901 to 2002 
increased temperatureYes. Multivariate 
statistical analyses 
find climate factors 
non-climate factors.Land clearing for 
cropland expansion,
increased pressure 
on wood resources 
(rural demography, 
urbanisation)highGonzalez (2001); 
Wezel and Lykke

(2006); Maranz (2009); 
Gonzalez et al. (2012); 
Hänke et al. (2016); 
Kusserow (2017); 
Ibrahim et al. (2018); 
Zida et al. (2020b)
Increased tree mortality at field 
sites across the SahelDecreased rainfall 
from 1901 to 2002, 
non-climate factors.Agricultural expansion, 
modified runoff on 
shallow soilshighHelldén (1984); 
Gonzalez, (2001); Wezel 
and Lykke (2006); 
Maranz (2009); Vincke 
et al. (2010); Hänke 
et al. (2016); Trichon 
et al. (2018); Zwarts 
et al. (2018); Wendling 
et al. (2019); Bernardino 
et al. (2020); Zida et al. 
(2020a)
Latitudinal biome shift of the 
SahelDecreased rainfall, 
non-climate factors.highBoudet (1977); 
Tucker and Nicholson 
(1999); Gonzalez, 
(2001); Hiernaux and 
Le Houérou (2006); 
Hiernaux et al. (2009a); 
Maranz (2009);

CCP3
2203Deserts, Semiarid Areas and Desertification Cr oss-Chapter Paper 3
Namib desertIncrease in woody plant cover 
and a shift of mesic species into more arid regionsIncrease in amount of fog from westward expansion of convective rainfall and increase in number of extreme rainfall events; elevated CO
2 and 
warming effects on the Benguela upwelling systemmediumMorgan et al. (2004); Haensler et al. (2010); Donohue et al. (2013); Rohde et al. (2019)
Southern Africa— Nama-KarooShifting rainfall seasonality (debate over whether it is cyclical or directional); elevated CO
2mediumDu Toit and O’Connor (2014); Du Toit et al. (2015); Masubelele et al. (2015a; 2015b)
Eastern Karoo has experienced a significant increase in the end of the growing season lengthShift in rainfall seasonality and increase in Mean Annual Precipitationlow Davis-Reddy (2018)

Woody encroachment observed throughout the Nama-Karoo in valley bottoms, ephemeral stream banks and the slopes of Karoo hillsRising concentration of CO
2Changing land use and herbivore managementmediumPolley et al. (1997); Morgan et al. (2004); Donohue et al. (2013); Ward et al. (2014); Masubelele et al. (2015a); Hoffman et al. (2018)
Southern Africa— Succulent KarooRange shift in tree aloe Aloidendron dichotomum with mortality in the warmer and drier range and increase in recruitment in the cooler southern range, populations have positive growth rates, possibly due to warming, although this finding has been challengedWarming and drying mediumFoden et al. (2007a); Jack et al. (2016)

Northern Africa—MoroccoIncreased vulnerability of oases and reduced ecosystem service provisionHigh temperature and reduced precipitation causing soil and water salinisation, drying up of surface water; hot winds and sandstormsAgricultural growth, high population growth and unregulated and indiscriminate land developmentmedium Karmaoui et al. (2014)
Reduced surface water availabilityIncreased temperature and reduced precipitationHigh demand (population growth) and land use changemediumRochdane et al. (2012); Choukri et al. (2020)
Reduction of resilience of Abies pinasapo–Cedrus atlantica forests to subsequent droughtsSuccessive droughts mediumNavarro-Cerrillo et al. (2020)
North American drylandsDrought adapted species are increasing in Chihuahuan desertsIncrease in aridity and increased interannual variation in climate trendsmediumCollins and Xia (2015); Rudgers et al. (2018)

Widespread woody plant encroachment; Prosopis sp. encroachment in arid desert regions (Chihuahuan and Sonoran Desert) at a rate of ~3% per decadeIncreasing temperature, elevated CO
2 and changing 
rainfallFire suppression and altered grazing/browsing regimeshighCaracciolo et al. (2016); Archer et al. (2017)
Plant desert community shift changes the albedo through the reduction in dark biocrustsWarming and drought medium Rutherford et al. (2000)

CCP3
2204Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
South 
Chihuahuan 
Desert— North 
and Central 
MexicoShrub encroachment of 
grassland (Berberis trifoliolata, 
Ephedra aspera, Larrea 
tridentata) changes dominant 
species in shrub areas; loss of 
less resistant shrubby species 
(Leucophyllum laevigatum, 
Lindleya mespiloides, 
Setchellanthus caeruleu); shrub 
encroachment of mesic and 
temperate areasDecreased rainfall, 
increase in 
temperature and
increase CO 2Urban growth, 
mechanised agriculture 
and changes in land 
usehighPérez-Sánchez et al. 
(2011); Castillón et al. 
(2015); Sosa et al. 
(2019)
Shifts in soil microbial 
community to being more 
abundant in fungi (Ascomycota 
and Pleosporales)Decreased rainfall 
and increase in 
temperatureChanges in land use lowVargas-Gastélum et al. 
(2015)

Limited ecological connectivity 
of shrubby populationsDecreased rainfall 
temperaturemedium Sosa et al. (2019)
Loss of cacti species 
(Echinocactus platyacanthus, 
Pediocactus bradyi, 
Coryphantha werdermannii, 
Astrophytum) due to decline 
in physiological performance, 
loss of seed banks and lower 
germination ratesDecreased rainfall 
temperatureCattle grazing, looting highAragón-Gastélum et al. 
(2014); Shryock et al. 
(2014); Martorell et al. 
(2015); Carrillo-Angeles 
et al. (2016); 
Aragón-Gastélum et al. 
(2018)
Arid and semiarid 
territories in 
ArgentinaDecreases in vegetation 
indexesDecreased rainfallHuman-induced land 
degradationlow Barbosa et al. (2015)
Argentina Chaco 
RegionDryland salinity Changes in rainfallLand use change, 
overexploitation of 
water resourcesmediumAmdan et al. (2013); 
Marchesini et al. (2017)
South America 
Arid DiagonalMarked reduction in 
streamflow from the Andes 
mountain ‘water towers’ due

to the persistent reduction in 
precipitationDecrease in 
precipitation in the 
upper Andes; the 
unprecedented 10-year 
extreme dry period has 
been called the ‘Mega- 
drought’highBianchi et al. (2017); 
Rivera and Penalba 
(2018); Masiokas 
et al. (2019); 
Rodríguez-Morales 
et al. (2019)
South American 
AndesExtensive glacier retreat across 
the AndesIncreasing 
sub-continental 
temperature and 
regional reduction in 
snow precipitationhighDussaillant et al. 
(2019); Falaschi et al. 
(2019); Masiokas et al. 
(2019)
Patagonian AndesWidespread tree mortality of 
Austrocedrus and Nothofagus 
forests in the dry ecotone 
forest-steppe across PatagoniaIncrease in extreme 
drought eventshighRodríguez-Catón et al. 
(2019)
Increase in elevation of the 
upper-forest Nothofagus 
treeline across PatagoniaIncrease in 
duration of the 
growing season at 
high elevation in the 
Patagonian Andeshigh Srur et al. (2016; 2018)
Central Asian arid 
landsShrub encroachment into arid

grasslands within the past 
10 yearsTemperature of central 
Asian arid regions 
experienced a sharp 
increase in 1997 and 
has been in a state of 
high variability since 
thenmedium Li et al. (2015)

CCP3
2205Deserts, Semiarid Areas and Desertification Cr oss-Chapter Paper 3
Loess Plateau, 
ChinaWidespread vegetation greening in the Loess Plateau region; soil moisture declining widely, and deficit in forests and orchards; Yellow River runoff decliningSignificant warming, slight increase in precipitationLand use and cover change, ecological restoration, mainly induced by Grain for Green ProjecthighJia et al. (2015); Wang et al. (2015); Deng et al. (2016); Jiao et al. (2016)
The Three-River Source Region of the Tibetan Plateau, ChinaRunoff increases, total water storage and groundwater increasing, Net Primary Productivity increasePrecipitation increasing and evapotranspiration slightly decreasingGrassland protection high Xu et al. (2019)
Semiarid
Australian arid 
landsWidespread greening Elevated CO
2 medium Donohue et al. (2013)

African savannaDoubling of tree cover from 1940–2010 in South Africa, changing land use and 20% increase in spread of woody areas into previously open areas in the last 20 yearsWarming, elevated CO
2, altered rainfall 
regimesRemoval of mega-herbivores, fire suppression, changed herbivore regimehighSkowno et al. (2017); Stevens et al. (2017); Venter et al. (2018); García Criado et al. 2020)
African savannaWidespread increase in tree cover across Africa with only three countries across the continent experiencing a net decline in tree coverWarming, changing rainfall, mention of CO
2Fire suppression high Venter et al. (2018)

African savannaBiodiversity responses to changes in vegetation structure (woody encroachment) causing declines in functional groups that are open area specialists, records for birds, rodents, termites, mammals, insectsWoody encroachment mediumBlaum et al. (2007); Blaum et al. (2009); Sirami and Monadjem (2012); Gray and Bond (2013); Péron and Altwegg (2015); Smit and Prins (2015)
African semiarid regions (savanna)Reduced tourism experience due to woody encroachmentWoody encroachment low Gray and Bond (2013)
North American drylands – sagebrush steppesSagebrush steppes are being invaded by non-native grassesIncrease in temperature and favourable climateshighBradley et al. (2016); Hufft and Zelikova (2016); Chambers (2018)
Shrub encroachment,(Prosopis glandulosa, Juniper ashei and Juniper pinchotti) occurring in the semiarid grasslands of the southern Great Plains at a rate of ~8% per decadeIncreasing temperature, elevated CO
2 and changing

Woody encroachment in sagebrush steppes (cold deserts) (Juniper occidentalis) at a rate of 2% per decadeWarming and associated decline in snowpack, less precipitation falling as snow and an increase in the rain fraction in winterhighChambers et al. (2014); Mote et al. (2018)
Central MexicoDesertification (as decreases in vegetation indexes)Decreased rainfall and increase in temperatureLand use change and intensificationmediumBecerril-Pina et al. (2015); Noyola-Medrano and Martínez-Sías (2017)

CCP3
2206Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
across drylands globally, with the greatest net declines in Africa 
(16%) and Asia (33%) (Abel et al., 2021), but with location-specific 
increases in vegetation-rainfall sensitivity, for example, in southern 
and eastern Africa and parts of the Sahel. Furthermore, NDVI declines 
were reported across the Sahel from 1999 to 2015 (Yuan et al., 2019; 
Zida et al., 2020a). However, field site monitoring showed a strong 
regeneration of the decimated woody populations except on shallow 
soil where the runoff system had evolved towards a web of gullies 
(Hiernaux et al., 2009a; Trichon et al., 2018; Wendling et al., 2019) .
Other site-specific impacts include tree mortality in southwestern 
Morocco (Le Polain de Waroux and Lambin, 2012), mortality of 
Austrocedrus and Nothofagus forests in the dry Patagonia forest-
steppe (Rodríguez-Catón et al., 2019) and a tree range contraction of

Aloidendron dichotmum in southern Africa (Foden et  al., 2007b). In 
Morocco, tree mortality was most highly correlated to an increase in 
aridity, measured by the Palmer Drought Severity Index (PDSI), which 
showed a statistically significant increase since 1900 due to climate 
change (Dai et al., 2004; Esper et al., 2007; Dai, 2011).
In deserts of the southwestern USA, a drought since 2000, mainly 
due to climate change (Williams et al., 2020), together with land use 
change, invasive plant species and wildfire (Syphard et al., 2017), has 
led to reductions in native desert plant species (Defalco et al., 2010; 
Conver et al., 2017) and perennial vegetation cover (Munson et al., 
2016a; 2016b). An increase in invasive exotic grasses has increased wildfires in these desert ecosystems in which fire had been rare 
(Brooks and Matchett, 2006; Abatzoglou and Kolden, 2011; Hegeman 
et al., 2014; Horn and St. Clair, 2017). In the Mojave Desert in the USA,

a loss of bird biodiversity has also been detected and attributed to 
increased aridity caused by climate change (Iknayan and Beissinger, 
2018; Riddell et al., 2019).
CCP3.2.1.6 Change in Herbaceous Cover
Changes in aridity (Rudgers et al., 2018) have caused some expansion 
of dominant grasses (often invasive) into desert shrublands. The 
spread of invasive Bromus tectorum may be enhanced by altered 
precipitation and freeze–thaw cycles (low confidence) (Collins 
and Xia, 2015; Rudgers et al., 2018). Arid grassland has expanded 
(between 10–100 km) into the eastern Karoo, South Africa (high 
confidence) (du Toit et al., 2015; Masubelele et al., 2015a; 2015b). 
Observations from 100-year-old grazing trials demonstrate that the 
increase in grassiness is a product of shift in rainfall seasonality and 
an increase in rainfall (Du Toit and O’Connor, 2014; du Toit et  al., 
2015; 2018; Masubelele et  al., 2015a;, 2015b). These changes are

causing an increase in fire frequency in these seldom burnt areas (du 
Toit et al., 2015). The Sahara was suggested to have expanded 10% 
from 1902 to 2013 (Thomas and Nigam, 2018), although herbaceous 
vegetation production has increased in general in the Sahel since 
the dry 1980s (Eklundh and Olsson, 2003; Anyamba and Tucker, 
2005; Herrmann et al., 2005; Hutchinson et al., 2005; Olsson et al., Region Observed changeClimate change 
Chinese drylandsWidespread greening trend of 
vegetation in China over the 
last three decades; regional 
differencesWarming, CO 2 increase
Rising atmospheric 
CO 2 concentration and 
nitrogen deposition 
are identified as the 
most likely causes of 
the greening trend 
in China, explaining 
85% and 41% of 
the average growing 
season Leaf Area Index 
trend
Negative impacts of 
climate change in 
north China and Inner 
Mongolia and and

positive impacts in 
the Qinghai-Xizang 
plateauEcological protection medium Piao et al. (2015)
Dry subhumid
African mesic 
savannasForest expansion into mesic 
savannasIncreased rainfall, 
elevated CO 2Fire suppression mediumBaccini et al. (2017); 
Aleman et al. (2018)
cerrado8% rate of woody cover 
increaseElevated CO 2 Fire exclusion highStevens et al. (2017); 
Rosan et al. (2019)
cerradoExpansion of forest into 
cerradoElevated CO 2 Fire exclusion highPassos et al. (2018); 
Australian 
savannas2% rate of woody cover 
increase and greening of 
drylandshighDonohue et al. (2013); 
Stevens et al. (2017); 
Bernardino et al. (2020)

CCP3
2207Deserts, Semiarid Areas and Desertification  Cross-Chapter Paper 3
2005; Fensholt et al., 2006; Dardel et al., 2014; Hiernaux et al., 2016; 
Stith et  al., 2016; Benjaminsen and Hiernaux, 2019; Hiernaux and 
Assouma, 2020).
Trends of land degradation (Section 16.4.1.2) and desertification (as 
demonstrated by loss of cover or reduced vegetation productivity) as 
an impact of changing climatic trends have been reported in Burkina 
Faso (Zida et  al., 2020), the northwestern regions of China during 
1975–1990 (Zhang et al., 2020) in Afghanistan (Savage et al., 2009), 
Iran (Mahmoudi et al., 2011; Kamali et al., 2017), Argentina (Barbosa 
et al., 2015) and India (Javed et al., 2012). Encroachment, re-greening 
and an increase of unpalatable plant species into rangeland areas 
(e.g., in East Africa and southern Africa’s Kalahari) all contribute to 
dryland degradation through the loss of open ecosystems and their 
services (Reed et al., 2015; Le et al., 2016; Chen et al., 2019b).

CCP3.2.1.7 Sand and Dust Storms
Soil dust emissions are highly sensitive to changing climate conditions 
but also to changing land use and management practices (high 
confidence). Distinguishing between the effects of these drivers is 
not straightforward, even in well-documented locations (Middleton, 
2019). There is limited evidence and low agreement about the impacts 
of climate change on sand and dust storms (SDS), with studies pointing 
to either substantial increases (+300%) or decreases (-60%) (Boucher et  al., 2013). Current climate models cannot adequately model the 
impact of climate change on SDS activity (Mirzabaev et  al., 2019). 
However, there is high confidence that land degradation, loss of 
vegetative cover and drying of water bodies in semiarid and arid areas 
will contribute to sand and dust activity (Mirzabaev et al., 2019).
SDS remain a major concern for desert areas under conditions of 
climate change and desertification (Middleton, 2017). Only about 20%

of deserts are covered by sand, but desert SDS provide an important 
feedback mechanism to climate (Pu and Ginoux, 2017), with literature 
showing that some areas have very frequent dust days (Figure CCP3.2; 
Ginoux et  al., 2012). In some locations, such as the USA, desert dust 
can be deposited downwind on snowpacks, hastening snowmelt and 
altering river hydrology (Painter et al., 2010). Deserts and other natural 
dryland surfaces produced 75–90% of atmospheric dust globally in the 
early 21st century, with the remainder from agricultural and other land 
dominated by human land use (Ginoux et al., 2012; Stanelle et al., 2014).
Recent changes in dust emissions and their attributions vary 
geographically. Warming in Iran over the period 1951–2013 has been 
associated with an increased frequency of dust events (Alizadeh-
Choobari and Najafi, 2018) and a trend (2000–2014) towards 
increased fine atmospheric mineral dust concentrations in the US

southwest has been linked to increasing aridity (Hand et  al., 2017). 
Conversely, increases in rainfall, soil moisture and vegetation linked to 
Natural areasObservations 2003–200 9
60%4 0% 20%020%4 0% 60%
Agricultural landsFrequency of high dust days during the dust season
Figure CCP3.2 |  Frequency of high dust days (dust optical depth >0.2) during the dust season, based on 2003–2009 remote sensing, the most recent data 
analysed, and divided into areas primarily under agriculture and areas dominated by natural land cover (Ginoux et al., 2012). Dust seasons: Africa (North), 
Year-round; Africa (South), September–February; America (North), March–May; America (South), December–February), Asia, March–May; Australia, September–February.

CCP3
2208Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
changes in circulation strength of the Indian summer monsoon since 
2002 have led to a substantial reduction of dust in the Thar Desert and 
surrounding region, showing agreement with findings from the Sahel 
and the West African Monsoon (Kergoat et  al., 2017). A decreasing 
trend in the number and intensity of SDS in spring (2007–2016) in 
East Asia has also responded to higher precipitation and soil moisture, 
related to a decrease in the intensity of the polar vortex, favouring 
higher vegetation cover during the period studied (An et al., 2018). 
Global climate change, transboundary movement of aeolian material 
by atmospheric flows from Central Asia, dynamics of the Caspian Sea 
regime, erosion, salinisation and the loss of land as a result of the 
placement of industrial facilities have expanded the land area prone 
to desertification in Russia. Desertification has been observed to some

extent in 27 sub-regions of the Russian Federation on territory of more 
than 100 million hectares (Kust et al., 2011; also recently confirmed by 
National Report, 2019). Eastern and south-eastern regions of Kalmykia, 
Russia, serve as dust sources, while dust and sand masses from areas 
of the Black Land sometimes move far beyond to parts of Rostov, 
Astrakhan, Volgograd and Stavropol regions. Agricultural land in these 
areas can become covered with dust and sand 10 cm or more thick, 
with negative impacts on yields (Tsymbarovich et al., 2020). High dust 
day frequency is also occurring in the High Latitude Dust (HLD) source 
areas not reported in Figure  CCP3.2, such as in Iceland, Patagonia, 
Canada, Alaska and, based on in situ measurements, in Antarctica 
(Dagsson-Waldhauserová et al., 2014; Bullard et al., 2016; Dagsson-
Waldhauserova and Meinander, 2019; Bachelder et al., 2020). Active 
HLD sources cover at least 500,000 km2 and produce at least 5% of

global dust budget (Bullard et al., 2016). HLD has negative impacts on 
the cryosphere via albedo changes and snow/ice melting (Boy, 2019; 
Dagsson-Waldhauserova and Meinander, 2019).
CCP3.2.1.8 Water Scarcity
Climate change and desertification have been linked to water loss 
(Bayram and Öztürk, 2014; Schwilch et  al., 2014; Mohamed et  al., 
2016), decreases in water quantity for irrigation and contamination 
of surface water bodies (Middleton, 2017). Increased runoff in areas 
in the Sahel with shallow soils increased water flows to lakes and 
the recharge of water tables (Favreau et  al., 2009; Gardelle et  al., 
2010; Descroix et  al., 2013; Kaptué et  al., 2015; Gal et  al., 2017). 
Water scarcity (Section  16.5.2.3.7) was among the first impacts 
of climate change recognised in North African countries such as 
Morocco which have extensive dryland areas, with countries such as 
Turkey, Libya, USA and China carrying out large-scale water transfer

projects (Sternberg, 2016; Stringer et al., 2021). The decrease in water 
availability in Morocco was substantial in terms of both surface water 
supply (Rochdane et al., 2012; Choukri et al., 2020) and groundwater 
(Bahir et al., 2020), threatening agricultural production.
CCP3.2.2 Observed Impacts of Climate Change on Human 
Systems in Desert and Semiarid Areas
Climate change and desertification, alongside other drivers of degra-
dation, reduce dryland ecosystem services, leading to losses of bio-
diversity, water, food and impacts on human health (Section CCP4.2.3) 
and well-being (high confidence) (Mirzabaev et al., 2019) resulting in disruption to the economic structures and cultural practices of affected 
communities (Elhadary, 2014; Middleton, 2017).
CCP3.2.2.1 Sand and Dust Storms
Desertification and SDS can cause substantial socioeconomic damage 
in drylands (UNEP , 1992; Opp et  al., 2021) over both the short and

long term. Short-term impacts occur on health, food production 
systems, infrastructure (damaging buildings, energy systems and 
communications), transport and related economic productivity, air 
and road traffic, and costs incurred in clearing sand and dust from 
deposition areas (Mirzabaev et al., 2019). In the Arab region increasing 
frequency of SDS events is projected to further exacerbate water 
scarcity and drought (ESCWA, 2017). Longer-term costs include loss of 
ecosystem services, biodiversity and habitat, chronic health problems, 
soil erosion and reduced soil quality (particularly through nutrient 
losses and deposition of pollutants), and disruption of global climate 
regulation (Middleton, 2018; Allahbakhshi et al., 2019). Dust deposition 
nevertheless can offer environmental and economic benefits, bringing 
important nutrients that improve and sustain soil fertility (Marticorena 
et al., 2017). Preventing and reducing SDS entails upfront investment

costs but full benefit–cost analyses of different measures compared 
to the costs of inaction are scarce and need to consider the likely 
frequency and magnitude of SDS events (Tozer and Leys, 2013).
CCP3.2.2.2 Human Health
The potential impacts of climate change, recurrent droughts and 
desertification on human health in drylands include: higher risks from 
water scarcity (linked to deteriorating surface and ground water quality 
and water-borne diseases; Stringer et al., 2021), food insecurity and 
malnutrition (Section  16.2.3.4) in the absence of sufficient imports, 
respiratory, cardiovascular and infectious diseases caused by SDS 
(Mirzabaev et  al., 2019), potential displacement and migration and 
mental health consequences (Chapter 7; Stringer et al., 2021) and heat 
stress (Dunne et al., 2013; Zhao et al., 2015; Russo et al., 2016). SDS 
negatively impact human health through various pathways, causing 
respiratory, cardiovascular diseases and facilitating infections (high

confidence) (Díaz et al., 2017; Goudarzi et al., 2017; Allahbakhshi et al., 
2019; Münzel et al., 2019). SDS can cause mortality and injuries related 
to transport accidents (Goudie, 2014). Research from China suggests 
that prenatal exposure to SDS can affect children’s cognitive function 
(Li et  al., 2018). The pollutants that are entrained and ingested or 
inhaled closely link to the land management strategies in source areas.
Droughts are among the natural hazards with the highest adverse 
impacts on human populations (Mishra and Singh, 2010; Arias et al., 
2021). Although droughts represented just 4% of hazard events, their 
impacts amounted to 31% of affected people (29 million) (Louvain, 
2019). Drought exposure relates to a higher risk of undernutrition 
(Section  16.5.2.3.6), among vulnerable populations (Kumar, 2016), 
particularly children (IFPRI, 2016) for whom the impacts can lead to 
lifelong consequences through stunted growth, impaired cognitive

ability and reduced future educational and work performance (UNICEF/
WHO/WBG, 2019). The corresponding costs of children stunting in 
terms of lost economic growth can be of the order of 7% of per capita 
income in developing countries (Galasso and Wagstaff, 2018).

CCP3
2209Deserts, Semiarid Areas and Desertification  Cross-Chapter Paper 3
CCP3.2.2.3 Agro-ecological Food Systems, Livelihoods and Food 
Security
Rising temperatures, variation in rainfall patterns and frequent extreme 
weather events associated with climate change have adversely 
affected agro-ecological food systems and pastoral systems in some 
drylands (Zhu et al., 2013; Amin et al., 2018), especially in developing 
countries (Haider and Adnan, 2014; Ahmed et  al., 2016; ur Rahman 
et  al., 2018) where desertification is a key challenge to agricultural 
livelihoods. Recurrent droughts in recent decades, coupled with wind 
erosion (particularly of fine sediment which gives soil its water-holding 
capacity and nutrients), affected vast areas in Argentina, leading to land 
abandonment and agricultural fields being covered by sand and invasive 
plants (Abraham et al., 2016). Temperature increases have contributed 
to reduced wheat yields in arid, semiarid and dry sub-humid zones of

Pakistan (Sultana et al., 2019). Agricultural production in the drylands 
of South Punjab is experiencing irreversible impacts since the grain 
formation phase has become swifter with a warmer climate, leading to 
improper growth and reduced yields (Rasul et al., 2011).
Aslam et  al. (2018) regard climate change impacts as particularly 
threatening to the livestock sector, water and food security, and 
the economy beyond agriculture in South Punjab, particularly as 
yields decrease. In the livestock sector across global drylands (WGI 
TS.4.3.2.10), observed impacts include reduction of plant cover in 
rangelands, reduced livestock and crop yields, loss of biodiversity 
and increased land degradation and soil nutrient loss (Van de Steeg, 
2012; Mganga et al., 2015; Ahmed et al., 2016; Mohamed et al., 2016; 
Eldridge and Beecham, 2018, Arias et  al., 2021), as well as injury 
and livestock death due to SDS. This is particularly worrisome for

traditional pastoralists who find themselves with fewer safety nets 
and more limited adaptive capacities than in the past, particularly 
where mobility, access and tenure rights are becoming restricted 
(Box CCP3.1) and where use of technologies such as mobile phones 
can result in mixed effects, as found in Morocco (Vidal-González and 
Nahhass, 2018). Observed SDS impacts can increase food production 
costs and threaten sustainability more generally (Middleton, 2017).
Woody plant encroachment and greening may be masking underlying 
land degradation processes and losses of ecosystem services, 
livelihood and adaptation options in pastoral livelihood systems (Reed 
et al., 2015; Chen et al., 2019a). Woody encroachment alters ecosystem 
services, particularly in rangelands, resulting in reduction of grass 
cover, hindering livestock production (Anadón et al. 2014), reducing 
water availability (Honda and Durigan 2016, Stringer et al., 2021) but

increasing availability of wood (Mograbi et al., 2019).
CCP3.2.2.4 Gender Differentiated Impacts
Impacts of desertification, climate change and environmental 
degradation, as well as vulnerability and capacity to adapt, are 
gendered. Differences are determined by socially structured gender-
specific roles and responsibilities, ownership of, access to and 
control over natural resources and technology, decision making, and 
capacity to cope and adapt to long-term changes (Mirzabaev et al., 
2019; Cross-Chapter Box GENDER in Chapter 18). Assessments of the 
gender dimension of desertification and climate change impacts and responses are scarce, and highly context specific. For example, in many 
lower income countries, rural women produce most of the household 
food, and are responsible for food preparation and collecting fuelwood 
and water from increasingly distant sources (Mekonnen et al., 2017; 
Droy, 2020). Drought and water scarcity particularly affect women and

girls in drylands because they need to spend more time and energy 
collecting water and fuelwood, have less time for education or income-
generating activities, and may be more exposed to violence (Sommer 
et al., 2014) and less able to migrate as an adaptation option. Women 
are also commonly excluded from family and community decision 
making on actions to address desertification and climate change, yet 
their engagement in climate adaptation is critical. International policy 
efforts are currently seeking to better recognise and address this 
challenge (Okpara et al., 2019).
CCP3.2.2.5 Climate Change, Migration and Conflict
Dryland populations pursuing traditional land-based livelihood 
options are generally mobile due to a highly fluctuating resource base 
(Box  CCP 3.1). Many rural dwellers in drylands also move to urban 
areas for seasonal work, which can have positive impacts in terms 
of remittances. While reasons for migration vary and can be positive

or negative, oppression and human rights abuses, lack of livelihood 
opportunities and food insecurity tend to be among the main push 
factors, while emerging opportunities at the rural–urban nexus present 
lucrative pull factors (Cross-Chapter Box MIGRATE in Chapter 7). In a 
survey in Libya in 2016, 80% of migrants interviewed said they had left 
home because of economic hardship (Hochleithner and Exner, 2018), 
which in drylands under water scarcity linked to climate change, would 
be exacerbated.
Causes of migration and violent conflict need to be seen in a wider 
historical, agrarian, political, economic and environmental context, 
in a multi-scalar perspective integrating levels of analysis from the 
local to the global (Glick Schiller, 2015). Quantitative studies tend to 
conclude that climate change has so far not significantly impacted 
migration including in drylands (Owain and Maslin, 2018), although 
with some disagreement (Lima et al., 2016; Missirian and Schlenker,

2017). In a study of the climate change–migration–conflict interface, 
Abel et al. (2019) found limited empirical evidence supporting a link 
between climatic shocks, conflict and asylum-seeking for the period 
2006–2015 from 157 countries. The authors found evidence of such a 
link for the period 2010–2012 relating to some countries affected by 
the Arab Spring and concluded that the impact of climate on conflict 
and migration is limited to specific time periods and contexts.
The same lack of general causality is largely concluded on the specific 
link between climate change and conflict (Buhaug et al., 2014; Buhaug 
et al., 2015; von Uexkull et al., 2016; Koubi, 2019), but a minority of 
quantitative studies argue for a stronger causal association (Hsiang 
et al., 2013). Mach et al. (2019) found considerable agreement among 
experts that climate variability and change have influenced the risk 
of organised armed conflict within countries, but they also agreed

that other factors, such as state capacity and level of socioeconomic 
development, played a much larger role. These factors also play a role 
in determining adaptation possibilities and in shaping the enabling 
environment (Section 8.5.2).

CCP3
2210Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
Qualitative case studies tend to frame conflict and migration within a 
larger political, economic and historical context. A number of studies 
from African drylands find that land dispossession is a key driver of both 
migration and conflict resulting from large-scale resource extraction or 
land encroachment, often associated with processes of elite capture 
and marginalisation (Benjaminsen and Ba, 2009; Benjaminsen et  al., 
2009; Cross, 2013; Glick Schiller, 2015; Nyantakyi-Frimpong and Bezner 
Kerr, 2017; Obeng-Odoom, 2017; Bergius et al., 2020). By undermining 
livelihoods, exacerbating poverty and setting rural population groups 
adrift, land dispossession in the Sahel may lead to increased migration 
to urban areas, to rural sites of non-farm employment (e.g., mines) 
(Chevrillon-Guibert et al., 2019) or out of the country. In addition, it

may lead to other types of reactions including violent resistance (Oliver-Smith, 2010; Cavanagh and Benjaminsen, 2015; Hall et al., 2015) as 
already seen in the Sahel in terms of the emergence of jihadist armed 
groups (Benjaminsen and Ba, 2019). Major drivers of the current 
crisis in Mali include decades of bureaucratic mismanagement and 
widespread corruption, the spill-over of jihadist groups from Algeria 
after the civil war there in the 1990s and the current civil war in Libya. 
Climate change has played a marginal role as a driver of conflicts in the 
Sahel (Benjaminsen et al., 2012; Benjaminsen and Hiernaux, 2019) but 
has potential to exacerbate the situation in the future with regards to 
migration and conflict (Owain and Maslin, 2018).Box CCP3.1 | Pastoralism and climate change
Pastoralism is a livestock-keeping system based on the herding of animals. Migrations often take place over long distances to track

variable and unpredictable plant growth that tends to be patchy in space and variable in time (Homewood, 2018). Pastoralism has a 
considerably lower carbon budget than other livestock-keeping systems. Research on pastoralism in the Sahel concluded that this system 
may be carbon neutral (Assouma et al., 2019), despite contributing directly to greenhouse gas emissions via methane enteric emissions 
and indirectly through faeces-driven CO 2, CH 4 and N 2O emissions during mineralisation (Assouma et al., 2017). Efforts to sedentarise and 
settle pastoralists in villages can lead to land degradation and higher overall emissions from the sector.
Pastoralists migrate with their animals in some of the most remote and marginal environments on the planet. Globally, mobile pastoralists 
number about 200 million households and use about 25% of the Earth’s landmass (Dong, 2016). Many pastoralists operate in non-

equilibrial environments that are unstable, fluctuating and generally uncertain, and are driven more by climatic variation than livestock 
numbers and grazing pressure (Behnke et al., 1993). Examples of such systems are grazing areas in the dry tropics (Sandford, 1983; Turner, 
1993; Sullivan and Rohde, 2002; Benjaminsen et al., 2006; Hiernaux et al., 2016) and rangelands in the Arctic (Behnke, 2000; Tyler et al., 
2008; Benjaminsen et al., 2015; Marin et al., 2020).
Over many generations, pastoralists have accumulated practical experience and knowledge to cope with uncertainty and value variability 
(Krätli and Schareika, 2010), mainly through a mobile and flexible approach. While pastoralists are also at risk of climate change impacts, 
they may be better able to adapt to a changing climate than other land users (Davies and Nori, 2008; Krätli and Schareika, 2010; Jones 
and Gutzler, 2016).

While pastoralists possess substantial adaptive capacity as a result of their Indigenous knowledge, this has been under pressure during 
the last few decades through continued loss of livestock corridors (essential to mobility) and pastures in general due to competing land 
uses, such as farming, mining, crop expansion and the establishment or extension of protected areas (Thébaud and Batterbury, 2001; 
Brockington, 2002; Benjaminsen and Ba, 2009; Upton, 2014; Johnsen, 2016; Tappan, 2016; Homewood, 2018; Weldemichel and Lein, 
2019; Bergius et al., 2020). Many of these competing land uses erect fences and exclude other uses, while property rights often privilege 
sedentary farming.
Modern states have typically tried to settle pastoralists and confine their movements within clearly defined boundaries, claiming that 
pastoral land use is neither ecologically sustainable nor economically productive. Based on such negative and often flawed views, stall-

feeding and ranching are often presented by policymakers as successful models of livestock keeping in contrast to the pastoral way of 
life (Steinfeld et al., 2006; Chatty, 2007).
Current pressures and processes of pastoral change are spatially variable and complex, and tend to result in further economic and 
political marginalisation of pastoralists, with adverse effects on livelihoods and landscapes. With climate change, which is projected to 
lead to higher temperatures and more frequent fluctuations in precipitation, maintaining flexibility and resilience in pastoral land use 
is essential. However, current processes of marginalisation, in addition to increased insecurity in some drylands (e.g., the Sahel), make 
pastoralists more vulnerable, and constrain them from fully employing their adaptive capacities (Davies and Nori, 2008). The skills and

capacities held by pastoralists may, however, offer lessons for society at large in its struggle to adapt to climate change and deal with 
increased uncertainty (Davies and Nori, 2008; Scoones, 2009; Nori and Scoones, 2019).

CCP3
2211Deserts, Semiarid Areas and Desertification  Cross-Chapter Paper 3
CCP3.3 Future Projections
CCP3.3.1 Projected Changes and Risks in Natural Systems
CCP3.3.1.1 Temperature
Globally, warming rates have been twice as high in drylands as in humid 
lands, because the sparse vegetation cover and lower soil moisture of 
dryland ecosystems amplify temperature and aridity increases (Huang 
et al., 2016). This enhanced warming is expected to continue in the 
future. Surface warming over drylands is projected to reach ~6.5°C 
(~3.5°C) under the high Representative Concentration Pathway (RCP) 
8.5 (low-moderate RCP4.5) emissions scenario by the end of this 
century, relative to the historical period (1961–1990) (Huang et  al., 
2016; Huang et al., 2017). Exploring the spatial variations between the 
aeolian desertification response in selected climate change scenarios, 
Wang et al. (2017) reported that temperature rise could trigger aeolian

desertification in West Asia, Central China and Mongolia. The number 
of extremely hot days with temperatures above 40°C is projected to 
increase considerably across the Arab region by the end of the 21st 
century (ESCWA, 2017).
CCP3.3.1.2 Rainfall, Evaporation and Drought
Drylands are highly sensitive to changes in precipitation and 
evapotranspiration. Potential evapotranspiration (PET) is projected to 
increase in all regions globally, under all RCPs, as a result of increasing 
temperatures and surface water vapour deficit (Mirzabaev et al., 2019). 
Simulations based on coupled land surface, energy, and water and 
vegetation models in the Central Sahel showed a strong response of 
the water budget. Under +2°C and +4°C warming scenarios, decreased 
evapotranspiration, runoff and drainage were found for all scenarios, 
except those with the highest precipitation (Léauthaud et al., 2015).
Globally, soil moisture declined over the 20th century (Gu et al., 2019), a

trend that is projected to continue under all emissions scenarios (WGI). 
Projected drier soils can further amplify aridity through feedbacks with 
land surface temperature, relative humidity and precipitation (Berg 
et al., 2016).
Drought conditions (frequency, severity and duration) are expected 
to substantially worsen in global drylands, driven by a higher 
saturation threshold and more intense and frequent dry spells under 
rising temperatures (Liu et  al., 2019a; 2019b). In a +1.5°C world, 
historical 50-year droughts (based on the Standardised Precipitation-
Evapotranspiration Index (SPEI)) could occur twice as frequently 
across 58% of global landmasses relative to the 1976–2005 period, 
an area that increases to 67% under 2°C warming (Gu et al., 2020). 
Multi-year drought events of magnitudes exceeding historical baselines 
will increase by 2050 in countries with drylands including Australia, 
Brazil, Spain, Portugal and the USA (Jenkins and Warren, 2015). The

magnitude of drought stress in different regions differs depending on 
the metric used. Projections based on the PDSI suggest drought stress 
will increase by more than 70% globally, while a substantially lower 
estimate of 37% is found when precipitation minus evapotranspiration 
(P – E) is used (Swann et al., 2016). However, the two metrics agree 
on increasing drought stress in regions with more robust decreases in precipitation, such as southern North America, northeastern South 
America (Section 12.3.1.1) and southern Europe (Section 13.1.3; Swann 
CCP3.3.1.3 Aridity
Studies based on the AI (the ratio of annual potential evapotranspiration 
to precipitation), almost always project conditions of increasing aridity 
under climate change, and associated widespread expansion of drylands 
(Huang et  al., 2016). The limitations of the AI are widely reported 
(Mirzabaev et al., 2019), with alternative indices that consider different

variables, including the Ecohydrological Index, PDSI, Standardised 
Precipitation Index and SPEI (Stringer et  al., 2021). AI projections 
indicate potentially severe aridification in the Amazon, Australia, Chile, 
the Mediterranean region, northern, southern and western Africa, 
southwestern USA and South America (medium confidence) (Feng and 
Fu, 2013; Greve and Seneviratne, 2015; Jones and Gutzler, 2016; Park 
et al., 2018). However, the AI does not incorporate potential changes to 
plant transpiration under increasing CO 2 concentration and therefore 
overestimates drought conditions and aridity. Additionally, it does not 
reflect seasonality in rainfall and evapotranspiration, which is important 
in regions where temperature and actual evapotranspiration are not 
increasing during the wet season when vegetation growth is occurring. 
Mirzabaev et al. (2019) concluded that while aridity will increase in 
some places (high confidence), there is insufficient evidence to suggest

a global change in dryland aridity (medium confidence). Nevertheless, 
a comparison of several metrics of aridity showed aridity increases for 
several hotspots such as the Mediterranean region and South Africa 
(Greve et  al., 2019). Under RCP8.5, aridity zones could expand by 
one-quarter of the 1990 area by 2100, increasing to over half of the 
global terrestrial area (Huang et al., 2016; Lickley and Solomon, 2018). 
Lower greenhouse gas emissions, under RCP4.5, could limit expansion 
to one-tenth of the 1990 area by 2100 (Huang et al., 2016). Aridity 
could expand substantially on all continents except Antarctica (Huang 
et  al., 2016), with expansion first manifesting in the Mediterranean 
region, southern Africa, southern South America and western Australia 
(Lickley and Solomon, 2018). In the Northern Hemisphere, aridity zones 
could expand poleward as much as 11° latitude (Rajaud and Noblet-
Ducoudré, 2017). By 2100, the population of dryland areas could

increase by 700  million people and, under RCP8.5, 3  billion people 
might live in areas with a 25% or greater increase in aridity (Lickley 
and Solomon, 2018). Many studies point to an increasing dryland area 
based on the AI, but there is low agreement on the actual amount 
and area of change (Feng and Fu, 2013; Scheff and Frierson, 2015; 
Huang et al., 2017). The inconsistency between studies is largely due 
to the substantial internal climate variability in regional precipitation. 
Changes in annual precipitation have been shown to range from −30% 
to 25% over drylands. Consistent changes in precipitation are only 
found at high latitudes, while total PET is projected to increase over 
most land areas (Feng and Fu, 2013). This leads to more consistent, 
widespread drying in the tropics, subtropics and mid-latitudes in most 
models (Feng and Fu, 2013; Cook et  al., 2014; Scheff and Frierson, 
2015; Zhao and Dai, 2015).

CCP3
2212Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
CCP3.3.1.4 Dryland Extent
Global dryland area (based on the AI) is projected to expand by ~10% 
by 2100 compared to 1961–1990 under a high emission scenario (Feng 
and Fu, 2013). However, there are significant regional differences in 
the drivers of dryland expansion and subsequent estimates of change 
in dryland extent. Subtropical drylands are projected to expand as 
the climate in these regions shifts from temperate to subtropical and 
aridity increases in currently sub-humid subtropical regions, resulting 
in the loss of temperature-controlled seasonal cycles (Schlaepfer et al., 
2017). Observed and projected warming and drying trends are most 
severe in transitional climate regions between dry and wet climates, 
with some exceptions (Nkrumah et al., 2019), which are often highly 
populated agricultural regions with fragile ecosystems (Cheng and

Huang, 2016). In contrast, P  –  E predicts decreasing drought stress 
across temperate Asia and central Africa (Swann et  al., 2016). 
Expansion of arid regions is anticipated in southwest North America, 
the northern fringe of Africa, southern Africa and Australia. The main 
areas of semiarid expansion are expected to occur in the north side 
of the Mediterranean, southern Africa, and North and South America. 
In contrast, India, eastern equatorial Africa and other areas of the 
southern Saharan regions are projected to have shrinking drylands 
(Biasutti and Giannini, 2006; Biasutti, 2013; Rowell et al., 2016). Future 
projections may underestimate dryland expansion, since the Coupled 
Model Intercomparison Project 5 (CMIP5) models underestimate 
historical warming (Huang et al., 2016) and overestimate precipitation 
over drylands, particularly in the semiarid and dry sub-humid regions 
(Ji et al., 2015). However, estimates vary depending on the metric used

(Swann et al., 2016; Berg et al., 2017b). Studies based on off-line aridity 
and drought metrics (calculated from model output of precipitation, 
evapotranspiration or temperature) project strong surface drying trends 
(Cook et  al., 2014; Scheff and Frierson, 2015; Zhao and Dai, 2015), 
while projections based on total soil water availability from CMIP5 
models show weaker and less extensive drying (Berg et al., 2017a). 
In contrast, projections in southern Africa may overestimate future 
drying, with systematic rainfall biases being found in the present-day 
climatology in models that simulate extreme future drying (Munday 
and Washington, 2019). Improvements in projections of future changes 
in aridity require better understanding of seasonality, land hydrology 
and the feedbacks between projected soil moisture decrease on land 
surface temperature, relative humidity and precipitation (Huang et al., 
2016).
Higher dust emissions are consistent with climate change projections

indicating an expansion in the global area of drylands (Feng and Fu, 
2013; Huang et al., 2016) and increased drought risk (Cook et al., 2014; 
Xu et al., 2019), but future trends in dust event frequency and intensity 
as a result of climate change are uncertain and will vary geographically 
(Jia, 2019). Combined effects of climate change and anthropogenic 
activities are projected to increase sand encroachment and extreme 
dust storms (Omar Asem and Roy, 2010; Sharratt et al., 2015; Pu and 
Ginoux, 2017) as a result of increased aridity, accelerating soil erosion 
(Section  4.4.8; Sharratt et  al., 2015) and loss of biomass (Sharratt 
et al., 2015; Middleton and Kang, 2017). Shifts in dust storm timings 
are also projected in some regions (Hand et  al., 2016). Dustiness is 
projected to increase in the southern US Great Plains in the late 21st 
century under the RCP8.5 climate change scenario but decrease over the northern Great Plains (Pu and Ginoux, 2017). A declining trend

in dust emission and transport from the Sahara under RCP8.5 was 
detected by Evan et al. (2016), but regional climate model experiments 
conducted by Ji et al. (2018) under the same scenario indicated that 
overall dust loadings would increase by the end of the 21st century 
over West Africa. New dust sources may emerge with changing climate 
conditions, as Bhattachan et al. (2012) indicate for the Kalahari Desert 
in southern Africa, due to vegetation loss and dune remobilisation. 
There is overall low confidence on future atmospheric dust loads at 
the global and regional scale. Models of future dust emissions are 
limited by the low accuracy of models of present anthropogenic dust 
emissions, which range from 10% to 60% of the total atmospheric 
dust load (Webb and Pierre, 2018). A global compilation of data 
from sedimentary archives (ice cores), remote sensing, airborne 
sediment sampling and meteorological station data estimated that

anthropogenic dust emissions have at least doubled over the past 
250 years (Hooper and Marx, 2018). While future emissions of natural 
dust sources are projected to decrease (Mahowald et  al., 2006) or 
remain stable (Ashkenazy et  al., 2012), when sources of human 
emissions are included, projections of future atmospheric dust loads 
suggest that emissions may increase (Stanelle et al., 2014).
The relative contribution of albedo and evapotranspiration to 
regional trends in surface temperature (Charney, 1975) remains 
unresolved, and may be determined by different mechanisms in 
different systems, depending on site-specific conditions such as snow 
coverage, vegetation and soil moisture (Yu et al., 2017). For example, 
the vegetation–albedo feedback mechanism may dominate in the 
Arctic (Blok et al., 2011; te Beest et al., 2016), while the vegetation–
evaporation feedback may drive change in other regions. Actions that

increase forest cover across Africa could thus, theoretically, moderate 
projected future temperature increases (Wu et al., 2016; Diba et al., 
2018), but with potentially negative effects on biodiversity (Chapter 
2). Soil drying exacerbates atmospheric aridity, which causes more 
soil drying in a self-reinforcing land–atmosphere feedback that could 
intensify under RCP8.5 (Zhou et al., 2019).
Changes to the composition, structure and functioning of natural 
communities in deserts and dryland ecosystems are key risks 
resulting from water stress, drought intensity and continued habitat 
degradation, greater frequency of wildfire, biodiversity loss and the 
spread of invasive species (Hurlbert et al., 2019). Not all these stresses 
occur at the same time in a particular environment, with some 
areas more exposed to, for example, wildfire than others, especially 
in areas with high amounts of dry herbaceous biomass. Grassland

composition may shift as C3 plants are replaced by C4 species, which 
have higher optimal temperatures and higher water use efficiency 
(although seasonality of precipitation also plays a role) (Knapp et al., 
2020). Many desert species have morphological, physiological and/
or behavioural adaptations to cope with climatic extremes, including 
rapid regeneration following droughts (Boudet, 1977; Hiernaux and Le 
Houérou, 2006), leaf dropping during the dry season to reduce water 
loss (Santos et  al., 2014), alongside long histories of adaptation to 
climate change (Brooks et al., 2005; Ballouche and Rasse, 2007), while 
many animals live near their physiological limits (Vale and Brito, 2015). 
Substantial ecological effects may occur when extreme events such 
as heatwaves or droughts are superimposed on the warming trend,

CCP3
2213Deserts, Semiarid Areas and Desertification  Cross-Chapter Paper 3
pushing species beyond their physiological and mortality thresholds 
(Hoover et al., 2015; Harris et al., 2018).
Climate change increases risks of continued range retractions of Karoo 
succulents in South Africa (Young et al., 2016), dry argan woodlands 
in Morocco (Alba-Sánchez et  al., 2015), epiphytic cacti in Brazil 
(Cavalcante and Duarte, 2019; Cavalcante et al., 2020) and other plant 
species exposed to higher aridity. Projected increases in heat and 
aridity could increase mortality of trees and shrubs in Sonoran Desert 
ecosystems in the USA (Munson et al., 2012; 2016b), reduce sagebrush 
in arid ecosystems of the western USA (Renwick et  al., 2018), and 
contribute to the replacement of perennial grasses with xeric shrubs in 
the southwestern USA (Bestelmeyer et al., 2018). CO 2 fertilization and 
warmer conditions, combined with changes in timing and availability

of moisture, could increase invasive grasses and wildfire in desert 
ecosystems of Australia and southwestern USA, where wildfire has 
historically been absent or infrequent (Abatzoglou and Kolden, 2011; 
Horn and St. Clair, 2017; Klinger and Brooks, 2017; Syphard et al., 2017). 
Trends of woody encroachment may continue in some North American 
and African drylands or at least not reverse (Higgins and Scheiter, 2012; 
Caracciolo et al., 2016). Impacts of woody encroachment on drylands 
may show a slight increase in carbon, but a decline in water and huge 
negative impacts on biodiversity, with a tendency for open ecosystem 
species to be most affected (Archer et al., 2017). Expansion of grasses 
into these arid shrublands has the potential to transform them rapidly, 
especially through the acceleration of the fire cycle (Bradley et  al., 
2016). While the impact of increased aridity may be offset by changing 
water use efficiency by plants under high CO 2 concentrations, limiting

the expansion of dryland ecosystems (Swann et al., 2016; Mirzabaev 
et al., 2019), increased plant growth in response to elevated CO 2, which 
results in increased water consumption, may counteract this. Increased 
water use efficiency is therefore not expected to counterbalance 
increased evaporative demand (Chapter 8). There is medium confidence  
that succulent species will be particularly vulnerable to increased heat 
and aridity due to reduced physiological performance, loss of seed 
banks, lower germination rates and increased mortality (Table CCP3.1; 
Musil et al., 2005; Aragón-Gastélum et al., 2014; 2017; Shryock et al., 
2014; Martorell et al., 2015; Carrillo-Angeles et al., 2016; Koźmińska 
et al., 2019).
CCP3.3.2 Projected Impacts on Human Systems
Across many drylands, human-induced causes of desertification, 
SDS, climate change and unsustainable land use are projected to 
become more pronounced over the next several decades with global

consequences. Future climate changes with increasing frequency, 
intensity and scales of droughts and heatwaves, are projected to further 
exacerbate the vulnerability and risk to humans from desertification 
(Hurlbert et al., 2019).
SDS exert a wide range of impacts on people, within deserts and 
semi-deserts but also outside dryland environments because of long-
range dust transport (Middleton, 2017). Research on the economic 
impacts of SDS is lacking, while studies that have been conducted 
lack consistency in data collection methods and analysis (Middleton, 
2019). Although projections are rarely modelled, estimated economic damages of increased dust-related health impacts and mortality under 
RCP8.5 could total USD 47 billion/year additional to the 1986–2005 
value of USD  13  billion/year in southwest USA (Allahbakhshi et  al., 
2019).
Projected impacts of climate change on the risk of food insecurity are 
a particular concern for the developing world drylands (Chapter 16;

Mirzabaev et al., 2019), potentially leading to the breakdown of food 
production systems, including crops, livestock and fisheries, as well as 
disruptions in food supply chains and distribution (Myers et al., 2017; 
Lewis and Mallela, 2018). Developing country drylands are particularly 
vulnerable due to a higher share of populations with lower income, 
lower physical access to nutritious food, social discrimination and 
other environmental factors that link to climate change. For example, 
countries such as Somalia, Yemen and Sudan faced recent and resurging 
challenges from an increase in desert locusts, the effects of which, in 
2020, extended from East Africa through the Arabian Peninsula and 
Iran as far as India and Pakistan. Meynard et al. (2020) note that under 
climate change, some areas suffering from previous outbreaks may see 
changes in formation of swarms of Schistocerca gregaria. Salih et al. 
(2020) recognise that attributing the 2020 swarms as a single event

to climate change remains challenging, but highlight that projected 
temperature and rainfall increases in deserts and strong tropical cycles 
can create conditions conducive to the development, aggregation, 
outbreak and survival of locusts. Mandumbu et  al. (2017) highlight 
how crop parasites such as Striga spp. in southern Africa may benefit 
from higher temperatures and rainfall activating dormant seeds, while 
high winds aid their dispersal. Combined with increasing risks of 
erosion and soil fertility losses (Striga is able to tolerate drought and 
a low nitrogen environment), this can have important impacts on the 
yields of key dryland crops such as maize and pearl millet.
Human responses can exacerbate desertification processes under 
climate change conditions, even in deserts. Exploitation of mineral 
resources (e.g., lithium mining in Chile’s Atacama Desert) can cause 
human population changes as people flock to the area for work (Liu

et al., 2019), increasing vulnerability due to, for example, soil erosion 
and salinisation, as well as increasing pressure on potable water for 
human consumption (Stringer et  al., 2021) and exhausting aquifers. 
Salinisation is projected to increase in the drylands due to climate 
change impacts in future (Mirzabaev et  al., 2019). For example, in 
India, about 7 million hectares of arable land area is currently affected 
by salt (Sharma et al., 2015; Sharma and Singh, 2015). It is projected 
that unsustainable use of marginal quality waters in irrigation and 
neglect of drainage, combined with climate change impacts, will 
accelerate land salinisation in India, rendering another 9  million 
hectares salty and less productive by 2050 (ICAR-CSSRI, 2015). This 
has important cost implications given that annually, 16.84  million 
tonnes of farm production valued at INR 230.19 billion is already lost 
in India due to salinity and associated problems (Sharma and Singh,

2015). The literature further shows evidence of desertification of oases 
and irrigated lands in parts of northern China’s drylands (Wang et al., 
2020), the Indian subcontinent’s deserts, as well as the Mesopotamian 
Arabian Desert (Ezcurra, 2006; Dilshat et al., 2015).

CCP3
2214Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
CCP3.4 Adaptations and Responses
Adaptations to climate change impacts in human systems vary 
depending on exposure to risks, types of risks and responses, 
underlying social vulnerabilities and adaptive capacities, including 
access to resources, the extent of adaptation responses and the 
potential of these responses to reduce risk/vulnerability (Chapter 16; 
Singh and Chudasama 2021). Adaptations tend to be applied locally, 
tackling symptoms of the problem and proximate drivers (e.g., of 
desertification), rather than distant or external drivers (Morris et al., 
2016; Adenle and Ifejika Speranza, 2021). Different groups require 
different kinds of supports and levers to enable them to follow adaptive 
pathways (Møller et al., 2017; Stringer et al., 2020) and face different 
barriers and limits to adaptation (Chapter 18). What constitutes an 
incremental adaptation in one location may be transformational in

another. Spatial patterns of dryland resilience and adaptive capacity 
can be partly explained by access to livelihood capitals (Mazhar et al., 
2021) and are shaped by prevailing structures and power dynamics. 
Supportive policies, institutions and good governance approaches can 
strengthen the adaptive capacities of dryland farmers, pastoralists 
and other resource users (high confidence) (Stringer et  al., 2017). 
Table  CCP3.2 provides examples of illustrative adaptation options 
responding to major challenges of climate change and desertification 
in deserts and semiarid areas. Some adaptations present no-regrets 
options while others tackle desertification and/ or climate changes to 
different extents.
Adaptations to climate change, desertification, drought management 
(Section  17.2.2.2) and sustainable development activities largely 
overlap in drylands, pointing to synergies between them (Reichhuber 
et  al., 2019). For example, support for communal and flexible land

tenure could bring about benefits across multiple dimensions, while 
attention to water as a limiting factor in drylands can link to multiple 
SDGs (Stringer et al., 2021), as well as adaptations in natural systems, 
where improved forecasting and anticipatory science and management 
can be appropriate (Bradford et  al., 2018). Currently, more than 
125 countries around the world, particularly in drylands, are setting land 
degradation neutrality (LDN) targets. LDN and its hierarchical response 
mechanisms of avoiding, reducing and reversing land degradation can 
provide an overarching resilience-based framework for adaptation at the 
national level (Mirzabaev et  al., 2019; Orr et  al., 2017b; Cowie et  al., 
2018) and support biodiversity conservation (Akhtar-Schuster et  al., 
2017). However, achieving LDN will require a transparent decision and 
prioritisation process (Dallimer and Stringer, 2018), anchored in a socio-

ecological systems approach (Okpara et al., 2018), with investment in 
all dimensions of an enabling environment, including inclusive policies 
and regulations, sustainable institutions, accessible finance and effective 
science–policy communications and interactions (Verburg et al., 2019; 
Allen et  al., 2020). LDN calls for integrated land use planning to 
ensure land uses are optimised at a landscape scale to help balance 
competition for limited land resources and harness multiple benefits 
(Cowie et al., 2018, Verburg et al., 2019), recognising that adaptations 
present synergies and trade-offs along various dimensions of sustainable 
development such as poverty reduction, enhancing food security and 
human health or providing improved access to clean energy, land, water, 
and finance (see Section 8.6). Distributional effects of adaptation options

also may vary between different socioeconomic groups within countries or locally among communities, pushing social justice concerns to the 
fore (Section 8.4). Measures promoting particular adaptations need to 
take into account such consequences, as well as the potential for some 
adaptations to become maladaptive at scale.
Natural systems are also able to adapt to climate change, be adapted 
and become more resilient to desertification. For example, the root 
network architecture of the hyper-arid Negev Desert acacia trees has 
enabled them to withstand intensive cultivation and climate change-
driven desertification (Winter et al., 2015), while vegetation-induced 
sand mounds (‘coppice dunes’) in the Arabian Desert have reduced 
desertification through reducing wind erosion and enriching sand 
desert land with water and nutrients (Quets et al., 2017). Vegetation 
cover of psammophyte shrub species (in the ‘desert oasis transitional

area’) surrounding the Dunhuang Oasis (northwest China) reduces oasis 
land degradation risk by reducing sand grain size and velocity of winds 
from the aeolian desert (Zhang et al., 2007); while land use planning in 
Israel’s Negev Desert taking a ‘sharing’ approach between cultivation 
and urbanisation has helped to minimise the external degrading 
effects of adjacent desert land ecosystems (Portnov and Safriel, 2004). 
Scholars are nevertheless questioning the wider suitability of tree 
planting in drylands, given concerns for water availability and other 
ecosystem services (Veldman et  al., 2015; 2019; Bond et  al., 2019). 
How natural dryland systems are managed following disturbances 
such as wildfire is important too. van den Elsen et al. (2020) found that 
establishing vegetation and mulch cover after a fire in a Mediterranean 
dryland ecosystem reduced soil erosion, helping maintain soil fertility 
and nutrients. However, different management objectives require

different adaptations. For example, adaptation measures that reduce 
land degradation through reforestation could increase vulnerability to 
fire if they exclude ecologically sound fire management or are based 
on plant species that are fire prone. Combinations of different land 
management practices and governance approaches tackling a range of 
different stresses appear to best support sustainability and adaptation 
over the long term (van den Elsen et al., 2020).
Collective action can facilitate the implementation of adaptation 
responses and help tackle challenges associated with upscaling of 
successful land-based adaptations (Thomas et  al., 2018). However, a 
lack of coordination between stakeholders and across sectors can be 
problematic (Amiraslani et al., 2018), showing the importance of multi-
stakeholder engagement (De Vente et  al., 2016). Multi-stakeholder 
engagement is recognised as an essential part of desertification

control, as well as vital in tackling climate change (Reed and Stringer, 
2016), with participation taking place to different extents in different 
drylands according to the prevailing governance system. In China, the 
Grain for Green programme is an example of a large-scale ecological 
restoration programme securing local engagement through payments 
for ecosystem services (Kong et al., 2021). Transdisciplinary stakeholder 
engagement involving researchers and central and local governments 
in the Heihe River Basin in China’s arid and semiarid northwest, using 
an interdisciplinary ‘web’ approach, enabled basin restoration. Multi-
stakeholder efforts saw improvement in the condition of Juyan Lake and 
the surrounding catchment, increasing both the lake surface area and 
groundwater in downstream locations (Liu et al., 2019).

CCP3
2215Deserts, Semiarid Areas and Desertification  Cross-Chapter Paper 3
Table CCP3.2 |  Synthesis of adaptation measures and responses to risks in deserts and semiarid areas. Appropriateness of measures is context dependent and some adaptations 
will be incremental or even maladaptive in some dryland contexts, while being transformational in other locations.
Challenge Adaptation measures and responses References
Soil erosionRainwater harvesting and soil conservation, grass reseeding, agroforestry
Use of different breeds of grazing animals, altered livestock rotation systems, use of new crop varieties, development of 
management strategies that reduce the risk of wildfireEldridge and Beecham (2018)
OvergrazingModification of production and management systems that involve diversification of livestock animals and crops, integration 
of livestock systems with forestry and crop production, and changing the timing and locations of farm operations

Improved breeds and feeding strategies and adoption of improved breeds for households without cows (both economic 
and environmental gain)Kattumuri et al. (2015); Shikuku et al. 
(2017)
Clearing 
of natural 
vegetationCarbon sequestration through decreasing vegetation clearing rates, reversal through revegetation, targeting for 
higher-yielding crops with better climate change adapted varieties, and improvement of land and water management
Agroforestry role in addressing various on-farm adaptation needs besides fulfilling many roles in agriculture, forestry and 
other land use-related mitigation pathways (assets and income from carbon, wood energy, improved soil fertility and 
enhancement of local climate conditions; provides ecosystem services and reduces human impacts on natural forests)
Implementation of co-benefits strategies including provision of incentives across multiple scales and time frames,

fostering multidimensional communication networks and promoting long-term integrated impact assessment
Achievement of triple-wins in sub-Saharan Africa through provision of development benefits by making payments for 
forest services to smallholder farmers, mitigation benefits by increasing carbon storage, and adaptation benefits by 
creating opportunities for livelihood diversificationKattumuri et al. (2017); Mbow et al. 
(2014); Suckall et al. (2014)
Invasive species 
and woody 
encroachmentClimate change is projected to facilitate the spread of invasive species that can have profound impacts on dryland 
ecosystem functioning leading to the loss of biodiversity
Biomass harvesting and selective clearing; utilising intense fires to manage encroachment, combined browsing and fire 
management
Rewilding in open ecosystems and reintroduction of mega-herbivores (e.g., in parts of Africa) to counter negative impact

of woody encroachment; chemical removal of undesirable encroached woody speciesMirzabaev et al. (2019); Davies and Nori 
(2008); Stafford et al. (2017); Cromsigt 
et al. (2018); Ding and Eldridge (2019)
DroughtsProactive drought risk mitigation compared with reactive crisis management approaches
Promoting collective action in livestock management, optimising livestock policies and feed subsidies, interventions in 
livestock markets during drought onset
Expanding sustainable irrigation and shifting to drought-resistant crops and crop varieties
Environmentally sustainable seawater desalination
Promoting behavioural changes for more efficient residential water use; moving away from water-intensive agricultural 
practices in arid areas; harvesting rainwater by local communities; empowering women and engagement in local 
climate adaptation planning, community-based early warning systems, Integrated Water Resources Management, water

governance benchmarking, and exploration of palaeo channels as freshwater sources using remote sensingMorton and Barton (2002); Abebe et al. 
(2008); Alary et al. (2014); Catley et al. 
(2014); Mohamed et al. (2016)
Grassland 
and savanna 
degradationPrescribed fire and tree cutting, invasive plant removal, grazing management, reintroduction of grasses and forbs, 
restoration of soil disturbanceFor review, see Buisson et al. (2019)
Rangeland 
degradation 
(decreasing 
fodder quality or 
yield, invasion by 
fodder poor value 
species/refusals)Promote local and regional herd mobility during the growing season to avoid intense grazing pressure on growing annual 
herbaceous vegetation of rangelands near settlements, water points, markets
Moderate grazing facilitates grass tillering and herbaceous flora diversity
Ecological restoration of grazing ecosystems by sowing a mixture of zone-typical dominant species and life forms of 
plants on severely degraded land; clearance of invasives

Ecological restoration of arid ecosystems by sowing a mixture of zone-typical dominant species and life forms of fodder 
plants with partial (ribbon) treatment of pasture lands
Ecological restoration of secondary salted irrigated soils using halophytesDe Vries and Djitèye (1982); Hiernaux 
et al. (1994); Hiernaux and Le Houérou 
(2006); Reed et al. (2015)
Poor livestock 
productivity 
(reproduction/
dairy/meat) in 
relation with poor 
seasonal nutritionPromote seasonal-regional herd mobility to optimise the use of complementary fodder resources (rangelands, browses, 
crop residues); implies institutionalised communal access, community agreements and infrastructures (water points, 
livestock path, grazing reserves, access to education, health care, markets for transhumant population); cross state 
boundary mobility implies international agreements such as promoted by N’djamena meeting (Declaration 2013)Turner (1993); Schlecht et al. (2004); 
Fernández-Rivera et al. (2005); Bonnet

and Herault (2011); Hiernaux et al. 
(2016)
Promote strategic supplementation of reproductive and young animals by the end of dry and early wet season
Secondary effect on excretion quantity/ quality to manure croplandsMany trials in research stations and on 
farm: for example Sangaré et al. (2002a; 
2002b); Osbahr et al. (2011); Sanogo 
(2011)
Decrease trend 
in cropland soil 
fertilityRotational corralling of livestock in field during the dry season (and on cleared fallow the following year in the wet 
season) to ensure maximum retrieval of organic matter and nutrients from faeces and urine deposited
Application of mineral N and P fertilizers as placed (per pocket) microdoses (50–80 kg/ha) to intensify staple crop 
production
Impact on soil fertility, rain use efficiency, vegetation cover, organic matter production and recycling
Legume association with cereals (millet–cowpea; sorghum–groundnut)

Adapting cultivars and cropping techniques (calendar, fertilization).Pieri (1989); Breman et al. (2001); 
Gandah et al. (2003); Manlay et al. 
(2004); Abdoulaye and Sanders (2005); 
Reij et al. (2005); Akponikpe (2008); 
Bagayoko et al. (2011); Bationo 
et al. (2011); Hiernaux et al. (2009b); 
Sendzimir et al. (2011); Turner and 
Hiernaux (2015); Weston et al. (2015; 
Reij and Garrity (2016)

CCP3
2216Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
In the short- to medium-term, monitoring, prediction and early warning 
can support adaptation and, for example, help reduce negative impacts 
of SDS by mobilising emergency responses. Daily dust forecasts 
enable preparation to minimise risks from SDS to both human and 
natural systems (e.g., the World Meteorological Organization Sand 
and Dust Storm Warning Advisory and Assessment System: https://
sds-was.aemet.es/forecast-products/dust-forecasts). Preparedness and 
emergency response procedures benefit from covering diverse sectors, 
such as public health surveillance, hospital services, air and ground 
transportation services, water and sanitation, food production systems 
and public awareness, suggesting the need for a coherent, multi-
sector governance approach. Longer-term actions include prioritising 
sustainable land management (Middleton and Kang, 2017), based on

IKLK and modern science (Verner, 2012), along with the investment of 
financial and human capital in supporting these measures. Devolved 
adaptation finance in dryland areas of, for example, Kenya (Nyangena 
and Roba, 2017) and Mali (Hesse, 2016) has yielded promising insights, 
highlighting the importance of climate information services and 
local government support for community prioritisation of adaptation 
activities. Such actions can enable substantial benefits for poor and 
marginalised men and women. Among international institutional 
measures, a global coalition to combat SDS was launched at the 
United Nations Convention to Combat Desertification Conference of 
Parties (UNCCD COP14) in 2019, which could help to better mobilise 
a global response to SDS. Similarly, there have been calls for increased 
investment in regional institutions such as the Desert Locust Control 
Organisation for Eastern Africa to both pre-empt and tackle locust

plagues (Salih et al., 2020), requiring transboundary cooperation.
There is high agreement and robust evidence that shifting emphasis 
to proactive risk mitigation, including solutions for drought, flooding, 
erosion and dust management, instead of exclusive focus on disaster 
management, reduces vulnerability and improves adaptive capacity 
(Section 16.4.3.2; 16.5.2.3.4; Sivakumar, 2005; Grobicki et al., 2015; 
Wieriks and Vlaanderen, 2015; Aguilar-Barajas et al., 2016; Runhaar et  al., 2016; Wilhite and Pulwarty, 2018; Wilhite, 2019). It also 
underscores the LDN response hierarchy avoid > reduce > reverse 
(Orr et  al., 2017a). Nevertheless, ex ante drought and flood risk 
mitigation has been adopted in limited dryland settings, despite it 
being preferable to increase preparedness before it happens, provide 
incentives for adaptation instead of insurance, provide insurance 
instead of relief and provide relief instead of regulation (Sivakumar,

2005). Yet, providing disaster relief is often more publicly visible and 
politically expedient, despite its social, economic and environmental 
challenges. The absence of proactive risk mitigation and resulting crisis 
management increases vulnerability, increases reliance on government 
support, reduces self-reliance and increases costs (Grobicki et al., 2015; 
Wilhite, 2019), as well as hindering progress towards the SDGs. In the 
case of drought and flooding, major obstacles for the transition from 
reactive management to proactive drought risk mitigation include path 
dependencies and lack of knowledge about relative costs and benefits 
of reactive versus proactive approaches. This lack of information can 
deter large-scale and long-term investments into proactive approaches 
(Mirzabaev, 2016).
A range of risk mitigation and adaptation measures can be taken, 
to address drought, desertification and other climate change-related

challenges in deserts and semiarid areas, some of which can be both 
proactive and reactive. These include inter alia:
• Implementing policies, public advocacy and social media 
campaigns that improve water use efficiency, especially in 
agriculture and industry, which can foster behavioural changes 
and reduce water consumption (Yusa et al., 2015; Tsakiris, 2017; 
Booysen et al., 2019).
• Integrating access to insurance, financial services, savings 
programmes and cash transfers into policies to increase the 
effectiveness of, for example, drought responses. Such efforts can 
result in significant cost savings (Berhane et al., 2014; Bazza et al., 
2018 ; Guimarães Nobre et al., 2019).Challenge Adaptation measures and responses References
Salinisation and 
groundwater 
depletionIndigenous and scientific adaptive practices to cope with salinity
Farmers in waterlogged saline areas harness subsurface drainage, salt tolerant crop varieties, land-shaping techniques and

agroforestry to adapt to salinity and waterlogging risks
Locally adapted crops and landraces and the traditional tree- and animal-based means to sustain livelihoods in face of 
salinisationSengupta (2002); Buechler and Mekala 
(2005); Wassmann et al. (2009); Singh 
(2010); Jnandabhiram and Sailen Prasad 
(2012); Manga et al. (2015); Sharma 
and Singh (2015); Gupta and Dagar 
(2016); Nikam et al. (2016); Bundela 
et al. (2017); Sharma and Singh (2017); 
Patel et al. (2020); Singh et al. (2020b); 
Sharma, (2016); Mirzabaev et al. (2019)
Sand and dust 
stormsUse of live windbreaks or shelterbelts, protection of the loose soil particles through the use of crop residues or plastic 
sheets or chemical adhesives, increasing the cohesion of soil particles by mechanical tillage operations or soil mulching
Use of perennial plant species that can trap sediments (sand and fallen dust) and form sandy mounds, such as Haloxylon 
salicornicum, Cyperus conglomerates, Lycium shawii and Nitraria retusa

In the Sahel, promote herbaceous (not woody plants) to trap sand: annuals such as Colocynthis vulgaris, Chrozophora 
senegalensis, Farsetia ramosissima; perennials such as Cyperus conglomeratus, Leptadenia hastate
In the Sahel, leaving at least part of the crop residues (stalks) on the soil during the dry season (100 kg dry matter per 
hectare has already had significant effect on wind erosion, many trials on millet in Niger); trampling by grazing livestock 
improves the partial burying of the residues
Improve monitoring, prediction and early warning to mobilise emergency responses for human systems and prioritise 
long-term sustainable land management measures; establish a Global Dust–Health Early Warning System (building on the 
Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) initiative); multi-sectoral preparedness and

response including public health, hospital services, air and ground transportation and communication servicesAhmed et al. (2016); Al-Hemoud et al. 
(2017); Sivakumar (2005); Hiernaux 
et al. (2009a); Hiernaux et al. (2016); 
Pierre et al. (2018); Lamers et al. (1995); 
Michels et al. (1998); Bielders et al. 
(2004),UNEP (2016); UNEP (1992)

CCP3
2217Deserts, Semiarid Areas and Desertification  Cross-Chapter Paper 3
• Developing robust early warning systems that provide information 
and improve knowledge surrounding drought and SDS to enable 
early recovery (Wilhite, 2019), also considering vulnerability and 
impact assessments (i.e., who is at greatest risk).
• Managing and storing water, including using methods that draw 
on Indigenous knowledge (Stringer et al., 2021), water transfers 
and trade, all of which can reduce costs and provide timely 
adaptations to drought, supporting agricultural productivity and 
rural livelihoods (Harou et al., 2010; Hurlbert, 2018).
• Implementing restoration, reclamation and landscape 
heterogeneity strategies, promoting ecosystem resilience to wind 
erosion and dust abatement (Duniway et  al., 2019), as well as 
restoring important ecosystem services at a catchment scale.
• Preventing soil erosion, providing of dust abatement and

enhancing biodiversity by changing grazing techniques (e.g., 
rotational grazing), facilitating herd mobility, protecting rangeland 
areas from fragmentation, promoting common tenure and access 
rights on grazing land, enabling rapid post-fire restoration efforts, 
minimum tillage, sustainable land management, integrated 
landscape management, planting and caring for non-irrigated 
indigenous trees and other vegetation (Middleton and Kang, 
2017).
• Creating drought-tolerant food crops through participatory plant 
breeding (Grobicki et  al., 2015) and investment in research and 
development of drought-resistant varieties (Basu et  al., 2017; 
Mottaleb et al., 2017; Dar et al., 2020), alongside adjusted planting and harvesting periods (Frischen et  al., 2020). Similar to other 
adaptations, the net economic benefits of ex ante resilient plant 
development far outweigh the research investment (Basu et  al., 
2017; Mottaleb et al., 2017; Dar et al., 2020).

Many of these measures can also support climate change mitigation 
efforts in drylands. Uptake of adaptation measures is often grounded 
in clear communications and information provision to support 
behavioural changes, taking into account local risk aversion and 
risk perceptions (Zeweld et al., 2018; Jellason et al., 2019). Building 
capacity by improving the knowledge base and access to information, 
as well as to financial and other resources, encourages vulnerable 
economic sectors and people to adopt more self-reliant measures that 
promote more integrated and sustainable use of natural resources 
(high confidence) (Sivakumar, 2005; Wieriks and Vlaanderen, 2015; 
Aguilar-Barajas et al., 2016; Middleton and Kang, 2017; Wilhite, 2019). 
Engaging natural resource users as active participants in planning 
and technology adoption using extension services, financial grants 
and services geared to the local area, can build resilience and drive

changes in practices (Webb and Pierre, 2018), while approaches such 
as Integrated Water Resources Management can support adaptation 
and drought risk management, including in dryland urban megacities 
(Stringer et  al., 2021) and in deserts and semiarid areas where 
precipitation trends remain stable yet other pressures on water are 
growing (Reichhuber et al., 2019).
FAQ CCP3.1 | How has climate change already affected drylands and why are they so vulnerable?
Human-caused climate change has so far had mixed effects across the drylands, leading to fewer trees and less biodiversity in some areas and 
increased grass and tree cover in others. In those dryland areas with increasing aridity, millions of people face difficulties in maintaining their 
livelihoods, particularly where there is water scarcity.
Drylands include the hottest and most arid areas on Earth. Human-caused climate change has been intensifying

this heat and aridity in some places, increasing temperatures more across global drylands than in humid areas. In 
areas which are hotter and drier, tree death has occurred and in some locations bird species have been lost. Climate 
change has reduced rainfall in some dryland areas and increased rainfall in other areas. Increased rainfall, combined 
with the plant-fertilizing effect of more carbon dioxide in the atmosphere, can increase grass and shrub production 
in dryland areas. Because water is scarce in drylands and aridity limits the productivity of agriculture, millions of 
people living in drylands have faced severe difficulties in maintaining their livelihoods. This challenge is exacerbated 
by non-climate change factors, such as low levels of infrastructure, remoteness and limited livelihood options that 
are less dependent on scarce natural resources. High temperatures in drylands increase the vulnerability of people

to potential heat-related illnesses and deaths from heat under continued climate change.
FAQ CCP3.2 | How will climate change impact the world’s drylands and their people?
Climate change is projected to lead to higher temperatures across global drylands. Many drylands also risk more irregular rainfall leading to 
increased irregularity in crop yields and increased water insecurity where less rainfall is projected, which may have profound implications for 
both dryland ecosystems and their human inhabitants.

CCP3
2218Cross-Chapter Paper 3 Deserts, Semiarid Areas and Desertification
There is, however, considerable uncertainty about the changes that may occur in drylands in the future and how 
people and ecosystems will be affected. In some drylands, higher temperatures and declining rainfall have increased 
aridity. However, this is not a global trend as many drylands are experiencing increases in vegetation cover and 
rainfall. Both the amount of rainfall and its seasonality have changed in many dryland areas, associated with 
natural variability and warming.
Most climate models project increased rainfall in tropical drylands, but more variability. High natural climatic 
variability in drylands makes predictions uncertain. Understanding future impacts is further complicated by many 
interacting factors such as land use change and urbanisation that affect the condition of drylands. Future trends

in sand and dust storm activity are also uncertain and will not be the same everywhere, but there will likely be 
increases in some regions (e.g., the USA) in the long term. The impacts of climate change in deserts and semiarid 
areas may have substantial implications globally: for agriculture, biodiversity, health, trade and poverty, as well as 
potentially, for conflicts and migration. Increasing temperatures and more irregular rainfall are expected to affect 
soil and water and contribute to tree death and loss of biodiversity. In other places, woody encroachment onto 
savannas may increase, in response to the combination of land use change, changes in rainfall, fire suppression 
and CO 2 fertilization. Crop yields are projected to decline in some areas, with adverse impacts on food security. The 
potential for conflicts and migration is primarily associated with socioeconomic development, while links to 
climate change remain uncertain and lack evidence.FAQ 5.1 (continued)

FAQ CCP3.3 | What can be done to support sustainable development in desert and semiarid areas, given projected 
climate changes?
Water is a major limiting factor in drylands. Many efforts to support sustainable development aim to improve water availability, access and 
quality, ranging from large engineering solutions that move or desalinise water, to herders’ migrations with their animals to locations that 
have water, to land management and water harvesting practices that conserve water and support land cover. These solutions draw on IKLK 
and innovative science, and can help to address multiple Sustainable Development Goals.
Different desert and semiarid areas can benefit from different incremental and transformational solutions to move 
toward sustainable development under climate change. In some dryland areas facing critical water shortages,

transformational adaptations may be needed; for example, large-scale water desalination when they have access to 
sea water, despite high energy use and negative environmental impacts of waste brine. In dryland agricultural areas 
across the world, incremental adaptations include water conservation measures, use of improved crop varieties or 
increasing herd mobility. What counts as a transformational change in some places may be incremental in others. 
Often solutions can target multiple development goals. For example, water harvesting can make water available 
during drought, buffering water scarcity impacts, while also supporting food production, agricultural livelihoods 
and human health. Land-based approaches, e.g. restoration of grassland, shrubland, and savanna ecosystems, are 
important for ensuring ecological integrity, soil protection and preventing livelihoods from being undermined

as a result of growing extreme weather events. It is important that policies, investments and interventions that 
aim to support sustainable development take into account which groups are likely to be most affected by climate 
change. Those people directly dependent on natural resources for their survival are generally most vulnerable 
but least able to adapt. The capacity to translate IKLK and experience into actions can require external support. 
Governments and other stakeholders can help by investing in early warning systems, providing climate information, 
realigning policies and incentives for sustainable management, investing in supporting infrastructures, alongside 
developing alternative livelihood options that are less exposed and sensitive to climate change. Involving all relevant 
stakeholders is important. For example, in China, the Grain for Green programme secured local engagement by

paying people to manage the environment more sustainably. At a global level, important groups have emerged to 
cooperate and offer solutions around issues such as sand and dust storms, and integrated drought management. 
Efforts are needed across all scales from local to global to support sustainable development in desert and semiarid 
areas, given projected climate changes.

CCP4
2235Mediterranean Region Cr oss-Chapter Paper 4
The Mediterranean region hosts exceptional biological diversity 
and socio-cultural richness originating from three continents. The nature of the semi-enclosed Mediterranean Sea and the complex topography imply unique physiographic and ecological features. The region has undergone continuous change in human activities over several millennia, and now hosts more than 500 million people with a high concentration of urban settlements and industrial infrastructure close to sea level. The region is the world’s leading tourist destination and one of its busiest shipping routes. Climate change strongly interacts with other environmental problems in the Mediterranean Basin, resulting from urbanisation, land use change, overfishing, pollution, biodiversity loss and degradation of land and marine ecosystems. {CCP4.1.1}
Previous Intergovernmental Panel on Climate Change (IPCC)

reports have never assessed the Mediterranean region as an entity – but they have nevertheless shown that virtually all parts of it are vulnerable and face significant risks due to climate change. Identified regional key risks include increased water scarcity (notably in the south and east) and droughts (in the north), coastal risks due to flooding, erosion and saltwater intrusions, wildfire, terrestrial and marine ecosystem losses, as well as risks to food production and security, human health, well-being and cultural heritage. {CCP4.1.2}
Surface temperature in the Mediterranean region is now 1.5°C 
above the pre-industrial level, with a corresponding increase in high-temperature extreme events (high confidence
1). Trends

in precipitation are variable across the basin (low confidence). Droughts have become more frequent and intense, especially in the north Mediterranean (high confidence). The sea surface has warmed by 0.29°C–0.44°C per decade since the early 1980s with stronger trends in the eastern basin. Sea level has risen by 1.4±0.2
 mm 
yr-1 during the 20th century (2.8±0.1
 mm 
yr-1 
over 1993–2018) (high confidence). Ocean acidity is increasing (medium confidence). {CCP4.1.3}
A growing number of observed impacts across the entire 
basin are now being attributed to climate change, along with major roles of other forcings of environmental change (high confidence). These impacts include multiple consequences 
of longer and/or more intensive heat waves, droughts, floods, ocean acidification and sea level rise, such as cascading impacts on marine and terrestrial ecosystems, as well as on land and sea use (agriculture, forestry, fisheries, tourism, recreation, etc.) and human health. {CCP4.1.4}

1 In this Report,  the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels 
2
 the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as

likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100%, and extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies within the 17–83% probability range.During the 21st century, climate change is projected to 
intensify throughout the region. Air and sea temperature and their extremes (notably heat waves) are likely
2 to continue to

increase more than the global average (high confidence). The projected annual mean warming on land at the end of the century is in the range of 0.9–5.6°C compared to the last two decades of the 20th century, depending on the emission scenario (high confidence). Precipitation will likely decrease in most areas by 4–22%, depending on the emission scenario (medium confidence). Rainfall extremes will likely increase in the northern part of the region (high confidence). Droughts will become more prevalent in many areas (high confidence). {CCP4.1.3}
Mediterranean Sea level is projected to rise further during the 
coming decades and centuries (high confidence), likely reaching 
0.15–0.33
 m in 2050,
 and 0.3–0.6
 m for shar
ed socioeconomic 
pathways (SSP) 1-1.9 and 0.6–1.1
 m 
for SSP5-8.5 in 2100 (relative

to 1995–2014) (medium confidence). Higher values cannot be excluded (low confidence) and the process is irreversible at the scale of centuries to millennia (high confidence). Coastal flood risks will increase in low-lying areas along 37% of the Mediterranean coastline that currently hosts 42 million people. The number of people exposed to sea level rise is projected to increase up to 2050, especially in the southern and eastern Mediterranean region, and may reach up to 130% compared to present in 2100 (medium confidence). Coastal settlements, World Heritage sites and ecosystems are at longer-term risk from sustained sea level rise over at least the coming three centuries (high confidence). {CCP4.1.3; CCP4.2; CCP4.3; SMCCP4.4}
Due to its particular combination of multiple strong climate

hazards and high vulnerability, the Mediterranean region is a hotspot for highly interconnected climate risks. The main economic sectors in the region (agriculture, fisheries, forestry, tourism) are highly vulnerable to climatic hazards, while socioeconomic vulnerability is also considerable. The low-lying areas are the most vulnerable areas for coastal climate-related risks (e.g., sea level rise, floods, erosion) and other consequent risks (e.g., saltwater intrusion and agriculture damage) (high confidence). Climate change threatens water availability, reducing river low flows and annual runoff by 5–70%, reducing hydropower capacity (high confidence). Yields of rain-fed crops may decrease by 64% in some locations (high confidence). Ocean warming and acidification will impact marine ecosystems, with uncertain consequences on fisheries (low confidence). Desertification will affect additional areas, notably in the south and southeast (medium confidence). Burnt area of forests may

increase by 96–187% under 3°C, depending on fire management. Beyond 3°C, 13–30% of the Natura 2000 protected area and 15–23% of Natura 2000 sites could be lost due to climate-driven habitat change (medium confidence). {CCP4.2; CCP4.3}

CCP4
2236Cross-Chapter Paper 4 Mediterranean Region
The adaptive capacity of ecosystems and human systems is 
expected to encounter hard limits due to the interacting, 
cumulative and cascading effects of droughts, heat waves, sea 
level rise, ocean warming and acidification (high confidence). 
Coastal protection can reduce risks from sea level rise in some regions, 
but the costs of such interventions and their consequences for coastal 
ecosystems are high (medium confidence). {CCP4.4.1} There is low 
confidence in the feasibility of adaptation options to sea level rise 
beyond 2100 or for large Antarctic ice melting. {CCP4.4.5}
Progress towards achievement of the United Nations Sustainable 
Development Goals differs strongly between Mediterranean 
sub-regions, with northwestern countries having stronger 
resilience than southern and eastern countries (high confidence).  
To equitably enhance regional adaptive capacity and sustainable

development, while safeguarding the rights of the most vulnerable 
people, regional cooperation can be strengthened with a focus on the 
link between adaptation, costs and financial limitation, and climate 
justice (high confidence). Cooperative policies across various sectors, 
involving all user groups and considering all regional and sectorial 
differences may enhance sustainable resource use in the region (high 
confidence). {CCP4.4.6}
Sharing and co-production of knowledge can support climate 
adaptation practices and enhance sustainability in the 
Mediterranean region (medium to high confidence). Currently 
incomplete knowledge of climate impacts and risks in the southern 
and eastern part of the basin hinders the implementation of adaptation 
measures, creating a need for implementable plans with enhanced and 
cooperative research and monitoring capacities between the north, 
south and southeast countries (high agreement). {CCP4.4}

CCP4
2237Mediterranean Region  Cross-Chapter Paper 4
CCP4.1 Climate Change in the Mediterranean 
Basin
CCP4.1.1 The Mediterranean Sea, Land and People
The Mediterranean Basin, known for its exceptional environmental and 
socio-cultural richness, comprises the semi-enclosed Mediterranean Sea 
and the countries and regions bordering it,3 which belong to Europe, 
Asia and Africa (Figure CCP4.1). The region has a unique historical and 
environmental identity (Abulafia, 2011), despite undeniable variations 
in the environment, socioeconomic conditions and cultural traditions. 
The countries in the Mediterranean Basin hosted approximately 
542 million people in 2020, a number which is expected to increase 
to 657 million by 2050 and 694 million by 2100. In 1950, only 23.7% 
of the Mediterranean population lived in countries of the south, this 
number increased to 41.2% in 2000, 46.3% in 2020, and is projected 
to reach 55.5% in 2050 and 64.6% in 2100 (UN DESA, 2019).

CCP4.1.2 Main Findings from Previous Assessments
All previous assessments of climate change for the Mediterranean Basin 
and its sub-regions indicate ongoing warming of the atmosphere and 
the sea, as well as projected warming and changes in rainfall (Stocker 
et  al., 2013; Cherif et  al., 2020). The projected increase in climate 
hazards, in combination with high regional vulnerability and exposure 
make it a prominent ‘climate change hotspot’ (Giorgi, 2006), with a 
large number of vulnerable natural systems and socioeconomic sectors 
(Field et al., 2014; MedECC, 2020). In addition to high temperatures, the 
main risk factor identified is drought, generally expected to increase 
in the region, significant already at global warming of only 1.5°C, 
reaching, for higher warming levels, intensities unprecedented during 
the past 10 ka (Hoegh-Guldberg et  al., 2018). In Southern Europe 
and North Africa, groundwater recharge and soil water content will

consequently decline, especially during summer (Kovats et al., 2014; 
Niang et al., 2014).
With the changing climate, marine ecosystems have already undergone 
changes in structure, including the spread of tropical species from the 
Atlantic Ocean and the Red Sea (high confidence) and mass mortality 
in at least 25 invertebrate species, threatening, along with ocean 
acidification, marine ecosystems, including seagrass meadows (Hoegh-
Guldberg et al., 2014; Nurse et al., 2014; Pörtner et al., 2014; Wong 
et al., 2014). Endemic marine species are at higher risk of extinction 
due to limited possibilities for migrating northward (Kovats et al., 2014; 
Poloczanska et al., 2014; Balzan et al., 2020). Southern and eastern 
Mediterranean coastal systems with narrow dune belts and often 
rapid urbanisation are vulnerable to both warming and sea level rise 
(Seneviratne et al., 2012; Wong et al., 2014; Balzan et al., 2020).
Most Mediterranean land ecosystems are impacted negatively by drier

conditions, causing the ranges of many endemic species to shrink, and 
the health and growth rates of trees to decline (Kovats et al., 2014; 
Niang et  al., 2014; Nurse et  al., 2014; Settele et  al., 2014). Climate 
change is expected to increase wildfire risk in the region (Kovats et al., 
3 By tradition, Portugal and Jordan are also considered Mediterranean countries, despite having no Mediterranean coastline.2014), although earlier estimates of burnt area have been reduced in 
the most recent assessments to approx. 40–100%, considering that 
prevention and mitigation actions have successfully reduced this risk so 
far (Balzan et al., 2020). Wetlands and mountain summits are hotspots 
for biodiversity loss and extinctions (medium confidence) (Jiménez 
Cisneros et al., 2014; Nurse et al., 2014; IPBES, 2018a; IPBES, 2018b; 
Balzan et  al., 2020). Along with unsustainable land use practices, 
climate change is projected to increase soil erosion in semiarid areas

(Jiménez Cisneros et al., 2014).
The increasing water scarcity was found to be a significant threat to 
agriculture (Jiménez Cisneros et al., 2014; Kovats et al., 2014; Niang 
et al., 2014; Mrabet et al., 2020). Associated with increased extreme 
temperatures, the Mediterranean is expected to become less attractive 
for tourism (Kovats et al., 2014; Nurse et al., 2014; Wong et al., 2014; 
Dos Santos et al., 2020). Several critical risks for human health increase 
due to climate change, including heat waves and vector-borne diseases 
(Kovats et al., 2014; Nurse et al., 2014; Linares et al., 2020). Adaptation 
options have been identified for many risks (buildings, water 
management, coastal protection, etc.) (Murray et al., 2012; Revi et al., 
2014; Wong et al., 2014). There are synergies between adaptation and 
mitigation, for example, renewable energies or nature-based solutions 
focused on the conservation and restoration of ecosystems (Nurse

et al., 2014; Hoegh-Guldberg et al., 2018; Vafeidis et al., 2020).
CCP4.1.3 Observed and Projected Climate Change
The Mediterranean Basin is located in a transition zone between mid-
latitude and subtropical atmospheric circulation regimes, with large 
topographic gradients. The analysis of observed climate changes and 
their impacts is strongly affected by the imbalance of observations 
between northern and southern countries, where available time series 
have often not allowed past climate evolution to be reconstructed over 
a sufficiently long-time scale (Cramer et al., 2018).
Since the 1980s, Mediterranean atmospheric warming has exceeded 
global average rates (high confidence) (WGI AR6 Chapter 11, 
Seneviratne et  al., 2021; Lionello and Scarascia, 2018; Cherif et  al., 
2020). Future annual and summer warming rates are projected to be 
20% and 50% larger than the global annual average, respectively. 
Summer warming is projected to be particularly strong in the north

(Figure CCP4.2, WGI AR6; Chapter 11, Seneviratne et al., 2021; Mariotti 
et al., 2015; Lionello and Scarascia, 2018). Temperature extremes and 
heat waves have increased in intensity, number, and length during 
recent decades, particularly in summer, and are projected to continue 
increasing (high confidence) (WGI AR6 Chapter 11, Seneviratne et al., 
2021; Zittis et  al., 2016; Hoegh-Guldberg et  al., 2018; Cherif et  al., 
2020).
Sea surface temperatures have increased in recent decades (high 
confidence), with regional variation between +0.29°C and +0.44°C 
per decade (Darmaraki et  al., 2019a), and stronger trends in the 
eastern basin (Iona et al., 2018; Pastor et al., 2019), involving the 
whole upper mixed layer (Rivetti et al., 2017). Towards the end of the

CCP4
2238Cross-Chapter Paper 4 Mediterranean Region
21st century, ocean warming in the range 0.8°C–3.8°C is projected 
near the surface (high confidence), 0.8°C–3.0°C at intermediate 
depth and 0.15°C–0.18°C in deeper waters (Darmaraki et al., 2019b; 
Soto-Navarro et al., 2020). The duration and intensity of marine heat 
waves have increased (high confidence) (Darmaraki et al., 2019a) and 
both parameters are projected to continue increasing in the future 
(Galli et  al., 2017). Under Representative Concentration Pathway 
(RCP) 8.5, at least one long-lasting marine heat wave is projected for 
every year by 2100, up to 3 months longer and about four times more 
intense than present-day events (WGI AR6 Chapter 9, Fox-Kemper 
et al., 2021; Darmaraki et al., 2019b). Salinity is projected to increase, 
with anomalies from +0.48 to +0.89 psu by the end of the century 
(medium confidence) (WGI AR6 Chapter 9, Fox-Kemper et al., 2021; 
Adloff et al., 2015).

Observed trends in annual precipitation are significant only in some 
areas and some periods, and they are stationary over the long term 
throughout the region (medium confidence) (WGI AR6 Chapter 11, 
Seneviratne et al., 2021; Figure CCP4.3; Harris et al., 2014; Lionello and 
Scarascia, 2018; Vicente-Serrano et al., 2020). Precipitation is projected 
to decrease (high confidence for global warming levels above 2°C) 
(Figure CCP4.2) by approximately 4% per 1°C global warming, for all seasons in the central and southern basin, and mostly in summer in 
the north (Mariotti et  al., 2015; Hertig and Tramblay, 2017; Lionello 
and Scarascia, 2018). Precipitation extremes have increased in some 
northern areas (medium confidence), and are projected to increase 
in the north (high confidence for global warming levels above 2°C), 
potentially accompanied by an increase in of flash floods (Llasat et al., 
2016), with no change in the south (low confidence) (WGI AR6 ATLAS,

Gutiérrez et  al. 2021; Figures CCP4.2; CCP4.3; Tramblay and Somot, 
2018; Lionello and Scarascia, 2020). These trends enhance the gradient 
between northern (already characterised by more intense events) and 
southern areas (where extreme precipitation events are comparatively 
milder) (Giorgi et  al., 2014; Jacob et  al., 2014; Vautard et  al., 2014; 
Lionello and Scarascia, 2020).
Widespread increase of evaporative demand and some decrease of 
precipitation explain the drying of the Mediterranean region during 
recent decades (high confidence) (WGI AR6 Chapter 11, Seneviratne 
et  al., 2021; Figure  CCP4.3) (Spinoni et  al., 2015; Gudmundsson and 
Seneviratne, 2016; Spinoni et al., 2017; Stagge et al., 2017; Caloiero et al., 
2018). Droughts are projected to become more severe, more frequent 
and longer under moderate emission scenarios, and strongly enhanced 
under severe emission scenarios (high confidence) (WGI AR6 Chapter 11, 3–52.5–3Major container ports
(million TEU)

Mediterranean (MED)The Mediterranean region
MjMj titi tt
1,000–5,000100–1,0000–100Major urban areas
(thousands of people)
-4,0004,000Elevation
(metres above sea level)
1–2.5
Above 5,000
Figure  CCP4.1 |   The Mediterranean region: Topography and bathymetry (colour bar in metres), main urban areas (population in thousands for 2020 from 
www.naturalearthdata.com), container ports (millions of TEU [twenty-foot container equivalent units] in 2017, from International Association of Ports and Harbours) and borders 
of the Mediterranean region used in WGI AR6 Chapter 10 (Doblas-Reyes et al., 2021).

CCP4
2239Mediterranean Region  Cross-Chapter Paper 4
Seneviratne et al. 2021; Hertig and Tramblay, 2017; Lehner et al., 2017; 
Ruosteenoja et al., 2018; Spinoni et al., 2018b; Grillakis, 2019; Lionello 
and Scarascia, 2020).
No trends in mid-latitude cyclones crossing the Mediterranean 
Basin have been detected for recent decades (Lionello et  al., 2016). 
For Mediterranean hurricanes (‘medicanes’), no observed trends are 
known because of insufficient monitoring. In the future, mid-latitude 
cyclones and medicanes are projected to decrease in frequency, but medicane intensity will likely increase (Cavicchia et al., 2014; Nissen 
et al., 2014; Romera et al., 2017).
Mediterranean waters have acidified since the pre-industrial period, 
more rapidly than the global ocean, due to faster ventilation times 
(high confidence) (Palmiéri et al., 2015). Acidification is projected to 
continue (virtually certain) (WGI AR6 Chapter 11, Seneviratne et al.,

2021), with a pH decrease of up to -0.46 in a high emission scenario 
(Goyet et al., 2016).Changes in climate impacts drivers
and present socio-ecological vulnerabilities + 1.5°C + 3.0°C
Summer
warming
Mean summer
(JJA)
(°C) Population
density
Annual count
of days with
maximum
≥ 40°C
Total
during the cold
(October to
March) season
(% change) 
Coastal
population
density≤ 0.80.8–1.51.5–2.32.3–3.03.0–3.8> 3.8
LowMediumHigh
Population
density LowMediumHigh
Annual
harvested
rainfed area SmallMediumLarge
(i)( j)
Built-up areaMaximum
one day
(% change)≤ -10-10–00–1010–20>20
≤ 1010–2020–3030–40> 40
≤ -10-10–00–1010–20>20
Mean sea
level rise
(Change
in meter)≤ 0.40.4–0.50.5–0.60.6–0.7> 0.7
(g) (h)(e) (f)(c) (d)(a) (b)
Figure CCP4.2 |  Changes in climate impact drivers with respect to the 1995–2014 period for 1.5°C (left column) and 3°C (right column) global warming:

mean summer (June to August) temperature (°C, a, b), number of days with maximum temperature above 40°C (days, c, d), total precipitation during the cold (October to March) 
season (%, e, f) and 1-day maximum precipitation (mm, g, h). Values based on CMIP6 global projections and SSP5-8.5. Sea level rise concerns the long term (2081–2100) and 
SSP1-2.6 for (i) and SSP3-7.0 for (j) (source: Annex I: Atlas).

CCP4
2240Cross-Chapter Paper 4 Mediterranean Region
Mediterranean mean sea level has risen by 1.4±0.2 mm  yr−1 during 
the 20th century (Wöppelmann and Marcos, 2012) and accelerated 
to 2.4±0.5 mm  yr−1 for 1993 to 2012 (Bonaduce et  al., 2016) and 
3.4 mm yr−1 for 1990 to 2009 in the northwest (medium confidence) 
(Calvo et al., 2011). The accelerating trend is robust, although different 
methods and time horizons yield slightly different rates of change 
(Meyssignac et al., 2011; Cazenave et al., 2018; von Schuckmann et al., 
2020). For 2150, sea level is likely to reach 0.52 m [0.32–0.81] for SSP1-
1.9, to 1.22 [0.91–1.78] for SSP5-8.5 relative to 1996–2014 (medium 
confidence) (WGI AR6 Chapter 9, Fox-Kemper et al., 2021; Figure FAQ 
CCP4.2; SMCCP4.4), with uncertain variation between sub-basins 
(Slangen et al., 2017). Melting processes in Greenland and Antarctica 
could result in even higher levels (low confidence, WGI AR6 Chapter 9,

Fox-Kemper et al., 2021; Cross-Chapter Box SLR in Chapter 3).
The Mediterranean Basin includes within small distances a large variety 
of climatic conditions that are likely to shift northwards with global 
warming. Consequently, ecoregions will be exposed to potentially 
unsuitable conditions: more arid climate for the Mediterranean forests 
of North Africa, more subtropical climate and temperate climate for 
the mountain forests of the Balkans and of the Alps, respectively, and 
Mediterranean climate for the temperate forests of North Anatolia 
(Figure CCP4.4; Lelieveld et al., 2012; Simpson et al., 2014).
CCP4.1.4 Detection and Attribution of Climate Change 
Impacts
New evidence published since Working Group II Assessment Report 5 
(WGII AR5) confirms that climate change is increasingly affecting many 
systems and sectors in the Mediterranean region (high confidence) 
(Figure CCP4.5; Chapters 9, 13 and 16). There is high confidence that

climate change has worsened heat waves and droughts (CCP4.1.3; 
Lionello et  al., 2014; Caloiero et  al., 2018; Mathbout et  al., 2018; 
Spinoni et al., 2019), and medium to high confidence that heat waves are impacting marine (Rivetti et al., 2014; Tsikliras and Stergiou, 2014; 
Stergiou et al., 2016; Corrales et al., 2017), freshwater and terrestrial 
ecosystems (Peñuelas et al., 2018; Bartsch et al., 2020; Carosi et al., 
2021), as well as agriculture (El-Maayar and Lange, 2013; Ortas and 
Lal, 2013; Ponti et  al., 2014; Garcia-Mozo et  al., 2015; Moore and 
Lobell, 2015; Oteros et  al., 2015; Di Lena et  al., 2018) and fisheries 
(Fortibuoni et al., 2015; Givan et al., 2018; IPBES, 2018a). Heat waves 
have also increased thermal discomfort, especially in urban areas 
(WGI AR6 Chapter 10, Doblas-Reyes et al., 2021; WGI AR6 Chapter 12, 
Ranasinghe et al., 2021; Zinzi and Carnielo, 2017). Despite increasing 
wildfire hazard, forest fires are generally decreasing in the European

part of the basin, due to more efficient risk management (medium 
confidence) (Turco et  al., 2016; 2017). Mixed trends of increasing 
and decreasing flash and river floods across the Mediterranean are 
reported, but there is low confidence in their attribution to climate 
change (Mediero et al., 2014; Baahmed et al., 2015; Gaume et al., 2016; 
Paprotny et al., 2018; Blöschl et al., 2019; Vicente-Serrano et al., 2019).
Flooding, erosion and salinisation are significant observed impacts 
in coastal regions, especially where subsidence is significant, such 
as in the region of Thessaloniki in Greece or the eastern Nile Delta 
in Egypt (Raucoules et  al., 2008; Frihy et  al., 2010), with only low 
confidence in attribution to climate change so far (Section SMCCP4.1). 
Coastal urbanisation and engineering protection are expanding in the 
Mediterranean, resulting in substantial impacts on coastal biodiversity 
(Masria et al., 2015; Carranza et al., 2020).

The attribution of impacts displays little variability across sub-regions, 
but confidence in attribution to climate change is higher in the north, 
due to the larger number of observations and studies in Europe. While 
land use and fisheries are still major non-climatic drivers of changing 
hazards and biodiversity losses (Aguilera et al., 2015; Turco et al., 2016; 
IPBES, 2018a; 2018b; Tramblay et  al., 2019; Vicente-Serrano et  al., 
2019), impacts of climate change are now being observed in all parts 
of the Mediterranean region (high confidence).Synthesis of observed and projected (1.5°C and 4.0°C global warming levels) changes in climate drivers 
affecting the Mediterranean region
SoutheastNortheastNorthwest
SouthwestMean warming
and heat extremes
Obs.1.5°C 4.0°CCold
extremes
Obs.1.5°C 4.0°CDry
conditions
Obs.1.5°C 4.0°CMean
Obs.1.5°C 4.0°CHeavy
Obs.1.5°C 4.0°C´Mean wind
spead
Obs.1.5°C 4.0°C
a
a
a
ab
bc
No change

Directionof changeIncrease Decrease Mixed
Confidence level:
ObservationsConfidence level:
ProjectionsHigh Medium Low High Medium Low High Medium Low High Medium LowThe magnitude and sign of trends depend substantially on time period and study 
region. Although precipitation is highly variable, it is stationary on the long term for 
the whole region.
Marginal increase in winter at the northern boundary of the subregions. 
There are subregional differences, with no change or even decrease over Iberia.a
b
c
Figure CCP4.3 |  Observed and projected (at global warming levels of 1.5°C and 3°C) direction of change of climate drivers and confidence levels for 
Mediterranean land sub-regions.

CCP4
2241Mediterranean Region Cr oss-Chapter Paper 4
Caucasus-Anatolian-Hyrcanian Temperate Forests
European-Mediterranean Montane Forests
Mediterranean Forests, Woodlands and Scrub
dry summer continental
continental
temperate oceanic humid subtropicaldesert and semiarid (cold)desert and semiarid (hot)Köppen-Geiger climate classification Terrestrial biodiversity hotspots
Mediterranean (MED)Bioclimatic regions (Köppen-Geiger classification) and terrestrial biodiversity hotspots
in the Mediterranean region
(a) 1985–2014
(b) 2076–21001,000 km
Figure CCP4.4 |  Climate and natural land ecosystems in the Mediterranean Basin, based on Köppen-Geiger climate types, for the baseline climate (a, 
1985–2014) and the future climate (b, 2076–2100, A1FI scenario (corresponding to global warming of approximately 4°C), based on (Rubel and Kottek, 2010), with the three terrestrial biodiversity hot spots that are present in the region (see WG2 Cross-Chapter Paper 1: Biodiversity Hotspots).

CCP4
2242Cross-Chapter Paper 4 Mediterranean Region
CCP4.2 Vulnerability of Mediterranean 
Countries to Climate Change
CCP4.2.1 The Specific Vulnerability of Mediterranean 
Countries
The Mediterranean region is predominantly vulnerable to the impacts 
of warming, notably prolonged and stronger heat waves, increased 
drought in an already dry climate and risk of coastal flooding (Section 
CCP4.1). southern and eastern countries are generally more vulnerable 
than countries in the north. Several countries (Tunisia, Algeria and Libya) 
are below the water scarcity threshold set by the Food and Agriculture 
Organization of the United Nations (FAO); others (Morocco) are close 
to the threshold for severe water stress. Uncertainties regarding the 
timing, duration, intensity and interval between extreme climatic events 
put some sectors, such as agriculture and tourism, at particular risk in 
the Mediterranean region (Section CCP4.3; Kallis, 2008; Kutiel, 2019).

CCP4.2.2 Economic Vulnerability
All Mediterranean countries are vulnerable to climate change across 
most socioeconomic sectors. In low-income countries of the Basin, a 
1.1-point reduction of gross domestic product (GDP) could occur as a 
consequence of 1°C rise warming (Radhouane, 2013). In Morocco, GDP 
impacts of climate change could be -3% to +0.4% by 2050 relative 
to 2003 (Ouraich and Tyner, 2018). In Middle East and North Africa 
(MENA) countries, approximately 10–13% of GDP loss is projected for 
an increase in global mean temperature of 4.8°C by 2100 (Kompas 
et al., 2018). In Southern Europe, mean labour productivity loss would 
shrink by approximately 2% under 2°C warming, along with a GDP 
loss of 0.1% by the 2030s, reaching 0.4% by the 2080s (Szewczyk 
Freshwater resources are vulnerable to climate change and growing 
demand, notably from agriculture (Section 4.1.3; Gudmundsson et al.,

2017; Zabalza-Martínez et al., 2018; Masseroni et al., 2020). The share 
of GDP and population exposed to high or very high water stress in 
MENA countries is 71% and 61%, respectively, compared to 22% 
and 36% in the world (World Bank, 2018). Freshwater resources are 
also vulnerable to sea level rise and associated salinisation (Ali and 
El-Magd, 2016; Wassef and Schüttrumpf, 2016; Twining-Ward et  al., 
2018). Due to the impact of climate change on water supplies (−14% to −6%), MENA countries are projected to experience high losses in 
GDP by 2050 (World Bank, 2016).
The agricultural sector is important for most Mediterranean economies, 
both in terms of GDP and employment, with its share of the total GDP 
in the region at 6.7% in 2016 (Kutiel, 2019). Water stress in southern 
countries is largely driven by growing demand from agriculture, with a 
potential water deficit of 28–47% by 2030 (Sebri, 2017). In Spain, 11

out of 15 river basin districts are under water stress due to demand 
from agriculture (Vargas and Paneque, 2019). In Greece, the largest 
agricultural region (Thessaly) where 70% of the irrigation water comes 
from groundwater, is under water stress (Gemitzi and Lakshmi, 2018). 
Water scarcity and high dependence on rain-fed agriculture make 
MENA countries vulnerable to warming and reduced rainfall, associated 
with high irrigation requirements (Dhehibi et  al., 2015; Fader et  al., 
2016; World Bank, 2016; 2018; Asseng et al., 2018). This is exacerbated 
by poverty and political instability (Price, 2017). For cropping systems 
in MENA countries, the Nile Valley and the western parts of North 
Africa on the Atlas Mountains are classified as the areas with highest 
vulnerability (ESCWA, 2017). Grassland and pastoral systems are also 
vulnerable to increasing drought, notably in the western part of the 
basin (Balzan et al., 2020). Increased heat stress in summer negatively

impacts animal health and welfare, i.e., increased incidence of diseases 
and mortality or lower fertility (Lacetera 2019).
As MENA countries are net food importers, they are not only vulnerable 
to the impact of climate change on food production in the Mediterranean 
region, but also the climate impacts on food production elsewhere, for 
example, in China and Russia (Waha et al., 2017). The agri-food sector 
in the Mediterranean region is also important for global food security 
because several large producing countries in the region, such as France, 
Italy and Morocco, are net exporters of many essential micronutrients 
to low- and lower-middle-income countries. Changing quantity and 
quality of production would have direct (availability) and indirect (price 
signals) impacts on their trade partners.
The economic value of fisheries in the Mediterranean Sea is over USD 
3.4 billion (Randone et al., 2017), with about 76,250 fishing vessels in

2019 (FAO, 2020), most of them (about 62%) in the eastern and central 
Mediterranean (FAO, 2018). Total employment on-board fishing vessels 
is 202,000 and six countries, Tunisia, Algeria, Turkey, Italy, Greece and 
Egypt, account for approximately 82% of total employment (FAO, 
2020). About 78% of the fish stocks in the Mediterranean are currently Attribution of observed impacts of climate change in the Mediterranean region
Southeast
Northeast
NorthwestSouthwestImpact level:
Confidence level:WildfiresThermal
discomfortWater
availability
and qualityPluvial
and river
flooding FisheriesMarine
eco-
systemsCoastal
flooding
and erosionAgriculture
and
viticultureTerrestrial
and 
eco-
systems
LowLowPositive
HighHighMixed
MediumMediumNegative
Figure CCP4.5 |  Attribution of observed impacts of climate change in the Mediterranean region (see SMCCP4.1 for supporting references).

CCP4
2243Mediterranean Region Cr oss-Chapter Paper 4
fished at unsustainable levels (Galli et al., 2015). The share of stocks 
in overexploitation has decreased from 88% in 2012 to 75% in 2018 (FAO, 2020). Nearly half of the catches consist of small pelagic species (anchovies, sardines, herrings), which are very vulnerable to increased seawater temperatures (FAOSTAT, 2019). Turkey is particularly sensitive to climate change in the fisheries sector (Turan et al., 2016; Hidalgo et al., 2018). Fisheries in northern countries are less vulnerable because they have a greater capacity to adapt (i.e., more assets, flexibility, learning potential and social organisation), while southern countries are more vulnerable (Ding et al., 2017). The reduction of fish availability directly impacts the income of employees, for example, in the Italian fisheries industry (Tulone et al., 2019).
Mediterranean forests are diverse and play a major ecological and

social role through significant ecosystem services, including wood, but also their recreational value and production of non-wood goods, such as mushrooms (Ding et al., 2016; Peñuelas et al., 2017; Gauquelin et al., 2018; Herrero et al., 2019). Many forests grow at the dry margin of their distribution area; therefore, projected drier conditions will affect their productivity and health (Doblas-Miranda et  al., 2017; Dorado-Liñán et  al., 2019; Sangüesa-Barreda et  al., 2019). Vulnerability to wildfire is a significant matter of concern, particularly in the northern and southwestern Mediterranean region (Ager et al., 2014; Gomes da Costa et al., 2020). In Córdoba (Spain), for example, fire suppression costs have increased by 66–87% in the last decade (Molina et  al., 2019).
The Mediterranean region accounts for one-third of global tourism with

330 million tourists in 2016 (Tovar-Sánchez et al., 2019). Before the COVID-19 crisis, international tourist arrivals were assumed to increase by 60% between 2015 and 2030, reaching 500 million. In 2015, tourism supported 15% of the total employment in the region (Randone et al., 2017). France, Spain, Italy and Greece are the top tourist destinations (UNWTO, 2016), but the highest growth was in Turkey, Croatia and Albania during 1995–2015 (MGI, 2017). The tourism industry is vulnerable to climate change, particularly in low-income countries (Dogru et al., 2016; Dogru et al., 2019). Coastal tourism in the region generates USD 300 billion annually followed by marine tourism (USD 110 billion) (Radhouane, 2013; Randone et al., 2017).
By providing around 550,000 jobs in the Mediterranean region, the

maritime transport and trade industry comprises approximately 20–40% of GDP . As a hub for trade, the Mediterranean, with approximately 600 ports of different sizes, accounts for 25% of all international seaborne trade, including 22% of oil trade. In the region, the shift to green energy to combat climate change would significantly influence the structure of foreign trade in terms of commodities and maritime energy transport flows (Manoli, 2021).
CCP4.2.3  Social and Human Vulnerability

With population growth, food demand in the region increases and will continue to do so, while regional food production on land and from the sea is threatened by climate change, creating the need for additional import. In MENA countries, livestock production increased by 25% in 1993–2013, causing animal feed imports to increase to about 32% of the total food import in 2014 (FAO, 2018), thereby increasing food import dependence of southern countries (INRA, 2015; Saladini et al., 2018). Sharp increases in international food prices since 2007 have caused inflation, trade deficits, fiscal pressure, increased poverty and political instability, all affecting food supply, notably in the south and east of the region (Harrigan, 2011; Kamrava and Babar, 2012; Ferragina and Canitano, 2015; Paciello, 2015).
Heat waves and other climatic extremes affect densely populated

urban centres and coastal regions, causing health risks for vulnerable groups, in particular those who live in poverty with substandard housing (Paz et al., 2016; Scortichini et al., 2018; Rohat et al., 2019). Nights with temperatures higher than 23°C have been increasing, with a corresponding increase in health risks (Royé, 2017). Human health is also vulnerable to other risks altered by climate change, either directly through droughts, floods, fires and so forth or indirectly through impacts on disease vectors, air pollution, water quality and food security (Negev et al., 2015). Cases of dengue fever were recently reported from several countries, and there is an apparent threat of outbreaks transmitted by Aedes mosquitoes in the northern Mediterranean (Semenza et al.,

2016; Semenza and Suk, 2017). The most vulnerable to climate impacts are the elderly, pregnant women, children, the chronically ill, the obese and people with cognitive impairment (Linares et al., 2015; Paravantis et al., 2017).
One-third of the Mediterranean population (about 150 million people) 
currently lives close to the sea, often in growing urban regions and with infrastructure vulnerable to sea level rise (Cross-Chapter Box SLR in Chapter 3; Briche et al., 2016; UN DESA, 2017). Future exposure to sea level rise is related to demographic growth. All SSPs project an increase of coastal population in the Mediterranean region to 2050. By 2100, coastal population could grow by up to 130%, mostly in the south, but it could also drop by 20% for SSP1 (Reimann et  al., 2018b). Overall, countries in the southeastern Mediterranean are most vulnerable to coastal risks, but the exposure is also high in the northern Mediterranean (Satta et al., 2017).

In terms of the number of people, Egypt, Libya, Morocco and Tunisia are 
the most exposed countries to sea level rise (World Bank, 2014), and this difference is projected to increase under SSP2-4 (Reimann et al., 2018a). Among MENA countries, Egypt is particularly exposed with several coastal cities at risk of inundation (Frihy et al., 2010; Solyman and Abdel Monem, 2020; Elshinnawy and Almaliki, 2021). In the Nile Delta, between 1500 and 2600
 km2 of land are projected to be exposed 
to flooding by 2100 by a sea level rise of 0.75
 m (median sea level 
rise 
scenario for SSP5-85) and additional subsidence up to 0.25
 m,
 
threatening around 6.3  million residents (Figure  CCP4.6; Ali and El-Magd, 2016; Solyman and Abdel Monem, 2020). Basin-wide economic losses are estimated at USD 5 billion, assuming a rise of sea levels by 1.26
 
m in 2100 (Frihy et al., 2010; World Bank, 2014).

The Mediterranean area is characterised by high human mobility, mostly within countries, but also between them (Cross-Chapter Box MIGRATE in Chapter 7; Charef and Doraï, 2016; Ben Youssef et al., 2017). In 2017, the value of remittances from migrants was about 16% of southern Mediterranean countries’ exports to the European Union (EU) (Alcidi et  al., 2019). Impacts of recent climate change, notably

CCP4
2244Cross-Chapter Paper 4 Mediterranean Region
drought and their effects on human livelihoods and vulnerability, may 
have contributed to migration decisions, although there is debate 
about the relative importance (Kelley et  al., 2015; Fröhlich, 2016; 
Hamed et  al., 2018; Ash and Obradovich, 2020). One study of five 
MENA countries estimated that extreme climate events account for 
about 10–20% of migration, with an expected increase of the role of 
environmental factors in the future as climatic conditions deteriorate 
further (Wodon et al., 2014).
Improved sharing and co-production of knowledge can support 
climate adaptation practices, ensure their implementation and thereby 
reduce vulnerability (Nguyen et al., 2019), for example, in the water 
sector (Iglesias and Garrote, 2015; Iglesias et al., 2018) and notably 
river management (Tàbara et al., 2018). The individual perception of 
climate risks is also a component of vulnerability (Nguyen et al., 2016).

Understanding the gap between perceptions and scientific evidence, 
and increasing risk perception and awareness, will be crucial to 
promote adaptive responses both at the individual and the collective 
level throughout the Mediterranean Basin (Macias et  al., 2015; 
Bodoque et al., 2016; Cramer et al., 2018).CCP4.3 Projected Climate Risks in the 
Mediterranean Basin
CCP4.3.1 Ocean Systems
With warming, marine primary production is projected to decrease in 
the western and increase in the eastern Mediterranean Sea (Macias 
et al., 2015). The diversity of copepods (species which dominate the 
meso-zooplankton communities feeding Mediterranean fishes) is 
projected to decline over most of the Mediterranean, albeit with 
regional variation (Benedetti et  al., 2018). Total marine biomass 
(and fishery potential) is projected to increase in the southeastern 
Mediterranean, whereas significant decreases are most likely in the

west (Moullec et al., 2019). The projected increase of marine heat waves 
in the Mediterranean Sea will add additional pressures to coastal and 
marine ecosystems. Warm-water fish species are expected to move 
northwards, while cold-water species will decline, and invasions of 
thermal-tolerant tropical species will increase (high confidence) (Lloret 
et al., 2015; Corrales et al., 2018). Fish species richness is predicted to 
increase in the eastern and decrease in the western Mediterranean by 
2050 but, by 2100, the cooler areas in the north will become a ‘cul-de-
sac’ for many species (Albouy et al., 2013; Burrows et al., 2014). Out 
of 75 endemic fish species, 14 are projected to go extinct, almost all 
of them benthic and demersal species (Ben Rais Lasram et al., 2010). 
The abundance of small and medium-sized pelagic fish (e.g., European Present-day and projected land below high-tides in the Nile delta, due to sea level rise and land subsidence
(a) Present-day conditions

0 6,000 Population density (people/km2)(b) 2100, at 0.43 m sea level rise (SSP1-2.6)
(d) 2100, at 1.7 m sea level rise (SSP5-high-end) (c) 2100, at 0.75 m sea level rise (SSP5-8.5)
Exposed area
Figure CCP4.6 |  Present-day and projected exposure to sea level rise in the Nile Delta, due to sea level change and land subsidence: (a) current exposure, 
(b) exposure for 2°C of global warming by 2100, (c) exposure for 3°C of global warming by 2100, (d) exposure for a high-end sea level rise scenario 
involving additional mass losses from the Antarctica ice sheet (Frihy et al., 2010; Ali and El-Magd, 2016; Kulp and Strauss, 2019); sea level scenarios from 
WG1 AR6 Chapter 9, Fox-Kemper et al., 2021; see Chapter 4 Supplementary Material for additional details.

CCP4
2245Mediterranean Region  Cross-Chapter Paper 4
anchovy) is projected to decline by 15–33% by 2100 (Stergiou et al., 
2016; Raybaud et al., 2017).
Heat waves will likely cause increasing mass mortality events of 
benthic species, mostly invertebrate organisms, such as corals, 
sponges, bivalves, ascidians and bryozoans, increasing the risks of 
abrupt collapse of endemic species (Kersting et al., 2013; Rivetti et al., 
2014; Rivetti et al., 2017; Garrabou et al., 2019; Garrabou et al., 2021). 
Deep-water corals live near their upper thermal tolerance and further 
warming could thus reduce their biotic potential and long-term survival 
(Nannini et al., 2015; Yasuhara and Danovaro, 2016; Marchini et al., 
2019), although there are some exceptions (Naumann et al., 2013) and 
also knowledge gaps (Maier et al., 2019). Warming has been shown to 
severely reduce the metabolism of some Mediterranean coral species 
(Gori et al., 2016). In summary, the observed shift in marine ecosystems

since 1980 is projected to continue and intensify, resulting in very high 
risks for marine ecosystems between 1.5°C–2°C global warming levels 
(GWL) (Figure CCP4.8; Chapters 3; 13; CCP1; Manes et al., 2021).
CCP4.3.2 Coastal Systems
Sea level rise is the origin of multiple risks for low-lying areas in the 
Mediterranean Basin; for example, the further increase in flooding at 
high tide in some locations, such as Venice (high confidence) (Chapter 
13; Cid et al., 2016; Pomaro et al., 2017). Currently, 37% of coastal 
areas are at moderate to high risk from coastal erosion and flooding 
(Satta et  al., 2017). Due to rapid urban development, many coastal 
assets are directly exposed to projected sea level rise and coastal 
hazards, with limited adaptation options and resilience of beaches 
(Section CCP4.2;Brown et al., 2016; Jiménez et al., 2017).
The Mediterranean is a micro-tidal sea, where storms may hit the 
coast over several hours or longer, and not only during high tides (Le

Cozannet et al., 2015; Sánchez-Arcilla et al., 2016; Sierra et al., 2016; 
Sayol and Marcos, 2018). Projected changes of winds, storms and 
waves are small, and confidence in these changes is limited by the 
quality of climate models applied to the Mediterranean (Calafat et al., 
2014; Androulidakis et al., 2015; Vousdoukas et al., 2017). Overall, sea 
level rise is projected to increase the risk of coastal flooding despite the 
potential slight reductions of marine storms (high confidence) (Lionello 
et al., 2017; Vousdoukas et al., 2017). Risks of erosion and flooding 
will be amplified with climate change, particularly in river deltas 
(Figure CCP4.6; Ali and El-Magd, 2016), on low-lying floodplains, on 
sandy beaches around the basin and in many coastal cities (Satta 
et  al., 2017). Impacts are projected to increase nonlinearly during 
the 21st century with higher sea level rise, because coastal flooding 
will progressively change from overtopping to overflow, high-tide

flooding and ultimately permanent flooding and shoreline retreat 
(high confidence) (Le Cozannet et  al., 2015; Sánchez-Arcilla et  al., 
2016; Sierra et al., 2016; Antonioli et al., 2017; Anzidei et al., 2017; Ciro 
Aucelli et al., 2017; Enríquez et al., 2017; Jiménez et al., 2017; Sayol 
and Marcos, 2018). These risks may be amplified further in areas with 
poor storm water management and sealed urban surfaces (Llasat et al., 
2013; Gaume et al., 2016).Combined with storm surges, sea level rise may disrupt Mediterranean 
port operations (Sánchez-Arcilla et al., 2016; Sierra et al., 2016), with 
risks depending on adaptation, physical protection measures and basin 
depth. Risks for deep ports are more limited (Sierra et al., 2017), while 
low-depth small harbours, common in the Mediterranean, could be 
significantly affected (Sierra et al., 2016). Sea level rise may enhance 
sandy beach erosion and thereby impact recreation and tourism (Bitan

and Zviely, 2018; Rizzetto, 2020), magnifying coastal degradation and 
pollution (Enríquez et al., 2017; Gössling et al., 2018).
CCP4.3.3 Inland Ecosystems
Beyond 3°C GWL, 13–30% of the Mediterranean Natura 2000 
protected area and 15–23% of Natura 2000 sites are projected to 
change towards more arid ecosystem types (Barredo et  al., 2016). 
Biodiversity and ecosystem services would be exposed to degradation 
of wetland hydrology, which could affect 19–32% of localities under 
a 1.5°C–2°C GWL (48–73% under higher warming), particularly in 
Spain, Portugal, Morocco and Algeria (Lefebvre et al., 2019). There is 
also a substantial shrinking of terrestrial and freshwater ecosystem 
habitats, in particular in Mediterranean islands (Chapters 2; 4; CCP1).
Increased aridity impacts forest ecosystems (Costa-Saura et al., 2017; 
García Sánchez et  al., 2018). Increasing heat waves, combined with 
drought and land use change, reduce fuel moisture, thereby increasing

fire risk, extending the duration of fire seasons and increasing the 
likelihood of large, severe fires (high confidence) (EEA, 2017; Lozano 
et  al., 2017; Peñuelas et  al., 2017; Varela et  al., 2019). Fires impact 
vegetation recovery after abandonment, thus transforming landscapes 
(González-De Vega et al., 2016). At warming levels of 1.5°C, 2°C and 
3°C, burnt area in Mediterranean Europe could increase by 40–54%, 
62–87% and 96–187%, respectively (Turco et  al., 2018b), although 
changes are highly site dependent and also affected by management 
(Caon et al., 2014; Wu et al., 2015; Parra and Moreno, 2018; Brotons and 
Duane, 2019; Hinojosa et al., 2019).
Desertification occurs in large parts of the region, generally due to 
unsustainable land use (Peñuelas et al., 2017). Increasing drought is 
projected to exacerbate desertification in North Africa and, under high 
warming, also southern Spain. In some areas, sclerophyllous vegetation

could replace deciduous forests (Guiot and Cramer, 2016). Increasing 
temperatures and drought could trigger dieback for some forest 
species such as Mediterranean oak (Sánchez-Salguero et  al., 2020), 
potentially also in combination with biotic factors such as pathogens 
(Matías et al., 2019).
CCP4.3.4 Water, Agriculture and Food Production
River runoff and low flows are expected to decrease (possibly by 12–
15% or more) in most locations due to reduced precipitation (Giuntoli 
et al., 2015; Roudier et al., 2016; Andrew and Sauquet, 2017; Gosling 
et al., 2017; Marchane et al., 2017; Marcos-Garcia et al., 2017; Marx 
et al., 2018; Yeste et al., 2021). Groundwater recharge is projected to 
decrease due to reduced inflow (WGI AR6 Chapter 11, Ranasinghe 
et al., 2021; Koutroulis et al., 2016; Guyennon et al., 2017; Braca et al.,

CCP4
2246Cross-Chapter Paper 4 Mediterranean Region
2019; Calvache et al., 2020). Water levels in lakes and availability of 
reservoirs are expected to decline by up to 45% in 2100 (Koutroulis 
et al., 2016; Masia et al., 2018; Okkan and Kirdemir, 2018; Braca et al., 
2019; Tramblay et al., 2020). The largest freshwater lake in the basin, 
Lake Beyşehir (Turkey), could dry out after 2070 (Bucak et al., 2017). In 
northern Africa, surface water availability is projected to be reduced by 
5–40% in 2030–2065 and by 7–55% in 2066–2095 from 1976–2005 
(Tramblay et al., 2018), with decreases of runoff by 10–63% by mid-
century in Morocco and Tunisia (Marchane et al., 2017; Dakhlaoui et al., 
2020). Reduced summer river flows and increasing water temperatures 
will constrain freshwater-cooled thermoelectric (including nuclear) 
power plants and hydropower plants, with possible reductions of 
production in the northern Mediterranean by 6–33% under 2°C and

by 20–60% beyond 3°C warming (Lobanova et al., 2016; Solaun and 
Cerdá, 2017; Payet-Burin et al., 2018; Tobin et al., 2018).These findings 
confirm the WGI AR6 Chapter 8 statement that drought duration and 
frequencies and water scarcity are projected to increase drastically 
between 1.5°C and 2°C of GWLs (Douville et al., 2021).
Climate change will likely reduce crop yields in many areas 
(Table  CCP4.1), mainly due to higher temperatures affecting crop 
phenology and the shortening of the crop growing season (high 
confidence). Additional irrigation will be needed for most crops, 
although the shortening of the growing season could reduce irrigation 
needs in some cases (Saadi et al., 2015). Irrigation needs could increase 
by 25% in northern and two-fold in southeastern Mediterranean (Fader 
et  al., 2016), with arid southern areas at risk of insufficient water 
resources by 2100. The use of supplemental irrigation for winter wheat

could become more common in northern Mediterranean (Saadi et al., 
2015; Ruiz-Ramos et al., 2018).Seawater intrusion is projected to cause additional risks in coastal 
aquifers, with severe impacts on agricultural productivity (Ali and El-
Magd, 2016; Wassef and Schüttrumpf, 2016; Pulido-Velazquez et  al., 
2018; Twining-Ward et al., 2018; Omran and Negm, 2020). While elevated 
atmospheric CO 2 concentration could be positive for photosynthesis 
and cereal yields (Dixit et al., 2018; Ben-Asher et al., 2019; Kapur et al., 
2019; Kheir et al., 2019), the net outcome for agricultural production is 
highly uncertain (Moriondo et al., 2016). The projected yield losses will 
likely reduce farm revenues, for example, in Morocco (Ouraich and Tyner, 
2018), Egypt (Abd El-Azeem, 2020), Greece (Georgopoulou et al., 2017) 
and Israel (Zelingher et  al., 2019). Given the growing water demand 
from agriculture and other users and the increasing competition

over water resources, adaptation efforts for water supply need to be 
enhanced (Guyennon et al., 2017; Zabalza-Martínez et al., 2018).
Climate-driven change in pelagic production (Section CCP4.3.1), 
together with overfishing, will likely increase risks for fishery landings 
(Hidalgo et  al., 2018). By 2060, more than 20% of exploited fishes 
and invertebrates currently found in eastern Mediterranean could 
become locally extinct (Jones and Cheung, 2015; Cheung et al., 2016; 
Balzan et  al., 2020). Thermophilic and/or thermal-tolerant tropical 
species may increasingly dominate the catch composition (Moullec 
et al., 2019), creating possible opportunities depending on technology 
and consumer acceptance of new species (Hidalgo et  al., 2018). 
Warming and acidification may weaken mussel shells, negatively 
impacting shellfish aquaculture (Martinez et  al., 2018). High losses 
of clawed lobster production by the end of the century are projected

under RCP4.5 (Boavida-Portugal et al., 2018). For much of the region, 
fisheries revenue may decrease by 15–30% by 2050 relative to 2000 
under RCP8.5 (Lam et al., 2016).Table CCP4.1 |  Projected risks for crop production in the Mediterranean Basin.
Crop Projected risk
Cereals and riceUnder 2°C warming and beyond, rain-fed wheat yield in most locations could decline by 2–59%, depending on agricultural practices (Chourghal et al., 2016; Dettori 
et al., 2017; Iocola et al., 2017; Brouziyne et al., 2018; Kheir et al., 2019). Under 1.5–3°C warming and reduced rainfall, yield decreases are also projected for maize 
(Georgopoulou et al., 2017; Iocola et al., 2017) and barley (Bouregaa, 2019; Cammarano et al., 2019), mainly due to the shortening of the crop growing season by up 
to 30 days due to higher temperatures (Saadi et al., 2015; Bird et al., 2016; Waha et al., 2017; Bouregaa, 2019). In Tunisia, cereal production may decrease by 0.79%

with a 1% decrease in precipitation (Zouabi and Peridy, 2015). Reductions of rice yields in parts of the region are projected in the absence of adaptation; for example, 
by 6–20% in southern France and Italy in 2070 under RCP8.5 (Bregaglio et al., 2017).
OlivesHigher temperatures and more frequent extreme heat events around flowering will likely affect phenology. While suitable areas for olive cultivation could extend 
northward and to higher elevations under the A1B scenario in 2036–2065 (Tanasijevic et al., 2014), negative consequences for several countries are expected, 
including southern Spain (Gabaldón-Leal et al., 2017; Arenas-Castro et al., 2020) and Tunisia (Ouessar, 2017) under 2°C warming. Under 1.5°C–2°C GWL, olive yields 
in northern Mediterranean locations could decrease by up to 21% (Brilli et al., 2019; Fraga et al., 2020). A 3°C warming could cause a 15–64% drop of production of 
rain-fed olives in Algeria (Bouregaa, 2019).

VegetablesYields could decline by up to 45% under current irrigation in some areas by 2050 under the A1B scenario (Zhao et al., 2015; Georgopoulou et al., 2017), while a lower 
availability of irrigation water would lead to further losses (Saadi et al., 2015) or even to non-viability of crops in some locations; for example, in Tunisia beyond 2°C 
warming (Bird et al., 2016).
Fruit treesFlowering of many fruit trees may be delayed, and chilling accumulation may be threatened. In Spain, under the A2 scenario, apples at maturity could be of inferior 
quality from mid-century, while after 2070, 28–72% of the years could have winters that do not fulfil chilling requirements (Rodríguez et al., 2019) Similar threats for 
other fruit trees were found beyond 3°C GWL (Funes et al., 2016).
Grapevines and 
orchardsClimate change could advance bud break and flowering, shortening the growing season by 20–35 days after 2060 under RCP8.5 (Fraga et al., 2016; Ramos, 2017;

Leolini et al., 2018; Ramos et al., 2018) and shifting maturation under high summer temperatures, thus affecting grape quality. Higher temperatures may increase 
evapotranspiration and therefore water deficit (Ramos et al., 2018). Some locations may suffer from high winter temperatures, causing a lack of chilling accumulation 
and ultimately missed bud break (Leolini et al., 2018). Early maturation may result in unbalanced wine quality through higher sugar and lower acids in the grape must 
after 2050 under RCP8.5 (Fraga et al., 2016; Koufos et al., 2018). Negative impacts of climate change on table quality vines and wine grape production in Southern 
Europe after 2040 under RCP8.5 have been projected (Cardell et al., 2019).
DatesIrrigation requirements for date palms in Tunisia under RCP8.5 could increase by 34% in 2050 from present to sustain date production (Haj-Amor et al., 2020), with 
adverse effects on groundwater resources.

CCP4
2247Mediterranean Region  Cross-Chapter Paper 4
Overall, reduced crop yields and fishery landings, combined with other 
factors such as rapid population growth and urbanisation, increasing 
competition for water and changing lifestyles, will likely impact food 
security, particularly in North Africa and the Middle East (Jobbins and 
Henley, 2015).
CCP4.3.5 Human Health and Cultural Heritage
Warming is projected to impact human health, mostly through increased 
intensity, frequency and duration of heat waves (high confidence) 
(Guerreiro et  al., 2018; Jacob et  al., 2018; Rohat et  al., 2019; Smid 
et al., 2019). Under current socioeconomic conditions, 53–93 million 
more people could be exposed to high or very high heat stress in 
northern Mediterranean by 2050 (Rohat et al., 2019) and heat-related 
excess mortality could increase by more than six-fold above 3°C GWL 
(Gasparrini et al., 2017). In MENA countries, the mortality risk of the

elderly in 2100 could be 8–20 times higher under RCP8.5 compared to 
1951–2005, and still 3–7 times higher under RCP4.5 (Ahmadalipour 
and Moradkhani, 2018). Deaths attributable to high temperatures in 
the northern Mediterranean could increase by 18–20,000 in 2050 
(50,000 in 2100) under RCP8.5 (1.4 and 2.6 times lower under RCP4.5) 
(Kendrovski et al., 2017).
Climate change and variability may also influence the emergence of 
vector-, food- and water-borne diseases (Negev et  al., 2015). Under 
RCP8.5, the epidemic potential of dengue fever in Southern Europe 
is projected to increase by 2100 (Liu-Helmersson et  al., 2019), as 
well as the risk of infections by West Nile virus in 2050 under A1B 
(Semenza et al., 2016). Climate-induced diseases could reduce labour 
productivity in the region by 2060, particularly in MENA countries 
(Dellink et al., 2019). Overall, there is still uncertainty in projections 
of the future severity and distribution of diseases because of climate

change due to the complex interactions between hosts, pathogens and 
vectors. Reductions in fruit and vegetable consumption as a result of 
climate change on food availability could lead to more than 20,000 
deaths in 2050 under RCP8.5 from diseases caused by malnutrition 
(Springmann et al., 2016).
Extreme high temperatures, hot days and nights and consequently 
cooling degree days will likely increase (high confidence) (Spinoni 
et al., 2018a; Coppola et al., 2021), with specific cooling needs in 
cities possibly increasing by 50–278% under 2°C GWL and 134–
375% beyond 3°C GWL (Cellura et  al., 2018). Urban heat island 
effects will further increase cooling needs (Salvati et al., 2017; Zinzi 
and Carnielo, 2017). Higher temperatures will increase thermal and 
chemical stress on materials used in many ancient buildings and 
sculptures, such as marble, stone and masonry (Bonazza et al., 2009; 
Leissner et al., 2015).

Many studies project a decrease of climatic comfort for tourism in the 
Mediterranean by 2071 to 2100, particularly during summer (Grillakis 
et al., 2016; Jacob et al., 2018; Braki and Anagnostopoulou, 2019). There 
is adaptive potential in the extension of the period with favourable 
climatic conditions for urban tourism in northern Mediterranean cities 
(Scott et al., 2016). Water scarcity may create additional constraints for 
tourism (Köberl et al., 2016).Cultural heritage sites in the region face risks from coastal flooding, 
with 37 out of 49 cultural World Heritage sites today facing risk from 
a 100-year flood, and 42 of them from coastal erosion (Reimann 
et al., 2018b). Sea level rise will increase these risks (high confidence) 
(Lionello, 2012; Rizzi et al., 2017; Reimann et al., 2018b; Ravanelli et al., 
2019; Tagliapietra et al., 2019). By 2100, 47 of the 49 United Nations 
Educational, Scientific and Cultural Organization (UNESCO) sites are

projected to be at risk from coastal flooding or erosion (Reimann 
et al., 2018b). Beyond 2100, sea levels are committed to rise further 
and represent an existential threat for the high number of coastal 
cultural heritage located in the Mediterranean (WGI AR6 Chapter 9, 
Fox-Kemper et al., 2021; Chapter 13; Cross-Chapter Box SLR in Chapter 
3; Marzeion and Levermann, 2014).
CCP4.3.6 Synthesis of Key Risks
For the Mediterranean Basin, all currently projected pathways of climate 
change will exacerbate climate-related risks in multiple systems and 
economic sectors, and for human health and well-being, amplifying 
current pressures on local ecosystems, economies and human well-
being (Figures CCP4.7; CCP4.8; Cramer et al., 2018; MedECC, 2020). 
While the majority of these risks apply across the entire region, many 
are specific for certain sub-regions or locations.
CCP4.4 Adaptation and Sustainable 
Development in the Mediterranean 
Basin
CCP4.4.1 Ocean and Coastal Systems

Adaptation options for climate change impacts on marine ecosystems 
and fisheries include improving and enlarging the regional network 
of marine protected areas, transnational management of marine food 
resources, sustainable fishery practices, developing collaborative 
monitoring, research and managing knowledge platforms for fisheries 
(Bjørkan et al., 2020; Raicevich et al., 2020) and sustainable aquaculture 
(Ehlers, 2016; Lacroix, 2016).
Adaptation options to sea level rise in the Mediterranean include 
nature-based solutions, such as beach and shore nourishment, dune 
restoration, or ecosystem-based adaptation and restoration in low-lying 
coasts, lagoons, estuaries and deltas (Aragonés et al., 2015; Aspe et al., 
2016; Loizidou et al., 2016; Danovaro et al., 2018). Engineering plays a 
major role for coastal adaptation too, through breakwaters, seawalls, 
dykes, surge barriers and submerged breakwaters (Sancho-García

et al., 2013; Becchi et al., 2014; Balouin et al., 2015; Masria et al., 2015; 
Tsoukala et  al., 2015; Bouvier et  al., 2017). Many engineering-based 
coastal adaptation imply large residual impacts on coastal ecosystems 
(high confidence) (Micheli et  al., 2013; Masria et  al., 2015; Cooper 
et al., 2016; Bonnici et al., 2018). A sea surface height control dam at 
the Strait of Gibraltar has been proposed for mitigating sea level rise 
in the Mediterranean, but this would likely involve major impacts on 
ecosystems and fisheries (Gower, 2015).

CCP4
2248Cross-Chapter Paper 4 Mediterranean Region
Key risks in the Mediterranean region
Very high
High
Moderate
UndetectableLevel of aditional
impact/risk due to
climate change
without
transformative
adaptationConfidence level
for transition•
••
•••
rangeGlobal
mean
change
01°C2°C3°C4°C
•••••••••
•••••
•••••••
•••••••••
••••••••
•••••••
••••••••
••••••
and
terrestrial
ecosystem sMarine
ecosystem sFood
productio n
and
securityWildfires Water quality
and
availabilit yHealth
and
wellbeingCoastal
risksDelayed
impacts of
sea level rise to
cultural heritage ,
infrastructures and
communities
Figure CCP4.8 |  Summary of key risks for the Mediterranean (Sections CCP4.3.2–8; Table SMCCP4.2a–h for details). Coastal risks include one burning ember 
displaying additional risks due to climate change as specific GWL are exceeded (Coastal risks), and one burning ember describing additional risks due to committed sea level rise

at timescales of centuries and millennia for long-living infrastructure and cultural heritage (WGI AR6 Chapter 9, Fox-Kemper et al., 2021; Marzeion et al., 2014; Marzeion and 
Levermann, 2014; Clark et al., 2016; see SMCCP4.2h).
Key risks in the Mediterranean and their location for SSP5-RCP8.5 by 2100
500–1,000Population
per km2
300–500
200–300
100–200
50–100>1,000
<50World
Heritage
sites
number
at risk
River
delta4 sites1 site Marine ecosystems
Terrestrial ecosystems
WildfiresInland flooding
Coastal riks
Agriculture and food production
Health and wellbeingWater and droughtConfidence
High
Medium
Low
Figure  CCP4.7 |   Key risks in the Mediterranean and their location across the Mediterranean region for SSP5-RCP8.5 by 2100 (Sections CCP4.3.2–6; 
Table SMCCP4.2a and b for details). Risks to World Cultural Heritage sites from flooding or erosion due to sea level rise in multiple locations (Section CCP4.3.5) and

Mediterranean river deltas are hotspots of vulnerability to climate change (Section CCP4.3.2). The population exposed to risks is mapped for an SSP5-8.5 pathway. Adaptation can 
reduce these risks (Section CCP4.4) (based on: Reimann et al., 2018a; 2018b; Wolff et al., 2018).

CCP4
2249Mediterranean Region  Cross-Chapter Paper 4
CCP4.4.2 Inland Ecosystems
In forests, adaptation to impacts of warming and drought may involve 
multiple forest management strategies, such as thinning (Fernández-
de-Uña et al., 2015; Giuggiola et al., 2016; Aldea et al., 2017; del Río 
et al., 2017; Gleason et al., 2017; Lechuga et al., 2017; Vilà-Cabrera 
et  al., 2018), increasing the share of drought-tolerant species and 
provenances (Hlásny et  al., 2014; Calvo et  al., 2016), or promoting 
mixed-species stands (Ruiz-Benito et  al., 2014; Guyot et  al., 2016; 
Sánchez-Pinillos et al., 2016; del Río et al., 2017; Jactel et al., 2017; 
Ratcliffe et al., 2017).
Adaptation options to increased fire risks include improved planning 
of residential development such as to avoid inevitable wildfire 
(Schoennagel et  al., 2017; Samara et  al., 2018), improved fire 
suppression capacities and strategies (Brotons et al., 2013; Regos et al.,

2014; Khabarov et al., 2016; Turco et al., 2018a; 2018b), managing and 
planning landscape matrix schemes to reduce fire risk (de Rigo et al., 
2017; Erdős et al., 2018), thinning, slash management and prescribed 
burning techniques (Fernandes et al., 2016; 2018; Khabarov et al., 2016; 
Regos et al., 2016; Piqué and Domènech, 2018; Samara et al., 2018; 
Vilà-Cabrera et  al., 2018; Duane et  al., 2019), as well as understory 
grazing (Varga et al., 2016; Vilà-Cabrera et al., 2018).
Adaptation of forest management generally requires improved 
monitoring systems of forest condition and natural disturbances 
(Hlásny et  al., 2014; Hengeveld et  al., 2015; Maes et  al., 2015), 
supported by participatory forest management and planning processes 
and local self-governance mechanisms (Bouriaud et al., 2013; 2015).
For freshwater ecosystems, adaptation options include hydrological 
and land use planning at basin scale, which can be complemented

with local conservation and restoration efforts, and the preservation 
of natural flow variability of rivers and streams (Aspe et  al., 2016; 
Loizidou et al., 2016; Cid et al., 2017; Menció and Boix, 2018; Morant 
et al., 2020).
CCP4.4.3 Water Management, Agriculture and Food 
Security
Adaptation options to address water shortages at the national scale 
include transboundary resource management (Escriva-Bou et  al., 
2017; Pulido-Velazquez et  al., 2018), promoting fair, equitable and 
sustainable water trade in international markets (Johansson et  al., 
2016; Lee et al., 2019), regional, national and basin-scale management 
plans for water resources (Wilhite et al., 2014; Paneque, 2015; Urquijo 
et  al., 2015; Estrela and Sancho, 2016; Vargas and Paneque, 2019), 
improved groundwater monitoring and strategic management (Pulido-
Velazquez et al., 2020), and economic instruments to manage water 
demand (prices policies, markets and subsidies).

Technical options include the reduction of losses in water distribution 
networks for drinking water and irrigation (Burak and Margat, 2016; 
Fader et al., 2016), desalinisation, often combined with generation of 
electricity (Papanicolas et al., 2016; Bonanos et al., 2017; Jones et al., 
2019), artificial recharge of groundwater and subterranean dams (Djuma et al., 2017; De Giglio et al., 2018; Missimer and Maliva, 2018; 
Baena-Ruiz et al., 2020), and waste water reuse (Kalavrouziotis et al., 
2015; Barba-Suñol et al., 2018; Cherfouh et al., 2018). On the demand 
side, options include changing diet and water consumption patterns 
(Blas et al., 2016; Gul et al., 2017; Blas et al., 2018), and enhancing 
water use efficiency in the tourism and food sectors (Hadjikakou et al., 
2013; Moresi, 2014).
In the agriculture sector, improved efficiency of irrigation practices can 
be achieved by changing surface water irrigation for other techniques

and shifting to more sustainable practices (Mrabet et  al., 2012; 
Benlhabib et al., 2014; Boari et al., 2015; Ćosić et al., 2015; Guilherme 
et al., 2015; Iglesias and Garrote, 2015; Cantore et al., 2016; Triberti 
et al., 2016; AbdAllah et al., 2018; Billen et al., 2018; Iglesias et al., 
2018; Malek and Verburg, 2018; Vargas and Paneque, 2019). Overall, 
the region could save 35% of water resources by improved irrigation 
techniques (Fader et al., 2016). However, maladaptive drip irrigation 
subsidies and developments can also result in the unsustainable use 
of groundwater resources and excessive agriculture intensification, 
indicating the need for careful strategic planning, regulation and 
monitoring of these options (Venot et al., 2017). In the livestock sector, 
adaptation options for heat wave-induced mortality of animals include 
the choice of more resistant genetic provenances (Rojas-Downing 
Other adaptation options in the agricultural sector include agro-

ecological techniques that increase the water retention capacity 
of soils (mulching, zero tillage, reduced tillage, etc.) (Aguilera et al., 
2013a; Aguilera et al., 2013b; Almagro et al., 2016; Sanz-Cobena et al., 
2017; Tomaz et  al., 2017; Bhakta et  al., 2019; García-Tejero et  al., 
2020) and promoting crop diversification, adapting the crop calendar 
and the use of new varieties adapted to evolving conditions. Many of 
these strategies for more sustainable production are also intended to 
address the food security risks and import dependence in the region. 
Other options are to manage nitrogen resources, food demand, change 
diets and reduce food waste (Billen et  al., 2018; Schils et  al., 2018; 
Billen et al., 2019; Garnier et al., 2019; Aguilera et al., 2020; Lassaletta 
et al., 2021).
CCP4.4.4 Human Health
In the Mediterranean region, adapting to increasing heat wave impacts 
involves local urban health adaptation plans, as well as increasing

the capacity of healthcare systems (Fernandez Milan and Creutzig, 
2015; Larsen, 2015; Paz et al., 2016; Liotta et al., 2018; Reckien et al., 
2018; Tsiros et al., 2018). Local urban adaptation strategies need to 
be integrative and address housing and infrastructure, the increase 
and design of urban green areas, education and awareness-raising 
of the most vulnerable communities, the implementation of early 
warning systems for extreme events and the surveillance of climate 
change induced diseases, the strengthening of local emergency and 
healthcare services, and the general strengthening adaptive capacity 
of the community and of the local institutions.

CCP4
2250Cross-Chapter Paper 4 Mediterranean Region
CCP4.4.5 Limits to Adaptation, Equity and Climate Justice
There is low confidence that the Mediterranean region can adapt to 
rapid sea level rise for the case of rapid Antarctic ice-sheets collapse, 
even in regions with high capabilities to adapt, such as the northwest 
Mediterranean (Poumadère et al., 2008). Residual coastal risks are 
still largely unquantified. For moderate levels of sea level rise, it is 
unlikely that these changes alone will exceed the technical limits of 
coastal adaptation over the 21st century (Hinkel et al., 2018). Beyond 
2100, continued sea level rise may require managed retreat in low-
lying Mediterranean areas, particularly in delta areas, such as the 
Nile (Figure CCP4.6). There is little knowledge on the potential for 
adaptation at these timescales.
Regional adaptation initiatives occur in a highly asymmetric geographic 
context characterised by contrasting demographic, environmental and

socioeconomic trends in the southern, eastern and northern parts of the 
Mediterranean Basin (Pausas and Fernández-Muñoz, 2012). Adaptation 
plans in Mediterranean countries are also limited by a lack of effective 
regional governance schemes (with the partial exception of European 
countries subject to the European directives and strategies), hampering 
the effective implementation of regionally harmonised adaptation 
strategies, plans and quantitative targets (UNEP/MAP , 2016; Sachs 
et al., 2019). Adaptation to sea level rise is essentially limited by social 
barriers along urban coasts in the northwest Mediterranean at present 
(Hinkel et al., 2018), while the adaptation dilemma involving economic 
and financial barriers is greater in peri-urban, rural and natural areas, 
as well as in the southern and eastern Mediterranean. In addition, 
limited regional monitoring of risks and adaptation options hampers 
adaptation in domains and sectors (Cramer et al., 2018).

In the Mediterranean region, vulnerability is strongly affected by 
equity: people most vulnerable to the effects of climate change are 
the elderly, especially women (Iñiguez et  al., 2016; Achebak et  al., 
2018) and children, who are often strongly affected by climate change 
(Watts et  al., 2019). An increase of heat waves poses a significant 
health risk especially for young children living in urban areas (UNICEF , 
2014; Perera, 2017; Royé, 2017) and for elderly women, in particular 
those affected by other conditions such as respiratory diseases (Sellers, 
2016; Achebak et al., 2018). Children and future generations in eastern 
Mediterranean countries are those most at risk of food insecurity, in 
both quantity and quality (Prosperi et al., 2014). In the region, many 
children are particularly vulnerable due to scarcity of drinking water 
and food, aggravated by droughts and flooding (Philipsborn and Chan, 
2018). The potential for adaptation to and preparation for vector-

borne diseases and other health risks, expected to increase with 
climate change, differs among Mediterranean countries (Negev et al., 
2015). Climate change in the Mediterranean region also impacts some 
groups disproportionately (e.g., poor farmers, urban migrants, seasonal 
workers) and livelihoods (Waha et al., 2017), favouring mobility and 
migration (Nori and Farinella, 2020).
To safeguard the rights of the most vulnerable people in the 
Mediterranean region, climate adaptation plans and measures must 
consider the cost of adaptation (Watts et al., 2019). In addition, some 
adaptation options can have side and residual effects, favouring some 
countries/groups over others. Climate-just adaptation options are those that promote fair solutions for all and take into account region-specific 
socioeconomic and geopolitical variabilities and vulnerabilities, such as 
the lack of inclusive and participatory approaches (Iglesias and Garrote,

2015) and pre-existing vulnerabilities, as in the case of Palestine (Jarrar, 
2015) and Syria (Gleick, 2014).
CCP4.4.6  Pathways for Sustainable Development
Climate-resilient sustainable development pathways are trajectories 
that combine adaptation and mitigation to realise the goal of 
sustainable development through iterative, continually evolving 
socioecological processes (Chapters 1; 18; Denton et  al., 2014). 
Transformative adaptation can be promoted through social and 
political processes, identifying the enabling conditions and strategies 
that facilitate structural changes (UNEP/MAP , 2016; Ramieri et  al., 
2018; EC, 2020; UNEP/MAP and Plan Bleu, 2020). The main options 
include ongoing structural change in the renewable energy system in 
this region, the production of renewable biological resources, measures 
towards increased water irrigation efficiency, behavioural changes in 
multiple sectors and improved regional governance (Table  CCP4.2; 
Cramer et al., 2018).

There also are risks for nonlinear climate change impacts in key 
socioeconomic and environmental processes, which could promote 
reactive changes and forced transformations (Table CCP4.3).
In the Mediterranean Basin, indicators for progress towards the 
Sustainable Development Goals (SDGs) show multiple directions of 
transformative change (Sachs et al., 2019). In some sectors, such as 
energy, there are general positive trends in sustainability (UNEP/MAP , 
2016), but there also are significant imbalances between northern and 
southern shores of the basin for most SDGs. Over the coming decades 
the Mediterranean Basin will likely experience sustained growth in 
renewable energy investments, accompanied by a shift in regional 
geographical patterns of energy demand (OME, 2018). However, 
future developmental pathways, solution space and feasible system 
transformations could be constrained by multiple factors for several

SDGs, such as social conflicts, lack of regional governance, limited 
action capacity and financial constraints (Figure CCP4.9; Table CCP4.3).
CCP4.4.7 Governance and Finance for Sustainable 
Several multilateral institutions are managing international 
environmental governance in the Mediterranean Sea, including, (a) the 
Barcelona Convention or Convention for the Protection of the Marine 
Environment and the Coastal Region of the Mediterranean (established 
under the United Nations Environment Programme, UNEP), (b) the 
General Fisheries Commission for the Mediterranean (GFCM, a 
subsidiary of the FAO) and (iii) the Agreement on the Conservation 
of Cetaceans in the Black Sea, Mediterranean Sea (ACCOBAMS, also 
under UNEP). These institutions act cooperatively pursuing synergies 
and greater effectiveness (Lacroix, 2016). The Mediterranean Action 
Plan (MAP) under the Barcelona Convention System involves 21

Mediterranean countries and the EU and promotes the Mediterranean

CCP4
2251Mediterranean Region  Cross-Chapter Paper 4
Table CCP4.2 |  Transformative adaptation and mitigation options for climate-resilient sustainable development in the Mediterranean Basin.
Code Sector Transformative option References
T1Energy, transport and 
tourismNational plans and regulations to decarbonise fuel sources and electricity grids on the supply 
side, for reducing energy demand and increasing efficiency and converting transport systems 
from fossil fuels to electricity.UNEP/MAP (2016); Bastianin et al. (2017); 
EEA (2018a; 2018b; 2019); OME (2018); CMI 
and EC (2019); Sachs et al. (2019); EC (2020); 
Simionescu et al. (2020)
T2 EnergyDeployment of large-scale Mediterranean transboundary renewable energy infrastructures 
and interconnections. Transboundary energy market integration schemes.EIB and IRENA (2015); Tagliapietra (2018); 
CMI and EC (2019); Zappa et al. (2019); CMI 
and EC (2020)

T3 EnergyDefinition of ‘Important Projects of Common European Interest’ pooling financial resources 
and funding large-scale innovation projects across borders in the Mediterranean. Green 
hydrogen projects in Mediterranean North Africa (especially Morocco) have already been 
suggested as strategic actions.CMI and EC (2019; 2020)
T4 Energy – financeEU Renewable Energy Financing Mechanisms such as calls for proposals for new renewable 
energy projects, including joint projects with third Mediterranean countries, joint support 
schemes, innovative technology projects or other projects that contribute to the enabling 
framework of the Renewable Directive 2018/2001. The mechanism can provide resources 
from payments by Member States, EU funds (European Green Deal Investment Plan, the 
Sustainable Finance Strategy, the Just Transition Fund, Connecting Europe Facility) or private 
sector contributions.CMI and EC (2019; 2020)

T5 WaterImproving efficiency of irrigation practices, including changing surface water irrigation for 
other techniques, use of remote sensing in intensive agriculture, optimisation of irrigation 
practices and other approaches. The Mediterranean region could save 35% of water by 
implementing improved irrigation techniques.Iglesias et al. (2011); Boari et al. (2015); 
Ćosić et al. (2015); Dhehibi et al. (2015); 
Guilherme et al. (2015); Iglesias and Garrote 
(2015); Cantore et al. (2016); Fader et al. 
(2016); Iglesias et al. (2017); Kang et al. 
(2017); AbdAllah et al. (2018); Iglesias et al. 
(2018); Malek and Verburg (2018); Vargas and 
Paneque (2019)
T6 WaterImprovement of water resource availability and quality. Desalinisation and co-generation of 
electricity and potable water in integrated Concentration Solar Power plants. Reduce climate 
impacts on nitrate and other pollutant concentrations through improved agriculture and

fertilizer management.Abufayed and El-Ghuel (2001); Elimelech 
and Phillip (2011); Aguilera et al. (2015); 
Papanicolas et al. (2016); Bonanos et al. 
(2017); Cramer et al. (2018); Jones et al. 
(2019); Lange (2019)
T7 WaterReduce/control water demand and use through efficiency management and/or modernisation 
in irrigation.Sanchis-Ibor et al. (2016); UNEP/MAP (2016)
T8 WaterWater demand management. Behavioural shifts in consumption and diet choice. Diet type 
influences the amount of water needed to produce and process food. Food waste implies the 
waste of the water used in the production cycle.Blas et al. (2016; 2018); Gul et al. (2017)
T9 Water Adaptation by increasing water trade in international markets (commodity markets).Antonelli et al. (2012); Hoekstra and 
Mekonnen (2012); Johansson et al. (2016); Lee 
T10 Food and fisheriesChanging diets, managing food demand and reducing food waste. Reductions in the demand

for livestock products.Bajželj et al. (2014); Havlík et al. (2014); Tilman 
and Clark (2014); Westhoek et al. (2014); 
Herrero et al. (2016); van Sluisveld et al. (2016)
T11 Food and fisheriesShift to more sustainable fishery practices. Collaborative monitoring, research and managing 
knowledge platforms.Bjørkan et al. (2020); Raicevich et al. (2020)
T12Human conflict, 
displacement, 
migration and securityImplementation of more effective Mediterranean regional policies and institutional 
frameworks for human rights protection, management of transboundary human migration, 
resolution of political and armed conflicts, increased internal displacements and food 
security.UNEP/MAP (2016)
T13 FinanceEnhanced Mediterranean transnational governance and financial bilateral and multilateral 
capacity. Increased finance for regional cooperation and development (above current levels, 
USD 8300 million yr-1).UNEP/MAP (2016); Midgley et al. (2018); Fosse 

T14 CoastalNature-based solutions aiming at reducing future coastal risks by restoring a buffer zone in 
coastal areas (e.g., through managed realignment), leaving space for sediments and coastal 
ecosystems, thus reducing the hazard and exposure to coastal flooding and erosion.Pranzini et al. (2015)

CCP4
2252Cross-Chapter Paper 4 Mediterranean Region
Strategy for Sustainable Development (MSSD), coordinated by the 
Mediterranean Commission on Sustainable Development (MCSD) 
(UNEP/MAP , 2016). MAP is primarily financed by national governments 
and the EU. Its financial capacity for regional environmental 
governance remains limited, with available annual funds in the range 
of 5–10 million Euro (Humphrey and Lucas, 2015).
Bilateral public climate finance in the Mediterranean area includes 
loans by multilateral development banks, bilateral official development 
aid and international climate fund projects (Midgley et  al., 2018; 
Tagliapietra, 2018). Bilateral public and private financial resources 
invested in international climate finance in southern Mediterranean 
countries are two orders of magnitude greater than the existing 
multilateral regional governance programmes for the environment 
(EC, 2018; Midgley et al., 2018; Fosse et al., 2019). The MSSD is a tool

for enhancing the governance of environmental issues, proposing the 
biannual reporting by the national parties of a set of quantitative 
indicators, including the commitments and obligations under the United 
Nations Framework Convention on Climate Change climate agreement, 
and other climate change mitigation and adaptation policy actions.Existing legal and institutional structures can facilitate coordination 
and collaboration across scales (DeCaro et  al., 2017). Legislative 
mechanisms, such as the rules governing water uses in time of 
drought, already exist in some Mediterranean countries, but they 
might not be suitable to cope with irreversible changes (e.g., the 
depletion of groundwater aquifers) or be flexible enough to respond 
to the needs of water users under a changing climate (Nanni, 2012). 
Although legislation can be recognised as a tool in support of adaptive 
water management, there is a need for better coordination among the

various legal provisions that define institutional roles and set out the 
mechanisms for the management of water resources across different 
scales (regional/national/sub-national) and sectors (agriculture, 
industry, urban, energy).SDG14: Protected areas Mean area that is
protected in marine sites important to biodiversity (%)SDG11: Urban pollution Annual mean 
concentration of particulate matter (PM2.5) (μg/m3)SDG7: Clean energy CO2 emissions from
fuel combustion / electricity output (MtCO2/TWh)Comparison between northern and southern Mediterranean countries
15 25 35 45 55 65 75 850 4 8 12 16 20 24 28
0 1.0 2.0
01020304050607080901000123456 7
65 70 75 80 85 90 95 100
323436384042444648505250 55 60 65 70 75 80 85 9095
1.0 2.0
10 20 30 40 50
0 1 2 3 4 530 40 50 60 70 80 90100 110
01 02030405060708090100
1.5 2.5 3.5 4.5 5.5 6.5 7.5
34 56 78 91 011121314SDG1: Poverty
Poverty headcount ratio at $3.20/day (% population)
SDG5: Gender equality

Ratio of female to male labour force participation rate
SDG9: Research and development
Research & development expenditure (% GDP)SDG2: Crop yield
Cereal yield (t/ha)
SDG6: Water access Population using at
least basic drinking water services (%)
SDG10: Inequality
Gini Coef ficient adjusted for top income (1–100)
SDG15: Endangered species
Red List Index of species survival (0–1)SDG3: Health coverage
Universal Health Coverage Tracer Index (0–100) %
SDG16: Conflict and violence
Homicides (per 100,000 population)SDG4: Education coverage
Lower secondary completion rate (%)
SDG8: Knowledge and infrastructure
Population using the internet (% )
SDG13: Emissions per capita
Energy-related emissions per capita (tCO2/capita)
SDG17: Governance capacity
Government Health & Education spending (% GDP)
0.82 0.86 0.9 0.94 0.98Sustainable
Goals Closer FurtherAchivement’ s mean and spread
**

This is the only indicator where southern mediterranean countries are ahead** Direction of axis reversed to harmonize direction towards goal.****
****Indicators for the achievement of the
Sustainability Development Goals in the Mediterranean region
Figure CCP4.9 |  Differences in present-day SDG indicator values between northern (blue) and southern (gold) Mediterranean countries. Yellow-shaded areas 
indicate better indicator values for the SDG descriptor. Red-shaded areas indicate poor performance on SDG values. Details of calculations and indicators in Table SMCCP4.3.

CCP4
2253Mediterranean Region  Cross-Chapter Paper 4
Table CCP4.3 |  Nonlinear processes that could force reactive changes and social transformations for climate-resilient sustainable development in the Mediterranean Basin. 
Nonlinearity implies the absence of a straight-line relationship between the independent variable and the response variable. In other words, changes in the output do not change in 
direct proportion to changes in the independent variable and the form of the relationship is often described by applying nonlinear mathematical models. Gradual changes induced 
by climate warming in thermal exposure or rainfall availability can induce nonlinear effects on social and ecological response variables.
Code Sector Processes References
P1 Agriculture and migrationAdverse nonlinear impacts of temperature on agricultural productivity can induce 
nonlinear effect on human migration. The temperature–migration relationship

is nonlinear and resembles the nonlinear temperature–yield relationship. These 
relationships affect mostly agriculture-dependent countries and especially people 
in those countries whose livelihoods depend on agriculture.Reuveny (2007); Schlenker and Roberts (2009); 
Cai et al. (2016)
P3 All societal sectorsThe increase in climatic impacts and catastrophic events is associated with 
nonlinear changes in economic and social impacts.Burke et al. (2014); Burke et al. (2015); Carleton 
and Hsiang (2016); Hsiang et al. (2017); Prahl 
et al. (2018); Coronese et al. (2019)
P4 All economic sectors Nonlinear temperature effects on labour conditions.Burke et al. (2014); Graff Zivin and Neidell 
(2014); Burke et al. (2015); Somanathan et al. 
(2018)
P5 All economic sectorsNonlinear temperature effects on GDP . Higher temperature may reduce GDP 
in Mediterranean agricultural countries more than non-agricultural countries.

Extreme heat over 30°C significantly reduces the GDP of agricultural countries 
but not the non-agricultural ones. GDP is a main determinant of international 
migration. The nonlinear relationship between GDP and temperature in 
agricultural countries provides indirect evidence for the agricultural linkage 
between temperature and migration.Dell et al. (2012); Burke et al. (2014; 2015); Cai 
et al. (2016)
P6 All societal sectors Nonlinear effects of temperature on human conflict.Baylis (2015); Burke et al. (2018); Koubi (2018); 
Baylis (2020)
P7 Food, health and demographyIn low-income areas of the Mediterranean Basin and sub-Saharan Africa regions 
higher poverty rates, malnutrition and elevated infant mortality are coupled 
with higher fertility, implying a higher rate of population growth that in turn 
can generate more poverty. These demographic cycles can in turn interact with

climatic impacts and conflict-induced displacement and migration processes.Vörösmarty et al. (2000); Barrios et al. (2006); 
Reuveny (2007); Hsiang et al. (2013); Ghimire 
et al. (2015); Brzoska and Fröhlich (2016); 
Cai et al. (2016); Cattaneo and Peri (2016); 
Grecequet et al. (2017); Waha et al. (2017); 
WFP (2017); Livi Bacci (2018); Raineri (2018); 
Scott et al. (2020)
P8 EnergyNonlinear effects of increased temperatures on energy demand and supply. High 
temperatures provoke demand surges while straining supply and transmission.Carleton and Hsiang (2016)
P9 Industry Nonlinear effects of temperature on industrial production. Hsiang and Meng (2015)
FAQ CCP4.1 | Is the Mediterranean Basin a ‘climate change hotspot’?
Is the Mediterranean ‘a geographical area characterised by high vulnerability and exposure to climate change’? Climate change projections for

the Mediterranean Basin indicate with very high consistency that the region will experience higher temperatures, less rainfall and continued sea 
level rise during the coming decades. Given that summers are already comparatively dry, these factors together will likely cause substantially 
drier and hotter conditions as well as coastal flooding, impacting people directly but also harming ecosystems on land and in the ocean.
For the Mediterranean Basin, climate models consistently project regional warming at rates about 20% above 
global means and reduced rainfall (–12% for global warming of 3°C). While it is not the region with the highest 
rate of expected warming on Earth, the Mediterranean Basin is considered particular in comparison to most other 
regions due to the high exposure and vulnerability of human societies and ecosystems to these changes: a ‘climate 
change hotspot’.

Rising temperatures trigger extensive evaporation of water from all wet surfaces, notably the sea, lakes and rivers, 
but also from soils. Along with decreasing rainfall, this evaporation leads to shrinking water resources on land, 
drier soils, reduced river flow, and significantly longer and more intensive drought spells. Since the Mediterranean 
climate is already relatively dry and warm in the summer, any additional drought (and also heat) will affect plants, 
animals and people significantly, and ultimately entire societies and economies.
In general, increasing temperatures and more intensive heat waves in the basin threaten human well-being, 
economic activities, and also many ecosystems on land and in the ocean. Extreme rainfall events, which despite the

CCP4
2254Cross-Chapter Paper 4  Mediterr anean Region
lower total rainfall are expected to increase in intensity and frequency in some regions, generate significant risks 
for infrastructure and people through flash floods. Warming also affects the ocean and its ecosystems, jointly with acidification caused by atmospheric carbon dioxide. Finally, sea level rise, currently accelerating because of global ice loss, threatens coastal ecosystems, historical sites and a growing human population.
per km2
300–500
200–300100–200
<50World
Heritage
sites
number
at risk
River
WildfiresInland floodingCoastal riks
Health and wellbeing Water and droughtConfidence
High
Medium
Low

Figure FAQ CCP4.1.1 |  Key risks across the Mediterranean region by 2100. The symbols above the map highlight risks enhanced by climate change 
which apply to the entire region with high confidence. Other risks are localised in the map.
Risks associated with projected climate change are particularly high for people and ecosystems in the Mediterranean 
Basin due to the unique combination of many factors, including:
•A large and growing urban population exposed to heat waves, with limited access to air conditioning
•A large and growing number of people living in settlements impacted by rising sea level
•Important and increasing water shortages, already experienced by 180 million people today
•Growing demand for water by agriculture for on irrigation
•High economic dependency on tourism, which is likely to suffer from increasing heat but also from the
consequences of international emission reduction policies on aviation and cruise-ship travel

•Loss of ecosystems in the ocean, wetlands, rivers and also uplands, many of which are already endangered byunsustainable practices (e.g., overfishing, land use change).FAQ CCP4.1(continued)

CCP4
2255Mediterranean Region  Cross-Chapter Paper 4
FAQ CCP4.2 | Can Mediterranean countries adapt to sea level rise?
The rates of observed and projected sea level rise in the Mediterranean are similar to the Northeast Atlantic, potentially reaching 1.1 metres 
at the end of the present century. Erosion, flooding and the impacts of salinisation are projected to be particularly severe due to the special 
conditions of the coastal zones in the region. Beyond a few tens of centimetres, adaptation to sea level rise will require very large investments 
and may be impossible in some regions.
Sea level in the Mediterranean has been rising by only 1.4 mm  yr-1 during the 20th century, more recently by 
2.4±0.5 mm yr−1 from 1993 to 2012, and it is bound to continue rising in the future. Future rates are projected to be 
similar to the global mean (within an uncertainty of 10–20 cm), potentially reaching 1.1 m or more around 2100 in

the event of 3°C of global warming (Figure FAQ CCP4.2; Table SMCCP4.4). Due to the ongoing ice loss in Greenland 
and Antarctica, this trend is expected to continue in coming centuries. Sea level rise already impacts extreme coastal 
waters around the Mediterranean and it is projected to increase coastal flooding, erosion and salinisation risks. 
These impacts would affect agriculture, fisheries and aquaculture, urban development, port operations, tourism, 
cultural sites and many coastal ecosystems.
Most of the Mediterranean Sea is a micro-tidal environment, which means that the difference between regular 
high and mean water levels (astronomical tides) is very small. Storm surges and waves can produce coastal floods 
that persist for several hours, causing particularly large impacts on sandy coasts and eventually also on coastal 
infrastructure. Mediterranean coasts are also characterised by narrow sandy beaches that are highly valuable for

coastal ecosystems and tourism. These beaches are projected to be increasingly affected by erosion and eventually 
disappear where sedimentary stocks are small.
Overall, Mediterranean low-lying areas of significant width occur along 37% of the coastline and currently host 
42 million inhabitants. The coastal population growth projected until 2050 mostly occurs in southern Mediterranean 
countries, with Egypt, Libya, Morocco and Tunisia being the most exposed countries to future sea level rise. The area 
at risk also hosts 49 cultural World Heritage sites, including the city of Venice and the early Christian monuments 
of Ravenna. The Mediterranean also includes areas subjected to sinking of the land (subsidence), including the 
eastern Nile Delta (Egypt) and the Thessaloniki flood plain (Greece), where local relative sea level rise can exceed 
10 mm yr-1 today.
Mediterranean mean sea level rise from 2020–2150
00.51.01.52.02.5
2020 2100 2050 2150Mean relative
sea level changes
(metres)

Median (dot) and 
likely (bar )
ranges in 2150Low confidenc e
83rd percentile for
SSP5-8.5
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5SSP1-1.9
Figure FAQ CCP4.2.1 |  Mediterranean Sea level projections. These projections translate the global estimates in WGI AR6 Chapter 9 to the Mediterranean 
Basin (Fox-Kemper et al., 2021). They assume that sea level change in the Mediterranean continues to be forced by Atlantic Sea level change seen at the Gibraltar 
Strait (Section CCP4.1) and thus follow the global mean beyond 2100. Vertical ground motions induced by glacial isostatic adjustments are also included, but not 
those due to other natural or anthropogenic processes such as tectonics or groundwater extractions. Intra-basin sea level changes are not included. Data available 
as supplementary material.

CCP4
2256Cross-Chapter Paper 4 Mediterranean Region
FAQ CCP4.3 | What is the link between climate change and human migration in the Mediterranean Basin?
Climate change already influences conflict and migrations occurring within countries or regions. However, climate is only one of the multiple 
factors affecting conflict and migration decisions across countries and regions. It is currently not possible to attribute particular conflicts or 
migrations to climate change and also in the future migration will most likely depend on the economic, social and governance context.
The Mediterranean Sea is the world’s most dangerous place for migrants, with more than 20,000 deaths reported 
since 2014. Although empirical evidence indicates that migration related to climate impacts is mostly internal to 
national borders, climate change is likely to contribute to migration in the Mediterranean Basin as one out of

several factors. Climate impacts contribute to migration flows particularly by affecting the economic and political 
drivers of migration.
Many migrants attempting to cross the Mediterranean to Europe originate from sub-Saharan Africa, a region 
heavily affected by climate change. In West Africa, for example, migration decisions are heavily influenced by 
perceptions of climate change and of its economic impact on resources and income. However, projections are 
uncertain, because climate impacts in Africa might both increase human suffering and thus enhance mobility, but 
they could also limit mobility of people through lack of financial resources.
The impacts of climate change on conflicts and security are increasingly documented, especially in Africa. Climate 
impacts may not in itself have caused social and political unrest but can contribute to them. The conflict in Syria has

occurred after the drought that marred the country in the years before, but there is no evidence for direct causal 
linkage. There is, however, high agreement that food insecurity and land degradation, which can be induced by 
climate change, are major drivers of political upheavals and instability in northern and sub-Saharan Africa.Adaptation to sea level rise in the Mediterranean includes engineering or soft/ecosystem-based protection, 
accommodation, and retreat or managed realignment. Despite various limitations, adaptation already happens 
today to some extent, as for example the coastal flood and erosion protections along the subsiding Nile Delta coast. 
Only massive coastal protection and other sustainable development policies could reduce the growing number 
of people exposed to sea level rise by 20%. It appears therefore likely that the number of people exposed could 
increase by up to 130% by 2100.

Without drastic mitigation of climate change, sea level rise is projected to accelerate and will require additional 
coastal engineering protection projects (e.g., dykes or groynes). Despite their efficiency for the few next decades, 
these engineering options have also adverse impacts for coastal ecosystems and may not ensure that the recreative 
value of Mediterranean coasts can be sustained (see Box 13.1 on Venice on the movable barriers protecting the 
Venice Lagoon). Among nature-based solutions, there are immediate benefits of restoring dunes and coastal 
wetlands to restore a buffer zone between coastal infrastructure and the sea and therefore reduce coastal risks 
(Cross-Chapter Box SLR in Chapter 3). Yet, this kind of protection is not feasible everywhere, particularly in urbanised 
areas, where it faces its limits. The limits for adaptation in the Mediterranean to further acceleration of sea level

rise have stimulated ideas of large-scale geoengineering projects such as surface height control dams at Gibraltar. 
However, such projects come with unknown risks for humans and ecosystems.FAQ CCP4.2 (continued)

CCP5
2275Mountains Cr oss-Chapter Paper 5
Mountains are highly significant regions in the context of climate 
change and sustainable development. They lie at the intersection of accelerated warming and large populations that depend directly or indirectly on them. They are regions of high biological and cultural diversity and provide vital goods and services to people living in and around mountain regions and in downstream areas. Building on the IPCC’s Fifth Assessment Report (AR5), Chapter 2, ‘High Mountain Areas’, of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Hock et al., 2019), and the IPCC Working Group I contribution to AR6 (IPCC, 2021), this Cross-Chapter Paper (CCP) assesses new evidence on observed and projected climate change impacts in mountain regions, their associated key risks and adaptation measures.
Observed changes, their impacts and adaptation responses in 
mountains

Climate change impacts in mountains and their attribution 
to human influence have increased in recent decades with observable and serious consequences for people and ecosystems in many mountain regions (high confidence
1). Observed changes 
include increasing temperatures, changing seasonal weather patterns, reductions in snow cover extent and duration at low elevation, loss of glacier mass, increased permafrost thaw and an increase in the number and size of glacier lakes (high confidence). {CCP5.2.7, Figure CCP5.4, SROCC Chapter 2, WGI Section 9.5}
The spatial distributions of many plant species have shifted

to higher elevations in recent decades, consistent with rising temperatures across most mountain regions (high confidence). Around two-thirds of treeline ecotones have also shifted upwards in recent decades, though these shifts are not ubiquitous and slower than expected based on rising temperatures (high confidence). Impacts on biological communities and animal species are also increasingly being reported, with species of lower elevations increasing in mountain regions, creating more homogeneous vegetation and increasing risks to mountain-top species (medium confidence). {CCP5.2.1; 2.4}
Climate and cryosphere change have negatively impacted the

water cycle in mountains, including variable timing of glacier melt and snowmelt stream discharge (high confidence). These changes have variable impacts on water availability for people and economies, contributing to increasing tensions or conflicts over water resources, especially in seasonally dry regions (medium confidence). Mountains are an essential source of freshwater 
for large and growing populations; the number of people largely or fully dependent on water from mountains has increased worldwide from approximately 0.6 billion in the 1960s to approximately 2 billion in the past decade, and globally two-thirds of irrigated agriculture depends on essential runoff contributions from mountains. {CCP5.2.2; Figure CCP5.2; SROCC Chapter 2; 4.2.2.3; 4.4.4.1}
1 In this report,  the summary terms ‘limited’, ‘medium’ and ‘robust’ are used to describe the available evidence; for the degree of agreement, low, medium, or high are applied. A level of confidence

is expressed using five qualifiers—very low, low, medium, high, and very high—which are set in italics, for example, medium confidence. For a given evidence and agreement statement, different 
confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.Climate-change-driven changes in precipitation, river flow regimes and landslides affect the production and use of energy in mountain regions, in particular hydropower (high confidence). Billions of USD in investment and assets of energy 
production are exposed to changing mountain hazards. The combined effects of climate change, hydropower development and other human interventions have exacerbated water security problems and social injustice (medium confidence). {CCP5.2.2, SROCC Chapter 2}
Observed climate-driven impacts on mountain ecosystem

services, agriculture and pastoralism are largely negative in most mountain regions (medium confidence). Agriculture has 
been negatively affected through increased exposure to hazards such as droughts and floods, changes in the onset of seasons, the timing and availability of water, increasing pests and decreasing pollinator diversity, which in turn have negatively influenced overall food production, dietary diversity and the nutritional value of food (medium confidence). Negative climate impacts on pastoralism, such as drought-induced degradation of rangelands and pastures, have affected livestock productivity and the livelihood of pastoralists, while other non-climatic factors, such as land use change and management, also play a role (medium confidence). {CCP5.2.3; CCP5.2.5; Table CCP5.2; SROCC Section 2.3.1.3.2; SROCC Section 2.3.7}
While contributing to poverty reduction in some mountain

regions, there is limited evidence of adaptations effectively contributing to the remediation of underlying social determinants of vulnerability, such as gender and ethnicity (medium confidence). Exposure and vulnerability exacerbate the negative effects of climate impacts on livelihoods and intertwine with power imbalances and gender and other inequalities (medium confidence). {CCP5.2.7; CCP5.3.2.2}
Observed changes in seasonality (timing and extent) are 
negatively affecting mountain winter tourism and recreation (high confidence) and variably affect tourism and recreation activities in other seasons (medium confidence). For winter

activities such as skiing, diminishing snow at lower elevations has challenged operating conditions (medium confidence), increasing the demand for and dependence on snow management measures such as snow-making (high confidence). Climate-induced hazards are negatively affecting some climbing, mountaineering and hiking routes (medium confidence). In some regions, options to change routes or shift seasons to reduce hazard exposure have been employed as adaptation strategies, with variable outcomes (medium confidence). In some cases, higher temperatures and extreme heat conditions at lower elevations have made some mountain destinations more appealing, increasing the potential for summer visitation demand (medium confidence). {CCP5.2.5; Table CCP5.2; SROCC Section 2.3.5}
Climate-related hazards, such as flash floods and landslides, 
have contributed to an increase in disasters affecting a growing number of people in mountain regions and areas

CCP5
2276Cross-Chapter Paper 5 Mountains
further downstream (high confidence). As a result, the number of 
disasters has increased; however, there is limited evidence that this is 
due to changes in the underlying hazard processes, pointing mainly 
to increasing levels of exposure (medium confidence). {CCP5.2.6; 
CCP5.2.7; CCP5.3.2.1}.
Adaptation responses to climate-driven impacts in mountain 
regions vary significantly in terms of goals and priorities, scope, 
depth and speed of implementation, governance and modes of 
decision-making and the extent of financial and other resources 
to implement them (high confidence). Observed adaptation 
responses in mountains are largely incremental and mainly focus on 
early warning systems and the diversification of livelihood strategies 
in smallholder agriculture, pastoralism and tourism. However, there is 
limited evidence of the feasibility and long-term effectiveness of these

measures in addressing climate-related impacts and related losses and 
damages, including in cities and settlements experiencing changing 
demographics. {CCP5.2.4; CCP5.2.7.2}
Projected impacts, key risks and limits to adaptation in mountains
Increasing temperatures will continue to induce changes 
in mountain regions throughout the 21st century, with 
expected negative consequences for mountain cryosphere, 
biodiversity, ecosystem services and human well-being (very 
high confidence). Many low-elevation and small glaciers around the 
world will lose most of their total mass at a 1.5°C global warming 
level (GWL) (high confidence). A large majority of endemic mountain 
species will be at risk of extinction; regions heavily relying on glacier 
melt and snowmelt for irrigation will face erratic water supply and 
increased food insecurity, whereas agriculture in some regions might 
see positive changes. Damages and losses from water-related hazards

such as floods and landslides are projected to increase considerably 
between 1.5°C and 3°C GWL. {CCP5.3.1}
Projected changes in hazards, such as floods and landslides, 
as well as changes in the water cycle, will lead to severe risk 
consequences for people, infrastructure and the economy in 
many mountain regions (high confidence). These risks will be more 
pervasive and increase more rapidly in south and central Asia and 
northwestern South America. However, nearly all mountain regions 
will face at least moderate and some regions even high risks at around 
2°C GWL (medium confidence). {CCP5.3.2.1, CCP5.3.2.2}
There is an increasing risk of local and global species extinctions 
where species are not able to move to higher elevations 
or other cooler locations (high confidence), with risks from 
extreme events such as wildfire potentially exacerbating those 
risks (medium confidence). The topographic variation in mountains,

such as elevation or aspect, may mean that some species will be 
able to survive in cooler microclimates. Mountain regions may act as 
refugia for some species from lower elevations if they can move into 
them. This may enable some species to persist in a region, though it 
may pose a threat to cold-adapted species, including endemics, which 
may be outcompeted (high confidence); invasive non-native species 
may become an increasing problem in some places. {CCP5.3.2.3, 
Box CCP5.1; CCP1.2.2.1; 2.6.6; 16.6.3.1}Climate change is projected to lead to profound changes 
and irreversible losses in mountain regions with negative 
consequences for ways of life and cultural identity (medium 
confidence). Intangible losses and loss of cultural values will become 
increasingly more widespread in mountain regions, mainly driven by a 
decline in snow and ice and an increase in intangible harm to people 
from hazards (medium confidence). However, there is limited evidence

on the magnitude of the consequences. {CCP5.3.2.4; 16.5.2.1; 
16.5.2.3.7}
Options for future adaptation and climate-resilient sustainable 
development in mountains
The current pace, depth and scope of adaptation are insufficient 
to address future risks in mountain regions, particularly at higher 
warming levels (high confidence). While the incremental nature of 
most implemented adaptations will not be sufficient to reduce severe risk 
consequences, options exist which offer practical and timely prospects to 
address risks before limits to adaptation are reached or exceeded. Reducing 
climate risks will depend on addressing the root causes of vulnerability, 
which include poverty, marginalisation and inequitable gender dynamics 
(high confidence). {CCP5.4.1, Figure  CCP5.7: CCP5.4.2, Cross-Chapter 
Box  DEEP in Chapter 17; Cross-Chapter Box  LOSS in Chapter 17; 17.3, 
17.6}
Adaptation decision-making processes that engage with and

incorporate people’s concerns and values and address multiple 
risks are more robust than those with a narrow focus on 
single risks (medium confidence). Risk management strategies 
that better integrate the adaptation needs of all affected sectors, 
account for different risk perceptions and build on multiple and 
diverse knowledge systems, including Indigenous knowledge and local 
knowledge, are important enabling conditions to reduce risk severity 
(medium confidence). {CCP5.2.6, CCP5.4.2; 17.3; 17.4; Cross-Chapter 
Box PROGRESS in Chapter 17; Cross-Chapter Box DEEP in Chapter 17}
Regional cooperation and transboundary governance in 
mountain regions, supported by multi-scale knowledge 
networks and monitoring programmes, enable long-term 
adaptation actions where risks transcend boundaries and 
jurisdictions (medium confidence). Collectively, they show potential 
to form an important component of the adaptation solution space in

mountains. There are increasing calls for more ambitious climate action 
in mountains, providing impetus for stronger cooperation within and 
across mountain regions and downstream areas (medium confidence). 
{CCP5.4.2; CCP5.4.3}
With warming above 1.5°C, the need for adaptation to address 
key risks in mountains becomes increasingly urgent (high 
confidence). Pathways and system transitions that strengthen 
climate-resilient sustainable mountain development are starting to 
receive attention, but current levels of resourcing are substantially 
insufficient to support timely action. {CCP5.4.2; CCP5.4.3; CCP5.5; 
18.1; 18.2}

CCP5
2277Mountains  Cross-Chapter Paper 5
CCP5.1 Point of Departure
Mountains are an extensive and significant typological region 
(Section  1.3.3 and Annex II: Glossary) in the context of climate 
change and sustainable development, with large populations directly 
or indirectly depending on them. Further, mountains are areas of 
high biological and cultural diversity that provide vital goods and 
services—such as water, food, energy, minerals, medicinal plants, 
tourism and recreation and aesthetic and spiritual values—to people 
living in and around these mountain regions and in downstream 
areas. Mountain regions are hotspots of climate-related losses in, for 
example, ecosystems, landscapes, culture and habitability, and while 
mountain people are adaptive, resourceful and independent, they live 
in highly fragile environments and in some regions under challenging 
socioeconomic circumstances that increase their vulnerability to 
climate change (Alfthan et al., 2018).

Chapter 2, ‘High Mountain Areas’, (Hock et  al., 2019) of the IPCC’s 
SROCC, presented an assessment of observed changes in the high 
mountain cryosphere, their impacts in situ and further downstream and 
the state of adaptation responses to these impacts. Before SROCC, the 
last time climate change in mountain regions had been systematically 
assessed in IPCC reports was in Chapter 5 of the Second Assessment 
Report (SAR) (Beniston et al., 1996). Projections made at the time for 
climate-related changes in mountain regions were expected towards 
the middle and the second half of the 21st century, rather than as 
early as recent decades (Haeberli and Beniston, 2021), underscoring 
the striking pace of change already observed in mountain regions.
Whereas SROCC focused on impacts from a changing climate on the 
high mountain cryosphere, this CCP on mountains synthesises key 
relevant content from across the AR6 WGII report with a broader

scope on the impacts of and adaptation to climate change in mountain 
regions as defined for this assessment (Figure CCP5.1, SMCCP5.1). It 
provides a wider assessment of the solution space and consequences 
for sustainable development due to climate change in mountain 
regions and downstream areas.
To define the geographical scope of the assessment in this CCP and 
to quantify the human population residing within these regions, the 
mountain characterisation given by Kapos et al. (2000) (Figure CCP5.1a 
and SMCCP5.1), minus Antarctica, Svalbard and Greenland (which fall 
under the assessment scope of CCP6 Polar Regions), was employed. 
This characterisation is consistent with the mountain region extents 
used in the AR6 WGI report (see AR6 WGI Atlas (Gutiérrez et al., 2021)) 
and yields a global mountainous area of 31.74  million km2, which 
corresponds to approximately 23.5% of the global land surface. In 
2015, a total of 1.28  billion people resided in mountain regions as

delineated for this CCP (SMCCP5.1).
The scope of the assessment presented in this CCP covers observed 
and projected climate change impacts in mountains, present, emerging 
and future key risks and observed adaptation responses, leading to 
an exploration of the adaptation solution space and climate resilient 
development (pathways) in mountains. Section 5.2 presents observed 
impacts and adaptation responses by synthesising information on 
mountains in the sectoral and regional chapters of WGII AR6, additional supporting evidence found in the literature, a detection and attribution 
assessment (SMCCP5.2) and a reanalysis of the mountain literature 
collected and synthesised in the Global Adaptation Mapping Initiative 
(GAMI) (SMCCP5.3). Section 5.3 presents an assessment of future key 
risks in mountains drawing from the regional and sectorial chapters and 
a key risks assessment carried out for this CCP (SMCCP5.4). Section 5.4

explores the solution space for future adaptation opportunities and 
constraints as well as climate resilient development in mountains. This 
CCP concludes with key assessment limitations and knowledge gaps 
and prospects for addressing these gaps in Section 5.5.
CCP5.2 Observed Impacts and Adaptation in 
Mountain Social-Ecological Systems
CCP5.2.1 Ecosystems and Ecosystem Services
Changes in climate over short distances in mountains are reflected 
in large ecological gradients. AR5 reported new evidence that plant 
species of mid and low elevations were starting to colonise higher 
elevations in mountains. Since AR5, new studies have been published 
(e.g., Steinbauer et  al., 2018; Payne et  al., 2020), including in some 
previously less well studied areas such as the Andes (e.g., Morueta-
Holme et al., 2015; Báez et al., 2016) and parts of Asia (e.g., Telwala 
et al., 2013; Artemov, 2018). There is now high confidence that many

plant species’ distributions have shifted to higher elevations in recent 
decades, consistent with climatic warming (Sections 2.4.2, 10.4.2.1.1, 
13.3.1.1). In recent years publications have also started to show similar 
trends in some animal species, including birds (Freeman et al., 2018; 
Bani et al., 2019; Lehikoinen et al., 2019) and snails (Baur and Baur, 
2013). Other climatic variables besides temperature can also affect 
elevational limits of species (Section  2.4.2) and sometimes in ways 
that contrast with temperature, for example increasing precipitation 
can allow some species to occur at lower elevations in dry climates 
(Crimmins et al., 2011; Coals et al., 2018). Tsai et al. (2015) reported 
large changes in the montane bird community in Taiwan, which they 
link to changes in weather patterns, including more severe typhoons. 
Changes in the amplitude and frequency of bank vole population 
waves in the Ilmen Nature Reserve in the Middle Urals can be linked to

longer frost-free periods (Kiseleva, 2020).
There are interactions with land use, for example a decrease in forest cover can 
exacerbate the effects of rising temperatures (Guo et al., 2018). In contrast, 
Bhatta et  al. (2018) showed a downward shift of species assemblages in 
Langtang National Park, Nepal, most likely related to interactions with land 
use, especially reduced grazing. Where glaciers retreat, new areas become 
available for pioneer species to colonise and new communities to form (Cuesta 
et al., 2019; Hock et al., 2019; Muhlfeld et al., 2020). The risk of extreme events 
such as wildfire, drought, floods and landslips is increasing in a wide range of 
places as a result of climate change, and the evidence of the disturbance they 
cause to ecosystems has grown in recent decades (Section 2.3.1, Box CCP5.1). 
The impacts of such extreme events may be greater than those of incremental 
changes.
For species at lower elevations, mountains may represent refugia to which

species can retreat. In this respect, Elsen et  al. (2018) highlighted the 
importance of protecting areas along elevational gradients. This applies

CCP5
2278Cross-Chapter Paper 5 Mountains
Delineation of mountain regions, population densities and projections
(a) Delineation of mountain regions and population densities in 2015
(c) Projected population changes in mountain regions for different SSPs from 2015 to 2100, per IPCC WGII Continental Region(b) Global population projections in mountain regions by 2100 for different SSPsAsia
Africa
EuropeIPCC WGII Continental Regions • Global population in mountain regions 1283 million people
• Global mountain area 31.74 million km2
Non-mountainous/ out of scope
regions. The assessment excludes
Svalbard, Greenland and Antarctica
Australasia: 0.380.53Asia: 15.92720.32
Europe: 2.27115.85
Central and South
America: 3.58138.26
Africa: 3.85227.8North America: 5.4263.75
Small 
Islands: 0.3216.58
05001000150020002500
SSP1SSP2SSP3
SSP4
SSP5
2020 2030 2040 2050 2060 2070 2090 2080 2100Population density in
mountain regions (people/km2 )

1–5 5–25 25–100 >100 <1
Mountain area (million km2)
characterised as a combination of 
elevation (greater than 300 m.a.s.l.), 
slope and relative relief (ruggedness),
based on Kapos (2000)<1515
15150750
Population in
mountain regions
(millions of people)
Africa
-100100300500Asia
-3000123Australasia
-1North America Europe
-10103050
-30-404080120
0Central and
South America Small Islands
Millions
of people
-10103050
-30Millions
of people
SSP1
SSP2
SSP3
SSP4
SSP5 -100100300500
-300-10103050
-30
Figure CCP5.1 |  Delineation of mountain regions in CCP Mountains, population numbers and densities in 2015 and their projections to 2100. 
(a) Population in mountain regions in 2015 aggregated per IPCC WGII Continental Regions, considering population densities, mountain areas and total population in mountain 
regions. 
(b) Population projections in mountain regions by 2100 for different Shared Socioeconomic Pathways (SSP) scenarios.

(c) Projected changes in population in mountain regions from 2015 to 2100 across five different SSP scenarios, per IPCC WGII Continental Region (SMCCP5.1 and Tables 
SMCCP5.1–5.4).

CCP5
2279Mountains  Cross-Chapter Paper 5
to freshwater and terrestrial habitats with mountain streams acting as 
potential refugia (Isaak et al., 2016). In contrast, species restricted to the 
highest elevations are increasingly at risk, including from competition with 
colonising species (Britton et al., 2016; Winkler et al., 2016). Mountain-
top species are often separated from potential new habitats by large 
areas with unsuitable climates, and tropical mountain species often 
have particularly narrow thermal tolerance and limited dispersal capacity 
(Polato et al., 2018).
The risks posed by non-native species may increase with climate 
change (Carboni et al., 2018; Shrestha et al., 2018; Thapa et al., 2018). 
Koide et al. (2017) found that non-native plant species on Hawaii were 
moving to higher elevations, whereas native species’ distributions 
were retracting at their lower elevational limit. Dainese et al. (2017)

found that non-native plant species spread to higher elevations 
approximately twice as fast as native species. Following recent climate 
warming, invasive Phyllostachys edulis and Phyllostachys bambusoides 
(Poaceae) bamboo species in Japan have shifted northwards and 
upslope in the last three decades (Takano et al., 2017). New evidence 
has shown that variations in microclimate, with topography and cold 
groundwater seeps, can provide micro-refugia small areas of locally 
suitable conditions where cold-adapted species can survive (Bramer 
et al., 2018; Muhlfeld et al., 2020) (Section 2.6.2). Some alpine species 
have thrived in recent years, and the range of microclimates may partly 
explain this (Rumpf et al., 2018).
Treeline elevation is linked to temperature (Paulsen and Körner, 2014) 
but may also be affected by water supply (Sigdel et al., 2018; Lu et al., 
2021) and land management. A recent summary of treeline shifts

worldwide found that 67% of studied alpine treelines had shifted 
upwards while 33% remained stable (based on 142 published studies), 
and 88.8% of the 143 undisturbed alpine treelines across the Northern 
Hemisphere had shifted upwards (Hansson et al., 2021; Lu et al., 2021). 
Since AR5, new evidence of shifting treeline ecotones has emerged 
for a wide variety of species in different locations, including in Siberia 
(Pospelova et al., 2017), various parts of the Ural Mountains (Shiyatov 
and Mazepa, 2015; Zolotareva and Zolotarev, 2017; Sannikov et al., 
2018), in the Canadian Rocky Mountains (Trant et al., 2020) and the 
Himalaya (Tiwari and Joshi, 2015; Chakraborty et  al., 2016; Gaire, 
2016; Yadava et al., 2017). Recent studies of treelines that have not 
or hardly shifted include those in the Himalaya (Singh et  al., 2015; 
Sigdel et al., 2018), eastern Tibetan Plateau (Wang et al., 2020) and 
the Andes (Lutz et al., 2014). Migration rates are not proceeding as fast

as warming rates, implying other processes also limit treeline ecotone 
response (e.g., Sigdel et al., 2020; Lu et al., 2021).
Whether treeline shifts occur, and if so at what rate, depends on a range 
of factors, including land use (especially livestock grazing and fire), 
species interactions, wildfires and climatic stress factors (wind, frost, 
drought, excess or shortage of snow) interacting with tree population 
processes (viable seed production, dispersal, seedling establishment, 
clonal propagation, growth, dieback, mortality). Differences in treeline 
shifts between north- and south-facing slopes have been demonstrated in 
the Rocky Mountains (Elliott and Cowell, 2015). Grigorieva and Moiseev 
(2018) showed that significant factors limiting the number of seedlings 
and shoots are the snow depth, the topsoil temperature dependent 
on it and the degree of competition from the parental tree stand and grass–shrub vegetation. In addition, land use and management exert an

influence in many mountains around the world. Suwal et al. (2016) found 
that elevational shifts in Himalayan silver fir in Nepal were larger when 
areas were protected from management. Similarly, Lutz et al. (2014) found 
faster treeline shifts in the Peruvian Andes in protected areas than that 
in other areas, where cattle grazing and fires are more frequent. Treeline 
ecotones can also change independently of climate change if land use 
changes (Vitali et al., 2019; Körner, 2020).
Changes in community composition are also happening within 
ecosystem types. Duque et  al. (2015) showed a change in the 
composition of northern Andean forests, and Feeley et  al. (2013) 
showed such a change in that of forests up to 2800 m in Costa Rica. 
In both cases the proportion of species adapted to warmer conditions 
increased, driven primarily by patterns of mortality, indicating that 
the changes in composition are mostly via range retractions, rather

than range shifts or expansions. An analysis of 200 forest inventory 
plots in the Andes likewise indicated a widespread, though not 
ubiquitous, thermophilisation of tree species’ composition (Fadrique 
et al., 2018). Within a period of 8 years (2003–2010), significant shifts 
in communities of vascular plants, butterflies and birds were found 
in Switzerland (Roth et  al., 2014). At lower elevations, communities 
of all species groups changed towards warm-dwelling species, 
corresponding to an average uphill shift of 8 m, 38 m and 42 m in 
plant, butterfly and bird communities respectively. However, rates of 
community change decreased with elevation in plants and butterflies, 
while bird communities shifted towards warm-dwelling species at all 
elevations (Roth et al., 2014).
Changes in mountain biodiversity and ecosystems have a wide 
range of impacts on ecosystem services and effects on people. Some 
mountain ecosystems, particularly those with peatlands or forests,

are important carbon stores, and climate change presents a risk to 
these in some locations (Dwire et al., 2018) (Sections 2.4.3.8, 2.4.4.4 
and 2.4.4.5). Palomo (2017) identified a wide range of threats to the 
lives, livelihoods and culture of mountain people as a consequence of 
the impacts of climate change on ecosystems. However, impacts are 
very heterogeneous between locations, even within the same region 
and ecosystem type (e.g., mountain forests in Europe) (Mina et  al. 
(2017) and are not necessarily all negative. In addition to changes 
in services, other impacts on humans from a changing climate may 
be mediated through species and ecosystems, for example changes 
in vector distribution shifting disease incidence into higher elevation 
areas (Escobar et al., 2016).
CCP5.2.2 Water and Energy
CCP5.2.2.1 Water
Water is a fundamental source of life in mountain regions; it is also 
a central element and ‘connector’ in coupled natural–human systems

and carries diverse meanings in different sociocultural contexts, 
including in indigenous ontologies (Boelens, 2014). In addition, water 
is a key component connecting upstream mountains and downstream 
lowlands (Salzmann et al., 2016; Di Baldassarre et al., 2018; Encalada 
et al., 2019). Mountains are of paramount importance as water towers

CCP5
2280Cross-Chapter Paper 5 Mountains
for people living there and for around two billion people living in 
connected lowland areas (Immerzeel et al., 2020; Viviroli et al., 2020).
Mountain river systems are especially sensitive to and affected by 
climate change and continuing anthropogenic disturbance, including 
water pollution, hydropower development, water withdrawals 
for agriculture and human consumption and biodiversity loss and 
ecosystem changes (high confidence) (Honda and Durigan, 2016; 
Encalada et al., 2019; Bissenbayeva et al., 2021; Chen et al., 2021). The 
effects of climate and cryosphere change in mountains on downstream 
water and river systems have been studied and quantified for many 
regions worldwide (Barnett et al., 2005; Huss, 2011; Lutz et al., 2014; 
O’Neel et al., 2015; Huss and Hock, 2018). Comprehensive approaches 
focusing on both water demand and supply aspects provide regionally 
or locally specified information on water availability, scarcity and

security (Buytaert et al., 2014; Drenkhan et al., 2015; Brunner et al., 
2019) (Chapter 4). Present and potential future hotspot regions of 
water scarcity that rely heavily on mountainous water sources include 
Central Asia, South Asia, tropical and subtropical western South 
America and southwestern North America (robust evidence, medium 
agreement) (Kummu et  al., 2016; Biemans et  al., 2019; Immerzeel 
et al., 2020; Viviroli et al., 2020).
Figure CCP5.2 represents different levels of dependences of lowland 
areas on mountain water. At a global scale, 68% of irrigated agricultural 
areas in lowlands depend on essential runoff contributions from the 
mountains. The dependence of lowland populations on essential 
mountain runoff contributions increased by a factor of more than three 
from the 1960s to the 2000s, with increases of up to ten-fold in some 
major river catchments (Viviroli et al., 2020).
Many mountain regions have one or more cryosphere components

(glaciers, permafrost and perennial or seasonal snow), and the 
mountain cryosphere is among the natural systems most sensitive 
to climate change worldwide (high confidence). The SROCC assessed 
a decline in all cryosphere components due to climate change over 
recent decades, i.e., for low-elevation snow cover (high confidence), 
permafrost (high confidence) and glaciers (very high confidence) (Hock 
et al., 2019). More recent studies using globally more complete data 
sets show a considerably higher glacier mass loss (267 ±16 Gt yr-1) for 
2000–2019 as compared to a (very likely2) range of 123 ±24 Gt yr-1 
for 2006–2015 in SROCC, with a mass loss acceleration of 48 ±16 Gt 
yr-1 per decade over 2000–2019 (Hugonnet et al., 2021). Assessment 
conclusions in SROCC found with high confidence that glacier 
shrinkage and snow cover changes over the past two decades have 
led to changes in the amount and timing of runoff in many mountain 
regions (Hock et al., 2019).

The effects of climate and environmental changes in upstream areas on 
downstream water quantity and quality, including nutrient, pollutant, 
heavy metals and sediment flux, have been assessed in only a limited 
number of catchments (Rakhmatullaev et al., 2009; Dong et al., 2015; 
Milner et al., 2017; Ilyashuk et al., 2018; Lane et al., 2019; Li et al., 
2 In this report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as 
likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, and exceptionally unlikely 0–1%. Additional terms (extremely likely: 95–100%, more likely than not >50–100%, and extremely unlikely 
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome

lies within the 17–83% probability range.2020; Chen et al., 2021). Groundwater contributions to streamflow are 
highly variable in mountains but can be substantial (up to 70 to 80% 
or more) during low-flow periods (Frisbee et al., 2011; Baraer et al., 
2015; Gordon et al., 2015; Käser and Hunkeler, 2016; Somers et al., 
2019). Groundwater may provide some resilience to loss of melt water 
from glacier and snow decline, but in the longer term groundwater 
recharge and contribution to streamflow are expected to decrease with 
ongoing climate change (medium confidence) (Somers and McKenzie, 
2020). In some mountain regions (e.g., in the Himalaya), springs are a 
particularly important source of water where large populations depend 
on them. Observations indicate a reduction of water provision from 
springs in recent years in the Himalaya, caused by multiple causal 
factors (human interventions, climatic) (Section 10.4.4.).

Both small-scale interventions (e.g., livestock grazing in sensitive 
high-elevation wetlands) and high-investment interventions (e.g., 
hydropower dams and plants) in upstream regions can strongly affect 
water availability, river connectivity, biodiversity and catchment 
management (Anderson et al., 2018; Ramsar Convention on Wetlands, 
2018; Encalada et al., 2019) and are often contested and have led to 
conflict (medium evidence, high agreement) (Drenkhan et  al., 2015; 
French et  al., 2015). Climate change often exacerbates tensions or 
conflicts between different users over water at local, national and 
transboundary or regional scales, and many tensions and social or 
political conflicts are documented, especially in seasonally dry regions, 
where large power inequalities exist among users, where clear and 
established regulations are lacking, and especially in transboundary 
settings (e.g., Central Asia, Hindu Kush Himalaya [HKH], Andes) (Carey

et al., 2014; Bocchiola et al., 2017; Yapiyev et al., 2017; Hock et al., 
2019; Mukherji et al., 2019).
Water plays a fundamental role in climate change adaptation in 
mountains. A majority of documented adaptation efforts in mountain 
regions address water-related aspects (precipitation variability 
and extremes, including drought, water availability, floods) (high 
confidence) (McDowell et  al., 2019, 2020). This is a robust finding 
across different mountain regions and adaptation project and 
programme types and is in line with findings for cryosphere-change-
related adaptation, as reported in SROCC (Hock et al., 2019). Water 
also plays a role in adaptation in other sectors, such as agriculture, 
disaster management, and tourism and recreation (McDowell et  al., 
2019). There is high confidence that water conservation efforts, 
including restoration and protection of particularly vulnerable areas 
(e.g., wetlands) and increase in efficiency in water use, are robust, low-

regret adaptation measures.
CCP5.2.2.2 Energy
Increasing temperatures and variability in precipitation and river flow 
affect energy availability and use in mountain regions. Mountain 
peoples, more so than national or global populations, are dependent 
on local sources of energy, accentuating climate adaptation cost 
and barriers (medium evidence, high agreement), while also offering

CCP5
2281Mountains  Cross-Chapter Paper 5
Box CCP5.1 | Wildfires and Mountain Ecosystems
Mountain ecosystems have long been known to be highly sensitive to the direct impacts of climatic warming and drying (Beniston et al., 
1994; Nogués-Bravo, 2009; Gottfried et al., 2012; Guisan et al., 2019). Furthermore, wildfires in these ecosystems, as in many others 
(Sections 2.4.4.2 and 2.5.3.2), are also expected to increase (Abatzoglou et al., 2019). This is because the occurrence and severity of fire 
are governed by four fundamental processes that are intricately linked to climate: 1) fuel biomass growth, 2) fuel moisture and type, 3) 
ignition source and 4) favourable weather conditions for fire spread (Bradstock, 2010).
In temperate and tropical mountain ecosystems, increases in fire activity are potentially linked to changing climate on most continents,

including Europe (Dupire et al., 2017), North America (Westerling, 2016; Halofsky et al., 2020; Burke et al., 2021), South America (Román-
Cuesta et al., 2014), Africa (Hemp, 2005), Asia (Tian et al., 2014) and Australia (Bradstock et al., 2014; Abram et al., 2021). In these 
ecosystems, fire frequency, severity and extent (i.e., the fire regime) are increasing because of climate-induced impacts on fuel moisture 
(Gergel et al., 2017; Littell et al., 2018), vegetation composition (i.e., fuel types) (Camac et al., 2017; Prichard et al., 2017; Zylstra, 2018), 
fire-conducive weather patterns and the length of fire seasons (Westerling, 2016; Fill et al., 2019; Di Virgilio et al., 2020).
Fire in mountain ecosystems alters many ecological processes and ecosystem services across all elevational zones, from foothill montane 
forests to high-elevation alpine (treeless) zones (Turner et al., 2003; Williams et al., 2008; Oliveras et al., 2014, 2018; Rocca et al., 2014).

However, the magnitude of short-term and long-term fire impacts depends on the degree of novelty of future fire regimes and the 
capacity of species to adapt to change (Camac et al., 2017, 2021; Archibald et al., 2018).
Montane and sub-alpine ecosystems have variable ecological responses to fire that are ultimately influenced by long-term, historical 
fire regimes and the evolutionary forces that have governed post-fire regeneration strategies of the biota. Two contrasting strategies in 
temperate forests are illustrated here. SE Australian mountain ash (Eucalyptus regnans) forests are adapted to a high-severity fire regime, 
consisting of infrequent (>100 years), large stand-replacing wildfires (Bowman et al., 2016). Mountain ash is a long-lived obligate seeder 
but is slow to reach reproductive maturity (>20 years) (Bowman et al., 2016). As such, natural post-fire regeneration takes decades to

centuries to recover to pre-fire conditions, and if fire reoccurs before reproductive maturity is reached, the species can be eliminated. By 
contrast, ponderosa pine (Pinus ponderosa) forests of the SW United States have evolved with a low- or mixed-severity fire regime, where 
fire is frequent (5–25 years), of low intensity, less likely to kill dominant stands and, thus, allow faster post-fire recovery (Prichard et al., 
2017). However, post-fire recovery times in this ecosystem are also becoming longer due to a century of effective fire suppression, shifting 
the fire regime to one which is more infrequent, of high intensity, extensive and stand replacing (Prichard et al., 2017).
Above the treeline, fire is less common than in foothill forests. Post-fire recovery times also tend to be shorter (Williams et al., 2008; 
Camac et al., 2013; Verrall and Pickering, 2019) because of the dual influences of low flammability traits coupled with the fact that

most alpine plant species exhibit strong resprouting strategies that have evolved in response to harsh climate conditions (Körner, 2003). 
However, fires in alpine treeless landscapes can still have long-term and catastrophic impacts on fire-sensitive vegetation types such 
as groundwater-dependent wetlands dominated by hygrophilous plants and peat soils (De Roos et al., 2018). Similar impacts can be 
severe on long-lived, slow-growing vegetation such as coniferous heathlands (Bowman et al., 2019) and highly restricted and threatened 
fauna (e.g., mountain pygmy possum) that depend on these plant communities (Gibson et al., 2018). Such fires have even been found 
to significantly impact sub-alpine treeline mortality rates (Fairman et al., 2017) and in some cases have resulted in treelines shifting to 
lower elevations (e.g., Hemp, 2005).
The long-term implications of a warmer global climate, coupled with more frequent and/or severe fires in mountain ecosystems, are

expected to be transformative for mountain biota. Fire-sensitive montane forests, such as Australia’s alpine ash (Eucalyptus delegatensis), 
are expected to become highly susceptible to population collapse and local extinction as intervals between fire events contract and 
become too short for species to reach reproductive maturity (Bowman et al., 2014; Enright et al., 2015)—an impact that will likely be 
further exacerbated by recruitment failure caused by post-fire drought and moisture deficiencies (Davies et al., 2019; Halofsky et al., 2020; 
Rodman et al., 2020). Fire and climate change are also likely to act synergistically in mountainous ecosystems, via positive feedbacks that 
increase fire frequency by changing vegetation composition to more flammable fuel types, thereby increasing landscape susceptibility 
to future fire (Camac et al., 2017; Tepley et al., 2018; Zylstra, 2018; Lucas and Harris, 2021). More frequent fires in these ecosystems will

also exacerbate native and exotic species invasions (Catford et al., 2009; McDougall et al., 2011; Gottfried et al., 2012; Kueffer et al., 
2013), faunal population declines (Ward et al., 2020), poor air quality (de la Barrera et al., 2018; Burke et al., 2021) and soil erosion and 
landslide risk (de la Barrera et al., 2018) and reduce freshwater catchment volumes and quality (Rust et al., 2018; Niemeyer et al., 2020), 
all of which will impact negatively on human health and well-being (Ebi et al., 2021).
Taken together, this evidence suggests that a significant risk exists of wildfire exacerbating other impacts of climate change on already 
vulnerable ecosystems in many mountain regions (medium confidence).

CCP5
2282Cross-Chapter Paper 5 Mountains
Light shade = Lowland areas
(b) Lowland population dependence on mountain water resources  (2041–2050, SSP2-RCP6.0 )
No surplus
Share of mountain
area in total
catchment area <5%Minor
NegligibleEssential but
vastly insufficient
Essential
but insufficient
Essential
and sufficient
SupportiveExtent of importance 
and dependence(a) Importance of mountain regions for lowland water resources  (2041–2050, SSP2-RCP6.0)Importance of mountain water resources for lowland areas and populations 
Light shade = Mountain regions
2040’s0123456
(billions of
people)
1960’s 1970’s 1980’s 1990’s 2000’s 2010’s 2020’s 2030’s 2040’sSSP2-RCP6.0
2040’sSSP1-RCP4.5 SSP3-RCP6.0
7(6.26 billion people) (5.87 billion people) (6.65 billion people)(c) Lowland population dependence on mountain water resources over tim e
Lowland

Figure CCP5.2 |  Dependence of land surface areas and population on mountain water resources, 1961–2050. Results are shown as decadal averages for lowland 
populations in each category of dependence on mountain water from no surplus and negligible to essential; 
(a) map of global mountain regions and their differentiated importance for lowland water resources; 
(b) map of lowland populations and their differentiated dependence on mountain water resources, both for the scenario combination SSP2-RCP6.0 and for the time period 
2041–2050; 
(c) number of lowland populations and their differentiated dependence on mountain water resources from 1960s to 2040s for three different scenario combinations (based on 
Viviroli et al., 2020).

CCP5
2283Mountains  Cross-Chapter Paper 5
opportunities for mountain-specific solutions (medium evidence, high 
agreement). In mountain regions, inadequate infrastructure (Tiwari 
et al., 2018), remoteness and reliance on traditional forms of energy 
that may be difficult to diversify (Dhakal et al., 2019) exacerbate the 
impacts of climate change on energy use and demand.
A review of the renewable energy transition in the context of 
adaptation across global mountain regions, including hydropower, 
wind, solar and biomass, shows that observed climate change impacts 
on these energy sources include altered seasonality, timing as related 
to snow and glacial melt runoff (30.9% of analysed cases), variable 
or declining precipitation and runoff (26.4%), increased flooding 
(15.5%), altered wind patterns (8.2%) and other/unspecified effects 
(19.1%) (Scott et al., 2019). The combined effects of climate change, 
hydropower development and further anthropogenic effects in

upstream mountain basins have increased and are expected to further 
negatively affect several aspects of ecosystem functioning and water 
security (e.g., negative effects on river geometry, water chemistry, 
sediment transport, fish composition and migration) (high confidence) 
(Anderson et al., 2018; Encalada et al., 2019; Lepcha et al., 2021).
With respect to hydropower, mountains play a unique role in the 
production of renewable energy for large downstream populations, but 
it also comes with important trade-offs affecting mountain ecosystems 
and populations (high confidence) (Farinotti et  al., 2019; Viviroli 
et al., 2020; Vaidya et al., 2021). Climate change requires adaptation 
in the hydropower sector; for instance, some advocate for increased 
water storage in dams and the importance of mountains for pumped 
hydropower storage systems (Gurung et al., 2016; Hunt et al., 2020), 
while others emphasise adaptive water management (Gaudard et al.,

2014; Caruso et al., 2017b). An example is the multi-purpose use of 
water strategies where water management storage is designed to 
accommodate different uses, including hydropower, agriculture and 
flood risk reduction (Haeberli et  al., 2016a; Drenkhan et  al., 2019) 
(Section 12.6.3). Hydropower is also especially vulnerable to glacier 
and snow decline (Schaefli et al., 2019) and is subject to risks from 
extreme events (Rangecroft et  al., 2013; Schwanghart et  al., 2016; 
Mishra et al., 2020; Shugar et al., 2021), social and political opposition 
(Ahlers et  al., 2015; Díaz et  al., 2017) and the resulting financial 
uncertainty for hydropower investors. There is still limited evidence 
on how climate change impacts wind, solar and biomass energy 
production and their use.
Overall, synergies between adaptation to climate change and renewable 
energy transition can be successfully generated where benefit-sharing

improves local involvement and support, adaptive capacity is enhanced, 
local health and livelihoods supported, Sustainable Development Goals 
(SDGs) met, environmental justice considered and sustainable mountain 
development pursued (high agreement, medium evidence).
CCP5.2.3 Food, Fibre and Other Mountain Ecosystem 
Products
There is high confidence that climate change is largely negatively 
impacting food, fibre and other ecosystem products, including agriculture 
(Porter et  al., 2014; Ingxay et  al., 2015; Upgupta et  al., 2015; Chirwa et al., 2017; Rojas-Downing et al., 2017; Chitale et al., 2018; Pretzsch 
et al., 2018; Barberán et al., 2019; Sultan et al., 2019; Huang and Hao, 
2020; Godde et al., 2021), and ecosystem services (Grêt-Regamey and 
Weibel, 2020) across many different mountainous regions, for example in 
Africa (Bondé et al., 2019; Musakwa et al., 2020), Asia (Guo et al., 2018; 
Sunderland and Vasquez, 2020), Europe (Nair, 2019), North America

(Hupp et  al., 2015; Prevéy et  al., 2020) and South America (Herman-
Mercer et al., 2020) (Sections 5.4, 5.4.1, 5.5.1, 5.6.2, 5.7, 5.11.1.1).
Ecosystem products are vital to support the livelihoods and 
economic prospects for communities living in and around mountains 
(Figure CCP5.3). For instance, collection and trade of caterpillar fungus 
contributed to 53.3–64.5% annual household cash income in Nepal 
(Shrestha and Bawa, 2014; Shrestha et al., 2019); 40–80% in Bhutan 
(Thapa et al., 2018) and 60–78% in Uttarakhand, India (Laha et al., 2018; 
Yadav et al., 2019) (Section 5.7.1). Livelihood support from ecosystem 
products in southern Malawi (Pullanikkatil et al., 2020), southwestern 
Ethiopian mountains (Nischalke et  al., 2017), Southern China (Min 
et al., 2017), Himalayan mountains (Nepal et al., 2018) and South Africa 
(Ngwenya et al., 2019) has been reported. Additionally, the sacredness 
of mountains in different religions and cultures is widely acknowledged

(Ceruti, 2019; Benedetti et al., 2021).
Climate change and its associated impacts on multiple ecosystem 
services and related products (timber production, carbon 
sequestration, biodiversity and protection against natural hazards) 
have been observed across European mountains, for example in the 
central Iberian Mountains (Spain), Western and Eastern Alps (France, 
Austria) and Dinaric Mountains (Slovenia) (Mina et al., 2017). Dumont 
et  al. (2015) demonstrated that climate change negatively affects 
forage nitrogen (N) content by 8% but increases total non-structural 
carbohydrate content by 25% in European mountains. Positive impacts 
have been reported on mushroom productivity in the mountains of 
Spain (Karavani et  al., 2018) (Section  5.7.3.3), yet negative impacts 
have been reported on the Ophiocordyceps in the Himalayan region 
(Hopping et  al., 2018), as well as on apple production in Himachal 
Pradesh, India, which declined by 9.4 t per hectare in the past two

decades (Das, 2021). Shifts in the richness of crop wild relatives from 
south to north and an increase in the numbers of threatened taxa with 
an increase of 1.5°C and 3°C temperature rise have been observed in 
European mountains (Phillips et al., 2017).
Medicinal and aromatic plants and their secondary metabolites are 
also observed to be affected by climate change (medium confidence) 
(Das et al., 2016; Zhang et al., 2019a). Phenological changes like early 
flowering and reduced vegetative phase are negatively affecting the 
productivity of such plants (Harish et  al., 2012; Gaira et  al., 2014; 
Maikhuri et  al., 2018). While increasing atmospheric temperature 
and CO 2 are reported to improve the biomass of Gynostemma 
pentaphyllum (Chang et  al., 2016) (Section  5.7.3.3), they adversely 
affect its antioxidant compounds/activity, health-promoting properties 
and phytochemical content (Gairola et al., 2010; Das et al., 2016; Kumar

et  al., 2020). Experimental trials have shown that when medicinal 
plants are stressed by drought, phytochemical content increases, either 
by a decrease in biomass or by an increase in the actual production of 
metabolites (medium confidence) (Selmar and Kleinwächter, 2013; Al-
Gabbiesh et al., 2015). The strong effects of climatic and non-climatic

CCP5
2284Cross-Chapter Paper 5 Mountains
Climate change and mountain social-ecological systemsCLIMATE CHANGEDIRECT IMP ACT INDIRECT IMP ACT
ADAPT ATION OPTIONS IN MOUNT AIN REGION SPeople in and around mountain regions depend 
on these services for livelihood, income
generation, food, health and well-beingMountain ecosystems provide
 vital services, including water , forest,
carbon storage and cultural values
Precipitation changes
(e.g. drying up springs,
erratic rainfall, cloud burst)
Decreasing regeneration potential 
Threatening native and endemic speciesShortage of fodder 
Declining livestock populationsClimate induced
hazards
(e.g. landslides)
Altering habitat conditions Increasing 
wildfire weather 
Decreasing quality and 
quantity of ecosystem products Decreasing 
pollinator diversity
Promote conservation of 
native flora and fauna
and their habitat restoration Promote water harvesting
(roof top, rain water)
and multi-purpose projects

for disaster risk management Promote mountain 
products (wild edibles, 
medicinal plants, 
cash crops, ecotourism)
Restoration of degraded 
land/wastelandPromote 
agroforestry practices
Crop diversification/
crop changeEducation and 
awareness-
buildingPoverty
Food, nutrition and health insecurity
Outmigration                       
Increase in area of fallow land
Help species to 
adapt and protect refugia[CCP5.2.1, CCP1, Ch 2.4]
[CCP5.2.3, CCP5.2.5,
Ch 2.4, Ch 8.2][CCP1, Ch 2.4,
Box 5.3]
[Ch 2.1, Ch 5.7, Ch 8.2.1, Ch16][Ch 2.2, Ch 4, Ch 5 (ES), CCP1.2.2]
[CCP5.2.3, CCP5.4.3, Box 2.2,
Ch 5.6.3, CCB-FEASEB]
[CCP5.4.1, Ch 2.2.6,
Ch 15.5.4, CCB-FEASEB][CCP5.2.6, CCP5.2.7.2, Ch 4.7.1.1][CCB-MIGRA TE][CCP-BOX 5.1, CCP1.2.2
WGI AR6 Ch 1 1.8.3][CCP5.2.2, CCP5.2.6,
Ch 4.2.6]
[CCP5.2.6, CCP1,
Ch 4.2.6, Ch 16]
[CCP-BOX 5.1, CCP5.2.1, CCP5.2.3] [CCP5.2.3, CCP5.2.5]
[CCP5.2.3, CCP5.2.5, 
Box 2.2, Ch 5.7]
[CCP5.2.7] [CCP5.2.1, CCP5.2.6, CCP5.2.7] [CCP5.2.7] [CCP5.2.7, CCP5.4.3]

Figure CCP5.3 |  Impact of climate change on mountain social-ecological systems, including ecosystem services and products, livelihoods of mountain 
people and examples of adaptation options to address direct and indirect impacts.

CCP5
2285Mountains  Cross-Chapter Paper 5
factors have been observed to affect the distribution of selected 
medicinal plant species in northern Thailand (Tangjitman et al., 2015), 
as well as in Egypt, sub-Saharan Africa, Spain, Central Himalaya, China 
and Nepal, with some species at risk of extinction (Munt et al., 2016; 
Yan et al., 2017; Brunette et al., 2018; Chitale et al., 2018; Zhao et al., 
2018; Applequist et al., 2020). Negative climate-related impacts on the 
distribution range of 41 medicinal plant species have been predicted 
for Spanish and Asian mountains (Munt et al., 2016) (Section 5.7.3.3), 
as has a decreasing size of fruits of Myrica esculenta in the Himalaya 
(Shah and Tewari, 2016).
CCP5.2.4 Cities, Settlements and Key Infrastructure
Mountain settlements and people are globally distributed and 
represent a significant proportion of the total global population that is 
exposed to the effects of climate change (Section CCP5.1, SMCCP5.1).

Cities with one or several million inhabitants located in mountainous 
environments or at high elevations are predominantly found in Latin 
America (e.g., El Alto and La Paz, Bolivia; Quito, Ecuador; Mexico 
City, Mexico; and Bogota, Colombia), Asia (e.g., Kabul, Afghanistan; 
Kathmandu, Nepal; Srinagar and Dehradun, India; Peshawar and 
Quetta, Pakistan; and Xining and Kunming, China) and Africa (e.g., 
Harare, Zimbabwe; and Addis Ababa, Ethiopia) (Wang and Lu, 2018; 
Balderas Torres et al., 2021; Ehrlich et al., 2021). Mountain regions are 
also host to many settlements with fewer than 500,000 inhabitants 
(Alfthan et  al., 2016). In many cases, particularly in developing 
countries, portions of the population also reside in informal and low-
income settlements (French et al., 2021), where rates of poverty and 
inequality exacerbate people’s vulnerability and exposure to climate-
related hazards such as landslides (Alfthan et  al. 2018) (Section

CCP5.2.5.1), environmental pollution or even pandemic diseases 
(Marazziti et al., 2021).
In many mountain regions, particularly in developing countries, the 
increasing urban population has put considerable pressure on water 
services and basic amenities for urban dwellers (Singh et al., 2021), 
for example in cities such as La Paz (Kinouchi et al., 2019), which are 
regions already under pressure due to the negative effects of climate 
change, coupled with poor water availability and governance (Chapter 
4; CCP5.2.2.1; FAQ CCP5.1; Hock et al. 2019). In many areas of the HKH 
region, water demand far exceeds municipal supply, and people cope 
with water insecurity in a variety of ways (Bharti et al., 2020; Sharma 
et al., 2020; Singh et al., 2020), such as by resorting to interbasin water 
transfers and deep pumping, to supply their water needs (Ojha et al., 
2020). Additionally, influxes of migrants, tourists and retirees, combined

with the growth of the incumbent population, place considerable stress 
on urban infrastructures that must supply adequate clean water and 
provide for sewage disposal (Prakash and Molden, 2020), which is also 
observable in other regions (Chapter 4; Section 6.4.7; Case Study 6.1 
in Chapter 6). Energy provision in and around mountain settlements 
is another key sector affected by climate-related impacts (Hock et al., 
2019; CCP5.2.2.2), which bears relevance for the adaptation prospects 
for urban mountain settlements (medium confidence).CCP5.2.5 Mountain Communities, Livelihoods, Health and 
Well-Being
People living in and around mountain regions strongly depend for 
their livelihoods, health and well-being on the ecosystem functions, 
services and resources available in these areas. Overall, subsistence 
agriculture and livestock remain key sources of livelihood in many 
mountain regions (FAO, 2019), with non-agricultural income sources

such as remittances, small businesses, medicinal plants, wage labour 
and tourism also contributing to these economies (Montanari and 
Koutsoyiannis, 2014; Palomo, 2017; Minta et  al., 2018). This section 
provides an illustrative overview of key reported observed impacts of 
climate change on mountain communities and adaptation responses 
(Table CCP5.1), as well as impacts on livelihood activities and economic 
sectors such as agriculture and pastoralism and tourism and recreation 
(Table CCP5.2), reported since AR5.
Other sections in this CCP provide detailed assessments that 
synthesise impacts associated with the detection of climate change 
and the attribution of those impacts to anthropogenic climate change 
(CCP5.2.7), projected impacts and key risks (CCP5.3) and adaptation 
responses to reduce those key risks (CCP5.4.1).
CCP5.2.6 Natural Hazards and Disasters
Climate- and weather-related disasters in mountain regions have

increased over the last three decades (medium confidence). Disaster 
frequency shows increasing trends in the HKH, the Andes and 
mountain regions in Africa, whereas no clear trends are observed for 
the European Alps and Central Asia (medium confidence) (Froude and 
Petley, 2018; Stäubli et al., 2018).
Floods, debris flows, landslides and avalanches are the most frequent 
hazards affecting the highest number of people in mountain regions 
(medium confidence) (Stäubli et  al., 2018). Landslides count among 
the deadliest hazards globally, with over 150,000 reported fatalities 
for the period 1995–2014 (Haque et al., 2019). There is high confidence 
that the number of fatalities from landslides has increased globally 
over the past 20 years (Froude and Petley, 2018; Haque et al., 2019), 
but there is limited evidence that this is due to changes in landslide 
event frequency and/or magnitude. Infrastructure expansion on

unstable terrain can increase disaster risk (Zimmermann and Keiler, 
2015; Huggel et al., 2019; Kirschbaum et al., 2019; Schauwecker et al., 
2019; Terzi et al., 2019; Motschmann et al., 2020a; Shugar et al., 2021). 
A study from western Nepal concludes that the exposure of people and 
infrastructure to hazards has been the main cause of disasters (Muñoz-
Torrero Manchado et al., 2021). Decreasing numbers of fatalities from 
disasters resulting from decreasing vulnerabilities have been reported 
in Europe and North America (Section 13.2.2.1) (Gariano and Guzzetti, 
2016; Strouth and McDougall, 2021). Evidence from Africa suggests 
that disasters from climate-induced natural hazards in mountain areas 
are often due to droughts, pests and changes in rainfall and associated 
impacts on smallholder farmers’ agricultural livelihoods (Shikuku et al., 
2017).

CCP5
2286Cross-Chapter Paper 5 Mountains
Table CCP5.1 |  Overview of key observed impacts and adaptation on mountain communities—livelihoods and poverty; migration, habitability and displacement; health and 
well-being.
Overview of key observed impacts on mountain communities and adaptation responses References and relevant AR6 WGII sections
Mountain livelihoods and poverty
Impacts –In some mountain regions, the incidence of poverty can be higher compared to other areas, with observed 
impacts of climate change intensifying the deterioration of socioeconomic conditions that support 
livelihoods, thereby exacerbating already existing conditions of non-climate-related vulnerabilities and 
livelihood insecurity (medium confidence).Gioli et al. (2019), Tiwari and Joshi (2012), Rasul and 
Hussain (2015), Hussain et al. (2019), McDowell and 
Hess (2012), FAO (2015, 2019), Shrestha et al. (2015), 
Motschmann et al. (2020a), Section 8.3
Responses and

adaptation –Diversification of livelihoods through integration of drought-resilient livestock and crops and changes in 
farming practices (i.e., water management or migration of crops from lowland to highland) with some 
shifting to non-agricultural livelihood options, reported for cases such as in the HKH, the Andes, Rwenzori 
Mountains of Uganda and Simien Mountains of Ethiopia.Ashraf et al. (2014), Hussain et al. (2016a), Skarbø and 
VanderMolen (2016), Nkuba et al. (2020), Yohannes et al. 
(2020), CCP5.4.1
Migration, habitability and displacement
Impacts –There is growing evidence of links between climate change impacts and migration and mobility through 
a complex web of causal links (medium confidence). In mountain contexts, migration and mobility 
are indirectly impacted by climate change through adverse effects on mountain livelihoods that are 
dependent on mountain ecosystem services.Wrathall et al. (2014), Hunter et al. (2015), Brandt

et al. (2016), Mastrorillo et al. (2016), Gautam (2017), 
Sagynbekova (2017), Cattaneo et al. (2019), Maharjan 
 –Extreme events are resulting in temporary and, in some cases, permanent displacement of populations 
in mountains (medium confidence), with hazards such as floods and mass movement (avalanche, 
flood, landslide) leading to population displacements (e.g., in Afghanistan, Pakistan, Peru, Thailand and 
Uganda).Iribarren Anacona et al. (2015), Stäubli et al. (2018), 
IDMC (2020), Wang et al. (2020)
 –Cases of entire settlements either abandoned or relocated due to prolonged slow onset events such as 
water shortage, drought and heat stress have been reported.Mueller et al. (2014), Nawrotzki and DeWaard (2016), 
Prasain (2018)
 –In contrast, place attachment is increasingly cited as one of the reasons for the immobility choices for 
some people. However, in some cases, vulnerability to climatic events contributes to the in-migration

decisions of vulnerable populations exposed to hazards from downstream to upland areas.Adams (2016), Dandy et al. (2019), Khanian et al. (2019), 
Islam et al. (2020)
Responses and 
adaptations –Migration, in turn, is often cited as a risk management strategy, where migration can lead to the 
diversification of livelihood options, improves access to information and resources and expands social 
networks, all of which can support households in their capacity to adapt to climate change impacts.
 –Migration is often gendered, with men migrating and leaving women to manage households at origin. 
Women’s capacities are often constrained due to institutional barriers and social norms, resulting in low 
adaptive capacity and increased vulnerability to hazards. Capacity-building interventions strengthen 
adaptation capacity and links to access institutional support ( medium confidence).Banerjee et al. (2018), Banerjee et al. (2019), Siddiqui

et al. (2019), Maharjan et al. (2020), Maharjan et al. 
(2021)
Health and well-being
Impacts –Direct links between climate change and health in mountain regions are reported in terms of physical 
injury or fatality due to exposure to climate-related hazards such as floods or landslides or to 
vector-borne diseases such as malaria or dengue fever reported at higher elevations with warming 
temperatures (medium confidence), such as in Mexico, Nepal, Ethiopia and Colombia.Dantés et al. (2014), Siraj et al. (2014), Dhimal et al. 
(2015), Wu et al. (2016), Equihua et al. (2017), Alfthan 
et al. (2018), Gilgel et al. (2019), Chapter 7
 –Indirect impacts on health of climate change are linked to water-borne diseases and pathogens 
associated with floods and droughts.Table 7.6
 –While reports on the ongoing challenges associated with the COVID-19 pandemic are emerging in 
relation to their compounding impacts on adaptive capacities, there is limited evidence to assess those

effects with respect to other climate-related impacts on health.Baiker et al. (2020), Cross-Chapter Box COVID in Chapter 
7
 –Mental health issues associated with climate-related impacts have been reported with respect to climate 
anxiety and ecological grief and their effects on the well-being of individuals. For example, the grief 
and loss associated with changes in glaciated landscapes, such as the ‘death’ of the Okjökull glacier in 
Iceland. However, there is limited evidence on mountain-specific cases and experiences that would allow 
for an assessment of the broader and longer-term impacts on mental health associated with a changing 
climate in mountains.Trombley et al. (2017), Cunsolo and Ellis (2018), Clayton 
(2020), Sideris (2020)
 –Other heightened vulnerability to climate-related impacts on health and well-being are also experienced 
by specific groups, for example Sami pastoralists facing changes in mountain snow cover that negatively

affect their reindeer herding, a key activity for their identity and spiritual health.Furberg et al. (2011), Section 7.1.7.2
adaptations –Approximately a fifth of observed adaptations reported in the GAMI mountain reanalysis address health 
and well-being as an aspect of vulnerability. This includes raising communities’ awareness of and coping 
strategies for climate-change-induced health issues.Furu and Van (2013), Section CCP5.4.1

CCP5
2287Mountains  Cross-Chapter Paper 5
Table CCP5.2 |  Overview of key observed impacts and adaptation on select livelihood activities and economic sectors—mountain agriculture and pastoralism, and tourism and 
recreation.
Overview of key observed impacts and adaptation on select livelihood activities and economic 
sectorsReferences and relevant AR6 WGII sections
Mountain agriculture and pastoralism
Impacts –Changes in temperature and seasonal precipitation patterns affect the timing and 
availability of water for agricultural activities (high confidence), for example in the 
Bolivian Andes; the Andean-Amazon foothills of Colombia, Ecuador and Peru; the High 
Atlas of Morocco; HKH; and the Golestan province of Iran.Rangecroft et al. (2013), Kaboosi and Kordjazi (2017), 
Hussain et al. (2018), Kalbali et al. (2019), Zkhiri et al. 
(2019), Beltrán-Tolosa et al. (2020), Torres-Batlló and 
Martí-Cardona (2020)

–Changes in temperature and seasonal precipitation patterns are reported to affect 
nutrient depletion of soils and increased incidence of pest attacks in crops (e.g., in 
cases in the HKH and in Peru); however, there is generally limited evidence on directs 
links specifically to climate-related changes in mountain regions.Oliver-Smith (2014), Hussain et al. (2016b)
 –Climate-induced hazards, such as erratic precipitation (rain, snow and hail), floods, 
droughts and landslides, have negatively affected the stable supply and transport of 
agricultural products in and out of remote mountain areas, such as in the Peruvian 
Altiplano and HKH.Hussain et al. (2016b), Gonzales-Valero (2018), Thapa and 
Hussain (2020)
 –Warming temperatures and changes in the timing of seasons and frost conditions 
needed for seeding certain tree crops impact lower-elevation mountain areas, such as 
in Oman.Buerkert et al. (2020)

–Drought conditions negatively affect mountain grasslands ( medium confidence), as 
reported in cases in Tyrol (Austria), Nepal, Afghanistan, Pakistan and China, which can 
contribute to a decline in agrobiodiversity.Ashraf et al. (2014), Zomer et al. (2014b), Grüneis et al. 
(2018), Adhikari et al. (2019), Chaudhary et al. (2020), 
Hussain and Qamar (2020)
 –In some cases, climate-related hazards lead to outmigration in mountain areas, with 
indirect negative impacts on labour deficits to support agricultural practices and 
productivity in mountain areas (medium confidence) (e.g., in Ghana, Tanzania, Thailand 
and HKH).Warner and Afifi (2014), Wester et al. (2019)
 –Positive impacts (favourable growing conditions) are reported for the production of 
some fruits and vegetables in the Gilgit-Baltistan province of Pakistan and for the 
production of traditional crops (e.g., local beans) in the Karnali region of Nepal.Hussain et al. (2016b), Thapa and Hussain (2020)

–Impacts on pastoralism include changes in growing conditions associated with 
warming temperatures and declining precipitation, which in turn lead to negative 
impacts on livestock productivity, food security and livelihoods of pastoralist 
communities, including drought-induced degradation of rangelands ( medium 
confidence) (e.g., in mountainous areas of Mongolia, Tanzania, Nepal and Ethiopia), 
which exacerbate impoverished conditions in pastoral communities.Batima et al. (2013), Rasul et al. (2014), Gentle and Thwaites 
(2016), Kimaro et al. (2018), Mekuyie et al. (2018), Tiwari 
Responses and adaptations –Recharging groundwater and adopting rainwater harvesting (including appropriate 
tillage methods to improve soil moisture), restoration and rehabilitation of land, 
diversification of agricultural crops (including introduction of stress resistant crop 
varieties), promotion of in situ (protected areas, conservation areas) and ex situ

(nurseries, gene banks, home gardens) conservation strategies, afforestation and 
agroforestry.Sections 4.7.1.1 and 5.6.3, Cross-Chapter Box FEASIB in 
Chapter 18
 –Local knowledge is used to help maintain the productive and cultural value of 
mountain agriculture and pastoralism, such as in the French and Italian Alps, Western 
Himalaya in India and the mountains of northern Morocco.Fassio et al. (2014), Kmoch et al. (2018), Das (2021)
 –Ecosystem- and community-based adaptations contribute to supporting the diversity 
and complementarily of management options, permaculture and local capacities to 
adapt and support ecosystem functions vital for agrobiodiversity ( medium confidence).Reid (2016), Grêt-Regamey and Weibel (2020), 
Cross-Chapter Box NATURAL in Chapter 2
Tourism and recreation
Impacts –Since SROCC, the literature on climate change impacts on winter skiing tourism 
has remained dominated by studies focused on future climate change impacts and

projected risks due to decreasing seasonal snow reliability (CCP5.3.1), most relevant 
when considering snow management and, in particular, snow-making.Hock et al. (2019), Sauri and Llurdés (2020), AR6 WG1 
Sections 9.5.3 and 12.4.10.4
 –Climate-induced hazards in mountains, such as rockfalls, negatively affect access to 
some climbing, mountaineering and hiking routes in summer ( medium confidence), 
with cases mainly reported in the European Alps.Hock et al. (2019), Mourey et al. (2019, 2020)
 –Higher temperatures and extreme heat conditions at lower elevations have made some 
mountain destinations more appealing for human comfort, increasing the potential 
summer visitation demand and opportunities for tourism and recreation in mountains, 
such as in the European Alps and the Catalan Pyrenees (medium confidence). However, 
there is limited evidence on similar trends in mountain regions outside of Europe.Serquet and Rebetez (2011), March et al. (2014),

Pröbstl-Haider et al. (2015), Steiger et al. (2016), Juschten 
et al. (2019a, b)

CCP5
2288Cross-Chapter Paper 5 Mountains
The characteristics of natural hazards in mountain areas have been 
widely explored, and evidence suggests that conditions favouring 
cascading impacts are a common feature (high confidence) 
(Section 8.2.1.1) (Zimmermann and Keiler, 2015; Huggel et al., 2019; 
Kirschbaum et al., 2019; Schauwecker et al., 2019; Terzi et al., 2019; 
Motschmann et  al., 2020a; Shugar et  al., 2021). Compound and 
cascading impacts have affected people, ecosystems and infrastructure 
and generate significant spillovers across numerous sectors, resulting 
in destructive impacts (Nones and Pescaroli, 2016; Kirschbaum et al., 
2019; Schauwecker et al., 2019).
Most adaptation responses to natural hazards in mountain regions are 
reactive to specific climate stimuli or post-disaster recovery (robust 
evidence, medium agreement) (McDowell et  al., 2019; Rasul et  al., 
2020). Hard structural measures such as dikes, dam reservoirs and

embankments have been widely employed to contain hazards, along 
with early warning systems, zonation and land management (Box 4.1, 
10.4.4.5, 12.5.3 and 13.2.2). Awareness raising, preparedness and 
disaster response plans are increasingly used in the context of more 
unpredictable hazard trends (see Cross-Chapter Box DEEP in Chapter 
17) (Allen et al., 2016, 2018; Hovelsrud et al., 2018). Ecosystem-based 
adaptations (EBAs) are widely implemented to mitigate risks from 
shallow landslides (e.g., afforestation and reforestation and improved 
forest management), floods (e.g., river restoration and renaturation) 
(Renaud et al., 2016; Klein et al., 2019b) and droughts (e.g., adapting 
watershed) (Renaud et  al., 2016; Klein et  al., 2019b; Palomo et  al., 
2021).
Evidence from different mountain regions shows that adaptation and 
risk reduction efforts are less successful if they focus on hazards or 
risks without considering diverse risk and value perceptions of the

affected people (medium confidence) (French et al., 2015; Allen et al., 
2018; Hovelsrud et  al., 2018; Kadetz and Mock, 2018; Klein et  al., 2019b). Previous experience and local social contexts of exposure to 
climate-related disasters affect people’s perceptions and influence the 
patterns associated with disaster risk management and associated 
coping strategies (high confidence) (SROCC Chapter 2 (Hock et  al., 
2019)), (Kaul and Thornton, 2014; Shijin and Dahe, 2015; Landeros-
Mugica et al., 2016; Wirz et al., 2016; Carey et al., 2017; Adler et al., 
2019).
Important synergies exist between disaster risk reduction, climate 
change adaptation and sustainable development in mountain regions 
(medium confidence) (Zimmermann and Keiler, 2015), where the 
multiple and diverse perceptions of risk and risk tolerance for natural 
hazards are relevant considerations (Schneiderbauer et  al., 2021). 
Global agreements for integrated disaster risk management and

climate change adaptation (Alcántara-Ayala et al., 2017), including the 
Sendai Framework for Disaster Risk Reduction 2015–2030 (UNISDR, 
2015), the SDGs (UN, 2015), the Paris Agreement (UNFCCC, 2015) and 
the New Urban Agenda-Habitat III (UN, 2016), create opportunities 
for synergies to address disaster risks (see also Section 6.3). Although 
these agreements are well established in international agendas, there 
is limited evidence of their implementation to address disaster risk 
reduction and adaptation in mountains (Alcántara-Ayala et al., 2017).
CCP5.2.7 Synthesis of Observed Impacts and Attribution 
and Observed Adaptations
CCP5.2.7.1 Observed Impacts and Attribution to Anthropogenic 
The assessment of observed impacts identified a large number of 
impacts across all major mountain regions of the world and for a large

variety of systems, based on more than 300 references (SMCCP5.2). Overview of key observed impacts and adaptation on select livelihood activities and economic 
Responses and adaptation –Diversification of tourism activities to non-snow activities has been reported as an 
adaptation approach to maintaining economic viability in some winter ski areas, partly 
due to the high cost of running snow-making infrastructure in winter, for example in 
the Pyrenees (Europe) and Australian Alps.Morrison and Pickering (2013), Sauri and Llurdés (2020)
 –In some cases, managing water resource availability and demand for snow-making is 
reported, with destination and large-scale governance highlighted as critical aspects for 
managing trade-offs, including overcoming conflicts arising from competing demands 
for environmental resources and land use (e.g., in French Alps and in Scandinavia).Demiroglu et al. (2019), Gerbaux et al. (2020)

–For snow management, examples exist of dedicated climate services designed to 
enable better-informed decision- making on appropriate long-term adaptation (e.g., 
through a dedicated Copernicus Climate Change Service or real-time early warning 
systems).Köberl et al. (2021), Morin et al. (2021)
 –Barriers to adaptation strategies such as snow-making, for instance in Switzerland, 
have been linked to perceived economic constraints on their implementation, as well as 
the social acceptability of these measures.Matasci et al. (2014), Moser and Baulcomb (2020)
 –Adaptation options to limit exposure to hazards in hiking, climbing or mountaineering 
activities include shifting the seasonal timing for these activities or changing routes 
entirely.Hock et al. (2019), Mourey et al. (2019, 2020)
 –In some cases, such as in Bolivia, Peru and New Zealand, and more recently reported 
in the French Alps, ‘last chance’ tourism has increased the appeal of some mountain

destinations, resulting in visitation demand to witness the effects of climate change on 
iconic mountain landscape features such as glaciers.Hock et al. (2019); Salim and Ravanel (2020)

CCP5
2289Mountains  Cross-Chapter Paper 5
The literature was assessed and the results classified on a per-region 
and per-system basis. Confidence statements on detection and 
attribution are based on expert judgement following IPCC guidelines 
(Section  1.3.4), building on evidence from multiple sources in the 
literature (Mach et al., 2017) (SMCCP5.2). Figure CCP5.4 provides an 
overview of the assessment results.
Climate change impacts have been documented in mountains on all 
continents. A wide range of human and natural systems have been 
affected by climate change to date, including the cryosphere, water 
resources, terrestrial and aquatic ecosystems, agriculture, tourism, 
energy production, infrastructure, health and life, migration, disasters 
and community and cultural values. The confidence levels for the 
detection of impacts are generally in the range of medium to high. The 
contribution of climate change to detected impacts varies depending

on the affected system and on climatic and non-climatic drivers. The 
highest levels of confidence for the attribution of detected impacts 
to anthropogenic climate change are related to the cryosphere. More 
generally, those impacts are more strongly driven by increasing 
temperatures and show higher confidence for attribution than 
those impacts driven mainly by precipitation changes. The level of 
contribution of climate change to observed impacts is predominantly 
medium or high, indicating the high sensitivity of natural and human 
systems in mountains to climate change. Furthermore, the vast majority 
of detected impacts imply negative impacts on natural and human 
systems (high confidence).
Local knowledge plays an important role in documenting impacts of 
climate change in mountain regions. Since IPCC AR5, the evidence 
for meaningful climate change impacts being reported using local 
knowledge sources has increased substantially (high confidence).

Similarly, important regional gaps present in the IPCC AR5 are 
addressed here (e.g., Africa), resulting in a much more comprehensive 
and regionally balanced assessment and perspective.
Furthermore, the science of attributing negative impacts of climate 
change to anthropogenic emissions or even individual polluters is 
becoming increasingly important for climate litigation (Marjanac et al., 
2017; McCormick et al., 2017; Otto et al., 2017; Setzer and Vanhala, 
2019), and there is emerging evidence that mountains are becoming 
sites of litigation cases, with cases, for instance, in Peru, Colombia 
and India (UNEP , 2017). Recent studies put litigation cases such as 
the Lliuya vs RWE (the German multi-national energy company) 
case, on the risk of glacier lake floods in Peru, in a broader context of 
differentiated responsibilities and justice (Huggel et al., 2020b).
CCP5.2.7.2 Synthesis of Observed Adaptation
Extending from recent assessments of observed adaptation in high

mountain areas (Hock et al., 2019; McDowell et al., 2019) new evidence 
for the geographically larger space for mountains assessed in this CCP 
is available from a mountain-specific reanalysis of the GAMI data 
set, which contains 423  articles reporting adaptation in mountains 
(Berrang-Ford et  al., 2021; McDowell et  al., 2021b) (SMCCP5.3), 
some of which also include those reported in Section CCP5.2. In 
these articles, adaptation measures in mountains are reported from 
all regions worldwide, with a preponderance from Asia and Africa. Of all reported adaptations, 91% involve individuals or households, 
frequently engaged in smallholder agriculture and/or pastoralism; local 
governments are also often involved (31%), as are sub-national or local 
civil society actors (29%), while private-sector involvement remains 
scarce (below 10%). Food, fibre and other ecosystem products (76%) 
and poverty, livelihoods and sustainable development (55%) are by far

the most often reported adaptations in mountains, followed by water 
and sanitation (28%) and health, well-being and communities (26%) 
(McDowell et al., 2021b) (SMCCP5.3.2).
Adaptation measures most commonly found include farming-
related changes (e.g., resilient or drought-tolerant crop varieties, 
irrigation techniques, crop storage and livestock insurance schemes), 
infrastructure development, Indigenous knowledge, community-
based capacity-building and ecosystem-based adaptation (high 
confidence) (McDowell et  al., 2021b) (SMCCP5.3.2). Nature-based 
solutions (NbS) are an adaptation component in the nationally 
determined contributions (NDCs) of many mountain countries around 
the world (UNEP , 2021). Furthermore, Indigenous knowledge and 
local knowledge are often reported as informing adaptation efforts, 
and Indigenous Peoples, marginalised people and gender issues are 
recognised in several national adaptation strategies, but autonomous

responses are often insufficiently understood (Mishra et al., 2019).
The GAMI-based reanalysis for mountains indicates that food security 
(75%), poverty (47%), consumption and production (36%), terrestrial 
and freshwater ecosystem services (19%) and clean water and sanitation 
(18%) are important aspects of vulnerability that adaptations address, 
with an emphasis on responses to climate-related shocks and stressors 
(McDowell et al., 2021b) (SMCCP5.2). The reanalysis also shows that 
more than 80% of adaptations in mountains are behavioural/cultural 
in nature, and more than 50% are ecosystem-based or technological 
or infrastructural.
About a third of the assessed adaptation activities are in the planning 
and early implementation stage, and around a fifth are in a stage of 
advanced implementation (McDowell et  al., 2021b) (SMCCP5.3.2). 
Several lines of evidence converge, indicating that most observed 
adaptations in mountains are incremental in nature and not

transformative (high confidence) (Mishra et al., 2019; McDowell et al., 
2021b) (SMCCP5.3.2). Nevertheless, some adaptation measures such 
as NbS were found to have important transformative potential in 
mountains if different knowledge types are combined, and community 
engagement and ecosystem management processes are in place 
(Palomo et al., 2021).
Overall, and consistent with the findings in SROCC, the systematic 
monitoring and evaluation processes that have been implemented 
to track adaptation progress remain limited, and there is limited 
evidence and prevailing uncertainties on the extent to which observed 
adaptation efforts reduce risks (Hock et  al., 2019; McDowell et  al., 
2021b; UNEP , 2021) (SMCCP5.3.2).
Limits to adaptation are found in a majority (>80%) of the assessed 
adaptation studies; around half of the studies reported soft limits, and 
less than a third identified both hard and soft limits to adaptation

(high confidence) (McDowell et al., 2021b) (SMCCP5.3.2). Soft limits

CCP5
2290Cross-Chapter Paper 5 Mountains
are frequently related to governance, economics and social/cultural 
constraints and can be overcome in principle through targeted efforts 
to address social conditions that impede adaptation planning and 
action. Hard limits are more frequently described as biophysical, 
such as precipitous declines in water supply. Examples of adaptation 
limits include lack of access to credit and markets, fixed livelihoods, 
insufficient awareness of climate risk, poor access to technology, the 
erosion of existing skills and knowledge, social inequities, lack of 
trust and social cohesion, inequitable gender norms and perceptions 
of conflict or scarcity. Furthermore, land tenure insecurity, poor 
integration of adaptation programmes across governing scales and a lack of decision-making power among vulnerable groups, along 
with inadequate funding for government-implemented adaptation 
programmes, are reported to limit adaptation (Mishra et  al., 2019;

McDowell et al., 2021b) (SMCCP5.3.2). Hard limits imply that further 
adaptation action is unfeasible, ineffective or unacceptable, resulting 
in inevitable losses and damages in mountain areas (medium evidence, 
medium agreement) (Huggel et al., 2019).
Overall, adaptation in mountain regions is taking place in various 
ways, in different sectors, scales, levels, quality, and effectiveness 
(high confidence). Most responses are incremental, with asymmetries Detection and attribution of observed impacts of anthropogenic climate change in mountain regions
Africa AsiaCentral and
South AmericaEuropeNorth 
AmericaAustralasia
Water
Terrestrial
and livestock
Tourism
Migration
Health
and life
Disasters
Community 
change and 
cultural valuesGlobal
Low Medium HighContribution of Climate Change
0–25 25–50 50–75% of negative impacts
75–100Number of
referencesAttribution Confidence
Low Medium HighDetection Confidence 
Low Medium High Very high107 29%
83 11%
52 21%

102 69%
30 17%
75 21%
23 87%743%7100%17 65% 27 22% 2 0%
813% 28 18% 2 0%
14 29% 956% 1 0%
57 89% 33 100%
1 0% 333% 1 0%
3100%
1100%
2100% 25 56% 4 0%
11 100%41%
9%
24%
67%
100%
33%
0%
75%91%34
35
17
3
4
6
10
121119
8
8
1
17
280%
0%
0%
0%
12%
0%8
2
3
5
60%
0%
0%
0%
0%
Not assessed% of local community
perception
Figure CCP5.4 |  Synthesis of detection and attribution of impacts of anthropogenic climate change on different natural and human systems in mountain 
regions. For each system and region assessed, the level of confidence for detection and for attribution to anthropogenic climate change is indicated. Also indicated is how strong 
the contribution of climate change is to the observed changes, considering climatic and non-climatic causal factors. Observed impacts were analysed in terms of negative impacts

(e.g., economic or non-economic damages, losses, contribution to increasing risks for society), where the numbers refer to the percentage of references indicating negative impacts 
for a given impact. The percentage of local community perception indicates the percentage of all literature references for a given system and region that account for local knowledge. 
The number of references refers to the total number of literature references considered for an impact on a specific system and region. ‘Not assessed’ refers to limited evidence in 
the literature (SMCCP5.2 and Table SMCCP5.5–5.14).

CCP5
2291Mountains Cr oss-Chapter Paper 5
of power among state, institutions and individuals, costs or capital 
requirements of adaptation, lack of coordinated planning, resistance to institutional change, household risk aversion, and lack of access to information inhibiting more transformational responses (SMCCP5.3.2). Aside from poverty reduction, there is limited evidence of adaptations effectively remediating the underlying social determinants of vulnerability (e.g., gender, ethnic identity).
CCP5.3  Pr ojected Impacts and Risks in 
Mountains
CCP5.3.1  Synthesis of Pr ojected Impacts

Declines and extinctions have been projected in a range of montane plants and animal species, including rare endemic species and sub-species due to climate change (medium evidence, high agreement) (Li et al., 2017; Ashrafzadeh et al., 2019; Brunetti et al., 2019; Zhang et al., 2019b; Manes et al., 2021). Up to 84% of endemic mountain species are found to be at risk of extinction (Manes et al., 2021). Using a simple model, Helmer et al. (2019) predict a large-scale contraction in the next 25 years of alpine ecosystems above tropical mountain cloud forest in the Andes due to tree invasion. Topographic complexity can smoothen and delay the transition of montane forests in terms of size and composition for warming up to 3°C GWL (Albrich et al., 2020).
Hydrological changes will determine how some ecosystems change,

more so than changes in temperature. For example, Dwire et al. (2018) found that changes in riparian areas, wetlands and forests were likely a result of climate change in the Blue Mountains in Oregon, USA, as a result of altered snowpack, hydrologic regimes, drought and wildfire. In the Bolivian Cordillera Real, wetland cover variations were associated with increases in extreme precipitation events and glacier melting over the 1984–2011 period but might be reversed with predicted future decreases in both total precipitation and glacier run-off (Dangles et al., 2017). About 30% of the wetland area in the Great Xing’an Mountains in northeastern China has been projected to disappear by 2050, with this value doubling by 2100 under the CGCM3-B1 scenario (Liu et al., 2011).
Climate change impacts on food, fibre and ecosystem products will be

highly variable across mountain regions (medium confidence) (Briner et al., 2013; Rasul and Hussain, 2015; Mina et al., 2017; Palomo, 2017; Said et al., 2019; Xenarios et al., 2019) (Sections 10.4, 12.3, 13.5 and 14.4). In some regions, tree crops that are cultivated at certain elevations may reach the limit of their agroclimatic plasticity, for instance for crop types that require winter chills and where projected growing conditions are too warm (Buerkert et al., 2020). In the European Alps, agricultural production in some areas may benefit from temperature rises, as total productivity in grasslands is projected to increase (Mitter et al., 2015; Grüneis et al., 2018), whereas some areas in Asia and South America heavily dependent on glacier- and snow-fed irrigation will be at risk of food insecurity (Rasul and Molden, 2019). In a study in Eastern Pamir, Mętrak et al. (2017) found that summer droughts and water changes lead to functional transformations of the wetland ecosystems which

can affect food security of the local population. Climate change affects the phenology of plants (Harish et al., 2012; Gaira et al., 2014; Maikhuri et al., 2018), secondary metabolites (Chang et al., 2016; Kumar et al., 2020) and pharmacological properties of medicinal plants (Gairola et al., 2010; Das et al., 2016).

Water resources in mountains and dependent lowlands will continue

to be strongly impacted by climate change throughout the 21st century (high confidence). The difference in impacts will be particularly strong in regions that greatly depend on glacier and snowmelt and, in pronounced dry seasons (high confidence), in regions including Central Asia, South Asia, tropical and subtropical western South America and southwestern North America (Huss and Hock, 2018; Hock et al., 2019; Immerzeel et al., 2020). Glaciers are expected to continue to lose mass throughout the 21st century, with higher mass loss under high emission scenarios (AR6 WGI Chapter 9 (Fox-Kemper et al., 2021)). Many low-elevation and small glaciers around the world will lose most of their total mass at 1.5°C GWL (high confidence) (Marzeion et  al., 2018; Vuille et al., 2018; Hock et al., 2019; Zekollari et al., 2020; Fox-Kemper et al., 2021) (WGI 9.5). For tropical and mid-latitude mountains, around half of the current ice mass can be preserved under low-emission scenarios, while between

two-thirds and up to more than 90% will be lost under high emission scenarios compared to the 2000s (medium confidence) (Schauwecker et al., 2017; Vuille et al., 2018; Hock et al., 2019; Fox-Kemper et  al., 2021) (WGI 9.5). Significant differences in impacts between the emission scenarios have also been assessed for declines in snow depth or mass at lower elevations [10 to 40% for RCP2.6 and 50 to 90% for RCP 8.5 by the end of the century (Hock et al., 2019)]. However, limitations in long-term climate, glaciological and hydrological monitoring data add uncertainty to the current understanding and adaptation support, for example, when peak water is reached in different mountain catchments (Salzmann et al., 2014; Hock et  al., 2019). Furthermore, context-specific sociocultural and economic factors can magnify or moderate impacts related to hydrological change (McDowell et al., 2021a).

The dependence of lowland populations on mountain water resources

will grow by mid-century across several climate and socioeconomic scenarios, and several seasonally dry or semiarid mountain regions (e.g., parts of South Asia, North America) are projected to be highly dependent on such resources (medium confidence) (Viviroli et al., 2020) (Figure  CCP5.2). Changing sediment, nutrient and pollutant flows due to climatic and non-climatic drivers will impact populations and economic sectors (medium evidence, high agreement). Hydropower in all mountain regions will experience higher fluxes of water and sediment in some seasons but lower water flow with demand from other water uses (e.g., irrigation) (Chevallier et al., 2011) in other seasons (Beniston and Stoffel, 2014; Gaudard et al., 2014; Majone et al., 2016; Caruso et al., 2017a, b; Patro et al., 2018). Recharge from groundwater and its buffer function is expected to decrease in the longer term (Somers and McKenzie, 2020). Glacier and snow depth or mass decline will impact current hydropower

facilities and production in various complex ways, requiring changes in hydropower management, with further potential for evidence-informed solutions (Gaudard et al., 2014; Schaefli, 2015; Schaefli et  al., 2019). On the other hand, deglaciation in mountain regions opens topographic space and, thus, potential for additional long-term hydropower development and production (Haeberli et  al., 2016a), with an estimated additional production of up to several hundred terawatt-hours per year, a potentially important contribution

CCP5
2292Cross-Chapter Paper 5 Mountains
to national energy supplies, in particular in the High Mountain Asia 
region (Farinotti et  al., 2019). However, water supply from glacier 
melt will decrease once source glaciers pass peak discharge (Huss 
and Hock, 2018), and the areas with available sediment will grow as 
glaciers shrink, posing potential risks to downstream populations and 
assets (high confidence) (Lane et al., 2019).
Since SROCC (Hock et al., 2019), several new studies have addressed 
projected impacts of future climate change on snow reliability in 
ski resorts, complementing previous findings or bridging existing 
knowledge gaps for winter tourism. This includes, in particular, 
new studies for China (An et  al., 2019; Fang et  al., 2019), showing 
that average ski seasons are projected to shorten (−4 to −61% for 
RCP4.5; −6 to −79% RCP8.5 in the 2050s) along with increases 
in snow-making water demand (27 to 51% for RCP4.5; 46 to 80%

for RCP8.5 in the 2050s), with large differences across the country. 
Changes in future snow reliability are projected across Europe at the 
national or pan-European scale (Demiroglu et al., 2019; Steiger and 
Scott, 2020; Morin et al., 2021), highlighting strong contrasts at the 
local (across ski resort size and/or elevation range, or local social or 
environmental context) and continental scales. Higher-latitude and 
high-elevation locations generally exhibit delayed declines in snow 
reliability compared to lower-latitude and lower-elevation locations 
(high confidence), consistent with assessment conclusions reached 
in SROCC (Hock et al., 2019). In general, climate change impacts and 
risks to ski tourism are found to be spatially heterogeneous, within and 
across local and international markets, with potential for significant 
disruptions to related socioeconomic sectors due to a growing 
mismatch between ski area supply and skier demand in the coming

decades (high confidence) (Fang et al., 2019; Hock et al., 2019; Steiger 
et al., 2021). These disruptions are plausible, even though a fraction of 
current ski resorts could technically operate in comparatively favourable 
locations (elevation, latitude) and operating models (business models, 
sociocultural assets and conditions, governance) (Steiger et al., 2020).
Severe damage and disruptions to people and infrastructure from 
floods are projected to increase in Northwestern South America 
(NWS), South Asia (SAS), Tibetan Plateau (TIB) and Central Asia (WCA) 
between 1.5°C and 3°C GWL, mainly driven by river floods and an 
increase in the number of glacial lakes with high potential for outburst 
(high confidence) (Drenkhan et al., 2019; Motschmann et al., 2020b; 
Furian et al., 2021; Zheng et al., 2021). For example, the formation of 
new lakes at the foot of steep icy peaks largely extends the hazard 
zones with respect to the earlier situation without lakes (Haeberli

et  al., 2016b). Projected changes in ice and snowmelt, as well as 
seasonal increases in extreme rainfall and permafrost thaw, will favour 
chain reactions and cascading processes, which can have devastating 
downstream effects well beyond the site of the original event (high 
confidence) (Cui and Jia, 2015; Beniston et al., 2018; Terzi et al., 2019; 
Vaidya et al., 2019; Shugar et al., 2021). The incidence of disasters is 
projected to increase in the future because some hazards will become 
more pervasive, with an increase in the exposure of people and 
infrastructure with future environmental and socioeconomic changes 
either contributing to reduce or enhance these disaster risks (medium 
confidence) (Klein et al., 2019b).CCP5.3.2 Key Risks Across Sectors and Regions
Key risks are derived from the detection and attribution assessment 
(CCP5.2.7) and from the projected impacts and risks (CCP5.3.1). 
The assessment is informed by evidence in the regional and sectoral

chapters and supports the key risk assessment in Chapter 16. Four key 
risks (KR1 to KR4) have been identified in this CCP and are presented in 
Sections CCP5.3.2.1–CCP5.3.2.4 (see SMCCP5.4 for methodology and 
references).
CCP5.3.2.1 KR1: People and Infrastructures at Risks from 
Landslides and Floods
The amount of people and infrastructure at risk of landslides will 
increase in regions where the frequency and intensity of rainfall events is 
projected to rise (Gariano and Guzzetti, 2016; Haque et al., 2019). Extreme 
precipitation in major mountain regions is projected to increase, leading 
to consequences such as floods and landslides (medium confidence). 
Rain-on-snow events, which can accelerate all flood stages and result 
in widespread consequence for societies, are projected to increase 
between 2°C and 4°C GWL (but decrease afterwards) (SROCC Chapter 
2 (Hock et al., 2019), AR6 WGI Chapter 12 (Ranasinghe et al., 2021)).

There is high confidence that glacial retreat, slope instabilities and heavy 
precipitation will affect landslides and flood activities, although for 
landslides there are considerable uncertainties in the direction of change 
(Patton et al., 2019, AR6 WGI Chapter 12 (Ranasinghe et al., 2021)).
Future risk consequences considered to be severe include, for example, 
an increase of 10–20% compared to present of the population exposed 
to landslide activities in certain regions (e.g., High Mountain Asia) 
(Kirschbaum et al., 2020). This does not consider the expected increase 
in landslide activity relating to glacier and permafrost changes (Picarelli 
et al., 2021, SROCC Chapter 2 (Hock et al., 2019)), so it is expected 
to be a conservative estimate. Other severe consequences are on 
average a projected twofold increase in the number of people exposed 
to inland flooding between 2°C and 4°C, with the highest increases 
in South Asia, Southeast Asia and South America (high confidence

in the direction of change and medium confidence in the absolute 
values because they are based on global studies) (Hirabayashi et al., 
2013; Allen et al., 2016; Arnell and Gosling, 2016; Zheng et al., 2021). 
Therefore, high to very high risks are expected between 2°C and 4°C 
GWL in several mountain regions (red and violet shaded bars in Figure 
CCP5.5). Many regions are projected to experience high risks due to 
the timing (potentially for severe consequences to happen sooner 
rather than later), the magnitude in terms of number of people and 
infrastructure affected and the persistence of hazard conditions (Figure 
CCP5.5, AR6 WGI Chapter 12 (Ranasinghe et al., 2021)). Comparatively, 
more severe risk consequences are expected under SSP3 and/or SSP4 
given the high population projections in certain regions compared to 
SSP1 (medium confidence) (Kirschbaum et al., 2020) (Figure CCP5.1).
CCP5.3.2.2 KR2: Risks to Livelihoods and the Economy from 
Changing Water Resources

KR2 encompasses the relative and absolute dependence on water 
resources for economic activities and livelihood sustainment in 
mountain regions and in lowlands (Table  SMCCP5.19). Particularly

CCP5
2293Mountains  Cross-Chapter Paper 5
affected by changes in water resources will be regions with (seasonally) 
high dependence on snow and glacier melt, i.e., arid and semiarid 
zones in the Andes, Central Asia and the Upper Indus Basin (Huss et al., 
2017; Huss and Hock, 2018; Viviroli et al., 2020) (Section CCP5.3.1).
Consequences that are considered severe refer to the magnitude (number 
of people and economic activities affected), timing (increase of water 
stress as early as mid-century in several regions) and likelihood (severe 
risk consequences are more likely where high population density is 
projected) (high confidence) (Figures CCP5.1 and CCP5.6, Section 4..5.7 
and 4.7) (Fuhrer et al., 2014; Wijngaard et al., 2018; Biemans et al., 2019; 
Immerzeel et al., 2020; Viviroli et al., 2020). Severe consequences are 
that by mid-century more than half of agricultural regions equipped for 
irrigation are projected to be dependent on mountain runoff and could

therefore be unsustainably using blue water (e.g., water from river, 
lakes and aquifers) (Viviroli et al., 2020) or that the number of people being water stressed will increase by 50% to 100% in areas already 
currently water stressed (Munia et al., 2020). Hotspot regions are those 
with large lowland populations depending on essential mountain water 
resource contributions and include river catchments such as the Ganges, 
Brahmaputra, Meghna, Yangtze, Nile, Niger, Indus, Euphrates-Tigris or 
Pearl (high confidence) (Viviroli et  al., 2020) (Figure  CCP5.6). Limited 
governance and integrated management of water resources, power and 
gender inequalities and level of disruption of local community practices 
also contribute to making risks more severe (medium confidence) (Lynch, 
2012; Boelens, 2014; Wijngaard et al., 2018; Scott et al., 2019; Immerzeel 
et  al., 2020). Consequences for hydropower are comparatively less

severe than for agriculture and domestic/municipal use, although this 
depends on region and timing (see also Section 5.2.2.2). For example, a 
study shows low risk to hydropower production in High Mountain Asia 
until the end of the century and even for warming levels beyond 3°C 
(robust evidence, moderate agreement) (Mishra et al., 2020).
People and infrastructure in mountain regions at risks of landslides and/or floods
for 1.3–1.7°C, 2.0–2.5°C and 4°C Global Warming Levels
(a) Risks in AR6 WGI reference regions 
(b) Risk and driving hazards in mountain regionsGlobal warming per subregion
Very high
High      
Medium      
Low Confidence
SAU
NZ
SAS
SEAF
NEAF
EAS
NWS
TIB
WCANWN
WNA
WCE
SWSRisk
Very high
Moderate
Not assessedHigh4ºC
2.0–2.5ºC
1.3–1.7ºC
Mountain regions
AR6 WGI 
reference regions
Dotted border between TIB and SAS is due to
discrepancies between studies referring to the Southern Himalaya as part of SAS,

and the new AR6 WGI reference region delineations which include most of the Southern Himalaya in TIB.
Global
warming
4ºC
2.0–2.5ºC
1.3–1.7ºCAfrica Asia AustralasiaCentral and
South AmericaNorth 
America EuropePrincipal hazards for 
which evidence was assessed
Glacial lake outburst flood
Landslide
River/Pluvial floodRain on SnowRisk
Very high
ModerateHigh
Figure CCP5.5 |  People and infrastructure in mountain regions at risk of landslides and/or floods for various GWLs. 
Panel a) shows the level of risk assessed per AR6 WGI reference region (AR6 WGI Atlas (Gutiérrez et al., 2021)). For some mountain regions, there is limited evidence for 
adequately assessing the level of risks against GWLs, so this is labelled ‘not assessed’. 
Panel b) shows the level of risk aggregated at the continent scale and the principal hazards for which evidence was available and assessed. Methodological details and traceability 
are provided in SMCCP5.4, Figure SMCCP5.1, Table SMCCP5.16 and SMCCP5.18.

CCP5
2294Cross-Chapter Paper 5 Mountains
Large-scale and transformative interventions can reduce the high-end 
impacts of changing water resources and in particular the risks of 
water scarcity (Section CCP5.4.1). These interventions have long lead 
times, are costly and may face institutional constraints (Section  4.7), 
resulting in adaptation shortfalls. Therefore, high-risk to very high-risk 
levels cannot be excluded in regions where other key risk characteristics, 
such as magnitude, timing and likelihood, are assessed as high due to 
potential losses (e.g., in many Asian regions) (Figure CC5.6, SMCCP5.4 
and Table SMCCP5.16).
CCP5.3.2.3 KR3: Risks of Ecosystem Change and Species 
Risks to mountain ecosystems and the services they provide to people 
are varied in magnitude, timing, likelihood and potential to adapt 
and place specific. However, many mountain ecosystems are already 
showing impacts of climate change (CCP5.3.1), reflecting the strong

influence climate exerts in many situations and indicating that risks are 
significant and immediate and will likely increase in the near as well 
as long term. There is robust evidence (high agreement) of vegetation 
zones and individual species shifting to higher elevations (CCP5.2.1 
and Section  2.4), and projections indicate that current trends will 
continue and accelerate at higher rates of warming (medium evidence, 
high agreement) (Section 2.5).
Many mountain species are at risk of range contraction and ultimately 
extinction if dispersal at the upper range limit is slower than losses due to mortality at the lower range limit (observed for trees in the neotropics 
(Feeley et al., 2013; Duque et al., 2015) or if mountains are not high 
enough to allow species to move to higher elevations. Ramirez-Villegas 
et al. (2014) modelled 11,012 species of birds and vascular plants in 
the Andes and found large decreases by 2050 (SRES-A2 scenario); in

the absence of dispersal, 10% of species could become extinct. Even 
assuming unlimited dispersal, most of the Andean endemics would 
become severely threatened. Other modelling studies have also 
projected declines in a range of communities and species, including 
rare endemics (Zomer et  al., 2014a; Rashid et  al., 2015; Bitencourt 
et al., 2016; Li et al., 2017; Rehnus et al., 2018; Ashrafzadeh et al., 2019; 
Zhang et al., 2019b; Cuesta et al., 2020; Hoffmann et al., 2020).
Many treelines will continue to shift to higher elevations with 
increasing temperatures (Chhetri and Cairns, 2018), although very 
few are changing as fast as the climate (Liang et al., 2016; Hansson 
et  al., 2021) and some are not moving or even shifting to lower 
elevations (CCP5.2.1). If treelines fail to shift uphill, this will pose a 
risk for species of the upper-montane forest that experience range 
contraction at their lower range limit but lack a suitable habitat to

expand into beyond their upper range limit (Rehm and Feeley, 2015). 
Changes in phenology can also pose risks to species and ecosystems 
(Chapter 2), including a potential desynchronisation of mutualistic 
relationships like pollination and increased freezing damage due to 
premature emergence from winter dormancy. In European broadleaf 
trees, for example, the upper elevational limits of different species 
involve a trade-off between maximising growing season length and 
Risks to livelihoods and the economy from changing mountain water resources
between 1.5°C and 2°C Global Warming Level in AR6 WGI reference regions
WSAFCAFWAFSAH
NEAF
SEAFARPEASESB
TIB
SAS
SEAWCAEEUWCE
MED
SAUNES
SESNWS
SWSCNA
NCAWNANWN
High      
Medium      
Low ConfidenceRisk
Very high
Moderate
LowHigh
AR6 WGI  reference regions

Figure CCP5.6 |  Risk levels assessed per AR6 WGI reference region (AR6 WGI Atlas (Gutiérrez et al., 2021)). The majority of studies assessed focus on impacts 
up to mid-century (2030–2060) and for RCP-2.6, RCP-4.5 and RCP-6.0, which was converted into the corresponding warming level range 1.5°C–2.0°C GWL (Cross-Chapter 
Box CLIMATE in Chapter 1). Methodological details are provided in Section SMCCP5.4, Figure SMCCP5.1, Table SMCCP5.17 and SMCCP5.19. Due to the limited evidence available 
to determine risks against high GWLs and the relatively high uncertainty associated with future irrigation trends for the second half of the century (e.g., Viviroli et al., 2020), 
assessment of risks associated with GWLs greater than 2.0°C GWL was not conducted.

CCP5
2295Mountains  Cross-Chapter Paper 5
limiting the risk of spring freeze damage (Vitasse et al., 2012; Körner 
and Spehn, 2016).
A wide range of mechanisms can cause changes within ecological 
communities, some of which are hard to predict, but an increasing 
number of studies illustrate some of the risks which are expected to 
be most common. If treelines shift upwards, this will pose a risk for 
alpine species, which cannot compete with trees. This may lead to the 
extinction of alpine species on mountains where there is insufficient 
room for the alpine zone to shift uphill. Shifts in species distributions, 
and in particular shifts in ecosystem types, can cause changes in 
ecosystem function, which may in turn have cascading impacts on 
people, for example leading to increased exposure to diseases such 
as malaria at high elevation (Section 2.4.2.7.2) as vector distribution 
changes and wider impacts on ecosystem services (Section 2.5.3) such

as water supply, flood alleviation and food.
CCP5.3.2.4 KR4: Risk of Intangible Losses and the Loss of 
Cultural Values
The risk of intangible losses and loss of cultural values is associated 
with the decline of ice and snow cover and temperature increase, as 
well as the increase in intangible harm from hazards such as floods 
and droughts (high agreement, medium evidence) (Diemberger et al., 
2015; Jurt et al., 2015; Vuille et al., 2018; Tschakert et al., 2019; Vander 
Naald, 2020). Losses are intangible because they characterise aspects 
which are difficult to quantify, i.e., loss of identity, loss of self-reliance, 
loss of rituals and traditions and place attachment (Allison, 2015; Baul 
and McDonald, 2015; Motschmann et al., 2020a; Schneiderbauer et al., 
2021). A global systematic analysis of case studies shows that this risk 
is more prevalent in the Andes, the Himalaya and the Alps (Tschakert 
et  al., 2019). Often mentioned across studies is the loss of intrinsic

memories and culture related to changes in world heritage landscapes 
and iconic sites (Jurt et  al., 2015; Sherry et  al., 2018; Bosson et  al., 
2019). Changes in hazard landscapes are also reported to contribute 
to the loss of peace of mind and loss of well-being (Diemberger et al., 
2015). Overall, there is limited evidence but medium agreement that 
the risk of intangible losses and the loss of cultural identity will rapidly 
increase and that consequences will go from reversible damage to 
irreversible losses (Tschakert et al., 2019).
CCP5.4 Options for Adaptation and Climate 
Resilient Development Pathways
CCP5.4.1 Synthesis of Adaptation Responses to Reducing 
(Key) Risks
More than half of the studies having a focus on mountains 
(423 articles) extracted from the GAMI data set report that adaptation 
responses contribute to reducing climate risks (Berrang-Ford et  al., 
2021; McDowell et al., 2021b) (SMCCP5.3.2). However, the extent of

adaptation in terms of time (i.e., speed), scale of change (i.e., scope) 
and depth of change (i.e., degree to which a change is substantial) 
is low in mountain regions, with the level of agreement across 
studies varying from one region to the other (medium confidence) 
(Figure  CCP5.7, SMCCP5.3.2). In regions where risk levels remain moderate, a low adaptation extent might be sufficient to constrain 
risks (Figures CCP5.5 and 5.6, Section 16.3.2.4).
Adaptation responses in mountains are mainly incremental changes 
from existing practices (high confidence) (McDowell et al., 2019, 2021b; 
Rasul et al., 2020), signalling that the potential of current and planned 
adaptation responses to reduce risks in the future will not be adequate 
to mitigate high to very high risks. For example, measures to contain 
floods or landslides (KR1) are designed with specific magnitudes and 
types in mind, often assuming stationarity of return periods (Montanari

and Koutsoyiannis, 2014; Gariano and Guzzetti, 2016). In the case of 
events showing decreasing return periods, risk mitigation standards 
need to be elevated to provide for more protection in the future 
(Felder et al., 2018; François et al., 2019). The portfolio of adaptation 
options to mitigate risks from changing water resources (KR2) is large 
but challenging and includes integrated catchment management, 
implementation of multiple use of water strategies, improved water 
governance (including community-based and participatory water 
governance), overcoming power inequalities among users and sectors 
and balancing economic pressure and sustainable development (high 
confidence) (Bekchanov and Lamers, 2016; Yapiyev et al., 2017; Jalilov 
et al., 2018; Drenkhan et al., 2019; Allen et al., 2020; Aggarwal et al., 
2021; Huang et al., 2021) (SMCCP5.3.2). There is limited evidence on 
the effectiveness of adaptation responses to reduce the severity of

ecosystem change (KR3) (also see Section 16.3.1). Prevention rather 
than control and eradication efforts can contribute to curbing biological 
invasions of alien species in the short turn, whereas colonisation by 
native trees following land use abandonment can be more effective 
in the long run (Carboni et  al., 2018). Reducing intensified grazing, 
agricultural expansion and conservation management in buffer zones 
of protected areas can limit the altitudinal range shift of endemic 
species (Kidane et al., 2019).
EbA has been effective in mountain regions at reducing risks 
from floods (e.g., restoration of buffer zones and floodplains) and 
landslides (e.g., protective forests) (Muccione and Daley, 2016; Klein 
et al., 2019b; Lavorel et al., 2019). Ecosystem-based measures have 
been implemented for water management purposes to supply clean 
water and improve water quality (Section  4.6.6). Furthermore, they 
provide scope for conservation and improvement of habitats, e.g.,

forest ecosystems (Nagel et  al., 2017; Lamborn and Smith, 2019) 
(high agreement, medium evidence). However, repeated and recurrent 
disturbances that increase recovery times can reduce the effectiveness 
of EbA (medium confidence) (Sebald et al., 2019; Scheidl et al., 2020).
Adaptation in mountain areas is currently constrained predominantly 
by soft limits related to existing social, economic and political 
conditions (high confidence) (Gioli et  al., 2014; Sansilvestri et  al., 
2016). Progress in overcoming soft limits is currently minimal due 
to insufficient engagement with socioeconomic and political issues 
in existing adaptation (medium confidence) (McDowell et  al., 2019, 
2021b) (Section 8.4.5.3, Cross-Chapter Box LOSS in Chapter 17). This 
is expected to lead to an expansion of residual risks as risk severity 
increases (McDowell et al., 2021b).

CCP5
2296Cross-Chapter Paper 5 Mountains
CCP5.4.2 Challenges, Opportunities and Solution Space 
for Adaptation in Mountains
The effects of climate change on mountain environments pose 
significant challenges for people, ecosystems and sustainable 
development, with issues such as difficult access, environmental 
sensitivity and socioeconomic marginalisation making adaptation 
particularly complex. Furthermore, varied and dynamic biophysical 
characteristics as well as high sociocultural diversity preclude one-
size-fits-all responses; adaptation planning and action in mountains 
rooted in context-specific socioecological and climatic realities are 
more effective (high confidence) (Hock et al., 2019; Lavorel et al., 2019; 
McDowell et  al., 2020). Despite these challenges, there is growing 
evidence of opportunities for advancing effective responses to climate 
risks in mountain areas (McDowell et al., 2020) (Section 16.3; Cross-
Chapter Box NATURAL in Chapter 2).

The solution space for adaptation represents a realm of possibility 
for addressing climate risks; it is shaped by both socioeconomic and 
climatic factors that influence who adapts, when they adapt and how 
they adapt to climate change (Haasnoot et al., 2020) (Sections 1.5.1 
and 17.4). The space includes both planned and autonomous responses 
(Hock et  al., 2019; McDowell et  al., 2019). Autonomous responses 
can be appropriate when local resilience is high (Mishra et al., 2019; 
Ford et al., 2020); however, many mountain communities continue to 
face socioeconomic challenges that constrain their adaptive capacity 
(high confidence). Planned adaptations are a critical component of 
the solution space, although external interventions can also reinforce, redistribute or create new vulnerabilities when they proceed without 
sincere engagement with local communities (Eriksen et al., 2021). The 
solution space also evolves as social and climatic conditions change

and can be capped by social and biophysical limits to adaptation that 
render further responses to climate change inaccessible, unfeasible 
or ineffectual. Such limits are already observed and are likely to 
become more widespread as climatic stressors move beyond historical 
experience (high confidence) (IPCC, 2018; Hock et al., 2019; McDowell 
et al., 2020) (Section 17.3; Cross-Chapter Box DEEP in Chapter 17).
Evidence shows the significant potential of adaptation actions such 
as NbS or multiple uses of water approaches but with a need to 
carefully evaluate environmental, economic and social co-benefits and 
trade-offs (high agreement, medium confidence) (Yang et  al., 2016; 
Drenkhan et  al., 2019; Lavorel et  al., 2019; McDowell et  al., 2019; 
Palomo et  al., 2021). The potential for adaptation to contribute to 
sustainable development and transformative change in mountains 
is also becoming increasingly evident (medium confidence) (Palomo

et  al., 2021), yet there is currently limited evidence with respect to 
the long-term effectiveness of adaptations in achieving such outcomes 
(Balsiger et al., 2020). To better achieve the adaptation potential in 
mountains, adaptation finance and private-sector inclusion and 
contribution are key enablers (high confidence) (Mishra et al., 2019; 
UNEP , 2021).
There is increasing recognition that inclusive and comprehensive 
adaptation approaches can be more successful (medium evidence, high 
agreement) (Allen et al., 2018; Hock et al., 2019; Huggel et al., 2020a, b). Extent of adaptation observed in mountain regions
SpeedGlobal
Level of adaptation extent
Low Medium High UnclearProportion of studies Dimensions
of adaptation
extent
Scope
Depth
South America EuropeNorth 
America Australasia
High confidence High confidence Medium confidence Low confidence Medium confidence Low confidence Medium confidence

Figure CCP5.7 |  Extent of planned and implemented adaptation actions observed in mountain regions shown in terms of three dimensions: i) speed (timeframe 
within which adaptations are implemented), ii) scope (scale of changes observed from adaptation action), and iii) its depth (i.e., degree to which a change reflects something new) 
(Section 16.3.2.4). The data are obtained from the Global Adaptation Mapping Initiative (GAMI) reanalysis for mountains (SMCCP5.3.2) (Berrang-Ford et al., 2021; McDowell et al., 
2021b).

CCP5
2297Mountains  Cross-Chapter Paper 5
Stakeholders such as local communities and government entities often 
prioritise different dimensions of climate-related risks (López et  al., 
2017; McDowell et al., 2020). Adaptation initiatives that identify locally 
relevant climate stressors and risks through knowledge co-production 
have the potential to be more acceptable and effective (medium 
evidence, high agreement) (Huggel et al., 2015; Muccione et al., 2016; 
Allen et al., 2018; Quincey et al., 2018; Balsiger et al., 2020; McDowell 
et al., 2020, 2021b) (Cross-Chapter Box DEEP in Chapter 17). However, 
tenable co-production requires recognition of the validity and integrity 
of diverse knowledges systems, including those held by Indigenous 
Peoples and local communities, as well as the provision of sufficient 
time and resources for meaningful engagement between stakeholder 
groups (Howarth and Monasterolo, 2016; Bremer and Meisch, 2017;

Schoolmeester and Verbist, 2018; McDowell et  al., 2019; Ford et  al., 
2020). Power imbalances and knowledge politics continue to impede 
the inclusion of historically underrepresented voices in adaptation 
planning and action (Ojha et al., 2016; Mills-Novoa et al., 2017). Citizen 
science plays an additional role in facilitating the inclusion of multiple 
knowledge traditions (Buytaert et  al., 2014; Dickerson-Lange et  al., 
2016; Tellman et al., 2016; Njue et al., 2019).
Progress in addressing climate risks requires targeting the root causes 
of vulnerability, which are often socioeconomic in origin and can 
include poverty, marginalisation and inequitable gender dynamics 
(high confidence) (Ribot, 2014; Carey et al., 2017; Shukla et al., 2018; 
McDowell et al., 2019). Promoting resilience in many mountain regions 
requires responses that address the social determinants of susceptibility 
to harm. Context-specific manifestations of such determinants

(and leverage points for positive action) can be identified through 
participatory processes with affected populations, with action on 
social determinants of climate change vulnerability having important 
co-benefits for equity, justice and sustainability. Addressing the root 
causes of vulnerability can also resolve soft limits to adaptation, 
thereby increasing the solution space (McDowell et al., 2020).
There is growing evidence of the potential for coordination and 
monitoring networks to overcome existing data deficiencies, to fill 
knowledge gaps and to streamline implementation, all of which 
currently impede adaptation in mountains (Salzmann et  al., 2014; 
Muccione et  al., 2016; Ryan and Bustos, 2019; McDowell et  al., 
2020; Shahgedanova et al., 2021; Thornton et al., 2021; Price et al., 
Accepted/In press). Furthermore, there is increasing evidence that key 
conventions related to mountains, such as the Alpine Climate Board

(SROCC section 2.4 (Hock et  al., 2019)), provide opportunities for 
accelerating adaptation efforts through mainstreaming responses 
into other policies aimed at addressing climate-related risks (medium 
confidence) (Balsiger et  al., 2020). Regional cooperation among 
countries and transboundary landscape and river basin governance 
initiatives are an important mechanism for advancing adaptation in 
mountains (high agreement, medium evidence) (Molden et al., 2017; 
Mishra et al., 2019; Balsiger et al., 2020), particularly as many mountain 
ranges and mountain ecosystem services are transboundary in nature.
Access to major adaptation support programmes such as through the 
UN Framework Convention on Climate Change (UNFCCC), national 
governments, multi- and bi-lateral aid arrangements, the private sector 
and non-governmental organisations (NGOs) has been relatively limited to support adaptation action in mountain regions, indicating

significant unutilised support options for increasing the solution space 
in mountains (McDowell et al., 2020). Enhanced uptake of available 
support and funding could help to ease the adaptation burden for 
mountain communities. This will require addressing soft limits to 
adaptation, which currently constrain the ability of actors to identify, 
access and mobilise resources for planned adaptations (McDowell 
More inclusive adaptation approaches, engagement with the root 
causes of vulnerability, improved coordination and monitoring 
activities and upscaling of support for adaptation are key enablers 
and are indicative of a substantial solution space for adaptation in 
mountain regions (high confidence). However, trajectories of climate 
change and the prospect of hard limits to adaptation, which are often 
biophysical in origin, portend climate futures that could overwhelm 
adaptation efforts. Success therefore hinges on increasing the quality

and quantity of adaptation efforts, including through transformative 
action, as well as enhanced mitigation efforts, consistent with the 
recommendations of IPCC SR 1.5C (IPCC, 2018) (Cross-Chapter 
Box PROGRESS in Chapter 17).
CCP5.4.3 Climate-Resilient and Sustainable Development 
in Mountains
With accelerating warming and compounding risks increasing above 
1.5°C warming, the need for climate resilient development in mountains 
is evident and intricately linked to achieving the SDGs and equity (high 
confidence). In this context, Chapter 18 draws attention to climate 
resilient development pathways (CRDPs) as processes that strengthen 
sustainable development and efforts to eradicate poverty and reduce 
inequalities while promoting fair and cross-scalar adaptation and 
mitigation. Pathways that strengthen climate-resilient sustainable 
mountain development are starting to receive attention (Chelleri 
et  al., 2016; Trabacchi and Stadelmann, 2016; AlpineConvention,

2021). This section treats four domains of emerging evidence related 
to climate resilient development in mountains: 1) climate actions that 
support both adaptation and mitigation, 2) Indigenous knowledge 
and local knowledge in support of climate resilient development, 3) 
climate resilient development in climate policy and planning and 4) 
mainstreaming of climate action into development pathways.
NbS can be pursued in mountains that will mitigate climate change 
and its impacts while at the same time contributing to improving 
livelihoods, social and economic well-being and sustainable 
environmental management (high confidence). A global review of 93 
NbS in mountains, such as afforestation, protection of existing forests, 
agroforestry and climate-smart agriculture, confirm the potential of 
NbS for change towards sustainable trajectories (Palomo et al., 2021). 
Agroforestry is widely cited for delivering on food security as well

as increasing resilience and mitigating climate change (Mbow et al., 
2014; Amadu et al., 2020; Gidey et al., 2020). Also, the prudent use 
of biomass for wood-based bioenergy in mountains can mitigate 
the impacts of climate change, reduce vulnerability to disturbance 
events such as fires and enhance rural socioeconomic development 
(Beeton and Galvin, 2017). Yet there can be trade-offs contingent

CCP5
2298Cross-Chapter Paper 5 Mountains
upon place-based and context-specific social and environmental 
factors, such as between the use of bio-energy, agricultural production 
and conservation concerns (Beeton and Galvin, 2017). Evidence 
from the world’s mountains highlights the importance of cross-
scale partnerships and interdisciplinary, bottom-up approaches that 
facilitate stakeholders in envisioning locally tailored, climate-resilient 
and sustainable development pathways (Chelleri et al., 2016; Capitani 
et al., 2019; Klein et al., 2019b; Pandey et al., 2021).
Mountains are the home of many cultures and diverse Indigenous 
knowledge and local knowledge (systems), which can and do provide 
strong support for place-based integrated adaptation and mitigation 
strategies (Merino et  al., 2019). Indigenous knowledge and local 
knowledge reinforce community adaptive capacity, yet governance 
structures and processes, including the deliberate design and

implementation of climate policy, can constrain that capacity from 
being realised (high confidence) (Hill, 2013; McDowell et  al., 2014; 
Wyborn et  al., 2015; Klepp and Chavez-Rodriguez, 2018; Lavorel 
et  al., 2019). Communities, particularly poor and remote mountain 
communities, are vulnerable to climate change, and there is a need for 
capacity-building in research, policy development and implementation 
to pursue climate resilient development (Manton and Stevenson, 
2014). Climatic stressors and socioeconomic changes are changing 
traditional genderscapes in mountain communities (Goodrich et  al., 
2019). There is increasing evidence on the roles that gendered 
diversity in knowledge, institutions and everyday practices can play in 
addressing barriers and creating opportunities for achieving resilience, 
adaptive capacity and sustainability in societies (Gioli et  al., 2014; 
Ravera et al., 2016; Su et al., 2017; Udas et al., 2018; Goodrich et al.,

2019; Sujakhu et al., 2019).
Concerning climate policy and planning for climate resilient 
development in mountains, a review of mountain-specific priorities 
in the National Adaptation Programmes of Action (NAPA) submitted 
to the UNFCCC shows that countries have prioritised improving 
agricultural outputs by introducing climate-smart crops and upgrading 
and building climate-resilient irrigation infrastructure (UNFCCC, 
2020c). Countries that have submitted their NAPAs to the UNFCCC 
have prioritised improving ecosystem resilience through conserving 
agro-biodiversity in mountains. Countries have also focused on 
achieving food security in mountain regions and laying foundations for 
food availability, stability, access and safety amid increasing climate 
risks (UNFCCC, 2020a).
In the NDCs where mountain regions are specifically mentioned, countries 
have prioritised climate-resilient solutions, including developing a low-

carbon green economy through implementing low-carbon transport 
systems and encouraging sustainable waste management practices, as 
well as developing infrastructure for climate-resilient agriculture, the 
sustainable management of forests and the biodiversity conservation. 
Several countries have specifically pledged to build climate-resilient 
mountain infrastructure taking into account future climate uncertainties. 
Countries have also identified the need for capacity-building of national 
stakeholders and have pledged to provide relevant climate information 
(UNFCCC, 2020b).Similar pledges have been announced in formal institutional arrange-
ments such as the Alpine Convention and the Carpathian Convention. 
The Alpine Convention’s climate action plan prioritises reaching a 
climate-neutral and climate-resilient Alps by 2050. For this, imple-
mentation pathways for specific sectors have been identified ensuring

coherence with global and regional goals such as the Paris agreement, 
SDGs, EU and climate legislation (AlpineConvention, 2021). Likewise, 
the Carpathian Convention’s working group on climate change has 
presented a long-term vision towards combating climate change 
through amending the article of the convention to focus specifically 
on climate change adaptation and mitigation (Carpathian Convention, 
2020).
Sustainable and climate-resilient mountain development is predicated 
on effective and timely climate action building on cross-scalar 
partnerships among researchers, stakeholders and decision makers 
to jointly identify desired futures and pathways and assess trade-
offs and synergies between climate action and the SDGs (high 
agreement, medium evidence) (Klein et  al., 2019a; Pandey et  al., 
2021). Understanding of the complexity of mountain ecosystems as 
well as path dependency from earlier and current decisions is of critical

importance for the sustainable future of mountain regions (Satyal et al., 
2017; Chanapathi and Thatikonda, 2020; Berkey et al., 2021). Framing 
pathways through questions such as for whom or for what is climate 
action positive and which trade-off should be accepted, and why can 
serve as a tool for addressing sustainable development goals while 
avoiding lock-ins or unsustainable path dependencies (Chelleri et al., 
2016). Increasingly, climate action is mainstreamed into sustainable 
development, which signifies a shift from climate policy as an end 
point to a continuing process for managing change and facilitating 
long-term sustainable development. The Ethiopian government’s 
climate-resilient green economy (CRGE) strategy is an example of such 
a shift (Simane and Bird, 2017) as are emerging initiatives to build 
back greener in response to COVID-19 impacts (Schipper et al., 2020).
CCP5.5 Key Assessment Limitations and 
Relevant Knowledge Gaps

The assessment presented in this CCP has several limitations, 
principally in terms of the amount of often fragmented and biased 
geographic coverage or lack of relevant thematic scope covered 
in the literature published since AR5 and SROCC. Key assessment 
limitations and relevant knowledge gaps identified in this CCP fall 
within the following broad categories: 1) detection and attribution of 
observed impacts to climate change, 2) limitations and uncertainties 
associated with predictive models of projected impacts and risks, 3) 
integrated and systems-oriented research on mountain ecosystem 
services and their limits under climate change and 4) measurable 
tracking of adaptation action implemented in mountain regions and 
their suitability for addressing climate risks. These are summarised in 
Table CCP5.3. While these limitations and assessment-relevant gaps 
in knowledge offer important caveats for the interpretation of this

assessment; they also highlight prospects to address and improve the 
evidence basis in future assessments.

CCP5
2299Mountains  Cross-Chapter Paper 5
Table CCP5.3 |  Summary of key assessment-relevant knowledge gaps and limitations identified in CCP5.
Key assessment-relevant knowledge gaps and limitations Relevant WGII report sections
Detection and attribution of observed impacts to climate change
Limited amount and 
scope of literature 
available on impacts for 
assessment of detection 
and attribution to climate 
change –While there is high confidence on the links between future impacts and risks associated with climate change, there 
is medium evidence available on robust detection and attribution of past changes in mountain regions.
 –Considerable assessment gaps exist given the limited scope (temporal, spatial or thematic coverage) and number of 
published studies reporting data and information that capture how mountain social-ecological systems function and 
their trends over recent decades, which may be applicable for the detection and attribution of changes to climatic 
change.

–Additionally, there are limitations in current methodologies in terms of including and accounting for other 
knowledge traditions with respect to the detection and attribution of impacts to climate change in mountain regions 
(e.g., Chakraborty and Sherpa, 2021).CCP5.2.7,
Figure CCP5.4,
SMCCP5.2
Consequences of shifting 
treelines and their 
interactions with other 
ecosystem functions –The net effects of ongoing climate change with treeline advance and vegetation change on ecosystem carbon 
exchange, or possible effects on mountain hydrology, remain unresolved in the literature.
 –Uncertainties remain regarding the effects of ecosystem-level carbon storage, given that above-ground biomass 
is higher in forests than in alpine vegetation and (new) trees may change soil carbon fluxes, for instance by 
introducing new soil organisms, thereby increasing soil carbon flux (e.g., Tonjer et al., 2021).

–The short- and long-term effects of combined warming and changed species cover on mountain soils are complex 
and insufficiently quantified (Hagedorn et al., 2019).CCP5.2.1,
CCP1-Biodiversity Hotspots
Limitations and uncertainties associated with predictive models of projected impacts and risks
SSPs –There are relevant knowledge gaps in the understanding of future vulnerabilities in mountain social-ecological 
systems in relation to highly variable and dynamic trends in projected demographic change, socioeconomic 
development pathways and demand for resources.CCP5.3.1; SMCCP5.1
Species distribution 
models (SDM) –SDMs, which rely on statistical correlations between occurrence records and environmental variables to make 
spatially explicit predictions, are commonly used to project climate change impacts on mountain ecosystems (Guisan 
 –However, they are associated with some limitations that can limit their utility in deriving reliable predictions

of future mountain vegetation distributions and, thus, ability to provide a sound basis for mountain nature 
conservation and climate change adaptation.
 –In particular, they only indicate the potential future species distributions based on static relationships between 
species and predictors in calibration data; in reality, vegetation dynamics will be heavily modulated by phenomena 
that are commonly overlooked by such models as changing species interactions and competition due to variance in 
response rates among different species, dispersal limitations and demographic processes (Scherrer et al., 2020).
 –In addition, SDMs are often limited by data availability and therefore tend to omit several environmental factors 
known to be important for plants, such as soil formation processes, disturbances (e.g., rockfalls, avalanches) and 
microclimatic conditions (Scherrer et al., 2011; Enright, 2014; Mod et al., 2015; Bråthen et al., 2018).

–More complex dynamic and process-based models are available but still rarely represent all potentially influential 
vegetation co-variates; applying both model types in conjunction holds potential (Horvath et al., 2021).CCP5.2.1
Quantifiable estimates 
of monetary costs and 
potential material losses –There is limited evidence on climate-related risks to economic sectors that are vital for mountain regions, specifically 
on quantifiable estimates of monetary costs and potential material losses for economic sectors and communities in 
mountains, adjacent lowlands and other regions dependent on these economic activities.CCP5.3.1
Other model limitations –Ecological models that could allow for better forecasts of the effectiveness of EbA as NbS, under different climate 
scenarios, have not been fully developed (Seddon et al., 2020).CCP5.4
Integrated and systems-oriented research on mountain ecosystem services and their limits under climate change

Water –Few assessment-relevant integrative studies are available in the published literature that address relevant aspects of 
water security, beyond water availability from glacier-fed meltwater, or snow, groundwater and other water stores (e.g., 
wetlands and sediments). Likewise, few studies address seasonality with respect to a more systems-oriented approach 
to supply (e.g., water availability) and demand (irrigated agriculture and other multiple uses and user groups).CCP5.2.2
Chapter 4
Measurable tracking of adaptation action implemented in mountain regions and their suitability for addressing climate risks
Conditions under which 
adaptation interventions 
work against stated goals –Few studies report on how adaptation measures and programmes function in mountain contexts that yield the 
outcomes reported (McDowell et al., 2020).
 –Despite transformative processes, to date there is limited evidence of how knowledge co-production activities

support the planning and implementation of successful adaptations in mountain areas.CCP5.4.2
Metrics and heuristics for 
tracking effectiveness –Adaptation responses to intangible losses and loss of cultural values are reported and take different forms, as 
demonstrated in studies from various world regions (de la Riva et al., 2013; Wang and Qin, 2015; Vander Naald, 
2020). However, there is limited evidence on their adequacy for addressing increasing losses, which remains largely 
unexplored in the available literature.CCP5.3.2.4;
Section 4.4.3.3

CCP5
2300Cross-Chapter Paper 5 Mountains
Methods and frameworks 
for monitoring and 
evaluation –Regarding adaptation efforts and effectiveness, there are considerable gaps in adequate monitoring and appropriate 
evaluation of successful implementation of diverse adaptation measures.
 –Across mountain areas, integrated monitoring of key environmental and socioeconomic variables, including 
international efforts towards the acquisition and sharing of data, offers prospects for supporting the tracking of 
impacts and adaptation responses, including community-based monitoring initiatives (Shahgedanova et al., 2021; 
Thornton et al., 2021).Section 17.5;
CCP5.4.2
Feasibility and suitability 
of adaptation options for 
managing climate risks –The feasibility of adaptation options for managing risks, for example those that could facilitate systems transitions

with respect to energy, remains largely unexplored in the literature, with limited evidence on how projected climate 
change could impact prospects to develop wind, solar or biomass energy production and use in mountain contexts.
 –Given assessments on observed adaptation (Section CCP5.2) and adaptation responses (Section CCP5.4), few 
studies report a ‘systems approach’ to the study and evaluation of adaptations that combine all relevant aspects 
of the risk framework (i.e., hazards, exposure and vulnerabilities), including how synergies and trade-offs are 
considered in context for managing risks.
 –There is limited evidence on the feasibility and long-term effectiveness of adaptation measures to address 
climate-related impacts and related losses and damages in cities and settlements experiencing changing 
demographics.CCP5.2.2.2; CCP5.4.2; CCP5.4.3
FAQ CCP5.1 | How is freshwater from mountain regions affected by climate change, and what are the

consequences for people and ecosystems?
Sources of freshwater from mountains, such as rainfall, snow and glacier melt, and groundwater are strongly affected by climate change, 
leading to important changes in water supply in terms of quantity and, partly, quality and timing (e.g., shifts and changes in seasonality). 
In many cases, the effects on ecosystems and people are negative, e.g., creating or exacerbating ecosystem degradation, water scarcity or 
competition or conflict over water.
River flow is a main source of freshwater both in mountain regions and downstream areas. Various sources contribute 
to it, including rainfall, snow and glacier melt and groundwater. Climate change affects these different sources in 
different ways. Climate change affects rainfall patterns, such as long-term increase or decrease, seasonal shifts or 
changes in rainfall intensity. Rising temperatures strongly influence snowmelt- and glacier-melt-generated river

discharge; the snowmelt season starts earlier, less snow mass is available for melt, and snowmelt contribution to 
river flow thus decreases over the year. Whether rising temperatures produce meltwater from glaciers depends on 
the state and characteristics of the glaciers and the catchment basin. The concept of ‘peak water’ implies that, first, 
as glaciers shrink in response to a warmer climate, more meltwater is released until a turning point (peak water), 
after which glaciers melt, and so its contribution to river flow decreases. In many mountain regions worldwide, 
glaciers and their basins have already passed peak water, and the runoff contribution of glaciers is on the decline. 
Glacier shrinkage not only influences river discharge but also water quality. In the Andes of Peru, for instance, it 
has been observed that retreating glaciers expose bedrock, resulting in more acid water because of minerals that

dissolve from the rock. Mountain ecosystems are also affected by changing freshwater availability. For instance, 
high-elevation wetlands in the tropical Andes critically depend on glacier meltwater during the dry season, and the 
disappearance of this freshwater source results in ecosystem degradation.
The effect of climate change on groundwater in mountains is insufficiently understood. Infiltrating water from 
glaciers and snowmelt plays an important role in groundwater recharge. Groundwater recharge is expected to 
decrease with continued climate change in several mountain regions. In the Himalaya many springs have already 
been observed to be in decline.
The availability of freshwater is a function of water supply and water demand, with the latter being determined by 
sectors such as agriculture, energy, industry or domestic use, as well as by competition among these sectors. Formal

and informal water extraction and use prevail, and competition includes issues of inequality, power relations and 
asymmetry. Consequently, the effects of climate change on water resources, people and ecosystems are strongly 
modulated and often exacerbated by socioeconomic development and related water resource management. For 
example, the increasing frequency and intensity of droughts in the European Alps, combined with declines and 
seasonal shifts of river runoff from snowmelt and glacier melt, are expected to result in growing competition among 
different sectors, such as hydropower, agriculture and tourism. Similar developments are projected or have already 
been observed in many other mountain regions. This situation calls for strengthening and improving negotiation 
formats for water management that are transparent, equitable and socially and environmentally just. Management

of water demand and strategies that entail multiple uses of water will become increasingly important in this context.

CCP5
2301Mountains  Cross-Chapter Paper 5
FAQ CCP5.2 | Do people in mountain regions, and further downstream, face more severe risks to water-related 
disasters due to climate change, and how are they coping?
Mountain regions have always been affected by either too much or too little water. Because of climate change, hazards are changing rapidly 
and becoming even more unpredictable. Whether or not these changes will result in more disasters locally and further downstream depends 
on several factors, not least the fact that more people are settling in exposed locations. People in mountains have a history of developing skills 
to live in a dangerous and dynamic environment, which will be invaluable in the future when combined with inclusive and long-term disaster 
risk reduction measures.
Water-related hazards in mountains include rainfall (pluvial) and river (fluvial) floods, extreme rainfall-induced

landslides, debris flows, ice and snow avalanches and droughts. When people are exposed and vulnerable to 
these hazards, disasters can result. Floods and landslides in mountains contribute to and count among the most 
devastating disasters globally, often resulting in significant losses such as high numbers of fatalities and economic 
and property damage. Climate change may alter rainfall frequency/intensity distributions, potentially leading to 
floods and droughts. Climate change may also lead to shifts in precipitation type, with more precipitation falling 
as rain rather than snow in the future, which will further impact both short- and long-term water storage and, 
therefore, will impact downstream ecosystems and cities.
Although climate change directly affects water-related hazards, studies indicate that above and beyond natural 
hazards, disaster risk and disasters are influenced to a major extent by vulnerability and exposure. This is of relevance

in mountains, where disaster risk is influenced by population growth, induced displacements, land use changes and 
inefficient water distribution systems. For example, current trends suggest that more people are settling in exposed 
locations, with more infrastructure being built and activities such as tourism and recreation being promoted, 
exacerbating this exposure.
Experiences in dealing with water-related disasters provide a basis on which to build adequate responses to 
increasing risks in the future. For example, upgrading infrastructure like dams and embankments can help address 
water shortages, but diversification of income-generating activities, such as subsistence farming moving away from 
certain drought-sensitive crops, can also help.
The risk perceptions of people also shape their behaviours in coping with disaster risks. For example, based on their 
longstanding observations and local knowledge, communities in the southern part of the Peruvian Andes identified

the shrinking of glaciers, more frequent and intense extreme weather events, more extreme temperatures and 
shortened rainy seasons as key challenges. The recognition of local knowledge is key to addressing these challenges, 
as well as providing a basis for the transformation of current systems. A lack of community involvement and 
participation in decision making on how to address disaster risk can contribute to mismatches between perceptions 
and behaviours in face of those risks, and the actions needed to reduce losses. Therefore, measures which are 
flexible, address the objectives and needs of all those affected by disasters and bring long-term benefits have more 
chances of being successful in dealing with future disaster risks.

CCP5
2302Cross-Chapter Paper 5 Mountains
FAQ CCP5.3 | Does climate change pose a risk to mountain species and ecosystems, and will this affect people?
Treeline position, bioclimatic zones and species ranges move up in elevation as the climate warms, increasing the risk of extinction for 
species isolated on mountain tops as a result of exceeding their physiological limits, loss of habitat or competition from colonising species. 
Additionally, climate change may alter the quality and quantity of food and natural products on which the livelihood of many mountain 
communities depends.
Mountain regions cover about a quarter of the Earth’s land surface, are scattered around the globe and may 
support a wide range of climates within short horizontal distances. Mountains have experienced above-average 
warming, and this trend is expected to continue. Mountains provide a variety of goods for people, are home to

many Indigenous Peoples and are attractive for tourism and recreational activities. Mountain regions support many 
different ecosystems, and some are very species rich. Mountain regions can be vast and diverse, and climate change 
and its impacts on ecosystems vary greatly from location to location.
With increasing average global temperatures, the climatic conditions under which plants and animals can thrive are 
shifting to higher elevations. The movement of some plant taxa towards mountain tops has been observed in recent 
decades. However, for species restricted to the highest elevations, there is nowhere to move to, meaning they are 
increasingly at risk of extinction. Climatic conditions may exceed the physiological limits for species and habitats 
may become unsuitable for others. There is also a risk from competition with colonising native species and invading 
non-native species, spreading to higher elevations, and some species cannot move quickly enough to keep pace

with changes in the climate. The most vulnerable species are those that reproduce and disperse slowly and those 
that are isolated on mountain tops, including endemic species, which may face global extinction. In other cases, 
species will be lost from some parts of their current range. Mountains can, however, allow other species to survive 
in areas where they otherwise would not because of small-scale variations in climate with elevation or different 
aspects of slopes.
Changes in snow cover and snow duration are related to changes in temperature and precipitation and are also 
critical for plants and animals. In particular, glacier retreat and changing snow patterns affect both streamflow 
dynamics (including extremes) and soil moisture conditions and can cause moisture shortages during the growing 
season. A change in snow patterns can critically affect animal movements in mountains. Other processes creating

stresses on mountain ecosystems are direct human impacts, such as the influence of grazing, tourism, air pollution 
and nitrogen deposition on alpine vegetation. In some cases, these impacts can be so large on the goods and 
services provided by alpine ecosystems that they can overshadow the effects of climate change or exacerbate its 
effects.
In many mountain regions, multiple sources of evidence point to tree expansions into treeless areas above (and in 
some cases below) the forest belt. This may increase forest productivity at the upper treeline. Treelines have moved 
up in the last 30–100 years in many mountain regions, including, for example, the Andes, Urals and Altai. At the 
same time, since the 1990s, treeline responses in different parts of the Himalaya have been highly variable, in some 
places advancing upslope, in others demonstrating little change and in yet others moving downwards. This can be

explained by site-specific complex interactions of the positive effects of warming on tree growth, drought stress, 
change in snow precipitation, land use change, especially grazing, and other factors. Treelines are affected by land 
use and management around the globe, and changing land use practices can supersede climate change effects in 
some mountain regions. An upward shift in the elevation of bioclimatic zones, decreases in the area of the highest 
elevation zones and an expansion of the lower zones can be expected by mid-century, for example in regions such 
as the Himalaya.
In some regions, the livelihoods of many local mountain communities depend on access to firewood, pastures, 
edible plants and mushrooms, and medicinal and aromatic plants. Climate change can alter the quality and quantity 
of these ecosystem services; however, the degree and direction of change are context specific. The appeal and

feasibility of mountains for tourism and recreational activities are also affected by climate change.

CCP5
2303Mountains Cr oss-Chapter Paper 5
FAQ CCP5.4 | What types of adaptation options are feasible to address the impacts of climate change in mountain 
regions under different levels of warming, and what are their limits?
The feasibility of adaptation to address risks in mountain regions is influenced by numerous factors, many of which are unique to mountain 
people and their environment. Adaptation efforts in mountains mainly consist of small, largely autonomous steps. Robust and flexible adaptation measures have a better chance of addressing risks, but eventually large systemic transformation will be needed in the face of higher levels of warming. Empirical evidence on what works and what does not is largely absent but urgently needed.
The term feasibility refers to climate goals and adaptation options that are possible and desirable. Feasibility is

influenced by factors such as economic viability, availability of technical resources, institutional support, social capital, ecological and adaptive capacity and biophysical conditions. Establishing the feasibility of options under changing climatic and socioeconomic conditions is not an easy task, mostly because even present feasibility is difficult to assess in mountains due to a lack of systematic information on opportunities and challenges of adaptation in practice.
Underlying environmental conditions, such as limited space, shallow soils, exposure to numerous hazards,

climate-sensitive ecosystems and isolation, make it particularly difficult to implement adaptation at scales relevant for implementation. Common adaptation options are often implemented at the individual, household or community level. These options are incremental and have generated observable results and outcomes. Adaptation actions that involve partial changes that do not dramatically alter established practices and behaviours seem to have better chances of being implemented than systemic or structural changes. Formal or planned adaptation efforts that are more institutionally driven constitute only a small proportion of observed adaptation in mountain regions. Where adaptation options are implemented, they often target not only climate change but an array of other issues, priorities and pressures experienced by and in those communities (e.g., livelihood diversification in farming practices).

Whether or not adaptation options are feasible says little about their effectiveness, i.e., the degree to which 
adaptation has been or will be successful in reducing the risks of negative impacts. Adaptation is difficult to disentangle from other factors that contribute to both increasing and decreasing risks. Since adaptation in mountains is often autonomous and unplanned, measuring its effectiveness is complex and missed by more conventional, formal or structured monitoring and evaluation frameworks.
Evidence suggests that promising measures undertaken in mountains are those that are robust under uncertain

futures, allow for adaptive planning and management and respond to multiple interests and purposes. For example, multi-purpose water reservoirs can alleviate multiple stressors and address several risks, such as those from natural hazards and water shortages. Capacity-building and awareness-raising can go a long way towards ensuring that these measures are also socially acceptable if combined with more structural and systemic changes. Indeed, transformations happen slowly in mountains and it is unlikely that small steps and incremental measures will be able to cope with more severe and pervasive risks.
Overall, empirical evidence on the effectiveness of adaptations at reducing risk is largely lacking but is urgently 
needed to better understand what works and what does not under certain circumstances.

CCP5
2304Cross-Chapter Paper 5 Mountains
FAQ CCP5.5 | Why are regional cooperation and transboundary governance needed for sustainable mountain 
development?
Regional cooperation and transboundary governance are key to managing our vast mountain resources because they do not necessarily share 
political boundaries. Mountain countries need to come together, share data and information, form joint management committees, jointly 
develop policies and take decisions that benefit all countries equitably. A lack of cooperation may lead to missed opportunities to address 
climate risks and adequately manage mountain resources, which could cause social unrest and spark conflict within and between countries.
Mountains are climate change hotspots that are highly susceptible to climate change. Due to rapidly changing 
climatic conditions, climate change is one of the major issues that would benefit from regional cooperation. The

transboundary management of mountains means shared legal and institutional frameworks for sharing the benefits 
and costs of managing mountain ranges across boundaries, whether local or district jurisdictions within countries or 
indeed across national boundaries.
The IPCC’s Special Report on Oceans and Cryosphere refers to governance as an ‘effort to establish, reaffirm or 
change formal and informal institutions at all scales to negotiate relationships, resolve social conflicts and realise 
mutual gains’. Governance is an act of governments, NGOs, private-sector institutions and civil society in establishing 
rules and norms for restricting the use of common goods. Institutions can guide, constrain and shape human 
interaction through direct control, incentives, and processes of socialisation. How do we apply the definitions of 
governance and institutions in the context of mountains? Since governance not only refers to government, which

is a formal arm of the state, the report also talks about other agencies such as community organisations, non-profit 
organisations or businesses that play a vital role in society and influence individual or collective decisions and help 
in preventing the overexploitation of resources.
To comprehend the processes of governance in mountain areas, we need to recognise how each of these agencies 
adds to the enduring task of enabling and managing change at the system level but also to preserving social 
structures and reconciling disputes. For the sustainable and resilient development of mountain regions, governance 
mechanisms may be different than those applied to the management of other resources, such as coastal zones 
or rivers. Mountains are also mostly transboundary and do not necessarily follow political boundaries. Mountain 
governance, therefore, is about managing resources across political boundaries for the benefit of all countries.

This includes downstream countries that also rely on resources such as water, silt and others from these mountain 
regions. These include high rangelands, biodiversity hotspots, forests and glaciers, for example.
There are several examples of regional cooperation in connection with the governance of shared resources in 
mountains. Some examples come from the Arctic (bottom-up and science-based evolution of Arctic cooperation), 
Southeast Europe (regionalisation of environmental benefits) and the HKH region (intergovernmental scientific 
institution for research and data sharing). Mountains share resources, so their management will benefit from 
cooperation among countries. Transboundary cooperation is needed not only to address transboundary climate risks 
and regional adaptation to climate change in mountains but also to work across countries to reduce greenhouse 

CCP6
2321Polar Regions Cr oss-Chapter Paper 6
Observed Impacts and Future Risks
Climate change impacts and cascading impacts in polar 
regions, particularly the Arctic, are already occurring at a magnitude and pace unprecedented in recent history (very high confidence), and much faster than projected for other world regions (high confidence
1).
The polar regions, notably the Arctic and maritime Antarctic, are experiencing impacts from climate change at magnitudes and rates that are among the highest in the world, and will become profoundly different in the near-term future (by 2050) under all warming scenarios (high confidence). In the Arctic, accelerated

sea ice loss (particularly during summer), increased permafrost thaw and extreme high temperatures have substantially impacted marine, freshwater and terrestrial sociological–ecological systems (very high confidence). Multiple physical, ecological and societal elements of polar regions are approaching a level of change potentially irreversible for hundreds of years, if not millennia (high confidence). Evidence of borealisation of terrestrial and marine systems is emerging (high confidence), and cascading impacts are ongoing and widespread yet challenging to quantify fully due to complexity and lags in ecological expression of change. Loss of multi-year sea ice and the occurrence of a seasonally ice-free Arctic Ocean by the middle of this century will result in substantial range contraction, if not the disappearance of several Arctic fish, crab, bird and marine mammal species, including possible extinction of seals and polar bears in certain regions (high confidence). In the Arctic,

permafrost thaw and snowfall decrease lead to profound hydrological changes, an overall greening of the tundra and regional browning of tundra and boreal forests (high confidence). {CCP6.1; Table CCP6.1; Table CCP6.2; CCP6.2.1; CCP6.2.2; Table CCP6.5}

Contractions of the polar climate zones lead to distribution 
shifts and changes in food webs, induce declines in many species (medium confidence) with impacts on subsistence harvests and commercial fisheries, and threaten global dependence on polar regions for substantial marine food production (high confidence). Climate change has induced food web changes resulting in population declines in polar sea birds, including penguins, and marine and terrestrial mammals (high confidence). Globally and regionally important harvested fish and invertebrate species are also contracting ranges and declining productivity, including Pacific cod, salmon, snow and king crab in the Arctic and krill in the Antarctic (medium confidence), with implications for global food systems (high confidence). {Table CCP6.2; CCP6.2.1; CCP6.2.3; Table CCP6.3; Table CCP6.4}
Loss of sea ice is rapidly expanding opportunities, but also

increasing risks for shipping and other economic industries in polar regions (very high confidence). Reduced sea ice enables greater access to high-latitudinal seas for industries, such as fisheries, 
levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.shipping, tourism (very high confidence) and Arctic maritime trade

and resource extraction (medium confidence). Navigational risks have grown due to increasingly mobile multi-year ice, poor hydrographic charting in newly open areas, and limited weather, water, ice and climate data and services (high confidence). Cascading risks from polar shipping growth include increased air emissions, underwater noise pollution, disruption to subsistence hunting and cultural activities in the Arctic (high confidence) and potential for invasive marine species and geopolitical tensions (medium confidence). {Table CCP6.3; CCP6.2.4; Box CCP6.1; Table CCP6.5; Table CCP6.6}
Increased permafrost thaw and flooding will disrupt economically

important transportation and supply-chain infrastructure to remote Arctic settlements (high confidence), increasing risks to economies, Arctic tourism and tourism to cultural heritage sites (medium confidence). Arctic permafrost thaw is projected to impact most infrastructure by the middle of this century, impacting millions of people and their economies, and costing billions in damages (high confidence). {CCP6.2.3; CCP6.2.4; Box  CCP6.1; CCP6.2.5; CCP6.3.1; Table CCP6.5; Table CCP6.6}
Climate change increasingly threatens many facets of Arctic

livelihoods, culture, identity, health and security, particularly for Indigenous Peoples (very high confidence). It has negatively impacted mental health and increased risks of injury, food insecurity and foodborne and waterborne disease, with risks amplified for those reliant on the environment for subsistence, livelihoods and identity (high confidence). Permafrost thaw, sea level rise and reduced sea ice protection have already damaged or destroyed many cultural heritage sites in some Arctic regions (very high confidence) and are projected to continue across all Arctic regions (very high confidence). {CCP6.2.3; Table CCP6.3; CCP6.2.4; CCP6.2.5; CCP6.2.6; Figure CCP6.3; Box CCP6.2; CCP6.3.1; Table CCP6.5; Table CCP6.6}
AdaptationAdaptations to manage climate change impacts and risks in 
polar regions are urgently needed (very high confidence), but implementation is uneven (high confidence), limits to adaptation are high and maladaptation is probable (high confidence).

Polar zones will continue to contract and diminish in extent 
under climate change, and local adaptations will be insufficient to achieve long-term resilience of polar systems (medium confidence). The pace and extent of change in polar regions is 
challenging the ability of social and natural systems to adapt (medium confidence). Management of different sectors with specific measures to reduce the potential for compounding risks and the development of climate-sensitive strategies would support the resilience of polar systems. Resilience of natural systems can be enhanced through strategies that maintain ecological connectivity over large spatial scales and reduce the particular impact of local extreme events on

CCP6
2322Cross-Chapter Paper 6 Polar Regions
biodiversity (medium confidence). {CCP6.2; Box  CCP6.1; CCP6.3; 
Table CCP6.5; Table CCP6.6; Figure CCP6.6; CCP6.4}
Timing, direction and scale of polar climate change impacts 
differ sub-regionally and will require adaptation strategies that 
are flexible, equitable, inclusive and integrated across sectors 
and governance arrangements to effectively reduce risks (high 
confidence). Governance around climate change planning, preparation 
and response has been limited in scope, and has often not considered 
interacting effects of climate change with other risks (high confidence). 
Reactive management strategies will not succeed in reducing risks 
in polar regions given the rapid change and increasing potential for 
extreme events (high confidence). Greater inclusivity of stakeholders 
and communities, along with using diverse sources of information, 
including Indigenous knowledge and local knowledge, can benefit

robust planning and decision making, and uptake of adaptations (high 
confidence). Effectiveness in preparing for and adapting to climate 
risks can benefit from improved climate, weather and ice forecasting 
services, tools for integrating climate change data and different 
types of knowledge into management processes and enhanced polar 
search, rescue and emergency response capabilities (high confidence). 
{CCP6.2.3.1; CCP6.3; Table CCP6.6; CCP6.4; Box CCP6.2; Box CCP6.3; 
Figure CCP6.8}
Climate Resilient Development
Climate resilience for Arctic Indigenous Peoples and local 
communities is dependent on Indigenous self-determination 
in climate-adaptation action (very high confidence), inclusive, 
coordinated and transboundary governance (high confidence) 
and ecosystem-based policies (high confidence) to effectively 
address climate change impacts and risks across scales and 
sectors, and to achieve a resilient, secure and equitable future.

Development of robust pathways for climate resilience in 
the Arctic can be accelerated by adaptation strategies and 
governance that reflect local conditions, cultures and adaptive 
capacities of communities and sectors (high confidence). 
Effectiveness of adaptation strategies will be enhanced by accounting 
for the geographic, climatic, ecological and cultural uniqueness of 
the polar regions (medium confidence). Colonialism can inhibit the 
development of robust climate adaptation strategies, and exacerbate 
climate risks (very high confidence). Inclusive decision making in 
establishing climate adaptations can foster resilience, reflect the unique 
environmental, cultural and economic imperatives of the region and 
support both market-based and sharing economies (high confidence). 
{Box CCP6.2; Table CCP6.6; CCP6.3.2; CCP6.4}
Indigenous self-determination in managing climate change 
impacts, adaptations and solutions can accelerate effective

robust climate resilient development pathways in the Arctic (very 
high confidence). Arctic Indigenous self-determination in decision 
making can establish robust climate resilience, especially in Indigenous 
communities, incorporating locally derived definitions of social and 
economic success, culturally legitimate institutions of government, 
strategic visioning and thinking and public-spirited, nation-building 
leadership (very high confidence). {Box CCP6.2; CCP6.3; CCP6.4}

CCP6
2323Polar Regions  Cross-Chapter Paper 6
CCP6.1 The Global Importance of Climate 
Change in Polar Regions
Polar regions (Figure CCP6.1) are considered flagship areas for climate 
change, since some of the most extreme climate change impacts 
that are projected to occur by 2050 elsewhere in the world have 
already been observed in the Arctic and Antarctic and have resulted 
in transformative and unprecedented change. Polar regions are not 
only home to cultural keystone species such as polar bears (Arctic) and 
penguins (Antarctic) but they also play fundamental roles in regulating 
the global climate system and in the provision of ecosystem services for the global community and for Arctic Indigenous Peoples and local 
communities in the region.
These changes are causing a suite of direct and cascading risks for 
all polar ecosystems with larger effects to date in the Arctic than the 
Antarctic (high confidence), due to larger and regionally more consistent

physical changes (Figure CCP6.2, Table CCP6.1; Chapter 3) (Meredith 
et  al., 2019; Ranasinghe et  al., 2021). In the Arctic, these changes 
affect every sector of society, impacting its 4,000,000 inhabitants, 
including 400,000 Indigenous People. The Antarctic has no permanent 
human settlements; however, many nations conduct field research, 
Polar regions and subregions
Arctic Region Names Antarctic Region NamesAR6 Boundary
Marine Ecosystem Assessment for the Southern Ocean (MEASO) RegionsSub Arctic
Low Arctic
High Arctic180oE
Aleutian Islands
Barents Sea
Baffin Bay
Beaufort Sea
Chukchi Sea
East Greenland Shel f
East Siberian Sea
Eastern Bering Sea
Faroe Plateau
Hudson Complex
Kamchatka Shel f
Kara SeaAI
BARS   
BB
BS
CS
EGS
ESS
EBS
FP
HBC
KAM
KSLaptev Sea
North Greenland Shelf
Labrador Sea
Sea of Okhotsk
West Greenland Shelf
North and East Iceland
South and W est Iceland
Southern Norway
N. Norway & Finnmark
North East Barents Sea
Canadian Arctic ArchipelagoLS
NGS
LAB
SOO
WGS
NEI

SWI
SN
NN
BARS
CAAWSEADML
EAEL
AS
RSEAWL
60oN180oE
APAO
CI
DV
WPEP
Atlantic
Central Indian
East Indian
East Pacific
West Pacific
Amundsen/Bellingshausen Se a
Antarctic Peninsula
Dry valley s
East Antarctic Enderby Land
East Antarctic Wilkes Land
East Antarctic Dronning Maud Land
Ross Sea
Weddell SeaAO
CI
EI
EP
WP
AS
AP
DV
EAEL
EAWL
EADML
RS
WSEICSAI
EBS
BS
HBC
BB
EGS NEI
SWINGSLAB
WGSESS
LS
KS
BARS
FPSNNNSOOKAM
Arctic
CAAPacific(a) Arctic (b) Antarctic 
Figure CCP6.1 |  Polar regions include the Arctic, Antarctica, Iceland, Greenland, Faroe Islands and some sub-Arctic areas (e.g., Bering Sea and Aleutian 
Islands as well as the Fennoscandian and Siberian boreal areas), and all sub-Antarctic areas. This CCP augments the geographical boundaries for the Arctic (Meredith 
et al., 2019) to also include the sub-Arctic boundary (as defined by the Arctic Biodiversity Assessment), northern boreal areas, parts of the Siberian taiga and southern Labrador.

The Antarctic region is delineated along the sub-Antarctic Front (Orsi et al., 1995). Geographic boundaries of the polar regions and important sub-regional locations are displayed, 
including five marine sectors as defined in the Marine Ecosystem Assessment for the Southern Ocean (MEASO), for example, Grant et al., (2021).

CCP6
2324Cross-Chapter Paper 6 Polar Regions
Table CCP6.1 |  Climatic impact drivers in the Arctic and Antarctic derived from WGI-AR6 chapters (indicated as WGI-9 (Fox-Kemper et al., 2021) and WGI-12 (Ranasinghe et al., 
2021)) and Meredith et al. (2019) (indicated as SROCC-3). Supplementary Material (SMCCP6.1) contains supplemental data for these drivers of projected changes (2021–2040, 
2041–2060, 2081–2100) derived from the WGI-AR6 Interactive Atlas (indicated as Atlas) (Gutiérrez et al., 2021) (https://interactive-atlas.ipcc.ch).
Driver Region Observed changes Projected changes
Marine and sea ice
Sea level (relative) ArcticNo consistent trend (increase in northwest America, decrease 
in northeast America, stable in Greenland and Arctic Russia) 
(WG1-12)Rise in all polar regions (except areas of substantial land 
uplift in northeast Canada, the west coast of Greenland) (high 
confidence);
Increase of extreme sea levels in Russian Arctic and northwest

America (high confidence) Greenland/Iceland and northeast 
America (given glacial isostatic adjustment) (medium confidence, 
WG1-12)
AntarcticRise in all polar regions (except areas of substantial land uplift in 
west Antarctica) (high confidence, WG1-12)
Sea surface temperature ArcticIncrease of ~0.5°C per decade during 1982–2017 in ice-free 
regions in summer (high confidence, SROCC-3)Further increases (high confidence, WG1-12)
AntarcticWarmed in northern areas of Southern Ocean but cooled in 
its southernmost regions since the 1980s (high confidence, 
SROCC-3)Circumpolar increases (high confidence, WG1-12)
Sea ice cover ArcticLoss (particularly of multi-year sea ice) accelerated since 2001 
(very likely, WG1-9)Will become sea ice free (< 1 × 106 km2) during summer before 
2050, irrespective of global warming level (likely, WG1-9)
AntarcticNo significant circumpolar trend from 1979–2018 (very high 
confidence), but decrease off the Antarctic Peninsula (high

confidence) and increases and decreases in other regions 
(medium confidence, WG1-9)Circumpolar decrease (low confidence due to limited 
understanding of driving processes, WG1-9)
Ocean surface pH Both polesDecrease since 1980 at rates of 0.003–0.026 pH units per decade 
in open polar zones (very likely, WG1-12)Further acidification by 0.1–0.6 pH by 2100 (Atlas), characterised 
by year-round conditions corrosive for aragonite minerals by 
2100 (very likely, SROCC-3)
Terrestrial, freshwater and ice
Atmospheric temperature ArcticIncrease of means higher than twice global mean, most 
pronounced in cold season (high confidence, WG1-12)Further increase (Table SMCCP6.1)
AntarcticWarmed from 1957 to 2016 at 0.2–0.3°C per decade in west 
Antarctica (very likely); no consistent change in east Antarctica 
(limited evidence, WG1-12)Region: future warming across continent (high confidence, 
WG1-12)

Extreme heat events Arctic Increase since 1979 (WG1-12)Polar amplification will drive further increases (high confidence, 
WG1-12)
AntarcticHeatwave across Antarctica (2020) (WG1-12, (Robinson et al., 
2020).Further increase, with >50 additional days above freezing by 
2100 (under RCP8.5, vs. 2014) over the Antarctic Peninsula but 
smaller changes over mainland Antarctica (medium confidence, 
WG1-12)
Fire weather (FW) ArcticOver four decades, fire season lengthened and number of fires 
increased in North America (WG1-12)FW index increases and more frequent fires in tundra regions 
(high confidence, WG1-12)
Precipitation Arctic Increase, highest during the cold season (likely, Atlas)
AntarcticIncreasing trend over the 20th century, while large interannual 
variability masks any existing trend since the end of 1970 
(medium confidence, Atlas)
Floods ArcticIncreasing river runoff, increasing heavy precipitation (high

confidence, WG1-12)Further increases in all variables (high confidence, WG1-12)
Snowfall ArcticRecent overall declines in snow extent and seasonal duration 
(high confidence, WG1-12)Higher % of precipitation as rain (fall and spring) ( high 
confidence, WG1-12)
Antarctic Increases in the 20th century (medium confidence, WG1-12) Further increases (over land) (likely, WG1-12)
Glaciers and IS ArcticLosses in glacier mass since 2000 (high confidence, WG1-12); 
losses in Greenland IS mass since 1980 at increasing rates ( high 
confidence, WG1-9)Further mass loss until 2100 under all warming scenarios 
(virtually certain, WG1-9 and -12)
AntarcticLosses in glacier mass since 2000 (high confidence, WG1-12); 
losses in Antarctic IS mass since 1992 (in west Antarctica but 
also parts of east Antarctica since 2000) (high confidence, 
WG1-9)Further mass loss until 2100 under all warming scenarios (likely, 
WG1-9 and -12)

CCP6
2325Polar Regions  Cross-Chapter Paper 6
operate seasonal and permanent stations and have an interest in the 
management of the region (Hughes et al., 2018; Grant et al., 2021). 
During summer, when Antarctic science, tourism and fishery activities 
are greatest, 4400 people live there, whereas only 1100 people live 
there over winter (Meredith et  al., 2019). Although adaptation is 
occurring in polar regions, it is uneven and sporadic and does not 
meet the risks posed by future climate change. Indigenous knowledge-
based solutions, inclusive ecosystem-based policies and integrated 
technologies demonstrate the potential to effectively address climate 
change impacts across scales and sectors, yet implementation barriers 
remain (CCP6.4.1).
This Cross Chapter Paper (CCP) assesses the impacts, risks and 
adaptation implications resulting from the physical and chemical 
changes in the polar regions that were detailed in the Intergovernmental

Panel on Climate Change (IPCC) Working Group I (WGI) contribution 
to the IPCC 6th Assessment Report (AR6) (IPCC, 2021). Several key 
WGI AR6 findings have important implications for natural and human 
systems in polar regions. Warming and wetting have persisted as key 
climatic impact drivers in polar regions (very high confidence) and 
will very likely2 continue to 2100 (Fox-Kemper et  al., 2021; Gulev 
et al., 2021) with cascading climate effects regarding heatwaves, fire, 
weather, floods and heavy precipitation, river runoff, snowfall, glaciers 
and ice sheets (IS), permafrost, lake, river and sea ice, relative sea level 
and coastal flooding and erosion (Gutiérrez et al., 2021; Ranasinghe 
et al., 2021) (Table CCP6.1). They represent major climate hazards in all 
key risks for polar regions (Table CCP6.5). Key points of departure for 
this CCP also include IPCC 5th Assessment Report (AR5) (IPCC, 2014) 
and the Polar Regions chapter in the Special Report on the Ocean and

Cryosphere in a Changing Climate (SROCC) (Meredith et  al., 2019). 
SROCC assessed physical, biological and social systems concerning the 
Arctic and Antarctic Oceans and cryosphere, and how they are affected 
by current and future climate change. This CCP assesses the rapidly 
increasing evidence that has been published since AR5 and SROCC, and 
advances previous IPCC assessments. First, results from the Coupled 
Model Intercomparison Projects (CMIP6) are an important advance 
since SROCC, which improve the certainty and resolution of projections 
of the main climate impact drivers and the risks they have for polar 
systems (Fox-Kemper et  al., 2021; Ranasinghe et  al., 2021). Second, 
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as

0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, for example, very likely. This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an 
outcome lies within the 17–83% probability range.building from the framework outlined in SROCC (Crate et al., 2019), 
scientific, Indigenous knowledge (IK) and local knowledge (LK) systems 
are included in this assessment. Importantly, Indigenous authors led 
the assessment of the impacts, adaptation and governance of climate 
change for Indigenous Peoples, which is an important advance since 
AR5 and represents an important step towards Indigenous self-
determination in international assessment processes (Ford et al., 2012; 
Ford et al., 2016; Hill et al., 2020).

Herein, observed impacts and future risks (CCP6.2), key risks and 
adaptation (CCP6.3) and climate resilient development pathways 
(CCP6.4) in the polar regions are assessed. The CCP describes how 
the implications of climate change impacts in the Arctic and Antarctic 
extend beyond their boundaries, in terms of transregional coupled 
ecological systems (CCP6.2.1, CCP6.2.2), global nutritional security 
(CCP6.2.3), global trade and shipping (CCP6.2.4) and cultural value 
(CCP6.2.5, CCP6.2.6). Given the synthetic and policy-facing mandate 
of CCPs and with SROCC as a key point of departure, this CCP is not 
intended to cover the full breadth of issues for polar regions but rather 
it highlights select key policy-relevant topics by synthesising and adding 
value to the relevant material from AR6 sectoral and regional chapters.
CCP6.2 Observed Impacts and Future Risks
CCP6.2.1 Marine and Coastal Ecosystems
CCP6.2.1.1 Warming and sea ice retreat cause shifts in

distribution ranges of species
In Arctic seas, warming and other climate impact drivers, primarily 
sea ice retreat, have led to range contractions of Arctic marine and 
ice-associated species and poleward expansions of boreal species 
(very high confidence) (Table  CCP6.2) (Bouchard and Fortier, 2020; 
Huntington et  al., 2020; Mueter et  al., 2020) even though light 
and energetics at seasonal extremes may limit some range shifts 
(limited evidence) (Ljungström et al., 2021). Altered conditions allow 
more microorganisms to move poleward and provide opportunities 
for invasive species (Cavicchioli et  al., 2019; Nielsen et  al., 2020; Driver Region Observed changes Projected changes
Permafrost ArcticRising permafrost temperatures over past three to four decades 
(high confidence, WG1-9); decreases in permafrost active 
layer thickness (very high confidence) (Biskaborn et al., 2019).

Submarine permafrost warming (medium confidence, WG1-9)Increases in temperature and active layer thickness (WG1-9); 
near-surface terrestrial permafrost extent will reduce under all 
scenarios by 2100 (virtually certain, WG1-9)
AntarcticRising permafrost temperatures over past three to four decades 
(high confidence, WG1-9)
Lake, river ice ArcticDeclines in seasonal lake ice cover thickness and duration over 
most Arctic lakes; declines in cold-season river ice extent (high 
confidence, WG1-12)Many lakes will lose >1 month lake ice cover by 2050 (medium 
confidence), Reductions in average Northern Hemisphere 
seasonal river ice duration of 6.10 days per 1°C GWL (WG1-12)
Coastal floods/erosion Arctic Increase (medium confidence, WG1-12) Further increase (high agreement-limited evidence, WG1-12)
Antarctic (Lack of studies, WG1-12)

CCP6
2326Cross-Chapter Paper 6 Polar Regions
Observed and projected climate changes across the Arctic and Antarctic
(a) (b)
+2°C
Global warming level+4°C
Global warming level
∆SI%
relative to
1850–1900
-0.30.10.30.5
-0.5-0.1
∆T °C
1986–2005
8°C
6°C
4°C
2°C
0°C
-70%-50%-30%-10%0%W5E5 (ERA5 adjusted)
1980–2015 observations
mean temperature trend
(°C decade-1)
+2°C
(c)
(d)
(e)(f)
(g)
(h)(i)
(j)
(k)(l)
(m)
(n)∆SST °C
1850–1900
8°C
6°C
4°C
2°C
0°C
Figure CCP6.2 |  Observed and projected climate changes across the Arctic (A, C–H) and Antarctic (B, I–N). Boundary lines in each plot are based on the polar regions 
defined in Figure CCP6.1. All data shown here are extracted from the IPCC WGI Interactive Atlas (Gutiérrez et al., 2021; Iturbide et al., 2021); data set details can be found in

the Atlas (https://interactive-atlas.ipcc.ch/). Arctic (A) and Antarctic (B) are observed temperature trends (°C per decade) over land for the period 1980–2015, derived from ERA5 
adjusted data set. Projected changes from an ensemble of CMIP6 projections: annual mean temperature over land is depicted for 2°C (C, I) and 4°C (F , L). Global warming levels 
(GWL) in the Arctic and Antarctic, respectively; annual mean sea surface temperature is depicted for 2°C (D, J) and 4°C (G, M) GWL in the Arctic and Antarctic respectively; annual 
sea ice (%) is depicted for 2°C (E, K) and 4°C (H, N) GWL in the Arctic and Antarctic, respectively.

CCP6
2327Polar Regions  Cross-Chapter Paper 6
Table CCP6.2 |  Summary of observed impacts (and projected risks of climate change for polar marine, terrestrial and freshwater ecosystems identified in Section 3.2.3 and Box 3.4 
in Chapter 3 of the IPCC SROCC (Meredith et al., 2019).
Affected systemHazard
*Cascading effectObserved impacts, future risks and natural adaptations identified in SROCC (confidence level)
Arctic marine ecosystems
Primary producers (PP-1)Sea ice loss
* Freshening
* StratificationImpact: timing (earlier and later blooms), distribution and magnitude (>30% increase in annual net primary production 
since 1998) (high confidence)
Acidification Adaptation: phytoplankton may compensate for decrease in pH
Zooplankton * PP-1 Impact: changing production and community composition (medium confidence)
Benthos * PP-1 Impact: changing production and biodiversity (medium confidence)

Acidification Risk: effects on zooplankton and pteropods depends on climate scenario and species’ sensitivity/adaptive capacity
FishWarming
* Prey changesImpact: northward expanding ranges of sub-Arctic/boreal species (e.g., Atlantic cod) in Bering Sea (Detection—high 
confidence, Attribution—medium confidence) negatively affecting Arctic polar cod (medium confidence)
* Prey declinesRisk: decreasing production of walleye pollock, Pacific cod and arrowtooth flounder, due to declines in large copepods 
(medium confidence)
Birds and marine mammals Sea ice lossImpact: phenological, behavioural, physiological and distributional changes; endemic marine mammals have little scope 
to move northwards in response to warming (high confidence)
Polar bearsSea ice timing, distribution, 
thicknessImpact: phenological shifts, and changes in distribution, denning, foraging behaviour and survival rates 
(high confidence)
Antarctic marine ecosystems
Primary productivitySea ice loss

* StratificationImpact: little overall change in biomass at circumpolar scale from 1998 to 2006, but sub-regional differences (medium 
confidence); changes difficult to detect and attribute to climate change
Microbes AcidificationImpact: detrimental effect on primary production and changes to the structure and function of microbial communities 
Antarctic krill WarmingImpact: declines in abundance in the South Atlantic sector (medium confidence); may not represent a long-term, 
climate-driven trend but a decline following a period of anomalous peak abundance ( low confidence)
Risk: southward range shift due to changes in the location of the optimum conditions for growth and recruitment, with 
decreases most apparent in the areas with the most rapid warming, such as the southwest Atlantic/Weddell Sea region 
Zooplankton Acidification Risk: vulnerability of pteropods through effects on eggs ( medium confidence)

Benthos Sea ice lossRisk: increase of biomass on the Antarctic continental shelf as productivity from longer phytoplankton blooms 
outweighs ice-scour mortality (low confidence)
Sea ice lossRisk: shallow-water communities may become dominated by macroalgae due to increases in the amount of light 
(possible loss of endemic species by 12% due to warming temperatures) (low confidence)
Fish Warming Risk: icefish may be displaced from shallow regions around sub-Antarctic islands ( low confidence)
Birds and marine mammals Sea ice coverImpact: predictability of foraging grounds and sea ice cover associated with climate are main drivers of population 
changes: increases for gentoo penguins (decreases for Adélie, chinstrap, king and Emperor penguins) (high confidence)
Arctic terrestrial and freshwater ecosystems
Vegetation Warming Impact: greening (high confidence)
Risk: decrease in tundra areal extent >50% by 2050; wood shrubs expected to increase (medium confidence)

Vertebrates Warming Impact: expanding range into Arctic
Freshwater primary productivity* Increased runoff
* Increased permafrost thawImpact: increased productivity in rivers, lakes and coastal areas
Risk: expected to mobilise stores of pollutants
Pathogens Warming Impact: expanding range into Arctic
Risk: mobilisation may increase in high latitudes, including anthrax from frozen carcasses possibly released from 
permafrost
Fish* Freshwater winter habitat
* Increased dischargeRisk: disruption of the life history of Arctic freshwater fish
* Warming freshwater Risk: may make some surface waters inhospitably warm for cold water fish species
Biodiversity Warming Impact: sub-Arctic biodiversity expanding into Arctic

CCP6
2328Cross-Chapter Paper 6 Polar Regions
Mustonen, 2021). Phytoplankton communities harbour increasing 
numbers of taxa, including harmful species (Lovejoy et  al., 2017) 
and the coccolithophore Emiliania huxleyi, which meanwhile forms 
regular blooms in the Barents Sea (Neukermans et  al., 2018; Silkin 
et al., 2020). Northward shifts of pelagic, benthic and demersal species 
and subsequent changes in Arctic community composition have been 
observed in the Bering, Greenland and Barents Seas (Grebmeier et al., 
2018; Mueter et al., 2020), as have higher numbers of economically 
important boreal species such as haddock and Pacific and Atlantic 
cod (CCP6.2.3). Cold-adapted Arctic fish species such as polar cod 
(Boreogadus saida) are expected to decline further and lose spawning 
habitats at GWL >1.5°C, mainly due to a lack of phenotypic plasticity, 
as well as increasing interspecific competition with and predation

from invading boreal species (Dahlke et al., 2018; Marsh and Mueter, 
2020). Numerous mammals and sea birds respond to changes in 
the distribution of their preferred habitats and prey by shifting their 
range, altering the timing or pathways for migration or switching 
prey (very high confidence) (Hamilton et al., 2017; Loseto et al., 2018; 
Meredith et al., 2019). Ice-breeding seals (e.g., harp seals – Pagophilus 
groenlandicus) often have little scope to shift distribution, leading to 
increases in strandings and pup mortality in years with little ice cover 
(medium confidence) (Table  CCP6.2) (Boveng et  al., 2020). Recent 
studies confirm that polar bears (Ursus maritimus) are negatively 
affected by changing ice and snow conditions with decreases in 
denning, foraging, reproduction, genetic diversity and survival rates 
(very high confidence) (Table CCP6.2) (Boonstra et al., 2020; Johnson 
and Derocher, 2020; Maduna et al., 2021).

In the Southern Ocean, southward range shifts are expected to result 
from increased warming coupled with the narrow thermal tolerance of 
cold-adapted Antarctic species (Convey and Peck, 2019; Morley et al., 
2019; Gutt et al., 2021). Such shifts have so far only been detected 
for Antarctic krill (Euphausia superba), with a poleward contraction of 
the highest densities of krill in the Atlantic sector (medium confidence) 
(Table CCP6.2); (Atkinson et al., 2019). Ocean warming is expected to 
put pressure on Antarctic phytoplankton (Pinkerton et al., 2021) and 
fish species unable to move further south in shelf areas, including 
waters off sub-Antarctic islands (low confidence) (Table  CCP6.2) 
(Caccavo et al., 2021). Off the Antarctic Peninsula and sub-Antarctic 
islands, invasive benthic invertebrates and macroalgae have already 
been detected (medium confidence) (Fraser et al., 2018; Avila et al., 
2020; Brasier et al., 2021), and projected changes will further favour

the spread of invasive species (Fraser et al., 2020; Macaya et al., 2020). 
On a local to regional scale, the benthic recolonisation of the newly 
exposed seabed after the disintegration of ice shelves shows typical 
succession patterns, with mass occurrences of few pioneer species followed by gradual shifts to a more diverse typical shelf community, 
driven by increasing pelagic primary production upon ice-shelf collapse 
and strengthening of the pelagic–benthic coupling (high confidence) 
(Brasier et al., 2021; Gutt et al., 2021). Range changes of Antarctic birds 
and marine mammals have been observed, which vary among sub-
regions and are mostly attributable to changes in sea ice extent and 
food availability (high confidence) (Table CCP6.2) (Gutt et al., 2018; 
Convey and Peck, 2019; Bestley et al., 2020). With projected sea ice 
retreat and associated change in prey distribution (Henley et al., 2020), 
foraging areas of sub-Antarctic sea birds and marine mammals will

shift southwards, leading to elevated pressure on populations due to 
higher foraging costs during the breeding season (medium confidence) 
(Ropert-Coudert et al., 2018; Bestley et al., 2020; Hindell et al., 2020; 
Hückstädt et al., 2020; Wege et al., 2021). These changes are particularly 
impacting emperor penguins (Aptenodytes forsteri) (Table  CCP6.2), 
with the projected population declining close to extinction by 2100 
under Business-As-Usual climate scenarios (medium confidence) 
(Jenouvrier et al., 2020; Trathan et al., 2020; Jenouvrier et al., 2021), 
whereas population decline is halted by 2060 under the 1.5°C climate 
scenario (low confidence) (Jenouvrier et al., 2020).
CCP6.2.1.2 Ocean warming and sea ice changes affect marine 
primary productivity
In the central Arctic Ocean, primary productivity remains low (medium 
confidence), mostly due to persisting nutrient and light limitations 
(Randelhoff and Guthrie, 2016; Ardyna and Arrigo, 2020). In inflowing

(Barents and Chukchi Sea) and interior shelf regions (Laptev, Kara, 
and Siberian Sea), changes in sea ice extent, thickness and seasonal 
timing have altered light and mixing regimes, causing increasing 
overall productivity in open-water and under-ice habitats, and in leads 
(high confidence) (Table CCP6.2) (Ardyna and Arrigo, 2020; Lannuzel 
et  al., 2020). Productivity changes are associated with the earlier-
onset phytoplankton spring blooms and the increasing occurrence 
of autumn blooms, particularly at lower latitudes of the Arctic (high 
confidence) (Table CCP6.2) (Tedesco et al., 2019; Ardyna et al., 2020). 
Ice algal communities are expected to change in productivity and 
species composition in response to the transition from a predominantly 
multi-year to a seasonal sea ice pack (high confidence) (Meredith 
et al., 2019; Tedesco et al., 2019; Lannuzel et al., 2020). Thinner sea ice 
increases the likelihood of surface flooding, resulting in the occurrence

of snow-infiltration algal communities, which have been described 
in the Atlantic sector of the Arctic Ocean (Fernández-Méndez et  al., 
2018) and observed by Indigenous Peoples off northern Greenland 
(Box  CCP6.2). The observed transition from marine-terminating to 
land-terminating glaciers has a negative impact on coastal ecosystems Affected systemHazard
Reindeer/caribou Climate factorsImpact: reindeer/caribou declined overall without adaptation ( high confidence), with climate affecting many aspects of 
their life history (medium confidence)
Risk: domesticated reindeer/caribou can be affected by fire, which reduces pasture, as well as by increased ice-on-snow, 
which can cause starvation
Antarctic terrestrial and freshwater ecosystems
Terrestrial biota * Increased coastal ice melt Impact: increasing coastal ice-free areas available for colonisation (high confidence)

Alien species Warming Risk: barriers to alien species reduce, affecting terrestrial biodiversity (medium confidence)

CCP6
2329Polar Regions  Cross-Chapter Paper 6
in Greenland (medium confidence) (Meire et al., 2017; Hopwood et al., 
2018) and Svalbard (Halbach et al., 2019), as land-terminating glacial 
meltwater input increases stratification, which hinders vertical mixing 
and lowers local productivity, whereas marine-terminating glaciers 
can trigger upwelling, which supplies nutrients and enables higher 
productivity in the summer (Hopwood et al., 2020). Macroalgae and 
seagrass are generally expanding in the Arctic (medium confidence), 
though there are negative trends in some regions, partly due to 
increased runoff and turbidity from melting glaciers (Hopwood et al., 
2020; Krause-Jensen et al., 2020). In the future Arctic Ocean, higher 
light availability in response to further sea ice decline and reduced 
deep mixing is projected to generally increase primary productivity 
(medium confidence), leading to an increase in phytoplankton biomass

from 2000 to 2100 by ~20% for SSP1-2.6 and ~30–40% for SSP5-
8.5 (Chapter 3) (Kwiatkowski et  al., 2020). However, productivity 
may increase less than predicted and eventually even decrease once 
nutrient limitation outweighs the benefits of higher light availability 
(low confidence) (Randelhoff et al., 2020; Seifert et al., 2020).
Despite large-scale environmental changes in the Southern Ocean, 
such as the deepening of the summer mixed layer (medium confidence) 
(Panassa et  al., 2018; Sallée et  al., 2021), and the expected impacts 
via altered nutrient entrainment, light availability and grazer encounter 
rates (Chapter 3) (Behrenfeld and Boss, 2014; Llort et  al., 2019), 
assessments indicated no consistent changes in primary production 
at the circumpolar scale, as sectors and regions show different 
trends (medium confidence). Although a global assessment found no 
overall changes in circumpolar primary production from 1998 to 2015

(Table  CCP6.2) (Gregg and Rousseaux, 2019), another study showed 
an overall increase in phytoplankton biomass in the mixed layer over 
the period 1997–2019 (Pinkerton et al., 2021). Primary productivity has 
increased in the Pacific sector and decreased in the Atlantic sector and 
the Ross Sea (low confidence) (Kahru et al., 2017; Henley et al., 2020; 
Pinkerton et al., 2021). Higher productivity has also been observed in 
regions where rapid environmental changes occurred, such as in the 
vicinity of retreating IS and declining sea ice cover off the Antarctic 
Peninsula (medium confidence) (Henley et  al., 2020; Rogers et  al., 
2020), although diversity of phytoplankton may decrease with warming 
temperatures and less sea ice (limited evidence) (Lin et al., 2021). In the 
future Southern Ocean, stronger upwelling due to strengthened westerly 
winds is projected to increase primary productivity at the circumpolar

scale in the Antarctic Zone and to the north of the sub-Antarctic Front, 
but not in the sub-Antarctic Zone (low to medium confidence) (Chapter 
3) (Henley et al., 2020; Kwiatkowski et al., 2020; Pinkerton et al., 2021). 
The largest changes are projected to occur after 2100 at 2–6°C warming 
of the surface ocean (Moore et al., 2018). Such an increase in Southern 
Ocean productivity will lead to a decline in global ocean productivity 
(medium confidence), due to nutrient trapping (Moore et  al., 2018) 
and altered ocean carbon uptake through ecosystem feedbacks (Hauck 
CCP6.2.1.3 Impacts of ocean acidification vary spatially and 
among biotas
In Arctic seas, areas with acidification levels corrosive to organisms 
forming CaCO 3 shells or skeletons expanded between the 1990s and 
2010 (high confidence), with instances of extreme aragonite under-saturation (Ding et al., 2017; Zhang et al., 2020). Key species of diatom

and picoeukaryote phytoplankton species yet appear relatively resilient 
to decreasing pH levels over a range of temperature and light conditions 
(medium confidence) (Table CCP6.2) (Thoisen et al., 2015; Wolf et al., 
2018; White et al., 2020). In contrast, there is evidence for species- and 
stage-specific sensitivities of zooplankton, pteropods and fishes (high 
confidence) (Table  CCP6.2) (Bailey et  al., 2016; Dahlke et  al., 2018; 
Thor et al., 2018). Warming, rising river-sediment discharge and coastal 
erosion in Arctic shelf regions are expected to increase the input of 
labile, often permafrost-derived organic carbon, the remineralisation 
of which further increases acidification rates (medium confidence) 
(Semiletov et al., 2016; AMAP , 2018b; Bröder et al., 2018). Interactions 
with other physical changes, such as warming or freshening, are 
expected to aggravate the impacts of ocean acidification (Chapter 3) 
(Falkenberg et al., 2018).

In the Southern Ocean, calcifying organisms are also most vulnerable 
to ocean acidification (high confidence) (Table CCP6.2), as evidenced 
by rates of calcification declining by 3.9% between 1998 and 2014 
(Freeman and Lovenduski, 2015). Calcifying species with low-
magnesium calcite or mechanisms to protect their skeletons are less 
vulnerable to the corrosive effects of acidification than those using 
aragonite or high-magnesium calcite (high confidence) (Figuerola et al., 
2021). In diatom-dominated communities, silicification diminishes 
with reduced pH levels, albeit with rates differing among taxa (low 
confidence) (Petrou et  al., 2019). Species-specific responses exist 
regarding growth and primary production, which are further strongly 
modulated by iron and light availability (high confidence) (Hoppe et al., 
2013; Trimborn et al., 2013; Hoppe et al., 2015; Henley et al., 2020; 
Seifert et al., 2020). A meta-analysis yielded different CO 2 thresholds for

Antarctic organismal groups; for example, negative impacts emerged 
at >1000 μ atm CO 2 in phytoplankton and at >1500 μ atm CO 2 in 
invertebrates, whereas bacterial abundance was positively affected by 
ocean acidification (Hancock et al., 2020). Species sensitivity can also 
differ strongly between life-cycle stages (Chapter 3.3.2). For instance, 
eggs and embryos of Antarctic krill are negatively impacted at >1250 
μatm CO 2 whereas adults can thrive even at 1000–2000 µatm CO 2 over 
one year (Kawaguchi et al., 2013; Ericson et al., 2018).
CCP6.2.1.4 Climate change alters food web dynamics
Climate change has transformed Arctic marine ecosystems from sea ice-
associated to open-water production regimes, with profound impacts 
on trophic energy transfer efficiencies and pathways (high confidence) 
(Behrenfeld et al., 2017; Meredith et al., 2019; Huntington et al., 2020) 
as well as benthic–pelagic coupling (medium confidence) (Birchenough

et  al., 2015; Degen et  al., 2016; Solan et  al., 2020). Shifts in bloom 
phenology favour small phytoplankton and smaller zooplankton over 
large lipid-rich macro-zooplankton, leading to longer, less efficient food 
chains (medium confidence) (Aarflot et  al., 2018; Feng et  al., 2018; 
Kimmel et al., 2018; Weydmann et al., 2018; Møller and Nielsen, 2020). 
In the Beaufort Sea and Svalbard waters, earlier spring phytoplankton 
blooms have resulted in a mismatch in dynamics between microalgae and 
herbivorous copepods (Renaud et al., 2018; Dezutter et al., 2019). In the 
Bering Sea, zooplankton declines following the particularly pronounced 
sea ice retreats in 2017 and 2018 were associated with reduced forage 
fish production (Duffy-Anderson et  al., 2019) as well as multi-trophic

CCP6
2330Cross-Chapter Paper 6 Polar Regions
mortality of ctenophore, fish, bird and mammal species, coupled with 
severe emaciation, reproductive failure, disease and high mortality rates 
of sea bird predators (Section 14.4.4.2) (Jones et al., 2019; Maekakuchi 
et  al., 2020; Piatt et  al., 2020; Romano et  al., 2020). Species range 
shifts have restructured higher trophic levels in Arctic food webs (high 
confidence) (Table  CCP6.2; CCP6.2.3.3 Chapter 3) (Huntington et  al., 
2020). In the northern Barents Sea, increased predation mortality for 
key species and incursions of boreal fish have induced entire ecosystem 
reorganisation (Degen et al., 2016; Pecuchet et al., 2020a; Pecuchet et al., 
2020b). Regional taxonomic and functional diversity increased with 
immigration of boreal species, although the ongoing decline in Arctic 
species suggests high species turnover (Table  CCP6.2) (Frainer et  al., 
2017). Recent marine heatwaves induced rapid and profound food web

changes unprecedented over the last four decades (Siddon et al., 2020).
Climate impacts on Arctic marine food webs will be profound and 
intensify with GWL (high confidence), regardless of mitigation 
scenarios due to multi-decadal lags in sea ice extent and atmospheric 
carbon (WGI) (Jones et al., 2020). However, the exact nature of these 
impacts remains unclear due to attenuating and amplifying dynamics 
of both top-down and bottom-up processes in polar food webs and 
the management of fisheries (high confidence) (Chapter 3) (Cavicchioli 
et al., 2019; Meredith et al., 2019). Projected sea ice loss is associated 
with a >50% decline in the density of large zooplankton species by 
2100 (relative to early 21st century levels) in the southern Bering Sea 
and a net increase in large zooplankton in the Northern Bering Sea 
in scenarios without carbon mitigation (Representative Concentration 
Pathway (RCP) 8.5), whereas these declines are roughly half the

magnitude under moderate mitigation scenarios (RCP4.5) (Hermann 
et al., 2019; Kearney et al., 2020). Warming is expected to reduce the 
quantity and quality of lipid-rich copepod prey (high confidence) (Aarflot 
et al., 2018; Kimmel et al., 2018; Bouchard and Fortier, 2020; Møller 
and Nielsen, 2020; Mueter et al., 2020), leading to declines in survival 
and growth of multiple upper-trophic level fish species; these impacts 
are amplified over time under low mitigation scenarios (RCP8.5) (high 
confidence) (CCP6.2.1.1) (Dahlke et  al., 2018; Holsman et  al., 2020; 
Mueter et al., 2020; Oke et al., 2020; Reum et al., 2020; Thorson et al., 
2020; Whitehouse et al., 2021). Marine mammals and sea birds will 
continue to attenuate climate change impacts by shifting their diets 
and behaviour (medium confidence) (Table CCP6.2) (Hamilton et al., 
2017; Lowther et  al., 2017; Lydersen et  al., 2017; Vihtakari et  al., 
2018; Boveng et  al., 2020). However, sea birds generally have low

temperature-mediated plasticity of reproductive timing, making them 
vulnerable to mismatches with their prey and limiting long-term 
adaptation (medium confidence) (Keogan et al., 2018; Kharouba and 
Wolkovich, 2020; Piatt et al., 2020; Samplonius et al., 2021).
Many factors have contributed to changes in Antarctic food webs, 
including historical exploitation of fish and marine mammals as well 
as changes driven by the ozone hole and climate factors (Meredith 
et al., 2019; Morley et al., 2020; Grant et al., 2021). Most documented 
changes resulting from warming and sea ice losses relate to shifts in 
ranges and dynamics of species, with most impacts occurring around 
the Antarctic Peninsula (CCP6.2.1.1; Table CCP6.2).
The projected general rise in primary production in Antarctic seas by 
2100 (CCP6.2.1.2) suggests a concomitant increase in the abundance of higher trophic species, but changes in the structure and function of

food webs will vary (McCormack et al., 2021; McCormack, accepted) 
depending on regional differences in changing drivers (Morley et al., 
2020; Cavanagh et al., 2021; Grant et al., 2021). Primary production 
in open water habitats is expected to be supported by smaller 
phytoplankton species in the future (Henley et al., 2020), which could 
increase the relative importance of the copepod-mesopelagic fish 
pathway (McCormack, accepted), because krill prefer larger diatoms as 
food (Siegel, 2016). The optimum habitat for Antarctic krill is expected 
to decline with a shortening of suitable season for krill growth and 
reproduction, particularly in the northern Scotia and Bellingshausen 
Seas (medium confidence) (Veytia et  al., 2020), although changes 
may be difficult to distinguish from natural variability until later in the 
century (Sylvester et al., 2021). More subtle and unpredictable changes 
may occur in the structure and relative importance of energy pathways

in the food webs (Trebilco et  al., 2020). Small mesopelagic fish are 
increasingly recognised for their importance as mid-trophic level species 
in the Southern Ocean, particularly in the sub-Antarctic zone (Caccavo 
et  al., 2021) and Central Indian Sector (Subramaniam et  al., 2020; 
McCormack et al., 2021). Although salps have long been considered 
to be competitors of Antarctic krill (Suprenand and Ainsworth, 2017; 
Rogers et al., 2020), they provide a third energy pathway in pelagic food 
webs and, given the changing ocean conditions and their preference 
for smaller phytoplankton, may increase in importance for copepods 
(low confidence) (Plum et al., 2020; Trebilco et al., 2020; McCormack 
et  al., 2021; Pauli et  al., 2021; McCormack, accepted). Declining ice 
shelves, such as those off the Antarctic Peninsula, will open up new 
pelagic and benthic habitats (CCP6.2.1.1) with expected increases in 
productivity of benthic assemblages in the new areas (Barnes, 2017;

Morley et al., 2020; Brasier et al., 2021; Gutt et al., 2021).
CCP6.2.2 Terrestrial and Freshwater Ecosystems
Since the publication of AR5 (IPCC, 2014) and SROCC (IPCC, 2019) and 
their findings (Table CCP6.2), more studies confirm rapid changes in 
Arctic terrestrial and freshwater systems including increased permafrost 
thaw, changes to tundra hydrology and vegetation (overall greening of 
the tundra, regional browning of tundra and boreal forests), coastal and 
riverbank erosion (high confidence) (Canadell et al., 2021; Mustonen 
and Shadrin, 2021), reduced duration of snow cover and river and lake 
ice, increased rain-on-snow events, and reduced land-ice extent and 
thickness (Bieniek et  al., 2018; Brown et  al., 2018). Climate change 
continues to alter vegetation and attendant biodiversity, with divergent 
regional trends across the Arctic due to disparities in local conditions 
and changes in growing seasons (Zhu et al., 2016; Taylor et al., 2020).

Warming facilitates woody vegetation growth in northeastern Siberia, 
western Alaska, and northern Quebec (Song et al., 2018; García Criado 
et al., 2020), as well as a northward expansion of shrub vegetation and 
sub-Arctic and boreal species (Davidson et al., 2020).
Further evidence shows that warming and changes to the Arctic 
hydrologic cycle increase the risk of wildfire (medium confidence) 
(Mustonen and Shadrin, 2021). Both the frequency of and the area 
burned by wildfires during recent years are unprecedented compared 
with the last 10,000  years (high confidence) (Meredith et  al., 2019; 
Irannezhad et al., 2020). Fire risk levels are projected to increase across

CCP6
2331Polar Regions  Cross-Chapter Paper 6
most tundra and boreal regions, and interactions between climate and 
shifting vegetation (Song et al., 2018) will influence future fire intensity 
and frequency (medium confidence) (Curtis et al., 2018).
For all warming scenarios, declines in snow cover in the Arctic by 2050 
(Table CCP6.1) may accelerate vascular plant, moss and lichen extinction 
rates (32% for Arctic–alpine and 12% for boreal species), especially after 
the tipping point of 20–30% decrease in snow cover duration is passed 
(Niittynen et al., 2018). Even though the overall regional water cycle 
will intensify, including increased precipitation, evapotranspiration 
and river discharge to the Arctic Ocean (Table  CCP6.1), snow and 
permafrost decline may lead to further soil drying (medium confidence) 
(Meredith et al., 2019). Glacial ice melt poses a risk to ecosystems and 
people through remobilisation of sequestered hazardous waste and

transported pollutants (Table CCP6.3) (Wang et al., 2019).
In the Antarctic, there is further high agreement since the publication of 
SROCC that melt and ice-free areas are causing increases in the rates of 
colonisation and utilisation of coastal environments by terrestrial biota 
and land-based colonies of seals and birds (Gutt et al., 2021), although 
colonisation rates remain variable (Ruiz-Fernandez et al., 2017; Bokhorst 
et al., 2021). Soil temperatures along the Antarctic Peninsula are now 
sufficient for germination of non-native plants; invasions by non-
endemic species are expected to increase with rising temperatures (high 
confidence) (Bokhorst et al., 2021), posing a risk to endemic polar species 
(medium confidence) (Chown and Brooks, 2019; Gutt et al., 2021).
Vegetation responses to warming are contingent on water availability 
and local temperature (medium confidence) (Guglielmin et al., 2014; 
Royles and Griffiths, 2015; Amesbury et al., 2017; Cannone et al., 2017;

Charman et  al., 2018; Robinson et  al., 2018; Stelling et  al., 2018), 
which vary greatly around Antarctica (Figure  CCP6.1) (Turner et  al., 
2020a). Antarctic terrestrial ecosystem responses to changes in water 
availability are not homogeneous (Ball and Levy, 2015; Sadowsky 
et al., 2016; Fuentes-Lillo et al., 2017; Gooseff et al., 2017; Schroeter 
et al., 2017; Lee et al., 2018). West Antarctica is showing evidence of 
greening in the dominant cryptogrammic vegetation, with greater 
growth in mosses (high confidence) (Casanova-Katny et  al., 2016; 
Amesbury et al., 2017; Shortlidge et al., 2017; Charman et al., 2018; 
Prather et al., 2019). Peatland ecosystems may increase on the west 
Antarctic Peninsula with future warming (low confidence) (Yu et al., 
2016; Loisel et  al., 2017). In contrast, some parts of East Antarctica 
and the subantarctic islands to the north have been experiencing a 
drying climate, with declining health of mosses and other vegetation

(high confidence) (Bergstrom et al., 2015; Bramley-Alves et al., 2015; 
Robinson et al., 2018; Bergstrom et al., 2021).
Antarctica encountered its first reported heatwave in 2020 (Table CCP6.1). 
Such abrupt heating can cause wide-ranging effects on biota, from flash-
flooding damage and dislodgement of plants to excess melt waters 
supplying moisture to arid Antarctic ecosystems. This suggests that 
increased melt may reverse the drying trend if plant communities remain 
connected to melt streams and there is sufficient precipitation (high 
agreement, limited evidence) (Bergstrom et al., 2021).
Warming of the Antarctic Peninsula has resulted in increased soil 
microbial abundance and biomass. However, this trend is not as great in southern colder locations (medium confidence) (e.g., Kim et  al., 
2018; Newsham et al., 2019), as the microbial community structure is 
affected by vegetation cover and water availability (high confidence) 
(Dennis et al., 2019; Newsham et al., 2019).

Antarctic terrestrial invertebrate communities on the West Antarctic 
Peninsula may be controlled more by vegetation and water 
availability than by air temperature (medium confidence) (Bokhorst 
and Convey, 2016; Knox et  al., 2016; Andriuzzi et  al., 2018; Prather 
et al., 2019; Newsham et al., 2020). Evidence from laboratory studies, 
field programmes and sedimentary records indicate that Antarctic 
freshwater ecosystems may become more productive under climate 
warming scenarios (medium confidence) (e.g., Schiaffino et al., 2011; 
Borghini et al., 2016; Píšková et al., 2019; Čejka et al., 2020).
CCP6.2.3 Food, Fibre and Other Ecosystem Products
Food and fibre production underpins regional identities, cultures and 
communities of practice and place in polar regions, are vital to local 
and distant economies (Table  CCP6.4) and represent for fisheries a 
critical source of global nutrition and food security (Hicks et al., 2019).

Since SROCC, there is further evidence that climate change alterations 
of polar ecosystems increasingly challenge production of, and access to, 
sufficient, healthy and nutritious food, posing risks to future food and 
nutritional security within and beyond polar regions (high confidence).
CCP6.2.3.1 Arctic subsistence resources
Subsistence harvest of fish, sea birds and marine mammals is the basis 
for economic, cultural and spiritual connections with Arctic marine 
systems (Box CCP6.2)(Fall et al., 2013; Haynie and Huntington, 2016; 
Raymond-Yakoubian et al., 2017; Slats et al., 2019), and nature-based 
livelihoods (e.g., caribou and reindeer (Rangifer tarandus) herding, 
fishing, hunting, trapping, small-scale forestry) are fundamental to 
Indigenous Peoples across the Arctic as they have been for millennia 
(Koivurova et  al., 2015; Betts, 2016; Gavin et  al., 2018; Raheem, 
2018; Mustonen and Shadrin, 2021). Climate change has impacted

Indigenous subsistence resources across the Arctic (very high 
confidence) (SMCCP6.2), and future food systems and ecological 
connections are at risk from future climate change hazards interacting 
with non-climate pressures, some of which are mediated or amplified by 
novel conditions and opportunities in Arctic regions (high confidence) 
(Moerlein and Carothers, 2012; Fall et al., 2013; Raymond-Yakoubian 
et  al., 2017; Meredith et  al., 2019; Slats et  al., 2019; Huntington 
et al., 2020; Huntington et al., 2021). Increasing heatwaves, wildfires, 
extreme precipitation, permafrost loss and rapid seasonal snow and ice 
thaw events will further threaten terrestrial subsistence food resources 
across the Arctic (high confidence) (Table CCP6.3). Although climate 
impacts and non-climate factors systematically undermine access to 
and productivity of subsistence resources, resilience is inherently high 
for Indigenous Peoples, illustrating critical elements underpinning

successful adaptation to climate change (Box  CCP6.2) (Huntington 

CCP6
2332Cross-Chapter Paper 6 Polar Regions
CCP6.2.3.2 Agriculture, forestry, livestock and aquaculture
In addition to reindeer herding, Arctic agriculture primarily consists 
of local production of cool season crops, forage, small grains and 
livestock (sheep and goats) (Westergaard-Nielsen et al., 2015; Natcher 
et  al., 2019). Short growing seasons, cold conditions, permafrost 
and moisture stress, especially along coasts, have historically limited 
production, but agriculture is generally increasing across the region 
(Westergaard-Nielsen et al., 2015). Although only ~0.2% of Alaska is 
farmland, area farmed and income from agriculture have increased 
2% and 80%, respectively, since 2012 (United States Department of 
Agriculture, 2017). It is likely that growing seasons have extended by 
1–3 days per decade in interior Alaska, although some coastal areas 
exhibit declines in growing season (Lader et al., 2018).
Arctic temperatures rarely exceed thermal tolerances for crops (e.g.,

35–38°C across corn, rice and grain), and warming will provide 
new opportunities for food and forage production in areas such as 
southwest Greenland and interior Alaska (Westergaard-Nielsen et al., 
2015; Tripathi et al., 2016; Lader et al., 2018). Higher atmospheric CO 2 
favours plant growth if soil quality and condition are sufficient, but 
benefits can be offset by increased heat and water stress associated 
with climate change (Tripathi et al., 2016; Unc et al., 2021). Growing seasons in Alaska will lengthen by 48–87 d yr-1 relative to historical 
growing season length (1981–2010), and the start of growing season 
is expected to shift 1–4  weeks earlier (Lader et  al., 2018). Feasible 
growing areas across the Arctic are expected to shift northward and 
increase within the 55°–69°N region (King et  al., 2018). Permafrost 
thaw (Table CCP6.1) increases drainage, which is a potential benefit, 
but can also increase erosion, subsidence and irregular surfaces,

inhibiting agriculture (Lader et al., 2018). Conversion of Arctic soils to 
croplands may also release carbon stored in vegetation and soils (Unc 
Arctic aquaculture contributes approximately 2% to global farm 
production (primarily Norwegian salmon (Salmo salar) as well as finfish 
in Iceland and Sweden and shellfish in Alaska), and will face increasing 
challenges from climate change (Troell et al., 2017) including increased 
frequency of storms (impacting sea farms), extreme temperatures 
and warmer conditions that favour pathogens, parasites and harmful 
algal blooms. Aquaculture feeds often depend on small pelagic fish 
or krill and supply may be affected by climate impacts on fisheries 
(Table  CCP6.6) (Troell et  al., 2017; Chen and Tung, 2018; Mørkøre 
et al., 2020). Integrated policies and coordination across multiple food 
production sectors in Arctic regions are needed to address climate

opportunities and challenges (Altdorff et al., 2021; Unc et al., 2021).Table CCP6.3 |  Illustrative examples of climate change impacts on subsistence resources in the Arctic.
Changing
driversObserved impacts and projected risks References
Snow, ice, river environmentsClimate change is disrupting subsistence harvests for Indigenous Peoples in Arctic communities that depend on 
snow, ice and river environments for travel and access to subsistence resources.(Wildcat, 2013; Meredith et al., 2019; 
Slats et al., 2019)
MultipleAcross the Canadian Arctic, multiple populations of reindeer and caribou are in decline, with 95% of assessed 
herds listed as rare, decreasing or ‘threatened’; reindeer and caribou abundances in the Alaska–Canada region 
have declined 56% over the past 20 years.(Russell et al., 2018)
MultipleReindeer herding is an important economic and Indigenous cultural activity in the Eurasian Arctic and is being

affected by non-climate and climate events, including changes to thaw cycles, drought and unpredictable 
summer weather, which threaten pasture areas in Siberia. Although changes in vegetation and the freeze–thaw 
cycle are impacting Sami reindeer herding, adaptive measures by herders have been effective at offsetting 
multiple climate and non-climate impacts.(Furberg et al., 2011; Uboni et al., 
2020; Mustonen and Shadrin, 2021)
Sea ice; winds; visibilityLoss of multi-year ‘mother ice’, declines in seasonal sea ice thickness and stability, and changes in winds and 
visibility have impacted the availability of, and access to, subsistence resources (high confidence) and have 
increased interactions between coastal communities and shipping, tourism and commercial fisheries, which 
directly impact human safety and well-being in Arctic communities (high confidence).(Stephenson and Smith, 2015; 
Brinkman et al., 2016; Melia et al., 
2016; Raymond-Yakoubian et al., 2017;

Ford et al., 2019; Slats et al., 2019; 
Huntington et al., 2020; Huntington 
et al., 2021)
MultipleMarine heatwave (MHW)-induced ecosystem changes contributed to widespread mortality events and declines 
in Northern Bering Sea sea birds and disrupted subsistence harvests in western Alaska.(Jones et al., 2019; Piatt et al., 2020; 
Siddon et al., 2020)
Storminess; sea ice; whale 
migration timing; shippingAlthough some communities have seen reduced whale harvests due to climate impacts on survival and 
productivity, changes in storminess and whale migration timing have lengthened the July harvest season 
for Inuvialuit from Inuvik, Aklavik and Tuktoyaktuk. Changes in Beluga migration routes have increased 
accessibility to communities of Ulukhaktok and Paulatuk. In Western Greenland, loss of sea ice has both 
reduced access to sealing and increased subsistence and commercial harvest of Atlantic cod, halibut and

other fish species. Increased impacts of noise and ship strikes associated with shipping are expected to impact 
subsistence species, especially seals and whales in Lancaster sound as well as the Pacific Arctic.(George et al., 2017; Hauser et al., 
2018; Loseto et al., 2018; Mustonen 
et al., 2018a)
Sea iceChanges in sea ice will continue to undermine subsistence resources and disrupt access by smaller scale 
commercial and subsistence-based ice-edge fishing.(Jacobsen et al., 2018; Ford et al., 
2019)
Shifting distributions; food 
web changesShifting species distributions and climate change mediated food web reorganisation pose a risk to near-shore 
subsistence harvests that are essential to sustaining Indigenous Peoples in Western Greenland and the 
Northern Bering, Beaufort and Chukchi Seas; for example, cod biomass in the Inuvialuit region is projected 
to decrease 17% by 2100 (RCP8.5). Climate-related declines in harvester access drive projected declines in

subsistence availability in Alaska.(Moerlein and Carothers, 2012; Fall 
et al., 2013; Brinkman et al., 2016; 
Loseto et al., 2018; Steiner et al., 2019; 
Marsh and Mueter, 2020; Ribeiro et al., 
2021)

CCP6
2333Polar Regions  Cross-Chapter Paper 6
CCP6.2.3.3 Commonalities in impacts and risks across polar 
fisheries
Fisheries play an increasingly important role in addressing global food 
and nutritional deficits (Section 3.6.3)(Béné et al., 2016; Ding et al., 
2017; Hicks et al., 2019; Costello et  al., 2020), especially as climate 
change has already reduced global yields from key crops (Myers 
et al., 2017; Ray et al., 2019; Thiault et al., 2019). Antarctic and Arctic 
systems support some of the world’s largest fisheries, including those 
for Antarctic krill and Arctic walleye pollock (Gadus chalcogrammus), 
which constitute a critical source of protein and macronutrients 
to a growing population of seafood consumers, as well as various 
aquaculture and livestock feeds (Cross-Chapter Box  MOVING PLATE 
in Chapter 5) (Table CCP6.4) (Huntington et al., 2013; Raheem, 2018; 
Hicks et al., 2019; Steiner et al., 2019; FAO, 2020; Cavanagh et al., 2021;

Grant et al., 2021; Murphy et al., 2021). Marine sources of protein and 
nutrition are important in transformational future scenarios where 
dietary shifts and provisioning policies provide multiple co-benefits 
to equity, food security and carbon mitigation (Springmann et  al., 
2016; Poore and Nemecek, 2018; Thiault et al., 2019; Kim et al., 2020). 
Shifting spatial distributions of fish stocks have led to transboundary 
management challenges in the Atlantic, Bering Sea and Arctic areas 
previously inaccessible due to sea ice (Table CCP6.6) (Gullestad et al., 
2020).Cascading and interacting effects of climate change impacts in polar 
regions (Table  CCP6.1) will reduce access to, and productivity of, 
future fisheries, and pose significant risks to regional and global food 
and nutritional security that increase with atmospheric carbon levels 
and declines in sea ice (high confidence) (Table CCP6.6). Although it 
is expected that fisheries will continue to contract poleward under

future warming (Cross-Chapter Box  MOVING PLATE in Chapter 5) 
(Table  CCP6.4) (Alabia et  al., 2018; Morley et  al., 2018; Stevenson 
and Lauth, 2019; Caccavo et al., 2021; Grant et al., 2021), global and 
regional models differ in their projections of fisheries catch potential 
for the polar regions under climate change. For example, some global-
scale models project increases in potential fishery yields in Arctic 
Canada (Cheung, 2018; Bindoff et al., 2019; Tai et al., 2019), whereas 
many observational studies and high-resolution regional projections 
suggest overall declines in biomass, productivity and yield associated 
with warming and loss of sea ice in multiple regions such as the Bering 
Sea (medium confidence) (Free et  al., 2019; Hollowed et  al., 2020; 
Holsman et al., 2020; Mueter et al., 2020; Reum et al., 2020). Reduced 
production of macronutrients and protein by polar marine sources 
will disproportionately impact people already experiencing food and

nutritional scarcity (Myers et al., 2017), marine-dependent communities 
within and beyond polar regions, and women and children who require 
higher quantities of macronutrients (high confidence).
FAQ CCP6.1 | How do changes in ecosystems and human systems in the polar regions impact everyone around the 
globe? How will changes in polar fisheries impact food security and nutrition around the world?
Polar regions are commonly known to be experiencing particularly fast and profound climate change, which strongly affects areas and people 
all around the world in several ways. Physical processes taking place in these regions are critically important for the global climate and sea 
level. Less known is that regional climate-driven changes of ecosystems and human communities will also have far-reaching impacts on a 
number of sectors of human societies at lower latitudes.

Climate change has triggered rapid, unprecedented and cascading changes in polar regions that have profound 
implications for ecosystems and people globally. Although physically remote from the largest population centres, 
polar systems are inextricably linked to the rest of the world through interconnected ocean currents, atmospheric 
interactions and weather, ecological and social systems, commerce and trade. The nutrient-rich waters of the polar 
regions fuel some of the most productive marine ecosystems on earth, which in turn support fisheries for species 
packed with vital macronutrients that are essential for human health and well-being. The largest most sustainable 
fisheries in the world are located in polar waters, where a mix of ice, seasonal light and cold nutrient-rich waters 
fuel schools of millions of fish that swell and retract in numbers across the years, reflecting interlaced cycles of

icy cold waters, lipid-rich prey and abundant predators. Polar systems thus exist in a productive balance that has 
supported vibrant ecocultural connections between Indigenous Peoples and the Arctic for millennia and has 
supported global food production and trade for centuries. Climate change increasingly destabilises this balance 
with uncertain outcomes for Indigenous Peoples and local residents in the Arctic as well as for the rest of the 
world. Triggered by warming oceans and air temperatures, accelerated melting of sea ice, glaciers and IS in polar 
regions in turn impacts ocean salinity, sea levels and circulation throughout the global ocean. Warming waters 
have also pushed cold-adapted species poleward, eroded the cold barrier between boreal and Arctic species, and 
induced rapid reorganisation of polar ecosystems. Studies increasingly indicate that the complex web of physical

and biological connections that have fuelled these productive regions will falter without the strong regulating 
influence of cryospheric change. At the same time, the global demand for food is increasing, particularly the 
demand for highly nutritious marine protein, placing increasing importance on stabilising polar ecological systems 
and minimising climate change impacts and risks.

CCP6
2334Cross-Chapter Paper 6 Polar Regions
Large-scale commercial fisheries are expected to continue to operate in 
polar regions (high confidence) (Barange et al., 2018; Cavanagh et al., 
2021; Grant et  al., 2021), and will shift poleward (high confidence) 
toward geopolitical and management boundaries (high confidence) 
(CCP6.3.2.3; Table  CCP6.6). Warming and climate impacts will 
continue to impact transboundary stocks and increase the potential 
for conflict in fisheries management (Pinsky et al., 2018; Mendenhall 
et  al., 2020; Palacios-Abrantes et  al., 2020; Sumaila et  al., 2020). 
Increased distances from ports to redistributed fishing grounds as well as increased frequency of storms and other extreme events are 
expected to increase risks and costs for fishery operations (medium 
confidence) and impact shore-based infrastructure and emergency 
response services (CCP6.2.4). Observed and expected increases in 
mobile ice combined with abrupt wind can create major hazards

for fish operators in Antarctica and the Arctic, with consequences to 
human safety and total revenue (Dawson and et al., 2017; Barber et al., 
2018; Grant et al., 2021). There will be increased demand for new port 
infrastructure across the Arctic (high confidence); new ports have Table CCP6.4 |  Climate change impacts on Arctic and Antarctic fisheries and fishing communities. Additional detail in Table SMCCP6.3.
Driver Observed impacts and projected risks References
Current and past climate change impacts
WarmingFisheries productivity declined in multiple stocks across the Arctic including the Eastern Bering Sea (EBS), while 
Atlantic cod and other fisheries have increased.(Free et al., 2019; Cheung and Frölicher, 
2020)
Extreme heatCommercially important fish species declined rapidly during recent MHWs (2016–2019), in the EBS due to 
reduced recruitment, increased metabolic demand and increased predation mortality, and it is probable that

climate impacts have contributed to the closure of Pribilof islands blue king crab ( Paralithodes platypus) 
fisheries.(Zheng and Ianelli, 2018; 
Duffy-Anderson et al., 2019; Stabeno 
et al., 2019; Basyuk and Zuenko, 2020; 
Reum et al., 2020; Thorson et al., 2020)
Temperature; shifting 
species distributionsIn the Barents Sea, northward redistribution of stocks led fisheries into previously unfished habitats, exposing 
benthic ecosystems to novel trawling impacts. Large-scale redistributions of Pacific cod (>1000 km per decade) 
and other groundfish species have challenged fisheries management in the EBS; ~50% of the biomass is now 
located in the Northern Bering Sea (NBS), outside of historical survey areas and in a region where bottom 
trawling is prohibited (although pelagic gear is permitted).(Christiansen et al., 2014; Jørgensen 
et al., 2019; Spies et al., 2019; 
Stevenson and Lauth, 2019)

OA, warming, windsShellfish species such as snow crab are undergoing range contractions poleward in the Barents Sea and NBS, 
with increased catches in the north and declines in the south.(Jørgensen et al., 2019; Fedewa et al., 
2020) (Cross-Chapter Box MOVING 
PLATE in Chapter 5)
Warming; poleward 
expansionPoleward expansion of Pacific salmon into Arctic watersheds and Greenland fjords presents both new 
opportunities and novel threats to key subsistence and commercial species such as Arctic char and Atlantic 
salmon.(Bilous and Dunmall, 2020; Nielsen 
et al., 2020)
Warming; harmful algal 
blooms (HABs)Altered seasonal freshwater habitats are impacting salmon productivity and phenology of important salmon 
resources in Alaska and in the Fennoscandian North, with subsequent community-specific impacts on 
commercial and subsistence resources.(Brattland and Mustonen, 2018; Cline 
et al., 2019; Mustonen and Feodoroff, 

Multiple; sea iceLosses of winter sea ice to the north and west of the Antarctic Peninsula have enabled krill fishing vessels to fish 
all year round in that area.(Meredith et al., 2019)
Future climate change impacts and risks
MultipleClimate change impacts on the ecology and physiology of polar cod species contribute to expected increases 
in biomass and catch potential under high to moderate mitigation (RCP2.6 and RCP4.5) and reductions 
in groundfish recruitment and yield under low mitigation (RCP8.5) scenarios (CCP6.2.2) across a range of 
multispecies models.(Laurel et al., 2016; Spencer et al., 2016; 
Lotze et al., 2018; Spencer et al., 2019; 
Dahlke et al., 2020; Grüss et al., 2020; 
Hollowed et al., 2020; Reum et al., 
2020; Thorson et al., 2020)
Climate × management 
interactionAssuming no climate adaptation in current EBM, 50% declines (relative to projections under persistent current

climate conditions) in EBS pollock and cod yield is likely under moderate carbon mitigation scenarios (RCP4.5), 
and very likely under low mitigation scenarios (RCP8.5).(Holsman et al., 2020; Reum et al., 
2020; Whitehouse et al., 2021)
Warming; ocean 
acidification (OA)Warming, OA, fish predators and thermal tolerance differentiate impacts across crab species in the Arctic; 
increased productivity and redistribution offshore is expected for tanner crab; red king crab and snow crab 
are projected to continue to shift north and decrease in productivity. OA is expected to impact demographics, 
altering harvest recommendations and biological reference points for some species of some shellfish and flatfish 
(e.g., red king crab, Northern rock sole) in projection simulations.(Punt et al., 2014; Sawatzky et al., 2020; 
Punt et al., 2021)
interactionMultiple rights-based fisheries operate in the Arctic, increasing investment in long-term sustainability but

reducing harvest portfolio diversity and increasing vulnerability to climate shocks.(Kasperski and Holland, 2013; Ojea 
et al., 2017)
Multiple; sea icePhysical and biological changes in Antarctic waters are expected to result in net declines in krill habitat and 
growth potential, although one study indicates a potential increase. Reduction in the Antarctic ice pack is as 
likely as not to increase total season length in areas near to land-based predators.(Melbourne-Thomas et al., 2016; 
Piñones and Fedorov, 2016; Klein et al., 
2018; Rogers et al., 2020; Veytia et al., 
2020)
Phytoplankton and 
temperatureProjected changes in primary production and temperature are expected to cause declines in krill growth and 
availability to predators; impacts may be countered by reducing fisheries, signifying a potential conflict between 
fisheries and top predators.(Piñones and Fedorov, 2016; Klein et al., 
2018)

CCP6
2335Polar Regions  Cross-Chapter Paper 6
already been proposed for the Northern Bering Sea, and small craft 
harbour investments are being considered across Arctic Canada and 
Greenland. Ecosystem-based management (EBM), increasing diversity 
and flexibility in harvest portfolios as well as access to high-resolution 
ecological forecasts and projections, and climate-informed advice will 
promote adaptation and climate resilience in fisheries (Dawson and 
et  al., 2017; Brooks et  al., 2018; Karp et  al., 2019; Hollowed et  al., 
2020). Coupling adaptation measures with global carbon mitigation 
strategies substantially decreases climate change risks to polar 
fisheries (very high confidence) (CCP6.3).
CCP6.2.4 Economic Activities
Climate change presents significant risks to economic activities in 
the polar regions (very high confidence) and simultaneously enables 
development possibilities for fisheries (CCP6.2.3.3), agriculture

(CCP6.2.3.2), the sharing and subsistence economy (CCP6.2.3.1) 
(SMCCP6.2) (high confidence), maritime trade (Box CCP6.1), natural 
resource development (CCP6.2.4.1) (medium confidence), tourism 
(CCP6.2.4.2) and transportation (including shipping) (CCP6.2.4.3; FAQ 
CCP6.2). Hundreds of billions of dollars are expected to be invested 
in the polar regions in the next several decades (Lloyd’s, 2012; 
Barnhart et al., 2016; Pendakur, 2017; Tsukerman et al., 2019), and, 
as this unfolds, there are opportunities to simultaneously implement 
adaptation strategies that support climate resilient development 
pathways in line with self-determination for Indigenous Peoples and 
local communities and locally derived visions of successful adaptation 
and development (CCP6.3.2, CCP6.4.3) (Jorgenson, 2007; Ritsema 
et al., 2015; Ready and Power, 2017; Larsen and Petrov, 2020).
CCP6.2.4.1 Changing access to natural resources with 
consequences for safety, economic development and

climate mitigation
Climate change is improving access to natural resources in the Arctic 
with consequences for human safety (high confidence), economic 
development (very high confidence) and global mitigation efforts 
(medium confidence). Reductions in sea ice combined with improved 
extraction and transportation technologies have increased accessibility 
to natural resources across the Arctic (Eliasson et  al., 2017; Dawson 
et al., 2018b; Stephen, 2018), a situation that could support continued 
global dependence on relatively cheap and abundant fossil fuels 
resources and contribute to further warming. By 2040 (RCP4.5) it is 
expected that sea ice will have receded enough to make gas production 
technologically feasible in the European off-shore Arctic (Petrick et al., 
2017). However, increased sea ice mobility, iceberg abundance, storm 
surge and surface wave action (Ng et al., 2018; Howell and Brady, 2019;

Casas-Prat and Wang, 2020) will also increase risks to ships servicing 
mines in a region that already exhibits disproportionately high accident 
rates (Council of Canadian Academies, 2016) (CCP6.3.1, Table CCP6.1). 
Season lengths for ship-based support to mines and extraction sites will 
increase with sea ice change, while access via ice roads will decrease 
with warming (Perrin et al., 2015; Council of Canadian Academies, 2016; 
Trofimenko et al., 2017; Southcott and Natcher, 2018). By 2050, climate 
change impacts to the Tibbitt to Contwoyto Winter Road servicing 
mines in the northeastern region of the Northwest Territories, Canada could cost between $55 million to $213 million CAD to maintain for 
a shorter period of time than at present (Perrin et al., 2015). Changes 
in submarine permafrost, critical to mining infrastructure, such as 
pipelines and offshore infrastructure (Bashaw et al., 2016; Paulin and 
Caines, 2016), are expected to increase production costs and impact

safety for workers (Riedel et al., 2017). By mid-century, regardless of 
emissions scenario, it is expected that risks from permafrost thaw will 
be disproportionately high for industrial infrastructure along major 
pipeline systems in Alaska and natural gas extraction areas in the 
Yamal-Nenets region in northwestern Siberia, Russia (Hjort et al., 2018).
CCP6.2.4.2 Changing demand, opportunities and risks for polar 
tourism
Climate change has increased risks to, and demand for, polar 
tourism experiences related to increased maritime accessibility (high 
confidence), lengthening of warm weather season lengths (very 
high confidence) and development of a ‘last chance tourism market’ 
(medium confidence). Reductions in sea ice extent have facilitated 
increased access for polar cruising (Dawson et al., 2018b; Stewart et al., 
2020). Demand for Arctic cruises has increased by 20.5% over the past 
5 years and resulted in 27.2 million passengers in 2018 (Shijin et al.,

2020). In the Antarctic, tourist numbers increased by 27% from 1992 to 
2018 and attracted 75,000 visitors in 2019–2020 (IAATO, 2020; Shijin 
et al., 2020), making it the largest economic sector in the entire region 
(Stewart et al., 2020). The recent increase in polar tourism is due in part 
to the development of a niche market called ‘last chance tourism’, which 
involves explicitly marketing vulnerable or vanishing destinations or 
features (i.e., glaciers, polar bears, landscapes) and encouraging tourists 
to see them ‘before they are gone’ (Dawson et al., 2018a; Groulx et al., 
2019). However, tourism development opportunities will also contend 
with ongoing risks related to the coronavirus disease 2019 (COVID-19) 
pandemic, which halted tourism globally in 2020–2021 (Frame and 
Hemmings, 2020; Lorenzo et  al., 2020), as well as those related to 
increased climatic risks limiting participation and reducing safety and

security. By 2100, under RCP8.5, snow cover season length suitable for 
winter recreational activities is projected to decrease by 21–49% in 
West Greenland (Schrot et al., 2019). Reduced sea ice and snow cover 
creates hazards for and could limit dog sledding, cross country skiing, 
snowmobiling and floe edge tours, with limited adaptation strategies 
available for low-elevation areas (Stephen, 2018; Palma et al., 2019).
CCP6.2.4.3 Risks and opportunities in transportation systems
Climate hazards create risks to transportation sectors with 
consequences for human safety (very high confidence), security (low 
confidence) and economic development (high confidence). Remote 
polar regions are highly reliant on transportation systems (air, road, 
sea) to support and service communities (Arctic) and scientific 
stations (Antarctic and Arctic). Changes in permafrost, snow, ice and 
precipitation patterns have increased the risk of rail infrastructure

and of using permanent roads and semi-permanent trails that service 
Antarctic research stations, connect Arctic communities and support 
Indigenous food harvesting activities (Calmels et  al., 2015; Council 
of Canadian Academies, 2016; Ford et al., 2019; Stewart et al., 2020). 
Warming temperatures have particularly decreased the reliability, 
safety level and season length of winter ice roads (Perrin et al., 2015;

CCP6
2336Cross-Chapter Paper 6 Polar Regions
Council of Canadian Academies, 2016; Gädeke et  al., 2021) in the 
northern Baltic (Finland) (Kiani et al., 2018), James Bay (Canada) (Hori 
et al., 2018a; Hori et al., 2018b) and Yakutia (Russia) (Mustonen and 
Shadrin, 2021). Dog sled travel in northwest Greenland has experienced 
shorter season lengths (Nuttall, 2020), Alaskan whale hunters have 
had difficulty finding suitable ice for safe harvest activities (Huntington 
et al., 2016; Nyland et al., 2017), and unpredictability in break-up and 
freeze-up of sea ice has compromised safe travel to and from culturally 
significant hunting and camping areas in Canada (Dawson et al., 2020; 
Simonee et al., 2021) and northeast Siberia (Ksenofontov et al., 2017; 
Mustonen and Shadrin, 2021). Fog (low confidence) and an increase in 
precipitation falling as ice pellets or hail (high confidence) (Kochtubajda 
et al., 2017) is expected to continue to cause operational delays and

create safety issues for aviation in the polar regions (Debortoli et al., 
2019).
CCP6.2.5 Arctic Settlements and Communities
Polar settlements range from large well-serviced cities such as Tromsø, 
Murmansk and Reykjavik, to remote fly-in Indigenous communities, 
to scientific outposts and research stations. Polar settlements are at 
significant risk from climate change through shoreline erosion, permafrost thaw and flooding (high confidence) (CCP6.2.2). Opportunities for 
community development in small communities are underestimated as 
they are emergent and unknown (highly likely) (CCP6.2.5).
Degradation of ice-rich permafrost can threaten the structural stability 
and functional capacities of community-based infrastructure (i.e., 
airports and roads; CCP6.2.5) and can have implications for local 
economies with coupled impacts for local livelihoods, health and well-
being (CCP6.2.5, CCP6.2.6) (high confidence). For instance, in Canada,

infrastructure damage from permafrost instability caused temporary 
closures of schools in Yukon, permafrost degradation contributed 
to runway damage at Iqaluit International Airport in Nunavut, and 
flooding from heavy rains resulted in thermal erosion of river banks that 
interrupted water and sewage service in Nunavut (Oldenborger and 
LeBlanc, 2015; Council of Canadian Academies, 2016; Lemmen et al., 
2016). In northeast Siberia, the floods of Alazaeya River attributed to 
thawing permafrost have severely affected Andreyushkino in Yakutia 
(Mustonen and Shadrin, 2021).
By 2050, 69% of fundamental human infrastructure in the Arctic is 
projected to be at risk under an RCP4.5 scenario, including more than 
1200 settlements and 36,000 buildings, leaving 4,000,000 people living 
in areas with high potential for thaw (Hjort et al., 2018). Widespread 
FAQ CCP6.2 |  Is sea ice reduction in the polar regions driving an increase in shipping traffic?

The polar seas have captured the imagination of global nations for centuries for its natural resource, tourism, scientific, and maritime trade 
potential. As the polar regions are warming at two to three times the rate of the global average leading to rapid reductions in sea ice extent 
and thickness, international attention has been reinvigorated and investments are being made by Arctic and non-Arctic nations alike with a 
view to utilise newly accessible seaways. Between 2013 and 2019, ship traffic entering the Arctic grew by 25% and the total distance travelled 
increased by 75%. Similar shipping growth trends are evident in the Antarctic, albeit to a lesser extent. Expected growth in Arctic shipping will 
influence a suite of cascading environmental and cultural risks with implications for Indigenous Peoples.
There has been debate among shipping stakeholders, rightsholders and experts about the extent to which climate

change and sea ice change is directly influencing increases in shipping activity in the polar regions relative to other 
social, technological, political and economic factors such as commodity prices, tourism demand, global economic 
trends, infrastructure support and service availability. Understanding the connection between climate change and 
polar shipping activity will allow for more reliable projections of possible future traffic trends and will aid in identify-
ing appropriate adaptation and infrastructure needs required to support future management of the industry. Recent 
studies have observed increasing statistical correlations between sea ice change and shipping trends in the polar 
regions, and many have concluded that although economic factors remain the main driver of shipping activities, 
followed by infrastructure availability, climate change does indeed play a varying but important role in influencing

operator intentions. The ‘opening of polar seaways’ due to sea ice reduction is indeed ‘enabling’ opportunities for 
polar shipping among all types of vessels due to increasingly accessible areas that were previously covered by multi-
year ice, but the extent to which climate change will specifically ‘drive’ an increase in shipping demand remains 
highly dependent on the vessel type and the reasons for operation. There are certain vessel types, such as those sup-
porting international trade, mining operations or community re-supply, where analysis shows no correlation or weak 
correlations with sea ice change, suggesting that climate change is enabling these types of ships via increased open 
water areas and season lengths but that it is not necessarily driving demand. Conversely, there are certain vessel 
types, such as yachts and cruise ships, where correlations between sea ice change and traffic increases are stronger,

and where there is evidence to suggest that these vessels are indeed driven to visit the polar regions because they 
perceive waterways as exotic and exciting due to being newly accessible or they want to have a Polar experience

CCP6
2337Polar Regions  Cross-Chapter Paper 6
before it disappears or is irreversibly changed as is the case with last chance tourists. As sea ice recedes and polar 
shipping opportunities grow, there will be an increased need to better identify and implement Indigenous self-deter -
mined and equitable shipping governance frameworks that facilitate benefits and minimise risks.
Ship traffic from 2012 to 2019 and minimum sea-ice extent from 1990 to 2019 in the Polar Regions
S60SIEFeb.2019SIEFeb.1990
N60SIESep.1990
SIESep.2019Sea-ice extent
Minimum in 2019
Minimum in 1990Average ship traffic density
Relatively low Relatively hig h1,000km
Minimum sea-ice extent (North of the 60th parallel)
Min. sea-ice extent 1990 vs 2019 :-29.9%
Min. sea-ice extent 2013 vs 2019: -17.8% All ship traffic 2013 vs 2019:  +70%Minimum sea-ice extent (South of the 60th parallel)
Min. sea-ice extent 2013 vs 2019 :-29.6%Sea-ice
extent
millions
of km2

2015 2010 2005 2000 1995 1990 2019 2015 2010 2005 2000 1995 1990 20190123456789
01234Min. sea-ice extent 1990 vs 2019: -10.5%
0.40.60.81.01.21.4
Sea-ice
extent
millions
of km2
2030405060
23456
2013 2014 2015 2016 2017 2018 2019Ship
traffic
nm
sailed
2013 2014 2015 2016 2017 2018 2019Sea-ice
extent
millions
of km2
23456
Ship
traffic
nm
sailed
All ship traffic 2013 vs 2019:  +62.6%
Figure FAQ CCP6.2.1 | Projected operational accessibility along Arctic maritime trade routes (Northwest Passage, Transpolar Route and 
Northern Sea Route) under future warming (left) and observed increases in commercial ship traffic along the routes from 2012 to 2019.Box FAQ CCP6.2 (continued)

CCP6
2338Cross-Chapter Paper 6 Polar Regions
Box CCP6.1 | Climate Change and the Emergence of Future Arctic Maritime Trade Routes
Discovering a viable maritime trade route linking the Atlantic and Pacific oceans through the Arctic has captured the collective global 
imagination for centuries (Bockstoce, 2018). Geographically shorter than southern trade routes via the Panama and Suez Canals, the Arctic 
presents the possibility for more economical and timely commercial trade, but has historically been limited by thick multi-year ice and other 
navigational challenges. Amplified warming in the Arctic has caused September sea ice extent to decline at a rate of −13% per decade 
(Serreze and Meier, 2019) and reduced sea ice thickness by 66% (2 m) between 1958–1976 and 2011–2018 (Kwok, 2018). Regardless of 
mitigation efforts, it is expected that before mid-century the Arctic will be seasonally ice free for the first time in 2,600,000 years (defined

as <1,000,000 km2) (Knies et al., 2014; SIMIP Community, 2020; Fox-Kemper et al., 2021; Lee et al., 2021) and will make Arctic maritime 
trade a reality (Eguíluz et al., 2016; Melia et al., 2016; Pizzolato et al., 2016; Bennett et al., 2020; Wei et al., 2020).
There are three identified trade routes in the Arctic: Northern Sea Route (NSR), Northwest Passages (NWP) and the Transpolar Sea Route 
(TSR). Over the last decade, economic trends and reductions in sea ice have facilitated significant increases in ship traffic in the NSR 
(Aksenov et al., 2017; Li et al., 2020), including a 79% increase in total transit tonnage from 2010 to 2017 (Babin et al., 2020) related 
mostly to domestic resource development. Relative to an early 21st century baseline, it is expected that the NSR will become 18% more 
accessible by mid-century (Stephenson et al., 2013) and could be navigable even for non-ice strengthened vessels for 101–118 days

annually by 2050 and 125–192 days by 2100 (Khon et al., 2017). The NWP has experienced a tripling of km travelled by ships since 1990, 
attributed mostly to resource extraction and increases in tourism opportunities (Johnston et al., 2017; Dawson et al., 2018a). The NWP 
could become 30% more accessible by 2050 compared with current conditions (Stephenson et al., 2013). Before 4°C global warming 
above pre-industrial, re-supply vessels (Polar Class 7) in the western NWP could gain an additional month of operating time, whereas 
the eastern NWP could gain just 2 weeks (Mudryk et al., 2021) due to the dynamic import of mobile and hazardous ice from the Arctic 
Ocean (Haas and Howell, 2015; Howell and Brady, 2019). Comparatively, the TSR has historically only been viable for nuclear icebreakers, 
submarines, and occasional military and scientific activity due to thick multi-year ice regimes (Bennett et al., 2020). However, this most

sought-after route offers the greatest reduction in sailing times compared with southern routes (19–24 days) of all Arctic Sea routes and 
could be 56% more accessible by mid-century compared with current conditions (Stephenson et al., 2013; Melia et al., 2016).
Arctic trade routes and projected operations related to sea-ice loss
Commercial ship traffic in the Northwest Passage (NWP)
from 2012 to 2019
Commercial ship traffic through the Transpolar Route
Commercial ship traffic in the Northern Sea Route
from 2012 to 2019250,000
200,000
150,000
100,000
50,000
0
250,000
200,000
150,000
100,000
50,000
0
8,000,000
7,000,000
6,000,000
5,000,000
4,000,000
03,000,000
2,000,000
1,000,000
2012 2018 2017 2016 2015 2014 2013 2019Increase of 277.4%
of commercial ship traffic
within NWP 
from 2012 (51,000 nm)
to 2019 (195,000 nm)
Increase of 171.3% 
of commercial ship traffic 
within Transpolar Route
from 2012 (61,000 nm)
to 2019 (173,000 nm)
Increase of 114.4%

within NSR
from 2012 (3.2 million nm)
to 2019 (6.9 million nm)
Note: Ship traffic estimates include vessels passing through the routes - including commercial vesels that do not take the full ro ute(s).
Nautical
miles
sailed
Figure Box CCP6.1.1 |  Arctic trade routes and projected operations related to sea ice loss.

CCP6
2339Polar Regions  Cross-Chapter Paper 6
permafrost thaw could increase the cost of infrastructure lifecycle 
replacement by 27% by mid-century under RCP8.5 (Suter et al., 2019). 
Northern Canada and Western Siberia are at particularly high risk, 
which are projected to cost additional annual spending of over 1% 
of annual gross regional product to maintain existing infrastructure 
(Suter et  al., 2019). For instance, under an RCP8.5 scenario, climate 
change could affect over 19% of structures and infrastructure assets in 
Russia, which would cost an estimated $84.4 billion USD to mitigate 
damages (Streletskiy et  al., 2019). Fifty-four percent of residential 
buildings are projected to be affected by significant permafrost 
degradation by the mid-century, costing an additional estimated 
$52.6  billion USD (Streletskiy et  al., 2019). Sea level rise (SLR) and 
reduced sea ice protection is projected to compound permafrost thaw

damages, including low lying coasts (e.g., along southern Beaufort 
Sea), low-lying barrier islands (e.g., along Chukchi Sea), and deltas 
(e.g., Mackenzie, Lena) (Fritz et al., 2017; Lantz et al., 2020). In Alaska, proactive adaptation was substantially cost-saving (reducing costs 
by $2.9  billion USD for RCP8.5 and $2.3  billion USD for RCP4.5), 
highlighting the financial benefit of investing in adaptation now (Melvin 
et al., 2017). Permafrost damage and SLR may result in tipping points, 
leaving some communities no longer habitable. In Alaska, USA, many 
communities at risk of flooding and storm surges are already engaged 
in community-led relocation planning processes (e.g., Shishmaref) 
(Melvin et al., 2017; Farbotko et al., 2020; Rosales et al., 2021).
Climate change has important intangible loss and damage implications 
in the Arctic, with negative impacts ranging from livelihoods to 
spirituality to solastalgia (i.e., distress caused by environmental change)

(Cunsolo and Ellis, 2018; Middleton et al., 2020b; Sawatzky et al., 2020; 
Mustonen and Shadrin, 2021). Permafrost thaw, SLR and reduced sea 
ice protection also presents risk to sociocultural assets, including 
heritage sites in all Arctic regions (very high confidence) (Friesen, Growth in Arctic maritime trade will result in increased emission of black carbon (Stephenson et al., 2018; Zhang et al., 2019; Wang et al., 
2021), increases in ship-source underwater noise impacts on marine mammals (Halliday et al., 2017), higher rates of accidents and 
incidents among vessels from increasing mobile sea ice and newly accessible ice-free waters that lack charting (Haas and Howell, 2015; 
Howell and Brady, 2019), impacts to cultural sustainability for Indigenous Peoples (Olsen et  al., 2019; Dawson et  al., 2020) (high 
confidence), the potential for the introduction and propagation of invasive species (Chan et al., 2019; Rosenhaim et al., 2019), and

sovereignty tensions with implications for global geopolitics (Drewniak et al., 2018) (medium confidence). Globalisation and the almost 
universal adherence to economic growth models among nations will continue to fuel maritime trade (Box 14.5). As sea ice decreases 
facilitates growth in Arctic maritime trade and transportation specifically, adaptation strategies designed to facilitate mitigation co-
benefits and that target the cascading implications and double exposure of climate change and Arctic shipping impacts will be essential 
in reducing risks (Ng et al., 2018; Pirotta et al., 2019; Bennett et al., 2020; Zeng et al., 2020). Electric and solar powered vessels, new 
engine and emission reduction technologies, investment in wind, water, ice and climate forecasting technologies and services (Haavisto 
et al., 2020; Stewart et al., 2020; Simonee et al., 2021), and efforts by the International Maritime Organization to reduce sulphur and the

use of heavy fuel oils (PAME, 2020; van Luijk et al., 2020) could play a key role in limiting emissions and reducing risks related to the 
environmental and cultural impacts of fuel spills in ice-infested Arctic waters. The development of low-impact shipping corridors (Chénier 
et al., 2017; Dawson et al., 2020) and multi-lateral agreements such as those implemented by the Arctic Council and Indigenous Peoples’ 
organisations on joint search and rescue (Arctic Council, 2011) and shared spill responsibilities (Arctic Council, 2013) represent important 
co-governance efforts that will be increasingly important in the future owing to projected climate-related risks.Box CCP6.1 (continued)
FAQ CCP6.3 | How have arctic communities adapted to environmental change in the past and will these 
experiences help them respond now and in the future?

For thousands of years, Arctic Indigenous Peoples and local communities have survived several major changes to the ecosystems on which they 
rely; however, the present changes in climate are more challenging than pre- and early historic changes in the Arctic, and polar communities 
will now face new unprecedented risks.
The challenges for responding to present change are due to the multiple imposed and simultaneous drivers 
combined with elimination and/or removal of endemic capacity to respond in culturally and locally appropriate 
ways. Adapting in the past may therefore inform and produce novel solutions for the present and convey baselines 
of important contextual information on significance of change. Arctic communities, especially Indigenous Peoples, 
have been marginalised in terms of their autonomous responses spaces and self-assessment that could be made 
without external pressures. Therefore, to increase the possibility of community-led adaptation, colonialism and

the resultant lack of upheld rights, resources and equity need to be solved simultaneously with the present climate 
change impacts. New research, governance, policy and collaborations are needed to effectively adapt to risks that 
are projected to emerge in the polar regions as a result of rapid climate change.

CCP6
2340Cross-Chapter Paper 6 Polar Regions
2015; Hollesen et al., 2016; Radosavljevic et al., 2016; O’Rourke, 2017; 
Hillerdal et  al., 2019; Fenger-Nielsen et  al., 2020; Jensen, 2020). A 
large number of archaeological sites are at risk from climate change in 
southwest Greenland; Yukon’s Beaufort coast, Canada; and Auyuittuq 
National Park Reserve, Nunavut, Canada (Westley et al., 2011; Hollesen 
et al., 2018; Irrgang et al., 2019; Fenger-Nielsen et al., 2020). Siberian 
nomadic reindeer herding and fishing livelihoods are vulnerable to 
permafrost thaw, which alters northern landscapes and lakes, as well 
as rain-on-snow events, and rapidly changes landscapes and terrestrial 
and aquatic habitats (Mustonen and Mustonen, 2016; Brattland 
and Mustonen, 2018; Mustonen and Huusari, 2020) (CCP6.2.2). The 
intangible loss and damage to nomadic cultures could cascade to 
losses of identity and social challenges (CCP6.2.6; Chapter 13).
CCP6.2.6 Human Health and Wellness in the Arctic

Climate change continues to have wide-ranging physical human health 
risks in the Arctic, particularly for Indigenous Peoples (high confidence); 
however, future projections of physical risks are nascent. Climate 
change has already challenged food and nutritional security (CCP6.2.5). 
Climate change also creates safety concerns for those who access the 
land, ice and water for food, cultural and recreational purposes, with 
changing environmental conditions linked to injury and death (Durkalec 
et al., 2014; Clark et al., 2016a; Clark et al., 2016b; Driscoll et al., 2016; 
Brattland and Mustonen, 2018). Foodborne disease risks are expected 
to increase in the Arctic, with warming temperatures linked to increased 
risk of microbial contamination of locally harvested foods (Grjibovski 
et  al., 2013; Harper et  al., 2015), chemical contamination of locally 
harvest foods (Hansen et al., 2015; Long et al., 2015; Alava et al., 2017),

compromised structural integrity and utility of ice cellars used to store 
locally harvested meat (Nyland et al., 2017; Markon et al., 2018), and 
new challenges to traditional food preparation techniques (Shadrin, 
2021). Waterborne disease risks have increased, with decreased 
drinking water quality and quantity, water treatment infrastructure 
failures and new waterborne pathogens emerging in the Arctic (Berner 
et al., 2016; Thivierge et al., 2016; Markon et al., 2018; Yoder, 2018; 
Masina et al., 2019; Sachal et al., 2019; Harper et al., 2020; Mustonen 
and Shadrin, 2021). Emerging environmental exposures to pathogens 
is also a concern. In 2016, a Nenets boy and over 200,000 reindeer died 
from anthrax linked to warming environments (Ezhova et al., 2021)—a 
risk which is projected to increase with climate change (Liskova et al., 
2021). Thawing permafrost increases smallpox risk in former nomadic 
campsites and graveyards (Mustonen and Shadrin, 2021; Shadrin,

2021). Arctic health systems—which are often already stressed—will 
be further challenged by climate change (Harper et  al., 2015; Clark 
and Ford, 2017), especially in conjunction with other system shocks 
(e.g., COVID-19) (Cross-Chapter Box COVID in Chapter 7) (Zavaleta-
Cortijo et al., 2020). While physical health impacts have been observed, 
research examining future health projections or evaluating the efficacy 
of health adaptations is rare (Dobson et al., 2015; Harper et al., 2020; 
Harper et al., 2021).
Climate change has negative, widespread and cumulative impacts 
on mental health in the Arctic, particularly for Indigenous Peoples 
(very high confidence) (Figure  CCP6.3). Climate-sensitive mental 
health outcomes are complex, overlapping and interrelated, and have multiple direct and indirect pathways stemming from acute (e.g., major 
storms, flooding, wildfires) and chronic (e.g., temperature increases,

sea ice loss, permafrost thaw) environmental conditions, and resulting 
disruptions to livelihoods, culture, food systems, social connections, 
health systems and economies (Cunsolo Willox et al., 2013a; Cunsolo 
Willox et al., 2013b; Cunsolo Willox et al., 2014; Beaumier et al., 2015; 
Durkalec et al., 2015; Hamilton et al., 2016; Clayton et al., 2017; Dodd 
et al., 2018; Jaakkola et al., 2018; Markon et al., 2018; ITK, 2019; Minor 
et al., 2019; Middleton et al., 2020a; Middleton et al., 2020b; Feodoroff, 
2021).
Negative mental health outcomes from climate change include: 
emotional reactions (e.g., sadness, fear, anger, distress and anxiety); 
psychosocial outcomes (e.g., depression, post-traumatic stress disorder 
and generalised anxiety); experiences with grief and loss (i.e., ecological 
grief); increased drug and alcohol usage, family stress and domestic 
violence; increased suicide ideation and suicides; loss of cultural

knowledge and continuity, disruptions to intergenerational knowledge 
transfer; and deterioration and loss of place-based identities and 
connections (i.e., solastalgia) (Cunsolo Willox et  al., 2013a; Cunsolo 
Willox et al., 2013b; Cunsolo Willox et al., 2014; Durkalec et al., 2015; 
Harper et al., 2015; Cunsolo and Ellis, 2018; Hayes et al., 2018; Jaakkola 
et al., 2018; Markon et al., 2018; Minor et al., 2019; Middleton et al., 
2020a; Feodoroff, 2021).
The negative mental health impacts from climate change are amplified 
among those most reliant on the environment for subsistence and 
livelihoods, those who already face chronic physical or mental health 
issues, and those facing socioeconomic inequities and marginalisation, 
particularly for Indigenous Peoples (high confidence). These climate 
change related mental health impacts are unequally distributed 
(Cunsolo Willox et  al., 2014; Minor et  al., 2019), and may vary by

gender (Beaumier et al., 2015; Harper et al., 2015; Feodoroff, 2021) and 
age (Petrasek MacDonald et al., 2013; Ostapchuk et al., 2015; Petrasek 
MacDonald et al., 2015; Kowalczewski and Klein, 2018).
Climate change will increase mental health risks in the Arctic in the 
future (medium confidence). Future risks include exposures to severe 
weather events and changing precipitation patterns, sea ice loss, 
wildfires and changing place attachment, as well as disruptions to 
underlying determinants of mental health and social support networks 
(Cunsolo Willox et  al., 2014; Cunsolo and Ellis, 2018; Markon et  al., 
2018; Council of Canadian Academies, 2019; ITK, 2019; Middleton 
et al., 2020a; Middleton et al., 2020b).
There is limited evidence assessing adaptation options that 
effectively reduce climate-related mental health risks, but developing 
or enhancing access to mental health resources and infrastructure is 
critical, such as land-based healing programmes, enhanced access

to culturally appropriate mental health resources, and climate-
specific counselling services to support individual and community 
psychosocial resilience, particularly among Arctic Indigenous Peoples 
(Cunsolo and Ellis, 2018; Middleton et al., 2020a). Incorporating a 
climate-sensitive mental health lens into mitigation and adaptation 
planning holds potential for increasing mental health and resilience 
in the Arctic, as well as supporting other social, economic and cultural 
co-benefits.

CCP6
2341Polar Regions Cr oss-Chapter Paper 6
Climate change impacts on mental health
and adaptation responsesin the Circumpolar North
2 1
Hazard:
Acute events 
(e.g., storms, floods, wildfires)
Chronic changes 
(e.g., sea ice loss, sea level rise, 
coastal erosion, permafrost melting, 
rising temperatures, changing 
seasonal and environmental norms, 
changes in wildlife and vegetation, 
changes in place)Vulnerability
Indigenous identity
Socio-economic inequities
Reliance on land-based 
livelihoods
Pre-existing health conditions 
(e.g., chronic physical and mental 
health outcomes)
Lack of access to 
health-sustaining resources
Gender (genders differentially 
affected)
Age (e.g., youth and Elders 
particularly at risk)Exposure
Direct exposure(s) (e.g., experiencing 
acute or chronic hazard events)
Indirect exposure(s) 
(e.g., disruptions to food systems, 
cultural activities, place-based 
knowledge sharing and livelihoods; 
displacement and relocation)
Vicarious exposure(s)

(e.g., seeing friends and family suffer, 
mediated experiences of climate 
change, anticipating future changes)Key adaptation responses
Scale of adaptation
Institutional
State and state-level actors: 
enhanced culturally-relevant mental health 
services; informed policies including 
incorporation of Indigenous leadership, 
knowledge, and culture; early intervention
Community
Community planning and design for current 
and future climate risks, increased 
climate-informed wellness programming, local 
climate-focused training for health providers
Individuals
Awareness, validation, preparedness, mental 
health supports, land-based programming3 4
Climate change risks to mental health and wellbeing
Mental illness 
(e.g., PTSD, depression, suicide)Diminished wellbeing
(e.g., stress, sadness, climate-related grief 
and anxiety, impaired mental functioning)Diminished social relations  
(e.g., domestic violence, loss of culture, 
reduced social capital)52. Vulnerability
Physiological factors

Social factors
3. Exposure
Direct exposure(s)
4. Response
Community
Individuals1. Hazard
Chronic changes5. Climate 
change risks
to mental health
and wellbeing
Figure CCP6.3 |  The pathways through which climate change impacts mental and emotional health in the Arctic.

CCP6
2342Cross-Chapter Paper 6 Polar Regions
Box CCP6.2 | Arctic Indigenous Self-determination in Climate Change Assessment and Decision 
Making
Similar to Indigenous Peoples globally (Cross-Chapter Box  INDIG in Chapter 18), climate change vulnerability for Arctic Indigenous 
Peoples is often rooted in colonialism, which has led to land dispossession and displacement, carbon-intensive economies, discrimination, 
racism, marginalisation and social, cultural and health inequities (Whyte, 2016; Whyte, 2017; Whyte et al., 2019; Chakraborty and Sherpa, 
2021). Therefore, effective responses to climate change risks for Indigenous Peoples are self-determined and underpinned by Indigenous 
knowledge (IK) (very high confidence).
IK systems are diverse among and within Arctic Indigenous Peoples, and reflect deep and rich knowledge that situates and contextualises

values, traditions, governance and practical ways of adapting to the ecosystem over millennia (Raymond-Yakoubian et al., 2017; Brattland 
and Mustonen, 2018). IK is a valuable source of knowledge; a method to detect change, evaluate risk and inform adaptation approaches; 
and a cultural ecological service (Brattland and Mustonen, 2018; Crate et al., 2019; Meredith et al., 2019) that is critical for decision 
making (Mustonen and Mustonen, 2016; Huntington et al., 2017). For instance, Kalaallit knowledge in Greenland has been used to detect 
and attribute long-term (over 50 years) marine change that reaches beyond scientific instrumental data (Mustonen et al., 2018b).
This Box was written by Indigenous authors, recognising that IK and LK are intellectual property (Cross-Chapter Box INDIG in Chapter 
18), alleviating the risk of this knowledge being misinterpreted (David-Chavez and Gavin, 2018; Hughes, 2018; Raymond-Yakoubian

and Daniel, 2018) and acknowledging that meaningful inclusion of Indigenous Peoples strengthens and supports Indigenous self-
determination (ITK, 2019). Self-determination signifies and values the capacity and decisions made by these peoples in their own right 
and from their own autonomous cultural positioning. Following the format used in SROCC, this Box prioritises Indigenous voices by 
presenting climate change assessments premised on IK and written by Indigenous Peoples.
Climate Change, Nomadic Lifestyles and Preservation of Traditions
Perspectives from the Yukaghir Council of Elders and Russian Association of Indigenous Peoples, Russia
Climate change threatens reindeer herding, hunting, fishing and gathering, which form the basis of Siberian Indigenous societies. 
Nomadic herding lifestyle is premised on IK which has accumulated over millennia. IK, including the ability to predict weather, has

played a substantial role in the adaptation to the extreme conditions. According to Shadrin (2021), present, rapid changes are changing 
Indigenous concepts of reality; they are increasingly finding themselves in situations where their experience and knowledge cannot help 
them. An Elder in Northeast Siberia explained that ‘nature does not trust us anymore’ (Mustonen and Shadrin, 2021).
A major problem for nomadic reindeer herding is the degradation of reindeer pastures (Mustonen and Shadrin, 2021). The expansion 
of willows and shrubs into the tundra has resulted in losses of pastures. In other nomadic communities, these changes have led to the 
expansion of moose into tundra area and effects of reindeer populations, as well as changes in wild reindeer migration routes leading to 
the destruction of domestic reindeer pastures (Mustonen and Shadrin, 2021).

Due to the steady changes in precipitation in recent years, a deeper than usual snow cover has formed in Northeast Siberia (Mustonen 
and Shadrin, 2021). This alters the capacity of reindeer to access lichen, their primary food source. Late onset of cold weather has led to 
difficulties in the herds moving to their winter pastures. In the summer, increased rainfall has led to waterlogging of low-lying pastures. 
The most important challenge is the instability of the weather (Mustonen and Shadrin, 2021). This includes frequent, never-before-seen 
warming, combined with rains in the late winter and early spring. Sharp temperature drops of over 30°C occurring within a few hours 
lead to formation of an ice crust on the ground which becomes a challenge for reindeer, especially in autumn, and are becoming more 
frequent. Furthermore, the number of summer storms and rapid cooling accompanied with snowfall during July has increased. Using IK

to predict weather is the basis of effective survival. It has become extremely difficult due to the unprecedented fast changing conditions 
(Mustonen and Shadrin, 2021) (Shadrin, 2021). All of these events lead to increased risks in the lives of Indigenous Peoples (Mustonen 
and Shadrin, 2021).
Climate change impacts Indigenous Peoples’ health. Degradation of the quality of surface waters has increased, resulting from new 
floods and the thawing of permafrost, which increases risk of gastrointestinal diseases (CCP6.2.8). The 2007 flood on Alazaya River was 
of special importance and was locally identified to have produced the first regional ‘climate refugees’ (Mustonen and Shadrin, 2021). 
Warming has expanded the distribution of new disease-carrying insects and ticks into new territories (Mustonen and Shadrin, 2021). 
Ancient cemeteries and campsites, as well as the burial sites of reindeer, become dangerous as permafrost thaws and coastal erosion 
proceeds.

CCP6
2343Polar Regions  Cross-Chapter Paper 6
Traditional food security is under threat. Permafrost-based storage facilities have deteriorated (CCP6.2.6) (Mustonen and Shadrin, 2021). 
There is an increase in the number of people who are forced to abandon the consumption of raw fish. As a result, the likelihood of losing 
cultural traditions is growing. These combined climate change impacts result in loss of IK and nomadic lifestyles, thus losing important 
aspects of their identity as distinct Indigenous Peoples (Mustonen and Shadrin, 2021).
Climate Change Impacts on Sámi Women
Perspectives from Sámi in Finland
Feodoroff (2021) stresses that many Sámi women are central to Indigenous-led adaptation. Indigenous women use their bodies as 
gauges of change. For example, the restoration work in Näätämöjoki River in Finland (Ogar et al., 2020; Feodoroff, 2021) is based on the

knowledge of traditional fishers and reindeer herders. IK and Western science offer possibilities to reflect on changes that the waters 
in Indigenous bodies have known of events of the past (Feodoroff, 2021). Changes in temperature, pain and the gradual passing of 
pain, waves and intrusions within Indigenous bodies are knowledges that are difficult to communicate according to Feodoroff (2021). 
Women are sensitive to receiving messages from their home environments. Feodoroff (2021) stresses that Indigenous conservation work 
is a bodily commitment. This realisation is linked with difficult questions of what or who controls Indigenous bodies. Feodoroff (2021) 
links present change with lingering impacts of global environmental damage that has not been dealt with or addressed. It may lead 
to real pain in Indigenous bodies and minds, causing feelings of being nauseated and ultimately causing fade-out, wilt, withering and 
extinguishment of Indigenous Peoples.

Adaptation Successes Underpinned by Inuit Knowledge
Perspectives from Inuit Circumpolar Council
Inuit have survived and thrived in Inuit Nunaat, their homelands, for millennia. In an environment that presents unique challenges, they 
have cultivated resourceful and innovative approaches tailored to their surroundings. Their values and knowledge guide their relationships 
with all that is within the Arctic, and this has informed their decisions and management practices that continue to be in place today (Inuit 
Circumpolar Council Alaska, 2020). They are experts in adaptation. Now more than ever, in the time of anthropogenic climate change, 
living in the fastest warming region on the planet requires this expertise and capacity.
The extraordinary developments in the field of IK have crystallised the main tenant of interaction with the natural world that is ‘integral

to a cultural complex that also encompasses language, systems of classification, resource use practices, social interactions, ritual and 
spirituality’ (UNESCO, 2017). Inuit have used their knowledge of the land and coastal seas to design technology, monitoring systems 
(Atlas of Community-Based Monitoring in a Changing Arctic, 2021) and new hunting routes that respond to the changes they face (Inuit 
Circumpolar Council, 2017; Nunavut Climate Change Center, 2018; SIKU, 2020). Such examples of ‘adaptation success’ across Inuit 
Nunaat have been showcased and celebrated nationally and internationally (Youth Climate Report, 2019), and all are underpinned by 
Inuit knowledge and pivot on their right to self-determination. This is also embodied, for example, in Canada; the National Inuit Climate 
Change Strategy outlines the collective Canadian Inuit plan for climate action, centring on Inuit-determined priorities to protect their

culture, language and way of life, and guiding partners in how to work with Inuit on implementing this strategy (ITK, 2019). Their action 
on adaptation also spans scales from local to international. As far back as 1977, Inuit have been organised and involved at the international 
level. Inuit were present at the Rio Earth Summit and have participated in diverse but interrelated United Nations conventions to protect 
their homelands (e.g., UNFCCC, CBD, Stockholm Convention). This history gives us unique insight and positions us as both leaders and 
partners with the ability to engage directly with governments, business and others.
However, while Inuit are often recognised as leaders in adaptation, too often the academic literature ends there, citing ‘successful Inuit-
led adaptation to climate change’ but not going further to explore towards what end this adaptation is designed. We have demonstrated

leadership and set an example for the world in how to respond to change, but successful adaptation is not enough; it is not the end goal.
Central to their significant capacity to adapt is that it is done in recognition of the need to move beyond adaptation. Indeed, Inuit-led 
adaptation action is founded on the intention of contributing to and moving towards reformation and eventual transformation of systems 
to create a ‘climate resilient’ Arctic. This concept has surfaced in academic climate change literature and discussion and has begun to 
filter into the climate policy arena, especially within the context of the current COVID-19 pandemic that challenges us all to think about 
our world differently. With acknowledgement that reform and transformation is needed, the question remains, ‘What does this look like?’Box CCP6.2 (continued)

CCP6
2344Cross-Chapter Paper 6 Polar Regions
Inuit have an answer. System reform and transformation is grounded in self-determination. It is based in a human rights framework and 
rooted in IK and culture. It recognises and respects interconnectedness and builds this into solutions. It demands collaboration and true 
partnership towards action. And it comes from thinking big and across scales. Shaping this change calls for willingness and support to 
rethink the current economic and governance models that have failed us. For example, decentralising governance and management, 
while it remains largely unconventional, has been shown to create some of the strongest systems we have. This is, in large part, due to 
the way in which decentralisation places more value and responsibility on the ‘self’ in self-determination. Decentralised processes in the 
Arctic have IK holders playing a key and lead role in determining, defining and deciding how to work towards positive change.

Across Inuit Nunaat, examples of direct management and control over lands, territories and resources have demonstrated that working 
from what is happening on the ground throughout their homelands, from their priorities and interests, has served to strengthen the 
health of their environment and their communities. For example, a comparative analysis on factors supporting and impeding Inuit food 
sovereignty between Alaska and the Inuvialuit Settlement Region found that the difference in outcomes within these regions is dependent 
on explicit respect for and recognition of the Inuit right of self-determination (Inuit Circumpolar Council Alaska, 2020). Furthermore, a 
new agreement achieved in Nunavut by the Qikiqtani Inuit Association related to the marine environment touted as an exemplary model 
for marine management is rooted in Inuit-determined structures and policies, and manifested by Inuit themselves (QIA, 2019).

Emphasis on decentralised management and substantial funding to do so at the grassroots level has been recognised by the IPCC 
previously in the SROCC. Ultimately, going beyond reform to system transformation requires, as Oren Lyons has stated, ‘value change for 
survival’ (Lyons, 2020). Valuing decentralisation, self-determination, Inuit knowledge, interconnectedness—core values held by Inuit—
can move us in a climate-resilient direction.Box CCP6.2 (continued)
CCP6.3 Key Risks and Adaptation
CCP6.3.1 Key Risks
Key risks arising from changing climate hazards are presented in 
Table CCP6.6 (details in SMCCP6.4). Changing levels and magnitude of 
climate hazards translate into different levels of risks for ecosystems, 
industry, society and infrastructure (Figure CCP6.4) (see Meredith et al., 
2019 Figures  3.5, 3.10 (Arctic), Figure  3.6 (Antarctic). In the Arctic, 
these risks are often also shaped by non-climatic factors (Huntington

et al., 2019; Ford et al., 2021), including ongoing colonial legacies, land 
dispossession, landscape fragmentation and resulting challenges in 
the valuing and meaningful use of IK and LK (Box CCP6.2) (Huntington 
et  al., 2019; Kelman and Næss, 2019; Ford et  al., 2020). Available 
literature enabled assessment of particular polar assets based on 
projected future risk and including consideration of non-climatic 
compounding factors under 1°C, 2°C, 3°C and 4°C global warming 
above pre-industrial level, including sea ice ecosystems, marine 
mammals, sea birds, fisheries, infrastructure (Arctic only), local mobility 
(Arctic only) and coastal erosion (Arctic only) (Figure CCP6.5) (details 
in SMCCP6.4).
CCP6.3.2 Adaptation
CCP6.3.2.1 Current adaptation
Across polar regions, adaptation responses to climate change impacts 
have ranged from rapid and incremental (e.g., shifting phenologies, 
alternative harvest or herding strategies) to large and transformative

(e.g., switching livelihoods, social–ecological system transformation) (Figure  CCP6.6). Some adaptation measures and opportunities 
induce novel risks to other sectors or systems resulting in cascading 
and compounding consequences that are sometimes hard to predict 
or prepare for (Huntington et  al., 2015) (Table  CCP6.6). Adaptation 
planning and implementation is greater in the Arctic than Antarctic 
regions, in part due to disparate magnitudes of realised climate impacts 
and change between regions (Figure CCP6.2; Table CCP6.1) but also 
because of the differing governance systems in place (Meredith et al., 
2019). In the Antarctic region, a climate action plan has been developed 
for terrestrial systems but not for the Southern Ocean (Meredith et al., 
2019), although strategies for adapting to climate change have been 
proposed, including incorporation of precaution in decision making 
(Constable et al., 2017). In the Arctic, climate change information is

increasingly integrated into research, policy and decision making 
including incorporation of climate change projections, forecasts and 
early warnings (AMAP , 2017; AMAP , 2018a; Marshall et al., 2019; Dorn 
and Zador, 2020; Hollowed et al., 2020; Stram et al., 2021).
The majority of adaptations in the Arctic are occurring at sub-regional 
levels in response to both observed and projected climate change, with 
evidence of increasing regional level action driven by climate planning 
processes of subnational governments (AMAP , 2017; Labbé et al., 2017; 
AMAP , 2018a; Canosa et al., 2020). Implemented adaptation includes 
alterations to building codes and infrastructure design (Shiklomanov 
et al., 2017; Flynn et al., 2019; Standards Council of Canada, 2020), 
surveillance (Ruscio et al., 2015; Ford and Clark, 2019; Meredith et al., 
2019), information sharing (Berner et al., 2016), changes to survey and 
monitoring design (Stevenson and Lauth, 2019), hazard mapping (Flynn

et  al., 2019), use of new technologies (Tejsner and Veldhuis, 2018; 
Galappaththi et al., 2019), the development of regional and municipal 
adaptation plans (Labbé et al., 2017), shifting stocks and changes in

CCP6
2345Polar Regions  Cross-Chapter Paper 6
fishery operations and location (Jørgensen et al., 2019; Fedewa et al., 
2020; Thompson et  al., 2020), alterations to subsistence harvesting 
activities (Anderson et al., 2018; Ford et al., 2018; Galappaththi et al., 
2019), co-production of knowledge (Raymond-Yakoubian and Daniel, 
2018) and application of IK for resource management (Robards et al., 
2018) and to monitor storms (Rosales et  al., 2021; Simonee et  al., 
2021). Pan-Arctic and national-level adaptation remains limited (Ford 
et al., 2014; Canosa et al., 2020), although there have been few efforts 
to examine the nature of adaptation responses in Arctic regions and 
large gaps in understanding.
Illustrative examples of direct and cascading risks, enabling principles 
of climate resilience pathways, anticipated future conditions (with 
certainty levels and compounding risks for key sectors within polar 
regions) are outlined in Table  CCP6.6. A list of adaptation options

responding directly to the challenges outlined for each sector, including 
an analysis of adaptation effectiveness and feasibility and cross 
referenced with KR assessed in this chapter (Table CCP6.5), is provided 
in Figure CCP6.6.
The need for self-determination for Indigenous Peoples and local 
communities in decision making and cooperation across Arctic nations 
to manage a rapidly changing Arctic is increasingly recognised, 
particularly in a shipping and wildlife management context where 
climate impacts will be transboundary and multi-sectoral (Spence, 
2017; Forbis and Hayhoe, 2018; Ford and Clark, 2019; Dawson et al., 
2020) (CCP6.2.6; Box CCP6.2). Effective Indigenous and community-
led adaptation efforts have been implemented across the Arctic to 
alleviate climate and non-climate stressors and build resilience 
through restoration and conservation (Huntington et  al., 2017; Brattland and Mustonen, 2018; Hudson and Vodden, 2020; Mustonen

and Feodoroff, 2020; Uboni et al., 2020; Huntington et al., 2021). For 
example, IK and science has been used by the Skolt Sámi in Finland to 
attenuate warming, drought and water quality impacts on salmonids 
through restoration of spawning and nursery habitats in the Vainosjoki 
River catchment (Brattland and Mustonen, 2018; Mustonen and 
Feodoroff, 2020; Ogar et  al., 2020). This ecological restoration of 
damaged habitats for fish represents community-led actions. In 
Aasiaat, Greenlandic hunters have implemented community-based 
oceanographic and ecological monitoring to convey IK observations 
of rapid change to the government and scientists. A special aspect of 
land use in the Russian North is the preservation of nomadic lifestyles 
of the Nenets and Chukchi (Mustonen and Mustonen, 2016), and 
while these traditional economies have undergone rapid change due 
to non-climate drivers, their land uses, observational frameworks and

cultural matrixes remain of high importance in the context of climate 
change. Endemic responses (self-agency from within the culture) 
and Indigenous governance enable adaptation to the rapid and 
accelerating changes under way (Mustonen et al., 2018a). Therefore, 
community-based monitoring and inclusion of IK in dialogue with 
science has been an effective mechanism to detect and respond to 
climate change.Table CCP6.5 |  Key risks (KR) and illustrative examples in polar regions identified through the processes described in Chapter 16 and SMCCP6.4.
Key risk Direct and indirect factors contributing to risk
KR1. Risk to marine ecosystems and species 
(CCP6.2.2; CCP6.2.3) –Warming, MHW, sea ice loss, glacial and IS melt, OA, invasive species, harmful algae blooms
 –Narrow thermal niches, altered marine habitat, hampered calcification, higher corrosivity for CaCO 3 shell/skeleton, phenological

mismatch, physiological/life history effects, sensitive food web relationships, reduced trophic (energy) transfer efficiencies, 
increased light availability, nutrient limitation, and changes to salinity, stratification, oxygen levels
KR2. Risk to terrestrial and freshwater ecosystems 
and species (CCP6.2.4) –Warming, hydrological changes, terrestrial heat waves, change in rain and snow events, increased wild- and mega-fire events in 
Arctic, permafrost thaw, and erosion
 –Vegetation browning/greening, narrow thermal niches, physiological/ life history effects, sensitive food web relationships, parasites 
and disease
KR3. Risk to commercial and private infrastructure 
(CCP6.2.6) –Permafrost freeze–thaw, extreme heat and precipitation, rapid warm-thaw events, storms, increased wave activity, storm surges, 
flooding, landslides and erosion
 –Roads, airstrips, railways, ports, commercial buildings, private homes, ice cellars, traditional snow/ice/water travel routes, other

infrastructure
 –Permafrost freeze–thaw and SLR impacting cultural assets, including cultural heritage sites
KR4. Risk to food and nutritional security (CCP6.2.5) –Warming, OA, sea ice loss, permafrost loss, changes to precipitation, wildfires, hydrological changes
 –Access to marine areas increased, to coastal and terrestrial areas decreased; effects on subsistence and commercial species
KR5. Increased polar shipping traffic with cascading 
risks for navigation, safety, ecosystems and culture 
(CCP6.2.4; CCP6.2.5; Box CCP6.2; FAQ CCP6.1) –Substantial reduction in sea ice extent and thickness
 –Marine subsistence species; coastal communities; Inuit hunters; ship operators; tourism operators; mining companies
KR6. Increased mental health challenges and 
impacts on Indigenous Peoples and culture 
(CCP6.2.6.4; CCP6.2.7; CCP6.2.8. Box CCP6.2; FAQ 
CCP6.3) –Warming temperature; heatwaves; ice changes; changes in snow cover; permafrost thaw; coastal erosion; changing landscapes

KR7. Risk from polar change for global processes 
and SLR (FAQ CCP6.1) –Reduction in Arctic sea ice, sheets and glaciers have implications for planetary albedo and ocean stratification and salinity, 
acceleration of global warming, potential effects on global overturning circulation and Northern Hemisphere weather patterns
 –Cultural and resource connections to global sustainable development

CCP6
2346Cross-Chapter Paper 6 Polar Regions
Rapid assessment of relative risk by sector and climate hazard for polar regions
based on an assessment of asset-specific vulnerability and exposure across climate hazards 
Mortality
MorbidityHeritage resources
Coastal infrastructureLabor productivitySafety and securityMarine transportationSolar resources
Fossil reourcesWind and wave resources
Recreation and tourism
Indigenous traditionsTransmission systemsHydro resourcesBioenergy resources
Livestock and pastureCities
Water transpiration
Water qualityCropsFisheries and aquacultureTransportation
Marine species
Desert ecosystemCoastal ecosystem
Polar ecosystem
Freshwater species
Land speciesMountain ecosystemCoral reef ecosystemFreshwater resourceHeat
and coldSnow
and iceCoastal
and oceanic Wind OtherWet
and dryMean temperatur e
Cold spell
Frost
Pluvial flood
Landslide
Aridity
Drought

Sand and dust stor m
Snow and land ic e
Hail
Coastal floo d
Current, eddies and circulation
Ocean and lake acidification
Radiation at surfaceAtmospheric CO2Permafrost
Lake, river and sea ic e
Heavy snow and ice stor m
Dissolved oxygen
Salinity and mixed layer
Water column temperatur e
Spring transition or bloomEnergy resourcesHealth and
communitiesPoverty and
livelihoodsCities and
infrastructureFood and
fibre Water OceansTerrestrial and
freshwaterVery high
High
Moderate
Undetectable
Not applicable
or not assessedLevel of risk
Figure CCP6.4 |  Rapid assessment for relative risk by sector (y-axis) and climate hazard (x-axis) for polar regions based on an assessment of asset-specific 
vulnerability and exposure across climate hazards (see SMCCP6.4 for methodological details). For each unique combination, the hazard by sector risk was ranked as

very high (very high risk and high confidence), high (significant impacts and risk, high to medium confidence), medium (impacts are detectable and attributable to climate change, 
medium confidence) or low/not detected/positive (risk is low or not detectable). Blank cells are those where the assessment was not applicable or not conducted. Risks identified 
through the rapid assessment were further evaluated in the chapter assessments (see corresponding sector text for full assessment of risk and impacts).

CCP6
2347Polar Regions  Cross-Chapter Paper 6
Relative risks to select assets in the Polar regions 
Global mean temperature
increase above pre-industrial
0°C1°C2°C3°C4°C
The ember colour gradient indicates the 
level of additional risk to society as a 
function of global temperature change. 
Confidence is provided for the change of 
risk level at given temperature ranges .Very high
High
Moderate
UndetectableLevel of risk Confidence
Low High•
•••••
Sea-ice
ecosystemsArctic Antarctic
Marine
mammalsArctic Antarctic
Sea birdsArctic Antarctic
FisheriesArctic Antarctic••••••••
•• •••
•••••••
•• •• •••
•• ••
•• ••
•• •• •••• •• ••
•
••• •• •
••• •• ••Arctic
Infra-
structureArctic
Local
mobilityArctic
Coastal
erosion
Figure CCP6.5 |  Burning ember of the relative risks to select assets in the polar regions as a function of global mean surface temperature increase since

pre-industrial times including: (1) sea ice ecosystems, (2) marine mammals, (3) sea birds, (4) fisheries, (5) infrastructure (Arctic only), (6) local mobility (Arctic 
only) and (7) coastal erosion (Arctic only). The supporting literature and methods are provided in SMCCP6.5
Table CCP6.6 |  Assessment of risks needing adaptation by sector in the polar regions.
Sector Direct and cascading risksEnabling principles of cli-
mate resilience pathwaysAnticipated future condi-
tions/level of certaintyCompounding risks (non-cli-
matic factors)
Coastal settlements
(CCP6.2.5)Change in extent of sea ice with 
more storm surges, thawing 
of permafrost, SLR and coastal 
erosionLocal leadership and 
community-led initiatives to 
initiate and drive processes, 
responsive agencies, established 
processes for assessments and 
planning, geographic optionsIncreasing number of 
communities needing relocation 
(medium confidence), rising 
costs for mitigating erosion (high

confidence)Limitations of government 
budgets, other disasters that may 
take priority, policies deficiencies 
for addressing mitigation and 
relocation
Human health
(CCP6.2.6)Increased food insecurity, 
waterborne disease, emerging 
pathogens, injury and death, and 
negative mental health outcomesResources to support public 
programmes; Indigenous 
self-determination; access 
to technology; supporting IK 
systems; interdisciplinary and 
integrated decision makingThe intersection of social 
determinants of health will 
modify or mediate climate change 
impacts on health (very high 
confidence)Underlying health conditions, 
advances in diagnosis and 
treatment, and other health 
system shocks (e.g., COVID)
Transportation (aviation, rail, road, 
ice roads)
(CCP6.2.4.3)Permafrost thaw, sea ice 
change, storm surge, coastal 
erosion, changing precipitation 
patterns (ice pellets, hail) and 
extreme events create risks to 
transportation infrastructure with 
consequences to navigation,

economics, safety and securityFinancial and human resources 
for: climate-resilient infrastructure 
research, development and 
implementation; improved 
weather, water, ice and climate 
forecasting at appropriate scales; 
improved communications 
infrastructure; local search and 
rescueLimits to adaptation exist 
(high confidence), but strategic 
investments in technologically 
innovative infrastructure that 
offers mitigation co-benefits 
will greatly enhance adaptation 
effectiveness (very high 
confidence)Level of local, regional and 
national infrastructure 
development, commitment 
of national and state level 
government to sustainable 
development pathways, global 
economic and political trends, 
commodity prices, unforeseen 
system shocks
Shipping
(Box CCP6.1; FAQ CCP2)Sea ice reduction leading to 
increased shipping related to 
trade, tourism, fisheries, resource 
development and re-supply with 
cascading risks from ships such 
as: increased under-water noise,

potential introduction of invasive 
species, fuel spill risks, release of 
black carbon and air emissions, 
impacts to cultural resources, 
implications for subsistence 
hunting and food security, 
increased accidents and incidentsFinancial support for ship-building 
technologies (e.g., low-emission 
fuels, propulsion technologies, hull 
strength); development of robust 
multi-national agreements (in 
addition to existing agreements); 
inclusion of Indigenous Peoples 
in decision making; investment in 
multi-national and longitudinal 
research on shipping impacts; 
and enhancing modern digital 
maritime chartingShip traffic will continue to grow 
in polar regions (high confidence), 
with Arctic trade routes becoming 
increasing accessible (very high 
confidence) albeit with more 
challenging navigation due to 
increases in mobile ice in the 
near-term compared with late 
century when ice is expected to 
diminish completely during the

shipping season (high confidence)Geopolitical and sovereignty 
debates; shipping insurance 
premiums; global economic 
trends; commodity prices; 
national policies and politics; 
level of infrastructure investment; 
availability of search-and-rescue 
assets, and modern charting
(CCP6.2.5)Loss and damage to infrastructure 
from permafrost thaw affecting 
stability of ground; coastal 
erosion; SLRResources for assessments, 
mitigation, and where needed, 
relocationIncreasing cost to maintain 
infrastructure and greater demand 
for technological solutions 
to prevent damages (high 
confidence)Strength of regional and national 
economies, other disasters that 
divert resources

CCP6
2348Cross-Chapter Paper 6 Polar Regions
Non-renewable resource 
extraction (Arctic only)
(CCP6.2.4.1)Reduced sea ice improves access 
to non-renewable resources 
in remote Arctic regions, while 
warming temperature and 
thawing permafrost affect 
production levels, quality, and 
reliability and season length of 
ice roads, leading to increased 
operational costsInvestment in climate-resilient 
infrastructure and low-emission 
transportation (shipping) and 
investment in solar powered ships 
and low-impact modular mining 
camp infrastructureIncrease in mining in newly 
accessible marine regions 
(medium confidence), frequent 
false starts (i.e., due to climatic 
and non-climatic factors) (high 
confidence) and high levels of 
operational uncertainty (i.e., 
commodity prices, economic

trends, climate risks) (very high 
confidence)Commodity prices; global 
economic trends and shocks; 
Indigenous rights and 
decisions; changing regulatory 
environments, geopolitics, global 
demand for resources
Tourism
(CCP6.2.4.2)Increased demand for polar 
tourism activities including 
development of ‘last chance 
tourism’ market; increased 
tourism improves economic 
conditions but leads to increased 
environmental and cultural 
impactsFinancial resources for service 
and infrastructure development; 
Indigenous self-determination and 
development of co-management 
approaches for natural and 
cultural attractions; development 
of multi-stakeholder/rightsholder 
tourism task teamsPolar tourism demand will 
continue to increase, especially 
for cruise and yacht experiences 
(high confidence) and enhance 
risks related to ship groundings, 
accidents and incidents (medium 
confidence)Limited search and rescue 
capacity, poor infrastructure, 
aging expedition cruise ship fleet,

uncharted waters, geological 
and sovereignty debates, global 
economic trends, unforeseen 
events (i.e., severe acute 
respiratory syndrome (SARS), 
COVID-19) altering tourism 
demand patterns
Reindeer herding
(CCP6.2.3.1; 6.2.3.2; 6.2.5; 
Box CCP6.2)Rain-on-snow events causing high 
mortality of herds, especially in 
the autumn season; shrubification 
of tundra pasture lowering forage 
qualityFlexibility in movement to 
respond to changes in pastures, 
secure land use rights; adaptive 
management; continued economic 
viability and cultural tradition; 
self-determination in decision 
making; adequate support for 
communication and technological 
services; Indigenous rights upheld 
and protectedIncreased frequency of extreme 
events and changing forage 
quality adding to vulnerabilities 
of reindeer and herders (high 
confidence); adaptation limits are 
being approachedChange in market value of meat; 
overgrazing; land use policies 
affecting access to pasture and

migration routes, property rights; 
cost of feed
Commercial fisheries
(CCP6.2.3)Loss of sea ice, warming waters 
and MHWs transform ecosystems 
in the Arctic with impacts on 
fisheries including declines in 
multiple regions; changes to 
Antarctic ecosystems affect 
southern fisheries productivity 
and distributionImplementation of adaptive 
management that is closely 
linked to monitoring, research 
and low cost and inclusive 
public participation in decisions, 
high-resolution forecast and 
projection tools, climate-informed 
survey and monitoring designChanges in availability and 
location of fishery resources 
will impact fish operations in 
the EBS and Barents Sea as 
well as the Convention for the 
Conservation of Antarctic Marine 
Living Resources area. Declines in 
catch impact livelihoods, coastal 
communities, and pose a risk to 
regional and global food and 
nutritional security (very high 
confidence)Changes in global demand for 
seafood, demand and markets,

changes in gear, changes in 
policies affecting property 
rights. Changes due to offshore 
development and transportation
Marine subsistence
(CCP6.2.3; CCP6.2.3.1)Changes in species distribution 
and abundance (not all negative); 
impediments to access of 
harvesting areas especially sea 
ice; increased interactions with 
shipping; safety; changes in 
seasonality; reduced harvesting 
success and process of food 
production (processing, food 
storage; quality); threats to culture 
and food securitySystems of adaptive 
co-management that allow 
for species switching, changes 
in harvesting methods and 
timing, secure harvesting rights, 
communication and relationship 
building, co-production of 
knowledgeChanges in distribution and 
abundance of resources combined 
with more regulations related to 
species at risk. Adaptation at the 
local, individual, and household 
level under low mitigation 
scenarios will be costly and 
possibly undermined by the scale

and pace of change, including 
climate shocks and extreme 
events (medium confidence)Changes in cost of fuel, land use 
affecting access, food preferences, 
harvesting rights; colonialism, 
international agreements to 
protect vulnerable species
Marine ecosystems
(CCP6.2.1)Warming, sea ice loss, OA 
resulting in poleward contraction 
of polar zones, invasive species 
introduction, displacement of 
polar species, and restructuring of 
food websReduce effects of external and 
compounding risks and increase 
application of EBM to meet 
biodiversity and management 
goals. Conservation of genetic 
diversity and biodiversity 
to preserve resilience, and 
supplementation and assisted 
migration may be neededWithout institutional investment 
in sustaining climate resilience 
in ecosystems across sectors, 
there is a high risk of failure (high 
confidence)Novel and expanding activities in 
ice-free areas (shipping; fishing), 
energy development and mineral 
extraction, increased tourism,

global markets and demand 
for polar resources, population 
growth and community relocation 
to coastal areas

CCP6
2349Polar Regions  Cross-Chapter Paper 6
Terrestrial and freshwater 
ecosystems
(CCP6.2.2)Warming, hydrology changes 
(reduced ice on lakes and rivers, 
flooding, snow) and permafrost 
thaw lead to impacts on polar 
terrestrial and freshwater systems, 
food webs, the distribution 
of polar fish, implications for 
peat systems with consequent 
changes on dependent animal 
assemblages and increasingly 
favourable conditions for 
parasites and pathogens. 
Increased risk of wildfires in the 
ArcticImproving biodiversity and 
redundancy to enhance resilience. 
Efforts to minimise and prevent 
extinctions; preservation of 
ecosystem processes and 
habitats during critical life 
stages; coordinated governance; 
measures and planning that 
consider dynamic interactions 
within and among social and

ecological systems are more 
effectiveWithout institutional investment 
in ecosystems across sectors 
there is a high risk of failure 
(high confidence). Arctic regions 
have greater understanding of 
resilience needs, but coordination 
is not widespread. Antarctic 
has established action plans to 
identify key management needs 
for conserving terrestrial and 
freshwater biota.Novel and expanding activities in 
extraction, increased tourism, 
1234567 1234567 1234567
Assessment of feasibility and effectiveness of adaptation options
by key risks in the polar regions
Implementation  of climate-informed  ecosystem-based management; diversif ication of ha rvest and li velihood

portfolios  to buffer climate shocks ; climate-informed  decision  suppor t tools; low co st inc lusive 
participator y decision-m aking to suppor t effective adaptationChange in gear , timing  of fishing or hunting, species switchin g; mobiliza tion to be invo lved in politi cal 
action; co-p roductio n of knowledg e; co-m anagement; Indigenous self dete rmination in decision making and 
resour ce managementChanges in movement pa tterns of herder s; policies  to ensu re free -range mo vements; supplement al 
feeding, protection and enhanc ement of grazing area sImplementation  of vo luntary guidelines  for activities , attractions, an d special site s; carrying capacity 
restrictions; development of strong indust ry associations ; strong local communit y leadershi p; inve stment

in se rvices and infrastructure with co-benefits for indust ry, scientists  and communities.Maritime  infrastructure in vestments, climate-resilien t rail syste ms to port-based extraction , cross-sectoral  
coordination  in of fshore extraction  areasUpdate d infrastructure codes; upgrades to infrastr ucture (e.g. inc reased  diameter culver ts and drainage  
system s; base -layer modi fication, thermosyphon  installation ; modi fied binder /sealant  application, 
base -layer strengthenin g; increased diameter roof drainage system s)Investment in ma ritime infrastructure , mult i-national agreements on ship safe ty, operations , and building ; 
investment in ship -building technologies inc luding solar powered ship s; robust  national  and inte rnational  risk 
mitigation  policy , suppor t for Arctic Council ship ping initiative, search -rescu e-spill responseInvestment in climate resilient technologies and infrastr ucture , dev elopme nt of new weat her, wate r, ice, and

climate fore casting te chniques , impr oving communic ations infrastructure , adherenc e to adap tive an d 
dynamic planning polic ies, investment in search and rescue, improved char tingHealth  surveillan ce programs , search  and rescue  capacity, communit y-based food programs (e.g. 
communit y-freezers ), on-the-land programming, communit y-based monito ring of trail co nditions , and 
cultural  programmin g
Ecosystem-based management; cross-se ctoral  integrated plannin g; conservation of climate refugi a; 
integrated planning and ec osystem-based management minimizing compoundin g pressure s; measures  to 
increase or conserve biodiv ersity and ecosystem connectivity ; enhanc ing biotic resis tance; and minimizing 
vectors of for inva sive speciesSpatia l and la nd co-m anagement; conservation and ecosystem-based solution s; protection  of climate refugi a; 
cross-sectoral  management  and coordination ; redu ce inva sive sp ecies  vectors, monitor and repo rt changes

in en vironmen tal conditionsMaintenanc e of er osion mitigation ; reloc ation planning,  secure  funding,  access to financin gKey Risks: Adaptation2080 –2100 2020 –2040F easibili ty
High Medium Low High Medium Low High Medium Low
Figure CCP6.6 |  Assessment of feasibility and effectiveness of adaptation options by KR in the polar regions (Table CCP6.6).

CCP6
2350Cross-Chapter Paper 6 Polar Regions
CCP6.3.2.2 Adaptation gaps
In a study of adaptation progress across the Arctic from 2004 to 2019, 
233 cases of adaptation were documented, with the majority of actions 
primarily behavioural and reactionary in nature and undertaken in 
the subsistence harvesting sector, with resource management, and 
infrastructure and transportation other prominent sectors where 
adaptation responses were documented to be occurring (Canosa et al., 
2020). The study found few changes in the profile of adaptation over 
time, except for an increase in responses being motivated solely by 
climate impacts, and few cases of transformational change, although 
it should be noted that a lack of data on adaptation actions makes 
documenting trends challenging. Human health is generally under-
represented in adaptation initiatives, along with adaptations being 
developed within larger Arctic settlements (Ford et al., 2014; Canosa

et al., 2020), and in many sectors decisions continue to be made without explicit inclusion of climate change impacts and risk in planning 
and design (high confidence) (Cherry et al., 2017; Lauta et al., 2018; 
Meredith et al., 2019). There is limited evidence of transformational 
adaptation taking place in the policy arena (e.g., U.S. Executive Order 
13990, 2021), but many examples of how impacts and responses 
to climate change have transformed social–ecological connections, 
traditions, markets, trade and livelihoods of Arctic residents and 
Indigenous Peoples (Ford et al., 2015).
CCP6.3.2.3 Maladaptation and limits to adaptation
In polar regions, multiple entities operate simultaneously to manage 
lands and resources, resulting in layered approaches and policies for 
the same sector or region, only some of which are synergistic and a 
few of which may counter each other (e.g., Southern Ocean: Solomonsz 
et al., 2021). Climate change and attendant uncertainty can undermine

Impact: Sea ice mobility & 
near-term shipping 
navigational hazards
Adaptation: Improved 
charting, enhanced local 
search & rescue,  
technological developments 
in ship-designImpact: Changes to cold water habitats & critical refugi a
Adaptation: Spatial management of climate refugia, EBMImpact: Climate impacts on caribou & 
reindeer subsistence resources
Adaptation: Indigenous 
co-management, protect & enhance 
grazing habitats, enhance forage
Impact: Loss of productivity in polar fisheries with impacts 
on food & nutritional security within & beyond polar regions
Adaptation: Diversify harvest portfolios,provide climate 
informed advice with EBM, nowcasts, & seasonal to  
decadal forecasting to improve harvest recommendations
Impact:  Changes to lower 
trophic pathways, ecosystem  
linkages, and carrying capacity
Adaptation: Climate-informed 
Ecosystem Based 
Management (EBM), 
measures to reduce pressureImpact: Novel conditions, increased activit y, &

stressed ecosystems favor invasive species
Adaptation: Increased detection, reporting, & 
control, preventative measures to reduce vectors,  
habitat protection to increase biotic resistance,  
regulations & quarantine to reduce introductions
Impact: Coastal erosion impacts 
coastal infrastructur e
Adaptation:  Infrastructure 
reinforcement, research & 
development on climate resilient  
infrastructure, climate informed 
planning and relocationImpact: Permafrost loss, erosion, loss of ice & snow  
routes impact homes, buildings, & critical infrastructure 
Adaptation: Co-management agreements and shared 
leadership approaches to managing self-determined  
sustainable development
Impact: Changes to cold water 
habitats and critical refugi a
Adaptation: Spatial management of  
climate refugia, EBM, conservatio n
Marine Protected Areas (MPA s)
Impact: decline in coastal ice 
habitats for Emperor penguins.  
Adaptation: Protection of  
important prey fields & 
ice-habitats to reduce

interference and increase 
resilience of the species.
Impact: Alterations to bio-psycho-social 
conditions & cultural continuity negatively 
disrupt mental health
Adaptation:  Place-specific mental health 
resources & training and infrastructure 
and incorporating a climate-sensitive 
mental health lens into climate change 
adaptatio n
Impact: Loss of multiyear ice, early break 
up, thinner more hazardous ice, loss of ice 
dependent species, shipping interactions
Adaptation: Nowcasting & real time 
observations of ice conditions,  
communication, sea ice mapping, data 
sharing, Indigenous co-management & 
oversight, enhanced search & rescue 
capacit y, conservationClimate change impacts, risks, and potential for adaptation in Arctic (upper panel)
and Antarctic (lower panel) social ecological systems.
Figure CCP6.7 |  Climate change impacts, risks and potential for adaptation in Arctic (upper panel) and Antarctic (lower panel) social–ecological systems.

CCP6
2351Polar Regions  Cross-Chapter Paper 6
FAQ CCP6.4 | When will climate change impacts in polar regions surpass our ability to adapt?
When environmental variability is within the range of the current adaptive management approaches, the social–ecological system can thrive. 
However, the rapidly changing polar systems are causing disruptions to societies, economies and ecosystems. The current management systems 
are yet to develop procedures for managing rapid change being experienced in warming waters, sea ice declines, permafrost thaw and erosion, 
and poleward shifts in species. These challenges are expected to become more pronounced within a few decades rather than later this century.
Polar regions are naturally dynamic environments. Ecosystems in polar regions, and the people who rely on them, 
have adapted to natural variability and dynamic nature of polar environments. Fish populations in polar regions

are known to exhibit cycles of productivity, and shift their distribution across hundreds of kilometres in response to 
changes in winter sea ice cover and concomitant summer ocean conditions. Management of the productive fisheries 
in polar regions is also designed to allow for these changes, using adaptive and ecosystem-based approaches that 
buffer populations from overexploitation and also stabilise fisheries, livelihoods and food resources. Indigenous 
Peoples diversify their subsistence harvest across species and resources and, therefore, similarly stabilise food and 
nutritional security. 
When environmental variability is within the range of these adaptive measures, the social–ecological system can 
thrive. Thus, there are fundamental components in place in polar regions already to help ecosystems and people 
adapt to some degree of climate change. However, as climate change impacts like warming waters, sea ice loss,

permafrost thaw and erosion systematically alter components of the system, shift species increasingly poleward, and 
disrupt linkages between species and people, the ability to adapt is reduced. There are critical tipping points (e.g., 
sea ice melt, permafrost thaw) where changes may cascade, self-reinforce and accelerate, outpacing adaptation 
actions and force natural and human systems irreversibly (on the scale of human existence) into novel regimes. 
The risk of crossing tipping points is greater and the probability much increased after mid-century under scenarios 
without global carbon mitigation (SSP5 8.5), where changes are largest and most rapid.stakeholder confidence in management, leading to less effective 
management even when scientific understanding is stable (Mumby 
et  al., 2017). In the Arctic, large landscapes, dispersed population 
centres, limited resources and settler colonialism are structural barriers

to effective planning, emergency response, and relief and recovery 
from climate impacts (medium confidence), which limit adaptation and 
sometimes exacerbate climate and non-climate pressures on social and 
ecological systems (Ford et  al., 2015; Ford et  al., 2020; Snook et  al., 
2020).
Adaptation strategies that are beneficial in the short term can 
result in long-term maladaptive outcomes. For Indigenous Peoples, 
strategies that fail to address colonialism, inequities and injustices 
undermine effective adaptation (Canosa et al., 2020; Schipper, 2020; 
Ford et al., 2021). Large ‘responsiveness gaps’ between impacts and 
implementation, approaches that fail to consider dynamic responses 
within social and ecological systems (which amplify or attenuate 
climate impacts), and a paucity of a priori planning can contribute 
to maladaptation (high confidence) (Pentz and Klenk, 2017; Turner 
et al., 2020b). For example, rationalisation (privatisation) can stabilise

fisheries and incentivise long-term sustainability under stationary 
conditions, yet also promote low diversity in harvest (or livelihood) 
portfolios, and when combined with behaviours to offset climate 
driven declines in yield (e.g., effort or price), rationalisation can create 
lock-in to declining stocks, increasing the risk of income variability 
and collapse (Kasperski and Holland, 2013; Pinkerton and Davis, 2015; 
Holland et al., 2017; Ojea et al., 2017; Anderies et al., 2019; Fisher et al., 2021). Policies that foster stewardship yet also allow for diversification 
in fisheries may further attenuate climate shocks to individual fisheries 
(Kasperski and Holland, 2013; Fisher et al., 2021) and stabilise catches 
(e.g., US Bering Sea pollock fleet (Watson and Haynie, 2018)). Inclusive 
and participatory decision making underpins long-term resilience 
to climate change (medium confidence)(Flynn et  al., 2018; Ford

et al., 2020), but a high cost of participation can disproportionately 
favour entities with strong investment, ample resources and extreme 
viewpoints such that decision outcomes are not in the broad interest 
of polar societies (Lynham et al., 2017).
There are significant limits to adaptation in the polar regions related 
to the rate of warming and cascading changes that are occurring, 
which is equivalent to double and sometimes triple the global average 
depending on the region (Bush and Lemmen, 2019; IPCC, 2021). The 
rapid pace of change, such as sea ice loss, can outpace ecological 
processes and induce substantial ecological shifts (CCP6.2) (medium 
confidence). The speed of climate change in the Arctic limits options 
for adaptation in communities who rely on a narrow resource base, 
when adaptation involves loss of culture and livelihoods, and when 
the costs of adaptation make it infeasible (medium confidence) (Ford

et al., 2015), such as for reindeer herding (Table CCP6.6; Figure CCP6.6; 
Figure  CCP6.7) (Meredith et  al., 2019). Adapting infrastructure in 
response to a rapidly changing cryosphere will be limited by available 
technologies and the relatively higher costs associated with updating 
infrastructure over vast polar regions (Schneider von Deimling et al., 
2021).

CCP6
2352Cross-Chapter Paper 6 Polar Regions
CCP6.4 Climate Resilient Development 
Pathways
The polar regions are expected to experience many economic 
development opportunities as a result of climate change, including 
increased accessibility for shipping and attractiveness for fisheries and 
tourism (CCP6.2.3.1, CCP6.2.4). For polar regions, equitable climate 
resilient development requires diverse perspectives in planning and 
implementation. In the Arctic, cultural, social and economic dimensions 
of Indigenous Peoples and local communities are critical (Ritsema 
et al., 2015; Huntington et al., 2021). For both poles, there are global 
cultural connections to polar systems (Roberts et al., 2021), along with 
important global and local needs for sustained ecosystems and their 
services, in the face of diminishing polar zonal conditions (Cavanagh 
et al., 2021; Murphy et al., 2021; Solomonsz et al., 2021).
Sustainable pathways integrating across sectors, and taking account of

local and global connections, can be readily achieved in polar regions 
to balance trade-offs between economic, ecological and sociocultural 
imperatives, yet challenges remain (Murphy et  al., 2021). Notably, 
terrestrial areas of greatest interest for infrastructure development, 
agriculture and visitation potential are often also the same areas that 
have been identified as culturally and ecologically significant (PEW, 
2016; Eliasson et  al., 2017; Grant et  al., 2021) (high confidence). 
Development of low-impact shipping corridors in Arctic Canada 
is an example of an effective mechanism where climate-resilient 
infrastructure can be prioritised and where regulation (voluntary and 
enforced) focused on cascading climatic risks can be implemented 
(Chénier et al., 2017; Dawson et al., 2020).
CCP6.4.1 Challenges to Climate Resilient Development 
Pathways
Decision making in polar regions is complicated by globalisation

processes and the complexity of governance arrangements from local 
to global instruments and differing stakeholder perspectives and 
needs (Hughes et  al., 2018; Stephen, 2018; Huntington et  al., 2021; 
Murphy et al., 2021; Solomonsz et al., 2021). Substantial interest in 
and management of polar resources from non-polar states can lead 
to decision making that lacks explicit consideration of local impacts 
and responses, thus reducing the effectiveness of adaptation and, in 
some cases, causing maladaptation. Participatory decision making 
is increasingly used in some sectors, but high costs of participation, 
a focus on consensus, and systematic erosion of resources can 
undermine outcomes (Mumby et  al., 2017; Parlee and Wiber, 2018; 
Mendenhall et al., 2020). In the Arctic, the societal burden of climate 
change impacts and risks will manifest at the local level, and thus 
the importance of local scale leadership and involvement in decision

making is essential for effective adaptation (AMAP , 2017).
Many losses and damages within Indigenous contexts are not able 
to be monetised but can be profound, such as loss of Indigenous 
languages (CAFF , 2013), loss of IK associated with nomadic lifestyles 
and cultures (Box  CCP6.2), and loss of geographical knowledge 
associated with an intimate knowledge of landscapes across seasons 
(Brattland and Mustonen, 2018), changing landscapes resulting in solastalgia and ecological grief (Cunsolo and Ellis, 2018), and some 
Indigenous practices and cultural assets, such as burial grounds, 
nomadic camp sites, graveyards, seasonal dwellings, and routes and 
pathways causing disruptions to mind and memory (Mustonen and 
Mustonen, 2016). Recognising these intangible losses and damages 
is critical for understanding how to achieve climate resilience in the 
Arctic (Tschakert et al., 2019; Sawatzky et al., 2020).
For the Antarctic, the governance for managing climate impacts is

emerging, particularly for terrestrial and nearshore habitats (high 
confidence) (Hughes et al., 2018; Chown and Brooks, 2019). However, 
it is poorly developed for marine ecosystems, despite its importance 
for decision making (Trebilco et al., 2020; Goldsworthy and Brennan, 
2021). A diversity of stakeholders is involved in developing evidence-
based management for the region, which presents challenges for 
adaptation planning (Solomonsz et al., 2021), particularly in identifying 
sustainable practices in a changing environment (Constable et  al., 
2017; Brooks et  al., 2018). Spatial management for enhancing the 
resilience of endemic polar biodiversity is increasingly proposed as the 
best option for managing risks of climate change (Chown and Brooks, 
2019).
CCP6.4.2 Inclusive, Integrated Co-management
Inclusive, low-cost participatory decision making can deliver equitable 
responses to climate change (high confidence). Land use, maritime

activities and subsistence fishery and other extracted resources in 
the polar regions are co-managed through multi-lateral and national 
government bodies. The capacity of governance systems in some 
Arctic regions to respond to climate change has strengthened recently 
(high confidence). Synthetic themes in adaptation for the Arctic have 
emerged from and since SROCC and include flexibility through diversity 
in livelihoods, and subsistence and harvest portfolios; co-management 
of resources; adaptive and ecosystem-based approaches; adoption 
of advanced technology, forecasts and longer-term projections to 
improve safety and resources management; and imperative need for 
low-cost, inclusive and participatory decision making (Kasperski and 
Holland, 2013; Brattland and Mustonen, 2018; Parlee and Wiber, 2018; 
Galappaththi et al., 2019; Holsman et al., 2020; Huntington et al., 2021; 
Melbourne-Thomas et al., 2021; Mustonen and Van Dam, 2021). This

was demonstrated in community-level adaptation by Pangnirtung Inuit 
to climate change impacts on fisheries (Galappaththi et  al., 2019). 
Inclusive approaches to co-management, especially those that enable 
diverse perspectives, embrace conflict, and address equity and justice 
across power holders, can help alleviate the risk and promote solutions 
(Raymond-Yakoubian et  al., 2017; Brattland and Mustonen, 2018; 
Parlee and Wiber, 2018; Raymond-Yakoubian and Daniel, 2018; Snook 
et  al., 2020). Integration across levels of management and diverse 
regional perspectives can reduce climate risks and support equitable 
adaptation measures (Allison and Bassett, 2015; Raymond-Yakoubian 
et  al., 2017; Raymond-Yakoubian and Daniel, 2018; Holsman et  al., 
2020).
Increased flexibility in management measures, greater investment 
in ecosystem monitoring, and more inclusive participatory methods 
and communication may help foster high levels of local investment

CCP6
2353Polar Regions Cr oss-Chapter Paper 6
and resilience and promote adaptive pathways (Cinner et  al., 2016;

Weymouth and Hartz-Karp, 2019), although explicit measures may be needed to reduce costs and increase representation, enhance transparency, embrace dissent and clarify accountability are needed as these are not inherent outcomes (Lynham et al., 2017; Parlee and Wiber, 2018). EBM, which includes provisions aimed at sustaining critical connections within and among social and ecological systems, enhances resilience and attenuates climate impacts on ecosystems and provisioning services; for example, EBM enhances climate resilience for Antarctic krill and Northeast Arctic cod fisheries (Troell et  al., 2017; Meyer et al., 2020) and forestalls fishery collapse in the Bering Sea in the near term (Holsman et al., 2020). Increasing likelihood of transboundary resources, interactions and novel commerce may strain existing regulatory and international agreements, suggesting that a priori governance agreements designed to manage climate risks and aimed at attenuating potential conflicts over

resources and regions may be important for resolving these issues (Parlee and Wiber, 2018; Mendenhall et al., 2020).CCP6.4.3  Enabling Climate Resilience in the Arctic: Self-

Climate change disproportionately impacts Indigenous Peoples (Box  CCP6.2), which directly affects their livelihoods, health and Sustainable Development Goal targets. For residents in the Arctic, a sustainable development pathway has been found to be highly effective if a self-determined development model is employed (very high confidence). Known determinants of Indigenous self-determined development in regions in the Arctic, include: (1) Indigenous self-determined decision making (and inherent sovereignty); (2) effective and culturally legitimate institutions of government; (3) strategic vision and strategic thinking; and (4) public-spirited, nation-building leadership (Cornell and Kalt, 1992; Cornell and Kalt, 1998; Cornell and Kalt, 2007; Ritsema et  al., 2015). For Indigenous Peoples, advances in recognition of self-governance, land and resource sovereignty, and resource co-management, particularly in the North American Arctic but also elsewhere, provide a strong basis for

responding to climate impacts (Ford et  al., 2015; Robards et  al., 2018). These developments expand the solution space (Haasnoot et al., 2020) for

Climate Resi l-
ient De velopment 
Principles 
in Polar Region sAccessible  & loca lly 
tailored climate 
informed tools & pr oducts , 
& regional fore casts & 
projections to suppor t 
climate-informed 
decision makingOvercoming 
colonialism throug h 
meaningful  & explicit 
inclusion of Indigenous  
knowledg e & Indi genous  
co-management in research  
& resource manage ment 
in the Arctic
Low cost, inc lusive,  
participator y decision  
making with explicit goals 
that consider gender , 
equity, and social  justice
Adequate & approp riate 
adaptation & em ergency 
response f inancing and 
plannin gAligned adap tation & 
carbon mitigation  
policies  that reduce risk & 
increase ef fectiveness 
of adap tationmeasuresCollabor ation & 
agreements among 
nation s; vertical & lateral 
coordination with (pol ycentric) 
governance in adapt ation 
plannin g & 
implemen tation Six principles that support climate resilient pathways in the polar regions

Figure CCP6.8 |  Six principles that support climate-resilient pathways in the polar regions.

CCP6
2354Cross-Chapter Paper 6 Polar Regions
responding to climate impacts, although historical and ongoing forms 
of colonialism in research and government institutions continues 
to undermine Indigenous self-determination and reinforce climate 
change vulnerability (Marino and Lazrus, 2015; Whyte et al., 2019; Ford 
et al., 2020; Snook et al., 2020). Readiness for adaptation across Arctic 
nations continues to be challenged by a number of factors, including 
the existence of pressing socioeconomic challenges, institutional 
and governmental barriers, lack of meaningful inclusion of IK in 
government planning and response, and lack of financial resources 
(Ford et al., 2015; Loring et al., 2016; AMAP , 2017; Ford et al., 2017; 
Birchall and Bonnett, 2019) (AMAP , 2018a). In Alaska, for example, the 
need to relocate high-risk villages has been recognised by researchers, 
decision makers and communities themselves for some time, and while

progress is being made in some locations (Ristroph, 2017), institutional 
barriers have generally resulted in negligible progress (Bronen and 
Chapin, 2013; Marino and Lazrus, 2015; Albert et al., 2018; Rosales 
CCP6.5 Summary and Conclusion
Rapid changes occurring in polar systems are clear and unequivocal, 
indicating that swift and effective responses are urgently needed 
to avoid substantial future impacts and reduce risks to polar social 
and ecological systems. Some underlying principles emerge from this 
assessment that appear fundamental to achieving climate resilient 
development in polar systems (Figure  CCP6.8) because they could 
facilitate rapid, equitable and just responses to achieve climate 
resilience. These principles include having locally relevant and 
accessible tools and services (e.g., regional forecasts and projections) 
to support climate-informed decision making, along with adequate 
and appropriate resourcing (including finance and integrated

planning) for climate adaptation and for responding to emergencies. 
Effective decision making processes integrate across sectors, all levels 
of governance, including through multi-national instruments, and, 
most importantly, apply low-cost and inclusive participatory processes 
to address gender, equitable and socially just outcomes. In the Arctic, 
there is evidence that overcoming colonialism through meaningful 
and explicit inclusion of IK in research and resource management, as 
well as co-management and self-determination in decision making, 
are effective measures to support equitable climate resilience across 
multiple sectors. Lastly, climate resilience is strongly dependent on 
both mitigation of climate change as well as effective adaptation to 
meet the challenges of unprecedented change in polar regions.

CCP7
2371Tropical Forests Cr oss-Chapter Paper 7
Over 420 million ha of forest were lost to deforestation from 
1990 to 2020; more than 90% of that loss took place in tropical areas (high confidence), threatening biodiversity, environmental services, livelihoods of forest communities and resilience to climate shocks (high confidence
1). Forty-five percent of the 
world’s forested areas are in the tropics, and they are among the most important regulators of regional and global climate, natural carbon sinks and the most significant repositories of terrestrial biomass. They are of immeasurable value to biodiversity, ecosystem services, social and cultural identities, livelihoods, and climate change adaptation and mitigation. {CCP7.2.1; CCP7.2.2; Box CCP7.2; Table CCP7.2}
Climate change affects tropical forests through warming and

increased occurrence of extreme events such as droughts and heatwaves, as well as more frequent fires, which increase tree mortality and reduce tree growth, limiting the ability of forests to regenerate (high confidence). Climate change is altering the structure and species composition of tropical tree communities (high confidence), including transitions from moist to drier forest in regions such as the Amazon (high confidence), and movement of species from lower to higher elevations (high confidence). Despite CO
2 fertilisation, 
ongoing climate change has weakened the carbon sink potential of tropical forests in Amazonia and, to a lesser extent, in Africa and Asia (medium confidence). {CCP7.2.3; CCP7.3}
Large-scale tropical deforestation affects regional to 
continental scale climates with significant impacts on forest resilience (high confidence). Deforestation generally reduces

rainfall and enhances temperatures, with effects depending on scales (high confidence), while often increasing surface runoff (medium confidence). Continued deforestation-driven landscape drying and fragmentation will aggravate fire risk and reduce forest resilience, leading to degradation or savannisation of the tropical forest biomes, in particular in combination with climate change (high confidence). {CCP7.3.6}
Implementing sustainable management strategies can improve

the ability of tropical forest ecosystems to adapt to climate change (high confidence), and the benefits of adaptation interventions often outweigh the costs (medium confidence). Adaptation of tropical forests to climate change provides an opportunity for tropical countries to develop forest policies that create incentives for environmental services such as carbon storage and biodiversity refugia. Forest restoration using a diverse mix of native species can help rebuild the climate resilience of tropical forests, but is best implemented alongside other sustainable forest management strategies and adaptation interventions (high confidence). {CCP7.5; Box CCP7.1}
1 In this Report,  the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.Community-based adaptation, built on Indigenous knowledge

and local knowledge (IK and LK) over centuries or millennia, is often identified as an effective adaptation strategy to climate change (high confidence). For successful adaptation of tropical forest communities, it is vital to consider IK and LK in addition to modern scientific approaches, together with consideration of non-climatic vulnerabilities (e.g., poverty, gender inequality and power asymmetries) (high confidence). Climate change vulnerability and adaptive capacity have a historical and geopolitical context, conditioned by value systems and development models. Transformative and sustainable practices are required for effective management of tropical forests (high confidence). {CCP7.4; Box CCP7.1}
Building resilience of tropical forests to climate change relies 
on adaptation in combination with reduction of direct and underlying drivers of deforestation and forest degradation (high confidence). Tropical deforestation is largely driven by agriculture,

both from subsistence farming and industrial agriculture (e.g., oil palm, timber plantations, soybeans, livestock) (high confidence). While poverty and population growth combined with poor governance often fuel subsistence agriculture (high confidence), industrial agriculture is often driven by international market forces for commodities and large-scale land acquisitions (high confidence). {CCP7.2.3}
Governance responses to addressing the direct and underlying

drivers of deforestation have been inadequate to reduce pressures, yet the urgency of tackling drivers of forest loss and degradation is increasing as climate impacts on forests and ecosystems increase (high confidence). Transformative levers towards improving environmental governance and resilience of tropical forests include: incentivising and building capacity for environmental responsibility and discontinuing harmful subsidies and disincentives; reforming segmented decision-making to promote integration across sectors and jurisdictions; pursuing pre-emptive and precautionary actions; managing for resilient social and ecological systems in the face of uncertainty and complexity; strengthening environmental laws and policies and their implementation; acknowledging land tenure and rights; and inclusive stakeholder participation (medium confidence). {CCP7.6}

CCP7
2372Cross-Chapter Paper 7 Tropical Forests
CCP7.1 Introduction
Climate change is already impacting tropical forests around the world, 
including through distributional shifts of forest biomes, changes in 
species composition, biomass, pests and diseases, and increases in 
forest fires (high confidence). These impacts are often compounded by 
non-climatic factors such as conversion of land for other uses, burning 
to clear land, mining, and road and infrastructure development. It is 
notable that, despite societal awareness and financial opportunities 
to restore forests (Brancalion and Chazdon, 2017), tropical forests are 
increasingly threatened. For instance, the conversion of tropical forests 
to large-scale agricultural production (mainly soybeans, oil palm, maize, 
cotton, livestock), is among the strongest drivers of species richness 
decline of both flora and fauna, thereby impacting the adaptation 
opportunities of ecosystems and local people to climate change (IPBES,

2018). Reducing direct and indirect drivers of deforestation and forest 
degradation is therefore critical to building, maintaining or enhancing 
the resilience of tropical forests against climate and non-climate 
drivers alike (high confidence).
With climate change-related drivers becoming increasingly important 
in the future, changes to tropical forests will most likely2 be aggravated 
overall, although some tropical forests may temporarily benefit, 
physiologically, from higher temperatures and changes in precipitation 
patterns. To the degree to which forests are affected by climate change 
and other drivers, their resilience against these stressors is diminishing 
leading to a reduction in the regulating, supporting, provisioning and 
cultural ecosystem services they provide (Alroy, 2017; Cadman et al., 
2017; Pörtner et al., 2021) (Chapter 2) (high confidence). This, in turn, 
is affecting the lives and livelihoods of millions of people who depend

on forests and their products, in particular forest dwelling communities, 
but also, via the teleconnections between forests and surrounding areas 
of influence, in socio-ecological systems outside the forests themselves.
While strong mitigation efforts are fundamental to minimising future 
climate impacts on forests, forest management can be improved in 
many places in support of enhancing the resilience of tropical forests, 
often with significant co-benefits for carbon storage, biodiversity, 
food security and ecosystem services (high confidence). Sustainable 
management practices allow forests to be utilised, frequently with 
equally high or even higher productivity levels, while keeping their core 
functions intact. While there are numerous approaches to managing 
forests and forest landscapes sustainably, an element that appears 
to be critical is property rights and tenure arrangements allowing 
stewards of the land, including Indigenous Peoples, securing long-

term access and utilisation of forest resources (medium confidence) 
(Rahman and Alam, 2016; Naughton-Treves, 2014).
Figure  CCP7.1 illustrates the interconnections of climate risks and 
non-climate drivers facing tropical forests. On the one hand, the rates 
and extent of deforestation and forest degradation result in loss of 
ecosystem services, biodiversity and human well-being and enhance 
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as 
likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100%, and extremely unlikely 
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome

lies within the 17–83% probability range.the vulnerability of the social-ecological system to the impacts of 
climate change. On the other, forest protection and sustainable forest 
management result in higher resilience of the ecosystem against 
climate impacts. This framing illustrates both the complexity and scale 
of the challenge and provides opportunities to reduce impacts at 
different scales by eliminating the underlying drivers, both climate and 
non-climate related, through policies and measures at global, national 
and subnational levels, involving state and non-state actors alike.
Building on what has been presented in IPCC AR5, SR15 and SRCCL, 
Section CCP7.2 first briefly describes the types and extent of tropical 
forest ecosystems, and then looks at current rates and drivers of 
deforestation and forest degradation. Section CCP7.3 presents current 
and projected climate change impacts on tropical trees and forests,

focusing primarily on drought, heat and fires, looking from physiological 
responses to risks, projected climate change impact and forest 
resilience. Section CCP7.4 addresses the impacts of climate change 
and tropical forest destruction on the livelihoods and well-being of 
communities and peoples living in or being strongly dependent upon 
tropical forests. This section includes a Box on Indigenous knowledge 
and local knowledge and community-based adaptation. Section 
CCP7.5 assesses adaptation options for the sustainable management 
of tropical forests drawing upon the protection, management and 
restoration framework, and includes a Box on the connection between 
sustainable forest management and the United Nations Sustainable 
Development Goals. Section CCP7.6, finally, assesses opportunities 
and challenges of tropical forest governance to maintain and enhance 
resilience against climate change impacts on forests.
CCP7.2 The Current State of Tropical Forests

In the most recent Global Ecological Zones map produced by the 
Food and Agriculture Organization (FAO) for the year 2010, tropical 
vegetation has been defined as encompassing regions which are frost-
free during all months in the year (FAO, 2012). Further, the tropical 
vegetation has been sub-classified into tropical rainforest, tropical 
moist forest, tropical dry forest, tropical shrubland, tropical desert 
and tropical mountain systems based on climate in combination with 
vegetation physiognomy and orographic zone (Table SMCCP7.1). IPCC 
has used the basic FAO classification in its National Greenhouse Gas 
Inventories Guidelines (IPCC, 2019a).
Since the FAO ecological zones represent potential biome extents, 
the present area under forest is assessed using the European Space 
Agency Climate Change Initiative Land Cover data set (ESA, 2017). 
The ESA data set provides a direct mapping to IPCC land categories

(e.g., ‘forest’), allowing for standardised and consistent reporting of 
existing forest and forest gain/loss in each ecological zone. The most 
extensive tropical ecological zone is the tropical rainforest (1459 
Mha or about 25% of all tropical ecological zones), followed by 
tropical desert (which is not further considered here), tropical moist

CCP7
2373Tropical Forests  Cross-Chapter Paper 7
forest, tropical shrubland, tropical dry forest and tropical mountain 
system (Table  CCP7.1; Figure  CCP7.2). Mangroves are not explicitly 
considered in the FAO classification. Tropical rainforest occurs largely 
in South America, Africa, and South and Southeast Asia, and is the 
most intact tropical forest biome (Table CCP7.1). Significant portions 
of tropical moist forest, which abut tropical rainforest in many regions 
but experience a longer dry season, have been lost in most regions 
(Table CCP7.2). Tropical moist forest typically grades into the highly 
threatened tropical dry forest ecological zone, of which only about a 
third exists under forest cover at present. Only about 44% of tropical 
mountain systems, which occur approximately above 1000 m above 
mean sea level, are presently under forest cover. While the FAO 
classification provides the potential tropical ecological zones (roughly,

‘vegetation types’), there are large differences in the extents of global 
tropical forest biomes which are still remaining as reported by different 
sources (Sayre et al., 2020; Ocón et al., 2021). These differences result 
from differences in biome definition, data source, the definition of 
‘forest’, and the method used for classifying remotely sensed data. For 
example, the reported global area of tropical dry forests ranges from 
105 to 645 Mha (Pan et al., 2013; Bastin et al., 2017; Ocón et al., 2021).CCP7.2.1 Distribution and Biodiversity of Tropical Forest 
Tropical forests are indisputably the areas with highest biological 
diversity on Earth, both in absolute and density (species per area) 
terms (Plotkin et  al., 2000). Estimates account that tropical forests 
harbour half or even more of world’s biodiversity (Kier et  al., 2009; 
Jenkins et al., 2013), even though this figure is highly uncertain owing

to varying estimates of undescribed species (Mora et al., 2011). For 
example, it is estimated that there are at least 40,000, but possibly 
more than 53,000 tree species in tropical forests (Slik et al., 2015). A 
vast majority of this biodiversity and Indigenous knowledge and local 
knowledge associated with its use remains poorly explored, presenting 
a vast unlocked genetic reserve at risk of loss, although many of 
today’s important medicines, foods and ecosystem products originate 
from tropical forests (Kouznetsov and Amado Torres, 2008; Calderon 
et al., 2009, Maia and Mourão, 2016).
Rates of global biodiversity loss in the past few decades have acelerated 
to levels that are, for some taxa, approaching the estimated rate of High vulnerability to climate change High resilience to climate change
Deforestation Conversion
Conversion
Habitat loss
Biodiversity declineForest protection
and restorationPractices of local and 
indigenous knowledge 
Climate friendly 
forest governance

Fires Food security and safeguardsClimate change threatens biodiversity and livelihoods of tropical forest communities
Sustainably managed tropical forest
Good forest ecosystem Unsecured livelihoods
Figure CCP7.1 |  Impacts of climate change and human disturbances on tropical forests lead to high risk of biodiversity loss and uncertainty of livelihoods 
for the majority of forest-dependent communities (left side). Good forest governance would increase the resilience of tropical forest through better adaptation to and 
mitigation of climate change (right side).

CCP7
2374Cross-Chapter Paper 7 Tropical Forests
Tropical ecological zones as defined by the FAO
Tropical dry forests
Tropical shrublandTropical desert
Tropical mountain systemTropical rain forests
Tropical moist forestsAreas classified as “Forest”
in the 2020 ESA Land Cover CCI Product
are overlaid in grey.
Figure CCP7.2 |  Colours represent tropical ecological zones as defined by the FAO (FAO, 2012). Areas classified as ‘forest’ in the 2020 ESA Land Cover CCI Product 
(ESA, 2017) are overlaid in grey.
Table CCP7.1 |  Areas in tropical ecological zones as defined by the FAO (FAO, 2012). 1Existing forest represents areas classified as ‘forest’ in the 2020 ESA Land Cover CCI Product 
(ESA, 2017). All units are in million hectares, except where indicated.
Ecological zone AfricaSouth 
AmericaNorth 
AmericaAsia Australia Oceania GlobalExisting 
forest1Existing 
forest (%)1
Tropical rainforest 399 659 48 323 3 13 1459 1140 78.2
Tropical moist forest 464 428 43 139 0 0 1077 509 47.3

Tropical dry forest 366 167 39 143 67 0 784 236 30.0
Tropical shrubland 595 11 0 116 85 0 808 60 7.4
Tropical desert 871 13 0 269 141 0 1296 6 0.4
Tropical mountain system 147 188 16 90 0 2 443 194 43.9
75% of taxa extinction found in Earth’s ‘big five’ mass extinction events 
(Barnosky et al., 2011; Díaz et al., 2019; Davison et al., 2021). Even 
though species–area relationships tend to overestimate extinction 
rates (He and Hubbell, 2011), there is evidence that species richness 
in tropical forests is alarmingly approaching or surpassing the taxa 
extinction value in this period (45% for dung beetles, 51% for lizards, 
65% for ants, and 80% for mammals) should deforestation and habitat 
loss continue at the current pace (Alroy, 2017; Ceballos et al., 2017). 
Moreover, there is reasonable understanding that these numbers are 
underestimated and, as such, tropical forest loss and degradation 
alone will precipitate a sixth mass extinction event (Giam, 2017). A

total of 13 out of the 25 global biodiversity hotspots for conservation 
are located in tropical forests, such as Brazil’s Atlantic Forest and 
India’s Western Ghats/Sri Lanka (Myers et al., 2000). While forest loss 
and degradation have been the main cause of tropical biodiversity loss 
in the past, climate change now arises as a major threat not only for 
individual tropical forest species or taxa—as already observed for frogs (Pounds et al., 2006)—but for whole communities (Esquivel-Muelbert 
et al., 2019), and even entire tropical forest ecoregions (Lapola et al., 
2018).
CCP7.2.2 Rates of Deforestation, Tropical Reforestation 
and Connections to Climate Resilience of 
Tropical Forests
More than 420 million ha of forest were lost globally in the 1990–
2020 period because of deforestation, and more than 90% of that loss 
took place in tropical areas (FAO, 2020). For the 2015–2020 period, 
the tropical deforestation rate decreased compared with 2010–2015,

being estimated at 10.2 Mha yr−1 (FAO, 2020). But reforestation and 
afforestation rates have also decreased, resulting in a tropical forests 
net loss rate of 7.3 Mha yr−1 in the 2015–2020 period. Overall, the net 
loss rate has slightly decreased (−4%) since 1990 (high confidence).

CCP7
2375Tropical Forests  Cross-Chapter Paper 7
However, a particularly high upward trend is observed in Central 
America and the Caribbean, while a small increase (2%) is observed 
in the tropical zone of Africa, during the periods from 2010–2015 to 
2015–2020 (Table CPP7.2).
CCP7.2.3 Drivers of Deforestation and Forest 
Degradation
Deforestation and forest degradation both affect carbon stocks, 
biodiversity loss and the provision of ecosystem services, leading to 
a reduction in resilience to climate change and exacerbating forest 
landscape vulnerability even in the absence of direct anthropogenic 
action (high confidence) (Barlow et  al., 2016; Aleixo et  al., 2019; 
Feng et  al., 2021; Saatchi et  al., 2021). There is also clear evidence 
of deforestation influencing temperatures and the hydrological cycle 
at local to regional scales resulting in reduced precipitation and 
evaporation and increased runoff relative to unaffected areas (high

confidence) [CCP7.3.6] (Jia et al., 2019; Douville et al., 2021). Negative 
trends in biodiversity and ecosystems are predicted to undermine 
80% of the Sustainable Development Goals targets related to poverty, 
hunger, health, water, cities, climate, oceans and land (IPBES, 2019). 
Therefore, besides greenhouse gas (GHG) mitigation, reducing the 
driving forces leading to deforestation and forest degradation is of 
the utmost importance for forest resilience, biodiversity protection, 
avoiding regional climatic changes and the provision of critical 
ecosystem services, and communities whose livelihoods depend on 
forests (high confidence) (Curtis et  al., 2018; IPBES, 2019; Jia et  al., 
2019; Seymour and Harris, 2019; Pörtner et al., 2021; Saatchi et al., 
2021).
Drivers of deforestation and forest degradation can be distinguished 
between proximate (i.e., direct) and underlying (i.e., indirect). Direct 
drivers, such as agriculture (including crops, livestock and plantation

forestry), infrastructure development (which often provides access to 
intact forests and catalyses deforestation) or timber extraction, are 
place-based and visible. They are influenced by underlying driving 
forces, such as demographic, economic, technological, political and institutional, or cultural factors, which typically form complex 
interactions and act at multiple scales, frequently without any direct 
connection to the areas of forest loss (Geist and Lambin, 2002).
Agriculture is by far the largest direct driver of tropical deforestation, 
with great differences between commercial and subsistence farming 
and large variation across regions (Figure  CCP7.3). Over 80% of 
tropical deforestation between 2000 and 2010 was caused by 
agriculture, proportionally ranging from ca. 75% in Africa and Asia to 
ca. 95% in the Americas (FAO and UNEP , 2020), but both the scale of 
deforestation and the relative contribution of different drivers have

changed considerably over time (high confidence) (Hosonuma et al., 
2012; Curtis et al., 2018; Seymour and Harris, 2019; FAO and UNEP , 
2020).
Forest degradation is more difficult to track, but can have large 
negative effects on carbon storage, provision of ecosystem services, 
and biodiversity (Griscom et al., 2017; Houghton and Nassikas, 2017). 
A recent analysis suggests that forest degradation is increasing and is 
now surpassing deforestation rates in the Brazilian Amazon (Aparecido 
Trondoli Matricardi et al., 2020). As with deforestation, drivers of forest 
degradation differ by region, such that timber extraction was by far the 
most important degradation driver in Latin America and Asia, whereas 
in Africa wood fuel consumption contributed to about half of forest 
degradation between 2000 and 2010 (Hosonuma et al., 2012).
Though not as visible as direct drivers, indirect or underlying causes 
can greatly influence direct drivers, and must be addressed to reduce

pressures on forests (high confidence) (e.g., FAO, 2016b; Fehlenberg 
et  al., 2017; Pendrill et  al., 2019b; Bos et  al., 2020; Junquera et  al., 
2020; Ken et  al., 2020; Kissinger, 2020; Siqueira-Gay et  al., 2020; 
Hoang and Kanemoto, 2021). Next to population growth, poverty and 
insecure land tenure (Ariti et  al., 2015; Arevalo, 2016; FAO, 2016a; 
Ken et al., 2020; Siqueira-Gay et al., 2020; Verma et al., 2021), many 
developing tropical countries identify weak forest sector governance 
and institutions, lack of cross-sectoral coordination, and illegal activity 
(related to weak enforcement) as critical underlying drivers (FAO, 
2016a; Ken et al., 2020; Kissinger, 2020) [CCP7.6].Table CCP7.2 |  Trends in net tropical forest loss, reforestation and expansion rates (1000 ha yr−1) from 2010–2015 and 2015–2020 periods by regions.
Net loss rate Reforestation rate Forest expansion rate
Region 2010–2015 2015–2020Observed
Trend2010–2015 2015–2020Observed

Trend
Africa 3911.37 3982.97 406.82 297.55 442.89 390.47
Asia and Oceania 1083.02 780.49 627.46 582.06 1227.15 1130.38
Central America 
and Caribbean59.4 122.45 51.36 44.51 104.74 41.34
South
America2663.96 2498.65 1081.9 846.24 447.88 297.19
Total 7717.76 7384.57 2167.49 1770.36 2222.66 1859.38
Trend direction Magnitude of trend (%)
Increase Decrease 0–25 25–50 >50
Details on the Table CCP7.2 elaboration are provided in the Supplementary Material (SMCCP7.1)

CCP7
2376Cross-Chapter Paper 7 Tropical Forests
International and market forces, particularly commodity markets and, 
increasingly, large-scale land acquisitions are also key underlying 
drivers (high confidence) (Assunção et al., 2015; Henders et al., 2015; 
Conigliani et al., 2018; Ingalls et al., 2018; Garrett et al., 2019; Pendrill 
et al., 2019b; Kissinger, 2020; Neef, 2020; Hoang and Kanemoto, 2021) 
[WGII Chapter 5.13]. Deforestation related to commodity imports is 
increasing, illustrating the growing influence of global markets in 
deforestation dynamics (Henders et al., 2015). Although some of this 
production is consumed domestically, 29–39% of deforestation was 
driven by international trade, primarily from Europe, China, the Middle 
East and North America (Pendrill et al., 2019a). While many developed 
countries, as well as China and India, have achieved net domestic 
forest gains, their consumption patterns have increased deforestation

embodied in their imports to varying degrees, frequently from 
biodiversity hotspots (Hoang and Kanemoto, 2021). Fifty percent of the 
biodiversity loss associated with consumption in developed economies 
occurs outside their territorial boundaries (Wilting et  al., 2017). The 
increasing prominence of medium- and large-scale clearings of forest 
between 2000 and 2012, particularly in Southeast Asia and South 
America, suggests the growing need for policy interventions targeting 
industrial-scale agricultural commodity producers (Austin et al., 2017). 
However, countries have been slow to address underlying drivers such 
as international demand for agricultural commodities. A review of 
43 countries’ REDD+ readiness documents found that proposed policy 
interventions largely missed the agricultural drivers identified (Salvini 
et al., 2014). An assessment of policy responses to rubber and coffee 
production highlights the challenges governments face in identifying

correlations between the direct drivers and related underlying drivers, 
with international drivers being the most challenging to address 
(Kissinger, 2020).
CCP7.3 Current and Projected Climate 
Change Impacts on Tropical Forests 
(Drought, Temperature, Extreme Events)
While early dynamic global vegetation models predicted biome shifts 
and contractions of tropical forests, more recent efforts have focused 
on biome changes at more regional scales, or on functional aspects 
of tropical forests, such as plant physiological and phenological 
changes, drought-related mortality, population dynamics, interspecies interactions and community responses, ecohydrology, risk of fire and 
related impacts, soil nutrient and microbe–plant interactions. Climate 
change is expected to increase temperatures across the tropics, with 
attendant variability in rainfall, and more extreme events such as 
intense storms, droughts and wildfires (Zelazowski et al., 2011; Malhi

et  al., 2014; Brando et  al., 2019). This could be expected to have 
structural and functional impacts on tropical forest biomes (Malhi 
et  al., 2014; Adams et  al., 2017). This section looks at responses of 
tropical trees and forests to current and future climate-change related 
pressures, focusing on physiological responses including growth, 
mortality and regeneration, fire risk and ecological vulnerability, as 
well as on climate effects of tropical forest loss.
CCP7.3.1 Tropical Tree Physiological Responses to 
With rising temperatures and atmospheric carbon dioxide, possibly 
accompanied by greater variability in soil moisture availability, a key 
question is how tropical forest trees respond physiologically (especially 
photosynthesis and respiration which determine net growth rates) and 
how well they can acclimate (i.e., able to adapt) to climate change 
(Dusenge et  al., 2019). Key climate factors influencing tree growth

on pan-tropical forests are precipitation, solar radiation, temperature 
amplitude and relative soil moisture (Wagner et al., 2014).
The temperature response of photosynthetic carbon uptake in tropical 
trees seems remarkably similar across moist and dry forest types, as 
well as for light-demanding, fast-growing species compared with 
shade-tolerant, slow-growing species (Slot and Winter, 2017). It is 
generally agreed that photosynthesis in tropical species can acclimate 
to moderate levels of warming but beyond this there would be no 
net gain in carbon (Slot and Winter, 2017). The factor that limits 
photosynthesis in different tropical forests will depend on water 
availability. In water-limited dry forests, photosynthesis may decline 
largely due to stomatal closure, while in wet forests the decline may 
largely be driven by warming-related changes to leaf biochemistry 
(Slot and Winter, 2017). A recent modelling approach suggests that

the limits of photosynthetic thermal acclimation may be an increase of 
about 2°C, in terms of maximum tolerated temperature, with enhanced 
tree mortality beyond this level of warming (Sterck et al., 2016).
Primary drivers of forest cover loss for the period 2001–2015
Forestry
WildfireUrbanization
Zero or minor lossCommodity driven deforestation
Shifting agriculture
Figure CCP7.3 |  Primary drivers of tropical forest cover loss for the period 2001–2015. Darker colour intensity indicates greater total quantity of forest cover loss. 
While some tropical forest cover loss is temporary, a large portion is related to deforestation. Source: Curtis et al. (2018). Cropped figure reprinted with permission from AAAS.

CCP7
2377Tropical Forests  Cross-Chapter Paper 7
A critical concern for plant function has been that higher temperatures 
will enhance respiration rates, potentially resulting in tropical forests 
becoming net carbon sources (rather than photosynthesis-driven 
carbon sinks) (Gatti et al., 2021). Some studies suggest that excessive 
respiration is less of a concern as respiration rates can acclimate 
to elevated temperatures over time (Lombardozzi et  al., 2015; Pau 
et  al., 2018). Thermal acclimation of respiration has been shown in 
a seasonally dry neotropical forest (Slot et  al., 2014), while models 
indicate that increases in plant respiration could halve by the end of 
the 21st century through acclimation, thereby partly ameliorating the 
potential release of carbon from tropical forests (Vanderwel et  al., 
2015). A contrary view is that plant physiological processes, such as 
the photosynthesis in tropical canopy trees, are already functioning

at levels close to or beyond their thermal optimum limits and that any 
further temperature increase would turn them from a sink into a carbon 
source (Mau et al., 2018). One of the most pressing questions regarding 
forest responses to increasing atmospheric CO 2 levels is whether trees 
experience enhanced growth rates as a result of the so-called CO 2 
fertilisation effect [Box 2.3 in IPCC 2019b]. Observed changes in the 
terrestrial carbon sink and process-based vegetation models indicate 
that tropical vegetation response to CO 2 fertilisation (Schimel et al., 
2015) is combined with other factors such as nitrogen deposition 
and length of the growing season, while aerosol-induced cooling 
may also have played a role in enhancing the carbon sink [Box 2.3 in 
IPCC 2019b]. Contrastingly, evidence for CO 2 fertilisation of growth 
in individual tropical tree species is generally lacking or controversial 
(Silva and Anand, 2013), or not as substantial as expected (Sampaio

et  al., 2021). It is, however, widely agreed that the intrinsic water-
use efficiency of a tree, that is, the amount of carbon assimilated as 
biomass per unit of water used, increases under elevated atmospheric 
CO 2 levels owing to the regulation of stomata (cells on the leaf surface 
which regulate the exchange of water and gases between the plant 
and the atmosphere) (Van Der Sleen et al., 2015; Bartlett et al., 2016; 
Rahman and Alam, 2016; Keeling et  al., 2017). Tropical dry forests 
(ca. 1000 mm annual rainfall) exhibit changes in water-use efficiency 
(WUE), relative to CO 2, at least twice as much as tropical moist forests 
(c. 4000 mm rainfall) (Adams et al., 2019).
Other key components in the forest system are plant–microbe–soil 
nutrient interactions, which play major roles in carbon cycling and 
plant photosynthetic response to increased atmospheric CO 2 and 
warming (Zhang et al., 2014; Singh and Singh, 2015; Du et al., 2019).

Phosphorus is generally a limiting factor in tropical forest soils, though 
this may be species-specific (Ellsworth et  al., 2017; Turner et  al., 
2018). Mycorrhizal fungi (both arbuscular and ectomycorrhizal) play 
major roles in water acquisition of host plant and their responses to 
drought in dry tropical forest (Lehto and Zwiazek, 2011) as well as in 
the capture and transfer of nutrients, especially nitrogen (which may 
otherwise become limiting), to host plants. Climate change factors can 
thus be expected to alter the nature of soil–plant interactions with 
consequences for the species composition and biodiversity of tropical 
ecosystems (Pugnaire et al., 2019; Terrer et al., 2019)CCP7.3.2 Climate-Related Mortality and Regeneration in 
Drought-related mortality of tropical trees shows complex patterns 
which could change forest community structure and composition 
with cascading effects on biodiversity (McDowell et al., 2020). During

drought, the mortality rate is enhanced in larger-sized trees in tropical 
forests (as is the case with all forests globally), with significant impacts 
on forest structure, carbon storage and regional hydrology (Bennett 
et al., 2015). The mortality rate of neotropical moist forest trees appears 
to be consistently increasing since the 1980s (McDowell et al., 2020), 
with plant functional types such as softwood, pioneer and evergreen 
species suffering higher mortality during years of extreme drought 
(Aleixo et al., 2019). Large trees (>30 cm diameter at breast height 
(dbh)) in tropical dry forests have much lower mortality rates than 
those reported for tropical moist forests (Suresh et al., 2010). Contrary 
to expectation, during prolonged droughts in these dry forests, deeper-
rooted tree species are more likely to die than shallow-rooted ones, 
which are more adapted to changes in soil moisture content, because

of water depletion in the deepest unsaturated zone (Chitra-Tarak et al., 
2018).
Regeneration of tropical tree seedlings and their response to a 
changing climate is inadequately understood. Experimental work 
suggests that tropical moist forest tree seedlings and saplings can 
acclimate photosynthetically to moderate levels of warming and, 
unlike adults, may even exhibit increased growth rates (Cheesman 
and Winter, 2013; Slot and Winter, 2018). Some moist forest seedlings 
also show plasticity to recurrent drought episodes by enhancing their 
growth rates when favourable moisture conditions return, while others 
fail to respond (O’Brien et al., 2017). The nature of response also seems 
to be mediated by neighbourhood diversity, with greater plasticity in 
more diverse communities (O’Brien et al., 2017). Seedlings in tropical 
dry forests subject to burning show enhanced growth rates post-fire 
and within two years attain similar height of seedlings in unburnt

areas (Pulla et al., 2015), though the environmental drivers of seedling 
growth post-fire are not well understood (Bhadouria et al., 2017).
The net outcome of the population dynamics processes of growth, 
mortality and regeneration is change in species composition as a 
consequence of a changing climate. In the Amazon forests, dry habitat-
affiliated genera have become more abundant among the newly 
recruited trees, while the mortality of moist habitat-affiliated genera 
has increased in places where the dry season has intensified most, 
thus driving a slow shift towards a drier forest type (Esquivel-Muelbert 
et al., 2019). A similar multi-decadal shift in West-African forest species 
composition towards more dry-affiliated species as a response to long-
term drying has been recorded (Aguirre-Gutiérrez et al., 2020). While 
upward shifts in the tree line and in the range of individual tree species 
have been recorded at several temperate mountain regions, evidence

from the tropics is rare. A large-scale study from 200 plot inventories of 
>2000 tree species across a ~3000 m elevation gradient in the Andean 
tropics and sub-tropics has shown that the relative abundances of tree 
species from lower, warmer locations were increasing at these sites 
indicating that ‘thermophilisation of vegetation’ (increased domination 
of plant species from warmer locations) was indeed taking place as 
expected (Fadrique et al., 2018) [Section 2.5.4.2.1 in Chapter 2].

CCP7
2378Cross-Chapter Paper 7 Tropical Forests
CCP7.3.3 Fire Risks from Climate Change in Tropical 
Forests
Temperature rise and prolonged droughts increase the danger of fires 
in drained peatlands and tropical forests in Southeast Asia and the 
Amazon (da Silva et al., 2018; Pan et al., 2018; Sullivan Martin et al., 
2020), resulting in large carbon emissions, which reached 11.3 Tg 
CO 2 day−1 during September–October 2015 (Huijnen et  al., 2016; 
Yin et al., 2020) and changes in forest composition and biodiversity 
(Asner et al., 2000; Hoffmann et al., 2003) (high confidence). In many 
cases, tree mortality due to fire is poorly recorded in the literature, 
but the available data suggest that fire-induced mortality has 
increased in recent years (Figure CCP7.2) (Malhi et al., 2014; Brando 
et al., 2019) (high confidence). While large forest and peat fires used 
to be associated mainly with El Niño-Southern Oscillation (ENSO)

events, there is now evidence that tropical rainforests in Indonesia 
may experience higher fire danger from increased temperatures even 
during non-drought years due to high evaporation rates of fragmented 
forests (Fernandes et al., 2017; McAlpine et al., 2018). The droughts of 
2007 and 2010 in the Amazonian region caused 12% and 5% of the 
southeastern Amazon forests to burn, respectively, as compared with 
<1% of these forests burning during non-drought years (Brando et al., 
2014; da Silva Júnior et al., 2019; Pontes-Lopes et al., 2021). Moreover, 
degraded forests in Ghana are more vulnerable to fires during droughts 
(Dwomoh et al., 2019).Factors other than solely climate also interact in enhancing the 
danger of tropical forest fires. For instance, the extent of burned area 
of rainforests in Borneo has shown that subsurface hydrology, (i.e., 
hydrological drought), interacts with meteorological drought and,

hence, fires have become more intense in recent decades following the 
progressive desiccation of the island over the past century (Taufik et al., 
2017). Bornean forest fire risk also increased through the interaction 
of drought with land use conversion for logging, oil palm and tree 
plantations, and human settlements (Sloan et  al., 2017). Similarly, 
simulations of future fire risks in the Amazon show that extensive 
land use change under the RCP 8.5 scenario results in 4- to 28-fold 
enhanced area of forest burned by fire by 2080–2100, as compared 
with 1990–2010, whereas in an RCP 4.5 scenario, the area burned 
would be enhanced by 0.9- to 5.4-fold (Le Page et al., 2017).
CCP7.3.4 Current Climate Risks for Tropical Forests
Impacts of climate change on tropical forest cover seem to correlate 
with climatic zone. Natural selection of drought tolerant species 
is observed in tropical dry forests under a prolonged water deficit

environment (Stan and Sanchez-Azofeifa, 2019). Tropical montane 
forests are highly sensitive to warming and associated changes in 
cloud cover and moisture, with evidence that such forests are already 60–70 70–80 80–90 90–100 10–20 50–60 30–40 40–50 20–30
Overall mortality (%)< 0
Fire-induced tree mortality
Drought-induced tree mortlaity(a) (b) (c)Observed tree mortality
in tropical forests
during 2000–2019
(a) South America (c) Asia(b) Africa
Overlapping points are distributed within circles centered at the origin.
Figure CCP7.4 |  Documented instances of tree mortality in tropical moist forests due to fire (1992–2016) and drought (1982–2005). These occurrences were 
associated with anomalies in precipitation and temperature over the study period. Adapted from Brando et al. (2019).

CCP7
2379Tropical Forests  Cross-Chapter Paper 7
being impacted through ‘browning’ (loss of biomass) from increased 
warming since the 1990s (Krishnaswamy et al., 2014).
Besides higher temperatures, current climate risks also depend on 
regional responses to a variety of climate events. For example, tropical 
biomes across the three continents may respond differently to ENSO 
events in terms of carbon fluxes and balance. During the 2015–2016 
ENSO event, different processes were dominant for the carbon fluxes 
anomaly in the tropical regions. In Asian forests, this anomaly was 
primarily derived from enhanced fire occurrence, in African forests 
through increased ecosystem respiration (from higher temperatures), 
and in South American forests by ecophysiological effects, through the 
gross primary production (GPP) expressed as reduced carbon uptake 
(Liu et al., 2017; van Schaik et al., 2018). It has also been shown that

the probability of drought spells at the beginning and end of the rainy 
season is higher in the areas with the highest deforestation (Leite-Filho 
et al., 2019). Furthermore, it has been observed that Amazon rainforest 
resilience is being lost faster in regions with less rainfall and in parts 
of the rainforest that are closer to human activity (IPCC, 2014; Seiler 
et al., 2015) (CCP7.3.6). Conversely, it has been pointed out, on the 
basis of vegetation indices, that temperature has a greater influence 
on resilience than does precipitation, and tropical forests are more 
resilient to climate change when they are more diverse (Feng et al., 
2021) (CCP7.3.6).
Biomes such as seasonally dry tropical forests subject to higher 
variability in rainfall or other climatic factors may be more resilient 
to fire and drought (Pulla et al., 2015; Liu et al., 2017), though there 
could be changes in species distributions as a result of disturbances

(Allen et  al., 2017). A regime of long-term, high rainfall variability 
seems to be critical in determining the overall resilience of tropical 
forests and savannas to climate disturbances (Ciemer et  al., 2019), 
highlighting the heterogeneity of the tropical landscape to climate 
risk. Similarly, forest composition, nutrient limitations and biodiversity 
can influence forest resilience to disturbances. Recent evidence 
suggests that the degree of forest disturbance also affects the 
mechanisms through which biodiversity influences forest functioning 
(Schmitt et al., 2020). Neotropical secondary forests also showed high 
resilience by maintaining their biomass through high productivity and 
rates of recovery following major disturbances (Poorter et al., 2016). 
However, the possibility of tropical forests reaching ‘tipping points’ 
in their resilience and experiencing rapid die-off cannot be ruled out 
(Verbesselt et al., 2016).
CCP7.3.5 Projected Impacts of Climate Change on

Tropical Forest
Climate change projections indicate increased warming and changes 
in rainfall patterns in the tropical region as elsewhere globally (IPCC, 
2021, AR6 WGI). These would have impacts on carbon stocks (Mitchard, 
2018; Hubau et al., 2020), water availability (Tamoffo et al., 2019), and 
structure and diversity (Malhi et al., 2014; McDowell et al., 2020) in 
tropical forests, amplified by deforestation (CCP7.3.6).
Tropical forests are critical repositories of global carbon; living tropical 
trees are estimated to hold 200–300 Pg C or about one-third of the levels in the atmosphere (Mitchard, 2018). CMIP5 and CMIP6 Earth 
System Models (ESM) project an increasing future tropical carbon sink, 
which is particularly strong in the scenarios with more pronounced 
increases in atmospheric CO 2 concentration (Koch et  al., 2021). 
However, major uncertainties regarding the ecophysiological processes 
governing carbon turnover and tree mortality under a changing

climate (Hartmann et al., 2015; Pugh et al., 2020), and the ecosystem-
level responses of tropical forests to elevated atmospheric CO 2 (Körner, 
2009) explain the contrast between observational data and modelling 
results (Rammig and Lapola, 2021). Observational data show that 
structurally intact old-growth tropical forests have been net sinks of 
atmospheric carbon in recent decades, but there is evidence that the 
capacity of such intact tropical forests to build up carbon stock may be 
limited as biomass peaked during the 1990s and has since weakened 
by 30% in the Amazon since the 1990s (high confidence), mainly due 
to increased tree mortality and faster carbon turnover, and the African 
tropical forest sink following this trend since about 2010 (Hubau et al., 
2020; Gatti et al., 2021). From a peak pan-tropical (Amazonia, Africa 
and Southeast Asia) forest sink of 1.26 Pg C yr−1 during the 1990s, it 
is projected to decline to an uptake of only 0.29 Pg C yr−1, reaching

zero in the Amazon, during the 2030s (Hubau et al., 2020). This decline 
will possibly be driven by the reduced rates of forest carbon uptake 
from the weakening global CO 2 fertilisation effect mediated by limiting 
soil nutrient, and reduced water availability and higher temperatures 
during extreme droughts (Qie et al., 2017; Fleischer et al., 2019; Wang 
et al., 2020), reinforced by deforestation and forest degradation [IPCC 
SRCCL, 2019].
Offline (uncoupled) vegetation model simulations indicate that the 
extensive tropical and subtropical forests of the Americas could 
gradually transit towards a savanna-like vegetation, with the most 
pronounced shifts (of up to 600 km northward) from relatively 
stable forests to savanna-forest transitions occurring in the eastern 
Amazonian region (Huntingford et al., 2013; Anadon et al., 2014; Nobre  
et  al., 2016) depending largely on the yet uncertain strength of the 
CO 2 fertilisation effect and future dry season length, with important

feedbacks on the flux of moisture from the forest to the atmosphere 
(Zemp et  al., 2017). More limited simulations for Central American 
rainforests under RCP 4.5 and 8.5 also support a transition in some 
areas to lower biomass tropical dry forest and savanna-like vegetation 
(Lyra et  al., 2017). Such transitions from one biome type to another 
will cause major changes in forest structure, species compositions and 
overall biodiversity. Additionally, the difficulty of species to migrate 
through highly fragmented tropical forested regions (such as West 
Africa or South and Southeast Asia) and ‘non-analogue climates’, under 
a climate change scenario, poses extra pressure on tropical biodiversity 
to adapt and survive (Pörtner et al., 2021). Even in expansive tracts of 
forests, such as in the Amazon, climate change is expected to become 
more important than deforestation by 2050 in causing the loss of tree 
species (Gomes et al., 2019). Tropical mountain biodiversity hotspots

(e.g., Andes, Himalayas) are particularly vulnerable to species loss due 
to elevation range shifts (Sekercioglu et al., 2008). Under a 2°C increase 
scenario, a substantial reduction of tropical montane cloud forest in 
Kenya is estimated (Los et al., 2019).

CCP7
2380Cross-Chapter Paper 7 Tropical Forests
CCP7.3.6 Climate Responses to Tropical Deforestation 
and Links to Forest Resilience
Since AR5, there has been meaningful advancement in understanding 
the climate effects of deforestation and concomitant changes in forest 
ecosystem resilience. The IPCC Special Report on Climate Change and 
Land (Jia et al., 2019) and IPCC AR6 WGI (Douville et al., 2021) both 
describe significant climate-related changes resulting from tropical 
deforestation (high confidence).
Deforestation generally reduces rainfall and enhances temperatures 
and landscape dryness; effects that increase with the scale of forest 
loss, whereas reforestation and afforestation generally reverses these 
effects (high confidence) (Lawrence and Vandecar, 2015; Alkama and 
Cescatti, 2016; Khanna et al., 2017; Jia et al., 2019; Staal et al., 2020; 
Douville et  al., 2021; Hofmann et  al., 2021; Leite-Filho et  al., 2021).

There is also medium evidence from observations and modelling that 
deforestation enhances surface runoff (Douville et al., 2021). Whereas 
quantitative information is much more limited for other tropical regions, 
past deforestation in the Amazon has led to a small reduction in rainfall 
of −2.3% to −1.3%, shortening and delay of the wet season, and an 
estimated 4% increase in dryness (Leite-Filho et al., 2020; Staal et al., 
2020; Douville et al., 2021).
Modelling studies estimate that large-scale tropical deforestation will 
contribute to average warming of the deforested areas with +0.61 ± 
0.48°C and will lead to large changes in diurnal temperature ranges 
owing to a reduction of nocturnal cooling (medium confidence) (Jia 
et  al., 2019). Large-scale deforestation will also strongly decrease 
average regional precipitation and evapotranspiration and further 
delay the onset of the wet season, enhancing the chance of dry spells

and intensifying dry seasons, but the magnitude of the decline depends 
on the scale and type of land-cover change (high confidence) (Zemp 
et al., 2017; Jia et al., 2019; Douville et al., 2021; Gatti et al., 2021).
Continued forest landscape drying and fragmentation in connection 
with deforestation may also enhance surface flow variability (Farinosi 
et al., 2019; Souza et al., 2019) and will aggravate the risk of forest 
dieback (Zemp et  al., 2017), elevate forest flammability (Alencar 
et  al., 2015) and increase fire incidence (high confidence) (Aragão 
et al., 2018; Jia et al., 2019; Silveira et al., 2020; dos Reis et al., 2021), 
ultimately leading to savannisation of many tropical rainforests (Sales 
et  al., 2020). However, compositional heterogeneity and diversity of 
forest assemblages increases resilience against climate-enhanced 
forest degradation (Réjou-Méchain et al., 2021).
For the Amazon, deforestation (ca. 40% of the region) in combination

with climate change will raise the prospect of passing a tipping point 
leading to large-scale savannisation of the rainforest biome, but   
uncertainty remains whether this will take place in the 21st century 
(Nobre et al., 2016; Jia et al., 2019; Douville et al., 2021). However, 
considering that the Amazon has already lost ca. 20% of its forests 
(Nobre et al., 2016), crossing the tipping point may not only create 
savannas of the deforested parts but may also result in precipitation 
reductions of 40% in non-deforested parts of the western Amazon due 
to a breakdown of the South American monsoonal circulation and the 
subsequent western cascade of precipitation and evapotranspiration (Boers et al., 2017). Other effects of forest degradation include loss 
of ecosystem services, biodiversity, carbon storage and Indigenous 
culture (Watson et al., 2018; Strassburg et al., 2019; Gatti et al., 2021), 
as well as potentially reduced hydropower capacity and agricultural

production (Sumila et  al., 2017), and increases in tropical diseases 
(Husnina et al., 2019).
The dearth of data for tropical forest regions other than the Amazon 
makes assessments of deforestation-related changes in temperature, 
precipitation and streamflow difficult (high confidence), and hampers 
estimates of tropical forest ecosystem health, biodiversity loss and 
vulnerability to current and future climatic and other pressures (high 
confidence). There is, hence, a strong need for increased investment in 
relevant data and research to narrow the knowledge gaps (Davison 
et  al., 2021). Nonetheless, conclusions based on a newly developed 
tropical vulnerability index synthesising remotely sensed land use and 
climate information indicate that forests in the Americas are already 
reaching critical levels to multiple stressors, while forests in Asia reveal 
vulnerability primarily to land-use change and African forests still show

relative resilience to climate change (Saatchi et al., 2021).
CCP7.4 Social-Economical Vulnerabilities 
of Indigenous Peoples and Local 
Communities Living in Tropical Forests
Around 800 million people live in or in the immediate vicinity of tropical 
forests (Keenan, 2015). Short-term impacts of climate change on 
biodiversity will exacerbate the inequalities affecting those livelihoods 
which heavily rely on forests (Pörtner et al., 2021).
Livelihoods, gender, land-use change and dependency on forest 
resources for food, fuel, housing and other needs have been identified 
as key elements of vulnerability in Indigenous Peoples and rural 
communities in Africa and South America (high confidence) (Nkem 
et  al., 2013; Field et  al., 2014; Newton et  al., 2016; Pearse, 2017; 
IPBES, 2018; Pörtner et al., 2021). Socioeconomic vulnerability varies 
depending on the level of dependency of forest food consumption 
(Rowland et al., 2017), livelihood strategies and settlement patterns.

In Cameroon (Nkem et  al., 2013), nomadic hunter-gatherers and 
sedentary communities showed differences in their vulnerability, 
driven by their preferences in forest settlement locations for farming, 
hunting, fishing, gathering, trapping and maintaining livestock.
Increasing temperatures, extreme climatic events, drought and fire will 
affect the proportion and frequency of forest resources availability. In 
communities of tropical America, Asia and Africa, social vulnerability 
factors identified include: deforestation pressures for agriculture 
expansion to cope with climate-induced food shortages, conflicts 
over access to forest land as a result of uncontrolled fire induced by 
higher drought frequency and severity, the availability of wild game, 
the work capacity, and the time consumed in work and gender-based 
differences (Blaser et al, 2011; Bele et al., 2013; IPCC, 2014). Although 
the size and quality of harvest in crops and non-timber forest products

(NTFPs) will be affected, the literature reports the use of NTFPs, 
hunting and fishing is less sensitive to climate change, and relevant 
for household incomes (Bele et al., 2013; Djoudi et al., 2013; Newton

CCP7
2381Tropical Forests  Cross-Chapter Paper 7
et al., 2016; Onyekuru and Marchant, 2016). Data from tropical forests 
document the contribution of NTFPs to local livelihoods (Issaka, 
2018), with well-established NTFPs such as Brazil nut (Bertholletia 
excelsa), rattan (Calamus and Daemonorops species), rubber (Hevea 
species) and açai (Euterpe oleracea) showing promise for sustainable 
harvesting strategies which could reduce socioeconomic vulnerability 
(Blaser et al., 2021).
The decrease of tropical forest area due to land-use change will put 
additional pressures, threatening livelihood practices, traditional land 
arrangements and customary rights of forest-dependent communities, 
and impacting the Sustainable Development Goals (SDG) of Climate 
Action and Life on Land (Djoudi and Brockhaus, 2011; Tiani et  al., 
2015; Hurlbert et  al., 2019). Globalised trade relations, agricultural 
expansion, illegal activities and violent conflicts have been identified as

important non-climatic drivers of forest degradation (high confidence) 
(Barr and Sayer, 2012; Rist et  al., 2012; Shanley et  al., 2012; Ruiz-
Mallén et al., 2017; IPBES, 2018; IPBES et al., 2018). Globally, about 
70% of tropical forest areas occur outside protected areas. In Latin 
America and the Caribbean, Indigenous Peoples and local communities 
have predominant ownership of tropical forest lands, while in West 
and Central Africa and Asia, forested areas are largely state-owned 
with exacerbating problems of governance, inequity and conflict with 
customary land tenure systems (Blaser et al., , 2011).
Further research by experts and local stakeholders and Indigenous 
Peoples is required to design more accurate and comprehensive 
indicators (Huong et  al., 2019). Solid evidence shows important 
knowledge and experiences that Indigenous Peoples and local 
communities contribute to disaster risk reduction and management

(IPBES, 2018a). Recognising the land rights of Indigenous Peoples 
is among the most cost-effective actions to address climate and 
biodiversity risks according to FAO and FILAC (2021). In Indigenous 
Peoples’ forest lands in the Amazon basin, deforestation rates are up 
to 50% lower than in other forested areas (Ding et  al., 2016), and 
Indigenous management is correlated with reduced carbon emissions 
(Blackman and Veit, 2018). Indigenous authors and local authors have 
pointed out the role of traditional systems of governance, knowledge 
and belief systems in the resilience of Indigenous Peoples and rural 
communities in the Amazonian and Andean regions, by regulating seed 
access and the conservation of agrobiodiversity and tropical forest 
(Camico et  al., 2021; Mustonen et  al., 2021). In the Philippines, the 
traditional land use system Muyong promotes sustainable agroforestry 
management based on customary land laws (Camacho et al., 2016).

Participation of local stakeholders and the inclusion of a gender 
perspective contribute to prioritising resource allocation and the 
development of effective legal frameworks for adaptation (Shah et al., 
2013; Tiani et al., 2015; Ihalainen et al., 2017; Collantes et al., 2018). 
There is a need to combine quantitative and qualitative methods, and 
increase research efforts to integrated approaches; including multi-
scalar and interdisciplinary assessments of vulnerability (Djoudi et al., 
2013; Guidi et al., 2018; FAO and CIFOR, 2019).CCP7.5 Adaptation Options, Costs and Benefits
Ecological adaptation and other spontaneous responses to climate 
change are discussed in Settele et al. (2015) and [AR6 WGII_Chapter 
2] Here we consider the role of humans in managing the adaptation 
of tropical forests to climate change. The focus is on human-assisted 
adaptation options that help to maintain tropical forest ecosystems

and not on the use of forests to supply provisioning services, such as 
timber, which is covered in [AR6 WGII_Chapter 5]. Forest management 
and agroforestry are discussed, but only with regard to their role in 
contributing to the adaptation of tropical ecosystems now and in the 
future. Maintaining ecosystems has a range of co-benefits for humans, 
including through ‘ecosystem-based adaptation’. These are explored 
in [Box 1.3; Cross-Chapter Box NATURAL in Chapter 2, Box CCP7.2]. 
Although there are a number of potentially valuable response 
options, it is clear that certain hazards, such as heatwaves, may be 
impossible to manage at the forest community level and require long-
term interventions at the landscape scale. Similarly, it will be difficult 
for forest managers to adapt to indirect climate-related ecosystem 
disturbances such as loss of pollination agents, invasive species or 
pest and diseases outbreaks (Allen et al., 2010; Anderegg et al., 2020).

Equally important in adapting to increased pressure from climate 
change are efforts to minimise disturbance from non-climatic stress 
factors (e.g., overharvesting, pollution and land use change; Malhi 
et al., 2014; Keenan, 2015; Barlow et al., 2016; Pörtner et al., 2021). 
Under some emissions scenarios, projected climate change impacts are 
of such severity that no adaptation measure is likely to protect natural 
forest systems; for example, with warming of 4°C, some tropical 
forests are at risk of die-back from high temperature (Malhi et  al., 
2014; Settele et al., 2015; Trumbore et al., 2015).
Actions to protect the extent or reduce the disturbance pressure 
on forest systems contribute to the capacity of these systems to 
respond to climate change (increasing resistance and resilience) (high 
confidence) (Millar et  al., 2007; Schmitz et  al., 2015; Settele et  al., 
2015; Sakschewski et al., 2016; Hisano et al., 2018). Furthermore, if

implemented sufficiently well, efforts to manage and restore forests 
also improve the capacity of forest systems to respond to future climate 
stressors (increasing resilience and responsiveness). Table  CCP7.3 
gives an overview of adaptation strategies for tropical forests 
within the framework of protect, manage, restore (Sayer et al., 2003; 
Pörtner et al., 2021). In assessing the available adaptation options, it 
can be useful to distinguish between actions focused on protecting 
forest extent, managing biodiversity, managing ecosystem function 
or restoring ecosystem services (Seppälä, 2009), Figure  CCP7.5 and 
Table  CCP7.4 give a detailed assessment of the major adaptation 
options in this context. Beyond these specific interventions, and in 
several cases underpinning them, there is an increasing awareness that 
effective management and adaptation of tropical forests requires an 
appreciation of IK, LK and CBA for implementation to be meaningful;

CCP7
2382Cross-Chapter Paper 7 Tropical Forests
Box CCP7.1 | Indigenous Knowledge and Local Knowledge and Community-Based Adaptation
Purely scientific knowledge, albeit indispensable, is insufficient to address climate change. Indigenous knowledge systems, embedded in 
social and cultural structures, are integral to climate resilience and adaptation (high confidence) (Ajani, 2013; Tengö et al., 2014; Hiwasaki  
et al., 2015; Roue and Nakashima, 2018) [AR5 WGII Section 12.3.3 (Adger et al. 2014), AR5 WGII Section 20.4.2 (Denton et al. 2014), SRCCL 
Section 4.8.1 (Olsson et al. 2019), SRCCL Section 4.8.2 (Olsson et al. 2019), SR15 Section 4.3.5.5 (de Coninck et al. 2018) ]. knowledge 
and local knowledge (IK and LK) and community-based adaptation (CBA) have received increasing recognition across all sectors (high 
confidence) (Reid and Huq, 2014; Wright et al., 2014; Moste, 2015) [SRCCL Section 4.1.6 (Olsson et al. 2019), SRCCL Section 5.3.5 (Mbow

et al. 2019), SR15 Box 4.3 (de Coninck et al. 2018)] (Figure Box CCP7.1.1). Forest Indigenous knowledge (IK) is closely linked to traditional 
land-use practices and local governance (Roberts et al., 2009); it is embodied in art, rituals, food, agriculture and customary laws, among 
others (Hiwasaki et al., 2015; Camico et al., 2021). CBA is a community-led process based on its desires, priorities, knowledge and capacities 
which empowers people as central players in climate change adaptation (Reid et al., 2009) [SRCCL 5.3.5].
CBA is related with concepts such as community and adaptive collaborative forest management. These approaches acknowledge the 
importance of cultural and socioeconomic ties between communities and forests, along with community’s authority and responsibility for 
forest sustainable management (Ajani, 2013; Ellis et al., 2015; Torres et al., 2015).
Role of IK and LK and CBA for Climate Change Adaptation in Tropical Forests

Local forest and Indigenous forest management systems have developed over long time periods, generating social practices and 
institutions that have supported livelihoods and cultures for generations (high confidence) (Seppälä, 2009; Martin et al., 2010; Parrotta 
and Agnoletti, 2012; Camico et al., 2021). Archaeological evidence shows that humans have manipulated tropical forests for at least 
45,000 years (high confidence). Indigenous Peoples usually consider themselves as parts of socio-ecosystems, protecting the forest by 
maintaining healthy socio-ecological relationships and successfully adapting to environmental change (Speranza et al., 2010; Swiderska 
et al., 2011; Parrotta and Agnoletti, 2012; Uprety et al., 2012; Mistry et al., 2016; Roberts et al., 2017) [AR5 WGII Setion 12.3.2 (Adger 
et al. 2014)].
CBA ensures community engagement in bottom-up management and adaptation approaches (Simane and Zaitchik, 2014; Keenan,

2015). IK, LK and CBA can enhance adaptation in many ways, including through knowledge generation, ecosystem monitoring, climate 
forecasting, increased resilience and response to climate extremes and slow-onset events (Speranza et al., 2010) [AR5 WGII Section 12.3.3 
(Adger et al. 2014); SRCCL Section 4.8.2 (Olsson et al. 2019)] ] (Figure Box CCP7.1.1).
Integration of IK and LK Systems, CBA and Modern Scientific Systems
Several authors have highlighted the need to foster a respectful dialogue between Indigenous knowledge (IK) and local knowledge 
(LK) and modern science towards a holistic research model (high confidence) (Berkes, 2010; Ajani, 2013; Tengö et al., 2014; Roue and 
Nakashima, 2018) [AR5 WGII Section 12.3.3 (Adger et al. 2014) , AR5 WGII Section 14.2.2 (Noble et al. 2014)], but few ecological 
studies have attempted this integration (Keenan, 2015; Vadigi, 2016). Examples in tropical forest ecosystems include topics such as

monitoring climate impacts; local climates; seed, water and land management resilience-increasing practices; and climate threats to 
traditional agriculture (Parrotta and Agnoletti, 2012; Fernández-Llamazares et al., 2017; Camico et al., 2021; Mustonen et al., 2021). A 
growing number of methods are available to help this dialogue [SRCCL Section 7.5.1 (Hurlbert et al. 2019)] (Reid et al., 2009; Tengö et al., 
2014; Tengö et al., 2017; Roue and Nakashima, 2018) (Figure Box CCP7.1.1). While there is expanding interest among decision makers, 
researchers, Indigenous Peoples and civil society on IK and LK (Hiwasaki et al., 2015; Maillet and Ford, 2016), gaps remain regarding links 
between place-and-culture dimensions and adaptive capacities (Ford et al., 2016).
Enhancing Adaptive Capacity through IK and LK and CBA: Lessons Learned
Useful lessons can be drawn from experience to effectively incorporate IK, LK and CBA in adaptation strategies. A number of barriers

to adaptation have also been recognised (Figure Box CCP7.1.1). Considering that IK and LK is increasingly threatened by colonisation, 
acculturation, dispossession of land rights, and environmental and social change, among others [AR5 WGII Section 12.3.3 (Adger et al. 
2014); SR15Section 4.3.5 (de Coninck et  al. 2018)] Seppälä (2009) highlighted the importance of supporting community efforts to 
document, vitalise and protect it. It is essential to consider goals, identity and livelihood priorities of Indigenous Peoples and local 
communities, including those beyond natural resource management (Reid et al., 2009; Diamond and Ansharyani, 2018; Zavaleta et al., 
2018). Adaptation processes are more likely to be transformational when they are locally driven (medium confidence: medium evidence, 
high agreement) (Chung Tiam Fook, 2015; Chanza and De Wit, 2016). This requires adaptive institutional frameworks, capable of

navigating the complex dynamic of socio-ecosystems (medium confidence: medium evidence, high agreement) (Locatelli et al., 2008; 
Simane and Zaitchik, 2014) [AR5 WGII Section 12.3.2 (Adger et al. 2014), SR15 Section 5.3.1 (Roy et al. 2018)]. It is important to consider 
power relations and priority differences to avoid causing social disruption and inequality. ‘We need to keep asking: Who benefits? Who 
loses? Who is empowered? Who is disempowered?’ (Reid et al., 2009).

CCP7
2383Tropical Forests  Cross-Chapter Paper 7
Finally, vulnerability and adaptive capacity have a historical and geopolitical context, conditioned by value systems and development 
models. Forest management strategies must take into account the wider picture if they seek to be not just temporally effective (at best), 
but transformative and sustainable over time (high confidence) (Chung Tiam Fook, 2015; Chanza and De Wit, 2016).
Figure Box CCP7.1.1 |  Main obstacles and barriers reported for 
successful IK, LK and CBA approaches in adaptation strategies 
and programs for tropical forests.
(a) Obstacles and barriers ranked by the number of references in which 
they were identified (one reference can identify more than one barrier, so 
numbers of references by barrier are not additive).
(b) Distribution of cases studies according to approach (IK and LK, CBA or 
a combination of both). One reference can include one or more case studies.

See countries included by continent and references in the Supplementary 
Material (SMCCP7.2).Obstacles and barriers for successful inclusion of
Indigenous Knowledge (IK), Local 
Knowledge (LK) and Community-Based 
Adaptation (CBA) approachesinto Adaptation 
strategies and 
programs in tropical forests
13 12 11 10 9 8 7 6 5 4 3 2 1
Number of referencesResearch on the sustainability of forest management practices 
is insufficientLand tenure, access and/or utilization rights by local communities are 
unclear or inadequateEconomic or market incentives are counterproductiveCommunity is insufficiently aware of climate change consequences and 
coping alternativesCommunities’ interest, perceptions and traditional knowledge and practices 
are not recognized or integrated with climate scenarios and planning
Community participation is inadequateGovernment institutions are inefficient, corrupt or lack commitmentNon-climatic drivers of vulnerability (e.g., poverty), or relevant social

dimensions (e.g., gender, ethnicity and economic conditions of the 
communities), are not considered or insufficiently addressed
Resources, preparedness, training and/or technical support are not 
sufficient to deal with climate change impacts
Loss of IK and LK due to limited intergenerational transmissionPlanning lacks a social-ecological perspective, failing to consider 
ecological and social dimensions in an integrated manneri.
ii.
iv.iii.
v.
vi.
vii.
viii.
ix.
xi.x.Latin America
6 total references
29 total case studies
(b) Types of case studies per region:(a) Types of obstacles/barriers:
Combination of IK, LK 
and CBA approaches
(235 case studies )Combination 
of IK and LK
(10)CBA only
(4)Asia
12 total references
157 total case studiesAfrica
3 total references
49 total case studiesBox CCP7.1 (continued)

CCP7
2384Cross-Chapter Paper 7 Tropical Forests
CCP7.5.1  Adaptation Options at Different Scales
To retain functioning tropical forests, adaptation will need to take 
place across many scales, from individual stands to interconnected 
landscapes, and upwards to regional and global policy changes. From a 
global perspective, the most effective adaptation and mitigation option 
is to reduce and reverse the loss of area in tropical forest ecosystems 
(Alkama and Cescatti, 2016; Griscom et al., 2017). Maximising tropical 
forest extent has well-described benefits in mitigating CO 2 emissions 
and in the role of forests regulating global climate (high confidence) 
(Smith et  al., 2014). For nations with tropical forests, adaptation is 
largely achieved through sustainable management of forested areas, 
enforcing the land rights/land tenure of Indigenous Peoples, and 
through establishment of protected areas (Table  CCP7.4; Seppälä,

2009; Pörtner et al., 2021). Some of this is achieved through schemes 
incentivising landowners to retain tree cover for the express purpose of 
mitigating climate change impacts (e.g., PES- Payments for Ecosystem 
Services, REDD+ Reducing Emissions from Deforestation and Forest Degradation). For nations outside of the tropics, there is a need to 
regulate the global drivers of forest loss, such as the consumption 
of agricultural commodities and of non-sustainable forest products 
(including timber) (CCP7.3; Henders et  al., 2015 Nolte et  al., 2017, 
Pendrill et al., 2019).
At a landscape scale, increasing forest cover and maintaining 
biodiversity friendly land-use outside forests increases ecosystem 
resilience to climate change (and other disturbances) and allows for 
climate-driven species migration, for example, ‘protect’ in Table CCP7.3 
(Schmitz et al., 2015; Aguirre and Sukumar, 2016). Ensuring forested 
areas are large and/or interconnected including the use of specific

climate refugia and climate corridors is recommended for climate 
adaptation (high confidence) (Schmitz et al., 2015; Settele et al., 2015; 
Simmons et  al., 2018; Pörtner et  al., 2021). For habitats or species 
pushed to the edge of their range, area-based conservation needs to 
take account of the future climate space and facilitate movement of 
species through connectivity or assisted migration (Seppälä, 2009; 
Table CCP7.3 |  Overview of adaptation strategies for tropical forests. This table includes key policy frameworks and common management approaches with potential for adapting 
native forests to increased disturbance from climate hazards. Details on each management approach and the associated literature are given in Table CCP7.4: Costs and Benefits of 
Adaptation Options in Tropical Forests.

Strategy Expected contribution to climate adaptationProtectProtected Areas Maintaining forest extent builds resistance and resilience to climate change (Seppälä et al. 2009; Schmitz et al. 2015).
Area-based conservation / 
Climate refugiaWhere forests are under threat from progressive warming, protection of less disturbance prone areas (e.g., higher altitude stands) allows for 
migration and recolonisation improving the ability of the whole ecosystem to respond to climate change (Schmitz et al. 2015; Pörtner et al. 2021).
Buffer zonesMaintaining buffer zones around protected forests builds resistance and resilience to climate change and allows for adjustment of boundaries, 
under future conditions (Seppälä et al. 2009; Schmitz et al. 2015).
Avoid deforestationReducing loss of trees due to non-climate stressors, protects forest extent and builds resistance and resilience to climate change (Locatelli et al. 
2010; Smith et al. 2019).

Public education / awarenessPublicising the role of forests in supporting human society can reduce anthropogenic pressures on forested areas (Seppälä et al. 2009; Hagerman 
& Pelai, 2018).ManageVulnerability assessment and 
monitoring programsRecognising changes in climate and in disturbance regimes allows for other management interventions, such as area-based conservation and 
assisted migration, to be implemented (Schmitz et al. 2015; Hagerman & Pelai, 2018).
Adaptive management / 
climate servicesAdaptive management along with information on the changing climate can improve the capacity of forest managers to respond to climate 
change (Seppälä et al. 2009; Tanner-McAllister et al. 2017).
Strengthen land tenureStrong land tenure, e.g., for Indigenous Peoples, often leads to more sustainable management of forested areas, so building resistance and 
resilience to climate change (Porter-Bolland et al. 2012; Garnett et al. 2018).
Conserve biodiversity, promote

mixed standsWithin managed forests, using diverse planting stock and managing for biodiversity improves resilience to disturbances from future climate 
changes (Keenan, 2015; Pörtner et al. 2021).
Fire prevention and managementThe use of fire suppression, fire breaks, controlled burning and water table maintenance can build resistance to climate change driven wildfires, 
in both managed and natural systems (Stephens et al. 2013; Musri et al. 2020; Bowman et al. 2020).
Sustainable forest managementWithin managed forests, vegetation control to manage tree density and stand conditions can build resistance to climate driven disturbance such 
as fire (Seppälä et al. 2009; Pörtner et al. 2021).RestoreIncrease connectivityProviding connection corridors between forested areas builds resilience and helps the system response to climate change. This can include 
thermal corridors that allow for species migration under progressive climate change (Schmitz et al. 2015; Hagerman & Pelai, 2018).

Forest restoration / 
assisted natural regenerationForest restoration helps restore forest extent and connectivity, and can reduce edge pressure, improving resilience and the capacity to respond 
to future climate stressors. In some cases, assisted migration and the use of planting stock selected for tolerance to climate change may be 
appropriate (Locatelli et al. 2015a; Pörtner et al. 2021).
Agroforestry / trees on farmIn degraded areas, such as buffer zones and mosaic landscapes, planted trees can reduce resource pressure on intact forest, improve soil 
conservation, regulate temperature and water cycles, and increase resilience through ecological processes (Jose, 2009; Lasco et al. 2014).
Indigenous and Local knowledge 
of ecosystemsIncorporating Indigenous and Local knowledge can improve the ability to protect and sustainably manage forest systems so building resilience 
(Seppälä et al. 2009; Porter-Bolland et al. 2012).

CCP7
2385Tropical Forests Cr oss-Chapter Paper 7
Schmitz et  al., 2015; Pörtner et  al., 2021). Maintaining functioning 
forest ecosystems is vital due to biophysical, biological (biodiversity-
driven) and socioeconomic interactions that contribute to ecosystem resilience (Pielke Sr et  al., 2011; Malhi et  al., 2014; Lawrence and 
Vandecar, 2015; Alkama and Cescatti, 2016; Sakschewski et al., 2016). 
Protecting forested areas can be achieved through vertical integration of policies at national, subnational and local levels and effective stakeholder empowerment (Meijer, 2015). Community-based and ecosystem-based adaptation approaches provide an overall strategy to help achieve these goals [Cross-Chapter Box NATURAL in Chapter 2] (Locatelli et al., 2010; Cerullo and Edwards, 2019). In addition to

conservation of tropical forests, restoration and afforestation can beeffective climate adaptation measures (e.g., ‘restore’ in Table CCP7.3)(Arora and Montenegro, 2011; Perugini et  al., 2017). The technical 
requirements for such adaptation measures are similar to those required for forest landscape restoration (Mansourian and Vallauri, 2005; Mansourian et al., 2017; Shimamoto et al., 2018; Philipson et al., 
2020). Agricultural intensification has been proposed as one method to reduce pressure on remaining forested land, although the overall carbon impact of such approaches must be considered (Cross-Chapter Box 6 in SRCCL, Shukla et al., 2019; Cerri et al., 2018; Kubitza et al., 
2018).
At the forest community level, adaptation options aim to protect 
the forest microenvironment and retain biodiversity through forest management (e.g., ‘manage’ in Table  CCP7.3) (Keenan, 2015; Jactel 
Framework to assess adaptation response options in tropical forests
Actual and expected

impacts on forests
Forest-based management
strategies with potential to
enhance adaptation to
climate change impacts
Adaptation
response
optionsChanges in the frequency 
and severity of forest  disturbance Alteration of plant and animal distributionIncreased mortality due to
climate change (including fire)
Strategies to
maintain the
extent of forestsStrategies to
facilitate natural adaptation
of biological diversityStrategies to
maintain the vitality
of forest ecosystemsStrategies to restore
the productive capacity
of forest ecosystems
4  Restore ecological
     connectivity
5  Tree planting with
     adaptes species6   Recognize IPLCs     tenure rights
7   Fire prevention/management8   Sustainable management 9  Agroforestry/trees
     on farms
10 Assisted natural
     regeneration1   Establish protected areas
2   Avoided deforestation3   Set aside HCVA/HCSA
     in working lands
Likely combinations of
response options in a
hypothetical landscape
as determined by

forests and tree cover
and tenure regimes
Comparison of economic
costs, and benefit-costs
ratios acros adaptation
response optionsTenure regimesState Collective Private
DegratedManagedIntact
Forest and tree coverRestoreManageProtect
Approaches12
73
6
84
9
510
Cost
rangeBenefit-cost
ratioEconomic costs:
Benefit-cost ratio:
High (>5,000 USD/ha) High (>1.5)
Medium (1,000–5,000 USD/ha) Medium (1–1.5)
Low (>1,000 USD/ha) Low (>1)High
HighHigh9
LowLow
Medium8
LowLowLow7
Low
/
Low6
Medium
Low
Medium5
Low
/
Low4
///3
Medium
/
Medium2
///1
Low
/
Medium10
/ = no data
Figure CCP7.5 |  Framework to assess adaptation response options in tropical forests by adopting a landscape perspective as determined by types of forests

and tree cover across different tenure regimes. HCVA, high conservation value areas; HCSA, high carbon stock areas; IPLC, Indigenous Peoples and local communities. The information supporting this figure originates from an extensive literature review that is included in this section, Table CCP7.4. The assessment of confidence levels is based on the judgement of the authors based on the reviewed literature and follows IPBES guidelines.

CCP7
2386Cross-Chapter Paper 7 Tropical Forests
et al., 2017). In protected areas, this would typically involve reinforcing 
existing conservation objectives through adaptive management 
(Salafsky et al., 2001; Ellis et al., 2015; Tanner-McAllister et al., 2017; 
Hagerman and Pelai, 2018), including support for natural regeneration 
(Chazdon et al., 2016). It is also possible to improve forest cover and 
interconnectivity through restoration or afforestation. There are many 
technical guides to improve the implementation and success rate of 
such approaches (Table CCP7.4) (Lamb and Gilmour, 2003; Shimamoto 
et al., 2018; Strassburg et al., 2019) and funding support specifically 
aimed at climate change adaptation and mitigation (e.g., REDD+). 
In some instances, climate change can alter climate suitability to the 
extent that managers need to allow for a transition to a new habitat 
type (e.g., from tropical forest to savanna), adaptive management can

help recognise and facilitate these transitions (Seppälä, 2009; Schmitz 
et  al., 2015; Lapola et  al., 2018). Depending on local conditions, it 
will be necessary to adapt to specific stress factors that are likely to 
increase in prevalence or severity because of climate change, such as 
heatwaves, drought events and forest fires (Allen et al., 2010; Malhi 
et  al., 2014; Seidl et  al., 2017). Although it is typically not possible 
to link individual events or adaptation measures to climate change, 
the effectiveness of technical interventions has been illustrated in a 
broader forest management context. Table CCP7.4 assesses the costs 
and benefits of different adaptation options based on the available 
literature. However, it should be noted that there is lack of information 
on many potential adaptation interventions, especially in the context 
of tropical forests (Locatelli et al., 2010; Bele et al., 2015; Keenan, 2015;

Hagerman and Pelai, 2018). The sections below and Figure  CCP7.5 
offer a framework for optimising management of complex tropical 
forest ecosystems within a landscape context, through a range of 
interconnected adaptation options.
CCP7.5.2 Adaptation Response Options
Forests will be affected by several climate change impacts that 
will require forest management towards fulfilling four objectives: 
maintain forest area; facilitate biodiversity adaptation; maintain 
healthy functioning forest ecosystems; and restore ecosystem services 
(including productive capacity) (Seppälä, 2009), which complement 
the more conventional approaches to protect, manage and restore 
forests (Sayer et  al., 2003). This is dependent on location-specific 
conditions that are defined by the type of forest and land tenure 
regimes or dominant actors across forest landscapes. The analysis 
here proposes 10 adaptation responses that focus on the adaptation

potential of tropical forests to climate change and are linked to the 
management objectives identified (Figure  CCP7.5). Each response 
option (1–10) implies variable economic costs and benefits, influenced 
by location-specific conditions, including several important non-
monetised benefits. The figure suggests the most relevant situations 
in which the different response options hold greater potential to meet 
the forest management objectives for addressing expected climate 
change impacts.
This assessment considers the economic costs and benefits of 10 
response options in their contribution to adaptation of tropical forests 
to climate change impacts but also includes non-market costs that are 
more difficult to quantify (e.g., cultural values), which are borne by different stakeholders (Chan et al., 2016; Pascual et al., 2017). Similarly, 
benefits also include the social and environmental benefits that result 
from adaptation options over extended time horizons. Economic costs

and benefit–cost ratios suggest the short-term economic potential 
of different options, but responsibly designed adaptation measures 
involving a combination of different response options and embracing 
a long-time horizon have the potential to provide significant social and 
climate benefits over the coming 50 years or more.
CCP7.5.3 Costs
The cost of implementing adaptation options varies widely and will 
change based on the location, time horizon and who bears the cost. 
As a result, most existing estimates are offered in broad ranges that 
include only partial cost estimates. Here we group the adaptation 
costs into three categories: low- (<USD 1000 ha−1), medium- (between 
USD 1000 ha−1 and USD 5000 ha−1) and high-cost options (>USD 
5000 ha−1).
• Low-cost options are those estimated to cost less than USD 
1000 ha−1 and include recognition of tenure rights of Indigenous 
Peoples and local communities (Hatcher, 2009), restoring ecological

connectivity (Crossman and Bryan, 2009; Torrubia et al., 2014), fire 
prevention and management (Griscom et al., 2017; Arneth et al., 
2019), assisted natural regeneration (Cury and Carvalho, 2011; Lira 
et al., 2012; MMA, 2017; Silva and Nunes, 2017) and sustainable 
forest management (Boltz et al., 2001; Holmes et al., 2002; Pokorny 
and Steinbrenner, 2005; Medjibe and Putz, 2012; Singer, 2016).
• Medium-cost options are those estimated to cost between USD 
1000 and USD 5000 ha−1 and include estimates for tree planting 
(Rodrigues, 2009; Campos-Filho et  al., 2013; Silva and Nunes, 
2017; Nello et al., 2019) and avoided deforestation (Kindermann 
et al., 2008; Overmars et al., 2014; Smith et al., 2019).
• High-cost options are those estimated to cost more than USD 
5000 ha−1 and include actions associated with agroforestry 
systems, particularly the most biodiverse systems (Raes et  al., 
2017; Nello et al., 2019).
• Costs per hectare are either not available or vary too widely for

several options, including protected areas (Balmford and Whitten, 
2003; Bruner et  al., 2004) and high-value conservation areas in 
working lands (Naidoo and Adamowicz, 2006). Griscom et  al., 
(2017) provided recent estimated costs for many of the above 
adaptation options; in most cases, these costs are much lower than 
other estimates referenced here, which are particularly focused on 
tropical forest landscapes.
While economic costs constitute an important factor in determining 
the feasibility of options, there are other factors that have an important 
influence on the viability of the options including opportunity costs, 
transaction costs and social feasibility, which are not included in 
this analysis. For example, options such as recognition of rights for 
Indigenous Peoples and local communities can be a low-cost option 
but often face political opposition (RRI, 2021), including from some 
conservation organisations; fire prevention and management require

political coordination across multiple governance levels (Fonseca-
Morello et al., 2017); and sustainable forest management can be seen

CCP7
2387Tropical Forests  Cross-Chapter Paper 7
as a less attractive option when compared with other more profitable 
land uses (Köthke, 2014). Table  CCP7.4 offers a more detailed 
assessment of the costs included, along with a reference to the costs 
for society.
CCP7.5.4 Benefits
Estimates of economic benefits across options tend to vary greatly, 
largely based on the scale of operations, and the market and 
institutional contexts in which they are implemented. The longer-term 
non-monetary benefits tend to be larger than has been acknowledged 
in the past (Chan et  al., 2016; Pascual et  al., 2017; UNEP , 2021). 
The shorter-term horizon of the economic benefits of adaptation 
options suggest that benefit-cost ratios of investments are higher 
in more biodiverse agroforestry systems in comparison with simpler 
ones (Miccolis et  al., 2016), and agroforestry system benefits are 
comparatively higher compared with commercial tree planting

depending on the species (Table CCP7.4; Nello et al., 2019).
All the objectives here support not only a large number of local people 
in fulfilling their livelihoods, but often provide services to distant urban 
populations as well. The benefits differ according to which of the four 
forest landscape management objectives is prioritised (Table CCP7.4):
• Objectives that seek to maintain the extent of forests contribute 
to improved landscape continuity, persistence of species and 
metapopulations (including floral recruitment) (Nordén et  al., 
2014), maintaining hydrological cycles (Creed et  al., 2011) and 
avoiding surface temperature increases (Perugini et al., 2017). In 
many cases high conservation value areas (HCVAs) are based on 
the presence of threatened or endemic species or dense, carbon-
rich forest ecosystems (e.g., primary forest) (Jennings et al., 2003).
• Objectives that prioritise natural regeneration and adaptation of

biological diversity allow greater opportunity for climate refugia 
(Morelli et  al., 2017; Simmons et  al., 2018), provide increased 
dispersal opportunities for different species (Christie and Knowles, 
2015), increase flora and fauna diversity, and may provide small 
benefits in reducing warming (Arora and Montenegro, 2011).
• Objectives to maintain and enhance the quality and persistence 
of vital forest ecosystems contribute to securing the provision of 
habitat, maintain soil structure and fertility, and regulate water 
quantity and quality (Imai et al., 2009; Putz et al., 2012).
• Objectives that prioritise the restoration of ecological productivity 
of degraded forest ecosystems and landscapes contribute to 
increased biodiversity conservation, soil structure and fertility, 
nutrient cycling, water infiltration/water recharge, erosion control 
and climate regulation (Seppälä, 2009; Shimamoto et  al., 2018; 
Pörtner et al., 2021).

CCP7.5.5 Strategic Approaches to Combine Response 
Options
While adaptation costs and benefits of response options differ, their 
benefit-to-cost ratios are almost always positive, particularly in 
the longer term (Müller and Sukhdev, 2018; Chausson et  al., 2020; Seddon et al., 2020; Baste et al., 2021). However, implementation of 
adaptation actions can be economically unviable if the benefits accrue 
over longer periods of time because development banks apply much 
higher discount rates to low income countries than the standard 
rates (Watkiss, 2015). Achieving conditions that do not disincentivise 
against, and rather encourage investments in nature-based solutions 
to protect, sustainably manage or restore tropical forest landscapes 
is therefore critical to enhancing their implementation (UNEP , 2021).
In addition, implementation of response options should consider 
equity aspects to ensure that the costs and benefits of actions within a

landscape are equitably distributed among public institutions, private 
enterprise and civil society (Verdone, 2015). Strategic approaches to 
restoring ecosystems can increase conservation gains and reduce 
costs (Shimamoto et al., 2018; Strassburg et al., 2019). Cost-effective 
solutions that consider multiple costs and benefits need a ‘compromise 
solution’ between short- and long-term social and economic gains. 
Pursuing spatial allocations for adaptation options has the potential 
to deliver greater benefits at lower costs, therefore aligning aims for 
tropical forest adaptation, species conservation and climate mitigation 
targets with the interests of farmers under short and long time horizons 
(Beatty et al., 2018).
CCP7.6 Governance of Tropical Forests for 
Resilience and Adaptation to Climate 
Change
Deforestation and forest degradation in tropical forests has grown 
in prominence as priorities for environmental governance in the face

of climate change, given the large share of forest and land use GHG 
emissions in the national profiles of tropical forest countries (high 
confidence) (Butt et al., 2015; IPCC, 2019b). This is reflected in Parties’ 
Nationally Determined Contributions to the Paris Climate Agreement 
(UNFCCC, 2021). Significant investments in REDD+ readiness, improved 
forest monitoring, assessments of drivers of deforestation and forest 
degradation and related policy responses, and stakeholder engagement 
have occurred over the past decade in countries across Africa, Asia-
Pacific, and Latin America and the Caribbean (Hein et al., 2018; UN-
REDD Programme, 2018; World Bank, 2018). Fifty-three percent of 
countries use the highest-quality remote sensing data for forest 
monitoring and reporting, covering 93% of forest cover (Nesha et al., 
2021). However, improved monitoring has not yet translated into forest 
governance effectiveness. Since the New York Declaration on Forests

was endorsed in 2014, average annual humid tropical primary forest 
loss has accelerated by 44% (NYDF , 2019). Policy responses towards 
conservation and ecosystem resilience are found to be insufficient 
to stem the direct and indirect drivers of nature deterioration (high 
confidence) (IPBES, 2019). For governance measures to be effective, it 
is necessary to alter the direct and underlying drivers that are leading 
to forest destruction or impeding the implementation of sustainable 
forest management practices and actions to restore degraded forests 
(high confidence) (Section CCP7.2.3; Section CCP7.5; UNFCCC, 2013).
Private sector commitments to reduce deforestation impacts in their 
commodity supply chains are growing, but evidence of impact is 
slim and inconclusive (Garrett et al., 2019; NYDF , 2019). Half of the

CCP7
2388Cross-Chapter Paper 7 Tropical Forests
occurs outside their territorial boundaries (Wilting et al., 2017), and 
trends in international trade in land-based production systems are 
increasing, with greatest impacts on tropical forests (Nyström et al., 
2019; Hoang and Kanemoto, 2021). In addition, in some cases the 
impacts of financialisation (e.g., correlation of commodity prices with 
stock market dynamics rather than pure demand) are found to be larger 
than those related to timber and agricultural commodity production 
dynamics (Girardi, 2015; Ouyang and Zhang, 2020; cross-reference to 
Chapter 5.13). Such factors present challenges for governance and 
policy responses.
The complexity of tackling drivers of forest loss and degradation will 
increase as climate impacts on forests and ecosystems intensify in 
the context of incomplete information and limited understanding of

risks (Helbing, 2013; Hughes et al., 2013; Springmann et al., 2018; Tu 
et al., 2019), necessitating novel approaches to forest governance for 
resilience (Keenan, 2015; Spathelf et al., 2018). Therefore, governance, 
defined as efforts that seek to influence the relationship between 
existing social processes and governance arrangements by using 
regulatory processes, mechanisms and organisations (Agrawal et al., 
2018), is a crucial process to convene stakeholders for decisions (FAO, 
2018a).
This section describes seven levers that support transformative 
environmental governance towards resilience of tropical forests by 
tackling the underlying indirect drivers, offering policy solutions and 
governance challenges and opportunities. The first five build on IPBES 
(2019), whereas the remaining two are drawn from the governance 
literature as highly relevant variables specific to the tropical forest 
context owing to their prominence in the international frameworks

developed over the past 10  years (Table  CCP7.5). Monitoring and 
finance are embedded in multiple levers. The levers include:
i) Developing incentives and increased capacity for environmental 
responsibility (particularly in relation to global targets such as 
the SDGs, Aichi Biodiversity Targets and the Paris Agreement) and 
discontinuing harmful subsidies and disincentives;
ii) Reforming sectoral and segmented decision-making to promote 
integration across sectors and jurisdictions to mainstream 
environmental objectives across institutions, within and among all 
relevant sectors;
iii) Pursuing pre-emptive and precautionary actions in regulatory and 
management institutions and businesses to avoid, mitigate and 
remedy the deterioration of nature and monitor outcomes;
iv) Managing for resilient social and ecological systems in the face of 
uncertainty and complexity;
v) Strengthening environmental laws and policies and their

implementation, and the rule of law more generally (Pörtner et al., 
2021);
vi) Acknowledging land tenure and rights to recognise the need of 
bringing human rights considerations into the climate change 
regime; and
vii) Enhancing inclusive stakeholder participation to ensure effective, 
efficient and equitable outcomes (Pasgaard et al., 2016).While the first five levers are relevant to environmental governance 
more broadly, the exploration of these levers in Table CCP7.5 is more 
specific to governance for forest resilience, drawing upon insights 
related to each transformation lever. Next to the governance solutions 
being implemented currently, indications of future challenges/
opportunities related to resilience in tropical forests are explored 
based on examples from the recent literature.

CCP7
2389Tropical Forests  Cross-Chapter Paper 7Table CCP7.4 |  Costs and benefits of adaptation options in tropical forests.
Climate 
change impactAdaptation 
measuresExpected contribution to adaptationContext/location 
of implementationEconomic costs Costs to society Benefits for forest ecosystemsBenefits/impacts 
to people
1. Forest management strategies to maintain the extent of forests
Changes in the 
frequency and 
severity of forest 
disturbanceAvoid 
deforestationForests counteract wind-driven degradation of 
soils, and contribute to soil erosion protection 
and soil fertility enhancement for agricultural 
resilience (Locatelli et al., 2015a). The impact 
of reduced deforestation may be higher when 
the large biophysical impacts on the water 
cycle (and thus drought) are taken into account 
(e.g., Alkama and Cescatti, 2016). Reducing 
deforestation and habitat alteration contribute 
to limiting infectious diseases (e.g., malaria)

(Karjalainen et al., 2010). Avoiding deforestation 
contributes to climate change mitigation due to 
reduced carbon emissions (Smith et al., 2019).In private lands 
(individual and 
collective) and in 
state lands, in areas 
with larger presence 
of intact forests or 
mosaic agriculture 
and forest lands 
under management.500–2600 USD ha−1 
(Kindermann et al., 
2008; Overmars et al., 
2014; Smith et al., 
2019).(1)
20–200 USD ha−1 
(Griscom et al., 2017; 
Arneth et al., 2019) 
(global estimate). –Opportunity costs associated 
with different alternatively 
productive land uses 
(Kindermann et al., 2008). –Landscape continuity, persistence of 
species and metapopulations (including 
floral recruitment) (Nordén et al., 2014).
 –Maintained hydrology (Creed et al., 
2011) and flood mitigation.
 –Avoided surface temperature increases 
(Perugini et al., 2017).
 –Protects other regulatory functions of 
forests, with positive impacts on human 
health.Potential to affect the lives

of 1–25 million people 
globally (low confidence) 
(Keenan, 2015; CRED, 2015; 
Smith et al., 2019).
Protect and/
or increase 
the size and 
number of 
protected 
areas, 
especially in 
‘high-value’ 
areasProtected areas play a key role for improving 
adaptation (Lopoukhine et al., 2012; Watson 
et al., 2014), through reducing water flow, 
stabilising rock movements, creating physical 
barriers to coastal erosion, improving resistance 
to fires and buffering storm damages. Primary 
forests sustain tropical biodiversity (Gibson et al., 
2011); thus, protecting intact forests preserves 
current patterns of biodiversity (Schmitz et al., 
2015).(2)Mainly established 
in state lands where 
there is dominance of 
intact forests, in some 
cases overlapping 
with Indigenous 
territories.Costs include recurrent 
management costs, 
system wide costs, 
and establishment 
costs. The cost per 
ha decreases with 
increased area 
(Balmford and Whitten, 
2003; Bruner et al.,

2004). –Potential land use and 
tenure conflicts over 
protected area expansion.
 –‘High value’ areas are often 
priority areas for human 
activity (e.g., lowlands) 
(Venter et al., 2014).
 –Management costs (Bruner 
et al., 2004). –May create additional dispersal 
corridors and support metapopulations 
for forest species increasing ecosystem 
resilience (Nordén et al., 2014).
 –Improved hydrology (Creed et al., 2011).
 –Protected areas contribute to income 
generation through tourism (Snyman 
and Bricker, 2019).Empirical studies of 
protected areas that use 
impact evaluation methods, 
provide evidence that parks 
help increase household 
incomes (Mullan et al., 
2009), poverty alleviation 
and environmental 
sustainability (Andam et al., 
2010).
Set aside 
high value 
conservation 
areas (HVCA) 
and high 
carbon stock 
areas (HCSA) 
in working 
landsSetting aside HCVA and HCSA within agriculture 
or tree-crop plantations has benefits for

preserving endemic species, and some ecological 
services (e.g., pollination services from insects) 
(Scriven et al., 2019).(3)Established in 
private intact and 
managed forest lands 
often allocated to 
mid- and large-scale 
plantations.Opportunity costs 
to landowners who 
would lose working 
land/productive 
area to HCVA or 
HCSA. Management 
costs (Naidoo and 
Adamowicz, 2006). –Opportunity costs to 
landowners who would lose 
working land/productive 
area to HCVA or HCSA.
 –Management costs (Naidoo 
and Adamowicz, 2006). –In many cases HCVA are based on the 
presence of threatened or endemic 
species or dense, carbon-rich forest 
ecosystems (e.g., primary forest) 
(Jennings et al., 2003).HCVA also provide 
ecosystem services, and 
therefore can contain 
valuable economic benefits; 
forests provide for some 
basic needs of local 
communities (health and 
subsistence) as well as 
traditional/cultural identity 
(Seppälä, 2009; Karjalainen 
et al., 2010).

2. Forest management strategies to facilitate adaptation of biological diversity
Alteration of 
plant and animal 
distributionRestore 
ecological 
connectivity 
through the 
establishment 
of corridorsConserve biodiversity by enabling natural 
migration of species to areas with more suitable 
climates (Malcolm et al., 2002), maintaining 
connectedness, especially between various 
protected areas, and ensuring that different 
stages of forest development are present 
(Seppälä, 2009). Building corridors creates 
landscape permeability for plant and animal 
movement (Schmitz et al., 2015).Corridors are 
implemented in 
managed lands across 
state, collective 
and private tenure 
regimes circumscribed 
to specific project 
targeted areas.60–1294 USD ha−1 (in 
USD 2019) (Crossman 
and Bryan, 2009; 
Torrubia et al., 2014) –Land use opportunity costs, 
financial costs of land 
acquisition and restoration 
(Naidoo and Adamowicz, 
2006).
 –Research and pilot costs of

different corridor connection 
methods (Naidoo and 
Adamowicz, 2006). –Landscape connectivity allows greater 
opportunity for climate refugia (Morelli 
et al., 2017; Simmons et al., 2018) and 
the restoration of ecosystem patches 
of native forests can provide dispersal 
opportunities for different species using 
alternate successional stages (Christie 
and Knowles, 2015).
 –Improved hydrology.Ecosystem services could be 
enhanced (e.g., hydrological 
benefits, soil conservation, 
health, recreational and 
cultural benefits through 
establishment and 
restoration of green spaces).

CCP7
2390Cross-Chapter Paper 7 Tropical ForestsClimate 
to people
Mixed 
planting 
with native 
species tree 
planting, with 
consideration 
of intraspecific 
genetic 
diversity of 
seedlingsReforestation is an important climate change 
adaptation response option (Reyer et al., 2009; 
Locatelli et al., 2015b; Ellison et al., 2017), and 
can potentially help a large proportion of the 
global population to adapt to climate change 
and related natural disasters. Native tree 
planting aimed at increasing resilience should 
include planting genotypes tolerant of drought, 
insects and/or disease, as well as increasing the 
genetic diversity within species used for planting 
and recognising provenance. Tree planting 
should avoid conversion of natural ecosystems 
including grasslands and savannahs (Bond and

Zaloumis, 2016).Tree planting is 
degraded lands 
across different state, 
collective and private 
landsPlanting of seedlings 
978–3450 USD 
ha−1 (in USD 2019) 
(Chabaribery et al., 
2008; Rodrigues, 2009; 
Campos-Filho et al., 
2013; MMA, 2017; 
Silva and Nunes, 2017; 
Nello et al., 2019)
20–200 USD ha−1
(Arneth et al., 2019), 
for reforestation and 
forest restoration 
(Griscom et al., 2017) 
(global estimate). –Loss of water yield (at 
least on an annual average 
basis) due to increased 
evapotranspiration
Reforestation helps 
maintaining base flow during 
the dry season may reduce the 
amount of water available for 
people downstream (Ellison 
 –Research costs on 
genetic varieties and 
implementation. –Better water retention capacity; reduced 
risk of erosion and landslides.
 –Carbon gain.
 –Increases both flora and fauna 
biodiversity.
 –In cases of reforestation/afforestation, 
small benefits in reducing warming

are expected (Arora and Montenegro, 
2011).
 –Increased potential for adaptive 
evolutionary responses within 
populations to the varied effects of 
climate change (drought, disease, etc.) 
(Puettmann, 2014).Reforestation/afforestation 
has the potential to impact 
the lives of >25 million 
people globally (medium 
confidence) (Reyer et al., 
2009; CRED, 2015; Sonntag 
et al., 2016; Griscom et al., 
2017; Smith et al., 2019) 
(global estimate). No 
availability of information 
on differentiated impacts 
from reforestation and 
afforestation.
3. Forest management strategies to maintain the vitality of forest ecosystems
disturbanceRecognising 
the rights of 
Indigenous 
Peoples 
and local 
communitiesGranting tenure rights to Indigenous People has 
the potential to maintain the forest, and ensure 
provision of ecosystem services, thus supporting 
local strategies for adaptation to climate change

threats (Porter-Bolland et al., 2012).Recognising local 
tenure rights 
takes place in 
land belonging 
to Indigenous 
Peoples and local 
communities across 
all different forest and 
trees conditions.0.05–9.96 USD ha−1 
(Hatcher, 2009). 
Include the costs of 
mapping, delimitation, 
and titling. RRI, 
(2021) estimates the 
following costs: 5 USD 
ha−1 for large projects, 
22.5 USD
ha−1 for medium, 
sub-national projects, 
and 50 USD ha−1 for 
small
investments. –Costs to local populations 
for protecting forest lands, 
and opportunity costs for 
avoiding land conversion 
(Hajjar et al., 2016). –Landscape continuity, persistence of 
floral recruitment) (Nordén et al., 2014).Some estimates indicate 
that Indigenous People 
manage or have tenure 
rights over at least 
~38 million km2 (Garnett 
et al., 2018) (global 
estimate). Recognition 
of rights often translates 
into positive social and 
environmental benefits 
(RRI, 2021), yet they may

differ depending on local 
conditions.
Increased 
mortality due to 
climate stresses 
(including fire)Within produc-
tion forests, 
practice 
sustainable 
logging by 
embracing 
reduced-im-
pact logging 
(RIL) and other 
practices.Some production forests can retain most 
ecosystem functions and services, and a similar 
species richness of animals, insects and plants 
to that found in nearby old-growth forest 
but can be more susceptible to defaunation 
and fire (Edwards et al., 2014). Sustainable 
forest management plays a role in adaptation 
by ensuring that through long-term forest 
management the diversity of forests is 
maintained as well as benefits from forest 
resource use (Putz et al., 2012). Improved forest 
management positively impacts adaptation by 
limiting the negative effects associated with 
pollution (of air and fresh water), diseases, and 
exposure to extreme weather events and natural 
disasters, e.g., (Smith et al., 2014). (4)SFM is undertaken at

a large scale in public 
forests allocated as 
concessions, and 
at smaller scales 
in private and 
community forests 
lands.70–160 USD ha−1 
(Singer, 2016)
169–345 USD ha−1 (in 
USD 2019)
(Boltz et al., 2001; 
Holmes et al., 
2002; Pokorny and 
Steinbrenner, 2005; 
Medjibe and Putz, 
2012)
(global estimate). –The tendency of 
interventions is a (direct 
or indirect) reduction of 
diversity because the natural 
interest of the forest owner 
is to favour commercial 
species. –Secures the provision of species habitat.
 –Soil structure and fertility.
 –Regulates water quantity and quality.
 –Carbon storage (Imai et al., 2009).The benefits of sustainable 
forest management have 
the potential to affect the 
lives of >25 million people 
(CRED, 2015; Smith et al., 
2019) (global estimate).

CCP7
2391Tropical Forests  Cross-Chapter Paper 7Climate 
to people
Reduce the 
incidence 
of fire 
hazard and 
improve fire 
managementAs fire hazard increases in some forests with 
climate change, adaptation measures to reduce 
fire hazard will be needed (Seppälä, 2009).Fire prevention and 
management is 
practiced in private 
lands (individual and 
collective) and state 
lands across managed 
and intact forest 
lands.<20 USD ha−1
Griscom et al., 2017; 
(global estimate). –Costs of fuel management 
and prescribed burns. 
-Costs of implementing fire 
management plans with 
many groups of stakeholders 
(Stephens et al., 2013). –Avoids forest degradation and 
deforestation.
 –Prevents biodiversity loss and species 
loss.
 –Protects local livelihoods and cultural

values.>5.8 million people affected 
by wildfire globally; max. 
0.5 million deaths yr-1 by 
smoke globally (medium 
confidence) (Johnston et al., 
2012; Doerr and Santín, 
2016; Smith et al., 2019) 
(global estimate).
4. Forest management strategies to restore the productive capacity of forest ecosystems
Increased 
climate stressesAssisted 
natural 
regeneration 
in degraded 
forest 
landscapesForest landscape restoration positively affects the 
structure and function of degraded ecosystems 
(Shimamoto et al., 2018). Forest restoration may 
enhance connectivity between forest areas and 
help conserve biodiversity hotspots (Locatelli 
et al. , 2015a; Ellison et al., 2017; Dooley and 
Kartha, 2018). Forest restoration may improve 
ecosystem functionality and services, provide 
microclimatic regulation for people and crops, 
wood and fodder as safety nets, soil erosion 
protection and soil fertility enhancement 
(Locatelli et al., 2015a). Land restoration can

reduce future risks (e.g., by protecting against 
hazards) and current vulnerability (e.g., by 
diversifying livelihoods) (Pramova et al., 2019). 
Natural forest regeneration contributes to climate 
mitigation through carbon removals (Lewis et al., 
2019), and this would imply less need for climate 
adaptation.Tree regeneration 
takes place in more 
degraded lands across 
different types of 
tenure regimes in 
public, community 
and private lands.Assisted natural 
regeneration 180–980 
USD ha−1 (in USD 
2019) (Cury and 
Carvalho, 2011; Lira 
et al., 2012; MMA, 
2017; Silva and Nunes, 
2017). –Opportunity costs of 
alternative land uses.
 –Costs of maintaining 
regenerating landscapes 
(e.g., exclusion plots).
 –Costs of facilitated dispersal 
or seeding (Naidoo and 
Adamowicz, 2006). –Uses microclimatic changes from 
regeneration to create emergent 
landscape restoration from available 
and present species in soil seed banks 
or dispersive capacity of local habitat 
patches.

–Increases potential area and influence 
of forest ecosystems even into 
marginal matrix habitat (Chazdon and 
Guariguata, 2016).The benefits of regeneration 
of degraded landscapes 
have the potential to impact 
disturbanceExpand 
agroforestry 
systems (AFs) 
in buffer zones 
and mosaic 
landscapesAgroforestry reduces pressure on intact forests 
and can enhance ecosystem services at the 
landscape level (Jose, 2009). It can also help 
to increase resilience to pests and diseases 
through ecological processes (Miccolis et al., 
2016). Agroforestry can reduce vulnerability to 
hazards like wind and drought, particularly for 
subsistence farmers (Thorlakson and Neufeldt, 
2012).Agroforestry has a 
large potential in 
collective forest lands,

both managed and 
degraded.7150–22,575 USD ha−1 
(in USD 2019) (Raes 
et al., 2017; Nello 
et al., 2019). –Opportunity costs of other 
land uses.
 –Costs of engaging in 
markets and/or developing 
markets for agroforestry 
products.
 –Risks of market saturation 
and supply/demand 
inconsistencies (Torres et al., 
2010; Mercer et al., 2014). –Biodiversity (habitat, migratory 
corridors, gene flow).
 –Soil structure and fertility, nutrient 
cycling.
 –Water infiltration/water recharge, 
erosion control.
 –Buffer strips can reduce the resource 
pressures on native ecosystems by 
providing income and resources for 
people (Vieira et al., 2009).Potential to improve 
farmers’ livelihoods 
and quality of life of 
2300 million people globally 
(medium confidence) (Lasco 
et al., 2014; Smith et al., 
This table draws on Appendix 6.1–6.4 from Seppala et al. (2009), pp. 71–77

(1) Agricultural expansion is the major driver of deforestation in developing countries. Cost of reducing deforestation is based on opportunity cost of not growing the most common crop in developing countries (maize) for 6 years to reach tree 
maturity, with yield of 8 t ha−1 (high); 5 tons ha−1 (medium), and 1.5 t ha−1, with a price of USD 329 t−1. Also, reduced deforestation practices have relatively moderate costs, but they require transaction and administration costs (Kindermann 
et al., 2008; Overmars et al., 2014).
(2) May not deal with displacement of wild species due to climate change.
(3) Fragments of disconnected HCVAs have less value to preserve ecological services.
(4) Forest management strategies may decrease stand-level structural complexity and may make forest ecosystems more susceptive to natural disasters like wind throws, fires and diseases (Seidl et al., 2017).

CCP7
2392Cross-Chapter Paper 7  T ropical Forests
Table CCP7.5 |  Levers of transformative change to tackle the underlying indirect drivers of forest deterioration for resilience.
Levers of trans-
formative changeBarriersCurrent governance and policy solutions and potential future challenges and 
opportunities with an orientation towards resilience in tropical forests
1. Incentives and 
capacity-building– P
opulation growth and corruption counteract governance 
effects (Enrici and Hubacek, 2016; Busch and Ferretti-Gallon, 2020; Fischer et al., 2020)
– Macroeconomic development favoured over ecosystem 
service provision—environment ministries under resourced and politically weak compared with those for economic and natural resource development (UNEP
, 2019)
– T
hough food systems are the major driver, many 
interconnected food system activities and effects do not have established governance regimes to address them (Clapp and Scott, 2018)
– Reliance on non-state mark

et-based approaches (e.g., 
zero-net deforestation) has not achieved necessary impact against stated targets, reporting is lacking (Lambin et al., 2018; Global Canopy, 2019)
– F
inance for forest mitigation is less than 1.5% of total since 
2010 (NYDF Assessment Partners, 2019), and amount for forest adaptation is even less (Micale et al., 2018).Current policy solutions
– REDD+ and payments for ecosystem services (PES)
– Corporate supply chain commitments (WWF and BCG
, 2021)
– Product certification and forest certification have mixed results in addressing deforestation 
(Blackman
 et al., 2018; van der Ven et al., 2018)
– Agricultural credit restrictions (Assunção
 et al., 2020)
– Protected areas and area-based conserv
ation measures (OECMs) (Maxwell et al., 2020)
– Clear performance indicators and monitoring systems to assess performance (Agraw
al et al., 
2018).
Future policy challenges/opportunities
– P

olicies that insulate the forest frontier from the influence of high commodity prices (Busch 
and Ferretti-Gallon, 2020)
– Project-level biodiversity responses link
ed to broader jurisdictional biodiversity targets 
(Simmonds et al., 2020)
– Ecological fiscal transfers to base portions of intergovernmental fiscal transfers on ecological 
indicators (Busch
 et al., 2021)
– F
inancial disclosure on risks, divestiture, environment-related investment mandates 
(Halvorssen, 2021)
– Identification of means for the forest-based bioeconomy (wood fuel
, timber) to be sustained 
(Dieterle and Karsenty, 2020)
– Incentives tow
ards less emissions-intensive inputs in manufactured products, such as bamboo 
(van der Lugt et al., 2018)
– Reducing imports of embedded deforestation (role of land-use telecoupling) (Gardner  
et al., 
2019)
 – Supply chain traceability and public reporting (Gardner  
et al., 2019; Global Canopy, 2019).

2. Cross-sectoral cooperation – Inherent vertical and horizontal fragmentation of policy arena
 – Challenge of silos between ministries (Nilsson
 et al., 2016)
 – P
olicy integration has a stronger chance of reforming existing 
policies and competing sectors than coordination, but is challenged to overcome sectoral fragmentation and reach international actors and markets (Kissinger et al., 2021).Current policy solutions
 – P
olicy coordination and integration (Candel and Biesbroek, 2016)
 – Jurisdictional and landscape approaches in targeted regions and commodity sectors/supply 
chains (Reed
 et al., 2017; von Essen and Lambin, 2021).
 – T
heories of change applied and testing of policy effectiveness (Meehan  et al., 2019; Bager 
 – Whole-of-government approaches to change mandates across ministries
 – Mainstreaming climate change into sectoral policies (Di Gregorio  
 – P

olicy mixes implemented as a bundle, policy instrument selection attuned to complexity of 
the problem (Henstra, 2015; Head, 2018).
3. Pre-emptive action – Complexity of the issues for any specific level of jurisdiction 
to grapple with scale mismatches (temporal
, spatial and 
institutional) and institutional inertia (Bai et al., 2016)
 – Reliance on path dependency rather than innov
ation (Beland 
Lindahl et al., 2017; Peters et al., 2018; Wieczorek, 2018)
 – Agenda setting and framing influences political and policy 
responses (Soto Golcher
 et al., 2018)
 – Problem denial and blame avoidance on the part of decision 
mak
ers (Howlett and Kemmerling, 2017).Current policy solutions
 – GHG emission cap-and-trade systems and carbon pricing (Green,
 2021)
 – Moratoria.
 – Identifying thresholds of concern,
 when critical thresholds of fast-changing variables are

triggered, and nonlinear responses erode the resilience of ecosystems (such as in the case of changing forest fire regimes) (Gillson et al., 2019)
 – Reduce loss and w
aste of biomass
 – Change in consumption patterns
, sharing and reuse
 – Shareholder divestiture due to climate/forest and biodiversity risk (Halvorssen,
 2021).
4. Decision-making in the context of resilience and uncertainty – Scope of problem identification limited (Beland Lindahl  
et al., 
2017)
 – Increasingly complex and network
ed world increases risks, 
but reduces our ability to understand and manage these risks (Helbing, 2013; Tu et al., 2019).Current policy solutions
 – F
orecasting, scenarios of future climate and forest condition, socioeconomic dimensions, 
science-policy dialogue (Bele et al., 2015) and thresholds for ecosystem shifts due to mortality (tipping points) (Verbesselt et al., 2016).

– Interdisciplinary and transdisciplinary approaches to data gathering and policy design 
(K
eenan, 2015)
 – ‘Robust’ decision-making approaches for adaptive forest management (Hörl  
 – Maintain diversity and redundancy,
 manage connectivity, and slow variables and feedbacks 
(Biggs et al., 2012)
 – Measurement and disclosure of climate and ecosystem risk (NBIM,
 2021).

CCP7
2393Tropical Forests  Cross-Chapter Paper 7
Box CCP7.2 | Contribution of Sustainable Tropical Forest Management to the SDGs
There is increasing evidence of positive impacts of resilient tropical forests, biodiversity and sustainable forest management in achieving 
SDGs, as presented in Table Box CCP7.2.1. However, there is also risk of unintended consequences based on conflicts between the use 
of forest-based goods and services, and effects on tropical forest resilience, ecosystem services and biodiversity (Baumgartner, 2019). 
For instance, substitution of fossil fuels and non-renewable resources with bio-based products can lead to deforestation and the loss of 
biodiversity (Carrasco, 2017) (Cross-Working-Group Box BIOECONOMY in Chapter 5). Deforestation as a result of increased agricultural 
production and productivity could hamper efforts in addressing long-term food security, particularly for forest-dependent people (Newton

et al., 2016; Section CCP7.2.3). Synergies and trade-offs depend very much on local contexts and are therefore presented in exemplary form.
IFAD (2016) estimated that there are 640  million people living below the poverty line in rural areas of 43 tropical countries. Poor 
communities rely on ecosystem services for their subsistence livelihoods, and often they have limited capacity to adapt to change, making 
them more vulnerable to climate change and other forms of changes (Bhatta, 2015). Managing forests sustainably benefits both urban 
and rural communities, including provision of food and fibre, and on watershed hydrology and agroforestry production, among others 
(Powell et al., 2013; Dawson et al., 2014; Clark and Nicholas, 2013, Mbow et al., 2014) (Table Box CCP7.2.1).Levers of trans-
5. Environmental law

and implementation –69% of agricultural conversion of tropical forests likely illegal 
between 2013 and 2019 (Dummett et al., 2021)
 –90% of countries (of 31 assessed), identify weak forest sector 
governance and institutions, conflicting policies beyond the 
forest sector, and illegal activity as main underlying drivers 
(Kissinger et al., 2012); corruption and illegality are identified 
as key factors in increasing forest loss (Piabuo et al., 2021)
 –Implementation and enforcement of environmental laws falls 
far short; primary obstacle is political will (UNEP , 2019)
 –Conflicting legal instruments, lack of clarity in 
implementation, monitoring and evaluation, responsibilities 
are poorly defined and fragmented across multiple agencies 
(Ranabhat et al., 2018)
 –Lack of sanctions, transparency and accountability (Bai et al., 
2016; Enrici and Hubacek, 2016)
 –Open-ended decision-making exacerbates political 
asymmetries (Holley and Sofronova, 2017).Current policy solutions

–Environmental laws and regulations (Head, 2018)
 –Trained prosecutors
 –Citizen rights to information (Bizzo and Michener, 2017).
 –Capacity and willingness to engage iterative processes for continuous effort in transparency and 
accountability (in implementing the Extractive Industry Transparency Initiative) (Lujala, 2018)
 –Regulatory frameworks as enablers to motivate and hold private sector initiatives to account 
(test effectiveness) (Begemann et al., 2021)
 –Nested and multi-level governance arrangements (Ravikumar  et al., 2015)
 –Diagnosing the political drivers of decision making through political economy assessment 
(Fritz et al., 2014).
6. Land tenure/rights –Though recognition of Indigenous self-determination is 
growing, many cases of legal recognition still lack full 
authority to govern (UN-DESA, 2021)
 –Free, Prior Informed Consent (FPIC).Current policy solutions

–Legal and constitutional recognition of rights, collective/communal rights (Safitri, 2015; 
Blackman et al., 2017; Gebara, 2018)
 –Indigenous land demarcation (Baragwanath and Bayi, 2020)
 –Community-based forest management (Pelletier et al., 2016).
 –Forest protection/climate and biodiversity is strongest when indigenous people hold collective 
legal titles to their lands (IPCC, 2019b) (in Latin America, deforestation rates are about 50% 
lower in Indigenous territories than in other forested areas) (FAO and FILAC, 2021).
7. Participation and 
stakeholder inclusion –Governments increasingly rely on highly autonomous 
semi-public or private organisations for policy results which 
weakens control of the process (Howlett et al., 2015), yet 
mediating between diverse values and interests of citizens, 
consumers, business and community is a determinant of 
policy effectiveness (Peters et al., 2018)

–Growing legal restrictions on civil society involvement in 
governance and access to funding (UNEP , 2019)
 –Institutional practices of stakeholder consultation in REDD+ 
not well operationalised (criteria and transparency often 
lacking) (Fujisaki et al., 2016).Current policy solutions
 –Multi-stakeholder dialogue combined with moratoria (e.g., Brazilian soy moratorium) (Gibbs 
et al., 2015)
 –Community-based monitoring (Slough et al., 2021).
 –Collaborative networks (Thomas et al., 2018)
 –Re-evaluating agency, social structures and the distribution of power to uphold rights 
(Delabre et al.)
 –Community engagement correlated to secure rights to resources (Pham  et al., 2015).

CCP7
2394Cross-Chapter Paper 7 Tropical Forests
Table Box CCP7.2.1 |  Examples from sustainable tropical forest management (STFM) in achieving SDGs.
SDGs Contribution of STFM to the goals Adaptation interventions Supporting references
1 No povertyArea of forest land with legal property 
status held by communitiesIn Mexico, community forest management (CFM) has played a pivotal role 
in forest cover and biodiversity conservation in the region where timber 
production and processing generate income and thereby offers a way out of 
poverty for families in communities with rights to forests.(Ellis et al., 2015)
Improve incomes through selling forest 
products or by generating employment for 
the poorestNon-timber forest products (NTFPs) are a significant source for socioeconomic, 
employment and income generation, particularly for tribal people.(Kumar, 2015)
Improve income through valuation of 
ecosystem servicesIn Cambodia, contribution of forest resources should be integrated into

payment for ecosystem services schemes, to provide more diversified income 
streams, insulating Indigenous People from shocks and stressors.(Nhem, 2018)
2 Zero hungerForests also provide food, which improves 
food security and nutritionIn Cameroon, forest fruits provide important macro- and micronutrients lacking 
from the family diets of rural people. Association between tree cover and the 
dietary diversity of children in the communities of 21 countries across Africa.(Fungo et al., 2015; 
Ickowitz et al., 2014)
3 Good health 
and well-beingMedicinal plants contribute to emotional 
and spiritual well-beingMedicinal plants and the associated Bhutanese traditional medicine are 
protected by the country’s constitution and receive both government support 
and acceptance by the wider public. These medicinal plants have been one of the 
drivers of the ‘gross national happiness’ and biodiscovery projects in Bhutan.(Wangchuk and Tobgay, 
2015)
Health co-benefits of preserving

biodiversityIn the Brazilian Amazon, interventions targeted specifically at preserving 
biodiversity in protected areas generate health co-benefits. From the 
perspectives of malaria, acute respiratory infection and diarrhoea, results 
suggest that the public health benefits of strict partnership agreements may 
offset some of their local costs. Nature is doing its part by providing a form of 
(human) capital for the rural poor and the politically voiceless.(Bauch et al., 2015)
4 Quality 
educationInclusive education that builds and 
reinforces positive attitudes to forestEncouraging and enabling pro-forest behaviour as well as strengthening 
education systems that respect, nurture and enable Indigenous Knowledge and 
Local Knowledge.(Kanowski, 2019; Tengö, 
2017; Vaidyanathan, 2014)
The value of social capital for maintaining sustainability of community forest 
management includes, among others, individual characteristics, procedural

knowledge and access to information. Initiatives to manage natural resources are 
likely to be more successful if the forest management program initiators consider 
several factors that influence the capacity development of resource users.(Lee, 2017)
5 Gender 
equalityWithin genders, other characteristics such 
as class, race, caste, culture, wealth, age 
and ethnicity influence responses and 
affect the impact of climate variability and 
change on livelihoodsDespite challenges, Nepal’s community forestry policy is considered one of the 
most progressive, as it allows women to exercise equal rights with men in the 
management and utilisation of community forests. Furthermore, women-only 
forestry groups have registered many success stories.(Lama et al., 2017; Agarwal, 
2015)
6 Clean water 
and sanitationRegulate water supply, water quality and 
water purificationEvidence from the Hindu Kush Himalayas require improved upstream–

downstream integration, transboundary cooperation and greater coordination 
of implementation of different SDGs. Greater efforts are required to make the 
communities struggling on the frontline of sustainable forest management 
more climate resilient.(Scott C.A., 2019; Amezaga, 
2019)
Forest concessions can make a positive contribution to this by minimising the 
negative impacts of harvesting operations on water access and by employing 
appropriate restoration techniques as required by the concession contract and 
national legislation.(Bruggeman et al., 2015)
7 Affordable 
and clean 
energyEnergy transitionsDecreased reliance on traditional wood fuels and increased use of 
forest-derived modern fuels (e.g., biofuel) are generally synergistic with 
achieving other SDGs, such as livelihoods strategies. However, modern wood 
fuels need improved stoves to ensure the energy is clean.(Jagger, 2019; 
Simangunsong et al., 2017)
8 Decent work 
and economic

growthStimulating economic growth and 
minimising forest lossSynergy potentials exist where growth strategies and associated policies target 
the forest section with NTFPs from natural forests, ecotourism and payments for 
environmental services.(Stoian, 2019)Box CCP7.2 (continued)

CCP7
2395Tropical Forests  Cross-Chapter Paper 7
Community forestry enterprises have the potential to make significant 
contributions by providing a solid institutional framework to efficiently translate 
SDGs into actions. It also improves forest management, social cohesion and 
rural incomes among local communities in developing countries.(Aryal, 2020; 
Vázquez-Maguirre, 2020; 
Baynes, 2015)
9 Industry 
innovation and 
infrastructureIntegration of small-scale business into 
value chains and marketsStrategies in relation to sustainable supply chains and tropical forest protection, 
i.e., Unilever and Instituto Centro de Vida (ICV), demonstrate both alignment 
and variability between and within organisations. Associated incentives could 
help balance the burden of responsibility for implementation between global 
and local actors of promoting zero deforestation.(Delabre et al., 2020)
10 Reduced

inequalitiesReduction in the number of poor 
households
Protect the workers and communities 
long-term and economic well-beingResults from Waseda–Bridgestone Initiative for Development of Global 
Environment (W-BRIDGE Initiative) in South Kalimantan province through 
capacity building delivered by academic partners. This initiative also increased 
land area ownership from 0.28 to 1.23 ha per household.
Rural agrarian communities in low-latitude tropical forests (e.g., communities 
in Southeast Asia, South America, Central Africa) adapting to chronically 
hotter temperatures in common ways, such as adjusting when and how they 
work. Decision makers should develop an understanding of these behavioural 
adaptations that are already being adopted before establishing broader 
adaptation strategies.(Hiratsuka, 2019)
(Masuda, 2019)
11 Sustainable 
cities and 
communitiesUpstream forests influence water supplies

to citiesWatershed condition is associated with measurable health outcomes downstream. 
Maintaining natural capitals within watersheds is an important public health 
investment especially for populations with low levels of built capital.(Herrera et al., 2017)
Evidence from the Marikina Watershed Integrated Resources Development 
Alliance in the Philippines working together with all stakeholders to restore 
Marikina Watershed to reduce disaster risk and urban resilience.(Devisscher, 2019)
Synergies delivered through sound urban forestry approaches could benefit not 
only urban dwellers but also forest communities. Community groups have also 
taken responsibility for urban forestry in the absence of strong government 
commitment.(Konijnendijk, 2018)
12 Responsible 
consumption 
and 
production)Generates materials for sustainable 
consumptionForest concessionaires can also increase the repurposing of waste to improve 
sustainable consumption. For instance, the logging company Congolaise

Industrielle des Bois produces electricity from sawmill wood waste.(Tegegne et al., 2019)
13 Climate 
actionEnhance resilience and adaptive capacities 
to climate change through forest 
managementMixed agroforestry systems offer opportunities to simultaneously meet the 
water, food, energy and income needs of densely populated rural and peri-urban 
areas in Indonesia.(van Noordwijk et al., 
2016)
Carbon-based conservationPayment for carbon-based conservation (eg., REDD+, Green Climate Fund) 
protecting peatlands from avoidable human impacts for favourable return from 
carbon conservation investments.(Roucoux et al., 2017)
REDD+ has mixed impacts on communities’ socio-ecological resilience. On one 
hand, increases in network ties and participation in decision making would 
enhance potential for local adaptability. However, restrictions on local forest 
practices could limit communities’ ability to manage uncertainty.(Hajjar, 2021)
14 Life below

water)Support numerous ecosystem services
Protection for aquatic macroinvertebrates 
habitatsComplex root systems serve as shelter as they protect juvenile fish from 
predators and provide food and nutrients for fish.
Mangroves contribute to fisheries production and have become one of the 
higher carbon stocks compared with other forests. The mangroves system of 
the Zambezi River Delta, Mozambique confirms the consistency of substantial C 
stocks typical of mangroves across a relatively large and hydrologically diverse 
area.
The riparian canopy of the tropical forest is significantly able to maintain 
in-stream temperature that is important to aquatic macroinvertebrates. The 
study of Gunung Tebu, Malaysia showed high diversity and abundance of 
steams invertebrates as the natural habitats are minimally impacted.(Friess, 2019)
(Stringer, 2015)
(Md rai, 2014)
15 Life on 
land)Community monitoring of their own forests 
or forest within communal jurisdiction,

sustainable certificationMainstreaming SFM in vast tracts of forest, thereby increasing the share of 
forest area under a forest management plan, including the proportion of forest 
area certified under independent forest certification schemes.(van Hensbergen, 2016)Box CCP7.2 (continued)

CCP7
2396Cross-Chapter Paper 7 Tropical Forests
FAQ CCP7.1 | How is climate change affecting tropical forests and what can we do to protect and increase their 
resilience?
Global warming, droughts, extreme rainfalls and sea level rise cause significant impacts on tropical forests.
In addition to climate change, tropical forests are experiencing non-climatic stressors. Conversion of forest into 
large-scale agriculture land and exploitation of timber and non-timber forest products are increasing pressure and 
amplifying the impacts of climate change on the remaining areas of tropical forests. These include biodiversity 
decline, increases of fires, large-scale ecosystem transformation (e.g., into savannah in southeastern Amazon) and 
increasing carbon emissions due to deforestation, forest conversion and forest degradation. Further, loss of forest 
resources leads to the decline of livelihoods of Indigenous Peoples and local communities. All nations need to

collaborate to implement collective actions to protect tropical forests.
Tropical forests are essentially important for the health of planet Earth. Tropical forests in Asia, Africa and South 
America regulate carbon, water and chemical cycles, which maintain a healthy climate and nutrient cycles for SDGs Contribution of STFM to the goals Adaptation interventions Supporting references
Even with tension between the management of resources for local goals 
and the need for public good values, still there are some communities that 
maintain strong control over their lands and resources in achieving desirable 
conservation outcomes and willing to see large tracts of land set aside, i.e., 
areas held to be sacred.(Sayer et al., 2015; Sheil, 
2015)
16 Peace, 
justice 
and strong 
institutionsAddressing complexity of implementing 
conservation policyTarget 16.7 calls for responsive, inclusive participatory and representative

decision making at all levels. Decentralisation in forest governance observed 
through community-based/collaborative forest management depends on the 
strength of underlying land tenure and use rights, as well as capacity to benefit 
from those rights.(Baynes, 2015; McDermott, 
2019; Myers, 2017; Nunan, 
2018)
By 2021, Thailand plans to increase use of renewable and alternative energy by 
25% including energy crops. Adequate forest protection is critical, as increasing 
demand for energy crops may drive demand for expanding agricultural 
production into public forests, benefitting some SDGs and threatening others.(Phumee, 2018)
Modern technologies in forest management 
control
Governance laws and policies provide 
access to justice for allTechnologies including remote sensing and geographic information systems 
(GIS) are interrelated as they support management actions in global forest 
resources management thus reducing exploitation through monitoring and 
evaluation activities.

The Forest Stewardship Council (FSC) and the Program for the Endorsement of 
Forest Certification (PEFC) significantly contribute to ensuring the legality of the 
timber supply chain. The (FAO, 2018b) considers the proportion of forest with 
secure tenure rights for forest dependent people and the local community in 
ensuring equal rights to economic resources for all.(Beckline, 2017)
(Gabay, 2019)
17 Partnership 
for the goalsCo-benefits derived from tropical forest 
conservationRaising awareness of the interconnectedness of tropical forests and the SDGs 
through multi-disciplinary collaboration will support more informed decisions of 
social, cultural, economic and policy interest.(Swamy, 2018; Bukoski 
et al., 2018)
Voluntary partnership agreements 
(VPAs) stabilise and reproduce the forest 
governance regime
Central bureaucracies promote forest 
benefits: countering conservationIn Ghana, the adoption of the VPA resulted in an improved the timber legality

assurance system (TLAS), strengthened social responsibility agreements (SRA) 
enforcement, updated forest management plans, artisanal milling strategies and 
technical transparent timber dights allocations.
Forest management units (FMUs) could be utilised to support 
conservation-oriented regimes with worldwide interests as well as domestic 
production-oriented regimes. For example, FMUs might potentially link up with 
global and domestic timber certification regimes under the Multistakeholder 
Forestry Programme (MFP3) initiative.(Hansen, 2018)
(Sahide, 2016)Box CCP7.2 (continued)

CCP7
2397Tropical Forests Cr oss-Chapter Paper 7
supporting life. Tropical forests are home to two-thirds of our world’s biodiversity, although they cover only about 
13% of the land on Earth, but it is not known exactly how many millions of living creatures, such as microorganisms, insects, amphibians, snakes, fish, birds, mammals and primates, live in tropical forests.
Approximately 1.3 billion people directly depend upon tropical forest resources to survive. Others are indirectly

dependent upon the health and provisioning of ecosystem services and goods from tropical forests. The forests provide many kinds of economic products, such as timber, medicines and food, and recreational services, such as nature trekking, bird and wildlife watching, to mention a few. Indigenous People and other forest-dependent communities have shown extraordinary knowledge on how to manage forest resources to meet their subsistence needs without causing forest degradation. This forest culture and wisdom are broken when the rate of forest extraction changes into unplanned and unsustainable large-scale transformation.
Deforestation and land-use changes in tropical forests cause not only physical and biological changes on flora

and fauna, but also rapid changes in cultures harming forest peoples. A degraded tropical forest is prone and more vulnerable to climate change. An increase in temperature in lowlands creates an unfavourable condition for optimum growths of many kinds of plant species which also affects several agricultural plants. Coffee farmers, for example, are forced to open new forest frontiers in highland areas to meet an optimum temperature for the growth of coffee.
The onset and duration of dry and rainy seasons also changes. A prolonged wet season has excessive rains which

cause flash floods and substantially disturbs the fruiting cycle of many plant species. Due to high rainfall and high humidity, most flowers of forest trees fail to mature, and hence essentially deplete fruit production. Most trees in tropical forests require a short period of a dry season to have a mass fruiting season. On the other hand, a prolonged dry season causes soils to dry in deeper layers, higher atmospheric demand for water vapour and enhanced forest fires. In the tropical humid forests, the majority of forest fires are anthropogenic. In Southeast Asia, peat fires cause large carbon emissions and haze pollution which harms locals and people in neighbouring countries. The impact on tropical forest comes also from the sea level rise which is due to changes in salinity and sedimentation rates, and the expansion of inundated areas leads to the decline of mangrove productivity.

Projected impacts of climate change on the tropical forest might be detrimental to safeguards of local communities 
and a significant number of flora and fauna in the tropics. In southeastern Amazon, reduction in precipitation, due to changes in the climate pattern, associated with intense deforestation and land cover change are leading to reduction of productivity in the remaining forest areas, and might lead to a large-scale change in the forest structure which can become a savannah. In Southeast Asia, in particular in Indonesia and Malaysia, prolonged dry seasons associated with the El Niño phenomenon cause extensive peat fires, releasing large amounts of carbon dioxide and creating various health problems related to haze pollution. Furthermore, climate change interacts with deforestation for agriculture (crops, livestock and plantation forestry), logging, mining or infrastructure development, exacerbating temperature and rainfall changes resulting in more degradation.

Climate change, together with forest fragmentation and deforestation, also harms wildlife. For example, 
the orangutan, an endemic species to tropical peat forests in Kalimantan and Sumatra, is classified as critically endangered. Many other endemic and unknown species of flora in tropical forests are in the same condition and could experience a mass extinction at a more rapid rate than the previous five mass extinctions on Earth. About 1.3 million Indigenous Peoples depending on the natural resources of the tropical forest would suffer from cultural disruption and livelihood change due to forest loss.
To protect tropical forests a collective action of all nations is needed. It requires a global effort to stop deforestation

and the conversion of tropical forests. The role of Indigenous Peoples and local communities as forest keepers must be strengthened. Economic incentives for protecting tropical forests, among other strategies, could facilitate collective actions towards a sustainable management of tropical forests. Sustainable, effective and just strategies to increase the resilience of tropical forests need to consider the complex political, social and economic dynamics involved, including the goals, identity and livelihood priorities of Indigenous Peoples and local communities beyond natural resource management. Strategies can benefit from integrating knowledge and know-how from traditional cultures, fostering transitions towards more sustainable systems.Box FAQ CCP7.1 (continued)

1
123Point of Departure and Key Concepts Chapter 1
The IPCC Working Group II contribution to the Sixth Assessment 
Report addresses the challenges of climate action in the context of sustainable development with a particular focus on climate change impacts, adaptation and vulnerability. This chapter frames the point of departure and key concepts building on the IPCC’s Fifth Assessment Report (IPCC, 2014a, b), the Special Report on Global Warming of 1.5°C (IPCC, 2018b), the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC, 2019b), and the Special Report on Climate Change and Land (SRCCL) (IPCC, 2019a); as well as the WGI contributions to the Sixth Assessment Report (IPCC, 2021a) and complements the contribution of the WGIII Sixth Assessment Report, which will be published after this report (IPCC, 2022).
Since IPCC AR5, human influence on the Earth’s climate has

become unequivocal, increasingly apparent and widespread, reflected in both the growing scientific literature and in the perception and experiences of people worldwide (high confidence). Current changes in the climate system and those expected in the future will increasingly have significant and deleterious impacts on human and natural systems. The impacts of climate change and extreme weather events have adversely affected, or caused the loss of ecosystems including terrestrial, freshwater, ocean and coastal ecosystems, including tropical coral reefs; reduced food security; contributed to migration and displacement; damaged livelihoods, health and security of people; and increased inequality. Climate change impacts are concurrent and interact with other significant societal changes that have become more salient since AR5, including a growing and urbanising global population; significant inequality and demands for social justice; rapid technological change; continuing poverty, land

and water degradation, biodiversity loss; food insecurity; and a global pandemic. {1.1.1; 1.3; Cross-Working Group Box ATTRIBUTION in Chapter 1}

Since AR5, climate action has grown in salience worldwide across 
all levels of government as well as among non-governmental organisations, small and large enterprises, and citizens (high confidence). At the international level the Paris Agreement and the Sustainable Development Goals (SDGs), along with other targets and frameworks, such as the Sendai Framework for Disaster Risk Reduction, the Convention on Biological Diversity (CBD) Aichi targets, the Addis Ababa Action Agenda for finance and the New Urban Agenda, provide overarching goals and policy context. These agreements also provide policy goals used by this IPCC Report to assess climate action across all levels of society. {1.1.2; 1.4.1; 1.4.3}
IPCC’s assessments have grown and changed substantially over

the last three decades. Compared to earlier IPCC assessments, this report emphasises a common risk-solution framing across all three Working Groups. This report focuses on solutions for risk reduction and adaptation; provides more integration across the natural and social sciences; applies a more comprehensive risk framework; assesses adaptation directly in the context of sectoral or regional risks; engages with different forms of knowledge, including Indigenous knowledge and local knowledge; and includes an increasing focus on equity and justice. {1.1.4; 1.4.2; Cross-Chapter Box ADAPT in Chapter 1}Adaptation plays a key role in reducing risks and vulnerability from climate change. Implementing adaptation and mitigation actions together with the SDGs helps to exploit synergies, reduce trade-offs and makes all three more effective. From a risk perspective, limiting atmospheric greenhouse gas (GHG) concentrations reduces climate-related hazards while adaptation and sustainable

development reduce exposure and vulnerability to those hazards. Adaptation facilitates development, which is increasingly hindered by impacts and risks from climate change. Development facilitates adaptation by expanding the resources and capacity to reduce climate risks and vulnerability. {1.1.3; 1.5.1; 1.5.3}

The concepts of risk and risk management have become 
increasingly central to climate change literature, research, practice and decision making (medium confidence). Risk, defined as the potential for adverse consequences for human and ecological systems, recognising the diversity of values and objectives associated with such systems, provides a framework for understanding the increasingly severe, interconnected and often irreversible impacts of climate change; how these impacts differentially affect different regions, sectors and populations; how to allocate resources best to manage the resulting risks and how to evaluate the responses that 
reduce residual risks for current and future generations, economies and ecosystems. {1.2.1; 1.3.1; 1.4.2}
The concepts of adaptation, vulnerability, resilience and 
risk provide overlapping, alternative entry points for the climate change challenge (high confidence). Vulnerability is

a component of risk, but also an important focus independently, improving understanding of the differential impacts of climate change on people of different gender, race, wealth, social status and other attributes. Vulnerability also provides an important link between climate adaptation and disaster risk reduction. Resilience, which can refer to either a process or outcome, encompasses not just the concept of maintaining essential function, identity and structure, but also maintaining a capacity for transformation. Such transformations bring forth questions of justice, power and politics. {1.2.1; 1.4.1}
Risks from climate change differ through space and time and

cascade across and within regions and systems. The total risk in any location may thus differ from the sum of individual risks if these interactions, as well as risks from responses themselves, are not considered (high confidence). The risks of climate change responses include the possibility of mitigation or adaptation responses not achieving their intended objectives or having trade-offs or adverse side effects for other societal objectives. Another core area of complexity in climate risk is the behaviour of systems, which includes multiple stressors unfolding together, cascading or compounding interactions within and across sectors and regions, and nonlinear responses and the potential for surprises. All of this is crucial for effective decision making and decision-support methods. The key risks assessed in this report become important in interaction with the cultures, values, ethics, identities, experiences and knowledge systems of affected communities and societies. {1.3.1}

Increasingly, impacts are detected and attributed to the changing 
climate. Improved understanding of deep history (palaeoclimate

1
124Chapter 1 Point of Departure and Key Concepts
and biotic responses) suggests that past climate changes 
have already caused substantial ecological, evolutionary and 
socioeconomic impacts (high confidence). Many recent impacts 
are not detected, due to a shortage of monitoring and robust 
attribution analysis (high confidence). Detection and attribution 
assessments inform risk assessment by demonstrating the sensitivity 
of a system to climate change, and they can inform loss and damage 
estimates including those involved in potential climate litigation 
cases. Robust detection and attribution methods now exist and play a 
significant role in increasing awareness and willingness to act among 
decision makers and the general population. {1.3.2.1; Cross-Working 
Group Box  ATTRIBUTION in Chapter 1; Cross-Chapter Box  PALEO in 
Chapter 1}
Narratives play an important role in communicating climate 
risks and motivating solutions. A narrative describes a chronological

chain of events, often with a premise and conclusions. In the AR6, as 
in previous IPCC assessments, climate change scenarios and related 
narratives (also called storylines) are central in the analysis, synthesis 
and communication of climate change impacts and of adaptation 
and mitigation responses. AR6 employs narratives to describe the 
assumptions, evolution and driving forces for the representative 
concentration pathways (RCPs) and shared socioeconomic pathways 
(SSPs) and links these to global warming levels (GWLs) as a complement 
to RCPs and SSPs for framing impacts (Cross-Chapter Box CLIMATE 
in Chapter 1). Narratives can also be enablers of transformation by 
communicating societal goals and the actions needed to achieve them 
{1.2.2; 1.3.3; 1.5.2}
AR6 highlights adaptation solutions and the extent to which 
they are successful and adequate at reducing climate risk, 
increasing resilience and pursuing other climate-related societal

goals. For adaptation, a solution is defined as an option which is 
effective, feasible and conforms to principles of justice. Effectiveness 
refers to the extent to which an action is anticipated or is observed to 
reduce climate-related risk. Feasibility refers to the extent to which a 
measure is considered possible and desirable in a particular context. 
A successful action is one observed to be effective, feasible and just. 
Adequacy refers to a set of solutions that together are sufficient to 
avoid dangerous, intolerable, or severe climate risks. {1.4}
Indigenous knowledge and local knowledge (IK and LK) can 
provide important understanding for acting effectively on 
climate risk and can help diversify knowledge that may enrich 
adaptation policy and practice (high confidence). Indigenous 
Peoples have been faced with adaptation challenges for centuries and 
have developed strategies for resilience in changing environments

that can enrich and strengthen current and future adaptation efforts. 
Valuing IK and LK is also important for recognition, a key component 
of climate justice. {1.3.2.3}
AR6 highlights three principles of climate justice: distributive 
justice, procedural justice and recognition. Distributive justice 
refers to the allocation of burdens and benefits among individuals, 
nations and generations. Procedural justice refers to who decides and 
participates in decision making. Recognition entails basic respect and 
robust engagement with and fair consideration of diverse cultures and perspectives. This report considers all three principles in the 
assessment of adaptation options and evaluates the extent to which 
better outcomes are obtained by choosing just ones. Since potential 
trade-offs exist among the principles, adaptation assessments will 
in general involve normative judgements, as well as science-based 
evidence. {1.4.1.1}

Concepts of justice and measures of well-being are increasingly 
used to evaluate the extent to which climate change adaptation 
is equitable and effective (medium confidence). AR6 employs 
evaluation frameworks based on both single and multi-criteria to 
assess adaptation effectiveness and consistency with principles of 
justice. Single criteria frameworks aggregate many attributes into a 
one number or ranking, often quantified using benefit–cost analysis 
or measures of social welfare. Existing decision processes often favour 
such single criteria, which also correlate well with many measures of 
social progress and sustainable development. Multi-criteria frameworks 
simultaneously report several different biophysical and socioeconomic 
attributes, which provides more information on potential trade-offs 
and synergies and can engage with emerging concepts of well-being. 
{1.4.1.1; 1.4.1.2}
The concepts of enablers, catalysts and the solution space help

AR6 assess ways to speed the implementation and expand the 
range of adaptation solutions. Many potential solutions have not 
yet been implemented, despite the gap between current and adequate 
levels of adaptation. Enablers enhance the feasibility of adaptation 
options and include governance, finance and knowledge. Catalysts 
accelerate and motivate the adaptation decision making process. 
The concept of solution space—defined as the space within which 
opportunities and constraints determine why, how, when and who 
adapts to climate risks—helps this in assessing how human choices 
and exogenous changes can expand and contract the set of effective, 
feasible and just solutions. {1.4.2}
Effective governance, adaptation finance and nature-based 
solutions are important enablers for expanding the solutions 
space and reducing adaptation gaps (high confidence). Actors at 
many scales and in many sectors are already adapting, and can take

additional and more significant adaptation action. These actors include 
individuals and households, communities, governments at all levels, 
private sector businesses, non-governmental organisations, religious 
groups and social movements. Many forms of adaptation (depending 
on the type of climatic risk and societal context) are likely to be more 
effective, cost-efficient and potentially also more equitable when 
organised collectively. Stronger governance and adaptation finance 
capabilities are usually associated with more ambitious adaptation 
plans and more effective implementation of such plans. {1.4.2; 1.5.2}
Monitoring and evaluation (M&E) of adaptation refers to a 
broad range of activities necessary for tracking adaptation 
progress over time, improving adaptation effectiveness and 
successful iterative risk management. Monitoring usually refers 
to continuous information gathering, whereas evaluation denotes

more comprehensive assessments of effectiveness and equity, often 
resulting in recommendations for decision makers. In some literatures, 
M&E refers solely to efforts undertaken after implementation. In

1
125Point of Departure and Key Concepts  Chapter 1
other literatures, M&E refers both to efforts conducted before and 
after implementation. Since AR5, a growing literature provides initial 
inventories of adaptation plans and implementation worldwide, but 
information on effectiveness remains scare (high confidence). { 1.4.3; 
Cross-Chapter Box ADAPT in Chapter 1}
The concept of limits to adaptation is dynamic in terms of 
the temporal, spatial and contextual dimensions of climate 
change risks, impacts and response. Socioeconomic, technological, 
governance and institutional systems or policies can be changed or 
transformed in response to the different dimensions of adaptation 
limits to climate change and extreme events. Adaptation limits can be 
soft or hard. Soft adaptation limits occur when options may exist but 
are currently not available to avoid intolerable risks through adaptive 
actions. Hard adaptation limits occur when no adaptive actions

are possible to avoid intolerable risks. The levels of GHG emissions 
reduction, adaptation and risk management measures are the key 
factors determining if and when adaptation limits are reached. When 
a limit (soft) is reached, then intolerable risks and impacts may occur 
and additional adaptations (incremental or transformational) would 
be required. Transformational adaptation can allow a system to extend 
beyond its soft limits and prevent soft limits from becoming hard 
limits. The loss and damage associated with the future climate change 
impacts, beyond the limits to adaptation, is an area of increasing 
focus. However, it is yet to be fully developed in terms of assessment 
methods, including non-economic values and identifying means to 
avoid and reduce both economic (loss of asset, infrastructure, land 
etc.) and non-economic (loss of societal beliefs and values, cultural 
heritage, biodiversity and ecosystem services) losses and damages. 
{1.4.4.1; 1.4.4.2}

Key concepts in this report provide a framework for assessing 
the urgency of climate change adaptation. Adaptation is urgent to 
the extent that soft adaptation limits are currently being approached 
or exceeded and that achieving levels of adaptation adequate to 
address these soft limits requires action at a speed and scale faster 
than that represented by current trends (high confidence). In addition, 
adaptation is urgent to the extent that any needed expansion of the 
future solution space requires near-term strengthening and expansion 
of enablers, such as governance, finance and information. {1.1.3; 1.4.4; 
1.5.1}
AR6 highlights the role of transformation in meeting the Paris 
Agreement, the SDGs and other policy goals. Transformation, 
and the related term transition, are pluralistic concepts, embracing 
the idea of major, fundamental changes in society or natural systems 
as opposed to changes that are minor, marginal or incremental. AR6

has a particular focus on transformational adaptation, which changes 
the fundamental attributes of a socioeconomic system in anticipation 
of climate change and its impacts. AR6 describes transitions in five 
systems: energy, land and ecosystems, urban and infrastructure, 
industrial and societal. In the past, transformations of such scale have 
been associated not only with technological and economic changes, 
but also with shifts in most aspects of society. {1.2.1.3; 1.4.4; 1.5.1}
Future transformation could be deliberate, envisioned and 
intended by at least some societal actors, who seek to expand the solution space, overcome soft limits to adaptation, reduce residual risk 
to tolerable levels and achieve societal goals. If such a transformation 
is not pursued or is not successful and risk remains above intolerable 
levels, a forced transformation may occur that is less consistent with 
societal goals. The literature describes incremental and transformational

change as linked processes. The transformational adaptation literature 
suggests shifts from incremental to transformational processes are 
made possible by knowledge and skills, as well adjustments to vision, 
agendas and coalitions achieved through monitoring and learning. 
The socio-ecological and sustainability transitions literature suggests 
that actors seeking deliberate transformation may take incremental 
steps that aim to induce societal tipping point behaviour in the 
near or longer term. Alternative pathways for pursuing deliberate 
transformations range from a focus on modernisation of sectors such 
as energy, agriculture and the use of natural resources to proposals 
for degrowth that intentionally aim to decrease both gross domestic 
product (GDP) and coupled GHG emissions. {1.2.1.3; 1.4.4; 1.5.1}
Transformation is understood as a collective action challenge 
among actors with both common and differing values interacting

with a mix of competition and cooperation. Significant innovations 
often begin in niches or protected spaces, sometimes introduced by 
new entrants or outsiders. The drivers of transformation are multi-
dimensional, involving social, cultural, economic, environmental, 
technical and political processes. The combination of these creates the 
potential for abrupt and systemic change, the stability of entrenched 
and interlocked power structures, and the importance of individual 
beliefs and behaviours. Decision frameworks that consider multiple 
objectives and multiple scenarios can avoid privileging some views 
over others and help multiple actors to identify resilient and equitable 
solutions to complex, deeply uncertain challenges. Nonetheless, 
common goals and narratives are both enablers of transformation and 
help align the activities of multiple, loosely co-ordinated actors. {1.5.2}
This report employs the climate resilient development concept

to inform co-ordinated implementation of adaptation and mit-
igation solutions to support sustainable development for all. 
As a transformation that emerges from the choices of many different 
actors, climate resilient development follows no single or preferred 
pathway and no single best combination of adaptation, mitigation 
and sustainable development strategies. All pathways involve complex 
trade-offs and synergies among different actions. The climate resilient 
development concept helps assess the extent to which solutions cur -
rently exist to meet societal goals or the extent to which an expanded 
solution space is required. The concept also helps assess the role of 
various actors, including governments, citizens, civil society, knowledge 
institutions, media, investors and businesses as well as the need for 
arenas of engagement in which they can interact. {1.2.3; 1.5.2; 1.5.3}

1
126Chapter 1 Point of Departure and Key Concepts
1.1 The Current Urgent Moment
1.1.1 A Changing Climate in a Changing World
Numerous additional significant climate-related changes have unfolded 
worldwide since the publication of the IPCC Fifth Assessment Report 
(AR5) in 2014 (IPCC, 2014a). Consistent with projections, multiple 
concurrent changes in the physical climate system have grown more 
salient, including increasing global temperatures, loss of ice volume, 
rising sea levels and changes in global precipitation patterns (AR6 WGI 
Chapter 1, Chen et al., 2021). The changes in the physical climate system, 
most notably more intensive extreme events, have adversely affected 
natural and human systems around the world. This has contributed to 
a loss and degradation of ecosystems, including tropical coral reefs; 
reduced water and food security; increased damage to infrastructure; 
additional mortality and morbidity; human migration and displacement;

damaged livelihoods; increased mental health issues; and increased 
inequality. Since AR5, a growing literature attributes change in specific 
climate variables to observed damages to specific localised human and 
natural systems in many regions of the world (Figure 1.1, also see Cross-
Working Group BOX ATTRIBUTION in Chapter 1).
Concurrently, since AR5, a growing share of people around the world 
perceive a changing climate, regard these changes as significant and 
consider climate action to be a matter of high urgency (Wilson and 
Orlove, 2019; Section  17.4.5). A survey, representing over half the 
world’s population, found that almost two-thirds of people across 
50 countries view climate change as an emergency (Flynn et al., 2021), 
compared to just over half across 23 countries in 2013 (Fagan, 2019). 
The highest level of support for climate action is among small island 
developing states (SIDS) (74%), followed by high-income countries

(72%), middle-income countries (62%) and, then, least developed 
countries (58%) (Flynn et al., 2021). Notably, after mid-2018, global 
media showed a large increase number of mentions of ‘global 
warming’, ‘climate change’ and similar terms (Thackeray et al., 2020). 
The business community now consistently includes climate change, 
including ‘climate action failure’ as a major risk (World Economic Forum, 
2021). In late 2019, protests calling for strengthened climate action 
reached an unprecedented level of over 6000 events in 185 countries, 
with a reported estimate of 7.6 million participants, largely led by the 
‘Fridays for Future’ youth movement (Chase-Dunn and Almeida, 2020).
Since AR5, governments, businesses and civil society have increasingly 
responded with planning and actions aimed at reducing current and 
future risks from climate change (Section 1.1.2, Chapters 16 and 17). 
Concern with climate change has increasingly motivated actions by

governments, the private sector and civil society (Hale et  al., 2021; 
Section 18.4.3). As described in this report, however, current climate 
policies and actions alone are not sufficient to meet stated policy goals 
(Section 1.1.3) (high confidence).
This report addresses the challenges of climate action in the context 
of sustainable development. Climate action takes place in a world 
already undergoing some of the most rapid and significant societal 
and environmental change in decades (IPCC, 2018b; Box 1.1). These 
include species and ecosystems loss due to land and sea use change 
and pollution (IPBES, 2019a); a growing and urbanising world population (Gerten et  al., 2019; van Vliet et  al., 2017); technology 
reshaping the workplace through automation (Schwab, 2017) and 
information dissemination through social media (Mavrodieva et  al., 
2019; Pearce et al., 2019); and increasing inequalities due to gender,

poverty, age, race and ethnicity (Cross-Chapter Box GENDER in Chapter 
18). Economic inequality grows within nations even as it has narrowed 
among them (UN Department of Economic and Social Affairs, 2020). 
International polycentric governance and non-state actors play an 
important role (Beck and Mahony, 2018; Sections 1.4.2 and 17.1.2.1). 
In 2020 and 2021, a global pandemic dramatically affected the lives of 
most of the world’s population, likely accelerating many of the changes 
already underway (Cross-Chapter Box COVID in Chapter 7).
The point of departure for this AR6 Working Group II report thus lies 
in rapid and significant changes in our climate and our world, growing 
attentiveness to those changes, a gap between current climate action 
and that needed to address policy goals, and a growing literature that 
improves understanding and informs potential responses. This chapter 
defines key concepts and the connections among them useful for

comprehending and evaluating these changes, the risks they generate, 
and options for incremental and transformative solutions that could 
reduce climate-related risks, impacts and vulnerability.
1.1.2  Policy Context
Since AR5, climate action has grown at all levels of governance as 
well as among non-governmental organisations, small and large 
enterprises, and citizens. Two international agreements—the United 
Nations Framework Convention on Climate Change (UNFCCC) Paris 
Agreement and the 2030 Agenda for Sustainable Development—
jointly provide overarching goals for climate action. The 2015 Paris 
Agreement frames direct local, national and private sector actions 
aligned with long-term goals addressing mitigation, adaptation and 
finance. For mitigation, the agreement calls for ‘holding the increase 
in global average temperature to well below 2°C above pre-industrial 
levels’, ‘pursuing efforts to limit the temperature increase to 1.5°C’ and

‘reaching net zero greenhouse gas (GHG) emissions in the second half 
of this century’ (UNFCCC, 2016). For adaptation, the agreement calls 
for ‘increasing the ability to adapt to the adverse impacts of climate 
change and foster climate resilience’ (UNFCCC, 2016, Article 2), as 
well as having a dedicated ‘global goal on adaptation’ (Lesnikowski 
et al., 2017; Persson, 2019). For finance, the agreement seeks to make 
‘financial flows consistent with a pathway towards low GHG emissions 
and climate resilient development’.
The 2030 Agenda for Sustainable Development, adopted in 2015 by 
UN member states, sets out 17 Sustainable Development Goals (SDGs), 
frames policies for achieving a more sustainable future and aligns efforts 
globally to prioritise ending extreme poverty, protecting the planet and 
promoting more peaceful, prosperous and inclusive societies. SDG 13 
(‘Climate Action’) provides benchmarks to align the Paris Agreement’s

call to ‘strengthen the global response to the threat of climate change, in 
the context of sustainable development and efforts to eradicate poverty’. 
Since AR5, several new international conventions have identified climate 
change adaptation and risk reduction as important global priorities for 
sustainable development, including the Sendai Framework for Disaster

1
127Point of Departure and Key Concepts  Chapter 1
Evidence of climate change impacts in many regions of the world
Number of weighted studies per grid cell
5 2 50 20 10 100 200 0.5 01
Figure 1.1 |  Evidence of climate change impacts in many regions of the world. Global density map shows climate impact evidence, derived by machine-learning from 
77,785 studies. Map colouring denotes the number of weighted studies per grid cell for all evidence on climate impacts (N= 77,785). Figure adopted from Callaghan et al. (2021). 
Risk Reduction (SFDRR) (Tozier de la Poterie and Baudoin, 2015; UNISDR, 
2015), the finance-oriented Addis Ababa Action Agenda (UN, 2015) and 
the New Urban Agenda (UN, 2017). For example, the SFDRR recognises 
some disasters as ‘exacerbated by climate change and increasing in 
frequency and intensity, significantly [impeding] progress towards 
sustainable development’ (UNISDR, 2015). The Convention on Biological

Diversity (CBD) is one of the key international legal instruments for 
sustainable development for ‘the conservation of biological diversity, the 
sustainable use of its components and the fair and equitable sharing 
of the benefits arising out of the utilisation of genetic resources’ (CBD, 
2011). The CBD and its Aichi targets recognises that biodiversity is 
affected by climate change, with negative consequences for human well-
being, but biodiversity, through ecosystem services, contributes to both 
climate change mitigation and adaptation (CBD, 2010). There is concern 
that many of the proposed post-2020 biodiversity targets of the CBD 
may not be met due to climate change impacts (post-2020 biodiversity 
targets from Chapter 2; Arneth et al., 2020).
At the national level, over 2,315 laws and policies that address climate 
change now exist in 196 countries and a number of territories as of 
May 2021 (Grantham Research Institute on Climate Change and the

Environment and Sabin Center for Climate Change Law, 2021). Sub-
national and non-state actors, including city and state governments 
and firms and investors, have also increasingly launched climate actions (Hale et al., 2021). Climate change litigation is gaining salience 
for both governments and corporations as the number of cases filed 
around the world grew from 834 between 1986 and 2014 to 1,006 
since 2015 and growing (Setzer and Higham, 2021).
1.1.3 Adaptation Efforts and Gaps
Adaptation to climate change plays a key role in reducing climate-
related risks along with mitigation and sustainable development. 
From a risk perspective (Section 1.2), emission reductions and carbon 
removal can both reduce GHG forcing and thus climate-related hazards, 
while adaptation and sustainable development reduce exposure and 
vulnerability to those hazards.
Important synergies and trade-offs exist among adaptation and 
mitigation actions (Section  1.5, Chapter 18). Limiting atmospheric

concentrations of GHGs reduces the extent of adaptation needed to 
keep risk within tolerable levels (Section  1.3, Chapter 16). From a 
global perspective, understanding adaptation and its limits can inform 
judgements about the best balance among levels of mitigation and 
adaptation. Such judgements underlie the mitigation goals of the 
Paris Agreement. From a more local perspective, there is a wide range 
of mitigation scenarios (Cross-Chapter Box  CLIMATE in Chapter 1),

1
128Chapter 1 Point of Departure and Key Concepts
Box 1.1 | Summary of IPCC AR5 and Special Report findings
The IPCC WGII AR6 builds upon key findings of the IPCC AR5, three subsequent special reports and the simultaneous assessment of 
the IPCC WGI and WGIII AR6. The findings and assessment approaches adopted across these reports have implications for the point of 
departure in the WGII AR6. They include the strong recognition of the urgency for climate action, the enhanced focus on risk and the 
aim to connect the search for near-term climate solutions with longer-term transitions. Headline conclusions of the IPCC AR5 include the 
following, directly quoted (IPCC, 2014a):
• Human influence on the climate system is clear.
• Recent climate changes have had widespread impacts on human and natural systems.
• Continued emission of greenhouse gases will cause further warming and long-lasting changes in all components of the climate

system, increasing the likelihood of severe, pervasive and irreversible impacts for people and ecosystems.
• Adaptation and mitigation are complementary strategies for reducing and managing the risks of climate change.
• Substantial emissions reductions over the next few decades can reduce climate risks in the 21st century and beyond, increase 
prospects for effective adaptation, reduce the costs and challenges of mitigation in the longer term and contribute to climate-resilient 
pathways for sustainable development.
• Effective implementation depends on policies and cooperation at all scales and can be enhanced through integrated responses that 
link adaptation and mitigation with other societal objectives.
Compared to previous IPCC assessments of impacts, adaptation and vulnerability, the IPCC WGII AR5 assessment highlighted new data 
for attributing observed climate changes and impacts (Cramer et al., 2014; IPCC, 2014c), a more formal approach to risk (IPCC, 2014c;

Jones et al., 2014), and an expanded assessment of adaptation (Chambwera et al., 2014; IPCC, 2014c; Klein et al., 2014a; Mimura et al., 
2014; Noble et al., 2014).
At the time of the IPCC AR5, very few scientific studies relevant to the impacts of global warming of 1.5°C above pre-industrial levels 
were available. In 2018, the IPCC concluded a Special Report on the impacts of global warming of 1.5°C levels and related global GHG 
emission pathways, following an invitation expressed in the Decision text of the Paris Agreement (UNFCCC, 2015a). The report assessed 
available literature on global warming of 1.5°C and on comparisons between global warming of 1.5°C and 2°C above pre-industrial 
levels. It also addressed possible pathways for achieving the ambitious goals of the Paris Agreement. Key findings from this report include 
the following, directly quoted (IPCC, 2018c):
• Global warming is likely to reach 1.5°C between 2030 and 2052 if it continues to increase at the current rate.

• Climate-related risks for natural and human systems are higher for global warming of 1.5°C than at present, but lower than at 2°C. 
Most adaptation needs will be lower for global warming of 1.5°C compared to 2°C.
• In model pathways with no or limited overshoot of 1.5°C, global net anthropogenic CO 2 emissions decline by about 45% from 2010 
levels by 2030 (40–60% interquartile range), reaching net zero around 2050 (2045–2055 interquartile range).
• Pathways reflecting current nationally stated mitigation ambitions as submitted under the Paris Agreement would not limit global 
warming to 1.5°C, even if supplemented by very challenging increases in the scale and ambition of emissions reductions after 2030.
In 2019, a Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) was published, motivated by the observation that 
many of the world’s people most exposed to risks caused by climate change live in the mountains or near the coast. Key findings include

the following, directly quoted (IPCC, 2019c):
• Over the last decades, global warming has led to widespread shrinking of the cryosphere and unabated ocean warming with an 
uptake of more than 90% of the excess heat in the climate system. Marine heatwaves have doubled in frequency since 1982 and the 
oceans acidify (virtually certain). Global mean sea level is rising, with acceleration in recent decades. Increases in tropical cyclone 
winds and rainfall exacerbate extreme sea level events and coastal hazards.
• All these trends have impacted ecosystems, food security, water resources, water quality, livelihoods, health and well-being, 
infrastructure, transportation, tourism and recreation, as well as the culture of human societies, particularly for Indigenous Peoples.
• The Greenland and Antarctic Ice Sheets are projected to lose mass at an increasing rate throughout the 21st century and beyond.

Projected ecosystem responses include losses of species habitat and diversity, and degradation of ecosystem functions. Warm-water 
corals are at high risk already and are projected to transition to very high risk even if global warming is limited to 1.5°C.
• Increased mean and extreme sea level, alongside ocean warming and acidification, are projected to exacerbate risks for human 
communities in low-lying coastal areas. People with the highest exposure and vulnerability are often those with lowest capacity to 
respond.

1
129Point of Departure and Key Concepts  Chapter 1
• Services provided by ocean and cryosphere-related ecosystems can be supported by protection, restoration, precautionary ecosystem-
based management of renewable resource use, and the reduction of pollution and other stressors.
• Coastal communities face challenging choices in crafting context-specific and integrated responses to sea level rise that balance 
costs, benefits and trade-offs of available options and that can be adjusted over time.
Also in 2019, IPCC published a Special Report on Climate Change and Land, addressing GHG fluxes in land-based ecosystems, land use 
and sustainable land management in relation to climate change adaptation and mitigation, desertification, land degradation and food 
security. Key findings include the following, directly quoted (IPCC, 2019c):
• Human use directly affects more than 70% of the global, ice-free land surface. Land also plays an important role in the climate system.

Climate change has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land 
degradation in many regions. Changes in land conditions, either from land use or climate change, affect global and regional climate.
• Pathways with higher demand for food, feed, and water, more resource-intensive consumption and production, and more limited 
technological improvements in agriculture yields result in higher risks from water scarcity in drylands, land degradation, and 
food insecurity. Most of the response options assessed contribute positively to sustainable development and other societal goals. 
Sustainable land management, including sustainable forest management, can prevent and reduce land degradation, maintain land 
productivity, and sometimes reverse the adverse impacts of climate change on land degradation. It can also contribute to mitigation 
and adaptation.

• Response options throughout the food system, from production to consumption, including food loss and waste, can be deployed and 
scaled up to advance adaptation and mitigation. All assessed modelled pathways that limit warming to 1.5°C or well below 2°C 
require land-based mitigation and land use change.
• The effectiveness of decision-making and governance is enhanced by the involvement of local stakeholders (particularly those most 
vulnerable to climate change including Indigenous Peoples and local communities, women, and the poor and marginalised) in policies 
for land-based climate change adaptation and mitigation.
• Near-term action to address climate change adaptation and mitigation, desertification, land degradation and food security can bring 
social, ecological, economic and development co-benefits.
In 2021, IPCC Working Group 1 published its Sixth Assessment Report on The Physical Science Basis. Key findings from the report include

the following, directly quoted from its Summary for Policymakers (SPM) quoted (IPCC, 2019c):
• The scale of recent changes across the climate system as a whole and the present state of many aspects of the climate system are 
unprecedented over many centuries to many thousands of years.
• Global surface temperature will continue to increase until at least the mid-century under all emissions scenarios considered. Global 
warming of 1.5°C and 2°C will be exceeded during the 21st century unless deep reductions in carbon dioxide and other greenhouse 
gas emissions occur in the coming decades.
• Many changes due to past and future greenhouse gas emissions are irreversible for centuries to millennia, especially changes in the 
ocean, ice sheets and global sea level.
• With further global warming, every region is projected to increasingly experience concurrent and multiple changes in climatic impact-

drivers. Changes in several climatic impact-drivers would be more widespread at 2°C compared to 1.5°C global warming and even 
more widespread and/or pronounced for higher warming levels. Low-likelihood outcomes, such as ice sheet collapse, abrupt ocean 
circulation changes, some compound extreme events and warming substantially larger than the assessed very likely range of future 
warming cannot be ruled out and are part of risk assessment.
• From a physical science perspective, limiting human-induced global warming to a specific level requires limiting cumulative CO 2 
emissions, reaching at least net zero CO 2 emissions, along with strong reductions in other greenhouse gas emissions. Strong, rapid 
and sustained reductions in emissions would also limit the warming effect resulting from declining aerosol pollution and would 
improve air quality.
Other assessment processes also inform the IPCC AR6. For example, a recent joint workshop between the Intergovernmental Science-

Policy Platform on Biodiversity and Ecosystem Services (IPBES) and IPCC, the first of its kind, made key observations relevant to the work 
of IPCC WGII AR6 (Pörtner et al., 2021). In this broad context, the workshop explored diverse facets of the interaction between climate 
and biodiversity, from current trends to the role and implementation of nature-based solutions and the sustainable development of 
human society. Key highlights of the workshop include the following, directly quoted from the workshop report:Box 1.1 (continued)

1
130Chapter 1 Point of Departure and Key Concepts
• Limiting global warming to ensure a habitable climate and protecting biodiversity are mutually supporting goals, and their achievement 
is essential for sustainably and equitably providing benefits to people.
• Several land- and ocean-based actions to protect, sustainably manage and restore ecosystems have co-benefits for climate mitigation, 
climate adaptation and biodiversity objectives.
• Measures narrowly focused on climate mitigation and adaptation can have direct and indirect negative impacts on nature and 
nature’s contributions to people.
• Measures narrowly focusing on protection and restoration of biodiversity have generally important knock-on benefits for climate 
change mitigation, but those benefits may be sub-optimal compared to measures that account for both biodiversity and climate.
• Treating climate, biodiversity and human society as coupled systems is key to successful outcomes from policy interventions.

• Transformative change in governance of socio-ecological systems can help create climate and biodiversity resilient development 
pathways.Box 1.1 (continued)
including those which meet or miss the Paris Agreement goals, and 
overshoot scenarios in which global mean temperature exceed targets 
for several decades before dropping to desired levels. Such scenarios 
inform assessments of the level of adaptation that may be required 
(Section 1.4; Chapter 17).
Adaptation and sustainable development are also interlinked 
(Section 18.1). Adaptation facilitates development, which is hindered 
by impacts and risks from climate change. Development facilitates 
adaptation by expanding the resources and capacity available to 
manage climate risks. Viewed from a climate justice perspective, some 
argue that a more just society is more capable of successful adaptation, 
while others argue that only adaptation that results in a more just

society can be judged successful (Section 1.4.1; Chapter 18).
Two concepts—adaptation gaps and limits to adaptation—help frame 
this report’s assessment of the extent to which current adaptation 
efforts are adequate to meet societal goals. Adaptation gaps are 
defined as ‘the difference between actually implemented adaptation 
and a societally set goal, determined largely by preferences related to 
tolerated climate change impacts and reflecting resource limitations 
and competing priorities’ (UNEP et  al., 2021). Limits to adaptation 
describe the extent to which no plausible level of adaptation can meet 
societal goals (Section 1.4.4). Within the limits, adaptation gaps can be 
closed by increased and more successful adaptation actions. Beyond 
the limits, only mitigation can close adaptation gaps.
Numerous climate-related impacts already cause severe damage in 
many places and are projected to increase in the future (Chapter

16). Adaptation can reduce these risks, often significantly, but limits 
to adaptation have already been reached or are being approached 
in some sectors and regions (Sections  16.3.1; 16.4). While natural 
systems worldwide are changing in response to climate change, many 
are not adapting sufficiently quickly to retain their resilience in the 
face of current and projected future climate change (Section 16.4). 
For human systems, numerous lines of evidence suggest that 
in many regions and sectors current infrastructure, settlement 
patterns, policies, practices and institutions remain inadequate for 
current changes in climate conditions (Section 16.2). Inadequate or 
insufficient adaptation to current conditions is called an adaptation 
deficit.In response, adaptation efforts have increased significantly since AR5 
(Sections 16.3; 17.2; 17.5.2; Cross-Chapter Box ADAPT in Chapter 1). 
Assessing the adequacy and effectiveness of these efforts as called for in

Article 7 of the Paris Agreement remains challenging (Section 1.3.2.2), 
because much adaptation is not recorded in the literature and because 
assessment depends on judgements of effectiveness (Section 1.4.1.2), 
judgements about societal goals including climate justice 
(Section  1.4.1.1), and expectations about future GHG concentration 
pathways and other socioeconomic conditions (Section  16.5, Cross-
Chapter Box DEEP in Chapter 17).
Knowledge about adaptation has significantly expanded since AR5 
(Cross-Chapter Box  ADAPT in Chapter 1). While understanding 
regarding the extent of adaptation gaps remains limited, the available 
evidence suggests significant adaptation gaps exist (high confidence). 
Many current adaptation efforts constitute adaptation planning, rather 
than implementation (Section  16.3). Most current implementation 
efforts represent incremental as opposed to transformational 
adaptation despite the proximity to adaptation limits (Sections 17.2;

17.5.2). Some current adaptation efforts are considered maladaptive 
because they increase some climate-related risks even if they reduce 
others (Sections 1.4.2.4; 17.5; Chapter 16). Gaps exist in key enablers 
of adaptation, such as finance (Cross-Chapter Box FINANCE in Chapter 
17). Given the long time scales involved with many adaptation actions 
and the potential to significantly reduce longer-term costs with near-
term actions, closing many adaptation gaps requires actions over the 
next few years by governments, business, civil society and individuals 
at a scale and speed significantly faster than that represented by 
current trends.
1.1.4 What is New in the History of Interdisciplinary 
Climate Change Assessment
Interdisciplinary climate change assessment, which has played a 
prominent role in science–society interactions on the climate issue 
since 1988, has advanced in important ways since AR5 (Mitchell et al.,

2006; Mach and Field, 2017; Oppenheimer et al., 2019). Building on a 
substantially expanded scientific and technical literature (Burkett et al., 
2014; Minx et  al., 2017), this AR6 report emphasises at least three 
broad themes.

1
131Point of Departure and Key Concepts  Chapter 1
First, this AR6 assessment has an increased focus on risk- and solutions-
frameworks. The risk framing can move beyond the limits of single 
best estimates or most-likely outcomes and include high-consequence 
outcomes for which probabilities are low or in some cases unknown 
(Jones et  al., 2014; Mach and Field, 2017). In this report, the risk 
framing for the first time spans all three Working Groups, includes 
risks from the responses to climate change, considers dynamic and 
cascading consequences (Section  1.3.1.1), describes with more 
geographic detail risks to people and ecosystems, and assesses such 
risks over a range of scenarios (Chapter 16). The focus on solutions 
encompasses the interconnections among climate responses, 
sustainable development, and transformation, and the implications 
for governance across scales within the public and private sectors 
(Section  17.5.2; Chapter 18). The assessment therefore includes

climate-related decision making and risk management, climate 
resilient development pathways, implementation and evaluation 
of adaptation, and also limits to adaptation, and loss and damage 
(Cross-Chapter Box LOSS in Chapter 17; Section 1.4.4). Specific focal 
areas reflect contexts increasingly important for the implementation 
of responses, such as cities (Chapter 6).
Second, emphases on social justice and different forms of expertise 
have emerged (Sections 1.4.1.1; 17.5.2). As climate change impacts 
and implemented responses increasingly occur, there is heightened 
awareness of the ways that climate responses interact with issues of 
justice and social progress. In this report, there is expanded attention 
to inequity in climate vulnerability and responses, the role of power 
and participation in processes of implementation, unequal and 
differential impacts, and climate justice. The historic focus on scientific

literature has also been increasingly accompanied by attention to and 
incorporation of Indigenous knowledge (IK), local knowledge (LK) and 
associated scholars (Section 1.3.2.3; Chapter 12).
Third, AR6 has a more extensive focus on the role of transformation in 
meeting societal goals (Section 1.5).
To support these three themes, this report assesses literature with 
an increasing diversity of topics and geographical areas covered. 
The diversity is encompassed through sectoral and regional chapters 
(Chapters 2–15), as well as cross-chapter papers and boxes. The 
literature also increasingly evaluates the lived experiences of climate 
change—the physical changes underway, the impacts for people 
and ecosystems, the perceptions of the risks, and adaptation and 
mitigation responses planned and implemented. In particular, scientific 
capabilities to attribute individual extreme weather and climate events 
to GHG emissions have gone from hypothetical to standard and routine

over the last three decades, and societal perceptions of these events 
and their impacts for people and ecosystems are now being studied as 
well (Figure 1.1; Cross-Working Group Box ATTRIBUTION in Chapter 1; 
see synthesis in Chapter 16).
Finally, climate change assessment has become increasingly integrative 
across multiple disciplines within the natural and social sciences. 
This report’s chapters combine experts across Working Groups 
and disciplines, such as natural and social sciences, engineering, 
humanities, law, and business administration. In this assessment cycle, 
the special reports (Allen et al., 2018; IPCC, 2019a; IPCC, 2019b) all emphasise such integration, and the chapter teams in the present 
report integrate disciplinary perspectives and also science–policy 
interactions inherent in climate change impacts, adaptation and 
vulnerability. There has been increasing real-time assessment of the 
assessment process itself, including interpersonal dynamics and how

they shape key findings (Oppenheimer et al., 2019). Additionally, best 
practices are being adopted from applied decision and policy analysis, 
decision support and co-production, in order to increase assessment 
relevance and usability for decision making (Hall et al., 2019; Mach 
et al., 2019). Methods of integration in this report include systematic 
review, meta-analysis, multi-criteria integration and expert elicitation 
(see synthesis in Chapter 16). The emphasis on knowledge for action 
has also included the role of public communication, stories and 
narratives within assessment and associated outreach (Section 1.2.2).
1.2 Different Entry Points for Understanding 
Climate Change Impacts, Adaptation and 
This section introduces key concepts used in this report and the 
connections between them that present different entry points for 
understanding climate change impacts, adaptation, and vulnerability.
1.2.1 Overlapping, Complementary Entry Points

Many actors from different research and practice communities engage 
with understanding and responding to climate risk. Not surprisingly, 
there thus exist alternative, overlapping and complementary entry 
points to the discussion widely used throughout the literature and this 
report.
The concepts of risk and risk management have in recent years 
been central to climate change research and practice related to 
impacts, adaptation, and vulnerability. The concepts provide a 
framework for understanding climate change and its increasingly 
severe, interconnected and irreversible impacts. They support the 
implementation of solutions that reduce adverse consequences, pursue 
opportunities and enable beneficial outcomes for people, economies 
and nature (IPCC, 2014c; IPCC, 2018c). All three AR6 Working Groups 
now apply a common risk framework (IPCC, 2020).
Additional concepts—adaptation, vulnerability, exposure, resilience

and transformation—also provide important framings for the climate 
change challenge.
Figure 1.2 displays the connections among many of the key concepts 
used in this report. This chapter, the Summary for Policymakers, 
Technical Summary and sectoral and regional chapters are organised 
around the concepts of risks (Section 1.3), solutions (Section 1.4) and 
transformation (Section 1.5).
Key concepts that contribute to an understanding of risk include its 
components hazards, exposure, and vulnerability (Section 1.2.1.1); the 
recognition that risks may be complex and cascading (Section 1.3.1.2); 
and the reasons for concern framework used to summarise the most

1
132Chapter 1 Point of Departure and Key Concepts
policy-relevant risks (Section 1.3.1.1). Key concepts that contribute to 
an understanding of feasible, effective, and just solutions (1.4.1) include 
the enablers of governance, finance and knowledge (Section  1.4.2); 
Transformation is supported by systems transitions in energy, land, 
infrastructure, industry and society (Section 1.5.1), which if successful 
can contribute to climate resilient development (Section 1.5).
The centre of Figure 1.2 shows societal goals of equity, health, well-
being and climate justice as articulated by the Paris Agreement, SDGs, 
and other policies and plans (Section 1.4.1). The limits to adaptation 
(Section  1.4.4), potential for maladaptation (Sections  1.4.2; 17.5.2) 
and loss and damage (Section  1.4.4.2) present barriers to reaching 
these goals.
The concept of vulnerability can provide a unique window into the 
effects of climate change on different communities, individuals and

ecosystems, in particular as human systems are affected by race, 
gender, wealth inequalities and other attributes (Section 1.2.1.2). The 
concept of adaptation can provide a unique window into the process 
of adjustment to climate change by human and natural systems (Section  1.2.1.3). Resilience (Section  1.3.1.4) is a broad concept, 
encompassing both outcomes and processes, an ability to maintain 
essential function and an ability to transform.
The ball and cup diagrams (Holling, 1973) in Figure 1.2 indicate that 
the current coupled human and natural system is not resilient, nor 
does it meet societal goals of equity, well-being and ecosystem health. 
Some types of transformation may prove inevitable (Section  1.5.1), 
either a deliberate transformation that results in a more resilient state 
consistent with societal goals or a forced transformation to a system 
state inconsistent with the goals.
1.2.1.1 Risk Framing

Risk in this report is defined as the potential for adverse consequences 
for human or ecological systems, recognising the diversity of values 
and objectives associated with such systems. In the context of climate 
change impacts, risks result from dynamic interactions between 
climate-related hazards with the exposure and vulnerability of the 
affected human or ecological system. In the context of climate change 
Solutions
RisksFeasible, effective,
and jus t
Enabling conditions
Limits, maladaptation,
loss and damageAdaptation
Hazard
Exposure
Reasons for concer n
Complex cascading riskSystem Transitions
Energy
Land, Oceans & Ecosystem s
Urban & Infrastructure
Industrial
SocietalConnecting key concepts in this report
ResilienceThe coupled humanThe coupled human
and natural system and natural system 
becomes more resilient becomes more resilient 
and more consistent with and more consistent with 
societal goalssocietal goals

VulnerabilityThe coupled humanThe coupled human
lacks sufficient lacks sufficient 
resilience and does not resilience and does not 
meet societal goalsmeet societal goals
The coupled humanThe coupled human
and natural system falls into and natural system falls into 
a state even less consistent a state even less consistent 
with societal goals, with societal goals, 
potentially leading to potentially leading to 
collapsecollapseenables
prompts
reducegeneratepromptsexpands
generatereduce
Climate Resilient
Human health & well-being
equity, justice
Planetary healthThe coupled human
and natural system 
becomes more resilient 
and more consistent with 
societal goals
The coupled human
lacks sufficient 
resilience and does not 
meet societal goals
and natural system falls into 
a state even less consistent 
with societal goals, 
potentially leading to 
collapse

Figure 1.2 |  Connecting key concepts in this report. The current coupled human and natural system is insufficiently resilient and does not meet societal goals of equity, 
well-being and ecosystem health. Meeting the objectives of the Paris Agreement, Sustainable Development Goals and other policy statements requires the system to move to a 
new and more resilient state. Key concepts used in this report help illuminate our current situation and potential solutions. These key concepts are usefully organised around the 
concepts of risk, solutions and transformation. Risk can prompt solutions and transformation. Both solutions and transformation seek to reduce some risks but may also generate 
others. Solutions can enable transformation, and transformation can expand the set of feasible solutions.

1
133Point of Departure and Key Concepts  Chapter 1
responses, risks result from the potential for such responses not 
achieving the intended objective(s), or from potential trade-offs or 
negative side effects (see Annex II: Glossary). Risk management is 
defined as plans, actions, strategies or policies to reduce the likelihood 
and/or magnitude of adverse potential consequences, based on 
assessed or perceived risks (see Annex II: Glossary).
Risk framing is increasingly used to assess climate change impacts on 
human and natural systems (Sections 1.2.4.1; 16.1; 17.3; Cross-Chapter 
Box CLIMATE in Chapter 1; IPCC, 2012; Mach and Field, 2017; O’Neill 
et al., 2017; Connelly et al., 2018; see also Oppenheimer et al., 2014). 
A risk framing reflects key dimensions of the climate challenge. These 
features include the changing likelihoods of many different outcomes 
(including adverse consequences and beneficial opportunities),

uncertainties that will persist, and different and contested values, 
priorities and goals (Jones and Preston, 2011; Mach et al., 2016). The 
IPCC AR6 and associated special reports apply a broad definition of 
risk. WGI Cross-Chapter Box 1.3. WGI uses the Climatic Impact Driver 
terminology, rather than hazard, to neutrally assess changing climatic 
conditions that are relevant to human and natural systems, leaving 
the determination of positive/negative consequences and resulting 
impacts and risks for WGII assessment (WGI Section  12.3). In most 
cases, throughout this WGII report, the term ‘risk’ refers to the risks of 
climate change impacts. The full assessment, however, incorporates all 
relevant risks from climate change impacts and responses.
The broad definition of risk involves quantitative and integrative 
understandings of risk ( Oppenheimer et al., 2014; Mach and Field, 2017; 
see also Section 17.3). Risk is sometimes defined as the probability

of a consequence, multiplied by the magnitude of that consequence, 
acknowledging both the diversity of possible consequences and 
the relevance of values. Yet it also applies in circumstances where 
probabilities cannot be fully quantified (e.g., Adger et al., 2013). For 
example, in some cases the probability and magnitude of consequences 
may be more uncertain, dependent on complex dimensions of the 
climate (e.g., a cyclone, high tide, and heatwave co-occurring) or the 
vulnerability of different communities (e.g., the ways in which social 
networks and community cohesion support the most vulnerable 
individuals during disasters) (Ford et al., 2018). The determinants of 
risk vary dynamically through space and time (Jurgilevich et al., 2017; 
Viner et  al., 2020). They interact, compound, and cascade (Dawson, 
2015; Adger et al., 2018; see also Section 16.1.2).
Risk framing supports connections with solutions (Jones and Preston,

2011; Mach et al., 2016; Adger et al., 2018). First, risk framing connects 
the present with the future. (Papathoma-Kohle et al., 2016). For instance, 
whether wildfire or drought, recent experiences have demonstrated 
limits to current response capacities relevant to future preparedness 
(e.g., evacuation of large communities on tight time frames or water 
management simultaneously responsive to intensifying drought and 
flooding). Second, risk framing emphasises that uncertainties and 
complex interactions are integral to decision making (Jones et  al., 
2014; Dawson, 2015). The uncertainties include high-impact, low-
probability outcomes and deep uncertainties for which core processes 
are not understood and meaningful probabilities cannot be applied 
(Adler et al., 2016; see also Section 17.2.1; Cross-Chapter Box DEEP 
in Chapter 17; Chapter 7 in SRCCL, IPCC, 2019a; Cross-Chapter Box 5 in SROCC, IPCC, 2019b). In these circumstances, risk assessment can

occur through tools used for risk management across contexts, such 
as insurance, business, social protection, security and policy planning, 
and decision making can be iterative and support dynamic adaptive 
pathways through time (Jones and Preston, 2011; Watkiss et al., 2015; 
Aven, 2016; see also Section 17.3.2)
Iterative risk management (Vervoort and Gupta, 2018) emphasises 
that anticipating and responding to climate change does not consist 
of a single set of judgements at a single point in time, but rather an 
‘ongoing cycle of assessment, action, reassessment, learning and 
response (USGCRP , 2018). It is consistent with most approaches 
applied for implementing adaptation (Jones and Preston, 2011; 
Jones et al., 2014). For instance, the Paris Agreement is organised as 
a polycentric process (see Section 1.4) of iterative risk management 
in which national governments pledge to take specific actions. Those 
actions are monitored and assessed, and nations asked to update their

pledges in light of that assessment.
1.2.1.2 Vulnerability
Vulnerability is a component of risk, but also an important focus 
independently. Vulnerability in this report is defined as the propensity 
or predisposition to be adversely affected. It encompasses a variety 
of concepts and elements, including sensitivity or susceptibility 
to harm and lack of capacity to cope and adapt (see Annex II: 
Glossary). Over the past several decades, approaches to analysing 
and assessing vulnerability have evolved. An early emphasis on top-
down, biophysical evaluation of vulnerability included—and often 
started with—exposure to climate hazards in assessing vulnerability. 
From this starting point, attention to bottom-up, social and contextual 
determinants of vulnerability, which often differ, has emerged, 
although this approach is incompletely applied or integrated across 
contexts (Bergstrand et al., 2015; Rufat et al., 2015; Spielman et al.,

2020; Taberna et al., 2020). Vulnerability is now widely understood 
to differ within communities and across societies, also changing 
through time (Kienberger et  al., 2013; Jurgilevich et  al., 2017; see 
also Chapter 16). In the WGII AR6, assessment of the vulnerability 
of people and ecosystems encompasses the differing approaches 
that exist within the literature, both critiquing and harmonising them 
based on available evidence. In this context, exposure is defined 
as the presence of people; livelihoods; species or ecosystems; 
environmental functions, services, and resources; infrastructure; 
or economic, social, or cultural assets in places and settings that 
could be adversely affected (Annex II: Glossary). Potentially affected 
places and settings can be defined geographically, as well as more 
dynamically, for example through transmission or interconnections 
through markets or flows of people.
Vulnerability is also a link between the climate risk and disaster risk

communities, recognising complementarities and differences between 
these communities. Disaster risk management is the set of processes 
that improve understanding of disaster risk, foster disaster risk reduction 
and transfer, and promote continuous improvement in disaster 
preparedness, response and recovery practices, increasing human security, 
well-being and sustainable development (see Annex II: Glossary). Climate 
risk and disaster risk are increasingly addressed together, bridging the

1
134Chapter 1 Point of Departure and Key Concepts
climate change adaptation and disaster risk reduction communities (e.g., 
IPCC, 2012; UNDRR, 2019, especially Chapter 13 in that report). Building 
on the scientific literature and adaptation and risk reduction practice, the 
IPCC Special Report on Extremes resulted in several major IPCC advances 
that continue through the present report, including emphasis on risk and 
climate-related extremes (e.g., Burton et al., 2012; Lavell et al., 2012) 
and re-conceptualisation of vulnerability to encompass both social 
and biophysical orientations (i.e., bridging contextual/bottom-up and 
climate-driven/top-down approaches) (Polsky et al., 2007; Cardona et al., 
2012). Linking disaster risk reduction and climate change adaptation can 
also be an important basis for discussion in climate negotiations on the 
allocation of funds needed for tackling climate change, especially in

developing countries and SIDS (Begum et al., 2014). The integration of 
disaster risk management and climate change adaptation in the IPCC 
AR6 is seen, for example, in the assessment of key risks within and 
across sectors and regions, along with global-scale reasons for concern, 
which is attuned to extreme events and disasters (Oppenheimer et al., 
2014; see also Chapter 16). Additionally, the assessment of adaptation 
has prioritised these interconnections (e.g., Mimura et  al., 2014), as 
have literature and practice especially in the context of sustainable 
development (e.g., Schipper et al., 2016).
1.2.1.3 Adaptation
Adaptation in this report is defined, in human systems, as the process 
of adjustment to actual or expected climate and its effects, in order 
to moderate harm or exploit beneficial opportunities. In natural 
systems, adaptation is the process of adjustment to actual climate 
and its effects; human intervention may facilitate adjustment to

expected climate and its effects (see Annex II: Glossary). Adaptation 
planning in human systems generally entails a process of iterative risk 
management. Different types of adaptation have been distinguished, 
including anticipatory versus reactive, autonomous versus planned 
and incremental versus transformational adaptation (Chapters 16–
18; IPCC WGII glossaries for the TAR, AR4, AR5, and AR6 (Annex II)). 
Adaptation is often seen as having five general stages: (a) awareness, 
(b) assessment, (c) planning, (d) implementation and (e) M&E (Moser 
and Boykoff, 2013; Jones et al., 2014; Mimura et al., 2014; Noble et al., 
2014; see also Section 17.4). Government, non-government and private 
sector actors have adopted a wide variety of specific approaches to 
adaptation that, to varying degrees, address these five general stages. 
Adaptation in natural systems includes ‘autonomous’ adjustments 
through ecological and evolutionary processes. It also involves the use

of nature through ecosystem-based adaptation. The role of species, 
biodiversity and ecosystems in such adaptation options can range 
from the rehabilitation or restoration of ecosystems (e.g., wetlands or 
mangroves) to hybrid combinations of ‘green and grey’ infrastructure 
(e.g., horizontal levees) (Chapters 2 and 3; IPBES, 2018).
The IPCC assessment of adaptation has evolved through time. The 
WGII AR4 included one chapter dedicated to adaptation, the WGII 
AR5 expanded to four and the WGII AR6 mainstreams adaptation 
comprehensively throughout the report. Adaptation science is rapidly 
evolving, including evaluation of adaptation effectiveness, feasibility, 
implementation and maladaptation, although major knowledge 
gaps persist in modelling and analysis (Cross-Chapter Box ADAPT in 
Chapter 1; Chapter 16; Section 1.4; Holman et al., 2019). The WGII AR6 emphasises assessment of observed adaptation-related responses to

climate change, governance and decision making in adaptation, and 
the role of adaptation in reducing key risks and global-scale reasons 
for concern, as well as limits to such adaptation (e.g., Chapters 16 
and 17). The assessment approach includes adaptation needs, options, 
planning and implementation across sectors and regions, as well as 
adaptation opportunities, constraints and also limits (Eisenack et al., 
2014; Klein et al., 2014b; Oberlack and Eisenack, 2014; Lehmann et al., 
2015; Roggero, 2015; Herrmann and Guenther, 2017; Oberlack, 2017; 
Sieber et al., 2018; Moser et al., 2019b; Capela Lourenço et al., 2019; 
Thaler et al., 2019; Russel et al., 2020; see also Chapters 16 and 17).
Since AR5, more adaptation has progressed (IPCC, 2014a; Lesnikowski 
et al., 2016; see also Sections 16.2.5 and 17.2) and the focus of activity 
has expanded to include social, institutional and governance dimensions 
beyond engineered and technical options and to decision processes

beyond technocratic, linear framings (IPCC, 2014a; see also Chapter 17). 
Adaptation includes increasing attention to implementation, M&E and 
learning through time, not just planning processes (Section 17.3 and 
17.5.1). On the one hand, an important advance has been recognition 
of generalised capacities, such as resources and knowledge, necessary 
for the feasibility of effective adaptation. Adaptation thereby strongly 
overlaps with risk management and with the building of resilience and 
sustainable development (Chapters 17 and 18).
1.2.1.4  Resilience, Including Connections with Development 
Pathways and Transformation
Resilience in this report is defined as the capacity of social, economic 
and environmental systems to cope with a hazardous event or trend 
or disturbance, responding or reorganising in ways that maintain their 
essential function, identity and structure, while also maintaining the 
capacity for adaptation, learning and transformation (see Annex II:

Glossary). Resilience is an entry point commonly used, although under 
a wide spectrum of meanings (Reghezza-Zitt et al., 2012; Flood and 
Schechtman, 2014; Aldunce et al., 2015; Tanner et al., 2015; Fisichelli 
et  al., 2016; Meerow et  al., 2016; Moser et  al., 2019a). Resilience 
as a system trait overlaps with concepts of vulnerability, adaptive 
capacity, and thereby risk. Resilience as a strategy overlaps with risk 
management, adaptation and also transformation (Woodruff et  al., 
2018; Moser et al., 2019a). Implemented adaptation is often organised 
around resilience as bouncing back and returning to a previous state 
after a disturbance (Fisichelli et al., 2016).
In much of the literature, resilience encompasses not just maintaining 
essential function, identity and structure, but also maintaining a capacity 
for adaptation, learning and transformation. Since the earliest framings 
of resilience around stability and persistence, ecology and allied fields

have come to recognise that while systems are often persistent in 
the face of disturbance, disturbance also creates opportunity for 
transformation and the emergence of new pathways (Section  1.5.2; 
Folke, 2006; Allen and Holling, 2010; Folke et al., 2010; Gelcich et al., 
2010; Stockholm Resilience Center, 2015; Doppelt, 2017). Across this 
literature, disturbance is framed as outside the system in question, for 
which the time frames and spatial scales of disturbances, impacts and 
responses are central to outcomes (Béné et al., 2011; Brown, 2014; 
Hamborg et al., 2020). Endogenous processes of transformation are

1
135Point of Departure and Key Concepts  Chapter 1
presented as emergent, characterised by thresholds and, as a result, 
very difficult to anticipate (Scheffer et al., 2001; Walker and Meyers, 
2004; Suding and Hobbs, 2009; Scheffer et  al., 2012; Hughes et  al., 
2013; Scheffer et  al., 2015). In the last 5  years (2016–2020), the 
concept of resilience has gained prominence as a core theme in the 
climate change adaptation literature (Nalau and Verrall, 2021).
Often, development and adaptation communities of practice default to 
persistence and stability in their use of resilience (Cote and Nightingale, 
2012; MacKinnon and Derickson, 2013). Such a framing aligns resilience 
with a long-standing but increasingly questioned belief that sustainable 
development can be achieved through incremental adjustments in 
behaviour and advances in technology that allow for the persistence of 
existing socioeconomic and socio-ecological arrangements (Klauer, 1999;

Banerjee, 2003; Redclift, 2005; UN Inter-agency Task Force on Financing 
for Development 2019; Chapter 18, Section 1.5). However, the literature 
increasingly suggests that the achievement of sustainable development 
will require transformative change in socio-ecological systems at scales 
ranging from the community to the globe. The concept of climate 
resilient development, initially introduced in AR5 and now a key focus in 
this report (see Chapter 18), engages with such transformations and the 
associated questions of justice, power and politics as shaped by internal, 
endogenous social factors and their interactions with other drivers of 
change (Eriksen et al., 2015; Nightingale, 2015b; Carr, 2019; Nightingale 
et al., 2019; see also Chapter 18).
1.2.2 Narratives, Storylines, Scenarios and Pathways
The concepts of narratives, storylines, scenarios and pathways play an 
important role in this report. While distinct concepts, they are inter-
related and sometimes confused.

A narrative is a story with a chronological order or, when cast in 
the form of an argument, with premises and conclusions (Roe, 1991; 
Adger et al., 2001). Narratives enable people to envision what various 
potential futures may mean for environments and livelihoods, and in 
this way facilitate the development of scenarios for the future (Miller 
et al., 2015). Narratives can also play a key role in enabling collective 
action (Section 1.5) by helping disparate groups co-create a common 
vision of a desirable future and achieve a common understanding of actions needed to move towards that future (Linnér and Wibeck, 2019; 
Muiderman et al., 2020).
A narrative contains a storyline in addition to a set of actors 
(Elliott, 2005). A storyline is a series of events including their 
causal connections within a narrative. The IPCC and climate change 
literature more broadly often use the terms storylines and narratives

interchangeably (O’Neill et al., 2017; see also WGI Cross-Chapter Box 6 
in Chapter 1; Sections 1.4.4; 10.5.3). A scenario storyline refers to a 
narrative description of a scenario including its main characteristics, 
relationships between driving forces and how these factors evolve 
(AR6 WGI Section 1.4.4.2, Chen et al., 2021). Storylines are used to 
assess risks related to low-likelihood, but high-impact events (Sutton, 
2018). In this use of the terms, narratives and storylines do not 
include specific actors. There is also a critical literature on the use of 
narratives and storylines based on projected scenarios, which points 
out the conservative character of these concepts whose performative 
effect tends to preserve the status quo and the current socioeconomic 
relationships. (Malm and Hornborg, 2014; Chollet and Felli, 2015; 
Lövbrand et al., 2015; Demortain, 2019; Theys and Cornu, 2019).
Standard research communication may fail to engage policymakers,

media and the public at large (WGI AR6 Section 1.2.4, Chen et al., 2021). 
Rather, policies and decision making tend to be based on narratives 
and storylines (Roe, 1994; Roe, 2017). Although mathematical models 
and narratives are often presumed to be antithetical, in practice they 
may be complementary and work together (Morgan and Wise, 2017). 
Communicating research insights through storylines and narratives 
may have a better chance of transmitting key messages. AR6 employs 
these communication tools in many places, for instance storylines 
for constructing and communicating regional climate information 
or climate services (WGI AR6 Chapter 10, Doblas-Reyes et al., 2021; 
WGI AR6 Chapter 12, Ranasinghe et  al., 2021) or ‘low likelihood 
high warming storylines’ (Chapter 4). To better communicate deep 
uncertainty in sea level rise projections, WGI uses storylines to describe 
the physical events that would have to unfold to generate its high-end

estimates (Cross-Chapter Box DEEP in Chapter 17).
Scenarios are defined in IPCC reports as plausible descriptions of how 
the future may develop, based on a coherent and internally consistent 
set of assumptions about key driving forces (e.g., rate of technological 
Cross-Chapter Box CLIMATE | Climate Reference Periods, Global Warming Levels and Common 
Climate Dimensions
Authors: Steven Rose (USA), Richard Betts (UK), Philippus Wester (Nepal/the Netherlands), Aris Koutroulis (Greece)
This Cross-Chapter Box sets out common climate dimensions to contextualise and facilitate AR6 WGII analyses, presentation, synthesis 
and communication of assessed, observed and projected climate change impacts across WGII chapters and cross-chapter papers. 
‘Common climate dimensions’ are defined as common global warming levels (GWLs), time periods and levels of other variables, as

needed by WGII authors for consistent communications. The set of climate variable ranges given below was derived from the AR6 WGI 
report and supporting resources, and helps to contextualise and inform the projection of potential future climate impacts and key risks. 
The information enables the mapping of climate variable levels to climate projections and vice versa, with ranges of results provided to 
characterise the physical uncertainties relevant to assessing climate impacts risk.

1
136Chapter 1 Point of Departure and Key Concepts
AR6 WGI Reference Periods, Climate Projections and Global Warming Levels
AR6 WGI adopts a common set of reference years and time periods to assess observed and projected climate change, namely the pre-
industrial period, the current ‘modern’ period and future reference time periods. The IPCC Glossary (2021b) defines the pre-industrial 
period as ‘the multi-century period prior to the onset of large-scale industrial activity around 1750. The reference period 1850–1900 is 
used to approximate pre-industrial global mean surface temperature (GMST).’ The ‘modern’ period is defined as 1995 to 2014 in AR6, 
while three future reference periods are used for presenting climate change projections, namely near term (2021–2040), mid-term (2041–
2060) and long term (2081–2100), in both the AR6 WGI and WGII reports. Importantly, the historical rate of warming assessed by WGI

in AR6 is different to that assessed in AR5 and Special Report on Global Warming of 1.5°C (SR1.5, IPCC, 2018b), due to methodological 
updates (see WGI Cross-Chapter Box 2.3 in Chapter 2 for details (Gulev, 2021)). This means that the ‘modern’ period is assessed as slightly 
warmer compared to 1850–1900 than it would have been with AR5-era methods. This also has implications for the projected timing of 
reaching policy-relevant levels of global warming, which need to be understood.
To explore and investigate climate futures, climate change projections are developed using sets of different input projections. These 
consist of sets of projections of GHG emissions, aerosols or aerosol precursor emissions, land use change, and concentrations designed to 
facilitate evaluation of a large climate space and enable climate modelling experiments. For AR5 (and the Coupled Model Intercomparison

Project (CMIP) 5 climate model experiments), the input projections were referred to as representative concentration pathways (RCPs). 
For AR6 (and the CMIP6 climate model experiments), new sets of inputs are used and referred to as SSP scenarios, where SSP refers to 
socioeconomic assumptions called the shared socioeconomic pathways (SSPs).
The RCPs are a set of four trajectories that span a large radiative forcing range, defined as increased energy input at surface level in Watts 
per square metre, ranging from 2.6 W m-2 (RCP2.6) to 8.5 W m-2 (RCP8.5) by the end of the 21st century, with RCP4.5 and RCP6.0 as 
intermediate scenarios, and RCP2.6 a peak and decline scenario reaching 3 W m-2 before 2100. A range of emissions scenarios compatible 
with each specific RCP was also assessed in AR5 (Ciais et al., 2013).
A core set of five SSP scenarios, namely SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, was selected in the AR6 WGI report to fill

certain gaps identified in the RCPs (see WGI Cross-Chapter Box 1.4 in Chapter 1 (Chen et al., 2021)). The first number in the label is the 
particular set of socioeconomic assumptions driving the emissions and other climate forcing inputs taken up by climate models and the 
second number is the radiative forcing level reached in 2100. WG1 Cross-Chapter Box 1.4 in Chapter 1 provides a comparison of this 
core set of SSP scenarios with scenarios used in previous reports, with SSP1–1.9 a low overshoot scenario consistent with limiting global 
average warming to 1.5°C, and SSP1-2.6 a scenario consistent with limiting warming to 2°C.
Also of importance to the impact literature and the WGII report are SSP-RCP combinations, that is, studies that employ climate outcomes 
based on RCPs and socio-economic assumptions based on SSPs. SSPs can be paired with a range of different RCPs because SSPs can be

combined with mitigation policy assumptions to produce a range of emissions pathways. In addition to the SSPs, there are many other 
emissions pathways and societies consistent with any global mean temperature outcome. These represent uncertainty and broad ranges 
of possibilities that affect climate change exposure and vulnerability (Rose and and M. Scott, 2020; Rose and Scott, 2018). Furthermore, 
there are large uncertainties in translating emissions scenarios into concentration pathways due to uncertainties in climate-carbon cycle 
feedbacks (Jones et al., 2013; Booth et al., 2017).
The plausibility of emissions levels as high as the emissions scenario conventionally associated with the RCP8.5 and SSP5-8.5 concentration 
pathways has been called into question since AR5, as has the emissions pathway feasibility of the low scenarios (Hausfather and Peters,

2020; Rose and and Scott, 2020). However, these views are contested (Schwalm et al., 2020, for RCP8.5) It is important to realise that 
emissions scenarios and concentration pathways are not the same thing and higher concentration pathways, such as RCP8.5 could arise 
from lower emissions scenarios if carbon cycle feedbacks are stronger than assumed in the integrated assessment models (IAMs) used to 
create the standard scenarios (Booth et al., 2017). In the majority of full-complexity Earth System Models, these feedbacks are stronger 
than in the IAMs (Jones et al., 2013), so the RCP8.5 concentration pathway cannot be ruled out purely through consideration of the 
economic aspects of emissions scenarios. Nonetheless, the likelihood of a climate outcome, and the overall distribution of climate 
outcomes, are a function of the emissions scenario’s likelihood. Note that the original RCPs were created explicitly to facilitate a broad

range of climate modelling experiments, with the expectation that other issues, such as socioeconomic uncertainty, could be subsequently 
explored (Moss et al., 2010).Cross-Chapter Box CLIMATE1 (continued)

1
137Point of Departure and Key Concepts  Chapter 1
An important feature of the AR6 cycle is a stronger emphasis on the use of future GWLs to support consistency and comparability across 
the three IPCC Working Groups’ contributions to the AR6 and improve communication. The common range of GWLs relative to the 1850 
to 1900 period, termed the ‘Tier 1’ range by WGI, are 1.5, 2.0, 3.0 and 4.0°C. The use of GWLs assists in the comparison of climate states 
across climate change scenarios (projections) and in assessing the broader literature, as well as for cross-chapter and cross-working 
group comparisons. They facilitate the integration of climate projections, impacts, adaptation challenges and mitigation challenges within 
and across the three Working Groups as there is a close connection between the level of global warming and climate change impacts. Of

particular interest is the timing of when the ‘Tier 1’ GWLs are reached, relative to the period 1850–1900, under the five SSP x–y scenarios, 
as well as RCP scenarios. For climate change impacts and adaptation responses, linking GWLs to RCP and SSP climate projections using 
a climate information translation resource is of great relevance for the WGII contribution to AR6.
AR6 WGII Common Climate Dimensions
WGII’s common climate dimensions include (a) a common range of GWLs from WGI, (b) common ranges for other climate variables, 
(c) information for translating climate variable levels to climate projections and vice versa. See Table Cross-Chapter Box CLIMATE.1 for 
global warming level ranges by time periods for RCP and SSP climate projections, and Table Cross-Chapter Box CLIMATE.2 for information 
regarding the timing for when GWLs are reached in climate projections. The common GWL range is based on WGI’s ‘Tier 1’ dimensions of

integration range: 1.5°C, 2°C, 3°C and 4°C. The first table illustrates the greater levels of projected global warming with higher emissions 
pathways, as well as the increasing uncertainty in the climate response over time for a given pathway. The second table illustrates 
significant uncertainty in the timing for passing GWL thresholds which can narrow for a given GWL, the higher the emissions pathway. 
Finally, given the importance of geographic heterogeneity in projected changes in future climate, Table Cross-Chapter Box CLIMATE.3a 
and 3b are provided with ranges for select climate variables (temperature, precipitation, ocean) by GWL and continent (or ocean biome). 
The ranges illustrate spatial heterogeneity in potential physical changes in levels and uncertainty that are relevant to assessing climate 
impacts risk. There is significantly more spatial heterogeneity than represented in the table that is relevant to local decision makers (see,

for instance, WGI Interactive Atlas).
The common climate dimensions can be used as a dimension of integration for impact studies in WGII, for example by providing a 
common framework for comparison of projected impacts for different studies (Figure Cross-Chapter Box CLIMATE.1). Moreover, GWL 
bands are needed in WGII to map the diverse temperature levels found across WGII’s literature. The GWL’s also facilitate integration 
with WGIII’s global emissions projections categorisation by global mean temperature (WGIII Chapter 3).
Table Cross-Chapter Box CLIMATE.1 |  GWL ranges by time periods for CMIP5 (RCP) and CMIP6 (SSP) climate projections (20-year averages). Temperature anomalies 
relative to 1850–1900. Full ranges for CMIP raw results (across all models and ensemble runs) and WGI AR6 assessed very likely (5–95%) ranges. Sources: Hauser et al. 
(2019); WGI SPM (IPCC, 2021a); Table SPM.1
Full ranges WGI AR6 assessed very likely (5–95%) ranges

Projection 2021–2040 2041–2060 2081–2100 2021–2040 2041–2060 2081–2100
RCP2.6 1.0 to 2.2 1.0 to 2.3 0.9 to 2.3 n/a n/a n/a
RCP4.5 1.1 to 2.2 1.4 to 2.7 1.8 to 3.3 n/a n/a n/a
RCP6.0 1.0 to 2.0 1.3 to 2.5 2.3 to 3.6 n/a n/a n/a
RCP8.5 1.1 to 2.6 1.7 to 3.7 3.0 to 6.2 n/a n/a n/a
SSP1–1.9 1.0 to 2.4 1.1 to 2.7 1.0 to 2.5 1.2 to 1.7 1.2 to 2.0 1.0 to 1.8
SSP1–2.6 1.0 to 2.4 1.2 to 2.9 1.3 to 3.1 1.2 to 1.8 1.3 to 2.2 1.3 to 2.4
SSP2–4.5 0.9 to 2.5 1.3 to 3.3 1.9 to 4.4 1.2 to 1.8 1.6 to 2.5 2.1 to 3.5
SSP3–7.0 1.0 to 2.6 1.5 to 3.7 2.7 to 6.2 1.2 to 1.8 1.7 to 2.6 2.8 to 4.6
SSP5–8.5 1.0 to 2.7 1.6 to 4.0 3.1 to 7.2 1.3 to 1.9 1.9 to 3.0 3.3 to 5.7Cross-Chapter Box CLIMATE1 (continued)

1
138Chapter 1 Point of Departure and Key Concepts
Cross-Chapter Box CLIMATE1 (continued)
Table Cross-Chapter Box CLIMATE.2 |  Timing for when 20-year average GWLs are reached in CMIP5 (RCP) and CMIP6 (SSP) climate projections. GWL anomalies relative to 1850–1900. Ranges based 
on CMIP raw results (all models and ensemble runs), and WGI AR6 assessed results. For each GWL and RCP/SSP , the earliest and latest 20-year window when a 20-year average GWL is reached across the CMIP models 
and ensemble members is reported, or the very likely (5–95%) assessed range is reported. ‘n.c.’ means the GWL is not reached during the period 2021–2100. Sources: Hauser et al. (2019); WGI TS Cross-Section Box 
Table TS.1(Arias et al., 2021)
CMIP5 full ranges
GWL RCP2.6 RCP4.5 RCP6.0 RCP8.5
4°C n.c. n.c. n.c. 2047–2066 to 2080–2099
3°C n.c. 2054–2073 to 2070–2089 2062–2081 to 2080–2099 2030–2049 to 2077–2096
2°C 2015–2034 to 2079–2098 2014–2033 to 2075–2094 2023–2042 to 2068–2087 2004–2023 to 2048–2067

1.5°C 1998–2017 to 2075–2094 1998–2017 to 2051–2070 2001–2020 to 2050–2069 1990–2009 to 2035–2054
CMIP6 full ranges
GWL SSP1–1.9 SSP1–2.6 SSP2–4.5 SSP3–7.0 SSP5–8.5
4°C n.c. n.c. 2061–2080 to 2081–2100 2046–2065 to 2081–2100 2042–2061 to 2081–2100
3°C n.c. 2050–2069 to 2068–2087 2034–2053 to 2081–2100 2030–2049 to 2081–2100 2027–2046 to 2079–2098
2°C 2009–2028 to 2063–2082 2008–2027 to 2075–2094 2009–2028 to 2080–2099 2008–2027 to 2060–2079 2008 - 2027 to 2055 - 2074
1.5°C 1997–2016 to 2058–2077 1997–2016 to 2073–2092 1997–2016 to 2051–2070 1997–2016 to 2042–2061 1997 - 2016 to 2038 - 2057
WGI AR6 assessed very likely (5–95%) ranges
4°C n.c. n.c. n.c. 2070–2089 to n.c. 2058–2077 to n.c.
3°C n.c. n.c. 2061–2080 to n.c. 2050–2069 to n.c. 2042–2061 to 2074–2093
2°C n.c. 2031–2050 to n.c. 2028–2047 to 2075–2094 2026–2045 to 2053–2072 2023–2042 to 2044–2063

1.5°C 2013–2032 to n.c. 2012–2031 to n.c. 2012–2031 to 2037–2056 2013–2032 to 2033–2052 2011–2030 to 2029–2048

1
139Point of Departure and Key Concepts  Chapter 1
Table Cross-Chapter Box CLIMATE.3a |  Projected continental level result ranges for select temperature and precipitation climate change variables by global warming level. Ranges are 5th and 
95th percentiles from SSP5–8.5 WGI CMIP6 ensemble results. There is little variation in the 5th and 95th percentile values by GWL across the SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 projections. Source: WGI AR6 
Interactive Atlas (https://interactive-atlas.ipcc.Chapter/).
Climate 
variableGlobal 
warming 
levelAll regions North America Europe AsiaCentra-South 
AmericaAfrica Australia Antarctica
Mean 
temperature (°C)4°C 12 to 15 8 to 11 5 to 9 12 to 14 24 to 27 26 to 29 24 to 27 −33 to −27
3°C 11 to 14 6 to 11 4 to 7 10 to 14 23 to 26 25 to 28 23 to 26 −35 to −26
2°C 10 to 13 5 to 9 3 to 6 8 to 12 22 to 25 24 to 27 22 to 25 −36 to −27
1.5°C 9 to 12 4 to 8 2 to 5 8 to 12 22 to 24 24 to 26 22 to 24 −36 to −27

Mean daily 
minimum 
temperature (°C)4°C 8 to 11 4 to 8 1 to 6 6 to 11 19 to 24 21 to 25 18 to 23 −38 to −29
3°C 6 to 11 2 to 8 0 to 5 4 to 10 19 to 22 19 to 23 17 to 21 −39 to −30
2°C 5 to 10 0 to 6 −2 to 4 3 to 9 17 to 21 18 to 22 16 to 20 −40 to −31
1.5°C 4 to 9 −1 to 5 −2 to 3 2 to 8 17 to 21 17 to 22 16 to 19 −41 to −32
Minimum of 
daily minimum 
(°C)4°C −12 to −5 −25 to −15 −22 to −14 −18 to −9 11 to 15 10 to 14 5 to 10 −64 to −48
3°C −13 to −6 −27 to −15 −24 to −15 −20 to −11 10 to 15 8 to 14 4 to 10 −64 to −50
2°C −15 to −8 −30 to −18 −27 to −17 −22 to −13 9 to 14 7 to 13 3 to 9 −65 to −51
1.5°C −16 to −9 −32 to −20 −28 to −19 −23 to −14 8 to 14 6 to 12 3 to 9 −66 to −51
Mean 
maximum daily 
temperature (°C)4°C 16 to 19 12 to 15 8 to 11 15 to 18 27 to 32 30 to 35 28 to 33 −31 to −25
3°C 15 to 19 11 to 15 7 to 11 14 to 18 27 to 32 30 to 37 27 to 34 −32 to −25
2°C 14 to 18 9 to 13 6 to 9 13 to 17 26 to 31 29 to 36 27 to 33 −33 to −25

1.5°C 13 to 17 8 to 12 5 to 9 12 to 16 25 to 30 28 to 35 26 to 33 −33 to −26
Maximum of 
daily maximum 
(°C)4°C 32 to 37 32 to 38 28 to 33 35 to 40 36 to 43 40 to 47 41 to 49 −12 to −5
3°C 31 to 39 31 to 38 28 to 34 35 to 41 35 to 44 39 to 51 41 to 54 −12 to −3
2°C 30 to 37 30 to 36 26 to 33 33 to 39 34 to 43 38 to 50 39 to 53 −13 to −4
1.5°C 29 to 36 29 to 35 25 to 31 32 to 39 33 to 42 38 to 49 39 to 52 −14 to −5
Number of days 
with maximum 
above 35°C—
bias adjusted4°C 81 to 106 36 to 50 11 to 22 57 to 77 138 to 194 153 to 210 140 to 168 0 to 0
3°C 66 to 87 27 to 40 6 to 15 44 to 59 100 to 153 131 to 183 124 to 147 0 to 0
2°C 52 to 68 19 to 29 4 to 8 33 to 45 61 to 106 116 to 151 102 to 124 0 to 0
1.5°C 45 to 58 16 to 24 2 to 5 30 to 39 43 to 85 107 to 133 94 to 115 0 to 0
Number of 
days with 
maximum 
above 40°C—
bias adjusted4°C 28 to 40 9 to 16 1 to 5 19 to 26 21 to 68 69 to 92 53 to 83 0 to 0

3°C 20 to 30 5 to 11 1 to 2 14 to 21 9 to 32 56 to 77 41 to 64 0 to 0
2°C 14 to 21 2 to 6 0 to 1 9 to 15 3 to 13 41 to 57 27 to 45 0 to 0
1.5°C 11 to 17 2 to 4 0 to 0 8 to 12 1 to 8 35 to 47 22 to 38 0 to 0

1
140Chapter 1 Point of Departure and Key Concepts
Climate 
warming 
Near-
surface total 
(mm d−1)4°C 2 to 3 2 to 3 2 to 2 2 to 3 4 to 5 2 to 3 1 to 2 1 to 1
3°C 2 to 3 2 to 3 2 to 2 2 to 3 3 to 5 2 to 3 1 to 2 1 to 1
2°C 2 to 3 2 to 3 2 to 2 2 to 3 3 to 5 2 to 3 1 to 2 1 to 1
1.5°C 2 to 3 2 to 3 2 to 2 2 to 3 3 to 5 2 to 3 1 to 2 1 to 1
Maximum 
1-day 
amount (mm)4°C 35 to 55 40 to 53 27 to 35 36 to 52 47 to 90 29 to 67 43 to 68 9 to 13
3°C 31 to 52 34 to 50 23 to 33 30 to 50 37 to 88 25 to 66 38 to 69 8 to 12
2°C 29 to 50 32 to 48 22 to 32 28 to 47 37 to 85 22 to 59 36 to 66 8 to 11
1.5°C 28 to 48 31 to 47 21 to 31 27 to 45 35 to 84 21 to 58 36 to 64 8 to 11
Maximum 
5-day 
amount (mm)4°C 79 to 99 75 to 93 53 to 71 81 to 105 118 to 168 68 to 113 81 to 124 20 to 29

3°C 66 to 99 68 to 87 48 to 68 70 to 101 97 to 165 60 to 118 76 to 129 19 to 27
2°C 64 to 93 65 to 84 47 to 65 66 to 95 93 to 162 55 to 107 73 to 122 18 to 26
1.5°C 63 to 91 63 to 83 46 to 64 64 to 93 92 to 160 52 to 105 74 to 119 18 to 25
Consecutive 
dry days 
(precipitation 
< 1 mm)4°C 36 to 80 23 to 31 26 to 38 35 to 68 31 to 88 48 to 146 45 to 109 44 to 99
3°C 36 to 88 21 to 33 25 to 43 35 to 76 29 to 82 49 to 160 40 to 127 45 to 120
2°C 37 to 88 21 to 32 24 to 40 36 to 74 29 to 77 49 to 161 38 to 128 45 to 127
1.5°C 36 to 87 22 to 31 25 to 37 36 to 74 28 to 77 49 to 159 40 to 125 46 to 131
Table Cross-Chapter Box CLIMATE.3b |  Projected sea surface temperature change ranges by global warming level and ocean biome (degrees Celsius). Ranges are 5th and 95th percentiles from SSP5–8.5 WGI CMIP6

ensemble results. There is little variation in the 5th and 95th percentile values by GWL across the SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 projections. Source: WGI Interactive Atlas (https://interactive-atlas.ipcc.
Chapter/).
Global 
warming 
levelAll ocean 
biomesNorthern 
Hemi-
sphere—high 
latitudesNorthern 
Hemisphere—
subtropicsEquatorialSouthern 
subtropicsSouthern Hem-
isphere—high 
latitudesGulf of 
MexicoEastern 
boundariesAmazon 
RiverArabian SeaIndonesian 
Flowthrough
4°C 1.9 to 2.4 2.0 to 3.3 2.2 to 2.8 2.1 to 3.0 1.8 to 2.4 1.3 to 2.0 2.1 to 2.8 2.1 to 2.7 1.7 to 2.5 2.3 to 2.9 1.9 to 2.7
3°C 1.3 to 1.7 1.2 to 2.2 1.4 to 2.4 1.4 to 2.2 1.2 to 1.7 0.7 to 1.4 1.5 to 2.3 1.4 to 2.1 1.2 to 2.0 1.6 to 2.2 1.3 to 1.9
2°C 0.6 to 1.0 0.5 to 1.4 0.7 to 1.4 0.7 to 1.3 0.5 to 1 0.3 to 0.8 0.6 to 1.4 0.6 to 1.3 0.6 to 1.3 0.6 to 1.3 0.5 to 1.2
1.5°C 0.2 to 0.7 0.1 to 0.9 0.2 to 1.0 0.2 to 0.8 0.2 to 0.6 0.1 to 0.5 0.2 to 1.0 0.2 to 0.9 0.2 to 0.9 0.2 to 0.9 0.1 To 0.8

1
141Point of Departure and Key Concepts  Chapter 1
Illustration of the use of Global Warming Levels as a dimension of integration for impact studies:
Projected changes in river flows in major basins at 4°C global warming from four different multi-model ensembles
-50%0%50%100%Tigris
Euphrates Nile Amu Darya HelmandGanges
Brahmaputr aMurray
Darling Mekong-50%0%50%100%Parana Amazon Amur Hwang Ho Yangtze Niger Congo-50%0%50%100%Colorado Mississippi Ri9o Grande Danube Volga Ob Baikal-50%0%50%100%Mackenzie Yukon Saskatchewan Tagus Rhine Elbe KemijokiCMIP5
CMIP6
HELIX
ISIMIP
CMIP5
CMIP6
HELIX
ISIMIP
CMIP5
CMIP6
HELIX
ISIMIP
CMIP5
CMIP6
HELIX
ISIMIP
CMIP5
CMIP6
HELIX
ISIMIP
CMIP5
CMIP6
HELIX
ISIMIP
CMIP5
CMIP6
HELIX
ISIMIP
Model agreement
66% 90% 95% 90% 95% 66% Drier WetterIncreased flow s
Decreased flowsMedian
95% confidence range
Figure Cross-Chapter Box CLIMATE.1 |  Illustration of the use of global warming levels (GWLs) as a dimension of integration for impact studies:

projected changes in river flows in major basins at 4°C global warming from four different multi-model ensembles. Results are shown for projected flow 
changes direct from Earth System Models (ESMs) in CMIP5 and CMIP6, for the Joint UK Land Environment Simulator (JULES) land surface model driven by meteorological 
outputs of the HadGEM3 and EC-Earth model in the High-End cLimate Impacts and eXtremes (HELIX) ensemble (Betts et al., 2018; Koutroulis et al., 2019), and nine 
hydrological models driven by a subset of five CMIP5 ESMs in the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP; Warszawski et al., 2014). Dots show results 
from individual models, blue for increased flows and red for decreased flows, black circles show the median for each ensemble, and black bars show the 95% confidence 
range in the median. See Figure 4.11 for further details.Cross-Chapter Box CLIMATE1 (continued)

1
142Chapter 1 Point of Departure and Key Concepts
To contextualise reported impacts by warming level for the influence of other determinants of risk, where appropriate and feasible (e.g., 
level of exposure/vulnerability, level of adaptation, time period), common time periods for the past and future can be aligned with WGI’s 
historical and projected time windows. Given differences in available literature, WGII chapters and CCPs (cross-chapter papers) 
contextualise impacts with respect to exposure, vulnerability and adaptation as appropriate.
Common ranges for other ‘climate’ variables, such as minimum and maximum temperatures and regional climates, are available based 
on WGI projections. They are based on feasible combinations with GWLs taken into consideration using the WGI Interactive Atlas. Climate 
information translation may have been necessary within chapters for mapping the WGII literature and assessments of the common

climate dimensions. WGII’s climate impacts literature is based primarily on climate projections around AR5 and earlier or assumed 
temperature levels, though some recent impacts literature uses newer climate projections based on the CMIP6 exercise. Thus, it was 
important to be able to map climate variable levels to climate projections of different vintages and vice versa and adjust variables, 
when possible, to a common reference year. WGII chapters and CCPs only provide climate impact information for the common climate 
dimensions that their literature supports and where there is sufficient evidence.
Interpretation of the update in projected time of reaching 1.5°C global warming from SR1.5 to AR6
In an assessment using multiple lines of evidence, including models, observational constraints and improved understanding of climate 
sensitivity, WGI project a central estimate of the 20-year average warming crossing the 1.5°C GWL in the early 2030s in all scenarios

assessed, except SSP5–8.5 (Lee et al., 2021). This is about 10 years earlier than the midpoint of the likely range (2030–2052) assessed in 
SR1.5, which assumed continuation of the observed warming rate reported at that time. However, this does not imply that the projected 
impacts of 1.5°C will be reached 10 years earlier, because roughly half of the 10-year difference is a result of updating the diagnosed 
historical rate of warming due to methodological advances, new datasets and other improvements (Gulev et al., 2021). The other half of 
the 10-year difference arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near 
term than was assessed as ‘current’ in SR1.5 (medium confidence).
The revised historical warming rate does not necessarily contribute to a change in timing of estimated impacts. It depends on how

impacts are calculated relative to climate. Because the revised historical warming results in a redefinition of the 1.5°C GWL relative to 
the modern time period (1995–2014) rather than a different level of overall change (Figure Cross-Chapter Box CLIMATE.2 in Chapter 1), 
impacts assessed relative to the modern time period are unaffected. There are, in effect ‘old’ and ‘new’ definitions of the 1.5°C GWL with 
different levels of impacts, and the impacts assessed for the ‘old’ 1.5°C GWL now apply to a different level of global warming. However, 
the timing of impacts assessed relative to pre-industrial (e.g., aggregate economic impact estimates), are affected and we are closer to 
impact levels associated with 1.5°C and 2°C.
To illustrate with a worked example: in SR1.5, the historical warming between 1850–1900 and the modern period of 2006–2015 was 
assessed as 0.87°C, implying that the 1.5°C GWL would be accompanied by impacts associated with 0.63°C warming from the modern

period. However, AR6 WGI (Gulev et al., 2021) revised the assessment of warming between 1850–1900 and 2006–2015 to 0.94°C, implying 
that the 1.5°C GWL would be accompanied by a slightly lower level of impacts associated with only 0.56°C warming from the modern 
period. So, while the redefined 1.5°C GWL would be reached earlier, it would also be accompanied by a lower level of impacts (Figure Cross-
Chapter Box CLIMATE.2 in Chapter 1). The impacts associated with the ‘old’ 1.5°C GWL would now be seen at 1.57°C global warming 
relative to 1850–1900, reached at the time of the ‘old’ 1.5°C GWL, if the same future level of warming were to be used as in SR1.5.
However, in addition to this redefinition of the historical warming rate, the assessed future warming in AR6 is also slightly faster than the 
continuation of reported recent warming used in SR1.5. This means that both the ‘old’ and ‘new’ 1.5°C GWLs are projected to be reached

earlier than they would have been using the SR1.5 method. This and the revised historical warming diagnosis contribute approximately 
equally to the assessment of 1.5°C global warming being reached about 10 years earlier than projected in SR1.5.
Central estimates of impacts associated with a specifically defined 1.5°C GWL could therefore be considered to be projected to be 
reached approximately 5  years earlier than implied by SR1.5. However, uncertainties in regional climate responses at a given GWL 
are large (Cross-Chapter Box CLIMATE in Chapter 1, Table CLIMATE.3a) and natural climate variability occurs in parallel with ongoing 
warming, so the potential for impacts higher than central estimates could be a more urgent consideration for risk assessments and 
adaptation planning than the earlier projected timing of reaching 1.5°C (high confidence). It should also be noted that individual years

may exceed 1.5°C above 1850–1900 sooner, but this is not the same as exceedance of the 1.5°C GWL which refers to the 20-year mean.Cross-Chapter Box CLIMATE1 (continued)

1
143Point of Departure and Key Concepts  Chapter 1
change, prices) and relationships (Annex II: Glossary). Scenarios are 
neither predictions nor forecasts but rather ‘foresights’, which imply 
envisioning challenging futures (Vervoort and Gupta, 2018). Scenarios 
are used to provide a view of the potential consequences and implications 
of developments and actions in a ‘what-if’ mode of exploring the future 
(AR6 WGIII Section  1.5.1; AR6 WGI Section  1.6.1, Chen et  al., 2021). 
They may be presented as numerical or mental models. Climate change 
scenarios are generated by climate modellers to highlight possible 
alternative GHG emission pathways and are used to develop and 
integrate projections of emissions and their climate change impacts 
and for analysing and contrasting climate policy choices. Cross-Chapter 
Box CLIMATE in Chapter 1 describes scenarios used in this report.
Pathways are one element of a larger scenario (O’Neill et  al., 2017),

focusing on just one element of a larger system of drivers, emissions 
or concentrations. Scenarios provide one means to represent deep 
uncertainty when there is disagreement or uncertainty about conceptual 
models (Cross-Chapter Box DEEP in Chapter 17; IPCC, 2019b). In addition, 
scenarios provide several important functions in decision support. A lack 
of strong association with probabilities enables scenarios to promote 
buy-in from parties to a decision who hold different expectations about 
the future, helping them to expand the range of futures and options 
they consider. The process of generating scenarios can serve as the focus 
of participatory stakeholder exercises and processes, and scenarios can 
also be used to support risk management by stress-testing alternative 
policies and identifying robust and adaptive policies under conditions of 
deep uncertainty (Cross-Chapter Box DEEP in Chapter 17).1.3 Understanding and Evaluating Climate 
Risks

Understanding of climate change has advanced in important ways 
that shape the AR6 assessment. This section describes advances in the 
understanding of the complex nature of climate change risks, the deep 
integration of social sciences, and increased utilisation of IK and LK. 
These multi-faceted dimensions of understanding climate change and 
evaluating risks are introduced here.
1.3.1 Nature of Climate Risk
Since AR5, understanding of the nature of climate risk has advanced 
substantially. AR6 assesses the serious, complex and cascading 
climate risks unfolding across sectors and regions. These risks are 
shaped by many societal factors including cultural norms and social 
practice, socioeconomic development, underlying physical and social 
vulnerability, and societal responses themselves (Section  1.2.1.1). 
Throughout, there is increased attention to the important role of 
different forms of knowledge, especially IK and LK, in the understanding

and the management of the changing climate.
1.3.1.1 The Nature of Climate Risk as Assessed in this Report
Greater understanding of climate-related risks is emerging; however, 
there are important shortcomings in the information for some regions 
and sectors, and for developing versus developed countries. These risks 
assume significance in interaction with the cultures, values, ethics, 
identities, experiences, and knowledge systems of affected communities 
and societies, as well as their governance, finances, capabilities and Warming
relative to modern period
used for impacts assessment
Modern period
Warming
from 1850–1900 
to modern period0.63°C0 .56°C0 .63°C
0.87°C0 .94°C0 .94°C1.5°C impact level in SR1. 5
1.5°C impact level in AR 6
1.5°C
warming
in SR1.5
1.5°C
warming
in AR6
1.5°C 
1.5°C 
1.57°C
warming
in AR6Definitions of the 1.5°C Global Warming Level in SR1.5 and AR6 WG1

Figure Cross-Chapter Box CLIMATE.2 |  Definitions of the 1.5°C global warming level (GWL) in SR1.5 (IPCC, 2018b) and AR6 WGI (IPCC, 2021a). GWLs 
are defined relative to 1850–1900 but impacts at the GWL are typically assessed in association with warming relative to a modern period 1995–2014, which in SR1.5 
was 2006–2015. Revised assessment of the historical warming between 1850–1900 and the modern period (0.87°C in SR1.5 to 0.94°C in AR6) has the effect of slightly 
reducing the warming between the modern period and the 1.5°C GWL (0.63°C in SR1.5 to 0.56°C in AR6), and the impacts at the GWL previously defined as 1.5°C in 
SR1.5 now occur at 1.57°C global warming with the AR6 definition. Warming values are central estimates. Heights of the bars are not to scale.Cross-Chapter Box CLIMATE1 (continued)

1
144Chapter 1 Point of Departure and Key Concepts
resources. The key risk assessment in the IPCC AR5 informed the long-
term temperature goal in the 2015 Paris Agreement—limiting the 
increase in global mean temperature to well below 2°C and pursuing 
efforts towards limiting warming to 1.5°C (Oppenheimer et al., 2014; 
Pachauri et al., 2014). The IPCC Special Report on Global Warming of 
1.5°C, responding to an invitation by UNFCCC, used new scientific 
information to provide a specific risk assessment associated with the 
ambitious warming levels targeted by the Paris Agreement (Hoegh-
Guldberg et al., 2019). The Special Reports on Oceans and Land further 
advanced the methods of transparent risk assessment (Zommers et al., 
2020). The current assessment expands significantly from the previous 
reports, aiming to inform and advance understanding of the following 
core themes: (a) the ways changes in vulnerability and exposure

modulate risks of climate change impacts and risk complexity in 
addition to warming; (b) the knowledge basis relevant to continued 
refinement of temperature goals; (c) the effectiveness of adaptation 
solutions; (d) the management of risks at higher levels of warming, 
should ambitious climate change mitigation be unsuccessful, including 
limits to adaptation; and (e) the benefits of climate change mitigation 
and emissions reductions (Section 16.1).
This report evaluates key risks—potentially severe risks—meriting 
society’s full attention globally and regionally across sectors, in order 
to inform judgements about dangerous anthropogenic interference 
with the climate system (Oppenheimer et al., 2014; Mach et al., 2016; 
see also Sections 16.1.2; 16.4; WGI Section 1.2.4.1). As described detail 
in Chapter 16, evaluation of key risks is based on expert judgement 
applied to all relevant lines of evidence, with a focus on the role of

societal values in determining the importance of a risk. Specific criteria 
considered relate to the magnitude of adverse consequences, including 
the potential for irreversibility, thresholds, or cascading effects; the 
likelihood of adverse consequences; the timing of the risk; and the 
ability to respond to the risk (Section 16.5.1).The key risk assessment conveys increasing urgency given the growing 
visibility of climate change impacts in the current world (Sections 1.1; 
16.1). Representative key risks emerging across sectors and regions 
include risks to coastal socio-ecological systems and terrestrial and 
ocean ecosystems; risks associated with critical infrastructure, networks 
and services; risks to living standards and human health; risks to food 
and water security; and risks to peace and migration (Section 16.5). 
Compared to the AR5, the emphasis on human dimensions of key 
climate-related risks has continued and increased, for instance, the

potentially severe impacts for cultural heritage (IPCC, 2014c; Pachauri 
et  al., 2014; see also Section  16.4). These human dimensions are 
essential for understanding vulnerability, impacts and risks central to 
ensuring human well-being, human security, sustainable development 
and poverty reduction in a changing climate.
To encompass the nature of climate risk, IPCC assessment since 
the Third Assessment Report has used five overarching domains, 
named ‘reasons for concern’, to assess increasing risk for societies 
and ecosystems under climate change (IPCC, 2014b; O’Neill et  al., 
2017; see also Section  16.5; WGI Section  1.2.4.1). The reasons for 
concern approach has enabled evidence to be combined with expert 
judgement, in order to provide a holistic assessment across multiple 
lines of evidence (O’Neill et al., 2017). The approach also respects the 
uncertainties inherent to climate risk and highlights the ways in which

values are relevant in connecting scientific knowledge to societal 
decision making and risk management. The different reasons for 
concern underscore that there is no single metric that can reflect all 
dimensions of climate-related risk and the diversity of consequences 
for lives and livelihoods, health and well-being, economic and 
sociocultural assets, infrastructure and ecosystems (Mach and Field, 
2017; see also Section 1.4.1.2).
The AR6 Reasons for Concern framework enables integration across 
key risks and representative key risks, including how risks vary with the 
High impac t
countriesHigh emissions
Climate
migrantsAir conditioning Urban greening
Energy
generation
GHG
emissionsHeat wavesHeat
deathsGHG
emissions
Frequency
of extreme
events
Disaster impacts in
memory affectin g
perceived riskEmissions
behaviour
changeEnergyHealth
WaterFood(a) Sectoral (b) Temporal (c) Spatial (d) Multiple response op tionsDifferent interactions can decrease or increase climate-related risks

Figure 1.3 |  Different interactions can decrease or increase climate-related risks. Key examples include interactions (a) among sectors, (b) through time, (c) across 
regions, or (d) between impacts and responses. The specific interactions indicated within each panel of this figure are illustrative, not comprehensive or indicative of relative 
importance. Source: (Simpson et. al. 2021)

1
145Point of Departure and Key Concepts  Chapter 1
magnitude of global warming, socioeconomic development pathways 
and levels of adaptation (Section  16.6). Risk levels are determined 
through a formal elicitation approach for both representative key 
risks and reasons for concern, following the authors’ assessment of 
the literature. The reasons for concern consider unique and threatened 
systems (RFC1), such as coral reefs or Arctic Sea ice systems that 
have especially high vulnerability and low capacity to adapt. They 
also include the role of extreme weather events (RFC2), such as heat 
waves, heavy rain, drought, coastal flooding or wildfires. The reasons 
for concern address both the distributional and the aggregate impacts 
of climate change (RFC3, RFC4), including the unfairness factor for 
populations that have contributed little in terms of historic emissions 
but that are disproportionately vulnerable to the impacts of a changing

climate. The final reason for concern relates to large-scale singular 
events, nonlinearities and tipping points (RFC5), including ice sheet 
collapse and ecosystem regime shifts.
1.3.1.2 The Complexities of Climate Risk
The AR6 assessment incorporates the inherently complex nature 
of climate risk, vulnerability, exposure and impacts, which include 
feedbacks, cascades, non-linear behaviour and the potential for surprise 
(Figures 1.3; 1.4). Many different overlapping and complementary terms 
and methods are used to evaluate and understand complex climate risk 
relevant to this report, such as aggregated, compounding or cascading 
risks, all of which are considered here as relevant to complex climate 
risk (Pescaroli and Alexander, 2018; Simpson et. al. 2021).
The dynamic nature of risk and its determinants is one important 
dimension of complexity. The risk of climate change impacts can be 
usefully understood as resulting from dynamic interactions among

climate-related hazards, the exposure and vulnerability of affected human and ecological systems, and also responses (see Section 1.2.1; AR6 
Glossary, IPCC, 2021b; WGI AR6 Cross-Chapter Box 2 in Chapter 1, Chen 
et al., 2021; Oppenheimer et al., 2014). The determinants of risk all can 
vary and change through space and time in response to socioeconomic 
development and decision making (Figures  1.4; 1.5; Section  16.1). 
Hazards are affected by current and future changes in climate, including 
altered climate variability and shifts in frequency and intensity of 
extreme events (WGI AR6 Chapter 12, Ranasinghe et al., 2021). Such 
hazards can be sudden, for example, a heat wave or heavy rain event, or 
slower onset, for example, land loss, degradation and erosion linked to 
multiple climate hazards compounding. The severity of climate change 
impacts will depend strongly on vulnerability, which is also dynamic and 
includes the sensitivity and adaptive capacity of affected human and

ecological systems (McDowell et al., 2016; Jurgilevich et al., 2017; Ford 
et al., 2018; Viner et al., 2020). As a result, risks vary at fine scale across 
communities and societies and also among people within societies, 
depending, for example, on intersecting inequalities and context-specific 
factors such as culture, gender, religion, ability and disability, or ethnicity 
(Kuruppu, 2009; Jones and Boyd, 2011; Carr and Thompson, 2014; 
also Section 16.1.4). The dynamic social distribution of impacts is the 
subject of increasing attention within climate assessment and responses, 
including the role of adaptation, iterative risk management and climate 
resilient sustainable development (Section 16.1).
Another core area of complexity in climate risk is the behaviour 
of complex systems, which includes multiple stressors unfolding 
together, cascading or compounding interactions, and non-linear 
responses and the potential for surprises (Kopp et  al., 2017; Clarke

et al., 2018; Yokohata et al., 2019). Risks and responses, including their 
determinants, can all interact dynamically in shaping the complexity 
of climate risk (Figure 1.4). The combined effects of multiple stressors 
or compound hazards and risks are unlikely to be assessed through Increasingly complex climate-related risks
(a) Compound: undirectional
(c) Cascade (d) Aggregate(b) Compound: bidirectional
ExposureHazard RiskColour definition of wheels corresponding to the Risk Propeller :
Figure 1.4 |  Increasingly complex climate-related risks. Risk results from interactions among the determinants of risk—hazard, vulnerability, and exposure, shaped by 
responses—which can interact in complex ways. Different risks and responses can compound in single  a) or multiple  b) directions, cascade (e.g., with one event triggering another;

c), and aggregate (e.g., with independent determinants of risks co-occurring; d). This complex nature of risk is central in the AR6 assessment. Figure adapted from Simpson et al. 
(2021).

1
146Chapter 1 Point of Departure and Key Concepts
RiskVulnerability
(b) AR6 additions: response risk and complexityRisk in IPCC assessment through time
Impacts
Emissions and Land-use ChanceSocioeconomic
processes
Socioeconomic
pathways
Adaptation and Mitigation actions
GovernanceClimate
Natural variability
Climate Change(a) The AR5 risk graphic
Key risk
- Representative key risk
- Variation by warming,  
   development and adaptation
   scenarios
- interactions across key risks
Reasons for concern
- Illustrative pathway s
- Global expert judgementVulnerability
ExposureHazardExposureHazard

1
147Point of Departure and Key Concepts  Chapter 1
(c) Future directions: response risks related to adaptation and mitigation
Exposure
ResponseHazard
Riskbi-directional uni-directionalaggregate
Figure 1.5 |  Risk in IPCC assessment through time. (a) An explicit risk framing emerged in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters 
to Advance Climate Change Adaptation (SREX) and WGII AR5 (IPCC, 2014a, b). (b) In the current assessment, the role of responses in modulating the determinants of risk is a 
new emphasis (the ‘wings’ of the hazard, vulnerability, and exposure ‘propellers’ represents the ways in which responses modulate each of these risk determinants). (c) As the risk 
assessment spans Working Groups, the differential role of risk determinants for risk related to impacts, adaptation, and vulnerability versus risk related to mitigation becomes an 
increasingly important feature of climate risk assessment as well as management.

simple addition of the independent effects and instead require system 
approaches to understanding risk. While some components may cancel 
each other out, others may nonlinearly increase risk. Nonlinearities can 
result from abrupt climate changes, tipping points or thresholds in 
responses, alternative stable states, low-probability/high-consequence 
outcomes or events that cannot be predicted based on current 
understanding (WGI Section 1.4.4.3).
The nature of climate risk also involves risks from responses 
themselves (Figure 1.5c). The risks of climate change responses include 
the possibility of responses not achieving their intended objectives or 
having trade-offs or adverse side effects for other societal objectives 
(Annex II: Glossary; Section  16.1). In particular, human responses 
may create novel hazards and unexpected side effects and entail 
opportunity costs and path dependencies (Boonstra, 2016). Such

feedback loops can unfold at local and global scales, including large-
scale interactions among climate, ecological and human systems with 
human behaviour and decision making affecting such interactions. 
Response risks can originate from uncertainty in implementation, 
maladaptation, action effectiveness, technology development or 
adoption, or transitions in systems (see Sections 1.4 and 1.5). Typical 
risks may be related to regulation, litigation, competition, sociopolitics 
or reputation. Interactions across responses can importantly involve 
co-benefits for other objectives, such as for human health and 
well-being which may be improved from both reduced air pollution 
(e.g., AR6 WGI Chapter 6, Szope et  al., 2021; WGIII, IPCC, 2022) 
and enhanced adaptation to climate change. The nature of risk also 
entails residual impacts that will occur even with ambitious societal responses, given limits to adaptation at sectoral and regional levels

(Section 1.4, 16.1, 16.4). In some cases, the losses will be irreversible.
Due to these complexities, the challenge of assessing risks of climate 
change is not well bounded, will be framed differently by individuals 
and groups, involves large and deep uncertainties, and will have 
unclear solutions and pathways to solutions (Rittel and Webber, 
1973; Renn, 2008; see also Sections  1.5.2; 17.2.1). Challenges also 
include the degree to which time is running out, there is no central 
authority, those seeking the solutions are also causing the problem, 
and the present is favoured over the future (Sun and Yang, 2016; see 
also Section 17.2.1). Both the needs for and the limits to adaptation 
responses fundamentally depend on progress achieved in reducing GHG 
emissions and limiting the magnitude of climate change that occurs, 
interlinked with socioeconomic development trajectories and the many 
social and political factors shaping climate risks and responses.

1.3.2 Assessing, Evaluating, and Understanding Climate 
Impacts and Risks
Multiple, diverse sources of information underlie our understanding of 
climate risks and response, including climate change science, diverse 
social sciences and IK and LK.

1
148Chapter 1 Point of Departure and Key Concepts
1.3.2.1 Detection and Attribution of Climate Change and its 
Impacts
Anthropogenic climate change is unequivocal and ongoing. The 
detection of specific changes in the climate and their diverse impacts 
on people and nature is advancing, with robust attribution of climate 
change to GHG emissions as well as to other contributing factors (e.g., 
socioeconomic development, land use change). In the AR6, advances 
include an increasing ability to link individual extreme weather 
and climate events to emissions of GHGs, increasing identification 
of impacts for societies and economies and strong linkages in the 
attribution methods across Working Groups (Cross-Working Group 
Box: ATTRIBUTION in Chapter 1).
Impacts occurring today can be put into context through understanding 
of long-term changes on Earth, introduced in Cross-Chapter 
Box  PALEO in Chapter 1. Climate has always varied and changed

in the past, and this change often caused substantial ecological, 
evolutionary and socioeconomic impacts. Adaptation of ecosystems 
and societies occurred through responses as diverse as migration to 
mass extinction. Humankind is at the verge of leaving the Holocene 
climatic envelope, in which all human achievement since the advent 
of agriculture has occurred. In some systems, the changes and losses 
will be irreversible.
1.3.2.2 Perceiving Climate Risk and Human Response
Since AR5, social science literature on how individuals and societies 
perceive and respond to climate risk has advanced dramatically (Renn, 
2008; Jones et al., 2014; Taylor et al., 2014; Neaves and Royer, 2017; Van 
Valkengoed and Steg, 2019). The literature is increasingly integrating 
and advancing long-standing scholarship on environmental and 
social governance, human dimensions of environmental change, risk 
perception and communication, and enabling conditions for effective

policymaking. These emergent literatures on climate risk, human 
action and solution are reflected in three broad areas of analysis: 
(a) root drivers (i.e., role of cultural norms and social practice, social 
structures and economic development status that shape physical 
and social vulnerability; (b) context-specific barriers and enablers 
(i.e., governance structures, institutional structure and function, risk 
perceptions, access to financing and knowledge availability and needs) 
and (c) the solution-proximate decision space (i.e., climate urgency and 
catalysing conditions, risk communication strategies, M&E strategies) 
(see Solecki et al., 2017; Jorgenson et al., 2019).
These three areas are deeply embedded in the social sciences and reflect 
fundamental questions of how and why humans and their institutions 
act and respond (Chapter 17). In the past two decades, these basic 
issues have been applied to research of climate change, dynamic risk

and adaptation. Underlying this analysis, particularly of root drivers, 
barriers and enablers, are assertions regarding the foundational 
properties of individual and collective behaviour (i.e., self-interest, 
optimisation, rationality, bounded rationality), how they are structured 
and how these properties can be revealed. This literature draws on 
several academic disciplines, including anthropology, economics, 
geography, political science, psychology, sociology and urban studies. 
Climate change social science research is often interdisciplinary or transdisciplinary and hence utilises a variety of methods to derive new 
knowledge (Orlove et al., 2020).
In contrast to previous assessments, AR6 is increasingly focused on the 
needs for and challenges of assessing the societal response to climate 
change. The accurate tabulation of adaptation, a key question for 
examining the solution space, is difficult (Chapter 16; Cross-Chapter

Box ADAPT in Chapter 1), since many forms of adaptation activity are 
under-represented in the peer-reviewed and grey literature. Moreover, 
the related question of assessing the effectiveness of adaptation, that 
is, the extent to which it reduces risk, is also difficult. Estimating risk 
reduction often involves counterfactuals, for instance, quantifying the 
damage a flood would have caused had a community not adapted prior 
to a storm or projecting the damage averted by today’s adaptation in 
some future storm (see Cross-Chapter Box PROGRESS in Chapter 17). 
Many socioeconomic drivers affect risk, so attribution for any observed 
or projected changes must be allocated among those that are due to 
adaptation and those due to economic development, cultural changes 
and other types of policies and trends. For instance, many measures of 
sustainable development overlap with those for adaptive capacity and 
both can reduce climate risk while also yielding benefits irrespective

of future climate regimes (UNEP , 2018). There are also many different 
goals for adaptation both among and within different jurisdictions, so 
that adaptation efforts deemed effective by some individuals may not 
be deemed effective by others (Dilling et al., 2019).
1.3.2.3 Indigenous Knowledge and Local Knowledge
While scientific knowledge is vital, IK and LK are also necessary for 
understanding and acting effectively on climate risk (IPCC, 2014a; IPCC, 
2019b, SROCC Chapter 1; see also Section 2.4). Indigenous knowledge  
refers to the understandings, skills and philosophies developed by 
societies with long histories of interaction with their natural surroundings 
(IPCC, 2019a). Local knowledge is defined as the understandings and 
skills developed by individuals and populations, specific to the places 
where they live (IPCC, 2019a). These definitions relate to the debates on 
the world’s cultural diversity (UNESCO, 2018a), which are increasingly

connected to climate change debates (UNESCO, 2018b). However, there 
is agreement that, in the same way that there is not a unique definition 
of Indigenous Peoples because it depends on self-determination (see 
below), there is not a single definition of neither IK and LK. Therefore, 
contextualisation is greatly needed. IK and LK will shape perceptions 
which are vital to managing climate risk in day-to-day activities and 
longer-term actions.
Such experience-based and practical knowledge is obtained over 
generations through observing and working directly within various 
environments. Knowledge may be place based and rooted in local 
cultures, especially when it reflects the beliefs of long-settled 
communities who have strong ties to their natural environments (Orlove 
et al., 2010). Other times, knowledge may be embedded in institutions 
or oral traditions that mobilise them across contexts, for example, as 
migrant populations bring their knowledge across different regions,

and have global relevance. Scientific insights often confirm IK and LK 
(Ignatowski and Rosales, 2013), but IK and LK also provides specific, 
alternative ways to understand environmental change. This includes 
tacit and embodied aspects of knowledge (Mellegård and Boonstra,

1
149Point of Departure and Key Concepts  Chapter 1
Cross-Working Group Box ATTRIBUTION | Attribution in the IPCC Sixth Assessment Report
Authors: Pandora Hope (Australia), Wolfgang Cramer (France/Germany), Maarten van Aalst (Netherlands), Greg Flato (Canada), Katja 
Frieler (Germany), Nathan Gillett (Canada/UK), Christian Huggel (Switzerland), Jan Minx (Germany), Friederike Otto (UK /Germany), 
Camille Parmesan (France/ UK /USA), Joeri Rogelj (UK /Belgium), Maisa Rojas (Chile), Sonia I. Seneviratne (Switzerland), Aimee Slangen 
(the Netherlands), Daithi Stone (New Zealand), Laurent Terray (France), Robert Vautard (France), Xuebin Zhang (Canada)
Changes in the climate system are becoming increasingly apparent, as are the climate-related impacts on natural and human systems. Attri-
bution is the process of evaluating the contribution of one or more causal factors to such observed changes or events. Typical questions ad-

dressed by the IPCC are, for example: ‘To what degree is an observed change in global temperature induced by anthropogenic GHG and aero-
sol concentration changes or influenced by natural variability?’ or ‘What is the contribution of climate change to observed changes in crop 
yields that are also influenced by changes in agricultural management?’ Changes in the occurrence and intensity of extreme events can also 
be attributed, addressing questions such as: ‘Have human GHG emissions increased the likelihood or intensity of an observed heat wave?’
methods used and provides recommendations for interpretation.
Attribution studies serve to evaluate and communicate linkages associated with climate change, for example: between the human-induced

increase in GHG concentrations and the observed increase in air temperature or extreme weather events (WGI Chapters 3, 10 and 11); or 
between observed changes in climate and changing species distributions and food production (e.g., Verschuur et al., 2021; WGII Chapter 2 
and others, summarised in Chapter 16) or between climate change mitigation policies and atmospheric GHG concentrations (WGI Chapter 5; 
WGIII Chapter 14). As such, they support numerous statements made by the IPCC (IPCC, 2013; IPCC, 2014c; WGI Section 1.3, Appendix 1A).
Attribution assessments can also serve to monitor mitigation and assess the efficacy of applied climate protection policies (e.g., Nauels 
et al., 2019; Banerjee et al., 2020; WGI Section 4.6.3), inform and constrain projections (Gillett et al., 2021; Ribes et al., 2021; WGI 
Section 4.2.3) or inform loss and damages estimates and potential climate litigation cases by estimating the costs of climate change

(Huggel et al., 2015; Marjanac et al., 2017; Frame et al., 2020). These findings can thus inform mitigation decisions, as well as risk 
management and adaptation planning (e.g., Climate & Development Knowledge Network, 2017).
The unambiguous framing of what is being attributed to what is a crucial first step for an assessment (Easterling et al., 2016; Hansen et al., 
2016; Stone et al., 2021), followed by the identification of the possible and plausible drivers of change and the development of a hypothesis 
or theory for the linkage (see Figure ATTRIBUTION.1 in Chapter 1). The next step is to clearly define the indicators of the observed change 
or event and note the quality of the observations. There has been significant progress in the compilation of fragmented and distributed 
observational data, broadening and deepening the data basis for attribution research (Poloczanska et al., 2013; Ray et al., 2015; Cohen

et al., 2018; WGI Section 1.5). The quality of the observational record of drivers should also be considered (e.g., volcanic eruptions: WGI 
Section 2.2.2). Impacted systems also change in the absence of climate change. This baseline and its associated modifiers such as agricultural 
developments or population growth need to be considered, alongside the exposure and vulnerability of people depending on these systems.
There are many attribution approaches, and several methods are detailed below. In physical and biological systems, attribution often builds 
on the understanding of the mechanisms behind the observed changes and numerical models are used, while in human systems other 
methods of evidence-building are employed. Confidence in the attribution can be increased if more than one approach is used and the 
model is evaluated as fit for purpose (Hegerl et al., 2010; Vautard et al., 2019; Otto et al., 2020a; Philip et al., 2020; WGI Section 1.5). Finally,

appropriate communication of the attribution assessment and the accompanying confidence in the result is needed (e.g., Lewis et al., 2019).2020) that may be crucial to foster local action and that are not easily 
captured in scientific knowledge (including cultural indicators, scales and 
interconnectedness between ecosystems). Multiple knowledge systems 
(i.e. IK, LK, disciplinary knowledge, technical expertise) may coevolve in 
iterative and interactive processes whereby they influence each other. However, at the same time, they may have specific characteristics so 
that they cannot be reduced to each other or subsumed by each other, 
and they all have relevance to understanding the interactions between 
society and climate (Bremer et al., 2019).

1
150Chapter 1 Point of Departure and Key Concepts
Attribution of changes in atmospheric GHG concentrations to anthropogenic activity
AR6 WGI Chapter 5 (Canadell et al., 2021) presents multiple lines of evidence that unequivocally establish the dominant role of 
human activities in the growth of atmospheric CO 2, including through analysing changes in atmospheric carbon isotope ratios and the 
atmospheric O 2:N2 ratio (WGI Section 5.2.1.1, Canadell et al., 2021). Decomposition approaches can be used to attribute emissions 
underlying those changes to various drivers such as population, energy efficiency, consumption or carbon intensity (Hoekstra and van 
den Bergh, 2003; Raupach et al., 2007; Rosa and Dietz, 2012). Combined with attribution of their climate outcomes, the attribution of 
the sources of GHG emissions can inform the attribution of anthropogenic climate change to specific countries or actors (Matthews,

2016; Otto et al., 2017; Skeie et al., 2017; Nauels et al., 2019) and, in turn, inform discussions on fairness and burden sharing (WGIII 
Chapter 14).
Changes in large-scale climate variables (e.g., global mean temperature) have been reliably attributed to anthropogenic and natural 
forcings (e.g., Hegerl et al., 2010; Bindoff and et al., 2014; WGI Section 1.3.4). The most established method is to identify the ‘fingerprint’ 
of the expected space–time response to a particular climate forcing agent such as the concentration of anthropogenically induced 
GHGs or aerosols, or natural variation of solar radiation. This technique disentangles the contribution of individual forcing agents to an 
observed change (e.g., Gillett et al., 2021). New statistical approaches have been applied to better account for internal climate variability

and the uncertainties in models and observations (e.g., Naveau et al., 2018; Santer et al., 2019; WGI Section 3.2). There are many other 
approaches, for example, global mean sea level change has been attributed to anthropogenic climate forcing by attributing the individual 
contributions from, for example, glacier melt or thermal expansion, while also examining which aspects of the observed change are 
inconsistent with internal variability (WGI Sections 3.5.2; 9.6.1.4).
such as regional land use change or aerosols, may enhance or reduce regional signals of change (Lejeune et al., 2018; Undorf et al., 2018; 
Boé et al., 2020; Thiery et al., 2020; see also WGI Sections 10.4.2; 11.1.6; 11.2.2). In general, regional climate variations are larger than

the variations in global mean climate, adding additional uncertainty to attribution (e.g., in regional sea level change, WGI Section 9.6.1). 
These statistical limitations may be reduced by ‘process-based attribution’, focusing on the physical processes known to influence the 
response to external forcing and internal variability (WGI Section 10.4.2).
New methods have emerged since AR5 to attribute the change in likelihood or characteristics of weather or climate events or classes of 
events to underlying drivers (National Academies of Sciences, 2016; Stott et al., 2016; Jézéquel et al., 2018; Wehner et al., 2019; Wang et 
al., 2020; WGI Sections 10.4.1; 11.2.2). Typically, historical changes, simulated under observed forcings, are compared to a counterfactual 
climate simulated in the absence of anthropogenic forcing. Another approach examines facets of the weather and thermodynamic status

of an event through process-based attribution (Hauser et al., 2016; Shepherd et al., 2018; Grose et al., 2020; WGI Section 10.4.1; WGI 
Chapter 11). Events where attributable human influences have been found include hot and cold temperature extremes (including some 
with widespread impacts), heavy precipitation, and certain types of droughts and tropical cyclones (e.g., Vogel et al., 2019; Herring et al., 
2021; WGI Section 11.9). Event attribution techniques have sometimes been extended to ‘end-to-end’ assessments from climate forcing 
to the impacts of events on natural or human systems (Otto et al., 2017; examples in Table 16.1; Section 16.2).
Attribution of observed changes in natural or human systems to climate-related drivers
The attribution of observed changes to climate-related drivers across a diverse set of sectors, regions and systems is part of each chapter

in the WGII contribution to the AR6 and is synthesised in WGII Chapter 16 (Section 16.2). The number of attribution studies on climate 
change impacts has grown substantially since AR5, generally leading to higher confidence levels in attributing the causes of specific 
impacts. New studies include the attribution of changes in socioeconomic indicators, such as economic damages due to river floods (e.g., 
Schaller et al., 2016; Sauer et al., 2021), the occurrence of heat-related human mortality (e.g., Vicedo-Cabrera et al., 2018; Sera et al., 
2020) or economic inequality (e.g., Diffenbaugh and Burke, 2019).Cross-Working Group Box (continued)

1
151Point of Departure and Key Concepts  Chapter 1
of possible and 
plausible causes Attribution assessmentPurposes: 
- evaluate and 
communicate causes
- monitor anthropogenic
 influence
- inform projections, 
policies and action
1
2
345Attribution in the IPCC: The process of evaluating the contribution of one 
or more causal factors to observed changes or events
the hypothesis. 
counterfactual, evaluate
methods and models, consider 
constraints
Figure Cross-Working Group Box ATTRIBUTION.1 | Schematic of the steps to develop an attribution assessment, and the purposes of such assessments.  
Methods and systems used to test the attribution hypothesis or theory include model-based fingerprinting, other model-based methods, evidence-based fingerprinting,

process-based approaches, empirical or decomposition methods and the use of multiple lines of evidence. Many of the methods are based on the comparison of the observed 
state of a system to a hypothetical counterfactual world that does not include the driver of interest to help estimate the causes of the observed response.
Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations from 
remote sensing, long-term in situ observations and monitoring efforts, teamed with LK, process understanding and empirical or dynamical 
modelling (Section 16.2; Stone et al., 2013; Cramer et al., 2014). The attribution of a change in a natural or human system (e.g., wild species, 
natural ecosystems, crop yields, economic development, infrastructure or human health) to changes in climate-related systems (i.e., climate

and ocean acidification, permafrost thawing or sea level rise) requires accounting for other potential drivers of change, such as technological 
and economic changes in agriculture affecting crop production (Hochman et al., 2017; Butler et al., 2018), changes in human population 
patterns and vulnerability affecting flood or wildfire induced damages (Huggel et al., 2015; Sauer et al., 2021), or habitat loss driving declines 
in wild species (IPBES, 2019b). These drivers are accounted for by estimating a baseline condition that would exist in the absence of climate 
change. The baseline might be stationary and be approximated by observations from the past, or it may change over time and be simulated 
by statistical or process-based impact models (Section 16.2; Cramer et al., 2014). Assessment of multiple independent lines of evidence, 
taken together, can provide rigorous attribution when more quantitative approaches are not available (Parmesan et al., 2013). These include

palaeodata, physiological and ecological experiments, natural ‘experiments’ from very long-term datasets indicating consistent responses 
to the same climate trend/event and ‘fingerprints’ in species’ responses that are uniquely expected from climate change (e.g., poleward 
range boundaries expanding and equatorial range boundaries contracting in a coherent pattern worldwide, Parmesan and Yohe, 2003). 
Meta-analyses of species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of 
climate change at an appropriate scale for attribution to anthropogenic climate change (Parmesan and Yohe, 2003; Parmesan et al., 2013).Cross-Working Group Box (continued)

1
152Chapter 1 Point of Departure and Key Concepts
Cross-Chapter Box PALEO | Vulnerability and Adaptation to Past Climate Changes
Authors: Wolfgang Kiessling (Germany, Chapter 3, Cross-Chapter Paper 1), Timothy A. Kohler (USA, Chapter 14), Wolfgang Cramer (France, 
Chapter 1, Cross-Chapter Paper 4), Gusti Anshari (Indonesia, Chapter 2), Jo Skeie Hermansen (Norway, CA Chapter 1), Darrell S. Kaufman 
(USA, WG 1, Chapter 2), Guy Midgley (South Africa, Chapter 16), Nussaïbah Raja (Mauritius, CA Chapter 3), Daniela N. Schmidt (UK/
Germany, Chapter 13), Nils Chr. Stenseth (Norway, Chapter 1), Sukumar Raman (India, Chapter 1)
Understanding how Earth’s biota have responded to past climate dynamics is essential to understanding current and future climate-
related risks, as well as the adaptive capacity and vulnerabilities of ecosystems and the human livelihoods depending on them. Here we

assess climate impacts on long geological time scales (Cross-Chapter Box PALEO in Chapter 1, Figure PALEO.1), as well as for the last 
70 kyr of Homo sapiens’ existence (Cross-Chapter Box PALEO in Chapter 1, Figure PALEO.2). Climate responses of natural and human 
systems are intertwined through the physiological limits of wild animals, livestock, plants and humans, subject to a slow evolutionary 
dynamic (Pörtner, 2021; Sections 2.6.1; 3.3).
Climate has always changed, often with severe effects on nature, including species loss
Observations provided by the historical, archaeological, and palaeontological records, together with paleoclimatic data, demonstrate 
that climatic variability has high potential to affect biodiversity and human society (high confidence). The evolution of the Earth’s biota 
has been punctuated by global biodiversity crises often triggered by rapid warming (high confidence) (Figure PALEO.1; Bond and Grasby,

2017; Benton, 2018; Foster et al., 2018;). These so-called hyperthermal events were marked by rapid warming of >1°C, which coincided 
with global disturbances of the carbon and water cycles, and by reduced oxygen and pH in seawater ( Foster et al., 2018; Clapham and 
Renne, 2019). Magnitudes of global temperature shifts in hyperthermal events were sometimes greater than those predicted for the 
current century but extended over longer periods of time. Rates inferred from paleo records that are coarsely resolved are inevitably lower 
than those from direct observations during recent decades, and caution must be exercised when describing the rate of recent temperature 
changes as unprecedented (Kemp et al., 2015). Mass extinctions, each with greater than 70% marine species extinctions, occurred when 
the magnitude of temperature change exceeded 5.2°C (Song et al., 2021), albeit species extinctions occurred at lower magnitudes of 
warming (medium confidence).

Adaptation options to rapid climate change are limited
Responses of biota to rapid climate change have included range shifts (very high confidence), phenotypic plasticity (high confidence), 
evolutionary adaptation (medium confidence), and species extinctions, including mass extinctions (very high confidence). While 
knowledge about the relative roles of these processes in promoting survival during times of climate change is still limited (Nogués-Bravo 
et al., 2018), they have influenced the evolutionary trajectories of species and entire ecosystems (high confidence), and also the course 
of human history (medium confidence). The combined ecological and evolutionary responses to ancient rapid warming events ranged 
from extinction of 81% of marine animal species and 70% of terrestrial tetrapod species on land at the end of the Permian period (~ 
252 million years ago, Ma) (Smith and Botha, 2005; Stanley, 2016) to low rates of species extinctions but biome- and range shifts on land

and in the ocean at the Palaeocene-Eocene Thermal Maximum (PETM, ~ 56 Ma) (Figure PALEO.1; Ivany et al., 2018; Fraser and Lyons, 
2020; Huurdeman et al., 2021). Temperature and deoxygenation were key drivers of past biotic responses in the oceans (Gibbs et al., 
2016; Penn et al., 2018; Section 3.3) (high confidence), whereas on land the interplay between temperature and precipitation is less well 
established in ancient hyperthermals (Frank et al., 2021) (medium confidence). Climate-driven extinction risk increased by up to 40% 
when a short-term climate change added to a long-term trend in the same direction, for example when a long-term warming trend was 
followed by rapid warming (Mathes et al., 2021).Impact attribution does not always involve attribution to anthropogenic climate forcing. However, a growing number of studies include 
this aspect (e.g., Diffenbaugh and Burke, 2019, for the attribution of economic inequality between countries; Frame et al., 2020, for the

attribution of damages induced by Hurricane Harvey; or Schaller et al., 2016, for flood damages).Cross-Working Group Box (continued)

1
153Point of Departure and Key Concepts  Chapter 1
Organismic traits associated with extinctions during ancient climate changes help identify present-day vulnerabilities and conservation 
priorities (Barnosky et al., 2017; Calosi et al., 2019; Reddin et al., 2020; Chapters 2; 3; Cross-Chapter Paper 1). Marine invertebrates and 
fishes are at greater extinction risk in response to warming than terrestrial ones because of reduced availability of thermal refugia in 
the sea (Pinsky et al., 2019) (high confidence). Terrestrial plants showed reduced extinction during past rapid warming compared to 
animals (high confidence), although they readily adjusted their ranges and reorganised vegetation types (Yu et al., 2015; Lindström, 2016; 
Heimhofer et al., 2018; Slater et al., 2019; Huurdeman et al., 2021).
Population range shifts including migrations are common adaptations to climate changes across multiple time scales and ecological

systems in the past and in response to current warming (high confidence). Poleward expansions and retractions ( Reddin et al., 2018; 
Williams et al., 2018; Fordham et al., 2020) as well as migration upslope and downslope in response to warming and cooling were 
common adaptations (Ortega-Rosas et al., 2008; Iglesias et al., 2018;). During warming periods, diversity loss was common near the 
equator (medium confidence)(Kiessling et al., 2012; Kröger, 2017; Yasuhara et al., 2020), while diversity gains and forest expansion 
occurred in high latitudes (Brovkin et al., 2021). Comparison of contemporary shells and skeletons with historical collections in museums 
(Barnes et al., 2011) and the analysis of skeletons of long-lived organisms (Cantin et al., 2010) indicate significant climate-induced 
change in organismic growth rates today (high agreement, medium confidence).
Humankind has responded to regional climate variability within a narrow Holocene climatic envelope

Early human evolution (beginning ~2.1 Ma) occurred in a highly variable climate characterised by glacial-interglacial cycles. This 
variability may have favoured key hominin adaptations such as bipedality, increased brain size, complex sociality, and more diverse 
tools (Potts, 1998; Potts et al., 2020) (medium confidence), but extinctions of five species of Homo have also been attributed partly 
to climate change (Raia et al., 2020) (low confidence). The ‘out-of-Africa’ dispersal of anatomically modern humans may have been 
driven by climate variability ( Timmermann and Friedrich, 2016; Tierney et al., 2017) (medium confidence, low agreement). Most late 
Pleistocene megafaunal extinctions are attributed to direct and indirect human impacts (Sandom et al., 2014), although some were 
likely accelerated by climate change (Wan and Zhang, 2017; Westaway et al., 2017; Carotenuto et al., 2018; Saltré et al., 2019) (low 

The emergence of agriculture (~10.2 ka) in southwest Asia was associated with stable (within ±1°C global mean annual on multi-century 
time scale; WGI Chapter 2) warm and moist conditions (Richerson et al., 2001; Rohling et al., 2019; Palmisano et al., 2021). Variability in 
resource availability and agricultural production, entrained by climatic variability, is implicated in the disruption and decline of numerous 
past human societies (medium confidence) (d’Alpoim Guedes and Bocinsky, 2018; Cookson et al., 2019; Jones, 2019; Park et al., 2019). 
These crises are partially caused by regional climate anomalies including Holocene ‘Rapid Climate Change Events’ (Rohling et al., 2019) 
not visible in the globally averaged conditions shown in Figure PALAEO.2. Such anomalies affected human population size (Clark et al., 
2019; Kuil et al., 2019; Riris and Arroyo-Kalin, 2019), health (Campbell and Ludlow, 2020) and social stability/conflict (Büntgen et al.,

2011; Kohler et al., 2014), and triggered migrations (D’Andrea et al., 2011; Schwindt et al., 2016; Chiotis, 2018; Pei et al., 2018) or retarded 
them (Betti et al., 2020; FAQ 14.2). Populations have also been impacted by sea level change in coastal areas (Turney and Brown, 2007; 
Cross-Chapter Box SLR in Chapter 3).
Evidence for widespread droughts ~4.2 ka, lasting for several centuries in some regions, has been tentatively linked to declines of the 
Akkadian Empire (Weiss, 2017; Carolin et al., 2019), the Indus Valley (Giosan et al., 2018; Sengupta et al., 2020), and the Egyptian 
Old Kingdom and Yangtze River Valley (Ran and Chen, 2019). Deteriorating climates often exacerbate accumulating weaknesses in 
social systems to which population growth and urban expansion contribute (Knapp and Manning, 2016; Lawrence et al., 2021; Scheffer 
et al., 2021). The rather narrow climatic niche favoured by human societies over the last 6000 years is poised to move on the Earth’s

surface at speeds unprecedented in this time span (IPCC, 2021a), with consequences for human well-being and migration that could be 
profound under high-emission scenarios (Xu et al., 2020). This will overturn the long-lasting stability of interactions between humans and 
domesticated plants and animals as well as challenge the habitability for humans in several world regions (Horton et al., 2021) (medium 
confidence).Cross-Chapter Box PALEO (continued)Cross-Chapter Box PALEO (continued)

1
154Chapter 1 Point of Departure and Key Concepts
Biological responses to six 
well-known ancient rapid warming 
events (hyperthermals) over the last 
300 million years
Biotic changes
21
3
456-5°C5°C10°C15°C20°C Temperature
anomaly
1
2
3
456Impact
None Minor MayorReef
crisisChanges in
vegetationRange
shiftExtinction
Figure Cross-Chapter Box PALEO.1 |  Biological responses to six well-known ancient rapid warming events (hyperthermals) over the last 300 million 
years. Temperature anomalies (mean temperature difference to pre-industrial 1850–1900, solid orange curve) derived from climate modelling (300–66 Ma) (Haywood 
et al., 2019) and deep-sea proxy data (66–0.1 Ma) (Hansen et al., 2013). Temperature peaks underneath the grey bars indicate well-known hyperthermals with temperature 
anomalies derived from temperature-sensitive proxy data (Foster et al., 2018). Error bars indicate uncertainties in peak warming events (ranges in the literature). Insets

show observed impacts to the biosphere. Q, Quaternary.
Climate change destroys unique natural archives and important cultural heritage sites
Climate change not only impacts past ecosystems and societies but also the remains they have left. The progressive loss of archaeological 
and historical sites and natural archives of paleo environmental data (WGI Chapter 2) constitutes often-overlooked impacts of climate 
change (Cross-Chapter Box SLR in Chapter 3; Anderson et al., 2017; Hollesen et al., 2018; Climate Change Cultural Heritage Working 
Group International, 2019). These archives include peat bogs and coastal archives lost to sea level rise, droughts and fires, degradation 
through permafrost thaw, and dissolution. The ancient cultural diversity documented by such sites is an important resource for future 
adaptation (Rockman and Hritz, 2020; Burke et al., 2021). Since many of these sites constitute anchors for IK, their loss is not just data

lost to science, it also interrupts intergenerational transmission of knowledge (Green et al., 2009).Cross-Chapter Box PALEO (continued)

1
155Point of Departure and Key Concepts  Chapter 1
Humankind is embarking on a trajectory beyond the global temperatures experienced 
since at least the advent of agriculture
-2.0Worldwide expansion of modern humans
Bronze
AgeIron
Age
Archaic4.2 ka BP1950 AD Persistent industrial
chemicals
1964 AD 1610 AD9.2 ka BP
3.2 ka BPc. 1750 AD
14C peak due to atmospheric nuclear detonation sc. 10 ka BP
Beginning of
agriculture
-4.0
-6.0Start of the
industrial revolution
Thousands of years before presentGlobal surface temperature relative to 1850–1900 (°C)8.2 ka BPClimatic variability of regional importanc e
Orbis spik eWorld
(millions)
10,000
5,000
Scenario
SSP1-2.6
SSP3-7.0SSP2-4.5
SSP5-8.5
02.04.0Megafaunal extinctio n
70 40 10 52 200 600 1000 1400 1800 1850 1900 1950 2000 2020 2041–2060 2081–2100Paleolithic Mesolithic Neolithic Historical IndustrialD igital Future projections

Figure Cross-Chapter Box PALEO.2 |  Humankind is embarking on a trajectory beyond the global temperatures experienced since at least the advent 
of agriculture. Global surface temperature change for the last 70,000 years (relative to 1850–1900; data from WGI Chapter 2) alongside projections (with 5–95% 
range; WGI Chapter 4) and major events in human societies. Global climatic parameters do not always capture regional variability of importance to specific societies. The 
‘Orbis Spike’ represents a pronounced dip in atmospheric CO 2 from the Law Dome ice core (Antarctica) (MacFarling Meure et al., 2006) marking the globalisation in biota 
and trade of the Columbian Exchange and population declines and afforestation in the Americas. This, and the 1964 14C peak, have been suggested as possible markers 
for the onset of the Anthropocene (Lewis and Maslin, 2015). Population trends from United Nations (2019).
Moreover, IK and LK may be particularly relevant to ensuring that

climate action does not cause further harm, and also addresses 
historical injustices committed against Indigenous Peoples and other 
marginalised social groups, recognising them as active agents of their 
own change (Nursey-Bray et  al., 2019). There are between 370 and 
500 million people in at least 90 countries belonging to about 5,000 
different ethnic groups that are classified as ‘Indigenous’ (Sangha et al., 
2019). Although there is no single, universal definition of Indigenous 
Peoples, core criterion within both the ILO Convention on Indigenous 
and Tribal Peoples (1989) and the UN Declaration on the Rights of 
Indigenous Peoples (UN, 2007) include: (a) self-determination and (b) 
the recognition that Indigenous Peoples as distinct social and cultural 
groups that retain collective ancestral ties to the lands they inhabited 
or to the lands from which they have been displaced. Indigenous 
Peoples attribute cultural and spiritual values to land, environmental

features and landscapes (ILO, 2013; ILO, 2019). Indigenous Peoples 
suffer disproportionally. For example, they are three times more likely 
to live in extreme poverty than non-Indigenous Peoples; they are also 
more likely to suffer discrimination and violence (UN, 2020). At the 
same time, Indigenous Peoples have long led climate change and 
environmental protection agendas.Indigenous Peoples have been faced with adaptation challenges 
for centuries and have developed coping strategies in changing 
environments (Coates, 2004). Along with other local groups, they hold 
relevant knowledge about the environment and environmental change, 
the impact of those changes on ecosystems and livelihoods, and 
possible effective adaptive responses (see Cross-Chapter Box  INDIG 
in Chapter 18). Therefore, the participation of Indigenous Peoples in 
climate change decisions and the inclusion of Indigenous knowledge 
in the IPCC assessment process should be of high priority (following

recommendations in UNESCO, 2018b, and UN, 2020). Furthermore, the 
participation of scientifically trained climate specialists with indigenous 
backgrounds is valuable to the work of IPCC because the assessment 
must reflect a diverse range of views and expertise (for examples of 
IK, see Cross-Chapter Box INDIG in Chapter 18). Article 31 of the UN 
Declaration on the Rights of Indigenous Peoples (2007) supports the 
inclusion of IK and LK in the IPCC assessment process, calling for the 
use of IK and LK to be protected and validated by Indigenous Peoples 
themselves and their inclusion as active participants in the assessment 
(Klenk et  al., 2017). Paying special attention to the mechanism 
whereby some forms of knowledge have been excluded in previous 
reports—such as the use of technical knowledge or acronyms, or the 
deployment of discipline-specific validation mechanism—is a first step Cross-Chapter Box PALEO (continued)

1
156Chapter 1 Point of Departure and Key Concepts
towards developing an inclusive assessment that reflects a wide range 
of voices.
The AR4 was the first IPCC report to explicitly discuss the value of IK 
and LK in adaptation and mitigation processes. AR5 recognised the 
importance of creating synergies across disciplines in the production 
of knowledge, acknowledging the importance of ‘non-scientific’ 
sources such as IK, which may not follow discipline conventions but 
nevertheless reflects the outcomes of learning across generations 
(Burkett et al., 2014). This also explains the importance of including IK 
and LK and diverse stakeholder interests and values in local decision 
making processes (Jones et  al., 2014). Such processes should be 
done in partnership with IK and LK knowledge holders and, when 
possible, be led by them (Inuit Tapiriit Kanatami, 2018). Recent IPCC 
reports have included distinct sections dedicated to IK and LK (e.g.,

IPCC, 2019b). The IPCC Special Report on Climate Change and Land 
(SRCCL) includes a section on ‘Local and Indigenous knowledge for 
addressing land degradation’ (2019a) and the IPCC Special Report 
on Ocean and Cryosphere (SROCC) describes LK as ‘what non-
Indigenous communities, both rural and urban, use on a daily and 
lifelong basis,’ a type of knowledge which is recognised as ‘multi-
generational, embedded in community practices and cultures, and 
adaptive to changing conditions’ (2019b). The IPCC Special Report 
on Global Warming of 1.5°C emphasised the high vulnerability of 
Indigenous Peoples to climate change. It stated that disadvantaged 
and vulnerable populations, including Indigenous Peoples and certain 
local communities, are at disproportionately higher risk of suffering 
adverse consequences with global warming of 1.5°C and beyond 
(IPCC, 2018b). The report also assessed evidence in relation to the 
importance of including IK and LK in adaptation options, explaining

their role in early warning systems and arguing that they are part of a 
range of approaches to catalyse wide-scale values and are consistent 
with adapting to and limiting global warming to 1.5°C (IPCC, 2018b).
Since AR5, several academic publications have directly addressed the 
challenges of including IK and LK in climate research ( Ford et al., 2016; 
Yeh, 2016; David-Chavez and Gavin, 2018) and demonstrated its value 
in building resilience to extreme events related to climate change 
(Janif et al., 2016; Olazabal et al., 2021). For instance, IK and LK has 
proved useful in land management methods that reduce wildfire risk 
(Nepstad et al., 2006; Cook et al., 2012; Welch et al., 2013; Mistry et al., 
2016). Since IK is traditionally communicated through storytelling 
and oral history, there are practical challenges to integrating it into 
an assessment that prioritises scientific knowledge. There is a need 
for increased critical engagement towards the co-production of

knowledge (Ford et al., 2016). Scholars now recognise the ontological 
and epistemological differences in approaches, understandings and 
effects of climate change (Yeh, 2016). One common strategy has 
been assessing Indigenous observations of climate change alongside 
scientific data (Klein et  al., 2014a) as a means to bridge the gap 
between scientific inquiry and Indigenous knowledge systems 
(Fernández-Llamazares et  al., 2017). The Intergovernmental Science-
Policy Platform on Biodiversity and Ecosystem Services (IPBES) and 
the CBD have helped illustrate how to bridge multiple knowledge 
systems, particularly those conceived from different ontologies. Rather 
than viewing IK as a single source of knowledge to be compared with 
scientific data, recent scholarship suggests assessments, such as the IPCC, directly involve Indigenous researchers (Yumagulova et al., 2019) 
to ensure ethical and equitable engagement with IK. Such partnership

with and leadership of Indigenous Peoples on climate research is also 
consistent with the UN Declaration on the Rights of Indigenous Peoples 
(e.g., Bawaka Country et al., 2015; Inuit Tapiriit Kanatami, 2018; Cross-
Chapter Box INDIG in Chapter 18).
1.3.3 Regional Assessment
As climate change is a multi-scale phenomenon, from the local to the 
global, the assessment of climate risks and climate change impacts 
is strongly spatial, with a focus on regional climate change. The term 
‘regions’ is used in different ways throughout the AR6 assessment as 
the use of the term varies across disciplines and context.
First, there are chapters dedicated to regional assessment in AR6 
WGII (Chapters 9–14 and Cross-Chapter Papers 4 and 6). Within 
the content of these and other chapters of AR6, the term region 
is often used to describe continental and sub-continental regions, 
oceanic regions, hemispheres, or more specific localities within these

geographic areas. Building on the continental domains defined in AR5 
WGII and to ensure consistency with WGI Chapter 12 (Ranasinghe 
et  al., 2021) and the WGI Atlas (Gutiérrez et  al., 2021), AR6 WGII 
uses a continental set of regions, namely Africa, Asia, Australasia, 
Europe, North America, Central and South America, Small Islands, 
Polar Regions and the Ocean.
Second, the term regions is used to categorise areas around the globe 
with common topographical characteristics or biological characteristics. 
For example, Chapter 2 introduces regions in its discussion of biomes, 
as in arid, grassland, savanna, tundra, tropical, temperate and boreal 
forested regions. Chapter 3 adds reference to an area’s orientation 
with bodies of water, using terms such as deltaic, coastal, intercoastal, 
freshwater and salty. In addition, Cross-Chapter Paper 2 uses a 
coastal region typology based on physical geomorphology considering

elevation, coastal type and topography (see Cross-Chapter Paper 2, p. 
5; Barragán and de Andrés, 2015; Kay and and Adler, 2017; Haasnoot 
et al., 2019a).
Third, CCPs are dedicated to typological regions, defined in the Annex 
II: Glossary as regions that share one or more specific features (known 
as ‘typologies’), such as geographic location (e.g., coastal), physical 
processes (e.g., monsoons), biological (e.g., coral reefs, tropical forests, 
deserts), geological (e.g., mountains) or anthropogenic (e.g., megacities), 
and for which it is useful to consider the common climate features. 
Typological regions are generally discontinuous (such as monsoon areas, 
mountains, deserts and megacities) and are specifically used to integrate 
across similar climatological, geological and human domains.
Understanding climate risks across regions also requires consideration 
of the capabilities of developing countries and scientists across country

contexts in conducting climate assessments. Substantial unevenness 
of available climate observations, risks assessments and scientific 
literature across regions and country capacities substantially challenges 
a globally comprehensive assessment (Connelly et al., 2018).

1
157Point of Departure and Key Concepts  Chapter 1
1.3.4 Evaluating and Characterising the Degree 
of Certainty in Assessment Findings
Since 1990, IPCC assessments have included designated terms and 
other approaches for communicating the expert judgements made 
by authors (Mastrandrea and Mach, 2011). The goal of such methods 
has been consistent treatment of uncertainties in assessing and 
communicating the current state of knowledge. Because terms such 
as ‘probable’ or ‘likely’ hold very different meanings to different 
people, a standardised approach is essential for enabling consistent 
interpretation (WGI Section  1.2.3.1). Since its 2001 assessment, 
IPCC authors have applied common guidance on expert judgement 
across the Working Groups (Moss and Schneider, 2000; IPCC, 2005). 
The AR5, iteratively building from past IPCC guidance, was the first 
report to apply a single framework consistently across the Working

Groups and their diverse topics and associated disciplines (Figure 1.3; 
Mastrandrea et al., 2010; Mastrandrea and Mach, 2011). The outcome was increased comparability of assessment conclusions across the full 
spectrum of the physical science basis of climate change and resulting 
impacts, risks and responses (Mach et al., 2017).
This framework for expert judgement is again being applied in the AR6 
and associated special reports in the assessment cycle (Mastrandrea 
et  al., 2010; see also WGI Box  1.1). Under the framework, the 
assessment of scientific understanding and uncertainties begins with 
evaluation of evidence and agreement—especially the type, amount, 
quality and consistency of evidence and the degree of agreement 
(steps 1–3 in Figure 1.6). Evidence assessed can reflect observations, 
experimental results, process-based understanding, statistical analyses 
or model outputs. Evidence is most robust when it consists of multiple

lines of consistent, independent and high-quality evidence. The 
degree of agreement considers the extent of established, competing 
or speculative explanations for a given topic or phenomenon across 
the scientific community. Together, this evaluation of evidence and 
4. Evaluate confidence based on 
evidence and agreement 6. Evaluate likelihood
EvidenceAgreement
Very low confidenc eVery high confidenc eHigh agreement
Robust evidenc e
5. Suff icient confidence and quantitative or  
probabilistic evidence? 
Present evidence and agreement Present confidence Present likelihood
It is very likely (90-100%) that the number  
of cold days and nights has decreased and 
the number of warm days and nights has  
increased on the global scale. Medium confidenc e
In many regions, changing preciptiation or melting 
snow and ice are altering hydrological systems, 
affecting water resources in terms of quantity and

quality (medium confidence). Behavior , lifestyle, and culture have a considerable 
influence on energy use and associated emissions,
with high mitigation potential in some sectors, in 
particular when complementing technological and 
structural change (medium evidence, medium 
agreement).Virtually certain that there has been a change 
(99-100% probability)
Likely that there has been a change
(66-100% probability)Low agreement
Medium agreemen t
Medium evidenc e
High confidenc e
Low confidenc eOutcome probability
Likely
Unlikely
90-100% 
66-100%
33-66% 
0-33% 
0-10%
0-1% Extremely unlikely 0-5% More likely than not >50-100% Extremely likely 95-100% Evaluation and communication of degree of certainty in AR5 ﬁndings 
yes no yes1. What evidence exists?
Consistenc yType

and scientific agreementStatistics
Models
3. Sufficient evidence and  
agreement to evaluate 
confidence? 
no
Figure 1.6 |  The IPCC AR5 and AR6 framework for applying expert judgement in the evaluation and characterisation of assessment findings. This illustration 
depicts the process assessment authors apply in evaluating and communicating the current state of knowledge. Guidance for the application of this framework is described in full 
detail in Mastrandrea et al. (2010). In addition to scientific knowledge, IK and LK is central to understanding and acting effectively on climate risk (Section 1.3.2.3). The diagram 
in this figure is reproduced from Mach et al. (2017).

1
158Chapter 1 Point of Departure and Key Concepts
agreement forms a traceable account for each key finding in the 
assessment. Subsequently, the framework proceeds to evaluation of 
levels of confidence, which integrate evidence and agreement (steps 
3–5 in Figure 1.6). Confidence reflects qualitative judgements of the 
validity of findings. It thereby facilitates, more readily, comparisons 
across assessment conclusions. Increasing evidence and agreement 
corresponds to increasing confidence (step 4 in Figure 1.6).
If uncertainties can be quantified, the framework involves a further 
option of characterising assessment findings with likelihood terms 
or more precise presentations of probability (steps 5–6 in Figure 1.6). 
The relevant probabilities can pertain to single events or broader 
outcomes. Probabilistic judgements can be based on statistical or 
modelling analyses, elicitation of expert views or other quantitative

analyses. Where appropriate, authors can present probability more 
precisely with complete probability distributions or percentile ranges, 
also considering tails of distributions important for risk management. 
Usually, likelihood assignments are underpinned by high or very high 
confidence in the findings.
Confidence is often most applicable in characterising key findings in 
WGII assessment (Mach et al., 2017). This tendency results from the 
diverse lines of evidence across disciplines relevant to climate change 
impacts, adaptation and vulnerability. By contrast, likelihood is more 
common in WGI assessment.
The guidance to authors additionally identifies other practices and 
approaches relevant in applying expert judgement and developing 
assessment findings (Mastrandrea et al., 2010; Mastrandrea and Mach, 
2011; Mach and Field, 2017). First, authors are encouraged to carefully 
consider appropriate generalisation within assessment findings,

emphasising insights that are integrative, nuanced and rigorous (IAC, 
2010; Mastrandrea et  al., 2010; NEAA, 2010). Second, authors are 
instructed to attend to potential biases, including in group dynamics, 
such as tendencies towards overconfidence and anchoring or Type I 
(false positive) error aversion (Mastrandrea et al., 2010; Brysse et al., 
2013; Anderegg et al., 2014; Morgan, 2014). Third, particular attention 
is drawn to the importance of evaluating and communicating ranges 
of potential outcomes to inform decision making and risk management 
(Mastrandrea et al., 2010). In some cases, deep uncertainties related to 
parameters or processes that are unknown or disagreed upon strongly 
benefit from dedicated methods of assessment and decision support 
(see Cross-Chapter Box  DEEP in Chapter 17). Fourth, the guidance 
explores the different ways that framings of conclusions can shape 
their interpretation by readers. Finally, the guidance underscores

the importance of reflecting upon all sources of uncertainty, which 
can include deep, difficult-to-quantify and easy-to-underestimate 
uncertainties arising from incomplete understanding of relevant 
processes or competing conceptualisations across the literature 
(Mastrandrea et  al., 2010). A detailed review of literature assessing 
IPCC uncertainty characterisation methods is provided in WGI 1.2.3.1.1.4 Societal Responses to Climate Change 
Risks
AR6 highlights the concept of solutions, defined as effective, feasible 
and just means of reducing climate risk, increasing resilience and 
pursuing other climate-related societal goals. This section introduces 
key concepts used in this report to assess the goals associated with 
adaptation, its process and governance, its implementation, M&E and 
its limits.
The term ‘solutions’ has various synonyms used across this and previous 
IPCC reports, including options, measures, actions and responses. All

denote policies, technologies, processes, investments or other activities 
undertaken in reaction to or with the intent of addressing some aspect 
of climate change (Chapter 17). The term ‘solutions’ has drawbacks, 
suggesting a finality, that is, the problem is solved. Solving climate 
change in this sense is not likely for the foreseeable future. In addition, 
the word ‘solutions’ sometimes denotes a narrow set of responses, 
such as ‘technical solution’, as opposed to more wide-ranging actions 
that might be involved in a transition to resilience. Nonetheless, AR6 
highlights the term ‘solutions’ because, compared to these other terms, 
when acted upon or incorporated in policy, it denotes effectiveness 
and some degree of progress at achieving desired goals.
Assessing successful adaptation is, however, difficult (Cross-Chapter 
Box ADAPT in Chapter 1). WGIII Section  1.6 summarises four broad 
analytic frameworks—aggregate efficiency, ethics and equity, transition

dynamics, and psychology and politics—relevant to mitigation and 
concludes that failure to integrate understanding across them has 
been a fundamental reason for inadequate progress to date in reducing 
GHG emissions. While the four analytic frameworks used in WGIII also 
all contribute to the understanding of adaptation, an integrated view 
remains elusive because adaptation differs strongly from mitigation. In 
particular, the goals of adaptation are harder to define and measure 
than those for mitigation. The feasibility, effectiveness and success of 
many adaptation actions depend more strongly on context. A different, 
often more diffuse, set of actors are involved, and it is often hard to 
distinguish what activities count as adaptation.
Given these challenges, this report provides an assessment of adaptation 
solutions based on the attributes justice, feasibility and effectiveness, 
as shown in Figure 1.7.
A solution is just when its outcomes, the process of implementing the

action and the process of choosing the action respects principles of 
distributive, procedural and recognitional justice (Section  1.4.1.1). 
Any assessment of justice depends on an understanding of potential 
outcomes of alternative options (Chapter 16), as well as processes 
of decision making (Chapter 17). Consideration of justice necessarily 
introduces normative elements into any assessment of what constitutes 
a solution.
A solution is effective to the extent it reduces climate risk. Effectiveness 
can refer to whether an adaptation-related action reduces risk 
(Section 1.4.1.2; Chapter 16) or the extent to which an action achieves its 
intended outcomes within a stated time frame (Chapter 17). Effectiveness 
can also include measures of economic efficiency, assessment of net

1
159Point of Departure and Key Concepts  Chapter 1
benefits over costs and the extent to which an action enhances broader 
and multi-dimensional measures of societal well-being (Section 1.4.1.2). 
Assessments of effectiveness will often involve uncertainty, which may 
affect judgements about the comparative effectiveness and justice 
of alternative options (Chapters 16; 17; Cross-Chapter Box  DEEP in 
Chapter 17). Assessment of effectiveness also involves consideration of 
maladaptation (Section 1.4.2.4) in which an action, often inadvertently, 
increases risk or vulnerability for some or all affected individuals or 
communities.A solution is feasible to the extent it is considered possible and 
desirable, taking into consideration barriers, enablers, synergies 
and trade-offs (Section 1.4.2). AR6 assesses the feasibility of a wide 
range of adaptation options (Cross-Chapter Box  FEASIB in Chapter 
18), building on the approach of the SR1.5 report, which uses five

dimensions of feasibility: geophysical, environmental-ecological, 
technological, economic, sociocultural, institutional. In addition, 
feasibility can also refer to a specific set of actions, so that feasibility 
in any particular situation may depend on specific conditions of 
governance capacity, financial capacity, public opinion, interest Assessing adaptation solutions and success
Figure 1.7 |  Assessing adaptation solutions and success. A solution is defined as an adaptation option which is effective, feasible and conforms to principles of justice. 
These attributes can be assessed ex ante during adaptation planning. During implementation, the overall success of a response can be judged via monitoring and evaluation of 
these attributes. Adaptation unfolds as an iterative learning process of assessment, implementation, monitoring, adjustment and learning. A set of responses is adequate to the

extent that they sufficiently reduce climate risk to levels considered tolerable. Adaptation may not fully avoid residual risks, but the more adequate the response, the less residual 
risk remains. Adaptation also has limits beyond which it is no longer possible to avoid intolerable risks and impacts.

1
160Chapter 1 Point of Departure and Key Concepts
group pressure, and the distribution of political and economic power 
(Chapter 17). For instance, a particular jurisdiction may find either 
of two options feasible when implemented alone but might lack the 
capacity to implement them both at the same time. Feasibility can be 
a context-dependent and time-varying attribute. Many solutions, for 
instance those that seek to unlock financing or build public support for 
certain actions, aim at increasing the feasibility of future adaptation 
responses (Sections 1.4.2; 1.5).
1.4.1 What is Equitable, Just and Effective Adaptation?
Articulating the goals of adaptation is an important initial step in the 
decision-making process (Jones et al 2014). Adaptation often involves 
trade-offs among various options of adaptation, mitigation and 
sustainable development, as well as judgements based on science, 
engineering and economics, and questions of distribution and democratic

participation (Jafry et al., 2018). Articulating the goals of adaptation at 
the international, national and local levels thus requires engaging with 
the concepts of equity, justice and effectiveness (high confidence).
1.4.1.1 Equitable Adaptation Informed by Concepts of Justice
Assessing climate action involves ethical considerations that the 
literature often describes as climate justice. The term ‘climate justice,’ 
however, has been used in different ways in different contexts and 
by different communities. Grassroot organisations and activists often 
focus on unequal global power relations, wealth, and interests within 
communities, within nations, and along the North–South divide, as well 
as the historical responsibility for climate change (Chatterton et al., 
2013). Some national governments also view climate justice as the 
right of developing countries to industrialise. Balancing these issues, 
international climate change negotiations have primarily focused on

current capacities and responsibilities for addressing climate change 
as reflected in the UNFCCC principle of ‘common but differentiated 
responsibilities’ (Fisher, 2015).
Since principles of justice are substantive normative commitments that 
have been debated for centuries, it would be unrealistic to expect a 
universal consensus. Nevertheless, there is broad agreement about the 
core issues. Just normative principles are ones that result in fair and 
equitable allocation of goods, vulnerabilities and risks (Caney, 2014; 
Schlosberg, 2009; Schlosberg, 2013; Jafry et al., 2018)
It is common to distinguish between distributive justice, procedural 
justice and recognition (Fraser, 1999; Schlosberg, 2003; Schlosberg, 
2009; Reckien et al., 2017; Forsyth, 2018; Olazabal et al., 2021). The first 
refers to the distribution of burdens and benefits; the second to who 
decides and participates in decision making; while recognition entails

basic respect and robust engagement with and fair consideration of 
diverse values, cultures, perspectives, and worldviews. Recognition 
is closely linked to distributive and procedural justice (Hourdequin, 
2016). Without recognition, actors may not benefit from the two other 
aspects of justice (medium confidence). Recognition thus represents 
both a normative principle as well as an underlying cause of unjust 
distribution and lack of democratic participation (Svarstad and 
Benjaminsen, 2020). However, recognition is still under-represented in climate justice compared to general scholarship and debate on justice 
principles (Chu and Michael, 2018; Benjaminsen et al., 2021).
Three principles of distributive justice are especially relevant to 
adaptation: fairness between individuals, fairness between states and 
fairness between generations (Fleurbaey et al., 2014). Fairness between 
individuals means that the distribution of goods, vulnerabilities and

risks of climate change should not fall on individuals for arbitrary 
reasons. It would be arbitrary if, say, a family were disproportionately 
affected by climate-induced drought by chance alone. Similarly, an 
adaptation action that protects some and creates risks for others is 
unfair if the final distribution of burdens and benefits is arbitrary.
The second consideration of distributive justice is international justice, 
or fairness between states. An important idea in international climate 
negotiations has been common but differentiated responsibilities 
(CBDR) and respective capabilities, stated in Principle 7 in the Rio 
Declaration (1992), as well as by the Kyoto Protocol (1997). The principle 
reflects the underlying idea that all countries must address climate 
change, but the form of climate action depends on the situation the 
country finds itself in. Developed countries may find themselves in a 
position where they can decarbonise more rapidly and ensure financial

flows, while the responsibilities of LDCs and SIDS may primarily come 
in the form of adaptation actions. This means that uneven distribution 
of wealth and power between (and within) countries is a key driver of 
climate injustice.
The third consideration of distributive justice relevant to climate 
adaptation is fairness between generations and the obligation to ensure 
that future generations are guaranteed at least a minimally decent life 
(Jonas, 1985; Llavador et al., 2010). For example, youth climate activists 
and political philosophers have argued that today’s children, as well 
as generations yet unborn, will be exposed to far greater risks than 
most living adults so that policymakers should work to avoid shifting all 
burdens of adaptation to future generations.
Procedural justice addresses the fairness of the processes by which 
decisions are made and the legitimacy of those making the decisions 
(Gutmann and Thompson, 2009; Kitcher, 2011). Criteria include

transparency, the application of neutral principles among parties, 
respect for participants’ rights and inclusive participation in decision 
making, which often takes the form of participatory processes. Article 
6 of the Framework Convention creates a binding commitment on 
parties to promote public participation in addressing climate change. 
Increased participation by civil society in climate policy discussion, 
including new forums such as the Local Communities and Indigenous 
People’s Platform of the UNFCCC work toward this goal (UNFCCC, 
2021). Genuine, not merely formal, participation requires communities 
be well-acquainted with the climate change risks they face and are 
given a full voice in the process of adaptation planning. Many local 
communities, especially those most vulnerable to climate change, 
remain excluded, which is inconsistent with principles of procedural 
justice. In addition to a normative principle, models of decision making

also suggest that diverse, representative decision makers can be 
expected to make better decisions than more limited groups (Hong 
and Page, 2004; Landemore, 2013; Singer, 2019).

1
161Point of Departure and Key Concepts  Chapter 1
In AR5 WGIII (IPCC, 2014a) discussions of justice and ethical concepts 
were combined with discussions of economic principles while the 
adaptation chapters did not explicitly discuss climate justice. This 
report moves beyond AR5 by connecting the assessment of policy 
choices to normative principles and showing how better outcomes are 
obtained by choosing just ones.
1.4.1.2 Equitable and Effective Adaptation Informed by 
Concepts and Measures of Well-being
Planning and assessment of effective and just adaptation require 
appropriate measures of both criteria. This report uses both single and 
multi-criteria measures. Local and regional decision makers employ 
benefit–cost analysis to efficiently allocate scarce resources among 
alternative adaptation efforts and among adaptation and other societal 
needs. Decision makers at national and global levels can employ 
measures of social welfare to consider trade-offs and synergies among

adaptation, mitigation, and development. Such measures can avoid 
wasteful allocation of resources and help avoid maladaptation. Such 
measures also prove useful because well-established approaches exist 
to evaluate such quantities, and because income is highly correlated 
with a wide range of indicators of social progress and climate change 
adaptation capacity (Dasgupta et al., 2018).
Aggregate, monetised economic measures are, however, insufficient 
to address issues of climate justice fully or to reflect that wide range 
of worldviews and values that different people bring to questions of 
climate action and development (Chambwera et al., 2014). While recent 
work has enriched the consideration of distributive justice in aggregate 
social welfare functions (Adler, 2012), multi-objective approaches that 
separately report several biophysical and socioeconomic attributes 
can prove valuable (Section  17.3.3). Many adaptation measures, in

particular those that encompass transformational social changes 
(Section 1.5), involve complicated trade-offs among multi-dimensional 
benefits and costs (Adger, 2016). Different people commonly value 
such trade-offs differently, particularly in heterogeneous societies. 
Multi-objective measures can thus enhance transparency, fairness, 
legitimacy and participation by highlighting the different outcomes 
that different people and communities might find important, making 
the specific trade-offs more transparent and explicit, and avoiding 
privileging any particular view on the appropriate trade-offs (Lempert 
et al., 2018; Siders, 2019b; Siders and Keenan, 2020).
The SDGs and Key Representative Risks (Chapter 16) exemplify such 
multi-criteria measures. In addition, many communities increasingly 
measure policy outcomes using multi-objective measures, often 
organised around the concept of well-being and designed to allocate

resources and implement policies to advance social progress (Lee et al., 
2015; City of Santa Monica, 2018). Similarly, the Human Development 
Index (HDI), which derives from the capabilities approach, combines 
income (as gross national income, GNI, and parity purchasing power, 
PPP) with an education and a health indicator and integrates human 
and socioeconomic factors (Herrero et  al., 2012; USEPA, 2016; Leal 
Filho et  al., 2018; Nagy et  al., 2018; UNDP , 2018). The inequality-
adjusted HDI value, or IHDI, can be interpreted as the level of human 
development when inequality is accounted for (UNDP , 2018).The multi-criteria concept of well-being has been increasingly 
employed as a structured framework for measuring social progress 
in many areas of public policy (Lamb and Steinberger, 2017) including 
climate and health (Chapter 7) and, to a lesser extent, in other areas 
of the climate change adaptation literature (Singh et al., 2021). Well-

being reflects the ability of a person to pursue and realise the goals 
that they value (Sen, 1985). The disaster risk management community 
employs well-being to evaluate mental health impacts in terms of 
peoples’ abilities to cope with trauma and loss because of natural 
disasters (Berry et  al., 2010; MacDonald et  al., 2015; Willox et  al., 
2015). The term appears in the literature with concepts such as 
human security (Koren and Butler, 2006; Adger, 2010; Pasgaard et al., 
2017), subjective well-being or happiness (Sekulova and van den 
Bergh, 2013; Rehdanz et al., 2015; Fanning and O’Neill, 2019), welfare 
(Gough, 2015) and living standards or quality of life (Degorska and 
Degorski, 2018; Rao and Min, 2018).
Recent work has used quantified measures of well-being and multi-
objective decision-support tools to balance among equity and efficiency 
objectives in disaster risk management (Section  1.5.2; Chapter 17;

Markhvida et al., 2020). Rather than focus on the economic value of 
lost assets, the well-being measure evaluates disaster impacts and 
recovery policies by considering the fraction of consumption lost at 
the household level for different income cohorts. Not surprisingly, poor 
households account for twice as much of the disaster losses when 
evaluated by effects on well-being rather than by asset losses. The 
most effective policy responses also differ when using well-being and 
asset loss-based measures. Ciullo et. al. (2020) compare flood control 
strategies using multi-objective decision criteria that include both 
benefit–cost and distributional components, show how the favoured 
strategy can depend on whether one seeks equitable risk or equitable 
risk reduction, and propose tools that can help embed both ethical 
and efficiency considerations in adaptation decisions. Widespread use 
of such approaches could strengthen consideration of climate justice

along with efficiency in the evaluation of climate risks and adaptation 
(Section 1.5.2; Dryzek et al., 2013).
1.4.2 Enabling and Governing Adaptation
Adaptation actions taken by individuals, social groups and organisations 
in response to climate and environmental stimuli depend, in part, on 
the options they have (see Chapters 16 and 17). Actions previously 
taken can reduce the scale of responses needed subsequently, increase 
the options available, reduce barriers to additional action and increase 
capacity to respond. Successful adaptation sufficient to meet the goals 
of the Paris Agreement and SDGs needs to involve actors at many 
scales and in many sectors, including individuals and households, 
communities, governments at all levels, private sector businesses, non-
governmental organisations, religious groups and social movements. 
This report highlights the increased range of societal actors engaged 
in adaptation and the need for multi-level and polycentric governance.

The section describes key concepts related to the process of adaptation 
and assessment of how human choices and exogenous changes can 
expand and contract the set of available solutions.

1
162Chapter 1 Point of Departure and Key Concepts
1.4.2.1 Adaptation Process and Expanding the Solution Space
Adaptation actions include those taken with the explicit intention of 
reducing climate risk, as well as actions taken without reference to 
climate change, for example, building community resilience irrespective 
of any particular hazard. Adaptation actions can include those aimed at 
reducing a specific risk or actions aimed at systemic changes, and also 
include adjustments to current practices or transformational changes. 
In addition, the success of adaptation in one place or jurisdiction can 
depend on activities in other places or jurisdictions.
Adaptation actions span a vast range of activities. Successful adaptation 
generally requires a portfolio of actions, often implemented by multiple 
actors in different sectors, often in different places and over time 
(Section  17.2.2). Useful taxonomies include categorising such actions

around representative key risks (Figure  17.3), and by human systems 
and scenarios of adaptation extent for four components of adaptation 
(depth, scope, speed and limits) (Table 16.2). As shown in Chapter 17, for 
instance, ecosystem-based adaptation, hardening buildings and physical 
barriers, and changes to zoning and planned retreat can reduce risks to 
coastal socio-ecological systems. Restoration and protection of forests, 
enhancing ecosystem connectivity through corridors and ecosystem-
based adaptation can reduce risks to terrestrial and ocean ecosystems. 
Increased use of grey, green and blue infrastructure and upgrading 
design standards, city plans and more redundancy in power systems and 
other networks can reduce risks associated with critical infrastructure. 
Insurance and diversified or changed livelihoods can reduce risks to living standards and equity. Improved health-care systems, disaster 
management and early warning can reduce risks to human health. Better

management of land, soil and fisheries, and changing diets and reducing 
food waste can reduce risks to food security. Improved water efficiency 
and policies to reduce demand can reduce risks to water security.
Previous IPCC reports have described in detail adaptation for individual 
actors as an iterative risk management process of scoping (identifying 
risks, vulnerabilities, objectives and decision-making criteria), 
analysis (identifying options, assessing risks, evaluating trade-offs) 
and implementation (implementing chosen options, monitoring, and 
reviewing and learning) (Jones et. al. 2014). This AR6 report expands 
the focus to consider adaptation processes with multiple actors and 
a richer temporal dimension in which actions taken at one time can 
expand or contract the set of feasible, effective and just options 
available at another time, thereby increasing or decreasing the ability of 
adaptation to reduce risks (Section 17.1). This AR6 report also expands

the focus to include decision processes the implement both adaptation 
and mitigation (Chapter 18), as well as heightened attention to M&E, 
which is a key prerequisite for successful iterative risk management 
and achieving effective and just adaptation outcomes at local to 
global levels (Sections  1.4.3; 17.5.2). The challenges of implications 
for adaptation, mitigation and sustainable development outcomes 
result from decision-making process at different levels (Von Stechow 
et al., 2015; Bertram et al., 2016). Overcoming these challenges often 
requires significant learning and innovative ways of linking science, 
practice and policy at all scales (Shaw and Kristjanson, 2014).
Box 1.2 | Financing as an example of enabler
According to the UNFCCC, adaptation finance includes public, private and alternative sources of finance for supporting adaptation

actions, whereby adaptation and resilience are often used interchangeably in this context. Adaptation finance constitutes a crucial 
enabling condition and shaper of the solution space, depending on other enabling conditions, such as proper planning, implementation 
and governance, which are also the triggers for investments and finance to flow, ensuring positive adaptation outcomes. Details of 
adaptation finance can be found in Chapter 17 (Cross-Chapter Box FINANCE in Chapter 17; Section 17.4). The adaptation and resilience 
options offer multiple benefits, including avoiding risks and losses, economic growth and well-being, as well as social and environmental 
benefits (Agrawal and Lemos, 2015; Bayleyegn et al., 2018; Global Commission on Adaptation, 2019). Hence, the rate of return on 
adaptation is large; for example, there is a huge potential of net benefits, that is USD 7.1 trillion while investing USD 1.8 trillion globally

in climate resilience and adaptation options, such as early warning systems, climate-resilient infrastructure, improved dryland agriculture 
crop production, global mangrove protection and building resilience of water resources (Global Commission on Adaptation, 2019). These 
net benefits resulted primarily from reducing future losses and risk, increasing productivity and innovation, and social and environmental 
benefits. Despite significant uncertainty and concerns over undue focus on efficiency (monetary), benefit-cost estimates often ignore 
important issues of non-economic values, effectiveness of risk reduction and climate justice (procedural/distributional).
The current public and private financial flows to adaptation are much smaller than needed (Cross-Chapter Box FINANCE in Chapter 17). Only 
a small portion of overall adaptation finance needs is likely to be covered by public sector finance. Private sector investment thus needs to

play a crucial role. Hence, tracking adaptation finance flows is important for enabling effective planning and prioritisation of investments, 
assessing whether needs are being met, and ensuring accountability towards funding commitments, such as the USD 100 billion promised 
to developing countries per year by 2020 under the Paris Agreement (Donner et al., 2016). Since AR5, significant progress has been made 
in tracking adaptation finance flows through UNFCCC channels, multi-lateral development banks and bilateral finance (Cross-Chapter 
Box FINANCE in Chapter 17), but large information gaps on adaptation finance via national public finance, commercial lenders, investors, 
asset managers and insurers, company finance, and individuals and households remain. That these financial flows do not occur suggests 
misaligned incentives and other governance challenges that could be addressed as part of a response to climate change (Chapter 17).

Across regions and sectors, financial constraints have been identified as the most common and important determinants leading to limits 
to adaptation (Chapter 16).

1
163Point of Departure and Key Concepts  Chapter 1
Two concepts—enabling conditions and catalysing conditions—
help frame this report’s assessment of factors that over time can 
help expand the set of available solutions (Section  17.4). Enabling 
conditions enhance the feasibility of adaptation and mitigation options 
(AR6 Glossary, IPCC, 2021b). Enablers include finance, technological 
innovation, strengthening policy instruments, institutional capacity, 
multi-level governance and changes in human behaviour and 
lifestyles. Chapter 17 (see also WGIII Figure  1.4) identifies three 
broad categories of enabling conditions: (Section 17.4): governance, finance and knowledge. Catalysing conditions motivate and accelerate 
the adaptation decision-making process, leading to more frequent 
and more substantial adaptation (Chapter 17). While enablers make 
adaptation more feasible and effective, catalysing conditions provide

an impetus for action. These later conditions include a sense of urgency 
(Section 17.4.5.1), system shocks, such as those from natural disasters, 
policy entrepreneurs and social movements.Box 1.3 | Nature-based solutions
Nature-based solutions (NbS) (Section 2.6; Cross-Chapter Box NATURAL in Chapter 2) provides an example of how innovative ideas 
can expand the climate solution space (IPCC, 2018b; Seddon et al., 2019). A commonly used definition of NbS is that of The World 
Conservation Union (IUCN), which defines it as ‘actions to protect, sustainably manage, and restore natural or modified ecosystems, that 
address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits’ (Cohen-
Shacham et al., 2016). In the context of IPCC, it focuses on NbS which deliver climate change adaptation or mitigation benefits. NbS

generally benefits biodiversity and supports its role in both climate mitigation and adaptation. While the carbon-sequestering mitigation 
role of increasing forest and tree cover has dominated much of the earlier discussions, the role of NbS in promoting adaptation of natural 
ecosystems and human societies to climate change is being increasingly emphasised. The details of different categories of ecosystem 
services in the ocean or on land, including biodiversity, food provision, other provisioning services (e.g., medicinal and commercial 
products), and regulating and cultural services are described in Chapters 2 and 3.
Forest restoration would certainly contribute substantially towards climate-proofing and achievement of several SDGs as well as the Paris 
Agreement. There is increasing evidence that diverse, native tree species plantations are more likely to be resilient to climate change in

contrast to fast-growing monocultures, (Hulvey et al., 2013) often of exotic species. At the same time, other natural ecosystems such as 
savannas, grasslands, peatlands, wetlands and mangroves have considerable value in acting as carbon sinks as well as providing other 
ecosystem services such as hydrological regulation, coastal protection, maintaining biodiversity and contributing to human livelihoods 
especially pastoralists and fishermen (Veldman et al., 2015; Conant et al., 2017; Leifeld and Menichetti, 2018; Seddon et al., 2019). 
Coastal and marine ecosystems including wetlands and mangroves have featured prominently in studies of NbS in climate adaptation 
and mitigation potential for ‘blue carbon’ sequestration (Inoue, 2019; Sections 3.6.2.1; 6.3.3; Cross-Chapter Paper 2.3.2.3). Agroecological 
practices such as agroforestry, intercropping, rotational grazing, organic manuring, and integrating livestock production with cropping

etc can also consider as NbS which contribute to both climate mitigation and adaptation (Altieri and Nicholls, 2017; Webb et al., 2017; 
Bezner Kerr et al., 2019; Leakey, 2020; Box 5.10).
There are concerns about large-scale conversion of non-forest land into forest plantations for the sole purpose of increasing carbon sinks 
through bioenergy with carbon capture and storage (BECCS) (Heck et al., 2018; Hanssen et al., 2020; Cross-Chapter Box in Chapter 2), 
which may actually result in negative carbon sink (Jackson et al., 2002; Mureva et al., 2018) and significant loss of overall biodiversity 
(Abreu et  al., 2017). Such large-scale afforestation may also lead to the dispossession of previous users, such as smallholders and 
pastoralists. Hence, when NbS include forest plantations or other large-scale conversion of land use, there is a risk that they result in 
maladaptation and malmitigation, including climate injustice (Seddon et al., 2019; Cousins, 2021).

Much of the conceptual framework for NbS has come from initiatives to bring environmental, social and economic dimensions to the 
same level of importance, particularly in the context of a highly urbanised society (Section 6.3.3; Faivre et al., 2017; Nesshöver et al., 
2017). Emphasis has been placed on urban storm water management (Section 2.6.5.2) and heat mitigation using measures, such as 
sustainable drainage systems, urban forests, parks and green roof-tops, apart from coastal defences using NbS (Section 13.6.2.3) This has 
triggered much debate on how distinct the concept of NbS is in relation to other similar concepts, such as ecosystem-based adaptation 
(EbA) approaches (Section 9.11.4.2). There are calls for an assessment framework for proving the ‘effectiveness and efficiency’ of NbS 
in providing superior ecosystem and societal benefits (Calliari et al., 2019). Instead, EbA can be treated as a subset of NbS (Chapter 2).

However, the time frame of EbA is also an important consideration; thus, grassland and forest restoration would operate at different 
time scales, while mangrove restoration can promote adaptation only at local to national scales, depending on the extent and nature of 
coastlines (Taillardat et al., 2018). Given the complex nature of plant and animal species adapting to climate change through dispersal 
and migration to more suitable habitat, this also means that landscape-scale approaches, as opposed to purely protected areas, are 
needed to promote adaptation, conserve and sustainably use biodiversity, and sustain livelihoods (Vos et al., 2008; Sukumar et al., 2016).

1
164Chapter 1 Point of Departure and Key Concepts
The concept of the solution space provides a framework for assessing 
how the options available for adaptation for any particular community 
are not constant over time and can depend on the past, current and 
future choices of many actors. The solution space is defined as the 
space within which opportunities and constraints determine why, 
how, when and who adapts to climate risks (Haasnoot et al., 2020). 
The concept aims to capture the dynamic inter-temporal, spatial and 
jurisdictional interconnections among adaptation actions. A larger 
solution space indicates people and organisations with more options 
for adapting to and reducing their risk from climate change. Both 
human choices and exogenous changes in human and natural systems 
affect the future solution space. For instance, changes such as the 
magnitude and rate of climate change may shrink the space. Economic

growth can generate more resources that expand the solution space as 
can implemented adaptation actions such as pilot projects, awareness 
raising and changes in laws and regulations.
AR5 used the concept of solution space in its SPM Figure  8 (IPCC, 
2014c). Several AR6 chapters, in particular Chapters 13, 14 and 18, use 
the concept to address challenges salient in AR6. In any assessment 
of solutions, what is feasible, effective and just depends not only 
on the potential solution itself but the particular biophysical and 
societal context in which it might occur (Section  17.5; Wise et  al., 
2014; Gorddard et  al., 2016). Solutions can also be space and time 
dependent because the biophysical and societal context can change 
over space and time (Section 18.1.4). In addition, the large gap that 
exists between current climate action and that needed to meet policy 
goals suggests that decision makers may not only seek to implement

available solutions but seek to actively expand the set of solutions 
(Chapters 17; 18). Finally, as used in this report, the concept of solution 
does not fully engage with questions of ‘by whom?’ and ‘for whom?’ In 
many cases solutions would necessarily be implemented by multiple, 
independent actors interacting with varying degrees of cooperation 
and competition (Sections 1.5.2).
1.4.2.2 Governing Adaptation
Governance and governing refer to the structures, processes and actions 
through which private and public actors interact to address societal 
goals. This includes formal and informal institutions and the associated 
norms, rules, laws and procedures for deciding, managing, implementing 
and monitoring policies and measures at any geographic or political 
scale, from global to local. Governance systems and the specific societal 
institutions through which they are organised are crucial to the feasibility

and success of climate change adaptation, both in terms of its effectiveness 
in reducing climate risk and vulnerability, as well as equity (including 
climate justice), with respect to incremental as well as transformational 
adaptation. This is why AR6 WGII pays even more attention than previous 
assessments to governance as an important enabling condition, and to 
the wide range of new actors beyond governments involved in planning, 
implementing, monitoring and evaluating adaptation action. The 
assessments in subsequent chapters of AR6 WGII show that successful 
and equitable collective adaptation efforts at different levels and scales, 
based on key principles of iterative risk management, require strong, 
usually multi-level. governance systems. Multi-level governance refers 
to the dispersion of governance across multiple levels of jurisdiction and

decision making, including global, regional, national and local, as well as trans-regional and trans-national levels (see also WG III Chapter 1). The 
concept emphasises that modern governance generally consists of, and 
is more flexible, when there are linkages of governance processes across 
different scales and levels. Multi-level governance is widely regarded as 
crucial, particularly for transformational adaptation, defined as ‘adapting 
to climate change resulting in significant changes in structure or function 
that go beyond adjusting existing practices including approaches that 
enable new ways of decision making on adaptation’ (IPCC SR1.5, IPCC, 
2018b; see also Section  1.5). The assessment in subsequent chapters 
also shows that public governance arrangements and institutions 
support most adaptation for addressing the main climate risks, though 
the importance of the private sector and community organisations in

adaptation is increasing. It also shows that polycentric governance tends 
to benefit adaptation.
The empirical literature on adaptation governance has advanced 
strongly since AR5. It shows that stronger general governance 
plans and more effective implementation of such plans (UNEP , 2014; 
Chen et al., 2016; Keskitalo and Preston, 2019b: 24; ND-GAIN, 2019; 
Oberlack, 2017; Oberlack and Eisenack, 2018; Woodruff and Regan, 
2019; UNEP , 2018; UNEP et al., 2021). Governance capabilities are, to a 
significant degree, but not exclusively, a function of available financial 
resources and technology. They are also a function of social capital 
and societal institutions, including well-functioning local, regional and 
national governments, and collaboration among these governmental 
actors and non-governmental stakeholders, including civil society and

the private sector. The literature also points to governance conditions 
that are likely to enable transformational adaptation (Maor et  al., 
2017; see also Sections 1.4.4; 1.5; Chapter 17).
Existing comparative data for adaptive capacity worldwide is at a rather 
coarse level of temporal and spatial resolution. It can, nonetheless, 
provide a very general picture of rates of change in adaptive capacity at 
the national scale, and differences between countries. Further empirical 
research is needed to identify the most important predictors of variation 
across countries and time, though the available data suggests that 
differing national income and education levels play a major role in 
accounting for differences in adaptive capacity (Andrijevic et al., 2020). 
Spatially more resolved (sub-national) data is needed because a large 
body of case study research suggests that there is strong variation within 
countries, particularly the large ones (e.g., India, China, Brazil, United

States) (Chapter 16; see also Nalau and Verrall, 2021 and Cross-Chapter 
Box  ADAPT). Moreover, higher degrees of adaptive capacity do not 
mean that adaptation action will follow automatically, nor that it will 
succeed in terms of equity and effectiveness in reducing vulnerability 
to climate change and enhancing well-being. How differences across 
and within countries in climate risk exposure translate into adaptation 
action, contingent on differences in adaptive capacity, can to some 
extent be inferred from case studies, but this remains to be studied at a 
larger, comparable and generalisable scale.
Governance capacity constitutes an important enabling condition 
not only because it facilitates the (efficient) organisation and 
implementation of adaptation action, but also because it contributes 
to learning. The latter is central to the process of adaptation as 
information regarding current and future climate conditions continues

1
165Point of Departure and Key Concepts Chapter 1
to evolve, as does understanding of appropriate response options and

the actors involved. In addition, norms, values and practices may change in response to changing conditions (Jones et al., 2014). Much learning by individuals, communities and organisations is unplanned (National Research Council, 2009), as is much current adaptation (Berrang-Ford, 2020), which can reduce near-term costs and administrative complexity, but may prove maladaptive (Section 1.4.2.4). Iterative risk management (Section 1.2.1.1) and related concepts such as risk governance (Renn, 2008) describe a planned learning process of ongoing assessment, action and reassessment. Iterative risk management can be as simple as scheduling future updates of assessments and plans, as with the 5-year updates of the global stocktake after 2023 called for in theParis Agreement, or encompass more elaborate learning processes,such as dynamic adaptive pathways (Haasnoot et al., 2019b; Cross-Chapter Box  DEEP in Chapter 17) which include specific near-termactions, specific trends to monitor and

specific contingency actions totake depending on the future conditions observed. While often moreeffective at meeting goals, such planned learning processes may poseimplementation and governance challenges (Metzger et al., 2021)

Mainstreaming adaptation into existing governance structures and

associated organisations and their investments, policies and practices can contribute to expanding the solution space and support efforts at transformative adaptation. For instance, urban planning can support adaptation by mainstreaming adaptation into city plans, such as land use planning, procuring resilient infrastructure and transportation, supporting health and social services, promoting community-based adaptation, and protecting and integrating biodiversity and ecosystem services into city planning (Section 17.4.3). Mainstreaming adaptation also shows many shortcomings, such as diminishing the visibility of dedicated, stand-alone adaptation approaches (Persson et  al., 2016), unequal distribution of adaptation efforts, diluting responsibilities for implementation (Nalau et  al., 2016; Reckien et  al., 2019), exhibiting disconnects between planning, investment and implementation, having limited policy coherence (Friend et  al., 2014; Bizikova et  al., 2015; England et  al., 2018;

Koch, 2018) and failing to adequately balance overlapping and/or competing policy objectives (Vij et al., 2018).

Finally, governing adaptation in ways that maximise the solution space 
and facilitate learning can help avoid maladaptation. Maladaptation refers to potentially adverse effects of certain forms of adaptation action, such as increased GHG emissions or increased vulnerability to climate change and diminished welfare of certain parts of a population now or in the future (Anguelovski et al., 2016; Keskitalo and Pettersson, 2016; Veldkamp et  al., 2017; Zimmermann et  al., 2018; Benzie et al., 2018; IPCC, 2018b; Munia et al., 2018; Nadin and Roberts, 2018; Prabhakar et al., 2018). Maladaptation is an example of response risk, which is increasingly highlighted in both AR6 WGII and 
LearningAssessments prior to implementation
Assessments of climate risks and vulnerabilities; identification, 
appraisal and selection of adaptation options
Monitoring and evaluation of implemented adaptation
Establishing criteria, methods, and procedures for 
monitoring and evaluation

Accounting for the use of resources
Identifying and evaluating effects on climate change  
impacts, risks, vulnerabilities, and resilience
Identifying and evaluating effects of adaptation activities on 
socio-economic wellbeing and equityTracking of adaptation finance
Tracking financial flows for adaptationMobilization and allocation of resources
Funding
Technology
Organizational capacity
Adaptation activities
Adaptation outcomes and impacts
Climate change impacts, risks, vulnerabilities, resilienceImplementation
ImplementationAdaptation assessment prior to implementation and M&E during and after implementationLearning
Learning
Figure 1.8 |  Adaptation assessment prior to implementation and M&E during and after implementation. Both systematic assessment of adaptation needs and

options and M&E of implemented adaptation are key to iterative climate risk management and to achieving effective and equitable adaptation. Most assessments to date have referred to aspects prior to implementation. There is much less systematic evidence on adaptation action, and even less evidence on adaptation outcomes and impacts and their implications for climate change impacts, risks, vulnerabilities and resilience. Figure 17.9 provides more detail on M&E.

1
166Chapter 1 Point of Departure and Key Concepts
WGIII (Section 1.3.1.2; see also IPCC Risk Guidance, Reisinger, 2020). 
One example is that adaptation action may set paths that limit the 
choices of future generations to adapt. This last characteristic refers 
to the lock-in effects of improperly designed and costly infrastructures 
that affect the ability of future generations to amend.
Maladaptation can result from many potential barriers, including 
administrative, human, financial and technical resource constraints 
(Hassanali, 2017; Pardoe et  al., 2018; Singh et  al., 2018); lack of 
transparency and/or capacity in governance (Friend et  al., 2014); 
unreliable information on climate impacts and the lack of key 
policy guidelines (Pilato et  al., 2018); entrenched institutional, legal 
and technical obstacles (Gao, 2018) and low literacy, including 
environmental and scientific literacy (Wright et  al., 2014); exclusion

of vulnerable groups (Forsyth, 2018); governance fragmentation (that 
is, a fragmentation of laws, regulations, and policy requirements); 
and limited cross-sectoral collaboration, meaning that there is 
limited coordination and that top-down planning approaches are not 
connected to local dynamics (Archer et al., 2014; Pardoe et al., 2018). 
This report draws attention to maladaptation challenges recognising 
that not all adaptation-related responses reduce risks (Chapter 16). 
Besides, maladaptation is the opposite of successful adaptation, which 
is associated with reduction of climate risks and vulnerabilities for 
humans and ecosystems, increased well-being and co-benefits with 
other sustainable development objectives. (Chapter 17)
1.4.3 Monitoring and Evaluation of Adaptation
M&E encompasses a broad range of activities serving multiple purposes, 
including tracking progress of adaptation efforts over time, understanding

equity and effectiveness of adaptation options and outcomes, and 
informing ongoing adaptation processes (Section  17.5.2.1). While 
monitoring and evaluation are often referred to jointly as ‘M&E,’ 
monitoring usually refers to continuous information gathering, whereas 
evaluation denotes more comprehensive assessments of effectiveness and equity, often resulting in recommendations for decision makers 
(Sections  17.5.1.1; 17.5.1.7). In some literatures M&E refers solely to 
efforts undertaken after implementation. In other literatures, M&E refers 
both to efforts conducted before and after implementation. As shown in 
Figure 1.8, M&E is essential to the process of iterative risk management, 
both in terms of adaptation assessment prior to implementation and 
M&E of implemented adaptation measures. AR6 highlights that M&E 
after implementation is much less common than adaptation assessment 
prior to implementation (Section 17.5.2.1; Berrang-Ford, 2020).

Tracking adaptation planning and policies: Since AR5, interest in M&E 
for tracking progress in adaptation has grown substantially at the 
local, national and global level. The Paris Agreement calls for a global 
stocktake every 5 years starting in 2023 (Cross-Chapter Box PROGRESS 
in Chapter 17). It also encourages states to monitor and evaluate their 
adaptation plans, policies, programmes and actions and provides 
guidance on communicating information about adaptation to the 
international community (UNFCCC, 2015b, Article 7.9d; UNFCCC, 
2018a, Decision 9/CMA.1; UNFCCC, 2018b, Decision 18/CMA.1).
Since AR5, a large number of case studies on individual local to national 
level adaptation measures have been published (see Chambwera 
et  al., 2014; Keskitalo and Preston, 2019b), as well as comparative 
studies across countries over multiple years (Lesnikowski et al., 2016; 
Biesbroek et  al., 2018; Biesbroek and Delaney, 2020). The existing

literature now allows for at least preliminary conclusions about where 
and why we observe adaptation efforts, as described in the sectoral, 
regional and synthesis chapters of this report.
While case studies provide context-specific insights, global inventories 
are essential for tracking global progress on adaptation (UNEP , 2018; 
UNEP et al., 2021; Cross-Chapter Box PROGRESS). Until recently, the 
dominant approach surveyed National Communications to the UNFCCC 
to measure the amount of adaptation planning activity worldwide 
(Gagnon-Lebrun and Agrawala, 2007; Lesnikowski et al., 2016). More 
recent assessments have focused also on the quality of local and 
Cross-Chapter Box ADAPT | Adaptation science
Authors: Johanna Nalau (Australia/Finland), Lauren Rickards (Australia), Tabea Lissner (Germany), Katharine J. Mach (USA), Lisa Schipper 
(Sweden/UK), Chandni Singh (India), William Solecki (USA), Adelle Thomas (Bahamas) and Edmond Totin (Benin)
High-level statements:

• Adaptation knowledge consists of a diverse set of sources including academic research, applied analysis, and practice and experience 
with projects and policy on the ground.
• Adaptation science encompasses both research ‘on adaptation’, documenting and analysing experiences of adaptation, and ‘for 
adaptation’, aiming to advance the planning and implementation of adaptation.
• The nature of adaptation research is diversifying and examines different approaches from local case studies to more global, 
transboundary, comparative and interactive perspectives, although critical conceptual and empirical gaps remain in defining 
effectiveness in adaptation and measuring adaptation progress.
This cross-chapter box complements the reviews of specific adaptation knowledge, content and progress described throughout WGII by 
providing a higher-level analysis of the shifting characteristics of and trends in adaptation research and its evolution over time.

1
167Point of Departure and Key Concepts  Chapter 1
The characteristics and diversity of adaptation knowledge
The knowledge base on adaptation has matured significantly since AR5. Whereas adaptation research was primarily academic during the 
1990s and 2000s, it now includes a proliferation of on-the-ground experience of how to adapt to climate change, increasingly documented 
in reports and papers. Furthermore, academic research on adaptation has diversified significantly. Understanding the characteristics and 
diversity of this knowledge base is key for it to effectively inform decision making and action on adaptation.
Academic work on adaptation now spans an increasing number of disciplines and countries and is published across diverse academic 
outlets and disciplines, with 28.5% annual average increase in adaptation specific publications (Nalau and Verrall, 2021). This expands

the range of considerations and perspectives within adaptation research and increases the challenge of identifying and synthesising all 
relevant research on adaptation in reviews or assessments (Berrang-Ford et al., 2015; Webber, 2016; Singh et al., 2020; Sietsma et al., 
2021). Also, large bodies of research and knowledge exist that support climate adaptation ideas, theoretical development and practical 
implementation, but are not explicitly framed as climate change adaptation (Dupuis and Biesbroek, 2013; Biesbroek et al., 2018; Keskitalo 
and Preston, 2019a). Therefore, debates still emerge about what actually counts as ‘adaptation’ (Dupuis and Biesbroek, 2013), and what 
knowledge is being assessed and measured for this purpose.
IPCC assessment reports combine two complementary approaches to adaptation research: that which is ‘on’ or ‘about’ adaptation and

that which is ‘for adaptation’. Both are needed because research ‘on adaptation’ helpfully investigates the phenomenon and processes of 
adaptation (e.g., via analyses of others’ adaptation practices and efforts), while research ‘for adaptation’ generates knowledge that can enable 
the planning and implementation of adaptation (e.g., action research as part of an adaptive capacity-building process) (Swart et al., 2014).
One of the contributions of research ‘on adaptation’ is to track and debate the broader trends, core characteristics and overall assumptions 
embedded in adaptation knowledge. This reflexive turn about the foundational assumptions is itself one emerging trend in adaptation 
research (e.g., Preston et al., 2013; Nalau et al., 2015; Juhola, 2016; Atteridge and Remling, 2017). This signals the influence of more 
social science in adaptation research and increased awareness of the practical value of being transparent and critically reflective about

the content, topics, frames and approaches that researchers use (Lacey et al., 2015; Nalau et al., 2021; Singh et al., 2021). For example, 
different conceptions of adaptation contribute to different definitions of ‘adaptation success’, different ideas about what ‘effective’ 
adaptation practice looks like and, thus, different conclusions about what is and is not working well (Berrang-Ford et al., 2019; Dilling 
et al., 2019; Magnan et al., 2020; Owen, 2020; Eriksen et al., 2021; Singh et al., 2021; Section 17.5.1.1). This diversity adds richness and 
options, but also poses challenges in constructing a conventional evidence base for decision and policymaking. Adaptation researchers 
are increasingly expected to offer clear and confident advice on adaptation success, yet are also increasingly aware of how context-
specific and contested success is (see also Lacey et al., 2015 on ethics).

Grey literature on adaptation is also proliferating, typically authored by organisations funding and implementing adaptation. This literature 
often documents a range of adaptation strategies (Sections 9.8.3; 10.4.6.4; 14.4.3.3; 17.2.1.) and lived experiences of adaptation efforts, 
including helping give voice to marginalised groups, and highlighting the importance of IK and LK (Sections 4.7.5.4; 15.6.4; Box 9.2; Cross-
Chapter Box INDIG in Chapter 18; Nunn et al., 2016; Petzold et al., 2020). However, most of the lessons learned through implementation of 
adaptation projects and programmes are still not captured in academic or even grey literature and thus remain less systematically analysed. 
Crucially, the large gaps in documentation of adaptation knowledge mean that a lack of published evidence about a given issue does not 
necessarily reflect its absence in real life—a qualification about adaptation research that readers of AR6 should appreciate.

The evolution of adaptation research trends
In the 1990s, climate change adaptation was constrained as a specific topic of inquiry by the dominant focus on mitigation of GHG 
emissions and the related assumption that successful mitigation would render unnecessary the need for adaptation beyond what human 
and natural systems could inherently manage (Pielke, 1998; Schipper, 2006; Schipper and Burton, 2009). Several key developments in the 
1990s included IPCC’s 2nd report (1996) and the establishment of several key journals including Climatic Change (1978), Mitigation and 
Adaptation Strategies to Global Change (1996) and the Global Environmental Change journal that strengthened more dedicated focus 
on climate change related research.
Many foundational papers on key concepts central to adaptation were published in the 1990s and early 2000s onwards (Burton, 1992;

Smit, 1993; Smithers and Smit, 1997; Parry and Carter, 1998; Fankhauser et al., 1999; Smit et al., 1999; Pittock and Jones, 2000; Klein, 2003; Cross-Chapter Box ADAPT (continued)

1
168Chapter 1 Point of Departure and Key Concepts
Adger et al., 2005), while adaptation began to gain more prominence in IPCC’s 3rd assessment (2001) and 4th assessment (2007). For 
example, the Canada Climate Programme report (Smit, 1993) set out many of the principles of adaptation and was highly influential 
charting these concepts in IPCC’s 3rd Assessment Report (Schipper and Burton, 2009). These papers and IPCC reports remain key 
foundations of climate adaptation science literature (Nalau and Verrall, 2021).
Helping to differentiate adaptation from mitigation during this period was a focus on theoretical principles and a framing of adaptation 
as local and context specific, in contrast to mitigation’s global character (Nalau et al., 2015; Westoby et al., 2020), leading to locally 
oriented adaptation research and practice, including the rise of community-based adaptation (Kirkby et al., 2017). Since AR5, however,

adaptation has extended beyond the local, recognising the ‘borderless’ character of many climate change risks and vulnerabilities (Benzie 
and Persson, 2019) and framing adaptation and global adaptation governance as a global public good (Persson, 2019). Encompassing 
this expanded scale is challenging for adaptation research compared to treating adaptation as a local issue, which fits more easily with 
social research methods. Adaptation now works across scales (Biesbroek et al., 2013; Dzebo and Stripple, 2015; Keskitalo and Preston, 
2019a) and attends simultaneously to both the opportunities and risks arising from climate change (Juhola, 2016; Keskitalo and Preston, 
2019a). This suggests that empirical adaptation research should incorporate multi-scalar research designs and methods.
A strong focus has been and remains on case studies of adaptation practice, but adaptation science literature reviews have become

common. Recent systematic reviews cover topics such as adaptation effectiveness (Owen, 2020), public participation and engagement 
(Hügel and Davies, 2020), the role of LK (Klenk et al., 2017), adaptive capacity (Mortreux and Barnett, 2017; Siders, 2019a; Mortreux et al., 
2020), evolution of adaptation science (Nalau and Verrall, 2021), empirical adaptation research in the Global South (Vincent and Cundill, 
2021), how cities are adapting (Reckien et al., 2018), how decisions can be made (Siders and Pierce, 2021), IK (Petzold et al., 2020) 
and SIDS (Robinson, 2020). Review papers have developed common methodologies for how to undertake robust reviews in adaptation 
research (Berrang-Ford et al., 2015; Biesbroek et al., 2018; Lesnikowski et al., 2019a; Singh et al., 2020), and noted an existing imbalance 
as the majority of the literature still originates from the Global North compared to Global South (Robinson, 2020; Nalau and Verrall, 2021; 
Sietsma et al., 2021).

At the same time, adaptation research is also challenged by increasing attention to transformational adaptation, which refers to fundamental 
changes going beyond existing practices, including new approaches to adaptation decision making (Section 1.5). Whereas AR5 noted 
transformational adaptation as an area of future research (Klein et al., 2014b), it has continued to grow in profile since then. Rather than 
a future or fringe consideration—for example, an extreme action necessitated by the limits of incremental adaptation— transformational 
adaptation is increasingly an option that decision makers are considering today. This increasing attention to transformational adaptation 
is driven by a growing recognition of climate risks and impacts, as well as the need for urgent, systemic action as laid out in the IPCC’s 
recent special reports (IPCC, 2018c). Yet what incremental and transformational adaptation look like, how they relate in practice and how

to appropriately choose incremental or transformational options is uncertain and increasingly debated ( Section 17.2.2.3; Termeer et al., 
2016; Few et al., 2017; Vermeulen et al., 2018; Magnan et al., 2020; Wilson et al., 2020). One of the main challenges is now to generate 
empirical evidence and policy relevant insights on transformational adaptation (e.g., Jakku et al., 2016). Transformative approaches are 
especially being discussed in the context of COVID-19 (Schipper et al., 2020; Cross-Chapter Box COVID in Chapter 7).
Increasingly reflective adaptation research
Another characteristic of recent adaptation research is a stronger focus on ethics, justice and power (Byskov et al., 2021; Coggins et al., 2021; 
Eriksen et al., 2021; Singh et al., 2021). Researchers and practitioners are increasingly impatient to address the root causes of vulnerability

and use inclusive climate adaptation processes to generate effective adaptation responses for marginalised and misrecognised groups 
(Tschakert et al., 2013; Eriksen et al., 2015; Scoones et al., 2015; Gillard et al., 2016; Wisner, 2016). Increasingly ambitious, normative 
adaptation research often challenges technological solutions that simply reinforce the existing status quo (Nightingale et al., 2019, p. 
2) and calls for ‘socially just pathways for change’. Here work on adaptation overlaps with mitigation, transitions and other large-scale 
social change, encouraging the move towards more systemic, integrated approaches that discern between options according to multiple 
criteria (Goldman et al., 2018).
Fundamental questions about equity and justice in adaptation include gender and intersectionality (see Cross-Chapter Box GENDER in 
Chapter 18; Section 1.4.1.1; Chapter 18;) and broader critiques of who participates in processes of adaptation planning and implementation,

who receives investments, who and what benefits from them, who makes key decisions regarding adjustments through time (Taylor et al., 
2014; Boeckmann and Zeeb, 2016; Nightingale et al., 2019; Pelling and Garschagen, 2019; Byskov et al., 2021; Eriksen et al., 2021) and Cross-Chapter Box ADAPT (continued)

1
169Point of Departure and Key Concepts  Chapter 1
national adaptation planning to better characterise its potential merits, 
shortcomings and effects (Woodruff and Regan, 2019; UNEP et  al., 
2021) and have compiled inventories of adaptation projects (Leiter, 
2021) and local adaptation policies (Reckien et al., 2018;Lesnikowski 
et al., 2019b; Olazabal et al., 2019; see also Section 6.4.6). Chapters 
16 and 17 of this report offer an initial synthesis, but efforts to compile 
a comprehensive global, empirical inventory of climate change 
adaptation remain in an early phase (e.g., Tompkins et  al., 2010; 
Berrang-Ford et  al., 2011; GEF , 2011; Ford et  al., 2015; Lesnikowski 
et al., 2016; Fankhauser, 2017; Leiter, 2021; Tompkins et al., 2018).
Improving effectiveness: Information regarding the effectiveness of 
adaptation remains scarce (UNEP et al., 2021), which hinders efforts 
to improve adaptation practice. A recent comprehensive review found

that only 2.3% of the close to 1700 articles identified by the Global 
Adaptation Mapping Initiative as documenting implemented adaptation 
provide evidence of risk reduction (Chapter 16; Berrang-Ford, 2020). 
However, there is limited but emerging evidence of the use of M&E by 
different actors to assess adaptation progress (Section 17.5.1).
Existing case studies use varying criteria for assessing effectiveness, 
complicating comparisons. Judgements regarding successful 
adaptation are contingent on the chosen scale and perspective (success 
for whom?) (Adger et al., 2005; Dilling et al., 2019) and on the level 
of risk, that is increasing climate risks may cause previously successful 
adaptation to become insufficient. Rather than a binary outcome 
(successful or unsuccessful), adaptation can be viewed on a continuum 
from successful adaptation to maladaptation (Section  17.5.2). 
Assessments of adaptation success need to account for distributional

effects and differential vulnerability, as well as consider connections 
across different scales and complex interactions with other change 
processes (Section 17.5.1). Recent literature has begun to identify how 
adaptation can better achieve its intended objectives (Eriksen et al., 
2021). For instance, inclusive M&E can legitimise and validate M&E 
and foster commitment and learning.Informing ongoing adaptation: Iterative risk management involves an 
ongoing cycle of assessment, action, learning and response in which 
M&E plays a central, enabling role (Section 1.2.1.1). Assessing the risk 
reduction provided by adaptation, both planned and implemented, 
often requires projections of anticipated future climate, socioeconomic 
conditions, and the effectiveness and implications for justice of each 
option (Section 17.4.4). Understanding the potential for maladaptation 
may also require such assessments (Section 1.4.2). Processes, such as

adaptive pathways, that involve anticipating future responses (Boxes 
11.4; 11.6; Sections  11.7; 17.3) entail monitoring to detect early 
warning of approaching thresholds or changes in the solution space 
(e.g., more rapid than expected sea level rise or new social acceptance 
of managed retreat) that suggest the need or opportunity to adjust or 
expand current adaptation efforts (Haasnoot et al., 2021).
1.4.4 Limits to Adaptation
The effectiveness of adaptation efforts also depends on the constraints 
and limits that human and natural systems face when confronted with 
increasingly higher levels of climate risks. The concept of adaptation 
limits strongly affects any appropriate balance among adaptation and 
mitigation actions in the sense that less mitigation makes adaptation 
harder or even infeasible. Adaptation limits refer to the point at which 
an actor’s objectives (or system needs) cannot be secured from intolerable

risks through adaptive actions (Annex II: Glossary). Adaptation limits 
can be soft or hard. Soft adaptation limits occur when options may 
exist but are currently not available to avoid intolerable risks through 
adaptive actions and hard adaptation limits occur when no adaptive 
actions are possible to avoid intolerable risks. Intolerable risks are those 
which fundamentally threaten a private or social norm—threatening, 
for instance, public safety, continuity of traditions, a legal standard or a 
social contract—despite adaptive action having been taken (Dow et. al. 
2013). Intolerable risks threaten core social objectives associated with 
health, welfare, security or sustainability (WGII AR5 Chapter 16, Klein 
et al., 2014b). Through the lens of resilience, hard limits represent the

range of change or disturbance beyond which a system cannot maintain how climate justice intersects with other justice agendas. Attention is also turning to relations and tensions between different adaptation 
approaches, scales, constraints, limits, losses, enablers and outcomes (Barnett et al., 2015; Pelling et al., 2015; Mechler and Schinko, 2016; 
Crichton and Esteban, 2017; Gharbaoui and Blocher, 2017; Deshpande et al., 2018; McNamara and Jackson, 2019). Evident here is an 
ongoing, serious knowledge gap around the long-term repercussions of adaptation interventions. There is growing awareness of the need 
to address the potential for maladaptation (Sections 1.4.2.4; 5.13.3; 15.5.1; 17.5.2; Chapter 4). Concerns about maladaptation have led 
to renewed calls to open the ‘black box’ of decision making to examine the influence of power relationships, politics and institutional

culture (Biesbroek et al., 2013; Eriksen et al., 2015; Goldman et al., 2018), including the power–adaptation linkage itself (Woroniecki 
et al., 2019), external factors outside the decision-making process (Eisenack et al., 2014) and the influence of leadership on adaptation 
processes and outcomes (Meijerink et al., 2014; Vignola et al., 2017).
All of these developments indicate that adaptation research is not only more reflexive about some of its central assumptions, methodologies 
and tools (Biesbroek et al., 2013; Conway and Mustelin, 2014; Nalau et al., 2015; Nightingale, 2015a; Porter et al., 2015; Eriksen et al., 
2015; Lubell and Niles, 2019; Woroniecki et  al., 2019; Singh et  al., 2021), but also cognisant of the need to critically consider its 
underpinning goals, purpose and impact in the world.Cross-Chapter Box ADAPT (continued)Cross-Chapter Box ADAPT (continued)

1
170Chapter 1 Point of Departure and Key Concepts
its essential function, identity and structure. Soft limits represent the 
range of change or disturbance of a system which can be sustained 
over time by innovation or policy changes. The level of GHG emissions 
factors determining if and when adaptation limits are reached.
1.4.4.1 Limits to Adaptation and Relation to Transformation
A species’ ability to adapt may be significantly impacted by the 
dynamics of interactions between the ecosystems and species, so that 
a species may reach its limit to adapt even in a gradually changing 
environment, leading to sudden changes in range fragmentation 
(Radchuk et  al., 2019). As human interventions affect the ability 
of species and ecosystems to adapt, a deeper understanding on 
ecosystems and species interactions and evolution in response to 
climate change is important in order to reduce future biodiversity losses

(Nadeau and Urban, 2019). Soft limits are usually associated with 
human systems whereas hard limits are more proximate for natural 
systems due to inability to adapt to biophysical changes (Chapter 16) 
(medium confidence). Many human and natural systems are near their 
soft adaptation limits for instance, terrestrial and aquatic species and 
ecosystems, coastal communities, water security, crop production, and 
human health (Chapters 2;3; 4; 5; 7; 16; Dow et al., 2013).
The concept of limits to adaptation is dynamic in terms of the temporal, 
spatial and contextual dimensions of climate change risks, impacts and 
responses (Chapter 17; Storch, 2018). Adaptation limits depend on a 
complex function of interactions between social, ecological, technological 
and climatic elements, which appear to have thresholds beyond which 
adaptation can be infeasible and represent limits to adaptation. Such 
thresholds are endogenous to society and hence contingent on ethics,

knowledge, attitudes, culture, governance, institutions and policies 
(Abrahamson et  al., 2009; Tschakert et  al., 2017). Since AR5, the 
evidence on limits to adaptation has been advanced across regions and 
sectors. Many adaptation constraints (financial, governance, institutional 
and policy, etc.) lead to soft adaptation limits (see Chapter 16 for 
detailed evidence on constraints and adaptation limits). The ability of 
actors to overcome these constraints including social constraints to 
behavioural changes, depends on additional adaptation implementation. 
(Abrahamson et  al., 2009; Juan, 2011; Di Virgilio et  al., 2019). Thus, 
socioeconomic, technological, governance and institutional systems or 
policies can be changed or transformed in responses to the different 
dimension of adaptation limits to climate change and extreme events.
When a limit (soft) is reached, then intolerable risks and impacts may 
occur, and additional adaptations (incremental or transformational)

are required.to reduce or avoid these risks and impacts (Chapters 
16; 17). IPCC SR1.5 defined incremental adaptation that maintains 
the essence and integrity of a system or process at a given scale, 
whereas transformational adaptation that changes the fundamental 
attributes of a socio-ecological system in anticipation of climate 
change and its impacts. When incremental adaptation is insufficient 
to avoid intolerable risks, transformational adaptation may be able 
to extend the potential to sustain human and natural systems (IPCC, 
2018a; Cross-Chapter Box  LOSS in Chapter 17; Klein et  al., 2014b). 
Transformational adaptation can allow a system to extend beyond 
its soft limits and prevent soft limits from becoming hard limits. This report provides evidence of assessing transformational adaptation in 
terms of scope, depth, speed and limits to adaptation (Chapter 16).
This report assesses adaptation limits (soft and hard) and residual risks

for some actors and systems (Chapter 16). Residual risk is the risk 
that remains following adaptation and risk reduction efforts (SROCC). 
Residual risk is also used as other terms such as ‘residual impacts’, 
‘residual loss and damage’ and ‘residual damage’. As noted in AR5 
WGII (IPCC, 2014a, b), the residual risk is larger or smaller depending 
on a society’s choices about the appropriate level of adaptation and 
its ability to achieve an appropriate level. The intersection of inequality 
and poverty presents significant adaptation limits, resulting in residual 
impacts for vulnerable groups, including women, youth, elderly, ethnic 
and religious minorities, Indigenous People and refugees (Section 8.4.5). 
An appropriate level of adaptation, which ideally reflects a balance 
between the desired level of risk and the actions needed to achieve 
that level of risk, depends on the solution space, the society’s views

on climate justice, the tolerance for climate-related risks, the society’s 
tolerance for the costs and other impacts of the actions needed to 
reduce risk. IPCC’s special reports stated that residual risks rise with 
increasing global temperatures from 1.5°C to 2°C (SR 1.5) and emerge 
from irreversible forms of land degradation (SRCCL). Among other risks, 
this report evidenced that, at risk to coastal flooding from sea level 
rise, nature-based adaptation measures (e.g., coral reefs, mangroves, 
marshes) reach hard limits beginning at 1.5 °C of global warming 
(Chapter 16). Residual risks may lead to exceeding the limits of 
adaptation, hence, this report underscores on the role of decision making 
on transformational adaptation for dealing with residual risk as well as 
soft and hard adaptation limits (Cross-Chapter Box  LOSS in Chapter 
17). Section 1.5 addresses transformational adaptation in the context 
of climate resilient development pathways since such adaptation is

inseparable from mitigation and sustainable development.
1.4.4.2 Emerging Importance of Loss and Damage
The concept of Loss and Damage (with capitalised letters, L&D) 
refers to the discussion point under the UNFCCC, which is to ‘address 
loss and damage associated with impacts of climate change, including 
are particularly vulnerable to the adverse effects of climate change’. 
Lowercase letters of losses and damages refer broadly to harm from 
(observed) impacts and (projected) risks (IPCC, 2018a). The IPCC report 
uses the latter for its assessment on loss and damage which may provide 
useful information for the former. L&D associated with climate change 
has gained importance supported by the robust scientific evidence on 
anthropogenic climate change amplifying the frequency, intensity and 
duration of climate-related hazards (Mechler et  al., 2019). Loss and

damage associated with those residual losses and damages that are felt 
beyond the adaptation actions taken imply a sense of limits to adaptation 
at a given time and within a spatial context (Tschakert et  al., 2017). 
IPCC’s SRCCL also underlined the unavoidable loss and damage due to 
changes in tropical and extratropical cyclones and marine heatwaves, 
where adaptation and resilience limits are being exceeded for the people 
and ecosystems (Cross-Chapter Box LOSS in Chapter 17; IPCC, 2019a).
Loss and damage has emerged as an important topic in international 
climate policy (Surminski and Lopez, 2015; Roberts and Pelling, 2016;

1
171Point of Departure and Key Concepts  Chapter 1
Boyd et al., 2017). It originated in assessing compensation for SIDS, 
related to sea level rise impacts. It has since become formalised under 
the UNFCCC, through the establishment of the Warsaw International 
Mechanism (UNFCCC, 2013) and Article 8 of the Paris Agreement 
(UNFCCC, 2015b). The Warsaw International Mechanism promotes 
the implementation of comprehensive risk management approaches, 
improves understanding of slow onset events, non-economic losses 
and human mobility (migration, displacement), and enhances action 
and support, including finance, technology and capacity building to 
avert, minimise and address loss and damage associated with climate 
change impacts, particularly on vulnerable and developing countries 
(UNFCCC, 2021). Different actors have defined loss and damage 
differently in reference to climate change impacts and responses 
(Surminski and Lopez, 2015; Roberts and Pelling, 2016; Boyd et  al.,

2017; McNamara and Jackson, 2019). These understandings include 
the following: (a) an adaptation and mitigation perspective linking all 
human-induced climate change impacts to potential loss and damage 
and a mandate to avoid dangerous anthropogenic interference; (b) a 
risk management perspective emphasising interconnections among 
disaster risk reduction, climate change adaptation and humanitarian 
efforts; (c) a limits to adaptation perspective focused on residual 
loss and damage beyond adaptation and mitigation; and (d) an 
existential perspective highlighting inevitable harm and unavoidable 
transformation for some people and systems. This report assesses the 
growing literature on loss and damage across sectors and regions 
linking with adaptation constraints and limits, GWL and incremental 
and or transformational adaptation to climate risks (Section  8.3.4; 
Cross-Chapter Box LOSS in Chapter 17; Box 10.7).

To assess the projected losses and damages, residual risks also need to 
be taken into account. The loss and damage associated with the future 
climate change impacts, beyond the limits to adaptation, is an area of 
increasing focus, although yet to be fully developed in terms of methods 
of assessment. This includes non-economic losses and damages, as well 
as identifying means to avoid and reduce both economic (loss of asset, 
infrastructure, land etc.) and non-economic (loss of societal beliefs and 
values, cultural heritage, biodiversity and ecosystem services) losses 
and damages (Fankhauser and Dietz, 2014; Andrei et al., 2015). There 
is increasing evidence of economic and non-economic losses due to 
climate extremes and slow onset events under observed increases in 
global temperatures (Section  8.3.4; Coronese et  al., 2019; Grinsted 
et al., 2019; Kahn et al., 2019), however assessment of non-economic 
losses and damages is lacking and needs more attention (Serdeczny

et al., 2016; Tschakert et al., 2019). The aggregate losses and damages 
would be higher if non-economic values are considered in such 
assessment (Laurila-Pant et al., 2015; McShane, 2017). To reduce or 
avoid loss and damage, there is a need for robust conceptual framework 
and analysis, focusing on future losses rather than past losses (Preston, 
2017). This should have an emphasis on avoiding versus addressing 
loss and damage and the role of justice (Boyd et al., 2017), clarity on 
detection and attribution (Sections 8.2.1; 8.3.3), effectiveness of risk 
management and adaptation (Cross-Chapter Box FEASIB in Chapter 
18; Section 1.4), the concepts of risk transfer, liability and financing 
(Cross-Chapter Box FINANCE in Chapter 17; Section 17.4.2) and the 
role of transformation (Section 1.5).1.5 Facilitating Long-Term Transformation
This report highlights that transformative system change is required to 
meet the SDGs (Chapter 18). Transformation is defined as ‘a change

in the fundamental attributes of a system including altered goals or 
values’ (SR 1.5). The related concept of transition is defined as ‘the 
process of changing from one state or condition to another in a given 
period of time’ (IPCC SR 1.5/SRCCL; also see Section 1.5.2).
Many time scales have been assessed that shape the context for any 
such transformations: including the present, 2030 and mid-century 
(Cross-Chapter Box CLIMATE in Chapter 1). In the present, significant 
changes in the climate system have already occurred in many places 
(WGI), while commensurate adaptation actions have in general been 
lacking (Chapter 16). By 2030, the SDGs call for significant societal 
changes, many of which would be more difficult to achieve without 
significant reductions in climate risk and impacts. By mid-century, 
global emissions pathways consistent with the Paris Agreement 1.5°C 
target drop to zero net GHG emissions with no overshoot and roughly a

decade later with overshoot (Cross-Chapter Box CLIMATE in Chapter 1). 
Pathways consistent with the Paris 2°C target drop to zero net emissions 
in the latter half of the 21st century. Even in low emission scenarios, 
temperatures, storm intensities, sea levels and other climate parameters 
are expected to continue to change for decades (IPCC, 2021a).
The concepts of transition and transformation help organise assessments 
of near- and longer-term adaptation actions that may prove feasible and 
effective in achieving societal goals related to climate and sustainable 
1.5.1 Understanding Transformation
Over the last 200  years, human society has undergone a rapid and 
profound transformation, with population and income per capita 
expanding by an order of magnitude or more after many millennia 
of relative stasis in living standards (Dasgupta et  al., 2018). The 
transformations associated with sustainable development and

managing climate risk may be of similar scale as these historic 
transformations. In the past, changes in technologies and economies 
of this scale are not separate from, but are necessarily embedded 
alongside changes in political, religious and social relationships 
(Polanyi, 1957). Future transformation may similarly involve such 
interlinked social, cultural, economic, environmental, technical and 
political factors (Chapter 18; Section 1.5.2). Technology-led, market-led 
or state-led transitions aimed at meeting Paris Agreement and SDGs 
may fail without integrating dimensions of social justice and addressing 
the social and political exclusion that prevent the disadvantaged from 
accessing such improvements and increasing their incomes (Burkett 
et al., 2014; Scoones et al., 2015) (medium confidence).
As used in the global environmental change literature, transformation is 
a pluralistic concept embracing many interpretations (Box 18.3), but all

focus on the general idea of fundamental change in society as opposed to 
change that is minor, marginal or incremental. Uses of the term can differ 
with respect to: (a) how the system undergoing change is conceptualised, 
(b) the extent to which change is continuous or discontinuous and the

1
172Chapter 1 Point of Departure and Key Concepts
time scales involved, (c) the extent to which transformation is guided 
towards desired goals or emerges without intent and (d) whether 
the usage focuses on descriptions of societal processes or includes 
normative judgements as to which outcomes should or should not 
occur (Feola, 2015) . The literature generally uses transformation as an 
analytic-descriptive concept, which aims to describe significant change 
in couple human–natural systems, or as a solutions-oriented concept, 
which aims to inform or contribute to societal change.
The IPCC Fifth Assessment cycle, starting with its Special Report 
on Managing the Risks of Extreme Events and Disasters to Advance 
Climate Change Adaptation (SREX), first highlighted the concept of 
transformation, drawing primarily on the solutions-oriented approaches 
of O’Brien (2011) and Pelling (2011). This Sixth Assessment report also

generally employs transformation as a solutions-oriented concept, with 
mention in almost all chapters and significant emphasis in the synthesis 
chapters.
The IPCC Sixth Assessment cycle also highlights the concept of 
transition, drawn from the sustainability transitions literature (Köhler 
et al., 2019). The 1.5 SR organises its assessments of feasibility and 
potential policy actions around transitions in four socio-technical 
system: energy, land, urban and infrastructure, and industrial systems 
(IPCC, 2018b, Chapter 4). This report adds a fifth system transition—a 
societal transition focused on attributes that drive innovation, the 
evolution of patterns of consumption and development and power 
relationships among societal actors (Section  18.1.4). The AR6 WGIII 
report is organised around six systems transitions: energy; agricultural, 
forestry, other land use; urban; buildings; transportation; and industry, 
which includes supply chains and the circular economy.

The literature offers multiple views on the relationships between 
transition and transformation (Box  18.3). The 1.5 SR suggests that 
transformation is needed to generate the four system transitions. 
In many literatures, transformation is considered a more expansive 
process than transition, with the former less exclusively focused on 
socio-technical systems and more engaged with questions of power, 
politics, capabilities, culture, identity and sense-making (Gillard et al., 
2016; Hölscher et  al., 2018; Linnér and Wibeck, 2019). This report 
generally takes this more expansive view of transformation to engage 
with issues of equity, climate justice and large-scale institutional and 
societal change (Box 18.3).
This WGII report has a particular focus on transformational 
adaptation (Section 1.4.4.1), which it views as laying on a continuum 
from incremental and transformational with no sharp division between 
them (Sections 1.5.2; 17.2.2.3).

The IPCC first highlighted the concept of transformational adaptation 
in SREX. SREX generally used the phrase transformation to refer to 
fundamental societal changes that advance climate adaptation, disaster 
risk management and sustainable development. Transformation 
was seen as one part of the solution space alongside options such 
as reducing vulnerability and exposure, and increasing resilience for 
managing risk. WGII of the IPCC Fifth Assessment Report used the 
phrase ‘transformational adaptation’ to contrast with ‘incremental 
adaptation’. That report used the former term to refer to: (a) adaptation at large scope or scale, (b) the type of adaptation that occurs once soft 
limits have been breached, or (c) change that addresses root causes of 
vulnerability as well as redressing long-standing inequities. The Fifth 
Assessment Report’s WGIII employed the concepts of transformation 
and transformation pathways to assess the large-scale societal

changes needed to meet GHG emission reduction goals.
This WGII report focuses on transformational adaptation as one 
component of climate resilient development in which adaptation, 
mitigation and development solutions are pursued together to exploit 
synergies and reduce trade-offs among these actions (Section 1.5.3; 
Chapter 18). Chapter 16 assesses the extent to which transformational 
adaptation is currently being implemented, using criteria including the 
scope, depth and speed of the adaptation actions, as well as the extent 
to which limits to adaptation have been considered (Section 16.3.2.4). 
Chapter 17 ranks potential adaptation options by where they lie on the 
incremental to transformational continuum (Section 17.2.2.4).
Societal transformation can arise without explicit intent as, arguably, 
did the industrial revolution and some of the trends re-making today’s 
society (see Section 1.1). In order to help policymakers achieve societal

goals, this report seeks to identify the conditions for deliberate 
transformations, that is, those envisioned and intended by at least 
some societal actors (Linnér and Wibeck, 2019).
Figure  1.9 connects several key concepts that this report employs 
to help distinguish pathways that lead to deliberative and forced 
transformations. As shown in the figure, adaptation goals might imply 
a desired level of adaptation: (a) accessible by actions within the 
solution space of the existing system or (b) beyond the solution space 
of the existing system. In the former case, incremental adaptation may 
stay within soft limits and hold risks to tolerable levels that avoid 
threatening private or social norms (also see Figure 17.6). In the latter 
case, deliberate transformational adaptation is necessary to reach the 
goals. Alternatively, if deliberate transformation does not successfully 
occur or hard limits are exceeded, the system may nonetheless

undergo some type of forced transformation which results in outcomes 
inconsistent with societal goals. While the figure shows single decision 
points, multiple actors are involved at each stage. Thus, some people 
may find themselves coping with what they regard as intolerable risks 
which are not otherwise avoided. Often such coping situations display 
significant inequities, with tolerable risks for powerful groups and 
intolerable ones for marginalised groups.
Multiple narratives describe pathways for pursuing deliberate 
transformations (Cavanagh and Benjaminsen, 2017). While building 
on the new ‘green economy’ framing that emerged with the Rio+20 
conference in 2012 (UNEP , 2011; De Mello and Dutz, 2012; OECD, 2012), 
these narratives reflect differing trade-offs among values and differing 
assumptions about the factors driving system change (see WGIII). 
The narratives range from ‘business-as-usual’ scenarios focused on

modernisation of sectors such as energy, agriculture and use of natural 
resources to more transformational propositions such as various green 
new deal proposals (European Commission, 2019), the new climate 
economy (Global Commission on the Economy and Climate, 2018), 
and ‘doughnut economics’ (Raworth, 2017). Some literature suggests 
significant benefits from such new climate economy proposals, claiming

1
173Point of Departure and Key Concepts  Chapter 1
Alternative Pathways to T ransformation
Are goals achievable with current solution space?
Copingyes
yes yesno
Is residual risk tolerable?
Tolerable risk Deliberative transformation 
consistent with goal sForced transformation potentially 
inconsistent with goals  Deliberate transformational adaptatio n
Does expanded solution space achieve goals?
Adaptation stays 
within soft limitsAdaptation exceeds 
hard limits 
and/or deliberate 
transformation failsno
Story line for figure:
This figure aims to connect the concepts of transformation, solution space, adaptation gap, limits to adaptation, and residual risk
Achieving policy goals may require expanding the solution space and pursuing a transformation towards a desired future
If such a transformation is not pursued or is not successful, we may be forced to undergo a transformation to a less desirable  futureno

Figure 1.9 |  Alternative pathways to transformation. Adaptation goals may be accessible by actions within or beyond the existing solution space. In the former case, 
incremental adaptation may stay within soft limits and hold risks to tolerable levels. In the latter case, deliberate transformational adaptation becomes necessary to achieve goals. If 
a successful deliberate transformation does not occur, the system may nonetheless undergo a forced transformation. Multiple actors are involved at each stage so that some people 
may nonetheless find themselves coping with what they regard as intolerable risks.
tens of trillions in economic benefits, tens of millions of new jobs and 
close to a million fewer premature deaths from pollution over the 
coming decade (Global Commission on the Economy and Climate, 2018).
Two contrasting schools of thought, called ecomodernism and degrowth 
(D'Alisa et  al., 2014), offer important bounding narratives for ‘green

economy’ approaches that aim achieve the SDGs and Paris Agreement 
goals.
Ecomodernism aims to decouple GHG emissions and other environmental 
impacts from GDP growth (WGIII Section 1.4.1; Desrochers and Szurmak., 
2020) through three primary strategies: (a) ‘green’ technological 
innovation, (b) resource efficiency or productivity improvements and (c) 
the sustainable intensification of land use in both rural and urban areas 
(Asafu-Adjaye et  al., 2015; Isenhour, 2016). Such efforts to mobilise 
large-scale investment in climate change adaptation and to decouple 
GDP growth from environmental impacts could generate substantial 
employment opportunities and open up profitable investment frontiers 
(Asafu-Adjaye et al., 2015; Adelman, 2018), which could help achieve 
SDG 8, which calls for accelerated annual growth rates of at least 7% in 
least developed countries and achieve SDG 10, which calls for ‘income 
growth of the bottom 40% of the population at a rate higher than the

national average’.
Degrowth proponents question the feasibility of decoupling at a scale 
and rate sufficient to meet Paris Agreement goals (Kallis, 2017; Parrique 
et  al., 2019; Gómez-Baggethun, 2020; Hickel and Kallis, 2020). Using 
precautionary principle-rooted arguments (Latouche, 2001), degrowth 
aims for intentional decreases in both GDP and coupled GHG emissions 
(Kallis, 2011) using policy mechanisms such a ‘cap and share’ framework 
for distributing emissions permits on an annually declining basis with 
legislation to prohibit the overshoot of established carbon budgets 
(Douthwaite, 2012; Kallis et al., 2012). Degrowth thus seeks to minimise reliance on negative emissions technologies, such as the large-scale 
deployment of BECCS (e.g., illustrative emissions reduction pathway 
labelled P4 in IPCC SR1.5, IPCC, 2018b; also WGIII Chapter 3) and 
aims to generate progress toward achieving the SDGs by prioritising

redistribution rather than GDP growth. SDGs potentially addressed by 
degrowth include universal basic income (SDGs 1 and 10), work-sharing 
to guarantee full employment (SDGs 8 and 10) and shifting taxation 
burdens from income to resource and energy extraction (SDGs 8 and 12).
The contrasting premises of ecomodernism and degrowth have 
prompted a series of mutual counterarguments. Degrowth scholars 
emphasise that global absolute decoupling is currently not proceeding 
fast enough to meet Paris Agreement targets (Ward et al., 2016; Moreau 
et  al., 2019; Haberl et  al., 2020). Ecomodernists point to important 
progress towards achieving absolute decoupling at the national or 
regional scale—as shown by Le Quéré et al. (2019) in 18 developed 
countries—and the future potential of emerging technologies and 
policy reforms (Asafu-Adjaye et al., 2015)
1.5.2 Enabling Transformation
As one important theme, this Sixth Assessment report assesses who

needs to take what actions and when in order that transformations 
unfold at sufficient speed and scale to meet the Paris Agreement, the 
SDGs and other policy goals. A number of literatures inform these 
assessments.
Various literatures describe multiple, co-evolving societal elements 
which organise themselves into stable regimes that, under some 
circumstances, can undergo significant change. The sustainability 
transitions literature provides one central focus for understanding such 
processes and potential intervention points for actors seeking change

1
174Chapter 1 Point of Departure and Key Concepts
(Köhler et  al., 2019). This literature identifies three, interacting scales: 
the micro, meso and macro.(Geels, 2004; Köhler et al., 2019) The micro 
level reflects changing individual choices, attitudes and motivations. The 
meso reflects socio-technical systems, ‘a cluster of elements, including 
technology, regulations, user practices and markets, cultural meanings, 
infrastructure, maintenance networks and supply networks’ (Geels, 2004, 
p. 436). The macro reflects the cultures, institutions, norms, governance 
and other broad organising features of society. The sustainability 
transitions literature generally focuses on change that originates and 
occurs within the meso scale, while the transformation literature focuses 
on change within and among all scales. This Working Group II report 
often considers three interacting scales labelled personal, practical and

political (O’Brien and Sygna, 2013). Working Group III often employs 
the multi-level perspectives framework (Geels, 2004) and the more 
actor-oriented three domains of decision making framework (WGIII 
Section 1.6.4; Grubb et al., 2014;) to describe related societal scales.
These literatures describe similar processes through which these 
interacting elements generate significant system change. In the 
sustainability transitions literature, the process begins with a stable 
system of actors, technologies and institutions (Köhler et  al., 2019). 
Radical innovations begin in niches or protected spaces, sometimes 
introduced by new entrants or outsiders. Successful innovations expand 
in scale, scope and geographically, and ultimately help generate 
new regimes. Incumbent actors can support or resist innovations 
through combinations of government policies, economic forces and 
institutional and behavioural pressures. Such processes can, but need

not, follow a common S-curve pattern of initial adoption, take-off, 
acceleration and stabilisation (Rotmans et al., 2001). The multi-level 
perspectives literature in WGIII similarly describes innovations as 
moving from niches to socio-technical regimes, at first mediated by 
and then potentially altering exogenous socio-technical landscapes 
(WGIII Section 1.6.4; Smith et al., 2010).
The socio-ecological systems literature, a main source of the resilience 
concept, focuses on the system elements of society and ecosystems, 
their interdependence and on how they change in response to shocks 
(Section 1.2.1.4). Coupled human and natural systems maintain their 
vital functions through what are called adaptive cycles that begin 
with growth, reach a period of stasis, experience a disruption and 
then reorganise. This repeating cycle can leave the system unchanged 
or transition it to new states. Human agency can alter system

characteristics so that after any disruption the system will reorganise 
into a different, more desired state, guide the reorganisation in desired 
directions after a system shock (such as a natural disaster) or provide 
the shock that catalyses a reorganisation.
These literatures view incremental and transformational change as 
linked processes. In the transformational adaptation literature, Park 
et  al. (2012) consider incremental and transformational adaptation 
as two concentric and linked action-learning cycles with similar steps 
that include monitoring and learning. Systems generally reside in the 
incremental cycle but can temporarily jump to the transformational cycle 
before returning to the incremental, albeit in a state with fundamentally 
changed attributes. Shifts from the incremental to transformational cycle 
are made possible by knowledge and skills, as well as adjustments to

vision, agendas and coalitions achieved through monitoring and learning. The incremental cycle is characterised by reactive responses to external 
drivers and performance evaluation relative to past performance. Shifts 
to the transformational cycle are characterised by more pro-active 
responses and more expansive problem framings.
The socio-ecological and sustainability transitions literature describes 
transitions as often nonlinear, characterised by tipping point behaviour 
with periods of relative stability interspersed with periods of more rapid 
change as thresholds are crossed (van Ginkel et al., 2020). Actors seeking 
transformation may take incremental steps that aim to induce such 
tipping point behaviour (Otto et al., 2020b). For instance, full accounting 
of climate risk in insurance and financial lending decisions could similarly 
act as such social tipping point interventions for adaptation (Hill and

Martinez-Diaz, 2019). Transformations need not, however, be equitable 
or smooth. Historical examples suggest the potential for rigidity traps, 
in which suppression of innovation and a high degree of connectivity in 
a system delay an eventual transformation, which, when it eventually 
occurs, unfolds as exceptionally harsh (Hegmon et al., 2008).
Many actors can contribute to launching or blocking significant system 
change. Pelling et al. (2015) highlights power relationships within and 
among activity spheres that influence the process of transformational 
adaptation and distribution of risks. In the sustainability transitions 
literature each set of actors—including those from academia, politics, 
industry, civil society and households—brings their own resources, 
capabilities, beliefs, strategies and interests, which affects their 
interest, objectives, ability to affect the process and their ability to 
affect others (Kern and Rogge, 2018).

There is no consensus in the literature on the best means for actors 
to pursue deliberate transformation (Section  1.5.1) and the extent 
to which actors can guide the process. The transitions and some 
transformation literature derive from a complex systems perspective ( 
Section 1.3.1.2; Köhler et al., 2019; Linnér and Wibeck, 2019) in which 
behaviours can be understood but not predicted (Chapter 17; Mitchell, 
2009). These literatures suggest that interventions in such systems will 
rarely result in them evolving along some pre-determined pathway. 
Rather, successful interventions more often resemble iterative 
processes of action, observation and response, which are described 
in the literature with terms such as iterative risk management 
(Section 17.2.1), clumsy solutions (Thompson and Rayner, 1998; Linnér 
and Wibeck, 2019), probe and response (Chapter 17; French, 2013) and 
what Young (2017) calls adaptive governance.

These literatures view transformation as a collective action challenge 
among actors with both common and differing values, interests and 
capabilities interacting over time with a mix of cooperation and 
competition (Young, 2017; Dasgupta et  al., 2018). Concepts such as 
radical incremental transformation (Göpel, 2016), direct incrementalism 
(Grunwald 2007) and progressive incrementalism (Levin et al., 2012) 
envision strategies in which actors pursue incremental actions in one or 
more niches that move the current system towards tipping points which, 
once crossed, will drive the system to a new state (Tàbara et al., 2018). The 
incremental actions aim to promote learning, remove barriers to change 
( Dasgupta et al., 2018; Baresi et al., 2020), create a series of wins that 
generate momentum and generate positive feedbacks (e.g., by creating 
constituencies) such that the speed and scale of the climate action

1
175Point of Departure and Key Concepts  Chapter 1
grows over time (Levin et al., 2012). However, incremental strategies 
can fail to move fast enough, can succumb to path-dependency that 
locks in initially helpful but long-term adverse responses (such as the 
well-known levee effect) (Sadoff 2015; Haasnoot 2019) or can result in 
a transition that meets some goals (e.g., environmental) but not others 
(e.g., equity) (high confidence).
This report describes decision frameworks and tools that can 
help those involved in such a process—acting independently or 
collectively—identify, evaluate, seek compromise on and then 
implement sequences of solutions that lead to pathways with more 
desirable outcomes and avoid pathways with less desirable outcomes 
(Section  17.3.1). For instance, adaptive (also called adaptation) 
pathways (Cross-Chapter Boxes SLR in Chapter 3 and DEEP in Chapter 
17) explicitly chart alternative sequences of actions including near-

term steps, indicators to monitor and contingency actions to take if 
pre-determined monitoring thresholds are breached. Employed in 
contexts with multiple actors and contested values, adaptive pathways 
frame deliberate transformation as both a near-term decision problem 
focused on physical, financial and natural resources, as well as a 
social change process of co-evolving knowledge, policies, institutions, 
values, rules and norms (Fazey et al., 2016). Transition management 
(Loorbach, 2010), rooted in the sustainability transitions literature, 
supports arenas of actors that co-produce visions of future change, 
plan pathways and recruit additional actors into the change process.
As a central feature, such frameworks and tools embrace: (a) multiple 
objectives and measures (Section  1.4.1.2) to help identify and 
consider trade-offs among parties with a diversity of interests, values 
and objectives and (b) multiple scenarios that enable stress-testing

of proposed actions to identify conditions in which they would fail 
to meet their goals and thus inform consideration of ways to make 
those actions more robust and resilient over multiple futures in the 
near and longer term (Chapter 17; Cross-Chapter Box DEEP in Chapter 
17). A focus on single or overly aggregated measures (Section 1.4.1.2) 
and single scenarios can privilege some actors’ views over others, 
reduce transparency and make it more difficult to identify resilient 
and equitable solutions to complex, deeply uncertain, non-linear and 
contested problems (Schoen and and Rein, 1994; Renn, 2008; Jones 
et al., 2014; Lempert and Turner, 2020) (medium confidence).
Nonetheless, most concepts of deliberate transformation also emphasise 
the importance of common goals and principles within a process of 
goal setting, acting on those goals, M&E and readjustment. Such goals 
encourage pro-active action, help align the activities of multiple, loosely

co-ordinated actors (Göpel, 2016; Dasgupta et  al., 2018) and provide 
benchmarks against which to measure progress (Young, 2017). The Paris 
Agreement and SDGs aim to provide such common goals for the world 
as a whole and implement what some have described as goal-based 
as opposed to rule-based governance for galvanising collective action 
(Sachs, 2015; Kanie and Biermann, 2017). As intended, many public 
sector, private sector and civil society actors have developed their own 
goals that aim to align with the Paris Agreement and the SDGs (see 
Section 1.1). The existence of goals that help people envision a future 
significantly different than present can be one, often key, difference 
between decision processes that pursue transformational as opposed to 
incremental change (Park et al., 2012; Chapter 17). Narratives that help explain where a community is, where it wants to go and how it intends 
to get there are an important enabler of transformation (Sections 1.5.1;

1.2.3; Linnér and Wibeck, 2019; Fazey et al., 2020).
1.5.3 Climate Resilient Development
Adaptation and mitigation can reduce climate-related risks. Implementing 
these two types of climate action together increases their effectiveness 
by exploiting synergies and reducing trade-offs among them. In 
addition, implementing adaptation and mitigation as an integral part of 
development can similarly make all three more effective (Section 18.2.3). 
The link between climate change and sustainable development has long 
been recognised and has been assessed in every Working Group II report 
since AR3. AR5 introduced the concept of climate resilient development 
to help assess development trajectories that include co-ordinated 
adaptation and mitigation actions aimed at reducing climate risk.
Building on AR5, this AR6 report expands the focus with increased 
attention to equity and the processes needed to follow such 
trajectories. AR6 thus defines climate resilient development as ‘a

process of implementing GHG mitigation and adaptation solutions 
to support sustainable development for all’ (Section 18.1.1). In AR6 
WGII, some chapters have a section dedicated to climate resilient 
development, emphasising the need for integrative and transformative 
solutions within a sector or region that address the uneven distribution 
of climate risks among different groups and geographies, as well as 
extend the goals of these solutions to more than reducing risk, such as 
in improving social, economic and ecological outcomes (Sections 2.6.7; 
4.1; 5.14.4; 6.4.8; 7.4.3.5; 7.4.5; 10.6; 11.8; 15.7; 17.6; Boxes 4.7; 13.3; 
8.10). Multiple chapters also employ the concept of climate resilient 
development to identify and balance trade-offs and make progress on 
achieving the SDGs (Sections 6.1.3; 7.4.5; 10.6; 13.11; 15.7; 16.6.4.3; 
Box 4.7; Chapter 18).
Climate resilient development requires large and equitable changes

in human and natural systems. As noted in Section 1.5.1, the SR1.5 
finds that four transitions in socioeconomic systems—energy, land 
and ecosystems, urban and infrastructure, and industrial—must occur 
at large scale and rapid rate in order to achieve climate resilient 
development. This report notes that transitions of such scale, even 
when beneficial for many people, can also impose significant adverse 
impacts on others, in particular on marginal and vulnerable populations 
(Section 18.1.2). This report identifies a fifth socioeconomic transition, 
that in societal systems that ‘drive innovation, preferences for 
alternative patterns of consumption and development, and the power 
relationships among different actors that engage in climate resilient 
development (Section 18.1.4). Such societal transitions are necessary 
to ensure the other four transitions unfold at sufficient speed and scale 
to meet the goals of Paris and the SDGs, as well as to ensure equity in

these transitions.
Introduced in the WGII AR5 (Olsson et al., 2014), and further addressed 
in SR1.5, climate resilient development pathways are trajectories 
that strengthen sustainable development and efforts to eradicate 
poverty and reduce inequalities, while promoting fair and equitable 
reductions of GHG emissions, and serve to steer societies towards low-

1
176Chapter 1 Point of Departure and Key Concepts
carbon, prosperous and ecologically safe futures. This report defines 
a climate resilient development pathway as a trajectory in time 
reflecting a particular sequence of actions and consequences against 
a background of autonomous developments leading to a specific 
future situation (Section  18.1.2). Such a pathway emerges from the 
spatially and temporally distributed choices of many different actors 
in government, business, civic organisations and households at the 
individual, community, national and international levels. As such, there 
is no single or preferred pathway for climate resilient development and 
no single best combination of adaptation, mitigation and sustainable 
development strategies. All pathways involve complex trade-offs 
and synergies among different actions (Sections  18.1.4; 18.2.2). All 
pathways are subject to hard-to-predict shocks, both adverse, such as

climate disasters, and beneficial, such as new technologies or shifts in 
public values. The pathway that emerges will represent the results of 
negotiation, cooperation and competition among actors at many scales, 
whose differing values and objectives would favour differing trajectories 
(Section 18.1.4). Individual actors at various scales will determine the 
mix of adaptation, mitigation and development appropriate for their 
development context and goals, while also influenced by the desire for 
their collective actions to become consistent with the global policy goals, 
such as those in the Paris Agreement and the SDGs (Section 18.1.2). The 
norms, institutions and power relationships that mediate such choices 
determine the extent to which the process unfolds consistent with 
principles of equity and social justice (high confidence).
Enabling conditions that can accelerate climate resilient development

include governance, finance, economy, and science, technology and information (Sections 18.4.2; 18.9.2). The pursuit of equity and justice 
are both an enabler and an outcome of climate resilient development 
(Section 18.1.4). Climate resilient development involves a process of 
action, learning and response (Section  1.5.2), so the capability for 
such monitoring and iterative risk management also represents an 
important enabling condition (Section 18.1.4.2). Governments have an 
important role to play in expanding the solution space, often focusing 
on technology, policy and finance. Expanding the solution space also 
requires a broader set of actors. Chapter 18 and other chapters in this 
report use the climate resilient development concept to highlight the 
role of citizens, civil society, knowledge institutions, media, investors 
and businesses, and the importance of expanding the arenas of 
engagement in which they interact.
1.6 Structure of the Report

The IPCC mandate involves the provisioning of available scientific 
information and evidence to inform climate action by multiple actors, 
notably governments (including international alliances) in the context 
of UNFCCC. Increasingly, IPCC assessments also aim to provide 
valuable information to non-governmental organisations, small and 
large enterprises, and citizens.
Figure  1.10 shows the structure of this report, organised into three 
sections comprising chapters and cross-chapter papers. This first 
chapter provides the point of departure for this assessment, defines 
key concepts and describes the connections among them.
Oceans and Coasts
Water
Food
Cities and Settlements
Health
Poverty and livelihoodsCh 2
Ch 3
Ch 4
Ch 5
Ch 6
Ch 7
Ch 8SECT ORS
Africa
Asia
Europe
North American
Small IslandsCh 9
Ch 10
Ch 11
Ch 12
Ch 13
Ch 14
Ch 15REGIONS
Mountains
Biodiversity hotspot s
Cities and Settlements by the Se a
Deserts

Polar regio n
Tropical Forest sCROSS CHAPTER P APERS
Key Risks
Decision Making
Climate Resilient DevelopmentCh 16
CH 17
CH 18POINT OF DEPA RTURE AND KE Y CONCEPT S Ch 1
SYNTHESIS CHAPTERS
Figure 1.10 |  Structure of the Working Group II Sixth Assessment Report

1
177Point of Departure and Key Concepts  Chapter 1
The next group of chapters assess risks, adaptation and sustainability 
for systems impacted by climate change from the vantage of seven 
sectoral chapters, The next group of chapters organises the assessment 
from the vantage of seven regional chapters.
In contrast to previous assessments, the current report embeds 
adaptation in each regional and sectoral chapter rather than in separate 
adaptation chapters, in order to reflect the increasing prevalence of 
adaptation and the extent to which many current risk, impacts and 
vulnerability estimates incorporate adaptation actions already taken. 
In addition, CCPs integrate across chapter topics, sectors and regions 
in particular types of geographies or ecosystems, termed typological 
regions, such as mountains, cities by the sea or tropical forests. CCPs

are a new element in WGII AR6 included at the request of governments to highlight and expand relevant material from chapters and beyond 
into one place to ensure the integrated treatment of particular systems 
or regions and to improve accessibility of the report to readers.
Finally, section three highlights sustainable development pathways 
integrating adaptation and mitigation through three synthesis 
chapters. The synthesis chapters summarise findings across all the 
chapters, CCPs and literatures on key risks, decision-making options 
and climate resilient development pathways that can lead from the 
current situation to a future more consistent with the stated policy 
goals. This report also assesses the adaptation options available, the 
extent to which such options can reduce risk, the effectiveness of 
current adaptation efforts, and their interactions with mitigation and 
sustainable development.

FAQ 1.1 | What are the goals of climate change adaptation?
The goals of climate change adaptation, as a broad concept, are to reduce risk and vulnerability to climate change, strengthen resilience, 
enhance well-being and the capacity to anticipate, and respond successfully to change. Existing international frameworks provide a high-level 
direction for coordinating, financing and assessing progress toward these goals. However, specifying the goals for specific adaptation actions 
is not straightforward because the impacts of climate change affect people and nature in many different ways requiring different adaptation 
actions. Thereby, goals that accompany these actions are diverse. Goals can relate to health, water or food security, jobs and employment, 
poverty eradication and social equity, biodiversity and ecosystem services at international, national and local levels.

Climate change adaptation entails the process of adjustment to actual or expected climate change and its effects 
in order to moderate harm or exploit beneficial opportunities. At a high level, international frameworks, including 
the Paris Agreement and the SDGs, have come to provide a direction for coordinating, financing and assessing 
global progress in these terms. The Paris Agreement calls for climate change adaptation actions, referring to these 
actions as those that reduce risk and vulnerability, strengthen resilience, enhance the capacity to anticipate and 
respond successfully, and ensure the availability of necessary financial resources, as these processes and outcomes 
relate to climate change. In addition, the Sustainable Development Goals include 17 targets (with a specific goal 
SDG 13 on climate action) to fulfil its mission to end extreme poverty by 2030, protect the planet and build more

peaceful, just and inclusive societies. These goals are difficult to reach without successful adaptation to climate 
change. Other notable frameworks that identify climate change adaptation as important global priorities include 
the SFDRR, the finance-oriented Addis Ababa Action Agenda and the New Urban Agenda.
While vital for international finance, coordination and assessment, the global goals set forth by these frameworks 
and conventions do not necessarily provide sufficient guidance to plan, implement or evaluate specific adaptation 
efforts at the community level. Specifying goals of adaptation is harder than setting goals for reducing emissions of 
climate-warming GHG emissions. For instance, emission reduction effort is ultimately measured by the total amount 
of GHGs in the Earth’s atmosphere. Instead, adaptation aims to reduce risk and vulnerability from climate change 
and helps to enhance well-being in each individual community worldwide.

Because the impacts of climate change affect people and nature in so many different ways, the specific goals of 
adaptation depend on the impact being managed and the action being taken. For human systems, adaptation 
includes actions aimed at reducing a specific risk, such as by fortifying a building against flooding, or actions aimed 
at multiple risks, such as requiring climate risk assessments in financial reporting in anticipation of different kinds 
of risk. At the local level, communities can take actions that include updating building codes and land use plans, 
improving soil management, enhancing water use efficiency, supporting migrants and taking measures to reduce 
poverty. For natural systems, adaptation includes organisms changing behaviours, migrating to new locations and 
genetic modifications in response to changing climate conditions. The goals for these adaptation actions can relate

to health, water or food security, jobs and employment, poverty eradication and social equity, biodiversity and 
ecosystem services, among others. Articulating the goals of adaptation thus requires engaging with the concepts of 
equity, justice and effectiveness at the international, national and local levels.

1
178Chapter 1 Point of Departure and Key Concepts
FAQ 1.2 | Is climate change adaptation urgent?
Climate impacts, such as stronger heat waves, longer droughts, more frequent floods, accelerating sea level rise and storm surges, are already 
being observed in some regions, and people around the world are increasingly perceiving changing climates, regarding these changes as 
significant and considering climate action as a matter of high urgency. Reducing climate risk to levels that avoid threatening private or social 
norms and ensuring sustainable development will require immediate and long-term adaptation efforts by governments, business, civil society 
and individuals at a scale and speed significantly faster than the current trends.
Current observed climate impacts and expected future risks include stronger and longer heat waves, unprecedented 
droughts and floods, accelerating sea level rise and storm surges affecting many geographies and communities.

People around the world are increasingly perceiving changing climates, regarding these changes as significant and 
considering climate action as a matter of high urgency. In particular, marginalised and poor people, as well as island 
and coastal communities, experience relatively higher risks and vulnerability. The available evidence suggests that 
current adaptation efforts may be insufficient to help ensure sustainable development and other societal goals in 
many communities worldwide even under the most optimistic GHG emissions scenarios.
Climate change adaptation is, therefore, urgent to the extent that meeting important societal goals requires 
immediate and long-term action by governments, business, civil society and individuals at a scale and speed 
significantly faster than that represented by current trends.
FAQ 1.3 | What constitutes successful adaptation to climate change?

The success of climate change adaptation is dependent on the extent to which relevant actions reduce risk and vulnerability, as well as achieve 
their respective goals. At a global scale, these goals are set and tracked according to international frameworks and conventions. At smaller 
scales, such as local and national, goals are dependent on the specific impacts being managed, the actions being taken and the relevant scale. 
While success can take shape as uniquely as goals can, the degree to which an adaptation is feasible, effective and conforms to principles of 
justice represents important attributes for measuring success across actions. Adaptation responses that lead to increased risk and impacts are 
considered maladaptation.
Altogether, adaptation success is dependent on the extent to which adaptation actions achieve their respective 
goals of reducing climate risk, increasing resilience and pursuing other climate-related societal goals. Viewed

globally, successful adaptation consists of actions anticipated to make significant contributions to meeting SDGs, 
such as ending extreme poverty, hunger and discrimination, and reduce risks to ecosystems, water, food systems, 
human settlements, and health and well-being. Viewed locally, successful adaptation consists of actions that help 
communities meet their diverse goals, including reducing anticipated current and future risks, enhancing capacity 
to adapt and transform, avoiding maladaptation, yielding benefits greater than costs and serving vulnerable 
populations, and arising from an inclusive, evidence-based and equitable decision process.
While success can be unique to an adaptation action, there are important attributes that constitute it as a successful 
solution. These include the extent to which an action is considered feasible, effective and conforms to principles of 
justice.

The degree to which an action is feasible is the extent to which it is appraised as possible and desirable, taking into 
consideration barriers, enablers, synergies and trade-offs. These considerations are based on financial or economic, 
political, physical, historical and social factors, depending on what is required for an action to be implemented. The 
degree to which an action is effective depends on the extent it reduces climate risk, as well as the extent an action 
achieves its intended goals or outcomes. An adaptation action can sometimes—usually inadvertently—increase risk 
or vulnerability for some or all affected individuals or communities. In some cases, such risk increases will be 
sufficient to call the actions maladaptation. The degree to which an action is just is when its outcomes, the process 
of implementing the action and the process of choosing the action respects principles of distributive, procedural

and recognitional justice. Distributive justice refers to the different distributions of benefits and burdens of an

1
179Point of Departure and Key Concepts Chapter 1
action across members of society; procedural justice refers to ensuring the opportunity for fairness, transparency, 
inclusion and impartiality in the decision making of an action; and recognitional justice insists on recognising and including those who are or may be most affected by an action.
These attributes of adaptation success can be assessed throughout the adaptation process of planning, 
implementation, Monitoring & Evaluation, adjustment and learning. However, at the same time, the success of many adaptation actions depends strongly on context and time. For instance, the effectiveness of adaptation will depend on the success of GHG mitigation efforts, as adaptation has strong synergies and trade-offs with mitigation effortsFAQ 1.3 (continued)
FAQ 1.4 | What is transformational adaptation?

Continuing and expanding current adaptation efforts can reduce some climate risks. But even with emission reductions sufficient to meet the Paris Agreement goals, transformational adaptation will be necessary.
Over six assessment reports, the IPCC has documented transformative changes in the Earth’s climate and ecosystems 
caused by human actions. These changes are now unequivocal and projected to become even more significant in the years and decades ahead. This AR6 report also highlights climate adaptation actions people are taking and can take in response to these significant changes in the climate system.
Some adaptation is incremental, which only modifies existing systems. Other actions are transformational,

leading to changes in the fundamental characteristics of a system. For instance, building a seawall to protect a coastal community from flooding might exemplify incremental adaptation. Changing land use regulations in that community and establishing a programme of managed retreat might exemplify transformational adaptation. There is no definitive line between incremental and transformational adaptation. Some incremental actions stay incremental. Others may expand the future space of solutions. For instance, including climate risk in mortgages and insurance might at first seem incremental but might lead to more transformational change over time.
Transformation can be deliberate, envisioned and intended by at least some societal actors, or forced, arising 
without explicit intent.
Deliberate transformational adaptation is not without risks because change can disturb existing power relationships

and can unfold in difficult to predict and unintended ways. But transformational adaptation is important to consider because it may be needed to avoid intolerable risks from climate change and to help meet development goals as articulated in the SDGs. In addition, some types of societal transformation may be inevitable and deliberate rather than forced transformation and may bring society closer to its goals.
Some types of transformation may be inevitable because the amount of transformational adaptation needed to

avoid intolerable risks depends in part on the level of GHG mitigation. Low concentration pathways consistent with Paris Agreement goals require deliberate transformations that lead to significant and rapid change in energy, land, urban and infrastructure, and industrial systems. Even with low concentration pathways, some transformational adaptation will be necessary to limit intolerable risks. But with higher concentration pathways, more extensive transformational adaptation would be required to limit (though not entirely avoid) intolerable risks. In such circumstances, insufficient deliberate transformation could lead to undesirable forced transformations.

1
180Chapter 1 Point of Departure and Key Concepts
FAQ 1.5 | What is new in this 6th IPCC report on impacts, adaptation and vulnerability?
Since IPCC Fifth Assessment Report, many new sources of knowledge have been employed to provide better understanding of climate change 
risks, impacts, vulnerability and also societal responses through adaptation, mitigation and sustainable development. This new, more integrative 
assessment focuses more on risk and solutions, social justice, different forms of knowledge including IK and LK, the role of transformation and 
the urgency of fast climate actions.
The IPCC Sixth Assessment Report (AR6) has played a prominent role in science–policy–society interactions on the 
climate issue since 1988 and has advanced in interdisciplinary climate change assessment since AR5. Many new 
sources of knowledge have been employed to provide better understanding of climate change risks, impacts,

vulnerability and also societal responses through adaptation, mitigation and sustainable development.
This AR6 assessment focuses more on risk and solutions. The risk framing for the first time spans all three Working 
Groups. It includes risks from the responses to climate change, considers dynamic and cascading consequences, 
describes with more geographic detail risks to people and ecosystems, and assesses such risks over a range of 
scenarios. The solutions framing encompasses the interconnections among climate responses, sustainable 
development and transformation, and the implications for governance across scales within the public and private 
sectors. The assessment therefore includes climate-related decision making and risk management, climate resilient 
development pathways, implementation and evaluation of adaptation, and also limits to adaptation and loss and 
damage.

The AR6 emphasises the emergent issue on social justice and different forms of knowledge. As climate change 
impacts and responses are implemented, there is heightened awareness of the ways that climate responses 
interact with issues of justice and social progress. In this report, there is expanded attention on inequity in climate 
vulnerability and responses, the role of power and participation in processes of implementation, unequal and 
differential impacts, and climate justice. The historic focus on scientific literature is also accompanied by increased 
consideration and incorporation of Indigenous knowledge and local knowledge and associated scholars.
The AR6 has a more extensive focus on the role of transformation and the urgency of fast climate actions in meeting 
societal goals. This report assesses extensive literatures with an increasing diversity of topics and geographical areas

with more sectoral and regional details. The literature also increasingly evaluates the lived experiences of climate 
change—the physical changes underway, the impacts for people and ecosystems, the perceptions of the risks, and 
adaptation and mitigation responses planned and implemented.
The assessment in AR6 is more integrative across multiple disciplines and combines experts across Working Groups, 
chapters, papers and disciplines, such as natural and social sciences, medical and health sciences, engineering, 
humanities, law and business administration. The emphasis on knowledge for action has also included the role of 
public communication, stories and narratives within assessment and associated outreach.

2
200Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Chapter 2, building on prior assessments1, provides a global 
assessment of the observed impacts and projected risks of climate 
change to terrestrial and freshwater ecosystems, including their 
component species and the services they provide to people. Where 
possible, differences among regions, taxonomic groups and ecosystem 
types are presented. Adaptation options to reduce risks to ecosystems 
and people are assessed.
Observed Impacts
Multiple lines of evidence, combined with the strong and 
consistent trends observed on every continent, make it very 
likely2 that many observed changes in the ranges, phenology, 
physiology and morphology of terrestrial and freshwater 
species can be attributed to regional and global climate changes, 
particularly increases in the frequency and severity of extreme 
events (very high confidence3) {2.3.1; 2.3.3.5; 2.4.2; 2.4.5; Table 2.2;

Table 2.3; Table SM2.1; Cross-Chapter Box EXTREMES in this chapter}. 
The most severe impacts are occurring in the most vulnerable species 
and ecosystems, characterised by inherent physiological, ecological or 
behavioural traits that limit their abilities to adapt, as well as those 
most exposed to climatic hazards (high confidence) {2.4.2.2; 2.4.2.6; 
2.4.2.8; 2.4.5; 2.6.1; Cross-Chapter Box EXTREMES in this chapter}.
New studies since the IPCC 5th Assessment Report (AR5) and 
the Special Report on Global Warming of 1.5°C (SR1.5) (with 
data for >12,000 species globally) show changes consistent 
with climate change. Where attribution was assessed (>4,000 
species globally), approximately half of the species had shifted 
their ranges to higher latitudes or elevations and two-thirds of 
spring phenological events had advanced, driven by regional 
climate changes (very high confidence). Shifts in species ranges 
are altering community make-up, with exotic species exhibiting a

greater ability to adapt to climate change than natives, especially in 
more northern latitudes, potentially leading to new invasive species 
(medium confidence) {2.4.2.3.3; 2.4.2.7}. New analyses demonstrate 
that prior reports underestimated impacts due to the complexity of 
biological responses to climate change (high confidence). {2.4.2.1; 
2.4.2.3; 2.4.2.4; 2.4.2.5; 2.4.5; Table 2.2; Table SM2.1; Table 2.3}
Responses of freshwater species are strongly related to changes 
in the physical environment (high confidence) {2.3.3; 2.4.2.3.2}. 
Global coverage of quantitative observations in freshwater ecosystems 
has increased since AR5. Water temperature has increased in rivers 
(up to 1°C per decade) and lakes (up to 0.45°C per decade) {2.3.3.1; 
Figure 2.2}. The extent of ice cover has declined by 25% and duration

1 Previous IPCC assessments include the AR5 (IPCC, 2013; IPCC, 2014b; IPCC, 2014c), the SR1.5 (IPCC, 2014a), the Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC, 
2019b) and the IPCC Sixth Assessment Report Working Group I (IPCC, 2021a).
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome 
lies within the 17–83% probability range.

3 In this report, the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high and very high, and is typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels 
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.by >2 weeks {2.3.3.4; Figure 2.4}. Changes in flow have led to reduced 
connectivity in rivers (high confidence) {2.3.3.2; Figure  2.3}. Indirect 
changes include alterations in river morphology, substrate composition, 
oxygen concentrations and thermal regime in lakes (very high 
confidence) {2.3.3.2; 2.3.3.3}. Dissolved oxygen concentrations have 
typically declined and primary productivity has increased with warming.

Warming and browning (increase in organic matter) have occurred in 
boreal freshwaters, with both positive and negative repercussions on 
water temperature profiles (lower vs. upper water) (high confidence) 
and primary productivity (medium confidence) as well as reduced water 
quality (high confidence) {2.4.4.1; Figure 2.5}.
Climate change has increased wildlife diseases (high 
confidence). Experimental studies provide high confidence in the 
attribution of observed increased disease severity, outbreak frequency 
and the emergence of novel vectors and their diseases into new areas 
to recent trends in climate and extreme events. Many vector-borne 
diseases and those caused by ticks, helminth worms and the chytrid 
fungus (Batrachochytrium dendrobatidis, Bd) have shifted polewards 
and upwards and are emerging in new regions (high confidence). In 
the high Arctic and at high elevations in Nepal, there is high confidence

that climate change has driven the expansion of vector-borne diseases 
(VBDs) that infect humans. {2.4.2.7, 7.2.2.1, 9.8.2.4, 10.4.7.1, 12.3.1.4, 
13.7.1.2, 14.4.6.4; Cross-Chapter Box ILLNESS in this chapter}
Forest insect pests have expanded northward, and the severity 
and extent of outbreaks have increased in northern North 
America and northern Eurasia due to warmer winters reducing 
insect mortality and longer growing seasons favouring more 
generations per year (high confidence) {2.4.2.1; 2.4.4.3.3}.
Local population extinctions caused by climate change have 
been widespread among plants and animals, detected in 47% 
of 976 species examined and associated with increases in the 
hottest yearly temperatures (very high confidence) {2.4.2.2}. 
Climate-driven population extinctions have been higher in tropical 
(55%) than in temperate (39%) regions, higher in freshwater (74%) 
than in marine (51%) or terrestrial (46%) habitats, and higher in

animals (50%) than in plants (39%). Extreme heat waves have led to 
local fish dying out in lakes and mass mortality events in birds, bats, 
mammals and fish {2.3.3.5, 2.4.2.7.2, Cross-Chapter Box EXTREMES 
in this chapter}. Intensification of droughts contributes to the 
disappearance of small or ephemeral ponds that often harbour rare 
and endemic species. {2.4.2.2; Cross-Chapter Box  EXTREMES in this 
chapter}
Global extinctions or near-extinctions have been linked to 
regional climate change in three documented cases {2.4.2.2}. The

2
201Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
cloud forest-restricted golden toad (Incilius periglenes) was extinct by 
1990 in a nature preserve in Costa Rica following successive extreme 
droughts (medium confidence). The white sub-species of the lemuroid 
ringtail possum (Hemibelideus lemuroides) in Queensland, Australia, 
disappeared after heat waves in 2005 (high confidence): intensive 
censuses found only 2 individuals in 2009. The Bramble Cay melomys 
(BC melomys, Melomys rubicola) was not seen after 2009 and was 
declared extinct in 2016, with sea-level rise (SLR) and increased storm 
surge associated with climate change being the most probable drivers 
(high confidence). Additionally, the interaction of climate change and 
chytrid fungus (Bd) has driven many of the observed global declines 
in amphibian populations and the extinction of many species (high 
confidence) {2.4.2.7.1}.
A growing number of studies have documented genetic evolution

within populations in response to recent climate change (very high 
confidence). To date, genetic changes remain within the limits of 
known variation for species (high confidence). Controlled selection 
experiments and field observations indicate that evolution 
would not prevent a species becoming extinct if its climate 
space disappears globally (high confidence). Climate hazards 
outside of those to which species have adapted are occurring on all 
continents (high confidence). More frequent and intense extreme 
events, superimposed on longer-term climate trends, have pushed 
sensitive species and ecosystems towards tipping points that are 
beyond the ecological and evolutionary capacity to adapt, causing 
abrupt and possibly irreversible changes (medium confidence). 
{2.3.1; 2.3.3; 2.4.2.6; 2.4.2.8; 2.6.1; Cross-Chapter Boxes ILLNESS and 
EXTREMES in this chapter}
Since AR5, biome shifts and structural changes within 
ecosystems have been detected at an increasing number

of locations, consistent with climate change and increasing 
atmospheric CO 2 (high confidence). New studies are documenting 
the changes that were projected in prior IPCC reports have now been 
observed, including upward shifts in the forest/alpine tundra ecotone, 
northward shifts in the deciduous/boreal forest ecotones, increased 
woody vegetation in the sub-Arctic tundra and shifts in the thermal 
habitat in lakes (high confidence). A combination of changes in grazing, 
browsing, fire, climate and atmospheric CO 2 is leading to observed 
woody encroachment into grasslands and savannah, consistent 
with projections from process-based models driven by precipitation, 
atmospheric CO 2 and wildfires (high confidence) {2.4.3; Table  2.3; 
Table SM2.1; Box 2.1; Figure Box 2.1.1; Table Box 2.1.1}. There is high 
agreement between the projected changes in earlier reports and the 
recent trends observed for areas of increased tree death in temperate

and boreal forests and woody encroachment in savannas, grasslands 
and tundra {2.5.4; Box  2.1; Figure Box  2.1.1; Table Box  2.1.1}. 
Observed changes impact the structure, functioning and resilience of 
ecosystems as well as ecosystem services, such as climate regulation 
(high confidence) {2.3; 2.4.2; 2.4.3; 2.4.4, 2.5.4, Figure 2.11, Table 2.5, 
Box 2.1; Figure Box 2.1.1; Table Box 2.1.1}.
Regional increases in the area burned by wildfire (up to double 
natural levels), tree mortality of up to 20%, and biome shifts 
of up to 20 km latitudinally and 300 m up-slope have been at-
tributed to anthropogenic climate change in tropical, temper -ate and boreal ecosystems around the world (high confidence), 
damaging key aspects of ecological integrity. This degrades the 
survival of vegetation, habitat for biodiversity, water supplies, carbon 
sequestration, and other key aspects of the integrity of ecosystems and 
their ability to provide services for people (high confidence). {2.4.3.1;

2.4.4.2; 2.4.4.3; 2.4.4.4; Table 2.3; Table SM2.1}
Fire seasons have lengthened on one-quarter of vegetated areas 
since 1979 as a result of increasing temperature, aridity and drought 
(medium confidence). Field evidence shows that anthropogenic 
climate change increased area burned by wildfire above natural 
levels in western North America in the period 1984–2017: 
a doubling above natural for the western USA and 11  times 
higher than natural in one extreme year in British Columbia 
(high confidence). In the Amazon, the Arctic, Australia and parts of 
Africa and Asia, burned area has increased, consistent with, although 
not formally attributed to, anthropogenic climate change. Wildfires 
generate up to one-third of ecosystem carbon emissions globally, 
a feedback that exacerbates climate change (high confidence). 
Deforestation, draining of peatlands, agricultural expansion or 
abandonment, fire suppression, and inter-decadal cycles such as the El

Niño-Southern Oscillation (ENSO), can exert a stronger influence than 
climate change on increasing or decreasing wildfire in some regions 
{2.4.4.2; Table 2.3; Table SM2.1; FAQ 2.3}. Increase in wildfire from the 
levels to which ecosystems are adapted degrades vegetation, habitat 
for biodiversity, water supplies and other key aspects of the integrity 
of ecosystems and their ability to provide services for people (high 
confidence). {2.4.3.1, 2.4.4.2, 2.4.4.3, 2.4.4.4; Table 2.3; Table SM2.1}
Drought-induced tree mortality attributed to anthropogenic 
climate change has caused up to 20% loss of trees in the period 
1945–2007 in three regions in Africa and North America (high 
confidence). It has also potentially contributed to over 100 other 
cases of drought-induced tree mortality across Africa, Asia, Australia, 
Europe, and North and South America (high confidence). Field 
observations have documented post-mortality vegetation shifts (high

confidence). Timber cutting, agricultural expansion, air pollution and 
other non-climate factors also contribute to tree death. Increases in 
forest insect pests driven by climate change have contributed to tree 
mortality and shifts in carbon dynamics in many temperate and boreal 
forest areas (very high confidence). The direction of changes in carbon 
balance and wildfires following insect outbreaks depends on the local 
forest insect communities (medium confidence). {2.4.4.3; Table  2.3; 
Table SM2.1}
Terrestrial ecosystems currently remove more carbon from 
the atmosphere, 2.5–4.3 Gt yr-1, than they emit (+1.6 ± 
0.7 Gt y-1), and so are currently a net sink of -1.9 ± 1.1 Gt y-1. 
Intact tropical rainforests, Arctic permafrost, peatlands and 
other healthy high-carbon ecosystems provide a vital global 
ecosystem service of preventing the release of stored carbon 
(high confidence). Terrestrial ecosystems contain stocks of ~3500

GtC in vegetation, permafrost, and soils, three to five times the amount 
of carbon in unextracted fossil fuels (high confidence) and >4 times the 
carbon currently in the atmosphere (high confidence). Tropical forests 
and Arctic permafrost contain the highest ecosystem carbon stocks in 
aboveground vegetation and in soil, respectively, in the world (high

2
202Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
confidence). Deforestation, draining, burning or drying of peatlands, 
and thawing of Arctic permafrost, due to climate change, has already 
shifted some areas of these ecosystems from carbon sinks to carbon 
sources (high confidence). {2.4.3.6; 2.4.3.8; 2.4.3.9; 2.4.4.4}
Evidence indicates that climate change is affecting many spe-
cies, ecosystems and ecological processes that provide eco-
system services connected to human health, livelihoods, and 
well-being (medium confidence). These services include climate 
regulation, water and food provisioning, pollination of crops, tourism 
and recreation. It is difficult to establish full end-to-end attribution 
from climatic changes to changes in a given ecosystem service and 
to identify the location and timing of impacts. The lack of attribution 
studies may delay specific adaptation planning, but there is evidence

that protection and restoration of ecosystems builds resilience of ser -
vice provision. {2.2; 2.3; 2.4.2.7; 2.4.4; 2.4.5; 2.5.3; 2.5.4; 2.6.3; 2.6.4; 
2.6.5; 2.6.6; 2.6.7; Cross-Chapter Boxes NATURAL, ILLNESS and EX-
TREMES in this chapter; Cross-Chapter Box COVID in Chapter 7; Cross-
Chapter Box MOVING PLATE in Chapter 5; Box 5.3; section 5.4.3.4}
Projected Risks
Climate change increases risks to fundamental aspects of ter -
restrial and freshwater ecosystems, with the potential for spe-
cies’ extinctions to reach 60% at 5°C global mean surface air 
temperature (GSAT) warming (high confidence), biome shifts 
(changes in the major vegetation form of an ecosystem) on 
15% (at 2°C warming) to 35% (at 4°C warming) of global land 
(medium confidence), and increases in the area burned by wild-
fire of 35% (at 2°C warming) to 40% (at 4°C warming) of glob-
al land (medium confidence). {2.5.1; 2.5.2; 2.5.3; 2.5.4; Figure 2.6;

Figure 2.7; Figure 2.8; Figure 2.9; Figure 2.11; Table 2.5; Table SM2.2; 
TableSM2.5; Cross-Chapter Box  DEEP in Chapter 17; Cross-Chapter 
Paper 1}
Extinction of species is an irreversible impact of climate change, 
with increasing risk as global temperatures rise (very high 
confidence). The median values for percentage of species at very 
high risk of extinction (categorized as “critically endangered” by IUCN 
Red List categories)(IUCN, 2001) are 9% at 1.5°C rise in GSAT, 10% 
at 2°C, 12% at 3.0°C, 13% at 4°C and 15% at 5°C (high confidence), 
with the likely range of estimates having a maximum of 14% at 
1.5°C and rising to a maximum of 48% at 5°C (Figure 2.7). Among 
the groups containing the largest numbers of species at a very high 
risk of extinction for mid-levels of warming (3.2°C) are: invertebrates 
(15%, and specifically pollinators at 12%), amphibians (11% overall, 
but salamanders are at 24%) and flowering plants (10%). All groups

fare substantially better at lower warming of 2°C, with extinction 
projections reducing to <3% for all groups, except salamanders that 
reduced to 7% (medium confidence) (Figure  2.8a). Even the lowest 
estimates of species’ extinctions (median of 9% at 1.5°C rise GSAT’) 
are 1000  times the natural background rates. Projected species’ 
extinctions at future global warming levels are consistent with 
projections from AR4, but assessed for many more species with much 
greater geographic coverage and a broader range of climate models. 
{2.5.1.3; Figure 2.6; Figure 2.7; Figure 2.8; Cross-Chapter Box DEEP in 
Chapter 17; Cross-Chapter Paper 1}Species are the fundamental unit of ecosystems, and the 
increasing risk of local losses of species increases the risks of 
reduced ecosystem integrity, functioning and resilience with 
increasing warming (high confidence) . As species become rare, their 
role in the functioning of the ecosystem diminishes (high confidence).

Loss of species locally reduces the ability of an ecosystem to provide 
services and lowers its resilience to climate change (high confidence). 
At 1.58°C GSAT warming, >10% of species are projected to become 
endangered (median estimate, with “endangered” equating to a high 
risk of extinction, sensu IUCN), and at 2.07°C this rises to >20% of 
species, representing a high and very high risk of biodiversity loss, 
respectively (medium confidence) {2.5.4; Figure  2.8b, Figure  2.11; 
Table 2.5; Table SM2.5}. Biodiversity loss is projected for more regions 
with increasing warming, and will be worst in northern South America, 
southern Africa, most of Australia and at northern high latitudes 
(medium confidence) {2.5.1.3; Figure 2.6}.
Climate change increases risks of biome shifts on up to 35% 
of global land at ≥4°C GSAT warming, that emission reductions 
could limit to <15% for <2°C warming (medium confidence). 
Under high-warming scenarios, models indicate shifts

of extensive parts of the Amazon rainforest to drier and lower-
biomass vegetation (medium  confidence), poleward shifts of 
boreal forest into treeless tundra across the Arctic, and upslope 
shifts of montane forests into alpine grassland (high confidence).  
Area at high risk of biome shifts from changes in climate and land use 
combined can double or triple compared to climate change alone 
(medium confidence). Novel ecosystems, with no historical analogue, 
are expected to become increasingly common in the future (medium 
confidence). {2.3, 2.4.2.3.3, 2.5.2; 2.5.4, Figure  2.11; Table  2.5; 
Table SM2.4; Table SM2.5}
The risk of wildfire increases along with an increase in global 
temperatures (high confidence). With 4°C GSAT warming by 
2100, wildfire frequency is projected to have a net increase of 
~30% (medium confidence). Increased wildfire, combined with soil 
erosion due to deforestation, could degrade water supplies (medium

confidence). For ecosystems with an historically low frequency of fires, 
a projected 4°C global temperature rise increases the risk of fires, with 
potential increases in tree mortality and the conversion of extensive 
parts of the Amazon rainforest to drier and lower-biomass vegetation 
(medium confidence). {2.5.3.2; 2.5.3.3}
Continued climate change substantially increases the risk 
of carbon stored in the biosphere being released into the 
atmosphere due to increases in processes such as wildfire, tree 
mortality, insect pest outbreaks, peatland drying and permafrost 
thaw (high confidence). These phenomena exacerbate self-reinforcing 
feedbacks between emissions from high-carbon ecosystems (that 
currently store ~3000–4000 GtC) and increasing global temperatures. 
Complex interactions of climate change, land use change (LUC), carbon 
dioxide fluxes and vegetation changes, combined with insect outbreaks 
and other disturbances, will regulate the future carbon balance of the

biosphere. These processes are incompletely represented in current earth 
system models (ESMs). The exact timing and magnitude of climate–
biosphere feedbacks and potential tipping points of carbon loss are 
characterised by large uncertainty, but studies of feedbacks indicate

2
203Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
that increased ecosystem carbon losses can cause large temperature 
increases in the future (medium confidence). (section 5.4, Figure 5.29 
and Table  5.4 in (Canadell et  al., 2021)), {2.5.2.7; 2.5.2.8; 2.5.2.9; 
2.5.3.2; 2.5.3.3; 2.5.3.4; 2.5.3.5; Figure  2.10; Figure  2.11; Table  2.4; 
Table 2.5; Table SM2.2 Table SM2.5}
Contributions of Adaptation Measures to Solutions
The resilience of biodiversity and ecosystem services to climate 
change can be increased by human adaptation actions including 
ecosystem protection and restoration (high confidence). 
Ecological theory and observations show that a wide range of actions 
can reduce risks to species and ecosystem integrity. This includes 
minimising additional stresses or disturbances; reducing fragmentation; 
increasing natural habitat extent, connectivity and heterogeneity; 
maintaining taxonomic, phylogenetic, and functional diversity and

redundancy; and protecting small-scale refugia where micro-climate 
conditions can allow species to persist (high confidence). Adaptation 
also includes actions to aid the recovery of ecosystems following 
extreme events. Understanding the characteristics of vulnerable 
species can assist in early warning systems to minimise negative 
impacts and inform management intervention. {2.3; Figure 2.1; 2.5.3.1, 
2.6.2, Table 2.6, 2.6.5, 2.6.7, 2.6.8}
There is new evidence that species can persist in refugia where 
conditions are locally cooler, when populations of the same 
species may be declining elsewhere (high confidence) {2.6.2}. 
Protecting refugia, for example, where soils remain wet during drought 
or fire risk is reduced, and in some cases creating cooler micro-climates, 
are promising adaptation measures {2.6.3; 2.6.5; Cross-Chapter Paper 
1; CCP5.2.1}. There is also new evidence that species can persist locally 
because of plasticity including changes in phenology or behavioural

changes that move an individual into cooler micro-climates, and 
genetic adaptation may allow species to persist for longer than might 
be expected from local climatic changes (high confidence) {2.4.2.6; 
2.4.2.8, 2.6.1}. There is no evidence to indicate that these mechanisms 
will prevent global extinctions of rare, very localised species already 
near their climatic limits or species inhabiting climate/habitat zones 
that are disappearing (high confidence). {2.4.2.8, 2.5.1, 2.5.3.1, 2.5.4, 
2.6.1, 2.6.2, 2.6.5}
Since AR5, many adaptation plans and strategies have been 
developed to protect ecosystems and biodiversity, but there is 
limited evidence of the extent to which adaptation is taking place 
and virtually no evaluation of the effectiveness of adaptation 
measures in the scientific literature (medium confidence). This 
is an important evidence gap that needs to be addressed, to ensure a 
baseline is available against which to judge effectiveness and develop

and refine adaptation in future. Many proposed adaptation measures 
have not been implemented (low confidence). {2.6.2; 2.6.3; 2.6.4; 
2.6.5; 2.6.6; 2.6.8; 2.7}
Ecosystem restoration and resilience building cannot prevent 
all impacts of climate change, and adaptation planning 
needs to manage inevitable changes to species distributions, 
ecosystem structure and processes (very high confidence). 
Actions to manage inevitable change include the local modification of micro-climate or hydrology, adjustment of site management plans 
and facilitating the dispersal of vulnerable species to new locations by 
increasing habitat connectivity and by active translocation of species. 
Adaptation can reduce risks but cannot prevent all damaging impacts 
so is not a substitute for reductions in greenhouse gas (GHG) emissions 
(high confidence). {2.2; 2.3; 2.3.1; 2.3.2; 2.4.5; 2.5.1.3; 2.5.1.4; 2.5.2; 
2.5.3.1; 2.5.3.5; 2.5.4; 2.6.1; 2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6; 2.6.8;

Cross-Chapter Box NATURAL in this chapter}
Ecosystem-based adaptation (EbA) can deliver climate change 
adaptation for people, with multiple additional benefits 
including those for biodiversity (high confidence). An increasing 
body of evidence demonstrates that climatic risks to people including 
floods, drought, fire and overheating, can be lowered by a range of 
EbA techniques in urban and rural areas (medium confidence). EbA 
forms part of a wider range of nature-based solutions (NbS); some 
have mitigation co-benefits, including the protection and restoration 
of forests and other high-carbon ecosystems as well as agro-ecological 
farming (AF) practices. However, EbA and other NbS are still not widely 
implemented. {2.2; 2.5.3.1; 2.6.2; 2.6.3; 2.6.4; 2.6.5; 2.6.6, 2.6.7; 
Table 2.7; Cross-Chapter Box NATURAL in this chapter; Cross-Chapter 
Paper 1}
To realise potential benefits and avoid harm, it is essential that

EbA is deployed in the right places and with the right approaches 
for that area, with inclusive governance (high confidence). 
Interdisciplinary scientific information and practical expertise, including 
Indigenous and local knowledge (IKLK), are essential to effectiveness 
(high confidence). There is a large risk of maladaptation where this does 
not happen (high confidence). {1.4.2; 2.2; 2.6; Table 2.7; Box 2.2; Figure 
Box 2.2.1; Cross-Chapter Box NATURAL in this chapter; Cross-Chapter 
Paper 1; 5.14.2}
EbA and other NbS are themselves vulnerable to climate change 
impacts (high confidence). They need to take account of climate 
change if they are to remain effective and they will be increasingly 
under threat at higher warming levels. NbS cannot be regarded as an 
alternative to, or a reason to delay, deep cuts in GHG emissions. (high 
confidence) {2.6.3, 2.6.5; 2.6.7; Cross-Chapter Box  NATURAL in this 
chapter}

Protection and restoration of natural and semi-natural ecosystems 
are key adaptation measures in view of the clear evidence that 
damage and degradation of ecosystems exacerbates the impacts 
of climate change on biodiversity and people (high confidence). 
Ecosystem services that are under threat from a combination of climate 
change and other anthropogenic pressures include climate change 
mitigation, flood risk management, food provisioning and water supply 
(high confidence). Adaptation strategies that treat climate, biodiversity 
and human society as coupled systems will be most effective. {2.3; 
Figure 2.1; 2.5.4; 2.6.2; 2.6.3; 2.6.7; Cross-Chapter Boxes NATURAL and 
ILLNESS in this chapter}
A range of analyses have concluded that ~30–50% of Earth’s 
surface needs to be effectively conserved to maintain biodiversity

2
204Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
and ecosystem services (high confidence). Climate change places 
additional stress on ecosystem integrity and functioning, adding 
urgency to taking action. Low-intensity sustainable management, 
including that performed by Indigenous Peoples, is an integral part 
of some protected areas, and can support effective adaptation and 
maintain ecosystem health. Food and fibre production in other areas 
will need to be efficient, sustainable and adapted to climate change to 
meet the needs of the human population. (high confidence) {Figure 2.1; 
2.5.4; 2.6.2; 2.6.3; 2.6.7}
Natural ecosystems can provide the storage and sequestration of 
carbon at the same time as providing multiple other ecosystem 
services, including EbA (high confidence), but there are risks 
of maladaptation and environmental damage from some 
approaches to land-based mitigation (high confidence). Plantation,

single-species forests in areas which would not naturally support forest, 
including savanna, natural grasslands and temperate peatlands, and 
replacing native tropical forests on peat soils, have destroyed local 
biodiversity and created a range of problems regarding water supply, 
food supply, fire risk and GHG emissions. Large-scale deployment of 
bioenergy, including bioenergy with carbon capture and storage (BECCS) 
through dedicated herbaceous or woody bioenergy crops and non-native 
production forests, can damage ecosystems directly or through increasing 
competition for land, with substantial risks to biodiversity. {2.6.3, 2.6.5, 
2.6.6, 2.6.7; Box  2.2; Cross-Chapter Box  NATURAL in this chapter; 
CCP7.3.2; Cross-Working Group Box BIOECONOMY in Chapter 5}
Terrestrial and aquatic ecosystems and species are often less 
degraded on land managed by Indigenous Peoples and local 
communities than on other land (medium confidence). Involving

indigenous and local institutions is a key element for developing 
successful adaptation strategies. IKLK includes a wide variety of 
resource-use practices and ecosystem stewardship strategies that 
conserve and enhance both wild and domestic biodiversity. {2.6.5; 
2.6.7; Cross-Chapter Box  NATURAL in this chapter; Chapter 15; 
Box 18.6; CCP2.4.1; CCP2.4.3; Box CCP7.1}
Increases in the frequency and severity of extreme events, 
that WGI has attributed to human greenhouse gas emissions, 
are compressing the timeline available for natural systems to 
adapt and also impeding our ability to identify, develop and 
implement solutions (medium confidence). There is now an 
urgent need to build resilience and assist recovery following extreme 
events. This, combined with long-term changes in baseline conditions, 
means that implementing adaptation and mitigation measures cannot 
be delayed if these are to be fully effective. {2.3; Cross-Chapter 
Box EXTREMES in this chapter}

2
205Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
2.1.1 Overview
We provide assessments of observed and projected impacts of climate 
change across species, biomes (vegetation types), ecosystems and 
ecosystem services, highlighting the processes that are emerging 
on a global scale. Where sufficient evidence exists, differences in 
biological responses across regions, taxonomic groups or types of 
ecosystems are presented, particularly when such differences provide 
meaningful insights into current or potential future autonomous or 
human-mediated adaptations. Human interventions that might build 
the resilience of ecosystems and minimise the negative impacts of 
climate change on biodiversity and ecosystem functioning are assessed. 
Such interventions include adaptation strategies and programmes to 
support biodiversity conservation and Ecosystem-based Adaptation 
(EbA). The assessments were done in the context of the Convention on

Biological Diversity (CBD) and sustainable development goals (SDGs), 
whose contributions to climate resilient development (CRD) pathways 
are assessed. This chapter highlights both the successes and failures of 
adaptation attempts and considers potential synergies and conflicts 
with land-based climate change mitigation. Knowledge gaps and 
sources of uncertainty are included to encourage additional research.
The Working Group II Summary for Policymakers of the AR5 stated that 
‘many terrestrial and freshwater species have shifted their geographic 
ranges, seasonal activities, migration patterns, abundances, and 
species interactions in response to ongoing climate change’ (IPCC, 
2014d). Based on long-term observed changes across the regions, 
it was estimated that approximately 20–30% of plant and animal 
species are at risk of extinction when global mean temperatures rise 
2–3°C above pre-industrial levels (Fischlin et  al., 2007). In addition,

the WGII AR5 Synthesis Report (IPCC, 2014e) broadly suggested that 
autonomous adaptation by ecosystems and wild species might occur, 
and proposed human-assisted adaptations to minimise negative 
climate change impacts.
Risk assessments for species, communities, key ecosystems and their 
services were based on the risk assessment framework introduced 
in the IPCC AR5 (IPCC, 2014b). Assessments of observed changes in 
biological systems emphasise detecting and attributing the impacts of 
climate change on ecological and evolutionary processes, particularly 
freshwater ecosystems, and ecosystem processes such as wildfires, that 
were superficially assessed in previous reports. Where appropriate, 
assessment of interactions between climate change and other human 
activities is provided.
Land use and land cover change (LULCC) as well as the unsustainable 
exploitation of resources in terrestrial and freshwater systems continue

to be major factors contributing to the loss of natural ecosystems and 
biodiversity (high confidence). Fertiliser input, pollution of waterways, 
dam construction and the extraction of freshwater for irrigation put 
additional pressure on biodiversity and alter ecosystem function (Shin 
et al., 2019). Likewise, for biodiversity, invasive alien species have been 
identified as a major threat, especially in freshwater systems, on islands 
and in coastal regions (high confidence) (IPBES, 2018b; IPBES, 2018e; 
IPBES, 2018c; IPBES, 2018d; IPBES, 2019). Climate change and CO 2 are expected to become increasingly important as drivers of change over 
the coming decades (Ciais et al., 2013; Settele et al., 2014; IPBES, 2019; 
IPCC, 2019c).
2.1.2  Points of Departure
Species diversity and ecosystem function influence each other 
reciprocally, while the latter forms the necessary basis for ecosystem 
services (Hooper et al., 2012; Mokany et al., 2016). Drivers of impacts

on biodiversity, ecosystem function and ecosystem services have been 
assessed in reports by the IPCC, the Food and Agriculture Organization 
(FAO), the Intergovernmental Platform on Biodiversity and Ecosystem 
Services (IPBES) and the Global Environmental Outlook (Settele et al., 
2014; FAO, 2018; IPBES, 2018b; IPBES, 2018e; IPBES, 2018c; IPBES, 
2018d; IPBES, 2019; UNEP , 2019; Secretariat of the Convention on 
Biological Diversity, 2020). Most recently, the IPCC Special Report on 
Climate Change and Land (SRCCL) provided an assessment on land 
degradation and desertification, GHG emissions and food security in 
the context of global warming (IPCC, 2019c), and the IPBES–IPCC joint 
report on biodiversity and climate change provided a synthesis of the 
current understanding of the interactions, synergies and feedbacks 
between biodiversity and climate change (Pörtner et  al., 2021). This 
chapter builds on and expands the results of these assessments.

Assessment of the impacts of climate change on freshwater systems 
has been limited in previous assessments, and inter-linkages between 
terrestrial and freshwater processes have not been fully explored 
(Settele et al., 2014; IPBES, 2019). Improved treatment of impacts on 
terrestrial and freshwater systems is critical, considering the revisions of 
international sustainability goals and targets, especially the conclusion 
that many of the proposed post-2020 targets of the CBD cannot be met 
due to climate change impacts (Arneth et al., 2020).
Previous reports highlighted the possibility of new ecosystem states 
stemming from shifts in thermal regimes, species composition, and 
energy and matter flows (Settele et  al., 2014; Shin et  al., 2019). 
Projecting such “tipping points” (see Glossary Appendix II) has 
been identified in previous reports as a challenge since monitoring 
programmes, field studies, and ecosystem and biodiversity modelling

tools do not capture the underlying species–species and species–
climate interactions sufficiently well to identify how biological 
interactions within and across trophic levels may amplify or dampen 
shifts in ecosystem states (Settele et al., 2014; Shin et al., 2019). Building 
on these previous analyses and the recent literature, Chapter 2 of AR6 
provides new insights compared to those of previous assessments by 
(i) emphasising freshwater aspects and the interlinkages between 
freshwater and terrestrial systems, (ii) assessing more clearly the link 
between biodiversity and ecosystem functioning, (iii) assessing the 
impacts associated with climate change mitigation scenarios versus 
those of climate change including interactions with adaptation, and 
(iv) where possible, places findings in the context of the United Nations 
(UN) SDGs 2030 and services for human societies.

2
206Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
2.1.3  Guide to Attribution and Traceability of 
Uncertainty Assessments
For biological systems, we use the framework for detection and 
attribution outlined in AR5, in which biological changes observed 
are not attributed to global but rather to local or regional climate 
changes (Parmesan et al., 2013; Cramer et al., 2014). However, global 
distribution of regional responses is desirable to achieve generality, and 
data in prior reports were concentrated from the Northern Hemisphere. 
The critique of ‘global’ studies by (Feeley et al., 2017) argues that their 
naming is misleading, that most of them are far from global, and that 
a considerable geographic and taxonomic bias remains. This bias is 
diminishing, as regional data from the Southern Hemisphere is added 
and there is now representation from every continent.
Overall confidence in attributing biological changes to climate change

can be increased in multiple ways (Parmesan et al., 2013), four of which 
we list here. First, confidence rises when the time span of biological 
records is long, such that decadal trends in climate can be compared 
with decadal trends in biological response, and long-term trends can be 
statistically distinguished from natural variability. Second, confidence 
can be increased by examining a large geographic area, which tends 
to diminish the effects of local confounding factors (Parmesan et al., 
2013; Daskalova et  al., 2021). Third, confidence is increased when 
there is experimental or empirical evidence of a mechanistic link 
between particular climate metrics and a biological response. Fourth, 
confidence is increased when particular fingerprints of climate change 
are documented that uniquely implicate climate change as the causal 
driver of the biological change (Parmesan and Yohe, 2003). These 
conditions constitute multiple lines of evidence, which, when they

converge, can provide very high confidence that climate change is the 
causal driver of an observed change in a particular biological species 
or system (Parmesan et al., 2013).
Important factors that may confound or obscure effects of climate 
change are the presence of invasive species, changes in land use 
(LULCC) and, in freshwater systems, eutrophication (IPCC, 2019a). The 
temporal and spatial scale of studies also affects estimates of impacts. 
The most extreme published estimates of biological change tend to 
be derived from smaller areas and/or shorter time frames (Daskalova 
et al., 2021); a recent large global analysis of data for 12,415 species 
found that differences in study methodology accounted for most of the 
explained variance in reported range shifts (Lenoir et al., 2020). The 
importance of LULCC is frequently stressed, but there is a paucity of 
studies actually quantifying the relative effects of climate change and

LULCC on species and communities. (Sirami et al., 2017) found only 13 
such studies: four concluded that effects of LULCC overrode those of 
climate change, four found that the two drivers independently affected 
different species and five found that they acted in synergy.
2.2 Connections of Ecosystem Services to 
Ecosystems provide services essential for human survival and well-being. 
The Millennium Ecosystem Assessment defined ecosystem services as 
‘the benefits people obtain from ecosystems’ including ‘provisioning services such as food and water; regulating services such as regulation 
of floods, drought, land degradation, and disease; supporting services 
such as soil formation and nutrient cycling; and cultural services such 
as recreational, spiritual, religious, and other nonmaterial benefits’ 
(Millennium Ecosystem Assessment, 2005).
The IPBES renamed the concept ‘nature’s contributions to people’

and broadened the definition to ‘the contributions, both positive and 
negative, of living nature (i.e., diversity of organisms, ecosystems, 
and their associated ecological and evolutionary processes) to the 
quality of life for people. Beneficial contributions from nature include 
such things as food provision, water purification, flood control, and 
artistic inspiration, whereas detrimental contributions include disease 
transmission and predation that damages people or their assets’ 
(IPBES, 2019). The concept was modified to include more social 
viewpoints and broaden the analyses beyond narrow economic 
stock-and-flow valuation approaches (Díaz et  al., 2018). IPBES 
developed a classification of 18 categories of ecosystem services (see 
Table 2.1).
When anthropogenic climate change affects ecosystems, it can also 
affect ecosystem services for people. Climate change connects to 
ecosystem services by means of three links, i.e., climate change–

species–ecosystems–ecosystem services. This chapter assesses these 
connections via all three links when end-to-end published scientific 
analyses are available for terrestrial and freshwater ecosystems. 
This type of robust evidence exists for some key ecosystem services 
(Section  2.5.3, 2.5.4), and is assessed in specific report sections: 
biodiversity habitat creation and maintenance (Sections  2.4, 2.5), 
regulation of detrimental organisms and biological processes 
(Sections 2.4.2.3, 2.4.2.7, 2.4.4, 2.5.3, 2.6.4, Cross-Chapter Box ILLNESS 
in this chapter), regulation of climate through ecosystem feedbacks in 
terms of carbon storage (Sections 2.4.4.4, 2.5.2.10, 2.5.3.4, 2.5.3.5) 
and albedo (Section  2.5.3.5) and the provision of freshwater from 
ecosystems to people (Section 2.5.3.6).
For ecosystem services that do not have published scientific 
information to establish unambiguous links to climate change, the 
climate–species–ecosystem links are assessed. Global ecological

assessments, including the Global Biodiversity Assessment (Heywood 
et  al., 1995), the Millennium Ecosystem Assessment (Millennium 
Ecosystem Assessment, 2005), and the IPBES Global Assessment 
Report (IPBES, 2019) have synthesised scientific information on the 
ecosystem–ecosystem services link, but a full assessment from climate 
change to ecosystem services is often impeded by limited quantitative 
studies that span this entire spectrum (see (Mengist et al., 2020) for a 
review of this gap in montane regions).
IPCC and IPBES are collaborating to address gaps in the knowledge 
about the effects of climate change on ecosystem services (Pörtner 
et  al., 2021). Table  2.1 provides a guide for finding information on 
climate change and individual ecosystem services in the AR6.

2
207Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Table 2.1 |  Connections of ecosystem services to climate change, indicating the 18 categories of nature’s contributions to people (IPBES, 2019), the most relevant sections in the 
AR6, and the level of evidence in this report for attribution to anthropogenic climate change of observed impacts on ecosystem services. The order of services in the table follows 
the order presented by IPBES and does not denote importance or priority. Connections denote observed impacts, future risks and adaptation. The order of connections follows the 
relevance or the order of sections. Numbers in parentheses refer to sections in this chapter.
Ecosystem service Connections to climate change
Habitat creation and maintenanceSpecies extinctions (2.4.2.2, 2.5.1.3), species range shifts (2.4.2.1, 2.4.2.5), ecological changes in freshwater ecosystems (2.3.3, 2.4.2.3.2, 2.4.4.1,

2.4.4.5.2, 2.5.1.3.2, 2.5.3.5, 2.5.4, 2.5.3.6, 2.5.5.8), vegetation changes (2.4.3, 2.4.4.2.5, 2.4.4.3, 2.4.4.4, 2.4.4.5.1, 2.5.2, 2.5.3.3), biome shifts 
(2.4.3.2, 2.5.4), wildfire (2.4.4.2, 2.5.3.2), tree mortality (2.4.4.3, 2.5.3.3)
(robust evidence)
Pollination and dispersal of seeds 
and other propagulesSpecies extinctions (2.4.2.2, 2.5.1.3), species range shifts (2.4.2.1, 2.4.2.5), phenology changes (2.4.2.4, 2.4.2.5). See also Box 5.3.
(medium evidence)
Regulation of air qualityWildfire (2.4.4.2, 2.5.3.2, Chapter 7), tree mortality (2.4.4.3, 2.5.3.3)
Regulation of climateEcosystem carbon stocks, emissions, and removals (2.4.4.4, 2.5.3.4, (Canadell et al., 2021), Amazon rainforest dieback (2.4.3.6, 2.4.4.3.2, 2.4.4.4.2, 
2.5.2.6, 2.5.2.10, 2.5.3.3), tundra permafrost thaw (2.4.4.4.4, 2.5.2.8, 2.5.3.5, 2.5.4), biome shifts (2.4.3, 2.5.2, 2.5.3.2.2), wildfire (2.4.4.2, 2.5.3.2), 
tree mortality (2.4.4.3, 2.5.3.3), primary productivity changes (2.4.4.5, 2.5.3.5)

Regulation of ocean acidificationOcean acidification (Canadell et al., 2021), changes in marine species distribution and abundance (Chapter 3)
Regulation of freshwater quantity, 
location and timingPhysical changes in freshwater systems (2.3.3), ecological changes in freshwater ecosystems (2.4.2.3.2, 2.4.4.1, 2.4.4.5.2, 2.5.1.3.2, 2.5.3.7), tree 
mortality (2.4.4.3, 2.5.3.3), freshwater supply from ecosystems (2.5.3.6)
Regulation of freshwater and 
coastal-water qualityCoastal ecosystem changes (Chapter 3), physical changes in freshwater systems (2.3.3), ecological changes in freshwater ecosystems (2.4.2.3.2; 
2.4.4.1, 2.4.4.5.2, 2.5.1.3.2, 2.5.3.7)
Formation, protection and 
decontamination of soils and 
sedimentsAgricultural ecosystem changes (Chapter 5), physical changes in freshwater systems (2.3.1), vegetation changes (2.4.3, 2.5.4), wildfire (2.4.4.2, 2.5.3.2)
Regulation of hazards and

extreme eventsCoastal ecosystem changes (Chapter 3), vegetation changes (2.4.3, 2.5.2), wildfire (2.4.4.2, 2.5.3.2), Summary of hazards (2.3), Cross-Chapter 
Box EXTREMES in this chapter
Regulation of detrimental 
organisms and biological 
processesInter-species interactions (2.4.2), control of disease vectors (2.4.2.7, 2.5.1, 2.6.4), insect-pest infestations (2.4.4.3), Cross-Chapter Box ILLNESS in 
this chapter
EnergyForestry plantation changes (Chapter 5), biomass changes in natural ecosystems (2.4.4.4), bioeconomy (Cross-Working Group Box BIOECONOMY 
in Chapter 5), tree mortality (2.4.4.3, 2.5.3.3)
(limited evidence)
Food and feedAgricultural ecosystem changes (Chapter 5), species extinctions (2.4.2.2, 2.5.1.3), species range shifts (2.4.2.1), nature-based services from natural 
ecosystems (Cross-Chapter Box NATURAL in this chapter), shifts in commercial food species (Cross-Chapter Box MOVING PLATE in Chapter 5)

Materials, companionship and 
labourForestry plantation changes (Chapter 5), species extinctions (2.4.2.2, 2.5.1.3), species range shifts (2.4.2.1), tree mortality (2.4.4.3, 2.5.3.3)
Medicinal, biochemical and 
genetic resourcesSpecies extinctions (2.4.2.2, 2.5.1.3), species range shifts (2.4.2.1)
Learning and inspirationAll observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems
Physical and psychological 
experiencesAll observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems. See also 5.4.3.4, Chapter 15, CCP6.
Supporting identitiesAll observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems. See also 5.4.3.4, Chapter 15, CCP6
Maintenance of optionsAll observed impacts (2.4) and future risks (2.5) in terrestrial and freshwater ecosystems, nature-based services from natural ecosystems

(Cross-Chapter Box NATURAL in this chapter, Cross-Chapter Box DEEP in Chapter 17, Cross-Chapter Box MOVING PLATE in Chapter 5.

2
208Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
2.3 Hazards and Exposure
In AR6, Working Group I (IPCC, 2021a) describes changes in physical 
climate systems using the term ‘climatic impact-drivers’ (CIDs), which 
can have detrimental, beneficial or neutral effects on a system. In 
contrast, the literature on natural systems tends to focus on hazards, 
which include natural or human-induced physical events, impacts, or 
trends with the potential to cause negative effects on ecosystems and 
environmental resources. Hazards are affected by current and future 
changes in climate, including altered climate variability and extreme 
events (Ranasinghe et al., 2021). Hazards can occur suddenly (e.g., a heat 
wave or heavy rain event), or more slowly (e.g., land loss, degradation 
and erosion linked to multiple climate hazards compounding). Observed 
exposure and risks to protected areas are assessed in Section 2.5.3.1.1.

See also Cross-Chapter Box EXTREMES in this chapter.
Non-climatic hazards such as LUC, habitat fragmentation, pollution and 
invasive species have been the primary drivers of change in terrestrial 
and freshwater ecosystems in the past (high confidence) (Figure 2.1). 
These impacts have been extensively documented in reports by 
the IPBES (2021). However, while climate change has not been the 
predominant influence to date, its relative impact is increasing (IPCC 
SRCCL), with greater interactive effects of non-climate and climate 
hazards now occurring (Birk et al., 2020).
2.3.1 Observed Changes to Hazards and Extreme Events
The major climate hazards at the global level are generally well 
understood (Ranasinghe et  al., 2021) (WGI AR6 Interactive Atlas). 
Increased temperatures and changes to rainfall and runoff patterns; 
greater variability in temperature, rainfall, river flow and water levels; 
and rising sea levels and the increased frequency of extreme events

means that greater areas of the world are being exposed to climate 
hazards outside of those to which they are adapted (high confidence) 
(Lange et al., 2020).
Extreme events are a natural and important part of many ecosystems, 
and many organisms have adapted to cope with long-term 
and short-term climate variability within the disturbance regime 
experienced during their evolutionary history (high confidence). 
However, climate changes, disturbance regime changes and the 
magnitude and frequency of extreme events such as floods, droughts, 
cyclones, heat waves and fire have increased in many regions (high 
confidence). These disturbances affect ecosystem functioning, 
biodiversity and ecosystem services (high confidence), but are, in 
general, poorly captured in impact models (Albrich et  al., 2020b), 
although this should improve as higher-resolution climate models 
that better capture smaller-scale processes and extreme events

become available (Seneviratne et  al., 2021). Extreme events pose 
huge challenges for EbA (IPCC, 2012). Ecosystem functionality, on 
which such adaptation measures rely, may be altered or destroyed 
by extreme episodic events (Handmer et al., 2012; Lal et al., 2012; Pol 
There is high confidence that the combination of internal variability, 
superimposed on longer-term climate trends, is pushing ecosystems to tipping points, beyond which abrupt and possibly irreversible changes 
are occurring (Harris et al., 2018a; Jones et al., 2018; Hoffmann et al., 
2019b; Prober et al., 2019; Berdugo et al., 2020; Bergstrom et al., 2021). 
Increases in the frequency and severity of heat waves, droughts and 
aridity, floods, fires and extreme storms have been observed in many 
regions (Seneviratne et al., 2012; Ummenhofer and Meehl, 2017), and 
these trends are projected to continue (high confidence) (Section 3.2.2.1,

Cross-Chapter Box  EXTREMES this Chapter) (Hoegh-Guldberg et  al., 
2018; Seneviratne et al., 2021).
While the major climate hazards at the global level are generally well 
described with high confidence, there is less understanding about the 
importance of hazards on ecosystems when they are superimposed 
(Allen et al., 2010; Anderegg et al., 2015; Seidl et al., 2017; Dean et al., 
2018), and the outcomes are difficult to quantify in future projections 
(Handmer et al., 2012). Simultaneous or sequential events (coincident 
or compounding events) can lead to an extreme event or impact, even 
if each event is not in themselves extreme (Denny et al., 2009; Hinojosa 
et al., 2019). For example, the compounding effects of SLR, extreme 
coastal high tide, storm surge, and river flow can substantially increase 
flooding hazard and impacts on freshwater systems (Moftakhari et al., 
2017). On land, changing rainfall patterns and repeated heat waves

may interact with biological factors such as altered plant growth 
and nutrient allocation under elevated CO 2, affecting herbivore rates 
and insect outbreaks leading to the widespread dieback of some 
forests (e.g., in Australian eucalypt forests) (Gherlenda et  al., 2016; 
Hoffmann et  al., 2019a). Risk assessments typically only consider a 
single climate hazard with no changing variability, thereby potentially 
underestimating the actual risk (Milly et al., 2008; Sadegh et al., 2018; 
Zscheischler et al., 2018; Terzi et al., 2019; Stockwell et al., 2020).
Understanding impacts associated with the rapid rate of climate 
change is less developed and more uncertain than changes in mean 
climate. High climate velocity (Loarie et al., 2009) is expected to be 
associated with distribution shifts, incomplete range filling and species 
extinctions (high confidence) (Sandel et al., 2011; Burrows et al., 2014), 
although not all species are equally at risk from high velocity (see

Sections 2.4.2.2, 2.5.1.3). It is generally assumed that the more rapid 
the rate of change, the greater the impact on species and ecosystems, 
but responses are taxonomically and geographically variable (high 
confidence) (Kling et al., 2020).
For example, strong dispersers are less at risk, while species with low 
dispersal ability, small ranges and long lifespans (e.g., many plants, 
especially trees, many amphibians and some small mammals) are 
more at risk (IPCC, 2014b; Hamann et  al., 2015) . This is likely to 
favour generalist and invasive species, altering species composition, 
ecosystem structure and function (Clavel et  al., 2011; Büchi and 
Vuilleumier, 2014). The ability to track suitable climates is substantially 
reduced by habitat fragmentation and human modifications of the 
landscape such as dams on rivers and urbanisation (high confidence). 
Freshwater systems are particularly at risk of rapid warming, given

their naturally fragmented distribution. Velocity of changes in surface 
temperature of inland standing waters globally was estimated as 
being 3.5 ± 2.3 km per decade from 1861 to 2005. From 2006 to 
2099, this is projected to increase from 8.7 ± 5.5 km (representative 
concentration pathway, RCP2.6) to 57.0 ± 17.0 km (RCP8.5) per

2
209Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
decade (Woolway and Maberly, 2020). Although the dispersal of the 
aerial adult stage of some aquatic insects can surpass these climate 
velocities, rates of change under mid- and high-emission scenarios 
(RCP4.5, RCP6.0, RCP8.5) are substantially higher than the known 
rates of the active dispersal of many species (Woolway and Maberly, 
2020). Many species, both terrestrial and freshwater, are not expected 
to be able to disperse fast enough to track suitable climates under mid- 
and high-emission scenarios (medium confidence) (RCP4.5, RCP6.0, 
RCP8.5; (Brito-Morales et al., 2018).2.3.2  Projected Impacts of Increases in Extreme Events
Understanding of the large-scale drivers and the local-to-regional 
feedback processes that lead to extreme events is still limited, and 
projections of extremes and coincident or compounding events remain

uncertain (Prudhomme et al., 2014; Sillmann et al., 2017; Hao et al., 2018; 
Miralles et al., 2019). Extreme events are challenging to model because 
they are, by definition, rare, and often occur at spatial and temporal scales 
much finer than the resolution of climate models (Sillmann et al., 2017; 
Zscheischler et al., 2018). Additionally, the processes that cause extreme 
events often interact, as is the case for drought and heat events, and they 
are spatially and temporally dependent, for example, soil moisture and 
temperature (Vogel et al., 2017). Understanding feedbacks between land A satellite-based record of
global land use change
(1982–2016)
(b) Long-term change estimates
Short
vegetation
gain
Short
lossBare
ground
gainTree
canopy
gainBare
ground
lossTree
canopy
loss(a) Mean annual estimates
      Tree canopy cover
      Short vegetation cover
      Bare ground cover
Data averaged
over 1.5° hexagonsSpatial resolution
 0.05°

Figure 2.1 |  Map of global land use change from 1982 to 2016.  Based on satellite records of global tree canopy (TC), short vegetation (SV) and bare ground (BG) cover 
(from Song et al., 2018).
(a) Mean annual estimates of cover (% of pixel area at 0.05° resolution).
(b) Long-term change estimates (% of pixel area at 1.5° resolution), with pixels showing a statistically significant trend (n = 35 years, two-sided Mann–Kendall test, P < 0.05) 
in TC, SV or BG. The dominant changes are TC gain with SV loss; BG gain with SV loss; TC gain with BG loss; BG gain with TC loss; SV gain with BG loss; and SV gain with TC loss. 
Grey indicates areas with no significant change between 1982 and 2016.

2
210Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
and atmosphere also remains limited. For example, positive feedbacks 
between soil and vegetation, or between evaporation, radiation and 
precipitation, are important in the preconditioning of extreme events 
such as heat waves and droughts, and can increase the severity and 
impact of such events (Miralles et al., 2019).
Despite recent improvements in observational studies and climate 
modelling (Santanello et  al., 2015; Stegehuis et  al., 2015; PaiMazumder 
and Done, 2016; Basara and Christian, 2018; Knelman et al., 2019), 
the potential to quantify or infer formal causal relationships between 
multiple drivers and/or hazards remains limited (Zscheischler and 
Seneviratne, 2017; Kleinman et  al., 2019; Miralles et  al., 2019; 
Yokohata et al., 2019; Harris et al., 2020). The mechanisms underlying 
the response are difficult to identify (e.g., responses to heat stress,

drought and insects), effects vary among species and at different life 
stages, and an initial stress may influence the response to further 
stress (Nolet and Kneeshaw, 2018). Additionally, hazards such as 
drought are often exacerbated by societal, industrial and agricultural 
water demands, requiring more sophisticated modelling of the 
physical and human systems (Mehran et al., 2017; Wan et al., 2017). 
Observations of past compound events may not provide reliable 
guides as to how future events may evolve, because human activity 
and recent climate change continue to interact to influence both 
system functioning and a climate state not previously experienced 
(Seneviratne et al., 2021)
2.3.3 Biologically Important Physical Changes in 
Freshwater Systems
Physical changes are fundamental drivers of change at all levels of 
biological organisation, from individual species, to communities, whole 
ecosystems. The climate hazards specific to freshwater systems not

documented elsewhere in AR6 are summarised here.
2.3.3.1 Observed Change in Thermal Habitat and Oxygen 
Since AR5, evidence of changes in the temperature of lakes and rivers 
has continued to increase. Global warming rates for lake surface 
waters were estimated as 0.21°C–0.45°C per decade between 1970 
and 2010, exceeding sea-surface temperature (SST) trends of 0.09°C 
per decade between 1980 and 2017 (robust evidence, high agreement) 
(Figure 2.2; (Schneider and Hook, 2010; Kraemer et al., 2015; O’Reilly 
et al., 2015; Woolway et al., 2020b). Warming of lake surface water 
temperatures was variable within regions (O’Reilly et  al., 2015) but 
more homogeneous than deep-water temperature changes (Pilla 
et al., 2020). Because temperature trends in lakes can vary vertically, 
horizontally and seasonally, complex changes have occurred in the 
amount of habitat available to aquatic organisms at particular depths 
and temperatures (Kraemer et al., 2021).

Changes in river water temperatures ranged from −1.21°C to +1.076°C 
per decade between 1901 and 2010 (medium evidence, medium 
agreement) (Hari et al., 2006; Kaushal et al., 2010; Jurgelėnaitė et al., 
2012; Li et al., 2012; Latkovska and Apsīte, 2016; Marszelewski and 
Pius, 2016). The more rapid increase in surface water temperature in lakes and rivers in regions with cold winters (O’Reilly et al., 2015) 
can, in part, be attributed to the amplified warming in polar and 
high-latitude regions (robust evidence, high agreement) (Screen and 
Simmonds, 2010; Stuecker et al., 2018).
Shifts in thermal regime: Since AR5, the trend that lake waters mix 
less frequently continues (Butcher et  al., 2015; Adrian et  al., 2016; 
Richardson et  al., 2017; Woolway et  al., 2017). This results from 
greater warming of surface temperatures relative to deep-water 
temperatures, and the loss of ice during winter which prevents inverse

thermal stratification in north temperate lakes (robust evidence, high 
agreement) (Adrian et  al., 2009; Winslow et  al., 2015; Adrian et  al., 
2016; Schwefel et al., 2016; Richardson et al., 2017).
Oxygen availability: increased water temperature and reduced mixing 
cause a decrease in dissolved oxygen. In 400 lakes, dissolved oxygen 
in surface and deep waters declined by 4.1 and 16.8%, respectively, 
between 1980 and 2017 (Jane et al., 2021). The deepest water layers 
are expected to experience an increase in hypoxic conditions by >25% 
due to fewer complete mixing events, with strong repercussions for 
nutrient dynamics and the loss of thermal habitat (robust evidence, 
high agreement) (Straile et  al., 2010; Zhang et  al., 2015; Schwefel 
2.3.3.2  Observed Changes in Water Level
Depending on how the intensification of the global water cycle affects 
individual lake water budgets, the amount of water stored in specific

lakes may increase, decrease or have no substantial cumulative effect 
(Notaro et  al., 2015; Pekel et  al., 2016; Rodell et  al., 2018; Busker 
et  al., 2019; Woolway et  al., 2020b). The magnitude of hydrological 
changes that can be assuredly attributed to climate change remains 
uncertain (Hegerl et al., 2015; Gronewold and Rood, 2019; Kraemer 
et  al., 2020). Attribution of water storage variation in lakes due to 
climate change is facilitated when such variations occur coherently 
across broad geographic regions and long time scales, preferably 
absent of other anthropogenic hydrological influences (Watras et al., 
2014; Kraemer et al., 2020). There is increasing awareness that climate 
change contributes to the loss of small temporary ponds which cover a 
greater global area than lakes (Bagella et al., 2016).
Lakes fed by glacial melt water are growing in response to climate 
change and glacier retreat (robust evidence, high agreement) (Shugar

et al., 2020). Water storage increases on the Tibetan Plateau (Figure 2.3a) 
have been attributed to changes in glacier melt, permafrost thaw, 
precipitation and runoff, in part as a result of climate change (Huang 
et al., 2011; Meng et al., 2019; Wang et al., 2020a). High confidence in 
attribution of these trends to climate change is supported by long-term 
ground survey data and observations from the Gravity Recovery and 
Climate Experiment (GRACE) satellite mission (Ma et al., 2010; Rodell 
et al., 2018; Kraemer et al., 2020).
In the Arctic, lake area has increased in regions with continuous 
permafrost, and decreased in regions where permafrost is thinner 
and discontinuous (robust evidence, high agreement) (See Chapter 4) 
(Smith et al., 2005; Andresen and Lougheed, 2015; Nitze et al., 2018; 
Mekonnen et al., 2021).

2
211Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
2.3.3.3 Observed Changes in Discharge
Analysis of river flows from 7250 observatories around the world 
covering the years 1971–2010 and identified spatially complex 
patterns, with reductions in northeastern Brazil, southern Australia 
and the Mediterranean, and increases in northern Europe (medium 
evidence, medium agreement) (Gudmundsson et  al., 2021). More 
than half of global rivers undergo periodic drying that reduces river 
connectivity (medium evidence, medium agreement). Increased 
frequency and intensity of droughts may cause perennial rivers to 
become intermittent and intermittent rivers to disappear (medium 
evidence, medium agreement), threatening freshwater fish in habitats 
already characterised by heat and droughts (Datry et al., 2016; Schneider 
et  al., 2017; Jaric et  al., 2019). In high-altitude/latitude streams, 
reduced glacier and snowpack extent, earlier snowmelt and altered

precipitation patterns, attributed to climate change, have increased 
flow intermittency (Siebers et al., 2019; Gudmundsson et al., 2021). 
Patterns in flow regimes can be directly linked to a variety of processes 
shaping freshwater biodiversity, so any climate change-induced 
changes in flow regimes and river connectivity are expected to alter species composition as well as having societal impacts (See Chapter 
3 in (IPCC, 2018b)) (Bunn and Arthington, 2002; Thomson et al., 2012; 
Chessman, 2015; Kakouei et al., 2018).
2.3.3.4 Observed Loss of Ice
Studies since AR5 have confirmed ongoing and accelerating loss of 
lake and river ice in the Northern Hemisphere (robust evidence, high 
agreement) (Figure 2.4). In recent decades, systems have been freezing 
later in winter and thawing earlier in spring, reducing ice duration by 
>2 weeks per year and leading to an increasing numbers of years with a 
loss of perennial ice cover, intermittent ice cover or even an absence of

ice (Adrian et al., 2009; Kirillin et al., 2012; Paquette et al., 2015; Adrian 
et al., 2016; Park et al., 2016; Roberts et al., 2017; Sharma et al., 2019). 
The global extent of river ice declined by 25% between 1984 and 2018 
(Yang et al., 2020). This trend has been more pronounced at higher 
latitudes, consistent with enhanced polar warming (large geographic 
coverage) (Du et al., 2017). Empirical long-term and remote-sensing 
data gathered in an increasingly large number of freshwater systems 
supports very high confidence in attributing these trends to climate (a) Observed trends in lakes for the period 1970–201 0
(b) Observed trends in rivers for the period 1901–201 0Global trends in lake and river surface water temperature
-60-40-20020406080
406080
-1.0°C -0.5°C 0.0 +0.5°C +1.0°CTemperature trend (°C change per decade)
-1.0°C -0.5°C 0.0 +0.5°C +1.0°C
Latitude
Latitude= Lake data point
= River data pointPolar region
Data restricted to the Northern HemispherePolar region

Temperate
regionTemperate
regionPolar region
Tropical
region
Figure 2.2 |  Observed global trends in lake and river surface water temperature.
(a) Left panel: map of temperatures of lakes (1970–2010).
(b) Left panel: map of temperatures of rivers (1901–2010). Note that the trends of river water temperatures are not directly comparable within rivers or to lakes, since time 
periods are not consistent across river studies. Right panels (a) and (b) depict water temperature trends along a latitudinal gradient highlighting the above average warming rates 
in northern Polar Regions (polar amplification). Data sources for lakes: (O’Reilly et al., 2015; Carrea and Merchant, 2019; Woolway et al., 2020a; Woolway et al., 2020b). Data 
sources for rivers: (Webb and Walling, 1992; Langan et al., 2001; Daufresne et al., 2004; Moatar and Gailhard, 2006; Lammers et al., 2007; Patterson et al., 2007; Webb and Nobilis,

2007; Durance and Ormerod, 2009; Kaushal et al., 2010; Pekárová et al., 2011; Jurgelėnaitė et al., 2012; Markovic et al., 2013; Arora et al., 2016; Latkovska and Apsīte, 2016; 
Marszelewski and Pius, 2016; Jurgelėnaitė et al., 2017).

2
212Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Nu RiverHuang Riv
erPanj River
LhasaLhasaDulanDulanDulanHotanHotanHotan
GarGarGarGolmudGolmudGolmud
LhasaNagchuNagchuNagchuDadu RiverZa River
Yarlung  River
Brahmputra RiverTongtian RiverIndus River
Sutlej River
Ganges Rive
rYamuna River(a) Change in water extent: Qinghai–Tibetan Plateau, Asia
250 km
(b) Regional median trend in annual mean river flow derived from 7,250 observatories around the world (period 1971–2010)≤ -10%≥ 10%
5%
0%
-5%Percent change
in water extent  
Change in 2000–2019
relative to 1984–1999
≤ -10%-5%0%5%≥ 10%Regional median trend
in percentage change
per decade
= Predefined regions
   used for grouping stations
= Colored polygons are defined 
by the convex hull around the 
station coordinates in the 
respective regionsChange in water extent in the Tibetan Plateau and annual mean global river flow
Figure 2.3 |  Change in water extent in the Tibetan Plateau and annual mean global river flow.

(a) Changes in water storage on the Tibetan Plateau. Map of the Qinghai–Tibetan Plateau, Asia, showing the percent change in surface water extent from 1984 to 2019 based 
on LANDSAT imagery. Increases in surface water extent in this region are mainly caused by climate change-mediated increases in precipitation and glacial melt (Source: EC JRC/
Google; (Pekel et al., 2016).
(b) Global map of the median trend in annual mean river flow derived from 7250 observatories around the world (in 1971–2010). Some regions are drying (northeast Brazil, 
southern Australia and the Mediterranean) and others are wetting (northern Europe), mainly caused by large-scale shifts in precipitation, changes in factors that influence 
evapotranspiration and alterations of the timing of snow accumulation and melt driven by rising temperatures (Source: (Gudmundsson et al., 2021).

2
213Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
(a) Future changes in lakes that 
experience intermittent winter ice cover 
in the Northern Hemisphere.
2,000 km(b) Future changes in river ice duration 
in the Northern Hemisphere.Annual winter iceTemperature projections 
relative to 1970–2010
Change in river ice duration
Days in 2080–2100
relative to 2009–2029Global ice cover trends of lakes and rivers
Intermittent winter ice (current)+ 3.2°C+ 2.0°C
+ 4.5°C
+ 8.0°C
No projection due to data paucity
2,000 km2,000 km2,000 km
up to minus 100 daysup to minus 80 daysno changeplus 7.5 days
up to minus 20 days
up to minus 40 days
up to minus 60 days
Reference period isolines
Days of river ice duration
in 2009–2029
Figure 2.4 |  Global ice cover trends of lakes and rivers.
(a) Spatial distribution of current (light grey areas) and future (coloured areas) Northern Hemisphere lakes that may experience intermittent winter ice cover with climate warming.

Projections were based on current conditions (1970–2010) and four established air temperature projections (Data source: (Sharma et al., 2019).
(b) Spatial distribution of projected change in Northern Hemisphere river ice duration under the RCP4.5 emission scenario by 2080–2100 relative to the period 2009–2029. White 
areas refer to rivers without ice cover in the period 2009–2029 (zero days). Reference period isolines indicate river ice duration in the period 2009–2029. Coloured areas depict 
loss of ice duration in days. Blue areas depict a projected increase in river ice duration. Grey land areas indicate a lack of Landsat-observable rivers (Data source: (Yang et al., 2020).

2
214Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
change. For the decline of glaciers, snow and permafrost, see Chapter 
4 (this report) and the Special Report on the Ocean and Cryosphere in 
a Changing Climate (IPCC, 2019b).
2.3.3.5  Extreme Weather Events and Freshwater Systems
Since AR5, numerous drastic short-term responses have been observed 
in lakes and rivers, to both expected seasonal extreme events and 
unexpected supra-seasonal extremes extending over multiple seasons. 
Consequences for ecosystem functioning are not well understood 
(Bogan et al., 2015; Death et al., 2015; Stockwell et al., 2020). Increasing 
frequencies of severe floods and droughts attributed to climate change 
are major threats for river ecosystems (Peters et al., 2016; Alfieri et al., 
2017). While extreme floods cause massive physical disturbance, 
moderate floods can have positive effects, providing woody debris

that contributes to habitat complexity and diversity, flushing fine 
sediments, dissolving organic carbon and providing important food 
sources from terrestrial origins (Peters et al., 2016; Talbot et al., 2018). 
Droughts reduce river habitat diversity and connectivity, threatening 
aquatic species, especially in deserts and arid regions (Bogan et al., 
2015; Death et al., 2015; Ledger and Milner, 2015; Jaric et al., 2019).
Rivers already under stress from human activities such as urban 
development and farming on floodplains are prone to reduced 
resilience to future extreme events (medium confidence) (Woodward 
et  al., 2016; Talbot et  al., 2018). Thus, the potential for floods to 
become catastrophic for ecosystem services is exacerbated by LULCC 
(Peters et al., 2016; Talbot et al., 2018). However, biota can recover 
rapidly from extreme flood events if river geomorphology is not greatly 
altered. If instream habitat is strongly affected, recovery, if it occurs,

takes much longer, resulting in a decline of biodiversity (medium 
confidence) (Thorp et al., 2010; Death et al., 2015; Poff et al., 2018).
However, not all extreme events will have a biological impact, 
depending, in particular, on the timing, magnitude and frequency 
of events and the antecedent conditions (Bailey and van de Pol, 
2016; Stockwell et  al., 2020; Jennings et  al., 2021; Thayne et  al., 
2021). For instance, an extreme wind event may have little impact 
on phytoplankton in a lake that was fully mixed prior to the event. 
Conversely, the effects of a storm on phytoplankton communities 
may compound when lakes have not yet recovered from a previous 
storm or if periods of drought alternate with periods of intense 
precipitation (limited evidence) (Leonard et al., 2014; Stockwell et al., 
2020).
In summary, extreme events (heat waves, storms and loss of ice) 
affect lakes in terms of water temperature, water level, light, oxygen

concentrations and nutrient dynamics, which, in turn, affect primary 
production, fish communities and GHG emissions (high confidence). 
These impacts are modified by levels of solar radiation, wind speed 
and precipitation (Woolway et al., 2020a). Droughts have a negative 
impact on water quality in streams and lakes by increasing water 
temperature, salinity, the frequency of algal blooms and contaminant 
concentrations, and reducing concentrations of nutrients and dissolved 
oxygen (medium confidence) (Peters et  al., 2016; Alfieri et  al., 
2017; Woolway et  al., 2020a). Understanding how these pressures 
subsequently cascade through freshwater ecosystems will be essential for future projections of their resistance and resilience towards extreme 
events (Leonard et al., 2014; Stockwell et al., 2020). See Table SM2.1 
for specific examples of observed changes.
2.3.3.6  Projected Changes in Physical Characteristics of Lakes 
and Rivers

Given the strength of relationship between past GSAT and warming 
trends at lake surfaces (Figure  2.2; Section  2.3.3.1) and projected 
increases in heat waves, surface water temperatures are projected to 
continue to increase (Woolway et al., 2021). Mean May to October lake 
surface temperatures in 46,557 European lakes were projected to be 
2.9°C, 4.5°C and 6.5°C warmer by 2081–2099 compared to the historic 
period (1981–1999) under RCP2.0, RCP6.0 and RCP8.5, respectively 
(Woolway et al., 2020a). Under RCP2.6, the average intensity of lake 
heat waves increases from 3.7°C to 4.0°C and the average duration 
from 7.7 to 27.0 days, relative to the historic period (1970–1999). For 
RCP8.5, warming increases to 5.4°C and duration increases dramatically 
to 95.5 days (medium confidence) (Woolway et al., 2021).
Worldwide alterations in lake mixing regimes in response to climate 
change are projected (Kirillin, 2010). Most prominently, monomictic

lakes—undergoing one mixing event in most years—will become 
permanently stratified, while lakes that are currently dimictic—mix-
ing twice per year—will become monomictic by 2080–2100 (medium 
confidence) (Woolway and Merchant, 2019). Nevertheless, predicting 
mixing behaviour remains an important challenge and attribution to 
climate change remains difficult (Schwefel et al., 2016; Bruce et al., 
2018).
Under climate projections of 3.2°C warming, 4.6% of the ice-covered 
lakes in the Northern Hemisphere could switch to intermittent winter 
ice cover (Figure  2.4a; (Sharma et  al., 2019). Unfrozen and warmer 
lakes lose more water to evaporation (Wang et al., 2018b). By 2100, 
global annual lake evaporation will increase by 16%, relative to 2006–
2015, under RCP8.5 (Woolway et al., 2020b). Moreover, melting of ice 
decreases the ratio of sensible to latent heat flux, thus channelling 
more energy into evaporation (medium confidence) (Wang et  al.,

2018b). In the periods 2009–2029 and 2080–2100, average duration 
of river ice is projected to decline by 7.3 and 16.7 days under RCP4.5 
and RCP8.5, respectively (Figure 2.4b; (Yang et al., 2020).
Projections of lake water storage are limited by the absence of reliable, 
long-term, homogenous and spatially resolved hydrologic observations 
(Hegerl et al., 2015). This uncertainty is reflected in the widely divergent 
projections in response to future climate changes in individual lakes 
(Angel and Kunkel, 2010; MacKay and Seglenieks, 2012; Malsy et al., 
2012; Notaro et al., 2015) . Selecting models that perform well when 
comparing hindcasted to observed past water storage variation often 
does little to reduce water storage projection uncertainty (Angel and 
Kunkel, 2010). This wide range of potential changes complicates lake 
management. For information on observed and projected changes in 
the global water cycle and hydrological regimes for streams, lakes,

wetland, groundwater and their implications on water quality and 
societies, see Chapter 4, this report, and (Douville et al., 2021). For the 
role of weather and climate extremes on the global water cycle, see 
(Seneviratne et al., 2021).

2
215Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Cross-Chapter Box EXTREMES | Ramifications of Climatic Extremes for Marine, Terrestrial, 
Freshwater and Polar Natural Systems
Authors: Rebecca Harris (Australia, Chapter 2, CCP3), Philip Boyd (Australia, Chapter 3), Rita Adrian (Germany, Chapter 2), Jörn Birkmann 
(Germany, Chapter 8), Sarah Cooley (USA, Chapter 3), Simon Donner (Canada, Chapter 3), Mette Mauritzen (Norway, Chapter 3), Guy 
Midgley (South Africa, Chapter 16); Camille Parmesan (France/USA/UK, Chapter 2), Dieter Piepenburg (Germany, Chapter 13, CCP6), 
Marie-Fanny Racault (UK/France, Chapter 3), Björn Rost (Germany, Chapter 3, CCP6), David Schoeman (Australia, Chapter 3), Stavana E. 
Strutz (USA/Chapter 2), Maarten van Aalst (the Netherlands, Chapter 16).
Increases in the frequency and magnitudes of extreme events, attributed to anthropogenic climate change by WGI (IPCC, 2021a), are now

causing profound negative effects across all realms of the world (marine, terrestrial, freshwater and polar) (medium confidence) (Fox-Kemper 
et al., 2021; Seneviratne et al., 2021) (Sections 2.3.1, 2.3.2, 2.3.3.5, 2.4.2.2, Chapter 3, Chapters 9–12, this report). Changes to population 
abundance, species distributions, local extirpations, and global extinctions are leading to long-term, potentially irreversible shifts in the 
composition, structure and function of natural systems (medium confidence) (Frolicher and Laufkotter, 2018; Harris et al., 2018a; Maxwell 
et al., 2019; Smale et al., 2019). These effects have widespread ramifications for ecosystems and the services they provide—physical 
habitat, erosion control, carbon storage, nutrient cycling and water quality—with knock-on effects for tourism, fisheries, forestry and other 
natural resources (2.4.3, 2.4.4, 2.5.1, 2.5.2, 2.5.3, 2.5.4) (Kaushal et al., 2018; Heinze et al., 2021; Pörtner et al., 2021).

Increasingly, the magnitude of extreme events is exceeding the values projected for mean conditions for 2100, regardless of emissions 
scenario (Figure Cross-Chapter Box  EXTREMES.1). This has collapsed the timeline that organisms and natural communities have to 
acclimate or adapt to climate change (medium confidence). Consequently, rather than having decades to identify, develop and adopt 
solutions, actions to build resilience and assist recovery following extreme events are required quickly if they are to be effective.
Recent extremes highlight the characteristics that enable natural systems to resist or recover from events, helping natural resource 
managers to develop solutions to improve the resilience of natural communities and identify the limits to adaptation (Bergstrom et al., 
2021).
Marine Heat Waves
Consensus is emerging that anthropogenic climate change has significantly increased the likelihood of recent marine heat waves (MHWs)

(medium confidence) (Oliver et al., 2018; Fox-Kemper et al., 2021). A widespread MHW occurred in the northeast Pacific in 2013–2015, 
with upper ocean temperature anomalies of up to 6.2°C relative to 2002–2012 (Gentemann et al., 2017). This event, termed the ‘Blob’, 
enhanced surface water stratification, decreasing nutrient supply, primary and community production and leading to widespread changes 
to open ocean and coastal ecosystems, with geographical shifts of key species across trophic levels, mass strandings of marine mammals, 
seabird mortalities and the closure of commercially important fisheries (Cavole et al., 2016; Piatt et al., 2020). The MHW reappeared in 
2019 (‘Blob 2.0’) (Amaya et al., 2020), with similarly high temperature anomalies extending from Alaska to California, but the ecological 
effects of this event are expected to differ because the Blob originated in winter, and Blob2.0 intensified in summer (Amaya et al.,

2020). Modelling suggests rapid shifts in the geographic distributions of important fish species in response to MHWs (Cheung and 
Frolicher, 2020), with projected decreased biomass and distributional shifts of fish at least four times faster and larger than the effects 
of decadal-scale mean changes throughout the 21st century under RCP8.5 (high confidence) (Cheung and Frolicher, 2020). MHWs can 
also dramatically increase CH 4 emissions from oceans, a significant positive feedback to global warming (see also Chapter 3, this report) 
(Borges et al., 2019).
The Arctic region is warming more than twice as fast as the global mean, and polar organisms and ecosystems are likely to be particularly 
vulnerable to heat waves due to their specific thermal niches and physiological thresholds and also the lack of poleward ‘refugia’ (high 
confidence). The consequences of MHWs are exacerbated by concomitant sea ice melting and the freshening of surface waters, leading

to secondary effects due to osmotic stress and failing pH homeostasis. Since sea ice-associated organisms are often critical components 
of polar food chains, cascading effects up to the top predators are expected. In 2015–2016, a MHW occurred in the Gulf of Alaska/Bering 
Sea (Walsh et al., 2018) which was unprecedented in terms of surface temperatures and ocean heat content, geographical extent, depth 
range and persistence, impacting the entire marine food web. Persistent warming favoured some phytoplankton species and triggered 
one of the largest algal blooms recorded in this region, with concomitant oyster farm closures due to uncommon paralytic shellfish-
poisoning events (Walsh et  al., 2018). There were also massive die-offs of common guillemots (Uria aalge) and puffins (Fratercula 
cirrhata), attributed to starvation resulting from warming-induced effects on food supply (Jones et al., 2019). A 2017 survey found a 71%

decline in the abundance of Pacific cod (Gadus macrocephalus) since 2015, likely due to an increase in metabolic demand and reduced 
prey supply during the MHWs (Barbeaux et al., 2020).

2
216Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
         How extinction risk is affected by changes in the frequency , duration, and magnitude
of extreme weather or climate events
Survivable Extremes
Healthy EnvironmentExtinction Extremes
1960 Year 1980 2000 2020 2040 2060 2080 2100Recovery Time:
DecreasingExtreme weather or climate event :
e.g. drought, flood, hurricane, heat waveeeux
es
yFrequency and duration of extreme events increases with increasing mean warming
Increasing Mean GSATDuration:
Figure Cross-Chapter Box EXTREMES.1 |  A conceptual illustration of how extinction risk is affected by changes in the frequency, duration and mag-
nitude of extreme weather or climate events (e.g,. drought, fire, flood and heat waves). Many organisms have adapted to cope with long- and short-term

climate variability, but as the magnitude and frequency of extreme events increases, superimposed on the long-term climate trend, the threshold between survivable 
extreme weather events (yellow) and extremes that carry a high risk of causing population or species extinctions (red) is crossed more frequently. This can lead to local 
extinction events with insufficient time between to enable recovery, resulting in long-term, irreversible changes to the composition, structure and function of natural 
systems. When the extreme event occurs over a large area relative to the distribution of a species (e.g., a hurricane impacting an island which is the only place a given 
species occurs), a single extreme event can drive the global extinction of a species.
Terrestrial Heat Waves
Heat waves are now regularly occurring that exceed the physiological thresholds of some species, including birds and other small

endotherms such as flying foxes (high confidence) (Sections 2.4.2.2, 2.4.2.6). Heat waves in Australia, North America and southern Africa 
have caused mass mortality events due to lethal hyperthermia and dehydration (Saunders et al., 2011; Conradie et al., 2020; McKechnie 
et al., 2021), reducing fitness (du Plessis et al., 2012; Andrew et al., 2017; Sharpe et al., 2019; van de Ven et al., 2019; van de Ven et al., 
2020), breeding success, and recruitment (Kennedy et al., 2013; Wiley and Ridley, 2016; Ratnayake et al., 2019) and affecting daily activity 
and geographic distributions (Albright et al., 2017). They also place enormous demands on wildlife management agencies and pose risks 
to human health (Welbergen et al., 2008).
Recent mortality events affected 14 species of bird and fruit bats (Epomophorus wahlbergi) in South Africa when maximum air 
temperatures exceeded 43–45°C in 2020 (McKechnie et al., 2021). Passerine birds seem more vulnerable to lethal hyperthermia, due to

the relative inefficiency of panting to lose heat (McKechnie et al., 2021) and also their small size, as heat tolerance generally increases 
with body mass (McKechnie et al., 2021). Several mass mortality events of flying foxes (Pteropus poliocephalus, P . alecto) have occurred 
in eastern Australia when maximum air temperatures exceeded 42° (Welbergen et al., 2008). Nineteen such events occurred between 
1994 and 2008, compared to three events prior to 1994. In January 2002, maximum temperatures exceeded the 30-year average mean 
daily maximum by up to 16.5° and killed >3500 individuals (Welbergen et al., 2008). In 2014, an estimated 45,500 flying foxes died in Cross-Chapter Box EXTREMES (continued)

2
217Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
a single day, when average maximum temperatures were ≥8°C above average (Bureau of Meteorology, 2014). Drought compounds the 
impacts, as mortality increases when water availability is low (Welbergen et al., 2008; Mo and Roache, 2020; McKechnie et al., 2021).
Antarctica encountered its first recorded heat wave in 2020. Record high temperatures occurred in East Antarctica (Robinson et al., 2020), 
with a maximum (9.2°) temperature ~7° above the mean maximum, and minimum temperatures > 0°. Record high temperatures (18.3°) 
were also recorded in West Antarctica (Robinson et al., 2020). It is too soon to know the impact on polar life, but such abrupt heating is 
expected to have wide-ranging effects on biota, from flash-flooding and dislodgement of plants, to excess meltwater supplying moisture

to arid polar ecosystems (Cross-Chapter Paper 6 Polar). Heat waves in Siberia in 2016, 2018 and 2020, with air temperature anomalies 
>6°, were associated with extensive wildfires, pest infestations and melting permafrost (Overland and Wang, 2021).
Freshwater Extremes
Heat waves, storms and floods affect the thermal regime and biogeochemical functioning of lakes and rivers (Woolway and Merchant, 2017; 
Vicente-Serrano et al., 2020). Extreme heat waves lead to abnormally high water temperatures (Till et al., 2019) and reduce the mixing of 
lakes (Woolway et al., 2021), causing a decrease in oxygen and deep-water oxygen renewal (Zhang et al., 2015). Ectotherms such as fish 
and invertebrates are particularly susceptible to such temperature and oxygen stress (Stoks et al., 2014). Their metabolic demands increase 
with rising temperature and a suitable habitat is eroded due to both high temperatures and lower oxygen concentrations in lakes and rivers.

Till et al. (2019) attributed 502 fish kill events in the Wisconsin lakes (USA) to warmer summers in lakes that experienced abnormally high 
water temperatures. Such events are predicted to double by 2041–2059 and increase four-fold by 2081–2099 compared to historical levels 
(Till et al., 2019). This anticipated increase in die-offs may facilitate warm-water fish species displacing cool-water species (Hansen et al., 
2017; Jennings et al., 2021). Floods mobilise nutrients and sediment, and aid dispersal of invasive species in rivers (Death et al., 2015), while 
drought extremes reduce river connectivity, threatening biodiversity in rivers (section 2.3.3.5) (Tickner et al., 2020).
Learnings from Recent Extremes
These examples show that the impact of an extreme event is a function of its characteristics and those of the exposed ecosystem. 
The timing, frequency, absolute magnitude and geographic extent of the extreme event, relative to antecedent conditions and the life

cycle, resistance and resilience of the natural community, all determine the biological response (Figure Cross-Chapter Box Extremes.1) 
(Hillebrand et al., 2018; Gruber et al., 2021). The impact appears to be greater when extreme events occur more frequently, particularly 
when the interval between events is insufficient to allow recovery to previous population sizes (e.g., frequent fires and coral bleaching) 
or coincides with vulnerable life-cycle stages, even when populations are adapted to cope with such disturbances. Events occurring over 
large spatial areas reduce the potential for recolonisation from nearby populations (e.g., regional droughts causing widespread declines). 
Often the magnitude of extreme events exceeds historical levels, so organisms are less likely to be adapted to them, particularly when 
several extremes coincide (e.g., high water temperature and drought) (Duke et al., 2017). When hazards occur simultaneously (compound

events), the impacts of extremes can be substantially aggravated, triggering a cascade of effects in ecosystems (Gruber et al., 2021).
Several characteristics of natural systems are associated with greater vulnerability to extreme events (Figure Cross-Chapter 
Box EXTREMES.2), knowledge of which can inform solutions to build resilience and aid recovery (Robinson et al., 2020). Resilience 
can be built prior to an event by minimising additional disturbances, such as water extraction from river systems, pollution of aquatic 
systems, fragmentation of land and LULCCs. Managing landscapes to reduce fragmentation and increase habitat extent, connectivity and 
heterogeneity, by increasing the number and extent of reserves, may provide local refugia from extreme events and enhance post-event 
recolonisation, but may be less effective for marine systems (Section 3.6). Maintaining taxonomic, phylogenetic and functional diversity

is important, as more diverse systems may be more stable in the face of disturbances (Pimm, 1984; García-Palacios et al., 2018).
Several characteristics increase vulnerability: low or narrow thermal tolerance, high habitat specificity, low dispersal ability, long 
generation times, low competitive ability and life-cycle constraints that limit recovery or recolonisation. Populations living close to one 
or more limiting factors near range edges are also vulnerable (Arafeh-Dalmau et al., 2019). Understanding these characteristics can 
inform management intervention to aid recovery following an extreme event. For instance, knowledge of the flying fox’s physiological 
temperature threshold led to successful interventions, including misting populations to reduce mortality (Mo and Roache, 2020), and the 
development of a ‘heat stress forecaster’, an online tool which uses weather forecasts to identify roosts at risk of extreme heat events

(Ratnayake et  al., 2019). This early warning system increases the preparedness of wildlife management and conservation agencies, 
enabling efficient allocation of management resources towards the locations that are likely to be the most affected.
Monitoring following extreme events can help identify immediate impacts and the potential for cascading interactions, such as changes to 
competitive interactions following range shifts, impacts on freshwater ecosystems following wildfires and the spread of invasive species. 
Ongoing monitoring of recovery and effectiveness of management intervention is important, focussing on habitat-forming species (e.g., Cross-Chapter Box EXTREMES (continued)

2
218Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
kelp, corals and dominant tree species) and keystone species (e.g., filter-feeders, macrophytes and top predators), as the loss of these 
species can lead to ecosystem tipping points, beyond which the system may not recover (Collins et al., 2019) (Sections 2.5.3; 3.4.4.1; 
3.4.4.1.4; chapters 9–15, this report).
The acute impacts of extreme events, in addition to the chronic stress of changing mean conditions, are accelerating and amplifying the 
biological effects of climate change. This amplification is being observed globally and in all realms where life exists. Extreme events are 
compressing the timeline available for natural systems to adapt, and impeding our ability to identify, develop and adopt solutions. Recent 
events highlight the urgent need to mitigate global GHG emissions and identify solutions to halt accelerating impacts on natural systems 
(Díaz et al., 2020).

Characteristics associated with vulnerability to 
extreme events
Landscape
/g122Small habitat patches
/g122/g3High fragmentation
/g122/g3Inhospitable matri xSolutions to build resilience and 
aid recovery
Post event
Monitor to assess recovery ,
identify management require d
Assess success of management  
/g122/g3Identify intervention options (e.g .
 translocation of warm-adapted
 genotypes, reseeding, removal of
 invasive or range shifting species)
/g122/g3Assess cost and likelihood of
 intervention success
/g122/g3Assess feasibility of ongoing
 management objectivesPre event
/g122/g3Reserve and protect to maintain   
 diversity and extent of natural system
/g122/g3Increase connectivity to improve   
 recolonisation
PP
/g122/g122
Characteristics of natural systems that affect vulnerability and help identify solutions
Event
/g122/g3Minimise population stressors
 (e.g. hunting, fisheries ’ quotas)
/g122/g3Identify vulnerable species and plan
 (e.g. save seed stock)

/g122/g3Forecast events and manage stressors  
 where possible (e.g. misting of flying-fox  
 colonies to reduce heat )/g122/g3Minimise disturbance and other   
 stressors (e.g. water extraction from  
 rivers, pollution of waterways and ocean)
/g122/g3Maximise heterogeneity to provide  
 refuges (thermal and structural )
/g122/g3Manage invasive specie s
Species, Populations
/g122/g3Population at range limit
/g122/g3Low genetic diversity
/g122/g3Low competitive abilit y
/g122/g3Poor dispersal abilit y
/g122/g3Narrow thermal toleranc e
/g122/g3Long generation timesHabitat
/g122/g3High disturbance levels
/g122/g3Low diversity
Figure Cross-Chapter Box EXTREMES.2 |  Characteristics of natural systems that affect vulnerability and help identify solutions—both prior to and after 
extreme events—to build resistance, resilience and recovery.Cross-Chapter Box EXTREMES (continued)

2
219Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
In summary, with ongoing climate warming and an increase in the 
frequency and intensity of extreme events, observed increases in water 
temperature, losses of ice and shifts in thermal regime are projected to 
continue (high confidence).
2.4 Observed Impacts of Climate Change 
on Species, Communities, Biomes, Key 
Ecosystems and Their Services
2.4.1 Overview
Global meta-analyses of terrestrial systems in AR3 and AR4 concentrated 
on long time frames (>20 years) and findings from relatively undisturbed 
areas, where confidence in attributing observed changes to climate 
change is high. Recent global and regional meta-analyses (AR5 and 
later) have been broader, including data from degraded and disturbed 
areas and studies with shorter time frames (Tables 2.2a,b).
By the time of AR5, >4000 species with long-term observational 
data had been studied in the context of climate change (Parmesan,

2006; Parmesan and Hanley, 2015). Since then, thousands of new 
studies and additional species have been added, leading to higher 
confidence in climate change attribution (Table 2.2) (Scheffers et al., 
2016; Wiens, 2016; Cohen et al., 2018; Feeley et al., 2020). Freshwater 
habitats have been under-represented in prior reports, but new 
long-term datasets, coupled with laboratory and field experiments, are 
improving our understanding. This assessment stresses observations 
from lakes and streams. As numbers of studies increase and data is 
increasingly extracted from areas with high LULCC, attribution is more 
difficult as habitat loss and fragmentation are known major drivers 
of changes in terrestrial and freshwater species (Ramsar Convention 
on Wetlands, 2018; IPBES, 2019; Tickner et  al., 2020). Due to the 
overwhelming volume of literature, the assessments for chapter 2 
concentrates on results from large continental or global-scale reviews

and meta-analyses. Most of the assessment of studies conducted in 
individual countries can be found in Regional chapters, but this chapter 
does include studies across very large countries or political entities 
that occupy much of a continent (e.g., Canada, the USA, Australia or 
Europe), or studies that provide rare or uniquely-relevant information.
2.4.2 Observed Responses to Climate Change by 
Species and Communities (Freshwater and 
Terrestrial)
2.4.2.1 Observed Range Shifts Driven by Climate Change
Poleward and upward range shifts were already attributable to climate 
warming with high confidence in AR5. Publication of observed range 
shifts in accord with climate change have accelerated since AR5 and 
strengthened attribution. Ongoing latitudinal and elevational range 
shifts driven by regional climate trends are now well-established globally 
across many groups of organisms, and attributable to climate change

with very high confidence due to very high consistency across a now 
very large body of species and studies and an in-depth understanding 
of mechanisms underlying physiological and ecological responses to climate drivers (Table  2.2; Table  2.3, Table  SM2.1) (Pöyry et  al., 2009; 
Chen et al., 2011; Grewe et al., 2013; Gibson-Reinemer and Rahel, 2015; 
MacLean and Beissinger, 2017; Pacifici et al., 2017; Anderegg et al., 2019). 
Range shifts stem from local population extinctions along warm-range 
boundaries (Anderegg et al., 2019) as well as from the colonisation of 
new regions at cold-range boundaries (Ralston et al., 2017).
Many studies since AR4 have tended not to be designed as attribution 
studies, particularly recent large-scale, multi-species meta-analyses. 
That is to say, all the data available was included in such studies 
(from both undisturbed and highly degraded lands and including 
very short-term data sets of <20 years), with little attempt to design

the studies to differentiate the effects of climate change from those 
of other potential confounding variables. These studies tended to 
find greater lag and a lower proportion of species changing in the 
directions expected from climate change, with the authors concluding 
that LULCC, particularly habitat loss and fragmentation, was impeding 
wild species from effectively tracking climate change (Lenoir and 
Svenning, 2015; Rumpf et al., 2019; Lenoir et al., 2020).
Attribution is strong for species and species-interactions for which 
there is a robust mechanistic understanding of the role of climate 
on biological processes (high confidence). Unprecedented outbreaks 
of spruce beetles occurring from Alaska to Utah in the 1990s were 
attributed to warm weather that, in Alaska, facilitated a halving of the 
insect’s life cycle from two years to one (Logan et al., 2003). Milder 
winters and warmer growing seasons were likewise implicated in

poleward expansions and increasing outbreaks of several forest pests 
(Weed et  al., 2013), leading to the current prediction that 41% of 
major insect pest species will further increase their damage as climate 
warms, and only 4% will reduce their impacts, while the rest will show 
mixed responses (Lehmann et al., 2020).
During their range shifts, forest pests remain climate-sensitive. For 
example, the distribution of the western spruce budworm is limited at its 
warm range edges by the adverse effects of mild winters on overwinter 
survival, and at its cool range by the ability to arrive at a cold-resistant 
stage before winter arrives (Régnière and Nealis, 2019). We might 
therefore expect tree mortality from insect outbreaks to be most severe 
at sites climatically less suitable for the plants, where plants would be 
under more stress. However, (Jaime et al., 2019), using separate species 
distribution models (SDMs) (MaxEnt) for the insects and plants, found

that observed mortality of Scots pine from bark beetles was highest at 
sites that were most climatically suitable for the trees as well as for the 
insects. In a study of tree mortality in California, bark beetles selectively 
killed highly stressed fir trees, but killed pines according to their size 
irrespective of stress status (Stephenson et al., 2019).
Range shifts in a poleward and upward direction, following expected 
trajectories according to local and regional climate trends, are strongly 
occurring in freshwater fish populations in North America (Lynch et al., 
2016b), Europe (Comte and Grenouillet, 2013; Gozlan et al., 2019) and 
Central Asia (Gozlan et al., 2019) (medium evidence, high agreement). 
Cold-water fish, such as coregonids and smelt have been negatively 
affected at the equatorial borders of their distributions (Jeppesen et al., 
2012). Upward elevational range shifts in rivers and streams have been

observed. Systematic shifts towards higher elevation and upstream were

2
220Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
found for 32 stream-fish species in France following regional variation 
in climate change (Comte and Grenouillet, 2013). Bull trout (Salvelinus 
confluentus) in Idaho (USA), were estimated to have lost 11–20% 
(8–16% per decade) of the headwater stream lengths necessary for 
cold-water spawning and early juvenile rearing, with the largest losses 
occurring in the coldest habitats (Isaak et al., 2010). Range contractions 
of the same species have been found in the Rocky Mountains watershed 
(Eby et  al., 2014). Likewise, the distribution of the stonefly Zapada 
glacier, endemic to the alpine streams of the Glacier National Park in 
Montana (USA), has been reduced over several decades by an upstream 
retreat to higher, cooler sites as water temperatures have increased and 
glacial masses have decreased (Giersch et al., 2015).
The melting of glaciers has led to a change in water discharge associated

with community turnover in glacier-fed streams (Cauvy-Fraunié and 
Dangles, 2019). For instance, glacier-obligate macro-invertebrates have 
started disappearing when glacial cover drops below approximately 
50% (robust evidence, high agreement), reviewed in (Hotaling et al., 
2017). For freshwater invertebrates, no meaningful trends have been 
detected in geographic extent or population size for most species 
(Gozlan et al., 2019).
An invasive freshwater cyanobacterium in lakes, Cylindrospermopsis 
raciborskii, originating from the tropics, has spread to temperate zones 
over the last few decades due to the climate change-induced earlier 
increase of water temperature in spring (Wiedner et al., 2007), aided 
by a competitive advantage in eutrophic systems (Ekvall et al., 2013; 
Urrutia-Cordero et al., 2016).
2.4.2.2 Observed Local Population and Global Species’ 
Extinctions Driven by Climate Change
Disappearances of local populations within a species range are more

frequent and better documented than whole species’ extinctions, 
and attribution to climate change is possible for sites with minimal 
confounding non-climatic stressors. Changes of temperature extremes 
are often more important to these local extinction rates than changes of 
mean annual temperature (high confidence) (see Sections 2.3.1, 2.3.2, 
2.3.3.5, 2.4.2.6, Cross-Chapter Box EXTREMES in this chapter) (Parmesan 
et al., 2013). A global meta-analysis of 236 species of birds, mammals, 
amphibians, fish, invertebrates and plants across 132 independent 
studies found that changes in population abundances were strongly 
related to temperature variability globally, and significantly related 
to precipiation variability in lower latitudes (Pearce-Higgins et  al., 
2015). In a global study of 538 diverse plant and animal species, sites 
with local extinctions were associated with smaller changes of mean 
annual temperature but larger and faster changes of hottest yearly

temperatures than sites where populations persisted (Román-Palacios 
and Wiens, 2020). Near warm range limits, 44% of species had suffered 
local extinctions. In both temperate and tropical regions, sites with local 
extinction had greater increases in maximum temperatures than those 
without: a T max increase of 0.456°C and 0.316°C versus a T mean increase 
of 0.153°C and 0.061°C for temperate (n  = 505 sites) and tropical (n  = 
76 sites), respectively (P < 0.001) (Román-Palacios and Wiens, 2020).
Wiens (2016)  assumed that population extinctions were primarily 
driven by climate change when they occurred at elevational or latitudinal ‘warm edge’ range limits, and were at relatively undisturbed 
sites stated by the authors to be under increasing climatic stress. By 
this criterion, climate-caused local extinctions were widespread among 
plants and animals globally, detected in 47% of 976 species examined. 
The percentage of species suffering these extinctions was higher in the

Tropics (55%) than in temperate habitats (39%), higher in freshwater 
(74%) than in marine (51%) or terrestrial (46%) habitats, and higher 
in animals (50%) than in plants (39%). The difference between plants 
and animals varied with latitude; in the temperate zone, a much higher 
proportion of animals than plants suffered range-limit extinctions 
(38.6% of 207 animal species vs. 8.6% of 105 plants, P < 0.0001) 
while at tropical sites, local extinction rates were (nonsignificantly) 
higher in plants (59% of 155 species) than in animals (52% of 349 
species), the reverse of their temperate-zone relationship. Rates varied 
across animal groups from 35% in mammals, to 43% in birds, 56% in 
insects and 59% in fish (Wiens, 2016).
Freshwater population extinctions are mainly due to habitat loss, 
the introduction of alien species, pollution, over-harvesting (Gozlan 
et  al., 2019; IPBES, 2019) and climate change-induced epidemic

diseases (Pounds et al., 2006)(see Section 2.4.2.7.1). Climate warming, 
particularly through the intensification and severity of droughts, 
contributes to the disappearance of small ponds which hold rare and 
endemic species (Bagella et al., 2016). Systematic data on the extent 
and biology of small ponds is, however, lacking on the global scale. 
Extreme heat waves can lead to large local fish kills in lakes (see 
Section 2.3.3.5), when water temperature and oxygen concentrations 
surpass critical thresholds and threatening cold-water fish and 
amphibians (Thompson et  al., 2012). Evidence of a local extinction 
of some invertebrate species with a 1.4°C–1.7°C rise in mean 
annual stream winter temperature from 1981 to 2005 was reported 
in Abrahams et  al. (2013). Population declines of specialist species 
in glacier-fed streams, such as the non-biting midge Diamesa davisi 
(Chironomidae), can be attributed to climate-change-driven glacier

retreat (Cauvy-Fraunié and Dangles, 2019), and the flatworm Crenobia 
alpina (Planariidae) has been reported as locally extinct in the Welsh 
Llyn Brianne river (Durance and Ormerod, 2010; Larsen et al., 2018).
Many high montane possums in Australia have low physiological 
tolerance to heat waves, with death occuring due to heat-driven 
dehydration at temperatures exceeding 29°C–30°C for >4–5 h over 
several days (Meade et al., 2018; Turner, 2020). Major declines have 
been recorded for several species, population extinctions have occured 
at lower elevations since the early 2000s, and the white sub-species of 
the lemuroid ringtail possum (Hemibelideus lemuroides) in Queensland, 
Australia, disappeared after heat waves in 2005 (high confidence): 
intensive censuses found only 2 individuals in 2009 (Chandler, 2014; 
Weber et al., 2021).
Two terrestrial and freshwater species have become extinct in 
the wild, with climate change implicated as a key driver. The cloud

forest-restricted golden toad (Incilius periglenes) was extinct by 1990 in 
a nature preserve in Costa Rica, driven by successive extreme droughts. 
This occurred in the absence of chytridiomycosis infection, caused by 
the fungal pathogen Bd, verified during field censuses of golden toad 
populations in the process of extinction as well as genetic analyses of 
museum specimens, although Bd was present in other frog species in

2
221Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
the region (medium evidence, high agreement) (Pounds et al., 1999; 
Pounds et al., 2006; Puschendorf et al., 2006; Richards-Hrdlicka, 2013). 
The interaction between expansion of chytrid fungus globally and 
local climate change is implicated in the extinction of a wide range 
of tropical amphibians (high confidence) (see Section 2.4.2.7.1 Case 
Study 2 Chytrid fungus and climate change).
The BC melomys (Melomys rubicola), the only mammal endemic to the 
Great Barrier Reef, inhabited a small (5-hectare) low-lying (<3-m-high) 
cay in the Torres Strait Islands, Australia. Recorded as having a 
population size of several hundred in 1978, this mammal has not 
been seen since 2009 and was declared extinct in 2016 (Gynther et al., 
2016). SLR and documented increases in storm surge and in tropical 
cyclones, driven by climate change, led to multiple inundations of the

island in the 2000s. Between 1998 and 2014, herbacious vegetation, 
the food resource for the BC melomys, declined by 97% in area (from 2.2 down to 0.065 hectares), and from 11 plant species down to 
two (Gynther et al., 2016; Watson, 2016; Woinarski, 2016; Woinarski 
et al., 2017). The island was unihabited with few non-climatic threats, 
providing high confidence in the attribution of extinction of the BC 
melomys to climate change-driven increases in the frequency and 
duration of island inundation (Turner and Batianoff, 2007; Woinarski 
et al., 2014; Gynther et al., 2016; Watson, 2016; Woinarski et al., 2017).
In the IUCN Red List (IUCN, 2019), 16.2% of terrestrial and freshwater 
species (n = 3,777 species) that are listed as endangered, critically 
endangered or extinct in the wild (n = 23,251 species) list climate 
change or severe weather as one of their threats.
In summary, local population extinctions caused by climate-change-driven

increases in extreme weather and climate events have been widespread 
among plants and animals (very high confidence), and the first clear 
FAQ 2.1 | Will species become extinct with climate change and is there anything we can do to prevent this?
Climate change is already posing major threats to biodiversity, and the most vulnerable plants and animals will probably go extinct. If climate 
change continues to worsen, it is expected to cause many more species to become extinct unless we take actions to improve the resilience of 
natural areas, through protection, connection and restoration. We can also help individual species that we care most about by reducing the 
stress that they are under from human activities, and even helping them move to new places as their climate space shifts and they need to 
shift to keep up.
Climate change has already caused some species to become extinct and is expected to drive more species to extinction.

Extinction of species has always occurred in the history of our planet, but human activities are accelerating this 
process, such that the estimated 10% of species that humans have driven to extinction in the past 10,000 years is 
roughly 1,000 times the natural background rate. Recent research predicts that climate change would add to that, 
with estimates that about one-third of all plant and animal species are at high risk of extinction by 2070 if climate 
change continues at its current rate. Species can adapt to some extent to these rapidly changing climate patterns. 
We are seeing changes in behaviour, dispersal to new areas as the climate becomes more suitable, and genetic 
evolution. However, these changes are small, and adaptations are limited. Species that cannot adapt beyond their 
basic climate tolerances (their ability to survive extremes of temperature or rainfall) or successfully reproduce in

a different climate environment from that in which they have evolved, will simply disappear. In the Arctic, for 
example, the sea ice is melting and, unless there are deep cuts in greenhouse-gas emissions, will probably disappear 
in summer within the century. This means that the animals that have evolved to live on sea ice—polar bears and 
some seals—will become extinct soon after the ice disappears.
Fortunately, there are some things we can do to help. We can take action to assist, protect and conserve natural 
ecosystems and prevent the loss of our planet’s endangered wildlife, such as:
‘Assisting’ the migration of species. This has many names, ‘assisted colonisation’, ‘assisted translocation’, ‘assisted 
migration’ and ‘assisted movement’. In effect, it is about helping endangered species to move to a new area with a 
good habitat for them to survive. ‘Passive’ assisted colonisation focusses on helping species move themselves, while

the most ‘active’ form implies picking up individuals and transporting them to a new location. This is different from 
reintroductions that are already a normal part of conservation programs. Climate-driven translocations constitute 
moving plants or animals to an area where they have never lived historically, a new location that is now suitable 
for them due to climate change.
This active form of ‘assisted colonisation’ has been controversial, because exotic species can become invasive 
when they are moved between continents or oceans. For example, no one would advocate moving polar bears to 
Antarctica, as they would likely feast on native penguins, thus causing another conservation problem. However, 
moving species only a few hundred kilometers avoids most adverse outcomes, and this is often all that is needed 
to help a wild plant or animal cope with lower levels of climate change. In extreme cases, another type of assisted

2
222Chapter 2  T errestrial and Freshwater Ecosystems and Their Services 
adaptation is to preserve species until we can stabilize then reverse climate change, and then reintroduce them to 
the wild. This might include moving them into zoos or into seed or frozen embryo banks.
Extending protected zones and their connectivity. The ability of species to move to new locations and track climate change are

very limited, particularly when a habitat has been turned into a crop field or a city. To help species move between their natural habitats, we can increase the connectedness of protected areas, or simply create small patches or corridors of semi-wild nature within a largely agricultural or inhabited region that encourages wildlife to move through an area, and in which they are protected from hunting and poisons. These semi-wild protected areas can be very small, like the hedgerows between fields in England that provide both a habitat for many flowers, birds and insects and corridors to move between larger protected areas. Alternatively, it can just be an abandoned field that is now growing ‘weeds’ and with a ban on use of pesticides or herbicides, hunting or farming. For instance, in the USA, private landowners get a tax break by making their land a ‘wildlife conservation’ area by using no pesticide, not cutting weeds too often, putting up brush piles and bird boxes for nesting by

Assisting, protecting and conserving natural ecosystems would help enhance biodiversity overall as well as aiding 
already endangered species. Diverse plant and animal communities are more resilient to disturbances, including climate change. A healthy ecosystem also recovers more quickly from increases in extreme events, such as floods, droughts and heat waves, that are a part of human-driven climate change. Healthy ecosystems are critical to prevent species’ extinctions from climate change, but are also important for human health and well-being, providing clean, plentiful water, cleaning the air, providing recreation and holiday adventures, and making people feel happier, calmer and more content.
Wildlife population size
No conservationLimited 
protected areasAssisted adaptationAssisted migrationExtended protected 
areas & connectivity
Endangered species 
go extinct & severelosses/declines inmost species
Ecosystems lose

functioning & resilienceHigh management of most at risk species to reduce non-climate stressorsActive assistance in relocating species to habitable climate spacesLarger, protected areas that are well connected to one another host higher biodiversity & allow natural movement as climate change reduces habitat qualityPossible actions to assist, protect, and conserve natural ecosystemsand prevent the loss of our planet’
s endangered wildlife
Conservation of endangered species in protected habitat
 Species outside of 
conservation areas are unprotected, some survive but many may decline or go extinct“Species banking” in zoos, captive breeding programs & seed banking for eventual re-introductionActive movement of individual members of a species to rescue declining populationsHigh biodiversity is more climate resilient & provides better climate mitigation & climateregulation*at least 30-50% of 
the Earth protected
Increasing conservation practices

Figure FAQ2.1.1 |  Possible actions to assist, protect and conserve natural ecosystems and prevent the loss of our planet’s endangered 
wildlife in the face of continued climate change. (Inspired by the Natural Alliance website© Chris Heward/GWCT).Box FAQ 2.1 (continued)

2
223Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
documentations of entire species driven extinct by recent climate 
change is emerging (medium confidence).
2.4.2.3 Observed Changes in Community Composition Driven 
by Climate Change
2.4.2.3.1 Overall patterns of community change
The most common type of community change takes the form of in situ  
decreases in cold-adapted species and increases in warm-adapted 
species (Bowler et  al., 2017; Hughes et  al., 2018; Kuhn and Gégout, 
2019; Feeley et al., 2020). This process has lead to increases of species 
richness on mountaintops and decreased richness at adjacent lower 
elevations (medium evidence, high agreement) (Forister et  al., 2010; 
Steinbauer et al., 2018). While it is also expected from observed range 
shifts of individual species that species richness should increase along 
tropical/temperate ecotones and along temperate/boreal ecotones, to

date this has not been well documented. Lewthwaite et al (Lewthwaite 
and Mooers, 2022) documented a small increase in local richness across 
Canada for 265 species of butterflies, but the stronger effect was an 
homogenization across the region, with generalist species generally 
expanding into new sites and leading to lower Beta-diversity (lower 
diversity among sites). In a study of 66 bumble bee species across North 
America and Europe, Soroye et al (Soroye et al., 2020) did not find the 
expected pattern, with most sites, regardless of latitude, declining in 
species richness, even when individual species benefited from warming 
or increased precipitation at some sites. Observed shifts in community 
composition have consequences for species’ interactions. Such indirect 
effects of climate change have been shown to often have greater 
impacts on species than the direct effects of climate itself, particularly 
for higher-level consumers (Cahill et al., 2013; Ockendon et al., 2014).

Analyses indicated that responses in range shifts and timing were 
lagging behind the changes expected from regional warming. This type 
of lag, where biological response is less than expected from known 
underlying physiology or general climatic limits, is called ‘climate debt’. 
Examples of climate debt, measured from community composition 
changes, come from birds and butterflies in Europe (Devictor et  al., 
2012) and lowland forest herbaceous plants in France (Bertrand 
et  al., 2011). The French study found that larger debts occurred in 
communities with warmer baseline conditions and that some of the 
apparent debt stemmed from the ability of species to tolerate warming 
in situ, so no range shift was observed.
Prominent changes in freshwater community composition, such as in-
creases in cyanobacteria and warm-tolerant zooplankton species, the 
loss of cold-water fish, gains in thermo-tolerant fish, macro-inverte-

brates, and floating macrophytes, are occurring (medium evidence, 
high agreement, medium confidence) (Adrian et  al., 2016; Hossain 
et  al., 2016; Short et  al., 2016; Huisman et  al., 2018; Gozlan et  al., 
2019). Geothermal streams have provided evidence about community 
structure and ecosystem function at high temperatures. A study of 14 
such habitats reported simplified food web structures and shortened 
pathways of energy flux between consumers and resources (high con-
fidence) (O’Gorman et al., 2019). Changes in the relative abundance of 
species, species composition and biodiversity due to warming trends, 
and non-climate-driven changes are to be expected in lakes and rivers globally. However, thus far, empirical evidence and mechanistic under -
standing to inform modelling is too limited to draw general conclu-
sions about the nature of current and future climate change-driven 
changes within entire food webs on a global scale (Urban et al., 2016).

2.4.2.3.2 Freshwater systems: mechanistic drivers and responses
Physical changes in lakes (see Section  2.3.3) have affected primary 
production (see Section  2.4.4.5.2), algal-bloom formation and 
composition, zooplankton and fish size distribution, and species 
composition (Urrutia-Cordero et al., 2017; Gozlan et al., 2019; Seltmann 
et al., 2019). Declines in the abundance of cold-stenothermal species, 
particularly the Arctic charr (Salvelinus alpinus), coregonids and smelt, 
and increases in eurythermal fish (e.g., the thermo-tolerant carp 
Cyprinus carpio, common bream, pike perch, roach and shad) have 
been observed in northern temperate lakes associated with warming 
trends (medium evidence, high agreement) (Jeppesen et  al., 2012; 
Jeppesen et al., 2014). These changes increase predation pressure on 
zooplankton and reduce grazing pressure on phytoplankton, which 
may result in higher phytoplankton biomass (De Senerpont Domis

et al., 2013; Jeppesen et al., 2014; Adrian et al., 2016). Reduction in 
lake mixing lowers the concentration of nutrients in the epilimnion and 
may lead to higher silicon-to-phosphorous ratios that negatively affect 
diatom growth (Yankova et al., 2017) or overall primary productivity 
(see Section 2.4.4.5.2).
In a study of 1567 lakes across Europe and North America, Kakouei 
(2021) identified climate change as the major driver of increases in 
phytoplankton biomass in remote areas with minimal LULCC. Greater 
temperature variability can be more important than long-term 
temperature trends as a driver of zooplankton biodiversity (Shurin 
et al., 2010). Reductions of winter severity attributed to anthropogenic 
climate change are increasing winter algal biomass, and motile and 
phototropic species, at the expense of mixotrophic species (Özkundakci 
et al., 2016; Hampton et al., 2017).
Tropical lakes are prone to loss of deep-water oxygen due to lake

warming, with negative consequences for their fisheries and their 
biodiversity (Lewis Jr, 2000; Van Bocxlaer et  al., 2012). Many ancient 
tropical lakes (Malawi, Tanganyika, Victoria, Titicaca, Towuti and Matano) 
hold thousands of endemic animal species (Vadeboncoeur et al., 2011).
Observed effects of climate change on freshwater invertebrates are 
variable (Knouft and Ficklin, 2017). In glacier-fed streams globally, climate 
change has caused community turnover and changes in abundances in 
terms of increased generalist and decreased specialist species (Lencioni, 
2018; Cauvy-Fraunié and Dangles, 2019). In turn, dragonflies in flowing 
waters, monitored during the warming period from 1988 through 
2006 in Europe, did not show consistent changes in their distribution 
(Grewe et al., 2013), reviewed in Knouft and Ficklin (2017). Long-term 
trends in the species composition and community structure of stream 
macro-invertebrates, specifically a general trend for decreases in species

characteristic of cold, fast-flowing waters and increases of thermophilic 
species typical of stagnant or slow-moving waters, have been attributed 
to climate change (robust evidence, high agreement) (Daufresne et al., 
2007; Chessman, 2015). A study of 14 geothermal streams reported 
simplified food web structures and shortened pathways of energy flux

2
224Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
between consumers and resources (O’Gorman et al., 2019). Macrophytes 
benefit from rising water temperatures, but increased shading from 
increased phytoplankton biomass could offset this (Hossain et al., 2016; 
Short et al., 2016; Zhang et al., 2017a).
2.4.2.3.3 Emergence of novel communities and invasive species
As climate change is increasing the movements of species into new 
areas, there is concern about how exotic species are being impacted, 
either by becoming invasive or by already invasive species gaining 
even more advantage over native species. Modelling predicts that the 
effects of climate warming on food web structure and stability favour 
the success of invading species (Sentis et al., 2021). Both simulated 
warming experiments (Zettlemoyer et  al., 2019) and long-term 
observations (Willis et  al., 2010) have found phenologies of exotic

species to respond more adaptively to warming than those of natives; 
in the long-term observations, the success of exotics was attributed 
to their greater phenological responsiveness. In an expert assessment 
of the future relative importance of different drivers of the impacts of 
biological invasions, climate change was named as the most important 
driver in polar regions, second-most important in temperate regions 
(after trade/transport), and third-most important in the tropics (after 
trade/transport and human demography/migration) (Essl et al., 2020).
However, not all exotic species become invasive. As novel climate 
conditions develop, novel communities made up of new combinations 
of species are emerging as populations and species adapt and shift 
their ranges differentially, not always with negative consequences 
(high confidence) (Dornelas et al., 2014; Evers et al., 2018; Teixeira and 
Fernandes, 2020). Novel communities differ in composition, structure,

function and evolutionary trajectories, as the proportions of specialists 
and generalists, native, introduced and range-shifting species change 
and species interactions are altered, ultimately affecting ecosystem 
dynamics and functioning (Lurgi et  al., 2012; Hobbs et  al., 2014; 
Heger and van Andel, 2019). The exact nature of novel communities is 
difficult to predict because species-level uncertainties propagate at the 
community level due to ecological interactions (Williams and Jackson, 
2007). However, observations, experimental mesocosms (Bastazini 
et al., 2021), and theoretical models (Lurgi et al., 2012; Sentis et al., 
2021) provide support that novel communities will continue to emerge 
with climate change (medium confidence).
2.4.2.4 Observed Phenological Responses to Climate Change
Since AR5, the number of studies of changes in phenology (timing 
of biological events) has increased substantially, aided by advances

in remote sensing (Piao et  al., 2019). Phenological studies have 
documented particularly consistent conclusions on responses of plants 
and animals to warming, including the advancement of spring events 
and the lengthening of growing seasons in temperate regions (via a 
combination of advancement of spring events and, to a lesser extent, 
the retardation of autumn events) (robust evidence, high agreement) 
(Table 2.2, Table 2.3, Table SM2.1) (Menzel et al., 2020). In the Tropics, 
by contrast, changes in precipitation have more strongly influenced 
animal phenology than have temperature changes (Cohen et  al., 
2018). A meta-analysis compared observed phenological advances in 
birds with expectations due to warming local climates, and concluded that the observed advances fell short of what was expected and that 
a substantial phenological climate debt had been generated (Radchuk 
Taxonomic groups have differed in their responses (Parmesan, 2007;

Thackeray et  al., 2010), and a few have completely diverged from 
general trends. For example, seabirds continue to breed with their 
pre-climate-change phenologies (Keogan et al., 2018). Newer reviews 
and analyses reveal differences in responses across continents and time 
intervals (Piao et al., 2019). Mean advance in days per decade for plants 
was 5.5 in China and 3.0–4.2 in Europe, but only 0.9 in North America 
(Piao et al., 2019). Mean values for the retardation of autumn leaf fall, 
which can be more influenced by photoperiod and less by temperature 
than spring leaf-out, were 0.36 days per decade in Europe (Menzel et al., 
2020), 2.6 days per decade in China and around 3 days per decade in 
the USA (medium evidence, high agreement) (Piao et al., 2019).
The rapid rate of the advancement of spring events in the 1990s 
slowed down in the 2000s, and stalled or even reversed in some 
regions (Menzel et al., 2020). Wang et al. (2019) noted, from remote

sensing, that during the ‘global warming hiatus’ from 1998 to 2012, 
there were no global trends in either spring green-up or autumn 
colouring. Annual crops, the timing of which is determined by farmers, 
were an exception. When natural systems were advancing fast prior to 
1998, farmers advanced more slowly, but during the natural ‘hiatus’, 
farmed crops advanced faster than wild plants and cultivated trees 
(Menzel et al., 2020). In a long (67 years) European time series (Menzel 
et al., 2020), autumn leaf colouring showed delays attributed to winter 
and spring warming in 57% of observations (mean delay of 0.36 days 
per decade); spring and summer phenologies advanced in 89% of 
wild plants, despite decreased winter chilling, with around 60% of 
trends significant and ‘strongly attributable’ to winter and spring 
warming; and the growing season lengthened in 84% of cases (mean 
lengthening 0.26 days yr-1) (Table 2.2).

Changes in freshwater systems are consistent with changes in terrestrial 
systems: earlier development of phytoplankton and zooplankton and 
earlier spawning by fish in spring as well as extension of the growing 
season are occurring (robust evidence, high agreement) (Adrian et al., 
2009; De Senerpont Domis et al., 2013; Adrian et al., 2016; Thackeray et al., 
2016). Phenological changes in lakes have been related to rising water 
temperatures, reduced ice cover and prolonged thermal stratification 
(increasing evidence and agreement since AR5; very high confidence). 
Crozier and Hutchings (2014) reviewed the phenological changes 
in fish and documented that changes in the timing of migration and 
reproduction, age at maturity, age at juvenile migration, growth, survival 
and fecundity were associated primarily with changes in temperature. 
The median return time of Atlantic salmon to rivers in Newfoundland 
and Labrador advanced by 12–21 days over the past decades, associated

with overall warmer conditions (Dempson et al., 2017).
2.4.2.5 Observed Complex Phenological and Range Shift 
Responses
Early meta-analyses tested the straightforward hypotheses that 
warming should shift timing earlier and ranges polewards. Once these 
trends had been established (IPCC, 2014b; Parmesan and Hanley,

2
225Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
2015), exceptions to them became a focus of study. For example, in 
northern regions of the Northern Hemisphere, the spring flowering of 
some plants was delayed instead of being advanced as to be expected 
with warming (Cook et al., 2012a; Cook et al., 2012b; Legave et al., 
2015). These turned out to be species requiring vernalisation (winter 
chilling) to speed their development in spring (Ettinger et al., 2020). 
For these plants, phenological changes result from the combined 
effects of advancement caused by spring warming and retardation 
caused by winter warming. Incorporating this level of complexity into 
analyses revealed that a greater proportion of species was responding 
to climate change than estimated according to the simple expectation 
that warming would always cause advancement (92% responding 
versus 72% from earlier analyses) (Cook et al., 2012b).

Animal species can show vernalisation equivalent to that in plants 
(Stålhandske et  al., 2017). However, a semi-global meta-analysis of 
terrestrial animals failed to detect the delaying effects of warming 
winters (Cohen et  al., 2018). The same animal-based meta-analysis 
contrasted phenological changes in temperate-zone animals, which 
are principally explained by changes in temperature, with those at 
lower latitudes, which tend to follow changes in precipitation (Cohen 
Vitasse et al. (2018), working with alpine trees, found that phenological 
delay with increasing elevation had declined from 34 days/1000 m in 
1960 to 22 days/1000 m in 2016, greatly reducing the differences in 
timing between trees growing at different elevations. This reduction 
was greatest after warmer winters, suggesting that winter warming is 
a principal cause of the overall trend.
Lian et al. (2020) observed that earlier spring leaf-out in the Northern

Hemisphere is causing increases in evapotranspiration that are not fully 
compensated by increased precipitation. The consequence is a greater 
soil moisture deficit in summer, expected to exacerbate impacts of 
heat waves as well as drought stress. In Arctic freshwater ecosystems, 
Heim et  al. (2015) demonstrated the importance of seasonal cues 
for fish migration, which can be impacted by climate change due to 
reduced stream connectivity and fragmentation, earlier peak flows and 
increased evapotranspiration.
Precipitation has also been implicated in exceptions to the rule 
that ranges should be shifting to higher elevations. In dry climates, 
increases in precipitation accompanying climate warming can facilitate 
downslope range shifts (Tingley et al., 2012).
Multiple responses can co-occur. Hällfors et  al. (2021), in a study 
of 289 Lepidoptera in Finland, found that, with warming, 45% had 
either shifted their range northward or advanced their flight season.

The 15% of species that did both (shifting northward by 113.1 km 
and advancing their flight period by 2.7 days per decade, on average, 
over a 20-year period) had the largest population increases, and the 
40% of species that showed no response had the largest population 
declines.2.4.2.6 Observed Changes to Physiology and Morphology 
Driven by Climate Change
Impacts on species physiology in terrestrial and freshwater systems 
have been observed, and attributed to climate change (medium 
confidence). These include changes in tolerance to high temperatures 
(Healy and Schulte, 2012; Gunderson and Stillman, 2015; Deery et al., 
2021), increased metabolic costs of living at elevated temperatures 
(Scheffers et  al., 2016) and shifts in sex ratios in species with 
temperature-dependent sex determination. For example, warmer 
temperatures have driven the masculinisation of lizard populations 
(Schwanz and Janzen, 2008; Schwanz, 2016; Edmands, 2021) and the

feminisation of turtle populations (Telemeco et al., 2009). Skewed sex 
ratios can lead to mate shortages, reduced population growth, reduced 
adaptive potential and increased extinction risk, because genetic 
diversity decreases as fewer individuals mate and heterozygosity is 
lost (Mitchell and Janzen, 2010; Edmands, 2021).
Behavioural plasticity (flexibility) such as nest-site selection can 
provide a partial buffer from the effects of increasing temperature by 
placing the individual in a slightly cooler microclimate, but there are 
environmental and physical limits to this plasticity (medium confidence) 
(Refsnider and Janzen, 2016; Telemeco et al., 2017). Plasticity in heat 
tolerance (e.g., due to reversible acclimation or acclimatisation) can 
also potentially compensate for rising temperatures (Angilletta Jr, 
2009), but ectotherms have relatively low acclimation in thermal 
tolerance and acclimation is expected to only slightly reduce the risk of

overheating in even the most plastic taxa (low confidence) (Gunderson 
and Stillman, 2015).
Geographic variation in thermal tolerance plasticity is expected to 
influence the vulnerability and range shifts of species in response to 
climate change (Gunderson and Stillman, 2015; Sun et al., 2021). In 
many ectotherms, plasticity in thermal tolerance increases polewards, 
as thermal seasonality increases (Chown et al., 2004), contributing to 
higher vulnerability to warming in tropical organisms (low confidence) 
(Huey et al., 2009; Campos et al., 2021). Some species have evolved 
extreme upper thermal limits at the expense of plasticity, reflecting 
an evolutionary trade-off between these traits (Angilletta et al., 2003; 
Stillman, 2003). The most heat-tolerant species, such as those from 
extreme environments, may therefore be at a greater risk of warming 
because of an inability to physiologically adjust to thermal change (low

confidence) (Bozinovic et al., 2011; Overgaard et al., 2014; Magozzi 
and Calosi, 2015).
Physiological changes have observable impacts on morphology, such as 
changes in body size (and length of appendages), and colour changes 
in butterflies, dragonflies and birds (medium confidence) (Galeotti 
et  al., 2009; Karell et  al., 2011). However, trends are not always 
linear or consistent across realms, taxonomic groups or geographic 
regions (Gotanda et  al., 2015). Some morphological changes arise 
in response to environmental changes, rather than as the result of 
genetic adaptation or selection for an optimal body type. For example, 
dietary changes associated with climate change have led to changes 
in chipmunk skull morphology (Walsh et al., 2016).

2
226Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Table 2.2 |  Global fingerprints of climate change impacts across wild species. (Updated from (Parmesan and Hanley, 2015). For each study for which data were made available, a 
response for an individual species or functional group was classified as (1) no response (i.e., no significant change in the measured trait over time), (2) if a significant change was 
found, the response was classified as either consistent or not consistent with expectations according to local or regional climate trends. Percentages are approximate and estimated 
for the studies as a whole. Individual analyses within the studies may differ. The specific metrics of climate change analysed for associations with biological change vary somewhat 
across studies, but most use changes in local or regional temperatures (e.g., mean monthly T or mean annual T), with some using precipitation metrics (e.g., total annual rainfall).

For example, a consistent response would be poleward range shifts in areas that are warming. Probability (P) of getting the observed ratio of consistent-to-not consistent responses 
by chance was <10–13 for (Parmesan and Yohe, 2003; Root et al., 2003; Root et al., 2005; Poloczanska et al., 2013) and <0.001 for Rosenzweig et al. (2008). The last collumn 
distinguishes studies that were designed for attribution to climate change (e.g. by analysing only long-term data from relatively undisturbed habitats (see section 2.1.3 and 2.4.1)
(Parmesan et al., 2013; Cramer et al., 2014) from those that analysed all available data, including data from areas highly-impacted by non-climate drivers (e.g. LULCC).
StudyN: total numbers of 
species, functional 
groups or studiesSpecies in given system:  
Terrestrial (T)  
Marine (M)  
Freshwater (F)Types of 
changeChanges documentedGeographical 
regionStudy allows for 
attribution to 
2.2a Observed phenological changes
(Parmesan and

Yohe, 2003)677 speciesT: 461 plants, 168 birds, 35 
insects; T/F: 9 amphibians; 
F: 2 fishSpring 
phenologyOverall: 9% delay; 27% no trend; 62% 
advance
Mean change 2.3 days per decade 
advanceGlobal Yes
(Menzel et al., 
2006)Agricultural crops, fruit 
trees, wild plants100% TSpring and 
autumn 
phenologyFrom 1971 to 2000, 48% responding as 
expected; spring advance 2.5 days per 
decade, mean autumn delay 0.2 days 
per decade, fruit ripening 2.4 days per 
decade advance; farming activities 
0.4 days per decade advanceEurope Yes
(Parmesan, 
2007)203 species T, FSpring 
phenologyOverall advance 2.8 days per decade
20 changes (delays), 153 advances, 8 
no change; significantly more advance 
at higher latitudesGlobal Yes
(Rosenzweig 
et al., 2008)55 studies (~100–200 
species)T: 65%
M: 13%
F: 22%Various90% of changes consistent with local/
regional climate changeGlobal Yes
(Thackeray et al., 
2010)726 taxaT: birds, moths, aphids, 
terrestrial plants; M and F: 
phytoplanktonSpring

phenology83.5% of ‘trends’ were advances; mean 
overall advance 3.9 days per decade; T 
plants 93% advancing, mean 5.8 days 
per decade; F plants 62% advancing, 
mean 2.3 days per decade; secondary 
consumers advanced less than primary 
consumers and producersUK No
(While and Uller, 
2014)59 populations, 
17 studiesT/F , 100% Amphibians Phenology35% statistically significant change; 
mean advance 6.1 ± 1.65 days 
per decade; range 17.5 days delay 
to 41.9 days advance; 65% (n = 
47 populations) found significant 
relationship between breeding 
phenology and temperature; higher 
latitudes advanced moreGlobal No
(Gill et al., 2015) 64 studiesT: 100%
treesDelay of autumn 
senescenceDelay averaged 0.33 days yr-1 and 
1.20 days per degree Celsius warming; 
more delay at low latitudes across 
Northern Hemisphere; high-latitude 
species driven more by photoperiod 
than low-latitude speciesGlobal No
(Ficetola and 
Maiorano, 2016)66 studies of 
temperature effects;

15 of precipitationT/F 100% amphibiansPhenology and 
abundancesPopulation dynamics driven by 
precipitation while breeding phenology 
driven by temperatureGlobal No
(Halupka and 
Halupka, 2017)28 species 
multi-brooded, 27 
species single-brooded, 
some species several 
populationsT 100% (birds)Phenology: 
length of 
breeding seasonShows differences in sign of response 
between single and multi-broods 
and migrants vs. residents; Season 
extended by 4 days per decade for 
multi-brooded, shortened by 2 days per 
decade for single-brooded;
Multi: 26 species; 15 of 34 populations 
significantly extended, none 
significantly reducedNorthern 
HemisphereYes

2
227Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Marine (M)  
(Kharouba et al., 
2018)88 species in 54 
pair-wise interactionsT: changes 
in relative 
phenologies of 
consumers and 
their resourcesAsynchrony between consumers and 
resources has increased in some cases 
and decreased in others, with no 
significant trend; the prediction that 
asynchronies should be increasing in 
general is not supported.Global No
(Cohen et al., 
2018)127 studies T: 100% animalsPhenological 
trends81% of 127 studies of animals show 
phenological change in direction of 
earlier spring; some studies were 
multi-species.
Mean advance since 1950: 2.88 days 
per decade.Europe
Australia
JapanNo
(Keogan et al., 
2018)145 populations, 209

time seriesT: Seabirds breeding sitesPhenological 
trendsNo change in breeding dates between 
1952 and 2015Global Yes
(Radchuk et al., 
2019)4835 studies, 1413 
speciesT: animals; T/F amphibiansPhenological 
trendsGreatest phenological advancements 
in amphibians, followed by insects and 
birds, in this order.Global but most 
in Northern 
HemisphereNo
(Piao et al., 
2019)Review T: PlantsSpring and 
autumn 
phenologiesRate of advance slowing down across 
Northern Hemisphere and reversed 
in parts of western North America in 
response to regional cooling since 
1980sGlobal No
2020)53 species in Germany, 
37 in Austria, 21 in 
Switzerland (includes 
overlaps)T: PlantsSpring and 
autumn 
phenologiesLong time series: 1951–2018. 
Autumn leaf colouring: mean 
delay 0.36 days per decade; spring 
phenology (leaf-unfolding) mean 
advance 0.24 days per decade; 
summer phenology (fruit ripening) 
mean advance 0.26 days per decade. 
Growing season length mean increase

0.26 days yr-1 but farming season 
length decreased by 0.02 days yr-1.Europe Yes
2.2b. Observed Changes In Distributions, Abundances And Local Population Extinctions
(Parmesan and 
Yohe, 2003)920 speciesT: 85.2%
M: 13.5%
F: 1.3%Distributions 
and abundances50% of species (460/920) showed 
changes in distribution or abundances 
consistent with local or regional 
climate changeGlobal Yes
(Root et al., 
2003)926 speciesT: 94%
M: 5.4%
F: 0.6%Distributions 
and abundances52% of species (483/926) showed 
et al., 2008)18 studiesT: 65%
M: 13%
F: 22%Distributions 
and abundances90% of studies showed changes in 
distribution or abundances consistent 
with local or regional climate changeGlobal Yes
(Pöyry et al., 
2009)48 species T: butterflies Range shiftsFrom 1992 to 2004, 37 ranges shifted 
poleward, 9 shifted equatorially, 2 
no change. Non-threatened species

expanded poleward by 84.5 km, 
threatened species showed no 
significant change (<2.1 km)Finland Yes

2
228Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Marine (M)  
(Tingley et al., 
2009)53 species T: birdsElevational 
range shiftsResurvey (2003–2008) of historical 
elevational transects (1911–1929). 
90.6% of species tracked their 
climate niche (temperature and/or 
precipitation) with regional climate 
change; Lower-elevation species (mean 
range centroid = 916 m) tracked only 
precipitation; high-elevation species 
(mean range centroid = 1944 m) 
tracked only temperature; species 
that tracked both temperature and 
precipitation had mid-elevation range 
centroids (1374–1841 m)California, USA Yes
(Chen et al., 
2011)24 taxonomic group × 
region combinations 
for latitude, 31 for 
elevationT >264
M >10
F >34Range shifts: 
elevation and

latitudeMean upward elevation shift 11.0 m 
per decade
Poleward shift 16.9 km per decadePseudo-global No
(Grewe et al., 
2013)90 species T/F DragonfliesShifts of 
northern range 
boundaries48 poleward shifts; 26 equatorial; 16 
no change from 1988 to 2006
Southern lentic (standing water) 
species expanded 116 km polewards; 
southern lotic (running water) and all 
northern species stayed stable.Europe No
(Mason et al., 
2015)21 animal groups, 
1573 speciesT: birds, Lepidoptera
T/F: OdonatesRange shifts in 3 
time periodsNorthward shifts 23 km per decade 
(1966–1975) and 18 km per decade 
(1986–1995), with significant 
differences among taxa in rates of 
changeUK Yes
(Gibson-
Reinemer and 
Rahel, 2015)13 studies, 273 species:
Plants, birds, mammals, 
marine inverterbratesT and MRange shifts in 
2 or 3 areas for 
each species;
shift measured 
as change of 
limit or centroid50% shifts of cold limits inconsistent 
with each other within species despite 
similar warming; species showing

inconsistent shifts (including stable vs. 
directional or different directions) = 
47% plants, 54% birds, 46% marine 
invertebrates, 60% mammals. Large 
difference in magnitude of range 
shifts when in same direction (mean 
difference 8.8 times)Global No
temperature effects; 
15 studies of 
precipitation effectsT/F 100% (amphibians)Phenology and 
precipitation, breeding phenology 
(Scheffers et al., 
2016)94 ecological processes T, F, MAll possible 
types and levels 
of ecological 
change82% of ecological processes affected 
by climate changeGlobal No
(Wiens, 2016) 976 species T, F, MPopulation 
extinction rates 
near warm 
latitudinal and 
elevational 
range limits47% of species suffered 
climate-related local extinctions: fish 
59%, insects 56%, birds 44%, plants 
39%, amphibians 37%, mammals 35%Global Yes
(Bowler et al., 
2017)1167 populations, 22 
communitiesT: 48%

M: 61%
F: 35%Abundance;
population 
trendsT species with warm-temperature 
preference performed better than 
cool preferers; F and M species: no 
effect of temperature preference on 
performance; 47% of species with 
significant abundance changes: 61% 
M, 48% T, 35% FEurope Yes

2
229Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Marine (M)  
(Pacifici et al., 
2017)873 mammals, 1272 
birdsT: 100% (birds and mammals)Multiple: 
range change, 
abundance, 
reproductive 
rate, survival, 
body massEstimated negative impacts (range 
contraction, reduced reproductive 
rates or other measures of fitness 
estimates) for IUCN-threatened species 
based on actual observed change in 
more common, related species; 47% 
threatened mammals and 23% birds 
negatively impacted by climate change 
in part of their rangesGlobal for birds; 
mammals North 
AmericaUnclear (complex 
methods)
(MacLean and 
Beissinger, 2017)21 studies
26 assemblages of 
taxonomically related 
speciesT: Plants and animalsRange shifts 
in latitude and

altitude related 
to species’ traits: 
dispersal, body 
size, habitat, diet 
specialization 
and historic 
range limitHigh-latitude/altitude range boundaries 
shifted less than lower-latitude/altitude 
boundaries. Author explanation is 
that habitat limits were reached (e.g., 
mountain tops). Magnitudes of shifts 
positively related to dispersal traits and 
habitat breadth.Global No
(Ralston et al., 
2017)46 species T: BirdsShifts in climate 
niche breadth, 
filling of 
climate space 
and overall 
abundanceSpecies increasing in abundance were 
also increasing breeding climate niche 
breadth and niche filling. Declining 
species were opposite: niche breadths 
narrowing and greater climate debt.North America No
(Rumpf et al., 
2019)1026 speciesT: plants, invertebrates, 
vertebratesComparison 
of rates of 
range limit 
shift at leading 
and trailing 
edgesNo difference in mean rate of 
shift of leading and trailing edges; 
elevational range sizes not changing

systematically. Greater lags in regions 
with faster warming.Global No
(Freeman et al., 
2018)975 species, 32 
elevational gradientsT: plants, endotherms, 
ectothermsComparison 
of rates of 
edgesMean change at warm limit 92 ± 
455 m per degree Celsius; cool limit 
131 ± 465 m per degree Celsius; (± SD, 
not significantly different from each 
other). Available area and range sizes 
decreased for mountaintop species.Global No
(Anderegg et al., 
2019)Meta-analysis
50 studies, >100 
speciesT: 100% woody plantsMortality at dry 
range edges100 individual species + a community 
of 828 species
mortality at range edges due to 
drought was 33% greater than for core 
populationsApparently 
globalYes;
drought not 
warming
(Román-Palacios 
and Wiens, 
2020)10 studies, 538 species, 
581 sitesT: plants and animalsAnalysis for 
drivers of 
extinctions at 
warm range 
edges44% of species had suffered

local population extinctions near 
warm-range limits. In temperate 
regions, sites with local extinction 
had greater increases in maximum 
temperature than those without 
(0.456°C vs. 0.153°C, P < 0.001, n 
= 505 sites) and smaller increases 
in mean temperatures (0.412°C vs. 
1.231°C, P < 0.001). In tropical regions, 
range edges with local extinction also 
temperatures (0.316°C vs. 0.061°C, P 
< 0.001, n = 76), but changes in mean 
temperatures were similar between 
edges with and without extinctions 
(0.415°C vs. 0.406°C, P = 0.9)Global Yes

2
230Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Decreased body size has been suggested as a general response of species 
to climate change in freshwater species, given the temperature-related 
constraints of metabolism with larger size. Reduced body size in 
response to global warming has been documented for freshwater 
bacteria, plankton and fish, as well as a shift towards smaller species 
(Daufresne et al., 2009; Winder et al., 2009; Jeppesen et al., 2010; Crozier 
and Hutchings, 2014; Jeppesen et al., 2014; Farmer et al., 2015; Rasconi 
et al., 2015; Woodward et al., 2016). However, the lack of systematic 
empirical evidence in fresh waters, and confounding effects such as 
interactions between temperature, nutrient availability and predation, 
limit generalisations in attributing observed body size changes to 
climate change (low confidence) (Pomati et al., 2020 Nutrients).
Evidence is weak for a consistent reduction in body size across

taxonomic groups in terrestrial animals (low confidence) (Siepielski 
et  al., 2019). Decreased body size in warmer climates (as higher 
surface area-to-volume ratios maximise heat loss) is expected, based 
on biogeographic patterns such as Bergmann’s rule, but both increases 
and decreases have been documented in mammals, birds, lizards 
and invertebrates and were attributed to climate change (Teplitsky 
and Millien, 2014; Gotanda et  al., 2015; Gardner et  al., 2019; Hill 
et al., 2021). Contrasting patterns (increased body size) may be due 
to short-term modifications in selection pressures (e.g., changes to 
predation and competition), variation in life histories or as a result 
of interactions with climate variables other than temperature (e.g., 
changes to food availability along with rainfall changes) and other 
disturbances (Yom-Tov and Yom-Tov, 2004; Gardner et al., 2019; Wilson 
et  al., 2019) or use of different body size measurements (linear vs.

volumetric dimensions) (Salewski et al., 2014).
Several lines of evidence suggest the evolution of melanism in 
response to climate change (low confidence), with colour changes 
associated with thermoregulation being demonstrated in butterflies 
(Zeuss et  al., 2014; MacLean et  al., 2016; MacLean et  al., 2019a), 
beetles (de Jong and Brakefield, 1998; Brakefield and de Jong, 2011; 
Zvereva et  al., 2019), dragonflies (Zeuss et  al., 2014) and phasmids 
(Nosil et al., 2018). Such changes may represent decreased phenotypic 
diversity and, potentially, genetic diversity (low confidence), but 
the consequences of climate change for the genetic structure and 
diversity of populations have not been widely assessed (Pauls et al., 
2013). Simplistically, the thermal melanism hypothesis suggests 
that lighter (higher-reflectance) individuals should be fitter and 
therefore be selected for in a warmer climate (Clusella-Trullas et al.,

2007). However, several biotic (e.g., thermoregulatory requirements, 
predator avoidance and signalling) and abiotic (e.g., UV, moisture 
and inter-annual variability) factors interact to influence changes in 
colour, making attribution to climate change across species and broad 
geographic regions difficult (Kingsolver and Buckley, 2015; Stuart-Fox 
et al., 2017; Clusella-Trullas and Nielsen, 2020).
Interactions between morphological changes and changes in phenology 
may facilitate or constrain adaptation to climate change (medium 
confidence) (Hedrick et al., 2021). For example, advancing phenology 
in migratory species may impose selection on morphological traits 
(e.g., wing length) to increase migration speed. If advancing spring 
phenology results in earlier breeding, this may offset the effect of rising 
temperatures in the breeding range and reduce the effect of increasing temperature on body size (Zimova et al., 2021). A study of 52 species of

North American migratory birds, based on >70,000 specimens, showed 
that spring migration phenology has advanced over the past 40 years, 
concurrent with widespread shifts in morphology (reduced body size 
and increased wing length), perhaps to compensate for the increased 
metabolic cost of flight as body size decreases (Weeks et al., 2020).
A lack of understanding of physiological constraints and mechanisms 
remains a barrier to predicting many of the ecological effects 
of climate change (Bozinovic et  al., 2011; Vázquez et  al., 2017; 
González-Tokman et  al., 2020). Many behavioural, morphological 
and physiological responses are highly species- and context-specific, 
making generalisations difficult (Bodensteiner et  al., 2021). Recent 
advances in mechanistic understanding (from experiments), in 
process-based modelling (including micro-climates and developmental 
processes) (Carter and Janzen, 2021) and in the sophistication of

niche models (Kearney et  al., 2009) have improved projections, but 
comprehensive tests of geographic patterns and processes in thermal 
tolerance and plasticity are still lacking, with studies limited to a few 
phylogenetically restricted analyses showing mixed results (Gunderson 
and Stillman, 2015). Improved understanding of the mechanistic basis 
for observed geographic patterns in thermal tolerance and plasticity 
is needed to identify the physiological limits of species, the potential 
for adaptation and the presence of evolutionary trade-offs, which will 
strongly influence population declines, species range shifts, invasive 
interactions and the success of conservation interventions (Cooke 
et al., 2021; Ryan and Gunderson, 2021).
2.4.2.7 Observed Impacts of Climate Change on Diseases of 
Wildlife and Associated Impacts on Humans
Assessment of changes in diseases of terrestrial and freshwater wild 
organisms was scarce in WGII AR4, AR5, IPCC SR1.5 and IPCC SRCCL.

Further, most emerging infectious diseases (EIDs) are zoonoses, that is, 
they are transmissible between humans and animals, and are climate 
sensitive (Woolhouse et al., 2001; Woolhouse and Gowtage-Sequeria, 
2005; McIntyre et  al., 2017; Salyer et  al., 2017). AR4 found 
weak-to-moderate evidence that disease vectors and their diseases 
had changed their distributions in concert with climate change, but 
attribution studies were lacking (Smith et al., 2014). In AR5, WGII AR5 
Chapter 11, geographic expansion of a few VBDs to higher latitudes 
and elevations were detected and associated with regional climate 
trends, but the non-climatic drivers were not well assessed, leading to 
a medium confidence in attribution (Smith et al., 2014)). Here, we build 
on previous assessments by focussing on changes in the population 
dynamics and geographic distribution of diseases in wild animals as 
well as diseases in humans and domestic animals that are harboured,

amplified and transmitted by wild animal reservoir hosts and vectors.
Increased disease incidence is correlated with regional climatic changes, 
as expected from a basic understanding of underlying biology and 
relationships between temperature, precipitation, and disease ecology 
(robust evidence, high agreement) (Norwegian Polar Institute, 2009; 
Tersago et al., 2009; Tabachnick, 2010; Paz, 2015; Dewage et al., 2019; 
Deksne et al., 2020; Shocket et al., 2020; Couper et al., 2021). Whether 
increases in diseases in wild and domestic animals correspond to an 
increased risk of disease in nearby human populations is complicated

2
231Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
by the potential buffering effects of the local medical system, access 
to health care and the socioeconomic status, education, behaviours 
and general health of the human population (see also Chapter 7 and 
Cross-Chapter Box ILLNESS in this chapter).
2.4.2.7.1 Direct effects of climate and climate change on 
reproduction, seasonality, the length of the growing season 
and the transmission of pathogens, vectors and hosts
VBDs require arthropod vector hosts (e.g., insects or ticks), while other 
infectious diseases (e.g., fungi, bacteria and helminths) have free-living 
life stages and/or complex life cycles that require intermediate 
hosts (e.g., snails), all of which have temperature-driven rates of 
development and replication/reproduction (robust evidence, high 
agreement) (Mordecai et al., 2013; Liu-Helmersson et al., 2014; Moran 
and Alexander, 2014; Bernstein, 2015; Marcogliese, 2016; Ogden and

Lindsay, 2016; Mordecai et  al., 2017; Short et  al., 2017; Caminade 
et al., 2019; Cavicchioli et al., 2019; Mordecai et al., 2019; Liu et al., 
2020; Rocklöv and Dubrow, 2020). Additionally, microbes such as 
bacteria thermally adapt to temperature changes through multiple 
mechanisms, indicating that warming will not reduce antibiotic 
resistance (MacFadden et  al., 2018; Pärnänen et  al., 2019; Shukla, 
2019; McGough et al., 2020; Rodriguez-Verdugo et al., 2020).
There is increasing evidence of the role of extreme events in disease 
outbreaks (very high confidence) (Tjaden et  al., 2018; Bryson et  al., 
2020). Heat waves have been associated with outbreaks of helminth 
pathogens, especially in sub-Arctic and Arctic areas. For example, a 
severe outbreak of microfilaremia, a VBD spread by mosquitoes and 
flies, plagued reindeer in northern Europe following extreme high 
temperatures (Laaksonen et  al., 2010). More frequent and severe

extreme events such as floods, droughts, heat waves and storms 
can either increase or decrease outbreaks, depending upon the 
region and disease (robust evidence, high agreement) (Anyamba 
et  al., 2001; Marcheggiani et  al., 2010; Brown and Murray, 2013; 
Paz, 2015; Boyce et al., 2016; Wu et al., 2016b; Wilcox et al., 2019; 
Nosrat et al., 2021). Heavy precipitation events have been shown to 
increase some infectious diseases with aquatic life-cycle components 
such as mosquito-borne, helminth, and rodent-borne diseases (robust 
evidence, high agreement) (Anyamba et al., 2001; Zhou et al., 2005; 
Wu et  al., 2008; Brown and Murray, 2013; Anyamba et  al., 2014; 
Boyce et al., 2016). Conversely, flooding also increases flow rate and 
decreases parasite load and diversity in other aquatic wildlife (Hallett 
and Bartholomew, 2008; Bjork and Bartholomew, 2009; Marcogliese, 
2016; Marcogliese et al., 2016) and can reduce mosquito abundance

by flushing them out of the system (Paaijmans et al., 2007; Paz, 2015).
Droughts reduce the aquatic habitat of some mosquito species while 
simultaneously increasing the availability of stagnant standing pools 
of water that are ideal breeding habitats for other species, such as 
dengue-vector Aedes mosquitoes (medium evidence, medium agree-
ment) (Chareonviriyaphap et al., 2003; Chretien et al., 2007; Padman-
abha et al., 2010; Trewin et al., 2013; Paz, 2015). Extreme drought has 
been associated with an increase in bluetongue virus haemorrhagic 
disease in wildlife in eastern North America, although the mechanisms 
involved were not identified (Christensen et al., 2020). Heat waves in 
some regions, especially coastal regions, have increased parasitism and decreased host richness and abundance, leading to population 
crashes (Larsen and Mouritsen, 2014; Mouritsen et al., 2018). Changes 
in temperature and precipitation, especially extreme events, can alter

community structure (Larsen et al., 2011) by increasing or decreasing 
parasites and their host organisms, and even altering host behaviour in 
ways that are advantageous to parasites (Macnab and Barber, 2012).
Climate change not only affects the occurrence of pathogens and 
their hosts in terms of geographic space but also impacts the 
temporal patterns of disease transmission. Warmer winters allow 
greater over-winter survival of arthropod vectors, which, coupled with 
lengthened transmission seasons, drive increases in vector population 
sizes, pathogen prevalence, and thus the proportion of vectors infected 
(robust evidence, high agreement) (Laaksonen et  al., 2009; Molnár 
et al., 2013; Waits et al., 2018). For example, a parasitic nematode lung 
worm (Umingmakstrongylus pallikuukensis) has shortened its larval 
development time by half (from two years to one year), which has 
increased infection rates in North American musk oxen (Norwegian 
Polar Institute, 2009).

Case Study 1: Climate change impacts on pathogenic helminths in 
Europe
Parasitic helminths can reduce growth and yield, kill livestock and 
infect humans and wildlife, leading to health, agricultural and 
economic losses (Fairweather, 2011; Charlier et  al., 2016; Charlier, 
2020). Attribution of increased incidence and risk of helminth disease 
to climate change is stronger than for other human diseases, thanks to 
long-term records and careful analysis of other anthropogenic drivers 
(e.g., LUC, agricultural/livestock intensification, and anti-helminthic 
intervention and resistance) (van Dijk et al., 2008; van Dijk et al., 2010; 
Fox et al., 2011b; Martínez-Valladares et al., 2013; Charlier et al., 2016; 
Innocent et al., 2017; Mehmood et al., 2017).
In Europe, evidence from laboratory studies, long-term surveillance, 
statistical analyses and modelling shows that multiple helminth 
pathogens and their host snails have extended their transmission

windows and increased their survival, fecundity, growth and 
abundances (robust evidence, high agreement). Furthermore, they 
have expanded or shifted their ranges poleward due to increases 
in temperature, precipitation and humidity (robust evidence, high 
agreement) (Lee et al., 1995; Pritchard et al., 2005; Poulin, 2006; van 
Dijk et al., 2008; van Dijk et al., 2010; Fairweather, 2011; Fox et al., 
2011b; Martínez-Valladares et al., 2013; Bosco et al., 2015; Caminade 
et  al., 2015; Caminade et  al., 2019). These documented changes in 
climate, hosts and pathogens have been linked to a higher incidence 
and more frequent outbreaks of disease in livestock across Europe 
(very high confidence).
Case Study 2: Chytrid fungus and climate change
Infection by the chytrid fungus, Bd (Batrachochytrium dendrobatidis), 
can cause chytridiomycosis in amphibians. Bd is widely distributed 
globally and has caused catastrophic disease in amphibians, associated

with the decline of 501 species and extinction of a further 90 species, 
primarily in tropical regions of the Americas and Australia (Scheele 
et al., 2019; Fisher and Garner, 2020). Bd successfully travelled with

2
232Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
high-elevation Andean frog species as they expanded their elevational 
ranges upward, driven by regional warming, to > 5200 m (Seimon 
New findings since AR5 from controlled laboratory experiments 
(manipulating temperature, humidity and water availability), intensive 
analyses of observed patterns of infection and disease in nature, and 
modelling studies have led to an emerging consensus that interactions 
between chytrids and amphibians are climate-sensitive, and that the 
interaction of climate change and Bd has driven many of the globally 
observed declines and extinctions of ~90 amphibian species (robust 
evidence, high agreement) (Rohr and Raffel, 2010; Puschendorf et al., 
2011; Rowley and Alford, 2013; Raffel et al., 2015; Sauer et al., 2018; 
Cohen et al., 2019a; Sauer et al., 2020; Turner et al., 2021).
The ‘thermal mismatch hypothesis’ posits that vulnerability to disease

should be higher at warm temperatures in cool-adapted species 
and higher at cool temperatures in warmth-adapted species and is 
generally supported (Pounds et al., 2006). However, the most recent 
studies reveal more complex mechanisms underlying amphibian 
disease–climate change dynamics, including variation in thermal 
preferences among individuals in a single amphibian population 
(robust evidence, high agreement) (Zumbado-Ulate et al., 2014; Sauer 
et al., 2018; Cohen et al., 2019b; Neely et al., 2020; Sauer et al., 2020).
Bd is not universally harmful; it has been recorded as endemic in frog 
populations that do not suffer disease, where it may be commensal 
rather than parasitic (Puschendorf et al., 2006; Puschendorf et al., 2011; 
Rowley and Alford, 2013). Projections of future impacts are difficult, as 
the virulence is variable across Bd populations and dependent upon the 
evolutionary and ecological history and evolutionary potential of both

a local amphibian population and the endemic or invading Bd (robust 
evidence, high agreement) (Retallick et al., 2004; Daskin et al., 2011; 
Puschendorf et al., 2011; Phillips and Puschendorf, 2013; Rowley and 
Alford, 2013; Zumbado-Ulate et al., 2014; Sapsford et al., 2015; Voyles 
et al., 2018; Bradley et al., 2019; Fisher and Garner, 2020; McMillan 
et  al., 2020). Further, specific local habitats might serve as regional 
climate refugia from chytrid infection (e.g., hot and dry) (medium 
evidence, high agreement) (Zumbado-Ulate et al., 2014; Cohen et al., 
2019b; Neely et al., 2020; Turner et al., 2021).
2.4.2.7.2 Changes in geographic distribution and connectivity 
patterns of pathogens
As species’ geographic ranges and migration patterns are modified 
by climate change (Section 2.4.2.1, Table 2.2), pathogens accompany 
them. Diverse vectors and associated parasites, pests and pathogens 
of plants and animals are being recorded at higher latitudes and

elevations in conjunction with regional temperature increases and 
precipitation changes (robust evidence, high agreement), although 
analysis of realised disease incidence often lacks the inclusion 
of non-climatic versus climate drivers, compromising attribution 
(Ollerenshaw and Rowlands, 1959; Purse et al., 2005; Laaksonen et al., 
2010; van Dijk et al., 2010; Alonso et al., 2011; Genchi et al., 2011; 
Pinault and Hunter, 2011; Jaenson et al., 2012; Loiseau et al., 2012; 
Kweka et al., 2013; Medlock et al., 2013; Dhimal et al., 2014a; Dhimal 
et al., 2014b seasonal; Siraj et al., 2014; Khatchikian et al., 2015; Hotez, 2016a; Hotez, 2016b; Bett et al., 2017; Mallory and Boyce, 2017; Strutz, 
2017; Booth, 2018; Dumic and Severnini, 2018; Carignan et al., 2019; 
Gorris et al., 2019; Le et al., 2019; Stensgaard et al., 2019b snails and; 
Brugueras et al., 2020; Gilbert, 2021).
At least six major VBDs affected by climate drivers have recently

emerged in Nepal and are now considered endemic, with climate 
change implicated as a primary driver as LULCC has been assessed 
to have a minimal influence on these diseases (high confidence) 
(Table SM2.1). There is increasing evidence that climate warming has 
extended the elevational distribution of Anopheles, Culex and Aedes 
mosquito vectors above 2000 m in Nepal (limited evidence, high 
agreement) (Dahal, 2008; Dhimal et al., 2014a; Dhimal et al., 2014b; 
Dhimal et al., 2015), with similar trends being recorded in neighbouring 
Himalayan regions (medium evidence, high agreement) (Phuyal 
et al., 2020; Dhimal et al., 2021). Host animals in novel areas may be 
immunologically naive, and therefore more vulnerable to severe illness 
(Bradley et al., 2005; Hall et al., 2016).
Case Study 3: Arctic and sub-Arctic disease expansion and 
intensification
High Arctic regions have warmed by more than double the global

average, >2°C in most areas (Sections 2.3.1.1.2, Figure 2.11, and Atlas 
11.2.1.2 in (IPCC, 2021a)). Experimental field ecology studies and 
computational models of Arctic and sub-Arctic regions indicate that 
milder winters have reduced the mortality of vectors and reservoir hosts 
and increased their habitat as forested taiga expands into previously 
treeless tundra (Table  SM2.1) (Parkinson et  al., 2014). Warmer 
temperatures and longer seasonal windows have allowed faster 
reproduction/replication, accelerated development and increased the 
number of generations per year of pathogens, vectors and some host 
animals, which, in turn, increases the populations of disease organisms 
and disease transmission (Sections 2.4.2.4, 2.4.4.3.3). Higher numbers 
of ticks, mosquitoes, Culicoides biting midges, deer flies, horseflies 
and Simuliidae black flies, that transmit a variety of pathogens, are 
being documented in high-latitude regions and where they have been

historically absent (robust evidence, high agreement) (Waits et  al., 
2018; Caminade et  al., 2019; Gilbert, 2021). In concert with these 
poleward shifts of hosts and vectors, pathogens, particularly tick-borne 
pathogens and helminth infections, have increased dramatically in 
incidence and severity from once-rare occurrences and have appeared 
in new regions (very high confidence) (Caminade et al., 2019; Gilbert, 
2021).
Zoonoses and VBDs that have been historically rare or never documented 
in the Arctic and sub-Arctic regions of Europe, Asia, and North America, 
such as anthrax, cryptosporidiosis, elaphostrongylosis, filariasis (Huber 
et al., 2020), tick-borne encephalitis and tularemia (Evander and Ahlm, 
2009; Parkinson et  al., 2014; Pauchard et  al., 2016), are spreading 
poleward and increasing in incidence, associated with warming 
temperatures (robust evidence, high agreement, very high confidence) 
(Table  SM2.1) (Omazic et  al., 2019). Recent anthrax outbreaks and

mass mortality events of humans and reindeer, respectively, have 
been linked to abnormally hot summer temperatures that caused the 
permafrost to melt and exposed diseased animal carcasses, releasing 
thawed, highly infectious Bacillus anthracis spores (medium evidence,

2
233Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
medium agreement) (Ezhova et al., 2019; Hueffer et al., 2020; Ezhova 
et  al., 2021). Multiple contributing factors conspired over different 
timescales to compound a 2016 anthrax outbreak occurring on the 
Yamal peninsula: (i) rapid permafrost thawing for 5 years preceding the 
outbreak, (ii) thick snow cover the year before the outbreak insulated 
the warmed permafrost and kept it from re-freezing, and (iii) anthrax 
vaccination rates had decreased or ceased in the region (Ezhova 
et al., 2019; Ezhova et al., 2021). These precursors converged with an 
unusually dry and hot summer that: (i) melted permafrost, creating an 
anthrax exposure hazard; (ii) increased the vector insect population; 
and (iii) weakened the immune systems of reindeer, thereby increasing 
their susceptibility (Waits et al., 2018; Hueffer et al., 2020).
Warmer temperatures have increased blood-feeding insect harassment

of reindeer with compounding consequences: (1) increased insect-bite 
rates lead to higher parasite loads, (2) time spent by reindeer in trying 
to escape biting flies reduces foraging while simultaneously increasing 
their energy expenditure, (3) the combination of (1) and (2) leads to 
poor body condition which subsequently leads to (4) reduced winter 
survival and fecundity (Mallory and Boyce, 2017). As temperatures 
warm and connectivity increases between the Arctic and the rest of the 
world, tourism, resource extraction and increased commercial transport 
will create additional risks of biological invasion by infectious agents 
and their hosts (Pauchard et al., 2016). These increases in introduction 
risk compounded with climate change have already begun to harm 
Indigenous Peoples dependent on hunting and herding livestock 
(horses and reindeer) that are suffering increased pathogen infection 
(high confidence) (Deksne et al., 2020; Stammler and Ivanova, 2020).

2.4.2.7.3 Biodiversity–disease links
Anthropogenic impacts, such as disturbances caused by climate 
change, can reduce biodiversity via multiple mechanisms and increase 
the risk of human diseases (limited evidence, low agreement), but more 
research is needed to understand the underlying mechanisms (Civitello 
et al., 2015; Young et al., 2017b; Halliday et al., 2020; Rohr et al., 2020; Glidden et al., 2021). Known wildlife hosts of human-shared pathogens 
and parasites overall comprise a greater proportion of local species 
richness (18–72% higher) and abundance (21–144% higher) at sites 
under substantial human use (agricultural and urban land) compared 
with nearby undisturbed habitats (Gibb et al., 2020).
Exploitation of wildlife and degradation of natural habitats have 
increased opportunities for a ‘spill over’ of pathogens from wildlife 
to human populations and also the emergence of zoonotic disease 
epidemics and pandemics (robust evidence, high agreement); animal

and human migrations driven by climate change have added to 
this increased risk (medium evidence, medium agreement) (see 
Section  2.4.2.1, Chapter 8, Cross-Chapter Box  MOVING PLATE in 
Chapter 5) (Patz et  al., 2004; Cleaveland et  al., 2007; Karesh et  al., 
2012; Altizer et al., 2013; Allen et al., 2017; Plowright et al., 2017; Faust 
et al., 2018; Carlson et al., 2020; Gibb et al., 2020; Hockings et al., 2020; 
IPBES, 2020; Volpato et  al., 2020; Glidden et  al., 2021). Agricultural 
losses and subsequent food scarcity, increasing due to climate change, 
can also lead to an increase in the use of bushmeat, and thus increase 
the risk of diseases jumping from wild animals to humans (medium 
evidence, high agreement) (Brashares et al., 2004; Leroy et al., 2004; 
Wolfe et al., 2004; Rosen and Smith, 2010; Kurpiers et al., 2016).
2.4.2.7.4 Implications of changes in diseases in wild animals for 
humans
Changes in temperature, precipitation, humidity and extreme events

have been associated with more frequent disease outbreaks, increases 
in disease incidence and severity, novel diseases and the emergence of 
vectors in new areas for wild animals, with a mechanistic understanding 
of the roles of these drivers from experimental studies providing high 
confidence for the role of climate change. However, attributing how 
this has impacted human infectious diseases remains difficult, and 
definitive attribution studies are lacking. The specific role of recent 
climate change is difficult to examine in isolation in most regions where 
human disease incidence has also been affected by LUC (particularly 
FAQ 2.2 | How does climate change increase the risk of diseases?
Climate change is contributing to the spread of diseases in both wildlife and humans. Increased contact between wildlife and human

populations increases disease risk, and climate change is altering where pathogens that cause diseases and the animals that carry them live. 
Disease risk can often be reduced by improving health care and sanitation systems, training the medical community to recognise and treat 
potential new diseases in their region, limiting human encroachment into natural areas, limiting wildlife trade and promoting sustainable and 
equitable socioeconomic development.
Diseases transmitted between humans and animals are called zoonoses. Zoonoses comprise nearly two-thirds of 
known human infectious diseases and the majority of newly emerging ones. COVID-19 is the most recent zoonosis 
and has killed millions of people globally while devastating economies. The risk posed by Emerging Infectious 
Diseases (EIDs) has increased because of: (1) the movement of wild animals and their parasites into new areas as a

result of climate change, global trade and travel; (2) human intrusion in natural areas and the conversion of natural 
areas for agriculture, livestock, the extraction of industrial/raw materials and housing; (3) increased wildlife trade 
and consumption; (4) increased human mobility resulting from global trade, war/conflicts and migration, made 
faster and extending farther due to fossil fuel-powered travel; and (5) widespread antimicrobial use, which can 
promote antibiotic-resistant infections (Figure FAQ2.2.1).

2
234Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
How diseases move from the wild into human populations
Climate ChangeStorms overwhelm or
 destroy water infrastructure
increasing fecal diseasessFloods increase mosquito
 habitat & cause
 sanitation problems
Heatwaves can
 increase pathogen &
 vector developmentDroughts may increase unsafe water
storage or usage leading to more
 fecal & mosquito-borne disease
Antibiotic overuseWildlife
tradeDiseases  Poor 
hygiene
Agricultural exposure
& wastePoor 
public health
& low vaccinationIncreased 
travel & tradePoverty
Weak worker
  protection
Wildlife stressed
& decliningEcosystem
   damageHuntingWar
Habitat
intrusionIncreases in extreme climate 
events increases disease risk
Low disease 
surveillanceSea level rise
 increases the risk
of cholera
Figure FAQ2.2.1 |  How diseases move from the wild into human populations. Climate change may increase diseases in nature, but whether or not this

leads to an increase in the risk of disease in humans depends upon a range of societal, infrastructural and medical buffers that form a shield protecting humans.
Climate change further increases risk by altering pathogen and host animal (1) geographic ranges and habitats; (2) 
survival, growth and development; (3) reproduction and replication; (4) transmission and exposure (5) behaviour; 
and (6) access to immunologically naïve animals and people who lack resistance to infection. This can lead to 
novel disease emergence in new places, more frequent and larger outbreaks, and longer or shifted seasons of 
transmission. Climate change is making it possible for many EIDs to colonise historically colder areas that are 
becoming warmer and wetter in temperate and polar regions and in the mountains. Vector-borne diseases (VBDs) 
are diseases spread by vectors such as mosquitoes, sand flies, kissing bugs and ticks. For example, ticks that carry the

virus that causes tick-borne encephalitis have moved into the northern subarctic regions of Asia and Europe. Viruses 
like dengue, chikungunya and Japanese encephalitis are emerging in Nepal in hilly and mountainous areas. Novel 
outbreaks of Vibrio bacteria seafood poisoning are being traced to the the Baltic States and Alaska where they were 
never documented before. Many scientific studies show that the transmission of infectious disease and the number 
of individuals infected depends on rainfall and temperature; climate change often makes these conditions more 
favourable for disease transmission.Box FAQ 2.2 (continued)

2
235Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Climate change can also have complicated, compounding and contradictory effects on pathogens and vectors. 
Increased rainfall creates more habitat for mosquitoes that transmit diseases like malaria, but too much rain washes 
away the habitat. Decreased rainfall also increases disease risk when people without reliable access to water use 
containers to store water where mosquitoes, such as the vectors of dengue fever Aedes aegypti and A. albopictus, 
lay their eggs. Hotter temperatures also increase mosquito-bite rate, parasite development and viral replication! 
Certain species of snails are intermediate hosts for many helminth parasites that make humans, livestock and wild 
animals sick. When it gets hot, the snails can produce 2–3 times as many infective larvae; however, if it becomes too 
hot, many pathogens and their vectors cannot survive or reproduce.

Humans also contract zoonoses directly through their skin, mucus membranes and lungs, when eating or butchering 
animals or when they come into contact with pathogens that are shed into the air or passed in urine and faeces and 
contaminate water, food, clothing and other surfaces. Any activity that increases contact with wildlife, especially 
in high-biodiversity regions like the Tropics and subtropics, increases disease risk. Climate change-related disease 
emergence events are often rare but may become more frequent. Fortunately, there are ways to reduce risks and 
protect our health, as described below.
Habitat and biodiversity protection. Human encroachment into natural areas, due to expansion of agriculture and livestock, 
timber harvests, extraction of resources and urban development, has increased human contact with wild animals 
and creates more opportunities for disease spill-over (transmission from an animal to a new species, including

humans). By conserving, protecting and restoring wild habitats, we can build healthier ecosystems that provide 
other services, such as clean air, clean and abundant water, recreation, spiritual value and well-being, as well as 
reduced disease spill-over. If humans must go into wild areas or hunt, they should take appropriate precautions such 
as wearing protective clothing, using insect repellant, performing body checks for vectors like ticks and washing 
their hands and clothing well.
Food resilience. Investing in sustainable agro-ecological farming will alleviate the pressure to hunt wild animals and 
reduce the conversion of more land to agriculture/livestock use. Stopping illegal animal trading and poaching and 
decreasing reliance on wild meats and products made from animal parts will reduce direct contact with potentially 
infected animals. This has the added benefit of increasing food security and nutrition, improving soil, reducing

erosion, preserving biodiversity and mitigating climate change.
Disease prevention and response. The level of protection against infection is linked directly to the level of development and 
wealth of a country. Improved education, high-quality medical and veterinary systems, high food security, proper 
sanitation of water and waste, high-quality housing, disease surveillance and alarm systems dramatically reduce 
disease risk and improve health. Utilising a One Biosecurity or One Health framework further improves resilience. 
Sharing knowledge within communities, municipalities, regionally and between national health authorities 
globally is important to assessing, preventing and responding to outbreaks and pandemics more efficiently and 
economically.
Humans are facing many direct and indirect challenges because of climate change. The increase in EIDs is one of our 
greatest challenges, due to our ever-growing interactions with wildlife and climatic changes creating new disease

transmission patterns. COVID-19 is a current crisis, and follows other recent EIDs: SARS, HIV/AIDS, H1N1 influenza, 
Ebola, Zika and West Nile fever. EIDs have accelerated in recent decades, making it clear that new societal and 
environmental approaches to wildlife interactions, climate change and health are urgently needed to protect our 
current and future well-being as a species.Box FAQ 2.2 (continued)

2
236Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
agricultural and urban expansion), changes in public health access 
and measures, socioeconomic changes, increased global movement 
of people and changes in vector and rodent control programs, 
supporting medium confidence in the role of climate change driving 
the observed changes in vector-borne and infectious human diseases 
globally. Exceptions are in areas noted above (the Arctic, sub-Arctic, 
and high-elevation regions), in which climate change fingerprints 
are strong and concurrent changes in non-climatic drivers are less 
pronounced than in other regions (high confidence for climate change 
attribution) (see Table SM2.1, Sections 5.5.1.3, 7.2.2.1, Cross-Chapter 
Box  ILLNESS this Chapter) (Harvell et  al., 2002; Norwegian Polar 
Institute, 2009; Tersago et al., 2009; Tabachnick, 2010; Altizer et al., 
2013; Garrett et al., 2013; Paz, 2015; Wu et al., 2016b; Caminade et al.,

2019; Dewage et al., 2019; Coates and Norton, 2020; Deksne et al., 
2020; Shocket et al., 2020; Couper et al., 2021; Gilbert, 2021).
2.4.2.8 Observed Evolutionary Responses to Climate Change
Previous sections document the tendency of species to retain 
their climate envelopes by some combination of range shift and 
phenological change (very high confidence). However, this tracking of 
climate change can be incomplete, causing species or populations to 
experience hotter conditions than those to which they are adapted, 
and thereby incur ‘climate debts’ (section 2.4.2.3.1) (Devictor et al., 
2012). The importance of population-level debt is illustrated by a study 
in which the estimated debt values were correlated with population 
dynamic trends in a North American migratory songbird, the yellow 
warbler, Setophaga petechia. Populations that were genetic outliers 
for their local climate space had larger population declines (greater

debt) than those with genotypes closer to the average values for that 
particular climate space. Debt values were estimated from genomic 
analyses independent of the population trends, and were distributed 
across the species’ range in a mosaic, not simply concentrated at 
range margins, rendering the results robust to being confounded by 
broad-scale geographical trends (Bay et al., 2018). Soroye et al. (2020) 
found similar results for 66 species of bumble-bees across Europe 
and North America, with declines in abundances spread throughout 
species’ ranges, but being greatest where populations already near 
their climate limits were being pushed beyond their climatic tolerances 
with climate change {2.4.2.3.1}.
In the absence of evolutionary constraints, climate debts can be 
cancelled by genetically based increases in thermal tolerance and the 
ability to perform in high ambient temperatures. In species already 
showing local adaptation to climate, populations currently living at

relatively cool sites should be able to evolve to adopt the traits of 
populations currently at warmer sites as their local experience of 
climate changes (Singer, 2017; Socolar et al., 2017).
An increasing number of studies document evolutionary responses to 
climate change in populations not at their warm range limits (Franks 
and Hoffmann, 2012). Organisms with short generation times should 
have a higher capacity to genetically track climate change than species 
with long generation times, such as mammals (Boutin and Lane, 2014). 
Indeed, observed evolutionary impacts have been mainly documented 
in insects, especially at expanding range margins (Chuang and 
Peterson, 2016) where evolutionary changes have increased dispersal ability (Thomas et al., 2001) and decreased host specialisation (Bridle 
et al., 2014; Lancaster, 2020) (medium evidence, medium agreement).
Away from range margins, individual populations experiencing

regional warming have evolved diverse traits related to climate 
adaptation. For example, pitcher-plant mosquitoes (Wyeomyia smithii) 
in Pacific Northwest America have evolved to wait for shorter days 
before initiating diapause. This adaptation to lengthening summers 
enables them to delay overwintering until later and add an extra 
generation each year (Bradshaw and Holzapfel, 2001). Among 26 
populations of Drosophila subobscura studied on three continents, 22 
experienced climate warming across two or more decades, and 21 of 
these 22 showed increasing frequency of the chromosome inversion 
characteristic of populations adapted to hot climates (robust evidence, 
high agreement) (Balanya et al., 2006).
However, for populations already at their warm range limits, their 
ability to track climate change in situ would require evolving to survive 
and reproduce outside their species’ historical climate envelope: 
abilities of wild species to do this is not supported by experimental

or observational evidence (medium evidence, high agreement) 
(Singer, 2017). Whether or not they can depends on the level of ‘niche 
conservatism’ operating at the species level (Lavergne et al., 2010). 
If a species whose range limits are determined by climate finds itself 
completely outside of its traditional climate envelope, extinction is 
expected in the absence of ‘evolutionary rescue’ (Bell and Gonzalez, 
2009; Bell et al., 2019).
To investigate the evolutionary potential of a species to survive in 
a novel climate entirely outside its traditional climate envelope, 
experiments have been carried out on ectotherms testing thermal 
performance, thermal tolerance and their evolvabilities (Castaneda 
et al., 2019; Xue et al., 2019). Tests of thermal performance have been 
complicated, as both long-term acclimation and trans-generational 
effects occur (Sgro et  al., 2016). However, the results to date have 
been consistent: despite widespread local adaptation to climate across

species’ ranges, substantial constraints exist regarding the evolution of 
greater stress tolerance (e.g., high temperatures and drought) at warm 
range limits (medium evidence, high agreement) (Hoffmann and Sgro, 
2011; MacLean et al., 2019b).
For example, as temperature was experimentally increased, the amount 
of genetic variance in the fitness of Drosophila melanogaster decreased; 
in hot environments, flies had low evolvability (Kristensen et al., 2015). 
The hypothesis that heat-stress tolerance is evolutionarily constrained 
is further supported by experiments in which 22 Drosophila spp. drawn 
from tropical and temperate climes were subjected to extremes of heat 
and cold. They differed, as expected, in cold tolerances, but not in heat 
tolerances nor in temperatures at which optimal performances were 
observed (MacLean et al., 2019b).
Plasticity (flexibility) in acclimating to thermal regimes helps organisms

adapt to environmental change. The form and extent of plasticity can 
vary among populations experiencing different climates (Kelly, 2019) 
and generate phenotypic values outside the prior range for the species, 
but plasticity itself has not yet been observed to evolve in response to 
climate change (Kelly, 2019).

2
237Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Relevant genetic changes in nature (e.g., affecting heat tolerance) have 
not yet been shown to alter the boundaries of existing genetic variation 
for any species. Further, a recent global analysis of 91 species found, 
on average, a 5.4–6.5% decline in genetic diversity within populations 
since the start of the Industrial Revolution, with much larger declines 
for island species (27.6–30.9% reductions) (Leigh et  al., 2019). In 
Leigh et al. (2019), genetic declines were documented in both common 
and already endangered species of fish, mammals, birds, insects, 
amphibians and reptiles. These declines in genetic diversity, though 
not caused by climate change, decrease the abilities of wild species 
to adapt to climate change via evolutionary responses. Evolutionary 
rescue of entire species has not yet been observed in nature, nor is 
it expected, based on experimental and theoretical studies (medium

evidence, high agreement).
Hybridisation between closely related species has increased in recent 
decades as one species shifts its range boundaries and positions itself 
more closely to the other. Hybrids between polar bears and brown 
bears have been documented in northern Canada (Kelly et al., 2010). 
In North American rivers, hybridisation between invasive rainbow trout 
and native cutthroat trout has increased in frequency as the rainbow 
trout has expanded into warming waters (Muhlfeld et  al., 2014). 
Whether climate-changed induced hybridisations can generate novel 
climate adaptations remains to be seen.
In summary, with our present knowledge, evolution is not expected to be 
sufficient to prevent the extinction of whole species if a species’ climate 
space disappears within the region they inhabit (high confidence).
2.4.3 Observed Changes in Key Biomes, Ecosystems and 
Their Services
2.4.3.1 Detection and Attribution for Observed Biome Shifts

Attribution for biome (major vegetation form of an ecosystem) shifts 
is complex because of their extensive, sometimes continental, spatial 
scale (Whittaker, 1975; Olson et  al., 2001; Woodward et  al., 2004). 
Therefore, non-climatic factors strongly influence biome spatial 
distributions (Ellis and Ramankutty, 2008).
The most robust attribution studies use data from many species, 
individual locations with minimal confounding factors, particularly 
observed recent LULCC, and scale up by analysing multiple locations 
across a large zone between biomes, providing multiple lines of 
evidence (Hegerl et  al., 2010; Parmesan et  al., 2013). Multivariate 
statistical analyses aid attribution studies by allowing the assessment 
of relative weights among multiple factors, including variables related 
to climate change (Gonzalez et  al., 2012). However, drivers often 
have strong, significant interactions with one another, complicating

quantitative assessment of the strength of individual drivers (Parmesan 
et al., 2013). In these cases, manipulative experiments are critical in 
assessing attribution to the drivers of climate change.
Certain biomes exhibit a relatively stronger relationship to climate; for 
example, Arctic tundra generally has a distinct ecotone with boreal 
conifer forest (Whittaker, 1975). In these areas, attribution of biome shifts to climate change are relatively straightforward, if human LULCC 
is minimal. However, other biomes, such as many grassland systems, 
are not in equilibrium with climate (Bond et  al., 2005). In these 
systems, their evolutionary history (Keeley et  al., 2011; Strömberg, 
2011; Charles-Dominique et  al., 2016), distribution, structure and 
function have been shaped by climate and natural disturbances, such 
as fire and herbivory (Staver et al., 2011; Lehmann et al., 2014; Pausas, 
2015; Bakker et al., 2016; Malhi et al., 2016). Disturbance variability is

an inherent characteristic of grassland systems, and suitable ‘control’ 
conditions are seldom available in nature. Furthermore, due to the 
integral role of disturbance, these biomes have been widely affected 
by long-term and widespread shifts in grazing regimes, large-scale 
losses of mega-herbivores and fire suppression policies (Archibald 
et al., 2013; Malhi et al., 2016; Hempson et al., 2017). It is necessary 
to conduct climate change attribution on a case-by-case basis for 
grasslands; such assessments are complex as direct climate change 
impacts from either inherent variation within disturbance regimes 
or directional changes in background disturbances are difficult to 
separate (detailed in Sections 2.4.3.2.1; 2.4.3.2.2; 2.4.3.5). Confidence 
in assessments is increased when the observed trends are supported by 
a mechanistic understanding of responses identified by physiological 
studies, manipulative field experiments, greenhouse studies and lab

experiments (Table SM2.1).
2.4.3.2 Global Patterns of Observed Biome Shifts Driven by 
2.4.3.2.1 Observed biome shifts predominantly driven by climate 
change
AR5 and a meta-analysis found that vegetation at the biome level 
shifted poleward latitudinally and upward altitudinally due to 
anthropogenic climate change in at least 19 sites in boreal, temperate 
and tropical ecosystems from 1700 to 2007 (Gonzalez et  al., 2010; 
Settele et  al., 2014). In these areas, temperature increased to 
0.4°C–1.6°C above the pre-industrial period (Gonzalez et  al., 2010; 
Settele et al., 2014). Field research since the AR5 detected additional 
poleward and upslope biome shifts over periods of 24–210 years at 
numerous sites (described below), but were not directly attributed to 
anthropogenic climate change as the studies were not designed or 
conducted properly for full attribution assessment.
Many of the recently detected shifts are nevertheless consistent

with climate change-induced temperature increases and observed in 
areas without agriculture, livestock grazing, timber harvesting and 
other anthropogenic land uses. For example, in the Andes Mountains 
in Ecuador, a biome shift was detected by comparing a survey by 
Alexander von Humboldt in 1802 to a re-survey in 2012, making this 
the longest time span in the world for this type of data (Morueta-Holme 
et al., 2015; Moret et al., 2019). Over 210 years, temperature increased 
by 1.7°C (Morueta-Holme et al., 2015) and the upper edge of alpine 
grassland shifted 100–450 m upslope (Moret et al., 2019).
Other biome shifts consistent with climate change and not substantially 
affected by local land use include: northward shifts in Canada of 
deciduous forest into boreal conifer forest, 5 km from 1970–2012 (Sittaro 
et al., 2017) and 20 km from 1970–2014 (Boisvert-Marsh et al., 2019)

2
238Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
and of temperate conifer into boreal conifer forest, 21 km from 1970–
2015 (Boisvert-Marsh and de Blois, 2021). Research detected upslope 
shifts of boreal and sub-alpine conifer forest into alpine grassland at 143 
sites on four continents (41 m from 1901–2018) (Lu et al., 2021) and at 
individual sites in Canada (54 m from 1900–2010) (Davis et al., 2020); 
China (300 m from 1910–2000) (Liang et al., 2016) (33 m from 1985–
2014) (Du et  al., 2018); Nepal (50 m from 1860–2000) (Sigdel et  al., 
2018); Russia (150 m from 1954–2006) (Gatti et al., 2019); and the USA 
(19 m from 1950–2016) (Smithers et al., 2018) (38 m from 1953–2015) 
(Terskaia et al., 2020). Other upslope cases include shifts of temperate 
conifer forest in Canada (Jackson et al., 2016) and the USA (Lubetkin 
et al., 2017), temperate deciduous forest in Switzerland (Rigling et al., 
2013) and temperate shrubland in the USA (Donato et al., 2016).

In summary, anthropogenic climate change caused latitudinal and 
elevational biome shifts in at least 19 sites in boreal, temperate and 
tropical ecosystems between 1700 and 2007, where temperature 
increased to 0.4°C–1.6°C above the pre-industrial period (robust 
evidence, high agreement). Additional cases of 5–20 km northward 
and 20–300 m upslope biome shifts between 1860 and 2016, under 
a mean global temperature increase of approximately 0.9°C above 
the pre-industrial period, are consistent with climate change (medium 
2.4.3.2.2 Observed biome shifts from combined land use change 
and climate change
Research has detected biome shifts in areas where agriculture, fire 
use or suppression, livestock grazing, harvesting of timber and wood 
for fuel and other local land use substantially altered vegetation, in 
addition to changes in climatic factors and CO 2 fertilisation. These 
studies were not designed or conducted in a manner to make climate

change attribution possible, although many vegetation changes 
are consistent with climate change. For example, a global review of 
observed changes in tree lines found that, globally, two-thirds of tree 
lines have shifted upslope in elevation over the past 50 years or more, 
((Hansson et al., 2021).
Upslope and poleward forest shifts have occurred where timber 
harvesting or livestock grazing has been abandoned, allowing the 
regeneration of trees at sites in Canada (Brice et al., 2019; Wang et al., 
2020b), France (Feuillet et al., 2020), Italy (Vitali et al., 2017), Spain 
(Ameztegui et al., 2016) and the USA (Wang et al., 2020b) as well as 
in mountainous areas across Europe (Cudlin et al., 2017). Intentional 
use of fire drove an upslope forest shift in Peru (Bush et  al., 2015) 
while mainly human-ignited fires drove the conversion of shrubland 
to grassland in a drought-affected area of the USA (Syphard et  al.,

2019b). In eastern Canada, timber harvesting and wildfire drove the 
conversion of mixed conifer–broadleaf forests to broadleaf-dominated 
forests (Brice et al., 2020; Wang et al., 2020b).
Shrub encroachment onto savanna has occurred at numerous sites, 
particularly across the Southern Hemisphere, mainly between 1992 
and 2010 (Criado et al., 2020). Globally, overgrazing initiates shrub 
encroachment by reducing grasses more than woody plants, while fire 
exclusion maintains the shrub cover (D’Odorico et al., 2012; Caracciolo 
et al., 2016; Bestelmeyer et al., 2018). The magnitude of woody cover change in savannas is not correlated with mean annual temperature 
change (Criado et  al., 2020); however, higher atmospheric CO 2 
increases shrub growth in savannas (Nackley et al., 2018; Manea and 
Leishman, 2019). A global remote-sensing analysis of biome changes 
from all causes, including agricultural and grazing expansion and

deforestation, estimated that 14% of pixels changed between 1981 
and 2012, although this approach can overestimate global changes, 
since it uses a new biome classification system which doubles the 
conventional biome classifications (Higgins et al., 2016). In addition to 
climate change, LULCC causes vegetation changes at the biome level 
(robust evidence, high agreement).
2.4.3.3 Observed Changes in Deserts and Arid Shrublands
Divergent responses to anthropogenic climate change are occurring 
within and across arid regions, depending on time period, location, 
detection methodology and vegetation type (see Cross-Chapter Paper 
3). Emerging shifts in ecosystem structure, functioning and biodiversity 
are supported by evidence from modelled impacts of projected climate 
and CO 2 levels. While observed responsiveness of arid vegetation 
productivity to rising atmospheric CO 2 (Fensholt et  al., 2012) may 
offset risks from reduced water availability (Fang et al., 2017), climate-

and CO 2-driven changes are key risks in arid regions, interacting with 
habitat degradation, wildfires and invasive species (Hurlbert et  al., 
2019).
Widespread vegetation greening, as projected in AR4, is occurring in 
arid shrublands (Zhang et al., 2019a; Maestre et al., 2021) as a result 
of increases in leaf area, woody cover and herbaceous production at 
desert–grassland interfaces (Gonsamo et al., 2021). Plant productivity 
in arid regions has increased (Fensholt et  al., 2012) because of 
improved water-use efficiency associated with elevated CO 2 (Norby 
and Zak, 2011; Donohue et al., 2013; Burrell et al., 2020; Gonsamo 
et  al., 2021) (medium evidence, high agreement), altered rainfall 
seasonality and amount (Rohde et  al., 2019; Zhang et  al., 2019a ) 
(robust evidence, high agreement), increases in temperature (Ratajczak 
et al., 2014; Wilcox et al., 2018) (robust evidence, high agreement) and 
heavy grazing (robust evidence, high agreement), with the relative

importance differing across locations (Donohue et al., 2013; Caracciolo 
et al., 2016; Archer et al., 2017; Hoffmann et al., 2019b; Rohde et al., 
2019). Woody-plant encroachment into arid shrublands is occurring 
with high confidence in North America (Caracciolo et al., 2016; Archer 
et al., 2017) and southern Africa (du Toit and O’Connor, 2014; Ward 
et  al., 2014; Masubelele et  al., 2015a; Hoffman et  al., 2019; Rohde 
et al., 2019), and with low confidence in central Asia (Li et al., 2015). 
In North America, sagebrush steppe changes have been attributed to 
increases in temperature and earlier snowpack melt (USGCRP , 2017; 
Mote et al., 2018; Snyder et al., 2019).
Non-native grasses are invading the sagebrush steppes (cold deserts) 
in North America (Chambers et  al., 2014) attributed to warming 
(Bradley et  al., 2016; Hufft and Zelikova, 2016). In the eastern 
semi-desert (Karoo) of South Africa, annual rainfall increases and a

rainfall seasonality shift (du Toit and O’Connor, 2014) are increasing 
grassiness as arid grasslands expand into semi-desert shrublands (du 
Toit et al., 2015; Masubelele et al., 2015b; Masubelele et al., 2015a) 
causing fire in areas seldom burned historically (Coates et al., 2016).

2
239Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Interactions of drought, warming and land management have caused 
vegetation mortality (see Section 2.4.4.3) and reduced vegetation cover 
in shrublands, as projected by AR4 (Burrell et al., 2020). Increased heat 
and drought are causing the health and abundance of succulent species 
to decline (Musil et al., 2009; Schmiedel et al., 2012; Aragón-Gastélum 
et al., 2014; Koźmińska et al., 2019). Hot droughts, in particular, have 
been shown to reduce population resilience (Koźmińska et al., 2019).
2.4.3.4 Observed Changes in Mediterranean-Type Ecosystems
Since AR5 (Settele et al. (2014), all five Mediterranean-type ecosystems 
(MTEs) of the world have experienced extreme droughts within the 
past decade, with South Africa and California reporting their worst on 
record (robust evidence, high agreement) (Diffenbaugh et  al., 2015; 
Williams et al., 2015a; Garreaud et al., 2017; Otto et al., 2018; Sousa

et  al., 2018). Climate change is causing these droughts to become 
more frequent and severe (medium evidence, medium agreement) 
(AghaKouchak et al., 2014; Garreaud et al., 2017; Otto et al., 2018; 
Seneviratne et al., 2021).
MTEs show a range of direct responses to various forms of water 
deficit, but have also been affected by increasing fire activity linked 
to drought (Abatzoglou and Williams, 2016), and interactions between 
drought or extreme weather and fire affecting post-fire ecosystem 
recovery (Slingsby et al., 2017). Responses include shifts in functional 
composition (Acácio et  al., 2017; Syphard et  al., 2019a), decline of 
vegetation health (Hope et al., 2014; Asner et al., 2016a), decline or 
loss of characteristic species (White et  al., 2016; Stephenson et  al., 
2019), shifts in composition towards more drought- or heat-adapted 
species and declining diversity (see also section 2.4.4.3) (Slingsby 
et al., 2017.; Harrison et al., 2018).

Declines in plant health and increased mortality in MTEs associated 
with drought have been widely documented (robust evidence, 
high agreement) (Section  2.4.4.3). Remote-sensing studies show 
drought-associated mortality in post-fire vegetation regrowth in the 
Fynbos of South Africa (Slingsby et al., 2020b), reduced canopy health 
in forests within MTE zones of South Africa (Hope et al., 2014) and 
declines in canopy water content in the forests of California (Asner 
et al., 2016a). Several studies reported climate-associated responses 
of dominant or charismatic species. High mortality in the Clanwilliam 
cedar tree between 1931 and 2013 occurred at lower, hotter elevations 
in the Fynbos of South Africa (White et al., 2016). Drought reduced 
growth and increased mortality of the holm oak, Quercus ilex, on 
the Iberian Peninsula of Spain (Natalini et  al. (2016). Portuguese 
shrublands experienced losses of many deciduous and evergreen oak

species, and an increasing dominance of pyrophytic xeric trees (Acácio 
et al., 2017). The 2012–2015 drought in California caused high-canopy 
foliage dieback of the giant sequoia (Sequoiadendron giganteum) 
(Stephenson et al., 2019), increased the dominance of oaks relative to 
pines as a result of the increased water deficit, and led to large-scale 
tree mortality due to interactions of drought and insect pest outbreaks 
(McIntyre et al., 2015; Fettig et al., 2019).
Species distribution or community composition changes have 
contributed to declines in diversity and/or shifts towards more drought- 
or heat-adapted species (medium evidence, high agreement). Two conifer species (Pinus longaeva and P . flexilis) shifted upslope 19 m from 
1950 to 2016 in the Great Basin, USA, (Smithers et al., 2018). Reduced 
winter precipitation caused native annual forbs to recede, resulting in 
long-lasting and potentially unidirectional reductions in diversity in a

Californian grassland (Harrison et al., 2018). More frequent extreme hot 
and dry weather between 1966 and 2010 caused a decline in diversity 
during the post-fire regeneration phase in the Fynbos of South Africa 
(Slingsby et al., 2017), resulting in shifts towards species with higher 
temperature preferences (Slingsby et  al., 2017). In Italy, Del Vecchio 
et al. (2015) observed increases in plant cover and thermophilic species 
in coastal foredune habitats between 1989 and 2012.
In southern California, USA, areas of forest and woody shrublands are 
shifting to grasslands, driven by a combination of climate and land use 
factors such as increased drought, fire ignition frequency and increases 
in nitrogen deposition (robust evidence, high agreement) (Jacobsen 
and Pratt, 2018; Park et al., 2018; Park and Jenerette, 2019; Syphard 
et al., 2019b).
The effects of climate change on heat, fuel and wildfire ignition limits

show spatial and temporal variation globally (see Section  2.3.6.1), 
but there have been a number of observed impacts on MTEs (medium 
evidence, high agreement). Climate change caused increases in fuel 
aridity and the area of land burned by wildfires across the western 
USA from 1985 to 2015 (Abatzoglou and Williams, 2016). Local and 
global climatic variability led to a 4-year decrease in the average fire 
return time in the Fynbos, South Africa, when comparing fires recorded 
in 1951–1975 and 1976–2000 (Wilson et al., 2010). In Chile, González 
et al. (2018) reported a significant increase in the number, size, duration 
and simultaneity of large fires during the 2010–2015 ‘megadrought’ 
when compared to the 1990–2009 baseline.
2.4.3.5 Observed Changes in Savanna and Grasslands
Savannas consist of co-existing trees and grasses in tropical and 
temperate regions (Archibald et al., 2019). The global trend of woody

encroachment reported in AR5 (Settele et al., 2014) is continuing (robust 
evidence, high agreement, very high confidence) (see Table  SM2.1), 
with increases occurring in temperate savannas in North America 
(10–20% per decade) and tropical savannas in South America (8% 
per decade), Africa (2.4% per decade) and Australia (1% per decade) 
(O’Connor et al., 2014; Espírito-Santo et al., 2016; Skowno et al., 2017; 
Stevens et al., 2017; McNicol et al., 2018; Venter et al., 2018; Rosan 
et al., 2019). Additionally, the forest expansion into mesic savannas 
reported in AR5 (Settele et  al., 2014) is continuing in Africa, South 
America and Southeast Asia (Marimon et al., 2014; Keenan et al., 2015; 
Baccini et al., 2017; Ondei et al., 2017; Stevens et al., 2017; Aleman 
et al., 2018; Rosan et al., 2019). Extreme high rainfall anomalies have 
also contributed to an increase in herbaceous and foliar production in 
the Sahel (Brandt et al., 2019; Zhang et al., 2019a).

New studies since AR5, using multiple study designs (experimental 
manipulations in lab and field, meta-analyses and modelling), attribute 
climate change increases in woody cover to elevated atmospheric 
CO 2 (Donohue et  al., 2013; Nackley et  al., 2018; Quirk et  al., 2019) 
and increased rainfall amount and intensity (robust evidence, high 
agreement) (Venter et al., 2018; Xu et al., 2018b; Zhang et al., 2019a).

2
240Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Direct quantification of climate-change drivers is confounded with 
local LUC such as fire suppression (Archibald, 2016; Venter et  al., 
2018), heavy grazing (du Toit and O’Connor, 2014; Archer et al., 2017), 
removal of native browsers and, specifically, loss of mega-herbivores 
in Africa (medium evidence, medium agreement) (Asner et al., 2016b; 
Daskin et  al., 2016; Stevens et  al., 2016; Davies et  al., 2018). The 
relative importance of the climate- and non-climate-related causes of 
woody plants varies between regions, but there is general consensus 
that the impacts of climate change, specifically, increasing rainfall and 
rising CO 2, are frequent and strong contributing factors of woody-cover 
increase (robust evidence, high agreement).
Extensive woody-cover increases in non-forested biomes is reducing 
grazing potential (Smit and Prins, 2015) as well as changing the

carbon stored per unit of land area (González-Roglich et  al., 2014; 
Puttock et  al., 2014; Pellegrini et  al., 2016; Mureva et  al., 2018) 
and the hydrological characteristics (Honda and Durigan, 2016; 
Schreiner-McGraw et  al., 2020). Woody-cover encroachment also 
reduces biodiversity by threatening fauna and flora adapted to open 
ecosystems (Ratajczak et  al., 2012; Smit and Prins, 2015; Pellegrini 
et al., 2016; Andersen and Steidl, 2019).
The global extent of grasslands is declining significantly because of 
climate change (medium confidence). In temperate and boreal zones, 
where about half of tree lines are shifting, they are overwhelmingly 
expanding poleward and upward, with an accompanying loss of 
montane and boreal grassland (robust evidence, high agreement) 
whereas tropical tree lines have been generally stable (medium 
evidence, medium agreement) (Harsch et al., 2009; Rehm and Feeley, 
2015; Silva et al., 2016; Andela et al., 2017; Song et al., 2018; Aide

et  al., 2019; Gibson and Newman, 2019). The Eurasian steppes 
experienced a 1% increase in woody cover per decade since 2000 (Liu 
et  al., 2021) and inner Mongolian grasslands in China experienced 
broad encroachment as well (Chen et  al., 2015). Climatic drivers of 
woody expansion in temperature-limited grasslands, particularly 
alpine grasslands, are most frequently attributed to warming (robust 
evidence, high agreement, high confidence) (D’Odorico et  al., 2012; 
Hagedorn et al., 2014), an increase in water and nutrient availability 
from thawing permafrost (medium evidence, high agreement) (Zhou 
et  al., 2015b; Silva et  al., 2016) and rising CO 2 (medium evidence, 
medium agreement) (Frank et al., 2015; Aide et al., 2019). Interactions 
of LULCCs such as land abandonment, grazing management shifts 
and fire suppression with climate change are contributing factors (Liu 
Remote sensing shows overall increasing trends in both the annual

maximum Normalized Difference Vegetation Index (NDVI) and annual 
mean NDVI in global grassland ecosystems between 1982 and 2011 
(Gao et al., 2016). Multiple lines of evidence indicate that changes in 
grassland productivity are positively correlated with increases in mean 
annual precipitation (Hoover et al., 2014; Brookshire and Weaver, 2015; 
Gang et al., 2015; Gao et al., 2016; Wilcox et al., 2017; Wan et al., 2018). 
Increasing temperatures positively impact grassland production and 
biomass, especially in temperature-limited regions (Piao et al., 2014; 
Gao et al., 2016). However, it is expected that grasslands in hot areas 
will decrease production as temperatures increase (limited evidence, 
low agreement) (Gang et al., 2015). Nevertheless, grassland responses to warming and drought are being ameliorated by increasing CO 2 
and associated improved water-use efficiency (Roy et al., 2016). For 
example, in a cool temperate grassland experiment, warming led to a

longer growing season and elevated CO 2 further extended growing by 
conserving water, which enabled most species to remain active longer 
(medium evidence, medium agreement) (Reyes-Fox et al., 2014).
2.4.3.6 Observed Changes in Tropical Forest
Overall declines of tropical forest cover (Kohl et al., 2015; Liu et al., 
2015; Baccini et al., 2017; Harris et al., 2021), with declines more than 
triple the gains (Harris et  al., 2021) have been driven primarily by 
deforestation and land conversion (robust evidence, high agreement) 
(Lewis et al., 2015; Curtis et al., 2018; Assis et al., 2019). In opposition 
to this general trend, expansion of tropical forest cover into savannas 
and grasslands has occurred in Africa, South America and Australia 
(Marimon et al., 2014; Baccini et al., 2017; Ondei et al., 2017; Stevens 
et al., 2017; Aleman et al., 2018; Staver, 2018; Rosan et al., 2019).
Specific examples of climate change-driven range shifts of tropical

deciduous forests upslope into alpine grasslands have been documented 
in the Americas (Chacón-Moreno et al., 2021; Jiménez-García et al., 
2021) and Asia (Sigdel et  al., 2018). However, tree line behaviours 
are diverse. A study in Nepal recorded that the tree line fomed by 
Abies spectabilis had been stable for more than a century, while the 
upper limit of large shrubs (Rhododendron campanulatum) had been 
advancing (Mainali et  al., 2020). In both the Andes (Harsch et  al., 
2009) and Himalayas (Singh et al., 2021), most tree lines have been 
stable, leading (Rehm and Feeley, 2015) to postulate a ‘grass ceiling’ 
that has been difficult for trees to penetrate. The tree line shifts that 
have occurred are probably driven by interactions between changing 
land use (e.g., fire suppression) and climate changes such as increased 
rainfall, warming and elevated CO 2 (via CO 2 fertilisation or increases 
in water-use efficiency) (medium evidence, medium agreement)

(Cernusak et al., 2013; Huang et al., 2013; Van Der Sleen et al., 2015; 
Yang et al., 2016).
Increases in productivity of tropical forests (Gatti et al., 2014; Brienen 
et al., 2015; Baccini et al., 2017), Africa and southeast Asia (Qie et al., 
2017) have been attributed to elevated CO 2 (robust evidence, medium 
agreement) (Ballantyne et al., 2012; Brienen et al., 2015; Sitch et al., 
2015; Yang et al., 2016; Mitchard, 2018). The rates of these increases 
have been slowing down in the central Amazon (Brienen et al., 2015; 
de Meira Junior et al., 2020) and Southeast Asia (Qie et al., 2017). In 
contrast, the carbon sink (and hence the rate of biomass gain) in intact 
African forests was stable until 2010 and has only recently started to 
decline, indicating asynchronous carbon sink saturation in Amazonia 
and Africa, the difference being driven by rates of tree mortality 
(Hubau et al., 2020). At the global level, Hubau et al. (2020) argue that

the carbon sink associated with intact tropical forests peaked in the 
1990s and is now in decline.
Declines in productivity are most strongly associated with warming 
(Sullivan et al., 2020), reduced growth rates during droughts (Bennett 
et al., 2015; Bonai et al., 2016; Corlett, 2016), drought-related mortality 
(Brando et al., 2014; Zhou et al., 2014; Brienen et al., 2015; Corlett, 
2016; McDowell et al., 2018), fire (Liu et al., 2017) and cloud-induced

2
241Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
radiation limitation (robust evidence, high agreement) (Deb Burman 
et al., 2020). Increases in the frequency and severity of droughts and 
shorter tree residence times due to increases in growth rates caused 
by elevated CO 2 may be additional interactive factors increasing tree 
mortality (Malhi et  al., 2014; Brienen et  al., 2015). Vulnerability to 
drought varies between tree species and sizes, with large, older trees 
at the highest risk of mortality (McDowell et al., 2018; Meakem et al., 
2018). Mortality risk also varies between forest types, with seasonal 
rainforests appearing to be the most vulnerable to drought (Corlett, 
2016).
Lianas (long-stemmed woody vines) generally negatively impact trees, 
significantly reducing the growth of heavily infested trees (Reis et al., 
2020). Lianas would benefit from climate change and disturbance

(LingZi et al., 2014; Hodgkins et al., 2018). The extent of their suitable 
niche can increase (Taylor and Kumar, 2016), thereby decreasing forest 
biomass accumulation (robust evidence, high agreement) (van der 
Heijden et al., 2013; Fauset et al., 2015; Estrada-Villegas et al., 2020).
Climate change continues to degrade forests by reducing resilience to 
pests and diseases, increasing species invasion, facilitating pathogen 
spread (Malhi et al., 2014; Deb et al., 2018) and intensifying fire risk 
and potential dieback (Lapola et  al., 2018; Marengo et  al., 2018). 
Drought, temperature increases and forest fragmentation interact to 
increase the prevalence of fires in tropical forests (robust evidence, 
high agreement). Warming increases water stress in trees (Corlett, 
2016) and, together with forest fragmentation, dramatically increases 
the desiccation of forest canopies—resulting in deforestation that 
then leads to even hotter and drier regional climates (Malhi et  al.,

2014; Lewis et al., 2015). Warming and drought increase the invasion 
of grasses into forest edges and increase fire risk (robust evidence, high 
agreement) (Brando et al., 2014; Balch et al., 2015; Lewis et al., 2015). 
Droughts and fires additively increase mortality and, consequently, 
reduce canopy cover and above-ground biomass (Cross-Chapter Paper 
7) (Brando et al., 2014, 2020; Balch et al., 2015; Lewis et al., 2015).
2.4.3.7 Observed Changes in Boreal and Temperate Forests
The AR5 found increased tree mortality, wildfire and plant phenology 
changes in boreal and temperate forests (Settele et  al., 2014). 
Expanding on these conclusions, this assessment, using analyses of 
causal factors, attributes the following observed changes in boreal 
and temperate forests in the 20th and 21st centuries to anthropogenic 
climate change: upslope and poleward biome shifts at sites in Asia, 
Europe and North America (Section 2.4.3.2.1); range shifts of plants

(Section 2.4.2.1); earlier blooming and leafing of plants (Section 2.4.2.4); 
poleward shifts in tree-feeding insects (Section  2.4.2.1); increases 
in insect pest outbreaks (Section  2.4.4.3.3); increases in the area 
burned by wildfire in western North America (Section  2.4.4.2.1); 
increased drought-induced tree mortality in western North America 
(Section  2.4.4.3.1); and thawing of the permafrost that underlies 
extensive areas of boreal forest (Section  2.4.3.9)(Section  2.3.2.5 in 
(Gulev et  al., 2021)). Atmospheric CO 2 from anthropogenic sources 
has also increased net primary productivity (NPP) (Section 2.4.4.5.1). 
In summary, anthropogenic climate change has caused substantial 
changes in temperate and boreal forest ecosystems, including biome 
shifts and increases in wildfire, insect pest outbreaks and tree mortality, at a global mean surface temperature (GMST) increase of 0.9°C above 
the pre-industrial period (robust evidence, high agreement).

Other changes detected in boreal forests and consistent with, but not 
formally attributed to, climate change, include increased wildfire in 
Siberia (Section 2.4.4.2.3), long-lasting smouldering below-ground fires 
in Canada and the USA (Scholten et al., 2021), tree mortality in Europe 
(Section 2.4.4.3.3) and post-fire shifts of boreal conifer to deciduous 
broadleaf tree species in Alaska (Mack et  al., 2021). From 1930 to 
1960, boreal forest growth became limited more by precipitation than 
temperature in the Northern Hemisphere (Babst et al., 2019).
For some vegetation, changes in land use and management have 
exerted more influence than climate change. These include upslope 
and poleward forest shifts in Europe following the abandonment of 
timber harvesting or livestock grazing (Section  2.4.3.2.2), changes 
in wildfire in Europe affected by fire suppression, fire prevention 
and agricultural abandonment (Section 2.4.4.2.3), and forest species

composition changes in Scotland due to nitrogen deposition from air 
pollution (Hester et al., 2019). Remote sensing suggests that the area 
of temperate and boreal forests increased in Asia and Europe between 
1982 and 2016 (Song et al., 2018) and in Canada between 1984 and 
2015 (Guindon et al., 2018), but forest plantations and regrowth are 
probable drivers (Song et al., 2018).
2.4.3.8 Observed Changes in Peatlands
Globally, peatland ecosystems store approximately 25% (600 ± 100 
GtC) of the world’s soil organic carbon (Yu et al., 2010; Page et al., 2011; 
Hugelius et  al., 2020) and 10% of the world’s freshwater resources 
(Joosten and Clarke, 2002), despite only occupying 3% of the global 
land area (Xu et al., 2018a). The long-term role of northern peatlands 
in the carbon cycle was mentioned for the first time in IPCC AR4 (IPCC, 
2007), while SR1.5 briefly mentioned the combined effects of changes 
in climate and land use on peatlands (IPCC, 2018b). New evidence

confirms that climate change, including extreme weather events (e.g., 
droughts; Section 8.3.1.6), permafrost degradation (Section 2.3.2.5), 
SLR (Section 2.3.3.3) and fire (Section 5.4.3.2) (Henman and Poulter, 
2008; Kirwan and Mudd, 2012; Turetsky et al., 2015; Page and Hooijer, 
2016; Swindles et al., 2019; Hoyt et al., 2020; Hugelius et al., 2020; 
Jovani-Sancho et al., 2021; Veraverbeke et al., 2021), superimposed on 
anthropogenic disturbances (e.g., draining for agriculture or mining; 
Section 5.2.1.1), has led to rapid losses of peatland carbon across the 
world (robust evidence, high agreement) (Page et  al., 2011; Leifeld 
et al., 2019; Hoyt et al., 2020; Turetsky et al., 2020; Loisel et al., 2021). 
Other essential peatland ecosystem services, such as water storage 
and biodiversity, are also being lost worldwide (robust evidence, high 
agreement) (Bonn et al., 2014; Martin-Ortega et al., 2014; Tiemeyer 

The switch from carbon sink to carbon source in peatlands globally is 
mainly attributable to changes in the depth of the water table, regardless 
of management or status (robust evidence, high agreement) (Lafleur 
et al., 2005; Dommain et al., 2011; Lund et al., 2012; Cobb et al., 2017; 
Evans et al., 2021; Novita et al., 2021). Across the temperate and tropical 
biomes, extensive drainage and deforestation have caused widespread 
water table draw-downs and/or peat subsidence, as well as high CO 2

2
242Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
emissions (medium evidence, high agreement). Climate change is 
compounding these impacts (medium evidence, medium agreement). 
For example, in Indonesia, the highest emissions from drained tropical 
peatlands were reported in the extremely dry year of the 1997 El Niño 
(810–2570 TgC yr-1) (Page et al., 2002) and the 2015 fire season (380 
TgC yr-1) (Field et al., 2016). These prolonged dry seasons have also 
led to tree die-offs and fires, which are relatively new phenomena at 
these latitudes (medium evidence, high agreement) (Cole et al., 2015; 
Mezbahuddin et al., 2015; Fanin and van der Werf, 2017; Taufik et al., 
2017; Cole et  al., 2019). Low soil moisture contributes to increased 
fire propagation (Section 12.4.2.2) (Dadap et al., 2019; Canadell et al., 
2021), causing long-lasting fires responsible for smoke and haze 
pollution (robust evidence, high agreement) (Ballhorn et  al., 2009;

Page et al., 2009; Gaveau et al., 2014; Huijnen et al., 2016; Page and 
Hooijer, 2016; Hu et al., 2018; Vadrevu et al., 2019; Niwa et al., 2021). 
Increases in fires and smoke lead to habitat loss and negatively impact 
regional faunal populations (limited evidence, high agreement) (Neoh 
et al., 2015; Erb et al., 2018b; Thornton et al., 2018).
In large, lowland tropical peatland basins that are less impacted by 
anthropogenic activities (i.e., the Amazon and Congo river basins), 
the direct impact of climate change is that of a decreased carbon 
sink (limited evidence, medium agreement) (Roucoux et  al., 2013; 
Gallego-Sala et  al., 2018; Wang et  al., 2018a; Dargie et  al., 2019; 
Ribeiro et al., 2021). As for the temperate and boreal regions, climatic 
drying also tends to promote peat oxidation and carbon loss to the 
atmosphere (medium evidence, medium agreement) (Section 2.3.1.3.4) 
(Helbig et al., 2020; Zhang et al., 2020). In Europe, increasing mean

annual temperatures in the Baltic, Scandinavia, and continental 
Europe (Section 12.4.5.1) have led to widespread lowering of peatland 
water tables at intact sites (Swindles et  al., 2019), desiccation and 
die-off of sphagnum moss (Bragazza, 2008; Lees et  al., 2019) and 
increased intensity and frequency of fires, resulting in a rapid carbon 
loss (Davies et al., 2013; Veraverbeke et al., 2021). Nevertheless, longer 
growing seasons and warmer, wetter climates have increased carbon 
accumulation and promoted thick deposits regionally, as reported for 
some North American sites (limited evidence, medium agreement) (Cai 
and Yu, 2011; Shiller et al., 2014; Ott and Chimner, 2016).
In high-latitude peatlands, the net effect of climate change on the 
permafrost peatland carbon sink capacity remains uncertain (Abbott 
et  al., 2016; McGuire et  al., 2018b; Laamrani et  al., 2020; Loisel 
et  al., 2021; Sim et  al., 2021; Väliranta et  al., 2021). Increasing air

temperatures have been linked to permafrost degradation and altered 
hydrological regimes (2.3.3.2; Figure  2.4a; 2.4.3.9; Box  5.1), which 
have led to rapid changes in plant communities and bio-geochemical 
cycling (robust evidence, high agreement) (Liljedahl et  al., 2016; 
Swindles et al., 2016; Voigt et al., 2017; Zhang et al., 2017b; Voigt et al., 
2020; Sim et  al., 2021). In many instances, permafrost degradation 
triggers thermokarst land subsidence associated with local wetting 
(robust evidence, high agreement) (Jones et  al., 2013; Borge et  al., 
2017; Olvmo et  al., 2020; Olefeldt et  al., 2021). Permafrost thaw in 
peatland-rich landscapes can also cause local drying through increased 
hydrological connectivity and runoff (Connon et al., 2014). In the first 
decades following thaw, increases in methane, CO 2 and nitrous oxide 
emissions have been recorded from peatland sites, depending on

surface moisture conditions (Schuur et al., 2009; O’Donnell et al., 2012; Elberling et  al., 2013; Matveev et  al., 2016; Euskirchen et  al., 2020; 
Hugelius et al., 2020). Conversely, some evidence suggests increased 
peat accumulation after thaw (Jones et al., 2013; Estop-Aragonés et al., 
2018; Väliranta et al., 2021). There is also a need to consider the impact 
of wildfire on permafrost thaw, due to its effect on soil temperature 
regime (Gibson et  al., 2018), as fire intensity and frequency have 
increased across the boreal and Arctic biomes (limited evidence, high 
agreement) (Kasischke et al., 2010; Scholten et al., 2021).
The CO 2 emissions from degrading peatlands is contributing to climate 
change in a positive feedback loop (robust evidence, high agreement). 
At mid-latitudes, widespread anthropogenic disturbance led to large 
historical GHG emissions and current legacy emissions of 0.15 PgC yr-1 
between 1990 and 2000 (limited evidence, high agreement) (Maljanen

et  al., 2010; Tiemeyer et  al., 2016; Drexler et  al., 2018; Qiu et  al., 
2021). About 80 million hectares of peatland have been converted to 
agriculture, equivalent to 72 PgC emissions in 850–2010 CE (Leifeld 
et al., 2019; Qiu et al., 2021). In Southeast Asia (SEA), an estimated 20–
25 Mha of peatlands have been converted to agriculture with carbon 
currently being lost at a rate of ~155 ± 30 MtC yr−1 (Miettinen et al., 
2016; Leifeld et al., 2019; Hoyt et al., 2020). Extensive deforestation 
and drainage have caused widespread peat subsidence and large CO 2 
emissions at a current average of ~10 ± 2 tonnes ha-1 yr-1, excluding 
fires (Hoyt et al., 2020), with values estimated from point subsidence 
measurements being as high as 30–90 tonnes CO 2 ha−1 yr−1 locally 
(robust evidence, high agreement) (Wösten et  al., 1997; Matysek 
et  al., 2018; Swails et  al., 2018; Evans et  al., 2019; Conchedda and 
Tubiello, 2020; Anshari et al., 2021). On average, at the global scale,

increases in GHG emissions from peatlands have primarily come from 
the compounded effects of LUC, drought and fire, with additional 
emissions from some thawing-permafrost peatlands (robust evidence, 
high agreement).
2.4.3.9 Observed Changes in Polar Tundra
Warming at high latitudes, documented in both AR4 and AR5, is 
leading to earlier snow and sea ice melt and longer growing seasons 
(IPCC, 2021a) which are continuing to alter tundra plant communities 
(medium evidence, high agreement) (Post et al., 2009; Gauthier et al., 
2013). Woody encroachment and increases in vegetation productivity, 
observed in both AR4 and AR5, are widespread and continuing. Both 
experiments and monitoring indicate that climate warming is causing 
increases in shrub, grass and sedge abundance, density, frequency, 
and height, with decreases in mosses and/or lichens (robust evidence, 
high agreement) (Myers-Smith et  al., 2011; Bjorkman et  al., 2018;

Bjorkman et al., 2019). Shrub growth is climate-sensitive and is greater 
in years with warmer growing seasons (Myers-Smith et  al., 2015). 
Plant species that prefer warmer conditions are increasing (Elmendorf 
et al., 2015; Bjorkman et al., 2018), plant cover is increasing and bare 
ground is decreasing in long-term monitoring plots (Bjorkman et al., 
2019; Myers-Smith et al., 2019). Animals such as moose, beavers and 
songbirds may already be responding to these vegetation changes 
by expanding their ranges northward or upslope into shrub tundra 
(Boelman et al., 2015; Tape et al., 2016a; Tape et al., 2016b; Tape et al., 
2018).

2
243Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
In addition to direct warming, indirect effects of climate change, first 
found in AR4 and AR5, continue, such as thawed permafrost, altered 
hydrology and enhanced nutrient cycling, and these processes are 
causing pronounced vegetation changes (medium evidence, medium 
agreement) (Schuur et  al., 2009; Natali et  al., 2012). Soil moisture 
status influences temperature sensitivity of plant growth and canopy 
heights (Myers-Smith et  al., 2015; Ackerman et  al., 2017; Bjorkman 
et al., 2018). In tundra ecosystems, permafrost thawing can decouple 
below-ground plant growth dynamics from above-ground dynamics, 
with below-ground root growth continuing until soils re-freeze in 
autumn (Cross-Chapter Paper 6) (Iversen et  al., 2015; Blume-Werry 
et al., 2016; Radville et al., 2016).
2.4.4 Observed Changes in Ecosystem Processes and 
Services
2.4.4.1 Observed Browning of Rivers and Lakes

In boreal coniferous areas, there has been an increase in the 
transporting of terrestrial-derived dissolved organic carbon (DOC) into 
rivers and lakes, which has caused increased opacity and a shift toward 
a brown colour (browning). There was little assessment of this in AR5. 
This process is driven by climate change, and stems from hydrological 
intensification, greening of the Northern Hemisphere and degradation 
of carbon sinks in peatlands (robust evidence, high agreement) 
(Solomon et al., 2015; Catalán et al., 2016; de Wit et al., 2016; Finstad 
et  al., 2016; Creed et  al., 2018; Hayden et  al., 2019). These factors 
enhance terrestrial productivity, alter vegetation communities and 
affect the hydrological control of the production and transport of DOC 
(Weyhenmeyer et al., 2016). Non-climate-related drivers of browning 
are: declining atmospheric sulphur deposition, forestry practices and 
LULCCs (see Table SM2.1 for detail).

Browning creates a positive feedback to climate by absorbing photo-
synthetically active radiation, which accelerates upper water (epil-
imnetic) warming (Solomon et al., 2015). Browning of lakes leads to 
shallower and more stable thermoclines, and thus overall deep water 
cooling (Solomon et al., 2015; Williamson et al., 2015), and can pro-
voke a transition of the seasonal mixing regime from a mixed lake 
(polymictic) to one that is seasonally stratified (Kirillin and Shatwell, 
2016).
The ecological responses of browning are a concomitant effect of climate 
change and nutrient status. Results from long-term, large-scale lake 
experiments have been variable, showing both strong synergistic effects 
(Urrutia-Cordero et  al., 2016) and no significant effects of browning 
on plankton community food webs (Rasconi et  al., 2015). Browning 
has driven a shift from auto- to heterotrophic/mixotrophic-based 
production (Urrutia-Cordero et  al., 2017) and supports heterotrophic

metabolism of the bacterial community (Zwart et al., 2016). Browning 
may also accelerate primary production through the input of nutrients 
associated with dissolved organic matter (DOM) in nutrient-poor lakes 
and increase cyanobacteria, which cope better with low light intensities 
(Huisman et al., 2018) and toxin levels (Urrutia-Cordero et al., 2016). 
However, the synergistic impacts of browning and climate change 
on aquatic communities depends on regional precipitation patterns (Weyhenmeyer et  al., 2016), watershed type (de Wit et  al., 2016) 
and the length of the food chain (Hansson et al., 2013). Quantitative 
attribution of browning to climate change remains difficult (medium 
evidence, medium agreement).
In summary, new studies since AR5 have explicitly estimated the 
effects of warming and browning on freshwaters in boreal areas, with 
complex positive and negative repercussions on water temperature 
profiles (lower vs. upper water) (high confidence) and primary

production (medium confidence).
2.4.4.2 Observed Changes in Wildfire
2.4.4.2.1 Detection and attribution of observed changes in wildfire
Wildfire is a natural and essential component of many forest and 
other terrestrial ecosystems. Excessive wildfire, however, can kill 
people, cause respiratory disease, destroy houses, emit carbon dioxide 
and damage ecosystem integrity (see Sections  2.4.4.2 and 2.4.4.4). 
Anthropogenic climate change increases wildfire by exacerbating its 
three principal driving factors: heat, fuel and ignition (Moritz et  al., 
2012; Jolly et  al., 2015). Non-climatic factors also contribute to 
wildfires—in tropical areas, fires are set intentionally to clear forest 
for agricultural fields and livestock pastures (Bowman et  al., 2020). 
Urban areas and roads create ignition hazards. Governments in many 
temperate-zone countries implement policies to suppress fires, even 
natural ones, producing unnatural accumulations of fuel in the form

of coarse woody debris and high densities of small trees (Ruffault and 
Mouillot, 2015; Hessburg et al., 2016; Andela et al., 2017; Balch et al., 
2017; Lasslop and Kloster, 2017; Aragao et al., 2018; Kelley et al., 2019). 
Globally, 4.2  million km2 of land per year burned on average from 
2002 to 2016 (Giglio et al., 2018), with the highest fire frequencies 
in the Amazon rainforest, deciduous forests and savannas in Africa 
and deciduous forests in northern Australia (Earl and Simmonds, 2018; 
Andela et al., 2019).
Since the AR5 and the IPCC Special Report on Land, published research 
has detected increases in the area burned by wildfire, analysed relative 
contributions of climate and non-climate factors and attributed burned 
area increases above natural (recent historical) levels to anthropogenic 
climate change in one part of the world, western North America 
(robust evidence, high agreement) (Abatzoglou and Williams, 2016;

Partain et  al., 2016; Kirchmeier-Young et  al., 2019; Mansuy et  al., 
2019; Bowman et  al., 2020). Across the western USA, increases in 
vegetation aridity due to higher temperatures from anthropogenic 
climate change doubled burned area from 1984 to 2015 over what 
would have burned due to non-climate factors including unnatural fuel 
accumulation from fire suppression, with the burned area attributed 
to climate change accounting for 49% (32–76%, 95% confidence 
interval) of cumulative burned area (Abatzoglou and Williams, 2016). 
Anthropogenic climate change doubled the severity of a southwest 
North American drought from 2000 to 2020 that has reduced soil 
moisture to its lowest levels since the 1500s (Williams et al., 2020), 
driving half of the increase in burned area (Abatzoglou and Williams, 
2016; Holden et al., 2018; Williams et al., 2019). In British Columbia, 
Canada, the increased maximum temperatures due to anthropogenic

climate change increased burned area in 2017 to its highest extent

2
244Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Synthesis of observed changes 
attributed to climate change in 
freshwater ecosystems
BrowningSurface water temperatur e
Animal body sizeIce durationShift in thermal regime
Lake productivity
Deep lake productivity
Ecosystem heterotrophyGrowing season lengt hOxygen concentration
Cyanobacteria blooms
Direction of change : Decrease Increase
= not applicablenaConfidence level:
Boreal
Polar / // naTemperateSub-tropicalna naTropical na // / na /
/
= limited dat a ///
/ nana
/ /
High Medium Low
Figure 2.5 |  Large-scale observed changes in freshwater ecosystems attributed to climate change over more than four decades. For description and references, 
see Sections 2.3.3, 2.4.2 and 2.5.3.6.2.
in the 1950–2017 record, seven to eleven times the area that would 
have burned without climate change (Kirchmeier-Young et al., 2019). 
In Alaska, USA, the high maximum temperatures and extremely low

relative humidity due to anthropogenic climate change accounted for 
33–60% of the probability of wildfire in 2015, when the area burned 
was the second highest in the 1940–2015 record (Partain et al., 2016). 
In protected areas of Canada and the USA, climate factors (temperature, 
precipitation, relative humidity and evapotranspiration) accounted for 
60% of burned area from local human and natural ignitions from 1984 
to 2014, outweighing local human factors (population density, roads 
and built area) (Mansuy et al., 2019).
In summary, field evidence shows that anthropogenic climate change 
has increased the area burned by wildfire above natural levels across 
western North America in the period 1984–2017, at GMST increases 
of 0.6°C–0.9°C, increasing burned area up to 11 times in one extreme 
year and doubling it (over natural levels) in a 32-year period (high 
2.4.4.2.2 Observed changes in wildfire globally

Regarding global terrestrial area as a whole, wildfire trends vary 
depending on the time period of analysis. From 1900 to 2000, global 
average fire frequency, based on field data, increased 0.4% but the 
change was not statistically significant (Gonzalez et  al., 2010). Fire 
frequency increased on one-third of global land, mainly from burning 
for agricultural clearing in Africa, Asia and South America, slightly less 
than the area of fire frequency decrease, mainly from fire suppression 
across Australia, North America and Russia (Gonzalez et  al., 2010). 
Analyses of the Global Fire Emissions Database document shows 
that, from 1996 to 2015, global burned area decreased at a rate of 
−0.7% yr-1 (Forkel et al., 2019) but the change was not statistically 
significant (Giglio et al., 2013). From 1998 to 2015, global burned area 
decreased at a rate of −1.4 ± 0.5% yr-1 (Andela et al., 2017). The area 
of fire increases was one-third of the area of decreases, due to reduced

vegetation cover from agricultural expansion and intensification 
(Andela et al., 2017) and from increased precipitation (Forkel et al., 2019). Furthermore, much of the decreasing trend derives from two 
years: 1998 with a high burned area and 2013 with low burned area 
(Forkel et al., 2019). Wildfire does not show a clear long-term trend for 
the world as a whole because of increases and decreases in different 
regions (medium evidence, medium agreement).
Where the global average burned area has decreased in the past two 
decades, higher correlations of rates of change in burning to human 
population density, cropland area and livestock density than to 
precipitation indicate that agricultural expansion and intensification 
were the main causes (Andela et  al., 2017). The global decrease of 
fire frequency from 2000 to 2010 is correlated with increasing human 
population density (Knorr et  al., 2014). The fire-reducing effect of

reduced vegetation cover following expansion of agriculture and 
livestock herding can counteract the fire-increasing effect of the 
increased heat and drying associated with climate change (Lasslop and 
Kloster, 2017; Arora and Melton, 2018; Forkel et al., 2019). The reduced 
burning needed after the initial clearing for agricultural expansion 
drives much of the decline in fires in the Tropics (Andela et al., 2017; 
Earl and Simmonds, 2018; Forkel et al., 2019). The human influence on 
fire ignition can be seen through the decrease documented on holy 
days (Sundays and Fridays) and traditional religious days of rest (Earl 
et al., 2015). Overall, human land use exerts an influence on wildfire 
trends for global terrestrial area as a whole that can be stronger than 
climate change (medium confidence).
2.4.4.2.3 Observed changes in wildfire in individual regions with 
complex attribution
While burned area has increased in parts of Asia, Australia, Europe and

South America, published research has not yet attributed the increases 
to anthropogenic climate change (medium evidence, high agreement).
In the Amazon, deforestation for agricultural expansion and the 
degradation of forests adjacent to deforested areas cause wildfire 
in moist humid tropical forests not adapted to fire (robust evidence, 
high agreement) (Fonseca et  al., 2017; van Marle et  al., 2017; da

2
245Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Silva et al., 2018; da Silva et al., 2021; dos Reis et al., 2021; Libonati 
et al., 2021). Roads facilitate deforestation, fragmenting the rainforest 
and increasing the dryness and flammability of vegetation (Alencar 
et  al., 2015). Extreme droughts that occur during warm phases of 
the ENSO and the Atlantic Multi-Decadal Oscillation combine with 
the degradation of vegetation to cause extreme fire events (robust 
evidence, high agreement) (Fonseca et al., 2017; Aragao et al., 2018; 
da Silva et al., 2018; Burton et al., 2020; dos Reis et al., 2021; Libonati 
et  al., 2021). In the State of Roraima, Brazil, distance to roads and 
infrastructure that enable deforestation and ENSO were the factors 
most explaining fire occurrence in the extreme 2015–2016 fire season 
(Fonseca et al., 2017). From 1973 to 2014, burned area increased in 
the Amazon, coinciding with increased deforestation (van Marle et al.,

2017). In the State of Acre, Brazil, burned area increased 36-fold from 
1984 to 2016, with 43% burned in agricultural and livestock settlement 
areas (da Silva et al., 2018). In the extreme fire year 2019, 85% of the 
area burned in the Amazon occurred in areas deforested in 2018 (Cardil 
et  al., 2020). Even though relatively higher moisture in 2019 led to 
burning below the 2002–2019 average across most of South America, 
burning in areas of recent deforestation in the Amazon were above the 
2002–2019 average, indicating that deforestation, not meteorological 
conditions, triggered the 2019 fires (Kelley et al., 2021; Libonati et al., 
2021). Furthermore, from 1981 to 2018, deforestation in the Amazon 
reduced moisture inputs to the lower atmosphere, increasing drought 
and fire in a self-reinforcing feedback (Xu et al., 2020). In the Amazon, 
deforestation exerts an influence on wildfire that can be stronger than 
climate change (robust evidence, high agreement).

In Australia, burned area increased significantly between the periods 
1950–2002 and 2003–2020 in the southeast state of Victoria, with the 
area burned in the 2019–2020 bushfires being the highest on record 
(Lindenmayer and Taylor, 2020). In addition to the deaths of dozens of 
people and the destruction of thousands of houses, the 2019–2020 
bushfires burned almost half of the area protected for conservation 
in Victoria, two-thirds of the forests allocated for timber harvesting 
(Lindenmayer and Taylor, 2020), wildlife and extensive areas of habitat 
for threatened plant and animal species (Geary et al., 2021). Generally, 
past timber harvesting did not lead to more severe fire canopy damage 
(Bowman et al., 2021b). Across southeastern Australia, the fraction of 
vegetated area that burned increased significantly in eight of the 32 
bioregions from 1975 to 2009, but decreased significantly in three 
bioregions (Bradstock et al., 2014). Increases in four bioregions were

correlated to increasing temperature and decreasing precipitation. 
Decreases in burned area occurred despite increased temperature 
and decreased precipitation. Analyses of climate across Australia 
from 1950 to 2017 (Dowdy, 2018; Harris and Lucas, 2019) and during 
periods with extensive fires in 2017 in eastern Australia (Hope et al., 
2019), in 2018 in northeastern Australia (Lewis et al., 2020), and in 
period 2019–2020 in southeastern Australia (Abram et  al., 2021; 
van Oldenborgh et al., 2021) indicate that temperature and drought 
extremes due to the ENSO, Southern Annular Mode and other natural 
inter-decadal cycles drive inter-annual variability of fire weather. While 
the effects of inter-decadal climate cycles on fire are superimposed on 
long-term climate change, the relative importance of anthropogenic 
climate change in explaining changes in burned area in Australia

remains unquantified (medium evidence, high agreement).In Africa, the rate of change of burned area on the continent as a 
whole ranged from a non-statistically significant −0.45% yr-1 in the 
period 2002–2016 (Zubkova et al., 2019) to a significant −1.9% yr-1 
in the period 2001–2016 (Wei et al., 2020). These decreases coincided 
with areas of agricultural expansion or areas where drought reduced 
fuel loads (Zubkova et  al., 2019; Wei et  al., 2020). It is possible, 
however, that the 500-m spatial resolution of Modis remote-sensing 
fire data underestimates the area burned in Africa by half, by missing 
small fires (Ramo et al., 2021). In the Serengeti-Mara savanna of east 
Africa, burned area showed no significant change from 2001 to 2014, 
although an increase in domestic livestock would tend to reduce the 
grass cover that fuels savanna fires (Probert et al., 2019).
In Mediterranean Europe, the area burned in the region as a whole

decreased from 1985 to 2011 (Turco et al., 2016), although the burned 
area for Spain did not show a significant long-term increase from 1968 
to 2010 (Moreno et al., 2014) whereas that for Portugal in 2017 was 
the highest in the period 1980–2017 (Turco et  al., 2019). Increased 
summer maximum temperature and decreased soil moisture explained 
most of the burned area observed, suggesting a contribution of climate 
change, but fire suppression, fire prevention, agricultural abandonment 
and reforestation as well as the reduction in forest area exerted 
even stronger influences on burned area than the climate across 
Mediterranean Europe (robust evidence, high agreement) (Moreno 
et al., 2014; Turco et al., 2017; Viedma et al., 2018; Turco et al., 2019).
In the Arctic tundra and boreal forest, where wildfire has naturally 
been infrequent, burned area showed statistically significant increases 
of ~50% yr-1 across Siberia, Russia, from 1996 to 2015 (Ponomarev

et al., 2016) and 2% yr-1 across Canada from 1959 to 2015 (Hanes 
et al., 2019). Wildfire burned ~6% of the area of four extensive Arctic 
permafrost regions in Alaska, USA, eastern Canada and Siberia from 
1999 to 2014 (Nitze et al., 2018). In boreal forest in the Northwest 
Territories, Canada and Alaska, USA, the area burned by wildfire 
increased at a statistically significant rate of 6.8% yr-1 in the period 
1975–2015, (Veraverbeke et al., 2017), with smouldering below-ground 
fires that lasted through the winter covering ~1% of burned area 
in the period 2002–2016 (Scholten et  al., 2021). While burned area 
was correlated with temperature and reduced precipitation in Siberia 
(Ponomarev et  al., 2016; Masrur et  al., 2018) and correlated with 
lightning, temperature and precipitation in the Northwest Territories 
and Alaska (Veraverbeke et  al., 2017), no attribution analyses have 
examined relative influences of climate and non-climate factors.

In Indonesia, deforestation and draining of peat swamp forests dries 
out the peat, providing substantial fuel for fires (Page and Hooijer, 
2016). Extreme fire years in Indonesia, including 1997, 2006 and 2015, 
coincided with extreme heat and aridity during the warm phase of the 
ENSO (Field et al., 2016). Fire-resistant forest in 2019 covered only 3% 
of peatlands and 4.5% of non-peatlands on Sumatra and Kalimantan 
(Nikonovas et al., 2020).
In Chile, the area burned in the summer of 2016–2017 was 14 times 
the mean for the period 1985–2016 and the highest on record 
(Bowman et al., 2019). While this extreme fire year coincided with the 
highest daily mean maximum temperature in the period 1979–2017 
(Bowman et al., 2019) in central Chile (the area of highest fire activity),

2
246Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
burned area from 1976 to 2013 showed the highest correlation with 
the precipitation cycles of the ENSO and the temperature cycles of the 
Antarctic Oscillation (Urrutia-Jalabert et al., 2018).
Overall, burned area has increased in the Amazon, Arctic, Australia and 
parts of Africa and Asia, consistent with, but not formally attributed 
to anthropogenic climate change (medium evidence, high agreement). 
Deforestation, peat draining, agricultural expansion or abandonment, 
fire suppression and inter-decadal cycles such as the ENSO exert a 
stronger influence than climate change on wildfire trends in numerous 
regions outside of North America (high confidence).
2.4.4.2.4 Observed changes in fire seasons globally
The IPCC AR6 WGI assessed fire weather (Ranasinghe et  al., 2021), 
while this chapter assesses the impacts of changes in fire weather: 
burned area and fire frequency. The global increases in temperature

from anthropogenic climate change have increased aridity and 
drought, lengthening the fire weather season (the annual period 
with a heat and aridity index greater than half of its annual range) 
on one-quarter of global vegetated area and increasing the average 
fire season length by one-fifth from 1979 to 2013 (Jolly et al., 2015). 
Climate change has contributed to increases in the fire weather season 
or the probability of fire weather conditions in the Amazon (Jolly et al., 
2015), Australia (Dowdy, 2018; Abram et  al., 2021; van Oldenborgh 
et  al., 2021), Canada (Hanes et  al., 2019), central Asia (Jolly et  al., 
2015), East Africa (Jolly et al., 2015) and North America (Jain et al., 
2017; Williams et  al., 2019; Goss et  al., 2020). In forest areas, the 
burned area correlates with fuel aridity, a function of temperature; in 
non-forest areas, the burned area correlates with high precipitation in 
the previous year, which can produce high grass fuel loads (Abatzoglou

et al., 2018). Fire use in agriculture and raising livestock or other factors 
have generated a second fire season on approximately one-quarter of 
global land where fire is present, despite sub-optimal fire weather in 
the second fire season (Benali et al., 2017). In summary, anthropogenic 
climate change, through a 0.9°C surface temperature increase since 
the pre-industrial period, has lengthened or increased the frequency of 
periods with heat and aridity that favour wildfire on up to one-quarter 
of vegetated area since 1979 (robust evidence, high agreement).
2.4.4.2.5 Observed changes in post-fire vegetation
Globally, fire has contributed to biome shifts (Section 2.4.3.2) and tree 
mortality (Sections 2.4.4.2, 2.4.4.3) attributed to anthropogenic climate 
change. Research since the AR5 has also found vegetation changes 
from wildfire due to climate change. Through increased temperature 
and aridity, anthropogenic climate change has driven post-fire changes

in plant regeneration and species composition in South Africa (Slingsby 
et al., 2017), and tree regeneration in the western USA (Davis et al., 
2019b). In the fynbos vegetation of the Cape Floristic Region, South 
Africa, post-fire heat and drought and the legacy effects of exotic plant 
species reduced the regeneration of native plant species, decreasing 
species richness by 12% from 1966 to 2010 and shifting the average 
temperature tolerance of species communities upward by 0.5°C 
(Slingsby et  al., 2017). In burned areas across the western USA, the 
increasing heat and aridity of anthropogenic climate change from 1979 
to 2015 pushed low-elevation ponderosa pine (Pinus ponderosa) and Douglas fir (Pseudotsuga menziesii) forests across critical thresholds of 
heat and aridity that reduced the post-fire tree regeneration by half 
(Davis et al., 2019b). In the southwestern USA, where anthropogenic 
climate change has caused drought (Williams et al., 2019) and increased

wildfire (Abatzoglou and Williams, 2016), high-severity fires have 
converted some forest patches to shrublands (Barton and Poulos, 2018). 
Field evidence shows that anthropogenic climate change and wildfire, 
together, altered vegetation species composition in the southwestern 
USA and Cape floristic region, South Africa, reducing post-fire natural 
regeneration and species richness of tree and other plant species, 
between 1966 and 2015, at GMST increases of 0.3°C–0.9°C (medium 
2.4.4.3 Observed Changes in Tree Mortality
2.4.4.3.1 Observed tree mortality globally
Anthropogenic climate change can cause tree mortality directly via 
increased aridity or drought (Section 2.4.4.3.3) or indirectly through 
wildfire (Section  2.4.4.2.1) and insect pests (Section  2.4.4.3.3). 
Catastrophic failure of the plant hydraulic system, in which a lack 
of water causes the xylem to lose hydraulic conductance, is the

principal mechanism of drought-induced tree death (Anderegg et al., 
2016; Adams et al., 2017; Anderegg et al., 2018; Choat et al., 2018; 
Menezes-Silva et al., 2019; Brodribb et al., 2020).
Up through the AR5 (Settele et al., 2014), detection and attribution 
analyses had found that anthropogenic climate change, with global 
temperature increases of 0.3°C–0.9°C above the pre-industrial period 
and the increases in aridity exceeding the effects of local non-climate 
change factors, caused three cases of drought-induced tree mortality 
of up to 20% in the period 1945–2007 in western North America (van 
Mantgem et al., 2009), the African Sahel (Gonzalez et al., 2012) and 
North Africa (le Polain de Waroux and Lambin, 2012). Increased wildfire 
and pest infestations, driven by climate change, also contributed to 
North American tree mortality (van Mantgem et al., 2009). In addition, 
a meta-analysis of published cases found that drought consistent with,

but not formally attributed to, climate change had caused tree mortality 
at 88 sites in boreal, temperate and tropical ecosystems (Allen et al., 
2010), with 49 additional cases found by the AR5 (Settele et al., 2014).
Since the AR5 (Settele et al., 2014), global meta-analyses found at least 
15 (Allen et al., 2015) and 25 (Hartmann et al., 2018) additional sites, 
respectively, of drought-induced tree mortality around the world. These 
and other global analyses found more rapid mortality than previously 
(Allen et  al., 2015), rising background mortality (Allen et  al., 2015), 
mortality increasing with drought severity (Greenwood et  al., 2017), 
mortality of tropical trees increasing with temperature (Locosselli et al., 
2020), mortality increasing with tree size for many species (Bennett 
et al., 2015), mortality predominantly at the dry edge of species ranges 
(Anderegg et al., 2019) and three-quarters of drought-induced mortality

cases leading to a change in the dominant species (Batllori et al., 2020). 
Multiple non-climate factors contribute to tree mortality, including 
timber cutting, livestock grazing and air pollution (Martinez-Vilalta 
and Lloret, 2016). Globally, tropical dry forests lost, from all causes, 
95,000 km2, 8% of their total area, from 1982 to 2016, the most 
extensive area of mortality of any biome (Song et al., 2018).

2
247Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
FAQ 2.3 | Is climate change increasing wildfire?
In the Amazon, Australia, North America, Siberia and other regions, wildfires are burning wider areas than in the past. Analyses show that 
human-caused climate change has driven the increases in burned area in the forests of western North America. Elsewhere, deforestation, fire 
suppression, agricultural burning and short-term cycles like El Niño can exert a stronger influence than climate change. Many forests and 
grasslands naturally require fire for ecosystem health but excessive wildfire can kill people, destroy homes and damage ecosystems.
1985 2000 2015 0246810
wildfires
with climate changemillion hectares
wildfireswithout climate change
(a) (b)Climate change and wildfires
Figure FAQ2.3.1 | (a) Springs Fire, May 2, 2013, Thousand Oaks, California, USA (photo by Michael Robinson Chávez, Los Angeles Times). (b) Cumulative area

burned by wildfire in the western USA, with (orange) and without (yellow) the increased heat and aridity of climate change.
Wildfire is a natural and essential part of many forest, woodland and grassland ecosystems, killing pests, releasing 
plant seeds to sprout, thinning out small trees and serving other functions essential for ecosystem health. Excessive 
wildfire, however, can kill people with the smoke causing breathing illnesses, destroy homes (Figure FAQ2.3.1a) and 
damage ecosystems.
Human-caused climate change increases wildfire by intensifying its principal driving factor, heat. The heat of 
climate change dries out vegetation and accelerates burning. Non-climate factors also cause wildfires. Agricultural 
companies, small-scale farmers and livestock herders in many tropical areas cut down forests and intentionally 
set fires to clear fields and pastures. Cities, towns and roads increase the number of fires that people ignite.

Governments in many countries suppress fires, even natural ones, producing unnatural accumulations of fuel in the 
form of coarse woody debris and dense stands of small trees. The fuel accumulations cause particularly severe fires 
that burn upwards into tree crowns.
Evidence shows that human-caused climate change has driven increases in the area burned by wildfire in the forests 
of western North America. Across this region, the higher temperatures of human-caused climate change doubled 
burned area from 1984 to 2015, compared with what would have burned without climate change (Figure FAQ2.3.1b). 
The additional area burned, 4.9 million hectares, is greater than the land area of Switzerland. Human-caused climate 
change drove a drought from 2000 to 2020 that has been the most severe since the 1500s, severely increasing the 
aridity of vegetation. In British Columbia, Canada, the higher maximum temperatures of human-caused climate

change increased burned area in 2017 to its widest extent in the 1950–2017 record, seven to eleven times the area 
that would have burned without climate change. Moreover, in national parks and other protected areas of Canada 
and the USA, most of the area burned from 1984 to 2014 can be attributed to climate factors (temperature, rainfall 
and aridity) and these outweigh local human factors (population density, roads and urban area).
In other regions, wildfires are also burning wider areas and occurring more often. This is consistent with climate 
change, but analyses have not yet shown if climate change is more important than other factors. In the Amazon, 
deforestation by companies, farmers and herders who cut down and intentionally burn rainforests to expand 
agricultural fields and pastures causes wildfires even in relatively moister years. Drought exacerbates these fires. In

Australia, much of the southeastern part of the continent has experienced extreme wildfire years, but analyses 
suggest that El Niño, a heat phenomenon that cycles up and down periodically, is more important than long-term

2
248Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
climate change. In Indonesia, intentional burning of rainforests for oil palm plantations and El Niño seem to be 
more important than long-term climate change. In Mediterranean Europe, fire suppression seems to have prevented 
any increasing trend in burned area but the suppression and abandonment of agricultural lands have allowed fuel 
to build up in some areas and contribute to major fires in years of extreme heat. In Canada and Siberia, wildfires 
are now burning more often in permafrost areas where fire was rare, but analyses are lacking regarding the relative 
influence of climate change. For the world as a whole, satellite data indicate that the vast amount of land converted 
from forest to farmland in the period 1998–2015 actually decreased the total burned area. Nevertheless, the 
evidence from the forests of western North America shows that human-caused climate change has, at least on one

continent, clearly driven increases in wildfire.Box FAQ 2.3 (continued)
In summary, anthropogenic climate change caused drought-induced 
tree mortality of up to 20% in the period 1945–2007 in western North 
America, the African Sahel and North Africa, via global temperature 
increases of 0.3°C–0.9°C above the pre-industrial period and increases in 
aridity, and it contributed to over 100 other cases of drought-induced tree 
mortality in Africa, Asia, Australia, Europe and North and South America 
(high confidence). Field observations document accelerating mortality 
rates, rising background mortality and post-mortality vegetation shifts 
(high confidence). Water stress, leading to plant hydraulic failure, is the 
principal mechanism of drought-induced tree mortality. Timber cutting, 
agricultural expansion, air pollution and other non-climate factors also 
contribute to tree death.
2.4.4.3.2 Observed tree mortality in tropical ecosystems

In the Brazilian Amazon, deforestation to clear agricultural land 
comprises the principal cause of tree mortality, reducing forest cover 
by an average of 13,900 km2 yr-1 from 1988 to 2020 (Assis et  al., 
2019). In addition, in a set of 310 Amazon field plots, an annual 
average temperature increase of 1.2°C from 1950 to 2018 (Marengo 
et  al., 2018) contributed to tree mortality of ~40% from 1983 to 
2011 (Brienen et al., 2015). In another set of plots, mortality among 
newly recruited trees of mesic genera increased and drought-tolerant 
genera became more abundant from 1985 to 2015 (Esquivel-Muelbert 
et al., 2019). In other plots, tree mortality did not show a statistically 
significant change from 1965 to 2016, but rose abruptly in severe 
drought years, mainly during warm phases of the ENSO (Aleixo et al., 
2019). Nearly half the area of the Amazon has experienced extremely 
dry conditions during ENSO warm phases; this can cause extensive

wildfire (Section 2.4.4.2.3). Wildfires can increase tree mortality rates 
by >600% above rates in non-burned areas, with the higher mortality 
persisting for up to a decade after a fire (Silva et al., 2018; Berenguer 
et al., 2021). Climate change has contributed to tree mortality in the 
Amazon rainforest (medium evidence, medium agreement).
In the African Sahel, field research has continued to detect tree 
mortality, ranging from 20 to 90% in the period 1965–2018 
(Kusserow, 2017; Trichon et al., 2018; Dendoncker et al., 2020), and 
declines in tree biodiversity, with up to 80% local losses of tree 
species in the period 1970–2014 (Hanke et  al., 2016; Kusserow, 
2017; Ibrahim et al., 2018; Dendoncker et al., 2020), consistent with, 
but not formally attributed to, climate change. In Algeria, mortality 
of the Atlas cedar (Cedrus atlantica) increased from 1980 to 2006, coinciding with a ~1°C spring temperature increase, but non-climate

factors were not examined (Navarro-Cerrillo et  al., 2019). Across 
southern Africa, nine of the 13 oldest known (1100–2500 years old) 
baobab trees (Adansonia digitata) have died since 2005, although 
the causes are unknown (Patrut et al., 2018). In South Africa, savanna 
trees experienced an order of magnitude increase in mortality, related, 
but not formally attributed to, decreased rainfall (Case et al., 2019). 
In Tunisia, insect infestations related, but not formally attributed to, 
hotter temperatures led to mortality of cork oaks (Quercus suber) 
(Bellahirech et al., 2019).
2.4.4.3.3 Observed tree mortality in boreal and temperate 
The most extensive research into tree mortality since the AR5 has been 
in the western USA, where anthropogenic climate change accounted 
for half the magnitude of a drought in the period 2000–2020 that has 
been the most severe since the 1500s, (Williams et al., 2020) and for

one-tenth to one-quarter of the magnitude of the 2012–2014 period of 
the severe drought in California that lasted from 2012 to 2016 (Williams 
et al., 2015a). Across the western USA, anthropogenic climate change 
doubled tree mortality between 1955 and 2007 (van Mantgem et al., 
2009). Lodgepole pine (Pinus contorta) mortality increased 700% 
from 2000 to 2013 (Anderegg et al., 2015) and piñon pine (P . edulis) 
experienced >50% mortality from 2002 to 2014 (Redmond et  al., 
2018). In montane conifer forest in California, anthropogenic climate 
change has increased tree mortality by one-quarter (Goulden and Bales, 
2019). One-quarter of the trees died in some areas, with mortality 
rates of ponderosa pine (P . ponderosa) and sugar pine (P . lambertiana) 
increasing to up to 700% of pre-drought rates (Stephenson et  al., 
2019; Stovall et  al., 2019). Substantial field evidence shows that 
anthropogenic climate change has caused extensive tree mortality in

North America (robust evidence, high agreement).
In western North America, increased infestations of bark beetles and 
other tree-feeding insects that benefit from higher winter temperatures 
(section 3.3.1.1 in (IPCC, 2021a)) and longer growing seasons 
(section 2.3.4.3.1 in (IPCC, 2021a)) have killed drought-stressed trees 
(Section 2.4.2.1) (Anderegg et al., 2015; Kolb et al., 2016; Lloret and 
Kitzberger, 2018; Redmond et al., 2018; Stephens et al., 2018; Fettig 
et al., 2019; Restaino et al., 2019; Stephenson et al., 2019). Increasing 
temperatures have allowed bark beetles to move further north and 
to higher elevations, survive through the winter at sites where they

2
249Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
would previously have died and reproduce more often (Raffa et al., 
2008; Bentz et al., 2010; Jewett et al., 2011; Macfarlane et al., 2013; 
Raffa et al., 2013; Hart et al., 2017; Stephenson et al., 2019; Teshome 
et  al., 2020; Koontz et  al., 2021). Under warmer conditions, some 
insects that were previously innocuous have become important agents 
of tree mortality (Stephenson et al., 2019; Trugman et al., 2021). Field 
observations show mixed effects of bark beetle-induced tree mortality 
on subsequent fire-caused tree mortality (Andrus et al., 2016; Meigs 
et  al., 2016; Candau et  al., 2018; Lucash et  al., 2018; Talucci and 
Krawchuk, 2019; Wayman and Safford, 2021). From 1997 to 2018, ~5% 
of the forest area in the western USA died from bark beetle infestations 
(Hicke et al., 2020). Under most circumstances, trees that have been 
weakened by drought are more vulnerable to being killed by bark

beetles (Anderegg et al., 2015; Kolb et al., 2016; Lloret and Kitzberger, 
2018; Redmond et al., 2018; Stephens et al., 2018; Fettig et al., 2019; 
Restaino et al., 2019; Stephenson et al., 2019; Koontz et al., 2021). In 
summary, climate change has contributed to bark beetle infestations 
that have caused much of the tree mortality in North America (robust 
evidence, high agreement) (see also Section 2.4.2.1).
Across Europe, rates of tree mortality in field inventories from 2000 to 
2012 were highest in Spain, Bulgaria, Sweden and Finland, positively 
correlated to maximum winter temperature and inversely correlated to 
spring precipitation (Neumann et al., 2017). Tree mortality in Austria, the 
Czech Republic, Germany, Poland, Slovakia and Switzerland doubled 
from 1984 to 2016, correlated with intensified logging and increased 
temperatures (Senf et al., 2018). Drought-related tree mortality rates 
from 1987 to 2016 were highest in the Ukraine, Moldova, southern

France and Spain (Senf et  al., 2020). Climate contributed to tree 
mortality across Europe from 1958 to 2001 (Seidl et  al., 2011). In 
addition, insect infestations related to higher temperatures (Okland 
et  al., 2019) have caused the extensive mortality of Norway spruce 
(Picea abies) across nine European countries (Marini et  al., 2017; 
Mezei et al., 2017). Across the Mediterranean Basin, a combination of 
drought, wildfire, pest infestations and livestock grazing (Peñuelas and 
Sardans, 2021) has driven tree mortality. In summary, climate change 
has contributed to tree mortality in Europe (high confidence) (see also 
Section 2.4.2.1).
2.4.4.3.4 Tree mortality and fauna
A global meta-analysis of 59  studies encompassing 631  cases of 
animal abundance changes in areas of tree mortality over the past 
7–59 years, primarly in North America and Australia, with a few sites 
in other regions (e.g. Europe). Overall, in areas with documented high

tree mortality, bird abundances increased (n=186 bird species), there 
was no significant trend for mammals (n=33 species), a slight trend 
towards declines in invertebrates (n=28 species), and insufficient 
information to categorize the responses of reptiles (n=20 species). 
However, within groups, significant differences appeared. Mammals 
that use trees as refugia showed declines with tree mortality (high 
confidence), but flying mammals (e.g. bats) increased (medium 
confidence). Ground-nesting, ground-foraging, tree-hole nesting 
and bark-foraging birds increased most, but nectar-feeding and 
foliage-gleaning birds declined (high confidence). Within invertebrates, 
declines were strongest in ground-foraging predators and detritivores 
(medium confidence) (Fleming et al., 2021).2.4.4.4 Observed Terrestrial Ecosystem Carbon
2.4.4.4.1 Observed terrestrial ecosystem carbon globally
Terrestrial ecosystems contain carbon stocks: 450 GtC (range 380–540

GtC) in vegetation, 1700 ± 250 GtC in soils that are not permanently 
frozen and 1400 ± 200 GtC in permafrost (Hugelius et al., 2014; Batjes, 
2016; Jackson et al., 2017; Strauss et al., 2017; Erb et al., 2018a; Xu 
et  al., 2021a). Ecosystem carbon stocks, totalling 3000–4000 GtC 
(from the lowest and highest estimates above), substantially exceed 
the ~900 GtC carbon in unextracted fossil fuels (see(Canadell et al., 
2021)).
Deforestation, draining of peatlands and the expansion of agricultural 
fields, livestock pastures and human settlements and other LULCCs 
emitted carbon at a rate of 1.6 ± 0.7 Gt yr-1 from 2010 to 2019, 
(Friedlingstein et  al., 2020), of which wildfires and peat burning 
emitted 0.4 ± 0.2 Gt yr-1 from 1997 to 2016 (van der Werf et al., 2017). 
Anthropogenic climate change has caused some of these emissions 
through increases in wildfire (Section  2.4.4.2.1) and tree mortality 
(Section 2.4.4.3.1), but the fraction of the total remains unquantified.

LUC produced ~15% of global anthropogenic emissions, from fossil 
fuels and land (Friedlingstein et  al., 2020). Terrestrial ecosystems 
removed carbon from the atmosphere through plant growth at a rate 
of -3.4 ± 0.9 Gt yr-1 from 2010 to 2019 (Friedlingstein et al., 2020).
Tropical deforestation and the draining and burning of peatlands 
produce almost all of the carbon emissions from LUC (Houghton and 
Nassikas, 2017; Friedlingstein et al., 2020), while forest growth accounts 
for two-thirds of ecosystem carbon removals from the atmosphere 
(Pugh et al., 2019b). Global terrestrial ecosystems comprised a net sink 
of -1.9 ± 1.1 Gt yr-1 from 2010 to 2019 (Friedlingstein et al., 2020), 
mainly due to growth in forests (Harris et al., 2021; Xu et al., 2021a), 
mitigating ~31% of global emissions from the burning of fossil fuels 
and LUC (Friedlingstein et al., 2020).
In summary, terrestrial ecosystems contain 3000–4000 GtC in

vegetation, permafrost and soils, three to five times the amount of 
carbon in unextracted fossil fuels and 4.4 times the carbon currently 
in the atmosphere (robust evidence, high agreement). Tropical 
deforestation, the draining and burning of peatlands and other LULCCs 
emit 0.9–2.3 GtC yr-1, ~15% of the global emissions from fossil 
fuels and ecosystems (robust evidence, high agreement). Terrestrial 
ecosystems currently remove more carbon from the atmosphere 
(-3.4±0.9 Gt yr-1) than they emit (+1.6±0.7 Gt yr-1), a net sink of 
-1.9±1.1 Gt yr-1 (Friedlingstein et al., 2020) . Thus, tropical rainforests, 
Arctic permafrost and other ecosystems provide the global ecosystem 
service of naturally preventing carbon from contributing to climate 
change (high confidence).
2.4.4.4.2 Observed stocks in high-carbon terrestrial ecosystems
The ecosystem that attains the highest above-ground carbon density 
in the world is the coast redwood (Sequoia sempervirens) forest in

California, USA, with 2600 ± 100 tonnes ha-1 carbon (Van Pelt et al., 
2016). The ecosystem with the second highest documented carbon 
density in the world is the mountain ash (Eucalyptus regnans) forest

2
250Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
in Victoria, Australia, with ~1900 tonnes ha-1 (Keith et al., 2009). In 
the Tropics, tropical evergreen broadleaf forests (rainforests) in the 
Amazon, the Congo and Indonesia attain the highest carbon densities, 
reaching a maximum of 230 tonnes ha-1 in the Amazon (Mitchard 
et al., 2014) and the Congo (Xu et al., 2017). Temperature increases 
reduce the tropical rainforest above-ground carbon density 9.1 tonnes 
ha-1 per degree Celsius, through reduced growth and increased tree 
mortality (Sullivan et al., 2020).
Tropical forests contain the largest vegetation carbon stocks in the 
world, with 180–250 GtC above and below ground (Saatchi et  al., 
2011; Baccini et al., 2012; Avitabile et al., 2016). The Amazon contains 
a stock of 45–60 GtC (Baccini et  al., 2012; Mitchard et  al., 2014; 
Englund et al., 2017).
Ecosystems with high soil carbon densities include the peat bogs in

Ireland with up to 3000 tonnes ha-1 (Tomlinson, 2005), the Cuvette 
Centrale swamp forest peatlands in Congo with an average of ~2200 
tonnes ha-1 (Dargie et al., 2017), the Arctic tundra with an average of 
~900 tonnes ha-1 (Tarnocai et al., 2009) and the mangrove peatlands 
in Kalimantan, Indonesia, with an average of 850 ± 320 tonnes ha-1 
(Murdiyarso et al., 2015). Arctic permafrost contains 1400 ± 200 GtC 
to a depth of 3 m, the largest soil carbon stock in the world (Hugelius 
et  al., 2014). Globally, peatlands contain 470–620 GtC (Page et  al., 
2011; Hodgkins et al., 2018), of which boreal and temperate peatlands 
contain 415 ± 150 GtC (Hugelius et al., 2020) and tropical peatlands 
contain 80–350 GtC (Page et al., 2011; Dargie et al., 2017; Gumbricht 
et al., 2017; Ribeiro et al., 2021). Other analyses increase the upper 
estimates for boreal and temperate peatlands to 800–1200 GtC 
(Nichols and Peteet, 2019; Mishra et al., 2021b).

Tropical forests and Arctic permafrost contain the highest ecosystem 
carbon stocks in above-ground vegetation and soil, respectively, in 
the world (robust evidence, high agreement). These ecosystems form 
natural sinks that prevent the emission to the atmosphere of 1400–
1800 GtC that would otherwise increase the magnitude of climate 
2.4.4.4.3 Biodiversity and observed terrestrial ecosystem carbon
High biodiversity and ecosystem carbon generally occur together, with 
rainforests in the Amazon, Congo and Indonesia containing the largest 
above-ground vegetation carbon stocks (Saatchi et al., 2011; Baccini 
et al., 2012; Avitabile et al., 2016) and the highest vascular plant species 
richness (Kreft and Jetz, 2007) in the world. Above-ground ecosystem 
carbon and animal species richness show high correlation but also 
high spatial variability (Strassburg et al., 2010). Above-ground carbon

is correlated to genus richness globally (Cavanaugh et al., 2014), but to 
species richness only in local areas (Poorter et al., 2015; Sullivan et al., 
2017). Species richness generally increases vegetation productivity 
in the humid tropics while tree abundance increases productivity in 
drier conditions (Madrigal-Gonzalez et al., 2020). Across the Amazon, 
~1% of tree species contain 50% of the above-ground carbon, due to 
abundance and maximum height (Fauset et al., 2015). Above-ground 
carbon in tropical forests shows positive correlations to vertebrate 
species richness (P values not reported) (Deere et al., 2018; Di Marco 
et  al., 2018). In logged and burned tropical forest in Brazil, species richness of plants, birds and beetles increased with carbon density up 
to ~100 tonnes ha-1 (Ferreira et al., 2018).
National parks and other protected areas which, in June 2021, covered 
15.7% of global terrestrial area (UNEP-WCMC et  al., 2021) contain

~90 GtC in vegetation and ~150 GtC in soil (one-fifth and one-tenth, 
respectively, of global stocks) and remove carbon from the atmosphere 
at a rate of ~0.5 Gt yr-1 (one-sixth of global removals) (Melillo et al., 
2016). The most strictly protected areas contain carbon at higher 
densities, but illegal deforestation and fires in some protected areas 
emit 38 ± 17 Mt yr-1 globally (Collins and Mitchard, 2017). In the 
Amazon, protected areas store more than half of the above-ground 
vegetation carbon stocks of the region, but account for only one-tenth 
of net emissions (Walker et al., 2020). Conservation of high biodiversity 
areas, particularly in protected areas, protects ecosystem carbon, 
prevents emissions to the atmosphere and reduces the magnitude of 
climate change (high confidence).
2.4.4.4.4 Observed emissions and removals from high-carbon 
terrestrial ecosystems
Most global deforestation is occurring in tropical forests (Pan et al.,

2011; Liu et al., 2015; Houghton and Nassikas, 2017; Erb et al., 2018a; 
Li et al., 2018; Harris et al., 2021), primarily as a result of clearing for 
agricultural land (Hong et al., 2021), causing primary tropical forest 
to comprise a net source of carbon from 2001 to 2019: emissions to 
the atmosphere 0.6 GtC yr-1, removals from the atmosphere -0.5 GtC 
yr-1 and net 0.1 GtC yr-1 (Harris et al., 2021). While wildfires emitted 
an average of 0.4 ± 0.2 GtC yr-1 from 1997 to 2016 (van der Werf 
et al., 2017), individual fire seasons can emit the same magnitude, such 
as the 0.4 GtC from the Amazon fires of 2007 (Aragao et al., 2018), 
the 0.5 GtC from the Amazon fires of 2015–2016 (Berenguer et al., 
2021) and the 0.2 Gt from the Australia fires of 2019–2020 (Shiraishi 
and Hirata, 2021). Wildfires thus account for up to one-third of annual 
average ecosystem carbon emissions, while major fire seasons can 
emit up to two-thirds of global ecosystem carbon (medium evidence,

medium agreement).
Primary boreal and temperate forests also comprised net sources in 
the period 2001–2019; however, when including all tree age classes, 
boreal, temperate and tropical forests were net sinks (boreal -1.6 ± 
1.1 Gt yr-1, temperate -3.6 ± 48 Gt yr-1), as growth exceeded permanent 
forest cover losses (Harris et  al., 2021), with boreal and temperate 
forests being much stronger sinks (Pan et al., 2011; Liu et al., 2015; 
Houghton and Nassikas, 2017). Estimates of carbon removals from 
remote sensing may provide more accurate estimates of boreal forest 
carbon balances than ESMs which overestimate regrowth after timber 
harvesting and other disturbance (Wang et  al., 2021a). Mortality 
of the boreal forest in British Columbia from mountain pine beetle 
infestations converted 374,000 km2 from a net carbon sink to a net 
carbon source (Kurz et al., 2008). Modelling suggests that a potential 
increase in water-use efficiency and regrowth could offset the losses in

part of the forest mortality area (Giles-Hansen et al., 2021).
The Amazon as a whole was a net carbon emitter in the period 2003–
2008 (Exbrayat and Williams, 2015; Yang et al., 2018b), primarily due 
to the expansion of agricultural and livestock areas, which caused

2
251Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
over two-thirds of deforestation from 1990 to 2005 (De Sy et  al., 
2015; De Sy et al., 2019). Four sites in the Amazon also showed net 
carbon emissions in the period 2010–2018, from deforestation and 
fire (Gatti et  al., 2021). In the Amazon, deforestation emitted 0.17 
± 0.05 GtC yr-1 from 2001 to 2015 (Silva Junior et  al., 2020) while 
fires emitted 0.12 ± 0.14 GtC yr-1 from 2003 to 2015 (Aragao et al., 
2018). An analysis of the Amazon carbon loss from deforestation and 
degradation estimated a loss of 0.5 Gt yr-1 in the period 2010 -2019, 
with degradation accounting for three-quarters (Qin et  al., 2021). 
Intact old-growth Amazon rainforest has been a net carbon sink from 
2000 to 2010 (-0.45 Gt yr-1, min. 0.31, max. 0.57) (Hubau et al., 2020) 
but may have become a net carbon source in 2010–2019 (0.67 Gt, for 
the entire period, uncertainty not reported) (Qin et al., 2021). These

factors combined—recent impacts of climate change on undisturbed 
forest, coupled with deforestation and agricultural expansion, along 
with associated intentional burning—have caused Amazon rainforest 
to become an overall net carbon emitter (medium confidence).
In Indonesia and Malaysia, draining and burning of peat swamp 
forests for oil palm plantations emitted 60–260 MtC yr-1 from 1990 
to 2015, converting peatlands in that period from a carbon sink to a 
carbon source (Miettinen et al., 2017; Wijedasa et al., 2018; Cooper 
et  al., 2020). Deforestation of mangrove forests caused 10–30% of 
deforestation emissions in Indonesia from 1980 to 2005 (Donato et al., 
2011; Murdiyarso et  al., 2015), even though mangroves comprised 
only 3% of Indonesia primary forest area in 2000 (Margono et  al., 
2014; Murdiyarso et al., 2015).
In North America, wildfire emitted 0.1 ± 0.02 GtC yr-1 from in the 
period 1990–2012, but regrowth was slightly greater, producing a net

sink (Chen et al., 2017). In California, USA, two-thirds of the 70 MtC 
emitted from natural ecosystems in 2001–2010 came from the 6% of 
the area that burned (Gonzalez et al., 2015). Anthropogenic climate 
change caused up to half of the burned area (Section 2.4.4.2.1).
In the Arctic, anthropogenic climate change has thawed permafrost 
(Guo et al., 2020), leading to emissions of 1.7 ± 0.8 GtC yr-1 in winter 
in the period 2003–2017 (Natali et al., 2019). Wildfires in the Arctic 
tundra in Alaska from ~1930 to 2010 caused up to a depth of 0.5 m 
of permafrost thaw (Brown et  al., 2015), exposing peatland carbon 
(Brown et al., 2015; Gibson et al., 2018) including soil carbon deposits 
up to 1600 years old (Walker et al., 2019).
Tropical deforestation, the draining and burning of peatlands and the 
thawing of Arctic permafrost due to climate change have caused these 
ecosystems to emit more carbon to the atmosphere than they naturally

remove through vegetation growth (high confidence).
2.4.4.5 Observed Changes in Primary Productivity
2.4.4.5.1 Observed changes in terrestrial primary productivity
The difference between photosynthesis by plants (gross primary 
productivity, GPP) and plant energy use through respiration is the 
net growth of plants (NPP), which removes CO 2 from the atmosphere 
and mitigates emissions from deforestation and other LUCs 
(Section 2.4.4.4). Global terrestrial NPP has exceeded emissions due to land use since the early 2000s, making terrestrial ecosystems a net 
carbon sink (Friedlingstein et al., 2020).
Global terrestrial NPP increased by 6% from 1982 to 1999 through 
increased temperature and increased solar radiation in the Amazon 
from decreased cloud cover (Nemani et al., 2003), and then decreased 
1% from 2000 to 2009, because of extensive droughts in the Southern 
Hemisphere (Zhao and Running, 2010). From 1999 to 2015, increased

aridity caused extensive declines in the NDVI globally, particularly 
semiarid ecosystems (Huang et  al., 2016), indicating widespread 
decreases in NPP (Yuan et al., 2019).
Global terrestrial GPP increased 2% from 1951 to 2010 and continued 
increasing at least until 2016, with increased atmospheric CO 2 
showing a greater influence than natural factors (Li et  al., 2017; 
Fernandez-Martinez et  al., 2019; Liu et  al., 2019a; Cai and Prentice, 
2020; Melnikova and Sasai, 2020). Global forest area increased 7% 
from 1982 to 2016, mainly from forest plantations and regrowth in 
boreal and temperate forests in Asia and Europe (Song et al., 2018); 
regrowth in secondary forests >20  years old, mainly in boreal, 
temperate and subtropical regions, generated a net removal of 
7.7 Gt yr-1 CO 2 from the atmosphere from 2001 to 2019 (Harris et al., 
2021). Vegetation growth that exceeds the modelled CO 2 fertilisation, 
gaps in field data and incomplete knowledge of plant mortality and

soil carbon responses introduce uncertainties into quantifying the 
magnitude of CO 2 fertilisation (Walker et al., 2021). A combination of 
CO 2 fertilisation of global vegetation and secondary forest regrowth 
has increased global vegetation productivity (medium evidence, 
The relative increase in GPP per unit of increased atmospheric CO 2 
declined from 1982 to 2015, indicating a weakening of any CO 2 
fertilisation effect (Wang et  al., 2020c). Increased growth from CO 2 
fertilisation has begun to shorten the lifespan of trees due to a trade-off 
between growth rate and longevity, based on analyses of tree rings of 
110 species around the world (Brienen et al., 2020). Furthermore, water 
availability controls the magnitude of NPP (Beer et  al., 2010; Jung 
et al., 2017; Yu et al., 2017), including water from precipitation (Beer 
et al., 2010), soil moisture (Stocker et al., 2019), groundwater storage

(Humphrey et al., 2018; Madani et al., 2020a) and atmospheric vapour 
(Novick et al., 2016; Madani et al., 2020b). Drought stress reduced NPP 
across tropical forests from 2000 to 2015 (Zhang et al., 2019b) and 
GPP in the tropics from 1982 to 2016 (Madani et al., 2020b). Drought 
stress has also reduced GPP in some semiarid and arid lands (Huang 
et al., 2016; Liu et al., 2019a). In addition, nitrogen and phosphorus 
constrain CO 2 fertilisation (Terrer et al., 2019), although phosphorus 
limitation of tropical tree growth is species-specific (Alvarez-Clare 
et al., 2013; Thompson et al., 2019). NPP has decreased during some 
time periods and in some regions where drought stress has exerted a 
greater influence than increased atmospheric CO 2 (medium evidence, 
2.4.4.5.2  Observed changes in freshwater ecosystem productivity
Temperature affects primary productivity by moderating phytoplankton 
growth rates, ice cover, thermal stratification and the length of growing

seasons (Rühland et al., 2015; Richardson et al., 2018). Global warming

2
252Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
has reinforced eutrophication, especially cyanobacteria blooms 
(Wagner and Adrian, 2009; Kosten et al., 2012; O’Neil et al., 2012; De 
Senerpont Domis et al., 2013; Adrian et al., 2016; Visser et al., 2016; 
Huisman et al., 2018) (very high confidence). Conversely, warming can 
reduce cyanobacteria in hypertrophic lakes (Richardson et al., 2019). 
Freshwater cyanobacteria may benefit directly from elevated CO 2 
concentrations (Visser et al., 2016; Ji et al., 2017; Huisman et al., 2018; 
Richardson et al., 2019).
Macrophyte growth in freshwaters is likely to increase with rising 
water temperatures, atmospheric CO 2 and precipitation (robust 
evidence, high agreement) (Dhir, 2015; Hossain et  al., 2016; Short 
et al., 2016; Reitsema et al., 2018). Nonetheless, primary productivity 
in rivers is variable and unpredictable (Bernhardt et al., 2018) because

seasonal variations in temperature and light are uncorrelated, frequent 
high-flow events reduce biomass of autotrophs and droughts can 
strand and desiccate autotrophs.
In large, nutrient-poor lakes, warming-induced prolonged thermal 
stratification can reduce primary production (medium confidence) 
(Kraemer et  al., 2017). Warming may reduce phytoplankton 
concentrations when temperature-induced increases in consumption 
of phytoplankton outpace increases in phytoplankton production (De 
Senerpont Domis et al., 2013). These decreases in productivity may be 
under-recognised responses to climate change.
Summary: There is robust evidence of an increase in primary production 
along with warming trends. However, increases or declines of algae 
cannot entirely be attributed to climate change; they are lake-specific 
and modulated through weather conditions, lake morphology, salinity, 
land use and restoration and biotic interactions (medium confidence)

(O’Beirne et al., 2017; Velthuis et al., 2017; Rusak et al., 2018; Ho et al., 
2019).
2.4.5 Conclusions on Observed Impacts
The consistency of patterns of biological change with expectations from 
regional or global warming processes, coupled with an understanding 
of underlying processes and the coherence of these patterns at 
both regional and global scales, all form multiple lines of evidence 
(Parmesan et al., 2013) that it is very likely that the observed range 
shifts and phenological changes in individual species can be attributed 
to regional and global climate changes (very high confidence) 
(Section  2.4.2, Table  2.2; Table  2.3; Table  SM2.1) (Parmesan et  al., 
2013).
Global and regional meta-analyses of diverse systems, habitats and 
taxonomic groupings document that approximately half of all species 
with long-term records have shifted their ranges poleward and/or 
upward in elevation and ~2/3 have advanced their timing of spring

events (phenology) (very high confidence) (Section  2.4.2, Table  2.2) 
(Parmesan and Hanley, 2015; Parmesan, 2019). Changes in abundance 
tend to match predictions from climate warming, with warm-adapted 
species significantly outperforming cold-adapted species in warming 
habitats (Feeley et al., 2020) and the composition of local communities 
becoming more ‘thermophilised’, that is, experiencing an ‘increase in relative abundance of heat-loving or heat-tolerant species’ (high 
confidence) (Section 2.4.2.3) (Cline et al., 2013; Feeley et al., 2020).
New studies since AR5, with more sophisticated analyses designed 
to capture complex responses, indicate that past estimates of the 
proportion of species impacted by recent climate change were 
underestimates due to unspoken assumptions that local or regional 
warming should lead solely to poleward/upward range shifts and 
advancements of spring timing (high confidence) (Duffy et al., 2019).

More complex analyses have documented cases of winter warming 
driving delayed spring timing of northern temperate species due to 
chilling requirements, and increased precipitation driving species’ 
range shifts downslope in elevation, and eastward and westward in 
arid regions (high confidence). Further new studies have shown that 
phenological changes have, in some cases, successfully compensated 
for local climate change and reduced the extent of range shifts (medium 
confidence). The limited number of studies of this type make it difficult 
to estimate the generality of these effects globally (Section  2.4.2.5, 
Table 2.2).
Responses in freshwater species are consistent with responses in 
terrestrial species, including poleward and upward range shifts, earlier 
timing of spring plankton development, earlier spawning by fish and the 
extension of the growing season (high confidence). Observed changes 
in freshwater species are strongly related to anthropogenic climate

change-driven changes in the physical environment (e.g., increased 
water temperature, reduced ice cover, reduced mixing in lakes, loss 
of oxygen and reduced river connectivity) (high confidence). While 
evidence is robust for an increase in primary production in nutrient 
rich lakes along with warming trends (high confidence), increasing or 
declining algal formations are lake-specific and are modulated through 
variability in weather conditions, lake morphology, changes in salinity, 
stoichiometry, land use and restoration measures and food web 
interactions. In boreal coniferous forest, there has been an increase 
in terrestrial-derived DOM transported into rivers and lakes as a 
consequence of climate change (which has induced increases in runoff 
and greening of the Northern Hemisphere) as well as from changes in 
forestry practices. This has caused waters to become brown, resulting in 
an acceleration of upper-water warming and an overall cooling of deep

water (high confidence). Browning may accelerate primary production 
through the input of nutrients associated with DOM in nutrient-poor 
lakes and increases the growth of cyanobacteria, which cope better 
with low light intensity (medium confidence) (Sections 2.4.2.1, 2.4.2.2, 
2.4.2.3, 2.4.2.4).
Field research since the AR5 has detected biome shifts at numerous 
sites, poleward and upslope, that are consistent with increased 
temperatures and altered precipitation patterns driven by climate 
change, and support prior studies that attributed such shifts to 
anthropogenic climate change (high confidence). New studies help 
fill previous geographic and habitat gaps, for example, documenting 
upward shifts in the forest/alpine tundra ecotone in the Andes, Tibet 
and Nepal, and northward shifts in the deciduous/boreal forest 
ecotones in Canada. Globally, woody encroachment into open areas 
(grasslands, arid regions and tundra) is likely being driven by climate

change and increased CO 2, in concert with changes in grazing and fire 
regimes (medium confidence) (Section 2.4.3).

2
253Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Climate change has driven, or is contributing to, increased tree mortality 
directly through increased aridity and droughts and indirectly through 
increased wildfires and insect pests in many locations (high confidence) . 
Analyses of causal factors have attributed increasing tree mortality at 
sites in Africa and North America to anthropogenic climate change, and 
field evidence has detected tree mortality due to drought, wildfires 
and insect pests in temperate and tropical forests around the world 
(high confidence). Water stress, leading to plant hydraulic failure, is a 
principal mechanism of drought-induced tree mortality, along with the 
indirect effects of climate change mediated by community interactions 
(high confidence)  (Section 2.4.4.3).
Terrestrial ecosystems sequester and store globally critical stocks of 
carbon, but these stocks are at risk from deforestation and climate

change (high confidence). Tropical deforestation and the draining and 
burning of peatlands produce almost all of the carbon emissions from 
LULCC. In the Arctic, increased temperatures have thawed permafrost 
at numerous sites, dried some areas and increased fires, causing net 
emissions of carbon from soils (high confidence) (Sections  2.4.4.4, 
2.5.3.4).
Globally, increases in temperature, aridity and drought have increased 
the length of fire seasons and doubled the potentially burnable land 
area (medium confidence). Increases in the area burned have been 
attributed to anthropogenic climate change in North America (high 
confidence). In parts of Africa, Asia, Australia and South America, 
the area burned has also increased, consistent with anthropogenic 
climate change. Deforestation, peat-burning, agricultural expansion 
or abandonment, fire suppression and inter-decadal cycles strongly 
influence fire occurrence. The areas with the greatest increases in the

length of the fire season include the Amazon, western North America, 
western Asia and East Africa (Section 2.4.4.2).
The changes in biodiversity and ecosystem health that we have 
observed, and project will continue, pose a risk of declines in human 
health and well-being (e.g., tourism, recreation, food, livelihoods and 
quality of life) (medium confidence). Clear attribution of these impacts 
is often not possible, but inferences can be made by comparison of 
the observed changes in biodiversity/ecosystem health and the known 
services from these particular ecosystems.
2.5 Projected Impacts and Risk for Species, 
Communities, Biomes, Key Ecosystems 
and Their Services
Under the risk assessment framework that was introduced in AR5 (IPCC, 
2014b), risk means the probability of harmful consequences resulting 
from climate change. It results from the interaction of vulnerability, 
exposure and hazard and can be represented as the probability of

occurrence of hazardous events or trends multiplied by the impacts if 
these events or trends occur (see Chapter 1, this report). The framework 
defines vulnerability as a pre-existing condition, incorporating the 
extent to which species or ecosystems are susceptible to, or unable 
to cope with, the adverse effects of climate change. Vulnerable 
species have limited adaptive capacity, stemming from physiological 
and behavioural constraints, limited dispersal abilities and restricted resource requirements or capacities for distributional and genetic 
changes (Foden et al., 2013; Cizauskas et al., 2017; Foden et al., 2019). 
Traits that render entire ecosystems vulnerable are harder to define, 
but it is clear that vulnerabilities are high in the coldest habitats, in 
those with limited geographic ranges such as low-lying islands and in 
specialised, restricted habitats such as serpentine outcrops in California 
(Anacker and Harrison, 2012) and dry meadows in Fennoscandia and

Tibet (Yang et al., 2018a). Ecosystem vulnerability can depend critically 
on the fates of plants that function as ‘foundation species’, providing 
community biomass above and below the ground, structuring habitat 
for fauna and providing ecosystem services such as erosion control 
(Camac et al., 2021).
2.5.1 Projected Changes at Species and Community 
Levels
2.5.1.1 Assessment of Models and Sources of Uncertainties
Methods for projecting the impacts of climate change on biodiversity 
can be classified into three types: (1) statistical models such as SDMs 
(Elith and Leathwick, 2009); (2) mechanistic or process-based models 
(Chuine and Régnière, 2017) and (3) trait-based models (Pacifici et al., 
2015). It is only recently that models have been developed looking 
at lower levels of warming like 1.5°C (Hoegh-Guldberg et al., 2018; 
Warren et al., 2018).
SDMs or niche-based models assess potential geographic areas of

suitable climate for the species in current conditions and then project 
them into future conditions (Trisurat, 2018; Vieira et al., 2018). There are 
limitations in all models and it is critical that modellers understand the 
assumptions, proper parameterization and limitations of each model 
technique, including differences between climate models, emission 
scenarios or RCPs and baselines (Araujo et al., 2019). Several systems 
automate the development of SDMs, including R-packages (Beaumont 
et al., 2016; Hallgren et al., 2016), and other model types (Foden et al., 
2019) and aid in the use of climate model data (Suggitt et al., 2017), 
including allowing for connectivity constraints (Peterson et al., 2013). 
Buisson et al. (2010) found most variation in model outputs stems from 
differences in design, followed by general circulation models (GCMs).
Mechanistic approaches, also known as process-based models, project

the responses of species to climate changes by explicitly incorporating 
known biological processes, thresholds and interactions (Morin and 
Thuiller, 2009; Maino et  al., 2016). Mechanistic models are able to 
accommodate a broad range of mechanisms of climate change impacts 
and include species-specific characteristics such as dispersal distances, 
longevity, fecundity, genetic evolution and phenotypic plasticity. 
However, sufficient knowledge is available for only a few well-studied 
species. Species’ traits have been used to more broadly estimate 
potential climate change impacts (Foden et al., 2013; Cizauskas et al., 
2017).
Most models are on a large scale (20–50 km), and so cannot capture 
micro-climatic refugia generated by diversities of slope aspect, elevation 
or shade (Suggitt et al., 2015; Suggitt et al., 2018). In analysing records of 
430 climate-threatened and range-declining species in England, (Suggett

2
254Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Table 2.3 |  Assessing uncertainty in detection and attribution of observed changes in terrestrial and freshwater species and ecosystems to climate change. The lines of evidence 
used to support given uncertainty statements, including confidence statements, of the attribution of key conclusions on observed biological changes to climate change and increased 
atmospheric CO2. Icons represent lines of evidence. This is a summary table that is fully detailed in Table SM2.1.
Lines of evidence: 
Paleo data Experiment Long-term observations Fingerprint of climate 
change respons eModels Complex statistical 
analysis
Key statement Region Period Lines of evidence Attribution to change
About half of all species where LULCC has been 
minimal have shifted their ranges, with 80-90% of 
movements being in the direction expected from 
regional warming trends, i.e., poleward and 
upward. Global Varies by study .
Range 20–260 yrs

medium evidence
high agreement
high confidenceWinter chilling-dependent species have delayed 
or not changed timing of spring events despite 
spring warming. Spring warming advancement 
countered by winter warming delay . When these 
species are taken into account, it is estimated that 
92% of species in these studies have responded 
to regional warming trends.Northern Europe, 
USAVaries by study . 
Range 26–46 yrsrobust evidence
very high confidenceAbout two-thirds of all species with long-term
(>20 yrs) records have shifted the timing of spring 
events in directions expected from regional winter 
and spring warming. GlobalVaries by study . 
Range 20–400 yrsDownslope elevational shifts and east/west shifts 
(shown for trees and birds) have been associated 
with regional increases in precipitation where 
precipitation has been shown to be the principal 
driver of a range boundary .USA
~40–60 yrslimited evidence
medium confidence
robust evidence

high confidenceAnthropogenic climate change, acting through 
increased heat and aridity at GMST  increases of 
0.6ºC–0.9ºC, increased the area burned by 
wildfire over natural levels. Burned area increased 
11-fold in one area in one extreme year and 
overall doubled over natural levels over a 32-year 
period.Western North 
America1984–2017
medium confidenceAnthropogenic climate change caused drought-
induced tree mortality from 5% to 20% in three 
regions (via GMST  increases of 0.3ºC–0.9ºC 
above the pre-industrial period and increases in 
aridity). Influence of climate change is greater 
than that of non-climate-change factors. North America, 
AfricaApproximately 
1945–2007
high confidenceAnthropogenic climate change caused latitudinal 
and elevational vegetation biome shifts in at least 
19 locations in boreal, temperate and tropical 
ecosystems between 1700 and 2007 (via local

temperature increases of 0.4ºC–1.6ºC above the 
pre-industrial period). Climate change is more 
important than non-climate-change factors.Global 1700–2007robust evidence
very high confidence

2
255Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Anthropogenic climate change and wildfire 
together altered vegetation species composition in 
at least two regions, reducing the post-fire natural 
regeneration and species richness of trees and 
other plant species, at GMST  increases of 
0.3ºC–0.9ºC.Western North 
America, Africa1966–2015
low agreement
high agreementThe most cold-adapted species are generally 
declining in population abundance and contracting 
their ranges poleward and upward (e.g., species 
that are sea ice-dependent, mountain top-restrict -
ed, or inhabit upper headwaters of rivers or coldest 
lakes).Arctic, Antarctic, 
Himalayas, 
Andes, Alps
high confidenceDiseases in wildlife, livestock, and humans have 
emerged in new areas that they have not

historically been found.Global Past 20–100 yearsExotic species are responding dif ferently than 
native species in both abundance changes and 
phenological changes, but not in a completely 
consistent fashion.North AmericaBeetles and moths shifting poleward and upward 
have brought new pest species into some forests; 
warming winters and longer growing season have 
increased destructive outbreaks of beetles and 
moths in temperate and boreal forests.North America, 
EuropeVaries by stud y high confidencemedium evidence
high confidenceWarming has amplified the trophic state lakes  
were already in. Eutrophic lakes have become 
more productive while oligotrophic lakes have 
tended to become more nutrient-limited.Global Past 20–50 years
medium confidenceWoody encroachment into open (grassland and 
desert) systems has occurred, with climate 
change one of the primary drivers, along with

changes in grazing and other land use.Global Past decades
high confidenceIn boreal, coniferous areas, changes in climate 
and forestry practices have caused an increase in 
the transport of terrestrial-derived DOM into rivers 
and lakes, leading to browning of waters.Boreal Past decades
high confidenceClimate change-induced warming has lead to 
shifts in the thermal regime of lakes.Global Past decades
limited evidence
medium agreement
low confidenceClimate change causes gains and losses of water  
levels of freshwater systems.Global Past decades
medium confidenceGreenhouse-gas (GHG) emissions from 
freshwater ecosystems are equivalent to around 
20% of CO2 emissions from global burning of 
fossil fuels.Global Past decades

2
256Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
In lakes, extremes of wind, air temperature, 
precipitation and loss of ice af fect the thermal 
regime, with repercussions for water temperature, 
transparency , oxygen and nutrient dynamics, all of 
which af fect ecosystem functionality .North America, 
EuropeVaries by study
high agreement 
high confidenceClimate change is causing poleward and upward 
range shifts of freshwater fish.North America, 
EuropePast decadesPast decadesLoss of biodiversity in streams can be directly 
attributed to climate change through increased 
water temperatures and hydrological changes 
such as increased peak discharges, flow alteration 
and droughts.GlobalClimate change-induced warming has lead to 
shifts in thermal regime of rivers and streams. 
Lowland rivers show a stronger thermal response

than high-altitude, cold water streams.North America, 
EuropePast decadesrobust evidence
high confidencemedium evidence
et  al., 2015; Suggitt et  al., 2018) showed that topographic diversity 
reduced population declines most strongly in areas experiencing the 
most local warming and in the species most sensitive to warming. Under 
these circumstances, topographic diversity reduced the risk of population 
extinction by 22% for plants and 9% for insects.
None of the modelling techniques are predictions of the future, 
they are rather projections of possible futures. To date, only a few 
studies have validated model performance against observations, but 
the studies that have been conducted do generally validate models 
using either SDMs or process-based models (Johnston et  al., 2013; 
Fordham et al., 2018). SDMs should be considered as hypotheses of 
what a future world might look like if the climate projections came

to pass. Suggestions have been made on how to start bringing more 
biotic interactions into SDMs (Early and Keith, 2019), but limited 
basic ecological understanding of interactions, along with limits on 
computation and funding, constrains how far and how fast these 
modelling techniques can advance.
2.5.1.2  Risk Assessment and Non-Modelling Approaches
In order to add realism and reliability to risk assessments at the species 
and community levels, non-modelling approaches, based on known 
biological traits or processes as well as on expert opinion (Camac 
et  al., 2021), are used to temper model outputs with ground-based 
validation. Trait-based assessment approaches use species’ biological 
characteristics as predictors of sensitivity, adaptive capacity and 
extinction risk due to climate change. Climate exposure can be 
estimated using GIS-based modelling, statistical programs or expert 
judgment (Chin et al., 2010). These trait-based approaches are widely

applied to predict responses of biodiversity to climate change because 
they do not require modelling expertise or detailed distibutional data 
(Pacifici et al., 2015; Willis et al., 2015). Most of these methods have not been independently validated and do not allow direct comparison 
of vulnerability and risk across taxonomic groups.
Some studies have combined two or three approaches for the 
assessment of climate change risk to biodiversity, in order to capture 
the advantages of each and avoid their limitations. Warren et al. (2013) 
used combinations of SDMs and trait-based approaches to estimate the 
proportions of species losing their climatically suitable ranges under 
the various future scenarios of climate and dispersal rate. Similarly, 
spatial projections of exposure to climate change were combined with 
traits to assess the vulnerability of sub-Saharan amphibians (Garcia 
et al., 2014). Laurance et al. (2012) combined 31 functional groups of

species and 21 potential drivers of environmental change, in order to 
assess both the ecological integrity and threats to protected tropical 
areas on a global scale. Keith et al. (2014) used a combination of three 
approaches (SDMs–trait–mechanistic) to determine how long before 
extinction a species would become eligible for listing as threatened, 
based on the IUCN Red List criteria.
2.5.1.3 Risk of Species’ Extinctions
2.5.1.3.1 Overview
This assessment of current findings is of studies across a range of taxa 
and modelling techniques. Extinction risk estimates whether or not a 
particular species may be at risk of extinction over the coming decades 
if climatic trends continue, and usually does not take into account other 
human-induced stressors (e.g., invasive species or pollution). It is not a 
prediction that a species will definitely become extinct because, even 
when complete loss of a species’ range is projected, the scale of the

model cannot estimate persistence in very small-scale micro-climatic 
refugia (that can be on the order of metres in size) (Suggitt et al., 2015; 
Suggitt et al., 2018). Individuals and populations can survive after the

2
257Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
conditions for successful reproduction are gone, leading to a lagged 
decline, called ‘extinction debt’ (see section 2.4.2.8) (Alexander et al., 
2018). Therefore, range loss is an established criterion for assessing 
endangerment status and risk of extinction. As a species range 
becomes smaller and occupied habitats become more isolated, the 
likelihood of a single stochastic event causing extinction increases. 
It is this combination of projected loss of climatically suitable space 
and additional stressors (especially LULCC of critical habitat) that is 
expected to drive future extinctions.
The IUCN Red List Criteria (IUCN, 2019) classifies a species as 
‘critically endangered’ if it has suffered a range loss of ≥80%, with 
a resulting likelihood of extinction of >50% in the near term (10–
100 yrs, depending upon generation length). A species is classified as

‘endangered’ if it has suffered a range loss of ≥50%, with a resulting 
likelihood of extinction of >20% in the near term (10–100  years). 
In this assessment, a species that is projected to become classified 
as ‘endangered’ is deemed to be at ‘high risk’ of extinction, and 
becoming classified as ‘critically endangered’ is deemed at ‘very high 
risk’ of extinction.
2.5.1.3.2 Projections for freshwater biodiversity
Because risk to freshwater species has been limited in past reports, this 
section provides details of freshwater risk. Lakes, rivers and freshwater 
wetlands cover approximately 7.7–9.1% of global land surface area; 
(Lehner et al., 2008; Fluet-Chouinard et al., 2015; Allen and Pavelsky, 
2018) and hold 9.5% of the Earth’s described animals (Balian et al., 
2008), with climate change indicated as a threat to 50–75% of fish 
(Xenopoulos et al., 2005; Darwall and Freyhof, 2015). Climate change 
is cited as a primary factor in species’ extinction risk due to changes in

water temperatures, stream flow, loss of cold water habitat, increased 
variability of precipitation and increased disease risk from warming 
temperatures (robust evidence, high agreement, high confidence) 
(Knouft and Ficklin, 2017; Pletterbauer et al., 2018; Jaric et al., 2019; 
Reid et al., 2019) adding to the stress of overexploitation and LULCC 
(Craig et al., 2017; IPBES, 2019).
Increased frequency of stream drying events, reducing hydrologic 
connectivity and limiting access of native fishes to spawning habitats 
is projected for RCP8.5 in Colorado, USA (medium evidence, medium 
agreement) (Jaeger et al., 2014). Cold-water habitats and associated 
obligate species are particularly vulnerable, and losses in these 
habitats have been both documented and projected, for example, 
in salmonids (Santiago et  al., 2016; Fullerton et  al., 2017; Merriam 
et al., 2017). River networks are projected to lose connections to cold

tributary refugia, that are important thermal refuges for cold water 
species (robust evidence, high agreement) (Isaak et al., 2016) during 
low flows (Merriam et al., 2017).
Community turnovers are expected in freshwaters as cold-adapted 
species lose and warm-adapted species gain climatically suitable 
habitat (Domisch et al., 2011; Domisch et al., 2013; Shah et al., 2014). 
While a number of warm-adapted species may experience range 
expansions, the majority of species are predicted to lose climatically 
suitable areas by, on average, 38–44%, depending on the emission 
scenario (A2a and B2a) (medium evidence) (Domisch et al., 2013).Molluscs are projected to be the most at-risk group, given their 
limited dispersal capability (Woodward et  al., 2010). Mediterranean 
freshwater fish are especially susceptible to climate change due to 
increasing flood and drought events and the risk of surpassing critical 
temperature thresholds (Santiago et  al., 2016; Jaric et  al., 2019). In

southern Europe, aquatic insects (Ephemeroptera, Plecoptera and 
Trichoptera) are endangered by climate change (Conti et  al., 2014). 
European protected areas are not expected to be sufficient under 
warming to provide habitat for the majority of rare molluscs and fish 
(Markovic et al., 2014). Observed trends agree with model projections 
in direction, but magnitude remains uncertain (medium evidence, 
medium agreement, medium confidence) (see Figure 2.8 for extinction 
risk globally for dragonflies, amphibians and turtles).
Regional threats from climate change have been reported for 40% of 
amphibians in China, (Wu, 2020), 33% of European freshwater fish 
species (Janssen et  al., 2016) and 56–69% of odonates in Australia, 
(Bush et al., 2014b). Assessment of site-specific extirpation for 88 aquatic 
insect taxa projected that climate change-induced hydrological alteration 
would result in a 30–40% loss of taxa in warmer, drier ecoregions and

a 10–20% loss in cooler, wetter ecoregions (medium evidence, medium 
agreement) (Pyne and Poff, 2017). In Africa’s Albertine Rift, 51% (n  = 
551) of fish are expected to be impacted by climate change, with 5.5% 
at a high risk due to their sensitivity and poor adaptative capability 
(medium evidence, high agreement) (Carr et al., 2013).
The GLOBIO-Aquatic model (Janse et  al., 2015 a) links models for 
demography, economy, LUCs, climate change, nutrient emissions, a 
global hydrological model and a global map of water bodies. It projects 
that changes in both water quality (eutrophication) and quantity (flow) 
will generate negative relations in freshwater ecosystems between 
the persistence of species originally present in each community and 
a constellation of stressors, including harmful algal blooms. Under a 
4°C rise by 2050, mean abundance of species is projected to decline 
by 70% in running water and by 80% in standing water (medium

evidence, high agreement, medium confidence) (Janse et al., 2015 a ).
2.5.1.3.3 Global projections of extinction risk
In previous reports, risk assessed from the literature was generally 
based on estimates of overall range contractions with climate change. 
In AR4, extinction risk was carefully quantified: ‘There is medium 
confidence that approximately 20–30% of species assessed so far are 
likely to be at increased risk of extinction if increases in global average 
warming exceed 1.5–2.5°C (relative to mean temperatures from 
1980–1999). As global average temperature increase exceeds about 
3.5°C, model projections suggest significant extinctions (40–70% of 
species assessed) around the globe.’ These estimates approximately 
correspond to 50–80% reductions in range size (depending upon 
study), that this assessment equates with a ‘high’ and ‘very high’ 
extinction risk, respectively (IPCC, 2007). AR5 stated: ‘a large fraction

of terrestrial and freshwater species face increased extinction risk 
under projected climate change during and beyond the 21st century, 
especially as climate change interacts with other pressures (high 
confidence)’ (Field et al., 2014). A series of multi-species and global 
analyses have been published since AR5, using both statistical models 
and trait-based approaches.

2
258Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Projected loss of terrestrial and freshwater biodiversity 
compared to pre-industrial period
+3.0°C
+2.0°C
+1.5°C+4.0°C
>75% 50% 25%Percentage of biodiversity lossFigure  2.6 |   Biodiversity loss for different areas at increasing levels of 
climate change. The higher the percentage of species projected to lose suitable 
climate in a given area, the higher the risk to ecosystem integrity, functioning and 
resilience to climate change. Warming levels are based on global levels (GSAT) above 
pre-industrial temperatures. Colour shading represents proportion of species for which 
the climate is projected to become sufficiently unsuitable that the species becomes 
locally ‘endangered’ and at high risk of local extinction within a given pixel across their 
current distributions at a given GSAT warming level, based on underlying data (Warren

et  al., 2018) (modelled n  = 119,813 species globally, with no dispersal, averaged 
over 21 CMIP5 climate models). Areas shaded in deep orange and red represent a 
significant risk of biodiversity loss (areas where climates become sufficiently unsuitable 
that it renders >50% and >75% of species at high risk of becoming locally extinct, 
respectively). The maps of species richness remaining have been overlaid with a 
landcover layer (2015) from the European Space Agency (ESA) Climate Change 
Initiative.  This landcover layer leaves habitats classified by the ESA as natural as 
transparent. Areas with a landcover identified as agriculture are 5% transparent, such 
that the potential species richness remaining if the land had not been converted for 
agriculture shows as pale shading of the legend colours (very pale yellow to very pale 
red). These paler areas represent biodiversity loss due to habitat destruction, but with

a potential to be restored, with yellow shading having the potential for restoration to 
greater species richness than orange or red shading.
In this chapter, risk to species, with implications for ecosystems, is 
assessed using three different approaches. First is an assessment of 
the geographic distributions of local species’ losses at different levels 
of GSAT warming, termed ‘local biodiversity loss’, measured as the 
proportion of species within a given location becoming classified as 
“endangered” or worse (sensu IUCN), and so at high risk of local 
population losses (local population extinctions) (Figure  2.6). This 
measure provides the best estimates of which sites are at most risk of 
losing substantial numbers of species locally, leading to degradation of 
that ecosystem’s ability to function.
Second is assessment of the proportions of species becoming

endangered globally (not just locally), so at high risk of global extinction of the species, termed ‘global biodiversity loss’ (Figure 2.8b). This metric 
(losing > 50% of suitable climate space across the species’ entire 
range) also serves to estimate a species’ becoming sufficiently rare 
that the species no longer fully contributes to ecosystem functioning, 
a state that often occurs decades before complete extinction (death of 
the last individual). The proportions of species becoming at high risk of 
global extinction is the foundation for the burning embers diagram on 
global biodiversity loss in Table 2.5 and Figure 2.11.
Third is an assessment of risk of the proportions of species becoming at 
very high risk of extinction globally at different levels of GSAT warming, 
measured using the IUCN criteria for ‘critically endangered’, and termed 
‘species’ extinction risk’ (Figure 2.7 and Figure 2.8a). This measure is

2
259Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
closest to assessing the complete loss of a species in the wild and 
can be used to compare to past (palaeo) extinction rates. These three 
approaches provide complementary information of the overall risks 
to individual species, to biodiversity at the community scale, and to 
ecosystem integrity and functioning at different levels of warming.
Risk of local biodiversity loss, estimated as the proportion of species 
in a given area projected to become endangered (sensu IUCN), and 
therefore at high risk of extinction, is projected to affect a greater 
number of regions experiencing increasing warming. About one-third 
of land area risks more than 50% of species becoming “endangered” 
by 4.0° GSAT warming (Figure 2.6). That is, the deep orange and red 
areas in Figure 2.6 are those areas for which >50% of species currently 
inhabiting those ecosystems are projected to lose >50% of their

climatically suitable habitat. Species’ losses are projected to be worst 
in northern South America, southern Africa, most of Australia and at 
northern high latitudes (medium confidence)  (Figure 2.6).
For risk of global biodiversity loss, at 1.58°C global warming (median 
estimate), >10% of species are projected to become “endangered”, and 
so at high risk of extinction (sensu IUCN). At 2.07°C (median) >20% of 
species are projected to become endangered. Ten-twenty percent losses 
represent high and very high risk of biodiversity losses, respectively, 
substantial enough to reduce ecosystem integrity and functioning 
(medium confidence) (Figure  2.8b) (see Section  2.5.4; Figure  2.11; 
Table 2.5, Table SM2.5).
Synthesis of modelled climate-driven extinction risk studies
010%20%30%40%50%60%70%
0 1°C 2°C 3°C 4°C 5°C
GSAT increase above preindustrialProportion of species at 
risk of extinction 
assessed in individual 
studies
0.95 
0.83 
0.50 
0.17 
0.05 Quantiles

Likely Very likelyAR4 WGII
extinction risk
assessment 
Figure 2.7 |  Global assessment of species’ extinction risks under different levels of warming.  Graph shows a synthesis of climate-driven models of individual species 
projected to become at very high risk of extinction globally (i.e, becoming “critically endandered” sensu IUCN by losing >80% of their suitable climate space or through estimates 
of extinction risk from process-based models). The relationship between modelled projections of extinction (expressed as a proportion of species at a risk of extinction assessed 
in individual studies) and GSAT increase above the pre-industrial average. Data (global sample size n = 178 modelled estimates) were taken from a number of sources, including 
digitization of data points in Figure 2 in the synthetic analysis of (Urban, 2015) (note: unweighted for sample size) n = 126; Table 4.1 of AR4 WGII Chapter 2 (Fischlin et al., 2007),

n = 40; (Hannah et al., 2020) n = 6; and (Warren et al., 2018) n = 6. The quantile regression (which is robust to the non-normal distribution of the response variable, and less 
sensitive to data outliers) was chosen as a descriptive statistic to fit quantile estimates for levels relevant to informing likely (between the 0.17 and 0.83 quantiles, shaded in orange) 
and very likely ranges (between the 0.05 and 0.95 quantiles, shaded in green) relating extinction risk to GSAT increase (quantile regression implemented using the Barrodale 
and Roberts algorithm in XLSTAT). The roughly equivalent estimate of this risk as expressed in AR4 (Fischlin et al., 2007) is indicated by the dotted block indicating the medium 
confidence statement ‘Approximately 20–30% of plant and animal species assessed so far (in an unbiased sample) are likely to be at increasingly high risk of extinction as global

mean temperatures exceed a warming of 2–3°C above pre-industrial levels (medium confidence).’ This box is open on the right side because AR4 estimates stipulated temperatures 
at or exceeding the given levels. Thick dark horizontal bars show the median values of percent of species at very high risk of extinction at 1.5°C, 2°C, 3°C, 4°C and 5°C, indicating 
that half of the data points lie above the bar and half below for a given level of global warming.Risk of global biodiversity loss differs among taxonomic groups. The 
percent of species projected at high risk of extinction was 49% for all 
insects, 44% for all plants and 26% for all vertebrates at ~3°C global 
rise in temperature (Figure 2.8b) (Warren et al., 2018). These estimates 
dropped considerably at lower levels of warming, down to 18%, 16% 
and 8% at 2°C; and 6%, 8% and 4% at 1.5°C (Figure 2.8b) (Warren 
et  al., 2018), so not entirely dissimilar to the numbers in AR4 
(Figure 2.7).

‘Species’ extinction risk’, estimated as at very high risk of extinction 
globally, i.e. becoming “critically endangered” (sensu IUCN) is shown 
in Figures 2.7 across 178 studies and in Figure 2.8a split by taxonomic 
group. The percentage of species at very high risk of extinction (median 
estimates and maximum likely range) will be 9% (max. 14%) at 1.5°C, 
10% (max. 18%) at 2°C, 12% (max. 29%) at 3.0°C, 13% (max. 39%) 
at 4°C and 15% (max. 48%) at 5°C (Figure 2.7). Maximum estimates 
of species at very high risk of extinction reach 60% within the 95% 
quartiles, ie the very likely range, for 5°C GSAT warming. Among the 
groups containing the largest numbers of species at a very high risk of 
extinction for mid-levels of projected warming (3.2°C rise in GSAT) are: 
invertebrates (15%), specifically pollinators (12%), amphibians (11%, 
but 24% for salamanders) and flowering plants (10%) (Figure 2.8a). 
All groups fare substantially better at 2°C, with extinction projections

reducing to <3% for all groups, except salamanders at 7% (medium 
confidence) (Figure 2.8a).

2
260Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Percent of species of different groups classified as being under risk of extinction
(a) Percent of species at very high risk of extinction (b) Percent of species at high risk of extinction
10th percentile 4.5°C 3.2°C 2°C 1.5°C
Non-passeriformes*Birds*Passeriformes*Non-passeriformesPasseriformesBirdsCarnivora*Bats*Ungulates*CarnivoraBatsUngulatesMammals*MammalsTurtlesSnakesReptilesFrogsSalamandersAmphibiansChordates*ChordatesDragonflies*Butterflies*DragonfliesAnts, bees, waspsButterfliesPollinatorsMoths*SpidersTrue bugsMothsBeetlesFliesAnnelid wormsInvertebratesPinesGrassesTimber speciesFlowering plantsPlantsFungi
0 70 60 50 40 30 20 10 80 90 100% 0 70 60 50 40 30 20 10 80 90 100%
* Represent potential benefits from adaptation, specifically dispersal at realistic rates.
Figure 2.8 |  Percent of species of different groups classified as being at risk of extinction.

(a) Species groups listed projected to be at a very high risk of extinction, corresponding to the IUCN Red List criteria for a species classified as ‘critically endangered’ (v3.1) by 
losing >80% of its climatically suitable range area.
(b) Species groups listed projected to be at a high risk of extinction, corresponding to the IUCN Red List criteria for a species classified as ‘endangered’ (v3.1) by losing >50% of 
its climatically suitable range area. For (a) and (b), values were calculated from the underlying data in (Warren et al., 2018). Values for each temperature are the mean values across 
21 CMIP5 models. The grey band represents the high end of extinction risk from the 10th percentile of the climate models to show the maximum range of values, while the low end 
(90th percentile, 1.5°C) is not shown as it is too small to appear on the plots. Taxa marked with * represent potential benefits from adaptation, specifically dispersal at realistic rates

(Warren et al., 2018); those with no * have dispersal rates that are essentially not detected in the spatial resolution of the models (20 km). See (Warren et al., 2018) for caveats 
and more details. Sample size for each group is as follows: 1) fungi (16187 species); 2) all plants (72399 species), broken down into sub-groups of plants: flowering plants (52310 
species), timber species (1328 species), grasses (3389 species) and pines (340 species); 3) all invertebrates (33,949 species), broken down into sub-groups of invertebrates: annelid 
worms (155 species), flies (4809 species), beetles (7630 species), moths (6910 species), true bugs (1728 species), spiders (2212 species), all pollinators (1755 species), butterflies 
(1684 species), ants/bees/wasps (5914 species), dragonflies (599 species); 4) Chordates (12642 species), broken down into major groups: 4i) all amphibians (1055 species),

broken down into sub-groups of amphibians: frogs (887 species) and salamanders (163 species); 4ii) reptiles (1850 species), snakes (1741 species) and turtles (94 species); 4iii) all 
mammals (1769 species), broken down into sub-groups of mammals: ungulates (80 species), bats (500 species), carnivores (107 species), 4iv) all birds (7968 species), broken down 
into sub-groups of birds: passeriforme birds (4744 species), and non-passeriforme birds (3224 species).

2
261Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Figure 2.8 also shows the benefits of dispersal in reducing extinction 
risk in birds, mammals, butterflies, moths and dragonflies (depicted 
with an asterix). While dispersal may benefit individual species, it poses 
additional risks to communities and ecosystems that species are moving 
into, as interactions between species are changed or eliminated.
Projected species extinctions at future global warming levels are in 
accord with projections from AR4, assessed on much larger numbers of 
species with much greater geographic coverage and a broader range 
of climate models. (Figure 2.7; Figure 2.8a). Even the lowest estimates 
of species extinction (median of 9% at 1.5°C warming, Figure 2.7) are 
1000 times the natural background rates (De Vos et al., 2015).
Using data from geological timescales, (Song et al., 2021) predicted 
that a warming of 5.2°C above pre-industrial levels would result in a

mass extinction comparable to that of the five mass extinctions over 
the past 540 Myr, on the order of 70–85% of species becoming extinct, 
in the absence of non-climatic stressor. (Mathes et  al., 2021) found 
evidence in the geological record that short-term rapid warming, 
on top of long-term warming trends, increases extinction risk by up 
to 40% over that expected from the long-term trend alone, with a 
biodiversity ‘memory’ of up to 60 Myr, indicating an additonal risk of 
multi-decadal overshoot.
Most of the large-scale studies that have been performed are 
for losses based on climate alone (Figures  2.6, 2.7, 2.8). However, 
climate is rarely the only stressor affecting species survival. Habitat 
loss is currently the largest driver of range loss and extinction risk 
for most species (IUCN, 2019). Communities in different regions are 
becoming more similar to each other as species tolerant of human 
activities prosper and spread, with many rare and endemic species

already having been driven to extinction, primarily by LULCC (Pimm 
et al., 2006). Thus, it will likely be the interaction of climate change 
and habitat conversion (often driven by climate change) that will 
ultimately determine the risk and ability of many species to survive 
over the next century.
2.5.1.4 Changing Risks of Diseases
Multiple studies predict increases in disease incidence or geographic 
and phenological changes of pathogens, vectors and reservoir host 
species due to climate change with or without other non-climatic 
variables (González et al., 2010; Moo-Llanes et al., 2013; Roy-Dufresne 
et al., 2013; Liu-Helmersson et al., 2014; Laporta et al., 2015; Ryan et al., 
2015; Haydock et al., 2016; Hoover and Barker, 2016; Prist et al., 2017; 
Blum and Hotez, 2018; Dumic and Severnini, 2018; Hundessa et al., 
2018; Ryan et al., 2019; Ryan et al., 2021). However, models predicting 
changes in infectious disease risk are complex and sometimes produce

conflicting results and lack consensus (Caminade et al., 2014; Giesen 
et  al., 2020). For example, malaria is projected to increase in some 
regions of Africa, Asia and South America by the end of the 21st 
century if public health interventions are not sufficient, but is also 
forecasted to decrease in some higher-risk areas (Cross-Chapter Box 
Illness in this chapter) (Peterson, 2009; Caminade et al., 2014; Ryan 
et al., 2015; Khormi and Kumar, 2016; Leedale et al., 2016; Murdock 
et al., 2016; Endo and Eltahir, 2020; Mordecai et al., 2020).While malaria risk is predicted to decrease in some lowland tropical 
areas as temperatures become too hot for vector or parasite 
development, other warm-adapted diseases, like dengue and Zika, 
transmitted by A. aegypti, are predicted to increase (Cross-Chapter 
Box Illness in this chapter, chapter 7) (Ryan et al., 2019; Ryan et al., 
2021). In more temperate regions, arboviruses and other VBDs with

wider thermal breadths, such as West Nile fever, Ross River fever and 
Lyme disease, are predicted to increase with climate warming (Ogden 
et al., 2008; Leighton et al., 2012; Shocket et al., 2018; Shocket et al., 
2020; Couper et al., 2021). Drought can exacerbate these effects of 
temperature (Paull et al., 2017).
A global analysis of 7346 wildlife populations and 2021 host–parasite 
combinations found that organisms adapted to cool and mild climates 
are likely to experience increased risks of outbreaks along with climate 
warming, while warm-adapted organisms may experience a lower 
disease risk, providing further support for predictions that climate 
change will increase the transmission of infectious diseases at higher 
latitudes across a taxonomically diverse array of pathogens (robust 
evidence, high agreement) (Cohen et al., 2020). A study examining the 
future risk of arboviruses (chikungunya, dengue, yellow fever and Zika

viruses) spread by A. aegypti and A. albopictus projected increased 
disease risk due to interactions of multiple variables, including 
increased human connectivity, urbanisation and climate change 
(Kraemer et al., 2019), although vector species’ ranges will broaden 
only slightly (Campbell et al., 2015).
In sum, climate change is expected to expand and redistribute the 
burden of vector-borne and other environmentally transmitted 
diseases of wild animals, domesticated animals and humans, by 
shifting many regions toward the thermal optima of VBD transmission 
for multiple parasites, thereby increasing risk of transmission, while 
pushing temperatures above optimal and towards upper thermal limits 
for other vectors and pathogens, thus decreasing their transmission 
(high confidence)(see also chapter 7) (Mordecai et al., 2019; Mordecai 
et al., 2020). These effects are mediated by other human impacts such 
as LUC, mobility, socioeconomic conditions and vector and pathogen

control measures (Parham et al., 2015; Tjaden et al., 2018).
2.5.2 Projected Changes at Level of Biomes and Whole 
2.5.2.1  Global Overview, Assessment of Ecosystem-Level 
Models and Sources of Uncertainties
Shifts in terrestrial biome and changes in ecosystem processes in 
response to climate change are most frequently projected with dynamic 
global vegetation models (DGVMs) or land-surface models that form 
part of ESMs, which use gridded climate variables, atmospheric CO 2 
concentration and information on soil properties as input variables. 
Since AR5, most DGVMs have been upgraded to capture carbon–
nitrogen cycle interactions (e.g., (Le Quéré et  al., 2018), many also 
include a representation of wildfire and fire–vegetation interactions 
(Rabin et  al., 2017) and a small number now also account for land 
management (e.g., wood removal from forests and crop fertilisation 
harvest of irrigation (Arneth et al., 2017). Other forms of disturbance,

2
262Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
such as tree mortality, in response to, for example, episodic weather 
extremes or insect pest outbreaks, are relatively poorly represented 
or not at all, although they demonstrably impact calculated carbon 
cycling (Pugh et al., 2019a). Simulated biome shifts are generally in 
agreement in projecting broad patterns on a global scale but vary 
greatly regarding the simulated trends in historical and future carbon 
uptake or losses, both regionally and globally (Chang et  al., 2017; 
Canadell et al., 2021).
Similar to other models, models to project large-scale changes in 
vegetation and ecosystem processes have to deal with structural 
uncertainty (associated with the choice and the representation of 
processes in models), input-data uncertainty (associated with variability 
in initial conditions and parameter values) and error propagation 
(associated with coupling models) (Rounsevell et  al., 2019). The

IPBES methodological assessment report on scenarios and models 
of biodiversity and ecosystem services provides a comprehensive 
overview over the relevant issues (Ferrier et al., 2016).
In order to assess performance, most models have been individually 
evaluated against a range of observations. Moreover, in the annual 
updates of the global carbon budgets, a model has to meet a small 
set of basic criteria to have its output included (Le Quéré et al., 2018). 
More systematic benchmarking approaches have also been proposed 
that utilise a range of different datasets (Kelley et  al., 2013; Chang 
et al., 2017) to assess multiple simulated processes. These methods, in 
principle, facilitate assigning quality scores to models based on their 
overall performance (Kelley et al., 2013). So far, this scoring does not 
yet allow a clear quality ranking of models, since individual DGVMs 
tend to score well for some variables and badly for others. A recent

comparison of global fire–vegetation model outputs was also able to 
clearly identify outliers when using a formalised benchmarking and 
scoring approach (Hantson et  al., 2020). However, benchmarking does not address sources of uncertainty and it would be advisable 
to perform ‘perturbed-physics’ experiments, in which multiple model 
parameters are varied in parallel more frequently as a means to test 
parameter-value uncertainty (Wramneby et  al., 2008; Booth et  al., 
2012; Lienert and Joos, 2018).
Species diversity impacts ecosystem functioning and hence ecosystem 
services (Hooper et al., 2012; Mokany et al., 2016). So far, however, 
integrated modelling of ecosystem processes and biodiversity across 
multiple trophic levels and food webs is in its infancy (Harfoot et al., 
2014). Whether or not the enhanced integration of state, function 
and functional diversity across multiple trophic levels in models will 
markedly alter projections of how ecosystems respond to climate

change thus remains an open research question.
Beyond dynamic simulation of biome shifts and carbon cycling, which 
are important aspects of climate regulation, DGVMs can also provide 
information on a number of variables closely linked to other ecosystem 
services such as water availability, air quality or food provisioning 
(Krause et al., 2017; Rabin et al., 2020). However, they are not intended 
to provide a comprehensive assessment of ecosystem services. For 
these, other approaches applied but, to date, these are mostly applied 
on regional scales and are only weakly dynamic (Ferrier et al., 2016).
2.5.2.2 Projected Changes Globally at the Biome Level
Climate change and the associated change in atmospheric CO 2 levels 
already exacerbate other human-caused impacts on the structure 
and composition of land and freshwater ecosystems, such as LULCC, 
nitrogen deposition and pollution. The relative importance of these

drivers for ecosystems over the coming decades will likely differ 
between biomes, but climate change and atmospheric CO 2 will be 
pervasive unless there is a rapid lowering of fossil-fuel emissions and 
Projected fraction of global terrestrial area that could experience a biome shift by 2100
5°C60Projected biome change (% of global land)
0
Global mean surface temperature change  relative to 1850–19004°C 3°C 2°C 1°C 0 6°C50
40
30
20
10Bergengren et al. (2011)
Alo and Wang (2008)
Gonzalez et al. (2010)
Scholze et al. (2006)
Sitch et al. (2008)
Li et al. (2018)
Warszawski et al. (2013)
Ostberg et al. (2018)
Eigenbrod et al. (2015)Climate change and
land use changeClimate change only
Figure 2.9 |  Projected fraction of global terrestrial area that could experience a biome shift by 2100. Shifts due to climate change (filled symbols) or a combination

of climate change and LUC (outline symbols), from publications in Supplementary Table SM2.3 (projected vulnerabilities and risks of ecosystems to biome shifts). Filled circles 
(Bergengren et al., 2011), filled squares (Alo and Wang, 2008), filled diamonds (Gonzalez et al., 2010), filled triangle pointing up (Scholze et al., 2006), filled triangle pointing down 
(Sitch et al., 2008), filled triangle on its side (Li et al., 2018), filled cross (Warszawski et al., 2013), outlined circle (Ostberg et al., 2018)and outlined diamond (Eigenbrod et al., 2015).

2
263Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
warming (high confidence) (Pereira et al., 2010; Warren, 2011; Ostberg 
et al., 2013; Davies-Barnard et al., 2015; Pecl et al., 2017; Ostberg et al., 
2018). Global vegetation and ESMs agree on climate change-driven 
shifts of biome boundaries of potentially hundreds of kilometres over 
this century, combined with several substantial alterations that take 
place within biomes (e.g., changes in phenology, canopy structure and 
functional diversity, etc.). Large discrepancies exist between models 
and between scenarios regarding the region and the speed of change 
(Gonzalez et al., 2010; Pereira et al., 2010; Pecl et al., 2017), but robust 
understanding is emerging in that the degree of impact increases 
in high-emission and high-warming scenarios (high confidence) 
(Figure 2.9).
Substantial changes in vegetation structure and ecosystem processes

are already happening (see Section 2.4). Many of these observations 
have already been projected to take place as early as at least IPCC AR3 
(Rosenzweig et al., 2007), and can they now be increasingly tested for 
their robustness with observational evidence. These multiple changes 
in response to warming (and changes in precipitation and increasing 
atmospheric CO 2 levels that go hand-in-hand with warming) are 
further expected for already relatively small additional temperature 
increases. In particular, in cold (boreal and tundra) regions, as well 
as in dry regions (high confidence), alterations of 2–47% of the areal 
extent of terrestrial ecosystems in scenarios of <2°C warming above 
pre-industrial levels have been projected, increasing drastically with 
higher-warming scenarios (Warren, 2011; Wårlind et al., 2014). More 
recent work, applying also probabilistic methods, confirm the risk 
of drastic changes in vegetation cover (e.g., forest to non-forest or

vice versa) at the end of the 21st century even for approximately 2°C 
warming scenarios, especially in tundra, and also in tropical forest and 
savannah regions, with more subtle changes (within a given biome 
type) likely to occur in all regions (Ostberg et al., 2013; Ostberg et al., 
2018). Model studies have found 5–20% of terrestrial ecosystems 
affected by warming of around 2°C–3°C, increasing to above one-third 
at a warming of 4°C–5°C (Ostberg et  al., 2013; Warszawski et  al., 
2013).
In general, vegetation types are projected to be moving into their 
‘neighbouring’ climates, depending on whether temperature or 
precipitation is expected to be the predominant factor and how 
vegetation interacts with the increasing CO 2 levels in the atmosphere 
(Wårlind et  al., 2014; Scheiter et  al., 2015; Schimel et  al., 2015; 
Huntzinger et  al., 2017). For instance, boreal or temperate forest 
vegetation is simulated to migrate polewards, closed tropical (moist)

forest is expected to transition towards dry tropical forest types, 
while climate-driven degradation might expand arid vegetation 
cover (Sections  2.5.2.2–2.5.2.9). However, ‘novel ecosystems’, that 
is, communities with no current or historical equivalent because of 
the novel combinations of abiotic conditions under climate change, 
are expected to be increasingly common in the future (medium 
confidence), although the regions where these novel ecosystems 
might emerge are still disputed (Reu et al., 2014; Radeloff et al., 2015; 
Ordonez et al., 2016). The possibility of these novel ecosystems and 
the communities that live within them are a challenge for current 
modelling of ecosystem shifts, and new approaches to conservation 
will be required that are designed to adapt to rapid changes in species 
composition and the ensuing challenges.2.5.2.3 Risk to Arid Regions
Shifts in arid system structure and functioning that have been

observed to date (Section 2.4.3.3) are projected to continue (medium 
confidence). These include widespread woody plant encroachment, 
notably in savanna systems in Africa, Australia and South America, 
and are attributed to interactions of LULCC, climate change and CO 2 
fertilisation effects (Fensholt et al., 2012; Fang et al., 2017; Stevens 
et  al., 2017). Arid Mongolian steppe grassland did not respond to 
experimentally elevated CO 2 (Song et al., 2019). Woody encroachment 
is projected to continue or not reverse in North American drylands 
(Caracciolo et  al., 2016) and southern African arid ecosystems 
(Moncrieff et al., 2014b). Dryland woody encroachment may increase 
carbon stocks, depending on emissions scenario (Martens et al., 2021), 
but reduce soil water and biodiversity of grassland-dependent species 
diversity (Archer et  al., 2017). Warm season (C4) grass expansion 
into arid shrublands risks sudden ecosystem transformation due

to introduced wildfire (Bradley et  al., 2016), a risk anticipated for 
grass-invaded desert ecosystems of Australia and the southwestern 
USA (Horn and St. Clair, 2017). Novel fire regimes in grassy shrublands 
have enhanced grass cover locally in the southern African Nama-Karoo 
(du Toit et al., 2015).
Range retractions are projected for endemic plants in southern Africa 
(Young et  al., 2016) and dry woodlands in Morocco (Alba-Sánchez 
et al., 2015). Increasing thermal stress is projected to increase woody 
plant mortality in the Sonoran Desert ecosystems (Munson et  al., 
2016) and facilitate perennial grass replacement by xeric shrubs in 
the southwestern USA (Bestelmeyer et  al., 2018). Ecological effects 
may occur rapidly when extreme events compound long-term trends 
(Hoover et  al., 2015), but evolve more slowly as opportunity costs 
accumulate due to warming (Cross-Chapter Paper 3) (Cunningham 
2.5.2.4  Risk to Mediterranean-Type Ecosystems (MTEs)

The regions containing MTEs all show high confidence in projected 
increases in the intensity and frequency of hot extremes and decreases 
in the intensity and frequency of cold extremes, and medium confidence 
in increasing ecological drought due to increased evapotranspiration 
(in all regions) and reduced rainfall (excluding California, USA, 
where model agreement is low) (see WGI Chapter 11). Projections 
also show a robust increase in the intensity and frequency of heavy 
precipitation in the event of ≥2°C warming for MTEs in South Africa, 
the Mediterranean Basin and California, USA, but are less clear for 
Australia and Chile (Seneviratne et al., 2021).
MTEs are characterised by the distinctive seasonal timing of 
precipitation and temperature, and the disruption of this regime is 
likely to be critical for their maintenance. Unfortunately, projections of 
changes in rainfall seasonality have received less attention and are far

more uncertain than many other aspects of climate change (Pascale 
et al., 2016; Breinl et al., 2020), thus limiting our ability to predict the 
ecological consequences of climate change in MTEs. Responses to 
experimental manipulation of rainfall seasonality show the potential 
for shifts in plant functional composition and diversity loss, but results 
vary with soil type (van Blerk et al., 2021).

2
264Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Unfortunately, global- and regional-scale dynamic vegetation models 
show a poor performance for large areas of MTEs, because they do not 
characterise shrub and Crassulacean acid metabolism (CAM)-photosyn-
thetic plant functional types well (Moncrieff et al., 2015). Furthermore, 
the grain of these models is too coarse for quantifying impacts to many 
vegetation formations which are patchy or of limited extent (e.g., small 
stands of trees). There is high confidence that observations of high mor -
tality in trees and other growth forms, reduced reproductive and recruit-
ment success, range shifts, community shifts towards more thermophilic 
species and type conversions are set to continue, due to either direct cli-
mate impacts through drought and other extreme weather events or to 
their interaction with factors like fire and pathogens (Sections 2.4.3.5,

2.4.3.6; 2.4.3.7; 2.4.4.2; 2.4.4.3; 2.5.2.5, 2.5.2.6, 2.5.2.7, 2.5.4).
Fire is a key driver across most MTEs due to summer-dry conditions. 
Climate projections for the MTEs translate into high confidence that 
periods of low fuel moisture will become more severe and prolonged, 
and that episodes of extreme fire weather will become more frequent 
and severe (see (Douville et al., 2021; Seneviratne et al., 2021)). This 
will lead to the birth of novel fire regimes in MTEs, characterised by 
an increase in the probability of greater burned area and extreme 
wildfire events (e.g., megafires), with associated loss of human life 
and property, long-term impacts on ecosystems and acceleration of 
the possible loss of resilience and capacity to recover (Abatzoglou and 
Williams, 2016; González et al., 2018; Boer et al., 2020; Moreira et al., 
2020; Nolan et al., 2020; Duane et al., 2021; Gallagher et al., 2021).
Fire is virtually certain to have additional impacts through compound

events (see Section  11.8 in (Seneviratne et  al., 2021)). Extreme 
post-fire weather is extremely likely to continue to impact diversity 
(Slingsby et  al., 2017), retard vegetation regrowth (Slingsby et  al., 
2020a) and accelerate vegetation shifts (Batllori et  al., 2019). Any 
increases in the intensity and frequency of heavy precipitation are 
highly likely to compromise soil stability in recently burnt areas 
(Morán-Ordóñez et  al., 2020). The impacts of fire often depend on 
interactions with non-climatic factors such as habitat fragmentation 
(Slingsby et  al., 2020b) and management (Steel et  al., 2015) or the 
spread of flammable exotic plantation forestry and invasive species 
(Kraaij et  al., 2018; McWethy et  al., 2018). Managing these factors 
provides opportunities for adaptation and mitigation (Moreira et al., 
2020). (See sections 2.4.4.2 and 2.5.3.2).
Human adaptation and mitigation responses to climate change

may create additional threats to MTEs. MTEs have dry summers by 
definition, posing a challenge for the year-round supply of water 
to growing human populations and agriculture. With recent major 
droughts in all MTEs (Section 2.4.3.6), there is increasing reliance on 
groundwater for the bulk of the water supply (Kaiser and Macleod, 
2018). The majority of groundwater systems have exceeded or are 
rapidly approaching their environmental flow limits (de Graaf et al., 
2019), threatening human populations and ecosystems that depend 
on these systems for their persistence through unfavourable climatic 
conditions (McLaughlin et al., 2017). Similarly, much of the MTEs are 
open shrublands and grasslands and proposed extensive tree-planting 
to sequester atmospheric CO 2 could result in a loss of biodiversity 
and threaten water security (Doblas-Miranda et al., 2017; Bond et al., 
2019).2.5.2.5 Risk to Grasslands and Savannas

Worldwide, woody cover is increasing in savannas (Buitenwerf et al., 
2012; Donohue et  al., 2013; Stevens et  al., 2017), as a result of 
interactions of elevated CO 2 and altered fire and herbivory impacts, 
some of which stems from LULCC (high confidence)(see Section 2.4.3.5; 
Cross-Chapter Paper 3.2) (Venter et al., 2018; Wu et al., 2021). In some 
regions, altered climate may also contribute (Cross-Chapter Paper 3.2). 
Elevated CO 2 benefits plants with C3 photosynthesis (often woody 
plants), more than C4 species (Moncrieff et al., 2014a; Scheiter et al., 
2015; Knorr et  al., 2016a). Increases in woody vegetation in grassy 
ecosystems could provide some carbon increase (medium confidence) 
(Zhou et al., 2017; Mureva et al., 2018), but is expected to decrease 
biodiversity (Smit and Prins, 2015; Abreu et al., 2017; Andersen and 
Steidl, 2019) and water availability (Honda and Durigan, 2016; Stafford 
et  al., 2017) and alter ecosystem services like grazing and wood

provision (high confidence) (Anadón et al., 2014b).
The relative importance of climate, disturbance (e.g., fire/herbivory) 
and plant feedbacks in shaping present and future savanna 
distribution varies between continents (Lehmann et  al., 2014), 
which makes projections of changing the biome extent challenging 
(Moncrieff et al., 2016). It has been shown that simulation studies that 
do not account for CO 2 interactions but only consider climate change 
impacts do not realistically capture the future distribution of savannas 
(high confidence) (Higgins and Scheiter, 2012; Moncrieff et al., 2016; 
Scheiter et al., 2020). Due to the continued strong effect of CO 2 on 
tree and shrub-to-grass ratios in future, models suggest a loss of 
savanna extent and conversion into closed canopy forest/thicket and 
an expansion of savanna-type vegetation into arid grasslands (Wårlind 
et al., 2014; Moncrieff et al., 2016). In arid savannas and their interface

to grasslands, survival of woody vegetation (which may be stimulated 
to grow by increasing CO 2) will depend on their capacity to survive 
potentially more severe and frequent droughts (Sankaran and Staver, 
2019). Across a range of models, for RCP4.5 future climate change 
and CO 2 concentrations, savanna expanse declines by around 50% 
(converting to closed canopy systems) by 2070 in Africa and South 
America, 25% in Asia and with small changes in Australia (Moncrieff 
et al., 2016; Kumar et al., 2021). Future fire-spread is expected to be 
reduced with increased woody dominance (Scheiter et al., 2015; Knorr 
et al., 2016b; Scheiter et al., 2020), feeding back to further increase 
tree-to-grass ratios (high confidence).
Like the tropical forest biome, savannas are at a high risk, given 
the projected climate changes in combination with LULCC (see 
Cross-Chapter Paper 3). About 50% of the Brazilian Cerrado has been

converted to agricultural land and pastures (Lehman and Parr, 2016), 
and African savannas have been proposed to follow a similar tropical 
agricultural revolution pathway to enhance agronomic prosperity 
(Ryan et  al., 2016). In fact, indirect climate change impacts arising 
from mitigation efforts may be particularly perilous to savannas; 
extensive tree-planting to restore ecosystems and remove CO 2 from 
the atmosphere, as pledged, for example, under the African Forest 
Restoration Initiative, could lead to carbon losses and the loss of 
biodiversity as well as damage the water balance if trees are planted 
on what was naturally grassland or savanna (Box 2.2; FAQ 2.6) (Bond 

2
265Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
2.5.2.6  Risk to Tropical Forests
Key factors affecting the future distribution of tropical humid and 
dry forests are amounts and seasonalities of precipitation, increased 
temperatures, prolonged droughts and droughted-moderated fires 
(robust evidence, high agreement) (Bonai et al., 2016; Corlett, 2016; 
Lyra et al., 2017; Anderson et al., 2018; da Silva et al., 2018; Fontes et al., 
2018; O’Connell et  al., 2018; Aguirre-Gutiérrez et  al., 2019; Bartlett 
et  al., 2019; Brando et  al., 2019; Stan and Sanchez-Azofeifa, 2019). 
The probability of severe drought is projected to quadruple in natural 
areas in Brazil with >2°C warming (Barbosa and Lakshmi Kumar, 
2016; Marengo et al., 2020). Most multi-model studies assuming rapid 
economic growth/business-as-usual scenarios (A2, A1B and RCP8.5) 
show an increase in future woody biomass and areas of woody cover

towards the end of the 21st century in temperate regions (Boit et al., 
2016; Nabuurs et al., 2017) and tropical forests in East Africa (Ross 
et al., 2021) but a decrease in the remaining tropical regions (Anadón 
et al., 2014a; Boit et al., 2016; Lyra et al., 2017; Nabuurs et al., 2017; 
Maia et al., 2020). Terrestrial species are predicted to shift to cooler 
temperatures and higher elevations (Pecl et al., 2017). Tropical species 
are more susceptible to climate warming than temperate species 
(Rehm and Feeley, 2016; Sentinella et al., 2020). This susceptibility will 
be exacerbated by road-building increasing the ease of access into 
forests (Brinck et al., 2017; Taubert et al., 2018; Bovendorp et al., 2019; 
Senior et  al., 2019). Furthermore, most tropical cloud forest species 
are unable to invade grasslands and this will increase the risk of 
extinctions in tropical cloud forests (Rehm and Feeley, 2015).
SLR as the result of climate change is likely to influence mangroves

in all regions, with greater impact on North and Central America, 
Asia, Australia and East Africa than on West Africa and South America 
(robust evidence, high agreement) (Alongi, 2015; Ward et al., 2016). 
On a small scale, mangroves are potentially moving landward (Di 
Nitto et al., 2014), while on a large scale they will continue to expand 
poleward (Alongi, 2015).
Most simulations predict a significant geographical shift of transition 
areas between tropical forests and savanna in the tropical and 
subtropical Americas and Himalayas (Anadón et  al., 2014a; Rashid 
et al., 2015). Forest dieback, as postulated for the Amazon region, does 
not occur in the majority of simulations (Malhi et al., 2009; Poulter et al., 
2010; Rammig et al., 2010; Higgins and Scheiter, 2012; Huntingford 
et  al., 2013; Davies-Barnard et  al., 2015; Sakschewski et  al., 2016; 
Wu et al., 2016a). Model projections of future biodiversity in tropical

forests are rare. Arguably, species are most vulnerable to climate 
change effects at higher altitudes or at the dry end of tropical forest 
occurrence (medium evidence, medium agreement) (Krupnick, 2013; 
Nobre et al., 2016; Trisurat, 2018). Tropical lowlands are expected to 
lose plant species as temperatures rise above species’ heat tolerance, 
but could also generate novel communities of heat-tolerant species 
(robust evidence, high agreement) (Colwell et al., 2008; Trisurat et al., 
2009; Trisurat et al., 2011; Krupnick, 2013; Zomer et al., 2014a; Zomer 
et al., 2014b; Sullivan et al., 2020; Pomoim et al., 2021).
Statistical models that correlate data on species abundance with 
information on human pressures, such as LUCs (Srichaichana et  al., 
2019), population density (Leclère et al., 2020) and hunting (Mockrin et  al., 2011), found in tropical and subtropical forests that birds, 
invertebrates, mammals and reptiles show a decline in their probability

of presence with declining forest cover, particularly pronounced in 
forest specialists or species with narrow ranges (Newbold et al., 2014). 
Different soil fauna groups showed different responses in abundance 
and diversity to climate change conditions (Coyle et al., 2017; Facey 
et al., 2017) but these responses can impact decomposition rates and 
biogeochemical cycles (medium evidence, low agreement).
Invasive plant species are predicted to expand upward by 500–
1,500 m in the western Himalayas (Thapa et al., 2018), and by 6–35% 
yr-1 from the current extent in South America (robust evidence, high 
agreement) (Bhattarai and Cronin, 2014). Global assessment (Wang 
et al., 2017) also revealed that ecoregions of high-elevation tropical 
forests and subtropical coniferous forests have a high risk of invasive 
plant expansion in low-CO 2-emission scenarios, with negative impacts 
on ecosystem functioning and local livelihoods (Shrestha et al., 2019).

The impact of unsustainable land use on tropical forests continues 
in all regions (see Cross-Chapter Paper 7). Projected climate changes 
will not only impact biodiversity but also the livelihoods of affected 
people (robust evidence, high agreement). Increased drought drives 
crop failures that cause local communities to expand their agricultural 
area by further clearing native forests (Desbureaux and Damania, 
2018). Climate change is projected to enlarge the area of suitability 
for booming tree crops such as oil palm, acacia, Eucalyptus and 
rubber (Koninck et  al., 2011; Cramb et  al., 2015; Nath, 2016; Hurni 
et al., 2017; Li et al., 2017; Varkkey et al., 2018). An increase of 8% 
in the area of rubber plantations in Yunnan Province, China, between 
2002–2010 to higher altitudes due to decreased environmental limits, 
has potentially increased pressure on the remaining biodiversity both 
within and outside of protected areas (Zomer et  al., 2014a). As a

consequence, the suitable area for mammals is projected to be reduced 
by 47.7% (RCP2.6) and 67.7% (RCP8.5) by 2070, with large variability 
depending on the different species (Cross-Chapter Paper 7) (Brodie, 
2016). To minimize these potential threats, the Yunnan provincial 
government has identified suitable areas for the establishment of 
national parks, including the Asian Elephant National Park since 2006. 
And the government of China developed a national park system in 
2013 across the country.
2.5.2.7 Risks to Boreal and Temperate Forests
As in the Arctic, warming substantially exceeding the global average 
has already been observed in the northern parts of the temperate 
and boreal forest zone (Gauthier et  al., 2015), and is projected to 
continue (see Cross-Chapter Paper 6 and (Lee et  al., 2021)). As a 
consequence, boreal tree species are expected to move northwards (or 
in mountainous regions, upwards) into regions dominated by tundra,

unless constrained by edaphic features, and temperate species are 
projected to grow in regions currently occupied by southern boreal 
forest (high confidence). In both biomes, deciduous trees are simulated 
to grow increasingly in regions currently dominated by conifers 
(Wårlind et al., 2014; Boulanger et al., 2017). These simulation results 
have been supported by observational examples. In eastern Siberia, 
fire disturbance of larch-dominated forest was followed by recovery 
to birch-dominated forest (Stuenzi and Schaepman-Strub, 2020). In

2
266Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Alberta, lodgepole pine (Pinus contorta) lost its dominant status after 
attacks by mountain pine beetles (Dendroctonus ponderosae) caused 
the canopy to switch to non-pine conifers and broadleaf trees (Axelson 
et al., 2018). In contrast to the examples above, some boreal forests 
have proven resilient to disturbances including recent unprecedented 
insect outbreaks (Campbell et al., 2019a; Prendin et al., 2020).
Reforestation, either natural or anthropogenic, leads to summer cooling 
and winter warming of the ground, while forest thinning or removal 
by fire has reverse effects, deepening the upper layer that is free of 
permafrost (Stuenzi et  al., 2021a). Interactions between permafrost 
and vegetation are important. For example, trees in the east Siberian 
taiga obtained water mostly from rain in wet summers and permafrost 
melt water in dry summers (Sugimoto et al., 2002), suggesting that

these forests will be particularly vulnerable to the combination of 
drought with the retraction of permafrost further underground due to 
climate warming.
2.5.2.8  Risk to Peatland Systems
The overall effect of climate change on the extent of northern peatlands 
is still debated (limited evidence, low agreement). It is expected that 
climate change will drive the expansion of high-latitude peatlands 
poleward of their present distribution due to warming, permafrost 
degradation and glacier retreat, which could provide new land and 
conditions favourable for peat development (limited evidence, medium 
agreement) (Zhang et al., 2017b), as seen during the last de-glacial 
warming (robust evidence, high agreement) (MacDonald et al., 2006; 
Jones and Yu, 2010; Ratcliffe et al., 2018). Peatland area loss (shrinking) 
near the southern limit of their current distribution or in areas where 
the climate becomes unsuitable is also expected (medium evidence,

medium agreement) (Section 2.3.4.3.2) (Finkelstein and Cowling, 2011 
Gallego-Sala and Prentice, 2013; Schneider et  al., 2016; Müller and 
Joos, 2020) (Müller and Joos, 2021), but they could persist if moisture 
is maintained via their capacity to self-regulate. In western Canada, 
a study suggests that peatlands may persist until 2100, even though 
the climate will be less suitable (Schneider et al., 2016). Simulations 
suggest that climate change-driven increases in temperature and 
atmospheric CO 2 could drive reductions in the northern peatland area 
up to 18% (SSP1–2.6), 41% (SSP2–4.5) and 61% (SSP5–8.5) by 2300 
(Müller and Joos, 2020). This is in contrast with the findings of northern 
peatland persistence and expansion under RCP2.6 and RCP6.0 
scenarios in 1861–2099 by another modelling study (Qiu et al., 2020). 
In the Tropics, the only available study suggests peatland area will 
increase until 2300, mainly due to increases in precipitation and the

CO 2 fertilisation effect (Müller and Joos, 2020; Müller and Joos, 2021).
The combination of changes in climate and land use represents a 
substantial risk to peatland carbon stocks, but full assessment is 
impeded because peatlands are yet to be included in ESMs (limited 
evidence, high agreement) (Loisel et al., 2021). It is expected that the 
carbon balance of peatlands globally will switch from sink to source 
in the near future (2020–2100), mainly because tropical peatland 
emissions, together with those from climate change-driven permafrost 
thaw, will likely surpass the carbon gain expected from climate 
change-driven enhanced plant productivity in northern high latitudes 
(Gallego-Sala et  al., 2018; Chaudhary et  al., 2020; Turetsky et  al., 2020; Loisel et  al., 2021) which are mainly caused by groundwater 
drawdown (robust evidence, medium agreement) (Hirano et al., 2014; 
Brouns et al., 2015; Cobb et al., 2017; Itoh et al., 2017; Evans et al.,

2021). The overall northern peatland carbon sink has been simulated 
to persist for at least 300 years under RCP2.6, but not under RCP8.5 
(Qiu et al., 2020).
Increases in the extent, severity and duration of fires are expected 
in all peatland regions in the future due to temperature increases 
(Section  4.3.1.1), changes in precipitation patterns (Section  4.3.1.2) 
and increases in ignition sources (e.g., lightning) (Section  5.4.3.2), 
with associated rapid carbon losses to the atmosphere (medium 
evidence, high agreement) (Dadap et  al., 2019; Chen et  al., 2021a; 
Nelson et al., 2021). For example, drought has been linked to fires in 
Southeast Asian peatlands (Field et al., 2009) and there are predicted 
decreases in mean summer precipitation (10–30%) for high and low 
RCPs, particularly over the Indonesian region, by the mid and late 
21st century (Section  12.4.2.2) (Tangang et  al., 2020; Taufik et  al., 
2020). During wet years, the fire probability in Indonesian peatlands

also significantly increases (by 15–40%) when temperatures in July 
to October surpass 0.5°C anomalies compared to the 1995–2015 
baseline (Fernandes et al., 2017). Overall, current evidence suggests 
that peat carbon losses via fire have the potential to be equal to, or 
greater than, losses due to human peatland drainage and disturbance 
(limited evidence, high agreement) (Turetsky et al., 2015).
Regarding permafrost peatlands, studies differ, with some projecting 
a net loss and others a net gain of carbon (medium evidence, low 
agreement) (Estop-Aragonés et al., 2018; Hugelius et al., 2020; Loisel 
et  al., 2021; Väliranta et  al., 2021). In some permafrost peatlands, 
prolonged and warmer growing seasons due to climate change 
(Section 2.3.4.3.1), along with increases in nitrogen deposition since 
1850 (Lamarque et al., 2013), are promoting plant primary productivity. 
Other studies indicate that increased nitrogen-mediated sequestration

could be exceeded by increased decomposition due to climate 
change-driven warming and fire (medium evidence, low agreement) 
(Natali et al., 2012; Vonk et al., 2015; Keuper et al., 2017; Burd et al., 
2018; Estop-Aragonés et al., 2018; Gallego-Sala et al., 2018; Serikova 
et al., 2018; Wild et al., 2019; Chaudhary et al., 2020; Hugelius et al., 
2020).
Any climate change- or human-driven degradation of peatlands will 
also entail losses in water storage (limited evidence, high agreement) 
(Wooster et  al., 2012; Hirano et  al., 2015; Cole et  al., 2019; Taufik 
et  al., 2019) and biodiversity (Harrison, 2013; Lampela et  al., 2017; 
Renou-Wilson et  al., 2019). The environmental archive contained in 
peat that preserves records of vegetation, hydrology, climate change, 
pollution and/or human disturbances is also being lost as peatlands 
degrade (Kasischke and Turetsky, 2006; MacDonald et  al., 2006; 
Turunen, 2008; Field et  al., 2009; Flannigan et  al., 2009; Jones and

Yu, 2010; Kasischke et al., 2010; Peterson et al., 2010; Finkelstein and 
Cowling, 2011; Rooney et al., 2012; Gallego-Sala and Colin Prentice, 
2013; Lamarque et al., 2013; Hirano et al., 2014; Brouns et al., 2015; 
Turetsky et  al., 2015; Miettinen et  al., 2016; Schneider et  al., 2016; 
Cobb et al., 2017; Fernandes et al., 2017; Itoh et al., 2017; Gallego-Sala 
et al., 2018; Greiser and Joosten, 2018; Ratcliffe et al., 2018; Dadap 
et al., 2019; Leifeld et al., 2019; Chaudhary et al., 2020; Hoyt et al.,

2
267Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
2020; Müller and Joos, 2020; Qiu et al., 2020; Tangang et al., 2020; 
Taufik et al., 2020; Turetsky et al., 2020; Chen et al., 2021a; Evans et al., 
2021; Loisel et al., 2021; Nelson et al., 2021; Qiu et al., 2021).
2.5.2.9 Risks to Polar Tundra Ecosystems
For boreal–tundra systems, AR5 projected the transformation of species 
composition, land cover and permafrost extent, decreasing albedo 
and increasing GHG emissions (medium confidence). SR1.5 classified 
tundra and boreal forests as particularly vulnerable to degradation 
and encroachment by woody shrubs (high confidence). The SROCC 
projected climate-related changes to arctic hydrology, wildfires and 
abrupt thaw (high confidence) and the broad disappearance of arctic 
near-surface permafrost this century, with important consequences for 
global climate (very high confidence). Chapter 2 of AR6 has focused

on new key findings about observed and projected changes in tundra 
vegetation and related hydrology, with implications for feedbacks to 
the climate system.
Due to the rapid warming at high northern latitudes, the Arctic tundra 
is one of the terrestrial biomes where climate change impacts are 
already clearly visible (Settele et al., 2014; Uboni et al., 2016). Climate 
models project that warming of the Arctic is likely to continue at more 
than double the global rate. Compared to the period 1995–2014, mean 
annual surface air temperatures in the Arctic tundra are projected to 
increase by 7.9°C–10°C by the end of the century in scenarios of 
high GHG emissions (RCP7.0 and RCP8.5). In scenarios of low GHG 
emissions (RCP1.9 and RCP2.6), the projected increase is 2.6°C–3.2°C 
(Lee et  al., 2021). The Arctic is also projected to have amongst the 
largest increases in precipitation globally, but with high uncertainty.

In contrast to climate change, LUC is projected to be very low in Arctic 
tundra systems (van Asselen and Verburg, 2013).
Models of vegetation response to climate project acceleration in the 
coming decades of observed increases in shrub dominance and boreal 
forest encroachment that have been driven by recent warming (Settele 
et  al., 2014), leading to a shrinking of the area of tundra globally 
(medium confidence) (Mod and Luoto, 2016; Gang et  al., 2017). 
Simulating changes in tundra vegetation is complicated by permafrost 
dynamics (e.g., the formation of thaw ponds and draining of existing 
ponds), changes in precipitation and low nutrient availability (which 
may promote the abundance of graminoids) (van der Kolk et al., 2016). 
Changes in vegetation, when combined with warming and increased 
precipitation effects on soil thawing and carbon cycling, are projected 
to modify GHG emissions and have biophysical feedbacks to regional

and global climate. High uncertainty in modelled carbon cycle changes 
arises from differences between the vegetation models (Nishina et al., 
2015; Ito et  al., 2016). In addition, climate change is expected to 
strongly interact with other factors, such as fire, to further increase 
uncertainty in projections of tundra ecosystem function (Jiang et al., 
2017).2.5.2.10 Committed Impacts of Climate Change on Terrestrial 
Ecosystems and Implications of Overshoot
Projections point to potentially large changes of canopy structure and 
composition within and across the terrestrial biomes in response to 
climate change and changes in atmospheric CO 2. These changes will 
contribute to altered ecosystem carbon uptake and losses, biophysical 
climate feedbacks (Sections  2.3.2; 2.4.4; 2.5.3.2; 2.5.3.3. 2.5.3.4, 
2.5.3.5, Figure 2.10, Table 2.4) and multiple other ecosystem services 
(Sections 2.5.3, 2.5.4) as well impacts on biodiversity (Sections 2.4.2,

2.4.3, 2.4.4, 2.4.5, 2.5.1.3, 2.5.1.4, 2.5.2, Figure Box  2.1.1, Table 
Box 2.1.1, Table SM2.4). Until now, most studies project changes over 
next decades until the end of this century.
However, there is an increasing body of literature that has found 
continued, longer-term responses of ecosystems to climate change, 
so-called ‘committed changes’, that arise from lags that exist in many 
systems. Many processes in ecosystems take more than a few decades 
to quasi-equilibrate to environmental changes. Therefore, the trends 
of changing vegetation cover identified in simulations of transient 
warming continue to show up in simulations that hold climate change 
at low levels of warming (medium confidence) (Boulton et al., 2017; 
Pugh et  al., 2018; Scheiter et  al., 2020). Such changes, which could 
tip ecosystems into an alternative state, could also be triggered by 
a ‘warming overshoot’ if global warming were to exceed a certain

threshold, even if mean temperatures afterwards decline again (Albrich 
et al., 2020a).
For instance, even if warming achieved by 2100 remained constant 
after 2100, such committed responses continue to occur. These include: 
(1) continued Amazon forest loss (Boulton et al., 2017), consistent with 
results in Pugh et al. (2018) that found continued tropical forest cover 
loss across a range of models and simulation setups, and (2) across 
Africa, an increased shift towards woody C3 vegetation was found in 
equilibrium state, the overall response depending on the atmospheric 
CO 2 concentration (Scheiter et  al., 2020). In Pugh et  al. (2018), the 
opposite was found for boreal forest cover, which showed a strong 
committed increase. The committed changes in vegetation composition 
correspond to large committed changes in terrestrial carbon uptake 
and losses (Boulton et al., 2017; Pugh et al., 2018; Scheiter et al., 2020),

and would plausibly also appear in other ecosystem functioning and 
services. These studies point to the importance of having not only a 
multi-decadal but also a multi-century perspective when exploring 
the impacts of political decisions on climate change mitigation taken 
now. Even if climate-warming targets are met, published evidence 
so far suggests that fundamental changes in some ecosystems are 
likely as these correspond to well-understood ecosystem physiological 
responses that trigger long-term changes in composition.

2
268Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Box 2.1 | Assessing Past Projections of Ecosystem Change against Observations
To assess future climate change impacts on ecosystems, we use models to project their future distribution. Comparing the trends in 
the observed changes against the projections can help assess the strength of the model projections. In this box, we compare observed 
trends of changes in ecosystem structure to projections highlighted in previous IPCC reports, specifically AR3 (IPCC, 2001), AR4 (Fischlin 
et al., 2007) and AR5 (Settele et al., 2014). We use this to assess how well the projections are matching up with observed changes. The 
map represents studies documenting observed changes in common plant functional groups (e.g., trees, grasses and shrubs). Studies 
documenting changes in plant functional groups were collated from published papers in natural and semi-natural areas. Studies

were included if climate change or interactions between climate change and land use showed a causal link to the observed change 
(Table SM2.4). Studies were excluded if the changes were only from landscape/land use transformation (e.g., deforestation). In each 
paper, we recorded the geographical location and type of functional change, and noted the causes. Observed changes are plotted onto a 
biome map derived from the WWF ecoregions database (Olson et al., 2001). Trends in changing plant functional types are good indicators 
of potential biome shifts and are used to assess how observations match up with projections.
Observed changes in the distribution of plant functional types
caused by climate change or combination of land use and climate change
Terrestrial biomes
Tropical broadleaf forests
Tropical coniferous forests
Temperate broadleaf forests
Temperate conifer forests
Boreal forests
Tropical grasslands/savannas/shrublandsTemperate grasslands/savannas/shrublands

Flooded grasslands
Montane grasslands
Tundra
Mediterranean type ecosystems
Deserts and xeric shrublandsShrub/woodland cover gainPlant functional type changes
Forest cover gain
Forest/woodland decline
Herbaceous cover lossHerbaceous cover gainForest cover change
Ti lbi
 Pl fi lh
Figure Box 2.1.1 |  Observed changes in the distribution of plant functional types that are caused by climate change or a combination of land use 
and climate change. Shifts in plant functional types are indicative of shift in biome function and structure. Based upon studies listed in Table SM2.4 and section 2.4.

2
269Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Table Box 2.1.1 |  Comparison of projections on biome change from AR3, AR4 and AR5 (IPCC, 2001; Fischlin et al., 2007; Settele et al., 2014), with observed changes 
in ecosystems as assessed in this report (see Section 2.4, Figure Box 2.1.1, Table SM2.4). Observed changes marked in bold show good agreement with past projections; 
those in red show mismatch with observations and projections.
Biome AR3 AR4 AR5 Observed trends 1990–2021
MTEsIncreased disturbance by fire 
and warming will cause a loss of 
unique habitatsLoss of 65% of area due 
to warming. Increased fire 
frequencies will favour 
resprouting plants. An increase 
in grass dominance. Forest 
expansion within MTEs due to 
elevated CO 2.Range contractions of all speciesIncrease in water deficit and fire activity 
(Sections 2.4.3.6, 2.4.4.2) causing a decline 
in diversity; tree mortality (Fig. Box 2.1.1) 
with resprouting trees worst affected.

Increasing dominance of grasses (often 
alien). Increasing dominance of deciduous 
over evergreen species (Fig. Box 2.1.1).
TundraTree and shrub encroachment 
into tundraIncreased woody plant growth 
due to longer and warmer 
growing seasons and shrub 
tundra replacing dwarf tundra
Poleward expansion of 
tundra into polar desert and 
encroachment of coniferous 
trees into tundraContinued woody expansion 
in tundra regions with reduced 
surface albedo due to less snow 
and more woody coverIncrease in woody shrub cover in tundra 
and expansion of boreal forest into 
tundra (Fig. Box 2.1.1, 2.4.3.4).
Boreal forestReduced productivity due to 
weather-related disturbances 
(e.g., increased fire risk).
Deciduous broadleaf tree 
encroachment into boreal forest.Extensive boreal tree spread into 
tundra.
Boreal forest dieback within 
boreal zone and contraction 
of boreal forest at southern 
ecotone with continental 
grasslandsExpansion into tundra and upslope

treeline advance (Section 2.4.3.8 and Fig 
Box 2.1.1).
Increased mortality due to drought, 
fire, beetle infestations (Sections 2.4.3.8, 
2.4.42.1, 2.4.4.3.1).
Tropical forestIncreasing CO 2 concentration 
would increase NPPIncreases in forest productivity 
and biomass through increased 
CO 2 with localised decreases 
in the Amazon. Shift in forest 
species composition.
Expansion of forest area into 
mesic savanna.Shift in the climate envelope of 
moist tropical forests but forests 
are less likely to undergo major 
retractions or expansions than 
suggested in AR4Expansion of tropical forest into 
savannas in Africa, Asia, South America 
(Section 2.4.3.7, Fig. Box 2.1.1).
Forest biomass increases (though 
slowing) (Section 2.4.4.4).
Forest degradation from drought, warming, 
fire and shorter residence time of trees 
(Section 2.4.3.7)
Shift in species composition towards 
species with more aridity-adapted traits 
(Section 2.4.3.7).
Temperate forestForest decline and increased

mortalityIncrease in tree mortality from 
drought-related declines.
A general increase of deciduous 
vegetation at the expense 
of evergreen vegetation is 
predicted at all latitudes.Map indicates a shift towards deciduous 
species in western North America (Fig. 
Tree death due to interactions of 
drought, pest outbreaks and fire (2.4.3.8, 
2.4.4.2.1., 2.4.4.3.1)
Grasslands and 
savannasIncreasing CO 2 concentration 
will increase NPPIncreased tree dominance in 
savannas and grasslands (from 
elevated CO 2), with C3 plants 
benefitting more than C4 plantsRising CO 2 will increase the 
likelihood of woodier states 
(but the transition will vary in 
different environments)Greening and encroachment across 
tropical and temperate savannas in 
Africa, Asia, Australia and America 
(Section 2.4.3.5).
Expansion of trees into grasslands and 
advancement of tree lines.
Signs of increased C4 grass productivity 
in drought conditions. Increased C3 grass

productivity (Section 2.4.3.5).
Desert/arid 
shrublandsAn increase in desert vegetation 
productivity was projected 
in southern Africa, the Sahel, 
central Australia, the Arabian 
Peninsula and parts of central 
Asia due to a positive impact of 
rising atmospheric CO 2Greening (increased leaf area index [LAI] 
and woody cover) and increased herbaceous 
production are occurring at desert–grassland 
interfaces (Cross-Chapter Paper 3)Box 2.1 (continued)

2
270Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Assessment: There is high agreement between observations and projections of tree death in temperate and boreal forests, with current 
projections (AR6) indicating this trend will continue (Sections 2.4.4.3, 2.5.3.3, 2.5.4). Forest death is most widely recorded in central 
Europe and western North America (Fig. Box 2.1.1). There is also very high agreement between observations and projections of woody 
encroachment in savannas, grasslands and tundra, with projections also indicating that this trend is likely to continue (Sections 2.4.3.5, 
2.4.3.9, 2.5.2.5, 2.5.2.9, 2.5.4). Observations of desert-greening show good agreement with earlier projections. Patterns of desertification 
are also occurring, although the geographical match between projections and observations shows moderate agreement, likely due to

the strong role of land use in this process. Projections of tropical forest expansion into mesic savannas and boreal forest expansion into 
tundra also show agreement with the observations.
Projections of the future of Mediterranean shrublands, deserts, xeric shrublands and temperate grassy systems are limited, making 
assessment of this relationship less clear. It is also unclear, due to limited observations, how widespread a shift there is from deciduous 
forest species to evergreen forest species. Some observations suggest this is occurring, but it is not clear how widespread this change is 
and if the geographical pattern is as projected.Box 2.1 (continued)
2.5.3 Risk Assessment of Ecosystems and Related 
Services
2.5.3.1 Risks in Protected Areas
15.7% of the global terrestrial area (UNEP-WCMC et  al., 2021), 
conserve greater biodiversity than adjacent unprotected areas (Gray

et al., 2016), and protect one-fifth of global vegetation carbon stocks 
and one-tenth of global soil carbon stocks (Section 2.4.4.4). This section 
assesses climate change specifically in protected areas. Even though it 
is included in a part of the chapter on projected risks, it includes both 
observed exposure and projected risks to gather the information on 
protected areas into one place.
2.5.3.1.1  Observed exposure of protected areas
In 2009, deforestation, agricultural expansion, overgrazing and 
urbanisation exposed one-third of the global protected area (6 million 
km2) to intense human pressure, a 6% increase from 1993 (Venter et al., 
2016; Jones et al., 2018). The exposure to observed climate change has 
not yet been quantified for protected areas globally, but research has 
analysed the spatial patterns and magnitudes of observed changes for 
the 360,000 km2 system of US national parks (Gonzalez et al., 2018)

including the first national park in the world. From 1895 to 2010, 
mean annual temperature of the US national park area increased 
at a rate of 1°C ± 0.2°C per century, double the rate of the whole 
USA, and precipitation decreased in 12% of the national park area, 
compared with 4% for the whole USA, due to a high fraction of US 
national park area being in the Arctic, at high elevations, and in the 
arid southwestern USA (Gonzalez et al., 2018). In addition, analyses of 
weather-station measurements in and near six South African national 
parks found that the maximum temperature increased at a rate of 
0.024°C ± 0.003°C yr-1 from 1960 to 2010 (Van Wilgen et al., 2016). 
While a substantial fraction of global protected area has been exposed 
to observed changes in human land cover, the global exposure to 
observed climate change is unquantified.2.5.3.1.2 Projected risks in protected areas
Under a climate change scenario of ~3.5°C temperature increase

by 2070, current climate could disappear from individual protected 
areas that comprise half the global protected area, and novel climates 
(climate conditions that are currently not present in an individual 
protected area) could emerge in half the global protected area 
(Hoffmann et al., 2019b). A lower-emissions scenario of ~1.5°C could 
reduce the disappearance of current climate conditions to 40% and the 
exposure to novel climates to 41% (Hoffmann et al., 2019b). Models 
project the highest exposure to novel climates in subtropical projected 
areas (Hoffmann and Beierkuhnlein, 2020). Projected disappearance 
of current climate conditions in protected areas is most extensive in 
Africa, Oceania, and North and South America (Elsen et al., 2020).
Projections indicate greater exposure of tropical rainforests, shrublands 
and grasslands, temperate conifer forests and grasslands, and tundra 
to novel climates (Hoffmann et al., 2019b; Elsen et al., 2020). A climate

change scenario of ~3.5°C temperature increase by 2100 could expose 
32% of the protected area in humid tropical forests (1.6 million km2 in 
2000) to climate that would be novel to humid tropical-forest protected 
areas; by 2050, the climate currently present in humid tropical-forest 
protected areas could disappear from 0.6  million km2 (12% of the 
current total area) (Tabor et al., 2018). High rates of deforestation and 
climate change combined could expose 2% of the humid tropical-forest 
protected area (Tabor et al., 2018). Regional analyses under RCP8.5 
also project the substantial disappearance of the current climate in 
protected areas in Bolivia, Chile and Peru (Fuentes-Castillo et  al., 
2020), Canada, Mexico and the USA (Batllori et al., 2017; Holsinger 
et al., 2019), China (Zomer et al., 2015), Europe (Nila et al., 2019) and 
Indonesia (Scriven et al., 2015). Projected climate change could expose 
an extensive part of the global protected area to disappearing and

novel climate conditions (high confidence) (Cross-Chapter Paper 1).
Continued climate change increases the risks to individual species and 
vegetation types in protected areas. Under a climate change scenario 
of 4°C temperature increase by 2100, the suitable climate for two 
species of baobab trees (Adansonia perrieri and A. suarezensis) in

2
271Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Madagascar could shift entirely out of the protected areas network 
(Vieilledent et al., 2013). Other species and vegetation types at risk 
from the partial disappearance of suitable climate in protected areas 
include Atlantic Forest amphibians in Brazil (Lemes et al., 2014), birds 
in Finland (Virkkala et al., 2013), birds and trees in Canada and Mexico 
(Stralberg et al., 2020), bog woodlands in Germany (Steinacker et al., 
2019), butterflies and mammals in Egypt (Leach et  al., 2013) and 
tropical dry forests in Mexico (Prieto-Torres et  al., 2016). Projected 
disappearance of suitable climate conditions in protected areas 
increase risks to the survival of species and vegetation types of 
conservation concern in tropical, temperate and boreal ecosystems 
(high confidence) (Cross-Chapter Paper 1).
Protected rivers, lakes and other freshwater protected areas require

inter-catchment connectivity to maintain species and population 
movements (Bush et al., 2014a; Hermoso et al., 2016; Thieme et al., 
2016), but dams and other barriers interrupt connectivity (Grill et al., 
2019). Climate change could also reduce freshwater connectivity 
(Section 2.3.3.3). Globally, over two-thirds of river reaches (by length) 
lack protected areas in their upstream catchments and nine-tenths 
of river reaches (by length) do not achieve full, integrated protection 
(Abell et al., 2017).
Terrestrial and freshwater protected areas can also serve as climate 
change refugia, that is, locations where suitable conditions may persist 
for the species into the future (e.g., Section 2.6.5.6). In Canada, Mexico 
and the USA, only a fraction of the protected area is located in potential 
climate change refugia under a 4°C temperature increase, estimated 
at 4% (Michalak et al., 2018) to 7% (Batllori et al., 2017). Potential

refugia from biome shifts due to climate change under temperature 
increases of 1.8°C–3.4°C cover <1% of the area of US national parks 
(Gonzalez et al., 2010), a fraction that diminishes to near zero when 
climate change is combined with habitat fragmentation due to LUC 
(Eigenbrod et al., 2015). Protected areas in boreal ecosystems could 
serve as refugia for species shifting north in Canada (Berteaux et al., 
2018) and Finland (Lehikoinen et al., 2019). Invasive species, habitat 
loss and other disturbances in protected areas could be lower than in 
unprotected areas across Europe (Gallardo et al., 2017), specifically in 
Spain (Regos et al., 2016), and also in Sri Lanka (Kariyawasam et al., 
2020). Protected areas conserve refugia from climate change under 
a temperature increase of 4°C, which is important for biodiversity 
conservation but is limited to <10% of the current protected area 
(medium confidence).
2.5.3.2 Risks to Ecosystems and Services from Wildfire

2.5.3.2.1 Future projections of wildfire globally
Continued climate change under high-emission scenarios that increase 
global temperature ~4°C by 2100 could increase global burned area 
by 50% (Knorr et al., 2016b) to 70% (Kloster and Lasslop, 2017) and 
global mean fire frequency by ~30% (Gonzalez et  al., 2010), with 
increases on one-third (Gonzalez et  al., 2010) to two-thirds (Moritz 
et al., 2012) and decreases on one-fifth (Gonzalez et al., 2010; Moritz 
et  al., 2012) of land globally. Lower emissions that would limit the 
global temperature increase to <2°C would reduce projected increases 
of global burned area to 30% (Lange et  al., 2020) to 35% (Kloster and Lasslop, 2017) and projected increases of fire frequency to ~20% 
(Gonzalez et al., 2010; Huang et al., 2015). Continued climate change 
could further lengthen fire weather seasons (Ranasinghe et al., 2021). 
Models combining projected climate change with potential agricultural

expansion project decreases in total burned area (Huang et al., 2015; 
Knorr et al., 2016b; Park et al., 2021). The area of projected increases in 
burned area and fire frequency due solely to continued climate change 
is higher for the world as a whole than the area of projected decreases 
(medium evidence, medium agreement).
Increased wildfire due to continued climate change increases risks 
of tree mortality (Sections  2.5.2.6, 2.5.2.7, 2.5.3.2), biome shifts 
(Section  2.5.2.2) and carbon emissions (Sections  2.5.2.10, 2.5.3.4). 
Wildfire and biome shifts under a projected climate change of 4°C 
above the pre-industrial period, combined with international trade 
and transport, cause high risks from invasive species across one-sixth 
of the global area including extensive high-biodiversity regions (Early 
Wildfire risks to people include death and destruction of their homes, 
respiratory illnesses from smoke (Ford et al., 2018; Machado-Silva et al.,

2020), post-fire flooding from areas exposed by vegetation loss and 
degraded water quality due to increased sediment flow (Dahm et al., 
2015) and the chemical precursors of carcinogenic trihalomethanes 
when water is later chlorinated for drinking (Section  2.5.3.7) (Uzun 
et al., 2020). Under RCP8.5 and shared socioeconomic pathway SSP3 
(high population growth, slow urbanisation), the number of people 
living in fire-prone areas could increase by three-quarters to 720 million 
in 2100, in a projected global population of 12.4 billion people (Knorr 
et al., 2016b). Lower emissions under RCP4.5 could reduce the number 
of people at risk by 70 million. In these projections, human population 
growth increases human exposure to wildfires more than increases 
in burned area (Knorr et  al., 2016b). A global temperature increase 
<2°C could increase global population exposure to wildfire by ~30% 
(Lange et al., 2020). Increased wildfire under continued climate change

increases the probability of human exposure to fire and risks to public 
health (medium evidence, high agreement).
2.5.3.2.2 Future projections of wildfire in high-risk areas
Regions identified by multiple global analyses as being at a high risk 
of increased burned area, fire frequency and fire weather include: the 
Amazon (Gonzalez et al., 2010; Huang et al., 2015; Knorr et al., 2016b; 
Burton et al., 2018; Abatzoglou et al., 2019), Mediterranean Europe 
(Gonzalez et al., 2010; Burton et al., 2018; Abatzoglou et al., 2019), 
the Arctic tundra (Moritz et al., 2012; Flannigan et al., 2013), Western 
Australia (Gonzalez et al., 2010; Burton et al., 2018; Abatzoglou et al., 
2019) and the western USA (Gonzalez et al., 2010; Moritz et al., 2012; 
Knorr et al., 2016b). Higher-resolution spatial projections indicate high 
risks of increased wildfire in the Amazon, Australia, boreal ecosystems, 
Mediterranean Europe and the USA with climate change (medium 

In the Amazon, climate change under RCP8.5, combined with high 
deforestation, could double the area of high fire probability (Fonseca 
et al., 2019), double the burned area by 2050 (Brando et al., 2020), 
increase the burned area by 400–2800% by 2100 (Le Page et al., 2017)

2
272Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
and increase fire intensity by 90% (De Faria et al., 2017). Lower GHG 
emissions (RCP4.5) and reduced deforestation could reduce the risk of 
fires to a one-fifth increase in the area of high fire probability (Fonseca 
et al., 2019) and a 100–500% increase in burned area by 2100 (Le 
Page et al., 2017). Moreover, increased fire, deforestation and drought, 
acting via vegetation–atmosphere feedbacks, increase the risk of 
extensive forest dieback and potential biome shifts of up to half of the 
Amazon rainforest to grassland, a tipping point that could release an 
amount of carbon that would substantially increase global emissions 
(Oyama and Nobre, 2003; Sampaio et al., 2007; Lenton et al., 2008; 
Nepstad et al., 2008; Malhi et al., 2009; Settele et al., 2014; Lyra et al., 
2016; Zemp et  al., 2017a; Zemp et  al., 2017b; Brando et  al., 2020). 
Continued climate change, combined with deforestation, increases

risks of wildfire and extensive forest dieback in the Amazon rainforest 
In Australia, climate change under RCP8.5 increases the risk of 
pyro-convective fire by 20–40 days in rangelands of Western Australia, 
South Australia and the Northern Territory (Dowdy et  al., 2019). 
Pyro-convective fire conditions could reach more frequently into the 
more populated areas of New South Wales, particularly at the start 
of the austral summer (Di Virgilio et al., 2019). GCMs do not agree, 
however, on the areas of projected fire increase in New South Wales 
(Clarke and Evans, 2019). Increases in heat and potential increases in 
wildfire threaten the existence of temperature montane rainforest in 
Tasmania, Australia (Mariani et al., 2019).
In Mediterranean Europe, climate change of 3°C of warming could 
double or triple the burned area whereas keeping the temperature 
increase to 1.5°C could limit the increase in burned area to 40–50%

(Turco et al., 2018). Under RCP8.5, the frequency of heat-induced fire 
weather could increase by 30% (Ruffault et  al., 2020). Severe fire 
followed by drought could cause biome shifts of forest to non-forest 
(Batllori et al., 2019) and tree mortality >50% (Dupire et al., 2019).
In the Arctic tundra, boreal forests and northern peatlands, 
including permafrost areas, climate change under the scenario of 
a 4°C temperature increase could triple the burned area in Canada 
(Boulanger et al., 2014), double the number of fires in Finland (Lehtonen 
et al., 2016), increase the lightning-driven burned area by 30–250% 
(Veraverbeke et al., 2017; Chen et al., 2021a), push half of the area 
of tundra and boreal forest in Alaska above the burning threshold 
temperature and double the burned area in Alaska (Young et  al., 
2017a). Thawing of Arctic permafrost due to a projected temperature 
of 4°C and the resultant wildfires could release 11–200 GtC which

could substantially exacerbate climate change (Section 2.5.2.9).
In the USA, climate change under RCP8.5 could increase the burned 
area by 60–80% by 2049 (Buotte et al., 2019) and the number of fires 
with an area >50 km2 by 300–400% by 2070 (Barbero et al., 2015). 
In montane forests, climate change under RCP8.5 increases the risk 
of fire-facilitated conversion of ~7% of forest to non-forest by 2050 
(Parks et al., 2019). In California, climate change under a scenario of 
a 4°C temperature increase could double fire frequency in some areas 
(Mann et al., 2016), but emission reductions that limit the temperature 
increase to ~2°C could keep this from increasing (Westerling et al., 
2011). Carbon dioxide fertilisation and increased temperature under climate change could increase invasive grasses and wildfire in desert 
ecosystems of the southwestern USA where wildfire has historically 
been absent or infrequent, and increase the mortality of the sparse

tree cover (Horn and St. Clair, 2017; Klinger and Brooks, 2017; Syphard 
et al., 2017; Moloney et al., 2019; Sweet et al., 2019).
In summary, under a high-emission scenario that increases global 
temperature 4°C by 2100, climate change could increase the global 
burned area by 50–70% and the global mean fire frequency by ~30%, 
with increases on one- to two-thirds and decreases on one-fifth of 
global land (medium confidence). Lower emissions that would limit the 
of burned area to ~35% and projected increases of fire frequency to 
~20% (medium confidence). Increased wildfire, combined with erosion 
due to deforestation, could degrade water supplies (high confidence). 
For ecosystems with an historically low fire frequency, a projected 
4°C rise in global temperature increases risks of fire, contributing 
to potential tree mortality and conversion of over half the Amazon

rainforest to grassland and thawing of the Arctic permafrost that 
could release 11–200 GtC that could substantially exacerbate climate 
change (medium confidence).
2.5.3.3  Risks to Ecosystems and Services from Tree Mortality
Under continued climate change, increased temperature, aridity, 
drought, wildfire (Section  2.5.3.2) and insect infestations 
(Section  2.4.4.3.3) will tend to increase tree mortality across many 
parts of the world (McDowell et al., 2020). Loss of boreal and temperate 
forest to fire, wind and bark beetles could cause more negative than 
positive effects for most ecosystem services, including carbon storage 
to regulate climate change (Sections 2.4.4.3, 2.5.2.6, 2.5.2.7, 2.5.3.4), 
water supply for people (Section  2.5.3.6.1), timber production and 
other forest products (Chapter 5) and protection from hazards (Thom 
and Seidl, 2016). In addition, deforestation in tropical and temperate 
forests can increase local temperatures by 0.3°C–2°C (Hesslerová

et al., 2018; Lejeune et al., 2018; Zeppetello et al., 2020) and this effect 
can extend up to 50 km (Cohn et al., 2019).
In Amazon rainforests, the relatively lower buffering capacity for 
plant moisture during drought increases the risk of tree mortality 
and, combined with increased heat from climate change and fire from 
deforestation, the possibility of a tipping point of extensive forest 
dieback and a biome shift to grassland (Oyama and Nobre, 2003; 
Sampaio et al., 2007; Lenton et al., 2008; Nepstad et al., 2008; Malhi 
et al., 2009; Salazar and Nobre, 2010; Settele et al., 2014; Lyra et al., 
2016; Zemp et al., 2017b; Brando et al., 2020). This could occur at a 
4°C–5°C temperature increase above that of the pre-industrial period 
(Salazar and Nobre, 2010). Under RCP8.5, half the Amazon tropical 
evergreen forest could turn into grassland through drought-induced 
tree mortality and wildfire, but lower emissions (RCP4.5) could limit

this loss to ~5% (Lyra et al., 2016). The decline in precipitation due 
to reduced evapotranspiration inputs after forest loss could cause 
additional Amazon forest loss of one-quarter to one-third (Zemp 
et al., 2017a). Similarly, in Guinean tropical deciduous forest in Africa, 
climate change under RCP8.5 could increase mortality 700% by 2100 
or 400% under lower emissions (RCP4.5; (Claeys et al., 2019). These 
projections indicate risks of climate change-induced tree mortality

2
273Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
reducing tropical forest areas in Africa and South America by up to 
half under a 4°C increase above the pre-industrial period, but a lower 
projection of a 2°C increase could limit the projected increases in tree 
mortality (robust evidence, high agreement).
Temperate and boreal forests possess greater diversity of physiological 
traits related to plant hydraulics, so they are more buffered against 
drought than tropical forests (Anderegg et al., 2018). Nevertheless, in 
temperate forests, drought-induced tree mortality under RCP8.5 could 
cause the loss of half the Northern Hemisphere conifer forest area by 2100 
(McDowell et al., 2016). In the western USA, under RCP8.5, one-tenth 
of forest area is highly vulnerable to drought-induced mortality by 2050 
(Buotte et  al., 2019). In California, increased evapotranspiration in 
Sierra Nevada conifer forests increases the potential fraction of the area

at risk of tree mortality by 15–20% per degree Celsius (Goulden and 
Bales, 2019). In Alaska, fire-induced tree mortality from climate change 
under RCP8.5 could reduce the extent of spruce forest (Picea sp.) by 
8–44% by 2100 (Pastick et  al., 2017). Under RCP8.5, tree mortality 
from drought, wildfire and bark beetles could reduce the timber 
productivity of boreal forests in Canada by 2100 below the current 
levels (Boucher et al., 2018; Chaste et al., 2019; Brecka et al., 2020). 
In Tasmania, projected increases in wildfire (Fox-Hughes et al., 2014) 
increase the risk of mortality of mesic vegetation (Harris et al., 2018b) 
and threaten the disappearance of the long-lived endemic pencil pine 
(Athrotaxis cupressoides) (Holz et  al., 2015; Worth et  al., 2016) and 
temperate montane rainforest (Mariani et al., 2019). These projections 
indicate risks of climate change-induced tree mortality reducing some 
temperate forest areas by half under emissions scenarios of 2.5°C–4°C

above the pre-industrial period (medium evidence, high agreement).
2.5.3.4 Risk to Terrestrial-Ecosystem Carbon Stocks
Globally, increasing atmospheric CO 2 enhances the terrestrial sink but 
temperature increases constrain it, reflecting the biological process 
understanding highlighted in previous IPCC reports (high confidence). 
Analyses of atmospheric inversion model output and spatial climate data 
indicate a sensitivity of net ecosystem productivity to CO 2 fertilisation of 
3.1 ± 0.1 Gt to 8.1 ± 0.3 Gt per 100 ppm CO 2 (~1°C increase) and a 
sensitivity to temperature of -0.5 ± 0.2 Gt to -1.1 ± 0.1 Gt per degree 
Celsius (Fernandez-Martinez et al., 2019). The future of the global land 
carbon sink (Section  2.4.4.4) nevertheless remains highly uncertain 
because (i) of regionally complex interactions of climate change and 
changes in atmospheric CO 2 with vegetation, soil and aquatic processes, 
(ii) episodic events such as heat waves or droughts (and related impacts

through mortality, wildfire or insects, pests and diseases) (Section 2.5.3.2, 
2.5.3.3) are so far only incompletely captured in carbon cycle models, 
(iii) the legacy effects from historic LUC and environmental changes 
are incompletely captured but likely to decline in future and (iv) lateral 
carbon transport processes such as the export of inland waters and 
erosion are incompletely understood and modelled (Pugh et al., 2019a; 
Friedlingstein et al., 2020; Krause et al., 2020; Canadell et al., 2021).
Enhanced carbon losses from terrestrial systems further limit the 
available carbon budget for global warming staying below 1.5°C (Rogelj 
et al., 2018). Analyses of satellite remote sensing and ground-based 
observations have indicated that, between 1982 and 2015, the CO 2 
fertilisation effect has already declined, implying a negative climate system feedback (Wang et al., 2020c). Peatlands, permafrost regions 
and tropical ecosystems are particularly vulnerable due to their

large carbon stocks, in combination with over-proportional warming, 
increases in heat waves and droughts and/or a complex interplay of 
climate change and increasing atmospheric CO 2 (Sections  2.5.2.8, 
2.5.2.9, 2.5.3.2).
Model projections suggest a reduction of permafrost extent and 
potentially large carbon losses for all warming scenarios (Canadell 
et al., 2021). Already a mean temperature increase of 2°C could reduce 
the total permafrost area extent by about 5–20% by 2100 (Comyn-Platt 
et al., 2018; Yokohata et al., 2020). Associated CO 2 losses in the order of 
15 Gt up to nearly 70 Gt by 2100 have been projected across a number 
of modelling studies (Schneider von Deimling et al., 2015; Comyn-Platt 
et  al., 2018; Yokohata et  al., 2020). Limiting the global temperature 
increase to 1.5°C versus 2°C could reduce projected permafrost 
CO 2 losses by 2100 by 24.2 Gt (median, calculated for a 3-m depth) 
(Comyn-Platt et al., 2018). Losses are possibly underestimated in the

studies that consider only the upper permafrost layers. Likewise, the 
actual committed carbon loss may well be larger (e.g., eventually a loss 
of approx. 40% of today’s permafrost area extent if climate is stabilised 
at 2°C above pre-industrial levels) due to the long time scale of warming 
in deep permafrost layers (Chadburn et al., 2017). It is not known at 
which level of global warming an abrupt permafrost collapse (estimated 
to enhance CO 2 emissions by 40% in 2300 in a high-emissions scenario) 
compared to gradual thaw (Turetsky et  al., 2020) would have to be 
considered an important additional risk. Large uncertainties arise also 
from interactions with changes in surface hydrology and/or northward 
migrating woody vegetation as climate warms, which could dampen 
or even reverse projected net carbon losses in some regions (McGuire 
et al., 2018a; Mekonnen et al., 2018; Pugh et al., 2018). Overall, there 
is low confidence on how carbon–permafrost interactions will affect

future carbon cycle and climate, although net carbon losses and thus 
positive (amplifying) feedbacks are likely (Sections  2.5.2.10, 2.5.3.5) 
(Shukla et al., 2019). See also WGI AR6 (Canadell et al., 2021) for a 
discussion on impacts of higher-emission and warming scenarios.
Peatland carbon is estimated as about 550–1000 Gt in northern 
latitudes (many of these peatlands would be found in permafrost 
regions) (Turetsky et al., 2015; Nichols and Peteet, 2019) and >100 Gt 
in tropical regions (Turetsky et al., 2015; Dargie et al., 2017). For both 
northern mid- and high-latitude and tropical peatlands, a shift from 
contemporary CO 2 sinks to sources were simulated in high-warming 
scenarios (Wang et  al., 2018a; Qiu et  al., 2020). Due to the lack of 
large-scale modelling studies, there is low confidence for climate change 
impacts on peat carbon uptake and emissions. The largest risk to tropical 
peatlands is expected to arise from drainage and conversion to forestry

or agriculture, which would outpace the impacts of climate change (Page 
and Baird, 2016; Leifeld et al., 2019; Cooper et al., 2020). The magnitude 
of possible carbon losses is uncertain, however, and depends strongly 
on socioeconomic scenarios (Sections 2.4.3.8, 2.4.4.2; 2.4.4.4.2, 2.5.2.8).
For tropical and subtropical regions, the interplay of atmospheric CO 2 
with precipitation and temperature becomes of particular importance 
for future carbon uptake, since in warm and dry environments, elevated 
CO 2 fosters plants with C3 photosynthesis and enhances their water-use 
efficiency relative to C4 species (Moncrieff et al., 2014a; Midgley and

2
274Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Bond, 2015; Knorr et al., 2016a). As a consequence, enhanced woody 
cover is expected to occur in the future, especially in mesic savannas, 
while in xeric savannas an increase in woody cover would occur in 
regions with enhanced precipitation (Criado et al., 2020). Even though 
semiarid regions have dominated the global trend in land CO 2 uptake 
in recent decades (Ahlström et  al., 2015), so far, most studies that 
investigated future climate change impacts on savanna ecosystems have 
concentrated on changes in the extent of land area affected (2.5.2.5) 
rather than on carbon cycling, with medium confidence for increasing 
woody cover:grass ratios (Moncrieff et al., 2014a; Midgley and Bond, 
2015; Moncrieff et al., 2016; Criado et al., 2020). Increases in woody 
vegetation in what is now grass-dominated would possibly come with 
a carbon benefit, for instance, it was found that a broad range of future

climate and CO 2 changes would enhance vegetation carbon storage on 
Australian savannas (Scheiter et al., 2015). Results from a number of 
field experiments indicate, however, that impacts on total ecosystem 
carbon storage may be smaller due to a loss in below-ground carbon 
(Coetsee et al., 2013; Wigley et al., 2020). Nunez et al. (2021) critique 
existing incentives to promote the invasion of non-native trees into 
treeless areas as a means of carbon sequestration, raising doubts about 
the effects on fire, albedo, biodiversity and water yield (see Box 2.2).
Substantial climate change-driven impacts on tropical tree cover and 
vegetation type are projected in all studies, irrespective of whether or 
not the degree amounts to a forest “dieback” (Sections 2.4.3.6, 2.4.4.3, 
2.5.2.6, 2.5.3.3) (Davies-Barnard et  al., 2015; Wu et  al., 2016a; Zemp 
et al., 2017a; Canadell et al., 2021) . Accordingly, models also suggest

a continuation of tropical forests acting as carbon sinks (Huntingford 
et al., 2013; Mercado et al., 2018). A recent study combining field plot 
data with statistical models (Hubau et al., 2020) indicates that, in the 
Amazonian and possibly also in the African forest, the carbon sink in 
above-ground biomass already declined in the three decades up to 2015. 
This trend is distinct in the Amazon whereas data from Africa suggests a 
possible decline after 2010. The authors estimate the vegetation carbon 
sink in 2030–2040 to decline to zero±0.205 PgC yr-1 in the Amazon and 
to 0.26±0.215 PgC yr-1 in Africa (a loss of 14% compared to the present). 
Their results suggest that, over time, CO 2 fertilisation is outweighed by 
the impacts of higher temperatures and drought that enhance tree 
mortality and diminish growth. The degree of thermal resilience of 
tropical forests is still uncertain, however (Sullivan et al., 2020).

The lack of simulation studies that seek to quantify all important 
interacting factors (CO 2, drought and fire) for future carbon cycling in 
savannas and tropical forests and the apparent disagreement between 
trends projected in models compared to data-driven estimates result 
in low confidence regarding the direction or magnitude of carbon flux 
and pool-size changes. Similar to tropical peatlands, given projected 
human population growth and socioeconomic changes, the continued 
conversion of forests and savannas into agricultural or pasture systems 
very likely poses a significant risk of rapid carbon loss which will amplify 
the climate change-induced risks substantially (high confidence) 
(2.5.2.10, 2.5.3.5) (Aragao et al., 2014; Searchinger et al., 2015; Aleman 
et al., 2016; Nobre et al., 2016).
The impacts of climate-induced altered animal composition and 
trophic cascades on land-ecosystem carbon cycling globally are as

yet unquantified (Schmitz et al., 2018), even though climate change is expected to lead to shifts in consumer–resource interactions 
that also contribute to losses of top predators or top herbivores 
(Sections  2.4.2.2, 2.5.1.3, 2.5.4; (Lurgi et  al., 2012; Damien and 
Tougeron, 2019). Cascading trophic effects triggered by top predators 
or the largest herbivores propagate through food webs and reverberate 
through to the functioning of whole ecosystems, notably altering 
productivity, carbon and nutrient turnover and net carbon storage 
(medium confidence) (Wilmers and Schmitz, 2016; Sobral et al., 2017; 
Stoner et al., 2018). Across different field experiments, the ecosystem 
consequences of the presence or absence of herbivores and carnivores 
have been found to be quantitatively as large as the effects of other 
environmental change drivers such as warming, enhanced CO 2, fire and 
variable nitrogen deposition (medium confidence) (Hooper et al., 2012;

Smith et  al., 2015). Some local and regional modelling experiments 
have begun to explore animal impacts on vegetation dynamics and 
carbon and nutrient cycling (Pachzelt et al., 2015; Dangal et al., 2017; 
Berzaghi et al., 2019). Turnover rate is the chief factor that determines 
future land-ecosystem carbon dynamics and hence carbon–climate 
feedbacks (Friend et al., 2014). To improve projections, it is imperative 
to better quantify the broader role of carnivores, grazers and browsers 
and the way these interact in global studies of how ecosystems 
respond to climate change. 
2.5.3.5 Feedbacks between Ecosystems and Climate
The possibility of feedbacks and interactions between climate drivers 
and biological systems or ecological processes was identified as a 
significant emerging issue in AR5, and has since also been highlighted 
in the SRCCL and the SR1.5. It is virtually certain that land cover 
changes affect regional and global climate through changes to albedo,

evapotranspiration and roughness (very high confidence) (Perugini 
et al., 2017). There is growing evidence that biosphere-related climate 
processes are being affected by climate change in combination 
with disturbance and LULCC (high confidence) (Jia et al., 2019). It is 
virtually certain that land surface change caused by disturbances such 
as forest fires, hurricanes, phenological changes, insect outbreaks and 
deforestation affect carbon, water and energy exchanges, thereby 
influencing weather and  climate (very high confidence) (Table  2.4; 
Figure  2.10) (Bright et  al., 2013; Brovkin et  al., 2013; Naudts et  al., 
2016; Prăvălie, 2018).
Feedbacks can be positive or negative (i.e., amplify or dampen the 
original forcing), vary spatially and seasonally, and act over large 
geographic areas and long time periods (more than decades), 
making them difficult to observe and quantify directly (Schimel et al., 
2015; Canadell et al., 2021). Due to the positive impacts of CO 2 on

vegetation growth and ecosystem carbon storage (high confidence) 
(Sections  2.4.4.4, 2.5.5.4) (Canadell et  al., 2021), the associated 
climate feedback is negative (i.e., increased removal of atmospheric 
CO 2 and dampened warming, compared to an absence of the 
feedback). By contrast, projected global losses of carbon in warmer 
climates (Canadell et al., 2021) imply a positive climate feedback. WGI 
(Canadell et  al., 2021) assesses an overall increase in land carbon 
uptake through the 21st century. However, the overall strength of 
the carbon cycle–climate feedback remains very uncertain. One of 
the underlying reasons may be complex interactions with ecosystem 
water balance and nitrogen and phosphorous availability, which

2
275Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
are poorly constrained by observational evidence and incompletely 
captured in ESMs (Section 2.5.2.10) (Huntzinger et al., 2017; Canadell 
Land ecosystems contribute substantially to global emissions of 
nitrous oxide and methane . As with CO 2, these emissions respond both directly and indirectly to atmospheric CO 2 concentration and climate 
change, and this gives rise to potential additional bio-geochemical 
feedbacks in the climate system. A large part of these emissions stem 
from land and water management, such as fertilizer application, 
rice production, aquaculture or animal husbandry (Jia et  al., 2019). 
However, nearly 60% of total nitrous oxide emissions (in 2007–2016) Table 2.4 |  Terrestrial and freshwater ecosystem feedbacks which affect the Earth’s climate system dynamics, according to (Prăvălie, 2018).
PerturbationImplications for warming/feedback mechanism

The Earth’s climate system dynamics
Phenological changes
(sections 2.4.2.4, 2.4.2.5)Increased primary productivity and plant growth with CO 2 fertilisation (Mao et al., 2016; Wang et al., 2018a); increasing growing season length 
(Peñuelas et al., 2009; Barichivich et al., 2013); reduced diurnal temperature range through evapotranspiration (mid latitudes) and albedo (high 
latitudes) caused by vegetation greening (Jeong et al., 2011); increased CO 2 storage in biomass (cooling) (Keenan et al., 2014); reduced albedo in 
snow-covered regions as canopies become taller and darker (warming); increased evapotranspiration, a key component of the global water cycle and 
energy balance which influences global rainfall, temperature and atmospheric motion (Zeng et al., 2017)
Insect outbreaks
(sections 2.4.4.2Reduced carbon uptake and storage (warming); increased surface albedo (cooling) (Landry et al., 2016); increased CO 2 emissions (warming); decreased

LAI and gross primary productivity (Ghimire et al., 2015), leading to reduced evapotranspiration and increased land surface temperature (Bright et al., 
2013)
Range shifts
(sections 2.4.2.1, 2.4.2.2, 2.4.2.3, 
2.4.2.5, 2.4.3)Reduced albedo in snow-covered regions as trees expand polewards (warming) (Chae et al., 2015); enhanced permafrost thawing; expansion of insect 
outbreak range, increasing forest impact (Pureswaran et al., 2018); biome-dependent changes in albedo and evapotranspiration regimes (Naudts et al., 
2016); reduction in snow and ice albedo in freshwater due to loss of ice (warming) (Lang et al., 2018)
Die-off and large-scale mortality 
events
(sections 2.4.2.2, 2.4.4.3)Decreased GPP; decline in carbon storage (warming); increased CO 2 emissions; increased solar radiation, reduced soil moisture and higher surface 
runoff; albedo effects (Lewis et al., 2011; Prăvălie, 2018)

(sections 2.4.3.6, 2.4.3.7)Reduced carbon storage (warming) (Pugh et al., 2019a); increase in (regional) surface air temperature due to reduced evaporation (less cooling); 
increased albedo in high-latitude systems (regional radiative cooling) (Loranty et al., 2014); increased air temperature and diurnal temperature 
variation (Alkama and Cescatti, 2016), locally and globally (Winckler et al., 2019); reduced precipitation (Perugini et al., 2017); decreased biogenic 
volatile organic compounds (BVOC) and aerosol emissions (warming through direct and indirect aerosol effects; cooling associated with reduction in 
atmospheric methane (Jia et al., 2019)
(sections 2.4.3.6, 2.4.3.7)Reduced carbon storage (warming) (de Paula et al., 2015; Bustamante et al., 2016; de Andrade et al., 2017; Mitchard, 2018)
Fragmentation Carbon losses because biomass is less developed at forest edges (Pütz et al., 2014; Chaplin-Kramer et al., 2015; Haddad et al., 2015)

Air pollutionDecreased plant productivity, transpiration and carbon sequestration in forests with lower biomass due to ozone toxicity (Sitch et al., 2007; Ainsworth 
et al., 2012); increased (regional) productivity due to increase in diffuse solar radiation caused by terrestrial aerosols (Xie et al., 2021)
Declining populations of 
megafaunaChanges to physical and chemical properties of organic matter, soils and sediments influence carbon uptake and storage (Schmitz et al., 2018); 
increased or decreased carbon storage biomass and carbon storage, with differences across biomes determined by floristic structure and animal size 
(Bello et al., 2015; Osuri et al., 2016; Peres et al., 2016; He et al., 2017; Berzaghi et al., 2018; Schmitz et al., 2018; He et al., 2019)
Fire
(sections 2.4.4.2, 2.5.3.2)Increased carbon and aerosol emissions (van der Werf et al., 2017); surface warming (Liu et al., 2019b); albedo effect dependent on ecosystem and

species-level traits (Rogers et al., 2015; Chen et al., 2018a) (initial albedo decreases post-fire; increased albedo where snow exposure is increased by 
canopy removal and species composition changes during recovery); black carbon deposition on snow and sea ice (short-term) (Randerson et al., 2006); 
indirect increases in carbon emissions due to soil erosion (Caon et al., 2014)
Changes in forest composition 
(sections 2.4.3.6, 2.4.3.7, 2.5.2.6, 
2.5.2.7)Reduced carbon storage due to the decline of biomass (warming) (McIntyre et al., 2015)
Woody encroachment in 
non-forested ecosystems
(sections 2.4.3.3, 2.4.3.4, 2.4.3.5, 
2.5.2.3, 2.5.2.4, 2.5.2.5, Box 2.1)Reduced production, increased water use, reduced albedo and altered land–atmosphere feedbacks; increased carbon storage in woody savannas (Zhou 
et al., 2017; Mureva et al., 2018); uncertain feedbacks to the carbon cycle (some suggest an increase, others a decrease)
NPP shifts

(section 2.4.4.5)Reduced albedo following high-latitude expansion of trees caused by photosynthetic enhancement of growth (cooling); increased photosynthesis and 
net ecosystem production (NEP) (Fernandez-Martinez et al., 2019); increased NPP in nutrient-limited ecosystems due to increased nitrogen deposition 
from agriculture and combustion (Du and de Vries, 2018; Schulte-Uebbing and de Vries, 2018); nutrient-limited lakes are likely to become less 
productive, while nutrient-rich lakes are likely to become more productive due to warming-induced prolongation of stable stratification (Adrian et al., 
2016; Kraemer et al., 2017)
Bio-geochemical shiftsDecline in carbon storage due to nitrogen limitation in nutrient-limited systems (warming) (Reich et al., 2014; Wieder et al., 2015); increased carbon 
storage on land (Peñuelas et al., 2013) and in lakes (Heathcote et al., 2015; Mendonça et al., 2017); increase in CO 2 and CH 4 emissions from freshwater

ecosystems due to increased eutrophication (DelSontro et al., 2018), the imbalance between losses and gains of CO 2 by photosynthesis and respiration 
(the metabolic theory of ecology), enhanced emissions from exposed river and lake sediments during droughts and re-wetting (Marcé et al., 2019; 
Keller et al., 2020), enhanced CH 4 ebullition of seasonally hypoxic lakes (Aben et al., 2017; DelSontro et al., 2018; Bartosiewicz et al., 2019; Beaulieu 
et al., 2019; Sanches et al., 2019) and increased transfer of organic carbon from land to water (particularly in permafrost areas) (Wauthy et al., 2018)

2
276Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Terrestrial ecosystem feedbacks which affect the Earth's climate system dynamics
Perturbations and implications for climate system dynamics for the three global forest biomes
= not applicable–
Magnitude of biological mechanisms /
Net effect in climate system dynamics:Low impact
on the climateEffect of biological mechanisms: IncreaseD ecrease Low effect
Strong cooling Moderate cooling Moderate warming Strong warming= not assesse d naImplications
Climatic
MixedBiological mechanism in
Tropical forestsBiological mechanism in
Temperate forestsBiological mechanism in
Perturbation
Die-off events
DefaunationDeforestation
Fragmentation
FiresPhenological shifts
Insect infestations
Composition shifts
Net Primary
Productivity shifts
Biogeochemical shiftsRange shiftsSnow
albedo
–
––
–
–
––
–
–
––Evaporative
cooling
–
––
–
–
––
–
–
–
––Carbon
storageEvaporative
coolingCarbon

coolingSnow
albedoCarbon
storageSnow
albedoNet
effectNet
effectNet
effect
na na na na na na na –
Figure  2.10 |  Terrestrial ecosystem feedbacks, which affect the Earth’s climate system dynamics. Perturbations and implications for climate system dynamics 
(warming/cooling) are shown for the three global forest biomes (adapted from Figure 5 in (Prăvălie, 2018). The strength of the mechanism is estimated in general terms, based on 
the magnitude of carbon storage and evaporative cooling processes that characterise each forest biome (Bonan, 2008). Carbon storage includes forest biomass, without accounting 
for carbon dynamics in soil, peat and underlying permafrost deposits. Implications of bio-geochemical shifts were only estimated in relation to the intensification of the carbon cycle 
and increase in biomass at high latitudes, assuming nitrogen availability for the stoichiometric demands of forest vegetation.

has been estimated to stem from natural ecosystems, especially in 
the Tropics (Tian et al., 2019; Canadell et al., 2021), while freshwater 
wetlands and peatlands are estimated to contribute between 83% 
(top-down estimates) and 40% (bottom-up estimates) of total natural 
CH 4 (and 31 and 20% of total methane emissions, respectively) for 
the period 2008–2017 (Canadell et al., 2021). Median CH 4 emissions 
from northern-latitude wetlands in 2100 were estimated to be 12.1 
and 13.5 PgC in emission scenarios leading to 1.5°C and 2°C warming, 
respectively (Comyn-Platt et al., 2018). Likewise, global warming has 
been attributed to soil N 2O emission increases since the pre-industrial 
period of 0.8 (0.3–1.3) TgN yr-1 (Tian et  al., 2020). Overall, climate 
feedbacks from future altered land ecosystem emissions of CH 4 or N 2O 
are uncertain, but are expected to be small (Canadell et al., 2021).
Changes in regional biodiversity are integral parts of ecosystem–cli-

mate feedback loops, including and beyond carbon cycle processes 
(Figure  2.10; Table  2.4). For instance, the impacts of climate-induced 
altered animal composition and trophic cascades on ecosystem carbon 
turnover (see Sections 2.4.4.4, 2.5.3.4) could be a substantive contribu-
tion to carbon–climate feedbacks (low confidence). Additional surface–atmosphere feedbacks that arise from changes in vegetation cover and 
subsequently altered albedo, evapotranspiration or roughness (often 
summarised as biophysical feedbacks) can be regionally relevant and 
could amplify or dampen vegetation cover changes (Jia et al., 2019).
Climate-induced shifts towards forests in what is currently tundra 
would be expected to reduce regional albedo especially in spring, but 
also during parts of winter when trees are snow-free (whereas tundra 
vegetation would be covered in snow), which amplifies warming 
regionally (high confidence) (Perugini et al., 2017; Jia et al., 2019). Trees

would also enhance momentum absorption compared to low tundra 
vegetation, thus impacting surface–atmosphere mixing of latent 
and sensible heat fluxes (Jia et al., 2019). Boreal forests insulate and 
stabilize permafrost and reduce fluctuations of ground temperature: 
the amplitude of variation of ground surface temperatures was 28°C at 
a forested site, compared to 60°C in nearby grassland (Section 2.5.2.7) 
(Bonan, 1989; Stuenzi et al., 2021a; Stuenzi et al., 2021b). Likewise, a 
shift in moist tropical forests towards vegetation with drought-tolerant 
traits could possibly reduce evapotranspiration, increase albedo, 
alter heat transfer at the surface and lead to a negative feedback to

2
277Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
precipitation (Section 2.5.2.6) (Jia et al., 2019). In savannas, restoration 
of woody vegetation has been shown to enhance cloud formation and 
precipitation in response to enhanced transpiration and turbulent 
mixing, leading to a positive feedback on woody cover (Syktus and 
McAlpine, 2016). While this has not yet been systematically explored, 
similar feedbacks might also emerge from a CO 2-induced woody cover 
increase in savannas (low confidence) (Section 2.5.2.5).
Since biophysical feedbacks can contribute to both surface temperature 
warming or cooling, analyses so far suggest that, on a global scale, the 
net impact on climate change is small (Perugini et al., 2017; Jia et al., 
2019), unless these feedbacks also accelerate vegetation mortality and 
lead to substantive carbon losses (Zemp et al., 2017a; Lemordant and 
Gentine, 2019). More than one-third of the Earth’s land surface has

at least 50% of its evapotranspiration regulated by vegetation, and in 
some regions between 40 and >80% of the land’s evaporated water is 
returned to land as precipitation. Locally, both directly human-mediated 
and climate change-mediated changes in vegetation cover can 
therefore notably affect annual average freshwater availability to 
human societies, especially if negative feedbacks amplify the reduction 
of vegetation cover, evapotranspiration and precipitation (medium 
confidence) (Keys et al., 2016; Keys and Wang-Erlandsson, 2018).
Since AR5, freshwater ecosystems (lakes, reservoirs, rivers and ponds) 
have been increasingly recognised as important sources of GHG 
emissions (CO 2, CH 4 and N 2O) into the atmosphere. Key mechanisms 
which contribute to rising GHG emissions from freshwater ecosystems 
are the temperature imbalance between photosynthesis and 
respiration (respiration increases more than photosynthesis with rising

temperature), CO 2 and CH 4 emissions from exposed sediments during 
droughts, increased transport of matter from land to water, changes in 
water retention time in rivers and lakes and the effects of temperature 
on lake stratification and anoxia that favour CH 4 emissions.
DelSontro et al. (2018) assembled the largest global data set to date 
on emission rates from lakes of CO 2, CH 4 and N 2O and found that 
they co-vary with lake size and trophic state. They estimated that 
moderate global increases in eutrophication of lakes could translate to 
5–40% increases in the GHG effect in the atmosphere. Moreover, they 
estimated that GHG emissions from lakes and impoundments in past 
decades accounted for 1.25–2.30 PgCO 2 yr-1 (DelSontro et al., 2018), 
thus around 20% of global CO 2 emissions from the burning of fossil 
fuels (9.4 PgCO 2 yr-1) (Friedlingstein et al., 2020).
Global warming will strongly enhance freshwater CH 4 emissions through

a disproportionate increase in ebullition (gas flux) by 6–20% per 1°C 
increase in water temperature (Aben et al., 2017). It can be expected that 
ongoing eutrophication enhanced by climate change-related increases 
in the release of sediment nutrients and the loading of organic carbon 
and nutrients from catchments will enhance CH 4 ebullition on a global 
scale (Aben et al., 2017; DelSontro et al., 2018; Bartosiewicz et al., 2019; 
Beaulieu et al., 2019; Sanches et al., 2019). The strongest increase in 
ebullition is expected in shallow waters where sediment temperatures 
are strongly related to atmospheric temperature (Aben et  al., 2017). 
Given that small ponds and shallow lakes are the most abundant 
freshwater ecosystems globally, these may become hot spots of CH 4 
ebullition in the future (Aben et al., 2017). On average, CH 4, CO 2 and N2O account for 75, 23 and 2% of the total CO 2-equivalent emissions, 
respectively, in lakes (DelSontro et al., 2018).

Furthermore, the exposure of lake and river sediments during droughts 
activates the decomposition of buried organic carbon. In dry river 
beds, mineralisation of buried organic matter is likely to increase with 
climate change as anoxic sediments are oxygenated downwards during 
drying, along with pulses of microbial activity following re-wetting 
of desiccated sediment. Conservative estimates indicate that adding 
emissions from exposed sediments of dry inland waters across diverse 
ecosystem types and climate zones to current global estimates of CO 2 
emissions could result in a 6% (~0.12 PgC yr−1) increase of total inland 
water CO 2 emission rates covering streams and rivers (334 mmol m-2 
day-1), lakes and reservoirs (320 mmol m-2 day-1) and small ponds 
(148 mmol m-2 day-1) (Marcé et al., 2019; Keller et al., 2020).
Overall, uncertainty as to the quantity of carbon fluxes within fresh-
water ecosystems and between terrestrial and freshwater systems, and

subsequent emissions to the atmosphere remains very high (Raymond 
et al., 2013; Catalán et al., 2016; Stanley et al., 2016; Evans et al., 2017; 
Drake et al., 2018; Seekell et al., 2018; Sanches et al., 2019; Bodmer 
et al., 2020; Keller et al., 2020; Canadell et al., 2021) (see Table SM2.1.). 
Projections of carbon fluxes are, for example, challenged by the com-
plex interaction between rising water temperatures, loss of ice, changes 
in hydrology, ecosystem productivity, increased extreme events and 
variation in terrestrial-matter transport. While we are still short of 
empirical data, particularly in the Tropics (DelSontro et al., 2018), im-
provements in sensor technology (Eugster et al., 2011; Gonzalez-Valen-
cia et al., 2014; Maeck et al., 2014; Delwiche et al., 2015) and the use 
of statistically robust survey designs (Beaulieu et al., 2016; Wik et al., 
2016) have improved the accuracy of measurements of GHG emissions

in freshwater ecosystems. Global networks such as the Global Lakes 
Ecological Observatory Network (GLEON) increasingly allow a global 
view of carbon fluxes, thereby improving estimates of the contribution 
of freshwater ecosystems to global GHG emissions to the atmosphere.
In summary for freshwater systems, Drake et  al. (2018) aggregated 
contemporary estimates of CO 2 and CH 4 emissions from freshwater 
ecosystems with global estimates made by Raymond et al. (2013), and 
arrived at an estimate of 3.9 PgC yr-1. Rivers and streams accounted 
for 85% and lakes and reservoirs for 15% of the emissions (Raymond 
et  al., 2013). This trend will continue under scenarios of nutrient 
loading to inland waters over the next century where increased CH 4 
emission of inland water has an atmospheric impact of 1.7–2.6 PgC/
CO 2-eq y−1, which is equivalent to 18–33% of annual CO 2 emissions 
from burning fossil fuels (medium evidence, medium agreement)

(Beaulieu et al., 2019). For comparison, annual uptake of CO 2 in land 
ecosystems is estimated as 3.4 (± 0.9) PgC yr−1 (Friedlingstein et al., 
2020). The freshwater numbers combine CO 2 and CH 4 and are thus not 
directly comparable. However, they are indicative of the importance 
of better accounting for freshwater systems in global carbon budgets.
2.5.3.6  Risks to Freshwater Ecosystem Services: Drinking Water, 
Fisheries and Hydropower
AR5 named water supply and biodiversity as freshwater ecosystem 
services vulnerable to climate change. We discuss the risks to these

2
278Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
and to additional services identified by model projections based 
both on climate-change scenarios (Schröter et  al., 2005; Boithias 
et al., 2014; Huang et al., 2019; Jorda-Capdevila et al., 2019) and on 
the Common International Classification of Ecosystem Services (high 
confidence) (CICES, 2018). The effects of floods, droughts, permafrost 
and glacier-melting on global changes in water quality, particularly with 
respect to contamination with pollutants, are described in Section 4.2.6.
2.5.3.6.1 Risks to the quantity and quality of drinking water
Forests and other vegetated ecosystems assist the production of 
drinkable water by facilitating the infiltration of rainfall and snowfall 
into the ground, where water either moves through the saturated soil 
zone to supply streams and other surface waters or infiltrates further 
to recharge groundwater aquifers (Ellison et  al., 2012; Bonnesoeur

et al., 2019). Globally, 4 billion people depend on forested watersheds 
for drinking water (Mekonnen and Hoekstra, 2016). Chapter 4 assesses 
the physical science of water supply, including precipitation, runoff 
and hydrology as well as the social aspects of human water use. This 
section assesses the ecological aspects of risks to freshwater supplies 
for people.
Diminished vegetation cover following wildfires (Section  2.5.3.2) 
and tree mortality (Section  2.5.3.3) can reduce long-term water 
infiltration, increase soil erosion and flash floods and release sediment 
that degrades drinking water quality. Widlfires increase impacts of 
extreme precipitation events due to climate change, which contribute 
to increased surface runoff and hence increased risks of land erosion, 
landslides and flooding (Ebel et  al., 2012; Robinne et  al., 2020). 
Under current conditions, nearly half the global land area is at a

moderate-to-high risk of water scarcity due to wildfires (Robinne et al., 
2018; Robinne et al., 2020). From 1984 to 2014, wildfires in the western 
USA affected 6–11% of stream and river length (Ball et  al., 2021). 
Under a high-emissions scenario of a 3.5°C temperature increase, 
post-fire erosion across the western USA could double sedimentation 
and degrade drinking water quality in one-third of watersheds by 2050 
(Sankey et al., 2017). In Brazil, post-fire vegetation loss tends to increase 
runoff, reduce infiltration and reduce groundwater recharge and flow 
of springs (Rodrigues et al., 2019). Runoff from wildfires can contain 
DOC precursors for the formation of carcinogenic trihalomethanes 
during chlorination of water for drinking (Uzun et al., 2020) as well 
as chromium, mercury, selenium and other toxic trace metals (Burton 
et al., 2016; Burton et al., 2019).
Net effects of deforestation and afforestation on runoff and water

supply depend on local factors, leading to conflicting evidence of 
effects of land cover change (Ellison et al., 2012; Chen et al., 2021b), 
but combinations of climate change and deforestation are projected 
to reduce water flows (Olivares et al., 2019). In southern Thailand, the 
combination of the conversion of forest to rubber plantations and a 
one-third increase in rainfall could increase erosion and sediment load 
by 15% (Trisurat et al., 2016). In the watershed that supplies São Paulo, 
Brazil, afforestation could increase water quantity and quality (Ferreira 
et al., 2019). In most regions with dry or Mediterranean subtropical 
climates, projected climate change can reduce surface water and 
groundwater resources (Doell et al., 2015). In northeast Spain, reduced 
precipitation and vegetation cover under the high-emissions scenario of a 3.5°C temperature increase could reduce drinking water supplies 
by half by 2100 (Bangash et al., 2013).

Changes in algal biomass development and the spread of cyanobacteria 
blooms, related to global warming, resemble those triggered by 
eutrophication with the well-known negative effects on the services 
lakes provide, particularly for drinking water provision and recreation 
(robust evidence, high agreement, high confidence) (Carvalho et al., 
2013; Adrian et al., 2016; Gozlan et al., 2019).
Based on a 10% increase in precipitation, (de Wit et  al., 2016) 
estimated an increased mobilisation of organic carbon from soils to 
freshwaters of at least 30%, demonstrating the importance of climate 
wetting for the carbon cycle. Browning negatively affects the taste of 
drinking water and this may be difficult to address (Kothawala et al., 
2015; Kritzberg et al., 2020). It also often reduces attractiveness for 
recreational purposes, especially swimming (Arthington and Hadwen, 
2003; Keeler et  al., 2015). Based on a worst-case climate scenario

until 2030, (Weyhenmeyer et al., 2016) projected an increase in the 
browning of lakes and rivers in boreal Sweden by a factor of 1.3. The 
chemical character of DOM, as modified by climate change (Kellerman 
et al., 2014), determines its amenability to removal by water treatment 
(Ritson et al., 2014). Therefore, in order to provide safe and acceptable 
drinking water, more advanced, more expensive and more energy/
resource-intensive technical solutions may be required (Matilainen 
In summary, climate change increases risks to the integrity of 
watersheds and the provision of safe, acceptable freshwater to people 
2.5.3.6.2 Risks to freshwater fisheries and biodiversity
Climate change will increase water temperatures and decrease 
dissolved oxygen levels (Section 2.3.1), impacting freshwater fisheries 
which form an important ecosystem service (Vári et al., 2022). People

living in the vicinity of cold lakes will be affected by projected losses 
of ice. In a worst-case scenario (an air temperatures increase of 8°C), 
230,400 lakes and 656 million people in 50 countries will be impacted 
(Reid et al., 2019; Sharma et al., 2019). Winter ice-fishing (Orru et al., 
2014), transportation via ice roads (Prowse et al., 2011) and cultural 
activities (Magnuson and Lathrop, 2014) are ecosystem services at 
stake from the ongoing loss of lake ice.
Eutrophication of central European lakes has wiped out a significant 
proportion of the endemic fish fauna (Vonlanthen et  al., 2012), so 
climate-induced further eutrophication is expected to represent an 
additional threat to fish fauna and commercial fisheries (Ficke et al., 
2007). Given that the ecological consequences of lake warming may 
be especially strong in the Tropics (Section 2.3.1.1), ecosystem services 
may be most affected there. Tropical lakes support important fisheries

(Lynch et al., 2016a; McIntyre et al., 2016) that provide a critical source 
of nutrition to adjacent human populations. These lakes are especially 
prone to the loss of deep-water oxygen due to warming, with adverse 
consequences for the productivity of fisheries and for biodiversity 
(medium evidence, medium agreement) (Lewis Jr, 2000; Van Bocxlaer 
et al., 2012).

2
279Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Tropical lakes tend to be hotspots of freshwater biodiversity (Vadebon-
coeur et al., 2011; Brawand et al., 2014; Sterner et al., 2020); ancient 
tropical lakes such as Malawi, Tanganyika, Victoria, Titicaca, Towuti 
and Matano hold thousands of animal species found nowhere else 
(Vadeboncoeur et al., 2011). While biodiversity and several ecosystem 
services can be considered synergistic (food webs, tourism and of aes-
thetic and spiritual value) (Langhans et al., 2019), others can be con-
sidered antagonistic in case of a strong ecosystem service demand 
(such as water abstraction, water use and food security in terms of 
overexploitation). Here, the balance between biodiversity and eco-
system services is key (Langhans et al., 2019), where biodiversity can 
be integrated into water policy by means of integrated water resource 
management (IWRM) towards NbS (Ligtvoet et al., 2017)

2.5.3.6.3 Risks to hydropower and erosion control
River banks, riparian vegetation and macrophyte beds play important 
roles in erosion control through reducing current velocities, increasing 
sedimentation and reducing turbidity (Madsen et al., 2001). Rates of 
flow in rivers affect inland navigation (Vári et  al., 2022). Changing 
seasonality in snow-dominated basins is expected to enhance 
hydropower production in winter but decrease it during summer (Doell 
et  al., 2015). Glacier melt changes hydrological regimes, sediment 
transport and bio-geochemical and contaminant fluxes from rivers 
to oceans, profoundly influencing ecosystem services that glacier-fed 
rivers provide, particularly the provision of water for agriculture, 
hydropower and consumption (Milner et  al., 2017). Loss of glacial 
mass and snowpack has already impacted flow rates, quantities and 
seasonality (Chapter 4, in this report) (Hock et al., 2019). Meltwater

yields from glacier ice are likely to increase in many regions during the 
next decades but decrease thereafter, as glaciers become smaller and 
smaller and finally disappear (Hock et al., 2019).
2.5.4  Key Risks to Terrestrial and Freshwater 
Ecosystems from Climate Change
Among numerous risks to terrestrial and freshwater ecosystems from 
climate change, this chapter identified five phenomena as the most 
fundamental risks of climate change to ecosystem integrity and the 
ecosystem services that support human well-being that are also 
quantified sufficiently to estimate risk thresholds with at least medium 
confidence : Biodiversity loss (global losses of species from ecosystems), 
ecosystem structure change, increased tree mortality, increased wildfire, 
and ecosystem carbon losses and (Table 2.5, Table SM2.5; Figure 2.11). 
These key risks form part of the overall assessment of key risks in 
Chapter 16. The AR5 chapter on terrestrial ecosystems (Settele et al.,

2014) had also identified three of these key risks—species extinctions, 
tree mortality and ecosystem carbon losses—and a fourth—invasion 
by non-native species. This chapter assesses, in multiple sections, the 
impacts of climate change on invasive species with respect to different 
processes or systems (e.g., in Section  2.4.2.3.3), and includes this 
aspect here in a new broader key risk of ecosystem structure change. 
The AR5 included wildfire as a mechanism of the key risk of ecosystem 
carbon loss. Based on additional research and field experience with 
major wildfires since then, this chapter sets wildfire apart as a specific 
key risk to ecosystem integrity and human well-being. These different measures of risk are interconnected, but approach the assessment of 
the risks to terrestrial and freshwater ecosytems from different angles, 
using complementary metrics.
Species are the fundamental unit of ecosystems. As species become

rare, their roles in the functioning of the ecosystem diminishes and 
disappears altogether if they become locally extinct (high confidence) 
(Isbell et al., 2015; Chen et al., 2018b; van der Plas, 2019; Wang et al., 
2021b). Loss of species and functional groups reduces the ability of 
an ecosystem to provide services, and lowers its resilience to climate 
change (high confidence) (Section  2.6.7) (Elmqvist et  al., 2003; 
Cadotte et al., 2011; Harrison et al., 2014; Carlucci et al., 2020). For 
example, among crop systems, a key factor to succesful pollination is 
the phylogenetic diversity of bee species available, more than total 
abundances (Drossart and Gérard, 2020). Because many species have 
obligate interactions with, or are resources for, other species (e.g., 
predators and their prey, insects and their host plants, plants and 
their mycorrhizae symbionts), the loss of one species affects the risk 
to another species, and, ultimately, ecosystem functioning (Mahoney

and Bishop, 2017)
Global rates of species extinction are accelerating dramatically 
(Barnosky et  al., 2011), with approximately 10% of species having 
been driven extinct by humans since the late Pleistocene, principally 
by overexploitation and habitat destruction, a rate estimated to be 
1000  times higher than pre-Anthropocene (natural) background 
extinction rates (De Vos et al., 2015). Therefore, this level—10%—of 
species becoming “endangered” (sensu IUCN),and therefore at high 
risk of extinction, due to the loss of suitable climate space (Figure 2.8b), 
is used here as a threshold, moving the risk to biodiversity from 
moderate to high, and twice that (20%) as the threshold from high 
to very high.
Key risks assessed here are interconnected. Extinction of species is an 
irreversible impact of climate change and has negative consequences 
on ecosystem integrity and functioning, and the risks increase steeply

with even small rises in global temperature (Section 2.5.1.3, Figure 2.6, 
Figure  2.7, Figure  2.8). Continued climate change substantially 
increases the risk of carbon losses due to wildfires, tree mortality from 
drought and insect pest outbreaks, peatland drying, permafrost thaw 
and changes in the structure of ecosystems; these could exacerbate 
self-reinforcing feedbacks between emissions from high-carbon 
ecosystems and increasing global temperatures (medium confidence). 
Thawing of Arctic permafrost alone could release 11–200 GtC (medium 
confidence). Complex interactions of climate changes, LULCC, carbon 
dioxide fluxes and vegetation changes will regulate the future carbon 
balance of the biosphere, processes incompletely represented in ESMs. 
The exact timing and magnitude of climate–biosphere feedbacks and 
the potential tipping points of carbon loss are characterised by broad 
ranges of the estimates, but studies indicate that increased ecosystem

carbon losses could cause extreme future temperature increases 
(medium confidence). (Sections  2.5.2.7, 2.5.2.8, 2.5.2.9, 2.5.3.2, 
2.5.3.3, 2.5.3.4, 2.5.3.5, Figure 2.10, Figure 2.11, Table 2.4, Table 2.5, 
Table SM2.2, Table SM2.5)

2
280Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Table 2.5 |  Key risks to terrestrial and freshwater ecosystems from climate change. This IPCC chapter assesses these as the most fundamental risks of climate change to ecosystem 
integrity and the ecosystem services that support human well-being. Climate factors include the primary variables governing the risk. Non-climate factors include other phenomena 
that can dominate or contribute to the risk. Detection and attribution comprise cases of observed changes attributed predominantly, or in part, to climate change, with some 
cases being attributed to anthropogenic climate change (Sections 2.4.2, 2.4.3, 2.4.4, 2.4.5, Table 2.2, Table 2.3, Table SM2.1). Adaptation includes options to address the risk 
(Section 2.6). Risk transitions (defined in Figure 2.11) indicate an approximate GSAT increase, relative to the pre-industrial period (1850–1900), to move from one level of risk to

the other as well as assessed confidence. Table SM2.5 provides details of the temperature levels for risk transitions. Both tables provides details for the key risk burning embers 
diagram (Figure 2.11).
Global biodiversity loss: Increasing numbers of plant and animal species at high extinction risk (species becoming endangered with projected loss of >50% of range). The transition from 
non-detectable risk to moderate risk was based on the observed documentation of hundreds of local population extinctions, major declines in many sub-species and two to 92 global species 
extinctions that are attributable to climate change (with medium confidence or higher). The transition from moderate risk to high risk of biodiversity loss is centred around 1.5°C, based on a 
few taxa that are known from their basic biology and habitat requirements to be at high risk of extinction (endangered) at 1.5°C, and on the increasing number of taxa that are projected to

have a high extinction risk affecting >10% of the species in that taxa (1000 times the natural background rates of extinction). The transition to very high risk of biodiversity loss comes from 
the increasing number of taxa projected to have >20% of species at a high risk of extinction. In the worst-case scenario (10th percentile of the models), some of the taxa show >50% of the 
species at a high risk of extinction. These assessments are also weighted by role the species in the taxa play in performing ecosystem services (both to the ecosystems and to humans, e.g., 
pollinators, detritivores). There is high confidence for the moderate risk threshold because it is based on observed trends attributed to climate change. There is medium confidence for future 
projections since, for the purpose of developing this burning embers diagram, these risk thresholds are based on one large study (covering >119,000 species) for which there were multiple

warming scenarios considered, and primarily on the loss of suitable climate. Based on Sections 2.4.2, 2.5.1, 2.6.1, 2.6.6, Table 2.3, Figure 2.6, Table SM2.1 and Table SM2.2.
Climate factors Non-climate factors Detection and attribution AdaptationRisk transitions
(confidence)
Shifts in geographic placements 
of climate space; loss of climate 
space globally; emergence of 
non-analogue climates, increases 
in extreme climate eventsLUC, habitat degradation (e.g. 
from pollution, fertilisation, and 
invasive species)Already observed: many cases 
of population extinctions; 2 to 
92 cases of species extinctions 
(2.4.2.2, 2.4.2.7.1);
species have tracked their climate 
niches raising confidence in SDM 
projections (2.4.2.1, 2.4.2.3, 
2.4.2.5)Habitat restoration, habitat 
creation, increased connectivity 
of habitats and protected areas, 
increase in protected areas, 
assisted colonisation0.8°C undetectable risk to 
moderate risk (high confidence)
1.58°C moderate risk to high risk

2.07°C high risk to very high risk 
Ecosystem structure change: increasing risk of large-scale changes in ecosystem structure. Ecosystem structural change with most information derived for tropical forests, boreal forests, 
savannas and tundra for both observations and future projections. The transition from non-detectable risk to moderate risk is based on detected changes attributable to climate change or 
to interactions between changing disturbance regime, climate and rising CO 2. These changes have already been observed at 0.5°C above pre-industrial levels, with shifts initially detected 
in boreal forests, tundra and tropical grassy ecosystems. The transition from moderate risk to high risk is centred around 1.5°C, based on widespread global observations (at a current GSAT

of 1.09°C above pre-industrial levels) that agree with projected future impacts with at least 10% area of key ecosystems being affected (Box 2.1). Overall, there is medium confidence in 
projections. This is based on existing observations and some projections that have a high confidence of risk for several ecosystems, but data and projections are not available for all biomes, 
thus lowering overall confidence to medium confidence. The transition from high risk to very high risk occurs when >50% of multiple ecosystems are projected to experience shifts in 
structure. (Sections 2.4.2.3, 2.4.3, 2.4.5, 2.5.2, Box 2.1, Figure Box 2.1.1, Table Box 2.1.1, Table SM2.2, Table SM2.3, Table SM2.4, Table SM2.5)
Increases in average and extreme 
temperatures, changes in 
precipitation volume and timing, 
increased atmospheric CO 2LUC, livestock grazing, 
deforestation, fire suppression,

loss of native herbivores, food, 
fiber and wood productionIndividual species range shifts, 
biome shiftsConservation of potential refugia, 
habitat restoration, increasing 
connectivity of habitats and 
protected areas, increase in 
protected areas, changes in 
grazing and fire management0.5°C undetectable rsik to 
1.5°C moderate risk to high risk 
2.5°C high risk to very high risk 
Tree mortality: tree mortality that exceeds natural levels degrades habitat for plant and animal species, increases carbon emissions and reduces water supplies for people. Anthropogenic 
climate change caused three cases of drought-induced tree mortality in the period 1945–2007 in western North America, the African Sahel and north Africa in temperate and tropical

ecosystems. Increased pest infestations and wildfires due to climate change also caused much of the recent tree mortality in North America. These changes occurred at GMST increases of 
0.3°C–0.9°C above those in the pre-industrial period. Models project increasingly extensive drought-induced tree mortality at continued temperature increases of 1°C–2°C. Models project 
risks of mortality of up to half the forest area in different biomes at temperature increases of 2.5°C–4.5°C. In Amazon rainforests, insufficient plant moisture reserves during drought increase 
the risk of tree mortality, and, combined with increased fire from climate change and deforestation, the risk of a tipping point of massive forest dieback and a biome shift to grassland. 
(Sections 2.4.4.3, 2.5.2.6, 2.5.3.3, 2.5.3.5)
Increase in temperature, decrease 
in precipitation, increase in aridity,

increase in the frequency and 
severity of droughtDeforestation, LUCTree mortality up to 20% in 
three regions in Africa and North 
AmericaReduce deforestation, reduce 
habitat fragmentation, encourage 
natural regeneration, restore 
fragmented habitats0.6°C undetectable risk to 
3.5°C high risk to very high risk 

2
281Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Key risks to terrestrial and freshwater ecosystems from climate change
2011–2020Very high
High
Moderate
UndetectableLevel of observed
impact or future riskConfidence level
••
•••
••••
LowMediumHighVery high
range
Very high = Very high probability of severe impacts or risks and 
the presence of significant irreversibility or the persistence of 
climate-related hazards, combined with limited ability to adapt;  
High = Significant and widespread impacts or risks;   
Moderate = Impacts or risks are detectable and attributable to 
climate change with at least medium confidence;  
Undetectable = Impacts or risks are undetectable. Global mean surface  
temperature change 
0234
1.55 °C••••• ••
loss••• •• ••
Structure
change••• •• ••
Tree
mortality••• •• ••
Wildfire
increase•• •• •
Carbon
loss1

Figure 2.11 |  Key risks to terrestrial and freshwater ecosystems from climate change. This IPCC chapter assesses these as fundamental risks of climate change to 
ecosystem integrity and the ecosystem services that support human well-being, based on observed impacts and future risks of: (far-left) “biodiversity loss” refers to losses of animal 
and plant species from different ecosystems globally, with resulting declines in ecosystem integrity, functioning and resilience (Section 2.4.2.1, 2.4.2.2, 2.5.1.3.3); (middle-left) 
“structure change” refers to major changes occurring in ecosystem structure (Sections 2.4.3, Box 2.1, 2.5.2, Figure 2.9, Figure Box 2.1.1, Table Box 2.1.1, Table SM2.5); (middle) 
“tree mortality” refers to tree mortality exceeding natural levels (2.4.4.3, 2.5.3.3); (middle- right) “wildfire increase” refers to wildfire exceeding natural levels (Section 2.4.4.2,

2.5.3.2); (far-right) “carbon loss” refers to ecosystem carbon losses that could occur abruptly and substantially raise atmospheric carbon dioxide (Sections 2.4.3.6–2.4.3.9, 2.4.4.4, 
2.5.2.6–2.5.2.10, 2.5.3.4, 2.5.3.5). This burning embers diagram shows impacts and risks in relation to changes in GSAT, relative to the pre-industrial period (1850–1900). Risk 
levels reflect current levels of adaptation and do not include more interventions that could lower risk. The compound effects of climate change, combined with deforestation, 
agricultural expansion and urbanisation as well as air, water and soil pollution and other non-climate hazards could increase risks. Tables 2.5 and SM2.5 provide details of the key 
risks and temperature levels for the risk transitions.Wildfire: increasing risk of wildfire that exceeds natural levels, damaging ecosystems, increasing human diseases and deaths and increasing carbon emissions. Field evidence shows that

anthropogenic climate change has increased the area burned by wildfire above natural levels across western North America in the period 1984–2017, increasing burned area up to 11 times 
in one extreme year and doubling burned area over natural levels in a 32-year period. Burned area has increased in the Amazon, the Arctic, Australia and parts of Africa and Asia, consistent 
with but not formally attributed to anthropogenic climate change. These changes have occurred at GMST increases of 0.6°C–0.9°C. Empirical and dynamic global vegetation models project 
increases in burned area and fire frequency above natural levels on all continents under continued climate change, the emergence of an anthropogenic signal from natural variation in 
fire weather for a third of the global area and increases of burned area in regions where fire was previously rare or absent, particularly the Arctic tundra and Amazon rainforest, at global

temperature increases of 1.5°C–2.5°C. Models project up to a doubling of burned area globally and wildfire-induced conversion of up to half the area of the Amazon rainforest to grassland 
at temperature increases of 3°C–4.5°C. (Sections 2.4.4.2, 2.5.3.2)
Increase in the magnitude and 
duration of high temperatures, 
decrease in precipitation, decrease 
in relative humidityDeforestation, agricultural 
burning, peatland burningIncreased burned area in western 
North America above natural 
levelsReduce deforestation, reduce the 
use of fire in tropical forests, use 
prescribed burning and allow 
naturally ignited fires to burn 
in targeted areas to reduce fuel 
loads, encourage settlement in 
non-fire-prone areas0.75°C undetectable risk to 
2.0°C moderate risk to high risk 
4.0°C high risk to very high risk 

Ecosystem carbon loss: increasing risk of ecosystem carbon losses that could substantially raise the atmospheric carbon dioxide level. Measurements have detected emissions of carbon 
from boreal, temperate and tropical ecosystems in places where increases in wildfire and tree mortality have been attributed to anthropogenic climate change, at GMST increases of 
0.6°C–0.9°C above the pre-industrial period. Many factors govern the carbon balance of ecosystems, so changes have not been attributed to climate change. Tropical forests and Arctic 
permafrost contain the highest ecosystem stocks of above- and below-ground carbon, respectively. Due to deforestation and forest degradation, primary tropical forests currently emit more 
carbon to the atmosphere than they remove. Wildfires in the Arctic are contributing to permafrost thaw and soil carbon release. An emissions scenario of 2°C increase could thaw ~15%

of permafrost area and emit 20–100 GtC by 2100. Under emissions scenarios of 4°C global temperature increase, models project possible tipping points of conversion of half the Amazon 
rainforest to grassland and thawing of Arctic permafrost that could release 11–200 GtC which could substantially exacerbate climate change (Sections 2.4.3, 2.4.4.3, 2.4.4.4, 2.5.2.7–10, 
2.5.3.2–5, Figure 2.9, Figure 2.10, Figure 2.11, Table 2.4, Table 2.5, Table SM2.2, Table SM2.3, Table SM2.5).
Increase in temperature, increase 
in aridity, increase in the 
frequency and severity of droughtDeforestation, road and 
infrastructure expansion, 
agricultural expansionLosses of carbon detected in 
boreal, temperate and tropical 
ecosystems due to wildfire and 
tree mortality, not formally 
attributed to climate changeReduce deforestation, especially 
in tropical forests, reduce road

and infrastructure expansion, 
especially in the Arctic, reduce the 
use of fire to clear agricultural 
land, increase protected areas0.75°C undetectable risk 
to moderate risk (medium 
confidence)
2°C: moderate risk to high risk 
4°C high risk to very high risk 
(low confidence)

2
282Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
FAQ 2.4 | How does nature benefit human health and well-being and how does climate change affect this?
Human health and well-being are highly dependent on the ‘health’ of nature. Nature provides material and economic services that are 
essential for human health and productive livelihoods. Studies also show that being in ‘direct contact with natural environments’ has direct 
positive effects on well-being, health and socio-cognitive abilities. Therefore, the loss of species and biodiversity due to climate change will 
reduce natural spaces and, in turn, decrease human well-being and health worldwide.
Human health and well-being are highly dependent on the ‘health’ of nature. Biodiversity—the variety of genes, 
species, communities and ecosystems—provides services that are essential for human health and productive

livelihoods, such as breathable air, drinkable water, productive oceans and fertile soils for growing food and fuels. 
Natural ecosystems also help store carbon and regulate climate, floods, disease, pollution and water quality. The 
loss of species, leading to reduced biodiversity, has direct and measurable negative effects on all of these essential 
services, and therefore on humankind. A recent demonstration of this is the decline of pollinator species, with 
potential negative effects on crop pollination, a fundamental ecosystem function crucial for agriculture. The loss of 
wild relatives of the domesticated varieties that humans rely on for agriculture reduces the genetic variability that 
may be needed to support the adaptation of crops to future environmental and social challenges.
Positive relationship between human health and well-being and nature conservation
Greater energy 
& vitalityHealthier 
blood pressure 
& heart rate
Improved 
mental healthPersonal

growthNature conservation
Improved human well-beingFeelings of
happiness & peaceNature stewardship
& protectionEcosystem 
conservation
& restorationFood security &
sustainabilityClean airClean
water
Fertile oceans & 
productive soils Climate 
regulation
Ecosystem
services
Contact
with nature
Figure FAQ2.4.1 |  The positive relationship between human health and well-being and nature conservation. Nature provides essential services 
to humans including material and economic services (i.e., ecosystem services) as well as cultural, experiential and recreational services, which, in turn, enhance 
human psychological and physical health and well-being. People who are more connected to nature are not only happier and healthier but are also more likely 
to engage in pro-nature behaviours, making the enhancement of human–nature connectedness worldwide a valuable win–win solution for humans and nature 
to face environmental challenges.

2
283Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
2.6  Climate Change Adaptation for Terrestrial 
and Freshwater Ecosystems
Adaptation to reduce the vulnerability of ecosystems and their services 
to climate change has been addressed in previous IPCC reports, 
with AR4 and AR5 recognising both autonomous adaptation and 
human-assisted adaptation to protect natural species and ecosystems. 
In AR5, Ecosystem-based Adaptation (EbA), adaptation for people, 
based on the better protection, restoration and management of the 
natural environment, was identified as an area of emerging opportunity, 
with a dedicated Cross-Chapter Box on the topic. In the SRCCL, 
conservation, EbA and related concepts were integrated throughout; 
SR1.5 also noted the role of EbA. Since the last assessment report, the 
scientific literature has expanded considerably, with growing interest 
in the concept of Nature-based Solutions (NbS). This section assesses

this new literature and its implications for the implementation of 
climate change adaptation.
Previous sections of this chapter have set out the vulnerability of 
natural and semi-natural ecosystems to climate change, and the risks 
this poses to both biodiversity and ecosystem services (also sometimes 
described as ‘Nature’s Contributions to People’). Natural systems 
respond to climatic and other environmental changes in a variety of 
ways. Individual organisms can respond through growth, movement 
and developmental processes. Species and populations genetically 
adapt to changing conditions and evolve over successive generations. 
Geomorphological features, such as the path of watercourses, can also 
change naturally in response to climate change. However, there is a 
limit to which these natural processes can maintain biodiversity and 
the benefits people derive from nature, partly due to intrinsic limits, 
but also because of the pressures that people exert on the natural

environment.Most of this section therefore focusses on human interventions to 
build the resilience of ecosystems, enable species to survive or to 
adjust management to climate change. Vulnerability is, in many cases, 
exacerbated by the degraded state of many ecosystems as a result of 
human exploitation and LUC, leading to the fragmentation of habitats, 
the loss of species and impaired ecosystem function. This interaction 
between climate change and environmental degradation means 
that protecting ecosystems in a natural or near-natural state will be 
an important pre-requisite for maintaining resilience and give many 
species the best chance of persisting in a changed climate (Belote 
et al., 2017; Arneth et al., 2020; Ferrier et al., 2020; França et al., 2020). 
Protection from degradation, deforestation and exploitation is also 
essential to maintain critical ecosystem services, including carbon 
storage and sequestration and water supply (Dinerstein et al., 2020;

It is worth briefly considering some key concepts that are relevant to 
adaptation in ecosystems. Adaptation for biodiversity and ecosystems 
can encompass both managing change and building resilience. We use 
the definition of ‘resilience’ set out Chapter 1: ‘the capacity of social, 
economic and environmental systems to cope with a hazardous event or 
trend or disturbance, responding or reorganising in ways that maintain 
their essential function, identity and structure while also maintaining 
the capacity for adaptation, learning and transformation’. This includes 
the concept of ‘resistance’, which is used in some ecological literature 
to distinguish systems which are resistant to change from those that 
recover quickly from change. We consider both interventions designed 
primarily to protect biodiversity and those intended to reduce the risks 
of climate change to people.
A variety of terms are used to describe using environmental management

to reduce the impacts of climate change on people in ways that also 
benefit biodiversity in the scientific literature, particularly EbA and NbS 
(see also Section 1.4). EbA is the use of biodiversity and ecosystem The number of species that can be lost before negative impacts occur is not known and is likely to differ in different 
systems. However, in general, more diverse systems are more resilient to disturbances and able to recover from 
extreme events more quickly. Biodiversity loss means there are fewer connections within an ecosystem. A simpler 
food web with fewer interactions means less redundancy in the system, reducing the stability and ability of plants 
and animal communities to recover from disturbances and extreme weather events such as floods and drought.
In addition to ‘material’ and economic services such as eco-tourism, nature also provides cultural services such as

recreation, spirituality and well-being. Specifically, being in ‘direct contact with natural environments’ (vs. an urban 
environment) has a high positive impact on human well-being (e.g., mood, happiness), psychological and physical 
health (energy, vitality, heart rate, depression) and socio-cognitive abilities (attention, memory, hyperactivity, 
altruism, cooperation). Therefore, the loss of species from climate change and urbanisation will reduce natural 
spaces, decrease biodiversity, and, in turn, decrease human well-being and health worldwide.
Finally, the extent to which humans consider themselves part of the natural world—known as human-nature 
connectedness—has been demonstrated to be closely associated with human health and well-being. Individuals 
who are more connected to nature are not only happier and healthier but also tend to engage more in pro-nature 
behaviours, making the enhancement of human–nature connectedness worldwide a valuable win–win solution for

humans and nature to face environmental challenges.Box FAQ 2.4 (continued)

2
284Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
services as part of an overall adaptation strategy to help people to 
adapt to climate change (Secretariat of the Convention on Biological 
Diversity, 2020). EbA aims to maintain and increase the resilience and 
reduce the vulnerability of ecosystems and people in the face of the 
adverse effects of climate change (Vignola et al., 2009). NbS is a broader 
term which is not restricted to climate change, and is also often used to 
refer to climate change mitigation; it has been defined by the IUCN as 
‘Actions to protect, sustainably manage and restore natural or modified 
simultaneously providing human well-being and biodiversity benefits’ 
(Cohen-Shacham et al., 2016). This widely accepted definition excludes 
actions which use the natural environment to solve human problems

but do not provide benefits for biodiversity, and is closely linked to the 
concept of the Ecosystem Approach. NbS is not a universally accepted 
term, but it is being increasingly used in the scientific literature. It is a 
concept which recognises the importance of biodiversity in ecosystem 
service provision, and offers the opportunity to address climate change 
and loss of biodiversity together in an efficient integrated way (Chong, 
2014; Seddon et al., 2020a; Ortiz et al., 2021). Given that the focus of 
this chapter is on adaptation, we primarily use the term EbA as it is 
more specific, but we do so understanding that it can be regarded as a 
subset of NbS. The wider concept of NbS for climate change adaptation 
and mitigation is covered in a cross-chapter box on the topic (see 
Cross-Chapter Box NATURAL in this chapter).
Whilst we distinguish between adaptation for biodiversity and EbA, it 
is important to recognise that the two are linked in that, if ecosystems

themselves are not resilient to climate change, they will not be able 
to provide adaptation benefits for people. The case for resourcing 
biodiversity conservation and building the resilience of ecosystems is 
also strengthened when there are direct benefits for people in addition 
to the more general benefits of biodiversity.
Ecosystems are specifically included in the adaptation goals set out in 
the Paris Agreement and are addressed in most national adaptation 
plans (Seddon et  al., 2020b). There are also now a large number 
of adaptation programmes and plans for local governments and 
governmental and non-governmental conservation organisations. 
Adaptation for and by ecosystems needs to be understood and 
developed in the wider contexts of conservation, climate resilient 
development and sustainable development. There are significant 
potential synergies but also conflicts between different objectives 
which require an integrated approach (covered further in 2.6.7).

2.6.1 Limits to Autonomous (Natural) Adaptation
Natural ecosystems often have a high degree of resilience and can, to 
some extent, adjust to change. Species can adjust through evolutionary 
adaptation, distribution change, behavioural change, developmental 
plasticity and ecophysiological adjustment. There are, however, limits 
to autonomous adaptation, because of intrinsic limitations, the rate 
at which the climate is changing and the degraded state of many 
None of the evolutionary changes either documented or theorised 
would enable a species to survive and reproduce in climate spaces that it does not already inhabit. It is very improbable that evolutionary 
responses would be sufficient to prevent species extinctions in the case 
of that species losing its climate space entirely on a regional or global 
scale (section 2.4.2.8) (Parmesan and Hanley, 2015). At the highest 
risk are the world’s most cold-adapted species (whose habitats are

restricted to polar and high mountain-top areas). Examples include the 
polar bear (Regehr et al., 2016), ‘sky-island’ plants in the Tropics (Kidane 
et  al., 2019), mountain-top amphibians in Spain (Enriquez-Urzelai 
et al., 2019), mountain-top lichens in the Appalachians (USA) (Allen 
and Lendemer, 2016) and silverswords in Hawaii (Krushelnycky et al., 
2013). However, there is potential for using evolutionary changes 
to enhance the adaptive capacity of target species, as is being done 
on the Great Barrier Reef by translocating symbionts and corals that 
have survived recent intense heat-induced bleaching events into areas 
that have had large die-off (Rinkevich, 2019). Multiple studies have 
assessed when and how evolution might be able to help wild species 
adapt to climate change (Ratnam et al., 2011; Sgro et al., 2011).
Some of the reasons cited in the literature as limits to autonomous 
adaptation are:
i) Genetic changes in populations require many generations and, for

many species, operate on longer timescales than those on which 
the climate is currently changing. In addition, experiments indicate 
there are strong constraints to ability to evolve beyond current 
climatic limits.
ii) Many species are moving to higher latitudes as the climate warms, 
but not all are keeping pace with changes in suitable climate space 
(Valladares et al., 2014; Mason et al., 2015). Such ‘climate debt’ 
(see sections 2.4.2.3.1, 2.4.2.8, 2.5.1.3.1) indicates an inability 
for non-genetic autonomous adaptation (e.g., evidence of limited 
ability for plastic responses, like those stemming from dispersal 
limitations, behavioural restrictions or physiological constraints).
iii) Some species have a low capacity for dispersal, which, combined 
with increased fragmentation of habitats, creates barriers to range 
shifts to match climate warming. Studies have shown that changes 
in the distribution of species and composition of communities are

limited by the presence of intensively managed agricultural land 
fragmenting natural habitats (Oliver et al., 2017).
There are a variety of mechanisms which promote the resilience of 
ecosystems through persistence, recovery and reorganisation (Falk 
et al., 2019). Changes in the balance of different plant species within 
a community can maintain the persistence of the community itself, 
maintaining its value as a habitat for other species and providing 
ecosystem services. In some cases, there are negative feedback 
mechanisms between biological and physical processes; for example, 
in peatlands, lowered water tables resulting from drier conditions 
can lead to reduced permeability of peat, increasing rates of water 
loss (Page and Baird, 2016). There are limits to this resilience and the 
concept of tipping points beyond which ecosystems change state, and 
returning to the original state has been subject of much recent research

(van Nes et al., 2016). There is clear evidence that the degradation of 
ecosystems has reduced their resilience and that restoration can help 
to reduce risks to biodiversity and ecosystem services, discussed below 
(see Section  2.6.2, 2.6.3). However, as the rate of climate change 
increases, the limits of this approach will start to be reached, and

2
285Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
losses (including some with potentially catastrophic consequences) 
cannot be prevented; this is discussed further in Section 2.6.6.
2.6.2 Adaptation for Biodiversity Conservation
A variety of approaches have been identified as potential adaptation 
measures which people can take to reduce the risks of climate change 
to biodiversity. (Heller and Zavaleta, 2009 ), quoted in AR5, identified 
113 categories of recommendation for adaptation from a survey of 
112 papers and reports. Since then, the literature has expanded with 
thousands of relevant publications. Whilst there is increasing interest in 
adaptation for biodiversity conservation and a wide range of plans and 
strategies, there is less evidence of these plans being implemented. 
Since AR5, a number of studies, predominantly from Europe and North 
America, have investigated the extent to which adaptation has been

integrated into conservation planning and is being implemented on a 
local and regional scale (Macgregor and van Dijk, 2014; Delach et al., 
2019; Prober et al., 2019; Clifford et al., 2020; Barr et al., 2021; Duffield 
et al., 2021). A common pattern in these studies is that vulnerability 
has been assessed and potential adaptation actions identified, but 
implementation has been limited beyond actions to improve ecological 
conditions which may increase resilience on a local scale.
To date, most scientific literature on adaptation to reduce the risks to 
biodiversity from climate change has been based on ecological theory 
rather than on observations or practical experience. A recent review 
(Prober et al., 2019) concluded that out of 473 papers on adaptation, 
only 16% presented new empirical evidence and very few assessed 
the effectiveness of actual adaptation actions. It is also the case 
that relatively little research is focused on local-scale management

interventions rather than on larger-scale strategies (Ledee et al., 2021), 
although there are some exceptions (Duffield et al., 2021).
Although direct assessments of the effectiveness of adaptation actions 
are rare, since AR5, there have been an increasing number of empirical 
analyses of how different land use and management can influence the 
vulnerability of species and habitats. As climate change often interacts 
with other factors including ecosystem degradation and fragmentation 
(Oliver et al., 2015a), actions to address these other interacting factors 
is expected to build resilience to climate change. Table 2.6 summarises 
evidence that supports the main categories of proposed adaptation 
measures. We have taken an inclusive approach and included studies 
that address extreme weather events such as droughts, which may 
be exacerbated by climate change, as well as long-term changes in 
climate variables. We have not distinguished between studies in

which climate change adaptation was an explicit focus and those in 
which lessons for adaptation can be learnt, that is, when studies were 
conducted for other reasons but inform the assessment of the impacts 
of actions identified as potential adaptation measures.
Many climate adaptation actions for biodiversity operate on the 
landscape scale (von Holle et  al., 2020). The total area of habitat, 
how fragmented it is, the size of habitat patches and the connectivity 
between them are inter-linked properties on this scale. A growing 
number of studies have investigated how these properties affect 
species ability to persist in situ and colonise new areas. Overall, larger areas of semi-natural habitat are associated with increased 
resilience to ongoing climate change and extreme events as well as 
the capacity to colonise new areas (Haslem et al., 2015; Oliver et al., 
2017; Papanikolaou et al., 2017). Larger habitat patches can support

larger populations of a given species, which are more likely to maintain 
themselves and recover from periods of adverse conditions. Inhabiting 
a large patch size has been found to increase the resilience of some 
populations of species to extreme events such as droughts (Oliver 
et al., 2015b). They are also more likely to provide a range of different 
resources and microclimate conditions, which may increase the 
chances of the persistence of species under climate change. A larger 
area of habitat may also enable greater connectivity between patches 
and increase the chances of species colonising new areas as they track 
climate change (Oliver et al., 2015b).
Protecting and restoring natural processes is a general principle for 
maintaining and building resilience to climate change for biodiversity 
(Timpane-Padgham et  al., 2017). One element of this is ensuring 
naturally functioning hydrology for wetlands and river systems

(Table 2.6), which is particularly important in a context of changing 
rainfall patterns and increased evapotranspiration. An important 
development in approaches to conservation over recent decades has 
been the concept of re-wilding (Schulte To Bühne et  al., 2021); this 
encompasses a number of elements of restoring natural processes, 
including the reintroduction of top predators, larger conservation 
areas, and less prescriptive outcomes than many previous conservation 
measures. There are elements of re-wilding which may well contribute 
to building resilience to climate change, but it will be increasingly 
important to factor climate change adaptation into the planning of 
re-wilding schemes (Carroll and Noss, 2021).
The most consistently cited climate change adaptation measure for 
species is increasing connectivity to facilitate the colonisation of new 
areas. This reflects the fact that many species’ habitats are highly

fragmented in areas with more intensive land management, which 
prevents them naturally changing their range to track changing 
climatic conditions. Advances and innovations in modelling techniques 
can support decision-making about connectivity (Littlefield et  al., 
2019). There is evidence from empirical and modelling studies that 
species can disperse more effectively in better-connected areas in 
terrestrial habitats (Keeley et al., 2018). The issues are different in more 
natural landscapes—species may still be threatened in intrinsically 
isolated habitats, such as mountain tops, but connectivity cannot 
be created here in the same way. Evidence suggests that increased 
connectivity will only benefit a subset of species, probably those 
which are intermediate-habitat specialists that are able to disperse 
(Pearce-Higgins and Green, 2014). Generalists do not require corridors 
or stepping stones, while many corridors or stepping stones will not be

of sufficient quality to be used by most habitat specialists. There should 
also be a caveat to the general principle that increasing connectivity is 
a benefit for climate change adaptation. It can increase the spread of 
invasive, pest and disease-causing species into newly suitable regions. 
In some places, isolated refugia may better allow vulnerable species 
and biological communities to survive.

2
286Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Table 2.6 |  Evidence to support proposed climate change adaptation measures for biodiversity. The evidence highlights that adaptation for biodiversity conservation is a broad 
concept, encompassing a wide range of actions. It includes targeted interventions to change the microclimate for particular species (e.g., by creating shade) to changing national 
conservation objectives to take account of changing distributions of species and communities. It includes targeted actions addressing both climate change and the protection and 
restoration of ecosystems, with multiple additional benefits including reduced vulnerability to climate change. Most of the studies are not direct tests of the impacts of adaptation 
actions which, as noted above, is an important gap in the evidence. There is also a major limitation in that reported studies are predominantly from Europe, North America and

Australasia, with little research in other regions.
Proposed adaptation measures 
for biodiversityUncertainty 
AssessmentComment Selected references
Protect large areas of natural and 
semi-natural habitatrobust evidence,
high agreementThere is considerable evidence that: intact systems provide better quality and 
quantity of ecosystem services; larger intact areas provide better ecosystem 
services; the risk of species’ extinctions from disturbances including climate 
change, is reduced by having large, connected populations; more biodiverse 
systems provide higher levels of ecosystem services and are more resilient to 
climate change than degraded systems that have lost species(Pimm et al., 2018; Dinerstein et al., 2019; 
Woodley et al., 2019; Brooks et al., 2020; 
Hannah et al., 2020; Luther et al., 2020; 
Zhao et al., 2020; Sala et al., 2021);
Increase connectivity in terrestrial 
habitats: corridors, stepping stonesmedium evidence,
medium

agreementThere is good evidence that some species move more quickly in more 
connected landscapes. However, not all species do and some of those that 
benefit are invasive/pest/disease species; to date, empirical evidence showing 
that connectivity has reduced climate change impacts on species is limited.(Keeley et al., 2018; Stralberg et al., 2019; 
von Holle et al., 2020)
Increase connectivity in river networkslimited evidence,
high agreementConnectivity is needed to maintain the movement of species and 
populations, but river reaches and catchments lack integrated protection(Hermoso et al., 2016; Thieme et al., 2016; 
Abell et al., 2017; Brooks et al., 2018)
Increase habitat patch size and 
expand protected areaslimited evidence,
high agreementGenerally increases resilience because of functioning natural processes, large 
species populations and refugial areas(Eigenbrod et al., 2015; Oliver et al., 
2015a)
Increase replication and 
representation of protected areaslimited evidence,

high agreementVarious benefits inferred, including a wider range of climatic and other 
conditions and less risk of extreme events affecting many rather than few 
areas. More sites available for colonisation by range-expanding species and 
better conditions to maintain species in situ under range contraction.(Mawdsley et al., 2009; Thomas et al., 
2012; Virkkala et al., 2014; Gillingham 
et al., 2015; Pavón-Jordán et al., 2020)
Protect microclimatic refugiamedium evidence,
high agreementLocally cool areas can be identified and there is evidence that species can 
survive better in such areas(Haslem et al., 2015; Suggitt et al., 2015; 
Isaak et al., 2016; Morelli et al., 2016; 
Merriam et al., 2017; Bramer et al., 2018; 
Suggitt et al., 2018; Massimino et al., 2020)
Creating shade to lower temperatures 
for vulnerable specieslimited evidence,
high agreementCreating shade (e.g., of watercourses) has been used as an adaptation

strategy, but improvements in species survival under warming conditions 
have yet to be demonstrated(Broadmeadow et al., 2011; Lagarde et al., 
2012; Patino-Martinez et al., 2012; Thomas 
et al., 2016)
Restoring hydrological processes 
of wetlands, rivers and catchments, 
including by raising water tables 
and restoring original channels of 
watercoursesmedium evidence,
high agreementWetland restoration is well established as a conservation measure in some 
countries. It can reduce vulnerability to drought with climate change, but 
evidence to demonstrate effectiveness as an adaptation measure is limited 
and requires the long-term monitoring of a range of sites. There is little 
restoration of degraded tropical peatlands to date(Carroll et al., 2011; Hossack et al., 2013; 
Dokulil, 2016; Timpane-Padgham et al., 
2017; Moomaw et al., 2018)
Restoration of natural vegetation 
dynamicsmedium evidence,
medium 
agreementIncludes reintroduction of native herbivores and reversing woody

encroachment of savannas. Benefits for biodiversity are well established in a 
wide range of different regions(Coffman et al., 2014; Valkó et al., 2014; 
Batáry et al., 2015; Smit et al., 2016; 
Stevens et al., 2016; Hempson et al., 2017; 
Bakker and Svenning, 2018; Cromsigt 
et al., 2018; Fulbright et al., 2018; 
Olofsson and Post, 2018)
Reduce non-climatic stressors to 
increase resilience of ecosystemslimited evidence,
medium agreementAs a general principle, climate change is recognised as a ‘threat multiplier’ 
but specific details are often unclear(Oliver et al., 2017; Pearce-Higgins et al., 
2019)
Assisted translocation and migration 
of specieslimited evidence,
medium agreementAssisted translocation has been commonly suggested as an adaptation 
measure, but there have been very few examples of this being trialled. 
Translocations have been carried out for other reasons and lessons for 
climate change adaptation have been inferred.(Willis et al., 2009; Brooker et al., 2018;

Skikne et al., 2020)
Intensive management for specific 
speciesmedium evidence,
medium agreementA variety of approaches including manipulating microclimate and competition 
between species to improve chances of survival under climate change(Angerbjörn et al., 2013; Greenwood et al., 
2016; Pearce-Higgins et al., 2019)
Ex situ conservation (seedbanks/
genetic stores, etc.)Not possible to 
assess at presentSeed banks have been established but their long-term effectiveness can only 
be evaluated at a later point(Christmas et al., 2016)
Adjusting conservation strategies and 
site objectives to reflect changing 
species’ distributions and habitat 
characteristicsrobust evidence,
high AgreementConservation management will need to take account of changes that 
cannot be prevented (e.g., the distribution of species and composition of 
communities) to protect and manage biodiversity as effectively as possible in 
a changing climate(Stein et al., 2013; Rannow et al., 2014;

Oliver et al., 2016; Stralberg et al., 2019; 
Duffield et al., 2021)
Softening the matrix of unsuitable 
habitats between patches to increase 
permeability for the movement of 
species in response to climate changelimited evidenceThere is potential for agri-environment schemes to do this in hostile farmed 
landscapes(Donald and Evans, 2006; Stouffer et al., 
2011)

2
287Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
There are many different approaches to increasing connectivity, 
ranging from increasing the overall area of suitable habitat to 
‘corridors’ and ‘stepping stones’, with different strategies likely to 
be more effective for different species and circumstances (Keeley 
et  al., 2018). Connectivity can also be important in increasing 
the resilience of populations to extreme climatic events (Newson 
et  al., 2014; Oliver et  al., 2015b). Within freshwater environments, 
the connectivity of watercourses is essential. Fluvial corridors are 
necessary to ensure the survival of migrating fish populations, even 
without climate change; with climate change, connectivity becomes 
crucial for relatively cold-adapted organisms to migrate upstream to 
colder areas. Connectivity is also important for the larvae of benthic 
invertebrates to be able to drift downstream and hence to disperse

(Brooks et  al., 2018); for adult benthic invertebrates, riparian and 
terrestrial habitat features can potentially affect dispersal. Connectivity 
within river and wetland systems for some species can also mediated 
by more mobile animal species such as fish and birds (Martín-Vélez 
et al., 2020). Which factors are the most important in either promoting 
their colonisation of new sites or persisting in situ will differ between 
species and locations. Some general principles have been recognised 
and can guide conservation policy and practice (Natural England and 
RSPB, 2020; Stralberg et al., 2020), but this will often require additional 
investigation and planning based on an individual understanding of 
the niches of specific species.
Managed translocation, that is, moving species from areas where the 
climate is becoming unsuitable to places where their persistence under 
climate change is more likely, has been discussed as an adaptation

option for many years. So far, there have been very few examples of 
this and it is likely to be a last resort in most cases, as it usually requires 
a large investment of resources, outcomes are uncertain and there may 
be adverse impacts on the receiving sites. Nevertheless, there are cases 
where it may be a be a viable option (Stralberg et al., 2019). This is 
discussed in more detail as a case study in Section 2.6.5.1.
The evidence that species can persist in microclimatic refugia where 
suitable conditions for them are maintained locally (e.g., because 
of variations in topography) has increased in recent years. This has 
opened up the potential to include refugia in conservation plans 
and strategies to facilitate the local survival of species (Jones et al., 
2016; Morelli et al., 2016; Morelli et al., 2020), for example, targeting 
management actions (Sweet et  al., 2019) aimed at supporting 
populations of species. This is likely to become an important aspect

of climate change adaptation for biodiversity conservation in future. It 
is also possible to manipulate microclimate, for example, by creating 
shelters for birds’ nests; see Case Study in 2.6.5.5 of African penguins; 
(Patino-Martinez et  al., 2012). One specific approach of this sort 
is the planting or retention of trees and wooded corridors to shade 
watercourses (Thomas et al., 2016). Riparian shading can also possibly 
help to reduce phytoplankton and benthic diatom growth in smaller 
streams and rivers (Halliday et al., 2016).
Refugia often refer to locally cool places in a landscape, such as 
shaded slopes or high elevations, but they can also include places 
where the supply of water may continue during dry periods (Morelli 
et al., 2016). Monitoring can reveal which streams, wetlands, springs 
and other aquatic resources retain suitable discharges, water quality, wetland area and ecological integrity, especially during dry years

(Cartwright et al., 2020). Measures to conserve drought refugia may 
include protecting springs and other groundwater-fed systems from 
groundwater extraction, contamination, salinisation, surface-water 
diversion, channelisation of streams, trampling by livestock, recreation 
activities and invasive species and the effects of disturbances in the 
surrounding landscape (Cartwright et al., 2020; Krawchuk et al., 2020). 
Restoration of degraded aquatic ecosystems can include removing 
flow-diversion infrastructure, excluding livestock, reducing other 
human impacts, geomorphic restructuring, removing invasive species 
and planting native riparian vegetation.
In fire-prone areas, fire suppression and management are a key element 
of protecting refugia (Section 2.6.5.8 below). In ecosystems in which 
a natural fire regime has been suppressed, restoration practices such 
as prescribed fires, thinning trees and allowing some wildfire where it

benefits the ecosystem can be introduced to reduce increasing risks 
from severe wildfires (Meigs et al., 2020).
Protected areas—areas of land set aside for species and habitat 
protection with legal protection from development or exploitation—
have been a cornerstone of nature conservation for many years. Their 
effectiveness under a changing climate has been the subject of debate 
and investigation. There is now a large body of evidence demonstrating 
that colonisations by range-shifting species are more likely to occur at 
protected sites than at non-protected sites for a wide range of taxa 
(e.g., Thomas et  al., 2012; Gillingham et  al., 2015) including across 
continents (Pavón-Jordán et  al., 2020). This is probably because, 
by protecting large areas of natural and semi-natural habitats, they 
provide suitable places for colonising species (Hiley et al., 2013) which 
may not be available in the surrounding landscape. Although the

evidence for protected areas being associated with reduced extinctions 
is weaker, the finding by Gillingham et  al. (Gillingham et  al., 2015) 
that protected sites were associated with reduced extinction rates at 
low latitudes and elevations is strongly suggestive that they can help 
species’ persistence in the face of climate change.
It is intrinsically difficult to assess the effectiveness of climate change 
adaptation measures, the benefit of which will be realised in the years 
and decades ahead (Morecroft et al., 2019). Nevertheless, taking into 
account the wide range of evidence reported above, including the 
theory, modelling and observations of the impacts of climate change 
in contrasting circumstances, it is possible to make an overarching 
assessment that appropriate adaptation measures can reduce the 
vulnerability of many aspects of biodiversity to climate change 
(robust evidence, high agreement). It is also clear, however, that to

be most effective and avoid unintended consequences, measures 
need to be carefully implemented by taking into account specific local 
circumstances (robust evidence, high agreement) and include the 
management of inevitable changes (robust evidence, high agreement). 
It is also clear that while there are now many plans and strategies for 
adapting biodiversity conservation to climate change, many have yet 
to be implemented fully (medium evidence, high agreement).

2
288Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
2.6.3 Ecosystem-Based Adaptation
A study published in 2020 found that, out of 162 intended nationally 
determined contributions (covering 189  countries) submitted to the 
United Nations Framework Convention on Climate Change (UNFCCC), 
as commitments to action under the Paris Agreement, 109 indicated 
‘ecosystem-orientated visions’ for adaptation, but only 23 used the 
term ‘ecosystem-based adaptation’ (Seddon et al., 2020b).
EbA includes a range of different approaches. Examples include 
restoring coastal and river systems to reduce flood risk and improve 
water quality, and the creation of natural areas within urban areas to 
reduce temperatures through shading and evaporative cooling. EbA 
is closely linked with a variety of other concepts such as ecosystem 
services, natural capital and disaster risk reduction (DRR). EbA 
was becoming a well-recognised concept at the time of AR5 but

implementation was still at an early stage in many cases. Since then, 
pilot studies have been assessed and EbA projects have been initiated 
around the world. The evidence base continues to grow (Table 2.7), 
and this has led to increasing confidence in approaches which have 
been shown to work leading to further expansion in some countries 
(Table 2.7). However, this is not uniform and there is relatively little 
synthesis across disciplines and regions (Seddon et  al., 2020a). 
Chausson et  al. (2020) used a systematic mapping methodology to 
characterise 386 published studies. They found that interventions 
in natural or semi-natural ecosystems ameliorated adverse climate 
change impacts in 66% of cases, with fewer trade-offs than for more 
artificial systems such as plantation forest. However, the evidence base 
has substantial gaps. Most of the evidence has been collected in the 
Global North, and there is a lack of robust, site-specific investigations

into the effectiveness of interventions compared to alternatives and 
more holistic appraisals that account for broader social and ecological 
outcomes.Restoring coasts, rivers and wetlands to reduce flood risk have probably 
seen the largest investment in EbA and it is becoming an increasingly 
accepted approach in some places (e.g., case studies in Sections 2.6.5.2, 
2.6.5.7), although significant social, economic and technical barriers 
remain (Wells et al., 2020; Bark et al., 2021; Hagedoorn et al., 2021). 
Natural flood management (NFM) encompasses a wide range of 
techniques in river systems and at the coast and has been used in 
varied locations around the world. In tropical and subtropical areas, 
the restoration of mangroves to reduce the risk of coastal flooding 
is a widely advocated, evidence-based approach (e.g., (Høye et  al., 
2013; Sierra-Correa and Kintz, 2015; Powell et al., 2019)). In temperate

regions, salt marsh is a similarly important habitat (Spalding et  al., 
2014). Both provide buffering against SLR and storm surges. Managed 
realignment of the coast, by creating new habitats, can lead to a loss 
of terrestrial and freshwater ecosystems, but it can protect them and 
the services they provide by reducing the risks of catastrophic failure 
from hard-engineered sea defences.
In river systems (Iacob et al., 2014), management of both the catchments 
and the channel itself is important: restoring natural meanders in 
canalised watercourses and allowing the build-up of woody debris 
can slow flows rates; restoring upstream wetlands or creating them 
in urban and peri-urban situations can store water during flood events 
if they are in the right place in a catchment (Acreman and Holden, 
2013; Ameli and Creed, 2019; Wu et al., 2020). There is less data on 
the potential for NFM in tropical compared to temperate catchments.

However, (Ogden et al., 2013) showed that flooding was reduced from 
a secondary forested catchment area compared to those which were 
pasture or a mosaic of forest, pasture and subsistence agriculture. EbA 
approaches to reduce flooding can be applied within urban areas as 
well as in rural catchments, as in Durban, South Africa (Section 2.6.5.7), 
but effectiveness will depend on EbA being implemented at a sufficient 
scale and in the right locations (Hobbie and Grimm, 2020; Costa et al., 
2021). This may, in turn, provide protection to downstream urban 
communities.
Table 2.7 |  Examples of key EbA measures with assessments of confidence. Note only adaptation-related services are shown; many measures also provide a range of other benefits 
to people. All also provide benefits for biodiversity.
EbA measuresConfidence 
assessmentEcosystem services 
for climate change 
adaptationClimate change 
impact addressedSocial benefits 
from adaptationRelevant 

and contextsSelected references
Natural flood risk 
management in river 
systems: restoring natural 
river courses (removing 
canalisation), restoring and 
protecting wetlands and 
riparian vegetationmedium evidence,
medium agreementFlood regulation; 
sediment retention; 
water storage; water 
purificationIncreased rainfall 
intensityReduction of flood 
damage
Increased water 
security (quality 
and supply)Multiple(Iacob et al., 2014; Meli 
et al., 2014; Dadson et al., 
2017; Rowiński et al., 2018; 
Burgess-Gamble et al., 2021)
Shade rivers and streams 
by restoration of riparian 
vegetation or trees.medium evidence,
high agreementProvision of fish stocksWarmer water 
temperaturesFood security;
income benefitsMultiple(Broadmeadow et al., 2011; 
Isaak et al., 2015; Williams 
et al., 2015b; Thomas et al., 
2016)
Managed realignment of 
coastlines; re-establishing 
and protecting coastal 
habitats including 
mangroves, saltmarshes, coral 
reefs and oyster reefsrobust evidence,

high agreementCoastal storm and 
flood protection;
coastal erosion control;
prevention of intrusion 
of salt waterSLR;
increasing storm 
energyProtection of 
life, property and 
livelihoods;
water securityCoastal(Høye et al., 2013; Spalding 
et al., 2014; Narayan et al., 
2016; Morris et al., 2018; 
Chowdhury et al., 2019; 
Powell et al., 2019)

2
289Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Agro-forestry and other 
agro-ecological/conservation 
practices on agricultural landmedium evidence,
medium agreementLocal climate 
regulation; soil 
conservation; soil 
nutrient regulation; 
water conservation; 
pest control; food 
provisioningHigh temperature or 
changing temperature 
regimes;
changing precipitation 
regimesFood security;
income benefitsMultiple(Vignola et al., 2015; Torralba 
et al., 2016; Paul et al., 2017; 
Blaser et al., 2018; Nesper 
et al., 2019; Verburg et al., 
2019; Aguilera et al., 2020; 
Tamburini et al., 2020)
Restore and maintain urban 
and peri-urban green space: 
trees, parks, local nature 
reserves, created wetlandsrobust evidence,
high agreementLocal climate 
regulation;

flood regulation;
water purification;
water storage;
erosion controlHigher temperatures 
and heat waves;
increased or reduced 
rainfall intensityCooler 
micro-climate;
reduced flood 
damage;
water securityUrban areas(Norton et al., 2015; Liquete 
et al., 2016; Liu, 2016; Bowler 
et al., 2017; Aram et al., 2019; 
Stefanakis, 2019; Ziter et al., 
2019)
Ecological restoration for 
reducing fire risk by restoring 
natural vegetation and 
herbivory and reinstating 
natural fire regimesmedium evidence,
high agreementRegulation of wildfiresMega-fires from 
increases in drought 
and heatReduce deaths 
and infrastructure 
damage from firesFire-adapted 
ecosystems(Waldram et al., 2008; 
Stephens et al., 2010; van 
Mantgem et al., 2016; 
Boisramé et al., 2017; Johnson 
et al., 2018; Parisien et al., 
2020a; Parisien et al., 2020b; 
Stephens et al., 2020)
Invasive non-native aquatic 
plant control to improve 
water securityrobust evidence,

high agreementWater provision Increasing droughts Water securityWater-scarce 
regions prone 
to an increase 
in droughts(van Wilgen and 
Wannenburgh, 2016)
Woody plant control (of 
encroaching biomass) in 
open grassy ecosystems to 
restore and maintain grassy 
vegetation (see 2.4.3.5)medium evidence,
medium agreementFodder biomass 
productionElevated CO 2 and 
increased rainfall 
promoting tree 
growthIncome through 
bush clearing, 
fuelwood supplies, 
restore grazingSavanna and 
grasslands(Haussmann et al., 2016)
Rangeland rehabilitation and 
management e.g. introducing 
livestock enclosures, 
appropriate grazing 
management, reintroducing 
native grassland speciesmedium evidence,
production; soil erosion 
control; soil formation; 
nutrient cycling; water 
retentionChanging 
precipitation and 
temperature regimes 
including prolonged 
dry seasons and 
increased drought 
frequencyFood security 
Water security,

income benefitsRangelands(Descheemaeker et al., 2010; 
Wairore et al., 2016; Kimiti 
Sustainable forestry of 
biodiverse managed forests, 
maintaining forest cover and 
protecting soilsmedium evidence,
medium agreementTimber productionIncreased frequency 
and severity of storms;
higher temperatures;
regimes (more 
intensive wet and dry 
periods);
increased incidents 
of wildfire, pest and 
disease outbreaksLivelihood and 
income benefitsBoreal, 
temperate, 
subtropical, 
tropical forests(Gyenge et al., 2011; Barsoum 
et al., 2016; Jactel et al., 2017; 
Cabon et al., 2018)
Watershed reforestation and 
conservation for hydrological 
servicesmedium evidence,
medium agreementFlood control; erosion 
control; water 
provisioning; water 
purificationChanging 
precipitation regimesFood security; 
Water security; 
Flood ProtectionBoreal, 
tropical forests(Filoso et al., 2017; 
Bonnesoeur et al., 2019)
Multi-functional forest

management and 
conservation to provide 
climate-resilient sources of 
food and livelihoods and 
protect water sourcesmedium evidence,
medium agreementTimber and non-timber 
forest production; 
fuel wood production; 
water provisioning; 
water purificationMultipleFood security; 
tropical forests(Lunga and Musarurwa, 2016; 
Strauch et al., 2016; Adhikari 
Slope re-vegetation for 
landslide prevention and 
erosion controlrobust evidence,
high agreementSoil retention; slope 
stabilisationIncreased rain 
frequencyReduced landslide 
damage; 
prevention of loss 
of lifeMontane 
and other 
steep-sloped 
regions(Fox et al., 2011a; Krautzer 
et al., 2011; Osano et al., 2013; 
Bedelian and Ogutu, 2017; 
Getzner et al., 2017; de Jesús 
Arce-Mojica et al., 2019)

2
290Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Protecting and restoring natural river systems and natural vegetation 
cover within catchments as well as integrating agro-ecological 
techniques into agricultural systems can also help to maintain 
and manage water supplies for human use, under climate change, 
including during periods of drought, by storing water in catchments 
and improving water quality (Taffarello et al., 2018; Agol et al., 2021; 
Khaniya et  al., 2021). Lara et  al. (2021) showed that replacing a 
non-native Eucalyptus plantation in Chile with native forest caused 
base flow to increase by 28–87% during the restoration period 
compared to pre-treatment, and found that it remained during periods 
with low summer precipitation.
EbA can operate on a range of different scales, from local to catchment 
to region. On the local scale, there is a variety of circumstances in 
which microclimates can be managed and local temperatures lowered

by the presence of vegetation (Table 2.7), and these EbA techniques are 
now being used more widely. In both urban and agricultural situations, 
shade trees are a traditional technique which can be applied to 
contemporary climate change adaptation. As reported in Section 2.6.2 
above, shading of watercourses can lower temperatures, which can 
allow species to survive locally; as well as supporting diversity, it can 
help to maintain important fisheries, including those of salmonid fish 
(O’Briain et al., 2020). Within cities, green spaces, including parks, local 
nature reserves and green roofs and walls can also provide cooling as 
a result of evapotranspiration (Bowler et al., 2010a; Aram et al., 2019; 
Hobbie and Grimm, 2020), although this may be reduced in drought 
Wildfire is an increasing risk for people as well as to ecosystems in many 
parts of the world. As discussed in Section 2.4.4.2, this is the result not

just of climate change but also past management practices, including 
fire suppression. Better fire management including reinstating more 
natural fire regimes can reduce risks.
EbA is usually a place-specific approach and a number of studies have 
documented how attempts to implement it without an understanding 
of local circumstances and the full engagement of local communities 
have been unsuccessful (Nalau et al., 2018). Since AR5, a number of 
studies have considered the factors that are important for environment 
adaptation programmes and projects (UNFCCC, 2015; Nalau et  al., 
2018; Duncan et al., 2020; EPA Network and ENCA, 2020; Townsend 
et al., 2020). Considering these sources, others described above and 
the case studies presented in Section 2.6.5, a number of requirements 
for effective implementation of EbA can be identified, including the 
following:
• Targeting of the right EbA measure in the right location
• Decision-making at the appropriate level of governance with

participation from all affected communities
• Integration of IKLK and capacity into decision-making and the 
management of projects
• Involvement of government and non-government stakeholders
• Full integration of EbA with other policy areas including agriculture, 
water resources and protection of natural resources
• Protection and, if possible, improvement of incomes of local people
• Effective institutional support to manage finances and the 
implementation of projects and programmes• Time—many EbA interventions take time to establish, for example, 
for trees to grow and wetlands to recover
• Monitoring of intended outcomes and other impacts and the 
communication of results
Whilst it is essential to develop place-specific EbA measures with the 
full engagement of local communities, it is worth noting that new 
opportunities may emerge that would not have been possible in the 
past. As the climate changes, novel ecosystems may emerge (with no

present day analogue) which have the potential to provide different 
adaptation benefits, and societies may be more willing to adopt 
transformative approaches (Colloff et al., 2017; Lavorel et al., 2020).
Increasingly, it is essential to integrate adaptation and the protection 
of biodiversity with land-based initiatives to mitigate climate change; 
this is discussed in more detail in Cross-Chapter Box  NATURAL in 
this chapter. The new IUCN standard (IUCN, 2020) offers a basis for 
assessing whether actions are true NbS and take into account the 
wider factors necessary for success.
Whilst policy interest is growing and there is an increasing deployment 
of EbA, there is still a long way to go in delivering its full potential 
(Huq and Stubbings, 2015) and significant institutional and cultural 
barriers remain (Huq et al., 2017; Nalau et al., 2018). Nevertheless, it is 
increasingly clear that EbA can offer a portfolio of effective measures

to reduce the risks for people of climate change at the same time as 
benefitting biodiversity (robust evidence, high agreement), providing 
that such measures are deployed with careful planning in a way 
that is appropriate for local ecological and societal contexts (robust 
evidence, high agreement). This chapter has identified risks to species, 
communities, ecosystems and ecosystem services from climate change, 
all of which increase with each increment of Global Warming Level 
(2.5.1, 2.5.2, 2.5.3, 2.5.4). There is therefore a risk to Ecosystem-based 
Adaptation measures in some circumstances and this risk increases 
progressively above 1.5°C of warming.
2.6.4 Adaptation for Increased Risk of Disease
Low-probability events can have a very high impact (e.g., the 
transmission of SARS-CoV-2 from wild animals to humans, causing 
the Covid-19 pandemic ). A robust disease risk reduction policy would 
include utilising One Biosecurity (Meyerson and Reaser, 2002; Hulme,

2020; MacLeod and Spence, 2020) or One Health (Monath et  al., 
2010; Deem et al., 2018; Destoumieux-Garzón et al., 2018; Zinsstag 
et  al., 2018) approaches with actions to reduce disease risk across 
multiple sectors and from a variety of anthropogenic drivers, including 
climate change, even if there is high uncertainty in projected risk 
(see Cross-Chapter Boxes ILLNESS in this chapter, COVID in Chapter 
7 and DEEP in Chapter 17). Kraemer et al. (2019) found that vector 
importation was a key risk factor and that the focus should be on 
preventing the introduction of invasive species. Furthermore, many 
neglected tropical diseases (NTDs) are also VBDs, and the UN SDG of 
good health and well-being explicitly calls for increased control and 
intervention with a focus on emergency preparedness and response 
(Stensgaard et al., 2019a).

2
291Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Online tools are being developed to warn conservation biologists 
when species of conservation concern are at a greater risk of disease 
outbreaks due to environmental changes, for example, for Hawaiian 
honeycreepers and avian malaria (Berio Fortini et al., 2020) and coral 
diseases (Caldwell et al., 2016). Forecasting models to warn of human 
disease outbreaks like malaria and dengue are also now available, with 
findings that multiple-model ensemble forecasts outperform individual 
models (Lowe et al., 2013; Lowe et al., 2014; Lowe et al., 2018; Zhai 
et al., 2018; Johansson et al., 2019; Tompkins et al., 2019; Muñoz et al., 2020; Colón-González et  al., 2021; Petrova et  al., 2021). Improving 
VBD and NTD public health responses will require multi-disciplinary 
teams capable of interpreting, analysing, and synthesising diverse 
components of complex ecosystem-based studies for effective

intervention (Mills et  al., 2010; Rubin et  al., 2014; Valenzuela and 
Aksoy, 2018), broad epidemiological and entomological surveillance 
(Depaquit et al., 2010; Lindgren et al., 2012; Springer et al., 2016) as 
well as community-based disease control programmes that build local 
capacity (Andersson et al., 2015; Jones et al., 2020b).
Cross-Chapter Box ILLNESS | Infectious Diseases, Biodiversity and Climate: Serious Risks Posed 
by Vector- and Water-Borne Diseases
Authors: Marie-Fanny Racault (UK/France, Chapter 3), Stavana E. Strutz (USA, Chapter 2), Camille Parmesan (France/UK/USA, Chapter 2), 
Rita Adrian (Germany, Chapter 2), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Sarah Cooley (USA, Chapter 3), Meghnath 
Dhimal (Nepal), Luis E. Escobar (Guatemala/USA), Adugna Gemeda (Ethiopia, Chapter 9), Nathalie Jeanne Marie Hilmi (Monaco/France,

Chapter 18), Salvador E. Lluch-Cota (Mexico, Chapter 5), Erin Mordecai (USA), Gretta Pecl (Australia, Chapter 11), A. Townsend Peterson 
(USA), Joacim Rocklöv (Germany/Sweden), Marina Romanello (UK/Argentina/Italy), David Schoeman (Australia, Chapter 3), Jan C. 
Semenza (Italy, Chapter 7), Maria Cristina Tirado (USA/Spain, Chapter 7), Gautam Hirak Talukdar (India, Chapter 2), Yongyut Trisurat 
(Thailand, Chapter 2)
Climate change is altering the life cycles of many pathogenic organisms and changing the risk of transmission of vector- and water-borne 
infectious diseases to humans (high confidence). The rearrangement and emergence of some diseases are already observed in temperate-
zone and high-elevation areas and coastal areas (medium confidence to high confidence, depending upon region). Shifts in the geographic 
and seasonal range suitability of pathogens and vectors are related to climatic-impact drivers (warming, extreme events, precipitation

and humidity) (very high confidence), but there are substantial non-climatic drivers (LUC, wildlife exploitation, habitat degradation, public 
health and socioeconomic conditions) that affect the attribution of the overall impacts on the prevalence or severity of some vector- and 
water-borne infectious diseases over recent decades (high confidence). Adaptation options that involve sustained and rapid surveillance 
systems as well as the preservation and restoration of natural habitats with their associated higher levels of biodiversity, both marine and 
terrestrial, will be key to reducing the risk of epidemics and the large-scale transmission of diseases (medium confidence).
Since AR5, further evidence is showing that climate-related changes in the geographic and seasonal range suitability of pathogens 
and vectors and the prevalence or new emergence of vector- and water-borne infectious diseases have continued across many regions

worldwide and are sustained over decadal timescales (low confidence to high confidence, depending upon region)(Sections 2.4.2.5, 
3.5.5.3, 7.2, 7.3, 9.10.1.2.1) (Harvell et al., 2009; Garrett et al., 2013; Burge et al., 2014; Guzman and Harris, 2015; Baker-Austin et al., 
2018; Watts et al., 2019; Semenza, 2020; Watts et al., 2021). Ecosystem-mediated infectious diseases at risk of increase from climate 
change include water-borne diseases associated with pathogenic Vibrio spp. (e.g., those causing cholera and vibriosis) and harmful algal 
blooms (e.g., ciguatera fish poisoning) (Sections 3.5, 5.12, Table SM3.3) (Bindoff et al., 2019); (Baker-Austin et al., 2013; Levy, 2015; Trtanj 
et al., 2016; Ebi et al., 2017; Mantzouki et al., 2018; Nichols et al., 2018), and VBDs associated with arthropods (e.g., malaria, dengue, 
chikungunya, Zika virus, West Nile virus and Lyme disease), helminths (e.g., schistosomiasis) and zoonotic diseases associated with cattle

and wildlife (e.g., leptospirosis) (low confidence to very high confidence, depending upon disease and region) (Sections 2.4.2.7, 3.5, 7.2, 
7.3, 9.10.1.1.1, 13.7.1.2, 14.4.6, Cross-Chapter Box COVID in Chapter 7; Table Cross-Chapter Box ILLNESS.1) (Hoegh-Guldberg et al., 
2018; Ebi et al., 2021).
The attribution of observed changes in disease incidence, partly or fully, to climatic-impact drivers remains challenging because of the 
difficulty of accurately capturing the contributions of multiple, interacting and often nonlinear underlying responses of host, pathogen and 
vector, which can be influenced further by non-climate stressors and the long history of anthropogenic disturbance. Disease emergence 
in new areas requires independent drivers to coincide (i.e., increasing climate suitability for pathogen or vector survival and competence/
capacity, and introduction of the pathogen, that is often via the mobility of human populations). Furthermore, the extent to which

changes in ecosystem-mediated diseases impact human health is highly dependent on local socioeconomic status, sanitation, medical 
systems and practices (Section 2.4.2.5, Figure FAQ2.3.1) (Gething et al., 2010; Lindgren et al., 2012; Mordecai et al., 2013; Liu-Helmersson 
et al., 2014; Bhatt et al., 2015; Morin et al., 2015; Ryan et al., 2015; Wesolowski et al., 2015; Stanaway et al., 2016; Yamana et al., 2016; 
Mordecai et al., 2017; Tesla et al., 2018; Ryan et al., 2019; Shah et al., 2019; Iwamura et al., 2020; Mordecai et al., 2020; Colón-González 
et al., 2021; Ryan et al., 2021).Thus, risk reduction is more effective when links between climate change, ecosystem change, health and 
adaptation are considered concurrently (Sections 2.4, 3.5.3, 7.2, 7.3, 4.3.3, 6.2.2.3, Table SM2.1).

2
292Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Table Cross-Chapter Box ILLNESS.1 |  Observed climate change impacts on cholera, dengue and malaria incidence. (1) Cholera: endemicity based on (Ali et al., 2015). 
Changes (2003–2018) in suitability for coastal Vibrio cholerae estimated from model observations driven by sea-surface temperature (SST) and chlorophyll a (CHL) 
concentration (Escobar et al., 2015; Watts et al., 2019); vulnerabilities based on Sigudu et al. (2015) Agtini et al. (2005) and Sack et al. (2003). (2) Dengue: endemicity 
based on Guzman and Harris (2015). (3) Malaria: endemicity based on Phillips et al. (2017) and the WHO Global Malaria Programme. Impacts of climate change on 
diseases and their vectors are most evident at the margins of current distributions. However, it is difficult to implicate climate change in areas with extensive existing

transmission and vector/pathogen abundance, and it is particularly difficult to distinguish from concurrent directional trends in disease control, changes in land use, water 
access, socioeconomic and public health conditions. As a result, while many studies indicate increasing climate suitability of some areas for cholera, and changes in disease 
incidence for dengue and malaria, the degree to which these changes can be attributed to climate change remains challenging. Uncertainty statements for malaria and 
dengue reflect the degree to which observed trends in disease incidence can be related to observed climate change in the given region. For cholera, confidence statements 
reflect the degree to which observed trends in disease or pathogen incidence and coastal area suitability for outbreaks can be linked to observed climate change drivers in

the given region. Acronyms: ONI (Oceanic Niño Index), Tmin (minimum temperature), SPI (Standardised Precipitation Index), LST (land surface temperature). Full references 
for this table can be found in Table SM2.6.
Cholera Dengue Malaria
Africa
Endemicity EndemicEndemic in sub-Saharan Africa but not 
South AfricaEndemic
Climate driversDisease incidence: northeast Africa, Central Africa and 
Madagascar: rainfall (medium confidence)
Southeast Africa: rainfall, LST, SST, Plankton (medium 
eastern South Africa: SST, CHL
(low confidence due to limited evidence)
West Africa: rainfall (floods), LST, SST (medium confidence)West Africa: temperature (medium 
East Africa: temperature (medium 
Direction of ChangeArea of coastline suitable for outbreak: northandwest 
Africa: increase (low confidence)
Central and East Africa: no change (low confidence)
South Africa: decrease (low confidence)Potentially expanding
Dengue and A. aegypti present but

underdetected in climatically suitable 
areasEast Africa: upward shift and increase in 
malaria and Anopheles spp. in highland 
areas (medium confidence)
Widespread decreases due to malaria 
control (medium confidence) and 
warming climate (low confidence)
VulnerabilitiesEastern South Africa: women of all ages more affected than 
men by outbreaks
Asia
 Endemicity EndemicEndemic in South Asia, Southeast Asia 
and East AsiaEndemic in South Asia, Southeast Asia, 
partially endemic in East Asia
 Climate driversDisease incidence: East Asia: SST, CHL, SLR (medium 
South Asia: SST, CHL, LST, rainfall (floods) (high confidence)South Asia: rainfall, temperature, 
Humidity (medium confidence)
Southeast Asia: rainfall, temperature 
East Asia: rainfall, temperature, Typhoons 
(low confidence)South Asia: rainfall, temperature (medium 

Direction of ChangeArea of coastline suitable for outbreak: increase (low 
confidence)Southeast Asia: increase (low confidence)
South Asia: increase (medium confidence)
East Asia: increase (low confidence)South Asia: increase (medium confidence)
VulnerabilitiesSoutheast Asia: infants (<9 years) with highest incidences 
of cholera
South Asia: older children and young adults (aged 
16–20 years) more frequently reported with cholera than 
non-cholera diarrhoea
 Endemicity Not endemic Partially endemic in northern Australia Not endemic
Climate drivers No evidence for disease incidence Rainfall, temperature (low confidence)
Direction of ChangeArea of coastline suitable for outbreak: no change (low 
confidence)Increase in sporadic outbreaks due to 
climate change (low confidence)No change
Central America
Endemicity Not endemic Endemic Partially endemic
Climate drivers No evidence for disease incidenceONI, SST, Tmin, temperature, rainfall,

2
293Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Direction of ChangeAreas of coastline suitable for outbreak: decrease (low 
confidence)Increasing due to climate (low 
Upward expansion of A. aegypti (low 
confidence)Overall decrease not linked to climate 
change. Focal increases due to human 
 Endemicity EpidemicEndemic in all regions except southern 
South AmericaEndemic
 Climate driversAbundance of coastal V. cholerae: northwestern South 
America:
SST, Plankton (low confidence)Temperature, precipitation, droughtNorthern South America: temperature 
northern and southeastern South 
America: Tmax, Tmin, humidity (low 
confidence)Increasing due to urbanisation and 
decreased vector control programmes, 
not strongly linked to climateHigher elevation regions: Increase (low 
Europe

Endemicity Not endemic Southern Europe: focal outbreaks Not endemic
 Climate driversNo evidence for disease incidence
Abundance of coastal V. cholerae: northern Europe: SST, 
Plankton (medium confidence)
confidence)Mediterranean regions of southern 
Europe: outbreaks (low confidence)No change
 Endemicity Not endemicPartially endemic in southern North 
AmericaNot endemic
Abundance of coastal V. cholerae: eastern North America: 
SST (low confidence due to limited evidence)Winter Tmin (low confidence)
confidence)Declining No change
 Endemicity EpidemicEndemic on many small islands in the 
TropicsEndemic on many small islands in the 
Tropics
 Climate driversDisease incidence: Caribbean: SST, LST, rainfall (low to

medium confidence)Caribbean: SPI, Tmin (low confidence)
Direction of ChangeArea of coastline suitable for outbreak: Caribbean and 
Pacific small islands: Decrease (low confidence)Increasing (low confidence)Decrease in Caribbean not linked to 
climate
Observed and projected changes
In aquatic systems, at least 30 human pathogens with water infection routes (freshwater and marine) are affected by climate change 
(Section 3.5.3, Table SM3.G) (Nichols et al., 2018) . Warming, acidification, hypoxia, SLR and increases in extreme weather and climate 
events (e.g., MHWs, storm surges, flooding and drought), which are projected to intensify in the 21st century (high confidence) (IPCC, 
2021b), are driving species’ geographic range shifts and global rearrangements in the location and extent of areas with suitable conditions 
for many harmful pathogens, including viruses, bacteria, algae, protozoa and helminths (high confidence) (Sections 2.3, 2.4.2.7, 3.5.5.3)

(Trtanj et al., 2016; Ebi et al., 2017; Manning and Nobles, 2017; Pecl et al., 2017; Mantzouki et al., 2018; Nichols et al., 2018; Bindoff et al., 
2019; IPCC, 2019b; Kubickova et al., 2019; Watts et al., 2019; Watts et al., 2020; Watts et al., 2021).
The incidence of cholera and Vibrio-related disease outbreaks have been shown to originate primarily in coastal regions, and then spread 
inland via human transportation. Our understanding of the impacts of climate-change drivers on the dynamics of Vibrio pathogens and 
related infections has been strengthened through improved observations from long-term monitoring programmes (Vezzulli et al., 2016) 
and statistical modelling supported by large-scale and high-resolution satellite observations (high confidence) ((Baker-Austin et al., 2013; 
Escobar et al., 2015; Jutla et al., 2015; Martinez et al., 2017; Semenza et al., 2017; Racault et al., 2019; Campbell et al., 2020).Cross-Chapter Box ILLNESS (continued)

2
294Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
The poleward expansion of the distribution of Vibrio spp. has increased the risk of vibriosis outbreaks from multiple species in northern 
latitudes. Specifically, the coastal area suitable for Vibrio infections in the past 5 years has increased by 50.6% compared with a 1980s 
baseline at latitudes of 40°N–70°N; in the Baltic region, the highest-risk season has been extended by 6.5 weeks over the same periods 
(Watts et al., 2021). Already, studies have noted greater numbers of Vibrio-related human infections and, most notably, disease outbreaks 
linked to extreme weather events such as heat waves in temperate regions such as Northern Europe (Baker-Austin et al., 2013; Baker-
Austin et al., 2017; Baker-Austin et al., 2018) (high confidence). By the end of the 21st century, under RCP6.0, the number of months of

risk of Vibrio illness is projected to increase in Chesapeake Bay by 10.4 ± 2.4%, with largest increases during May and September, which 
are the months of strong recreational and occupational use, compared to a 1985–2000 baseline (Jacobs et al., 2015; Davis et al., 2019a). 
In the Gulf of Alaska, the coastal area suitable for Vibrio spp. is projected to increase on average by 58 ± 17.2% in summer under RCP6.0 
by the 2090s, compared to a 1971–2000 baseline (low to medium confidence) (Jacobs et al., 2015).
The coastal area suitable for V. cholerae (the causative agent for cholera) has increased by 9.9% globally compared to a 2000s baseline 
(Escobar et al., 2015; Watts et al., 2019). However, in the case of V. cholerae and cholera disease incidence, climate change is more difficult 
to implicate because outbreaks require independent drivers to coincide (i.e., introduction of pathogenic strains of V. cholerae in the waters

via mobility of human-infected populations) and observed trends are difficult to separate from concurrent directional trends in disease 
control, sanitation and water access, socioeconomic and public health conditions.
On land, increased global connectivity and mobility, unsustainable exploitation of wild areas and species and land conversion (agricultural 
expansion, intensification of farming, deforestation and infrastructure development), together with climate change-driven range shifts 
of species and human migration (Cross-Chapter Box  MOVING PLATE in Chapter 5), have modified the interfaces between people 
and natural systems (IPBES, 2018a). Climate-driven increase in temperature, the frequency and intensity of extreme events as well as 
changes in precipitation and relative humidity have provided opportunities for rearrangements of disease geography and seasonality,

and emergence into new areas (high confidence) (Section 2.4.2.7). In particular, malaria has expanded into higher elevations in recent 
decades and, although attributing this to climate change remains challenging (Hay et  al., 2002; Pascual et  al., 2006; Alonso et  al., 
2011; Campbell et al., 2019c), evidence that the elevational distribution of malaria has tracked warmer temperatures is compelling for 
some regions (Siraj et al., 2014). Models based on both empirical relationships between temperature and the Anopheles mosquito and 
Plasmodium parasite traits that drive transmission (Mordecai et al., 2013; Yamana and Eltahir, 2013; Johnson et al., 2015) and existing 
mosquito distributions (Peterson, 2009) predict that warming will increase the risk of malaria in highland East Africa and Southern Africa, 
while decreasing the risk in some lowland areas of Africa, as temperatures exceed the thermal optimum and upper thermal limit for

transmission (Peterson, 2009; Yamana and Eltahir, 2013; Ryan et al., 2015; Watts et al., 2021).
In contrast to malaria, dengue has expanded globally since 1990, particularly in Latin America and the Caribbean, South Asia and sub-
Saharan Africa (Stanaway et al., 2016). While urbanisation, changes in vector control and human mobility play roles in this expansion (Gubler, 
2002; Åström et al., 2012; Wesolowski et al., 2015), the physiological suitability of temperatures for dengue transmission is also expected to 
have increased as climates have warmed (Colón-González et al., 2013; Liu-Helmersson et al., 2014; Mordecai et al., 2017; Rocklöv and Tozan, 
2019). Models predict that dengue transmission risk will expand across many tropical, subtropical and seasonal temperate environments 
with future warming (Åström et al., 2012; Colón-González et al., 2013; Ryan et al., 2019; Iwamura et al., 2020; Watts et al., 2021)).

During the 21st century, public health adaptation measures (Figure Cross-Chapter Box ILLNESS.2) have been put in place in attempts 
to control or eradicate a variety of infectious diseases by improving surveillance and early detection systems; constraining pathogen, 
vector, and/or reservoir host distributions and abundances; reducing the likelihood of transmission to humans; and improving treatment 
and vaccination programmes and strategies (robust evidence, high agreement) (Chinain et al., 2014; Adrian et al., 2016; Friedman et al., 
2017; Konrad et  al., 2017; Semenza et  al., 2017; Borbor-Córdova et  al., 2018; Rocklöv and Dubrow, 2020). In addition, the effective 
management and treatment of domestic and waste-water effluent, through better infrastructure and preservation of aquatic systems 
acting as natural water purifiers, have been key to securing the integrity of the surrounding water bodies, such as groundwater, reservoirs

and lakes, and agricultural watersheds as well as protecting public health (high confidence) (Okeyo et al., 2018; Guerrero-Latorre et al., 
2020; Kitajima et al., 2020; Sunkari et al., 2021). The preservation and restoration of natural ecosystems, with their associated higher levels 
of biodiversity, have been reported as significant buffers against epidemics and large-scale pathogen transmission (medium confidence) 
(Johnson and Thieltges, 2010; Ostfeld and Keesing, 2017; Keesing and Ostfeld, 2021). Furthermore, the timely allocation of financial 
resources and sufficient political will in support of a ‘One Health’ scientific research approach, recognising the health of humans, animals 
and ecosystems as interconnected (Rubin et al., 2014; Whitmee et al., 2015; Zinsstag et al., 2018), holds potential for improving surveillance 
and prevention strategies that may help to reduce the risks of further spread and new emergence of pathogens and vectors (medium

confidence) (Destoumieux-Garzón et al., 2018; Hockings et al., 2020; Volpato et al., 2020; Hopkins et al., 2021; Pörtner et al., 2021).Cross-Chapter Box ILLNESS (continued)

2
295Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Type Description of adaptation optionsAdaptation measures to reduce risks of ecosystem-mediated diseases under climate change
ConfidenceClimate
impact
Capacity
buildingTrain health & environmental  
officials to rapidly respond to 
newly emerging disease risksIncrease public awareness of the 
health risks from pathogens & vector sInvest in robust healthcare systems 
with good facilities, access & 
epidemic protocols++Warning
systemsBuild & maintain early surveillance  
systems of pathogens af fecting 
humans, wildlife & farm animals  Establish seasonal & dynamic forecasts 
of disease outbreaks with detailed risk  
mappingCreate early warning systems 
targeted to the appropriate scale 
(local, regional, and international )+++
Report cases in near-real time for 
efficient response & resource  
mobilization to mitigate outbreaksDiagnostic
abilitiesIncrease technological & lab 
capacity & train personnel to rapidly

diagnose & raise case awarenes sRespond rapidly to disease emergence
events with adequate public health  
& medical resources+++
Public 
policyIncrease international cooperation 
and build policies within a One 
Health/One Biosecurity frameworkPromote large-scale public health 
programs for disease/vector eradicationReach herd-level immunity via 
vaccination for pathogens with  
few host species++
FinancingUse Green recovery funds to tackle
biodiversity loss & climate changeUse national funds for Nature-based 
Solution projects to conserve habitats 
& improve water servicesProvide funds to tribal/local groups 
engaging in sustainable agricultural,
forest & water management+
++ ManagementAlign planning with Sustainable
Development Goals & climate 
targetsBuild sustainabile long-term  
observation & monitoring systemsInstitute environmental regulations & 
sustainable agriculture, livestock & 
fisheries’  farming practices++ TechnologyPriotitize using non-insecticid e

based control for vector sUtilize control alternatives (avoiding 
use of antibiotics, synthetic chemicals
& drugs when possible)Build robust vector/pathogen genetic 
surveillance & control programmes
+++ InfrastructureCreate urban forests & green spaces  
with vector control integrated into 
the design Improve & maintain drinking water 
access, & sewage/drainage system sBuild and outfit homes/buildings with
high quality materials to
prevent vectors from entering
++Nature-based 
SolutionsRestore and conserve natural  
habitats (e.g., reforestation)Decrease habitat fragmentation & 
limit human proximity to risky 
environmentsUse ecosystem-based management  
to regulate pathogen & vector  
populations
++Co-benefits 
from 
mitigationReductions in local emissions from  
energy system s
Pathogen, 
host/vector 
distributions & 
abundancePathogen-host 
transmission 
cycle occurrence 
and efficiencyLikelihood of 
transmission to

humansPromotion of clean transport systems Improved access to food, water & 
energy++Changes 
in
practicesDiversify diets & create more  
resilient food systemsReduce the wildlife trade Decrease reliance on bushmeat and
wild animal product s
Evidence
Low --------- HighAgreement
Low --------- High+ +++
Figure Cross-Chapter Box ILLNESS.1 |  Adaptation measures to reduce risks of climate change impact on water- and vector-borne diseases.  Impacts 
are identified at three levels: (1) on pathogen, host/vector distributions and abundance; (2) on pathogen-host transmission cycle occurrence and efficiency; and (3) on the 
likelihood of transmission to humans. Adaptation typology is based on (Biagini et al., 2014; Pecl et al., 2019). For each type of adaptation, examples are provided with 
their level of evidence and agreement.Cross-Chapter Box ILLNESS (continued)

2
296Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
2.6.5 Adaptation in Practice: Case Studies and Lessons 
Learned
Adaptation plans for biodiversity and EbA have been adopted in many 
places and on different scales, but it is difficult to get a systematic 
overview of adaptation in practice. We have therefore reviewed a 
series of contrasting case studies to illustrate some of the key issues. 
There is a pressing need for more thorough monitoring and evaluation 
of adaptation to assess effectiveness. Climate change adaptation 
is conceptually difficult to measure, but it is possible to test which 
techniques work in reducing vulnerability and to monitor their 
deployment (Morecroft et al., 2019).
Adaptation can take place on a range of scales, with specific projects 
nested within overarching national strategies. Small-scale projects 
can be adaptation-focused, but on the larger scale, adaptation is

often integrated with wider objectives. Within an urban or peri-urban 
context, the benefits of natural and semi-natural areas for health 
and well-being help to justify support for EbA. Economic well-being 
is also an important factor in many cases whether, as in Durban, 
South Africa (Section  2.6.5.7), it provides new job opportunities 
or, as in the Andes (Section  2.6.5.4), it supports long-established 
agricultural practices. Action on the ground often depends on factors 
on a range of scales, for example, a local plan, a national strategy 
and international funding. In Durban, partnerships between local 
communities, local authorities and the academic community were 
essential, together with an international context. Nevertheless, there 
are examples of communities using traditional or local knowledge (LK) 
to adapt to changing circumstances, with little or no external input, 
(Section 2.6.5.4); Their scope for adaptation is, however, often limited

by factors beyond their direct control.
Specific interventions to protect species from climate change, such 
as the case of African penguins in South Africa (Section 2.6.5.5) and 
threatened plant species in the Tasmanian Wilderness World Heritage 
Area (Section 2.6.5.8), are rare. However, in countries where nature 
reserves are actively managed or where ecosystem restoration projects 
are progressing, local practitioners may use their knowledge to adapt 
to weather conditions and their associated effects (e.g., fire and water 
shortages). This is good practice, but it may not be sufficient to address 
the likely future changes in climate (Duffield et  al., 2021). Training 
and resources to support conservation practitioners are becoming 
available. Examples include the Climate Change Adaptation Manual 
in England, UK (Section 2.6.5.2), and The Alliance for Freshwater Life 
(https://allianceforfreshwaterlife.org) which provides expertise for the

sustainable management of freshwater biodiversity (Darwall et  al., 
2018).
Adaptation is widely recognised as important for national conservation 
policies and is being considered in a variety of countries (Section 2.6.5.2, 
2.6.5.3). Adaptation in this strategic context includes decisions about 
the selection and objectives for protected areas, for example, identifying 
places which can act as refugia. It can also mean recognising where 
protected areas remain important but will support a changing range 
of species and ecosystems. This is important for directing resources 
effectively and ensuring that the management of the sites remains 
appropriate. There are, however, often major uncertainties, and the extent to which there will be a need for more radical measures will 
depend on the success of reducing GHG emissions globally. A global 
rise of 1.5°C–2°C would require relatively incremental adjustments to 
conservation management in many parts of the world, but a 3°C–4°C

rise would require radical, transformational changes to preserve many 
species and maintain ecosystem services (Morecroft et al., 2012).
Whilst adaptation strategies for conservation are relatively common, 
at least on an outline level, their implementation is slow in most 
places. This may partly reflect a lack of resources for conservation 
in many parts of the world; however, another barrier is that people 
often value protected sites in their present form. Actions which might 
jeopardise this are inevitably a last resort. Initiatives to engage wider 
communities in discussions are likely to be essential in gaining support 
for such changing approaches.
EbA and adaptation for biodiversity are intrinsically linked and the 
largest-scale interventions for adaptation in ecosystems have tended 
to bring together both elements. For example, adaptation to reduce 
the risk of flooding by habitat creation and using natural processes

(Section  2.6.5.2, Cross-Chapter Box  SLR in Chapter 3), such as 
re-naturalising straightened river systems or creating wetlands for 
water storage, offers the potential to meet multiple objectives and has 
increased the overall funding available for ecosystem restoration.
2.6.5.1 Case Study: Assisted Colonisation/Managed Relocation 
in Practice
Scale: Global
Issue: Helping species move in order to track shifting climate space
Managed relocation (assisted migration and colonisation) is the 
movement of species, populations or genotypes to places outside the 
areas of their historical distribution (Hoegh-Guldberg et  al., 2008), 
and it may be an option where they are not able to disperse and 
colonise naturally. It requires careful consideration of scientific, ethical, 
economic and legal issues between the object of relocation and the 
receiving ecosystem (Hoegh-Guldberg et al., 2008; Richardson et al., 
2009; Schwartz et al., 2012).

Individual cases show that assisted migration can be successful. Anich 
and Ward (2017) extended the geographic breeding range of a rare 
bird, Kirtland’s warbler, Setophaga kirtlandii, by 225 km by using 
song playbacks to attract migrating individuals. Wadgymar (2015) 
successfully transplanted an annual legume, Chamaecrista fasciculata, 
to sites beyond its current poleward range limit, while Liu (2012) found 
that all but one of 20 orchid species survived when transplanted to 
higher elevations than their current range limits. After introducing 
two British butterfly species to sites ∼ 65 and ∼ 35 km beyond their 
poleward range margins, Willis (2009) observed that both of these 
populations grew, expanded their ranges and survived for at least the 
8 year span of the study.
Butterflies have been favoured subjects for assisted migration in 
response to regional climate warming, since they are easy to move and 
their range dynamics have been extensively studied. The Chequered

Skipper, Carterocephalus palaemon, became locally extinct in England

2
297Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
in the 1970s, in an area not close to either the species’ poleward or 
equatorial range limits. Nonetheless, Maes (2019) considers climate a 
crucial parameter for reintroduction, using SDMs for both choosing the 
source population in Belgium and introduction site.
The success of assisted migration for conservation purposes has 
been variable. Bellis et  al. (2019) identified 56 successes and 33 
failures among 107 translocations of insects undertaken explicitly for 
conservation purposes. They concluded that failure was most strongly 
associated with the low numbers of individuals being released. Another 
potential source of failure is local adaptation: there is good evidence 
that adaptive differences among potential source populations can be 
important. For example, the transplants of Chamaecrista fasciculata 
were more successful when sourced from the most poleward existing

sites, while individuals from more equatorial habitats performed poorly 
even when artificially warmed (Wadgymar et al., 2015).
2.6.5.2 Case Study: Adaptation for Conservation and Natural 
Flood Management in England, UK
Scale: National
Issue: National approach to adaptation in the natural environment
Threats to biodiversity from climate change in England include range 
retractions of cold-adapted species and the effects of more frequent 
extreme weather events such as drought. These threats are exacerbated 
by land use and management, for example, fragmenting habitats, 
draining land and straightening rivers. There are also risks to people, 
which are exacerbated by environmental factors, including flooding 
and over-heating in urban areas. The National Adaptation Programme 
provides a broad policy framework for England and includes a chapter 
on the natural environment. There are also adaptation plans produced 
by public bodies such as Natural England and the UK Environment

Agency, with a wide range of responsibilities including flood defence. 
The principles of adaptation to climate change are well established in 
the UK conservation community and resources are available. Natural 
England has published a Climate Change Adaptation Manual jointly 
with the Royal Society for the Protection of Birds (a major conservation 
NGO) (Natural England and RSPB, 2020) and a spatial mapping tool for 
vulnerability to climate change (Taylor et al., 2014).
Duffield et al. (2021) found that awareness of the need for adaptation 
was common amongst nature reserve managers and that they 
were implementing actions that might build resilience to climate 
change, such as restoring ecosystem processes and reducing habitat 
fragmentation. There was recognition that it will be necessary to 
change the management objectives of protected sites to adjust to 
changing circumstances, but little implementation of such changes.

The main example of managing change was at the coast where the 
SLR is causing transitions from terrestrial and freshwater systems to 
coastal and marine ones.
A range of EbA approaches are starting to contribute to adaptation in 
England, but the best-developed is Natural Flood Management (NFM): 
restoring natural processes and natural habitats to reduce flood 
risk (Wingfield et  al., 2019). Over the last decade, a series of NFM 
projects have been established in local areas. The Environment Agency collated the evidence base for NFM (Burgess-Gamble et al., 2021) and 
was able to draw on 65 case studies (Ngai et al., 2017) covering the 
management of rivers and floodplains, woodlands, runoff, and coasts 
and estuaries.
NFM includes a broad range of techniques, some of which deliver 
real benefits for biodiversity and allow natural ecological processes 
to become re-established. Others, such as creating ‘woody debris

dams’—barriers artificially constructed from tree trunks and branches 
in watercourses to slow the flow of water— have fewer benefits, 
although they may be good for some species. Dadson et  al. (2017) 
concluded that ‘the hazard associated with small floods in small 
catchments may be significantly reduced’ by NFM techniques. However, 
they noted that the most extreme flood events may overwhelm any 
risk management measures, and failed to find clear evidence of NFM 
reducing flood risk downstream in large catchments.
Challenges in deploying large-scale NFM remain, which partly reflects 
the length of time necessary to demonstrate the effectiveness of pilot 
studies and build confidence; building stakeholder support is important 
(Huq et  al., 2017). There are now a number of examples of where 
collaborative initiatives between local communities, landowners and 
government agencies have been successful in establishing effective 
NFM schemes (Short et al., 2019).

2.6.5.3 Case Study: Protected Area Planning in Response to 
Climate Change in Thailand
Issue: Protected area network planning
Many countries in the Association of Southeast Asian Nations (ASEAN) 
are expanding protected area networks to meet the Aichi Target 11 of 
at least 17% of terrestrial area protected, and it is important to take 
the effects of climate change into account. Existing protected areas in 
Thailand cover approximately 21% of the land area and it is one of 
the few tropical countries that has achieved the Aichi Target 11. Most 
protected areas in Thailand were established on an ad hoc basis to 
protect remaining forest cover and, as a result, they do not represent 
diverse habitats and their associated species (Chutipong et al., 2014; 
Tantipisanuh, 2016) so they may not be resilient to the interacting 
impacts of future land use and climate change (Klorvuttimontara et al., 
2011; Trisurat, 2018).

Recent research conducted in northern Thailand indicated that the 
existing protected areas (31% of the regional area) cannot secure the 
viability of many medium-sized and large mammals. The climate space 
of most species will shift substantially, bringing a risk of extinction. 
Results, based on a spatial distribution model and network flow, 
determined there was a need for expansion areas of 5,200 km2 in size, 
or 3% of the region, to substantially minimise the high level of risk 
and increase the average coping capacity of the protection of suitable 
habitats from 82%—the current plan—to 90%. These results were 
adopted by Thailand’s Department of National Parks, Wildlife and Plant 
Conservation, and included in the National Wildlife Administration and 
Conservation Plan for 2021–2031.

2
298Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
2.6.5.4 Case Study: Effects of Climate Change on Tropical High 
Andean Social Ecological Systems
Scale: Regional
Issue: Complex ramifications of glacial retreat on vegetation, animals, 
herders and urban populations
Accelerated warming is shrinking tropical glaciers at rates unseen since 
the middle of the Little Ice Age (Rabatel et al., 2013; Zemp et al., 2015). 
Climate-driven upwards migration of species, associated with warming 
and glacier retreat, has modified species distribution and richness and 
community composition along the Andes altitudinal gradient (Seimon 
et al., 2017; Carilla et al., 2018; Zimmer et al., 2018; Moret et al., 2019). 
Climate-driven glacier retreat alters hydrological regimes, directly 
impacting Andean pastoralists (López-i-Gelats et  al., 2016; Postigo, 
2020; Thompson et al., 2021) and the provision of water to lowland

regions (Vuille et al., 2018; Hock et al., 2019; Orlove et al., 2019; Rasul 
and Molden, 2019). The drying of wetlands has modified alpine plant 
communities, which are relevant for storing carbon, regulating water 
and providing food for local livestock; this has led to negative impacts 
on herders’ livelihoods (Dangles et al., 2017; Polk et al., 2017; Postigo, 
2020) and affecting the wild vicuña and the domesticated alpaca and 
llama. The wool from Vicuña (Vicugna vicugna) and alpaca (V. pacos) 
is an important source of income for indigenous communities and the 
llama (Lama glama) is their main source of meat. Vicuña are adjusting 
their feeding behaviour and spatial distribution as vegetation migrates 
upwards (Reider and Schmidt, 2020), causing them to roam outside 
protected areas and become vulnerable to illegal poaching.
Andean herders have responded to the drying of grasslands by 
increasing livestock mobility, accessing new grazing areas through

kinship and leases, creating and expanding wetlands through building 
long irrigation canals (several kilometres in length), limiting the 
allocation of wetlands to new households and sometimes cultivating 
grasses (Postigo, 2013; López-i-Gelats et al., 2015; Postigo, 2020). These 
adaptive responses to regional climate change are enabled by deeply 
embedded indigenous institutions that have traditionally governed 
Andean pastoralists, but they have become severely compromised 
by national socioeconomic pressures (Valdivia et  al., 2010; Postigo, 
2019; Postigo, 2020). For instance, the quality of water and local 
pastoralists’ access to it and control of it have declined, due to new 
mining concessions granted in the headwaters of Andean watersheds 
(Bebbington and Bury, 2009) and the diversion of water to areas of 
lowland coastal desert for agricultural irrigation (Mark et al., 2017).
Glacier mass and runoff in the Tropics are projected to diminish by

>70% and >10%, respectively, by 2100, under mean of RCP2.6, 4.5 
and 8.5 (Huss and Hock, 2018; Hock et al., 2019). In Peru, montane 
ice-field meltwater provides 80% of the water resources for the 
arid coast where half the population lives (Thompson et  al., 2021). 
Increasing variability of precipitation has compromised rain-fed 
agriculture and power generation, particularly in the dry season, 
exacerbating pressures for new sources of water (Bradley et al., 2006; 
Bury et  al., 2013; Buytaert et  al., 2017). There is therefore a risk of 
increasing conflicts between adaptation to climate change to benefit 
human and natural communities in the high Andes and maintaining 
water provisioning for lowland agricultural and urban areas.2.6.5.5  Case Study: Helping African Penguins Adapt to Climate 
Change
Scale: Regional/local
Issue: Adaptation for a threatened species
The African penguin, Spheniscus demersus, is the only resident penguin

species on mainland Africa. It breeds in a handful of colonies in South 
Africa and Namibia. In 2017, the penguins of Cape Town’s Boulders 
Beach colony attracted almost one million visitors, providing 885 jobs 
and USD 18.9 M in revenue (Van Zyl and Kinghorn, 2018). Ninety-six 
percent of the population of this species has been lost since 1900, with 
a 77% decline in the last two decades (Sherley et al., 2018). By 2019, 
only 17,700 pairs remained (Sherley et al., 2020). The species is listed 
as endangered on the IUCN Red List (Birdlife International, 2018) and 
if this trajectory persists, the African penguin will become functionally 
extinct in the near future (Sherley et al., 2018).
Historically, hunting and the collection of eggs and guano were the main 
threats, but three aspects of climate change now predominate. Firstly, an 
eastward shift of several hundred kilometres in the distributions of their 
main prey species, anchovies and sardines, has reduced food availability

(Roy et al., 2007; Crawford et al., 2011). While adult penguins typically 
forage up to 400 km from their colonies, they are restricted to a ~20-
km radius from their colonies during breeding months (Ludynia et al., 
2012; Pichegru et al., 2012). The resulting food shortage at this critical 
time is compounded by competition with commercial fisheries and 
environmental fluctuations (Crawford et al., 2011; Pichegru et al., 2012; 
Sherley et al., 2018). This has impacted adults’ survival and their ability 
to raise high-quality offspring (Crawford et al., 2006; Crawford et al., 
2011; Sherley et al., 2013; Sherley et al., 2014).
The increasing frequency and intensity of heat waves recorded in recent 
decades presents a second threat (van Wilgen and Wannenburgh, 2016; 
Van Wilgen et al., 2016; Mbokodo et al., 2020). Nests were historically 
built in insulated guano burrows, but are now frequently sited on open 
ground (Kemper et al., 2007; Pichegru et al., 2012; Sherley et al., 2012).

High temperatures frequently expose the birds to severe heat stress, 
causing adults to abandon their nests and resulting in the mortality 
of eggs and chicks (Frost et al., 1976; Shannon and Crawford, 1999; 
Pichegru et  al., 2012). Intensifying storm surges and greater wave 
heights can cause nest flooding (Randall et al., 1986; de Villiers, 2002).
The African penguin’s survival in the wild is dependent on the 
success of adaptation action. Increasing access to food resources is 
a management priority (Birdlife International, 2018). One approach 
is to reduce fishing pressure immediately around breeding colonies. 
An experiment excluding fishing around colonies since 2008 has 
demonstrated positive effects (Pichegru et al., 2010; Pichegru et al., 
2012; Sherley et al., 2015; Sherley et al., 2018; Campbell et al., 2019b). 
A second approach is to establish breeding colonies closer to their prey. 
An ongoing translocation initiative aims to entice birds eastwards, to

recolonise an extinct breeding colony and potentially establish a new 
one (Schwitzer et al., 2013; Sherley et al., 2014; Birdlife International, 
2018). Penguin ‘look-alikes’ or decoys, constructed from rubber and 
concrete, have been placed at the site of the extinct colony, and, along 
with call play-backs, these give the illusion of an established penguin

2
299Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
colony (Morris and Hagen, 2018). This approach has not yet proven 
successful.
To promote on-site adaptation to heat extremes and flooding, initiatives 
are underway to provide cooler nesting sites that also provide storm 
protection and are sufficiently above the high-water level (Birdlife 
International, 2018; Saving Animals From Extinction, 2018). Artificial 
nest boxes of various designs and constructed from a range of materials 
have been explored, in combination with the use of natural vegetation. 
Some designs have proven successful, increasing breeding success 
(Kemper et al., 2007; Sherley et al., 2012), but the same designs have 
had less success at other locations (Pichegru, 2013; Lei et al., 2014).
Hand-rearing and releasing African penguin chicks, including from 
eggs, has long proven valuable because moulting parents, being 
shore-bound, are unable to feed late-hatching chicks. Since 2006, over

7,000 orphaned chicks have been released into the wild as part of the 
Chick Bolstering Project, with a success rate of 77% (Schwitzer et al., 
2013; Sherley et al., 2014; Klusener et al., 2018; SANCCOB, 2018). A 
new project at Boulders Beach aims to use real-time weather station 
data, within-nest temperatures and known thresholds of penguin 
heat stress as triggers for implementing a Heat Wave Response Plan. 
Drawing on well-established chick-rearing facilities and a large body 
of expertise, this includes removing heat-stressed eggs and birds, 
hand-rearing and/or rehabilitation and release. It is hoped that such 
birds can be released at the proposed new colony site.
2.6.5.6 Case Study: Conserving Climate Change Refugia for the 
Joshua Tree in Joshua Tree National Park, CA, USA
Scale: Local
Issue: Possible extirpation of a plant species from a national park
Joshua Tree National Park conserves 3200 km2 of the Mojave and

Sonoran Desert ecosystems. The climate of the national park is arid, 
with an average summer temperature of 27.3°C ± 0.7°C and average 
annual precipitation of 170 ± 80 mm yr-1 in the period 1971–2000 
(Gonzalez et  al., 2018). From 1895 to 2017, the average annual 
temperature increased at a significant (P < 0.0001) rate of 1.5°C ± 
0.1°C per century and the average annual precipitation decreased at a 
significant (P = 0.0174) rate of -32 ± 12% per century (Gonzalez et al., 
2018). Anthropogenic climate change accounts for half the magnitude 
of a 2000–2020 drought in the southwestern USA, the most severe 
since the 1500s (Williams et al., 2020).
The national park was established to protect ecosystems and cultural 
features unique to the region, particularly the Joshua tree (Yucca 
brevifolia), a tall, tree-like yucca that provides habitat for birds and 
other small animals and holds cultural significance. The national 
park protects the southernmost populations of the Joshua tree.

Palaeo-biological data from packrat (Neotoma spp.) middens and 
fossilised dung of the extinct Shasta ground sloth (Nothrotheriops 
shastensis) show that Joshua trees grew 13,000–22,000  years ago 
across a wider range, extending as far as 300 km south into what 
is now México (Holmgren et  al., 2010; Cole et  al., 2011). A major 
retraction of this range began ~11,700  years ago, coinciding with 
warming of approximately 4°C, caused by Milankovitch cycles, which marked the end of the Pleistocene and the beginning of the Holocene 
(Cole et al., 2011), suggesting a sensitivity of Joshua trees of 300 km 
of latitude per 4°C.
Under an emissions scenario that could increase park temperatures 
by >4°C by 2100, the suitable climate for the Joshua tree could shift 
northwards and the species become extirpated from the park (Sweet 
et al., 2019). Plant mortality would increase from drought stress and 
wildfires, which have been rare or absent in the Mojave, but which

invasive grasses have fuelled and may continue to fuel (Brooks and 
Matchett, 2006; DeFalco et al., 2010; Abatzoglou and Kolden, 2011; 
Hegeman et al., 2014).
The national park had been trying to conserve the species wherever 
in the park it was found. The future risk of extirpation prompted 
adaptation of conservation efforts to focus on protecting potential 
refugia, where suitable conditions may persist for the species into the 
future (Barrows et al., 2020). The national park used spatial analyses 
of suitable climate to identify potential refugia under all emissions 
scenarios, except for the highest (Barrows and Murphy-Mariscal, 2012; 
Sweet et  al., 2019). The park prioritises the refugia for removal of 
invasive grasses and fire control (Barrows et al., 2020) and works to 
restore refugia that have burned in fires, using native plants, including 
nursery-grown Joshua tree seedlings. The park and its partners are

monitoring plant species composition and abundance in the refugia 
for early warnings of any changes (Barrows et al., 2014).
2.6.5.7 Case Study: Ecosystem Based Adaptation in Durban, 
South Africa
Issue: EbA in a city and surrounding area
Durban was an early pioneer of EbA in a city context, establishing a 
Municipal Climate Protection Programme (MCPP) in 2004 (Roberts et al., 
2012). The city, situated in a global biodiversity hotspot (World Bank, 
2016), has a rapidly growing population (approximately 3.5 million) and 
is highly fragmented (Roberts et al., 2013). High levels of development, 
particularly in peri-urban areas, have encroached into natural habitats 
(World Bank, 2016). Degradation of the natural resource base in this 
way has direct economic and financial costs, is threatening Durban’s 
long-term sustainability and is exacerbated by climate change (World 
Bank, 2016; eThekwini Municipality, 2020). The impacts of climate

change are anticipated to increase unless appropriate mitigation and 
adaptation interventions are prioritised (eThekwini Municipality, 2020). 
High rates of poverty, unemployment and health problems have pushed 
Durban to explore a climate change adaptation work stream within its 
MCPP (Roberts et al., 2013; Roberts et al., 2020b).
A single approach to adaptation is likely to be insufficient (Archer et al., 
2014), and community-based adaptation should be integrated as part 
of a package of tools applied at the city level. Durban’s climate change 
adaptation work stream is composed of three separate components: 
municipal adaptation (adaptation activities linked to the key functions 
of local government), community-based adaptation (CbA, focused on 
improving the adaptive capacity of local communities), and a series 
of urban management interventions (addressing specific challenges

2
300Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
such as the urban heat island, increased storm-water runoff, water 
conservation and SLR) (Roberts et al., 2013).
Lessons learnt from Durban’s experience include the importance of 
meaningful partnerships, long-term financial commitments (Douwes 
et  al., 2015) and significant political and administrative will (Roberts 
et  al., 2012; Roberts et  al., 2020b). Securing these requires strong 
leadership (Douwes et al., 2015), including from local champions (Archer 
et al., 2014), even when EbA is considered cost-effective (Roberts et al., 
2012). Projects for the restoration of natural habitats are seen as an ideal 
tool, as they combine mitigation outcomes with an increased adaptation 
capacity, not only reducing the vulnerability of ecosystems and 
communities (Douwes et al., 2016) but creating economic opportunities. 
These include direct job creation (Diederichs and Roberts, 2016; Douwes

and Buthelezi, 2016) with various spin-offs such as better education for 
schoolchildren (Douwes et al., 2015). Indirect benefits, including better 
water quality and reduced flooding, are generated as a result of improved 
ecosystem service delivery (Douwes and Buthelezi, 2016). In areas that 
are already developed, opportunities for green-roof infrastructure can 
yield reductions in roof storm-water runoff (by approx.. 60 ml/m2/min 
during a rainfall event), slow the release of water over time and reduce 
temperatures on roof surfaces (Roberts et al., 2012).
2.6.5.8 Case Study: Protecting Gondwanan Refugia against Fire 
in Tasmania, Australia
Issue: Protection of rare endemic species
The Tasmanian Wilderness World Heritage Area (TWWHA) has a high 
concentration of ‘palaeo-endemic’ plant species which are restricted 
to living in cool, wet climates and fire-free environments, but recent 
wildfires have burnt substantial stands that are unlikely to recover

(Harris et al., 2018b; Bowman et al., 2021a). The fires led to government 
inquiries and a fire-fighting review, which have suggested changes to 
management as climate change will make such fires more likely in the 
future (AFAC, 2016; Press, 2016; AFAC, 2019).
Most of the TWWHA is managed as a wilderness zone and is currently 
carried out in a manner that allows natural processes to predominate. 
The exclusion of fire from stands of fire-sensitive trees such as the 
pencil pine, Athrotaxis cupressoides, is part of this management 
strategy, possible in the past due to the moisture differential and lower 
flammability of these areas. However, in recent years, the threat posed 
by extensive and repeated wildfires and increasing awareness that fire 
risk is likely to increase (Fox-Hughes et al., 2014; Love et al., 2017; Love 
et al., 2019) have meant that more direct management intervention 
has been implemented. There has been a realisation that a ‘hands

off’ approach to managing the threat will not be sufficient to protect 
the palaeo-endemics. Not only is fire-fighting difficult in this remote 
wilderness area, but limited resources mean that fire managers must 
prioritise where fires will be fought when many fires are threatening 
towns and lives across the state simultaneously.
After the wildfires in 2016 caused extensive damage (Bowman et al., 
2021a), significant efforts and resources were spent trying to protect 
the remaining stands of pencil pine during the 2019 fires, using new approaches including the strategic application of long-term fire 
retardant and the installation of kilometres of sprinkler lines (AFAC, 
2019). These approaches are thought to have been effective at halting 
the fire and protecting high-value vegetation in some situations. 
Impact reports are currently being finalised to quantify the extent 
of fire-sensitive vegetation communities that have been affected.

However, there is concern that these interventions may have adverse 
effects on the values of the TWWHA if applied widely, so while 
research is ongoing, these will only be applied in strategic areas (e.g., 
fire retardant is not being applied to some areas).
The TWWHA Management Plan (2016) emphasises Aboriginal fire 
management as an important value of the area, along with Aboriginal 
knowledge of plants, animals, marine resources and minerals (ochre 
and rock sources), and the connection with the area as a living and 
dynamic landscape. Fire management planning aims to protect 
important sites from fire and ensure that management does not 
impact Aboriginal cultural values (DPIPWE, 2016). Increasingly, there 
is an acknowledgment that the cessation of traditional fire use has led 
to changes in vegetation and there are calls to incorporate Aboriginal 
burning knowledge into the fire management of the TWWHA.
2.6.5.9 Case Study: Bhojtal Lake, Bhopal, India

Issue: Protection of water resources and biodiversity
The city of Bhopal, the capital of Madhya Pradesh state in central India, 
is dependent for its water supply on Bhojtal, a large man-made lake 
bordering the city (Everard et al., 2020). Bhojtal is also an important 
conservation site, with its wetlands protected under the Ramsar 
convention and diverse flora and fauna (WWF , 2006). It also provides 
a wide range of other benefits to people, including tourism, recreation, 
navigation and subsistence and commercial fisheries, supporting the 
livelihoods of many families (Verma, 2001).
Climate change in Bhopal may pose ecological and socioeconomic 
stresses due to changes in rainfall and weather patterns (Ministry of 
Environment et  al., 2019), and exacerbated by a series of problems 
such as waste-water discharge, illegal digging of bore wells and 
unsustainable water extraction/exploitation (Everard et  al., 2020).

Ecosystem service provision at Bhojtal was assessed using the Rapid 
Assessment of Wetland Ecosystem Services (RAWES) approach, 
including an analysis of the lake’s water quality. Information on the 
geology, hydrology and catchment ecology of the lake was collected 
and a baseline biodiversity assessment was conducted.
The Lake Bhopal Conservation and Management Project (JICA, 2007) 
was developed with the following actions:
i) Desilting and dredging; deepening and widening of spill channel; 
prevention of pollution (sewage scheme); management of shoreline 
and fringe area; improvement and management of water quality
ii) Soil and water conservation measures using vegetative and 
engineering structures, particularly at upper ridges of watersheds; 
construction of small check dams or percolation tanks for recharge 
purposes in areas marked for ‘drainage line recharge measures’
iii) Afforestation initiatives

2
301Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Implementation of these measures with the help of local communities 
improved the lake’s health. NbS are more resilient adaptation measures 
towards climate change. Restoration not only reduced water stress but 
also provides multiple societal benefits in the urban area (Kabisch 
2.6.5.10 Case Study: Addressing the Vulnerability of Peat Swamp 
Forests in Southeast Asia
Issue: Protecting peatland biodiversity, carbon and ecosystem services 
from climate change and land degradation
Peatlands in SEA have undergone extensive logging, drainage and 
land use conversion that have caused habitat loss for endemic species, 
i.e., the orangutan (Pongo spp.) (Gregory et al., 2012; Struebig et al., 
2015). Prolonged droughts associated with El Niño (Section 4.4.3.2) 
compound the effects of drainage, leading to large recurrent fires

(Langner and Siegert, 2009; Gaveau et al., 2014; Putra et al., 2019). 
Under RCP8.5, it is projected that by the end of this century, the 
annual rainfall over SEA will decrease significantly (by 30%), and the 
number of consecutive dry days will increase significantly (by 60%) 
over Indonesia and Malaysia (Supari et al., 2020). Peat degradation 
and losses to fire result in high GHG emissions (Miettinen et al., 2016) 
as well as haze pollution which is a trans-boundary problem in the 
region (Heil et al., 2007).
Improving the resilience of SEA peatlands to fire and climate 
change through restoration is extremely difficult and presents 
many challenges. The Indonesian government has tasked the Badan 
Restorasi Gambut (Peatland Restoration Agency) to restore peatlands 
(Darusman et al., 2021; Giesen, 2021). Other local initiatives exist, such 
as fire management programmes and restoration projects (Puspitaloka 
et al., 2020). Since 2016, the government of Indonesia has re-wetted

~380,000 hectares of degraded peatlands, mainly by blocking canals 
and flooding, but less than 2000 hectares have been successfully 
restored to sustaining native plant species common to peat swamp 
forests (Giesen, 2021). Replanting native trees has had relatively little 
success (Lampela et al., 2017) because such trees have low tolerance 
to prolonged inundation and no fire adaptation strategies (Page et al., 
2009; Roucoux et  al., 2013; Dohong et  al., 2018; Cole et  al., 2019; 
Luom, 2020; Giesen, 2021).
The barriers to successful management are complex, and include the 
disparity in time frames between ecological restoration and political/
socioeconomic needs (Harrison et  al., 2020) and an over-focus on 
fire-fighting rather than fire prevention (Mishra et  al., 2021a). Early 
protection of peat forests has been highlighted as a more effective 
management strategy than restoration, not only on islands in SEA 
but also in areas like Papua New Guinea, which may be targeted for

the expansion of estate crop plantations (Neuzil et al., 1997; Dennis, 
1999; Anshari et  al., 2001; Anshari et  al., 2004; Hooijer et  al., 2006; 
Heil et al., 2007; Page et al., 2009; Page et al., 2011; Posa et al., 2011; 
Miettinen et  al., 2012; Wetlands International, 2012; Biagioni et  al., 
2015; Miettinen et al., 2016; Rieley and Page, 2016; Adila et al., 2017; 
Cole et al., 2019; Vetrita and Cochrane, 2019; Harrison et al., 2020; Hoyt 
et al., 2020; Ruwaimana et al., 2020; Ward et al., 2020; Cole et al., 2021).2.6.6 Limits to Adaptation Actions by People
The evidence summarised above (Sections  2.6.2–2.6.4) shows that 
by restoring ecosystems it is possible to increase their resilience to 
climate change, including the resilience of the populations of species 
they support and of human communities. However, changes to healthy 
ecosystems and biodiversity are already happening as described in this 
chapter (robust evidence, high agreement) and further changes are

inevitable even in scenarios of low GHG emissions (robust evidence, 
high agreement). Planning to manage the consequences of inevitable 
changes and prioritise investments in conservation actions where they 
have the best chance of succeeding (e.g., Section 2.6.5.6) will be an 
increasingly necessary component of adaptation (robust evidence, 
high agreement) (Table 2.6).
It is possible to help species survive by active interventions such as 
translocation, but, as described above (Section  2.6.5.1), this is not 
straightforward, is not suitable for all species and is resource-intensive. 
Modifying local microclimate or hydrological conditions can work for 
some species (Sections 2.6.2, 2.6.5.5), but is likely to be less effective at 
higher levels of climate change. It will also be less successful for larger 
species and more mobile ones. The microclimate of a tree is much more 
closely coupled with wider atmospheric conditions than that of a small

plant or animal in the boundary layer, and mobile species like birds and 
large mammals range over large areas rather than being confined to 
discrete locations where conditions can be manipulated.
There is potential for using evolutionary changes to enhance the 
adaptive capacity for target species, as is being done on the Great 
Barrier Reef where symbionts and corals that have survived recent 
intense heat-induced bleaching events are being translocated into 
areas that have had large die-off. However, known limitations to 
genetic adaptations preclude species-level adaptations to climates 
beyond their ecological and evolutionary history (Sections  2.2.4.6; 
2.6.1). All of these interventionist approaches are constrained by 
requiring significant financial resources and expertise. They also 
require a high level of understanding of individual species autecology, 
which can take years to acquire, even when resources are available. Ex

situ conservation (e.g., seed banks) may be the only option to conserve 
some species, especially as levels of warming increase, but this will not 
be feasible for all species.
While the science of restoration has generated many successes, 
some habitats are very difficult to restore, making certain decisions 
effectively irreversible. For example, Acacia nilotica was introduced 
into Indonesia in the 1850s for gum arabic, with planting expanded 
for fire breaks in the 1960s. This tree became invasive and has already 
replaced >50% of the savanna habitat in the Baluran National Park, 
with complete replacement expected in the near future. This shift from 
savanna to acacia forest is causing large declines in native species, 
including the charismatic wild banteng, Bos javanicus, and the wild 
dog (dhole, Cuon alpinus) (Caesariantika et  al., 2011; Padmanaba 
et al., 2017; Zahra et al., 2020). Multiple approaches to controlling the

spread of this acacia have been ineffective, highlighting the difficulty 
of reversing the decision to plant this tree (Zahra et al., 2020). Another 
example is the difficulties in restoring the tropical peat forests of SEA 
(Section 2.6.5.10).

2
302Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
EbA, when implemented well, can reduce risks to people but there are 
limits. For example, an extreme flood event may exceed the capacity of 
natural catchments to hold water or slow its flow (Dadson et al., 2017), 
and urban shade trees and green spaces can make a few degrees 
difference to temperatures experienced by people but this may not be 
enough in the hottest conditions.
In general, adaptation measures can substantially reduce the adverse 
impacts of 1°C–2°C of global temperature rise, but beyond this losses 
will increase (IPCC, 2018b), including species extinctions and changes 
like major biome shifts which cannot be reversed on human time scales. 
Some adaptation measures will also become less effective at higher 
temperatures. Whilst adaptation is essential to reduce risks, it cannot 
be regarded as a substitute for effective climate change mitigation 

2.6.7 Climate Resilient Development
CRD is the subject of Chapter 18. This section briefly assesses some of 
the fundamental issues for CRD relating to ecosystems. An overview of 
the importance of specific ecosystem services for CRD is presented in 
Box 18.7 in Chapter 18. A large body of evidence has demonstrated the 
extent to which human life, well-being and economies are dependent 
on healthy ecosystems and also the range of threats that these are 
faced with (high confidence) (IPBES, 2019; Dasgupta, 2021; Pörtner 
et al., 2021). An analysis of 64 studies found a strong positive synergy 
among eight critical regulating services of healthy ecosystems, 
including climate regulation, water provisioning, pest and disease 
control and adjacent-crop pollination (Lee and Lautenbach, 2016). 
The health of ecosystems is, in turn, reliant upon the maintenance 
of natural levels of species’ richness and functional diversity (high

confidence) (Lavorel et al., 2020) (see Section 2.5.4). A meta-analysis 
of 74 studies documented that the mechanism for increased ecosystem 
stability is increased asynchrony among species, which itself is a 
product of greater species diversity (Xu et al., 2021b). Responding to 
these threats requires the protection and restoration of natural and 
semi-natural ecosystems, together with sustainable management of 
other areas.
The CBD set the Aichi 2020 Target at 17% of each country to be protected 
for biodiversity. Analyses suggest that 30% or even 50% of land and 
sea needs to be protected or restored to confer adequate protection 
for species and ecosystem services (high confidence) (Dinerstein et al., 
2019; Woodley et al., 2019; Brooks et al., 2020; Hannah et al., 2020; 
Zhao et al., 2020; Sala et al., 2021). Hannah et al. (2020) estimated 
that limiting warming to 2°C and protecting 30% of high-biodiversity

regions (in Africa, Asia and Latin America) reduced the risk of species’ 
extinctions by half (medium confidence). The placement of protected 
areas is as important as the total area (Pimm et al., 2018), and the 
quality of the protection (strictness and enforcement) is as important 
as the official land designation (Shah et al., 2021). Pimm et al. (2018) 
found that many small protected areas are successful because they are 
in areas of very high biodiversity containing species with small range 
sizes, while many large regions identified as wild are often of low 
biodiversity value even though they may have a high mitigation value 
(e.g., the high Arctic tundra). A global meta-analysis of 89 restoration projects found that biodiversity increased by 44% and ecosystem 
services by 25% after restoration but values remained lower than in 
intact reference systems (Rey Benayas et al., 2009).
There is also increasing evidence, reported in this chapter, that the loss

and degradation of natural and semi-natural habitats exacerbates the 
impacts of climate change and climatic extreme events on biodiversity 
and ecosystem services (high confidence) (e.g., in (Ogden et al., 2013; 
Eigenbrod et al., 2015; Struebig et al., 2015; Stevens et al., 2016; Oliver 
et al., 2017; McAlpine et al., 2018; Taffarello et al., 2018; Lehikoinen 
et  al., 2019; Birk et  al., 2020; Chapman et  al., 2020; Agol et  al., 
2021; Khaniya et al., 2021; Lara et al., 2021; Lehikoinen et al., 2021). 
Considering these two sets of evidence together, it is clear that climate 
change adaptation and ecosystem degradation both need to be 
addressed if either is to be tackled successfully (robust evidence, high 
agreement) as a number of recent publications concluded (Haddad 
et al., 2015; Hannah et al., 2020; Arneth et al., 2021; Pörtner et al., 
2021). Taking this combined body of evidence, the assessment is that

the protection and restoration of natural and semi-natural ecosystems 
are key adaptation measures (robust evidence, high agreement) 
(Section 2.5.4).
Large-scale protection and restoration of ecosystems can also make 
a significant contribution to climate change mitigation (Dinerstein 
et al., 2020; Roberts et al., 2020a; Soto-Navarro et al., 2020). Globally, 
there is a 38% overlap between areas of high carbon storage and high 
intact biodiversity (mainly in the peatland tropical forests of Asia, the 
western Amazon and the high Arctic), but only 12% of this is protected 
(high confidence) (see also sections 2.4.4.4.1, 2.4.4.4.3, 2.5.3.4) 
(Soto-Navarro et  al., 2020). Peatlands are particularly important 
carbon stores but are threatened by human disturbance, LULCC (Leifeld 
et al., 2019) and fire (sections 2.4.3.8, 2.5.2.8) (Turetsky et al., 2015). 
Restoration of peatlands is not only an efficient climate solution in

terms of emissions of GHGs (Nugent et al., 2019), it may also increase 
ecosystem resilience (Glenk et al., 2021). Global restoration efforts are 
ongoing to target degraded temperate peatlands in the Americas and 
Europe (Chimner et al., 2017) in recognition of their importance for 
climate change mitigation (Paustian et al., 2016; Bossio et al., 2020; 
Humpenöder et al., 2020; Drever et al., 2021; Tanneberger et al., 2021). 
It has been estimated that the global GHG-saving potential of peatland 
restoration is similar to the most optimistic sequestration potential 
from all agricultural soils (Leifeld and Menichetti, 2018). However, 
the pressure on peatlands from human activity remains high in many 
parts of the world (Humpenöder et al., 2020; Tanneberger et al., 2021). 
Currently, the rapid destruction of tropical peatlands overshadows any 
current restoration efforts in temperate peatlands or any potential 
carbon gain from natural high-latitude peatlands (Roucoux et al., 2017;

Wijedasa et al., 2017; Leifeld et al., 2019) (Sections 2.4.3.8, 2.4.4.4.2, 
2.4.4.4.4, 2.5.2.8, 2.5.3.4).
Recent studies have highlighted the importance of ensuring that 
ecosystem protection is not implemented in a way which disadvantages 
those who live in or depend on the most intact ecosystems (Mehrabi 
et al., 2018; Schleicher et al., 2019) or risk food security. The actual 
area of land to be protected and the balance between sustainable use 
and protection will need careful planning and targeting to where it can 
have the most benefit (Pimm et al., 2018). It will also be important to

2
303Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
ensure that protection measures are effective in preventing damage 
(Shah et al., 2021).
At a local level, EbA can often provide a wide range of additional benefits 
for sustainable development in both rural and urban areas (Wilbanks, 
2003; Nelson et  al., 2007; Cohen-Shacham et  al., 2016; Hobbie and 
Grimm, 2020; Martín et al., 2020). A number of the case studies above, 
such as in Durban and at Bhojtal Lake, illustrate this (section 2.6.5). A 
key element of CRD is ensuring that actions taken to mitigate climate 
change do not compromise adaptation, biodiversity and human needs. 
This depends on choosing appropriate actions for different locations 
(Box  2.2, Cross-Chapter Box  NATURAL in this chapter). A particularly 
notable case of this is the creation of woodland described in Box 2.2: 
re-afforestation of previously forested areas can provide multiple benefits

(Lee et al., 2018; Lee et al., 2020) including those for climate change 
mitigation, adaptation and biodiversity. However, planting trees where 
they would not naturally grow can create multiple problems including 
the loss of native biodiversity and the disruption of hydrology (Box 2.2). 
It is also the case that protection of existing natural forest ecosystems is 
the highest priority for reducing GHG emissions (Moomaw et al., 2019) 
and restoration may not always be practical (see Section  2.6.5.10). 
(Sections  2.4.3.6, 2.4.3.7, 2.4.4.3, 2.4.4.4, 2.5.2.6, 2.5.2.7, 2.5.3.3, 
Box 2.2, Cross-Chapter Box NATURAL in this chapter)In some cases, actions supported by international donors and 
presented as addressing climate change adaptation and mitigation in 
the natural environment can have damaging consequences for people 
and nature as well as failing to deliver adaptation and mitigation. One 
example of this was presented by Work et al. (2019), who reviewed

three climate change mitigation and adaptation projects in Cambodia: 
an irrigation project, a protected-area forest management project 
and a reforestation project. In each case, they found evidence of the 
rights of local communities being violated, maladaptation and the 
destruction of biodiverse habitats. They concluded that the potential 
for maladaptation and adverse social and environmental impacts had 
been ignored by international donors and the national authorities, and 
that there was a need for much stricter accountability mechanisms. 
Moyo et al. (2021), using case studies from South Africa, documented 
greater success of ecosystem restoration projects when they embraced 
broader SDGs, particularly enhancement of people’s livelihoods. Better 
assessment of the impacts of adaptation and mitigation measures 
on people and ecosystems, before they are implemented, will be 
increasingly necessary to avoid unintended and damaging consequences

as their deployment is scaled up (Larsen, 2014; Enríquez-de-Salamanca 
et al., 2017; Pour et al., 2017). This applies to ostensibly nature-based 
approaches as well as more engineering-based ones.
Cross-Chapter Box NATURAL | Nature-Based Solutions for Climate Change Mitigation and 
Authors: Camille Parmesan (France/USA/UK, Chapter 2), Gusti Anshari (Indonesia, Chapter 2, CCP7), Polly Buotte (USA, Chapter 4), 
Donovan Campbell (Jamaica, Chapter 15), Edwin Castellanos (Guatemala, Chapter 12), Annette Cowie (Australia, WGIII Chapter 12), 
Marta Rivera Ferre (Spain, Chapter 8), Patrick Gonzalez (USA, Chapter 2, CCP3), Elena López Gunn (Spain, Chapter 4), Rebecca Harris 
(Australia, Chapter 2, CCP3), Jeff Hicke (USA, Chapter 14), Rachel Bezner Kerr (USA/Canada, Chapter 5), Rodel Lasco (Philippines, Chapter 
5), Robert Lempert (USA, Chapter 1), Brendan Mackey (Australia, Chapter 11), Paulina Martinetto (Argentina, Chapter 3), Robert Matthews

(UK, WGIII, Chapter 3), Timon McPhearson (USA, Chapter 6), Mike Morecroft (UK, Chapter 2, CCP5), Aditi Mukherji (India, Chapter 4), 
Gert-Jan Nabuurs (the Netherlands, WGIII Chapter 7), Henry Neufeldt (Denmark/Germany, Chapter 5), Roque Pedace (Argentina, WGIII 
Chapter 3), Julio Postigo (USA/Peru, Chapter 12), Jeff Price (UK, Chapter 2, CCP1), Juan Pulhin (Philippines, Chapter 10), Joeri Rogelj 
(UK/Belgium, WGI Chapter 5), Daniela Schmidt (UK/Germany, Chapter 13), Dave Schoeman (Australia, Chapter 3), Pramod Kumar Singh 
(India, Chapter 18), Pete Smith (UK, WGIII Chapter 12), Nicola Stevens (South Africa, Chapter 2, CCP3), Stavana E. Strutz (USA, Chapter 
2), Raman Sukumar (India, Chapter 1), Gautam Hirak Talukdar (India, Chapter 2, CCP1), Maria Cristina Tirado (USA/Spain, Chapter 7), 
Christopher Trisos (South Africa, Chapter 9)
Nature-based solutions provide adaptation and mitigation benefits for climate change as well as contributing to other

sustainable development goals (high confidence). Effective nature-based climate change mitigation stems from inclusive 
decision-making and adaptive management pathways that deliver climate-resilient systems serving multiple sustainable 
development goals. Robust decision-making adjusts management pathways as systems are impacted by ongoing climate 
change. Poorly conceived and poorly designed nature-based mitigation efforts have the potential for multiple negative 
impacts, including competing for land and water with other sectors, reducing human well-being and failing to provide 
mitigation that is sustainable in the long term (high confidence).
The concept of Nature-based Solutions (NbS) is broad and under debate, but has become prominent in both the scientific literature and 
policy since AR5, and includes earlier concepts like EbA. The key point is that these are actions benefitting both people and biodiversity

(IUCN, 2020) (WGII Glossary). In the context of climate change, NbS provide adaptation and mitigation benefits in ways that support 
wild species and habitats, often contributing to other sustainable development goals (robust evidence, high agreement) (Griscom et al., 
2017; Keesstra et al., 2018; Hoegh-Guldberg et al., 2019; IPCC, 2019a; Lewis et al., 2019; Lavorel et al., 2020; Malhi et al., 2020; Seddon 
et al., 2020b) (AR6 WGIII Chapter 12; Sections 2.2, 2.5.4, 2.6.3, 2.6.5, 2.6.7). Well-designed and implemented NbS mitigation schemes 
can increase carbon uptake or reduce GHG emissions at the same time as protecting or restoring biodiversity and incorporating elements

2
304Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
of food provisioning (Mehrabi et al., 2018). A variety of measures can be part of NbS, ranging from the protection of natural terrestrial, 
freshwater and marine ecosystems to the restoration of degraded ones (this Cross-Chapter Box; Section 13.3) and more sustainable 
management of naturally regenerating ecosystems used for food, fibre and energy production (Figure Cross-Chapter Box NATURAL.1, 
Chapter 5 in this report, Cross-Working Group Box BIOECONOMY in Chapter 5). Agro-ecological practices mitigate and adapt to climate 
change and can promote native biodiversity (high confidence) (Sinclair et al., 2019; Snapp et al., 2021).
The Role of Restoration in Nature-Based Solutions
Where natural ecosystems have been degraded or destroyed, re-establishing them and restoring natural processes can be a key

action for adaptation and mitigation, and the science of restoration is well established (de los Santos et al., 2019; Duarte et al., 2020) 
(Section 13.4.1). Such restoration activities need to adapt to ongoing climate change risks for the landscape and oceans and the species 
composition of biological communities. Indeed, the impacts of climate change may overwhelm attempts at restoration/conservation of 
previous or existing ecosystems, particularly when the ecosystem is already near its tipping point, as is the case with tropical coral reefs 
(Bates et al., 2019; Bruno et al., 2019).
Land (e.g., forests) and oceans (e.g., fisheries) managed for products using sustainable practices (whether applied by individuals, states 
or Indigenous Peoples) can also be carbon- and biodiversity-rich, and thus considered effective NbS (Paneque-Gálvez et al., 2018; Soto-

Navarro et al., 2020). Indigenous Peoples and private forest owners manage, use or occupy at least one-quarter of the global land area, 
over one-third of which overlaps with protected areas, thus combining both protection and production (Jepsen et al., 2015; Garnett et al., 
2018; IPBES, 2019; Santopuoli et al., 2019).
The protection/restoration of natural systems including reducing non-climate stressors, and the sustainable management of semi-natural 
areas emerge as necessary actions for adaptation to minimise extinctions of species, the reaching of tipping points that cause regime 
shifts in natural system and the loss of whole ecosystems and their associated benefits for humans (Scheffer et al., 2001; Folke et al., 2005; 
Luther et al., 2020) (Chapters 2 and 3 in this report; AR6 WGIII Chapter 7). Such measures are critical for the conservation of biodiversity

and the provision of ecosystem goods and services in the face of projected climate change (Duarte et  al., 2020). Supporting local 
livelihoods and providing benefits to indigenous local communities and millions of private landowners, together with their active 
engagement in decision-making, are critical to ensuring support for NbS and their successful delivery (high confidence) (Chapter 5 in this 
report; Figure Cross-Chapter Box NATURAL.1)(Ceddia et al., 2015; Blackman et al., 2017; Nabuurs et al., 2017; Smith et al., 2019a; Smith 
et al., 2019b; Jones et al., 2020a; McElwee et al., 2020; Cao et al., 2021).
Forests
Intact natural forest ecosystems are major stores of carbon and support large numbers of species that cannot survive in degraded habitats 
(very high confidence). Extensive areas of natural forest ecosystems remain in tropical, boreal and (to a lesser extent) temperate biome

regions, but in many regions they are managed (sustainably and unsustainably) or have been degraded or cleared. Deforestation and land 
degradation continue to be a source of global GHG emissions (very high confidence) (Friedlingstein et al., 2019). Protection of existing 
natural forests and sustainable management of semi-natural forests that continue to provide goods and services are highly effective NbS 
(Bauhus et al., 2009) (high confidence).
Natural forests and sustainably managed biodiverse forests play important roles in climate change mitigation and adaptation while 
providing many other ecosystem goods and services (very high confidence) (Bradshaw and Warkentin, 2015; Favero et al., 2020; Mackey 
et al., 2020). Contributions of natural forests to climate change mitigation are estimated at a median of 5–7 GtCO 2 yr-1 (Roe et al., 
2019). Forests influence the water cycle on a local, regional and global scale (Creed and van Noordwijk, 2018), reducing surface runoff,

increasing infiltration to groundwater and improving water quality (Bruijnzeel, 2004; Zhou et al., 2015a; Ellison et al., 2017; Alvarez-
Garreton et al., 2019). Recent evidence shows that downwind precipitation is also influenced by evapotranspiration from forests (Keys 
et al., 2016; Ellison et al., 2017). Protecting existing natural forests and sustainably managing production forests in a holistic manner can 
optimise the provision of the many functions forests fulfil for owners, conservation, mitigation and for society as a whole (Bauhus et al., 
2009; Nabuurs et al., 2013).
Reforestation of previously forested land can help to protect and recover biodiversity and is one of the most practical and cost-effective 
ways of sequestering and storing carbon (high confidence) (Nabuurs et al., 2017; Hoegh-Guldberg et al., 2018; Paneque-Gálvez et al., 
2018; Smith et al., 2018; Cook-Patton et al., 2020; Cowie et al., 2021; Drever et al., 2021). This can be achieved through planting or

by allowing natural colonisation by tree and shrub species. The most effective method to deploy depends upon local circumstances 
(e.g., the presence of remnant forest cover) or socio-cultural and management objectives. Reforestation with climate-resilient native or 
geographically-near species restores biodiversity at the same time as sequestering large amounts of carbon (Lewis et al., 2019; Rozendaal Cross-Chapter Box NATURAL (continued)

2
305Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
et al., 2019). It can also restore hydrological processes, thereby improving water supply and quality (Ellison et al., 2017) and reducing the 
risk of soil erosion and floods (high confidence) (Locatelli et al., 2015).
Climate change may mean that, in any given location, different species will be able to survive and become dominant and restoring the 
former composition of forests may not be possible (Sections 2.4, 2.5). Severe disturbances such as insect/pathogen outbreaks, wildfires 
and droughts, which are an increasing risk, can cause widespread tree mortality resulting in sequestered forest carbon being returned to 
the atmosphere (Anderegg et al., 2020; Senf and Seidl, 2021), suggesting that we need to adapt (Sections 2.4, 2.5, 13.3 14.4.1, Box 14.1). 
Adaptation measures, such as increasing the diversity of forest stands through ecological restoration rather than monoculture plantations

can help to reduce these risks (high confidence). When plantations are established without effective landscape planning and meaningful 
engagement including free prior and informed consent, they can present risks to biodiversity and the rights, well-being and livelihoods of 
indigenous and local communities as well as being less climate-resilient than natural forests (very high confidence) (Section 5.6) (Corbera 
et al., 2017; Mori et al., 2021).
Afforesting areas such as savannas and temperate peatlands, which would not naturally be forested, damages biodiversity and increases 
vulnerability to climate change (high confidence), so cannot be considered a nature-based solution and can even exacerbate GHG 
emissions (Sections 2.4.3.5, 2.5.2.5, Box 2.2 in this chapter). Remote sensing-based assessments of the suitability of land for planting 
trees can overestimate potential, due to their failure to adequately distinguish between degraded forest and naturally open areas (Bastin

et al., 2019; Veldman et al., 2019; Bastin et al., 2020; Sullivan et al., 2020).
Peatlands
Peatlands are naturally high-carbon ecosystems, which have built up over millennia. Draining, cutting and burning peat lead to oxidation 
and the release of CO 2 (very high confidence). Re-wetting by blocking drainage and preventing cutting and burning can reverse this 
process on temperate peatlands (medium confidence) but takes many years (Bonn et al., 2016). Trees are naturally found on many tropical 
peatlands and restoration can involve removing non-native species like the oil palm and re-establishing natural forest. However, peatland 
tropical forest is difficult to fully restore, and native pond-fish, vital as a local food, often do not return. Protecting intact peat forests, 
rather than attempting to restore cleared forest, is by far the more effective pathway, in terms of cost, CO 2 mitigation and the protection

of food sources (Kreft and Jetz, 2007). Naturally treeless temperate and boreal peatlands have, in some cases, been drained to enable 
trees to be planted, which then leads to CO 2 emissions, and restoration requires the removal of trees as well as re-blocking drainage (high 
confidence) (Sections 2.4.3.8, 2.5.2.8, 2.6.5.10).
Blue Carbon
Blue carbon ecosystems (mangroves, saltmarshes and seagrass meadows; see Glossary Appendix II) often have high local rates of carbon 
accumulation and sequestration (Section 3.5.5.5) (Macreadie et al., 2019). However, quantification of their overall mitigation value is 
difficult due to the variable production of CH 4 and N 2O (Adams et al., 2012; Rosentreter et al., 2018; MacLean et al., 2019b), uncertainties 
regarding the provenance of the carbon accumulated (Macreadie et al., 2019) and the release of CO 2 by biogenic carbonate formation

in seagrass ecosystems (Saderne et al., 2019). Therefore, blue carbon strategies, referring to climate change mitigation and adaptation 
actions based on the conservation and restoration of blue carbon ecosystems, can be effective NbS, with evidence of the recovery of 
carbon stocks following restoration, although their global or regional carbon sequestration potential and net mitigation potential may be 
limited (medium confidence) (Sections 3.6.3.1.6, 13.4.3) (section 5.6.2.2.2 in (Canadell et al., 2021)) (Duarte et al., 2020).
They can also significantly attenuate wave energy, raise the seafloor (thereby counteracting the effects of SLR) and buffer storm surges 
and erosion from flooding (high confidence) (Sections 13.2.2, 13.10.2). Additionally, they provide a suite of cultural (e.g., tourism and the 
livelihoods and well-being of native and local communities), provision (e.g., mangrove wood, edible fish and shellfish) and regulation

(e.g., nutrient cycling) services (high confidence) (Section 3.5.5.5). These services have motivated the implementation of management 
and conservation strategies of these ecosystems (Sections 3.6.3.1.6, 13.4.2). Blue carbon strategies are relatively new, with many of them 
experimental and small-scale; there is therefore only limited evidence of their long-term effectiveness. There is also limited information 
on the potential emission of other GHGs from restored blue carbon ecosystems, although reconnecting hydrological flow in mangroves 
and restoring saltmarshes are effective interventions to reduce CH 4 and CO 2 (limited evidence, medium agreement) (Kroeger et al., 2017; 
Al-Haj and Fulweiler, 2020).
Urban Nature-Based Solutions
NbS can be a key part of urban climate adaptation efforts. Direct human adaptation benefits may stem from the cooling effects of urban

forests and green spaces (parks and green roofs), from coastal wetlands and mangroves reducing storm surges and flooding and from 
sustainable drainage systems designed to reduce surface flooding as a result of extreme rainfall as well as the general benefits to human Cross-Chapter Box NATURAL (continued)

2
306Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
health and well-being (high confidence) (Sections 2.2, 2.6, Chapter 6) (Kowarik, 2011; Frantzeskaki et al., 2019; Keeler et al., 2019). Not 
all green schemes are considered ‘Nature-Based Solutions’ if they do not benefit biodiversity, but carefully designed urban greening can 
be effective NbS. Careful planning also helps limit negative equity consequences such as benefitting wealthy neighbourhoods more than 
poor neighbourhoods (Geneletti et al., 2016; Pasimeni et al., 2019; Grafakos et al., 2020). Effective planning should also consider what is 
appropriate for the climate and conditions of each city. For example, some trees emit volatiles (e.g., isoprene) which, in the presence of 
certain atmospheric pollutants, can increase surface ozone which can, in turn, cause human respiratory problems (Kreft and Jetz, 2007).

Wetland restoration close to human settlements needs to be paired with mosquito control to prevent negative impacts on human health 
and well-being (Stewart-Sinclair et al., 2020), but it has been shown to provide better filtration and toxicity reduction with a lower 
environmental impact than other forms of waste-water treatment (Vymazal et al., 2021), including ‘green roofs’ and ‘green walls’ (Chapter 
6 in this report) (Addo-Bankas et al., 2021).
Agro-Ecological Farming
AF is a holistic approach that incorporates ecological and socioeconomic principles, many of which have been shown to have a positive 
impact on biodiversity and on the resilience of human and natural systems to climate change (chapter 5, this report). It strives to enhance 
biodiversity, soil health and synergies between agro-ecosystem components, reduces reliance on synthetic inputs (e.g., pesticides), builds

on IKLK and fosters social equity (e.g., supporting fair, local markets) (HLPE, 2019; Wezel et al., 2020). AF practices include inter-cropping; 
the mobility of livestock grazing across landscapes; organic agriculture; and the integration of livestock, fish and cropping, cover crops 
and agro-forestry (Sections 5.14, FAQ 12.5, FAQ 13.5).
Agro-forestry, cover crops and other practices that increase vegetation cover and enhance soil organic matter, carefully managed and 
varying by agro-ecosystem, mitigate climate change (high confidence) (Zomer et al., 2016; Aryal et al., 2019; Nadège et al., 2019). Global 
meta-analyses demonstrate agro-forestry as storing 20–33% more soil carbon than conventional agriculture (De Stefano and Jacobson, 
2018; Shi et al., 2018) and reducing the spread of fire (Sections 5.6, 13.5.2, 7.4.3, Box 7.7). Minimising synthetic inputs such as nitrogen-

based fertilizers reduces emissions (Gerber et al., 2016). Cover crops can reduce N 2O emissions and increase soil organic carbon (Abdalla 
et al., 2019). Conservation farming (no-till with residue retention and crop rotation) increases soil organic carbon, particularly in arid 
regions (Sun et al., 2020). Silvo-pastoral systems (pastures with trees) and other practices that increase vegetation cover and enhance soil 
organic matter increase sequestered carbon in vegetation and soils (Zomer et al., 2016; Aryal et al., 2019; Nadège et al., 2019; Ryan, 2019). 
Agro-ecologically improved management of land for crops and grazing has significant mitigation potential, estimated at 2.8–4.1 GtCO 2-eq 
yr-1 (Smith et al., 2020) (Sections 5.10, 5.14, Box 5.10, Cross Working-Group Box BIOECONOMY in Chapter 5; WGIII 7.4.3, Box 7.7).
AF enhances adaptation to climate change, including resilience to extreme events. Building organic matter improves the water-holding

capacity of soils and buffers against drought; increased perenniality and high levels of ground cover reduce soil erosion during storms; 
agro-forestry shelters livestock and crops during heat waves; landscape complexity and agro-biodiversity increase resilience to disease and 
pests and stabilise livestock production; and restoration of oyster reefs provides thermal refugia and storm surge protection (Henry et al., 
2018; Kremen and Merenlender, 2018; Kuyah et al., 2019; Gilby et al., 2020; Niether et al., 2020; Richard et al., 2020; Howie and Bishop, 
2021; Snapp et al., 2021). Livestock mobility enables adjustment to increased climatic variability while maintaining the productivity of 
pastoral systems (Turner and Schlecht, 2019; Scoones, 2020). The adoption of agro-ecology principles and practices will therefore be highly 
beneficial to maintaining healthy, productive food systems under climate change (high confidence) (Sections 5.4.4, 13.5.2, FAQ 12.4).

AF practices such as hedgerows and poly-cultures maintain habitat and connectivity for biodiversity, thus aiding the ability of wild 
species to respond to climate change via range shifts, and support ecosystem functioning under climate stress compared to conventional 
agriculture (high confidence) (Section 5.4.4.4) (Buechley et al., 2015; Kremen and Merenlender, 2018; Albrecht et al., 2020). Increasing 
farm biodiversity benefits pollination, pest control, nutrient cycling, water regulation and soil fertility (Beillouin et al., 2019; Tamburini 
et al., 2020; Snapp et al., 2021). Biodiverse agro-forestry systems increase ecosystem services and biodiversity benefits compared to 
simple agro-forestry and conventional agriculture (high confidence), with up to 45% more biodiversity and 65% more ecosystem services 
compared to conventional production of timber and crops and profits from livestock in the Atlantic Forest in Brazil (Santos et al., 2019),

including benefits for birds and local tree species (Braga et al., 2019) and meaning there are fewer invasive exotic plants species (de 
Almeida Campos Cordeiro et al., 2018). AF includes the conservation of semi-natural woodlands, which can conserve bird predators of 
insect pests (Gonthier et al., 2019). The richness and abundance of insect species, including essential pollinators, are increased by organic 
farming (Sections 5.10, 12.6) (Kennedy et al., 2013; Haggar et al., 2015; Lichtenberg et al., 2017).
AF significantly improves food security and nutrition by increasing access to healthy, diverse diets and raising incomes for food producers, 
due to the increased biodiversity of crops, animals and landscapes (high confidence) (Garibaldi et al., 2016; D’Annolfo et al., 2017; Isbell 
et al., 2017; Dainese et al., 2019; Kerr et al., 2021). Livestock mobility improves the site-specific matching of animals’ needs with food Cross-Chapter Box NATURAL (continued)

2
307Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
availability (Damonte et al., 2019; Mijiddorj et al., 2020; Postigo, 2021), and can generate a form of re-wilding that restores lost ecosystem 
functioning (Gordon et al., 2021). Conservation of crop wild relatives in situ supports the genetic diversity of crops for a range of future 
climate scenarios (Redden et al., 2015).
System-level agro-ecological transitions require policy support for experimentation and exchange of knowledge by farmers, community-
based participatory methodologies and market and policy measures, for example, public procurement, local and regional market support, 
regulation or payments for environmental services (Mier y Terán Giménez Cacho et al., 2018; HLPE, 2019; Snapp et al., 2021). Scientific 
consensus about the food security and environmental implications of agro-ecological transitions on a global scale is lacking. Yields of agro-

forestry and organic farming can be lower than high-input agricultural systems but, conversely, AF can boost productivity and profit, varying 
according to the time frame and the socioeconomic, political or ecosystem context (medium confidence) (Section 5.14) (Muller et al., 2017; 
Barbieri et al., 2019; Smith et al., 2019b; Smith et al., 2020). Such contrasting results and the limited investment in agro-ecological research 
to date mean it is paramount to assess the global and regional impacts of agro-ecological transitions on food production, ecosystems and 
economies in the context of climate change adaptation (Section 5.14) (DeLonge et al., 2016; Muller et al., 2017; Barbieri et al., 2019).
Starting point :
Evaluating ecosystem status
Degraded
(few or no ecosystem services )
Ecological & social benefits 
of recovering the syste m
Potential for green,
Green, climate  
resilient building  
developmen tClimate change  
causing transition 
changePotential fo r

restoration
Potential for low intensity  
agroecological farming or 
aquaculture
Potential conversion to  
biomass or industrial  
food productio n
Industrial bioenergy/biomass
or industrial food producctio nLow intensity agroecological 
farming or aquacultureSustainabl y
managed systemLevel of management 
requiredLevel of carbon capture 
& storageHealthy
(low impact management)Healthy / Undisturbed
(natural and healthy semi-natural systems)
Restore Assess for inclusion in  
regional conservation  
planning based on 
usual criteri a
Protect & connect to build
climate change resilience & • Assess costs-benefits for 
conservation, mitigation, food & 
social values
• Assess feasibility of keeping  
options open for restoration 
• Delay irreversible actionslow high
low highlow high
high
low highcosts > benefits benefits > costs
(multiple options)high low naturally
    lownaturally
   high

high cos tDecision-making framework to co-maximise adaptation and mitigation benefits from natural systems
low
Closed / Fewer options Open / More options
Periodic re-evaluation can help to choose pathways forward even as systems are being impacted by on-going climate change (gradient from poor to good health)
natural carbon storage 
Figure  Cross-Chapter  Box  NATURAL.1 |   Decision-making framework to co-maximise adaptation and mitigation benefits from natural systems.  
Decision-making pathways are designed to add robustness in the face of uncertainties in future climate change and its impacts. Emphasis is on keeping open as many 
options as possible, for as long as possible, with periodic re-evaluation to aid in choosing pathways forward, even as systems are being impacted by ongoing climate change.
Conclusions
NbS provide adaptation and mitigation benefits for climate change as well as contributing to achieving other sustainable development

goals (high confidence). NbS avoid further emissions and promote CO 2 removal, by using approaches that yield long-lasting mitigation 
benefits and avoid negative outcomes for other sustainable development goals. Poorly conceived and poorly designed mitigation efforts 
have the potential for multiple negative impacts: (1) cascading negative effects on long-term mitigation by promoting short-term Cross-Chapter Box NATURAL (continued)

2
308Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
sequestration over existing long-term accumulated carbon stocks; (2) being detrimental for biodiversity, undermining conservation 
adaptation; and (3) eroding other ecosystem services important for human health and well-being (high confidence). Conversely, well-
designed and implemented mitigation efforts have the potential to provide co-benefits in terms of climate change adaptation as well as 
providing multiple goods and services, including the conservation of biodiversity, clean and abundant water resources, flood mitigation, 
sustainable livelihoods, food and fibre security and human health and well-being (high confidence). A key aspect of such ‘smart’ climate 
mitigation is the implementation of inclusive and adaptive management pathways (Section 1.4.2). These entail acceptance of the uncertainty

inherent in projections of future climate change, especially at the regional or local level, and using decision-making processes that keep 
open as many options as possible for as long as possible, with periodic re-evaluation to aid in choosing pathways forward, even as systems 
are being impacted by ongoing climate change (Figure Cross-Chapter Box NATURAL.1; Cross-Chapter Box DEEP in Chapter 17; Section 1.4.2).
Table Cross-Chapter Box NATURAL.1 |  Assessment of benefits and trade-offs between mitigation and strategies for both biodiversity and human adaptation to future 
climate change. Best practices highlight approaches that lead to maximal positive synergy between mitigation and adaptation; worst practices are those most likely to 
lead to negative trade-offs for adaptation. Many best practices have additional societal benefits beyond adaptation, such as food provisioning, recreation and improved

water quality. Mitigation Potential (Mit. Pot.) and Restoration Potential (Rest. Pot.) are considered.
System Mit. Pot. Rest. Pot.Best practices and adap-
tation benefitsWorst practices and nega-
tive adaptation trade-offsAdditional societal 
benefitsReferences
Forests
Boreal forests medium mediumMaintain or restore species and 
structural diversity, reduce fire 
risk, spatially separate wood 
production and sustainably 
intensify management in some 
regionsVery large-scale clear cuts, 
aiming for one or few tree 
species, although boreal is 
characterised by few tree 
species and a natural fire riskProviding goods and 
services, jobs and 
improved air quality and 
hydrology(Drever et al., 2021)
Temperate 
forestsvery high highMaintain or restore natural 
species and structural diversity, 
leading to more biodiverse and 
resilient systemsPlanting large-scale non-native 
monocultures which would lead 
to loss of biodiversity and poor 
climate change resilienceProviding goods and

improved hydrology and 
biodiversitySections 2.4.3; 2.5; 
Box 2.2 ; (Nabuurs et al., 
2017; Roe et al., 2019; 
Favero et al., 2020)
Tropical wet 
forestshigh moderateMaintain or restore natural 
high biodiversity, more resilient 
to climate changePlanting non-native 
monocultures, loss of 
biodiversity, poor climate 
change resilience, soil erosionIndigenous foods, 
medicines and other 
forest products, including 
sustainable selective 
loggingSection 2.4.3 (Edwards 
et al., 2014)
Tropical dry 
forestshigh moderateIntegrated landscape 
managementPlanting non-native 
change resilience, soil erosion(Foli et al., 2018)
Tropical 
peatland 
forestsvery high lowIntegrated landscape 
managementCutting native rainforest and 
planting palm oil for biodiesel 
results in very high carbon 
emissions from exposed peat 
soilsForest pond fish are 
a major food for local

communitiesSection 2.4.3; 2.5; (Smith 
et al., 2019b)
Blue carbonAR6 WGI 5.6.2.2.2
(Canadell et al., 2021)
Mangroves moderate highConservation, restoration 
of hydrological flows, 
re-vegetation with native 
plants, livelihood diversification, 
landscape planning for 
landward and upstream 
migrationPotential NH 4 emissionsImproved fisheries and 
biodiversity, coastal 
protection against 
SLR and storm surges, 
recreation and cultural 
benefitsSections 3.4.2.5; 3.5.5.5; 
3.6.3.1; (Macreadie et al., 
2019; Duarte et al., 2020; 
Sasmito et al., 2020)
Saltmarshes moderate highConservation, reduction of 
nutrient loads, restoration 
of hydrological flows and 
sediment delivery, re-vegetation 
with native plants, landscape 
planning for landward and 
upstream migrationPotential NH 4 emissionsImproved fisheries and 
biodiversity, protection 
against SLR and storm 
surges, recreational and 
cultural benefitsSections 3.4.2.5; 3.5.5.5; 
3.6.3.1; (Macreadie et al.,

2
309Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Seagrasses moderate highConservation, restoration, 
improve water quality and 
reduce local stressors (reduction 
of industrial sewage, anchoring 
and trawling regulation)Potential NH 4 emissionsImproved fisheries and 
from shoreline erosion, 
recreational benefitsSection 3.4.2.5; 3.5.5.5; 
3.6.3.1; (de los Santos 
et al., 2019; Macreadie 
et al., 2019; Duarte et al., 
2020)
Urban ecosystems
Urban forestsmoderate 
to high*moderateIntegrated landscape 
management. Species richness 
(including exotics) can be high.Monoculture of an exotic tree 
lowers resilience and reduces 
biodiversityRecreation and 
aesthetics, stormwater 
absorption benefits, heat 
mitigation, air quality 
improvementsChapter 6, this report
Urban 
wetlandsmod-

erate*moderateIntegrated landscape 
managementRecreation and 
absorption, heat 
mitigation, coastal flood 
protectionChapter 6, this report
Urban 
grasslandsmod-
managementfertilised commercial grass 
monocultures often require 
irrigation and are less resilient 
to droughts than native, mixed 
grasses and forbsRecreation and 
mitigationChapter 6, this report
Open grasslands and savanna
Boreal and 
temperate 
peatlandshigh moderateBlock drainage channels, raise 
water levels to their natural 
condition, remove planted 
trees, re-vegetation of bare 
peat, no fires, increased 
biodiversity resilience, reduced 
flood riskInappropriate hydrological 
restoration, e.g., flood surface 
depth greater than natural 
depth leading to methane 
emissionsImproved water quality 
in some conditionsSections 2.4.3; 2.5;(Bonn 
et al., 2016; Nugent, 
2019; Taillardat et al., 
2020)
Tropical

savannas and 
grasslands 
(including 
rangelands)moderate highControl of feral herbivores, 
reintroduce indigenous burning, 
reintroduce native herbivores 
and controlled grazing, 
strategic design of water holes, 
community-based natural 
resource management, grass 
reseeding, clearing of invasive 
and encroaching woody plantsAfforestation,over-grazing/
stocking, no burning, 
inappropriate placement and 
design of watering points. All 
lead to loss of biodiversity and 
resilience, soil erosion and 
water insecurity.Improved grazing 
potential for livestock 
and dairy production, 
sustainable wildlife 
harvests, increased water 
security, income from 
eco-tourism, medicinal 
plants, fuel wood, 
enhanced food securitySections 2.4.3; 2.5; 
Box 2.1; (Stafford et al., 
2017; Moura et al., 2019; 
Shackelford et al., 2021; 
Stringer et al., 2021; 
Wilsey, 2021)
Temperate 
grasslands and 
rangelandsmoderate 
to highmoderate 
to highIntegrated landscape 
management, sustainable

grazing, community-based 
natural resource management, 
native grassland species are 
more resistant to drought than 
introduced speciesMonocultures (especially 
of introduced species), 
over-fertilising with chemical 
or organic amendments, 
failure to manage human–
wildlife clashes, failure to 
distribute income equitably, 
inadequate enabling policy to 
facilitate integrated landscape 
managementSustainable harvest of 
wildlife, livestock and 
dairy production, wild 
fruits, medicinal plants, 
construction material, 
fuel wood, income from 
ecotourismSections 2.4.3; 2.5, 
Box 2.1; (Farai, 2017; 
Baker et al., 2018; 
Homewood et al., 2020; 
AF and 
aquaculturehighhigh 
(context-
specific)Biodiverse systems on the 
landscape scale, participatory 
adaptation to context, short 
value chains, farmer incentives, 
biodiversity synergies, reduced 
climate riskPoorly chosen species, practices 
and amendments can lead 
to low yields. Simplified 
agro-forestry systems and

industrial-scale organic 
agriculture lack a holistic 
system-wide approach. 
Over-fertilising with organic 
amendments.Food security, human 
health, livelihoods, 
socio-cultural benefits, 
e.g., culturally 
appropriate foodsSections 5.4, 5.10, 5.12, 
5.14 ; (Coulibaly et al., 
2017; HLPE, 2019; 
Quandt et al., 2019; 
Sinclair et al., 2019; 
Smith et al., 2019b; 
Muchane et al., 2020; 
Reppin et al., 2020)Cross-Chapter Box NATURAL (continued)

2
310Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
FAQ 2.5 | How can we reduce the risks of climate change to people by protecting and managing nature better?
Damage to our natural environment can increase the risks that climate change poses to people. Protecting and restoring nature can be a way 
to adapt to climate change, with benefits for both humans and biodiversity. Examples include reducing flood risk by restoring catchments 
and coastal habitats, the cooling effects of natural vegetation and shade from trees and reducing the risk of extreme wildfires by better 
management of natural fires.
Protecting and restoring natural environments, such as forests and wetlands, can reduce the risks that climate change 
poses to people as well as supporting biodiversity, storing carbon and providing many other benefits for human health

and well-being. Climate change is bringing an increasing number of threats to people, including flooding, droughts, 
wildfire, heat waves and rising sea levels. These threats can, however, be reduced or aggravated, depending on how 
land, sea and freshwater are managed or protected. There is now clear evidence that ‘Nature-based Solutions’ (NbS) 
can reduce the risks that climate change presents to people. ‘Ecosystem-based Adaptation’ (EbA) is a part of NbS and 
includes:
• Natural flood management: As warm air holds more water and, in some places, because of changing seasonal rainfall 
patterns, we are seeing more heavy downpours in many parts of the world. This can create serious flooding 
problems, with loss of life, homes and livelihoods. The risk of flooding is higher where natural vegetation has been 
removed, wetlands drained or channels straightened. In these circumstances, water flows quicker and the risk of

flood defences being breached is increased. Restoring the natural hydrology of upstream catchments by restoring 
vegetation, creating wetlands and re-naturalising watercourse channels and reinstating connections with the 
floodplain can reduce this risk. In a natural catchment with trees or other vegetation, water flows slowly overland 
and much of it soaks into the soil. When the water reaches a watercourse, it moves slowly down the channel, both 
because of the longer distance it travels when the channel bends and because vegetation and fallen trees slow the 
flow. Wetlands, ponds and lakes can also hold water back and slowly release it into river systems.
• Restoring natural coastal defences: Rising sea levels as a result of climate change mean that coasts are eroding at a fast 
rate and storm surges are more likely to cause damaging coastal flooding. Natural coastal vegetation, such as

saltmarshes and mangrove swamps can, in the right places, stabilise the shoreline and act as a buffer, absorbing the 
force of waves. On a natural coast, the shoreline will move inland and as the sea level rises, the coastal vegetation 
will gradually move inland with it. This contrasts with hard coastal defences such as sea walls and banks, which can 
be overwhelmed and fail. In many places, however, coastal habitats have been cleared and where there are hard 
sea defences behind the coastal zone, the vegetation disappears as the coast erodes rather than moving inland. 
This is often referred to as ‘coastal squeeze’ as the vegetation is squeezed between the sea and the sea wall. 
Restoring coastal habitats and removing hard sea defences, can help reduce the risks of catastrophic flooding.
• Providing local cooling: Climate change is bringing higher temperatures globally, which can result in heat waves

that affect people’s health, comfort and agriculture. In cities, this can be a particular problem for health as 
temperatures are typically higher than in the countryside. Trees give shade, which people, in both rural and 
urban areas, have long used to provide cool places for themselves, for growing crops such as coffee and for 
livestock. Planting trees in the right place can be a valuable, low-cost natural-based solution to reduce the effects 
of increasing heat, including reducing water temperatures in streams and rivers which can help to maintain 
fisheries. Trees and other vegetation also have a cooling effect as a result of water being lost from their leaves 
through evaporation and transpiration (i.e., the loss of water through pores in the leaves, known as stomata). 
Natural areas, parks, gardens in urban areas can help reduce air temperatures by up to a few degrees.

• Restoring natural fire regimes: Some natural ecosystems are adapted to burning, such as savannas and some temperate 
and boreal forests. Where fire has been suppressed or non-native species of trees are planted in more open 
habitats, there is a risk that potential fuel accumulates, which can result in larger and hotter fires. Solutions can 
include restoring natural fire regimes and removing non-native species to decrease the vulnerability of people 
and ecosystems to the exacerbated fire risk that climate change is bringing due to higher temperatures and, in 
some places, changing rainfall patterns.
NbS, including protecting and restoring mangroves, forests and peatlands, also play an important part in reducing 
greenhouse gas emissions and taking carbon dioxide out of the atmosphere. They can also help people in a wide 
range of other ways, including through providing food, materials and opportunities for recreation. There is

increasing evidence that spending time in natural surroundings is good for physical and mental health.

2
311Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
Erosion
Soil health
Ocean acidi/f_ication Over/f_ishing
Reduced /f_isheries catch
Coral bleaching and degradation
Marine Protected Areas (MP As)Agroecology
Sustainable /f_isheriesMangroveSediment
 provis
ionHuman well-being
Seagrass
Reef ecosystemUpward migration of high
elevation grasslands
Upward migration of
lower elevation specie s
Low biodiversityInvasive plants
Water catchments
invaded by inva sive plants
Woody encroachment
Reduced
food and w ater
security Little or no
perennial grass coverOvergrazing
Fire suppressionEncroachment
by woody plantsDeforestation
High-emission energy 
Deserti/f_ication
Excessive fertilisers
in wa terwaysIntensive agriculture
Coastal /f_loodingIntensive
animal
productionUnsustainable
tourismRetreat and loss of glaciers
and permafrost thaw
Coastal erosionPoor water
qualityReforestation
Sustainable
tourism
Susta inable
tourism
Diverse, indigenous

trees and shrubs
Rainwa ter
harvesting
Mixed,  diverse crops
& livestockHigh biodiversity
Water
scarcity Informal settlements
Sustainabl
emobilityUnsustainable
mobilit
yLow-emission
energy production
Water
security
intact
peatlandsHealthy soilsHealthy soils Landslides
Rich wildlife
Coastal erosion w ashed to ocean
Green Cities an
dSettlementsFlooding(a) Human activities that degrade ecosystems also drive global w arming
 and negatively impact nature and people(b) Human activities that protect,  conserve and restore ecosystems
 contribute to climate resilient developmentEcosystem health in/f_luences prospects for climate resilient development
Global goods transportation
Figure FAQ2.5.1 |  Different NbS strategies that contribute to climate resilient developmentBox FAQ 2.5 (continued)

2
312Chapter 2  T errestrial and Freshwater Ecosystems and Their Services 
Another aspect of the benefits to people from ecosystems that needs 
to be taken into account in CRD is increasingly strong evidence of the benefits of natural environments for human health and well-being beyond the provision of basic necessities such as food and water (Bratman et  al., 2019; Marselle et  al., 2021). Meta-analyses of 162  studies involving 51,738 people documented that individuals with high levels of contact with nature throughout their lives felt significantly happier, healthier and more satisfied with their lives, and engaged in more pro-nature behaviours than those with little or no contact with nature (high confidence) (Capaldi et  al., 2014;

Mackay and Schmitt, 2019; Pritchard et  al., 2020; Whitburn et  al., 2020). Meta-analyses of manipulative human trials across 65 studies documented a significant increase in positive feelings and attitudes and a decline in negative feelings after experimental treatments involving nature (medium confidence) (Bowler et al., 2010b; McMahan 
and Estes, 2015; Soga et al., 2017). In the context of CRD improving the extent to which humans see themselves as part of the natural world—known as human-nature connectedness (HNC)—increasing access to natural areas, particularly within urban areas, can provide additional health, cultural and recreation benefits of NbS as well as increasing public engagement and support (robust evidence, high

agreement) (Wilbanks, 2003; Nelson et al., 2007; Bowler et al., 2010b; Capaldi et al., 2014; McMahan and Estes, 2015; Cohen-Shacham et al., 2016; Soga et al., 2017; Mackay and Schmitt, 2019; Work et al., 2019; Hobbie and Grimm, 2020; Pritchard et al., 2020; Whitburn et al., 2020).
2.6.8 F easibility of Adaptation Options

The IPCC (2018a) defined feasibility as ‘the degree to which climate goals and response options are considered possible and/or desirable’ (IPCC, 2018b) and set out an approach to assessing the feasibility of pathways to limit the global temperature rise to 1.5°C. Singh et  al. (2020) developed this approach for adaptation, recognising six different dimensions of feasibility: economic, technological, institutional, socio-cultural, environmental/ecological and geophysical (Table 2.9). Feasibility is considered more fully in other chapters of this report, including Cross-Chapter Box FEASIB in Chapter 18. Adaptation for biodiversity conservation and EbA encompasses a large range of approaches and techniques (Sections  2.6.2, 2.6.3) and will vary in different contexts globally, as illustrated by the range of case studies (Section 2.6.5). It is important to take into account specific regional and local circumstances as well as the type of adaptation action envisaged before making a

feasibility assessment. It is also important to note that what is a feasible adaptation response at one point in time may change with the level of warming experienced—some techniques will be become less effective at higher levels of warming. With global temperature rises of <2°C, in many cases, it will be realistic to build resilience and maintain species and ecosystems in situ, but, at higher levels of warming, this will become increasingly difficult; managing inevitable change, including the consequences of loss and damage, will be important (Prober et al., 2019). Similarly, to be effective at higher levels of warming may require the adaptation of the EbA approaches themselves (Calliari et al., 2019; Martín et al., 2021; Ossola and Lin, 2021). We have therefore not attempted a global-scale assessment of the feasibility of adaptation options, but rather present some key cross-cutting considerations in assessing feasibility for adaptation of and through ecosystems.

Many of the necessary techniques for climate change adaptation for 
biodiversity and EbA have been demonstrated and shown to provide a wide range of additional benefits. This does, however, depend on deploying the right techniques in the right place (Box  2.2) as well as engaging local communities (see Section  2.6.6). There is also a challenge where there is high demand for land for other purposes, especially for agriculture and urban development. Table 2.8 summarises the main feasibility considerations, drawing on previous sections. An assessment of constraints on EbA by Nalau et  al. (2018) addressed similar issues.
A key element of economic feasibility is the cost of adaptation options.

Costs of adaptation vary greatly depending on the actions taken, the location, the methods used, the need for ongoing maintenance and whether land purchase is necessary. At its simplest, adaptation may be a matter of taking account of actual or potential climate change impacts in the course of conservation planning and have little or no additional cost. For example, if a species of conservation concern colonises or starts to use a new area as a result of climate change, like migrant waterfowl shifting the locations where they overwinter (Pavón-Jordán et al., 2020), protection or habitat management may be redirected there. At the other extreme, large-scale restoration can incur significant costs, for example, between 1993 and 2015, the EU-LIFE nature programme invested 167.6 million Euro in 80 projects, which aim to restore over 913
 km2 of peatland habitats in Western European

countries (Andersen et  al., 2017). This is equivalent to <2% of the remaining peatland area, much of which has been affected to at least some extent by human pressures, and restoring the total affected area will cost considerably more.If NbS are to be effective, it is important that the right adaptation actions are carried out in the right place and that 
local communities play an active part in making decisions about their local environment. When they are not part of the process, conflicts can emerge and benefits can be lost.
While NbS help us to adapt to climate change and reduce the amount of greenhouse gases in the atmosphere, it is 
important to note that there are limits to what they can do. To provide a safe environment for both people and nature, it will be essential to radically reduce greenhouse gas emissions, especially those from fossil-fuel burning in the near future.Box FAQ 2.5 (continued)

2
313Terrestrial and Freshwater Ecosystems and Their Services  Chapter 2
Table 2.8 |  Considerations in assessing the feasibility of ecosystem restoration for climate change adaptation, according to Singh et al. (2020)
Feasibility 
characteristicsFeasibility indicators Factors relevant to ecosystem restoration
EconomicMicro-economic viability
Costs are highly variable, depending on techniques and whether land purchase is required. Costs 
will depend on local rates for labour and materials.Economic benefits to local communities where employment is created and where loss from extreme events are avoided (Section 2.6.4; De Groot et al., 2013).Macro-economic viability
Socioeconomic vulnerability reduction potentialEmployment and productivity enhancement potential
TechnologicalTechnical resource availability Techniques are available for restoration of most ecosystems (Sections 2.6.2; 2.6.3), although this

can be very difficult to achieve in some circumstances and take a long time, e.g., the restoration of peat swamp forests (Section 2.6.5.10).Successful implementation may also require skills which are in short supply and training may be required.Risks mitigation potential (stranded assets, unforeseen impacts)
InstitutionalPolitical acceptability
This will vary according to local factors. It should, however, be noted that EbA and adaptation for conservation have been implemented in a wide range of different countries (see the case studies in Section 2.6.5). In many cases, the EbA can meet multiple policy objectives but falls between different decision-makers’ responsibilities.Legal, regulatory feasibility
Institutional capacity and administrative feasibilityTransparency and accountability potential
Socio-culturalSocial co-benefits (health, education)
Multiple benefits to local communities are possible, but full engagement and/or leadership of the

affected members of these communities has been shown to be critical. IKLK can provide important insights (Section 2.6.6).Socio-cultural acceptability
Social and regional inclusivenessBenefits for gender equityInter-generational equity
Environmental/
ecologicalEcological capacity It is important to assess the benefits for ecosystems in relation to other potential options. In particular, for some EbA approaches, it may be possible to achieve a range of different outcomes for biodiversity.Adaptive capacity/potential
GeophysicalPhysical feasibilityAppropriate measures need to be designed to take account of local geophysical conditions, e.g., catchment characteristics, which define where some habitats can occur. This is also critical for ensuring the effectiveness of EbA in reducing natural hazards.LUC enhancement potential
Hazard risk reduction potential
Box 2.2 | Risks of Maladaptive Mitigation

To hold global temperature rise to well below 2°C and pursue efforts to limit it to 1.5°C as required by the Paris Agreement requires

major changes in land use and management. There are many opportunities for NbS, which can provide climate change mitigation and adaptation in ways that protect and restore biodiversity and provide a wide range of benefits to people (Cross-Chapter Box NATURAL in this chapter). There are also new technologies and approaches to develop the bioeconomy in ways which will provide many benefits (Cross-Working Group Box BIOECONOMY in Chapter 5). Nevertheless, renewable energy is a large and essential element of climate change mitigation and there are adverse impacts on biodiversity associated with some types of renewable energy, including wind and solar technologies (Rehbein et al., 2020). However, one of the most serious conflicts emerging is that between land-based approaches to mitigation and the protection of biodiversity, particularly as a result of afforestation strategies and potentially large areas devoted to bioenergy, including bioenergy with carbon capture and storage (BECCS). It

is important to recognise the impacts of climate change mitigation at the same time as assessing the direct impacts of climate change, and ensure that adaptation and mitigation are joined up.

BECCS is an integral part of all widely accepted pathways to keeping global temperature rise to 1.5°C (IPCC, 2018b). This requires large

areas of land, which can be in conflict with the need to produce food and protect biodiversity (Smith et al., 2018). One study examined the combined impacts of climate change and LULCC for bioenergy, and found that severe impacts on species were likely if bioenergy was a major component of strategies for climate change mitigation (Hof et al., 2018). A study on the potential impacts of bioenergy production and climate change on European birds found that one scenario for land conversion for bioenergy to meet a 2°C target would have less impact on species range loss than a global temperature increase of 4°C, but noted that if bioenergy were the only mitigation option it would 'very likely result in the negative effects of bioenergy outweighing the positive effects' (Meller et al., 2015). To avoid the worst impacts of BECCS, it will need to be carefully targeted according to context and local conditions, and other mitigation strategies prioritised so that its use can be minimised (IPCC,

2
314Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
De Groot et al. (2013) analysed 94 restoration projects globally and 
found costs varied by several orders of magnitude, but in terrestrial 
and freshwater ecosystems mostly in the range of USD 100–10,000 
per hectare. They did, however, estimate that the majority of these 
projects provided net benefits and should be considered as high-yield 
investments. Some methods can be much cheaper than others, even in 
the same type of ecosystems in the same country; the estimated cost of 
restoring forest cover in Brazil varied between a mean of USD 49 using 
natural regeneration compared to a mean of USD 2041 per hectare using planting (Brancalion et  al., 2019). When assessing costs, it is 
also important to take into account the benefits delivered by different 
options, both in economic terms and regarding other wider benefits.
The ‘technological’ dimension of feasibility in the context of ecosystems

can be regarded as the range of techniques available and the capacity 
to implement them. As described in Sections 2.6.2 and 2.6.3 above, 
a wide range of techniques have been developed and are starting to 
be implemented. There is good evidence to support adaptation for Reforestation of previously forested areas can bring multiple benefits, but planting trees in places where they do not naturally grow can 
have serious environmental impacts, including potentially exacerbating the effects of climate change. Savannas are amongst the 
ecosystems at risk from afforestation programmes. Savannas are grass-dominated, high-diversity ecosystems with endemic species 
adapted to high-light environments, herbivory and fire (Staver et al., 2011; Murphy et al., 2016). Interactions between climate change, 
elevated CO 2 and the disruption of natural disturbance regimes have led to the widespread encroachment of woody plants (Stevens et al.,

2016), causing a fundamental shift in ecosystem structure and function with loss of grass and reduced fire frequency (Archibald et al., 
2009) and stream flow (Honda and Durigan, 2016) (Sections  2.4.3.5, 2.5.2.5, Box  2.1, 2.5.4, TAble 2.5, Figure  2.11). Afforestation 
exacerbates this degradation (Bremer and Farley, 2010; Veldman et al., 2015; Abreu et al., 2017). Global-scale analyses aimed at identifying 
degraded forest areas suitable for reforestation (Veldman et al., 2019) cannot reliably separate naturally grassy ecosystems with sparse 
tree cover from degraded forests, so local information is essential to ensure tree planting is targeted where it can benefit most and avoid 
harm. Figure Box 2.2.1 indicates where these issues are most likely to arise.
Regions where savannas at potential risk from afforestation
Grassy biomes Grassy biomes at risk of afforestation and forest expansion

Figure Box 2.2.1 |  Regions where savannas are at potential risk from afforestation. Based on (Veldman et al., 2015)
A similar issue can occur in naturally treeless peatlands which can be afforested if they are drained, but this leads to the loss of distinctive 
peatland species and communities as well as high GHG emissions (Wilson et al., 2014). The mitigation benefits of growing timber are 
reduced or become negative in these conditions due to the CO 2 emissions from the oxidation of the drained peat—they can become a 
net carbon source rather than a carbon sink (Simola et al., 2012; Crump, 2017; Goldstein et al., 2020). (Sections 2.4.3.8, 2.5.2.8)Box 2.2 (continued)

2
315Terrestrial and Freshwater Ecosystems and Their Services   Chapter 2
FAQ 2.6 | Can tree planting tackle climate change?
Restoring and preventing further loss of native forests is essential for combatting climate change. Planting trees in historically unforested areas 
(grasslands, shrublands, savannas and some peatlands) can reduce biodiversity and increase the risks of damage from climate change. It is 
therefore essential to target tree planting to the appropriate locations and use appropriate species. Restoring and protecting forests reduces 
human vulnerability to climate change, reduces air pollution, stores carbon and builds the resilience of natural systems.
Like all living plants, trees remove carbon dioxide from the atmosphere through the process of photosynthesis. In trees, 
this carbon uptake is relatively long-term, since much of it is stored in the trees’ woody stems and roots. Therefore,

tree planting can be a valuable contribution to reducing climate change. Besides capturing carbon, planting trees 
can reduce some negative impacts of climate change by providing shade and cooling. It can also help prevent erosion 
and reduce flood risk by slowing water flow and improving ground water storage. Restoring forest in degraded areas 
supports biodiversity and can provide benefits to people, ranging from timber to food and recreation.
There are some areas where replacing lost trees is useful. These include forest that has been recently cut down and 
where reforestation is usually practical. However, it is very important to correctly identify areas of forest that are 
degraded or have definitely been deforested. Reforesting places, especially where existing native forest patches 
occur, brings benefits both in sucking up carbon from the atmosphere and helping us to adapt to climate change.

Plantations of a non-native species, although offering some economic benefits, do not usually provide the same 
range of positive impacts, generally have lower biodiversity, reduced carbon uptake and storage, and are less 
resilient to climate change.
Reforestation options include the natural regeneration of the forest, assisted restoration, enrichment planting, 
native-tree plantations, commercial plantations and directed tree planting in agro-forestry systems and urban areas. 
Reforestation with native species usually contributes to a wide range of sustainability goals, including biodiversity 
recovery, improved water filtration and groundwater recharge. It can reduce the risks of soil erosion and floods. In 
cities, planting trees can support climate change adaptation by reducing the heat of the area, and promote a wide 
range of social benefits such as providing shade and benefitting outdoor recreation. Urban trees can also lower

energy costs by reducing the demand for conventional sources of cooling like air-conditioning, especially during 
peak-demand periods. It is therefore important to recognise that there are a wide range of different planting and 
forest management strategies. The choice will depend on the objectives and the location.
Not everywhere is suitable for tree planting. It is particularly problematic in native non-forested ecosystems. These 
natural ecosystems are not deforested and degraded but are instead naturally occurring non-forested ecosystems. 
These areas vary from open grasslands to densely wooded savannas and shrublands. Here, restoring the natural 
ecosystems instead of afforesting them will better contribute to increasing carbon storage and increasing the area’s 
resilience to climate change and other environmental changes. It is important to remember that, just because a

tree can grow somewhere, it does not mean that it should. These systems are very important in their own right, 
storing carbon in soils, supporting rich biodiversity and providing people with important ecosystem services such as 
grasslands for animal grazing. Planting trees in these areas destroys the ecosystem and threatens the biodiversity 
which is adapted to these environments. They can also impact on ecosystem services such as forage for livestock, on 
which many people rely.
Many of these open areas also occur in low-rainfall areas. Planting trees there uses a lot of water and can cause 
reductions in stream flow and groundwater. Many of these locations also burn regularly, and planting trees threatens 
the establishing trees but can also increase the intensity of the fires from that of a grass-fuelled fire to that of 
a wood-fuelled fire. Swapping grassy ecosystems for forests may contribute to warming, as forests absorb more

incoming radiation (warmth) than grasslands. Aside from the negative impacts to adaptation, it is also questionable 
just how much carbon can be sequestered in these landscapes as planting trees in grassy ecosystems can reduce carbon 
gains. Furthermore, a high below-ground carbon store prevents carbon loss to fire in these fire-prone environments.
Another example is peatlands. Peat stores an incredible amount of carbon; maintaining and restoring peatlands 
is therefore important to reduce atmospheric carbon. However, the restoration actions depend on what type of 
peatland it is and where it is located. Many temperate and boreal peatlands are naturally treeless. Here, planting trees 
is often only possible following drainage, but draining and planting (especially of non-native species) destroys native 
biodiversity and releases GHGs. Many peatlands, especially in the Tropics, are naturally forested, and restoring them

requires re-wetting and restoring the natural tree cover (see Figure FAQ2.2.1) which will increase carbon storage.

2
316Chapter 2  T errestrial and Freshwater Ecosystems and Their Services 
biodiversity and EbA in general terms and, in many cases, adaptation 
draws on techniques for habitat creation and restoration which have been developed to meet other objectives. However, feasibility needs to be assessed alongside the likely effectiveness: a feasible but ineffective scheme is of no value and the evaluation of success for specific interventions remains poorly developed (Morecroft et  al., 2019). It is therefore often important to proceed with the use of pilot studies, good monitoring and the evaluation of outcomes to build confidence before greater deployment of approaches. A linked technical area is the availability of specialist skills and knowledge to implement adaptation; this can vary considerably according to the type of adaptation measure.
Institutional dimensions are dealt with more fully in other chapters,

but in the specific context of the natural environment it is notable that EbA is relevant to a wide range of organisations and policy objectives, in addition to environmental departments, NGOs and agencies which There are actions we can perform instead of planting trees in non-forested ecosystems, and these include:
•Address the causes of deforestation, forest degradation and widespread ecosystem loss
•Reduce carbon emissions from fossil fuels
•Focus on ecosystem restoration over tree planting. For example, in restoring tropical grassy ecosystems, we can
look at actions that cut down trees, enhance grass regrowth and restore natural fire regimes. We then havea much better chance of both enhancing carbon capture and reducing some of the harmful effects of climatechange.

In between the two extremes of where planting trees is highly suitable and areas where it is not, it is important to remember that the context matters and that decisions to (re)forest should look beyond simply the act of planting trees. W
e can consider what the ecological, social and economic goals are of tree planting. It is then important to verify 
the local context and decide what restoration action will be most effective. It is also more efficient and effective to conserve existing forests before worrying about reforesting.
Basic biome specific guidelines when planting in natural and semi-natural vegetation
Was there a 
forest here?Is restoration 
possible?Will benefits 
outweigh
the costs?
Plant a native tree 
adapted to the local 
environment!
Is the land naturally unforested? Many people mistakenly assume open areas like grasslands, savannas and some peatlands are degraded forests.
Check with local experts to determine what 
the historical ecosystem was.

Planting trees in areas where they don’t 
belong can stress local water supplies,harm native biodiversity, damage peoples’ 
livelihoods and reduce resilience to climate change. Can the forest regenerate naturally? Has the local community been consulted and are they supportive/involved in the decision making process?
Is it better to use the land for livestock 
grazing or agriculture because it would be too difficult/costly to restore?Will the trees benefit ecosystem services? Will they help with flood protection, reduce erosion, carbon storage, heat mitigation, provide food or timber products? 
Or will trees displace people, reduce water 
supply,  reduce biodiversity, or harm food 
production?
What are the costs associated with 
restoration and who will pay for them? Will trees survive climate change impacts like increased and more severe fires or droughts?

Figure FAQ2.6.1 |  Some places are more appropriate for tree planting than others and caution needs to be applied when planting in different 
biomes, with some biomes being more suitable than others. This figure highlights some basic biome-specific guidelines when planting in natural and semi-natural vegetation.Box FAQ 2.6 (continued)

2
317Terrestrial and Freshwater Ecosystems and Their Services  Chapter 2
traditionally deliver conservation. Upscaling implementation is likely to 
be dependent on this wider range of interests. There can, however, be problems, in that appropriate geographies for decision-making about ecosystems (e.g., a catchment) may not directly map onto governance arrangements.
Socio-cultural factors are important for adaptation of the natural 
environment. Reviewing the constraints of EbA, Nalau et  al. (2018) found that risk perceptions and cultural preferences for particular types of management approaches were frequently identified in studies.
In the IPCC feasibility assessment framework, one integral dimension

is ‘environmental/ecological’. In this respect, adaptation by and for ecosystems should perform well, and this may be a reason to prefer EbA to other approaches when there is an alternative. It should, however, be noted that sometimes apparently environmentally positive approaches such as forest creation can be done in ways which are damaging (Section 2.6.7 and Box 2.2) and the impacts need to be critically assessed for local circumstances.
Geophysical dimensions are important for ecosystems as they have 
typically shaped which ecosystems can occur where, and feasibility will depend on implementing adaptation options in places where they are appropriate. Palaeo-ecological studies can help inform potential options (Wingard et al., 2017)
2.7 Reducing Scientific Uncertainties to 
Inform P
olicy and Management Decisions
Research since the AR5 (Settele et  al., 2014) has increased the

understanding of climate change impacts and vulnerability in ecosystems. Evidence gaps remain and geographic coverage of research is uneven. This section assesses gaps in ecosystem science where research is necessary for environmental policies and the management of natural resources, including under the UNFCCC and the CBD.
2.7.1  Observed Impacts
Detection and attribution efforts have increased since AR5, but there are some key impacts of high societal importance that would benefit from more detailed and sophisticated attribution studies. For example, while it is clear that diseases have altered considerably in both wild animals and humans in some areas (high confidence in detection),

there are many regions that are under-studied, and few regions that provide robust assessments of the role of climate change, particularly with respect to human infectious diseases. While wildfire has been robustly linked to climate change in some regions, there are still a lack of attribution studies in some regions that have experienced large burns recently, and only one fire impact—the increase of the area burned by wildfire in western North America in the period 1984–2017 (Section 2.4.4.2.1) —has been formally attributed to anthropogenic climate change. Global changes in soil and freshwater ecosystem carbon over time remain uncertainties in global carbon stocks and changes (Section  2.4.4.4); due to the physical inability to conduct repeat-monitoring and the lack of remote sensing to scale up point measurements, no global methods can yet produce repeating spatial estimates of soil carbon stock changes.
Despite the growing understanding of the importance of ecosystem

services, this assessment found limited research on the observed impacts of climate change for 14 of 18 ecosystem services (Table 2.1).
2.7.2  Pr ojected Risks

A challenge for future projections that continues from previous IPCC reports is accurately characterising and quantifying the interactions of climate change vs. other, non-climate factors that cause ecological change, including LULCC (particularly deforestation, agricultural expansion, and urbanisation) and air and water pollution. Interactions can be particularly complex for invasive species, pests, pathogens and human infectious diseases. Modelling of risks at the species level requires comprehensive databases of the physiological, life-history, and reproduction of individual species, and modeling the impact of changes in species’ compositions requires a mechanistic understanding of functional traits relevant to ecosystem integrity, functioning and resilience to climate change. Taxa that particularly lack this basis for model projections include fungi and bacteria. For numerous plant and animal species, research into genotypic and phenotypic diversity as a source of ecosystem

Soil plays a vital role in ecosystem function, is the habitat of a large 
number of species and is a large carbon store which is currently a major source of GHG emissions; it is therefore a priority for climate change research (Hashimoto et  al., 2015). Major uncertainties remain in our understanding of soil functions. ESMs predict that soil respiration will increase with rising temperatures (Friedlingstein et al., 2014). However, there is evidence of acclimation post-increase (Carey et al., 2016) as the opposite response of decrease in respiration with warming (Li et al., 2013; Reynolds et al., 2015). Long-term, large-scale field observations combined with a better conceptual understanding of the factors governing soil process responses to climate change are needed. A better understanding of plant–water relations is also necessary, including the response of plant transpiration to increased CO
2, climate warming and changes in soil moisture and groundwater 
elevation.

2.7.3 Adaptation and Climate Resilient Development
There are significant evidence gaps in developing adaptation, both for biodiversity conservation and EbA. In particular, while many adaptation measures have been proposed and implementation is starting, there are very few evaluations of success in the scientific literature (Morecroft et al., 2019; Prober et al., 2019). As detailed in Section 2.6.2, there is a strong body of literature on conceptual approaches to climate change adaptation for biodiversity but very little empirical testing of which approaches actually work best. Going forward, it is important to put in place good monitoring and evaluation of adaptation strategies. For EbA, there are good examples of measuring changes in response to new adaptation measures, but these remain relatively rare globally.

2
318Chapter 2 Terrestrial and Freshwater Ecosystems and Their Services 
Human factors which promote or hinder adaptation are important as 
well as the technical issues. Only a few studies incorporate climate 
change and ecosystem services in integrated decision-making, and 
even fewer aim to identify solutions robust to uncertainty (Runting 
Over the last decades, losses due to natural disasters including those 
from events related to extreme weather have strongly increased 
(Mechler and Bouwer, 2015). There is a need for better assessment of 
global adaptation costs, funding and investment (Micale et al., 2018). 
Potential synergies between international finance for disaster risk 
management (DRM) and adaptation have not yet been fully realised. 
Research has almost exclusively focused on normalising losses for 
changes in exposure, but not for vulnerability, which is a major gap, 
given the dynamic nature of vulnerability (Mechler and Bouwer, 2015).

3
381Oceans and Coastal Ecosystems and Their Services Chapter 3
Ocean and coastal ecosystems support life on Earth and many 
aspects of human well-being. Covering two-thirds of the planet, the ocean hosts vast biodiversity and modulates the global climate system by regulating cycles of heat, water and elements, including carbon. Marine systems are central to many cultures, and they also provide food, minerals, energy and employment to people. Since previous assessments
1, new laboratory studies, field observations 
and process studies, a wider range of model simulations, Indigenous knowledge, and local knowledge have provided increasing evidence on the impacts of climate change on ocean and coastal systems, how human communities are experiencing these impacts, and the potential solutions for ecological and human adaptation.
Observations: vulnerabilities and impactsAnthropogenic climate change has exposed ocean and coastal

ecosystems to conditions that are unprecedented over millennia (high confidence
2), and this has greatly impacted life in the 
ocean and along its coasts (very high confidence). Fundamental changes in the physical and chemical characteristics of the ocean acting individually and together are changing the timing of seasonal activities (very high confidence), distribution (very high confidence) and abundance (very high confidence) of oceanic and coastal organisms, from microbes to mammals and from individuals to ecosystems, in every region. Evidence of these changes is apparent from multi-decadal observations, laboratory studies and mesocosms, as well as meta-analyses of published data. Geographic range shifts of marine species generally follow the pace and direction of climate warming (high confidence): surface warming since the 1950s has shifted marine taxa and communities poleward at an average (mean ±  very 
likely
3 range) of 59.2  ±  15.5  km per decade (high confidence),

with substantial variation in responses among taxa and regions. Seasonal events occur 4.3 ± 1.8
 d to 7.5 
± 1.5
 d earlier per decade 
among planktonic organisms (
very high confidence) and on average 
3 ± 2.1
 d earlier per decade for fish (
very high confidence). Warming, 
acidification and deoxygenation are altering ecological communities by increasing the spread of physiologically suboptimal conditions for many marine fish and invertebrates (medium confidence). These and other responses have subsequently driven habitat loss (very high confidence), population declines (high confidence), increased risks of species extirpations and extinctions (medium confidence) and rearrangement of marine food webs (medium to high confidence, depending on ecosystem). {3.2, 3.3, 3.3.2, 3.3.3, 3.3.3.2, 3.4.2.1, 3.4.2.3–3.4.2.8, 3.4.2.10, 3.4.3.1, 3.4.3.2, 3.4.3.3, Box 3.2}

1 Previous IPCC assessments include the IPCC Fifth Assessment Report (AR5) (IPCC, 2013; IPCC, 2014b; IPCC, 2014c; IPCC, 2014d), the Special Report on Global Warming of 1.5°C (SR1.5) (IPCC, 2018), 
the Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC, 2019b) and the IPCC Sixth Assessment Report Working Group I (WGI AR6).
2
 the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high and very high, and is typeset in italics (e.g., medium confidence). For a given evidence and agreement statement, different confidence levels 
3
 In

this Report, the following terms are used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as likely 
as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100% and extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics (e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies within the 17–83% probability range.Marine heatwaves lasting weeks to several months are exposing

species and ecosystems to environmental conditions beyond their tolerance and acclimation limits (very high confidence). WGI AR6 concluded that marine heatwaves are more frequent (high confidence), more intense and longer (medium confidence) since the 1980s, and since at least 2006 very likely attributable to anthropogenic

climate change. Open-ocean, coastal and shelf-sea ecosystems, including coral reefs, rocky shores, kelp forests, seagrasses, mangroves, the Arctic Ocean and semi-enclosed seas, have recently undergone mass mortalities from marine heatwaves (very high confidence). Marine heatwaves, including well-documented events along the west coast of North America (2013–2016) and east coast of Australia (2015–2016, 2016–2017 and 2020), drive abrupt shifts in community composition that may persist for  years (very high confidence), with associated biodiversity loss (very high confidence), collapse of regional fisheries and aquaculture (high confidence) and reduced capacity of habitat-forming species to protect shorelines (high confidence). {WGI AR6 Chapter  9, 3.2.2.1, 3.4.2.1–3.4.2.5, 3.4.2.7, 3.4.2.10, 3.4.2.3, 3.4.3.3.3, 3.5.3}
At local to regional scales, climate change worsens the impacts on

marine life of non-climate anthropogenic drivers, such as habitat degradation, marine pollution, overfishing and overharvesting, nutrient enrichment and introduction of non-indigenous species (very high confidence). Although impacts  of multiple climate and

non-climate drivers can be beneficial or neutral to marine life, most are detrimental (high confidence). Warming exacerbates coastal eutrophication and associated hypoxia, causing ‘dead zones’  (very high confidence), which drive severe impacts on coastal and shelf-sea ecosystems (very high confidence), including mass mortalities, habitat reduction and fisheries disruptions (medium confidence). Overfishing exacerbates effects of multiple climate-induced drivers on predators at the top of the marine food chain (medium confidence). Urbanisation and associated changes in freshwater and sediment dynamics increase the vulnerability of coastal ecosystems like sandy beaches, salt marshes and mangrove forests to sea level rise and changes in wave energy (very high confidence). Although these non-climate drivers confound attribution of impacts to climate change, adaptive, inclusive and evidence-based management reduces the cumulative pressure on ocean and coastal ecosystems, which will

decrease their vulnerability to climate change (high confidence). {3.3, 3.3.3, 3.4.2.4–3.4.2.8, 3.4.3.4, 3.5.3, 3.6.2, Cross-Chapter Box SLR in Chapter 3}

Climate-driven impacts on ocean and coastal environments have 
caused measurable changes in specific industries, economic losses, emotional harm and altered cultural and recreational activities around the world (high confidence). Climate-driven 
movement of fish stocks is causing commercial, small-scale, artisanal

3
382Chapter 3 Oceans and Coastal Ecosystems and Their Services
and recreational fishing activities to shift poleward and diversify 
harvests (high confidence). Climate change is increasing the geographic 
spread and risk of marine-borne pathogens like Vibrio sp. (very high 
confidence), which endanger human health and decrease provisioning 
and cultural ecosystem services (high confidence). Interacting climate-
induced drivers and non-climate drivers are enhancing movement 
and bioaccumulation of toxins and contaminants into marine food 
webs (medium evidence, high agreement), and increasing salinity 
of coastal waters, aquifers and soils (very high confidence), which 
endangers human health (very high confidence). Combined climate-
induced drivers and non-climate drivers also expose densely populated 
coastal zones to flooding (high confidence) and decrease physical 
protection of people, property and culturally important sites (very

high confidence). {3.4.2.10, 3.5.3, 3.5.5, 3.5.5.3, 3.5.6, Cross-Chapter 
Box SLR in Chapter 3}
Projections: vulnerabilities, risks and impacts
Ocean conditions are projected to continue diverging from a 
pre-industrial state (virtually certain), with the magnitude 
of  warming, acidification, deoxygenation, sea level rise and 
other climate-induced drivers depending on the emission 
scenario (very high confidence), and to increase risk of regional 
extirpations and global extinctions of marine species (medium 
confidence). Marine species richness near the equator and in the Arctic 
is projected to continue declining, even with less than 2°C warming 
by the end of the century (medium confidence). In the deep ocean, 
all global warming levels will cause faster movements of temperature 
niches by 2100 than those that have driven extensive reorganisation 
of marine biodiversity at the ocean surface over the past 50  years

(medium confidence). At warming levels beyond 2°C by 2100, risks of 
extirpation, extinction and ecosystem collapse escalate rapidly (high 
confidence). Paleorecords indicate that at extreme global warming 
levels (>5.2°C), mass extinction of marine species may occur (medium 
confidence). {Box 3.2, 3.2.2.1, 3.4.2.5, 3.4.2.10, 3.4.3.3, Cross-Chapter 
Box PALEO in Chapter 1}
Climate impacts on ocean and coastal ecosystems will be 
exacerbated by increases in intensity, reoccurrence and 
duration of marine heatwaves (high confidence), in some cases, 
leading to species extirpation, habitat collapse or surpassing 
ecological tipping points (very high confidence). Some habitat-
forming coastal ecosystems including many coral reefs, kelp forests and 
seagrass meadows, will undergo irreversible phase shifts due to marine 
heatwaves with global warming levels >1.5°C and are at high risk this 
century even in <1.5°C scenarios that include periods of temperature

overshoot beyond 1.5°C (high confidence). Under SSP1-2.6, coral 
reefs are at risk of widespread decline, loss of structural integrity and 
transitioning to net erosion by mid-century due to increasing intensity 
and frequency of marine heatwaves (very high confidence). Due to 
these impacts, the rate of sea level rise is very likely to exceed that 
of reef growth by 2050, absent adaptation. Other coastal ecosystems, 
including kelp forests, mangroves and seagrasses, are vulnerable to 
phase shifts towards alternate states as marine heatwaves intensify 
(high confidence). Loss of kelp forests are expected to be greatest 
at the low-latitude warm edge of species’ ranges (high confidence). 
{3.4.2.1, 3.4.2.3, 3.4.2.5, 3.4.4}Escalating impacts of climate change on marine life will further 
alter biomass of marine animals (medium confidence), the 
timing of seasonal ecological events (medium confidence) 
and the geographic ranges of coastal and ocean taxa (medium

confidence), disrupting life cycles (medium confidence), 
food webs (medium confidence) and ecological connectivity 
throughout the water column (medium confidence). Multiple 
lines of evidence suggest that climate-change responses are very 
likely to amplify up marine food webs over large regions of the ocean. 
Modest projected declines in global phytoplankton biomass translate 
into larger declines of total animal biomass (by 2080–2099 relative 
to 1995–2014) ranging from (mean ±  very likely range) −5.7 ± 4.1% 
to −15.5 ± 8.5% under SSP1-2.6 and SSP5-8.5, respectively (medium 
confidence). Projected declines in upper-ocean nutrient concentrations, 
likely associated with increases in stratification, will reduce carbon 
export flux to the mesopelagic and deep-sea ecosystems (medium 
confidence). This will lead to a decline in the biomass of abyssal meio- 
and macrofauna (by 2081–2100 relative to 1995–2014) by −9.8% and

−13.0% under SSP1-2.6 and SSP5-8.5, respectively (limited evidence). 
By 2100, 18.8 ± 19.0% to 38.9 ± 9.4% of the ocean will very likely 
undergo a change of more than 20 d (advances and delays) in the 
start of the phytoplankton growth period under SSP1-2.6 and SSP5-
8.5, respectively (low confidence). This altered timing increases the risk 
of temporal mismatches between plankton blooms and fish spawning 
seasons (medium to high confidence) and increases the risk of fish-
recruitment failure for species with restricted spawning locations, 
especially in mid-to-high latitudes of the Northern Hemisphere (low 
confidence). Projected range shifts among marine species (medium 
confidence) suggest extirpations and strongly decreasing tropical 
biodiversity. At higher latitudes, range expansions will drive increased 
homogenisation of biodiversity. The projected loss of biodiversity 
ultimately threatens marine ecosystem resilience (medium to high

confidence), with subsequent effects on service provisioning (medium 
to high confidence). {3.2.2.3, 3.4.2.10, 3.4.3.1–3.4.3.5, 3.5, WGI AR6 
Section 2.3.4.2.3}
Risks from sea level rise for coastal ecosystems and people 
are very likely to increase tenfold well before 2100 without 
adaptation and mitigation action as agreed by Parties to the Paris 
Agreement (very high confidence). Sea level rise under emission 
scenarios that do not limit warming to 1.5°C will increase the risk of 
coastal erosion and submergence of coastal land (high confidence), 
loss of coastal habitat and ecosystems (high confidence) and worsen 
salinisation of groundwater (high confidence), compromising coastal 
ecosystems and livelihoods (high confidence). Under SSP1-2.6, 
most coral reefs (very high confidence), mangroves (likely, medium 
confidence) and salt marshes (likely, medium confidence) will be 
unable to keep up with sea level rise by 2050, with ecological impacts

escalating rapidly beyond 2050, especially for scenarios coupling high 
emissions with aggressive coastal development (very high confidence). 
Resultant decreases in natural shoreline protection will place 
increasing numbers of people at risk (very high confidence). The ability 
to adapt to current coastal impacts, cope with future coastal risks and 
prevent further acceleration of sea level rise beyond 2050 depends on 
immediate implementation of mitigation and adaptation actions (very 
high confidence).  {3.4.2.1, 3.4.2.4, 3.4.2.5, 3.4.2.6, 3.5.5.3, Cross-
Chapter Box SLR in Chapter 3}

3
383Oceans and Coastal Ecosystems and Their Services  Chapter 3
Climate change will alter many ecosystem services provided 
by marine systems (high confidence), but impacts to human 
communities will depend on people’s overall vulnerability, 
which is strongly influenced by local context and development 
pathways (very high confidence). Catch composition and diversity 
of regional fisheries will change (high confidence), and fishers who are 
able to move, diversify and leverage technology to sustain harvests 
decrease their own vulnerability (medium confidence). Management 
that eliminates overfishing facilitates successful future adaptation of 
fisheries to climate change (very high confidence). Marine-dependent 
communities, including Indigenous Peoples and local peoples, will be 
at increased risk of losing cultural heritage and traditional seafood-
sourced nutrition (medium confidence). Without adaptation, seafood-

dependent people face increased risk of exposure to toxins, pathogens 
and contaminants (high confidence), and coastal communities 
face increasing risk from salinisation of groundwater and soil (high 
confidence). Early-warning systems and public education about 
environmental change, developed and implemented within the local 
and cultural context, can decrease those risks (high confidence). 
Coastal development and management informed by sea level rise 
projections will reduce the number of people and amount of property 
at risk (high confidence), but historical coastal development and 
policies impede change (high confidence). Current financial flows are 
globally uneven and overall insufficient to meet the projected costs 
of climate impacts on coastal and marine social–ecological systems 
(very high confidence). Inclusive governance that (a) accommodates 
geographically shifting marine life, (b)  financially supports needed

human transformations, (c)  provides effective public education and 
(d) incorporates scientific evidence, Indigenous knowledge and local 
knowledge to manage resources sustainably shows greatest promise 
for decreasing human vulnerability to all of these projected changes in 
ocean and coastal ecosystem services (very high confidence). {3.5.3, 
3.5.5, 3.5.6, 3.6.3, Box 3.4, Cross-Chapter Box ILLNESS in Chapter 2, 
Cross-Chapter Box SLR in Chapter 3}
Solutions, trade-offs, residual risk, decisions and governance
Humans are already adapting to climate-driven changes in 
marine systems, and while further adaptations are required even 
under low-emission scenarios (high confidence), transformative 
adaptation will be essential under high-emission scenarios 
(high confidence).  Low-emission scenarios permit a wider array 
of feasible, effective and low-risk nature-based adaptation options 
(e.g., restoration, revegetation, conservation, early-warning systems

for extreme events and public education) (high confidence). Under 
high-emission scenarios, adaptation options (e.g., hard infrastructure 
for coastal protection, assisted migration or evolution, livelihood 
diversification, migration and relocation of people) are more uncertain 
and require transformative governance changes  (high confidence). 
Transformative climate adaptation will reinvent institutions to 
overcome obstacles arising from historical precedents, reducing current 
barriers to climate adaptation in cultural, financial and governance 
sectors (high confidence). Without transformation, global inequities will 
likely increase between regions (high confidence) and conflicts between 
jurisdictions may emerge and escalate. {3.5, 3.5.2, 3.5.5.3, 3.6, 3.6.2.1, 
3.6.3.1, 3.6.3.2, 3.6.3.3, 3.6.4.1, 3.6.4.2, 3.6.5, Cross-Chapter Box SLR 
in Chapter 3, Cross-Chapter Box ILLNESS in Chapter 2}Available adaptation options are unable to offset climate-

change impacts on marine ecosystems and the services they 
provide (high confidence). Adaptation solutions implemented at 
appropriate scales, when combined with ambitious and urgent 
mitigation measures, can meaningfully reduce impacts (high 
confidence). Increasing evidence from implemented adaptations 
indicates that multi-level governance, early-warning systems for 
climate-associated marine hazards, seasonal and dynamic forecasts, 
habitat restoration, ecosystem-based management, climate-adaptive 
management and sustainable harvesting tend to be both feasible 
and effective (high confidence). Marine protected areas (MPAs), as 
currently implemented, do not confer resilience against warming and 
heatwaves (medium confidence) and are not expected to provide 
substantial protection against climate impacts past 2050 (high 
confidence). However, MPAs can contribute substantially to adaptation 
and mitigation if they are designed to address climate change,

strategically implemented and well governed (high confidence). 
Habitat restoration limits climate-change-related loss of ecosystem 
services, including biodiversity, coastal protection, recreational use and 
tourism (medium confidence), provides mitigation benefits on local to 
regional scales (e.g., via carbon-storing ‘blue carbon’ ecosystems) (high 
confidence) and may safeguard fish-stock production in a warmer 
climate (limited evidence). Ambitious and swift global mitigation 
offers more adaptation options and pathways to sustain ecosystems 
and their services (high confidence). {3.4.2, 3.4.3.3, 3.5, 3.5.2, 3.5.3, 
3.5.5.4, 3.5.5.5, 3.6.2.1, 3.6.2.2, 3.6.2.3, 3.6.3.1, 3.6.3.2, 3.6.3.3, 3.6.5, 
Figure 3.24, Figure 3.25}
Nature-based solutions for adaptation of ocean and coastal 
ecosystems can achieve multiple benefits when well designed 
and implemented (high confidence), but their effectiveness 
declines without ambitious and urgent mitigation (high

confidence). Nature-based solutions, such as ecosystem-based 
management, climate-smart conservation approaches (i.e., climate-
adaptive fisheries and conservation) and coastal habitat restoration, 
can be cost-effective and generate social, economic and cultural co-
benefits while contributing to the conservation of marine biodiversity 
and reducing cumulative anthropogenic drivers (high confidence). 
The effectiveness of nature-based solutions declines with warming; 
conservation and restoration alone will be insufficient to protect coral 
reefs beyond 2030 (high confidence) and to protect mangroves beyond 
the 2040s (high confidence). The multidimensionality of climate-change 
impacts and their interactions with other anthropogenic stressors calls 
for integrated approaches that identify trade-offs and synergies across 
sectors and scales in space and time to build resilience of ocean and 
coastal ecosystems and the services they deliver (high confidence).

{3.4.2, 3.5.2, 3.5.3, 3.5.5.3, 3.5.5.4, 3.5.5.5, 3.6.2.2, 3.6.3.2, 3.6.5, 
Figure 3.25, Table 3.SM.6}
Ocean-focused adaptations, especially those that employ nature-
based solutions, address existing inequalities, and incorporate 
just and inclusive decision-making and implementation processes, 
support the UN Sustainable Development Goals (SDGs) (high 
confidence). There are predominantly positive synergies between 
adaptation options for Life Below Water (SDG14), Climate Action 
(SDG13) and social, economic and governance SDGs (SDG1–12, 16–17) 
(high confidence), but the ability of ocean adaptation to contribute to the

3
384Chapter 3 Oceans and Coastal Ecosystems and Their Services
SDGs is constrained by the degree of mitigation action (high confidence). 
Furthermore, existing inequalities and entrenched practices limit 
effective and just responses to climate change in coastal communities 
(high confidence). Momentum is growing towards transformative 
international and regional governance that will support comprehensive, 
equitable ocean and coastal adaptation while also achieving SDG14 
(robust evidence), without compromising achievement of other SDGs. 
{3.6.4.0, 3.6.4.2, 3.6.4.3, Figure 3.26}.

3
385Oceans and Coastal Ecosystems and Their Services  Chapter 3
3.1 Point of Departure
The ocean contains approximately 97% of Earth’s water within a 
system of interconnected basins that cover 71% of its surface. Coastal 
systems mostly extend seaward from the high-water mark, or just 
beyond, to the edge of the continental shelf and include shores of soft 
sediments, rocky shores and reefs, embayments, estuaries, deltas and 
shelf systems. Oceanic systems comprise waters beyond the shelf edge, 
from ~200 m to nearly 11,000 m deep (Stewart and Jamieson, 2019), 
with an average depth of approximately 3700 m. The epipelagic zone, 
or upper 200 m of the ocean, is illuminated by sufficient sunlight to 
sustain photosynthesis that supports the rich marine food web. Below 
the epipelagic zone lies the barely lit mesopelagic zone (200–1000 m), 
the perpetually dark bathypelagic zone (depth >1000 m) and the 
deep seafloor (benthic ecosystems at depths >200 m), which spans

rocky and sedimentary habitats on seamounts, mid-ocean ridges and 
canyons, abyssal plains and sedimented margins. Semi-enclosed seas 
(SES) include both coastal and oceanic systems.
The ocean sustains life on Earth by providing essential resources 
and modulating planetary flows of energy and materials. Together, 
harvests from the ocean and inland waters provide more than 20% of 
dietary animal protein for more than 3.3 billion people worldwide and 
livelihoods for about 60 million people (FAO, 2020b). The global ocean 
is centrally involved in sequestering anthropogenic atmospheric CO 2 
and recycling many elements, and it regulates the global climate system by redistributing heat and water (WGI AR6 Chapter  9; Fox-Kemper 
et al., 2021). The ocean also provides a wealth of aesthetic and cultural 
resources (Barbier et al., 2011), contains vast biodiversity (Appeltans 
et al., 2012), supports more animal biomass than on land (Bar-On et al.,

2018) and produces at least half the world’s photosynthetic oxygen 
(Field et al., 1998). Ecosystem services (Annex II: Glossary) delivered 
by ocean and coastal ecosystems support humanity by protecting 
coastlines, providing nutrition and economic opportunities (Figure 3.1; 
Selig et al., 2019) and providing many intangible benefits. Even though 
ecosystem services and biodiversity underpin human well-being and 
support climate mitigation and adaptation (Pörtner et al., 2021b), there 
are also ethical arguments for preserving biodiversity and ecosystem 
functions regardless of the beneficiary (e.g., Taylor et al., 2020). This 
chapter assesses the impact of climate change on the full spectrum of 
ocean and coastal ecosystems, on their services and on related human 
activities, and it assesses marine-related opportunities within both 
ecological and social systems to adapt to climate change.
Previous IPCC Assessment Reports (IPCC, 2014b; IPCC, 2014c; IPCC,

2018; IPCC, 2019b) have expressed growing confidence in the 
detection of climate-change impacts in the ocean and their attribution 
to anthropogenic greenhouse gas emissions. Heat and CO 2 taken up by 
the ocean (high to very high confidence) (IPCC, 2021b) directly affect 
marine systems, and the resultant “climatic impact-drivers (CIDs) (e.g., 
ocean temperature and heatwaves, sea level, dissolved oxygen levels, 
acidification; Annex II: Glossary, WGI Figure  SPM.9; IPCC, 2021b) 
Estimated relative human dependence on marine ecosystems
Coastal protection
Nutritional dependence
Economic dependence
Overall dependenceRelative
dependence for:
dence
pendenc e
ependence
Figure 3.1 |  Estimated relative human dependence on marine ecosystems for coastal protection, nutrition, fisheries economic benefits and overall. Each

bar represents an index value that semi-quantitatively integrates the magnitude, vulnerability to loss and substitutability of the benefit. Indices synthesize information on people’s 
consumption of marine protein and nutritional status, gross domestic product, fishing revenues, unemployment, education, governance and coastal characteristics. Overall 
dependence is the mean of the three index values after standardisation from 0–1. (Details regarding component indices are found in Table 1 and Supplementary Material of Selig 
et al., 2019.) The overall index does not include the economic benefits from tourism or other ocean industries, and data limitations prevented including artisanal or recreational 
fisheries or the protective impact of salt marshes (Selig et al., 2019). Values for reference regions established in the WGI AR6 Atlas (Gutiérrez et al., 2021) were computed as 
area-weighted means from original country-level data (Table S6 in Selig et al., 2019).

3
386Chapter 3 Oceans and Coastal Ecosystems and Their Services
also influence ocean and coastal systems (Section 3.2; Cross-Chapter 
Box  SLR in Chapter  3; Cross-Chapter Box  EXTREMES in Chapter  2; 
Figure  3.SM.1), from individual biophysical processes to dependent 
human activities. Several marine outcomes of CIDs are themselves 
drivers of ecological change (e.g., climate velocities, stratification, 
sea ice changes). This chapter updates and extends the assessment 
of SROCC (IPCC, 2019b) and WGI AR6 by assessing the ecosystem 
effects of the CIDs in WGI AR6 Figure SPM.9 (IPCC, 2021b) and their 
biologically relevant marine outcomes (detailed in Section 3.2), which 
are referred to collectively hereafter as ‘climate-induced drivers’4.
Detrimental human impacts on ocean and coastal ecosystems are not 
only caused by climate. Other anthropogenic activities are increasingly 
affecting the physical, chemical and biological conditions of the ocean

(Doney, 2010; Halpern et al., 2019), and these ‘non-climate drivers5’ also 
alter marine ecosystems and their services. Fishing and other extractive 
activities are major non-climate drivers in many ocean and coastal systems 
(Steneck and Pauly, 2019). Many activities, such as coastal development, 
shoreline hardening and habitat destruction, physically alter marine spaces 
(Suchley and Alvarez-Filip, 2018; Ducrotoy et al., 2019; Leo et al., 2019; 
Newton et al., 2020; Raw et al., 2020). Other human activities decrease 
water quality by overloading coastal water with terrestrial nutrients 
(eutrophication) and by releasing runoff containing chemical, biological 
and physical pollutants, toxins, and pathogens (Jambeck et  al., 2015; 
Luek et al., 2017; Breitburg et al., 2018; Froelich and Daines, 2020). Some 
human activities disturb marine organisms by generating excess noise 
and light (Davies et al., 2014; Duarte et al., 2021), while others decrease

natural light penetration into the ocean (Wollschläger et  al., 2021). 
Several anthropogenic activities alter processes that span the land–sea 
interface by changing coastal hydrology or causing coastal subsidence 
(Michael et al., 2017; Phlips et al., 2020; Bagheri-Gavkosh et al., 2021). 
Atmospheric pollutants can harm marine systems or unbalance natural 
marine processes (Doney et al., 2007; Hagens et al., 2014; Lamborg et al., 
2014; Ito et  al., 2016). Organisms frequently experience non-climate 
drivers simultaneously with climate-induced drivers (Section  3.4), and 
feedbacks may exist between climate-induced drivers and non-climate 
drivers that enhance the effects of climate change (Rocha et al., 2015; 
Ortiz et al., 2018; Wolff et al., 2018; Cabral et al., 2019; Bowler et al., 2020; 
Gissi et al., 2021). SROCC assessed with high confidence that reduction 
of pollution and other stressors, along with protection, restoration and

precautionary management, supports ocean and coastal ecosystems 
and their services (IPCC, 2019b). This chapter examines the combined 
influence of climate-induced drivers and primary non-climate drivers on 
many ecosystems assessed.
Detecting changes and attributing them to specific drivers has been 
especially difficult in ocean and coastal ecosystems because drivers, 
responses and scales (temporal, spatial, organisational) often overlap 
and interact (IPCC, 2014b; IPCC, 2014c; Abram et al., 2019; Gissi et al., 
2021). In addition, some marine systems have short, heterogeneous 
or geographically biased observational records, which exacerbate the 
interpretation challenge (Beaulieu et  al., 2013; Christian, 2014; Huggel 
et al., 2016; Benway et al., 2019). It is even more challenging to detect

4 We henceforth use the term ‘climate-induced drivers’ in reference to all drivers of ecological change that are related directly to climate change (IPCC, 2021a) as well as those that emerge in response 
to CIDs.
5 We henceforth use the term ‘non-climate drivers’ in reference to drivers of ecological change that are not caused by climate change.and attribute climate impacts on marine-dependent human systems, 
where culture, governance and society also strongly influence observed 
outcomes. To assess climate-driven change in natural and social systems 
robustly, IPCC reports rely on multiple lines of evidence, and the 
available types of evidence differ depending on the system under study 
(Section  1.3.2.1, Cross-Working Group Box  ATTRIB). Lines of evidence 
used for ocean and coastal ecosystems for this and previous assessments 
include observed phenomena, laboratory and field experiments, long-
term monitoring, empirical and dynamical model analyses, Indigenous

knowledge (IK) and local knowledge (LK), and paleorecords (IPCC, 2014b; 
IPCC, 2014c; IPCC, 2019b). The growing body of climate research for ocean 
and coastal ecosystems and their services increasingly provides multiple 
independent lines of evidence whose conclusions support each other, 
raising the overall confidence in detection and attribution of impacts over 
time (Section 1.3.2.1, Cross-Working Group Box ATTRIB in Chapter 3).
Natural adaptation to climate change in ocean and coastal systems 
includes an array of responses taking place at scales from cells to 
ecosystems. Previous IPCC assessments have established that many 
marine species ‘have shifted their geographic ranges, seasonal 
activities, migration patterns, abundances and species interactions 
in response to climate change’ (high confidence) (IPCC, 2014b; 
IPCC, 2014c), which has had global impacts on species composition, 
abundance and biomass, and on ecosystem structure and function

(medium confidence) (IPCC, 2019b). Warming and acidification have 
affected coastal ecosystems in concert with non-climate drivers (high 
confidence), which have affected habitat area, biodiversity, ecosystem 
function and services (high confidence) (IPCC, 2019b). Confidence 
has grown in these assessments over time as observational datasets 
have lengthened and other lines of evidence have corroborated 
observations. AR5 and SROCC assessed how physiological sensitivity 
to climate-induced drivers is the underlying cause of most marine 
organisms’ vulnerability to climate (high confidence) (Pörtner et  al., 
2014; Bindoff et al., 2019a). Since those assessments, more evidence 
supports the empirical physiological models of tolerance and plasticity 
(Sections 3.3.2, 3.3.4) and of interactions among multiple (climate and 
non-climate) drivers at individual to ecosystem scales (Sections 3.3.3, 
3.4.5). New experimental evidence about evolutionary adaptation

(Section 3.3.4) bolsters previous assessments that adaptation options 
to climate change are limited for eukaryotic organisms. Tools such 
as ecosystem models can now constrain probable ecosystem states 
(Sections 3.3.4, 3.3.5, 3.4). Observations have increased understanding 
of how extreme events affect individuals, populations and ecosystems, 
helping refine understanding of both ecological tolerance to climate 
impacts and ecological transformations (Section 3.4).
Human adaptation to climate impacts on ocean and coastal systems 
spans a variety of actions that change human activity to maintain 
marine ecosystem services. After AR5 concluded that coastal 
adaptation could reduce the effects of climate impacts on coastal 
human communities (high agreement, limited evidence) (Wong et  al., 
2014), SROCC confirmed that mostly risk-reducing ocean and coastal 
adaptation responses were underway (Bindoff et al., 2019a). However,

3
387Oceans and Coastal Ecosystems and Their Services  Chapter 3
FAQ 3.1 | How do we know which changes to marine ecosystems are specifically caused by climate change?
To attribute changes in marine ecosystems to human-induced climate change, scientists use paleorecords (reconstructing the links between 
climate, evolutionary and ecological changes in the geological past), contemporary observations (assessing current climate and ecological 
responses in the field and through experiments) and models. We refer to these as multiple lines of evidence, meaning that the evidence comes 
from diverse approaches, as described below.
Emissions of greenhouse gases like carbon dioxide from human activity cause ocean warming, acidification, oxygen 
loss, and other physical and chemical changes that are affecting marine ecosystems around the world. At the same

time, natural climate variability and direct human impacts, such as overfishing and pollution, also affect marine 
ecosystems locally, regionally and globally. These climate and non-climate impact drivers counteract each other, add 
up or multiply to produce smaller or larger changes than expected from individual drivers. Attribution of changes in 
marine ecosystems requires evaluating the often-interacting roles of natural climate variability, non-climate drivers, 
and human-induced climate change. To do this work, scientists use
• paleorecords: reconstructing the links between climate and evolutionary and ecological changes of the past;
• contemporary observations: assessing current climate and ecological responses;
• manipulation experiments: measuring responses of organisms and ecosystems to different climate conditions; and
• models: testing whether we understand how organisms and ecosystems are impacted by different stressors, and

quantifying the relative importance of different stressors.
Paleorecords can be used to trace the correlation between past changes in climate and marine life. Paleoclimate is 
reconstructed from the chemical composition of shells and teeth or from sediments and ice cores. Changes to sea 
life signalled by changing biodiversity, extinction or distributional shifts are reconstructed from fossils. Using large 
datasets, we can infer the effects of climate change on sea life over relatively long time scales⎯ usually hundreds to 
millions of years. The advantage of paleorecords is that they provide insights into how climate change affects life 
from organisms to ecosystems, without the complicating influence of direct human impacts. A key drawback is that 
the paleo and modern worlds do not have fully comparable paleoclimate regimes, dominant marine species and 
rates of climate change. Nevertheless, the paleorecord can be used to derive fundamental rules by which organisms,

ecosystems, environments and regions are typically most affected by climate change. For example, the paleorecord 
shows that coral reefs repeatedly underwent declines during past warming events, supporting the inference that 
corals may not be able to adapt to current climate warming.
Contemporary observations over recent decades allow scientists to relate the status of marine species and 
ecosystems to changes in climate or other factors. For example, scientists compile large datasets to determine 
whether species usually associated with warm water are appearing in traditionally cool-water areas that are rapidly 
warming. A similar pattern observed in multiple regions and over several decades (i.e., longer than time scales 
of natural variability) provides confidence that climate change is altering community structure. This evidence is 
weighed against findings from other approaches, such as manipulation experiments, to provide a robust picture of

climate-change impacts in the modern ocean.
In manipulation experiments, scientists expose organisms or communities of organisms to multiple stressors, for 
example, elevated CO 2, high temperature, or both, based on values drawn from future climate projections. Such 
experiments will involve multiple treatments (e.g., in different aquarium tanks) in which organisms are exposed 
to different combinations of the stressors. This approach enables scientists to understand the effects of individual 
stressors as well as their interactions to explore physiological thresholds of marine organisms and communities. 
The scale of manipulation experiments can range from small tabletop tanks to large installations or natural ocean 
experiments involving tens of thousands of litres of water.
Ecological effects of climate change are also explored within models developed from fundamental scientific principles

and observations. Using these numerical representations of marine ecosystems, scientists can explore how different 
levels of climate change and non-climate stressors influence species and ecosystems at scales not possible with 
experiments. Models are commonly used to simulate the ecological response to climate change over recent decades 
and centuries. Convergence between the model results and the observations suggests that our understanding of the 
key processes is sufficient to attribute the observed ecological changes to climate change, and to use the models to 
project future ecological changes. Differences between model results and observations indicate gaps in knowledge 
to be filled in order to better detect and attribute the impacts of climate change on marine life.

3
388Chapter 3 Oceans and Coastal Ecosystems and Their Services
Using peer-reviewed research spanning the full range of scientific approaches (paleorecords, observations, 
experiments and models), we can assess the level of confidence in the impact of climate change on observed 
modifications in marine ecosystems. We refer to this as multiple lines of evidence, meaning that the evidence comes 
from the diverse approaches described above. This allows policymakers and managers to address the specific actions 
needed to reduce climate change and other impacts.
Coral reefsImpacts
Coral
Bleaching
Limited MediumEvidence availability
RobustMangroves
Beaches
Fisheries
Mangrove
diebackExamples of well-known impacts of anthropogenic climate change
Paleorecor d Observations ExperimentsEvidence of impactsExamples of
nature-based adaptation
solutions
Restoration and 
Recovery of sand dunes
Ecosystem-based
Beach
erosion
Species shifts
Model

Figure FAQ3.1.1 |  Examples of well-known impacts of anthropogenic climate change and associated nature-based adaptation. To attribute changes 
in marine ecosystems to anthropogenic climate change, scientists use multiple lines of evidence including paleorecords, contemporary observations, manipulation 
experiments and models.Box FAQ 3.1 (continued)

3
389Oceans and Coastal Ecosystems and Their Services  Chapter 3
overlapping climate-induced drivers and non-climate drivers confound 
implementation and assessment of the success of marine adaptation, 
revealing the complexity of attempting to maintain marine ecosystems 
and services through adaptation. SROCC assessed with high confidence  
that while the benefits of many locally implemented adaptations exceed 
their disadvantages, others are marginally effective and have large 
disadvantages, and overall, adaptation has a limited ability to reduce the 
probable risks from climate change, being at best a temporary solution 
(Bindoff et al., 2019a). SROCC also concluded that a portfolio of many 
different types of adaptation actions, effective and inclusive governance, 
and mitigation must be combined for successful adaptation (Bindoff 
et al., 2019a). The portfolio of adaptation measures has now been defined 
(Section 3.6.2), and individual and combined adaptation solutions have

been implemented in several marine sectors (Section 3.6.3). Delays in 
marine adaptation have been partly attributed to the complexity of 
ocean governance (Section 3.6.4; Cross-Chapter Box 3 and Figure CB3.1 
in Abram et  al., 2019) and to the low priority accorded the ocean in 
international development goals (Nash et al., 2020), but in recent years 
the ocean is being increasingly incorporated in international climate 
policy and multilateral environmental agreements (Section 3.6.4).This chapter assesses the current understanding of climate-induced 
drivers, ecological vulnerability and adaptability, risks to coastal 
and ocean ecosystems, and human vulnerability and adaptation 
to resulting changes in ocean benefits, now and in the future 
(Figure 3.2). It starts by assessing the biologically relevant outcomes 
of anthropogenic climate-induced drivers (Section  3.2). Next, it 
sets out the mechanisms that determine the responses of ocean

and coastal organisms to individual and combined drivers from the 
genetic to the ecosystem level (Section 3.3). This supports a detailed 
assessment of the observed and projected responses of coastal and 
ocean ecosystems to these hazards, placing them in context using the 
paleorecord (Section 3.4). These observed and projected impacts are 
used to quantify consequent risks to delivery of ecosystem services 
and the socioeconomic sectors that depend on them, with attention 
to the vulnerability, resilience and adaptive capacity of social–
ecological systems (Section 3.5). The chapter concludes by assessing 
the state of adaptation and governance actions available to address 
these emerging threats while also advancing human development 
(Section  3.6). Abbreviations used repeatedly in the chapter are 
defined in Table 3.1.
WGII AR6 Chapter 3 concept map
3.1 3.2
3.33.4
3.5
3.6Heat, CO2
(see WGI)Heatwaves
Deoxygenation
Acidification Organisms
PopulationsOcean and coastal systems

BiodiversityEcosystem
services
3.6
Implementation
Barriers
Enablers
Pollution
(non-climate)SDGs
3.4
Production
Seasonality
Biodiversity3.3
Acclimation
Evolution
ExtinctionChanging properties
Changing mechanisms3.5
Abundance
Quality
ReliabilityO2
3.2
Figure 3.2 |  WGII AR6 Chapter 3 concept map. Climate changes both the properties (top of wave; Sections 3.1–3.6) and the mechanisms (below wave; Sections 3.2–3.6) 
that influence the ocean and coastal social–ecological system. The Sustainable Development Goals (top right) represent ideal outcomes and achievement of equitable, healthy and 
sustainable ocean and coastal social–ecological systems.

3
390Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.1 |  Abbreviations frequently used in this chapter, with brief definitions
Abbreviation Definition
ABNJAreas beyond national jurisdiction: the water column beyond the exclusive economic zone called the high seas and the seabed beyond the limits of the continental shelf; 
established in conformity with United Nations Convention on the Law of the Sea
AMOC Atlantic meridional overturning circulation (WGI AR6 Glossary, IPCC, 2021a)
AR5 The IPCC Fifth Assessment Report (IPCC, 2013; IPCC, 2014b; IPCC, 2014c; IPCC, 2014d)
CBDConvention on Biological Diversity: an international legal instrument that has been ratified by 196 nations to conserve biological diversity, sustainably use its 
components and share its benefits fairly and equitably
CE Common era
CID Climatic impact-driver (WGI AR6 Glossary, IPCC, 2021a)
CMIP5, CMIP6 The Coupled Model Intercomparison Project, Phase 5 or 6 (WGI AR6 Glossary, IPCC, 2021a)

EbAEcosystem-based adaptation: the use of ecosystem management activities to increase the resilience and reduce the vulnerability of people and ecosystems to climate 
change
EBUS Eastern boundary upwelling system (WGI AR6 Glossary, IPCC, 2021a)
EEZExclusive economic zone: the area from the coast to 200 nautical miles (370 km) off the coast, where a nation exercises its sovereign rights and exclusive management 
authority
ESMEarth system model: a coupled atmosphere–ocean general circulation model (AOGCM, WGI AR6 Glossary, IPCC, 2021a) in which a representation of the carbon cycle is 
included, allowing for interactive calculation of atmospheric CO 2 or compatible emissions
Fish-MIPThe Fisheries and Marine Ecosystem Model Intercomparison Project: a component of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) that explores the 
long-term impacts of climate change on fisheries and marine ecosystems using scenarios from CMIP models

GMSL/GMSLR Global mean sea level/global mean sea level rise (sea level change, WGI AR6 Glossary, IPCC, 2021a)
HAB Harmful algal bloom: an algal bloom composed of phytoplankton known to naturally produce biotoxins that are harmful to the resident population as well as humans
ICZMIntegrated coastal zone management: a dynamic, multidisciplinary and iterative process to promote sustainable management of coastal zones (European Environmental 
Agency)
IKLK Indigenous knowledge and local knowledge (SROCC Glossary, IPCC, 2019a)
MHW Marine heatwaves (WGI AR6 Glossary, IPCC, 2021a)
MPAMarine protected area: an area-based management approach, commonly intended to conserve, preserve or restore biodiversity and habitat, protect species or manage 
resources (especially fisheries)
NbSNature-based Solution: actions to protect, sustainably manage and restore natural or modified ecosystems that address societal challenges effectively and adaptively,

simultaneously providing human well-being and biodiversity benefits (IUCN, 2016)
NDC Nationally determined contribution by parties to the Paris Agreement
NPPNet primary production: the difference between how much CO 2 vegetation takes in during photosynthesis (gross primary production) and how much CO 2 the plants 
release during respiration
OECMOther effective area-based conservation measures: a conservation designation for areas that are achieving the effective in situ conservation of biodiversity outside of 
protected areas
OMZ Oxygen minimum zone (WGI AR6 Glossary, IPCC, 2021a)
pCO 2 Partial pressure of carbon dioxide. For seawater, pCO 2 is used to measure the amount of carbon dioxide dissolved in seawater.
pH Potential of hydrogen (WGI AR6 Glossary, IPCC, 2021a)
POC Particulate organic carbon: a fraction of total organic carbon operationally defined as that which does not pass through a filter pore size ≥ 0.2 µm

SDGSustainable Development Goals: the 17 global goals for development for all countries established by the United Nations through a participatory process and elaborated 
in the 2030 Agenda for Sustainable Development
SES Semi-enclosed sea: a gulf, basin or sea surrounded by land and connected to another sea by a narrow outlet
SIDS Small Island Developing States (WGI AR6 Glossary, IPCC, 2021a)
SLR/RSLR/RSL Sea level rise/relative sea level rise/relative sea level (sea level change, WGI AR6 Glossary, IPCC, 2021a)
SR15 The IPCC Special Report on 1.5°C (IPCC, 2018)
SROCC The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019b)
SSP/RCP Shared Socioeconomic Pathway/Representative Concentration Pathway (Pathways; IPCC, 2021a)
SST Sea surface temperature (WGI AR6 Glossary, IPCC, 2021a)
Ωaragonite Saturation state of seawater with respect to the calcium carbonate mineral aragonite, used as a proxy measurement for ocean acidification

3
391Oceans and Coastal Ecosystems and Their Services  Chapter 3
Table 3.2 |  Overview of the main global ocean climatic impact-drivers and their observed and projected trends from WGI AR6, with corresponding confidence levels and links to 
WGI chapters where these trends are assessed in detail
Climatic impact-
drivers (hazards)Observed trends over the historical 
periodWGI section Projected trends over the 21st century WGI section
Ocean temperature
Ocean warming‘At the ocean surface, temperature has on 
average increased by 0.88 [0.68–1.01] °C from 
1850–1900 to 2011–2020.’2.3.3.1, 9.2.1
(Fox-Kemper et al., 2021; 
Gulev et al., 2021)Ocean warming will continue over the 
21st century (virtually certain), with the 
rate of global ocean warming starting to 
be scenario-dependent from about the 
mid-21st century (medium confidence).9.2.1 (Fox-Kemper et al., 
2021)
Marine heatwaves 
(MHWs)MHWs became more frequent (high confidence), 
more intense and longer (medium confidence)

over the 20th and early 21st centuries.Box 9.2 (Fox-Kemper 
et al., 2021)MHWs will become ‘4 [2–9, likely range] times 
more frequent in 2081–2100 compared with 
1995–2014 under SSP1-2.6, and 8 [3–15, likely 
range] times more frequent under SSP5-8.5.’Box 9.2 (Fox-Kemper 
Climate velocities Not assessed in WGI Not assessed in WGI
Sea level
Global mean sea 
level (GMSL)‘Since 1901, GMSL has risen by 0.20 [0.15–
0.25] m’, and the rate of rise is accelerating.2.3.3, 9.6.1 (Fox-Kemper 
et al., 2021; Gulev et al., 
2021)There will be continued rise in GMSL throughout 
the 21st century under all assessed SSPs 
(virtually certain).4.3.2.2, 9.6.3 (Fox-Kemper 
et al., 2021; Lee et al., 
2021)
Extreme sea levelsRelative sea level rise is driving a global increase 
in the frequency of extreme sea levels (high 
confidence).9.6.4 (Fox-Kemper et al., 
2021)Rising mean relative sea level will continue to 
drive an increase in the frequency of extreme sea

levels (high confidence).9.6.4 (Fox-Kemper et al., 
2021)
Ocean circulation
Ocean stratification‘The upper ocean has become more stably 
stratified since at least 1970 […] (virtually 
certain).’9.2.1.3 (Fox-Kemper et al., 
2021)‘Upper-ocean stratification will continue to 
increase throughout the 21st century (virtually 
2021)
Eastern boundary 
upwelling systems‘Only the California current system 
has experienced some large-scale 
upwelling-favourable wind intensification since 
the 1980s (medium confidence).’9.2.5 (Fox-Kemper et al., 
2021)‘Eastern boundary upwelling systems will 
change, with a dipole spatial pattern within 
each system of reduction at low latitude and 
enhancement at high latitude (high confidence).’9.2.5 (Fox-Kemper et al., 
2021)
Atlantic overturning 
circulation (AMOC)There is low confidence in reconstructed and 
modelled AMOC changes for the 20th century.2.3.3.4, 9.2.3 (Fox-Kemper 
et al., 2021; Gulev et al.,

2021)The AMOC will decline over the 21st century 
(high confidence, but low confidence for 
quantitative projections).4.3.2.3, 9.2.3 (Fox-Kemper 
2021)
Sea ice
Arctic sea ice 
changes‘Current Arctic sea ice coverage levels are the 
lowest since at least 1850 for both annual mean 
and late-summer values (high confidence).’2.3.2.1, 9.3.1 (Fox-Kemper 
2021)‘The Arctic will become practically ice-free in 
September by the end of the 21st century under 
SSP2-4.5, SSP3-7.0 and SSP5-8.5[…](high 
confidence).’4.3.2.1, 9.3.1 (Fox-Kemper 
2021)
Antarctic sea ice 
changesThere is no global significant trend in 
Antarctic sea ice area from 1979 to 2020 (high 
confidence).2.3.2.1, 9.3.2 (Fox-Kemper 
2021)There is low confidence in model simulations of 
future Antarctic sea ice.9.3.2 (Fox-Kemper et al., 
2021)
Ocean chemistry

Changes in salinityThe ‘large-scale, near-surface salinity contrasts 
have intensified since at least 1950 […] 
(virtually certain).’2.3.3.2, 9.2.2.2 
Gulev et al., 2021)‘Fresh ocean regions will continue to get fresher 
and salty ocean regions will continue to get 
saltier in the 21st century (medium confidence).’9.2.2.2 (Fox-Kemper et al., 
2021)
Ocean acidificationOcean surface pH has declined globally over the 
past four decades (virtually certain).2.3.3.5, 5.3.2.2 (Canadell 
2021)Ocean surface pH will continue to decrease 
‘through the 21st century, except for the 
lower-emission scenarios SSP1-1.9 and SSP1-2.6 
[…] (high confidence).’4.3.2.5, 4.5.2.2, 5.3.4.1 
(Lee et al., 2021; Canadell 
Ocean 
deoxygenationDeoxygenation has occurred in most open 
ocean regions since the mid-20th century (high 
confidence).2.3.3.6, 5.3.3.2 (Canadell 

2021)Subsurface oxygen content ‘is projected to 
transition to historically unprecedented condition 
with decline over the 21st century (medium 
confidence).’5.3.3.2 (Canadell et al., 
2021)
Changes in nutrient 
concentrationsNot assessed in WGI Not assessed in WGI

3
392Chapter 3 Oceans and Coastal Ecosystems and Their Services
3.2 Observed Trends and Projections of 
Climatic Impact-Drivers in the Global 
Ocean
3.2.1 Introduction
Climate change exposes ocean and coastal ecosystems to changing 
environmental conditions, including ocean warming, SLR, acidification, 
deoxygenation and other climatic impact-drivers (CIDs), which have 
distinct regional and temporal characteristics (Gruber, 2011; IPCC, 
2018). This section aims to build on the WGI AR6 assessment (Table 3.2) 
to provide an ecosystem-oriented framing of CIDs. Updating SROCC, 
projected trends assessed here are based on a new range of scenarios 
(Shared Socioeconomic Pathways, SSPs), as used in the Coupled Model 
Intercomparison Project Phase 6 (CMIP6; Section 1.2.2).
3.2.2 Physical Changes
3.2.2.1 Ocean Warming, Climate Velocities and Marine 
Heatwaves
Global mean SST has increased since the beginning of the 20th century

by 0.88°C (very likely range: 0.68–1.01°C), and it is virtually certain  
that the global ocean has warmed since at least 1971 (WGI AR6 
Section  9.2; Fox-Kemper et  al., 2021). A key characteristic of ocean 
temperature change relevant for ecosystems is climate velocity, a 
measure of the speed and direction at which isotherms move under 
climate change (Burrows et al., 2011), which gives the rate at which 
species must migrate to maintain constant climate conditions. It has 
been shown to be a useful and simple predictor of species distribution 
shifts in marine ecosystems (Chen et al., 2011; Pinsky et al., 2013; Lenoir 
et  al., 2020). Median climate velocity in the surface ocean has been 
21.7 km per decade since 1960, with higher values in the Arctic/sub-
Arctic and within 15° of the Equator (Figure 3.3; Burrows et al., 2011). 
While climate velocity has been slower in the mesopelagic layer (200–
1000 m) than in the epipelagic layer (0–200 m) over the past 50 years,

it has been shown to be faster in the bathypelagic (1000–4000 m) and 
abyssopelagic (>4000 m) layers (Figure 3.4; Brito-Morales et al., 2020), 
suggesting that deep-ocean species could be as exposed to effects of 
warming as species in the surface ocean (Brito-Morales et al., 2020).
Marine heatwaves (MHWs) are periods of extreme seawater temperature 
relative to the long-term mean seasonal cycle, that persist for days to 
months, and that may carry severe consequences for marine ecosystems 
and their services (WGI AR6 Box  9.2; Hobday et  al., 2016a; Smale 
et  al., 2019; Fox-Kemper et  al., 2021). MHWs became more frequent 
over the 20th century (high confidence) and into the beginning of the 
21st century, approximately doubling in frequency (high confidence) 
and becoming more intense and longer since the 1980s (medium 
confidence) (WGI AR6 Box 9.2; Fox-Kemper et al., 2021). These trends in 
MHWs are explained by an increase in ocean mean temperatures (Oliver

et al., 2018), and human influence has very likely contributed to 84–
90% of them since at least 2006 (WGI AR6 Box 9.2; Fox-Kemper et al., 
2021). The probability of occurrence (as well as duration and intensity) 
of the largest and most impactful MHWs that have occurred in the past 
30 years has increased more than 20-fold due to anthropogenic climate 
change (Laufkötter et al., 2020).(a) Warming rate
(b) Climate velocity
(c) Change in total marine heatwaves-3 -2 -1 0123
-240 -120 0 120 240
-60 -40 -20 02 04 06 0°C century-1
km decade-1
DaysObserved surface ocean warming, surface climate 
velocity and reconstructed changes in marine 
heatwaves over the last 100 years
Figure 3.3 |  Observed surface ocean warming, surface climate velocity and 
reconstructed changes in marine heatwaves (MHWs) over the past 100 years.  
(a) Sea surface temperature trend (degrees Celsius per century) over 1925–2016 from

Hadley Centre Sea Ice and Sea Surface Temperature 1.1 (HadISST1.1; (b) surface climate 
velocity (kilometres per decade) over 1925–2016 computed from HadISST1.1 and 
(c) change in total MHW days for the surface ocean over 1925–1954 to 1987–2016 
based on monthly proxies. (Data from Oliver et al., 2018).

3
393Oceans and Coastal Ecosystems and Their Services  Chapter 3
Ocean warming will continue over the 21st century (virtually certain), 
with the rate of global ocean warming starting to be scenario-
dependent from about the mid-21st  century (medium confidence). 
At the ocean surface, it is virtually certain that SST will continue to 
increase throughout the 21st century, with increasing hazards to many 
marine ecosystems (WGI AR6 Box 9.2; Fox-Kemper et al., 2021). The 
future global mean SST increase projected by CMIP6 models for the 
period 1995–2014 to 2081–2100 is 0.86°C (very likely range: 0.43–
1.47°C) under SSP1-2.6, 1.51°C (1.02–2.19°C) under SSP2-4.5, 2.19°C 
(1.56–3.30°C) under SSP3-7.0 and 2.89°C (2.01–4.07°C) under SSP5-
8.5 (WGI AR6 Section 9.2.1; Fox-Kemper et al., 2021). Stronger surface 
warming occurs in parts of the tropics, in the North Pacific, and in the 
Arctic Ocean, where SST increases by >4°C in 2080–2099 under SSP5-

8.5 (Kwiatkowski et al., 2020). The CMIP6 climate models also project 
ocean warming at the seafloor, with the magnitude of projected changes 
being less than that of surface waters but having larger uncertainties 
(Kwiatkowski et al., 2020). The projected end-of-the-century warming 
in CMIP6 as reported here is greater than assessed with Coupled Model 
Intercomparison Project  5 (CMIP5) models in AR5 and in SROCC for 
similar radiative forcing scenarios (Figure 3.5; Kwiatkowski et al., 2020), 
because of greater climate sensitivity in the CMIP6 model ensemble 
than in CMIP5 (WGI AR6 Chapter 4; Forster et al., 2020; Lee et al., 2021).
Marine heatwaves will continue to increase in frequency, with a likely 
global increase of 2–9 times in 2081–2100 compared with 1995–2014 under SSP1-2.6, and 3–15  times under SSP5-8.5, with the largest 
increases in tropical and Arctic oceans (WGI AR6 Box  9.2; Frölicher 
et al., 2018; Fox-Kemper et al., 2021).

3.2.2.2 Sea Level Rise and Extreme Sea Levels
Global mean sea level (GMSL) (Cross-Chapter Box SLR in Chapter 3) 
has risen by about 0.20 m since 1901 and continues to accelerate (WGI 
AR6 Section 2.3.3.3; Church and White, 2011; Jevrejeva et al., 2014; 
Hay et  al., 2015; Kopp et  al., 2016; Dangendorf et  al., 2017; WCRP 
Global Sea Level Budget Group, 2018; Kemp et al., 2018; Ablain et al., 
2019; Gulev et al., 2021).
Most coastal ecosystems (mangroves, seagrasses, salt marshes, shallow 
coral reefs, rocky shores and sandy beaches) are affected by changes in 
relative sea level (RSL, the change in the mean sea level relative to the 
land; Section 3.4.2). Regional rates of RSL rise differ from the global 
mean due to a range of factors, including local subsidence driven 
by anthropogenic activities such as groundwater and hydrocarbon 
extraction (WGI AR6 Box 9.1; Fox-Kemper et al., 2021). In many deltaic 
regions, anthropogenic subsidence is currently the dominant driver

of RSL rise (WGI AR6 Section 9.6.3.2; Tessler et al., 2018; Fox-Kemper 
et al., 2021). RSL rise is driving a global increase in the frequency of 
extreme sea levels (high confidence) (WGI AR6 Section 9.6.4.1; Fox-
Kemper et al., 2021).
(a)
-240 -120 0 120 240km decade-1Historical and projected climate velocity
Epipelagic
0–200m
Mesopelagic
200–1000m
Bathypelagic
1000–4000mHistorical SSP1-2.6 SSP5-8.5
(d)
(g)(b)
(e)
(h)(c)
(f)
(i)
Figure 3.4 |  Historical and projected climate velocity.  Climate velocities (in kilometres per decade) are shown for the (a,d,g) historical period (1965–2014), and the last 
50 years of the 21st century (2051–2100), under (b,e,h) SSP1-2.6 and (c,f,i) SSP5-8.5. Also shown are the epipelagic (0–200 m), mesopelagic (200–1000 m) and bathypelagic 
(1000–4000 m) domains. Updated figure from Brito-Morales et al. (2020), with Coupled Model Intercomparison Project 6 models used in Kwiatkowski et al. (2020).

3
394Chapter 3 Oceans and Coastal Ecosystems and Their Services
GMSL rise through the middle of the 21st  century exhibits limited 
dependence on emissions scenario; between 1995–2014 and 2050, 
GMSL is likely to rise by 0.15–0.23 m under SSP1-1.9 and 0.20–0.30 m 
under SSP5-8.5 (WGI AR6 Section  9.6.3; Fox-Kemper et  al., 2021). 
Beyond 2050, GMSL and RSL projections are increasingly sensitive to 
the differences among emission scenarios. Considering only processes 
in which there is at least medium confidence (e.g., thermal expansion, 
land-water storage, land-ice surface mass balance and some ice-sheet dynamic processes), GMSL between 1995–2014 and 2100 is likely to 
rise by 0.28–0.55 m under SSP1-1.9, 0.33–0.61 m under SSP1-2.6, 
0.44–0.76 m under SSP2-4.5, 0.55–0.90 m under SSP3-7.0 and 0.63–
1.02 m under SSP5-8.5 (Figure 3.5). Under high-emission scenarios, ice-
sheet processes in which there is low confidence and deep uncertainty

might contribute more than one additional metre to GMSL rise by 2100 
(WGI AR6 Chapter 9; Fox-Kemper et al., 2021).Projected trends in climatic impact-drivers for ocean ecosystems
01°C2°C3°C4°C5°C
∆Sea level (metres) ∆pH ∆O2 (mmol m-3)∆NO-
3 (mmol m-3)∆ SST(a) (b) (c) (d)
1.2 1.0 0.8 0.6 0.4 -0.1 -0.2 -0.3 -0.4 -0.5 0 -0.5 -0.15 -0.20 -0.25 -0.10 0 -0.4 -1.2 -1.6 -2.0 -0.8
(e)
(f)(g)
(h)CMIP5
RCP2.6
RCP4.5
RCP6.0
RCP8.5CMIP6
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5
SSP3-7.0SSP2-4.5∆SST  > 2°C
∆NO-
3 < -1 mmol m-3
∆SST  > 2°C and ∆NO-
∆pH < -0.3
∆02 < -30 mmol m-3
Figure 3.5 |  Projected trends in climatic impact-drivers for ocean ecosystems. Panels (a,b,c,d) represent Coupled Model Intercomparison Project 5 (CMIP5) Representative 
Concentration Pathway (RCP) and CMIP6 Shared Socioeconomic Pathway (SSP) end-of-century changes in (a) global sea level; (b) average surface pH, (c) subsurface (100–600 m)

dissolved oxygen concentration and (d) euphotic-zone (0–100 m) nitrate (NO 3) concentration against anomalies in sea surface temperature. All anomalies are model-ensemble 
averages over 2080–2099 relative to the 1870–1899 baseline period (from Kwiatkowski et al., 2020), except for sea level, which shows model-ensemble median in 2100 relative 
to 1901 (from AR6 WGI Chapter 9). Error bars represent very likely ranges, except for SLR where they represent likely ranges. Very likely ranges for pH changes are too narrow 
to appear in the figure (see text). Panels (e,f,g,h) show regions where end-of-century projected CMIP6 surface warming exceeds 2°C, where surface ocean pH decline exceeds 
0.3, where subsurface dissolved oxygen decline exceeds 30 mmol m-3 and where euphotic-zone (0–100 m) nitrate decline exceeds 1 mmol m-3 in (e) SSP1-2.6, (f) SSP2-4.5,

(g) SSP3-7.0 and (h) SSP5-8.5. All anomalies are 2080–2099 relative to the 1870–1899 baseline period. (Modified from Kwiatkowski et al., 2020).

3
395Oceans and Coastal Ecosystems and Their Services  Chapter 3
Rising mean RSL will continue to drive an increase in the frequency of 
extreme sea levels (high confidence). The expected frequency of the 
current 1-in-100-year extreme sea level is projected to increase by a 
median of 20–30 times across tide-gauge sites by 2050, regardless of 
emission scenario (medium confidence). In addition, extreme-sea-level 
frequency may be affected by changes in tropical cyclone climatology 
(low confidence), wave climatology (low confidence) and tides (high 
confidence) associated with climate change and sea level change (WGI 
AR6 Section 9.6.4.2; Fox-Kemper et al., 2021).
3.2.2.3 Changes in Ocean Circulation, Stratification and Coastal 
Upwelling
Ocean circulation and its variations are key to the evolution of the 
physical, chemical and biological properties of the ocean. Vertical 
mixing and upwelling are critical factors affecting the supply of

nutrients to the sunlit ocean and hence the magnitude of primary 
productivity. Ocean currents not only transport heat, salt, carbon and 
nutrients, but they also control the dispersion of many organisms and 
the connectivity between distant populations.
Ocean stratification is an important factor controlling biogeochemical 
cycles and affecting marine ecosystems. WGI AR6 Section  9.2.1.3 
(Fox-Kemper et  al., 2021) assessed that it is virtually certain that 
stratification in the upper 200 m of the ocean has been increasing 
since 1970. Recent evidence has strengthened estimates of the rate 
of change (Yamaguchi and Suga, 2019; Li et al., 2020a; Sallée et al., 
2021), with an estimated increase of 1.0 ± 0.3% (very likely range) 
per decade over the period 1970–2018 (high confidence) (WGI AR6 
Section  9.2.1.3; Fox-Kemper et  al., 2021), higher than assessed in 
SROCC. It is very likely that stratification in the upper few hundred

metres of the ocean will increase substantially in the 21st century in all 
ocean basins, driven by intensified surface warming and near-surface 
freshening at high latitudes (WGI AR6 Section 9.2.1.3; Capotondi et al., 
2012; Fu et al., 2016; Bindoff et al., 2019a; Kwiatkowski et al., 2020; 
Fox-Kemper et al., 2021).
Contrasting changes among the major eastern boundary coastal 
upwelling systems (EBUS) were identified in AR5 (Hoegh-Guldberg 
et al., 2014). While SROCC assessed with high confidence that three 
(Benguela, Peru-Humboldt, California) out of the four major EBUS have 
experienced upwelling-favourable wind intensification in the past 
60 years (Sydeman et al., 2014; Bindoff et al., 2019a), WGI AR6 revisited 
this assessment based on evidence showing low agreement between 
studies that have investigated trends over past decades (Varela et al., 
2015). WGI AR6 assessed that only the California Current system has

undergone large-scale upwelling-favourable wind intensification since 
the 1980s (medium confidence) (WGI AR6 Section  9.2.1.5; García-
Reyes and Largier, 2010; Seo et al., 2012; Fox-Kemper et al., 2021).
While no consistent pattern of contemporary changes in upwelling-
favourable winds emerges from observation-based studies, numerical 
and theoretical work projects that summertime winds near poleward 
boundaries of upwelling zones will intensify, while winds near 
equatorward boundaries will weaken (high confidence) (WGI AR6 
Section  9.2.3.5; García-Reyes et  al., 2015; Rykaczewski et  al., 2015; 
Wang et  al., 2015; Aguirre et  al., 2019; Fox-Kemper et  al., 2021). Nevertheless, projected future annual cumulative upwelling wind 
changes at most locations and seasons remain within ±10–20% of 
present-day values (medium confidence) (WGI AR6 Section  9.2.3.5; 
Continuous observation of the Atlantic meridional overturning

circulation (AMOC) has improved the understanding of its variability 
(Frajka-Williams et  al., 2019), but there is low confidence in the 
quantification of AMOC changes in the 20th century because of low 
agreement in quantitative reconstructed and simulated trends (WGI 
AR6 Sections 2.3.3, 9.2.3.1; Fox-Kemper et al., 2021; Gulev et al., 2021). 
Direct observational records since the mid-2000s remain too short to 
determine the relative contributions of internal variability, natural 
forcing and anthropogenic forcing to AMOC change (high confidence) 
(WGI AR6 Sections 2.3.3, 9.2.3.1; Fox-Kemper et al., 2021; Gulev et al., 
2021). Over the 21st century, AMOC will very likely decline for all SSP 
scenarios but will not involve an abrupt collapse before 2100 (WGI 
AR6 Sections 4.3.2, 9.2.3.1; Fox-Kemper et al., 2021; Lee et al., 2021).
3.2.2.4 Sea Ice Changes
Sea ice is a key driver of polar marine life, hosting unique ecosystems

and affecting diverse marine organisms and food webs through its 
impact on light penetration and supplies of nutrients and organic 
matter (Arrigo, 2014). Since the late 1970s, Arctic sea ice area has 
decreased for all months, with an estimated decrease of 2 million km2 
(or 25%) for summer sea ice (averaged for August, September and 
October) in 2010–2019 as compared with 1979–1988 (WGI AR6 
Section 9.3.1.1; Fox-Kemper et al., 2021). For Antarctic sea ice there is 
no significant global trend in satellite-observed sea ice area from 1979 
to 2020 in either winter or summer, due to regionally opposing trends 
and large internal variability (WGI AR6 Section 9.3.2.1; Maksym, 2019; 
CMIP6 simulations project that the Arctic Ocean will likely become 
practically sea ice free (area below 1 million km2) for the first time before 
2050 and in the seasonal sea ice minimum in each of the four emission

scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5 and SSP5-8.5 (Figure 3.7; WGI 
AR6 Section 9.3.2.2; Notze and SIMIP Community, 2020; Fox-Kemper 
et al., 2021). Antarctic sea ice area is also projected to decrease during 
the 21st century, but due to mismatches between model simulations 
and observations, combined with a lack of understanding of reasons 
for substantial inter-model spread, there is low confidence in model 
projections of future Antarctic sea ice changes, particularly at the 
regional level (WGI AR6 Section  9.3.2.2; Roach et  al., 2020; Fox-
3.2.3 Chemical Changes
3.2.3.1  Ocean Acidification
The ocean’s uptake of anthropogenic carbon affects its chemistry 
in a process referred to as ocean acidification, which increases the 
concentrations of aqueous CO 2, bicarbonate and hydrogen ions, and 
decreases pH, carbonate ion concentrations and calcium carbonate 
mineral saturation states (Doney et  al., 2009). Ocean acidification

3
396Chapter 3 Oceans and Coastal Ecosystems and Their Services
affects a variety of biological processes with, for example, lower 
calcium carbonate saturation states reducing net calcification rates 
for some shell-forming organisms and higher CO 2 concentrations 
increasing photosynthesis for some phytoplankton and macroalgal 
species (Section 3.3.2).
Direct measurements of ocean acidity from ocean time series, as well as 
pH changes determined from other shipboard studies, show consistent 
decreases in ocean surface pH over the past few decades (virtually 
certain) (WGI AR6 Section 5.3.2.2; Takahashi et al., 2014; Bindoff et al., 
2019a; Sutton et al., 2019; Canadell et al., 2021).
Since the 1980s, surface ocean pH has declined by a very likely rate 
of 0.016–0.020 per decade in the subtropics and 0.002–0.026 per 
decade in the subpolar and polar zones (WGI AR6 Section  5.3.2.2; 
Canadell et al., 2021). Typically, the pH of global surface waters has

decreased from 8.2 to 8.1 since the pre-industrial era (1750 CE), a 
trend attributable to rising atmospheric CO 2 (virtually certain) (Orr 
et al., 2005; Jiang et al., 2019).
Ocean acidification is also developing in the ocean interior (very high 
confidence) due to the transport of anthropogenic CO 2 to depth by 
ocean currents and mixing (WGI AR6 Section 5.3.3.1; Canadell et al., 
2021). There, it leads to the shoaling of saturation horizons of aragonite 
and calcite (high confidence) (WGI AR6 Section 5.3.3.1; Canadell et al., 
2021), below which dissolution of these calcium carbonate minerals 
is thermodynamically favoured. The calcite or aragonite saturation 
horizons have migrated upwards in the North Pacific (1–2 m yr–1 over 
1991–2006) (Feely et al., 2012) and in the Irminger Sea (10–15 m yr–1 
for the aragonite saturation horizon over 1991–2016) (Perez et  al., 
2018). In some locations of the western Atlantic Ocean, calcite

saturation depth has risen by ~300 m since the pre-industrial era 
due to increasing concentrations of deep-ocean dissolved inorganic 
carbon (Sulpis et al., 2018). In the Arctic, where some coastal surface 
waters are already undersaturated with respect to aragonite due to 
the degradation of terrestrial organic matter (Mathis et  al., 2015; 
Semiletov et al., 2016), the deep aragonite saturation horizon shoaled 
on average 270 ± 60 m during 1765–2005 (Terhaar et al., 2020).
Detection and attribution of ocean acidification in coastal environments 
are more difficult than in the open ocean due to larger spatio-temporal 
variability of carbonate chemistry (Duarte et  al., 2013; Laruelle 
et al., 2017; Torres et al., 2021) and to the influence of other natural 
acidification drivers such as freshwater and high-nutrient riverine 
inputs (Cai et al., 2011; Laurent et al., 2017; Fennel et al., 2019; Cai 
et  al., 2020) or anthropogenic acidification drivers (Section  3.1) like

atmospherically deposited nitrogen and sulphur (Doney et  al., 2007; 
Hagens et  al., 2014). Since AR5, the observing network in coastal 
oceans has expanded substantially, improving understanding of both 
the drivers and amplitude of observed variability (Sutton et al., 2016). 
Recent studies indicate that two more decades of observations may be 
required before anthropogenic ocean acidification emerges over natural 
variability in some coastal sites and regions (WGI AR6 Section 5.3.5.2; 
Sutton et al., 2019; Turk et al., 2019; Canadell et al., 2021).
Mean open-ocean surface pH is projected to decline by 0.08 ± 0.003 
(very likely range), 0.17 ± 0.003, 0.27 ± 0.005 and 0.37 ± 0.007 pH units in 2081–2100 relative to 1995–2014, for SSP1-2.6, SSP2-4.5, 
SSP3-7.0 and SSP5-8.5, respectively (Figure 3.5; WGI AR6 Section 4.3.2; 
Kwiatkowski et al., 2020; Lee et al., 2021). Projected changes in surface 
pH are relatively uniform in contrast with those of other surface-

ocean variables, but they are largest in the Arctic Ocean (Figure 3.6; 
WGI AR6 Section  5.3.4.1; Canadell et  al., 2021). Similar declines in 
the concentration of carbonate ions are projected by Earth system 
models (ESMs; Bopp et al., 2013; Gattuso et al., 2015; Kwiatkowski 
et al., 2020). The North Pacific, the Southern Ocean and Arctic Ocean 
regions will become undersaturated for calcium carbonate minerals 
first (Orr et al., 2005; Pörtner et al., 2014). Concurrent impacts on the 
seasonal amplitude of carbonate chemistry variables are anticipated 
(i.e., increased amplitude for pCO 2 and hydrogen ions, decreased 
amplitude for carbonate ions; McNeil and Sasse, 2016; Kwiatkowski 
and Orr, 2018; Kwiatkowski et al., 2020).
Future declines in subsurface pH (Figure  3.6) will be modulated by 
changes in ocean overturning and water-mass subduction (Resplandy 
et al., 2013), and in organic matter remineralisation (Chen et al., 2017).

In particular, decreases in pH will be less consistent at the seafloor than 
at the surface and will be linked to the transport of surface anomalies 
to depth. For example, >20% of the North Atlantic seafloor deeper 
than 500 m, including canyons and seamounts designated as marine 
protected areas (MPAs), will experience pH reductions >0.2 by 2100 
under RCP8.5 (Gehlen et al., 2014). Changes in pH in the abyssal ocean 
(>3000 m deep) are greatest in the Atlantic and Arctic Oceans, with 
lesser impacts in the Southern and Pacific Oceans by 2100, mainly due 
to ventilation time scales (Sweetman et al., 2017).
3.2.3.2  Ocean Deoxygenation
Ocean deoxygenation, the loss of oxygen in the ocean, results from 
ocean warming, through a reduction in oxygen saturation, increased 
oxygen consumption, increased ocean stratification and ventilation 
changes (Keeling et  al., 2010; IPCC, 2019a). In recent decades, 
anthropogenic inputs of nutrients and organic matter (Section 3.1) have

increased the extent, duration and intensity of coastal hypoxia events 
worldwide (Diaz and Rosenberg, 2008; Rabalais et al., 2010; Breitburg 
et al., 2018), while pollution-induced atmospheric deposition of soluble 
iron over the ocean has accelerated open-ocean deoxygenation (Ito 
et al., 2016). Deoxygenation and acidification often coincide because 
biological consumption of oxygen produces CO 2. Deoxygenation can 
have a range of detrimental effects on marine organisms and reduce 
the extent of marine habitats (Sections 3.3.2, 3.4.3.1; Vaquer-Sunyer 
and Duarte, 2008; Chu and Tunnicliffe, 2015).
Changes in ocean oxygen concentrations have been analysed from 
compilations of in situ data dating back to the 1960s (Helm et  al., 
2011; Ito et al., 2017; Schmidtko et al., 2017). SROCC concluded that 
a loss of oxygen had occurred in the upper 1000 m of the ocean 
(medium confidence), with a global mean decrease of 0.5–3.3% (very

likely range) over 1970–2010 (Bindoff et al., 2019a). Based on new 
regional assessments (Queste et  al., 2018; Bronselaer et  al., 2020; 
Cummins and Ross, 2020; Stramma et al., 2020), WGI AR6 assesses 
that ocean deoxygenation has occurred in most regions of the open 
ocean since the mid-20th century (high confidence), but it is modified 
by climate variability on interannual and inter-decadal time scales

3
397Oceans and Coastal Ecosystems and Their Services  Chapter 3
(medium confidence) (WGI AR6 Sections  2.3.3.6, 5.3.3.2; Canadell 
et al., 2021; Gulev et al., 2021). New findings since SROCC also confirm 
that the volume of oxygen minimum zones (OMZs) are expanding at 
many locations (high confidence) (WGI AR6 Section 5.3.3.2; Canadell 
The most recent estimates of future oxygen loss in the subsurface 
ocean (100–600 m), using CMIP6 models, amount to −4.1  ±  4.2 
(very likely range), −6.6 ± 5.7, −10.1 ± 6.7 and −11.2 ± 7.7% in 
2081–2100 relative to 1995–2014 for SSP1-2.6, SSP2-4.5, SSP3-7.0 
and SSP5-8.5, respectively (Figure  3.5; Kwiatkowski et  al., 2020). 
Based on these CMIP6 projections, WGI AR6 concludes that the 
oxygen content of the subsurface ocean is projected to decline to 
historically unprecedented conditions over the 21st century (medium 
confidence) (WGI AR6 Section 5.3.3.2; Canadell et al., 2021). These

declines are greater (by 31–72%) than simulated by the CMIP5 
models in their Representative Concentration Pathway (RCP) 
analogues, a likely consequence of enhanced surface warming and 
stratification in CMIP6 models (Figure 3.5; Kwiatkowski et al., 2020). 
At the regional scale and for subsurface waters, projected changes 
are not spatially uniform, and there is lower agreement among 
models than they show for the global mean trend (Bopp et al., 2013; 
Kwiatkowski et al., 2020). In particular, large uncertainties remain for 
these future projections of ocean deoxygenation in the subsurface 
tropical oceans, where the major OMZs are located (Cabré et  al., 
2015; Bopp et al., 2017).
3.2.3.3  Changes in Nutrient Availability
The availability of nutrients in the surface ocean often limits primary 
productivity, with implications for marine food webs and the biological 
carbon pump. Nitrogen availability tends to limit phytoplankton

productivity throughout most of the low-latitude ocean, whereas 
dissolved iron availability limits productivity in high-nutrient, low-
chlorophyll regions, such as in the main upwelling region of the Southern 
Ocean and the Eastern Equatorial Pacific (high confidence) (Moore 
et  al., 2013; IPCC, 2019b). Phosphorus, silicon, other micronutrients 
such as zinc, and vitamins can also co-limit marine phytoplankton 
productivity in some ocean regions (Moore et  al., 2013). Whereas 
some studies have shown coupling between climate variability and 
nutrient trends in specific regions, such as in the North Atlantic (Hátún 
et  al., 2016), North Pacific (Di Lorenzo et  al., 2009; Yasunaka et  al., 
2014) and tropical (Stramma and Schmidtko, 2021) Oceans, very few 
studies have been able to detect long-term changes in ocean nutrient 
concentrations (but see Yasunaka et al., 2016).
Future changes in nutrient concentrations have been estimated

using ESMs, with future increases in stratification generally leading 
to decreased nutrient levels in surface waters (IPCC, 2019b). CMIP6 
models project a decline in the nitrate concentration of the upper 
100 m in 2080–2099 relative to 1995–2014 of −0.46  ±  0.45 (very 
likely range), −0.60 ± 0.58, −0.80 ± 0.77 and −1.00 ± 0.78 mmol m–3 
under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively (Figure  3.5; 
Kwiatkowski et al., 2020). These declines in nitrate concentration are 
greater than simulated by the CMIP5 models in their RCP analogues, 
a likely consequence of enhanced surface warming and stratification 
in CMIP6 models (Figure 3.5; Kwiatkowski et al., 2020). It is concluded that the surface ocean will encounter reduced nitrate concentrations 
in the 21st century (medium confidence).
3.2.4 Global Synthesis on Multiple Climate-induced 
Drivers
In the 21st  century, ocean and coastal ecosystems are projected to

face conditions unprecedented over past centuries to millennia (high 
confidence) (Section 3.2; WGI AR6 Chapters 4, 9; Fox-Kemper et al., 
2021; Lee et al., 2021), with increased temperatures (virtually certain) 
and frequency and severity of MHWs (very high confidence), stronger 
upper-ocean stratification (high confidence), continued rise in GMSL 
throughout the 21st century (high confidence) and increased frequency 
of extreme sea levels (high confidence), further acidification (virtually 
certain), oxygen decline (high confidence) and decreased surface 
nitrate inventories (medium confidence).
The rates and magnitudes of these changes largely depend on the extent 
of future emissions (very high confidence), with surface ocean warming 
and acidification (very likely range) at +3.47°C ± 1.28°C and −0.44 pH 
units ± 0.008 pH units in 2080–2099 (relative to 1870–1899) for SSP5-
8.5 compared with +1.42°C ± 0.53°C and −0.16 pH units ± 0.003 pH

units for SSP1-2.6 (Figure 3.5; Kwiatkowski et al., 2020).
3.2.4.1  Compound Changes in the 21st century
Earth system models project distinct regional evolutions of the 
different CIDs over the 21st century (very high confidence) (Figures 3.5, 
3.6, 3.7; Kwiatkowski et al., 2020). Tropical and subtropical oceans are 
characterised by projected warming and acidification, accompanied by 
declining nitrate concentrations in equatorial upwelling regions. The 
North Atlantic is characterised by a high exposure to acidification and 
declining nitrate concentrations. The North Pacific is characterised by 
high sensitivity to compound changes, with high rates of warming, 
acidification, deoxygenation and nutrient depletion. In contrast, the 
development of compound hazards is limited in the Southern Ocean, 
where rates of warming and nutrient depletion are lower. The Arctic 
Ocean is characterised by the highest rates of acidification and

warming, strong nutrient depletion, and it will likely become practically 
sea ice free in the September mean for the first time before the year 
2050 in all SSP scenarios (high confidence) (Figures  3.5, 3.6, 3.7; 
Sections 3.2.2, 3.2.3).
In general, the projected changes in climate-induced drivers are less 
in absolute terms in the deep-sea (mesopelagic and bathypelagic 
domains and deep-sea habitats) than in the surface ocean and in 
shallow-water habitats (e.g., kelp ecosystems, warm-water corals) 
(very high confidence) (Figures 3.6, 3.7; Mora et al., 2013; Sweetman 
et al., 2017). The mesopelagic domain will be nevertheless exposed to 
high rates of deoxygenation (Figure 3.6) and high climate velocities 
(Figure 3.4; Section 3.2.2.1), as well as impacted by the shoaling of 
aragonite or calcite saturation horizon (Section  3.2.3.2). Significant 
differences in projected trends between the SSPs show that mitigation

strategies will limit exposure of deep-sea ecosystems to potential 
warming, acidification and deoxygenation during the 21st  century 
(very high confidence) (Figure 3.6; Kwiatkowski et al., 2020).

3
398Chapter 3 Oceans and Coastal Ecosystems and Their Services
Projected trends across open-ocean systems
(a)
(b)
(c)
(d)
Figure 3.6 |  Projected trends across open-ocean systems. Projected annual and global (a) average warming, (b) acidification, (c) changes in dissolved oxygen concentrations 
and (d) changes in nitrate (NO 3) concentrations for four open-ocean systems, including the epipelagic (0–200 m depth), mesopelagic (200–1000 m), bathypelagic (>1000 m) 
domains and deep benthic waters (>200 m). All projections are based on Coupled Model Intercomparison Project 6 models and for three Shared Socioeconomic Pathways (SSPs): 
SSP1-2.6, SSP2-4.5 and SSP5-8.5 (Kwiatkowski et al., 2020). Anomalies in the near-term (2020–2041), mid-term (2041–2060) and long-term (2081–2100) are all relative to 
1985–2014. Error bars represent very likely ranges.

3
399Oceans and Coastal Ecosystems and Their Services  Chapter 3
3.2.4.2  Time of Emergence
Anthropogenic changes in climate-induced drivers assessed here 
exhibit vastly distinct times of emergence, which is the time scale 
over which an anthropogenic signal related to climate change is 
statistically detected to emerge from the background noise of natural climate for a specific region (Christensen et  al., 2007; Hawkins and 
Sutton, 2012). SROCC concluded that for ocean properties, the time 
of emergence ranges from under a decade (e.g., surface ocean pH) to 
over a century (e.g., net primary production; see Section 3.4.3.3.4 for 
time of emergence of biological properties; Bindoff et al., 2019a).
Projected trends across coastal-ocean ecosystems
(a)
(b)
(c)
(d)
(e)
Figure 3.7 |  Projected trends across coastal-ocean ecosystems. Projected (a) warming, (b) acidification, (c) changes in dissolved oxygen concentrations, (d) changes in

nitrate (NO 3) concentrations and (e) changes in summer sea ice cover fraction (September and north of 66°N for the Northern Polar Oceans, and March and south of 66°S for the 
Southern Polar Ocean) for five coastal-ocean ecosystems. All projected trends are for the surface ocean, except oxygen concentration changes that are computed for the subsurface 
ocean (100–600 m depth) for the upwelling ecosystems and the polar seas. All projections are based on Coupled Model Intercomparison Project 6 (CMIP6) models and for three 
Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5 and SSP5-8.5 (Kwiatkowski et al., 2020). Anomalies in the near term (2020–2041), mid term (2041–2060) and long term 
(2081–2100) are all relative to 1985–2014. Error bars represent very likely ranges. Coastal seas are defined on a 1° × 1° grid when bathymetry is less than 200 m deep. Distribution

of warm-water corals is from UNEP-WCMC et al. (2018). Distribution of kelp ecosystems is from OBIS (2020). Upwelling areas are defined according to Rykaczewski et al. (2015).

3
400Chapter 3 Oceans and Coastal Ecosystems and Their Services
The literature assessed in SROCC mainly focused on surface ocean 
properties and gradual mean changes. Since then, the time of 
emergence has also been investigated for subsurface properties, ocean 
extreme events and particularly vulnerable regions, such as the Arctic 
Ocean (Hameau et al., 2019; Oliver et al., 2019; Burger et al., 2020; 
Landrum and Holland, 2020; Schlunegger et al., 2020), but subsequent 
assessments are low confidence due to limited evidence. Below 
the surface, changes in temperature typically emerge from internal 
variability prior to changes in oxygen; however, in about a third of the 
global thermocline, deoxygenation emerges prior to warming (Hameau 
et al., 2019). Permanent MHW states, defined as when SST exceeds 
the MHW threshold continuously over a full calendar year, will emerge 
during the 21st  century in many parts of the surface ocean (Oliver

et al., 2019). Ocean acidification extremes have already emerged from 
background natural internal variability during the 20th century in most 
of the surface ocean (Burger et al., 2020). In the Arctic, anthropogenic 
sea ice changes have already emerged from the background internal 
variability, and anthropogenic alteration of air temperatures will 
emerge in the early- to mid-21st century (Landrum and Holland, 2020).
3.2.4.4  Perspectives from Paleoclimatology Data
Paleoclimatology observations are useful to assess multiple hazards 
of environmental change while excluding direct anthropogenic 
impacts (Section 3.4.3.3). Ancient intervals of rapid climate warming 
that occurred between 300 and 50  million  years ago (Ma) were 
triggered by the release of greenhouse gases (high confidence). The 
sources of greenhouse gases varied but include volcanic degassing 
from continental flood basalts and methane hydrates stored in

marine sediments and soils (Foster et al., 2018). Six extreme ancient 
hyperthermal events are known from the last 300 Ma, when tropical 
SSTs reached 1.5°C–10°C warmer than pre-industrial conditions, and 
with substantial impacts on ancient life (Cross-Chapter Box  PALEO 
in Chapter  1). Warming and deoxygenation in the oceans were 
closely associated in hyperthermal events (high confidence), with 
anoxia reaching the photic zone and abyssal depths (Kaiho et  al., 
2014; Müller et al., 2017; Penn et al., 2018; Weissert, 2019), whereas 
ocean acidification has not been demonstrated consistently (medium 
confidence) (Hönisch et al., 2012; Penman et al., 2014; Clarkson et al., 
2015; Harper et al., 2020a; Jurikova et al., 2020; Müller et al., 2020).
Greenhouse gases also contributed substantially to shaping the longer-
term climate trends over the past 50 million years, although changes 
in continental configuration and ocean circulation as well as planetary

orbital cycles were equally important (WGI AR6 Cross-Chapter Box 2.1 
in Chapter 2; Westerhold et al., 2020; Gulev et al., 2021). There is little 
evidence for ocean acidification in the past 2.6 Ma (low confidence) 
(Hönisch et al., 2012), but ocean ventilation was highly sensitive to 
even modest warming such as observed in the past 10,000  years 
(medium confidence) (Jaccard and Galbraith, 2012; Lembke-Jene et al., 
2018).3.3 Linking Biological Responses to Climate-
induced Drivers
3.3.1 Introduction
This section assesses new evidence since AR5 (Pörtner et al., 2014) and 
SROCC (Bindoff et al., 2019a) regarding biotic responses to multiple 
environmental drivers. It assesses differential sensitivities among life 
stages within individual organisms, changing responses across scales 
of biological organisation and the potential for evolutionary adaptation 
to climate change (e.g., Przeslawski et  al., 2015; Boyd et  al., 2018;

Reddin et al., 2020), providing examples and identifying key gaps and 
uncertainties that limit our ability to project the ecological impact of 
multiple climate-induced drivers (Figure 3.8a). The assessment includes 
physiological responses to single environmental drivers and their 
underlying mechanisms (Section 3.3.2), the characteristics of multiple 
drivers and organisms’ responses to them (Section 3.3.3), short-term 
acclimation and longer-term evolutionary adaptation of populations 
(Section  3.3.4), and it concludes with an assessment of progress in 
upscaling laboratory findings to ecosystems within in situ settings 
(Figure 3.8b; Section 3.3.5).
3.3.2 Responses to Single Drivers
Anthropogenic CO 2 emissions trigger a suite of changes that alter 
ocean temperature, pH and  CO 2 concentration, oxygen concentration 
and nutrient supply at global scales (Section  3.2). The response 
pathways of these climate-induced drivers have been investigated 
primarily as single variables.

Temperature affects the movement and transport of molecules and, 
thereby, the rates of all biochemical reactions; thus, ongoing and 
projected warming (Section 3.2.2.1) that remains below an organism’s 
physiological optimum will generally raise metabolic rates (very 
high confidence) (Pörtner et  al., 2014). Beyond this optimum (T opt; 
Figure  3.9), metabolism typically decreases sharply, finally reaching 
a critical threshold (T crit) beyond which enzymes become thermally 
inactivated and cells undergo oxidative stress. Local and regional 
adaptation affect the heat tolerance thresholds of organisms. For 
example, organisms adapted to thermally stable environments (e.g., 
tropical, polar, deep sea) are often more sensitive to warming than 
those from thermally variable environments (e.g., estuaries) (very 
high confidence) (Section  3.4; Sunday et  al., 2019; Collins et  al., 
2020). Heat tolerance also decreases with increasing organisational

complexity (Storch et al., 2014; Pörtner and Gutt, 2016) and is lower in 
eggs, embryos and spawning fish than for their larval stages or adults 
outside the spawning season (high confidence) (Dahlke et al., 2020b). 
By altering physiological responses, projected changes in ocean 
warming (Section 3.2.2.1) will modify growth, migration, distribution, 
competition, survival and reproduction (very high confidence) 
(Messmer et al., 2017; Dahlke et al., 2018; Andrews et al., 2019; Pinsky 
et al., 2019; Anton et al., 2020).
Altered seawater carbonate chemistry (Section 3.2.3.1) affects specific 
processes to varying degrees. For example, higher CO 2 concentrations 
can increase photosynthesis and growth in some phytoplankton,

3
401Oceans and Coastal Ecosystems and Their Services  Chapter 3
macroalgal and seagrass species (high confidence) (Pörtner et al., 2014; 
Seifert et al., 2020; Zimmerman, 2021), while lower pH levels decrease 
calcification (high confidence) (Pörtner et al., 2014; Falkenberg et al., 
2018; Doney et  al., 2020; Fox et  al., 2020; Reddin et  al., 2020) or 
silicification (low confidence) (Petrou et al., 2019). Organisms’ capacity 
to compensate for or resist acidification of internal fluids depends 
on their capacity for acid–base regulation, which differs due to 
organisms’ wide-ranging biological complexity and adaptive abilities 
(low to medium confidence) (Vargas et  al., 2017; Melzner et  al., 
2020). Detrimental impacts of acidification include decreased growth 
and survival, and altered development, especially in early life stages 
(high confidence) (Dahlke et al., 2018; Onitsuka et al., 2018; Hancock 
et al., 2020), along with lowered recruitment and altered behaviour in

animals (Kroeker et al., 2013a; Wittmann and Pörtner, 2013; Clements 
and Hunt, 2015; Cattano et al., 2018; Esbaugh, 2018; Bednaršek et al., 
2019; Reddin et al., 2020). For finfish, laboratory studies of behavioural 
and sensory consequences of ocean acidification showed mixed results 
(Rossi et al., 2018; Nagelkerken et al., 2019; Stiasny et al., 2019; Velez 
et  al., 2019; Clark et  al., 2020; Munday et  al., 2020). Calcifiers are 
generally more sensitive to acidification (e.g., for growth and survival) 
than non-calcifying groups (high confidence) (Kroeker et  al., 2013a; 
Wittmann and Pörtner, 2013; Clements and Hunt, 2015; Cattano et al., 
2018; Bednaršek et al., 2019; Reddin et al., 2020; Seifert et al., 2020). 
For calcifying primary producers, including phytoplankton and coralline 
algae, ocean acidification has different, often opposing effects, for 
example, decreasing calcification while photosynthetic rates increase

(high confidence) (Riebesell et al., 2000; Van de Waal et al., 2013; Bach 
et al., 2015; Cornwall et al., 2017b; Gafar et al., 2019).
Oxygen concentrations affect aerobic and anaerobic processes, 
including energy metabolism and denitrification. Projected decreases 
in dissolved oxygen concentration (Section 3.2.3.2) will thus impact 
organisms and their biogeography in ways dependent upon their 
oxygen requirements (Deutsch et  al., 2020), which are highest for 
large, multicellular organisms (Pörtner et al., 2014). The upper ocean 
generally contains high dissolved-oxygen concentrations due to 
air–sea exchange and photosynthesis, but in subsurface waters, 
deoxygenation may impair aerobic organisms in multiple ways 
(Oschlies et al., 2018; Galic et al., 2019; Thomas et al., 2019; Sampaio 
et  al., 2021). Many processes contribute to lowered oxygen levels: 
altered ventilation and stratification; microbial respiration enhanced

by nearshore eutrophication; and less oxygen solubility in warmer 
waters. For example, deoxygenation in highly eutrophic estuarine and 
coastal marine ecosystems (Section 3.4.2) can result from accelerated 
microbial activity, leading to acute organismal responses. Under 
hypoxia (oxygen concentrations ≤2 mg  l–1; Limburg et  al., 2020), 
physiological and ecological processes are impaired and communities 
undergo species migration, replacement and loss, transforming 
community composition (very high confidence) (Chu and Tunnicliffe, 
2015; Gobler and Baumann, 2016; Sampaio et  al., 2021). Hypoxia 
can lead to expanding OMZs, which will favour specialised microbes 
and hypoxia-tolerant organisms (medium confidence) (Breitburg 
et  al., 2018; Ramírez-Flandes et  al., 2019). As respiration consumes 
oxygen and produces CO 2, lowered oxygen levels are often interlinked 
with acidification in coastal and tropical habitats (Rosa et al., 2013;

Gobler and Baumann, 2016; Feely et al., 2018) and is an example of a 
compound hazard (Sections 3.2.4.1, 3.4.2.4).Increased density stratification and mixed-layer shallowing, caused by 
warming, freshening and sea ice decline, can alter light climate and 
nutrient availability within the surface mixed layer (high confidence) 
(Section  3.2.2.3). As light and nutrient levels drive photosynthesis, 
changes in these drivers directly affect primary producers, often in 
different directions (Matsumoto et al., 2014; Deppeler and Davidson, 
2017). Decreased upward nutrient supply is expected to decrease 
primary production in the low-latitude ocean (medium confidence) 
(Section  3.4.4.2.1; Moore et  al., 2018a; Kwiatkowski et  al., 2019). 
Alternatively, higher mean underwater light levels resulting from 
changes in sea ice and/or mixed layer shallowing can increase primary 
production in high-latitude offshore regions, provided nutrient levels

remain sufficiently high (medium confidence) (Section 3.4.4.2.1; Cross-
Chapter Paper 6; Vancoppenolle et al., 2013; Deppeler and Davidson, 
2017; Tedesco et al., 2019; Ardyna and Arrigo, 2020; Lannuzel et al., 
2020). In some parts of the open Southern Ocean, where iron limitation 
largely controls primary productivity (Tagliabue et al., 2017), changes 
in wind fields will deepen the summer mixed-layer depth (Panassa 
et al., 2018), entrain more nutrients, and raise primary productivity in 
the future (medium confidence) (Cross-Chapter Paper 6; Hauck et al., 
2015; Leung et al., 2015; Moore et al., 2018a; Kwiatkowski et al., 2020).
Climate-induced drivers fluctuate on time scales ranging from diurnal 
to annual, with potential consequences for organismal responses 
(Figure 3.10), but these fluctuations are commonly not incorporated 
experimentally. Experiments that simulate natural fluctuations in 
drivers, especially beyond tidal or diel cycles, can result in more

detrimental impacts than those based on quasi-constant conditions 
(Eriander et al., 2015; Sunday et al., 2019), but can also ameliorate 
effects (Comeau et al., 2014; Laubenstein et al., 2020; Cabrerizo et al., 
2021), confirming that the influence of environmental variability 
requires evaluation (Dowd et al., 2015). Marine heatwaves exacerbate 
the impacts of rising mean temperatures, with major ecological 
consequences (very high confidence) (Frölicher et al., 2018; IPCC, 2018; 
Arafeh-Dalmau et al., 2020; Laufkötter et al., 2020). Higher temperature 
variability decreased phytoplankton growth and calcification in 
Emiliania huxleyi relative to a stable warming regime (Wang et  al., 
2019b). Diel fluctuations (i.e., over 24 h) in carbonate chemistry 
superimposed on current and future pCO 2 levels influenced diatom 
species differently, depending on their habitat (Li et  al., 2016). CO 2 
fluctuations overlaid on changing mean values also altered phenotypic

evolutionary outcomes of picoeukaryotic algae (Schaum et al., 2016). 
In the bivalve Mytilus edulis, fluctuating pH regimes exerted higher 
metabolic costs (Mangan et  al., 2017), while salinity fluctuations 
might be more influential than pH fluctuations in other bivalves (Velez 
et al., 2016). The amplitude of diel and seasonal pH and CO 2 changes 
are projected to increase in the future due to lowered CO 2 seawater 
buffering capacity (very high confidence) (Section 3.2.3.1; Burger et al., 
2020), which can impose additional stress on organisms.
3.3.3 Responses to Multiple Drivers
Each organism encounters a unique combination of local and climate-
induced drivers, which vary in space and time. The contribution of 
these drivers to an organism’s overall biological response, and thereby 
also potential risks for the organism, depends on the intensity and

3
402Chapter 3 Oceans and Coastal Ecosystems and Their Services
(a)
(b)
Single driver with outcome(s), underlying mechanism(s) and projectionsMultiple drivers with outcomes (cum. ef fect, sign, magnitude)Multiple drivers with outcomes, assessment of interactionsMultiple drivers with demarcate d
physiological roles of each driverMultiple drivers with mechanistic
understanding and projectionsCumulative effects 
Mechanistic understanding 
and projections
Influence of fluctuations ,
extremes, adaptation, etc.Influence of populations ,
communities, foodwebsEcosystem
Community
Species
Single
Double
SP
ACE
MultipleAdapted
AcclimatedDRIVERTIME3.3.33.3.5
3.3.43.3.4
3.3.2Box3.1
3.3.5ECOSYSTEM SERVICESPOLICYThe state of knowledge regarding ecological responses to environmental drivers in experimental settings
Figure 3.8 |  The state of knowledge regarding ecological responses to environmental drivers in experimental settings.

(a) Schematic indicates where themes are discussed within Section 3.3, and how they jointly inform policy. (Adapted from Riebesell and Gattuso, 2014).
(b) The hierarchy of accumulating physiological knowledge (grey layers), from single (e.g., Pörtner et al., 2012) to multiple drivers, and from simple outcomes (e.g., Sciandra et al., 
2003), interactions among drivers (e.g., Crain et al., 2008) and identification of physiological roles of drivers (e.g., Bach et al., 2015) to mechanistic understanding of drivers (e.g., 
Thomas et al., 2017). At present, the upper grey layer has been achieved, in full, for two drivers (e.g., temperature and nutrient concentrations), with validation of dual controls 
on phytoplankton growth rate (Thomas et al., 2017). Hatched layers denote major advances since WGII AR5 Chapter 6 (Pörtner et al., 2014). The green layer indicates the level of

understanding potentially needed to project the response of marine life subjected to multiple drivers. Red horizontal arrows indicate the influence of confounding factors on our 
current understanding, including population genetics, fluctuating oceanic conditions or extreme events.

3
403Oceans and Coastal Ecosystems and Their Services  Chapter 3
Physiological process rate
Driver A (e.g. temperature)
Driver A (e.g. temperature)Driver B (e.g. pH
)Driver B (e.g
. pH)Organismal responses to single and multiple drivers
(a)
(b)
(c)
Driver (e.g. temperature)Physiological process ratePlasticit y
Stress StressTopt
Tcrit TcritRmax
Drivers (e.g. elevated temperature and lowered pH)ControlPhysiological process rateSynergistic
A+B< A&BAdditive
A+B= A&BAntagonistic
A+B> A&B
AB
ControlAB
ControlABA&B A&B A&B
Figure 3.9 |  Organismal responses to single and multiple drivers.
(a) The generic temperature–response curve shows physiological process rates as a nonlinear function of a particular driver (e.g., temperature) with maximum rates (R max) and 
temperature optima (T opt). The driver range that keeps physiological rates above a certain threshold represents the organism’s range of phenotypic plasticity, while below that

threshold, the critical temperature (T crit), physiological performance is so low as to constitute stressful conditions. 
(b) The response curve for one driver can depend on other drivers, here exemplified for temperature and pH in the central panel. This interaction causes rates as well as optima to 
change with pH (left) and temperature (right), indicated by the coloured lines. (c) Impacts of multiple drivers on processes can be additive (blue), synergistic (red) or antagonistic 
(green), that is, the cumulative effects of two (or more) drivers are equal to, larger than or smaller than the sum of their individual effects, respectively. Potential experimental 
outcomes affected by additive, synergistic and antagonistic interactions are shown for scenarios where drivers increase rates (left), decrease rates (centre) or cause opposite

responses (right), showing how experimental outcomes can mask these mechanistic interactions. (For a quantitative analysis of effects of driver pairs on animals, see Figure 3.SM.2.) 
(Adapted from Crain et al., 2008 and Piggott et al., 2015).

3
404Chapter 3 Oceans and Coastal Ecosystems and Their Services
duration of its exposure to these drivers and associated sensitivities. 
Both geographic location (e.g., polar, tropical) and marine habitat (e.g., 
benthic, pelagic) strongly affect the combination of climate and non-
climate drivers to which organisms are exposed. Non-climate drivers 
(Section 3.1) can dominate outcomes or amplify vulnerability to climate-
induced drivers, with mostly detrimental effects such as extirpation 
(very high confidence) (Section 3.4; Boyd et al., 2018; Gissi et al., 2021), 
and unique feedbacks may exist between climate change and drivers 
like habitat loss or invasive species that further confound climate-
change effects (Ortiz et al., 2018; Wolff et al., 2018; Gissi et al., 2021). 
Individual responses are further influenced by an organism’s behaviour, 
trophic level and life-history strategy (Figure 3.10; Przeslawski et al.,

2015; Boyd et al., 2018). Evidence is increasing that some life-history 
stages are more sensitive to specific drivers than others (Dahlke et al., 
2020b). To identify the most influential drivers for an organism requires 
targeting key traits (e.g., calcification, reproduction). The trophic level 
of the organism must also be considered, because autotrophs directly 
depend on light and nutrients while invertebrates are often more 
sensitive to changes in oxygen or altered prey, but temperature plays 
a key role for both groups (Figure 3.10b).
Co-occurring environmental drivers often cause complex organismal 
responses (high confidence) (Pörtner et  al., 2014). Individual drivers 
can have detrimental, neutral or beneficial effects, depending on the 
relationship between driver and physiological process (Section 3.3.2; 
Figure 3.9a). Multiple drivers can have interactive effects, where the 
response to one driver alters the sensitivity to another, and outcomes

cannot be deduced from individual drivers’ effects (Figure  3.9b). 
Impacts of multiple drivers can be additive, synergistic or antagonistic 
(Figure 3.9c; Crain et al., 2008; Piggott et al., 2015; Boyd et al., 2018; 
Bindoff et al., 2019a). Well-controlled laboratory studies on multiple-
driver effects have revealed insights into the mode of action of 
individual drivers and their interdependence (Kroeker et al., 2017; Gao 
et al., 2019; Reddin et al., 2020; Seifert et al., 2020; Green et al., 2021b; 
Sampaio et  al., 2021). Understanding the outcomes of interactive 
drivers is important for robustly assessing risks to organisms under 
different climate-change scenarios.
3.3.3.1 Effects of Multiple Drivers on Primary Producers
Warming and rising CO 2 concentrations enhance growth and/or 
photosynthetic rates in many species of cyanobacteria, picoeukaryotes, 
coccolithophores, dinoflagellates and diatoms (high confidence)

(Fu et  al., 2007; Sett et  al., 2014; Hoppe et  al., 2018a; Wolf et  al., 
2018; Brandenburg et al., 2019), and the optimum pCO 2 for growth 
and/or primary production shifts upward under warming (medium 
confidence) (Sett et al., 2014; Hoppe et al., 2018a). Warming and ocean 
acidification appear to jointly favour the proliferation and toxicity of 
harmful algal bloom (HAB) species (limited evidence, high agreement) 
(Section  3.5.5.3; Bindoff et  al., 2019a; Brandenburg et  al., 2019; 
Griffith et  al., 2019a; Wells et  al., 2020), but a 2021 analysis found 
no uniform global trend in HABs or their distribution over 1985–2018 
once field data were adjusted for regional variations in monitoring 
effort (Hallegraeff et al., 2021). The predominantly detrimental impacts 
of ocean acidification on coccolithophores can partly be offset by 
warming (Seifert et al., 2020) but also be exacerbated, depending on

the magnitudes of drivers (D’Amario et al., 2020). For non-calcifying macroalgae, responses are highly species specific and often indicate 
synergistic interactions between warming and acidification (Kram 
et  al., 2016; Falkenberg et  al., 2018). Ocean acidification poses a 
large risk for coralline algae that is further amplified by warming 
(medium confidence) (Section 3.4.2.2; Cornwall et al., 2019). However, 
temperatures up to 5°C above ambient do not decrease calcification 
(Cornwall et  al., 2019), and there is limited evidence that some 
species have the physiological capacity to resist acidification via pH 
upregulation at the calcification site (Cornwall et  al., 2017a). For 
seagrass, warming beyond a species’ thermal tolerance will limit 
growth and impact germination, but ocean acidification appears to 
increase thermal tolerance of some eelgrass species by increasing the 
photosynthesis-to-respiration ratio (medium confidence) (Egea et al.,

2018; Scalpone et al., 2020; Zimmerman, 2021).
Thermal sensitivity of pelagic primary producers changes with nutrient 
supply (high confidence) (Thomas et al., 2017; Marañón et al., 2018; 
Fernández et al., 2020). Phosphorus limitation lowers the temperature 
optimum for growth of phytoplankton, making these organisms more 
prone to heat stress (Thomas et al., 2017; Bestion et al., 2018). This 
trend may hold for open-ocean phytoplankton, which are often iron-
limited (medium confidence) (Boyd, 2019). Such temperature-nutrient 
interactions might be especially relevant during summer MHWs 
(Section  3.2.2.1; Cross-Chapter Box  EXTREMES in Chapter  2; IPCC, 
2018; Holbrook et  al., 2019; DeCarlo et  al., 2020; Hayashida et  al., 
2020), when primary producers are often nutrient-limited and near their 
thermal limits. Increasingly frequent and intense MHWs along with 
projected decreases in nutrient availability (Section 3.2.3.3) may push

some primary producers beyond tolerance thresholds. Temperature–
nutrient interactions can also alter the photosynthesis-to-respiration 
ratio in phytoplankton (Marañón et al., 2018). Overall, rising metabolic 
rates due to warming will be restricted to primary producers in high-
nutrient regions (medium confidence) (Thomas et al., 2017; Marañón 
et al., 2018). For zooxanthellae-containing corals, nutrient supply from 
upwelling or from runoff can increase coral susceptibility to bleaching 
during warm-season MHWs (DeCarlo et al., 2020; Wooldridge, 2020).
The effects of ocean acidification on growth, metabolic rates or 
elemental composition of primary producers changes with nutrient 
availability and light conditions (high confidence) (Gao et al., 2019; 
Seifert et  al., 2020). While interactions with nutrients are often 
additive in phytoplankton, diatoms revealed predominantly synergistic 
interactions (Seifert et al., 2020). Growth or photosynthesis of some

diatom and HAB species, for instance, are stimulated by ocean 
acidification only if nutrients are replete (Hoppe et al., 2013; Boyd et al., 
2015b; Eberlein et al., 2016; Griffith et al., 2019a). Interactions with 
light are more complex because relative effects of ocean acidification 
are larger under limiting irradiances, while saturating light levels 
decrease beneficial or detrimental effects on these processes (Kranz 
et al., 2010; Garcia et al., 2011; Rokitta and Rost, 2012; Heiden et al., 
2016). For the coccolithophore Emiliania huxleyi, for example, the 
impacts of ocean acidification are less detrimental under high light 
availability, which could partly explain why this species is moving 
poleward (Winter et al., 2014; Kondrik et al., 2017; Neukermans et al., 
2018), although acidification is more pronounced in polar waters 
(Section 3.2.3.1; Cross-Chapter Paper 6). Under excess light, however, 
the detrimental impacts of ocean acidification are amplified for many

3
405Oceans and Coastal Ecosystems and Their Services  Chapter 3
Effect of environmental drivers varies depending on several factors
pH
Time (month)0 128.7
8.6
8.5
8.4
8.3
8.2
8.1
8.0
7.9
7.8Adult
(Benthic)Larva
(Pelagic)Juvenile
(Benthic)Juvenile
(Benthic)(a)
PreyCO2PreyCO2CO2Sunlight
Nutrients Trace element s
O2
OxygenO2
PreyCO2
CO2 Temperature pHWater column
(epipelagic) Phytoplankton
Sea floor
(benthic) Water column
(mesopelagic) (b)
Figure 3.10 |  The effect of environmental drivers differs depending upon organisms’ life history, and trophic strategy or habitat. 
(a) pH variability differs for benthic invertebrates, such as sea urchins (in blue), and their pelagic larvae (in green); pH fluctuations over the annual cycle can be much larger in the 
water column (due to primary production) relative to the seafloor. Variability associated with behaviour and life stage strongly defines organisms’ niches and sensitivities to present 
and future conditions.

(b) Examples of organisms that are influenced by different suites of drivers that are set jointly by their habitat (e.g., benthic versus epipelagic settings) and trophic strategy (e.g., 
nutrients for phytoplankton, prey characteristics for grazers).

3
406Chapter 3 Oceans and Coastal Ecosystems and Their Services
species (high confidence) (Gao et  al., 2012; Li and Campbell, 2013; 
Zhang et al., 2015; Kottmeier et al., 2016; Gafar et al., 2019). Lowered 
photo-physiological capacity to cope with high-light stress and avoid 
photodamage (Gao et al., 2012; Li and Campbell, 2013; Hoppe et al., 
2015; Kvernvik et  al., 2020) is also consistent with observations 
that dynamic light regimes can become more stressful under ocean 
acidification (Jin et al., 2013; Hoppe et al., 2015). Given the expected 
mixed-layer shallowing in some regions (Section 3.2.2.3), the exposure 
to overall higher mean irradiances could shift the effects of acidification 
from beneficial to detrimental for some primary producers, depending 
on species and organismal traits (medium confidence) (Gao et  al., 
2019; Seifert et al., 2020).
Studies investigating two drivers provide most of the information on

the wide range of interactive effects of drivers on phytoplankton (Gao 
et al., 2019; Seifert et al., 2020), although climate change alters several 
oceanic drivers concurrently (Section 3.2). The few experimental studies 
that have addressed three or more drivers (Xu et al., 2014; Boyd et al., 
2015b; Brennan and Collins, 2015; Brennan et al., 2017; Hoppe et al., 
2018b; Moreno-Marín et  al., 2018) indicate that one or two drivers 
generally dominate the cumulative outcome, with others playing a 
subordinate role (medium confidence). In these studies, temperature 
had a disproportionately large influence, while other drivers differed 
in importance, depending on the type of primary producer, ecosystem 
characteristics and selected driver values.
3.3.3.2 Effects of Multiple Drivers on Animals
When changing CO 2 concentrations affect marine ectotherms, they 
typically combine additively or synergistically with warming (medium

confidence) (e.g., Lefevre, 2016; Reddin et al., 2020; Sampaio et al., 
2021), and their cumulative effects can lead to detrimental, neutral or 
beneficial effects (high confidence) (Figure 3.9a; Bennett et al., 2017; 
Büscher et al., 2017; Dahlke et al., 2017; Foo and Byrne, 2017; Johnson 
et al., 2017b; Cominassi et al., 2019). Higher ocean CO 2 influences the 
thermal tolerance of species adapted to extreme but stable habitats 
in tropical and polar regions, more than that of thermally tolerant 
generalists (high confidence) (Byrne et al., 2013; Schiffer et al., 2014; 
Flynn et al., 2015; Kunz et al., 2016; Pörtner et al., 2017; Kunz et al., 
2018; Bindoff et  al., 2019a; but see Ern et  al., 2017), especially in 
early life stages (Dahlke et  al., 2020a). In thermal generalists from 
temperate and subtropical species, warming and ocean acidification 
generally have detrimental effects on growth and survival (e.g., Gao

et al., 2020), but warming can also alleviate the detrimental effects 
of ocean acidification by increasing metabolic rate and/or growth 
(Garzke et  al., 2020), provided that other conditions (e.g., thermal 
niche, food availability) are beneficial. For example, larval growth and 
survival of Australasian snapper (Pagrus auratus) appear to benefit 
from combined acidification and warming (but see Watson et al., 2018; 
McMahon et al., 2020), introducing major uncertainties to population 
modelling (Section 3.3.4; Parsons et al., 2020).
As with ocean acidification, reduced oxygen availability further 
alters the influence of warming on metabolic rates (high confidence). 
Acidification and hypoxia can contribute to a decrease or shift in thermal 
tolerance, while the magnitude of this effect depends on the duration 
of exposure (Tripp-Valdez et al., 2017; Cattano et al., 2018; Calderón-Liévanos et al., 2019; Schwieterman et al., 2019). Warming and hypoxia

are mostly positively correlated and tolerances to both phenomena 
are often linked after long-term acclimation (e.g., Bouyoucos et  al., 
2020). Acute short-term heat shocks can impair hypoxia tolerance, 
for instance, in intertidal fish (McArley et al., 2020). This is relevant 
for shallow waters, specifically for MHWs (Section  3.2.2.1; Hobday 
et al., 2016a; IPCC, 2018; Collins et al., 2019a). Ocean acidification can 
increase hypoxia tolerance in some cases, possibly by downregulating 
activity (Faleiro et  al., 2015) and/or changing blood oxygenation 
(Montgomery et al., 2019). Other studies, however, reported additive 
negative effects of acidification and warming on hypoxia tolerance 
(Schwieterman et al., 2019; Götze et al., 2020), in line with the oxygen- 
and capacity-limited thermal tolerance (OCLTT) hypothesis presented 
in AR5 (Pörtner et  al., 2014): Warming causes increased metabolic 
rates and oxygen demand in ectotherms, which at some point exceed

supply capacities (which also depend on environmental oxygen 
availability) and reduce aerobic scope. In consequence, expansion of 
OMZs and other regions where warming, hypoxia and acidification 
combine will further reduce habitat for many fish and invertebrates 
(high confidence) (Sections 3.4.3.2, 3.4.3.3).
Food availability modulates, and may be more influential than, 
other driver responses by affecting the energetic and nutritional 
status of animals (Cole et  al., 2016; Stiasny et  al., 2019; Cominassi 
et al., 2020). Laboratory studies conducted under an excess of food 
risk underestimating the ecological effects of climate-induced 
drivers, because increased feeding rates may help mitigate adverse 
effects (Nowicki et  al., 2012; Towle et  al., 2015; Cominassi et  al., 
2020). Lowered food availability from reduced open-ocean primary 
production (Sections 3.2.3.3, 3.4.4.2.1) will act as an additional driver,

amplifying the detrimental effects of other drivers. However, warming 
and higher CO 2 availability may increase primary productivity in some 
coastal areas (Section 3.4.4.1), ameliorating the adverse direct effects 
on animals (e.g., Sswat et al., 2018). Due to the few studies addressing 
food availability under multiple-driver scenarios (Thomsen et al., 2013; 
Pistevos et al., 2015; Towle et al., 2015; Ramajo et al., 2016; Brown 
et al., 2018a; Cominassi et al., 2020), there is medium confidence in its 
modulating effect on climate-induced driver responses.
Animal behaviour can be affected by ocean acidification, warming 
and hypoxia. While warming and hypoxia mostly induce avoidance 
behaviour, potentially leading to migration and habitat compression 
(Section 3.4; McCormick and Levin, 2017; Limburg et al., 2020), the 
effects of acidification appear more complex. Some studies reported 
that acidification dominates behavioural effects (Schmidt et al., 2017),

although outcomes vary with experimental design and duration 
of exposure (low confidence, low agreement) (Maximino and de 
Brito, 2010; Munday et  al., 2016; Laubenstein et  al., 2018; Munday 
et  al., 2019; Sundin et  al., 2019; Clark et  al., 2020; Munday et  al., 
2020; Williamson et  al., 2021). Behaviour represents an integrated 
phenomenon that can be influenced both directly and indirectly 
by multiple drivers. For instance, increased pCO 2 can directly act 
on neuronal signalling pathways (e.g., Gamma-aminobutyric acid 
hypothesis; Nilsson et  al., 2012; Thomas et  al., 2020) and influence 
learning (Chivers et al., 2014), vision (Chung et al., 2014), and choice 
and escape behaviour (Watson et al., 2014; Wang et al., 2017b). There 
is further evidence that observed alterations in fish olfactory behaviour

3
407Oceans and Coastal Ecosystems and Their Services  Chapter 3
under ocean acidification may result from physiological and molecular 
changes of the olfactory epithelium, influencing olfactory receptors 
(Roggatz et al., 2016; Porteus et al., 2018; Velez et al., 2019; Mazurais 
et al., 2020). Temperature mainly drives metabolic processes and thus 
energetic requirements, which can indirectly influence behaviour, 
including increased risk-taking during feeding (Marangon et  al., 
2020). Ocean warming also accelerates the biochemical reactions 
and metabolic processes that are primarily influenced by acidification. 
It is therefore difficult to generalise to what extent co-occurring 
ocean warming ameliorates or exacerbates effects of acidification on 
behaviour (Laubenstein et al., 2019); outcomes depend upon species 
and life stage (Faleiro et al., 2015; Chan et al., 2016; Tills et al., 2016; 
Wang et al., 2018b; Jarrold et al., 2020), interactions between species

(e.g., Paula et  al., 2019) along with confounding factors including 
food availability and salinity (medium confidence) (Ferrari et  al., 
2015; Pistevos et al., 2015; Pimentel et al., 2016; Pistevos et al., 2017; 
Horwitz et al., 2020).
While hypoxia can dominate multiple-driver responses locally (Sampaio 
et  al., 2021), warming is the fundamental physiological driver for 
most marine ectotherms, globally, as it directly affects their entire 
biochemistry and energy metabolism. Other influential drivers include 
ocean acidification, salinity (high confidence) (Lefevre, 2016; Whiteley 
et al., 2018; Reddin et al., 2020) or food availability/quality (medium 
confidence) (Nagelkerken and Munday, 2016; Gao et  al., 2020). 
Fluctuating and decreasing salinity may aggravate the detrimental 
effects of warming and elevated CO 2, because dilution with freshwater 
lowers acid–base buffering capacity, resulting in lower pH and calcium

carbonate saturation state (Dickinson et al., 2012; Shrivastava et al., 
2019; Melzner et al., 2020).
3.3.4 Acclimation and Evolutionary Adaptation
Climate change is and will continue to be a major driver of natural 
selection, causing important changes in fitness-related (e.g., growth, 
reproduction, survival) and functional (e.g., body/cell size, morphology, 
physiology) traits, and in the genetic diversity of natural populations 
(medium confidence) (Pauls et  al., 2013; Merilä and Hendry, 2014). 
Climate-change impacts will continue to be exacerbated by interactions 
with non-climate drivers such as habitat fragmentation or loss, pollution 
or resource overexploitation, which limit the adaptive potential of 
populations to future conditions (Trathan et al., 2015; Gaitán-Espitia 
and Hobday, 2021). However the ultimate responses to complex 
change are conditioned by the rate and magnitude of environmental 
change, organisms’ capacity for acclimation, the degree of local

adaptation of natural populations and populations’ potential for 
adaptive evolution (Figure 3.11; Pespeni et al., 2013; Calosi et al., 2017; 
Vargas et al., 2017). These controlling factors are mainly determined 
by local environmental conditions encountered by populations across 
their geographic distribution (Boyd et al., 2016). In highly fluctuating 
environments (e.g., upwelling regions, coastal zones), multiple drivers 
can change and interact across temporal and spatial scales, generating 
geographic mosaics of tolerances and sensitivities to environmental 
and climate change in marine organisms (medium confidence) (Pespeni 
et al., 2013; Boyd et al., 2016; Vargas et al., 2017; Li et al., 2018a). A 
further challenge for marine life lies in its ability to cope with extreme events such as MHWs (Cross-Chapter Box  EXTREMES in Chapter  2). 
The interplay between the abruptness, intensity, duration, magnitude 
and reoccurrence of extreme events may alter or prevent evolutionary

responses (e.g., adaptation) to climate change and the potential for 
acclimation to extreme conditions such as MHWs (Cheung and Frölicher, 
2020; Coleman et al., 2020a; Gurgel et al., 2020; Gruber et al., 2021).
Some studies have documented higher phenotypic plasticity and 
tolerance to ocean warming and acidification in marine invertebrates 
(Dam, 2013; Kelly et  al., 2013; Pespeni et  al., 2013; Gaitán-Espitia 
et al., 2017a; Vargas et al., 2017; Li et al., 2018a), seaweeds (Noisette 
et  al., 2013; Padilla-Gamiño et  al., 2016; Machado Monteiro et  al., 
2019) and fish (medium confidence) (Sandoval-Castillo et  al., 2020; 
Enbody et  al., 2021) living in coastal zones characterised by strong 
temporal fluctuations in temperature, pH, pCO 2, light and nutrients. 
For these populations, strong directional selection with intense and 
highly fluctuating conditions may have favoured local adaptation 
and increased tolerance to environmental stress (low confidence, low

evidence) (Hong and Shurin, 2015; Gaitán-Espitia et al., 2017b; Li et al., 
2018a).
Other mechanisms acting within and across generations can influence 
selection and inter-population tolerances to environmental and 
climate-induced drivers. For instance, transgenerational effects and/or 
developmental acclimation, both ‘carry-over effects’ (where the early-
life environment affects the expression of traits in later life stages or 
generations), can influence within- and cross-generational changes 
in the tolerances of marine organisms (medium confidence) to ocean 
warming (Balogh and Byrne, 2020) and acidification (Parker et al., 2012). 
Over longer time scales, increasing tolerance to these drivers may be 
mediated by mechanisms such as transgenerational plasticity (Murray 
et al., 2014), leading to locally adapted genotypes as seen in bivalves 
(Thomsen et al., 2017), annelids (Rodríguez-Romero et al., 2016; Thibault

et al., 2020), corals (Putnam et al., 2020) and coralline algae (Cornwall 
et  al., 2020). However, transgenerational plasticity is species specific 
(Byrne et al., 2020; Thibault et al., 2020) and, depending on the rate and 
magnitude of environmental change, it may either be insufficient for 
evolutionary rescue (Morgan et al., 2020) or could induce maladaptive 
responses (i.e., reduced fitness) in marine organisms exposed to multiple 
drivers (medium confidence, low evidence) (Figure  3.11; Griffith and 
Gobler, 2017; Parker et al., 2017; Byrne et al., 2020).
Acclimation to environmental pressures and climate change via 
phenotypic plasticity (Section  3.3.3; Collins et  al., 2020) enables 
species to undergo niche shifts such that their present-day climatic 
niche is altered to incorporate new or shifted conditions (Fox et al., 
2019). Although plasticity provides an adaptive mechanism, it is 
unlikely to provide a long-term solution for species undergoing

sustained directional environmental change (e.g., global warming) 
(medium confidence) (Fox et  al., 2019; Gaitán-Espitia and Hobday, 
2021). Beyond the limits for plastic responses (Figure  3.9; DeWitt 
et al., 1998; Valladares et al., 2007), genetic adjustments are required 
to persist in a changing world (Figure  3.11; Fox et  al., 2019). The 
ability of species and populations to undergo these adjustments (i.e., 
adaptive evolution) depends on extrinsic factors including the rate 
and magnitude of environmental change (important determinants 
of the strength and form of selection; Hoffmann and Sgrò, 2011;

3
408Chapter 3 Oceans and Coastal Ecosystems and Their Services
Munday et al., 2013), along with intrinsic factors such as generation 
times and standing genetic variation (Mitchell-Olds et  al., 2007; 
Lohbeck et al., 2012). Accurately assessing the degree of acclimation 
and/or adaptation across space and time is difficult and constrains 
studying adaptive evolution in natural populations. There is a major 
gap in climate-change biology related to the study of evolutionary 
responses in complex and long-lived multicellular organisms. Insights 
on organismal acclimation, adaptation and evolution rely on studies 
of small, short-lived marine organisms, such as phytoplankton, which 
divide rapidly and contain high genetic variation in large populations. 
(Schaum et al., 2016; Cavicchioli et al., 2019; Collins et al., 2020).
Experimental evolution suggests that microbial populations can 
rapidly adapt (i.e., over 1–2  years) to environmental changes

mimicking projected effects of climate change (medium confidence). 
Phytoplankton adaptive mechanisms include intraspecific strain sorting 
and genetic changes (Bach et al., 2018; Hoppe et al., 2018b; Wolf et al., 2019). The evolutionary responses of microbes are conditioned by the 
number and characteristics of interacting drivers (low confidence) 
(Brennan et al., 2017). For example, in a high-salinity adapted strain of 
the phytoplankton Chlamydomonas reinhardtii, the selection intensity 
and the adaptation rate increased with the number of environmental 
drivers, accelerating the adaptive evolutionary response (Brennan 
et al., 2017). For this and other phytoplankton species, a few dominant 
drivers explain most of the phenotypic and evolutionary changes 
observed (Boyd et  al., 2015a; Brennan and Collins, 2015; Brennan 
Adaptation can be impeded, delayed or constrained in eukaryotic 
microbial populations as a result of reduced genetic diversity and/or the

presence of functional and evolutionary trade-offs (Aranguren-Gassis 
et al., 2019; Lindberg and Collins, 2020; Walworth et al., 2020). In the 
marine diatom Chaetoceros simplex, a functional trade-off between 
high-temperature tolerance and increased nitrogen requirements 
Micro-evolutionary dynamics in response to environmental chang e
Original environment
High stress new environment Low stress new environment
Within generation
Across generations (>2)
Maintained populationDecrease fitness, 
population size, extinctionAcclimation/Plasticity
Selection SelectionStress respons e
 {
{Fitness
High
Low
Figure 3.11 |  Micro-evolutionary dynamics in response to environmental change. Simplified conceptual framework shows two main eco-evolutionary trajectories for 
natural populations over time (vertical axis from top to bottom). If environmental stress is low, rapid responses (within a generation) through plastic phenotypic adjustments and

selection (across generations) sustain fitness, enhancing maintenance of viable populations across generations. In contrast, if environmental stress is high, ongoing phenotypic 
plasticity and acclimation may be insufficient to buffer the negative effects, exacerbating the loss of fitness (change of colour to orange/yellow/red). Ultimately, very high stress 
conditions accelerate population decline, enhancing the risk of species extinction.

3
409Oceans and Coastal Ecosystems and Their Services  Chapter 3
underlies inhibited thermal adaptation under nitrogen-limited 
conditions (low confidence) (Aranguren-Gassis et  al., 2019). When 
selection is strong due to unfavourable environmental conditions, 
microbial populations can encounter functional and evolutionary 
trade-offs evidenced by reducing growth rates while increasing 
tolerance and metabolism of reactive oxygen species (Lindberg and 
Collins, 2020). Other trade-offs can be observed in offspring quality 
and number (Lindberg and Collins, 2020). These findings contribute 
towards a mechanistic framework describing the range of evolutionary 
strategies in response to multiple drivers (Collins et al., 2020), but other 
hazards, such as extreme events (e.g., MHWs), still need to be included 
because their characteristics may alter the potential for adaptation of 
species and populations to climate change (Gruber et al., 2021).

3.3.5 Ecological Response to Multiple Drivers
Assessing ecological responses to multiple climate-induced drivers 
requires a combination of approaches, including laboratory- and 
field-based experiments, field observations (e.g., natural gradients, 
climate analogues), study of paleo-analogues and the development 
of mechanistic and empirical models (Clapham, 2019; Gissi et  al., 
2021). Experimental studies of food-web responses are often limited 
to an individual driver, although recent manipulations have used a 
matrix of >1000-l mesocosms to explore ecological responses to both 
warming and acidification (see Box  3.1; Nagelkerken et  al., 2020). 
Hence, complementary approaches are needed to indirectly explore 
the mechanisms underlying ecosystem responses to global climate 
change (Parmesan et al., 2013). Observations from time series longer 
than modes of natural variability (i.e., decades) are essential for

revealing and attributing ecological responses to climate change (e.g., 
Section 3.4; Barton et al., 2015b; Brun et al., 2019). Also, paleorecords 
provide insights into the influence of multiple drivers on marine 
biota (Cross-Chapter Box  PALEO in Chapter  1; Reddin et  al., 2020). 
Specifically, associations between vulnerabilities and traits of marine 
ectotherms in laboratory experiments correspond with organismal 
responses to ancient hyperthermal events (medium confidence) 
(Reddin et  al., 2020). This corroboration suggests that responses to 
multiple drivers inferred from the fossil record can help provide insights 
into the future status of functional groups, and hence food webs, under 
rapid climate change.
Multi-species and integrated end-to-end ecosystem models are 
powerful tools to explore and project outcomes to the often-interacting 
cumulative effects of climate change and other anthropogenic drivers

(Section 3.1; Kaplan and Marshall, 2016; Koenigstein et al., 2016; Peck 
and Pinnegar, 2018; Tittensor et  al., 2018; Gissi et  al., 2021). These 
models can integrate some aspects of the knowledge accrued from 
manipulation experiments, paleo- and contemporary observations, help 
test the relative importance of specific drivers and driver combinations, 
and identify synergistic or antagonistic responses (Koenigstein et al., 
2016; Payne et al., 2016; Skogen et al., 2018; Tittensor et al., 2018). 
As these models are associated with wide-ranging uncertainties 
(SM3.2.2; Payne et  al., 2016; Trolle et  al., 2019; Heneghan et  al., 
2021), they cannot be expected to accurately project the trajectories 
of complex marine ecosystems under climate change; hence, they are 
most useful for assessing overall trends and in particular for providing a plausible envelope of trajectories across a range of assumptions 
(Fulton et al., 2018; Peck et al., 2018; Tittensor et al., 2018). On a global

scale, ecosystem models project a −5.7  ±  4.1% (very likely range) 
to −15.5  ±  8.5% decline in marine animal biomass with warming 
under SSP1-2.6 and SSP5-8.5, respectively, by 2080–2099 relative to 
1995–2014, albeit with significant regional variation in both trends 
and uncertainties (medium confidence) (Section  3.4.3.4; Tittensor 
et  al., 2021). Biological interactions may exacerbate or buffer the 
projected impacts. For instance, trophic amplification (strengthening 
of responses to climate-induced drivers at higher trophic levels) may 
result from combined direct and indirect food-web-mediated effects 
(medium confidence) (Section 3.4.3.4; Lotze et al., 2019). Alternatively, 
compensatory species interactions can dampen strong impacts on 
species from ocean acidification, resulting in weaker responses at 
functional-group or community level than at species level (medium 
confidence) (Marshall et al., 2017; Hoppe et al., 2018b; Olsen et al.,

2018; Gissi et al., 2021). Globally, the projected reduction of biomass 
due to climate-induced drivers is relatively unaffected by fishing 
pressure, indicating additive responses of fisheries and climate change 
(low confidence) (Lotze et al., 2019). Regionally, projected interactions 
of climate-induced drivers, fisheries and other regional non-climate 
drivers can be both synergistic and antagonistic, varying across regions, 
functional groups and species, and can cause nonlinear dynamics with 
counterintuitive outcomes, underlining the importance of adaptations 
and associated trade-offs (high confidence) (Sections 3.5.3, 3.6.3.1.2, 
4.5, 4.6; Weijerman et al., 2015; Fulton et al., 2018; Hansen et al., 2019; 
Trolle et al., 2019; Zeng et al., 2019; Holsman et al., 2020; Pethybridge 
et al., 2020; Gissi et al., 2021).
Given the limitations of individual ecological models discussed above, 
model intercomparisons, such as the Fisheries and Marine Ecosystem

Model Intercomparison Project (Fish-MIP; Tittensor et al., 2018) show 
promise in increasing the robustness of projected ecological outcomes 
(Tittensor et  al., 2018). Model ensembles include a greater number 
of relevant processes and functional groups than any single model 
and thus capture a wider range of plausible responses. Among the 
global Fish-MIP models, there is high (temperate and tropical areas) 
to medium agreement (coastal and polar regions) on the direction of 
change, but medium (temperate and tropical regions) to low agreement 
(coastal and polar regions) on magnitude of change (Lotze et al., 2019; 
Heneghan et al., 2021). Although model outputs are validated relative 
to observations to assess model skills (Payne et  al., 2016; Tittensor 
et  al., 2018), the Fish-MIP models under-represent some sources of 
uncertainty, as they often do not include parameter uncertainties and 
do not usually include impacts of ocean acidification, oxygen loss

or evolutionary responses because there remains high uncertainty 
regarding the influences of these processes across functional groups. 
Ensemble model investigations like Fish-MIP have also identified gaps 
in our mechanistic understanding of ecosystems and their responses 
to anthropogenic forcing, leading to model improvement and more 
rigorous benchmarking. These investigations could inspire future 
targeted observational and experimental research to test the validity 
of model assumptions (Payne et al., 2016; Lotze et al., 2019; Heneghan 
et  al., 2021). The state of the art in such experimental research is 
presented in Box 3.1.

3
410Chapter 3 Oceans and Coastal Ecosystems and Their Services
3.4 Observed and Projected Impacts of 
Climate Change on Marine Systems
3.4.1 Introduction
Ocean and coastal ecosystems and their resident species are under 
increasing pressure from a multitude of climate-induced drivers and 
non-climate drivers (Section  3.1; Figure  3.12; Bindoff et  al., 2019a). 
This section builds from the assessment of biological responses to 
climate-impact drivers (Section  3.3) to examine the new evidence 
about climate-change impacts at the level of marine ecosystems. It 
focuses on detection and attribution of observed changes to marine 
ecosystems and the projected changes under different future climate 
scenarios. This assessment considers emerging evidence on the effects 
of multiple non-climate drivers and physiological acclimation and/or 
evolutionary adaptation on these observations and projections.
The section focuses first on coastal ecosystems and seas (Section 3.4.2),

which have high spatial variability in physical and chemical 
characteristics, are affected by many non-climate drivers (Section 3.1; 
Figure 3.12) and support rich fisheries, high biodiversity and high levels 
of species endemism. The assessment begins with warm-water coral 
reefs (Section 3.4.2.1) because these highly threatened systems are at 
the vanguard of research on acclimation and evolutionary adaptation 
among coastal ecosystems. It follows with the other shallow, nearshore 
ecosystems dominated by habitat-forming species (e.g., rocky shores, 
kelp systems) and then nearshore sedimentary systems (estuaries, 
deltas, coastal wetlands and sandy beaches), before moving on to 
semi-enclosed seas, shelf seas, upwelling zones and polar seas.The section continues on to oceanic and cross-cutting changes 
(Section  3.4.3), which influence large areas of the epipelagic zone 
(<200 m depth) while also affecting the mesopelagic (200–1000 m),

the perpetually dark bathypelagic (depth >1000 m) and the deep 
seafloor (benthic ecosystems at depths >200 m) zones. Assessed in this 
section are species range shifts (Section 3.4.3.1), phenological shifts 
and trophic mismatches (Section 3.4.3.2), changes in communities and 
biodiversity (Section 3.4.3.3.2), time of emergence of climate-impact 
signals in ecological systems from background natural variability 
(Section 3.4.3.3.4) and changes in biomass, primary productivity and 
carbon export (Sections 3.4.3.4–3.4.3.6).
3.4.2 Coastal Ecosystems and Seas
3.4.2.1 Warm-Water Coral Reefs
Warm-water coral reef ecosystems house one-quarter of the marine 
biodiversity and provide services in the form of food, income and 
shoreline protection to coastal communities around the world. These 
ecosystems are threatened by climate-induced and non-climate drivers, 
especially ocean warming, MHWs, ocean acidification, SLR, tropical

cyclones, fisheries/overharvesting, land-based pollution, disease 
spread and destructive shoreline practices (Hoegh-Guldberg et  al., 
2018a; Bindoff et al., 2019a; Hughes et al., 2020). Warm-water coral 
reefs face near-term threats to their survival (Table 3.3), but research 
on observed and projected impacts is very advanced.
Box 3.1 | Challenges for Multiple-Driver Research in Ecology and Evolution
The majority of the examples in Section 3.3 are from studies mimicking projected conditions in the year 2100 that report the responses 
of an individual species or strain to multiple drivers. This powerful generic experimental approach has largely been restricted to single 
species because it is logistically complex to conduct experiments that straddle multiple trophic levels, and that also include more than 
two drivers (see Figure Box 3.1.1b); the need for multiple replicates, drivers and treatment levels greatly increase the work required

(Parmesan et al., 2013; Boyd et al., 2018). It is challenging to apply this experimental approach to communities or ecosystems (see Figure 
Box 3.1.1). To date, most research on community or ecosystem response to climate-induced drivers has been in large-volume (>10,000 l) 
mesocosms (Riebesell and Gattuso, 2014), or at natural analogues such as CO 2 seeps, in which only one driver (ocean acidification) is 
altered (see (4) in Figure Box 3.1.1). Only very recently have two drivers been incorporated into climate-change manipulation studies 
examining responses of primary producers to secondary consumers (see (5) in Figure Box 3.1.1a; Nagelkerken et al., 2020). Therefore, 
‘natural experiments’ from the geological past (Reddin et al., 2020) provide insights into how food webs and their constituents respond to 
complex change involving multiple drivers. Contemporary observations are occasionally long enough (>50 years) to capture community

responses to complex climate change. For example, Brun et al. (2019) reported a shift in zooplankton community structure in the North 
Atlantic (1960–2014), with major biogeochemical ramifications.
Conducting sufficiently long manipulation experiments to study the effect of adaptation on organisms is equally difficult (see Figure 
Box 3.1.1b), with much research restricted to multi-year studies of the microevolution of fast-growing (more than one division per day) 
phytoplankton species responding to single drivers (Lohbeck et al., 2012; Schaum et al., 2016). In a few experimental evolution studies 
(see (7) in Figure Box 3.1.1a; Brennan et al., 2017), multiple drivers have been used, but none have used communities or ecosystems (see 
Figure Box 3.1.1b). Nevertheless, the fossil record provides limited evidence of adaptations to less rapid (relative to present day) climate

change (Jackson et al., 2018). Despite the need to explore ecological or biogeochemical responses to projected future ocean conditions, 
logistical challenges require that assessments of climate-change impacts at scales larger than mesocosms use large-scale, long-term in 
situ observational studies (as documented in Section 3.4).

3
411Oceans and Coastal Ecosystems and Their Services  Chapter 3
YEAR OF PUBLICATION1
2
3
4+DRIVER NUMBER
SP
AC
EEcosystem
Community
SpeciesAcclimatio n
20002005201020152020(a)
(b)AcclimatedAdapted
Want to knowSP
ACE
Want to knowWant  to know
TIMEEcosystem
Community
Species
Single
Double
Multiple
3
276
14
MultipledriversEcosystem
AdaptedPOLICY
5
DRIVERKnowledge gaps between current scientific understanding and that needed to inform policy
Figure Box 3.1.1 |  Knowledge gaps between current scientific understanding and that needed to inform policy. The conceptual space relating driver 
number, (Driver axis), ecological organisation (Space axis) and evolutionary acclimation state (Time axis), modified from Riebesell and Gattuso (2014).
(a) Spheres indicate suites of studies that illustrate the progress of research, including multiple drivers: (1) one species and one driver (Hutchins et al., 2013); (2) one

species and multiple drivers (five; Boyd et al., 2015a). Ecology: (1) one driver, one species; (3) one driver, planktonic community (Moustaka-Gouni et al., 2016); (4) one 
driver (high-CO 2 seep) and (benthic) ecosystem (Fabricius et al., 2014); (5) two drivers and nearshore ecosystem (Nagelkerken et al., 2020). Evolution: (1) acclimated 
organism and one driver; (6) adapted organisms and one driver (Listmann et al., 2016); (7) adapted organism and multiple drivers (Brennan et al., 2017).
(b) Trends in research trajectories since 2000 from a survey of 171 studies (Boyd et al., 2018). Note the dominance of multiple-driver experiments at the species level 
(lower left cluster); the focus on acclimation (red triangle) rather than adaptation (blue dot); and the focus of investigation on three or fewer drivers. (Redrawn from Boyd 
et al., 2018).Box 3.1 (continued)

3
412Chapter 3 Oceans and Coastal Ecosystems and Their Services
Global analyses published since AR5 show that mass coral bleaching 
events and disease outbreaks have increased due to more frequent 
and severe heat stress associated with ocean warming (very high 
confidence, virtually certain) (Donner et al., 2017; Hughes et al., 2018a; 
DeCarlo et al., 2019; Sully et al., 2019; Tracy et al., 2019). The mass 
coral bleaching, which occurred continuously across different parts of 
the tropics from 2014 to 2016, is considered the longest and most 
severe global coral bleaching event on record (Section  10.4.3; see 
Box 15.2; Eakin et al., 2019). The Great Barrier Reef underwent mass 
bleaching three times between 2016 and 2020 (see Box 11.2; Pratchett 
et al., 2021), validating past model projections that some warm-water 
coral reefs would encounter bleaching-level heat stress multiple times 
per decade by the 2020s (Hoegh-Guldberg, 1999; Donner, 2009).

Heat stress and mass bleaching events caused decreases in live coral 
cover (virtually certain) (Graham et al., 2014; Hughes et al., 2018b), loss 
of sensitive species (extremely likely) (Donner and Carilli, 2019; Lange 
and Perry, 2019; Toth et al., 2019; Courtney et al., 2020), vulnerability 
to disease (extremely likely) (van Woesik and Randall, 2017; Hadaidi 
et al., 2018; Brodnicke et al., 2019; Howells et al., 2020) and declines 
in coral recruitment in the tropics (medium confidence) (Hughes 
et  al., 2019; Price et  al., 2019). Recent observations also suggest 
that excess nutrients can increase the susceptibility of corals to heat 
stress (DeCarlo et  al., 2020). Changes in coral community structure 
due to bleaching have caused declines in reef carbonate production 
(high confidence) (Perry and Morgan, 2017; Lange and Perry, 2019; 
Perry and Alvarez-Filip, 2019; Courtney et al., 2020; van Woesik and Cacciapaglia, 2021) and in reef structural complexity (high confidence,

very likely) (Couch et al., 2017; Leggat et al., 2019; Magel et al., 2019), 
which increases water depth, reduces wave attenuation and increases 
coastal flood risk (Yates et al., 2017; Beck et al., 2018). Corals may 
also lose reproductive synchrony through climate change (Shlesinger 
and Loya, 2019), adding to their vulnerability. Bleaching and other 
drivers promote phase shifts to ecosystems dominated by macroalgae 
or other stress-tolerant species (very high confidence) (Graham et al., 
2015; Stuart-Smith et al., 2018), leading to changes in reef-fish species 
assemblages (high confidence) (Richardson et  al., 2018; Robinson 
et al., 2019a; Stuart-Smith et al., 2021).
Ocean acidification and associated declines in aragonite saturation 
state (Ω aragonite ) decrease rates of calcification by corals and other 
calcifying reef organisms (very high confidence), reduce coral settlement 
(medium confidence) and increase bioerosion and dissolution of reef

substrates (high confidence) (Hoegh-Guldberg et  al., 2018a; Bindoff 
et  al., 2019a; Kline et  al., 2019; Pitts et  al., 2020). Warming can 
exacerbate the coral response to ocean acidification (Kornder et al., 
2018) and accelerate the decrease in coral skeletal density (Guo et al., 
2020). In addition, reefs with lower coral cover and a higher proportion 
of slow-growing species, because of bleaching, are more sensitive to 
acidification (net dissolution occurs Ωaragonite   =  2.3 for 100% coral 
cover, and Ω aragonite   >3.5 for 30% coral cover; Kline et  al., 2019). 
However, experimental evidence suggests that coral responses to 
ocean acidification are species specific (medium confidence) (Fabricius 
et al., 2011; DeCarlo et al., 2018; Comeau et al., 2019). Evidence from 
Summary assessment of observed hazards to coastal ecosystems and seas
Climate-induced drivers
Warm water coral reefs
Rocky shores
Kelp systems
Mangroves
Saltmarshes
Seagrases
Sandy beaches
Semi-enclosed seas

Shelf seas
EBUS*
Arctic region
Antarctic regionSystem WarmingMarine
heat-
wavesOcean
acidificationSea-
level Storms OxygenSea
iceSummary
riskPrimary non-climate drivers
Marine
pollution
Fisheries/
harvestin g
Nutrient en-
hancementHabitat
degradation
Non-
indigenous
speciesAssessment’s
corresponding
section in
the report
Estuaries, lagoons3.4.2.1
3.4.2.2
3.4.2.3
3.4.2.5
3.4.2.6
3.4.2.7
3.4.2.8
3.4.2.9
3.4.2.103.4.2.4Ecological
adaptive
capacity
Low
Low
Moderate to hig h
Low to moderate
Moderate
Moderate
Low
Moderate
LowLow
Low/
/
/
/
/
/
/
/
/
//
na
na
na
na
na
na
na
na
nanana
na
na
na
nana
na
nanana
na
nananana
* Eastern Boundary
Upwelling Systems= no data or not assessednaStrength  of impact
High to
very highMedium Low to
very lowConfidence  level
= not applicable/ Mixed
directionHigh Medium Low High Medium Low
Figure 3.12 |  Summary assessment of observed hazards to coastal ecosystems and seas as assessed in Section 3.4.2.

3
413Oceans and Coastal Ecosystems and Their Services  Chapter 3
experiments suggests that crustose coralline algae, which contribute 
to reef structure and integrity and may be resistant to warming at 
the RCP8.5 level by 2100 (Cornwall et  al., 2019), are also sensitive 
to declines in Ωaragonite  (high confidence) (Section  3.4.2.3; Fabricius 
et al., 2015; Smith et al., 2020). The integrated effect of acidification, 
bleaching, storms and other non-climate drivers on corals, coralline 
algae and other calcifiers can further compromise reef integrity and 
ecosystem services (Rivest et al., 2017; Cornwall et al., 2018; Perry and 
Alvarez-Filip, 2019).
Since SROCC, there have been advances in experimental, field and 
modelling research on the projected response of coral cover and 
reef growth to bleaching and ocean acidification (Cziesielski et  al., 
2019; Morikawa and Palumbi, 2019; Cornwall et al., 2021; Klein et al.,

2021; Logan et al., 2021; McManus et al., 2021), and on the effect of 
possible human interventions like assisted evolution on coral resilience 
(Section 3.6.3.2.2; Condie et al., 2021; Hafezi et al., 2021; Kleypas et al., 
2021). New model projections incorporating physiological acclimation, larval dispersal and evolutionary processes find limited ability to adapt 
this century at rates of warming at or exceeding that in RCP4.5 (high 
confidence, very likely) (Bay et  al., 2017; Kubicek et  al., 2019; Matz 
et  al., 2020; McManus et  al., 2020; Logan et  al., 2021; McManus 
et al., 2021). For example, a global analysis (Logan et al., 2021) finds 
that increased thermal tolerance via evolution or switching to more 
stress-tolerant algal symbionts enable most (73–81%) coral to survive 
through 2100 under RCP2.6, but coral-dominated communities with a 
historical mix of coral taxa still disappear (0–8% coral survival) under

RCP6.0 in simulations with adaptive mechanisms (Figure 3.13). Due 
to the impacts of warming, and to a lesser extent ocean acidification, 
global reef carbonate production is estimated to decline 71% by 
2050 in SSP1-2.6, and the rate of SLR is estimated to exceed that of 
reef growth for 97% of reefs assessed, without adaptation by corals 
and their symbionts (WGI AR6 Table 9.9; Cornwall et al., 2021; Fox-
Kemper et al., 2021). The increased water depth due to coral loss and 
reef erosion, as well as reduced structural complexity, will limit wave 
attenuation and exacerbate the risk of flooding from SLR on reef-
Table 3.3 |  Summary of previous IPCC assessments of coral reefs
Observations Projections
AR5 (Hoegh-Guldberg et al., 2014; Wong et al., 2014)
Coral reefs are one of the most vulnerable marine ecosystems ( high confidence), and more 
than half of the world’s reefs are under medium or high risk of degradation.

Mass coral bleaching and mortality, triggered by positive temperature anomalies ( high 
confidence), is the most widespread and conspicuous impact of climate change. Ocean 
acidification reduces biodiversity and the calcification rate of corals ( high confidence) 
while at the same time increasing the rate of dissolution of the reef framework ( medium 
‘In summary, ocean warming is the primary cause of mass coral bleaching and mortality 
(very high confidence), which, together with ocean acidification, deteriorates the balance 
between coral reef construction and erosion (high confidence).’‘Coral bleaching and mortality will increase in frequency and magnitude over the next 
decades (very high confidence).’ Analysis of the Coupled Model Intercomparison Project 5 
ensemble projects the loss of coral reefs from most sites globally by 2050 under mid to high 
rates of warming (very likely).

‘Under the A1B CO 2 emission scenario, 99% of the reef locations will experience at least one 
severe bleaching event between 2090 and 2099, with limited evidence and low agreement 
that coral acclimation and/or adaptation will limit this trend.’
‘The onset of global dissolution [of coral reefs] is at an atmospheric CO 2 [concentration] of 
560 ppm (medium confidence) and dissolution will be widespread in 2100’ (Representative 
Concentration Pathway, RCP8.5, medium confidence).
‘A number of coral reefs could therefore keep up with the maximum rate of sea level rise 
(SLR) of 15.1 mm yr–1 projected for the end of the century [...], but lower net accretion [...] 
and increased turbidity will weaken this capability (very high confidence).’
SR15 (Hoegh-Guldberg et al., 2018a; IPCC, 2019c)
‘Climate change [...] has emerged as the greatest threat to coral reefs, with temperatures of 
just 1°C above the long-term summer maximum for an area (reference period 1985–1993)

over 4–6 weeks being enough to cause mass coral bleaching [...] and mortality ( very high 
confidence).’
Predictions of back-to-back bleaching events have become reality over 2015–2017 as have 
projections of declining coral abundance (high confidence).‘Multiple lines of evidence indicate that the majority (70–90%) of warm water (tropical) 
coral reefs that exist today will disappear even if global warming is constrained to 1.5°C 
(very high confidence).’
Coral reefs, for example, are projected to decline by a further 70–90% at 1.5°C ( high 
confidence) with larger losses (>99%) at 2°C (very high confidence).
SROCC (Bindoff et al., 2019a)
‘New evidence since AR5 and SR15 confirms the impacts of ocean warming and acidification 
on coral reefs (high confidence), enhancing reef dissolution and bioerosion (high confidence), 
affecting coral species distribution and leading to community changes ( high confidence). The

rate of SLR (primarily noticed in small reef islands) may outpace the growth of reefs to keep 
up, although there is low agreement in the literature (low confidence).’
‘Reefs are further exposed to other increased impacts, such as enhanced storm intensity, 
turbidity and increased runoff from the land (high confidence). Recovery of coral reefs 
resulting from repeated disturbance events is slow ( high confidence). Only few coral reef 
areas show some resilience to global change drivers ( low confidence).’‘Coral reefs will face very high risk at temperatures 1.5°C of global sea surface warming 
‘Almost all coral reefs will degrade from their current state, even if global warming remains 
below 2°C (very high confidence), and the remaining shallow coral reef communities will 
differ in species composition and diversity from present reefs ( very high confidence). This 
will greatly diminish the services they provide to society, such as food provision (high

confidence), coastal protection (high confidence) and tourism (medium confidence).’
‘The very high vulnerability of coral reefs to warming, ocean acidification, increasing storm 
intensity and SLR under climate change, including enhanced bioerosion (high confidence), 
points to the importance of considering both mitigation and adaptation.’

3
414Chapter 3 Oceans and Coastal Ecosystems and Their Services
fringed shorelines and reef islands (Yates et al., 2017; Beck et al., 2018; 
Harris et al., 2018). Local coral reef fish species richness is projected 
to decline due to the impacts of warming on coral cover and diversity 
(high confidence), with declines up to 40% by 2060 in SSP5-8.5 (Strona 
These observed and projected impacts are supported by geological 
and paleo-ecological evidence showing a decline in coral reef extent 
and species richness under previous episodes of climate change and 
ocean acidification (Kiessling and Simpson, 2011; Pandolfi et al., 2011; 
Kiessling et al., 2012; Pandolfi and Kiessling, 2014; Kiessling and Kocsis, 
2015). Major reef crises in the past 300 million years were governed 
by hyperthermal events (medium confidence) (Section  3.2.4.4; 
Cross-Chapter  Box  PALEO in Chapter  1) longer in time scale than 
anthropogenic climate change, during which net coral reef accretion

was more strongly affected than biodiversity (medium confidence).
In response to the global-scale decline in coral reefs and high 
future risk, recent literature focuses on finding thermal refuges and 
identifying uniquely resilient species, populations or reefs for targeted 
restoration and management (Hoegh-Guldberg et  al., 2018b). Reefs 
exposed to internal waves (Storlazzi et al., 2020), turbidity (Sully and 
van Woesik, 2020) or warm-season cloudiness (Gonzalez-Espinosa 
and Donner, 2021) are expected to be less sensitive to thermal 
stress. Mesophotic reefs (30–150 m) have also been proposed as 
thermal refugia (Bongaerts et  al., 2010), although evidence from 
recent bleaching events, subsurface temperature records and species 
overlap is mixed (Frade et al., 2018; Rocha et al., 2018b; Eakin et al., 
2019; Venegas et al., 2019; Wyatt et al., 2020). A study of 2584 reef 
sites across the Indian and Pacific oceans estimated that 17% had

sufficient cover of framework-building corals to warrant protection, 
54% required recovery efforts and 28% were on a path to net erosion 
(Darling et al., 2019). There is medium evidence for greater bleaching 
resistance among reefs subject to temperature variability or frequent 
heat stress (Barkley et  al., 2018; Gintert et  al., 2018; Hughes et  al., 
2018a; Morikawa and Palumbi, 2019), but with trade-offs in terms of 
diversity and structural complexity (Donner and Carilli, 2019; Magel 
et  al., 2019). There is limited agreement about the persistence of 
thermal tolerance in response to severe heat stress (Le Nohaïc et al., 
2017; DeCarlo et al., 2019; Fordyce et al., 2019; Leggat et al., 2019; 
Schoepf et al., 2020). Recovery and restoration efforts that target heat-
resistant coral populations and culture heat-tolerant algal symbionts 
have the greatest potential of effectiveness under future warming 
(high confidence) (see Box 5.5 in SROCC Chapter 5; Bay et al., 2017;

Darling and Côté, 2018; Baums et  al., 2019; Bindoff et  al., 2019a; 
Howells et al., 2021); however, there is low confidence that enhanced 
thermal tolerance can be sustained over time (Section  3.6.3.3.2; 
Buerger et  al., 2020). The effectiveness of active restoration and 
other specific interventions (e.g., reef shading) are further assessed in 
Section 3.6.3.3.2.
In summary, additional evidence since SROCC and SR15 (Table  3.3) 
finds that living coral and reef growth are declining due to warming 
and MHWs (very high confidence). Coral reefs are under threat of 
transitioning to net erosion with >1.5°C of global warming (high 
confidence), with impacts expected to occur fastest in the Atlantic 
Ocean. The effectiveness of conservation efforts to sustain living coral area, coral diversity and reef growth is limited for the majority 
of the world’s reefs with >1.5°C of global warming (high confidence) 
(Section 3.6.3.3.2; Hoegh-Guldberg et al., 2018b; Bruno et al., 2019;

Darling et al., 2019).
3.4.2.2 Rocky Shores
Rocky shore ecosystems refer to a range of temperate intertidal 
and shallow coastal ecosystems that are dominated by different 
foundational organisms, including mussels, oysters, fleshy macroalgae, 
hard and soft corals, coralline algae, bryozoans and sponges, which 
create habitat for species-rich assemblages of invertebrates, fish, 
marine mammals and other organisms. Rocky shores provide services 
including wave attenuation, habitat provision and food resources, and 
these support commercial, recreational and Indigenous fisheries and 
shellfish aquaculture.
Observations since AR5 and SROCC (Table 3.4) find increased impacts 
of ocean warming on rocky shores. This includes extirpation of species 
at the warm edge of their ranges (Yeruham et al., 2015; Martínez et al., 
2018), extension of poleward range boundaries (Sanford et al., 2019), 
mortality from climate extremes (Seuront et  al., 2019), reduction in

survival at shallower depths (Sorte et al., 2019; Wallingford and Sorte, 
2019) and reorganisation of communities (Wilson et al., 2019; Mulders 
and Wernberg, 2020; Albano et al., 2021). Data collected after MHWs 
find ecological phase shifts (moderate evidence, high agreement) 
(e.g., California; Rogers-Bennett and Catton, 2019; McPherson et al., 
2021) and homogenisation of communities (limited evidence) (e.g., 
Alaska; Weitzman et al., 2021). For example, the collapse of sea star 
populations in the Northeast Pacific due to a MHW-related disease 
outbreak (Hewson et al., 2014; Menge et al., 2016; Miner et al., 2018; 
Schiebelhut et  al., 2018), including 80–100% loss of the common 
predatory sunflower star, Pycnopodia helianthoides (very high 
confidence) (Harvell et al., 2019), triggered shifts from kelp- to urchin-
dominated ecosystems (Schultz et  al., 2016; Gravem and Morgan, 
2017; McPherson et al., 2021).
Multiple lines of evidence find that foundational calcifying organisms

such as mussels are at high risk of decline due to both the individual 
and synergistic effects of warming, acidification and hypoxia (high 
confidence) (Sunday et al., 2016; Sorte et al., 2017; Sorte et al., 2019; 
Newcomb et  al., 2020). Warmer temperatures reduce mussel and 
barnacle recruitment (e.g., northwest Atlantic; Petraitis and Dudgeon, 
2020) and the upper vertical limit of mussels (e.g., northeast Pacific, 
Harley, 2011; and southwest Pacific, Sorte et al., 2019). Experiments 
show that ocean acidification negatively impacts mussel physiology 
(very high confidence), with evidence of reduced growth (Gazeau 
et  al., 2010), attachment (Newcomb et  al., 2020), biomineralisation 
(Fitzer et  al., 2014) and shell thickness (Pfister et  al., 2016; McCoy 
et  al., 2018). Net calcification and abundance of mussels and other 
foundational species, including oysters, are expected to decline due to 
ocean acidification (very high confidence) (Kwiatkowski et al., 2016;

Sunday et al., 2016; McCoy et al., 2018; Meng et al., 2018), causing 
the reorganisation of communities (high confidence) (Kroeker et al., 
2013b; Linares et al., 2015; Brown et al., 2016; Sunday et al., 2016; 
Agostini et al., 2018; Teixidó et al., 2018). Experiments indicate that 
acidification can interact with warming and hypoxia to increase the

3
415Oceans and Coastal Ecosystems and Their Services  Chapter 3
Coral reef futures
with and without adaptation
- (b) Low diversity reef (c) Degraded coral reef
Moderate coverc omposed offew,
heat-tolerant taxa; lower complexit y
and growth rate; lower fish diversityLimited cover with fewspecies; low
complexity withlimited growth ;low
fishbiomass anddiversit y(i) No adaptation (ii) With adaptation% Healthy reefs100
ca
b80
60
40
20
0
1950 2000 2050 2100a
b
c
High coralcover anddiversit y;high 
physicalcomplexity andreefgrowth ;
high fish biomass anddiversit y
(a) Historical coral reef 
Mean coral reef SST anomaly 
3°C 2.5°C 2°C 1.5°C 1°C 0.5°C0RCP2.6 RCP4.5 RCP6.0 RCP8.5
Figure  3.13 |   Coral reef futures, with and without adaptation. Graphs are based on a model of coral-symbiont evolutionary dynamics from Logan et  al. (2021),

which simulates two coral types and symbiont populations for 1925 reef cells worldwide, from 1950 to 2100 drawn from simulations with National Oceanic and Atmospheric 
Administration–Geophysical Fluid Dynamics Laboratory Earth System Model (ESM2M) under four RCPs. Top panels show the simulated fraction of cells with healthy reefs, when 
both coral types are not in a state of severe bleaching or mortality, (i) without adaptive responses and (ii) with adaptive responses (symbiont evolution). Colours indicate maximum 
monthly sea surface temperature increase across all reef cells, versus a 1861–2010 baseline. Panels (a,b,c) depict snapshots of coral reef conditions at time points in the future, 
each with different levels of warming, drawn from the model-projected cover of the two coral types and from a literature assessment (Section 3.4.2.1; Hughes et al., 2018b; Bindoff 
et al., 2019a; Darling et al., 2019; Leggat et al., 2019; Cornwall et al., 2021).

detrimental effects on mussels (Gu et al., 2019; Newcomb et al., 2020). 
In regions where food is readily available to mussels, detrimental 
effects of ocean acidification may be dampened (Kroeker et al., 2016); 
however, recent findings are inconclusive (Brown et al., 2018a).
Coralline algae, foundational taxa that create habitat for sea urchins 
and abalone, form rhodolith beds in temperate to Arctic habitats 
and bind together substrates, are expected to be highly susceptible 
to ocean acidification because they precipitate soluble magnesium 
calcite (Kuffner et  al., 2008; Williams et  al., 2021). Damage from 
acidification varies among species and regions, and can be due to 
direct physiological stress (Marchini et al., 2019) or interactions with 
non-calcifying competitors such as fleshy macroalgae (Smith et al., 
2020). Experiments indicate that warming reduces calcification 
by coralline algae (high confidence) (Cornwall et  al., 2019) and

exacerbates the effect of acidification (Kim et  al., 2020; Williams 
et al., 2021).In contrast to warm-water coral reefs, there are no regional or global 
numerical models of rocky shore ecosystem response to projected 
climate change and acidification. Experiments suggest that existing 
genetic variation could be sufficient for some mussels (Bitter et al., 
2019) and coralline algae (Cornwall et  al., 2020) to adapt over 
generations to ocean acidification. Populations exposed to variable 
environments often have a greater capacity for phenotypic plasticity 
and resilience to environmental change [e.g., urchins (Gaitan-
Espitia et al., 2017b) and coralline algae (Section 3.3.2; Rivest et al., 
2017; Cornwall et al., 2018)]. Although parental conditioning within 
and across generations is an acclimatisation mechanism to global 
change, there is limited evidence from experimental studies that this 
is applicable for marine invertebrates on rocky shores (Byrne et al., 
2020).

This assessment concludes that MHWs attributable to climate change 
(Section 3.2.2.1) can cause fatal disease outbreaks or mass mortality 
among some key foundational species (high confidence) and contribute

3
416Chapter 3 Oceans and Coastal Ecosystems and Their Services
FAQ 3.2 | How are marine heatwaves affecting marine life and human communities?
Heatwaves happen in the ocean as well as in the atmosphere. Marine heatwaves (MHWs) are extended periods of unusually warm ocean 
temperatures relative to the typical temperatures for that location and time of year. Due to climate change, the number of days with MHWs has 
increased by 54% over the past century. These MHWs cause mortalities in a wide variety of marine species, from corals to kelp to seagrasses 
to fish to seabirds, and have consequent effects on ecosystems and industries like aquaculture and fisheries.
Extreme events in the ocean can have damaging effects on marine ecosystems and the human communities that 
depend on them. The most common form of ocean extremes are MHWs, which are becoming more frequent and

intense due to global warming. Because seawater absorbs and releases heat more slowly than air, temperature 
extremes in the ocean are not as pronounced as over land, but they can persist for much longer, often for weeks 
to months over areas covering hundreds of thousands of square kilometres. These MHWs can be more detrimental 
for marine species, in comparison with land species, because marine species are usually adapted to relatively stable 
temperatures.
A commonly used definition of MHWs is a period of at least 5 days whose temperatures are warmer than 90% 
of the historical records for that location and time of year. Marine heatwaves are described by their abruptness, 
magnitude, duration, intensity and other metrics. In addition, targeted methods are used to characterize MHWs 
that threaten particular ecosystems; for example, the accumulated heat stress above typical summer temperatures,

described by ‘degree heating weeks’, is used to estimate the likelihood of coral bleaching.
Over the past century, MHWs have doubled in frequency, become more intense, lasted for longer and extended 
over larger areas. Marine heatwaves have occurred in every ocean region over the past few decades, most markedly 
in association with regional climate phenomena such as the El Niño/Southern Oscillation. During the 2015–2016 El 
Niño event, 70% of the world’s ocean surface encountered MHWs.
Such MHWs cause mortality of a wide variety of marine species, from corals to kelp to seagrasses to fish to seabirds, 
and they have consequent effects on ecosystems and industries such as mariculture and fisheries. Warm-water coral 
reefs, estuarine seagrass meadows and cold-temperate kelp forests are among the ecosystems most threatened by 
MHWs since they are attached to the seafloor (see FAQ 3.2). Unusually warm temperatures cause bleaching and

associated death of warm-water corals, which can lead to shifts to low-diversity or algae-dominated reefs, changes 
in fish communities and deterioration of the physical reef structure, which causes habitat loss and increases the 
vulnerability of nearby shorelines to large-wave events and SLR. Since the early 1980s, the frequency and severity 
of mass coral bleaching events have increased sharply worldwide. For example, from 2016 through 2020, the Great 
Barrier Reef experienced mass coral bleaching three times in 5 years.
Mass loss of kelp from MHWs effects on the canopy-forming species has occurred across ocean basins, including 
the coasts of Japan, Canada, Mexico, Australia and New Zealand. In southern Norway and the northeast USA, 
mortality from MHWs contributed to the decline of sugar kelp over the past two decades and the spread of turf 
algal ecosystems that prevent recolonisation by the original canopy-forming species.

One of the largest and longest-duration MHWs, nicknamed the ‘Blob’, occurred in the Northeast Pacific Ocean, 
extending from California north towards the Bering Sea, from 2013 through 2015. Warming from the MHW persisted 
into 2016 off the West Coast of the USA and into 2018 in the deeper waters of a Canadian fjord. The consequent 
effects of this expansive MHW included widespread shifts in abundance, distribution and nutritional value of 
invertebrates and fish, a bloom of toxic algae off the West Coast of the USA that impacted fisheries, the decline of 
California kelp forests that contributed to the collapse of the abalone fishery, and mass mortality of seabirds.
The projected increase in the frequency, severity, duration and areal extent of MHWs threaten many marine species 
and ecosystems. These MHWs may exceed the thermal limits of species, and they may occur too frequently for the

species to acclimate or for populations to recover. The majority of the world’s coral reefs are projected to decline 
and begin eroding due to more frequent bleaching-level MHWs if the world warms by more than 1.5°C. Recent 
research suggests possible shifts to more heat-tolerant coral communities but at the expense of species and habitat 
diversity. Other systems, including kelp forests, are most threatened near the edges of their ranges, although more 
research is needed into the effect of re-occurring MHWs on kelp forests and other vulnerable systems.
The projected ecological impacts of MHWs threaten local communities and Indigenous Peoples, incomes, fisheries, 
tourism and, in the case of coral reefs, shoreline protection from waves. High-resolution forecasts and early-warning

3
417Oceans and Coastal Ecosystems and Their Services  Chapter 3
systems, currently most advanced for coral reefs, can help people and industries prepare for MHWs and also collect 
data on their effects. Identifying and protecting locations and habitats with reduced exposure to MHWs is a key 
scientific endeavour. For example, corals may be protected from MHWs in tidally stirred waters or in reefs where 
cooler water upwells from subsurface. Marine protected areas and no-take zones, in addition to terrestrial protection 
surrounding vulnerable coastal ecosystems, cannot prevent MHWs from occurring. But, depending on the location 
and adherence by people to restrictions on certain activities, the cumulative effect of other stressors on vulnerable 
ecosystems can be reduced, potentially helping to enhance the rate of recovery of marine life.
Anomalous atmospheric pattern
Reduced cooling by atmosphere
Reduced cold water advection

Association with El Niño'Blob' causesHow are marine heatwaves affecting marine life and human communities?
Ecological impacts Cascading effects Socioeconomic 
impacts
Species shift
Decrease of original inhabitants
New species invasions
Harmful algae blooms
Prey plankton decreaseEcosystem structure change
New species interactions
Food limitation to megafauna
and seabirds
Mass mortality eventsFishing closures and delays
Whale entanglements in fishing 
gear
Substitution of fisheries
Prohibition of aquaculture 
products
Food security issues
54%Due to climate change, ocean heatwave days have increased by  
54% over the past centur y. Marine heatwaves cause mass mortalities 
in a wide variety of marine species.
Needs for seasonal prediction | real-time monitoring | real-time predictio nNeeds for real-time monitoring | monitoring responseNeeds for education | adaptation

Figure FAQ3.2.1 |  Impact pathway of a massive extreme marine heatwave, the northwest Pacific ‘Blob’, from causal mechanisms to initial effects, 
resulting nonlinear effects and the consequent impacts for humans. Lessons learnt from the Blob include the need to advance seasonal forecasts, real-time 
predictions, monitoring responses, education, possible fisheries impacts and adaptation.Box FAQ 3.2 (continued)

3
418Chapter 3 Oceans and Coastal Ecosystems and Their Services
to ecological phase shifts (medium confidence). The upper vertical 
limits of some species will also be constrained by climate change (high 
confidence). Experimental evidence since previous assessments further 
indicates that acidification decreases abundance and richness of 
calcifying species (high confidence), although there is limited evidence 
for acclimation in some species. Synergistic effects of warming and 
acidification will promote shifts towards macroalgal dominance in 
some ecosystems (medium confidence) and lead to reorganisation of 
communities (medium confidence).
3.4.2.3 Kelp Ecosystems
Kelp are temperate, habitat-forming marine macroalgae or seaweeds, 
mostly of the order Laminariales, which extend across one-
quarter of the world’s coastlines (Assis et al., 2020; Jayathilake and 
Costello, 2020). The perennial species form dense underwater forest

canopies and three-dimensional habitat that provides refuge for 
fish, crustaceans, invertebrates and marine mammals (Filbee-Dexter 
et al., 2016; Wernberg et al., 2019). Kelp ecosystems support fisheries, 
aquaculture, fertiliser and food provision, including for local and 
Indigenous Peoples, along with regulating services in the form of wave 
attenuation and habitat provision. Kelp aquaculture can also buffer against local acidification (Xiao et al., 2021) and contribute to carbon 
storage (Froehlich et al., 2019).
Recent research (Straub et al., 2019; Butler et al., 2020; Filbee-Dexter 
et  al., 2020b; Tait et  al., 2021) supports the findings of previous 
assessments (Table 3.5) that kelp and other seaweeds in most regions 
are undergoing mass mortalities from high temperature extremes 
and range shifts from warming (very high confidence). Kelp are 
highly sensitive to the direct effect of high temperature on survival

(Nepper-Davidsen et  al., 2019) and indirect impact of temperature 
on herbivorous species (Ling, 2008; Vergés et  al., 2016), upwelling 
and nutrient availability (Carr and Reed, 2015; Schiel and Foster, 
2015). Synergies between warming, storms, pollution and intensified 
herbivory (due to removal or loss of predators including sea stars and 
otters that constrain herbivory by fish and urchin populations) can 
also cause physiological stress and physical damage in kelp, reducing 
productivity and reproduction (Rogers-Bennett and Catton, 2019; 
Beas-Luna et al., 2020; McPherson et al., 2021).
Trends in kelp abundance since 1950 are uneven globally (Krumhansl 
et  al., 2016; Wernberg et  al., 2019), with population declines (e.g., 
giant kelp Macrocystis pyrifera in Tasmania, Butler et al., 2020; and 
Table 3.4 |  Summary of previous IPCC assessments of rocky shores
AR5 (Wong et al., 2014)

‘Rocky shores are among the better-understood coastal ecosystems in terms of potential 
impacts of climate variability and change. The most prominent effects are range shifts of 
species in response to ocean warming (high confidence) and changes in species distribution 
and abundance (high confidence) mostly in relation to ocean warming and acidification.’
‘The dramatic decline of biodiversity in mussel beds of the Californian coast has been 
attributed to large-scale processes associated with climate-related drivers [...] ( high 
confidence).’‘The abundance and distribution of rocky shore species will continue to change in a warming 
world (high confidence). For example, the long-term consequences of ocean warming on 
mussel beds of the northeast Pacific are both positive (increased growth) and negative 
(increased susceptibility to stress and of exposure to predation) ( medium confidence).’
‘Observations performed near natural CO 2 vents in the Mediterranean Sea show that

diversity, biomass and trophic complexity of rocky shore communities will decrease at future 
pH levels (high confidence).’
SR15 (Hoegh-Guldberg et al., 2018a)
‘Changes in ocean circulation can have profound impacts on [temperate] marine ecosystems 
by connecting regions and facilitating the entry and establishment of species in areas where 
they were unknown before (‘tropicalization’ ...) as well as the arrival of novel disease agents 
(medium agreement, limited evidence).’‘In the transition to 1.5°C, changes to water temperatures are expected to drive some 
species (e.g., plankton, fish) to relocate to higher latitudes and cause novel ecosystems to 
assemble (high confidence). Other ecosystems (e.g., kelp forests, coral reefs) are relatively 
less able to move, however, and are projected to experience high rates of mortality and loss 
‘In the case of ‘less mobile’ ecosystems (e.g., coral reefs, kelp forests, intertidal communities),

shifts in biogeographic ranges may be limited, with mass mortalities and disease outbreaks 
increasing in frequency as the exposure to extreme temperatures increases’ (high agreement, 
robust evidence).
Intertidal rocky shores ecosystems are highly sensitive to ocean warming, acidification and 
extreme heat exposure during low tide emersion ( high confidence).
‘Sessile calcified organisms (e.g., barnacles and mussels) in intertidal rocky shores are highly 
sensitive to extreme temperature events and acidification ( high confidence), a reduction 
in their biodiversity and abundance have been observed in naturally acidified rocky reef 
ecosystems (medium confidence).’‘Intertidal rocky shores are also expected to be at very high risk (transition above 3°C) under 
the RCP8.5 scenario (medium confidence). These ecosystems have low to moderate adaptive 
capacity, as they are highly sensitive to ocean temperatures and acidification.’

‘Benthic species will continue to relocate in the intertidal zones and experience mass 
mortality events due to warming (high confidence). Interactive effects between acidification 
and warming will exacerbate the negative impacts on rocky shore communities, causing 
a shift towards a less diverse ecosystem in terms of species richness and complexity, 
increasingly dominated by macroalgae (high confidence).’

3
419Oceans and Coastal Ecosystems and Their Services  Chapter 3
sugar kelp Saccharina latissima in the North Atlantic, Filbee-Dexter 
et al., 2020b) more common than increases or no change (e.g., giant 
kelp Macrocystis pyrifera in southern Chile; Friedlander et al., 2020). 
Warming is driving range contraction and extirpation at the warm edge 
of species’ ranges and expansions at the cold range edge (very high 
confidence) (Smale, 2019; Filbee-Dexter et al., 2020b). Local declines 
in populations of kelp and other canopy-forming seaweeds driven by 
MHWs and other stressors have caused irreversible shifts to turf- or 
urchin-dominated ecosystems, with lower productivity and biodiversity 
(high confidence) (Filbee-Dexter and Scheibling, 2014; Filbee-Dexter 
and Wernberg, 2018; Rogers-Bennett and Catton, 2019; Beas-Luna 
et al., 2020; Stuart-Smith et al., 2021), ecosystems dominated by warm-
affinity seaweeds or coral (high confidence) (Vergés et al., 2019), and

loss of genetic diversity (Coleman et al., 2020a; Gurgel et al., 2020).
Species distribution models of kelp project range shifts and local 
extirpations with increasing levels of warming (Japan: Takao et  al., 
2015, Sudo et  al., 2020; Australia: see Table  11.6, and Assis et  al., 
2018, Martínez et  al., 2018, Castro et  al., 2020; Europe: de la Hoz 
et al., 2019; North America: Wilson et al., 2019; South America: see 
Figure  12.3). There is high agreement on the direction but not the 
magnitude of change (Martínez et al., 2018; Castro et al., 2020), but 
effects of MHWs are not simulated. Where the length of higher-latitude 
coastlines is limited, range contractions are projected to occur, even 
with 2°C of global warming (i.e., SSP1-2.6) due to loss of habitat at 
the warm edge of species’ ranges (Martínez et  al., 2018). Poleward 
expansion of warm-affinity herbivores, including urchins, could further

reduce warm-edge kelp populations (Castro et al., 2020; Mulders and Wernberg, 2020). Evidence from natural temperate CO 2 seeps suggests 
that ocean acidification at levels above those in RCP4.5 in 2100 could 
offset the increase in urchin abundance (Coni et  al., 2021). Genetic 
analyses suggest that kelp populations at the midpoint of species’ 
ranges will have lower tolerance of warming than that implied by 
species distribution models, without assisted gene flow from warm-
edge populations (King et al., 2019; Wood et al., 2021).
While reducing non-climate drivers can help prevent kelp loss from 
warming and MHWs, there is limited potential for restoration of kelp 
ecosystems after transition to urchin-dominant ecosystems (high 
confidence). Current restoration efforts are generally small scale 
(<0.1 km2) and less advanced than those in ecosystems like coral 
reefs (Coleman et al., 2020b; Eger et al., 2020; Layton et al., 2020).

Although abundance of herbivores limits kelp populations, there is 
limited evidence that restoring predators of herbivores by creating 
marine reserves, or directly removing grazing species, will increase 
kelp forest resilience to warming and extremes (Vergés et al., 2019; 
Wernberg et  al., 2019). Active reseeding of wild kelp populations 
through transplantation and propagation of warm-tolerant genotypes 
(Coleman et  al., 2020b; Alsuwaiyan et  al., 2021) can overcome low 
dispersal rates of many kelp species and facilitate effective restoration 
(medium confidence) (Morris et al., 2020c).
Building on the conclusions of SROCC, this assessment finds that kelp 
ecosystems are expected to decline and undergo changes in community 
structure in the future due to warming and increasing frequency and 
intensity of MHWs (high confidence). Risk of loss of kelp ecosystems 
Table 3.5 |  Summary of previous IPCC assessments of kelp ecosystems

‘Kelp forests have been reported to decline in temperate areas in both hemispheres, a loss 
involving climate change (high confidence). Decline in kelp populations attributed to ocean 
warming has been reported in southern Australia and the north coast of Spain.’‘Kelp ecosystems will decline with the increased frequency of heatwaves and sea 
temperature extremes as well as through the impact of invasive subtropical species (high 
‘Climate change will contribute to the continued decline in the extent of [...] kelps in the 
temperate zone (medium confidence) and the range of [...] kelps in the Northern Hemisphere 
will expand poleward (high confidence).’
Observed movement of kelp ecosystems not assessed.‘In the transition to 1.5°C of warming, changes to water temperatures will drive some 
species (e.g., plankton, fish) to relocate to higher latitudes and cause novel ecosystems to

‘Kelp forests have experienced large-scale habitat loss and degradation of ecosystem 
structure and functioning over the past half century, implying a moderate to high level of 
risk at present conditions of global warming (high confidence).’
‘The abundance of kelp forests has decreased at a rate of ~2% per year over the past half 
century, mainly due to ocean warming and marine heat waves [...], as well as from other 
human stressors (high confidence).’
‘Changes in ocean currents have facilitated the entry of tropical herbivorous fish into 
temperate kelp forests decreasing their distribution and abundance ( medium confidence).’
‘The loss of kelp forests is followed by the colonisation of turfs, which contributes to the

reduction in habitat complexity, carbon storage and diversity (high confidence).’Kelp forests will face moderate to high risk at temperatures above 1.5°C global sea surface 
warming (high confidence).
‘Due to their low capacity to relocate and high sensitivity to warming, kelp forests are 
projected to experience higher frequency of mass mortality events as the exposure to 
extreme temperature rises (very high confidence).’
‘Kelp forests at low latitudes [...] will continue to retreat as a result of intensified extreme 
temperatures, and their low dispersal ability will elevate the risk of local extinction under 
RCP8.5 (high confidence).’

3
420Chapter 3 Oceans and Coastal Ecosystems and Their Services
and shifts to turf- or urchin-dominated ecosystems are highest at the 
warm edge of species’ ranges (high confidence) and risks increase 
under RCP6.0 and RCP8.5 by the end of the century (high confidence).
3.4.2.4 Estuaries, Deltas and Coastal Lagoons
Estuaries, deltas and lagoons encounter environmental gradients over 
small spatial scales, generating diverse habitats that support myriad 
ecosystem services, including food provision, regulation of erosion, 
nutrient recycling, carbon sequestration, recreation and tourism, 
and cultural significance (D’Alelio et  al., 2021; Keyes et  al., 2021). 
Although these coastal ecosystems have historically been sensitive to 
erosion-accretion cycles driven by sea level, drought and storms (high 
confidence) (Peteet et al., 2018; Wang et al., 2018c; Jones et al., 2019b; 
Urrego et  al., 2019; Hapsari et  al., 2020; Zhao et  al., 2020b), they

were impacted for much of the 20th century primarily by non-climate 
drivers (very high confidence) (Brown et  al., 2018b; Ducrotoy et  al., 
2019; Elliott et al., 2019; He and Silliman, 2019; Andersen et al., 2020; 
Newton et  al., 2020; Stein et  al., 2020). Nevertheless, the influence 
of climate-induced drivers has become more apparent over recent 
decades (medium confidence) (Table 3.6).
Estuarine biota are sensitive to warming (high confidence), with recent 
responses including changes in abundance of some fish stocks (Erickson 
et al., 2021; Woodland et al., 2021), poleward shifts in distributions 
of fish species, communities and associated biogeographic transition 
zones (Table 12.3; Franco et al., 2020; Troast et al., 2020), recruits of 
warm-affinity species persisting into winter (Kimball et al., 2020) and 
changes in seasonal timing of peaks in species abundance (Kimball 
et al., 2020). MHWs can be more severe in estuaries than in adjacent

coastal seas (Lonhart et al., 2019), causing conspicuous impacts (very 
high confidence), including mass mortality of intertidal vegetation 
(Section  3.4.2.5), range shifts in algae and animals (Lonhart et  al., 
2019) and reduced spawning success among invertebrates (Shanks 
Relative SLR extends the upstream limit of saline waters (high confidence) 
(Harvey et  al., 2020; Jiang et  al., 2020) and alters tidal ranges (high 
confidence) (Idier et al., 2019; Talke et al., 2020). Elevated water levels 
also alter submergence patterns for intertidal habitat (high confidence) 
(Andres et al., 2019), moving high-water levels inland (high confidence) 
(Peteet et al., 2018; Appeaning Addo et al., 2020; Liu et al., 2020e) and 
increasing the salinity of coastal water tables and soils (high confidence) 
(Eswar et  al., 2021). These processes favour inland and/or upstream 
migration of intertidal habitat, where it is unconstrained by infrastructure,

topography or other environmental features (high confidence) (Kirwan 
and Gedan, 2019; Parker and Boyer, 2019; Langston et al., 2020; Magolan 
and Halls, 2020; Saintilan et al., 2020). The spread of ‘ghost forests’ along 
the North American east coast (Kirwan and Gedan, 2019) and elsewhere 
(Grieger et  al., 2020) illustrates this phenomenon. Along estuarine 
shorelines, changing submergence patterns and upstream penetration 
of saline waters interact synergistically to stress intertidal plants, 
changing species composition and reducing above-ground biomass, 
in some cases favouring invasive species (Xue et al., 2018; Buffington 
et al., 2020; Gallego-Tévar et al., 2020). Overall, changing salinity and 
submergence patterns decrease the ability of shoreline vegetation to 
trap sediment (Xue et al., 2018), reducing accretion rates and increasing the vulnerability of estuarine shorelines to submergence by SLR and 
erosion by wave action (medium confidence) (Zhu et al., 2020b).

Drought and freshwater abstraction can reduce freshwater inflows 
to estuaries and lagoons, increasing salinity, reducing water quality 
(Brooker and Scharler, 2020) and depleting resident macrophyte 
communities (Scanes et al., 2020b). Changes in freshwater input and 
SLR, combined with land-use change, can alter inputs of land-based 
sediments, causing expansion (Suyadi et al., 2019; Magolan and Halls, 
2020) or contraction (Andres et al., 2019; Appeaning Addo et al., 2020; 
Li et  al., 2020b) of intertidal habitats. The same phenomena alter 
salinity gradients, which are the primary drivers of estuarine species 
distributions (high confidence) (Douglass et  al., 2020; Lauchlan and 
Nagelkerken, 2020). Extreme reduction of freshwater input can extend 
residence time of estuarine water, leading to persistent HABs (Lehman 
et al., 2020) and converting estuaries to lagoons if the mouth clogs 
with sediment (Thom et al., 2020).

Acidification of estuarine water is a growing hazard (medium confidence) 
(Doney et al., 2020; Scanes et al., 2020a; Cai et al., 2021), and resident 
organisms display sensitivity to altered pH in laboratory settings 
(medium confidence) (Young et  al., 2019a; Morrell and Gobler, 2020; 
Pardo and Costa, 2021). However, attribution of the biological effects 
of acidification is difficult because many biogeochemical processes 
affect estuarine carbon chemistry (Sections 3.2.3.1, 3.3.2). Warming can 
exacerbate the impacts of both acidification and hypoxia on estuarine 
organisms (Baumann and Smith, 2018; Collins et al., 2019b; Ni et al., 
2020). These effects are further complicated by eutrophication, with high 
nitrogen loads associated with lower pH (Rheuban et al., 2019). Warming 
(including MHWs) and eutrophication interact to decrease estuarine 
oxygen content and pH, increasing the vulnerability of animals to MHWs

(Brauko et al., 2020) and exacerbating the incidence and impact of dead 
zones (medium confidence) (Altieri and Gedan, 2015). The impacts of 
storms on estuaries are variable and are described in SM3.3.1.
All these impacts are projected to escalate under future climate 
change, but their magnitude depends on the amount of warming, 
the socioeconomic development pathway and implementation of 
adaptation strategies (medium confidence). Modelling studies (Lopes 
et al., 2019; Rodrigues et al., 2019; White et al., 2019; Zhang and Li, 
2019; Hong et al., 2020; Krvavica and Ružić, 2020; Liu et al., 2020e; 
Shalby et  al., 2020) suggest that responses of estuaries to SLR will 
be complex and context dependent (Khojasteh et  al., 2021), but 
project that salinity, tidal range, storm-surge amplitude, depth and 
stratification will increase with SLR (medium confidence), and that 
marine-dominated waters will penetrate farther upstream (high

confidence). Without careful management of freshwater inputs, 
sediment augmentation and/or the restoration of shorelines to more 
natural states, transformation and loss of intertidal areas and wetland 
vegetation will increase with SLR (high confidence) (Doughty et  al., 
2019; Leuven et al., 2019; Yu et al., 2019; Raw et al., 2020; Shih, 2020; 
Stein et al., 2020), with small, shallow microtidal estuaries being more 
vulnerable to impacts than deeper estuaries with well-developed 
sediments (medium confidence) (Leuven et al., 2019; Williamson and 
Guinder, 2021). Warming and MHWs will enhance stratification and 
deoxygenation in shallow lagoons (medium confidence) (Derolez et al., 
2020) and will continue to drive range shifts among estuarine biota

3
421Oceans and Coastal Ecosystems and Their Services  Chapter 3
(medium confidence) (Veldkornet and Rajkaran, 2019; Zhang et  al., 
2020c), resulting in extirpations where thermal habitat is lost and 
potentially generating new habitat for warm-affinity species (limited 
evidence, medium agreement) (Veldkornet and Rajkaran, 2019).
3.4.2.5 Vegetated Blue Carbon Ecosystems
Mangroves, salt marshes and seagrass beds (wetland ecosystems) are 
considered ‘blue carbon’ ecosystems due to their capacity to accumulate 
and store organic-carbon rich sediments (see Box 3.4; Macreadie et al., 2019; Rogers et  al., 2019) and provide an extensive range of other 
ecosystem services (see Box 3.4). Because these ecosystems are often 
found within estuaries and along sheltered coastlines, they share 
vulnerabilities, climate-induced drivers (Table  3.7) and non-climate 
drivers with estuaries and coastal lagoons (Section 3.4.2.4).
Since AR5 and SROCC, syntheses have emphasised that the

vulnerability of rooted wetland ecosystems to climate-induced drivers 
is exacerbated by non-climate drivers (high confidence) (Elliott et al., 
2019; Ostrowski et al., 2021; Williamson and Guinder, 2021) and climate Table 3.6 |  Summary of previous IPCC assessments of estuaries, deltas and coastal lagoons
Humans have impacted lagoons, estuaries and deltas (high to very high confidence), but 
non-climate drivers have been the primary agents of change ( very high confidence).
In estuaries and lagoons, nutrient inputs have driven eutrophication, which has modified 
food-web structures (high confidence) and caused more-intense and longer-lasting hypoxia, 
more-frequent occurrence of harmful algal blooms and enhanced emissions of nitrous oxide 
(high confidence).
In deltas, land-use changes and associated disruption of sediment dynamics and land 
subsidence have driven changes that have been exacerbated by relative SLR and episodic

events, including river floods and oceanic storm surges ( very high confidence).
Increased coastal flooding, erosion and saltwater intrusions have led to degradation of 
ecosystems (very high confidence).Future changes in climate impact-drivers such as warming, acidification, waves, storms, 
sea level rise (SLR) and runoff will have consequences for ecosystem function and services 
in lagoons and estuaries (high confidence), but with regional differences in magnitude of 
change in impact drivers and ecosystem response.
Warming, changes in precipitation and changes in wind strength can interact to alter 
water-column salinity and stratification (medium confidence), which could impact water 
column oxygen content (medium confidence).
Land-use change, SLR and intensifying storms will alter deposition-erosion dynamics, 
impacting shoreline vegetation and altering turbidity ( medium confidence). Together

with warming, these drivers will alter the seasonal pattern of primary production and the 
distribution of biota throughout the ecosystems (medium to high confidence), impacting 
associated ecosystem services.
The projected impacts of climate change on deltas are associated mainly with pluvial floods 
and SLR, which will amplify observed impacts of interacting climate and non-climate drivers 
Estuaries, deltas and lagoons were not assessed in this report.Under both a 1.5°C and 2°C of warming, relative to the pre-industrial era, deltas are 
expected to be highly threatened by SLR and localised subsidence ( high confidence). The 
slower rate of SLR associated with 1.5°C of warming poses smaller risks of flooding and 
salinisation (high confidence), and facilitates greater opportunities for adaptation, including 
managing and restoring natural coastal ecosystems and infrastructure reinforcement 

[Intact coastal ecosystems] ‘may be effective in reducing the adverse impacts of rising 
sea levels and intensifying storms by protecting coastal and deltaic regions ( medium 
‘Natural sedimentation rates are expected to be able to offset the effect of rising sea levels, 
given the slower rates of SLR associated with 1.5°C of warming (medium confidence). Other 
feedbacks, such as landward migration of wetlands and the adaptation of infrastructure, 
remain important (medium confidence).’
Increased seawater intrusion caused by SLR has driven upstream redistribution of marine 
biotic communities in estuaries (medium confidence) where physical barriers, such as the 
availability of benthic substrates, do not limit availability of suitable habitats (medium 
Warming has driven poleward range shifts in species’ distributions among estuaries (medium 

Interactions between warming, eutrophication and hypoxia have increased the incidence 
of harmful algal blooms (high confidence), pathogenic bacteria, such as Vibrio species, (low 
confidence) and mortalities of invertebrates and fish communities ( medium confidence).‘Salinisation and expansion of hypoxic conditions will intensify in eutrophic estuaries, 
especially in mid and high latitudes with microtidal regimes ( high confidence).’
‘The effects of warming will be more pronounced in high-latitude and temperate shallow 
estuaries with limited exchange with the open ocean [...] and seasonality that already leads 
to dead zone development [...] (medium confidence).’
Interaction between SLR and changes in precipitation will have greater impacts on shallow 
than deep estuaries (medium confidence).
Estuaries characterised by large tidal exchanges and associated well-developed sediments 
will be more resilient to projected SLR and changes in river flow ( medium confidence).

Human activities that inhibit sediment dynamics in coastal deltas increase their vulnerability 
to SLR (medium confidence).

3
422Chapter 3 Oceans and Coastal Ecosystems and Their Services
variability (high confidence) (Day and Rybczyk, 2019; Kendrick et al., 
2019; Shields et al., 2019). Global rates of mangrove loss have been 
extensive but are slowing (high confidence) at least partially due to 
management interventions (Friess et al., 2020b; Goldberg et al., 2020). 
From 2000 to 2010 mangrove loss averaged 0.16% yr–1, globally, but 
with greatest loss in Southeast Asia (high confidence) (Hamilton and 
Casey, 2016; Friess et al., 2019; Goldberg et al., 2020) and ubiquitous 
fragmentation leaving few mangroves intact (Bryan-Brown et  al., 
2020). Salt-marsh ecosystems have also suffered extensive losses (up 
to 60% in places since the 1980s), especially in developed and rapidly 
developing countries (medium confidence) (Table 12.3; Gu et al., 2018; 
Stein et al., 2020). Similarly, 29% of seagrass meadows were lost from 
1879–to 2006 due primarily to coastal development and degradation

of water quality, with climate-change impacts escalating since 1990 (medium confidence) (Waycott et al., 2009; Sousa et al., 2019; Derolez 
et al., 2020; Green et al., 2021a). Local examples of habitat stability or 
growth (e.g., de los Santos et al., 2019; Laengner et al., 2019; Sousa 
et al., 2019; Suyadi et al., 2019; Derolez et al., 2020; Goldberg et al., 
2020; McKenzie and Yoshida, 2020) indicate some resilience to climate 
change in the absence of non-climate drivers (high confidence). 
Nevertheless, previous declines have left wetland ecosystems more 
vulnerable to impacts from climate-induced drivers and non-climate 
drivers (high confidence) (Friess et al., 2019; Williamson and Guinder, 
2021).
Warming and MHWs have affected the range, species composition 
and survival of some wetland ecosystems. Warming is allowing some,

but not all (Rogers and Krauss, 2018; Saintilan et al., 2018), mangrove Table 3.7 |  Summary of previous IPCC assessments of mangroves, salt marshes and seagrass beds
Seagrasses occurring close to their upper thermal limits are already stressed by climate 
‘Increased CO 2 concentrations have increased seagrass photosynthetic rates by 20% ( limited 
evidence, high agreement).’Climate change will drive ongoing declines in the extent of seagrasses in temperate waters 
(medium confidence) as well as poleward range expansions of seagrasses and mangroves, 
especially in the Northern Hemisphere (high confidence).
Beneficial effects of elevated CO 2 will increase seagrass productivity and carbon burial rates 
in salt marshes during the first half of the 21st century, but there is limited evidence that this 
will improve their survival or resistance to warming.

As a result, interactions between climate change and non-climate drivers will continue to 
cause declines in estuarine vegetated systems (very high confidence).
Vegetated blue carbon systems were not assessed in this report.Intact wetland ecosystems can reduce the adverse impacts of rising sea levels and 
intensifying storms by protecting shorelines (medium confidence), and their degradation 
could reduce remaining carbon budgets by up to 100 GtCO 2.
Under 1.5°C of warming, natural sedimentation rates are projected to outpace SLR 
(medium confidence), but ‘other feedbacks, such as landward migration of wetlands and the 
adaptation of infrastructure, remain important (medium confidence).’
SROCC (Bindoff et al., 2019a; Oppenheimer et al., 2019)
Coastal ecosystems, including salt marshes, mangroves, vegetated dunes and sandy beaches, 
can build vertically and expand laterally in response to SLR, though this capacity varies

across sites (high confidence). These ecosystems provide important services that include 
coastal protection and habitat for diverse biota. However, as a consequence of human 
actions that fragment wetland habitats and restrict landward migration, coastal ecosystems 
progressively lose their ability to adapt to climate-induced changes and provide ecosystem 
services, including acting as protective barriers (high confidence).’
Warming and SLR-driven salinisation of wetlands are causing shifts in the distribution 
of plant species inland and poleward. Examples include mangrove encroachment into 
subtropical salt marshes (high confidence) and contraction in extent of low-latitude seagrass 
meadows (high confidence).
Plants with low tolerance to flooding and extreme temperatures are particularly vulnerable, 
increasing the risk of extirpation (medium confidence).
Extreme-weather events, including heatwaves, droughts and storms, are causing mass

mortalities and changes in community composition in coastal wetlands ( high confidence).
Severe disturbance of wetlands or transitions among wetland community types can favour 
invasive species (medium confidence).
The degradation or loss of vegetated coastal ecosystems reduces carbon storage, with 
positive feedbacks to the climate system (high confidence).‘Seagrass meadows (high confidence) [...] will face moderate to high risk at temperature 
above 1.5°C global sea surface warming.’
‘The transition from undetectable to moderate risk in salt marshes [...] takes place between 
0.7°C–1.2°C of global sea surface warming (medium/high confidence), and between 
0.9°C–1.8°C (medium confidence) in sandy beaches, estuaries and mangrove forests.’
‘The ecosystems at moderate to high risk under future emission scenarios are mangrove 
forests (transition from moderate to high risk at 2.5°C–2.7°C of global sea surface warming),

estuaries and sandy beaches (2.3°C–3.0°C) and salt marshes (transition from moderate 
to high risk at 1.8°C–2.7°C and from high to very high risk at 3.0°C–3.4°C) ( medium 
‘Global coastal wetlands will lose between 20–90% of their area depending on emissions 
scenario with impacts on their contributions to carbon sequestration and coastal protection 
(high confidence).’
Estuarine wetlands will remain resilient to modest rates of SLR where their sediment 
dynamics are unconstrained. But SLR and warming are projected to drive global loss of 
up to 90% of vegetated wetlands by the end of the century under the RCP8.5 ( medium 
confidence), especially if landward migration and sediment supply are limited by human 
modification of shorelines and river flows (medium confidence).
‘Moreover, pervasive coastal squeeze and human-driven habitat deterioration will reduce the 
natural capacity of these ecosystems to adapt to climate impacts ( high confidence).’

3
423Oceans and Coastal Ecosystems and Their Services  Chapter 3
species to expand their ranges poleward (high confidence) (Friess 
et  al., 2019; Whitt et  al., 2020). This expansion can affect species 
interactions (Guo et  al., 2017; Friess et  al., 2019), and enhance 
sediment accretion and carbon storage rates in some instances 
(medium confidence) (Guo et al., 2017; Kelleway et al., 2017; Chen 
et al., 2018b; Coldren et al., 2019; Raw et al., 2019). Drought, low sea 
levels and MHWs can cause significant die-offs among mangroves 
(medium confidence) (Lovelock et  al., 2017b; Duke et  al., 2021). 
Seagrasses are similarly vulnerable to warming (high confidence) 
(Repolho et al., 2017; Duarte et al., 2018; Jayathilake and Costello, 
2018; Savva et al., 2018), which has been attributed as one cause of 
observed changes in distribution and community structure (medium 
confidence) (Hyndes et  al., 2016; Nowicki et  al., 2017). MHWs,

together with storm-driven turbidity and structural damage, can 
cause seagrass die-offs (high confidence) (Arias-Ortiz et  al., 2018; 
Kendrick et al., 2019; Smale et al., 2019; Strydom et al., 2020), shifts 
to small, fast-growing species (high confidence) (Kendrick et  al., 
2019; Shields et  al., 2019; Strydom et  al., 2020) and ecosystem 
collapse (Serrano et al., 2021).
The sensitivity of salt marshes and mangroves to RSLR depends on 
whether they accrete inorganic sediment and/or organic material at 
rates equivalent to rising water levels (very high confidence) (Peteet 
et al., 2018; FitzGerald and Hughes, 2019; Friess et al., 2019; Gonneea 
et  al., 2019; Leo et  al., 2019; Marx et  al., 2020; Saintilan et  al., 
2020). Otherwise, wetland ecosystems must migrate either inland or 
upstream, or face gradual submergence in deeper, increasingly saline 
water (very high confidence) (Section  3.4.2.4; Andres et  al., 2019;

Jones et  al., 2019b; Cohen et  al., 2020; Mafi-Gholami et  al., 2020; 
Magolan and Halls, 2020; Sklar et al., 2021). Ability to migrate depends 
on local topography, the positioning of anthropogenic infrastructure 
and structures placed to defend such infrastructure (Schuerch et al., 
2018; Fagherazzi et  al., 2020; Cahoon et  al., 2021). Submergence 
drives changes in community structure (high confidence) (Jones et al., 
2019b; Yu et al., 2019; Douglass et al., 2020; Langston et al., 2020) 
and functioning (high confidence) (Charles et  al., 2019; Buffington 
et al., 2020; Stein et al., 2020), and will eventually lead to extirpation 
of the most sensitive vegetation (medium confidence) (Schepers et al., 
2017; Scalpone et al., 2020) and associated animals (low confidence) 
(Rosencranz et  al., 2018). The impacts of storms on wetlands are 
variable and described in SM3.3.1.As noted in SROCC, given the diversity of coastal wetlands as well

as the dependence of their future vulnerability to climate change on 
adaptation pathways (Krauss, 2021; Rogers, 2021), projections of 
future impacts based on shoreline elevation estimated from satellite 
data and CMIP5 projections (Spencer et  al., 2016; Schuerch et  al., 
2018) vary greatly. Although all approaches have individual strengths 
and weaknesses (Törnqvist et al., 2021), paleorecords provide some 
clarity because they yield estimates of wetland responses to changes in 
climate in the absence of other anthropogenic drivers and are therefore 
inherently conservative. On the basis of paleorecords (Table 3.8), we 
assess that mangroves and salt marshes are likely at high risk from 
future SLR, even under SSP1-1.9, with impacts manifesting in the mid-
term (medium confidence). Under SSP5-8.5, wetlands are very likely 
at high risk from SLR, with larger impacts manifesting before 2040 
(medium confidence). By 2100, these ecosystems are at high risk of

impacts under all scenarios except SSP1-1.9 (high confidence), with 
impacts most severe along coastlines with gently sloping shorelines, 
limited sediment inputs, small tidal ranges and limited space for inland 
migration (very high confidence) (Cross-Chapter Box SLR in Chapter 3; 
Schuerch et al., 2018; FitzGerald and Hughes, 2019; Leo et al., 2019; 
Schuerch et al., 2019; Raw et al., 2020; Saintilan et al., 2020).
For seagrasses, recent projections for climate-change impacts vary 
by species and region. Warming is projected to increase the habitat 
available to Zostera marina on the east coast of the USA by 2100 but 
contract its southern range edge by 150–650 km under RCP2.6 and 
RCP8.5, respectively (Wilson and Lotze, 2019). Other species, such 
as Posidonia oceanica in the Mediterranean, might lose as much as 
75% of their habitat by 2050 under RCP8.5 and become functionally 
extinct (low confidence) by 2100 (Chefaoui et  al., 2018). Observed

impacts of MHWs (Kendrick et al., 2019; Strydom et al., 2020; Serrano 
et al., 2021) indicate that increasing intensity and frequency of MHWs 
(Section 3.2.2.1) will have escalating impacts on seagrass ecosystems 
(high confidence). Habitat suitability can also be reduced by moderate 
RSLR, due to its impact on light attenuation (medium confidence) 
(Aoki et al., 2020; Ondiviela et al., 2020; Scalpone et al., 2020).
Overall, warming will drive range shifts in wetland species (medium to 
high confidence), but SLR poses the greatest risk for mangroves and 
salt marshes, with significant losses projected under all future scenarios 
by mid-century (medium confidence) and substantially greater losses Table 3.8 |  Estimates of vulnerability of coastal wetlands to sea level rise (SLR) on the basis of sediment cores
Region Habitat Reference Rates of SLR at which habitat loss isWGI AR6 Table 9.9 median estimate (and 
likely range) of SLR (Fox-Kemper et al., 2021)

Likely Very likely 2040–2060 2080–2100
Global Mangrove Saintilan et al. (2020) 4.2a6.1SSP1-1.9:
4.2 (2.9–6.1) mm yr–14.3 (2.5–6.6) mm yr–1
Southeastern USA Salt marsh Törnqvist et al. (2020) 3.5b4.2bSSP5-8.5:
7.3 (5.7–9.8) mm yr–112.2 (8.8–17.7) mm yr–1
UK Salt marsh Horton et al. (2018) 4.6a7.1a
Notes:
(a) Estimate digitised from published figure
(b) Published figure digitised and remodelled as binomial generalised linear model (number drowned as compared with not drowned)

3
424Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.9 |  Summary of previous IPCC assessments of sandy beaches.
‘Globally, beaches and dunes have in general undergone net erosion over the past century 
or longer.’
‘Attributing shoreline changes to climate change is still difficult owing to the multiple natural 
and anthropogenic drivers contributing to coastal erosion.’‘In the absence of adaptation, beaches, sand dunes and cliffs currently eroding will continue 
to do so under increasing sea level (high confidence).’
‘Coastal squeeze is expected to accelerate with a rising sea level. In many locations, finding 
sufficient sand to rebuild beaches and dunes artificially will become increasingly difficult and 
expensive as present supplies near project sites are depleted ( high confidence).’
‘In the absence of adaptation measures, beaches and sand dunes currently affected by

erosion will continue to be affected under increasing sea levels ( high confidence).’
Coastal ecosystems are already impacted by the combination of SLR, other climate-related 
ocean changes and adverse effects from human activities on ocean and land ( high 
confidence). Attributing such impacts to SLR, however, remains challenging due to 
the influence of other climate-related and non-climate drivers such as infrastructure 
development and human-induced habitat degradation ( high confidence). Coastal 
ecosystems, including salt marshes, mangroves, vegetated dunes and sandy beaches, can 
build vertically and expand laterally in response to SLR, though this capacity varies across 
sites (high confidence) as a consequence of human actions that fragment wetland habitats 
and restrict landward migration. Coastal ecosystems also progressively lose their ability 
to adapt to climate-induced changes and provide ecosystem services, including acting as

protective barriers (high confidence).
‘Loss of breeding substrate, including mostly coastal habitats such as sandy beaches, can 
reduce the available nesting or pupping habitat for land-breeding marine turtles, lizards, 
seabirds and pinnipeds (high confidence).’
‘Overall, changes in sandy beach morphology have been observed from climate-related 
events, such as storm surges, intensified offshore winds and from coastal degradation 
caused by humans (high confidence), with impacts on beach habitats (e.g., benthic 
megafauna) (medium confidence).’‘Sandy beach ecosystems will increasingly be at risk of eroding, reducing the habitable area 
for dependent organisms (high confidence).’
‘Sandy shorelines are expected to continue to reduce their area and change their topography 
due to SLR and increased extreme climatic erosive events. This will be especially important in 
low-lying coastal areas with high population and building densities ( medium confidence).’

‘Assuming that the physiological underpinning of the relationship between body size and 
temperature can be applied to warming (medium confidence), the body size of sandy beach 
crustaceans is expected to decrease under warming (low evidence, medium agreement).’
Sandy beaches transition from undetectable to moderate risk between 0.9°C and 1.8°C 
(medium confidence) of global sea surface warming and from moderate to high risk at 
2.3°C–3.0°C of global sea surface warming (medium confidence).
‘Projected changes in mean and extreme sea levels and warming under RCP8.5 are expected 
to result in high risk of impacts on sandy beach ecosystems by the end of the 21st century 
(medium confidence), taking account of the slow recovery rate of sandy-beach vegetation, 
the direct loss of habitats and the high climatic sensitivity of some fauna.’
‘Under RCP2.6, the risk of impacts on sandy beaches is expected to be only slightly higher

than the present-day level (low confidence). However, pervasive coastal urbanisation lowers 
the buffering capacity and recovery potential of sandy beach ecosystems to impacts from 
SLR and warming, and thus is expected to limit their resilience to climate change ( high 
‘Coastal squeeze and human-driven habitat deterioration will reduce the natural capacity of 
these ecosystems to adapt to climate impacts (high confidence).’
by 2100 under all scenarios except SSP1-1.9 (high confidence). MHWs 
pose the greatest risk to seagrasses (high confidence). In all cases, 
losses will be greatest where accommodation space is constrained or 
where other non-climate drivers exacerbate risk from climate-induced 
drivers (very high confidence).
3.4.2.6 Sandy Beaches
Sandy beaches comprise unvegetated, fine- to medium-grained 
sediments in the intertidal zones that line roughly one-third of the 
length of the world’s ice-free coastlines (Luijendijk et al., 2018). The

amenity value of beaches as cultural, recreational and residential 
destinations has driven extensive urbanisation of beach-associated 
coastlines (Todd et al., 2019). Beaches also provide habitat for many 
resident species, nesting habitat for marine vertebrates, filtration 
of coastal waters and protection of the coastline from erosion 
(McLachlan and Defeo, 2018). These soft-sediment coastal ecosystems 
are particularly vulnerable to habitat loss caused by erosion, especially 
where landward transgression is inhibited by infrastructure (Table 3.9).
Since SROCC, observed trends in coastal erosion continue to be 
obscured by beach nourishment that replaces eroded sediment or by coastal protection of areas at risk of erosion (Section  3.6.3.1.1; 
Cross-Chapter  Box  SLR in Chapter  3). Nevertheless, RSLR, increases 
in wave energy and/or changes in wave direction, disruptions to 
sediment supplies (including sand mining) and other anthropogenic

modifications of the coast have driven localised beach erosion (very 
high confidence) at rates up to 0.5–3 m yr–1 (Vitousek et al., 2017a; 
Vitousek et  al., 2017b; Cambers and Wynne, 2019; Enríquez-de-
Salamanca, 2020; Sharples et  al., 2020). Corresponding analyses 
of coarse-scale (30-m resolution) global data estimate that 15% of 
tidal flats (including beaches) have been lost since 1984 (medium 
confidence) (Mentaschi et al., 2018; Murray et al., 2019) but with a 
corresponding number of the world’s beaches accreting (28%) as 
eroding (24%) (medium confidence) (Luijendijk et al., 2018).
Progress is being made towards models that can project beach 
erosion under future scenarios despite inherent uncertainties and 
the presence of multiple confounding drivers in the coastal zone 
(Vitousek et al., 2017b; Le Cozannet et al., 2019; Cooper et al., 2020a; 
Vousdoukas et al., 2020b; Vousdoukas et al., 2020a). In the interim,

models with varying levels of complexity estimate local loss of beach 
area to SLR by 2100 under RCP8.5-like scenarios, assuming minimal 
human intervention, ranging 30–70% (low confidence) (Vitousek

3
425Oceans and Coastal Ecosystems and Their Services  Chapter 3
et al., 2017b; Mori et al., 2018; Ritphring et al., 2018; Hallin et al., 
2019; Kasmi et  al., 2020). Within regions, projected impacts scale 
negatively with beach width and positively with the magnitude of 
projected SLR. None of these local studies, however, considered 
high-energy storm events, which are known to also impact sandy 
coasts (high confidence) (e.g., Burvingt et  al., 2018; Garrote et  al., 
2018; Duvat et al., 2019; Sharples et al., 2020), and model structure 
often had more influence on projected shoreline responses than did 
physical drivers (Le Cozannet et al., 2019). Nevertheless, the most-
advanced available models, which incorporate multiple coastal 
processes, including SLR, project that without anthropogenic barriers 
to erosion, 13.6–15.2% and 35.7–49.5% of the world’s beaches likely  
risk undergoing at least 100 m of shoreline retreat (relative to 2010)

by 2050 and 2100, respectively (low confidence) (Vousdoukas et al., 
2020b). Aggregating these trends regionally suggests that relative 
rates of shoreline change under RCP4.5 and RCP8.5 diverge strongly 
after mid-century, with trends towards erosion escalating under 
RCP8.5 by 2100 (medium confidence) (Figure 3.14; Vousdoukas et al., 
2020b). This trend supports the WGI AR6 assessment that projected 
SLR will contribute to erosion of sandy beaches, especially under high-emissions futures (high confidence) (WGI AR6 Technical Summary; 
Arias et al., 2021).
For beach fauna, emerging evidence links range shifts, increasing 
representation by warm-affinity species and mass mortalities to ocean 
warming (limited evidence, high agreement) (McLachlan and Defeo, 
2018; Martin et  al., 2019). But even amongst the best-studied taxa, 
such as turtles, vulnerability to warming (high confidence) and SLR 
(medium confidence) anticipated on the basis of theory (Poloczanska

et al., 2009; Saba et al., 2012; Pike, 2013; Laloë et al., 2017; Tilley et al., 
2019) yields only a few detected impacts in the field associated mainly 
with feminisation (female-skewed sex ratios driven by warmer nest 
temperatures) (Jensen et al., 2018; Colman et al., 2019; Tilley et al., 2019), 
phenology (Monsinjon et al., 2019), reproductive success (Bladow and 
Milton, 2019) and inter-nesting period (Valverde-Cantillo et al., 2019). 
Moreover, although established vulnerabilities imply high projected 
future risk for turtles (high confidence) (e.g., Almpanidou et al., 2019; 
Monsinjon et al., 2019; Patrício et al., 2019; Varela et al., 2019; Santidrián 
Tomillo et  al., 2020), many populations remain resilient to change 
(Fuentes et al., 2019; Valverde-Cantillo et al., 2019; Laloë et al., 2020; (a) RCP4.5 2050 (b) RCP4.5 2100
(c) RCP8.5 2050

200m 200m 50m 50mShoreline retreat (erosion) Shoreline advance (accretion)(d) RCP8.5 2100 Relative trends in projected regional shoreline change (advance/retreat relative to 2010)
Figure 3.14 |  Relative trends in projected regional shoreline change (advance/retreat relative to 2010). Frequency distributions of median projected change by 
(a,c) 2050 and (b,d) 2100 under (a,b) RCP4.5 and (c,d) RCP8.5. Projections account for both long-term shoreline dynamics and sea level rise and assume no impediment to inland 
transgression of sandy beaches. Data for small island states are aggregated and plotted in the Caribbean. (Data are from Vousdoukas et al., 2020b.) Values for reference regions 
established in the WGI AR6 Atlas (Gutiérrez et al., 2021) were computed as area-weighted means from original country-level data. (For model assumptions and associated debate, 
see Vousdoukas et al., 2020a and Cooper et al., 2020a.)

3
426Chapter 3 Oceans and Coastal Ecosystems and Their Services
Lamont et al., 2020), perhaps because variation in sand temperatures 
at nesting depth among beaches very likely exceeds the magnitude of 
warming anticipated by 2100, even under RCP8.5 (medium confidence) 
(Bentley et al., 2020a). As expected for a taxon with a long evolutionary 
history, turtles display natural adaptation, not only by virtue of broad 
geographic distributions that include natural climate-change refugia 
(Boissin et al., 2019; Jensen et al., 2019), but also because some initial 
responses to warming might counteract anticipated impacts. For 
example, although feminisation poses a significant long-term risk to 
turtle populations (high confidence), it might contribute to population 
growth in the near to mid-term (medium confidence) (Patrício et  al., 
2019). Resilience to climate change might be further enhanced by range 
extensions, alterations in nesting phenology and fine-scale nest-site

selection (medium confidence) (Abella Perez et al., 2016; Santos et al., 
2017; Almpanidou et al., 2018; Rivas et al., 2019; Laloë et al., 2020).
New literature since SROCC on climate impacts and risks has been 
scarce for most beach taxa besides turtles. (The impacts of storms on 
beach fauna are variable and are described in SM3.3.1.) Nevertheless, 
theoretical sensitivity to warming (Section  3.3.2), together with the 
projected loss of habitat under future climate scenarios, suggest 
substantial impacts for populations and communities of beach fauna 
into the future (high confidence). These impacts will be exacerbated by 
coastal squeeze along urbanised coastlines (high confidence), albeit 
with magnitudes that cannot yet be accurately projected (McLachlan 
and Defeo, 2018; Le Cozannet et al., 2019; Leo et al., 2019).
3.4.2.7 Semi-Enclosed Seas
This section assesses impacts on five SES, or seas larger than 
200,000 km2 with single entrances <120 km wide, including the Persian

Gulf, the Red Sea, the Black Sea, the Baltic Sea and the Mediterranean 
Sea. These SES are largely landlocked and are thus heavily influenced by surrounding landscapes, local and global climate-induced drivers, 
as well as non-climate drivers (Section  3.1), making them highly 
vulnerable to cumulative threats. Key climate-induced drivers in SES 
are warming, increasing frequency and duration of MHWs, acidification 
and the increasing in size and number of OMZs (Figure 3.12; Hoegh-
Guldberg et  al., 2014). In AR5, SES were recognised as regionally 
significant for fisheries and tourism but highly exposed to both local 
and global stressors, offering limited options for organisms to migrate 
in response to climate change (Table 3.10).
Since AR5, there is evidence for increasing frequency and duration of 
MHWs, extreme-weather events and a diversity of threats across depth 
strata causing mass-mortality events, local extirpations and coral reef

decline (high confidence) (Section 3.4.2.1; SM3.3.2; Buchanan et al., 
2016a; Shlesinger et al., 2018; Wabnitz et al., 2018b; Garrabou et al., 
2019). In most SES, non-climate drivers, including pollution, habitat 
destruction and especially overfishing, are decreasing the local 
adaptive capacity of organisms and the ability of ecosystems to cope 
with climate-change impacts (high confidence) (Cramer et al., 2018; 
Hidalgo et  al., 2018; Ben-Hasan and Christensen, 2019). The SLR is 
accelerating faster than expected (high confidence) (Kulp and Strauss, 
2019), posing a key risk to SES’ coastal ecosystems and the services 
they provide in urban areas, including drinking water provision, 
housing and recreational activities, among others (Hérivaux et  al., 
2018; Reimann et al., 2018).
The size and number of OMZs are increasing worldwide and in most 
SES (high confidence) (Global Ocean Oxygen Network, 2018), with 
growing impacts on fish species diversity and ecosystem functioning. In

the Persian Gulf and Red Sea, increasing nutrient loads associated with 
coastal activities and warming has increased the size of OMZs (high 
confidence) (Al-Said et al., 2018; Lachkar et al., 2019). OMZs represent 
an even greater problem in the Black and Baltic seas, with broad 
Table 3.10 |  Summary of past IPCC assessments of semi-enclosed seas (SES)
AR5 (Hoegh-Guldberg et al., 2014)
‘The surface waters of the SES exhibit significant warming from 1982, and most CBS [coastal 
boundary systems] show significant warming since 1950. Warming of the Mediterranean has 
led to the recent spread of tropical species invading from the Atlantic and Indian oceans.’
‘SES are highly vulnerable to changes in global temperature on account of their small 
[seawater] volume and landlocked nature. Consequently, SES will respond faster than most 
other parts of the ocean (high confidence).’

‘The impact of rising temperatures on SES is exacerbated by their vulnerability to other 
human influences such as over-exploitation, pollution and enhanced runoff from modified 
coastlines. Due to a mixture of global and local human stressors, key fisheries have 
undergone fundamental changes in their abundance and distribution over the past 50 years 
(medium confidence).’‘Projected warming increases the risk of greater thermal stratification in some regions, which 
can lead to reduced O 2 ventilation [of underlying waters] and the formation of additional 
hypoxic zones, especially in the Baltic and Black seas (medium confidence).’
‘Changing rainfall intensity can exert a strong influence on the physical and chemical 
conditions within SES, and in some cases will combine with other climatic changes to 
transform these areas. These changes are likely to increase the risk of reduced bottom-water 
O2 levels to Baltic and Black Sea ecosystems (due to reduced solubility, increased

stratification, and microbial respiration), which is very likely to affect fisheries.’
Persian Gulf, Red Sea: ‘Extreme temperature events, such as heat waves, are projected to 
increase (high confidence) [... and] temperatures are very likely to increase above established 
thresholds for mass coral bleaching and mortality (very high confidence).’
Semi-enclosed seas were not assessed in this report.‘Projections from multiple fish species distribution models for multiple fish species show 
hotspots of decrease in species richness in the Indo-Pacific region, and semi-enclosed seas 
such as the Red Sea and Persian Gulf (medium evidence, high agreement).
In addition, geographic barriers, such as land boundaries [...] or lower oxygen water in 
deeper waters, are projected to limit species range shifts in SES, resulting in a larger relative 
decrease in species richness (medium confidence).’

3
427Oceans and Coastal Ecosystems and Their Services  Chapter 3
implications for ecosystem function and services (Levin et  al., 2009), 
especially where actions to reduce nutrient loading from land have 
been unable to reduce the OMZ coverage (high confidence) (Carstensen 
et al., 2014; Miladinova et al., 2017; Global Ocean Oxygen Network, 
2018). In the Baltic Sea, OMZs are affecting the extent of suitable 
spawning areas of cod, Gadus morhua ( high confidence) (Hinrichsen 
et al., 2016), while in the Black Sea, the combined effect of OMZs and 
warming is influencing the distribution and physiology of fish species, 
and their migration and schooling behaviour in their overwintering 
grounds (medium confidence) (Güraslan et al., 2017). Cascading effects 
on food webs have been reported in the Baltic, where detrimental 
effects of changing oxygen levels on zooplankton production, pelagic 
and piscivorous fish are influencing seasonal succession and species

composition of phytoplankton (high confidence) (Viitasalo et al., 2015).
In the Mediterranean Sea (Cross-Chapter  Paper  4), the increase in 
climate extremes and mass-mortality events reported in AR5 has 
continued (very high confidence) (Gómez-Gras et al., 2021). Extreme-
weather events (including deep convection; González-Alemán et al., 
2019) and MHWs have become more frequent (Darmaraki et al., 2019) 
and are associated with mass mortality of benthic sessile species 
across the basin (high confidence) (Garrabou et  al., 2019; Gómez-
Gras et al., 2021). Since AR5, in the Persian Gulf and Red Sea, extreme 
temperatures, together with disease and predation, have continued 
to cause bleaching-induced mortality of corals, along with declines 
in the average coral-colony size (high confidence) (Burt et al., 2019). 
Poleward migration and tropicalisation of species (Section 3.4.2.3) has 
also continued in the Mediterranean, and these phenomena have also

become an issue in the Black Sea (high confidence) (Boltachev and 
Karpova, 2014; Hidalgo et al., 2018). Climate impacts on phytoplankton 
production and phenology show high spatial heterogeneity across the 
Mediterranean Sea (medium evidence) (Marbà et al., 2015b; Salgado-
Hernanz et  al., 2019), with consequent effects on the diversity and 
abundance of zooplankton and fish species (medium confidence) 
(Peristeraki et al., 2019). Changes in primary production and a decrease 
in river runoff have also altered the optimum habitats for small 
pelagic fish in the Mediterranean, from the local to the basin scale 
(Piroddi et al., 2017). Evidence of impacts from ocean acidification is 
increasing, with the rates of coral calcification showing major decline 
in the Red Sea (medium confidence) (Section  3.4.2.1; Steiner et  al., 
2018; Bindoff et  al., 2019a). In the Mediterranean Sea, evidence of 
acidification events have been reported at the local scale (Hassoun

et  al., 2015), with impacts on bivalves and coralligenous species 
(medium confidence) (Lacoue-Labarthe et al., 2016).
Climate models project increasing frequency and intensity of MHWs 
(high confidence) (Section  3.2.2.1), which will exacerbate warming-
driven impacts in the Red Sea and Persian Gulf regions, and erode 
the resilience of Red Sea coral reefs (high confidence) (Osman et al., 
2018; Genevier et al., 2019; Kleinhaus et al., 2020). In the Persian Gulf 
region, extreme temperatures, >35°C (Pal and Eltahir, 2016), have 
been linked with high rates of extirpation and a decrease in fisheries 
catch potential (medium confidence) (Wabnitz et  al., 2018b). In the 
Mediterranean Sea, east–west gradients in rates of warming are 
projected to trigger spatially different changes in primary production, 
which combined with the increasing arrival of non-indigenous species,

may trigger biogeographic changes in fish diversity, increasing in the eastern and decreasing in the western Mediterranean (medium to 
high confidence) (Albouy et al., 2013; Macias et al., 2015). Projections 
also show greater impacts from SLR than originally expected in the 
Mediterranean and Baltic (e.g., Dieterich et  al., 2019; Thiéblemont 
et al., 2019). In the Baltic Sea, under high nutrient load and warming 
climate scenarios, eutrophication is projected to increase in the future 
(2069–2098) compared with historical (1976–2005) periods. In 
contrast, under continued nutrient load reductions following present 
management regulations, environmental conditions and ecological 
state will continue to improve independently of the climate-warming 
scenarios (low to medium confidence) (Saraiva et al., 2019).
3.4.2.8 Shelf Seas
Shelf seas overlie the continental margin, often with maximum depths 
of <200 m, and represent 7% of the global ocean by area (Simpson

and Sharples, 2012). These ecosystems are found offshore of every 
continent, generate 10–30% (Mackenzie et al., 2000; Andersson and 
Mackenzie, 2004) of global marine net primary production and play a 
key role in global biogeochemical cycling, including the export of land-
borne carbon and nutrients (Johnson et al., 1999; Nishioka et al., 2011; 
Li et al., 2019) to the deep ocean and recycling of fixed nitrogen back 
to the atmosphere via denitrification (Devol, 2015). The shelf seas are 
home to several of the world’s major industrial capture fisheries, such 
as those of the mid-Atlantic Bight, Scotian Shelf, Eastern Bering Sea 
Shelf and North Brazil Shelf (Barange et al., 2018), and support other 
marine industries, including aquaculture, extractive industries (oil, gas 
and mining), shipping and renewable energy installations.
Similar to other coastal ecosystems, evidence since SROCC (Table 3.11) 
suggests that shelf-sea ecosystems and the fisheries and aquaculture

they support are sensitive to the interactive effects of climate-induced 
drivers, as well as non-climate drivers, including nutrient pollution, 
sedimentation, fishing pressure and resource extraction (Table  3.12; 
Figure  3.12). Changes in freshwater, nutrient and sediment inputs 
from rivers due to both climate-induced and non-climate drivers can 
influence productivity and nutrient limitation, ecosystem structure, 
carbon export and species diversity and abundance (Balch et  al., 
2012; Picado et  al., 2014), and can result in reduced water clarity 
and light penetration (Dupont and Aksnes, 2013; McGovern et  al., 
2019). Seasonal bottom-water hypoxia occurs in some shelf seas (e.g., 
northern Gulf of Mexico, Bohai Sea, East China Sea) due to riverine 
inputs of freshwater and nutrients, promoting stratification, enhanced 
primary production and organic carbon export to bottom waters (high 
confidence) (Zhao et al., 2017; Wei et al., 2019; Del Giudice et al., 2020;

Große et al., 2020; Jarvis et al., 2020; Rabalais and Baustian, 2020; 
Song et al., 2020a; Xiong et al., 2020; Zhang et al., 2020a).
Key risks to shelf seas include shifts or declines in marine micro- and 
macro-organism abundance and diversity driven by eutrophication, 
HABs and extreme events (storms and MHWs), and consequent effects 
on fisheries, resource extraction, transportation, tourism and marine 
renewable energy (Figure 3.12). The combined effects of deoxygenation 
and warming can affect the metabolism, growth, feeding behaviour 
and mobility of fish species (Section 3.3.3). The increasing availability 
of observations mean that ecosystem changes in shelf seas can be 
increasingly attributed to climate change (high confidence) (Liang

3
428Chapter 3 Oceans and Coastal Ecosystems and Their Services
et al., 2018; Maharaj et al., 2018; Ma et al., 2019; Meyer and Kröncke, 
2019; Bargahi et  al., 2020; Bedford et  al., 2020; Friedland et  al., 
2020b; Mérillet et  al., 2020). Eutrophication and seasonal bottom-
water hypoxia in some shelf seas have been linked to warming (high 
confidence) (Wei et al., 2019; Del Giudice et al., 2020) and increased 
riverine nutrient loading (high confidence) (Wei et al., 2019; Del Giudice 
et al., 2020). Since SROCC, some severe HABs have been attributed to 
extreme events, such as MHWs (Section 14.4.2; Roberts et al., 2019; 
Trainer et al., 2019); however, a recent worldwide assessment of HABs 
attributed the increase in observed HABs to intensified monitoring 
associated with increased aquaculture production (high confidence) 
(Hallegraeff et al., 2021).
Since SROCC, changes in the community structure and diversity of 
plankton, macrofauna and infauna have been detected in some shelf

seas, although attribution has been regionally specific (e.g., bottom-
water warming or hypoxia) (Meyer and Kröncke, 2019; Rabalais and 
Baustian, 2020). Detection of the picoplankton Synechococcus spp. in 
the North Sea is potentially linked to a summer decrease in copepod 
stocks and declining food-web efficiency (low confidence) (Schmidt 
et al., 2020). The seasonally distinct phytoplankton assemblages in the 
North Sea have begun to appear concurrently and homogenise (Nohe 
et al., 2020). Changes in abundance, species composition and size of 
zooplankton have been detected in some shelf seas (Yellow Sea, North 
Sea, Celtic Sea and Tasman Sea), including a decline in stocks of larger 
copepods, increased abundances of gelatinous and meroplankton, and 
a shift to smaller species due to warming, increased river discharge, 
circulation change and/or extreme events (high confidence) (Wang 
et al., 2018a; Bedford et al., 2020; Evans et al., 2020; Shi et al., 2020;

Edwards et al., 2021).Ocean warming has shifted distributions of fish (Free et  al., 2019; 
Franco et  al., 2020; Pinsky et  al., 2020b; Fredston et  al., 2021) and 
marine mammal species (Salvadeo et al., 2010; García-Aguilar et al., 
2018; Davis et al., 2020) poleward (high confidence) or deeper (low 
to medium confidence) (Section 3.4.3.1; Nye et al., 2009; Pinsky et al., 
2013; Pinsky et al., 2020b). Warming has also tropicalised the pelagic 
and demersal fish assemblages of mid- and high-latitude shelves 
(high confidence) (Montero-Serra et  al., 2015; Liang et  al., 2018; 
Maharaj et al., 2018; Ma et al., 2019; Friedland et al., 2020a; Kakehi 
et al., 2021; Punzón et al., 2021). Fisheries catch composition in many 
shelf-sea ecosystems has become increasingly dominated by warm-
water species since the 1970s (high confidence) (Cheung et al., 2013; 
Leitão et al., 2018; Maharaj et al., 2018; McLean et al., 2019). Warming

has taxonomically diversified fish communities along a latitudinal 
gradient in the North Sea but has homogenised functional diversity 
(McLean et al., 2019). However, in some regions, changing predator 
or prey distributions, temperature-dependent hypoxia, population 
changes, evolutionary adaptation and other biotic or abiotic processes, 
including species’ exploitation, confound responses to climate-induced 
drivers, which must therefore be interpreted with caution (Frank et al., 
2018). For example, although, most species’ range edges are tracking 
temperature change on the northeast shelf of the USA (medium 
confidence) (Fredston-Hermann et  al., 2020; Fredston et  al., 2021), 
range edges of others are not.
A wide range of responses by fish and invertebrate populations 
to warming have been observed. The majority of responses have 
been detrimental, with the direction and magnitude of the response 
depending on ecoregion, taxonomy, life history and exploitation history

(Free et al., 2019; Yati et al., 2020). For example, fisheries productivity Table 3.11 |  Summary of past IPCC assessments of shelf seas
‘Primary productivity, biomass yields and fish capture rates have undergone large changes 
within the ECS [East China Sea] over the past decades ( limited evidence, medium agreement, 
low confidence).’
‘Changing sea temperatures have influenced the abundance of phytoplankton, benthic 
biomass, cephalopod fisheries and the size of demersal trawl catches in the northern 
SCS [South China Sea] observed over the period 1976–2004 ( limited evidence, medium 
agreement).’
‘Concurrent with the retreat of the ‘cold pool’ [...] on the northern Bering Sea shelf, 
[...] bottom trawl surveys of fish and invertebrates show a significant community-wide 
northward distributional shift and a colonisation of the former cold pool areas by sub-Arctic 
fauna (high confidence).’

‘Observed changes in the phenology of plankton groups in the North Sea over the past 
50 years are driven by climate forcing, in particular regional warming (high confidence).’‘Global warming will result in more frequent extreme events and greater associated 
risks to ocean ecosystems (high confidence). In some cases, [...] projected increases will 
eliminate ecosystems, and increase the risks and vulnerabilities to coastal livelihoods [and 
the vulnerabilities for food security including that of Southeast Asia] (medium to high 
confidence). Reducing stressors not related to climate change represents an opportunity 
to strengthen the ecological resilience within these regions, which may help them [biota] 
survive some projected changes in ocean temperature and chemistry.’
Changes in eutrophication and hypoxia are likely to influence shelf seas, but there is low 
confidence in the understanding of the magnitude of potential changes and impacts on

ecosystem functioning, fisheries and other industries.
‘Species composition of fisheries catches since the 1970s in many shelf seas ecosystems of 
the world is increasingly dominated by warm-water species (medium confidence).’
‘Estuaries, shelf seas and a wide range of other intertidal and shallow-water habitats play 
an important role in the global carbon cycle through their primary production by rooted 
plants, seaweeds (macroalgae) and phytoplankton, and also by processing riverine organic 
carbon. However, the natural carbon dynamics of these systems have been greatly changed 
by human activities (high confidence).’‘Direct anthropogenic impacts include coastal land-use change; indirect effects include 
increased nutrient delivery and other changes in river catchments, and marine resource 
exploitation in shelf seas. There is high confidence that these human-driven changes will

continue, reflecting coastal settlement trends and global population growth.’

3
429Oceans and Coastal Ecosystems and Their Services  Chapter 3
has strongly decreased in the North Sea (Free et al., 2019), and fisheries 
yields have also decreased in the Celtic Sea, attributed primarily to 
warming and secondarily to long-term exploitation (Hernvann and 
Gascuel, 2020; Mérillet et al., 2020). Conversely, fish species diversity 
and overall productivity have increased in the Gulf of Maine, even with 
warming (Le Bris et al., 2018; Friedland et al., 2020a; Friedland et al., 
2020b). Fisheries yields have decreased in the Yellow Sea, East China 
Sea and South China Sea partially due to overexploitation (Ma et al., 
2019; Wang et al., 2019c), with warming exerting more influence on 
the yield of cold-water species than on temperate- and warm-water 
groups (Ma et  al., 2019). The combined effects of exploitation and 
multi-decadal climate fluctuations make it difficult to assess global 
climate-change impacts on fisheries yields (Chapter 5; Ma et al., 2019;

Bentley et al., 2020b; Johnson et al., 2020).
Since AR5, increasing spatio-temporal extent of hypoxia has been 
projected due to enhanced benthic respiration and reduced oxygen 
solubility from warming (Del Giudice et  al., 2020). Similar to the 
open ocean, large shifts in the phenology of phytoplankton blooms 
have been projected for shelf seas throughout subpolar and polar 
waters (medium confidence) (Henson et al., 2018a; Asch et al., 2019). 
Zooplankton, which are important prey for many fish species and sea 
birds, are expected to decrease in abundance on the northeast shelf 
of the USA (Grieve et  al., 2017); however, responses vary by shelf 
ecosystem (Chust et  al., 2014b). Trends towards tropicalisation will 
continue in the future (high confidence) (Cheung et al., 2015; Stortini 
et al., 2015; Allyn et al., 2020; Maltby et al., 2020; Costa et al., 2021), 
but uncertainty of future projections of fisheries production increases

substantially beyond 2040 (Maltby et al., 2020). Nevertheless, shelf-
sea fisheries at lower latitudes are most vulnerable to climate change 
(Monnereau et  al., 2017). Under future climate change marked by 
more frequent and intense extreme events and the influences of 
multiple drivers, more flexible and adaptive management approaches 
could reduce climate impacts on species while also supporting industry 
adaptation (high confidence) (Section 3.6.3.1.2; Shackell et al., 2014; 
Stortini et al., 2015; Hare et al., 2016; Stortini et al., 2017; Greenan 
et al., 2019; Ocaña et al., 2019; Maltby et al., 2020).3.4.2.9 Upwelling Zones
Eastern boundary upwelling systems (EBUS) comprise four important 
social–ecological systems in the Pacific (California and Peru-Humboldt) 
and Atlantic (Canary and Benguela) ocean basins. Each is characterised 
by high primary production, sustained by wind-driven upwelling that 
draws cold, nutrient-rich, generally low-pH and low-oxygen water to

the surface (Bindoff et  al., 2019a). Despite their small relative size, 
the primary productivity in EBUS supports a vast biomass of marine 
consumers, including some of the world’s most productive fisheries 
(Pauly and Zeller, 2016), along with many species of conservation 
significance (Bakun et al., 2015).
Although upwelling is important in many other oceanic regions, we 
focus here on the most documented examples provided by the EBUS. Yet 
even here, observed changes in upwelling, temperature, acidification 
and loss of oxygen (Seabra et  al., 2019; Abrahams et  al., 2021; 
Gallego et al., 2021; Varela et al., 2021) cannot be robustly attributed 
to anthropogenic climate change, and projected future changes in 
upwelling are expected to be relatively small and variable among and 
within EBUS (Section 3.2.2.3; WGI AR6 Chapter 9; Fox-Kemper et al., 
2021). We therefore have few updates to assessments provided by AR5

and SROCC (Table 3.13) and restrict our brief assessment to the limited 
amount of new evidence (Figure 3.12).
The California EBUS is arguably the best-studied of the four ecosystems 
in terms of robust projections of climate change, although even here, 
there is limited evidence and low agreement among projections. For 
example, trends in outputs from high-resolution, downscaled models in 
the California EBUS generally reflect those from underlying coarser-scale 
ESMs, but projections for physical variables are more convergent among 
modelling approaches than are those for biogeochemical variables (high 
confidence) (Howard et al., 2020a; Pozo Buil et al., 2021). Models agree 
on general warming in the California EBUS, with concomitant declines 
in oxygen content (medium confidence) (Howard et al., 2020b; Fiechter 
et al., 2021; Pozo Buil et al., 2021). But implications for the future spatial

distribution of species, including for some fisheries resources (Howard Table 3.12 |  Synthesis of interactive effects and their influence on shelf-sea ecosystems and the fisheries and aquaculture they support
Factor Example of effect Example references
TemperatureAltered habitats for species, change in plankton, fish and 
macrofauna community structure, influence on species 
growth, thermal stress, altered diversity, altered productivity 
and altered phenologyLiang et al. (2018); Maharaj et al. (2018); Ma et al. (2019); 
Meyer and Kröncke (2019); Yan et al. (2019); Bargahi et al. 
(2020); Bedford et al. (2020); Denechaud et al. (2020); 
Friedland et al. (2020b); Mérillet et al. (2020); Nohe et al. 
(2020)
pH Acidification with hypoxia Zhang and Wang (2019)
SalinityChange in species distribution due to altered salinity front 
distributionLiu et al. (2020c)
Oxygen concentration Deoxygenation Wei et al. (2019); Del Giudice et al. (2020)

River discharge Change in plankton community structure Shi et al. (2020)
Nutrient pollutionEnhanced primary production, change in plankton 
community structureKong et al. (2019); Nohe et al. (2020)
Sedimentation Modified ocean chemistry Hallett et al. (2018)
Fishing pressureIncreased vulnerability leading to changes in community 
structureMaharaj et al. (2018); Wang et al. (2019c); Hernvann and 
Gascuel (2020)
Resource extraction Contamination, change in benthic community structure Hall (2002)

3
430Chapter 3 Oceans and Coastal Ecosystems and Their Services
et  al., 2020b; Fiechter et  al., 2021), are confounded by local-scale 
oceanographic processes (Siedlecki et al., 2021) and by lateral input of 
anthropogenic land-based nutrients (Kessouri et al., 2021), suggesting 
that such projections should be accorded low confidence.
More generally, changes in upwelling intensity are observed to affect 
organismal metabolism, population productivity and recruitment, and food-web structure (medium confidence) (van der Sleen et  al., 
2018; Brodeur et al., 2019; Ramajo et al., 2020). But low confidence 
in projected trends in upwelling make it difficult to extrapolate these 
results to understand potential changes in the ecology of EBUS. 
Projected changes in fish biomass within EBUS (Carozza et al., 2019) 
are therefore accorded low confidence. Finally, although MHWs are

an important emerging hazard in the global ocean, with intensity, Table 3.13 |  Summary of previous IPCC assessments of eastern boundary upwelling systems (EBUS)
AR5 (Hoegh-Guldberg et al., 2014; Lluch-Cota et al., 2014)
‘[EBUS] are vulnerable to changes that influence the intensity of currents, upwelling and 
mixing (and hence changes in sea surface temperature, wind strength and direction), as well 
as O 2 content, carbonate chemistry, nutrient content and the supply of organic carbon to 
deep offshore locations (high confidence).’
Climate-change-induced intensification of ocean upwelling in some EBUS, as observed in 
past decades, may lead to regional cooling, rather than warming, of surface waters and 
cause enhanced productivity (medium confidence), but also enhanced hypoxia, acidification 
and associated biomass reduction in fish and invertebrate stocks. Owing to contradictory

observations, there is currently uncertainty about the future trends of major upwelling 
systems and how their drivers will shape ecosystem characteristics ( low confidence).
‘Declining O 2 and shoaling of the aragonite saturation horizon through ocean acidification 
increase the risk of upwelling water being low in pH and O 2, with impacts on coastal 
ecosystems and fisheries [...]. These risks and uncertainties are likely to involve significant 
challenges for fisheries and associated livelihoods along the west coasts of South America, 
Africa and North America (low to medium confidence).’
‘There is robust evidence and medium agreement that the California Current has 
experienced [...] an increase of the overall magnitude of upwelling events from 1967 to 
2010 (high confidence). This is consistent with changes expected under climate change 
yet remains complicated by the influence of decadal-scale variability (low confidence).’

Declining oxygen concentrations and shoaling of the hypoxic boundary layer likely ‘reduced 
the available habitat for key benthic communities as well as fish and other mobile species. 
Together with the shoaling of the saturation horizon, these changes have increased the 
incidence of low O 2 and low pH water flowing onto the continental shelf (high confidence; 
40 to 120 m), causing problems for industries such as the shellfish aquaculture industry.’
Despite its apparent sensitivity to environmental variability, there is limited evidence of 
ecological changes in the Benguela Current EBUS due to climate change.‘Like other ocean sub-regions, [EBUS] are projected to warm under climate change, with 
increased stratification and intensified winds as westerly winds shift poleward (likely). 
However, cooling has also been predicted for some [EBUS], resulting from the intensification 
of wind-driven upwelling.’

‘There is medium agreement, despite limited evidence, that upwelling intensity and 
associated variables (e.g., temperature, nutrient and O 2 concentrations) from the Benguela 
system will change as a result of climate change.’
Any projected increase in upwelling intensity has potential disadvantages. ‘Elevated primary 
productivity may lead to decreasing trophic transfer efficiency, thus increasing the amount 
of organic carbon exported to the seabed, where it is virtually certain to increase microbial 
respiration and hence increase low O 2 stress.’
SROCC (Bindoff et al., 2019a; IPCC, 2019c; IPCC, 2019d)
‘Increasing ocean acidification and oxygen loss are negatively impacting two of the four 
major upwelling systems: the California Current and Humboldt Current (high confidence). 
Ocean acidification and decrease in oxygen level in the California Current upwelling system 
have altered ecosystem structure, with direct negative impacts on biomass production and

species composition (medium confidence).’
‘Three out of the four major Eastern Boundary Upwelling Systems (EBUS) have shown 
large-scale wind intensification in the past 60 years ( high confidence). However, the 
interaction of coastal warming and local winds may have affected upwelling strength, with 
the direction of changes [varying] between and within EBUS (low confidence). Increasing 
trends in ocean acidification in the California Current EBUS and deoxygenation in California 
Current and Humboldt Current EBUS are observed in the last few decades (high confidence), 
although there is low confidence to distinguish anthropogenic forcing from internal climate 
variability. The expanding California EBUS OMZ [oxygen minimum zone] has altered 
ecosystem structure and fisheries catches (medium confidence).’
‘Overall, EBUS have been changing with intensification of winds that drives the upwelling, 
leading to changes in water temperature and other ocean biogeochemistry ( medium

‘The direction and magnitude of observed changes vary among and within EBUS, with 
uncertainties regarding the driving mechanisms behind this variability. Moreover, the high 
natural variability of EBUS and their insufficient representation by global ESMs [Earth 
system models] gives low confidence that these observed changes can be attributed to 
anthropogenic causes.’‘Anthropogenic changes in EBUS will emerge primarily in the second half of the 21st century 
(medium confidence). EBUS will be impacted by climate change in different ways, 
with strong regional variability with consequences for fisheries, recreation and climate 
regulation (medium confidence). The Pacific EBUS are projected to have calcium carbonate 
undersaturation in surface waters within a few decades RCP8.5 (high confidence); combined 
with warming and decreasing oxygen levels, this will increase the impacts on shellfish

larvae, benthic invertebrates, and demersal fishes (high confidence) and related fisheries and 
aquaculture (medium confidence).’
‘The inherent natural variability of EBUS, together with uncertainties in present and future 
trends in the intensity and seasonality of upwelling, coastal warming and stratification, 
primary production and biogeochemistry of source waters poses large challenges in 
projecting the response of EBUS to climate change and to the adaptation of governance of 
biodiversity conservation and living marine resources in EBUS (high confidence).’
‘Given the high sensitivity of the coupled human–natural EBUS to oceanographic changes, 
the future sustainable delivery of key ecosystem services from EBUS is at risk under climate 
change; those that are most at risk in the 21st century include fisheries ( high confidence), 
aquaculture (medium confidence), coastal tourism (low confidence) and climate regulation 
(low confidence).’

‘For vulnerable human communities with a strong dependence on EBUS services and 
low adaptive capacity, such as those along the Canary Current system, unmitigated 
climate-change effects on EBUS (complicated by other non-climatic stresses such as social 
unrest) have a high risk of altering their development pathways (high confidence).’

3
431Oceans and Coastal Ecosystems and Their Services  Chapter 3
frequency and duration increasing strongly (Section  3.2.2.1), the 
number of MHW days  yr–1 within EBUS has been increasing more 
slowly (or decreasing faster, in the case of the Peru-Humboldt system) 
than in surrounding waters (Varela et al., 2021). Notwithstanding these 
trends, EBUS remain vulnerable both to MHWs (high confidence) (Sen 
Gupta et al., 2020) and to their long-lasting impacts (high confidence) 
(Arafeh-Dalmau et  al., 2019; Harvell et  al., 2019; McPherson et  al., 
2021). On this basis, the suggestion that EBUS may represent refugia 
from MHWs is accorded low confidence.
Despite low confidence in detailed projections for ecological changes 
in EBUS, the WGI assessment (WGI AR6 Chapter  9; Fox-Kemper 
et  al., 2021) that upwelling-favourable winds will weaken (or be 
present for shorter durations) at low latitude but intensify at high

latitude (high confidence), albeit by no more than 20% in either case 
(medium confidence), presents some key risks to associated EBUS 
ecosystems. These risks include potential decreases in provisioning 
services, including fisheries and marine aquaculture (Bertrand et al., 
2018; Kifani et al., 2018; Lluch-Cota et al., 2018; van der Lingen and 
Hampton, 2018), and cultural services such as nature-based tourism 
(Section 3.5).
3.4.2.10  Polar Seas
The polar seas cover ~20% of the global ocean and include the deep 
Arctic Ocean and surrounding shelf seas as well as the Southern 
Ocean south of the polar front. They play a significant role in ocean 
circulation and absorption of anthropogenic CO 2 (Meredith et  al., 
2019). The Arctic is characterised by polar seas surrounded by land, 
while the Antarctic comprises continental Antarctica surrounded 
by the Southern Ocean. These high-latitude ecosystems share key 
properties, including strong seasonality in solar radiation and sea

ice coverage. Sea ice regulates water-column physics, chemistry and 
biology, air–sea exchange and is a critical habitat for many species. 
In spring, when solar radiation returns and sea ice melts, intense 
phytoplankton blooms fuel food webs that include rich communities 
of both resident and summer-migrant species, with typically high 
dependency on a few key species for trophic transfer (Meredith et al., 
2019; Rogers et al., 2020). Over the past two decades, Arctic Ocean 
surface temperature has increased in line with the global average, 
while there has been no uniform warming across the Antarctic (high 
confidence) (WGI AR6 Chapter 9; Fox-Kemper et al., 2021). Thus, the 
rate of change due to warming, and associated sea ice loss, is greater 
in the Arctic than in the Antarctic (high confidence) (Section  3.2; 
Table  3.14; WGI AR6 Chapter  9; Fox-Kemper et  al., 2021). Both 
Arctic and Antarctic regions have a long history of living resource

extraction, including some of the largest fisheries on the globe in 
terms of catches. However, only the Arctic hosts human populations, 
holding a rich Indigenous knowledge and local knowledge (IKLK) on 
these social–ecological systems (Cross-Chapter  Paper 6; Meredith 
Previous assessments of polar seas (Table 3.14) concluded that climate 
change has already profoundly influenced polar ecosystems, through 
changing species distributions and abundances from primary producers 
to top predators, including both ecologically and economically important 
species (high confidence), and that it will continue to do so (Table 3.14).Since SROCC, evidence demonstrates that warmer oceans, less sea 
ice and increased advection results in increasing primary production 
in the Arctic, albeit with regional variation (high confidence), while 
trends remain spatially heterogeneous and less clear in the Antarctic 
(medium confidence) (Cross-Chapter  Paper 6; Del Castillo et  al.,

2019; Lewis et al., 2020; Pinkerton et al., 2021; Song et al., 2021a). 
Furthermore, climate warming influences key mechanisms determining 
energy transfer between trophic levels including (a) altered size 
spectra, (b) shifts in trophic pathways, (c) phenological mismatches 
and (d) increased top-down trophic regulation (Table 3.15); however, 
the scale of impacts from changes in these mechanisms on ecosystem 
productivity in warming polar oceans remains unresolved and is hence 
Major community shifts, both gradual and abrupt, are observed in polar 
oceans in response to warming trends and MHWs (Arctic only) (high 
confidence) (Figure 3.12; Cross-Chapter Paper 6; Beaugrand et al., 2019; 
Meredith et al., 2019; Huntington et al., 2020). In general, abundances 
and ranges of Arctic fish species are declining and contracting, while 
ranges of boreal fish species are expanding, both geographically and

in terms of feeding interactions and ecological roles (high confidence) 
(Huserbråten et al., 2019; Meredith et al., 2019; Huntington et al., 2020; 
Pecuchet et al., 2020a), with variable outcomes for large commercial 
fish stocks (Cross-Chapter Paper 6; Kjesbu et al., 2014; Holsman et al., 
2018; Free et al., 2019). The extreme seasonal solar radiation cycles of 
these high latitudes may both act as a barrier for species immigration 
and change predator–prey dynamics in previously ice-covered areas, 
factors not currently considered in projections (limited evidence) 
(Kaartvedt and Titelman, 2018; Ljungström et al., 2021). Responses by 
marine mammals and birds to the ongoing changes in polar ecosystems 
are both positive and negative (Meredith et al., 2019; Bestley et al., 
2020). Phenological, behavioural, physiological and distributional 
changes are observed in marine mammals and birds in response to 
altered ecological interactions and habitat degradation, especially to

loss of sea ice (high confidence) (see Box  3.2; Cross-Chapter  Paper 
6; Beltran et  al., 2019; Cusset et  al., 2019; Descamps et  al., 2019; 
Meredith et al., 2019; Huntington et al., 2020). Reproductive failures 
and declining abundances attributed to warmer polar oceans and 
less sea ice cover are observed in populations of polar bears, Ursus 
maritimus, seals, whales and marine birds (high confidence) (see 
Box  3.2; Duffy-Anderson et  al., 2019; Ropert-Coudert et  al., 2019; 
Bestley et al., 2020; Chambault et al., 2020; Molnár et al., 2020; Stenson 
et al., 2020). The ongoing changes in polar marine ecosystems can lead 
to temporary increases in biodiversity and functional diversity (e.g., 
due to immigration of boreal species in the Arctic, high confidence), 
but reduced trophic-transfer efficiencies and functional redundancy, 
with uncertain consequences for ecosystem resilience and vulnerability 
(limited evidence, low agreement) (Griffith et al., 2019b; Alabia et al.,

2020; du Pontavice et al., 2020; Alabia et al., 2021; Frainer et al., 2021).
Calcareous polar organisms are among the groups most sensitive 
to ocean acidification (high confidence) (Section  3.3.2). Niemi et  al. 
(2021) reports that >80% of sampled sea snail, Limacina helicina, a key 
species in pelagic food webs, displayed signs of shell dissolution in the 
Amundsen Gulf. However, bacteria, phytoplankton, zooplankton and 
benthic communities are found to be detrimentally impacted, resilient 
or even positively influenced by ocean acidification in observational

3
432Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.15 |  Examples of mechanisms influencing the transfer of energy between lower trophic levels in warmer polar oceans
Mechanism Examples References
Altered size spectraShifts towards smaller algal cells and zooplankton in warmer and more 
stratified oceans results in longer and less-efficient food chains, with lower 
lipid content.Aarflot et al. (2018); Kimmel et al. (2018); Weydmann et al. 
(2018); Hop et al. (2019); Møller and Nielsen (2020); Spear 
et al. (2020); but see Dong et al. (2021) and Vernet et al. 
(2017) for opposite trends.
Shifts in trophic pathwaysChanges in microbial food-web interactions, including strengthening of the 
microbial loop, may reduce overall productivity. Transitions from sea ice 
algae to open-water phytoplankton production may reduce benthic–pelagic 
coupling and benthic production; transition from autotroph to heterotroph

benthic production with increased water turbidity; shifts from krill-dominated 
to salp-dominated ecosystems in the Antarctic may have negative impacts on 
higher trophic levels.Cross-Chapter Paper 6; Fujiwara et al. (2016); Onda et al. 
(2017); Vernet et al. (2017); Grebmeier et al. (2018); Moore 
et al. (2018b); Cavan et al. (2019); Vaqué et al. (2019); 
Yurkowski et al. (2020); Braekcman et al. (2021)
Phenological mismatchesMismatches in timing arise between spring phytoplankton blooms and 
zooplankton recruits.Søreide et al. (2010); Renaud et al. (2018); Dezutter et al. 
(2019)
Increased top-down trophic regulationIncreased predation efficiency and top-down regulation of zooplankton 
by zooplanktivorous fish (due to more light with less sea ice) disconnects 
zooplankton and phytoplankton production.Langbehn and Varpe (2017); Kaartvedt and Titelman (2018); 
Hobbs et al. (2021)Table 3.14 |  Summary of previous IPCC assessments for polar seas

Poleward species distributional shifts are due to climate warming (medium to high 
Impacts of shifts in ocean conditions affect fish and shellfish abundances in the Arctic (high 
Changes in sea ice and the physical environment to the west of the Antarctic Peninsula are 
altering phytoplankton stocks and productivity, and krill (high confidence).Some marine species will shift their ranges in response to changing ocean and sea ice 
conditions in the polar regions (medium confidence).
Loss of sea ice in summer and increased ocean temperatures are expected to impact 
secondary pelagic production in some regions of the Arctic Ocean, with associated changes 
in the energy pathways within the marine ecosystem (medium confidence).
Ocean acidification has the potential to inhibit embryo development and shell formation of 
some zooplankton and krill in the polar regions, with potentially far-reaching consequences

to food webs in these regions (medium confidence).
Shifts in the timing and magnitude of seasonal biomass production could disrupt coupled 
phenologies in the food webs, leading to decreased survival of dependent species (medium 
‘A fundamental transformation is occurring in polar organisms and ecosystems, driven by 
climate change (high confidence).’‘The losses in sea ice at 1.5°C and 2°C of warming will result in habitat losses for organisms 
such as seals, polar bears, whales and seabirds. There is high agreement and robust evidence 
that phytoplankton species will change because of sea ice retreat and related changes in 
temperature and radiation, and this is very likely to benefit fisheries productivity [in the 
Arctic spring bloom system].’
‘‘Unique and threatened systems’ (RFC1), [including Arctic and coral reefs], display a 
transition from high to very high risk of transition at temperatures between 1.5°C and 2°C

of global warming, as opposed to at 2.6°C of global warming in AR5 (high confidence).’
Climate-induced changes in seasonal sea ice extent and thickness as well as ocean 
stratification are altering marine primary production ( high confidence), with impacts on 
ecosystems (medium confidence).
Changes in the timing, duration and magnitude of primary production have occurred in both 
polar oceans, with marked regional or local variability (high confidence).
In both polar regions, climate-induced changes in ocean and sea ice conditions have 
expanded the range of temperate species and contracted the range of polar fish and 
ice-associated species (high confidence).
Ocean acidification will affect several key Arctic species (medium confidence).Future climate-induced changes in the polar oceans, sea ice, snow and permafrost will 
drive habitat and biome shifts, with associated changes in the ranges and abundance of

ecologically important species (medium confidence).
Projected range expansion of sub-Arctic marine species will increase pressure for high-Arctic 
species (medium confidence), with regionally variable impacts.
Both polar oceans will be increasingly affected by CO 2 uptake, causing corrosive conditions 
for calcium carbonate shell-producing organisms (high confidence), with associated impacts 
on marine organisms and ecosystems (medium confidence).
The projected effects of climate-induced stressors on polar marine ecosystems present risks 
for commercial and subsistence fisheries, with implications for regional economies, cultures 
and the global supply of fish, shellfish, and Antarctic krill (high confidence).

3
433Oceans and Coastal Ecosystems and Their Services  Chapter 3
and experimental studies (Section 3.3; Hildebrandt et al., 2016; Thor 
et al., 2018; Ericson et al., 2019; McLaskey et al., 2019; Meredith et al., 
2019; Petrou et  al., 2019; Renaud et  al., 2019; Brown et  al., 2020; 
Hancock et  al., 2020; Henley et  al., 2020; Johnson and Hofmann, 
2020; Torstensson et  al., 2021). While fish larval stages may be 
sensitive, adult fish are expected to have low vulnerability to projected 
acidification levels (Section  3.3.3; Hancock et  al., 2020), although 
reduced swimming capacity in polar cod in an ocean acidification 
experiment has been observed (Kunz et  al., 2018). Polar organisms’ 
sensitivity to ocean acidification may increase with increasing light 
levels due to the loss of sea ice (algae; Donahue et al., 2019; Kvernvik 
et  al., 2020), temperature stress (pteropods; Johnson and Hofmann, 
2020) or indirectly via increased heterotrophic bacterial productivity

(limited evidence) (Vaqué et  al., 2019). Due to limited mechanistic 
understanding of observed effects, and mixed responses among Arctic 
species, future impacts of ocean acidification are assigned medium 
confidence for polar species and low confidence for outcomes for polar 
ecosystems (Meredith et al., 2019; Green et al., 2021b).
While levels of pollutants in biota (e.g., persistent organic pollutants, 
mercury) have generally declined over the past decades, recent 
increasing levels are associated with release from reservoirs in ice, 
snow and permafrost, and through changing food webs and pathways 
for trophic amplification (medium confidence) (see Box 3.2; Ma et al., 
2016; Amélineau et al., 2019; Foster et al., 2019; Bourque et al., 2020; 
Kobusińska et al., 2020). Also, a warmer climate, altered ocean currents 
and increased human activities elevate the risk of invasive species in 
the Arctic (medium confidence), potentially changing ecosystems in

this region (high confidence) (Chan et al., 2019; Goldsmit et al., 2020). 
In the remote Antarctic, there is a lower risk of invasive species (limited 
evidence) (McCarthy et al., 2019; Holland et al., 2021).
Fisheries are largely sustainably managed yet are expanding polewards 
following sea ice melt in the Arctic (high confidence) (Fauchald 
et  al., 2021) and possibly in the Antarctic (limited evidence) (Santa 
Cruz et al., 2018). Tourism is increasing and expanding in both polar 
regions, while shipping and hydrocarbon exploration are growing in 
the Arctic, increasing the risks of compound effects on vulnerable 
and already stressed populations and ecosystems (high confidence) 
(Sections  3.6.3.1.3, 3.6.3.1.4; Cross-Chapter  Paper 6; Hauser et  al., 
2018; Meredith et  al., 2019; Helle et  al., 2020; Rogers et  al., 2020; 
Cavanagh et al., 2021).
Ensemble global model projections indicate future increases in primary

production and total animal biomass towards 2100 under RCP2.6 (~5 
and 50%, respectively) and RCP8.5 (~10 and 70%, respectively), in the 
Arctic (Bryndum-Buchholz et  al., 2019; Lotze et  al., 2019; Nakamura 
and Oka, 2019), highlighting opportunities for, and possibly conflicts 
over, new ecosystem services (Section  3.5). For the Southern Ocean, 
no overall trends are apparent, but greater variability in both primary 
production and total animal biomass are projected under RCP2.6, with 
an ~5 and 15% increase in primary production and total animal biomass 
under RCP8.5, respectively (Bryndum-Buchholz et al., 2019; Lotze et al., 
2019; Nakamura and Oka, 2019). All projections presented exhibit 
high inter-model variability and hence uncertainty (Heneghan et  al., 
2021). Furthermore, regional models project significant distributional 
shifts and wide-ranging trends (i.e., relatively stable, increasing and declining) in productivity for key ecological and commercial species,

and functional groups, with weak to strong dependence on emission 
scenarios, indicating low confidence in future outcomes for polar 
marine ecosystems and associated ecosystem services (Section  3.5; 
Piñones and Fedorov, 2016; Griffiths et  al., 2017; Klein et  al., 2018; 
Hansen et al., 2019; Meredith et al., 2019; Steiner et al., 2019; Tai et al., 
2019; Alabia et  al., 2020; Holsman et  al., 2020; Reum et  al., 2020; 
Veytia et  al., 2020; Sandø et  al., 2021). Potentially highly influential 
tipping points associated with Arctic sea ice melt and Antarctic ocean 
circulation change adds to this uncertainty (Cross-Chapter  Paper 6; 
Heinze et  al., 2021). Nevertheless, increasing evidence supports that 
sustainable and adaptive ecosystem-based fisheries practices can 
reduce detrimental impacts of climate change on harvested populations 
(medium confidence) (Section 3.6.3.1.2; Klein et al., 2018; Free et al., 
2019; Hansen et al., 2019; Holsman et al., 2020).

3.4.3 Oceanic Systems and Cross-Cutting Changes
The oceanic zone, comprising >99% of the ocean’s volume, is highly 
exposed to climate-induced drivers because of its proximity to the 
atmosphere (Section 3.2; Pörtner et al., 2014; Bindoff et al., 2019a), while 
its relative distance from human settlements and coastal ecosystems 
decreases variability and interactions, and permits many phenomena to 
be detected clearly and attributed to climate change. This section assesses 
how climate-driven changes influence oceanic biological systems over 
very large spatial scales and notes how impacts on the epipelagic zone 
affect the mesopelagic, bathypelagic and deep seafloor ecosystems.
3.4.3.1 Biogeography and Species Range Shifts
3.4.3.1.1 Observed species range shifts
Since previous assessments (Table 3.16), poleward range shifts have 
remained a ubiquitous response to climate change (high confidence), 
moving species from warmer regions into higher-latitude ecosystems

(Fossheim et  al., 2015; Kumagai et  al., 2018; Burrows et  al., 2019; 
Lenoir et al., 2020).
Thermal tolerances of epipelagic populations drive biogeographic 
change (Figures 3.10, 3.15), but the strength and direction of range 
shifts tend to be modulated by both climate-induced and non-climate 
drivers (Pinsky et al., 2020b), including: (a) interactive effects of hypoxia 
and ocean acidification (Sampaio et al., 2021); (b) oceanic dispersal 
barriers (Choo et al., 2021), food and critical habitat availability (Alabia 
et  al., 2020; Tanaka et  al., 2021); (c)  geographic position, including 
depth (Mardones et al., 2021); and (d) ocean currents (Sunday et al., 
2015; Chapman et  al., 2020; Fuchs et  al., 2020). The difference 
between physiological thermal tolerances (Section  3.3.2) and local 
environmental conditions determines safety margins against future 
climate warming in ectotherms (Pinsky et al., 2019). Acclimation and

evolution (Section  3.3.4) and life-history stage (Section  3.3.3) also 
alter species’ thermal tolerances. Biogeographic responses are further 
modulated by other interacting factors (Table 3.17).
A global meta-analysis of range shifts (Lenoir et  al., 2020) that 
included data from 951 species (over half of which exhibited median

3
434Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.16 |  Summary of previous IPCC assessments of biogeography and species range shifts
AR5 (Hoegh-Guldberg et al., 2014; Pörtner et al., 2014)
The distribution and abundance of many fishes and invertebrates have shifted poleward and/
or to deeper, cooler waters (high confidence).
On average, species’ distributions have shifted poleward by 72.0 ± 0.35 km per decade (high 
confidence).Spatial shifts of marine species due to projected warming will cause high-latitude invasions 
and high local-extinction rates in the tropics and semi-enclosed seas ( medium confidence).
‘Ocean warming has contributed to observed changes in biogeography of organisms ranging 
from phytoplankton to marine mammals (high confidence).’
‘The direction of the majority of the shifts of epipelagic organisms are consistent with a

response to warming (high confidence)’ but are also shaped by oxygen concentrations and 
ocean currents across depth, latitudinal and longitudinal gradients (high confidence).
Geographic ranges have shifted since the 1950s by 51.5 ± 33.3 km per decade (mean 
and very likely range) and 29.0 ± 15.5 km per decade for organisms in the epipelagic and 
seafloor ecosystems, respectively.‘Recent model projections since AR5 and SR15 continue to support global-scale range shifts 
of marine fishes at rates of tens to hundreds of km per decade in the 21st century, with rate 
of shifts being substantially higher under RCP8.5 than RCP2.6.’
range shifts consistent with climate change) estimates that marine 
species are moving poleward at a rate of 59.2 km per decade (very 
likely range: 43.7–74.7 km per decade), closely matching the local 
climate velocity (high confidence). In some cases, warming-related 
distribution shifts were followed by density-dependent use of these

areas, influencing associated fisheries (Baudron et al., 2020), and in 
others, warming influenced competitive interactions: in the Arctic-
Boreal Barents Sea, warming-induced increases in cod (Gadus morhua) abundance reduces haddock (Melanogrammus aeglefinus) abundance 
(Durant et al., 2020).
Biogeographic shifts lead to novel communities and biotic interactions 
(high confidence) (Zarco-Perello et al., 2017; Pecuchet et al., 2020b), 
with concomitant changes in ecosystem functioning and servicing 
(high confidence) (Vergés et al., 2019; Nagelkerken et al., 2020; Peleg 
et al., 2020). For instance, temperature-driven changes in distribution Range 
expansionRange 
contractionWarm extreme
Dispersal
Present range
Future range
ColonisationCool extremeSchematic of range-shift dynamics in marine ectotherms in response to climate warming

Figure 3.15 |  Range-shift dynamics in marine ectotherms in response to climate warming. As the ocean warms, conditions at the edge of the species’ distribution 
may become warmer than the maximum thermal tolerance of the species (Figure 3.9), causing local populations to undergo a gradual decline in performance, a decreasing 
population size and ultimately their extirpation, resulting in a range contraction. Conversely, at the cool extreme of the distribution, habitats beyond the current range of the 
species will become thermally suitable in the future (i.e., within the species’ thermal tolerance range) and, providing the species can disperse to those locations, allow for the 
colonisation and consolidation of new populations and subsequent range expansion. These are processes conditioned by multiple drivers that interact with warming to ultimately

define range-shift responses, some of which are described in Table 3.17. Note that physiological thermal tolerances relate to body temperatures of the organism rather than 
ambient temperatures.

3
435Oceans and Coastal Ecosystems and Their Services  Chapter 3
Table 3.17 |  Synthesis of selected processes conditioned by multiple environmental drivers that interact with warming to ultimately define range-shift responses
Factor Effect Example references
Evolution and acclimationEvolution of thermal tolerances and acclimation under local 
climatic conditions can increase resilience to future climate 
warming, slowing the loss of species at trailing (warm) 
range edges.Palumbi et al. (2014); Miller et al. (2020a)
Marine heatwaves (MHWs)MHWs can influence the evolution of thermal tolerances 
by eliminating genotypes that are intolerant of elevated 
temperatures.Buckley and Huey (2016); Sunday et al. (2019)
MHWs can produce widespread die-offs of shallow-water 
benthic organisms triggering extensive contractions of their 
ranges.Smale and Wernberg (2013)
MHWs can facilitate range expansions by opening niches

and/or enhancing recruitment of warm-affiliated species.Leriorato and Nakamura (2019); Thomsen et al. (2019); 
Monaco et al. (2021)
Cold waves can halt or even reverse range expansions at 
leading edges.Leriorato and Nakamura (2019)
Ocean currentsOcean currents can influence range dynamics through their 
effect on dispersal, depending on their magnitude, direction 
and seasonal patterns.Hunt et al. (2016); Kumagai et al. (2018); Fuchs et al. (2020)
Where currents align with spatial gradients of warming, 
range expansions track thermal changes more closely. 
Conversely, directional mismatches result in consistently 
slower expansion rates and larger response lags, an effect 
more acute for benthic organisms relying on passive 
dispersion of larvae and propagules.García Molinos et al. (2017)
Rates of range contraction across taxa decreased 
(increased) under directional agreement (mismatch) with 
ocean currents, possibly associated with enhanced (reduced)

flows of adaptive genes to warming in downstream 
(upstream) populations within the distributional range.García Molinos et al. (2017)
Climatic refugiaAreas of locally stable climatic conditions, such as deeper 
waters or regions with internal tides or localised upwelling, 
can buffer the effects of regional warming, facilitating 
species persistence and conserving genetic diversity at 
rear-edge populations.Smith et al. (2014); Assis et al. (2016); Lourenço et al. (2016); 
Wyatt et al. (2020)
Distributional shifts into deeper, cooler habitats can offer an 
effective alternative response to latitudinal shifts, because 
sharper thermal gradients mean that vertical displacements, 
needed to compensate for the same amount of warming, 
are several orders of magnitude smaller than planar 
displacements.Smith et al. (2014); Assis et al. (2016); Lourenço et al. (2016)
Oxygen availabilityOxygen supersaturation may extend ectotherm survival to

extreme temperatures and increase thermal tolerances by 
compensating for the increasing metabolic demand at high 
temperatures.Giomi et al. (2019)
Oxygen deprivation increases metabolic demand and 
respiration rates. Shallowing of oxygen-dead zones and 
subsequent hypoxic avoidance can render deep thermal 
refuges unsuitable for organisms.Brown and Thatje (2015); Roman et al. (2019); Hughes et al. 
(2020)
Habitat availability and qualityThe availability and quality of habitat (underwater light 
conditions, adequate substrate, nutrient and food supply) 
set limits to the distribution of organisms and range-shift 
dynamics (e.g., resilience of populations to climate warming 
and the consolidation of range expansions).Krause-Jensen et al. (2019); Tamir et al. (2019)
Biotic interactions, including food availabilitySpecies interactions can confer resilience to warming by 
retarding habitat degradation and buffering the impacts of

warming on organisms.Falkenberg et al. (2015); Giomi et al. (2019)
Changes in biotic interactions (e.g., altered predation 
rates, food availability, competition or trophic mismatches) 
induced by climate warming can modify range-shift 
dynamics.Selden et al. (2018); Westerbom et al. (2018); Figueira et al. 
(2019); Pinsky et al. (2020b); Monaco et al. (2021)

3
436Chapter 3 Oceans and Coastal Ecosystems and Their Services
and abundance of copepods, the dominant zooplankton, were 
observed between 1960 and 2014 in the North Atlantic. These changes 
subsequently affect biogenic carbon cycling through alteration of 
microbial remineralisation and carbon sequestration in deep water 
(medium confidence) (Section 3.4.3.6; Pitois and Fox, 2006; Brun et al., 
2019).
3.4.3.1.2 Observed vertical redistributions
Epipelagic isotherms have recently (1980–2015) deepened at an 
average of 6.6 ± 18.8 m per decade (Pinsky et al., 2019), but there 
is low agreement on whether species move deeper in pursuit of 
thermal refuge. Prior studies suggested range shifts to depth (Dulvy 
et al., 2008; Pinsky et al., 2013; Yemane et al., 2014), but increasing 
evidence suggests that fish and planktonic communities across large 
parts of the North Atlantic, sub-Arctic and northeast Pacific Ocean 
redistribute horizontally with horizontal climate velocity, except where

vertical temperature gradients are particularly steep. There is low 
confidence for temperature-driven depth shifts in the epipelagic zone 
(Burrows et al., 2019; Campana et al., 2020; Caves and Johnsen, 2021). 
At the same time, decreasing oxygen concentrations and the vertical 
expansion of OMZs have already decreased suitable habitat of pelagic 
fishes, including tuna and billfishes, by ~15% primarily due to vertical 
compression of environmental niches (Stramma et al., 2012; Deutsch 
3.4.3.1.3 Projected changes in species range shifts
Continued changes in the biogeography of marine predators and prey 
are anticipated under future climate change, with climate velocity in 
the epipelagic zone during 2050–2100 under RCP8.5 projected to be 
sevenfold faster than that during 1955–2005 (medium confidence) 
(Figure 3.4; Brito-Morales et al., 2020). This has substantial ecological 
implications, as projections suggest near elimination of overlaps

between the distributions of certain predator–prey pairs in the 
northeast Atlantic Ocean when their current joint distributions (1989–
2014) are compared with those projected (2037–2062) under RCP8.5 
(Sadykova et al., 2020).
Deepening of epipelagic isotherms is projected to accelerate over 
2006–2100 to rates of 8.5 m per decade under RCP4.5 and 32 m 
per decade under RCP8.5 (Jorda et  al., 2020). Although vertical 
redistribution of thermal niches is three to four orders of magnitude 
slower than horizontal displacement, maximum depth limits imposed 
by the seafloor and photic layer (both of which are projected to be 
reached in this century) will likely vertically compress suitable habitat 
for most marine organisms (medium confidence) (Dueri et al., 2014; 
Jorda et al., 2020).
Projections from coupled biogeochemical and ecosystem models 
suggest a general decline in mesopelagic biomass (Lefort et al., 2015), 
although this may vary among ocean basins. The volume of OMZs

have been expanding at many locations (high confidence), and the 
confidence) (Section 3.2.3.2; WGI AR6 Section 5.3.3.2; Canadell et al., 
2021) at a rate of 10–15 µM per decade in OMZs (Section  3.2.3.2; Breitburg et  al., 2018). Oxygen availability and the effects of ocean 
acidification (Sections  3.3, 3.4.2) on zooplankton might become a 
dominant constraint in the upper ocean’s metabolic index, which is 
projected to decrease globally by 20% by 2100 (Deutsch et al., 2015; 
Steinberg and Landry, 2017). In addition, extremely rapid acceleration 
of climate velocities projected in the mesopelagic under all emissions 
scenarios suggest that species in this ocean stratum will be even 
more exposed to future warming than species in the epipelagic 
(Figure 3.4; Brito-Morales et al., 2020). But projections also suggest

that warming-related increases in trophic efficiency lead to a 17% 
increase in the biomass of the deep-scattering layer (zooplankton 
and fish in the mesopelagic) by 2100 (low confidence) (Bindoff et al., 
2019a). Observational studies appear to show that mesopelagic fishes 
adapted to warm water increased in abundance and distribution in the 
California Current associated with warming and the expansion of OMZ 
(Koslow et al., 2019), suggesting that some mesopelagic fish stocks 
might be resilient to a changing climate (medium confidence).
3.4.3.2 Phenological Shifts and Trophic Mismatches
3.4.3.2.1  Observed changes
SROCC reported high confidence in phenological shifts towards earlier 
onset of biological events (Table 3.18), with phenological shifts among 
epipelagic species attributed to ocean warming (high confidence).
Since SROCC, field data have continued to show that the phenology 
of biological events in the ocean is very likely (high to very high

confidence) advancing in response to climate change, with 71.9% 
of published observations consistent with these anticipated effects 
(Figure 3.16a,b; Table 3.19), although most reports (95.6%) were from 
the Northern Hemisphere (Figure  3.16a). Biological events that are 
shifting earlier in response to climate change include phytoplankton 
blooms (Scharfe and Wiltshire, 2019; Chivers et  al., 2020) such as: 
(a)  those of HAB species (Forsblom et  al., 2019; Bucci et  al., 2020); 
(b) peaks in zooplankton abundance (Chevillot et al., 2017; Forsblom 
et al., 2019); (c) the migration (Otero et al., 2014; Kovach et al., 2015; 
Chust et al., 2019) and spawning of commercial fish (McQueen and 
Marshall, 2017; Kanamori et  al., 2019), including crabs and squid 
(Henderson et al., 2017); and (d) breeding of marine reptiles (Mazaris 
et al., 2008; Cherkiss et al., 2020). Moreover, different trophic levels 
within epipelagic food webs are responding at different rates (very

high confidence) (Table  3.19; Figure  3.16b,c), with greater and 
more consistent responses by lower trophic levels (phytoplankton, 
holozooplankton and meroplankton) but less consistent, weaker 
and more varied responses by higher trophic levels. There were too 
few independent time series to make robust estimates for benthic 
invertebrates, plants, marine reptiles and mammals. This differential 
response across trophic levels could lead to trophic mismatches 
(Neuheimer et  al., 2018), where predators and their prey respond 
asynchronously to climate change (Edwards and Richardson, 2004; 
Rogers and Dougherty, 2019; Rubenstein et al., 2019; Émond et al., 
2020), with potential population-level consequences, including 
declines in fish recruitment (Burthe et al., 2012; Chevillot et al., 2017; 
McQueen and Marshall, 2017; Asch et al., 2019; Durant et al., 2019; 
Régnier et  al., 2019). Available evidence also suggests that feeding

relationships could modulate species responses to climate change,

3
437Oceans and Coastal Ecosystems and Their Services Chapter 3
as seen in breeding of surface-feeding and deeper-diving seabirds 
(Descamps et al., 2019). These differential responses could determine ‘winners’ and ‘losers’ under future climate change (Lindén, 2018).
3.4.3.2.2
 Projected changes
T
he CMIP6 ESM ensembles project that, by 2100, 18.8  ±  19.0% 
(mean  ±  very likely range) and 38.9  ±  9.4% of the ocean surface 
will very likely undergo a change of 20
 d or more (adv
ance or delay) 
in the start of the phytoplankton growth period under SSP1-2.6 and 
SSP5-8.5, respectively (Figure  3.17a,b) (low confidence  due to the

dependence with the projected changes in phytoplankton biomass, the trends of which are reported with low confidence) (Section 3.4.3.4; SROCC Section  5.2.3; Bindoff et  al., 2019a). Phytoplankton growth is projected to begin later in the Northern Hemisphere subtropics, and earlier at high latitudes in some regions around the Antarctic Peninsula, and over large areas in the Northern Hemisphere (low to medium confidence as there are improved constraints from historical 
variability in this region and consistency with CMIP5-based-studies results) (Henson et  al., 2018b; Asch et  al., 2019). There is  high 
agreement in model projections that the start of the phytoplankton Table 3.18 |  Summary of previous IPCC assessments of phenological shifts and trophic mismatches
AR5 WGII (Hoegh-Guldberg et al., 2014; Larsen et al., 2014)

‘Changes to sea temperature have altered the phenology, or timing of key life-history events such as plankton blooms, and migratory patterns, 
and spawning in fish and invertebrates, over recent decades (medium confidence). There is medium to high agreement that these changes pose

significant uncertainties and risks to fisheries, aquaculture and other coastal activities.’The highly productive high-latitude spring bloom systems in the northeast Atlantic are responding to warming (medium evidence, high agreement), with the greatest changes being observed since the late 1970s in the phenology, distribution and abundance of plankton assemblages, and the reorganisation of fish assemblages, with a range of consequences for fisheries (high confidence).‘Observed changes in the phenology of plankton groups in the North Sea over the past 50 years are driven by climate forcing, in particular regional warming (high confidence).’‘On average, spring events in the ocean have advanced by 4.4 ± 0.7 days per decade (mean ± SE).’‘Shifts in the timing and magnitude of seasonal biomass production could disrupt matched phenologies in the food webs, leading to decreased survival of dependent species (medium confidence). If the timing of primary and secondary production is no longer

matched to the timing of spawning or egg release, survival could be impacted, with cascading implications to higher trophic levels. This impact would be exacerbated if shifts in timing occur rapidly (medium confidence).’‘There is medium to high confidence that climate-induced disruptions in the synchrony between timing of spawning and hatching of some fish and shellfish and the seasonal increases in prey availability can result in increased larval or juvenile mortality or changes in the condition factor of fish and shellfish species in the Arctic marine ecosystems.’Projections of phenological shifts and trophic mismatches were not assessed in this report.

‘Phenology of marine ectotherms in the epipelagic systems related to ocean warming (high confidence) and the timing of biological events has 
shifted earlier (high confidence).’‘Timing of spring phenology of marine organisms is shifting to earlier in the year under warming, at an average rate of 4.4 ± 1.1 days per decade, although it is variable among taxonomic groups and among ocean regions.’Projections of phenological shifts and trophic mismatches were not assessed in this report.
WGI AR6 Chapter 2 (Gulev et al., 2021)
‘Phenological metrics for many species of marine organisms have changed in the last half century (high confidence), though many regions and

many species of marine organisms remain under-sampled or even unsampled. The changes vary with location and with species (high confidence). There is a strong dependence of survival in higher trophic-level organisms (fish, exploited invertebrates, birds) on the availability of food at various stages in their life cycle, which in turn depends on the phenologies of both ( high confidence). There is a gap in our understanding of how the varied 
responses of marine organisms to climate change, from a phenological perspective, might threaten the stability and integrity of entire ecosystems.’Projections of phenological shifts and trophic mismatches were not assessed in this report.
growth period will  very likely advance in the Arctic Ocean under a 
high-emission scenario for CMIP5 and CMIP6 (Figure 3.17b; Henson

et al., 2018b; Asch et al., 2019; Tedesco et al., 2019; Lannuzel et al., 2020). The CMIP6 ensemble projections further show limited changes in phenology across most of the Southern Ocean but large regional variations in the tropics (Figure  3.17). Overall, the regional patterns are qualitatively similar under SSP1-2.6 and SSP5-8.5 but with greater magnitude and larger areas under SSP5-8.5 (low confidence).
At latitudes >40°N, temperature-linked phenology of fish reproduction 
with high geographic fidelity to spawning grounds (geographic spawners) is projected to change at double the rate of that for phytoplankton, which will likely cause phenological mismatches resulting in increased

risk of starvation for fish larvae (medium to high confidence) (WGI AR6 Section 2.3.4.2.3; Asch et al., 2019; Durant et al., 2019; Régnier et al., 2019; Gulev et al., 2021; Laurel et al., 2021). Furthermore, under RCP8.5, trophic mismatch events exceeding ±30 days (Asch et al., 2019) leading to fish-recruitment failure are expected to increase tenfold for 
geographic spawners across much of the North Atlantic, North Pacific and Arctic Ocean basins (low confidence) (Neuheimer et al., 2018). In contrast, temporal mismatches between fish that relocate spawning

3
438Chapter 3 Oceans and Coastal Ecosystems and Their Services
grounds in response to environmental variations (environmental 
spawners) and phytoplankton blooms are projected to remain shorter 
and less varied, suggesting that across ocean basins, range shifts by 
environmental spawners may increase their resilience. Nevertheless, this 
compensation mechanism might fail at locations where phytoplankton 
bloom phenology is not controlled by temperature-driven water-column 
stratification, leading to a possible sixfold local increase in extreme 
mismatches under climate change (Asch et al., 2019).
3.4.3.3 Changes in Community Composition and Biodiversity
3.4.3.3.1 Evidence of natural adaptive capacity based on species’ 
responses to past climate variability
Responses to abrupt climate change in the geological past suggest 
that adaptive capacity is limited for marine animals (Cross-
Chapter  Box  PALEO in Chapter  1). Temperatures during the last

Interglacial (~125 ka), which were warmer than today, led to poleward 
range shifts of reef corals (medium confidence) (Kiessling et al., 2012; 
Jones et al., 2019a). Temperature has also driven marine range shifts 
over multi-million-year time scales (medium confidence) (Gibbs et al., 
2016; Reddin et al., 2018). Warming climates, even with low ocean-
warming rates, inevitably decreased tropical marine biodiversity 
compared with middle latitudes (high confidence) (Mannion et  al., 
2014; Crame, 2020; Yasuhara et al., 2020; Raja and Kiessling, 2021).
The paleorecord confirms that marine biodiversity has been vulnerable 
to climate warming both globally and regionally (very high confidence) 
(Cross-Chapter  Box  PALEO in Chapter  1; Stanley, 2016). In extreme 
cases of warming (e.g., >5.2°C), marine mass extinctions occurred 
in the geological past, and there may be a relationship between 
warming magnitude and extinction toll (medium confidence) (Song

et al., 2021b). A combination of warming and spreading anoxia caused 
marine extinctions in ancient episodes of rapid climate warming (high 
confidence) (Bond and Grasby, 2017; Benton, 2018; Penn et al., 2018; 
Them II et al., 2018; Chen and Xu, 2019). The role of ocean acidification 
in ancient extinctions is yet to be resolved (low confidence) (Clapham 
and Payne, 2011; Clarkson et al., 2015; Jurikova et al., 2020; Müller 
3.4.3.3.2 Observed and projected changes in contemporary 
community structure and biodiversity
Ocean temperature is a major driver of species richness in the 
global ocean at evolutionary time scales (Tittensor et  al., 2010; 
Chaudhary et al., 2021). This, together with temperature-driven range 
and phenology shifts evident across taxa and ocean ecosystems 
(Sections  3.4.3.1, 3.4.3.2), suggests that recent ocean warming 
(Section 3.2.2.1) should alter biodiversity at regional to global scales.

Since previous assessments (Table 3.20), the most common evidence 
supporting these expected changes is replacement of cold-adapted 
species by warm-adapted species within an ecosystem as waters warm 
(Worm and Lotze, 2021). Known as tropicalisation (Section 3.4.2.3), this 
phenomenon has been attributed to ocean warming (medium to high 
confidence) in communities as diverse as kelp, invertebrates, plankton 
and fish (Burrows et al., 2019; Flanagan et al., 2019; Ajani et al., 2020; 
Villarino et al., 2020; Punzón et al., 2021; Smith et al., 2021).Observed responses to climate change
Based on a systematic Web of Science Review
to update the previous assessment
Low confidence High confidence(b)
Shift (days per decade) Consistent (%)
-20-100100255075100
(c)
Phytoplankton Zooplankto
nMeroplankton Invertebrate
sPlants Fish Reptiles Seabirds MammalsNot consistentConsistent(a)
58 64
5451457214
5677
43109
83854
Figure 3.16 |  Observed responses to climate change based on a systematic

Web of Science review of marine phenology studies exceeding 19 years in 
length to update the assessment in WGII AR5 Chapter 30 (Hoegh-Guldberg 
et al., 2014). Error bars indicate 95% confidence limits (i.e., the extremely likely range).
(a) Global data shows changes in seasonal cycles of biota that are attributed (at 
least partly) to climate change (blue, n = 297 observations), and changes that are 
inconsistent with climate change (white, n = 116 observations). Each circle represents 
the centre of a study area.
(b) The proportion of phenological observations (showing means and extremely likely 
ranges) that are attributed to climate change (i.e., generally showing earlier timing) by 
taxonomic group.
(c) Observed shifts in timing (days per decade, showing means and extremely likely 
ranges), by taxonomic group, that are attributed to climate change. Negative shifts are 
earlier, positive shifts are later. (Details and additional plots are presented in 3.SM.3.3,

3
439Oceans and Coastal Ecosystems and Their Services  Chapter 3
Table 3.19 |  Assessment of phenological shifts by taxon based on time series from field observations spanning at least 19 years published over the past 25 years
TaxonRate of consistency of 
observations with climate 
changeEstimated mean rate of 
change in seasonal timingConfidence Notes
Phytoplankton78.41%
(n = 85)−7.5 d per decade
(n = 83)Very high confidenceEvidence most robust for changes in timing of 
blooms in the North Atlantic (e.g., Chivers et al., 
2020) and Baltic (e.g., Scharfe and Wiltshire, 2019; 
Wasmund et al., 2019), with limited evidence from 
the Southern Hemisphere.
Holozooplankton79.74%
(n = 77)−4.27 d per decade
(n = 58)Very high confidenceEvidence most robust in the northeast Atlantic 
(e.g., Chevillot et al., 2017), but sparse elsewhere.
Meroplankton (taxa that 
are only temporarily in 
the plankton)81.06%
(n = 72)−4.34 d per decade

(n = 64)Very high confidenceIncludes earlier peak abundance of fish larvae in 
upwelling systems (e.g., Asch, 2015).
Benthic invertebrates72.34%
(n = 5)−8.5 d per decade
(n = 5)Low confidence (limited 
evidence, medium agreement)Evidence is limited, uncertainty levels are high. 
Rate of consistency of responses with climate 
change is not significantly different from random 
chance.
Plants100%
(n = 1)No estimate available Very low confidenceJust a single study for seagrasses, and only for 
consistency (Diaz-Almela et al., 2007).
Fish65.48%
(n = 109)−3.02 d per decade
(n = 43)Very high confidenceIncludes earlier appearance of migratory fish 
in estuaries (e.g., Chevillot et al., 2017), earlier 
spawning migrations for anadromous fish 
such as salmon (e.g., Rubenstein et al., 2019), 
earlier migrations for sole (e.g., Fincham et al., 
2013) and tuna (e.g., Dufour et al., 2010), and 
earlier spawning of key commercial demersal 
(bottom-dwelling) species such as cod (e.g.,

McQueen and Marshall, 2017).
Marine reptiles100.0%
(n = 4)−2.89 d per decade
(n = 4)Low confidence (limited 
evidence, low agreement)Evidence is limited, uncertainty levels are high. 
Mean phenological shift is not significantly 
different from zero.
Seabirds42.36%
(n = 56)+0.77 d per decade
(n = 51)Very low confidence (limited 
evidence, low agreement)Neither the rate of consistency with climate change 
nor the phenological shift differ significantly 
from null expectations (50% consistency and no 
shift). Many seabirds are breeding earlier (Byrd 
et al., 2008; Sydeman et al., 2009), while breeding 
among others in temperate and polar regions has 
been delayed, which has been linked to later sea 
ice breakup or limited prey resources (Barbraud 
and Weimerskirch, 2006; Wanless et al., 2009; 
Chambers et al., 2014). Although the response 
of lifecycle events for many seabird species is 
variable in direction, there has usually been a 
more complex driver associated with climate that

has been considered to be responsible (Sydeman 
et al., 2015). For many species, seasonal timing is 
moving earlier, especially in the Arctic (e.g., Byrd 
et al., 2008; Descamps et al., 2019), but for many 
species in the Southern Ocean, it is not (Barbraud 
and Weimerskirch, 2006; Chambers et al., 2014). 
This could be because of a much slower rate of 
warming in most of the Southern Ocean than in 
the Arctic.
Marine mammals100.0%
(n = 4)−0.34 d per decade (n = 4)Very low confidence (limited 
evidence, low agreement)All studies of phenological changes for marine 
mammals have focused on whales (e.g., Ramp 
et al., 2015; Hauser et al., 2017; Loseto et al., 
2018) or polar bears (e.g., Cherry et al., 2013; 
Atwood et al., 2016; Escajeda et al., 2018) 
and have related timing to aspects of sea ice 
dynamics, highlighting the complexity of such 
processes. Mean phenological shift is not 
significantly different from zero at the global 
scale.

3
440Chapter 3 Oceans and Coastal Ecosystems and Their Services
At local to regional scales, tropicalisation often increases species 
richness where warm-water species extend their ranges to overlap with 
existing communities and decreases species richness where warming 
waters extirpate species (medium to high confidence) (Friedland et al., 
2020a; Chaudhary et  al., 2021; Worm and Lotze, 2021). Latitudinal 
estimates from catalogued observations show declining species richness 
in equatorial waters over the past 50 years, with concomitant increases 
in species richness at mid-latitudes; the pattern is especially prominent 
in free-swimming pelagic species (Figure 3.18; Chaudhary et al., 2021). 
Similar patterns among marine animals have been described previously 
for historical warming events (Song et  al., 2020b). Tropicalisation is 
associated with increased representation of herbivorous species (Vergés 
et  al., 2016; Zarco-Perello et  al., 2020; Smith et  al., 2021), although

observations and theory suggest that dietary generalism can also favour 
range-shifting species (Monaco et al., 2020; Wallingford et al., 2020).
At the community level, the magnitude and shape of projected future 
biodiversity changes differ depending on which groups are considered 
(medium confidence) (Chaudhary et al., 2021). Molecular-based richness measures indicate that the most dramatic increases in diversity relative 
to current conditions are expected for photosynthetic eukaryotes 
and copepods in the Arctic Ocean (Ibarbalz et  al., 2019). However, 
component eukaryotic taxa, for example diatoms (Busseni et al., 2020), 
are projected to lose diversity by 2100 under RCP8.5. Ecosystem models 
project a decline in nutrient supply that drives the disappearance of 
less-competitive and larger phytoplankton types, leading to extinction 
of up to 30% of diatom types, particularly in the Northern Hemisphere, 
by 2100 under RCP8.5 (Henson et al., 2021). Models further suggest

that high latitudes are likely to encounter entirely novel phytoplankton 
communities by 2100 under RCP8.5 (100% change in community 
composition; Dutkiewicz et al., 2019; Reygondeau et al., 2020). At the 
polar edges, the increased richness is projected to coincide with high 
species turnover and increasing dominance of smaller phytoplankton 
types (Henson et  al., 2021). These imply pronounced changes to 
both the oceans’ ecological and biogeochemical function, as regions 
dominated by small phytoplankton typically support less-productive 
food webs (Section 3.4.3.4; Stock et al., 2017; Armengol et al., 2019) 
and sequester less particulate organic carbon (POC) in the deep ocean Projected phytoplankton phenology
(a) SSP1-2.6
40 30 -30 -10 01 0 -40 -20 20
ΔTiming of initiation (days)(c) SSP5-8.5
14
12
10
8
6
4
2
014
(b) SSP1-2.6
(d) SSP5-8.512
10
8
6
4
2
0
40 20 0 -20 -40 -60 60
ΔTiming of initiation (days)Density Density

Figure 3.17 |  Projected phytoplankton phenology. (a,c) Spatial patterns and (b,d) density distributions of projected change in phytoplankton phenology by 2100 under 
Shared Socioeconomic Pathway (SSP)1-2.6 and SSP5-8.5, respectively. Difference in the start of the phytoplankton growth period is calculated as 2090–2099 minus 1996–2013. 
Negative (positive) values indicate earlier (later) start of the phytoplankton growth period by 2100. The ensemble projections of global changes in phytoplankton phenology include, 
under SSP1-2.6 and SSP5-8.5, respectively, a total of five Coupled Model Intercomparison Project 6 Earth system models containing coupled ocean biogeochemical models that 
cover a wide range of complexity (Kwiatkowski et al., 2019). (The phenology calculations are based on Racault et al., 2017, using updated data.)

3
441Oceans and Coastal Ecosystems and Their Services  Chapter 3
(Section  3.4.3.5; Mouw et  al., 2016; Cram et  al., 2018) than areas 
dominated by larger size classes (high confidence).
The profound climatic and environmental changes projected for the 
Arctic region by 2100 (Cross-Chapter Paper 6) are also anticipated to 
alter the composition of apex assemblages like marine mammals (see 
Box 3.2; Albouy et al., 2020). Under both RCP2.6 and 8.5 scenarios 
the most vulnerable marine mammal species will be the North Pacific 
right whale (Eubalaena japonica, listed as an endangered species; 
IUCN, 2020) and the grey whale (Eschrichtius robustus, which has 
critically endangered subpopulations; IUCN, 2020). The extinction of 
the most-vulnerable species will disproportionately eliminate unique 
and important evolutionary lineages as well as functional diversity, 
with consequent impacts throughout the entire marine ecosystem

(Section  3.3.4).    More generally, future warming and acidification 
simulated in mesocosm experiments support projections of a 
substantial increase in biomass and productivity of primary producers 
and secondary consumers, but a decrease by >40% of primary 
consumers (Nagelkerken et al., 2020). On longer time scales, alteration 
of energy flow through marine food webs may lead to ecological 
tipping points (Wernberg et al., 2016; Harley et al., 2017) after which 
the food web collapses into shorter, bottom-heavy trophic pyramids 
Global projections anticipate a likely future reorganisation of marine 
life of variable magnitude, contingent on emission scenario (Beaugrand 
et  al., 2015; Jones and Cheung, 2015; Barton et  al., 2016; García 
Molinos et al., 2016; Nagelkerken et al., 2020; Henson et al., 2021). Table 3.20 |  Summary of previous IPCC assessments of community composition and biodiversity

The paleoecological record shows that global climate 
changes comparable in magnitudes to those projected for 
the 21st century under all scenarios resulted in large-scale 
biome shifts and changes in community composition, and 
that for rates projected under RCP6 and 8.5 those changes 
were associated with species extinctions in some groups 
Loss of corals due to bleaching has a potentially critical 
influence on the maintenance of marine biodiversity in the 
tropics (high confidence).Spatial shifts of marine species due to projected warming will cause high-latitude invasions and high local-extinction rates in 
the tropics and semi-enclosed seas (medium confidence).
Species richness and fisheries catch potential are projected to increase, on average, at mid and high latitudes (high 
confidence) and decrease at tropical latitudes (medium confidence).

‘Shifts in the geographical distributions of marine species [...] cause changes in community composition and interactions [...]. 
Thereby, climate change will reassemble communities and affect biodiversity, with differences over time and between biomes 
and latitudes (high confidence).’
‘Models are currently useful for developing scenarios of directional changes in net primary productivity, species distributions, 
community structure, and trophic dynamics of marine ecosystems, as well as their implications for ecosystem goods and 
services under climate change. However, specific quantitative projections by these models remain imprecise (low confidence).’
‘Ocean warming has contributed to observed changes in 
biogeography of organisms ranging from phytoplankton 
to marine mammals (high confidence), consequently 
changing community composition (high confidence), and 
in some cases altering interactions between organisms and

ecosystem function (medium confidence).’Poleward range shifts are projected to decrease species richness in tropical oceans, counterbalanced by increases in mid- to 
high-latitude regions, leading to global-scale species turnover (medium confidence on trends, low confidence on magnitude 
because of model uncertainties and the limited number of published model simulations). ‘The projected intensity of species 
turnover is lower under low-emission scenarios (high confidence).’
‘Projections from multiple fish species distribution models show hotspots of decrease in species richness in the Indo-Pacific 
region, and semi-enclosed seas such as the Red Sea and Persian Gulf (medium evidence, high agreement). In addition, 
geographic barriers, such as land, [bounding the] poleward species range edge in semi-enclosed seas or low-oxygen water 
in deeper waters are projected to limit range shifts, resulting in a larger relative decrease in species richness (medium 

‘The large variation in sensitivity between different zooplankton taxa to future conditions of warming and ocean acidification 
suggests elevated risk on community structure and inter-specific interactions of zooplankton in the 21st century (medium 
Marine organism redistributions projected under RCP4.5 and RCP8.5 
include extirpations and range contractions in the tropics, strongly 
decreasing tropical biodiversity, and range expansions at higher 
latitudes, associated with increased diversity and homogenisation 
of marine communities (Figure  3.18b). Under continuing climate 
change, the projected loss of biodiversity may ultimately threaten 
marine ecosystem stability (medium confidence) (Albouy et al., 2020; 
Nagelkerken et  al., 2020; Henson et  al., 2021), altering both the 
functioning and structure of marine ecosystems and thus affecting 
service provisioning (medium confidence) (Section 3.5; Ibarbalz et al., 
2019; Righetti et al., 2019).

However, biodiversity observations remain sparse, and statistical and 
modelling tools can provide conflicting diversity information (e.g., 
Righetti et  al., 2019; Dutkiewicz et  al., 2020) because correlative 
approaches assume that the modern-day relationship between 
marine species distribution and environmental conditions remains 
the same into the future, whereas mechanistic models permit 
marine species to respond dynamically to changing environmental 
forcing. Moreover, existing global projections of future biodiversity 
disproportionately focus on the effects sea surface temperature 
(Thomas et  al., 2012), typically overlooking other factors such 
as ocean acidification, deoxygenation and nutrient availability 
(Section 3.2.3), and often failing to account for natural adaptation 
(e.g., Section 3.3.4; see Box 3.1; Barton et al., 2016; Henson et al., 
2021).

3
442Chapter 3 Oceans and Coastal Ecosystems and Their Services
3.4.3.3.3 Abrupt ecosystem shifts and extreme events
Climate-change-driven changes in ocean characteristics and the 
frequency and intensity of extreme events (Section 3.2) increase the 
risk of persistent, rapid and abrupt ecosystem change (very high 
confidence), often referred to as ecosystem collapses or regime shifts 
(AR6 WGI Chapter 9; Collins et al., 2019a; Canadell and Jackson, 2021; 
Ma et  al., 2021). Such abrupt changes include altering ecosystem 
structure, function and biodiversity outside the range of natural 
fluctuations (Collins et al., 2019a; Canadell and Jackson, 2021). They 
can involve mass-mortality events and ‘tipping points’ or ‘critical 
transitions’, where strong positive feedbacks within an ecosystem lead 
to self-sustaining change (Figure 3.19a; Scheffer et al., 2012; Möllmann 
et  al., 2015; Biggs et  al., 2018). Abrupt ecosystem shifts have been

observed in both large open-ocean ecosystems and coastal ecosystems 
(Section 3.4.2), with dramatic social consequences through significant 
loss of diverse ecosystem services (high confidence) (Section 3.5; Biggs 
et al., 2018; Pinsky et al., 2018; Beaugrand et al., 2019; Collins et al., 
2019a; Filbee-Dexter et al., 2020b; Huntington et al., 2020; Trisos et al., 
2020; Turner et al., 2020b; Canadell and Jackson, 2021; Ma et al., 2021; 
Ruthrof et al., 2021). A summary of previous assessments of abrupt 
ecosystem shifts and extreme events is provided in Table 3.21.
Abrupt ecosystem shifts are associated with large-scale patterns 
of climate variability (Alheit et  al., 2019; Beaugrand et  al., 2019; 
Lehodey et al., 2020), some of which are projected to intensify with 
climate change (medium confidence) (WGI AR6 Chapter  1; Wang 
et al., 2017a; Collins et al., 2019a; Chen et al., 2021). Over the past 60  years, abrupt ecosystem shifts have generally followed El Niño/

Southern Oscillation events of any strength, but some periods had 
geographically limited ecological shifts (~0.25% of the global ocean 
in 1984–1987) and others more extensive shifts (14% of the global 
ocean in 2012–2015) (medium confidence) (Figure 3.19b; Beaugrand 
et al., 2019). Typically, interacting drivers, such as eutrophication and 
overharvest, reduce ecosystem resilience to climate extremes (e.g., 
MHWs, cyclones) or gradual warming, and hence promote ecosystem 
shifts (high confidence) (Figure 3.19a; Rocha et al., 2015; Biggs et al., 
2018; Babcock et al., 2019; Turner et al., 2020b; Bergstrom et al., 2021; 
Canadell and Jackson, 2021; Tait et al., 2021). Also, shifts in different 
ecosystems may be connected through common drivers or through 
cascading effects (medium confidence) (Rocha et al., 2018a).
Recent MHWs (Section 3.2.2.1) have caused major ecosystem shifts 
and mass mortality in oceanic and coastal ecosystems, including

corals, kelp forests and seagrass meadows (Sections 3.4.2.1, 3.4.2.3, 
3.4.2.5, 3.4.2.6, 3.4.2.10; Cross-Chapter  Box  MOVING SPECIES in 
Chapter 5; Cross-Chapter Box EXTREMES in Chapter 2), with dramatic 
declines in species foundational for habitat formation or trophic flow, 
biodiversity declines, and biogeographic shifts in fish stocks (very 
high confidence) (Table  3.15; Cross-Chapter  Box  MOVING SPECIES 
in Chapter  5; Canadell and Jackson, 2021). Three major bleaching 
episodes on Australia’s Great Barrier Reef in 5  years corresponded 
with extreme temperatures in 2016, 2017 and 2020 (Pratchett et al., 
2021). Between 1981 and 2017, MHWs have increased more than 20-
fold due to anthropogenic climate change (Section 3.2.2.1; WGI AR6 
Chapter 9; Laufkötter et al., 2020; Fox-Kemper et al., 2021), increasing 
Changes in the latitudinal distribution of marine species richness
(a) Observed species richness (b) Projected changes in species richness
-50°Latitude
0°50°

2000 4000 6000 0
Species richness % change in species richness-50°0°50°1955–19741975–19941995–2015
RCP8.5RCP4.5
-100 -50 50 100 150 200 250 0
Figure 3.18 |  Changes in the latitudinal distribution of marine species richness.
(a) Observed species richness for three historical periods. The observed latitudinal patterns in species richness are for a suite of taxonomic groups based on 48,661 marine species 
(Chaudhary et al., 2021).
(b) Projected changes in species richness under RCP4.5 and RCP8.5 are calculated as differences by grid cell by 2100 relative to 2006. Latitudinal global median (5° moving 
average). (Based on Figure 1b,c in García Molinos et al., 2016.) The projected latitudinal patterns in changes in species richness under climate change are based on a numerical 
model that includes species-specific information across a suite of taxonomic groups, based on 12,796 marine species (García Molinos et al., 2016).

3
443Oceans and Coastal Ecosystems and Their Services  Chapter 3
Box 3.2 | Marine Birds and Mammals
Marine birds (seabirds and shorebirds) and mammals include charismatic species and species that are economically, culturally and 
ecologically important (Sydeman et al., 2015; Albouy et al., 2020; Pimiento et al., 2020). Their long generation times and slow population 
growth suggests limited evolutionary resilience to rapid climate change (Section 3.3.4; Sydeman et al., 2015; Miller et al., 2018). According 
to the Red List Species Assessments of the International Union for Conservation of Nature (IUCN, 2020), the greatest current hazards to 
these groups include human use of biological resources and areas, invasive species and pollution (see Figure Box 3.2.1; Dias et al., 2019; 
Lusseau et al., 2021). Impacts of climate change and severe weather are ranked among the five most-important hazards influencing

131 and 45 bird and mammal species, respectively (see Figure Box 3.2.1 for selection of species), including 24 bird and 7 mammal 
species that are currently listed as endangered, critically endangered or threatened. Furthermore, according to these IUCN assessments, 
climate change and severe weather are expected to impact an additional 122 and 18 marine bird and mammal species over the next 
50–100 years, respectively (see Figure Box 3.2.1; Dias et al., 2019).
0Hazard assessment for marine birds and mammals
(a) Number of marine bird species
Residential
and
commercial
developmentAgriculture
and
aquacultureEnergy
and miningTransportation 
and service
corridorsBiological
resource
useHuman
intrusions
and
disturbanceNatural
system
modificationsInvasive
and other
problematic
species, genes
and diseasesPollution Geological
eventsClimate
change
and
severe
weatherOther
options050100150200250300350
120
100
80
60
40

20(b) Number of marine mammal speciesAdditional species expected to be exposed to these threats over the next 50–100 years Species currently impacted by different hazards
Figure Box 3.2.1 |  Hazard assessment for marine birds and mammals. Number of (a) marine birds and (b) mammals currently impacted by different hazards 
(blue), and numbers of additional species expected to be exposed to these threats over the next 50–100 years (red), as assessed in the International Union for Conservation 
of Nature Red List (IUCN, 2020). Seabird species include species in the key orders Sphenisciformes, Pelecaniformes, Suliformes, Anseriformes, Procellariiformes and 
Charadriiformes categorised as inhabitants of marine ecosystems (n = 483 species, assessed in the period 2016–2019). Marine mammal species include the species 
reviewed by Lusseau et al. (2021) (n = 136 species, assessed in the period 2008–2019).

Marine birds and mammals are vulnerable to climate-induced loss of breeding and foraging habitats such as sea ice (Section 3.4.2.12), 
sandy beaches (Section 3.4.2.6), salt marshes (Section 3.4.2.5) and seagrass beds (high confidence) (Section 3.4.2.5; Sydeman et al., 2015; 
Bindoff et al., 2019a; Ropert-Coudert et al., 2019; Von Holle et al., 2019; Albouy et al., 2020; Amano et al., 2020; Bestley et al., 2020; Grose 
et al., 2020). With warming, shorebird population abundances decline in the tropics, likely due to heat stress and habitat loss, and increase 
at higher latitudes (Amano et al., 2020). Marine mammals dependent on sea ice habitat are particularly vulnerable to warming (medium 
confidence) (Albouy et al., 2020; Bestley et al., 2020; Lefort et al., 2020), yet vulnerability can differ between populations. Ongoing sea ice

3
444Chapter 3 Oceans and Coastal Ecosystems and Their Services
loss is decreasing some polar bear populations while others remain stable, likely related to past harvesting history, regional differences in 
sea ice phenology and ecosystem productivity (Hamilton and Derocher, 2019; Molnár et al., 2020). Nevertheless, even under an intermediate 
emission scenario RCP4.5, increasing ice-free periods will likely reduce both recruitment and adult survival across most polar bear 
populations over the next four decades, threatening their existence (medium confidence) (see Figure Box 3.2.2; Molnár et al., 2020).
Climate change is affecting marine food-web dynamics (high confidence) (Sections 3.4.2, 3.4.3), and the vulnerability and adaptive 
capacity of marine birds and mammals to such changes is linked to the species’ breeding and feeding ecology. Higher-vulnerability

species include central-place foragers (confined to, for example, breeding colonies fixed in space), diet and habitat specialists, and species 
with restricted distributions such as marine mammal populations in SES (medium confidence) (McMahon et al., 2019; Ropert-Coudert 
et al., 2019; Albouy et al., 2020; Grose et al., 2020; Sydeman et al., 2021). Surface-feeding and piscivorous marine birds appear to be more 
vulnerable to food-web changes than diving seabirds and planktivorous seabirds (medium confidence) (Sydeman et al., 2021). During 
the 2014–2015 Pacific heatwave, around 1 million piscivorous common murres died along a 1500 km coastal stretch in the Pacific USA 
due to reduced prey availability (Jones et al., 2018b; Piatt et al., 2020). Marine birds are vulnerable to phenological shifts in food-web 
dynamics, as they have limited phenotypic plasticity of reproductive timing, with potentially little scope for evolutionary adaptation

(medium confidence) (Keogan et al., 2018), although changes in reproduction timing are observed in several species (Section 3.4.4.1; 
Sydeman et al., 2015; Descamps et al., 2019; Sauve et al., 2019). There is limited evidence of marine mammals’ capacity to adapt to 
shifting phenologies, but observed responses include changes in the onset of migrations, moulting and breeding (Section 3.4.4.1; Ramp 
et al., 2015; Hauser et al., 2017; Beltran et al., 2019; Bowen et al., 2020; Szesciorka et al., 2020).
Modelled risk timelines for demographic impacts on circumpolar polar bear subpopulations
and associated confidence assessments, due to extended fasting periods with loss of sea ice
(a) RCP4.5 (b) RCP8.5
Chukchi SeaWestern Hudson Bay
Southern Hudson  bay
Davis Strait
Foxe Basin
Baffin Bay
Southern Beaufort Sea
Laptev Sea
Kara Sea
East Greenland Sea
Northern Beaufort Sea
Queen Elizabeth Islands
1980 2000 2020 2040 2060 2080 1980 2000 2020 2040 2060 2080

Risk of demographic effects:
Cub recruitment
Adult male survival
Adult female survival
Very likely Likely Possible Inevitable
Figure  Box  3.2.2 |   Modelled risk timelines for demographic impacts on circumpolar polar bear subpopulations, and associated confidence 
assessments, due to extended fasting periods with loss of sea ice. Years of first impact on cub recruitment (yellow), adult male survival (blue) and adult female 
survival (red) are shown for the (left) RCP4.5 and (right) RCP8.5. (Data from Molnár et al., 2020).Box 3.2 (continued)

3
445Oceans and Coastal Ecosystems and Their Services  Chapter 3
Increased emergence of infectious disease in mammals and birds is expected with ocean warming, due to new transmission pathways 
from changing species distributions, higher species densities caused by habitat loss and increased vulnerability due to environmental 
stress on individuals (limited evidence) (Sydeman et al., 2015; VanWormer et al., 2019; Sanderson and Alexander, 2020). Marine birds and 
mammals are likely to suffer from increased mortalities due to increasing frequencies of HABs, and of extreme weather, at sea, on sea ice, 
and in terrestrial breeding habitats (Broadwater et al., 2018; Gibble and Hoover, 2018; Ropert-Coudert et al., 2019; Grose et al., 2020). Also, 
climate-change driven distributional shifts have strengthened interactions with other anthropogenic impacts, through, for example,

increasing risks of ship strikes and bycatch (medium confidence) (e.g., Hauser et al., 2018; Krüger et al., 2018; Record et al., 2019; Santora 
et al., 2020).Box 3.2 (continued)
the risk of abrupt ecosystem shifts (high confidence) (Figure  3.19a; 
Cross-Chapter Box EXTREMES in Chapter 2; van der Bolt et al., 2018; 
Garrabou et al., 2021; Wernberg, 2021).
Ecosystems can recover from abrupt shifts (e.g., Babcock et al., 2019; 
Christie et al., 2019; Medrano et al., 2020). However, where climate 
change is a dominant driver, ecosystem collapses increasingly cause 
permanent transitions (high confidence), although the extents of 
such transitions depend on the emission scenario (Trisos et al., 2020; 
Garrabou et al., 2021; Klein et al., 2021; Pratchett et al., 2021; Wernberg, 
2021). Over the coming decades, MHWs are projected to very likely 
become more frequent under all emission scenarios (Section 3.2; WGI 
AR6 Chapter  9; Fox-Kemper et  al., 2021), with intensities and rates

too high for recovery of degraded foundational species, habitats or 
biodiversity (medium confidence) (Babcock et  al., 2019; Garrabou 
et al., 2021; Klein et al., 2021; Serrano et al., 2021; Wernberg, 2021). 
Emission pathways that result in temperature overshoot above 1.5oC 
will increase the risks of abrupt and irreversible shifts in coral reefs and 
other vulnerable ecosystems (Section 3.4.4).3.4.3.3.4 Time of emergence: species exposure to altered 
environments
Since SROCC, more studies have assessed the time of emergence for 
climate-induced drivers (Section  3.2.3) and the ecosystem attributes 
through which the impacts manifest. However, as in previous assessments 
(Table  3.22), the time of emergence for a given driver or ecosystem 
attribute depends on the reference period, the definition of the signal 
emergence threshold and the spatio-temporal scales considered (see 
Box 5.1 in SROCC; Kirtman et al., 2013; Bindoff et al., 2019a).

Anthropogenically driven changes in chlorophyll-a  concentrations 
across an ensemble of 30 ESMs are expected to exceed natural variability 
under RCP8.5 by 2100 in ~65–80% of the global oceans, when the 
natural variability is calculated using the ensemble’s standard deviation 
(Schlunegger et al., 2020); however, if two standard deviations are used, 
then significant trends in chlorophyll-a  concentration are expected 
under RCP8.5 across ~31% of the global oceans by 2100 (Dutkiewicz 
et  al., 2019). In contrast, the anthropogenic signal in phytoplankton 
community structure, which has a lower natural variability, will emerge 
under RCP8.5 across 63% of the ocean by 2100 when two standard 
deviations are used (limited evidence) (Dutkiewicz et al., 2019).Table 3.21 |  Summary of previous IPCC assessments of observed and projected abrupt ecosystem shifts and extreme events
Observations of abrupt ecosystem shifts and extreme

events were not assessed in this report.‘Warming and acidification will lead to coral bleaching, mortality, and decreased constructional ability (high confidence), 
making coral reefs the most vulnerable marine ecosystem with little scope for adaptation. Temperate seagrass and kelp 
ecosystems will decline with the increased frequency of heatwaves and sea temperature extremes as well as through the 
impact of invasive subtropical species (high confidence).’
SROCC (Collins et al., 2019a)
‘Marine heatwaves (MHWs), periods of extremely high 
ocean temperatures, have negatively impacted marine 
organisms and ecosystems in all ocean basins over the last 
two decades, including critical foundation species such as 
corals, seagrasses and kelps (very high confidence).’‘Marine heatwaves are projected to further increase in frequency, duration, spatial extent and intensity (maximum

temperature) (very high confidence). Climate models project increases in the frequency of marine heatwaves by 2081–2100, 
relative to 1850–1900, by approximately 50 times under RCP8.5 and 20 times under RCP2.6 ( medium confidence).’
‘Extreme El Niño and La Niña events are projected to likely increase in frequency in the 21st century and to likely intensify 
existing hazards, with drier or wetter responses in several regions across the globe. Extreme El Niño events are projected to 
occur about twice as often under both RCP2.6 and RCP8.5 in the 21st century when compared to the 20th century (medium 
‘Limiting global warming would reduce the risk of impacts of MHWs, but critical thresholds for some ecosystems (e.g., kelp 
forests, coral reefs) will be reached at relatively low levels of future global warming (high confidence).’

3
446Chapter 3 Oceans and Coastal Ecosystems and Their Services
The time of emergence of climate impacts on ecosystems will be 
modulated jointly by species-specific adaptation potential (Section 3.3.4; 
Jones and Cheung, 2018; Collins et al., 2020; Gamliel et al., 2020; Miller 
et  al., 2020a), speed of range shifts and spatial reorganisation (high 
confidence) (Sections  3.3, 3.4.2, 3.4.3). These ecosystem responses 
complicate projections of the time of emergence of environmental 
properties that impact biogeochemical cycling (Schlunegger et  al., 
2019; Schlunegger et  al., 2020; Wrightson and Tagliabue, 2020), 
ecosystem structure and biodiversity (Figure 3.20a,c; Dutkiewicz et al., 
2019; Trisos et al., 2020), and higher trophic levels, including fisheries 
targets (Cheung and Frölicher, 2020). Better accounting for multiple 
interacting factors in ESMs (see Box 3.1) will provide insight into how 
marine ecosystems will respond to future climate (high confidence).

6 SROCC reported that declines in total marine animal biomass have been recomputed using 1995–2014 as the baseline period and the very likely ranges (5–95%) are now computed from the model  
ensemble ranges assuming a normal distribution.The time of emergence of ecosystem responses supports planning 
for specific time-bound actions to reduce risks to ecosystems 
(Section 3.6.3.2.1; Bruno et al., 2018; Trisos et al., 2020). Although under 
RCP8.5, climate refugia from SST after 2050 are primarily in the Southern 
Ocean in tropical waters, these refugia are mainly from deoxygenation 
(Bruno et  al., 2018). Marine assemblages in these places will be 
exposed to unprecedented temperatures after 2050, peaking in 2075 
(Figure 3.20a,b; Trisos et al., 2020). In contrast, changes in phytoplankton 
community structure will emerge earlier, primarily in the Pacific Ocean 
subtropics and through much of the North Atlantic Ocean (Figure 3.20c,d;

Dutkiewicz et  al., 2019). Under RCP8.5, changes in phytoplankton 
community structure and, to a lesser extent, exposure of marine species 
to unprecedented temperatures, will emerge earlier in marine protected 
areas (MPAs), covering ~7.7% of the global oceans (Section 3.6.2.3.2.1; 
UNEP-WCMC and IUCN, 2020; UNEP-WCMC and IUCN, 2021) as 
compared with non-MPAs (Figure 3.20b,d). Such assessment can support 
planning for future MPA placement and extent. Because MPAs can 
serve as refugia from non-climate drivers (Sections  3.6.2.3, 3.6.3.2.1), 
they facilitate opportunities for adaptation among marine species and 
communities in coastal oceans (Section 3.4.2).
3.4.3.4 Biomass
3.4.3.4.1 Observed changes
Observed changes in biomass in the global ocean, beyond those for 
phytoplankton (Table 3.23), have not routinely been attributed to climate-
induced drivers, but rather to the compound effects of multiple drivers,

especially fishing (Christensen et al., 2014; Palomares et al., 2020). We 
therefore do not assess observed changes in ocean biomass here.
3.4.3.4.2 Projected changes
Based on an ensemble of CMIP5 ESMs, SROCC projected declines in 
global zooplankton biomass by 2100 dependent on emission scenario 
(low confidence) (Table 3.23). The new CMIP6 ESM ensemble projects 
a decline in global zooplankton biomass by −3.9  ±  8.2% (very 
likely range) and −9.0  ±  8.9% in the period 2081–2100 relative to 
1995–2014 under SSP1-2.6 and SSP5-8.5, respectively (Figure 3.21d; 
Kwiatkowski et  al., 2020), thus reinforcing the SROCC assessment 
albeit with greater inter-model uncertainties.
Using an ensemble of global-scale marine ecosystem and fisheries 
models (Fish-MIP) (Tittensor et  al., 2018) with the CMIP5 ESM 
ensemble, SROCC concludes that projected ocean warming and 
decreased phytoplankton production and biomass will reduce global

marine animal biomass during the 21st century (medium confidence). 
The simulated declines (with very likely range) are −3.5 ± 4.8% and 
−14.0  ±  14.6% under RCP2.6 and RCP8.5, respectively, by 2080–
2099 relative to 1995–2014 (SROCC Section  5.2.3; Bindoff et  al., 
2019a; Lotze et al., 2019)6. Updated Fish-MIP simulations with CMIP6 
(Figure 3.21g,h,i) confirm the projected decline in total marine animal 
biomass in the 21st century (Tittensor et al., 2021). The simulated Observed ecological regime shifts
and their drivers in the oceans
960 1970 1980 1990 2000 2010Regime 2Current state Critical threshold
Regime 1
2,5005,0007,50010,000Shift in magnitude and extent of abrupt community shifts
in the global ocean(a)
0Shock
(b)
Figure 3.19 |  Observed ecological regime shifts and their drivers in the oceans.
(a) A conceptual representation of ecosystem resilience and regime shifts. Shift 
from Regime 1 to Regime 2 can be triggered by either a large shock (i.e., an abrupt

environmental transition) or gradual internal or external change that erodes the dominant 
balancing feedbacks, reducing ecosystem resilience (indicated by the shallower dotted line, 
relative to the deeper ‘valley’ reflecting higher resilience). (Based on Biggs et al., 2018).
(b) The sum of the magnitude and extent of the abrupt community shifts that has been 
estimated at each geographic cell in the global ocean during 1960–2014, calculated as 
the ratio of the amplitude of the change in a particular year to the average magnitude 
of the change over the entire time series (thus, is dimensionless). (Based on Beaugrand 

3
447Oceans and Coastal Ecosystems and Their Services  Chapter 3
FAQ 3.3 | Are we approaching so-called tipping points in the ocean and what can we do about it?
A tipping point is a threshold beyond which an abrupt or rapid change in a system occurs. Tipping points that have already been reached in 
ocean systems include the melting of sea ice in the Arctic, thermal bleaching of tropical coral reefs and the loss of kelp forests. Human-induced 
climate change will continue to force ecosystems into abrupt and often irreversible change, without strong mitigation and adaptation action.
Where are we reaching tipping points in the ocean and what can we do about it?
Coral transitio nIce cover transition
Kelp transitionSmall Island developing states  Indigenous people s
Small scale fisher sWomen/vulnerable groups  
Warmin g
Extrem eevents
Sealevel risePacific EquatorialPacific EquatorialCalifornia EBUSCalifornia EBUS

Indian E CBCIndian E CBCAtlantic NEAtlantic NE Pacific NWPacific NW
Pacific NEPacific NE
SE AsiaSE Asia
Pacific SWPacific SWPacific SWPacific SWIndianIndian
EastEastIndianIndian
WestWest
AntarcticaAntarcticaArcticArctic
Atlantic NWAtlantic NW
CaribbeanCaribbean
SESSESPacific NEPacific NE
Touris m
Fisherie s
Transpor t
Climat echange hazard s Social vulnerability Economic vulnerability TransitionPacific EquatorialCalifornia EBUS
Indian E CBCAtlantic NE Pacific NW
Pacific NE
SE Asia
Pacific SWPacific SWIndian
EastIndian
West
Atlantic NW
Caribbean
SESPacific NE
IIIc
e
c
o
v
e
ttrtr
an
ititsitiii
o
n
= no data
Figure FAQ3.3.1 |  Global map with examples of tipping points that have been passed in ocean systems around the world. Tipping points in ecological 
systems are linked to increasing impacts and vulnerability of dependent human communities. SES: semi-enclosed sea; EBUS: eastern boundary upwelling system; CBC; 
coastal boundary current.

A gradual change in water temperature or oxygen concentration can lead to a fundamental shift in the structure and/
or composition of an ecosystem when a tipping point is exceeded. For example, all species have upper temperature 
limits below which they can thrive. In the tropics, prolonged warm temperatures can cause fatal ‘bleaching’ of 
tropical corals, leading reef ecosystems to degrade and become dominated by algae. In temperate regions, MHWs 
can kill or reduce the growth of kelp, threatening the other species that depend on the tall, canopy-forming marine 
plants for habitat. In the Arctic, rising temperatures are melting sea ice and reducing the available habitat for 
communities of ice-dependent species.
Once a tipping point is passed, the effects can be long-lasting and/or irreversible over time scales of decades or 
longer. An ecosystem or a population can remain in the new state, even if the driver of the change returns to

previous levels. For example, once a coral reef has been affected by bleaching, it can take decades for corals to

3
448Chapter 3 Oceans and Coastal Ecosystems and Their Services
grow back, even if temperatures remain below the bleaching threshold. Crossing a tipping point can cause entire 
populations to collapse, causing local extinctions.
Tipping points are widespread across oceanic provinces and their ecosystems for climate variables like water 
temperature, oxygen concentration and acidification. Evidence suggests that ocean tipping points are being 
surpassed more frequently as the climate changes; scientists have estimated that abrupt shifts in communities of 
marine species occurred over 14% of the ocean in 2015, up from 0.25% of the ocean in the 1980s. Other human 
stressors to the ocean, including habitat destruction, overfishing, pollution and the spread of diseases, combine 
with climate change to push marine systems beyond tipping points. As an example, nutrient pollution from land 
together with climate change can lead to low-oxygen coastal areas referred to as ‘dead zones’.

Human communities can also experience tipping points that alter people’s relationships with marine ecosystem 
services. Indigenous Peoples and local communities may be forced to move from a particular location due to SLR, 
erosion or loss of marine resources. Current activities that help sustain Indigenous Peoples and their cultures may 
no longer be possible in the coming decades, and traditional diets or territories may have to be abandoned. These 
tipping points have implications for physical and mental health of marine-dependent human communities.
Adaptation solutions to the effects of ecological tipping points are rarely able to reverse their environmental 
impacts, and instead often require human communities to transform their livelihoods in different ways. Examples 
include diversifying income by shifting from fishing to tourism and relocating communities threatened by flooding

to other areas to continue their livelihoods. Tipping points are being passed already in coral reefs and polar systems, 
and more will probably be reached in the near future given climate-change projections. Nevertheless, the chances 
of moving beyond additional tipping points in the future will be minimised if we reduce greenhouse gas emissions 
and we also act to limit other human impacts on the ocean, such as overfishing and nutrient pollution.Box FAQ 3.3 (continued)
Table 3.22 |  Conclusions from previous IPCC assessments about projected time of emergence on coastal, ocean and deep-sea systems
Oceanic systems and chapter 
subsectionProjections
Coastal (Section 3.4.2)‘Multiple [climate-impact drivers] will emerge [...] in the 21st century under RCP8.5, while the time of emergence will be later and with less [climatic 
hazards] under RCP2.6. [Non-climate] impacts such as eutrophication add to, and in some cases, exacerbate these large-scale slow climate drivers

beyond biological thresholds at local scale (e.g., deoxygenation)’ (Section 5.3.7 in SROCC; Bindoff et al., 2019a).
Epipelagic (Section 3.4.3)‘Observed range shifts in response to climate change in some regions such as the north Atlantic are strongly influenced by warming due to both 
multi-decadal [climate change and] variability, suggesting that there is a longer time of emergence of range shifts from natural variability and a need 
for longer biological time series for robust attribution’ (Section 5.2.3.1.1 in SROCC; Bindoff et al., 2019a).
Open ocean (Section 3.4.3)‘[The timing] for five primary drivers of marine ecosystem change (surface warming and acidification, oxygen loss, nitrate concentration and net 
primary production change) are all prior to 2100 for over 60% of the ocean area under RCP8.5 and over 30% under RCP2.6 (very likely)’ (Figure 1 in 
Box 5.1 in SROCC, Box 5.1 in SROCC, Executive Summary in SROCC Chapter 5; Bindoff et al., 2019a).

‘Anthropogenic signals are expected to remain detectable over large parts of the ocean, even for the RCP2.6 scenario for pH and SST, but are likely 
[to be less conspicuous] for nutrients and NPP [net primary production] in the 21st century. For example, for the open ocean, the anthropogenic pH 
signal in Earth System Models’ (ESM) historical simulations is very likely to have emerged for three-quarters of the ocean prior to 1950, and it is very 
likely over 95% of the ocean has already been affected, with little discernible difference between scenarios’ (Executive Summary in SROCC Chapter 
5, Box 5.1 in SROCC; Bindoff et al., 2019a). ‘The climate signal of oxygen loss will very likely emerge by 2050 with a very likely range of 59–80% by 
2031–2050 and rising with a very likely range of 79–91% of the ocean area by 2081–2100 (RCP8.5 emissions scenario). The emergence of oxygen

loss is smaller in area for the RCP2.6 scenario in the 21st century and by 2090 the [area where emergence is evident is declining].’ It has also been 
shown that signatures of altered oxygen solubility or utilisation may emerge earlier than for oxygen levels (Executive Summary in SROCC Chapter 5, 
Box 5.1 in SROCC; Bindoff et al., 2019a).
Deep sea (Box 3.3)‘Emergence of risk is expected to occur later at around the mid-21st century under RCP8.5 for abyssal plain and chemosynthetic ecosystems (vents 
and seeps)’ (Section 5.2.5 in SROCC; Bindoff et al., 2019a). ‘All deep seafloor ecosystems are expected to be subject to at least moderate risk under 
RCP8.5 by the end of the 21st century, with cold water corals undergoing a transition from moderate to high risk below 3°C’ (SM5.2 in SROCC; 
Bindoff et al., 2019b).

3
449Oceans and Coastal Ecosystems and Their Services  Chapter 3
declines (with very likely range) are −5.7 ± 4.1% and −15.5 ± 8.5% 
1995–2014 (Figure 3.21g), showing greater declines and lower inter-
model uncertainties (Tittensor et al., 2021). These declines result from 
combined warming and decreased primary production (with low 
confidence in future changes in primary production; Section 3.4.3.5) 
and are amplified at each trophic level within all ESM and marine 
ecosystem model projections across all scenarios (medium confidence) 
(Kwiatkowski et al., 2019; Lotze et al., 2019; Tittensor et al., 2021). 
However, there is limited evidence about how underlying food-web 
mechanisms amplify the climate signal from primary producers to 
higher trophic levels, and several putative mechanisms have been 
proposed (Section 3.4.4.2.2; Chust et al., 2014a; Stock et al., 2014;

Kwiatkowski et al., 2019; Lotze et al., 2019; Heneghan et al., 2021). 
As assessed in SROCC, the biomass projections contain considerable 
regional variation with declines in tropical to temperate regions 
and strong increases in total animal biomass are projected in polar 
regions under high-emission scenarios, with climate-change effects that are spatially similar but less pronounced under lower-emission 
scenarios (Figure 3.21b,c,e,f,h,i; Tai et al., 2019; Tittensor et al., 2021).
SROCC assessed that reduced food supply to the deep sea will 
drive a reduction in abyssal seafloor biota by 2100 for RCP8.5 
(Table  3.23). Simulations from one size-resolved benthic biomass 
model coupled to an ocean-biogeochemistry model forced with the 
CMIP5 ESM HadGEM2-ES (Yool et al., 2017) project a decline in the 
globally integrated total seafloor biomass of −1.1 and −17.6% by 
2100 under RCP2.6 and RCP8.5, respectively (limited evidence, high

agreement). In waters shallower than 100 m, total benthic biomass 
is projected to increase by 3.2% on average by 2100 under RCP8.5, 
primarily driven by warming-increased growth rates (Yool et  al., 
2013), while at depths >2000 m (representing 83% of the ocean 
seafloor), declines of −32% arise from climate-driven decreases in 
surface primary production and POC flux to the seafloor (Yool et al., 
2013; Kelly-Gerreyn et al., 2014; Yool et al., 2015; Yool et al., 2017). 
These patterns are qualitatively similar under RCP2.6, except in the Time of exposure to altered environments
(a)
2100 2020 2040 2060 2000 2080(c)
(b)
(d)
Relative proportion of area Relative proportion of area
MPA
Non-MPA
2100 2020 2040 2060 2000 2080
Figure 3.20 |  Time of exposure to altered environments.
(a) Simulated spatial variation in the time of exposure of marine species to unprecedented temperatures under RCP8.5. Time of exposure is quantified as the median year after which

local species are projected to encounter temperatures warmer than the historical maximum within their full geographic range for a period of at least 5 years. This estimate is based on 
22 Coupled Model Intercomparison Project 5 (CMIP5) models, and is drawn from data presented by Trisos et al. (2020). Only regions that have times of emergence by 2100 are shown.
(b) The distribution in the time of exposure to unprecedented temperatures within marine assemblages (Trisos et al., 2020) under RCP8.5 in marine protected areas (in turquoise) 
and in non-marine protected areas (in purple). Values were calculated after regridding to equal-area 0.5° hexagons.
(c) Time of emergence for phytoplankton community-structure changes (based on a proxy–ecosystem-model reflectance at 500 nm) under RCP8.5. Only regions with statistically

significant (p  < 0.05) trends that are presently largely ice free and have times of emergence by 2100 are shown. (Based on the results of one numerical model from Dutkiewicz et al., 2019).
(d) The distribution in the time of emergence for changes in phytoplankton community structure (same proxy as in panel c) (Dutkiewicz et al., 2019) under RCP8.5 in marine 
protected areas (in turquoise) and in non-marine protected areas (in purple). Values were calculated after regridding to equal-area 0.5° hexagons.

3
450Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.23 |  Summary of previous IPCC assessments of changes in open ocean and deep-sea biomass
Measure Observations Projections
AR5 WGII 
(Hoegh-Guldberg 
et al., 2014; 
Pörtner et al., 
2014)
Chlorophyll-a/
phytoplankton 
biomass‘Phytoplankton biomass: the approximately 15-year archived time series of 
satellite-chlorophyll (as a proxy of phytoplankton biomass) is too short to reveal trends 
over time and their causes’ (WGII AR5 Section 6.1.2; Pörtner et al., 2014).
‘Chlorophyll concentrations measured by satellites have decreased in the subtropical 
gyres of the North Pacific, Indian and North Atlantic oceans by 9, 12 and 11%, 
respectively, over and above the inherent seasonal and interannual variability from 1998 
to 2010 (high confidence; p ≤ 0.05). Significant warming over this period has resulted 
in increased water-column stratification, reduced mixed-layer depth and possibly

decreases in nutrient availability and ecosystem productivity (limited evidence, medium 
agreement). The short time frame of these studies against well-established patterns of 
long-term variability leads to the conclusion that these changes are about as likely as not 
due to climate change’ (WGII AR5 Chapter 30; Hoegh-Guldberg et al., 2014).‘Owing to contradictory observations there is currently uncertainty about 
the future trends of major upwelling systems and how their drivers 
(enhanced productivity, acidification and hypoxia) will shape ecosystem 
characteristics (low confidence)’ (WGII AR5 Chapter 6 Executive Summary; 
Pörtner et al., 2014).
Animal biomass Observed changes in animal biomass were not assessed in this report.‘The climate-change-induced intensification of ocean upwelling in some 
eastern boundary systems, as observed in the last decades, may lead 
to regional cooling, rather than warming, of surface waters and cause

enhanced productivity (medium confidence), but also enhanced hypoxia, 
acidification and associated biomass reduction in fish and invertebrate 
stocks’ (WGII AR5 Chapter 6 Executive Summary; Pörtner et al., 2014).
SROCC (Bindoff 
et al., 2019a)
biomass‘[Changes reported] in overall open-ocean chlorophyll levels (a proxy of phytoplankton 
biomass) of less than ±1% yr–1 for individual time periods. Regionally, trends of 
±4% between 2002 and 2015 for different regions are found when different satellite 
products are merged, with increases at high latitudes and moderate decreases at low 
latitudes’ (SROCC Section 5.2.2.6; Bindoff et al., 2019a).Projected changes in chlorophyll-a/phytoplankton biomass were not 
assessed in this report.
Animal biomassObserved changes in open-ocean and deep-sea biomass were not assessed in this 
report.‘There is high agreement in model projections that global zooplankton

biomass will very likely reduce in the 21st century, with projected decline 
under RCP8.5 almost doubled that of RCP2.6 (very likely). However, 
the strong dependence of the projected declines on phytoplankton 
production (low confidence) and simplification in representation of the 
zooplankton communities and food web render their projections having 
low confidence.’
The global biomass of marine animals, including those that contribute to 
fisheries, is projected to decrease by 4.3 ± 2.0% (95% confidence interval) 
and 15.0 ± 5.9% under RCP2.6 and RCP8.5, respectively, by 2080–2099 
relative to 1986–2005, while the decrease is around 4.9% by 2031–2050 
across all RCP2.6 and RCP8.5 (very likely). Regionally, total animal biomass 
decreases largely in tropical and mid-latitude oceans ( very likely).
‘Projected decrease in upper-ocean export of organic carbon to the deep 
seafloor is expected to result in a loss of animal biomass on the deep

seafloor by 5.2–17.6% by 2090–2100 compared to the present (2006–2015) 
under RCP8.5 with regional variations (medium confidence). Some increases 
are projected in the polar regions, due to enhanced stratification in the 
surface ocean, reduced primary production and shifts towards small 
phytoplankton (medium confidence). The projected impacts on biomass in the 
abyssal seafloor are larger under RCP8.5 than RCP4.5 (very likely).’
WGI AR6 
Chapter 2 (Gulev 
biomassThe multi-sensor time series of chlorophyll-a concentration has been updated to cover 
two decades (1998–2018).
‘Global trends in chlorophyll-a for the last two decades are insignificant over large areas 
of the global oceans, but some regions exhibit significant trends, with positive trends in 
parts of the Arctic and the Antarctic waters (>3% yr–1), and both negative and positive 
trends (within ±3% yr–1) in parts of the tropics, subtropics and temperate waters.’

‘In the last two decades, the concentration of phytoplankton at the base of the marine 
food web, as indexed by chlorophyll concentration, has shown weak and variable trends 
in low and mid-latitudes and an increase in high latitudes ( medium confidence).’Projected changes in open-ocean and deep-sea biomass were not assessed 
in this report.

3
451Oceans and Coastal Ecosystems and Their Services  Chapter 3
Pacific and Indian Ocean basins, where some increased total seafloor 
biomass is projected (Yool et al., 2013). Updated simulations with the 
same benthic biomass model (Kelly-Gerreyn et al., 2014) forced with 
the CMIP6 ESM UKESM-1 project declines in total seafloor biomass of 
−9.8 and −13.0% by 2081–2100 relative to 1995–2014 for SSP1-2.6 
and SSP5-8.5, respectively (Figure 3.21j,k,l). These projected changes 
in benthic biomass are based on limited evidence. Development of 
ensemble projections forced with a range of ESMs and a benthic 
model that considers the ecological roles of temperature (Hunt and 
Roy, 2006; Reuman et  al., 2014), oxygen (Mosch et  al., 2012) and 
ocean acidification (Andersson et al., 2011) will provide opportunities 
to better quantify uncertainty in projected declines in total seafloor 
biomass under climate change.
Overall, ocean warming and decreased phytoplankton production

and biomass will drive a global decline in biomass for zooplankton 
(low confidence), marine animals (medium confidence) and seafloor 
benthos (low confidence), with regional differences in the direction 
and magnitude of changes (high confidence). There is increasing 
evidence that responses will amplify throughout the food web and 
at ocean depths, with relatively modest changes in surface primary 
producers translating into substantial changes at higher trophic levels 
and for deep-water benthic communities (medium confidence).
3.4.3.5 Changes in Primary Production and Biological Carbon 
Export Flux
3.4.3.5.1 Observed changes in primary production
Analyses of satellite-derived primary production over the past two 
decades (1998–2018) showed generally weak and negative trends (up 
to −3.0%) at low and mid latitudes (Kulk et  al., 2020). In contrast, 
positive trends occurred in large areas of the South Atlantic and South

Pacific Oceans, as well as in polar and coastal (upwelling) regions (up 
to +4.5%; Cross-Chapter Paper 6; Kulk et al., 2020). Data-assimilating 
ocean biogeochemical models estimate a global decline in primary 
production of 2.1% per decade in the period 1998–2015, driven by 
the shoaling mixed layer and decreasing nitrate concentrations (Gregg 
and Rousseaux, 2019). This is consistent with previous assessments 
that identified ocean warming and increased stratification as the main 
drivers (high confidence) affecting the regional variability in primary 
production Bindoff et al. (2019). However, as noted in SROCC and WGI 
AR6 Chapter 2 (Table 3.24; Gulev et al., 2021), observed interannual 
changes in primary production on global and regional scales are 
nonlinear and largely influenced by natural temporal variability, 
providing low confidence in the trends.
3.4.3.5.2 Projected changes in primary production
Across 10 CMIP5 and 13 CMIP6 ESM ensembles, global mean NPP is

projected to decline by 2080–2099 relative to 2006–2015, under all 
RCPs and SSPs (Kwiatkowski et al., 2020). However, under comparable 
radiative forcing, the CMIP6 multi-model mean projections of primary 
production declines (mean  ±  SD: −0.56  ±  4.12% under SSP1-2.6, 
and −3.00 ± 9.10% under SSP5-8.5) are less than those of previous 
CMIP5 models (3.42 ± 2.47% under RCP2.6, and 8.54 ± 5.88% under 
RCP8.5) (WGI AR6 Section 5.4.4.2; Kwiatkowski et al., 2020; Canadell et al., 2021). The inter-model uncertainty associated with CMIP6 NPP 
projections is larger than in CMIP5, and it is consistently larger than 
the scenario uncertainty. For each SSP across the CMIP6 ensemble, 
individual models project both increases and decreases in global 
primary production, reflecting a diverse suite of bottom-up and top-
down ecological processes, which are variously parameterised across 
models (Laufkötter et  al., 2015; Bindoff et  al., 2019a). Furthermore,

accurate simulation of many of the biogeochemical tracers upon which 
NPP depends (e.g., the distribution of iron; Tagliabue et  al., 2016; 
Bindoff et al., 2019a) remains a significant and ongoing challenge to 
ESMs (high confidence) (Séférian et al., 2020).
Regionally, multi-model mean changes in primary production show 
generally similar patterns of large declines in the North Atlantic and 
the western equatorial Pacific, while in the high latitudes, primary 
production consistently increases in CMIP5 and CMIP6 by 2100 
(Cross-Chapter Paper 6; Kwiatkowski et al., 2020). In the Indian Ocean 
and subtropical North Pacific, which were regions of consistent NPP 
decline in CMIP5 projections (Bopp et al., 2013), the regional declines 
are reduced in magnitude, less spatially extensive and are typically 
less robust in CMIP6. Further assessment of simultaneous changes in 
processes such as nutrient advection, nitrogen fixation, the microbial

loop and top-down grazing pressure (WGI AR6 Section  5.4.4.2; 
Laufkötter et al., 2015; Bindoff et al., 2019a; Canadell et al., 2021) are 
required to fully understand the regional primary production response 
in CMIP6 (Kwiatkowski et al., 2020). Given the regional variations in 
the estimates of primary production changes and the uncertainty in the 
representation of the dominant drivers, there remains low confidence 
in the projected global decline in NPP .
3.4.3.5.3 Observed processes driving changes in global export flux
The SROCC medium confidence assessment that warming, stratification, 
declines in productivity and changes in plankton community in the 
epipelagic zone result in reduced export of primary production to deeper 
layers (Table 3.24) is supported by subsequent literature (Bach et al., 
2019; Leung et al., 2021). Particulate organic carbon export efficiency 
is constrained by altered mixing and nutrient availability (Boyd et al.,

2019; Lundgreen et  al., 2019), particle fragmentation (Briggs et  al., 
2020) as well as viral, microbial and planktonic community structure 
(Fu et al., 2016; Guidi et al., 2016; Flombaum et al., 2020; Kaneko et al., 
2021) and metabolic rates (Cavan et al., 2019). These processes are 
strongly interlinked, and their net effect on primary production export 
from the upper ocean remains difficult to quantify observationally 
(Boyd et  al., 2019). Since SROCC, there is increasing evidence that 
ocean deoxygenation can alter zooplankton community structure 
(Wishner et al., 2018), zooplankton respiration rates (Cass and Daly, 
2014; Cavan et  al., 2017) and patterns of diel vertical migration 
(Aumont et  al., 2018), which may focus remineralisation of organic 
carbon at the upper margins of OMZs (Section 3.4.3.4 on depth shifts 
due to OMZ; Bianchi et al., 2013; Archibald et al., 2019).
Data on export flux from the upper ocean are limited either in coverage

and consistency (ship-board sampling) or duration (sediment traps), 
and are subject to considerable spatial variability (as shown in 
satellite observations (Boyd et al., 2019). As a result, trends are weak, 
inconsistent and often not statistically significant (Lomas et al., 2010;

3
452Chapter 3 Oceans and Coastal Ecosystems and Their Services
Cael et al., 2017; Muller-Karger et al., 2019; Xie et al., 2019). Deep-
ocean fluxes are similarly equivocal (Smith et al., 2018; Fischer et al., 
2019; Fischer et al., 2020). In coming years, an increasing number of 
Argo floats equipped with bio-optical sensors should help improve 
estimates of particle flux spatio-temporal variability (e.g., Dall’Olmo 
et al., 2016).Projected changes
SROCC and WGI AR6 reported global declines in POC export flux, from 
−8.9 to −15.8% by 2100 relative to 2000 under RCP8.5 in CMIP5 
models, and −2.5 to −21.5% (median value: −14%) between 1900 
and 2100 under SSP5-8.5 in CMIP6 models (Table  3.24; WGI AR6 
5.4.4.2; Bindoff et al., 2019a; Canadell et al., 2021). In CMIP5 model 
runs, the decrease in the sinking flux of organic matter from the upper 
ocean into the ocean interior was strongly related to the changes in Projected change in marine biomass

ΔPhytoplankton (%) ΔZooplankton (%) ΔAnimal (%)SSP1-2.6 SSP5-8.5
40% 30% -30% -10%% 0 10% -40% -20% 20%
Biomass anomaly(a)
(d)
(g)(b) (c)
(f)
(i)(e)
(h)
(j) (l) (k)ΔBenthic (%)0
-10
-20
-3010
2025 2050 2075 2100SSP5-8.5SSP1-2.6
0
-10
-20
-30100
-10
-20
-30100
-10
-20
-3010
Figure 3.21 |  Projected change in marine biomass. Simulated global biomass changes of (a,b,c) surface phytoplankton, (d,e,f) zooplankton, (g,h,i) animals and (j,k,l) seafloor 
benthos. In (a,d,g,j), the multi-model mean (solid lines) and very likely range (envelope) over 2000–2100 relative to 1995–2014, for SSP1-2.6 and SSP5-8.5. Spatial patterns of 
simulated change by 2090–2099 are calculated relative to 1995–2014 for (b,e,h,k) SSP1-2.6 and (c,f,i,l) SSP5-8.5. Confidence intervals can be affected by the number of models 
available for the Coupled Model Intercomparison Project 6 (CMIP6) scenarios and for different variables. Only one model was available for panel (j), so no confidence interval is

calculated. For panels (a–f), the ensemble projections of global changes in phytoplankton and zooplankton biomasses updated based on Kwiatkowski et al. (2019) include, under 
SSP1-2.6 and SSP5-8.5, respectively, a total of nine and ten CMIP6 Earth system models (ESMs). For panels (b,c,e,f), unhatched areas represent regions where at least 80% of 
models agree on the sign of biomass anomaly. For panels (g,h,i), the ensemble projections of global changes in total animal biomass updated based on Tittensor et al. (2021) include 
six to nine published global fisheries and marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Tittensor et al., 2018; Tittensor et al., 
2021), forced with standardised outputs from two CMIP6 ESMs. For panels (j,k,l), globally integrated changes in total seafloor biomass have been updated based on Yool et al. 
(2017) with one benthic model (Kelly-Gerreyn et al., 2014) forced with the CMIP6 ESM UKESM-1.

3
453Oceans and Coastal Ecosystems and Their Services  Chapter 3
stratification that reduce net nutrient supply (Fu et al., 2016; Bindoff 
et  al., 2019a), especially in tropical regions, and the projections 
for global export production changes are reported with medium 
confidence. Increasing model complexity with more widespread 
representation of ocean biogeochemical processes between CMIP5 
and CMIP6, and inclusion of more than one or two classes of phyto- 
and zooplankton, will provide opportunities to improve assessments 
of how climate-induced drivers affect different components of 
biological carbon pump in the epipelagic ocean, as well as changes in 
the efficiency and magnitude of carbon export in the deep ocean (high 
confidence) (see Box 3.3; Le Quéré et al., 2016; Séférian et al., 2020; 
Wright et al., 2021).
3.4.4 Reversibility and Impacts of Temporary Overshoot 
of 1.5°C or 2°C Warming
Scenarios limiting warming to the 1.5°C and 2°C limits in the Paris

Agreement can involve temporarily exceeding those warming levels 
before declining again (WGI AR6 Section 4.6.2.1; Lee et al., 2021). The 
effect of such ‘overshoot’ on marine and coastal ecosystems depends 
on the reversibility of both the response of climate-induced drivers 
and the response of organisms and ecosystems to the climate impact-drivers during the overshoot period. WGI AR6 assessed that temporary 
overshoot of a 2°C warming threshold has irreversible effects on global 
mean sea level and also effects on ocean heat content that persist 
beyond 2100 (WGI AR6 Section 4.6.2.1; Lee et al., 2021). Model results 
indicate that sea surface temperatures (high confidence), Arctic sea ice 
(high confidence), surface ocean acidification (very high confidence) 
and surface ocean deoxygenation (very high confidence) are reversible 
within  years to decades if net emissions reach zero or below (WGI 
AR6 Table 4.10; Lee et al., 2021). Although changes in these surface

ocean variables are reversible, habitat-forming ecosystems, including 
coral reefs and kelp forests, may undergo irreversible phase shifts with 
>1.5°C warming (Sections 3.4.2.1, 3.4.2.3), and are thus at high risk 
this century in 1.5°C or 2°C scenarios involving overshoot (Tachiiri 
et al., 2019). In an overshoot scenario in which CO 2 returns to 2040 
levels by 2100 (SSP5-3.4-OS; O’Neill et al., 2016), SST and Arctic sea 
ice do not fully return by 2100 to levels prior to the CO 2 peak (medium 
confidence) (WGI AR6 Section 4.6.2.1; Lee et al., 2021), suggesting that 
reversal of marine ecological impacts from 21st century climate impacts 
would extend into the 22nd century or beyond (McManus et al., 2021). 
Models also indicate that global sea level rise, as well as warming, 
ocean acidification and deoxygenation at depth, are irreversible for 
centuries or longer (very high confidence)  (WGI AR6 Section 4.6.2.1 and

Table 4.10; Palter et al., 2018; Li et al., 2020c; Lee et al., 2021).Table 3.24 |  Summary of previous IPCC assessments of ocean primary production and carbon export flux
Process Observed impacts Projected impacts
Open-ocean
primary production‘Past open-ocean productivity trends, including those 
determined by satellites, [are appraised with low 
confidence] due to newly identified region-specific drivers of 
microbial growth and the lack of corroborating in situ time 
series datasets.’‘Net primary productivity (NPP) is very likely to decline by 
4–11% by 2081–2100, relative to 2006–2015, across CMIP5 
models for RCP8.5, but there is low confidence for this 
estimate due to the medium agreement among models and 
the limited evidence from observations. The tropical ocean 
NPP will very likely to decline by 7–16% for RCP8.5, with 
medium confidence as there are improved constraints from 
historical variability in this region.’

Open-ocean carbon export‘Analyses of long-term trends in primary production and 
particle export production, as well as model simulations, 
reveal that increasing temperatures, leading to enhanced 
stratification and nutrient limitation, will have the greatest 
influence on decreasing the flux of particulate organic 
carbon (POC) to the deep ocean. However, different 
lines of evidence (including observation, modelling and 
experimental studies) provide low confidence on the 
mechanistic understanding of how climate drivers affect 
different components of the biological pump in the 
epipelagic ocean, as well as changes in the efficiency and 
magnitude of carbon export in the deep ocean.’‘The projected changes in export production can be larger 
than global primary production because they are affected 
by both, the NPP changes, but also how shifts in food-web 
structure modulates the ‘transfer efficiency’ of particulate 
organic material, which then affects the sinking speed and

lability of exported particles through the ocean interior to 
the sea floor.’
‘As export production is a much better understood net 
integral of changing net nutrient supply and can be 
constrained by interior ocean nutrient and oxygen levels, 
there is medium confidence in projections for global [export 
production] changes [based on CMIP5 model runs].’
WGI AR6 Chapters 2, 5 (Canadell et al., 2021; Gulev et al., 
2021)
primary productionGlobal ocean marine primary production is estimated to 
be 47 ± 7.8 PgC yr–1 with low confidence because of the 
small number of recent studies and the insufficient length 
of the time series analysed. A small decrease in productivity 
is evident globally for the period 1998–2015, but regional 
changes are larger and of opposing signs (low confidence) 
(WGI AR6 Section 2.3.4.2.2; Gulev et al., 2021).‘In CMIP5 models run under RCP8.5, [POC] export flux 
is projected to decline by 1–12% by 2100 (Taucher and

Oschlies, 2011; Laufkötter et al., 2015). Similar values are 
predicted in 18 CMIP6 models, with declines of 2.5–21.5% 
(median: −14%) [...] between 1900 and 2100 under 
the SSP5-8.5 scenario. The mechanisms driving these 
changes vary widely between models due to differences in 
parameterisation of particle formation, remineralisation and 
plankton community structure’ (WGI AR6 Section 5.4.4.2; 

3
454Chapter 3 Oceans and Coastal Ecosystems and Their Services
Box 3.3 | Deep-Sea Ecosystems
Deep-sea ecosystems include all waters below the 200-m isobath as well as the underlying benthos, and they provide habitats for highly 
diversified and specialised biota, which play a key role in the cycling of carbon and other nutrients (see Figure Box 3.3.1; Thurber et al., 
2014; Middelburg, 2018; Snelgrove et al., 2018). The deep sea covers >63% of Earth’s surface (Costello and Cheung, 2010) and is exposed 
to climate-driven changes in abyssal, intermediate and surface waters that influence sinking fluxes of particulate organic matter (high 
confidence) (see Figure Box 3.3.1; Sections 3.1, 3.2.1, 3.2.2, 3.4.3.4; WGII AR5 Section 30.5.7; SROCC Sections 5.2.3, 5.2.4; Hoegh-Guldberg 
et al., 2014; Bindoff et al., 2019a). These ecosystems are also influenced by non-climate drivers, especially fisheries, oil and gas extraction

(Thurber et al., 2014; Cordes et al., 2016; Zhang et al., 2019a); cable laying (United Nations, 2021); and mineral resource exploration (Hein 
et al., 2021); with proposed large-scale deep-sea mining a potential future source of impacts (Danovaro, 2018; Levin et al., 2020).
Ocean warming alters biological processes in deep-sea ecosystems in ways that affect deep-sea habitat, biodiversity and material 
processing. Enhancement of microbial respiration by warming attenuates sinking POC, which has been associated with the globally 
projected declines in total seafloor biomass of −9.8 and −13.0% by 2081–2100 relative to 1995–2014 under SSP1-2.6 and SSP5-8.5, 
respectively (limited evidence) (Section  3.4.3.4). Additionally, climate-change-driven oxygen loss (Section  3.2.3.2; Luna et  al., 2012; 
Belley et al., 2016) and geographic shifts in predator distributions (Section 3.4.3.1) are anticipated to affect deep-sea biodiversity (limited

evidence, high agreement) (Smith et al., 2012; Morato et al., 2020). Complex responses of some bathyal crustacean assemblages to 
environmental change suggest an increase in phylogenetic diversity but limited decreases in abundances with temperature (Ashford 
et al., 2019). Acute mortality of some reef-forming cold-water corals to laboratory-simulated warming (Lunden et al., 2014) suggests 
that both long-term warming and the increase of MHWs in intermediate and deep waters (Elzahaby and Schaeffer, 2019) could pose 
significant risk to associated ecosystems (high confidence). Thermal tolerance thresholds (lethal and sub-lethal) of scleractinians in 
laboratory settings depend on their geographic position and capacity for thermal adaptation, as well as other factors including food, 
oxygen and pH (medium to high confidence) (Naumann et al., 2013; Hennige et al., 2014; Lunden et al., 2014; Naumann et al., 2014;

Georgian et al., 2016; Gori et al., 2016; Maier et al., 2016; Büscher et al., 2017).
The extension and intensification of deep-water acidification (Section 3.2.3.1) has been identified as a further key risk to deep-water coral 
ecosystems (medium confidence) (Bindoff et al., 2019a). Literature since SROCC supports this assessment (Morato et al., 2020; Puerta 
et al., 2020), although scleractinians and gorgonians are found in regions undersaturated with respect to aragonite (Thresher et al., 2011; 
Fillinger and Richter, 2013; Baco et al., 2017). Laboratory experiments on reef-forming scleractinians show variable results, with regional 
acclimation potential and population-genetic adaptations (Georgian et al., 2016; Kurman et al., 2017). Desmophyllum pertusum7and 
Madrepora oculata maintain calcification in moderately low pH (7.75) and near-saturation of aragonite (Hennige et al., 2014; Maier et al.,

2016; Büscher et al., 2017), but lower pH (7.6) and corrosive conditions lead to net dissolution of D. pertusum skeletons (high confidence) 
(Lunden et al., 2014; Kurman et al., 2017; Gómez et al., 2018). Experiments suggest that D. dianthus is more sensitive to warming than 
acidification and when both are high, as projected under climate change. Warming appears to compensate for declines in calcification, 
with fitness also sensitive to food availability (Bramanti et al., 2013; Movilla et al., 2014; Gori et al., 2016; Baussant et al., 2017; Büscher 
et al., 2017; Schönberg et al., 2017; Höfer et al., 2018; Maier et al., 2019).
In OMZ regions (Section 3.2.3.2), benthic species distributions (Sperling et al., 2016; Levin, 2018; Gallo et al., 2020), abundance and 
composition of demersal fishes in canyons (De Leo et al., 2012) and deep-pelagic zooplankton (Wishner et al., 2018) follow oxygen

gradients, indicating that deep-sea biodiversity and ecosystem structure will be impacted by extension of hypoxic areas (medium 
confidence). Fossil records show benthic population collapse and turnover when oxygen ranged from oxic to mildly or severely hypoxic 
(Cross-Chapter Box PALEO in Chapter 1; Moffitt et al., 2015). Regional extirpations among cold-water corals in the paleorecord were 
associated with substantial declines in oxygen, coincident with abrupt warming and altered properties of intermediate water-masses 
(Wienberg et al., 2018; Hebbeln et al., 2019). Despite mortality and functional impacts from low oxygen concentrations observed in 
aquaria (Lunden et al., 2014), recent observations of the deep-water coral D. pertusum suggest adaptive capacity to hypoxia among 
specimens from OMZ regions that are highly productive (low confidence) (Hanz et al., 2019; Hebbeln et al., 2020).

Chemosynthetic ecosystems could be particularly prone to oxygen decline (low to medium confidence). Projected OMZ expansion in the 
vicinity of seep communities could favour sulphide-tolerant species, as suggested from fossil records (Moffitt et al., 2015), but this will 
exclude large symbiont-bearing foundation species of methane-seep ecosystems (Fischer et al., 2012; Sweetman et al., 2017). Projected 
warming, or shifts in warm-current circulation along continental margins, could enhance dissociation of buried methane hydrates (Phrampus 
and Hornbach, 2012; Phrampus et al., 2014), either increasing anaerobic methane oxidation (Boetius and Wenzhöfer, 2013), which benefits 
seep communities, or increasing gas fluxes, which would decrease anaerobic methane oxidation rates and exclude chemosynthetic fauna. 
7 Previously named Lophelia pertusa

3
455Oceans and Coastal Ecosystems and Their Services  Chapter 3
Environmental niche models (FAO, 2019; Morato et  al., 2020; Puerta et  al., 2020) project that under RCP8.5, >50% of present-day 
scleractinian habitats in the North Atlantic Ocean will become unsuitable by 2100, with greater impacts on D. pertusum than on D. 
dianthus or M. oculata. For gorgonians, corresponding habitat loss is likely >80%. Much less is known about the environmental niches of 
deep-sea sponges, preventing a similar assessment (Kazanidis et al., 2019; Puerta et al., 2020).
Climate-driven impacts further limit the resilience of deep-sea ecosystems to impacts from human activities (high confidence) (Levin and 
Le Bris, 2015; Rogers, 2015; Sweetman et al., 2017). However, assessing cumulative climatic and non-climatic impacts is challenging for 
these data-poor environments (Ashford et al., 2018; Levin, 2018; Armstrong et al., 2019; Heffernan, 2019; Kazanidis et al., 2020; Orejas

et al., 2020), where lack of knowledge increases the possibility of overlooking ecosystem vulnerabilities and risks (Levin, 2021). A paucity 
of information about the natural variability and historical trends of these habitats prevents robust assessment of adaptive capacities and 
potential vulnerabilities to extreme events (Aguzzi et al., 2019; Levin et al., 2019; Chapron et al., 2020; Danovaro et al., 2020; Le Bris and 
Levin, 2020; Levin, 2021). The spatial resolution of CMIP5 models is too coarse to robustly project changes in mesoscale circulation at the 
seafloor (Sulpis et  al., 2019), on which deep-sea ecosystems depend for organic material supplies and dispersal of planktonic and 
planktotrophic larvae (high confidence) (Fox et al., 2016; Mitarai et al., 2016; Dunn et al., 2018). Higher-resolution modelling from CMIP6 
(Orr et  al., 2017), multi-annual and high-frequency records of ocean bottom-water properties (Meinen et  al., 2020), and better

understanding and accounting of biogeochemical mechanisms of organic carbon transport to the ocean interior is expected to improve 
this capacity (Boyd et al., 2019; Séférian et al., 2020).
Schematic of the combination of climate-induced drivers in different deep-ocean ecosystems
200 m
2000–3000 m
4000–6000 m
8000–1 1000 mSubmarine 
canyon, deep fjord
Continental slope
Continental marginWarming
AcidificationChange in POC
and O2fluxesWarming
MHW
Volcanic ar c
TrenchAbyssal plain
Abyssal hillsSeamounts
Oceanic ridges
Flank, crest, 
hotspots & faultsSubmarine
canyonAcidificationDeoxygenation
Decreased 
POC fluxHypoxia
 extension/shrinking
Decreased
POC fluxXX
Surface waters
Intermediate waters 
Abyssal/hadal waters
Biological drivers           
Reduced/increased surface NPP  and sinking rate
Reduced vertical migratory abundance and activity
Mortality of reef-building corals and reef dissolution
Reduction of chemosynthetic production ratesX
XPhysical drivers

Reduced/increased dense shelf-water cascading
Reduced/increased dense shelf-water open convection
Change in vertical mixing and in sub-mesoscale circulation
Reduced/increased upwellingChemosynthesis-based ecosystemsCold-water coral habitatsVulnerable benthic habitats
Bathyal and abyssal benthos
XX
Figure Box 3.3.1 |  The combination of climate-induced drivers in different deep-ocean ecosystems. (Key physical and biological drivers of change in the 
deep-sea and benthic habitats with specific vulnerabilities are discussed in Section 3.4.3.3.)Box 3.3 (continued)

3
456Chapter 3 Oceans and Coastal Ecosystems and Their Services
3.5 Vulnerability, Resilience, and Adaptive 
Capacity in Marine Social–Ecological 
Systems, Including Impacts on Ecosystem 
Services
3.5.1 Introduction
This section  assesses the impacts of climate change on ecosystem 
services (Table 3.25; Chapter 1) and the outcomes on social–ecological 
systems, building on previous assessments (Table 3.26). Section 3.5.2 
assesses how changes in biodiversity influence ecosystem services. 
Then Sections  3.5.3 and 3.5.4 assess provisioning services (food 
and non-food), Section  3.5.5 assesses supporting and regulating 
services, and Section  3.5.6, cultural services. Where evidence exists, 
the section evaluates how the vulnerability and adaptive capacity of 
social–ecological systems govern the manifestation of impacts on each 
ecosystem service.
3.5.2 Biodiversity
Climate change is a key agent of biodiversity change in numerous ocean

and coastal ecosystems (very high confidence) (Table 3.26; Worm and 
Lotze, 2021), and climate change and biodiversity loss reinforce each 
other (Pörtner et al., 2021b). Biodiversity has changed in association 
with ocean warming and loss of sea ice (Sections 3.4.2.10, 3.4.3.3.3; 
Section  CCP6 2.4.2), SLR (Section  3.4.2; Cross-Chapter  Box  SLR in 
Chapter 3), coral bleaching (Section 3.4.2.1), MHWs (Sections 3.4.2.1–
3.4.2.5) and upwelling changes (high confidence) (Section  3.4.2.9). 
Overlapping non-climate drivers (Section 3.1) also decrease ocean and 
coastal ecosystem biodiversity (very high confidence) (O’Hara et  al., 
2021; Pörtner et al., 2021b). There is medium confidence that local and 
regional marine biodiversity losses from climate disrupt ecosystem 
services provided by specific ocean and coastal species or places 
(Sections 3.5.3–3.5.6; Figure 3.23; Table 3.26; see Box 3.3; Dee et al., 
2019a; Hossain, 2019; Smale et al., 2019; Teixeira et al., 2019; Martin

et al., 2020; Pathak, 2020; Weiskopf et al., 2020; Zunino et al., 2020; 
Archer et al., 2021). However, adaptive capacity varies greatly among 
ecosystems, and ecological functions sometimes remain, despite 
changes in species assemblages, as in certain coral reef communities 
(Richardson et  al., 2020). Projected changes in biodiversity due to 
climate change (Section 3.4.3.3.3) are expected to alter the flow and 
array of ocean and coastal ecosystem services (high confidence) (Smale 
et  al., 2019; Cavanagh et  al., 2021; Ruthrof et  al., 2021; Worm and 
Lotze, 2021), but data gaps hinder developing projections of ecosystem 
service changes detailed enough to support decision making (Rosa 
Non-indigenous marine species are major agents of ocean and coastal 
biodiversity change, and climate and non-climate drivers interact to 
support their movement and success (high confidence) (Iacarella 
et  al., 2020). At times, non-indigenous species act invasively and

outcompete indigenous species, causing regional biodiversity shifts 
and altering ecosystem function, as seen in the Mediterranean region 
(high confidence) (e.g., Mannino et al., 2017; Bianchi et al., 2019; Hall-
Spencer and Harvey, 2019; Verdura et al., 2019; García-Gómez et al., 
2020; Dimitriadis et  al., 2021). Warming-related range expansions of non-indigenous species have directly or indirectly decreased 
commercially important fishery species and nursery habitat (Booth 
et al., 2018). Non-indigenous species outperform indigenous species 
in coastal zones experiencing warming and freshening (McKnight 
et al., 2021). Non-climate drivers, especially marine shipping in newly 
ice-free locations (Chan et  al., 2019), fishing pressure (Last et  al., 
2011), aquaculture of non-indigenous species (Mach et  al., 2017; 
Ruby and Ahilan, 2018) and marine pollution and debris (Gall and 
Thompson, 2015; Carlton et  al., 2018; Carlton and Fowler, 2018;

Lasut et al., 2018; Miralles et al., 2018; Rech et al., 2018; Therriault 
et al., 2018), promote range shifts and movement of non-indigenous 
species (high confidence). Non-climate drivers can also intensify 
the ecological effects of non-indigenous species (Geraldi et  al., 
2020). Invasive marine species can alter species behaviour, reduce 
indigenous species abundance, reduce water clarity, bioaccumulate 
more heavy metals than indigenous species and inhibit ecosystem 
resilience in the face of extreme events (medium confidence) 
(McDowell et al., 2017; Geburzi and McCarthy, 2018; Anton et al., 
2019; Ruthrof et al., 2021). Risks from invasive species to the sources 
of other ecosystem services or aquatic goods, including valuable 
materials, mining activities, shipping or ocean energy installations, 
have not been evaluated.
Reducing risk to ecosystem functions and services that depend on 
biodiversity requires an integrated approach that acknowledges

the close linkages between the climate and biodiversity crises and 
common governance challenges (Pörtner et  al., 2021b). Climate-
focused solutions that employ nature-based solutions (NbS), 
technological interventions and socio-institutional interventions 
(Section 3.6.2) can also safeguard biodiversity (Pörtner et al., 2021b), 
which in turn will help ocean and coastal ecosystems adapt to climate 
impacts as well as help sustain the services they provide to people 
(Sections 3.5.3–3.5.6).
3.5.3 Food Provision
Globally, about 17% of humans’ average per capita intake of animal 
protein in 2017 came from marine and freshwater wild-caught and 
aquacultured aquatic animals (Costello et  al., 2020; FAO, 2020a). 
Per capita intake of seafood is 50% or more in some Small Island 
Developing States (SIDS) (Vannuccini et al., 2018), and consumption per 
capita is 15 times higher in Indigenous Peoples than non-Indigenous 
Peoples (Cisneros-Montemayor et  al., 2016). Fishery products also

supply critical dietary micronutrients worldwide (Section  3.5.4.1; 
Hicks et al., 2019; Vianna et al., 2020). Marine and freshwater fisheries 
and aquaculture provide livelihoods for an estimated 10–12% of the 
world’s population (Barange et  al., 2018). Fishing and aquaculture 
provide women and their families with substantial amounts of food 
and income (Harper et  al., 2020b), because at least 11% of small-
scale fishers (Harper et al., 2020b) and up to half of all fishery and 
aquaculture workers (FAO, 2018) are women. This section assesses 
how climate-driven alterations of the abundance or nutritional quality 
of food from the sea could affect humans. Aquaculture, catch potential 
changes and human adaptations to changes in wild and cultured 
harvests are assessed in Section 5.9.

3
457Oceans and Coastal Ecosystems and Their Services  Chapter 3
Ocean and coastal fauna are moving towards higher latitudes globally 
due to warming (high confidence) (Section  3.4.3.1; Table  3.26), 
challenging fishers and fisheries management (high confidence) as 
fishers also move poleward and diversify harvests (medium evidence, 
high agreement) (Sections  3.4.3.3.3, 5.8.4; Table  3.26; Leitão et  al., 
2018; Liang et al., 2018; Ottosen et al., 2018; Peck and Pinnegar, 2018; 
Pinsky et al., 2018; Erauskin-Extramiana et al., 2019; Free et al., 2019; 
Gianelli et  al., 2019; Scott et  al., 2019; Smith et  al., 2019; Gervais 
et al., 2021). Model hindcasts have identified temperature-associated 
fisheries reductions worldwide (Free et  al., 2019), and they have 
implicated overfishing as the primary non-climate driver increasing 
fishery vulnerability (Section  5.8.4; Peck and Pinnegar, 2018; Das Table 3.25 |  Ocean and coastal ecosystem servicesa

Ecosystem service category Components Ocean and coastal examples
Provisioning Food and feed Status of harvested marine fish, invertebrates, mammals and plants.
Medicinal, biochemical and genetic resourcesExistence of, and access to, biological resources that could offer future prospects for development, 
including marine fish, invertebrates, mammals, plants, microbes and viruses.
Materials and assistanceExistence of, and access to, minerals, shells, stones, coral branches and dyes used to create other 
goods; availability of marine organisms to exhibit in zoos, aquariums and as pets.
EnergyExistence of, and access to, sources of energy, including oil and gas reserves; solar, tidal and 
thermal ocean energy; and biofuels from marine plants.
Supporting and regulating Habitat creation and maintenanceStatus of nesting, feeding, nursery and mating sites for birds, mammals and other marine life, and

of resting and overwintering places for migratory marine life or insects. Connectivity of ocean 
habitats.
Dispersal and other propagulesAbility of marine life to spread gametes and larvae successfully by broadcast spawning 
reproduction, and ability of adults to disperse widely.
Regulation of climateStatus of carbon storage and sequestration, methane cycling in wetlands, and dimethyl sulphide 
creation and destruction.
Regulation of air qualityStatus of aquatic processes that maintain and balance CO 2, oxygen, nitrogen oxides, sulphur 
oxides, volatile organic compounds, particulates and aerosols.
Regulation of ocean acidification 
(Section 3.2.3.1)Status of chemical and biological aquatic processes that maintain and balance CO 2 and other 
acids/bases.
Regulation of freshwater quantity, location and 
timingStatus of water storage by coastal systems, including groundwater flow, aquifer recharge and 
flooding responses of wetlands, coastal water bodies and developed spaces.

Regulation of freshwater and coastal water 
qualityStatus of chemical and biological aquatic processes that retain and filter coastal waters, capture 
pollutants and particles, and oxygenate water (e.g., natural filtration by sediments including 
adsorbent minerals and microbes).
Regulation of organisms detrimental to humans 
and marine lifeStatus of grazing that controls harmful algal blooms and algal overgrowth of key ecosystems. 
Environmental conditions that suppress marine pathogens.
Formation, protection and decontamination of 
soils and sedimentsStatus of chemical and biological aquatic processes that capture pollutants and particles (e.g., 
adsorption by minerals, microbial breakdown of pollutants).
Regulation of hazards and extreme events Ability of coastal environments to serve as wave-energy dissipators, barriers and wave breaks.
Regulation of key elementsStatus of aquatic processes that maintain and balance stocks of carbon, nitrogen, phosphorus and

other elements critical for life.
Cultural Physical and psychological experiencesExistence of, and access to, recreational opportunities including visiting beaches and coastal 
environments; and aquatic activities such as fishing, boating, swimming and diving.
Supporting identitiesExistence of, and access to, cultural, heritage and religious activities, and opportunities for 
intergenerational knowledge transfer; sense of place.
Learning and inspiration Existence of educational opportunities and characteristics to be emulated, as in biomimicry.
Maintenance of optionsExistence of opportunities to develop new medicines, materials, foods, and resources, or to adapt 
to a warmer climate and emergent diseases.
Notes:
(a) Adapted from IPBES (2017), with examples made specific to ocean and coastal ecosystems by the authors of Chapter 3
et al., 2020). Catch composition is changing in many locations fished 
by smaller-scale, less-mobile commercial, artisanal and recreational

fisheries (high confidence) (Booth et al., 2018; Townhill et al., 2019; 
Young et  al., 2019b; Robinson et  al., 2020; Champion et  al., 2021). 
Limited exceptions have been noted, with wild harvests in some 
places remaining stable or increasing (e.g., Arreguín-Sánchez, 2019; 
Robinson et al., 2019b; Kainge et al., 2020). Where possible, fishers are 
maintaining harvests by broadening catch diversity, traveling poleward 
and changing gear and strategies (high confidence) (Section 3.6.3.1.2; 
Barange et  al., 2018; Dubik et  al., 2019; Townhill et  al., 2019). 
Fisheries and aquaculture adaptations, including management, are 
comprehensively assessed in Sections 3.6.3.1.2, 5.8.4 and 5.9.4.

3
458Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.26 |  Conclusions from previous IPCC assessments about observed and projected climate impacts on ocean and coastal biodiversity and ecosystem services
Ecosystem service and 
chapter subsectionObserved impacts Projected impacts
All (Section 3.5)Climate change has affected marine ‘ecosystem services with 
regionally diverse outcomes, challenging their governance (high 
confidence). Both positive and negative impacts result for food 
security through fisheries (medium confidence), local cultures 
and livelihoods (medium confidence), and tourism and recreation 
(medium confidence). The impacts on ecosystem services have 
negative consequences for health and well-being ( medium 
confidence), and for Indigenous Peoples and local communities 
dependent on fisheries (high confidence) (1.1, 1.5, 3.2.1, 5.4.1, 5.4.2,

Figure SPM.2)’ (SROCC SPM A.8; IPCC, 2019c).‘Long-term loss and degradation of marine ecosystems compromises the 
ocean’s role in cultural, recreational, and intrinsic values important for 
human identity and well-being (medium confidence) (3.2.4, 3.4.3, 5.4.1, 
5.4.2, 6.4)’ (SROCC SPM B.8; IPCC, 2019c).
Biodiversity (Section 3.5.2)‘[Climate] Impacts are already observed on [coastal ecosystem] 
habitat area and biodiversity, as well as ecosystem functioning 
and services (high confidence) (4.3.2, 4.3.3, 5.3, 5.4.1, 6.4.2, 
Figure SPM.2)’ (SROCC SPM A.6; IPCC, 2019c).‘Risks of severe impacts on biodiversity, structure and function of 
coastal ecosystems are projected to be higher for elevated temperatures 
under high compared to low emissions scenarios in the 21st century and 
beyond’ (SROCC SPM B.6; IPCC, 2019c).
Food provision (Section 3.5.3)‘Warming-induced changes in the spatial distribution and abundance 
of some fish and shellfish stocks have had positive and negative

impacts on catches, economic benefits, livelihoods, and local culture 
(high confidence). There are negative consequences for Indigenous 
Peoples and local communities that are dependent on fisheries 
(high confidence). Shifts in species distributions and abundance 
has challenged international and national ocean and fisheries 
governance, including in the Arctic, North Atlantic and Pacific, in 
terms of regulating fishing to secure ecosystem integrity and sharing 
of resources between fishing entities (high confidence) (3.2.4, 3.5.3, 
5.4.2, 5.5.2, Figure SPM.2)’ (SROCC SPM A.8.1; IPCC, 2019c).‘Future shifts in fish distribution and decreases in their abundance and 
fisheries catch potential due to climate change are projected to affect 
income, livelihoods, and food security of marine resource-dependent 
communities (medium confidence). Long-term loss and degradation 
of marine ecosystems compromises the ocean’s role in cultural,

recreational, and intrinsic values important for human identity and 
well-being (medium confidence) (3.2.4, 3.4.3, 5.4.1, 5.4.2, 6.4)’ (SROCC 
SPM B.8; IPCC, 2019c).
Non-food consumable provisioning 
services (Section 3.5.4.1)Observed impacts on non-food provisioning services not previously 
assessed.‘Reductions in marine biodiversity due to climate change and other 
anthropogenic stressors (Tittensor et al., 2010), such as ocean 
acidification (CBD, 2009) and pollution, might reduce the discovery 
of genetic resources from marine species useful in pharmaceutical, 
aquaculture, agriculture, and other industries (Arrieta et al., 2010), 
leading to a loss of option value from marine ecosystems’ (WGII AR5 
Section 6.4.1.2; Pörtner et al., 2014)
Renewable energy (Section 3.5.4.2)Observed impacts on ocean renewable energy not previously 
assessed.‘Ocean renewable energy can support climate change mitigation, and 
can comprise energy extraction from offshore winds, tides, waves,

thermal and salinity gradient and algal biofuels. The emerging demand 
for alternative energy sources is expected to generate economic 
opportunities for the ocean renewable energy sector (high confidence), 
although their potential may also be affected by climate change ( low 
confidence) (5.4.2, 5.5.1, Figure 5.23)’ (SROCC SPM C.2.5; IPCC, 2019c).
Habitat creation and maintenance
(Section 3.5.5.1)‘[Climate] Impacts are already observed on [coastal ecosystem] 
Figure SPM.2)’ (SROCC SPM A.6; IPCC, 2019c).
‘In polar regions, ice associated marine mammals and seabirds have 
experienced habitat contraction linked to sea ice changes (high 
confidence)’ (SROCC SPM A.5.2; IPCC, 2019c).‘In the Southern Ocean, the habitat of Antarctic krill, a key prey species 
for penguins, seals and whales, is projected to contract southwards

under both RCP2.6 and RCP8.5 (medium confidence) (3.2.2, 3.2.3, 
5.2.3)’ (SROCC SPM B5.3; IPCC, 2019c).
‘Ocean warming, oxygen loss, acidification and a decrease in flux of 
organic carbon from the surface to the deep ocean are projected to 
harm habitat-forming cold-water corals, which support high biodiversity, 
partly through decreased calcification, increased dissolution of skeletons, 
and bioerosion (medium confidence)’ (SROCC SPM B5.4; IPCC, 2019c).
Climate regulation and air quality
(Section 3.5.5.2)‘Global ocean heat content continued to increase throughout [the 
1951 to present] period, indicating continuous warming of the entire 
climate system (very high confidence)’ (WGI AR6 TS1.2.3; Arias et al., 
2021).‘The increase in global ocean heat content (TS2.4) will likely continue 
until at least 2300 even for low-emission scenarios’ (WGI AR6 Box TS.9; 
‘Land and ocean have taken up a near-constant proportion (globally

about 56% yr–1) of CO 2 emissions from human activities over the past 
six decades, with regional differences (high confidence)’ (WGI AR6 
SPM A1.1; IPCC, 2021b).‘While natural land and ocean carbon sinks are projected to take up, 
in absolute terms, a progressively larger amount of CO 2 under higher 
compared to lower CO 2 emissions scenarios, they become less effective, 
that is, the proportion of emissions taken up by land and ocean decrease 
with increasing cumulative CO 2 emissions. This is projected to result in 
a higher proportion of emitted CO 2 remaining in the atmosphere (high 
confidence)’ (WGI AR6 SPM B4.1; IPCC, 2021b).

3
459Oceans and Coastal Ecosystems and Their Services  Chapter 3
Ocean acidification and deoxygenation caused by climate change 
are thought to influence fishing and aquaculture harvests, but 
limited evidence prevents assessing their present global impact on 
harvests. Substantial economic losses in the North American Pacific 
Coast shellfish aquaculture industry in the 2000s assessed in SROCC (Bindoff et  al., 2019a) and WGII AR5 (Pörtner et  al., 2014) remain 
the clearest example of human harm from ocean acidification. 
Technology-based adaptations (Section  3.6.3) have minimised 
aquaculture losses from ocean acidification, including early-warning 
systems to guide hatchery operations and culturing resilient shellfish Ecosystem service and 
Observed impacts on marine organisms’ contribution to climate 
regulation not previously assessed.‘The effect of climate change on marine biota will alter their

contribution to climate regulation, that is, the maintenance of the 
chemical composition and physical processes in the atmosphere and 
oceans (high confidence) (Beaumont et al., 2007)’
(WGII AR5 Section 6.4.1.3; Pörtner et al., 2014).
Provision of freshwater, maintenance 
of water quality, regulation of 
pathogens (Section 3.5.5.3)Observed climate impacts on salinisation of coastal soil and 
groundwater not previously assessed.‘In the absence of more ambitious adaptation efforts compared to 
today, and under current trends of increasing exposure and vulnerability 
of coastal communities, risks, such as erosion and land loss, flooding, 
salinisation, and cascading impacts due to mean sea level rise and 
extreme events are projected to significantly increase throughout 
this century under all greenhouse gas emissions scenarios ( very high 
confidence)’ (SROCC SPM B9.1; IPCC, 2019c).
‘Global warming compromises seafood safety (medium confidence)

through human exposure to elevated bioaccumulation of persistent 
organic pollutants and mercury in marine plants and animals 
(medium confidence), increasing prevalence of waterborne Vibrio 
sp. pathogens (medium confidence), and heightened likelihood of 
harmful algal blooms (medium confidence)’ (SROCC SPM B.8.3; IPCC, 
2019c).‘[Risks from marine-borne pollutants and pathogens] are projected to 
be particularly large for human communities with high consumption 
of seafood, including coastal Indigenous communities (medium 
confidence), and for economic sectors such as fisheries, aquaculture, and 
tourism (high confidence) (3.4.3, 5.4.2, Box 5.3)’ (SROCC SPM B.8.3; 
‘Since the early 1980s, the occurrence of harmful algal blooms 
(HABs) and pathogenic organisms (e.g., Vibrio) has increased 
in coastal areas in response to warming, deoxygenation and 
eutrophication, with negative impacts on food provisioning, tourism,

the economy and human health (high confidence)’ (SROCC Chapter 5 
Executive Summary; Bindoff et al., 2019a).‘Overall, the occurrence of HABs, their toxicity and risk on natural and 
human systems are projected to continue to increase with warming 
and rising CO 2 in the 21st century (Glibert et al., 2014; Martín-García 
et al., 2014; McCabe et al., 2016; Paerl et al., 2016; Gobler et al., 2017; 
McKibben et al.; 2017; Rodríguez et al., 2017; Paerl et al., 2018; Riebesell 
et al., 2018) (high confidence)’ (SROCC Box 5.4; Bindoff et al., 2019a).
Regulation of physical hazards
(Section 3.5.5.4)‘Coastal ecosystems are already impacted by the combination of sea 
level rise, other climate-related ocean changes, and adverse effects 
from human activities on ocean and land (high confidence)... Coastal 
and near-shore ecosystems including saltmarshes, mangroves, and 
vegetated dunes in sandy beaches,...provide important services

including coastal protection...(high confidence)’ (SROCC Chapter 4 
Executive Summary; Oppenheimer et al., 2019).‘The decline in warm water coral reefs is projected to greatly 
compromise the services they provide to society, such as...coastal 
protection (high confidence)...’ (SROCC SPM B.8.2; IPCC, 2019c).
Ocean and coastal carbon storage
(Section 3.5.5.5)‘Recent observations show that ocean carbon processes are starting 
to change in response to the growing ocean sink, and these changes 
are expected to contribute significantly to future weakening of the 
ocean sink under medium- to high-emission scenarios. However, the 
effect of these changes is not yet reflected in a weakening trend of 
the contemporary (1960–2019) ocean sink (high confidence)’ (WGI 
AR6 Chapter 5 Executive Summary; Canadell et al., 2021).‘Emission scenarios SSP4-6.0 and SSP5-8.5 lead to warming of the 
surface ocean and large reductions of the buffering capacity, which will

slow the growth of the ocean sink after 2050. Scenario SSP1-2.6 limits 
further reductions in buffering capacity and warming, and the ocean 
sink weakens in response to the declining rate of increasing atmospheric 
CO 2. There is low confidence in how changes in the biological pump will 
influence the magnitude and direction of the ocean carbon feedback’ 
(WGI AR6 Chapter 5 Executive Summary; Canadell et al., 2021).
‘Mangrove, seagrass, and salt marsh ecosystems offer important 
carbon storage and sequestration opportunities (limited evidence, 
medium agreement), in addition to ecosystem goods and services 
such as protection against coastal erosion and storm damage and 
maintenance of habitats for fisheries species’ (WGII AR5 Technical 
Summary).‘…under high emission scenarios, sea level rise and warming are 
expected to reduce carbon sequestration by vegetated coastal 
ecosystems (medium confidence); however, under conditions of slow

sea level rise, there may be net increase in carbon uptake by some 
coastal wetlands (medium confidence)’ (SROCC Chapter 5; Bindoff et al., 
2019a).
Cultural services (Section 3.5.6)‘Climate change impacts on marine ecosystems and their services 
put key cultural dimensions of lives and livelihoods at risk ( medium 
confidence), including through shifts in the distribution or abundance 
of harvested species and diminished access to fishing or hunting 
areas. This includes potentially rapid and irreversible loss of culture 
and local knowledge and Indigenous knowledge, and negative 
impacts on traditional diets and food security, aesthetic aspects, and 
marine recreational activities (medium confidence)’ (SROCC SPM 
B.8.4; IPCC, 2019c).‘Future shifts in fish distribution and decreases in their abundance and 
income, livelihoods, and food security of marine resource-dependent

of marine ecosystems compromises the ocean’s role in cultural, 
well-being (medium confidence)’ (SROCC SPM B.8; IPCC, 2019c).

3
460Chapter 3 Oceans and Coastal Ecosystems and Their Services
strains (Section 5.9.4; Barton et al., 2015a). Laboratory studies show 
that ocean acidification decreases the fitness, growth or survival of 
many economically and culturally important larval or juvenile shelled 
mollusks (high confidence) (Cao et  al., 2018; Onitsuka et  al., 2018; 
Stevens and Gobler, 2018; Griffith et al., 2019a; Mellado et al., 2019) 
and of several valuable wild-harvest crab species (Barton et al., 2015a; 
Punt et al., 2015; Miller et al., 2016; Swiney et al., 2017; Gravinese 
et al., 2018; Tomasetti et al., 2018; Long et al., 2019; Trigg et al., 2019). 
Ocean acidification alters larval settlement and metamorphosis of fish 
in laboratory studies (high confidence) (Cattano et al., 2018; Espinel-
Velasco et al., 2018), suggesting possible changes in fish survival and 
thus fishery characteristics. Deoxygenation can decrease size and

abundance of marine species and suppress trophic interactions (Levin, 
2003), decrease the diversity within marine ecosystems (Sperling et al., 
2016) while temporarily increasing catchability and increasing the risk 
of overfishing (Breitburg et  al., 2018) and decrease the ecosystem 
services provided by specific fisheries (Orio et al., 2021). The chronic 
effects of deoxygenation on wild fisheries are complex and highly 
interactive with co-occurring drivers and overall ecosystem responses 
(medium evidence, high agreement) (Townhill et al., 2017; Rose et al., 
2019). Detecting and attributing marine ecosystem responses to 
ocean acidification and deoxygenation outside of laboratory studies 
remains challenging because of the strong influence of co-occurring 
environmental changes on natural systems (Section 3.3.5; Rose et al., 
2019; Doo et al., 2020).
Ocean and coastal organisms will continue moving poleward under

RCP8.5 (high confidence) (Section 3.4.3.1.3; Figure 3.18), and this is 
expected to decrease fisheries harvests in low latitudes and alter species 
composition and abundance in higher latitudes (high confidence) 
(Table 3.26; Figure 3.23; Asch et al., 2018; Morley et al., 2018; Tai et al., 
2019; Erauskin-Extramiana et al., 2020; Shelton et al., 2021). Species 
that succeed in new ranges or conditions may offer opportunities to 
diversify regional fisheries or aquaculture (Sections  3.6.3.1.2, 5.8.4, 
5.9.4; Bindoff et  al., 2019a), or they may outcompete indigenous 
species and act as invasive species (Sections 3.4.2.10, 3.5.2).
Temperature will continue to be a major driver of fisheries changes 
globally, but other non-climate factors like organism physiology and 
ecosystem response (Section 3.3) and fishing pressure (Chapter 5), as 
well as other climate-induced drivers like acidification, deoxygenation 
and sea ice loss (Section 3.2), will play critical roles in future global

and local fisheries changes (high confidence). Warming, acidification 
and business-as-usual fishing policy under RCP8.5 are projected 
to place around 60% of global fisheries at very high risk (medium 
confidence) (Cheung et  al., 2018). Model intercomparison showed 
that ocean acidification and protection affect ecosystems more than 
fishing pressure, and ecological adaptation will significantly determine 
impacts on fishery biomass, catch and value until approximately 2050 
(medium confidence) (Olsen et  al., 2018). Ecosystem responses to 
warming water, fishing pressure, food-web changes, MHWs and sea 
ice algal populations have been responsible for highly variable or 
collapsing populations of Northern Hemisphere high-latitude forage 
fish species including sand lances (Ammodytes spp.), Arctic cod 
(Boreogadus saida), capelin (Mallotus catervarius) and herring (Clupea 
spp.) (Lindegren et  al., 2018; Steiner et  al., 2019; Arimitsu et  al.,

2021; Suca et al., 2021). Declining stocks of forage fish are expected to have detrimental effects on seabirds, pelagic fish and marine 
mammals (medium confidence) (Lindegren et al., 2018; Steiner et al., 
2019), which may harm dependent human communities, including 
Arctic Indigenous Peoples (low confidence) (Arctic Monitoring and 
Assessment Programme, 2018; Steiner et al., 2019). Modelled fishery 
futures and revenue depend on environmental scenario, fishing-
fleet composition and management, and ocean acidification and 
temperature responses of harvested species (high confidence) (Punt 
et al., 2014; Punt et al., 2015; Seung et al., 2015; Fernandes et al., 2017; 
Rheuban et al., 2018; Tai et al., 2019; Punt et al., 2020). Detrimental 
effects of ocean acidification are projected to begin emerging in specific 
fisheries by 2030 (limited evidence, high agreement) [(southern Tanner 
crab (Chionoecetes bairdi) (Punt et al., 2015); sea scallop (Placopecten

magellanicus) (Rheuban et  al., 2018); Northeast Arctic cod (Gadus 
morhua) (Hänsel et al., 2020); Arctic fisheries (Lam et al., 2016)]. At the 
same time, projected hypoxic conditions of ~2 mg l–1 of oxygen will 
be consistently detrimental across taxonomic groups, developmental 
stages and climate regions (high confidence) (Sampaio et al., 2021). 
Ecosystem-based management (Section 3.6.3.1.2) shows promise for 
decreasing risk from interacting climate and non-climate drivers to 
forage species and fished species.
3.5.4 Other Provisioning Services
3.5.4.1 Non-Food Consumable Products
The interaction of climate and non-climate drivers endangers the 
supply of non-food consumable products developed from marine 
organisms (limited evidence, high agreement). This broad class 
includes nutraceuticals (derived from fish, krill, shellfish, seaweeds and 
microbes), food preservatives or additives (derived from crustaceans, 
fish, microalgae and seaweeds, and cyanobacteria), pharmaceuticals

(derived from fish, shellfish, microbes, cyanobacteria, corals and 
sponges) or cosmetic products (derived from sponges, phytoplankton 
and seaweeds, fish etc.) (Freitas et  al., 2012; Dewapriya and Kim, 
2014; Leal and Calado, 2015; Stengel and Connan, 2015; Greene 
et al., 2016; Ciavatta et al., 2017; Gutiérrez-Rodríguez et al., 2018). 
But biodiversity changes, warming, acidification and non-climate 
drivers (especially fishing pressure) may decrease the availability 
of these organisms or the potency of the compounds they produce 
(Section  5.7.5.1; Figure  3.23; Table  3.26; Webster and Taylor, 2012; 
Mehbub et al., 2014; Kotta et al., 2018; Martins et al., 2018; Conrad 
et al., 2021). Observed and projected declines and movement of fish 
stocks due to fishing pressure and climate change impacts (IPCC, 
2019b) have generated concerns that the supply and safety of fish and 
krill oil for human dietary supplements may decline (Section 5.7.5.1;

Gribble et al., 2016; Lloret et al., 2016). This risk can be lowered by 
technological adaptations (Section 3.6.2.2), such as increasing the use 
of alternative sources like marine phytoplankton, macroalgae, marine 
microbes (Dewapriya and Kim, 2014; Greene et al., 2016; Dave and 
Routray, 2018; Nguyen et al., 2020) and underutilised resources such 
as fish, seal, crab and shrimp byproducts (Dave and Routray, 2018), 
and by improving extraction and processing efficiency (Cashion et al., 
2017). Climate effects on non-food consumable products could be 
widespread yet poorly detected, complicating assessment of impacts, 
risks and vulnerability reduction.

3
461Oceans and Coastal Ecosystems and Their Services  Chapter 3
There is insufficient evidence to develop global projections of future 
climate impacts on humans through changes in non-food consumable 
marine products, but specific local examples have been investigated, 
such as the Arctic ooligan (eulachon; Thaleichthys pacificus), a small 
smelt fish. Ooligan grease has been used by Indigenous Peoples of the 
North Pacific coast (Phinney et al., 2009) for at least 5000 years to treat 
stomach aches, colds and skin conditions, and as a traditional food 
source high in omega-3 fatty acids (Byram and Lewis, 2001; Cranmer, 
2016; Patton et al., 2019). Analysis of remains have shown that ooligan 
could comprise up to 67% of traditional historical fisheries catches 
(Patton et al., 2019). Because ooligan spawning relies on the timing 
of the spring freshet, and because the species has declined in the past 
25 years due to fishing pressure and predation, the species may be at

risk from combined climate-induced and non-climate drivers (medium 
confidence) (Talloni-Álvarez et  al., 2019). Projections under RCP2.6 
or RCP8.5 estimate reductions by 21 or 31% by 2050 in essential 
nutrients from traditional seafood for Indigenous Peoples in Canada, 
relative to 2000, with a modelled nutritional deficit that includes non-
traditional dietary substitutions (Marushka et al., 2019).
3.5.4.2 Non-Consumable Goods
Limited evidence about climate impacts exists for valuable non-food 
aquatic materials. Ocean warming and acidification harm red coral 
(Corallium rubrum) (Bramanti et al., 2013) and communities hosting 
black coral (Antipatharian spp.), both used for jewellery (Ross et al., 
2020). While no-take MPAs (Section  3.6.3.2) enhance red-coral 
structural complexity, they only weakly compensate for warming effects 
(Cerrano et al., 2013; Montero-Serra et al., 2019). Antipatharian spp. 
are not well studied or monitored (Gress and Andradi-Brown, 2018).

Acidification and warming negatively impact pearl oysters (Welladsen 
et al., 2010; Liu and He, 2012; Liu et al., 2012; Hoegh-Guldberg et al., 
2014; Zhang et  al., 2019b). For example, projected climate impacts 
for 2035 would decrease the average net present value of French 
Polynesia’s pearl aquaculture industry by 29.1% compared with the 
present (Hilsenroth et  al., 2021). Climate impacts on ornamental 
species sought by aquarists have not been well studied (Dee et  al., 
2019b).
Decreasing the vulnerability of renewable-energy installations, 
particularly wind turbines, to climate risks (Table 3.26; Bindoff et al., 
2019a) could include technological adaptations (Section 3.6.2.2) such 
as storm ‘survival mode’ settings (Penalba et al., 2018); preparation 
for hazards such as icing, SLR, drifting sea ice and wave activity (Neill 
et al., 2018; Goodale and Milman, 2019; Solaun and Cerdá, 2019); and 
biofouling (medium confidence) (Want and Porter, 2018; Joyce et al.,

2019; Vinagre et al., 2020), which is expected to increase in response 
to warming and acidification (medium confidence) (Dobretsov et al., 
2019; Khosravi et  al., 2019; Liu et  al., 2020d; Lamim and Procópio, 
2021). Macroalgae and fish-processing byproducts are being tested 
for biofuel use (Greene et al., 2016; Alamsjah et al., 2017; Saifuddin 
and Boyce, 2017; Sakthivel et al., 2018; Sudhakar et al., 2019; Nguyen 
et al., 2020; Ramachandra and Hebbale, 2020; Tan et al., 2020), but 
weather variability could pose financial risk to this sector (Kleiman 
et al., 2021).3.5.5 Supporting and Regulating Services
Ocean and coastal regulating services are detailed in Table 3.25. The 
economic value of all regulating ecosystem services in 2015 was 
estimated at 29.1 trillion USD, with water- and climate-regulating 
services contributing the most (Balasubramanian, 2019).
3.5.5.1 Habitat Creation and Maintenance, and Larval Dispersal

Climate impacts have already altered ocean and coastal habitats 
(Section 3.4.2; Table 3.26; Gissi et al., 2021) in ways that have led to 
species range shifts, biodiversity changes, phenology changes and 
regime shifts (Section  3.4.3) from the surface ocean to the seafloor 
(very high confidence) (see Box 3.3; Figure 3.22). Continued ocean and 
coastal habitat impacts are projected, and their severities will depend 
on emissions scenario and co-occurring drivers (Section 3.4.3; Qiu et al., 
2019) or extremes (e.g., Babcock et  al., 2019). Warming and physical 
circulation are projected to change larval dispersal, a habitat-related 
service (Bashevkin et  al., 2020), but identifying probable outcomes 
remains challenging owing to the high variability among species, 
locations and recruitment (Schilling et  al., 2020; King et  al., 2021; Le 
Corre et al., 2021; Raventos et al., 2021). Climate risks to habitat can 
be decreased by reducing non-climate drivers, preserving ecosystems or

restoring habitat (Sections 3.6.2, 3.6.3.2). Risk to larval dispersal cannot 
be meaningfully addressed at scale by human-implemented adaptations; 
instead, declines in this service will pressure natural systems to adapt via 
physiological plasticity or evolution (Section 3.3; Bashevkin et al., 2020).
3.5.5.2 Climate Regulation and Air Quality
Climate regulation by the ocean depends on physical and biogeochemical 
processes (Sections 3.2–3.4) that create, move, and store heat, water 
vapour and other climate-active compounds including CO 2, methane 
and dimethyl sulphide (WGI AR6 Chapter 6; Szopa et al., 2021). Over 
the 21st century, ocean heat and CO 2 uptake will continue (WGI AR6 
SPMB4.1, B5.1; IPCC, 2021b) and sea ice loss from warming will allow 
some additional CO 2 uptake (Armstrong et  al., 2019), but the ocean 
will take up a smaller fraction of CO 2 emissions as atmospheric CO 2 
concentrations rise (high confidence) (Table 3.26; WGI AR6 SPM B4.1; 
IPCC, 2021b).

There is very limited evidence on climate-driven air-quality changes in 
the coastal zone. Increased humidity decreases the lifetime of ozone 
and increases particulate matter and indoor mould levels (USGCRP , 
2016), potentially affecting near-shore air quality. However, coastal-
zone air pollution can enhance coastal-climate impacts by increasing 
the risk of acid rain, which worsens ocean acidification (nitrogen oxides, 
sulphur oxides and mercury; Doney, 2010; Northcott et al., 2019).
3.5.5.3 Provision of Freshwater, Maintenance of Water Quality 
and Regulation of Pathogens
The salinities of many estuaries, deltas, coastal freshwater aquifers 
and soils around the world are increasing, and this decrease in water 
quality is endangering human health and agricultural yields (very 
high confidence) (Section  3.4.2.4; Table  3.26; Bindoff et  al., 2019a; 
Bouderbala, 2019; Rahman et  al., 2019; Naser et  al., 2020; Rakib

3
462Chapter 3 Oceans and Coastal Ecosystems and Their Services
et al., 2020; Mastrocicco and Colombani, 2021). Coastal salinisation 
is attributed to regionally varying combinations of climate-induced 
drivers, like SLR and storm-related flooding by seawater, and non-
climate drivers, like water withdrawal and land-use changes (very high 
confidence) (Islam et al., 2019; Rahman et al., 2019; Paldor and Michael, 
2021). Monitoring-related adaptations (Section  3.6.2.2.2), including 
advances in modelling and monitoring, are providing decision-relevant, 
regional-scale information (Colombani et  al., 2016; Mukhopadhyay 
et  al., 2019; Slama et  al., 2020; Corwin, 2021). For example, new 
projections indicate which drinking-water intake stations on China’s 
Pearl River Estuary will be unable to meet demands by 2100 due to 
SLR and drought (Wang and Hong, 2021), while others show that SLR 
effects on seawater intrusion into the coastal aquifer in Kerala, India,

under both RCP4.5 and RCP8.5 scenarios are negligible (Sithara et al., 
2020). Salinisation-associated changes may disproportionately burden 
women responsible for securing drinking water and fuel, such as in 
the Indian Sundarbans (Mukhopadhyay et al., 2019). Salinisation will 
continue to endanger coastal water and soil quality in the future (high 
confidence) (Islam et  al., 2019; Paldor and Michael, 2021), but the 
evidence assessed above shows that subsequent impacts to human 
health and agriculture will depend heavily on regional variations in 
environment and human behaviour (medium confidence).
Together, climate-induced and non-climate drivers can mobilise 
toxins and contaminants in ways that harm human and marine 
species health (very high confidence) (see Box  3.2), and climate 
change is altering these relationships (high confidence) (Table  3.26; 
Bindoff et al., 2019a). Under warming or ocean acidification, marine

molluscs exposed to pharmaceuticals via wastewater experience 
more detrimental biological consequences or greater bioaccumulation 
(limited evidence, high agreement) (Costa et al., 2020a; Costa et al., 
2020b; Dionísio et al., 2020; Freitas et al., 2020; Kibria et al., 2021). 
Physical circulation, temperature and biogeochemical characteristics 
(Bowman et al., 2020; Liu et al., 2020a; Liu et al., 2020b; Sun et al., 
2020; Zhang et al., 2020b) control the ubiquitous oceanic distribution of 
methylmercury, and ocean acidification- and warming-driven changes 
in planktonic speciation and interactions can promote additional food-
web bioaccumulation of methylmercury (Tada and Marumoto, 2020; 
Wu et al., 2020b; Zhang et al., 2020b; Zhang et al., 2021a). Interactions 
among drivers also matter: temperature plus overfishing increased 
tissue methylmercury concentrations in Atlantic bluefin tuna from 
the 1970s to the 2000s more than the decreases in the late 1990s

and 2000s from lower environmental mercury levels (Schartup et al., 
2019). This appears true for persistent organic pollutants as well, but 
their bioaccumulation is related more to temperature effects on animal 
behaviour than on pollutant dynamics (Houde et  al., 2019; Wagner 
et  al., 2019; Kalia et  al., 2021). By 2100 under RCP8.5, productivity 
changes and community structure shifts are expected to increase 
methylmercury concentrations in polar oceans and high-latitude 
phytoplankton and decrease it in low latitudes (Zhang et al., 2021a). 
The estimated average global cost of mercury-related health effects by 
2050, mainly from seafood consumption during 2010–2050, will be 
19 trillion USD (2020), assuming a 3% discount rate, if methylmercury 
emissions are not reduced (Zhang et al., 2021b).
Since previous assessments, evidence has increased that climate

impacts, such as warming, extreme weather and SLR, are increasing the geographic spread and risk of marine-borne human pathogen 
outbreaks, including Vibrio spp. ( very high confidence) (Table  3.26; 
Bindoff et al., 2019a; Logar-Henderson et al., 2019; Froelich and Daines, 
2020; Montánchez and Kaberdin, 2020; Semenza, 2020; Ferchichi 
et  al., 2021). Climate change affects at least 30 human pathogens 
with aquatic-system infection routes (e.g., ingestion of contaminated 
water or seafood, or contact with wounds; Table  3.SM.2; Cross-
Chapter Box ILLNESS in Chapter 2; Nichols et al., 2018). Conditions 
favourable for Vibrio cholerae are increasing globally, which raises the 
risk to humans (Cross-Chapter Box ILLNESS in Chapter 2). Increased 
storm-related flooding and SLR further increase human encounters with 
Vibrio spp. (Froelich and Daines, 2020). Aquatic diseases, particularly 
Vibrio spp., have caused large economic losses in aquaculture by

decreasing the quality or survival of cultured species (Lafferty et al., 
2015; Novriadi, 2016). Temperature-based model projections show that 
all Canadian shellfish beds will experience conditions that promote 
high risk of Vibrio spp. growth by 2100 for both RCP4.5 and RCP8.5 
scenarios (Ferchichi et al., 2021). Climate-induced drivers may increase 
Vibrio spp. loads in seafood species: laboratory-simulated heatwaves 
increase Vibrio spp. abundance in Pacific oyster (Crassostrea gigas) 
(Green et al., 2019) and simulated ocean acidification increases hard 
clam (Mercenaria mercenaria) susceptibility to Vibrio spp. infection 
(Schwaner et al., 2020). Projected increases in temperature, extreme 
and variable rainfall conditions, coastal flooding and SLR (Section 3.2; 
Cross-Chapter  Box  SLR in Chapter  3) strongly increase the risk of 
frequent and severe aquatic human pathogen outbreaks in ocean 
and coastal areas that will continue to harm human health and cause

economic losses (high confidence) (Cross-Chapter  Box  ILLNESS in 
Chapter 2; Froelich and Daines, 2020; Semenza, 2020; Ferchichi et al., 
2021). Section 3.6.3.1.5 assesses human adaptations to increasing risk 
of marine-borne pathogens.
Climate-driven changes in temperature, salinity (from ice melt and 
precipitation changes), deoxygenation and ocean acidification can 
alter dynamics of infectious diseases that target ocean and coastal 
species by increasing hosts’ susceptibility or pathogens’ abundance 
or virulence (high confidence) (Burge and Hershberger, 2020; Byers, 
2021). Coral and urchin diseases have increased over time driven 
by warming-related declines in organism recovery and survival or 
immunity (medium confidence) (Cohen et al., 2018; Tracy et al., 2019). 
Seagrass and sea star wasting disease outbreaks have occurred 
under combinations of ocean warming or MHWs and non-climate 
drivers (e.g., eutrophication, bottom trawling), but attribution of

these outbreaks to specific drivers is still not resolved (Harvell et al., 
2019; Jakobsson-Thor et al., 2020; Krause-Jensen et al., 2021). Disease 
outbreaks threaten marine biodiversity, species that create habitat or 
dampen wave action, and keystone species (Harvell and Lamb, 2020). 
Attributing observed changes in marine disease patterns to climate 
remains extremely difficult owing to interacting climate and non-
climate drivers (Burge and Hershberger, 2020) and lack of baseline 
data (Tracy et al., 2019). Projected increases in the frequency, duration 
and intensity of warming events would reduce survival and recovery 
of some species from hot events, reduce immunity of other species to 
pathogens, extend poleward ranges of some pathogens and increase 
infection risk when host species congregate in scarce habitat (Cohen 
et al., 2018). Pathogens that target ocean and coastal organisms may 
themselves be sensitive to future climate conditions or subsequent

3
463Oceans and Coastal Ecosystems and Their Services  Chapter 3
ecosystem changes, which challenges development of projections 
(Cohen et al., 2018; Burge and Hershberger, 2020).
New examples have illustrated how toxic HABs interfere with regulating, 
provisioning (Section  3.5.3) and cultural ecosystem services (Section  3.5.6) 
in interconnected ways (limited evidence, high agreement). A massive 
toxic Pseudo-nitzschia spp. bloom in 2013–2016 along the USA West 
Coast triggered Dungeness crab, rock crab and razor clam fishery 
closures to protect human consumers (Sections 3.6.2, 3.6.3.1.5; McCabe 
et al., 2016), and this disproportionately harmed fishers, especially small-
vessel owners, and fishing-support service industries, primarily through 
lost revenue (Ritzman et al., 2018; Moore et al., 2019; Trainer et al., 2019; 
Jardine et al., 2020; Moore et al., 2020a). Toxic Alexandrium spp. blooms 
promoted by climate-driven coastal extremes (e.g., MHWs, stratification,

runoff) in Tasmania, Australia, in 2012 and Chile in 2016 caused fish kills, 
shellfish product recalls, substantial economic losses, and human sickness 
and death (Trainer et al., 2019). The Chile event caused an estimated loss 
of 800  million USD in the farmed salmon industry (Díaz et  al., 2019) 
and resulted in a series of large, long-lasting regional protests calling 
for national aid (Delgado et al., 2019). New evidence, however, suggests 
that the perceived global increase in harmful algal blooms results from 
better monitoring and more detrimental bloom impacts, rather than a 
climate-linked mechanism (Hallegraeff et al., 2021).
Natural and engineered systems have long been used effectively 
to manage precipitation and wastewater safely (see  Box  4.5), and 
maintaining and enhancing them is a key nature-based adaptation 
strategy for coastal communities (Section 3.6.2.3; Cross-Chapter Paper 
2). Estimated values of water purification and stormwater management

provided by coastal ecosystems are in the hundreds to thousands 
of USD per hectare [e.g., 272  Euro per 0.01 km2  yr–1 from the 
Mediterranean’s sandy coastline (Hérivaux et  al., 2018); 1100–2800 
USD per 0.01 km2  yr–1 from the state of Maryland, USA (Campbell 
et al., 2020b); 600 USD per 0.01 km2 yr–1 in Zhuzhou City, China (Zhan 
et al., 2020)]. Both wild and cultured organisms also provide filtration 
services. Seagrasses’ ability to purify water is well recognised by coastal 
residents and ocean resource users in tropical and temperate locations 
(Ambo-Rappe et al., 2019; Quevedo et al., 2020; Heckwolf et al., 2021; 
McKenzie et  al., 2021a). Globally, aquacultured shellfish remove an 
estimated 49,000 tonnes of nitrogen and 6000 tonnes of phosphorus 
from coastal waters, worth a potential 1.20 billion USD, and they may 
help improve existing engineered wastewater treatment systems (van 
der Schatte Olivier et al., 2020). Climate change, especially episodic

extreme rains and RSLR (Romero-Lankao et al., 2014), is challenging 
management and design of wastewater and stormwater systems (high 
confidence) (Flood and Cahoon, 2011; Trtanj et  al., 2016; Hummel 
et al., 2018; Kirshen et al., 2018; Nazarnia et al., 2020; Reznik et al., 
2020; McKenzie et  al., 2021b) and the integrity of coastal landfills 
(Beaven et al., 2020). Without substantial adaptation that addresses 
projected wastewater management challenges and community needs 
(Section  4.2.6.1; Kirshen et  al., 2018; Kirchhoff and Watson, 2019; 
Kool et al., 2020; Nazarnia et al., 2020; Hughes et al., 2021), coastal 
water quality in many areas will decrease because of more frequent 
or severe releases of untreated wastes (high confidence) (Flood and 
Cahoon, 2011; Hummel et  al., 2018; Hughes et  al., 2021; McKenzie 
et al., 2021b), and this will have harmful consequences for human and coastal ecosystem health (high confidence) (Section  4.2.6.1; Cross-

Chapter Box ILLNESS in Chapter 2; Bindoff et al., 2019a).
3.5.5.4 Regulation of Physical Hazards
Coastal ecosystems physically protect people and property from storms 
and flooding, and climate change threatens this protection function 
(Figure  3.22; Table  3.26). Increasingly detailed models show how 
warm-water coral reefs (Reguero et  al., 2019; Storlazzi et  al., 2019; 
Reguero et al., 2021) mangroves (Blankespoor et al., 2017; Menéndez 
et  al., 2020; Trégarot et  al., 2021) and wetlands (Sun and Carson, 
2020) prevent billions of US dollars of direct and indirect damage to 
private and public property and shield millions of people from flooding 
each year. Protection by mangroves provides more economic benefits 
in higher-income nations and shields more people in lower-income 
nations (Menéndez et  al., 2020). Seagrasses (James et  al., 2020; 
James et al., 2021), kelp (Morris et al., 2020b; Zhu, 2020), suspended

shellfish aquaculture (Gentry et al., 2020; Zhu et al., 2020a), oyster reefs 
(Chowdhury et al., 2019), coastal wetlands (Möller, 2019; Keimer et al., 
2021) and sandy coastlines (Section 3.4.2.6) Hérivaux et al., 2018) also 
measurably decrease wave energy. Non-climate drivers [e.g., invasive 
species (James et al., 2020), sediment-supply changes (Ganju, 2019; Ladd 
et al., 2019; Ilia, 2020), erosion and storm damage (Mehvar et al., 2019; 
Bacopoulos and Clark, 2021)], acting together with climate-induced 
drivers and associated impacts [e.g., SLR (Cross-Chapter  Box  SLR in 
Chapter 3), changes in plant biodiversity (Section 3.5.2; Lee Smee, 2019; 
Silliman et al., 2019; Schoutens et al., 2020), MHWs (Section 3.4.3.7) 
and acidification (Section 3.4.2.1)], compromise physical protection by 
coastal ecosystems (very high confidence). (See Cross-Chapter Box SLR 
in Chapter  3 and Sections  3.6.3.1 and 3.6.3.2.2 for assessment of 
adaptations that address this ecosystem service.)

3.5.5.5 Regulation of Carbon Cycling in Ocean and Coastal 
Current and future total carbon storage and cycling in the ocean are 
governed by past and future CO 2 emissions trajectories (Table 3.26), 
but regional ocean and coastal carbon stocks and cycling vary over 
time and space due to processes being altered by climate, including 
ocean circulation, sea ice cover, coastal upwelling and thermal 
stratification (Section 3.2.2.3); ocean primary production and export 
(Sections  3.2.3, 3.4.4); and marine ecosystem biodiversity (high 
confidence) (Section 3.5.2; Figure 3.22). Quantifying regional carbon 
fluxes and stocks is still challenging and relies on indirect measures 
(e.g., Fennel et  al., 2019; Clay et  al., 2020), especially in coastal 
ecosystems where drivers interact. Carbon cycling and storage co-
occurs with other regulating services such as habitat provision, 
water-quality maintenance and coastal protection (Ouyang et  al.,

2018), particularly in vegetated coastal ecosystems (see Box  3.4). 
Adaptations to support regional carbon cycling and storage generally 
focus on area-based management and conservation (Section 3.6.3.2), 
but interventions to enhance ocean carbon storage are being explored 
for mitigation (WGIII AR6 Chapter 7).

3
464Chapter 3 Oceans and Coastal Ecosystems and Their Services
Box 3.4 | Blue Carbon Ecosystems
Climate change and other anthropogenic drivers, including eutrophication, land-use changes and overexploitation, directly and indirectly 
threaten blue carbon ecosystems (Annex II: Glossary). Commonly considered blue carbon ecosystems include vegetated coastal ecosystems 
(Sections 3.4.2.3–3.4.2.5), whose mangroves, salt marshes and seagrass beds host rooted, vascular plants known to store large amounts of 
carbon for long periods and to be amenable to management (Lovelock and Duarte, 2019). Other ocean and coastal taxa, including rooted 
or floating macroalgae (e.g., non-vascular multicellular kelp or seaweed genera such as Macrocystis spp., Sargassum spp. or Laminaria spp. 
(Filbee-Dexter and Wernberg, 2020), phytoplankton and even pelagic fauna (e.g., finfish or whales; Chami et al., 2019), have also been

proposed as blue carbon ecosystems. Terrestrial vascular-plant-derived material can also carry and store significant amounts of carbon in 
marine environments (Cragg et al., 2020). There is increasing evidence about the coverage and carbon content of macroalgal, planktonic 
and faunal taxa, but low agreement about their long-term carbon-storage potential and manageability (Alongi, 2018b; Wernberg and 
Filbee-Dexter, 2018; Lovelock and Duarte, 2019; Ortega et al., 2019; Pfister et al., 2019; Queirós et al., 2019; Filbee-Dexter et al., 2020a; 
Gallagher, 2020; Mariani et al., 2020; Thorhaug et al., 2020; van Son et al., 2020; Bach et al., 2021; Bayley et al., 2021; Cavanagh et al., 2021; 
Frontier et al., 2021; Martin et al., 2021; Pedersen et al., 2021; Weigel and Pfister, 2021). This section focuses on the array of ecosystem 
services and adaptation opportunities provided by vegetated coastal blue carbon ecosystems, where consensus and evidence are most

abundant. Mitigation potential of blue carbon ecosystems is assessed with land-based mitigation options in WGIII AR6 Section 7.4.
Carbon storage and burial in mangroves, salt marshes and seagrass meadows (see Table Box 3.4.1) help regulate ocean and coastal carbon 
cycling and may contribute to nature-based mitigation, although regional estimates vary widely based on climatic and edaphic conditions 
(WGIII AR6 Section  7.4). In addition, coastal vegetated ecosystems provide substantial and interdependent regulating, provisioning 
and cultural ecosystem services. These services include: (a) disproportionately high biodiversity per unit area (Pörtner et al., 2021a); 
(b) abundant habitat (Section 3.5.5.1) and nurseries for aquatic, terrestrial, aerial and microbial species; (c) natural filtration of waste 
and stormwater runoff into the coastal ocean (Sections 3.5.5.3, 4.2.7; Cross-Chapter Box ILLNESS in Chapter 2); (d) coastal protection

(Section 3.5.5.4; Ouyang et al., 2018; Quevedo et al., 2020); (e) food and natural materials (Sections 3.5.3, 3.5.4); and (f) support for 
tourism, livelihoods and cultural activities (Section  3.5.6). Global estimates of services provided by coastal blue carbon ecosystems 
depend on the quality of available mapping, which is currently best developed for mangroves (Macreadie et al., 2019), and improving for 
salt marshes and seagrasses (McOwen et al., 2017; McKenzie et al., 2020; Young et al., 2021).
Table Box 3.4.1 |  Estimates of organic carbon storage and burial rates in mangroves, salt marshes and seagrass meadowsa
Mangroves Salt marshes Seagrass meadows
Carbon stocks (MgC ha–1)856 ± 64.2 [79–2208] (Kauffman et al., 
2020)317.2 ± 38.2 [27–1900] (Alongi, 2018c)139.7 [9.1–628] (Fourqurean et al., 2012; 
Alongi, 2018d)
Carbon burial rate (g C m–2 yr–1) 194 ± 30 [6.2–1722] (Wang et al., 2020) 168 ± 14 [1.2–1167.5] (Wang et al., 2020)220.7 ± 40.2 [–2094 to 2124] (Alongi, 
2018d)

Global carbon burial rate 
(TgC yr–1)41 (Wang et al., 2020) 12.63 (Wang et al., 2020) 35.31 (Alongi, 2020)
Global areal coverage (Mha) 13.7 (Richards et al., 2020) 5.5 (McOwen et al., 2017) 16 (McKenzie et al., 2020)
(a) Estimates are the mean ± 95% confidence interval, where available (indicating the extremely likely range) and range. Carbon stocks for mangroves 
include above- and below-ground storage up to 3 m depth (sampling period 2007–2017). The estimates for salt-marsh and seagrass stocks are soil 
stocks up to 1 m depth (observations spanning 1983–2016 for salt marshes and until 2016 for seagrass meadows). Date ranges for the burial rates are: 
1989–2020, 1975–2020 and 1956–2016 for mangroves, salt marshes and seagrass meadows, respectively.
Coastal vegetated ecosystems are vulnerable to harm from multiple climate-induced and non-climate drivers, and together these have

reduced wetland area globally (high confidence) (Section 3.4.2.5) and endangered the services provided by these ecosystems (high 
confidence). Loss of coastal vegetated ecosystems changes biodiversity (Sections 3.5.2, 3.4.2.3–3.4.2.5; Numbere, 2019; Parreira et al., 
2021), increases risk of damage and erosion from SLR and storms (Sections 3.4.2.3–3.4.2.5; Cross-Chapter Box SLR in Chapter 3; Galeano 
et al., 2017) and impacts provisioning (Sections 3.5.3–3.5.4; Li et al., 2018b; Maina et al., 2021). These changes also strongly determine 
the quantity and longevity of blue carbon storage (high confidence) (Macreadie et al., 2019; Lovelock and Reef, 2020). Specific site 
characteristics and ecosystem responses to climate change will determine future local blue carbon storage or loss (high confidence) (see 
Table Box 3.4.2). For instance, poleward migration of mangroves to areas dominated by salt marshes is expected to increase carbon

3
465Oceans and Coastal Ecosystems and Their Services  Chapter 3
storage (Kelleway et al., 2016); however, this change in the dominant vegetation and associated faunal changes can modify carbon stocks 
and sequestration, as well as other ecosystem services (Martinetto et al., 2016; Kelleway et al., 2017; Smee et al., 2017; Macreadie et al., 
2019; Macy et al., 2019). Landward range expansion of mangroves, marshes and seagrass in response to gradual RSLR can enhance 
carbon sequestration (Section 3.4.2.5; Cross-Chapter Box SLR in Chapter 3; Macreadie et al., 2019), but coastal squeeze can limit this 
(Phan et al., 2015; Schuerch et al., 2018) and RSLR can either submerge and bury or erode and release stored blue carbon (Section 3.4.2.5; 
Macreadie et al., 2019; Lovelock and Reef, 2020). Gains and losses of mangrove habitat area (and therefore carbon storage) projected for

nations under RCP4.5 and RCP8.5 depend primarily on the combination of SLR rate, adaptation scenario (including coastal development) 
and island or continental status (Lovelock and Reef, 2020). The influence of warming, MHWs and acidification on seagrass meadows 
(Kendrick et al., 2019; Strydom et al., 2020), and associated coralligenous reefs (Zunino et al., 2019), suggests that future warming and 
especially MHWs will cause more widespread loss of services from these ecosystems (Section 3.4.2.5). Loss of blue carbon ecosystems 
will not only halt carbon storage but also release stored carbon: emissions after 2000 due to global mangrove deforestation have been 
estimated at 23.5–38.7 Tg Cyr–1 (Ouyang and Lee, 2020). Mitigation estimates for avoided conversion and restoration of coastal wetlands 
and the implications of the impacts of climate change are assessed in WGIII AR6 Section 7.4.

To date, initiatives aiming to restore coastal wetland ecosystems primarily address ecosystem characteristics other than carbon storage 
(Herr et al., 2017; de los Santos et al., 2019; Lovelock and Duarte, 2019; Friess et al., 2020a). But recovery of coastal vegetated ecosystems is 
expected to bring back the full suite of ecosystem services they provide, not just carbon storage (medium confidence) (Marbà et al., 2015a; 
Burden et al., 2019; Friess et al., 2020a), making coastal restoration a low-risk action that offers both adaptation and mitigation benefits 
(Steven et al., 2020; Gattuso et al., 2021). Successful restoration requires using appropriate plant species in suitable environmental settings 
(Wodehouse and Rayment, 2019; Friess et al., 2020a) with favourable geomorphology and biophysical conditions (Cameron et al., 2019; Ochoa-
Gómez et al., 2019) and considering social, economic, policy and operational constraints (Section 3.6.3.2.2; Cross-Chapter Box NATURAL in

Chapter 2), now and in the future (high confidence) (Duarte et al., 2020; Lovelock and Reef, 2020). Nevertheless, restored spaces may not 
store carbon at rates equal to those of undisturbed spaces (Yang et al., 2020), and it may take decades to determine or achieve carbon-storage 
outcomes of restoration (Sasmito et al., 2019; Duarte et al., 2020; Oreska et al., 2020). Integration improves efforts to restore or conserve 
coastal wetland ecosystems to accomplish both adaptation and mitigation outcomes (Steven et al., 2020). Government-led conservation of 
blue carbon ecosystems as part of national and subnational climate strategies (e.g., Friess et al., 2020a; Kelleway et al., 2020; Wedding et al., 
2021) benefits from coordination with private activities, such as incentivising conservation with payments for ecosystem services (Muenzel 
and Martino, 2018; Friess et al., 2020a). Moreover, successful area-based protection measures consider both environmental and social issues

(Section 3.6.3.2). Continued integration and alignment of policies at international to local levels (Section 3.6.5) will also support achieving 
the adaptation and mitigation benefit of blue carbon spaces (Friess et al., 2020a; Steven et al., 2020; Wu et al., 2020a).
Table Box 3.4.2 |  Examples of vegetated blue carbon ecosystem carbon-storage gains and losses in response to climate-induced drivers, and key actions contributing 
to maintained and/or increased carbon storagea
Mangroves Salt marshes Seagrasses
Landward expansion by vegetation +C +C +C
Coastal squeeze −C −C −C
Loss of low-lying or submerged land or vegetation −C −C −C
Human adaptation to increase accommodation space +C +C
Extreme storms
Erosion, loss of area, subsidence −C −C 0 to −C
Enhanced sedimentation +C +C +C
Vegetation damage and mortality −C to +C −CBox 3.4 (continued)

3
466Chapter 3 Oceans and Coastal Ecosystems and Their Services
Warming
Increased productivity +C +C
Vegetation mortality −C
Increased decomposition of soil −C −C to +C
Poleward expansion of mangroves +C −C
Poleward expansion of seagrasses +C
Poleward expansion of bioturbators ∆C
Change in dominant species ∆C
Rising concentrations of atmospheric CO 2
Increased productivity of some species ∆C ∆C +C
Biodiversity loss −C
Altered precipitation
Reduced productivity −C −C
Increased remineralisation −C −C
Low-salinity events 0 to −C
Key actions to sustain blue carbon storage
Protect ecosystems X X X
Develop alternative livelihoods X
Provide space for landward migration X X
Restore hydrological connections X X
Maintain or restore sediment supply X X
Restore ecosystems X X
Plant indigenous species X
Reduce nutrient inputs X

(a) ‘+C’ indicates potential positive effects on blue carbon stocks, ‘−C’ indicates potential negative effects, ‘0’ indicates no effects and ‘∆C’ indicates 
positive potential or negative effects. Effects on carbon stocks are from Section 3.4.2.5, Macreadie et al. (2019), Lovelock and Reef (2020) and Wang et al. 
(2020). Key actions to sustain blue carbon storage are from Duarte et al. (2020) and Wedding et al. (2021).Box 3.4 (continued)

3
467Oceans and Coastal Ecosystems and Their Services  Chapter 3
3.5.6 Cultural Services
Cultural services provided by ocean and coastal ecosystems help maintain 
psychological well-being, cultural development, human identities, 
educational opportunities and reserves that could support development 
of future goods or activities (Table 3.25). Most recent studies of ocean 
and coastal cultural services simply detail local benefits using replicable 
methods (e.g., Drakou et al., 2018; Folkersen, 2018; Förster et al., 2019; 
Lau et al., 2019; Pouso et al., 2019; Weitzman, 2019; Yang et al., 2019), 
focusing on diverse ocean and coastal environments and ecosystems 
(Jobstvogt et al., 2014; Balzan et al., 2018; Drakou et al., 2018; Ingram 
et al., 2018; Pouso et al., 2018; Zapata et al., 2018; Ghermandi et al., 
2019; Pouso et al., 2019; Tanner et al., 2019; Turner et al., 2019; Ortíz 
Liñán and Vázquez Solís, 2021). Cultural ecosystem services may directly

benefit from marine development activities, such as marine aquaculture 
(e.g., Alleway et al., 2018), and indirectly benefit from marine activities 
that increase biodiversity (e.g., Causon and Gill, 2018). Cultural services 
are generally quantified using interviews and revealed-preference or 
stated-preference valuation (National Research Council, 2005; Sangha 
et  al., 2019), but people often are especially reluctant to evaluate 
cultural ecosystem services in monetary terms, given the spiritual and 
community linkages to these services (Oleson et al., 2018).
Additional evidence since previous assessments (Table 3.26) confirms 
that climate-change impacts on ocean and coastal cultural ecosystem 
services have already disrupted people’s place-based emotional 
attachments and cultural activities (limited evidence, high agreement) 
(Figure  3.22). Bleaching and mortality of corals in the Great Barrier 
Reef have induced measurable ‘reef grief’, a type of solastalgia, among

reef visitors and researchers (Conroy, 2019; Curnock et  al., 2019; 
Marshall et al., 2019). The mental health of people in Tuvalu (Gibson 
et  al., 2020), Alaska (Allen, 2020) and Honduras (Kent and Brondo, 
2020) have suffered from both the experience of climate impacts 
on ocean and coastal ecosystems (e.g., SLR and changes in fisheries 
and wildlife), and the anticipation of more in the future. The climate-
associated MHWs and harmful algal bloom events in 2014–2016 in 
the US Pacific Northwest (Moore et al., 2019) prevented seasonal razor 
clam harvests culturally important to Indigenous Peoples and the local 
community (Section 3.5.5.3; Crosman et al., 2019). Sea level rise and 
storm-driven coastal erosion endanger coastal archaeological and 
heritage sites around the world (very high confidence) (Hoque and 
Hoque, 2008; Carmichael et al., 2018; Reimann et al., 2018; Elliott and 
Williams, 2019; Ravanelli et  al., 2019; Anzidei et  al., 2020; Chemeli

et al., 2020; García Sánchez et al., 2020; Harkin et al., 2020; Hil, 2020; 
Rivera-Collazo, 2020).
Disruptions in ocean and coastal ecosystem services partly attributable 
to climate change have also caused economic losses (limited evidence, 
high agreement). Water-quality deterioration over 24  years in a 
temperate bay in the USA due to nutrient enrichment and warming 
caused 0.08–0.67  million USD per decade in lost recreational 
shellfish revenues (Luk et  al., 2019). In southwestern Florida, where 
nutrient enrichment, lake hydrology, and rainfall conditions control 
cyanobacterial HAB formation (Havens et al., 2019), toxic HAB events 
deterred visitors and recreation, leading to lodging and restaurant 
revenue losses (Bechard, 2020), decreased domestic and international 
arrivals and overall visitor spending (a 99 million USD loss from August to October 2018; Scanlon, 2019), and lost recreational spending from 
loss of boat-ramp access (a 3  million USD economic loss from June

to September 2018; Alvarez et al., 2019). In Cornwall, England, HABs 
from 2009 to 2016 disrupted residents’ sense of place, identity and 
well-being by interrupting recreational and economic activities, and 
by creating feelings of uncertainty and unease around the safety or 
dependability of future ocean-related activities (Willis et  al., 2018). 
Increasingly abundant Sargassum spp. floating macroalgae from the 
central Atlantic Ocean and Caribbean Sea, whose proliferation has been 
attributed to high sea surface temperatures and nutrient enrichment 
(Wang et al., 2019a), has substantially disrupted beach tourism in the 
Caribbean and Mexico and imposes millions of dollars of clean-up costs 
annually on affected beaches (Milledge and Harvey, 2016).
Observed disruption of ocean and coastal cultural services by climate 
impacts, plus increasingly severe and widespread projected climate-
change impacts on ocean and coastal ecosystems, imply that the risk

to cultural ecosystem services will remain constant or even increase 
(medium confidence) (Figure 3.22; Table 3.26). Recent studies assert 
that cultural ecosystem services are at risk from climate change (high 
confidence) (Singh et al., 2019a; Koenigstein, 2020). However, limited 
evidence and complex social–ecological interactions (e.g., Ingram et al., 
2018) challenge development of specific projections. For instance, the 
little auk (Alle alle) in the North Water Polynya is traditionally harvested 
by Indigenous Inughuit for food and community-wide celebrations and 
seasonal activities, but harvests are threatened to an undetermined 
degree as the seabird competes for food with recovering bowhead 
whale (Balaena mysticetus) populations and northward range shifts 
of capelin (Mallotus villosus) due to warming (Mosbech et al., 2018). 
Section 3.6 assesses the cultural implications of implemented human 
adaptations.
3.6 Planned Adaptation and Governance to

Achieve the Sustainable Development 
Goals
3.6.1 Introduction
Human adaptation comprises an array of measures (adaptation options; 
IPCC, 2014a) that modulate harm or exploit opportunities from climate 
change (Section  1.2.1.3). Adaptation options that respond to key 
ocean and coastal risks (Section 3.4) focus on individuals, livelihoods 
and economic sectors that benefit from ocean and coastal ecosystem 
services (Section 3.5). AR5 concluded that local adaptation measures 
would not alone be enough to offset global effects of increased 
climate change on marine and coastal ecosystems, and that mitigation 
of emissions would also be necessary (high confidence) (Table 3.27; 
Pörtner et  al., 2014; Oppenheimer et  al., 2019). SROCC assessed 
that ecosystem-based adaptation, including MPAs (high confidence) 
(Bindoff et  al., 2019a) and adaptive management, are effective to 
reduce climate-change impacts (IPCC, 2018; IPCC, 2019b), but that

existing marine governance is insufficient to provide an effective 
adaptation response in the marine ecosystem (high confidence) (IPCC, 
2019c).

3
468Chapter 3 Oceans and Coastal Ecosystems and Their Services
Observed global influence of climate-induced drivers on ecosystem services
Food provision
Other provisioning
Climate and air quality
Ocean carbon storage
Blue carbon storage
Cultural servicesEcosystem service WarmingMarine
heat-
acidification /
CO2Sea-
level
rise StormsOxygen
lossSea
ice
lossPrimary non-climate drivers
Marine
pollution
Non-
section in
the report
Habitat creation and maintenance3.5.2
3.5.3
3.5.4
3.5.5.3
3.5.5.5
Box 3.4
3.5.63.5.5.1Biodiversit y
lossObserved
impact
3.5.5.2
3.5.5.4Regulation of water quality/pathogensna
na
na
na
na
nana
nanana
nana
na na
na
na na nana na na na
na
na
na/
/
= no data or not assessednaStrength  of impact
PositiveConfidence  level

directionHigh Medium Low High Medium Low Negative
Figure 3.22 |  Observed global influence of climate-induced drivers on ecosystem services. Symbols show whether the observed impact of the climate-induced drivers 
on a group of ecosystem services is positive (beneficial), negative (detrimental) or mixed (usually resulting from location, the presence of interacting drivers or changing effects 
over time). The ‘observed impact’ indicates the total effect of all climate-induced drivers on a specific ecosystem service, using expert judgement based on summary statements 
throughout Section 3.5.  Tick marks represent the presence of co-occurring drivers non-climate drivers that affect the service. No assessment indicates that not enough evidence is 
available to assess the direction of impact.
This section builds on the SROCC assessment of the portfolio 
of available solutions, their applicability and their effectiveness 
in reducing climate-change-induced risks to ocean and coastal

ecosystems. Section  3.6.2 assesses the set of planned adaptation 
measures. Section  3.6.3 assesses implementation of adaptation 
solutions and the enablers, barriers and limitations that affect their 
feasibility. Section  3.6.4 evaluates the contribution of planned 
adaptation to the Sustainable Development Goals (SDGs) and other 
policy-relevant frameworks, and Section 3.6.5 synthesises emerging 
evidence about best practices.
3.6.2 Adaptation Solutions
Adaptation in ocean and coastal ecosystems continues to be informed 
primarily by theory, as there is still limited evidence about implemented 
solutions (high agreement) (Seddon et  al., 2020) and their success 
across regions, especially in low-income nations (Chausson et  al., 
2020). Adapting to climate change depends on society’s ability and 
willingness to anticipate the change, recognise its effects, plan to 
accommodate its consequences (Ling and Hobday, 2019; Wilson et al.,

2020b) and implement a coordinated portfolio of informed solutions. 
Here, the complete portfolio of adaptation solutions is assessed using 
the taxonomy of Abram et al. (2019): (1) socio-institutional adaptation, 
(2) built infrastructure and technology, and (3) marine and coastal 
nature-based solutions (NbS) (Figure 3.23).3.6.2.1 Socio-Institutional Adaptation
Increasing evidence shows that an effective solution portfolio includes 
social and institutional adaptation (Figure 3.23, top; Table 3.28). Social 
adaptation to climate change is already occurring, as people use 
strategies ranging from accommodating change, to coping, adapting 
and transforming their livelihoods (Béné and Doyen, 2018; Fedele 
et al., 2019; Galappaththi et al., 2019; Barnes et al., 2020; Ojea et al., 
2020; Green et al., 2021c). Although management and institutions have 
major roles in adaptation (Gaines et al., 2018; Barange, 2019), marine 
governance is impeded by increasing numbers of often-competing

users and uses (Boyes and Elliott, 2014); sector-led, fragmented efforts 
(Nunan et al., 2020); and a legal framework less clear than those on 
land (Crespo et al., 2019; Guggisberg, 2019). Future social responses 
depend on warming levels and on the institutional, socioeconomic 
and cultural constructs that allow or limit livelihood changes (medium 
confidence) (Chapter 18; Galappaththi et al., 2019; Ford et al., 2020; 
Green et  al., 2021c). Both social and institutional transformations 
are needed to change the structures of power, culture, politics and/
or identity associated with marine ecosystems (Section 1.5.2; Wilson 
et  al., 2020b). Ideally, institutional and social adaptation will work 
together to sustain knowledge systems and education, enhance 
participation and social inclusion, facilitate livelihood support and 
transformational change of dependent coastal communities, provide 
economic and financial instruments, and include polycentric and multi-

level governance of transboundary management (Fedele et al., 2019; 
Fulton et al., 2019).

3
469Oceans and Coastal Ecosystems and Their Services  Chapter 3
FAQ 3.4 | Which industries and jobs are most vulnerable to the impacts of climate change in the oceans?
The global ocean underpins human well-being through the provision of resources that directly and indirectly feed and employ many millions 
of people. In many regions, climate change is degrading ocean health and altering stocks of marine resources. Together with over-harvesting, 
climate change is threatening the future of the sustenance provided to Indigenous Peoples, the livelihoods of artisanal fisheries, and 
marine-based industries including tourism, shipping and transportation.
The ocean is the lifeblood of the planet. In addition to regulating planetary cycles of carbon, water and heat, 
the ocean and its vast resources support human livelihoods, cultural practices, jobs and industries. The impacts

of climate change on the ocean can influence human activities and employment by altering resource availability, 
spreading pathogens, flooding shorelines and degrading ocean ecosystems. Fishing and mariculture are highly 
exposed to change. The global ocean and inland waters together provide more than 3.3 billion people at least 
20% of the protein they eat and provide livelihoods for 60 million people. Changes in the nutritional quality or 
abundance of food from the oceans could influence billions of people.
Substantial economic losses for fisheries resulting from recent climate-driven harmful algal blooms and marine 
pathogen outbreaks have been recorded in Asia, North America and South America. A 2016 event in Chile caused 
an estimated loss of 800 million USD in the farmed-salmon industry and led to regional government protests. The 
recent closure of the Dungeness crab and razor clam fishery in the USA due to a climate-driven algal bloom harmed

84% of surveyed residents from 16 California coastal communities. Fishers and service industries that support 
commercial and recreational fishing experienced the most substantial economic losses, and fishers were the least 
able to recover their losses. This same event also disrupted subsistence and recreational fishing for razor clams, 
important activities for Indigenous Peoples and local communities in the Pacific Northwest of the USA.
Other goods from the ocean, including non-food products like dietary supplements, food preservatives, pharmaceuticals, 
biofuels, sponges and cosmetic products, as well as luxury products like jewellery coral, cultured pearls and aquarium 
species, will change in abundance or quality due to climate change. For instance, ocean warming is endangering the 
‘candlefish’ ooligan (Thaleichthys pacificus), whose oil is a traditional food source and medicine of Indigenous Peoples

of the Pacific Northwest of North America. Declines in tourism and real estate values, associated with climate-driven 
harmful algal blooms, have also been recorded in the USA, France and England.
Small-scale fisheries livelihoods and jobs are the most vulnerable to climate-driven changes in marine resources and 
ecosystem services. The abundance and composition of their harvest depend on suitable environmental conditions and 
on IKLK developed over generations. Large-scale fisheries, though still vulnerable, are more able to adapt to climate 
change due to greater mobility and greater resources for changing technologies. These fisheries are already adapting 
by broadening catch diversity, increasing their mobility to follow shifting species, and changing gear, technology and 
strategies. Adaptation in large-scale fisheries, however, is at times constrained by regulations and governance challenges.

Jobs, industries and livelihoods which depend on particular species or are tied to the coast can also be at risk from 
climate change. Species-dependent livelihoods (e.g., a lobster fishery or oyster farm) are vulnerable due to a lack 
of substitutes if the fished species are declining, biodiversity is reduced, or mariculture is threatened by climate 
change or ocean acidification. Coastal activities and industries ranging from fishing (e.g., gleaning on a tidal flat) 
to tourism to shipping and transportation are also vulnerable to sea level rise and other climate-change impacts on 
the coastal environment. The ability of coastal systems to protect the shoreline will decline due to sea level rise and 
simultaneous degradation of nearshore systems, including coral reefs, kelp forests and coastal wetlands.
The vulnerability of communities to losses in marine ecosystem services varies within and among communities.

Tourists seeking to replace lost cultural services can adapt by engaging in the activity elsewhere. But communities 
who depend on tourism for income or who have strong cultural identity linked to the ocean have a more 
difficult time. Furthermore, climate-change impacts exacerbate existing inequalities already experienced by some 
communities, including Indigenous Peoples, Pacific Island countries and territories and marginalised peoples, such 
as migrants and women in fisheries and mariculture. These inequities increase the risk to their fundamental human 
rights by disrupting livelihoods and food security, while leading to loss of social, economic and cultural rights. 
These maladaptive outcomes can be avoided by securing tenure and access rights to resources and territories for 
all people depending on the ocean, and by supporting decision-making processes that are just, participatory and 
equitable.

3
470Chapter 3 Oceans and Coastal Ecosystems and Their Services
Cultural–supporting identitie s Cultural–physical and psychological well being Cultural–recreation
Provisioning–fisheries Provisioning –fiber and foo d Provisioning –medicinal and option valu e
of sedimentsRegulation of  
salinit yRegulation of
 hazardsRegulation
of freshwaterRegulation
of OA
Sea level rise3
O2
Deoxygenation4
Coastal population Small-scale fisheries Maricultur e Blue energy Maritime transportation Tourism Large-scale fisheries
Indigenous Peoples Small island developing states Small scale fishers and shellfish gatherers
11
22
2 22 3
3
1 2 3very high high medium/lowVulnerabilityWhich livelihoods and economic sectors are most vulnerable to the impacts
of climate change in the oceans?
4 4 44
44
1213 2 1
of sedimentsRegulation of 
salinity
Supporting-Habita t
creationRegulation of
 hazardsRegulation of freshwaterRegulation of 
air qualityRegulation of 
climate

Regulation of OASupporting-dispersalEcosystem services Hazards Sector s Livelihoods Systems
 sss
Rl ti f
RRegReg llaulatitiotiononofff
Ocean acidificationpH1
Ocean warming2Regulation
of climat eRegulation of
air qualit ySupporting
Habitat creationSupporting
dispersa l
Figure FAQ3.4.1 |  Illustration of vulnerable ocean and coastal groups, the climate-induced hazards they experience, and anticipated outcomes 
for human systems.Box FAQ 3.4 (continued)

3
471Oceans and Coastal Ecosystems and Their Services  Chapter 3
A key adaptation solution is improving access to credit and insurance in order to buffer against variability in resource 
access and abundance. Further actions that decrease social and institutional vulnerability are also important, such as 
inclusive decision-making processes, access to resources and land for Indigenous Peoples, and participatory approaches 
in management. For the fishing industry, international fisheries agreements and investing in sustainable mariculture 
and fisheries reforms is often recommended. Immediate adaptations to other challenges, such as harmful algal blooms, 
frequently include fishing-area closures; these can be informed by early-warning forecasts, public communications; and 
education. These types of adaptations are more effective when built on trusted relationships and effective coordination

among involved parties, and are inclusive of the diversity of actors in a coastal community.Box FAQ 3.4 (continued)
Table 3.27 |  Conclusions from previous IPCC assessments about implemented adaptation, enablers and limits, and contribution to Sustainable Development Goals (SDGs)
AR5 SR15 SROCC
Degree of implementation (Section 3.6.3.1)‘The analysis and implementation of coastal 
adaptation towards climate-resilient and 
sustainable coasts has progressed more
significantly in developed countries than in 
developing countries (high confidence)’
(Wong et al., 2014).‘Adaptation (to SLR) is
already happening (high confidence) and 
will remain important over
multi-centennial time scales’
(Hoegh-Guldberg et al., 2018a).‘A diversity of adaptation responses to 
coastal impacts and risks have been 
implemented around the world, but mostly 
as a reaction to current coastal risk or 
experienced disasters (high confidence)’
(Oppenheimer et al., 2019).
Conservation and restoration

(Section 3.6.3.2)‘With continuing climate change, local 
adaptation measures (such as conservation) 
or a reduction in human activities (such as
fishing) may not sufficiently offset 
global-scale effects on marine ecosystems 
(high confidence)’
(Pörtner et al., 2014).‘Existing and restored natural coastal 
ecosystems may be effective in reducing 
the adverse impacts of rising sea levels and 
intensifying storms by protecting coastal 
and deltaic regions (medium confidence)’
(Hoegh-Guldberg et al., 2018a).‘Ecosystem restoration may be able to 
locally reduce climate risks (medium 
confidence) but at relatively high cost and 
effectiveness limited to low-emissions 
scenarios and to less-sensitive ecosystems 
(Bindoff et al., 2019a).
Enablers, barriers and limits of adaptation 
(Section 3.6.3.3)‘Adaptation strategies for ocean regions 
beyond coastal waters are generally 
poorly developed but will benefit from 
international legislation and expert

networks, as well as marine spatial 
planning (high agreement)’
(Hoegh-Guldberg et al., 2014).‘Lower rates of change [associated with a 
1.5°C temperature increase] enhance the 
ability of natural and human systems to 
adapt, with substantial benefits for a wide 
range of terrestrial, freshwater, wetland, 
coastal and ocean ecosystems (including 
coral reefs) (high confidence)’
(Hoegh-Guldberg et al., 2018a).‘There are a broad range of identified 
barriers and limits
for adaptation to climate change in 
ecosystems and human systems (high 
confidence). Limitations include [...] 
availability of technology, knowledge and 
financial support, and existing governance 
structures (medium confidence)
Existing ocean-governance structures 
are already facing multi-dimensional, 
scale-related challenges because of climate 
change [...] (high confidence)’
SDGs and other policy frameworks 
(Section 3.6.4)‘Overall, there is a strong need to

develop ecosystem-based monitoring and 
adaptation strategies to mitigate rapidly 
growing risks and uncertainties to the 
coastal and oceanic industries, communities 
and nations (high agreement)’
(Hoegh-Guldberg et al., 2014).‘Adaptation strategies can result in 
trade-offs with and among the SDGs 
(medium evidence, high agreement)’
(Roy et al., 2018).‘Achieving [the SDGs] and charting Climate 
Resilient Development Pathways depends in 
part on ambitious and sustained mitigation 
efforts to contain SLR coupled with 
effective adaptation actions to reduce SLR 
impacts and risk (medium evidence, high 
agreement)’

3
472Chapter 3 Oceans and Coastal Ecosystems and Their Services
Adaptation solutions for ocean and coastal ecosystems
Socio-Institutional Adaptation
Built infrastructure and technology
Marine and coastal nature-based solutions-
Figure 3.23 |  Adaptation solutions for ocean and coastal ecosystems that address climate-change risk in different ocean ecosystems, communities and 
economic sectors. Box colour indicates confidence in the solution’s potential to reduce mid-term risks (based on the amount of evidence and agreement supporting the solutions; 
see SM3.5.1 for full assessment). The feasibility and effectiveness of each solution (low, medium or high) indicates its ability to support ecosystems and societies as they adapt to 
climate change impacts, based on Table 3.SM.3.

3
473Oceans and Coastal Ecosystems and Their Services  Chapter 3
Table 3.28 |  Assessment of socio-institutional adaptation solutions to reduce mid-term climate impacts in oceans and coastal ecosystemsa
SolutionConfidence in solution 
(mid-term potential)Contribution to adaptation Selected referencesExamples of implementa-
tion
Knowledge diversity High confidenceConsideration of IK and LK systems is beneficial 
to communities, increases their resilience and 
is relevant and transferable beyond the local 
scale.Norström et al. (2020); Petzold 
et al. (2020); Gianelli et al. (2021); 
Schlingmann et al. (2021)Ecotourism (Section 3.6.3.1.3), 
conservation (Section 3.6.3.2.1)
Socially inclusive policies High confidencePolicies that promote participation of a 
diversity of groups are able to address existing 
vulnerabilities in coastal communities and 
promote adaptation and transformational 
change.Brodie Rudolph et al. (2020); 
Ford et al. (2020); Friedman et al.

(2020)Finance (Section 3.6.3.4.2)
Participation Medium confidenceParticipation in decision making and adaptation 
processes is recommended across a range of 
different hazards and contexts, and has the 
potential to improve adaptation outcomes.Brodie Rudolph et al. (2020); 
Claudet et al. (2020a); Hügel 
and Davies, 2020); Sumaila et al. 
(2021)Fisheries and mariculture 
(Section 3.6.3.1.2), Indigenous 
Peoples (Section 3.6.3.4.1)
Livelihood diversification Medium confidenceLivelihood diversification in communities 
dependent on marine and coastal ecosystems 
reduces climate risks and confers flexibility to 
individuals, which is key to adaptive capacity.Blanchard et al. (2017); Cinner 
and Barnes (2019); Shaffril et al. 
(2020); Owen (2020); Pinsky 
(2021); Taylor et al. (2021)Fisheries and mariculture 
(Section 3.6.3.1.2), 
coastal communities 
(Cross-Chapter Box SLR 
in Chapter 3), tourism 
(Section 3.6.3.1.3)
Mobility Medium confidenceWhen individuals are given the choice about

mobility, they may elect to use this response 
to minimise climate risks and benefit their 
livelihoods.Barnett and McMichael (2018)Fisheries and mariculture 
(Section 3.6.3.1.2)
Migration Low confidenceMigration often involves different spatial and 
temporal scales than mobility. Migration could 
be considered an adaptation solution for some 
coastal and island populations in the cases of 
extreme events, but also as a response to more 
gradual changes (e.g., coastal erosion from sea 
level rise).Maharjan et al. (2020); Biswas 
and Mallick (2021); Zickgraf 
(2021)Coastal livelihoods 
(Section 3.6.3.1.1)
Finance and market 
mechanismsHigh confidenceFinancial mechanisms and credit provision for 
marine-dependent livelihoods are effective for 
overcoming impacts from SLR, extreme events 
and other climate-induced drivers.Shaffril et al. (2017); Dunstan 
et al. (2018); Hinkel et al. (2018); 
Moser et al. (2019); Sainz et al. 
(2019); Woodruff et al. (2020)Economic dimensions

(Section 3.6.3.4.2)
Disaster response 
programmesHigh confidenceDisaster response programmes confer resilience 
to communities and contribute to adaptation, 
when designed to be inclusive, participatory 
and adaptive.Nurhidayah and McIlgorm (2019)Climate services 
(Section 3.6.3.4.3), 
tourism cruise-ship sector 
Multi-level ocean 
governanceHigh confidenceThe multi-scale nature of ocean and coastal 
climate-change risk demands adaptation 
solutions at multiple levels of governance that 
consider the objectives and perceptions of all 
stakeholders to support local implementation of 
broad strategies.Miller et al. (2018); Gilfillan 
(2019); Holsman et al. (2019); 
Obura et al. (2021)Policy frameworks 
(Section 3.6.4.3)
Institutional transboundary 
agreementsMedium confidenceInstitutional agreements for the management 
of transboundary marine resources are key 
for a sustainable future given current impacts 
on marine species distribution due to climate

change.Engler (2020); Mason et al. 
(2020); Oremus et al. (2020); 
Melbourne-Thomas et al. (2021)Fisheries (Section 3.6.3.1.2; 
Cross-Chapter Box MOVING 
SPECIES in Chapter 5)
(a) Confidence is assessed in SM3.5.1. Feasibility and effectiveness are assessed in Figure 3.24.

3
474Chapter 3 Oceans and Coastal Ecosystems and Their Services
Table 3.29 |  Assessment of built infrastructure and technology solutions to reduce mid-term climate impacts in oceans and coastal ecosystemsa
tion
Accommodation and 
relocationHigh confidenceAsset modification and relocation of 
livelihoods to adapt to sea level rise, extreme 
events and coastal erosion.Hanson and Nicholls (2020); 
Monios and Wilmsmeier (2020); 
Zickgraf (2021)Cross-Chapter Box SLR in 
Chapter 3, coastal development 
Protection and beach and 
shore nourishmentMedium confidenceProtection of coastal ecosystems with 
interventions, such as beach and shore 
nourishment, is a common response to 
beach erosion around the world, and an 
alternative to hard protection structures such 
as seawalls.Pinto et al. (2020); de Schipper

et al. (2021); Elko et al. (2021)Cross-Chapter Box SLR in 
Early-warning systems High confidenceEarly-warning systems can support decision 
making, limit economic losses from extreme 
events and aid in the enterprise and 
development of adaptive management 
systems.Bindoff et al. (2019); Collins et al. 
(2019a); Winter et al. (2020); 
Neußner (2021)Coastal development 
(Section 3.6.3.1.1), human health 
(Section 3.6.3.1.5)
Seasonal and dynamic 
forecastsHigh confidenceThe proliferation of real-time and seasonal 
forecasts of temperature extremes, marine 
heatwaves, storm surges, harmful algal 
blooms and the distribution of living marine 
resources greatly contribute to adaptation 
through monitoring, early-warning systems, 
adaptive management and ecosystem-based 
management.Payne et al. (2017); Hazen et al. 
(2018); Fernández-Montblanc 
et al. (2019); Holbrook et al. 
(2020); Winter et al. (2020); Bever

et al. (2021); Davidson et al. 
(2021); Spillman and Smith (2021)Fisheries and mariculture 
(Section 3.6.3.1.2), marine 
protected areas (MPAs) 
(Section 3.6.3.2.1), climate 
services (Section 3.6.3.2.4)
Monitoring systems Medium confidenceMonitoring systems that address 
climate-induced drivers, ecosystem impacts 
and social vulnerabilities in marine social–
ecological systems are key for adaptation.Nichols et al. (2019); Claudet et al. 
(2020a); Wilson et al. (2020a)MPAs (Section 3.6.3.2.1), climate 
services (Section 3.6.3.2.4), 
fisheries (Section 3.6.3.1.2)
Habitat development Low confidenceAccelerates the recovery of damaged 
ecosystems and promotes ecological or 
biological adaptation to future climate 
change.Jones et al. (2018a); 
Boström-Einarsson et al. (2020); 
Kleypas et al. (2021)Restoration (Section 3.6.3.2.2)
Active restoration High confidenceReintroduces species or augments existing 
populations, for example, propagating and

transplanting heat-tolerant coral species.Boström-Einarsson et al. (2020); 
Rinkevich (2021)Restoration (3.6.3.2.2)
Assisted evolution High confidenceManipulates species’ genes to accelerate 
natural selection.Bulleri et al. (2018); National 
Academies of Sciences (2019); 
Morris et al. (2020c)Restoration (Section 3.6.3.2.2)
(a) Confidence is assessed in SM3.5.1. Feasibility and effectiveness are assessed in Figure 3.24.3.6.2.2 Built Infrastructure and Technology
Engineering and technology support marine and coastal adaptation 
(Table  3.29). Built infrastructure includes engineered solutions that 
protect, accommodate or relocate coastal assets using hard engineering, 
like seawalls, and soft engineering, such as beach and shore nourishment 
(Cross-Chapter Box SLR in Chapter 3). Technological tools include early-
warning systems for extreme events (Bindoff et al., 2019a; Collins et al., 
2019a), improved forecast and hindcast models (Winter et  al., 2020;

Davidson et  al., 2021; Spillman and Smith, 2021) and environmental 
monitoring (Claudet et  al., 2020a; Wilson et  al., 2020a; Melbourne-
Thomas et al., 2021) that support informed decision making (Tommasi 
et  al., 2017; Rilov et  al., 2020; A. Maureaud et  al., 2021). Emerging 
adaptation technologies, such as habitat development, active restoration 
and assisted evolution (Boström-Einarsson et al., 2020; Kleypas et al., 
2021), intend to accelerate recovery of damaged ecosystems and 
promote ecological adaptation to climate change (Jones et al., 2018a; 
Boström-Einarsson et al., 2020; Kleypas et al., 2021).3.6.2.3 Marine and Coastal Nature-Based Solutions
The ocean and coastal adaptation portfolio (Figure  3.23) also 
includes marine and coastal NbS (Table 3.30). NbS that contribute 
to climate adaptation, also known as ecosystem-based adaptations 
(EBA), are cross-cutting actions that harness ecosystem functions to

restore, protect and sustainably manage marine ecosystems facing 
climate-change impacts, while also benefiting social systems and 
human security (Abelson et  al., 2015; Barkdull and Harris, 2019) 
and supporting biodiversity (high confidence) (Annex II: Glossary; 
Cross-Chapter  Box  NATURAL in Chapter  2; Seddon et  al., 2021). 
NbS are expected to contribute to global adaptation and mitigation 
goals (high confidence) (Beck et  al., 2018; Cooley et  al., 2019; 
Hoegh-Guldberg et al., 2019b; Menéndez et al., 2020; Morris et al., 
2020a) by protecting coastal environments from SLR and storms 
(Cross-Chapter  Box  SLR; Reguero et  al., 2018), and by storing 
substantial quantities of carbon (Sections  3.4.2.5, 3.6.3.1.5; WGIII 
AR6 Chapter 7; Howard et al., 2017; Chow, 2018; Smale et al., 2018;

3
475Oceans and Coastal Ecosystems and Their Services  Chapter 3
Singh et al., 2019b; Soper et al., 2019). Marine NbS are cost-effective, 
can generate social, economic and cultural co-benefits, and can 
contribute to the conservation of biodiversity in the near- to mid-
term (high confidence) (Secretariat of the Convention on Biological 
Diversity, 2009; Gattuso et  al., 2018; Barkdull and Harris, 2019; 
McLeod et al., 2019).
3.6.3 Implementation and Effectiveness of Adaptation 
and Mitigation Measures
This section assesses implemented adaptations introduced in 
Section  3.6.2 for selected marine sectors (Section  3.6.3.1) and 
ecosystems (Section  3.6.3.2), using case studies to emphasise 
characteristics that enable or inhibit adaptation (Section  3.6.3.3). 
The feasibility and effectiveness of these adaptations are assessed 
in Figure 3.24.3.6.3.1 Degree of Implementation and Evidence of 
Effectiveness Across Sectors
3.6.3.1.1 Coastal community development and settlement

Coastal adaptation often addresses the risk of flooding and erosion from 
SLR, changes in storm activity and degradation of coastal ecosystems 
and their services (high confidence) (Sections 3.4.2, 3.5; Oppenheimer 
et al., 2019). Without coastal protection, people and property will be 
increasingly exposed to coastal flooding after 2050, especially under 
RCP8.5 (Cross-Chapter Box SLR in Chapter 3; Bevacqua et al., 2020; 
Kirezci et  al., 2020). This section assesses adaptation responses for 
coastal ecosystems, addressing loss of natural coastal protection 
(Sections  3.4.2.1, 3.4.2.4–3.4.2.6), and the need for relocation 
(Section 3.6.2.1.2). Adaptation responses specific to SLR are assessed 
in detail in Cross-Chapter Box SLR in Chapter 3, while adaptation in 
coastal cities and settlements is assessed in Chapter 6.
Table 3.30 |  Assessment of marine and coastal nature-based solutions to reduce mid-term climate impacts in oceans and coastal ecosystemsa

tion
Habitat restoration High confidenceMarine habitat restoration increases biodiversity 
and protects shorelines and coastal livelihoods 
from climate oceanic hazards.Colls et al. (2009); Arkema et al. 
(2017); Espeland and Kettenring 
(2018); McLeod et al. (2019)Restoration (Section 3.6.3.2.2)
Marine protected areas 
(MPAs) and other 
effective area-based 
conservation measures 
(OECMs)High confidenceMPAs and MPA networks that are carefully 
designed to address climate change, strategically 
placed and well enforced, hold great potential to 
deliver adaptation outcomes. OECMs can confer 
climate resilience to dependent communities 
outside of MPAs.Section 3.4.3.3.4; Queirós 
et al. (2016); Roberts et al. 
(2017); Maxwell et al. (2020a); 
Arafeh-Dalmau et al. (2021); 
Gurney et al. (2021); Sala et al. 
(2021)Conservation (Section 3.6.3.2.1)

Conservation of climate 
refugiaMedium confidenceProtecting areas that retain climate and 
biodiversity conditions for longer durations 
under climate change can increase the resilience 
of marine ecosystems to warming and marine 
heatwaves (MHWs), and facilitate marine species 
range shifts.Section 3.4.3.3.4; 
SPECIES in Chapter 5; Rilov et al. 
(2020); Wilson et al. (2020a); 
Arafeh-Dalmau et al. (2021)Conservation (Section 3.6.3.2.1)
Transboundary marine 
spatial planning (MSP) 
and integrated coastal 
zone management 
(ICZM)Low confidenceTransboundary MSP and ICZM that incorporate 
climate-change impacts and adaptation in their 
design can support climate adaptation and foster 
international ocean cooperation.Rosendo et al. (2018); Tittensor 
et al. (2019); Frazão Santos et al. 
(2020); Rilov et al. (2020); Pinsky 
et al. (2021)Tourism (Section 3.6.3.1.3), 
conservation, (Section 3.6.3.2.1.)

Sustainable harvesting High confidenceSustainable harvesting is a nature-based solution 
that contributes to adaptation by safeguarding 
the provision of marine food and cultural services 
while reducing the ecological vulnerability of 
marine ecosystems.Gattuso et al. (2018); Burden and 
Fujita (2019); Duarte et al. (2020)Fisheries and mariculture 
Climate-adaptive 
managementHigh confidenceIncorporating climate-adaptive management 
allows climate knowledge and information 
available for the system to be iteratively updated 
in the management plan. It also facilitates 
consideration of species distribution shifts and 
other climate-change responses.Cross-Chapter Box MOVING 
(2019); Free et al. (2020); Wilson 
et al. (2020a); Melbourne-Thomas 
et al. (2021)Fisheries and mariculture 
(Section 3.6.3.1.2), conservation, 
(Section 3.6.3.2.1), restoration 
(Section 3.6.3.2.2)

management (EbM)High confidenceEbM focuses on ecosystems. By incorporating 
many of the above tools, ecosystem-based 
adaptation benefits adaptation of marine 
ecosystems and supports provision of ecosystem 
services to people.Fernandino et al. (2018); 
Lowerre-Barbieri et al. (2019)Fisheries and mariculture 

3
476Chapter 3 Oceans and Coastal Ecosystems and Their Services
Coastal conservation tends to involve cost-effective, low-impact actions 
that aim to support both adaptation and mitigation by conserving a 
wide array of ecosystem functions and services (Gattuso et al., 2018; 
Gattuso et al., 2021), and that are achievable by nations with extensive 
coastlines or low-income status (Herr et  al., 2017; Taillardat et  al., 
2018). Where coastlines are undeveloped, the lowest-risk option is to 
avoid new development, but elsewhere, coastal conservation includes 
protection of key assets, accommodation of SLR, advancing defences 
seawards or upwards, or planned retreat from the coast (Cross-
Chapter Box SLR in Chapter 3).
Hard-engineered structures like seawalls are generally more costly 
than nature-based adaptations (high confidence) (Hérivaux et  al., 
2018; Haasnoot et  al., 2019; Nicholls et  al., 2019; Oppenheimer 
et al., 2019) and can lock communities into engineered responses in

the future (Cross-Chapter Box SLR in Chapter 3), creating trade-offs 
with mitigation goals, which constitutes maladaptation (Nunn et al., 
2021) that carries ecological and cultural costs (Sections  3.4.2.4, 
3.4.2.6, 3.5.6). As a result, there is high agreement on the importance 
of shifting from hard infrastructure to soft infrastructure for coastal 
defence (Toimil et al., 2020; Nunn et al., 2021). The common remedy for 
beach erosion is beach nourishment (Oppenheimer et al., 2019; Pinto 
et al., 2020; Elko et al., 2021), which provides rapid results but poorly 
quantified trade-offs between efficacy, long-term cost, utility to beach 
users and ecological damage (de Schipper et al., 2021).
Since SROCC, coastal adaptation using NbS, like restoration of coastal 
vegetation, has advanced substantially (Cohen-Shacham et al., 2019; 
Kuhl et al., 2020; Kumar et al., 2020; Morris et al., 2020a). Field and 
modelling studies confirm that wetland restoration and preservation

are key actions to restore coastal protection and reduce community 
vulnerability to flooding (very high confidence) (see also Section 3.6; 
Chapter  15; Cross-Chapter  Box  SLR in Chapter  3; Jones et  al., 2020; 
Menéndez et al., 2020; Van Coppenolle and Temmerman, 2020), while 
maintaining coastal ecosystem services (Section  3.5). Restoring coral 
reefs, oyster reefs and mangroves (Section  3.6.2.1) and protecting 
macrophyte meadows dissipates wave energy (Section  3.4.2.1; Yates 
et  al., 2017; Beck et  al., 2018; Wiberg et  al., 2019; Menéndez et  al., 
2020), accretes sediment and elevate shorelines, which reduces 
exposure to waves and storm surges, and offsets erosional losses 
(medium confidence) (Kench and Mann, 2017; Pomeroy et  al., 2018; 
Dasgupta et al., 2019; James et al., 2019; Morris et al., 2019; David and 
Schlurmann, 2020; Masselink et al., 2020). However, irreversible regime 
shifts in ocean ecosystems due to SLR and extreme events, such as MHWs,

can limit or compromise restoration in the long term (high confidence) 
(Section 3.4.3.3.3; Cross-Chapter Box SLR in Chapter 3; Marzloff et al., 
2016; Johnson et  al., 2017a). Under all warming scenarios, coastal 
wetlands will be impacted by warming and MHWs (Sections 3.2.2.1, 
3.2.4.5; Cross-Chapter  Box  9.1 in WGI Chapter  9; Fox-Kemper et  al., 
2021), while also being pressed inland by RSLR (Section 3.4.2.5; Cross-
Chapter Box SLR in Chapter 3). Therefore, restoration and conservation 
are more successful when non-climate drivers are also minimised (high 
confidence) (Brodie et al., 2020; Duarte et al., 2020; Liu et al., 2021).
For highly exposed human settlements, migration is an adaptation 
option (e.g., for some island populations under extreme circumstances), 
but there are important uncertainties (Section 15.3.4.6), as international regimes develop around human rights, migration (Scobie, 2019a), 
displacement (George Puthucherril, 2012) and the implications for

national sovereignty (Yamamoto and Esteban, 2014) of disappearing 
land spaces caused by climate change. Colonial power dynamics can 
influence climate-change responses (Chapter 18), for example, when 
external funders favour migration over local desires to adapt in place 
to preserve national identity and sovereignty (Bordner et  al., 2020). 
Examples of relocation within livelihoods’ customary land show some 
successes (Section 15.3.4.6).
Evidence since SROCC (Section, 5.5.2.3.3; Bindoff et  al., 2019a) 
continues to show that built infrastructure cannot address all of the 
adaptation challenges that coastal communities face. Coastal squeeze 
creates tensions between coastal development, armouring and habitat 
management (Sections  3.4.2.4–3.4.2.6). Managed realignment is 
the best option to reduce risks from SLR (high confidence) (Cross-
Chapter  Box  SLR in Chapter  3) but requires transformative changes 
in coastal development and settlement (Felipe Pérez and Tomaselli,

2021; Fitton et al., 2021; Mach and Siders, 2021; Siders et al., 2021). 
Implementation of protective measures varies among nations and 
lack of financial resources limits the options available (very high 
confidence) (Cross-Chapter Box SLR in Chapter 3; Hinkel et al., 2018; 
Klöck and Nunn, 2019).
3.6.3.1.2 Fisheries and mariculture
SROCC (Bindoff et  al., 2019a) assessed adaptation in fisheries and 
mariculture (marine aquaculture), and socioeconomically focused 
updates are provided in Section 5.8.4 and Cross-Chapter Box MOVING 
SPECIES in Chapter 5. Here, we present a brief synthesis of how fisheries 
and mariculture adaptations interact with the natural environment, 
with further detail and supporting material in SM3.5.2.
Mobility allows fishing fleets and fishers to adapt to shifts in marine 
species distributions (high agreement) (Sections 3.4.3.1, 3.5.3; Peck 
and Pinnegar, 2018; Pinsky et al., 2018; Frazão Santos et al., 2020)

but with limits and unintended consequences (Pinsky and Fogarty, 
2012; Bell et  al., 2021). Diversification of target species, harvest 
tactics and employment sectors, including transitions from fisheries 
to mariculture and ecotourism, allows some fishers to accommodate 
some impacts on their livelihoods (Miller et al., 2018; Robinson et al., 
2020; Gonzalez-Mon et  al., 2021). Technology and infrastructure 
adaptations can improve marine harvest efficiency, reduce risk and 
support resource management goals (Friedman et al., 2020; Bell et al., 
2021; Melbourne-Thomas et al., 2021), but their ability to overcome 
climate-change impacts remains uncertain (Bell et  al., 2020). 
Improving capacity to predict anomalous conditions in coastal and 
marine ecosystems (Jacox et al., 2019; Holbrook et al., 2020; Jacox 
et al., 2020), storm-driven flooding in reef-lined coasts (Scott et al., 
2020; Winter et  al., 2020) and fisheries stocks (Payne et  al., 2017;

Tommasi et  al., 2017; Muhling et  al., 2018) can improve forecasts 
of coastal and marine resources; these can enhance sustainability 
of wild-capture fisheries under climate change (high confidence) 
(Blanchard et  al., 2017; Tommasi et  al., 2017; Pinsky et  al., 2020a; 
Bell et  al., 2021). Limiting overexploitation is the central goal of 
fishery management, and it very likely benefits fisheries adaptation 
to climate change (Burden and Fujita, 2019; Free et al., 2019; Sumaila

3
477Oceans and Coastal Ecosystems and Their Services  Chapter 3
Cross-Chapter Box SLR | Sea Level Rise
Authors: Gonéri Le Cozannet (France, Chapter 13, CCP4), Judy Lawrence (New Zealand, Chapter 11), David S. Schoeman (Australia, 
Chapter 3), Ibidun Adelekan (Nigeria, Chapter 9), Sarah R. Cooley (USA, Chapter 3), Bruce Glavovic (New Zealand/South Africa, Chapter 18, 
CCP2), Marjolijn Haasnoot (The Netherlands, Chapter 13, CCP2), Rebecca Harris (Australia, Chapter 2), Wolfgang Kiessling (Germany, 
Chapter  3), Robert E. Kopp (USA, WGI), Aditi Mukherji (Nepal, Chapter  4), Patrick Nunn (Australia, Chapter  15), Dieter Piepenburg 
(Germany, Chapter 13), Daniela Schmidt (UK/Germany, Chapter 13), Craig T. Simmons (Australia), Chandni Singh (India, Chapter 10, 
CCP2), Aimée Slangen (The Netherlands, WGI), Seree Supratid (Thailand, Chapter 4).
Sea level rise is already impacting ecosystems, human livelihoods, infrastructure, food security and climate mitigation at the coast and

beyond. Ultimately, it threatens the existence of cities and settlements in low-lying areas, and some island nations and their cultural 
heritage (Chapters 9–15; Cross-Chapter Papers 2, 4; Oppenheimer et al., 2019). The challenge can be addressed by mitigation of climate 
change and coastal adaptation.
Current impacts of sea level rise
The rate of global mean SLR was 1.35 mm yr–1 (0.78–1.92 mm yr–1, very likely range) during 1901–1990, faster than during any century 
in at least 3000 years (high confidence) (WGI AR6 Chapter 9; Stanley and Warne, 1994; Woodroffe et al., 2016; Fox-Kemper et al., 2021). 
Global mean SLR has accelerated to 3.25 mm yr–1 (2.88–3.61 mm yr–1, very likely range) during 1993–2018 (high confidence). Extreme 
sea levels have increased consistently across most regions (WGI AR6 Chapter 9; Fox-Kemper et al., 2021). The largest observed changes

in coastal ecosystems are being caused by the concurrence of human activities, waves, current-induced sediment transport and extreme 
storm events (medium confidence) (Chapters 3, 15, 16; Takayabu et al., 2015; Mentaschi et al., 2018; Duvat, 2019; Murray et al., 2019; 
Oppenheimer et al., 2019). Early impacts of accelerating SLR detected at sheltered or subsiding coasts include chronic flooding at high tides, 
wetland salinisation and ecosystem transitions, increased erosion and coastal flood damage (Chapters 3, 11, 13–16; WGI AR6 Chapter 9; 
Sweet and Park, 2014; Moftakhari et al., 2015; Nunn et al., 2017; Oppenheimer et al., 2019; Sharples et al., 2020; Fox-Kemper et al., 2021; 
Strauss et al., 2021). The exposure of many coastal populations and ecosystems to SLR is high: economic development is disproportionately 
concentrated in and around coastal cities and settlements (virtually certain) (Chapters 3, 9–15; Cross-Chapter Papers 2, 4).

Projected risks to coastal communities, infrastructure and ecosystems
Risks from SLR are very likely to increase by one order of magnitude well before 2100 without adaptation and mitigation action as 
agreed by parties to the Paris Agreement (very high confidence). Global mean SLR is likely to continue accelerating under SSP1-2.6 and 
more strongly forced scenarios (Figure BoxSLR1; WGI AR6 Chapter 9; Oppenheimer et al., 2019; Fox-Kemper et al., 2021), increasing the 
risk of chronic coastal flooding at high tide, serious flooding during extreme events such as swells, storms and hurricanes, and erosion, 
and coastal ecosystem losses across many low-lying and erodible coasts (very high confidence) (Chapters 3, 9–15; Cross-Chapter Paper 
2; Hinkel et al., 2014; McLachlan and Defeo, 2018; Kulp and Strauss, 2019; Vousdoukas et al., 2020b). The compounding of rainfall, river

flooding, rising water tables, coastal surges and waves are projected to exacerbate SLR impacts on low-lying areas and rivers further 
inland (Chapters 4, 11–15; Bevacqua et al., 2020).
There is high confidence that coastal risks will increase by at least one order of magnitude over the 21st century due to committed SLR 
(Hinkel et al., 2013; Hinkel et al., 2014; IPCC, 2019b). Exposure of population and economic assets to coastal hazards is projected to 
increase over the next decades, particularly in coastal regions with fast-growing populations in Africa, Southeast Asia and Small Islands 
(medium evidence) (Chapters 9–15; Cross-Chapter Papers 2, 4; Neumann et al., 2015; Jones and O’Neill, 2016; Merkens et al., 2016; 
Merkens et al., 2018; Rasmussen et al., 2020). For RCP8.5, 2.5–9% of the global population and 12–20% of the global gross domestic 
product is projected to be exposed to coastal flooding by 2100 (Kulp and Strauss, 2019; Kirezci et al., 2020; Rohmer et al., 2021). Above

3°C global warming levels (GWL) and with low adaptation, SLR may cause disruptions to ports and coastal infrastructure (Camus et al., 
2019; Christodoulou et al., 2019; Verschuur et al., 2020; Yesudian and Dawson, 2021), which in turn may cascade and amplify across 
sectors and regions, generating impacts to financial systems (Chapters 11, 13; Mandel et al., 2021). Depending on the hydrogeological 
context, SLR causes salinisation of groundwater, estuaries, wetlands and soils, adding constraints to water management and livelihoods 
in agriculture sectors, for example, in deltas (Chapters 9, 15; Cross-Chapter Paper 4; Oppenheimer et al., 2019; Nicholls et al., 2020).
Coastal ecosystems can migrate landward or grow vertically in response to SLR, but their resilience and capacity to keep up with SLR 
will be compromised by ocean warming and other drivers, depending on regions and species, for example, above 1.5°C for coral reefs

(high confidence) (Chapters 3, 16; IPCC, 2018; Melbourne et al., 2018; Perry et al., 2018; IPCC, 2019b; Cornwall et al., 2021). Sediments 
and space for landward retreat are crucial for mangroves, salt marshes and beach ecosystems (high confidence) (Chapter 3; Peteet et al., 
2018; Schuerch et al., 2018; FitzGerald and Hughes, 2019; Friess et al., 2019; Leo et al., 2019; Schuerch et al., 2019). Loss of habitat 
is accompanied by loss of associated ecosystem services, including wave-energy attenuation, habitat provision for biodiversity, food 
production and carbon storage (Chapter 3; Cross-Chapter Box NATURAL in Chapter 2).

3
478Chapter 3 Oceans and Coastal Ecosystems and Their Services
Under a high-emissions, low-likelihood/high-impact scenario, where low confidence ice-sheet mass loss occurs, global mean SLR could 
exceed the likely range by more than one additional metre in 2100 (Figure BoxSLR1b; Cross-Chapter Box DEEP in Chapter 17; WGI AR6 
Technical Summary and Chapter 9; Arias et al., 2021; Fox-Kemper et al., 2021). This is a reason for concern given that rapid SLR after the 
last glacial–interglacial transition caused a drowning of coral reefs (high confidence) (Camoin and Webster, 2015; Sanborn et al., 2017; 
Webster et  al., 2018), extensive loss of coastal land and islands, habitats and associated biodiversity (high confidence) (AR6 WGI 
Chapter 9; Fruergaard et al., 2015; Fernández-Palacios et al., 2016; Hamilton et al., 2019; Helfensdorfer et al., 2019; Kane and Fletcher,

2020; Fox-Kemper et al., 2021), and triggered Neolithic migrations in Europe and Australia (medium confidence) (Cross-Chapter Box PALEO 
in Chapter 1; Turney and Brown, 2007; Brisset et al., 2018; Williams et al., 2018).
At centennial time scales, projected SLR represents an existential threat for island nations, low-lying coastal zones and the communities, 
infrastructure, and cultural heritage therein (Chapters 9–15; Cross-Chapter Paper 4). Even if climate warming is stabilised at 2°C to 
2.5°C GWL, coastlines will continue to reshape over millennia, affecting at least 25 megacities and drowning low-lying areas where 
0.6–1.3 billion people lived in 2010 (medium confidence) (WGI AR6 Chapter 9; Marzeion and Levermann, 2014; Clark et al., 2016; Kulp 
and Strauss, 2019; Fox-Kemper et al., 2021; Strauss et al., 2021).
Solutions, opportunities and limits to adaptation

The ability to adapt to current coastal impacts, to cope with future coastal risks and to prevent further acceleration of SLR beyond 2050 
depends on immediate mitigation and adaptation actions (very high confidence). The most urgent adaptation challenge is chronic flooding 
at high tide (Chapters  10, 11, 13–15). Reducing the acceleration of SLR beyond 2050 will only be achieved with fast and profound 
mitigation of climate change (Nicholls et al., 2018; Oppenheimer et al., 2019). Until 2050, adaptation planning and implementation needs 
are projected to increase significantly in most inhabited coastal regions (see Figure BoxSLR1; WGI AR6 Chapter 9; IPCC, 2019b; Fox-Kemper 
et al., 2021). For SSP1-2.6 and more strongly forced scenarios, SLR rates continue to increase (WGI AR6 Chapter 9; Fox-Kemper et al., 2021), 
and so do the scale and frequency of adaptation interventions needed in coastal zones (Figure BoxSLR1; Haasnoot et al., 2020).

Risks can be anticipated, planned and decided upon, and adaptation interventions can be implemented over the coming decades 
considering their often long lead- and lifetimes, irrespective of the large uncertainty about SLR beyond 2050 (high confidence) 
(Figure  BoxSLR1; Cross-Chapter  Box  DEEP in Chapter  17; Cross-Chapter  Paper 2; Chapters  11, 13; Haasnoot et  al., 2018; Stephens 
et al., 2018; Stammer et al., 2019). Adaptation capacity and governance to manage risks from projected SLR typically require decades 
to implement and institutionalise (high confidence) (Chapters 11, 13; Haasnoot et al., 2021). Without considering both short- and long-
term adaptation needs, including beyond 2100, communities are increasingly confronted with a shrinking solution space, and adverse 
consequences are disproportionately borne by exposed and socially vulnerable people (Chapters 1, 8). Sea level rise is likely to compound

social conflict in some settings (high confidence) (Oppenheimer et al., 2019).
Coastal impacts of SLR can be avoided by preventing new development in exposed coastal locations (Chapters 3, 9–15; Cross-Chapter Paper 
2; Doberstein et al., 2019; Oppenheimer et al., 2019). For existing developments, a range of near-term adaptation options exists, including: 
(a) engineered, sediment- or ecosystem-based protection; (b) accommodation and land-use planning, to reduce the vulnerability of people 
and infrastructure; (c) advance through, for example, land reclamation; and (d) retreat through planned relocation or displacements and 
migrations due to SLR (Chapters 9–15; Cross-Chapter Paper 2; Oppenheimer et al., 2019). Only avoidance and relocation can remove 
coastal risks for the coming decades, while other measures only delay impacts for a time, have increasing residual risk or perpetuate risk

and create ongoing legacy effects and virtually certain property and ecosystem losses (high confidence) (Cross-Chapter Paper 2; Siders 
et al., 2019). Large-scale relocation has immense cultural, political, social and economic costs, and equity implications, which can be 
reduced by fast implementation of climate mitigation and adaptation policies (Chapter 8; Cross-Chapter Paper 2; Gibbs, 2015; Haasnoot 
et al., 2021). While relocation may currently appear socially unacceptable, economically inefficient or technically infeasible today (Lincke 
and Hinkel, 2021), it becomes the only feasible option as protection costs become unaffordable and the limits to accommodation become 
obvious (Chapters 11, 13, 15; Hino et al., 2017; Siders et al., 2019; Strauss et al., 2021). Effective responses to rising sea level involve 
locally applicable combinations of decision analysis, land-use planning, public participation and conflict resolution approaches; together

these can anticipate change and help to chart adaptation pathways, over time addressing the governance challenges due to rising sea 
level (high confidence) (Oppenheimer et al., 2019).
Ecosystem-based adaptation can reduce impacts on human settlements and bring substantial co-benefits, such as ecosystem services 
restoration and carbon storage, but they require space for sediment and ecosystems and have site-specific physical limits, at least above 
1.5°C GWL (high confidence) (Cross-Chapter Box NATURAL in Chapter 2; Chapters 3, 9, 11, 15; Herbert et al., 2015; Brown et al., 2019; Van 
Coppenolle and Temmerman, 2019; Watanabe et al., 2019; Neijnens et al., 2021). For example, planting and conserving vegetation helps Cross-Chapter Box SLR (continued)

3
479Oceans and Coastal Ecosystems and Their Services  Chapter 3
sediment accumulation by dissipating wave energy and reducing impacts of storms, at least at present-day sea levels (high confidence) 
(Temmerman et al., 2013; Narayan et al., 2016; Romañach et al., 2018; Laengner et al., 2019; Leo et al., 2019). Coastal wetlands and 
ecosystems can be preserved by landward migration (Schuerch et al., 2018; Schuerch et al., 2019) or sediment supply (VanZomeren et al., 
2018), but they can be seriously damaged by coastal defences designed to protect infrastructure (Chapters 3, 13; Cooper et al., 2020b). 
Sediment nourishment can prevent erosion, but it can also negatively impact beach amenities and ecosystems through ongoing dredging, 
pumping and deposition of sand and silts (VanZomeren et al., 2018; de Schipper et al., 2021; Harris et al., 2021).
2020 2100 2050 2150Sea level rise challenges the timing of coastal adaptation planning and implementation

2020 2100 2050 2150meters of global
sea level riseEcosystem-based adaptatio nSediment-based protectionElevating houses*Protect levees*Protect barrier s*Planned relocation
2020 2100 2050 2150SSP1-2.6 (p50)
SSP2-4.5 (p50)
SSP5-8.5 (p50)
SSP5-8.5 (p95)
(low confidence process)
not assessed beyond 3m(a) Typical timescales of coastal risk management
Indicative time for planning and implementation Typical intended lifetime of measure s
(b) Sea-level rise projections
95th percentile and
83rd percentile 
(low confidence processes)SSP1-2.6
SSP2-4.5
SSP3-7.0
(c) Projected sea level rise demands earlier or larger adaptation actions and reduces the lifetimes of measures
≈15 years≈15 years≈30 years≈50 years≈100 year s≈100 year s*Measures with long-living societal legac y
SSP5-8.5
Illustrative example with measures for 0.5m of additional sea level rise :
≈120 Lifetime ≈90 year s
Lifetime ≈60 year s
Lifetime ≈30 year sLifetime ≈75 year s ≈70
≈40
≈20≈20≈ 15 ≈10≈ 10 ≈1017th–83rd / 5th–95th
percentile

medium and low
confidenc e
ranges in 2150
Figure Cross-Chapter BoxSLR.1 |  The challenge of coastal adaptation in the era of sea level rise (SLR): (a) typical time scales for the planning, implementation 
(grey triangles) and operational lifetime of current coastal risk-management measures (blue bars); (b) global sea level projections, which are representative of relative SLR 
projected for 60–70% of global shorelines, within ±20% errors (WGI AR6 Chapter 9; Fox-Kemper et al., 2021); (c) frequency of illustrative adaptation decisions to +0.5 m 
of SLR under different SSP-RCP scenarios. In response to accelerated SLR, adaptation either occurs earlier and faster, or accounts for higher amounts of SLR (e.g., to +1 m 
instead of to +0.5 m). Adaptation to +0.5 m from today’s sea levels have a lifetime of 90 years for SSP1-2.6, but lifetime is reduced to 60 years for SSP5-8.5 and 30 years

for a high-end scenario involving low confidence processes. Adaptations to +0.5 m are comparable to, for example, the Thames Barrier in the United Kingdom or the Delta 
Works in the Netherlands, which primarily had an intended lifetime of 100–200 years. Adaptation measures to +0.2 m may include nourishment or wetland or setback zones.Cross-Chapter Box SLR (continued)

3
480Chapter 3 Oceans and Coastal Ecosystems and Their Services
There is increasing evidence that current governance and institutional arrangements are unable to address the escalating risks in low-
lying coastal areas worldwide (high confidence). Barriers to adaptation, such as decision making driven by short-term thinking or vested 
interests, funding limitations and inadequate financial policies and insurance, can be addressed equitably and sustainably through 
implementation of suites of adaptation options and pathways (Chapters  11, 13, 17–18; Cross-Chapter  Paper 2). Improved coastal 
adaptation governance can be supported by approaches that consider changing risks over time, such as ‘dynamic adaptation pathways’ 
planning (Chapters  11, 13, 18; Cross-Chapter  Box  DEEP in Chapter  17). Integrated coastal zone management and land-use and 
infrastructure planning are starting to consider SLR by, for example, monitoring early signals (Haasnoot et al., 2018; Stephens et al., 2018;

Kool et al., 2020), updating sea level projections (Stephens et al., 2017; Hinkel et al., 2019; Kopp et al., 2019; Stammer et al., 2019), 
considering uncertainties of sea level projections and coastal impacts (e.g., Stephens et al., 2017; Jevrejeva et al., 2019; Rohmer et al., 
2019), as well as engaging with communities, practitioners and scientists, recognising the values of current and future generations (e.g., 
Nicholls et al., 2014; Buchanan et al., 2016b). While there is high agreement that the majority of adaptation needs are not met yet, there 
is robust evidence of SLR increasingly being considered in coastal adaptation decision making and being embedded in national and local 
guidance and regulations (Nicholls et al., 2014; Le Cozannet et al., 2017; Lawrence et al., 2018; Kopp et al., 2019; McEvoy et al., 2021).Cross-Chapter Box SLR (continued)
and Tai, 2020). Conventional tools include catch and size limits, spatial

management and adaptive management. Ecosystem-based fisheries 
management outperforms single-species management (Fulton et al., 
2019), is widely legislated (Bryndum-Buchholz et  al., 2021) and 
can reduce climate impacts in fisheries in the near-term, especially 
under low-emission scenarios (Karp et  al., 2019; Holsman et  al., 
2020). Transboundary agreements on shifting fisheries will reduce 
the risk of overharvesting (medium confidence) (Gaines et al., 2018). 
Permits tradable across political boundaries could also address this 
challenge, but limited evidence is available regarding their efficacy 
(Cross-Chapter  Box  MOVING SPECIES in Chapter  5; Pinsky et  al., 
2018). Climate-smart conservation (Section  3.6.32.1) under the 
negotiations on areas beyond national jurisdiction (ABNJ) (Pinsky 
et al., 2018; Tittensor et al., 2019; Frazão Santos et al., 2020), and in 
the Convention on Biological Diversity (CBD) areas designed as other

effective area-based conservation measures (OECMs) (Tittensor et al., 
2019), provide further benefits. Despite the potential for adaptive 
management to achieve sustainable fisheries, outcomes will very 
likely be inequitable (Gaines et al., 2018; Lam et al., 2020), with lower-
income countries suffering the greater biomass and economic losses, 
increasing inequalities, especially under higher-emission scenarios 
(high confidence) (Boyce et  al., 2020). Flexible and polycentric 
governance approaches have facilitated some short-term successes in 
achieving equitable, sustainable fisheries practices, but these may be 
challenging to implement where other governance systems, especially 
hierarchical systems, are well established (Cvitanovic et al., 2018; Bell 
3.6.3.1.3 Tourism
Coastal areas, coastal infrastructure and beaches, sustaining tourism 
that contributes significantly to local economies (James et  al., 2019;

Ruiz-Ramírez et al., 2019), are under threat from development, SLR and 
increased wave energy during storms (high confidence) (Sections 3.4.2.4–
3.4.2.6, 3.5.6, SM3.3.1; Lithgow et al., 2019; Ruiz-Ramírez et al., 2019). 
Engineered solutions, such as seawalls and revetments, have traditionally 
been used to address coastal erosion (Section  3.6.3.1.1), but soft 
infrastructure approaches, including beach nourishment, submerged breakwaters and groins, and NbS (Section 3.6.2.1), are becoming more 
common, partly due to demand from the tourism industry (medium 
confidence) (Pranzini, 2018; Pranzini et al., 2018).
Elsewhere, interactions between tourism and climate impacts worsen 
outcomes for coastal and ocean environments (Section  3.6.3.1.4). 
Climate change is opening up new cruise-ship routes in the Arctic 
(Sun et  al., 2018), increasing the number of visitors and associated 
stressors, such as litter, to previously undisturbed areas (Anfuso et al.,

2020; Hovelsrud et al., 2020; Suaria et al., 2020). Risk reduction for 
cruise-ship tourism includes disaster response management, improved 
mapping and passenger codes of conduct ensuring social, cultural and 
ecological sustainability (Stewart et al., 2015; Dawson et al., 2016).
Marine ecotourism, integrating conservation, education and provision 
of benefits to local communities (Donohoe and Needham, 2006) can 
provide significant economic benefits (Wabnitz, 2019) and is among 
the most common livelihood alternatives to support both marine 
conservation and climate-change adaptation (Kutzner, 2019; Pham, 
2020; Prasetyo et al., 2020). Ecotourism can enhance social and political 
will for marine conservation (Cisneros-Montemayor and Sumaila, 
2014) and facilitates integration of local and Indigenous Peoples in 
employment, ownership and industry governance. The community 
of Cabo Pulmo, Mexico, self-imposed an MPA and replaced fishing

with ecotourism, which now generates millions of USD yr–1, sustains 
locally owned and operated tour companies and has increased some 
fish populations tenfold (Knowlton, 2020). In Misool, Indonesia, local 
ecotourism incorporates IK by including local communities’ preferences 
and sustainable resource use (Prasetyo et al., 2020).
Unintended consequences of ecotourism, such as detrimental ecological 
impacts on reefs (Giglio et al., 2020), sharks, marine birds (Monti et al., 
2018) and whales (Higham et al., 2016; Barra et al., 2020; Hoarau et al., 
2020), can be minimised by relying on evidence-based management 
of associated activities (Blumstein et  al., 2017). Public perception of 
climate-change connections to tourism can create obstacles (Meynecke 
et al., 2017; Atzori et al., 2018) such as deterring long-term investment 
in SIDS tourism initiatives (Santos-Lacueva et  al., 2017), or benefits

3
481Oceans and Coastal Ecosystems and Their Services  Chapter 3
like inclining tourists to participate in conservation projects (Curnock 
et al., 2019; Miller et al., 2020b; Ziegler et al., 2021). Social and cultural 
networks may decrease climate vulnerability, as with Indigenous 
tourism operators in SIDS (Parsons et  al., 2018). Tourism-based 
adaptation can also be improved by equitable access to resources 
as well as recognition and inclusion of all stakeholders during policy 
planning and implementation. The principles of marine spatial planning 
(Papageorgiou, 2016) provide for effectively incorporating stakeholders 
and could inform development of activities to assess climate-associated 
risks (e.g., Tzoraki et  al., 2018; Loehr, 2020). The recent decrease in 
global tourism due to the COVID-19 pandemic may offer opportunities 
to transform existing practices to more sustainable approaches (Cross-
Chapter Box COVID in Chapter 7; Gössling et al., 2021).

3.6.3.1.4 Maritime transport
Increased maritime transport and cruise-ship tourism in the Arctic are 
already impacting local and Indigenous Peoples, revealing conflicts 
over the uses of the ocean and the governance needed to support local 
people and a sustainable blue economy (high confidence) (Debortoli 
et al., 2019; Palma et al., 2019; Berman et al., 2020; Dundas et al., 2020). 
While shipping and its associated environmental impacts are projected 
to grow (Palma et al., 2019; Dawson et al., 2020), adaptation efforts 
are only at the planning stage (Debortoli et al., 2019). Increased Arctic 
traffic due to ice loss can benefit trade, transportation and tourism 
(medium confidence), but will also affect Arctic marine ecosystems 
and livelihoods (high confidence) (Palma et al., 2019; Dawson et al., 
2020). Increasing search-and-rescue activities (Ford and Clark, 2019) 
reveal capacity gaps to support future demands (Ford and Clark, 2019;

Palma et al., 2019). The Low-Impact Shipping Corridors initiative has 
been developed as an adaptation strategy in the Arctic, although with 
limited inclusion of IKLK (Dawson et al., 2020).
Relative SLR and the increased frequency and severity of storms 
are already affecting port activity, infrastructure and supply chains, 
sometimes disrupting trade and transport (Monios and Wilmsmeier, 
2020), but these hazards are not systematically incorporated into 
adaptation planning (medium evidence) (Monios and Wilmsmeier, 
2020; O’Keeffe et  al., 2020). Climate-change impacts that increase 
food insecurity, income loss and poverty can exacerbate maritime 
criminal activity, including illegal fishing, drug trafficking or piracy 
(medium evidence) (Germond and Mazaris, 2019). A transformational 
adaptation approach to address climate impacts on maritime activities 
and increase security (Germond and Mazaris, 2019) would relocate

ports, change centres of demand, reduce shipping distances or shorten 
supply chains (medium agreement) (Walsh et al., 2019; Monios and 
Wilmsmeier, 2020) as well as decrease marginalisation of vulnerable 
groups, develop polycentric governance systems and eliminate 
maladaptive environmental policies and resource loss (Belhabib et al., 
2020; O’Keeffe et al., 2020).
3.6.3.1.5 Human health
Health-focused adaptations to climate-driven changes in ocean and 
coastal water quality (Section 3.5.5.3) leverage mainly technology and 
infrastructure (Section 3.6.2.2) to improve water-quality monitoring and 
forecasting to inform socio-institutional adaptation (Section  3.6.2.1) and NbS (Section 3.6.2.3). Seafood quality and safety are decreasing 
due to climate-driven increases in marine-borne diseases (Cross-
Chapter Box ILLNESS in Chapter 2), toxic HABs or toxin bioaccumulation 
(high agreement) (Karagas et al., 2012; Krabbenhoft and Sunderland,

2013; Rafaj et  al., 2013; Curtis et  al., 2019; Schartup et  al., 2019; 
Thackray and Sunderland, 2019). Future exposure to seafood-borne 
contaminants also depends partly on consumers’ seafood preferences 
(Elsayed et  al., 2020) and seafood supply (Sunderland et  al., 2018). 
Reducing this risk by decreasing seafood consumption increases the risk 
of eating less nutritious foods, and loss of cultural practices (Chapter 5; 
Cross-Chapter Box MOVING SPECIES in Chapter 5; Donatuto et al., 2011; 
Bindoff et  al., 2019a). Models incorporating high-resolution satellite 
images, field survey data, meteorological observations and historical 
records can provide early-warning forecasts of HABs or conditions that 
favour microbial pathogen outbreaks (Cross-Chapter  Box  ILLNESS in 
Chapter  2; Semenza et  al., 2017; Franks, 2018; Hattenrath-Lehmann 
et al., 2018; Borbor-Cordova et al., 2019; Davis et al., 2019; Campbell

et  al., 2020a; Davidson et  al., 2021). Forecasts facilitate preventive 
public health measures (World Health Organisation and United Nations 
Children’s Fund, 2017), or seafood harvest guidance (Maguire et  al., 
2016; Leadbetter et al., 2018; Anderson et al., 2019; Bolin et al., 2021), 
reducing risks of disease outbreaks, waste and contaminated seafood 
entering the market (medium confidence) (Cross-Chapter Box ILLNESS 
in Chapter  2; Nichols et  al., 2018). Monitoring of water quality and 
seafood safety (Cross-Chapter Box ILLNESS in Chapter 2), paired with 
effective public communication and education (Ekstrom et al., 2020), 
inform individual and local adaptations, including use of (a) personal 
protective equipment, (b) seafood selection and preparation (Elsayed 
et al., 2020; Froelich and Daines, 2020; Fielding et al., 2021), (c) income 
diversification (Section  3.6.2.1; Moore et  al., 2020b), (d)  public

education (Borbor-Cordova et al., 2019) or (e) community-level actions 
to decrease risk from coastal aquifer and soil salinisation (Slama et al., 
2020; Mastrocicco and Colombani, 2021), HAB toxins (Ekstrom et al., 
2020) and other contaminants (e.g., methylmercury, metals, persistent 
organic pollutants) in seafood (Chan et al., 2021). A full assessment of 
climate-change impacts on human health is found in Chapter  7 and 
Cross-Chapter Box ILLNESS in Chapter 2.
3.6.3.2 Cross-Cutting Solutions for Coastal and Ocean 
SROCC concluded that protection, restoration and pollution reduction 
can support ocean and coastal ecosystems (high confidence), and that 
EbA lowers climate risks locally and provides multiple societal benefits 
(high confidence) (IPCC, 2019c). This section updates the assessment 
of the effectiveness of these strategies for addressing climate impacts.
3.6.3.2.1  Area-based protection: MPAs for adapting to climate 
change

Marine protected areas are the most widely implemented area-based 
management approach (Section  3.6.2.3.2), commonly intended 
to conserve, preserve or restore biodiversity and habitats, protect 
species or manage resources (especially fisheries) (National Research 
Council, 2001). By August 2021, 7.74% of the ocean was protected 
(in both MPAs and OECMs) (UNEP-WCMC and IUCN, 2021), primarily 
within nations’ exclusive economic zones (EEZs). These MPAs support

3
482Chapter 3 Oceans and Coastal Ecosystems and Their Services
adaptation by sustaining nearshore ecosystems that provide natural 
erosion barriers (Sections  3.4.2.1–3.4.2.5; Cross-Chapter  Box  SLR in 
Chapter 3), ecosystem function (Cheng et al., 2019), habitat, natural 
filtration, carbon storage, livelihoods and cultural opportunities 
(Sections 3.5.5, 3.5.6; Erskine et al., 2021), and help ecosystems and 
livelihoods recover after extreme events (Roberts et al., 2017; Aalto 
et al., 2019; Wilson et al., 2020a). However, in 2021 only 2.7% of the 
ocean was in fully or highly protected MPAs (Marine Conservation 
Institute, 2021), the hard-to-achieve states that most effectively rebuild 
biomass and fish community structure (Sala and Giakoumi, 2017; Bergseth, 2018; Zupan et al., 2018; Ohayon et al., 2021). Only 1.18% 
of ABNJ is protected (UNEP-WCMC and IUCN, 2021), mostly due to 
governance limitations (O’Leary and Roberts, 2017; Vijayaraghavan,

2021), but calls to protect more ABNJ emphasise the need to protect 
the habitats of long-range pelagic fish and marine mammals, maintain 
the ocean’s regulating functions and minimise impacts from uses such 
as maritime shipping or deep-sea mining (Table 3.30).
Marine protected areas are theorised to facilitate ecological climate 
adaptation and contribute to SDG14 (Life Below Water) (Table  3.30; 
Assessment of feasibility and effectiveness of adaptation solutions for ocean and coastal ecosystems
Marine
and coastal
Nature-based
Solutions
(NbS)Habitat restoration
Marine Protected Areas and OECMs*
Conservation of climate refugia
Sustainable harvesting
Ecosystem-based managementTransboundary marine spatial planning and ICZM**
Climate adaptive managementBuilt
and technologyAccommodation and relocation
Protection and nourishment
Early-warning systems
Monitoring systems
Active restoration
Assisted evolutionSeasonal and dynamic forescasts
Habitat developmentSocio-

institutional
adaptationKnowledge diversity
Socially inclusive policies
Participation
Mobility
Finance and market mechanisms
Disaster response programs
Multi-level ocean governance
Institutional transboundary agreementsSolutionsPrimary solutions
for sectors at riskFisheries &
maricultur e
Tourism
Maritime
transport
Overall feasibility confidence level
Coastal
settlement s
Health
Livelihood diversification
Migration
Feasibility/Effectiveness
High Medium LowFeasibility dimensions
Institutional &
geophysica l
Socio-
ecologica lTechnical
& economic
(overall)
Effectiveness
**Integrated coastal zone management*Other effective area-based conservation measuresAssessment’s
section in
the report
3.6.2.1
3.6.2.2
3.6.2.3
High Medium Low High Medium Low
Figure 3.24 |  Assessment of feasibility and effectiveness of adaptation solutions for ocean and coastal ecosystems. Feasibility dimensions assessed include:

technical and economic capacity to deliver and implement the solution; the institutional and geophysical capacity to implement a solution; and associated social and ecological 
implications that make a solution more feasible. The general feasibility level is obtained from assessment of the three dimensions together. Note that feasibility is assessed for marine 
and coastal ecosystems as a whole and not by ecosystem type or region. Feasibility dimensions and assessment are updated and adapted from IPCC (2018) and Singh et al. (2020). 
Effectiveness: ability of the adaptation solution to reduce climate-change mid-term risks. The main solutions are assessed per sector. (Underlying data are available in Table 3.SM.3.)

3
483Oceans and Coastal Ecosystems and Their Services  Chapter 3
Figure  3.26; Bates et  al., 2014; Lubchenco and Grorud-Colvert, 2015; 
Gattuso et  al., 2018) because they alleviate non-climate drivers and 
promote biodiversity (i.e., ‘managed resilience hypothesis’) (Bruno 
et  al., 2019; Maestro et  al., 2019; Cinner et  al., 2020). Current MPAs 
offer conservation benefits such as increases in biomass and diversity 
of habitats, populations and communities (high confidence) (Pendleton 
et al., 2018; Bates et al., 2019; Stevenson et al., 2020; Lenihan et al., 2021; 
Ohayon et al., 2021), and these benefits may last after some (possibly 
climate-enhanced) disturbances (e.g., tropical cyclones) (McClure et al., 
2020). But current MPAs do not provide resilience against observed 
warming and heatwaves in tropical-to-temperate ecosystems (medium 
confidence) (Bates et al., 2019; Bruno et al., 2019; Freedman et al., 2020;

Graham et al., 2020; Rilov et al., 2020). There is robust evidence that 
processes around MPA design and implementation strongly influence 
whether outcomes are beneficial or harmful for adjacent human 
communities (McNeill et al., 2018; Zupan et al., 2018; Ban et al., 2019).
Current placement and extent of MPAs will not provide substantial 
protections against projected climate change past 2050 (high 
confidence), as the placement of MPAs has been driven more often 
by political expediency (e.g., Leenhardt et al., 2013) than by managing 
key drivers of biodiversity loss (Cockerell et al., 2020; Stevenson et al., 
2020) or climate-induced drivers (Bruno et  al., 2018). Only 3.5% of 
the area currently protected will provide refuges from both SST 
and deoxygenation by 2050 under both RCP4.5 and RCP8.5 (Bruno 
et al., 2018), and MPAs are more exposed to climate change under 
RCP8.5 than non-MPAs (Section 3.4.3.3.4; Figure 3.20d). Community

thermal tolerances will be exceeded by 2050 in the tropics and by 
2150 for many higher-latitude MPAs (Bruno et al., 2018). Most MPA 
design has focused on the surface ocean, but MPAs are assumed to 
protect the entire water column and benthos. Climate-induced drivers 
(Section  3.2) throughout the water column and rapidly accelerating 
climate velocities at depths below 200 m (Johnson et al., 2018; Brito-
Morales et al., 2020) are projected to affect virtually all North Atlantic 
deep-water and open-ocean area-based management zones in the 
next 20–50 years (Johnson et al., 2018), and the conservation goals of 
benthic MPAs in the North Sea are not expected to be fulfilled (Weinert 
et al., 2021). Heightened risk of non-indigenous species immigration 
from vessel traffic plus climate change further endangers MPA success 
(Iacarella et  al., 2020), a particular concern in the Mediterranean 
(D’Amen and Azzurro, 2020; Mannino and Balistreri, 2021), where

the current MPA network is already highly vulnerable to climate 
change (Kyprioti et  al., 2021). This new evidence supports SROCC’s 
high confidence assessment that present governance arrangements, 
including MPAs, are too fragmented to provide integrated responses 
to the increasing and cascading risks from climate change in the ocean 
(SROCC SPMC1.2; IPCC, 2019c).
Strategic conservation planning can yield future MPA networks 
substantially more ready for climate change (e.g., Section  3.6.3.1.5; 
SROCC SPM C2.1; IPCC, 2019c; Frazão Santos et al., 2020; Rassweiler 
et  al., 2020). Global protection is increasing (Worm, 2017; Claudet 
et  al., 2020b) as nations pursue international targets (e.g., SDG14, 
Life Below Water aimed to conserve 10% of the ocean by 2020), 
and the UN CBD proposes to protect 30% by 2030 (Section  3.6.4; 
SM3.5.3; CBD, 2020). A growing body of evidence (Tittensor et  al.,

2019; Zhao et  al., 2020a; Pörtner et  al., 2021b; Sala et  al., 2021) underscores the urgent need to pursue biodiversity, ecosystem-
service provision and climate-adaptation goals simultaneously, while 
acknowledging inherent trade-offs (Claudet et al., 2020a; Sala et al., 
2021). Frameworks to create ‘climate-smart’ MPAs (Tittensor et  al., 
2019) generally include: (a) defining conservation goals that embrace 
resource vulnerabilities and co-occurring hazards; (b)  carefully 
selecting adaptation strategies that include IKLK while respecting 
Indigenous rights and accommodating human behaviour (Kikiloi et al., 
2017; Thomas, 2018; Yates et  al., 2019; Failler et  al., 2020; Wilson 
et al., 2020a; Croke, 2021; Reimer et al., 2021; Vijayaraghavan, 2021); 
(c)  developing protection that is appropriate for all ocean depths 
(Brito-Morales et al., 2018; Frazão Santos et al., 2020; Wilson et al., 
2020a), especially considering climate velocity (Arafeh-Dalmau et al.,

2021); (d) using dynamic national and international management tools 
to accommodate extreme events or species distribution shifts (Gaines 
et al., 2018; Pinsky et al., 2018; Bindoff et al., 2019a; Scheffers and 
Pecl, 2019; Tittensor et al., 2019; Cashion et al., 2020; Crespo et al., 
2020; Frazão Santos et al., 2020; Maxwell et al., 2020b), which could 
build on dynamic regulations already in place for fishing or ship strikes 
(Maxwell et  al., 2020b); and (e)  seeking to increase connectivity 
(Wilson et al., 2020a), using genomic or multi-species model insights 
(Xuereb et al., 2020; Friesen et al., 2021; Lima et al., 2021).
There is growing international support for a 30% conservation target 
for 2030 (Gurney et al., 2021), which will need efforts beyond protected 
areas. For example, OECMs recognise management interventions that 
sustain biodiversity, irrespective of their main objective (Maxwell et al., 
2020b; Gurney et al., 2021). There is high agreement on the potential

of OECMs to contribute to conservation and equity, for example, by 
recognising Indigenous territories as OECMs (Maxwell et al., 2020b; 
Gurney et al., 2021); however, the capacity of these conservation tools 
to provide adaptation outcomes remains unexplored.
In summary, MPAs and other marine spatial-planning tools have great 
potential to address climate-change mitigation and adaptation in 
ocean and coastal ecosystems, if they are designed and implemented 
in a coordinated way that takes into account ecosystem vulnerability 
and responses to projected climate conditions, considers existing and 
future ecosystem uses and non-climate drivers, and supports effective 
governance (high confidence).
3.6.3.2.2  Ecological restoration, interventions and their limitations
Restoration of degraded ecosystems is a common NbS increasingly 
deployed at local scales in response to climate change (Cross-
Chapter Box NATURAL in Chapter 2; Duarte et al., 2020; Bertolini and

da Mosto, 2021; Braun de Torrez et al., 2021). Despite covering limited 
areas and having uncertain efficacy under future climate change 
(Gordon et al., 2020), these actions have successfully restored marine 
populations and ecosystems at regional to global scales (Duarte et al., 
2020), and enhanced livelihoods and the well-being of coastal peoples 
as well as the biodiversity and resilience of ecological communities 
(Silver et al., 2019; Gordon et al., 2020; Braun de Torrez et al., 2021). 
Technology-based approaches, such as active restoration, assisted 
evolution and ecological forecasting, can aid in moving beyond 
restoring ecosystems (Section 3.6.2.3) towards enhancing resilience, 
reviving biodiversity and guarding against loss of foundational,

3
484Chapter 3 Oceans and Coastal Ecosystems and Their Services
ornamental or iconic species (Bulleri et al., 2018; Collins et al., 2019a; 
da Silva et al., 2019; National Academies of Sciences, 2019; Boström-
Einarsson et  al., 2020; Fredriksen et  al., 2020; Morris et  al., 2020c; 
Kleypas et al., 2021).
Local restoration projects often target vegetated ecosystems like 
mangroves, seagrasses and salt marshes that are valued and used 
by coastal communities (Veettil et al., 2019; Duarte et al., 2020; Wu 
et  al., 2020a; Bertolini and da Mosto, 2021). Detail on mangroves 
and corals as EbA and protection/restoration hotspots is provided in 
SM3.8. Common and effective actions (Sasmito et  al., 2019; Duarte 
et  al., 2020; Oreska et  al., 2020) include securing accommodation 
space (Sections 3.4.2.4–3.4.2.5), restoring hydrological (Kroeger et al., 
2017; Al-Haj and Fulweiler, 2020) and sediment dynamics; managing 
harvesting (particularly in mangroves); reducing pollution (especially

in seagrasses) (de los Santos et al., 2019); and replanting appropriate 
species in suitable environmental settings (Wodehouse and Rayment, 
2019; Friess et al., 2020a). Although efficacy is context dependent (Zeng 
et al., 2020; Krause-Jensen et al., 2021) and implementation is most 
often local (Alongi, 2018a), such projects facilitate tangible community 
engagement in climate action. Moreover, because these ecosystems 
sequester disproportionate amounts of carbon (blue carbon) (Annex II: 
Glossary; see Box 3.4), restoration supports climate-change mitigation 
(Lovelock and Reef, 2020; Gattuso et  al., 2021). Yet, constraints 
remain. For instance, Southeast Asia has 1.21 million km2 of terrestrial, 
freshwater and mangrove area biophysically suitable for reforestation, 
which could mitigate 3.43 ± 1.29 Pg CO 2e yr−1 through 2030; however, 
reforestation is only feasible in a small fraction of this area (0.3–18%)

given financial, land-use and operational constraints (Zeng et al., 2020). 
Nevertheless, the multiple benefits offered by ecosystem restoration 
will likely outweigh competing costs and increase its relevance as part 
of adaptation-strategy portfolios (Silver et  al., 2019; Wedding et  al., 
2021), national carbon-accounting systems and nationally determined 
contributions by parties to the Paris Agreement (Friess et al., 2020a; 
Wu et al., 2020a).
Restoration efficacy of coral reefs, kelp forests and other habitat-
forming coastal ecosystems (Sections 3.4.2.2–3.4.2.6) are jeopardised 
by the near-term nature of climate-driven risks (McLeod et al., 2019; 
National Academies of Sciences, 2019; Coleman et  al., 2020b). 
Modelling studies indicate that available practices will not prevent 
degradation of coral reefs from >1.5°C of global average surface 
warming (Figure  3.25; National Academies of Sciences Engineering

and Medicine, 2019; Condie et al., 2021; Hafezi et al., 2021). Proposed 
interventions include assisted migration (Boström-Einarsson et  al., 
2020; Fredriksen et al., 2020; Morris et al., 2020c), assisted evolution 
(Bay et  al., 2019; National Academies of Sciences, 2019) and other 
engineering solutions like artificial shading and enhanced upwelling 
(Condie et al., 2021; Kleypas et al., 2021).
Transplanting heat-tolerant coral colonies can increase reef resistance 
to bleaching (Morikawa and Palumbi, 2019; Howells et  al., 2021) 
but potentially lower species diversity and alter ecosystem function 
(Section  3.4.2.1). Genetic manipulation or assisted evolution that 
propagates genes from heat-tolerant populations could enhance 
restoration of corals (Anthony et al., 2017; Epstein et al., 2019) and 
kelp (medium agreement, limited evidence) (Coleman and Goold, 2019; Coleman et  al., 2020b; Fredriksen et  al., 2020; Wade et  al.,

2020). Managed breeding of corals has also had limited success in the 
laboratory and at small local scales (National Academies of Sciences, 
2019). There is also limited evidence that physiological interventions, 
such as algal-symbiont or microbiome manipulation, could increase 
coral thermal tolerance in the field (National Academies of Sciences, 
2019). Employing the natural adaptive capacity of species or 
individuals in active restoration for corals and kelps with current 
technology involves fewer risks than assisted evolution or long-
distance relocation (high confidence) (Filbee-Dexter and Smajdor, 
2019; National Academies of Sciences, 2019). More ambitious 
engineered interventions like reef shading remain theoretical and 
not scalable to the reef level (Condie et al., 2021). Debate continues 
on how to apply planned adaptation in cost-effective ways that will 
accomplish the intended goals (National Academies of Sciences, 2019; 
Duarte et al., 2020; Kleypas et al., 2021).

Models show that a combination of available management approaches 
(restoration, reducing non-climate drivers) and speculative interventions 
(e.g., enhanced corals, reef shading) can contribute to sustaining some 
coral reefs beyond 1.5°C of global warming with declining effectiveness 
beyond 2°C of global warming (medium confidence) (Figure  3.25; 
WGII Chapter  17). These proposed interventions are also currently 
theoretical and impractical over large scales; for example, engineered 
solutions like reef shading are untested and not scalable at the reef 
level (Condie et al., 2021). Existing projects suggest that restoration 
and ecological interventions to habitat-forming ecosystems have the 
additional benefits of raising local awareness, promoting tourism, 
and creating jobs and economic benefits (Fadli et al., 2012; Boström-
Einarsson et al., 2020; Hafezi et al., 2021), provided communities are 
involved in planning, operation and monitoring (Boström-Einarsson 

3.6.3.3 Enablers, Barriers and Limitations of Adaptation and 
Not only is mitigation necessary to support ocean and coastal 
adaptation (Pörtner et  al., 2014; Oppenheimer et  al., 2019), but the 
global emission pathways also impose limits to ocean and coastal 
adaptation, with lower warming levels enabling greater effectiveness 
of adaptations (high confidence) (Figure  3.25). Chapter  17 broadly 
assesses the limits to adaptation, while this section focuses on barriers 
and limits to adaptation imposed by cultural (Section  3.6.3.3.1), 
economic (Section  3.6.3.3.2) and governance (Section  3.6.3.3.3) 
dimensions (Hinkel et al., 2018). Globally, these factors more strongly 
influence ocean development than does local natural resource 
availability (Cisneros-Montemayor et  al., 2021), and are key to 
avoiding maladaptation. This section also assesses enablers and limits 
to mitigation (Section 3.6.3.3.4).
3.6.3.3.1 Sociocultural dimensions (culture, ethics, identity,

behaviour)
Every coastal community values marine ecosystems for more than 
the material and intangible resources they deliver, or the physical 
protection they offer (Díaz et al., 2018). Cultural services that provide 
identity, spiritual and cultural continuity, religious meaning or options

3
485Oceans and Coastal Ecosystems and Their Services  Chapter 3
for the future (e.g., genetic or mineral resources) (Bindoff et al., 2019a) 
are not substitutable. Furthermore, interactions between climate 
impacts and existing inequalities can threaten the human rights of 
already-marginalised peoples by disrupting livelihoods and food 
security, which further erodes people’s social, economic and cultural 
rights (Finkbeiner et  al., 2018). For instance, European colonisation 
and ongoing development blocked the Cucapá Indigenous People’s 
access and rights to resources in the Colorado River Delta, USA, over 
the 20th century. Recent reallocation of water rights and fishing access 
is allowing the Cucapá people to reconstruct their cultural identity 
(Sangha et al., 2019), but future climate-change impacts could reverse 
the community’s recovery of their cultural heritage. Adaptations that 
consider local needs may help sustain cultural services (Ortíz Liñán and

Vázquez Solís, 2021).Interactions with oceans are fundamental to the identities of many coastal 
Indigenous Peoples (Norman, 2017), and this influences Indigenous 
responses to climate hazards and adaptation. Around 30  million 
Indigenous Peoples live along coasts (Cisneros-Montemayor et  al., 
2016). Seafood consumption among Indigenous Peoples is much higher 
than for non-Indigenous populations, and marine species support many 
cultural, medicinal and traditional activities contributing to public health 
(Section 3.5.3.1; Kenny et al., 2018). Perpetuation of Indigenous cultures 
depends on protecting marine ecosystems and on adapting to changes 
in self-led ways (Section  3.5.6; Sangha et  al., 2019) that promote self-
determination (von der Porten et  al., 2019). Indigenous resurgence, or 
reinvigorating Indigenous ways of life and traditional management, can 
include marine resource protection and ocean-sector development founded

on culturally appropriate strategies and partnerships that are consistent Implemented and potential future adaptations in ocean and coastal ecosystems
(a) Implemented nature-based solutions
Area protected  (million km2)
0510152025Rebuilt stocks (%)
012345678Number of interventions
050100150200250300350
MPA total
No take
High Seas MPAMarine conservation
Rebuilt stock sSustainable
fisheries harvestSaltmarshesSeagrass meadowsMangroves
Oyster reefsHabitat restoration
Coral reefs
1960 1970 1980 1990 2000 2010 2020(b) Adaptation pathways in coral reefs
Enhanced coral
Reef shadingLow
risk
Diversify
livelihoodsBest current
Coral restoration
High
riskVery high confidence
Medium confidence
Very high
(c) Adaptation pathways in mangroves
1.5°C 1°CWarming level
2°C 3°C 4°C
2050 2090 2030SSP1-2.6
2030SSP5-8.52050Payment for eco-
system service/
carbon market
Hard
infrastructureLow
risk
Migration and
relocationConservation
High
risk
Low

confidenceDiversify
livelihoodsRestoration and/
or revegetation
Effectiveness likely
Effectiveness more likely than not  to likely
Effectiveness unlikely to more likely than notCircles denote when
other measures must
also be implementedHigh
Very high
High
Medium
Low
2090
Figure 3.25 |  Implemented and potential future adaptations in ocean and coastal ecosystems.
(a) Global implementation since 1970 of (top) cumulative habitat-restoration projects (Duarte et al., 2020), (middle) cumulative area-based conservation protected area (MPA 
total) (Boonzaier and Pauly, 2016), no-take areas (UN Environment World Conservation Monitoring Centre et al., 2018; UNEP-WCMC, 2019) and (bottom) percentage of total fish 
stocks rebuilt (Kleisner et al., 2013).
(b) Adaptation pathways for coral reefs to maintain healthy cover (line weight: solid lines, likely effectiveness; dashed lines, more likely than not to likely; dotted lines = unlikely to

more likely than not), with confidence noted for each intervention (Section 3.4.2.1, 3.6.3.2; Anthony et al., 2019; National Academies of Sciences, 2019)
(c) As in (b), but for mangrove ecosystems. (Underlying data are available in Tables SM3.4–3.6.)

3
486Chapter 3 Oceans and Coastal Ecosystems and Their Services
FAQ 3.5 | How can nature-based solutions, including marine protected areas, help us to adapt to climate-driven 
changes in the oceans?
Coastal habitats, such as mangroves or vegetated dunes, protect coastal communities from sea level rise and storm surges while supporting 
fisheries, sequestering carbon and providing other ecosystem services as well. Efforts to restore, conserve and/or recover these natural habitats 
help people confront the impacts of climate change. These marine nature-based solutions (NbS), such as Marine protected areas (MPAs), 
habitat restoration and sustainable fisheries, are cost-effective and provide myriad benefits to society.
In the oceans, NbS comprise attempts to recover, restore or conserve coastal and marine habitats to reduce the 
impacts of climate change on nature and society. Marine habitats, such as seagrasses and coral reefs, provide

services like food and flood regulation in the same way as forests do on land. Coastal habitats, such as mangroves or 
vegetated dunes, protect coastal communities from sea level rise and storm surges while supporting fisheries as well 
as recreational and aesthetic services. Seagrasses, coral reefs and kelp forests also provide important benefits that 
help humans adapt to climate change, including sustainable fishing, recreation and shoreline protection services. 
By recognising these services and benefits of the ocean, NbS can improve the quality and integrity of the marine 
Nature-based solutions offer a wide range of potential benefits, including protecting ecosystem services, supporting 
biodiversity and mitigating climate change. Coastal and marine examples include MPAs, habitat restoration, habitat 
development and maintaining sustainable fisheries. While local communities with limited resources might find

NbS challenging to implement, they are generally ‘no-regret’ options, which bring societal and ecological benefits 
regardless of the level of climate change.
Carefully designed and placed MPAs, especially when they exclude fishing, can increase resilience to climate change by 
removing additional stressors on ecosystems. While MPAs do not prevent extreme events, such as marine heatwaves 
(FAQ3.2), they can provide marine plants and animals with a better chance to adapt to a changing climate. Current 
MPAs, however, are often too small, too poorly connected and too static to account for climate-induced shifts in 
the range of marine species. MPA networks that are large, connected, have adaptable boundaries and are designed 
following systematic analysis of future climate projections can better support climate resilience.
Habitat restoration and development in coastal systems can support biodiversity, protect communities from

flooding and erosion, support the local economy and enhance the livelihoods and well-being of coastal peoples. 
Restoration of mangroves, salt marshes and seagrass meadows provide effective ways to remove carbon dioxide 
from the atmosphere and at the same time protect coasts from the impacts of storms and SLR. Active restoration 
techniques that target heat-resistant individuals or species are increasingly recommended for coral reefs and kelp 
forests, which are highly vulnerable to marine heatwaves and climate change.
Sustainable fishing is also seen as an NbS because managing marine commercial species within sustainable limits 
maximises the catch and food production, thus contributing to the UN’s Sustainable Development Goal 2 (Zero 
Hunger). Currently, the oceans provide 17% of the animal protein eaten by the global population, but the 
contribution could be larger if fisheries were managed sustainably. Aquaculture, such as oyster farming, can be an

efficient and sustainable means of food production and also provide additional benefits like shoreline protection. 
Through NbS that conserve and restore marine habitats and species, we can sustain marine biodiversity, respond to 
climate change and provide benefits to society.with traditional norms and beneficial to local communities (von der 
Porten et al., 2019). Successful adaptation would simultaneously improve 
ecosystem health and address current and historical inequities (Bennett, 
2018). Examples include practicing traditional resource management, 
protecting traditional territories, engaging with monitoring, collaborations 
with non-Indigenous partners and reinvesting benefits into capacity-
building within communities (von der Porten et al., 2019; Equator Initiative, 
2020). The legitimacy of different adaptation strategies depends on local and Indigenous Peoples’ acceptance, which is based on cultural values

(Adger et al., 2017); financial gain cannot compensate for loss of IK or LK 
(Wilson et al., 2020b). Palau’s recent goal of shifting seafood consumption 
away from reef fishes (Remengesau Jr., 2019) as well as limiting and 
closely monitoring the expansion of ecotourism was prompted by the 
cultural importance of protecting these reefs and associated traditional 
fisheries for local consumption, a recognition of the importance of tourism 
and the hazard of climate change (Wabnitz et al., 2018a).

3
487Oceans and Coastal Ecosystems and Their Services  Chapter 3
NatureBasedSolutionsNatureBasedSolutionsMarine protected areas
(MPA)
Marine habitatsContributions of nature-based solutions in the oceans to the Sustainable Development Goals
Contributions to Sustainable Development Goals
ConservationConservationCoral reefs
ecosystemsRestoration
Sustainablefisheries
Figure FAQ3.5.1 |  Contributions of nature-based solutions (NbS) in the oceans to the Sustainable Development Goals. The icons at the bottom show 
the Sustainable Development Goals to which NbS in the ocean possibly contribute.Box FAQ 3.5 (continued)
Adaptations implemented at the local level that consider IKLK systems 
are beneficial (high confidence) (Nalau et  al., 2018; Sultana et  al., 
2019). Studies in SIDS and the Arctic have shown how IKLK facilitate 
the success of EbA (Nalau et  al., 2018; Peñaherrera-Palma et  al., 
2018; Raymond-Yakoubian and Daniel, 2018), reinforce and improve

institutional approaches and enhance the provision of ecosystem 
services (Ross et  al., 2019; Terra Stori et  al., 2019). Perspectives on adaptation also vary among groups of age, race, (dis)ability, class, caste 
and gender (Wilson et al., 2020b), so engaging different groups results 
in more robust and equitable adaptation to climate change (Cross-
Chapter Box GENDER in Chapter 18; McLeod et al., 2018). Some coastal 
communities have developed substantial social capital and dense local 
networks based on trust and reciprocity (Petzold and Ratter, 2015), 
with individual and community flexibility to learn, adapt and organise

3
488Chapter 3 Oceans and Coastal Ecosystems and Their Services
themselves to help local adaptation governance (Silva et  al., 2020). 
Recent evidence suggests that policies supporting local institutions 
can improve adaptation outcomes (medium confidence) (Berman et al., 
2020). Coastal communities can be engaged using novel approaches 
to co-generate adaptation solutions (van der Voorn et al., 2017; Flood 
et  al., 2018) that benefit education (Koenigstein et  al., 2020) and 
engagement in adaptation processes (Rumore et al., 2016). Successful 
adaptation implementation in line with climate resilient development 
pathways (WGII Chapter 18) depends on bottom-up, participatory and 
inclusive processes (Section 3.6.1.2.1) that engage diverse stakeholders 
(Basel et al., 2020; McNamara et al., 2020; Ogier et al., 2020; Williams 
et  al., 2020) and protect Indigenous customary rights (Farbotko and 
McMichael, 2019; Ford et al., 2020), empower women and give rights to

climate refugees (McLeod et al., 2018).
3.6.3.3.2 Economic dimensions (planning, finance, costs)
Finance is a key barrier globally for ocean health, governance and 
adaptation to climate change (high agreement) (Annex II: Glossary; 
Cross-Chapter  Box  FINANCE in Chapter  17; Hinkel et  al., 2018; 
Miller et al., 2018; Wabnitz and Blasiak, 2019; Woodruff et al., 2020; 
Sumaila et al., 2021). Global adaptation finance was estimated to total 
30 billion USD yr–1 in 2017–2018, or 5% of all climate finance (CPI, 
2019), with no tracking specifically for coastal or marine adaptation in 
low- to middle-income countries. Marine-focused adaptation finance 
is difficult to trace and label due to the cross-sectoral nature of many 
projects (Blasiak and Wabnitz, 2018) and the lack of clear definitions 
about what qualifies as adaptation or as new and additional finance 
(Donner et  al., 2016; Weikmans and Roberts, 2019). Finance for

marine conservation from Overseas Development Assistance doubled 
between 2003 and 2016, reaching 634 million USD in 2016, similar to 
the level provided by philanthropic foundations (Berger et al., 2019). 
Yet coastal adaptation to SLR alone is projected to cost hundreds of 
billions of USD yr–1, depending on the model and emission scenario 
(e.g., Wong et al., 2014; Nicholls et al., 2019). Economic and financing 
barriers to marine adaptation are often higher in low- to middle-
income countries, where resources influence governance and constrain 
options for implementation and maintenance (high confidence) 
(Hinkel et al., 2018; Klöck and Nunn, 2019; Tompkins et al., 2020), and 
impacts on their coastal and marine ecosystems could total several 
percentage points of their gross domestic product (Wong et al., 2014). 
Current financial flows are insufficient to meet the costs of coastal and 
marine impacts of climate change (very high confidence) and ocean-

focused finance is unevenly distributed, with higher flows within, and 
to, developed countries (very high confidence).
Development assistance can help resolve resource constraints, 
but additional governance and coordination challenges can arise 
from short-term, project-based funding, shifting the priorities of 
donor institutions and the pressures placed on human resources in 
the receiving nation (Parsons and Nalau, 2019; Nunn et  al., 2020). 
Innovative policy instruments, such as concessional loans, tax-policy 
reforms, climate bonds and public-debt forgiveness, can supplement 
traditional financial instruments (Bisaro and Hinkel, 2018; McGowan 
et al., 2020). Mechanisms for solving the persistent problem of securing 
upfront investments for coastal protection and other adaptation 
measures (Bisaro and Hinkel, 2018; Moser et al., 2019; Kok et al., 2021) include integrating adaptation investments into insurance schemes

(Reguero et  al., 2020) and using debt financing to bridge the time 
until benefits are realised (Ware and Banhalmi-Zakar, 2020). Insurance 
mechanisms that link payments to losses from a trigger event (e.g., 
MHW) can confer resilience to marine-dependent communities 
(Sumaila et  al., 2021). All innovative financial instruments are most 
effective when they are inclusive and reach vulnerable groups and 
marginalised communities (low evidence, high agreement) (Claudet 
et al., 2020a; Sumaila et al., 2021).
Countries with large ocean areas within their EEZs have opportunities 
to develop ‘blue–green economies’ to reduce emissions and finance 
adaptation pathways (Chen et al., 2018a; Lee et al., 2020). Shifting from 
grants to results-based financing can help attract more private capital 
to ocean adaptation (Lubchenco et  al., 2016; Claudet et  al., 2020a). 
Public–private partnerships can also increase ocean adaptation finance

(Goldstein et al., 2019; Sumaila et al., 2021). For example, the financial 
benefits that biodiversity conservation confers to seafood harvest 
resilience could be used to leverage industry participation in adaptation 
and conservation finance (Barbier et al., 2018). Connecting restoration 
of blue carbon ecosystems with offset markets (e.g., Vanderklift 
et  al., 2019) shows potential, but uncertainties remain about the 
international emissions trading under the UN Framework Convention 
on Climate Change and climate impacts on blue carbon ecosystems 
(Section 3.6.3.1.6; Lovelock et al., 2017a; Macreadie et al., 2019).
Transparency, coherence between different actors and initiatives, and 
project monitoring and evaluation enhance success in adapting and 
achieving SDG14 (Life Below Water) (Blasiak et al., 2019). Maladaptation 
(WGII Chapter  16; Magnan et  al., 2016) is a common risk of current 
project-based funding due to the pressure to produce concrete results

(medium confidence) (Parsons and Nalau, 2019; Nunn et  al., 2020; 
Nunn et al., 2021). Maladaptation can be avoided through a focus on 
building adaptive capacity, community-based management, drivers of 
vulnerability and site-specific measures (low confidence) (Magnan and 
Duvat, 2018; Piggott-McKellar et al., 2020; Schipper, 2020). More research 
is needed to identify ways that governance and financing agreements can 
help overcome financial barriers and sociocultural constraints to avoid 
maladaptation in coastal ecosystems (high confidence) (Hinkel et  al., 
2018; Miller et al., 2018; Piggott-McKellar et al., 2020; Schipper, 2020).
3.6.3.3.3 Governance dimension (institutional settings, decision 
making)
Ocean governance has become increasingly complex as new initiatives, 
new international agreements, institutions and scientific evidence 
arise at global, national and subnational scales (high agreement) 
(Bindoff et al., 2019a; Scobie, 2019b), limiting the present effectiveness

of adaptation (IPCC, 2019c). Marine climate governance is within 
the normatively contested marine governance space (Frazão Santos 
et al., 2020), which is influenced by geopolitics (Gray et al., 2020) and 
profit maximisation (Flannery et  al., 2016; Haas et  al., 2021) in ways 
that can entrench exclusionary processes in decision making, science 
management and funding (Levin et  al., 2018). This limits just and 
inclusive ocean governance (Bennett, 2018), perpetuates historical and 
cultural extractive practices and climate inaction, and leaves little space 
for Indigenous-led adaptation frameworks and approaches (Nursey-Bray

3
489Oceans and Coastal Ecosystems and Their Services  Chapter 3
et al., 2019). At the national level, ocean governance for climate-change 
adaptation is often transversal, requiring consideration of biophysical and 
environmental conditions (Furlan et al., 2020) while fitting into existing 
economic (Kim, 2020) and political processes. Adaptation governance 
that couples existing top-down structures with decentralised and 
participatory approaches generates shared goals and unlocks required 
resources and monitoring (Gupta et al., 2016; Haas et al., 2021).
Communities and governments at all levels increasingly use decision-
making frameworks (e.g., structured decision making) or decision-
analysis tools to evaluate trade-offs between different responses, 
rather than applying generic best practices to different physical, 
technical or cultural contexts (high confidence) (Watkiss et al., 2015; 
Haasnoot et al., 2019; Palutikof et al., 2019). Increased effort has also

been devoted to developing climate services (actionable information 
and data products) that bridge the gap between climate prediction 
and decision making (Hewitt et al., 2020). Climate services have the 
potential to inform decision making related to disaster-risk reduction, 
adaptation responses, marine environmental management (e.g., 
fisheries management and MPA management) and ocean-based 
climate mitigation (e.g., renewable-energy installations) (Le Cozannet 
et  al., 2017; Gattuso et  al., 2019; Gattuso et  al., 2021). Although 
improving observational and modelling capacity is important to 
developing ocean-focused services, particularly in high-risk regions 
like SIDS where regional climate projections are scarce (WGI AR6 
Chapter  9; Morim et  al., 2019; Fox-Kemper et  al., 2021), data are 
not the only limiting factor in decision making (Weichselgartner and 
Arheimer, 2019). Focusing on user engagement, relationship building

and the decision-making context ensures that climate services are 
useful to, and used by, different stakeholders (high confidence) (Soares 
et  al., 2018; Mackenzie et  al., 2019; Weichselgartner and Arheimer, 
2019; Findlater et al., 2021; West et al., 2021).
3.6.3.3.4 Mitigation
Ocean and coastal NbS can contribute to global mitigation efforts, 
especially with ocean renewable energy and restoration and 
preservation of carbon ecosystems (see Box  3.4; Section  3.6.2.3). 
Technological, economic and financing barriers presently hamper 
development of renewable ocean energy (AR6 WGIII Chapter 6). Such 
development could help small nations reliant on imported fuel meet 
their climate-mitigation goals and decrease risk from global fuel-
supply dynamics (Millar et al., 2017; Chen et al., 2018a), but progress 
is limited by lack of investment (Millar et al., 2017; Lee et al., 2020) or 
equipment (Aderinto and Li, 2018; Rusu and Onea, 2018). Wave-energy

installations, possibly co-located with wind turbines (Perez-Collazo 
et al., 2018), are promising for both low- to middle-income nations and 
areas with significant island or remote coastal geographies (Lavidas 
and Venugopal, 2016; Bergillos et al., 2018; Jakimavičius et al., 2018; 
Kompor et al., 2018; Penalba et al., 2018; Saprykina and Kuznetsov, 
2018; Lavidas, 2019). Wave-energy capture may also diminish storm-
induced coastal erosion (Abanades et al., 2018; Bergillos et al., 2018). 
Tidal energy is a relatively new technology (Haslett et al., 2018; Liu et al., 
2018; Neill et al., 2018) with limiting siting requirements (Mofor et al., 
2013). Ocean renewable-energy expansion faces other technological 
obstacles including lack of implementable or scalable energy-capture 
devices, access to offshore sites, competing coastal uses, potential environmental impacts and lack of power-grid infrastructure at the 
coast (Aderinto and Li, 2018; Neill et al., 2018).

3.6.4 Contribution to the Sustainable Development 
Goals and Other Relevant Policy Frameworks
The impacts of climate change on ocean and coastal ecosystems and 
their services threaten achievement of the UN SDGs by 2030 (high 
confidence), particularly ocean targets (Table 3.31; Nilsson et al., 2016; 
Pecl et al., 2017; IPCC, 2018; Singh et al., 2019a; Claudet et al., 2020a). 
Nevertheless, local to international decision-making bodies have 
assigned the lowest priority to SDG14, Life Below Water (Nash et al., 
2020).
3.6.4.1 Climate Mitigation Effects on Ocean-Related SDGs
SROCC underscored the need for ambitious mitigation to control 
climate hazards in the ocean to achieve SDGs (medium evidence, high 
agreement) (Bindoff et al., 2019a; Oppenheimer et al., 2019). Delays in 
achieving ocean-dependent SDGs observed in SROCC and SR15 can be 
addressed with ambitious planned adaptation and mitigation action 
(high agreement) (Hoegh-Guldberg et al., 2019b). Since the ocean can

contribute substantially to the attainment of mitigation targets aiming 
to limit warming to 1.5°C above pre-industrial levels (Hoegh-Guldberg 
et al., 2019b), and to adaptation solutions facilitating attainment of 
social and economic SDGs, climate policy is treating the ocean less as 
a victim of climate change and more as a central participant in solving 
the global climate challenge (Cooley et  al., 2019; Hoegh-Guldberg 
et al., 2019a; Dundas et al., 2020).
Relationships between Climate Action (SDG13) targets and SDG14 
targets are mostly synergistic (Figure  3.26; Fuso Nerini et  al., 2019). 
Responding to climate-change impacts requires transformative 
governance (high confidence) (Chapters  1, 18; Collins et  al., 2019a; 
Brodie Rudolph et al., 2020; Claudet et al., 2020a), especially for extreme 
events and higher-impact scenarios (e.g., higher emissions) (Fedele et al., 
2019), and for achieving SDGs through one of the global ecosystems

transitions (Chapter 18; Sachs et al., 2019; Brodie Rudolph et al., 2020). 
Opportunities to transform ocean governance exist in developing new 
international and local agreements, regulations and policies that reduce 
the risks of relocating ocean and coastal activities (Section 3.6.3.1.1) 
or in reinventing established practices (Section  3.6.3.3.3). Policy 
transformations improving ocean sustainability under SDG14 also help 
address SDG13 (Brodie Rudolph et al., 2020; Dundas et al., 2020; Claudet, 
2021; Sumaila et al., 2021). Emergent situations, such as the COVID-19 
pandemic, may provide opportunities to implement transformative 
‘green recovery plans’ that support achievement of the SDGs and NDCs 
(Cross-Chapter Box COVID in Chapter 7).
3.6.4.2  Contribution of Ocean Adaptation to SDGs
Marine-focused adaptations show promise in helping achieve 
social SDGs, especially when they are designed to achieve multiple 
benefits (medium confidence) (Figure  3.26; Ntona and Morgera,

2018; Claudet et al., 2020a). Technology- and infrastructure-focused 
adaptations (Section  3.6.2.2) can help relieve coastal communities

3
490Chapter 3 Oceans and Coastal Ecosystems and Their Services
from risks associated with poverty (SDG1), hunger (SDG2), health 
and water sanitation (SDG3 and SDG6), and inequality (SDG10) by 
supporting aquaculture (Sections  3.5.3, 3.6.3.1), alerting the public 
about poor water quality (Sections 3.5.5.3, 3.6.3.1) and empowering 
marginalised groups, such as women and Indigenous Peoples, with 
decision-relevant information (medium evidence, high agreement) 
(Sections  3.5.5.3, 3.6.3.1). Effectively implemented and managed 
marine NbS (Section 3.6.2.3) contribute to attainment of social SDGs 
by: (a)  preserving biodiversity (Carlton and Fowler, 2018; Warner, 
2018; Scheffers and Pecl, 2019), which benefits most ocean and 
coastal ecosystem services (Section 3.5.3; Figure 3.22); (b) increasing 
marine fishery and aquaculture sustainability (Section  3.6.3); (c) 
including vulnerable people and communities in management

(Section  3.6.3.2.1); (d)  lowering risk of flooding from storms and 
SLR (Cross-Chapter  Box  SLR in Chapter  3; Sections  3.6.3.1.1); and 
(e) implementing spatial-management tools that make room for 
new uses like renewable-energy development (Section  3.6.3.3.4). 
Nature-based solutions can therefore help support achievement of 
No Poverty (SDG1) (Ntona and Morgera, 2018), Zero Hunger (SDG2), 
Good Health and Well-Being (SDG3) (Duarte et al., 2020), Affordable 
and Clean Energy (SDG7) (Fuso Nerini et  al., 2019; Levin et  al., 
2020) and Reduced Inequality (SDG10). Socio-institutional marine 
adaptations (Section  3.6.2.2) that support current livelihoods and 
help develop alternatives can contribute to attainment of social SDGs 
by enhancing social equity and supporting societal transformation 
(medium confidence) (Cisneros-Montemayor et al., 2019; Pelling and 
Garschagen, 2019; Nash et  al., 2021). Even societal changes that

are not directly marine related can decrease human vulnerability to 
ocean and coastal climate risks by improving overall human adaptive 
capacity (Section 1.2).
Marine adaptation also shows promise for helping support achievement 
of economic SDGs (medium confidence) (Figure  3.26). Marine NbS 
could help blue-economy frameworks achieve Decent Work and 
Economic Growth (SDG8) (Lee et al., 2020) by sustainably and equitably 
incorporating ecosystem-based fisheries management, restoration or conservation (Sections  3.6.3.1.2, 3.6.3.2.1, 3.6.3.2.2; Voyer et  al., 
2018; Cisneros-Montemayor et  al., 2019; Cohen et  al., 2019; Okafor-
Yarwood et  al., 2020). Nature-based solutions that involve active 
restoration or accommodation can contribute to Sustainable Cities and 
Communities (SDG11) and Infrastructure (SDG9) (Section  3.6.3.1.1). 
Newly developed marine industries and livelihoods associated with 
NbS might support attainment of Sustainable Communities (SDG11)

(Cisneros-Montemayor et al., 2019). Finance and market mechanisms 
to support disaster relief or ocean ecosystem services, such as blue 
carbon or food provisioning, and innovations (SDG9) including new 
technologies like vessel-monitoring systems (Kroodsma et  al., 2018), 
can contribute to Responsible Consumption and Production (SDG12) 
(Sumaila and Tai, 2020). Blue-economy growth that includes sustainable 
shipping, tourism, renewable ocean energy and transboundary fisheries 
management (Pinsky et al., 2018) have the potential to contribute to 
Economic Development (SDG8), affordable and clean energy (SDG7) as 
well as global mitigation efforts (SDG13) (Hoegh-Guldberg et al., 2019b; 
Duarte et  al., 2020). Participatory approaches and co-management 
systems (Section 3.6.2.1) in many maritime sectors can contribute to 
SDG11 and SDG12 while helping align the blue economy and the SDGs 
(high agreement) (Lee et al., 2020; Okafor-Yarwood et al., 2020).

Developing marine adaptation pathways that offer multiple benefits 
requires transformational adaptation (high confidence) (Claudet 
et al., 2020a; Friedman et al., 2020; Wilson et al., 2020b; Nash et al., 
2021) that avoids risky and maladaptive actions (Magnan and 
Duvat, 2018; Ojea et  al., 2020). Ocean and coastal extreme events 
and other hazards disproportionately harm the most vulnerable 
communities in SIDS, tropical and Arctic regions, and Indigenous 
Peoples (Chapter 8.2.1.2). Presently implemented adaptation activity, 
at the aggregate level, adversely affects multiple gender targets under 
SDG5 (high confidence) (Cross-Chapter Box GENDER in Chapter 18). 
Although women make up over half of the global seafood production 
workforce (fishing and processing sectors), provide more than half the 
artisanal landings in the Pacific region (Harper et al., 2013), dominate

some seafood sectors such as seaweed (Howard and Pecl, 2019) and Table 3.31 |  Sustainable Development Goals, grouped into broader categories as discussed in this sectiona
Category Goal
SocietySDG1: No Poverty
SDG2: Zero Hunger
SDG3: Good Health and Well-Being
SDG4: Quality Education
SDG5: Gender Equality
SDG6: Clean Water and Sanitation
SDG7: Affordable and Clean Energy
EconomySDG8: Decent Work and Economic Growth
SDG9: Industry, Innovation and Infrastructure
SDG10: Reduced Inequality
SDG11: Sustainable Cities and Communities
SDG12: Responsible Consumption and Production
EnvironmentSDG13: Climate Action
SDG14: Life Below Water
SDG15: Life on Land
GovernanceSDG16: Peace and Justice Strong Institutions
SDG17: Partnerships to Achieve the Goals
(a) See http://sdgs.un.org/goals

3
491Oceans and Coastal Ecosystems and Their Services Chapter 3
shellfish harvesting (Turner et  al., 2020a) and account for 11% of

global artisanal fisheries participants (Harper et al., 2020b), they are often not specifically counted in datasets and excluded from decision making and support programmes (Cross-Chapter  Box  GENDER in Chapter  18; Harper et  al., 2020b; Michalena et  al., 2020). Targeted efforts to incorporate knowledge diversity, and include artisanal fishers, women and Indigenous Peoples within international, regional and local policy planning, promote marine adaptation that supports achievement of gender equality (SDG5) and reduces inequalities (SDG10) (limited evidence, high agreement) (FAO, 2015). Integrated planning, financing and implementation can help overcome these limitations (Section  3.6.3.3.2; Cross-Chapter  Box  FINANCE in Chapter  17), ensuring that marine adaptations do not compromise overall human equity or specific SDGs (Österblom et al., 2020; Nash et al., 2021), but are in fact fully synergistic with these goals (Bennett et al., 2021).

3.6.4.3  Relev ant Policy Frameworks for Ocean Adaptation
The intricacy, scope, time scales and uncertainties associated with climate change challenge ocean governance, which already is extremely complex because it encompasses a variety of overlapping spatial scales, concerns and governance structures (see Figure CB3.1 in SROCC Chapter 1; Prakash et al., 2019). Assessment of how established global agreements and regional, sectoral or scientific bodies address climate adaptation and resilience, and how current practices can be improved, is found in SM3.5.3.
There is growing momentum to include the ocean in international

climate policy (robust evidence), paving the way for a more integrated approach to both mitigation and adaptation. Following adoption of the Paris Agreement in 2015, the UN SDGs (Table  3.31) came into force in 2016, including SDG14 specifically dedicated to Life Below Water (Table 3.31). In 2017, the first UN Ocean Conference was held (United Nations, 2017), the UNFCCC adopted the Ocean Pathway to increase ocean-targeted multilateral climate action (COP23, 2017) and the UN Assembly declared 2021–2030 the Decade for Ocean Science for Sustainable Development (Visbeck, 2018; Lee et al., 2020). Next, 14 world leaders formed the High-Level Panel for a Sustainable Ocean Economy to produce the New Ocean Action Agenda, founded on 100% sustainable management of national ocean spaces by 2025 (Ocean Panel, 2020). All of these initiatives position oceans centrally within the climate-policy and biodiversity-conservation landscapes and seek to develop a coherent effort and common frameworks to

achieve marine sustainability (Visbeck, 2018; Lee et  al., 2020), new economic opportunities (Konar and Ding, 2020; Lee et al., 2020), more equitable outcomes (Österblom et  al., 2020) and decisive climate mitigation and adaptation (Hoegh-Guldberg et al., 2019a), to achieve truly transformative change (Claudet et al., 2020a).

There is high confidence in the literature that multilateral environmental agreements need better alignment and integration to support achievement of ambitious international development, climate mitigation and adaptation goals (Swilling et al., 202; Duarte et al., 2020; Friedman et al., 2020; Conservation International and IUCN, 2021; Pörtner et al., 2021b; Sumaila et al., 2021). The ocean targets of the CBD (e.g., the Post-2020 Global Biodiversity Framework), the SDGs (Agenda 2030) and the Paris Agreement are already inclusive and synergistic (Duarte et al., 2020). However, specific policy instruments and sectors within them could be additionally integrated, especially to address such cross-cutting impacts 
Synergies and trade-offs between Sustainable Development Goals (SDGs) Climate Action(13), Life Below 
Water (14), and social, economic and governance SDGs
Agreement
Indivisible Reinforcing Enabling Consistent ConstrainingSDG interactions

High Medium LowHigh Medium LowSDG14: Life Below Water targets
14.1. Reduce pollution
14.2 Protection and restoration
14.3 Reduce ocean acidification
14.4 Sustainable fishing14.5 Conservation14.6 No overfishing subsidies14.7 Sustainable resources
14.B Small-scale fisheries
14.C Sea lawClimate
action
(SDG13)
to
SDG14SDG14 to Social SDGs SDG2
SDG3SDG1
SDG5
SDG6SDG4
SDG7SDG14 to Economic SDGs
SDG9
SDG10SDG8
SDG12SDG 11
SDG16
SDG17SDG14 to
Governance SDGs
14.A Knowledge
Figure 3.26 |  Synergies and trade-offs between SDG13 Climate Action, SDG14 Life Below Water and social, economic and governance SDGs.  Achieving SDG13 
provides positive outcomes and supports the achievement of all SDG14 targets. In turn, meeting SDG14 drives mostly positive interactions with social, economic and governance

SDGs. The interaction types, ‘Indivisible’ (inextricably linked to the achievement of another goal), ‘Reinforcing’ (aids the achievement of another goal), ‘Enabling’ (creates conditions that further another goal), ‘Consistent’ (no significant positive or negative interactions) and ‘Constraining’ (limits options on another goal), follow Nilsson et al.’s (2016) scoring system based on the authors’ assessment, and agreement denotes consistency across author ratings. (Full data are available in Table 3.SM.7.)

3
492Chapter 3 Oceans and Coastal Ecosystems and Their Services
as ocean acidification and deoxygenation (Gallo et  al., 2017; Bindoff 
et  al., 2019a), increasing plastic pollution (Ostle et  al., 2019; Duarte 
et al., 2020), high-seas governance (Johnson et al., 2019; Leary, 2019) or 
deep-sea uses (Wright et al., 2019; Levin et al., 2020; Orejas et al., 2020). 
National adaptation plans present opportunities to synergistically build 
on mitigation to support equitable development (Morioka et al., 2020), 
economic planning (Dundas et  al., 2020; Lee et  al., 2020) and ocean 
stewardship (von Schuckmann et al., 2020). Alignment of multilateral 
agreements is expected to increase mitigation impact as well as increase 
adaptation options (Section  3.6.3; Figure  3.25; Roberts et  al., 2020). 
Opportunities to improve multilateral environmental agreements and 
policies beyond UNFCCC and CBD processes are discussed in SM3.5.3,

and an assessment of commercial species-management initiatives and 
needs is in Chapter 5.
3.6.5  Emerging Best Practices for Ocean and Coastal 
Climate Adaptation
There is robust evidence that a combination of global and local solutions 
offers the greatest benefit in reducing climate risk (Gattuso et al., 2018; 
Hoegh-Guldberg et al., 2019a; Hoegh-Guldberg et al., 2019b). Ambitious 
and swift global mitigation offers more adaptation options and pathways 
to sustain ecosystems and their services (Figure 3.25). Some solutions 
target both mitigation and adaptation (e.g., blue carbon conservation; 
Cross-Chapter  Box  NATURAL in Chapter  2;  see Box  3.4), and cross-
cutting solutions simultaneously support several ocean-related sectors 
(e.g., area-based measures support fishing, tourism; Section 3.6.3.2.1) or 
ecosystem functions (e.g., NbS support coastal protection, biodiversity, 
habitat, etc.; Section 3.6.3.2.2; Sala et al., 2021). Combined solutions

also leverage a variety of existing policies and governance systems 
(Section 3.6.4.3; Duarte et al., 2020) to advance climate mitigation and 
adaptation. Even communities that face the limits of adaptation, like 
those who must relocate to cope with rising seas (McMichael et  al., 
2019; Bronen et  al., 2020), urgently require solutions that combine 
scientific projections, IKLK, cultural and community values, and ways to 
preserve cultural identity to support planning and implementation of 
relocation (McMichael and Katonivualiku, 2020).
Nature-based solutions are showing promising results in achieving 
adaptation and mitigation outcomes across marine and coastal 
ecosystems (Sections  3.6.3.2.1–3.6.3.2.2), but NbS have different 
degrees of readiness in marine ecosystems (Duarte et al., 2020). Habitat 
restoration and recovery are highly effective in specific settings and 
conditions (McLeod et al., 2019). Restoring and conserving vegetated

coastal habitats (Sections  3.4.2.4–3.4.2.5) represent robust NbS, 
especially in the tropics, and particularly when paired with restoration and 
conservation of terrestrial ecosystems (robust evidence) (e.g., peatlands 
and forests; WGIII AR6 Chapter 7; Hoegh-Guldberg et al., 2019b; Duarte 
et al., 2020; Griscom et al., 2020). Although most of the focus on NbS 
efficacy has been on coastal and shelf ecosystems (Section  3.6.3.2), 
recent advances point to an emerging role of NbS beyond coastal waters 
in the form of area-based management tools in marine areas beyond 
national jurisdiction (Section 3.6.2.3; Gaines et al., 2018; Pinsky et al., 
2018; Crespo et al., 2020; O’Leary et al., 2020; Visalli et al., 2020; Wagner 
et al., 2020), because sustainable fisheries and aquaculture and climate-
responsive MPAs have high potential to adapt (Tittensor et al., 2019).Adaptation efforts (Sections  3.6.3.1–3.6.3.2) have three common

characteristics that facilitate implementation and success, and 
contribute to climate resilient development pathways (Chapter  18). 
First, availability of multiple types of information (e.g., monitoring, 
models, climate services; Section 3.6.3.3) exposes the magnitude and 
nature of the adaptation challenge. Well-developed observation and 
modelling capabilities (Reusch et al., 2018) offer insights on climate-
associated risks at different time scales (Cvitanovic et al., 2018; Hobday 
et  al., 2018), and this facilitates adaptation within multiple areas 
(e.g., industries over shorter time scales, societies over longer scales) 
(Hobday et  al., 2018). Environmental data have supported building 
societal and political (socio-institutional) will to adopt national and 
subnational adaptive management principles (Hobday et  al., 2016b; 
Champion et al., 2018; McDonald et al., 2019). However, incorporating 
IKLK at the same time provides more diverse social–environmental

insight (Section 3.6.3.4.1; Goeldner-Gianella et al., 2019; Petzold and 
Magnan, 2019; Wilson et al., 2020b). This can help align adaptation 
solutions with cultural values and increase their legitimacy with 
Indigenous and local communities (Chapter 1.3.2.3), achieving climate 
resilient development pathways (Chapter 18; Adger et al., 2017; Nalau 
et al., 2018; Peñaherrera-Palma et al., 2018; Raymond-Yakoubian and 
Daniel, 2018; Wamsler and Brink, 2018). Second, implementation of 
multiple low-risk options (Hoegh-Guldberg et al., 2019a; Gattuso et al., 
2021) such as economic diversification (Section 3.6.2.1) can provide 
culturally acceptable livelihood alternatives and food supplies (e.g., 
fishing to ecotourism and mariculture) (Froehlich et al., 2019) while also 
providing environmental benefits (e.g., seaweed mariculture’s potential 
carbon storage co-benefits) (WGIII AR6 Chapter  7; Hoegh-Guldberg 
et al., 2019a; Gattuso et al., 2021). Third, inclusive governance that is

well aligned to the systems at risk from climate change is fundamental 
for effective adaptation (Barange et al., 2018). Solutions implemented 
within polycentric governance systems (Section  3.6.3; Bellanger 
et al., 2020) benefit from synergies between knowledge, action and 
social–ecological contexts and stimulate governance responses at 
appropriate spatio-temporal scales (Cvitanovic and Hobday, 2018). 
Governance aligned with Indigenous structures and local structures 
supports successful outcomes that prioritise the concerns and rights 
of involved communities (Section 3.6.3; Mawyer and Jacka, 2018) and 
better leverages existing social organisation (i.e., network structures), 
learning processes and power dynamics (Barnes et al., 2020).
There is an opportunity to improve current practices when developing 
new ocean and coastal adaptation efforts so that they routinely contain 
these successful characteristics and resolve technical, economic,

institutional, geophysical, ecological and social constraints (Figure 3.25; 
Section  3.6.3.3; IPCC, 2018; Singh et  al., 2020). Enhancements are 
needed in human, technical and financial resources; regulatory 
frameworks (Ojwang et  al., 2017); political support (Rosendo et  al., 
2018); institutional conditions and resources for fair governance (Gupta 
et al., 2016; Scobie, 2018); political leadership; stakeholder engagement; 
multidisciplinary data availability (Gopalakrishnan et al., 2018); funding 
and public support for adaptation (Cross-Chapter  Box  FINANCE in 
Chapter 17; Ford and King, 2015); and incorporating IKLK in decision 
making (Nalau et al., 2018; Jabali et al., 2020; Petzold et al., 2020). As 
climate change continues to challenge ocean and coastal regions, there 
is high confidence associated with the benefits of developing robust, 
equitable adaptation strategies that incorporate scientific projections,

3
493Oceans and Coastal Ecosystems and Their Services Chapter 3
employ portfolios of low-risk options, internalise IKLK and address social 
aspects of governance from international to local scales (Finkbeiner et al., 2018; Gattuso et al., 2018; Miller et al., 2018; Raymond-Yakoubian and Daniel, 2018; Cheung et al., 2019; Gattuso et al., 2021).
AcknowledgementsWe acknowledge the kind contributions of Rita Erven (GEOMAR

Helmholtz Centre for Ocean Research Kiel, Germany), Miriam Seifert (Alfred Wegener Institute for Polar and Marine Research, Germany), Sebastian Rokitta (Alfred Wegener Institute for Polar and Marine Research, Germany), Amy Marie Campbell (National Oceanography Centre, Southampton/Centre for Environment, Fisheries and Aquaculture Science, UK), Mariana Castaneda-Guzman (Virginia Polytechnic Institute and State University, USA), Stephen Goult (Plymouth Marine Laboratory/National Centre for Earth Observation, UK), Josh Douglas (Plymouth Marine Laboratory, UK), Carl Reddin (Museum für Naturkunde, Berlin, Germany) and the PML Communications and Graphics Team (Plymouth Marine Laboratory, UK) who assisted in drafting figures and tables.

4Water Chapter 4
555Executive Summary
This chapter assesses observed and projected climate-induced changes 
in the water cycle, their current impacts and future risks on human and natural systems and the benefits and effectiveness of water-related adaptation efforts now and in the future.
Currently, roughly half of worlds ~8 billion people are estimated to 
experience severe water scarcity for at least some part of the year due to climatic and non-climatic factors (medium confidence
1). Since 
the 1970s, 44% of all disaster events have been flood-related. Not surprisingly, a large share of adaptation interventions (~60%) are forged in response to water-related hazards (high confidence). {4.1, Box 4.1, 4.2.1.1, 4.2.1.2, 4.2.2, 4.2.4, 4.2.5, 4.2.6, 4.3.8, 4.6, 4.7}
Intensification of the hydrological cycle due to human-induced

climate change is affecting physical aspects of water security (high confidence), thereby exacerbating existing water-related vulnerabilities caused by other socioeconomic factors. {4.2, 4.2.1.1, 4.2.1.2, 4.2.1.3, 4.2.2, 4.2.4, 4.2.5, 4.2.6, 4.3}
Nearly half a billion people live in unfamiliarly wet areas, where 
the long-term average precipitation is as high as previously seen in only about one in six years (medium confidence). Approximately 163  million people live in unfamiliarly dry areas now (medium confidence). {4.2.1.1}
The intensity of heavy precipitation has increased in many regions since 
the 1950s (high confidence). Substantially more people (~709 million) live in regions where annual maximum one-day precipitation has increased than regions where it has decreased (~86 million) (medium confidence). At the same time, more people (~700 million) are also experiencing longer dry spells than shorter dry spells since the 1950s (medium confidence). {4.2.1.1}

During the last two decades, the global glacier mass loss rate exceeded 
0.5 meters water equivalent per year (high confidence), impacting humans and ecosystems, including cultural uses of water among vulnerable high mountain and polar communities (high confidence). {4.2.2, 4.3.8}
There is a clear trend of increases in streamflow in the northern 
higher latitudes (high confidence), with climatic factors being more important than direct human influence in a larger share of major global basins (medium confidence). At the same time, groundwater in aquifers across the tropics has experienced enhanced episodic recharge from intense precipitation and flooding events (medium confidence), with implications for sectoral water use. {4.2.3, 4.2.6, 4.3.1, 4.3.4}

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.Extreme weather events causing highly impactful floods and 
droughts have become more likely and (or) more severe due to anthropogenic climate change (high confidence). {4.2.4, 4.2.5, Cross-Chapter Box DISASTER in Chapter 4}
Anthropogenic climate change has contributed to the increased 
likelihood and severity of the impact of droughts (especially agricultural and hydrological droughts) in many regions (high confidence). Between 1970 and 2019, 7% of all disaster events worldwide were drought-related. Yet, they contributed to 34% of disaster-related deaths, mostly in Africa. {4.2.5, 4.3.1, 4.3.2, Cross-Chapter Box DISASTER in Chapter 4}

Several recent heavy rainfall events, such as in western Europe, China, 
Japan, the USA, Peru, Brazil and Australia that led to substantial flooding, were made more likely by anthropogenic climate change (high confidence). There is high confidence that the warming in the last 
40–60 years has led to ~10
 d earlier spring floods per decade
. Between 
1970 and 2019, 31% of all economic losses were flood-related. {4.2.4, Cross-Chapter Box DISASTER in Chapter 4}
There is increasing evidence of observed changes in 
the hydrological cycle on people and ecosystems. A significant share of those impacts are negative and felt disproportionately by already vulnerable communities (high confidence). {4.3.1, 4.3.2, 4.3.3, 4.3.4, 4.3.5, 4.3.6, 4.3.8}
Agriculture and energy production have been impacted by changes

in the hydrological cycle (high confidence). Between 1983 and 2009, approximately three-quarters of the global harvested areas (~454  million hectares) experienced yield losses induced by meteorological drought, with the cumulative production losses corresponding to USD 166  billion. There is medium confidence that current global thermoelectric and hydropower production has been negatively affected due to droughts with ~4–5% reduction in plant utilisation rates during drought years compared to long-term average values since the 1980s. {4.3.1, 4.3.2}
Climate change and changes in land use and water pollution are

key drivers of loss and degradation of freshwater ecosystems (high confidence), with impacts observed on culturally significant terrestrial and freshwater species and ecosystems in the Arctic and high-mountain areas (high confidence). In addition, precipitation and extreme weather events are linked to increased incidence and outbreaks of water-related diseases (high confidence). {4.3.3, 4.3.4, 4.3.5, 4.3.8}
Changes in water-related hazards disproportionately impact vulnerable 
populations such as the poor, women, children, Indigenous Peoples and the elderly in all locations, especially in the Global South, due to systemic inequities stemming from historical, socioeconomic and political marginalisation (medium confidence). {4.3.1, 4.3.3, 4.3.4, 4.3.8}

4Chapter 4 Water
556Water-related risks are projected to increase with every degree 
of global warming (high confidence), and more vulnerable and 
exposed regions and peoples are projected to face greater 
risks (medium confidence). {Box 4.1, 4.4.1, 4.4.1.1, 4.4.4, 4.5.4, 
4.5.5, 4.5.6, Box 4.2}
Climate change impacts via water availability changes are projected to 
increase with every degree of global warming (high confidence), but 
there are high regional uncertainties. Between 3 and 4 billion people 
are projected to be exposed to physical water scarcity at 2°C and 4°C 
global warming levels (GWL), respectively (low confidence). {Box.4.1; 
4.4.1, 4.4.3, 4.4.5, 4.6.1}
By 2100, one third of the 56 large-scale glacierised catchments are 
projected to experience a mean annual runoff decline by over 10%, 
with the most significant reductions in central Asia and the Andes (low 
confidence). Expected impacts may be felt by roughly 1.5 billion people

who are projected to critically depend on runoff from the mountains by 
the mid-21st century (RCP6.0 scenario). {4.4.2, 4.4.3, 4.5.8}
By 2050, environmentally critical streamflow is projected to be affected 
in 42–79% of the world’s watersheds, causing negative impacts on 
freshwater ecosystems (medium confidence). Modified streamflow is 
also projected to affect inflows to urban storage reservoirs and increase 
the vulnerability of urban water services to hydro-meteorological 
extremes, particularly in less developed countries (high confidence). 
{4.4.6, 4.5.4, 4.4.5}
Future water-related impacts of climate change on various sectors of 
the economy are projected to lower global gross domestic product 
(GDP) (ranging from 0.49% of GDP by mid-century (SSP3) to less than 
0.1% (RCP8.5, SSP5), with higher projected losses expected in low- 
and middle-income countries (medium confidence). {4.7.5}
Drought and flood risks and societal damages are projected

to increase with every degree of global warming (medium 
confidence). {4.4.4, 4.4.5, 4.4.7, 4.5.1, 4.5.2}
Drought risks are projected to increase over the 21st century in many 
regions (very high confidence), increasing economy-wide risks (high 
confidence). With RCP6.0 and SSP2, the global population exposed 
to extreme-to-exceptional total water storage drought is projected to 
increase from 3% to 8% over the 21st century (medium confidence). 
{4.4.5}
The projected increase in precipitation intensity (high confidence) will 
increase rain-generated local flooding (medium confidence). Direct 
flood damages are projected to increase by four to five times at 4°C 
compared to 1.5°C (medium confidence). {Box  4.1, 4.4.1, 4.4.1.1, 
4.4.4, 4.5.4, 4.5.5}
At 4°C global warming, by the end of the century, approximately 10% 
of the global land area is projected to face simultaneously increasing 
high extreme streamflow and decreasing low extreme streamflow,

affecting roughly over 2.1 billion people (medium confidence). {4.4.3} 
The increase in extreme events is projected to compromise the efficacy of WaSH services and slow progress towards reductions in WaSH-
related disease burdens (medium confidence). {4.5.3}
Limiting global warming to 1.5°C would reduce water-related 
risks across regions and sectors (high confidence). {4.4.2, 4.4.5, 
4.5.2, 4.5.3, 4.5.4, 4.5.6, 4.5.7, 4.6.1, 4.7.2}
Projected increases in hydrological extremes pose increasing risks, with 
a potential doubling of flood risk between 1.5°C and 3°C of warming 
and an estimated 120–400% increase in population at risk of river 
flooding at 2°C and 4°C, respectively. Projected losses include a 1.2- to 
1.8-fold increase in GDP loss due to flooding between 1.5°C and 2°C 
warming (medium confidence). {4.4.3, 4.4.4, 4.4.5, 4.5.6, 4.6.1, 4.7.2}
Over large areas of northern South America, the Mediterranean, 
western China and high latitudes in North America and Eurasia, extreme

agricultural droughts are projected to be at least twice as likely at 
1.5°C global warming, 150 to 200% more likely at 2°C warming, and 
over 200% at 4°C (medium confidence). Due to the combined effects 
of water and temperature changes, risks to agricultural yields could 
be three times higher at 3°C compared to 2°C (medium confidence). 
{4.5.1, 4.6.1}
In Mediterranean parts of Europe, hydropower potential reductions of 
up to 40% are projected under 3°C warming, while declines below 
10% and 5% are projected under 2°C and 1.5°C warming levels, 
respectively.
Climate-induced hydrological changes are projected to increase 
migration in the last half of the century, with an almost seven-fold 
increase in asylum seekers to the European Union (EU) for RP4.5 
compared to RCP2.6. The number of internally displaced people in sub-
Saharan Africa, South Asia and Latin America increased almost five 
times for RCP8.5 compared to RCP2.6 (low confidence). {4.5.7}

Observed water adaptation responses have multiple benefits 
(high confidence), yet evidence of effectiveness of adaptation 
in reducing climate risks is not clear due to methodological 
challenges (medium confidence). {4.6, 4.7.1, 4.7.3}
A large share of adaptation interventions (~60%) are shaped in 
response to water-related hazards (high confidence) and involve 
water interventions (irrigation, rainwater harvesting, soil moisture 
conservation). Adaptation responses in developing countries tend to 
be autonomous, incremental and focused on managing water-related 
risks in agriculture. In contrast, responses are more policy-oriented and 
urban-focused in developed countries (high confidence). {4.6.2, box 
4.3, 4.6.5, 4.7.1, 4.7.2}
Irrigation helps stabilise and increase crop yields and is often a 
preferred strategy for farmers and policymakers for risk reduction, but 
irrigation is also associated with a range of adverse outcomes, including

groundwater over-extraction (medium confidence). In addition, large-
scale irrigation also affects local to regional climates, both in terms of 
temperature and precipitation change (high confidence). {4.2.6, 4.6.2, 
Box. 4.2}.

4Water  Chapter 4
557Water adaptation measures tend to have positive economic outcomes 
in developing countries and positive environmental outcomes in 
developed countries (high confidence). Roughly one third and 
one fourth of case studies on water adaptation also documents 
maladaptation and co-benefits, respectively (high confidence). A 
significant knowledge gap remains in knowing if observed adaptation 
benefits also translate to climate risk reduction, if so, by how much 
and under what conditions (medium confidence). {4.7.1, 4.7.2, 4.7.4}
Future projected adaptations are effective in reducing risks 
to a varying extent (medium confidence), but effectiveness 
falls sharply beyond 2°C, emphasizing the need for limiting 
warming to 1.5°C (high confidence). {4.6, 4.7.2, 4.7.3}
Adaptations that are beneficial now (e.g., crop- and water-related 
ones) are also projected to effectively reduce specific future risks to 
a moderate to a large extent (medium confidence). However, residual

impacts remain for some options and regions at all levels of warming, 
and the overall effectiveness decreases at higher warming levels (high 
confidence), further emphasizing the need for limiting warming to 
1.5°C. {Box 4.2, 4.7.1, 4.7.2, 4.7.3, 4.7.4}
At warming levels beyond 1.5°C, the potential to reach biophysical 
limits to adaptation due to limited water resources are reported for 
small islands (medium confidence) and regions dependent on glaciers 
and snowmelt (medium confidence). {4.7.4}
Water security is critical for meeting Sustainable Development 
Goals (SDGs) and systems transitions needed for climate 
resilient development, yet many mitigation measures have 
a high water footprint which can compromise SDGs and 
adaptation outcomes (high confidence). {4.1, Box 4.4, 4.6, 
4.6.2, 4.6.3, 4.7, 4.7.1, 4.7.4, 4.7.5.7}
Water features prominently in nationally determined contributions 
(NDCs) and national adaptation plans (NAPs) of most countries. SDGs

cannot be met without adequate and safe water (high confidence), 
and water is fundamental to all systems transition (high confidence). 
{4.1, 4.7, 4.7.1, 4.8, 4.8.7}
Water garners a significant share of public and private adaptation funds 
(high confidence). However, barriers remain for low-income countries 
to access funds (medium confidence), and there is insufficient evidence 
on benefits for marginalised groups (medium confidence). {4.8.2}
Many mitigation measures, such as carbon capture and storage, bio-
energy and afforestation and reforestation, can have a high-water 
footprint (high confidence). The water intensity of mitigation must 
be managed in socially and politically acceptable ways to increase 
synergies with SDGs, improve water security and reduce trade-offs 
with adaptation (medium confidence). {4.7.6}A common set of enabling principles underpinned by strong 
political support can help meet the triple goals of water

security, sustainable and climate resilient development (high 
confidence). {4.8, 4.8.3, 4.8.4., 4.8.5, 4.8.6, 4.8.7}
Many countries and social groups most threatened by climate 
change have contributed the least to the problem and do not have 
the adequate resources to adapt (high confidence). Water adaptation 
policies enabled through ethical co-production between holders of 
Indigenous knowledge, local knowledge and technical knowledge 
(medium confidence), through cooperation and coordinated actions 
among multiple actors, including women and all marginalised groups, 
at various levels of governance (medium confidence) is needed for 
effective transitions towards climate resilient development. {4.8, 4.8.3, 
4.8.4, 4.8.5, 4.8.6}

5584.1 Centrality of Water Security in 
Climate Change and Climate Resilient 
Water security is defined as ‘the capacity of a population to safeguard 
sustainable access to adequate quantities of acceptable quality water 
for sustaining livelihoods, human well-being, and socio-economic 
development, for ensuring protection against water-borne pollution 
and water-related disasters, and for preserving ecosystems in a climate 
of peace and political stability’ (Grey and Sadoff, 2007). Risks emanating 
from various aspects of water insecurity have emerged as a significant 
global challenge. The Global Risks Report by the World Economic Forum 
lists water crisis as one of the top five risks in all its reports since 2015 
(WEF , 2015; WEF , 2016; WEF , 2017; WEF , 2018; WEF , 2019; WEF , 2020). 
Water also features prominently in the SDGs (Section 4.8) and plays a 
central role in various systems transitions needed for climate resilient

development. Most SDGs cannot be met without access to adequate 
and safe water (Ait-Kadi, 2016; Mugagga, 2016). In addition, without 
adequate adaptation, future water-related impacts of climate change 
on various sectors of the economy are projected to lower the global 
GDP by mid-century, with higher projected losses expected in low- and 
middle-income countries (World Bank, 2017; GCA, 2019).
There are at least four reasons for the centrality of water security in 
adapting to, and mitigating climate change.
First, approximately half the world’s population (~4  billion out of 
~8 billion people) are assessed as being currently subject to severe water 
scarcity for at least some part fo the year (medium confidence) due to 
climatic and non-climate factors (Box 4.1). Water insecurity arises from 
many factors, both environmental and societal. Environmental factors 
include too little freshwater due to drought or pollution, and too much

water, due to extreme precipitation and flooding, and are being affected 
by climate change. Societal factors include economic and governance-
related barriers to water access or protection from water-related damages. 
Currently, many people are experiencing climate change on a day-to-day 
basis through water-related impacts such as the increased frequency 
and intensity of heavy precipitation (high confidence) (Section  4.2.1.1, 
Seneviratne et al., 2021 ); accelerated melting of glaciers (high confidence) 
(Section 4.2.2, Douville et al., 2021); changes in frequency, magnitude and 
timing of floods (high confidence) (Section 4.2.4, Seneviratne et al., 2021 
); more frequent and severe droughts in some places (high confidence) 
(Section 4.2.5, Seneviratne et al., 2021); decline in groundwater storage 
and reduction in recharge (medium confidence) (Section 4.2.6, Douville 
et  al., 2021) and water quality deterioration due to extreme events

(medium confidence) (Section  4.2.7). For example, since the 1970s, 
44% of all disaster events have been flood-related (WMO, 2020). With 
the added stressor of climate change, globally, a larger fraction of land 
and population are projected to face increased water scarcity due to 
climate change. For example, at an approximately 2°C GWL, between 
0.9 and 3.9 billion people are projected to be at increased exposure to 
water stress, depending on regional patterns of climate change and the 
socioeconomic scenarios considered (Koutroulis et al., 2019).
Second, while climate change directly affects freshwater availability 
across space and time, it also affects water requirements for different 
uses, such as irrigation, potentially adding to existing societal challenges (Bijl et  al., 2018). Vulnerability to water-related impacts 
of climate change and extreme weather are already felt in all major 
sectors and are projected to intensify in the future, for example, in

agriculture (high confidence) (Sections  4.3.1, 4.5.1); energy and 
industry (high confidence for observed drought impacts and projected 
impacts) (Sections  4.3.2, 4.5.2); water for health and sanitation 
(high confidence about links to precipitation extremes and disease 
outbreaks) (Sections  4.3.3, 4.5.3); water for urban, peri-urban and 
municipal sectors (medium confidence) (Sections  4.3.4, 4.5.4) and 
freshwater ecosystems (high confidence in climate change as a driver 
in degradation of freshwater ecosystems) (Sections  4.3.5, 4.5.5). 
Agriculture and irrigation account for the most significant proportion 
of consumptive water use and account for 60–70% of total water 
withdrawals (Hanasaki et al., 2018; Burke et al., 2020; Müller Schmied 
et al., 2021). Globally, 10% of the most water-stressed basins account 
for 35% of global irrigated calorie production (Qin et al., 2019), and 
food production is at risk in those basins and worldwide due to

changes in hydrological components of climate change. Lack of access 
to clean water and sanitation has been one of the leading causes of 
water-borne diseases. In 2017, approximately 2.2 billion people lacked 
access to safe drinking water, and roughly 4.2 billion people could not 
access safe sanitation (WHO and UNICEF , 2019). Inequities in access to 
safe water are being amplified during the current COVID-19 pandemic 
(Box 4. 5 and Cross-Chapter Box COVID in Chapter 7). The same 10% 
of most water-stressed basins also account for 19% of global thermal 
electricity generation (Qin et al., 2019), and globally, both production 
of hydropower and thermal power has been negatively affected by 
droughts and other extreme events. Globally, between 16% and 39% 
of cities experienced surface-water deficits between 1971 and 2000. 
If environmental flow requirements (EFRs) are accounted for, these 
numbers increase to 36% and 63%, respectively. Even under a scenario

where urban water gets the highest priority, more than 440.5 million 
people in cities globally are projected to face a water deficit by 2050 
(Flörke et  al., 2018). The situation is particularly precarious in the 
Global South, where most of the population lacks access to piped 
water (WRI, 2019).
Third, a large majority (~60%) of all adaptation responses documented 
since 2014 are about adapting to water-related hazards like droughts, 
floods and rainfall variability (Berrang-Ford et  al., 2021b) (high 
confidence). Water-related adaptation action features prominently in 
NDC pledges by a large majority of countries in both Global North 
and Global South (GWP , 2018). These adaptation responses and their 
current benefits and effectiveness in reducing water-related risks in 
the future are systematically assessed in this chapter (Sections  4.6, 
4.7.1, 4.7.2 and 4.7.3). These adaptation measures aim to reduce 
impacts of water-related hazards through responses such as irrigation,

water and soil moisture conservation, rainwater harvesting, changes 
in crops and cultivars, improved agronomic practices, among others 
(Sections 4.6.2; 4.7.1). Only ~20% of all documented case studies on 
observed water-related adaptations measure outcomes (positive or 
negative), but the link between positive outcomes and climate risk 
reduction is unclear and remains challenging to assess (Section 4.7.1) 
(medium confidence). On the other hand, most of the future projected 
water-related adaptations are more effective at lower GWLs (1.5°C) 
than at higher GWLs, showing the importance of mitigation for future 
adaptations to remain effective (high confidence).

559Finally, while limiting global warming to 1.5°C would minimise the 
increase in risks in the various water use sectors and keep adaptation 
effective, many mitigation measures can potentially impact future 
water security. For example, bioenergy with carbon capture and storage 
(BECCS) and afforestation and reforestation can have a considerable 
water footprint if done at inappropriate locations (Section 4.7.6, see 
also Canadell et  al., 2021). Therefore, minimising the risks to water 
security from climate change will require a full-systems view that 
considers the direct impacts of mitigation measures on water resources 
and their indirect effect via limiting climate change (high confidence).
This chapter draws on previous IPCC reports and new methodologies 
(Section 4.1.1 and SM4.1, SM4.2) and assesses the impacts of climate 
change on natural and human dimensions of the water cycle with 
a particular focus on water-related vulnerabilities and adaptation

responses (Figure 4.1). Section 4.2 assesses observed changes in the 
hydrological cycle, and Section 4.3 focuses on their societal impacts 
and detects which parts of these changes are directly attributable to 
climate change. Section 4.4 assesses projected risks of changes in the 
hydrological cycle on various components of the hydrological cycle, and 
Section 4.5 assesses the same for sectoral risks. Projections and risks 
assessments for future impacts are framed in terms of GWLs and time 
horizons, as these are useful for informing mitigation policy under the 
Paris Agreement and informing adaptation planning. Sections 4.6 and 
4.7 assesses current and future water-related adaptation responses in 
reducing climate and associated impacts and risks and looks at limits 
to adaptations, especially in a future warmer world. Finally, Section 4.8 
outlines the enabling principles for meeting water security, SDGs and 
climate resilient development.

4.1.1 Points of Departure and Advancements since AR5
The Fifth Assessment Report (AR5, Jiménez Cisneros et  al., 2014) 
concluded that for each degree of global warming, approximately 7% 
of the global population, under a scenario of moderate population 
growth, was projected to be exposed to a decrease of renewable water 
resources of at least 20%. In addition, AR5 reported negative impacts on 
streamflow volumes, its seasonality (specifically in cryospheric zones), 
a decline in raw water quality (medium evidence, high agreement) and 
projected reduction in renewable surface water and groundwater in 
most dry tropical regions. AR5 projected an increase in meteorological, 
agricultural and hydrological droughts in dry regions (medium 
confidence) (Jiménez Cisneros et al., 2014).
The Special Report on Global Warming of 1.5°C (SR1.5) assessed that 
limiting global warming to 1.5°C is expected to substantially reduce

the probability of extreme droughts, precipitation deficits and risks 
associated with water availability in some regions (medium confidence). 
On the other hand, higher risks to natural and human systems in a 2.0°C 
world would mean increased vulnerability for the poor, showing that 
socioeconomic drivers are expected to have a more significant influence 
on water-related risks and vulnerabilities than changes in climate alone 
(medium confidence) (Hoegh-Guldberg et al., 2018).
Figure 4.1 |  Chapter structure.Structure of Chapter 04
Section 4.7
Benefits and Effectiveness of W ater-Related
Adaptations, Their Limits and T radeoffs
/circle6Current adaptation responses, Benefits, Co-benefits, and Maladaptation
/circle6Projections of future ef fectiveness and residual risks
/circle6Comparison of Current and Future Adaptation
/circle6Limits to Adaptation, Loss and Damage
/circle6Costs of Adaptation losses and losses due to non-adaptation

/circle6Tradeof fs and Synergies between Water-related Adaptation and Mitigation
Enabling Conditions for Achieving W ater Security ,
Sustainable and Climate Resilient Development
through System T ransformations
/circle6Appropriate Technologies
/circle6Adequate and Appropriate Financing
/circle6Gender , Equity and Social Justice
/circle6Inclusion of Indigenous Knowledge and Local Knowledge
/circle6Participative, Cooperative and Bottom-up Engagement
/circle6Polycentric W ater Governance
/circle6Strong Political SupportSection 4.6
Key Risks and Adaptation
/circle6Key Risks
/circle6Agriculture
/circle6Energy and Industry
/circle6WaSH
/circle6Urban and Peri-urban
/circle6Freshwater Ecosystems
/circle6Conflicts
/circle6Mobility and Migration
/circle6Indigenous, Local and Traditional Water UsesCentrality of water security
/circle6 Points of departure
/circle6 Climatic and non-climatic driver sSection 4.1
4.2 Observed
4.4 Projected
Changes in Hydrological Cycle

/circle6P, ET and Soil Moisture
/circle6Cryosphere
/circle6Runof f and Streamflow
/circle6Floods
/circle6Droughts
/circle6Groundwater
/circle6Water Qualit y
/circle6Soil Erosion and Sediment Load
4.3 Observed
4.5 Projected
Sectoral Impacts
/circle6Indigenous, Local and
/circle6Traditional Cultural UsesObserved Impacts and Projected Risks of
Climate Change to W ater

560Box 4.1 | Implications of Climate Change for Water Scarcity and Water Insecurity
Water scarcity and water insecurity are related concepts but not identical, and each has a range of interpretations leading to some overlap. 
Water scarcity can be broadly described as a mismatch between the demand for fresh water and its availability, quantified in physical terms. 
Water security/insecurity is a broader concept with definitions beyond physical water scarcity, encompassing access to water services, safety 
from poor water quality and flooding, and appropriate water governance that ensures access to safe water (Sadoff et al., 2020). Metrics 
of water security include both physical and socioeconomic components and are a tool for comparison between different locations and 
countries regarding relative levels of water security in the context of water-related risks. Some definitions of water scarcity also incorporate

these broader issues. For example, ‘economic water scarcity’ has been defined as a situation where ‘human, institutional, and financial 
capital limit access to water, even though water in nature is available locally to meet human demands’ (Comprehensive Assessment of Water 
Management in Agriculture, 2007). Economic water scarcity can also occur where infrastructure exists, but water distribution is inequitable 
(Jaeger et al., 2017). Much of the literature exploring the impacts of climate change on water security, however, focuses on quantifying 
physical water scarcity. Discussions in this box consider physical water scarcity as a quantifiable measure of water availability compared to 
its demand and consider the societal elements of economic water scarcity to be part of the more comprehensive concept of water insecurity.
Physical water scarcity

Definitions of water scarcity have evolved to take account of a broader set of factors. For example, physical water scarcity indicates that 
an insufficient quantity of water is available to meet requirements. A commonly used measure of physical water scarcity is the Falkenmark 
index which measures the amount of renewable freshwater available per capita (Falkenmark et al., 1989; White et al., 2014). However, 
the Falkenmark index is now regarded as an incomplete measure, as it does not account for water needed for non-human needs (as 
quantified with EFRs). Therefore, EFRs have begun to be incorporated in recent water scarcity assessments (Liu et al., 2016; Liu et al., 
2017b). Quality-induced water scarcity is an additional factor beginning to be considered (Liu and Zhao, 2020) .
Using a Water Scarcity Index (WSI) defined as the ratio of demand and availability, accounting for EFRs, it is estimated that 4 billion

people live under conditions of severe water scarcity for at least one month per year (Figure Box 4.1.1a; Mekonnen and Hoekstra, 2016). 
Nearly half of these people live in India and China. Although regions with high water scarcity are already naturally dry (virtually certain2), 
human influence on climate is leading to reduced water availability in many regions. It is very likely that global patterns of soil moisture 
change are being driven by human influence on climate, and an overall global decline in soil moisture is attributable to greenhouse 
forcing [4.2.1.3]. Climate change patterns of streamflow change include declines in western North America, northeast South America, the 
Mediterranean and South Asia (medium confidence) [4.2.3]. However, quantification of the contribution of anthropogenic climate change 
to current levels of water scarcity is not yet available.

Water demand is projected to change as a direct result of socioeconomic changes. For example, the global water demand for domestic, 
industrial and agricultural uses, at present about 4600 km³ yr–1, is projected to increase by 20–30% by 2050 (Greve et  al., 2018), 
depending on the socioeconomic scenario. Changes in water availability and demand have been projected in several studies using climate 
models and socioeconomic scenarios (e.g., Arnell and Lloyd-Hughes, 2014; Gosling and Arnell, 2016; Greve et al., 2018; Koutroulis et al., 
2019). In such studies, the projected changes in water availability arise from differences in precipitation and evapotranspiration (ET). 
However, both precipitation and evapotranspiration are also subject to very high uncertainty in key processes such as regional climate 
change patterns (Uhe et al., 2021) and the influence of vegetation responses to elevated CO 2 on transpiration (Betts et al., 2015).

Human factors are projected to be the dominant driver of future water scarcity on a global scale (Graham et al., 2020a). However, at 
regional scales, high uncertainty in climate changes means that reduced water availability is more likely than not in many major river 
basins and remains a risk in most basins even where the central estimate is for increased water availability due to climate change 
(Figure  4.16). Such substantial uncertainties in projected water scarcity are crucial factors causing water management policies and 
planning challenges in the future. Therefore, locations projected to see significant increases in water scarcity with large uncertainty can 
be considered to be subject to the highest challenges for water management policy (Figure Box 4.1.1b; Greve et al., 2018).

2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as 
likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely than not >50–100%, and Extremely unlikely 
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome 

561Geographical distributions of current water scarcity and levels of challenge
for policies addressing future change
Water
scarcity
Index
in 2050
(median)Policy
challenges
Medium High
Low Medium>0.4
<0.4
Stable Increase
Uncertainty change
present day to 2050(b) Local levels of policy challenges for addressing water scarcity by 2050(a) Number of months per year with severe water scarcity
Number of months
in which water scarcity is >100%
012
10–11
8–9
6–7
4–5
2–3
1
No data
Figure Box 4.1.1 |  Geographical distributions of current water scarcity and levels of challenge for policies addressing future change.
(a) The number of months per year with severe water scarcity (ratio of water demand to availability > 1.0). Reproduced from Mekonnen and Hoekstra (2016).
(b) Local levels of policy challenges for addressing water scarcity by 2050, considering both the central estimate (median) and the change uncertainty in projections of a

Water Scarcity Index (WSI) from the present day to 2050 (Greve et al., 2018). Projections used five CMIP5 climate models, three global hydrological models from ISIMIP 
and three Shared Socioeconomic Pathways (SSPs). Levels of policy challenges refer to the scale and nature of policies to address water scarcity and range from monitoring 
and reviewing risks (‘low’) through transitional changes in water systems (‘medium’) to transformational changes (‘high’). Low policy challenges arise when the projected 
water scarcity in 2050 is lower (<0.4), and the level of uncertainty remains relatively stable in future projections. Medium policy challenge arises when either the central 
estimate of water scarcity remains low but uncertainty increases, or the uncertainty is stable but the central estimate of water scarcity for 2050 is higher (>0.4). High

policy challenges arise when the central estimate of water scarcity is higher and the uncertainty increases. White areas show grid points defined as non-water-scarce (75th 
quantile of the WSI < 0.1 at all times) or very low average water demand. Reproduced from Greve et al. (2018).Box 4.1 (continued)

562Water security and insecurity
Unlike physical water scarcity, water security or insecurity cannot be quantified in absolute terms. However, relative levels of water 
security in different places can be compared using metrics representing critical aspects of security (Gain et al., 2016; Young et al., 2019), 
ideally with thresholds for secure/insecure compared with local experience to assess validity (Young et al., 2019).
Gain et al. (2016) define a Global Water Security Index (GWSI) metric on a scale of 0 to 1 combining indicators of relative levels of availability 
of freshwater, accessibility to water services, water management and water quality and safety (including flood risk, which can affect water 
quality as well as being a direct physical hazard). Global application of this index indicates large worldwide differences in water security

arising from different combinations of reasons (Figure Box 4.1.2a). In North Africa, the Middle East, large parts of the Indian sub-continent 
and north China, low water security arises predominantly from low water availability. However, many areas with relatively high water 
availability have relatively low levels of water security due to other factors. In 2015, 29% of the world’s population did not have access to safe 
drinking water (Ritchie and Roser, 2019). In large parts of South and Southeast Asia, significant contributions to water insecurity came from 
increased flood risk and deteriorated water quality (Burgess et al., 2010; Ward et al., 2017; Farinosi et al., 2018). Water availability is relatively 
high across most of Africa, but water security is relatively low due to low accessibility, management and safety/quality standards. Most 
people in Africa do not have access to safe drinking water and improved sanitation (Marson and Savin, 2015; Naik, 2017; Armah et al., 2018).

In contrast, some areas with high physical water scarcity, such as some parts of the USA, Australia and southern Europe, show relatively 
high water security levels due to good governance, safety/quality and accessibility. Nevertheless, marginalised groups such as Indigenous 
Peoples experience reduced access to water even within regions in the Global North. For example, in both Canada and the USA, many 
Indigenous Peoples living on reserves lack access to piped water (Collins et al., 2017; Hanrahan, 2017; Marshall et al., 2018) and (or) are 
on boil water advisories (Patrick et al., 2019). In Australia, 25–40% of Aboriginal people live in remote rural areas with poor access to 
clean water (Bowles, 2015; NCCARF , 2018).
Global Water Security Index
and its components for the present day, and factors affecting future change in water security
(a) Present-day (b) Future
Water quality and safety Water availabilit yAggregated global water security index

Accessibility to water servises Water managementFuture water security
Risks to water 
availability , quality and 
safety increase with 
higher levels of global 
warmingCompetition for water 
from climate mitigation 
methods,e.g.,bioenergy 
with carbon capture 
and storage (BECCS) 
and af forestationAction on water 
accesssibility and 
management as 
climate change 
adaptationMore
secure
Less
secure
Figure Box 4.1.2 |  Global Water Security Index (GWSI) and its components for the present day, and factors affecting future change in water security.Box 4.1 (continued)

563The Special Report on Oceans and Cryosphere in a Changing Climate 
(SROCC) confirmed findings from AR5, with robust evidence of 
declines in snow cover and negative mass balance in most glaciers 
globally. Glacier melting seriously threatens water supply to mountain 
communities and millions living downstream through water shortages, 
jeopardising hydropower generation, irrigation and urban water uses 
(Hock et al., 2019b). Additionally, Arctic hydrology will be affected by 
permafrost changes, negatively impacting Arctic communities’ health 
and cultural identity (Meredith et al., 2019).
The Special Report on Climate Change and Land (SRCCL) stated 
that groundwater over-extraction for irrigation is causing depletion 
of groundwater storage (high confidence). The report also noted 
that precipitation changes, coupled with human drivers, will have 
a role in causing desertification, and water-driven soil erosion is

projected to increase due to climate change (medium confidence). 
The population vulnerable to impacts related to water is projected 
to increase progressively at 1.5°C, 2°C and 3°C of global warming, 
with half of those impacted residing in South Asia, followed by 
Central Asia, West Africa and East Asia. SRCCL stated that improved irrigation techniques (e.g., drip irrigation) and moisture conservation 
(e.g., rainwater harvesting using indigenous and local practices) could 
increase farmers’ adaptive capacity (high confidence) (Mirzabaev 
The Sixth Assessment Report (AR6) Working Group I (WGI) (Douville 
et  al., 2021) concluded that anthropogenic climate change has 
increased atmospheric moisture and precipitation intensity (very likely 
by 2–3% per 1°C) (high confidence), increased terrestrial ET (medium 
confidence) and contributed to drying in dry summer climates including 
in the Mediterranean, southwestern Australia, southwestern South

America, South Africa and western North America (medium to high 
confidence), and has caused earlier onset of snowmelt and increased 
melting of glaciers (high confidence) since the mid-20th century. The 
report also stated with high confidence that the water cycle variability 
and extremes are projected to intensify, regardless of the mitigation 
policy. The share of the global population affected by water-related 
hazards and water availability issues is projected to increase with the 
intensification of water cycle variability and extremes. They concluded 
with high confidence that strong and rapid mitigation initiatives are (a) Top: a global map of local values of GWSI constructed from the following components with their subjectively weighted contribution to the combined metric indicated 
in brackets. Middle left: relative effectiveness of water management (15%), comprising a World Governance Index at country scale (itself representing six components:

voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law and control of corruption) and 
indicators of transboundary legal frameworks and political tensions at a river-basin scale. Middle right: relative accessibility to water services (20%), including drinking 
water and sanitation. Bottom left: relative water quality and safety (20%), including a Water Quality Index and Flood Frequency Index. Bottom right: relative availability of 
fresh water (45%), comprising a Water Scarcity Index, Drought Index and the groundwater depletion rate. Data for the components do not apply to the same set of dates 
but are generally applicable to recent decades up to 2010. White areas indicate no data available for at least one component. For further details, see Gain et al. (2016).
(b) Factors through which climate change or action on mitigation or adaptation could influence water security.

The discrepancy between physical water scarcity and overall water insecurity is a function of socioeconomic vulnerabilities and governance 
gaps. Therefore, improving societal aspects of water management will be key in adapting to climate change-driven increases in water 
scarcity in the future (high confidence).
Future water security will depend on the magnitude, rate and regional details of future climate change and non-climatic factors, including 
agricultural practices, water demand and governance. In many cases, climate change may not be the dominant factor affecting water 
security. Nevertheless, climate change poses clear risks to water security in many regions through potential impacts on water availability, 
quality and flooding. The range of possible outcomes is extremely large, and assessing the likelihood of particular outcomes depends on

consideration of uncertain regional climate changes and uncertain socioeconomic futures. Uncertainty in future water scarcity projections 
makes climate change risks to water security and planning for adaptation challenging. Limiting climate change to lower levels of global 
warming would reduce the risks to water security arising from climate change, partly because uncertainties in regional climate change 
are smaller at lower levels of warming.
In summary, roughly half of the world’s population are assessed as currently subject to severe water scarcity for at least some part 
of the year due to climatic and non-climatic factors, and this is projected to be exacerbated at higher levels of warming (medium 
confidence). General water insecurity issues are seen worldwide, particularly in South Asia, North China, Africa and the Middle East, due 
to high population densities often coupled with low water availability, accessibility, quality and governance (high confidence). Areas with

high water availability can also be water-insecure due to increased flood risk, deteriorated water quality, and poor governance (high 
confidence). Future water security will depend on the evolution of all these socioeconomic and governance factors and future regional 
climate change (high confidence). The main climate change contribution to water insecurity is the potential for reduced water availability, 
with a secondary contribution from increased flooding risk (medium confidence). Future socioeconomic conditions are a crucial driver of 
water insecurity, implying the need for further adaptation to some level of future climate change (medium confidence). However, policy 
challenges are high in many regions, with uncertainty in the regional climate outcomes being a key factor (high confidence).Box 4.1 (continued)

564needed to avert the manifestation of climate change in all components 
Building on these previous reports, this chapter advances understanding 
climate change-induced hydrological changes and their societal 
impacts and risk in several key ways.
First, since AR5, the methodology of climate change impact studies 
has advanced and these methodological advances are described in 
SM4.1. AR6 uses new projections (CMIP6) based on the SSPs and other 
scenarios, and we assess those results in this chapter alongside those 
using other projections and scenarios.
Second, this chapter follows the developments set in motion by 
SR1.5, SRCCL and SROCC to incorporate Indigenous knowledge (IK), 
traditional knowledge (TK) and local knowledge (LK). SR1.5 stated 
that disadvantaged and vulnerable populations, including Indigenous 
Peoples and certain local communities, are at disproportionately

higher risk of suffering adverse consequences due to global warming 
of 1.5°C or more (Roy et al., 2018). SRCCL highlighted the enhanced 
efficacy of decision-making and governance with the involvement 
of local stakeholders, particularly those most vulnerable to climate 
change, such as Indigenous Peoples (Arneth et  al., 2019). SROCC 
found adaptation efforts have benefited from the inclusion of IK and 
LK (IKLK) (Abram et al., 2019). In this chapter, we engage directly with 
Indigenous contributing authors and use multiple evidence-based 
approaches, as undertaken by the IPBES (Tengö et  al., 2014; Tengö 
et al., 2017). This approach is guided by the understanding that the co-
production of knowledge (between scholars and local communities) 
about water and climate change vulnerability, impacts and adaptation 
has the potential to lead to new water knowledge and context-
specific governance strategies (Arsenault et  al., 2019; Chakraborty

and Sherpa, 2021). Additionally, shifting beyond the exclusive use 
of technical knowledge and Western viewpoints redresses the 
shortcomings of resource- and security-oriented understandings to 
water and acknowledges the more holistic and relational approaches 
common to IKLK (Section 4.8.4) (Stefanelli et al., 2017; Wilson, 2019; 
Chakraborty and Sherpa, 2021).
Finally, grounded in the AR6 goal to expand the solution space, this 
chapter advances the understanding of adaptation in the water sector 
since AR5 by deploying a meta-analysis of adaptation measures. 
The meta-analysis focuses on both current adaptation responses 
(Section  4.7.1) and future projected adaptation responses, which 
have been modelled (Section  4.7.2). The meta-review assesses the 
outcomes of current adaptation responses and effectiveness of 
future projected adaptations in reducing climate and associated 
risks. Studies derived from the Global Adaptation Mapping Initiative

(GAMI) database (Berrang-Ford et al., 2021a) (see Chapter 16) were 
coded systematically following a meta-review protocol developed 
specifically for this assessment (Mukherji et al., 2021; SM4.2). A similar 
meta-review protocol was also developed to assess effectiveness of 
adaptations to reduce projected climate risks (Section 4.7.2; SM4.2).4.1.2 Climatic and Non-Climatic Drivers of Changes in 
the Water Cycle
The water cycle is affected by both climatic and non-climatic factors 
(Douville et  al., 2021). Radiative forcing by changes in greenhouse 
gas (GHG) concentrations, aerosols and surface albedo drives global 
and regional changes in evaporation and precipitation (Douville et al., 
2021). A warmer atmosphere holds more moisture, increasing global 
and regional mean precipitation, and more extreme precipitation 
(Allan et  al., 2014; Giorgi et  al., 2019; Allan et  al., 2020). Regional 
precipitation responses vary according to changes in atmospheric

circulation. Geographical variation in aerosols drives changes in 
atmospheric circulation, affecting precipitation patterns such as the 
Asian monsoon (Ganguly et al., 2012; Singh et al., 2019). (Section 4.2.1)
Warming increases glacier melt and is expected to decrease snowfall 
globally and lead to shorter snow seasons with earlier but less rapid 
snowmelt. It can also lead to local increases in snowfall intensity (Allan 
et  al., 2020). These changes affect the seasonality of river flows in 
glacier-fed or snow-dominated basins. (Section 4.2.2)
Rising atmospheric CO 2 generally decreases plant transpiration, 
affecting soil moisture, runoff, stream flows, the return of moisture 
to the atmosphere and surface temperature (Skinner et  al., 2017). 
However, in some regions, these can be offset by increased leaf area 
(‘global greening’) driven by elevated CO 2, land use change, nitrogen 
deposition and effects of climate change itself (Zhu Z. et al., 2016; Zeng

Z. et al., 2018). Increased ozone can impact plant functioning, reducing 
transpiration (Arnold et al., 2018). (Section 4.2.1)
Direct human interventions include abstraction of surface water and 
groundwater for drinking, irrigation and other freshwater uses, as well 
as streamflow impoundment behind dams and large-scale inter-basin 
transfers (Zhao et al., 2015; Donchyts et al., 2016; McMillan et al., 2016; 
Shumilova et al., 2018). The consequences of these interventions are 
substantial and are discussed below briefly. In addition, these direct 
human interventions can change due to various societal and economic 
factors, including changes in land use and urbanisation (Sections 4.3 
and 4.5).
Irrigation can reduce river flows and groundwater levels via abstraction 
and increase local precipitation (Alter et  al., 2015; Cook et  al., 2015), 
alter precipitation remotely through moisture advection (de Vrese et al.,

2016) and change the timing of monsoons through land–sea temperature 
contrasts (Guimberteau et al., 2012) (Box 4.3). Land cover change affects 
ET and precipitation (Li et al., 2015; Douville et al., 2021), interception 
of precipitation by vegetation canopies (de Jong and Jetten, 2007), 
infiltration (Sun et  al., 2018a) and runoff (Bosmans et  al., 2017). Land 
cover impacts on the hydrological cycle are of similar magnitude as 
human water use (Bosmans et al., 2017).
Urbanisation decreases land surface permeability (Choi et al., 2016), 
which can increase fast runoff and flooding risks and reduce local 
rainfall by decreasing moisture return to the atmosphere (Wang et al., 
2018). But urbanisation can also increase the sensible heat flux driving 
greater or more extreme precipitation (Kusaka et  al., 2014; Niyogi 
et al., 2017). (Section 4.3.4)

565River discharge
46±10%Land evaporation 74±10%
Energy productionWater, Sanitation &
Hygiene (W aSH)Water fluxes in natural and human systems. Units in thousands of km3 per year The water cycle, including direct human interventions
(Snow , Glaciers, and Permafrost)
Municipal water
withdrawal 
0.4±12%
Groundwater 
recharge 13±60%Groundwater
withdrawal
0.7±17%Green water use 19±20%
Irrigation from
surface water 1.9±6%
Industrial water
withdrawal 0.8±1 1%Ocean to land
water vapour 
transport 46±10%
Water Quality
discharge to
ocean 4.5±70%Freshwater
ecosystemIrrigation from
ground water
0.6±17%Land Precipitation 120±10%
Figure 4.2 |  The water cycle, including direct human interventions. Water fluxes on land precipitation, land evaporation, river discharge, groundwater recharge and

groundwater discharge to the ocean from Douville et al. (2021). Human water withdrawals for various sectors are shown from Hanasaki et al. (2018), Sutanudjaja et al. (2018), Burek 
et al. (2020), Droppers et al. (2020) and Müller Schmied et al. (2021). Green water use (Abbott et al., 2019) refers to the use of soil moisture for agriculture and forestry. Irrigation 
water use (called blue water) is not included in green water use.
In summary, radiative forcing by GHG and aerosols drives changes in 
ET and precipitation at global and regional scales, and the associated 
warming shifts the balance between frozen and liquid water (high 
confidence). Rising CO 2 concentrations also affect the water cycle via 
plant physiological responses affecting transpiration, including via 
reduced stomatal opening and increased leaf area (high confidence 
regarding the individual processes; medium confidence regarding 
their net impact). Land cover changes and urbanisation affect both

the climate and land hydrology by altering the exchanges of energy 
and moisture between the atmosphere and surface (high confidence) 
and changing the permeability of the land surface. Direct human 
interventions in river systems and groundwater systems are non-
climatic drivers with substantial impacts on the water cycle (high 
confidence) and have the potential to change as part of societal 
responses to climate change (Figure 4.2).
4.2 Observed Changes in the Hydrological 
Cycle Due to Climate Change
All components of the global water cycle have been modified due to 
climate change in recent decades (high confidence) (Douville et  al., 
2021), with hundreds of millions of people now regularly experiencing 
hydrological conditions that were previously unfamiliar (Sections 4.2.1.1, 
4.2.4, 4.2.5). Extensive records from weather stations, satellites and 
radar clearly show that precipitation patterns have shifted worldwide.

Three major shifts documented are (a) some regions receiving more annual or seasonal precipitation and others less, (b) many regions have 
seen increased heavy precipitation, and many have seen either increases 
or decreases in dry spells and (c) some regions have seen shifts towards 
heavier precipitation events separated by more prolonged dry spells 
(Section  4.2.1.1). Observationally based calculations suggest that ET 
has changed in response to changes in precipitation and increasing 
temperatures, resulting in changing patterns of soil moisture worldwide 
which are now detectable by satellite remote sensing (Sections 4.2.1.2, 
4.2.1.3). Rising temperatures have caused profound and extensive 
changes in the global cryosphere, with mountain glaciers, land ice and 
snow cover shrinking, causing substantial, permanent impacts on the 
ways of life of people in these regions, particularly Indigenous Peoples 
with strong cultural links to long-term or seasonally frozen environments

(Sections 4.2.2, 4.3.8). Groundwater recharge in spring may have been 
reduced due to shorter snowmelt seasons, although the dominant impact 
on groundwater has been non-climatic and through intensification of 
irrigation (Section 4.2.6). The global-scale pattern of streamflow changes 
is now attributable to observed historical climate change, with human 
land and water use insufficient by themselves to explain the observed 
streamflow changes at global scales (Section 4.2.3). Numerous examples 
of extreme hydrometeorological events, including heavy precipitation, 
flooding, drought and wildfire events causing deaths, high levels of 
economic damage and extensive ecological impacts, have been shown 
to have been made more likely by human influence on climate through 
increased GHG concentrations in the atmosphere (Sections 4.2.1.1, 4.2.4, 
4.2.5). Overall, there is a clear picture of human alteration of the global

water cycle, which is now affecting societies and ecosystems across the

566
Observed mean and extreme precipitation changes
and people experiencing the emergence of historically unfamiliar precipitation and changes in extreme precipitation
(a) Trend in annual precipitation (b) Level of unfamiliarity of annual precipitation
(h) Population density in regions of emerging Rx1day changes 1950–2018 (i) Population density in regions of emerging CDD changes 1950–2018
Changes in precipitation (% per °C of global warming)
-50-202050
Emerging
Unfamiliar
Unusual
Unknown
(c) Population density in regions of em erging precipitation changes
(d) Trend in Dec-Jan-Feb precipitation (e) Trend in Jun-Jul-Aug precipitation
(f) Rx1day-Ann, Linear Trend 1950–2018 (g) CDD-Ann, Linear Trend 1950–20180EmergingUnfamiliarUnusualUnknown
DryWet
Signal/noise ratio Frequency before climate change
0.5 ~1 in 3 years Emerging
1 ~1 in 6 years Unfamiliar
2 ~1 in 50 years Unusual

3 ~1 in 1,000 years UnknownPopulation per square degree in regions of increasing/reducing precipitation
1 million10 million
100 thousand
10 thousand
1 thousand
100
10
1 million
10 million100 thousand10 thousand1 thousand10010
-50-202050Changes in precipitation (% per °C of global warming)
Trend (mm/10 year)
-4124
0.5
0
-0.5
-1
-2Trend (days/10 year)
-8248
1
0
-1
-2
-4
Population per square degree in regions of increasing/decreasing Rx1day
1 thousand
100
10
1 million
10 million100 thousand10 thousand1 thousand10010Population per square degree in regions of increasing/decreasing CDD
1 thousand
100
10
1 million
Figure 4.3 |  Observed mean and extreme precipitation changes and people experiencing the emergence of historically unfamiliar precipitation and changes 
in extreme precipitation.

567world. This section describes changes in the hydrological cycle through a 
lens of societal impacts.
4.2.1 Observed Changes in Precipitation, 
Evapotranspiration and Soil Moisture
4.2.1.1 Observed Changes in Precipitation
AR6 WGI (Douville et  al., 2021) concluded that GHG forcing has 
driven increased contrasts in precipitation amounts between wet and 
dry seasons and weather regimes over tropical land areas (medium 
confidence), with a detectable precipitation increase in the northern 
high latitudes (high confidence). GHG forcing has also contributed 
to drying in dry summer climates, including the Mediterranean, 
southwestern Australia, southwestern South America, South Africa 
and western North America (medium to high confidence) (Figure 4.3). 
AR6 WGI (Seneviratne et al., 2021) also concluded that the frequency 
and intensity of heavy precipitation events have likely increased at the 
global scale over most land regions with good observational coverage.

Heavy precipitation has likely increased on the continental scale 
over North America, Europe and Asia. Regional increases in heavy 
precipitation frequency and (or) intensity have been observed with 
at least medium confidence for nearly half of the AR6 WGI climatic 
regions (Figure 4.3). Human influence, in particular GHG emissions, is 
likely the main driver of the observed global-scale intensification of 
heavy precipitation in land regions
Large numbers of people live in regions where the annual mean 
precipitation is now ‘unfamiliar’ compared to the mean and variability 
between 1891 and 2016 (Figure  4.3c). “Unfamiliar” is defined as 
the long-term change being greater than one standard deviation in 
the annual data (Figure  4.3b). In 2020, approximately 498  million 
people lived in unfamiliarly wet areas, where the long-term average 
precipitation is as high as previously seen in only about one in 
6 years (medium confidence) (Figure 4.3c). These areas are primarily

in mid and high latitudes (Hawkins et al., 2020). On the other hand, 
approximately 163 million people lived in unfamiliarly dry areas, mostly 
in low latitudes (medium confidence). Due to high variability over time, 
the signal of long-term change in annual mean precipitation is not 
distinguishable from the noise of variability in many areas (Hawkins 
et al., 2020), implying that the local annual precipitation cannot yet be 
defined ‘unfamiliar’ by the above definition.Notably, many regions have seen increased precipitation for part of 
the year and decreased precipitation at other times (high confidence) 
(Figure  4.3d,e), leading to small changes in the annual mean 
precipitation. Therefore, the numbers of people seeing unfamiliar 
seasonal precipitation levels are expected to be higher than those 
quoted above for unfamiliar annual precipitation changes (medium 
confidence). Still, quantified analysis of this is not yet available.

The intensity of heavy precipitation has increased in many regions (high 
confidence), including much of North America, most of Europe, most 
of the Indian sub-continent, parts of northern and southeastern Asia, 
much of southern South America, parts of southern Africa and parts of 
central, northern and western Australia (Figure 4.3 f) (Dunn et al., 2020; 
Sun et al., 2020). Conversely, heavy precipitation has decreased in some 
regions, including eastern Australia, northeastern South America and 
western Africa. The length of dry spells has also changed, with increases 
in annual mean consecutive dry days (CDD) in large areas of western, 
eastern and southern Africa, eastern and southwestern South America, 
and Southeast Asia, and decreases across much of North America 
(Figure 4.3g). Precipitation extremes have changed in some places where 
annual precipitation shows no trend. Some regions such as southern 
Africa and parts of southern South America are seeing increased heavy

precipitation and longer dry spells. Many regions with changing extremes 
are highly populated, such as the Indian sub-continent, Southeast Asia, 
Europe and parts of North America, South America and southern Africa 
(Figure 4.3h,i). Substantially more people (~709 million) live in regions 
where annual maximum one-day precipitation has increased than in 
regions where it has decreased (~86  million) (medium confidence). 
However, more people are experiencing longer dry spells than shorter 
dry spells: approximately 711 million people live in places where annual 
mean CDD is longer than in the 1950s, and ~404 million in places with 
shorter CDD (medium confidence) (Figure 4.3i).
In summary, annual mean precipitation is increasing in many regions 
worldwide and decreasing over a smaller area, particularly in the 
tropics. Nearly half a billion people live in areas with historically 
unfamiliar wet conditions, and over 160  million in areas with

historically unfamiliar dry conditions (medium confidence). Over 
700  million people experience heavy precipitation significantly 
more intense than in the 1950s, but less than 90 million experience 
decreased heavy precipitation. Compared to the 1950s, 711 million 
people now experience longer dry spells and 404 million experience 
shorter dry spells.(a) Percentage changes in annual mean precipitation over land (1891–2019) per °C global warming in the Global Precipitation Climatology Centre (GPCC) v2020 data set 
(Schneider et al., 2017; Schneider et al., 2020). Green shows increasing precipitation; orange shows decreasing precipitation.
(b) Levels of unfamiliarity of wetter and drier climates, classified in terms of the ratio of the signal S of change to the noise N of variability, where the latter is defined as one

standard deviation in annual data with the trend removed, that is, occurs approximately one in 6 years. Grey regions are either unobserved (oceans) or deserts (<250 mm year–1). 
Stippling indicates where the signal of change is not significant. See Hawkins et al. (2020) for further details.
(c) Population densities in regions with annual precipitation classified as “emerging”.
(d) Precipitation trends from the GPCC data set in December, January and February (mm day–1 decade–1).
(e) As (d) for June-July-August.
(f) Changes in annual maximum 1-day precipitation (Rx1day) in the HadEX3 data set (Dunn et al., 2020).
(g) Trend in annual mean consecutive dry days (CDD), 1950–2018, in HadEX3.
(h) Population densities per grid box where the trend in Rx1day is significantly different from zero.
(i) Population densities per grid box where the trend in CDD is significantly different from zero. Stipples in (h) and (i) show where HadEX3 data is available. Population data in (c),

568Table 4.1 |  Trends in global evapotranspiration for different periods between 1981–1982 and 2009–2013.
Trend (mm yr-2) Period Data source Author(s)
+0.54 1981 to 2012 Observations (Zhang Y . et al., 2016)
+1.18 1982 to 2010 Observations (Mao et al., 2015)
+0.93 ± 0.31 1982 to 2010 LSMs (Mao et al., 2015)
+0.88 1982 to 2013 Remote-sensing data (Zhang K. et al., 2015)
+1.5 1982 to 2009 Remote-sensing and surface observations (Zeng et al., 2014)
4.2.1.2 Observed and Reconstructed Changes in 
WGI (Douville et al., 2021) conclude with high confidence that global 
terrestrial annual ET has increased since the early 1980s, driven by 
both increasing atmospheric water demand and vegetation greening 
(medium confidence), and can be partly attributed to anthropogenic 
forcing (high confidence).
Regional changes in ET depend on changes in both the climate and the 
properties of the land surface and ecosystems. The latter also responds

to changes in climate and atmospheric composition. For example, a 
warming climate increases evaporative demand (Huang M et al., 2015; 
Berg et al., 2016), although seasonal rainfall totals (Hovenden et al., 
2014) affect the amount of soil moisture available for evaporation. 
Since transpiration accounts for much of the land-atmosphere water 
flux (Good et al., 2015), vegetation changes also play a significant role 
in overall changes in ET.
With higher CO 2, the increase in evaporative demand can, to 
some extent, be counteracted by reduced stomatal conductance 
(‘physiological effect’), which reduces transpiration and increases 
leaf-level water use efficiency (WUE), but is highly species-specific. 
There is evidence for recent increases in leaf-scale WUE from tree 
rings (14 ± 10%, broadleaf to 22 ± 6%, evergreen over the 20th 
century: (Frank et al., 2015)), carbon isotopes (30 to 35% increase 
in 150  years: (van der Sleen et  al., 2014)), and satellite-based

measurements (1982–2008) combined with data-driven models 
(Huang M et  al., 2015). WUE is also affected by aerodynamic 
conductance (Knauer et al., 2017), nutrient limitation (Medlyn et al., 
2015; Donohue et al., 2017), soil moisture availability (Bernacchi and 
VanLoocke, 2015; Medlyn et  al., 2015), and ozone pollution (King 
et al., 2013; Frank et al., 2015).
Higher CO 2 also increases photosynthesis rates, though this may not 
be maintained in the longer term (Warren et al., 2015; Adams et al., 
2020), particularly where temperatures exceed the thermal maxima for 
photosynthesis (Duffy et  al., 2021). Higher photosynthesis increases 
leaf area index (LAI) (‘structural effect’) and therefore transpiration; 55 
± 25% of observed increases in ET (1980–2011) have been attributed 
to LAI change (Zeng Z. et al., 2018). Increases in ET driven by increased 
LAI (from satellite observations 1982–2012) are estimated at 0.32 ±

0.07 mm month–1 per decade, generating a climate forcing of −0.31 
Wm–2 per decade (Zeng et al., 2017).
Overall regional transpiration change depends on the balance between 
the physiological and structural effects (e.g., Tor-ngern et  al., 2015; Ukkola et al., 2015). In dry regions, ET may increase due to increasing 
LAI (Huang M et  al., 2015), but in some densely vegetated regions, 
the stomatal effect dominates (Mao et  al., 2015). Reductions in 
transpiration due to rising CO 2 concentrations may also be offset by a 
longer growing season (Frank et al., 2015; Mankin et al., 2019). Other 
factors modulate the transpiration effect both temporally and spatially, 
for example, additional vegetation structural changes (Kim et al., 2015; 
Domec et al., 2017), vegetation disturbance and age (Donohue et al., 
2017) and species (Bernacchi and VanLoocke, 2015).
Recent studies report global ET increases from the early 1980s to

2009 and 2013 (Table  4.1). Calculations informed by observations 
suggest that ET has increased in most regions, with statistically 
significant (p<0.05) trends of up to 10 mm yr-2 observed in large parts 
of North America and northern Eurasia. Larger increases in ET are 
also observed in several regions, including northeast Brazil, western 
central Africa, southern Africa, southern India, southern China, and 
northern Australia. Decreases of around 10 mm yr-2 are reported for 
western Amazonia and central Africa (Miralles et al., 2014), although 
not across all data sets (Zeng et  al., 2018). In estimates of past 
changes in long-term drying or wetting of the land surface driven by 
climate, uncertainties in ET observations or reconstructions make a 
more substantial contribution to the overall uncertainty than observed 
changes in precipitation (Greve et al., 2014). Other changes in ET are 
also driven strongly by land cover changes and irrigation (Bosmans 

The contribution of changes in WUE to observed changes in ET is a 
key knowledge gap. WGI assigned low confidence to this contribution. 
Estimating large-scale transpiration response to increased CO 2 based 
on leaf-level responses of WUE is not straightforward (Bernacchi and 
VanLoocke, 2015; Medlyn et al., 2015; Tor-ngern et al., 2015; Walker 
et  al., 2015; Kala et  al., 2016) and new methodological approaches 
are needed.
In summary, there is high confidence that ET increased by between 
approximately 0.5 and 1.5 mm yr-2 between the 1980s and early 2010s 
due to warming-induced increased atmospheric demand worldwide 
and greening of vegetation in many regions. Increases in many areas are 
10 mm yr–2 or more, but in some tropical land areas, ET has decreased 
by 10 mm yr–2. Plant stomatal responses to rising CO 2 concentrations 
may play a role, but there is low confidence in quantifying this. Changes 
in land cover and irrigation have also changed regional ET (medium 

5694.2.1.3 Observed and Estimated Past Changes in Soil Moisture 
and Aridity
AR6 WGI (Douville et al., 2021) find that a global trend in soil moisture 
is detectable in a reanalysis and is attributable to GHG forcing, and 
conclude that it is very likely that anthropogenic climate change 
affected global patterns of soil moisture over the 20th century.
Changes in soil moisture and land surface aridity are due to changes 
in the relative balance of precipitation and ET. Soil moisture is also 
affected by irrigation. Regional trends derived from satellite remote 
sensing products show increases and decreases in annual surface soil 
moisture of up to 20% or more between the late 1970s and late 2010s 
(Figure  4.4). For example, using the ESA CCI SM v03.2 COMBINED 
products (van der Schalie et al., 2021), approximately 0.9 billion people 
live in regions with decreasing surface soil moisture, and 2.1  billion

people live in regions with increasing surface soil moisture (Figure 4.4, 
b). However, there are disagreements between data sets on the direction 
of change in some regions (Seneviratne et al., 2010; Feng and Zhang, 
2015; Feng, 2016), so quantification is subject to low confidence.
Analysis of changes in P–ET estimates for 1948–2005 (Greve et al., 
2014) suggests that geographical variations in soil moisture trends are more complex than the ‘wet get wetter, dry get drier’ (WGWDGD) 
paradigm. This is also supported by remote sensing data, with ESA 
CCI data for 1979–2013 showing only 15% of land following the 
WGWDGD paradigm for soil moisture (Feng and Zhang, 2015). Defining 
arid, humid and transitional areas according to precipitation and 
temperature regimes, all three classes of regions see more widespread 
trends of declining soil moisture than increasing soil moisture (Feng 
and Zhang, 2015). In the ESA CCI product, increasing soil moisture

trends are mainly seen in humid or transitional areas and are rare in 
arid regions (Table 4.2)
Reconstructions of historical soil moisture trends with data-driven 
models and process-based land surface models indicate drier dry 
seasons predominantly in extratropical latitudes, including Europe, 
western North America, northern Asia, southern South America, 
Australia and eastern Africa, consistent with climate model simulations 
of changes due to human-induced climate change (Padrón et al., 2020). 
Furthermore, reduced water availability in the dry season is generally 
a consequence of increasing ET rather than decreasing precipitation 
(Padrón et al., 2020).
While observationally based data for soil moisture are now more widely 
available, regional trends remain uncertain due to disagreements 
Table 4.2 |  Proportions of arid, transitional and humid areas with drying and wetting trends in surface soil moisture from remote sensing, 1979–2013 (Feng and Zhang, 2015).

Areas % of the area with a drying trend % of the area with a wetting trend
Arid 38.4 2.9
Transitional 13.0 10.5
Humid 16.3 8.1Global patterns of changes in soil moisture and people in regions with significant changes
Surface soil moisture change (%)(a) Observed change in surface soil moisture 1978–2018
20(b) Population density in regions of surface soil moisture changes
30 10 0 -10 -20 -30Population density in regions of reducing/increasing soil moisture
1 thousand
100
10
1 million
Figure 4.4 |  Global patterns of changes in surface soil moisture and people in regions with significant changes.
(a) Percentage changes in annual mean surface soil moisture (0–5 cm) for 1978–2018 from satellite remote sensing: the “COMBINED” product of European Space Agency Climate

Change Initiative Soil Moisture (ESA CCI SM v03.2), which blends data products from two microwave instruments, a scatterometer measuring radar backscattering and a radiometer 
measuring brightness temperature (van der Schalie et al., 2021).
(b) The population density in 0.25° grid boxes with trends of significantly increasing and decreasing soil moisture from (a). Stippling indicates where changes are not significant.

570between data sets, so confident assessments of soil moisture changes 
remain a knowledge gap.
In summary, global mean soil moisture has slightly decreased, but 
regional changes vary, with both increases and decreases of 20% 
or more in some regions (medium confidence). Drying soil moisture 
trends are more widespread than wetting trends, not only in arid 
areas but also in humid and transitional areas (medium confidence). 
Reduced dry-season water availability is driven mainly by increasing 
transpiration (medium confidence)
4.2.2 Observed Changes in the Cryosphere (Snow, 
Glaciers and Permafrost)
AR5 reported a decrease in snow cover over most of the Northern 
Hemisphere, decreases in the extent of permafrost and increases in its 
average temperature, and glacier mass loss in most parts of the world 
(Jiménez Cisneros et al., 2014). SROCC (IPCC, 2019c) stated with very 
high or high confidence (a) reduction in seasonal snow cover (snow cover

extent decreased by 13.4% per decade for 1967–2018); (b) glacier mass 
budget of all mountain regions (excluding the Canadian and Russian 
Arctic, Svalbard, Antarctica, Greenland) was 490 ± 100 kg m–2 yr–1 in 
2006–2015; (c) warming of permafrost (e.g., permafrost temperatures 
increased by 0.39°C in the Arctic for 2007–2017). Tourism and recreation 
activities have been negatively impacted by declining snow cover, 
glaciers and permafrost in high mountains (medium confidence).
Recent studies confirmed with high confidence that snow cover extent 
continues to decrease across the Northern Hemisphere in all months 
of the year (see Douville et al. (2021); Eyring et al. (2021); Fox-Kemper 
et al. (2021) for more details). From 1922 to 2018, snow cover extent in 
the Northern Hemisphere peaked in the 1950s to 1970s (Mudryk et al., 
2020) and has consistently reduced since the end of the 20th century 
(Hernández-Henríquez et  al., 2015; Thackeray et  al., 2016; Mudryk

et al., 2017; Beniston et al., 2018; Hammond et al., 2018; Thackeray 
et al., 2019; Mudryk et al., 2020). The consistently negative snow-mass 
trend of approximately 5 Gt yr−1 in 1981–2018 for all winter-spring 
months (Mudryk et al., 2020), including 4.6 Gt yr−1 decrease of snow 
mass across North America and a negligible trend across Eurasia, 
has been observed (Pulliainen et al., 2020). Negative trends in snow-
dominated period duration of 2.0–6.5 weeks per decade was detected 
from surface and satellite observations during 1971–2014 (Allchin 
and Déry, 2017), mainly owing to earlier seasonal snowmelt (Fox-
Kemper et  al., 2021). The observed decrease of snow cover metrics 
(extent, mass, duration) led to changes in runoff seasonality and has 
impacted water supply infrastructure (Blöschl et al., 2017; Huss et al., 
2017), particularly in southwestern Russia, western USA and central 
Asia. In these regions, snowmelt runoff accounts for more than 30%

of irrigated water supplies (Qin et  al., 2020). Negative impacts on 
hydropower production due to changes in the seasonality of snowmelt 
have also been documented (Kopytkovskiy et al., 2015).
0.5-meter water equivalent (m w.e.) per year compared to an average 
of 0.33 m w.e. yr–1 in 1950–2000. This volume of mass loss is the 
highest since the start of the entire observation period (very high confidence) (Zemp et  al., 2015; Zemp et  al., 2019; Hugonnet et  al., 
2021) (also see Douville et al. (2021; Fox-Kemper et al. (2021; Gulev 
et  al. (2021) for more details). Regional estimates of glacier mass 
balance are also mostly negative (Dussaillant et al., 2019; Menounos 
et al., 2019; Zemp et al., 2019; Douville et al., 2021; Fox-Kemper et al., 
2021; Hugonnet et al., 2021), except for West Kunlun, eastern Pamir 
and northern Karakoram (Brun et al., 2017; Lin et al., 2017; Berthier

and Brun, 2019). Changes in glacier metrics estimated in post-SROCC 
publications are summarised in Figure 4.5.
Regional and global decreasing trends in glacier mass loss are about 
linear until 1990, after which they accelerated, especially in western 
Canada, the USA, and the southern Andes (WGMS, 2017). There is a 
worldwide growth in the number, total area and total volume of glacial 
lakes by around 50% between 1990 to 2018 due to the global increase 
in glacier melt rate (Shugar et al., 2020) (Shugar et al., 2020) that can 
potentially increase risks of glacial lake outburst floods (GLOFs) with 
significant negative societal impacts (Ikeda et  al., 2016). A drop in 
glacier runoff has happened in the regions where the glaciers have 
already passed their peak water stage, for example, in the Canadian 
Rocky Mountains, European Alps, tropical Andes and North Caucasus 
(Bard et al., 2015; Hock et al., 2019b; Rets et al., 2020). There is medium

confidence that the accelerated melting of glaciers has negatively 
impacted glacier-supported irrigation systems worldwide (Buytaert 
et al., 2017; Nüsser and Schmidt, 2017; Xenarios et al., 2019). Varying 
impacts on hydropower production (Schaefli et al., 2019) and tourism 
industry in some places due to cryospheric changes have also been 
documented (Hoy et al., 2016; Steiger et al., 2019).
Permafrost changes mainly refer to changes in temperature and active 
layer thickness (ALT) (Hock et  al., 2019b; Fox-Kemper et  al., 2021; 
Gulev et  al., 2021). Permafrost temperature near the depth of zero 
annual temperature amplitude increased globally by 0.29 ± 0.12°C 
during 2007–2016, by 0.39 ± 0.15°C in the continuous permafrost 
and by 0.20 ± 0.10°C in the discontinuous permafrost (Biskaborn 
et  al., 2019). Thus, permafrost has been warming during the last 3–4 
decades (Romanovsky et  al., 2017) with a rate of 0.4°C–1.4°C per

decade throughout the Russian Arctic, 0.1°C–0.8°C per decade in 
Alaska and Arctic Canada during 2007–2016 (Biskaborn et  al., 2019) 
and 0.1°C–0.24°C per decade in the Tibetan plateau (Wu et al., 2015). 
The ALT has also been increasing in the European and Russian Arctic and 
high-mountain areas of Eurasia since the mid-1990s (Hock et al., 2019b; 
Fox-Kemper et al., 2021; Gulev et al., 2021). Unfortunately, unlike glaciers 
and snow, the lack of in situ observations on permafrost still cannot be 
compensated for by remote sensing. Still, some methodological progress 
on this front has been happening recently (Nitze et al., 2018).
There is high confidence that degradation of the cryospheric components 
is negatively affecting terrestrial ecosystems, infrastructure and 
settlements in the high-latitude and high-altitude areas (Fritz et al., 2017; 
Oliva and Fritz, 2018; Streletskiy et al., 2019). Similarly, communities in

the north polar regions and the ecosystems on which they depend for 
their livelihoods are at risk (Mustonen, 2015; Pecl et al., 2017; Mustonen 
and Lehtinen, 2020) (Figure 4.6).
In summary, the cryosphere is one of the most sensitive indicators of 
climate change. There is high confidence that cryospheric components

571
Elevation change
Mass balance change
Mass change
-1 0 -267 ± 16 GT  YR-1Kenai Peninsula (2)European Alps (4,5)Central Asia (1)
Asia South East (1)
Asia South W est (1)
Tropical Andes (3)EC:
EC:
EC:
EC:
EC:EC:EC:
EC:
EC:
EC:EC:
EC:EC:EC:EC:
EC:
EC:
EC:MC:
MC:
MC:
MC:
MC:
MC:MB:MB:MB:MB:MB:
MC:MC:MC:MC:
MC:MC:MC:
MC:
MC:
MC:
MC:
MC:MC:MC:-1 0 
-1 0 
-1 0 
-1 0 
-1 0 
-1 0 -1 0 -1 0 -1 0 -1 0 -1 0 -1 0 
-1 0 
-1 0 -1 0 -1 0 
-1 0 
-1 0 
-1 0 -1 0 
P: 2000–2019P: 2000–2019
P: 2000–2019
P: 2000–2019P: 2000–2019P: 2000–2019P: 2000–2019P: 2000–2019P: 2000–2019
P: 2000–2019 P: 2000–2019
P: 2000–2018P: 2000–2019
P: 2000–2016
P: 2000–2014
P: 2000–2018P: 2005–2014−0.34 ± 0.03-0.5 ± 0.04-0.68 ± 0.1 1
-0.24 ± 0.03 -0.23 ± 0.04
-9.6 ± 2.1
-0.56 ± 0.08
-6.9 ± 1.4
-0.16 ± 0.06
-4.6 ± 1.7
-0.53 ± 0.1 1
-0.9 ± 0.2
-0.42 ± 0.24
-1.0 ± 0.5-10.4 ± 1.9
-0.61 ± 0.16
-1.3 ± 0.4
-0.64 ± 0.16
-0.7 ± 0.2-1.7 ± 0.4

-1.02 ± 0.25
−1.7 ± 0.4
-0.62 ± 0.08
-0.74 ± 0.20
-0.69 ± 0.13
-.54 ± 0.38
-1.34 ± 0.24-35.5 ± 5.8
-0.37 ± 0.03
-0.21 ± 0.06
-10.5 ± 1.7
-7.0 ± 2.1
-1.03 ± 0.06
-9.4 ± 1.4−30.6 ± 4.8
-0.77 ± 0.06
-26.5 ± 4.3
-0.62 ± 0.1 1
-7.6 ± 1.7
-0.91 ± 0.08
-66.7 ± 10.9
-0.94 ± 0.12
-3.84 ± 0.50
-0.84 ± 0.12
-0.75 ± 0.23
-20.7 ± 4.1
-21.9 ± 5.8EC:
EC:MC:
MC:-1 0 -1 0 
P: 2000–2019-0.85 ± 0.23
-0.7 ± 0.02
-0.2 ± 0.04
-20.9± 4.9
Global T otal
for 2000–2019 (1)Arctic Canada North (1)
Arctic Canada South (1)
Western Canada & US A(1)
Alaska (1)
Southern Andes (1,3)Greenland Periphery (1)
Svalbard (1,6)
Iceland (1)Scandinavia (1)
Central Europe (1)Russian Arctic (1)
North Asia (1)
Caucasus & Middle East (1)High Mountain Asia
Low Latitudes (1)
New Zealand (1)
Antarctic Periphery (1)
Mass change rateMB = Mass balance (M W .E. YR-1)EC = Elevation change (M YR-1)
EC:-0.46 ± 0.02 MC = Mass change rate for 2000–2019 66 GT  YR-135 GT  YR-12 GT  YR-1= 95% confidence intervals
P = Period

(1,2,3,4,5,6) = Publications reference detailed in caption text
Figure  4.5 |   Global and regional estimates of changes in glacier characteristics (elevation, m yr–1; mass Gt yr–1, mass balance, m.w.e. yr–1) and 95% 
confidence intervals of the estimates. Results are taken from the post-SROCC publications, which are labelled in the chart titles as 1 – (Hugonnet et al., 2021); 2 – (Yang 
et al., 2020); 3 – (Dussaillant et al., 2019); 4 – (Davaze et al., 2020); 5 – (Sommer et al., 2020); 6 – (Schuler et al., 2020).
(glaciers, snow, permafrost) are melting or thawing since the end of 
the 20th and beginning of the 21st century. Widespread cryospheric 
changes are affecting humans and ecosystems in mid-to-high latitudes 
and the high-mountain regions (high confidence). These changes are 
already impacting irrigation, hydropower, water supply, cultural and 
other services provided by the cryosphere, and populations depending

on ice, snow and permafrost.4.2.3 Observed Changes in Streamflow
AR5 (Jiménez Cisneros et al., 2014) concluded with medium evidence 
and high agreement that trends in annual streamflow have generally 
followed observed changes in regional precipitation and temperature 
since the 1950s. AR6 WGI (Eyring et  al., 2021; Gulev et  al., 2021) 
(12.4.5) conclude with medium confidence that anthropogenic climate 
change has altered local and regional streamflow in various parts of 
the world, but with no clear signal in the global mean.
Between the 1950s and 2010s, stream flows showed decreasing 
trends in parts of western and central Africa, eastern Asia, southern 
Europe, western North America and eastern Australia, and increasing

572trends in northern Asia, northern Europe, and northern and eastern 
North America (Dai, 2016; Gudmundsson et al., 2017; Gudmundsson 
et al., 2019; Li et al., 2020b; Masseroni et al., 2020). Significant spatial 
heterogeneity is also found in streamflow changes at the regional 
scale. Significant declines occurred at 11% of stations and significant 
increases at 4% of stations, with most decreases occurring in southern 
Canada (Bonsal et al., 2019). An increasing trend (1950–2010) is found 
in the northern region, mainly due to climate warming. Mixed trends 
are found in other regions.
The spatial differences in annual mean streamflow trends around 
the world are influenced by climatic factors, particularly changes in precipitation and evaporation (Zang and Liu, 2013; Greve et al., 2014; 
Hannaford, 2015; Ficklin et  al., 2018), as well as by anthropogenic 
forcing (Gudmundsson et al., 2016; 2017; 2021).. Other factors (e.g.,

land use change and CO 2 effects on vegetation) dominate in some 
areas, especially dryland regions (Berghuijs et  al., 2017b). Human 
activities can reduce runoff through water withdrawal and land use 
changes (Zaherpour et  al., 2018; Sun et  al., 2019a; Vicente-Serrano 
et  al., 2019), and human regulation of streamflows via impounding 
reservoirs can also play a major role (Hodgkins et al., 2019).
Streamflow trends are attributed to varying combinations of climate 
change and direct human influence through water and land use in Map of selected observed impacts on cultural water uses of Indigenous Peoples of the cryosphere
Quelcaya pastoralistsSaami
Hazard: Winter temperature increase; 
summer precipitation increase
Water Impact: Harder and deeper 
snow; increased ice formation; river 
flooding and wet ground
Human Impact: Pasture degradation; 
impacts on herding and winter feed
(Forbes et al. 2019; Rasmus et al. 
2020)Jagshung pastoralists
Hazard:  Temperature increas e

Water Impact: Glacier melt
Human Impact: Pasture loss to 
inundation; loss of livestock 
(Nyima and Hoping, 2019)
Dokpa
Hazard: T emperature increas e
Water Impact: Decreasing snowfall
loss of sheep (Ingty , 2017)
Gurung
Hazard: Te mperature increase
Water Impact: Decreasing snowcover; 
increasing snowmelt
Human Impact: Deterioration of 
culturally-significant water sources; 
changes in camp locations and routes 
(Gentle and Thwaites, 2016)Manangi 
Hazard: T emperature increase;  
precipitation increase
Water Impact: Glacier retreat; 
decreased permanent snowcover
Human Impact: Spiritual loss due to 
declining snowcover
(Konchar et al., 2015)Aymara
Water Impact: Glacier loss
Human Impact: Degradation of 
pastures; outmigration
(Yager et al. 2019)Kashechewan
First Nation
Water Impact: Flooding
Human Impact: Negative ef fects on 
culturally-significant species
(Khalafzai et al. 2019)Inuit

Hazard: T emperature increase;
increased precipitation
Water Impact: Changing ice 
conditions; early snowmelt
Human Impact: Tr ail access
(Ford et al. 2019); decline of culturally 
significant species
(Cunsolo, et al. 2020)Alaskan Natives
Hazard: T emperature Increas e
Water Impact: Permafrost melt; 
thinning ice; temperature increase 
freshwater lakes
Human Impact: Negative ef fects
on availability and access to 
(Norton-Smith et al. 2016)
Hazard:  Temperature increase;  
rainfall reduction; increasing rainfall 
variability
Water Impact: Decreased snow
and ice
Human Impact: Decreasing access 
to culturally-significant water sources; 
deterioration of pastures
(Postigo 2020)
Figure 4.6 |  Map of selected observed impacts on cultural water uses of Indigenous Peoples of the cryosphere. Map location is approximate; text boxes provide

names of the Indigenous Peoples whose cultural water uses have been impacted by climate change; changed climate variable; impact on water; and specific climate impact on 
cultural water use (Section 4.3.7).

573different basins worldwide, with conclusions on the relative contribution 
of climatic and anthropogenic factors sometimes depending on the 
methodology (Dey and Mishra, 2017). Precipitation explains over 80% 
of the changes in discharge of large rivers from 1950 to 2010 in northern 
Asia and northern Europe, where the impact of human activities is 
relatively limited (Li et al., 2020b). In northwest Europe, precipitation 
and evaporation changes explain many observed trends in streamflow 
(Vicente-Serrano et al., 2019). In several polar areas in northern Europe 
(e.g., Finland), North America (e.g., British Columbia in Canada) and 
Siberia, many studies reported increased winter streamflow primarily 
due to climate warming, for instance, more rainfall instead of snowfall 
and more glacier runoff in the winter period (e.g., Bonsal et al., 2020) 
(Section 4.2.2). A similar phenomenon of the earlier snowmelt runoff is

also found in North America during 1960–2014 (Dudley et al., 2017). 
Thus, climate drivers largely explain changes in the average and 
maximum runoff of predominantly snow-fed rivers (Yang et al., 2015a; 
Bring et al., 2016; Tananaev et al., 2016; Frolova et al., 2017b; Ficklin 
et al., 2018; Magritsky et al., 2018; Rets et al., 2018).
In contrast, in southwestern Europe, land cover changes and increased 
water demands by irrigation are the main drivers of streamflow 
reduction (Vicente-Serrano et  al., 2019) (Section  4.3.1). In addition, 
the human intervention also contributed to the increase of the winter 
streamflow due to the release of water in the winter season for 
hydropower generation in large rivers in the northern regions (Rawlins 
et al., 2021). In some regions, the impact of human activities on runoff 
and streamflow outplays the climate factors, for example, in some 
typical catchments with area near to or less than 15000 km2 in China 
(Zhai and Tao, 2017).

Shi et  al. (2019) found that in 40 major basins worldwide, both 
climatic and direct human impact contribute to observed flow 
changes to varying degrees. Climate change or variability is the main 
contributor to changes in basin-scale trends for 75% of rivers, while 
direct human effects on streamflow dominate for 25%. However, this 
does not consider attribution of the climate drivers to anthropogenic 
forcing. Using time series of low, mean and high river flows from 7250 
observatories around the world (1971–2010) and global hydrological 
models (GHMs) driven by Earth System Model (ESM) simulations with 
and without anthropogenic forcing of climate change, Gudmundsson 
et  al. (2021) also found direct human influence to have a relatively 
small impact on global patterns of streamflow trends. Gudmundsson 
et al. (2021) further identified anthropogenic climate change as a causal 
driver of the global pattern of recent trends in mean and extreme river

flow (Figure 4.7). Overall, the sign of observed trends and simulations 
accounting for human influence on the climate system was found to 
be consistent for decreased mean flows in western and eastern North 
America, southern Europe, northeast South America and the Indian sub-
continent, and increased flows in northern Europe. Similar conclusions 
were drawn for low and high flows, except for the Indian sub-continent. 
However, in some regions, the observed trend was opposite to that 
simulated with anthropogenic climate forcing. Thus, human water and 
land use alone did not explain the observed pattern of trends.
Although there are different observational and simulated runoff 
and streamflow data sets (e.g., Global Runoff Data Centre, GRDC), 
it is still challenging to obtain and update long-term river discharge records in several regions, particularly Africa, South and East Asia (Dai, 
2016). When observed data are scarce, hydrological models are used

to detect trends in runoff and streamflow. However, simulations of 
streamflow can differ between models depending on their structures 
and parametrisations, contributing to uncertainties for trend detection, 
especially when considering human intervention (e.g., Caillouet et al., 
2017; Hattermann et al., 2017; Smith et al., 2019b; Telteu et al., 2021).
In summary, both climate change and human activities influence the 
magnitude and direction of change in runoff and streamflow. There are no 
clear trends of changing streamflow on the global level. However, trends 
emerge on a regional level (a general increasing trend in the northern 
higher latitude region and mixed trend in the rest of the word) (high 
confidence). Climatic factors contribute to these trends in most basins 
(high confidence). They are more important than direct human influence 
in a larger share of major global basins (medium confidence), although 
direct human influence dominates in some (medium confidence).

Overall, anthropogenic climate change is attributed as a driver to the 
global pattern of change in streamflow (medium confidence).
4.2.4 Observed Changes in Floods
AR6 WGI Chapter 11 (Seneviratne et  al., 2021) assessed with high 
confidence the increase in the extreme precipitation and associated 
increase in the frequency and magnitude of river floods. However, 
there is low confidence in changes in the river flooding regionally, 
which is strongly dependent upon complex catchment characteristics 
and land use patterns. SROCC (Hock et al., 2019b) summarised with 
high confidence that changes in the cryosphere have led to changes in 
frequency, magnitude and location of rain-on-snow floods, snowmelt 
floods and glacier-related floods.
There is high confidence that the frequency and magnitude of river 
floods have changed in the past several decades in some regions 
mentioned below (and in WGI 11.5.2; SM4.1) with impacts across

human and natural systems (Section  4.3). A global flood database 
based on in situ measurement and satellite remote-sensing during 
1985–2015 show that floods have increased 4-fold and 2.5-fold in the 
tropics and northern mid-latitudes, respectively (Najibi and Devineni, 
2018). Estimates of flood exposure using satellite-derived inundation 
area and high-resolution population data showed a 20–24% increase 
during 2000–2018 (Tellman et al., 2021). Analyses of in situ streamflow 
measurement showed both increases and decreases in the frequency 
of river floods for 1960–2010 in Europe (Berghuijs et  al., 2017a; 
Blöschl et al., 2019a) and the USA (Berghuijs et al., 2017a), an overall 
increase in China, Brazil and Australia (Berghuijs et al., 2017a) but 
decrease in some areas in the Mediterranean (Tramblay et al., 2019) 
and southern Australia (Ishak et al., 2013; Do et al., 2017). Warming 
in the last 40–60 years has led to a 1–10-d earlier per decade spring

flood occurrence depending on the location (the most frequent being 
2–4 d per decade) (high confidence) (Yang L. et  al., 2015; Blöschl 
et al., 2017; Dudley et al., 2017; Solander et al., 2017; Rokaya et al., 
2018; Kireeva et al., 2020).

574
Pre−industrial climateObserved changes in river flows and attribution to externally forced climate change
(a) Percentage changes in flow in individual rivers 1971–2010
% -change decade-1
≤-20 -10 01 0 ≥20
Observed streamflow trend Simulated streamflow trend with human water and land use
Historical climate change(b)
Low Low Low
Mean Mean
High HighMean
High
Regional trend (% decade-1)
≤-10 -5 05 ≥10
Figure 4.7 |  Observed changes in river flows and attribution to externally forced climate change.
(a) Percentage changes in flow in individual rivers 1971 to 2010. Black box outlines show climatic regions with at least 80 gauging stations with almost complete daily 
observations over 1971–2010, using the SREX (Seneviratne et al., 2012) regions.
(b) Left column: observed regional median trends from 1971 to 2010 in SREX regions with at least 80 gauging stations with almost complete daily observations over that period.

Middle column: trends simulated by eight global hydrological models driven by four CMIP5 Earth System Models, with human water and land use from 1971 to 2020 and the 
pre-industrial control climate state. Right column: same as the middle column but with ESM-simulated climates from 1971 to 2010 with both anthropogenic forcings (greenhouse 
gases, aerosols and land use) and natural external forcings (solar variability and volcanic eruptions). Top row: low flows (annual 10th percentile). Middle row: mean flows. Bottom 
row: high flows (annual 90th percentile). Reproduced from Gudmundsson et al. (2021).

575Table 4.3 |  Selected major heavy-precipitation events from 2014 to 2021 that led to flooding and their impacts. Studies were selected for presentation based on the availability 
of scientific literature with impacts information and do not necessarily represent the most severe events. Impactful events are included even if not found to have a component 
attributable to climate change. This is not a systematic assessment of event attributions studies and their physical science conclusions. ‘Sign of influence’ indicates whether 
anthropogenic climate change was found to have made the event more or less likely, and ‘mechanism/magnitude of influence’ quantifies the change in likelihood and the processes 
or quantities involved.
Year Country/region ImpactAnthropogenic climate change in-
fluence on the likelihood of an event
Reference
Sign of 
influenceMechanism/magnitude of 
influence
2021Germany, Belgium, 
Luxembourg and 
neighbouring

countriesAt least 222 fatalities, substantial damage to transport and 
communications infrastructure and houses, severe disruption to 
businesses and livelihoods.,IncreaseOne-day rainfall intensity 
increased by 3–19%, the 
likelihood of event increased by a 
factor between 1.2 and 9.Kreienkamp et al. (2021)
2019Canada (Ottawa)Thousands of people evac uated, extended states of emergency, and 
about $200 mil lion in insured lossesIncreaseSpring maximum 30-d rainfall 
accumulation in 2019 was 
three times as likely with 
anthropogenic forcing.Kirchmeier-Young et al. 
(2021)
Southern ChinaOver 6 million people across several southern China provinces 
were affected by heavy rains, floods and landslides. These extremes 
caused at least 91 deaths, collapsed over 19,000 houses, damaged 
around 83,000 houses and affected 419,400 ha of crops (China 
Ministry of Emergency Man agement 2020). The direct economic 
loss was estimated to be more than 20 billion RMB (equivalent to

3 billion USD)DecreaseAnthropogenic forcings have 
reduced the likelihood of heavy 
precipitation in southern China 
like the 2019 March–July event 
by about 60%.Li et al. (2021b)
2018USA (Mid-Atlantic) One fatality, $12 million damages Increase 1.1 to 2.3 times more likely Winter et al. (2020)
Central western 
ChinaPersistent heavy rain led to floods, landslides and house collapse 
affecting 2.9 million people. The direct economic loss of over USD 
1.3 billion.Decrease~47% reduction in the 
probabilityZhang et al. (2020b)
Northwestern ChinaExtreme flooding in the Upper Yellow River basin affected about 
1.4 million people and led to 30 deaths and disappearances.Decrease 34% reduction in the probability Ji et al. (2020)
Japan237 fatalities, more than 6000 buildings destroyed by floods and 
landslidesIncrease 7% increase in total precipitation Kawase et al. (2020)
Australia (Tasmania) $100 million in insurance claims Unknown Unknown Tozer et al. (2020)

2017PeruWidespread flooding and landslides affected 1.7 million people, 
177 fatalities, estimated total damage of $3.1 billionIncrease At least 1.5 times more likely Christidis et al. (2019)
Uruguay and BrazilDirect economic loss in Brazil of USD 102 million, displacement of 
more than 3500 people in UruguayIncreaseAt least double, with a most likely 
increase of about fivefoldde Abreu et al. (2019)
North-East 
BangladeshFlash flood affected ~850,000 households, ~220,000 ha of nearly 
harvestable Boro rice damaged. Crop failure contributed to a record 
30% rice price hike compared to the previous year.IncreaseDoubled the likelihood of the 
2017 pre-monsoon extreme 6-d 
rainfall eventRimi et al. (2019)
China7.8 million people affected 34 fatalities, about 0.8 million people 
displaced, 605,000 hectares of crops affected, 116,000 hectares 
without harvest. 32,000 houses collapsed, 41,000 were severely 
damaged. Direct economic loss 24.12 billion Chinese Yuan (~ USD

3.6 billion)IncreaseDoubled the probability from 
0.6% to 1.2%Sun et al. (2019b)
2016South ChinaWidespread severe flooding, waterlogging, and landslides in the 
Yangtze–Huai region.Increase1.5-fold (0.6 to 4.7) increase in 
the probabilitySun and Miao (2018)
China (Wuhan) 237 fatalities, 93 people missing, at least USD 22 billion in damage Increase Approximately 60% of the risk Zhou et al. (2018a)
China (Yangtze 
River)Direct economic loss of about USD 10 billion IncreaseIncreased probability by 38% 
(± 21%)Yuan et al. (2018)
AustraliaFlooding and wild weather impacted some agriculture and power 
generation.None Minimal Hope et al. (2018)
2015 India (Chennai) City declared a disaster area. Damages estimated as $3 billion. None Nonevan Oldenborgh et al. 
(2017a)
2014 Indonesia (Jakarta)26 reported deaths, thousands of buildings flooded, much 
infrastructure damaged. Losses up to USD 384 millionUnclear2-d rain event approximately 
2.4 times more likely compared 
to 1900, but cause not

576
(d) Population exposed to river flooding (risk)(c) Population distribution (exposure)(a) Flood water (hazard)Risk of historical (1961–2005) and projected (2051–2070) river flooding
Low
<5Moderate
5–50High
50–100Very high
>100Population
density
(persons/km2)Low
>5Moderate
5–50High
density
(persons/km2)Flood water
depth (metres)
Vulnerability (b)
Hazards (a)
Exposure (c)Risk (d)Low
<1Moderate
1–5High
5–10Very high
>10
Projected
with
adaptationHistorical Projected
Historical Projected
HistoricalProjected
with no
adaptation
(b) Flood protection standard (vulnerability)
Flood
protection level
(Return period)
Low
>100Moderate
50–100High
5–50Very high
<5

577Figure 4.8 | 
(a) Modelled mean global fluvial flood water depth (Tanoue et al., 2016; Tanoue et al., 2021) based on a land surface model and a river and inundation model driven by reanalysis 
climate forcing of five CMIP5 GCMs (metres). The annual maximum daily river water was allocated along elevations, and inundation depth was calculated for each year and 
averaged for the target period. 
(b) Local flood protection standard (return period) at sub-country scale (Scussolini et al., 2016) based on published reports and documents, websites and personal communications 
with experts. Note that the vulnerability of this map reflects local flood protection such as complex infrastructure and does not fully reflect the other source of vulnerabilities, 
including exposure.
(c) Population distribution per 30 arc second grid cell (Klein Goldewijk et al., 2010; Klein Goldewijk et al., 2011).

(d) Population exposed to flood (number of people where inundation occurs) per 30 arc-second grid cell. Population under inundation depth > 0 m (a) was counted when the 
return period of annual maximum daily river water exceeds the flood protection standard (c) calculated by the authors. All values are averages for the period 1958–2010 for the 
past and 2050–2070 for the future.
Between 1970 to 2019, 44% of all disasters and 31% of all economic 
losses were flood related (WMO, 2021). Observed flood risks changes 
in recent decades are often caused by human factors such as increased 
urbanisation and population growth rather than climate change 
alone (Tramblay et al., 2019). There is medium confidence that flood 
vulnerability varies among various regions and countries (Jongman 
et al., 2012; Scussolini et al., 2016; Tanoue et al., 2016) (Figure 4.8), 
reflecting differences in GDP , severity and characteristics of hazard and

political and social conditions (Rufat et al., 2015). Flood vulnerability 
has decreased with economic development in many regions, while 
increased exposure has elevated risk in some places (Mechler, 2016; 
Tanoue et al., 2016). Global annual mean expected damage considering 
the current flood protection standard is estimated to be USD 54 million 
under the climate of 1976–2005 and unevenly distributed (Alfieri et al., 
2017). Similar estimation using different models shows an increase of 
flood exposure in the past (USD 31 million for 1971–1990 and USD 
45 million for 1991–2010 without population change as fixed in 2010) 
(Tanoue et al., 2016) (Section 4.7.5).
The link between rainfall and flooding is complex. While observed 
increases in extreme precipitation have increased the frequency and 
magnitude of pluvial floods and river floods in some regions, floods 
could decrease in some regions due to other factors. These factors

could include soil wetness condition, cryospheric change, land cover 
change and river system management, adaptation measures or water 
usage within the river basin (WGI FAQ8.2). For example, in the USA 
and Europe, a study indicated that major (e.g., 25–100-year return 
period) floods did not show significant long-term trends (Hodgkins 
et  al., 2019). Nevertheless, anthropogenic climate change increased 
the likelihood of a number of major heavy precipitation events and 
floods that resulted in disastrous impacts in southern and eastern 
Asia, Europe, North America and South America (Table  4.3) (high 
confidence). Davenport et al. (2021) demonstrated that anthropogenic 
changes in precipitation extremes had contributed one third of the 
cost of flood damages (from 1988 to 2017) in the USA. Anthropogenic 
climate change has altered 64% (eight out of 22 events increased, 
eight decreased) of floods events with significant losses and damages

during 2010–2013 (Hirabayashi et  al., 2021a). Gudmundsson et  al. 
(2021) attributed observed change in extreme river flow trends to 
anthropogenic climate change (Section  4.2.3). Although there is 
growing evidence on the effects of anthropogenic climate change 
on each event, given the relatively poor regional coverage and high 
model uncertainty, there is low confidence in the attribution of human-
induced climate change to flood change on the global scale.In snow-dominated regions, 1~10 d earlier spring floods per decade 
due to warmer temperature are reported for the last decades (high 
confidence), such as in Europe (Morán-Tejeda et al., 2014; Kormann 
et  al., 2015; Matti et  al., 2016; Vormoor et  al., 2016; Blöschl et  al., 
2017), the European part of Russia (Frolova et al., 2017a; Frolova et al., 
2017b; Kireeva et al., 2020), Canada (Yang L. et al., 2015; Burn et al., 
2016; Rokaya et al., 2018) and the USA (Mallakpour and Villarini, 2015; 
Solander et al., 2017).

There is a knowledge gap in how ice-related floods, including glacier-
related and ice-jam floods, respond to ongoing climate change. Despite 
the increase in the number of glacial lake studies (Wang and Zhou, 
2017; Harrison et al., 2018; Begam and Sen, 2019; Bolch et al., 2019), 
changes in the frequency of occurrence of glacier-related floods 
associated with climate change remain unclear (medium confidence). 
Studies show that the compound occurrence of high surges and high 
river discharge has increased in some regions (WGI Chapter 11), but 
few studies quantify changes and impacts. Increases in precipitation 
from tropical cyclones (WGI Chapter 11) and associated high tide 
are expected to exacerbate coastal flooding. However, more studies 
are required to quantify their impacts. In addition, limitations in the 
duration of data hinder the assessment of trends in low-likelihood 
high-impact flooding (WGI BOX 11.2).
In summary, the frequency and magnitude of river floods have

changed in the past several decades with high regional variations 
(high confidence). Anthropogenic climate change has increased the 
likelihood of extreme precipitation events and the associated increase 
in the frequency and magnitude of river floods (high confidence). There 
is high confidence that the warming in the last 40–60 years has led 
to a maximum of 10 days earlier spring floods per decade, shifts in 
timing and magnitude of ice-jam floods and changes in frequency and 
magnitude of snowmelt floods.

5784.2.5 Observed Changes in Droughts
There are different types of droughts, and they are interconnected in 
terms of processes (Douville et  al., 2021). Meteorological droughts  
(periods of persistent low precipitation) propagate over time into 
deficits in soil moisture, streamflow and water storage, leading to a 
reduction in water supply (hydrological drought). Increased atmospheric 
evaporative demand increases plant water stress, leading to agricultural 
and ecological drought.
Hydrological drought can result in shortages of drinking water and 
cause substantial economic damages. Agricultural drought threatens 
food production through crop damage and yield decreases (e.g., Section  4.3.1) (high confidence) and consequent economic impacts 
(Table  4.4). For example, drought in India in 2014 was reported to 
have led to an estimated USD 30 billion in losses (Ward and Makhija, 
2018). Ecological drought increases the risks of wildfire (Table 4.4).

Cascading effects of droughts can include health issues triggered by a 
lack of sanitation (Section 4.3.3); can cause human displacements and 
loss of social ties, sense of place and cultural identity; and migration 
to unsafe settlements (medium confidence) (Serdeczny et al., 2017) 
(Section  4.3.7). Between 1970 and 2019, only 7% of all disaster 
events were drought-related, yet they contributed disproportionately 
to 34% of disaster-related death, mostly in Africa (WMO, 2021). 
Nevertheless, IK, TK and LK have increased drought resilience among 
crop and livestock farmers, for example, in South Africa (Muyambo 
(d) Drought risk
Risk
IndexCurrent global drought risk
averages for period 1901–2010
Exposure (c)Risk (d)Desert or cold region
No data
Exposure
Index(c) Drought exposure
Low
(<0.25)Moderate
(0.25–0.5)High
(0.5–0.75)Very high
(0.75–1.0)Hazard
Index(a) Drought hazard
Low
(0.75–1.0)
Low

(0.75–1.0)(b) Drought vulnerability
IndexLow
Figure 4.9 |  Current global drought risk and its components.
(a) Drought hazard computed for the events between 1901 and 2010 by the probability of exceedance the median of global severe precipitation deficits, using precipitation data 
from the Global Precipitation Climatology Centre (GPCC) for 1901–2010.
(b) Drought vulnerability is derived from an arithmetic composite model combining social, economic and infrastructural factors proposed by the United Nations International 
Strategy for Disaster Risk Reduction (UNISDR, 2004).
(c) Drought exposure computed at the sub-national level with the non-compensatory Data Envelopment Analysis (DEA) model (Cook et al., 2014).

(d) Drought risk based on the above components of hazard, vulnerability and exposure, scored on a scale of 0 (lowest risk) to 1 (highest risk) with the lowest and highest hazard, 
exposure and vulnerability (Carrão et al., 2016).

579et al., 2017), Uganda (Mfitumukiza et al., 2020) and India (Patel et al., 
2020) (Section 4.8.4).
When hazard, vulnerability and exposure are considered together, 
drought risk is lower for sparsely populated regions, such as tundra 
and tropical forests, and higher for populated areas and intensive 
crop and livestock farming regions, such as southern and central Asia, 
southeastern South America, central Europe and the southeastern 
USA (Figure  4.9). Dynamics in exposure and vulnerability are rarely 
addressed (Jurgilevich et al., 2017; Hagenlocher et al., 2019). Quantifying 
economic vulnerability to drought in terms of damages as a percentage 
of exposed GDP , Formetta and Feyen (2019) show a disproportionate 
burden of drought impact on low-income countries, but with a clear 
decrease in global economic drought vulnerability between 1980–1989 
and 2007–2016, including a convergence between lower-income and

higher-income countries due to stronger vulnerability reduction in 
less-developed countries. Nevertheless, during 2007–2016, economic 
vulnerability to drought was twice as high in lower-income countries 
compared to higher-income countries (Formetta and Feyen, 2019).
AR6 WGI (Douville et  al., 2021; Seneviratne et  al., 2021) found that 
increasing agricultural and ecological droughts trends are more evident 
than increasing trends in meteorological drought in several regions due 
to increased evaporative demand. Therefore, WGI concluded with high 
confidence that the increased frequency and the severity of agricultural/
ecological droughts over the last decades in the Mediterranean and 
western North America can be attributed to anthropogenic warming.
In addition, there is high confidence in anthropogenic influence on 
increased meteorological drought in southwestern Australia and 
medium confidence that recent drying and severe droughts in southern

Africa and southwestern South America can be attributed to human 
influence. Increased agricultural/ecological and (or) meteorological and 
(or) hydrological drought is also seen with either medium confidence 
or high confidence in the trend but with low confidence on attribution 
to anthropogenic climate change in western, northeastern and central 
Africa; central, eastern and southern Asia; eastern Australia; southern 
and northeastern South America and the South American monsoon 
region; and western and central Europe. Finally, decreased drought in 
one or more categories is seen with medium confidence in western 
and eastern Siberia; northern and central Australia; southeastern South 
America; central North America and northern Europe, but with low 
confidence in attribution to anthropogenic influence, except in northern 
Europe, where anthropogenic influence on decreased meteorological 
drought is assessed with medium confidence.

Major drought events worldwide have had substantial societal and 
ecological impacts, including reduced crop yields, shortages of drinking 
water, wildfires causing deaths of people and very large numbers of 
animals, impacting the habitats of threatened species, and widespread 
economic losses (Table 4.4, Cross-Chapter Box DISASTER in Chapter 
4). In addition, anthropogenic climate change was found to have 
increased the likelihood or severity of most such events examined in 
event attribution studies.
Although long-term drought trends are clearer for agricultural or 
ecological drought compared to meteorological droughts (Douville et  al., 2021; Seneviratne et  al., 2021), most attribution studies for 
individual extreme events focus on meteorological (precipitation) 
drought and sometimes also consider temperature anomalies. A 
complete examination of drought relevant to societal impacts often 
requires consideration of hydrological and agricultural drought, so

extreme event attribution conclusions relating to precipitation alone 
may not fully capture the processes leading to societal effects. There 
is, therefore, a critical knowledge gap in the attribution of changes in 
drought indicators more closely related to societal impacts such as soil 
moisture and the availability of fresh water supplies.
In summary, droughts can have substantial societal impacts (virtually 
certain), and agricultural and ecological drought conditions in particular 
have become more frequent and severe in many parts of the world but 
less frequent and severe in some others (high confidence). Drought-
induced economic losses relative to GDP are approximately twice as 
high in lower-income countries compared to higher-income countries, 
although the gap has narrowed since the 1980s, and at the global 
scale there is a decreasing trend of economic vulnerability to drought 
(medium confidence). Nevertheless, anthropogenic climate change has

contributed to the increased likelihood or severity of drought events in 
many parts of the world, causing reduced agricultural yields, drinking 
water shortages for millions of people, increased wildfire risk, loss of 
lives of humans and other species and loss of billions of dollars of 
economic damages (medium confidence).
4.2.6 Observed Changes in Groundwater
AR5 concluded that the extent to which groundwater abstractions 
are affected by climate change is not well known due to the lack of 
long-term observational data (Jiménez Cisneros et  al., 2014). AR6 
(Douville et  al., 2021) confirmed that, despite considerable progress 
since AR5, limitations in the spatio-temporal coverage of groundwater 
monitoring networks, abstraction data and numerical representations of 
groundwater recharge processes continue to constrain understanding of 
climate change impacts on groundwater.
Globally, groundwater use has societal and economic benefits, providing

a critical buffer against precipitation variability. Groundwater irrigation 
has ensured food security, livelihood support and poverty alleviation, 
for example, in India (Sekhri, 2014), Bangladesh (Salem et al., 2018) 
and sub-Saharan Africa (Taylor et al., 2013a; Cuthbert et al., 2019b). 
Groundwater is a safe drinking water source during natural hazard-
induced disasters (Richts and Vrba, 2016). However, groundwater over-
exploitation leads to the attenuation of societal benefits, including 
reduced agricultural production (Asoka and Mishra, 2020; Jain et al., 
2021), decrease in adaptive capacity of communities (Blakeslee et al., 
2020) and water quality deterioration (Mas-Pla and Menció, 2019). 
Loss of traditional water systems based on groundwater, such as 
foggara in Tunisia (Mokadem et al., 2018), qanat in Pakistan (Mustafa 
and Usman Qazi, 2008), aflaj in Oman (Remmington, 2018) and spring 
boxes in the Himalayas (Kumar and Sen, 2018), also leads to loss of

cultural values for local communities.
Even though global groundwater abstraction (789 ± 30 km3 yr−1) is 
just about 6% of the annual recharge (~13,466 km3) (Hanasaki et al.,

580Table 4.4 |  Selected major drought events from 2013 to 2020 and their societal impact. Studies were selected for presentation based on the availability scientific literature impacts 
information and do not necessarily represent the most severe events.Impactful events are included even if not found to have a component attributable to climate change. This is not 
a systematic assessment of event attributions studies and their physical science conclusions. ‘Sign of influence’ indicates whether anthropogenic climate change was found to have 
made the event more or less likely, and ‘mechanism/magnitude of influence’ quantifies the change in likelihood and the processes or quantities involved.
YearCountry/
regionImpactInfluence of anthropogenic climate change on the 
likelihood of an eventReference
Sign of in-
fluenceMechanism/magnitude of influence
2019/2020 AustraliaWildfires burning ~97,000 km2 across southern and

eastern Australia; 34 human fatalities; 5900 buildings 
destroyed; millions of people affected by hazardous 
air quality; between 0.5 and 1.5 billion wild animals 
and tens of thousands of livestock killed; at least 
30% of habitat affected for seventy taxa, including 
21 already listed as threatened with extinction, over 
USD 110 billion financial lossIncreaseExtreme high temperatures causing drying of fuel. 
The likelihood of extreme heat at least doubled 
due to the long-term warming trend, and the 
likelihood of Fire Weather Index as severe or 
worse as observed in 2019/2020 by at least 30%, 
despite no attributable increase in meteorological 
(precipitation) drought.van Oldenborgh 
et al. (2020); 
Ward et al. (2020); 
Haque et al. 
(2021)
2019Western Cape, 
South AfricaWa ter supply was reduced to 20% of capacity in 
January 2018. Agricultural yields in 2019 declined 
by 25%.IncreaseAnthropogenic greenhouse forcing at least 
doubled the likelihood of drought levels seen in

2015–2019, offsetting anthropogenic aerosol 
forcing.Kam et al. (2021)
Yunnan, 
southwestern 
ChinaWater scarcity affected nearly 7 million residents and 
re sulted in crop failure over at least 1.35 × 104 km2 
cropland. More than 94% of the total area in the 
province was drought-stricken, and around 2 million 
people faced drinking water shortages, with a direct 
economic loss of about 6.56 billion RMB.IncreaseAnthropogenic influence increased the risk of 2019 
March–June hot and dry extremes over Yunnan 
province in southwestern China by 123–157% and 
13–23%, respectively.Wang et al. 
(2021b)
Southwestern 
ChinaOver 640,100 hectares of crops with rice, corn and 
potatoes were extensively damaged. Over 100 rivers 
and 180 reservoirs dried out. Over 824,000 people 
and 566,000 head of livestock experienced a severe 
lack of drinking water, with a direct economic loss of 
2.81 billion Chinese yuan (USD 400 million).IncreaseAnthropogenic forcing has likely increased

the likelihood of the May–June 2019 severe 
low-precipitation event in southwestern China by 
approximately 1.4 to 6 times.Lu et al. (2021)
South ChinaA lightning-caused forest fire in Muli County killed 31 
firefighters and burned about 30 ha of forest.IncreaseAnthropogenic global warming increased the 
weather-related risk of extreme wildfire by 
7.2 times. In addition, the El Niño event increased 
risk by 3.6 times.Du et al. (2021)
Middle and 
lower reaches 
of the Yangtze 
River, ChinaReduced agriculture productivity and increased load 
on power system supplies and transportations, and 
on human healthDecreaseAnthropogenic forcing reduced the probability of 
rainfall amount in the extended rainy winter of 
2018/2019 by ~19%, but exerted no influence on 
the excessive rainy days.Hu et al. (2021)
2018 South ChinaShrinking reservoirs, water shortages. Area and yield 
for early rice reduced by 350 thousand hectares and

1.28 million tons relative to 2017IncreaseLikelihood increased by 17 times in the 
HadGEM3-A model. However, the event did not 
occur without human influence in the CAM5 
model.Zhang et al. 
(2020)
China (Beijing)A record 145 consecutive dry days (CDD), severe 
drought, increased risk of wildfiresIncreaseThe likelihood of the record 145 CDD was 
increased by between 1.29 and 2.09 times by 
anthropogenic climate change and between 1.43 
and 4.59 times by combining the La Niña event 
and a weak Arctic polar vortex.Du et al. (2021)
2017USA (Northern 
Great Plains)“billion-dollar disaster”; widespread wildfires (one 
of Montana’s worst wildfire seasons on record) 
compromised water resources, destruction of 
property, livestock sell-offs, reduced agricultural 
production, agricultural losses of USD 2.5 billionIncrease1.5 times more likely due to increased ET (minimal 
anthropogenic impact on precipitation)Hoell et al. (2019)
East AfricaExtensive drought across Tanzania, Ethiopia, Kenya

and Somalia contributed to extreme food insecurity 
approaching near-famine conditions.Increase Likelihood doubled Funk et al. (2019)
2016 Southern AfricaMillions of people were affected by famine, disease 
and water shortages. In addition, a 9-million-tonne 
cereal deficit resulted in 26 million people in need of 
humanitarian assistance.IncreaseAnthropogenic climate change likely increased the 
intensity of the 2015/2016 El Niño, and a drought 
of this severity would have been very unlikely 
(probability ~9%) in the pre-industrial climate.Funk et al. (2018)

581YearCountry/
Sign of in-
2016 BrazilTrês Marias, Sobradinho, and Itaparica reservoirs 
reached 5% of volume capacity. Ceará registered 
39 (of 153) reservoirs empty. Another 42 reached 
inactive volume; 96 (of 184) Ceará municipalities 
experienced water supply interruption.Not found Not foundMartins et al. 
(2018)
2016 ThailandSevere drought affected 41 Thai provinces, had 
devastating effects on major crops, such as rice 
and sugar cane, and incurred a total loss in the 
agricultural production of about half a billion USD.IncreaseThe record temperature of April 2016 in Thailand 
would not have occurred without the influence 
of both anthropogenic forcings and El Niño. 
Anthropogenic forcing has contributed to drier 
Aprils, but El Niño was the dominant cause of low 
rainfall.Christidis et al. 
(2018)
2015Washington

state, USAUSD 335 million loss for the agricultural industry IncreaseSnowpack drought resulted from exceedingly high 
temperatures despite normal precipitationFosu et al. (2016)
2014 São Paulo, BrazilIn January 2015, the largest water supply system 
used for Sao Paulo, Cantareira, sank to a water 
volume of just 5% of capacity, and the number 
of people supplied fell from 8.8 million people to 
5.3 million people, with other systems taking over 
supplies for the remainder.No impactAnthropogenic climate change is not found to be a 
major influence on the hazard, whereas increasing 
population and water consumption increased 
vulnerability.Otto et al. (2015)
2014Southern Levant, 
SyriaWhile the extent to which the 2007/2008 drought in 
the Levant region destabilised the Syrian government 
was not clear, ‘there is no questioning the enormous 
toll this extreme event took on the region’s 
population. The movement of refugees from both 
the drought and war-affected regions into Jordan

and Lebanon ensured that the anomalously low 
precipitation in the winter of 2013/2014 amplified 
impacts on already complex water and food 
provisions.’IncreaseThe persistent drought in the 2014 rainy season
was unprecedented for the critical January–
February period in the observational
record, and was made ~45% more likely by 
anthropogenic climate change.Bergaoui et al. 
(2015)
2013–2014Mediterranean 
coastal Middle 
East, northward 
through Turkey 
and eastward 
through 
Kazakhstan, 
Uzbekistan and 
KyrgyzstanThe eastern (main) basin of the Aral Sea dried up for 
the first time in modern history.UnclearHigh western Pacific sea surface temperatures 
(SSTs) linked to drought in the Middle East and 
central-southwest Asia, and the SSTs in that region 
showed a strong warming trend.Barlow and Hoell 
(2015)
2014 East Africa Some isolated food security crises IncreaseAnthropogenic warming contributed to the 2014 
East African drought by increasing East African

and west Pacific temperatures, and increasing 
the gradient between standardised western and 
central Pacific SST, causing reduced rainfall, ET and 
soil moisture.Funk et al. (2018)
2018), a few hotspots of groundwater depletion have emerged at local 
to regional scales since the end of 20th century to the beginning of 
the 21st century due to intensive groundwater use for irrigation. The 
variability in groundwater storage is a function of human abstraction 
and natural recharge, which is in turn controlled by local geology 
(Green, 2016). In humid regions, precipitation influences recharge, 
and linear associations between precipitation and recharge are often 
observed (Kotchoni et al., 2019); for example, over humid locations in 
sub-Saharan Africa (Cuthbert et al., 2019b).
A global review (Bierkens and Wada, 2019) of groundwater storage 
changes highlights that estimates of depletion rates at the global scale are variable. These estimates range from approximately 113 to 510 km3

yr−1 and variation in estimates is due to methods and spatio-temporal 
scales considered (high confidence). Global hydrological models 
(Herbert and Döll, 2019) show that human-induced groundwater 
depletion at rates exceeding 20 mm yr–1 (2001–2010) is occurring in the 
major aquifers systems such as the High Plains and California Central 
Valley aquifers (USA), Arabian aquifer (Middle East), North-Western 
Sahara Aquifer System (North Africa), Indo-Gangetic Basin (India) and 
North China Plain (China) (high confidence). Groundwater depletion at 
lower rates (<10 mm yr–1) is taking place in the Amazon Basin (Brazil) 
and Mekong River Basin (South East Asia), primarily due to climate 
variability and change (high confidence). A global-scale analysis

582(Shamsudduha and Taylor, 2020) of GRACE satellite measurements 
(2002–2016) for the 37 world’s large aquifer systems reveals that 
trends in groundwater storage are mostly nonlinear and declines are 
not secular (high confidence). There are strong statistical associations 
between changes in groundwater storage and extreme annual 
precipitation from 1901 to 2016 in the Great Artesian Basin (Australia) 
and the California Central Valley aquifer (USA). Groundwater recharge 
of high magnitudes can be generated from intensive precipitation 
events. On the other hand, recharge can become more episodic, mostly 
in arid to semiarid locations (robust evidence, medium agreement). For 
example, in central Tanzania, seven rainfall events between 1955 and 
2010 generated 60% of total recharge (Taylor et al., 2013b). Similarly, 
in southern India (Asoka et  al., 2018) and the southwestern USA 
(Thomas et al., 2016), focused recharge via losses from ephemeral river

channels, overland flows, and floodwaters is documented (Cuthbert 
In cold regions, where snowmelt dominates the local hydrological 
processes, Irannezhad et  al. (2016) and Vincent et  al. (2019) show 
high recharge to aquifers from glacial meltwater, while Nygren et al. 
(2020) report a decrease in groundwater recharge due to a shift in 
main recharge period from spring (snowmelt) to winter (rainfall). In 
Finland, a sustained reduction (almost 100 mm in 100 years) of long-
term snow accumulation combined with early snowmelt has reduced 
spring recharge (Irannezhad et al., 2016) (medium confidence).
Data from ground-based long-term records in the Indo-Gangetic Basin 
reveals that sustainable groundwater supplies are constrained more 
by extensive contamination (e.g., arsenic, salinity) than depletion 
(MacDonald et  al., 2016). Many low-lying coastal aquifers are 
contaminated with increased salinity due to land use change, rising

sea levels, reduced stream flows and increased storm surge inundation 
(Lall et al., 2020). Nearly 26 million people are currently exposed to 
very high (>1500 μ S cm–1) salinity in shallow groundwater in coastal 
Bangladesh (Shamsudduha and Taylor, 2020).
Groundwater-dependent ecosystems (GDEs), such as terrestrial 
wetlands, stream ecosystems and estuarine and marine ecosystems 
(Kløve et al., 2014), support wetlands and biodiversity, provide water 
supply and baseflows to rivers, offer recreational services and help 
control floods (Rohde et al., 2017). Globally, 10–23% of the watersheds 
have reached the environmental flow limits due to groundwater 
pumping (de Graaf et al., 2019). A recent study of 4.2 million wells 
across the USA shows that induced groundwater recharge in nearly 
two thirds of these wells could reduce stream discharges, thereby 
threatening GDEs (Jasechko et al., 2021). Work (2020) found reduced

spring flow due to increased groundwater abstraction in 26 out of 56 
springs studied in Florida (USA). GDEs in semiarid and arid regions 
tend to have much longer groundwater response times and may be 
more resilient to climate change than those in humid areas where 
groundwater occurrence is mostly at shallow levels (Cuthbert et al., 
2019a; Opie et al., 2020). However, groundwater depletion impacts on 
the full range of ecosystem services remain understudied (Bierkens and 
Wada, 2019).
A better understanding of and incorporating subsurface storage 
dynamics into ESMs will improve climate–groundwater interactions under global warming (Condon et al., 2020). Long-term groundwater-
level monitoring data are of critical importance (Famiglietti, 2014) for 
understanding the sensitivity of recharge processes to climate variability 
and, more critically, calibration and validation of hydrological models 
(Goderniaux et al., 2015). GRACE satellite-derived groundwater storage

estimates provide important insights at a regional scale (Rodell et al., 
2018) but overlook more localised depletion or short-term storage 
gains. Low- and middle-income countries such as central Asia and sub-
Saharan Africa lack such monitoring networks, which is a significant 
knowledge gap.
In summary, groundwater storage has declined in many parts of the 
world, most notably since the beginning of the 21st century, due to 
the intensification of groundwater-fed irrigation (high confidence). 
Groundwater in aquifers across the tropics appears to be more resilient 
to climate change as enhanced recharge is observed to occur mostly 
episodically from intense precipitation and flooding events (robust 
evidence, medium agreement). In higher altitudes, warmer climates 
have altered groundwater regimes and may have led to reduced spring 
recharge due to reduced duration and snowmelt discharges (medium 
4.2.7 Observed Changes in Water Quality

and high agreement that climate change affected water quality, 
posing additional risks to drinking water quality and human health 
(Field et  al., 2014b), particularly due to increased eutrophication at 
higher temperatures or release of contaminants due to extreme floods 
(Jiménez Cisneros et al., 2014). In addition, SROCC (Hock et al., 2019b; 
Meredith et  al., 2019) assessed that glacier decline and permafrost 
degradation impacts water quality through increases in legacy 
contaminants (medium evidence, high agreement).
Warming temperatures and extreme weather events can potentially 
impact water quality (Khan et  al., 2015). Water quality can be 
compromised through algal blooms that affect the taste and odour of 
recreational and drinking water and can harbour toxins and pathogens 
(Khan et al., 2015). Warming directly affects thermal water regimes,

promoting harmful algal blooms (Li et  al., 2018; Noori et  al., 2018) 
(Section  4.3.5). Additionally, permafrost degradation leads to an 
increased flux of contaminants (MacMillan et al., 2015; Roberts et al., 
2017; Mu et al., 2019). The increased meltwater from glaciers (Zhang 
et  al., 2019) releases deposited contaminants and reduces water 
quality downstream (Zhang et al., 2017; Hock et al., 2019b).
Floods intensify the mixing of floodwater with wastewater and 
the redistribution of pollutants (Andrade et  al., 2018). In addition, 
contaminated floodwaters pose an immediate health risk through 
waterborne diseases (Huang et al., 2016b; Paterson et al., 2018; Setty 
et al., 2018). Wildfires, along with heavy rainfalls and floods, can also 
affect turbidity, which increases drinking water treatment challenges 
and has been linked to increases in gastrointestinal illness (de Roos et al., 
2017). Droughts reduce river dilution capacities and groundwater levels

(Wen et al., 2017) increasing the risk of groundwater contamination 
(Kløve et al., 2014). More generally, contaminated water diminishes its

583aesthetic value, compromising recreational activities, reducing tourism 
and property values and creating challenges for management and 
drinking water treatment (Eves and Wilkinson, 2014; Khan et al., 2015; 
Walters et al., 2015).
Between 2000 and 2010, ~10% of the global population faced 
adverse water quality issues (van Vliet et al., 2021). Adverse drinking 
water quality has been associated with extreme weather events 
in countries located in Asia, Africa and South and North America 
(Jagai et  al., 2015; Levy et  al., 2016; Huynh and Stringer, 2018; 
Leal Filho et al., 2018; Abedin et al., 2019) (medium evidence, high 
agreement). Dilution factors in 635 of 1049 US streams fell extremely 
low during drought conditions. Additionally, the safety threshold for 
endocrine-disrupting compound concentration exceeded in roughly 
a third of streams studied (Rice and Westerhoff, 2017). Natural acid 
rock drainage, which can potentially release toxic substances, has

experienced intensification in an alpine catchment of the Central 
Pyrenees due to climate change and severe droughts in the last 
decade. River length affected by natural acid drainage increased 
from 5 km in 1945 to 35 km in 2018 (Zarroca et al., 2021). Threefold 
increases in contaminants and fivefold increases in nutrients have 
been observed in water sources after wildfires (Khan et al., 2015). Due 
to permafrost thawing, the concentration of major ions, especially 
SO 42− in two high Arctic lakes, has rapidly increased up to 500% and 
340% during 2006–2016 and 2008–2016, respectively (Roberts et al., 
2017). The exports of dissolved organic carbon (DOC), particulate 
organic carbon and mercury in six Arctic rivers were reported to 
increase with significant deepening of active layers caused by climate 
warming during 1999–2015 (Mu et al., 2019). Sustained warming in 
Lake Tanganyika in Zambia during the last ∼ 150 years reduced lake

mixing, which has depressed algal production, shrunk the oxygenated 
benthic habitat by 38% and further reduced fish and mollusc yield 
(Cohen et  al., 2016). From 1994 to 2010, coastal benthos at King 
George Island in Antarctica have observed a remarkable shift primarily 
linked to ongoing climate warming and the increased sediment runoff 
triggered by glacier retreats (Sahade et al., 2015). The recovery time 
of macroinvertebrates from floods was found longer in cases of pre-
existing pollution problems (Smith et al., 2019a).
In summary, although climate-induced water quality degradation 
due to increases in water and surface temperatures or melting of 
the cryosphere has been observed (medium confidence), evidence of 
global-scale changes in water quality is limited because many studies 
are isolated and have limited regional coverage.
4.2.8 Observed Changes in Soil Erosion and Sediment 
Load
AR5 established potential impacts of climate change on soil erosion

and sediment loads in mountain regions with glacier melt (low to 
medium evidence) (Jiménez Cisneros et al., 2014). SRCCL (Olsson et al., 
2020) reported with high confidence that rainfall changes attributed 
to human-induced climate change have already intensified drivers of 
land degradation. Nonetheless, attributing land degradation to climate 
change alone is challenging because of the role of land management 
practices (medium evidence, high agreement).Climate change impacts soil erosion and sedimentation rates both 
directly from increasing rainfall or snowmelt intensity (Vanmaercke 
et al., 2014; Polyakov et al., 2017; Diodato et al., 2018; Golosov et al., 
2018; Li et al., 2020a; Li et al., 2020b) and indirectly from increasing 
wildfires (Gould et al., 2016; Langhans et al., 2016; DeLong et al., 2018), 
permafrost thawing (Schiefer et al., 2018; Lafrenière and Lamoureux, 
2019; Ward Jones et al., 2019) and vegetation cover changes (Micheletti

et al., 2015; Potemkina and Potemkin, 2015; Carrivick and Heckmann, 
2017; Beel et  al., 2018). In addition, accelerated soil erosion and 
sedimentation have severe societal impacts through land degradation, 
reduced soil productivity and water quality (Section 4.2.7), increased 
eutrophication and disturbance to aquatic ecosystems (Section 4.3.5), 
sedimentation of waterways and damage to infrastructure (Graves et al., 
2015; Issaka and Ashraf, 2017; Schellenberg et al., 2017; Hewett et al., 
2018; Panagos et al., 2018; Sartori et al., 2019) (medium confidence).
In the largest river basin of the Colombian Andes, regional climate change 
and land use activities (ploughing, grazing and deforestation) caused 
a 34% erosion rate increase over 10  years, with the anthropogenic 
soil erosion rate exceeding the climate-driven erosion rate (Restrepo 
and Escobar, 2018). Sedimentation increases due to soil erosion in 
mountainous regions burned by wildfires, as a result of warming and

altered precipitation, is documented with high confidence in the USA 
(Gould et al., 2016; DeLong et al., 2018), Australia (Nyman et al., 2015; 
Langhans et al., 2016), China (Cui et al., 2014) and Greece (Karamesouti 
et al., 2016) and can potentially damage downstream aquatic ecosystems 
(Section 4.3.5) and water quality (Section 4.2.7) (Cui et al., 2014; Murphy 
et al., 2015; Langhans et al., 2016) (medium confidence). In Australia, for 
instance, sediment yields from post-fire debris flows (113–294 t ha–1) 
are 2–3 orders of magnitude higher than annual background erosion 
rates from undisturbed forests (Nyman et al., 2015). The positive trend 
in sediment yield in small ponds in the semiarid southwestern USA over 
the last 90 years was not entirely related to the rainfall or runoff trends, 
but was a result of complex interaction between long-term changes in 
vegetation, soil and channel networks (Polyakov et al., 2017).

Regional climate changes (precipitation decrease) and human 
activities (landscape engineering, terracing, large-scale vegetation 
restoration, soil conservation) over the Loess Plateau (China) caused 
a distinct stepwise reduction in sediment loads from the upper-middle 
reach of the Yellow River, with 30% of the change related to climate 
change (Tian et al., 2019). Substantial increases in sediment flux were 
identified on the Tibetan Plateau (Li et al., 2020a; Li et al., 2021a), for 
example, the sediment load from the Tuotuohe headwater increased by 
135% from 1985–1997 to 1998–2016, mainly due to climate change 
(Li et al., 2020a). In 1986–2015, the sedimentation rate in dry valley 
bottoms of the Southern Russian Plain was two-2–5 times lower than 
in 1963–1986 due to the warming-induced surface runoff reduction 
during spring snowmelt (Golosov et  al., 2018). Declining erosion 
trends are primarily associated with soil conservation management

in northern Germany (Steinhoff-Knopp and Burkhard, 2018) and 
reforestation in southwestern China (Zhou et al., 2020).
The climate change impact on erosion and sediment load varies 
significantly over the world (Li et al., 2020b) (high confidence). There 
was a statistically significant correlation between sediment yield and air 
temperature for the non-Mediterranean region of western and central

584Europe (Vanmaercke et al., 2014) and northern Africa (Achite and Ouillon, 
2016). Still, such correlation is yet to be found for the other European 
rivers (Vanmaercke et  al., 2015). Increased sediment and particulate 
organic carbon fluxes in the Arctic regions are caused by permafrost 
warming (Schiefer et al., 2018; Lafrenière and Lamoureux, 2019; Ward 
Jones et al., 2019). Potemkina and Potemkin (2015) demonstrate that 
regional warming and permafrost degradation have contributed to an 
increased forested area over the last 40–70 years, reducing soil erosion 
in eastern Siberia. The sediment dynamics of small rivers in the eastern 
Italian Alps, depending on extreme floods, is sensitive to climate change 
(Rainato et  al., 2017). In the northeastern Italian Alps, precipitation 
change during 1986–2010 affected soil wetness conditions, influencing 
sediment load (Diodato et  al., 2018). Regional warming in northern

Africa (Algeria) dramatically changed river streamflow and increased 
sediment load over four decades (84% more every decade compared to 
the previous) (Achite and Ouillon, 2016).
A long-term global soil erosion monitoring network based on the unified 
methodological approach is needed to correctly evaluate erosion rates, 
detect their changes and attribute them to climate or other drivers.
In summary, in the areas with high human activity, factors other than 
climate have a more significant impact on soil erosion and sediment 
flux (high confidence). On the other hand, in natural conditions, for 
example, in high latitudes and high mountains, the influence of climate 
change on the acceleration of the erosion rate is observed (limited 
4.3 Observed Sectoral Impacts of Current 
Hydrological Changes
The intensification of the hydrological cycle due to anthropogenic 
climate change has multifaceted and severe impacts for cultural,

economic, social and political pathways. In this section, we assess 
burgeoning evidence since AR5 which shows that environmental 
quality, economic development and social well-being have been 
affected by climate-induced hydrological changes since many aspects 
of the economy, environment and society are dependent upon water 
resources. We advance previous IPCC reports by assessing evidence 
on the impacts of climate change-induced water insecurity for energy 
production (Section  4.3.2), urbanisation (Section  4.3.4), conflicts 
(Section 4.3.6), human mobility (Section 4.3.7) and cultural usage of 
water (Section 4.3.8).
Integrating qualitative and quantitative data, we show that it is evident 
that societies heightened exposure to water-induced disasters—such as 
floods and droughts—and other hydrological changes have increased 
vulnerability across most sectors and regions, with few exceptions. 
Through the assessment of literature relying on IK, we are also able

to present evidence on how observed changes impact particularly 
Indigenous Peoples, local communities and marginalised groups, such 
as women, people without social protections and minorities.
Importantly, we note that climate change-induced hydrological changes 
are, for most sectors, one of the several factors, often coupled with 
urbanisation, population growth and heightened economic disparities, that have increased societal vulnerability and required communities 
across the globe to alter their productive and cultural practices.
4.3.1 Observed Impacts on Agriculture
AR5 concluded with high confidence that agricultural production was 
negatively affected by climate change, with droughts singled out as a 
major driver of food insecurity. In contrast, evidence of floods on food 
production was limited (Porter et al., 2014).
Globally, 23% of croplands are irrigated, providing 34% of global calorie 
production. Of these lands, 68% experience blue water scarcity at the

least one month yr–1 and 37% up to five months yr–1. Such agricultural 
water scarcity is experienced in mostly drought-prone areas in low-
income countries (Rosa et al., 2020a). Approximately three quarters of 
the global harvested areas (~454 million hectares) experienced drought-
induced yield losses between 1983 and 2009, and the cumulative 
production losses corresponded to USD 166 billion (Kim et al., 2019). 
Globally, droughts affected both harvested areas and yields, with a 
reported cereal production loss of 9–10% due to weather extremes 
between 1964 and 2007. Yield losses were greater by about 7% during 
recent droughts (1985–2007) due to greater damage—reducing 
harvested area—compared to losses from earlier droughts (1964–1984), 
with 8–11% greater losses in high-income countries than in low-income 
ones (Lesk et al., 2016). Globally, between 1961 and 2006, it has been 
estimated that 25% yield loss occurred, with yield loss probability

increasing by 22% for maize, 9% for rice and 22% for soybean under 
drought conditions (Leng and Hall, 2019). Mean climate and climate 
extremes are responsible for 20–49% of yield anomalies variance, with 
18–45% of this variance attributable to droughts and heatwaves (Vogel 
et al., 2019). Drought has been singled out as a major driver of yield 
reductions globally (high confidence) (Lesk et  al., 2016; Meng et  al., 
2016; Zipper et al., 2016; Anderson et al., 2019; Leng and Hall, 2019).
Yields of major crops in semiarid regions, including the Mediterranean, 
sub-Saharan Africa, South Asia and Australia, are negatively affected 
by precipitation declines in the absence of irrigation (Iizumi et al., 
2018; Ray et al., 2019), but this trend is less evident in wetter regions 
(Iizumi et al., 2018). Precipitation and temperature changes reduced 
global mean yields of maize, wheat and soybeans by 4.1, 1.8 and 
4.5%, respectively (Iizumi et  al., 2018). Of the global rice yield

variability of ~32%, precipitation variability accounted for a larger 
share in drier South Asia than in wetter East and Southeast Asia 
(Ray et al., 2015). Between 1910 and 2014 agro-climatic conditions 
became more conducive to maize and soybean yield growth in the 
American Midwest due to increases in summer precipitation and 
cooling due to irrigation (Iizumi and Ramankutty, 2016; Mueller et al., 
2016) (Box 4.3). In Australia, between 1990 and 2015, the negative 
effects of reduced precipitation and rising temperature led to yield 
losses, but yield losses were partly avoided because of elevated 
CO 2 atmospheric concentration and technological advancements 
(Hochman et  al., 2017a). Overall, temperature-only effects are 
stronger in wetter regions like Europe and East and Southeast Asia, 
and precipitation-only effects are stronger in drier regions (Iizumi 
et al., 2018; Ray et al., 2019) (medium evidence, high agreement).

In Asia, the gap between rain-fed and irrigated maize yield widened

585from 5% in the 1980s to 10% in the 2000s (Meng et al., 2016). In 
North America, yields of maize and soybeans have increased (1958–
2007), yet meteorological drought has been associated with 13% of 
overall yield variability. However, yield variability was not a concern 
where irrigation is prevalent (Zipper et  al., 2016). However, when 
water scarcity has reduced irrigation, yields have been negatively 
impacted (Elias et  al., 2016). In Europe, yields have been affected 
negatively by droughts (Beillouin et  al., 2020), with losses tripling 
between 1964 and 2015 (Brás et al., 2021). In West Africa, between 
2000 and 2009, drought, among other altered climate conditions, 
led to millet and sorghum yield reductions between 10 and 20% 
and 5 and 15%, respectively (Sultan et al., 2019). Between 2006 and 
2016, droughts contributed to food insecurity and malnutrition in 
northern, eastern and southern Africa, Asia and the Pacific. In 36% of

these nations—mainly in Africa—where severe droughts occurred, 
undernourishment increased (Phalkey et  al., 2015; Cooper et  al., 
2019). An attribution study showed that anthropogenic emissions 
increased the chances of October–December droughts over the 
region by 1.4–4.3 times and resulted in below-average harvests in 
Zambia and South Africa (Nangombe et  al., 2020). Root crops, a 
staple in many tropics and subtropical countries, and vegetables are 
particularly prone to drought, leading to smaller fruits or crop failure 
(Daryanto et al., 2017; Bisbis et al., 2018). Livestock production has 
also been affected by changing seasonality, increasing frequency of 
drought, rising temperatures and vector-borne diseases and parasites 
through changes in the overall availability, as well as reduced 
nutritional value, of forage and feed crops (Varadan and Kumar, 
2014; Naqvi et al., 2015; Zougmoré et al., 2016; Henry et al., 2018; 
Godde et al., 2019) (medium confidence).

Floods have led to harvest failure and crop and fungal contamination 
(Liu et  al., 2013; Uyttendaele et  al., 2015). Globally, between 1980 
and 2018, excess soil moisture has reduced rice, maize, soybean and 
wheat yields between 7 and 12% (Borgomeo et al., 2020). Changes 
in groundwater storage and availability, which are affected by the 
intensity of irrigated agriculture, also negatively impacted crop yields 
and cropping patterns (Section 4.2.6, Box 4.3, 4.7.2). Moreover, extreme 
precipitation can lead to increased surface flooding, waterlogging, 
soil erosion and susceptibility to salinisation (high confidence). For 
example, in Bangladesh, in March and April 2017, floods affected 
220,000 ha of a nearly harvest-ready summer paddy crop and resulted 
in almost a 30% year-on-year increase in paddy prices. An attribution 
study of those pre-monsoon extreme rainfall events in Bangladesh 
concluded that anthropogenic climate change doubled the likelihood

of the extreme rainfall event (Rimi et  al., 2019). Moreover, floods, 
extreme weather events and cyclones have led to animal escapes and 
infrastructure damage in aquaculture (Beveridge et al., 2018; Islam and 
Hoq, 2018; Naskar et al., 2018; Lebel et al., 2020) (see Section 5.9.1).
Worldwide, the magnitudes of climate-induced water-related hazards 
and their impact on agriculture are differentiated across populations 
and genders (Sections 4.3.6; 4.8.3). Evidence shows that hydroclimatic 
factors pose high food insecurity risks to subsistence farmers, whose 
first and only source of livelihood is agriculture, and who are situated 
at low latitudes where the climate is hotter and drier (Shrestha and 
Nepal, 2016; Sujakhu et  al., 2016). Historically, they have been the 
most vulnerable to observed climate-induced hydrological changes (Savo et al., 2016). Indigenous and local communities, often heavily 
reliant on agriculture, have a wealth of knowledge about observed

changes. These are important because they shape farmers’ perceptions, 
which in turn shape the adaptation measures farmers will undertake 
(Caretta and Börjeson, 2015; Savo et al., 2016; Sujakhu et al., 2016; Su 
et al., 2017) (Section 4.8.4) (high confidence).
In summary, ongoing climate change in temperate climates has 
some positive impacts on agricultural production. In subtropical/
tropical climates, climate-induced hazards such as floods and 
droughts negatively impact agricultural production (high confidence). 
People living in deprivation and Indigenous Peoples have been 
disproportionally affected. They often rely on rain-fed agriculture in 
marginal areas with high exposure and high vulnerability to water-
related stress and low adaptive capacity (high confidence).
4.3.2 Observed Impacts on Energy and Industrial Water 
Use
and high agreement that hydropower negatively impacts freshwater

ecosystems. SROCC (IPCC, 2019a) concluded with medium confidence 
that climate change has led to both increases and decreases in annual/
seasonal water inputs to hydropower plants.
Water is a crucial input for hydroelectric and thermoelectric energy 
production, which together account for 94.7% of the world’s current 
electricity generation (Petroleum, 2020). Climate change impacts 
hydropower production through changes in precipitation, evaporation, 
volume and timing of runoff; and impacts cooling of thermoelectric power 
plants through reduced streamflow and increased water temperatures 
(Yalew et al., 2020). In addition, extreme weather events, like tropical 
cyclones, landslides and floods, damage energy infrastructure (MCTI, 
2020; Yalew et al., 2020), while high temperature and humidity increase 
the energy requirement for cooling (Maia-Silva et al., 2020).
With 1308 GW installed capacity in 2019, hydropower became the

world’s largest single source of renewable energy (IHA, 2020) (also see 
Figure  6.12, WGIII). While hydropower reduces emissions relative to 
fossil fuel-based energy production, hydropower reservoirs are being 
increasingly associated with GHG emissions caused by submergence 
and later re-emergence of vegetation under reservoirs due to water 
level fluctuations (Räsänen et  al., 2018; Song et  al., 2018; Maavara 
et  al., 2020). A recent global study concluded that reservoirs might 
emit more carbon than they bury, especially in the tropics (Keller et al., 
2021) (medium confidence).
In Ghana, between 1970 and 1990, rainfall variability accounted for 
21% of interannual variations in hydropower generation (Boadi and 
Owusu, 2019). In Brazil’s São Francisco River, following drought events 
in 2016 and 2017, hydropower plants operated with an average capacity 
factor of only 23% and 17%, respectively (de Jong et  al., 2018). In

Switzerland, increased glacier melt contributed to 3–4% of hydropower 
production since 1980 (Schaefli et al., 2019) (Section 4.2.2). In the USA, 
hydropower generation dropped by nearly 27% for every standard 
deviation increase in water scarcity. Equivalent social costs of loss in

586hydropower generation between 2001 and 2012 were approximately 
USD 330,000 (at 2015 value) per month for every power plant that 
experienced water scarcity (Eyer and Wichman, 2018). Globally, for the 
period 1981–2010, the utilisation rate of hydropower was reduced by 
5.2% during drought years compared to long-term average values (van 
Vliet et al., 2016a). Thus, there is a growing body of evidence of negative 
impacts of extreme events on hydropower production (high confidence).
Impacts of water scarcity on thermoelectric plants are more 
unequivocal than hydropower plants. For example, a scenario-based 
simulation study showed that 32% of the world’s coal-fired power 
plants (CFPPs) plants are currently experiencing water scarcity for 
at least five months or more in a year (Rosa et  al., 2020c). In the 
UK, almost 50% of freshwater thermal capacity is lost on extreme 
high-temperature days, causing losses in the range of average GBP

29–66 million yr–1. For ~20% of particularly vulnerable power plants, 
these losses could increase to GBP 66–95 million yr–1 annualised over 
30  years (Byers et  al., 2020). Globally, for the period 1981–2010, 
the utilisation rate of thermoelectric power was reduced by 3.8% 
during drought years compared to long-term average values (van 
Vliet et  al., 2016a), and none of the studies reported increases in 
thermoelectric power production as a consequence of climate change 
In the energy sector, a large number of studies document the impact of 
extreme climate events (e.g., droughts or extreme temperature days) 
on production of hydropower and thermoelectric power, yet there are 
limited studies that measure trends in energy production due to long-
term climate change. This remains a knowledge gap.
Mining in regions already vulnerable to climate change-induced water 
scarcity is under threat, leading some countries like El Salvador to

ban metal mining completely (Odell et al., 2018). Likewise, food and 
agro-processing companies are aware of water-related threats to their 
operations, with 77% of 35 publicly traded companies evaluated in 
2019 explicitly citing water as a risk factor in their annual reports, 
up from 59% in 2017 (CDP , 2018; CERES, 2019). Changes in water 
availability affect the mining, electrical, metal and agro-processing 
sectors (UNIDO, 2017; Odell et  al., 2018; Frost and Hua, 2019), but 
these impacts are less understood due to the lack of studies.
In summary, there is high confidence that climate change has had 
negative impacts on hydro and thermal power production globally due 
to droughts, changes in the seasonality of river flows, and increasing 
ambient water temperatures.
4.3.3 Observed Impacts on Water, Sanitation 
and Hygiene (WaSH)
AR5 showed that local changes in temperature and rainfall had altered 
the distribution of some water-related diseases (medium confidence),

and extreme weather events disrupt water supplies, impacting 
morbidity, mortality and mental health (very high confidence) (Field 
et  al., 2014b). In addition, melting and thawing of snow, ice and 
permafrost (Section 4.2.2) have also adversely impacted water quality, 
security and health (high confidence) (IPCC, 2019a) (Section 4.2.7).Literature since AR5 confirms that temperature, precipitation and 
extreme weather events are linked to increased incidence and 
outbreaks of water-related and neglected tropical diseases (Colón-
González et al., 2016; Levy et al., 2016; Azage et al., 2017; Harp et al., 
2021) (high confidence). For example, the rainy season in Senegal has 
been associated with an 84% increase in relative risk of childhood 
diarrhoea, and an additional wet day per week was associated with 
up to 2% increases in diarrhoeal disease in Mozambique (Thiam et al., 
2017; Horn et al., 2018). In Ecuador, increases of 1.5 cases of diarrhoea

per 1000 were associated with heavy rainfall after dry periods, while 
a decrease of one case per 1000 was associated with heavy rain after 
wet periods (Carlton et al., 2014). Floods have been associated with 
22% increases in relative risk of diarrhoea in China (Liu et al., 2018c). 
In addition, higher levels of faecal contamination of drinking water 
and hands (i.e., lack of WaSH) has been statistically significantly 
associated with increased child diarrhoea (Goddard et al., 2020).
In 2020, 2 billion people lacked access to uncontaminated water, while 
771 million lacked basic sanitation services, primarily in sub-Saharan 
Africa and rural areas (WHO and UNICEF , 2021). Even in high-income 
countries, poor-quality drinking water can be a health issue (Murphy 
et al., 2014). For example, in a sampled population in Canada, reported 
exposure to exposure routes for waterborne illness included 7% from 
private wells and 71.8% from municipal water (David et  al., 2014).

Drinking water treatment can be compromised by degraded source 
water quality and extreme weather events, including droughts, storms, 
ice storms and wildfires that overwhelm or cause infrastructure damage 
(Sherpa et al., 2014; Khan et al., 2015; Howard et al., 2016; White et al., 
2017) (high confidence). Adverse health effects are exacerbated due to 
the absence of adequate WaSH, particularly in poorer households (Khan 
et al., 2015; Kostyla et al., 2015; Cissé et al., 2016), WaSH infrastructure 
failure (Khan et  al., 2015; Wanda et  al., 2017) or inadequate WaSH 
facilities in emergency shelters (Alam and Rahman, 2014). For example, 
WaSH coverage decreased from 65% to 51% due to damage from floods 
and earthquakes in Malawi (Wanda et al., 2017). Loss of electricity also 
impacts WaSH service delivery (Cashman, 2014), and infrastructure 
damage caused by climate hazards may reverse progress on universal 
access to WaSH (Kohlitz et al., 2017) (limited evidence, high agreement).

In addition, wastewater outflows have been associated with a 13% 
increased relative risk of gastrointestinal illness through contaminated 
drinking water sources (Jagai et  al., 2015) (limited evidence, high 
agreement). Harmful algal blooms represent an emerging health risk, 
but lack of monitoring and reporting prevent risk exposure assessments 
(Carmichael and Boyer, 2016; Nichols et  al., 2018) (limited evidence, 
high agreement). Chemical contaminants (e.g., nitrates, arsenic) have 
been linked to non-communicable diseases, including neurological 
disorders, liver and kidney damage, and cancers (Jones Rena et  al., 
2016), and to some water-related diseases (e.g., schistosomiasis) (low 
Water insecurity and inadequate WaSH have been associated with 
increased disease risk (high confidence), stress and adverse mental 
health (limited evidence, medium agreement), food insecurity and 
adverse nutritional outcomes, and poor cognitive and birth outcomes

(limited evidence, medium agreement) (Workman and Ureksoy, 2017; 
Sclar et al., 2018; Boateng et al., 2020; Rosinger and Young, 2020; Wutich 
et  al., 2020). Climate-induced water scarcity and supply disruptions

587disproportionately impact women and girls. The necessity of water 
collection takes away time from income-generating activities, child care 
and education (Yadav and Lal, 2018; Schuster et  al., 2020) (medium 
evidence, medium agreement). Consumption of larger volumes of 
water is essential for healthy women during pregnancy, lactation and 
caregiving, which increases the amount of water that has to be fetched. 
Fetching of water is associated with increased risk of sexual abuse, 
demand for sexual favours at controlled water collection points, physical 
injuries (e.g., musculoskeletal or from animal attacks), domestic violence 
for not completing daily water-related domestic tasks (limited evidence, 
high agreement), and poorer maternal and child health (Mercer and 
Hanrahan, 2017; Pommells et al., 2018; Anwar et al., 2019; Collins et al., 
2019a; Geere and Hunter, 2020; Venkataramanan et al., 2020) (medium

evidence, high agreement). Menstrual hygiene management is a public 
health issue but poorly linked to climate change, despite relationships 
between lack of adequate WaSH, poor menstrual hygiene, and urinary 
tract infections (Ellis et al., 2016; Pouramin et al., 2020). Water insecurity 
also affects emotional, spiritual and cultural relationships that are often 
critical to Indigenous health (Wilson et al., 2019) (limited evidence, high 
agreement).
There are gaps in data on climate-driven water-related disease 
burden for both infectious and non-communicable diseases. Increased 
demands for water and WaSH services for infectious diseases, such as 
HIV/AIDS and COVID-19 (Box 4.4) exacerbate existing vulnerabilities 
and inequities (Stanley et  al., 2017; Armitage and Nellums, 2020a; 
Rodriguez-Lonebear et  al., 2020). Additionally, limited research has 
been undertaken to quantify the effects of climate-compromised WaSH 
on health and well-being.

In summary, WaSH-related household water insecurity and disease 
incidence are products of geography, politics, social and environmental 
determinants, vulnerability and climate change (Bardosh et al., 2017; 
Stoler et al., 2021).
4.3.4 Observed Impacts on Urban and Peri-Urban Sectors
All previous IPCC reports have focused on future water-related risks to 
urban areas due to climate change rather than documented observed 
impacts.
Climate extremes have profound implications for urban and peri-urban 
water management, particularly in an increasingly urbanised world (high 
confidence). Over half (54%) of the global population currently lives in 
cities (WWAP , 2019), and global urbanisation rates continue to increase 
across all SSPs (Jiang and O’Neill, 2017). Using observed station data 
for 217 urban areas worldwide, Mishra et al. (2015) noted that 17% of 
cities experienced statistically significant increases (p  value < 0.05) in

the frequency of daily precipitation extremes from 1973 to 2012 and 
hypothesised that such observed climate changes in urban areas were 
largely due to large-scale changes rather than local land cover changes.
Since AR5, factors such as rapid population growth, urbanisation, 
ageing infrastructure and changes in water use have also magnified 
climate risks, such as drought and flooding, and contributed to 
urban and peri-urban water insecurity (medium agreement, medium evidence) (Section 4.1.2). For example, despite an increase in flooding 
events from 1.1 flood events yr–1 (1986–2005) to five flood events 
yr–1 (2006–2016) in Ouagadougou (Burkina Faso), analyses of rainfall 
indices showed few have significant trends at a 5% level over the 
period 1961–2015 and that the generalised extreme value distribution 
fit the time series of annual maximum daily rainfall (Tazen et al., 2019). 
On the other hand, long-term annual variations of maximum hourly

precipitation in Shanghai (China) increased significantly during 1916–
2014, especially from 1981.
Advances in the attribution of extreme weather events have made 
it possible to determine the causal relationship between droughts, 
floods and climate change for some cities, particularly those with 
long hydro-meteorological records (Bader et  al., 2018; Otto et  al., 
2020). Attribution analysis shows that urbanisation contributed to the 
increase in both frequencies of local and abrupt heavy rainfall events 
in the city, at a rate of 1.5 and 1.8 10 yr–1, respectively (Liang and 
Ding, 2017). A multi-method attribution showed that the likelihood 
of prolonged rainfall deficit in Cape Town (South Africa) during 
2015–2017 was made more likely by a factor of 3.3 (1.4–6.4) due 
to anthropogenic climate change (Otto et  al., 2018). These results 
show that climate change has impacted the return time of extreme 
droughts in the Western Cape, exceeding the capacity of the existing

water supply system to cope (Otto et  al., 2018) (Box  9.4; 9.8.2). 
In Baton Rouge (USA), a rapid attribution study showed that the 
probability of an event such as the intense precipitation and flash 
flooding of August 2016 has increased by at least a factor of 1.4 
due to radiative forcing (USA) (van der Wiel et al., 2017). In Houston 
(USA), a study found that the combination of urbanisation and climate 
change nearly doubled peak discharge (84%) during Hurricane Harvey 
(August 2017), suggesting that land use change magnified the effects 
of climate change on catchment response to extreme precipitation 
events (Sebastian et  al., 2019) (14.4.3.1; Box  14.5 The Economic 
Consequences of Climate Change in North America, Cross-Chapter 
Box DISASTER in Chapter 4). According to a multi-method approach, 
the 2014/2015 drought event in Sao Paulo (Brazil) was more likely to 
have been driven by water use changes and population growth than

climate change (Otto et  al., 2015) (Cross-Chapter Box  DISASTER in 
Chapter 4).
The science of weather event attribution requires high-quality 
observational data and climate models that are currently available 
only in highly developed countries (Otto et  al., 2020). In addition, 
further research is necessary to determine the impacts of climate 
change on water-related extremes in the urban areas of developing 
countries (Bai et  al., 2018). For example, a combination of 
observational analysis and global coupled climate models showed 
that the 2015 flooding event in Chennai (India) could not be attributed 
to anthropogenic climate change, with the effects of that being 
relatively small in the region due to the impact of GHG increases 
being largely counteracted by those of aerosols (van Oldenborgh 
et  al., 2017a) (Section  4.2.5). Further research is also required to 
determine the impacts of climate change on water-related extremes

in informal settlements where vulnerability to water insecurity is high 
due to poverty, overcrowding, poor-quality housing and lack of basic 
infrastructure (Scovronick et al., 2015; Grasham et al., 2019; Williams 
et al., 2019; Satterthwaite et al., 2020).

588Cross-Chapter Box DISASTER | Disasters as the Public Face of Climate Change
Authors: Aditi Mukherji (India, Chapter 4), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Caroline Zickgraf (Contributing 
Author), Paulina Aldunce (Chile, Chapter 7), Liliana Raquel Miranda Sara (Peru, Chapter 12), William Solecki, (USA, Chapter 17), Friederike 
Otto (UK, WGI), François Gemenne (France, WGI), Martina Angela Caretta (Sweden, Chapter 4);, Richard Jones (UK, WGI); Richard Betts (UK, 
Chapter 4), Maarten van Aalst (the Netherlands, Chapter 16), Jakob Zscheischler (Switzerland), Kris Murray (UK), Mauro E. González (Chile).
Some extreme weather events are increasing in frequency and (or) severity as a result of climate change (Seneviratne et al., 2021) (high 
confidence). These include extreme rainfall events (Roxy et al., 2017; Myhre et al., 2019; Tabari, 2020); extreme and prolonged heat leading

to catastrophic fires (Bowman et al., 2017; Krikken et al., 2019; van Oldenborgh et al., 2020); and more frequent and stronger cyclones/
hurricanes and resulting extreme rainfall (Griego et al., 2020). These extreme events, coupled with high vulnerability and exposure in 
many parts of the world, turn into disasters and affect millions of people every year. New advances enable the detection and attribution 
of these extreme events to climate change (Otto et al., 2016; Seneviratne et al., 2021), with the most recent study saying that heavy rains 
leading to devastating floods in western Europe that captured the world’s attention in July 2021 were made more likely due to climate 
change (Kreienkamp et al., 2021). Most WGII chapters (this volume) report various extreme event-induced disasters and their societal 
impacts. This cross-chapter box brings together authors from WGI and WGII to emphasise that disasters following extreme events have

become the most visible and public face of climate change (Solecki and Rosenzweig, 2014). These disasters reflect immediate societal and 
political implications of rising risks (high confidence), but also provide windows of opportunity to raise awareness about climate change 
and to implement disaster-reduction policies and strategies (high confidence) (Albright, 2020; Boudet et al., 2020).
Here, we document eight catastrophic climate-related disasters that took place between 2017 and 2021. These disasters resulted in the 
loss of lives and livelihoods and had adverse impacts on biodiversity, health, infrastructure and the economy. These disasters provided 
important rallying points for discussions around climate change, equity and vulnerability in some cases. These disasters also offer valuable 
lessons about the role of effective climate change adaptation in managing disaster risks and the importance of Loss and Damage

mechanisms in global negotiation processes (Jongman et al., 2014; Mechler et al., 2014; Cutter and Gall, 2015).
Case 1. Compounded events and impacts on human systems: Cyclones Idai and Kenneth in Mozambique in 2019
While individual events alone can lead to major disasters, when several events occur in close spatial and temporal proximity, impacts 
get compounded, with catastrophic results (Zscheischler et al., 2018; Zscheischler et al., 2020). In March 2019, Cyclone Idai (category 
2) was the deadliest storm on record to strike the African continent, with the coastal city of Beira in Mozambique being particularly 
hard hit with at least 602 deaths (CRED, 2019; Zehra et al., 2019; Phiri et al., 2020). Nationally, Idai caused massive housing, water 
supply, drainage and sanitation destruction, but its impact extended to South Africa through disruption of the regional electricity grid

(Yalew et al., 2020). In April 2019, amidst heightened vulnerabilities in the aftermath of cyclone Idai, cyclone Kenneth (category 4) hit 
the country, affecting 254,750 people and destroying more than 45,000 homes (Kahn et al., 2019). These circumstances caused the 
rapid spread of cholera, which triggered a massive vaccination programme to control the epidemic (Kahn et al., 2019; Lequechane et al., 
2020). While there were no specific detection and attribution studies for Idai and Kenneth, overall, there is high confidence that the 
rainfall associated with tropical cyclones is more intense because of global warming. However, there remain significant uncertainties 
about the impact of climate change on the numbers and strength of tropical cyclones per se (Walsh et al., 2019; Zhang G. et al., 2020).
Case 2. COVID-19 as the compounding risk factor: Cyclone Amphan in India and Bangladesh, 2020

Cyclone Amphan hit coastal West Bengal and Bangladesh on 20 May 2020. It was the first supercyclone to form in the Bay of Bengal since 
1999 and one of the fiercest to hit West Bengal, India, in the last 100 years. The cyclone intensified from a cyclonic storm (category 1) to 
a supercyclone (category 5) in less than 36 hours (Balasubramanian and Chalamalla, 2020). Several hours before and on 20 May, extreme 
rain events resulted in heavy cumulative rainfall, flash flooding and landslides in several adjoining districts (Mishra and Vanganuru, 2020). 
As per the initial estimates, about 1600 km2 area in the mangrove forests of Sundarbans were damaged, and over 100 lives were lost. 
Earlier cyclones in the region have shown that impacts of these events are gendered (Roy, 2019). The cyclone damage was somewhat 
lessened due to the delta’s mangroves (Sen, 2020). The estimated damage was USD 13.5 billion. Cyclone Amphan was the largest source

of displacement in 2020, with 2.4 million displacements in India alone, of which 800,000 were pre-emptive evacuations by authorities

589(IDMC, 2020). Because it happened amidst the COVID-19 crisis, evacuation plans were constrained due to social distancing norms (Baidya 
et al., 2020). Social media played an important role in disseminating pre-cyclone warnings and information on post-cyclone relief work 
(Crayton et al., 2020; Poddar et al., 2020).
Case 3. Further exacerbating inequities in human systems: Hurricane Harvey, USA, 2017
Hurricane Harvey, a category 4 hurricane, made landfall on Texas and Louisiana in August 2017, causing catastrophic flooding and 80 deaths 
and inflicting $125 billion (2017 USD) in damage, of which $67 billion (2017 USD) was attributable to climate change (Frame et al., 2020). 
Several studies estimated the return period of the rainfall associated with this event and assessed that human-induced climate change 
increased the likelihood by a factor of approximately three using a combination of observations and climate models (Risser and Wehner,

2017; van Oldenborgh et al., 2017b). The impacts of Hurricane Harvey were exacerbated by extensive residential development in flood-prone 
locations. A study showed that urbanisation increased the probability of such extreme flood events several folds (Zhang W. et al., 2018) 
through the alteration of ground cover and disruption and redirection of water flow. Water quality in cities also deteriorated (Horney et al., 
2018; Landsman et al., 2019), and 85% of flooded land subsided at a rate of 5 mm yr–1 following the event (Miller and Shirzaei, 2019). 
Notably, the impacts of Harvey were unequally distributed along racial and social categories in the greater Houston area. Neighbourhoods 
with larger Black, Hispanic and disabled populations were the worst affected by the flooding following the storm and rainfall (Chakraborty 
et al., 2018; Chakraborty et al., 2019; Collins et al., 2019b). In addition, racial and ethnic disparities were shown to impact post-disaster needs,

ranging from household damage to mental health and recovery (Collins et al., 2019b; Flores et al., 2020; Griego et al., 2020).
Case 4. Impacts worsened due to sociocultural and political conditions: The “Coastal Niño” in Peru, 2017
The Coastal Niño event of 2017 led to extreme rainfall in Peru, which was made more likely by at least 1.5 times as compared to pre-
industrial times due to anthropogenic climate change and Coastal Niño (Christidis et al., 2019) and comparable to the El Niño events of 
1982–1983 and 1997–1998 (Poveda et al., 2020). This event showed evidence of larger anomalies in flood exposure (Muis et al., 2018; 
Christidis et al., 2019; Rodríguez-Morata et al., 2019) and sediment transport (Morera et al., 2017). In Peru, this Niño event led to USD 
6 to 9 billion of monetary losses, more than a million inhabitants were affected, 6614 km of roads were damaged, 326 bridges were

destroyed, 41,632 homes were damaged or became uninhabitable and 2150 schools and 726 health posts were damaged (French and 
Mechler, 2017; French et al., 2020), leaving half of the country in a state of emergency (Christidis et al., 2019). Furthermore, institutional 
and systemic sociocultural and political conditions at multiple levels significantly worsened disaster risk management which hampered 
response and recovery (French et al., 2020). Citizens and zero-order responders proved to be more effective and quicker than national 
disaster risk management response (Briones et al., 2019).
Case 5. Triggering institutional response for future preparedness: Mega-fires of Chile, 2017
The mega-fire that occurred in Chile in January 2017 had the highest severity recorded on the planet (CONAF , 2017), burning in three 
weeks an area close to 350,000 hectares in south-central Chile. These events have been associated with the prolonged ongoing drought

that has persisted for more than one decade and with the increase in heat waves (González et al., 2018; Miranda et al., 2020). This 
extreme drought and the total burned area of the last decades have been attributed to anthropogenic climate change in at least 25% 
and 20% of their severity, respectively (Boisier et al., 2016). The mega-fire of summer 2017 resulted in 11 deaths, more than 1500 houses 
burned and the destruction of the small town of Santa Olga. The smoke from these fires exposed 9.5 million people to air pollution, 
causing an estimated 76 premature deaths (Bowman et al., 2017; González et al., 2020). The direct costs incurred by the State exceeded 
USD 360  million (González et  al., 2020). The 2017 mega-fires led to a series of institutional responses such as management plans 
that include preventive forestry techniques, regulatory plans containing rural–urban interface areas, an emergency forest fire plan, and 
promotion of native species (González et al., 2020).

Case 6. Loss of human lives and biodiversity: Bushfires in Australia, 2019/2020
In the summer of 2019/2020, bushfires in Australia killed 417 people due to smoke and killed between 0.5 and 1.5 billion wild animals and 
tens of thousands of livestock (van Oldenborgh et al., 2020). These fires also destroyed approximately 5900 buildings and burnt 97,000 km2 of Cross-Chapter Box DISASTER (continued)

590vegetation, which provided habitat for 832 species of native vertebrate fauna. Seventy taxa had more than 30% of their habitat impacted, 
including 21 already identified as threatened with extinction (Ward et al., 2020). In addition, millions of people experienced levels of smoke 
20 times higher than the government-identified safe level. The year 2019 had been Australia’s warmest and driest year on record. In the 
summer of 2019/2020, the seasonal mean and mean maximum temperatures were the hottest by almost 1°C above the previous record. Eight 
of the 10 hottest days on record for national mean temperatures occurred in December 2019. While the prevailing weather conditions were 
strongly influenced by the Indian Ocean Dipole pressure pattern, with a contribution from weakly positive ENSO conditions in the Pacific, the 
fact that Australia is approximately 1°C warmer than the early 20th century demonstrates links to anthropogenic climate change. Eight climate

models using event attribution methodologies (comparison of simulations with present-day and pre-industrial forcings) indicates that 
anthropogenic climate change made the heat conditions of December 2019 more than twice as likely (van Oldenborgh et al., 2020).
Case 7. Improved preparedness reduced mortality: Heatwave in Europe, 2019
In 2019, Europe experienced several record-breaking heatwaves. In June, the first one featured record heat for that time in early summer, 
with temperatures of 6°C–10°C above normal over most of France and Germany, northern Spain, northern Italy, Switzerland, Austria 
and the Czech Republic (Climate, 2019). The second heatwave also resulted in all-time records for Belgium, Germany, Luxembourg, the 
Netherlands and the UK in July. Attribution studies (Vautard et al., 2020) demonstrated that these would have had extremely small odds

in the absence of human-induced climate change or would have been 1.5°C–3°C colder without human-induced climate change. This 
study concluded that state-of-the-art climate models underestimate the trends in local heat extremes compared to the observed trend. 
Since the 2003 heatwave, which resulted in tens of thousands of deaths across Europe, many European countries have adopted heatwave 
plans, including early warning systems. Therefore, mortality in 2019 was substantially lower than it might have been. Unfortunately, 
mortality is not registered systematically across Europe, and therefore, comprehensive analyses are missing. But even based on the 
countries that provide the numbers, more specifically France, Belgium and the Netherlands, the European heatwave of 2019 resulted in 
over 2500 deaths (CRED, 2019). Despite their deadliness and the fact that climate change increases the frequency, intensity and duration

of heatwaves globally (Perkins-Kirkpatrick and Lewis, 2020), heatwaves are not consistently reported in many countries (Harrington and 
Otto, 2020), rendering it currently impossible to estimate climate change impacts on lives and livelihoods comprehensively.
Case 8. Loss of human lives and property: Floods in Europe in 2021
From 12 to 15 July 2021, extreme rainfall in Germany, Belgium, Luxembourg and neighbouring countries led to severe flooding. The 
severe flooding was caused by very heavy rainfall over a period of 1–2 d, wet conditions prior to the event and local hydrological factors. 
The observed rainfall amounts in the Ahr/Erft region and the Belgian part of the Meuse catchment substantially exceeded previous 
records for observed rainfall. An attribution study (Kreienkamp et al., 2021) focused on the heavy rainfall rather than river discharge and

water levels, because sufficient hydrological data was not available, partly because hydrological monitoring systems were destroyed 
by the event. Considering a larger region of western Europe between the northern side of the Alps and the Netherlands, in any given 
location, one such event can be expected every 400 years on average in the current climate. The floods resulted in least 222 fatalities and 
substantial damage to houses, roads, communication infrastructure, motorways, railway lines and bridges.
Table Cross-Chapter Box DISASTER.1 |  Summarising impacts, losses and damages, displacement and climate change detection and attribution of these seven disaster 
case studies.
Name of the disaster 
eventImpacts, losses and damages; and displacement Climate change detection and attribution
Cyclones Idai and 
Kenneth, March and April 
2019, Mozambique, Africa254,750 affected people, and more than 45,000 houses were 
destroyed. Sparked cholera outbreaks that resulted in 6600 cases and

over 200 deaths. More than 500,000 people were displaced in 2019. 
As of 31 December 2019, more than 132,000 people were internally 
displaced in Mozambique (IDMC, 2020).There are no detection and attribution studies on Idai and Kenneth, 
but it is known that rainfall associated with tropical cyclones are now 
more intense because of global warming, but there remain significant 
uncertainties concerning changes in the number and strength of the 
cyclones themselves (Walsh et al., 2019; Zhang G. et al., 2020).
Cyclone Amphan, May 
2020, West Bengal, India 
and BangladeshAbout 1600 km2 area in the mangrove forests of Sundarbans were 
damaged. The city of Kolkata lost a substantial portion of its green 
cover due to Amphan. The estimated damage was USD 13.5 billion. 
Cyclone Amphan was the largest source of displacement in 2020, with 
2.4 million displacements in India and a similar number in Bangladesh. 
Out of these 2.4 million, roughly 800,000 were pre-emptive

evacuations or organised by the authorities (IDMC, 2020).The combined decline of both aerosols (due to COVID-19-related 
lockdowns) and clouds may have contributed to the increased sea 
surface temperature, further compounding the climate change-related 
warming of the oceans (Vinoj and Swain, 2020). However, there are no 
attribution studies on tropical cyclones in the Indian Ocean.Cross-Chapter Box DISASTER (continued)

591Name of the disaster 
Hurricane Harvey, 2017, 
USACatastrophic flooding and many deaths inflicted $125 billion (2017 
USD). In addition, economic costs due to the rainfall are estimated 
at $90 billion, of which $67 billion are attributed to climate change 
(Frame et al., 2020).Several attribution studies found that the rainfall associated with 
Harvey has increased by a factor of three, while intensity in rainfall 
and wind speed also increased due to human-induced climate change 
(Emanuel, 2017; Risser and Wehner, 2017; Patricola and Wehner, 2018; 
van Oldenborgh et al., 2020).
Coastal Niño 2017, PeruUSD 6–9 billion monetary losses with 114 deaths, 414 injuries and 
1.08 million inhabitants affected. In addition, 6614 km of improved 
roads were damaged, 326 bridges destroyed, 41,632 homes destroyed 
or uninhabitable, and 242,433 homes, 2150 schools and 726 health

centres damaged.Clear anthropogenic climate change fingerprint detected. For example, 
while the anomalously warm ocean favoured extreme rainfall of March 
2017 in Peru, the human influence was estimated to make such events 
at least 1.5 times more likely (Christidis et al., 2019).
Mega-fires in Chile, 
January 2017The mega-fire that occurred in Chile in January 2017 burned in three 
weeks an area close to 3500 km2 in south-central Chile. As a result, 
thousands of people were displaced.There is no attribution study on the fires in Chile (yet). Still, there is an 
increasing number of attribution studies on wildfires worldwide, finding 
that because climate change has increased the likelihood of extreme 
heat, which is part of the fire weather, the likelihood of wildfire weather 
conditions has increased too (Krikken et al., 2019; van Oldenborgh et al., 
2020).
Australian bushfires of 
2019/2020Killed 417 people due to smoke, and between 0.5 and 1.5 billion wild

animals and tens of thousands of livestock. Destroyed ~5900 buildings 
and burnt 97,000 km2 of vegetation that provided habitat for 832 
species of native vertebrate fauna.Anthropogenic climate change made the extreme heat condition of 
December 2019 more than twice as likely (van Oldenborgh et al., 2020).
Heatwaves of Europe, 
2019Record heat in several European countries, and deadliest global 
disaster of 2019, with over 2500 deaths (CRED, 2019)There have been many attribution studies on heatwaves in Europe, 
finding that human-induced climate change is increasing the frequency 
and intensity of heatwaves. In the case of 2019, the observed heat 
would have been extremely unlikely without climate change. The studies 
also find that climate models underestimate the increase in heat waves 
in Europe compared to observed trends (Vautard et al., 2020).
Floods in western Europe 
(Germany, Belgium), July 
2021Severe flooding resulting in at least 222 fatalities and substantial

damage to houses, roads, communication infrastructure, motorways, 
railway lines and bridges. Some communities were cut off for days due 
to road closures, inhibiting emergency responses, including evacuation.Climate change was found to have increased the intensity of the 
maximum 1-d rainfall event in the summer season in this large region 
by about 3–19% compared to a global climate 1.2°C cooler than at the 
present day. The increase was similar for the 2-d event. The likelihood 
of such an event today was found to have increased by a factor 
between 1.2 and 9 for both the 1-d and 2-d events in the large region 
(Kreienkamp et al., 2021).
Disaster risk reduction needs to be a central component of adaptation and mitigation for meeting Sustainable 
Development Goals and for a climate-resilient future
Disasters resulting from extreme events are increasingly experienced by a large section of human population (Hoegh-Guldberg et al.,

2018). Disasters expose inequalities in natural and managed systems and human systems as they disproportionately affect poor and 
marginalised communities like ethnic minorities, people of colour, Indigenous Peoples, women and children. Therefore, disaster risk 
reduction is fundamental for climate justice and climate resilient development (UNISDR, 2015). Far from being disconnected policy 
objectives, disaster risk reduction and climate change mitigation/adaptation are two sides of the same coin as recognised explicitly by the 
Paris Agreement and Sendai Framework of 2015. There can be no sustainable development without disaster risk reduction, as explicitly 
recognised by the SDGs of 2015. Furthermore, disaster events can increase awareness among citizens and provide a platform for all 
important stakeholders, including climate activists, to come together, and give a clarion call for the urgency of climate action.

In summary, disasters are a stark illustration of the potential for extreme weather events to impact people and other species. With the 
frequency, severity and (or) likelihood of several types of extreme weather increasing, disasters can increasingly be regarded as ‘the 
public face of climate change’ (high confidence). Detection and attribution studies make the climate change fingerprint of several types 
of disasters increasingly clear (high confidence). Moreover, existing vulnerabilities and exposures play an important role in turning 
extreme events into disasters, further exacerbating existing racial, gender and social inequalities (high confidence). Therefore, disaster risk 
reduction needs to be central to adaptation and mitigation efforts to meet the SDGs and the Paris Agreement for a climate-resilient future.Cross-Chapter Box DISASTER (continued)

592In summary, water-related hazards such as drought and flooding have 
been exacerbated by climate change in some cities (high confidence). 
Further research is necessary to determine the extent and nature of 
water-related climate change impacts in the urban areas of developing 
countries (high confidence).
4.3.5 Observed Impacts on Freshwater Ecosystems
The loss and degradation of freshwater ecosystems have been widely 
documented, and SRCCL assessed with medium confidence the loss of 
wetlands since the 1970s (Olsson et al., 2020).
The links between air and water temperatures and ecological processes 
in freshwater ecosystems are well recognised. Increasing temperatures 
affect wetlands by influencing biophysical processes, affecting feeding 
and breeding habits and species’ distribution ranges, including their 
ability to compete with others. Increased temperatures can also 
cause deoxygenation in the lower depths of the water columns and

throughout the entire water column if heating destabilises the water 
column. Under extreme heat, often associated with minimal rainfall or 
water flows, the drying of shallower areas and the migration or death 
of individual organisms can occur (Dell et al., 2014; Miller et al., 2014; 
Scheffers et al., 2016; Szekeres et al., 2016; Myers et al., 2017; FAO, 
2018a) (high confidence). A global systematic review of studies since 
2005 shows that climate change is a critical direct driver of freshwater 
ecosystems impacts through increasing temperatures or declining 
rainfall, for example, by causing physiological stress or death (thermal 
stress, dehydration or desiccation), limiting food supplies, or resulting 
in migration of animals to other feeding or breeding areas, and possibly 
increased competition with animals already present in those migrating 
locations {Diaz et  al. 2019; Dziba et  al. 2018}. Other drivers include

land use changes, water pollution, extraction of water, drainage and 
conversion, and invasive species, which to varying extents interact 
synergistically with climate change or are exacerbated due to climate 
change (Finlayson et al., 2017; Ramsar Convention, 2018).
The Global Wetland Outlook (Ramsar Convention, 2018) reported that 
between 1970 and 2015, the area of freshwater wetlands declined by 
approximately 35% (Davidson and Finlayson, 2018), with high levels of 
the overall percentage of threatened species recorded in Madagascar 
and Indian Ocean islands (43%); in Europe (36%); in the tropical Andes 
(35%); and New Zealand (41%) (Ramsar Convention, 2018). Where 
long-term data are available, only 13% of the wetlands recorded in 
and around the year 1700 remained by 2000. However, these data 
may overestimate the rate of loss (Davidson, 2014) (limited evidence, 
medium agreement). Many wetland-dependent species have seen a

long-term decline, with the Living Planet Index showing that 81% 
of populations of freshwater species are in decline and others being 
threatened by extinction (Davidson and Finlayson, 2018; Darrah et al., 
2019; Diaz et al., 2019) (high confidence).
Temperature changes lead to changes in the distribution patterns of 
freshwater species. Poleward and up-elevation range shifts due to 
warming temperatures tend to ultimately lead to reduced range sizes. 
Freshwater species in the tropics are particularly vulnerable (Jezkova and 
Wiens, 2016; Sheldon, 2019). Systematic shifts towards higher elevation and upstream were found for 32 stream fish species in France (Comte 
and Grenouillet, 2013). In North America, for the bull trout (Salvelinus 
confluentus) a reduction in the number of occupied sites was documented 
in a watershed in Montana (Eby et  al., 2014). Other impacts include 
disruption of seasonal movements of migratory waterbirds that regularly

visit freshwater ecosystems, with adverse impacts on their feeding and 
breeding (Finlayson et al., 2006; Bussière et al., 2015). Keystone species, 
such as the beaver (Caster Canadensis) in North America, have been 
moving into new areas as the vegetation structure has changed in 
response to higher temperatures enabling shrubs to establish in the Arctic 
and alpine tundra ecosystems (Jung et al., 2016). Increased occurrence 
and intensity of algal blooms have occurred due to the interactive effects 
of thermal extremes and low dissolved oxygen concentrations in water 
(Griffith and Gobler, 2020) (Section 4.2.7). A global review found that 
almost 90% of all studies reviewed documented a decline in salmonid 
populations in North America and Europe, and identified knowledge gaps 
elsewhere (Myers et al., 2017). Another review (Pecl et al., 2017) found 
declines in Atlantic salmon in Finland and poleward shift in coastal fish

species, while another review (Scheffers et al., 2016) noted hybridisation 
between freshwater species like invasive rainbow trout (Oncorhynchus 
mykiss) and native cutthroat trout (O. clarkia).
Lakes have been warming, as shown by an increasing trend of summer 
surface water temperatures between 1985 and 2009 of 0.34°C per 
decade (O’Reilly et al., 2015). However, responses of individual lakes to 
warming were very dependent on local characteristics (O’Reilly et al., 
2015), with warming enhancing the impacts of eutrophication in some 
instances (Sepulveda-Jauregui et al., 2018). For example, temperature 
increases led to lower oxygen concentrations in eutrophic coastal 
wetlands due to phytoplankton and microbial respiration (Jenny et al., 
2016) and stimulated algal blooms (Michalak, 2016) and affected the 
community structure of fish and other biotas (Mantyka-Pringle et al., 
2014; Poesch et al., 2016).
Rising temperatures have a strong impact in the arctic zone, where the

southern limit of permafrost is moving north and leading to changes 
in the landscape (Arp et al., 2016; Minayeva et al., 2018). Thawing of 
the permafrost leads to increased erosion and runoff and changes in 
the geomorphology and vegetation of arctic peatlands (Nilsson et al., 
2015; Sun et al., 2018b). Permafrost thawing has led to the expansion 
of lakes in the Tibetan Plateau (Li et al., 2014). As northern high-latitude 
peatlands store a large amount of carbon, permafrost thawing can 
increase methane and carbon dioxide emissions (Schuur et al., 2015; 
Moomaw et al., 2018). This represents a major gap in our understanding 
of the rates of change and their consequences for freshwater ecosystems.
The extent of past degradation due to multiple drivers is important, as 
climate change is expected to interact synergistically and cumulatively 
with these (Finlayson et al., 2006), exacerbate existing problems for 
wetland managers and potentially increase emissions from carbon-rich

wetland soils (Finlayson et al., 2017; Moomaw et al., 2018). Freshwater 
ecosystems are also under extreme pressure from changes in land use 
and water pollution, with climate change exacerbating these, such as 
the further decline of snow cover (DeBeer et al., 2016) and increased 
consumptive use of fresh water, and leading to the decline, and 
possibly extinction, of many freshwater-dependent populations (high 
confidence). Thus, differentiating between the impacts of multiple

593drivers is needed, especially given the synergistic and cumulative 
nature of such impacts, which remains a knowledge gap.
In summary, climate change is one of the key drivers of the loss and 
degradation of freshwater ecosystems and the unprecedented decline 
and extinction of many freshwater-dependent populations. The 
predominant key drivers are changes in land use and water pollution 
4.3.6 Observed Impacts on Water-Related Conflicts
According to AR5, violent conflict increases vulnerability to climate 
change (Field et  al., 2014a) (medium evidence, high agreement). 
Furthermore, the IPCC SRCCL (Hurlbert et  al., 2019) concluded with 
medium confidence that climatic stressors can exacerbate the negative 
impacts of conflict.
Since AR5, only a few studies focused specifically on the association 
between observed changes in the hydrological cycle linked to climate 
change and conflicts (Zografos et al., 2014; Dinar et al., 2015).  Some

studies associate conflicts with local abundance of water (Salehyan 
and Hendrix, 2014; Selby and Hoffmann, 2014; de Juan, 2015), mainly 
because of political mobilisation around abundant waters and the need 
for developing new rules of allocation among competing users. Others 
provide evidence that the increase in water availability in some areas 
compared with a decrease in other surrounding areas can affect the risk 
of a conflict in a region (de Juan, 2015) (low to medium confidence). 
However, the large majority acknowledges reduction of water availability 
due to climate change as having the potential to exacerbate tensions (de 
Stefano et al., 2017; Waha et al., 2017), especially in regions and within 
groups dependent on agriculture for food production (von Uexkull et al., 
2016; Koubi, 2019) (high confidence). Particularly representative is the 
case of Syria, where drought aggravated existing water and agricultural

insecurity (Kelley et al., 2015). However, whether drought caused civil 
unrest in Syria remains highly debated (Gleick, 2014; Kelley et al., 2017; 
Selby et al., 2017; Ash and Obradovich, 2019). Additionally, there is no 
consensus on the causal association between observed climate changes 
and conflict (Hsiang Solomon et  al., 2013; Burke et  al., 2015; Selby, 
2019). However, evidence suggests that changes in rainfall patterns 
amplify existing tensions (Abel et  al., 2019); examples include Syria, 
Iraq (Abbas et  al., 2016; von Lossow, 2016) and Yemen (Mohamed 
et  al., 2017) (medium confidence). There is also medium evidence  
that in some regions of Africa (e.g., Kenya, Democratic Republic of the 
Congo), there are links between observed water stress and individual 
attitude for participating in violence, particularly for the least resilient 
individuals (von Uexkull et al., 2020) (medium confidence). A reverse

association from conflict to climate impacts has also been observed 
(Buhaug, 2016). For example, conflict-affected societies cannot address 
climate-change impacts due to other associated vulnerabilities such as 
poverty, food insecurity and political instability.
For transboundary waters, the probability of inter-state conflict can 
both increase and decrease (Dinar et al., 2019) depending on climatic 
variables (e.g., less precipitation) and other socioeconomic and political 
factors, such as low levels of economic development and political 
marginalisation (Koubi, 2019). Climate change concerns also play a role in stimulating cooperative efforts, as in the case of the Ganges-
Brahmaputra-Meghna River Basin (Mirumachi, 2015; Link et al., 2016) 
(medium confidence). More generally, there is some evidence that 
when hydrological conditions change in transboundary river basins, 
formal agreements (e.g., water treaties or river basin organisations)

can enhance cooperation (de Stefano et al., 2017; Dinar et al., 2019) 
(medium evidence, high agreement). Still, more cooperation does 
not necessarily reduce the risk of conflict, especially when water 
variability increases beyond a certain threshold (low evidence, medium 
agreement) (Dinar et al., 2015; Dinar et al., 2019).
In summary, there is no consensus on the causal association between 
observed climate change and conflicts. Still, evidence exists that those 
tensions can be amplified depending on climatic variables and other 
concomitant socioeconomic and political factors.
4.3.7 Observed Impacts on Human Mobility and 
Migration
AR5 (Adger and Pulhin, 2014) found links between climate change 
and migration in general (medium evidence, high agreement), but 
provided no assessment of climate-induced hydrological changes and 
migration specifically. Likewise, SRCCL (Mirzabaev et al., 2019; Olsson 
et al., 2020) and SROCC (Hock et al., 2019b) noted that migration is

complex and that migration decisions and outcomes are influenced 
by a combination of social, demographic, economic, environmental 
and political factors and contexts (see Cross-Chapter Box MIGRATE in 
Chapter 7). This chapter confirms this evidence, focusing on climate-
induced hydrological changes.
Climate-induced hydrological changes can, through slow-onset (e.g., 
drought) or rapid-onset (e.g., flood) events, influence human mobility 
and migration through effects on the economy and livelihoods 
(Adger et al., 2018). There is medium confidence that climate-induced 
hydrological changes have affected bilateral migration (Backhaus 
et  al., 2015; Cattaneo and Peri, 2016; Falco et  al., 2019). However, 
there is medium evidence and low agreement on the effects on the 
movements of refugees globally (Missirian and Schlenker, 2017; Owain 
and Maslin, 2018; Abel et al., 2019; Schutte et al., 2021).
There is robust evidence that floods and droughts have, mainly through

adverse impacts on agriculture (Mastrorillo et  al., 2016; Nawrotzki 
and Bakhtsiyarava, 2017; Bergmann et  al., 2021; Zouabi, 2021) 
(Section  4.6.2), both increased and decreased the risk of temporary 
or permanent migration (Obokata et al., 2014; Afifi et al., 2016; Thiede 
et al., 2016; Murray-Tortarolo and Salgado, 2021; Wesselbaum, 2021). 
However, migration effects depend on the nature of the hydrological 
change, for example, whether it is a slow-onset or rapid-onset event 
(Kaczan and Orgill-Meyer, 2020), the perception of change (Koubi et al., 
2016; de Longueville et al., 2020) or the socioeconomic situation of 
the affected communities (Ocello et al., 2015; Afifi et al., 2016; Thiede 
et al., 2016) (robust evidence; medium agreement).
The Internal Displacement Monitoring Centre (IDMC) estimates that 
an average of 12 million new displacements happen each year due to 
droughts and floods alone. By the end of 2020, there were 7 million

594people displaced due to natural disasters, including drought and floods 
(IDMC, 2020). Furthermore, household water insecurity has also been 
singled out as a driver of migration, given its physical, mental health 
and socioeconomic effects (Stoler et al., 2021) (medium confidence).
More research is needed to understand better the contexts in which 
climate-induced hydrological changes affect the likelihood of migration 
or alter existing patterns (Obokata et al., 2014; Gray and Wise, 2016; 
Cattaneo et al., 2019).
In summary, climate-induced hydrological changes can increase 
and decrease the likelihood of migration (robust evidence, medium 
agreement). The outcome is determined mainly by the socioeconomic, 
political and environmental context (medium confidence).
4.3.8 Observed Impacts on the Cultural Water Uses 
of Indigenous Peoples, Local Communities and 
Traditional Peoples
AR5 concluded with high confidence that the livelihoods and cultural

practices of the diverse Indigenous Peoples of the Arctic have been 
impacted by climate change (Larsen et al., 2014). SROCC found with 
high confidence that cryospheric and associated hydrological changes 
have affected culturally significant terrestrial and freshwater species 
and ecosystems in high-mountain and polar regions, thus impacting 
residents’ livelihoods and cultural identity, including Indigenous 
Peoples (Hock et al., 2019b; IPCC, 2019a; Meredith et al., 2019). SROCC 
also concluded that IKLK are vital in determining community responses 
to environmental risk. The report further noted that IKLK helps increase 
adaptive capacity and reduces long-term vulnerability, but did not 
assess climate-related impacts on cultural water uses on low-lying 
islands (Oppenheimer et al., 2019).
Freshwater (including ice and snow) has diverse meanings and 
symbolic representations, as well as associated practices, management

and reciprocal responsibilities for many Indigenous Peoples, local 
communities and traditional peoples (Cave and McKay, 2016; Craft, 
2018; Hansen and Antsanen, 2018; Ngata, 2018; Chiblow 2019; 
Wilson et al., 2019; Moggridge and Thompson, 2021). Climate-driven 
hydrological changes are affecting culturally significant terrestrial and 
freshwater species and ecosystems, particularly for Indigenous Peoples, 
local communities and traditional peoples in the Arctic, high mountain 
areas, and small islands (high confidence). These climate impacts on 
cultural water uses are influencing travel, hunting, herding, fishing and 
gathering practices, which have negative implications for livelihoods, 
cultural traditions, economies and self-determination (Table 4.5).
Some of these losses may be classified as non-economic losses and 
damages, such as loss of culture and traditions (Thomas and Benjamin, 
2018b; McNamara et al., 2021). The vulnerability of these cultural uses

to climate change is exacerbated by historical and ongoing processes 
of colonialism and capitalism, which dispossessed Indigenous Peoples 
and disrupted culturally significant multi-species relationships (Whyte, 
2017; Whyte, 2018; Wilson et al., 2019; Whyte, 2020; Rice et al., 2021) 
(14.4.7.3; 9.13.2.4). Despite these significant structural barriers, there is 
medium confidence that some Indigenous Peoples, local communities and traditional peoples are adapting to the risks of climate-driven 
hydrological changes to cultural water uses and practices (Section 4.6.9).
There is high confidence that the prospect of loss (anticipatory grief) 
due to climate-related hydrological change, such as inundation 
or relocation, affects Indigenous Peoples, local communities and 
traditional peoples. These communities are especially susceptible to 
detrimental mental health impacts because of the implications of 
climate change for their cultural, land-based practices (du Bray et al.,

2017). For example, fears of cultural loss in Tuvalu (Gibson et al., 2019) 
are resulting in worry, anxiety and sadness among local people, with 
similar responses reported in Fiji and other Pacific islands (du Bray 
et al., 2017; Yates et al., 2021) (Box 15.1).
There is high confidence that glacier retreat and increasing glacier 
runoff variability are negatively affecting cultural beliefs and practices 
in high-mountain areas. For example, the loss of glaciers threatens the 
ethnic identity of the Indigenous Manangi community of the Annapurna 
Conservation Area of Nepal (Konchar et al., 2015; Mukherji et al., 2019). 
Likewise, ice loss in the Cordillera Blanca in the Peruvian Andes has 
challenged traditional approaches of interacting with the glaciers 
(Motschmann et al., 2020) (Section 4.2.2). There is high confidence that 
cryospheric changes in high-mountain areas also impact traditional 
pastoral practices by altering seasonal conditions, pasture quality and

water availability. For example, pasture quality in India (Ingty, 2017); Tibet 
Autonomous Region, People’s Republic of China (Nyima and Hopping, 
2019); and Bolivia (Yager et al., 2019) has been negatively impacted by 
climate-related hydrological changes, leading some Indigenous herders 
to diversify livestock, while herders in Nepal (Popular and Rik, 2016) 
and Peru (Postigo, 2020) have altered their routes in response to local 
water scarcity. Local communities in high-mountain areas understand 
these hydrological changes through cultural and spiritual frameworks 
(medium evidence, high agreement). For instance, in the Peruvian Andes 
and the Hindu Kush Himalaya, changing ice is attributed to a lack of 
spiritual devotion (Drenkhan et al., 2015; Konchar et al., 2015; Scoville-
Simonds, 2018). Communities in the Peruvian Andes also interpret 
climate impacts in the broader context of socioeconomic and political 
injustice and inequality (Drenkhan et al., 2015; Paerregaard, 2018).

In polar areas, there is high confidence that the appearance of land 
previously covered by ice, changes in snow cover, and thawing permafrost 
are contributing to changing seasonal activities. These include changes 
in accessibility, abundance and distribution of culturally important 
plant and animal species. These changes are harming the livelihoods 
and cultural identity of Indigenous Peoples, local communities and 
traditional peoples. In northern Fennoscandia, for example, reindeer 
herders reported experiences of deteriorated foraging conditions due 
to changes in the winter climate (Forbes et  al., 2019; Rasmus et  al., 
2020). In addition, Inuit and First Nations communities in Canada (Ford 
et  al., 2019; Khalafzai et  al., 2019) and Alaskan Natives and Native 
American communities in the USA (Norton-Smith et al., 2016) identified 
disruption to access routes to traditional hunting grounds and climate-
related stresses to culturally important species.

Further research is needed to provide culturally informed integrated 
assessments of climate change impacts on Indigenous Peoples’, local 
communities’ and traditional uses of water in the context of multiple

595Table 4.5 |  Selected observed impacts on cultural water uses of Indigenous Peoples (also see Figure 4.6).
RegionIndigenous 
PeoplesClimate hazardWater-related 
impactSituated knowledge Reference
Asia ManangiIncreased 
temperatures; 
increased precipitationGlacier retreat; 
decreased permanent 
snow coverManangi villagers reported a deep sense of spiritual loss associated 
with the decline of mountain snows and the receding glacier, which 
some attributed to a lack of spiritual devotion.Konchar et al. (2015); 
Mukherji et al. (2019)
Asia GurungIncreased 
temperaturesDecreasing snow; 
increased snowmeltIndigenous Gurung herders reported water scarcity in traditional 
water sources such as streams and wells along traditional livestock 
migration routes. As a result of these changes, they have altered their 
routes and camp locations.Popular and Rik 
(2016)
Asia DokpaIncreased

temperaturesDecreasing snowfallDokpa herders reported that pasture conditions have deteriorated 
due to shallower snowpack, shorter winters and erratic rainfall, which 
has impacted sheep populations. As a result of these changes, Dokpa 
herders are replacing traditionally important sheep with yaks, which 
are more tolerant to poor-quality pasturage.Ingty (2017)
AsiaJagshung 
pastoralistsIncreased 
temperaturesGlacier meltDue to the expansion of the majority of large lakes on the Tibetan 
Plateau, herders in Jagshung Village have lost large areas of pastures 
to inundation. As a result, the quality of nearby feed has also 
deteriorated, which has led to reduced livestock populations and 
productivity.Nyima and Hopping 
(2019)
Central and 
South AmericaAymaraIncreased 
temperaturesGlacier lossDecreasing rain and snow have led to degraded and dry peatland 
pastures (bofedales). This reduction of pasture contributes to 
out-migration, over-grazing and the loss of ancestral practices and

community commitment to pasture management (Table 12.5).Yager et al. (2019)
South AmericaQuelcaya 
temperatures; reduced 
rainfall; increasing 
variabilityDecreased snow 
and icePastoralists reported water scarcity in traditional water sources along 
migration routes. As a result, women pastoralists had to herd livestock 
farther to find water. Pastoralists also reported the deterioration of 
pasture due to decreasing water availability (Table 12.5).Postigo (2020)
Europe SaamiIncreased winter 
temperature; 
Increased summer 
precipitationHarder and deeper 
snow cover; increased 
ice formation; flooding 
rivers and wet groundChanges in the quality of winter pastures (especially decreased access 
to forage and the amount of forage) have increased the number of 
working hours and altered reindeer herding practices.
Rainy summers increase the difficulty of gathering and moving

reindeer to round-up sites and limit hay production for supplementary 
winter feed (13.8.1.2).Forbes et al. (2019); 
Rasmus et al. (2020)
North AmericaKashechewan 
First NationIncreased 
temperaturesFloodingThe timing and extent of spring flooding have changed, which, 
combined with inadequate infrastructure, have increased the 
frequency and risk of flooding for the Kashechewan community.
Earlier snowmelt has also affected the migration patterns of migratory 
birds and reduced the duration of traditional hunting and harvesting 
camps for culturally important species (14.4.6.7, 14.4.7.1).Khalafzai et al. 
(2019)
North America InuitIncreased 
temperatures (an 
average of 2.18°C 
from 1985 to 2016)Changing ice 
conditionsTrail access models showed that overall land and water trail access 
in the Inuit Nunangat had been minimally affected by temperature 
increase between 1985 and 2016. However, these findings illustrate

that although Inuit are developing new trails and alternative forms of 
transport, these changes could negatively impact cultural identity and 
well-being (14.4.6.7, 14.4.7.1).Ford et al. (2019)
increased precipitationEarly snowmeltInuit in Labrador, Canada, are grieving the rapid decline of culturally 
significant caribou, which is partly due to rising temperatures in the 
circumpolar north and the associated changes to caribou habitat 
and migration. In addition, the decline of this species is negatively 
affecting their sense of cultural identity because of the importance of 
hunting and cultural continuity (14.4.6.7, 14.4.7.1).Cunsolo et al. (2020)
North AmericaAlaskan 
NativesIncreasing 
temperaturesIncreasing temperature 
of freshwater lakes; 
permafrost melt; 
thinning iceIn Alaska, permafrost melting and the shorter ice season make it more 
difficult for hunters to access traditional hunting grounds. Increased

temperatures are changing the habitats and migration patterns of 
culturally important freshwater species. Declining fish health and 
populations threaten requirements of treaty rights and tribal shares of 
harvestable fish populations 14.4.6.7, 14.4.7.1.Albert et al. (2018); 
Norton-Smith et al. 
(2016)

596RegionIndigenous 
Small islands iTaukei Sea level riseFlooding, inundation 
and salt-water 
intrusionThe village of Vunidogola was relocated in response to inundation, 
storm surges and flooding, which villagers found emotionally and 
spiritually distressing. Although the village was relocated as a single 
unit and on customary lands, the shift away from the coast has 
impacted spiritual relationships, as the ocean is an integral part of 
village culture (15.6.5).Charan et al. (2017); 
Piggott-McKellar 
et al. (2019a)
Small islands iTaukei Sea level riseCoastal erosion; 
inundationVillagers of Viti Levu reported their grief at the potential loss of their 
traditions and livelihoods. In addition, they are concerned as to how 
climate change is affecting their cosmology and cultural traditions 
and understand possible relocation as another source of cultural loss 
(15.6.5).du Bray et al. (2017);

McNamara et al. 
(2021)
Small islands Funafuti Sea level riseCoastal erosion; 
inundationIn addition to climate impacts and stresses affecting Tuvalu, 
the potential for further environmental hardships in the future 
exacerbated worry and distress for local people, who are anxious 
about future cultural loss arising from sea level rise (15.6.5).Gibson et al. (2019); 
Yates et al. (2021); 
(2021)
stresses, disparities and inequities (Yates et  al., 2021). In the Arctic, 
for example, increased rates of development and resource extraction, 
including hydropower dams, mining, fisheries and sport hunting, all 
threaten water quality, habitat condition and the ecosystem services 
provided by Arctic freshwaters (Mustonen and Mustonen, 2016; Knopp 
In summary, the cultural water uses of Indigenous Peoples, local 
communities and traditional peoples are being impacted by climate 
change (high confidence), with implications for cultural practices and

food and income security, particularly in the Arctic, high-mountain 
areas and small low-lying islands.
4.4 Projected Changes in the Hydrological 
The terrestrial hydrological cycle is projected to intensify through a higher 
exchange of water between the land surface and the atmosphere. A rise of 
near-surface atmospheric water capacity is projected because of greater 
warming leading to changes in the atmospheric circulation patterns, the 
intensification of the convection processes, and the increased temperature 
of the underlying surface. Continuation of projected warming and other 
physical mechanisms will further accelerate the melting of snow cover 
and glaciers and thawing of permafrost (high confidence).
Methodologically, the projected changes in the hydrological cycle due to 
climate change are assessed directly from climate models or hydrological 
system models driven by the climate models’ projections (SM4.1). The

latter is simulated by the CMIP-based multi-model experiments carried 
out under the scenarios of future climate forcing and socioeconomic 
changes (e.g., RCPs, SSPs scenarios) or the pre-assigned global warming 
levels over the 21st century. Since AR5, there has been an improvement 
of the physical basis of the climate change impact projections owing 
to the advances in modelling clouds, precipitation, surface fluxes, 
vegetation, snow, floodplains, groundwater and other processes 
relevant to the water cycle (Douville et al., 2021) (SM4.1).
The subsections highlight the projected responses of these hydrological 
systems/processes to multiple drivers, high variability and the uncertainty of the projections, depending on regions, seasons, temporal 
and spatial scales, and the influence of the non-climatic factors.
4.4.1 Projected Changes in Precipitation, 
4.4.1.1 Projected Changes in Precipitation

WGI (Douville et al., 2021) concludes with high confidence that without 
large-scale reduction in GHG emissions, global warming is projected 
to cause substantial changes in the water cycle at both global and 
regional scales. However, WGI also noted large uncertainties in many 
aspects of regional water cycle projections by climate models. Water 
cycle variability and extremes are projected to increase faster than 
average changes in most regions of the world and under all emission 
scenarios (high confidence). The concept of ‘wetter regions get wetter, 
drier regions get drier’ from AR5 (Collins et al., 2013) is assessed by 
AR6 WGI (Douville et al., 2021) as too simplistic. WGI (Seneviratne 
et al., 2021) further concludes that heavy precipitation will generally 
warming.
In the CMIP6 multi-model ensemble, as in previous generations of 
ensembles, the projected changes in annual mean precipitation vary

substantially across the world. Importantly, in most land regions, the 
future changes are subject to high uncertainty even in the sign of the 
projected change (high confidence). Figure 4.10 illustrates this using 
the 5th, 50th and 95th percentile changes across the ensemble at 
individual grid points. For any given location, the range of projected 
changes generally increases with global warming (high confidence).
For example, in parts of the Indian sub-continent, the projected changes 
in mean precipitation at 1.5°C global warming range from a 10–20% 
decrease to a 40–50% increase. The multi-model median change is close 
to zero. Most other regions show a smaller range of changes (except for 
very dry regions where a small absolute change in precipitation appears 
as a larger percentage change). Nevertheless, across most global land 
regions, both increases and decreases in precipitation are projected 
across the ensemble. At 1.5°C global warming, a complete consensus

597on increased precipitation is seen only in the central and eastern Sahel, 
south-central Asia, parts of Greenland and Antarctica, and the far 
northern regions of North America and Asia, with projected increases 
in the latter ranging up to 20–30%. No land regions see a complete 
consensus on decreased precipitation, but South America, southern 
Africa and the Mediterranean region show a stronger consensus towards 
reduced precipitation.
The geographical patterns of local agreement/disagreement in 
projected precipitation change remain broadly similar with increased 
global warming, but the range of uncertainty generally increases 
(high confidence). For example, in northeastern Amazonia, the driest 
projections increase from a 10% decrease at 1.5°C global warming 
to a 40% decrease at 4°C global warming. In comparison, the wettest 
projections remain at up to a 10% increase. In the far north of North

America and Asia, the higher end of projected increases in precipitation 
extends to approximately 40–60%. A few regions are projected to 
see a shift in the consensus on the sign of the change. These include 
parts of the Indian sub-continent where at 4°C global warming, the 
projected changes shift to a consensus on increased precipitation 
ranging between a few percent to over 70%.Notably, the multi-model median change in precipitation is relatively 
small in many regions—less than 10% over most of the global land 
surface at 1.5°C global warming. In contrast, in many locations, the 
5th to 95th percentile range can include changes that are much larger 
changes than the median and also changes that are relatively large 
but opposite in sign. At 4°C global warming, the median projected 
changes are larger, ranging from a 20% decrease to a 40% increase 
(excluding very dry areas, where percentage changes can be much

larger due to very small baseline values), but nevertheless often remain 
a poor indicator of the range of changes across the ensemble. Therefore, 
use of the median or mean projected changes for future adaptation 
decisions could substantially underestimate the risk of large changes in 
precipitation. It could mean that the risk of the opposite sign of changes 
is not accounted for. Indeed, for mean precipitation, different multi-
model ensembles can show different levels of significance of the central 
estimate of change (Uhe et  al., 2021: Figure  4.11a). Consequently, 
information on the range of possible outcomes can be valued by users 
for effectively informing risk assessments (Lowe et al., 2018).
There is a stronger consensus on changes in heavy precipitation 
than mean precipitation within individual ensembles such as CMIP6 
GWL
4°C
GWL
2°C
GWL
1.5°C
Precipitation change (%)

relative to 1981–2010-80 -60 06 08 0 -40 -20 40 20Projected percentage changes in annual mean precipitation
95th percentile local 50th percentile local 5th percentile local
Figure 4.10 |  Projected percentage changes in annual mean precipitation at global warming levels (GWLs) of 4°C (top), 2°C (middle) and 1.5°C (bottom) 
for the CMIP6 multi-model ensemble of GCMs driven by the SSP5-8.5 scenario. For any given GWL, similar ranges of changes are seen with other scenarios that reach 
that GWL, and the difference between scenarios is smaller than the ensemble uncertainty (Seneviratne et al., 2021). The distribution of outcomes is shown at local scales with the 
5th, 50th and 95th percentile precipitation changes in individual grid boxes. Note that these are uncertainties at the individual point and are not spatially coherent, that is, they

do not represent plausible global patterns of change. Results for 1.5°C, 2°C and 4°C global warming are defined as 20-year means relative to 1850–1900 and use 40, 40 and 31 
ensemble members, respectively, due to some members not reaching 4°C global warming.

598(Figure  4.12) and especially between the means of the different 
ensembles (Figure 4.11b). At 4°C global warming, the 50th percentile 
projection is for increased annual maximum 1-d precipitation over 
virtually all global land, with the median increase being over 20% 
for a majority of the land. The 95th percentile increase is 20–40% 
over most mid-latitude areas and at least 40-–70% over the tropics 
and subtropics, exceeding 80% over western Amazonia, central 
Africa and most of the Indian sub-continent. The 5th percentile also 
shows an increase over most global land; in other words, decreased 
heavy precipitation has less than a 5% probability in these regions 
(Figure  4.12a), although decreases remain possible but of low 
probability in some regions, particularly northern South America and 
northern and western Africa. At the 50th and 95th percentiles, similar 
global patterns of change are projected at 2°C and 1.5°C global

warming, with smaller local magnitudes (Figure  4.12e,f,h,i). At the 
5th percentile, decreased Rx1day is seen over much larger land areas 
(Figure 4.12d,g), which may be a result of internal climate variability 
being relatively larger than the long-term trend at lower GWLs. In 
CMIP5, precipitation extremes are projected to be more likely to 
increase than to decrease on average over both the humid and arid 
regions of the world, but with larger uncertainty in arid areas (Donat 
In the 50th percentile projections at 4°C global warming, dry 
spells are projected to become up to 40 d longer in South America 
and southern Africa and up to 20 d shorter in large parts of Asia 
(Figure 4.13a,b,c). In most regions, the projected changes in dry spell 
lengths are highly uncertain. In southern Africa, the increase in dry 
spell length ranges from 10 d to over 40 d. In northeast Asia, dry spells are projected to become shorter by up to 20–30 d. In much

of South America, dry spells could increase by over 40 d or decrease 
by over 10 d. Similar global patterns with smaller magnitudes of 
change are projected for 2°C and 1.5°C global warming in all three 
percentiles (Figure 4.13d,e,f,g,h,i).
Taken together, these projections of more intense precipitation and 
changes in the length of dry spells give a clear picture of increasingly 
volatile precipitation regimes, with many regions seeing both longer 
dry spells and heavier events when precipitation does occur (high 
The critical knowledge gap for precipitation projections is the ability 
to make precise projections. With such large uncertainties in many 
regions, climate model projections can inform risk assessments, but 
cannot provide confident predictions of specific outcomes.
In summary, the annual mean precipitation range is projected to 
increase or decrease by up to 40% or more at 4°C global warming

over many land areas. The ranges of projected precipitation changes 
are smaller at lower levels of global warming (high confidence). 
Either an increase or decrease is possible in most regions, but there 
is an agreement among models on the increase in the far north 
(high confidence). There is a stronger model consensus on heavy 
precipitation increasing with global warming over most land areas 
(high confidence). There are widely varying projections of change in dry 
spell length (high confidence), but in regions with increasing projected 
dry spells, the potential increase is larger at higher levels of global 
warming (high confidence).(a) Mean precipitation (b) Extreme precipitationAgreement between different multi-model ensembles on significant changes in annual mean precipitation
and annual maximum 1-day precipitation, at 2°C global warming
Number of ensembles projecting drying
50 34 43 2 1 21 5Number of ensembles projecting wetting

= Different ensembles project significant changes in opposite directions = CMIP6 disagrees with 3 of the other 4 ensembles on the significance of the change
Figure 4.11 |  Agreement between different multi-model ensembles on significant changes in (a) annual mean precipitation and (b) annual maximum 1-d 
precipitation (Rx1day) at 2°C global warming (Uhe et al., 2021). Using central estimates from five ensembles of climate models (CMIP5, CMIP6, HAPPI, HELIX and UKCP18) 
using different models and different experimental designs for the ensembles, the maps show the number of ensembles for which the central estimate shows a significant drying or 
wetting change at 2°C global warming relative to pre-industrial levels. The different ensembles reach 2°C global warming at different times. The projected changes are aggregated

over the new climatic regions defined for IPCC AR6 (Iturbide et al., 2020). Hatched regions show where different ensembles project significant changes in opposite directions, i.e., 
there is no agreement on either drying or wetting. Regions with thick outlines are where CMIP6 disagrees with three of the other four ensembles on the significance of the change, 
highlighting where over-relying on CMIP6 alone may not fully represent the level of confidence in the projections.

5994.4.1.2 Projected Changes in Evapotranspiration
AR5 (Collins et  al., 2013) found that the CMIP5 model projections 
of ET increases or decreases followed the same pattern over land 
as precipitation projections, with additional impacts of reduced 
transpiration due to plant stomatal closure in response to rising CO 2 
concentrations. AR6 WGI (Douville et al., 2021) assessed that it is very 
likely that ET will increase over land, with regional exceptions in drying 
areas.
In most CMIP5 and CMIP6 models, projected ET changes are driven not 
just by meteorological conditions and soil moisture but also by plant 
physiological responses to elevated CO 2, which themselves influence 
meteorology and soil moisture through surface fluxes (Halladay and 
Good, 2017; Lemordant and Gentine, 2019). Elevated CO 2 causes 
stomatal closure which decreases ET, but also increases leaf area 
index (LAI) which in turn increases ET, but these do not necessarily

compensate (Skinner et al., 2017). Higher LAI increases transpiration, 
depleting soil moisture but increasing shading, thus reducing soil evaporation (Skinner et  al., 2017), but LAI may not increase in 
areas where it is already high (Lemordant et  al., 2018). Projected 
ET decreases from physiological effects alone are widespread but 
greatest in tropical forests (Swann et  al., 2016; Kooperman et  al., 
2018).
Future changes in regional ET are therefore highly uncertain. The 
CMIP6 multi-model ensemble projects changes in ET varying both 
in magnitude and sign across the ensemble members (Figure 4.14). 
At 4°C global warming, the ensemble median projection shows 
increased ET of approximately 25% in mid/high latitudes but 
decreases of up to 10% across most of tropical South America, 
southern Africa and Australia. These CMIP6 ensemble projections 
resemble ET changes projected by the CMIP5 ensemble, except 
over central Africa and Southeast Asia (Berg and Sheffield, 2019).

However, the ensemble ranges are wide and include both increases 
and decreases in projected ET in many locations, with mid-latitude 
ET increases being up to approximately 50% and ET decreases in 
southern Africa being up to approximately 30%. Projected changes 
GWL
4°C
GWL
2°C
GWL
1.5°C
Change (%)
-80 08 0 -40 40Projected percentage changes in annual maximum daily precipitation (Rx1day) 
(a)(a) (b) (c)
(d) (e) (f)
(g) (h) (i)112
Simulation s
127
Simulation s37
Figure 4.12 |  Projected percentage changes in annual maximum daily precipitation (Rx1day) averaged over 20 years centred at the time of first passing 
(a–c) 4°C, (d–f) 2°C and (g–i) 1.5°C global warming levels (GWLs) relative to 1851–1900. Results are based on simulations from the CMIP6 multi-model ensemble under

the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. Uncertainties in the projections are quantified with the (a, d, g) 5th, (b, e, h) 50th and (c, f, i) 95th percentile 
local values from the ensemble at each GWL. Note that these are uncertainties at the individual point and are not spatially coherent, that is, they do not represent plausible global 
patterns of change. The 50th percentile maps (b, e, h) present the same data over land as Figure 11.16 of Seneviratne et al. (2021). The numbers on the left indicate the number of 
simulations included at each warming level, including multiple realisations from some models with varying initial conditions, depending on data availability. Results for the 1.5°C 
GWL include 37 unique models. Fewer models and realisations are available for the 2°C and 4°C GWLs, as fewer scenarios and/or models reach those warming levels. For individual

models, the global patterns of changes are very similar across scenarios, and any differences between scenarios are smaller than the ensemble uncertainty for an individual scenario. 
The CMIP6 projections of changes in mean and extreme precipitation are discussed in more detail by WGI (Doblas-Reyes et al., 2021; Seneviratne et al., 2021).

600are proportionally smaller at lower levels of global warming, while 
patterns of change remain similar.
The relative importance of the physiological and radiative effects of 
CO 2 on future ET is a crucial knowledge gap, partly because many 
ESM land surface schemes still use representations of this process 
based on older experimental studies. Furthermore, large-scale 
experimental studies using free-air CO 2 enrichment (FACE) techniques 
to constrain the models have not yet been performed in certain 
critical ecosystems, such as tropical forests. Finally, uncertainties in 
equilibrium climate sensitivity (ECS) imply uncertainties in the CO 2 
concentration accompanying any given level of warming (Betts and 
McNeall, 2018).
In summary, the sign of projected ET change depends on region, but 
there is medium confidence that ET will increase in the global mean 
and mid/high latitudes and decrease in northern South America and

southern Africa. In addition, the impacts of rising CO 2 concentrations 
on plant stomata and leaf area play a role in model projections of ET 
change (high confidence), but there is low confidence in their overall 
contribution to global ET change.4.4.1.3 Projected Changes in Soil Moisture
AR5 (Collins et al., 2013) mainly focused on surface (upper 10 cm) soil 
moisture, summarising multi-model projections of 21st century annual 
mean soil moisture changes as broadly decreasing in the subtropics 
and Mediterranean region and increasing in east Africa and central 
Asia across the RCPs, with the changes tending to become stronger 
as global warming increases. AR6 WGI (Douville et  al., 2021) draw 
broadly similar conclusions based on new ESMs, noting that compared 
to CMIP5, the CMIP6 models project more consistent drying in the 
Amazon basin, Siberia, westernmost North Africa and southwestern 
Australia. WGI (Douville et al., 2021) also note that soil moisture in

the upper 10 cm shows more widespread drying than in the total soil 
column.
The CMIP6 multi-model ensemble of ESMs show varying levels of 
consensus on projected changes in surface soil moisture with global 
warming (Figure 4.15). As in CMIP5 (Cheng et al., 2017), uncertainties 
are substantial, often associated with uncertainties in projected 
regional precipitation changes (Section 4.4.1.1), and in most regions, 
both increases and decreases are projected across the ensemble. In the 
far north of North America and Asia, projected changes in soil moisture 
at 4°C global warming range from a 20–30% decrease to an increase 
of 30–40%. In northern mid-latitudes, projections range from a 10–
20% decrease to an increase of 20–30%, except for eastern North 
GWL
4°C
GWL
2°C
GWL
1.5°C
-40 04 0 -20 20Projected changes in annual consecutive dry days (CDD)
(d) (e) (f)
(g) (h) (i)114
129

Simulation s38
Figure 4.13 |  As Figure 4.12 for projected changes in annual consecutive dry days (CDD), the highest number of days yr–1 with precipitation < 1 mm. The 50th 
percentile maps (b, e, h) present the same data as Figure 11.19a,b,c of Seneviratne et al. (2021).

601America, where the projected changes (both increases and decreases) 
are less than 10%, and western Europe and the Mediterranean where 
there is a stronger consensus towards decreased soil moisture of up 
to 25%. South America, southern Africa and Asia also show a stronger 
consensus towards decreased soil moisture of up to 40% or more in 
some regions.
Most CMIP6 models simulate direct CO 2 effects on plant transpiration, 
which has been shown to be a strong influence on projected future 
changes in soil moisture (Milly and Dunne, 2016). Approaches 
that neglect this process project greater decreases in soil moisture 
availability than the climate models (Roderick et  al., 2015; Swann 
et  al., 2016). Therefore, although several studies project increased 
global aridity and dryland expansion (Feng and Fu, 2013; Sherwood 
and Fu, 2014; Huang et  al., 2016a), these may overestimate future 
drying (Berg et al., 2017). Nevertheless, land surface models, including

vegetation responses to CO 2, still project reduced soil moisture in many 
regions (Grillakis, 2019).
A critical knowledge gap concerns the relative importance of 
climate and CO 2 physiological effects on soil moisture, in relation 
to uncertainties in climate sensitivity. For a given level of global warming, the relative importance of climate effects and the direct 
effects of CO 2 on transpiration depend on the CO 2 concentration 
accompanying that level of warming (Betts and McNeall, 2018). 
Some CMIP6 models have very high climate sensitivities (Meehl 
et al., 2020), which are assessed as being of low probability on the 
basis of other lines of evidence (Sherwood et al., 2020). This means 
that the CO 2 concentration accompanying specific global warming 
levels may be too low and lead to overly large projections of soil 
moisture decrease in those models.
In summary, projected soil moisture changes increase with levels of

global warming (high confidence), although there remains substantial 
disagreement on specific regional changes. In the CMIP6 multi-
model ensemble at 4°C global warming, decreased soil moisture of 
up to 40% is projected in Amazonia, southern Africa and western 
Europe in all models (high confidence). In all other regions, there is 
no consensus on the sign of projected soil moisture changes, and 
projected changes at 4°C global warming include decreases of up to 
30% and increases of up to 40%. Projected changes are smaller at 
lower levels of global warming, with similar geographical patterns 
of change.
GWL
4°C
GWL
2°C
GWL
1.5°C
Evapotranspiration change (%)
relative to 1981–2010-100 -50 75 -75 -25Projected percentage changes in annual mean evapotranspiration (ET) 
50 25 0 100

Figure 4.14 |  Projected percentage changes in annual mean ET at global warming levels (GWLs) of 4°C (top), 2°C (middle) and 1.5°C (bottom) for the 
CMIP6 multi-model ensemble of GCMs driven by SSP5-8.5 concentrations. The distribution of outcomes is shown at local scales with the 5th, 50th and 95th percentile 
ET changes in individual grid boxes. Note that these are uncertainties at the individual point and are not spatially coherent, that is, they do not represent plausible global patterns 
of change. Results for 1.5°C, 2°C and 4°C global warming are defined as 20-year means relative to 1850–1900 and use 40, 40 and 31 ensemble members, respectively, due to 
some members not reaching 4°C global warming.

6024.4.2 Projected Changes in the Cryosphere (Snow, 
AR5 noted that global glacier mass loss is very likely to increase further 
during the 21st century (Jiménez Cisneros et al., 2014). According to the 
SROCC (Hock et al., 2019b), it is very likely that glaciers will continue 
to lose mass throughout the 21st century: from 18% (by 2100, relative 
to 2015) for RCP2.6 to 36% for RCP8.5. AR5 (Collins et  al., 2013) 
and SROCC (Meredith et  al., 2019) reported with high confidence 
that permafrost would continue to thaw in the 21st century, but 
the projections are uncertain. Constraining warming to 1.5°C would 
prevent the thawing of a permafrost area of 1.5 to 2.5  million km2 
compared to thawing under 2°C (medium confidence) (IPCC, 2018b). 
AR5 (Collins et al., 2013) and SROCC (Meredith et al., 2019) concluded 
that Northern Hemisphere snow extent and mass would likely reduce

by the end of the 21st century, both in plain and mountain regions. AR6 
assessed with medium confidence that under RCP2.6 and RCP8.5 from 
2015 to 2100, glaciers are expected to lose 18% and 36% of their early 
21st-century mass, respectively (AR6 WGI, (Fox-Kemper et al., 2021)).Global glacier mass loss since 2015 and 2100 was projected to be 18 
± 13% by 2100 with 0.9 – 2.3°C global warming and 36 ± 20% with 
3.2 – 5.4°C global warming (Marzeion et al., 2020), which corresponds 
with previous findings (Radić et al., 2014; Hock et al., 2019a; Shannon 
et al., 2019). The regional glacier loss rate projections are unevenly 
distributed worldwide and considerably vary between scenarios (Huss 
and Hock, 2018; Hock et  al., 2019a). In most regions, ‘peak water’ 
has already been reached or is expected to be reached before mid-
century (with an earlier ‘peak water’ for RCP2.6 scenario compared 
with RCP8.5) (Huss and Hock, 2018; Pritchard, 2019; Marzeion et al.,

2020; Rounce et al., 2020). The influence of the expected subsequent 
decrease in glacier runoff by the end of the 21st century will be more 
pronounced during droughts and dry seasons (Farinotti et al., 2016; 
Huss and Fischer, 2016; Hanzer et al., 2018; Brunner et al., 2019).
Such changes in runoff could potentially lead to water shortages for 
over 200 million people in the high mountains of Asia (Pritchard, 2019; 
Shahgedanova et al., 2020). There is medium confidence that under a 4°C 
warming scenario, 40% of current irrigated demand in sub-basins relying 
primarily on snowmelt runoff would need to be supplemented from other 
water sources (Qin et  al., 2020). Basins where such alternate sources 
GWL
4°C
GWL
2°C
GWL
1.5°C
Soil moisture change (%)
relative to 1981–2010-40 -30 03 04 0 -20 -10 20 10Projected percentage changes in annual mean total column soil moisture

Figure 4.15 |  Projected percentage changes in annual mean total column soil moisture relative to 1981–2010 at global warming levels (GWLs) of 4°C (top), 
2°C (middle) and 1.5°C (bottom) for the CMIP6 multi-model ensemble of GCMs driven by SSP5-8.5 concentrations. The distribution of outcomes is shown at local 
scales with the 5th, 50th and 95th percentile soil moisture changes in individual grid boxes. Note that these are uncertainties at individual points and are not spatially coherent, 
that is, they do not represent plausible global patterns of change. Results for 1.5°C, 2°C and 4°C global warming are defined as 20-year means relative to 1850–1900 and use 
34, 34 and 26 ensemble members, respectively, due to some members not reaching 4°C global warming. Fewer models are shown here than in Figure 4.10 on precipitation and 
Figure 4.14 on ET because some do not provide soil moisture output.

603are not available will face agricultural water scarcity (Section  4.5.1). 
Globally, 1.5 billion people are projected to critically depend on runoff 
from the mountains by the mid-21st century under the RCP6.0 scenario 
(Viviroli et  al., 2020). Furthermore, there is medium confidence that 
projected changes in snow and glacier melt runoff will affect water 
inputs to hydropower, leading to a decline in hydroelectricity production 
in mountain basins, for example, in India (Ali et al., 2018), Switzerland 
(Schaefli et al., 2019) and the USA (Lee et al., 2016) (Section 4.5.2) (IPCC 
AR6 WGI, 2021) (Sections 9.5.1.3 and 8.4.1.7.1).
Projections of snow cover metrics [IPCC AR6 WGI, 2021 (Section 9.5.3.3)] 
suggest a further decrease in snow water equivalent (SWE) and snow 
cover extent (SCE), though the inter-model spread is considerable (Lute 
et al., 2015; Thackeray et al., 2016; Kong and Wang, 2017; Henderson

et  al., 2018) (high confidence). The projected CMIP6 SCE and SWE 
changes share the broad features of the CMIP5 projections: SCE is 
expected to decrease in the Northern Hemisphere by approximately 
20%, relative to the 1995–2014 mean value, around 2060 and stabilise 
afterwards under the RCP2.6 scenario, while the RCP8.5 scenario 
leads to snow cover losses up to 60% by 2100 (Mudryk et al., 2020). 
Regionally, the SWE loss will probably lead to more frequent snow 
droughts; for example, the frequency of consecutive snow droughts is 
projected to increase to 80–100% of years at 4°C warming in western 
Canada (Shrestha et  al., 2021) and 42% of years under the RCP8.5 
scenario in the western USA (Marshall et al., 2019) by 2100. Thus, by the 
mid- to late 21st century, for more than 2/3 of snow-dominated areas in 
the western USA, the ability to predict seasonal droughts and prepare 
robust water management plans will decline (Livneh and Badger, 2020) 
(Section 4.4.5).

There is a high agreement between the CMIP6 projections and the 
previous findings that permafrost will undergo increasing thaw and 
degradation during the 21st century worldwide (Fox-Kemper et  al., 
2021) . The CMIP6 models project that the annual mean frozen volume 
in the top 2 m of the soil could decrease by 10–40% for every degree 
increase of global temperature (Burke et  al., 2020; Yokohata et  al., 
2020b). The CMIP5-based equilibrium sensitivity of permafrost extent 
to stabilised global mean warming is established to be about 4.0 × 
106 km2 °C–1 (Chadburn et  al., 2017). The southern boundary of the 
permafrost is projected the move to the north: 1°–3.5° northward 
(relative to 1986–2005) at the level of 1.5°C temperature rise (Kong 
and Wang, 2017).
The observational knowledge gaps (Section  4.2.2) impede efforts 
to calibrate and evaluate models that simulate the past and future 
evolution of the cryosphere and its social impacts.

In summary, in most basins fed by glaciers, runoff is projected to 
increase initially in the 21st century and then decline (medium 
confidence). Projections suggest a further decrease in seasonal snow 
cover extent and mass in mid to high latitudes and high mountains 
(high confidence), though the projection spread is considerable. 
Permafrost will continue to thaw throughout the 21st century (high 
confidence). There is medium confidence that future changes in 
cryospheric components will negatively affect irrigated agriculture 
and hydropower production in regions dependent on snowmelt 
runoff.4.4.3 Projected Changes in Streamflow
AR5 (Jiménez Cisneros et  al., 2014) concluded that increases in the 
mean annual runoff are projected in high latitudes and the wet tropics 
and decreases in dry tropical regions, but with very considerable 
uncertainty. Both the patterns of change and uncertainties were found 
to be primarily driven by projected changes in precipitation. SR1.5

(Hoegh-Guldberg et  al., 2018) concluded with medium confidence 
that areas with either positive or negative changes in mean annual 
runoff/streamflow are projected to be smaller for 1.5°C than for 2°C of 
global warming. AR6 WGI (Douville et al., 2021) conclude with medium 
confidence that global runoff will increase with global warming 
but with significant regional and seasonal variations. WGI further 
concluded with high confidence that runoff will increase in the high 
northern latitudes and decrease in the Mediterranean and southern 
Africa. However, there was medium confidence that runoff will increase 
in central and eastern African regions and decrease in Central America 
and parts of southern South America. The magnitude of the change 
is projected to increase with emissions. There is medium confidence 
that the seasonality of runoff and streamflow will increase with global 
warming in the subtropics. In snow-dominated regions, there is high

confidence that peak flows associated with spring snowmelt will occur 
earlier in the year and medium confidence that snowmelt-induced 
runoff will decrease with reduced snow, except in glacier-fed basins 
where runoff may increase in the near term.
Changes in runoff and streamflow are projected over most of the 
ice-free land surface with all recent climate and hydrological model 
ensembles (Figure  4.16). Changes in streamflow could increase the 
number of people facing water scarcity or insecurity (high confidence) 
(Schewe et  al., 2014; Gosling and Arnell, 2016; McMillan et  al., 
2016). Projections of future runoff at basin scales show considerable 
uncertainty in many regions, including differences in signs in many 
regions (Figure  4.16). This uncertainty is driven by uncertainties in 
regional precipitation patterns and hydrological models (Koirala 
et al., 2014; Asadieh et al., 2016), including vegetation responses to

CO 2 and its effects on ET (Betts et al., 2015). This uncertainty in future 
water availability contributes to the policy challenges for adaptation, 
for example, for managing risks of water scarcity (Greve et al., 2018; 
Box  4.1). In many regions, some models project large changes in 
runoff/streamflow but with low consistency between models on the 
sign of the change (Figure 4.16). In streamflow projections driven by 
11 CMIP5 models with the RCP8.5 scenario, strong model consistency 
(agreement by at least 10 models) is only seen over 21% of global 
land (Koirala et al., 2014). Consensus on the sign of projected change 
is smaller with the RCP4.5 scenario.
Considering a wider set of projections, the consensus on increased flows 
becomes stronger at higher GWLs in (for example) the Yukon, Mackenzie, 
Kemijoki, Amur, Hwang Ho, Yangtze, Mekong, Ganges-Brahmaputra, 
Nile, Zaire and Parana basins (Figure 4.16). The consensus on decreased

flows becomes stronger for higher GWLs in (for example) the Colorado, 
Tagus, Helmand, Tigris-Euphrates and Amazon. However, in both cases, 
some models have projected changes of the opposite sign to the 
consensus. Moreover, the distribution of projected outcomes becomes 
notably broader at higher GWLs in (for example) the Mississippi, Yangtze 
and Amazon. Therefore, even with a strong global climate change signal,

604
Wetter DrierM odel agreemen t-GWL 4°C
66% 66% 90% 95% 90% 95%Median changeModel agreement
on the sign of change
-75% to 100%
range
Global Warming LevelMedian (circle)
95% CI for themedian (bar)
CongoProjected changes in the annual mean run-off in selected river basins
Figure 4.16 |  Projected changes in the annual mean runoff in selected river basins at global warming levels (GWLs) of 1.5°C, 2°C and 4°C in a combined 
ensemble. For each named basin, the sinaplot dots show individual model outcomes for percentage increased flows (blue) and decreased flows (red) at each GWL. Black circles 
show the ensemble median, and black bars show the 95% confidence range in the median. See inset with the Rio Grande sinaplot for additional guidance on interpretation. In the 
map, the colours in the basins show the percentage model agreement on the sign of the projected change in streamflow at the 4°C GWL. The combined ensemble is comprised of

four multi-model ensembles: the CMIP5 multi-model ensemble of GCMs driven with RCP8.5; the CMIP6 multi-model ensemble of GCMs driven with SSP5-8.5; varying combinations 
of hydrological models with five GCMs in the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP); and the JULES land ecosystems and hydrology model driven by GCMs 
from the HELIX study (Betts et al., 2018; Koutroulis et al., 2019). In CMIP5 and CMIP6, the projected runoff changes are directly from the GCM land surface schemes without bias 
correction. In ISIMIP and HELIX, bias-corrected climate model outputs were used to drive the hydrology models. A comparison of the projected changes at the 4°C GWL for the four 
individual ensembles is shown in Figure Cross-Chapter Box CLIMATE.1 in Chapter 1.

605uncertainties in changes in mean runoff/streamflow can remain large or 
even increase. Nevertheless, since projected changes typically increase 
with global warming, limiting warming to 1.5°C or 2°C substantially 
reduces the potential for either large increases or decreases in mean 
streamflow compared to 3°C or 4°C (Warszawski et al., 2014; Falkner, 
2016; Gosling et al., 2017; Figure 4.16) (high confidence).
In CMIP5, strong model consistency on changes in high and low 
streamflows is seen with similar global patterns to the mean flows, but 
over smaller areas (Koirala et al., 2014). By the end of the 21st century, 
with RCP8.5, increases in mean, high and low flows are projected for 
the Lena, and mean and low flows for the MacKenzie (Gelfan et al., 
2017; Pechlivanidis et  al., 2017; Döll et  al., 2018). Increased mean 
and high flows are projected in the Ganges, high flow in the Rhine 
and Mississippi, while decreasing mean and low flows are projected

in the Rhine (Krysanova et al., 2017; Pechlivanidis et al., 2017; Vetter 
et al., 2017). Decreases in mean, high and low flows are projected for 
the Tagus (Krysanova et al. 2017; Vetter et al. 2017). Low flows are 
projected to decrease in the Mediterranean region and increase in the 
Alps and northern Europe (Marx et al., 2018). A general shift in the 
runoff distribution towards more extreme low runoff is projected in 
Mexico, western USA, western Europe, southeastern China and the 
West Siberian Plain, and more extreme high runoff is projected in 
Alaska, northern Canada and large parts of Asia (Zhai et al., 2020).
While projected changes in high and low flows are similar to those in 
mean flows in many regions, this is not the case everywhere. When a 
single hydrological model and a sample of climate models are selected 
to explore uncertainties systematically, approximately 56% of the 
global population is projected to be affected by increased extreme high

flows at 1.5°C warming, rising to 61% at 2°C warming (Zhai et al., 
2020). Those affected by extreme low flows decrease is projected to 
remain close to 45% at both 1.5°C and 2°C warming. However, these 
results are based on the median of the ensemble projections, so they 
are subject to high uncertainty. At 1.5°C  global warming, 15% of 
the population is projected to be affected concurrently by decreased 
extreme low flows and increased extreme high flows, increasing 
to 20% at 2°C warming. In 25 combinations of five CMIP5 climate 
models and five global hydrological models under the RCP8.5 scenario 
reaching approximately 4°C GWL at the end of the century, 10% of the 
global land area is projected to face simultaneously increasing high 
extreme streamflow and decreasing low extreme streamflow. These 
regions include the British Isles and the shores of the North Sea, large 
parts of the Tibetan Plateau, South Asia and western Oceania, and

smaller regions of Africa and North and South America, affecting over 
2.1  billion people with 2015 population distributions (Asadieh and 
Krakauer, 2017). With 11 CMIP5 models driving a single hydrological 
model, simultaneous increases in high flows and decreases in low 
flows are projected over 7% of global land (Koirala et al., 2014).
By the end of the 21st century, global changes in streamflow extremes 
are projected to be approximately twice as large with RCP8.5 (over 
4°C GWL) than with RCP2.6 (approximately 2°C GWL) (Asadieh and 
Krakauer, 2017).
Glacier retreat and associated runoff changes represent a major global 
sustainability concern (Section 4.4.2). By 2100, using an ensemble of 14 CMIP5 climate models driven by the RCP4.5 scenario, one third of 
the 56 large-scale glacierised catchments are projected to experience 
a mean annual runoff decline by over 10%, with the most significant 
reductions in central Asia and the Andes (Huss and Hock, 2018). Thus,

communities dependent on glacier runoff are particularly vulnerable 
Societal impacts of change in runoff spread throughout several 
socioeconomic sectors, such as agriculture, health and energy 
production, affecting overall water security (Wang et  al., 2021a). 
Decreases in runoff may lead to water scarcity and result in increased 
multi-sectoral effects in sub-Saharan Africa (Serdeczny et  al., 2017), 
western Africa, the Middle East, Mexico, Northeastern Brazil, central 
Argentina, Mediterranean Africa and Europe (Gosling and Arnell, 2016; 
Greve et al., 2018), and southeastern Australia (Barnett et al., 2015).
In summary, mean and extreme streamflow changes are projected over 
most of the ice-free land surface (high confidence). The magnitude of 
streamflow change is projected to increase with global warming in 
most regions (high confidence), but there is often high uncertainty on

the sign of change. There is high confidence that mean streamflows 
will increase in the northern high latitudes and decrease in the 
Mediterranean and southern Africa. Annual mean runoff in one third of 
assessed glacierised catchments is projected to decline by at least 10% 
by 2100 under RCP4.5, with the most significant reductions in central 
Asia and the Andes (medium confidence). Elsewhere, projections include 
both increased and decreased flows. Substantial fractions of ensemble 
projections disagree with the multi-model mean (high confidence), with 
implications for long-term planning for water management. With 1.5 
and 2°C global warming, approximately 15 and 20% of the current 
global population, respectively, would experience both an increase 
in high streamflows and a decrease in low streamflows (medium 
confidence). At 4°C global at the end of the century, 10% of the global 
land area is projected to simultaneously experience an increase in high

extreme streamflow and decrease in low extreme streamflow.
4.4.4 Projected Changes in Floods
SR1.5 (Hoegh-Guldberg et al., 2018) concluded with medium confidence 
that global warming of 2°C would lead to an expansion of the area 
affected by flood hazards, compared to conditions at 1.5°C global 
warming. Both AR5 (Jiménez Cisneros et al., 2014) and SROCC (Hock 
et al., 2019b) concluded that spring snowmelt floods would be earlier 
(high confidence), and hazards from floods involving meltwater will 
gradually diminish, particularly at low elevation (medium confidence). 
SROCC (Hock et al., 2019b) and AR6 WGI Chapter 9 stated that given 
limited evidence and the complexity of the process, the changes of 
glacier-related floods under climate change are not clear. AR6 WGI 
Chapters 8 and 11 summarised that there is medium confidence for a 
general increase in flooding due to warming, but there are significant 
regional and seasonal variations.

floods are projected to change at a global scale. For example, the 
frequency of river floods is projected to increase in many regions, 
including Asia, central Africa, western Europe, Central and South

606America and eastern North America, and decrease in northern North 
America, southern South America, the Mediterranean and eastern 
Europe in 2050 and beyond (Koirala et al., 2014; Arnell et al., 2016) 
(Figure  4.17). There is low agreement in projections in changes 
to snowmelt flood magnitude. A negative trend in snowmelt flood magnitude, together with an increase in rain-fed winter floods, is 
projected with medium confidence, for example, in mid-latitude and 
low-altitude basins of Scandinavia (Arheimer and Lindström, 2015; 
Vormoor et al., 2016) and throughout Europe as a whole (Kundzewicz 
et al., 2017), and northeastern North America (Arnell and Lloyd-Hughes, 
(a) SSP1-2.6 (b) SSP2-4.5Projected changes in river flooding Changes in 2071–2100 relative to 1970–2000
Return period (years)
Decrease
flood
frequencyIncreased
flood
frequency1,000 500 250 125 105 95 75 50 25 5
High model agreement(c) SSP5-8.5
3% 4% 2% 1% 0Relative exposed population
X
X
XX

Historical1.5°C2.0°C3.0°C4.0°C
Global
X
X
XX
AustralasiaX
X
XHistorical1.5°C2.0°C3.0°C4.0°C
AsiaX
X
XX
Africa
= Constant population scenario of CMIP6= SSP5 population scenario of CMIP6
= Constant population scenario of CMIP5(d) Global and regional potential exposure of the population
3% 2% 1% 0Relative exposed population
X
X
XX
NorthAmericaX
X
XX
EuropeX
X
XX
Central andSouthAmerica
Figure 4.17 |  Multi-model median return period (years) in the 2080s for the 20th-century 100-year river flood, based on a global river and inundation model, 
CaMa-Flood, driven by runoff output of nine CMIP6 Models in the SSP1-2.6 (a), SSP2-4.5 (b) and SSP5-8.5 (c) scenario respectively. All changes are estimated in

2071–2100 relative to 1970-–2000. A dot indicates regions with high model consistency (more than seven models out of nine show the same direction of change).
(d) Global or regional potential exposure (% to the total population affected by flooding) under different global warming levels with a constant population scenario and climate 
of CMIP5-HELIX (circle, Alfieri et al., 2017) and CMIP6 (triangle, Hirabayashi et al., 2021b), and with the population scenario of SSP5 and climate of CMIP6 (bar chart, Hirabayashi 
et al., 2021b). Inundation is calculated when the magnitude of flood exceeds current flood protection (Scussolini et al., 2016). Note that number of GCMs used to calculate global 
warming level (GWL) 4.0 is less than that for other GWLs, as the global mean temperature change of some GCMs did not exceed 4°C.

6072014). With medium confidence, a positive trend is projected in high-
latitude basins, for example, for large Arctic rivers such as Lena and 
Mackenzie (Eisner et al., 2017; Gelfan et al., 2017; Pechlivanidis et al., 
2017) and high-altitude upstreams, such as the Ganges, Brahmaputra, 
Salween, Mekong and the upper Indus Basin (Lutz et al., 2014) and 
alpine catchments (Hall et al., 2014). Moderate decreasing trends or 
insignificant changes are projected for snowmelt floods in the Fraser 
River Basin of British Columbia (Shrestha et al., 2017).
There is high confidence that climate change and projected socioeconomic 
development would increase exposure in inundation areas (Figure 4.17), 
resulting in a large increase in direct flood damages as several times 
more in all warming levels (Table 4.6). Alfieri et al. (2017) estimated a 
120 and 400% increase in population affected by river flooding for 2°C

and 4°C warming, respectively, and a 170% increase in damage for 2°C 
warming without socioeconomic impact development (Section  4.7.5). 
Dottori et al. (2018) estimated the same but with a 134% increase in 
fatalities with population increase under the SSP3 scenario. The highest 
numbers of people affected by river flooding are projected for countries 
in southern, eastern and southeastern Asia, with tens of millions of people per year per country projected to be affected (Figure 4.17; Alfieri 
et al., 2017; Hirabayashi et al., 2021b). Kinoshita et al. (2018) showed 
that climate change contributes a 2.8–28.8% increase in global fatality 
for the period 2071–2100 compared to 1991–2005, but socioeconomic 
change (~131.3% increase) and associated vulnerability change (~72.1% 
reduction) have a greater impact of the projected flood-related fatality 
rate than climate change alone. Winsemius et al. (2016) discussed that

projected flood damage could be reduced to 1/20th in absolute value 
with adequate adaptation strategies. Direct flood damages are projected 
to increase by 4–5 times at 4°C compared to 1.5°C, highly depending on 
scenarios and assumptions (Table 4.6; Box 4.7).
In all climate scenarios projected, earlier snowmelt leads to earlier 
spring floods (high confidence), for example, in northern and eastern 
Europe (Gobiet et al., 2014; Hall et al., 2014; Etter et al., 2017; Lobanova 
et al., 2018), northern North America (Vano et al., 2015; Musselman 
et al., 2018; Islam et al., 2019b), large Arctic rivers (Gelfan et al., 2017; 
Pechlivanidis et al., 2017) and high-altitude Asian basins (Lutz et al., 
2014; Winsemius et al., 2016). There is high confidence that snowmelt

floods will occur 25–30 d earlier in the year by the end of the 21st Table 4.6 |  Projected economic impact by river flooding in billion USD in different emission scenarios or for different global warming levels (GWLs). The percentage of the total 
GDP of the region is given in brackets.
Description The economic impact in billion USD (% of GDP) Reference
No adaptation with current flood protection, no 
economic development (fixed at the level of 2010), 
USD at 2010 purchasing power parity (PPP), mean 
of 7 GCMs with the RCP8.5 scenario –Current (1976–2005): 75 (0.11%)
 –GWL 1.5°C: 145 (0.22%)
 –(Asia 92, Australasia 8, Europe 29, Africa 7, North America 3, Central and South America 5)
 –GWL 2°C: 172 (0.26%)
 –(Asia 114, Australasia 7, Europe 32, Africa 9, North America 4, Central and South America 7)
 –GWL 3°C: 249 (0.37%)
 –(Asia 176, Australasia 9, Europe 38, Africa 11, North America 4, Central and South America 11)
 –GWL 4°C: 343 (0.51%)

–(Asia 241, Australasia 19, Europe 55, Africa 9, North America 6, Central and South America 14)Alfieri et al. (2017), with 
regional aggregation 
and currency conversion
No adaptation with current flood protection, USD 
at 2010 PPP , mean of five CMIP5 GCMs and 10 
hydrological models –Current (1976–2005): 142 (0.21%)
 –GWL 1.5°C, SSP3: 370 (0.55%), SSP5: 485 (0.72%)
 –GWL 2°C, SSP3: 597 (0.89%), SSP5: 888 (1.32%)
 –GWL 3°C, SSP3: 1024 (1.52%), SSP5: 1616 (2.40%)Dottori et al. (2018) with 
currency conversion
No adaptation and no flood protection, mean value 
in 2030 (2010–2030) and 2080 (2010–2080), USD 
at 2010 PPP , mean of five CMIP5 GCMs –Current (1960–1999): 1,032 (1.6%)
 –RCP2.6, SSP1: 2030: 2366 (1.44%), 2080: 7429 (1.43%)
 –RCP6.0, SSP3: 2030: 1987 (1.44%), 2080: 3353(1.14%)
 –RCP8.5, SSP5: 2030: 2304 (1.37%), 2080: 3684(1.77%)Winsemius et al. (2016)
Partial adaptation (protected against 100-year 
floods in high-income countries, against 5-year

floods for all others), mean value in 2030 
(2010–2030) and 2080 (2010–2080), USD at 2010 
PPP , mean of five CMIP5 GCMs –Current (1960–1999): 163 (0.25%)
 –RCP2.6, SSP1: 2030: 558 (0.34%), 2080: 851 (0.48%)
 –RCP6.0, SSP3: 2030: 418 (0.29%), 2080: 413(0.32%)
 –RCP8.5, SSP5: 2030: 418 (0.33%), 2080: 441 (0.57%)Winsemius et al. (2016)
A model calibrated to fit reported damages, future 
vulnerability scenarios considering autonomous 
adaptation, USD at 2005 PPP , mean of 11 CMIP5 
GCMs, –Current (1991–2005): 14 (0.044%)
 –RCP2.6, SSP1: 2081–2100, 121 (0.037%)
 –RCP6.0, SSP2: 2081–2100, 133 (0.042%)
 –RCP8.5, SSP3: 2081–2100, 130 (0.063%)Kinoshita et al. (2018)
No adaptation and current flood protection, USD at 
2005 PPP , mean of five CMIP5 GCMs –Current (1961–2005): 102 (0.39%)
 –RCP2.6, SSP1: 2020–2100, 2333 (0.99%)
 –RCP4.5, SSP2: 2020–2100, 2221 (0.99%)
 –RCP6.0, SSP3: 2020–2100, 1328 (0.80%)
 –RCP8.5, SSP5: 2020–2100, 4007 (1.21%)Tanoue et al. (2021)

Optimised adaptation, USD at 2005 PPP , mean of 
five CMIP5 GCMs –Current (1961–2005): 102 (0.39%)
 –RCP2.6, SSP1: 2020–2100, 1621 (0.69%)
 –RCP4.5, SSP2: 2020–2100, 1567 (0.70%)
 –RCP6.0, SSP3: 2020–2100, 872 (0.52%)
 –RCP8.5, SSP5: 2020–2100, 2558 (0.77%)Tanoue et al. (2021)

608century with RCP8.5, but there is only low agreement in the projected 
magnitude of snowmelt flood (Arheimer and Lindström, 2015; Vormoor 
et al., 2016; Islam et al., 2019b).
Challenges to projecting flood risk are large because of the complexity of 
the projecting snowmelt, high-intensity rainfall and soil wetness in large 
river basins. Even though increases in the number and area of glacier 
lakes may cause increases in glacier-related floods (Section  4.2.2), 
knowledge of the frequency or magnitude of glacier-related projected 
floods is limited. Some local studies indicate that the severity of ice-jam 
flooding is projected to decrease (Rokaya et al., 2019; Das et al., 2020), 
but a model study in Canada projected increases in damage of ice-jam 
floods (Turcotte et al., 2020). While most flood risk projections do not 
consider the impact of urban expansion, Güneralp et al. (2015) estimate 
that urban areas exposed to flooding will increase by a factor of 2.7

due to urban growth by 2030 (Section  4.5.4). Given the significant 
differences in assumption in flood protection, exposure or vulnerability 
scenario among studies, uncertainties in the global estimation of flood 
losses and damages are large are large (Table 4.6, 4.7.5).
Floods and their societal impacts, especially the enhancement of hazards 
and increase in vulnerability, depend on complex political, economic 
and cultural processes (Carey et al., 2017; Caretta et al., 2021). Thus, 
assessments that analyse long-term flood impacts need to account 
for the interplay of water and society relations. Unfortunately, such 
studies remain scarce (Pande and Sivapalan, 2017; Ferdous et al., 2018; 
Caretta et  al., 2021). In particular, projected socioeconomic, cultural 
and political impacts on the vulnerable group are understudied, as is 
their resourcefulness through LK, adaptive capacity and community-
led adaptation (Sections  4.6.9; 4.8.4; Cross-Chapter Box  INDIG in 
Chapter 18).

In summary, there is high confidence that the magnitude and frequency 
of floods are projected to increase in many regions, including Asia, 
central Africa, western Europe, Central and South America and eastern 
North America, and decrease in northern North America, southern South 
America, the Mediterranean and eastern Europe. Projected increases in 
flooding pose increasing risks, with a 1.2–1.8 and 4–5 times increase 
in global GDP loss at 2°C and 4°C compared to 1.5°C warming, 
respectively (medium confidence). Without adaptation, projected 
increases in flooding are 1.4 to 2.5 and 2.5 to 3.9 times in global GDP 
loss at 2°C and 3°C compared to 1.5°C warming, respectively (medium 
confidence). However, regional differences in risks are large because 
of the strong influence of socioeconomic conditions and significant 
uncertainty in flood hazard projection. In small river basins and urban 
areas, there is medium confidence that projected increases in heavy

rainfall would contribute to increases in rain-generated local flooding. 
However, the snowmelt floods are projected to decrease (medium 
confidence) and occur 25–30 d earlier in the year by the end of the 
21st century with RCP8.5 (high confidence).
4.4.5 Projected Changes in Droughts
AR6 WGI (Douville et  al., 2021) concluded that the total land area 
subject to increasing drought frequency and severity would expand 
(high confidence), and in the Mediterranean, southwestern South America and western North America, future aridification will far exceed 
the magnitude of change seen in the last millennium (high confidence). 
WGI (Seneviratne et al., 2021) also find many consistencies among 
projections of climate change effects on different forms of drought 
(meteorological, agricultural/ecological and hydrological drought, 
4.2.5), but also significant differences in some regions, particularly in 
the levels of confidence in projected changes.

Many studies focus on precipitation-based drought indices (Carrão 
et al., 2018), but higher evaporative demands and changes in snow 
cover are additional drivers of hydrological, agricultural and ecological 
drought (medium confidence) in many regions of the world (Koirala 
et al., 2014; Prudhomme et al., 2014; Touma et al., 2015; Wanders et al., 
2015; Zhao and Dai, 2015; Naumann et al., 2018; Cook et al., 2020a). 
Furthermore, these droughts (hydrological, agricultural and ecological) 
are often modulated by prevailing soil and hydro-morphological 
characteristics. Therefore, the choice of drought definition can affect 
the magnitude and even the sign of the projected drought change.
In a study with multiple climate models, global water models and 
scenarios, the choice of drought definition was the dominant source 
of uncertainty in the sign of projected change in drought frequency 
in over 17% of global land by 2070–2099, including several major

wheat- and maize-growing areas where agricultural (soil moisture) 
drought is of high importance (Satoh et al., 2021). Cook et al. (2020a) 
noted that in the CMIP6 projections, soil moisture and runoff drying 
are more robust, spatially extensive and severe than precipitation, 
resulting in the frequency of agricultural drought increasing over wider 
areas than for meteorological drought. At 1.5°C global warming, the 
likelihood of extreme agricultural (soil moisture) drought is projected 
to at least double (100% increase) over large areas of northern South 
America, the Mediterranean, western China and high latitudes in 
North America and Eurasia (Figure 4.18, left column). The likelihood 
is projected to increase by 150–200% in these regions at 2°C global 
warming, with an expansion of the affected areas, and increase by 
over 200% at 4°C global warming. Agricultural drought likelihood also 
increases by 100–250% at 4°C global warming in southwestern North

America, southwest Africa, southern Asia and Australia. The likelihood 
of extreme drought is projected to decrease in central North America, 
the Sahel, the Horn of Africa, the eastern Indian sub-continent and parts 
of western and eastern Asia. Using eight global hydrological models 
driven by a subset of four of the CMIP5 climate models, Lange et al. 
(2020) projected a 370% (30–790%) increase of the global population 
annually exposed to agricultural (soil moisture) droughts in response 
to 2°C global warming. Therefore, it is essential to consider the drought 
type when applying drought projections to impact and risk in decision-
making, especially for informing adaptation. For example, if responses 
are explicitly tailored to agricultural (soil moisture) drought changes, 
projected changes in a meteorological (precipitation) drought metric 
may not provide accurate information.
Compared to CMIP5, the CMIP6 ensemble projects more consistent

drying in the Amazon basin (Parsons, 2020), more extensive declines in 
total soil moisture in Siberia (Cook et al., 2020a) and stronger declines in 
westernmost North Africa and southwestern Australia. Projected declines 
in soil moisture in these geographies would cause a significant risk of 
agricultural drought. Also, importantly, projected changes in drought in

609many regions depend on the season and may not be evident in annual 
mean changes. For example, in northwestern Asia, hydrological (runoff) 
drought frequency is projected to decrease by 50–100% in autumn and 
winter but increase by up to 250% in spring and summer (Cook et al., 
2020a). In contrast, meteorological (precipitation) drought frequency is 
projected to increase by up to 350% throughout the year.
Drought projections are subject to uncertainties due to limits of 
predictability and understanding of the relevant biophysical processes. Uncertainties in regional climate changes are significant in many 
regions (see Figure 4.10, Figure 4.13, Figure 4.15), and in climate model 
ensembles, the range of regional outcomes generally increases with 
global warming. This widening of the range of outcomes can contribute 
to the increased likelihood of extreme droughts across the ensemble 
as a whole (Figure 4.18, right column). The response of transpiration

to elevated CO 2 is also a significant uncertainty. The inclusion of CO 2 
physiological effects leads to smaller projected increases in agricultural, 
ecological or hydrological drought (Milly and Dunne, 2016; Yang et al., 
Projected changes in the likelihood of an extreme single-year agricultural (soil moisture) drought event
Changes in likelihood of extreme agricultural (soil moisture) drought years
+4.0°C
+4°C
+2°C
+1.5°C
Drier Wetter
Monsoon
(SAM)
(MED)
West Southern
Africa (WSAF)
Z-ScoreProbability distribution of annual soil moisture anomalies
SAM
WSAFMEDSAM
WSAFMED
+2.0°C +1.5°C 1995–2014 +250% +200% +150% +100% +50% 0 -50% -100%
Figure 4.18 |  Projected changes in the likelihood of an extreme single-year agricultural (soil moisture) drought event, with extreme drought defined as the 
driest 10% of years from 1995 to 2014, using total soil moisture projections pooled from the CMIP6 ensemble following Cook et al. (2020a). All ensemble

members are treated as equally likely potential outcomes, and likelihoods are calculated using the whole ensemble. Left: Percentage change in the likelihood of extreme drought at 
GWLs of 4°C (top), 2°C (middle) and 1.5°C (bottom), with ‘extreme drought’ defined locally as the 10th percentile in individual grid boxes. Right: probability distribution functions 
of regional mean soil moisture anomalies for the climatic regions Mediterranean (MED), South American Monsoon (SAM) and West Southern Africa (WSAF) (Iturbide et al., 2020), at 
1.5°C, 2°C and 4°C GWLs. The solid vertical line shows the baseline, that is, the 50th percentile in 1995–2014. The dashed vertical line shows the 10th percentile for 1995–2014, 
defining ‘extreme drought’ at the regional scale. Projections used the SSP5-8.5 scenario to maximise the number of ensemble members at higher GWLs, but global patterns of

change are very similar for all scenarios (Cook et al., 2020a), and for any given GWL, similar results can be expected with other scenarios (Seneviratne et al., 2021).

6102020). However, the level of uncertainties in representing the effects of 
CO 2 is still very high, precluding conclusive results in a global analysis 
(de Kauwe et al., 2013; Prudhomme et al., 2014; Yang et al., 2016). 
Most CMIP6 climate models include CO 2 physiological effects, but 
many hydrological models used for impacts studies do not.
Terrestrial water storage (TWS) is the sum of continental water stored 
in canopies, snow and ice, rivers, lakes and reservoirs, wetlands, soil 
and groundwater (Pokhrel et  al., 2021). TWS drought can therefore 
be considered to be a combination of agricultural, ecological and 
hydrological drought. The proportion of the global population exposed 
to TWS drought is projected to increase with ongoing climate change 
(Figure 4.19). By the late 21st century, under RCP6.0, the global land 
area in extreme-to-exceptional TWS drought is projected to increase 
from 3% to 7% (Pokhrel et  al., 2021), with increasing uncertainty

over time. Combined with a medium population growth scenario 
(SSP2), this leads to the global population in this level of drought 
increasing from 3% to 8%, again with increasing uncertainty over 
time. Hydrological droughts can also be driven by direct human impact 
via water abstraction (Javadinejad et al., 2019).
Critical knowledge gaps include uncertainties in regional drought due 
to regional climate change uncertainties, challenges in constraining 
plant physiological responses to atmospheric CO 2, and the uncertainties 
in modelling the role of different population projections in projecting 
regional drought risk.
In summary, the likelihood of drought is projected to increase in many 
regions over the 21st century (high confidence) even with strong 
climate change mitigation, and more severely in the absence of this. 
Different forms of drought broadly show similar patterns of projected 
change in many regions (high confidence), but the frequency of

agricultural drought is projected to increase over wider areas than for 
meteorological drought (medium confidence). Clarity on the definition 
of drought is therefore important for informing decision-making. With 
the RCP6.0 and SSP2 scenarios, the global population exposed to 
extreme-to-exceptional terrestrial water storage drought is projected 
to increase from 3% to 8% over the 21st century.4.4.6 Projected Changes in Groundwater
AR5 concluded that the range of projected future changes in 
groundwater storage was large, from statistically significant declines 
to increases due to several uncertainties in existing models (Jiménez 
Cisneros et al., 2014). AR6 (Douville et al., 2021) concluded with high 
confidence that projected increases in precipitation alone cannot ensure 
an increase in groundwater storage under a warming climate unless 
unsustainable trends in groundwater extraction are also reversed.
Projected impacts of climate change on groundwater systems are

commonly simulated using models at local to global scales (Bierkens and 
Wada, 2019). The relations between climate change and groundwater 
are more complex than those embedded in current numerical models 
(Cuthbert et  al., 2019b). For instance, groundwater systems register 
effects of drought with several years of lag effect, and aquifer response 
times to changes in hydraulic forcing also vary across aquifers (Cuthbert 
et al., 2019a). For instance, long groundwater response times can buffer 
drought impacts and lengthen recovery times to sustained drought 
events (Van Lanen et al., 2013; Opie et al., 2020).
Global total and non-renewable groundwater withdrawals are 
projected to increase from 952 km3 year–1 (2010) to 1621 km3 year–1 
(2099) and from 304 km3 year–1 (2010) to 597 km3 year–1 (2099), 
respectively (Bierkens and Wada, 2019). At the same time, groundwater 
depletion is projected to increase from approximately 204 (± 30) km3

year–1 in 2000 to 427 (± 56) km3 year–1 by 2099 (Wada, 2016). Much 
of the projected depletion is a function of increased future abstraction 
of groundwater for irrigation and increased ET (Condon et al., 2020) 
in a warmer climate. For example, the projected doubling of average 
water use by 2050 in Tunisia is attributed partly (3.8–16.4%) to climate 
change and mainly to socioeconomic policies (Guermazi et al., 2019). 
Similarly, groundwater depletion in the Bengal Basin and North China 
Plain is more due to irrigation development than climate change per se 
(Leng et al., 2015; Kirby et al., 2016).
A recent synthesis of modelling studies conducted in various climates 
showed that out of 33 studies, 21 reported a decrease in the projected Projected changes in the area under drought and population affected
Percentage of global population Percentage of global land area 
5%10%15%20%25%30%
0

2006 2020 2030 2040 2050 2060 2070 2080 2090 2099 2006 2020 2030 2040 2050 2060 2070 2080 2090 2099Extreme-exceptionalModerate-severeDroughts
change under RCP6.0**
relative to 1976–2005
= Population projections 
based on Shared 
Socio-Economic 
Pathway 2 (SSP2)*
= ~1.3°C to 2.5°C Global 
Warming Level 
between 2041–2060**
**
Figure 4.19 |  Projected changes in the area under drought and population affected, defined with changes in the Terrestrial Water Storage–Drought Severity 
Index (TWS-DSI) projected with seven terrestrial hydrology models driven by four CMIP5 climate models using RCP6.0.
(a) Fractional global land area under moderate-to-severe drought (top), defined as −0.8 ≤ TWS-DSI < −1.6, and extreme-to-exceptional drought (bottom), defined as TWS-DSI < −1.6.
(b) Fraction of global population exposed to moderate-to-severe (top) and extreme-to-exceptional (bottom) drought, using the SSP2 population projection. Dark lines show the

ensemble means; shaded areas indicate uncertainty as ± 1 standard deviation. Reproduced from Pokhrel et al. (2021).

611groundwater recharge or storage, eight reported an increase and the 
rest showed no substantial change (Amanambu et al., 2020). A global-
scale multi-model ensemble study projected decreasing recharge in 
southern Chile, Brazil, central continental USA, the Mediterranean 
and East China, but consistent and increasing recharge for northern 
Europe and East Africa (Reinecke et al., 2021). In continental Spain, a 
modelling study (Pulido-Velazquez et al., 2018) projected significant 
reductions in groundwater recharge in the central and southeast 
region but a small and localised increase in east and northeastern 
areas. In subarctic Alaska, increased contribution of glacier melts to 
streamflow and aquifer recharge under a warming climate is projected 
(Liljedahl et  al., 2017). In contrast, over the Iranian and Anatolia 
Plateaus, groundwater recharge is projected to reduce by ~77% in 
the spring season (March–May) due to a decrease in snowfall (Wu

et al., 2020). Overall, several recent studies of climate change impacts 
on groundwater in different parts of the world have concluded that 
projected groundwater recharge could either increase or decrease, 
and results are often uncertain (high confidence) (Meixner et al., 2016; 
Zaveri et al., 2016; Hartmann et al., 2017; Mehran et al., 2017; Tillman 
et al., 2017; Kahsay et al., 2018; Herbert and Döll, 2019).
Wu et al. (2020) reported a projected increase in future groundwater 
storage in the semiarid regions of northwest India, North China Plain, 
the Guarani Aquifer in South America and Canning Basin in Australia 
due to significant increases in projected precipitation, but the models 
do not consider local hydrogeological characteristics. However, the 
projected irrigation expansion could negate this positive gain in 
groundwater storage (Sishodia et al., 2018; Wu et al., 2020). In drylands 
(e.g., playas in the southwestern USA), where focused groundwater

recharge processes dominate, greater recharge is projected to 
occur from the increased number of significant runoff-generating 
extreme precipitation events in the future (McKenna and Sala, 2018). 
Overall, an emerging body of studies have projected amplification of 
episodic recharge in the tropics and semiarid regions due to extreme 
precipitation under global warming (medium confidence).
Climate change is also projected to impact groundwater-dependent 
ecosystems and groundwater quality negatively (medium confidence). 
Projected increase in precipitation intensity and storms can contaminate 
groundwater by mobilising contaminants such as chemical fertilisers, 
pesticides and antibiotics and leaching of human waste from pit 
latrines into groundwater (Amanambu et al., 2020; Lall et al., 2020). By 
2050, environmentally critical streamflow is projected to be affected in 
42–79% of the world’s watersheds. The majority of these watersheds

currently experience intensive groundwater use, and changes in critical 
streamflow are projected to negatively impact aquatic ecosystems (de 
Graaf et al., 2019). Using a global synthesis of 9404 data points from 
32  countries across six continents, McDonough et  al. (2020) report 
increases in DOC concentrations in groundwater following projected 
changes in precipitation and temperature. For example, hotspots 
of high DOC concentration (increases of up to 45%) are associated 
mainly with increased temperatures in the wettest quarter of the year 
in the southeastern USA under RCP8.5 scenarios.
The projected rise in sea levels can lead to saline intrusion into aquifers 
in low-lying areas and small islands and threaten coastal ecosystems 
and livelihood resilience; for example, in already vulnerable countries like Bangladesh and vulnerable ecosystems like the mangrove forest of 
Sundarbans (Befus et al., 2020; Dasgupta et al., 2020; Shamsudduha

et  al., 2020). However, hydrogeological properties, aquifer settings 
and impacts of over-abstraction are more important determinants of 
salinisation of coastal aquifers than slowly rising sea levels (Michael 
et al., 2013; Taylor et al., 2013a). The projected contribution of global 
groundwater depletion to sea level rise is expected to increase from 
0.57 (± 0.09) mm year–1 in 2000 to 0.82 (± 0.13) mm year–1 by 2050, 
driven by a growing trend in groundwater extraction (Wada, 2016). 
However, several uncertainties around model parametrisation remain 
(Wada et al., 2017).
There are several knowledge gaps in our understanding of the global-
scale sensitivity of groundwater systems to climate change and resulting 
feedbacks (Maxwell and Condon, 2016; Cuthbert et al., 2019a). There 
are process uncertainties in groundwater recharge simulation due to the 
potential impact of atmospheric CO 2 on vegetation and resulting changes

in ET (Reinecke et al., 2021). There are uncertainties in impact models 
due to poor representation of recharge pathways (diffuse compared to 
focused) and inability to adequately capture feedbacks among climate, 
land use and groundwater systems (Meixner et al., 2016). Finally, there 
are gaps in long-term observational data, especially in less-developed 
countries (Amanambu et al., 2020), making it challenging to evaluate 
the performance of impact models (Gleeson et al., 2020).
In summary, groundwater abstraction is projected to deplete the long-
term, non-renewable storage as withdrawals are projected to increase 
significantly in all major aquifers worldwide (medium evidence, high 
agreement). In the tropics and semiarid regions, growing precipitation 
intensification under global warming may enhance the resilience 
of groundwater through increased episodic recharge (medium 
confidence). However, in the semiarid areas, over-abstraction continues

to be a threat to groundwater storage and can nullify the benefits of 
increased future recharge.
4.4.7 Projected Changes in Water Quality
AR5 concluded that climate change was projected to reduce water quality 
(Jiménez Cisneros et  al., 2014). SR1.5 assessed with low confidence  
differences in projected impacts under 1.5°C compared with 2°C of 
warming (Hoegh-Guldberg et  al., 2018). In addition, SROCC reported 
water quality degradation due to the release of legacy contaminants in 
glaciers and permafrost (medium confidence) (Hock et al., 2019b). The 
AR6 WGI Report does not explicitly mention water quality issues.
Water insecurity due to water quality degradation is projected to 
increase under climate change due to warming, enhanced floods and sea 
level rise (Arnell and Lloyd-Hughes, 2014; Dyer et al., 2014; Whitehead 
et al., 2015) (medium confidence). Drought-driven diminishing river and 
lake levels (Jeppesen et al., 2015) and continued water abstraction for

irrigation (Aragüés et al., 2015) may contribute to the salinisation of 
soil and water. In addition, warming is projected to disrupt the historical 
sequestration of contaminants in permafrost in the Arctic and mountain 
regions (Bond and Carr, 2018).

612Quantitative projections on climate-induced water quality degradation 
are sparse. Aminomethylphosphonic acid and glyphosate are 
projected to exceed drinking water quality standards in dry years 
in a high-emissions scenario in the Meuse River in Europe by 2050 
(Sjerps et al., 2017). From 2020 to 2050, based on scenarios RCP2.6, 
RCP4.5 and RCP8.5, the incidences of total nitrogen pollution are 
projected as 97.3, 97.1 and 94.6%, respectively, in drought–flood 
abrupt alternation months compared to 69.3, 69.7 and 67.5% in 
normal months in the Luanhe River basin in China (Bi et al., 2019). 
From 2012 to 2050, freshwater river area is expected to decrease from 
40.8% to 17.1–19.7% under different sea level rise scenarios in the 
southwest coastal zone of Bangladesh (Dasgupta et al., 2013). Under 
the warming scenario of +4.8°C increase by the end of the century, the 
average nutrient abundance is projected to triple in a shallow lake in

the northwest of England (Richardson et al., 2019).
While there is some understanding of the potential effect of glacier 
and permafrost degradation on water quality, projections are lacking. 
Research is limited mainly in Europe and North America, and quantifying 
the future water quality changes is still incipient.
In summary, climate change is projected to increase water pollution 
incidences, salinisation and eutrophication due to increasing 
drought and flood events, sea level rise and water temperature rise, 
respectively, in some local rivers and lakes, but there is a dearth of 
exact quantification at a global scale (medium confidence).
4.4.8 Projected Changes in Soil Erosion and Sediment 
Load
AR5 stated that soil erosion and sediment load are projected to change 
(low confidence) due to warming and increased rainfall intensity (Jiménez 
Cisneros et al., 2014). SRCCL concluded that future climate change will

increase, with medium confidence, the potential for water-driven soil 
erosion in many dryland areas, causing soil organic carbon decline 
(Mirzabaev et al., 2019). SR1.5 (Hoegh-Guldberg et al., 2018) concluded 
that because of the complex interactions among climate change, land 
cover, soil management, etc., the differences between mean annual 
sediment load under 1.5°C and 2°C of warming are unclear.
Globally, climate change is estimated to be responsible for 30–66% 
increase of soil erosion by 2070, while socioeconomic developments 
impacting land use may lead to ± 10% change of soil erosion (Borrelli 
et al., 2020). At a regional scale, different effects of the climate change 
impact on soil losses are found owing to the ensemble experiments 
with climate models coupled with regional/local models of soil 
erosion and sediment yield. In the 21st century, the soil erosion rates 
are projected to increase for the European countries (Czech Republic

(Svoboda et al., 2016), Belgium (Mullan et al., 2019), Spain (Eekhout 
et  al., 2018; Eekhout and de Vente, 2019a; Eekhout and De Vente, 
2019b), Germany (Gericke et al., 2019)) by 10–80% depending on the 
emission scenario and time period of the projection, as well as for the 
USA (Garbrecht and Zhang, 2015) and Australia (Yang et al., 2015b; 
Zhu et al., 2020). Only a few studies demonstrated decreasing trend 
in soil erosion, for example, up to 9% with RCP8.5 scenario in Greece 
(Vantas et al., 2020). Sediment yield is projected to both increase (5–16% with the SRES A1, B1, B2 scenarios in Vietnam and Laos (Giang 
et al., 2017), 11% with the RCP8.5 scenario and 8% with the SRES 
A2 scenario in the USA (Yasarer et al., 2017 and Wagena et al., 2018, 
respectively), 19–37% with the RCP4.5, RCP8.5 scenarios in Burkina 
Faso (Op de Hipt et  al., 2018)) and decrease (30% with the SRES 
A1B scenario in the southwest USA (Francipane et al., 2015), 8–11%

with the SRES A1B scenario in Spain (Rodríguez-Blanco et al., 2016), 
11–52% with the RCP4.5, RCP8.5 scenarios in Ethiopia (Gadissa et al., 
2018), 13–62% with the RCP2.6, RCP8.5 scenarios in Canada (Loiselle 
et al., 2020)) over the different regions of the world in the 21st century.
Post-fire sedimentation is projected to increase for nearly nine tenths 
of watersheds by >10% and for more than one third of watersheds by 
>100% by the 2041 to 2050 decade in the western USA with the SRES 
A1B scenario (Sankey et al., 2017).
In summary, soil losses mainly depend on the combined effects of 
climate and land use changes. Herewith, recent studies demonstrate 
increasing impact of the projected climate change (increase of 
precipitation, thawing permafrost) on soil erosion (medium confidence).
4.5 Projected Sectoral Water-Related Risks
Observed sectoral water-related impacts have been documented across 
world regions. Climate change is projected to further exacerbate many

of these risks, especially at warming levels above 1.5°C (Figure 4.20). For 
some sectors and regions, climate change may also hold the potential 
for beneficial outcomes, though feedback and cascading effects 
as well as risks of climate extremes are not always well understood 
and often underestimated in impact projections. Risks manifest as a 
consequence of the interplay of human and natural vulnerability, sector-
specific exposure as well as the climate hazard as a driver of climate 
change. Challenges to water security are driven by factors across these 
components of risk, where climate change is but one facet of driving 
water insecurity in the face of global change. While the focus of this 
chapter is on climate change and its effects on water security, for many 
sectors and regions the dynamics of socioeconomic conditions is a core 
driver. They play an essential role in understanding and alleviating water

security risks. The following sections outline sectoral risks for both, risks 
driven by water-related impacts, such as drought, flood or changes in 
water availability, as well as risks with effects on water uses, mainly 
focusing on changing water demand as a consequence of climate 
change. It therefore does not cover all climate change-driven risks to 
the respective sectors, but is limited to those that stand in relation to 
water. The focus within this chapter is on global to regional processes 
(additional regional to local information in Table SM4.4; Figure 4.20 as 
well as across regional chapters of this report).
4.5.1 Projected Risks to Agriculture
AR5 concluded that overall irrigation water demand would increase by 
2080, while the vulnerability of rain-fed agriculture will further increase 
(Jiménez Cisneros et al., 2014). SR1.5 concluded that both the food and 
the water sectors would be negatively impacted by global warming

with higher risks at 2°C than at 1.5°C, and these risks could coincide

613spatially and temporally, thus increasing hazards, exposures and 
vulnerabilities across populations and regions (medium confidence). 
SR1.5 further reinforced AR5 conclusions in terms of projected crop 
yield reductions, especially for wheat and rice (high confidence), loss of 
livestock and increased risks for small-scale fisheries and aquaculture 
(medium confidence) (Hoegh-Guldberg et al., 2018), conclusions which 
are further corroborated by SRCCL (Mbow et al., 2019).
Climate change impacts agriculture through various pathways (5.4 – 
Crop-based Systems), with projected yield losses of up to 32% by 2100 
(RCP8.5) due to the combined effects of temperature and precipitation. 
Limiting warming could significantly reduce potential impacts (up 12% 
yield reduction by 2100 under RCP4.5) (Ren et  al., 2018a). Though 
overall changes differ across models, regions and seasons, differences 
in impacts between 1.5°C and 2°C can also be identified (Ren et al.,

2018a; Ruane et al., 2018; Schleussner et al., 2018). Globally, 11% (± 
5%) of croplands are estimated to be vulnerable to projected climate-
driven water scarcity by 2050 (Fitton et al., 2019).
Overall drought-driven yield loss is estimated to increase by 9–12% 
(wheat), 5.6–6.3% (maize), 18.1–19.4% (rice) and 15.1–16.1% 
(soybean) by 2071–2100, relative to 1961–2016 (RCP8.5) (Leng and 
Hall, 2019). In addition, temperature-driven increases in water vapour 
deficit could have additional negative effects, further exacerbating 
drought-induced plant mortality and thus impacting yields (Grossiord 
et  al., 2020) (see also Cross-Chapter Box  1 in Chapter 5 of WGI 
report). Currently, global agricultural models do not fully differentiate 
crop responses to elevated CO 2 under temperature and hydrological 
extremes (Deryng et al., 2016) and largely underestimate the effects of 
climate extremes (Schewe et al., 2019).
Flood-related risks to agricultural production are projected to increase

over Europe, with a mean increase of expected annual output losses of 
approximately €11 million (at 1.5°C GWL); €12 million ( at 2°C GWL) 
and €15 million (at 3°C GWL) relative to the 2010 baseline (Koks et al., 
2019). In parts of Asia, where flooding impacts on agriculture are 
already significant, projections indicate an increase in damage to area 
under paddy by up to 50% in Nepal, 16% in the Philippines, 55% in 
Indonesia, 23% in Cambodia and Vietnam and 13% in Thailand (2075–
2099 compared with 1979–2003; RCP8.5) (Shrestha et al., 2019a).
Global crop water consumption of green water resources (soil 
moisture) is projected to increase by about 8.5% by 2099 relative to 
1971–2000 as a result of climate drivers (RCP6.0), with additional 
smaller contributions by land use change (Huang et  al., 2019) 
(Sections 4.4.1.3, 4.4.8). In India, a substantial increase in green and 
blue water consumption is projected for wheat and maize, with a slight

reduction of blue water consumption for paddy fields (Mali et al., 2021). 
Temperate drylands, especially higher latitude regions, may become 
more suitable for rain-fed agriculture (Bradford et al., 2017). Locally 
and regionally, however, some of those areas with currently larger 
areas under rain-fed production, for example, in Europe, may become 
less suitable for rain-fed agriculture (Table 1 to 4.5.1) (Bradford et al., 
2017; Shahsavari et al., 2019).
While global crop models and estimates of yield impacts often focus 
on major staple crops relevant for global food security, crops of high economic value are projected to become increasingly water dependent. 
For example, climate-driven yield increases for tea are projected for 
various tea-producing regions if no water limitations and full irrigation 
is assumed, but decreases in yields are projected under continued 
present-day irrigation assumptions (Beringer et al., 2020). Water-related

impacts on global cotton production are highly dependent on the CO 2-
fertilisation effect, with increases projected for higher CO 2 concentration 
if no water limitations are implemented. However, substantial decreases 
in cotton production are projected if lower or no fertilisation effects are 
accounted for due to increasing water limitations (Jans et al., 2018). 
Reductions in economically valuable crops will probably increase the 
vulnerability of population groups, especially small-holder farmers with 
limited response options (Morel et al., 2019).
To stabilise yields against variations in moisture availability, irrigation 
is the often the most common adaptation response (Section  4.6.2, 
Box  4.3). Projections indicate a potentially substantial increase in 
irrigation water requirements (Boretti and Rosa, 2019). Increasing 
agricultural water demand is driven by various factors, including 
population growth, increased irrigated agriculture, cropland expansion

and higher demand for bio-energy crops for mitigation (Chaturvedi 
et al., 2015; Grafton et al., 2015; Turner et al., 2019; 4.7.6). Depending 
on underlying assumptions and the constraints on water resources 
implemented in the global agricultural models, irrigation water 
requirements are projected to increase two- to three-fold by the end of 
the century (Hejazi et al., 2014; Bonsch et al., 2015; Chaturvedi et al., 
2015; Huang et al., 2019). While the combined effects of population 
and land use change as well as irrigation expansion account for the 
significant part of the projected increases in irrigation water demand 
by the end of the century, around 14% of the increase is directly 
attributed to climate change (RCP6.0) (Huang et al., 2019).
With various degrees of water stress being experienced under current 
conditions and further changes in regional water availability projected, 
as well as continuing groundwater depletion as a consequence of

over-abstraction for irrigation purposes (Sections  4.2.6 and 4.4.6), 
limitations to major irrigation expansion will occur in some regions, 
including South and Central Asia, the Middle East and parts of North and 
Central America (Grafton et al., 2015; Turner et al., 2019). Constraining 
projections of available irrigation water through consideration of 
environmental flow requirements further reduces the potential for 
irrigation capacity and expansion (Bonsch et  al., 2015). Changes in 
land use and production patterns, for example, expansion of rain-fed 
production and increasing inter-regional trade, would be required to 
meet growing food demand while preserving environmental flow 
requirements, though this may increase local food security-related 
vulnerabilities (Cross-Chapter Box  INTERREG in Chapter 16) (Pastor 
et al., 2014). Where climate impacts on yields are not a consequence 
of water limitations (mainly for C4 crops), irrigation cannot offset

negative yield impacts (Levis et al., 2018).
Over 50% of the global lowlands equipped for irrigation will depend 
heavily on runoff contributions from the mountain cryosphere by 
2041–2050 (SSP2–RCP6.0) and are projected to make unsustainable 
use of blue water resources (Viviroli et al., 2020). Projected changes in 
snowmelt patterns indicate that for all regions dependent on snowmelt 
for irrigation during warm seasons, alternative water sources will have

614to be found for up to 20% (at 2°C GWL) and up to 40% (at 4°C GWL) 
of seasonal irrigation water use, relative to current water use patterns 
(1986–2015) (Qin et al., 2020). Regional studies further corroborate 
these global findings (Biemans et al., 2019; Malek et al., 2020). Basins 
where such alternate sources are not available will face agricultural 
water scarcity.
Elevated CO 2 concentrations play an important role in determining 
future yields in general and have the potential to beneficially affect 
plant water use efficiency (Deryng et  al., 2016; Ren et  al., 2018a; 
Nechifor and Winning, 2019). The elevated CO 2 effects are projected to 
be most prominent for rain-fed C3 crops (Levis et al., 2018). Combined 
results from field experiments and global crop models show that CO 2 
fertilisation could reduce consumptive water use by 4–17% (Deryng 
et al., 2016). To account for uncertainties, global agricultural models

provide output with and without account for CO 2 fertilisation effects, 
though recent progress on reducing model uncertainty indicates that 
non-CO 2 model runs may no longer be needed for adequate projections 
of yield impacts (Toreti et al., 2019).
Due to the complex interactions among determinants for livestock 
production, the future signal of water-related risks to this sector is 
unclear. Globally, 10% (± 5%) of pasture areas are projected to be 
vulnerable to climate-induced water scarcity by 2050 (Fitton et  al., 
2019). Water use efficiency gains through elevated CO 2 concentrations 
have the potential to increase forage quantities, though effects of 
nutritional values are ambiguous (Augustine et al., 2018; Derner et al., 
2018; Rolla et  al., 2019). In addition, spatial shifts in temperature/
humidity regimes may shift suitable regions for livestock production, 
opening up new suitable areas for some regions or encouraging shifts

in specific breeds better adapted to future climatic regimes (Rolla et al., 
2019) (5.5 – Livestock Systems and 5.10 Mixed Systems).
Projections of climate impacts on freshwater aquaculture are limited 
(5.9.3.1 – Projected Impacts; Inland freshwater and brackish aquaculture). 
In particular, in tropical regions, reductions in water availability, 
deteriorating water quality, and increasing water temperatures pose 
risks to terrestrial aquaculture, including temperature-related diseases 
and endocrine disruption (Kibria et  al., 2017, Section  4.4.7). On the 
other hand, freshwater aquaculture in temperate and arctic polar 
regions may benefit from temperature increases with an extension of 
the fish-growing season (Kibria et al., 2017).
Global crop models, which provide the basis for most projections 
of agricultural risk, continue to have limitations in resolving water 
availability. For example they do not fully resolve the effects of elevated

CO 2 for changing water use efficiency (Durand et al., 2018), potentially 
overestimating drought impacts on maize yield (Fodor et al., 2017) and 
may underestimate limitations to further expansion of irrigation (Elliott 
et al., 2014; Frieler et al., 2017b; Winter et al., 2017; Jägermeyr and 
Frieler, 2018; Kimball et al., 2019; Yokohata et al., 2020a).
In summary, agricultural water use is projected to increase globally 
due to cropland expansion and intensification and climate change-
induced changes in water requirements (high confidence). Parts of 
temperate drylands may experience increases in suitability for rain-
fed production based on mean climate conditions; however, risks to rain-fed agriculture increase globally because of increasing variability 
in precipitation regimes and changes in water availability (high 
confidence). Water-related impacts on economically valuable crops will 
increase regional economic risks (medium evidence, high agreement).

Regions reliant on snowmelt for irrigation purposes will be affected by 
substantial reductions in water availability (high confidence).
4.5.2 Projected Risks to Energy and Industrial Water Use
AR5 concluded with high confidence that climate-induced changes, 
including changes in water flows, will affect energy production, and the 
actual impact will depend on the technological processes and location 
of energy production facilities (Arent et  al., 2014). SR1.5 concluded 
with high confidence that climate change is projected to affect the 
hydropower production of northern European countries positively. 
However, Mediterranean countries like Greece, Spain and Portugal are 
projected to experience approximately a 10% reduction in hydropower 
potential under a 2°C warming level, which could be reduced by half 
if global warming could be limited to 1.5°C (Hoegh-Guldberg et al., 
2018). In addition, SROCC concluded with high confidence that an

altered amount and seasonality of water supply from snow and glacier 
melt is projected to affect hydropower production negatively (IPCC, 
2019a).
Since AR5, a large number of studies have modelled future changes 
in hydropower production due to climate-induced changes in volume 
and seasonality of streamflow and changes in sediment load due 
to accelerated melting of cryosphere at both global (van Vliet et al., 
2016b; Turner et al., 2017) and regional scales (Tarroja et al., 2016; Ali 
et al., 2018; de Jong et al., 2018; Tobin et al., 2018; Arango-Aramburo 
et al., 2019; Carvajal et al., 2019; Arias et al., 2020; Meng et al., 2021).
For hydropower production at a global scale, Turner et  al. (2017) 
projected an uncertainty in the direction of change in global hydropower 
production to the tune of +5% to −5% by the 2080s, under a high-
emissions scenario. On the other hand, van Vliet et al. (2016b) projected 
an increase in global hydropower production between +2.4% to +6.3%

under RCP4.5 and RCP8.5, respectively, by the 2080s, as compared to a 
baseline period of 1971–2000, but with significant regional variations 
(high confidence). For example, regions like central Africa, India, central 
Asia and northern high-latitude areas are projected to see more than 
20% increases in gross hydropower potential (high confidence). On the 
other hand, southern Europe, northern Africa, southern USA and parts 
of South America, southern Africa and southern Australia are projected 
to experience more than 20% decreases in gross hydropower potential. 
The Mediterranean region is projected to see almost a 40% reduction 
in hydropower production (high confidence) (Turner et al., 2017). On 
the other hand, northern Europe and India are projected to add to their 
hydropower production capacity due to climate change by mid-century 
(high confidence) (van Vliet et al., 2016b; Turner et al., 2017; Emodi 

In hydropower plants located in the Zambezi basin, electricity output 
is projected to decline by 10–20% by 2070 compared to baseline 
(1948–2008) under a drying climate; only marginal increases are 
projected under a wetting climate (Spalding-Fecher et al., 2017). In the

615Mekong Basin, the total hydropower generation is projected to decline 
by 3.0% and 29.3% under 1.5°C and 2°C, respectively (Meng et al., 
2021). In this context, 1.5°C will come up in 2036 under RCP2.6 and 
in 2033, under RCP6.0; and 2°C will come up in 2056 under RCP6.0 
(Frieler et al., 2017a). In India, hydropower production is projected to 
increase by up to 25% by the end of the 21st century due to increased 
temperature and precipitation under the RCP8.5 scenario. However, 
hydropower production is projected to decline in plants located in 
snow-dominated rivers due to earlier snowmelt (Ali et al., 2018). In 
Colombia, hydropower production is projected to decrease by ~10% 
under the RCP4.5 dry scenario by 2050 (Arango-Aramburo et al., 2019). 
In a sub-basin of the Amazon River (one of the hydropower hotspots 
in Brazil), dry-season hydropower potential is projected to decline by 
−7.4 to −5.4% from historical baseline conditions under RCP4.5 (Arias

et al., 2020). In the São Francisco basin of Brazil, hydropower production 
is projected to reduce by −15% to −20% by 2100 under the IPCC A1B 
scenario (de Jong et al., 2018), which will affect the Brazilian energy 
mix in the future. In Ecuador, under various policy pathways and dry 
and wet scenarios under RCP4.5, hydropower production is projected 
to increase by +7% to +21% or decline by −25% to −44% by 2050 
(Carvajal et al., 2019). In Europe, different impacts are projected across 
different sub-regions (WGII, Chapter 13, Table 13.7- Projected climate 
change risks for energy supply in Europe by 2100). In northern Europe, 
up to 20% of hydropower potential increases are projected under 3°C 
warming; increases of up to 15% and 10% are projected under 2°C and 
1.5°C warming levels. In Mediterranean parts of Europe, hydropower 
potential reductions of up to −40% are projected under 3°C warming; 
while reductions below −10% and −5% are projected under 2°C

and 1.5°C warming levels, respectively (van Vliet et al., 2016b; Tobin 
et al., 2018). Hydropower plants in Switzerland are projected to lose 
~1.0 TWh of hydroelectricity production per year by 2070–2090 due to 
net glacier mass loss in the earlier part of the century (Schaefli et al., 
2019). In the Italian Alps, under the warmest scenario of RCP8.4, up 
to 4% decreases in hydropower production are projected (Bombelli 
et  al., 2019). The magnitude of change differs significantly among 
models. In California, USA, the average annual hydropower generation 
is expected to decline by 3.1% under RCP4.5 by 2040–2050, compared 
to the baseline 2000–2010 (Tarroja et al., 2016). In the Skagit River 
basin in the USA, hydropower generation is projected to increase by 
19% in the winter/spring and decline by 29% in summer by the 2080s 
(Lee et al., 2016).
Apart from climate impacts on hydropower production, climate-
induced flood loads and reservoir water level change may lead to

dam failure under RCP2.6 and RCP4.5 scenarios (Fluixá-Sanmartín 
et al., 2018; Fluixá-Sanmartín et al., 2019) (medium confidence). For 
example, the incidence of 100-year floods in the Skagit River basin 
in the USA and peak winter sediments are projected to increase by 
49% and 335%, respectively, by 2080, necessitating fundamental 
changes in hydropower plant operation. Nevertheless, some risks, such 
as floods, will remain unmitigated even with changes in hydropower 
operation rules (Lee et al., 2016). Overall, impacts of future extreme 
events on energy infrastructure have been less studied than impacts of 
gradual changes (Cronin et al., 2018). Furthermore, future hydropower 
development may also impact areas of high freshwater megafauna 
in South America, South and East Asia and in the Balkan region, and 
sub-catchments with a high share of threatened freshwater species are particularly vulnerable (Zarfl et al., 2019). Therefore, future hydropower

dams will need to be sited carefully (Dorber et al., 2020).
There is high confidence that changes in future cooling water 
availability are projected to affect thermoelectric production capacity 
negatively at global (van Vliet et al., 2016b; Zhou et al., 2018b) and 
regional scales (Bartos and Chester, 2015; Behrens et al., 2017; Ganguli 
et al., 2017; Zhou et al., 2018b; Emodi et al., 2019). Global mean water 
temperature is projected to increase by +1°C for RCP2.6 and +2.7°C for 
RCP8.5 (van Vliet et al., 2016b). Correspondingly, global cooling water 
sufficiency is projected to decline by −7.9% to −11.4% by 2040–2069 
and −11.3% to −18.6% by 2070–2090 (Zhou et al., 2018b), thereby 
impacting thermoelectric power production.
In Asia, under a 2°C global warming scenario, coal power plants’ 
annual usable capacity factor in Mongolia, Southeast Asia and parts 
of China and India are projected to decrease due to water constraints

(Wang et  al., 2019b). In the EU, an assessment of 1326 thermal 
electric plants in 818 basins projected that the number of basins with 
water stress would increase from 47 in 2014 to 54 in 2030 (Behrens 
et  al., 2017) with consequent impacts on cooling water supplies. In 
the western USA, by 2050, vulnerable power plants are projected to 
lose 1.1–3.0% of average summer generation capacity, which could 
rise to 7.2 to 8.8% loss under a 10-year drought condition (Bartos 
and Chester, 2015). Further, 27% of thermoelectric production in the 
USA may be at severe risk of low-capacity utilisation due to water 
stress by 2030 (Ganguli et al., 2017). Thermoelectric plant capacity on 
the hottest summer day in the USA and EU is projected to fall by 2% 
under a 2°C global warming and by 3.1% under a 4°C global warming, 
requiring overbuilding of electricity infrastructure by 1–7% given the 
current energy mix portfolio (Coffel and Mankin, 2020). A systematic

review showed consistent decreases in mid to end of the century in 
thermal power production capacity due to insufficiency of cooling 
water in southern, western and eastern Europe (high confidence); 
North America and Oceania (high confidence), central, southern 
and western Asia (high confidence) and western and southern 
Africa (medium confidence) (Emodi et al., 2019). Overall, apart from 
emissions benefits, moving away from thermal power generation to 
other renewable energy will also lower the chances of climate-induced 
curtailment of energy production (high confidence).
Global freshwater demand for the energy sector is projected to increase 
under all 2°C scenarios due to the rapid increase in electricity demand 
in developing countries (Fricko et al., 2016). Despite the water shortage 
and climate change impacts, industry and energy sectors’ share in global 
water demand has been projected to rise to 24% by 2050 (UN Water,

2020), which will increase the competition among various water-use 
sectors (Boretti and Rosa, 2019). Furthermore, mining activities, which 
are highly dependent on sufficient water availability, are also at risk 
due to climate change (Aleke and Nhamo, 2016). Given that some of 
the intensely mined regions, such as the Atacama Desert in Chile, are 
already water-scarce, even small changes in rainfall could destabilise 
water-intensive mining operations and affect the production and 
processing activities at mines (Odell et al., 2018). Overall, there is a 
lack of literature on the impact of climate change on future mining 
activities and other water-intensive industries.

616In summary, globally, hydropower and thermoelectric power capacities 
are projected to increase and decrease, respectively, due to changes 
in river runoff and increases in ambient water temperatures (high 
confidence). In the future, freshwater demand for energy and industrial 
sectors is projected to rise significantly at the global level, triggering 
competition for water across sectors. Although climate change also 
poses risks to mining and other water-intensive industries, quantifying 
these risks is difficult due to limited studies.
4.5.3 Projected Risks to Water, Sanitation and Hygiene 
(WaSH)
Climate-related extreme events impact WaSH services and local water 
security. While not WaSH-specific, AR5 showed that more people would 
experience water scarcity and floods (high confidence) and identified 
WaSH failure due to climate change as an emergent risk (medium 
confidence) leading to higher diarrhoea risk (Field et  al., 2014b). In

addition, both SR1.5 (IPCC, 2018a) and SRCCL (IPCC, 2019b) projected 
the risk from droughts, heavy precipitation, water scarcity, wildfire 
damage and permafrost degradation to be higher at 2°C warming than 
1.5°C (medium confidence), and all these could potentially impact 
water quality and WaSH services.
Waterborne diseases result from complex causal relationships between 
climatic, environmental and socioeconomic factors that are not fully 
understood or modelled (Boholm and Prutzer, 2017) (high confidence). 
WaSH-related health risks are related to extreme events, harmful 
algal blooms and WaSH practices (Chapter 7 WGII 7.3.2). In addition, 
changes in thermotolerance and chlorine resistance of certain viruses 
have been observed in laboratory experiments simulating different 
temperatures and sunlight conditions (Carratalà et al., 2020), increasing 
potential health risks even where traditional water treatment exists

(Jiménez Cisneros et  al., 2014) (low confidence). Studies show that 
degraded water quality increases the willingness to pay for clean 
water regardless of national economic status. However, payment for 
clean, potable water, particularly in low- and middle-income countries, 
can represent a significant percentage of people’s income, limiting 
economic well-being and the possibility for re-investment in other 
livelihoods or activities (Constantine et al., 2017; van Houtven et al., 
2017; Price et al., 2019).
Collectively, drinking water treatment, sanitation and hygiene interrupt 
disease transmission pathways, particularly for water-related diseases. 
However, WaSH systems themselves are vulnerable to extreme events 
(Section 4.3.3). For example, sewage overflows resulting from heavy 
rainfall events are expected to increase waterborne disease outbreaks 
(Khan et al., 2015). High diarrhoeal disease burdens mean that small

changes in climate-associated risk are projected to have significant 
impacts on disease burdens (Levy et  al., 2018). For example, up to 
2.2 million more cases of E. coli by 2100 in Bangladesh under a 2.1°C 
GWL are projected (Philipsborn et al., 2016), while up to an 11-fold 
and 25-fold increase by 2050 and 2080, respectively, under a 2–4°C 
GWL, in disability-adjusted life years, associated with cryptosporidiosis 
and giardiasis in Canada is projected (Smith et al., 2015). In addition, 
an additional 48,000 deaths of children under 15 years of age globally 
from diarrhoea by 2030 are also projected (WHO, 2014). Notably, high levels of treatment compliance and boiling water before consumption 
offset the projected impact of climate change on giardiasis in Canada 
in the 2050 scenario, but could not wholly offset the projected impact 
in 2080 (Smith et  al., 2015). Climate change impacts on WaSH-
attributable disease burden are also projected to delay China’s progress

towards disease reduction by almost 9% under RCP8.5 (Hodges et al., 
2014). Disruptions in the drinking water supply can lead to increased 
household water storage, potentially increasing vector larvae breeding 
habitats (see Section  3.6.3). In combination with the projected 
expansion of vector ranges given climate change (Liu-Helmersson et al., 
2019), there is the potential for increased risk of vector-borne disease 
during periods of water shortage or natural disasters (Section 4.3.3). 
Moreover, energy requirements for water and wastewater treatment 
are indirectly responsible for GHG emissions, while the breakdown of 
excreta contributes directly to emissions (Box 4.5, Section 4.7.6). These 
contributions need to be better articulated and accounted for as part of 
the WaSH and climate change dialogue (Dickin et al., 2020).
In summary, climate change is expected to compromise WaSH services, 
compounding existing vulnerabilities and increasing water-related

health risks (medium evidence, high agreement). Therefore, additional 
research is required on disease-, country-, and population-specific risks 
due to future climate change impacts (Baylis, 2017; Bhandari et  al., 
2020; Harper et al., 2020).
4.5.4 Projected Risks to Urban and Peri-Urban Sectors
AR5 reported with medium confidence that climate change would 
impact residential water demand, supply and management (Revi et al., 
2014). According to AR5, water utilities are also confronted by changes 
to the availability of supplies, water quality and saltwater intrusion 
into aquifers in coastal areas due to higher ambient and water 
temperatures (medium evidence, high agreement), altered streamflow 
patterns, drier conditions, increased storm runoff, sea level rise and 
more frequent forest wildfires in catchments (Jiménez Cisneros 
et al., 2014). SR1.5 found with medium confidence that constraining 
warming to 1.5°C instead of 2°C might mitigate risks for water

availability, but socioeconomic drivers could affect water availability 
more than variations in warming levels, while the risks differ across 
regions (Hoegh-Guldberg et al., 2018).
In nearly a third of the world’s largest cities, water demand may exceed 
surface water availability by 2050, based on RCP6.0 projections and 
the WaterGAP3 modelling framework (Flörke et al., 2018). Under all 
SSPs, the global volume of domestic water withdrawal is projected to 
reach 700–1500 km3 yr–1 by 2050, indicating an increase of 50 to 250%, 
compared to the 2010 water use intensity (400–450 km3 yr–1) (Wada 
et  al., 2016). Increasing water demand by cities is already spurring 
competition between cities and agricultural users for water, which is 
expected to continue (Garrick et al., 2019) (Section 4.5.1). By 2030, 
South and Southeast Asia are expected to have almost three quarters 
of the urban land under high-frequency flood risk (10.4.6). South Asia,

South America and mid-latitudinal Africa are projected have the largest 
urban extents exposed to floods and droughts (Güneralp et al., 2015). 
An analysis of 571 European cities from the Urban Audit database 
(using RCP8.5 projections without assessing urban heat island effects)

617found drought conditions are expected to intensify (compared to the 
historical period 1951–2000) in southern European cities, particularly 
in Portugal and Spain (Guerreiro et  al., 2018; Section CCP4.3.3). 
Changes in river flooding are projected to affect cities in northwestern 
European cities and the UK between 2051 and 2100 (Guerreiro et al., 
2018) (Sections 6.2.3.2, CCP2.2.1, CCP2.2.3).
Globally, climate change is projected to exacerbate existing challenges 
for urban water services. These challenges include population growth, 
the rapid pace of urbanisation and inadequate investment, particularly 
in less developed economies with limited governance capacity (high 
confidence) (Ceola et  al., 2016; van Leeuwen et  al., 2016; Reckien 
et al., 2017; Tapia et al., 2017; Veldkamp et al., 2017). More specifically, 
in Arusha (Tanzania), a combination of urban growth modelling, 
satellite imagery and groundwater modelling projected that rapid

urbanisation would reduce groundwater recharge by 23–44% of 2015 
levels by 2050 (under business as usual and an RCP8.5 scenario), 
causing groundwater levels to drop up to 75 m (Olarinoye et  al., 
2020). Flood risk modelling showed a median increase in flood risk 
of 183% in 2030 based on baseline conditions in Jakarta (Indonesia) 
with flood risks increasing by up to 45% due to land use changes 
alone (Budiyono et al., 2016). A probabilistic analysis of surface water 
flood risk in London (UK) using the UKCP09 Weather Generator (with 
10th and 90th percentile uncertainty bounds) found that the annual 
damage is expected to increase from the baseline by 101% and 128% 
under 2030 and 2050 high-emission scenarios, respectively (Jenkins 
Modified streamflow is projected to affect the amount and variability 
of inflow to urban storage reservoirs (high confidence), which may ex-
acerbate existing challenges to urban reservoir capacity, such as sed-

imentation and poor water quality (Goharian et al., 2016; Howard et al., 
2016; Yasarer and Sturm, 2016). For example, in Melbourne (Australia), 
a combination of stochastic hydro-climatological modelling, rainfall-
runoff modelling and climate model data projects a mean precipitation 
shift over catchments by −2% at 1.5°C and −3.3% at 2°C, relative to 
1961–1990. Considering an annual water demand of 0.75 of the mean 
yearly inflow, the median water supply shortage risk was calculated to 
be 0.6% and 2.9% at 1.5°C and 2°C warming levels, respectively. At 
the higher demand level of 0.85 of the mean annual inflow, the median 
water shortage risk is higher, between 9.6% and 20.4% at 1.5°C and 
2°C warming, respectively, without supply augmentation desalination 
(Henley et al., 2019).
As climate change poses a substantial challenge to urban water 
management, further refinement of urban climate models, downscaling 
and correction methods (e.g., Gooré Bi et  al., 2017; Jaramillo and

Nazemi, 2018) is needed. Additionally, given that 90% of urban growth 
will occur in less developed regions, where urbanisation is largely 
unplanned (UN-Habitat, 2019), further research is needed to quantify 
the water-related risks of climate change and urbanisation on informal 
settlements (Grasham et al., 2019; Satterthwaite et al., 2020, 4.5.3).
In summary, rapid population growth, urbanisation, ageing infrastructure 
and changes in water use are responsible for increasing the vulnerability 
of urban and peri-urban areas to extreme rainfall and drought, 
particularly in less developed economies with limited governance capacity (high confidence). In addition, modified stream flows due to 
climate change (Section 4.4.3) are projected to affect the amount and 
variability of inflows to storage reservoirs that serve urban areas and 
may exacerbate challenges to reservoir capacity, such as sedimentation 
and poor water quality (high confidence).

4.5.5 Projected Risks to Freshwater Ecosystems
AR5 concluded that climate change is projected to be an important 
stressor on freshwater ecosystems in the second half of the 21st 
century, especially under high-warming scenarios of RCP6.0 and 
RCP8.5 (high confidence), even though direct human impacts will 
continue to be the dominant threat (Settele et al., 2014). Rising water 
temperatures are also projected to cause shifts in freshwater species 
distribution and worsen water quality problems (high confidence), 
especially in those systems that already experience high anthropogenic 
loading of nutrients (Settele et al., 2014).
Changes in precipitation and temperatures are projected to affect 
freshwater ecosystems and their species through, for example, direct 
physiological responses from higher temperatures or drier conditions 
or a loss of habitat for feeding or breeding (Settele et al., 2014; Knouft 
and Ficklin, 2017; Blöschl et al., 2019b). In addition, increased water

temperatures could lead to shifts in the structure and composition of 
species assemblages following changes in metabolic rates, body size, 
timing of migration, recruitment, range size and destabilisation of food 
webs. A review of the impact of climate change on biodiversity and 
functioning of freshwater ecosystems found that under all scenarios, 
except the one with the lowest GHG emission scenario, freshwater 
biodiversity is expected to decrease proportionally to the degree of 
warming and precipitation alteration (Settele et  al., 2014) (medium 
These are several examples of such projected changes. Due to higher 
water temperatures, changes in macroinvertebrates and fish are 
projected under all future warming scenarios (Mantyka-Pringle et al., 
2014). Decreased abundance of many fish species, such as salmonids, 
under higher temperatures, is also projected, although the effects 
between species are variable (Myers et  al., 2017). Poleward and

shifts of freshwater species are projected as they try to stay within 
preferred cooler environmental conditions (Pecl et  al., 2017). Other 
anticipated changes include physiological adjustments with impacts 
on morphology with some species shrinking in body size because 
large surface-to-volume ratios are generally favoured under warmer 
conditions (Scheffers et al., 2016) and changes in species communities 
and food webs as a consequence of increases in metabolic rates in 
response to increased temperatures with the flow-on effects for 
many ecosystem processes (Woodward et al., 2010). Changes in the 
seasonality of flow regimes and variability (Blöschl et  al., 2019b) 
and more intermittent flows (Pyne and Poff, 2017) are also projected 
and could result in decreased food chain lengths through the loss of 
large-bodied top predators (Sabo et al., 2010) and changes in nutrient 
loadings and water quality (Woodward et al., 2010). The impacts on

freshwater systems in drylands are projected to be more severe (Jaeger 
et al., 2014; Gudmundsson et al., 2016). Changes to snow and glacier 
melting, including the complete melting of some glaciers (Leadley

618et al., 2014; Kraaijenbrink et al., 2017), are projected to reduce water 
availability and cause declines in biodiversity in high altitudes through 
local extirpations and species extinctions in regions of high endemism. 
Lake nutrient dynamics are expected to change, for example, at 
2°C warming, and net increase in CH 4 emissions by 101–183% in 
hypereutrophic lakes and 47–56% in oligotrophic lakes in Europe are 
projected (Sepulveda-Jauregui et al., 2018). Similarly, under the high-
GHG emission scenario, lake stratification is projected to begin 22.0 
± 7.0 d earlier and end 11.3 ± 4.7 d later by the end of this century 
(Woolway et al., 2021). While overall future trends on climate change 
on freshwater species and habitats are largely negative, evidence 
indicates that different species are projected to respond at different 
rates, with interactions between species expected to be disrupted and

which may result in novel biological communities and rapid change in 
ecological processes and functions (Pecl et al., 2017).
These impacts are expected to be most noticeable where significant 
air temperature increases are projected, leading to local or regional 
population extinctions for cold-water species because of range 
shrinking, especially under the RCP4.5, 6.0 and 8.5 scenarios (Comte 
and Olden, 2017). The consequences for freshwater species are projected 
to be severe with local extinctions as the freshwater ecosystems dry. 
In the Americas, under all scenarios that have been examined, the risk 
of extinction of freshwater species is projected to increase above that 
already occurring levels due to biodiversity loss caused by pollution, 
habitat modification, over-exploitation and invasive species (IPBES, 
2019). Freshwater ecosystems are also at risk of abrupt and irreversible 
change, especially those in the higher latitudes and altitudes with

significant changes in species distributions, including those induced 
by melting permafrost systems (Moomaw et al., 2018; IPBES, 2019).
While changes in the species distribution across freshwater ecosystems 
are projected, the extent of change and the ability of individual species 
or populations to adapt are not widely known. Species that cannot move 
to more amenable habitats may become extinct, whereas those who 
migrate may relocate. An unknown outcome could be establishing novel 
ecosystems with new assemblages of species, including invasive alien 
species, in response to changes in the environment with the prospect of 
irreversible changes in freshwater ecosystems (Moomaw et al., 2018).
In summary, changes in precipitation and temperatures are projected 
to affect all types of freshwater ecosystems and their species. Under 
all scenarios, except the one with the lowest GHG emission scenario, 
freshwater biodiversity is expected to decrease proportionally to the

degree of warming and precipitation change (medium evidence, high 
4.5.6 Projected Risks to Water-Related Conflicts
AR5 concluded with medium confidence that climate change can 
indirectly increase the risks of violent conflicts, though the link to 
hydrological changes were not spelled out (Jiménez Cisneros et  al., 
2014). Furthermore, according to IPCC SR1.5 (Hoegh-Guldberg et al., 
2018), if the world warms by 2°C–4°C by 2050, rates of human conflict 
could increase, but again, the role of hydrological change in this was 
not explicit (medium confidence).The impact of climate change on shared water resources might 
increase tensions among states, particularly in the absence of strong 
institutional capacity (Petersen-Perlman et  al., 2017; Dinar et  al., 
2019). On the other hand, although the mere existence of formal 
agreements does not necessarily reduce the risks of conflicts, robust 
treaties and institutions can promote cooperative events, even under

hydrological stress (Link et  al., 2016).  Yet, since both conflictive 
and cooperative events are possible under conditions of climatic 
variability, whether conflict arises or increases depends on several 
contextual socioeconomic and political factors, including the adaptive 
capacity of the riparian states (Koubi, 2019), the existence of power 
asymmetries (Dinar et  al., 2019) and pre-existing social tensions 
At the intra-state level, analysis suggests that additional climate 
change will increase the probability of conflict risks, with 13% 
increase probability at the 2°C GWL and 26% probability at the 4°C 
GWL scenario (Mach et al., 2019). However, to date, other factors are 
considered more influential drivers of conflict, including lack of natural 
resource use regulations (Linke et al., 2018b), societal exclusion (von 
Uexkull et  al., 2016; van Weezel, 2019), poor infrastructures and a

history of violent conflict (Detges, 2016) (high confidence). In addition, 
medium-high evidence exists that climate change imposes additional 
pressures on regions that are already fragile and conflict-prone 
(Matthew, 2014; Earle et al., 2015) (medium agreement).
Recent research indicates that climatic change can multiply tensions 
in regions dependent on agriculture when coupled with other 
socioeconomic and political factors (Koubi, 2019), including a low level 
of human development (Ide et al., 2020) and deterioration of individual 
living conditions (Vestby, 2019). On the other side, intergroup 
cohesion (De Juan and Hänze, 2020) and policies that improve 
societal development and good governance reduce the risk of conflict 
associated with the challenges to adaptation to climate change (Hegre 
et al., 2016; Witmer et al., 2017) (medium confidence) at both the intra-
state and inter-state level.
Increased risk of conflict between different sectors (agriculture,

industry, domestic) and needs (urban, rural) is projected to arise 
in several river basins due to climate change and socioeconomic 
developments, including urbanisation (Flörke et  al., 2018). Future 
climatic conditions and population growth are expected to exert 
additional pressures on managing already stressed basins such as the 
Nile, the Indus, Colorado, the Feni, the Irrawaddy, the Orange and the 
Okavango (Farinosi et al., 2018). In addition, recent scenario analysis 
in global transboundary basins supports the finding that there is 
more potential for conflict in areas already under water stress, such 
as central Asia and the northern parts of Africa (Munia et al., 2020) 
In summary, the impact of climate change on water resources might 
increase tensions, particularly in the absence of strong institutional 
capacity. However, whether conflict arises or increases depends on 
several contextual socioeconomic and political factors. Evidence

exists that climate change imposes additional pressures on regions 
already under water stress or fragile and conflict-prone (medium 

6194.5.7 Projected Risks to Human Mobility and Migration
SR1.5 found with medium confidence that migration is expected to 
increase with further warming, but that there are major knowledge 
gaps preventing more detailed assessments (Hoegh-Guldberg et  al., 
2018). However, as in AR5, there was no specific focus on hydrological 
changes-induced migration.
In general, the projected population growth in at-risk areas, especially 
in low-income countries, is expected to increase future migration 
and displacement (McLeman et  al., 2016; Rigaud et  al., 2018). For 
example, a study looking at potential flood exposure found that low-
income countries, particularly in Africa, are at higher risk for flood-
induced displacement (Kakinuma et  al., 2020). One model, focusing 
on slow-onset climate impacts, such as water stress, crop failure 
and sea level rise, projected between 31–72 million people (RCP2.6, 
SSP4) and 90–143 million people (RCP8.5, SSP4) internally displaced

by 2050 in sub-Saharan Africa, South Asia and Latin America (Rigaud 
et  al., 2018). Another estimate, incorporating temperature increase 
and precipitation, projects that asylum applications to the EU could 
increase by between 0.098 million (RCP4.5) and 0.66 million (RCP8.5) 
yr–1, as a consequence of temperature increases in agricultural areas 
of low-income countries (Missirian and Schlenker, 2017) (limited 
evidence; medium agreement).
More detailed local and regional models are needed, incorporating 
migrant destinations (Abel et al., 2019) and immobility (Zickgraf, 2018).
In summary, research that projects future migration changes due to 
climate-induced hydrological changes is limited and shows significant 
uncertainties about the number of migrants and their destinations 
(limited evidence; medium agreement).
4.5.8 Projected Risks to the Cultural Water Uses of 
Indigenous Peoples, Local Communities and 

AR5 found that climate change will threaten cultural practices 
and values, although the risks vary across societies and over time 
(medium evidence, high agreement). Furthermore, AR5 concluded 
that significant changes in the natural resource base on which many 
cultures depend would directly affect the cultural core, worldviews, 
cosmologies and symbols of indigenous cultures (Adger and Pulhin, 
2014). SR1.5 concluded with high confidence that limiting global 
warming to 1.5°C, rather than 2°C, will strongly benefit terrestrial and 
wetland ecosystems and their services, including the cultural services 
provided by these ecosystems (Hoegh-Guldberg et al., 2018). SROCC 
found with high confidence that cultural assets are projected to be 
negatively affected by future cryospheric and associated hydrological 
changes (Hock et al., 2019b).
There is high confidence that the cultural water uses of Indigenous 
Peoples, local communities and traditional peoples are at risk of

climate change-related hydrological change (Table 4.7). Climate-driven 
variations in streamflow, saltwater intrusion and projected increases 
in water temperature will exacerbate declines of culturally important species and lead to variations or depletion of culturally important places 
and subsistence practices. For example, in New Zealand, the increasing 
risk of flood events may impact culturally important fish species for 
Māori (Carter, 2019), while habitat changes may shift the distribution 
of culturally significant plants (Bond et al., 2019). In Australia, Yuibera 
and Koinmerburra Traditional Owners fear the saltwater inundation of 
culturally significant sites and waterholes (Lyons et  al., 2019), while 
the flooding of culturally significant wetlands will negatively affect the 
Lumbee Tribe (USA) (Emanuel, 2018). Moreover, changes in the carrying 
capacity of ice, snow quality and formation will probably increase the

physical risks to Saami practising reindeer herding (Jaakkola et al., 2018).
Further research is necessary to assess the extent and nature of 
climate-driven risks to cultural water uses in the context of broader 
socioeconomic, cultural and political challenges facing diverse 
Indigenous Peoples and local and traditional communities. In addition, 
given the significance of IKLK to adaptive capacity and community-led 
adaptation, the potential risks of climate-related hydrological changes 
to diverse cultural water uses warrant closer study (Sections  4.6.9, 
4.8.4, Cross-Chapter Box INDIG in Chapter 18).
In sum, there is high confidence that climate-driven hydrological 
changes to cultural water uses and culturally significant ecosystems 
and species are projected to pose risks to the physical well-being of 
Indigenous Peoples, local communities and traditional peoples.
4.6 Key Risks and Adaptation Responses in 
Various Water Use Sectors

Anthropogenic climate change has impacted every aspect of the water 
cycle (Section  4.2), and risks are projected to intensify with every 
degree of global warming (Section 4.4), with impacts already visible in 
all sectors of the economy and ecosystems (Section 4.3) and projected 
to intensify further (Section  4.5). In response to climate- and non-
climate-induced water insecurity, people and governments worldwide 
are undertaking various adaptation responses across all sectors. In 
addition, there are several projected studies for future adaptation 
responses. We draw upon a list of 359 case studies of observed 
adaptation and 45  articles on projected future adaptation. Further 
information on selection and inclusion criteria is available in SM4.2. 
In this section, we document those adaptation responses (current 
and future) in different water use sectors. In the next (Sections 4.7.1, 
4.7.2, 4.7.3), benefits of current adaptation and effectiveness of future

adaptation are discussed.
4.6.1 Key Risks Related to Water
The preceding sections have outlined the various pathways along 
which climate affects water resources and water-using sectors. In 
synthesis, fundamental changes in observed climate are already visible 
in water-related outcomes (high confidence), including ~500 million 
people experiencing historically unfamiliar precipitation regimes 
(Section 4.2.1.1); cryospheric changes impacting various societal and 
ecosystem components (Section  4.2.2); increasing vulnerability to 
flood impacts, driven by both by climate and socioeconomic factors

620Table 4.7 |  Selected projected risks to Indigenous Peoples’ uses of water.
PeopleClimate hazard Water-related risk Situated knowledge Reference
Asia IfugaoIncreased temperatures; 
increasing rainfall (wet 
season); decreasing rainfall 
(dry season)Flooding (wet season); 
water deficit (dry season)Increases in future wet season rainfall pose increase risks of 
excess surface water runoff and potential for soil erosion, which 
may cause the collapse of Ifugao rice terraces. Reductions in 
future dry season rainfall and warmer temperatures indicate 
significant water deficits during the growing season of local 
tinawon rice.Soriano and 
Herath (2020)
AustralasiaYuibera and 
Koinmerburra 
Traditional 
Owner groupsSea-level rise FloodingCulturally important coastal waterholes, wetlands and sites 
are at risk of saltwater inundation due to rising sea levels. If 
inundated, Traditional Owners may not be able to maintain

cultural connections to these important sites (11.4.1).Lyons (2019 }
Australasia Māori Increased precipitation FloodingIncreasing flood events may negatively impact spawning 
and fishing sites of the culturally important īnaka (whitebait; 
Galaxias maculates) in the Waikōuaiti River (11.4.2).Carter (2019)
Australasia MāoriIncreased temperature; 
precipitation variabilityEcosystem
changeChanges in temperature and precipitation are projected to 
shift the range of wetland plants (Kūmarahou and Kuta) in 
New Zealand, which may decrease access to these culturally 
significant species, which are used for medicinal and weaving 
purposes. The changing distribution of these plants may lead 
to a loss of Indigenous knowledge and affect inter-tribal 
reciprocity and gifting practices (11.4.2).Bond et al. (2019)
Central 
and South 
AmericaWarao Sea level rise FloodingThe partial or total inundation of the Orinoco Delta will result in

the loss of freshwater wetlands and species, which will produce 
rapid shifts in the culturally significant lands and resources of 
the Warao. Among the affected species is the Mauritia palm, on 
which Warao culture and livelihoods are based.Vegas-Vilarrúbia 
et al. (2015)
Europe SaamiIncreased temperatures; 
changes in precipitationWinter thawReindeer herding is culturally important for Saami and provides 
a means to maintain traditions, language and cultural identity, 
thus constituting an essential part of Saami physical and 
mental well-being. More frequent ice formation on soil and 
snow, which will reduce the availability and quality of winter 
forage for reindeer, will negatively impact reindeer herding and 
thus Saami identity and well-being (13.8.1.2).Jaakkola et al. 
(2018); (Markkula 
North 
AmericaLumbee TribeIncreased temperatures; 
increased rainfall variabilityFloodingClimate-related degradation and flooding of wetlands and

streams in the Lumbee River watershed will negatively affect 
cultural practices of fishing and harvesting that rely on access 
to and resources obtained from the area.Emanuel (2018)
(Section  4.2.4); and as climate change-driven increases in drought 
impacts (Section 4.2.5).
Further increases in risks are projected to manifest at different 
levels of warming. Climate change is impacting all components of 
the hydrological cycle, but the water use sectors are also facing the 
consequences of climate change, given the central role of water for all 
aspects of human and environmental systems (Section 4.1, Box 4.1). 
Therefore, risks to water security are also identified as a representative 
key risk (RKR) (WGII, Chapter 16, Section 16.5.2.3.7).
Approximately 4  billion people globally face physical water scarcity 
for at least one month yr–1 which is driven by both climatic and non-
climatic factors (Mekonnen and Hoekstra, 2016). Increases in physical

water scarcity are projected, with estimates between 800 million and 
3 billion for 2°C global warming and up to approximately 4 billion for 
4°C global warming (Gosling and Arnell, 2016). Projected increases in 
hydrological extremes pose increasing risks to societal systems globally 
(high confidence), with a potential doubling of flood risk between 1.5°C 
and 3°C of warming (Dottori et al., 2018) and an estimated 120–400% increase in population at risk of river flooding at 2°C and 4°C, respectively 
(Alfieri et al., 2017). Also projected are increasing risks of fatalities and 
socioeconomic impacts (Section  4.4.4). Similarly, a near doubling of 
drought duration (Naumann et al., 2018) and an increasing share of the 
population affected by various types, durations and severity levels of 
drought are projected (high confidence) (Section 4.4.5). Increasing return 
periods of high-end hydrological extremes pose significant challenges

to adaptation, requiring integrated approaches to risk management, 
which take the various economic and non-economic, as well as direct 
and indirect losses and damages into account (Jongman, 2018).
Increasing sectoral risks are reported across regions and sectors with 
rising temperatures and associated hydrometeorological changes 
(Cross-Chapter Box  INTEREG in Chapter 16). Risks to agricultural 
yields due to combined effects of water and temperature changes, for 
example, could be three times higher at 3°C compared to 2°C (Ren 
et  al., 2018b), with additional risks as a consequence of increasing 
climate extremes (Leng and Hall, 2019). In addition, climate-driven 
water scarcity and increasing crop water demands, including for 
irrigation, pose additional challenges for agricultural production

621Regional synthesis of assessed changes in water and consequent impacts
Direction of change
Human systems
and ecosystemsDecrease Increase Mixed
Positive Negative MixedObserved ImpactsProjected Impacts
High Medium LowPhysicalConfidence level
= Not assesse d EmptyMean precipitation
Runoff and
streamflow
Drought
Soil Erosion and
sediment load
Energy
Water, sanitation
& hygiene (WaSH)
Urban and
municipal sectorAgriculture
EcosystemsAsia AfricaCentral and
South
AmericaAustral-
asia EuropeNorth
AmericaSmall
Island
StatesMedite-
rranean Arctic AntarcticHuman systemsP hysicalEco-
systemsObs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.Proj. Obs.
and pluvial floodGlobal
Obs.Proj.
(a) Regional changes
and impacts of 
selected variables
Physical change Hydrological Impact on natural and human systemsMediterraneanNorth AmericaArctic
Europe
Asia
AustralasiaAfrica

AntarcticSmall Islands
AmericaMediterranean
MediterraneanNorth AmericaArctic
Europe
Asia
AmericaMediterranean(b) Physical changes, impacts on ecosystems, and impacts on human systems
Observed
ImpactsProjected
Impacts
High confidence  in change
Medium confidence  in change
Cryosphere Flood Drought Water quality WaSH Urban water Ecosystem Agriculture
Figure 4.20 |  Regional synthesis of changes in water and consequent impacts assessed in this chapter.

622(a) Regional changes and impacts of selected variables. Confidence levels higher than medium are shown.
(b) Assessment result of all variables. For each region, physical changes, impacts on ecosystems and impacts on human systems are shown. For physical changes, upward/
downward triangles refer to an increase/decrease, respectively, in the amount or frequency of the measured variable, and the level of confidence refers to confidence that the change 
has occurred. For impacts on ecosystems and human systems, plus or minus marks depict whether an observed impact of hydrological change is positive (beneficial) or negative 
(adverse), respectively, to the given system, and the level of confidence refers to confidence in attributing an impact on that system to a climate-induced hydrological change. The

hydrological impact may be different to the overall change in the system; for example, over much of the world, crop yields have increased overall, largely for non-climatic reasons, 
but in some areas, hydrological impacts of climate change are countering this. Circles indicate that within that region, both increase and decrease of physical changes are found, 
but are not necessarily equal; the same holds for cells showing ‘both’ assessed impacts. Cells assigned ‘n.a.’ indicates variables not assessed due to limited evidences. Decrease 
(increase) in water quality refers to adverse (positive) change in quality. Agriculture refers to impacts on crop production. Note: Energy refers to impacts on hydro and thermoelectric 
power generation. Ecosystems refers to impacts on freshwater ecosystem.
in many regions (high confidence). Regional water-related risks to 
agricultural production are diverse and vary strongly across regions

and crops (Section 4.5.1). As there are limitations to how well global 
agricultural models can represent available water resources (Elliott 
et al., 2014; Jägermeyr et al., 2017), water limitations to agricultural 
production may well be underestimated. For example, the potential for 
irrigation, commonly assumed to play an important role in ensuring 
food security, could be more limited than models assume (Box 4.3).
With higher levels of warming, risks to water-dependent energy production 
increase substantially across regions (van Vliet et al., 2017). While there 
are increasing potentials of ~2–6% for hydropower production by 2080 
(medium confidence), risks to thermoelectric power production increase 
for most regions (high confidence), for example, with potentially near 
doubling of the risk to European electricity production from 1.5°C to 3°C 
(Tobin et al., 2018). Shifting to a higher share of renewable sources less

dependent on water resources for energy production could substantially 
reduce the vulnerability of this sector (Section 4.5.2).
Increasing hydrological extremes also have consequences for the 
maintenance and further improvement of the provision of WaSH 
services (medium confidence). Risks related to the lack or failure of 
WaSH services under climate change include increased incidence 
and outbreaks of water-related diseases, physical injuries, stress, 
exacerbation of the underlying disease, and risk of violence, which 
is often gendered (Section  4.5.3). Although globally, the regional 
potential infestation areas for disease-carrying vectors could be five 
times higher at 4°C than at 2°C (Liu-Helmersson et al., 2019), climate 
projections suggest up to 2.2 million more cases of E. coli by 2100 (2.1°C 
increase) in Bangladesh (Philipsborn et al., 2016), up to an 11-fold and 
25-fold increase by 2050 and 2080, respectively (2°C–4°C increase),

in disability-adjusted life years associated with cryptosporidiosis and 
giardiasis in Canada (Smith et  al., 2015), and an additional 48,000 
deaths of children under 15 years of age globally from diarrhoea by 
2030 (WHO, 2014).
Increasing water demand in conjunctions with changing precipitation 
patterns will pose risks to urban water security by mid-century, 
with water demand in nearly a third of the world’s largest cities 
potentially exceeding surface water availability by 2050 (RCP6.0) 
(Flörke et  al., 2018) and the global volume of domestic water 
withdrawal projected to increase by 50–250% (Wada et al., 2016) 
(Section  4.5.4). Globally, climate change will exacerbate existing 
challenges for urban water services, driven by further population 
growth, the rapid pace of urbanisation and inadequate investment, 
particularly in less developed economies with limited governance 
capacity (high confidence).Risks to freshwater ecosystems increase with progressing climate

change, with freshwater biodiversity decreasing proportionally with 
increasing warming if 1.5°C is exceeded (medium evidence, high 
agreement). Risks include range shift, a decline in species population, 
extirpation and extinction (Section 4.5.5).
The potential for climate change to influence conflict is highly contextual 
and depends on various socioeconomic and political factors. However, 
water-specific conflicts between sectors and users may be exacerbated 
for some regions of the world (high confidence) (Section 4.5.7).
Human migration takes many forms and can be considered a consequence 
and impact of climate change and an adaptation response (Section 4.5.8). 
Projections indicate a potentially substantial increase in internal and 
international displacement due to water-related climate risks (Missirian 
and Schlenker, 2017; Rigaud et  al., 2018). In the context of water-
related adaptation, short-term migration as an income diversification

approach is commonly documented. However, permanent relocation 
and fundamental changes to livelihoods are more transformational and 
yet can be associated with tangible and intangible losses (Mechler et al., 
2019). In the context of climate-induced hydrological change, increased 
vulnerability among migrants and the risk of trapped populations poses 
significant additional risks. However, quantifications that disentangle 
different climate drivers and show specific risks emanating from 
hydrological change are unavailable (Rigaud et al., 2018).
Hydrological change, especially increasing extreme events, pose risks 
to the cultural uses of water of Indigenous Peoples, local communities 
and traditional peoples (high confidence), with implications for the 
physical well-being of these groups (high confidence). Increasing risks 
are documented across groups and regions; however, partly due to 
the unquantifiable nature of these risks, the lack of research funding

for the social dimensions of climate change, particularly in the Global 
South, and the systemic underrepresentation of marginalised groups in 
scientific research, quantitative projections are limited (Section 4.5.8).
Adaptation is already playing an integral part in reducing climate 
impacts and preparing for increasing climate risk, and it will grow in 
importance evermore with increasing risks at higher levels of warming. 
The remaining subsections describe these adaptation responses.
4.6.2 Adaptation in the Agricultural Sector
AR5 reported a range of available hard and soft adaptation options 
for water-related adaptation in the agricultural sector. However, the 
evidence on the effectiveness of these adaptation responses, now and

623Box 4.2 | Observed Risks, Projected Impacts and Adaptation Responses to Water Security in Small 
Island States
AR5 and SR1.5 recognised the exceptional vulnerability of islands, especially concerning water security and potential limits to adaptation 
that may be reached due to freshwater resources (Klein et al., 2014; Hoegh-Guldberg et al., 2018; Roy et al., 2018).
Small islands are already regularly experiencing droughts and freshwater shortages (high confidence) (Holding et al., 2016; Pearce et al.,

2018; Gheuens et al., 2019; MacDonald et al., 2020). Freshwater supply systems vary from household or small community systems such as rainwater harvesting systems and private wells to large public water supply systems using surface, groundwater and, in some cases, desalinated water (Alsumaiei and Bailey, 2018b; Falkland and White, 2020). In many cases, communities rely on more than one water source, including a strong reliance on rainwater and groundwater (Elliott et al., 2017; MacDonald et al., 2020). Groundwater resources in freshwater lenses (FWLs) are essential in providing access to freshwater resources, especially during droughts when the collected rainwater is insufficient (Barkey and Bailey, 2017; Bailey et al., 2018), leading to greater risks of water-borne diseases, with significant effects on nutrition (Elliott et al., 2017; Savage et al., 2020), and improper sanitation poses additional risks to the limited groundwater resources (MacDonald et al., 2017). Drought events have

also severely affected FWL recharge (Barkey and Bailey, 2017), with extraction rates further threatening available groundwater volumes (Post et al., 2018). In conjunction with sea level rise, this poses serious risks to groundwater salinisation (Alsumaiei and Bailey, 2018b; Storlazzi et al., 2018; Deng and Bailey, 2019). In addition, FWLs are threatened by climate change due to changes in rainfall patterns, extended droughts and wash-over events caused by storm surges and sea level rise (high confidence) (see Chapter 15) (Chui and Terry, 2015; Alsumaiei and Bailey, 2018a; Alsumaiei and Bailey, 2018b; Post et al., 2018; Storlazzi et al., 2018; Deng and Bailey, 2019). After small-scale wash over events, the FWLs have been shown to recover to pre-wash over salinity levels within a month (Oberle et al., 2017).

Due to wash-over events exacerbated by sea level rise and lens thinning due to pumping, recovery time for FWLs is projected to take

substantially longer (Oberle et al., 2017; Alsumaiei and Bailey, 2018a; Storlazzi et al., 2018). Projections indicate that atolls may be unable to provide domestic freshwater resources due to the lack of potable groundwater by 2030 (RCP8.5+ ice-sheet collapse), 2040 (RCP8.5) or the 2060s (RCP4.5) (Storlazzi et al., 2018). Projections of future freshwater availability in small islands further underline these substantial risks to island water security (Karnauskas et al., 2016; Karnauskas et al., 2018). Population growth, changes in rainfall patterns and agricultural demand are projected to increase water stress in small islands (Gohar et al., 2019; Townsend et al., 2020). While some islands are projected to experience an increase in rainfall patterns, this may refer to shorter intense rainfall events, thereby increasing the risk of flooding during the wet season, while not decreasing their risk of droughts during dry periods (Aladenola et al., 2016; Gheuens et al., 2019). In addition,

projected shifts in the timing of the rainfall season might pose an additional risk for water supply systems (Townsend et al., 2020).

Observed adaptation during drought events includes community water sharing (Bailey et al., 2018; Pearce et al., 2018) as well as using

alternative water resources such as water purchased from private companies (Aladenola et al., 2016), desalination units (Cashman and Yawson, 2019; MacDonald et al., 2020) or accessing deeper or new groundwater resources (Pearce et al., 2018). Rainwater harvesting to adapt the water supply system in the Kingston Basin in Jamaica was able to significantly alleviate water stress, for example. Still, it would not fill the total supply gap caused by climate change (Townsend et al., 2020). Likewise, groundwater sustainability with increasing climate change in Barbados cannot be ensured without aquifer protection, leading to higher optimised food prices if no additional adaptation measures are implemented (Gohar et al., 2019). The potential of using multiple water sources is rarely assessed in future water supply projections in small islands (Elliott et al., 2017). In the Republic of Marshall Islands, more than half of all interviewed households have already had to migrate once due to a

water shortage (MacDonald et al., 2020). In Cariacou, Grenada, increases in migration rates have been observed following drought events (Cashman and Yawson, 2019). with long-term cross border and internal migration shown to be having significant impacts on well-being, community-cohesion, livelihoods and people-land relationships (Yates et al., 2021).

In sum, small islands are already regularly experiencing droughts and freshwater shortages (high confidence). For atoll islands, freshwater 
availability may be severely limited as early as 2030 (low confidence). The effects of temperature increase, changing rainfall patterns, sea level rise and population pressure combined with limited options available for water-related adaptation leave small islands partially water-insecure currently, with increasing risks in the near-term and at warming above 1.5°C (high confidence).

624in the future, was not assessed (Noble et al., 2014; Porter et al., 2014). 
Assessing the feasibility of different irrigation measures as adaptation, 
SR1.5 (de Coninck et al., 2018) found mixed evidence, depending on 
the applied methodology.
There is high confidence that water-related adaptation is occurring in 
the agricultural sector (Acevedo et  al., 2020; Ricciardi et  al., 2020), 
and water-related adaptation in the agricultural sector makes up 
the majority of documented local, regional and global evidence of 
implemented adaptation (high confidence) (Section 4.7.1, Figure 4.23 
and Figure 4.24, Table 4.8). However, while there is increasing evidence 
of adaptation and its benefits across multiple dimensions, the link 
between adaptation benefits and climate risk reduction is unclear due 
to methodological challenges (medium confidence) (Section 4.7.1). On 
the other hand, while it is methodologically possible to measure the

effectiveness of future adaptation in reducing climate risks, the main 
limitation here is that not all possible ranges of future adaptations 
can be modelled given the limitations of climate and impact models 
(high confidence) (Section  4.7.2). Furthermore, findings on current 
adaptation are constrained by what is documented in peer-reviewed 
articles. At the same time, there may be a range of options implemented 
on the ground by local governments or as a part of corporate social 
responsibility that is not published in peer-reviewed publications.
Water and soil conservation measures (e.g., reduced tillage, contour 
ridges or mulching) are frequently documented as adaptation responses 
to reduce water-related climate impacts (Kimaro et  al., 2016; Traore 
et al., 2017). This measure features in all continents’ top four adaptation 
responses except Australasia (Figure  4.27). Especially for rain-fed 
farming, which currently is the norm in most of Africa, large parts of

Central and South America and Europe, water and soil conservation 
measures and various components of conservation agriculture are some 
of the most frequently used adaptation responses (Jat et  al., 2019). 
This measure is deemed to have economic benefits and benefits for 
vulnerable communities who adopt this measure (high confidence) 
and benefits in terms of water saving and positive ecological and 
sociocultural benefits (medium confidence). However, this measure can 
be sometimes maladaptive (low evidence, medium agreement) and can 
have mitigation co-benefits (low evidence, high agreement) (Figure 4.29). 
Furthermore, water- and soil management-related measures show high 
potential efficacy in reducing impacts in a 1.5°C world, with declining 
effectiveness at higher levels of warming (Figure 4.28 and Figure 4.29)
Changes in cropping patterns, the timing of sowing and harvesting, crop 
diversification towards cash crops and the adoption of improved crop

cultivars that can better withstand hazards like floods and drought are 
among the most used adaptation responses by farmers. This is among 
the top two measures in Asia and Africa (Figure 4.27). Extra income 
allows households to re-invest in improved agricultural techniques 
and improved cultivars (Taboada et  al., 2017; Khanal et  al., 2018b). 
Beneficial outcomes are documented in terms of increases in incomes 
and yields and water-related outcomes (medium confidence, from 
robust evidence, but medium agreement), but benefits to vulnerable 
communities are not always apparent on the whole (Figure FAQ4.4.1). 
Changes in cropping patterns and systems are also among those 
adaptation options assessed for their potential to reduce future climate 
impacts, though effectiveness is shown to be limited (Brouziyne et al., 2018; Paymard et al., 2018). Assessments of the future effectiveness 
of crop rotation systems for adaptation show a continued reduction in

required irrigation water use, though studies of effectiveness beyond 
2°C global mean temperature increase are not available (Kothari et al., 
2019; Yang et al., 2019b) (Figure 4.28 and Figure 4.29).
Conservation agriculture and climate-smart agriculture (includes 
improved cultivars and agronomic practices) have proven to increase 
soil carbon, yields and technical efficiency (Penot et  al., 2018; Salat 
and Swallow, 2018; Ho and Shimada, 2019; Makate and Makate, 2019; 
Okunlola et al., 2019). Some water-related measures in conservation 
agriculture include allowing for shading and soil moisture retention, 
with the co-benefit of reducing pest attacks (Thierfelder et al., 2015; 
Raghavendra and Suresh, 2018; Islam et  al., 2019a). Especially for 
traditional food grains in small-holder agriculture, improved practices 
such as modern varieties or integrated nutrient management can play 
an important role in making production more resilient to climate stress

(Handschuch and Wollni, 2016). This measure is also among the top four 
most frequent adaptation measures in all continents except Australia 
and North America (Figure 4.27). In addition, this measure is shown to 
have positive economic benefits (high confidence) and also benefits 
on other parameters (medium confidence) (Figure  FAQ4.4.1). Such 
approaches are also among those most frequently assessed for their 
effectiveness in addressing future climate change, but show limited 
effectiveness across warming levels (Figure 4.28 and Figure 4.29).
The use of non-conventional water sources, that is, desalinated and 
treated waste water, is emerging as an important component of 
increasing water availability for agriculture (DeNicola et  al., 2015; 
Martínez-Alvarez et al., 2018b; Morote et al., 2019). While desalination 
has a high potential in alleviating agricultural water stress in arid 
coastal regions, proper management and water quality standards

for desalinated irrigation water are essential to ensure continued 
or increased crop productivity. In addition to the energy intensity 
(Section 4.7.6), risks of desalinated water include lower mineral content, 
higher salinity, crop toxicity and soil sodicity (Martínez-Alvarez et al., 
2018b). Similarly, waste-water reuse can be an important contribution 
to buffer against the increasing variability of water resources. However, 
waste-water guidelines that ensure the adequate treatment to reduce 
adverse health and environmental outcomes due to pathogens or 
other chemical and organic contaminants will be essential (Angelakis 
and Snyder, 2015; Dickin et al., 2016) (Box 4.5; 4.6.4).
IKLK are crucial determinants of adaptation in agriculture for many 
communities globally. Indigenous Peoples have intimate knowledge 
about their surrounding environment and are attentive observers of 
climate changes. As a result, they are often best placed to enact successful

adaptation measures, including shifting to different crops, changing 
cropping times or returning to traditional varieties (Mugambiwa, 2018; 
Kamara et al., 2019; Nelson et al., 2019) (Section 4.8.4).
Migration and livelihood diversification is often an adaptation response 
to water-related hazards and involves securing income sources away 
from agriculture, including off-farm employment and temporary or 
permanent migration, and these are particularly important in Asia 
and Africa (Figure 4.27). Income and remittances are sometimes re-
invested, for instance, for crop diversification (Rodriguez-Solorzano,

6252014; Musah-Surugu et  al., 2018; Mashizha, 2019). While there is 
extensive documentation on the benefits of migration, the quality of 
studies is such that links between migration and subsequent benefits 
are not clear, making our conclusion of benefits from this measure as 
having medium confidence. On the other hand, there is more rigorous 
evidence on the maladaptive nature of migration as an adaptation 
measure (Figure  FAQ4.4.1). However, adverse climatic conditions, 
especially droughts, have been found to reduce international migration, 
as resources are unavailable to consider this option (Nawrotzki and 
Bakhtsiyarava, 2017), resulting in limits to adaptation (Ayeb-Karlsson 
et al., 2016; Brottem and Brooks, 2018; Ferdous et al., 2019). In addition, 
it is difficult to model this option in future climate adaptation models.
Policies, institutions and capacity building are important adaptation

measures in agriculture and often have beneficial outcomes, but the 
quality of studies precludes a high degree of certainty about those 
impacts (Figure  FAQ4.4.1). Access to credits, subsidies or insurance 
builds an important portfolio of reducing reliance on agricultural 
income alone (Rahut and Ali, 2017; Wossen et al., 2018). Training and 
capacity building are essential tools to ensure effective adaptation in 
agriculture, increasing food security (Chesterman et al., 2019; Makate 
and Makate, 2019). Through better understanding, the implementation 
of available responses reduces exposure to climate impacts. In 
addition, public regulations, including water policies and allocations 
and incentive instruments, and availability of appropriate finance 
play an essential role in shaping and enabling (Sections 4.8.5, 4.8.6, 
4.8.7), but also limiting (Section 4.8.2), water-related adaptation for 
agriculture (see also Chapter 17).

Water-stressed regions already rely on importing agricultural resources, 
thus importing water embedded in these commodities (D’Odorico 
et al., 2014). Virtual water trade will continue to play a role in reducing 
water-related food insecurity (Cross-Chapter Box INTERREG in Chapter 
16) (Pastor et al., 2014; Graham et al., 2020b).
While an increasing body of literature documents water-related 
adaptation in the agricultural sector, both in reducing current climate 
impacts and addressing future climate risk, knowledge gaps remain 
about assessing the effectiveness of such measures to reduce impacts 
and risks. Additional considerations on co-benefits of trade-offs for 
overall sustainable development are not always sufficiently considered 
in the available literature.
In sum, water-related adaptation in the agricultural sector is widely 
documented, with irrigation, agricultural water management, crop 
diversification and improved agronomic practices among the most

common adaptation measures adopted (high confidence). However, 
the projected future effectiveness of available water-related adaptation 
for agriculture decreases with increasing warming (medium evidence, 
4.6.3 Adaptation in Energy and Industrial Sectors
While AR5 (Arent et al., 2014) had looked at demand and supply changes 
in the energy sector due to climate change, none of the AR5 chapters 
had assessed adaptations in the energy sector per se. A modelling study by van Vliet et al. (2016b) demonstrated that increasing the efficiency of 
hydropower plants by up to 10% could offset the impacts of decreased 
water availability in most regions by mid-century, under both RCP2.6 
and RCP8.5 scenarios (medium confidence). Changing hydropower 
operation protocol and plant design can be effective adaptation 
measures, yet may be insufficient to mitigate all future risks related to 
increased floods and sediment loads (Lee et al., 2016).

van Vliet et al. (2016b) projected that even a 20% increase in efficiency 
of thermoelectric power plants might not be enough to offset the 
risks of water stress by mid-century (medium confidence). Therefore, 
thermoelectric power plants will need additional adaptation measures 
such as changes in cooling water sources and alternative cooling 
technologies (van Vliet et al., 2016c). In China, many CFPPs in water-
scarce North China have adopted air cooling technologies (Zhang 
et al., 2016a). In Europe, wet/dry cooling towers (Byers et al., 2016) and 
seawater cooling (Behrens et al., 2017) have been the preferred options. 
Overall, freshwater withdrawals for adapted cooling systems under all 
scenarios are projected to decline by −3% to −63% by 2100 compared 
to the base year of 2000 (Fricko et al., 2016) (medium confidence).
Diversifying energy portfolios to reduce water-related impacts on 
the energy sector is another effective adaptation strategy with high

mitigation co-benefits. A modelling study from Europe shows that 
for a 3°C scenario, an energy mix with an 80% share of renewable 
energy can potentially reduce the overall negative impacts on the 
energy sector by a factor of 1.5  times or more (Tobin et  al., 2018). 
In addition, hydropower can also play a role in compensating for the 
intermittency of other renewable energies (François et al., 2014). For 
example, integrating hydro, solar and wind power in energy generation 
strategies in the Grand Ethiopian Renaissance Dam can potentially 
deliver multiple benefits, including decarbonisation, compliance with 
environmental flow norms and reduce potential conflicts among Nile 
riparian countries (Sterl et al., 2021). Furthermore, reducing the share 
of thermoelectric power with solar and wind energy (Tobin et al., 2018; 
Arango-Aramburo et al., 2019; Emodi et al., 2019) can be synergistic 
from both climate and water perspectives, as solar and wind energy

have lower water footprints (high confidence).
Indigenous Peoples, mountain communities and marginalised minorities 
often bear the brunt of environmental and social disruptions due to 
hydropower. As a consequence, hydropower operators face resistance 
prior to and during construction. Benefit-sharing mechanisms help 
redistribute some of the gains from hydropower generation to the 
communities in the immediate vicinity of the project. For instance, 
sharing of hydropower revenues and profits to fund local infrastructure 
and pay dividends to local people has been practiced in Nepal and in 
some countries of the Mekong basin to enhance the social acceptability 
of hydropower projects (Balasubramanya et al., 2014; Shrestha et al., 
2016) (low confidence).
Most water-intensive industries are increasingly facing water stress, 
making the reuse of water an attractive adaptation strategy (see 
Box  4.5). For example, Singapore, where the share of industrial

water use is projected to grow from 55% in 2016 to 70% in 2060, is 
increasing its NEWater (highly treated wastewater) supply share from 
30% to 55% to meet the growing demand of industrial and cooling

626Box 4.3 | Irrigation as an Adaptation Response
Irrigation has consistently been used as a crop protection and yield enhancement strategy and has become even more critical in a 
warming world (Siebert et al., 2014). Approximately 40% of global yields come from irrigated agriculture, with a doubling of irrigated 
areas over the last 50 years and now constituting around 20% of the total harvested area (FAO, 2018b; Meier et al., 2018; Rosa et al., 
2020b). Thus, irrigation is one of the most frequently applied adaptation responses in agriculture and features centrally in projections of 
adaptation at all scales. Expansions of irrigated areas over the coming century are projected, leading to shifts from rain-fed to irrigated 
agriculture in response to climate change (Malek and Verburg, 2018; Huang et al., 2019; Nechifor and Winning, 2019). However, there

are regional limitations to this expansion due to renewable water resource limitations, including water quality issues (Zaveri et al., 2016; 
Turner et al., 2019). Depending on the specific spatial, temporal and technological characteristics of irrigation expansion, up to 35% of 
current rain-fed production could sustainably shift to irrigation with limited negative environmental effects (Rosa et al., 2020b).
Irrigation increases resilience and productivity relative to rain-fed production by reducing drought and heat stress on crop yields and by 
lowering ET demand by cooling canopy temperatures (Siebert et al., 2014; Tack et al., 2017; Li and Troy, 2018; Zaveri and B. Lobell, 2019; 
Agnolucci et al., 2020; Rosa et al., 2020b). Large-scale irrigation also affects local and regional climates (Cook et al., 2020b). While cooling 
effects, including reduction of the extreme heat due to irrigation, have been observed (Qian et al., 2020; Thiery et al., 2020), increases in

humid heat extremes because of irrigation with potentially detrimental health outcomes have also been reported (Krakauer et al., 2020; 
Mishra et al., 2020). For the heavily irrigated North China Plain, a night-time temperature increase overcompensated daytime cooling 
effects, leading to an overall warming effect (Chen and Jeong, 2018). In addition, modification of rainfall patterns has been linked to 
irrigation (Alter et al., 2015; Kang and Eltahir, 2019; Mathur and AchutaRao, 2020). For example, increases in extreme rainfall in central 
India in recent decades has been linked to the intensification of irrigated paddy cultivation in northwest India (Devanand et al., 2019).
Different irrigation techniques are associated with significant differences in irrigation water productivity (Deligios et  al., 2019), and 
replacing inefficient systems can reduce average non-beneficial water consumption by up to 76% while maintaining stable crop yields

(Jägermeyr et al., 2015). Several adjustments can improve water use efficiency, including extending irrigation intervals, shortening the 
time of watering crops or reducing the size of the plot being irrigated (Caretta and Börjeson, 2015; da Cunha et al., 2015; Dumenu and 
Obeng, 2016). Deficit irrigation is an important mechanism for improving water productivity (Zheng et al., 2018) and increasing regional 
crop production under drying conditions (Malek and Verburg, 2018). Access to irrigation can also play a role in alleviating poverty, 
contributing to reducing vulnerability and risks (Balasubramanya and Stifel, 2020). However, the diversity of irrigation-related techniques 
and the consequent differences in effect and water-use intensity is often underreported (Vanschoenwinkel and Van Passel, 2018).
The use of water-saving technologies like laser levelling, micro-irrigation, efficient pumps and water distribution systems (Kumar et al.,

2016); increasing irrigation efficiency (Wang et al., 2019a) through improved agronomic practices (Kakumanu et al., 2018) and economic 
instruments like water trading in developed countries like Australia (Kirby et al., 2014) are known to reduce water application rates and 
increase yields, and ‘save’ water at the plot level, but may exacerbate basin-scale water scarcity (van der Kooij et al., 2013; Zhou et al., 2021).
Asia accounts for 69–73% of the world’s irrigated area. However, irrigation currently plays a relatively minor role in most of Africa, except in 
the contiguous irrigated area along the Nile basin and North Africa and South Africa (Meier et al., 2018). In India, long-term data (1956–1999) 
on the irrigated area shows that farmers adjust their irrigation investments and crop choices in response to medium-run rainfall variability

(Taraz, 2017). da Cunha et al. (2015) report that farmers’ income tends to be higher on irrigated lands in Brazil. In Bangladesh, farmers invest 
a part of their increased incomes in improving irrigation access (Delaporte and Maurel, 2018). The severity of drought increases the likelihood 
of farmers adopting supplementary irrigation in Bangladesh (Alauddin and Sarker, 2014). In Vietnam, irrigation improvement had the highest 
positive impact on crop yield among all farm-level adaptive practices (Ho and Shimada, 2019). In South Africa, access to irrigation was one of 
the most important predictors of whether or not farmers would adopt a whole suite of other adaptation responses (Samuel and Sylvia, 2019).
Irrigation is also associated with adverse environmental and socioeconomic outcomes, including groundwater over-abstraction, aquifer 
salinisation (Foster et al., 2018; Pulido-Bosch et al., 2018; Quan et al., 2019; Blakeslee et al., 2020) and land degradation (Singh et al.,

2018). Further, while irrigation expansion is one of the most commonly proposed adaptation responses, there are limitations to further 
increases in water use, as many regions are already facing water limitations under current climatic conditions (high confidence) (Rockström 
et al., 2014; Steffen et al., 2015; Kummu et al., 2016).
Projections of the future effectiveness of irrigation indicate a varying degree of effectiveness depending on the region and specific type 
and combination of approaches used. At the same time, overall residual impacts increase at higher levels of warming (Section 4.7.1.2). 
Uncertainties in regional climate projections and limitations in the ability of agricultural models to fully represent water resources are 
important limitations in our understanding of the potential of further irrigation expansion (Section 4.5.1) (Greve et al., 2018).

627In light of the volume of irrigated agriculture globally, and the projected increase in water requirements for food production, increasing 
water productivity and thus improving the ratio of water used per unit of agricultural output is necessary globally to meet agricultural 
water demand (Section  4.5.1) (Jägermeyr et  al., 2015; Jägermeyr et  al., 2017). For example, assuming a doubling of global maize 
production by 2050 increased water productivity could reduce total water consumption compared to the baseline productivity by 20–
60% (Zheng et al., 2018). Under economic optimisation assumptions, shifts towards less water-intensive and less climate-sensitive crops 
would be optimal in terms of water use efficiency and absolute yield increases; however, this could pose risks to food security as 
production shifts away from main staple crops (Nechifor and Winning, 2019). Shifting currently rain-fed production areas to irrigation will

be an important element in ensuring food security with increasing temperatures, though investment in storage capacities to buffer 
seasonal water shortage will be essential to ensure negative environmental impacts are minimised (Rosa et al., 2020b).Box 4.3 (continued)
activities (PUB, 2016). In addition, the mining industry has also adopted 
water adaptations measures, such as water recycling and reuse; using 
brackish or saline sources; and working with regional water utilities 
to reduce water extraction and improve water use efficiency (Northey 
et al., 2017; Odell et al., 2018).
In summary, energy and industrial sector companies have undertaken 
several adaptation measures to reduce water stress, with varying 
effectiveness levels. However, residual risks will remain, especially at 
higher levels of warming (medium confidence).
4.6.4 Adaptation in the Water, Sanitation and Hygiene 
Sector
AR5 pointed to adaptive water management techniques (limited

evidence, high agreement) (Field et  al., 2014b), while SR1.5 
documented the need for reducing vulnerabilities and promoting 
sustainable development and disaster risk reduction synergies (high 
confidence) (IPCC, 2018a). WaSH has also been identified as a low-
regrets adaptation measure (Cutter et al., 2012).
Access to appropriate, reliable WaSH protects against water-related 
diseases, particularly after climate hazards such as heavy rainfalls and 
floods (Carlton et al., 2014; Jones et al., 2020). WaSH interventions have 
been demonstrated to reduce diarrhoea risk by 25–75% depending 
on the specific intervention (Wolf et  al., 2018) (high confidence). 
Conversely, inadequate WaSH is associated with an estimated annual 
loss of 50 million daily adjusted life years (Prüss-Ustün et al., 2019), 
of which 89% of deaths are due to diarrhoea, and 8% of deaths from 
acute respiratory infections (Chapter 7 WGII 7.3.2), making universal

access to WaSH (i.e., achievement of SDG 6.1, 6.2) a critical adaptation 
strategy (high confidence). However, not all WaSH solutions are suited 
to all climate conditions (Sherpa et  al., 2014; Howard et  al., 2016), 
so health outcome improvements are not always sustained under 
changing climate impacts (Dey et al., 2019) (medium evidence, high 
agreement). As such, WaSH infrastructure also needs to be climate-
resilient (Smith et al., 2015; Shah et al., 2020). In addition to new WaSH 
infrastructure design and implementation, expansion and replacement 
of existing infrastructure offer opportunities to implement climate-
resilient designs and reduce greenhouse emissions (Boholm and 
Prutzer, 2017; Dickin et al., 2020) (medium evidence, high agreement).Effective adaptation strategies include protecting source water and 
managing both water supply and demand. Source water protection 
(Shaffril et al., 2020) has proven effective in reducing contamination.

Improved integrated (urban) water resources management (Kirshen 
et al., 2018; Tosun and Leopold, 2019) and governance (Chu, 2017; 
Miller et al., 2020) and enhanced ecosystem management (Adhikari 
et  al., 2018b) lead to policies and regulations that reduce water 
insecurity and, when developed appropriately, reduce inequities 
(medium confidence). Supply (source) augmentation, including dams, 
storage and rainwater/fog harvesting, can increase the supply or 
reliability of water for drinking, sanitation and hygiene (DeNicola 
et  al., 2015; Pearson et  al., 2015; Majuru et  al., 2016; Poudel and 
Duex, 2017; Lucier and Qadir, 2018; Goodrich et  al., 2019) (high 
confidence). For example, rainwater harvesting in an Inuit community 
increased water for hygiene by 17%, reduced water retrieval efforts 
by 40% and improved psychological and financial health (Mercer 
and Hanrahan, 2017). However, climate change impacts will affect

amounts of rainwater available. A recent study concluded that 
domestic water demand met through rainwater harvesting generally 
improves under climate change scenarios for select communities 
in Canada and Uganda, with the exception of drier summers in 
some areas of Canada (Schuster-Wallace et  al., 2021). Further, it 
is important to recognise that many of these interventions require 
financial investments that make them inaccessible to the poorest 
(Eakin et  al., 2016). Demand for water can be decreased through 
reductions in water loss from the system (e.g., pipe leakage) (Orlove 
et  al., 2019) and water conservation measures (Duran-Encalada 
et al., 2017) (medium confidence).
During periods of water insecurity, people often implement 
maladaptive strategies (Magnan et al., 2016), that is, strategies that 
can increase the risk of adverse health impacts, increase exposure 
to violence or cause malnutrition (Kher et al., 2015; Pommells et al.,

2018; Collins et al., 2019a; Schuster et al., 2020) (medium evidence, 
high agreement). Examples include walking further, using less safe 
water sources, prioritising drinking and cooking over personal/
household hygiene, or reducing food/water intake. Conversely, some 
rebalancing of gender roles can occur when women and girls cannot 
source sufficient water, with men building additional water supply 
or storage infrastructure or fetching water (Singh and Singh, 2015; 
Magesa and Pauline, 2016; Shrestha et al., 2019b). Some adaptation 
strategies create unintended health threats such as increased odds

628Box 4.4 | COVID-19 Amplifies Challenges for WaSH Adaptation
While COVID-19 is an airborne disease (see Cross-Chapter Box COVID in Chapter 7), public health responses to the COVID-19 pandemic 
and the associated socioeconomic and environmental impacts of these measures intersect with WaSH (Armitage and Nellums, 2020a). 
Notably, COVID-19 and climate change act as compound risks in the context of water-induced disasters, exacerbating existing threats to 
sustainable development (Neal, 2020; Pelling et al. 2021).
The principal WaSH response to COVID-19 relates to hand hygiene, an infection control intervention that requires access to sufficient, 
clean and affordable water beyond cooking, hydration and general sanitation needs, as outlined in SDG6 (Armitage and Nellums, 2020a). 
However, despite significant progress, more than 800 million people in central and southern Asia, and 760 million in sub-Saharan Africa,

lack basic hand-washing facilities in the home (UNICEF , 2020). Notably, one in four healthcare facilities in select low- and middle-income 
countries lacks basic water access, and one in six lacks hand-washing facilities (WHO, 2019) (Section 4.3.3). Moreover, household water 
insecurity also impacts marginalised and minority groups in the Global North (Deitz and Meehan, 2019; Rodriguez-Lonebear et al., 2020; 
Compound disasters have arisen due to either the co-occurrence of drought, storms or floods and COVID-19. COVID-19 acts as a 
stress multiplier for women and girls in charge of water collection and minorities and disabled people who are not engaged in water 
management (Phillips et al., 2020; Rodriguez-Lonebear et al., 2020). Across the world, existing inequalities deepened due to lockdowns, 
which further limited access to clean water and education for women and girls, and reinstated gendered responsibilities of child, elderly

and sick care, which had been previously externalised (Cousins, 2020; Neal, 2020; Zavaleta-Cortijo et al., 2020). Accordingly, COVID-19 
has further steepened the path to reach SDGs 2, 3, 4, 5 and 11 (Lambert et al., 2020; Mukherjee et al., 2020; Neal, 2020; Pramanik et al., 
2021). In addition, the pandemic exacerbated food insecurity in drought-affected eastern and southern Africa (Phillips et al., 2020; Mishra 
et al., 2021). As the twin risk of COVID-19 and hurricanes on the US Gulf Coast (Pei et al., 2020; Shultz et al., 2020) and cyclone Amphan 
in Bangladesh (Pramanik et al., 2021) showed, increased hand washing, additional WaSH and evacuation and shelter infrastructures 
proved essential for preventing further spread of COVID-19 (Baidya et al., 2020; Ebrahim et al., 2020; Guo et al., 2020; Mukherjee et al., 
2020; Pei et al., 2020; Shultz et al., 2020; Pramanik et al., 2021). Moreover, while immediate steps can be taken during disaster response

to minimise climate-attributable loss of life, climate adaptation requires long-term strategies that intersect with pandemic preparedness 
(Phillips et al., 2020).
Public health responses to COVID-19 geared towards infection control and caring for the sick can trigger increased water demand where 
population numbers and density are high (Mukherjee et al., 2020; Sivakumar, 2021). As COVID-19 has highlighted the importance of 
WaSH (Section 4.3.3), this pandemic could also result in long-term positive outcomes in community resilience, improved infection control 
and health protection while addressing longer-term environmental challenges of climate change (Phillips et al., 2020).(1.55) of mosquito larvae in water storage pots (Ferdousi et al., 2015), 
which could have even more significant impacts in the future given 
projected range expansion for vectors as a result of climate change 
(Liu-Helmersson et al., 2019). Other unintended consequences include

pathogen contamination (Gwenzi et al., 2015) and time or financial 
trade-offs (Schuster et al., 2020) (medium evidence, high agreement). 
Wastewater reuse for irrigation may have adverse health impacts if 
wastewater is not treated (Dickin et al., 2016). Conversely, especially 
where women are responsible for domestic and productive water 
management, adaptive agricultural water strategies, such as water-
efficient irrigation or low-water crops, mean that less water from finite 
water supplies are used for agriculture, leaving more water locally 
available for domestic purposes (see section 4.6.2). These co-benefits 
across sectors become important community water stress adaptations 
(Chinwendu et al., 2017), with water savings from one use leading to 
more water available for other uses. This can reduce domestic water 
burdens and, therefore, gender inequities (Section  4.8.3) (limited 
evidence, high agreement). Further analyses of co-benefits, particularly

employing a gender lens, are required to improve adaptation strategies 
(McIver et al., 2016).In summary, ensuring access to climate-resilient WaSH infrastructure 
and practices represents a key adaptation strategy that can protect 
beneficiaries against water-related diseases induced by climate change 
(high confidence). Better management of water resources, supply 
augmentation and demand management are important adaptation 
strategies (high confidence). Reliable, safe drinking water reduces 
adverse physical and psychological impacts of climate-related water 
stress and extreme events (robust evidence, medium agreement). 
WaSH infrastructure expansion and replacement provide opportunities 
to redesign and increase resilience in rural and urban contexts (limited 
4.6.5 Adaptation in Urban and Peri-Urban Sectors
AR5 reported that although case studies of the potential effectiveness 
of adaptation measures in cities are growing, not all considered how

adaptation would be implemented in practice (Jiménez Cisneros et al., 
2014). Furthermore, AR5 concluded that more attention had been 
given to adaptations that help ensure sufficient water supplies than

629to increasing the capacity of sewage and drainage systems to adapt to 
heavier rainfall or sea level rise (Revi et al., 2014).
Since AR5 knowledge on urban adaptation has advanced, even though 
there is still limited documentation of water adaptation in urban contexts 
as compared to other adaptation responses (Figure 4.23.) The majority 
of case studies on urban adaptation are also from developed countries, 
most commonly in Europe and Australasia (Figure 4.24). Water-related 
urban and peri-urban climate change adaptation can involve ‘hard’ 
engineering structures (grey), managed or restored biophysical systems 
(green and blue) or hybrid approaches that combine these strategies 
(Ngoran and Xue, 2015; Palmer et  al., 2015) (Figure  4.21, also see 
Figure 4.22 for types of urban adaptation options).
In most regions, hybrid adaptation approaches are underway. For 
example, sustainable urban drainage systems (SUDS) are a common

adaptation measure that can reduce flooding and improve stormwater 
quality while reducing the urban heat island effect (e.g., Chan et al., 
2019; Loiola et  al., 2019; Song et  al., 2019; Huang et  al., 2020; Lin 
et al., 2020) (Box 4.6; 12.5.5.3.2; 12.7.1). Municipal, catchment and 
local community plans to minimise water-related climate risks are 
another form of adaptation (Stults and Larsen, 2018). Plans involve 
supply augmentation (Chu, 2017; Bekele et  al., 2018), as well as 
floodplain management, land use planning, stakeholder coordination 
and water demand management (Andrew and Sauquet, 2017; Flyen 
et al., 2018; Robb et al., 2019; Tosun and Leopold, 2019), with some 
US cities including strategies to address social inequalities that climate 
change may exacerbate (Chu and Cannon, 2021).
Such adaptation measures are concentrated in more developed countries 
(Olazabal et al., 2019). For example, about 80% of European cities with

more than 500,000 inhabitants have either mitigation and/or adaptation 
plans (Reckien et al., 2018). In contrast, a survey of cities with more than 
one million inhabitants found 92% of Asian cities, 89% of African cities 
and 87% of Latin American cities did not report adaptation initiatives 
(Araos et al., 2016) (12.5.8.1). Autonomous adaptation measures (e.g., 
elevating housing and drainage maintenance) are pursued to reduce 
flood risk in urban Senegal (Schaer, 2015), Kenya (Thorn et al., 2015), 
Brazil (Mansur et al., 2018) and Guyana (Mycoo, 2014) (Box 4.7; 9.8.5.1; 
12.5.5.3; FAQ12.2).
Further studies are required to ascertain the effectiveness of adaptation 
measures implemented since AR5, particularly for the growing 
populations of informal and peri-urban settlements. For example, in 
urban Africa, such informal settlements are sites of political contestation 
as residents resist municipal relocation strategies for flood alleviation

(Douglas, 2018). In addition, the growing complexity of challenges 
facing urban water management, such as climate change, urbanisation 
and environmental degradation, warrants a transformative shift away 
from prevailing siloed approaches of water supply, sanitation and 
drainage to more integrated systems that enhance adaptive capacity 
(Ma et al., 2015; Franco-Torres et al., 2020).
In summary, although water-related adaptation is underway in the 
urban, peri-urban and municipal sectors of some nations, governance, 
technical and economic barriers remain in implementing locally informed 
strategies, particularly in developing countries (high confidence).4.6.6 Adaptation for Communities Dependent on 
Freshwater Ecosystems
AR5 concluded that some adaptation responses in the urban and 
agricultural sectors could negatively impact freshwater ecosystems 
(medium confidence) (Settele et al., 2014).
Adaptation measures to cope with changes in ecosystems, including

freshwater ecosystems, such as ecosystem-based adaptation (EbA) 
interventions have gained wide recognition at the global policy level 
(Reid, 2016; Barkdull and Harris, 2019; Piggott-McKellar et al., 2019b).
These have been implemented in many locations around the world, 
yet, challenges remain, including improving the evidence base of their 
effectiveness, scaling up of these interventions, mainstreaming across 
sectors and receiving more adaptation finance (medium confidence).
A systematic review of 132 academic papers and 32 articles from non-
peer-reviewed literature (Doswald et  al., 2014) provided a comprehensive 
global overview of EbA, which showed that EbA interventions were 
used in various ecosystems, including inland wetlands (linked to 30 
publications). An investigation of EbA effectiveness by Reid et  al. 
(2019), where nine case studies covering South Asia, Africa and South 
America were associated with freshwater systems, concluded that

EbA enabled the enhancement of the adaptive capacity or resilience 
to climate change, particularly for the more vulnerable groups in the 
community. An assessment of the potential for EbA in three sub-
basins of the Murray–Darling Basin, Australia, concluded that EbA can 
augment catchment management practices but that there were also 
institutional challenges (Lukasiewicz et  al., 2016). In urban settings, 
EbA has been associated with ecological structures for reducing risks, 
including the use of urban wetlands (Barkdull and Harris, 2019). EbA is 
a subset of NbS that is rooted in climate change adaptation and covers 
both mitigation and adaptation (Pauleit et  al., 2017) (Section  4.6.5, 
Box  4.6). Although adaptation measures for freshwater ecosystems 
have been implemented in many places (Shaw et al., 2014; Lukasiewicz 
et al., 2016; Karim and Thiel, 2017; Milman and Jagannathan, 2017; 
FAO, 2018a; Piggott-McKellar et al., 2019b), the evidence base for the

effectiveness of these measures to cope with changes in freshwater 
ecosystems needs improvement. These measures also require further 
financial support, mainstreaming across sectors and the scaling up of 
individual measures (medium confidence).
In summary, adaptation measures to cope with changes in freshwater 
ecosystems have been implemented in many locations around the world. 
However, challenges remain, including improving the evidence base of 
their effectiveness, scaling up these interventions, mainstreaming across 
4.6.7 Adaptation Responses for Water-Related Conflicts
AR5 concluded with high confidence that challenges for adaptation 
actions (though not water) are particularly high in regions affected by 
conflicts (Field et  al., 2014a). Although climate–conflict linkages are 
disputed (Section 4.3.6), the potential for synergies between conflict risk

reduction and adaptation to climate change exists (Mach et al., 2019). For 
example, discourses around climate–conflict inter-linkages can present

630evaporationtranspirationprecipitation
run-off
wetlands
groundwater rechargeinfiltrationwater tablestream 
restoration(a) Green and/or Blue Strategies
lake
parks, grass
(b) Grey Strategies
increased run-off
concrete storage basin
lower water tablepiped stormwaterimpervious surfaceextensive
impervious
surfacestorm
water
drainwastewater
treatment
plantwater filtration plant
pipes, canals
(c) Hybrid Strategies
groundwater recharge
infiltrationriparian
buffer
french draingreen roofs
reduced
run-off
bioswale
water table maintainedbioswales
water treatment wetlandporous
pavement
constructed
wetlandsStrategies for Urban W ater Adaptation
Figure 4.21 |  Strategies for urban water adaptation.
(a) Green and blue strategies of urban water adaptation prioritise ecosystem restoration, such as wetlands restoration.

(b) Grey water strategies are hard engineering approaches to urban water adaptation, including infrastructure such as pipes and canals, with extensive areas of impervious surfaces.
(c) Hybrid approaches combine green, blue and grey adaptation strategies, such that ecosystem functions are complemented by engineered infrastructure, such as constructed 
wetlands, green roofs and riparian buffers. Green and blue and hybrid approaches are variously classified in terms of a circular economy, water sensitive urban design, nature-based 
solutions (NbS), integrated urban water management, and ecological infrastructure. Adapted from Depietri and McPhearson (2017).

631Box 4.5 | Reduce, Remove, Reuse and Recycle (4Rs): Wastewater Reuse and Desalination as an 
Adaptation Response
Circular economies can increase the available sustainable adaptation space by moving away from a linear mode of production of ‘extract-
produce-use-discard’ to a ‘4Rs’ closed loop to reduce pollution at the source, remove contaminants from wastewater, reuse treated 
wastewater and recover valuable by-products (UN Water, 2017; see WGIII 11.3.3).
It is estimated that 380  billion m3 of wastewater is produced annually worldwide, which equals about 15% of agricultural water 
withdrawals. The recovery of nitrogen, phosphorus and potassium from wastewater can offset 13.4% of the global agriculture demand 
for these nutrients (Jiménez and Asano, 2008; Fernández-Arévalo et  al., 2017). Recycling human waste worldwide could satisfy an 
estimated 22% of the global demand for phosphorus (UN Water, 2017). It has been estimated that some 36 million ha worldwide (some

12% of all irrigated land) reuse urban wastewater, mainly for irrigation. However, only around 15% is adequately treated (Thebo et al., 
2017), thus the need to invest in sustainable, low-cost wastewater treatment to protect public health. The irrigation potential of this 
volume of wastewater stands at 42 million ha. Wastewater production is expected to increase globally to 574 billion m3 by 2050, a 51% 
increase compared to 2015, mainly due to a growing urban population (Qadir et al., 2020). Water reuse with treated wastewater for 
potable and non-potable purposes can be practised in a manner that is protective of public health and the environment (WHO, 2006; 
WHO, 2017). For example, when implemented with sufficient treatment standards, the use of recycled water for the irrigation of crops is 
protective of public health (Blaine et al., 2013; Paltiel et al., 2016), as was determined by an appointed panel of experts in the state of

California (Cooper et al., 2012). However, there are several barriers to the adoption of wastewater reuse; these include technical barriers 
and public health aspects related to microbiological and pharmaceuticals risks (Jiménez and Asano, 2008; Jaramillo and Restrepo, 2017; 
Saurí and Arahuetes, 2019). These are currently being addressed by strengthening regulatory standards, with, for example, 11 out of 22 
Arab States adopting legislation permitting the use of treated wastewater (WHO, 2006; US EPA, 2017; WHO, 2017; EC, 2020). Benefits of 
wastewater reuse usually outweigh the costs (Stacklin, 2012; Hernández-Sancho et al., 2015; UN Water, 2017).
Desalination is particularly important in arid and semiarid climates, coastal cities and small island states (Box 4.2). There were 16,000 
operational desalination plants globally in 2017, with a daily desalinated water production of 95 million m3 d-1 (IDA, 2020). In 2012,

desalinated water was equivalent to 0.6% of the global water supply, and 75.2 TWh of energy per year was used to generate desalinated 
water; in other words, about 0.4% of the worldwide electricity consumption (IRENA, 2012). Unfortunately, only 1% of total desalinated 
water uses renewable sources (IRENA, 2012; Amy et  al., 2017; Balaban, 2017; Martínez-Alvarez et  al., 2018a; Jones et  al., 2019) 
(Section 4.7.6). Desalination has already helped to meet urban and peri-urban water supply, particularly during annual or seasonal 
drought events, with half of the world’s desalination capacity in the Arab region (UN Environment, 2019; UN Water, 2021). In addition, 
seawater desalination could help address water scarcity in 146 (50%) large cities (including 12 (63.2%) megacities) (He et al., 2021). 
Desalination is also being adopted for irrigation. For example, in the island of Gran Canary (Spain), 30% of the agricultural surface area

is irrigated with desalinated water to irrigate high-value crops (Burn et al., 2015; Martínez-Alvarez et al., 2018a; Monterrey-Viña et al., 
2020). The expected growth of desalination, if not coupled with renewable energy (RE), causes a projected 180% increase in carbon 
emissions by 2040 (GCWDA, 2015; Pistocchi et al., 2020). There have been advances in large-scale and on-farm renewable desalination 
(Abdelkareem et al., 2018). Using renewable energy to decarbonise desalination has meant that the projected global average levelled 
cost of water could decrease from €2.4 m–3 (2015) to approximately €1.05 m–3 by 2050, considering unsubsidised fossil fuel costs (Caldera 
and Breyer, 2020). Desalination will be maladaptive if fossil fuel is used (Tubi and Williams, 2021).
In summary, a resilient circular economy is central to deliver access to water and sanitation, with, wastewater treatment, desalination

and water reuse as viable adaptation options compatible with the Paris Agreement, while safeguarding ecological flows according to the 
SDG6 targets for climate resilient development (medium evidence, high agreement).
opportunities for peace building and cooperation (Matthew, 2014; 
Abrahams, 2020). Indeed, adaptation efforts are needed in the context 
of conflict, where the pre-existing vulnerability undermines the capacity 
to manage climatic stresses. In addition, adaptive capacity depends on 
contextual factors such as power relations and historical, ethnic tensions 
(Petersen-Perlman et al., 2017; Eriksen et al., 2021), which need to be 
adequately considered in the design of adaptation strategies.
Some adaptation options, such as water conservation, storage and 
infrastructure, voluntary migration, planned relocation due to flood risk/
sea level rise, and international water treaties, can reduce vulnerability to climate change and conflicts. However, on the other hand, these

adaptation options sometimes may have unintended consequences 
by increasing existing tensions (Milman and Arsano, 2014); displacing 
climate hazards to more vulnerable and marginalised groups (Milman 
and Arsano, 2014; Mach et  al., 2019), for example, pastoralists 
(Zografos et al., 2014); and favouring some over others, such as industry 
over agriculture (Iglesias and Garrote, 2015)¸ upstream countries over 
downstream countries (Veldkamp et al., 2017), and men over women 
(Chandra et  al., 2017). Such unintended consequences may happen 
when adaptation measures intended to reduce vulnerability produce 
maladaptive outcomes by rebounding or shifting vulnerability to

632Box 4.6 | Nature-based Solutions for Water-Related Adaptation
In the context of climate change-induced water insecurity, nature-based solutions (NbS) are an adaptation response that relies on natural 
processes to enhance water availability and water quality and mitigate risks associated with water-related disasters while contributing 
to biodiversity (IUCN, 2020).
Until recently, NbS have been considered mainly for mitigation (Kapos et al., 2020; Seddon et al., 2020). Yet, NbS increase the low-cost 
adaptation options that expand the adaptation space due to their multiple co-benefits (Cross-Chapter Box NATURAL in Chapter 2). 
Furthermore, a meta-review of 928 NbS measures globally shows that NbS largely addresses water-related hazards like heavy precipitation 
(37%) and drought (28%) (Kapos et al., 2020).
Natural infrastructure (green and blue) uses natural or semi-natural systems, for example, wetlands, healthy freshwater ecosystems, etc.,

to supply clean water, regulate flooding, enhance water quality and control erosion (6.3.3.1 to 6.3.3.6.). Grey infrastructure can damage 
biophysical and hydrological processes, seal soils and bury streams. Compared with grey physical infrastructure, natural infrastructure 
is often more flexible, cost-effective and can provide multiple societal and environmental benefits simultaneously (McVittie et al., 2018; 
UN Water, 2018; IPBES, 2019). There is increasing evidence and assessment methods on the role of NbS for climate change adaptation 
and disaster risk reduction at different scales (Chausson et al., 2020; Seddon et al., 2020; Cassin and Matthews, 2021) (Section 4.6.5).
At the landscape scale, there is evidence that impacts from fluvial and coastal floods can be mitigated through water-based NbS like 
detention/retention basins, river restoration and wetlands (Thorslund et  al., 2017; Debele et  al., 2019; Huang et  al., 2020). Several

examples show the effectiveness of floodplain restoration, natural flood management and making room for the river measures (see 
FAQ2.5, Hartmann et al., 2019; Mansourian et al., 2019; Wilkinson et al., 2019) (medium evidence, high agreement). Likewise, the use 
of managed aquifer recharge (MAR) in both urban and rural settings will be crucial for groundwater-related adaptation (Zhang et al., 
2020a).
At the urban and peri-urban scale, the use and effectiveness of NbS is a crucial feature to build resilience in cities for urban stormwater 
management and heat mitigation (Depietri and McPhearson, 2017; Carter et al., 2018; Huang et al., 2020; Babí Almenar et al., 2021) (high 
confidence). NbS have been used for stormwater management by combining water purification and retention functions (Prudencio and 
Null, 2018; Oral et al., 2020). NbS have also been used to mitigate impacts from high-impact extreme precipitation events by integrating

large-scale NbS investment plans into urban planning in cities like New York and Copenhagen, highlighting the importance of blended 
finance and investment (including insurance) to mainstream NbS investments (Liu and Jensen, 2017; Rosenzweig et al., 2019; Lopez-
Gunn et al., 2021). According to the CDP database, one in three cities use NbS to address climate hazards, and this trend is growing 
(Kapos et al., 2020).
NbS are cost-effective and can complement or replace grey solutions (Cross-Chapter Box FEASIB in Chapter 18, 3.2.3) (Chausson et al., 
2020). Moreover, estimates of NbS are increasingly based on integrated economic valuations that incorporate co-design with stakeholders 
to incorporate LK (Pagano et al., 2019; Giordano et al., 2020; Hérivaux and Le Coent, 2021; Palomo et al., 2021) (medium evidence, high 
agreement). Yet, the performance of NbS themselves may be limited at higher GWLs (Calliari et al., 2019; Morecroft et al., 2019).

More knowledge is needed on the long-term benefits of NbS, particularly to hydro-meteorological hazards (Debele et al., 2019). There 
is still low evidence for slow-onset events, including the applicability of NbS to manage highly vulnerable ecosystems and in agriculture 
(Sonneveld, 2018),
In summary, there is growing evidence on NbS effectiveness as an adaptation measure and its critical role for transformative adaptation 
to address climate change water-related hazards and water security (medium evidence, high agreement). Moreover, several NbS– —for 
example, natural (blue and green) and grey infrastructure—can help address water-related hazards such as coastal hazards, heavy 
precipitation, drought, erosion and low water quality (high confidence).
other actors (Juhola et al., 2016). For example, in the Mekong River 
basin, the construction of dams and water reservoirs contributes to 
the adaptation efforts of the upstream Southeast Asia countries

while increasing current/future vulnerability to floods and droughts in 
downstream countries and can emerge as a cause of conflict (Earle 
et al., 2015; Ngô et al., 2016).Furthermore, adaptation in the context of water-related conflicts 
is also constrained by economic, institutional and political factors, 
competition for development (Anguelovski et  al., 2014) and gender 
considerations (Sultana, 2014; Chandra et al., 2017), which need to be 
taken into account when designing adaptation plans/measures.

6334.6.8 Adaptations Through Human Mobility and 
Migration
AR5 noted that whether migration is adaptive or maladaptive 
depends on the context and the individuals involved; however, it 
did not focus specifically on hydrological change-induced migration 
(Noble et al., 2014). Migration is often regarded as a transformational 
adaptation strategy in response to climate-induced hydrological 
changes (Gemenne and Blocher, 2017) but rarely as the primary or 
only adaptation measure (Wiederkehr et  al., 2018; de Longueville 
et al., 2020; Cross-Chapter Box MIGRATE in Chapter 7). Migration is 
among one of the top five adaptation responses documented in Asia 
and Africa (Figure 4.27) and confers several benefits to migrants, yet 
maladaptations are also documented (Figure 4.29). This strategy is not 
available to everyone. Vulnerable populations exposed to hydrological 
changes may become trapped due to a lack of economic and social

capital required for migration (Adams, 2016; Zickgraf, 2018) (medium 
Spontaneous migration, undertaken without outside assistance, 
has shown the potential to improve the resilience of migrants and 
communities (Call et  al., 2017; Jha et  al., 2018a), but may also lead 
to increased vulnerability and insecurity in some instances (Adger 
et al., 2018; Linke et al., 2018a; Singh and Basu, 2020). Migration is 
not a viable strategy for everyone, but age, gender and socioeconomic 
status play a significant role in encouraging or inhibiting the chances 
of successful migration (Maharjan et al., 2020; Bergmann et al., 2021; 
Erwin et al., 2021). Migration has increased vulnerability among women 
and female-headed households (Patel and Giri, 2019), but has also 
triggered gender-positive processes such as increased female school 
enrolment (Gioli et al., 2014) (medium confidence). Remittances, that is, 
transfers of money from migrants to beneficiaries in sending areas, may

reduce vulnerability and increase adaptive capacity to climate-induced 
hydrological changes (Ng’ang’a et al., 2016; Jha et al., 2018b) (medium 
confidence). Managed retreat refers to the planned and assisted 
moving of people and assets away from risk areas, such as government- 
or community-led resettlement (Hino et  al., 2017; Maldonado and 
Peterson, 2018; Tadgell et al., 2018; Arnall, 2019). Such initiatives may 
reduce exposure to risk (Lei et al., 2017). However, they often fail to 
include affected populations in the process and may lead to greater 
impoverishment and increased vulnerability (Wilmsen and Webber, 
2015) (medium confidence).
More research on how to ensure migration becomes a successful 
adaptation strategy is needed (McLeman et  al., 2016). In addition, 
impacts on women, youth and marginalised groups (McLeman et al., 
2016; Miletto, 2017) and immobility issues need more attention 
(Zickgraf, 2018).

In summary, measures that facilitate successful migration and inclusive 
resettlement may facilitate adaptation to climate-induced hydrological 
changes (medium confidence).4.6.9 Adaptation of the Cultural Water Uses of 
AR5 reported that religious and sacred values inform actions taken to 
adapt to climate change (Noble et  al., 2014). Neither AR5 nor SR1.5 
reviewed adaptation of indigenous, local and traditional uses of 
water. SROCC highlighted the context-specific adaptation strategies 
of vulnerable communities in coastal, polar and high-mountain areas, 
reporting that adaptive capacity and adaptation limits are not only 
physical, technical, institutional and financial, but also culturally informed 
(Hock et al., 2019b; Meredith et al., 2019; Oppenheimer et al., 2019).
There is high confidence that some Indigenous Peoples, local 
communities and traditional peoples could adapt, and are adapting

to climate-driven hydrological changes and their impacts on culturally 
significant sites, species, ecosystems and practices in polar, high-
mountain and coastal areas, where sufficient funding, decision-making 
power and resourcing exist (e.g., Golden et al., 2015; Bunce et al., 2016; 
Anderson et al., 2018). However, there is also high confidence that there 
are significant structural barriers and limits to their adaptation, and 
that the outcomes of some adaptation strategies can be uneven and 
maladaptive (medium evidence, high agreement) (Sections 4.7.4; 4.8.3). 
These barriers include the lack of recognition of Indigenous Peoples’ 
sovereignty and exclusion of Indigenous Peoples from decision-making 
institutions (Ford et al., 2017; Labbé et al., 2017; Eira et al., 2018; McLeod 
et al., 2018; MacDonald and Birchall, 2020) (14.4.4.2.2; 13.8.1.2). At the 
same time, the rate and scale of climate change can impede the ability

of vulnerable communities to turn their adaptive capacity into effective 
adaptation responses (Ford et al., 2015; Herman-Mercer et al., 2019).
There is high confidence that local people are adapting to the cultural 
impacts of climate-driven glacier retreat and decline in snow cover 
and ice in polar and high-mountain areas. However, there is also high 
confidence that such adaptation can be detrimental and disrupt local 
cultures. For example, in the Peruvian Andes, concerns about water 
availability for ritual purposes has led to restrictions on pilgrims’ removal 
of ice and limiting the size of ritual candles to preserve the glacier 
(Paerregaard, 2013; Allison, 2015). Relatedly, some local people have 
questioned the cosmological order and have reoriented their spiritual 
relationships accordingly (Paerregaard, 2013; Carey et  al., 2017). In 
Siberia (Mustonen, 2015) and northern Finland (Turunen et al., 2016), 
community-led decisions among herders favour alternative routing,

pasture areas and shifts in nomadic cycles in response to changing 
flood events and permafrost conditions (Box 13.2). However, loss of 
grazing land and pasture fragmentation pose adaptation limits, and 
some strategies such as supplementary feeding and new technologies 
may further affect cultural traditions of herding communities (Risvoll 
and Hovelsrud, 2016; Jaakkola et al., 2018).
There is high confidence that relocation (managed retreat) is an 
adaptation response for communities in areas impacted by, or at 
risk of, inundation and other hydrological changes (15.3.4.7; 15.5.3). 
However, relocation can be culturally, socially, financially, politically 
and geographically constrained due to the importance of cultural 
relationships with traditional, customary or ancestral lands (high 
confidence) (Albert et  al., 2018; Narayan et  al., 2020; Yates et  al.,

6342021). Among Pacific islands, for example, the prospect of migration 
raises concerns about the loss of cultural identity and IK and practices, 
which can impact emotional well-being (Yates et al., 2021).
As cultural beliefs influence risk perception, there is medium confidence 
that some cultural understandings can foster a false sense of security 
among Indigenous Peoples, local communities and traditional peoples 
regarding climate-driven hydrological changes. For example, some 
members of the Rolwaling Sherpa community in Nepal believe that 
mountain deities protect them from GLOFs (Sherry and Curtis, 2017)
(Section 4.2.2). Elsewhere, such as in the islands of Fiji and St. Vincent, 
cultural beliefs can diminish human agency because change is viewed as inevitable and beyond human intervention (Smith and Rhiney, 
2016; Currenti et al., 2019). Yet such cultural beliefs are not necessarily 
maladaptive, as they potentially support other resilience factors, such

as IKLK (Section 4.8.5; Ford et al., 2020), as well as cultural connections 
and social ties (Yates et al., 2021).
In sum, although some Indigenous Peoples, local communities and 
traditional peoples can adapt, and are adapting to climate-driven 
hydrological changes, and their impacts on and risks to culturally 
significant practices and beliefs (medium confidence), these strategies 
are constrained by structural barriers and adaptation limits (high 
Box 4.7 | Flood-Related Adaptation Responses
Floods, due to their rapid onset and destructive force, require specific adaptation measures. Historically, to address flood damages and 
risk protection, retreat and accommodation were most common, emphasizing protecting and retreating (Wong et al., 2014; Bott and 
Braun, 2019). Figure 4.22 identifies five major adaptation strategies from a meta-review of water-related adaptation responses that helps 
in protecting, retreating and accommodating (Section 4.7.1).

Globally, structural measures for flood protection through hard infrastructure are the most common measures as they directly manage 
flood hazards by controlling flow through streams and prevent water overflow (Andrew and Sauquet, 2017; Duží et al., 2017). These 
measures include dikes, flood control gates, weirs, dams, storage and proper waste management (Barua et al., 2017; Egbinola et al., 
2017). Infrastructure measures require high maintenance, such as dredging and clearing channels and overpasses (Egbinola et al., 2017). 
A negative aspect of protective infrastructural measures is that, while they eliminate the hazard up to a certain magnitude (Di Baldassarre 
et al., 2013), they also generate an illusion of no risk by diminishing frequent floods (Duží et al., 2017; Logan et al., 2018). In addition, specific 
engineering solutions that might be introduced from other localities without proper contextual adjustments may lead to maladaptation

(Mycoo, 2014; Pritchard and Thielemans, 2014). NbS (Box 4.6) have shifted infrastructure measures from purely grey onto mixed engineering 
and environmental measures. Examples include SUDS, which aid in decreasing flow peaks and are affordable, aesthetically pleasing and 
socially acceptable while also reducing heat and hence the production of storms (Chan et al., 2019) (Section 4.6.5).
Non-structural or soft measures for flood adaptation include human actions that generate capacities, information and, therefore, 
awareness of floods (Du et al., 2020). Soft measures aim to integrate flood resilience within city management and planning (Wijaya, 
2015; Andrew and Sauquet, 2017; Abbas et al., 2018). Social support between members of a community and economic mechanisms 
such as loans or remittances are soft measures that promote recovery or resilience to floods (Barua et al., 2017; Musah-Surugu et al.,

2018; Bott and Braun, 2019). Communities with heightened awareness and knowledge of floods are probably going to elect political 
leaders that will affect flood protection and policies that include adaptation (Abbas et al., 2018). Soft measures can be an anchoring 
factor for policies that promote early warning systems, infrastructure, flood-resilient housing and environmental restoration (Andrew 
and Sauquet, 2017; Abbas et al., 2018). However, soft measures, especially at large scale, may also lead to maladaptation, such as lack 
of synchronisation between international, national and local levels (Hedelin, 2016; Lu, 2016; Jamero et al., 2017), and can further be 
hampered by bureaucracy (Pinto et al., 2018).
Early warning systems (EWS) are defined as integrated systems of hazard monitoring, forecasting and prediction, disaster risk assessment, 
communication and preparedness activities systems to enable individuals, communities, governments and businesses to take timely action

to reduce disaster risks in advance of hazardous events (UNISDR, 2021). By this definition, EWS are directly dependent on soft and hard 
infrastructure measures that increase capacity and reduce hazard (Abbas et al., 2018). Aside from the capacity dependent on soft measures 
and the monitoring infrastructure, communication at all scales, from national weather services to local leaders, needs to be effective for 
prompt action (Devkota et al., 2014). In many cases, EWS might be the only option to reduce flood casualties (Kontar et al., 2015).
Accommodating floods has gained popularity as the effects of climate change become more apparent and as notable hydroclimatic 
events exceed the limitations of protective measures (Pritchard and Thielemans, 2014). NbS measures like wetland restoration can act as 
modern infrastructure protection with clear mitigation co-benefit and provide opportunities for accommodating floods. For example,

initiatives such as ‘Room for the River’ consider flood safety combined with other values such as landscape, environment and cultural 
values (Zevenbergen et al., 2015). A popular EbA measure has been wetland restoration, which can control flood peaks, serve as storage

635ponds in addition to restoring the environment (Pinto et al., 2018; Saroar, 2018). However, its effectiveness under different conditions is 
yet to be assessed (Wamsler et  al., 2016). Flood resilient housing is another form of accommodating and living with floods. These 
comprise mostly of elevated homes or different flood protection measures considering vegetation around the house to make those flood 
resilient (Ling et al., 2015; Abbas et al., 2018; Ferdous et al., 2019).
Despite different degrees of effectiveness, no flood adaptation measure is uniquely effective to eliminate flood risk. Adaptation to 
floods needs to be considered at a local level, considering the types of floods, community’s capacities and available livelihoods (Fenton 
et al., 2017a). Ideally, flood adaptation strategies need to include short-term actions linked to long-term goals, be flexible, consider

multiple strategies and interlink investment agendas of stakeholders (Zevenbergen et al., 2015). Most importantly, flood adaptation and 
management options have been proven effective to reduce loss of human lives, but not entirely at sustaining livelihoods and reducing 
infrastructure damages (Rahman and Alam, 2016; Bower et al., 2019; Ferdous et al., 2019).Box 4.7 (continued)
4.7 Benefits and Effectiveness of Water-
Related Adaptations, Their Limits and 
Trade-Offs
The previous section documented adaptation responses in water use 
sectors we assess in this chapter (Section 4.6), and noted that in many 
instances, effectiveness of those responses is not clear. While there 
are thousands of case studies of implemented adaptation responses 
(observed adaptation) to water insecurity, there is a lack of synthesised 
understanding about the effectiveness and benefits of adaptation 
(Berrang-Ford et al., 2021a) and whether or not those benefits also

translate into climate risk reduction (Singh et al., 2021). In contrast, 
literature on the effectiveness of future projected adaptation in reducing 
climate risks is limited in number. Yet, even then, the findings are not 
synthesised across various options to make an overall assessment of 
the effectiveness of future projected adaptation. In this section, we 
draw on two meta-review protocols (see SM4.2 for a description of 
each protocol) and assess the benefits of current adaptation and 
effectiveness of future projected adaptation in reducing climate risks. 
We also assess limits to adaptation and trade-offs and synergies 
between adaptation and mitigation.
4.7.1 Current Water-Related Adaptation Responses, 
Benefits, Co-benefits and Maladaptation
AR5 (Jiménez Cisneros et al., 2014) concluded that developing countries 
needed a larger share of adaptation investments for anticipatory 
adaptation in the water sector (medium evidence, high agreement)

and that adaptive water management measures were critical in 
addressing climate-related uncertainty. Noble et  al. (2014) listed 
various examples of adaptation options, and water-related adaptation 
featured prominently in almost all categories. They also discussed the 
challenges of developing metrics for measuring adaptation outcomes 
and stressed the importance of transformational adaptation instead of 
incremental adaptation. Finally, SR1.5 (de Coninck et al., 2018) made 
one of the first attempts to systematically assess the feasibility of 
adaptation options (Singh and Basu, 2020).4.7.1.1 Current Water-Related Adaptation Responses
We define an adaptation response as a water-related adaptation if the 
hazard is water-related (e.g., floods, droughts, extreme rainfall events, 
groundwater depletion, melting and thawing of cryosphere, Figure 4.25) 
or the adaptation intervention is water-related (e.g., irrigation, rainwater

harvesting, soil moisture conservation, etc.). Adaptation responses were 
implemented across all water use sub-sectors assessed in this chapter 
(Section  4.6, Figure  4.23). Given the overall interest in assessing 
adaptations that documents outcomes, we limited our analysis to a set 
of 359 unique articles that measure outcomes of adaptation across pre-
defined outcome categories (SM4.2, Table SM4.5; Berrang-Ford et al., 
2021a; Mukherji et  al., 2021). A total of 1054 adaptation responses 
were documented in the 359 case studies; these were categorised 
into 16 categories (Figure  4.22). These adaptation responses are not 
always specific to long-term climate change impacts (that is, changes 
in annual mean fluxes), but rather respond to changes in variability 
in the water cycle and specific water hazards. Adaptation to internal 
variability is needed to increase the resilience to projected water cycle 
changes because water cycle changes primarily manifest as changes in

variability (Douville et al., 2021).
There is high confidence that a significant share of water-related 
adaptations is occurring in the agriculture sector. Agriculture accounts 
for 60–70% of total water withdrawals (Hanasaki et al., 2018; Burek 
et al., 2020; Müller Schmied et al., 2021) and supports the livelihoods 
of a large majority of people in the developing countries. Within the 
agriculture sector, there is high confidence in the quality and quantity 
of evidence of adaptation responses such as improved cultivars and 
agronomic practices, on-farm irrigation and water management and 
water and soil moisture conservation, and medium confidence, derived 
from robust evidence, and medium agreement for other most other 
adaptation responses (Figure  4.23 and Figure  4.24). Most of these 
adaptation case studies are from Asia and Africa, and agriculture is 
the predominant sector where most of these adaptation responses

are being implemented (high confidence) (Section  4.6.2). However, 
the sectoral nature of adaptation responses varies across continents. 
Agriculture is the most important sector in all continents, except 
Europe and Australasia, where most adaptation occurs in the urban 
sector (high confidence) (Figure 4.24).

636
Map of selected observed impacts on cultural water uses of Indigenous Peoples of the cryosphere
Figure 4.22 |  Decision tree, documenting the classification of water-related adaptation responses across 48 subcategories into 16 intermediate and 8 larger categories. We use the 16 intermediate categories of 
adaptation responses for further analysis in this section.

637Quantity of evidence on current water-related adaptation responses 
Improved cultivars and agronomic practices
 Changes in cropping pattern and crop systems
On farm irrigation and water management
Water and soil moisture conservation
Collective action, policies, institutions
Migration and off-farm diversification
Economic/financial incentives
Training and capacity building
Agro-forestry and forestry interventions
Flood risk reduction measures
Livestock and fishery related
Indigenous knowledge and local knowledge based adaptations
Urban water management
Energy related adaptations
Water, ssanitation and hygiene (WASH) related adaptations
Any other (includes coping)Water-related adaptation responses
= not assessed or no datanaQuantity of evidence
Confidence  in evidenceAgri-
cultureWater-
induced
disaster sHuman
mobility
and
migratio n
na
naUrban
and
peri-
urbanFreshwater
eco-
systems
naOthersEnergy 
and
industry
naHigh Medium Low High Medium Low

Figure  4.23 |   Sectoral distribution of documented water-related adaptation responses (observed adaptation) across the 16 categories derived from 
Figure 4.22. The quantity of evidence is derived from the number of papers in a particular adaptation response category where high is > 40 papers, medium is 10–40 papers and 
low is < 10 papers. Confidence in evidence relates to the way the article links outcomes of adaptation with the adaptation response. Category 1: studies causally link adaptation 
outcomes to the adaptation response by constructing credible counterfactuals; category 2: studies correlate responses and outcomes without causal attribution; category 3: studies 
describe adaptation outcomes without making any causal or correlation claims between adaptation outcomes and adaptation responses. High confidence: more than 67% of the

studies fall in categories 1 and 2; medium confidence: 50–67% of the studies are in categories 1 and 2, and low confidence is less than 50% of studies are in categories 1 and 2.
The top four adaptation responses in terms of frequency of documentation 
are changes in the cropping pattern and crop systems (145 responses), 
improved crop cultivars and agronomic practices (139 responses), 
irrigation and water management practices (115 responses) and 
water and soil conservation measures (102 responses). These top four 
responses provide several benefits such as higher incomes and yields, 
better water use efficiencies and related outcomes (high confidence) 
(Table 4.9 and Figure 4.27). However, those benefits are incremental, 
that is, they help improve crop production and incomes, at least in the 
short run, but may not automatically lead to transformative outcomes 
and climate risk reductions (Pelling et al., 2015; Fedele et al., 2019). One

way to move from incremental to transformative adaptation could be 
to invest gains from incremental adaptation in education and capacity 
building to improve overall adaptive capacity (Vermeulen et al., 2018). 
Responses such as migration, including spontaneous and planned 
relocation, are also relatively well documented (medium confidence), as 
are responses such as collective action, training and capacity building 
and economic and financial measures for increasing adaptive capacities 
(medium confidence). These categories of adaptation can potentially 
lead to transformative outcomes, such as a shift to livelihoods that are 
less exposed to climate hazards. However, transformative pathways are 
not always straightforward (Pahl-Wostl et al., 2020) (Table 4.8).
Droughts, followed by precipitation variability and extreme precipitation, 
are the two most common hazards against which adaptation responses 
are forged. The other three top hazards are general climate impacts,

heat-related hazards and inland and riverine flooding (Figure  4.25). 
The majority of the adaptation responses across all categories were introduced by individuals and households, followed by the civil society, 
and hence autonomous (Figure  4.26). The private sector (defined as 
profit-making companies and distinct from individual farmers and 
households) has played a relatively minor role in initiating adaptation 
responses. However, the low participation of the private sector in 
initiating adaptation responses could be partly an artefact of the nature 
of documentation.
4.7.1.2 Benefits, Including Co-benefits of Water-related 
Adaptation Responses and Resulting Maladaptation
There is no consensus in the literature about ways of measuring the 
effectiveness of current adaptation responses in reducing climate-
related impacts (Singh et al., 2021). However, various methodologies, 
including feasibility assessment, have been deployed (Williams et al.,

2021). Given the methodological challenges in defining and measuring 
the effectiveness of adaptation in reducing climate risks, in this section, 
we focus on outcomes of water-related adaptation across several 
dimensions. A total of 359 studies were identified to contain sufficiently 
robust evidence of documented adaptation outcomes to form the basis 
of this assessment (SM4.2, Table  SM4.5; Berrang-Ford et  al., 2021a; 
Mukherji et al., 2021). Positive outcomes denote benefits of adaptation, 
while negative outcomes may mean that adaptation was not effective 
in bringing any benefits or that it was maladaptive (Schipper, 2020).
We assess outcomes across five indicators: (a) economic and financial 
indicators, such as improvements in crop yields and resulting incomes; 
increase in profits, higher savings or lesser losses from hazards; (b)

638
Observed water-related adaptation responses that measure outcomes
12 0 10 30 40Number of studies
Small islands
and States
(5)Australasia
(6)North
America
(19)Europe
(21)Central and
(26)Africa
(132)Asia
(150)
Agriculture Water-induced disasters Urban and peri-urban Freshwater ecosystems Human mobility and migration Energy and industry Other s
Figure 4.24 |  Location of case studies on water-related adaptation which measure adaptation outcomes (n = 359) and their sectoral distribution across 
all regions. Circles denote the number of case studies in a particular location in the continent. The pie chart shows the sectors in which adaptation is taking place. The sectors 
correspond to water use sectors described in Sections 4.3, 4.5 and 4.6 of this chapter.
impacts on vulnerable people, for example, on women, children and 
Indigenous Peoples; (c) water-related impacts, for example, improved

water use efficiency, water saving, reduction in water withdrawals and 
application; (d) ecological and environmental impacts such as lesser 
energy use, better soil structures and better thermal comfort.; (e) 
institutional and sociocultural impacts such as improved social capital 
and stronger communities of practice, equity; and strengthening of local 
institutions or national policies. Of these 359 studies, 319 documented 
beneficial outcomes across one or more indicators, while the remaining 
40 presented no beneficial outcomes. Illustrative examples are shown 
in Table  4.9, while the distribution of these responses with positive 
outcomes is shown in Figure  4.27, and indicates that economic 
benefits of adaptation are more common in developing countries, 
while benefits along ecological dimensions are more common in the 
developed countries,
Co-benefits are defined as mitigation benefits resulting from an

adaptation response (Deng et al., 2017). Around a quarter of papers that 
documented positive adaptation outcomes also reported mitigation co-
benefits. Agroforestry, community forests and forest-based adaptations 
are the most oft-cited examples of mitigation co-benefits (Bhatta et al., 
2015; Etongo et  al., 2015; Weston et  al., 2015; Pandey et  al., 2017; Sain et al., 2017; Sánchez and Izzo, 2017; Wood et al., 2017; Adhikari 
et  al., 2018a; Hellin et  al., 2018; Aniah et  al., 2019; Quandt et  al., 
2019; also see Box 5.11). Other examples include mitigation benefits 
of climate-smart agricultural practices that reduce input intensity and 
help in carbon sequestration (Arslan et al., 2015; Somanje et al., 2017), 
retrofitting buildings in urban areas with energy-efficient devices for 
lowering electricity bills and emissions (Fitzgerald and Lenhart, 2016) 
and reuse of treated wastewater for irrigation and urban uses (Morote 
et al., 2019) (Box 4.5, 4.7.6).

Not all adaptation responses reduce risks, and some may have long-
term maladaptive outcomes, even if they are beneficial in the short term. 
Maladaptation often stems from poor planning and implementation of 
adaptation responses and because of not addressing the root causes 
of vulnerability (Schipper, 2020; Eriksen et al., 2021). Of the 319 case 
studies where adaptation response was found to have some beneficial 
outcomes, around one third of them also mentioned the possibility of 
maladaptation. Migration can often have maladaptive outcomes because 
migration can exacerbate the inherent vulnerabilities of migrants 
(Section  4.6.8). For example, slum dwellers in cities may earn higher 
incomes, but their quality of life worsens (Ayeb-Karlsson et al., 2016). 
In some instances, even wage rates in migration hotspots can remain

639Table 4.8 |  Illustrative examples of case studies of water-related adaptation responses where outcomes were measured (n = 359). These cases include instances where adaptation 
benefits were positive, negative or neutral. Examples also include studies with or without causal and correlation links between adaptation response and outcomes (categories 1, 2 
and 3 studies as described in the caption of Figure 4.23). The purpose of the table is to provide a list of illustrative examples to showcase the wide range of adaptation responses 
that are being implemented. Table 4.9 zooms into examples where adaptation had positive benefits on any of the selected parameters described in Section 4.7.1.2.
Name of the adaptation response 
(number of documented responses 
in that category)Description of adaptation response Sources
Changes in the cropping pattern and crop 
systems
(145 responses)Changes in cropping pattern; e.g., the introduction of sugarcane and rice in Costa Rica;

crop diversification in Ethiopia and Zimbabwe; crop diversification in TanzaniaSingh et al. (2014); Warner et al. (2015); Asmare 
et al. (2019); Lalou et al. (2019); Makate et al. 
(2019)
Changes in the timing of sowing and harvesting, e.g., in China; India and Pakistan Yu et al. (2014); Macchi et al. (2015)
On-farm diversification, e.g., an integrated crop-livestock system in France Havet et al. (2014)
Improved crop cultivars and agronomic 
practices (139 responses)Improved crop cultivars, e.g., short-duration paddy varieties in Nepal; saline-tolerant 
rice cultivar in Bangladesh; drought-tolerant maize varieties in Malawi, Nigeria, 
Zimbabwe and UgandaKabir et al. (2016); Wossen et al. (2017); Khanal 
et al. (2018a); Makate et al. (2019)
Improved agronomic practices, e.g., conservation agriculture to conserve soil moisture 
in Malawi and Tanzania; climate-smart agricultural practices in Zambia; alternate

wetting and drying and direct seeding of rice in IndiaThierfelder et al. (2015); Kimaro et al. (2016); Traore 
et al. (2017); Kakumanu et al. (2019)
Irrigation and water management 
practices
(115 responses)Irrigation, e.g., construction of local irrigation infrastructure in Chile; funding of 
community wells in Canada; drilling of borewells in Thailand; irrigation in Ethiopia; 
spate irrigation in Sudan; night-time irrigation scheduling to reduce evaporative 
demand in the UKHurlbert and Pittman (2014); Ferchichi et al. 
(2017); Rey et al. (2017); Pak-Uthai and Faysse 
(2018); Fadul et al. (2019); Lemessa et al. (2019); 
Lillo-Ortega et al. (2019); Torres-Slimming et al. 
(2020)
On-farm water management and water-saving technologies, e.g., use of surface 
pipes for irrigation water conveyance in China; drip irrigation in China; and use of 
water-saving measures in IndiaHong and Yabe (2017); Tan and Liu (2017); Deligios 
et al. (2019); Rouabhi et al. (2019)

Water and soil conservation (102 
responses)On-farm water and soil conservation measures, e.g., in Burkina Faso; terraces and 
contour bunds in EthiopiaWest Colin et al. (2016); Kosmowski (2018)
Water harvesting through on-sand dams in Kenya; in situ and ex situ water harvesting 
in Uganda and IndiaNgigi et al. (2018); Sullivan-Wiley and Short 
Gianotti (2018); Kalungu et al. (2021)
Watershed conservation programmes, e.g., in Ethiopia Siraw et al. (2018)
Revival of water bodies; e.g., creation of artificial lakes in Portugal Santos et al. (2018)
Collective action, policies and institutions 
(95 responses)Collective action and cooperation; e.g., grassroots-level collective action for conflict 
resolution in Guatemala; collective decision to reduce water withdrawals during 
drought in JapanHellin et al. (2018); Tembata and Takeuchi (2018)
Community-based adaptation in Bangladesh, community-based management of 
rangelands in MongoliaFernández-Giménez et al. (2015); Roy (2018)

Local institutions, e.g., multi-stakeholder platforms for disaster risk reduction and 
agriculture in Peru and several African countries; Adaptation Learning Programme.Mapfumo et al. (2017); Lindsay (2018)
Water dispute resolution; e.g., water conflict mitigation in Costa Rica. Kuzdas et al. (2016)
Institutional and policy reforms; e.g., local water and land use planning instruments in 
Australia; the Dutch Delta Programme in the Netherlands; implementation of EU Flood 
Directives in SwedenFallon and Sullivan (2014; Zevenbergen et al. 
(2015; Hedelin (2016)
Migration and off-farm diversification (92 
responses)Spontaneous migration, e.g., voluntary relocation in the Solomon Islands and rural to 
urban migration in Ethiopia and Pakistan.Birk and Rasmussen (2014); Iqbal et al. (2018)
Employment and remittances, e.g., in Senegal. Romankiewicz et al. (2016)
Planned relocation; e.g., the Massive Southern Shaanxi Migration Programme in China;

resettlement of flood-prone communities in Bangladesh.Islam et al. (2014); Lei et al. (2017)
Off-farm diversification; e.g., migration to towns and engaging in off-farm labour 
wage-earning in Niger, Ghana Bangladesh; shifting to non-pastoral livelihoods in 
EthiopiaMussetta et al. (2016); Basupi et al. (2019)
Livestock and fishery-related (63 
responses)Livestock related, e.g., livestock species diversification in Ethiopia and Kenya; insuring 
livestock in Pakistan; changes in range management practices in the USA.Opiyo et al. (2015); Yung et al. (2015); Wako et al. 
(2017); Rahut and Ali (2018)
Fishery related, e.g., non-destructive fishery gears and techniques in Ghana and 
TanzaniaYang et al. (2019a)

640Name of the adaptation response 
Training and capacity building (57 
responses)Information, training and capacity building; e.g., climate information services in 
Kenya and Senegal; training contributed new learning about digging canals to avoid 
prolonged water logging in the Philippines; soil conservation training programme in 
EthiopiaBacud (2018); McKune et al. (2018); Chesterman 
Agroforestry and forestry-related 
responses (56 responses)Agroforestry-related measures in India, Kenya, Nigeria; farmer-managed natural 
regeneration (FMNR) in Ghana.Weston et al. (2015); Pandey et al. (2017); Fuchs 
et al. (2019); Okunlola et al. (2019)
Forestry related; e.g., coastal afforestation by planting salinity-resistant trees in 
Bangladesh and ColombiaPandey et al. (2016); Barrucand et al. (2017); Barua 
et al. (2017)
Economic and financial incentives (54

responses)Insurance; rice crop insurance programme in Indonesia; agricultural insurance 
programme in South Africa.Dewi et al. (2018); Elum et al. (2018)
Micro-finance and credit programmes, e.g., in Bangladesh. Fenton et al. (2017b)
Social safety nets; e.g., food-based safety net programmes in Brazil, food for work 
programmes in Ethiopia.Mesquita and Bursztyn (2017); Sain et al. (2017); 
Tesfamariam and Hurlbert (2017); Gao and Mills 
(2018)
Subsidies and incentives, e.g., farm input subsidy programme in Malawi; financing 
programmes in Canada to help producers with resources to improve/maintain the 
quality of soil, water, biodiversity for drought mitigation.Hurlbert (2014); Kawaye and Hutchinson (2018)
Water markets and tariffs; e.g., urban water tariffs in Zaragoza, Spain; informal 
groundwater markets in China.Kayaga and Smout (2014); Zhang et al. (2016b)
Payment for ecosystems services, e.g., in Mexico Newsham et al. (2018)

IKLK based adaptations (41 responses)Use of TK of Konda Reddy’s in India to shift agroforestry practices; and among Khasia 
and Tripura communities in Bangladesh; use of local ecological knowledge is by 
small-scale fisher-farmers in the Amazon floodplains, Brazil; traditional water sharing 
system ‘bethma’ in Sri Lanka; indigenous methods of water harvesting in IndiaSarkar et al. (2015); Burchfield and Gilligan (2016); 
Kodirekkala (2018); Ahmed and Atiqul Haq (2019)
Flood risk reduction measures include (40 
responses)Non-structural measures for flood management; e.g., changes in day-to-day practices 
in Indonesia; place-specific social structures in the UK.Petzold (2018); Bott and Braun (2019)
Structural measures for flood management; improvement of the drainage system in 
Indonesia; flood walls in Beira, Mozambique; dredging and construction of culverts in 
Nigeria.Bahinipati and Patnaik (2015); Wijaya (2015); 
Egbinola et al. (2017); Spekker and Heskamp 
(2017)

Early warning systems; e.g., flood forecasting in Nepal, Indonesia, Nigeria.Ajibade and McBean (2014); Devkota et al. (2014); 
Sari and Prayoga (2018)
Flood-resilient housing; e.g., houses on stilts in Guyana, in Pakistan, Vietnam, 
Philippines.Mycoo (2014); Ling et al. (2015); Abbas et al. 
(2018)
Wetland restoration; e.g., in the USA and Netherlands Zevenbergen et al. (2015); Pinto et al. (2018)
Urban water management (22 responses)Urban water management, e.g., incorporating low impact development and urban 
design features for sustainable urban drainage systems in Spain and Malaysia; demand 
management and tariff reforms in several European countries.Flyen et al. (2018); Rodríguez-Sinobas et al. (2018); 
Stavenhagen et al. (2018); Chan et al. (2019)
Green infrastructure; e.g., ecological stormwater management and re-naturalisation 
processes in Sweden; pavement watering in France, Ghana, India, Kenya, BangladeshHendel and Royon (2015); Wamsler et al. (2016);

Tauhid and Zawani (2018); Birtchnell et al. (2019)
Desalinisation for water supplies in Spain Martínez-Alvarez et al. (2016); Morote et al. (2019)
Energy-related adaptations (eight 
responses)Hydropower related; e.g., hydropower benefit-sharing in the Mekong basin and NepalBalasubramanya et al. (2014); Suhardiman et al. 
(2014); Shrestha et al. (2015)
Other renewable energy-related, e.g., “Raising Water and Planting Electricity project” 
in Taiwan province of ChinaLin and Chen (2016)
WaSH-related adaptations (five responses)Hand washing and hygiene, e.g., provision of latrines and washing hands with soap in 
BangladeshDey et al. (2019)
Safe drinking water and sanitation; e.g., piped water supply in China Su et al. (2017)
Any other including coping strategies (20 
responses)Reduction in consumption, selling off assets, etc.; e.g., selling of household property 
and livestock in Nigeria; consumption smoothing in Ghana; reducing consumption in

641
Water-related hazards
and adaptations in response
Economic or financial incentives
Livestock and fishery-related
Indigenous knowledge & local knowledge based adaptations
Water, sanitation and hygiene (WASH) related adaptations
= not assessed or no datanaDroughtPrecipitation
and
extremesHeat
relatedInland
and
riverine
floodsSoil
erosion
and
sediment
load
changeGround-
water
availabilit y
changePoor
water
qualityCryo-
sphere
change Storms PestsCoastal
hazardsGeneral
climat e
impactsOther
hazards

Strength
of evidenceHigh Low MediumConfidence
in evidencenanana
nana
nana nana
nana
nanana
Figure 4.25 |  Water-related adaptations and climate hazards against which adaptation responses are forged. Evidence and confidence are derived in the same 
way as in Figure 4.23.

= not assessed or no datanaConfidence  in evidenceIndividual
and
householdsCivil
societ yGovernment
(local and
sub-
national)Govern-
ment
(national)International
or
multi-
national
governance
institutionsPrivate
sectorNot
availableO thers
nana
nana
na
na
na
na
na
na
nana
nana
naAdaptation responses initiated by different actors
Strength  of evidence
Figure 4.26 |  Water-related adaptations and their initiators. The initiator of adaptation is defined broadly and includes the entities who initiate a response, implement that 
response or engage in that response in any way, including leading, financing or enabling. Evidence and confidence are derived in the same way as in Figure 4.23.

642Table 4.9 |  Illustrative examples of adaptation responses and their benefits across different outcome indicators. All these studies are either category 1 or category 2 studies in that the 
link between adaptation response and the outcome is either causal or correlated with one another. These benefits notwithstanding, links of adaptation benefits to climate and associated 
risk reduction are not always clear. Some of these adaptation responses can have beneficial outcomes in one of the five parameters, but can have maladaptive outcomes in others.
Hazard Adaptation responsesOutcome 
categoryAdaptation outcome Reference
Droughts, floods, 
and general climate 
impacts in NepalImproved crop cultivars, agronomic 
practices, irrigation, soil water 
conservation measures
Economic 
and financial 
outcomesFarming households that adapted produced about 33% more rice than 
households that did not adapt after controlling for all heterogeneity.Khanal et al. 
(2018a)

Increased rainfall 
variability in IndiaFarmer’s training on agronomic 
measures, for example, alternate 
drying and wetting (ADW), modified 
system of rice intensification (MSRI) 
and direct-seeded rice (DSR)The capacity building and water saving increased crop yields by 960 kg ha–1, 
930 kg ha–1 and 770 kg kg–1 through the adoption of AWD, MSRI and DSR, 
respectively. The three practices have increased farmers’ income and decreased 
the cost of cultivation by up to USD 169 ha–1.Kakumanu et al. 
(2019)
Droughts and 
changes in the 
seasonality of rainfall 
in PakistanAdjusting sowing time of wheatHousehold income and wheat yields were higher for households who adjusted 
the sowing time to cope with climate risks than those who did not, after 
controlling for other factors.Rahut and Ali 
(2017)
Droughts in North 
China PlainsIrrigationAdding one extra irrigation could increase wheat yield by up to 12.8% in a 
severe drought year.Wang et al. 
(2019a)
Soil degradation;

extreme rainfall 
events and high 
runoff causing 
erosion in MaliSoil and water conservation using 
contour ridges and improved millet 
and sorghum cultivarsMillet grain yield during 2012–2014 was statistically higher in contour ridge 
terrace plots than the control, with yield differences ranging from 301 kg ha–1 in 
2012 to 622 kg ha–1 in 2013. Improved varieties produced on average 55% more 
yield than the local ones.Traore et al. 
(2017)
Drought, floods, 
hailstorm and erratic 
rainfall, EthiopiaOn-farm agricultural water 
managementThe net revenue from adopting a combination of agricultural water management 
and modern seeds or inorganic fertiliser is significantly higher by 7600 and 1500 
Birr ha–1, respectively, than adopting modern seeds or inorganic fertiliser alone. 
Birr is the Ethiopian currency.Teklewold et al. 
(2017)
Droughts and general 
climate impacts, 
South AfricaCrop insurance and irrigationFarmers who insured their farm business and had access to irrigation had

relatively higher net revenue than those who did not, but this link is not causal. 
Instead, it shows causality could go either way, including those farmers who 
were better off getting their business insured.Elum et al. (2018)
Droughts and floods 
in KenyaMigrationRemittance income enables uptake of costlier adaptation measures such as 
a change in livestock species, which also have higher returns for households. 
Therefore, the study was not causal in its inference.Ng’ang’a et al. 
(2016)
Droughts in Nigeria Drought-tolerant varietiesPer capita, food expenditure of those who adopted drought-tolerant maize was 
significantly lower than those who did not after controlling for everything else 
and causal inference.Wossen et al. 
(2017)
General climate 
impacts, including 
rainfall variability in 
BrazilAgroforestry systems as land use in 
rural municipalitiesThe land value in the municipalities with agroforestry was higher than that of the

municipalities where the agroforestry scheme was not implemented.Schembergue 
Water quality 
deterioration due to 
floods in BangladeshWater, sanitation and health WaSH 
programme
Outcomes for 
vulnerable 
peopleChildren: prevalence of childhood diarrhoea reduced by 35% in midline 
prevalence, 8.9% and by 73% in end line prevalence, 3.6% compared to baseline 
prevalence 13.7%.. Inferences are causal.Dey et al. (2019)
Droughts in 
ZimbabweAdoption of drought-tolerant maize 
varieties by smallholder farmersSmallholder farmers: Smallholder farmers practising conservation agriculture 
(CA) were as likely to adopt drought-tolerant maize varieties as other farmers 
and thus benefit from increased yields and incomes.Makate et al. 
(2019)
droughts in NigerCrop diversificationPoor households: Crop diversification mainly benefits the most vulnerable 
households; the impact on the poorest group ranges from double to triple the

impact on the wealthiest group.Asfaw et al. 
(2018)
climate impacts 
in Malawi and 
ZimbabweConservation agriculture; 
drought-tolerant maize and 
improved legume varietiesFemale farmers: Yield and income effects on the adoption of conservation 
agriculture and improved varieties of maize and legumes were both positive for 
men and women.Makate et al. 
(2019)
Historically 
widespread and 
severe droughts in 
Ethiopia in 1999, 
2002, 2003, 2005 
and 2008.Government safety net programme 
called Productive Safety Net 
Programme (PSNP)Poor households: PSNP transfers reduce chronic poverty level from 15.7% to 
10.6% and increase the never poor share from 11.5% to 15.8%.Gao and Mills 
(2018)

643Hazard Adaptation responsesOutcome 
Droughts in
KenyaWater harvesting structures, for 
example, sand dams
Water-related 
outcomesSand dams increase groundwater storage in riverbanks by up to 40%, which is 
maintained throughout the year.Ryan and Elsner 
(2016)
Millennium drought 
in AustraliaWater tradingIrrigation application rates fell in the dairy industry from 4.2 million litres ha–1 in 
2000–2001 to 3.5 million litres ha–1 in 2005–2006Kirby et al. (2014)
Droughts, floods 
and soil erosion and 
sediment load in a 
river basin in FranceAgreement signed between water 
and electricity utilities and farmersAgreement between water and electricity utilities to compensate farmers for 
reducing water use resulted in a decrease in water demand from 310 Mm3 in 
1997 to 220 Mm3 in 2012 in the Durance Valley irrigation system in France.Andrew and 
Sauquet (2017)
Drought in IndiaThe reducing area under irrigated

rice cropReduced rice irrigation resulted in over 60 mm ha–1 of water savings compared 
to irrigated rice crops on that land.Hochman et al. 
(2017b)
Floods due to 
cyclonic storms and 
tidal inundation in 
BangladeshPlanting of vetiver grass for 
stabilising coastal embankments
Ecological and 
environmental 
outcomesHouseholds that planted vetiver grass around their homestead and nearby road 
managed to save their houses and assets from the recent cyclonic storm and 
tidal inundation.Barua et al. (2017)
rural municipalitiesTrees planted as a part of the agroforestry programme provide thermal comfort 
to both animals and humans.Schembergue 
Drought in 2015 in 
EthiopiaContour ridge terraces as soil water 
conservation measureContour ridge terraces primarily controlled water runoff and soil erosion and

acted as a buffer during the 2015 Ethiopian drought.Kosmowski (2018)
Drought and rainfall 
variability in PakistanClimate-smart agricultural practices
Institutional 
and 
sociocultural 
outcomesFarmers who adopted climate-smart practices also tended to form a better 
relationship with local extension agents and reached out to them more 
frequently. Again, however, causality might as well lie the other way round.Imran et al. (2019)
Droughts, MexicoStrengthening of local water users’ 
associations through external 
assistance programmesLocal water user’s associations were able to reduce water abstractions during 
years of severe droughts.Villamayor-Tomas 
and García-López 
(2017)
Rainfall variability 
in NigerCommunity-based adaptation 
and through adaptation learning 
programmesMore robust social networks where women were able to take important 
decisionsVardakoulias and 
Nicholles (2015)
low due to the high volume of the migrant population (Fenton et al.,

2017b); as such, it does not help buffer consumption against rainfall 
shocks (Gao and Mills, 2018). Migration also has gendered impacts, with 
girls from migrating families being taken out of school (Gioli et al., 2014) 
or interrupting children’s education overall (Warner and Afifi, 2014). In 
planned relocation from vulnerable urban slums, relocation sites can be 
far from job sites and increase social conflicts (Tauhid and Zawani, 2018).
Adaptation responses that focus on improving incomes through 
production intensification can have maladaptive outcomes. An oft-
cited example of this is groundwater overuse as a result of irrigation 
intensification. There is widespread evidence of groundwater overuse 
in many countries in Africa (Mapfumo et al., 2017), in the Middle East 
and North Africa (Petit et  al., 2017; Daly-Hassen et  al., 2019), in Asia 
(Burchfield and Gilligan, 2016; Zhang et  al., 2016b; Kattumuri et  al.,

2017), in Spain (Petit et al., 2017) and in Australia (Kirby et al., 2014) 
(Sections  4.2.6, 4.6.2, Box  4.3). Intensification-based approaches also 
increase costs of cultivation (Mussetta et al., 2016; Wang and Chen, 2018; 
Quan et al., 2019), and can lead to more use of fertilisers and herbicides 
(Thierfelder et al., 2015; Sujakhu et al., 2016; Khanal et al., 2018a; Yamba 
et al., 2019). Diversification away from food crops can also compromise 
domestic food security (Kloos and Renaud, 2014; Brüssow et al., 2017).
Even interventions that have positive carbon co-benefits like forestry 
and agroforestry can have maladaptive consequences on land and 
water resources, especially if inappropriate species (Etongo et  al., 2015) with higher water demands are grown (Krishnamurthy et  al., 
2019) (Section 4.7.6).
In summary, current adaptation responses have benefits across several 
dimensions. In developing countries, most adaptation measures

improve economic outcomes (high confidence). Adaptation responses 
also have benefits in terms of water outcomes and environmental 
and ecological parameters, and these benefits are more commonly 
manifested in developed countries (high confidence). Of the papers 
assessed for water-related adaptation, roughly one fourth reported 
adaptation co-benefits (high confidence). In contrast, one third of 
studies reported maladaptive outcomes, now or in the future (high 
confidence), emphasizing the importance of looking at synergies and 
trade-offs. Despite many adaptation case studies, there is a knowledge 
gap in understanding if the benefits of adaptation also translate into a 
reduction of climate impacts, and if so, to what extent, and under what 
conditions (high confidence). In view of this critical knowledge gap, 
our assessment is limited to benefits of current adaptation responses.
4.7.2 Projections of Future Effectiveness of Adaptation 
Responses

Several adaptation options have been shown to have beneficial effects 
on societally relevant outcomes under current climate conditions 
(Section 4.7.1.2) and will remain critical to adapt to future climate change.

644Observed water-related adaptation responses with positive outcomes
Changes in cropping pattern and crop systems (n=133)
Migration and off-farm diversification (n=84)
Water and soil moisture conservation (n=100)
On-farm irrigation and water management (n=109)
Collective action, policies, institutions (n=88)
Indigenous knowledge & local knowledge based adaptations (n=39)
Economic/financial incentives (n=49)
Agro-forestry and forestry interventions (n=53)
Urban water management (n=19)
Flood risk reduction measures (n=31)
Livestock and fishery related (n=59)
Training and capacity building (n=55)
Remaining categoriesImproved cultivars and agronomic practices (n=128)Adaptation response categories (n = number of case studies)
Asia Africa Australasia Europe North AmericaEcological
Economic Water

Institutional Vulnerable60 60 41 01 01 0(c) Beneficial outcomes of adaptation per region across five dimensions. Innerlines correspond to the top six adaptation response categories from previous panel .(a) Map depicting 319 case studies of current water related adaptation responses with documented beneficial outcomes of adaptat ion
(b) Fraction of top six adaptation responses to total responses
100% 0 25% 50% 75%Top six response categories per region as fraction of total responses
Asia 431 responses
Australasia 12 responses
Central and
South America65 responses
Europe 42 responses
Africa 374 responses
North America 42 responses
Small islands and States 7 responses12 0 10 30 40Number of studies
Figure 4.27 |  Top panel: location of case studies of water-related adaptation responses (996 data points from 319 studies). In these 996 data points, at least one

positive outcome was recorded in one of the five outcome indicators. These outcome indicators are economic/financial, outcomes for vulnerable people, ecological/environmental, 
water-related, and sociocultural and institutional. Middle panel: the top six documented adaptation options per region as a fraction of the total of reported studies, with grey bars 
containing the share of all other adaptation responses. In most instances, the top six adaptation categories include nearly 3/4 of the studies. Bottom panel: The spider diagrams 
show the number of studies reporting beneficial outcomes for one or more dimensions for the top six adaptation options identified in each region. Due to a small number of studies 
in small island states, a spider diagram was not generated for the small island states.

645However, there is limited quantitative information on the future viability 
of available responses to reduce projected climate impacts effectively. 
However, the context-specific nature of adaptation on the ground and 
the uncertainties associated with future climate outcomes, both in terms 
of policy decisions around mitigation and model-inherent uncertainties, 
make long-term projections of adaptation effectiveness of limited use 
for decision-making on the ground. However, such projections are still 
needed to understand the efficacy of current technical and managerial 
solutions to reduce climate risk. Consequently, an increasing body of 
literature focuses on the effectiveness of specific interventions to reduce 
projected climate risks in a local to regional setting.
This section provides a quantitative aggregate assessment of 
effectiveness of projected water-related climate adaptations at different

levels of GWLs (SM4.2). Effectiveness is defined as the potential of a 
given adaptation measure to address projected changes in climate and 
return the system under analysis to baseline conditions. If the measure 
cannot fully compensate for the projected climate risk, residual risks 
remain, defined as the fraction of risk remaining after adaptation. For 
example, in many regions, projected temperature-driven yield loss 
can be reduced by shifting to or increasing irrigation. However, yields 
often do not always fully return to baseline conditions without climate 
change, leaving residual risk after adaptation. Assessed options are 
limited to technical solutions, which have quantitative entry points to 
global climate impact models.
Most adaptation projections focus on water-related interventions in 
the agricultural sector, including irrigation-related responses, shifting 
planting dates, changing crops and cultivars, and water and soil

conservation. Sectoral projections of adaptation effectiveness are 
limited in forestry- and agroforestry-related responses, flood protection 
measures (excluding here options that are solely related to effects of sea 
level rise), urban water-related adaptation and energy-related responses. 
The majority of assessed studies focus on comparing different variations 
of one or several response options in terms of timing or duration, for 
example, a shift in planting dates of 10 d and 20 d, relative to present-day 
practice and provide results for a range of scenarios and (or) timeframes.
A total of 45 studies were identified for this assessment, based on their 
quantitative assessment of the effects of adaptation on projected impacts 
(SM4.2 for the method of future projected effectiveness assessment). 
From each study, the distinct combinations of specific variations of 
adaptations, scenarios and timeframes assessed were considered as

individual data points, providing a total of 450 unique data points for 
the assessment (Table SM4.6). The study-specific temperature increase 
was classified relative to the 1850–1900 baseline for each data point, 
based on the model and scenario specifications provided and grouped 
into outcomes at 1.5°C, 2°C, 3°C and 4°C. The effectiveness is assessed 
based on the fraction of risk that an option can reduce. Co-benefits 
are defined as a situation where outcomes improve relative to baseline 
conditions, whereas maladaptive outcomes describe a situation where 
risks increase after adaptation has been implemented.
Several studies assess the future effectiveness of improved cultivars and 
agronomic practices, such as changing fertiliser application or switching 
to drought-resistant crops (five studies; 85 data points). Results show 
a range of effectiveness levels across regions and warming levels and vary depending on the tested response options (Qin et al., 2018)

(Figure  4.29), with moderate to small effectiveness, large residual 
impacts or potential maladaptive outcomes, as well as decreasing 
effectiveness with increasing warming (Figure 4.28) (high confidence). 
For studies testing results across a range of scenarios, approaches show 
increasingly mixed (Qin et al., 2018) and limited effects (Amouzou et al., 
2019) with higher warming, with overall reductions across warming 
levels for most tested responses (Qin et al., 2018).
Changes in cropping patterns and crop systems (Figure  4.28) (five 
studies; 31 data points) indicate limited potential to reduce projected 
climate risks, with the majority of studies providing results of up to 1.5°C 
of warming and limited evidence for higher warming levels. At 1.5°C, 
effectiveness in Africa is mostly insufficient, with substantial maladaptive 
potential (Brouziyne et al., 2018). Over Asia, effectiveness is mostly small

at 1.5°C with substantial residual impacts, further reducing to insufficient 
effectiveness at large residual risks at 4°C (Figure  4.28 Projected 
effectiveness) (robust evidence; medium agreement) (Boonwichai et al., 
2019; Dai et  al., 2020; Mehrazar et  al., 2020). Amongst the options 
related to changes in cropping patterns and crop systems, shifting 
planting dates is projected to retain moderate to high residual risks 
under some specifications in Iran (Paymard et al., 2018) and Morocco 
(Brouziyne et al., 2018), while high effectiveness is reported for similar 
specifications in Thailand (Boonwichai et al., 2019), Australia (Luo et al., 
2016), Morocco ((Brouziyne et al., 2018) and Iran (Mehrazar et al., 2020). 
Of the assessed adaptation options, changes in cropping patterns and 
cropping systems appear least effective in reducing climate risk, with 
decreasing effectiveness at higher levels of warming.
Studies assessing the future effectiveness of irrigation-related

responses (Figure  4.28) focus on a range of specific approaches, 
including increasing irrigation efficiency, deficit irrigation, irrigated 
area expansion or shifting from rain-fed to irrigated agriculture, as 
well as specific types of irrigation (21 studies; 103 data points). As a 
frequently implemented option with direct entry points to agricultural 
models, this option provides the most robust set of data points across 
regions and warming levels. For all regions, a reduction in effectiveness 
is apparent from 1.5°C to higher levels of warming, leading to increased 
residual risk with increasing warming (high confidence). Irrigation 
can increase yield relative to present day, showing co-benefits for 
some regions, though the share of co-benefits decreases with higher 
warming (high confidence) (Figure 4.28). However, since many of these 
studies rely on global agricultural models which do not fully represent

the actual availability of water, further expansion of irrigation at the 
scale assumed in those studies may not be realistic (Sections 4.3.1.2. 
4.3.1.3) (Elliott et al., 2014).
A wide range of water and soil management-related options 
(Figure 4.28), including mulching, no tilling or contour farming, has been 
assessed for future effectiveness (eight studies; 49 data points). Results 
underline the context-specific nature and need to carefully adjust the 
specific options to a regional setting, with variations of options leading 
to effective outcomes or residual impacts within individual studies (Qiu 
et al., 2019) and across regions and warming levels.
Similar to observed adaptation, studies assessing combinations of the 
agricultural adaptation options outlined above (11 studies; 36 data

646Projected effectiveness of water-related adaptation
Effectiveness to reduce projected climate risk and residual risk 
retained after adaptation
Energy related adaptationsChanges in cropping pattern and crop system s
On farm irrigation and water managemen tMultiple agricultural adaptation options
On farm irrigation and water managemen t
On farm irrigation and water managemen tFlood risk reduction measuresOn farm irrigation and water managemen t
Water and soil moisture conservationMultiple agricultural adaptation optionsImproved cultivars and agronomic practices 
Changes in cropping pattern and crop system sAgro-forestry and forestry

Water and soil moisture conservationMultiple agricultural adaptation optionsImproved cultivars and agronomic practices On farm irrigation and water managemen tChanges in cropping pattern and crop system s
Multiple agricultural adaptation optionsFlood risk reduction measuresAgro-forestry and forestry
On farm irrigation and water managemen tChanges in cropping pattern and crop system s
Flood risk reduction measuresAgro-forestry and forestry
Multiple agricultural adaptation options
Changes in cropping pattern and crop system s
On farm irrigation and water managemen tImproved cultivars and agronomic practices Energy related adaptationsAgro-forestry and forestryEnergy related adaptations

Water and soil moisture conservationEnergy related adaptations
Improved cultivars and agronomic practices 
Water and soil moisture conservationImproved cultivars and agronomic practices AustralasiaCentral and
South AmericaA siaA frica
Flood risk reduction measuresImproved cultivars and agronomic practices 
Changes in cropping pattern and crop system sOn farm irrigation and water management
Agro-forestry and forestryEnergy related adaptations
Changes in cropping pattern and crop system sOn farm irrigation and water managementWater and soil moisture conservatio n

Agro-forestry and forestryEnergy related adaptationsUrban water
Agro-forestry and forestryWater and soil moisture conservatio nUrban waterFlood risk reduction measures
Water and soil moisture conservatio n
Water and soil moisture conservatio nEnergy related adaptations
On farm irrigation and water managemen tOn farm irrigation and water managemen t
On farm irrigation and water managemen tEnergy related adaptations

Improved cultivars and agronomic practices Water and soil moisture conservatio n
Water and soil moisture conservatio nAgro-forestry and forestry
Agro-forestry and forestry
Improved cultivars and agronomic practices Agro-forestry and forestry
Flood risk reduction measuresEurope North Americ a GlobalEffectiveness Residual impact
LargeModerateSmallNegligibleNegligibleSmallModerateLargeCo-benefit
Maladaptatio nUrban waterMultiple agricultural adaptation options
Changes in cropping pattern and crop system sWater and soil moisture conservation

High
Medium
Low1.5°C 2°C 3°C 4°CAdaptation options 
per Global Warming LevelGlobal
distribution
Figure 4.28 |  Projected effectiveness of adaptation options in returning the system to a study-specific baseline state relative to the projected climate 
impact; and level of residual risk retained after adaptation, relative to baseline conditions. Regional summaries are based on IPCC regions. Warming levels refer to the

647points) show the highest effectiveness across agricultural adaptation 
outcomes and generally project moderate to high effectiveness 
with the potential for co-benefits (Figure 4.28). Though maladaptive 
outcomes are also documented, residual risks are limited, also at 
higher levels of warming. Therefore, developing integrated plans of 
synergistic options linked to adequate monitoring and evaluation 
approaches and designed to adjust to changing conditions 
continuously is desirable to minimise climate risk and ensure food 
security (Babaeian et al., 2021).
Globally, agroforestry-related adaptation (four studies; 18 data points) 
is moderately to highly effective, with the potential for substantial 
co-benefits at 1.5° and 2°C of warming, with a sharp decline in 
effectiveness at 3°C and 4°C and a substantial increase in residual risk 
and maladaptive outcomes (Figure 4.28).
Flood risk-related adaptation (four studies; 47 data points) is associated

with the potential for substantial co-benefits relative to present-day 
flood risk, indicating a current adaptation gap larger than for other 
impact areas. These co-benefits decline with increasing warming. 
Limits to the tested options become increasingly apparent at 3°C and 
4°C of warming, where residual risks increase for most assessed cases 
(Figure 4.28).
Adaptation projections for urban water risks as well as the energy 
sector are limited to one study each, with one data point for urban 
adaptation (Rosenberger et al., 2021) and 80 data points for different 
variations of adaptation outcomes across regions and scenarios for 
the energy sector (van Vliet et  al., 2016c). Sustainable stormwater 
management, focusing on a combination of nature-based solutions, is 
shown to be highly effective and yields co-benefits at 3°C. However, 
these results were gained in a specific case study setting in a European 
city with limited generalizability (Figure 4.28).

The assessment of adaptation in the hydropower and thermoelectric 
power-generation sector indicates high effectiveness and co-benefits 
across all regions for 1.5°C, with decreasing effectiveness and 
increasing residual risks for 2°C and 3°C of warming and highest 
reductions in effectiveness for Central and South America (Figure 4.28).
Quantitative projections of future adaptation depend on available impact 
models to analyse the effect of specific adaptation interventions. However, 
since not all possible future adaptation responses can be incorporated 
in climate impact models, this is a major limitation to assessing the full 
scope of options available in the future. For example, many frequently 
implemented measures showing effective outcomes, such as behavioural 
and capacity building-focused responses or migration and off-farm 
diversification (Section  4.7.1.2), are not incorporated in quantitative 
water-related climate impact projection models. In addition, projections

of future adaptation depend on currently available technologies or 
approaches, but new methods and technologies will probably emerge. Thus, improving the representation of adaptation in future projections is 
a significant knowledge gap that remains to be addressed.
Whether specific adaptation responses are shown to be effective 
and even lead to co-benefits or are associated with residual impacts 
is highly contextually, location- and crop-specific. In addition, the 
specific climate-impact-scenario combinations play an important role 
in determining assessed outcomes.
In practice, responding to increasing climate risk will need to be context-
specific and sufficiently agile to respond to ever-changing realities on the 
ground. The adaptive pathways approach underline that a sequence of 
different options responding to climate change over time may be most 
effective (Babaeian et al., 2021). In addition, impact models generally

underestimate or underrepresent climate extremes (Schewe et  al., 
2019), limiting the ability of the present analysis to reflect adaptation 
requirements to extremes, which are likely to push systems to their limits 
(Section 4.7.4). While currently known structural adaptation responses 
can reduce some of the projected risks across sectors and regions, 
residual impacts remain at all levels of warming, and effectiveness 
decreases at higher levels of warming. Adaptation generally performs 
more effectively at 1.5°C, though residual damages are projected at this 
warming level across sectors and regions (high confidence). A range of 
options also shows the potential for further increasing negative effects 
(maladaptation) across sectors, regions and warming levels, further 
underlining the need for contextualised approaches.
4.7.3 Comparing Current and Future Water-Related 
Adaptation Responses
Water-related adaptation is being observed across sectors and regions

(Section 4.6), and beneficial outcomes are documented across different 
dimensions (Section  4.7.1). A limited set of frequently documented 
adaptation responses is also represented in quantitative projections 
of adaptation effectiveness (Section  4.7.2, Figure  4.29). However, 
due to the largely different assessment methodologies for measuring 
beneficial outcomes for current adaptations and effectiveness to reduce 
impacts for future adaptations, comparing current and future adaptation 
outcomes is not straightforward. For current adaptation responses, 
beneficial outcomes may or may not translate to climate risk reduction, 
making risk reduction potential of observed adaptation a significant 
gap in our current understanding. The large diversity of outcomes across 
regions and assessed options becomes apparent for future adaptation 
options, with the group of ‘inconclusive’ outcomes indicating a large 
spread of results across regions. This underlines the contextual nature

of adaptation and boundary conditions for implementation that can 
determine the success of adaptation outcomes, now or in the future.
Documented implemented adaptations show several beneficial 
outcomes, with most studies (319 of 356) documenting positive rather global mean temperature (GMT) increase relative to an 1850–1900 baseline. For each data point, the study-specific GMT increase was calculated to show effectiveness at 1.5°C, 
2°C, 3°C and 4°C. Based on the ability of an implemented option to return the system to its baseline state, the effectiveness is classified based on the share of risk the option can 
reduce: large (>80%); moderate (80–50%); small (<50–30%); insufficient (<30%). Where the system state is improved relative to baseline, co-benefits are identified. Residual 
impacts show the share of remaining impacts after adaptation has been implemented: negligible (<5%); small (5 to <20%); moderate (20 to <50); large (≥50%). Where risks

increase after adaptation, data points are shown as maladaptation. All underlying data is provided in Table SM4.6.

648
Strength  of evidence as fraction of studies
High Medium LowImproved cultivars and agronomic practices
Energy-related adaptations
Multiple agricultural options 
Any other (includes coping)Water-related adaptation responses:
Current beneficial outcomes, co-benefits with 
mitigation, and maladaptive outcomes of responses 
and future effectiveness of adaptation and residual 
risk under different levels of global warming.
Water-related adaptation responses

Mitigation co-benefits
Maladpative outcomesImproved outcomesEconomic or financial
For vulnerable people
Water-related
Ecological or environmental
Institutional & socio-culturalEffectiveness
potential 
to reduce
projected
risk
1.5 2.0 3.0 4.0Residual risk
residual risk
remaining
after
1.5 2.0 3.0 4.0Current Future
Confidence  in evidence as fraction of studies
High Medium LowStrength of effectiveness / residual risk
Confidence  as number of studies= not assessednana na
High Medium Low High Medium Lowna na na na na
High Med. Low Insufficient/
NegligibleIncon-
clusivena
na
na
na
na
na
na
na
naAssessment results under different
levels of global warming (+°C)
na
na
na
na
na
na
na
na
nana
na
na
na
na
na
na
nana
na
na
na
nana
na
na
na
nana
na
na
na
nana
na
na
nana
na
na
na
nana
na
na
nana
na
na
na
na
na
na
nana
na
na
na
nana
na
na
na
na

Figure 4.29 |  The panel on the left side shows observed benefits of adaptation. Observed outcomes are reported across five dimensions of benefits, co-benefits and 
maladaptation outcomes. Benefits are measured across five dimensions. Strength of evidence is high if >80% of adaptation responses in that category have at least one beneficial 
outcome; medium if between 50 and 80% of adaptation responses in that category have at least one beneficial outcome, and low if <50% of adaptation responses have at 
least one beneficial outcome. Confidence in evidence relates to the way the article links outcomes of adaptation with the adaptation response. Category 1: studies causally link 
adaptation outcomes to the adaptation response by constructing credible counterfactuals; category 2: studies correlate responses and outcomes without causal attribution; category

3: studies describe adaptation outcomes without making any causal or correlation claims between adaptation outcomes and adaptation responses. High confidence: more than 
67% of the studies fall in categories 1 and 2; medium confidence: 50–67% of the studies are in categories 1 and 2; low confidence is less than 50% of studies are in categories 
1 and 2. The panel on the right-hand side shows the effectiveness of future adaptations. Future outcomes are assessed in terms of their effectiveness to reduce climate impacts at 
1.5°C, 2°C, 3°C and 4°C of global temperature increase relative to 1850–1900. Effectiveness is defined as the fraction of adaptation that the option is able to reduce; residual risk 
is the fraction of risk remaining after adaptation. If >66% of assessed data points agree on the effectiveness class, a response–temperature combination is shown as belonging to

that class. Where results diverge, the result is inconclusive, with studies showing high and low effectiveness across regions and studies. Confidence is based on the number of data 
points available for each response–temperature combination with high confidence: 5 or more data points; medium confidence: 2–4 data points; low confidence: 1 data point. Also, 
see Figure 4.28 for further explanations, and Tables SM4.5 and SM4.6 provide underlying data.
than negative outcomes. However, there may be a positive reporting 
bias in the literature, as positive outcomes are more likely to be 
reported than negative ones. Also, positive outcome in one parameter 
does not preclude negative outcomes in others, so maladaptation 
is still possible even when an adaption has some positive benefits 
(Section  4.7.1.2). In addition, much of the adaptation happening 
on the ground may not be published in peer-reviewed publications and, therefore, not covered by the literature assessed in this report.

Further, there is limited knowledge about the effectiveness of current 
adaptation in reducing climate-related risks due to documentation and 
methodological challenges elaborated in Section 4.7.1.2 (SM4.2).
In contrast, evaluating the effectiveness for future projected adaptations 
is methodologically possible (Section  4.7.2, and SM4.2), but every

649adaptation that is happening now cannot be modelled for the future. 
Therefore, projections of future adaptation effectiveness are limited 
to those options that can be incorporated into (global) quantitative 
climate impact models. Unfortunately, an extensive range of options, 
such as capacity building or training, migration and employment, which 
are essential building blocks in the portfolio of available (water-related) 
adaptation options, are currently not quantitatively represented in 
adaptation projections. In addition, the future will probably bring 
further development in technical solutions, which are currently also 
not modelled. While implementing the modelled technical options may 
be feasible in general, several barriers and constraints (Section 4.7.4) 
and enabling conditions, which influence adaptation action in practice, 
are not included in current modelling studies. Therefore, the modelling

studies may present optimistic assessments of adaptation effectiveness 
for the future.
ones) are also projected to be effective to varying extents in reducing 
future risks, with the degree of effectiveness strongly depending on 
future GWLs. For example, beyond a certain level of warming (2°C 
and upwards), the effectiveness of most options is projected to 
reduce, and residual impacts are projected to increase. Reduction in 
the effectiveness of future adaptation at higher global warming levels 
emphasises the need for limiting warming to 1.5°C, as space for 
adaptation solution starts to shrink beyond that for most options for 
which future projections exists (high confidence).
To sum up, there are two significant knowledge gaps in our understanding 
of water-related adaptations. First, the nature of literature on current 
adaptation makes it challenging to infer their effectiveness in reducing

climate risks, even though the benefits of adaptation are clear (high 
confidence). Second, not all adaptation responses that are possible in 
the future can be modelled because of inherent limitations to what can 
be modelled. Thus, advancement in tools and metrics for measuring 
the effectiveness of current adaptation in reducing climate risks and 
suitable downscaled climate and impact models that incorporate 
economic, social, cultural and management aspects for an extensive 
range of future adaptation options is needed.
4.7.4 Limits to Adaptation and Losses and Damages
The core constraints identified in AR5 (Klein et al., 2014) for freshwater-
related adaptation were lack of governance, financial resources and 
information, while water availability was singled out as a core constraint 
to diversifying options for water-dependent sectors. SR1.5 showed 
that increasing aridity and decreased freshwater availability, including

limited groundwater supply in fossil aquifers in conjunction with rising 
sea levels may pose hard limits to adaptation for small islands (Roy 
et al., 2018). SR1.5 also shows that water-related risks can be reduced 
substantially by limiting warming to 1.5°C (high confidence) (Hoegh-
Guldberg et  al., 2018), thereby also reducing the potential to reach 
hard limits to adaptation. SROCC highlighted that several barriers 
and limits to adapt to reduced water availability in mountain areas, 
such as lack of finance and technical knowledge (Hock et al., 2019b). 
The SRCCL further highlighted the critical importance of water-related 
climate change adaptation and potential limits to adaptation in the land sector when extreme forms of desertification lead to a complete 
loss of land productivity (high confidence) (Mirzabaev et al., 2019).
Institutional constraints, including path dependency and lengthy decision-
making processes, remain major limitations to successful adaptation

globally (high confidence) (Barnett et al., 2015; Oberlack, 2017), as well 
as for the water sector (Kingsborough et al., 2016; Oberlack, 2017; Azhoni 
and Goyal, 2018). For example, a lack of institutional support has limited 
the ability of farmers to implement adaptation, even if information about 
the benefits is acknowledged (Nambi et al., 2015). A lack of inter-sectoral 
coordination and communication within institutions and conflicting 
interests between water sectors limit the potential for integrated 
policies. For all water-related adaptation options, which have shown to 
be effective in one or more dimensions (Section  4.7.1.2), governance 
and institutional constraints were identified to be the most commonly 
encountered to a moderate or significant extent (Figure 4.30). Water–
energy–food nexus approaches can help overcome these inter-sectoral 
barriers (Box 4.8) (Rasul and Sharma, 2016; Ernst and Preston, 2017). In

addition, trade-offs between different policy goals must be considered 
to ensure the broader significance of the implemented adaptation 
strategies, such as water quality implication of adaptation efforts in the 
agricultural or energy sectors (Section 4.7.6) (Fezzi et al., 2015).
The lack of financial and technological resources constrains adaptation 
implementation (Castells-Quintana et al., 2018; Iglesias et al., 2018) 
and was identified as significant or moderate across all water-related 
adaptation responses, with significant constraints especially present 
in options related to the agricultural sector (Figure 4.30). For example, 
financial resources were significant constraints to implementing 
Climate Smart Agriculture in Guatemala, a relevant adaptation strategy 
to improve food security, resilience, and low emission development 
(Sain et al., 2017).
While financial barriers played an important role in adopting new

technologies at the farm level in Spain, acceptance, common 
understanding and awareness were amongst the most frequently 
identified barriers across different adaptation options (Esteve et  al., 
2018). Limitations in knowledge and understanding of complex 
processes, feedback effects and interconnections in the water sector 
pose challenges to effective adaptation and adaptation decision-
making (Kundzewicz et  al., 2018). Such constraints are identified 
as moderate across the range of options assessed in this chapter 
(Figure  4.30). For tropical and mountainous regions and the African 
continent, in particular, significant uncertainties in available data 
and a lack of reliable climate projections remain one of the biggest 
obstacles in long-term adaptation planning (Antwi-Agyei et al., 2015), 
especially in the water sector (Watson et al., 2017; Azhoni and Goyal, 
2018; Hirpa et  al., 2018; González-Zeas et  al., 2019). There is also

often a discrepancy between the level of awareness among different 
stakeholders, for example, between affected farmers whose agency is 
limited by the lack of knowledge by local authorities (Chu, 2017).
For some regions of the world, such as small islands (Karnauskas 
et al., 2016; Karnauskas et al., 2018) (Box 4.2) and the Mediterranean 
(Cross-Chapter Paper 4) (Schleussner et  al., 2016), aridity increases 
have the potential to pose hard adaptation limits. In mountain and 
polar regions, changes in the cryosphere (Sections 4.2.2, 4.4.2) may

650limit water availability for irrigation systems that depend on melt-
water (Section 4.5.1) (Qin et al., 2020). Biophysical limits may also be 
reached through impacts of hydrological extremes, such as crop loss as 
a consequence of extreme precipitation events (Huggel et al., 2019; van 
der Geest et al., 2019). Such limits are reported to a limited to moderate 
extent across all adaptation options assessed (Figure 4.30). However, 
knowledge gaps remain about physical and biological constraints to 
adaptation in the water sector. Climate impacts, such as droughts in 
East Africa or glacier melt in the cryosphere, indicate that biophysical 
limits to adaptation may exist, even under current climate conditions 
(Figure 4.31) (Warner and van der Geest, 2013; Huggel et al., 2019; van 
der Geest et al., 2019). A lack of investment in relevant infrastructure, 
such as dikes for example, as well as maladaptive effects of certain

measures could increase existing risks and exacerbate impacts (van 
der Geest et al., 2019).
Integrated approaches, such as linking land use and water policies (Mehdi 
et  al., 2015), inter-institutional networks (Azhoni et  al., 2017), nexus 
approaches (Box 4.8) (Conway et al., 2015) as well as consideration of 
linkages to the SDGs (Section 4.8) (Gunathilaka et al., 2018) are crucial to 
overcoming constraints in water adaptation. In addition, monitoring and 
evaluating the effectiveness of adaptation measures, policies and actions 
can contribute to knowledge, awareness and data to support adaptation 
implementation in the future (Sections 4.7.1; 4.8) (Klostermann et al., 2018). Although the information on climate change adaptation that has 
beneficial impacts, including enabling conditions and success factors 
specific to the water sector, is emerging, significant knowledge gaps 
remain (Section 4.7.1.2) (Gotgelf et al., 2020). Further understanding the

constraints and limits that exist with regard to adaptation in the water 
sector is becoming urgent in light of increasing slow (e.g., droughts) and 
rapid (e.g., floods) onset impacts associated with climate change.
Taking action towards adaptation critically determines the outcomes 
and impacts of climate change processes across space and time. Where 
efforts to reduce risk do not effectively occur, losses and damages 
occur as a consequence of climate change, some of which can have 
irreversible and existential effects (van der Geest and Warner, 2015; 
Page and Heyward, 2016; Thomas and Benjamin, 2018a; Mechler 
et al., 2019). Water-related impacts that occurred despite implemented 
adaptation have been documented across all world regions (high 
confidence) (Figure 4.31).
Advances in climate change attribution (Section 4.2; SM4.3; Figure 4.20) 
show the direct effects of anthropogenic climate change, also with 
regard to climate extremes. These advances also provide the basis

for climate litigation (Marjanac and Patton, 2018) to hold countries/
companies accountable for climate change impacts, for example, 
concerning risks of glacial lake outburst in Peru (Frank et al., 2019).Adaptation constraints for water related adaptation across different dimensions
Types of constraints
Water-related adaptation responses BiologicalInformation
and
awarenessHuman
capacityGovernance,
institutions
and policies Financial Economic Cultural Technological Physical
Livestock and fishery-related adaptation

Other (includes coping)
(Percentage of articles that identified the constraint)Extent to which a constraint is present Insufficent
(<5%)Limited
(<20%)Moderate
(20–50%)Significant
(>50%)
Figure 4.30 |  Adaptation constraints manifest across a range of dimensions and here are assessed based on a meta-review of water-related adaptation 
(Section 4.7.1, SM4.2, and Table SM4.5). Where less than five articles are available for assessment, data is insufficient to assess the extent to which a constraint is present. 
Where less than 20% of the articles reporting on the respective adaptation option identify the presence of a constraint, it is classified as ‘limited’, where 20 to 50% report on a 
specific constraint it is considered as ‘moderate’. Where more than 50% of articles report on the presence, the constraint is considered ‘significant’. This assessment is based on

the available peer-reviewed literature assessing adaptation benefits in the water sector—in practice, these or other constraints may still be significant, but have not have been 
identified in peer-review sources.

651Temperature increase,  
permafrost thawing Outmigration Loss of livelihoods, ecosystems 
and infrastructureHazard Adaptation Losses and Damages
Loss of food security Pastoralism (moving cattle to regions with 
abundant pasture)Drought, climate variability 
Changing monsoon patterns Irrigation channels, water sharing 
mechanisms, crop diversificationLoss of access to water and loss of crops
Floods, cyclones, surges, coastal flooding Physical protection, creating buf fers, build 
safety netsLoss of livelihoods, harvest failure and 
damage to infrastructure
Floods, landslides, typhoons Food relief, temporary shelter and loaning 
moneyLoss of livelihoods, infrastructure and 
Changes in glacier runof f, permafrost 
thawing, GLOFsOutmigration, new livelihoods (e.g. tourism) Loss of cultural heritage, loss of income,
loss of livesExamples of regional studies
where communities experienced negative 
impacts despite or beyond implemented

adaptation have been documented
CA
B
D
E
F
G
Global CA
BD
E
F
Global GG
Hurricanes, storms, droughts Using savings, borrowing, government 
assistanceLoss of income sources, loss of access to 
finance, depletion of assets, health problems, 
damage to housing and agriculture Drought Loss of food security Crop-livestock integration, soil fertility 
management, crop variety , migration
Landslides Diversify livelihoods, physical barriers, 
house adjustments, migrationLoss of life, loss of property and infrastructure 
Floods, storms and drought Irrigation, diversification of crops, regenera -
tion of degraded forests, animal husbandry Loss of food security , crops, income, 
livelihoods and land
Freshwater scarcity , aridity , cyclones,
El Nino, floodingSeasonal work and outmigration, early 
warning systemsLoss of life, livelihoods, homes, crops,  
contamination of drinking water , displacement

Figure 4.31 |  Examples of regional studies where communities experienced negative impacts despite or beyond implemented adaptation have been 
documented. Panels indicate the climate hazard that leads to the need for adaptation, the adaptation option implemented and the recorded impacts per region (A – Arctic 
(Landauer and Juhola, 2019), B – Africa (van der Geest et al., 2019), C – Caribbean (Lashley and Warner, 2015), D – South Asia (Kusters and Wangdi, 2013; van der Geest and 
Schindler, 2016; Bhowmik et al., 2021), E – Southeast Asia (Acosta et al., 2016; Beckman and Nguyen, 2016), F – Pacific the Small Island States (Gawith et al., 2016; Handmer 
and Nalau, 2019), G – Global effect: Mountain Cryosphere (Huggel et al., 2019)). Presented examples are limited to the available peer-reviewed literature that focuses explicitly

on impacts that have been documented despite documented evidence that adaptation in relation to water hazards had previously been implemented. Section 4.3 provides a full 
assessment of observed impacts across sectors and regions.

652A further increase in the frequency and/or intensity of water-related 
extremes (Section  4.4) will also increase consequent risks and 
associated losses and damages (Section 4.5), primarily for exposed 
and vulnerable communities globally (Bouwer, 2019). After assessing 
the future potential of currently available technologies to reduce 
projected water-related climate impacts, there is evidence that 
residual impacts will remain after adaptation for most adaptation 
options and levels of warming, with increasing residual risks at higher 
warming levels (Section 4.7.2). Financial, technical and legal support 
will be needed when hard limits are transgressed and loss and damage 
occurs (Mechler et  al., 2020). Knowledge gaps remain regarding 
quantified information on limits and constraints to adaptation in the 
water sector.
In summary, institutional constraints (governance, institutions, policy),

including path dependency and financial and information constraints, 
are the main challenge to adaptation implementation in the water sector 
(high confidence). Water-related losses and damages that manifest 
despite or beyond implemented adaptation have been observed across 
world regions, primarily for exposed and vulnerable communities (high 
confidence). Hard limits to adaptation due to limited water resources 
will emerge for small islands (medium evidence, high agreement) and 
regions dependent on glacier- and snowmelt (medium evidence, high 
4.7.5 Costs of Adaptation and Losses due to Non-
Estimating adaptation costs for climate change impacts on the various 
water use sectors is vital for decision-making, budgeting, and resource 
allocation (Chambwera et  al., 2014). However, in AR5, studies on 
adaptation costs for water were deemed to have ‘limited coverage’ 
and mainly focused on ‘isolated case studies’; costs in agriculture were

‘extremely limited’ (Chambwera et al., 2014).
One estimate on observed losses due to climate change from the 
UK notes that almost 50% of freshwater thermal capacity is lost on 
extreme high-temperature days, causing losses in the range of average 
GBP 29–66 million/year (Byers et al., 2020). However, global estimates 
of current losses because of climate change impacts on water resources 
remain few. Most of the evidence is focused on projected damages 
rather than actual ones (World Bank, 2016; Rozenberg and Fay, 2019).
Without adaptation, water-related impacts of climate change are 
projected to reduce global GDP by 0.49% in 2050 under SSP3, with 
significant regional variations for the Middle East (14%); Sahel (11.7%); 
Central Asia (10.7%), and East Asia (7%) (World Bank, 2016). In Asia, 
water-related impacts of climate change on all sectors of the economy 
are projected to reduce GDP by 0.9% (in high-income Asia) to 2.7% (in

low-income Asia) by 2050 without adaptation or mitigation. Under the 
A1B scenario, real GDP is projected to fall by 0.78% by 2030 in South Asia 
(Ahmed and Suphachalasai, 2014). In Sub-Saharan Africa, damages from 
floods in 2100 are projected at 0.5% of GDP under a 2°C temperature rise 
without adaptation; and will be non-uniformly spread across countries 
(Markandya, 2017; Dottori et al., 2018). In Europe, annual damages due 
to coastal flooding are projected at €93 billion by 2100 under RCP8.5-SSP3 (Ciscar et al., 2018). Global direct damages from fluvial floods are 
projected to rise to €1250 billion yr–1 under a 3°C global warming level 
and SSP5 socioeconomic scenario (Dottori et al., 2018). A model-based 
study of selected water-related sectors like fluvial and coastal flooding, 
agricultural productivity of major crops, hydroelectric power generation, 
and thermal power generation provides much conservative estimates of

GDP loss (Takakura et al., 2019). The study shows that without adaptation, 
loss of global GDP could be 0.094% under RCP8.5 and SSP5 and 0.013% 
under RCP2.6 and SSP1 scenarios in 2090 (2080–2099), with regional 
values for Africa (0.017 to 0.286%), Asia (0.015 to 0.104%), Australasia 
(-0.012 to 0.003%), North America (-0.002 to 0.005%) and South and 
Central America (0.011 to 0.055%) (Takakura et al., 2019). So, while there 
is general agreement about negative impacts on GDP due to water-related 
risks in the future, the magnitude of GDP loss estimates varies substantially 
and depends on various model assumptions (high confidence). Updating 
costs while improving the modelling of uncertainties is essential for 
evidence-based decision-making (Ginbo et al., 2020).
Costs of water-related infrastructure in adaptation have received 
attention at the global and regional level to bridge the ‘adaptation gap’ 
(Hallegatte et al., 2018; UNEP , 2018; Dellink et al., 2019; GCA, 2019). For

example, (Rozenberg and Fay, 2019) estimated that subsidising capital 
costs to extend irrigation to its full potential would cost 0.13% of the GDP 
per year of low-and middle-income countries between 2015 and 2030. 
The coastal and riverine protection cost was between 0.06% and 1% of 
these countries’ GDP per year over the same period. Projected economic 
damage due to coastal inundation was USD 169–482  billion in 2100 
under RCP8.5-SSP3 without adaptation, but USD 43–203 billion cost to 
raise dike height will reduce 40% of the total damage (Tamura et al., 
2019). Hard infrastructure for river floods, costing $4–9 billion yr–1, can 
reduce damage by USD 22–74 billion yr–1 (Tanoue et al., 2021). Damages 
are estimated to be up to six-time larger than the cost of implementing 
efficient adaptation measures (H2020., 2014). (GCA, 2019) reported that 
investing USD 1.8 trillion globally, for example, in early warning systems,

climate-resilient infrastructure; dryland crop production; mangrove 
protection; and improving the resilience of water resources between 
2020 and 2030 could generate USD 7.1 trillion in benefits.
Comparatively, less attention has been paid to low-regret options, 
especially at the national and local levels. Conservation agriculture and 
integrated production systems, early-warning systems, restoration of 
wetlands, and zoning are postulated to have lower investment and lock-
in costs than engineering-based options (Mechler, 2016; Cronin et al., 
2018; Johnson et al., 2020). However, they require regular maintenance 
and high technical and human capacity, which are likely to vary by 
scale, location, and context (Chandra et al., 2018; Khanal et al., 2019; 
Mutenje et al., 2019; Rahman and Hickey, 2019). Global studies suggest 
improvements in returns on adaptation investments by delivering 
better services and reducing water wastage through appropriate water

pricing and regulations (Damania et al., 2017; Bhave et al., 2018). For 
example, under scenarios SSP1 and SSP3, water pricing and regulation 
are projected to reverse losses in expected 2050 global GDP of 0.49% 
to gains of 0.09%. GDP losses are projected to drastically reduce in the 
Middle East, eliminated in the Sahel and Central Africa, and reversed 
into gains in Central Asia and East Africa, with benefits concentrated 
in worst-affected regions (World Bank, 2016). More local and national 
studies are needed to identify low regret options and their benefits and

653actual costs (Blackburn and Pelling, 2018; Abedin et al., 2019; Brown 
et al., 2019; Momblanch et al., 2019; Page and Dilling, 2020) (limited 
In summary, climate change impacts on water resources are projected to 
lower GDP in many low-and middle-income countries without adequate 
adaptation measures (high confidence). However, estimating the exact 
quantum of future GDP loss due to water-related impacts of climate 
change is fraught with several methodological challenges. Adaptation 
measures that focus on reducing water-related impacts of climate change 
will help stem losses further. Still, more work needs to be done on actual 
benefits and costs of adaptation strategies and residual impacts and risks 
of delaying adaptation action (medium confidence). In addition, better 
evidence on the costs and benefits of low-regret solutions, such as water 
pricing, increasing water use efficiency through technology and service

improvements, and enhanced support for autonomous adaptation, is 
also needed for informed decision-making (high confidence).4.7.6 Trade-Offs and Synergies between Water-Related 
Adaptation and Mitigation
In AR5, there was medium evidence and high agreement that some 
adaptation and mitigation measures can lead to maladaptive outcomes, 
such as a rise in GHG emissions, while further exacerbating water scarcity 
leading to increased vulnerability to climate change, now or in the future 
(Noble et al., 2014). In addition, SR1.5 (Hoegh-Guldberg et al., 2018; IPCC, 
2018a) and SRCCL (IPCC, 2019b) reiterated the challenge of trade-offs 
that may undermine sustainable development. Conversely, adaptation, 
when framed and implemented appropriately, can synergistically reduce 
emissions and enhance sustainable development.
Different mitigation pathways can either increase or decrease water 
withdrawals or water consumption (or both, or either) depending on

the specific combination of mitigation technologies deployed (high 
confidence) (Fricko et al., 2016; Jakob and Steckel, 2016; Mouratiadou 
et al., 2016; Fujimori et al., 2017; Parkinson et al., 2019). For example, 
the impacts of climate change mitigation on future global water 
demand depend largely on assumptions regarding socioeconomic and 
water policy conditions and range from reduction of 15,000 km3 to an 
increase of more than 160,000 km3 by the end of century (Mouratiadou Box 4.8 | Water-Energy-Food (WEF) Nexus Approaches for Managing Synergies and Trade-Offs
The WEF nexus is an approach that recognises that water, energy and food are linked in a complex web of relationships in the hydrological, 
biological, social, and technological realms (D’Odorico et  al., 2018; Liu et  al., 2018b; Märker et  al., 2018). For instance, agricultural 
production requires significant energy inputs due to intensive groundwater pumping (Siddiqi and Wescoat, 2013; Gurdak, 2018; Putra

et al., 2020). Similarly, hydropower production often has trade-offs with irrigation, affecting food production, carbon emission and forest 
protection (Meng et al., 2020). New technologies, such as desalination plants for urban water supply against future climate change and 
drought, are also very energy-intensive (Caldera et al., 2018) (Box 4.5). Quantifying the complex interdependencies among food, energy 
and water is critical to achieving the SDGs and reducing trade-offs (Liu et al., 2018a; Liu et al., 2018b; UN, 2019). A key benefit of the 
nexus approach is to leverage the interconnection of WEF and achieve the most efficiency in the overall systems. Hence, this approach 
allows for widening the set of salient stakeholders and, therefore, solution possibilities that may otherwise not be possible in single-
domain efforts and helps connect these stakeholders to achieve synergistic goals (Ernst and Preston, 2017; Mercure et al., 2019).

The WEF nexus approach thus opens up possibilities for strategic interventions across sectors through a better understanding of 
trade-offs (Albrecht et al., 2018). Policies and strategies aiming to cope with climate change may amplify rather than reduce negative 
externalities and trade-offs within the nexus: low carbon transition, the shift to non-conventional water resources, and agricultural 
intensification, all implemented to mitigate and adapt to climate change, are not always nexus-smart. Hence, a nexus approach that 
integrates management and governance across these three sectors can enhance WEF security by minimising trade-offs and maximising 
synergies between sectors. At the same time, renewable energy offers the opportunity to decouple water and food production from fossil 
fuel supply, leading to several advantages from both a socioeconomic and environmental point of view (Cipollina et al., 2015; Pistocchi

et al., 2020). WEF nexus approaches can achieve overall system efficiency when maximising the use and recovery of water, energy, 
nutrients and materials (Pistocchi et al., 2020; Tian et al., 2021). These types of holistic system thinking of WEF show promising strategies 
to catalyse transformative changes. Suppose that the specific types and extent of WEF linkages in a region are well understood. In that 
case, it becomes possible to intervene through one element to cause an effect on another connected component that may have proven 
difficult for direct intervention (Mukherji, 2020).
Several challenges remain for sound operationalisation of the nexus, notably insufficient data, information and knowledge in understanding 
the WEF inter-linkages and lack of systematic tools to address trade-offs involved in the nexus and to generate future projections (Liu

et al., 2017a; Liu et al., 2018b). There are recent signs of progress in developing models and tools for addressing the nexus trade-offs, 
for example, the bioenergy–water nexus (Ai et al., 2020). There is a need to move beyond viewing the WEF nexus as a way of problem 
identification to seek integrated solutions to interconnected problems.

654et  al., 2016). This section assesses some of the mitigation and 
adaptation measures from a water trade-off and synergy lens.
Solar pumps for irrigation are increasingly introduced where 
conventional energy is not available (Senthil Kumar et  al., 2020) or 
supply is intermittent or expensive (Shah et al., 2018), for example, in 
Africa (Schmitter et al., 2018), Europe (Rubio-Aliaga et al., 2016) and 
South Asia (Sarkar and Ghosh, 2017). Solar pumps can replace diesel 
and electric pumps (Rajan et al., 2020), potentially reduce 8–11% of 
India’s carbon emissions (~45.3–62.3 MMT of CO 2) attributable to 
groundwater pumping while also boosting agricultural productivity 
(Gupta, 2019). However, in the absence of incentives to deter 
groundwater over-exploitation (Shah et  al., 2018), solar pumps may 
exacerbate groundwater depletion (Closas and Rap, 2017; Gupta, 2019) 
(low evidence, medium agreement).
In many places, treatment and reuse of wastewater from urban

residential and industrial sources may be the principal supply 
option under acute water scarcity (US EPA, 2017) and help reduce 
other freshwater withdrawals (Tram Vo et  al., 2014; Diaz-Elsayed 
et  al., 2019). While reuse may recover valuable nutrients, capture 
energy as methane, and save water, effluent containing heavy 
metals may degrade land and surface and groundwater quality 
and pose a salinisation risk in semiarid regions (medium evidence, 
high agreement). Agricultural reuse of poor-quality wastewater will 
become increasingly necessary, but treatment is energy-intensive and 
may contribute to further GHG emissions (Qadir et al., 2014; Salgot 
and Folch, 2018) (Box 4.5).
Desalination of seawater or brackish water is an adaptation measure 
in many coastal water-scarce regions (Hanasaki et  al., 2016; Jones 
et al., 2019). Solar desalination is developing rapidly, and it lessens the 
carbon footprint of conventional, fossil-fuel-powered desalinisation

plants (Pouyfaucon and García-Rodríguez, 2018) (also see Box  4.5). 
However, the desalinisation process is energy-intensive (Caldera et al., 
2018); it ejects brine that is difficult to manage inland, has high salinity 
and other contaminants (Wilder et al., 2016) (medium evidence, high 
agreement) (Box 4.5).
Negative-emission technologies, such as direct air capture (DAC) of 
CO 2, could reduce emissions up to 3 GtCO 2/year by 2035, equivalent 
to 7% of 2019 global emissions. However, they can increase net water 
consumption by 35 km3 yr–1 in 2050 (Fuhrman et  al., 2020) under 
the low-overshoot emissions scenario. According to other estimates, 
capturing 10 Gt CO 2 could translate to water losses of 10–100 km3, 
depending on the technology deployed and climatic conditions 
(temperate vs. tropical) (Chapter 12, WGIII). Some DAC technologies 
that include solid sorbents also produce water as a by-product, but not

in quantities that can offset total water losses (Beuttler et al., 2019; 
Fasihi et al., 2019) (medium confidence).
Developing countries are projected to witness the highest increase in 
future energy demand under 2°C global warming leading to significant 
increases in water use for energy production (Fricko et  al., 2016) 
(Section 4.5.2). Results from a simulation study on retrofitting coal-
fired power plants built after 2000 with carbon capture and storage 
(CCS) technologies show an increase in global water consumption, currently at 9.66 km3 yr–1, by 31–50% (to 12.66 km3 yr–1 and 14.47 km3 
yr–1, respectively) depending on the cooling and CCS technology 
deployed, and hence are best deployed in locations which are not 
water scarce (Rosa et  al., 2020c) (medium confidence). In Asia, the 
near-term mitigation scenario with high CCS deployment increases 
the average regional water withdrawal intensity of coal generation

by 50–80% compared to current withdrawals (Wang et  al., 2019b). 
Carbon can be ‘scrubbed’ from thermoelectric power plant emissions 
and injected for storage in deep geological strata (Turner et al., 2018), 
but this can lead to pollution of deep aquifers (Chen et al., 2021) and 
have health consequences (low confidence).
Bio-energy crop with carbon capture and storage (BECCS) involves 
CO 2 sequestration as biofuel or forest bioenergy (Creutzig et al., 2015). 
BECCS has profound implications for water resources (Ai et al., 2020), 
depending on factors including the scale of deployment, land use, and 
other local conditions. Evaporative losses from biomass irrigation and 
thermal bioelectricity generation are projected to peak at 183 km3 
yr–1 in 2050 under a low overshoot scenario (Fuhrman et al., 2020). 
(Senthil Kumar et  al., 2020) projected that while BECCS strategies 
like irrigating biomass plantations can limit global warming by the

end of the 21st century to 1.5°C, this will double the global area and 
population living under severe water stress compared to the current 
baseline. Both BECCS (Muratori et al., 2016) and DAC can significantly 
impact food prices via demand for land and water (Fuhrman et  al., 
2020). The direction and magnitude of price movement will depend on 
future carbon prices, while vulnerable people in the Global South will 
be most severely affected (medium evidence, high agreement).
Afforestation and reforestation are considered one of the most 
cost-effective ways of storing carbon. An additional 0.9  billion ha 
of canopy cover in suitable locations could store 205 Gt of carbon 
(Bastin et  al., 2019), but this estimate is deemed unrealistic. 
Aggressive afforestation and reforestation efforts can result in 
trade-offs between biodiversity, carbon sequestration, and water 
use (Smith et  al., 2008). In northern China, ecological restoration

by regreening drylands resulted in several environmental and 
social benefits (Mirzabaev et  al., 2019) but also led to increased 
freshwater use in some pockets (Zhao et  al., 2020). Afforestation 
and reforestation with appropriate broad-leaf species in temperate 
Europe (Schwaab et al., 2020) can offer water quality and quantity-
related benefits, mitigate extreme heat, and buffer against drought 
(Staal et al., 2018). A global assessment on forest and water showed 
that forests influence the overall water cycle, including downstream 
water availability via rainfall-runoff dynamics and downwind water 
availability via recycled rainfall effects (Creed and van Noordwijk, 
2018). The study concluded that afforestation and reforestation 
should be concentrated (Ellison et  al., 2017) in water-abundant 
locations (to offset downstream impacts) and where transpiration 
can potentially be captured downwind as precipitation (Creed et al.,

2019) (Cross-Chapter Box NATURAL in Chapter 2). Overall, extensive 
BECCS and afforestation/reforestation deployment can alter the 
water cycle at regional scales (high confidence) (Cross-Chapter 
Box 5.1 in Chapter 5, WGI, (Canadell et al., 2021)).
On the other hand, demand-side mitigation options, such as dietary 
changes to more plant-based diets, reduced food waste (Aleksandrowicz

655et al., 2016; Springmann et al., 2018; Kim et al., 2020), can reduce water 
use (medium evidence, high agreement).
In summary, many adaptation and mitigation measures have synergistic 
or maladaptive consequences for water use, depending on associated 
incentives, policies, and governance that guide their deployment. 
Many mitigation measures have a considerable water footprint 
(high confidence), which must be managed in socially and politically 
acceptable ways to reduce the water intensity of mitigation while 
increasing synergies with sustainable development (medium evidence, 
4.8 Enabling Principles for Achieving Water 
Security, Sustainable and Climate 
Resilient Development Through Systems 
Transformations
Sustainable development is a global policy priority and commitment, 
as is keeping temperatures well below 2°C as per the Paris Agreement. 
Water is central to almost all SDGs (Box 4.1). Water is explicitly referred

to in SDG6 (clean water and sanitation) and SDG11 (sustainable 
communities and cities) (UN, 2015) (Section 4.1). SDG1 (no poverty) 
is statistically linked to SDG6 (clean water and sanitation) (Pradhan, 
2019), since reducing poverty can help increase adaptive capacity in 
line with the Paris Agreement adaptation goals (see Chapter 1 and 
Chapter 18). SDG2 (zero hunger) cannot be achieved without access to 
adequate water for agriculture. Meeting SDG3 (health and well-being) 
will rely on access to basic infrastructure like water and sanitation 
(Delany-Crowe et al., 2019; see Cross-Chapter Box HEALTH in Chapter 
7, Sections  4.3.3, 4.3.5), while SDG7 (affordable and clean energy) 
will need water for hydropower production under a changing climate 
(Berga, 2016; Byers et  al., 2016) (Section  4.5.2). Meeting SDG11 
(sustainable cities and communities) will require reducing the impacts 
from water-related disasters.

Water is also fundamental to all systems transitions, namely, transitions 
in energy, industrial, land and ecosystem and urban systems. Within 
energy and industrial system transitions, water stress for electricity 
generation has already caused impacts (Section 4.3.2). Therefore, water 
efficiency measures are increasingly applied in both energy and industrial 
systems with benefits for mitigation and adaptation (Section  4.6.3). 
Water is inextricably entwined with land and ecosystems transitions, 
with forested areas and ecosystems being integral components of the 
water cycle, regulating streamflow, fostering groundwater recharge 
and contributing to atmospheric water recycling (Takata and Hanasaki, 
2020) (Section 4.2). However, mitigation action of large afforestation, 
can have negative water impacts (Cross-Chapter Box 1 in Chapter 5 
of WGI report, 4.7.6), making it imperative to consider water footprint 
of land- and forest-based mitigation (Muricho et  al., 2019; Seddon

et al., 2020) (Section 4.7.6). Sustainable forest management and NBS 
are promising alternatives for good water management (Muricho 
et al., 2019; Seddon et al., 2020). Water will also play a crucial role in 
sustainable urban transitions. Cities are already facing water-related 
impacts (Section  4.3.4), which are projected to intensify with every 
degree of global warming (Flörke et  al., 2018; Nazemi and Madani, 
2018) (Section 4.5.4). Mitigation and adaptation measures in urban spaces, such as green infrastructure (Liu and Jensen, 2018), sustainable 
water supply management through recycling of wastewater and storm 
water runoff (Box 4.5) and NbS like sponge cities, are fundamentally 
about water (Box 4.6).
Thus, water remains central to achieving SDGs and will play a fundamental 
role in systems transitions needed for climate resilient development. We 
outline a set of seven enabling principles that are needed to achieving

water security and will also help in achieving SDGs and facilitate systems 
transitions.
4.8.1 Appropriate Technologies
AR5 concluded that successful adaptation across all sectors depends 
on access to technology, and technology transfer can play an essential 
role in building up adaptive capacity (Noble et  al., 2014). SR1.5 
discussed the role of efficient irrigation technologies in adaptation (de 
Coninck et al., 2018).
Technologies that reduce carbon emissions by promoting the efficient 
use of water can support successful adaptation (Biagini et al., 2014), 
provided they do not have adverse distributional outcomes (medium 
evidence, high agreement). Water management in agriculture has long 
seen the use of technology. For example, the use of technology to improve 
access to water, for example, through the diffusion of groundwater 
pumps in the 1970s in South Asia, had several livelihood benefits, but 
made agriculture more carbon-intensive (Zaveri et  al., 2016). More

recently, technology has been used to improve water use efficiency 
in agriculture through the adoption of drip and sprinkler irrigation 
(Zhuo and Hoekstra, 2017; Grafton et  al., 2018), and the use of the 
Internet of Things (IoT) (Keswani et al., 2019). In addition, innovations 
to reuse water through various wastewater recovery technologies 
(Diaz-Elsayed et  al., 2019; Capodaglio, 2020), create potable water 
through desalinisation (Caldera et al., 2018) and reuse of wastewater in 
agriculture (Salgot and Folch, 2018) are also on the rise (Box 4.5). Solar 
technologies are increasingly used for irrigation, wastewater recovery, 
desalinisation and water harvesting (Algarni et al., 2018; Pouyfaucon 
and García-Rodríguez, 2018; Tu et al., 2018; Zhao F . et al., 2020). Machine 
learning and artificial intelligence technologies (Doorn, 2021) have 
started being used in many water-use sectors, such as urban settings

(Nie et al., 2020), wastewater management (Abdallah et al., 2020; Ben 
Ammar et al., 2020) and agricultural water management, but mostly 
in high-income countries mostly on an experimental basis (Tsang and 
Jim, 2016; González Perea et al., 2018). Technology is being increasingly 
used in hydrological sciences for measurements and monitoring 
(SM4.1), as well as for creating comprehensive hydrometeorological 
warning systems (Funk et al., 2015). Lack of technology and knowledge 
transfer, especially related to remote sensing, is an adaptation barrier in 
states with less resources (Funk et al., 2015).
Adoption of technologies depends on the availability of finance 
(Section 4.8.2). The effectiveness of technology in reducing climate-related 
risks depends on its appropriateness to the local context (Biagini et al., 
2014; Mfitumukiza et al., 2020) and other factors, including institutional 
and governance frameworks (high confidence). Water technologies

656some cases. For example, efficient irrigation technologies like drip and 
sprinkler irrigation, while reducing water application rates per unit of 
land, can increase overall water extraction by increasing total land 
under irrigation (van der Kooij et al., 2013; Grafton et al., 2018; Mpanga 
and Idowu, 2021). Water-related technologies can also have adverse 
distributional outcomes when gains from technology adoption accrue 
disproportionately to a small section of the population; for example, 
only rich and male farmers can adopt high-cost technologies like solar 
irrigation pumps (Gupta, 2019) (medium confidence).
In summary, technology is an important part of water adaptation 
response, and outcomes of technology adoption are mediated through 
other societal factors, including institutions, governance frameworks 
and equity and justice issues (medium evidence, high agreement).
4.8.2 Adequate and Appropriate Financing

Although AR5 did not explicitly mention finance for water-related 
adaptation actions, it considered urban adaptation (Revi et al., 2014) 
and risk financing (Arent et al., 2014). SR1.5 (de Coninck et al., 2018) 
discussed governance and finance limitations, while SRCCL discussed 
finance in adapting to floods and droughts (Hurlbert et al., 2019).
Mitigation garners the significant share of committed climate finance. 
For example, of the total USD 15.4 billion climate finance commitments 
through ‘green bonds’, 79% accrued to mitigation and the rest to 
adaptation (World Bank, 2017). However, within adaptation finance, 
water garners a significant share of adaptation funds, with 13% of 
the Adaptation Fund’s investments were for water management, 
12% for coastal management and 10% for disaster risk reduction 
(Adaptation Fund, 2018). Similarly, within the urban adaptation 
landscape, which got ~3–5% of total adaptation finance flows of USD

30.8 billion tracked in 2017–2018 (Richmond et al., 2021), water and 
wastewater management projects received the largest share of urban 
adaptation finance (USD 761  million annually) followed by disaster 
risk management (USD 323 million) (Richmond et al., 2021). However, 
more frequent tracking of public financing is required, with a greater 
focus on transparency and accountability (Ciplet et  al., 2018; Khan 
et al., 2020) and justice and social equity (Emrich et al., 2020) (also see 
Cross-Chapter Box FINANCE in Chapter 17).
Private financing remains a minor source of adaptation financing 
(World Bank, 2019). Around 39% of green bonds issued in 2017 
were for water, wastewater and solid waste management (World 
Bank, 2017). In 2018, USD 100.5  billion of water-themed bonds 
were issued, mainly in Europe (63%), the Asia Pacific (19.6%) and 
North America (14.9%) (Filkova et al., 2018; World Bank, 2019). Such 
financing focuses on returns and scale (Cholibois, 2020), and as such,

local needs, especially those of the poor, may not be adequately 
represented (Manuamorn et  al., 2020; Williams, 2020) (medium 
COVID-19 will probably affect adaptation financing in water. Countries 
will be fiscally stretched to finance public investments domestically 
and through international development aid (Barbier and Burgess, 
2020). However, investments in flood and drought management (Phillips et al., 2020) and water and sanitation (Armitage and Nellums, 
2020b; Bhowmick et  al., 2020) are critical for building resilience 
against pandemics, and are also crucial elements of adaptation in 
water. Therefore, integrated approaches that achieve both goals need 
to be deployed (Barbier and Burgess, 2020; Newell and Dale, 2020) 
(Box 4.4., Cross-Chapter Box COVID in Chapter 7).
In summary, water garners a significant share of public and private 
adaptation funds (high confidence). However, current COVID-19-

related cuts in adaptation financing may further impede developing 
countries’ ability to invest in adequate water adaptation.
4.8.3 Gender, Equity and Social Justice
SR1.5 acknowledged that the adaptive capacity of a population was 
going to reduce with each degree of warming and that vulnerability to 
climate change was due to gender, race and level of education, which 
can compound existing and future vulnerabilities (IPCC, 2018a).
Gender, class, race, age, physical ability and educational level determine 
access to water and financial and societal resources, potentially 
adverting climate-induced water hazards, reducing vulnerability and 
facilitating adaptation. However, insufficient attention has been given 
to the role of improving equity in access to water (Abedin et al., 2019; 
Eakin et al., 2020). Not all water adaptation strategies are accessible 
to the poorest, who may turn to maladaptive strategies if their access

to water is negatively affected (Eakin et  al., 2016). Consequently, 
there have been calls for mainstreaming equity considerations into 
adaptation (Blackburn and Pelling, 2018) (medium evidence, high 
agreement). It has been shown that people living in poverty, racial 
minorities and those ageing are more vulnerable to climate-induced 
water hazards and that their adaptive capacity is limited (Szewrański 
et al., 2018; Winsemius et al., 2018; Nyantakyi-Frimpong, 2020; Erwin 
et al., 2021). Among these categories, gender is the one that has been 
most analysed in the context of water and climate change.
Women’s water rights are hampered by societal patriarchal norms 
that prevent women from accessing water and participating in water 
management. Gender power relations effectively limit women’s 
decision-making power, mobility and access to resources, including 
water, which makes them more vulnerable to climate-related hazards

(Caretta and Börjeson, 2015; Djoudi et al., 2016; Sultana, 2018; Yadav 
and Lal, 2018). In most societies in developing countries, women and 
girls are in charge of fetching water. The necessity of water collection 
takes away time from income-generating activities and education (high 
confidence) (Fontana and Elson, 2014; Kookana et al., 2016; Yadav and 
Lal, 2018). In addition, the distances women and girls would have 
to walk as a result of growing water scarcity due to climate change 
may increase (limited evidence, high confidence) (Becerra et al., 2016) 
(Sections 4.3.3, 4.5.3). Numerous studies substantiate a male bias in 
information access, employment opportunities, resource availability 
and decision-making in water-related adaptation measures (Huynh 
and Resurreccion, 2014; Sinharoy and Caruso, 2019).
Although women are often depicted as victims of climate change-
induced water scarcity (Huynh and Resurreccion, 2014; Djoudi

657et  al., 2016; Gonda, 2016; Yadav and Lal, 2018), they are also 
proactive adaptation actors (Singh and Singh, 2015) (Cross-Chapter 
Box GENDER in Chapter 18). Notably, women are not a homogenous 
group, and local gender roles are not immutable or generalisable (Carr 
and Thompson, 2014; Djoudi et al., 2016; Gonda, 2016; Sultana, 2018). 
Coping responses and adaptation mechanisms to climate change are 
profoundly gendered. Women and men approach the diversification of 
agricultural and pastoral livelihoods differently in response to climate 
change (Caretta and Börjeson, 2015; Kankwamba et al., 2018; Singh 
et  al., 2018; Basupi et  al., 2019). For example, reliance on women’s 
self-help groups and associations has proven successful in ensuring 
women’s participation in decision-making in adaptation interventions 
as a response to climate change-induced shifting precipitation patterns 
and increasing droughts (Chu, 2017; Mersha and van Laerhoven, 2018;

Phuong et al., 2018; Walch, 2019). Studies feature water harvesting, 
crop diversification, cash transfer programmes and food subsidies as 
adaptation measures that enhance gender equality. Adaptation to 
climate change in these instances promoted gender equality because 
it allowed women to reap the benefits of these new measures in terms 
of economic and health well-being (Tesfamariam and Hurlbert, 2017; 
Lindoso et al., 2018; Walch, 2019).
Meanwhile, adaptation interventions such as drip irrigation, the 
adoption of more labour-intensive crops and livelihood diversification 
through male out-migration have proven to increase women’s burden 
(Caretta and Börjeson, 2015; Kattumuri et al., 2017). Hence, a lack of 
gender-sensitive analysis before implementing water management 
projects can lead to maladaptation and increase gender vulnerability 
(Phan et al., 2019; Eriksen et al., 2021) (high confidence).
Acknowledging and understanding the implications of climate-related

water adaptation policies in terms of equity and justice is a prerequisite 
for ensuring their legitimacy and inclusiveness and promotes social justice 
(Carr and Thompson, 2014; Djoudi et al., 2016; Jost et al., 2016; Sultana, 
2018). Furthermore, integrating the principle of gender inclusivity in 
adaptation is morally and ethically appropriate and effective because 
women hold much of the local and TK in many agricultural communities 
and can fruitfully provide insights on how to design and implement 
adaptation responses (Fauconnier et al., 2018; James, 2019).
In summary, there is high confidence that the effects of climate 
change-induced water insecurity are not evenly felt across populations. 
Particularly vulnerable groups are women, children, disabled and 
Indigenous Peoples whose ability to access adequate water is limited 
and varies across race, ethnicity and caste. Equity and justice are 
central to climate change adaptation and sustainable development, as

the world’s poorest people and countries feel the adverse impacts of a 
changing climate most acutely. These groups can become even more 
vulnerable due to adaptation actions that are not equitable.
4.8.4 Inclusion of Indigenous Knowledge and Local 
Knowledge
AR5 concluded that there is robust evidence that mutual integration 
and co-production of local, traditional and scientific knowledge increase 
adaptive capacity and reduce vulnerability (Adger and Pulhin, 2014). SROCC stated with medium confidence that IKLK provide context-
specific and socioculturally relevant understandings for effective climate 
change responses and policies (Abram et al., 2019). SRCCL found that 
IKLK contribute to enhancing resilience against climate change and 
combating desertification (medium confidence). The combination of IKLK 
with new sustainable land management techniques, SRCCL stated with 
high confidence, can contribute to raising resilience to the challenges of

climate change and desertification (Mirzabaev et al., 2019).
There is high confidence that adaptation efforts benefit from the 
inclusion of IKLK (Mustonen et al., 2021). IKLK can inform how climate 
change impacts and risks are understood and experienced. Holders of 
IKLK can also help to develop place-based and culturally appropriate 
adaptation strategies that meet their community’s expectations 
(Comberti et  al., 2019; Martinez Moscoso, 2019) (Cross-Chapter 
Box INDIG in Chapter 18).
There is high confidence that genuine partnerships with Indigenous 
Peoples and local communities can assist in decolonising approaches 
to freshwater management (Arsenault et al., 2019; Wilson et al., 2019), 
which recognise the importance of knowledge that is not grounded 
on the technocratic division between nature and society (Goldman 
et al., 2018). There is also high confidence that Indigenous Peoples-
led freshwater management can facilitate culturally inclusive decision-

making and collaborative planning processes at the local and national 
levels (Somerville, 2014; Harmsworth et al., 2016; Parsons et al., 2017). 
However, market-based models of water rights regimes can impede 
the ability of Indigenous Peoples to exercise their rights and deploy 
traditional ecological knowledge regarding freshwater protection 
(Nursey-Bray and Palmer, 2018) (medium evidence, high agreement).
Community-led actions and restoration measures are helping to 
ameliorate climate impacts and provide ‘safe havens’ to affected 
freshwater species (high confidence). For example, the Skolt Sámi 
of Finland have introduced adaptation measures to aid survival of 
culturally significant Atlantic salmon stocks in the Näätämö watershed. 
Atlantic salmon had declined as northern pike, which preys on juvenile 
salmon, expanded its range in response to warmer water temperatures. 
Indigenous co-management measures included increasing the catch of

pike and documenting important sites (such as lost spawning beds) 
to ensure that ecological restoration encourages further habitat and 
increased salmon reproduction (Pecl et  al., 2017; Mustonen and 
Feodoroff, 2018).
Community-led applications of IKLK in conjunction with external 
knowledge and funding can improve water security (high confidence). 
For example, Borana pastoralists in Ethiopia (Iticha and Husen, 2019) 
and Ati and Suludnon people (Philippines) (Nelson et al., 2019) utilise 
both IK and technical information for weather forecasting, while 
Calanguya people (Philippines) collaborated with local government 
and nongovernmental organisations (NGOs) to diversify crops and 
protect the watershed (Gabriel and Mangahas, 2017). With assistance 
from municipalities, Indigenous Peoples are rehabilitating springs and 
traditional water wells in Bangladesh hill tracts (Sultana et al., 2019) and 
Micronesia (McLeod et al., 2019). In response to changing cryospheric

conditions in the Peruvian Andes, indigenous Quechua farmers use IK and 
technical information in community-led research to preserve biocultural

658knowledge and emblematic crops (Sayre et al., 2017). In Galena, Alaska 
(USA), a flood-preparedness and response programme has benefitted 
from the long-term cooperation between emergency management 
and tribal officials (Kontar et  al., 2015) (12.5.3.2 Main concepts and 
approaches). IKLK can enhance the visibility of Indigenous Peoples 
and local communities that are excluded from official decision-making 
processes. In southwest Burkina Faso, for example, Indigenous Peoples 
are using IKLK to balance (and sometimes resist) official technical 
estimates of water availability, which enhances their political visibility 
and enables them to address water scarcity (Roncoli et al., 2019).
There are structural and institutional challenges in knowledge co-
production between holders of IKLK and ‘technical’ knowledge. These 
challenges include issues of water rights, language, extractive research 
practices (Ford et al., 2016; Simms et al., 2016; Stefanelli et al., 2017;

Arsenault et al., 2019) and colonial uses of IKLK (Castleden et al., 2017), 
which can produce distrust among holders of IKLK (David-Chavez and 
Gavin, 2018). In addition, some IK is sacred and cannot be shared with 
outsiders (Sanderson et al., 2015).
In summary, IKLK are dynamic and have developed over time to 
adapt to climate and environmental change in culturally specific and 
place-based ways (high confidence). Ethical co-production between 
holders of IKLK and technical knowledge is a key enabling condition 
for successful adaptation measures and strategies pertaining to water 
security, as well as other areas (medium evidence, high agreement). 
Knowledge co-production is a vital and developing approach to the 
water-related impacts of climate change that recognises the culture, 
agency and concerns of Indigenous Peoples and local communities. It 
is critical to developing effective, equitable and meaningful strategies

for addressing the water-related impacts of global warming (Cross-
4.8.5 Participative, Cooperative and Bottom-Up 
Engagement
Participation, cooperation and bottom-up engagement are critical 
to optimal adaptation (medium evidence, high agreement). There is 
high confidence that many of the countries and social groups most 
threatened by climate change have contributed the least to global 
emissions and do not have not the resources to adapt. Effective 
participation of these actors in climate change adaptation planning in 
the water sector can contribute to more just adaptation actions (high 
There is medium evidence and high agreement that optimal adaptation 
depends critically on inter-state cooperation (Banda, 2018), which in 
turn requires trust and norms of reciprocity among all those involved 
(Ostrom, 2014). Reciprocity is central to international cooperation on 
climate change, where actors are more inclined to cooperate when

they perceive that the expected outcome will be fair in terms of costs 
and benefits of implementation (Keohane and Oppenheimer, 2016). 
Indeed, cooperation at the international level is less probable to occur 
if participants do not trust each other (Hamilton and Lubell, 2018). 
In climate-related water adaptation, transboundary cooperation is 
essential, as 60% of global freshwater resources contained in 276 river and lake basins are shared between countries (Timmerman et al., 
2017). Yet, more than 50% of the world’s 310 international river basins 
lack any type of cooperative framework (McCracken and Meyer, 2018).
SDG6 on water and sanitation includes a specific indicator (6.5.2) 
to assess cooperation over transboundary waters. While the 
methodology for measuring this indicator is debated, it is clear that 
its composition will influence international and national water policy 
and law (McCracken and Meyer, 2018) and possibly help build an

environment of trust among riparian states. Moreover, although the 
2030 Agenda for Sustainable Development (A/RES/70/1) makes it 
clear that without the participation of local communities (e.g., SDG6, 
Target 6b) and women (e.g., SDG5, Target 5.5), the SDGs will not be 
met; the involvement of these actors in formal water governance 
processes and water management is still limited (Fauconnier et al., 
2018). This is due partly to the absence, in many regions of the 
world, of adequate legal, regulatory and institutional frameworks 
for effective stakeholder participation, partly to the influence of local 
social and cultural contexts, which can discourage inclusive water 
governance (Andajani-Sutjahjo et al., 2015; Dang, 2017). Yet, inclusion 
and effective participation in bottom-up decision-making processes 
of those disproportionately affected by climate change—including 
women and Indigenous Peoples—is particularly important to ensure

the legitimacy and inclusiveness of the decision-making process 
and the design of socially just adaptation actions (Shi et al., 2016). 
Moreover, incentives for bottom-up and participative decision-making 
in the water sector can facilitate effective stakeholder engagement 
(OECD, 2015), which helps build public confidence and trust in water 
governance.
4.8.6 Polycentric Water Governance
SR1.5 concluded with high confidence that cooperation and coordinated 
actions at various governance levels are vital to ensuring participation, 
transparency, capacity building and learning among different actors 
(IPCC, 2018a). According to SRCCL, adaptive governance builds on 
multi-level and polycentric governance (Hurlbert et al., 2019), where 
efforts taken by multiple actors across different scales provide learning 
opportunities for all (Hurlbert, 2018).
Polycentrism is characterised by the absence of a unique centre of

authority. Therefore, the legitimacy of the decisions taken by multiple 
decision-makers at different levels of water governance derives from 
the perceived fairness of the decision-making process (Baldwin et al., 
2018) and the inclusion of women, Indigenous Peoples and young 
people (Iza, 2019) (medium confidence). Evidence-based approaches 
can also enhance the legitimacy of polycentric governance (Boelens 
et  al., 2015; Arriagada et  al., 2018) by generating knowledge to 
support localised and multi-leveled decision-making, as in the case 
of water user communities in Peru (Buytaert et  al., 2014; Buytaert 
The advantages of polycentric approaches to climate governance 
include improved communication, inclusiveness, consensus and better 
outcomes (Ostrom, 2014; Cole, 2015; Keohane and Victor, 2016; 
Morrison et al., 2017; Tormos-Aponte and García-López, 2018) (high

659agreement). However, polycentric governance systems require cross-
scale information sharing, coordination and democratic participation 
to work appropriately (Pahl-Wostl and Knieper, 2014; Carlisle and 
Gruby, 2017; Morrison et al., 2017; Biesbroek and Lesnikowski, 2018; 
Frey et al., 2021) (high confidence). For example, efficient information 
sharing has been necessary to implement groundwater governance in 
transboundary contexts (Albrecht et al., 2017).
Empirical studies that examined the potential of polycentric 
governance to address water challenges in the face of climate change 
showed that polycentrism could encourage and support participatory, 
decentralised and deliberative adaptation. These, in turn, can produce 
better environmental outcomes and improve water governance 
outcomes (high confidence). Polycentric water governance can be an 
effective enabler for adaptation when it ensures interconnectedness

with multiple public and private actors across the different sectors 
(e.g., irrigation users, domestic users, industrial users, watershed 
institutions, etc.) and across different levels (e.g., local, regional and 
national governments) to help come up with well-coordinated water 
adaptation responses (high confidence) (Pahl-Wostl and Knieper, 2014; 
McCord et al., 2017; Baldwin et al., 2018; Hamilton and Lubell, 2018; 
Kellner et al., 2019).
Questions remain about the extent to which polycentrism can result 
in either greater climate justice or exacerbate existing inequalities 
due, for example, to existing power inequalities which may affect the 
performance and effectiveness of a polycentric system (Pahl-Wostl 
and Knieper, 2014; Morrison et al., 2017; Hamilton and Lubell, 2018; 
Okereke, 2018). For instance, historical inequities and injustices due 
to settler colonialism and top-down water policies, governance and

laws (Collins et al., 2017; Arsenault et al., 2018; Johnson et al., 2018; 
Robison et  al., 2018) have resulted in long-term water insecurity in 
many indigenous communities in North America (Simms et al., 2016; 
Medeiros et  al., 2017; Conroy-Ben and Richard, 2018; Diver, 2018; 
Emanuel, 2018) (high confidence) (Section 4.6.9). Additionally, studies 
highlight that power dynamics can undermine the success of those 
initiatives. For example, in the Sao Paulo water crisis, polycentric 
governance did not fully realise its potential when it was guided by 
authoritarian governance favouring political interests over social, 
territorial and environmental justice (Frey et  al., 2021). Likewise, in 
the Thau basin (France), the most important and influential actors 
shaped policy measures in response to climate change, thus limiting 
the potential for radical changes in water use (Aubin et al., 2019).
In summary, polycentric governance can enable improved water

governance and effective climate change adaptation (medium 
confidence). However, it can also exacerbate existing inequalities as 
long as less powerful actors, such as women, Indigenous Peoples and 
young people, are not adequately involved in the decision-making 
process (high confidence).
4.8.7 Strong Political Support
According to AR5 (Jiménez Cisneros et al., 2014), barriers to adaptation 
in the water sector include lack of institutional capacity, which, together 
with political support, constitutes one of the feasibility dimensions towards limiting global warming to 1.5°C (de Coninck et al., 2018). 
As the IPCC SROCC (IPCC, 2019a) and SRCCL (Shukla et  al., 2019) 
suggest, limited institutional support can challenge adaptation efforts 
in water management.
Climate adaptation planning approaches can be constrained by 
several economic, institutional, developmental and political barriers 
(Anguelovski et  al., 2014; Eisenack et  al., 2014), including strong

political support, that is, the lack of collective willingness to take 
action. Despite the ongoing accumulation of scientific evidence as to 
the seriousness of the impact of climate change on water resources, 
state action has not always been effective. There are now a rising 
number of case laws addressing the state’s failure to implement 
adaptation policies and resultant climate change litigation (Setzer 
and Vanhala, 2019; Peel and Osofsky, 2020), including in the water 
sector, as in the leading case Leghari v Federation of Pakistan (2015 
WP . No. 25501/201), in which a farmer sued the national government 
for failure to carry out national climate change policies impacting on 
the constitutional right to life (Preston, 2016).
The 2015 Paris Agreement made a significant impact on the status 
quo, with almost all the countries agreeing to limit global warming 
to 2°C or less. The preparation of NDCs under the Paris Agreement

contributed positively to national climate policies and helped focus on 
the centrality of water in adaptation planning (Röser et al., 2020). In 
total, 92% of countries that mention adaptation in NDCs also include 
water (GWP , 2018). Low-income countries make specific reference to 
rain-fed or irrigated agriculture and livestock. In contrast, middle- and 
high-income countries include developing management, governance 
mechanisms and increased disaster risk reduction in their NDC pledges 
(GWP , 2018). Floods were the critical climate hazards identified in 
the adaptation components of NDCs, followed by droughts (85 out 
of 137 countries for floods and 80 out of 137 for drought). Also, the 
water sector was identified as the top priority sector for adaptation 
actions in the NDCs for 118 out of 137 countries, followed closely by 
the agricultural sector with 100 out 137 (GWP , 2018) based on data 
from UNFCCC (2017). Many developing countries have included

quantitative targets for adaptation in the water sector (Pauw et al., 
2018). Similarly, water-related impacts and adaptation often feature 
prominently in NAPs (DEFRA, 2018).
Evidence suggests that adaptation failure in the water sector is due 
to policy and regulatory failures (Keohane and Victor, 2016; Oberlack 
and Eisenack, 2018; Javeline et al., 2019), reflecting political myopia 
(Muller, 2018; Empinotti et al., 2019; Pralle, 2019) (high confidence).
International donors and supranational/transnational legislation 
(e.g., EU law) can support the capacity of national and sub-national 
governments to act and remove possible barriers to the effective 
implementation of climate change adaptation policies in the water 
sector, including obstacles posed due to lack of financial support 
for the developing countries (Massey et al., 2014; Tilleard and Ford, 
2016; Biesbroek et  al., 2018; Rahman and Tosun, 2018) (medium 

660Frequently Asked Questions
FAQ 4.1 | What is water security, and how will climate change affect it?
Water is essential for all societal and ecosystems needs. Water security is multi-dimensional and not just about water availability. Water 
needs to be available in sufficient quantity and quality and needs to be accessible in an acceptable form. Accordingly, a situation of water 
security indicates the availability and accessibility of sufficient clean water to allow a population to sustainably ensure its livelihoods, health, 
socioeconomic development and political stability. Many socioeconomic factors, such as population growth and food consumption patterns, 
play an important role in determining water security. Still, climate change is increasingly shown to be an important contributor to water 
insecurity worldwide, with some regions more at risk than others.

Climate change can affect these different dimensions of water security in different ways. Most directly, climate 
change is affecting the overall availability of water across regions and during important seasons. More extended 
periods of dry spells and droughts are already affecting water availability, especially in the arid areas of India, 
China, the USA and Africa. Other extremes, such as heavy precipitation and flooding, can affect water quality, 
making water unsafe for drinking, for example. In coastal regions and small islands, the combined effects of higher 
sea levels and more intense storms affect water security by increasing the salinisation of groundwater resources. 
Indirect effects of climate change on water security include impacts on infrastructure for the provision and recovery 
of water resources, which can affect the safe access to adequate water resources, both in terms of quality and 
quantity.

In terms of assessing the extent of water scarcity, studies estimate that currently, between 1.5 and 2.5  billion 
people live within areas exposed to water scarcity globally. These numbers are projected to increase continuously, 
with estimates of up to 3 billion at 2°C and up to 4 billion at 4°C by 2050. Many socioeconomic factors, such as 
population growth and food consumption patterns, determine water scarcity. Still, climate is increasingly shown to 
be an important component that drives scarcity across the world. Water scarcity is often a seasonal occurrence, and 
climate change is projected to increase seasonal extremes. Often, consecutive years with drier conditions lead to a 
long-term decrease in groundwater tables, affecting water availability directly and soil moisture in the longer term.
As an essential component of water security, climate change will affect water quality in different ways. Drier

conditions lead to a reduction in water availability, causing a potential increase in the concentration of contaminants. 
Increasing runoff and floods can wash pollutants into water bodies. With climate change projected to increase the 
variability of rain over space and time, such impacts on water quality are becoming increasingly likely. Higher 
temperatures add to deteriorating water quality by reducing oxygen levels.
Another critical component to ensure secure access to water resources is adequate water infrastructure for access, 
disposal and sanitation. Unfortunately, increasing extremes due to climate change, especially floods and increasing 
storm activity, have great potential to damage such infrastructure, especially in developing world regions, where 
infrastructure is much more susceptible to damage and pollution.
There are substantial differences in the distribution of risks across regions, with some areas facing a much higher

risk burden than others. Also, projections of the potential impacts of climate change on water security vary across 
regions. However, patterns of projected water-related extremes are emerging more clearly globally with increasing 
confidence.

661Frequently Asked Questions
FAQ 4.2 | Which places are becoming wetter and which are becoming drier, and what risks do these bring to 
people?
Due to climate change, substantial numbers of people are now living in climates with average precipitation levels significantly different to the 
average over the 20th century. Nearly half a billion people are living in unfamiliar wet conditions, mostly in mid- and high latitudes, and over 
160 million people are living in unfamiliar dry conditions, mostly in the tropics and subtropics. In addition to changes in average precipitation, 
precipitation patterns over time are also changing, as well as river flows. Societal impacts and increased risks from both wetter and drier 
conditions are starting to emerge.
Some parts of the world are becoming wetter, and some are becoming drier, in terms of either changes in precipitation

and/or the water available in the soil, in rivers or underground. Soil moisture, river water and groundwater are 
affected by changes in precipitation and also by changes in evaporation, which is affected by temperature and by 
uptake by vegetation.
All these factors are affected by climate change. Rising temperatures drive higher evaporation, which dries the 
landscape, although this can be offset in some areas by reduced uptake of water from the soil by plants in response 
to rising CO 2 concentrations. A warming climate brings more precipitation overall, although changes in global wind 
patterns mean that some areas are seeing less precipitation.
As a result, substantial numbers of people are now living in climates with average precipitation levels significantly 
different to the average over the 20th century. Nearly half a billion people are living in unfamiliar wet conditions,

mostly in mid- and high latitudes, and over 160  million in unfamiliar dry conditions, mostly in the tropics and 
subtropics (Figure FAQ4.2.1).
Population density in regions of
emerging precipitation changes
Regions of
increasing
1 thousan d
100
10
per square
degreeRegions of
decreasin g
Figure FAQ4.2.1 |  Numbers of people seeing increases and decreases in precipitation.
In addition to changes in average precipitation, the patterns over time are also changing, such as the length of 
dry spells and the amount of precipitation falling in heavy events. Again, these changes vary across the world due 
to shifting wind patterns. Approximately 600 million people live in places with longer dry spells than in the 1950s, 
mostly in West Africa, south Asia and parts of South America. Approximately 360 million people experience shorter 
dry spells, in North America, northern Asia and other parts of South America.

In contrast, far more people (about 600 million people) are seeing heavier precipitation than less heavy precipitation 
(80 million). A more widespread increase in heavy precipitation is expected in a warming world, where the warmer 
atmosphere takes up more moisture and hotter ground drives more intense storms.
River flows are also changing in many parts of the world, often due to changes in precipitation, although direct 
human impacts are also important. Generally, the most widespread increased river flows are seen in high latitudes, 
while decreasing flows are seen in mid- and low latitudes, although there are major exceptions to these trends and 
data is sparse in many regions (Figure FAQ4.2.2).

662Observed changes in mean river flows from 1971–201 0
≤-20 -10 01 0≥ 20
% -change decade
-1
≤-20
 -10
 0
 10
 ≥20
river flowsDecreasing
river flows
Figure FAQ4.2.2 |  Observed changes in mean river flows from 1971 to 2010
Some of these changes are starting to have impacts on society. For example, increasing rainfall in the USA has 
led to increased crop yields. Heavy rainfall and long periods of rainfall lead to flooding, causing deaths, injuries, 
infrastructural damage, spread of disease, disruptions to employment and education, psychological trauma and 
territorial displacement. The weather conditions associated with many recent major flooding events were made 
more likely by climate change, although non-climatic factors remain the dominant driver of increased flooding.
Drier soils have made heatwaves more severe. A drying of the landscape has increased the length of the fire season

across much of the world, contributing to unprecedented severity of wildfires in recent years. In recent years, 
several major drought events with impacts on agriculture were made more likely by climate change.
Overall, the general picture is of increased average precipitation and/or longer periods of precipitation in the mid 
and high latitudes, but decreased precipitation and/or longer times between precipitation across much of the 
tropics and subtropics. Where heavy precipitation is changing, this is mostly towards increasing intensity. Societal 
impacts and increased risks from both wetter and drier conditions are starting to emerge.
FAQ 4.3 | How will climate change impact the severity of water-related disasters, such as droughts and floods?
Climate change will lead to populations becoming more vulnerable to floods and droughts due to an increase in the frequency, magnitude

and total area affected by water-related disasters. Floods and droughts will also affect more people in the course of this century as a result 
of population growth and increased urbanisation, especially if warming cannot be limited to 1.5°C. The impact of floods and droughts are 
expected to increase across all economic sectors, resulting in negative outcomes for the global production of goods and services, industry 
output, employment, trade and household consumption. Floods will pose additional risks to people’s lives and health through inundation, 
facilitating the further spread of waterborne diseases. At the same time, droughts can have adverse health impacts due to the limited 
availability of food and water for drinking and hygienic purposes. All losses, both in terms of lives and in economic terms, will be more limited 
in a 1.5°C than in a 3°C warmer world.
Anthropogenic land use changes and climate change will exacerbate the intensity, frequency and spatial extent of

floods and droughts, leading to populations becoming more vulnerable. According to projections, these increases FAQ 4.2 (continued)

663in extreme events will be more significant with higher levels of global warming. However, the location and severity 
of floods and droughts are context-dependent and complex phenomena.
The processes that lead to droughts include lack of or less frequent precipitation, increased evapotranspiration 
and decreased soil moisture, snow cover, runoff and streamflow. For example, warming temperatures may result 
in higher evapotranspiration, in turn leading to drier soils. In addition, reduced soil moisture diminishes the 
amount of water filtering into rivers in both the short and long term while also increasing the aridity that can 
foster the conditions for fire. Moreover, decreased snow cover represents less runoff supply to downstream areas 
during warmer seasons. Depending on this process and the propagation of a meteorological drought onto further 
systems, a drought can be defined as hydrological, agricultural or ecological. Agricultural drought threatens food

production through crop damage and yield decreases, and consequent economic impacts, and therefore, can be 
the most impactful to humans. Geographically, the likelihood of agricultural drought is projected to increase across 
most of southern Africa, Australia, the majority of Europe, the southern and western USA, Central America and the 
Caribbean, northwest China, parts of South America, and the Russian Federation; but due to increased precipitation, 
it is projected to decline in southeastern South America, central Africa, central Canada, western India and the south 
of the Arabian Peninsula.
Flood hazard natural processes usually result from increases in heavy precipitation events, but they can also be 
caused by saturated soils, increased runoff and land use changes. A warming climate usually causes greater energy 
for the intense upward motion for storm formation and increases evapotranspiration, which leads to heavier

precipitation. Many places around the world will experience more-than-average rainfall, which may increase soil 
moisture. Wetter soils saturate faster during precipitation events, resulting in increased runoff that can muddy the 
waters and lead to floods. Anthropogenic land use changes, such as urbanisation, deforestation, grasslands and 
agricultural extension, can also reduce the amount of water infiltrating the soil and leading to frequent flooding. 
Floods are expected to increase in Asia, the USA and Europe, particularly in areas dependent on glacier water where 
melting will lead to earlier spring floods. Additionally, fluvial floods are projected to be more frequent in some 
regions in central Africa and northern high latitudes and less frequent in the southern areas of North America, 
southern South America, the Mediterranean, parts of Australia and southern parts of Europe.

Globally, socioeconomic development will lead to heightened societal hazards. Due to population growth and 
increased urbanisation, floods and droughts will affect more people in the course of this century, especially if 
warming cannot be limited to 1.5°C. All losses, both in lives and in economic terms, will be more limited in a 1.5°C 
than in a 3°C warmer world. The impacts of floods and droughts are expected to increase across all economic sectors, 
from agriculture to energy production, resulting in negative outcomes for our global production of goods and 
services, industry output, employment, trade and household consumption. Landslides, sinkholes and avalanches 
arising from heavy rainfall events will increasingly threaten infrastructure and agricultural production. In cities, 
increased flood frequency could disrupt waste management systems, resulting in the clogging of waterways. In

addition, unprecedented flood magnitudes could overwhelm hydraulic infrastructure, affecting the energy, industry 
and transportation sectors. An expansion in inundation area, coupled with urban sprawl, would increase flood 
damage. Floods will pose additional risks to people’s lives and health through inundation, thus facilitating the spread 
of waterborne diseases. At the same time, drought can have adverse health impacts due to the limited availability of 
food and water for drinking and hygienic purposes. Although there are no agreed-upon projections for migration 
and displacement due to water-related disasters, it is known that drought and desertification cause harvest failures, 
which may lead subsistence farmers to relocate to urban areas. Whether temporary or permanent, displacement is 
often mired with diminished safety, loss of social ties, and a weakened sense of place and cultural identity.

Finally, vulnerable groups such as people living in poverty, women, children, Indigenous Peoples, uninsured workers 
and the elderly will be the most affected by water-related disasters.FAQ 4.3 (continued)

664Frequently Asked Questions
FAQ 4.4 | Globally, agriculture is the largest user of water. How will climate change impact this sector, and how can 
farmers adapt to these changes?
Climate-induced changes in the global hydrological cycle are already impacting agriculture through floods, droughts and increased rainfall 
variability, which have affected yields of major crops such as maize, soybeans, rice and wheat. These changes are projected to continue in a 
warmer world, which will cause yields of rain-fed crops to decline and reduce the amount of water available for irrigation in water-stressed 
regions. Farmers already use adaptation and coping strategies to manage agricultural water use. Some of the most important adaptation 
responses are the application of irrigation, on-farm water and soil conservation; changing cropping patterns; adopting improved cultivars; and

improved agronomic practices. In many parts of the world, farmers increasingly use Indigenous knowledge and local knowledge to inform their 
decisions of what to grow, when to grow and how much to irrigate. To offset the risks of market-related volatility coupled with climate change, 
farmers also adopt economic and financial instruments such as index-based crop insurance. Training and capacity-building programmes and 
social safety nets are other forms of adaptation that farmers are using to respond to these changes.
Worldwide, and especially in developing countries, agriculture (including crop cultivation and livestock and fisheries) 
is the largest water user, accounting for 50–90% of all water use. Moreover, a substantial part of the water used in 
agriculture is ‘consumptive’ use, which means that the water is ‘consumed’ for crop growth and is not immediately 
available for other uses. This is different from other sectors, such as energy production, where only a fraction of

the water is consumed, and other downstream users can reuse the rest. Agriculture also accounts for a large share 
of employment in developing countries, with 60–80% of the rural population dependent on agriculture for their 
livelihoods. Agriculture provides food security for all. This makes farmers and agriculture particularly vulnerable to 
climate change.
Climate-induced changes in the global hydrological cycle are already impacting agriculture through floods, 
droughts and increased rainfall variability. For example, loss in yields has been reported for major crops such as 
maize (by 4.1%), soybeans (by 4.5%), rice (by 1.8%) and wheat (by 1.8%) due to changes in precipitation between 
1981 and 2010. In addition, drought has affected both the area under cultivation and the yields of major crops. 
According to one estimate, globally, there has been a loss of 9–10% of total cereal production due to droughts

and other weather extremes. Similarly, floods are one of the significant reasons for crop losses worldwide. Climate 
change-induced losses in livestock and fisheries have also been documented. In some parts of the world, especially 
in cold temperate zones, agro-climatic zones have become more conducive to yield growth in crops like maize and 
soybean due to increases in summer precipitation. Yet, negative impacts far outweigh positive impacts.
Projected impacts on agriculture due to changes in water availability are also severe. For example, yields of rain-fed 
crops such as maize are projected to decline by one fifth to one third by the end of the century. In contrast, many 
areas which currently support multiple crops may become unsuitable for rain-fed farming or support only one crop 
in a year. Irrigation, which is often one of the most effective adaptive strategies against water-induced stress, is also

projected to be affected by a reduction of the amount of water available for irrigation in some parts of the world 
that are already water-stressed or as a result of groundwater depletion in places such as India, North China and the 
northwestern USA. Overall, future droughts and floods will pose a major risk to food security, and agriculture and 
impacts will be more severe on countries and communities that are already food insecure.
Given that farmers are already dealing with variability in the amount and timing of rainfall. In many places, demand 
for agricultural water is greater than supply, and farmers are using many adaptations and coping strategies to meet 
water demands for their crops, fish and livestock. Some of the most popular adaptation responses around crops and 
water include:
• changing cropping patterns to less water-intensive crops, and changes in the timing of sowing and harvesting 
to respond to unfamiliar trends in the onset of rains

• adoption of improved cultivars, such as drought and flood-resistant seed varieties
• improved agronomic practices, including conservation agriculture that helps reduce water application rates
• irrigation and water-saving technologies such as efficient irrigation and on-farm water management techniques
• on-farm water and soil moisture conservation
Most of these measures are beneficial across multiple indicators (water saving, increased incomes, etc.); however, whether 
they also reduce climate-related risks is not well understood and remains a knowledge gap. Irrigation and changes in crop 
choices and cultivars are also shown to be effective for future adaptation, especially at 1.5°C global warming, but much 
less effective at 2°C and 3°C when these responses will not mitigate a large part of the climate risk. Most of these

665adaptation measures mentioned above are autonomous. However, some, such as improved seeds and cultivars, are 
supported by national agricultural research agencies, international research coalitions such as the CGIAR [Consultative 
Group on International Agricultural Research], and private seed companies. In many parts of the world, farmers are also 
increasingly using IKLK to inform these decisions of what to grow, when to grow and how much to irrigate.
Improved cultivars
and agronomic practices Water related adaptation responses
Changes in cropping pattern 
and crop systems
Livestock and Fishery related
On farm irrigation 
and water managementWater and soil moisture conservation
 Migration & off-farm diversification
 Economic/financial incentives
Collective action, policies,
interventions

IK and LK based adaptationsFlood risk reductionAdaptation improves economic or financial outcomes Adaptation improves outcomes
for vulnerable people Adaptation improves waterrelated outcomes
Adaptation improves ecologicalor environmental outcomes Adaptation improves institutional
and socio-cultural outcomes
Adaptation has mitigation co-benefitsWater related adaptation responses in agriculture sector:
benefits, co-benefits with mitigation, and possible maladaptation
Evidence 
(number/quantity of case studies on the topic )Confidence 
(% of studies which establish causality or correlatio n
between adapation response and outcome)Sector
Agriculture, terrestria l
Water
Policy,  Governments, Societ y
Forestry intervention
Indigenous and local knowledgeHigh, > 67%
Medium, 50-67%
Low, < 50%High, > 40
Low, < 10Medium, 10 to 40
Adaptation  leads to 
maladpative outcomes

Figure FAQ4.4.1 |  Water-related adaptation responses in agriculture sector: benefits, co-benefits with mitigation, and possible maladaptationFAQ 4.4 (continued)

666Given the predominance of market economies worldwide, most farmers also depend on the market to sell their 
produce, and market fluctuations affect their incomes. In addition, market-related volatility coupled with climate 
change is a source of increased risk for farmers. Several economic and financial instruments are being used with 
varying levels of success to offset some of these interlinked impacts. Index-based crop insurance is one such 
instrument that compensates farmers for losing crops due to hazards such as floods and droughts. However, several 
limitations in their implementation remain.
In cases of severe droughts and floods, which have debilitating impacts on already poor and vulnerable populations, 
national governments provide social safety programmes, such as food or cash-for-work programmes, which are 
shown to be successful in reducing risks for the most vulnerable people, even though there are often concerns

with targeting efficiency. Providing training and capacity building of farmers to adopt new farming practices and 
technologies to manage risk better are also known to be effective when the training is conceptualised, targeted and 
implemented in consultation with farmers. Planned adaptation practices include managing weather and market 
risks through insurance products, social safety nets for vulnerable populations, and providing the right mix of 
training and capacity building. These adaptation practices are generally implemented by civil society, governments 
and the private sector.FAQ 4.4 (continued)
FAQ 4.5 | Which principles can communities implement to sustainably adapt to the ways that climate change is 
impacting their water security?
For communities to sustainably adapt to climate impacts on water security, their participation, cooperation and bottom-up engagement are

critical in all stages of decision-making processes. In addition to enhancing the legitimacy of the decision-making process, the community’s 
involvement can increase the equitability and effectiveness of the adaptation approach. As water insecurity disproportionately affects 
marginalised social groups, their participation in water governance and implementation can help improve their water security. Combining 
and integrating local, indigenous and traditional ecological knowledge with Western understandings of climate change can enhance the 
effectiveness of adaptation measures and strategies while ensuring that the adaptation is equitable and just. Improving water security is 
fundamental to achieving many of the 17 Sustainable Development Goals (SDGs).
For decades, communities worldwide have already been adapting to climate change-induced hydrological changes 
to maintain their livelihood and safety. Adaptation is a multi-faceted process that is implemented differently

depending on the sector affected by changes in the hydrological cycle and the region where these changes happen. 
For instance, farmers in the semiarid areas might adapt to changing rain patterns through irrigation (see also 
FAQ4.4). At the same time, urban dwellers can adopt measures such as rainwater harvesting and other nature-based 
solutions. Several principles have been documented as crucial for achieving sustainable adaptation as they support 
communities in becoming more resilient to climate change. However, these principles can be implemented singularly 
or in tandem, and it is essential to acknowledge that long-term adaptation success is context-specific. Therefore, it 
is critical to involve local communities in co-designing effective adaptation responses.
For communities to sustainably adapt to climate impacts on water security, participation, cooperation and bottom-up

engagement are critical in all stages of the decision-making processes, from planning to full implementation. Many 
of the countries and social groups most threatened by climate change have contributed least to global warming 
and do not have access to adequate resources to adapt. Effective participation of these actors in water-related 
climate change adaptation planning can contribute to more equitable adaptation actions. The involvement of the 
most vulnerable in the design of adaptation responses makes it more probable that these solutions will suit their 
needs and have therefore a higher chance of being effective. Accessible, inclusive and well-coordinated efforts to 
enhance water security will improve the legitimacy of water governance and work synergistically with reducing 
inequalities (UN SDG, SDG 10) and encouraging more sustainable communities (SDG 11). Communities can also be

involved in sector-specific adaptation responses. These are often water-related and help ensure that climate action 
(SDG 13) is well aligned with clean water and sanitation (SGD 6).

667The participation of traditionally excluded groups such as women and marginalised communities and Indigenous 
Peoples and ethnic minorities contributes to more equitable and socially just adaptation actions. Water insecurity disproportionately affects these marginalised groups, and their participation in water governance and implementation can help alleviate this burden.
Recognising the importance of Indigenous knowledge and local knowledge in improving water security is vital 
to ensuring that decisions and solutions align with the interests of Indigenous Peoples and local peoples and benefit their communities culturally and economically. Furthermore, the effectiveness of adaptation measures and strategies improves when Indigenous knowledge and local knowledge and traditional ecological knowledge are combined and integrated with technical understandings of climate change.

The climate adaptation plans led by national governments and local authorities will only be accepted and adequately 
implemented when supported by the community. Therefore, strong political and societal support is necessary to ensure effective policy changes, whether local or national. Significantly, access to financial assistance from private and public sources expands the range of strategies that communities can consider for enhancing their water security.
These principles are also conducive to the achievement of the United Nations SDGs. Actions that reduce climate risk 
and enhance water security can positively interact with sustainable development objectives (synergies). Therefore, improving water security is fundamental to achieving many of the 17 SDGs.FAQ 4.5 (continued)

5
717Food, Fibre and Other Ecosystem Products Chapter 5
Current Impacts
Climate change impacts are stressing agriculture, forestry, 
fisheries and aquaculture, increasingly hindering efforts to meet human needs (high confidence
1). Human-induced warming

has slowed growth of agricultural productivity over the past 50 years in mid and low latitudes (medium confidence). Crop yields are compromised by surface ozone (high confidence). Methane emissions have negatively impacted crop yields by increasing temperatures and surface ozone concentrations (medium confidence). Warming is negatively affecting crop and grassland quality and harvest stability (high confidence). Warmer and drier conditions have increased tree mortality and forest disturbances in many temperate and boreal biomes (high confidence), negatively impacting provisioning services (medium confidence). Ocean warming has decreased sustainable yields of some wild fish populations (high confidence). Ocean acidification and warming have already affected farmed aquatic species (high confidence). {5.2.1, 5.4.1, 5.5.1, 5.6.1, 5.7.1, 5.8.1, 5.9.1}
Warming has altered the distribution, growing area suitability

and timing of key biological events, such as flowering and insect emergence, impacting food quality and harvest stability (high confidence). It is very likely
2 that climate change is altering 
the distribution of cultivated, wild terrestrial, marine and freshwater species.  At higher latitudes, warming has expanded potential area but has also altered phenology (high confidence), potentially causing plant–pollinator and pest mismatches (medium confidence). At low latitude, temperatures have crossed upper tolerance thresholds, more frequently leading to heat stress (high confidence). {5.4.1, 5.7.4, 5.8.1, Cross-Chapter Box MOVING PLATE this chapter , 5.12.3.4}
Climate-related extremes have affected the productivity of all

agricultural and fishery sectors, with negative consequences for food security and livelihoods (high confidence). The frequency of sudden food production losses has increased since at least mid-20th century on land and sea (medium evidence, high agreement). Droughts, floods and marine heatwaves contribute to reduced food availability and increased food prices, threatening food security, nutrition and livelihoods of millions (high confidence). Droughts induced by the 2015–2016 El Niño, partially attributable to human influences (medium confidence), caused acute food insecurity in various regions, including eastern and southern Africa and the dry corridor of Central America (high confidence). In the northeast Pacific, a recent 5-year warm period impacted the migration, distribution and abundance of key fish resources (high confidence). Increasing variability in grazing systems has negatively affected animal fertility, mortality and herd recovery rates, reducing livestock keepers’

expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g.,  medium confidence. For a given evidence and agreement statement, different confidence levels 
2
 the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as 
likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100%, and extremely unlikely

0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely. This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies within the 17–83% probability range.
3
 Disability-adjusted life years or DAL
Ys.(medium confidence). { 5.2.1, 5.4.1, 5.4.2, 5.5.2,5.8.1, 5.9.1, 5.12.1, 
5.14.2, 5.14.6, Cross-Chapter Box MOVING PLATE this chapter; WGI AR6 Sections 11.2–11.8}
Climate change impacts everybody, but vulnerable groups, 
such as women, children, low-income households, Indigenous or other minority groups and small-scale producers, are often at higher risk of malnutrition, livelihood loss, rising costs and competition over resources (high confidence). Increasing competition for land, energy and water exacerbates impacts of climate change on food security (high confidence). {5.4.2.2, 5.5.2.6; 5.8.2.2, 5.9.2.1, 5.12.2, 5.12.3.1; 5.12.3.2; 5.12.3.3; 5.13.1, 5.13.3, 5.13.4}

Projected ImpactsClimate change will make some current food production areas 
unsuitable (high confidence). Current global crop and livestock 
areas will increasingly become climatically unsuitable under a high-emission scenario (high confidence) (e.g., 10% by 2050, over 30% by 2100 under SSP-8.5 versus below 8% by 2100 under SSP1-2.6). Increased, potentially concurrent climate extremes will periodically increase simultaneous losses in major food-producing regions (medium confidence). {5.2.2, 5.4.1, 5.4.3, 5.5.2, 5.5.3, Cross-Chapter Box MOVING PLATE in this chapter, Section 5.12.4; WGI Section 11.8}
Impacts on food availability and nutritional quality will increase 
the number of people at risk of hunger, malnutrition and diet-related mortality (high confidence). Climate change will increase

the number of people at risk of hunger in mid-century, concentrated in Sub-Saharan Africa, South Asia and Central America (high confidence) (e.g., between 8 million under SSP1-6.0 and 80 million people under SSP3-6.0). Increased CO
2 concentrations will reduce nutrient density of 
some crops (high confidence). Climate change will increase loss of years of full health
3 by 10% in 2050 under Representative Concentration 
Pathway (RCP) 8.5 because of undernutrition and micronutrient deficiencies (medium evidence, high agreement). {5.2.2, 5.4.2, 5.4.3, 5.12.1.2, 5.12.4; Cross-Chapter Box MOVING PLATE this chapter}
Climate change will increasingly expose outdoor workers and

animals to heat stress, reducing labour capacity, animal health, and dairy and meat production (high confidence). The number of days with climatically stressful conditions for outdoor workers will increase by up to 250 workdays per year by century’s end in some parts of South Asia, tropical sub-Saharan Africa and parts of Central and South America under Shared Socioeconomic Pathway (SSP) 5-8.5, with negative consequences such as reduced food productivity, higher costs and prices (medium confidence). From early- to end-century, cattle, sheep, goats, pigs and poultry in the low latitudes will face 72–136 additional days per year of extreme stress from high heat and humidity

5
718Chapter 5 Food, Fibre and Other Ecosystem Products
under SSP5-8.5. Meat and milk productivity will be reduced (medium 
confidence). {5.5.3.4; 5.12.4}
Climate change will further increase pressures on terrestrial 
ecosystem services supporting global food systems (high  
confidence). Climate change will reduce the effectiveness of pollinator 
agents as species are lost from certain areas, or the coordination of 
pollinator activity and flower receptiveness is disrupted in some regions 
(high confidence). Greenhouse-gas emissions will negatively impact air, 
soil and water quality, exacerbating direct climatic impacts on yields 
(high confidence).  {5.4.3, Box 5.3, Box 5.4, 5.5.3.4; 5.7.1, 5.7.4, 5.10.3}
Climate change will significantly alter aquatic food provisioning 
services and water security with regional variances (high 
confidence). Climate change will reduce marine fisheries and 
aquaculture productivity, altering the species that will be fished or

cultured, and reducing aquaculture habitat in tropical and subtropical 
areas (high confidence). Global ocean animal biomass will decrease by 
5–17% under RCP2.6 and 8.5, respectively, from 1970 to 2100 with 
an average decline of 5% for every 1°C of warming, affecting food 
provisioning, revenue value and distribution (medium confidence). 
Global marine aquaculture will decline under warming and 
acidification from 2020 to 2100, with potential short-term gains for 
temperate finfish and overall negative impacts on bivalve aquaculture 
from habitat reduction (50–100% for some countries in the Northern 
Hemisphere) (medium confidence). Changes in precipitation, sea level, 
temperature and extreme climate events will affect food provisioning 
from inland and coastal aquatic systems (high confidence). Sea 
level rise and altered precipitation will increase coastal inundation 
and water conflicts between water-dependent sectors, such as rice

production, direct human use and hydropower (medium confidence). 
{5.8.3, 5.9.3, 5.13, Cross-Chapter Box SLR in Chapter 3}
The occurrence and distribution of pests, weeds and diseases, 
including zoonoses, in agricultural, forest and food systems 
(terrestrial and aquatic) will be altered, and their control will 
become costlier  (medium confidence). Changes in the rates of 
reproduction and distribution of weeds, insect pests, pathogens and 
disease vectors will increase biotic stress on crops, forests and livestock, 
and will increase the risk of biodiversity loss and ecosystem degradation 
(medium evidence, high agreement). Risks will increase for climate-
driven emerging zoonoses (medium evidence, high agreement). {5.4.1.3, 
5.9.4, Cross-Chapter Box MOVING PLATE this chapter}
Forest production systems will have variable responses to 
climate change across regions, with negative effects being more 
predominant in tropical forests (high confidence). In temperate and

boreal regions, some productivity gains are projected, but tree mortality 
will increase in some areas (high confidence). In tropical forests, change 
in species composition and forest structure will lower production 
(medium confidence). Some models project a possible increase in global 
wood supply and lowering of average wood prices, but they do not 
account for the negative impacts of extreme events and thus possibly 
overestimate the wood supply (medium confidence). {5.6.2}
Climate change will negatively impact food safety (high con-
fidence). Higher temperatures and humidity will favour toxigenic fungi, plant and animal-based pathogens, and harmful algal blooms (HABs) 
(high confidence).  More frequent  and intense  flood events and in-
creased melting of snow and ice will increase food contamination (high 
confidence). Incidence and severity of HABs and water-borne diseases 
will increase, as will indirect effects from infrastructure damage during

extreme events (high confidence). {5.4.3, 5.5.2.3, 5.8.1, 5.8.2, 5.8.3, 
5.9.1, 5.11.1, 5.11.3, 5.12.3; Cross-Chapter Box ILLNESS in Chapter 2}
Many autonomous adaptation options have been implemented 
in both terrestrial and aquatic systems, but on-farm adaptations 
are insufficient to meet Sustainable Development Goal (SDG) 2 
(high confidence). Autonomous responses include livestock and farm 
management, switching varieties/species and altered timing of key 
farm activities such as planting or stocking (high confidence). However, 
because of limited adaptive capacities and non-climatic compounding 
drivers of food insecurity, SDG2 will not be met (high confidence). 
{Table 5.1, 5.4.4; 5.5.4, 5.9.4, 5.10.4; 5.12.4}
Various adaptation options are currently feasible and effective 
at reducing climate impacts in different socio-cultural, economic 
and geographical contexts (high confidence), but some lack 
adequate economic or institutional feasibility or information on

limits (medium confidence). Feasible and effective options include 
cultivar improvements, community-based adaptation, agricultural 
diversification, climate services, adaptive eco-management in fisheries 
and aquaculture. There is limited evidence, medium agreement on the 
institutional feasibility or cost effectiveness of adaptation activities, 
and the limits to such adaptations. {5.4.4, 5.5.4, 5.6.3, 5.8.4, 5.9.4, 
5.10.4, 5.11.4, 5.12.4, 5.14.1}
Ecosystem-based approaches such as diversification, land 
restoration, agroecology and agroforestry have the potential 
to strengthen resilience to climate change with multiple co-
benefits, but trade-offs and benefits vary with socio-ecological 
context (high confidence). Ecosystem-based approaches support 
long-term productivity and ecosystem services such as pest control, 
soil health, pollination and buffering of temperature extremes (high 
confidence), but potential and trade-offs vary by socioeconomic context,

ecosystem zone, species combinations and institutional support 
(medium confidence). {5.4.4.4, 5.6.3, 5.10.4, 5.14.1, Cross-Chapter 
Box NATURAL in Chapter 2; Cross-Working Group Box BIOECONOMY 
this chapter}
Bio-based products as part of a circular bioeconomy have potential 
to support adaptation and mitigation, with sectoral integration, 
transparent governance and stakeholder involvement key to 
maximising benefits and managing trade-offs (high confidence). 
A sustainable bioeconomy relying on bioresources will need to be 
supported by technology innovation and international cooperation 
and governance of global trade to disincentivise environmental and 
social externalities (medium confidence). {Cross-Working Group 
Box BIOECONOMY this chapter}
Sustainable resource management in response to distribution 
shifts of terrestrial and aquatic species under climate change is

5
719Food, Fibre and Other Ecosystem Products  Chapter 5
an effective adaptation option to reduce food and nutritional 
risk, conflict and loss of livelihood (medium confidence). Adaptive 
transboundary governance and ecosystem-based management, livelihood 
diversification, capacity development and improved knowledge-sharing 
will reduce conflict and promote the fair distribution of sustainably 
harvested wild products and revenues (medium confidence). Other 
options include shared quotas and access rights considering trade-offs, 
shifting livelihoods to follow target species, new markets for emerging 
species, and technology {Cross Chapter Box MOVING PLATE this chapter, 
5.8.4, 5.14.3.4}
Implemented adaptation in crop production will be insufficient to 
offset the negative effects of climate change (high confidence). 
Currently available management options have the potential to 
compensate global crop production losses due to climate change up

to ~2°C warming, but the negative impacts even with adaptation 
will grow substantially from the mid-century under high temperature 
change scenarios (high confidence). Regionally, the negative effects 
will prevail sooner where current temperatures are already higher as 
in lower latitudes (high confidence). {5.2.2, 5.4.3, 5.4.4, 5.8.4, 5.9.4, 
5.14.2.4}
Supportive public policies will enhance effectiveness and/or 
feasibility of adaptation in ecosystem provisioning services 
(medium confidence). Policies that support system transitions 
include shifting subsidies, removing perverse incentives, regulation 
and certification, green public procurement, investment in sustainable 
value chains, support for capacity-building, access to insurance 
premiums, payments for ecosystem services, and social protection, 
among others (medium confidence). {5.4.4.3; 5.4.4.4; 5.10.4.4; 
5.12.6; 5.13.4; 5.14.1.3; 5.14.2.4; Box  5.13, Cross-Working Group 
Box BIOECONOMY in Chapter 2}

Harnessing youth innovation and vision alongside other SDGs 
such as gender equity, Indigenous knowledge, local knowledge, 
and urban and rural livelihoods, will support effective climate 
change adaptation to ensure resilient economies in food 
systems (high confidence). Adaptation strategies that address 
power inequities lead to co-benefits in equity outcomes and resilience 
for vulnerable groups (medium confidence). Indigenous knowledge 
and local knowledge facilitate adaptation strategies for ecosystem 
provisioning, especially when combined with scientific knowledge using 
participatory and community-based approaches (high confidence). 
{5.4.4.3, Table 5.6, 5.6.3, 5.8.4, 5.9.2, 5.9.4.1, 5.9.5, 5.10.2.2, 5.12.7, 
5.12.8, 5.13.4, 5.13.5, 5.14.1.1, 5.14.1.2, 5.14.1.4,5.14.2.1, Box 5.13, 
5.14.2.2 }
Policy decisions related to climate change adaptation and 
mitigation that ignore or worsen risks of adverse effects 
for different groups and ecosystems increase vulnerability,

negatively affect capacity to deal with climate impacts, and 
impede sustainable development (medium confidence with 
robust evidence, medium agreement) . Lacking sufficient stakeholder 
participation, large-scale land acquisitions have had mostly negative 
implications for vulnerable groups and climate change adaptation (high 
confidence). Policy and programme appraisal of adaptation options 
that consider the risks of adverse effects across different groups at different scales and use inclusive rights-based approaches help avoid 
maladaptation (medium confidence). Successful forest adaptation 
involves recognition of land rights and cooperation with Indigenous 
Peoples and other local communities who depend on forest resources 
(high confidence). {5.6.3; 5.12.3, 5.13.1; 5.13.2; 5.14.2.1}
Financial barriers limit implementation of adaptation options in 
agriculture, fisheries, aquaculture and forestry, and vastly more 
public and private investment is required (high confidence).

Public-sector investment in adaptation of agriculture, forestry and 
fisheries has grown four-fold since 2010, but adaptation costs will be 
much higher to meet future adaptation needs (medium confidence). 
Expanding access to financial services and pooling climate risks will 
enable and incentivise climate change adaptation (medium confidence). 
{5.14.3, 5.14.5., Cross-Chapter Box FINANCE in Chapter 17}
Climate resilient development pathways offer a way forward to 
guide climate action in food system transitions, but operationa-
lisation is hampered by limited indicators and analyses (medium 
confidence). Robust analyses are needed that detail plausible path-
ways to move towards more resilient, equitable and sustainable food 
systems in ways that are socially, economically and environmentally 
acceptable through time (high confidence). Appropriate monitoring and 
rapid feedback to food system actors will be critical to the success of

many current and future adaptation actions (high confidence). {5.14.4}

5
720Chapter 5 Food, Fibre and Other Ecosystem Products
5.1 Introduction
5.1.1 Scope of the Chapter
This chapter assesses the scientific literature produced after the 
Intergovernmental Panel on Climate Change (IPCC) 5th Assessment 
Report (AR5) dealing with past, current and future climate change 
effects on managed ecosystems that provide provisioning and cultural 
services. It spans low- and high-intensity production systems for food, 
feed, fibre and other ecosystem products.
Climate change has already had global impacts, including in high-
income countries. Special emphasis is placed on the assessment of 
vulnerabilities of particular groups that are context- and location-
specific, such as Indigenous Peoples and other minorities, women and 
small-scale food producers. The report builds on the IPCC AR5 and 
recent Special Reports. This chapter combines food systems, fibre, wood 
and other products from ecosystems previously detailed in separate

chapters of AR5, with an increased focus on ecosystem services, 
including the long-term sustainability of the global food system 
(Figure 5.1). The chapter focuses on key climate risks, implementation 
and outcomes of adaptation solutions for different groups as well as 
limits to adaptation.
5.1.2  Starting Point: AR5 and Recent IPCC Special Reports
AR5 Chapter 7 (Porter et  al., 2014) reported with high confidence 
that food production systems were being negatively impacted by 
climate change, including both terrestrial and aquatic food species 
(Porter et al., 2014). Increased temperatures will have large negative 
impacts on the food production system under 2°C warming by late 
20th century, with temperatures exceeding 4°C posing even greater 
risk to global food security (Porter et al., 2014). Adaptation options are 
needed to reduce the risk from climate change, but there was limited 
information of their effectiveness.

The 1.5°C Special Report concluded that climate-related risks to food 
security will rise under 1.5°C and will increase further under 2°C 
or higher. Above 1.5°C, currently available adaptation options will 
be much less effective and site-specific limits to adaptation will be 
reached for vulnerable regions and sectors. There was high confidence 
that limiting warming to 1.5°C will result in smaller net reductions in 
yields of major crops affecting food availability and nutrition, and that 
rising temperatures will adversely affect livestock via changes in feed 
quality, fertility, production, spread of diseases and water availability.
The IPCC Special Report on Climate Change and Land (SRCCL) expanded 
beyond the 1.5°C report to provide more in-depth information on 
climate change interactions with food security, desertification and 
degradation. There was high confidence that climate risks, both for 
slow changes and extreme events, are interlinked with ecosystem

services, health and food security, often cascading and potentially 
reinforcing effects. Climate change already affects all dimensions of 
food security, namely availability, access, utilisation and stability, by 
disrupting food production, quality, storage, transport and retail. These 
effects exacerbate competition for land and water resources, leading to increased deforestation, biodiversity reduction and loss of wetlands. 
With high certainty, limiting global warming would lower future 
risks related to land, such as water scarcity, fire, vegetation shifts, 
degradation, desertification and food insecurity and malnutrition, 
particularly for those most vulnerable today: small-scale food 
producers in low-income countries, Indigenous communities, women, 
and the urban poor. SRCCL assessed a range of adaptation pathways 
to increase food resilience.
The IPCC Special Report on the Ocean and Cryosphere in a Changing 
Climate (SROCC) identified climate change impacts of warming,

deoxygenation and acidification of the ocean and reductions in snow, 
sea ice and glaciers as having major negative impacts on fisheries and 
crops watered from mountain runoff and agriculture. These impacts 
affect food provisioning of food and directly threaten livelihoods and 
food security of vulnerable coastal communities and glacier-fed river 
basins. Climate change impacts on fisheries will be particularly high in 
tropical regions, where reductions in catch are expected to be among 
the largest globally, leading to negative economic and social effects 
for fishing communities and with implications for the supply of fish 
and shellfish (high confidence). While specific impacts will depend on 
the level of global warming and mitigative action to improve fisheries 
and aquaculture management, some current management practices 
and extraction levels may not be viable in the future.
5.1.3 Chapter Framework
This chapter is taking a food systems approach similar to the food

security chapter in SRCCL (Mbow et al., 2019), with close attention to 
food system linkages, interactions and impacts on ecosystem services 
and biodiversity (Steffen et al., 2015; Raworth, 2017; Gerten et al., 2020). 
Food security is defined as “a situation that exists when all people, at 
all times, have physical, social and economic access to sufficient, safe 
and nutritious food that meets their dietary needs and food preferences 
for an active and healthy life” (FAO, 2020). Food insecurity is often 
experienced as chronic hunger reported in the annual UN Food Security 
and Nutrition in the World (FAO, 2020), when a person is unable to 
consume enough food over an extended period. The chapter gives special 
attention to climate change impacts on acute food insecurity, which can 
occur at any time with a severity that threatens lives, livelihoods or both, 
regardless of the causes, context or duration, as a result of shocks risking

determinants of food security and nutrition, and used to assess the need 
for humanitarian action (IPC Global Partners, 2019).
Climate change directly affects food systems, and the impacts on 
terrestrial or aquatic food production will become increasingly negative, 
although regionally some changes may be beneficial in the near future 
(Porter et al., 2014). Current food system trajectories are leading to 
biodiversity loss and land and aquatic ecosystem degradation without 
delivering food security, nutrition, and sustainable and healthy 
livelihoods to many (Steffen et al., 2015). Addressing climate change 
in isolation ignores these interconnections, which is why the chapter 
considers integrated adaptation solutions to allow humanity to thrive 
in the long term. At the same time, social foundations of equality, 
justice and political participation are crucial in order to move towards 
a safe operating space for humanity (Raworth, 2017). The SDGs

5
721Food, Fibre and Other Ecosystem Products  Chapter 5
provide the most comprehensive set of metrics of humanity’s progress 
in achieving equitable and thriving socio-ecological systems. Therefore, 
while the focus of this chapter is climate change impacts, vulnerability 
and adaptation of food systems, feed, fibre and other ecosystem 
products, other environmental and social challenges are considered 
concomitantly.
Food system and natural systems interact via political, economic, 
social, cultural and demographic factors in complex ways, leading 
to food security and sustainability outcomes. The food system has a 
supply (production) and demand (consumption) side, connected via 
processing, trade and retail, with loss and waste streams all along the 
food chain. Natural ecosystems provide multiple services (regulating, supporting, provisioning, cultural) to the food system. Food security 
and nutrition strongly depend on the driving forces connecting food

and natural systems while at the same time positively or negatively 
influencing them. Climate change frequently exacerbates the effects of 
other drivers of change, further limiting the environment within which 
humanity can safely operate and thrive. The chapter assesses how 
climate change affects the four pillars of food security and nutrition 
and how these effects can be mediated by various factors, including our 
adaptation responses, social equity, underlying ecosystem services and 
governance (Figure 5.1). Adaptation solutions are a major emphasis 
of this chapter, including many ecosystem-based adaptation options 
(Table  5.1), which fall under the broader umbrella of nature-based 
solutions (Seddon et al., 2020).Adaptation and mitigation
Climate resilient development pathwaysCollective resource
managementLand
restorationGenetic
improvementsClimate
diversificationPolicy and
planningBio-base
productsLivelihood
diversificationGender
equity
Youth
innovationAnimal
feedsDietary

choice
Early warning
systemsCommunity
basedSpecies
diversificationAlternative
energiesTechnology
improvementsBest
practicesWaste and
tradeMixed
productionKnowledge systems
(Indigenous, local)
Sustainable Development GoalsGreenhouse
gas emissions
Maladaptation
Barriers
Trade-offsConceptual framework of Chapter 5
Climate change drivers
Rising
temperaturesAltered
precipitationAcidification
increaseOxygen
decreaseSea level
riseExtreme
eventsIncreased storms
and cyclonesIncreased climate
variabilityDroughts
 and floods
Food, fibre and other ecosystem products
Climate change impacts
Air, soil and
water qualityCrop and
animal
yieldsGrassland 
qualityTree
mortalityDistribution
shiftsAltered
phenologyPlant and 
animal
fertilitySuitable
habitatFood
nutritional
qualityHeat
stressPests and
pollinatorsBiodiversity
lossEcosystem
Food
availabilityFood
securityLivelihoods Land use
competitionVulnerabilit y
of marginal
groups Hunger Mal-
nutritionHeat stress 
for
workersFood

pricesFood
safetyRisks for people
Diet-related
mortality
Figure 5.1 |  Conceptual framework of Chapter 5.

5
722Chapter 5 Food, Fibre and Other Ecosystem Products
Table 5.1 |  Adaptation strategies assessment in food, fibre and other ecosystem provisioning services.
Adaptation strategies/options Systems Benefits Constraints or enablers Confidence Relevant sections
 –Ecosystem-based integrated 
approaches such as agroecology 
that increase soil organic 
matter, enhance soil and water 
conservation, and diversify food 
production systems
 –Certain types of urban agricultureCrops –Improve resilience of food systems
 –Provide mitigation measures and 
co-benefits in health, ecosystem 
services and other SDGs
 –Improve productivity and yield 
stabilitySecure tenure arrangements are 
often critical for delivering successful 
ecosystem-based adaptationHigh(5.4.4.5, 
5.6.3, 5.12.3, 
Cross-Chapter 
Box NATURAL 
in Chapter 2, 
5.14.3.6, 5.14.3.11; 
Box HEALTH in 
Chapter 7)
 –Increasing agroecosystem 
diversification through-expanding 
crop, animal, fish and other species

genetic diversity-varying spatial and 
temporal arrangements including 
mixed planting, crop rotations, 
integrated crop, livestock and 
agroforestry systemsCrops, 
livestock, 
aquaculture, 
mixed, 
systems –Increase resilience, productivity, 
and sustainability of farming 
systems under climate changePolicies and technologies that support 
diversification at landscape and 
farm levels: programmes that reward 
farmers for diversification practices, 
reduced incentives for intensified 
monocultures, extension support 
and market infrastructure for diverse 
crops, and productivity research on a 
greater variety of crops with support 
for post-harvest processing and 
regional marketsHigh(5.4.4.4, 5.14.3.1, 
5.14.3.6)
 –Changing the relative emphasis on 
crops and livestock
 –Changing crop varieties and 
livestock breeds and speciesCrops–
livestock 
mixed system 
particularly 
in the tropics 
and subtropics –Increase resilienceGender inequalities can act as a risk

multiplierMedium (5.5.4; 5.10.4)
 –Indigenous and local knowledge 
including participatory plant 
breeding or community-based 
adaptationCrops, forestry, 
fisheries –Increase resilience and 
sustainability of food, fibre, 
forest and small-scale fisheries 
productionIndigenous knowledge and 
local knowledge can facilitate 
adaptation when combined with 
scientific knowledge and utilised in 
management regimesHigh(5.4.4.5, 5.6.3, 
5.14.3)
 –Land restoration
 –Agroforestry
 –Silvo-pastureForestry  –Improve resilience and productivityPartnerships between key 
stakeholders such as researchers, 
forest managers, and Indigenous and 
local forest dependent communities 
will facilitate sustainable forest 
managementMedium (5.6.3)
 –Improved management practices 
that consider fish stocks and 
the ecosystem (ecosystem-
based management, adaptive 
management, co-management, 
adaptive eco-management, and 
active adaptive management)
 –Adopting complementary 
productive activities to reduce

economic dependence on fisheries
 –Developing capacity
 –Improving information flows 
in adaptive co-management 
transboundary resource 
 –Gear or vessel modificationsFisheries –Promote sustainable harvesting 
and fair distribution of wild fish 
products and revenues
 –Proactive dynamic fisheries 
management and diversification 
based on scientific, Indigenous 
and local knowledge will facilitate 
adaptive fisheries planning and 
reduce conflict (national and 
international) over resourcesMedium(5.14.3.4; 
Box MOVING PLATE 
this chapter)
 –Adaptation options that incorporate 
ecological knowledge and risk into 
management decisions in the near 
and long termAquaculture –Enhance sustainable aquaculture 
productionGovernance that recognises 
unexploited biological and 
socioeconomic food system synergies 
and equity would lead to positive 
adaptation strategy development and 
implementation, but options may be 
limited for those most at risk due to

5
723Food, Fibre and Other Ecosystem Products  Chapter 5
Ecosystem-based adaptation, defined as the ‘use of ecosystem 
management activities to increase the resilience and reduce the 
vulnerability of people and ecosystems to climate change’ (Campbell 
et al., 2009), has at its core the recognition that there are unexploited 
synergies in agricultural systems that can increase productivity and 
resilience. These can result from increasing biodiversity, adding organic 
matter to soils, integrating livestock and aquatic species, including 
aquaculture, into farming practices, broadening landscape practices to 
exploit crop–forestry synergies, supporting beneficial insect populations 
and altering pest management practices that have unintended negative 
consequences. In addition, the chapter considers socioeconomic 
strategies to build resilience in the food system, strengthening local and regional economies, building on Indigenous and local knowledge,

and addressing social inequity, through inclusive, participatory and 
democratic governance of food systems (HLPE, 2019; Wezel et al., 2020).
5.2 Observed Impacts and Key Risks
5.2.1 Detection and Attribution of Observed Impacts
Detection and attribution of climate change impacts on the food system 
remain challenging because many non-climate drivers are involved 
(Porter et  al., 2014) but have been improved by recently developed Adaptation strategies/options Systems Benefits Constraints or enablers Confidence Relevant sections
 –Effective linkage of freshwater 
aquatic food provisioning 
management to the adaptation 
plans of other water-using sectors, 
considering trade-offs of production 
with community nutritional needsFreshwater 
fisheries and 
aquaculture 
systems –Reduce the risk of food insecurity 
and livelihood loss for those reliant 
on freshwater for inland fisheries 
and aquacultureChanging precipitation patterns 
will increase competition for limited

freshwater suppliesMedium (5.8.4, 5.9.4.)
 –Agricultural production systems that 
integrate crops, livestock, forestry, 
fisheries and aquacultureMixed system –Increase food production per unit 
of land
 –Reduce climate risks
 –Reduce GHG emission
 –Confer buffering capacity
 –Increasing household resilience, 
though the benefits and challenges 
depend on local contextUncertainties exist concerning the 
scalability of integrated systems; 
their uptake faces particular 
barriers around risk, land tenure, 
social inclusion, information and 
management skill, and the nature and 
timing of benefit flowsHigh (5.10.4)
 –Investments in improved humidity 
and temperature control in storage 
facilities for perishable items, and 
changes in public policy that control 
international trade and domestic 
market transactionsPost-harvest –Improve food utilisation and 
access and thereby resilience to 
climate changeThe extent to which adaptation 
activities beyond harvest are

cost-effective, and the limits to such 
adaptation, are location-specific and 
largely unknownMedium (5.11.4)
 –Integrated multi-sectoral food 
system adaptation approaches 
that address food production, 
consumption and equity issues
 –Nutrition and gender-sensitive 
agriculture programmes, adaptive 
social protection and disaster risk 
management are examplesProduction 
and 
post-harvest –Protect vulnerable groups against 
livelihood risks
 –Enhance responsiveness to 
extreme eventsDifferentiated responses based on 
food security level and climate risk 
can be effectiveMedium (5.12.4)
 –Rights-based approaches, including 
legislation, gender transformative 
approaches to agriculture, 
recognition of rights to land, seeds, 
fishing areas and other natural 
resources, and community-based 
adaptationProduction 
and 
post-harvest –Improved food security and 
nutrition for marginalised groups
 –Increased resilience through 
capacity-building of marginalised 
groups

–Address questions of access to 
resources for marginalised groupsFocus on meaningful participation 
in governance, design and 
implementation of adaptation 
strategies of those groups who are 
vulnerable, including gender. Can 
be conflicts and trade-offs, such as 
between addressing land rights or 
traditional fishing groundsMedium (5.12.4)
 –Climate services Production –Can support decision makers in 
agriculture by providing tailored 
information that can inform 
the implementation of specific 
adaptation optionsFor some high- and medium-income 
countries, evidence suggests 
that climate services have been 
underutilised. In low-income 
countries, use of climate services 
can increase yields and incomes 
and promote changes in farmers’ 
practices, but low confidence that 
climate services are delivering on their 
potential, whether they are being 
accessed by the vulnerable, and how 
these services are contributing to food 
security and nutritionMedium (5.14.1)

5
724Chapter 5 Food, Fibre and Other Ecosystem Products
climate model outputs tailored for impact attribution (Iizumi et  al., 
2018; Moore, 2020; Ortiz-Bobea et al., 2021).
Climate change has caused regionally different, but mostly negative, 
impacts on crop yields and quality and marketability of products (high 
confidence) (see Section 5.4.1 for observed impacts). There is medium 
evidence and high agreement that the effects of human-induced 
climate warming since the pre-industrial era has had significantly 
negative effects on global crop production, acting as a drag on the 
growth of agricultural production (Iizumi et  al., 2018; Moore, 2020; 
Ortiz-Bobea et al., 2021). One global study using an empirical model 
estimated the negative effect of anthropogenic warming trends from 
1961 to 2017 to be on average 5.3% for three staple crops (5.9% 
for maize, 4.9% for wheat and 4.2% for rice) (Moore, 2020). Another 
study using a process-based crop model found a yield loss of 4.1%

(0.5–8.4%) for maize and 4.5% (0.5–8.4%) for soybean between 
1981 and 2010 relative to the non-warming condition, even with 
CO 2 fertilisation effects (Iizumi et al., 2018). Human-induced warming 
trends since 1961 have also slowed down the growth of agricultural 
total factor productivity by 21% (Ortiz-Bobea et al., 2021). Regionally, 
heat and rainfall extremes intensified by human-induced warming in 
West Africa have reduced millet and sorghum yields by 10–20%, and 
5–15%, respectively (Sultan et al., 2019).
Methane emissions significantly impact crop yields by increasing 
temperatures as a greenhouse gas (GHG) and surface ozone 
concentrations as a precursor (medium confidence) (Shindell, 2016; 
Van Dingenen, 2018; Shindell et al., 2019). Shindell (2016) estimated a 
net yield loss of 9.5±3.0% for four major crops due to anthropogenic 
emissions (1850–2010), after incorporation of the positive effect of 
CO 2 (6.5±1.0%) and the negative effects of warming (10.9±3.2%) and

tropospheric ozone elevation (5.0±1.5%). Although these estimates 
were not linked with historical yield changes, more than half of the 
estimated yield loss is attributable to increasing temperature and 
ozone concentrations from methane emissions, suggesting the 
importance of methane mitigation in alleviating yield losses (medium 
confidence) (Section 5.4.1.4).
Climate change is already affecting livestock production (high 
confidence) (Section 5.5.1). The effects include direct impacts of heat 
stress on mortality and productivity, and indirect impacts have been 
observed on grassland quality, shifts in species distribution and range 
changes in livestock diseases (Sections 5.5.1.1–5.5.1.3). Quantitative 
assessment of observed impacts is still limited.
In aquatic systems, more evidence has accumulated since AR5 on 
warming-induced shifts (mainly poleward) of species (high confidence) 
(Section  5.8.1, Cross-Chapter Box  MOVING PLATE this chapter),

causing significant challenges for resource allocation between 
different countries and fishing fleets. Quantitative assessments of 
climate change impacts on production are still limited, but Free et al. 
(2019) estimated a 4.1% global loss of the maximum sustainable yield 
of several marine fish populations from 1930 to 2010 due to climate 
change. The effects of climate change on aquaculture are apparent 
but diverse, depending on the types and species of aquaculture (high 
confidence) (Section  5.9.1). Temperature increases, acidification, salt 
intrusion, oxygen deficiency, floods and droughts have negatively impacted production via reduced growing suitability, mortalities or 
damages to infrastructure (Section 5.9.1).
The impacts of climate change on food provisioning have cascading 
effects on key elements of food security, such as food prices, 
household income, food safety and nutrition of vulnerable groups 
(Peri, 2017; Ubilava, 2018; 5.11, 5.12). Climate extreme events are

frequently causing acute food insecurity (Section 5.12.3, FSIN, 2021). 
There is growing evidence that human-induced climate warming 
has amplified climate extreme events (Seneviratne et al., 2021), but 
detection and attribution of food insecurity to anthropogenic climate 
change is still limited by a lack of long-term data and complexity of 
food systems (Phalkey et al., 2015; Cooper et al., 2019). A recent event 
attribution study by Funk (2018) demonstrated that anthropogenic 
enhancement of the 2015/2016 El Niño increased drought-induced 
crop production losses in Southern Africa. Human-induced warming 
also exacerbated the 2007 drought in southern Africa, causing 
food shortages, price spikes and acute food insecurity in Lesotho 
(Verschuur et al., 2021).Cascading impacts of climate hazards 
on food and nutrition
Climatic
hazardsMore frequent and intensi/f_ied 
heat/droughts, /f_loods
Reduced
soil moisture 
& health
Labour 
capacity
Access to 
foodFood
availability ,

consumption
+utilizationHousehold 
income
Quality
of life
Food yield 
and quality 
losses
Food prices
Malnutri-
tionInfectious 
diseasesDrought Heat
Food
system
Figure 5.2 |  Cascading impacts of climate hazards on food and nutrition. 
The factors involved the impacts on crop production and prices (black arrows) and 
interaction among food-health interaction (white arrows). Adapted and revised from 
(Phalkey et al., 2015).

5
725Food, Fibre and Other Ecosystem Products  Chapter 5
5.2.2 Key Risks
Key risks in this chapter are grouped into those related to food 
security, food safety and dietary health, livelihoods of people in 
related sectors and ecosystem services (Table  16.9). Determining 
when a risk is considered severe is challenging to quantify because 
of the complexity of the food system, uncertainty about the effects 
and ethical challenges.
Current levels of food insecurity are already high in some parts of 
the world, and often exacerbated by short-term food shortages and 
price spikes caused by weather extremes partly linked to climate 
change (Sections 5.2.1, 5.12.3, 16.5.2). Climate change will increase 
malnourished populations through direct impacts on food production 
and have cascading impacts on food prices and household incomes, 
all of which will reduce access to safe and nutritious food (high 
confidence) (Figure 5.2, 5.12).

Extreme climate events will become more frequent and force some of 
the current food production areas beyond the safe climatic space for 
production (high confidence) (Sections 5.4.3, 5.5.2). Globally, 10% of 
the currently suitable area for major crops and livestock is projected 
to be climatically unsuitable in mid-century and 31–34% by the end 
of the century under SSP5-8.5 (Kummu et al., 2021). Adverse effects 
of climate change on food production will become more severe when 
global temperatures rise by more than 2°C (Sections 5.4.4.1, 5.12.4.1). 
One study estimated that the heat stress from projected 3°C warming 
above baseline (1986–2005) would reduce labour capacity by 30–50% 
in Sub-Saharan Africa and Southeast Asia, leading to a 5% increase 
in crop prices because of higher labour cost and production losses, 
thereby undermining food availability, access and livelihood (de Lima 
et  al., 2021). Thiault et  al. (2019) projected that, by 2100, climate

change under RCP8.5 could have negative impacts on both agriculture 
and marine fisheries productivity in countries where 90% of the world 
population live. A global analysis of shellfish aquaculture estimated 
that habitat suitability will decline beyond 2060 globally, but much 
sooner in some Asian countries (Stewart-Sinclair et al., 2020; 5.9.1). 
These negative effects in the second half of the century will be much 
less under RCP2.6.
Climate change impacts will increase the number of people at risk of 
hunger, in 2050 ranging from 8 million people under SSP1 to 80 million 
people under SSP3 scenarios (RCP6.0), compared with a world with no 
climate change (Mbow et al., 2019). Estimates also vary depending on 
the adaptation and mitigation assumptions (Hasegawa et  al., 2018; 
Janssens et al., 2020). Geographically, nearly 80% of the population at 
risk of hunger are projected to reside in Africa and Asia (Nelson et al.,

2018). Projections of risk of hunger beyond 2050 are limited, but it will 
grow from the mid-century towards the end of the century, with more 
people at risk under RCP8.5 compared with RCP4.5 (Richardson et al., 
2018). Regional disparity is projected to increase, particularly under a 
high-emission scenario.
Climate change will increase the costs and management challenges 
of providing safe food. The safety challenges arise from contamination 
caused by increased prevalence of pathogens, HAB and toxic inorganic 
bioaccumulation (high confidence) (Sections  5.8, 5.9, 5.11, 5.12). Micronutrient deficiency is prevalent across many regions and will 
continue to be a problem at least during the first half of the century 
(Nelson et al., 2018), with significant implications for human health 
(Section 5.12.4).
Food security and healthy balanced diets will also be undermined 
by reduced livelihoods and health of people in agriculture and food-

related sectors (Sections  5.12.3, 5.12.4), diminished ecosystem 
services provided by pollinators, the soil biome (Section  5.4.3) and 
water systems, and climate-mitigation related policies that solely focus 
on reducing GHG emissions without considering their potential to 
increase competition with food production for scarce land and water 
(Section 5.13.3).
5.3 Methodologies and Associated 
Uncertainties
Chapter text draws on previous IPCC reports, other reports (i.e., High 
Level Panel of Experts (HLPE), Food and Agriculture Organization (FAO), 
and Intergovernmental Science-Policy Platform on Biodiversity and 
Ecosystem Services (IPBES)), and literature published since 2014. This 
section highlights key trends in research topics and methods since AR5.
5.3.1 Methodologies for Assessing Impacts and Risks
Since AR5, there are more examples of observed impacts from past 
climate change in cropping systems (Section 5.4.1), pastoral systems

(Section  5.5.1), forests (Section  5.6.1), fisheries (Section  5.8.1) and 
mixed farming systems (Section 5.10.1). These assessments of observed 
impacts make use of historical data on climate, production area and 
yield to attribute the role of climate in driving changes in suitability, 
production, yield, food quality or total factor productivity (Ortiz-Bobea 
et al., 2021). Observations across the global food systems have been 
analysed (Cottrell et al., 2019), with the advantage that unexpected 
impacts due to changes in seasonality and biotic interactions can be 
detected. Quantitative analysis is only possible in places with adequate 
historical data; in many cases, studies rely on qualitative assessments, 
often drawing on farmers’ perceptions of climate impacts.
Projecting future climate impacts relies on modelling that combines 
climate data with data from experimental studies testing how species 
respond to each climate factor. In cropping and forest systems, a

network of experimental studies with plants exposed to elevated CO 2 
concentrations, ozone and elevated temperature provides data on the 
fundamental responses to climate and atmospheric conditions (i.e., 
free-air carbon dioxide enrichment (FACE) and temperature free-air 
controlled enhancement (T-FACE) systems). FACE results have been 
combined and assessed more extensively since AR5 (Bishop et  al., 
2014; Haworth et al., 2016; Kimball, 2016; Ainsworth and Long, 2021). 
Field-based FACE studies have several advantages over more enclosed 
testing chambers, although results from more controlled experiments 
and coordination between different methods continue to give new 
insights into crop responses to climate change and variability (Drag 
et al., 2020; Ainsworth and Long, 2021; Sun et al., 2021). Experimental 
results have limitations and can be difficult to scale up (Porter

5
726Chapter 5 Food, Fibre and Other Ecosystem Products
et  al., 2014; Haworth et  al., 2016), but generally the conclusions 
follow known plant responses (Lemonnier and Ainsworth, 2018). As 
highlighted in AR5, there is a scarcity of FACE infrastructure in the 
tropics and subtropics (Leakey et al., 2012; Lemonnier and Ainsworth, 
2018; Toreti et al., 2020). One area that has been investigated further 
is the negative impact of elevated CO 2 on crop nutritional value, 
which has important implications for human nutrition (Scheelbeek 
et al., 2018; Smith and Myers, 2018; Toreti et al., 2020; Ainsworth and 
Long, 2021). Increasingly, experimental studies seek to examine the interaction between climatic factors such as temperature, drought and 
ozone, or the responses of understudied food systems, crop species, 
cultivars and management interventions (Kimball, 2016; Ainsworth 
and Long, 2021). The use of experimental data to improve projections

has also expanded in other systems. There has been an increased 
focus on the impact of warming on livestock health and productivity 
(5.5.3).  Aquatic system studies have incorporated projected impacts 
on physiology, distribution, phenology and productivity (5.8.3).Table 5.2 |  A comparison of modelling approaches and their application in climate change impact projections. Model types are categorised by: food system, with labels representing 
the food systems from this chapter where each model type is used ({CROP}, {TREE}, {LIVES}, {FISH}, {MIX}, {FOOD}); scale over which each model type is usually applied 
local [()], regional [(  )], global [(    )], or a combination of these); and sensitivity to climate change where the colour intensity indicates the ability of each model type to incorporate 
each of the listed factors. After Van Wijk et al. (2014), Kanter et al. (2018) and Thornton (2018). Integrated assessment models are discussed in the main text.

Description Applications for each food-system ScaleSensitivity to climate change 
factors and responses
Climate
CO 2
Biotic
System responsesEmpirical
Agroclimatic 
indicesUse simple equations to link agricultural performance 
to key climate factors, such as drought or heat 
stress, or summarise agricultural requirements using 
multiple environmental descriptors.Comparing regions; matching crops to regions; early 
warning systems: e.g Agro-ecological zones, Ecocrop, 
Palmer Drought Severity Index {CROP}.((  ))Statistical 
modelsUse quantitative associations between agricultural 
performance and climate, based on past 
observations. Can include projections for biotic 
factors such as pest and disease.Productivity and production area projections; annual 
climate variability; attribution: e.g. Traditional: 
regression, statistical emulators {CROP} {TREE} {LIVES}  
{FISH}; e.g. Spatial suitability models /niche models:

MaxEnt, CLIMEX, Ecocrop {CROP} {TREE} {FISH}.((  ))Process-based (dynamic simulation models)
Vegetation 
focussedUse combinations of land-surface energy and soil 
water balance models to simulate the growth of crop 
species along with natural vegetation, typically using 
plant and crop functional types.Productivity projections; interactions with non-climate 
variables (e.g. CO2): e.g. PEGASUS, Agro-IBIS, DayCent, 
LPJmL, LPJ-GUESS, ORCHIDEE {CROP} {TREE}.(    )Species focussedUse mechanistic models based on the known 
responses of species to key environmental 
descriptors over time. Typically based on detailed 
information for a particular species within a 
region, but also applied to mixed systems such as 
agroforestry and globally.Productivity projections; matching tree species 
to locations; species interactions; interactions 
with non-climate variable s (e.g.CO 2); adaptation 
projections: e.g. point-based versions: APSIM, 
AquaCrop, DayCent, DSSAT, EPIC, Infocrop, SARRA-H,

STICS {CROP} IBIS {TREE} LIVSIM, RUMINANT {LIVES} 
Fish-MIP {FISH} Yield-SAFE, WaNuLCAS, Hi-sAFe {MIX}; 
e.g. global gridded version: pDSSAT, pAPSIM, GEPIC, 
GLAM, MCWLA, PEGASUS, SARRA-O {CROP}.((  ))Integrated Models
Optimization 
methodsMathematical representations of systems with regard 
to key indicators, constraints, and objectives. Allows 
prioritisation of different climate change response 
options using the defined indicators.Adaptation projections; food security projections; 
livelihood projections; trade-offs; live cycle assessment: 
e.g. Global Timber Model {TREE} CSAP toolkit, 
FarmDESIGN {CROP} {MIX} {FOOD}()Economic (Econometric, 
Economic surplus)Used to integrate the broad impacts of climate 
change with other economic drivers, to quantify the 
economic costs and assess the value of adaptation/
mitigation interventions.Adaptation projections; food security projections; 
livelihood projections: e.g. GFPM {TREE} FUND 3.8,

DICE 2010, IMPACT {FOOD}(    )Household and village 
modelsUse detailed site-specific data to generate rules that 
describe the current behaviour of stakeholders such 
as households or villages. Can be integrated with 
other model approaches to consider climate response 
and adaptation interventions.Adaptation projections (case specific); behavioural 
responses; trade-offs; participatory monitoring: e.g. 
DECUMA, PALM, MPMAS, MIDAS, TOA-MD {LIVES} 
{MIX} {FOOD}(())

5
727Food, Fibre and Other Ecosystem Products  Chapter 5
Modelling approaches differ widely and serve different purposes 
(Table 5.2; Porter et al., 2014; Jones, 2017a). The use of process-based and 
statistical modelling alongside remote sensing and other spatial data 
has grown. Projections increasingly draw on a combination of modelling 
approaches and coordinated efforts for model intercomparisons and 
ensemble techniques, using standardised emission scenarios (RCPs). 
For major crops, models of global yield impacts from CO 2 concentration, 
air temperature and precipitation have been refined and compared 
(Challinor et al., 2014; Iizumi et al., 2017; Ruane et al., 2017; Zhao et al., 
2017; Rojas et al., 2019). Despite advances since AR5, modelling is still 
constrained by limited data from field experiments (Ruane et al., 2017). 
Increasingly, studies attempt to incorporate effects of elevated CO 2, 
ozone and climate extremes (Barlow et al., 2015; Schauberger et al.,

2019a; Vogel et  al., 2019), as well as attempts to incorporate more 
complex interactions with soil and crop management (Basso et  al., 
2018; Smith et al., 2020b). However, only a few models consider crop 
protein content and other quality factors (Nuttall et al., 2017; Asseng 
et al., 2019). Some models take account of the impacts of climate on 
the timing of key biological events (phenology) in the target species; 
however, incorporating biotic interactions with pests, pathogens and 
pollinators remains a challenge (Table 5.2; Sections 5.4.1, 5.4.3).
In addition to productivity projections, research also draws on climate 
suitability estimates (Table  5.2). These compare the known climate 
suitability of species and habitats with projected climate conditions 
across different locations. Such projections are useful especially for 
incorporating movement of pests and pathogens but cannot be applied 
in isolation if non-climate constraints are not considered. As different

research groups use different assumptions and data inputs, more 
coordination is needed if suitability projections are to be compared 
globally (SM5.3).
Increasingly, projections look across different disciplines and across 
multiple components of the food system, including livestock, fisheries 
and mixed farming systems (Campbell et al., 2016; Mbow et al., 2019). 
Major timber species have been modelled, with projected impacts 
on productivity, duration of rotation and distribution (i.e., climate 
suitability) (Albert et  al., 2018). Livestock systems are influenced 
by plant productivity projections via their feedstock, for example, 
rangeland cattle impacted by changes in net primary production 
(NPP) (Boone et al., 2018). Direct climate impacts on animals are also 
projected, using indices based on direct observations (Section 5.5.3). 
Since AR5, Fisheries and Marine Ecosystem Model Intercomparison 
Project (Fish-MIP) has allowed for global intercomparisons and

ensemble projections of marine fisheries, and projections capturing 
interactions from multiple food systems (e.g., Inter-Sectoral Impact 
Model Intercomparison Project (ISI-MIP); Sections 5.8, 5.10).
Global simulations have uncovered important differences between 
regions (Deryng et al., 2016; Blanchard et al., 2017). Efforts to coordinate 
and combine regional and global modelling studies allow for greater 
insight into regional differences in climate change impacts, such as 
the Coordinated Global and Regional Assessments (CGRA) performed 
by the Agricultural Model Intercomparison and Improvement Project 
(AgMIP) (Blanchard et al., 2017; Müller et al., 2017; Rosenzweig et al., 
2018; Ruane et al., 2018; Lotze et al., 2019). Increasingly, multi-model 
intercomparisons are used to evaluate global gridded crop models’ performance and sensitivity to temperature, water, nitrogen and CO 2 
within AgMIP , with the focus mostly on major annual crops (Valdivia

et al., 2015; Ruane et al., 2017; Müller et al., 2021a). Differences in 
model type, structures and input data can result in large variation in 
projections, particularly for the response of crops to elevated CO 2 and 
temperature (5.4.3.1); methods for quantifying and minimising this 
uncertainty have been developed, but improvement is still needed ( 
Asseng et al., 2015; Li et al., 2015; Zhao et al., 2017; Folberth et al., 
2019; Tao et al., 2020; Müller et al., 2021a; Ruane et al., 2021). The 
use of multi-model intercomparisons has widened the range of 
uncertainties but has increased the robustness of impact assessments 
(Asseng et al., 2013; Challinor et al., 2014; Zhao et al., 2017). Model 
outputs are strongly influenced by decisions over which factors to 
include; for example, including drought impacts can result in positive 
yield projections switching to neutral or negative values (Gray et al., 
2016; Jin et  al., 2018). Models are also limited in their ability to

incorporate socioeconomic drivers and extreme events (Porter et al., 
2014; Campbell et al., 2016; Ruane et al., 2017; Jagermeyr and Frieler, 
2018; Webber et al., 2018; Schewe et al., 2019).
For long-term projections and integrated assessments, a large 
component of uncertainty remains in the ability to represent 
socioeconomic responses to climate change and the degree to which 
these will mitigate or exacerbate climatic changes (Valdivia et al., 2015; 
Prestele et al., 2016; Arneth et al., 2019). This includes the potential 
adaptation responses of food producers. Models that incorporate 
alternative socioeconomic responses offer one solution (e.g., AgMIP) 
(Nelson et  al., 2014; Von Lampe et  al., 2014; Wiebe et  al., 2015; 
Rosenzweig et al., 2018; van Zeist et al., 2020). Another approach is 
the use of solution-oriented scenarios to compare the effectiveness 
of adaptation options (Le Mouël and Forslund, 2017; Arneth et  al.,

2019), or to quantify the time period in which adaptation responses 
will become essential (Challinor et al., 2016; Rojas et al., 2019). Others 
point to the necessity of managing food systems within the context of 
uncertainty (Campbell et al., 2016).
5.3.2 Methodologies for Assessing Vulnerabilities and 
Methods for monitoring vulnerability and adaptation are under-
researched but have increased since AR5. Increasingly, projections 
move from individual crops to assessing risks across the food systems 
and the relative vulnerability of different systems (Campbell et  al., 
2016; Gil et  al., 2017; Lipper et  al., 2017; Richardson et  al., 2018). 
Adaptation options can be considered as parameters in integrated 
models, such as those used in ISI-MIP , while others use systematic 
assessments of case studies, such as the application of agent-based 
household models to assessments of adaptation in livestock systems

(Section 5.5.4). Quantitative studies are less common than qualitative 
assessments, and there is a need to combine modelling and qualitative 
approaches more effectively (Beveridge et al., 2018a; Vermeulen et al., 
2018).
The food system is dynamic, with changes in management practices 
driven by many factors, including climate adaptation (Iizumi, 2019; 
Iizumi et al., 2021a). Adaptation potential, such as expected advances

5
728Chapter 5 Food, Fibre and Other Ecosystem Products
in crop breeding, are often not explicitly accounted for in modelling 
studies, but more recent studies do quantify the potential for 
adaptation (Iizumi et al., 2017; Tao et al., 2017; Aggarwal et al., 2019; 
Minoli et al., 2019). To account for this complexity, case studies rely on 
data derived from the perception and practices of stakeholders who 
are engaged in adaptation (usually autonomous adaptation) (Hussain 
et al., 2016; Lipper et al., 2017; Ankrah, 2018; Sousa-Silva et al., 2018). 
Case studies use a range of different indicators to monitor climate 
response options, making quantitative comparisons more difficult (Gil 
et al., 2017; Vermeulen et al., 2018). However, systematic comparisons 
have provided valuable insights (Descheemaeker et al., 2018; Shaffril 
et al., 2018; Aggarwal et al., 2019; Bene et al., 2019); for example, the 
sustainable livelihood framework has been applied widely to diverse

aquatic systems (Bueno and Soto, 2017; Barange and Cochrane, 2018) 
and the Livelihood Vulnerability Index is well used across systems 
(Section 5.14). Coordinated efforts such as the AgMIP also provide 
systematic assessments (Blanchard et al., 2017; Lipper et al., 2017; 
Antle et  al., 2018). Nonetheless, the full effectiveness of different 
adaptation options is difficult to assess given that many impacts 
have not yet occurred (due to the cumulative nature of impacts and 
the inertia in the climate system) (Stocker et al., 2013; Zickfeld et al., 
2013).
Transformation of the food system that addresses all dimensions 
of ecosystem services is discussed in this chapter, including risk 
management and the communication of uncertainties (Section 5.14). 
The focus is on flexible approaches to risk and uncertainty, assessing 
trends, drivers and trade-offs under different future scenarios (Campbell 
5.4 Crop-Based Systems

Crops such as cereals, vegetables, fruit, roots, tubers, oilseeds and 
sugar account for about 80% of the dietary energy supply (FAO, 
2019 f). Crops are a significant source of food and income for about 
600 million farms in the world, 90% of which are family farms (Lowder 
et al., 2019). Previous assessment reports focused on yields of staple 
crops such as maize, wheat and rice, but studies are emerging on 
climate change impacts on other crops.
5.4.1 Observed Impacts
5.4.1.1 Observed impacts on major crops
AR5 Chapter 7 (Porter et al., 2014) stated with confidence that warmer 
temperatures have benefited agriculture in the high latitudes, and 
more evidence has been published to support this statement. Typical 
examples include pole-ward expansion of growing areas and reduction 
of cold stress in East Asia and North America (Table SM5.1).
Recent warming trends have generally shortened the life cycle of major 
crops (high confidence) (Zhang et al., 2014; Shen and Liu, 2015; Ahmed

et al., 2018; Liu et al., 2018c; Tan et al., 2021). Some studies, however, 
observed prolonged crop growth duration despite the warming trends 
(Mueller et al., 2015; Tao et al., 2016; Butler et al., 2018; Zhu et al., 2018b) because of shifts in planting dates and/or adoption of longer-duration 
cultivars in mid-to-high latitudes. Conversely, in mid-to-low latitudes in 
Asia, a review study found that farmers favoured early maturing cultivars 
to reduce risks of damages due to drought, flood and/or heat (Shaffril 
et  al., 2018), suggesting that region-specific adaptations are already 
occurring in different parts of the world (high confidence).
Global yields of major crops per unit land area have increased 2.5- to 
3-fold since 1960. Plant breeding, fertilisation, irrigation and integrated 
pest management have been the major drivers, but many studies have 
found significant impacts from recent climate trends on crop yield (high

confidence) (Figure 5.3; see Section 5.2.1 for the change attributable to 
anthropogenic climate change).
Climate impacts for the past 20–50 years differ by crops and regions. 
Positive effects have been identified for rice and wheat in Eastern Asia, 
and for wheat in Northern Europe. The effects are mostly negative 
in Sub-Saharan Africa, South America and Caribbean, Southern Asia, 
and Western and Southern Europe. Climate factors that affected long-
term yield trends also differ between regions. For example, in Western 
Africa, 1°C warming above preindustrial climate has increased heat 
and rainfall extremes, and reduced yields by 10–20% for millet and 
5–15% for sorghum (Sultan et al., 2019). In Australia, declined rainfall 
and increased temperatures reduced yield potential of wheat by 
27%, accounting for the low yield growth between 1990 and 2015 
(Hochman et  al., 2017). In Southern Europe, climate warming has

negatively impacted yields of almost all major crops, leading to recent 
yield stagnation (Moore and Lobell, 2015; Agnolucci and De Lipsis, 
2020; Brás et al., 2021).
Ortiz-Bobea et al. (2021) analysed agricultural total factor productivity 
(TFP), defined as the ratio of all agricultural outputs to all agricultural 
inputs, and found that, while TFP has increased between 1961 and 2015, 
the climate change trends reduced global TFP growth by a cumulative 
21% over a 55-year period relative to TFP growth under counterfactual 
non-climate change conditions. Greater effects (30–33%) were 
observed in Africa, Latin America and the Caribbean (Figure 5.3).
Climate variability is a major source of variation in crop production 
(Ray et  al., 2015; Iizumi and Ramankutty, 2016; Frieler et  al., 2017; 
Cottrell et al., 2019)(Table SM5.1). Weather signals in yield variability 
are generally stronger in productive regions than in the less productive

regions (Frieler et al., 2017), where other yield constraints exist such 
as pests, diseases and poor soil fertility (Mills et  al., 2018; 5.2.2). 
Nevertheless, yield variability in less productive regions has severe 
impacts on local food availability and livelihood (high confidence) 
(FAO, 2021).
Climate-related hazards that cause crop losses are increasing (medium 
evidence, high agreement) (Cottrell et al., 2019; Mbow et al., 2019; 
Brás et al., 2021; FAO, 2021; Ranasinghe et al., 2021). Drought-related 
yield losses have occurred in about 75% of the global harvested area 
(Kim et al., 2019b) and increased in recent years (Lesk et al., 2016). 
Heatwaves have reduced yields of wheat (Zampieri et al., 2017) and 
rice (Liu et  al., 2019b). The combined effects of heat and drought 
decreased global average yields of maize, soybeans and wheat by 
11.6%, 12.4% and 9.2%, respectively (Matiu et al., 2017). In Europe,

5
729Food, Fibre and Other Ecosystem Products  Chapter 5
crop losses due to drought and heat have tripled over the last five 
decades (Brás et al., 2021), pointing to the importance of assessing 
multiple stresses. Globally, floods also increased in the past 50 years, 
causing direct damages to crops and indirectly reduced yields by 
delaying planting, which cost 4.5  billion USD in the 2010 flood in 
Pakistan and 572  million USD in the 2015 flood in Myanmar (FAO, 
2021).
5.4.1.2 Observed impacts on other crops (vegetables, fruit, nut 
and fibre)
The impact of climate change on these diverse crop types is under-
researched and uncertain (Manners and van Etten, 2018; Alae-Carew 
et al., 2020); there are reports of positive impacts in some cases, but 
overall the observed impacts are negative across all crop categories 
(Figure 5.3).
Above-ground annual crops consumed as vegetables, fruits or salad are 
essential for food security and nutrition (5.12). In temperate regions,

climate change can result in higher yields (Potopová et al., 2017; Bisbis 
et  al., 2018), while in subtropical/tropical regions, negative impacts 
from heat and drought take precedence (Scheelbeek et  al., 2018). 
Different species have different sensitivities to heat and drought 
(Prasad et  al., 2017; Scheelbeek et  al., 2018) and to combinations 
of stresses (Zandalinas et  al., 2018). Above-ground vegetables are 
especially vulnerable to heat and drought stress during pollination and fruit set, resulting in negitive impacts on yield (Daryanto et al., 2017; 
Sita et al., 2017; Brás et al., 2021) and harvest quality (Mattos et al., 
2014; Bisbis et al., 2018). Growers have already seen negative impacts 
from the expansion of pest and disease agents due to warming 
(Section 5.4.1.3; Figure 5.3).
Below-ground vegetables include starchy roots and tubers that form a 
regular diet in many parts of the tropics and subtropics. Warming and

climate variability has altered the rate of tuber development, with yield 
impacts varying by location, including yield increases in some cases 
(Shimoda et al., 2018; Ray et al., 2019). These crops are considered 
stress tolerant but are more sensitive to drought than cereals (Daryanto 
et  al., 2017). Impacts on water supply are critical as root crops are 
water-demanding for long periods, and highly sensitive to drought and 
heat events during tuber initiation (Dua et al., 2013; Potopová et al., 
2017; Brás et al., 2021).
Among perennial tree crops, only grapevine, olive, almond, apple, 
coffee and cocoa have received significant research attention. Concerns 
about climate impacts on harvest quality are widespread (Figure 5.3) 
(Barnuud et al., 2014; Bonada et al., 2015). In higher-latitude regions, 
the primary concern is the effect of temperature variability on harvest 
stability, pests and diseases and phenology (including fulfilment of

winter chill requirements and risks due to early emergence in spring), 
(El Yaacoubi et al., 2014; Ramírez and Kallarackal, 2015; Santos et al., 
2017; Gitea et  al., 2019). In lower-latitude regions, information is 
Synthesis of observed impacts 
on crop yields and productivity
Impact level: Mixed Positive Negative = not assessedna Confidence level: High Medium LowNorthern Africa
Sub-Saharan Africa
Western Asia
South-eastern Asia
Western Europe
Northern Europe
Northern AmericaAustralia and New Zealand
Global (average of regional data)Agricultural
total factor
productivityAll
cropsMajor crop species
Rice
Wheat
SoybeanMaize
na naCrop categoriesCereals
Vegetables
na
na
na
na
na
na
Leafy crops
Soft fruit
Root crops
Tree fruitsand nuts
na
na
nanana
na
nana
na
nanana
na
nana
nana
na
naLegumes
Latin America and the Caribbean nana
na
na
na

Figure 5.3 |  Synthesis of literature on observed impacts of climate change on productivity by crop type and region. The figure draws on >150 articles categorized 
by: agriculture total factor productivity including literature estimating all agricultural outputs in a region; major crop species including literature assessing yield changes in the 
four major crops; crop categories including productivity changes (yield, quality and other perceived changes) in a range of crops with different growth habits. The assessment uses 
literature published since AR5, although the timespan often extends prior to 2014. The direction of the effect and the confidence are based on the reported impacts and attribution, 
and on the number of articles. See SM5.1 and SM5.2 for details.

5
730Chapter 5 Food, Fibre and Other Ecosystem Products
limited, but studies are focused on increased tree mortality and yield 
loss due to drought, heat and impacts from variability in the timing of 
the wet and dry seasons (Glenn et al., 2013; Ramírez and Kallarackal, 
2015); see Box 5.7). In fruit trees, warming and climate variability have 
already affected fruit quality, such as acidity and texture in apples, or 
skin colour in grape berries (Sugiura et al., 2013; Sugiura et al., 2018). 
The reliability and stability of harvests has been impacted by climate 
variability, changes in the distribution of pests and pathogens (Seidel, 
2014; Bois et al., 2017), and the mismatch of important phenological 
events (such as bud emergence and flowering) (Guo and Shen, 2015; 
Legave et  al., 2015; Ito et  al., 2018; Vitasse et  al., 2018). Perennial 
crops are particularly vulnerable to these impacts as they are exposed

throughout the year, with little potential for growers to adjust planting 
date or location. Negative impacts via disruption to phenology and 
pest dynamics are best studied in grapevine (see Box 5.2).
Among the fibre crops, cotton is particularly well studied. As cotton 
is heat tolerant and yield increases with extra plant growth, positive 
effects of increasing temperature are expected, but observed impacts 
have been mixed due to negative impacts on phenology and plant 
water status (Traore et al., 2013; Chen et al., 2015a; Cho and McCarl, 
2017). Negative impacts of climate change due to proliferation of the 
pest cotton bollworm are widely reported (Ouyang et al., 2014; Huang 
and Hao, 2020).
The impacts of climate change on water availability (rainfall and 
irrigation supply) are an emerging issue. Increased occurrence of 
drought combined with limited access to irrigation water is already 
a key constraint; for example, Californian almonds are predicted to

increase their potential geographical range under climate warming 
(Parker, 2018), yet a trend of increasing drought has already resulted in 
trees being removed due to lack of access to irrigation water (Keppen 
and Dutcher, 2015; Kerr et al., 2018; Reisman, 2019).
5.4.1.3 Observed impacts on pests, diseases and weeds
AR5 and SRCCL (IPCC, 2019) indicated that more frequent outbreaks 
and area expansion of pests and diseases are serious concerns under 
climate change but are under-researched because of the difficulties in 
assessing multi-species interactions (Porter et al., 2014; Mbow et al., 
2019). High-quality historical and current observational data to detect 
changes in pests and diseases attributable to recent trends in climate 
are still limited.
Bebber (2013) found significant poleward expansions of many 
important groups of crop pests and pathogens since 1960, with an 
average shift of 2.7 km yr−1. Different pest species populations respond

differently to ongoing climate change, with some shifting, contracting 
or expanding their current distribution range and others persisting or 
disappearing in their current range (high confidence). These asymmetric 
distribution changes can create novel species combinations or 
decouple existing ones (Pecl et al., 2017; Hobbs et al., 2018), but their 
consequences on future crop production and food security are hard to 
predict. Multi-species climate change experiments are rare (Bonebrake 
et al., 2018), but one study shows that under future climates different 
pest assemblages of interacting species may alter levels of damage 
to crops compared with that by only one species (Crespo-Perez et al., 2015). Some studies highlight the importance of location-specific 
species interactions for more realistic projections of pest distribution, 
performance and damage to crops, which in turn would allow more 
effective prevention and pest control strategies (Wilson et al., 2015;

Carrasco et al., 2018).
Weeds are recognised as a primary constraint on crop production 
(Oerke, 2006), rangelands (DiTomaso et al., 2017) and forests (Webster 
et al., 2006). Climate change could favour the growth and development 
of weeds over crops with negative consequences for desired plants in 
managed systems (medium evidence, high agreement) (Peters et al., 
2014; Ziska and McConnell, 2016). First, changes in temperature 
and precipitation alter the range, composition and competitiveness 
of native and invasive weeds (Bradley et  al., 2010). Second, rising 
concentrations of CO 2 enhance growth of C 3 species (~85% of plant 
species, including many weeds) (Ogren and Chollet, 1982; Ziska, 2003), 
and increase plant water use efficiency with potentially strong effects 
on invasive plant species establishment (Smith et  al., 2000; Belote 
et al., 2004; Blumenthal et al., 2013).
Some invasive species within unmanaged areas will expand further,

proliferate and be more competitive under climate change as they may 
benefit from increased resource ability (e.g., additional CO 2, enhanced 
precipitation) (Bradley et  al., 2010; Kathiresan and Gualbert, 2016; 
Merow et al., 2017; Ramesh et al., 2017; Waryszak et al., 2018), which 
will make chemical weed control more problematic (medium evidence, 
high agreement) (Waryszak et  al., 2018; Ziska, 2020). The range of 
other invasive weeds may become static, or even decline (Bradley 
et al., 2016; Buckley and Csergo, 2017). A recent meta-analysis also 
supports that invasive plants respond more favourably to elevated CO 2 
concentrations and elevated temperatures than native plants (Korres 
et al., 2016; Liu et al., 2017). Movement of invasive species into low-
fertility areas, however, could provide resource opportunities, especially 
if agriculture in those areas is limited (Randriambanona et al., 2019).
Rising CO 2 concentrations and climate change could reduce herbicide

efficacy (medium evidence, high agreement). These reductions may 
be associated with physical environmental changes (precipitation, 
wind speed) that influence herbicide coverage (Ziska, 2016) as well 
as direct effects of CO 2 on plant biochemistry and herbicide resistance 
(Refatti et  al., 2019). Increasing CO 2 levels and altered temperature 
and precipitation are therefore projected to affect all aspects of weed 
biology (Peters et  al., 2014; Ziska and McConnell, 2016), including 
establishment (Bradley et  al., 2016), competition (Fernando et  al., 
2019), distribution, (Castellanos-Frías et al., 2016) and management 
(Waryszak et al., 2018).
A warmer climate increases the need for pesticides (Shakhramanyan 
et  al., 2013; Ziska, 2014; Delcour et  al., 2015; Zhang et  al., 2018). 
Increases in temperature and CO 2 concentration may reduce pesticide 
efficiency by altering its metabolism, or accelerating detoxification

(Matzrafi et  al., 2016; Matzrafi, 2019). Intense rainfall also reduces 
persistence (Delcour et  al., 2015). Invasive pests and pathogens 
impose an additional cost for the society (Bradshaw et al., 2016). Rapid 
and large-scale dispersal of pests is already a major threat to food 
security, as exemplified by the recent outbreak of desert locusts (see 
Box 5.8), indicating the importance of international cooperation. Taken

5
731Food, Fibre and Other Ecosystem Products  Chapter 5
Box 5.1: Evidence for Simultaneous Crop Failures Due to Climate Change
Simultaneous yield losses across major producing regions can be a threat to food security but had not been quantified by the time of AR5. 
Large-scale sea surface temperature (SST) oscillations greatly influence global yield of major crops (high confidence) (Anderson et al., 
2019b; Najafi et al., 2019; Ubilava and Abdolrahimi, 2019; Heino et al., 2020; Iizumi et al., 2021b) and food prices (Ubilava, 2018). Some 
studies showed that crop yields in different regions covaried with SST oscillations, suggesting occurrences of tele-connected yield failures 
(crop losses caused by related factors in distant regions; Table Box 5.1.1) (medium confidence). Evidence of synchronised crop failures 
increasing with ongoing climate change is still limited.
Table Box 5.1.1 |  A summary of peer-review papers detecting synchronised yield losses.
Regions/ 
commoditiesPeriod

studiedObserved impacts Climate driverEvidence 
for multiple 
breadbasket 
failuresEvidence for 
increasing 
risks due 
to multiple 
failuresReference
Global 
breadbaskets 
for maize, rice, 
sorghum and 
soybean1961–2013Not only yields of each crop covaried in many countries, but 
also those of different crops, maize in particular, covaried 
with other crops.SST anomalies, 
atmospheric and 
oceanic in- dices, 
air temperature 
anomalies and 
Palmer Drought 
Severity IndexHigh NANajafi et al. 
(2019)
Global 
breadbaskets for 
wheat, soybean 
and maize1980–2010Climate modes (El Niño-Southern Oscillation (ENSO), the 
Indian Ocean Dipole (IOD), tropical Atlantic variability 
(TAV) and the North Atlantic Oscillation (NAO)) account 
for 18%, 7% and 6% of global maize, wheat and soybean 
production variability, respectively. ENSO events sometimes 
offset yield reductions in some places by increases in other 
places (e.g., soybean yields in the USA and southeast South 
America).

Since 1961, ENSO in 1983 was the only climate mode that 
showed global synchronous crop failures.Climate modes Medium (1983) NAAnderson 
et al. (2019b)
Global 
and maizeClimate modes induce yield variability in major 
breadbaskets, e.g., ENSO affects about half of maize and 
wheat areas. IOD and ENSO influence wheat in Australia. 
ENSO affects soybean in northern South America.Climate modes Medium NAHeino et al. 
(2020)
67 maize 
producing 
countries1961–2017SST anomalies from the 1980–2010 base period in 
the Niño3.4 region, a rectangular area bounded by 
120°W–170°W and 5°S–5° is used as a driver. Maize 
yields are tele-connected among the southeastern tier of 
Sub-Saharan Africa, as well as Central America, South Asia 
and Australia. A 1° increase in SST reduced maize yield by 
up to 20% in these countries.Climate 
modes (SST), 
precipitationMedium NAUbilava and 
Abdolrahimi 
(2019)
Global 
breadbasket (the 
USA, Argentina, 
Europe, Russia/

Ukraine, China, 
India, Australia, 
Indonesia and 
Brazil)1967–2012Likelihood of simultaneous climate risks increased from 
1967–1990 to 1991–2012 in the global breadbasket (lower 
25th yield deviation percentile events at province level) for 
wheat, soybean and maize, but not rice.
Likelihood of simultaneous climate risks increased from 
1967–1990 to 1991–2012 in China (lower 25th yield 
deviation percentile events at province level).Unspecified Medium MediumGaupp et al. 
(2020)
Global 1961–2008Synchronous yield losses among major breadbaskets within 
each commodity, such as maize and soybean, decreased 
between 1961 and 2008. In contrast, synchronous yield 
variation between crops has increased. Under a scenario 
of synchronisation of all four crops, the global maximum 
production losses for rice, wheat, soybean and maize are 
estimated to reach between −17% and −34%.Unspecified Medium MediumMehrabi and 
Ramankutty 
(2019)

5
732Chapter 5 Food, Fibre and Other Ecosystem Products
together, the need for control of pests, disease and weeds will increase 
under climate change (medium evidence, high agreement). The use of 
toxic agricultural chemicals also has human health and environmental 
risks (Whitmee et al., 2015; IPBES, 2019). Surveillance for monitoring 
pest distribution and damages, climate-relevant pest risk analysis, and 
climate-smart strategies for controlling pests with minimal impacts on 
human and environmental health are important tools in the face of 
climate change (IPPC Secretariat, 2021).
5.4.1.4 Observed impacts of ozone on crops
Tropospheric (i.e., the lowest 6–10 km of the atmosphere) ozone 
exacerbates negative impacts of climate change (high confidence) 
(Mattos et al., 2014; Chuwah et al., 2015; McGrath et al., 2015; Bisbis 
et al., 2018; Mills et al., 2018; Scheelbeek et al., 2018). Ozone is an 
air pollutant and short-lived GHG that affects air quality and global

climate. It is a strong oxidant that reduces physiological functions, 
yield and quality of crops and animals. Surface ozone concentration 
has increased substantially since the late 19th century (Cooper et al., 
2014; Forster et al., 2021; Gulev et al., 2021; Szopa et al., 2021) and in some locations and times reaches levels that harm plants, animals and 
human (high confidence) (Fleming et al., 2018).
Mills (2018) estimated global distributions of current yield losses 
of major crops due to ozone, pest and diseases, heat, and aridity 
(Figure  5.4). Ozone-induced yield losses in 2010–2012 averaged 
12.4%, 7.1%, 4.4% and 6.1% for soybean, wheat, rice and maize, 
respectively. Spatial variation in yield losses is similar among different 
stresses; areas with a large loss due to ozone are also at high risk 
of yield losses due to pest and diseases and heat. Many vegetable 
crops are also susceptible to ozone, which will adversely impact quality

and quantity (Mattos et al., 2014; Bisbis et al., 2018; Scheelbeek et al., 
2018).
The estimated yield loss does not account for interactions with other 
climatic factors. Temperatures enhance not only ozone production but 
also ozone uptake by plants, exacerbating yield and quality damage. 
Burney (2014) estimated current yield losses due to the combined 
effects of ozone and heat in India at 36% for wheat and 20% for rice. 
Schauberger et  al. (2019a) found global yield losses, ranging from 
2% to 10% for soybean and 0% to 39% for wheat with a model that 
Yield Constraint Score for the effect of five crop stresses on global production of soybean and wheat
The yield constraint score integrates the five stress depicted below which provide an indication of where each stress is predic ted to be 
affecting crop yield globally and the magnitude of the effect.
(a) Soybean (Glycine max )
Yield
constraint
scoreYield
score
Pests & 
diseasesSoil
nutrients
Heat
   stress Aridity

Pests & 
nutrients
Heat
Ozone
Higher stress Lower stressOzone(b) Wheat (Tr iticum aestivu m)
Figure 5.4 |  The global effects of five biotic and abiotic stresses on soybean and wheat. All data are presented for the 1 × 1° (latitude and longitude) grid squares 
where the mean production of soybean or wheat was >500 tonnes (0.0005 Tg). The effect of each stress on yield is presented as a Yield Constraint Score (YCS) on a scale of 1–5, 
where 5 is the highest level of stress from ozone, pests and diseases, heat stress and aridity (Mills et al., 2018). Data are available at Sharps et al. (2020). See Annex I: Global to 
Regional Atlas for all four crops.

5
733Food, Fibre and Other Ecosystem Products  Chapter 5
accounts for temperature, water and CO 2 concentration on ozone 
uptake.
5.4.2 Assessing Vulnerabilities within Production 
Systems
Since AR5, vulnerability assessment has become a pivotal component 
of risk analysis associated with climate hazards, climate change and 
climate variability (UNDRR, 2019). Vulnerability assessment can be 
sectoral or regional but involves social and ecological indicators. This 
section presents examples of vulnerability assessment to climatic 
hazards and social vulnerabilities.
5.4.2.1 Vulnerability to climatic hazards
Drought is a major risk component in cropping systems globally, 
with substantial economic loss (Kim et al., 2019b), livelihood impacts 
(Shiferaw et al., 2014; Miyan, 2015) and ultimately health risks such as 
malnutrition (Phalkey et al., 2015; Cooper et al., 2019). Vulnerability to 
drought can be estimated with a range of indicators (Hagenlocher et al.,

2019). Meza (2020) showed that drought risks could be exacerbated 
or moderated by regional differences in vulnerability (Figure 5.5). For 
instance, high-level risks observed in southern Africa, western Asia and central Asia result from high vulnerability (low coping capacity), 
whereas risk levels are relatively low despite the high exposure by 
relatively high adaptive capacity to drought in other regions.
Regional-scale assessment also highlights the importance of adaptive 
capacity. For instance, rice and maize production in Viet Nam Mekong 
Delta has high exposure to multiple climate hazards such as flooding, 
sea level rise, salinity intrusion and drought (Parker et al., 2019). Risks 
can be moderated by a relatively high adaptive capacity because of 
infrastructure, resources and high education levels (Parker et  al., 
2019). Another regional study demonstrated that erratic rains and 
high temperatures in southern and southeastern Africa increased

the vulnerability of agricultural soils, thereby exacerbating impacts 
of prolonged and frequent droughts (Sonwa et  al., 2017a; See also 
Box 5.4).
Farm-scale assessment exemplifies context-sensitive vulnerability 
to climate hazards. Studies of coffee growers in Central America 
demonstrated that key vulnerability indicators varied greatly between 
regions and between farms, ranging from a lack of labour, postharvest 
infrastructure, conservation practices and transport that limits access 
to market, technical and financial assistance (Baca et  al., 2014; 
Bouroncle et al., 2017). These region- and scale-specific vulnerability 
Rainfed agriculture: Drought risks, hazards, exposure and vulnerability indicators
Observed period 1986–2015
Low High
Areas with no crops
Areas with no data
Data averaged over 1.5° hexagonsIndicator scores for rainfed agriculture(c) Drought risk index(a) Hazard and exposure indicator score (b) Vulnerability index

Figure 5.5 |  Hazard and exposure indicator score (a), vulnerability index (b) and drought risk index (c), for rainfed agricultural systems between 1986 and 
2015. Drought hazard indicator is defined as the ratio of actual crop evapotranspiration to potential crop evapotranspiration, calculated for 24 crops. Vulnerability index is the 
country-scale weighted average of a total of 64 indicators including social and ecological susceptibility indicators, and coping capacity. Risk index is calculated by multiplying hazard/
exposure indicator score and vulnerability index (Meza et al., 2020).

5
734Chapter 5 Food, Fibre and Other Ecosystem Products
indicators assist in identifying ways to enhance resilience to climate 
hazards (high confidence).
5.4.2.2 Inequities in cropping systems—other crops and 
regional disparities
While those working with major crops have benefited from the release 
of new cultivars, those growing other crops are typically reliant 
on a heritage cultivars or landraces. While Indigenous knowledge 
and local smallholder knowledge and practices play an important 
role in supporting agrobiodiversity which provides genetic diversity 
resistant to climate-related stresses, a global and national focus in 
international research, subsidies and support for a few crop species 
has contributed to an overall decline in agrobiodiversity (FAO, 2019e; 
Song et al., 2019) Similarly, there is a lack of agronomic innovation and 
research to service ‘minor’ crops (Moriondo et al., 2015; Manners and 
van Etten, 2018). Even some high-value commodities grown outside

high-income countries suffer from imbalances in the focus of available 
credit, research and innovation (Section 5.4.4.3; Glover, 2014; Fischer, 
2016; Farrell et al., 2018). There is a possibility that a lack of adaptive 
capacity and policy support will drive these growers to move away 
from these diverse crops, further reducing the resilience of food 
systems by increasing risk of crop loss from pests, disease and drought 
and potential loss of Indigenous or local knowledge (Section 5.13.5, 
Table Box  5.1.1). In the Andean Altiplano of Bolivia, for example, 
Indigenous farmers have traditionally managed a diverse set of native 
crops which are drought and frost-tolerant, using cultural practices 
of seed selection and exchange, but have faced an increase in pests and diseases and a decline of traditional crops due to climate-change-
related stresses, out-migration and intensification drivers (Meldrum 
5.4.2.3 Gender and other social inequities

Social inequities such as gender, ethnicity and income level, which 
vary by time and place and may overlap, can compound vulnerability 
to climate change for producers within cropping systems (high 
confidence) (Table 5.3, Arora-Jonsson, 2011; Djoudi et al., 2013; Carr 
and Thompson, 2014; Mbow et al., 2019; Rao et al., 2019a; Nyantakyi-
Frimpong, 2020a). Rather than binary and static categories (i.e., men 
versus women), social vulnerabilities are dynamic and intersect; to 
understand vulnerability, the specific socio-cultural identities and 
political and environmental context need to be studied in relation to 
climate stress (Thompson-Hall et al., 2016; Rao et al., 2019a; Nyantakyi-
Frimpong, 2020a).
5.4.3 Projected Impacts
5.4.3.1 Advances in the characterisation of the effects of 
elevated atmospheric CO 2
Elevated CO 2 concentrations stimulate photosynthesis rates and 
biomass accumulation of C 3 crops, and enhance crop water use

efficiency of various crop species, including C 4 crops (high confidence) 
(Kimball, 2016; Toreti et al., 2020). Perennial crops and root crops may Table 5.3 |  Examples of social inequities in cropping systems that compound climate change vulnerability.
Social inequity How social inequity increases vulnerability to climate change in cropping systems
Gender inequity can create and worsen social 
vulnerability to climate change impacts within cropping 
systems (high confidence) (Carr and Thompson, 
2014; Sugden et al., 2014; Nyantakyi-Frimpong and 
Bezner-Kerr, 2015; Rao et al., 2019a; Ebhuoma et al., 
2020; Nyantakyi-Frimpong, 2020a; see Cross-Chapter 
Box GENDER in Chapter 18). –Men and women have different access to and decision-making control over resources such as seeds, systemic differences in 
land tenure and agricultural employment, and their responsibilities, workloads and response to climate stresses differ due

to systemic gender inequities and socio-cultural norms, which intersect with other inequities (e.g., income level, ethnicity) to 
compound vulnerability (Rao et al., 2019a; Ebhuoma et al., 2020; Nyantakyi-Frimpong, 2020a).
 –In a study in northern Ghana, for example, poor widows with poor health had fewer resources to rely on during droughts 
than married women, particularly those married to local leaders; in contrast, due to gendered expectations, during floods 
low-income men suffered greater consequences (Nyantakyi-Frimpong, 2020a).
 –Adaptation strategies such as migration can compound that vulnerability, but importantly, the specific gendered 
vulnerability intersects with other inequities which are context specific (Sugden et al., 2014; Nyantakyi-Frimpong, 2020a; 
Cross-Chapter Box MIGRATE in Chapter 7).
Globally, smallholder food producers are more 
vulnerable than large-scale producers to climate change

impacts (high confidence). –Smallholder food producers are more vulnerable in part because of limited policy, infrastructure and institutional support, 
low credit access, viable markets and limited political voice in policy debates (HLPE, 2013; Karttunen et al., 2017; Mbow 
et al., 2019; Nyantakyi-Frimpong, 2020a).
 –Smallholder producers’ vulnerability may be increased by heavy reliance on one crop for income, particularly if the crop 
requires significant capital investments (medium confidence) (Toufique and Belton, 2014; Craparo et al., 2015; Ovalle-Rivera 
 –For example, smallholder coffee producers in southern Mexico and Central America are more vulnerable due to a range of 
factors, including unstable and low coffee prices, limited institutional support for small-scale producers, low negotiation 
capacity and access to markets, and heavy reliance on one crop for income (Economic Commission for Latin America and

the Caribbean and System, 2014; Ovalle-Rivera et al., 2015; Ruiz Meza, 2015; Hannah et al., 2017; Bacon et al., 2021). Pest 
and disease outbreaks such as coffee leaf rust, extreme climatic events, ongoing conflict, poor governance and low viability 
of livelihoods increased migration and high levels of food insecurity for this group (Robalino et al., 2015; Hannah et al., 
2017; Donatti et al., 2019) which also varied by institutional- and farm-level responses, land size and income level (Quiroga 
et al., 2020; Bacon et al., 2021).
Farmworkers are another social group with heightened 
vulnerability to climate change (medium confidence). –Farmworkers often experience job insecurity, food insecurity, poor working conditions, poverty and social marginalisation. 
Climate change impacts can compound their vulnerability, for example by worsening working conditions through increased

temperatures and humidity (Section 5.12.3.1), or increase unreliability of work due to rainfall irregularity, flooding or 
drought, and can put them more at risk during climatic extreme events such as wildfires (Turhan et al., 2015; Greene, 2018; 
Mendez et al., 2020; Tigchelaar et al., 2020).

5
735Food, Fibre and Other Ecosystem Products  Chapter 5
have a greater capacity for enhanced biomass under elevated CO 2 
concentrations, although this does not always result in higher yields 
(Glenn et al., 2013; Kimball, 2016).
Recent FACE studies found that the effects of elevated CO 2 are greater 
under water-limited conditions (medium confidence) (Manderscheid 
et al., 2014; Fitzgerald et al., 2016; Kimball, 2016), which was generally 
reproduced by crop models (Deryng et  al., 2016). However, drought 
sometimes negates the CO 2 effects (Jin et al., 2018).
There are significant interactions between CO 2, temperature, cultivars, 
nitrogen and phosphorous nutrients (Kimball, 2016; Toreti et al., 2020): 
positive effects of rising CO 2 on yield are significantly reduced by higher 
temperatures for soybean, wheat and rice (medium confidence) (Ruiz-
Vera et al., 2013; Cai et al., 2016; Gray et al., 2016; Hasegawa et al.,

2016; Obermeier et al., 2016; Purcell et al., 2018; Wang et al., 2018). In 
above-ground vegetables, elevated CO 2 can in some cases reduce the 
impact of other climate stressors, while in others the negative impacts 
of other abiotic factors negate the potential benefit of elevated CO 2 
(Bourgault et  al., 2017; Bourgault et  al., 2018; Parvin et  al., 2018; 
Parvin et al., 2019). Significant variation exists among cultivars in yield 
response to elevated CO 2, which is positively correlated with yield 
potential in rice and soybean, suggesting the potential to develop 
cultivars for enhanced productivity under future elevated [CO 2] 
(Ainsworth and Long, 2021).
Elevated CO 2 reduces some important nutrients such as protein, iron, 
zinc and some grains, fruit or vegetables to varying degrees depending 
on crop species and cultivars (high confidence) (Mattos et al., 2014; 
Myers et al., 2014; Dong et al., 2018; Scheelbeek et al., 2018; Zhu et al.,

2018a; Jin et al., 2019; Ujiie et al., 2019). This is of particular relevance 
for fruit and vegetable crops given their importance in human 
nutrition (high confidence) (see Section 5.12.4 for potential impacts 
on nutrition; Nelson et  al., 2018; Springmann et  al., 2018). Recent 
experimental studies (Section 5.3.2), however, show some complex and 
counteracting interactions between CO 2 and temperature in wheat, 
soybean and rice; heat stress negates the adverse effect of elevated 
CO 2 on some nutrient elements (Macabuhay et al., 2018; Kohler et al., 
2019; Wang et al., 2019b). The CO 2 by temperature interaction for grain 
quality needs to be better understood quantitatively to predict food 
nutritional security in the future.
5.4.3.2 Projected impacts on major crop production
AR5 Chapter 7 estimated global crop yield reduction due to climate 
change to be about 1% per decade (Porter et al., 2014), similar to the

previous assessment reports (Porter et al., 2019). Additional research 
confirms that climate change will disproportionately affect crop 
yields among regions, with more negative than positive effects being 
expected in most areas, especially in currently warm regions, including 
Africa and Central and South America (high confidence).
A systematic literature search between 2014 and 2020 resulted in 
about 100 peer-reviewed papers that simulated crop yields of four 
major crops (maize, rice, soybean and wheat) using Coupled Model 
Intercomparison Project Phase 5 (CMIP5) data (Hasegawa et  al., 
2021b). Most studies focus on the relative change in crop yields due to climate change but do not consider technological advances. 
Nevertheless, they provide useful insights into time-, scenario- and 
warming-degree-dependent impacts of climate change.
The impact of climate change on crop yield without adaptation projected

in the 21st century is generally negative even with the CO 2 fertilisation 
effects, with the overall median per-decade effect being −2.3% for 
maize, −3.3% for soybean, −0.7% for rice and −1.3% for wheat, which 
is consistent with previous IPCC assessments (Porter et al., 2014). The 
effects vary greatly within each crop, timeframe and RCP , but show a 
few common features across crops (Figure  5.6a). Differences in the 
projected impacts between RCPs are not pronounced by mid-century. 
From then onward, the negative effect becomes more pronounced 
under RCP8.5, notably in maize. Rice yields show less variation across 
models than other crops presumably because simulations are mostly 
under irrigated conditions. A part of the uncertainty in the projection is 
due to regional differences (Figure 5. 6b). Negative impacts on cereals 
are projected in Africa and Central and South America at the end of the 
century, which agrees with the previous studies (Aggarwal et al., 2019;

Porter et al., 2019).
The differences due to regions, RCPs and timeframes are related to 
the current temperature level and degree of warming (Figure 5.7). The 
projected effects of climate change are positive where current annual 
mean temperatures (T ave) are below 10°C, but they become negative 
with Tave above around 15°C. At T ave > 20°C, even a small degree of 
warming could result in adverse effects. In maize, negative effects 
are apparent at almost all temperature zones. A new study using 
the latest climate scenarios (Coupled Model Intercomparison Project 
Phase 6, CMIP6) and global gridded crop model ensemble projected 
that climate change impacts on major crop yields appear sooner than 
previously anticipated, mainly because of warmer climate projections 
and improved crop model sensitivities (Jägermeyr et al., 2021).
As noted in Section 5.3.1, most simulations do not fully account for 
responses to pests, diseases, long-term change in soil, and some climate

extremes (Rosenzweig et al., 2014), but studies are emerging to include 
some of these effects. For example, based on the temperature response 
of insect pest population and metabolic process, global yield losses of 
rice, maize and wheat are projected to increase by 10–25% per degree 
Celsius of warming (Deutsch et al., 2018). Rising temperatures reduce 
soil carbon and nitrogen, which in turn exacerbate the negative effects 
of +3°C warming on yield from 9% to 13% in wheat and from 14% to 
19% in maize (Basso et al., 2018).
A few studies have examined possible occurrences of tele-connected 
yield losses (5.4.1.2) using future climate scenarios. Tigchelaar (2018) 
estimated that, for the top four maize-exporting countries, the 
probability that simultaneous production losses greater than 10% 
occur in any given year increases from 0% to 7% under 2°C warming 
and to 86% under 4°C warming. Gaupp (2019) estimated that risks of

simultaneous failure in maize would increase from 6% to 40% at 1.5°C 
and to 54% at 2°C warming, relative to the historical baseline climate. 
Large-scale changes in SST are the major factors causing simultaneous 
variation in climate extremes, which are projected to intensify under 
global warming (Cai et al., 2014; Perry et al., 2017). Consequently, risks 
of simultaneous yield losses in major food-producing regions will also

5
736Chapter 5 Food, Fibre and Other Ecosystem Products
increase with global warming levels above 1.5°C (medium confidence). 
Further examination is needed for the effects of spatial patterns of 
these extremes on breadbaskets in relation to SST anomalies under 
more extreme climate scenarios.
Future surface ozone concentration is highly uncertain (Fiore et al., 
2012; Turnock et al., 2018); it is projected to increase under RCP8.5 and 
decrease under other RCPs depending largely on different methane 
emission trajectories because methane is an important precursor 
of ozone. Methane, therefore, reduces crop yield both from climate 
warming and ozone increase (Avnery et  al., 2013). Shindell (2016) estimated yield losses of four major crops (to be 25±11% by 2100 
under RCP8.5, as a net balance of the positive effect of CO 2 (15±2%) 
and negative effects of warming (35±10%) and ozone (4.0±1.3%), 
and that 62% of the yield loss was attributable to methane. This

points to the importance of reducing methane and other precursors 
of ozone as an effective adaptation strategy (medium evidence, high 
agreement).Africa
Asia
Europe
North AmericaCentral and
Africa
Asia
Europe
Africa
Asia
Europe
South AmericaNear future
Mid centur y
End of centur y
Near future
End of centur yProjected yield changes relative to the baseline period (2001–2010)
without adaptation and with CO2 fertilization effectsNumbers are the number of simulations
RCP2.6 RCP4.5 RCP8.5 RCP8.5 RCP4.5 RCP2.6
5055 85 55
61123 9158 77 56
651189 243 47 48
45 51-100-500
77 44 52 50
-100-50050
10 10 99 7
-100-5005010 17 61 32
62 21 31 6313 17 61 46
63 53 18 1314 17 61 44
71 73 19 1233 91 61 31 21 31 16 1
3313 44 4Maize
WheatSoybeanRiceYield
change
(%)(a)
At different time periods(b)

At different regions at end of century
49496868 104104 7373 9292 8484
1818 1515 1919 1414
1717 33337777 6565
77 7
4968 104 73 92 84
18 15 19 14
17 3377 65
7
Figure 5.6 |  Projected yield changes relative to the baseline period (2001–2010) without adaptation and with CO 2 fertilisation effects (Hasegawa et al., 
2021b). The box is the interquartile range (IQR), and the middle line in the box represents the median. The upper and lower end of whiskers are median 1.5 × IQR ± median. Open 
circles are values outside the 1.5 × IQR. (a) At different time periods (near future, NF , baseline to 2039; mid-century, MC, 2040–2069; end-century, EC, 2070–2100) under three 
RCPs, and (b) at different regions at EC.

5
737Food, Fibre and Other Ecosystem Products  Chapter 5
5.4.3.3 Projected impacts on other crops
Yield projections for crops other than cereals indicate mostly 
negative impacts on production due to a range of climate drivers 
(high confidence), with yield reductions similar to that of cereals 
expected in tropical, subtropical and semi-arid areas (Mbow et  al., 
2019). Springmann et al. (2016), compared the projected global food 
availability for different food groups under the SSP2 2050 scenario and found reductions in availability were similar in cereals, fruit and 
vegetables, and root and tubers (with legumes and oilseed crops 
showing a smaller reduction).
Fruit and vegetables have not been subject to extensive or coordinated 
yield projections (Figure 5.8). Yield projections have been performed for 
individual crops and locations (Ruane, 2014; Adhikari et al., 2015; Awoye

et al., 2017; Ramachandran et al., 2017), but more often crop suitability Projected yield changes relative to the baseline period (2001–2010)
without adaptation and with CO2 fertilization effects Numbers are the number of simulations
Maize Wheat Soybean RiceYield change (%)
at mid century
-100-50050Yield change (%) 
at end of centuryYield change (%)(a) As a function of current annual temperature (Tave) 
(b) As a function of global temperature rise from the baseline period by three current annual temperature (Tave) levelsCurrent annual temperature (Tave °C)
Temperature rise (°C)Tave 20<
Tave 10<20
Tave <10RCP2.6
RCP4.5
RCP8.534 183451245421
33 10323625331716 205320535368
61960335656649291 814
978 1235 365194563411
35 1752765319 9
-10 05 1015202530 -10 05 1015202530 -10 05 1015202530 -10 05 101520253053
56
45 322 105 52
≤ 1.5 ≤ 2.5 ≤ 3.5 3.5<89 342 137 76
≤ 1.5 ≤ 2.5 ≤ 3.5 3.5<75 92 41 5
≤ 1.5 ≤ 2.5 ≤ 3.5 3.5<5353 435 1159 2

≤ 1.5 ≤ 2.5 ≤ 3.5 3.5<53Maize Wheat Soybean Rice
Figure 5.7 |  Projected yield changes relative to the baseline period (2001–2010) without adaptation and with CO 2 fertilisation effects (Hasegawa et al., 
2021b). (a) Mid-century (MC, 2040–2069) and end-century (EC, 2070–2100) projections under three RCP scenarios as a function of current annual temperature (T ave), (b) as a 
function of global temperature rise from the baseline period by three T ave levels. See Figure. 5.6 for legends.

5
738Chapter 5 Food, Fibre and Other Ecosystem Products
models have been used (SM5.3). Zhao (2019) introduced a modelling 
approach that could be used to generate yield projections for a wider 
range of annual crops. The discussion here also draws on reviews of more 
restricted experimental studies. Negative impacts of climate change on 
crop production are expected across many cropping systems (Figure 5.8). 
Apart from the direct effects of elevated carbon dioxide, most changes 
are expected to have negative effects on crop production. Changes in 
temperature and rainfall are most often mentioned as drivers of climate 
impacts, but expected changes in phenology, pests and diseases are 
also raising concerns. Scheelbeek et al. (2018) synthesised projections 
for vegetables and legumes, based on their response to climate factors 
under experimental conditions; in most cases, the magnitude of the 
changes is comparable to the RCP8.5 2100 forecasts. Scheelbeek et al.

(2018) projected yield changes of: +22.0% (+11.6% to +32.5%) for a 
250 ppm increase in CO 2 concentration; −34.7% (−44.6% to −24.9%) 
for a 50% reduction in water availability; −8.9% (−15.6% to −2.2%) 
for a 25% increase in ozone concentration; −31.5% for a 4°C increase 
in temperature (in papers with a baseline temperature of >20°C). 
Overall, impacts are expected to be largely negative in regions where 
the temperature is currently above 20°C, while some yield gains are 
expected in cooler regions (provided that water availability and other 
conditions are maintained). Scheelbeek et al. (2018) did not consider 
changes in pest and disease pressure, which are projected to increase 
with warming (see SM5.3).
Systematic assessments of climate response for root crops as a group 
are lacking (Raymundo et al., 2014; Knox et al., 2016; Manners and 
van Etten, 2018). Climate suitability is projected to increase for tropical

root crops (SM5.3), and some studies have found that root crops will 
be less negatively impacted than cereals, but there is no consensus on 
this (Brassard and Singh, 2008; Adhikari et al., 2015; Schafleitner, 2016; 
Manners et al., 2021). For potato, Raymundo et al. (2018) projected 
global yield reductions of 2–6% by 2055 under different RCPs, but 
with important differences among regions; tuber dry weight may 
experience reductions of 50–100% in marginal growing areas such 
as central Asia, while increases of up to 25% are expected in many 
high-yielding environments. Projections show yield increases of 6% per 100 ppm elevation in CO 2 but declines of 4.6% per degree Celsius 
and 2% per 10% decrease in rainfall (Fleisher et al., 2017). Jennings 
et al. (2020) projected an overall increase in global potato production, 
but only if widespread adoption of adaptation measures is achieved. 
Although increases in CO 2 could produce positive yield responses,

the effects of temperature may offset these potential benefits (Dua 
et al., 2013; Raymundo et al., 2014). Warming offers the potential of 
longer growing seasons but can also have negative impacts through 
disrupted phenology and interactions with pests (Figure 5.8, Bebber, 
2015; Pulatov et al., 2015).
Global yield modelling is lacking for woody perennial crops. 
Experimental studies suggest negative impacts on yields due to reduced 
water supply and increased soil salinity, as well as from warming and 
ozone (although evidence was limited for these) (Alae-Carew et al., 
2020). Increasing CO 2 is expected to increase yields, but only where 
other factors, such as warming, do not become yield-limiting (Alae-
Carew et al., 2020). Many local projections include large uncertainty 
because of a lack of observational data and reliable parametrisation 
(Moriondo et  al., 2015; Mosedale et  al., 2016; Kerr et  al., 2018; 
Mayer et  al., 2019b). Most perennial crop models have found large

negative impacts on yield and suitability, although CO 2 fertilisation 
and phenology are not always considered (Lobell and Field, 2011; 
Glenn et al., 2013). Perennial crops are often grown in dryland areas 
where rainfall or irrigation water can be critical (Mrabet et al., 2020). 
Valverde (2015) found that yield losses in the Mediterranean region 
were largely driven by reduced rainfall, with maximum estimated 
yield losses of 5.4% for grape, 14.9% for olive and 27.2% for almond 
under a relatively hot and dry scenario (by 2041–2070). Moriondo 
(2015) highlight the need for perennial crop models to incorporate 
phenology and extreme climate events. Equally challenging is the need 
to estimate the impact of biotic changes, particularly climate-driven 
movement of pests and diseases (Ponti et al., 2014; Bosso et al., 2016; 
Schulze-Sylvester and Reineke, 2019).Synthesis of literature on the projected impacts of climate change on different cropping systems
Vegetable
Legumes

Soft fruit
Root crops
Tree fruits
and nutsAnnual
(above ground)
Annual (below ground)
PerennialAverage confidence in attribution
= not assesse d naAverage direction of impact
Low Medium HighClimate
change
(general)Temperature
(incl. heat
stress)Precipitatio n
(Incl.
drought,
flood)Ecosystem
servicesCombined
(sum of
all drivers ) Crop type Life history strategyPhenology
& seasonsCarbon
dioxid ePests &
diseases
na
na
na na na na na na na na na
na na na
na
Allna
Mixed Positive Negative
Figure 5.8 |  Synthesis of literature on the projected impacts of climate change on different cropping systems. The assessment includes projections of impacts on 
crop productivity over a range of emission scenarios and time periods. The projected impacts are disaggregated by the different climate and climate-related drivers. Impacts are

reported as positive, negative or mixed. The assessment draws on >60 articles published since AR5. The confidence is based on the evidence given in individual articles and on the 
number of articles. See SM5.2 information for details.

5
739Food, Fibre and Other Ecosystem Products  Chapter 5
Box 5.2: Case Study: Wine
Wine-growing regions cover 7.4 million ha, with a value of 35 billion USD in 2018 (OIV, 2019). Important regions (Italy, France, Spain, 
USA, Argentina, Australia, South Africa, Chile, Germany, China, Argentina) are located in areas where mean annual temperature roughly 
varies between 10°C and 20°C (Schultz and Jones, 2010; Mosedale et al., 2016).
Temperature is the primary determinant for vine development. Recent warming trends have advanced flowering, maturity and harvest 
(high confidence) (Koufos et al., 2014; Cook and Wolkovich, 2016; Hall et al., 2016; Ruml et al., 2016; van Leeuwen and Destrac-Irvine, 
2017; Koufos et  al., 2020; Wang et  al., 2020b; Wang and Li, 2020), and wine-growing regions have expanded outside the normal 
temperature bounds of locally grown varieties (limited evidence, high agreement) (Kryza et al., 2015; Irimia et al., 2018). Milder winters

have affected harvest in ice-wine growing regions (Pickering et al., 2015). Higher temperatures have mixed effects depending on site, but 
generally decrease grape quality (Barnuud et al., 2014; Morales et al., 2014; Sweetman et al., 2014; Kizildeniz et al., 2015; Kizildeniz et al., 
2018). Warming increases sugar accumulation and decreases acidity (Leolini et al., 2019). Secondary metabolites are negatively affected 
(Biasi et al., 2019; Teslić et al., 2019). Developmental phases are projected to proceed faster in response to warming (high confidence) 
(Fraga et al., 2016a; Fraga et al., 2016b; García de Cortázar-Atauri et al., 2017; Costa et al., 2019; Molitor and Junk, 2019; Sánchez, 2019). 
However extreme high temperatures may have inhibitory effects on development (Cuccia et al., 2014).
In some cases, irrigation is required, and more frequent droughts are a key concern for yield and fruit quality (Morales et al., 2014;

Bonada et al., 2015; Kizildeniz et al., 2015; Salazar-Parra, 2015; Kizildeniz et al., 2018; Funes et al., 2020). Water stress reduces shoot 
growth and berry size, and increases tannin and anthocyanin content (van Leeuwen and Darriet, 2016). However, controlled water stress 
produces positive impacts on wine quality, increasing skin phenolic compounds (van Leeuwen and Destrac-Irvine, 2017). The level of 
stress will depend on soil type, texture and organic matter content (Fraga et al., 2016a; Fraga et al., 2016b; Bonfante, 2017; García de 
Cortázar-Atauri et al., 2017; Leibar et al., 2017; Costa et al., 2019; Molitor and Junk, 2019; Sánchez, 2019). Increases in water demands 
with potential negative effects from increased soil salinity are among the most common effects of climate change in irrigated regions 
(medium evidence, high agreement) (Mirás-Avalos et al., 2018; Phogat et al., 2018).

Rising CO 2 will have mixed effects on vine growth and quality (medium evidence, high agreement) (Martínez-Lüscher et  al., 2016; 
Edwards et al., 2017; van Leeuwen and Destrac-Irvine, 2017). Rising CO 2 concentrations will negatively affect wine quality by reducing 
anthocyanin concentration and colour intensity (Leibar et al., 2017).
Suitability responses to warming are region-specific. In regions where low temperature is a limiting factor, warming will enable growers 
to grow a wider range of varieties and obtain better-quality wines (high confidence) (Fuhrer et al., 2014; Mosedale et al., 2015; Mosedale 
et al., 2016; Meier et al., 2018; Jobin Poirier et al., 2019; Maciejczak and Mikiciuk, 2019). Subtropical and Mediterranean regions will 
experience major declines in fruit quality for high-quality wines (high confidence) (Resco et al., 2016; Lazoglou et al., 2018; Cardell et al.,

2019; Fraga et al., 2019a; Fraga et al., 2019b; Teslić et al., 2019). These changes will also affect wine tourism (Nunes and Loureiro, 2016).
Impacts on suitability may reshape the geographical distribution of wine regions. Viability of the wine-growing regions will depend on the 
knowledge of local climatic variability (Neethling et al., 2019; Rességuier et al., 2020) and the implementation of adaptation strategies 
such as use of adapted plant material rootstocks, cultivars and clones, viticultural techniques (e.g., changing trunk height, leaf area to 
fruit weight ratio, timing of pruning), irrigation, enological interventions to control alcohol and acidity, and policy incentives and support 
(Callen et al., 2016; Ollat and Leeuwen, 2016; van Leeuwen and Destrac-Irvine, 2017; Merloni et al., 2018; Alikadic et al., 2019; del Pozo 
et al., 2019; Fraga et al., 2019b; Santillan et al., 2019; Morales-Castilla et al., 2020; Marín et al., 2021).
Box 5.3: Pollinators

Climate change will reduce the effectiveness of pollinator agents as species are lost from certain areas, or the coordination of pollinator 
activity and flower receptiveness is disrupted in some regions (high confidence) (Potts et al., 2010; Gonzalez-Varo et al., 2013; Polce 
et al., 2014; Kerr et al., 2015; Potts et al., 2016; Settele et al., 2016; Giannini et al., 2017; Mbow et al., 2019). A modelling study estimates 
that complete removal of pollinators could reduce global fruit supply by 23%, vegetables by 16%, and nuts and seeds by 22%, leading 
to significant increases in nutrient-deficient population and malnutrition-related diseases (Smith and Haddad, 2015), highlighting the 
importance of this ecosystem service for human health.

5
740Chapter 5 Food, Fibre and Other Ecosystem Products
Bees are an essential agricultural pollinator, widely recognised for their role in the fertilisation of many domesticated plants. The observed 
widespread decline in native bees and honeybee colony numbers, particularly in the USA and Europe, has been associated with a number 
of environmental stressors in addition to climate change, such as neonicotinoids and varroa mites, and has raised concerns regarding 
plant–pollinator networks, the stability of pollination services, global food production and the prevalence of malnutrition (Williams and 
Osborne, 2009; Potts et al., 2010; Chaplin-Kramer et al., 2014).
Any climatic influence on floral phenology or physiology could, potentially, alter bee biology. At present, there is evidence that climate-
change-induced asynchrony in pollen and pollinators can occur (Stemkovski et al., 2020). In addition, the nutritional composition of floral

pollen may also affect bees’ health at the global level (low evidence). For example, goldenrod (Solidago spp.), a ubiquitous pollen source 
for bees just prior to winter, has experienced a ~30% drop in protein since the onset of CO 2 emissions from the industrial revolution 
(Ziska et al., 2016).
Climate extremes could pose risks to pollinators when species tolerance is exceeded, with subsequent reduction in populations and 
potential extirpation (Nicholson and Egan, 2020; Soroye et al., 2020). The rate of climate change may induce potential mismatches in 
the timing of flowering and pollinator activity depending on the species (Bartomeus et al., 2011). For instance, Miller-Struttmann (2015) 
showed that long-tongued bumblebees may be at a disadvantage as warming temperatures are reducing their floral hosts, making 
generalist bumblebees more successful.

Overall, there is medium confidence that long-term mutualisms may be impacted directly by CO 2 increases in terms of nutrition, or by 
temperature and other climatic shifts that may alter floral emergence relative to pollinator life cycles. Additional research is needed to 
further our understanding of the biological basis for these effects, and their consequence for pollination services.Box 5.3 (continued)
Table 5.4 |  Projected impacts on CES from climate change.
Region CES Climate change scenario Projected impacts from climate change References
Central Chile, South AmericaAesthetic experience of scenic 
beauty in vine-growing region.RCP2.6 and 8.5.Increased temperature, reduced precipitation and 
increased fires will damage scenic beauty of vineyards. 
Participatory scenario analysis estimated reduction in 
aesthetic experience from scenic beauty by 18–28% by 
2050 for RCP2.6, with greater impacts under RCP8.5.Martinez-Harms 
Mountainous regions of

AustriaCultural and aesthetic 
experiences in alpine pastures 
and diverse agricultural 
landscapes.Temperature +1.5°C from 2008 
to 2040 and four precipitation 
scenarios (high, similar, 
seasonal shift and low).Some decline in CES, with trade-offs between diversity 
and CES and provisioning services depending upon the 
scenario.Kirchner et al. (2015)
Forest and agricultural 
landscapes in southern 
Saxony-Anhalt in GermanyRecreation, scenic landscape 
beauty and spiritual value of 
agricultural landscapes and 
forests.Regional scenarios, do not 
specify RCPs.Not anticipated to be significantly changed by climate 
change under most scenarios, except for intensification 
scenario, which would lead to a decline in the forest 
cultural services as they provide important historical and 
cultural ties.Gorn et al. (2018)
Northeast Austria floodplains 
(grasslands and wetlands)Tourism, recreation, cultural 
heritage.Increased temperature by 2050 
and 2100 and seasonal shifts in

precipitation.Increased agricultural intensification due to shifts in 
climate and decline in CES is predicted, based on farmer 
interviews.Probstl-Haider et al. 
(2016)
Mount Kenya, KenyaTourism, recreation, spiritual 
and cultural values.Not specified.Glacier disappearance may lead to reduced mountain 
trekking and other tourism and recreational activities.Evaristus (2014)
PhilippinesNature-based tourism in 
agri-tourism.Not specified.Risk of typhoon, drought and strong wind, grass fire, 
heavy rains. Anticipated to increase vulnerability in terms 
of human health services and energy use in tourism.Hidalgo (2015)

5
741Food, Fibre and Other Ecosystem Products  Chapter 5
For cotton, experimental studies suggest positive impacts from rising 
CO 2 and temperature (Zhang et  al., 2017a; Jans et  al., 2021), but 
projections show mixed impacts on yield, including large negative 
impacts in warmer regions due to heat, drought and the interaction of 
temperature with phenology (Yang et al., 2014; Williams et al., 2015; 
Adhikari et  al., 2016; Rahman et  al., 2018). Climate change is also 
expected to increase the demand for irrigation water, which will likely 
limit production (Jans et al., 2021). There are also concerns that fibre 
quality may deteriorate (e.g., air permeability of compressed cotton 
fibers) (Luo et al., 2016).
Higher temperatures and altered moisture levels are expected to 
present a food safety risk, particularly for above-ground harvested 
vegetables (Figures  5.8; 5.10). Warmer and wetter weather is anticipated to increase fungal and microbial growth on leaves and fruit,

while altered flooding regimes increase the risk of crop contamination 
(Liu et al., 2013; Uyttendaele et al., 2015). This is also true for perennial 
crops; for example, warming and climate variability can increase fungal 
contamination of grapes, including that associated with mycotoxins 
(Battilani, 2016; Paterson, 2018).
5.4.3.4 Observed and projected impacts on cultural ecosystem 
service
Cultural ecosystem services (CES) are those non-material benefits, 
such as aesthetic experiences, recreation, spiritual enrichment, social 
relations, cultural identity, knowledge and other values (Millennium 
Ecosystem Assessment, 2005), which support physical and mental health Box 5.4: Soil Health
Soil health, defined as an integrative property that reflects the capacity of soil to respond to land management, continues to support 
provisioning ecosystem services (Kibblewhite et al., 2008). Climate change will have significant impacts on soil health indicators such

as soil organic matter (SOM). For example, precipitation extremes can reduce soil biological functions, and increase surface flooding, 
waterlogging, soil erosion and susceptibility to salinisation (Herbert et al., 2015; Chen and Mueller, 2018; Akter et al., 2019; Sánchez-
Rodríguez et al., 2019).
The most significant threat to soil health is the loss of SOM (FAO and ITPS, 2015). SOM holds a great proportion of the nutrients, and 
regulates important soil physical, chemical and biological processes, such as cation exchange capacity, pH buffering, soil structure, 
water-holding capacity and microbial activity (FAO and ITPS, 2015). Soils also hold the largest terrestrial organic carbon stock, three to 
four times greater than the atmosphere (Stoorvogel et al., 2017). At the global scale, climate and vegetation are the main drivers of soil 
organic carbon (SOC) storage (Wiesmeier et al., 2019). While organic matter input is the primary driver of SOC stocks (Fujisaki et al.,

2018), temperature and soil moisture play a key role in SOC storage at the local scale (Carvalhais et al., 2014; Doetterl et al., 2015). Soil 
type, land use and management practices also play important roles at the local scale.
Increase in soil temperature will negatively impact SOC, but primarily in higher latitudes (medium confidence) (Carey et al., 2016; Qi et al., 
2016; Feng et al., 2017; Gregorich et al., 2017; Hicks Pries et al., 2017; Melillo et al., 2017; Hicks Pries et al., 2018). Experiments have 
shown that warming can accelerate litter mass loss and soil respiration (Lu et al., 2013) and reduces the soil recalcitrant C pool (Chen 
et al., 2020). SOC losses may speed up soil structural degradation, changes in soil stoichiometry and function (Hakkenberg et al., 2008; 
Tamene et al., 2019), with downstream effects on aquatic ecosystems. The rate and extent of SOC losses vary greatly depending on the

scale of measurement (local to global), soil properties, climate, land use and management practices (Sanderman et al., 2017; Wiesmeier 
Adoption of practices that build SOC can improve crop resilience to climate-change-related stresses such as agricultural drought. Iizumi 
and Wagai (2019) found that a relatively small increase in topsoil (0–30 cm) SOC could reduce drought damages to crops over 70% of 
the global harvested area. The effects of increasing SOC are more positive in drylands owing to more efficient use of rainwater, which 
can increase drought tolerance (Iizumi and Wagai, 2019). Similarly, Sun et al. (2020) found that, relative to local conventional tillage, 
conservation agriculture has a win-win outcome of enhanced C sequestration and increased crop yield in arid regions. However, the 
impact of no-till may be minimal if not supplemented with residue cover and cover crops. As such, this is a highly debated area where

some authors argue that no-till has limited effect and the evidence outside drylands is weak. Furthermore, the use of crop residues is 
constrained by its alternative uses (e.g., fuel, livestock feed, etc.) in much of the developing world. Practices that build up SOC may 
encourage soil microbial populations, which in turn can increase yield stability under drought conditions (Prudent et al., 2020).
Soil C sequestration is an important strategy to improve crop and livestock production sustainably that could be applied at large scales 
and at a low cost, if there was adequate institutional support and labour, using agroforestry, conservation agriculture, mixed cropping 
and targeted application of fertilizer and compost (high confidence) (Paustian et al., 2016; Kongsager, 2018; Nath et al., 2018; Woolf et al., 
2018; Corbeels et al., 2019; Kuyah et al., 2019; Corbeels et al., 2020; Muchane et al., 2020; Sun et al., 2020; Nath et al., 2021). For example,

a widespread adoption of agroforestry, conservation agriculture, mixed cropping and balanced application of fertilizer and compost by 
India’s small landholders could increase annual C sequestration by 70–130 Tg CO 2e (Nath et al., 2018; Nath et al., 2021).

5
742Chapter 5 Food, Fibre and Other Ecosystem Products
and human well-being (Chan et al., 2012; Triguero-Mas et al., 2015). 
CES in agricultural and wild landscapes include recreational activities, 
access to wild or cultivated products, and cultural foods, spiritual rituals, 
heritage and memory dimensions, and aesthetic experiences (Daugstad 
et  al., 2006; Calvet-Mir et  al., 2012; Ruoso et  al., 2015). Relative to 
other ecosystem services, CES in agricultural landscapes have been less 
researched (Merlín-Uribe et al., 2012; Milcu et al., 2013; Bernues et al., 
2014; Plieninger et al., 2014; van Berkel and Verburg, 2014; Ruoso et al., 
2015; Quintas-Soriano et al., 2016). Agricultural heritage is a key aspect 
of CES and plays an important role in maintaining agrobiodiversity 
(Hanaček and Rodríguez-Labajos, 2018).
Climate change is projected to have negative impacts on CES (medium 
confidence) (Table 5.4). There is limited evidence that climate change has

been the main driver affecting CES of agroecosystems confounded by 
other drivers such as migration and changing farming patterns (Hanaček 
and Rodríguez-Labajos, 2018; Dhakal and Kattel, 2019). Recent studies 
observed declines in CES in alpine pastures and floodplains in Europe in 
part due to climate change impacts (Probstl-Haider et al., 2016; Schirpke 
et al., 2019). Another study estimated that the scenic beauty enjoyed by 
those who visit the vineyards in central Chile will decline by 18–28% 
by 2050 owing to a combination of reduced precipitation, increased 
temperatures and natural fire cycles (Martinez-Harms et  al., 2017). 
More research is needed, however, particularly on cultural heritage and 
spiritually significant places and in low-income countries.
5.4.4 Adaptation Options
Adaptation strategies in crop production range from field and farm-
level technical options such as crop management and cultivar/crop

options to livelihood diversification and income protection such 
as index-based insurance. This section assesses crop management 
options for different crop types. Feasibility of adaptation options in 
various systems is addressed in Section 5.14.
5.4.4.1 Adaptation options for major crops
Crop management practices are the most commonly studied adaptation 
measures (Shaffril et  al., 2018; Hansen et  al., 2019a; Muchuru and 
Nhamo, 2019), but quantitative assessments are mostly limited to 
existing agronomic options such as changes in planting schedules, 
cultivars and irrigation (Beveridge et al., 2018a; Aggarwal et al., 2019). 
This section draws on the global data set used in Section  5.4.3.2 
(Hasegawa et  al., 2021b) to estimate adaptation potential, defined 
as the difference in simulated yields with and without adaptations. A 
caveat to the analysis is that the data set includes management options 
if the literature treats them as adaptation. They include intensification

measures such as fertilizer and water management, not allowing for 
physical and economic feasibility.
The overall adaptation potential of existing farm management 
practices to reduce yield losses averaged 8% in mid-century and 
11% in end-century (Figure  5.9), which is insufficient to offset the 
negative impacts from climate change, particularly in currently warmer 
regions (Section 5.4.3.2). Emission scenarios, crop species, regions and 
adaptation options do not show discernible differences. Combinations of two or more options do not necessarily have greater adaptation 
potential than a single option, though a fair comparison is difficult 
in the data set from independent studies. One regional study in West 
Africa found that currently promising management would no longer 
be effective under future climate, suggesting the need to evaluate 
effectiveness under projected climate change.
A global-scale meta-analysis estimated a 3–7% yield loss per degree

Celsius increase in temperature (Zhao et  al., 2017). Two global-scale 
studies using multiple global gridded crop models found that growing-
season adaptation through cultivar changes offsets global production 
losses up to 2°C of temperature increase (Minoli et al., 2019; Zabel et al., 
2021). While these studies do not account for CO 2 fertilisation effects, 
another global-scale study with the CO 2 fertilisation effects (Iizumi 
et  al., 2020) showed that residual damage (climate change impacts 
after adaptation) would start to increase almost exponentially from 
2040 towards the end of the century under RCP8.5. The cost required 
for adaptation and due to residual damage is projected to rise from 
USD 63 billion at 1.5°C to USD 80 billion at 2°C and to USD 128 billion 
at 3°C (Iizumi et al., 2020). All these global studies project that risks 
and damages are greater in tropical and arid regions, where crops are 
exposed to heat and drought stresses more often than in temperate

regions (Sun et al., 2019; Kummu et al., 2021; SM5.4). There are still 
large uncertainties in the crop model projections (Müller et al., 2021a), 
but these multiple lines of evidence suggest that warming beyond 
+2°C (projected to be reached by mid-century under high-emission 
scenarios) will substantially increase the cost of adaptation and the 
residual damage to major crops (high confidence). The residual damage 
will prevail much sooner in currently warmer regions, where the effect 
of even a modest temperature increase is greater (Section 5.4.3.2).
Most crop modelling studies on adaptation are still limited to a handful 
of options for each crop type (Beveridge et al., 2018a). A range of other 
options are possible not just to reduce yield losses but to diversify 
risks to livelihoods, which are partially assessed in Sections 5.4.4.4 and 
5.14.1. Current modelling approaches are not suited for the assessment 
of multiple dimensions of adaptation options. New studies are

emerging that evaluate multiple options for productivity, sustainability 
and GHG emission (Xin and Tao, 2019; Smith et al., 2020b), but local- 
and household-scale assessment, taking account of future climatic 
variability, needs to be enhanced (Beveridge et al., 2018a).
5.4.4.2 Adaptation options for other crops
Across this diverse group of cropping systems, distinct adaptation 
options and adaptation limits have emerged (Figure  5.10; Acevedo 
et  al., 2020; Berrang-Ford et  al., 2021b). Some crop types have 
already seen widescale implementation of climate adaptation (e.g., 
grapevines), while others show little evidence of preparation for 
climate change (e.g., leafy salad crops). Many adaptation responses 
are shared with the major crops, but prominent options such as plant 
breeding are underutilised and there is a lack of evidence for assessing 
adaptation for many crops (Bisbis et  al., 2018; Gunathilaka et  al.,

2018; Manners and van Etten, 2018). Figure  5.10 assesses several 
adaptation options based on the perceived importance of each in 
the literature. Fruit and vegetable crops tend to be more reliant on 
ecosystem services in the form of pollination, biocontrol and other

5
743Food, Fibre and Other Ecosystem Products  Chapter 5
resources (water, nutrients, microbes, etc.), and ecosystem-based 
adaptation options are prominent. The range of crops means that 
there is great potential for crop switching, but cultural and economic 
barriers will make such options difficult to implement, with barriers 
to entry for production and marketing (Waha et  al., 2013; Magrini 
et  al., 2016; Kongsager, 2017; Rhiney et  al., 2018). Perennial crops 
are exposed to a wide range of climate factors throughout the year 
and have significant barriers to implementing some of the common 
adaptation options, such as relocation or replacing tree species/
cultivar; agronomic interventions on-farm are well used in high-
value tree crops and provide some climate resilience, but longer-term 
options will be needed (Glenn et  al., 2013; Mosedale et  al., 2016; 
Gunathilaka et al., 2018; Sugiura, 2019).
Many fruit and vegetable crops are water demanding, and adaptation

responses relating to water management and access to irrigation water are crucial. Rainwater storage and deficit irrigation techniques 
are frequently mentioned as adaptation options and can minimise the 
burden on off-farm water supplies (Bisbis et al., 2018; Acevedo et al., 
2020).
5.4.4.3 Cultivar improvements
As stated in AR5, cultivar improvements are one effective counter -
measure against climate change (Porter et al., 2014; Challinor et al., 
2016; Atlin et  al., 2017). Plant breeding biotechnology for climate 
change adaptation draws upon modern biotechnology and con-
ventional breeding, with the latter often assisted by genomics and 
molecular markers. Plant breeding biotechnology will contribute to 
adaptation for large-scale producers (high confidence). However, in 
addition to inconsistencies in meeting farmer expectations, a variety

of socioeconomic and political variables strongly influence, and limit, Adaptation potential defined as the difference between yield impacts with and without adaptation in projected impacts
(a)
Mid-century (2040–2069) End-century (2070–2100)
334 97 33 12 22 51 1 346 134 29 81 26 21 83Soil organi c
matter managemen tCombined
Cultivar
Fertiliser
Irrigation
Planting date
Soil organic
matter management
Tillage
Others
Combined
Cultivar
Tillage
Others-250255075Adaptation potential (%)RCP2.6
RCP4.5
RCP8.5Maize
32 31 15 39 19
30 39 15 39 23Africa
Asia
Europe
North America-250255075
-250255075Adaptation potential (%)Rice
9124 33
37 125 32 43Africa
Asia
Europe
South AmericaSoybean
99 5
99 3Africa
Asia
Europe
South AmericaMid-century (2040–2069) End-century (2070–2100)Wheat
23 72 39 48 97
23 75 39 48 95Africa
Asia
Europe

(b)
Adaptation optionsNumbers are the number of simulationsRegions
Figure 5.9 |  Adaptation potential, defined as the difference between yield impacts with and without adaptation in projected impacts (Hasegawa et al., 
2021b). (a) Projections under three RCP scenarios by regions and (b) by options at mid-century (MC, 2040–2069) and end-century (EC, 2070–2100). n is the number of 
simulations. See Figure 5.6 for legends.

5
744Chapter 5 Food, Fibre and Other Ecosystem Products
uptake of climate-resilient crops (Acevedo et al., 2020; Rhoné et al., 
2020).
Genome sequencing significantly increases the rate and accuracy 
for identifying genes of agronomic traits that are relevant to climate 
change, including adaptation to stress from pests and disease, 
temperature and water extremes (high confidence) (Brozynska et al., 
2016; Scheben et al., 2016; Voss-Fels and Snowdon, 2016). Access to 
this information where it is needed and in practical timeframes, as well 
as the expertise to use it, will limit the sharing of benefits by the most 
vulnerable groups and countries (high agreement, limited evidence) 
(Heinemann et al., 2018).
Genetic improvements for climate change adaptation using modern 
biotechnology have not reliably translated into the field (Hu and Xiong, 
2014; Nuccio et al., 2018; Napier et al., 2019), but good progress has 
been made by conventional breeding. Desirable traits that adapt plants

to environmental stress are inherited as a complex of genes, each of 
which makes a small contribution to the trait (Negin and Moshelion, 
2017). Adaptation by conventional breeding requires making rapid 
incremental changes in the best germplasm to keep pace with the 
environment (Millet et al., 2016; Atlin et al., 2017; Cobb et al., 2019). 
Further improvements would be difficult without in situ and ex situ 
conservation of plant genetic resources to maintain critical germplasm 
for breeding (Dempewolf et al., 2014; Castañeda-Álvarez et al., 2016).
Despite the advances in sequencing, phenotyping remains a significant 
bottleneck (Ghanem et al., 2015; Negin and Moshelion, 2017; Araus 
and Kefauver, 2018); the emergence of high-throughput phenotyping 
platforms may reduce this bottleneck in future. Emerging modern 
biotechnology such as gene/genome editing may in the future increase

the ability to better translate genetic improvements into the field (medium agreement, limited evidence) (Puchta, 2017; Yamamoto 
et al., 2018; Friedrichs et al., 2019; Kawall, 2019; Zhang et al., 2019b).
Other breeding approaches assisted by genomics have been making 
steady gains in introducing traits that adapt crops to climate 
change (high confidence). DNA sequence information is used to 
identify markers of desirable traits that can be enriched in breeding 
programmes, as well as to quantify the genetic variability in species 
(Gepts, 2014; Brozynska et al., 2016; Voss-Fels and Snowdon, 2016). 
However, breeding for smallholder farmers and the stresses caused by 
climate change are unlikely to be addressed by the private sector and 
will require more public investment and adjusting to the local social-
ecological system (Glover, 2014; Heinemann et  al., 2014; Acevedo 
et al., 2020). Modern biotechnology has not demonstrated the scale

neutrality needed to serve smallholder-dominated agroecosystems, 
due to a combination of the kinds of traits and restrictions that come 
from the predominant intellectual property rights instruments used in 
their commercialisation, as well as the focus on a small number of 
major crop species (medium confidence) (Fischer, 2016; Montenegro 
de Wit et al., 2020).
Globally, there is a notable lack of programmes aimed specifically at 
breeding for climate resilience in fruits and vegetables, although there 
have been calls to begin this process (Kole et al., 2015). Breeding for 
climate resilience in vegetables has great potential given the range 
of crop species available. Tolerance to abiotic stress is reasonably 
advanced in pulses (Araújo et  al., 2015; Varshney et  al., 2018), but 
examples of translation to commercial cultivars are still limited 
(Varshney et  al., 2018; Varshney et  al., 2019). The infrastructure for

germplasm collection, maintenance, testing and breeding lags behind 
that of major crops (partly because of the large number of species 
involved) (Keatinge et al., 2016; Atlin et al., 2017).
Synthesis of literature on the implementation of on-farm adaptation options across different cropping systems
Vegetable
Legumes
Soft fruit
Root crops
Tree fruits & nutsAnnual
PerennialCrop type Life history strategy
All
= not assesse d naAdaptation option’s implementation status
Untested TestedPlant
breeding/
bio-
technologyClimate
servicesShift
planting
areaShift
planting
dateShift
crop/
cultivarConservation
agricultureAgro-
ecology/
agroforestry AgronomyWater
manegementOn-farm
diversityCombined
sum of
listed
options
na na
na na
na na
na
na
Figure 5.10 |  Synthesis of literature on the implementation of on-farm adaptation options across different cropping systems. Adaptation options that have

been implemented by growers are considered ‘tested’, while those that have not are considered ‘untested’. Untested options are those that appear in studies as suggestions by 
stakeholder or experts but were not implemented within the study. The assessment draws on >200 articles published since AR5. The confidence is based on the evidence given in 
individual articles and on the number of articles. See SM5.2 for details.

5
745Food, Fibre and Other Ecosystem Products  Chapter 5
Table 5.5 |  PPB as cultivar improvement adaptation method.
RegionCrop(s) used for 
breedingResults
West AfricaSorghum and pearl 
millet –Released sorghum and millet varieties which were selected for climate variability (e.g., drought), low soil fertility, pest and disease 
resistance, gendered preferences for processing, and nutrition (Camacho-Henriquez et al., 2015; Weltzien et al., 2019).
 –Farmers who adopted these varieties increased yield, income and food security, alongside increased technical knowledge of plant 
breeding, and increased breeders’ understanding of local farmers’ varietal requirements (Trouche et al., 2016).
 –Joint learning with scientists led to increased genetic gain both in terms of operational scale and focused breeding for diverse 
farmer priorities (Weltzien et al., 2019).

South America (Andes) Potato –PPB with Indigenous Quechua and Aymara farmers resulted in potato varieties with traits from wild relatives, with yield stability, 
higher yields under low input use and disease resistance under climate change impacts such as increased hail or frost events and 
upward expansion of pests and diseases (Camacho-Henriquez et al., 2015; Scurrah et al., 2019).
Asia (southwest China) Maize –PPB done primarily with women farmers, led to 1500 landraces safeguarded, 12 farmer-preferred varieties released and 30 landraces 
released, bred for improved yield (15–20% increases), drought resistance, taste, market potential and other priority traits (Song 
 –Studies suggest PPB improved farmer knowledge, income and access to resilient seeds, and strengthened institutions such as 
women-led farmer cooperatives and Farmers’ Seed Network of China (Song et al., 2019).

Table 5.6 |  Agroecosystem diversification practices, climate change adaptation mechanisms, trade-offs, co-benefits and constraints to implementation.
Agroecosystem diversification practice and
mechanism for climate change adaptationBenefits, trade-offs and constraints to implementation with examples
Crop diversification
 –Diversifying revenue streams and food supply (portfolio 
effect).
 –Can impact multiple plant and soil biological and 
physicochemical properties associated with building 
SOM, improving soil structure and water conservation. –Crop diversification reduces cereal crop sensitivity to precipitation variability, yield losses and crop insurance payouts 
under drought (high confidence) (McDaniel et al., 2014; Williams et al., 2016; Iizumi and Wagai, 2019; Renwick et al., 2020; 
Huang et al., 2021; Kane et al., 2021).
 –For example, a study in Canada comparing diversified rotations and monoculture corn found significant positive yield

impacts, yield stability and increased SOC under both RCP4.5 and RCP8.5 by 2100 (Jarecki et al., 2018).
 –Diverse agroecosystems with a range of native, neglected and introduced species, often maintained through Indigenous 
knowledge and farmer seed systems, offer adaptation opportunities in some regions (medium evidence, high agreement) 
(Bezner Kerr, 2014; Westengen and Brysting, 2014; Camacho-Henriquez et al., 2015; Ghosh-Jerath et al., 2015; Adhikari 
et al., 2017; Li and Siddique, 2018; Scurrah et al., 2019).
 –Diversified landscapes can also enhance CES, by supporting cultural heritage crops, recreational and aesthetic experiences 
(medium confidence) (Novikova et al., 2017; Martínez-Paz et al., 2019; Alcon et al., 2020).
 –Diversified cropping systems often require new knowledge, equipment access to inputs and viable markets for new 
products (van Zonneveld et al., 2020). Barriers to diversification, or those which support agroecosystem simplification,

include environmental constraints such as elevation or soil type, along with institutional constraints such as low research 
investment, limited policy support, subsidies that encourage monocrops, poor market access, market instability and limited 
access to seeds (Kaushal and Muchomba, 2015; DeLonge et al., 2016; Burchfield and de la Poterie, 2018).
Legume diversification can be effective for both 
mitigation and adaptation, by reducing use of nitrogen 
derived from fossil fuels, and meat consumption, and 
providing ecosystem services through nutrient cycling, 
increasing soil biological activity and erosion control 
(Snapp et al., 2019). –Can increase food security and nutrition by increasing cereal productivity and stability in intercropped systems, diversify 
diets and increase income in crop sales (high agreement, medium evidence) (Snapp et al., 2019; Steward et al., 2019;

Renwick et al., 2020), but legume production may be constrained by pest, disease, limited access to genetic material, 
market access and food preferences (Anders et al., 2020).
Organic amendments, no/low tillage or crop residue 
retention may increase diversity in soil biological 
organisms, which might be important in building resilience 
to multiple stresses such as drought and pest pressure 
(Furze et al., 2017; Blundell et al., 2020; de Vries et al., 
2020; Stefan et al., 2021; Yang et al., 2021). –Higher organic matter does not consistently improve soil hydraulic properties (Minasny and McBratney, 2018; Basche and 
DeLonge, 2019).
 –Can decrease yield variability under dry conditions and increase rainfed annual crop yield productivity (high 
agreement) (Pittelkow et al., 2014; Williams et al., 2016; Williams et al., 2018; Degani et al., 2019; Steward et al., 2019; 
Bowles et al., 2020; Marini et al., 2020; Sanford et al., 2021).

Livestock integration. Inclusion of legumes and other 
forage into crop rotation allows mixed crop and livestock 
operations to mitigate farm-level risk and ecosystem 
buffering. –Benefits to productivity and stability of annual crop yields in some contexts (see Section 5.10.3, high agreement, medium 
evidence) (Stark et al., 2018; Peterson et al., 2020; de Albuquerque Nunes et al., 2021).
Traditional and locally adapted mixed cropping and 
agroforestry practices which include leguminous trees 
can improve soil fertility and microclimate (Sida et al., 
2018; Amadu et al., 2020).Benefits: resilience to extreme events such as hurricanes can be promoted by supporting ecosystem functions to mitigate 
impacts and accelerate recovery (high agreement, medium evidence) (Altieri et al., 2015; Simelton et al., 2015; Sida et al., 
2018; Perfecto et al., 2019).
 –Can increase food security, livelihoods and productivity, but local context and resource availability must be considered to

optimise species arrangement and benefits and can have considerable implementation barriers and costs (high confidence) 
(see Sections 5.10.3, 5.14 and Cross-Chapter Box NATURAL in Chapter 2). (Altieri et al., 2015; Simelton et al., 2015; Sida 
et al., 2018; Perfecto et al., 2019).

5
746Chapter 5 Food, Fibre and Other Ecosystem Products
Participatory plant breeding (PPB) facilitates interaction between 
Indigenous and local knowledge systems and scientific research and 
can be an effective adaptation strategy in generating varieties well 
adapted to the socio-ecological context and climate hazards (high 
confidence) (Table  5.5, Westengen and Brysting, 2014; Humphries 
et al., 2015; Anderson et al., 2016; Migliorini et al., 2016; Leitão et al., 
2019; Ceccarelli and Grando, 2020; Singh et al., 2020).
5.4.4.4 Integrated approach to enhance agroecosystem 
resilience
Diversifying agricultural systems is an adaptation strategy that can 
strengthen resilience to climate change, with socioeconomic and 
environmental co-benefits, but trade-offs and benefits vary by socio-
ecological context (high confidence) (Table  5.6, M’Kaibi et  al., 2015; 
Bellon et  al., 2016; Jones, 2017b; Schulte et  al., 2017; Jarecki et  al.,

2018; Jones et al., 2018; Luna-Gonzalez and Sorensen, 2018; Sibhatu 
and Qaim, 2018; Renard and Tilman, 2019; Rosa-Schleich et al., 2019; 
Bozzola and Smale, 2020; Mulwa and Visser, 2020). Crop diversification 
alongside livestock, fish and other species can be applied at various 
scales in a range of systems, from rainfed or irrigated to urban and home 
gardens in multiple spatial and temporal arrangements such as mixed 
planting, intercrops, crop rotation, diversified management of field 
margins, agroforestry (Section 5.10.1.3) and integrated crop livestock 
systems (Section 5.10.1.1, Isbell et al., 2017; Kremen and Merenlender, 
2018; Dainese et al., 2019; Rosa-Schleich et al., 2019; Hussain et al., 
2020; Renwick et al., 2020; Tamburini et al., 2020; Snapp et al., 2021; 
see Section 5.14 and Cross-Chapter Box NATURAL in Chapter 2).
Diversification improves regulating and supporting ecosystem services 
such as pest control, soil fertility and health, pollination, nutrient

cycling, water regulation and buffering of temperature extremes (high 
confidence) (Barral et al., 2015; Prieto et al., 2015; Tiemann et al., 2015; 
Schulte et al., 2017; Beillouin et al., 2019a; Dainese et al., 2019; Kuyah 
et al., 2019; Tamburini et al., 2020), which can in turn mediate yield 
stability and reduced risk of crop loss according to socio-ecological 
contexts and time since adoption (high confidence) (Prieto et al., 2015; 
Roesch-McNally et al., 2018; Sida et al., 2018; Williams et al., 2018; 
Birthal and Hazrana, 2019; Degani et al., 2019; Amadu et al., 2020; 
Bowles et al., 2020; Li et al., 2020; Sanford et al., 2021).
Agroecosystem diversification often has variable impacts depending 
on crop combination, agro-ecological zone and soil types, and rigorous 
assessments of adaptive gains with traditional and locally diversified 
systems and potential trade-offs still need to be conducted across 
socio-ecological contexts.  The quantitative upstanding will assist in

enhancing multiple benefits of diversification tailored for each condition 
(Table  5.6). Progress is also needed via breeding and/or agronomy 
to adapt underutilised as well as major food crops to diversified 
agroecosystems and optimise management of nutrients, pest and 
disease pressure and other socio-ecological constraints (Araújo et al., 
2015; Foyer et al., 2016; Adams et al., 2018; Pang et al., 2018).
Managing for diversity and flexibility at multiple scales is central to 
developing adaptive capacity. Policies to support diversification include 
shifting subsidies towards diversified systems, public procurement for 
diverse foods for schools and other public institutions, investment in shorter value chains, lower insurance premiums and payments 
for ecosystem services that include diversification (Sorensen et  al., 
2015; Guerra et al., 2017; Nehring et al., 2017; Valencia et al., 2019). 
Integrated landscape approaches involving multiple stakeholders

(Reed et  al., 2016) including urban governments can support 
diversification at a regional scale through public and private sector 
investment in extension services, regional supply chains, agritourism 
and other incentives for diversified landscapes (Milder et  al., 2014; 
Münke et  al., 2015; Sorensen et  al., 2015; Pérez-Marin et  al., 2017; 
Caron et al., 2018; 5.14.1.5).
5.5 Livestock-Based Systems
Livestock systems may be classified as industrial (monogastric, 
ruminant), grassland-based in which crop-based agriculture is absent 
or minimal (pastoralism, agro-pastoralism), mixed rainfed combining 
mostly rainfed cropping with livestock, and mixed irrigated systems 
with a significant proportion of irrigated cropping interspersed with 
livestock. Livestock systems are located widely across all regions of the 
world, and animal-sourced food provides humans with 39% of their 
protein and 18% of their calorie intake (FAO, 2019 f). Some 400 million

people depend on livestock for a substantial part of their livelihood 
(Robinson et al., 2011).
5.5.1 Observed Impacts
Climate change affects livestock productivity and production in many 
ways (Porter et  al., 2014; Rojas-Downing et  al., 2017). Evidence is 
accumulating that rising temperatures are increasing heat stress in 
domestic species and affecting productivity (high confidence) (Das 
et al., 2016b; Godde et al., 2021).
5.5.1.1 Pastoral systems
Many grassland-based livestock systems are vulnerable to climate 
change and increases in climate variability (high confidence) (Dasgupta 
et  al., 2014; Sloat et  al., 2018; Stanimirova et  al., 2019). Decadal 
vegetation changes from warming and drying trends have been 
detected in North American grasslands, with implications for species 
composition, rangeland quality and economic viability of grazing 
livestock (Rondeau et  al., 2018; Reeves et  al., 2020). Feed quality

in South Asian grasslands has been negatively affected, reducing 
food security (Rasul et  al., 2019). Increased grassland degradation 
has been observed in parts of Inner Mongolia (Nandintsetseg et al., 
2021). Changing seasonality, increasing frequency of drought and 
rising temperatures are affecting pastoral systems globally (high 
confidence). These and other drivers are reducing herd mobility, 
decreasing productivity, increasing incidence of vector borne diseases 
and parasites, and reducing access to water and feed (high agreement, 
medium evidence) (López-i-Gelats et  al., 2016; Vidal-González and 
Nahhass, 2018; de Leeuw et al., 2020).

5
747Food, Fibre and Other Ecosystem Products  Chapter 5
5.5.1.2 Livestock distribution and climate variability
There is limited evidence of observed distributional changes in 
livestock species due to climate changes. Asian buffalo and yak breeds 
in China over the past 50 years have shifted distribution partly because 
of increases in heat stress (Wu, 2015; Wu, 2016). Nepalese cattle 
numbers have declined, attributed to increases in the number of hot 
days (Koirala and Shrestha, 2017).
Climate variability has been identified as the primary cause of 
vegetation cover changes on the Tibetan Plateau since 2000 (Lehnert 
et  al., 2016). Increasing inter-annual variability is a driver of farm 
extensification in Mediterranean dairy systems (Dono et al., 2016). In 
Australian rangelands (Godde et al., 2019) and dairy systems (Harrison 
et  al., 2016; Harrison et  al., 2017), increasing rainfall variability 
contributes more to stocking rate and profitability variability than

changes in mean rainfall.
5.5.1.3 Diseases and disease vectors
Climate change is affecting the transmission of vector-borne diseases 
(Hutter et al., 2018; Semenza and Suk, 2018) and parasites (Rinaldi 
et al., 2015) in high latitudes (high confidence). Different processes link 
climate change and infectious diseases in domesticated livestock. Some 
show a positive association between temperature and range expansion 
of arthropod vectors that spread the bluetongue virus. Others show a 
contraction, such as tsetse flies that transmit trypanosome parasites 
of several livestock species. Positive associations have been found 
between temperature and the spread of pathogens such as anthrax, 
and droughts and ENSO weather patterns and Rift Valley fever 
outbreaks in East Africa (Bett et al., 2017). Observed range expansion 
of economically important tick disease vectors in North America 
(Sonenshine, 2018) and Africa (Nyangiwe et al., 2018) are presenting

new public health threats to humans and livestock.
5.5.2 Assessing Vulnerabilities
5.5.2.1 Rising temperature and heat stress
Most domestic livestock have comfort zones in the range 10–30°C, 
depending on species and breed (Nardone et  al., 2006). At higher 
temperatures, animals eat 3–5% less per additional degree of 
temperature, reducing their productivity and fertility. Heat stress 
suppresses the immune and endocrine system, enhancing susceptibility 
of the animal to disease (Das et  al., 2016b). Recent stagnation in 
dairy production in West Africa and China may be associated with 
increased periods of high daily temperatures (low confidence) 
(Rahimi et al., 2020; Ranjitkar et al., 2020). Increases in the productive 
capacity of domestic animals can compromise thermal acclimation 
and plasticity, creating further loss. Escalating demand for livestock 
products in low-to-middle-income countries (LMICs) may necessitate

considerable adaptation in the face of new thermal environments 
(medium confidence) (Collier and Gebremedhin, 2015; Theusme et al., 
2021). Heat effects on productivity have been summarised for pigs 
(da Fonseca de Oliveira et al., 2019), sheep and goats (Sejian et al., 
2018), and cattle (Herbut et  al., 2019). The direct effects of higher temperatures on the smaller ruminants (sheep and goats) are relatively 
muted, compared with large ruminants; goats are better able to cope 
with multiple stressors than sheep (Sejian et al., 2018). Under SSP5-8.5 
to mid-century, land suitability for livestock production will decrease 
because of increased heat stress prevalence in mid and lower latitudes 
(high confidence) (Thornton et al., 2021).
5.5.2.2 Livestock water needs
Livestock production may account for 30% of all water (blue, green 
and grey) used in agriculture (Mekonnen and Hoekstra, 2010) and can 
negatively affect water quality. Cropland feed production accounts

for 38% of crop water consumption (Weindl et al., 2017). High-input 
livestock systems may consume more water than grazing or mixed 
systems, though water used per kg beef produced, for example, depends 
on country, context and system (Noya et al., 2019). In systems where 
feed production is rainfed, livestock and crop water productivity may 
be comparable (Haileslassie et al., 2009). Direct water consumption by 
livestock is <1–2% of global water consumption (Hejazi et al., 2014). 
Rising temperatures increase animal water needs, potentially affecting 
access of herders and livestock to drinking water sources (Flörke et al., 
2018).
5.5.2.3 Rising temperatures and livestock disease
Climate change will have effects on future distribution, incidence 
and severity of climate-sensitive infectious diseases of livestock (high 
confidence) (Bett et al., 2017). In an assessment of climate sensitivity 
of European human and domestic animal infectious pathogens, 63%

were sensitive to rainfall and temperature, and zoonotic pathogens 
were more climate-sensitive than human- or animal-only pathogens 
(McIntyre et al., 2017). Over the last 75 years, >220 emerging zoonotic 
diseases, some associated with domesticated livestock, have been 
identified, several of which may be affected by climate change, 
particularly vector-borne diseases (Vaillancourt and Ogden, 2016; 
see Cross-Chapter Box  ILLNESS in Chapter 2). Walsh et  al. (2018) 
identified both temperature and rainfall as influential factors in 
predicting increasing anthrax outbreaks in northern latitudes. Growing 
infectious disease burdens in domesticated animals may have wide-
ranging impacts on the vulnerability of rural livestock producers in the 
future, particularly related to human health and projected increases 
in zoonoses (high confidence) (Bett et  al., 2017; Heffernan, 2018; 
Rushton et al., 2018; Meade et al., 2019).
5.5.2.4 Livestock and socioeconomic vulnerability to climate

change
There is limited evidence about the role of livestock in addressing 
socioeconomic vulnerability. Although agriculture in parts of North 
America has become more sensitive to climate over the last 50 years, 
livestock have helped to moderate this effect, being less sensitive to 
increasing temperatures than some specialised crop systems (Ortiz-
Bobea et  al., 2018). Increasing frequency and severity of droughts 
will affect the future economic viability of grassland-based livestock 
production in the North American Great Plains (Briske et  al., 2021). 
Purchasing more forage and selling more livestock have reduced 
household vulnerability in semi-arid parts of China over the last

5
748Chapter 5 Food, Fibre and Other Ecosystem Products
35  years (Bai et  al., 2019). A greater focus on sheep production 
away from cropping has increased the resilience of farming systems 
in Western Australia in low-rainfall years, although with mixed 
environmental effects (Ghahramani and Bowran, 2018). More insights 
are needed as to where and how livestock can affect the vulnerability 
of farmers and pastoralists.
5.5.2.5 Effects of climate on the health and vulnerability of 
livestock keepers
Vulnerability to the health impacts of climate change will be shaped 
by existing burdens of ill health and is expected to be highest in poor 
and socioeconomically marginalised populations (high agreement, 
limited evidence) (Labbé et al., 2016). In addition to projected changes 
in infectious disease burdens, labour capacity in a warming climate is 
anticipated to decrease further, beyond the >5% drop estimated since

2000 (Watts et al., 2018). Loss of labour capacity may greatly increase 
the vulnerability of subsistence livestock keepers (high agreement, 
limited evidence).
5.5.2.6 Gender and other social inequities
Vulnerability to climate change depends on demography and social 
roles (Mbow et al., 2019). Gender inequities can act as a risk multiplier, 
with women being more vulnerable than men to climate-change-
induced food insecurity and related risks (high confidence) (Cross-
Chapter Box  GENDER in Chapter 18). Women and men often have 
differential and unequal control over different productive assets and 
the benefits they provide, such as income from livestock (Ngigi et al., 
2017; Musinguzi et  al., 2018). Indigenous livestock keepers can be 
more vulnerable to climate change, partly due to ongoing processes of 
land fragmentation (Hobbs et al., 2008), historical land dispossession, 
discrimination and colonialisation, creating greater levels of poverty

and marginalisation (Stephen, 2018). Adaptation actions may also be 
affected by gender and other social inequities (Balehey et al., 2018; 
Dressler et al., 2019). Men and women heads of household may access 
institutional support for adaptation in different ways (Assan et  al., 
2018). Further research is warranted to evaluate alternative gendered 
and equity-based approaches that can address differences in adaptive 
capacity within communities.
5.5.3 Projected Impacts
There is limited evidence on future impact of climate change on 
livestock production, particularly in LMICs (Rivera-Ferre et al., 2016).
5.5.3.1 Impacts on rangelands, feeds and forages
Uncertainties persist regarding estimates of net primary productivity 
(NPP) in grazing lands (Fetzel et  al., 2017; Chen et  al., 2018b), so 
estimation of climate change impacts on grasslands is challenging. 
Mean global annual NPP is projected to decline 10 gC m−2 yr−1 in

2050 under RCP8.5, although herbaceous NPP is projected to increase 
slightly (Boone et al., 2018; see Figure 5.11). Similar estimates were 
made by Havlik et  al. (2014): large increases in projected NPP in 
higher northern latitudes (21% increase in the USA and Canada) and large declines in western Africa (−46%) and Australia (−17%). 
The cumulative effects of impacts on forage productivity globally are 
projected to result in 7–10% declines in livestock numbers by 2050 for 
warming of ~2°C, representing a loss of livestock assets ranging from 
USD 10 to 13 billion (Boone et al., 2018). Changes to African grassland 
productivity will have substantial, negative impacts on the livelihoods 
of >180 million people.
Increases in above-ground NPP , and woody cover at the expense 
of grassland, are projected in some of the tropical and subtropical 
drylands (Doherty et al., 2010; Ravi et al., 2010; Saki et al., 2018), in 
Mediterranean wood pastures (Rolo and Moreno, 2019) and in the

northern Great Plains of North America (Klemm et al., 2020). Godde 
et al. (2021) projected that woody encroachment would occur on 51% 
of global rangeland area by 2050 under RCP8.5. The future makeup of 
grasslands under climate change is uncertain, given the variation in 
responses of the component species, though this variation may provide 
a climate buffer (Jones, 2019) (low confidence). C4 grass species are 
regarded as less responsive to elevated carbon dioxide than C3 species, 
though this is not always the case (Reich et al., 2018).
There are other interactions between climate change and grazing 
effects on grasslands. Li (2018a) reported strong negative responses 
of NPP and species richness to 4°C warming, a 50% precipitation 
decrease, and high grazing intensity. Changes in grassland composition 
will inevitably change their suitability for different grazing animal 
species, with switches from herbaceous grazers such as cattle to goats

and camels to take advantage of increases in shrubland (Kagunyu 
and Wanjohi, 2014). Rangeland feed quality may also be reduced via 
invasive species of lower quality than native species (Blumenthal et al., 
2016).
Warming and water deficits impair the quality and digestibility of a 
C4 tropical forage grass, Panicum maximum, because of increases in 
leaf lignin (Habermann et al., 2019). A metanalysis by Dellar (2018) 
of climate change impacts on European pasture yield and quality 
found an increase in above-ground dry weight under increased 
CO 2 concentrations for forbs, legumes, graminoids and shrubs 
with reductions in N concentrations in all plant functional groups. 
Temperature increases will increase yields in alpine and northern areas 
(+82.6%) but reduce N concentrations for shrubs (−13.6%) and forbs 
(−18.5%).
Increased temperatures and CO 2 concentrations may increase 
herbaceous growth and favour legumes over grasses in mixed pastures

(He et al., 2019). These effects may be modified by changes in rainfall 
patterns, plant competition, perennial growth habits and plant–animal 
interactions. The cumulative effect of these factors is uncertain. Large, 
persistent declines in forage quality are projected, irrespective of 
warming, under elevated CO 2 conditions (600 ppm and +1.5°C day/3°C 
night temperature increases) in North American grasslands (Augustine 
et al., 2018). Rising CO 2 concentrations may result in losses of iron, zinc 
and protein in plants by up to 8% by 2050 (Smith and Myers, 2018). 
Little information is available on possible impacts on carbon-based 
micronutrients, such as vitamins. About 57% of grasses globally are C3 
plants and thus susceptible to CO 2 effects on their nutritional quality 
(Osborne et al., 2014). These impacts will result in greater nutritional

5
749Food, Fibre and Other Ecosystem Products  Chapter 5
stress in grazing animals as well as reduced meat and milk production 
(quality and quantity) (high confidence, medium evidence).
5.5.3.2 Impacts of increased temperature on livestock
Recent research confirms the seriousness of the heat stress issue 
(medium evidence, high agreement). Considerable increases are 
projected during this century in the number of ‘extreme stress’ days per 
year for cattle, chicken, goat, pig and sheep populations with SSP5-8.5 
but many fewer with SSP1-2.6 (Thornton et al., 2021: Figure 5.12; see 
Cross-Chapter Box MOVING PLATE in this chapter). Resulting impacts 
on livestock production and productivity may be large, particularly for 
cattle throughout the tropics and subtropics and for goats in parts 
of Latin America and much of Africa and Asia. Pigs are projected to

be particularly affected in the mid-latitudes of Europe, East Asia and Projected plant responses in the rangelands to enhanced CO2 fertilization 
Changes in 2050 under RCP8.5 relative to 1971–200 0
(k) Central America(f) Central Asia(a) Eastern Africa
(g) Eastern Asia
(m) Eastern Europe(b) Middle Africa
(n) North america(c) Northern Africa
(l) South America(d) Southern Africa
(h) Southern Asia(e) We stern Africa
(i) We stern Asia
(j) Australia & New Zealand
(j) Australia & New Zealand(k) Central America(f) Central Asia(a) Eastern Africa

-40-200204060
-40-200204060-60
-60
-60Annual
net primary
productivity% change % change % changeMean change 
across regions
Above ground
live biomass
Bare ground
Shrub coverBelow ground
Tree coverHerbaceous
productivity
Soil carbon
Herb cover*
*Truncated bar reaches 82 %a bc
def g
hi
jk
lm
n
Figure 5.11 |  Regional percent changes in land cover and soil carbon from ensemble simulation results in 2050 under emissions scenario RCP8.5 compared 
with 1971–2000. Plant responses were enhanced by CO 2 fertilisation. The larger chart (lower left) shows mean changes for all rangelands, and all charts are scaled to −60% to 
+60% change. Shown are annual net primary productivity (ANPP), herbaceous net primary productivity (HNPP), bare ground, herbaceous (herb), shrub, and tree cover, SOC (soil

carbon), above-ground live biomass and below-ground live biomass. Regions as defined by the United Nations Statistics Division. The bar for above-ground live biomass in Western 
Asia (*) is truncated and is 82% (Boone et al., 2018).
North America. Lallo et al. (2018) estimated that global warming of 
1.5°C and 2°C may exceed limits for normal thermo-regulation of 
livestock animals and result in persistent heat stress for animals in the 
Caribbean. Breed differences in heat stress resistance in dairy animals 
are now being quantified (Gantner et al., 2017), as are effects on sow 
reproductive performance in temperate climates (Wegner et al., 2016). 
Estimates of losses in milk production due to heat stress in parts of 
the USA, UK and West Africa to the end of the century range from 
1% to 17% (Hristov et  al., 2018; Fodor et  al., 2018; Wreford and 
Topp, 2020; Rahimi et al., 2020). Much larger losses in dairy and beef

production due to heat stress are projected for many parts of the 
tropics and subtropics: these could amount to USD 9 billion per year 
for dairy and USD 31 billion per for beef to end-century under SSP5-
8.5, approximately 5% and 14% of the global value of production of 
these commodities in constant 2005 dollars.

5
750Chapter 5 Food, Fibre and Other Ecosystem Products
In many LMICs, poultry contribute significantly to rural livelihoods, 
including via modest improvements in nutritional outcomes of house-
hold children (de Bruyn et al., 2018). Rural poultry are generally as-
sumed to be hardy and well adapted to stressful environments, but 
little information exists regarding their performance under warmer 
climates or interactions with other production challenges (Nyoni et al., 
2019).
5.5.3.3 Impacts on livestock diseases
The impacts of climate change on livestock diseases remain highly un-
certain (medium evidence, high agreement). Bett et al. (2017) showed 
positive associations between rising temperature and expansion of the geographical ranges of arthropod vectors such as Culicoides imicola, 
which transmits the bluetongue virus. A 1-in-20-year bluetongue out-
break at present-day temperatures is projected to increase in frequen-

cy to 1-in-5 to 1-in-7 years by the 2050s, under RCP4.5 and RCP8.5, 
although animal movement restrictions can prevent devastating out-
breaks (Jones et al., 2019).
The prevalence and occurrence of some livestock diseases are positively 
associated with extreme weather events (high confidence). There are 
high risks of future Rift Valley fever (RVF) outbreaks under both RCP4.5 
and RCP8.5 this century in East Africa and beyond (Taylor et al., 2016; 
Mweya et al., 2017).
Early 21st Century
Cattle
Goats
Chicken
Days per year when livestock is under extreme stress
Sheep
Pigs
1 366 days
 0
(No days)No
livestockSSP5-8.5, End 21st Century  SSP1-2.6, End 21st Century
Temperature and humidity driven “extreme stress” for livestock
Figure 5.12 |  Change in the number of days per year above ‘extreme stress’ values from the early 21st century (1991–2010) to end of century (2081–2100),

estimated under SSP1-2.6 and SSP5-8.5 using the Temperature Humidity Index (THI). Mapped for species current global distribution (Gilbert et al., 2018) (grey areas, 
no change). (Thornton et al., 2021), Also see Annex I: Global to Regional Atlas.

5
751Food, Fibre and Other Ecosystem Products  Chapter 5
Few studies explicitly consider the biotic and abiotic factors that 
interact additively, multiplicatively or antagonistically to influence 
host–pathogen dynamics (Cable et al., 2017). Integrative concepts that 
aim to improve the health of people, animals and the environment such 
as One Health may offer a framework for enhancing understanding 
of these complex interactions (Zinsstag et  al., 2018). Much remains 
unknown concerning disease transmission dynamics under a warming 
climate (Heffernan, 2018), highlighting the need for effective 
monitoring of livestock disease (Brito et al., 2017; Hristov et al., 2018).
5.5.3.4 Impacts on livestock and water resources
Water resources for livestock may decrease in places because of 
increased runoff and reduced groundwater resources, as well as 
decreased groundwater availability in some environments (AR5). 
Increased temperatures will cause changes in river flow and the amount

of water stored in basins, potentially leading to increased water stress 
in dry areas such as parts of the Volta River Basin (Mul et al., 2015). 
Toure (2017) estimated decreases in groundwater recharge rates of 
49% and of stored groundwater by 24% to the 2030s in the Klela 
Basin in Mali under both RCP4.5 and RCP8.5, with potentially serious 
consequences for water availability for livestock and irrigation.
Water intake by livestock is related to species, breed, animal size, age, 
diet, animal activity, temperature and physiological status of animals 
(Henry et al., 2018). Direct water use by cattle may increase by 13% for 
a temperature increase of 2.7°C in a subtropical region (Harle et al., 
2007). Changes in water availability may arise because of decreased 
supply or increased competition from other sectors. Availability 
changes may be accompanied by shifts in water quality, such as 
increased levels of microorganisms and algae, that can negatively

affect livestock health (Naqvi et  al., 2015). In arid lands, projected 
decreases in water availability will severely compromise reproductive 
performance and productivity in sheep (Naqvi et al., 2017). In higher-
input livestock systems, water costs may increase substantially owing 
to increased competition for water (Rivera-Ferre et al., 2016).
5.5.3.5 Livestock and climate variability
Information on future climate variability changes on livestock system 
productivity does not exist yet. Increases in climate variability may 
increase food insecurity in the future, mediated through increased 
crop and livestock production variability (Thornton and Herrero, 2014) 
in LMICs. Rainfall variability increases in pastoral lands have been 
linked to declining cattle numbers (Megersa et al., 2014). Changes in 
future climate variability may have large negative impacts on livestock 
system outcomes (Sloat et al., 2018; Stanimirova et al., 2019); these

effects can be larger than those associated with gradual climate 
change (limited evidence, medium agreement) (Godde et al., 2019). 
In grasslands, Chang et al. (2017) (Europe) and Godde et al. (2020) 
(globally) projected increases in biomass inter-annual variability, 
the worst effects occurring in rangeland communities that are 
already vulnerable. Ways in which climate variability impacts have 
been addressed in the past, such as via herd mobility, may become 
increasingly unviable in the future (Hobbs et al., 2008).5.5.3.6 Societal impacts within the production system
Livestock play important social (Kitalyi et  al., 2005) and cultural 
(Gandini and Villa, 2003) roles in many societies. Climate change will 
negatively affect the provisioning of social benefits in many of the 
world’s grasslands (medium confidence). Examples include moving to 
semi-private land ownership models, driven in part by climate change, 
that are changing social networks and limiting socio-ecological

resilience in pastoral systems in East Africa (Kibet et al., 2016; Bruyere 
et  al., 2018) and Asia (Cao et  al., 2018a); altering traditional food, 
resource and medicine sharing mechanisms in West Africa (Boafo 
et  al., 2016); and the limited ability of current livestock systems to 
satisfy societies’ demand for CES in Northwest Europe (Bengtsson 
et  al., 2019). The societal impacts of climate change on livestock 
systems may interact with drivers of change and increase herders’ 
vulnerability via processes of sedentarisation and land fragmentation, 
both of which may result in decreased animal access to rangelands 
(Adhikari et al., 2015; Cross-Chapter Box MOVING PLATE this chapter). 
Stronger linkages are needed between ecosystem service and food 
security research and policy to address these challenges (Gentle and 
Thwaites, 2016; Bengtsson et al., 2019).
5.5.4 Adaptation in Livestock-Based Systems
Livestock adaptation options are increasingly being studied with

methods such as agent-based household models (Hailegiorgis et  al., 
2018), household models that disaggregate climate scenarios as 
well as differentiate farms of varying types and farmer attributes 
(Descheemaeker et al., 2018), new meso-scale grassland models (Boone 
et al., 2018) and modelling approaches that capture decision making at 
the farm level for sample populations (Henderson et al., 2018).
Many grassland-based livestock systems have been highly resilient 
to past climate risk, providing a sound starting point for current 
and future climate change adaptation (Hobbs et  al., 2008). These 
adaptations include more effective matching of stocking rates with 
pasture or other feed production; adjusting herd and watering point 
management to altered seasonal and spatial patterns of forage 
production; managing diet quality, which also helps reduce enteric 
fermentation in ruminants and thus GHG emissions (using diet

supplements, legumes, choice of introduced pasture species and 
pasture fertility management); more effective use of silage, rotational 
grazing or other forms of pasture spelling; fire management to control 
woody thickening; using better-adapted livestock breeds and species; 
restoration of degraded pastureland; migratory pastoralist activities; 
and a wide range of biosecurity activities to monitor and manage the 
spread of pests, weeds and diseases (Herrero et al., 2015; Godde et al., 
2020). Combining adaptations can result in increases in benefits in 
terms of production and livelihoods over and above those attainable 
from single adaptations (high confidence) (Bonaudo et  al., 2014; 
Thornton and Herrero, 2015; ul Haq et al., 2021).
The adaptations that livestock keepers have been undertaking in Asia 
(Hussain et  al., 2016; Li et  al., 2017) and Africa (Belay et  al., 2017; 
Ouédraogo et al., 2017) are largely driven by their perceptions of climate

change. Keeping two or more species of livestock simultaneously on

5
752Chapter 5 Food, Fibre and Other Ecosystem Products
the same farm can confer economic and sustainability benefits to 
European farmers (Martin et al., 2020). Some livestock producers are 
changing and diversifying management practices, improving access to 
water sources, increasing uptake of off-farm activities, trading short-
term profits for longer-term resilience benefits and migrating out of 
the area (Hussain et al., 2016; Berhe et al., 2017; Merrey et al., 2018; 
Thornton et al., 2018; Espeland et al., 2020). Others are adopting more 
climate-resilient livestock species such as camels (Watson et al., 2016a), 
using climate forecasts at differing time scales, and benefitting from 
innovative livestock insurance schemes, though challenges remain in 
their use at scale (Dayamba et al., 2018; Hansen et al., 2019a; Johnson 
In West Africa, cattle and small ruminant producers and traders are 
changing strategies in response to emerging market opportunities as

well as to multiple challenges including climate change (Gautier et al., 
2016; Ouédraogo et al., 2017). Niles (2017) found that reduced food 
insecurity in 12  countries was associated with livestock ownership, 
providing cash for food purchases. Livestock ownership or switching 
to smaller, local breeds does not automatically translate into positive 
nutrition outcomes for women and children, although it may if 
communities see such animals as suitable for husbandry by women 
(Chanamuto and Hall, 2015); the relationship is complex (Nyantakyi-
Frimpong and Bezner-Kerr, 2015; Dumas et al., 2018).
Options for adapting domestic livestock systems to increased exposure 
to heat stress (Table 5.7) include breeding and crossbreeding strategies, 
species switching, low-cost shading alternatives and ventilation and building-design options (Chang-Fung-Martel et al., 2017; Godde et al., 
2021). In utero exposure to heat stress may increase adaptive capacity

in later life, though the underlying mechanisms are incompletely 
understood (Skibiel et  al., 2018). For confined livestock systems in 
temperate regions, the economic consequences of adapting to heat 
stress are still being quantified.
New research is investigating the prospects for accelerating traditional 
and novel breeding processes for animal traits that may be effective in 
improving livestock adaptation as well as production (Stranden et al., 
2019; Barbato et al., 2020). Even if the technical challenges of using 
new tools such as CRISPR-Cas9 for genome editing in livestock are 
overcome, the granting of societal approval to operate in this research 
space may be elusive (Herrero et al., 2020; Menchaca et al., 2020).
5.5.4.1 Contributions of Indigenous knowledge and local 
knowledge
Indigenous knowledge has a role to play in helping livestock 
keepers adapt (medium confidence), though the transferability of 
this knowledge is often unclear. Pastoralists’ local knowledge of

climate and ecological change can complement scientific research 
(Klein et al., 2014), and local knowledge can be mobilised to inform 
adaptation decision making (Klenk et  al., 2017). While Indigenous 
weather forecasting systems among pastoralists in Ethiopia (Balehegn 
et al., 2019; Iticha and Husen, 2019) and Uganda (Nkuba et al., 2020) 
are effective, synergies can be gained by combining traditional 
and modern knowledge to help pastoralists adapt. Sophisticated Table 5.7 |  Selected adaptations to heat stress in livestock systems.
Adaptation Example Reference
Breeding for heat stress 
toleranceSheep and cattle farming systems in southern Australia under IPCC Special Report on Emissions Scenarios (SRES) A2. 
Projected not to improve livestock productivity by 2070, even in drier locations.Moore and Ghahramani (2014)
‘Slick hair’ breedingIn the Caribbean, introduction of a ‘slick hair’ gene into Holstein cows by crossbreeding with Senepols to increase

thermo-tolerance and productivity. An integrated approach to heat stress adaptation will still be needed, including 
shading strategies, for example.(Ortiz-Colón et al. (2018)
CrossbreedingCrossbreeding with Indigenous sheep breeds as an adaptation option in Mongolia produced some benefits in 
productivity and improved adaptation to winter cold. Best combined with other improved management interventions. In 
general, effectiveness of crossbreeding as an adaptation strategy will be dependent on context.Wilkes et al. (2017)
Species switchingSwitching from large ruminants to more heat-resilient goats for dairy production in Mediterranean systems to adapt to 
increasing heat stress.
Switching from cattle to more heat- and drought-resilient camels in pastoral systems of southern Ethiopia as an 
adaptation to increasing drought.Silanikove and Koluman (2015)
Wako et al. (2017)

Shading, fanning, bathingLow-capital relief strategies (shading with trees or different types of shed; bathing animals several times each day; 
installing electric fans in sheds) are effective at reducing heat stress impacts on household income in smallholder dairy 
systems in India.
Different tree arrangements in silvopastoral systems in Brazil were effective in reducing thermal loads by up to 22% for 
animals compared with full-sun pasture.York et al. (2017)
Pezzopane et al. (2019)
Ventilation and cooling 
systemsA wide range of different ventilation systems, cooling systems and building designs for confined and seasonally confined 
intensive livestock systems (pigs, poultry, beef, dairy) in temperate regions. Economic consequences and profitability of 
different options under different RCPs are still being assessed.Vitt et al. (2017)
Derner et al. (2018),
Hempel and Menz (2019), 
Mikovits et al. (2019), 
Schauberger et al. (2019b)
In utero exposure to heat

stressPotential as an adaption option is uncertain, as there are different effects of in utero heat stress exposure and the 
mechanisms are not completely understood:
 –Cows may be better adapted to heat stress conditions at maturity via improved regulation of core body temperature
 –Cow milk yield at first lactation was reduced
 –Nutrient partitioning and carcass composition were altered in pigsAhmed et al. (2017)
Monteiro et al. (2016),
Boddicker et al. (2014)

5
753Food, Fibre and Other Ecosystem Products  Chapter 5
knowledge of feed resources among agro-pastoralists in West Africa 
is being used to increase system resilience (Naah and Braun, 2019). 
Understanding local knowledge for adaptation can present research 
challenges, for which new multi-disciplinary research methods may be 
needed (Reyes-Garcia et al., 2016; Roncoli et al., 2016). In particular, 
the complexities of knowledge, practice, power, local governance and 
politics need to be addressed (Hopping et al., 2016; Scoville-Simonds 
5.6 Forestry Systems
Forests play a vital role in the ecology of the planet, including 
climate regulation, and provide a range of important ecosystem 
services within their local landscape. Moreover, they are essential 
to the well-being of millions of people around the world. Forests are 
sources of food contributing about 0.6% of global food consumption 
and provide important products, such as timber and non-timber

forest products (NTFPs) (FAO, 2014). Indigenous Peoples and local 
communities are estimated to manage at least 17% of total carbon 
(or 293 × 109 Mg) stored in forest in 64 assessed countries (RRI, 
2018a). While small in number, numerous local communities around 
the world are highly or entirely dependent on forests for their food 
supply (Karttunen et  al., 2017). An estimated 9% of the world’s 
rural population is lifted above the extreme poverty line because 
of income from forest resources (World Bank, 2016). Additionally, 
forest income plays a particularly important role in diversifying the 
income sources of poor households, reducing their vulnerability to 
loss from one source of income. This section covers an assessment 
of the impacts of climate change on forestry production systems and 
the adaptation options available. Non-timber forest products will be 
covered in the next section.5.6.1 Observed Impacts
The IPCC AR5 stated that there is high confidence that numerous

plants and animal species have already migrated, changed their 
abundance, and shifted their seasonal activities as a result of climate 
change (Settele et al., 2014). The report highlighted the widespread 
deaths of trees in many forested areas of the world. Forest die back 
could significantly affect wood production among other impacts.
The SRCCL (Barbosa et al., 2019) concluded that climate change will 
have positive and negative effects on forests, with varying regional 
and temporal patterns. For example, the SRCCL noted the increasing 
productivity in high-latitude forests such as those in Siberia. In contrast, 
negative impacts are already being observed in other regions such as 
increasing tree mortality due to wildfires.
In the past years, tree mortality continued to increase in many parts 
of the world. Large pulses of tree mortality were consistently linked 
to warmer and drier than average conditions for forests throughout

the temperate and boreal biomes (high confidence) (Sommerfeld 
et al., 2018; Seidl et al., 2020). Long-term monitoring of tropical forests 
indicates that climate change has begun to increase tree mortality and 
alter regeneration (Hubau et al., 2020; Sullivan et al., 2020). Climate-
related die back has also been observed due to novel interactions 
between the life cycles of trees and pest species (Kurz et  al., 2008; 
Lesk et  al., 2017; Sambaraju et  al., 2019). A recent example of the 
impacts of climatic extremes is the European drought of 2018 (Buras 
et al., 2020), which led to a significant browning of the vegetation and 
resulted in widespread tree mortality (high confidence) (Brun et  al., 
2020; Schuldt et  al., 2020). This brought markets for conifer timber 
close to collapse in parts of Europe, posing considerable challenges 
for timber-based forestry and leading to cascading impacts on society 
(Hlásny et  al., 2021). Overall, there is robust evidence and medium

agreement that provisioning services of boreal and temperate forests 
are affected negatively by forest disturbances, while for cultural Box 5.5: Alternative Sources of Protein for Food and Feed
Alternative protein sources for human food and livestock feed are receiving considerable attention. Laboratory or ‘clean meat’ is one 
potential contributor to the human demand for protein in the future (SRCLL). Such technology may be highly disruptive to existing value 
chains but could lead to significant reduction in land use for pastures and crop-based animal feeds (Burton, 2019; Rosenzweig et al., 
2020). The impacts on GHG emissions depend on the meat being substituted and the trade-off between industrial energy consumption 
and agricultural land requirements (Mattick et al., 2015; Alexander et al., 2017; Rubio et al., 2020b; Santo et al., 2020). Livestock feeds

can make use of other protein sources: insects are generally rich in protein and can be a significant source of vitamins and minerals. Black 
soldier fly, yellow mealworm and the common housefly have been identified for potential use in feed products in the EU, for example 
(Henchion et al., 2017). Replacing land-based crops in livestock diets with some proportion of insect-derived protein may reduce the 
GHG emissions associated with livestock production, though these and other potential effects have not yet been quantified (Parodi 
et al., 2018; Section 5.13.2). Other sources are high-protein woody plants such as paper mulberry (Du et al., 2021) and algae, including 
seaweed. While microalgae and cyanobacteria are mainly sold as a dietary supplement for human consumption, they are also used as a 
feed additive for livestock and aquaculture, being nutritionally comparable to vegetable proteins. The potential for cultivated seaweed as

a feed supplement may be even greater: some red and green seaweeds are rich in highly digestible protein. Asparagopsis taxiformis, for 
example, also decreases methane production in both cattle and sheep when used as a feed supplement (Machado et al., 2016; Li et al., 
2018b). Novel protein sources may have considerable potential for sustainably delivering protein for food and feed alike, though their 
nutritional, environmental, technological and socioeconomic impacts at scale need to be researched and evaluated further.

5
754Chapter 5 Food, Fibre and Other Ecosystem Products
services only limited evidence with medium agreement exists (Thom 
and Seidl, 2016).
Increasingly, climate impacts on the recovery of forests after disturbance 
are observed: using data from the past 20 years and 33 wildfires, it 
has been shown that post-fire regeneration of Pinus ponderosa and 
Pseudotsuga menziesii in the western USA has declined because of 
climate change and increased severity of fires (Davis et  al., 2019). 
However, the observed patterns of post-disturbance recovery vary with 
region, with reduced tree regeneration reported for the western USA 
(Stevens-Rumann and Morgan, 2019; Turner et al., 2019) but robust 
recovery observed in Canada (White et al., 2017) and Central Europe 
(medium confidence) (Senf et al., 2019).
Also, the distribution and traits of trees are increasingly influenced by 
climate change, with impacts for local ecosystem service supply. In the

USA, a study of 86 tree species/groups over the past three decades 
showed that more tree species have shifted westward (73%) than 
poleward (62%) in their abundance (Fei et al., 2017). This was due more 
to changes in moisture availability than to changes in temperature. 
As climate has warmed, trees are growing faster with longer growing 
seasons. However, a study of forests in Central Europe revealed that 
wood density has decreased since the 1870s (Pretzsch et al., 2018). 
This means that increasing tree growth might not directly translate to 
increased total biomass and carbon sequestration.
5.6.2 Projected Impacts
AR5 stated that other stressors such as human-driven land use change 
and pollution will continue to be the main causes of forest cover change 
in the next three decades (Settele et al., 2014). In the second half of 
this century, it was projected that climate change will be a strong 
stressor of change in forest ecosystems. Many forest species may not

be able to move fast enough to adjust to new climate conditions. In 
some cases, a warmer climate could lead to extinction of species.
The SR15 concluded that limiting warming to 1.5°C will be more 
favourable to terrestrial ecosystems, including forests, relative to a 2°C 
warming (Hoegh-Guldberg et al., 2018). In general, a 2°C warming could 
lead to two times more area of biome shifts compared with a 1.5°C 
warming. As a result, keeping a cooler average global temperature 
will lead to lower extinction risks. The special report supports the AR5 
conclusion that a warmer planet will impact wide swaths of forests 
adversely. For example, higher temperatures will promote fire, drought 
and insect disturbances. Consistent with AR5, SRCCL projected that 
tree mortality will increase with climate change (Barbosa et al., 2019). 
In addition, forests will be more exposed to extreme events such as 
extreme heat, droughts and storms. The incidence of forest fires will 
likewise increase.

Additional evidence since the above reports were published supports 
their overall conclusions. For example, at the global scale, modelling the 
vulnerability of 387 forest ecoregions under future climate change (to 
2080 using the average of five Global Circulation Models (GCMs) and 
RCP4.5 and 8.5) across different biomes, biogeographical realms and 
conservation statuses showed that 8.8% of global forest ecoregions are highly vulnerable in a low-greenhouse-gas-concentration scenario, 
and 32.6% of the global forest ecoregions are highly vulnerable in 
a high-greenhouse-gas-concentration scenario (Wang et  al., 2019a). 
Furthermore, a recent synthesis of the literature suggests that climate 
change will result in younger and shorter forests globally (McDowell 
et al., 2020). In Asia, a systematic review of climate change impacts 
on tropical forests revealed that future climate may lead to changes 
in species distribution and forest structure and composition as well as

phenology (Deb et al., 2018).
Overall, studies indicate both negative and positive climate change 
impacts on forest production systems. Some forests in the USA 
could benefit slightly from CO 2 fertilisation (using IGSM-CAM and 
MIROC3.2 till 2100) resulting in increased productivity especially for 
hardwoods (Beach et al., 2015). A study across Europe showed that 
both productivity gains (mostly in Northern and Central Europe, up to 
+33%) and losses (predominately in Southern Europe, up to −37%) 
are possible until the end of the 21st century (Reyer et al., 2017). The 
study further indicated that disturbances would reduce gains and 
exacerbate losses of productivity throughout Europe under climate 
change (Reyer et al., 2017). For Central and Eastern Canada, decreasing 
biomass production is projected as a result of increasing disturbance 
from wildfire and drought (Brecka et  al., 2020). Climate-induced 
disturbances could also reduce the temporal stability of ecosystem

service supply (Albrich et al., 2018), increasing the volatility of timber 
markets (medium confidence). More broadly, climate change could 
lead to abrupt changes and the crossing of tipping points, resulting 
in profoundly altered future forest development trajectories (Turner 
et al., 2020). Some studies suggest that such threshold could already 
be crossed at relatively low warming levels of +2°C (Elkin et al., 2013; 
Albrich et  al., 2020), with substantial implications for ecosystem 
service supply (limited evidence, high agreement).
Regional studies on the potential future effects of climate change 
on forest production systems indicate diverse impacts. In Germany, 
drier conditions in 2070 (RCP8.5; GCMs INM-CM4, ECHAM6 and 
ACCESS1.0) are expected to benefit the mean annual increment at 
biological rotation age of Scots pine and oak, while beech might 
suffer losses of up to 3 m3 ha–1 yr–1 depending on climate scenario

and region (Albert et al., 2018). In India, 46% of the forest grid points 
were found to have high, very high or extremely high vulnerability 
under future climate in the short term (2030s) under both RCP4.5 
and 8.5, increasing to 49% and 54%, respectively, in the long term 
(2080s) (Sharma et al., 2017). In addition, forests in the higher rainfall 
zones show lower vulnerability as compared with drier forests under 
future climate, which is in contrast to dry forests in Central and South 
America cited above. Warming and drying trends are projected to 
reduce timber production in the neotropics in some cases (Hiltner 
et al., 2021). Also in India, a study using CMIP5 (RCP4.5 and 8.5 with 
two time slices 2021–2050 and 2070–2099) shows how forests in 
five districts in Himachal Pradesh in Western Himalayan region are 
vulnerable to global warming (Upgupta et al., 2015). In the Guiana 
Shield, climate projections under RCP2.5 and 8.5 led to decreasing

the basal area, above-ground fresh biomass, quadratic diameter, tree 
growth and mortality rates of tropical forests (Aubry-Kientz et  al., 
2019). In Central Africa, projections under RCP4.5 and 8.5 showed 
a general increase in growth, mortality and recruitment leading to

5
755Food, Fibre and Other Ecosystem Products  Chapter 5
a strong natural thinning effect, with different magnitudes across 
species (Claeys et al., 2019).
On a global and regional scale, there is limited evidence and high 
agreement (medium confidence) that climate change will increase 
global and regional supply of timber and other forest products. To date, 
there are eight studies assessing the total economic impacts of climate 
change on the forestry sector at the global level. Some of them have 
assumed only flow effects of climate change by using the projected 
changes in yields of forest types from integrated economic models 
(Perez-Garcia et  al., 1997; Perez-Garcia et  al., 2002; Buongiorno, 
2015), while other studies have assumed both flow and stock effects 
by accounting for changes in forest yields, die back effects and biome 
migration (Sohngen et al., 2001; Lee and Lyon, 2004; Tian et al., 2016; 
Favero et al., 2018; Favero et al., 2021).

According to these studies, global timber supply will increase as the 
result of an increase in global forest growth under climate change 
scenarios (medium confidence). Some studies indicate that timber 
supply is projected to increase more in tropical and subtropical areas 
because of the assumed availability of short-rotation species which 
might could adaptation easier for forest owners in these regions 
relative to others (Sohngen et al., 2001; Perez-Garcia et al., 2002; Tian 
et al., 2016), while others indicate that temperate areas will experience 
the largest increase in supply (Favero et al., 2018; Favero et al., 2021). 
The results are very sensitive to the climate change scenarios tested, 
the climate and vegetation models used and the climate drivers that 
are considered. For example, Tian et al. (2016) and Favero et al. (2018; 
2021) used the same economic model (the global timber model) but 
different climate scenarios and vegetation models, obtaining different

results.The increasing supply induces lower global timber prices (medium 
confidence). Studies estimate that the prices will decline between 
1% and 38% in 2100 with respect to a no climate change scenario 
depending on the model and the climate change scenario assumed 
(climate change is represented as a change in GHG concentration, 
global average temperature or radiative forcing) (Favero et al., 2018; 
Favero et  al., 2021). Clearly, further studies are needed considering 
a wider set of vegetation and climate models and incorporating the 
impacts of extreme events (such as droughts and wildfires).
There are a number of national and regional scale studies exploring 
the impact of climate change on yields and markets of wood products, 
with mixed results. In Finland, it is projected that timber yield in the 
north will increase in Scots pine and birch stands by 33–145% and 
42–123%, compared with the current climate, depending on the

GCM and thinning regime using a 90-year rotation (10 individual 
GCM projections under the RCP4.5 and RCP8.5 forcing scenarios) 
(ALRahahleh et  al., 2018). However, in Norway spruce stands, yield 
could decline by up to 35%, under GFDL-CM3 RCP8.5 and increase 
by up to 39%, under CNRM-CM5 RCP8.5, compared with the current 
climate.
In Germany, timber harvest was projected to increase slightly (<10%) 
in 2045 using the process-based forestry model (4C) driven by three 
management strategies (nature protection, biomass production and a 
baseline management) and an ensemble of regional climate scenarios 
(RCP2.6, RCP4.5, RCP8.5) (Gutsch et  al., 2018). Similarly, average 
production of pulpwood in slash pine stands in the southeastern USA 
are projected to increase by 7.5 m3 ha−1 for all climatic scenarios using 
the Physiological Processes Predicting Growth (3-PG) forest growth 
model by 2100 (RCP4.5 and RCP8.5; CanESM2) (Susaeta and Lal, 2018).

Box 5.6: Contributions of Indigenous and Local Knowledge: An Example
Indigenous and local people have long histories of adaptation to climate hazards in forests (see Eriksen and Hankins, 2014; Neale et al., 
2019; Bourke et al., 2020; Long et al., 2020; Williamson, 2021 for notable examples in Australia and North America). In this section, we 
present a North American example of an indigenous adaptation practice developed by the Karuk Tribe in northern California. The Karuk 
Climate Adaptation Plan focuses on the use of cultural fire as climate adaptation, places a central importance on restoring human 
ecological caretaking responsibilities, and emphasises the need for collaboration, public education and policy advocacy to achieve these 
outcomes.
The Karuk Climate Adaptation Plan utilises a combination of Western science and Karuk traditional ecological knowledge. The plan

centres on 22 focal species as cultural indicators as cues for human responsibilities and the particular techniques of fire application 
across seven habitat management zones (e.g., multiple forest types as well as riverine, riparian and montane systems). These adaptations 
range from specific prescriptions for the use of fire to lower river temperatures in acute scenarios (David et al., 2018), to protocols for 
treatment of grasslands and the use of high elevation meadows as fuel breaks. The plan also includes chapters on adaptations for tribal 
sovereignty, the mental and physical health effects of the changing climate and the protection of critical tribal infrastructure.
One aspect of Indigenous fire knowledge featured in the Karuk Climate Adaptation Plan is the culture-centric perspective on vegetation 
zones which are organised in relation to the elevation band in which smoke inversions occur (Figure Box 5.6.1). Within this system,

burn timing follows a gradient that tracks the reproductive life cycles of season and elevational migrant species, the calving of elk and 
the nesting of birds. Within this system, elevational migrants are indicators of when to stop burning at one location and move upslope, 
following receding snows.

5
756Chapter 5 Food, Fibre and Other Ecosystem Products
5.6.3 Adaptation
AR5 notes that natural ecosystems have built-in adaptation ability 
(Settele et  al., 2014). However, this capacity will not be enough to 
prevent loss of forest ecosystem services because of projected climate 
change in this century under RCP6.0 and 8.5. Management actions 
could reduce the risks of impacts to forest ecosystems but only up to 
a certain point.
A systematic review of literature revealed that successful adaptation in 
forest management can be achieved if there are partnerships between 
key stakeholders such as researchers, forest managers and local actors (Keenan, 2015). Such partnerships will lead to a shared understanding 
of climate-related challenges and more effective decisions. Forest 
managers in some countries of the world seem to have high awareness 
of climate change (van Gameren and Zaccai, 2015; Seidl et al., 2016; 
Sousa-Silva et  al., 2016). However, they need more information on

how they can adjust their practices in response to climate change. 
Institutional and policy context needs to be considered to facilitate 
adaptation by forest managers (Sousa-Silva et  al., 2016; Andersson 
et al., 2017).The plan also calls for the restoration of Indigenous fire science in emergency scenarios such as when rivers become too hot for salmon. 
With such fires localised, smoke inversions cool water temperatures through a variety of mechanisms, including shading river systems and 
reducing evapo-transpiration, thereby increasing stream flow (David et al., 2018).
Seasonality and elevation dynamics of cultural indicators in Karuk Cultural Management Zones
Figure Box 5.6.1 |  Seasonality and elevation dynamics of cultural indicators in Karuk Cultural Management Zones based in Karuk traditional 
ecological knowledge.Box 5.6 (continued)

5
757Food, Fibre and Other Ecosystem Products  Chapter 5
5.6.3.1 Adaptation measures in sustainable forest management
A wide range of measures exist to adapt sustainably managed 
forests of the boreal and temperate zone to climate change (Kolström 
et al., 2011; Gauthier et al., 2014; Keenan, 2015). Evidence emerging 
since the last assessment report further bolstered the notion that 
adapting the tree species composition to more warm-tolerant and less 
disturbance-prone species can significantly mitigate climate change 
impacts (high confidence) (Duveneck and Scheller, 2015; Seidl et al., 
2018). Assisting the establishment of species in suitable habitats is 
one option to achieve climate-adapted tree species compositions 
(Benito-Garzón and Fernández-Manjarrés, 2015; Iverson et al., 2019). 
Furthermore, increasing the diversity of tree species within stands can 
have positive effects on tree growth and reduce disturbance impacts

(high confidence) (Neuner et al., 2015; Jactel et al., 2018; Ammer, 2019). 
Some studies also suggest a positive effect of increased structural 
diversity, such as on forest resilience (moderate confidence) (Lafond 
et al., 2013; Koontz et al., 2020). Managing for continuous forest cover 
can also help to maintain the forest microclimate and buffer tree 
regeneration and the forest floor community against climate change 
(high confidence) (De Frenne et  al., 2013; Zellweger et  al., 2020). 
Reducing stocking levels, such as through thinning, has been found 
to effectively mitigate drought stress (Gebhardt et  al., 2014; Elkin 
et  al., 2015; Bottero et  al., 2017), yet effects vary with species and 
ecological context (robust evidence, medium agreement) (Sohn et al., 
2016; Castagneri et al., 2021). Also shortened rotation periods have 
been suggested in response to climate-induced increases in growth 
and disturbance (Jönsson et al., 2015; Schelhaas et al., 2015). However,

recent evidence suggests that these measures diminish in efficiency 
under climate change and can have corollary effects on other important 
forest functions such as carbon storage and habitat quality (medium 
confidence) (Zimová et al., 2020). Also, measures targeting landscape 
structure and composition have proven effective for increasing the 
climate resilience of forest systems (medium confidence) (Aquilue 
et  al., 2020; Honkaniemi et  al., 2020). While an increasing number 
of adaptation measures exist for sustainably managed forests, many studies highlight that the lead times for adaptation in forestry are 
long and that some vulnerabilities might remain also after adaptation 
measures have been implemented. Furthermore, the costs and benefits 
of adaptation measures relative to other goals of sustainable forest 
management, such as the conservation of biological diversity, have 
to be considered (Felton et al., 2016; Zimová et al., 2020; see Cross-

Chapter Paper 7.5 Adaptation Response Options).
5.6.3.2 Linking adaptation and mitigation through Reducing 
Deforestation and Forest Degradation plus
Reducing Deforestation and Forest Degradation plus (REDD+) is 
a climate mitigation strategy which could also provide important 
climate change adaptation co-benefits; for example, sustainable forest 
management could provide long term livelihoods to local communities 
and enhance resilience to climate risks (Turnhout et al., 2017). However, 
major challenges related to REDD+ implementation and forest use 
remain such that it has not been implemented successfully at scale 
(Table 5.8).
5.7 Other Natural Products
Natural products such as medicinal plants, wild food (plants, animals, 
mushrooms) and resins (e.g., gum arabic and frankincense) have high 
commercial value and contribute an important source of livelihood 
in some regions. One in six persons globally live in or near forests,

and many depend on forest resources for some of their livelihood and 
needs, particularly in low- and middle-income countries (Vira et  al., 
2016; Newton et al., 2020). The FAO has estimated that in 2011 non-
wood forest products, including medicinal plants, contributed over 
88 billion USD to the global economy (FAO, 2014). Greater diversity 
in local knowledge and Indigenous knowledge of natural resources 
supports resilience in the face of hazards, especially in environments 
with high levels of uncertainty (Berkes et al., 2003; Blanco and Carriere, 
2016).Table 5.8 |  Challenges and solutions for REDD+
Challenges with REDD+ implementation Solutions for successful forest management
Legal: lack of carbon rights in national legislations (Sunderlin et al., 2018; RRI, 2018b); 
unclear forestland tenure systems (Resosudarmo et al., 2014).There is high confidence that implementing social safeguards such as a Free Prior and

Informed Consent (FPIC) is vital to adequately involving Indigenous Peoples and local 
communities in REDD+ (White, 2014; Raftopoulos and Short, 2019). Indigenous Peoples, 
consisting of at least 370 million people, manage or have tenure rights over a quarter of 
the world’s land surface (around 38 million km2) encompassing about 40% of the world’s 
protected areas (Garnett et al., 2018; RRI, 2018a).
Food security and livelihoods: negative impacts of REDD+ on food security, agroforestry and 
swidden agriculture (Fox et al., 2014; Holmes et al., 2017).There is high agreement that REDD+ and other green adaptation and mitigation efforts need 
to cooperate with Indigenous Peoples and other local communities who depend on forest 
resources for their livelihoods and food security (Wallbott, 2014; Mccall, 2016; Brugnach 
et al., 2017; Vanclay, 2017; Garnett et al., 2018; Paneque-Galvez et al., 2018; Sunderlin et al., 
2018; Schroeder and Gonzalez, 2019).

Political and socio-cultural: land acquisition or ‘green grabbing’ (Asiyanbi, 2016; Corbera 
et al., 2017); (mis)communicating the concept of carbon (Kent and Hannay, 2020); and 
lack of influence of Indigenous and local communities’ representation in global and 
national REDD+ negotiations (Wallbott, 2014; Dehm, 2016). In the absence of social and 
environmental safeguards, REDD+ could drive large-scale land acquisitions by states and 
corporations, resulting in global land grabs (or green grabbing), negatively affecting the food 
security, livelihoods and tenure rights of Indigenous and local communities (limited evidence, 
high agreement) (Carter et al., 2017; Lund et al., 2017; Borras et al., 2020).There is low confidence as to whether community forestry is compatible with REDD+ (Hajjar 
et al., 2021). This is mainly due to lack of carbon payments and the variety of approaches to 
REDD+. There is high confidence that restoring land access and rights via transfer of formal

land titles to Indigenous and local communities improves biodiversity conservation and 
carbon sequestration.

5
758Chapter 5 Food, Fibre and Other Ecosystem Products
5.7.1 Medicinal Plants
The World Health Organization lists traditional medicine as an essential 
component of culturally appropriate healthcare (WHO, 2013). Medicinal 
plants make up the primary source of medicine for 70–95% of people 
in low- and middle-income countries and are used widely in wealthier 
countries (Applequist et al., 2020). Continued use of medicinal plants 
ensures millions of rural people have access to effective treatments 
for day-to-day illness and infection and thus improves their health and 
resilience to climate change.
Indigenous Peoples largely depend on medicinal plants for their 
healthcare need in different parts of the world (de Boer and Cotingting, 
2014; Silva et al., 2020). Medicinal and aromatic plants can support 
the economy and generate livelihood options for rural people through 
preparing and selling traditional medicine; collecting from wild;

and trade for income generation (Fajinmi et  al., 2017; Zahra et  al., 
2020). Income from medicinal plant collection increases livelihood 
diversification, which is widely accepted to improve resilience.
5.7.2 Resin and Gum
Resin and gum are economically important natural products, 
contributing 14–23% total household income in parts of Ethiopia and 
Sudan (Abtew et al., 2014; Fikir et al., 2016), Cambodia (Sakkhamduang 
et al.) and India (Tewari et al., 2017). They are an important source of 
raw material for many industries. For instance, in Africa, the genera 
Boswellia and Commiphora, which provide frankincense and myrrh 
resins, provide significant income generation and export value (Tilahun 
et  al., 2015). Populations of many species that provide gums and 
resins are declining under pressure from unsustainable harvesting and 
deforestation, and climate change may threaten them further.
In Sri Lanka, Boswellia serrata Roxb. is critically endangered or

possibly extinct (Weerakoon and Wijesundara 2012). In India, B. 
serrata populations are ‘vulnerable’ (Chaubey et  al., 2015; Brendler 
et al., 2018), and declining in the Western Ghats (Soumya et al., 2019). 
Invasion of Lantana camara and Prosopis juliflora has resulted in 
poor regeneration of Commiphorawightii in central India (Jain and 
Nadgauda, 2013). Other resin-producing species under threat include: 
Daemonoropsdraco (dragon’s blood resin) in Indonesia (Yetty et  al., 
2013; Widianingsih et  al., 2019), Pinus merkusii (tusam) in Sumatra 
(Indonesia) (Hartiningtias et al., 2020), Pinus pinaster in Spain, Pinus 
massoniana in China (Génova et  al., 2014; Chen et  al., 2015b) and 
Pistacia atlantica in Iran (Yousefi et al., 2020).
5.7.3 Wild Foods
Wild foods can include both native and introduced species that are not 
cultivated or reared but may be under various degrees of management 
by humans and may include escapees of species that are cultivated

in some contexts (Powell et  al., 2015). Information on the use and 
importance of wild foods for nutrition is growing but remains limited 
(FAO, 2019e). The AR4 covered wild food briefly in the polar regions 
and noted the inter-related nature of climate change and Indigenous knowledge loss in reducing access to wild food (Anisimov et al., 2001). 
AR5 did not address wild foods and other natural products. There is 
large variation in the importance of wild foods (Powell et al., 2015; 
Rowland et al., 2017; Dop et al., 2020). A recent survey of 91 countries 
found that 15 reported regular use of wild foods by most of the 
population, and 26 reported regular use of wild foods by a subsection 
of the population (FAO, 2019e). While they contribute little to food 
energy intake, their contribution to nutrition can be significant because 
most wild and forest foods (vegetables, fruits, mushrooms, insects and 
meat) are rich in proteins and micronutrients (Powell et al., 2015). The

impacts of climate change on wild foods will vary in time and space 
and among species.
5.7.4 Observed and Projected Impacts
5.7.4.1 Medicinal plants
Research is limited on the effects of climate change on the distribution, 
productivity or availability of medicinal plants (Applequist et al., 2020), 
but some are facing threats due to climate change (Phanxay et al., 2015; 
Chirwa et al., 2017; Chitale et al., 2018). Climate change is projected to 
impact some medicinal plant species through changes in temperature, 
precipitation, pests and pathogens; unsustainable harvest of high-value 
species will significantly exacerbate these impacts (medium evidence, 
high agreement) (Applequist et  al., 2020). Table  5.9 highlights that 
climate change impacts on medicinal plant species will vary greatly 
by species. Medicinal plants that grow in arid environments are also 
highly susceptible to climate-induced change (Applequist et al., 2020).

Arctic medicinal species may also be particularly at risk due to climate 
change (Cavaliere, 2009).
Changes in range distribution will interact with detailed local 
knowledge and Indigenous knowledge needed to harvest and use 
medicinal plants. Northward range shifts, for example, may mean 
certain plants still exist, but not where they have traditionally been 
important as medicine, and possibly moving suitable ranges outside of 
areas where plants species have sufficient protection (Kaky and Gilbert, 
2017). Climate-induced phenological changes are already observed as 
a threat to some species (Gaira et  al., 2014; Maikhuri et  al., 2018). 
Other major climate-induced impacts on medicinal plants will be via 
the phytochemical content and pharmacological properties of medical 
plants (Gairola et al., 2010; Das et al., 2016a). Experimental trials have 
shown that drought stresses increase phytochemical content, either 
by decreasing biomass or increasing metabolites production (high

confidence) (Selmar and Kleinwachter, 2013; Al-Gabbiesh et al., 2015).
5.7.4.2 Wild food
5.7.4.2.1 Wild food in the Arctic, North America and Europe
Changes to the availability, abundance, access and storage of wild 
foods associated with changing climate are exacerbating high rates of 
food insecurity (high confidence) (Ford, 2009; Beaumier and Ford, 2010; 
Herman-Mercer et al., 2019). Wild foods are central to the food systems 
of communities throughout the Arctic and sub-Arctic (Kuhnlein et al., 
1996; Ballew et al., 2006; Kuhnlein and Receveur, 2007; Johnson et al.,

5
759Food, Fibre and Other Ecosystem Products  Chapter 5
Table 5.9 |  Observed and predicted impacts of climate change on selected medicinal plant species.
Region Species Observed and projected impacts of climate changeAssessment of 
evidence and level 
of agreement
Egypt, Sub-Saharan 
Africa, Spain, 
Central Himalaya, 
China, NepalGeneral assessment of 
medicinal plantsHabitat suitability and/or range distribution will shift or may be lost (Munt et al., 2016; Yan et al., 
2017; Brunette et al., 2018; Chitale et al., 2018; Zhao et al., 2018; Applequist et al., 2020), including 
in high-elevation meadows which are home to some of the most threatened plant populations 
and contain a high number of and higher proportion of species used as medicine compared with 
lower-elevation habitats (Salick et al., 2009; Brandt et al., 2013).Medium confidence
Hindukush 
HimalayaGynostemmapentaphyllumThe elevated CO 2 and temperature can increase biomass, but the health-promoting properties such as

total antioxidants, phenols and flavonoids are expected to decrease (Chang et al., 2016).Medium confidence
Arctic Golden root (Rhodiola rosea)Population decline has been associated with drying of stream beds and alpine meadows, which are 
predicted to become more severe under climate change
(Cavaliere, 2009; Brinkman et al., 2016).Medium confidence
North AmericaAmerican ginseng 
(Panax quinquefolius)Modelling of the combined impact of climate change (warming) and harvesting pressure indicates a 
nonlinear increase in extinction risk (Souther and McGraw, 2014).Medium confidence
Asia Gentiana rigescensA model evaluating future climate impacts shows a westward range shift and major loss of highly 
suitable habitats. Modelling also shows a potential decline in quality (chemical concentration of iridoid 
glycoside, which is highest in highly suitable habitats) due to climate change (Shen et al., 2021).Medium confidence

Africa AlstoniabooneiModelling indicates that the range for this species remains relatively stable, with a possible modest 
expansion at the northern and southern margins of the range (Asase and Peterson, 2019).Medium confidence
Asia Homonoia ripariaModelling of future climate scenarios in Yunnan Province, China projects that habitat suitability 
improves (Yi et al., 2016). Modelling of future climate scenarios across the whole species range in 
China shows that both the suitable area and suitability of the habitat increase (Yi et al., 2018).Medium confidence
Asia NotopterygiumincisumModelling for future climate change shows areas of suitable habitat will significantly decrease; 
however, the area of marginally suitable habitat will remain relatively stable (Zhao et al., 2020).Medium confidence
HimalayasHimalayan yew Taxus 
wallichianaModelling shows projected shrink in climatic niche of the species by 28% (RCP4.5) and 31% (RCP8.5),

highlighting the vulnerability to climate change impacts (Rathore et al., 2019).Medium confidence
Iran Daphne mucronataModelling of future climate change projects disappearance of the species below 2000 m, significant 
change in distribution between 2000 and 3000 m and no change above 3000 m (Abolmaali et al., 2018).Medium confidence
Central AmericaPericón or Mexican Mint 
Marigold
Tagetes lucidaModels predict range to contract somewhat and shift northward (Kurpis et al., 2019). Medium confidence
Africa Rooibos tea Aspalathus linearisModelling of future climate scenarios shows substantial range contraction of both wild and cultivated 
tea, with range shifts southeastwards and upslope (Lotter and Maitre, 2014).Medium confidence
Himalayas Lilium polyphyllumHabitats of this species will shrink by 38–81% under future climate scenarios and shift towards the 
southeast region in Western Himalaya, India (Dhyani et al., 2021).Medium confidence

Iran Fritillaria imperialisModelling shows 18% and 16.5% of the habitats may be lost due to climate change by 2070 under RCP4.5 
and RCP8.5, respectively. Further, it is observed that, under the current climatic conditions, the suitable 
habitat may become unsuitable in the future, resulting in local extinction (Naghipour Borj et al., 2019).Medium confidence
Himalayas/ China Snow lotus (Saussurea spp.)Climate change is a significant threat to this species (Law and Salick, 2005). Laboratory and field trials 
show considerable plasticity and a wide thermal range for germination, which may help compensate 
for range reductions under climate change (Peng et al., 2019).Medium confidence
North Africa Atlas cedar Cedrus atlanticaModelling shows a significant and rapid contraction of distribution range, upward elevational range 
shift, increased fragmentation, and possible disappearance in many North African localities (Bouahmed 
et al., 2019).Medium confidence

Asia/South Korea Paeonia obovataModelling of climate change scenarios shows significant loss of suitable habitat and possible 
disappearance of P . obovatain in South Korea after 2080 (Jeon et al., 2020).Medium confidence
Iran Salvia hydrangeaA projected loss of habitat in the southeast of the range will not be compensated by the northward or 
upward elevational range migration (Ardestani and Ghahfarrokhi, 2021).Medium confidence
Patagonian, 
ArgentinaValeriana carnosaModelling for future climate scenarios projects a 22% loss of the suitable habitat (Nagahama and 
Bonino, 2020).Medium confidence
Western Ghats, 
IndiaKokum
Garcinia indicaPredictions of climate change impact on habitat suitability indicate drastic reduction in the suitability 
by over 10% under RCP8.5 for the years 2050 and 2070 (Pramanik et al., 2018).Medium confidence
Himalaya Ophiocordyceps sinensisA decline of the species is largely due to over harvesting, but ecological modelling indicates that

climate warming is also contributing to this decline (Hopping et al., 2018).High confidence
Pacific islandsNoni (Morindacitrifoli), 
naupaka (Scaevola spp.), kukui 
(Aleurites moluccana) and milo 
(Thespesia populnea)May be less susceptible to climate change as they are fast growing, have high reproduction rates, grow 
at sea level (and are often salt-tolerant) and have significant room for range shifts (Cavaliere, 2009).Low confidence

5
760Chapter 5 Food, Fibre and Other Ecosystem Products
2009) and play an essential role in people’s physical and emotional 
health (Section CCP6.2.5; 2.8) (high confidence) (Loring and Gerlach, 
2009; Cunsolo Willox et al., 2012). Wild foods consumed in the Arctic 
and northern regions include animals and a wide variety of plant 
foods (Wein et al., 1996; Ballew et al., 2006; Kuhnlein and Receveur, 
2007). Wild foods contribute most of important nutrients in the diets 
of northern and Arctic people (Johnson et al., 2009; Wesche and Chan, 
2010; Kenny et al., 2018). However, the use of traditional wild foods is 
declining across the region, lowering diet quality (Rosol et al., 2016). 
Indigenous communities in the Arctic perceive climate change related 
impacts on traditional wild foods, and availability and access to wild 
foods are forecast to continue to decline (Brinkman et al., 2016). Some 
communities hold positive views of the new opportunities a warmer

climate will bring, seeing them as a favourable trade-off relative to 
the loss of some forms of subsistence hunting (Nuttall, 2009). Climate 
change is causing ecological changes that impact Arctic wild food 
availability and abundance in many different ways, including changes 
to breeding success, migration patterns and food webs (Table  5.10, 
Markon et al., 2018).
Climate-change-induced impacts of access to wild foods are also 
of concern in Arctic regions (high confidence). Coastal and inland 
communities of Alaska found that 60% of climate impacts on food 
security listed by hunters were related to access (Brinkman et  al., 
2016). Reduced duration, thickness and quality of sea ice are some of 
the most cited impacts of climate change on wild food consumption 
(Ford, 2009; Laidler et  al., 2009; Downing and Cuerrier, 2011; 
Huntington et al., 2017; Nuttall, 2017; Fawcett et al., 2018; Ford et al., 
2018; Markon et al., 2018). Lack of snowfall reduces and delays the

ability to travel on land using snowmobiles (Downing and Cuerrier, 
2011), impacting safety of travel, time needed and costs of accessing 
wild foods (Cold et al., 2020).
Rising temperatures and humidity are also impacting wild food storage 
and increasing the risk of food-borne diseases (Cozzetto et al., 2013; 
Nuttall, 2017; Markon et al., 2018). Changes in AT and humidity can 
mean that whale and fish meat no longer dry properly, or meat may 
spoil before hunters can get it home (Downing and Cuerrier, 2011; 
Nuttall, 2017). Traditional permafrost ice cellars are no longer reliable 
(Downing and Cuerrier, 2011; Nyland et al., 2017; Herman-Mercer et al., 
2019). Climate-related environmental change compounded with social, 
economic, cultural and political change have had complex but overall 
negative impacts on wild foods (Section CCP6.4, Lujan et al., 2018) .
Communities across other (non-Arctic) parts of North America and

Europe also report declining availability of wild foods, with climate 
change among the perceived drivers for decline (medium confidence) 
(Table 5.10, Serrasolses et al., 2016; Smith et al., 2019a). Even when 
climate change may not always be the primary driver of loss of these 
wild food resources, climate may interact with other stressors to 
exacerbate loss of wild foods (Lynn et al., 2013; Reo and Parker, 2013).
5.7.4.2.2 Wild food in the arid and semi-arid environments
Wild foods are also impacted by climate change in arid and semi-arid 
landscapes around the world (medium evidence, high agreement) 
(Table 5.10). A number of wild species are important traditional foods of Indigenous Peoples or local communities across arid regions of 
North America (Messer, 1972; Kuhnlein and Calloway, 1977; Santos-
Fita et al., 2012; Vinyeta et al., 2016), South America (e.g., Argentina; 
Ladio and Lozada, 2004; Altrichter, 2006; Eyssartier et  al., 2011),

Australia (Scelza et al., 2014), the Mediterranean Basin (Hadjichambis 
et al., 2008; Powell et al., 2014), India and the Himalayas (Pingle, 1975; 
Gupta and Sen, 1980; Delang, 2006; Bhatt et al., 2017).
Wild foods such as baobab, shea and nere from plants and animals 
make an important contribution to diets and nutrition in arid and semi-
arid regions of Africa (Boedecker et al., 2014; Leßmeister et al., 2015; 
Bélanger and Pilling, 2019) and are being impacted by climate change 
(Moseley et al., 2015; Sango and Godwell, 2015; Hitchcock, 2016) (see 
Chapter 9). There has been little published research on the impacts 
of climate change on wild food in arid regions of Australia, although 
Aboriginal elders in one report suggested that climate-related changes 
are impacting wild food (Memmott et al., 2013).
5.7.4.2.3 Wild food in tropical humid environments
Wild foods are important to many communities that live in and 
adjacent to humid tropical forests, but climate change impacts are

mixed (Table  5.10, Dounias et  al., 2007; Colfer, 2008; Powell et  al., 
2015; Rowland et  al., 2017; Reyes-García et  al., 2019). In some 
humid tropical forest regions, bushmeat is particularly important 
(Golden et al., 2011; Nasi et al., 2011; Fa et al., 2015; Powell et al., 
2015; Rowland et al., 2017). In humid tropical regions, the impact of 
climate change on wild food availability, access and consumption is 
currently unclear and research is limited. There are, however, important 
interrelationships between climate change and wild food use in 
humid forests. For example, the loss of large mammals to bushmeat 
consumption and global trade will likely slow the regeneration of 
tropical forests in which a large number of tree species are dependent 
on large mammals for seed dispersal (Brodie and Gibbs, 2009). 
Conversely, others argue that bushmeat provides local communities 
with an important incentive to support local maintenance of forest

cover and, thus, carbon sequestration (Bennett et al., 2007).
5.8 Ocean-Based and Inland Fisheries Systems
The livelihoods of 10–12% of the world’s population depend on 
fisheries and aquaculture (FAO, 2020c). Globally, fish provide more 
than 3.3 billion people with 20% of their average per capita intake of 
animal proteins, reaching 50% or more in countries such as Bangladesh, 
Cambodia, The Gambia, Ghana, Indonesia, Sierra Leone, Sri Lanka and 
several Small Island Developing States (FAO, 2020c). Between 1961 
and 2017, the average annual apparent global food fish consumption 
increased (3.1% per year; from 9.0 kg per person in 1961 to 20.5 kg in 
2018), exceeding the rate of increase in consumption of meat from all 
terrestrial animals combined (2.1% annually, currently around 40 kg 
per person) (FAO, 2020d). Fish are a rich source of protein and specific 
vitamins and minerals (Khalili Tilami and Sampels, 2018), and are an

essential food source in regions in need of nutritious, affordable food 
(Thilsted et al., 2016; FAO et al., 2018; Hicks et al., 2019; Cross-Chapter 
Box MOVING PLATE this chapter).

5
761Food, Fibre and Other Ecosystem Products  Chapter 5
Table 5.10 |  Observed and predicted impacts of climate change on selected wild food species.
Arctic regionRinged seals
(Pusahispida)Drastic declines in population size and major changes in population structure (Hammill, 2009; Reimer 
et al., 2019); habitat (dependent on snow cover or ice breathing holes for lairs) will decline by 
approximately 70%, and significantly reduce survival rates of pups (Freitas et al., 2008).High confidence
Arctic regionBearded seal (Erignathus 
barbatus)Climate change affects the availability and stability of at least 11 ice-associated species, including 
bearded seal. Potential impacts due to climate change will reduce available habitat for birthing (Moore 
and Huntington, 2008; Fink, 2017).Medium evidence, 
Arctic regionWalrus

(Odobenus rosmarus)Declines in the climate-vulnerable Pacific walrus populations, induced by overharvesting (Taylor et al., 
2018); however, the species is considered highly vulnerable to loss of sea ice (Lydersen, 2018). Possible 
diet changes (related to climate-induced changes in food web) raise concerns about the health of the 
population (Clark et al., 2019).High confidence
Arctic regionNarwhal
(Monodon monoceros)The impacts of climate change on other sea ice-associated marine mammals are somewhat less clear 
(Moor et al., 2017). Climate change may threaten narwhal given their vulnerability to ice entrapment 
(Laidre and Heide-Jorgensen, 2005) and the narrow range of prey in their diet (Heide-Jørgensen, 2018). 
In Greenland, hunters report that narwhal now frequent fjords and other areas where manoeuvring a 
boat is difficult (Nuttall, 2017).Low evidence, 
Arctic regionBeluga

(Delphinapterus leucas)Belugas are thought to be less sensitive to climate change than some other sea mammals but can 
perish in large groups from ice entrapment. Climate impacts likely increased human activity (noise) 
(O’Corry-Crowe, 2009). Changes in migrating timing have been documented (Hsiang et al., 2017).Low evidence, low 
agreement
Arctic regionBowhead
(Balaena mysticetus)The movements of some whale species are linked to SSTs (Moore and Huntington, 2008; Chambault 
et al., 2018). Some whale hunting communities are now reporting that whales pass by at a time of year 
when launching boats is impaired by rough weather and poor sea ice conditions (Noongwook et al., 
2007; Huntington et al., 2017).Medium confidence
Arctic regionOther sea ice associated marine 
mammals (harp seal, hooded 
seal)The impacts of climate change on other sea ice associated marine mammals are somewhat less clear 
(Moor et al., 2017).Low confidence
Arctic and 
northern regionsReindeer and caribou

(Rangifer tarandus)Large herbivores are highly dependent on their food sources such as mosses, lichens and grasses which 
are sensitive to climate change (Istomin and Habeck, 2016).
Combined impacts of climate change and other inter-related factors suggest significant declines in 
caribou and reindeer populations, although to varying extents from one population to another (Kenny 
et al., 2018; Mallory and Boyce, 2018).
Warming has led to increased plant productivity and associated increases in body mass of some 
reindeer populations (Albon et al., 2017; Mallory and Boyce, 2018).
Increasing primary production, warming will also change the plant composition, leading to increases in 
woody/shrubby vegetation which will have negative nutritional consequences for caribou and reindeer 
(Elmendorf et al., 2012; Mallory and Boyce, 2018). The loss of lichens, a key winter food source, due 
to increased wildfire or replacement by grasses and herbs that die back in the winter, may also be

detrimental to caribou and reindeer, although there is currently no consensus on this among experts 
(Mallory and Boyce, 2018).
Rain on snow and icing events during winter, which are predicted to become more frequent, have been 
documented to lead to large increases in arctic herbivore mortality because they create an ice barrier 
making access to food more difficult (Putkonen and Roe, 2003; Tyler, 2010; Stien et al., 2012; Hansen 
et al., 2013; Forbes et al., 2016). Rain-on-snow events may also impact reproductive success, although 
recent research suggests this relationship in not straightforward (Douhard et al., 2016).
Increased summer insect harassment is also predicted to increase and further stress large herbivores 
both by the additional parasitic load and by decreasing the amount of time spent grazing as animals 
seek to outrun pests (Mallory and Boyce, 2018).
Finally, many caribou and reindeer populations rely on sea and freshwater ice to facilitate their movement

and migration; loss of ice may make some populations no longer viable (Mallory and Boyce, 2018).Medium confidence
Arctic and 
northern regionsMoose (Alces alces)The distributional changes of Rangifer populations might be affected by the range expansions and 
the northward expansion of moose (Mallory and Boyce, 2018). This is due to increases in productivity 
on the tundra and more frequent wildfire activity resulting in improved habitat quality for moose 
northward.Medium confidence
North AmericaGeese (Branta canadensis, 
Answer spp., Branta spp.)Phenological mismatch developing between the berries and migration timing may mean that Canadian 
geese no longer stop near some communities (Downing and Cuerrier, 2011).Medium confidence

5
762Chapter 5 Food, Fibre and Other Ecosystem Products
Arctic and 
northern regionsBerries
(Vaccinium spp., Rubus spp. 
and others)Berries are among the most important and widely consumed wild foods of plant origins in Arctic and 
northern regions (Vaara et al., 2013; Hupp et al., 2015; Boulanger-Lapointe et al., 2019).
Berry production will be impacted by climate change, including snow cover, rainfall, soil moisture, air 
temperature and availability of insect pollinators (Herman-Mercer et al., 2020) and possible risk from 
sea-level-rise-associated soil salinisation (Cozzetto et al., 2013).
Increased growth of woody shrub vegetation, driven by increased temperatures, can also make moving 
across the land difficult, impairing access to berry patches (Boulanger-Lapointe et al., 2019). Conversely,

a recent modelling experiment suggested that the >2°C warming experienced by Arctic communities 
over the past three decades has had minimal impact on overall trail access (Ford et al., 2019).
In Alaska, communities perceive berry abundance as declining and/or becoming more variable (Kellogg 
et al., 2010; Hupp et al., 2015). In a Gwich’in community in Canada, Parlee and Berkes (2005) recorded 
that local women perceived climate change, especially extreme weather events, as the greatest risk to 
traditional berry patches (cranberry, blueberry and cloudberry).
The expansion of trees and shrubs may cause shading and negatively impact the productivity of berry 
plants (Downing and Cuerrier, 2011; Lévesque et al., 2012).
Berries are predicted to be increasingly susceptible to negative impacts of invasive species (which 
compete for pollinators) (Spellman and Swenson, 2012) and infections (Turner and Clifton, 2009) as

climate change progresses. Suitable area of huckleberry (Vaccinium membranaceum) would shrink by 
5–40% by the end of the 21st century (Prevéy et al., 2020).
Phenological shifts are also important. Many communities report changes in phenology including failed 
ripening or ‘all of the berries are ripening at the same time’ (Turner and Clifton, 2009; Herman-Mercer 
et al., 2020). Competition with growing populations of geese is viewed by many communities to be an 
important threat to berry harvesting. (Boulanger-Lapointe et al., 2019). In Labrador, Canada, changes in 
permafrost, vegetation, water and weather have had an impact on cloudberry (bakeapple) productivity, 
phenology and patch fragmentation. Moreover, changes in summer settlement patterns (which are now 
farther from berry patches) are making it more difficult for people to respond to variations in growth 
and timing (Anderson et al., 2018).

In Montana, USA, Crow Nation elders have noted that many of their important berry resources have 
been impacted by climate change, either because they bud earlier and are then vulnerable to cold 
snaps, or the timing of fruit production has changed (with many now ripening at the same time) (Doyle 
et al., 2013). Similarly, the Wabanaki Nations in Maine and Eastern Canada worry that climate change 
will impact berry resources already under pressure from dwindling territory and pollution (Lynn et al., 
2013).High confidence
North America 
(Washington State, 
USA)Salmon (Salmonidae)Indigenous communities in Washington State, USA report devastation of their salmon fishery due to 
loss of glacial run off and associated warming river and stream temperatures; potential damage to 
shellfish resources due to sea level rise and ocean acidification (Lynn et al., 2013). The Karuk people in

California have also experienced losses in salmon (Lynn et al., 2013; Vinyeta et al., 2016).Medium confidence
(California)Acorns from oak trees (Querus)In the arid southwest of the USA, wild foods are less widely consumed today, but their revitalisation 
is important to identity and well-being of many Indigenous People. The Karuk people of the Klamath 
River in California have experienced an almost complete loss of two key traditional wild foods: salmon 
and acorns, foods which once made up 50% of a traditional Karuk diet (Lynn et al., 2013; Vinyeta et al., 
2016), as well as huckleberry (Vinyeta et al., 2016). Using regional climate models, Kueppers (2005) 
showed a major reduction in the range of two species of oak in California that are used in traditional 
diets. Increasing frequency of severe fires in the western USA threaten a number of traditional wild 
food resources, especially acorns (Vinyeta et al., 2016).Medium confidence
North AmericaWild rice

(Zizania spp.)Significant reductions in wild rice area in Great Lakes have been associated with mining, dams and 
other activities, but climate change may lead to further reductions (Cozzetto et al., 2013; Lynn et al., 
North AmericaCamas tuber
(Camassia quamash)Historic changes in fire regimes, linked to changes in climate, are believed to have altered availability of 
the important camas tuber (Camassia quamash) (Lepofsky et al., 2005).Medium confidence
North AmericaWapato tuber
(Sagittaria latifolia)The aquatic Sagittaria latifolia (the roots of which are consumed by Indigenous groups across North 
America) is vulnerable to both water salinity and temperature (Delesalle and Blum, 1994).Medium confidence
North AmericaSpringbeauty (Claytonia 
lanceolate)Claytonia lanceolata is particularly vulnerable to changes in snow melt and other climatic changes 
owing to advancement in the flowering (Renner and Zohner, 2018).Medium confidence

North AmericaSeaweed (Porphyra abbottiae, 
among others)In British Columbia, Canada, Indigenous People Gitga’at elders noted that the ripening of an important 
edible seaweed (Porphyra abbottiae) had rarely synchronised with weather patterns that enabled 
them to process it in the traditional way (drying on rocks and then ripening and re-drying) (Turner and 
Clifton, 2009).Low confidence
Africa Baobab (Adansonia digitata)Baobab is thought to be vulnerable to climate change because it is long-lived, it can take up to 23 years 
to start fruiting and leaf harvesting is often so intensive that it depresses fruit production. Modelling 
study using different records model shows that the percentage of present distribution predicted to be 
suitable in the future ranged from 5% to 91% (Sanchez et al., 2011).Low confidence

5
763Food, Fibre and Other Ecosystem Products  Chapter 5
Overall capture fishery production has remained relatively static since 
the 1990s, reaching 96.4  million tonnes in 2018, with over 87% of 
the production coming from marine environments and the rest from 
inland fisheries (FAO, 2020c). Finfish represent 85% of global marine 
seafood production, with small pelagic fishes (anchovies, sardines 
and herrings) as the major contributor. Almost 60% of the total 
global marine catches come from China, Peru, Indonesia, the Russian 
Federation, the USA, India, Viet Nam, Japan, Norway and Chile (FAO, 
2020c). Inland fisheries are found on every continent other than 
Antarctica and provide 158 million people the equivalent of all dietary 
animal protein (McIntyre et  al., 2016). Inland production accounted 
for 12 million tonnes in 2018, with nearly 70% of capture from low-

income Asian and African countries (Harrod et al., 2018a).The aquaculture and fisheries’ share of gross domestic product 
(GDP) varies mostly from 0.01% to 10% (Cai et  al., 2019), but the 
relative importance in countries’ economies and welfare is greater in 
several low-income countries, especially in many African and Pacific 
Island states. Approximately 60 million people are directly employed 
in fisheries value chains, from harvesting to distribution (Vannuccini 
et al., 2018); around 95% of them are in small-scale fisheries of low- 
and middle-income countries, and almost half are women.Region Species Observed and projected impacts of climate changeAssessment of 
Africa Shea (Vitellaria paradoxa)Shea (Vitellaria paradoxa) was expanded through human intervention and is linked to human 
migration; fruit traits such as fruit size and shape, pulp sweetness and kernel fat content are

determined both by temperature and rainfall, as well as human selection for preferred traits (Maranz 
and Wiesman, 2003). There is limited and conflicting evidence of the impacts of climatic conditions 
and future projected climate variations on V. paradoxa (Tom-Dery et al., 2018). Mixed evidence of the 
impact of climate and rainfall on fruit production and timing is reported (Tom-Dery et al., 2018). Fruit 
production was negatively correlated with mean annual temperature and positively correlated with 
annual rainfall (Bondé et al., 2019).Limited evidence, 
North Africa
(Morocco)Argan (Argania spinosa)Climate change projections suggest a 32% decrease in habitat suitable for Argania spinosa under some 
scenarios (Alba-Sánchez et al., 2015; Moukrim et al., 2019).Medium confidence
Asia (Nepal)Fruit species and vegetables 
(e.g., Asparagus racemosus, 
Urticadioica)In Nepal, Thapa (2015) reports phenological changes in semi-domesticated fruit species, as well as

decreased availability of a number of wild plants that can be consumed as vegetables.Limited evidence, 
Worldwide, most 
important in 
Europe and AsiaMushroomsWild mushrooms production (including truffles) is closely linked to climate factors including temperature 
and precipitation as well as tree growth and carbohydrate production (Tahvanainen et al., 2016). Some 
species are sensitive to high temperatures (Büntgen et al., 2012; Le Tacon et al., 2014; Ágreda et al., 
2015; Bradai et al., 2015; Taye et al., 2016; Alday et al., 2017; Karavani et al., 2018; Büntgen et al., 2019; 
Thomas and Buntgen, 2019). Models for some varieties suggest ‘declines of 78–100% in European 
truffle production are likely for 2071–2100’ (Thomas and Buntgen, 2019). For some species in northern 
Europe, the season is expanding (starting earlier and/or ending later), likely linked to warming (Büntgen

et al., 2012; Le Tacon et al., 2014; Ágreda et al., 2015; Bradai et al., 2015; Taye et al., 2016; Alday et al., 
2017; Karavani et al., 2018; Büntgen et al., 2019; Thomas and Buntgen, 2019).
Matsutake mushroom (Tricholoma matsutake), highly prized in China, is sensitive to timing and amount 
of precipitation and temperature (Yang et al., 2012), and suitable habitat for this species is predicted 
to significantly decrease and highly suitable habitat to nearly disappear under various climate change 
scenarios (Guo et al., 2017).High confidence
(California)Acorns, nuts and berries and 
other fire-dependent wild foodsLow-intensity traditional burning practices increased pyro-diversity (Vinyeta et al., 2016). Climate 
change will exacerbate the risks posed by exotic pathogens that attack oak species and further reduce 
access to acorns as well as other foods founds in oak ecosystems (Voggesser et al., 2013).High confidence
(Amazon region)Aguaje, (Mauritia

felxuosa), Brazilian nut 
(Bertholletiaexcelsa), fishing 
and hunting in generalLocal communities perceived a lower yield of aguaje due to drought (Hofmeijer et al., 2013). In another 
study from the Colombian Amazon, wild food use was reported to be vulnerable to extreme climate 
events which impact species migration patterns or restrict access to fishing and hunting rounds 
(Torres-Vitolas et al., 2019). In some humid regions, the range of some wild food species may be 
extended by climate change, such as the Brazilian nut (Bertholletiaexcelsa) (Thomas et al., 2014).Medium confidence
Small islands 
(Papua New 
Guinea)Sweet potatoIncreases in the ENSO were associated with drought which increased sweet potato losses (Jacka, 2016) 
in highlands humid forest.Limited evidence, 
Australasia 
(Australia)General wild foodsAboriginal communities in North Queensland, a humid tropical region of northern Australia, reported

some climate impacts on wild foods, although primarily for marine resources and those found in dry 
forest ecosystems (McIntyre-Tamwoy et al., 2013).Limited evidence, 
Asia (Indonesia) Sago (Metroxylon sagu)People in a sago-dependent community in Papua Indonesia viewed climate variation as less important 
than other factors (logging, mining, infrastructure), but still expressed concerns about salinity of water 
supplies, floods and reduced hunting success (Boissière et al., 2013).Limited evidence, 

5
764Chapter 5 Food, Fibre and Other Ecosystem Products
5.8.1 Observed Impacts
Ocean systems are already facing significant impacts of climate 
change. At the ocean surface, temperature has on average increased 
by 0.88 [0.68–1.01] °C from 1850–1900 to 2011–2020 (Fox-Kemper 
et  al., 2021; Gulev et  al., 2021). Marine heatwaves have increased 
in frequency over the 20th century, with an approximate doubling 
since the 1980s (high confidence), and their intensity and duration 
have also increased (medium confidence) (IPCC, 2021, Box  9.2). In 
the Northeast Pacific, for example, an intense and long-lasting marine 
heatwave during 2013–2015 bridged to the strong 2015–2016 El Niño 
(Tseng et al., 2017) resulted in over 5 years of warmer-than-normal 
temperatures affecting the migration, distribution and abundance 
of several marine species, including fisheries resources (Cornwall, 
2019; Jiménez-Quiroz et  al., 2019). The surface open ocean pH has

declined globally over the last 40 years by 0.003–0.026 pH per decade 
(virtually certain), and a decline in the ocean interior pH has been 
observed in all ocean basins over the past two to three decades (high 
confidence) (Gulev et al., 2021). The ocean is losing dissolved oxygen 
(very likely) in the range of 0.5–3.3% between 1970 and 2010 for the 
0–1000 m depth stratum (Bindoff et al., 2019; Canadell et al., 2021), 
salt content is being redistributed (very likely) (Liu et al., 2019a; Gulev 
et al., 2021) and vertical stratification is increasing (virtually certain) 
(HLPE, 2017a; Fox-Kemper et al., 2021; Ranasinghe et al., 2021). There 
is high confidence that all these new physical, chemical and biological 
conditions affect marine organisms’ physiology, distribution and 
ecology, with an overall shift in biomass and species composition 
affecting ecosystem structure and function (Chapter 3). Under climate 
change, freshwater ecosystems are highly exposed to eutrophication,

species invasion and rising temperatures (Lynch et al., 2016; Hassan 
et al., 2020). Major threats to wetland fisheries include water stress, 
sedimentation, weed proliferation, sea level rise and loss of wetland 
connectivity (Naskar et al., 2018).
Changes in aquatic ecosystems directly affect humans by altering 
livelihood, cultural identity and sense of self, and seafood provision, 
quality and safety. The state of marine fishery resources has 
continued to decline, with the proportion of fish stocks at biologically 
unsustainable levels of exploitation increasing from 10% in 1974 to 
34.2% in 2017 (FAO, 2020d). There is medium confidence that fisheries 
production declines in different world regions can be partly attributed 
to climate change, along with overfishing and other socioeconomic 
factors. It has been estimated that, from 1930 to 2010, the amount 
of fish that can be sustainably harvested from several marine fish

populations has decreased by 4.1% globally due to ocean warming, 
with some regions (East Asian Marginal Seas, the North Sea, the Iberian 
Coast and the Celtic-Biscay Shelf), experiencing losses of 15–35% 
(Free et al., 2019). There is regional variation such as redistribution of 
fishing grounds, due to climate-induced fish species migrations (Cross-
Chapter Box MOVING PLATE this chapter). In Tanzania, for example, 
most small-scale fishers (75%) have reported shifting fishing grounds 
from nearshore to offshore areas during the last decade, due to 
perceived combined effects of overfishing and environmental impacts 
(Silas et al., 2020). Observed impacts in some inland aquatic systems 
indicate substantial productivity reductions (medium confidence). 
For example, sustained warming in Lake Tanganyika during the last 
∼150 years has affected the biological productivity by strengthening and shallowing stratification of the water column (Cohen et al., 2016).

Still, over 60% of the published reports on directly observed impacts 
of climate change on freshwater biota are on salmonids in North 
America and Europe, highlighting significant literature gaps for other 
fish species and regions (Myers et al., 2017a).
There is low confidence in climate change affecting the nutritious value 
of seafood. Contrasting evidence suggests that ocean warming and 
acidification could be altering the nutritional quality of commercial 
mollusks, primarily by reducing healthy fatty acids content (Tate 
et al., 2017; Ab Lah et al., 2018; Lemasson et al., 2019), but Coleman 
(2019) found no significant changes in a widely distributed coastal 
fish species.
In terms of food safety, there is high confidence that climate change 
increases the trends in seafood consumption related illnesses due to 
biological agents such as algae-produced toxins, ciguatera and Vibrio 
(Cross-Chapter Box  ILLNESS in Chapter 2, Sections  5.11 and 5.12).

Increased surface water warming changes the occurrence, intensity, 
species composition and toxicity of marine and freshwater algae and 
bacteria, and expansion to areas where they had not been reported 
before (Botana, 2016; McCabe et al., 2016; Griffith et al., 2019). There 
is limited evidence suggesting that risks linked to the bioaccumulation 
of chemicals are also of concern, such as neurotoxic methylmercury 
(MeHg) and heavy metals, due to water quality and trophic changes 
induced by climate change (Shi et al., 2016; Schartup et al., 2019).
5.8.2 Assessing Vulnerabilities
In the absence of adaptive measures, climate-induced changes in 
the abundances and distributions of fish will impact the provision, 
nutrition and livelihood security of many people (high confidence) as 
well as regional and global trade patterns (medium confidence).
5.8.2.1 Food security: provision and nutrition
The importance of seafood in food security and nutrition is

increasing, largely due to its contribution as high-quality food (high 
confidence) (Hicks et  al., 2019), as seafood contains unique long-
chain polyunsaturated fatty acids (LC-PUFAs) and highly bioavailable 
essential micronutrients—vitamins (A, B and D) and minerals (calcium, 
phosphorus, iodine, zinc, iron and selenium). These compounds, often 
not readily available elsewhere in diets, have beneficial effects for 
adult health and child cognitive development (HLPE, 2014). Changes 
in marine and freshwater fish production can have significant 
consequences for human nutrition (Colombo et  al., 2020). These 
changes are of particular concern in regions with few nutrition 
alternatives, such as low-income countries in Africa, Asia, Australasia, 
and Central and South America (high confidence) (Ding et al., 2017; 
Kibria et al., 2017).
Freshwater ecosystems that support most inland fisheries are under 
continuing threat from changes in land use, water availability and

pollution and other pressures that will be exacerbated by climate 
change (high confidence) (Section 4.3.5). Declines in dissolved oxygen 
in freshwater are 2.75–9.3 times greater than observed in the world’s

5
765Food, Fibre and Other Ecosystem Products  Chapter 5
oceans (Jane et al., 2021). These systems have a relatively low buffering 
capacity and are therefore more sensitive to climate-related shocks 
and variability (Harrod et al., 2018b). Freshwater faunae are projected 
to be highly vulnerable; in the tropics because organisms are closer 
to approaching their thermal physiological limits and in the northern 
hemisphere (30–50°N) because the rate of temperature change is 
faster (Comte and Olden, 2017). The worldwide spatial confluence of 
productive freshwater fisheries and low food security highlights the 
critical role of rivers and lakes in providing locally sourced, low-cost, 
nutritious food sources (McIntyre et al., 2016).
Deltas and other wetland fisheries are extremely vulnerable to climate 
change and home to a large and growing proportion of the world’s 
population. In India, Ghana and Bangladesh, where three of the most

populated Deltaic systems are located, subsistence fisheries provide 
12–60% of the animal protein in people’s diets (Lauria et al., 2018).
The concern over aquatic food products’ safety due to climate change 
is increasing (high confidence). A strong positive relationship exists 
between specific bacterial growth rates and temperature, including 
pathogenic species of the genera Vibrio, Listeria, Clostridium, 
Aeromonas, Salmonella, Escherichia and others, whose distributional 
area is expanding with changing climate conditions (Cross-Chapter 
Box ILLNESS in Chapter 2, Section 5.12.1).
5.8.2.2 Social vulnerabilities, including gender and 
marginalised groups and cultural services
There is high confidence that climate change is and will continue 
to be a threat to the livelihood of millions of fishers, with the most 
vulnerable being those with fewer opportunities and less income 
(Barange and Cochrane, 2018; Section 3.4.3). The social vulnerability

can differ largely between locations, even between relatively close 
coastal or inland communities (Bennett et al., 2014; Maina et al., 2016; 
Ndhlovu et  al., 2017; Martins et  al., 2019) and among inhabitants 
within a location, depending on factors such as access to other 
economic activities, education, health, adults in the household, and 
political connections (high confidence) (Senapati and Gupta, 2017; 
Abu Samah et al., 2019; Lowe et al., 2019).
Indigenous coastal communities consume 1.5–2.8  million metric 
tonnes of fish per year (about 2% of global yearly commercial 
marine catch), and reach a per capita consumption estimated to be 
15 times greater than that of non-Indigenous country populations 
(Cisneros-Montemayor et  al., 2016). There is high confidence that 
some Indigenous fishing communities are particularly vulnerable to 
climate change through a reduced capacity to conduct traditional 
harvests because of limited access to, or availability of, fish resources

(Weatherdon et al., 2016), with consequences that include dietary 
shifts with significant nutritional and health implications (Marushka 
et al., 2019), displacement and loss of cultural identity (Sullivan and 
Rosenberg, 2018) and loss of social, economic and cultural rights 
(Finkbeiner et al., 2018). Areas of high risk for Indigenous Peoples 
include the Arctic, coastal communities with a high dependency 
on marine and freshwater fisheries, and Small Island States and 
Territories (Finkbeiner et  al., 2018; Hanich et  al., 2018, Section 
CCP6.2.5.1).Women play a crucial role along the entire fisheries value chain, 
providing labour force in industrialised and small-scale fisheries all 
around the world (FAO, 2020d). For small-scale fisheries alone, women 
represent about 11% of the labour force, and their activity is generally 
in subsistence fisheries, highlighting their role in household food 
security (Harper et al., 2020). In general, gendered division of labour

tends to cause lower salaries for women and different perception and 
experience of risk to climate change impacts (high confidence) (Lokuge 
and Hilhorst, 2017).
5.8.2.3 Management, economic and geopolitical vulnerabilities
Local, national, regional and international fisheries are mostly 
underprepared for geographic shifts in marine animals driven by 
climate change over the coming decades (high confidence) (Pinsky 
et al., 2018; Oremus et al., 2020; Pinsky et al., 2020). With fisheries 
distribution changes, sometimes into areas dedicated to different 
historical uses or new ventures, the current management regimes 
will face constraining legal frameworks (Farady and Bigford, 2019; 
Pinsky et al., 2020), which will demand interventions in the form of 
policies, programmes and actions, at multiple scales (Cross-Chapter 
Box MOVING PLATE this chapter). Coordinated fisheries management 
can substantially expand capacity to respond to a changing climate

(Pinsky et al., 2020), but a great deal of political will, capacity building 
and collective action will be necessary (high confidence) (Teslić et al., 
2017; Burden and Fujita, 2019; Section 5.8.4).
Today, approximately half the world’s population (~4  billion out of 
7.8 billion people) are assessed as being currently subject to severe 
water scarcity for at least 1  month per year (medium confidence) 
(Box 4.1), and freshwater inland fisheries are particularly vulnerable 
as they are given lower priority for water resources than other sectors 
(high confidence). In some cases, this situation results in the total 
loss of freshwater fisheries. Examples include diversion of water 
for agriculture, shifts from food provision to recreational fisheries, 
conserving biodiversity, and the requirement for high-quality water for 
drinking water supply (Section 5.13, Harrod et al., 2018a).
There is high confidence that climate change increases the risk of

conflicts due to the redistribution of stocks and their abundance 
fluctuations, with subsequent impacts on resource sharing (Spijkers and 
Boonstra, 2017; Pinsky et al., 2018; Spijkers et al., 2018; Mendenhall 
et al., 2020; Pinsky et al., 2020). High vulnerability and lack of adaptive 
capacity to climate change impacts (including fisheries-dependent 
livelihoods, attachment to place, and pre-existing tensions) increase 
the risk of conflicts, including among fishery area users and authorities 
(Ndhlovu et  al., 2017; Shaffril et  al., 2017; Spijkers and Boonstra, 
2017; Mendenhall et  al., 2020). Similarly, shifts in the distribution 
of transboundary fish stocks under climate change alter the current 
sharing of resources between countries and create conflicts as well 
as new opportunities (Cross-Chapter Box MOVING PLATE this chapter, 
Spijkers and Boonstra, 2017; Pinsky et al., 2018).

5
766Chapter 5 Food, Fibre and Other Ecosystem Products
5.8.3 Projected Impacts
There is medium confidence that climate change will reduce global 
fisheries’ productivity (Section  3.4.4.2.3), with more significant 
reductions in tropical and subtropical regions and gains in the poleward 
areas (Bindoff et al., 2019; Oremus et al., 2020). Through an ensemble 
of marine ecosystem models and Earth System Models, mean global 
animal biomass in the ocean has been estimated to decrease by 5% 
under the RCP2.6 emissions scenario and 17% under RCP8.5 by 2100, 
with an average decline of 5% for every 1°C of warming (Lotze et al., 
2019), affecting food provision, revenue distribution, and potentially 
hindering the rebuilding of depleted fish stocks (Britten et al., 2017). The 
projected declining rates result in a 5.3–7% estimated global decrease 
in marine fish catch potential by 2050 (Cheung et al., 2019), particularly 
accentuated in tropical marine ecosystems and affecting many low-

income countries (Barange and Cochrane, 2018; Bindoff et al., 2019; 
Cross-Chapter Box MOVING PLATE this chapter). Projections indicate 
that by 2060 the number of exclusive economic zones (EEZs) with 
new transboundary stocks will increase to 46 under strong mitigation 
RCP2.6, and up to 60 EEZs under the RCP8.5 GHG emissions scenario 
(Pinsky et al., 2018). Similarly, by combining six intercompared marine 
ecosystem models, Bryndum-Buchholz et  al. (2019) projected that 
under the RCP8.5 scenario a total marine animal biomass decline 
of 15–30% would occur in the North and South Atlantic and Pacific 
and the Indian Ocean by 2100. In contrast, polar ocean basins would 
experience a 20–80% increase. In the eastern Bering Sea, simulations 
based on RCP8.5 predict declines of pollock (>70%) and cod (>35%) 
stocks by the end of the century (Holsman et al., 2020). Temperate tunas 
(albacore, Atlantic bluefin and southern bluefin) and the tropical bigeye

tuna are expected to decline in the tropics and shift poleward by the 
end of the century under RCP8.5, while skipjack and yellowfin tunas are 
projected to increase abundance in tropical areas of the eastern Pacific 
but decrease in the equatorial western Pacific (medium confidence) 
(Erauskin-Extramiana et al., 2019). In the western and central Pacific, 
redistribution of tropical tuna due to climate change is projected to 
affect license revenues from purse seine fishing and shift more fishing 
into high seas areas (Bell et al., 2018; Table 15.5). For the east Atlantic, 
observational evidence indicates that not only will tuna distribution 
change with temperature anomalies, but also fishing effort distribution 
(Rubio et al., 2020a). There is medium confidence that climate change 
will create new fishing opportunities when exploited fish stocks shift 
their distribution into new fishing regions in enclosed seas, such as the

Mediterranean and the Black Sea (Hidalgo et al., 2018; Pinsky et al., 
2018). However, in general, where land barriers constrain the latitudinal 
shifts, the expected impacts of climate change are population declines 
and reduced productivity (high confidence) (Oxenford and Monnereau, 
2018). Besides direct impacts on the abundance of fisheries-targeted 
species, climate-change-induced proliferation of invasive species could 
also affect fisheries’ productivity (low confidence) (Mellin et al., 2016; 
Goldsmith et al., 2019).
Shifting marine fisheries will affect national economies (high 
confidence) (Bindoff et al., 2019). It has been suggested that, without 
government subsides, fishing is already non-profitable in 54% of the 
international waters (Sala et  al., 2018). Projections are that fishing 
maximum revenue potential from landed catches will decrease further

by 10.4% (±4.2%) by 2050 relative to 2000 under RCP8.5, close to 35% greater than the decrease projected for the global maximum catch 
potential (7.7±4.4%); (Lam et al., 2016). The global revenue potential 
loss for that period ranges from USD 6 to 15 billion (depending on the 
model), but impacts may be amplified at the regional scale for fisheries-
dependent and low-income countries. The maximum revenue potential 
percentage decrease in the EEZ under RCP8.5 is estimated to be over 
2.3 times larger than that of the high seas (Lam et al., 2016). Ocean 
acidification is also expected to drive large global economic impacts 
(medium confidence) (Cooley et  al., 2015; Fernandes et  al., 2017; 
Macko et al., 2017; Hansel et al., 2020), and there is high confidence 
that the integrated economic consequences of all interacting climate 
change-related factors would result in even larger losses. Changes in 
the frequency and intensity of extreme events will also alter marine

ecosystems and productivity. Marine heatwaves can lead to severe 
and persistent impacts, from mass mortality of benthic communities 
to decline in fisheries catch (IPCC, 2021, Box 9.2). These events have 
very likely doubled in frequency between 1982 and 2016 and have also 
become more intense and longer (Smale et al., 2019; Laufkotter et al., 
2020); for all future scenarios Earth System Models project even more 
frequent, intense and longer-lasting marine heatwaves (Eyring et al., 
2021; IPCC, 2021, Box 9.2).
In addition to temperature and water availability stress, climate 
change will bring new water quality challenges in freshwater systems, 
including increased dissolved organic carbon and toxic metal loads 
(high confidence) (Chen et al., 2016). Harrod et al. (2018a) found that 
the two major inland fishery producers (China and India) will face 
significant stress in the future, a large group of countries that produce

around 60% of total yield is projected to face medium stress, and a 
small group of 17 countries has the least severe repercussions (medium 
confidence). Climate warming may enhance northward colonisation of 
water bodies of commercial freshwater species in the Arctic, where 
there are few ecological competitors (medium confidence) (Campana 
et  al., 2020) but at the same time may also accentuate the age-
truncation effect of harvesting, elevating the population’s vulnerability 
to environmental perturbations (Smalås et  al., 2019). Detailed 
information on many of the most important inland fisheries is limited.
In terms of food safety, major concerns linked to climate change 
include the continued trend of increasing HABs, and the quantity of 
pollutants reaching aquatic systems (Box 3.3; Section 5.11).
5.8.4 Adaptation
Adaptation options in land- and aquatic-based culturing food 
production systems include both governance actions and changes

in the factors of production (Section  5.4.4, 5.5.4, Reverter et  al., 
2020). In contrast, adaptation options in fisheries are primarily 
concentrated in the socioeconomic dimension, especially governance 
and management (Brander et al., 2018; Holsman et al., 2019), and 
given the scale of the problem, there are relatively few intentional, 
well-documented examples of implemented tactical responses (Bell 
The proportion of fisheries operating at levels that are considered 
biologically unsustainable by the FAO has increased from 10% in 1974

5
767Food, Fibre and Other Ecosystem Products  Chapter 5
to 34.2% in 2017 (FAO, 2020d). There is high confidence that reducing 
stresses on marine ecosystems reduces vulnerability to climate 
change and augments resilience (Barange, 2019; Woodworth-Jefcoats 
et al., 2019; Ogier et al., 2020). Specifically, overfishing is the most 
critical non-climatic driver affecting the sustainability of fisheries, 
and therefore improving management could help rebuild fish stocks, 
reduce ecosystem impacts and increase the adaptive capacity of 
fishing (high confidence); (Barange, 2019; Das et al., 2020). Pursuing 
sustainable fisheries practices under a low-emissions scenario would 
decrease risk by 63%; in contrast, under the most extreme RCP8.5, 
both profit and harvest decline relative to today even under the most 
optimistic assumptions about global fisheries management reforms 
(Gaines et al., 2018; Sumaila et al., 2019; Free et al., 2020).

One adaptation strategy in the fishing sector is developing the capacity 
to recognise and respond to new opportunities that might arise from 
climate change by establishing a policy and planning setting that 
augments the fishers’ flexibility to change target species of fisheries or 
even engage in different productive activities. A key element would be 
the design and implementation of management schemes that consider 
flexible permits, sharing quotas, rethinking boundaries and reference 
points in response to system changes (Brander et  al., 2018; Cross-
Chapter Box MOVING PLATE this chapter). Large-scale distribution and 
productivity changes of commercial fish species will demand the ability 
to implement cooperative fishing strategies (Cisneros-Montemayor 
et al., 2020; Østhagen et al., 2020), and adjust multi-lateral treaties 
and other legal instruments used for managing shared transboundary 
ecosystems (Butler et al., 2019; Cross-Chapter Box MOVING PLATE this 
chapter).

There is high confidence that making climate change and adaptive 
capacity a mainstream consideration in global, regional, environmental 
and fisheries governance structures can improve the response capacity 
to ocean change (Gaines et al., 2018; Bindoff et al., 2019; Holsman 
et al., 2020; Ojea et al., 2020). For example, spatial management that 
includes strategies such as Territorial Use Rights for Fishing (TURFs), 
locally managed marine areas (LMMAs) and customary tenure is an 
approach that has climate change adaptation potential in small-scale 
fisheries but will require adjustments in governing and managing 
institutions that allow them to be more dynamic and flexible (Le Cornu 
et al., 2018). In regions where some of these measures have already 
been tested, institutional, legal, financial and logistical barriers to 
successful adaptation have been encountered, such as market failures 
stemming from uncertainty around new or emerging species, or policy

barriers derived from the fact that the creation of scientific information 
needed to change regulations is likely slower than the pace of changes 
in stocks (Peck and Pinnegar, 2018).
Adaptation capacity is limited by the financial capacity of some 
countries (Bindoff et al., 2019). For example, in West African fisheries, 
adaptation costs associated with replacing the loss of coastal 
ecosystems and productivity is estimated to require 5–10% of 
countries’ GDP (Zougmoré et al., 2016). For Pacific Islands and Coastal 
Territories, fisheries adaptation will require significant investment from 
local governments and the private sector (Rosegrant et al., 2016), and 
reducing dependence on or finding alternatives to vulnerable marine 
resources (Johnson et al., 2020; Mabe and Asase, 2020).Adaptive capacity is strongly associated with social capital (i.e., the 
networks, shared norms, values and understandings that facilitate

cooperation within or among groups) (high confidence) (Stoeckl 
et al., 2017; D’agata et al., 2020) and depends on to what extent 
stakeholders are aware of climate change and their perception of risk 
(Ankrah, 2018; Martins and Gasalla, 2018; Chen, 2020). Improving 
information flows allows for a more efficient co-management 
implementation (medium confidence) (Vasconcelos et  al., 2020). 
Utilisation of local and Indigenous knowledge has the potential 
to facilitate adaptation (Bindoff et  al., 2019), not only because it 
represents actual experiences and autonomous adaptations, but 
also because it facilitates reaching shared understanding among 
stakeholders and adoption of solutions. Challenges to hybridising local 
ecological knowledge and scientific knowledge include differences 
in stakeholder or governance perceptions about the validity of each 
knowledge set and issues of expertise and trust (Harrison et  al., 
2018). Engaging Indigenous Peoples and local communities as

partners across climate research ensures this knowledge is utilised, 
enhancing the usefulness of assessments (Bindoff et al., 2019) and 
facilitating the co-construction and implementation of sustainable 
solutions (medium confidence) (Braga et al., 2020; Bulengela et al., 
2020). Building climate resilience in the fishing sector also involves 
recognising gender and other social inequities (Call and Sellers, 
2019), and ensuring that all stakeholders are equally involved in the 
adaptation plans, including their design and the capacity-building 
training programmes.
There is high confidence that, for the freshwater fisheries systems, the 
most immediate adaptation option is the effective linkage of fisheries 
management to the adaptation plans of other sectors, especially 
water management (hydropower, irrigation and the commitment to 
maintaining environmental flows) (Harrod et  al., 2018a; Kao et  al., 
2020). In some regions, organisations are already addressing this issue;

for example, The Office of Water (OW) in the USA is aimed at ensuring 
that drinking water is safe while ecosystem is conserved to provide 
healthy habitat for fish, plants and wildlife; however, success strongly 
depends on the possibility of integrating the jurisdictional framework 
of different agencies (Poesch et  al., 2016), the implementation of 
effective monitoring programmes (Paukert et  al., 2016) and finding 
ways to incentivise the early restoration of degraded systems (Ranjan, 
2020).

5
768Chapter 5 Food, Fibre and Other Ecosystem Products
Cross-Chapter Box: MOVING PLATE: Sourcing Food When Species Distributions Change
Authors: H. Gurney-Smith (Canada/UK), W. Cheung (Canada), S. Lluch Cota (Mexico), E. Ojea (Spain), C. Parmesan (France/UK/USA), J. 
Pinnegar (UK) P . Thornton (Kenya/UK), M-F . Racault (UK/France), G. Pecl (Australia), E.A. Nyboer (Canada), K. Holsman (USA), K. Miller 
(USA), J. Birkmann (Germany), G. Nelson (USA) and C. Möllmann (Germany)
This Cross-Chapter Box, the ‘moving plate’, addresses climate-induced shifts and domesticated production suitability of food species 
consumed by people. Marine, freshwater and terrestrial systems are already experiencing species shifts in response to climate change 
(very high confidence) (see also Sections 2.4.2.1. and 3.4.3., Figure MOVING PLATE.1 this chapter), with subsequent impacts on food

provisioning services, pests and diseases (high confidence) (see Box 5.8 and Cross-Chapter Box ILLNESS in Chapter 2). This Box highlights 
food insecurity and malnutrition of vulnerable peoples under climate change for both wild and domesticated aquatic and terrestrial 
species, and discusses challenges for adaptation and the roles that management (transboundary and ecosystem-based) can play to 
enable food security, reduce conflicts and prevent resource over-extraction.
Range contractions, shifts or extirpations are projected for terrestrial and aquatic species under warming, with greater warming leading 
to larger shifts and losses, where mitigation would therefore benefit climate refugia and reduce projected biodiversity declines (Smith 
et al., 2018; Warren et al., 2018). Marine species are moving poleward faster than terrestrial and freshwater species, despite faster

warming on land (Pecl et al., 2017; Lenoir et al., 2019; Woolway and Maberly, 2020), leading to new or exacerbated socioeconomic 
conflicts within and between countries (see Figure MOVING PLATE.1 this chapter, see Sections 13.5.2.2., 15.3.4.4., FAQ 15.3., Mendenhall 
et al., 2020). There is large variation in the magnitude and pattern of species shifts, even among similar species within a region, leading 
to changes in communities in a given region (Brown et al., 2016; Pecl et al., 2017). The number of extreme heat stress days are projected 
to increase for domesticated species like cattle (see Figure MOVING PLATE.1 this chapter), leading to shifts in suitable habitat for raising 
livestock in the open with associated impacts in animal productivity and the costs of adapting in Africa, Asia, and Central and South 
America (Thornton et al., 2021).
Nutritional dependency, cultural importance, livelihood, or economic reliance on shifting species will increase impacts of climate change,

especially for small-scale fishers (marine and freshwater), farmers, women and communities highly dependent on local sources of food 
and nutrition (high confidence) (see Figures MOVING PLATE.1 and MOVING PLATE.3 this chapter, Sections 3.5.3., 8.2.1.2. and 15.3.4.4, 
McIntyre et al., 2016; Blasiak et al., 2017; Kifani et al., 2018; Bindoff et al., 2019; Atindana et al., 2020; Hasselberg et al., 2020; Farmery et al., 
2021). Micronutrient concentrations from marine fisheries vary with species, providing higher concentrations of calcium, iron and zinc in 
tropical regions and higher concentrations of omega-3 fatty acids in polar regions (Hicks et al., 2019). While consumption of smaller species 
rich in micronutrients may provide significant benefits against deficiencies in Asia and Africa, local dietary changes in fish consumption may 
be linked to food preferences, fish availability due to international trade or illegal fishing and competing usage of fish (see Figure MOVING

PLATE.3 this chapter, Hicks et  al., 2019; Sumaila et  al., 2020; Vianna et  al., 2020). Industrial fleets are likely to switch target species 
(Belhabib et al., 2016) and inhibit small-scale fishers via illegal, unreported or unregulated fishing in EEZs (Belhabib et al., 2019; Belhabib 
et al., 2020). Extreme events can exacerbate issues, as fisheries are frequently increasingly exploited as a coping mechanism under times 
of crisis, increasing illegal fishing activities and conflict among maritime users (Pomeroy et al., 2016; Mazaris and Germond, 2018). Spatial 
conflicts between artisanal and commercial foreign fishing fleets are already occurring in Ghana (Penney et al., 2017), and from climate-
induced tropical tuna shifts in the Western and Central Pacific Ocean Islands (see Section 15.3.4.4., (Bell et al., 2018a)). Properly managed 
small-scale fisheries can reduce poverty and improve localised food security and nutrition in low-income countries but will likely require

restriction in the number of fishers, boat size or fishing days (Purcell and Pomeroy, 2015; Hicks et al., 2019).
Shifting species have negative implications for the equitable distribution of food provisioning services, increasing the complexity of 
resolving sovereignty claims and climate justice (high confidence) (Allison and Bassett, 2015; Ayers et al., 2018; Baudron et al.; Ojea 
et  al., 2020; Palacios-Abrantes et  al., 2020). Higher-latitude countries generally have higher GHG emissions and will benefit from 
poleward-migrating resources from tropical poorer and lower-emitting GHG countries (Free et al., 2020). In this context, climate justice 
supporting fishing arrangements could offset socioeconomic impacts from exiting species (Mills, 2018; Lam et  al., 2020) and have 
negative implications particularly for small-scale operators (Farmery et al., 2021), However, considerations of climate justice have not

been used by Regional Fisheries Management Organizations (RFMOs) allocation shares to date (Engler, 2020). Species shifting from one 
historical jurisdiction to another may result in an incentivised depletion of the resource by the country the stock is shifting away from; 
reforming management to allocate resource sharing of quotas and permits or stock-unrelated side payments in bilateral or multilateral 
cooperative agreements may compensate or prevent loss (Diekert and Nieminen, 2017; Free et al., 2020; Ojea et al., 2020; Østhagen et al., 
2020; Cross-Chapter Paper Polar 6.2.).
Strong governance, ecosystem-based and transboundary management are considered fundamental to ameliorate the impacts of climate 
change (high confidence) but may be limited in effectiveness by the magnitude of change projected under low or no mitigation scenarios

5
769Food, Fibre and Other Ecosystem Products  Chapter 5
(see Sections 2.6.2., 14.4.2.2. and 15.3.4.4., Harrod et al., 2018c; Pinsky et al., 2018; Holsman et al., 2020; Ojea et al., 2020). Flexible and 
rapid policy reform and management adaptation will help to meet sustainability targets (Nguyen et al., 2016; Pentz and Klenk, 2020), and 
may only be available for countries with the scientific, technical and institutional capacity to implement these (high confidence) (Peck and 
Pinnegar, 2018; Figures MOVING PLATE.2 and 3 this chapter). Other adaptation options include ‘follow the food’ thereby migrating 
further (Belhabib et al., 2016), provision of alternative livelihoods (Thiault et al., 2019; Cross-Chapter Box MIGRATE in Chapter 7, Free 
et al., 2020), increasing ecosystem resilience by rebuilding coastal mangroves (Tanner et al., 2014; and Box 1.3) and riparian areas of

freshwater ecosystems (Mantyka-Pringle et al., 2016) and autonomous adaptations, such as harvesting gear modifications to access new 
target species (Harrod et al., 2018c; Kifani et al., 2018), practice change, and early-warning systems (see Section 11.3.2.3; Pecl et al., 2019; 
Melbourne-Thomas et al., 2021). Adaptive capacity will change with country, region, scale (commercial, recreational, Indigenous) of 
fishery, jurisdiction, and resource dependence (see Figure MOVING PLATE.2 this chapter for adaptation options for marine, freshwater 
and terrestrial systems). While shifting fishing fleets or herding may be an adaptation option to follow resources, limits to feasibility 
include institutional, legal, financial and logistical barriers such as costs of sourcing food and operational economic viability (Belhabib 
et al., 2016); this could potentially lead to maladaptation through increased GHG emissions from fuel usage and cultural displacement

from traditional fishing and herding lands. Overall, decreases in GHG emissions under future scenarios would reduce increases in global 
temperatures and limit species shifts, thereby lowering the likelihood of conflicts and food insecurity (high confidence).
Coastal Regions of the Gulf of Guinea: Ghanian Fisheries
Marine fisheries in Ghana are dominated by artisanal fishers with overfished stocks, high nutritional fish dependency, high illegal fishing, 
low governance capacity (−0.21 2018, (World Bank, 2019)) and low climate awareness in regional fisheries management (Figure MOVING 
PLATE.3 this chapter; see Chapter 9; Nunoo et al., 2014; Belhabib et al., 2015; Belhabib et al., 2016; Kifani et al., 2018; Belhabib et al., 
2019). Artisanal fishing plays a pivotal role in reducing poverty and food insecurity, and the impacts of climate change will risk developing

poverty traps (see Section 8.4.5.6., (Kifani et al., 2018)). Climate change induced species redistribution is a large risk to Ghanian fisheries, 
with projections of over 20 commercial fish species exiting the region with no new species entering under RCP4.5 by 2100 (Oremus et al., 
2020), and has already seen increases in warmer-water species with declining stocks. Adaptation options being applied are extending 
fishing ranges, increasing fishing effort (and cost) to access declining fish (with government fuel incentives) (Kifani et al., 2018; Muringai 
et al., 2021), developing aquaculture for alternative livelihoods, implementing fleet monitoring to reduce illegal fishing, and developing 
a robust Fisheries Information and Management System that accounts for environmental and climate drivers (Johnson et al., 2014; FAO, 
2016; Kassi et al., 2018). However, fisheries remain insufficiently regulated, there is a lack of a skilled workforce, and there is low access

to credit; collectively, these factors limit options for artisanal fishers to find alternative sustainable employment (FAO, 2016).
Shifting Distributions of Freshwater Fishery Resources: Knowledge Gaps
Freshwater fisheries provide the primary source of animal protein and essential micronutrients for an estimated 200 million people 
globally and are especially important in tropical developing nations (see Section  9.8, Lynch et  al., 2017; Funge-Smith and Bennett, 
2019.). There is evidence that freshwater fishes have undergone climate-induced distribution shifts (Comte and Grenouillet, 2015; see 
Section 9.8.5.1.), and further shifts are projected as water temperatures rise and hydrological regimes change, with the largest effects 
predicted for equatorial, subtropical and semi-arid regions (Barbarossa et al., 2021). Currently, the effects of distribution shifts on local

fishery catch potential, food security and/or nutrition have not been quantified for any major inland fishery, representing a key knowledge 
gap for anticipating future adaptation needs for freshwater fishing societies. However, studies on fishers’ perceptions of climate-induced 
changes in fishery catch rates have revealed that using local knowledge to adjust management practices (see Chapter 12 Central and 
South America this volume; Oviedo et al., 2016) and shifting gears, fishing grounds and target species (see Section 9.8.5.3.; Musinguzi 
et al., 2016) can be effective adaptation options.
Terrestrial Species Shifts
There is robust evidence of shifts that terrestrial species have shifted poleward in high latitudes, with general declines of sea-ice dependent 
as well as some extreme-polar-adapted species (high confidence) (Arctic and Siberian Tundra, see Section 2.4.2.2., Cross-Chapter Paper

6), with often deleterious effects on the food security and traditional knowledge systems of Indigenous societies (Horstkotte et al., 2017; 
Pecl et al., 2017; Mallory and Boyce, 2018; Forbes et al., 2020). Recent decades have seen declines in Arctic reindeer and caribou (see 
Section 2.5.1., Cross-Chapter Paper 6), and adaptation responses include utilisation of Indigenous knowledge with scientific sampling to 
maintain traditional management practices (Pecl et  al., 2017; Barber et  al.; Forbes et  al., 2020). Preserving herder livelihoods will 
necessitate novel solutions (supplementary feeding, seasonal movements), where governance, ecological and socioeconomic trade-offs 
will be balanced at the local level (Horstkotte et al., 2017; Pecl et al., 2017; Mallory and Boyce, 2018; Forbes et al., 2020). Wild meat 
consumption plays a critical, though not well understood, role in the diets and food security of several hundred million people (medium

evidence), for example in lower latitudes such as Central Africa and the Amazon basin (Bharucha and Pretty, 2010; Godfray et al., 2010; 
Nasi et al., 2011; Friant et al., 2020). Although illegal in many countries, wild meat hunting occurs either in places where there is no or Cross-Chapter Box: MOVING PLATE (continued)

5
770Chapter 5 Food, Fibre and Other Ecosystem Products
limited domesticated livestock production, or in places where shock events such as droughts and floods threaten food supply, forcing 
increased reliance on wild foods including bush meat (Mosberg and Eriksen, 2015; Bodmer et al., 2018). Appropriate management of wild 
meat for reliant peoples under projected climate change will necessitate incorporating social justice elements into conservation and 
public health strategies (see Cross-Chapter Box ILLNESS in Chapter 2, Cross-Chapter Box COVID in Chapter 7, Friant et al., 2020; Ingram, 
2020; Pelling et al., 2021).
(a) Ocean sensitivity within FAO regions and projected average fishing resource shifts in location
(b) Projected changes in the number of extreme heat stress days for cattle from early to end of century
Potential movement of cattle
from areas of high heat stress risk

to areas of lower heat stress riskGlobal vulnerabilities to current and projected climate change for living marine resources and cattle
Current cattle distributio nSmall-scale 
fisheriesProjected shifts by 2100
fisheries0.9
0.5
0.0Ocean
0.10.20.30.40.60.70.81.0
Examples of vulnerabilities,
conflicts and opportunities
for marine resource usage New Arctic fisheries opportunities with poleward fish migrations
Climate regime shift triggers reform of cooperative managemen t
under Pacific Salmon Treaty 
Species exits and increased competition between small-scale and 
large-scale fisherie si
ii
iiiConflicts over climate-induced migrations of commercial stock s
and food sovereignt y
High regional dependency on tuna for economy and food security
New opportunities for migrating species of commercial interestiv
v
viRCP2.6 RCP8.5
Catch
proportion
<40%
40–70%
>70%i
ii
iii
i
ii
iii
IPCC climate regionsAdditional days per yea r
of extreme heat stres s
10–50 days
50–100 day s

>100 days
Figure Cross-Chapter Box MOVING PLATE.1 | Global vulnerabilities to current and projected climate change for living marine resources and cattle. Cross-Chapter Box: MOVING PLATE (continued)

5
771Food, Fibre and Other Ecosystem Products Chapter 5
(a)Ocean areas are delineated into F AO (Food and Agricultural Organization of the United Nations) regions. Ocean sensitivity is calculated from aggregated sensitivities 
from Blasiak et al. (2017) S1 country data based on number of fishers, fisheries exports, proportions of economically active population working as fishers, total fisheries
landings and nutritional dependence, which was subsequently re-analysed for each FAO region depicted here. Arrows denote projected average commercial andartisanal fishing resource shifts in location under RCP2.6 and under RCP8.5 (dark-blue and red arrows, respectively) scenarios by 2100. Text boxes highlight examplesof vulnerabilities (Bell et al., 2018a), conflicts (Miller et al., 2013; Blasiak et al., 2017; Østhagen et al., 2020) or opportunities for marine resource usage (Robinson et al., 2015; Stuart-Smith et al., 2018; Meredith et al., 2019).
(b)

Projected changes in the number of extreme heat stress days for cattle from early (1991–2010) to end of century (2081–2100) 
under SSP1-2.6 and SSP5-8.5, shown 
as arrows rooted in the most affected area in each IPCC sub-region pointing to the nearest area of reduced or no extreme heat stress. Arrows are shown only for sub-
regions where >1 million additional animals are affected. Areas in green are those with >5000 animals per 0.5° grid cell in the eary 21st century (Thornton et al., 2021).
Common adaptation options, limitations and potential for adaptation in aquatic and terrestrial species with climate-induced movement 
of food species and reliant peoplesAdapting food livelihoods to species shifts
optionsTransboundary
managementMonitoring
shiftsEcosystem-based
managementEconomic
incentivesTechnology
& knowledgeMobilityAlternative
speciesAlternative
livelihoodsInternational
Limits to
adaptationInsufficient member
state involvement,
consensus & enforcementInsufficient

scientific capacity
& consensusPower dynamics
(sovereignty rights),
inequalitiesLow acces
to credit &
financial capacityLow access
to new & existing
technologyRestricted right
to roam, fisheries tenure
& access, especially for
vulnerable groupsLack of training,
education,
income
alternativesMarineRMFOs
- EEZ fisheries
TB agreementsAdaptive
fisheries
managementShared quotas,
establish fishing 
rights, use of IK & LK Gear/vesselmodificationEcosystem-based &
multi-species managementShifting
fleetsNew markets
for incoming
speciesAquaculture
/ tourismTerrestrialTB protected 
areas &
managementClimate data
tracking &
CITESAccess rights,
tenure &
licensesHerding,
utilization of
IK & LKSufficient
capacity &
enforcementShiftingherdingNew markets
speciesAgro-ecological
livestock &
farmingFreshwaterFisheries
agreements &
TB management Fisheries
considered
in freshwater
usage
(e.g., hydroelectric)Adaptive/
ecosystem-based
fisheries
managementAccess rights,
tenure 
licensesSufficient

capacity &
enforcementNew markets
for emerging
fisheriesReduction of
post-harvest
lossesAquaculture
/ tourism
MaladaptationInternational
conflicts,
unbalanced
negotiations,
sovereignty &
climate justice
issues
leading to
overharvestIllegal &
unreported
harvest,
species
trafficking &
poachingPower
dynamics,
top-down
policy
making &
agreement,
failure to
adapt policiesPerverse
subsidies,
increased
inequitiesData poor
assessments,
uninformed
decision-making Regional /
community
increased
inequities
due to accessOverexploitation,
industry
re-location,
human relocation
necessitated by
food/livelihood 
insecurityResource
detachment,
loss of cultural
identity, 
knowledge &
social prideAdaptation outcomesIndividual
Figure Cross-Chapter Box MOVING PLATE.2 | Common adaptation options, limitations and potential for adaptation and maladaptation in aquatic

and terrestrial species with climate-induced movement of food species and reliant peoples.Cross-Chapter Box: MOVING PLATE (continued)

5
772Chapter 5 Food, Fibre and Other Ecosystem Products
(a) Documented fisheries adaptive capacity to climate change
(b) Regional seafood-relevant micronutrient deficiency risk (Calcium, Iron, Zinc, Vitamin A)Current fisheries adaptive capacity and regional micronutrient deficiency risks related to seafood-
relevant micronutrients in human diets
0.9
0.5
0.00.10.20.30.40.60.70.81.0Adaptive
Capacity Index
90
50
010203040607080100Nutritional
dependence
No data
Figure Cross-Chapter Box MOVING PLATE.3 | Global documented fisheries adaptive capacity to climate change and regional seafood micronutrient 
deficiency risk. Ocean areas are delineated into FAO (Food and Agricultural Organization of the United Nations) regions. Fisheries management adaptive capacity is a 
function of: averaged GDP World Development Indicators for 2018 (World Bank, 2020); climate awareness assessments of 30 of the FAO recognised most recent RFMOs

with direct fisheries linkages (see Supplementary Material SM5.5); governance effectiveness index based on six aggregate indicators (voice and accountability, political 
stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, control of corruption) from 2018 World Governance Indicator (World 
Bank, 2019) data; and heterogeneity of countries within each FAO zone (highly heterogeneous regions are less likely to establish sustainable and efficient fisheries 
management for the entire FAO zone). Land area represents the percentage regional averaged seafood micronutrient deficiency risk of calcium, iron, zinc and vitamin A 
from 2011 data (Beal et al., 2017).
In terrestrial, marine and freshwater systems, human populations already impacted by poverty and hunger experience greater risk under 
climate change. Future food security will depend on access to other sustainable sources either via transnational agreements or resource/

livelihood diversification. Sudden shocks across food production systems (Cottrell et al., 2019) can lead to increases in fisheries harvest Cross-Chapter Box: MOVING PLATE (continued)

5
773Food, Fibre and Other Ecosystem Products  Chapter 5
and wild meat consumption, and following food species may result in community relocations or disruption and loss of access to historical 
places of attachment (high confidence) (Pecl et al., 2017; Lenoir et al., 2019; Meredith et al., 2019; Melbourne-Thomas et al., 2021; see 
Cross-Chapter Box  MIGRATE in Chapter 7). Ecosystem-based management approaches exist for terrestrial, marine and freshwater 
systems, but have proved successful only with early engagement of local small-scale, subsistence fishers/harvesters, utilising Indigenous 
knowledge and local knowledge and needs, in addition to those of larger-scale operators (high confidence) (Huntington et al., 2015; 
McGrath and Costello, 2015; Huq and Stubbings, 2016; Huq et al., 2017; Raymond-Yakoubian et al., 2017; Nalau et al., 2018; Raymond-

Yakoubian and Daniel, 2018; Pecl et al., 2019; Planque et al., 2019). Currently, there are large regional differences in climate literacy in 
RFMOs (Sumby et al., 2021) which, when combined with low governance and GDP per capita, will limit adaptation capacity and increase 
vulnerabilities, particularly for tropical and subtropical regions already at increased risk due to poleward species migrations (see 
Figure MOVING PLATE.3 this chapter). Trade will be an alternative to compensate for the moving plate but has specific risks that can 
amplify inequities and maladaptation (Asche et al., 2015; Vianna et al., 2020).Cross-Chapter Box: MOVING PLATE (continued)
5.9 Ocean-Based and Inland Aquaculture 
Systems
Global aquaculture provides more fish for human consumption 
than wild capture fisheries, with projected provisioning of 60% by 
2030 (FAO, 2018c). Aquaculture can contribute to SDGs by reducing 
poverty and food insecurity, filling increasing aquatic food demand

shortages from declining capture fisheries production (medium 
confidence) (Figure  5.13a and c, World Bank, 2013; Béné et  al., 
2016; Hambrey, 2017; Beveridge et  al., 2018b; Kalikoski et  al., 
2018; Belton et  al., 2020) and improving social inequities for poor 
rural communities (Béné et al., 2016; FAO, 2018c; Vannuccini et al., 
2018; Pongthanapanic et  al., 2019). Global aquaculture production 
reached 82  million tonnes (Mt) of food fish, crustaceans, molluscs 
and other aquatic animals from inland (51 Mt) and marine (31 Mt) 
systems, and 32 Mt of aquatic plants in 2018 (FAO, 2020d). China, 
India, Indonesia, Vietnam, Bangladesh, Egypt, Norway and Chile are 
major production regions (FAO, 2020d). The range of species, farming 
methods and environments makes aquaculture the most diverse, 
long-standing farming practice in the world, with an estimated global 
sectoral value of USD 250 billion in 2018 (Figure 5.13b and 5.14d,

Bell et  al., 2019; Harland, 2019; FAO, 2020d; Houston et  al., 2020; 
Metian et al., 2020), but it is dominated by 20 finfish, 9 mollusc and 6 
crustacean species (FAO, 2020). Inland aquaculture in freshwater and 
coastal ponds accounts for 85–90% of farmed production (Beveridge 
et al., 2018b; Naylor et al., 2021). Globally, 20.5 million people are 
engaged in aquaculture (FAO, 2020d), where marine finfish farming is 
primarily conducted by high-income countries and inland production 
is dominated by small-scale producers in lower-middle-income 
countries (Vannuccini et al., 2018).
5.9.1 Observed Impacts
Marine aquaculture food production is being impacted directly 
and indirectly by climate change (high confidence) (Bindoff et  al., 
2019). Ocean pH and oxygen levels are declining, whereas global 
warming, sea level rise and extreme events are increasing (Cross-
Chapter Box  SLR in Chapter 3, Canadell et  al., 2021; Eyring et  al.,

2021; Fox-Kemper et al., 2021; Lee et al., 2021;). Marine heatwaves have been increasing in both incidence and longevity over the past 
century (Frolicher and Laufkotter, 2018; Oliver et  al., 2018; Bricknell 
et al., 2021), with productivity consequences for marine aquaculture 
(mariculture), carbon sequestration and local species extinctions (high 
confidence) (Weatherdon et al., 2016; Smale et al., 2019). Temperature 
increases related to El Niño climatic oscillations have caused mass fish 
mortalities either through warming waters (e.g., Pacific threadfin in 
Hawaii (McCoy et  al., 2017)) or associated HABs (e.g., 12% loss of 
Atlantic salmon as well as other fish and shellfish in Chile in 2016, 
with estimated USD 800 million in losses (high confidence) (Clement 
et al., 2016; Apablaza et al., 2017; Leon-Munoz et al., 2018; Trainer 
et al., 2020)). Increases in sea lice parasite infestations on salmon are 
related to higher salinity and warmer waters (medium confidence)

(Groner et al., 2016; Soto et al., 2019). Ocean acidification is having 
negative impacts on the sustainability of mariculture production (high 
confidence) (Bindoff et al., 2019), with observed impacts on shellfish 
causing significant production and economic losses for regions, 
estimated at losses of nearly USD 110 million by 2015 in the Pacific 
Northwest (Barton et al., 2015; Ekstrom et al., 2015; Waldbusser et al., 
2015; Zhang et al., 2017b; Doney et al., 2020). Ocean oxygen levels are 
declining due to climate change (Hoegh-Guldberg et al., 2018; IPCC, 
2021), and decreased oxygen (hypoxia) has negative impacts on fish 
physiology (Cadiz et al., 2018; Hvas and Oppedal, 2019; Martos-Sitcha 
et al., 2019; Perera et al., 2021), fish growth, behaviour and sensitivity 
to concurrent stressors (high confidence) (Stehfest et al., 2017; Abdel-
Tawwab et al., 2019).
Observed impacts on inland systems have generally been site and

region specific (high confidence) (Hoegh-Guldberg et al., 2018; Sainz 
et  al., 2019; Lebel et  al., 2020). Salinity intrusions into freshwater 
aquaculture systems have changed oxygen and water quality of inland 
ponds, resulting in mortalities in areas such as India and Bangladesh 
(medium confidence) (Dubey et al., 2017; Dabbadie et al., 2018). Rapid 
changes in temperature, precipitation, droughts, floods and erosion 
have created significant production losses for aquatic farmers in 
Cambodia, Laos, Myanmar, Thailand, Viet Nam and Ghana (medium 
confidence) (Asiedu et  al., 2017; Pongthanapanic et  al., 2019; Lebel 
et  al., 2020). Algal blooming and inland lake browning related to 
warming was found to negatively affect fish biomass (van Dorst et al., 
2018). Observed indirect effects of climate change on aquaculture

5
774Chapter 5 Food, Fibre and Other Ecosystem Products
include extreme weather events that damage coastal aquaculture 
infrastructure or enable flooding, both leading to animal escapees 
(e.g., fish, shrimp), damaged livelihoods and interactions with wild 
species (high agreement, medium evidence) (Beveridge et al., 2018b; 
Dabbadie et  al., 2018; Kais and Islam, 2018; Pongthanapanic et  al., 
2019; Ju et al., 2020).Global and regional aquaculture production
(b) Diversity of aquaculture groups cultured in 2016
62%
Fish12%
Crustacea
1% Amphibians and reptile s2% Aquatic invertebrates11%
Molluscs7%
Aquatic
plants
Africa
Americas
Europe
Oceania(c) Aquaculture share of total
fisheries production
18% 18% 18% 18%(a) World aquaculture and capture fisheries production
250,000
200,000
150,000
100,000
50,000
0
1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018Capture
fisheriesAquaculture
productionMarine
Inland
Marine
InlandTonnes
Tonnes of

(Log scale)
110010,0001,000,000100,000,000Fish Crustacea Molluscs Aquatic plants
Brackish
Marine(d) Global aquaculture species production in 2018
Africa
AsiaAmerica s
Europe
Oceani a
Africa
Europe
Oceani a
Africa
Europe
Oceani a
Africa
Europe
Oceani aAsiaChina alone
75%
China
exclude d
42%
Figure 5.13 |  Global and regional aquaculture production. (a) World wild capture fisheries and aquaculture inland (freshwater and brackish) and marine production from 
1950 to 2018; (b) diversity of aquaculture groups cultured in 2016; (c) regional aquaculture share of total fisheries production; and (d) global aquaculture species production in 
2018 by region and type (freshwater, brackish or marine) on a logged scale (FAO, 2018c; FAO, 2020c; FAO, 2020d).
5.9.2 Assessing Vulnerabilities
Aquaculture vulnerability assessments have shown that countries from 
both high and low latitudes are highly vulnerable to climate change,

where vulnerability is driven by particular exposures, economic reliance, 
type of production sector (freshwater, brackish, marine) and adaptive 
capacity (high confidence) (Handisyde et al., 2017; Soto et al., 2018). 
Regional aquaculture vulnerabilities and risk mitigation potentials 
for the major FAO reporting regions are shown in Figure 5.14. Best

5
775Food, Fibre and Other Ecosystem Products  Chapter 5
practice guidelines for assessments exist (Brugère et  al., 2019; FAO, 
2020d), but in practice most only cover some climatic drivers (medium 
agreement, limited evidence) (Soto et al., 2018). Holistic vulnerability 
assessments include ecosystem services (Custódio et al., 2020; Gentry 
et al., 2020) and farming practices which can exacerbate production 
pressures (stocking densities, eutrophication, fish stress) (Soto et al., 
2018; Sainz et al., 2019). Common vulnerabilities to inland and marine 
aquaculture include increasing incidence and toxicity of HABs related 
to warming waters, causing fish kills and product consumption risks, 
negatively impacting the productivity and stability of production 
sectors and reliant communities (high confidence) (Soto et al., 2018; 
Aoki et al., 2019; Bannister et al., 2019).
There is high confidence that inland aquaculture in Southeast Asia

is highly vulnerable to climate change, due to fluctuations in water 
resources either through climatic variability in precipitation, flooding 
or salinity inundation or through competition (Handisyde et al., 2017; 
Nguyen et al., 2018; Soto et al., 2018; Islam et al., 2019; Nguyen et al., 
2019b; Prakoso et  al., 2020). Studies in Bangladesh and Indonesia 
highlighted regional and species-specific vulnerabilities (Prakoso et al., 
2020) and roles of governance in vulnerability reduction (Islam et al., 
2019).
In the marine sector, vulnerability models (Brugère and De Young, 
2015; Handisyde et  al., 2017) have been adapted and applied to 
semi-quantitative spatial risk assessments for Chilean Atlantic 
salmon, where analysis of exposure threat coupled with mortality 
and temperature farm data could enhance salmon production (Soto 
et al., 2019). Vulnerability assessments in Korea (RCP8.5 temperature 
increase of 4–5°C by 2100) (Kim et al., 2019a) and the USA (ocean

acidification, Barton et al., 2015; Ekstrom et al., 2015) found major 
exposure-related vulnerabilities for seaweeds and shellfish, with 
reduced vulnerabilities under higher production control and adaptive 
capacity. Global bivalve vulnerability assessments (RCP8.5 by 2100) 
show high vulnerabilities for major producing countries related to 
cyclones (China, Japan, South Korea, Thailand, Viet Nam and North 
Korea), regional risk of high sensitivity and low adaptive capacity 
(Chile, Peru, Spain, Italy), with few major producers (France, the 
Netherlands and USA) anticipated to remain moderately vulnerable 
by 2100 (Stewart-Sinclair et al., 2020).
Climate uncertainty and data limitations hinder vulnerability assess-
ments (high confidence), so broader vulnerabilities and qualitative as-
sessments can be used (Brugère and De Young, 2015; Soto et al., 2018; 
Brugère et al., 2019; Cochrane et al., 2019). Filling data gaps with mon-

itoring (high confidence), increasing governmental support to assist 
particularly vulnerable small- and medium-scale farmers with increased 
costs associated with risk management and uncertainty (medium con-
fidence) and the early inclusion of community stakeholders (high agree-
ment, medium evidence) can reduce vulnerabilities (Handisyde et al., 
2017; Dabbadie et al., 2018; Soto et al., 2018; Bindoff et al., 2019; Co-
chrane et al., 2019).5.9.2.1 Gender and other social vulnerability and roles in 
There are regional differences in women’s roles, responsibilities 
and involvement in adaptation strategies in the aquaculture sector. 
Women comprise 14% of the 2018 global aquaculture workforce 
of 20.5 million (FAO, 2020c), representing up to 42% of the salmon 
workforce in Chile (Chávez et al., 2019), predominantly in processing 
roles (Gopal et  al., 2020). In the majority of lower-middle-income 
countries, seaweed culture is dominated by women in family-owned

businesses as in Zanzibar and the Philippines (Brugere et  al., 2020; 
Ramirez et  al., 2020), where women are not always paid directly 
but contribute to family incomes (high confidence) (Msuya and 
Hurtado, 2017; Brugere et  al., 2020; Ramirez et  al., 2020). In India, 
women collect stocking juveniles and assist in pond construction; in 
Bangladesh, women do the same tasks as men; and in Ghana, women 
undertake post-harvest fishing activities (Lauria et al., 2018). Women 
employed in aquaculture cooperatives gained adaptive capacity, which 
reduced gender inequities (medium confidence) (Farquhar et al., 2018; 
Gonzal et al., 2019), but lack of financial access for women can create 
gender inequity at larger commercial scales (Gurung et al., 2016; Call 
and Sellers, 2019). Women in aquaculture experience competing roles 
between employment, childcare and home duties (high confidence) 
(Morgan et al., 2015; Lauria et al., 2018; Chávez et al., 2019; see Cross-

Chapter Box  GENDER in Chapter 18) and differ from men in terms 
of perceptions of environmental risk, climate change and adaptation 
behaviour, with limited contributions to decision making (medium 
confidence) (Barange and Cochrane, 2018). Therefore, effective 
climate aquaculture adaptation options need to address gender 
inequity, such as suitable technology designs that fit with social 
norms and access to credit to facilitate independent uptake (medium 
evidence, high agreement) (Morgan et al., 2015; Oppenheimer et al., 
2019). Generalised best practices for gender-sensitive approaches to 
adaptation are relevant for aquaculture (UNFCCC, 2013).
5.9.3 Projected Impacts
Projected impacts on regional inland and marine aquaculture production 
are summarised in Figure 5.15.
5.9.3.1 Inland freshwater and brackish aquaculture
Predicted sea level and temperature rise will result in coastal 
inundation into brackish and inland aquaculture systems (high

confidence) (Mehvar et al., 2019; Nhung et al., 2019; Oppenheimer 
et  al., 2019; Fox-Kemper et  al., 2021), with negative impacts on 
aquaculture production in Viet Nam, East Africa and Jamaica (medium 
confidence) (Lebel et  al., 2018; Nguyen et  al., 2018; Bornemann 
et  al., 2019). Precipitation and temperature changes will cause 
drought and flooding, negatively affecting near-shore fishpond 
productivity (limited evidence) (Canevari-Luzardo et  al., 2019), but 
provide competitive advantages to non-native shrimp in Australia 
(limited evidence) (Cerato et  al., 2019). Warming and acidification 
will increase HAB toxicity in freshwater systems, but responses may 
be strain-specific (Griffith and Gobler, 2020; Hennon and Dyhrman, 
2020). As for molluscs in marine systems, projected climate change

5
776Chapter 5 Food, Fibre and Other Ecosystem Products
Assessment of inland freshwater and brackish aquaculture, and marine aquaculture
vulnerabilities and mitigation potential
Mitigation confidence levelVulnerability level Vulnerability confidence level
Medium LowMitigation potential High Medium LowVulnerabilityFood security at local level
Land use conflict
Social inequality
Feed conversion efficency
Low greenhouse gas emission speciesWater use conflict
Alternative energies
Mitigation(a) Inland aquaculture (freshwater and brackish)
VulnerabilityFood security at local level
Coastal and marine use conflict
Food conversion efficiency
Low greenhouse gas emission speciesMitigation(b) Marine aquacultureAfrica
(Sub-Saharan)
Tilapia,
Catfish,
Carp
Seaweed,
Prawn,
Mussel sNorthern
America
Catfish,
Crawfish,
Trout
Oysters,
Salmon,
ClamsLatin America
and the
Caribbean
Tilapia, Pacu ,
Salmonids ,
Carp
Shrimp,
Salmon,
Mussels,
SeaweedEurope
Carp,

Salmonids
Salmon,
Sea Bream,
Seabass,
Mussels,
OystersAsia-
Pacific
Tilapia, Catfish ,
Prawn, Crayfish,
Carp, Crab
Molluscs, Shrimp ,
Seaweed,
Milkfish, Crab,
Grouper,
Sea BreamAfrica
(Near East and
Northern)
Tilapia,
Trout,
Carp
Mullet,
Shrimp,
Sea BreamSource of feeds
Source of feeds
High
Figure 5.14 |  Assessment of inland freshwater and brackish aquaculture (salinities of <10 ppm and/or no connection to the marine environment) (a) and marine 
aquaculture vulnerabilities and mitigation potential per major FAO production zones (b). See SM5.6 (Tables SM5.5, 5.6, 5.9, 5.10) for assessment methodologies.
in freshwater and brackish systems may limit the availability of wild-
sourced juveniles from fisheries (Beveridge et  al., 2018). Projected 
impact studies for the inland and small-scale aquatic sectors are very 
limited (Halpern et al., 2019; Galappaththi et al., 2020b); therefore, 
this is a noted knowledge gap.
5.9.3.2 Marine Aquaculture

5.9.3.2.1 Finfish culture
Global projections of ocean warming, primary productivity and ocean 
acidification predict suitable habitat expansions and short-term growth 
benefits for finfish aquaculture for some regions (medium confidence) 
(see Figure 5.15) until thermal tolerances or productivity constraints 
are exceeded by 2090 (Beveridge et  al., 2018b; Dabbadie et  al., 2018; Froehlich et al., 2018a; Catalán et al., 2019; Thiault et al., 2019; 
Falconer et  al., 2020a). Sensitivities for marine finfish may be high 
even under +1.5–2.0°C (medium confidence) (Gattuso et al., 2018), 
resulting in finfish farms moving northward to maintain productivity 
(e.g., Arctic (Troell et al., 2017)). Downscaled projections of regionally 
specific tolerances (Klinger et al., 2017) may be particularly useful for 
management and planning; a 0.5°C rise is predicted for Chilean salmon 
aquaculture (Soto et  al., 2019), and potential negative impacts on

productivity in Norway by 2029 have been projected (limited evidence) 
(Falconer et al., 2020a). Marine heatwaves are predicted to increase in 
occurrence, intensity and persistence under RCP4.5 or RCP8.5 by 2100 
(Oliver et al., 2019; Bricknell et al., 2021), with risk partly mitigated 
by husbandry (medium confidence) (McCoy et  al., 2017). Generally, 
negative impacts are predicted for marine species, with residual risk 
increasing with level of exposure (Sara et  al., 2018; Smale et  al.,

5
777Food, Fibre and Other Ecosystem Products  Chapter 5
2019), where warming will affect oxygen solubility and reduce salmon 
culture capacity (limited evidence) (Aksnes et  al., 2019, Chapter 3) 
and combine with increasing incidence of HABs (high confidence) 
resulting in negative impacts for food security and nutrition and health 
(Oppenheimer et al., 2019; Colombo et al., 2020; Glibert, 2020; Raven 
et  al., 2020). Climate change is predicted to affect the incidence, magnitude and virulence of finfish disease such as Vibriosis (Barber 
et  al., 2016; Mohamad et  al., 2019a; Mohamad et  al., 2019b), but 
specific host–pathogen–climate relationships are not yet established 
(high confidence) (Slenning, 2010; Marcogliese, 2016; Montanchez 
et al., 2019; Bandin and Souto, 2020; Behringer et al., 2020; Filipe et al., 
2020; Montanchez and Kaberdin, 2020). Projected climate change will 
Confidence levelAssessment of projected impacts of climate change

on inland freshwater and brackish aquaculture, and marine aquaculture
Direction of impact
Negative Very
negativeNeutral
or mixed
effectsVery
positivePositive(a) Inland aquaculture (freshwater and brackish)
(b) Marine aquacultureAfrica
Tilapia,
Catfish,
Carp
Seaweed,
Prawn,
America
Catfish,
Crawfish,
Trout
Oysters,
Salmon,
and the
Caribbean
Salmonids,
Carp
Shrimp,
Salmon,
Mussels,
Carp,
Salmonids
Salmon,
Sea Bream,
Seabass,
Mussels,
Pacific
Carp, Crab
Seaweed,
Grouper,
(East and
Northern)
Tilapia,
Trout,
Carp
Mullet,
Shrimp,
Sea BreamGlobal warming
Deoxygenation 
Freshwater availablility
Precipitation changes (including droughts)
Eutrophication 
Harmful algal blooms
Food safety
Floods 
Cyclones/hurricanes/extreme events
Pathogens and parasites
Juvenile availability
Aquaculture feed

Precipitation changes (including droughts and freshwater discharge)
Acidification 
Extreme wave heights
Cyclones/hurricanes/severe storms
Circulation patterns and strength
Primary productivity
Low Medium High
Figure 5.15 |  Assessment of projected impacts of climate change on inland freshwater and brackish aquaculture (salinities of <10 ppm and/or no connection to 
the marine environment) (a) and marine aquaculture (b) per major FAO production zones. See SM5.6 (Tables SM5.7, 5.11) for assessment methodologies.

5
778Chapter 5 Food, Fibre and Other Ecosystem Products
Table 5.11 |  Projected impacts of climate on specific inland, brackish and marine culture systems and species.
Exposure Scenario RegionProduction 
systemSpecies Impact Reference
Temperature increaseRCP4.5 and RCP8.5 by 
2050Northern 
ThailandInland Nile tilapia Reduced productivity Lebel et al. (2018)
Precipitation change 
(drought, hurricane, 
heavy rainfall)– Jamaica Inland TilapiaReduced productivity, infrastructure 
damageCanevari-Luzardo et al. 
(2019)
Temperature increase4°C increase, B2, A1B 
by 2100Australia InlandFreshwater 
shrimpIncreased production in non-native 
zonesCerato et al. (2019)
Temperature increase, 
ocean acidification, 
primary productivity 
declinesCMIP5 RCP8.5 in 20-year 
increments to 2090Global Marine Finfish speciesIncreased suitable habitat 
expansion for regions (Russia, 
Norway, USA Alaska, Denmark, 
Canada). By 2100, reduction in 
productivity for major producers

(Norway, China)Froehlich et al. (2018a),
Thiault et al. (2019)
Temperature increase2–5°C increase under 
RCP8.5Europe Marine Atlantic salmon Increased growth Catalán et al. (2019)
Temperature increase RCP4.5 to 2029 Norway Marine Atlantic salmon Growth threshold reached by 2029 Falconer et al. (2020a)
Temperature increaseDownscaled CM2.6 by 
2050Global MarineAtlantic salmon, 
cobia and sea 
breamIncreased or decreased growth 
rates depending on regionKlinger et al. (2017)
increments to 2090Global Marine ShellfishOverall declines in suitable habitat 
globally, up to 50–100% reductions 
in regions in China, Thailand and 
CanadaFroehlich et al. (2018a)
Temperature increaseCMIP5 RCP8.5 by 2050, 
2100Italy Marine ClamsNegative impacts for juvenile 
timing, spatial distribution, and 
qualityGhezzo et al. (2018)
Temperature increaseCMIP5 RCP2.6 and

RCP8.5 by 2035, 2070France Marine OystersIncrease incidence of oyster 
mortality; increase by 2035 to 
annual occurrence by 2070Thomas et al. (2018)
Temperature increaseRCP2.6 and RCP8.5 by 
2050Global Marine ShellfishSpecies reduction (10–40%) in 
tropical and subtropical regions, 
with increase (40%) in higher 
latitudesOyinlola et al. (2020)
ocean acidificationEcopath with RCP8.5 by 
2100 (2.8°C warming 
and pH 7.89)USA Marine ShellfishReduction primary productivity 
and subsequent bivalve carrying 
capacityChapman et al. (2020)
stratification changeRCP8.5 by 2088–2099 Spain Marine MusselsDecline in mussel optimal culture 
conditions of 60% in upper and 
30% in deeper waters by 2099Des et al. (2020)
ocean acidificationRCP2.6 and 8.5 by 
2070–2090Global Marine ShellfishUnder RCP8.5, a decline in 
shellfish production due to primary 
productivity reduction in tropical 
regions and gains in high latitudes.

Under RCP2.6, marine production 
will have net gainThiault et al. (2019)
Temperature increase 4°C increase Global MarineVibrio spp. 
(mortality 
causative agent)Increased virulence Montanchez et al. (2019)
Temperature increase 
(marine heatwave)5°C increase Global Marine Oysters Increased oyster mortality Green et al. (2019)
Ocean acidification ~2000 ppm CO 2 Global Marine Oysters Impaired immune function Cao et al. (2018b)
Ocean acidificationRCP8.5 in 20-year 
increments to after 2099USA Marine ShellfishRegional projected vulnerabilities; 
southern Alaska and Pacific 
Northwest at more immediate riskEkstrom et al. (2015)
Ocean acidification A1B and RCP8.5 by 2100 UK Marine ShellfishRegional projected vulnerabilities; 
Wales and England at more 
immediate riskMangi et al. (2018)

5
779Food, Fibre and Other Ecosystem Products  Chapter 5
also increase competition for feed ingredients between aquatic and 
terrestrial animal production systems (see Section 5.13.2.).
5.9.3.2.2 Shellfish culture
Globally, there is overall high confidence that suitable shellfish 
aquaculture habitat will decline by 2100 under projected warming, 
ocean acidification and primary productivity changes, with 
significant negative impacts for some regions and species before 
2100 (Table 5.9, Froehlich et al., 2018a; Ghezzo et al., 2018). Shellfish 
growth will increase with warming waters until tolerances are 
reached, such as through extreme El Niño events (high confidence) 
(Beveridge et al., 2018b; Dabbadie et al., 2018; Liu et al., 2018b; Liu 
et al., 2020). Rising temperatures and ocean acidification will result 
in losses of primary productivity and farmed species from tropical and 
subtropical regions, and gains in higher latitudes (high confidence)

(Froehlich et  al., 2018a; Aveytua-Alcazar et  al., 2020; Chapman 
et al., 2020; Des et al., 2020; Oyinlola et al., 2020), but net marine 
production gains could be achieved under strong mitigation (Thiault 
et al., 2019). Shellfish Vibrio infections will increase with warming 
waters and extreme events, increasing shellfish mortalities (medium 
confidence) (Green et al., 2019; Montanchez et al., 2019), with ocean 
acidification impairing immune responses (limited evidence) (Cao 
et al., 2018b). Bivalve larvae are known to be highly vulnerable to 
ocean acidification (high confidence) (see Section 3.3, Bindoff et al., 
2019), with projected regional and species-specific levels of impact 
(high confidence) (Ekstrom et al., 2015; Zhang et al., 2017b; Mangi 
et  al., 2018) (Greenhill et  al., 2020). Ocean acidification is also 
projected to weaken shells, affecting productivity and processing 
(high confidence) (Martinez et al., 2018; Cummings et al., 2019) and

dependent livelihoods (Doney et al., 2020).
5.9.3.2.3 Aquatic plant culture
There is medium confidence that cultivated seaweeds are predicted 
to suffer habitat loss resulting in population declines and northward 
shifts (Table 5.11).
5.9.3.2.4 Societal impacts within the production system
Marine aquaculture provides distinct ecosystem services through 
provisioning (augmenting wild fishery catches), regulating (coastal 
protection, carbon sequestration, nutrient removal, improved water clarity), habitat and supporting (artificial habitat) and cultural (livelihoods 
and tourism) services (Gentry et  al., 2020), which vary with species, 
location and husbandry (Alleway et al., 2019). Projected thermal increases 
of 1.5°C will reduce ecosystem services, further reduced under 2°C 
warming, with associated increases in acidification, hypoxia, dead zones, 
flooding and water restrictions (medium confidence) (Hoegh-Guldberg

et al., 2018). Sudden production losses from extreme climate events can 
exacerbate food security challenges across production sectors, including 
aquaculture, increasing global hunger (high confidence) (Cottrell et al., 
2019; Food Security Information Network, 2020). While aquaculture 
provides positive influences such as food security and livelihoods, there 
are negative concerns over environmental impacts (including high 
nutrient loads from sites) and socioeconomic conflicts (Alleway et al., 
2019; Soto et  al., 2019), and adoption of ecosystem approaches is 
dependent on particular user groups and regions (Gentry et al., 2017; 
Brugère et al., 2019; Gentry et al., 2020). In coastal Bangladesh, projected 
saline inundation to wetland ecosystem services will result in ecosystem 
services losses of raw materials and food provisioning, ranging from 
USD 0 to 20.0 million under RCP2.6 to RCP8.5 scenarios (Mehvar et al.,

2019). Mangrove deforestation for shrimp farming in Asia negatively 
impacts ecosystem services and reduces climate resilience (medium 
confidence) (Mehvar et al., 2019; Nguyen and Parnell, 2019; Reid et al., 
2019; Custódio et al., 2020), while mangrove reforestation efforts may 
have some effectiveness in re-creating important nursery grounds for 
aquatic species (low confidence) (Gentry et al., 2017; Chiayarak et al., 
2019; Hai et al., 2020). Families are highly vulnerable to climate change 
where nutritional needs are being met by self-production, such as in 
Mozambique, Namibia (Villasante et al., 2015), Zambia (Kaminski et al., 
2018) and Bangladesh (high confidence) (Pant et  al., 2014). Climate 
change will therefore affect multiple ecosystem services where ultimately 
decisions on balance or trade-offs will vary with regional perceptions of 
service value (high confidence).
5.9.4 Aquaculture Adaptation
5.9.4.1 Adaptation planning

Aquaculture is often viewed as an adaptation option for fisheries 
declines, thereby alleviating food security from losses of other climate 
change impacts (Sowman and Raemaekers, 2018; Johnson et  al., 
2020) such as Pacific Islands freshwater aquaculture, Bangladesh crop-
aquaculture systems or Viet Nam rice–fish cultivations (Soto et  al., Exposure Scenario RegionProduction 
Ocean acidificationRCP2.6 and RCP8.5 by 
2300East China Marine ShellfishCarbonate saturation projected to 
decrease by 13% and 72% under 
RCP2.6 and RCP8.5 respectively, 
projecting decreased shellfish 
productivityRCP2.6 and RCP8.5 
by 2300 (Zhang et al., 
2017b)
Increased temperatureRCP2.6 and RCP8.5 by 
2100North Sea Marine SeaweedNorthward population shift by 110–
163 km and 450–635 km under 
RCP2.6 and RCP8.5, respectivelyWestmeijer et al. (2019)
Increased temperatureRCP4.5 and RCP8.5 by 
2090Japan Marine KelpHabitat decline to 30–51% and 
0–25% under RCP4.5 and RCP8.5,

5
780Chapter 5 Food, Fibre and Other Ecosystem Products
2018). Many adaptations are specific to regions, countries or sectors, 
implemented on a regional to national scale (FAO, 2018c; Galappaththi 
et al., 2020b). Adaptation likelihood (potential), effectiveness and risk 
of maladaptation was assessed per major FAO production region for 
inland, brackish and marine aquaculture (Figure  5.16) production 
systems. Potential adaptation measures to reduce production loss can 
be built upon existing adaptation planning and guidelines, to reduce 
the risk of maladaptation including feedback loops (e.g., FAO, 2015; 
Bueno and Soto, 2017; Dabbadie et  al., 2018; FAO, 2018c; Poulain 
et al., 2018; Brugère et al., 2019; Pham et al., 2021; Soto et al., 2021). 
Large climate change adaptation strategies for the aquaculture sector 
exist, such as in the USA (Link et al., 2015), Australia (Hobday et al., 
2017) and South Africa (Department of Environmental Affairs, 2016).

Lower-income countries often lack financial, technical or institutional 
capacity for adaptation planning (Galappaththi et  al., 2020b), but 
examples include Bangladesh and Myanmar (FAO, 2018c), with 
programmes offering adaptation funding (Dabbadie et  al., 2018). 
Early participation of stakeholders in adaptive planning has promoted 
action and ownership of results (high confidence), such as in India 
and the USA (Link et al., 2015; FAO, 2018c; Soto et al., 2018) Early 
outreach, education and knowledge gap assessments raise awareness, 
where utilisation of local knowledge and Indigenous knowledge 
and scientific involvement support informed adaptive planning and 
uptake for all stakeholders (high confidence) (Cooley et al., 2016; FAO, 
2018c; Rybråten et al., 2018; Soto et al., 2018; McDonald et al., 2019; 
Galappaththi et al., 2020b), as perceptions of climate risk and capacity 
will vary (Tiller and Richards, 2018). Supporting the active involvement

of women helps address gender inequity and perceived risk, 
particularly for smallholder farmers (high confidence) (Morgan et al., 
2015; Barange and Cochrane, 2018; FAO, 2018c; Avila-Forcada et al., 
2020). However, regional and national political influences, financial 
and technical capacity, governance planning and policy development 
will ultimately support or hinder adaptation for aquaculture (high 
confidence) (Cooley et  al., 2016; FAO, 2018c; Galappaththi et  al., 
2020b; Greenhill et al., 2020).
5.9.4.2 Species selections and selective breeding
Adaptation options at the operational level include species selections, 
such as cultivation of brackish species (shrimp, crabs) during dry seasons, 
and rice-finfish in wetter seasons in Thailand (Chiayarak et al., 2019), 
use of salt-tolerant plants in Viet Nam (Nhung et al., 2019; Paik et al., 
2020), converting inundated rice paddies into aquaculture, rotating 
shrimp, and rice culture (high confidence) (Chiayarak et  al., 2019).

Species diversification through co-culture, integrated aquaculture–
agriculture (e.g., rice–fish) or integrated multi-trophic culture (e.g., 
shrimp–tilapia–seaweed or finfish–bivalve–seaweed) may maintain 
farm long-term performance and viability by: creating new aquaculture 
opportunities; promoting societal and environmental stability; reducing 
GHG emissions through reduced feed usage and waste; and carbon 
sequestration (medium confidence) (see Section  5.10, Ahmed et  al., 
2017; Bunting et al., 2017; Gasco et al., 2018, Soto et al., 2018; Ahmed 
et al., 2019; Dubois et al., 2019; FAO, 2019c; Li et al., 2019; Freed et al., 
2020; Galappaththi et al., 2020b; Prasko et al., 2020; Tran et al., 2020). 
In practice, most aquaculture operations concentrate on single-species 
systems (Metian et al., 2020), and barriers such as land availability, 
freshwater resources and lack of credit access may limit the uptake and success of integrated adaptation approaches to climate change

(Ahmed et al., 2019; Tran et al., 2020; Kais and Islam, 2021).
Selective breeding can promote climate resilience (medium 
confidence) (Klinger et al., 2017; Fitzer et al., 2019), and operations 
have already intentionally, or unintentionally, selected for production 
traits for changing conditions (de Melo et al., 2016; Tan and Zheng, 
2020). Exposure of broodstock to future climate conditions may or 
may not confer advantages to offspring (moderate evidence,  low 
agreement) (Parker et al., 2015; Griffith and Gobler, 2017; Thomsen 
et al., 2017; Durland et al., 2019). Traditional pedigree developments 
require extensive phenotypic data, but genomic selections can rapidly 
select for robust climate-associated traits (Sae-Lim et  al., 2017; 
Gutierrez et al., 2018; Zenger et al., 2018; Houston et al., 2020; Tan and 
Zheng, 2020). Genomic resources are available for salmon, rainbow 
trout, coho, carp, tilapia, seabass, bream, turbot, flounder, catfish,

yellow drum, scallops, oysters and shrimp, but have been developed 
for disease and growth selections rather than climate resistance 
(Dégremont et  al., 2015a; Dégremont et  al., 2015b; Abdelrahman 
et al., 2017; Gjedrem and Rye, 2018; Gutierrez et al., 2018; Guo et al., 
2018; Liu et al., 2018a; FAO, 2019d; Houston et al., 2020), although 
bivalve selections for ocean acidification and warming resiliency are 
underway (Tan and Zheng, 2020). Targeted genome editing could 
modify phenotypes of major aquaculture species (Li et  al., 2014a; 
Elaswad et al., 2018; Yu et al., 2019; Houston et al., 2020), but uptake 
is dependent upon national regulatory and public approvals. Local 
adaptations within species with higher climate resiliencies may assist 
in selections (Thomsen et al., 2017; Falkenberg et al., 2019; Scanes 
et al., 2020; Toomey et al., 2020), but highlight the need to consider 
specific farming environments for selective processes (Houston et al.,

2020). Projections of climate on aquaculture production traits are not 
well understood (Lhorente et al., 2019); therefore, genetic diversity 
needs to be maintained to ensure population fitness (high confidence) 
(Bitter et al., 2019; Lhorente et al., 2019; Visch et al., 2019; Houston 
et al., 2020; Mantri et al., 2020).
5.9.4.3 Farm site selection, infrastructure and husbandry
Land-based aquaculture systems including hatcheries may reduce 
exposure to climatic extremes (due to better control of the culture 
environment), limit water usage, reduce juvenile reliance and buffer 
climate effects using optimal diets (high confidence) (Barton et  al., 
2015; Reid et al., 2019; Cominassi et al., 2020). However, land-based 
aquaculture requires large capital and operational costs and use of 
land, increasing conflicts between land and water use, have increased 
energy demands (increasing GHG if fossil fuels are the primary energy

source), require necessary expertise and will not reduce outgrowing 
exposures (high confidence) (see Section 5.13, Beveridge et al., 2018b; 
Soto et al., 2018; Tillotson et al., 2019; Costello et al., 2020; Prakoso 
Geographical selection of marine farm sites may prevent climate 
productivity declines (medium confidence) (Froehlich et  al., 2018a; 
Sainz et al., 2019; Oyinlola et al., 2020), particularly for temperature-
related mortality hotspots (Garrabou et  al., 2019), HAB occurrences 
(Dabbadie et  al., 2018) or extreme events (Liu et  al., 2020; Wu 
et al., 2020). However, while downscaled climate forecasts facilitate

5
781Food, Fibre and Other Ecosystem Products  Chapter 5
localised adaptation planning (Falconer et al., 2020a), such projections 
are rare (Whitney et al., 2020). GIS can be used for climate adaptive 
planning along with routine site assessments (Falconer et al., 2020b; 
Galappaththi et  al., 2020b; Jayanthi et  al., 2020). Building coastal 
protection, stronger cages and mooring systems, and deeper ponds and using sheltered bays can reduce escapees and mortalities related 
to flooding, increased storms and extreme events (medium confidence) 
(Dabbadie et al., 2018; Bricknell et al., 2021; Kais and Islam, 2021). 
Inshore aquaculture in low-lying areas prone to sea level salinity 
intrusion (e.g., Mekong delta and Viet Nam) have already implemented 
Assessment of the likelihood and effectiveness of a range of adaptation options
for potential implementation in the near-term for inland freshwater and brackish aquaculture, and marine aquaculture systems

Maladapation confidence levelAdaptation (likelihood of
implementation and effectiveness)Adaptation confidence level
High Medium High MediumMaladaptation (risk of) High Medium LowCombined food production
Biotechnology
Tolerant species/strain selections
Governance - national
Insurance and financial support
Early warning systems
Aquaculture feedsGender equity
Governance - local(a) Inland aquaculture (freshwater and brackish)
Tilapia,
Catfish,
Carp
Seaweed,
Prawn,
MusselsNorthern
America
Catfish,
Crawfish,
Trout
Oysters,
Salmon,
and the
Caribbean
Carp
Shrimp,
Salmon,
Mussels,
Carp,
Salmonids
Salmon,
Sea Bream,
Seabass,
Mussels,
Pacific
Carp, Crab
Seaweed,
Milkfish, Crab ,
Grouper,
(East and
Northern)
Tilapia,
Trout,
Carp
Mullet,
Shrimp,
Sea BreamOptimizing fisheries - aquaculture interactions

Best practice implementation
On-farm adaptation approachesSpatial planningAdap. Malad. Adap. Malad. Adap. Malad. Adap. Malad. Adap. Malad. Adap. Malad.
Combined food production
Governance - local
Optimizing fisheries - aquaculture interactions
LowLow
Figure 5.16 |  Assessment of the likelihood and effectiveness of a range of adaptation options for potential implementation in the near term (next decade) 
for inland freshwater and brackish aquaculture (salinities of <10 ppm and/or no connection to the marine environment) (a) and marine aquaculture systems (b) per major 
FAO production zone. See SM5.6 (Tables SM5.8, 5.12) for assessment methodologies.

5
782Chapter 5 Food, Fibre and Other Ecosystem Products
adaptation measures, such as conversion of land to mixed plant–
animal systems (Nguyen et al., 2019a), conversion of freshwater ponds 
to brackish or saline aquaculture (Galappaththi et al., 2020b), building 
of dams and dykes (Renaud et al., 2015) and intensification of shrimp 
or fish pond culture to reduce water and land usage (Nguyen et al., 
2019b; Johnson et  al., 2020). Other adaptation options for limited 
water supply are government equitable water allocations and water 
storage (high confidence) (Bunting et  al., 2017; Galappaththi et  al., 
2020b).
Feed formulations and improved feed conversion can reduce climate-
associated stress for freshwater species, significantly reducing waste 
and increase sustainability (medium confidence) (FAO, 2018c; Gasco 
et al., 2018; Chen and Villoria, 2019). Projected decreases in fish meal 
and global targets of limiting warming to under 2°C may increase

the ratio of plant-based diets but reduce fish nutritional content 
(see Sections  5.10 and 5.13, Hasan and Soto, 2017; Johnson et  al., 
2020). Companies provide insurance in major production areas, but 
aquaculture is considered high risk with large levels of small claims 
(Secretan et al., 2007). Insurance covers natural disasters and disease, 
helping to reduce and cope with climate-induced risk, enabling faster 
livelihood recoveries and preventing poverty (high agreement, limited 
evidence) (Xinhua et al., 2017; Kalikoski et al., 2018; Soto et al., 2018). 
For example, small-scale shrimp farmers were willing to pay higher 
premiums to manage risk, after participation in government pilot 
insurance schemes, ensuring greater pay-outs if a mortality event 
occurred (Nyguyen and Pongthanapanic, 2016; Pongthanapanic et al., 
2019). Technological innovations are more widely implemented in 
larger operations, with Internet access promoting adoption at the

farm site (Joffre et  al., 2017; Salazar et  al., 2018). Improved farm 
management is a key opportunity (high confidence) to reduce climate 
risks on aquaculture, where Best Management Practices can increase 
resiliency (Soto et  al., 2018) and lower additional risk from non-
climatic stressors (Gattuso et al., 2018; Smith and Bernard, 2020), and 
decision-tree frameworks can provide adaptation choices when events 
occur (Nguyen et al., 2016).
5.9.4.4 Early-warning and monitoring systems
Globally, monitoring is increasing to fill scientific uncertainties 
(Goldsmith et  al., 2019) but is not often at spatial scales which 
facilitate farm or regional adaptation management (Whitney et al., 
2020) or data complexities prevent direct uptake by operators, 
resource managers and policymakers (medium confidence) (Soto 
et al., 2018; Gallo et al., 2019). Specialised industry portals (Pacific 
shellfish) and government-established monitoring programmes

(Chilean salmon) and other observational networks (e.g., Global 
Ocean Acidification Observing Network (GOA-ON)) can provide 
real-time monitoring and early-warning event alerts and facilitate 
aquaculture decision making (medium confidence) (Cross et al., 2019; 
Farcy et al., 2019; Soto et al., 2019; Tilbrook et al., 2019; Bresnahan 
et  al., 2020; Peck et  al., 2020). Seasonal forecasting, downscaled 
models and early-warning systems provide valuable regional or 
farm site risk information (Hobday et al., 2018; Galappaththi et al., 
2020b; Whitney et al., 2020), but monitoring will need to be useful 
for farmers, involve farmers, and be accurate, timely, cost-effective, 
reviewed and maintained in order to ensure uptake (high confidence) (Soto et  al., 2018). Early-warning systems for HABs enable rapid 
decision making and risk mitigation (medium confidence), such 
as ocean colour monitoring in South Africa (Smith and Bernard,

2020), where early harvesting and additional husbandry were used 
to minimise production and economic losses (Pitcher et al., 2019). 
New tools, strategies and observations are needed to predict HAB 
occurrences and range shifts with changing climate (high confidence) 
(Schaefer et al., 2019; Tester et al., 2020), as there is uncertainty on 
drivers of incidence and toxicity (Wells et al., 2020).
5.9.5 Contributions of Indigenous, Traditional and Local 
Knowledge
Indigenous mariculture practices, such as intertidal clam gardens, 
have been occurring for thousands of years, providing knowledge of 
traditional practices still applicable to mariculture (Deur et al., 2015; 
Jackley et  al., 2016; Poulain et  al., 2018; Bell et  al., 2019; Toniello 
et  al., 2019). Indigenous groups differ in opinions on aquaculture 
acceptability, implications for coastal management and territorial rights 
(high confidence) (Young et al., 2019). Such perceptions may determine

culturally appropriate types and benefits of aquaculture (employment, 
food diversification, income, building autonomy and skillsets), such as 
in Australia (Petheram et  al., 2013) and Canada (Young and Liston, 
2010). Marginalised people, like small-scale aquaculture farmers in 
lower-income and lower-middle-income countries, are often overlooked 
and are not represented at a governance level (Barange et al., 2014; 
Kalikoski et  al., 2018). Therefore policy, economic, knowledge and 
other support must ensure representation with traditional and other 
stakeholder ecological knowledge at national, regional and local levels 
to facilitate climate change adaptation and safeguard human rights for 
poor and vulnerable groups (high confidence) (Kalikoski et al., 2018; 
Poulain et al., 2018).
5.10 Mixed Systems
The food and livelihoods of many rural people depend on combinations 
of crops, livestock, forestry and fisheries, and still information on these

mixed systems is scarce. Rural households in low- and middle-income 
countries earn almost 70% of their income through mixed production 
systems (Angelsen et al., 2014). These systems produce about half of 
the world’s cereals, most of the fruits, vegetables, pulses, roots and 
tubers, and most of the staple crops and livestock products consumed 
by poor people in lower-income countries (Herrero et al., 2017). They 
can help in adapting to climatic risks and reducing GHG emissions 
by improving nutrient flows and improving the recycling of nutrients 
within the production system and by increasing food production and 
diet quality per unit of land and diversifying income sources (Smith 
et  al., 2019c). Indigenous groups often practice mixed production, 
integrating crops, animals, fisheries, forestry and agroforestry through 
traditional ecological knowledge.
Some evidence exists of the buffering capacity that integrated

systems can provide in the face of climate change (Gil et al., 2017). 
This buffering, often affecting the farming system as a whole rather 
than the individual agricultural enterprises involved, applies to some

5
783Food, Fibre and Other Ecosystem Products Chapter 5
aquaculture–agriculture systems as well as to crop–livestock systems 
(Bunting et al., 2017; Stewart-Koster et al., 2017). In some situations, there may be trade-offs and constraints at the household level that affect this resilience-conferring ability: for instance, mixed systems often need relatively high levels of management skill, and extra labour may be required (van Keulen and Schiere, 2004; Thornton and Herrero, 2015). The diversification of food production systems offers promise for enhanced resilience at the global level (Kremen and Merenlender, 2018; Dainese et  al., 2019; Section  5.4.4.4), though policies need to provide adequate incentives for resource efficiency, equity and environmental protection (Havet et  al., 2014; Thornton and Herrero, 2014; Troell et al., 2014).
5.10.1  Observed Impacts
5.10.1.1
 Mixed crop–livestock systems

Overall, there is high confidence that farm strategies that integrate mixed crop–livestock systems can improve farm productivity and have positive sustainability outcomes (Havet et  al., 2014; Thornton and Herrero, 2014; Herrero et al., 2015; Thornton and Herrero, 2015; HLPE, 2019). The scale of the improvement varies between regions and systems and is moderated by overall demand in specific food products and the policy context. Integrated crop–livestock systems present opportunities for the control of weeds, pests and diseases. They can also provide a range of environmental benefits, such as increased soil carbon and soil water retention, increased biodiversity and reduced need for inorganic fertilizers (Havet et al., 2014; Thornton and Herrero, 2014; Herrero et al., 2015; Thornton and Herrero, 2015; HLPE, 2019).
Research indicates that mixed crop–livestock systems are often more

resilient to climate change (medium confidence). In the southern Afar region of Ethiopia, crop–livestock households were more resilient than livestock-only households to climate-induced shock (Mekuyie et  al., 2018). However, the benefits of managing crop–livestock interactions 
in response to climate change depend on local context. For example, in higher-rainfall zones in Australia, Nie et al. (2016) found some yield reductions and difficulty in maintaining groundcover. The systematic review of Gil et al. (2017) concluded that the integration of crop and livestock enterprises as an adaptation measure can enhance resilience (FAQ 5.1).
Reconfiguration of mixed farming systems is occurring. In semi-

arid eastern Senegal, Brottem and Brooks (2018) found increasing reliance on livestock production mostly because of changing climate conditions. Many poorer households are having to rely on migration to compensate for shortfalls in crop production arising from a changing climate. Some farmers have successfully shifted to crop–livestock systems in Australia, where they have allocated land and forage resources in response to climate and price trends (Bell et al., 2014).
Mixed livestock–crop systems may increase burdens on women,

require managing competing uses of crop residues, and have higher requirements of capital and management skills. These factors can be challenging in many lower-income countries (Rufino et al., 2013; Thornton and Herrero, 2015; Jost et  al., 2016; Thornton, 2018). The policy actions needed for the successful operation of mixed crop–livestock systems may be similar across widely different situations: good access to credit inputs and capacity building needed to facilitate uptake (Hassen et al., 2017; Marcos-Martinez et al., 2017), and good levels of market infrastructure (Ouédraogo et al., 2017; Iiyama et al., 2018).
5.10.1.2  Mixed crop–aquatic systems

Households may have a mix of aquatic and land-based food production, contributing to food security and nutrition and income generation (Freed et al., 2020; see also discussion of aquaponics and hydroponics in Section  5.10.4.3. and combined rice–aquatic species production in Section  5.9.4). Failures in agricultural outputs due to climate-associated factors may result in diversification to fisheries as a way of alleviating food production shortfalls; for example, fisheries landings may dramatically increase after agricultural failures following hurricanes, which can subsequently create overfishing collapses (Cottrell et al., 2019). Where climatic impact drivers affect multiple sectors, adaptation may become more difficult because of the interacting challenges (Cottrell et al., 2019). One study of 12 countries with high food insecurity levels found that fish-reliant households utilised as much land as those not reliant on fish (Fisher et al., 2017). To meet food security requirements,

most of these households needed to both farm and fish, illustrating the interdependence of aquatic–terrestrial food systems.

5.10.1.3  Agroforestry systems
Agroforestry is frequently mentioned as a strategy to adapt to and mitigate climate change and address food security (de Coninck et al., 2018; Smith et  al., 2019c). There is strong evidence of net positive biophysical and socioeconomic effects of agroforestry systems under both smallholder and large-scale mechanised production systems (Quandt et  al., 2017; Hoegh-Guldberg et  al., 2018; Sida et  al., 2018; Wood and Baudron, 2018; Table 5.10; Cross-Chapter Box NATURAL in Chapter 2; Quandt et al., 2019). Many of these effects also reduce climate risk. At the same time, agroforestry systems are subject to impacts from climate change, potentially reducing the benefits they provide. Still, there is limited evidence of observed climate impacts on agroforestry systems, and modelling climate impacts is more complex for agroforestry than for single cropping systems (Luedeling et al., 2014).
5.10.2  Assessing V ulnerabilities
5.10.2.1

Assessing vulnerability in mixed systems
Important information gaps exist concerning the costs and benefits of many adaptation options in mixed systems, where the interactions between farming enterprises may be complex. Among communal crop–livestock farmers in Eastern Cape Province of South Africa, Bahta (2016) reported high levels of vulnerability to drought and highlighted the need for more coordination between monitoring agencies in terms of reliable early-warning information that can be communicated appropriately, between farmers’ organisations and the private sector to facilitate adaptation options that can overcome feed shortages such

5
784Chapter 5 Food, Fibre and Other Ecosystem Products
as fodder purchases in times of drought, and between government 
departments at the national and provincial level that address the 
concerns and needs of affected communities. Nyamushamba (2017) 
reviewed the use of indigenous beef cattle breeds in smallholder 
mixed production systems in southern Africa. Some of these breeds 
exhibit adaptive traits such as drought and heat tolerance and 
resistance to tick-borne diseases. However, their adaptation potential 
in crossbreeding programmes is essentially unknown, as most African 
cattle populations are still largely uncharacterised.
5.10.2.2 Social vulnerabilities
As in other production systems, Indigenous groups, gender, race and 
other social categories can result in heightened vulnerability to climate 
change in mixed production systems owing to historical and current 
marginalisation and discrimination (high confidence) (Parraguez-

Vergara et  al., 2016; Baptiste and Devonish, 2019; Moulton and 
Machado, 2019; Popke and Rhiney, 2019; Fagundes et al., 2020). A study 
of the Mapuche Indigenous group in Chile found that marginalisation 
and discrimination worsened their vulnerability and observed impacts 
of climate change because they had less access to services and lower 
incomes and were not as high a priority as other groups (Parraguez-
Vergara et  al., 2016). Among fisherfolk on Lake Wamala, Uganda, 
Musinguzi (2018) found evidence of considerable diversification to crop 
and livestock production as a means of increasing households’ food 
security and income, but women had greater workloads and less control 
over new income sources than men. Ngigi (2017) evaluated adaptation 
actions within households in rural Kenya and found that women tended 
to adopt adaptation strategies related to crops, and men to livestock 
and agroforestry activities. Chingala (2017) found substantial gender-

and age-related differences in control of access to animal feed, animal 
health and water resources in beef producers in mixed crop–livestock 
systems in Malawi. In a review of agriculture–aquaculture systems in 
coastal Bangladesh, Hossain et al. (2018) showed that existing policies 
and adaptation mechanisms are not adequately addressing gender 
power imbalances, and women continue to be marginalised, leading 
to increasing feminisation of food insecurity. Such studies highlight the 
need to consider gender and other social inequities when examining 
adaptation in mixed production systems, particularly in situations in 
which men and women have different levels of control over productive 
assets (Cross-Chapter Box GENDER in Chapter 18).
5.10.3 Projected Impacts
The impacts of climate change on risk in mixed farming systems are 
projected to be dependent on market, ecosystem and policy context 
(medium evidence, low agreement). In mixed crop–livestock farms in

a semi-arid region of Zimbabwe, Descheemaeker (2018) found that 
feeding forages and grain could alleviate dry-season feed gaps to the 
2050s, but their effectiveness depended on the household’s livestock 
stocking density. In comparing different commercial production 
systems, Tibesigwa (2017) found that, under South African conditions, 
climate change to the 2050s will reduce productivity across the 
agricultural sector, with the largest impacts occurring in specialised 
commercial crop farms owing to their relative lack of diversity. Mixed 
farming systems were the least vulnerable in terms of relative effects on farm output; this applied to commercial and subsistence sectors 
(Tibesigwa et al., 2017). Other studies suggest increased risk in mixed 
systems in semi-arid conditions. In northern Burkina Faso, Rigolot (2017) 
examined different crop fertilisation and animal supplementation 
levels under RCP8.5 to the 2050s. They found that, although aggregate

profits could be increased via moderate levels of inputs, the use of 
external inputs may increase risk because of marginal costs exceeding 
marginal benefits in lower rainfall years. In the Western Australian 
wheat belt, Thamo (2017) assessed climate-change-induced shifts in 
farm profitability to the 2050s. For most options, the adverse effects 
on profitability were greater than the advantageous effects, profit 
margins being much more sensitive to climate change than production 
levels. However, in the same system, Ghahramani (2018) evaluated 
adaptation options to 2030 and found that a shift to a greater reliance 
on livestock could be profitable, even in years with low rainfall.
Risk management in integrated production systems may constitute a 
barrier to uptake of adaptation options (Rigolot et al., 2017). Watson 
(2018) highlighted the current lack of financial risk management tools 
that could be used in smallholder coastal communities. Alongside other

risk management tools such as weather-based index insurance, risk 
pooling may find wide application in different farming systems as an 
effective adaptation measure (medium agreement, limited evidence) 
(Hansen et al., 2019a).
Climate change impacts on productivity of agroforestry systems are 
similar to individual perennial crops, although there is limited research on 
tree crops (see Section 5.4.1.2). Impacts include increased temperature 
or water stress, an increase in pathogens affecting crops, changes to 
pollinator abundance, and changes in the nutrient content of one or 
more of the agroforestry components. Many tree products such as fruits 
and nuts are grown in agroforestry settings. The quality and nutrition of 
these products and other specialty crops are often negatively affected 
by rising temperatures, ambient CO 2 concentrations and tropospheric 
ozone (Ahmed and Stepp, 2016). There is also evidence that the fungus

coffee rust will be positively affected by climate change (Avelino et al., 
2015; Bebber et al., 2016), with adverse effects on coffee agroforestry 
systems.
While shade trees can ameliorate increasing stand temperatures that 
will significantly impact arabica coffee (Ovalle-Rivera et  al., 2015; 
Schroth et al., 2015), the opposite can also be true. Comparing shade 
and full-sun coffee systems in Ghana, Abdulai (2018) concluded 
that the leguminous tree species providing shade and additional 
nitrogen led to soil water competition with the coffee trees during 
severe drought, resulting in enhanced coffee mortality. On the other 
hand, experimentally induced drought in a soybean-intercropping 
agroforestry system in eastern Canada led to crop losses in the 
monocropping system only, whereas N-fixation declined in both 
systems (Nasielski et  al., 2015). Thus, balancing the synergies and 
trade-offs of multiple component systems is necessary based on local

context. While species diversification can enhance resilience to climate 
shocks, lack of water can constrain the implementation of agroforestry 
practices in arid locations (Apuri et al., 2018).
For people reliant on both agriculture and fisheries for food production, 
regional differences in productivity effects of climate change are

5
785Food, Fibre and Other Ecosystem Products  Chapter 5
expected; populations in LMICs that are already vulnerable will be 
most affected by simultaneous reductions in fisheries and agricultural 
productivity (Blanchard et  al., 2017). Twelve out of 17 high-income 
countries in Europe showed projected increases in agricultural 
production where adaptive capacity is higher, and agricultural and 
food fisheries’ dependence was lower. Some LMIC countries (Nigeria, 
Cameroon, Ghana and Gabon) showed relative reductions in both 
fisheries and agricultural production, where food insecurity, human 
population growth and fisheries overexploitation rates are high 
(Blanchard et al., 2017). Model projections under the RCP6.0 scenario 
show decrease in marine and terrestrial production to 2050 in 87 out 
of the 119 coastal countries studied, even though there is a wide variance in adaptive capacity and relative and combined dependencies

on fisheries and agriculture (Blanchard et al., 2017). A projected 2050 
move towards greater consumption of cultured seafood and less meat 
showed that aquaculture requires less feed crops and land, but was 
regionally dependent upon differing patterns of production, trade and 
feed composition (Froehlich et al., 2018b).Box 5.7: Perspectives of Crop and Livestock Farmers on Observed Changes in Climate in the Sahel
The Sahel region of West Africa has experienced some of the most severe multi-decadal rainfall variations in the world: excessive rainfall 
in the 1950s–1960s followed by two decades of deficient rainfall, leading to a large negative trend until the mid-to-late 1980s with 
a decrease in annual rainfall of between 20% and 30%. Recently, there has been a partial recovery of annual rainfall amounts, more 
significant over the central than the western Sahel. This recovery is characterised by new rainfall features, including false starts and early

cessation of rainy seasons, increased frequency of rainy days, increased precipitation intensity and more frequent and longer dry spells 
(Salack et al., 2015; Sanogo et al., 2015; Salack et al., 2016; Biasutti, 2019). The Sahel is experiencing a new era of rainfall extremes 
(Bichet and Diedhiou, 2018; Panthou et al., 2018), suggesting an intensification of the hydrological cycle (Doblas-Reyes et al., 2021).
The ways in which crop and livestock farmers in the Sahel have responded to climatic variability have been studied widely (Sissoko, 2011; 
Gonzalez et al., 2012; Jalloh et al., 2013; Gautier et al., 2016; Sultan and Gaetani, 2016; Zougmoré et al., 2016; Segnon, 2019). Local 
communities have developed an extensive Indigenous ecological knowledge system, enabling them to make use of ecosystem services to 
support their livelihoods and to survive environmental change (Nyong et al., 2007; Mertz et al., 2009; Lahmar et al., 2012; Segnon et al.,

2015). These knowledge systems have been crucial in people’s resilience to and recovery from major environmental change, such as the 
severe drought period experienced in the region in the 1970s and 1980s (Nyong et al., 2007; Lahmar et al., 2012; Segnon et al., 2015; 
Gautier et al., 2016; Zouré et al., 2019). As climate change became evident and a primary concern on the global agenda, interest in local 
people’s knowledge and understanding of climate change has also increased (Mertz et al., 2009; Tambo and Abdoulaye, 2013; Traore 
et al., 2015; Kosmowski et al., 2016; Sanogo et al., 2017; Segnon, 2019).
There is no simple understanding of crop and livestock farmers’ response in the Sahel to rainfall variability. Nielsen and Reenberg (2010) 
developed human–environment timelines for the period 1950–2008 for a small village in northern Burkina Faso, relating livelihood

diversification and crop–livestock management changes that map closely to local rainfall variability, such as fields abandoned in dry 
years and intense animal manure use in wet years. Although they found a significant correlation between crop–livestock management 
practice changes and major climatic events, the climate is only one of many interacting factors that influence local adaptation strategies 
(Mortimore, 2010; Nielsen and Reenberg, 2010; Sendzimir et al., 2011). Robust attribution of observed changes to specific change drivers 
remains a challenge.
Crop and livestock farmers’ knowledge and perceptions of increases in temperature and temperature-related stressors (heatwaves, 
number of extreme hot or cold days) are consistent with the observed meteorological data (Mertz et al., 2009; Mertz, 2012; Tambo and 
Abdoulaye, 2013; Traore et al., 2015; Sanogo et al., 2017; Segnon, 2019). Their perceptions of changes in rainfall amounts have not

always been consistent with the observational record (Mertz, 2012; Segnon, 2019). Nevertheless, their perception of increases in dry spell 
occurrence during the rainy season and changes in rainfall pattern (onset, cessation, intensity and distribution) were consistent with the 
recent observations (Barbier et al., 2009; Ouédraogo et al., 2010; Tambo and Abdoulaye, 2013; Salack et al., 2015; Traore et al., 2015; 
Kosmowski et al., 2016; Salack et al., 2016; Segnon, 2019). Rainfall patterns within the season, rather than the total amounts of rainfall, 
matter more for crop and livestock farmers in the Sahel (Segnon, 2019).
Crop and livestock farmers in the Sahel have a sophisticated understanding of the local climate. There is considerable potential to 
harness this knowledge, coupled with an enabling institutional environment, in developing policies and adaptation plans (Rasmussen

et al., 2018); the Sahel is a region where meteorological stations and observed data are scarce (Buytaert et al., 2012; Nkiaka et al., 2017). 
A deeper understanding of the resilience of local ecological knowledge systems, in light of the hydro-climatic intensification currently 
experienced in the region and future changes, may well provide further insights into their long-term effectiveness.

5
786Chapter 5 Food, Fibre and Other Ecosystem Products
5.10.4 Adaptation Strategies
5.10.4.1 Increasing integration and diversity within mixed systems
There is medium confidence in the effectiveness of changing 
the nature of the integration between crops and livestock as an 
adaptation: moving from crops to livestock, moving from livestock to 
crops, and moving from one species of livestock to others, for example 
(Roy et al., 2018). Such transitions that increase integration between 
farm enterprises may contribute to risk reduction and increased food 
security. In areas with adequate rainfall and relatively limited rainfall 
variability under climate change, where agricultural diversity is the 
greatest, transitions towards more diverse and integrated systems may 
bring substantial adaptation benefits (Waha et al., 2018).
Barriers to increasing integration and diversification include policies 
which support cereals and crop specialisation, lack of markets, limited

post-harvest processing, limited technical or biophysical research on 
implementation and poor market infrastructure (Keatinge et al., 2015; 
Bodin et  al., 2016; Garibaldi et  al., 2016; Bassett and Koné, 2017; 
Kongsager, 2017; Rhiney et  al., 2018; Roesch-McNally et  al., 2018; 
Clay and King, 2019; Ickowitz et al., 2019). Proactive policy and market 
development are needed to reduce these barriers (Clay and King, 2019; 
Ickowitz et al., 2019; See 5.14.3.8 for Insurance).
5.10.4.2 Agroforestry as an adaptation–mitigation strategy for 
mixed systems
Agroforestry, the purposeful integration of trees or shrubs with crop 
or livestock systems, increases resilience against climate risks through 
a range of biophysical and economic effects (high confidence). 
Traditional agroforestry has been practiced for millennia and provides 
prime examples of sustainable agroecological production systems 
meeting the production, income and socio-cultural needs of farming

communities within their ecological niches, but market forces have often led to their demise (McNeely and Schroth, 2006; Plieninger and 
Schaar, 2008; García-Martínez et  al., 2016; Krčmářová and Jeleček, 
2016; Coq-Huelva et al., 2017; Paudel et al., 2017; Doddabasawa et al., 
2018; Maezumi et al., 2018; Lincoln, 2020). The wide range of options 
to associate different trees with crops, livestock and aquaculture 
allows agroforestry to be practiced in most regions, including those 
with precipitation regimes ranging from semi-arid to humid. While 
most agroforestry systems occur in smallholder settings, there are 
examples of successful industrial-scale mechanised agroforestry 
systems (Feliciano et al., 2018; Lovell et al., 2018). Agroforestry delivers 
medium to large benefits to all five land challenges described in the 
SRCCL—climate change mitigation, adaptation, desertification, land 
degradation and food security—and is considered to have broad

adaptation and moderate mitigation potential compared with other 
land challenges (Smith et  al., 2019c). Agroforestry is also able to 
deliver multiple biophysical and socioeconomic benefits (Table 5.12).
The adoption and maintenance of agroforestry practices require 
appropriate incentives or the removal of barriers (high confidence). 
Agroforestry adoption has been limited to date in both higher-income 
and lower-income countries. Several constraints need to be carefully 
addressed for successful scaling-up of agroforestry systems, including 
costs of establishment, limited short-term benefits, lack of reliable 
financial support to incentivise longer-term returns on investments, land 
tenure, knowledge of and experience with trees and the management of 
multiple component systems, and inadequate market access, (Coulibaly 
et al., 2017; Iiyama et al., 2017; Jacobi et al., 2017; Kongsager, 2017; 
Hernández-Morcillo et  al., 2018; Iiyama et  al., 2018; Lincoln, 2019).

Kongsager (2017) and Roupsard et al. (2020) also highlight the need 
for vertical integration of measures from local to national scales to 
successfully address local barriers to adoption. Although there are few 
studies evaluating the long-term performance of agroforestry systems 
(Coe et al., 2014; Meijer et al., 2015; Brockington et al., 2016; Kongsager, 
2017; Toth et al., 2017), the available results suggest that successful 
adoption of agroforestry practices depends strongly on the local 
Table 5.12 |  Some of the biophysical and socioeconomic benefits of agroforestry.
Contribution Pathway References
Increased food security and household 
incomeDiversification of production, avoiding trade-offs between crop and 
tree productsNath et al. (2016), Coulibaly et al. (2017), Montagnini and Metzel 
(2017), Waldron et al. (2017), Blaser et al. (2018), Sida et al. (2018), 
Quandt et al. (2019), Amadou et al. (2020)

Increased productivity per unit of land Introduction of multiple species leading to higher land equivalency ratios van Noordwijk et al. (2018), Reppin et al. (2019)
Improved biophysical site propertiesVia limiting soil erosion, facilitating water infiltration, increasing 
nutrient use efficiency, improving soil physical properties, improving 
crop nutritional quality, modifying the site micro-climate, and helping 
to buffer against extreme eventsNguyen et al. (2013); Carsan et al. (2014), Rosenstock et al. (2014), 
Quandt et al. (2017), Hoegh-Guldberg et al. (2018), Sida et al. (2018), 
Wood and Baudron (2018), de Leeuw et al. (2020), Muchane et al. 
(2020), Nyberg et al. (2020)
Enhanced biodiversity and supporting 
ecosystem servicesVia integrating different perennial and annual species in different 
spatial or temporal associations, thereby providing greater habitat

diversity for other species, including pollinators and predatorsMcNeely and Schroth (2006), Imbach et al. (2017), Isbell et al. (2017), 
Sonwa et al. (2017b), Tran and Brown (2019)
Enhanced CES Enhanced recreational, cultural and spiritual uses Nyberg et al. (2020)
Carbon dioxide removalVia enhanced above-ground carbon sequestration compared with 
most cropping or livestock systems, ranging from 2.6 to 10 Mg C ha−1 
yr−1 depending on regional and climatic conditions (>0.7 Gt CO 2e yr−1 
globally between 2000 and 2010)Ramachandran Nair et al. (2009), Zomer et al. (2016), Rochedo et al. 
(2018), Wolz et al. (2018), Crous-Duran et al. (2019), Platis et al. (2019)
Enhanced gender balance Via providing women with more diversified income sources Kiptot et al. (2014), Ngigi et al. (2017), Benjamin et al. (2018)
Strengthened urban and peri-urban 
agricultural systemsVia provision of regulating and provisioning ecosystem services such as

shade, water infiltration, new food and livelihood opportunitiesBorelli et al. (2017)
See Section 5.12

5
787Food, Fibre and Other Ecosystem Products  Chapter 5
enabling environment, including appropriate markets, technologies and 
delivery systems (medium evidence,  high agreement).
5.10.4.3 Links between crops and aquaponics–hydroponics as 
Hydroponic systems produce plants in a soilless environment requiring 
mineral fertilizers to meet plant nutritional needs, whereas aquaponics 
combines an aquaculture production system with hydroponics, where 
fish waste provides nitrogen, phosphorous and potassium for plant 
growth and nitrifying and mineralising bacteria act as filters (Goddek 
et al., 2015; Pérez-Urrestarazu et al., 2019; Ghamkhar et al., 2020). The 
relative environmental impact of hydroponic systems is lower com-
pared with conventional systems owing to the significant reductions 
in land use and fertilizer usage (high confidence) (Goddek et al., 2015; 
Datta et al., 2018; Pantanella, 2018; Suhl et al., 2018; El-Essawy et al.,

2019; Jaeger et al., 2019; Monsees et al., 2019; Mupambwa et al., 2019; 
Pérez-Urrestarazu et al., 2019; Ghamkhar et al., 2020). While studies 
indicate that aquaponics and hydroponics have higher yields and a 
lower environmental footprint than conventional agriculture (medium 
confidence), aquaculture and heated greenhouse production (Panta-
nella, 2018; Romeo et al., 2018), aquaponic production may need to 
be coupled or decoupled or have double-recirculation systems to meet 
the different requirements of farmed fish and crop species (Pantanella, 
2018; Suhl et al., 2018; Mupambwa et al., 2019). Aquaponics and hy-
droponics are a promising adaptation option for urban agriculture, with 
benefits including a protected growing environment from climate ex-
tremes, reduced GHG emissions related to food transportation, reduced 
food waste, rainwater harvesting and use of food waste (medium agree-
ment, limited evidence) (Goddek et al., 2015; Al-Kodmany, 2018; Clin-

ton et al., 2018; Weidner and Yang, 2020). Such systems show promise 
for reducing food production environmental footprints and increasing 
food security, particularly in arid or water-stressed environments (Doyle 
et al., 2018; Mupambwa et al., 2019). Barriers to aquaponics and hydro-
ponics adoption include market acceptance of cultured fish species and 
desirability of plant crops, lack of expertise, legal constraints or high 
investment costs and financial feasibility (Bosma et al., 2017; Al-Kod-
many, 2018; Datta et al., 2018; Pantanella, 2018; El-Essawy et al., 2019; 
Martin and Molin, 2019; Pérez-Urrestarazu et al., 2019; Specht et al., 
2019). There is high confidence ( high agreement,  medium evidence) 
that a major barrier to hydroponic and aquaponics adoption is the re-
quirement for skilled operators (Goddek et al., 2015; Bosma et al., 2017; 
Datta et al., 2018; McHunu et al., 2018; Pantanella, 2018), which could

be mitigated by decoupling systems and disciplines (Pantanella, 2018). 
As yet, these systems are not widely implemented and information on 
their climate change impacts is limited.
5.10.4.4 Transitions in and between mixed systems as 
adaptation strategy
Transitions in and between the different elements of integrated 
agricultural systems can be an effective adaptation option (medium 
confidence). Havlik et  al. (2014) projected that, by 2030, market-
driven autonomous transitions towards more efficient production 
systems would increase ruminant meat and milk productivity by 
up to 20% and decrease emissions by 736 MtCO 2e y−1, most of this 
arising through avoided emissions from the conversion of 162 Mha of natural land. Weindl et al. (2015) assessed the implications of several 
climate projections on land use change to 2045 and found that shifts 
in livestock production towards mixed crop–livestock systems would

represent a resource- and cost-efficient adaptation option, reducing 
global agricultural adaptation costs and abating deforestation by 
about 76 million ha globally. Both studies suggest that public policy 
support for transitioning livestock production systems to increase their 
efficiency could be an important lever for reducing adaptation costs and 
contributing to emissions reductions. This policy support could include 
modified regulatory and certification frameworks that incentivise 
livestock producers to adapt and mitigate (Weindl et al., 2015).
Recent reviews have summarised literature on production system 
transitions, driven at least partly by a changing climate or changing 
climate variability, that sometimes involves substantial shifts in 
enterprises and land configurations. These reviews found several cases 
of transitions affecting pastoral and mixed systems, with a range of 
responses including intensification, diversification and sedentarisation

as well as the abandonment of agriculture (see Section  5.14.3.1, 
Vermeulen et al., 2018; Thornton et al., 2019). The consequences of these 
system transitions have been mixed; in some cases, the household-level 
outcomes have been beneficial, while in others not. Policy environments, 
defined in terms of multi-level governance structures and institutions, 
are critical enablers of change. The vulnerability of many crop–livestock 
keepers to climate change is particularly affected by property and grazing 
rights (high confidence). Identifying the winners and losers from changes 
in land ownership and the use of communal lands in the coming decades 
is a key challenge for the research agenda, particularly as climate change 
impacts in the marginal lands intensify (Reid et al., 2014).
5.11 The Supply Chain from Post-harvest to 
Food
The food system is more than just the production of food. It includes 
domestic and international transportation, storage, processing, market

infrastructure and institutions that make up value chains, as well as the 
food environment in which consumers make food purchasing decisions 
(HLPE, 2017a). Climate change impacts along the value chain alter 
availability, access and stability of food security. Nutrition-dense 
foods tend to be more perishable and are thus more vulnerable to 
limitations of food storage and transportation infrastructure (Ickowitz 
et  al., 2019). Climate-change-related damage to food in storage 
(e.g., electricity failures and loss of cold storage) and transportation 
infrastructure (e.g., extreme weather events damaging roads and other 
infrastructure) could significantly decrease availability and increase 
the cost of highly perishable, nutritious foods such as fruits, vegetables, 
fish, meat and dairy.
This discussion of the post-harvest food system (i.e., after production 
or catch) focuses on three key elements—food safety, storage, and

domestic and international transactions—that could see significant 
climate change impacts, either directly or indirectly. Higher temperatures 
and humidity can increase post-harvest loss from pests and diseases, 
increase occurrence of food-borne diseases and contamination, and 
raise the cost of refrigeration and other forms of preservation. Extreme 
weather events can cause disruptions to food transport networks

5
788Chapter 5 Food, Fibre and Other Ecosystem Products
and storage infrastructure. Changes in regional weather can cause 
production centres to shift locations, potentially requiring changes in 
storage and processing locations. Prices to producers and consumers 
will change, although directions and magnitudes are determined by 
local conditions and policies.
Food loss is the harvest not used by industry or for food. Food waste is 
the subset of food loss that is potentially recoverable for food use. As a product moves in the post-harvest chain to end users, post-harvest 
food loss from climate change can occur from improper handling to 
damage from microorganisms, insects, rodents or birds. Post-harvest 
losses in quality can be the result of stresses and damage to a plant or 
animal before harvest, including from climate change (Hodges et al., 
2011; Medina et  al., 2015a). Food waste caused by climate change 
may occur at both retail units and homes because fresh ingredients

and freshly prepared foods are vulnerable to quality reduction and Box 5.8: Climate Adaptation and Maladaptation in Cocoa and Coffee Production
Coffee and cocoa are important crops in low-latitude regions where agriculture is projected to be heavily impacted by climate change. 
Both crops are at risk from climate change impacts by 2050 (Baca et al., 2014; Ovalle-Rivera et al., 2015; Chemura et al., 2016; Schroth 
et al., 2016; Bacon et al., 2017; Schreyer et al., 2018; de Sousa et al., 2019; Lahive et al., 2019; Pham et al., 2019; Cilas and Bastide, 2020). 
Chocolate and coffee are notable among foods in that their carbon footprint ranges from negative to high, as these industries include 
both low-input agroforestry systems that have many co-benefits, and high-input monoculture systems where crops are grown without 
shade, in some cases on sites that have been deforested (Poore and Nemecek, 2019). While the coffee industry in many countries has

already transitioned from agroforestry to full-sun production (Jha et al., 2014), the cocoa industry is at a turning point, with many growers 
deciding whether to move to the potentially more productive ‘full-sun system’, despite a general view that the agroforestry system is 
more resilient to climate change impacts (Rajab et al., 2016; Schroth et al., 2016; Farrell et al., 2018; Niether et al., 2020).
Shade-grown cocoa and coffee agroforestry systems provide an array of ecosystem services, including regulating pests and diseases, 
maintaining soil fertility, maintaining biodiversity and carbon sequestration (high confidence) (Jha et al., 2014; Rajab et al., 2016; Cerda 
et al., 2017; Pham et al., 2019). For example, a comparison of Indonesian cocoa stands found that total carbon stocks above and below 
ground were five times higher in multi-shade agroforestry stands compared with monoculture stands (57 compared with 11 Mg C ha−1),

and total NPP was twice as high (18 compared with 9 Mg C ha−1 yr−1). The extra carbon sequestration was achieved without any notable 
difference in cocoa yield (Rajab et al., 2016). At higher levels of shade, there can be negative impacts on the yield of the understory crop, 
but careful management of shade trees allows for both crops to thrive (Andreotti et al., 2018; Blaser et al., 2018; Niether et al., 2020).
Cocoa grown under shade in some situations may be more resilient to climate change (Schwendenmann et al., 2010; Schroth et al., 
2016). Schwendenmann et  al. (2010) implemented drought experimentally in the field and found shade trees increased drought 
resilience. Shade trees insulate the understory crop from the warming and drying sun (Schroth et al., 2016). On the other hand, full-sun 
cocoa systems may be more climate resilient in some cases (Abdulai et al., 2018), as interactions between understory trees and shade

trees are complex; in addition to shade effects, evapotranspiration and root interactions must be considered (Niether et al., 2017; 
Wartenberg et al., 2020). Moving to a full-sun system may also involve additional inputs in irrigation, fertilizer and labour. Neither (2020) 
reviewed the literature comparing the two cocoa production systems and concluded that the agroforestry system was superior in terms 
of climate adaptation.
The choice of cropping system will have wide-reaching consequences for climate vulnerability and climate justice. Coffee and cocoa 
are often a main source of income for small-scale producers who are among the most vulnerable to climate hazards (Bacon et al., 
2014; Schroth et al., 2016). Most of their produce is exported by large corporations and sold to relatively better-off consumers. In the 
context of climate justice, underlying structural inequities (socioeconomic, ethnicity, gender, caste), marginality and poverty help to shape

the vulnerabilities of small-scale farmers to climate hazards (Beckford and Rhiney, 2016; Schreyer et al., 2018). Climate change may 
compound their vulnerability, if for example the loss of pollination services leads to a reduction in productivity (Avelino et al., 2015). 
Adaptation needs to consider the inequities associated with the commodity chain, and the adaptative capacity of producers as they 
seek to move into the more advanced processing stages of the commodity chain to realise higher returns from their exports (Ovalle-
Rivera et al., 2015). Blue Mountain Coffee is a ‘specialty’ coffee associated with a protected area forest ecosystem that attracts a high 
price premium owing to its distinct flavour and aroma. The livelihoods of coffee farmers in this region are characterised by multiple 
socioeconomic, environmental and institutional stressors related to climate change, pests, plant diseases and production costs. Some

coping strategies employed by these coffee farmers have increased their susceptibility to future climate impacts (Guido et al., 2019). Davis 
(2017) showed that these coffee farmers’ food security challenges could be alleviated by improved marketing of fruit tree products under 
shade coffee farming systems. Adaptation measures in such systems need to consider co-benefits and negative trade-offs, especially 
in vulnerable communities, to avoid widening further the inequities, rural livelihood loss, migration and marginalisation, and ensure 
progress towards the SDGs (high confidence).

5
789Food, Fibre and Other Ecosystem Products  Chapter 5
spoilage from exposure to higher temperatures and humidity. Food 
waste also contributes to climate change by utilising resources that 
emit GHGs (Galford et al., 2020).
5.11.1 Current and Future Climate Change Impacts on 
Food Safety
Emerging food safety risks from climate change include those posed 
by toxigenic fungi, plant- and marine-based bacterial pathogens, HABs 
and increased use of chemicals (plant protection products, veterinary 
drugs) potentially leaving residues in food (European Food Safety 
Authority Panel on Plant Protection Products and their Residues et al., 
2017; Deeb et al., 2018; Mbow et al., 2019; FAO et al., 2020).
Mycotoxins, produced by toxigenic fungi found on many crops, 
contaminate food and feed and cause a wide range of adverse 
impacts to human and animal health. Climate change can affect the 
growth and geographical expansion of these fungi (high confidence)

(Wild et al., 2015; Battilani, 2016; FAO and WHO, 2016; Watson et al., 
2016b; Alshannaq and Yu, 2017; Chen et al., 2018a; Avery et al., 2019; 
Milicevic et al., 2019; Van der Fels-Klerx et al., 2019; FAO, 2020a; FAO 
Aspergillus flavus is a fungus that infects a range of crops and 
can reduce grain quality. Several strains also produce aflatoxin, a 
particularly problematic mycotoxin. Increasing CO 2 and drought stress 
has little effect on growth of Aspergillus but significantly increases the 
production of aflatoxin (Medina et al., 2015b).
In Europe, one estimate is that the risk of aflatoxin contamination 
will increase in maize in a +2°C temperature scenario in Europe, with 
nearly 40% of Europe exceeding the current legal limits (Battilani and 
Toscano, 2016). In Malawi, maize aflatoxin levels above European 
Union (EU) legal thresholds are possible for most of the country by 
mid-21st century (Warnatzsch and Reay, 2020). The occurrence of

toxin-producing fungi will increase and expand from tropical and 
subtropical areas into new regions and where appropriate capacity 
for surveillance and risk management is lacking (medium confidence) 
(Miller, 2016). The increase in toxigenic fungi in crops, and consequent 
contamination of staple foods with mycotoxins, will increase the risks 
of human and animal exposure (high confidence) (Botana and Sainz, 
2015; Rose and Wu, 2015; Battilani, 2016; Avery et al., 2019; Bosch 
et al., 2019; Milicevic et al., 2019; Moretti et al., 2019; Van der Fels-
Klerx et al., 2019; FAO, 2020a).
In aquatic systems, toxins produced during HABs also cause food 
safety problems (high confidence) (Botana, 2016; Estevez et al., 2019; 
Section 5.8). Increased poleward expansion of Vibrio in coastal mid- 
to high-latitude areas has been observed (Baker-Austin et al., 2017). 
Vibrio-related mortalities from finfish consumption are expected to

rise with climate change (water temperature, salinity, oxygen and 
pH) (medium confidence) (Mohamad et al., 2019a; Mohamad et al., 
2019b). For shellfish species, oxygen deficits (Mohamad et al., 2019b), 
sea level rise (Deeb et al., 2018) and temperature (Green et al., 2019) 
will be most important for food safety.Food safety is also anticipated to worsen from increased contaminant 
bioaccumulation under climate-induced warming (high confidence) 
(Sections 3.5.8, 3.5.9, 5.8, 5.9, Bindoff et al., 2019;), with changes in 
pathogen, parasite, fungi and virus abundance and virulence (Bondad-
Reantaso et al., 2018). Coastal communities who depend on fisheries 
for livelihoods and nutrition are especially vulnerable (Hilmi et  al., 
2014; Golden et al., 2016; Bindoff et al., 2019).
Occurrence of bacterial pathogens such as Salmonella and 
Campylobacter will increase with rising temperatures (high 
confidence). Foodborne pathogen risks will increase through multiple

mechanisms, though in general the impacts of climate change on 
different pathogens are uncertain (Akil et al., 2014; Hellberg and Chu, 
2016; Lake and Barker, 2018). Even species within a genus can be 
affected differently. For example, higher CO 2 levels depress the growth 
rate of F . graminearum, an economically important pathogen on barley 
but have little effect on F . verticillioides, which is the most reported 
fungal species infecting maize.
Increases in rainfall intensity will have some effect on the transport 
of heavy metals by enhancing runoff from soil and increasing the 
leaching of heavy metals into water systems, with magnitudes 
dependent on local conditions (high confidence) (Joris et  al., 2014; 
Wijngaard et al., 2017). Methyl mercury (MeHg) is highly neurotoxic 
and nephrotoxic and bioaccumulates and biomagnifies through the 
food web via dietary uptake (fish, seafood, mammals) (Fort et  al., 
2016). Ocean warming facilitates methylation of mercury, and the

subsequent uptake of methyl mercury in fish and mammals has been 
found to increase by 3–5% for each 1°C rise in water temperature 
(Booth and Zeller, 2005; FAO, 2020a). A changing climate will release 
mercury from snow and ice, raising the amount of mercury in aquatic 
ecosystems, although its importance relative to industrial sources is 
unknown (Morrissey et al., 2005).
Increased frequency of inland floods has been associated with 
contamination of food with toxic and fat-soluble persistent organic 
pollutants (POPs), polychlorinated biphenyls (PCBs) and dioxins (Lake 
et al., 2014; Tirado, 2015; Alava et al., 2017). Exposure to POPs can 
lead to serious health effects, including certain cancers, birth defects 
and impairments to the immune, reproductive and neurological 
systems.
Climate change–contaminant interactions may alter the bioaccumulation 
and biomagnification of POPs and PCBs as well as MeHg (Alava et al.,

2017). Of particular concern is the pollution risk influenced by climate 
change in Arctic ecosystems and the bioamplification of POPs and 
MeHg in seafoods resulting in long-term contamination of traditional 
foods in Indigenous communities (Tirado, 2015; Alava et al., 2017).
The high risk associated with emerging zoonoses (animal diseases 
that can infect humans) and alterations in the distribution, survival 
and transmission of vectors and associated pathogens and parasites 
could lead to an increased use of veterinary drugs and more rapid 
development of microbial resistance (European Food Safety Authority 
et al., 2020; FAO, 2020a) and higher veterinary drug residues in food 
of animal origin, potentially posing health issues for humans (Beyene 
et al., 2015; FAO et al., 2018; European Food Safety Authority et al.,

5
790Chapter 5 Food, Fibre and Other Ecosystem Products
2020). These outcomes will depend, at least in part, on the extent of 
changes in current regulatory systems for veterinary drugs. Pre-harvest 
stress on animals can increase the contamination of meat products 
with zoonoses. Climate change may also increase rodent populations 
and rodent-born zoonoses (Naicker, 2011). Extreme weather events 
that cause flooding, such as hurricanes or extreme rain events, increase 
the chance of inundating areas that contain waste from animal farms 
where antibiotics are used for production, increasing the spread of 
antibiotic-resistant bacteria into the surrounding environment (FAO, 
2020a).
5.11.2 Current and Future Climate Change Impacts on 
Food Loss in Storage, Distribution and Processing
The potential for climate-change-based food losses exists in all 
parts of the food system—post-harvest storage, distribution and 
processing—with the potential for impacts in one part of the system

to be passed on to other elements (Davis et  al., 2021). Storing a 
product destined for food use makes it available in times other than 
immediately after harvest, which is especially important for products 
with a pronounced seasonal availability or that are not available 
from other regions with different seasons. Storage of fresh products 
(meat, fish, fruits and vegetables) even with the best cold storage 
technology results in some quality loss relatively quickly. Higher 
temperatures increase the cost of maintaining quality. One estimate 
is that an increase in outdoor temperature from 17°C to 25°C 
increases cold storage power consumption by about 11% (James and 
James, 2010). Post-harvest storage of roots and cereals is subject 
to physical and quality losses from damage by mice, rats and birds 
and by microorganisms such as the toxigenic fungi discussed above, 
all of which are expected to increase in warmer and more humid 

The higher temperatures and humidity will generally raise storage 
costs and lower the quantity and quality of stored product, reducing 
producer incomes and raising consumer prices (high agreement, 
medium evidence) (Mbow et al., 2019). For example, in the US state of 
Michigan, climate change will shorten the period of reliably cold local 
storage of potato by 11–17  days and 14–20  days further south by 
mid-century and by 15–29 days and 31–35 days, respectively, by late 
century. These changes would increase future demand for ventilation 
and/or refrigeration immediately after harvest and again in spring and 
early summer (Winkler et al., 2018).
Insects are a main source of food loss. Climate change can alter 
insect damage in at least two ways: increases in reproductive rate 
from temperature increases and changes in pheromone effectiveness 
(high confidence). Increasing temperature up to about 40°C raises the

rates of insect food digestion and reproduction (Deutsch et al., 2018), 
but temperatures above that level are fatal for many insects (Neven, 
2000). Most insects rely on pheromones to facilitate reproduction. 
Higher temperatures, but also increases in atmospheric CO 2 and O 3 
levels, can affect this process. Insect species that rely on long-range 
chemical signals (such as ladybirds, aphids, bark beetles and fruit 
flies) will be most impacted, because these signals suffer from longer 
exposure to processes that reduce pheromone effectiveness (Medina et  al., 2015b; Moses et  al., 2015; Boullis et  al., 2016; Verheecke-
Vaessen et al., 2019).
There are several potential pathways for climate change impacts on 
processing that would negatively affect quality and appearance, but 
with limited research to date. For example, some studies have indicated 
that recent increases in temperature have decreased the appearance

and milling quality of rice in the USA and East Asia, owing to increased 
occurrence of chalky grains (Lyman et al., 2013; Morita et al., 2016; 
Masutomi et al., 2019; Ishigooka et al., 2021). Impacts on quality of 
perennial crops and annual fruits and vegetables are discussed above 
(Section 5.4.3 and Box 5.2).
5.11.3 Current and Projected Impacts on Transportation 
and Distribution: Domestic and International Trade
Regional differences in resource availability are a key underlying 
driver of domestic and international trade. Climate change can change 
resource availability, in terms of both quantity and quality, altering 
trade flows, prices and incomes of producers. Climate change can also 
affect food access, and its stability can be affected through climate-
change-driven disruption of infrastructure (FAO et  al., 2018; Mbow 
et al., 2019). Extreme events are expected to become more common 
as climate change progresses. Recent examples illustrate the potential

for trade disruptions. In March 2019, Cyclone Idai affected 1.7 million 
people in Mozambique and 920,000 in neighbouring Malawi, according 
to United Nations (UN) officials. The World Food Program reported that 
satellite imagery of flooding in central Mozambique showed an ‘inland 
ocean’ the size of Luxembourg with potentially large impacts on 
distribution of existing supplies, and uncertain effects on future food 
production and availability. The extreme rainfall events in the US state 
of Iowa in spring 2019 destroyed large numbers of well-built grain 
silos. In addition, major road and bridge damage required rebuilding.
Trade plays a sizeable role in global food supplies. More than 1 billion 
people relied on international food trade in the early 21st century 
(Fader et al., 2013; Pradhan, 2014). Domestic and international trade 
flows can be dramatically affected by climate change impacts (medium 
evidence, high confidence) (Nelson et al., 2014; Pradhan, 2014; Wiebe

et al., 2015). Since the impacts of climate change will not be uniform, 
profitable locations for exports production will change. In addition, the 
effects of increasing local weather variability caused by climate change 
means increasing variability of food availability for domestic use and 
international trade. Finally, extreme events driven by climate change 
can disrupt transportation along the food value chain. Countries more 
at risk of natural hazards that disrupt transportation and distribution, 
and with less extensive routes, are more vulnerable to climate change 
impacts. A global multi-hazard risk assessment (Koks et  al., 2019) 
suggests surface and river flooding, which are projected to increase 
in a warmer climate, are the main hazards for road and railway 
infrastructure, increasingly disrupting international and domestic 
transportation of agricultural commodities.
Climate change impacts will increase most global prices relative to

early 21st century levels, with varying effects on the cost of food 
imports (high confidence) (Nelson et  al., 2014; Wiebe et  al., 2015;

5
791Food, Fibre and Other Ecosystem Products  Chapter 5
Fujimori et  al., 2018; Lee et  al., 2018). For example, analysis using 
results from one study (using CMIP5 data for RCP8.5 and SSP2) found 
that net food importing countries in the early 21st century would 
see expenditures on food imports decrease by USD 36 billion in mid-
century in real terms with climate change over a no climate change 
scenario. (Table 5.13).
Global economic models with a focus on agriculture provide a 
perspective on the range of potential changes in market outcomes 
because of climate change. In one study comparing several SSPs to 
a future with no climate change to one with impacts from RCP8.5, 
2050 yields with climate changes impacts are 17% smaller on average 
than those without climate change. Adaptation by farmers reduce 
that to an 11% decline. The change in 2050 prices of all crops and 
regions after climate change impacts and farm-level adaptation is a

mean 20% increase (Nelson et al., 2014). Substantial differences arise 
from both the heterogeneous impacts of climate change over crops 
and geography and the diversity of modelling approaches in the GCM 
and crop models. A later study with more socioeconomic scenarios and 
fewer models got roughly similar results (Wiebe et al., 2015), as did a 
modelling study focused on food security in South Asian countries (Cai 
Most climate scenario modelling to date does not incorporate increasing 
variability nor the use of storage, a critical tool to manage variability. 
Two recent studies are exceptions. In one, climate change generally 
reduces mean yields and increases their variability in the Midwestern 
USA and causes modest increases in price volatility (Thompson et al., 
2018). A second study (Chen and Villoria, 2019) focuses on maize net 
importers across Africa, Asia and Latin America during 2000–2015.

A 1% increase in the ratio of imports to total consumption reduces 
domestic price variability by 0.29%. A 1% increase in stocks at the 
beginning of the season is correlated with a 0.22% reduction in the 
coefficient of variation.
5.11.4 Adaptation in the Post-harvest Supply Chain
The SRCCL (Mbow et  al., 2019) findings on adaptation support 
targeting food value chains and intervention types to the needs of 
specific locations. Furthermore, adaptation choices will need to be 
dynamic as climate change impacts are expected to worsen over time.
As discussed above and in Section 6.2.5, climate change is expected 
to cause increasingly severe effects on infrastructure needed for food 
security: roads and harbours for transport, water storage facilities 
for irrigation and storage facilities able to withstand climate-related damage. Three categories of adaptation could be considered: adoption 
of technologies already in use elsewhere, including Indigenous and

local knowledge, or available or near ready that become profitable as 
impacts become more severe; development of new technologies; and 
taking advantage of changing comparative advantage across regions. 
Specific examples of post-harvest technical adaptation options that 
are already available but could be more widely adopted include solar 
driers, cold storage facilities and transport and use of ultrasonic 
humidification of selected fruits and vegetables, a technology that has 
been shown in Europe to reduce losses in each post-harvest stage by 
20% or more (Fabbri et al., 2018). Hermetic storage containers using 
community-based farmer research networks to scale out (Singano et al., 
2020; Wenndt et al., 2021) also show promise. Another innovation is to 
introduce Aspergillus fungi that do not produce aflatoxins in biocontrol 
formulations, as is being undertaken in the Aflasafe project in Kenya 
(Bandyopadhyay et al., 2016).

International trade changes are a potentially important adaptation 
mechanism for both the short-term effects of climate variability and 
long-term changes in comparative advantage with globally substantial 
benefits but that are distributed unevenly (Mosnier et al., 2014; Baldos 
and Hertel, 2015; Fuss et  al., 2015; Costinot et  al., 2016; Hertel and 
Baldos, 2016; Gouel and Laborde, 2021). One estimate is that, with a 
reduction in tariffs as well as institutional and infrastructural barriers, 
the negative impacts of climate change globally would be reduced 
by 64%, with hunger-affected import-dependent regions seeing the 
greatest benefit. However, in hunger-affected export-oriented regions, 
partial trade integration might lead to increased exports at the expense 
of domestic food availability (Janssens et  al., 2020). It is possible for 
policy changes that result in increased trade flows to also increase the

potential for maladaptation, for example by encouraging conversion of 
environmentally sensitive areas to agriculture (Fuchs et al., 2020; 5.13.3).
As discussed in Section  5.4, climate change is expected to increase 
variability in yields. As long as the variability is not correlated across 
regions, trade flows within a year can partially compensate, with 
in-period exports from countries less affected to those that are. 
Alterations in trade flow patterns to accommodate these impacts will 
reduce the negative effects so long as this variability is not correlated 
across regions (UK, 2015; Janetos et al., 2017).
In terms of food safety impacts, Lake and Barker (2018) highlight a range 
of approaches to enhance preparedness for more serious foodborne 
disease effects from climate change: adoption of novel surveillance 
methods to speed up detection and improve intervention in foodborne 
outbreaks; genotype-based approaches to surveillance of food pathogens

to enhance spatiotemporal resolution in tracing and tracking of illness; 
improving integration of plant, animal and human surveillance systems 
under the rubric of One Health, increased commitment to cross-border 
and global information initiatives; and improved clarity regarding the 
governance of complex societal issues such as the conflict between food 
safety and food waste and strong user-centric (social) communications 
strategies to engage diverse stakeholder groups.
The range of potential adaptation approaches from production to 
transportation to reduce food loss and waste is captured in Figure 5.17 
(Galford et al., 2020).Table 5.13 |  Net exports of agricultural products, by net exporting and net importing 
countries, 2010 and 2050 (billion constant parity US dollars), based on analysis in 
Beach et al. (2019).
2010 2050
Net importers in 2010
 No climate change –301 −838
 Climate change −301 −802

5
792Chapter 5 Food, Fibre and Other Ecosystem Products
Food loss and waste (FLW) interrelationships by value change stage
Plan cultivation  schedules 
to time  harvest for  
maximum shelf life.
Improve physical  handling 
to reduce  physical  
deterioration of  products.
Sort freshly  harvested 
items to  separate 
damaged.Production Harvesting Processing Storage Transportation
PP
too
mm
Imm
too
dee
SSo
itee
daa
HH
 PP
 SSt
 TT
Maintain hygiene  during 
food handling  and storage 
through  improved 
packaging.
Promote climate  controlled 
facilities  and units.
Maintain cold chain  from 
harvest to  retail.
Utilize pest and  contamina -
tion  reduction practices.Improve  infrastructure for  
physical travel.
Develop centralized  
distribution  facilities.
Schedule  transportation for  
ideal storage  conditions for  
products while in  transit.Modify the  product's form 
or  make-up for longer  
preservation.
Dry product to  achieve 
optimal  moisture levels for  
storage.

Reduce  contamination  
through use of  proper 
practices,  standards, and 
tools.Select crop varieties  that 
have longer  shelf life, meet  
buyer quality requirements,  
and  have improved  drought 
or salinity  tolerance.
Select crop and  livestock 
types,  species or breeds  
that are disease  tolerant.
Figure 5.17 |  Examples of food loss and waste (FLW) interventions at five stages in the food value change (Galford et al., 2020).
The importance of reducing food loss and waste due to climate change 
is widely recognised, but literature on cost-effective reductions is 
sparse, particularly in low-income countries (Parfitt et  al., 2010). A 
list of farm and post-harvest methods to reduce food loss (Sheahan 
and Barrett, 2017) includes potential farm interventions such as 
varietal choice, education in harvest and post-harvest handling, 
hermetic storage technologies (see above), chemical sprays and 
integrated pest management techniques in storage. The evidence on

their effectiveness, especially in the face of increased climate change 
impacts, is limited.
5.12 Food Security, Consumption and Nutrition
5.12.1 Introduction
Food security and nutrition are key desired outcomes of food systems. 
Climate change is already contributing to reduced food security and 
nutrition and will continue to do so (high confidence) (Sections 5.4, 5.5, 
5.8, 5.9, 5.10). Climate change impacts affect all four dimensions of 
food security: availability, access, utilisation and stability (Table 5.14), 
through both direct and indirect pathways.
Global food security improved dramatically in the 20th century even 
as global population increased from 2 to 6 billion. While some may 
assume that global food security is primarily provided by large-scale 
producers, research since AR5 has shown the sizeable role of small and 
mid-sized food producers in Asia, Africa and Latin America contributing 
to global food security and nutrition, while being highly vulnerable to

climate change impacts on food security (Samberg et al., 2016; Herrero 
et al., 2017; FAO et al., 2018; Ricciardi et al., 2018). In 2019, more than 
750 million people in the world, almost 1 in 10 people, suffered from 
severe food insecurity, a figure which has risen since 2014 in every 
region except North America and Europe(FAO et al., 2020). Overnutrition, a result of high-calorie unbalanced 
diets, is also rising, with over 2 billion adults overweight or obese (FAO 
et al., 2018; Swinburn et al., 2019; FAO et al., 2020; Venkatesh Mannar 
et al., 2020; WHO, 2021). Many low- and middle-income countries now 
have both high under- and overnutrition rates (FAO et al., 2018).
There are multiple drivers of food security, including changing dietary 
patterns, urbanisation and population growth (HLPE, 2017b; FAO et al., 
2018; Swinburn et al., 2019). Vulnerability to climate change impacts 
on food insecurity and malnutrition is worsened by other underlying

causes, including poverty, multiple forms of inequity (e.g., gender, 
racial, income), low access to water and sanitation, macroeconomic 
shocks and conflict (Smith and Haddad, 2015; Clay et al., 2018; FAO 
et  al., 2018; Cook et  al., 2019). Climate change frequently acts to 
compound these drivers of food insecurity (Table 5.14).
The coronavirus disease 2019 (COVID-19) pandemic has increased 
vulnerability to food insecurity and malnutrition of particular groups 
and sectors in the food system, including low-income households, 
farmworkers, food service workers, informal food market sellers and 
low-income countries dependent on food imports (Cross-Chapter 
Box COVID in Chapter 7). Climate change will compound pandemic 
vulnerabilities in the food system (high agreement, low evidence) 
(HLPE, 2020; UNDRR (United Nations Office for Disaster Risk 
Reduction, Regional Office for Asia and Pacific), 2020; WFP-FSIN, 
2020). The pandemic may also increase coordination among sectors

and a willingness to address food system weaknesses made visible 
by the impacts of COVID-19 (Blay-Palmer et al., 2020; Cohen, 2020; 
Ramos et al., 2020).
Ecosystem services, the provisioning, supporting and regulating 
mechanisms we all depend on for food security and nutrition, are 
also undermined by climate change impacts (Section 5.4.3). Even in 
the absence of climate change, our current food system threatens to

5
793Food, Fibre and Other Ecosystem Products  Chapter 5
Table 5.14 |  Impacts from climate change drivers on the four dimensions of food security. Adapted from Table 5.1 in SRCCL.
CIDs and mechanism for food security impacts Examples of regions and groups most affected References
Food security dimension: Availability
Increased heat and drought reduce crop and animal 
productivity and soil fertility and increase land degradation for 
some regions and crops.Countries in which a large proportion relies on agriculture for 
livelihoods.
Food production systems that rely on rainfed agriculture and 
pastoral rangeland. Urban populations and the poor.FAO et al. (2018), Dury et al. (2019), Mbow et al. 
(2019), Section 5.4 and 5.5).
Extreme heat affects crop productivity. Combined with high 
humidity reduces agricultural labour capacity and animal 
productivity.Countries and sectors that rely extensively on outdoor manual 
agricultural labour and experience high temperatures and

humidityZander et al. (2015), Kjellstrom et al. (2016), Ioannou 
et al. (2017), Mitchell et al. (2017), FAO et al. (2018), 
Flouris et al. (2018), Kjellstrom et al. (2018), Levi 
et al. (2018).
Increasing temperatures and precipitation changes 
increase and shift crop and livestock pests and diseasesEast African pastoral groups who experienced increased 
livestock morbidity and mortality from RVF in El Niño years.Bebber (2015), FAO et al. (2018), Mbow et al. (2019), 
Sections 5.4.1.3 and 5.5.1.3
Increasing temperatures and drought stress has led to 
higher post-harvest losses due to mycotoxins.Tropical and subtropical regions with limited food safety 
surveillanceMiller (2016), FAO et al. (2018), Section 5.11
Rising ocean temperatures, marine heatwaves and ocean 
acidity has reduced availability of fish in coastal communities.Coastal people and coastal areas of tropical countries with high

dependence on fisheries, e.g., West African coastal communitiesHilmi et al. (2014), Golden et al. (2016), Bindoff et al. 
(2019), Section 5.8 and 5.9
Increased number and intensity of extreme events such 
as cyclones lead to reduced food production and distribution 
from crop damage, increased pest incidence and transportation 
disruption.Delta regions where there are high populations and are 
often important food production regions, e.g., Cyclone Nargis 
in Myanmar estimated to reduce crop production by 19%, 
production declined for subsequent 3 years.Omori et al. (2020)
Increased atmospheric CO 2 concentrations increase total 
plant biomass and plant sugar content, which can increase 
crops as well as pests and weeds. High CO 2 also reduces 
transpiration during drought, which can increase plant drought 
resistance.All regions are anticipated to have increased atmospheric CO 2 
concentrations, but due to impacts of other CIDs (e.g., drought,

heat stress, pests), the impacts on crop growth, forage and 
subsequent food availability are mixed.Iizumi et al. (2018); Canadell et al. (2021), 
Ranasinghe et al. (2021), Cross-Chapter Box MOVING 
PLATE this chapter)
Food security dimension: Access
Increased drought and flood events and increased pests and 
disease from rising temperatures lead to loss of agricultural 
income due to reduced yields, and higher costs of production 
inputs such as water. Reduced ability to purchase food leads to 
lower dietary diversity and consumption levels.Low-income smallholder farmers and pastoralists in Ethiopia, 
Mali, Niger, Malawi, Zambia and Tanzania.Saronga et al. (2016), Giannini et al. (2017), FAO 
et al. (2018) Mbow et al. (2019) Omori et al. (2020)
Increase in number and intensity of extreme weather 
events (e.g., droughts, floods) lead to increased food prices, 
which often leads to lower dietary diversity as well as lower 
consumption levels.Low-income consumers.

Women and girls.FAO et al. (2018), Mbow et al. (2019), Ilboudo Nébié 
et al. (2021)
Extreme events (e.g., floods) disrupt food storage and 
transport networks, reducing access and availability of food 
supplies.Countries dependent on food imports, e.g., Small Island 
Developing States. Poor households living in flash flood and 
saline zones in Bangladesh who rely on monocropped rice. 
Women and children may experience greater impacts from 
extreme events.Toufique and Belton (2014), FAO et al. (2018), Hickey 
and Unwin (2020), Algur et al. (2021)
Food security dimension: Utilisation (food quality and safety)
Increased temperatures reduce food safety caused by 
microorganisms, including increased mycotoxins in food and 
feed.Countries with limited food safety surveillance systems. FAO et al. (2018), Mbow et al. (2019), Section 5.11
Climate change extreme events make fruits and vegetables 
relatively unaffordable compared with less-nutrient-dense

foods.Urban low-income households and rural households who 
purchase the majority of their food. Children in regions such as 
West Africa, with lower access to diverse food types as a result 
of climate impact drivers, e.g., drought.An et al. (2018), Algur et al. (2021), Baker and 
Anttila-Hughes (2020), Niles et al. (2021)
Rising air temperature, ocean warming and high 
CO 2 conditions increase risk of food poisoning and pollutant 
contamination of food through increased prevalence of 
pathogens, HAB and increased contaminant bioaccumulation 
and threaten human health.Low-income tropical countries where current ability to reduce 
and monitor mycotoxin contamination is limited. Coastal 
Indigenous Peoples and other poor populations in coastal areas 
of tropical countries with high dependence on fisheries, e.g., 
west African coastal communitiesGolden et al. (2016), Bindoff et al. (2019), 
Sections 5.7, 5.8, 5.9, 5.11
Increased atmospheric CO 2 concentrations reduce

nutritional quality of grains, some fruits and vegetables.Low-income households who have limited access to range of 
diverse foods.Mbow et al. (2019), Section 5.4
acidity reduce fish populations, which reduces consumption of 
fish high in iron, zinc, omega-3 fatty acids and vitamins in areas 
where fish populations decline.Coastal areas of tropical countries; coastal Indigenous Peoples 
and other groups who rely on fisheries.Golden et al. (2016); Bindoff et al., 2019; Section 5.7, 
5.8, 5.9

5
794Chapter 5 Food, Fibre and Other Ecosystem Products
exceed planetary, regional or local boundaries of long-term sustainable 
development (Campbell et al., 2017). Climate change will make efforts 
to reduce this threat more difficult to achieve (medium confidence), 
though many solutions to enhancing food security are also potential 
climate change adaptation responses (Sections  5.4, 5.6, 5.8, 5.10, 
5.14).
5.12.2 Mechanisms for Climate Change Impacts on Food 
Security
Climate change is increasing the number of people experiencing 
food insecurity through greater incidence and severity of climatic 
impact drivers (CIDs), (Seneviratne et al., 2021) such as extreme heat, 
drought and floods. Increasing CO 2 concentrations have positive 
effects on food and forage crops by enhancing photosynthesis and 
alleviating drought stresses (5.4.3.1, 5.5.3.1) but have negative 
effects on nutrient concentrations in food crops. Ocean acidification

is also caused by increasing CO 2, causing negative impacts on aquatic 
systems. Tropospheric ozone concentrations already hinder crop 
production (Section  5.4.1.4). Several CIDs increase the number of 
people experiencing food insecurity (high confidence) (SROCC 2019, 
FAO et al., 2018; Mbow et al., 2019; Baker and Anttila-Hughes, 2020; 
Table 5.12).
Vulnerability to climate impacts on food security and nutrition varies 
by region and group. Countries that experience CIDs such as extreme 
heat, severe drought or floods and have a large proportion of the 
population dependent on rainfed agriculture or livestock for their 
livelihoods and food supply have experienced rising food insecurity 
due to climate change impacts (FAO et al., 2018; Cooper et al., 2019; 
Mbow et  al., 2019). Children in Sub-Saharan Africa are particularly 
at risk of undernutrition and mortality from increasing temperatures 
(Belesova et al., 2019; Baker and Anttila-Hughes, 2020). An additional

estimated 5.9 million children became underweight because of rising 
temperatures in 51 countries affected by ENSO intensity in 2015–2016 (Anttila-Hughes et  al., 2021). Low-income urban households and 
marginalised groups such as landless and ethnic minorities are at risk 
of increased food insecurity due in part to climate change extreme 
events such as extended drought, floods or cyclones that interrupt 
supply chains and impact livelihoods (Rodriguez-Llanes et al., 2016; 
FAO et al., 2018; Algur et al., 2021). A systematic review in India found 
that women often experience greater workloads and stress during 
drought events (Algur et al., 2021).
In the subsequent sections, the four dimensions of food security 
will be discussed in relation to observed and projected impacts and 
vulnerabilities (Table 5.14).
5.12.3 Observed Impacts
5.12.3.1 Impacts on food availability
All food production systems (crops, livestock, marine, fish, mixed,

aquaculture) have been undermined by climate change and are 
expected to experience larger impacts in the future as described in 
earlier sections (see Sections  5.4.1, 5.5, 5.8, 5.9, 5.10). In addition, 
sudden production losses from extreme climate events can reduce food 
security (FAO et al., 2018; Cottrell et al., 2019; FAO et al., 2020; Anttila-
Hughes et al., 2021). For example, a 2007 drought-induced crop failure 
in southern Africa led to severe food insecurity in Lesotho because 
of the land-locked country’s dependence on imports from South 
Africa that aggravated food availability and access under conditions 
of declining food production and land degradation (Verschuur et al., 
2021). Pest and disease outbreaks in both crops and livestock due 
to climate change (Sections  5.4.1, 5.5.1) have also impacted food 
availability and access (see Box  5.8 Desert Locust case study). Loss 
in labour productivity from climate-change-related heat stress is a

growing problem.CIDs and mechanism for food security impacts Examples of regions and groups most affected References
Food security dimension: Stability
Increased frequency and severity of extreme events (e.g., 
droughts and heatwaves) lead to greater instability of supply 
through production losses and disruption to food transport.Landlocked countries; low-income countries reliant on imports; 
low-income households in areas prone to floods.Toufique and Belton (2014), FAO et al. (2018), Algur 
et al. (2021), Section 5.11
disease from rising temperatures lead to unstable incomes 
from agriculture and fisheries.Small-scale producers (crops and livestock) and fishersRuiz Meza, (2015), FAO et al. (2018), Sections 5.8, 
5.9
Climate change extreme events increase food prices due to 
climate shocks.Low-income countries reliant on imports; urban low-income 
households and rural households who purchase the majority

of their food.Bene et al. (2015), Peri (2017), Mbow et al. (2019), 
Section 5.11
Increased drought and flood events and increased pests 
and disease from rising temperatures cause widespread crop 
failure. Rising ocean temperatures, marine heatwaves and 
ocean acidity lead to dramatic decline in fisheries, contributing 
to migration and conflict.Coastal communities in West Africa, Southeast Asia and other 
tropical countries highly dependent on fisheries.Golden et al. (2016), Bindoff et al. (2019) Mbow 
Reduced frost days and snow days will increase stability 
of food security in some temperate regions since there will be 
less loss of food crops to frost damage and a longer growing 
season. However, they also raise pest and disease risks due to 
increased range and overwintering.Australia, most Asian regions, Europe, Central and South 
America and North America.
The benefits of yield gains at high latitudes may be tempered by

greater risks of pests and pathogen damages.Jones and Barbetti (2012), IPPC Secretariat (2021), 
Ranasinghe et al. (2021)

5
795Food, Fibre and Other Ecosystem Products  Chapter 5
Climate change affects agricultural labour productivity through 
increased intensity and frequency of heat stress events, with 
those performing physical labour in high humidity and ambient 
temperatures most vulnerable to heat stress (high confidence) (Hsiang 
et al.; FAO et al., 2018; Kjellström et al., 2019; Antonelli et al., 2020; 
Shayegh et  al., 2020). Labour capacity, supply and productivity loss 
in moderate outdoor work due to heat stress is estimated between 
2% and 14%, depending on the location and indicator (Ioannou et al., 
2017; Kjellstrom et al., 2018), with an overall estimate of 5.3% loss 
in productivity for outdoor work between 2000 and 2015 (medium 
confidence) (Watts et  al., 2018) but as high as 14% in low-income 
tropical countries (Antonelli et al., 2020; Shayegh et al., 2020). Highly 
vulnerable occupation groups affected by heat stress include farmers,

farmworkers and livestock keepers working outdoors in low-income 
tropical countries (high confidence) (Zander et  al., 2015; Kjellstrom 
et  al., 2016; Flouris et  al., 2018; Kjellstrom et  al., 2018; Levi et  al., 
2018). Farmworkers and small-scale food producers in high- and 
middle-income countries involved in outdoor labour are also affected 
by heat stress (Zander et al., 2015; Gosling et al., 2018; Szewczyk et al., 
2018; Watts et  al., 2021). There is also evidence that heat stress is 
affecting labour supply through variation in nutrition intake (Antonelli 
5.12.3.2 Impacts on food access (physical, economic and socio-
cultural) and vulnerabilities
Increased extreme events (e.g., droughts, floods and tropical storms; 
Seneviratne et  al., 2021) due to climate change are key drivers of 
recent rises in food insecurity rates and severe food crises in some

regions (high confidence) (Section  5.4.1, Yeni and Alpas, 2017; FAO et  al., 2018; Cooper et  al., 2019; Baker and Anttila-Hughes, 2020; 
Bogdanova et al., 2021; Ilboudo Nébié et al., 2021). Extreme weather 
events reduce physical and economic access to food, increase food 
prices, and compound underlying conditions of food insecurity and 
malnutrition such as low access to diverse healthy foods and safe 
water (FAO et  al., 2018; Niles et  al., 2021). Increased incidence of 
severe drought conditions since 2005 is contributing to food insecurity 
in affected regions, including Africa, Asia and the Pacific (Chapter 7, 
Phalkey et al., 2015; FAO et al., 2018; Cooper et al., 2019; Ilboudo Nébié 
et  al., 2021; Verschuur et  al., 2021;). In Arctic western Siberia, high 
temperatures, melting ice and forest and tundra fires have degraded 
reindeer pastures; Indigenous Peoples have reduced traditional diets 
and increased purchased food with increases in hypertension and

related health impacts (Bogdanova et al., 2021).
There is growing evidence that anthropogenic climate warming has 
already  intensified climate extreme events induced by large-scale 
SST oscillations such as ENSO (Herring et al., 2018; Seneviratne et al., 
2021). For example, the 2015–2016 El Niño, the strongest in the past 
145 years, induced severe droughts in Southeast Asia and eastern and 
southern  Africa, some intensified by anthropogenic warming (Funk 
et al., 2018). As a result, 20.5 million people faced acute food insecurity 
in 2016 (FSIN, 2017) and an estimated additional 5.9 million children 
became underweight (Anttila-Hughes et al., 2021).
Weather extreme events increased food prices and food price volatility 
(Peri, 2017), thereby worsening food insecurity (Shiferaw et al., 2014; 
Bene et al., 2015; Miyan, 2015; FAO et al., 2018; Ilboudo Nébié et al., 
2021). Rising food prices can affect conflict, political instability and

migration (Bush and Martiniello, 2017), but the relationship between 
Box 5.9: Desert Locust Case Study: Climate as Compounding Effect on Food Security
At the end of 2019, desert locust swarms infested Eastern Africa and caused widespread damage to crops and pastures, threatening food 
security and livelihoods (Kimathi et al., 2020; Salih et al., 2020). The FAO estimates that over 200,000 ha of crop and pastureland were 
damaged, rendering 2 million people in the region acutely food insecure (IGAD, 2020). The desert locust infestation was facilitated by two 
tropical cyclones that created desert lakes in a usually dry region of Saudi Arabia. Moist soils, warm temperatures and ample vegetation 
provided a suitable environment for desert locust breeding and migration to Yemen and Somalia, where the pest remained uncontrolled 
due to conflict and spread to neighbouring countries. A series of political and socioeconomic weaknesses such as armed conflict, limited

financial resources and lack of early actions compounded the impact of the current invasion and made it the most damaging in 70 years 
(Meynard et al., 2020; Salih et al., 2020).
Although desert locusts have been here for centuries, this recent outbreak can be linked to a unique feature of the positive IOD event, 
in part caused by long-term trends in SSTs (Wang et al., 2020a). The warming of the western Indian Ocean has increased frequency and 
intensity of severe weather, including tropical cyclones (Roxy et al., 2014; Murakami H, 2017; Roxy et al., 2017). Under a 1.5°C warmer 
climate, extreme positive IODs are anticipated to occur twice as often, which could also increase the occurrence of pest outbreaks (Cai 
Climate change increases the need for robust adaptation measures, such as transnational early-warning systems, biological control

mechanisms, crop diversification and further technological innovations in areas of sound and light stimulants, remote sensing, and 
modelling for tracking and forecasting of movement (Maeno and Ould Babah Ebbe, 2018; Peng et al., 2020). The desert locust outbreak 
and the role of the Indian Ocean warming show that the impacts of climate change can increase unpredictable events. Extreme weather 
events act as a compounding effect, exacerbated further by weak governance systems, political instability, limited financial resources and 
poor early-warning systems (Meynard et al., 2020).

5
796Chapter 5 Food, Fibre and Other Ecosystem Products
climate change, political instability and conflict is often mediated by 
other underlying factors such as poor governance (Chapter 7.2.7, 
Mach et al., 2019; Selby, 2019).
Low-income urban and rural households who are net food buyers 
are particularly affected by food price increases, with reduction in 
consumption of diverse food groups (high confidence) (Green et al., 
2013; Villasante et  al., 2015; FAO et  al., 2018). Depending on the 
context, particular groups, including women, ethnic and religious 
minorities, will be more vulnerable to worsening food insecurity from 
climate change impacts (Clay et  al., 2018; Jantarasami et  al., 2018; 
Nature climate change Editorials, 2019; Algur et  al., 2021 and see 
Cross-Chapter Box  GENDER in Chapter 18). Indigenous Peoples are 
often more vulnerable to climate change, due to conditions of poverty, 
limited resources, discrimination and marginalisation (high confidence)

(Smith and Rhiney, 2016; Vinyeta et al., 2016; Jantarasami et al., 2018). 
Indigenous Peoples may experience loss of culturally significant foods 
and declining traditional ecological knowledge (Dounias and Ichikawa, 
2017; Ross and Mason, 2020; 5.7).
5.12.3.3 Impacts on food utilisation and vulnerabilities
Food utilisation refers to the way the body most effectively uses food, 
and includes food preparation, food quality and intra-household 
distribution. Food utilisation is affected by climate change in several 
ways: food safety, dietary diversity and food quality (Aberman and 
Tirado, 2014).
Climate change have increased food safety risks (high confidence), 
including foodborne zoonotic animal diseases (5.5), and marine toxins 
from HABs (Sections  5.8, 5.9) and mycotoxins (Section  5.11). Other 
foodborne and waterborne infectious diseases such as cholera are 
further covered in Chapter 7.
Weather variability and extreme events (Seneviratne et al., 2021) have

reduced availability and access to diverse foods to sell and to purchase 
in rural markets, thereby reducing access to affordable, diverse foods 
for both rural small-scale producers and net consumers, particularly 
for landlocked and low-income countries (high confidence) (Pant 
et al., 2014; Villasante et al., 2015; Alston and Akhter, 2016; FAO et al., 
2018; Park et al., 2019; Niles et al., 2021) and otherwise marginalised 
communities (Algur et al., 2021). One study of 87 countries and 150 
extreme events estimated that low-income food deficit and landlocked 
countries had reduced nutrient supply ranging from −1.6 to −7.6% 
of average supply, a significant portion of a healthy child’s average 
dietary intake (Park et al., 2019).
Rural children in low-income countries are at particular risk of 
undernutrition from climate change impacts, due to a combination 
of factors: potential reduction in food quantity and quality from

heat impacts; greater exposure from outdoor play and agricultural 
activities; and increased likelihood of heat exhaustion and vector-
borne and diarrheal diseases (Oppenheimer and Anttila-Hughes, 2016). 
A study of child growth data in 30 countries in Africa between 1993 
and 2012 found that increased temperature was significantly related 
to children’s wasting (Baker and Anttila-Hughes, 2020). Another 
study examined 30 years of climate data and child dietary diversity outcomes in 19 countries, and found that higher-than-average annual 
temperatures correlated with declines in child diet diversity at levels 
equal to or greater than other factors which often are the focus of 
policy, such as market access or education (Niles et al., 2021).
5.12.3.4 Impacts on food stability
Climate change has already changed the start and duration of the 
growing season and increased variability of rainfall in some places, 
with impacts on food intake and nutritional status and income for

low-income and small-scale producers (medium evidence, high 
agreement, (FAO et al., 2018; Cooper et al., 2019). Evidence to date 
suggests that climate change has negative impacts on the stability 
of food supply over the medium to long term, thereby affecting food 
stability (Myers et  al., 2017b). Increasing number and intensity of 
adverse weather events, driven by climate change (Seneviratne et al., 
2021), are important factors decreasing food stability, through reduced 
availability, increased local price volatility, reduced livelihoods for food 
producers and disruption to food transport (Toufique and Belton, 2014; 
Verma et al., 2014; Ruiz Meza, 2015; Clay et al., 2018; FAO et al., 2018; 
Mbow et al., 2019).
5.12.4 Projected Impacts on Food Security
5.12.4.1 Food availability and access
Climate change will have negative effects on food security and nutrition 
in 2050 (high agreement, medium evidence) (Amjath-Babu et al., 2016;

Springmann et al., 2016; Lloyd et al., 2018; Richardson et al., 2018; see 
Chapter 7; Hasegawa et al., 2021a). How many people are affected 
will depend considerably on non-climatic drivers of food security (van 
Dijk et al., 2021), but modelling studies agreed that climate change 
would increase the risk of food insecurity. For example, one study 
comparing an RCP8.5 scenario with one that has zero climate impacts 
estimates 65 million additional people (10% increase) will experience 
food insecurity due to climate change impacts in 2050 (modelling 
results in Nelson et al., 2018). Another study accounting for climate 
extreme events estimates that, by 2050, the number of people at risk 
of hunger will increase by 20% and 11% under high- and low-emission 
scenarios, respectively, owing to a once-per-100-year extreme climate 
event (Hasegawa et  al., 2021a). Sub-Saharan Africa and South Asia 
in this study were projected to be at the greatest risk, with triple the

amount of South Asia’s current food reserves needed to offset such 
an extreme event. Models suggest that food security and malnutrition 
impacts will be much more severe from 2050 onwards relative to pre-
2050, but the scale and extent of the impacts will strongly depend on 
the GHG emission scenario (FAO, 2018a; Richardson et al., 2018). Due 
to CIDs and non-climate drivers of food insecurity, Sub Saharan Africa 
is projected to be the hardest hit, followed by South Asia and Central 
and South America, but contingent on adaptation level (Richardson 
et al., 2018; Hasegawa et al., 2021a).
Without adaptive measures, heat stress impacts on agricultural labour 
will increase with climate change (high confidence) (Im et al., 2017; 
Levy and Roelofs, 2019; Hertel and de Lima, 2020). Climate-change-
related heat stress will reduce outdoor physical work capacity on a

5
797Food, Fibre and Other Ecosystem Products  Chapter 5
global scale. Depending on GHG concentrations, some regions will 
experience losses of 200–250 outdoor workdays per year at century’s 
end. Using results from one study reporting experimental procedures to 
assess loss of work capacity (Foster et al., 2021), regions hardest hit in 
an SSP5-8.5 scenario include much of South Asia, tropical Sub-Saharan 
Africa and parts of Central and South America (Figure 5.18). de Lima 
et al. (2021) projected that negative impacts of warming on crop yields 
and labour capacity would affect crop production and cost for workers 
and labour-saving mechanisation, raising food price by 5% at +3° from 
the baseline period (1986–2005) globally, with significant implications 
for vulnerable regions (sub-Saharan Africa and Southeast Asia). Large 
uncertainties, however, exist around population diversity and adaptive 
capacity (Vanos et al., 2019). Agricultural labour productivity impacts

Days per year when outdoor 
physical work capacity is 
reduced by at least 40%
1 day366 daysTemperature and humidity-driven reduction in first-hour physical capacity for outdoor work
Upper insets and arrows point to the only locations across the globe where the first hour loss of physical work capacity* is 40% for the 
early century and end century SSP1-2.6 scenario. Other locations will have large capacity losses over the course of a work day . End century 
impacts will be much greater and more widespread under SSP5-8.5.
Historical (1991–2010) SSP1 2.6 (2081–2100)
SSP5 8.5 (2081–2100)
No days
* The research for the representation of lost physical work capacity was undertaken in a controlled environment. The worker was on a treadmill operating at a constant speed for 
one hour in a room with controlled temperature and humidit y. These conditions approximate work in a field with no wind (which would reduce heat ef fects) and no direct exposure

to solar radiation (which would worsen heat effects). In addition, work capacity declines as hours in the field extend beyond one hour. Research is underway to take these additional 
factors into account.
Figure 5.18 |  The number of days per year where physical work capacity (PWC) is less than 60% based on average daily air temperature and relative humidity 
(Foster et al., 2021). PWC is defined as the maximum physical work output that can be reasonably expected from an individual performing moderate-to-heavy work in a ‘cool’ 
reference environment of 15°C. Values plotted are from the early (A) and end of century (B) for SSP5-8.5 using ensemble means from the ISI-MIP CMIP6 data set. See SM5.4 for details.
of heat attributed to climate change are expected to be worse in low- 
and middle-income countries (Kjellstrom et  al., 2016). Adaptation 
options needed to protect agricultural worker productivity outdoors 
and reduce occupational heat illnesses and deaths include cooled

working environments, improved surveillance systems and education 
on the need to monitor (high confidence) (Xiang et al., 2016; Quiller 
et al., 2017; Flouris et al., 2018; Day et al., 2019; Vanos et al., 2019). 
Currently available options, however, are more difficult to achieve in 
lower-income economies (Kjellstrom et al., 2016; Im et al., 2017).
Under higher-emission scenarios, food availability will be further 
reduced after 2050, due to the potential for widespread crop failure 
and decline in livestock and fisheries stocks (Mbow et al., 2014; Kelley

5
798Chapter 5 Food, Fibre and Other Ecosystem Products
et al., 2017; Challinor et al., 2018; Hendrix, 2018; Bindoff et al., 2019). 
At +3°C from the preindustrial era, all food production sectors will 
experience greater, and pronounced, losses due to climate change 
compared with +1.5°C or +2°C (see Sections  5.2, 5.4.3, 5.8.3 and 
5.9.3).
Food insecurity from food price spikes due to reduced agricultural 
production associated with climate impact drivers such as drought 
can lead to both domestic and international conflict, including political 
instability (Abbott et al., 2017; Bush and Martiniello, 2017; WEF , 2017; 
D’Odorico et  al., 2018; de Amorim et  al., 2018;Chapter 7.2.7). While 
climate change impacts, including drought impacts on food security, 
are important risk factors for conflict, other key drivers are often more 
influential, including low socioeconomic development, limited state 
capacity, weak governance, intergroup inequities and recent histories of

conflict (medium confidence) (Mach et al., 2019; Selby, 2019; Chapter 7.2.7). The interaction between extreme weather events, conflict and 
human migration may increase vulnerability of particular communities 
of low-income countries (WEF , 2017; D’Odorico et al., 2018; de Amorim 
et al., 2018; Chapter 7). Further research is needed to better understand 
how increased drought risk under future climate change might affect 
food prices and water availability (Abbott et al., 2017).
5.12.4.2 Projected Impacts on Food Safety and Quality
Increasing levels of CO 2 directly contribute to reduced food quality 
by reducing levels of protein, iron, zinc and some vitamins, varying 
by crop species and cultivars (high confidence) (Section 5.4.3, Myers 
et al., 2014; Smith and Haddad, 2015; Bisbis et al., 2018; Scheelbeek 
et al., 2018; Weyant et al., 2018; Zhu et al., 2018a). Higher levels of CO 2 
are predicted to lead to 5–10% reductions in a wide range of minerals

and nutrients (Loladze, 2014). Climate warming will also reduce food 
quality of seafood, by changing the LC-PUFA content in phytoplankton 
(Section 5.8; Hixson and Arts, 2016).
5.12.4.3 Reaching Sustainable Development Goal 2
Current projections indicate that it is highly likely that the UN SDG2 
(‘Zero Hunger’) by 2030 will not be achieved, with climate impacts on 
one of several drivers of food security and nutrition preventing this goal, 
including in Africa, Small Island States and South Asia (high confidence) 
(FAO et al., 2018; Otekunrin et al., 2019; Singh et al., 2019; Atukunda 
et al., 2021; Kumar et al., 2021; Vogliano et al., 2021). Integrated policy 
strategies that consider synergies and trade-offs between different 
food system components would strengthen the likelihood of meeting 
SDG2 goals (Dyngeland et al., 2020; Lipper et al., 2020; Vogliano et al., 
2021) (Grosso et al., 2020). Adaptation options which address climate

risks for food security and nutrition are discussed below.
5.12.5 Adaptation Options for Food Security and 
Nutrition
Since AR5, there has been increased research on adaptation options 
that address climate risks for food security and nutrition. In this section, 
cultivar improvements, urban and peri-urban agriculture, changing 
dietary patterns, integrated multi-sectoral approaches and rights-based 
approaches are assessed for their potential as an adaptation option 
that addresses food security and nutrition. Feasibility and effectiveness 
assessment of several options is in Section 5.14.
5.12.5.1 Potential, barriers and challenges for genetically 
modified crops to address food security and nutrition
While biotechnology can be used as an adaptation strategy 
(Section  5.4.4.3), there is low confidence that genetically modified 
(GM) crops can increase food security and nutrition in smallholder 
farming systems relative to alternative agronomic strategies (National

Academies of Sciences Engineering and Medicine, 2016; Qaim, 2016). 
Some underline their potential in building resilience to changing 
climatic conditions, in the form of enhanced drought/heat tolerance, 
pest/disease protection and/or reduced land usage, thus serving to 
bolster food security and nutrition (Sainger et al., 2015; Muzhinji and Box 5.10: Food Safety Interactions with 
Food Security and Malnutrition
Climate change significantly increases the future food 
safety risks (high confidence) (Sections  5.8.2, 5.8.3, 5.11.1, 
Box  5.9). Increasing temperatures and drought stress are 
expected to lead to greater aflatoxin contamination of food 
crops.  Aflatoxins, a major foodborne hazard, contaminate 
staple crops and are associated with various health risks, 
including stunting in children and cancer (Koshiol et al., 2017). 
In LICs, children with high exposure to aflatoxins were found to 
be more likely to suffer from micronutrient (zinc and vitamin A)

deficiencies (Watson et al., 2016b). Climate change is expected 
to cause decreases in micro- and macronutrient content of 
foods, leading to an increased burden of infectious diseases, 
diarrhea and anaemia, with an estimated 10% increase in 
disability-adjusted life years (DALYs) by 2050 associated with 
undernutrition and micronutrient deficiencies  (Aberman and 
Tirado, 2014; Smith and Myers, 2018; Weyant et al., 2018; Zhu 
et al., 2018a; Ebi and Loladze, 2019; FAO, 2020a; Sulser et al., 
2021b).
Children in low-income countries will be at greater risk of 
undernutrition from these multiple climate change impacts, 
including lower food availability, quality and safety and 
increased risk of diarrheal disease (high confidence) (Aberman 
and Tirado, 2014). One study of 30 countries in Africa estimated 
that, by 2100, increased temperatures under RCP8.5 could 
increase children’s wasting by 37% in western Africa and 25% 
in southern Africa (Baker and Anttila-Hughes, 2020).

The combination of climate change and the presence of arsenic 
in paddy rice fields is expected to increase the toxic heavy metal 
content of rice and reduce production by 2100, threatening 
food security and food safety mainly in low-income countries 
where rice is the main staple (Neumann et al., 2017; Muehe 
et al., 2019; Farhat et al., 2021).

5
799Food, Fibre and Other Ecosystem Products  Chapter 5
Ntuli, 2021). Others suggest that the empirical evidence supporting 
GM crops as a climate-resilience strategy remains thin (Leonelli, 2018). 
Technical and social barriers and potential solutions are summarised 
in Table 5.15.
5.12.5.2 Urban and peri-urban agriculture, vertical and 
horizontal
Urban areas have more than half of the global population and consume 
about 70% of the total food supply (FAO, 2019b). The urban population 
is projected to grow further to about 70% of the global population 
by 2050 (UN, 2018). Direct evidence supporting climate resilience of 
urban and peri-urban agriculture (UPA) is limited and contextual, but 
there is medium confidence of multi-functional benefits from UPA, 
depending on regions and types of UPA (Artmann and Sartison, 2018; 
Kareem et  al., 2020). UPA takes different forms of production, and 
can be broadly classified into four categories, depending on operating

characteristics and capital inputs (Table 5.16) (Goldstein et al., 2016). 
Controlled environments can protect crops, livestock and fish from 
extreme weather events or pest and disease outbreak (Mohareb 
et al., 2017). Innovative indoor farming such as vertical farming can 
be highly productive with minimal water and nutrient supply but can 
be capital intensive with high energy demand (O’Sullivan et al., 2019), 
and those with aquaponics can be water demanding (Love et  al., 
2015). Currently, commodities are often limited to crops with short 
growing seasons such as leafy vegetables. Vertically grown crops are 
more expensive than field-grown produce and, thus, not accessible 
for low-income urban dwellers (Al-Kodmany, 2018). Community and 
institutional unconditioned (outdoor) farms and gardens are better 
positioned to provide increased access to healthy food to those who 
need it (Eigenbrod and Gruda, 2015; Goodman and Minner, 2019).

Many UPA farmers are migrant workers or other socially marginalised 
racial and ethnic groups and often limited by access to land (Lawanson et al., 2014; Horst et al., 2017). There is high agreement that proactive 
policies for urban design accounting for food–energy nexus and social 
inclusion including addressing questions of governance and rights to 
green urban spaces are necessary to enhance food provisioning and to 
gain multiple functions of UPA (Lwasa et al., 2014; Horst et al., 2017; 
Mohareb et al., 2017; Siegner et al., 2018; O’Sullivan et al., 2019; Titz 
and Chiotha, 2019; Halvey et al., 2020).
5.12.6 Changing Dietary Patterns
Dietary change in regions with excess consumption of calories and 
animal-sourced foods to a higher share of plant-based foods with 
greater dietary diversity and reduced consumption of animal-sourced 
foods and unhealthy foods (as defined by scientific panels such as 
EAT-Lancet) has both mitigation and adaptation benefits along with

reduced mortality from diet related non-communicable diseases, 
health, biodiversity and other environmental co-benefits (high 
confidence) (Springmann et al., 2016; Springmann et al., 2018; Branca 
et  al., 2019; Henry et  al., 2019; Searchinger et  al., 2019; Swinburn 
et  al., 2019; Willett et  al., 2019; Rosenzweig et  al., 2020; Chapter 
7.4.2.1.3 and WGIII Chapter 12). Reducing food waste, especially of 
environment- and climate-costly foods would further extend these 
benefits (Rosenzweig et al., 2020 and see Section 5.11).
Dietary behaviour is complex: shaped by the broader food system 
(HLPE, 2017a), the food environment (Herforth and Ahmed, 2015; 
Turner et  al., 2018) and socio-cultural factors (Fischler, 1988). Since 
most food-related decisions are made at a subconscious level 
(Marteau et al., 2012), achieving dietary change for personal health 
reasons has proven difficult; it seems unlikely that dietary change for

climate will be achieved without careful attention to the factors that 
shape dietary choice and behaviour. Food environments, defined as 
‘the physical, economic, political and socio-cultural context in which Table 5.15 |  Barriers, challenges and potential solutions for GM crops.
Barriers and challenges Examples and potential solutions to barriers
Major challenges as a food security and nutrition adaptation include the introgression of GM 
traits into host varieties (Dowd-Uribe, 2014), and confusion around proper growing practices 
that can accelerate resistance (Iversen et al., 2014; Fischer et al., 2015). The combination 
of the kinds of traits and restrictions that come from the predominant intellectual property 
rights instruments used in their commercialisation, and concentration of plant and animal 
breeding industry (Bonny, 2017) mean that benefits from released GM crops tend to be 
captured disproportionately by farmers with more land, wealth and education (Afidchao

et al., 2014; Ali and Rahut, 2018; Azadi et al., 2018) but also increase debt levels for growers 
(Dowd-Uribe, 2014; Leguizamón, 2014).
Underlying gender inequities also play a critical role in shaping food security and nutrition 
outcomes associated with the introduction of GM crops, in part due to unequal control 
over income and agricultural decision making; in some cases, women reported decreased 
workload and enhanced decision-making power (Gouse et al., 2016), while in others the 
introduction of GM crops could increase workload and devalue womens’ role as seed savers 
(Carro-Ripalda and Astier, 2014; Addison and Schnurr, 2016).
Major hurdles for GM crops include translating promising research results into real-world 
farming systems and consumer trust in the food product. Experimental programmes have 
been dogged by issues, including complications with the introgression of GM traits into 
high-performing varieties (Dowd-Uribe and Schnurr, 2016; Stone and Glover, 2017), strict

management regimes that clash with the realities of smallholder agricultural systems 
(Iversen et al., 2014; Whitfield et al., 2015), and a lack of attention to farmer decision making 
(Schnurr, 2019).One case study is the Water Efficient Maize for Africa (WEMA) programme, a public–private 
partnership that transplants a cold shock protein B, known as Droughtgard, into maize in 
order to mitigate yield losses from drought. Proponents suggest that this GM venture, which 
will be distributed free to smallholder farmers, represents the best strategy for ensuring 
stable yields in the face of climatic change across Africa (Kyetere et al., 2019). Critics argue 
that WEMA maize is not a good fit with the smallholder farming systems it is designed to 
benefit, with particular concerns around how farmers will access the extra inputs, credit and 
labour that WEMA maize requires to be successful (Schnurr, 2019).

Emergent genome-edited crops are considered a more precise, accessible and accelerated 
means of targeting stressors that matter to poor farmers, but evidence is limited (Kole 
et al., 2015; Haque et al., 2018; Zaidi et al., 2019). A more iterative and flexible adaptation 
approach beyond just genomic improvement to tackle the multiplicity of factors limiting 
smallholder production is anticipated to increase the likelihood that these promising 
technologies can enhance food security and nutrition (medium confidence) (Giller et al., 
2017; Stone, 2017; Montenegro de Wit, 2019).
To address food security and nutrition, future breeding needs to move from just enhancing 
agronomic traits of a single crop to improving multiple traits of multiple crops suited to 
local conditions that will increase climate resilience of farming systems. To make breeding 
technologies scale-neutral, the policy structure needs to support and protect smallholders 

5
800Chapter 5 Food, Fibre and Other Ecosystem Products
consumers engage with the food system to make their decisions about 
acquiring, preparing and consuming food’ (HLPE, 2017a): 28), include 
food availability, accessibility, price/ affordability, food characteristics, 
desirability, convenience and marketing.
There are a range of options to change dietary patterns, but more 
research is needed in this area, adjusted to the regional, socioeconomic 
and cultural context. Studies of policy instruments to change diets 
include changes in subsidies, taxes, marketing regulation and efforts 
to change the retail physical environment. Subsidies directed at staple 
foods and animal sourced foods could be shifted towards diversified production of plant-based foods in order to change the relative price of 
foods and, thus, dietary choice (Franck et al., 2013; Harris et al., 2021). 
Taxes on animal-sourced foods that are climate-costly and unhealthy, as

defined by scientific panels such as the EAT-Lancet report, could similarly 
impact relative price (Mbow et al., 2019; Willett et al., 2019). Regulation 
of marketing could change desirability of climate-unfriendly and 
unhealthy foods (Willett et al., 2019). Many of the same strategies used 
to increase sales by conventional food marketing efforts hold potential 
to change the desirability and people’s preferences for plant foods which 
are strongly shaped by social–cultural norms. Studies have shown that 
changes to the number, placing or prevalence of vegetarian options on a Table 5.16 |  Urban agriculture classifications based on operating characteristics and capital inputs (Goldstein et al., 2016; O’Sullivan et al., 2019), and a summary of literature 
search on positive and negative aspects.
Summary of urban and peri-urban agriculture and evidence for improved food security and nutrition

Urban agriculture has two components: vertical (e.g., grown on or in buildings) and horizontal (grown on land within urban boundaries, in backyards and marginal spaces). The 
horizontal component of urban and peri-urban agriculture (UPA) has gained attention because of multiple functions that could improve food systems and ecosystem services 
under climate change (Revi et al., 2014; Artmann and Sartison, 2018; FAO, 2019b; Mbow et al., 2019; Chapter 6).
UPA cannot fully feed urban dwellers within its boundaries but can make an important contribution to local food security and nutrition (medium confidence) (Martellozzo et al., 
2014; Badami and Ramankutty, 2015; Algert et al., 2016; Mohareb et al., 2017; Clinton et al., 2018; Kriewald et al., 2019). UPA is also expected to play important roles in eco-
system functions in addition to alleviating food shocks caused by natural disasters and reducing food mileage.
Categories and Description Synergies Trade-offs

Ground-based Unconditioned  –Multi-species cropping can increase access to diverse healthy 
foods and reduce food costs for low-income households (Algert 
et al., 2016; Horst et al., 2017).
 –Green cover helps to attenuate heat island effects, and reduce 
runoff and flood risks (Lwasa et al., 2015; Di Leo et al., 2016; 
Gondhalekar and Ramsauer, 2017; Artmann and Sartison, 2018; 
Small et al., 2019).
 –Green garden spaces can reduce vulnerability to heat stress and 
food insecurity for low-income neighbourhoods and address 
racial inequities in access to green spaces if UA governance 
addresses equity concerns (Horst et al., 2017; Titz and Chiotha, 
2019; Halvey et al., 2020; Hoffman et al., 2020).
 –Multi-species cropping helps to conserve biodiversity (Lovell, 
2010; Goldstein et al., 2016).
 –Skill building and job opportunities (Lovell, 2010; Mok et al., 2014; 
Horst et al., 2017), sometimes in regions and for groups that have

been socially and economically disadvantaged (Horst et al., 2017).
 –CES benefits through cultivation of specific crops, cultural 
learning, sharing culinary and garden knowledge and 
strengthening social networks for socially marginalised ethnic, 
racial groups (Horst et al., 2017; Nadeau et al., 2019).
 –UPA provides social and health co-benefits such as increased 
social interaction and physical and mental health benefits (Horst 
et al., 2017; White and Bunn, 2017).
 –Can divert organic waste produced in cities as compost, to reduce 
water contamination and input costs (Menyuka et al., 2020). –Can increase the value of land and thereby push out 
lower-income households via gentrification (Horst et al., 2017).
 –Unconditioned UPA is under strong pressure from other lucrative 
land use demands and can be difficult to maintain without 
addressing urban social inequities, (Martellozzo et al., 2014; 
Horst et al., 2017; White and Bunn, 2017).

–Yields are lower than conventional, rural production, and water 
demand is high (Goldstein et al., 2016; Bisaga et al., 2019).
 –Air, soil and water quality in urban areas can disturb crop 
production and reduce food safety (Eigenbrod and Gruda, 
2015; Titz and Chiotha, 2019), and create health risks from 
contamination (Mok et al., 2014), causing mixed or even 
negative public perceptions against the produce (Specht et al., 
2019; Menyuka et al., 2020). Trace metal contamination in soils 
and plants is an increased risk in outdoor UPA (Eigenbrod and 
Gruda, 2015; Titz and Chiotha, 2019).
 –May provide limited job and income opportunities in low-income 
urban areas (Daftary-Steel et al., 2015; Biewener, 2016).
 –Outdoor fields are exposed to rising temperatures and urban 
heat islands (Chapman et al., 2017). Low water availability may 
be another limit for UPA as a form of adaptation (Kareem et al., 
2020; Tankari, 2020). In coastal cities, sea level rise and flooding

from climate change impacts may make significant portions of 
cities unuseable for UPA (Algert et al., 2016; Kareem et al., 2020).Traditional, peri-urban field farms, market 
gardens, community farms, community 
gardens, home gardens.
Building-integrated Unconditioned
Rooftop gardens, balcony agriculture, and 
green wall, but production quantity is small.
Ground-based Conditioned  –Controlled environments can protect crops, livestock and fish 
from extreme weather events or pest and disease outbreak 
(Mohareb et al., 2017).
 –Some building-integrated conditioned farms can utilise 
wastewater and waste heat from buildings or other urban source 
(De Zeeuw et al., 2011; Thomaier et al., 2015; Mohareb et al., 
2017).
 –Innovative indoor farming such as vertical farming (VF) is highly 
productive with minimal water and nutrient supply, but highly 
energy-demanding (O’Sullivan et al., 2019).
 –Some initiatives combine with social justice goals and use

abandoned buildings in low-income neighbourhoods to grow 
diverse food types for addressing food security of low-income 
groups (Thomaier et al., 2015; Horst et al., 2017). –Power outages and/or system failure can easily destroy the 
production system (Small et al., 2019).
 –Initial costs and energy requirements are substantially higher 
than those of unconditioned farms (Goodman and Minner, 2019; 
O’Sullivan et al., 2019).
 –GHG emissions may be higher than conventional rural 
agriculture (Santo et al., 2016) and full mitigation potential only 
realised with low-energy systems (WGIII, 12.4).
 –Commodities are often limited to short-cycled crops such as leafy 
vegetables and herbs, and the produce is more expensive, making 
it difficult for the urban poor to access (O’Sullivan et al., 2019).Horticultural farms using glasshouses or 
polyhouses. Often exist on the city fringes.
Aquaponics that grow fish in aquaculture 
systems and re-use nutrient-rich wastewater.

One of the few options that provide proteins 
in urban farms.
Building integrated Conditioned
Rooftop glasshouses, fully indoor, artificially lit 
plant factories. Recent advancements include 
production using vertical stacks to produce 
more food per land area.
Indoor aquaculture is also included.

5
801Food, Fibre and Other Ecosystem Products  Chapter 5
menu (Bacon and Krpan, 2018; Kurz, 2018; Garnett et al., 2019; Gravert 
and Kurz, 2019), the relative price of vegetarian options (Garnett et al., 
2021) and the ‘access’ (order and distance) to vegetarian options in 
the retail physical environment (Garnett et  al., 2020) can all increase 
consumption of plant-based foods and decrease meat consumption 
(Bianchi et al., 2018). Studies on food environment ‘nudging’ methods 
found that making the vegetarian meal option the default during 
conference registration or on a meal plan significantly reduced meat 
consumption (Campbell-Arvai et al., 2012; Hansen et al., 2019b). Studies 
simply educating people about the negative health and environmental/
climate outcomes of meat consumption have been found to have very 
little impact (Byerly et al., 2018). More research is needed to understand 
the potential for motivational crowding in shaping pro-climate dietary

choice, as has been demonstrated in development (Agrawal et al., 2015) 
and conservation interventions (Rode et al., 2015).
5.12.7 Integrated Multisectoral Food Security and 
Nutrition Adaptation Options
Integrated multi-sectoral strategies that incorporate social protection 
are effective adaptation responses (high confidence) (Gros et  al., 
2019; Ulrichs et  al., 2019; Medina Hidalgo et  al., 2020; Daron et  al., 
2021; Ilboudo Nébié et al., 2021; Verschuur et al., 2021; 7.4.2, Cross-
Chapter Box-GENDER in Chapter 18). Social protection programmes, 
such as cash transfers, weather index insurance and asset-building 
activities such as well construction, can support short-term responses to 
acute food insecurity in response to extreme events but can also build 
adaptive capacity longer term (Table 5.16, Costella et al., 2017; Ulrichs 
et al., 2019). An assessment of an adaptive social protection programme 
in the Sahel found that tailored seasonal forecasting can improve

responsiveness to climate-related extreme events, but investment in 
capacity building and dialogue between forecasters, community groups 
and humanitarian organisations is needed (Daron et al., 2021). Forecast-
based financing, which automatically disperses funds when threshold 
forecasts are reached for an extreme event (Coughlan de Perez et al., 
2016), used in Bangladesh prior to a 2017 flood event allowed low-
income, flood-prone communities to access better-quality food in the 
short term without accruing debt (Gros et al., 2019).
Differentiated responses based on food security level and climate risk 
can be effective. A study of drought impacts on food security in Senegal 
between 1997 and 2016 recommended different adaptation strategies 
based on whether the region was a higher risk of acute short-term food 
insecurity and/or faced higher risk of drought (Table 5.16; Ilboudo Nébié 
et  al., 2021). Given identified linkages between higher temperatures

and extreme events with declines in child dietary diversity, safeguarding 
diverse diets is one important adaptation priority (Niles et al., 2021). 
Humanitarian responses are appropriate for short-term acute hunger, 
while in the medium term, home-grown school feeding programmes 
with diverse foods can support child nutrition and learning, and with 
local procurement can also increase income and food security of 
smallholder farmers (Ilboudo Nébié et al., 2021). Farmer associations 
can manage regional staple food storehouses, in which farmers store 
their harvest and receive credit, and can sell their harvest later in 
the season and pay back the credit with interest, strengthening local 
supplies and farmer income (Ilboudo Nébié et al., 2021).A study in Lesotho examined the extent to which climate change 
increased the likelihood of an acute drought in 2007, and a related 
food crisis (Verschuur et al., 2021). Given land degradation, reliance on

rainfed agriculture and food imports from neighbouring South Africa, 
the study recommended crop diversification, increased use of drought 
tolerant crop varieties and expanded trade partners in the medium to 
long term, to both strengthen regional food production and reduce 
risk of crop failure and the likelihood of climate-induced drought 
from trade partners reducing food imports (Verschuur et al., 2021). A 
longitudinal study of smallholder coffee farmers in Nicaragua found 
that crop diversification, alongside crop management and varietal 
improvement, would help farmers strengthen food security long 
term in the face of climate hazards such as drought and coffee leaf 
rust (Bacon et al., 2021). Another medium- to long-term adaptation 
response is to address systemic gender, land tenure and other social 
inequities as part of an inclusive approach (Bezner Kerr et al., 2019; 
Khatri-Chhetri et al., 2020; Bacon et al., 2021). This long-term strategy

could be part of a human-rights-based approach (HRBA, 5.12.8)
5.12.8 Incorporating Human Rights-Based Approaches 
into Food Systems
A human rights-based approach (HRBA), endorsed by the UN, is one 
strategy for addressing core inequities that are key drivers for food 
insecurity and malnutrition of particular groups such as low-income 
consumers, children, women, small-scale producers and different 
regions of the world (FAO, 2013; Claeys and Delgado Pugley, 2017; 
Caron et  al., 2018; Le Mouël et  al., 2018; Springmann et  al., 2018; 
Tramel, 2018; HLPE, 2019; Willett et al., 2019). Climate change impacts, 
mitigation and adaptation approaches can also worsen inequities 
(Eastin, 2018; Borras et  al., 2020). HRBA includes core principles of 
participation, accountability, non-discrimination, transparency, human 
rights, empowerment and rule of law, which can be integrated into 
policymaking and implementation as part of transforming the food

system (FAO, 2013; Caron et al., 2018; Toussaint and Martínez Blanco, 
2020). The right to well-being can serve as the overarching umbrella 
of HRBA to addressing climate change within food systems and 
includes a right to health, right to food, cultural rights, the rights of 
the child and the right to healthy environment (Swinburn et al., 2019). 
An HRBA has a specific focus on those groups who are vulnerable 
due to poverty, discrimination and historical inequities and involves 
meaningful participation of vulnerable groups in governance, design 
and implementation of adaptation and mitigation strategies, including 
gender responsiveness and integration of Indigenous Peoples’ 
knowledge (UNHRC 2017; Caron et al., 2018; Mills, 2018). There can 
be conflicts and trade-offs, such as between addressing land rights or 
traditional fishing grounds, the right to food, and addressing climate 
justice concerns (Mills, 2018; Borras et al., 2020; Section 5.13). Adaptation

strategies that incorporate HRBA include legislation, programmes that 
address gender inequities in agriculture, agroecology, recognition of 
rights to land, fishing areas and other natural resources, protection 
of culturally significant seeds, and community-based adaptation that 
explicitly involves marginalised groups in governance (Mills, 2018; 
Tramel, 2018; Huyer et al., 2019; Borras et al., 2020; Section 5.14).

5
802Chapter 5 Food, Fibre and Other Ecosystem Products
5.13 Climate-Change-Triggered Competition, 
Trade-offs and Nexus Interactions in Land 
and Ocean
This section presents information about the impacts generated by 
competition and trade-offs in food systems and discusses opportunities 
and challenges associated with the use of the Nexus framework.
5.13.1 Impacts of Global Land Deals on Land Use, 
Vulnerable Groups and Adaptation to Climate 
Change
Land deals, also known as large-scale land acquisitions (LSLAs), 
describe recent changes in access to land globally (Borras et  al., 
2011). Since 2000, at least 160  million hectares have been under 
negotiation (Land Matrix, 2021). Land deals surged after the 2007–
2008 food price crisis and farmland investment boom (Fairbairn, 
2014), with a diverse range of drivers (Arezki et al., 2015; Zoomers 
and Otsuki, 2017; Conigliani et al., 2018) including land-based climate

change interventions (Dunlap and Fairhead, 2014; Davis et al., 2015a; 
Hunsberger et al., 2017; Franco and Borras, 2019). Examples are the 
expansion of biofuel crops (e.g., Yengoh and Armah, 2016; Aha and 
Ayitey, 2017), afforestation and reforestation (A/R) projects (Olwig 
et  al., 2016; Richards and Lyons, 2016; Scheidel and Work, 2018), 
REDD+ (Bayrak and Marafa, 2016; Ingalls et al., 2018), conservation Table 5.17 |  Examples of adaptation responses to drought and floods by food security level and time frame. Adapted from Ilboudo Nébié et al. (2021) Table 4, with information 
from Bahadur et al. (2015); Costella et al. (2017); Gros et al. (2019); Ulrichs et al. (2019); Medina Hidalgo et al. (2020); Bacon et al. (2021); and Verschuur et al. (2021).
Food insecurity level and time frame of adaptation
Adaptation response to drought or floodsAcute, 
short termModerate, 
medium termChronic, 
long termResilience type
Forecast-based financing (provides unconditional cash in advance

of extreme event)X
Anticipatory: people and systems are better 
prepared for climate shock by reduced 
exposure or vulnerability.Early-warning systems/climate services and education for disaster 
preparationX X X
Social protection programmes with regular provisions which allow 
for asset building, e.g., savings, building of informal networks, 
purchase of livestockX X
Humanitarian food aid and malnutrition treatment X X
Absorptive capacity: people or systems cope 
with climate-related shocks or systems while 
and immediately after they occur.Home-grown nutrition-sensitive school feeding programmes X X
Social protection programmes with short-term targeted response, 
e.g., short-term cash transfers, food assistance for asset building 
such as wellsX
Weather index insurance program X X X
Regional grain banks run by farmer associations X X
Adaptive capacity: can adjust to long-term 
climate risks and disasters, reduce vulnerability

to future shocks.Savings, credit and local food procurement support for smallholder 
farmersX X
Agroecosystem diversification, other agroecological practices to 
strengthen ecosystem services in long term (see Box 5.10)X X
Rainwater evacuation infrastructure combined with flood 
management and waste collection and urban gardeningX X
Drought- or flood-resistant crop varieties X X
Expand trade partners beyond climactically connected partners X X
Gender transformative or responsive agriculture programmes X X
areas (Lunstrum, 2016; Schleicher et  al., 2019), renewable energy 
installations (e.g., Sovacool, 2021) or natural disaster management 
(e.g. Uson, 2017).
Land deals raise important social justice questions (Franco et  al., 
2017; Hunsberger et  al., 2017; Borras and Franco, 2018b; Borras 
et  al., 2020; Sekine, 2021) (high confidence). Specific impacts of 
land deals vary according to their purpose, location, actors, land use

history and procedural aspects. However, multi-case analyses identify 
severe adverse impacts (Table 5.18). LSLAs are a significant driver of 
tropical forest loss (Davis et al., 2020), increasing emissions through 
deforestation (Liao et al., 2021) and industrialisation of agriculture 
(Rosa et  al., 2021). LSLAs entail large water appropriations (Breu 
et  al., 2016; Chiarelli et  al., 2016; Adams et  al., 2019), affecting 
local populations’ access to water and food security (Dell’Angelo 
et  al., 2018; Veldwisch et  al., 2018). By increasing exported crops, 
and limiting local populations’ access to land, LSLAs produce food 
security risks (Marselis et al., 2017; Müller et al., 2021b). Negative 
livelihoods impacts arise through enclosure of assets, elite capture 
(Oberlack et  al., 2016), crowding out of small farmers (Nolte and 
Ostermeier, 2017) and reducing local populations’ access to commons 
(Dell’Angelo et al., 2016; Giger et al., 2019). Indigenous People are

affected, facing high levels of violence in land acquisition conflicts 
(Dell’Angelo et al., 2021). The social burdens of land deals tend to be 
gendered (e.g., Fonjong et al., 2016; Nyantakyi-Frimpong and Bezner 
Kerr, 2017; Atuoye et al., 2021).

5
803Food, Fibre and Other Ecosystem Products  Chapter 5
Local populations can experience declining access to livelihood 
resources and deteriorating food security, increasing gendered 
vulnerabilities (Yengoh et  al., 2015; Faye and Ribot, 2017; Atuoye 
et  al., 2021). Vulnerable groups displaced by land deals may face 
higher exposure to climate change (Dell’Angelo et  al., 2017). 
LSLAs affecting common-pool resources governed by Indigenous 
institutions jeopardise the resilience and adaptive capacity of local 
socio-ecological systems (Dell’Angelo et al., 2016; D’Odorico et al., 
2017; Hak et  al., 2018; Haller, 2019; Haller et  al., 2020). Growing land tenure insecurity may force farmers to engage in unsustainable 
farming and forestry practices (Aha and Ayitey, 2017; Gabay and 
Alam, 2017) and hinder agroecological innovations to manage 
climate risks (Nyantakyi-Frimpong, 2020b). Social justice concerns 
and vulnerability of local populations can be addressed by promoting

land redistribution and recognition, particularly for customary lands 
of Indigenous and ethnic minorities, and land restitution to those 
who were forcibly displaced (Franco et al., 2015; Borras and Franco, 
2018a).
Table 5.18 |  Adverse social and ecological risks and impacts of agricultural land deals on land use and vulnerable groups.
Land use dimensions Impacts and implications References (2014 to present)
ForestryDirect and indirect land use change provoked by LSLAs accelerates deforestation of tropical forests 
globally (medium confidence).Multi-case analyses
Davis et al. (2020)
Case study examples
Davis et al. (2015b)
Scheidel and Work (2018), Magliocca et al. (2020)
Energy use and accessExpected land use changes provoked by agricultural LSLAs have high fossil-energy footprints. LSLAs 
may adversely affect local populations’ access to energy resources ( medium confidence).Multi-case analyses
Rosa et al. (2021)

Carbon emissionsLSLAs have high carbon footprints resulting from deforestation and industrialisation of agriculture 
(medium confidence).Multi-case analyses
Liao et al. (2021)
Johansson et al. (2020)
Liao et al. (2020)
Water use and accessLSLAs frequently involve water appropriations, which may affect access to water, traditional 
agriculture and the human right to food of local populations ( medium confidence).Multi-case analyses
Breu et al. (2016)
Chiarelli et al. (2016)
Dell’Angelo et al. (2018)
Adams et al. (2019)
Tejada and Rist (2018)
Food security and nutritionLSLAs pose food security risks by re-orienting crop production to nutrient-poor crops predominantly 
destined for export, and/or excluding local populations from agricultural land ( high confidence).Multi-case analyses
Cristina Rulli and D’Odorico (2014)
Mechiche-Alami et al. (2021)
Marselis et al. (2017)
Müller et al. (2021b)
Conceptual studies

Häberli and Smith (2014)
Shete and Rutten (2015)
Mabe et al. (2019)
Bruna (2019)
Hules and Singh (2017)
Moreda (2018)
Atuoye et al. (2021)
LivelihoodsLSLAs often lead to adverse livelihood impacts and increased livelihood vulnerability of local 
populations (high confidence).Multi-case analyses
Davis et al. (2014)
Oberlack et al. (2016)
Nolte and Ostermeier, 2017)
Vandergeten et al. (2016)
Schoneveld (2017)
Zoomers and Otsuki (2017)
Richards and Lyons (2016)
Yengoh and Armah (2016)
Gyapong (2020)

5
804Chapter 5 Food, Fibre and Other Ecosystem Products
5.13.2 Trade-offs Generated by Agricultural 
Intensification and Expansion
Agricultural intensification seeks to increase agricultural productivity 
per input unit, reducing the pressure on land use and generating 
positive impacts in GHG emissions (Mbow et al., 2019), but valuing 
the final effect requires common metrics in terms of carbon capture or 
emission reductions (Searchinger et al., 2018). It has been suggested 
to address multiple SDGs (SDG2, SDG13, SDG15) but only occasionally 
leads to simultaneous positive ecosystem service and well-being 
outcomes (Rasmussen et al., 2018). When the process relies only on 
increasing input use, there is a risk of generating adverse outcomes 
that may override positive effects, such as CO 2 emissions, (McGill et al., 
2018), NOx emissions (Hickman et  al., 2017), soil salinisation and 
groundwater depletion (Doody et al., 2015; Daliakopoulos et al., 2016;

Fragaszy and Closas, 2016; Foster et  al., 2018; Flörke et  al., 2019). 
Agricultural intensification could meet short-term food security and 
livelihood goals, but reduces biological and landscape diversity, and ecosystem services (high confidence) (Campbell et al., 2017; Balmford 
et  al., 2018; Springmann et  al., 2018; Ickowitz et  al., 2019; Mbow 
et  al., 2019). Agricultural intensification can also affect livelihoods 
of small-scale producers, compromising food security. It can increase 
low-waged casual farm work, increasing gender and income inequity 
(Bigler et al., 2017; Clay and King, 2019; Table 5.18).
Land available for provisioning ecosystem services is declining in 
many places because of agricultural expansion, bioenergy crops 
and reforestation for mitigation (Kongsager, 2018), with adverse 
climate impacts (Froese and Schilling, 2019). Cropland expansion can 
deteriorate biodiversity (Delzeit et  al., 2017), water quality (Ayala

et al., 2016) and carbon storage (Goldstein et al., 2012) and increase 
water demands (Yokohata et al., 2020).
A systems-based perspective on land use is needed to address 
climate change impacts on nutrition security and ecosystem services 
(Springmann et  al., 2018; IPCC, 2019b; Willett et  al., 2019). Land Land use dimensions Impacts and implications References (2014 to present)
Indigenous People and 
commonsLSLAs often have adverse impacts on Indigenous peoples and lands, including land encroachment, 
dispossession, and displacement.
Land deals frequently target common land and may increase the vulnerability of customary, 
traditional, and Indigenous systems common property, while reducing their adaptive capacity (high 
confidence).Multi-case analyses
Dell’Angelo et al. (2016)
Giger et al. (2019)
Dell’Angelo et al. (2021)
Haller et al. (2020)
Olwig et al. (2016)
Moreda (2017)
Montefrio (2017)
Scheidel and Work (2018)
Konforti (2018)

Pietilainen and Otero (2019)
Mingorría (2018)
Bukari and Kuusaana (2018)
Haller (2019)
Hak et al. (2018)
Gabay and Alam (2017)
Imbong (2021)
GenderImpacts and implications of land deals are sometimes experienced in different ways by different 
genders (high confidence).Case study examples
Tsikata and Yaro (2014)
Yengoh et al. (2015)
Fonjong et al. (2016)
Nyantakyi-Frimpong and Bezner Kerr (2017)
Elmhirst et al. (2017)
Bottazzi et al. (2018)
Ndi (2019)
Osabuohien et al. (2019)
Porsani et al. (2019)
Impacts on other climate 
change mitigation and 
adaptation initiativesLSLAs may undermine mitigation and adaptation initiatives and other land uses relevant for climate 
change mitigation and adaptation (high confidence).Multi-case analyses
Carter et al. (2017)
Borras et al. (2020)
Nyantakyi-Frimpong (2020b)
Rodríguez-de-Francisco et al. (2021)
Other environmental

impactsLSLAs are projected to provoke global environmental change; LSLAs are a potential driver of slope 
instability; LSLAs affect natural habitats such as tiger landscapes; LSLAs jeopardize biodiversity (low 
confidence).Lazarus (2014)
Chiarelli et al.(2021)
Debonne et al.(2019)
Balehegn (2015)

5
805Food, Fibre and Other Ecosystem Products  Chapter 5
sparing sets aside some land for conservation purposes and intensifies 
production on farmland (Balmford et  al., 2018; Benton et  al., 2018; 
IPCC, 2019b), with potential to offset GHG emissions (Lamb et  al., 
2016). Alternatively, ‘land sharing’ approach employs principles such 
as minimising fossil-fuel-based inputs, maximising synergies, and 
addressing climate change mitigation and adaptation as well as 
biodiversity (Kremen and Miles, 2012; Kremen, 2015; Kremen and 
Merenlender, 2018; HLPE, 2019; Section  5.14, Box on Agroecology). 
Community-managed initiatives can address biodiversity and 
ecosystem conservation, livelihoods, food provisioning and other 
ecosystem services (Kremen and Merenlender, 2018; HLPE, 2019).
The concept of sustainable intensification has emerged, looking for 
enhancements in environmental outcomes, while maintaining or 
increasing agricultural systems performance. There is a potential to find

synergies between agricultural production and landscape systems if 
systems are designed to operate within planetary boundaries (Rockström 
et al., 2017; Liao and Brown, 2018; Pretty, 2018; Pretty et al., 2018).
5.13.3 Competition between Food Systems in Land and 
Ocean
Livestock and aquaculture feeds utilise crops such as soyabean 
and maize, with food conversion efficiencies similar in chicken and 
Atlantic salmon, and higher in pigs and cattle (Troell et  al., 2014; 
Fry et al., 2018b; Fry et al., 2018a). Use of wild fish meal and oil has 
been decreasing, partly due to concerns regarding vulnerable small 
pelagic fish stocks (Bindoff et  al., 2019). The instability of wild fish 
stocks has increased terrestrial crop feed components (Troell et  al., 
2014; Blanchard et al., 2017; FAO, 2017; Cottrell et al., 2018). The use 
of wild fish in fish feeds that may have been directly consumed may 
put low-income households at risk of food insecurity (Troell et  al.,

2014). An increasing demand for aquaculture products intensifies 
competition for feed supplies (medium confidence) (Troell et al., 2014; 
Blanchard et al., 2017). Increases in demands for animal protein and 
shifts to pescatarian diets will increase the existing competition for 
land resources, particularly in low- and medium-income countries, 
with negative impacts on food security (Makkar, 2018), but may be 
mitigated by dietary changes, novel feeds and food waste usage for 
aquatic systems (Berners-Lee et  al., 2018; Hua et  al., 2019; Cottrell 
et al., 2020).Competition over use of major aquaculture feed crops (Fry et al., 2016) 
with terrestrial livestock (Troell et al., 2014), and fish use by terrestrial 
livestock, will also place pressure on fish and crop resources (medium 
confidence) (Cottrell et al., 2018). Increases in feed prices will affect 
fish and meat prices (Troell et al., 2014), and changes in agriculture

will be needed to satisfy aquaculture demands (Blanchard et al., 2017). 
Aquaculture and livestock dietary components may also compromise 
crops and forage fish that provide essential nutrients for low-income 
households increasing nutritional insecurity, in regions of sub-Saharan 
Africa, Asia and Latin America (Troell et al., 2014). Waste fish products 
can supplement fish meal and oil to reduce competition for feed, as 
well as reducing use of fish that could go to human consumption 
(medium confidence) (Little et al., 2016; Shepherd et al., 2017; Dave 
and Routray, 2018; Naylor et al., 2021). Use of algae, bacteria, yeast 
and insect diets could replace fishmeal for aquaculture (Cohen et al., 
2018; Hua et al., 2019; Cottrell et al., 2020), not affecting nutritional 
profiles (Campanaro et al., 2019), and fish could be reared on waste 
by-products of other food production systems (Bava et  al., 2019). 
Complete fish oil substitutions with microalgae may be possible

without compromising omega-3 contents, but energy usage in diet 
production should be considered Cottrell et al. (2020). Substitutions 
of plant-based and alternative feeds may decrease food conversion 
efficiencies (Cottrell et al., 2020), affect omega-3 content of farmed 
seafood (Fry et al., 2016; Shepherd et al., 2017), be problematic for 
the fish themselves (Little et al., 2016; Naylor et al., 2021) and lead to 
reduced productivity (Shepherd et al., 2017).
Competition will be heightened by other climate impacts, such as 
changes in water availability. Water usage is relatively high in animal 
production (Abraham et al., 2014; Sultana et al., 2014; de Miguel et al., 
2015; Palhares and Pezzopane, 2015; Weindl et  al., 2017). In some 
areas, increased demand for plant-based animal feeds will be affected 
by sea level rise and competing usage of available freshwater with 
other users, and ecosystem needs (Karttunen et al., 2017).
5.13.3.1 Agricultural and river runoff

Flooding on agricultural land will enhance nutrient runoff, creating 
eutrophication and increasing harmful phytoplankton blooms, 
affecting fisheries and aquaculture, human health and ecosystem 
biodiversity. Changes in precipitation, monsoons, runoff and flood 
potential combine with deforestation and poor sewage treatment, Table 5.19 |  Case studies of trade-offs and negative outcomes associated with agricultural intensification on biodiversity and ecosystem services.
Ecosystem service Trade-offs/negative outcomes References
Provisioning: water quality Negative impacts on ephemeral wetlands Dalu et al. (2017)
Provisioning: water availability Contribution to water scarcity Satgé et al. (2019)
Supporting: soil Increasing erosion risk Govers et al. (2017)
Regulating: climate Reduced SOC sequestration Olsen et al. (2019)
Regulating: pest controlReduced level of biological control of pests; reduced 
number of insectivorous birdsEmmerson et al. (2016)

Cultural: recreational Reduction of river wildlife DeBano et al. (2016)
Biodiversity Reduced global biodiversity Newbold et al. (2015), Egli et al. (2018), Beckmann et al. (2019)
Biodiversity Reduction of taxonomic diversity Jeliazkov et al., (2016), Kehoe et al. (2017), Banerjee et al. (2019)
Biodiversity Negative impacts on mean population stability Olivier et al. (2020)

5
806Chapter 5 Food, Fibre and Other Ecosystem Products
resulting in larger volumes of nutrients and freshwater reaching coastal 
ecosystems (Jin et al., 2018; Nasonova et al., 2018; Tamm et al., 2018). 
Rising surface temperatures, ocean acidification and eutrophication 
will increase pathogenic Vibrio bacterial loads in marine organisms, 
with potential transfer to humans (Hernroth and Baden, 2018). 
Shallow and microtidal estuaries will be more vulnerable to changing 
river runoffs and saltwater intrusions, eutrophication and hypoxia 
(high confidence) (IPCC, 2019c).
5.13.4 Maladaptation Responses and Sustainable 
Solutions
Maladaptation can result in three types of outcomes (Juhola et al., 2016): 
(1) rebounding vulnerability: short term adaptations that decrease 
adaptive capacity and hinder future choices; (2) shifting vulnerability: 
larger-scale adaptation actions that produce spill-over effects in other 
locations; (3) eroding sustainable development: adaptation strategies

which increase emissions and deteriorate environmental conditions 
and/or social and economic values (Tables 5.20 and 5.21).
Existing climate policies do not adequately consider trade-offs, 
adaptive limits, cumulative costs and potential risks of maladaptation 
(robust evidence, medium agreement) (Dovie, 2017; Holsman et  al., 
2019; IPCC, 2019b; Work et  al., 2019; Thomas, 2020: Table  5.19). 
Government policies are seldom coordinated across scales and often 
focused on regional short-term risks (medium evidence, medium 
agreement) (Dovie, 2017; Holsman et al., 2019; Rahman and Hickey, 
2019; Butler et al., 2020). Past development trajectories and dominant 
political economic structures may narrow adaptation pathways, be 
restrictive and increase the vulnerability of particular groups (Paprocki, 
2018; Quan et al., 2019; Rahman and Hickey, 2019; Work et al., 2019).
Case Studies of Maladaptation
Large-Scale Irrigation Project in Navarre, Spain

Many small-scale producers could not afford the irrigation investment 
and had to sell or rent their land to those who joined the irrigation 
project. Many large-scale farmers using irrigation switched to corn 
and forage and dropped crops with high labour costs. Water costs are 
now paid to a private company, and small-scale farmers lost access 
to communal water rights. The project increased inequity and land 
concentration and lowered crop diversity, with small-scale producers 
more vulnerable to climate change. Large-scale intensive farmers are 
more exposed to crop price volatility than to climate vulnerability but 
have greater access to subsidies and water rights (Albizua et al., 2019).
Constraining Adaptation: Previous Agricultural 
Development Pathways in India
Government policies in colonial and postcolonial India invested in 
infrastructure, export production and synthetic input use (Gupta, 
1998; Davis, 2001), setting the stage for current development

trajectories and closing out other adaptive options. Although such policies increased national food production, they failed to 
address high levels of malnutrition, worsening regional inequities, 
degraded natural resources and an agrarian debt crisis (Singh, 2000; 
Gupta et  al., 2016; Gajjar et  al., 2019). Agricultural livelihoods are 
increasingly considered unviable, with lower adaptive capacity of 
farmers, high debt levels (Gupta et al., 2016), and Indigenous and local 
knowledge loss and denigration (Kumar, 2016) alongside lower crop 
diversification (Srivastava et al., 2016). Government institutions aimed 
at infrastructure often lack adaptive capacity needed to address rural 
livelihoods (Singh et al., 2017; Gajjar et al., 2019).
Adaptation options that consider adverse effects for different 
groups reduce the risk increasing vulnerability, negatively affecting 
socioeconomic factors to deal with climate impacts, or impeding efforts

to implement SDGs (high confidence) (Juhola et al., 2016; Antwi-Agyei 
et al., 2018; Paprocki and Huq, 2018; Holsman et al., 2019; IPCC, 2019b; 
Stringer et al., 2020). Adaptation methods considering historical roots 
of current vulnerabilities can identify viable solutions, which are 
difficult to undertake because of path dependencies (high confidence) 
(Ribot, 2014; Albizua et al., 2019; Gajjar et al., 2019; Paprocki, 2019; 
Thomas, 2020). Planning techniques that model outcomes for different 
groups from different adaptation options could be put in place to 
diminish maladaptation risks (Rodríguez et al., 2019).
Inclusive planning initiatives such as community-based anticipatory 
adaptation combined with ‘two-way learning’ that considers future 
scenarios and different adaptation pathways can prevent maladaptation 
(high confidence) (Dovie, 2017; Bezner Kerr et al., 2019; Neset et al., 
2019a; Rahman and Hickey, 2019; Work et al., 2019; Butler et al., 2020;

Nunn et al., 2020; Piggott-McKellar et al., 2020; Westoby et al., 2020; 
Table  5.20). Promising policy management tools combine temporal 
scales and mitigation–adaptation interactions and consider political 
dynamics, socioeconomic impacts and trade-offs for vulnerable groups, 
long-term support for policy leaders, efforts to establish livelihood 
‘niches’ and ongoing participatory evaluation (Dovie, 2017; Holsman 
et al., 2019; Rahman and Hickey, 2019; Work et al., 2019; Butler et al., 
2020). A focus on the most disadvantaged groups can help small-
scale producers at higher risk to prevent maladaptation (FAO, 2018c). 
Governance mechanisms have emerged that consider food security, 
socio-cultural factors, and land and water rights, using participatory, 
inclusive ‘two-way learning’ methods that involve vulnerable people 
alongside government (IPCC, 2018; Holsman et al., 2019; IPCC, 2019b; 
Rahman and Hickey, 2019; Butler et al., 2020).

5.13.5 Climate Change and Climate Response Impacts on 
Indigenous People
Indigenous people and ethnic minorities, many of them having special 
cultural associations to local foods, are particularly vulnerable to 
climate change owing to changes in the availability of wild foods, crop 
failure and food production losses or increased food prices (Norton-
Smith et al., 2016; Otto et al., 2017).
Changes in sea level rise or coastal erosion can reduce ecosystem services 
to a point where either subsidies are used to enable human populations 
to remain in their place of attachment, or ultimately to displace coastal

5
807Food, Fibre and Other Ecosystem Products  Chapter 5
Table 5.20 |  Summary of the emerging literature on potential risks of maladaptation.
Description of 
adaptation strat-
egyPotential negative impactsMaladaptation typology 
(1 = rebounding vul-
nerability, 2 = shifting, 
3 = eroding SDGs) and 
confidence levelRegions and 
countries 
affectedGroups affected References
Agricultural 
intensification to 
increase productivity, 
in places with heavy 
rainfall events or rising 
pest/disease incidenceIncreases GHG emissions, water pollution, 
possible insect resistance and costs to 
farmers; possibly increases inequities.
May constrain adaptation policy options 
for development pathways due to lock-ins 
and trade-offs which entrench inequities.1, 2, 3
Robust evidence, medium 
agreementUSA, Africa, Asia 
(India, China), 
EuropeFarmers, pastoralists/
nearby communities who 
rely on water; small-scale 
farmers who cannot afford 
inputs; policymakers.Gajjar et al. (2019),

Guodaar et al. (2019), 
Houser and Stuart (2019), 
Neset et al. (2019b), Quan 
et al. (2019), Young and 
Ismail (2019)
Livelihood 
diversification into 
charcoal productionIncreases GHG emissions and 
deforestation rates1, 3
mediumAfrica (Northern 
Ghana), South 
America (Peru)Small-scale food producers; 
Indigenous communitiesAntwi-Agyei et al. (2018), 
Zavaleta et al. (2018), 
Young and Ismail (2019)
Irrigation projects or 
programmes that are 
either large-scale and/
or rely on groundwaterReduces long-term potential for 
hydropower and groundwater availability, 
can increase salinisation and cost of water.
Can increase cost of farming and 
debt levels of farmers, squeezing out 
small-scale producers.
Can reduce water availability for 
aquaculture.1, 2, 3
highCentral 
China, India, 
Mediterranean 
areas, Europe, 
USAFood producers who rely 
on irrigation; consumers 
who rely on hydropower or 
groundwater; small-scale 
diversified producers who 
cannot afford irrigation;

aquaculture.Doody et al. (2015), Herbert 
et al. (2015), Barik et al. 
(2016), Daliakopoulos 
et al. (2016) Fragaszy 
and Closas (2016) Dalin 
et al. (2017), Foster et al. 
(2018) Hanaček and 
Rodríguez-Labajos (2018), 
Albizua et al. (2019), Flörke 
et al. (2019) Gajjar et al. 
(2019), Zhang et al.(2019a)
Investment in 
improved cultivars or 
shift to different cropsMay displace local varieties; reduces 
diversity if too much policy/extension 
emphasis falls on a few varieties; may 
increase risk of crop loss from pests, 
disease or drought if reliant on a few 
varieties; may increase fertilizer use; 
may lead to loss of Indigenous or local 
knowledge.1, 3
mediumSouth America 
(Bolivia, Pacific 
Islands, AsiaSmall-scale food producers; 
Indigenous communitiesMcleod et al. (2018), 
Meldrum et al. (2018), 
Neset et al. (2019b) 
Rahman and Hickey (2019)
MigrationCan increase the workload of people 
left behind (often women), worsen rural 
livelihoods and food insecurity; can

lead to worsened living conditions, food 
security and poverty in precarious urban 
conditions; may increase vulnerability to 
flooding in urban locations.
May affect mental health by disrupting 
existing social ties.1, 3
highAsia, Africa, 
South AmericaSmall-scale low-income 
food producers or rural 
workers; womenBettini et al. (2017), 
Paprocki (2018), Chen 
et al. (2019), Jacobson 
et al. (2019), Michael et al. 
(2019), Young and Ismail 
(2019), Singh and Basu 
(2020), Torres and Casey 
(2017)
Coastal sea walls, 
embankments, canals, 
riverbed draining and 
dikes to reduce flood 
riskCan degrade coastal mangroves, 
deplete open freshwater fisheries, cause 
sedimentation of rivers, reduce fish 
diversity and increase flooding risk for 
particular vulnerable groups; may divert 
funds from other more sustainable 
measures.1, 2, 3
highAsia, South 
Pacific Islands, 
West AfricaCoastal communities 
dependent on mangroves 
and fisheries; low-income 
rural households with

seasonal dependence on 
inland fisheriesDovie (2017), 
Owusu-Daaku (2018), 
Freduah et al. (2019), IPCC 
(2019c), Rahman and 
Hickey (2019), Nunn et al. 
(2020) Seddon et al. (2020), 
Thomas (2020)
River regulation for 
hydropowerMay have negative impacts on inland 
fisheries.2, 3 GlobalSmall-scale inland fisheries 
and low-income rural 
households with seasonal 
dependence on inland 
fisheriesFAO (2018c)
Government 
policies to manage 
coastal fisheries 
which promote 
overcapitalisation of 
fisheries, including 
index insuranceGovernment confiscation of fishing nets 
to prevent rapid decline of fish population 
can worsen livelihoods for small-scale 
fishers; subsidies of pre-mixed fuel to 
allow fishers to stay out longer due to 
shifting fish populations may increase 
total number of fishers and total fish 
catch; insurance payments may benefit 
larger-scale fishing fleets and push out 
small-scale fishers.1, 3
mediumWest AfricaCoastal small-scale fishery

communitiesFAO (2018b), Freduah et al. 
(2019), Holsman et al. 
(2019), Sainsbury et al. 
(2019)

5
808Chapter 5 Food, Fibre and Other Ecosystem Products
countries 
Consultative 
stakeholder systems 
in fisheries or flood 
managementMay encourage inertia in the system 
due to a few powerful stakeholders 
participating in the consultative process.2
lowNorth America, 
AsiaCoastal fisheriesHolsman et al. (2019), 
Climate servicesMay reinforce existing inequities if 
climate services are attuned to powerful 
stakeholders in industry, services are 
privatised, there are limited ways to 
get input from vulnerable groups and 
planning budgets that use climate services 
are constrained.1, 2, 3
mediumNorth America Coastal fisheries, farmingFurman et al. (2014), 
Webber (2017), Nost 
(2019)
Nature-based solutions 
mitigation and

adaptation strategies 
such as reforestation 
or afforestationCan displace local communities’ access 
to land for food production and other 
ecosystem services, have negative impacts 
on Indigenous rights, reduce biodiversity 
and may not reduce GHG as much as 
conserving natural forests and wetlands or 
agroecological systems such as agroforestry 
or other means to increase soil C.2, 3
mediumAfrica, Asia, and 
South America, 
e.g., Indonesia, 
Amazon, 
west-central 
AfricaIndigenous communities; 
small-scale producers 
and forest-dependent 
communitiesLunstrum et al. (2016), 
Work et al. (2019), Seddon 
et al. (2020), Cross-Working 
Group Box BIOECONOMY 
Social safety nets 
provide funds which 
increase consumption 
of processed, 
purchased food and 
erode Indigenous 
knowledgeDecline in Indigenous knowledge of 
and collective approaches to seasonal 
adaptation strategies in hunting, fishing 
and food production; shift in dietary

patterns to more processed and non-local 
foods; reduction in farming. Reduced 
capacity to respond to hazards through 
dispersed settlement, e.g., hunting, 
fishing, wild food collection. Increased 
population density increases deforestation 
and vulnerability.1, 3
lowSouth America 
(Amazonian 
region of Peru), 
Africa (South 
Africa)Indigenous communitiesLemos et al. (2016), 
Zavaleta et al. (2018)
Community-based 
adaptation strategiesLocal gender and other social inequities 
can lead to ‘elite capture’ that reinforces 
inequity; power dynamics between the 
funding agency and local participants 
can make local community involvement 
tokenistic. There may be inadequate 
attention to socio-cultural preferences 
and structural factors which foster 
maladaptation such as inappropriate 
crops or animals used.1, 3
highPacific Islands, 
Africa, AsiaSmall-scale food producers; 
Indigenous communities, 
other vulnerable groups 
such as women and 
low-caste groupsMcNamara and Buggy

(2017) Jamero et al. 
(2018), Singh (2018) 
Bezner Kerr et al. (2019) 
Piggott-McKellar et al. 
(2020), Westoby et al. 
(2020)
Digital agriculture for 
increased precision 
and efficient use of 
fertilizers, pesticides, 
waterCould lead to net job losses, particularly 
for those with lower levels of education; 
increased surveillance and employer 
scrutiny of lower-skilled workers in fields, 
greenhouses and processing plants and 
warehouses; separate workers from 
employees and companies who collect 
data. Overall increased racial, income 
inequities and unequal working conditions.2, 3
Europe, Asia, 
parts of AfricaFarmworkers; small-scale 
food producers who cannot 
afford digital technologies; 
rural communities(Furman et al. (2014), Rotz 
Increased credit 
access for livelihood 
diversificationHigh interest rates, tight return policies 
could increase debt loads for low-income 
households, which could rebound

vulnerability. Household may invest in 
livelihood strategies which are vulnerable 
to climate change impacts, or which 
increase GHG.1, 3
lowAsia 
(Bangladesh)Low-income landless 
people or small-scale 
producersRahman et al. (2018)
AquacultureLarge-scale coastal aquaculture can 
increase soil salinisation and reduce land 
available for other food production and 
increase migration.2, 3
lowAsia 
(Bangladesh)Small-scale mixed systems 
including rice production 
and other rural livelihoodsPaprocki (2018), Paprocki 
and Huq (2018)

5
809Food, Fibre and Other Ecosystem Products  Chapter 5
residents, thereby removing connections to places of intrinsic value. For 
example, the United Houma Nation in Louisiana is experiencing coastal 
land loss, sea level rise and strong Gulf hurricanes, which leads to the 
relocation of some tribes causing loss of Houma identity (Sullivan and 
Rosenberg, 2018). Another example is the relocation of Alaska Native 
communities due to climate change (Hamilton et al., 2016)
Expansion of agriculture can bring distress to Indigenous communities 
because of environmental deterioration and the stress associated with 
relocation or displacement (Otto et al., 2017). A/R programmes can also 
bring inequities to Indigenous communities (Godden and Tehan, 2016) 
and even violent displacement with tragic results (Celentano et  al., 
2017). A/R programmes can negatively affect a range of substantial 
and procedural Indigenous Peoples’ rights entrenched in international

human rights law (Table 5.22) and their potential for climate change 
adaptation (high confidence).
A significant proportion of land targeted for A/R projects is inhabited 
and used by Indigenous Peoples and local communities (Cagalanan, 
2016). Indigenous Peoples have rights to and/or manage at least 
37.9  million km2 of land and influence land management across 
at least 28.1% of the land area (Garnett et  al., 2018). At least a 
quarter of the global land area is traditionally owned, managed, 
used or occupied by Indigenous Peoples, overlapping with 35–40% 
of the area that is formally protected (Garnett et al., 2018; Brondizio 
et  al., 2019). In many cases, A/R is implemented in areas where 
tenure rights are insecure and Indigenous Peoples’ rights are at 
risk of being disregarded (Naughton-Treves and Wendland, 2014; 
Kohler and Brondizio, 2017; Garnett et al., 2018) (medium evidence, 
high agreement). Many projects are also found in areas where

complex socio-political contexts challenge management (Jurjonas 
and Seekamp, 2019). It is anticipated that A/R projects will create 
huge pressures on existing land uses and generate further land use 
conflicts (Aggarwal, 2014; Robinson et al., 2014; Paul et al., 2016; 
Brancalion and Chazdon, 2017; Pye et al., 2017; Bond et al., 2019). 
In addition, many afforestation projects are conducted in regions 
that are not bio-climatically suitable, leading to the degradation of 
ecosystems that are key to local livelihoods (Veldman et al., 2015; 
Robinson et al., 2016b).Until 2010, most A/F projects had technical, carbon-related goals 
and did not consider issues of livelihoods, community involvement 
or broader ecosystem impacts (Wolde et  al., 2016). New strategies 
such as nature-based solutions (Seddon et al., 2020) and forest and 
landscape restoration (Brancalion and Chazdon, 2017) integrate a 
larger set of social and environmental objectives. Indigenous Peoples

enjoy a range of co-benefits of A/F initiatives such as improved habitat, 
fire management or protection from climatic shocks such as drought 
(Robinson et al., 2016b; Seddon et al., 2020), provided they are able to 
manage carbon funds collectively, meet the monitoring and reporting 
requirements, and protect forests from illicit uses and natural disasters 
(Wolde et al., 2016).
Policies and safeguards attached to specific A/R initiatives determine 
their impact (high confidence) (Talor, 2015; West, 2016; Brancalion and 
Chazdon, 2017). In countries where there is a great level of devolution 
of rights to Indigenous Peoples, there is a risk that the A/R agenda 
will lead to recentralisation (limited evidence,  medium agreement) 
(Bayrak and Marafa, 2016). Some A/R initiatives specify the need to 
respect the rights of Indigenous Peoples and local communities and 
protect biodiversity (medium evidence,  high agreement) (Seddon 

Local communities’ ability to participate in project design, implementation 
and monitoring is directly linked to the autonomy and independence 
of local institutions (Pye et al., 2017), their ability to formulate by-laws 
(Wolde et al., 2016) and handle funds in a transparent way (medium 
evidence, high agreement) (Witasari, 2016). It is further dependent on 
cohesion in the community (Cagalanan, 2016), the existence of clear 
rules delineating community membership and the presence of elders and 
community members with relevant local knowledge (Robinson et  al., 
2016b), and gender and out-migration dynamics affecting participation 
structures (robust evidence, medium agreement) (Cormier-Salem and 
Panfili, 2016; Witasari, 2016; Wolde et al., 2016; Jurjonas and Seekamp, 
2019).Table 5.21 |  Strategies to avoid maladaptation (adapted from Magnan, 2014; Lim-Camacho et al., 2015; Sovacool et al., 2015; FAO, 2018b; Paprocki and Huq, 2018; Sainsbury 

Type of maladaptation Strategies
Environmentali) Prevent negative effects on ecosystem services in situ (e.g., habitat degradation, pollution) that increase exposure to climate hazards.
ii) Avoid increasing pressure on other socio-ecological systems.
iii) Ensure ecosystems’ protective role as natural buffer zones is sustained against current and future climate-related hazards, such as storms, floods and sea 
level rise.
iv) Provide some duplication and ensure flexibility of adaptation strategies to reduce risk because of uncertainties about climate change impacts and 
ecosystem response (e.g., agrobiodiversity to reduce pest outbreaks).
Socio-culturali) Consider local social characteristics and cultural values that could affect risks and environmental dynamics.
ii) Support local skills and knowledge related to climate-related hazards.
iii) Support capacity-building for new skills needed by local communities.

Political-economici) Consider the political dynamics and power imbalances and create inclusive processes to involve the most vulnerable and disadvantaged groups in 
ii) Work to reduce socioeconomic inequities, poverty and food insecurity.
iii) Support livelihood diversification.
iv) Focus on the impacts of adaptation on the poorest, structurally disadvantaged and vulnerable groups, and take power imbalances into account.
v) Work across the full supply chain to consider linkages and possible ripple effects.

5
810Chapter 5 Food, Fibre and Other Ecosystem Products
5.13.6 Increased Presence of Financial Actors in the 
Agrifood System
Financial actors, markets, institutions and incentives have gained 
importance in agricultural commodities and farmland markets in the 
past two decades (Clapp and Isakson, 2018; Fairbairn, 2020). New 
types of investment vehicles such as commodity index funds that track prices of commodities and farmland have emerged, and the use 
of older vehicles such as forward and futures markets has increased 
(Schmidt and Pearson, 2016; Clapp and Isakson, 2018). These trends are 
connected to climate change as financial investments are influenced 
by the likelihood that climate change will increase commodity and 
farmland price variability (medium confidence) (Cotula, 2012; Isakson, 
2014; Tadesse et al.).Table 5.22 |  Indigenous rights recognised in international human rights law negatively affected by A/R projects.
Negative impacts of monoculture plantations (and

other A/R projects)Indigenous Peoples’ 
rights affectedDegree of certainty References
Local community not informed, not adequately consulted, not 
provided means for meaningful participation in project design, 
implementation, and monitoring (with specific attention to 
women and poor households); disruption or non-recognition 
of local or traditional institutions; elite capture; no access to 
third-party grievance mechanisms.Right to self-determination; 
consultation and free, prior 
and informed consent (FPIC); 
participationMedium evidence, high 
agreementAggarwal (2014), Maraseni et al. (2014), Ravikumar 
et al. (2015), Bayrak and Marafa (2016), Loaiza et al. 
(2016), Vijge et al. (2016), Pye et al. (2017), Ryngaert 
(2017), Wolde et al. (2016), Brancalion and Chazdon 
(2017), Seddon et al. (2020)
Evictions and displacement; dispossession; livelihood precarity; 
and criminalisation of forest-dwelling people.Right not to be forcibly 
removedMedium evidence, high

agreementMingorría (2014), Richards and Lyons (2016), 
Witasari (2016), Corbera et al. (2017), Pye et al. 
(2017), Sarmiento Barletti et al. (2020), Brancalion 
and Chazdon (2017)
Loss, transfer or acquisition of land. A/R projects involve 
changes in land use for medium to long term and often lack 
consideration for local dynamics including land tenure and 
competition with agriculture or conservation.Rights to land and territoryLimited evidence, high 
agreementAggarwal (2014), Robinson et al. (2014), Bayrak and 
Marafa (2016), Pye et al. (2017), Bond et al. (2019)
A/R projects exacerbate conflicts, accentuate uneven power 
relations, increase existing inequities within communities, 
exclude the poor and deepen structural injustices, including 
racism and stigmatisation.Rights to land and territoryLimited evidence, low 
agreementAggarwal (2014)
Forest expansion intensifies already acute land shortages for 
growing food and forces villagers to take their animals for

grazing to new areas as a result of forests being fenced off.Rights to land and territory 
(with implications for food 
security)Limited evidence, high 
agreementLyons et al. (2014), Wolde et al. (2016), Brancalion 
and Chazdon (2017), Mousseau and Teare (2019)
Decreased stream flows and water yields; exacerbated water 
scarcity.Right to waterRobust evidence, high 
agreementVeldman et al. (2015), Aitken and Bemmels (2016), 
Brancalion and Chazdon (2017), Pye et al. (2017), 
Bond et al. (2019), Seddon et al. (2020)
Pollution of lakes with agrochemicals; heavy chemical use, 
including the spread of pesticides, herbicides and fertilizers by 
aircraft and other means causing runoff into rivers.Right to a healthy 
environmentMedium evidence, high 
agreementRichards and Lyons (2016), Johansson and Isgren 
(20179, Pye et al. (2017)
Encroachment on other ecosystems with devastating impacts 
on biodiversity; pressures on ecologically sensitive ecosystems

such as wetlands; reduction in seed-dispersing animals; planted 
tree species becoming invasive, introducing pests and diseases.Right to a healthy 
environment, right to foodMedium evidence, high 
agreementRichards and Lyons (2016), Holmes et al. (2017), 
Seddon et al. (2020), Ennos et al. (2019)
Loss of habitat, degradation of savannas, native grasslands 
(grassy biomes) or mangroves wrongly characterised as 
degraded land suitable for afforestation.Right to a healthy 
environment, right to foodRobust evidence, high 
agreementVeldman et al: (2015), Cormier-Salem and Panfili 
(2016), Brancalion and Chazdon (2017), Bond et al. 
(2019), Seddon et al. (2020)
Direct negative health impacts; loss of traditional medicine. Right to healthLimited evidence, medium 
agreementDotchamou et al. (2016), Johansson and Isgren 
(2017)
A/R projects affect burial sites as, for many communities, the 
forest is also the resting place for deceased ancestors.Right to cultural identity and

to main and control their 
traditional knowledgeLimited evidence, high 
agreementLyons et al. (2014), Gabriel and Mangahas (2017), 
Mousseau and Teare (2019)
Loss of traditional or Indigenous ecological knowledge and 
forest management practicesRight to cultural identity and 
traditional knowledgeLimited evidence, medium 
agreementBayrak and Marafa (2016)
Increased labour burden. Benefit sharing by direct cash transfer 
or in-kind modalities tends to not compensate lost income 
opportunities. Some projects bring employment opportunities, 
but these are short term and limited and rarely viable if the 
opportunity cost of land and labour is considered. Poor farmers 
may drop out to regain access to their land for uses that 
provide cash returns in the shorter term.Right to an adequate 
standard of living; right to 
decent work; right to benefit 
sharingMedium evidence, medium 
agreementBoyd et al. (2007), Aggarwal (2014), Cagalanan 
(2016), Witasari (2016), Corbera et al. (2017), Pye

5
811Food, Fibre and Other Ecosystem Products  Chapter 5
Financial investors pool their investments through intermediaries, 
alongside other dynamic forces in the global economy, making 
unambiguous assessments of their effect difficult (Clapp, 2014; Clapp, 
2017). However, assessment of the broader trends at the interface of 
financial investment, food system dynamics and climate change shows 
potential connections.
Climate-induced variability in food production has the potential to 
introduce a new level of uncertainty into food and farmland markets, 
encouraging financial investment into products to capitalise on price 
volatility and to hedge risks. The new financial instruments enable 
investors to speculate more easily on the direction of food and land 
prices, especially when they are volatile (Ouma, 2014; Baines, 2017).
5.13.7 Climate Change Interactions with other Drivers—
Food–Water–Health–Energy–Security Nexus

Linkages between food security and nutrition with water and energy 
as well as other important socio-environmental issues are increasingly 
being described within a nexus framework (see also Chapters 3, 4, 6 
and 7), with food systems frequently located at the centre of nexus 
concepts (Caron et al., 2018).
Climate change will affect the food–energy–water (FEW) nexus, 
commonly in the form of risk multiplier (high confidence) (e.g., Conway 
et al., 2015; Barik et al., 2016; Keairns et al., 2016; Abbott et al., 2017; 
Ebhuoma and Simatele, 2017; Caron et  al., 2018; D’Odorico et  al., 
2018; de Amorim et  al., 2018; Mpandeli et  al., 2018; Nhamo et  al., 
2018; Soto Golcher and Visseren-Hamakers, 2018; Yang et al., 2018; 
Amjath-Babu et al., 2019; Froese and Schilling, 2019; Mercure et al., 
2019; Momblanch et  al., 2019; Pastor et  al., 2019; Xu et  al., 2019). 
Xu et  al. (2019) modelled the irrigation demand for large-scale

maize production in Northeast China and concluded that increasing 
droughts under future climate change would lead to extreme shortage 
of irrigation water without adaptive measures. Barik et  al. (2016) 
described how the growing demand for food in India has led to more 
irrigation with a reduction in groundwater levels in some regions.
Increasing demands for food, energy and water can lead to domestic 
and international conflict, including political instability and migration, 
often in the context of drought (high confidence) (Abbott et al., 2017; 
Bush and Martiniello, 2017; WEF , 2017; D’Odorico et  al., 2018; de 
Amorim et al., 2018). de Amorim et al. (2018) conclude that the FEW 
nexus is susceptible to many global risks, including extreme weather 
events and human migrations, and predominantly endangers vulnerable 
communities of less developed countries. There is emerging evidence that 
food and water insecurity enhances social conflicts, including protests

and violent riots, at least partially, by accelerating existing grievances 
(Heslin, 2021; Koren et al., 2021). Closer coordination at global, regional 
and national levels could be recommended to manage these risks.
Meeting growing demands for food, water and energy under a changing 
climate requires technical solutions and behavioural change as well as 
greater coordination across multilateral institutions and governance. 
Supply-side solutions focus on enhancing production, reducing food 
waste and loss or lowering water demand through both technological approaches (e.g., breeding, improved irrigation) and agroecological 
approaches, such as agroforestry, underutilised and more adapted 
crops, and transition towards a circular economy (Alexander et  al., 
2015; Obersteiner et al., 2016; D’Odorico et al., 2018; Nhamo et al., 
2018; Soto Golcher and Visseren-Hamakers, 2018). Demand-side 
solutions focus primarily on changes in consumer behaviour towards

healthier diets with lower carbon footprints, particularly reduction of 
meat consumption (Alexander et al., 2015; Obersteiner et al., 2016). 
Improving the coordination of multilateral organisations could result 
in improved cross-boundary management of natural resources, 
particularly related to water (Conway et al., 2015; Nhamo et al., 2018; 
Soto Golcher and Visseren-Hamakers, 2018).
As relationships between individual subsystems are systemic, integrated 
solutions would result in better outcomes across the FEW nexus (strong 
agreement). Obersteiner et  al. (2016) concluded that single-sector 
policies can create strong trade-offs with other policy targets and SDGs, 
whereas strategies that reduce pressure on food production systems 
diminish trade-offs between FEW nexus components. This suggests that 
achieving multiple SDGs will require balancing societal demands in the 
context of finite natural resources (Jägermeyr et al., 2017; Amjath-Babu

et al., 2019; Momblanch et al., 2019).
Despite concluding that integrated solutions addressing the systemic 
connections between the FEW nexus would improve development and 
environmental outcomes, there are limitations of integrating multiple 
frameworks, both in terms of describing the complexities and in finding 
solutions (Leck et al., 2015; Weitz et al., 2017; Wichelns, 2017; Shannak 
et al., 2018). Leck et al. (2015) and Weitz et al. (2017) indicate that 
evidence of successful implementation and improved outcomes based 
on the application of nexus concepts is rare.
5.14 Implementation Pathways to Adaptation 
and Co-benefits
5.14.1 State of Adaptation of Food, Feed, Fibre and Other 
Ecosystem Products
Since AR5, several adaptation reviews have been done (Ford et  al., 
2015; Lesnikowski et  al., 2016). In a review of 1159 peer-reviewed 
sources, Berrang-Ford et al. (2021b) found that observed adaptations 
in food, fibre and other ecosystem products have consisted mainly of

changes in autonomous behaviour changes, such as changing planting 
time, followed by technological/infrastructure and ecosystem-based 
adaptation approaches, the majority of which have occurred in Africa 
and Asia (Figures  5.20 and 5.21, Table  5.22). Several adaptation 
options addressed multiple SDGs (e.g., 2, 6, 8, 12) (Figure 5.21).
Assessment of adaptation options was done for 15 potential options 
for land and ecosystem transitions (SM5.7, Figure  5.22a). Several 
adaptation options have high to medium feasibility, with robust 
evidence, high agreement about the adaptive capacity resilience 
building potential of options in relation to climate change impact 
drivers (high confidence). Policy and planning and production shifts 
have limited evidence for feasibility. Most options are technically 
and physically feasible, with generally high political and social

5
812Chapter 5 Food, Fibre and Other Ecosystem Products
acceptability and environmental feasibility, but have limited evidence 
for institutional feasibility. Most adaptation options have medium to 
high microeconomic feasibility (high confidence) but limited evidence 
for macroeconomic viability.
Among five effectiveness indicators (SM5.7, Figure  5.22b), most 
options have robust evidence of reduced risk vulnerability to climate 
change, with low scores for local governance, substitution of plant or 
animal type, community forest management, livelihood diversification 
and climate services. Higher-scored options to reduce risk included 
increasing biodiversity (at landscape and field level), community seed 
banks, conventional breeding (plant and animals), mixed systems and agroecological approaches (medium confidence), suggesting 
multiple co-benefits of these options. Most options have high scores 
for enhancing social well-being and economic and environmental

benefits (medium confidence) but limited evidence for strengthening 
institutions for most options. There were low scores for potential 
maladaptation (medium confidence).
5.14.1.1 Nature-based solutions or ecosystem-based adaptation
There is growing evidence that nature-based solutions (NBS), which 
emphasise ecological approaches and biodiversity conservation 
(Chapter 1), have high potential to transform land and aquatic systems Table 5.23 |  State of adaptation in food, fibre and other ecosystem products by actors and vulnerabe groups (source: GAMI database; Berrang-Ford et al., 2021a)).
Actors N (%) Vulnerable groups Planned, N (%) Implemented, N (%)
International or multi-national governance institutions   72 (6%) Women 134 (12%) 118 (10%)
National government 264 (23%) Youth 22 (2%) 24 (2%)
Local government 267 (23%) Elderly 31 (3%) 28 (2%)
Sub-national government   89 (8%) Low income 201 (17%) 258 (22%)
Private sector corporations   56 (5%) Disabled   2 (0%)   3 (0%)

Private sector SMEs   80 (7%) Migrants 12 (1%) 18 (2%)
Civil Society — international/multi-national/national 117 (10%) Indigenous 95 (8%) 85 (7%)
Civil Society — sub-national or local 257 (22%) Ethnic minorities 32 (3%) 32 (3%)
Individuals or households 1087 (94%)State of adaptation across region and category of adaptation responseEvidence(a) Africa (b) Asia
(c) AustralasiaEvidence
050100150200(d) Central and South America
(e) EuropeEvidence
050100150200(f) North America (g) Small Island StatesAdaptation response categoriesGlobal
evidence
Infrastructure and technological 293
Sustainable intensification 241
Nature-based adaptation 313
Social, economic and institutional 423(f)
(d)(a)(e)(b)
(c)(g)
Figure 5.19 |  State of adaptation by region and type of response (based on 1159 peer-reviewed references that addressed adaptation in food, fibre and

other ecosystem products sector; source: Global Adaptation Mapping Initiative (GAMI) database (Berrang-Ford et al., 2021a). The bars indicate the amount of 
evidence for the category x region.

5
813Food, Fibre and Other Ecosystem Products  Chapter 5
into climate-resilient systems (medium evidence, high agreement) 
(Albert et al., 2017; Brugère et al., 2019; Galappaththi et al., 2020b; 
Snapp et al., 2021; Cross-Working Group Box BIOECO; Cross-Chapter 
Box NATURAL in Chapter 2).
5.14.1.2 Climate services
Climate services, understood as the production, translation, 
communication and use of climate information in decision-making 
processes, can contribute to adaptation efforts in agricultural systems 
(medium agreement, low evidence). Climate services can support 
decision makers in agriculture by providing tailored information 
that can inform the implementation of specific adaptation options 
(Vaughan, 2018; Buontempo et al., 2019; Dobardzic et al., 2019; Hank 
For some high- and medium-income countries, evidence suggests that 
climate services have been underutilised (Mase and Prokopy, 2014), 
with limited evidence in these countries of the impact of climate

services on yields, income, and food security and nutrition. In low-020406080100
020406080100Evidence Evidence Evidence(f)
(c)(g)Agricultural diversification
Climate-smart strategies
Cultivar & species improvement
Insurance
Livelihood diversification & migration
Shifting harvest locationAgricultural diversification
Insurance
Shifting harvest location
Agricultural diversification
Insurance
Shifting harvest location(g) Small Island StatesState of adaptation across region and specific adaptation options
Adjustment of planting dates
Fertilizer
Other financial mechanismsAdjustment of planting dates

Other financial mechanisms
Other financial mechanisms(c) Australasia(a) Africa (b) Asia
(d) Central and South America
(e) Europe (f) North America
Figure 5.20 |  Observed adaptation across regions in food, fibre and other ecosystem products based on the GAMI database (Berrang-Ford et al., 2021a). 
The bars indicate the number of evidence for the options x region.
income countries, use of climate services can increase yields and 
incomes and promote changes in farmers’ practices (low confidence) 
(Roudier et  al., 2014; Roudier et  al., 2016; Tarchiani et  al., 2017; 
Ouedraogo et al., 2018). There is low confidence that climate services 
are delivering on their potential, whether they are being accessed 
by the vulnerable, and how these services are contributing to food 
security and nutrition (Ouedraogo et al., 2018; Vaughan et al., 2019).

Improved design and delivery of climate services can enhance 
effectiveness (medium confidence). Ways to enhance the impact of 
climate services include integrating information from multiple sources 
at different scales (Bouroncle et al., 2019), participatory collection and 
analysis of climate information (Loboguerrero AM, 2018; Tesfaye et al., 
2019; Rossa, 2020), and making forecast information available in local 
languages and as verbal communications for farmers who cannot read 
(Nkiaka et al., 2019).
In countries with limited climate data, crowd sourcing (outsourcing 
data collection to the public) (Minet et  al., 2017) and digital tools 
present an opportunity for addressing climate risk (medium confidence)

5
814Chapter 5 Food, Fibre and Other Ecosystem Products
Adaptation options addressing the Sustainable Development Goals
Infrastructure and technological
Sustainable intensification
Nature-based adaptation
Social, economic and institutional
Cultivar and species improvement
Insurance
= no data available/Sustainable
Goals
SDG
12SDG
11SDG
9SDG
8SDG
6SDG
4SDG
3SDG
2SDG
1
Amount of evidence
>300 1SDG
5
/SDG
10
/SDG
16
//
/SDG
14
/
/SDG
15
/SDG
7
/
/Adaptation
categories
options
High Medium Low High Medium Low Limitedor none
Figure 5.21 |  How different response types address the SDGs based on GAMI.
(Osgood et al., 2018; Thornton, 2018; Partey et al., 2020; Sotelo et al.,

2020). Bundling additional services such as market information with 
climate information may be effective at plugging information gaps 
(low confidence) (Chatuphale and Armstrong, 2018; Tsan et al., 2019; 
Tesfaye et al., 2019)
There may be inequality in access to climate services; their use may tend 
to benefit large-scale operations and disadvantage small- and medium-
scale farmers and others who face issues of access due to social and 
economic inequity; also some groups such as pastoralists have not 
yet benefitted from climate services (high confidence) (Furman et al., 
2014; Muema et  al., 2018; Awazi et  al., 2019; Nyantakyi-Frimpong, 
2019; Paudyal et al., 2019; Vaughan et al., 2019; Nidumolu et al., 2020; 
Partey et  al., 2020). Other challenges include technology ignorance, 
data privacy and security, data access permissions, software and 
system compatibility, and understanding how to use and derive value 
from accessed data (Chatuphale and Armstrong, 2018; Drewry et al.,

2019). More work is needed to understand the factors that prevent 
farmers and fishers from benefitting from this new information. Recent 
assessments suggest that access to, and value of, climate and weather 
information can be enhanced by the development of digital tools 
(including radio, text messages, etc.) appropriate to the specific needs 
of different vulnerable groups, as well as by including these groups in their development and building their capacity (medium confidence) 
(Camacho and Conover, 2019; Gumucio et al., 2020; Sultan et al., 2020).
5.14.1.3 Insurance as a climate impact risk management tool
Insurance is a financial adaptation strategy increasingly used in 
agriculture and aquaculture. A relatively new approach to agricultural 
insurance risk is the use of financial derivative products, such as index-
based agricultural insurance (IBAI), marketed by financial institutions 
to farmers to help them deal with weather-related production risks

(Isakson, 2015; Jensen and Barrett, 2017). The basic idea is to rely on 
easily observed weather indices, such as precipitation or temperature, 
that co-vary with farm production. Insurance payments are received 
when the metric trigger for a region is reached, eliminating the need 
to collect farm-specific information. Proponents of index insurance 
argue that it can resolve the information costs and incentive problems 
inherent in rural financial markets, such as adverse selection, and allow 
provision of insurance coverage at a fraction of the costs of loss-based 
polices (Jensen and Barrett, 2017). Buyers of index policies do not have 
to prove their ownership of assets with weather-related losses. This 
lowers transactions costs and makes it more affordable to insure small 
plots of land.

5
815Food, Fibre and Other Ecosystem Products  Chapter 5
Box 5.11: Agroecology as a Transformative Climate Change Adaptation Approach
Agroecological approaches can increase food system resilience (robust evidence, medium agreement), while some agroecological practices 
such as agroforestry can provide mitigation measures (medium confidence) (Section 5.10.4.2, Table Box 5.11.1, Altieri et al., 2015; Martin 
and Willaume, 2016; HLPE, 2019; Bezner Kerr et al., 2021; Snapp et al., 2021). Studies testing agroecological approaches have shown 
robust evidence, medium agreement of increasing adaptation effectiveness through reducing risk, improving food security and yield 
stability, reducing input costs, and other supporting and provisioning ecosystem services (Section 5.4.4.4 Diacono et al., 2017; Pandey 
et al., 2017; Schulte et al., 2017; Calderón, 2018; Bezner Kerr et al., 2019; Côte et al., 2019; Rosa-Schleich et al., 2019; Bezner Kerr et al.,

2021; Snapp et al., 2021). Effective locally relevant agroecological approaches involve participatory processes, co-creation of knowledge 
with farmers and attention to social inequities (Bezner Kerr et al., 2021; Santoso et al., 2021; Snapp et al., 2021). To address smallholder 
vulnerability to climate change impacts, however, additional policy support beyond agroecology will be needed that is context specific; 
for example, addressing farmer capacity, limited political power to access land, water, seeds and other key natural resources, structural 
gender inequities, policy and market disincentives that support large-scale monocultures (high confidence) (Anderson et al., 2019a; HLPE, 
2019; Holt-Giménez et al., 2021; Snapp et al., 2021).(a) Feasibility
(b) Effectiveness
Adaptation categories
Assessement scoreAdaptation option
Shift in production
Policy and planning
Genetic improvement
Collective resource

Agronomic management (farm level)
diversificationSubstitution change plant or animal type
Regional local, food systems strengthening
Local governance
Community based adaptation
Diversification of livelihoods
Conventional breeding
Community seed/feed/fodder banks
Community forest management
Improving weather forecasting & early warning systems
Mixed systems
Agroecological approaches
On-farm diversification 
Landscape diversificationOrganic management
High
(3)Medium Low
(1)
Economic
SocialGeophysica lEnvironmental
TechnologicalMicroeconomic viabilit y
Macroeconomic viabilit y
Ecological capacit y
Adaptive capacity/resilience building potential
Physical feasibilit y
Transparency & accountability potential
Political acceptabilit y
Legal, regulatory feasibilit y
Institutional capacity &  administrative feasibilit y
Socio-cultural acceptabilit y
Technical reources availabilit y
Reduced risk vulnerabilit y
Enhanced social wellbeing
Improved environment

Increased economic resource s
Strengthen institutions
Maladaptiv e2.4
2.7
2.7
1.8
2.6
2.22.8
3.0
2.5
2.2
2.9
2.82.4
2.4
2.4
2.6
2.5
2.32.7
3.0
3.02.6
2.8
3.0
2.9
3.0
2.93.0
2.6
3.0
2.5
3.02.8
2.9
2.9
2.9
2.8
2.92.9
2.0
2.4
2.62.3
2.0
2.6
2.8
2.82.3
2.12.2
2.33.0
2.6
2.6
2.1
2.6
2.72.7
2.7
2.6
1.8
1.9
2.62.8
2.52.7
2.6
2.2
1.0
1.5
2.52.6
2.6
2.6
1.8
2.02.6
1.9
1.8
2.02.7
2.5
2.5
3.0
2.3
3.02.6
2.5
3.0
2.5
2.72.5
2.0
2.6
2.3
2.3
2.3
2.62.4
2.0
2.7
2.3
2.4
2.41.77
1.5
1.7
2.7
1.3
2.62.1
2.4
1.4
2.0
2.5
2.32.3
2.4
2.12.6
2.7
2.9
2.8
2.82.7
2.5
3.02.8
3.0
2.9
2.9
2.82.75
2.3
2.5
2.9
2.62.4
2.6
2.6
2.1
2.5
2.32.3
2.12.2
2.31.0
1.0
1.1
1.3
1.0
1.3
1.01.2
1.6
1.5
1.1
1.33.02.82.92.17
2.5
2.7 2.7 2.8 2.5 2.1 2.5 2.1 2.2 1.9 2.2 2.1 2.4 2.8 2.8 2.2 2.6 1.1
Figure 5.22 |  Assessment of 11 feasibility indicators (six categories), five effectiveness indicators and maladaptation of adaptation options based on 287

peer-reviewed papers. See SM5.7 for methods and data. Scores ranging from 1 (low) to 3 (high) were obtained by averaging five or more papers for each option and indicator. 
Blank cells were not assessed because of insufficient literature.

5
816Chapter 5 Food, Fibre and Other Ecosystem Products
Table Box 5.11.1 |  Dimensions of agroecological transitions as a transformative climate change adaptation strategy, benefits, trade-offs and constraints to implementation.
Different dimensions of agroecological transitions as a 
transformative climate change adaptation strategyLinks to climate change impacts, benefits, trade-offs and constraints to imple-
mentation with examples
Environmental: Agroecology can support long-term productivity and 
resilience of food systems by sustaining ecosystem services such as 
pollination, SOC, pest and weed control, soil microbial activity, crop 
yield stability, water quality and biodiversity (high confidence, see 
Section 5.4.4.4, Cross-Working Group Box BIOECONOMY this chapter and 
Cross-Chapter Box NATURAL in Chapter 2). (Isbell et al., 2017; Kremen and 
Merenlender, 2018; LaCanne and Lundgren, 2018; Beillouin et al., 2019b;

Dainese et al., 2019; Rosa-Schleich et al., 2019; Snapp et al., 2021). –Biodiversity of functional species groups and responses to climate hazards play an important 
role in building stability and productivity in agroecological systems (5.4.4.4). A 5-year study, for 
example, in Asia, Africa and Latin America found that smallholder farmers (<2 ha) increased yields 
by 25% through promoting pollination (Garibaldi et al., 2016).
 –Landscape complexity is an important feature of agroecology which can increase resilience to 
extreme events, such as pest and disease outbreaks or floods, and provide multi-purpose benefits 
(Sections 5.4.4; 5.10.4.2) (Paolotti et al., 2016; Reed et al., 2016; Kremen and Merenlender, 2018; 
LaCanne and Lundgren, 2018; Rosa-Schleich et al., 2019; Holt-Giménez et al., 2021).
 –Context-specific: some agroecological systems and practices have lower average crop productivity

than conventional systems, while others can have higher overall crop productivity and farm 
profitability (LaCanne and Lundgren, 2018; Barbieri et al., 2019; Rosa-Schleich et al., 2019).
Socio-cultural: Effective locally relevant agroecological approaches involve 
participatory processes, co-creation of knowledge with farmers and 
attention to social inequities, in doing so building farmer capacity (HLPE, 
2019; Bharucha et al., 2020; Holt-Giménez et al., 2021; Snapp et al., 2021). –Agroecology can emphasise social justice concerns, including gender inequities, considered crucial 
for climate change adaptations in food production to have positive impacts on food security 
and nutrition (Cross-Chapter Box GENDER in Chapter 18; (Smith and Haddad, 2015; HLPE, 2019; 
Sylvester and Little, 2020).
 –In some contexts, agroecological systems can draw on and support Indigenous knowledge, 
farming systems, networks and socio-cultural values (Catacora-Vargas et al., 2017).

Food security and nutrition: Agroecological practices can increase 
household food security and nutrition for producer households, with 
more evidence in low- and medium-income countries ( high confidence) 
(Darrouzet-Nardi, 2016; Demeke et al., 2017; Jones, 2017a; Kangmennaang 
et al., 2017; Pandey et al., 2017; Luna-Gonzalez and Sorensen, 2018; 
Bezner Kerr et al., 2019; Boedecker et al., 2019; Mulwa and Visser, 2020; 
Bezner Kerr et al., 2021; Santoso et al., 2021). –Combinations of practices, such as intercropping, crop rotation and crop diversification, often 
outperform individual practices for yield and food security outcomes (Beillouin et al., 2019b; 
Bezner Kerr et al., 2021).
 –Agroecological systems more effectively support food security and nutrition when complemented 
by nutrition and health education, participatory research and other public policies and 
programmes which address access to knowledge (high confidence; (HLPE, 2019; Bezner Kerr et al., 
2021; 7.4).

Economic: Agroecology can support socioeconomic resilience, through 
reducing reliance on purchased inputs, enhancing local and regional 
economies (HLPE, 2019; Bharucha et al., 2020; Holt-Giménez et al., 2021). –Multi-level policies and programmes that support urban and peri-urban networks with 
agroecological producers, including farmers’ markets, public procurement (e.g., school meals, 
hospitals), incentives for short food value chains, and participatory guarantee certification 
schemes which build producer–consumer networks are all ways to support agroecological 
transitions by consumers (high confidence) (Catacora-Vargas et al., 2017; Pérez-Marin et al., 2017; 
Mier y Terán Giménez Cacho et al., 2018; Anderson et al., 2019a; HLPE, 2019; Borsatto et al., 2020; 
González de Molina, 2020).
 –Transitions to agroecology at a global scale, however, may require considerable dietary shifts 
which vary by region, and have implications for total food production and farm-level revenues,

especially in the short term (medium confidence, (Muller et al., 2017; Seufert and Ramakutty, 
2017; Barbieri et al., 2019; Rosa-Schleich et al., 2019; Smith et al., 2019b; Smith et al., 2020a).
 –To address smallholder vulnerability to climate change impacts, additional policy support beyond 
agroecology will be needed that is context specific; for example, addressing farmer capacity, 
limited political power to access land, water, seeds and other key natural resources, structural 
gender inequities, policy and market disincentives that support large-scale monocultures 
(Anderson et al., 2019a; Holt-Giménez et al., 2021; Snapp et al., 2021).
Long-term investment: Timeframes are an important consideration, as an 
agroecological transition involves multiple overlapping stages, of reducing 
chemical inputs, experimenting with and applying new agroecological 
practices and adjusting them, redesigning the farm, strengthening short

value chains and producer networks (Gliessman, 2014; Padel et al., 2020). –In the short term, without policy support, the costs of implementing agroecological practices at 
the farm scale can outweigh ecological and adaptation benefits, although the timeframe required 
is context-specific (Padel et al., 2020).
 –In the long term, implementing agroecological practices can increase yields, yield stability 
and farm profitability, reduce risks, and build resilience alongside ecological, health and social 
co-benefits, but impacts are context-specific (Section 5.4.4.4, Rosa-Schleich et al., 2019; Bezner 
Kerr et al., 2021; Snapp et al., 2021).
 –In Malawi, for example, studies indicate that smallholder producers using agroecological practices 
improved food security and nutrition, livelihoods and provisioning ecosystem services after 2 years 
(Kangmennaang et al., 2017; Bezner Kerr et al., 2019; Kansanga et al., 2021), while in the UK,

farmers transitioning to agroecological practices took 3 or more years to realise benefits (Padel 
et al., 2020).Box 5.11 (continued)

5
817Food, Fibre and Other Ecosystem Products  Chapter 5
The creation of index insurance requires significant prior research 
and extensive data that may not be available or sufficient in lower-
income countries, including identifying the most appropriate farm and 
climate variables to include and financial and regulatory support from 
the public sector (Economic Commission for Latin America and the 
Caribbean and Central American Agricultural Council of the Central 
American Integration System, 2013; Economic Commission for Latin 
America and the Caribbean and System, 2014). Some insurance 
providers bundle it with other services, such as fertilizer use or seeds 
that may not be useful to particular farmers and can increase their 
overall capital costs (Isakson, 2015). Although proponents see IBAI 
as a way to mitigate farmers’ risks associated with more variable 
weather patterns (Greatrex et al., 2015), critics argue that derivative-

based insurance products tend to benefit wealthier farmers and fail in 
assisting the poorest and most marginalised farmers (Isakson, 2015; 
Taylor, 2016). Thus far, there is low agreement and medium evidence 
regarding the adaptation potential of derivatives-based insurance 
products, signalling a need for further research in this area.
5.14.1.4 Community-based adaptation approaches
Community-based adaptation (CbA) strategies, which involve locally 
driven, place-based adaptation approaches, can help build adaptive 
capacity to climate change impacts, but require explicit attention to 
power dynamics, respect for local and Indigenous knowledge systems, 
adequate resources, future climatic trends and coordination at 
multiple levels of governance to be effective (high confidence) (Spires 
et  al., 2014; Fernández-Giménez et  al., 2015; Nagoda, 2015; Ashley 
et al., 2016; Berner et al., 2016; Ensor et al., 2016; Avtar et al., 2019;

Lam et al., 2019; Silwal et al., 2019; McNamara et al., 2020; Piggott-
McKellar et al., 2020; Rossa, 2020; Uchiyama et al., 2020). Since AR5, there is strong evidence that participation of local stakeholders in 
adaptation planning and implementation improves communities’ 
capacity to monitor and respond to climate change impacts on food, 
fibre and forestry systems, provided that adequate resources and local 
knowledge on climate change exist. Participatory monitoring of climate 
change impacts and participatory scenario development to develop 
community action plans are examples, which can help strengthen 
community preparation for and response to climate impacts.
Community-based monitoring of forests, coral reefs, seagrass and 
mangroves are examples of local natural resource assessment that 
can support food security and livelihoods while informing regional and 
national climate change planning tools (Carter et  al., 2014; Gevaña

et al., 2018; Avtar et al., 2019). Negotiation among many stakeholders 
at multiple scales, including inclusive mechanisms to address power 
inequities in governance structures and communities, may be needed 
for CbA to be effective (Avtar et al., 2019; McNamara et al., 2020). 
Indigenous knowledge and community-based management of 
fisheries and aquaculture in the Arctic and Asia (Roux et  al., 2019; 
Chen and Cheng, 2020; Galappaththi et al., 2020a; Schott et al., 2020; 
Galappaththi et al., 2021) provide adaptive strategies for sustainable 
use. (Iticha and Husen, 2019). Community-based climate services in 
the Andes (managed through a collaboration of smallholder producers 
and an international partnership) built capacity and knowledge of 
climate change dynamics as well as trust in local climate institutions, 
providing meaningful information for regional responses to climate 
change impacts (Rossa, 2020). Community-based participatory

scenario planning can help identify multiple climate stressors and 
vulnerabilities to develop effective adaptation plans (Fernández-
Giménez et al., 2015; Bennett et al., 2016; Cross-Chapter Box MOVING 
PLATE this chapter).Different dimensions of agroecological transitions as a 
Policy tools: Investment in agroecological approaches that are designed 
for socio-ecological context, farmer-led schools, co-learning platforms, and 
networks of farmers, scientists, private sector and civil society can support 
agroecological transitions at a regional scale (high confidence) (Coe 
et al., 2014; Catacora-Vargas et al., 2017; Pérez-Marin et al., 2017; Mier 
y Terán Giménez Cacho et al., 2018; Anderson et al., 2019a; González de 
Molina, 2020; Lampkin et al., 2020; Padel et al., 2020; Snapp et al., 2021).

Policies can provide incentives (e.g., price premiums, access to credit, 
extension service, taxes, regulation) to support agroecological transitions 
by producers (HLPE, 2019; Rosa-Schleich et al., 2019; Gerard et al., 2020; 
SAPEA, 2020). –Farm scale and landscape diversity can affect the capacity for producers to implement 
agroecological systems. Small to mid-sized farms can more effectively integrate agroecological 
methods such as increasing landscape diversity, on-farm diversity and intercrops (medium 
confidence) (Garibaldi et al., 2016; Herrero et al., 2017; HLPE, 2019). Barriers to adopting 
agroecological practices for small to mid-sized farms include limited market options, subsidy 
and policy disincentives, lack of extension support, knowledge and insecure land tenure (Jacobi 
et al., 2017; Kongsager, 2017; Hernández-Morcillo et al., 2018; Iiyama et al., 2018; Anderson et al., 
2019a; Gerard et al., 2020).

–Barriers for large farms to transition to agroecological practices include knowledge gaps, cost, 
significant infrastructure and farm design changes, labour, psycho-social adjustments, policy 
disincentives and market lock-ins (Hill, 2014; Rosa-Schleich et al., 2019; Lampkin et al., 2020).
 –Some policies and initiatives support large-sized farms to transition to agroecology (Zhou et al., 
2014; Liebman and Schulte, 2015; Ajates Gonzalez et al., 2018; Bellon and Ollivier, 2018; Lampkin 
et al., 2020; Padel et al., 2020).
Other drivers of agroecological transitions can include crises 
(environmental, economic or social), social movements, changing 
socio-cultural values, addressing social inequities, and discourse 
(Pérez-Marin et al., 2017; Mier y Terán Giménez Cacho et al., 2018; 
Anderson et al., 2019a).Further research could provide context-specific information about economic and ecological benefits

of some practices and combinations, with effective policies to support their implementation ( high 
confidence) (HLPE, 2019; Rosa-Schleich et al., 2019; Snapp et al., 2021). Institutional support to 
monitor the ecosystem services climate change mitigation and adaptation impact of agroecological 
systems can inform policy, using systematic methods and indicators (e.g., Barrios et al., 2020; Mottet 
et al., 2020) including annual reporting to the United Nations Framework Convention on Climate 
Change (UNFCCC) (Snapp et al., 2021).Box 5.11 (continued)

5
818Chapter 5 Food, Fibre and Other Ecosystem Products
An assessment of 32 different CbA initiatives in the Pacific Islands, 
including addressing risks to food security, found high-performing 
projects had six key entry points: effective methods to improve 
adaptive capacity, appropriate to the local context, which moved 
beyond narrow geographical definitions of community to consider 
equity of impact, and ecosystem-based approaches, jointly addressing 
climatic and non-livelihood pressures and consideration of future 
climatic trends (McNamara et al., 2020). Low-performing initiatives, in 
contrast, were not sustained; these overlooked future climatic trends 
in their initiatives, such as beehive susceptibility to climate extremes, 
and had dependent, unequal relationships that lacked genuine local 
approval or ownership and did not fit local values and context (Spires 
et al., 2014; McNamara et al., 2020; Piggott-McKellar et al., 2020). CbA

initiatives can also suffer from not having adequate local knowledge of 
potential strategies to address future climatic scenarios, and may lead 
to maladaptation, increasing socioeconomic inequities in communities 
(Nagoda, 2015). Addressing inequity in power dynamics and building 
technical adaptive capacity of local people are some of the ways that 
CbA initiatives can support more resilient food systems (McNamara 
5.14.1.5 Local and regional food systems’ strengthening and 
food sovereignty
Food sovereignty brings together adaptation options based on 
agroecological methods, access to resources, collective and CbA (HLPE, 
2019). Addressing food security and nutrition in light of climate change 
impacts and vulnerabilities is considered to arise from a mixture of 
globalised supply chains and local production, not one or the other 
(Blesh et al., 2019; Stringer et al., 2020). Evidence on strengthening

local and regional food systems with a food sovereignty approach, 
in terms of access to resources (land, seeds, water), shortened food 
chains and CbA strategies suggest that these strategies can positively 
contribute to climate change adaptation in many contexts (medium 
confidence) (SRCCL) but can also lead to conflict especially regarding 
management of mobile resources such as fisheries (Section 5.8, Cross-
Chapter Box MOVING PLATE this chapter). All these options can build 
adaptation through actions that strengthen local capacities and the 
power to act within food systems. Securing and recognising tenure 
for Indigenous Peoples (Hurlbert et al., 2019) and local communities 
(Oates et al., 2020) can improve their ability to adapt by increasing 
the incentive to invest in resilient infrastructure and sustainable 
land management practices. Community seed banks and networks 
strengthen local seed systems and realise farmers’ rights favouring

access to a variety of local genetic resources, with landraces often 
more adapted to the local social, cultural and ecological environment 
and needs, and better adapted to harsh environments without external 
inputs (Mousseau, 2015; Bisht et al., 2018; Maharjan and Maharjan, 
2018; Otieno et  al., 2018; Mbow et  al., 2019). This plays a key role 
in PPB (Section 5.4.4.5; FAO, 2019e). The integration of informal and 
formal seed system elements is important for the adaptive capacity of 
smallholder farmers (Westengen and Brysting, 2014; Westengen and 
Berg, 2016; FAO, 2019e).
Strengthening both local and regional food systems is a strategy 
to increase resilience (Schipanski et  al., 2016; Palmer et  al., 2017), 
resource use efficiency (Mu et  al., 2019) and self-reliance (medium evidence, low agreement) (Griffin et al., 2015; Chapin et al., 2016; Karg 
et al., 2016). Collective trademarks (Quiñones-Ruiz et al., 2015) and

participatory guarantee systems (Niederle et al., 2020) are examples 
of innovative institutional strategies to strengthen local and regional 
food systems. In the urban context, the city region food system (CRFS) 
approach is motivated by reducing dependence on international trade 
and associated instability and to facilitate local decision making (Karg 
et  al., 2016). CRFS includes a network within a regional landscape 
around one urban centre and surrounding peri-urban and rural regions 
(Blay-Palmer et al., 2018). UPA is promoted as an effective strategy to 
adapt to climate change in different contexts (see Section  5.12.5.3, 
Dubbeling, 2015; Lwasa et  al., 2015). To cope with the effects of 
climate change, strengthening regional food systems is becoming 
an explicit part of urban and regional policy, being tested in many 
different cities worldwide (Dubbeling et al., 2017; Blay-Palmer et al., 
2018; Berner et al., 2019; Sellberg et al., 2020; van der Gaast et al.,

2020). Strengthening both local and regional food systems has to be 
balanced against limitations and trade-offs, since modelling exercises 
of regionalisation scenarios show urban agriculture cannot achieve 
food security in areas with rapid population growth (Le Mouël et al., 
2018). Furthermore, international trade can compensate in cases 
where the regional system fails due to extreme events or other related 
climate shocks (Section 5.11.8).
5.14.2 Enabling Conditions for Implementing Adaptation
5.14.2.1 Addressing social inequities in food systems
Addressing gender and other social inequities (e.g., racial, ethnicity, 
age, income, geographic location) in markets, governance and control 
over resources is a key enabling condition for climate-resilient 
transitions in land and aquatic ecosystems (high confidence) (Pearse, 
2017; Vermeulen et  al., 2018; Blesh et  al., 2019; Rao et  al., 2019b; 
Cross-Chapter Box GENDER in Chapter 18, Section 5,13,1; Tavenner

et  al., 2019). Adaptation strategies can have negative impacts on 
marginalised social groups and worsen socioeconomic inequities 
unless explicit efforts are made to address unequal power dynamics and 
differences in access to resources in agricultural, fisheries, aquaculture, 
livestock and forestry systems (high confidence) (Glemarec, 2017; 
Haji and Legesse, 2017; Nagoda and Nightingale, 2017; Nightingale, 
2017; Rao et al., 2019b; Huyer and Partey, 2020; Mikulewicz, 2020; 
Taylor and Bhasme, 2020; Eriksen et al., 2021). Technical approaches to 
adaptation that ignore inequities can worsen them; see, for example, 
the case study on Climate Smart Agriculture (Box  5.12). Enabling 
environments support inclusive decision making, capacity building, 
shifts in social rules, norms and behaviours and access to resources 
for marginalised groups for climate change adaptation (e.g., Tschakert 
et al., 2016; Ziervogel, 2019; Eriksen et al., 2021; Garcia et al., 2021).

5.14.2.2 Incorporating Indigenous knowledge and local 
knowledge
Indigenous knowledge (IK) and local knowledge (LK), while an 
important component of many adaptation strategies (Reyes-García, 
2014; Roue, 2018), continues to be marginalised in food systems; 
greater integration will increase effectiveness (high confidence)

5
819Food, Fibre and Other Ecosystem Products  Chapter 5
(Ford et  al., 2015; Brugnach et  al., 2017; Figueroa-Helland et  al., 
2018). Where  Indigenous  Peoples have access to and control over 
their lands and natural resources, food systems can potentially be 
more sustainably managed and more resilient (high confidence) 
(Rumbach and Foley, 2014; O’Connell-Milne, 2015; Camacho et  al., 
2016; Janhiainen, 2017; Kihila, 2018). For example, on Solomon 
Islands, community-based adaptation combining with IK-informed 
community mapping helped boost agricultural yields sustainably (Leon 
et al., 2015), and in China people living in rich plant resource regions 
have used their wild plants IK to complement the decrease of crop 
yields during extreme droughts to ensure food security (Zhang et al., 
2016). These cases have led scientists and local communities to call 
for more practical actions to bridge local knowledge, IK and formal

science (Borquez et al., 2017; Klenk et al., 2017; Mukhopadhyay, 2017; 
Olorunfemi, 2017; Reyes-Garcia et  al., 2019). Despite this increased 
public and scientific recognition, IK is often not acknowledged or used.
Effective adaptation requires a more holistic approach that includes 
the recognition of Indigenous rights, governance systems and laws 
(high confidence) (Robinson et al., 2016a; Brugnach et al., 2017; Magni, 2017; McMillen et al., 2017; McNeeley, 2017; Pearce et al., 2018), and 
to couple IK with proactive and regionally coherent adaptation plans, 
actions and cooperation (Shaffer, 2014; Melvin et al., 2017; Forbis Jr. 
and Hayhoe, 2018; Makondo and Thomas, 2018).
Supporting Indigenous groups’ knowledge and other excluded social 
groups can help preserve and harness underutilised resources to 
enhance nutritional and economic security, with careful measures in 
protecting Indigenous intellectual rights and avoiding commodification

exploitation (Nakashima et  al., 2012; Nandal and Bhardwaj, 2014; 
Ghosh-Jerath et al., 2015; Ebert, 2017). In some regions, there has been a 
loss of IK about food systems, reducing adaptive capacity (Richards et al., 
2019; Panikkar and Lemmond, 2020). Knowledge exchange between 
Indigenous elders and youth can support adaptive capacity (Osterhoudt, 
2018; Richards et al., 2019; Zin et al., 2019). Education utilising IK and 
LK can help prevent maladaptation options (high confidence) (Melvin 
et  al., 2017; Taremwa, 2017; Forbis Jr. and Hayhoe, 2018; Narayan 
et al., 2020). There are examples of integrating IK and LK into resource 
management systems and school curricula and in local institutions with 
existing decision-making process to strengthen their capacity to address 
Box 5.12: Is Climate-Smart Agriculture Overlooking Gender and Power Relations?

Climate-smart agriculture (CSA) is an approach that aims to increase agricultural productivity, enhance food security, adapt to climate 
change and, where possible, reduce GHG emissions. The effective implementation of climate-smart practices is conceptually linked to an 
enabling environment in which policies, institutions and finance can re-orient agricultural systems, thereby supporting development and 
enhancing food security in a changing climate (Lipper et al., 2014; Karttunen et al., 2017). However, the concept has received criticism 
based on the absence of conceptual clarity of the interrelations between productivity, food security, adaptation and mitigation (Arenas-
Sanchez et al., 2019) and because of limited evidence on the efficacy of CSA for achieving adaptation and mitigation outcomes at a global 
scale (Arslan et al., 2015; Lamanna et al., 2016; Chandra et al., 2018). Some argue that CSA operates within an apolitical framework that

tends to minimise issues concerning power, inequity and access, and is overly focused on technical approaches (Taylor, 2017; HLPE, 2019). 
CSA is explicitly referenced by more than 30 countries in their Intended Nationally Determined Contributions (INDCs) (Ross et al., 2016), 
but measuring the degree of its implementation still represents a challenge.
There is low agreement, medium evidence on the relationship between CSA and equity (Allen, 2018; Karlsson et al., 2018). CSA can 
potentially benefit women if they are able to take advantage of improvements in productivity, food security and adaptation decision 
making as a result of the implementation of CSA practices. Nevertheless, these advantages can be unequally realised given male 
domination in receiving information and extension services, as well as financial or resource access (Jost et al., 2016). Some (Huyer and

Partey, 2020) argue that CSA may undermine gender equity (Collins, 2018), entrench and solidify power (Haapala, 2018), and result in the 
disproportional allocation of new labour-intensive activities to women (Jost et al., 2016). Uptake of some climate-smart technologies can 
further marginalise the most disadvantaged local groups (Roncoli et al., 2009; Haapala, 2018). Unequal sharing of benefits and burdens 
with respect to emission reduction costs among different agricultural groups has also been observed (Budiman, 2019).
In contrast, emerging research points to the potential of CSA as a supporting condition for gender equity, provided that equity and power 
concerns are explicitly included in the approach (Chanana-Nag and Aggarwal, 2020). Some CSA technologies and practices, such as direct 
seeding, green manuring and laser land levelling, can have a significant role in reducing the gender gap in labour burden for women

in agriculture (Khatri-Chhetri et al., 2020). The use of participatory approaches can facilitate community-based adaptation of gender-
sensitive CSA practices (Rosimo, 2018). CSA may also empower both men and women: in two villages in India, CSA adoption empowered 
both sexes in decision making and use and control of income (Hariharan et al., 2018).
In general CSA programmes have tended to overlook questions of inequity (medium confidence), including limited attention to social 
conditions that promote Business-As-Usual pathways, although this is now changing. Addressing questions of rights, social injustice, 
unequal power relations and inequity would help make CSA-related policy responses more effective in addressing vulnerability (Chandra 
et al., 2017; Clapp and Isakson, 2018; Karlsson et al., 2018; Westengen et al., 2018; Ellis and Tschakert, 2019; Eriksen et al., 2019; 
Westengen et al., 2019).

5
820Chapter 5 Food, Fibre and Other Ecosystem Products
climate change (Huaman and Valdiviezo, 2014; McNamara and Prasad, 
2014; Abah et  al., 2015; Mistry and Berardi, 2016; Tschakert et  al., 
2017; McNeeley et al., 2018; McNeeley et al., 2020). However, there are 
limitations of IK and LK to address future climate impacts. Therefore, 
it is important that science-based knowledge and  other knowledge 
coalesce to produce solutions that are sustainable and viable in the face 
of projected impacts of climate change. Community-based adaptation 
approaches can integrate IK and LK and more formal knowledge 
systems, provided efforts to establish relationships of respect, trust and 
common understanding between different stakeholders involved (Herath 
et al., 2015; Camacho et al., 2016; Fidelman et al., 2017; Inaotombi and 
Mahanta, 2019; Lam et al., 2019).
5.14.2.3 System transformation and policy enablers
Recent literature highlights the future challenges of producing the

quantities of food needed to feed a growing world population in a 
way that satisfies nutritional needs, benefits everyone equally and 
equitably, and minimises the negative impacts of food systems on the 
environment and the natural resource base. There is broad agreement 
that current trajectories towards the SDGs and countries’ commitments 
under the Paris Agreement are slow and that transformation of food 
systems is needed (medium agreement, robust evidence) (Campbell 
et al., 2018; Brondizio et al., 2019; Dury et al., 2019; EAT-LANCET, 2019; 
FAO, 2019 f; Food and Land Use Coalition, 2019; Sachs et al., 2019; Searchinger, 2019a; Searchinger T, 2019b; Loboguerrero et al., 2020; 
Meridian Institute, 2020; Steiner A, 2020).
transformations, driven at least in part by a changing climate or 
changing climate variability. Such transformations may involve 
sometimes substantial shifts in farm and livelihood enterprises

and land configurations, including intensification, diversification, 
sedentarisation and abandonment of agriculture (Vermeulen et  al., 2018; 
Thornton et al., 2019). Relevant literature is summarised in Table 5.24, 
showing reported farmers’ perceptions of the drivers of change and 
the different outcomes of these changes. The consequences of these 
production system transitions have been mixed; in about 40% of cases, 
the outcomes at household level have been unequivocally beneficial. 
In the other cases, there were detrimental effects on livelihoods, or 
a mixture of positive and negative effects. The effects on nutritional 
security reported in these studies were limited. Different enablers of 
change appear critical if transitions are to have positive outcomes. 
Policy environments, defined in terms of multi-level governance 
structures and institutions, are a key driver of systems change, as well 
as being enablers of and barriers to adaptation responses (Xu et al.,

2008; Namgay et al., 2014; Galvin et al., 2015; Schmidt and Pearson, 
2016; Liao and Fei, 2017). Policies around property and grazing rights 
are directly linked to small-scale food producer vulnerability, and 
land ownership changes will pose a key challenge as climate change 
Box 5.13: Supporting Youth Adaptation in Food Systems
Young people are key agents in agrifood systems: both a vulnerable group, and one that can foster systemic change (high confidence) 
(Brooks et al., 2019; Figure X; IFAD, 2019; Flynn and Sumberg, 2021; HLPE, 2021). Food systems are the largest source of employment for 
young people, but do not always provide adequate livelihoods or decent working conditions (HLPE, 2021). Regions with more youthful 
populations—such as Sub-Saharan Africa, South Asia and Central America—are both highly vulnerable to climate change impacts and

reliant on agriculture, forestry, aquaculture and fisheries for livelihoods (Brooks et al., 2019; IFAD, 2019; HLPE, 2021). Rural youth in 
these sectors are particularly vulnerable, often with less access to land, water, capital and other resources, shaped by family and social 
relations, and fewer opportunities (high confidence) (Chingala et al., 2017; Ricker-Gilbert and Chamberlin, 2018; IFAD, 2019; Yeboah et al., 
2020; Flynn and Sumberg, 2021; Nhat Lam Duyen, 2021). In these vulnerable regions, climate change compounds other drivers such as 
poverty to increase youth out-migration to urban areas or other regions (medium confidence) (Zin et al., 2019; Weinreb et al., 2020; HLPE, 
2021; Stoltz et al., 2021; Voss, 2021), which can further worsen rural economies. Young low-income rural women may be particularly 
marginalised and vulnerable due to systemic gender inequities in access to land, credit, employment, institutions and other resources

(medium confidence) (Sah Akwen, 2017; IFAD, 2019; Flynn and Sumberg, 2021).
Youth play a critical role in all sectors of the food system (HLPE, 2021; Figure Box 5.13.1), and some are actively pursuing work and 
innovation in agrifood systems (medium confidence) (Sah Akwen, 2017; 2019; Yeboah et al., 2020; Flynn and Sumberg, 2021). Climate 
change impacts may reduce youth employment options in food systems in some regions, while they are often politically marginalised 
(Brooks et al., 2019; IFAD, 2019; HLPE, 2021). At the same time, due to heightened awareness about climate change, youth may be 
more willing to apply climate adaptation strategies (medium confidence) (Ali and Erenstein, 2017; Jiri et al., 2017; Sah Akwen, 2017; 
Chamberlin and Sumberg, 2021; Doherty et al., 2021). Agrifood policy implementation of adaptation strategies could increase inclusive

participation of youth to meet their needs (HLPE, 2021). Inclusive investments in water management, infrastructure, agrifood science, 
and policies that increase youth access to land, credit, knowledge, education, skills and other crucial resources can support dignified and 
rewarding agrifood employment (Ahsan and Mitra, 2016; Brooks et al., 2019; HLPE, 2021). Digital technologies can support agrifood 
adaptations, but digital divides must be overcome to avoid worsening inequities (HLPE, 2021). Initiatives which protect and strengthen 
youth engagement and employment in the all points of the food system, including recognition of youth’s critical role and agency through 
rights-based approaches, can support sustainable food transitions (HLPE, 2021). Harnessing youth innovation and vision to address 
climate change alongside other SDGs such as gender inequity and rural poverty will be a crucial strategy to ensure resilient economies in

food systems (high confidence) (Laube, 2016; Brooks et al., 2019; IFAD, 2019; Abay et al., 2021; HLPE, 2021).

5
821Food, Fibre and Other Ecosystem Products  Chapter 5
Food Systems
Food Environment s
Consumer
behaviourDiets
Climate
WaterHealth
LandConsumer
Family member
Corporat e
advertisingCorporat e
advertisingEducator
Media influencerYouth as agent of change
Collective action: associations, cooperatives, trade unions, social movements
FOOD SUPPLY CHAINSYouth agency, engagement and employment in food systems
Farm and primary 
Extension 
agentCaregiver ParentCottage 
entrepreneur Water
ScientistCoop 
memberRetail 
workerTeacherPlantation 
worker
Farmer, 
pastoralist, 
fisherDriverMigrant/-
seasonal  
workerInformal 
market wat StudentStorage and tradeProcessing 
and packagingRetail and 
marketing
Own-account worker/entrepreneurs
Wage worker
Family labour
Volunteer/intern/apprenticeEnvironmentt
and natural 
resources
Adapted from source: Elaborated by authors based on HLPE (2017, 2020a)Nutritional 
requirements 
specific to life 
course stage
Malnutrition 
(including

undernutrition, 
overweight and 
obesity
Figure Box 5.13.1 |  Youth agency, engagement and employment in food system (HLPE, 2021).Box 5.13 (continued)

5
822Chapter 5 Food, Fibre and Other Ecosystem Products
Table 5.24 |  Agricultural and livelihood system transformations from systematic searches of the literature, which are at least partially attributable to climatic factors and that 
involve increased or decreased system integration, and major consequences of the change. Information in the table is from the references cited. Sources: updated from (Vermeulen 
et al., 2018; Thornton et al., 2019).
Underlying production 
systemPrimary drivers of change 
as statedMajor processes of change as 
reportedConsequences of change, 
if reportedReference
Extensive grassland-based systems
Extensive grassland-based, 
northwest ChinaGovernment policy, climateSedentarisation
Diversification (crops, wages)Income decline, asset holding decline Liao and Fei, (2017)
Peruvian AndesMultiple climatic and non-climatic 
driversDiversification (wages, livestock assets, 
land)
ExtensificationLivestock accumulation in wealthy

households, asset diversification in 
poorer householdsLópez-i-Gelats et al., 
(2015)
BhutanGovernment policy, labour 
constraints, climateSedentarisation
Diversification (crops)
ExitIncreased risk, loss of cultural identity, 
improved market access, livelihood 
‘lock-in’ (inability to change rapidly)Namgay et al., (2014)
Borana, EthiopiaIncrease in climate variability, 
resource degradationLivestock herd diversification (more 
small stock and camels, fewer cattle)Enhanced household resilience Megersa et al., (2014)
Tibetan PlateauGovernment policy, climateSedentarisation
Diversification (crops, off-farm wages, 
trade)Increased food production, increased 
disease burdenXu et al. (2008)
Extensive grassland-based, Afar, 
EthiopiaGovernment policy, climateSedentarisation
Diversification (crops)Weakened institutions and cultural 
practices, deteriorating natural 
resourcesSchmidt and Pearson 
(2016)

Kajiado, KenyaGovernment policy, climate, 
population growthSedentarisation
Diversification (crops, wages, 
remittances)
IntensificationNutritional status remains poor Galvin et al. (2015)
Mongolian AltaiGovernment policy, climateSedentarisation
Diversification (cashmere sales, forest 
products)Fodder shortages, forest over-use, 
unsustainable land use systemLkhagvadorj et al. 
(2013)
Extensive grassland based, 
MongoliaIncreasing drought, grassland 
degradationDiversification (decreases in sheep and 
goats, increases in cattle, decreases in 
grain production, increases in fruit and 
vegetable production)
Exit from agricultureIncreased household income from 
off-farm employment, more diverse 
dietsDu et al. (2016)
northern KenyaClimate change and variability Diversification (crops, wages, migration)Decreasing adaptive capacity, 
over-dependence on local knowledge 
for adaptationOgalleh et al. (2012)

Extensive systems with crops
Extensive with crops, Eastern 
Cape, South AfricaMultipleIntensification (richer households)
Exit and abandonment (poorer 
households)
Livelihood diversificationWildlife conflicts, loss of cultural identityShackleton et al. 
(2013)
Extensive with crops, Peruvian 
highlandsEconomic globalisation, climate 
changeDiversification (dairy production, wage 
migration)
Conversion (away from staple crops to 
feed production)
Intensification (feed production)Reduced vulnerability to climate 
change, but potential loss of 
both agrobiodiversity and food 
self-sufficiency identified by the authorLennox (2015)
Extensive with crops, East Africa ClimateDiversification (crops, livestock, wages)
Intensification (crops, intercrops)Increasing household vulnerability Rufino et al. (2013)
Extensive with crops, GhanaClimate variability, temperature 
changeDiversification (off-farm activities) Reduced vulnerabilityAntwi-Agyei et al. 
(2018)
Extensive smallholder cropping,

NepalAnnual and seasonal warming. 
Increased precipitation with 
changes in patterns.Diversification and integration (from 
growing buckwheat and barley to 
vegetables and fruit trees)Increased household resilience due to 
diversification of productionKonchar et al. (2015)
Extensive smallholder mixed 
system, NigerDroughts and famines, and land 
degradationLarge-scale regeneration of native trees 
and shrubs in the arable landscapeIncreased household income, effects on 
household food security not yet knownHaglund et al. (2011)

5
823Food, Fibre and Other Ecosystem Products  Chapter 5
impacts in the marginal lands intensify (Reid et al., 2014). Collective 
action at multiple scales and effective governance structures are also 
a key enabler of transformational change, for helping community 
initiatives overcome economic, social and technical barriers, and to 
strengthen social capital and farmer knowledge (Haglund et al., 2011; 
Reed et al., 2017; Vermeulen et al., 2018; Fedele et al., 2019). Market 
development has been shown to be a critical factor for successful 
adaptation at scale in sub-Saharan Africa (Ouédraogo et  al., 2017; 
Iiyama et  al., 2018; Totin et  al., 2018). At the same time, financing 
mechanisms may be a crucial enabler for different food system actors: 
de-risking agricultural production and food system investments for 
producers and input suppliers, for example, that address core market 
failures and compensate actors for extra short-term costs that can

lead to longer-term benefits, particularly for small-scale producers and 
businesses with comparatively low access to technologies and services 
(Vermeulen et al., 2018; Millan, 2019; see Section 5.14.2.5).
The examples in Table 5.24 highlight the uneven impact of adaptation 
programmes and projects in general, due in part to differences in 
institutional support and failure of policies to take into account 
inequities (Clay and King, 2019; Nightingale et al., 2020). Focusing on 
transformational adaptation, Vermeulen (2018) suggested the need 
to expand the remit of adaptation planning to consider the multi-
functionality of agriculture and a system-wide view of food production 
and consumption. Several authors argue that transformational change 
must address the personal, practical and political spheres, in view 
of the role of power relations and worldviews in shaping practices, 
food security and inequity (O’Brien, 2015; Nightingale, 2017; O’Brien,

2018; Eriksen et al., 2019; Gosnell et al., 2019). If it involves new or 
unfamiliar technology, transformation may also be highly disruptive, 
and the added vulnerabilities of food system actors at risk will need to 
be addressed (Herrero et al., 2020; see Box 5.5).
‘Transformation’, defined by IPCC (2019a) as ‘a change in the 
fundamental attributes of natural and human systems’, is defined 
here as a redistribution of at least a third in the primary factors of 
production (land, labour, capital) and/or the outputs and outcomes of 
production (the types and amounts of production and consumption of 
4 For reference, the SEI Aid-Atlas (https://aid-atlas.org) only reports flows where adaptation is the principal objective, and therefore adaptation spending on agriculture, forestry and fisheries for the same 
period is significantly lower with USD 16.52 billion, that is, 21.4% of total adaptation spending.goods and services arising from multi-functional agricultural systems)

(Vermeulen et al., 2018; Thornton et al., 2019).
5.14.2.4 Finance needs and strategies for adaptation
Current understanding of finance flows and needs for adaptation in 
crop agriculture, livestock, fisheries, aquaculture and forest products 
relies primarily on top-down projections, with limited data (UNFCCC, 
2018; Buchner et  al., 2019; Jachnik et  al., 2019). By one estimate, 
in 2017/2018, agriculture, forestry and land use received 24% of 
public adaptation finance (totaling USD 7 billion; half via multilateral 
development finance institutions and one-quarter from governments) 
and 35% of international grants (with 71% used for adaptation) 
(Buchner et al., 2019). According to data from OECD (2020), finance 
flows for agriculture, forestry and fisheries have risen fairly linearly 
from ca. USD 1.46 billion in 2010 (the year the Rio marker on climate 
change adaptation was introduced) to ca.  5.5  billion in 2018. Over

the entire tracked period, the three subsectors combined received a 
total of USD 29.82 billion for activities with principal and significant 
adaptation components.4 However, the data set only includes climate-
related development finance from bilateral, multilateral and private 
philanthropic sources, whereas private sector finance flows are not 
captured as this is notoriously difficult to track (UNEP , 2016; OECD, 
2020; cross-ref to Cross-Chapter Box FINANCE in Chapter 17). Most of 
the funding (85%) was directed towards agriculture, with forestry (12%) 
and fisheries (3%) receiving significantly less, but across the subsectors, 
there is consistency in the sense that policy and administrative 
management and development receive the lion’s share of support, 
which is predominantly given in the form of grants (72%), while debt 
instruments (26%) and equity and shares in collective investment 
vehicles (2%) contribute less. From a regional perspective, 80% were

directed to Africa (47%), Asia-Pacific (27%), and Latin America and 
Caribbean States (7%), whereas Eastern Europe and Western Europe 
and Other States received (2%) each and 17% were destined for 
‘developing countries’ without regional tags. Finally, it is noteworthy 
that 38% of adaptation finance in agriculture, forestry and fisheries is 
marked as also having mitigation benefits, and roughly a quarter of 
funding is reported as having principal or significant gender objectives.Underlying production 
Other mixed coastal and forest systems
Coastal rice-based, BangladeshIncreased salinity due to reduced 
dry season flows from rivers in 
India, use of groundwater for 
irrigationDiversification (from rice cultivation to 
aquaculture of shrimp and prawn)Increased household income, increased 
engagement of women, increased

human disease vulnerabilityFaruque et al. (2017)
Smallholder cropping systems, 
coastal BangladeshIncreasing frequency and severity 
of floods since 2008Diversification (re-allocation of land 
from crops to aquaculture)
Exit (migration away from village)Mixed impacts on household incomes 
and seasonal migration frequencyFenton et al. (2017)
Smallholder mixed cropping in 
forested landscapes in IndonesiaFloods, drought, crop and 
livestock diseaseDiversification (re-allocation of land 
from forests to rubber plantations and 
rice)
Intensification (agroforestry)
Extensification (reforestation, forest 
protection)Locally, increased household incomes in 
general; more widely, some trade-offs 
with biodiversity, water, carbon stocksFedele et al. (2018)

5
824Chapter 5 Food, Fibre and Other Ecosystem Products
Whether current levels of growth in adaptation finance for agriculture, 
forestry and fisheries is keeping up with estimated needs cannot be 
assessed because of the large uncertainties that surround adaptation 
cost estimates (Cross-Chapter Box FINANCE in Chapter 17). There is, 
hence, high agreement that better assessment of adaptation costs 
of climate impacts requires considerably more research (Watkiss, 
2015; Diaz and Moore, 2017). A recent study focusing on investments 
needed to offset the effects of climate change on the prevalence 
of hunger concludes that investments in agricultural research and 
development (R&D) have to increase from USD 1.62 billion to USD 
2.77 billion per year between 2015 and 2050 (Sulser et al., 2021a). 
In addition to agricultural R&D, significant investment increases in 
water and infrastructure in the range of USD 12.7 billion and USD

10.8  billion are required, respectively, a considerable portion of 
which is relevant to the food system. In total, Sulser et al. (2021a) 
estimate that annual investment between USD 21.47  billion and 
USD 29.8  billion are needed to avoid sliding back from climate-
change-related increases in the prevalence of hunger but recognise 
the shortcomings of their approach and acknowledge that ‘a full 
analysis of adaptation to climate change in agriculture would 
require including many other social, economic, and environmental 
dimensions’. For comparison, World Bank (2010) estimated global 
costs of USD 70–100  billion per year for agriculture, forestry 
and fisheries, infrastructure, water resources, health, ecosystem 
services, coastal zones and extreme weather events to adapt to an 
approximately 2°C warmer world between 2010 and 2050. While the 
World Bank includes more sectors, more recent publications consider 
the resulting figures to be significantly too low (Baarsch et  al.,

2015; UNEP , 2016; Rossi and Miola, 2017; Hallegatte et  al., 2018; 
Markandya and González-Eguino, 2019; Chapagain et al., 2020; WGII 
Cross-Chapter Box FINANCE in Chapter 17). Therefore, despite the 
methodological and data challenges, further efforts are needed to 
better capture the economic risks of climate change and provide 
estimates of adaptation costs at global to national scales as well as 
across sectors (Watkiss, 2015; Diaz and Moore, 2017).
agriculture, fisheries, aquaculture and forestry (high confidence) (Shukla et al., 2019; FAO et al., 2020). Finance strategies can contribute 
to adaptation in these sectors in different ways (Table 5.25) and to 
different degrees. Standardised strategies have not yet been developed 
for specific adaptation needs, and in current practice, finance strategies 
are opportunistically deployed, with developing countries facing

particular challenges due to under-developed financial mechanisms 
(Omari-Motsumi et al., 2019).
Many types of financial instruments are employed by diverse actors 
(Table 5.26) guided by their mandates (e.g., development, commerce), 
capacity (investor, intermediary, donor) and risk appetite. Actors 
within a sector or local production area can coordinate their financial 
strategies towards common objectives (e.g., reduced supply chain 
loss) or participate in joint financial action such as blended finance 
structures that combine commercial and concessionary finance to 
catalyse additional private investment, enrich the pipeline of bankable 
projects, and test business models (FAO, 2020b).
Expanding access to financial services and pooling climate risks 
can enable and incentivise climate change adaptation (medium 
confidence) (Shukla et  al., 2019). To mobilise financial instruments 
(Table  5.27) towards adaptation needs, individual actors can apply

an adaptation lens to existing or new activities, accounting for 
investment characteristics (e.g., development stage; cash flow profile), 
requirements (e.g., amount; risk–return) and context (e.g., regulatory 
landscape) (Havemann et al., 2020). Risk-layering can match financial 
instruments to severity and probability climate risks (Hochrainer-
Stigler and Reiter, 2021).
5.14.2.5 Constraints on adaptation finance for food, feed, fibre 
and other ecosystem products
Flow of adaptation finance in the agriculture, fisheries, aquaculture 
and forestry sectors is impeded by weak measurement and 
benchmarking of financial and resilience outcomes (Kramer et  al., 
2019; Negra et  al., 2020), and challenges in assessing repayment 
capacity of investee producers and companies (medium confidence).

Immature information systems (e.g., weak analytics, fragmented Table 5.25 |  Potential adaptation finance strategies for categories of climate-related risks in the agriculture, fisheries, aquaculture and forestry sectors.
Finance strategies Reduced food availability Low food safety / dietary health Diminished livelihoods Declining ecosystem services
Reduce vulnerabilityAvoid staple failure: Vouchers to 
producers for improved production 
inputsDiversify production strategies: Invest 
in alternative crops/species/harvest 
methodsIncrease producer capacity: Fund 
technical assistance programmesIncentivise improved management: 
Improved access to credit based on 
environmental performance
Anticipate/minimise 
impactsMinimise impact of extreme 
weather: Fund early-warning 
systemsDiversify products in supply chains: 
Finance processing equipment for 
alternative food productsModerate food price spikes: 
National food reservesMinimise resource depletion:  
Subsidise micro-lending for

water-efficient technologies
Steer capital towards 
climate resilienceDevelop climate-resilient production 
technologies:  Fund R&D for 
improved genetics (crops, fish, 
livestock) and managementBuild nutrition-sensitive food systems: 
Finance early-stage market building for 
diversified food productsIncrease resilience of supply chain 
infrastructure: Finance improved 
storage and transport facilitiesDisincentivise low-resilience 
production: Screen investments 
based on climate risk disclosures
Pool climate-related 
risksDistribute climate-related risks: 
Securitise investments in production 
systemsDe-risk diversified food supply chains: 
Invest in producer aggregation to 
improve supply chain efficiencyInsure against supply chain 
risks: Subsidised index insurance 
programmesDetect high-risk production systems: 
Invest in supply chain monitoring/
traceability mechanisms
Compensate for 
climate-related 
impactsCompensate for production losses: 
Financial transfers to affected

producersAvoid food shortages: Subsidise food 
importationAvoid selling off productive assets: 
Fund social support for low-income 
householdsEcological restoration: Direct 
development aid to land 
rehabilitation projects

5
825Food, Fibre and Other Ecosystem Products  Chapter 5
standards) (Woodard et al., 2019; Negra et al., 2020) inhibit effective 
due diligence and impact assessment, contributing to uncertainty 
and low investor confidence (Havemann et al., 2020; NGFS, 2020). 
Improved characterisation of adaptation finance strategies (e.g., 
insurance, subsidies, blended finance) requires increased transaction 
volume (Millan et al., 2019) and analysis of financial (e.g., risk–return 
profile, investor demand) and resilience (e.g., reduced vulnerability) 
effects.
Use of climate-resilient financial strategies and instruments is limited 
by weak incentives, which commonly take the form of high upfront 
costs (Verdolini et  al., 2018), high transaction and intermediation 
costs (Havemann et al., 2020) and relatively long pay-off time. Tenant 
producers may not experience benefits from adaptation investments 
(Woodard et al., 2019). Investors seek low-risk, liquid investments and

credit-worthy counterparties (Havemann et al., 2020), yet small- and 
medium-sized producers and supply chain actors often lack access 
to formal credit. Given limited experience and weak information for 
adaptation finance, sub-optimal outcomes may include imbalanced 
allocation of public and private finance (e.g., to less vulnerable regions 
and producers; to lower-resilience investments; to short-term benefits) 
as well as inequitable division of risks and returns (e.g., within blended 
finance structures) (Clapp, 2017; World Bank, 2018; Attridge and 
Engen, 2019). Additionally, while risk-sharing finance strategies can 
deliver adaptation benefits, they do not inherently reduce overall risk 
and commonly cover only specified types of risks (Kellett and Peters, 
2014; Watson et al., 2015).Table 5.26 |  Potential adaptation finance objectives for major actors in agriculture, fisheries, aquaculture and forestry sectors.
Actors Potential adaptation finance objectives

Private sector: Focused on capturing positive externalities (i.e., lower risks or costs) from adaptation investments (Woodard et al., 2019). Major considerations include fiduciary 
responsibilities; expected rates of return (i.e., risk-adjusted; benchmarked to comparable investments); investment characteristics (e.g., liquidity, structure, size) and contribution to investor 
portfolio; material business risks (e.g., supply chain reliability; stranded assets); cost control (e.g., product losses; insurance); legal compliance; and sectoral requirements (e.g., climate risk 
disclosure) (Havemann et al., 2020).
Production companies or cooperatives –Supply chain transactions (e.g., trade finance)
 –Sustainable agricultural infrastructure (e.g., capital investment in storage or processing facilities to reduce exposure to climate risks)
 –Developing or accessing advisory services (weather data; agronomic information) (Orchard, 2019)

–Risk management (e.g., insurance/reinsurance; budget reserves)
Financial investors and intermediaries 
(e.g., banks, asset managers, 
venture capital; non-bank financial 
institutions) –Ownership shares in established companies (i.e., private equity) or large publicly traded companies (i.e., listed equities)
 –Debt issuance (e.g., working capital; catastrophe bonds; emergency loans)
 –Real estate investment
 –Financial derivatives
 –Technological research and development
 –(Impact investors) Bespoke non-financial sustainability objectives (e.g., fairtrade products; financial inclusion) (Havemann et al., 2020)
Public sector: Encompassing nearly commercial (e.g., specialised commodity boards; bond issuances), partially subsidised (e.g., low-interest loans) and fully subsidised (e.g., R&D; grants)

investments. Major considerations include avoiding negative impacts to citizens (e.g., food price spikes) and specific constituencies (e.g., catastrophic losses to producers) and maintaining/
enhancing public revenues (i.e., taxes from economic activity in agriculture, fisheries, aquaculture and forestry).
Government agencies and multilateral 
institutions –Strengthen enabling environments for sustainable production and ecosystem protection (e.g., price transparency; information exchange; 
international coordination)
 –Support demonstration projects for sustainable land and resource management (e.g., grants)
 –Disaster risk reduction (e.g., national disaster funds; social protection programmes; contingent credit lines; sovereign/sub-sovereign insurance 
(Global Commission on Adaptation, 2019)
 –Increase resilience through early-warning systems, infrastructure, and capacity building (e.g., climate change adaptation funds)

–Increase revenues for adaptation activities (e.g., income/luxury taxes)
 –Reduce production risks (e.g., agricultural subsidies)
 –Promote advanced technology implementation (e.g., tax incentives)
 –Coordinate and align donor funding with national priorities (e.g., multi-donor national climate change funds)
 –Incentivise and de-risk commercial investments (e.g., interest rate reduction programmes, structured financing, guarantee funds) 
(Woodard et al., 2019)
Methods to strengthen adaptation finance include updating 
regulations and policies to support adaptation finance instruments 
(e.g., climate accounting standards), requiring climate-risk disclosure, 
improved information-sharing among public and private sector actors 
and devolving funding to local actors (medium confidence) (Global 
Commission on Adaptation, 2019; Millan et al., 2019).
5.14.3 Climate Resilient Development Pathways
Climate resilient development pathways (CRDPs) introduced in AR5

(Denton, 2014) can briefly be described as ‘development trajectories 
that integrate adaptation and mitigation to realise the goal of 
sustainable development’ (see IPCC (2019a)) for a more extensive 
definition). Several characteristics were proposed in SR1.5 by which 
such CRDPs could be identified: consistency with principles of 
sustainable development; ability to deliver poverty reduction; ability 
to enhance social, gender, racial, ethnic and intergenerational equity; 
ability to deliver resilience to climate change and other shocks and 
stresses; and ability to protect species, biodiversity and ecosystem 
goods and services. There is an increasing literature, assessed in SR1.5, 
on adaptation pathways approaches, generally for specific regions, 
locations and subsectors.
Two recent examples directly related to agriculture and food are the 
following: sustaining agrarian livelihoods to mid-century of Nicaraguan

small-scale coffee producers using analyses of suitability and coffee

5
826Chapter 5 Food, Fibre and Other Ecosystem Products
Table 5.27 |  Major types of financial instruments suitable to adaptation finance in agriculture, fisheries, aquaculture and forestry sectors (adapted from Havemann et al., 2020).
Financial instrument Description
Equity: Ownership stake in a company (e.g., agricultural technology company; processing company) or collective investment vehicle (e.g., agriculture fund; Timber Investment Management 
Organization; commodity index fund) providing returns (via dividends and/or sale of equity shares) corresponding to business-related risk (e.g., higher return for higher risk and/or lower 
liquidity)
Listed equities Ownership of shares in a company listed in a public market
Private equity Ownership of shares in a company or other assets
Junior or risk-absorbing equity Ownership of lower-tier shares in a company (e.g., common stock) or collective investment vehicle (e.g., first-loss tranche)

Debt: Capital provided directly or indirectly (via banks or other third-party institutions) to users with defined repayment terms (i.e., timeframe, interest rate); more likely to deliver adaptation 
benefits when coupled with capacity building (e.g., technical assistance, education, analytics) (Woodard et al., 2019)
Loan, bond, note, credit lineDirect or indirect provision of capital (e.g., operating loans; dedicated credit line for agricultural trade); concessionary loans may allow for 
below-market interest rates
Soft loan Direct interest-free loan (e.g., funds provided in advance of good/service delivery)
Emergency loanLending in response to climate risks or impacts with repayment terms (e.g., return period) that consider necessary relief, recovery and 
reconstruction
Catastrophe bondRisk transfer instrument in which insurers or reinsurers provide high interest payments to investors in exchange for a payout (and repayment

deferment or forgiveness) activated by specific events (e.g., extreme weather)
Impact bond Subsidised investment providing capital upfront or based on defined outcomes
Subordinated loanConcessionary capital with a junior position (i.e., accepting higher risk of non-repayment and / or lower rate of return on investment) relative to 
other investors
Securitised investments Aggregation of equity or debt to offer marketable securities to a wider pool of investors with different risk–return appetites
Guarantees: Commercial and concessionary guarantees that provide compensation for losses due to specified risks (e.g., political risk, performance risk); more likely to deliver adaptation 
benefits when linked to robust underwriting standards and verification protocols (Woodard et al., 2019)
Credit guarantee Compensation for specified losses incurred by agricultural lenders

Payment, performance, surety bondsDe-risking mechanism for transactions between providers and buyers of goods/services; may be used in trade finance and other forms of 
intermediation
Insurance: Policies and other financial instruments that provide compensation for losses based on defined terms and conditions.
Production insuranceCompensation for specified losses related to production (e.g., insurance indexed to specific weather events) or supply chains (e.g., shipping 
insurance)
Market and price insurance Compensation for specified market-related losses (e.g., price or currency fluctuation)
Grants: Concessionary funding provided by public or philanthropic entities to support climate adaptation costs or outcomes (no expectation of repayment)
Direct supportFunding for provision of goods (e.g., fertilizer, seeds, nursery stock) or services (e.g., technical assistance, product storage) to producers, local

companies or intermediaries (e.g., for agronomic or business management expertise); can reduce credit risk when part of blended finance 
arrangements
Performance-based grantsGrants or other concessionary funding contingent on achievement of defined adaptation outcomes (with possible third-party verification 
requirement); may support development and testing of new approaches (i.e., design funding; challenges/prizes)
Governmental instruments
Policy incentivesPublic policies designed to stimulate adaptation action among targeted groups (e.g., producers, consumers, agri-businesses, financiers) including 
direct or indirect subsidies (e.g., producer payments, tax breaks, health insurance), procurement policies (e.g., low carbon and sustainability criteria; 
nutrition-sensitive school feeding programmes) and other fiscal measures (e.g., infrastructure development; funding R&D in climate-resilient 
practices or technologies) (Shukla et al., 2019)

Development aidInternational or domestic programmes that directly or indirectly fund adaptation actions including financial transfers (e.g., producer support or 
anti-poverty programmes) and subsidised credit (medium confidence) (Shukla et al., 2019)
Planning grants Financial support to governments for adaptation planning (e.g., via readiness programmes)
Other instruments
FintechData analytics and risk analysis models used to better assess borrowers’ repayment risk (e.g., due to crop failure) and reduce transaction costs 
(e.g., streamlined lending processes); applications may include financial inclusion (e.g., micro-financing; lending to small- and mid-size operators), 
alternative repayment programmes (e.g., for larger capital borrowing), insurance (e.g., more granular risk assessment) or digital strategies (e.g., 
crowdfunding, smallholder credit) (Agyekumhene et al., 2018)

Payment for Ecosystem Services (PES)Funds delivered to land and resource managers in exchange for compliance with specified sustainability practices or environmental outcomes; 
PES depends on willing payers (i.e., direct and indirect beneficiaries of ecosystem services such as governments, companies, conservation groups, 
philanthropies)

5
827Food, Fibre and Other Ecosystem Products Chapter 5
quality changes under an IPCC Special Report on Emissions Scenarios

(SRES) A2 emissions scenario (Läderach et al., 2017); and development of participatory pathways to mid-century under RCPs 4.5 and 8.5 support regional adaptation planning in Hawke’s Bay, New Zealand for agricultural producers and rural communities (Cradock-Henry et al., 2020). CRDPs mentioned in SROCC include shifting from providing coastal defences to adapting to seawater inundation in coastal regions (Renaud et al., 2015) and retreating coastal megacities (Solecki et al., 2017). Pathway frameworks continue to be used to frame the broad-scale challenges of development and climate change, thereby linking different types of food system actor with different responses through time using a variety of approaches, top-down and participatory, qualitative and quantitative (Butler et  al., 2016; Antle et  al., 2017; Thornton and Comberti, 2017; Collste et al., 2019; Loboguerrero et al., 2020; Stringer et al., 2020).
While there is consensus that the concept of CRDPs is useful, there

are major challenges in identifying, operationalising, monitoring and evaluating them (Lin et al., 2017; Bloemen et al., 2018). Management approaches seldom integrate across spatio-temporal scales and may be unable to address unidirectional change and extreme events (Holsman et  al., 2019). The socioeconomic complexities and implications of pursuing integrated outcomes make it difficult to evaluate synergies and trade-offs associated with different actions in local contexts through time (Thornton and Comberti, 2017; Ellis and Tschakert, 2019; Holsman et  al., 2019; Orchard, 2019). Case studies by Lo (2019) of transformation in a fishing town in south China and by Gajjar (2019) on undesirable path dependencies in development trajectories in urban and rural India show that overall adaptive capacity of populations may be decreased though politicisation and entrenchment of existing inequities, severely limiting the possibilities for future adaptation. A further challenge of implementation

is timely detection of tipping points and abrupt exposure events in both climate and environmental systems (Lenton et al., 2019; Trisos et al., 2020), which may alter the efficacy of current and planned adaptation actions, necessitating a switch to other, more transformational strategies; in such cases, re-energising food system actors’ commitment to adaptation action may well be needed (Bloemen et al., 2018).

Integrated modelling of CRDPs will increasingly be needed to throw

light on key SDG synergies and trade-offs into the future (Bleischwitz et al., 2018). In investigating possible future pressures on land under the SSPs, Doelman (2018) projected that the largest changes take place in sub-Saharan Africa in SSP3 and SSP4, mostly because of continued high population growth coupled with (projected) sluggish increases in agricultural efficiency, among other things, leading to expansion of agricultural land for crop and livestock production and reduced food security. Lassaletta (2019) evaluated global pig production in the SSPs and concluded that the future sustainability of pig systems will depend on production efficiency improvements coupled with other factors such as use of alternative feed sources and use of slurries on cropland. Such studies will be increasingly important for quantifying the potential trade-offs and synergies between different SDGs, to guide adaptation (and mitigation) action along CRDPs in the future. The current lack of widely

accepted and simple-to-measure indicators for tracking progress in adaptation is a significant hurdle to overcome. There is a large literature on the desirable characteristics of future global food systems, but much less on robust analysis that explicitly addresses and evaluates the pathways towards these desired futures. Gerten (2020) estimates that 10.2 billion people can be supported within key planetary boundaries via spatially redistributed cropland and dietary changes, among other actions. There are few, if any, analyses for detailing the plausible pathways to move towards such a future in ways that are socially, economically and environmentally acceptable through time; whether such pathways could indeed be made climate-resilient is unknown. Appropriate monitoring and rapid feedback to food system actors on what is working and why will be critical to the successful operationalisation of adaptation actions within CRDPs (Bosomworth and Gaillard, 2019).

5
828Chapter 5 Food, Fibre and Other Ecosystem Products
Cross-Working Group Box BIOECONOMY: Mitigation and Adaptation via the Bioeconomy
Authors: Henry Neufeldt (Denmark/Germany), Göran Berndes (Sweden), Almut Arneth (Germany), Rachel Bezner Kerr (USA/Canada), 
Luisa F Cabeza (Spain), Donovan Campbell (Jamaica), Jofre Carnicer Cols (Spain), Annette Cowie (Australia), Vassilis Daioglou (Greece), 
Joanna House (UK), Adrian Leip (Italy/Germany), Francisco Meza (Chile), Michael Morecroft (UK), Gert-Jan Nabuurs (the Netherlands), 
Camille Parmesan (UK/USA), Julio C Postigo (USA/Peru), Marta G. Rivera-Ferre (Spain), Raphael Slade (UK), Maria Cristina Tirado von der 
Pahlen (USA/Spain), Pramod K. Singh (India), Peter Smith (UK)
Summary Statement
The growing demand for biomass offers both opportunities and challenges to mitigate and adapt to climate change and 
natural resource constraints (high confidence). Increased technology innovation, stakeholder integration and transparent

governance structures and procedures at local to global scales are key to successful bioeconomy deployment maximising 
benefits and managing trade-offs (high confidence).
Limited global land and biomass resources accompanied by growing demands for food, feed, fibre and fuels, together with prospects for a 
paradigm shift towards phasing out fossil fuels, set the frame for potentially fierce competition for land5and biomass to meet burgeoning 
demands even as climate change increasingly limits natural resource potentials (high confidence).
Sustainable agriculture and forestry, technology innovation in bio-based production within a circular economy and international 
cooperation and governance of global trade in products to reflect and disincentivise their environmental and social externalities can 
provide mitigation and adaptation via bioeconomy development that responds to the needs and perspectives of multiple stakeholders to

achieve outcomes that maximise synergies while limiting trade-offs (high confidence).
There is high confidence that climate change, population growth and changes in per capita consumption will increase pressures on 
managed as well as natural and semi-natural ecosystems, exacerbating existing risks to livelihoods, biodiversity, human and ecosystem 
health, infrastructure and food systems (Conijn et al., 2018; IPCC, 2018; IPCC, 2019b; Lade et al., 2020). At the same time, many global 
mitigation scenarios presented in Intergovernmental Panel on Climate Change (IPCC) assessment reports rely on large greenhouse gas 
(GHG) emissions reduction in the Agriculture, Forestry, and Other Land Use (AFOLU) sector and concurrent deployment of reforestation/
afforestation and biomass use in a multitude of applications (Rogelj et al., 2018; AR6 WGIII Chapter 3 and Chapter 7; Canadell et al., 
2021; Lee et al., 2021)

Given the finite availability of natural resources, there are invariably trade-offs that complicate land-based mitigation unless land 
productivity can be enhanced without undermining ecosystem services (e.g., Obersteiner et  al., 2016; Campbell et  al., 2017; Caron 
et al., 2018; Conijn et al., 2018; Heck et al., 2018; WRI, 2018; Smith et al., 2019c). Management intensities can often be adapted to local 
conditions with consideration of other functions and ecosystem services, but at a global scale the challenge remains to avoid further 
deforestation and degradation of intact ecosystems, in particular of biodiversity-rich systems (Cross-Chapter Box on NBS-NATURAL in 
Chapter 2), while meeting the growing demands. Further, increased land use competition can affect food prices and impact food security 
and livelihoods (To and Grafton, 2015; Chakravorty et al., 2017), with possible knock-on effects related to civil unrest (Abbott et al., 2017; 
D’Odorico et al., 2018).

Developing New Bio-Based Solutions while Mitigating Overall Biomass Demand Growth
Many existing bio-based products have significant mitigation potential. Increased use of wood in buildings can reduce GHG emissions 
from cement and steel production while providing carbon storage (Churkina et al., 2020). Substitution of fossil fuels with biomass in 
manufacture of cement and steel can reduce GHG emissions where these materials are difficult to replace. Dispatchable power based on 
biomass can provide power stability and quality as the contribution from solar and wind power increases (WGIII Chapter 6), and biofuels 
can contribute to reducing fossil fuel emissions in the transport and industry sectors (WGIII Chapter 10 and Chapter 11). The use of bio-
based plastics, chemicals and packaging could be increased, and biorefineries can achieve high resource-use efficiency in converting

biomass into food, feed, fuels and other bio-based products (Aristizábal-Marulanda and Cardona Alzate, 2019; Schmidt et al., 2019). There 
is also scope for substituting existing bio-based products with more benign products. For example, cellulose-based textiles can replace 
cotton, which requires large amounts of water, chemical fertilizers and pesticides to ensure high yields.
5 For lack of space, the focus is on land only, although the bioeconomy also includes sea-related bioresources.

5
829Food, Fibre and Other Ecosystem Products  Chapter 5
While increasing and diversified use of biomass can reduce the need for fossil fuels and other GHG-intensive products, unfavourable 
GHG balances may limit the mitigation value. Growth in biomass use may in the longer term also be constrained by the need to protect 
biodiversity and ecosystems’ capacity to support essential ecosystem services. Biomass use may also be constrained by water scarcity and 
other resource scarcities and/or challenges related to public perception and acceptance due to impacts caused by biomass production 
and use. Energy conservation and efficiency measures and deployment of technologies and systems that do not rely on carbon, such 
as carbon-free electricity supporting, inter alia, electrification of transport as well as industry processes and residential heating (IPCC, 
2018; UNEP , 2019), can constrain the growth in biomass demand when countries seek to phase out fossil fuels and other GHG-intensive

products while providing an acceptable standard of living. Nevertheless, demand for bio-based products may become high where full 
decoupling from carbon is difficult to achieve (e.g., aviation, bio-based plastics, and chemicals) or where carbon storage is an associated 
benefit (e.g., wood buildings, bioenergy with carbon capture and storage (BECCS), biochar for soil amendments), leading to challenging 
trade-offs (e.g., food security, biodiversity) that need to be managed in environmentally sustainable and socially just ways.
Changes on the demand side as well as improvements in resource-use efficiencies within the global food and other bio-based systems 
can also reduce pressures on the remaining land resources. For example, dietary changes towards more plant-based food (where 
appropriate) and reduced food waste can provide climate change mitigation along with health benefits ( WGIII Chapter 7.4 and 12.4,

Willett et al., 2019) and other co-benefits with regard to food security, adaptation and land use (Mbow et al., 2019; Smith et al., 2019c; 
WGII Chapter 5). Advancements in the provision of novel food and feed sources (e.g., cultured meat, insects, grass-based protein feed and 
cellular agriculture) can also limit the pressures on finite natural resources (WGIII Chapter 12.4, Parodi et al., 2018; Zabaniotou, 2018).
Box Cross-Working Group Box BIOECONOMY .1: Circular Bioeconomy
Circular economy approaches (WGIII-12.6) are commonly depicted by two cycles, where the biological cycle focuses on regeneration in 
the biosphere and the technical cycle focuses on reuse, refurbishment and recycling to maintain value and maximise material recovery 
(Mayer et al., 2019a). Biogenic carbon flows and resources are part of the biological carbon cycle, but carbon-based products can be

included in, and affect, both the biological and the technical carbon cycles (Kirchherr et al., 2017; Winans et al., 2017; Velenturf et al., 
2019). The integration of circular economy and bioeconomy principles has been discussed in relation to organic waste management 
(Teigiserova et al., 2020), societal transition and policy development (Directorate-General for Research Innovation, 2018; Bugge et al., 
2019) as well as coronavirus disease 2019 (COVID-19) recovery strategies (Palahi et al., 2020). To maintain the natural resource base, 
circular bioeconomy emphasises sustainable land use and the return of biomass and nutrients to the biosphere when it leaves the 
technical cycle.
Biomass scarcity is an argument for adopting circular economy principles for the management of biomass as for non-renewable 
resources. This includes waste avoidance, product reuse and material recycling, which keep down resource use while maintaining product

and material value. However, reuse and recycling is not always feasible, such as when biofuels are used for transport and bio-based 
biodegradable chemicals are used to reduce ecological impacts where losses to the environment are unavoidable. A balanced approach 
to management of biomass resources could take departure in the carbon cycle from a value-preservation perspective and the possible 
routes that can be taken for biomass and carbon, considering a carbon budget defined by the Paris Agreement, principles for sustainable 
land use and natural ecosystem protection.
Land Use Opportunities and Challenges in the Bioeconomy
Analyses of synergies and trade-offs between adaptation and mitigation in the agriculture and forestry sectors show that outcomes 
depend on context, design and implementation, so actions have to be tailored to the specific conditions to minimise adverse effects

(Kongsager, 2018). This is supported in literature analysing the nexus between land, water, energy and food in the context of climate 
change, which consistently concludes that addressing these different domains together rather than in isolation would enhance synergies 
and reduce trade-offs (Obersteiner et al., 2016; D’Odorico et al., 2018; Soto Golcher and Visseren-Hamakers, 2018; Froehse and Schilling, 
2019; Momblanch et al., 2019).
Nature-based solutions addressing climate change can provide opportunities for sustainable livelihoods as well as multiple ecosystem 
services, such as flood risk management through floodplain restoration, saltmarshes, mangroves or peat renaturation (Cross-Chapter 
Box NATURAL in Chapter 2; UNEP , 2021). Climate-smart agriculture can increase productivity while enhancing resilience and reducing 
GHG emissions inherent to production (Lipper et al., 2014; Singh and Chudasama, 2021). Similarly, climate-smart forestry considers the

whole value chain and integrates climate objectives into forest sector management through multiple measures (from strict reserves Cross-Working Group Box BIOECONOMY (continued)

5
830Chapter 5 Food, Fibre and Other Ecosystem Products
to more intensively managed forests) providing mitigation and adaptation benefits (Nabuurs et al., 2018; Verkerk et al., 2020; WGIII 
Section 7.3)
Agroecological approaches can be integrated into a wide range of land management practices to support a sustainable bioeconomy and 
address equity considerations (HLPE, 2019). Relevant land use practices, such as agroforestry, intercropping, organic amendments, cover 
crops and rotational grazing, can provide mitigation and support adaption to climate change via food security, livelihoods, biodiversity 
and health co-benefits (Ponisio et al., 2015; Garibaldi et al., 2016; D’Annolfo et al., 2017; Bezner Kerr et al., 2019; Clark et al., 2019; 
Córdova et al., 2019; HLPE, 2019; Mbow et al., 2019; Renard and Tilman, 2019; Sinclair et al., 2019; Bharucha et al., 2020; Bezner Kerr

et  al., 2021;WGII Cross-Chapter Box  NATURAL in Chapter 2). Strategic integration of appropriate biomass production systems into 
agricultural landscapes can provide biomass for bioenergy and other bio-based products while providing co-benefits such as enhanced 
landscape diversity, habitat quality, retention of nutrients and sediment, erosion control, climate regulation, flood regulation, pollination 
and biological pest and disease control (WGIII Chapter12 Box on UNCCD-LDN, Christen and Dalgaard, 2013; Asbjornsen et al., 2014; 
Holland et al., 2015; Ssegane et al., 2015; Dauber and Miyake, 2016; Milner et al., 2016; Ssegane and Negri, 2016; Styles et al., 2016; 
Zumpf et al., 2017; Cacho et al., 2018; Alam and Dwivedi, 2019; Cubins et al., 2019; HLPE, 2019; Olsson et al., 2019; Zalesny et al., 2019; 
Englund et al., 2020). Such approaches can help limit environmental impacts from intensive agriculture while maintaining or increasing 
land productivity and biomass output.

Intensive agriculture vs. agroecological agriculture
Figure Cross-Working Group Box BIOECONOMY .1 | Left: High-input intensive agriculture, aiming for high yields of a few crop species, with large 
fields and no semi-natural habitats. Right: Agroecological agriculture, supplying a range of ecosystem services, relying on biodiversity and crop and animal diversity 
instead of external inputs, and integrating plant and animal production, with smaller fields and presence of semi-natural habitats. Credit: Jacques Baudry (left); Valérie 
Viaud (right), published in van der Werf et al. (2020).
Transitions from conventional to new biomass production and conversion systems include challenges related to cross-sector integration 
and limited experience with new crops and land use practices, including needs for specialised equipment (WGII Chapter 5.10, Thornton 
and Herrero, 2015; HLPE, 2019). Introduction of agroecological approaches and integrated biomass/food crop production can result in

lower food crop yields per hectare, particularly during transition phases, potentially causing indirect land use change, but can also support 
higher and more stable yields, reduce costs, and increase profitability under climate change (Muller et al., 2017; Seufert and Ramakutty, 
2017; Barbieri et  al., 2019; HLPE, 2019; Sinclair et  al., 2019; Smith et  al., 2019c; Smith et  al., 2020a). Crop diversification, organic 
amendments and biological pest control (HLPE, 2019) can reduce input costs and risks of occupational pesticide exposure and food 
and water contamination (Gonzalez-Alzaga et al., 2014; European Food Safety Authority Panel on Plant Protection Products and their 
Residues et al., 2017; Mie et al., 2017), reduce farmers’ vulnerability to climate change (e.g., droughts and spread of pests and diseases 
affecting plant and animal health; Delcour et al., 2015; FAO, 2020a) and enhance provisioning and sustaining ecosystem services, such as

pollination (D’Annolfo et al., 2017; Sinclair et al., 2019).
Barriers towards wider implementation include absence of policies that compensate landowners for providing enhanced ecosystem 
services and other environmental benefits, which can help overcome short-term losses during the transition from conventional practices 
before longer-term benefits can accrue. Other barriers include limited access to markets, knowledge gaps, financial, technological or Cross-Working Group Box BIOECONOMY (continued)

5
831Food, Fibre and Other Ecosystem Products  Chapter 5
labour constraints, lack of extension support and insecure land tenure (Jacobi et al., 2017; Kongsager, 2017; Hernández-Morcillo et al., 
2018; Iiyama et  al., 2018; HLPE, 2019). Regional-level agroecology transitions may be facilitated by co-learning platforms, farmer 
networks, private sector, civil society groups, regional and local administration, and other incentive structures (e.g., price premiums, 
access to credit, regulation) (Coe et al., 2014; Pérez-Marin et al., 2017; Mier y Terán Giménez Cacho et al., 2018; HLPE, 2019; Valencia 
et al., 2019; SAPEA, 2020). With the right incentives, improvements can be made with regard to profitability, making alternatives more 
attractive to landowners.
Governing the Solution Space
Literature analysing the synergies and trade-offs between competing demands for land suggest that solutions are highly contextualised

in terms of their environmental, socioeconomic and governance-related characteristics, making it difficult to devise generic solutions 
(Haasnoot et al., 2020). Aspects of spatial and temporal scale can further enhance the complexity, for instance where transboundary 
effects across jurisdictions or upstream–downstream characteristics need to be considered, or where climate change trajectories might 
alter relevant biogeophysical dynamics (Postigo and Young, 2021). Nonetheless, there is broad agreement that taking the needs and 
perspectives of multiple stakeholders into account in a transparent process during negotiations improves the chances of achieving 
outcomes that maximise synergies while limiting trade-offs (Ariti et al., 2018; Metternicht, 2018; Favretto et al., 2020; Kopáček, 2021; 
Muscat et  al., 2021). Yet differences in agency and power between stakeholders or anticipated changes in access to or control of

resources can undermine negotiation results even if there is a common understanding of the overarching benefits of more integrated 
environmental agreements and the need for greater coordination and cooperation to avoid longer-term losses to all (Aarts and Leeuwis, 
2010; Weitz et al., 2017). There is also the risk that strong local participatory processes can become disconnected from broader national 
plans, and thus fail to support the achievement of national targets. Thus, connection between levels is needed to ensure that ambition for 
transformative change is not derailed at the local level (Aarts and Leeuwis, 2010; Postigo and Young, 2021).
Decisions on land uses between biomass production for food, feed, fibre or fuel, as well as nature conservation or restoration and other 
uses (e.g., mining, urban infrastructure), depend on differences in perspectives and values. Because the availability of land for diverse

biomass uses is invariably limited, setting priorities for land use allocations therefore first depends on making the perspectives underlying 
what is considered as ‘high-value’ explicit (Fischer et al., 2007; Garnett et al., 2015; de Boer and van Ittersum, 2018; Muscat et al., 2020). 
Decisions can then be made transparently based on societal norms, needs and the available resource base. Prioritisation of land use for 
the common good therefore requires societal consensus-building embedded in the socioeconomic and cultural fabric of regions, societies 
and communities. Integration of local decision making with national planning ensures local actions complement national development 
objectives.
International trade in the global economy today provides important opportunities to connect producers and consumers, effectively 
buffering price volatilities and potentially offering producers in low-income countries access to global markets, which can be seen as an

effective adaptation measure (Baldos and Hertel, 2015; Costinot et al., 2016; Hertel and Baldos, 2016; Gouel and Laborde, 2021; WGII 
Chapter 5.11).. But there is also clear evidence that international trade and the global economy can enhance price volatility, lead to food 
price spikes and affect food security due to climate and other shocks, as seen recently due to the COVID-19 pandemic (WGII Chapter 5.12, 
Cottrell et al., 2019; WFP-FSIN, 2020; Verschuur et al., 2021). The continued strong demand for food and other bio-based products, mainly 
from high- and middle-income countries, therefore, requires better cooperation between nations and global governance of trade to more 
accurately reflect and disincentivise their environmental and social externalities. Trade in agricultural and extractive products driving land 
use change in tropical forest and savanna biomes is of major concern because of the biodiversity impacts and GHG emissions incurred in

their provision (WGII Cross-Chapter Paper 7, Hosonuma et al., 2012; Forest Trends, 2014; Henders et al., 2015; Curtis et al., 2018; Pendrill 
et al., 2019; Seymour and Harris, 2019; Kissinger et al., 2021).
In summary, there is significant scope for optimising use of land resources to produce more biomass while reducing adverse effects (high 
confidence). Context-specific prioritisation, technology innovation in bio-based production, integrative policies, coordinated institutions 
and improved governance mechanisms to enhance synergies and minimise trade-offs can mitigate the pressure on managed as well as 
natural and semi-natural ecosystems (medium confidence). Yet, energy conservation and efficiency measures, and deployment of 
technologies and systems that do not rely on carbon-based energy and materials, are essential for mitigating biomass demand growth as 
countries pursue ambitious climate goals (high confidence).Cross-Working Group Box BIOECONOMY (continued)

5
832Chapter 5 Food, Fibre and Other Ecosystem Products
FAQ 5.1: How is climate change (already) affecting people’s ability to have enough nutritious food?
Climate change has already made feeding the world’s people more difficult. Climate-related hazards have become more common, disrupting 
the supply of crops, meat and fish. Rapid changes in weather patterns have put financial strain on producers, while also raising prices and 
limiting the choices and quality of produce available to consumers.
Most of our food comes from crops, livestock, aquaculture and fisheries. Global food supply increased dramatically 
in the last century, but ongoing climate change has begun to slow that growth, reducing the gains that would have 
been expected without climate change. Regionally, negative effects are apparent in regions closer to the equator, 
with some positive effects further north and south.

Climate impacts are also negatively affecting the quality of produce, from changes in micronutrient content to 
texture, colour and taste changes that reduce marketability. With warmer and more humid condition, many food 
pests thrive, food decays more quickly, and food contains more toxic compounds produced by fungi and bacteria.
Warming of the oceans has reduced potential fish catch. The increased carbon dioxide in the atmosphere has led to 
ocean acidification, which is already impacting the production of farmed fish and shellfish. Changes in local climate 
have forced producers to shift to new locations, changing what they grow or where they work (e.g., pole-ward 
shifting fishing grounds).
Climate hazards have increased over the past 50 years and are the major cause of sudden losses of production 
(food production shocks). Food shocks occur following droughts, heatwaves, floods, storms and outbreaks of

climate-related pests and combine to cause multiplying impacts. Climate hazards sometimes disrupt food storage 
and transport, which impairs the food supply.
All of these negative impacts can lead to increased food prices, and reduced income for producers and retailers as 
there are fewer products to sell. Together, these impacts threaten to reduce the supply of varied, nutrient-rich foods 
to poor populations that already suffer ill health.

5
833Food, Fibre and Other Ecosystem Products  Chapter 5
Crops(a)
Livestock(b)
Fisher ies(c)
Aquaculture(d)
0.00 .2 0.40 .6Shockfrequency
0.000.020.040.06
1970 1980 1990 2000 2010Shockfreq.(e)
1970 1980 1990 2000 2010Shockfreq.(f)
1970 1980 1990 2000 2010Shockfreq.(g)
YearShockfreq.(h)Trends in food production shocks in different food supply sectors from 1961-2013
Figure FAQ5.1.1 |  Trends in food production shocks in different food supply sectors from 1961 to 2013 (Cottrell et al., 2019). The red lines in 
the time series are the annual shock frequency, and the dashed line is the decadal mean.Box FAQ 5.1 (continued)

5
834Chapter 5 Food, Fibre and Other Ecosystem Products
FAQ 5.2: How will climate change impact food availability by mid and late century and who will suffer most?
Climate change impacts will worsen over time, with the period after mid-century seeing more rapid growth in negative impact than in the 
early part of this century. The impacts will be global, but people with fewer resources, and those who live in regions where impacts will worsen 
more rapidly, will be hurt the most.
Climate change impacts will worsen over time, but the extent depends on how rapidly greenhouse-gas emissions 
grow. If the current rate of emissions continues, the impacts will worsen, especially after mid-century, with rapid 
growth in the number and severity of extreme weather events. Yields of plants, animals and aquaculture will 
decline in most places, and marine and inland fisheries will suffer. Food production in some regions will become

impossible, either because the crops or livestock there cannot survive in the new climatic conditions, or it is too hot 
and humid for farm workers to be in the fields.
After harvest, agricultural production passes through the agricultural value chain, supplying animal feeds, industrial 
uses and international markets, with some stored for use in the future. Each of these transitions will be affected 
by climate change. Food storage facilities will face more challenges in dealing with spoilage. Transportation of 
perishable fruits, vegetables and meats will become costlier to maintain quality. Households and food services will 
need to spend more on food preservation.
Low-income countries and poor people are at higher risk, as they have limited social safety nets and suffer more 
from rising food prices and an unstable food supply. But large famers will also be hurt. Rural communities, especially

smallholder farmers, pastoralists and fishers, are extremely vulnerable because their livelihoods mainly depend on 
their production. The urban poor will have to spend more on food.
A flood, for example, may force low-income families out of their homes, affect their employment and reduce their 
access to food supplies, with prices often rising after natural disasters. Families will have less access to safe water 
supplies, and this combination of lower food supplies, uncertain employment, displacement from home and rising 
food costs will increase the number of children who are undernourished.
Impacts of climate change in the food system
Yields reduced
Producer income fallsPests and disease damage 
reduce quality and quantityLosses of perishable items to 
higher temperatures/humidity
More expense to marketing 
systemMore spoilage,
reduced availability
Impacts in other sectors 
reduce income available
Maize yields fall by 23 % in the 
21st century with high GHG

emissions (SSP585)Aflatoxin contamination will 
increase in maize in a +2°C 
temperature scenario in EuropeIncrease in temperature from 
17°C to 25 °C increases cold 
storage power consumption by 
about 11%Uptake of methyl mercury in fish 
and mammals has been found 
to increase by 3-5% for each 
1°C rise in water temperatureProduction (farm, 
pasture, fisheriesPost-harvest, 
storageTransportationConsumption-quality 
and availability
Examples (taken from individual sectors of chapter 5)
Figure FAQ5.2.1 |  Impacts of climate change on the food system.

5
835Food, Fibre and Other Ecosystem Products  Chapter 5
FAQ 5.3: Land is going to be an important resource for mitigating climate change: how is the increasing 
competition for land threatening global food security and who will be affected the most?
Climate change will affect food production. Meeting future food needs requires greater land shares unless we change what we eat and how 
we grow food. Additionally, large-scale land projects that aim to mitigate climate change will increase land competition. Less land will then 
be available for food production, increasing food insecurity. People at greater risk from land competition are smallholder farmers, Indigenous 
Peoples and low-income groups.
Why is land important?
Land is a limited resource on which humans and ecosystems depend on to grow plants, which capture carbon 
dioxide and release oxygen, and provide food, timber and other products. We also have cultural, recreational and

spiritual connections to land.
Climate impacts will increase competition for land use
Reforestation will reduce climate 
impacts but increase land competition
Sea level rise will lead to coastal 
flooding and land use changeCompetition of food production for 
people, livestock and fish
Figure FAQ5.3.1 |  Climate impacts will increase competition for land use, reducing coastal land for crops and affecting food security for 
vulnerable groups. Adaptation methods like coastal aquaculture and mangrove reforestation reduce climate effects but may increase land competition.

5
836Chapter 5 Food, Fibre and Other Ecosystem Products
Why will climate change affect land use?
Climate change results in more frequent heatwaves, extreme rainfall, drought and rising sea levels, which negatively 
affect crop yields. More land is thus needed to grow crops, increasing land competition with other food systems 
that use crops to feed their animals (e.g., livestock, fish). Where land will be flooded, humans cannot grow crops, 
but food production could be adapted to grow seafood instead. Extensive land allocations aiming at reducing 
carbon emissions, such as afforestation, reduce land availability for food. Unless carefully managed, competition 
for land will increase food prices and food security.
Solutions to reduce land competition and protect food security
Sustainable land management allows land to remain productive and support key functions. Other land practices

include growing cover crops to improve soil quality. Governments can provide incentives to producers to grow 
alternative foods and use sustainable practices. Making sure that vulnerable groups (e.g., low-income communities, 
Indigenous people and small-scale producers) strengthen land tenure rights will help protect food security.
Food by-products used as alternative food sources and other products reduce waste and increase sustainability. 
Dietary changes are another important solution. People that eat high amounts of meat or unhealthy foods could 
reduce consumption of these foods and have more diverse diets. These dietary changes will benefit their health and 
reduce pressure on land. Regulated labelling, education and other policies which encourage healthy diets can 
support these shifts.Box FAQ 5.3 (continued)
FAQ 5.4: What are effective adaptation strategies for improving food security in a warming world?

A variety of adaptation options exist to improve food security in a warming world. Examples of adaptation for crop production include crop 
management and livelihood diversification. For livestock-based systems, an example is matching number of animals with the production 
capacity of pastures. For fisheries, eliminating overfishing is an effective adaptation practice. For mixed cropping and nature-based systems, 
an appropriate adaptation is agroforestry.
Adaptation strategies to enhance food security vary from farm-level interventions to national policies and 
international agreements. They cover the following dimensions of food security: availability, access, utilisation 
(food quality and safety) and stability.
For the production of crops, adaptation strategies include field and farm-level options such as crop management, 
livelihood diversification and social protection such as crop insurance. The most common field management options

are changes in planting schedules, crop varieties, fertilizers and irrigation. For example, farmers can shift their 
planting schedules in response to the early or late onset of the rainy season. Moreover, there are new crop insurance 
schemes that are based on changes in weather patterns.
For livestock-based systems, adaptation options include matching the number of animals with the production 
capacity of pastures; adjusting water management based on seasonal and spatial patterns of forage production; 
managing animal diet; more effective use of fodder, rotational grazing; fire management to control woody 
thickening of grass; using more suitable livestock breeds or species; migratory pastoralist activities; and activities to 
monitor and manage the spread of pests, weeds and diseases.
For ocean and inland fisheries, adaptation options are primarily concentrated in the socioeconomic dimension and

governance and management. In general, eliminating overfishing could help rebuild fish stocks, reduce ecosystem 
impacts, and increase fishing’s adaptive capacity. Aquaculture is often viewed as an adaptation option for fisheries 
declines. However, there are adaptation strategies specific to aquaculture, including proper species selections at the 
operational level, such as the cultivation of brackish species (shrimp, crabs) in inland ponds during dry seasons and 
rice–freshwater finfish in wetter seasons.
For so-called mixed farming systems that produce a combination of crops, livestock, fish and trees, these systems’ 
inherent diversity provides a solid platform for adaptation. A good example is agroforestry, the purposeful 
integration of trees or shrubs with crop or livestock systems, which increases resilience against climate risks.

5
837Food, Fibre and Other Ecosystem Products Chapter 5
Overall, nature-based systems or ecosystem-based strategies in food systems, such as agroecology, can be a useful 
adaptation method to increase wild and cultivated food sources. Agroecological practices include agroforestry, intercropping, increasing biodiversity, crop and pasture rotation, adding organic amendments, integration of livestock into mixed systems, cover crops and minimising toxic and synthetic inputs with adverse health and environmental impacts.Box FAQ  5.4 (continued)
FAQ 5.5: Climate change is not the only factor threatening global food security: other than climate action, what 
other actions ar
e needed to end hunger and ensure access by all people to nutritious and sufficient food all year 
round?

Our food systems depend on many factors other than climate change, such as food production, water, land, energy and biodiversity. People’s access to healthy food can be also be affected by factors such as poverty and physical insecurity. We are all stakeholders in food systems, whether as producers or consumers, and we can all contribute to the goal of a food-secure world by the choices we make in our everyday lives.
Today more than 820 million people are hungry, and hunger is on the rise in Africa. Two billion people experience

moderate or severe food shortages, and another 2 billion suffer from overnutrition, a state of obesity or being overweight from unbalanced diets, with related health impacts such as diabetes and heart disease. The changing climate is already affecting food production. These effects are worsening, affecting food production from crops, livestock, fish and forests in many places where people already do not have enough to eat. Food prices will be affected as a result, with increasing risk that poorer people will not be able to buy enough for their families. Food quality will increasingly be affected too.
Our ability to grow and consume food depends on many factors other than climate change. There are tight 
connections between food production, water, land, energy and biodiversity, for example. Other factors like gender inequity, poverty, political exclusion, remoteness from urban centres and physical insecurity can all affect people’s access to healthy food.

Food systems are complicated (Figure FAQ5.5.1). To improve food production, supply and distribution, we need

to make changes throughout the food supply chain. For instance: improving the way farmers access the inputs needed to grow food; improving the ways in which food is grown, with climate and market information, training and technical know-how, water-saving and water-harvesting technologies; adopting new low-cost and less carbon-intensive storage and processing methods; and creating local networks of producers and processors For food consumers, we could consider shifts to different diets that are healthier and make more efficient use of natural resources; depending on context, these could involve rebalancing consumption of meat and highly processed foods, reducing food loss and waste, and preparing food in more energy-efficient ways. Policymakers can enable such actions through appropriate price and trade policies, implementing policies for sustainable and low-emission agriculture, providing safety nets where needed, and empowering women, youth and other socially disadvantaged groups.

Our food systems need to be robust and sustainable; otherwise we will not be able to manage the additional 
pressures imposed on them by climate change. We can all contribute to this goal.

5
838Chapter 5 Food, Fibre and Other Ecosystem Products
Conceptual framework of food systems for diets and nutrition
Biophysical & 
environmental driversInnovation, 
technology & 
infrastructure driversPolitical & economic 
driversSocio-cultural drivers
Food environments
Food availability & access
Affordabilit y
Promotion, advertising, 
Food quality & safetyConsumer behaviour
Choosing where and what 
food to acquire, prepare, 
cook, store, eatDiets
Quantity , quality , 
diversity , safety Social, 
economic, 
impactsFood supply chains
Production systems
Storage & distribution
Processing & packaging
Retail & markets
Political, programme & institutional actions
Sustainable Development Goals
Availability Access Utilization
Demographic drivers
Nutrition & 
health outcomes
Figure FAQ5.5.1 |  Conceptual framework of food systems for diets and nutrition (modified from HLPE, 2017a).Box FAQ 5.5 (continued)

6
909Cities, Settlements and Key Infrastructure Chapter 6
In all cities and urban areas, the risk faced by people and assets 
from hazards associated with climate change has increased (high confidence
1). Urban areas are now home to 4.2 billion people, the 
majority of the world’s population. Urbanisation processes generate vulnerability and exposure which combine with climate change hazards to drive urban risk and impacts (high confidence). Globally, the most rapid growth in urban vulnerability and exposure has been in cities and settlements where adaptive capacity is limited, especially in unplanned and informal settlements in low- and middle-income nations and in smaller and medium-sized urban centres (high confidence). Between 2015 and 2020, urban populations globally grew by more than 397 million people, with more than 90% of this growth taking place in less developed regions. {Box 6.1; 6.1.4; 6.2.1; 6.3.2; 6.3.3.4; 6.2.2.2; 6.4.4}

The documentation of climate-related events and observed 
human and economic losses have increased since AR5 for urban areas and human settlements. Observed losses arise from single, compound, cascading and systemic events (medium evidence, high agreement). Losses from single events include the direct impact

of heat stress on human health. Compound event losses arise from the interaction of single climate hazards with at least one other hazard driver such as heat with poor air quality (e.g., from traffic fumes or wildfire), flooding with poor water quality (e.g., from contaminated runoff and flood water) or land subsidence. Cascading impacts are observed when damages in one place or system reduce resilience and generate impacts elsewhere (e.g., when flood waters damage energy infrastructure causing blackouts and knock on financial and human impacts). Losses become systemic when affecting entire systems and can even jump from one system to another (e.g., drought impacting on rural food production contributing to urban food insecurity) (medium confidence). In some cases, maladaptive responses to hazards have exacerbated inequality in the distribution of impacts, for example shifting risk from one community to another. {Figure 6.2; 6.2.6; 6.3.4.1; 6.4.5; Cross-Chapter Paper 2; Cross-Working

Evidence from urban and rural settlements is unequivocal; climate 
impacts are felt disproportionately in urban communities, with the most economically and socially marginalised being most affected (high confidence). Vulnerabilities are shaped by drivers of inequality, including gender, class, race, ethnic origin, age, level of ability, sexuality and non-conforming gender orientation, framed by cultural norms, diverse values and practices (high confidence). Intersections between these drivers shape unique experiences of vulnerability and risk and the adaptive capacities of groups and individuals. Robust adaptation plans are those developed in inclusive ways. However, few adaptation plans for urban areas and infrastructure are being developed through consultation and co-production with diverse and marginalised urban communities. The concerns and capacities of marginalised communities are rarely considered in planning (medium

expressed using five qualifiers: very low, low, medium, high and very high, and typeset in italics, for example, medium confidence. For a given evidence and agreement statement, different confidence 
levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.confidence). {Box  6.3, Box  6.4; 6.4.3.1; 6.4.5.2, Case Study 6.7; 
Cross-Working Group Box URBAN in Chapter 6}
The COVID-19 pandemic has had a substantial impact on 
urban communities and climate adaptation (medium evidence, high agreement). The pandemic has revealed both systemic

under-investment resulting in multiple, persistent health-related vulnerabilities (many of which also exacerbate climate change risk) and co-benefits for urban interventions to reduce future pandemic and climate change risk. The COVID-19 pandemic is estimated to have pushed an additional 119 to 124  million people into poverty in 2020, with South Asia and Sub-Saharan Africa each contributing roughly two-fifths of this total (medium confidence). At city level, community groups, non-governmental organisations (NGOs) and local governments face challenges to bring agencies already working on social and economic development into coordinated action to reduce urban vulnerabilities and manage risks. COVID-19 and climate change impacts are exacerbated by widening social inequality. Addressing the causes of social vulnerability creates opportunity for transformative adaptation. {Box  6.4; 6.1.4; 6.2.2.4; 6.2.5; 6.4.1.3; Case Study 6.4; Cross-Chapter Box COVID in Chapter 7}

The number of people expected to live in urban areas highly 
exposed to climate change impacts has increased substantially (high confidence).  An additional 2.5  billion people are projected

to be living in urban areas by 2050, with up to 90% of this increase concentrated in the regions of Asia and Africa. Projections of the number of people expected to live in urban areas highly exposed to climate change impacts have increased. Sea level increase and increases in tropical cyclone storm surge and rainfall intensity will increase the probability of coastal city flooding,  with more than a billion people located in low-lying cities and settlements expected to be at risk from coastal-specific climate hazards by 2050 (high confidence).  Sea level rise, increases in tropical cyclone storm surges and more frequent and intense extreme precipitation will increase the number of people, area of urban land, and damages from flood hazard (high confidence). The main driver for increased heat exposure is the combination of global warming and population growth in already-warm centres, and the majority of the population exposed to heatwaves will live in urban centres. An additional

350 million people living in urban areas are estimated be exposed to water scarcity from severe droughts at 1.5°C warming, and 410.7 million at 2°C warming. {6.1; 6.2.2; Cross-Chapter Paper 2}

Many more cities have developed adaptation plans since AR5, 
but only a limited number of these have been implemented (medium confidence). Many of these plans focus narrowly on climate risk reduction, missing opportunities to advance co-benefits of climate mitigation and sustainable development, compounding inequality and reducing well-being (medium confidence). However, an increasing array of adaptation options are available. Nature-based solutions are now mainstream urban adaptation options and there remains considerable scope for their wider application. Social-policy-based adaptation, including education and the adaptation of health systems offers

6
910Chapter 6 Cities, Settlements and Key Infrastructure
considerable future scope. Options of adapting physical infrastructure 
are similarly advancing, though at times constrained by existing 
infrastructure design and location. The greatest gaps between policy 
and action are in failures to manage adaptation of social infrastructure 
(community facilities, services and networks) and failure to address 
complex interconnected risks for example in the food–energy–water–
health nexus or the inter-relationships of air quality and climate risk 
(medium confidence). Barriers to implementing plans include lack of 
political will and management capacity, limited financial means and 
mechanisms (especially for smaller urban settlements) and competing 
priorities (limited evidence, high agreement). {6.3.1, 6.4.3; 6.4.5; 
6.4.5.1; 6.4.5.2; Figure 6.5}
The shift from urban planning to action in ways that identify and 
advance synergies and co-benefits of mitigation, adaptation and

Sustainable Development Goals (SDGs) has occurred slowly and 
unevenly (high confidence). While there is ambition for joined-up 
policy, action and research, this is still the exception. One area of 
sustained effort is community-based adaptation planning and resilience 
actions which have potential to be better integrated to enhance 
well-being and create synergies with the Sustainable Development 
Goal ambitions of leaving no one behind. Complex trade-offs and gaps 
in alignment between mitigation and adaptation over scale and across 
policy areas where sustainable development is hindered or reversed 
also remain. {6.1.1, Table 6.2; 6.1.5; 6.4.1.4; 6.4.3; 6.4.4}
Urban adaptation gaps exist in all world regions and for all 
hazard types, although exposure to the limits to adaptation is 
unevenly distributed. Governance capacity, financial support 
and the legacy of past urban infrastructure investment 
constrain how all cities and settlements are able to adapt

(high confidence). Critical capacity gaps exist at city and community 
levels that hinder adaptation. These include the limited ability to 
identify social vulnerability and community strengths; the absence of 
integrated planning to protect communities; and the lack of access 
to innovative funding arrangements and limited capability to manage 
finance and commercial insurance (medium confidence). These can 
be addressed through enhanced locally accountable decision making 
with sufficient access to science, technology and local knowledge to 
support widespread application of adaptation solutions. {6.3.1, 6.4.3; 
6.4.5; 6.4.5.1; 6.4.5.2; Figure 6.4; Figure 6.5}
Slow uptake of monitoring and evaluation frameworks 
constrains potential for developing climate resilient urban 
development pathways (medium confidence). A lack of agreement 
on metrics and indices to measure urban adaptation investment, 
impacts and outcomes, reduces the scope for sharing lessons and

joined-up action across interconnected sectors and places in the face 
of compound and systemic risks. These constraints affect the potential 
for climate resilient development pathways. Limits to adaptation are 
often most pronounced in rapidly growing towns and cities and smaller 
settlements including those without dedicated local government. At 
the same time, legacy infrastructure in large and mega-cities, designed 
without taking climate change risk into account, constrains innovation 
leading to stranded assets and increasing numbers of people unable to 
avoid harm, including heat stress and flooding, without transformative 
adaptation. {6.2.5; 6.3.3.3; 6.3.7; Figure 6.4; 6.4.4; 6.4.6 FAQ6.5}City and local governments are key among multiple actors 
facilitating climate change adaptation in cities and settlements 
(medium confidence). City and local governments can invest directly 
and work in partnership with community, private sector and national

agencies to address climate risk. Private and business investment in key 
infrastructure, housing construction and through insurance requirements 
can also drive widespread adaptive action, though at times excluding 
the priorities of the poor (medium confidence). Networked community 
actions can also go beyond neighbourhood-scale improvements to 
address widespread vulnerability. Such actions include fostering roles 
of intermediaries and multiple spaces for networked governance 
across scales of decision-making, improving development processes 
through an understanding of social and economic systems, foresight, 
experimentation and embedded solutions, and social learning. 
Transnational networks of local government can also enhance city level 
capacity, share lessons and advocacy (medium confidence). {Table 6.2; 
6.3.3.4; 6.3.3.5; 6.4.1; 6.4.1.1; Case Study 6.2; FAQ 6.5}
Globally, decisions about key infrastructure systems and urban

expansion drive risk creation and potential action on climate 
change (high confidence). Urban infrastructure concentrates and 
connects populations, physical assets and energy use. Urban expansion 
and the compromising of green infrastructure and ecosystem services 
reduce adaptive capacity and can increase risk: the urban heat island, a 
product of expansion, can add 2°C to local warming. How settlements 
and key infrastructure are planned, designed and maintained 
determines patterns of exposure, social and physical vulnerability 
and capacity for resilience. Unplanned rapid urbanisation, including 
peri-urban development, is a major driver of risk, particularly where 
cities and settlements are expanding into land that is prone to coastal 
flooding or landslides, or where there is inadequate water to meet 
the needs of growing populations. Urban decision making processes 
equally shape how far low- and zero-carbon development can meet

social needs; enhancing well-being while enabling climate change 
mitigation and advancing the SDGs. {6.1.3; 6.2.3; 6.2.4; 6.3.3; 6.3.4; 
6.3.5; 6.4.6; Cross-Working Group Box URBAN in Chapter 6}
Investment in urban adaptation has not kept pace with inno-
vations in policy and practice (medium confidence). Knowledge 
transfer and innovation in adaptation has broadened advances in so-
cial and ecological infrastructures including disaster risk management, 
social policy and green/blue infrastructure, especially where these are 
integrated with grey/physical infrastructure (medium evidence, high 
agreement). Innovation has also taken place at the interface of differ -
ence systems, for example information and communications technolo-
gy (ICT) and water or energy, although financial investment has been 
slow to recognise and support these activities. Adaption finance con-
tinues to be directed at large-scale grey/physical engineering projects,

neglecting maintenance and reproducing risk of stranded assets if 
climate change risk accelerates beyond planned-for levels. Finance de-
ployed at the interface of multiple, integrated adaptation measures 
can support climate resilient development (high confidence). Access to 
finance is most difficult for city, local and non-state actors, and in con-
ditions where governance is fragile. {6.3.3; 6.3.4; 6.3.6; 6.4.5; 6.4.5.2; 
Table 6.10; Table 6.11; Box 6.8; Case Study 6.2; Case Study 6.3; Case 
Study 6.5}

6
911Cities, Settlements and Key Infrastructure  Chapter 6
Global urbanisation offers a time-limited opportunity to work 
toward widespread and transformational adaptation and climate 
resilient development (high confidence). Current dominant models 
of energy intensive and market-led urbanisation build high carbon 
dependency and high vulnerability into cities, but this need not be the 
case. Integrated development planning that connects innovation and 
investment in social, ecological and grey/physical infrastructures can 
significantly increase the adaptive capacity of urban settlements and 
cities. Transitioning cities to low carbon development and equitable 
resilience may lead to trade-offs with dominant models of economic 
growth based on housing and infrastructure investment. Integrated 
planning approaches are important for climate resilient development to 
enable planning and monitoring of interactions between development,

mitigation and adaptation. Urban adaptation measures can offer 
a considerable contribution to climate resilient development. This 
potential is realised by adaptations that extend predominant physical 
infrastructure approaches to also deploy nature-based solutions and 
social interventions. The most consistent limit for all infrastructure types 
is in risk transfer. Current adaptation approaches in cities, settlements 
and key infrastructure tend to move risk from one sector or place to 
others. Multi-level leadership and institutional capacity, together with 
financial resources (including climate finance) to support inclusive 
and sustainable adaptation in the context of multiple pressures and 
interconnected risks, can help to ensure that global urbanisation of an 
additional 2.5  billion people by 2050 reduces rather than generates 
climate risk (medium confidence). {Table  6.7; Table  6.5; 6.1.3; 6.3.6; 
6.3.5.2; 6.4.7; Box 6.5; Cross-Working Group Box URBAN in Chapter 6;

Cross-Chapter Paper 2}
Intersectional, gender-responsive and inclusive action can ac-
celerate transformative climate change adaptation. The great-
est gains in well-being in urban areas can be achieved by 
prioritising investment to reduce climate risk for low-income 
and marginalised residents and targeting informal settlements 
(high confidence). These approaches can advance equity and envi-
ronmental justice over the long term in ways more likely to lead to 
outcomes that reduce vulnerability for all urban residents. Participatory 
planning for infrastructure provision and risk management to address 
climate change and underlying drivers of risk in informal and under -
serviced neighbourhoods, the inclusion of Indigenous knowledge and 
local knowledge, communication and efforts to build local leader -
ship, especially among women and youth, are examples of inclusive 
approaches with co-benefits for equity. Providing opportunities for

marginalised people, including women, to take on leadership and par -
ticipation in local projects can enhance climate governance and its 
outcomes (high confidence). Since AR5 (IPCC, 2014), social movements 
in many cities, including movements led by youth, Indigenous and eth-
nic communities have also heightened public awareness about the 
need for urgent, inclusive action to achieve adaptation that can also 
enhance well-being. {6.1.5; 6.3.5; 6.4.1.2; 6.4.7; Box 6.6, Case study 
6.2; Case study 6.4, FAQ6.3}
City and infrastructure planning approaches that integrate 
adaptation into everyday decision making are supported by the 
2030 Agenda (the Paris Agreement, the SDGs, the New Urban 
Agenda and the Sendai Framework for Disaster Risk Reduction) 
(high confidence). The 2030 Agenda provides a global framework for city and community level action to be points of alignment between 
Nationally Determined Contributions, National Adaptation Plans and

the SDGs. City and local action can complement, and at times go 
further than national and international interventions. Similarly, the 
Convention on Biological Diversity offers a global agreement through 
which nature-based solutions can be viewed as benefits for biodiversity, 
social justice and climate resilience. However, there is no specific global 
agreement that addresses informality and city-level climate adaptation. 
More comprehensive and clearly articulated global ambitions for city 
and community adaptation will contribute to inclusive urbanisation, 
by addressing the root causes of social and economic inequalities 
that drive social exclusion and marginalisation, so that adaptation 
can directly support the 2030 Sustainable Development Agenda (high 
confidence). {6.1.1; Table 6.2; 6.2.3.2; 6.4.1.4; Case Study 6.4}

6
912Chapter 6 Cities, Settlements and Key Infrastructure
6.1 Introduction and Points of Departure
6.1.1 Background and Chapter Outline
Cities and urbanising areas are currently home to over half the world’s 
population. What happens in cities is crucial to successful adaptation 
(Grafakos et  al., 2019). By 2050, over two thirds of the world’s 
population is expected to be urban, many living in unplanned and 
informal settlements and in smaller urban centres in Africa and Asia 
(high confidence) (UNDESA, 2018). Between 2015 and 2020, urban 
populations globally have grown by about 397  million people, with 
more than 90% of this growth taking place in less developed countries 
(UNDESA, 2018). Projections of the number of people expected to live 
in urban areas highly exposed to climate change impacts have also 
increased, exacerbating future risks under a range of climate scenarios. 
Rates of population growth are most pronounced in smaller and

medium-sized settlements of up to 1 million people (UNDESA, 2018).
Since AR5, there has been increasing understanding of the 
interdependence of meta-regions, large, small and rural settlements 
which may be connected through key infrastructure (Lichter and Ziliak, 
2017), including national and trans-national infrastructure investments 
(Hanakata and Gasco, 2018). Almost all the world’s non-urban 
population and its provisioning ecosystems are impacted by urban 
systems through connecting infrastructure and family and kinship ties, 
remittances and trade arrangements that influence flows of water, 
food, fibre, energy, waste and people (Trundle, 2020; McIntyre-Mills 
and Wirawan, 2018; Zhang et al., 2019; Nerini et al., 2019; Friend and 
Thinphanga, 2018). Many rural places are so deeply connected to 
urban systems that risks are observed to cascade from one to the other, 
for example, when drought in arable zones leads to food insecurity in

cities, or where flood damage to urban transport infrastructure leads 
to prolonged isolation of small towns and rural settlements (Friend 
and Thinphanga, 2018; McIntyre-Mills and Wirawan, 2018). A focus 
of this chapter is the experience of a range of urban settlements, 
from small to large, and the connecting infrastructure and formal and 
informal networks and systems that join them to each other. There are 
close synergies with Chapters 7 (Health, Well-being and the Changing 
Structure of Communities) and 8 (Poverty, Livelihoods and Sustainable 
Development). There are further important synergies with Working 
Group III Chapter 8 (Urban Systems and Other Settlements) and the 
Cross-Chapter Paper 2: Cities and Settlements by the Sea.
Well-planned climate adaptation can have far reaching co-benefits 
for sustainable development and community well-being (Nerini et al., 
2019; Tonmoy et  al., 2020). However, the varied success of cities’

responses to the global COVID-19 pandemic underscores how social 
and economic conditions, built environments and local planning 
can exacerbate or reduce vulnerability and long-term sustainable, 
community well-being (Megahed and Ghoneim, 2020; Plastrik et al., 
2020; Hepburn et al., 2020; Sarkis et al., 2020).
Many of the significant sustainable development initiatives that have 
been proposed and implemented in the last five years recognise the 
critical importance of cities, settlements and key infrastructure in 
responding to the crisis of climate change (Zhang et al., 2019; Nerini 
et al., 2019). There is widespread acceptance of the need for far-reaching responses by actors from the local to the global scales to make human 
settlements and infrastructure more resilient (UNDP , 2021). There is 
recognition also of the considerable capacity in settlements to meet 
climate change challenges, if the governance, financial and social

conditions are in place (Carter et al., 2015; MINURVI, 2016). And yet 
the implementation of climate adaptation planning lags behind climate 
mitigation efforts in urban communities (Sharifi, 2020; Grafakos et al., 
2019; Nagendra et al., 2018).
Since the publication of AR5, there has been rapid expansion in 
policy, practice and research related to climate change and human 
settlements. The 2030 Agenda for Sustainable Development (the SDGs) 
agreed in September 2015, was preceded by the Sendai Framework for 
Disaster Risk Reduction 2015-30 and followed shortly afterwards by 
the Paris Agreement (December 2015) (United Nations, 2015b). These 
make explicit mention of ‘mainstreaming of disaster risk assessments 
into land use policy development and implementation, including urban 
planning’ (Sendai Framework) (UNISDR, 2015). The agreements identify 
‘sustainable cities and communities’ (SDG11) and ‘cities and subnational

authorities’ (Paris Agreement) as important actors in integrating climate 
and development goals (Sanchez Rodriguez, Ürge-Vorsatz and Barau, 
2018). However not all urban SDGs have measurable targets yet, 
or data, particularly in regard to children and youth, the elderly and 
disabled (Klopp and Petretta, 2017; Reckien et al. , 2017; Nissen et al. , 
2020). Clear procedures for linking climate adaptation in communities 
at all scales to the SDGs is lacking (Major, Lehmann and Fitton, 2018; 
Sanchez Rodriguez, Ürge-Vorsatz and Barau, 2018).
The New Urban Agenda (NUA) (October 2016), with its focus on 
housing and sustainable urban development, commits its signatories 
to building resilient and responsive cities that foster climate change 
mitigation and adaptation (United Nations, 2016b). This agreement 
followed the Geneva UN Charter on Sustainable Housing, endorsed 
by 56 member states of the United Nations Economic Commission

for Europe (United Nations, 2015d). The NUA aims to ensure access 
to decent, adequate, affordable and healthy housing for all, while 
reducing the impact of the housing sector on the environment and 
increasing resilience to extreme weather events (United Nations, 
2016b). Voluntary, networked action led by cities was also illustrated 
by a November 2019 call to Mayors and youth climate activists to sign 
a voluntary pledge in a ‘Race to Zero’ ahead of the Conference of the 
Parties 26, which included endorsing principles of a New Green Deal 
(C40, 2019). Other voluntary, global, urban efforts have been led by 
the scientific community including the Research and Action Agenda 
on Cities and Climate Change Science which aims to promote research 
and reports (Prieur-Richard, Walsh and Craig, 2019).
These collaborative global changes are reflected in the bodies of 
literature assessed for this report. In AR5, the section on ‘human

settlements, industry, and infrastructure’ contained three chapters: 
urban areas; rural areas; and key economic sectors and services. 
This chapter covers the full range of human settlements: from small 
settlements in predominantly rural areas, to large metropolises in 
both high-income and low-income countries. It also assesses evidence 
of climate change impacts, vulnerability and adaptation on a range 
of urban infrastructures, including infrastructure that incorporates 
socio-economic and ecosystem dimensions (see Section 6.1.3).

6
913Cities, Settlements and Key Infrastructure  Chapter 6
This assessment also considers new literature about how enabling 
environments can support adaptation in ways that are also sensitive to 
Indigenous knowledge and Local knowledge (see below Section 6.1), 
social justice (6.4.3.4)) and climate mitigation (Section 6.3.5.2). It builds 
on the findings of AR5 which highlighted the concentration of global 
climate risks in urban areas, the complex causal chains that mediate 
climate impacts for smaller settlements and rural areas, and the multiple 
issues shaping and influencing economic sectors and infrastructure. 
This integrated chapter enables a more detailed analysis of the 
inter-connected drivers of risk that affect urban people and settlements 
of different sizes. This discussion also highlights the inter-connections 
within and between urban areas, and between different types of 
infrastructure and how these complex relationships accentuate or limit

the effects of climate change and the institutional structures that play a 
critical role in mediating and govern these relationships.
This chapter has five main sections. The first elaborates on changes in the 
international policy context since 2014, highlighting the implications 
that this has for responses to climate change in cities, settlements and 
key infrastructure. Section 6.2 is focused on observed and projected 
climate risks, paying particular attention to the ways in which these 
are created through processes of urbanisation and infrastructural 
investment. Section 6.3 takes an integrated and holistic approach to an 
assessment of adaptive actions relevant to key infrastructures (those 
that form the material basis for resilience in cities and settlements, 
drive economies and are essential for human well-being). Section 6.4 
assesses the enabling conditions and leadership qualities associated 
with adaptation processes that can also meet the equity agenda of

the SDGs, to leave no-one behind, including the role of governance, 
finance, institutions and emerging literature around the limits of urban 
Case studies highlight how climate and other issues interrelate to 
create (or reduce) urban risk within and between scales of decision 
making. They illustrate how multiple levels of governance and formal 
and informal decision making sectors influence how risk production/
reduction plays out across a range of urban contexts and networks.
6.1.2 Points of Departure
The AR5 conceptualised cities and settlements as complex 
interdependent systems that could be engaged in supporting climate 
change adaptation (Revi et  al., 2014 8.8.2). Effective municipal 
governance systems and cooperative multi-level governance supported 
adaptation action. The AR5 report expressed medium confidence 
that governance interventions can help develop synergies across 
geographical and institutional scales. Urban areas face challenges of

infrastructure investment and maintenance, land use management, 
livelihood creation and ecosystem services protection. AR5 also 
considered how urban localities can encourage incremental and 
transformative adaptation, build resilience and support sustainable 
development. The assessment identified the need for multi-level and 
multi-partner action in rapidly growing cities where institutions and 
infrastructure are still not established to meet the growing demands of 
the cities. However, there was only medium confidence that adaptation 
action was happening in the AR5 review period.The framing of ‘key economic sectors and services’ in AR5 focused 
primarily on three infrastructural areas (energy, water services, 
transport) and on primary and secondary economic activities 
(including recreation and tourism, insurance and financial services). 
Cities, settlements and key infrastructure are also referred to in the

IPCC special reports released since AR5. The Special Report on Global 
Warming of 1.5°C (IPCC, 2018) examines impacts of global warming 
on urban systems and infrastructure in the context of advancing 
sustainable development and eradicating poverty. It highlights the 
risks facing residents of unplanned and informal urban settlements, 
many of which are exposed to a range of climate-related hazards 
(Sections  3.4.8 and 4.4.1.3). The Special Report on Global Warming 
of 1.5°C also identifies green infrastructure, sustainable land use 
and planning, and sustainable water management as key adaptation 
options that can reduce risks in urban areas (SPM C2.4; C2.5), and 
highlights ‘urban and infrastructure’ as one of four system transitions 
required to limit warming to 1.5°C to create an enabling environment 
for adaptation (Section  4.3.3). Innovative governance arrangements 
that go beyond formal ‘government’ and political arrangements and

that include non-state actors, networks and informal institutions 
were identified as important in addressing climate change and 
implementing responses to 1.5°C-consistent pathways (Special Report 
on Global Warming of 1.5°C [Sections 4.4.1 and 5.6.2]). In addition, 
the Special Report on Global Warming of 1.5°C mentions, with high 
confidence, the climate-related health effects of urban heat islands, 
urban heatwaves and increasing risks from some vector-borne 
diseases (illnesses caused by pathogens and parasites in human 
populations) (SPM B5.2). The report also notes both trade-offs and 
important co-benefits of sustainable development in pursuit of climate 
resilient development pathways that achieve ambitious mitigation 
and adaptation in conjunction with poverty eradication and efforts to 
reduce inequalities (SPM D6).
The Special Report on Oceans and Cryosphere (SROCC) similarly 
emphasizes the role governance plays in reducing disaster risk,

through planning, and zoning (IPCC, 2019b). It identifies vulnerability 
factors such as poverty, which can undermine resilience and 
sustainable development in urban communities (IPCC, 2019b SPM 
C3.1,and Section 2.3.2) The SROCC report shows that the emerging 
climate-related challenges are impacting the accessibility and 
availability of vital resources and blurring the public and private 
boundaries of risk and responsibility (Cross-Chapter Box 3). According 
to the SROCC report, new governance arrangements are emerging 
to address these challenges, including participatory and networked 
structures, and institutions linking formal and informal networks 
involving state, private sector, Indigenous and civil society actors 
(Cross-Chapter Box  3). The SROCC report calls for place-specific 
action because there is no single climate governance panacea 
for the ocean, coasts and cryosphere (Cross-Chapter Box  3). The 
SROCC report highlights evidence of the importance of inclusivity,

fairness, deliberation, reflexivity, responsiveness, social learning, the 
co-production of knowledge, and respect for ethical and cultural 
diversity in climate-related urban decision making (Cross-Chapter 
Box 3). In addition, the Special Report on Climate Change and Land 
notes that urbanisation can intensify extreme rainfall events over the 
city or downwind of urban areas and have can significant consequences 
for heat island effects on loss of food production, posing additional

6
914Chapter 6 Cities, Settlements and Key Infrastructure
risks to the food system (IPCC, 2019b SPM A5.3 and Cross-Chapter 
Box 4 in Chapter 2).
An additional research bridge between AR5 and AR6 was the IPCC 
Cities and Climate Change Science conference held in Edmonton, 
Canada, in March 2018 (Prieur-Richard, Walsh and Craig, 2019). This 
generated a ‘Global Research and Action Agenda on Cities and Climate 
Change Science’ (Prieur-Richard, Walsh and Craig, 2019), which 
highlights six topical research areas where more evidence is needed 
to inform action: finance; informality; uncertainty; urban planning 
and design; built and green/blue infrastructure; and sustainable 
consumption and production. These areas are addressed in specific 
sections of this chapter or as cross-cutting themes. The Cross-Working 
Group Box URBAN in this chapter provides a linkage with perspectives 
from Working Group III.
6.1.3 Terminology and Definitions

This chapter covers both ‘cities and settlements’ and ‘key infrastructure’.
Definitions of ‘urban’ have become more nuanced since the AR5 
review with the publication of the OECD report ‘A new perspective 
on urbanisation’ (OECD and European Commission, 2020). This report 
presents two new global definitions of urbanisation reflecting the 
degree of urbanisation on a continuum of cities, towns and semi-dense 
areas, and rural areas. The OECD estimates almost half the world’s 
population (48%) live in cities, while just 24% live in rural areas and 
28% live in towns and semi-dense areas (28%). In addition, the OECD 
report defines metropolitan areas as functional urban areas together 
with their surrounding commuting zones ‘to capture the full extent’ of a 
city’s working population. Metropolitan areas account for 54% of total 
world population, with the OECD estimating that commuting zones 
representing 17% of the overall metropolitan population, rising to 31%

in high-income countries. In the context of these global definitions, 
this chapter identifies ‘cities and settlements’ as concentrated human 
habitation centres (along a dynamic continuum from rural to urban) 
(Murali et al., 2019; Ward and Shackleton, 2016) (Figure 6.1) that are 
fundamentally inter-connected to other urban centres and rural areas 
as nodes within broader networks.
Key infrastructure is used here to refer to ‘critical nodes and arteries’ 
that comprise urban energy, food, water, sewerage, health, transport 
and communication systems (Steele and Legacy, 2017; Maxwell et al., 2018; Bassolas et  al., 2019). Key or critical infrastructure provides 
much of the material basis of cities and settlements, as well as the 
mechanisms for enabling flows of people, goods, data, waste, energy 
(through urban metabolism processes of consumption and production) 
and capital, between urban regions and rural areas (Blay-Palmer et al.,

2018; Dijst et  al., 2018). An overview of this process of ‘planetary’ 
urbanisation is provided in Box  6.1. The balance of accumulated 
scientific knowledge on climate risks, impact and adaptation has 
been generated from studies in large and medium-sized cities of 
1 million or more. While these larger cities continue to grow rapidly 
(UNDESA 2018), settlements of more than 5  million people contain 
less than a quarter of the world’s urban population, and more than 
half of the world’s urban residents live in settlements of 1 million or 
less (Table 6.1). There is a key gap in knowledge, especially concerning 
urban enabling environments and how smaller settlements can be 
supported to accelerate equitable and sustainable adaptation in the 
face of financial and governance constraints (Birkmann et al., 2016; 
Shi et al., 2016; Dulal, 2019; Rosenzweig et al., 2018b).
This chapter takes a comprehensive approach to understanding ‘key

infrastructure’ as expressed in social, nature-based and physical 
infrastructure. Social infrastructure includes the social, cultural, and 
financial activities and institutions, as well as associated property, 
buildings and artefacts and policy domains such as social protection, 
health and education that support well-being and public life (Frolova 
et al., 2016; Latham and Layton, 2019). Nature-based infrastructure 
focuses on solutions to risk, applying natural assets such as trees or 
open water, physical infrastructure describes engineering approaches 
and grey/physical infrastructure refers to engineered assets that provide 
one or multiple services required by society, such as transportation or 
wastewater treatment ([IISD, N.D.]; see also Annex II: Glossary).
This approach is based on a framing of cities and settlements as 
complex systems where social, ecological and physical processes 
interact in planned and unplanned ways. This chapter therefore builds

on the AR5 Chapter 10 (Arent et al., 2014) conception of key economic 
sectors and services (e.g., energy, water, transport, waste, sanitation 
and drainage) by positioning these within three major categories of 
infrastructure: social, nature based and physical (see Section  6.3). 
Where adaptation challenges can be responded to by more than 
one approach, sometimes working together, this is noted (see also 
Sections  17.2 and 17.4). This approach allows an understanding of 
adaptation that is not constrained to the administrative boundaries 
of cities and settlements, but that includes the networks and flows 
Table 6.1 |  Proportion of the urban population in different size class urban areas (UN-DESA 2018). Each column indicates the percentage of urban residents in that region living 
in cities of that size class.
Proportion (by region) of 
urban population living in 
cities with population sizeAfrica AsiaLatin America 
and the CaribbeanEuropeNorthern 
AmericaOceania World

10 million + 8 15 17 4 10 0 13
5–10 million 6 9 3 5 17 0 8
1–5 million 22 22 25 16 30 60 22
500,000–1 million 9 10 8 11 13 2 10
300,000–500,000 6 6 6 8 7 11 6
Under 300,000 48 38 40 57 24 27 41

6
915Cities, Settlements and Key Infrastructure  Chapter 6
that connect peri-urban communities, metropolitan regions, suburban 
settlements and more rural places (see Box 6.1). Both formal provision 
of infrastructure services by government and informal provision 
by communities and individuals are considered at risk from climate 
change, as are existing adaptation pathways and actions.
Cities are complex entities where social, ecological and physical systems 
interact in planned and unplanned ways (Markolf et  al. , 2018). The 
complexity of cities, settlements and key infrastructure (Figure 6.2) where 
multiple functional systems continuously interact makes it difficult to 
distinguish risks (Box 6.1). The literature often resolves this by offering 
discrete assessments for specific sectors (see Section 6.3). This fragmented 
approach to understanding climate change associated impacts and 
risks is then reflected also in siloed approaches to risk management

and adaptation financing (see Section 6.4). Recent literature notes that 
resilience planning has begun to overcome this tendency by presenting 
climate change impacts, losses and damages, and urban processes, as 
unfolding together in interacting and cascading pathways (Fraser et al. , 
2020; Eriksen et al. , 2020) (Figure 6.2). The chapter reflects this change in 
the literature by presenting climate change impacts through a series of 
risk assessments, including by hazard type, through indirect impacts on 
health or food security, key infrastructure systems, land use and human 
mobility, water flows and on structural conditions, such as poverty and 
justice in the city (see Sections 6.3 and 6.4). In a departure from AR5 
we also consider the consequential interactions of climate risks, impacts, 
adaptation and climate mitigation (see also Cross-Working Group 
Box URBAN in Chapter 6).
The IPCC 1.5°C Special Report commented that ‘The extent of risk

depends on human vulnerability and the effectiveness of adaptation for regions (coastal and non-coastal), informal settlements, and 
infrastructure sectors (energy, water, and transport) (high confidence)’ 
(IPCC, 2018).We take this statement as a starting point for assessing 
the risks to cities, settlements and key infrastructure, with infrastructure 
extended as noted above. Risks from climate change are understood 
as the product of climate change associated hazards impacting on 
exposed and vulnerable people and assets (including biodiversity). 
Adaptation can, in some cases, reduce exposure and susceptibility 
and enable recovery and scope for transformation toward long-term 
equitable and sustainable development. Risks describe both present 
conditions and future prospects. Direct attribution of hazards to 
climate change remains limited to temperature extremes and sea level 
rise, though we consider all hydrometeorological hazards as systems

associated with climate change processes.
This chapter also assesses conditions supporting incremental and 
transformative adaptation (Section 6.4). Incremental and transformative 
adaptation are both important but serve distinct roles in the interaction 
of urban systems, climate risk and risk management, and in advancing 
social justice, just transitions and climate resilient development (see 
Section 6.4). Climate resilient development pathways are an emerging 
concept in the literature since the AR5 (Schipper et al. , 2020). Climate 
resilient development is an iterative process of systemic change that 
integrates both mitigation and adaptation efforts (see Annex II: Glossary). 
Initial studies highlight the way rapid urbanisation and precarious urban 
housing and land tenure can undermine climate resilient development, 
while human settlements that are managed to protect housing tenancy 
and land tenure rights can advance land use planning and social learning

while reducing inequalities and vulnerability and enhancing resilient 
development (Mitchell, Enemark and Van der Molen, 2015; Bellinson Unambiguously rural settlements, 
with low levels of density and a high proportion of 
inhabitants engaged in agricultural livelihoodsRural 
settlements
Populations of rural settlements range from farmsteads
to a few hundred inhabitantsIntermediate or 
ambiguous settlements
‘Large villages’, ‘small towns’, and ‘small urban 
centres’. The proportion of the population in rural 
and urban areas is influenced by each nation’ s 
definition of ‘urban areas’
Populations range from a few hundred
to 20,000 inhabitantsUrban  
settlements 
Unambiguously urban centres with much of the 
economically active population deriving their living 
from manufacturing or services
In virtually all nations, settlements with 20,000+ 
inhabitants are considered as urbanDefining ‘urban’  and ‘rural’  in relation to cities and settlements

Figure 6.1 |  Defining ‘urban’ and ‘rural’ in relation to cities and settlements

6
916Chapter 6 Cities, Settlements and Key Infrastructure
and Chu, 2019; Ürge-Vorsatz et al., 2018). The benefits of integrating 
decision making across scales for climate resilient development is also 
highlighted in Section 6.4. How households engage with communities 
and neighbourhoods and larger units within cities, and how cities 
(both formal and informal) interact with sub-national and national 
actors is also discussed, as is the role of finance and community-based 
organisations (CBOs)/NGOs in the governance process.
6.1.4 Global Urban Trends
Since AR5, many cities and other settlements, particularly unplanned 
and/or informal in Asia and Africa, have continued to grow at rapid 
rates (van den Berg , Otto and Fikresilassie 2021). Elsewhere, in Latin 
America in particular, while growth is less rapid, inequality persists. 
As a result, cities and settlements are crucial both as sites of potential 
action on climate change, and sites of increased exposure to risk

(medium evidence, high agreement).
Patterns and trends for urban population growth were described in 
detail in AR5. Between 2015 and 2020, urban populations globally 
have grown by more than 397 million people, with more than 90% of 
this growth taking place in less developed regions (UNDESA, 2018). The latest population projections from UNDESA (2018) reinforce the 
trends identified previously, with even higher estimates for global 
urban populations. The 2012 data used in AR5 projected a global urban 
population of 4984 million in 2030 and 6252 million in 2050; the 2018 
revisions project 5167 million and 6680 million respectively. Particularly 
noteworthy is the higher projection provided for sub-Saharan Africa’s 
urban population: increasing from 596  million to 666  million in 
2030, and from 1069 million to 1258 million in 2050. These figures 
highlight the continued trend toward larger urban populations, and the

particular significance of this in areas which currently have relatively 
small proportions of their populations living in towns and cities; this 
is also true in some Small Island States (e.g., the Solomon Islands) 
(McEvoy et al., 2020). The proportion of the global urban population 
living in megacities (with populations of more than 10 million people) 
is expected to continue growing slowly (to 16% of the urban total, or 
862  million people, living in 48 agglomerations) by 2035 (UNDESA, 
2018). The size and form of these megacities presents particular 
challenges with climate change impacts, in areas including air quality 
(Baklanov, Luisa and Molina, 2016), flooding (Januriyadi et al., 2018), 
and temperature increase (Darmanto et al., 2019) (see Section 6.2.3).
While there are few analyses of urban trends at the global scale, an 
additional 2.5 billion people are projected to be living in urban areas Climat e Impacts Cascade Thr ough Infr astructure
Transport

TransportUrban service s
Urban service sSocial infr astructur e
Social infr astructureTraffic management
systems disrupted
Goods and 
people unable to travelGoods and 
people unable to tr avel
Reduced budget
Failure to  
maintainPublic services compr omised
Social wellbeing erodedIncreased reliance on formal support services  
exceeds capacitySocial services  
disrupte dCommunication
disrupted
Communication
disruptedSocial services 
disruptedSupply broken
Loss of human skill sEner gy 
supply
Social 
infrastructur eA flash flood damages energy supply, for 
example by flooding an electricity 
sub-station. This direct impact of the flood 
cascades rapidly to produce compound 
impacts on social infrastructure through 
compromising urban services, breaks in IT 
services and shutdown in traffic 
management.
The chronic impacts of everyday flooding damage 
social infrastructure over time as livelihoods, local 
health and education services are eroded. These

impacts cascade through reduced city tax income at 
a time when there is increased demand for urban 
services including public transport, out-migration of 
skilled workers reduce the skill base to maintain IT 
and nature based solutions such
as public parks. These impacts in turn
constrain social infrastructrue.Rapid onset event, e.g. flood or storm surge
Slow-onset or chronic impacts, e.g. recurrent food price shocks or everyday flooding
Figure 6.2 |  The interconnected nature of cities, settlements and infrastructure

6
917Cities, Settlements and Key Infrastructure  Chapter 6
by 2050, with up to 90% of this increase concentrated in the regions 
of Asia and Africa, particularly in India, China and Nigeria, where 35% 
of this urban growth is projected to occur (UNDESA, 2018). Growth 
rates are slowing down in North America, South America and Europe 
(UNDESA, 2018). Much global growth continues to outstrip the ability 
of governments or the private sector to plan, fund and provide for 
sustainable urban infrastructure and this is most marked in low-income 
and informal settlements (Angel et al., 2016). Rural migration as a driver 
of urbanisation is discussed in 6.2.4.3, and literature has documented 
the way urban expansion and the conversion of agricultural land is 
also driven by investment incentives and weak planning policies 
(Colsaet, Laurans and Levrel, 2018; Woodworth and Wallace, 2017). At 
the same time, early evidence suggests that, at least in some locations,

out-migration from cities occurred as a result of the COVID-19 
pandemic (Rajan, Sivakumar and Srinivasan, 2020), but the evidence 
is not clear and in some cases may have increased migration to other 
megacities (Chow et al., 2021). There is also growing recognition that 
poor planning has exacerbated the concentrated of deprivation in 
specific locations, deepening a cycle of exclusion and marginalisation 
(UNDESA, 2020).
One critical element of global urban trends which has received growing 
attention is informality (see also Prieur-Richard, Walsh and Craig, 2019). 
Informality is one of the key defining features of cities and settlements 
in the Global South (see Annex II: Glossary; Banks, Lombard and Mitlin, 
2020; Myers, 2021; UN-Habitat, 2016c). In almost all nations in the 
Global South, more than half the urban workforce work in informal 
employment; the proportions are particularly high in South Asia (82% 
in informal employment) and sub-Saharan Africa (66%) (Chen, Roever

and Skinner, 2016; Chen, 2014). The term ‘informal settlement’ refers to urban settlements or neighbourhoods that developed outside the formal 
system that is meant to record land ownership and tenure and without 
meeting a range of regulations relating to planning and land use, built 
structures and health and safety. Informality is a broader concept than 
‘slums’, which are usually defined using measures of housing quality, 
provision of services and overcrowding. While most countries do not 
generate formal statistics on the number of people living in informal 
settlements, UN Habitat provides regional and global estimates of the 
number of urban households that are ‘slum’ households and therefore 
likely to include most residents of informal settlements. These estimates 
suggest that there were 1034 million slum dwellers in 2018, including 
some 56% of the urban population in sub-Saharan Africa and more 
than 30% of the urban population of South Asia (UN-Habitat, 2020).

Informality is particularly important in understanding climate risks 
and responses in cities and settlements, and also in relation to key 
infrastructure (Trundle, 2020; Taylor et al. , 2021).
Evidence since AR5 confirms that occupants of informal settlements 
are particularly exposed to climate events given low-quality housing, 
limited capacity to adapt, and limited or no risk-reducing infrastructure 
(high confidence) (Melore and Nel, 2020; Twinomuhangi et al., 2021; 
Satterthwaite et  al., 2020; Patel et  al., 2020a)(see Section  6.2 and 
case study). The impacts of COVID-19 are also increasingly impacting 
high-density informal and slum settlements where social distancing 
and access to water for handwashing are limited (Bhide, 2020; Pinchoff 
et al., 2021; Tagliacozzo, Pisacane and Kilkey, 2021; Wilkinson, 2020). 
This compounds pre-existing vulnerability to climate change associated 
hazards. Box  6.1 expands on trends in informality as part of global

urbanism, peri-urbanisation and suburbanisation, with implications for 
the global distribution of climate risks and adaptive capacity.
Box 6.1 | Planetary Urbanisation and Climate Risk
The scale, reach and complexity of contemporary urbanisation compounds climate risks and conditions adaptation (high confidence) 
(Miller and Hutchins, 2017; Rosenzweig et al., 2018b). Urbanisation manifests as a heterogeneous and plural process with varied spatial 
manifestations (Oswin, 2018) that extends beyond cities and settlements, defining actions elsewhere in what has been called ‘planetary 
urbanization’ (Brenner, 2014b). While the concept of planetary urbanisation is contested, for example for a predominantly Eurocentric 
focus (Vegliò, 2021), the concept has reflected human urbanisation as a mega-trend of urban expansion and landuse intensification 
(Capon, 2017; Lauermann, 2018). Three dimensions of planetary urbanisation are currently shaping adaptation actions: the new forms

and scales of urbanisation, the blurring of boundaries around clearly demarcated territories, and the fragmentation of the urban hinterland 
into units that serve productive functions for the reproduction of urban space under capitalism (Brenner and Schmid, 2017).
Planetary scale urbanisation challenges current understandings of spatial settlements and how risk affects urban communities (limited 
evidence, medium agreement) (Ruddick et al., 2018). Massive urbanisation manifests in large agglomerations such as metropolitan areas 
and urban regions, conurbations with unique risk challenges, particularly when interacting with other drivers of vulnerability (Adetokunbo 
and Emeka, 2015; Maragno, Pozzer and Musco, 2021). Experiences of regional collaboration to scale adaptation to metropolitan areas 
have shown to be effective, particularly facilitating information and technology exchanges and institutional cooperation (Shi, 2019;

Lundqvist, 2016), but may face challenges such as addressing administrative and fiscal requirements and enrolling local populations in 
a meaningful participation process (Shi, 2019). For example, the coordination of planning policies in the Vienna–Bratislava metropolitan 
region, further divided by an international border, demonstrates that institutional coordination alone is not sufficient to deliver effective 
spatial governance: instead, meaningful spatial policies required the involvement of multiple actors (Patti, 2017). In addition to 
institutional coordination, adaptation in rapidly urbanising areas requires understanding how these processes magnify risk and condition 
urban responses (see also Section 6.3).
Urban expansion processes affect human settlements everywhere, regardless of their size. Figure 6.1 represents a continuum of settlements

from high- to low-density areas (Ward and Shackleton, 2016). Urban and rural areas are not always clearly differentiated (Brenner, 2014a;

6
918Chapter 6 Cities, Settlements and Key Infrastructure
Brenner and Schmid, 2017). For example, in 2010/2011, drought-exacerbated wildfires across Russia’s agricultural hinterland not only led 
to increased air pollution in Moscow and other large cities in the region, it also disrupted global supply chains of wheat and caused 
skyrocketing global food prices (Zscheischler et al., 2018). Floods in Bangkok, Thailand in 2011 destroyed many foreign-owned factories, 
leading to a global shortfall in different types of IT equipment (Levermann, 2014).
Rural areas provide ecosystem services that benefit cities directly, including through reducing hazards (runoff, and temperature) and 
through carbon storage, and can be maintained through urban markets and other inputs (Gebre and Gebremedhin, 2019). Most urban 
areas extend into dispersive peri-urban areas where urban and rural land uses co-exist (Simon, 2016) and/or suburban areas which are

lower density and primarily residential in function. Moreover, the urban and rural differentiation creates normative expectations at the 
heart of planning conflicts and constraints of urban governance (Taylor, Butt and Amati, 2017). Expanding peri-urban areas pose specific 
structural constraints to addressing risks. In Bogotá, Colombia, a study found marked inequalities as more impoverished families had 
restricted access to peri-urban forests, trees and tree services (Escobedo et al., 2015). Factors such as limited land ownership and tenure 
insecurity in peri-urban areas hinder people’s ability to invest in permanent infrastructure to buffer themselves from flood events, as 
witnessed in the slums in Nairobi (Thorn, Thornton and Helfgott, 2015). Building resilience and adaptation via community mobilisation 
may not be effective in peri-urban areas shaped by migration, agricultural intensification and industrialisation (Wandl and Magoni, 2017).

At the same time, actions to improve access to peri-urban services almost always improve resilience (Simon, 2016) Evidence from Kampala, 
Addis Ababa, Dar es Salaam, Douala, Ibadan, Nairobi, Dakar and Accra shows that urban and peri-urban agriculture and forestry can 
support adaptation (Lwasa et al. , 2014). In the metropolitan area of Milan, multi-functional agriculture supports a local, more sustainable 
food chain (Magoni and Colucci, 2017). Since communities in peri-urban areas are often transitory, efforts toward creating social capital by 
promoting civic engagement are crucial to facilitate collective action (Narain et al. , 2017). For example, adaptation actions can help to build 
the capacity of the community to engage with service providers (Harris, Chu and Ziervogel, 2018; Ziervogel et al. , 2017), as demonstrated 
in parts of peri-urban Kolkata, India and Khulna, and Bangladesh (Gomes and Hermans, 2018; Gomes, Hermans and Thissen, 2018).

Urbanisation on an immense scale blurs the boundaries that previously defined cities and settlements (Arboleda, 2016a; Shaw, 2015; 
Brenner, 2014a; OECD and European Commission, 2020; Schmid, 2018; Davidson et al., 2019; Wu and Keil, 2020). For example, peri-urban 
areas typically extend over multiple government jurisdictions (Wandl and Magoni, 2017). Adaptation actions can be difficult to plan, 
coordinate, implement and evaluate in these transboundary contexts (Solecki et al., 2018; Srivastava, 2020; Fünfgeld, 2015; Rukmana, 
2020; Carter et al., 2018). In Medellín, Colombia, a 46-mile-long green belt is being built to stop urban expansion while also protecting 
urban forests, providing access to green spaces, and reducing urban heat island effects (Anguelovski et al., 2016). However, large-scale 
infrastructure projects such as this one require coordination between regional transport authorities and the different municipalities in

charge of housing and public services, in addition to consulting communities on their social impact (Chu, Anguelovski and Roberts, 2017). 
Local and regional authorities have competing mandates, such as a competition for taxpaying residents in peri-urban, commuting zones, 
and different infrastructure investment logics, political drivers and constituent needs. Smaller discrete infrastructure projects that actively 
engage local populations may provide better opportunities to build resilience across fragmented spaces (Santos, 2017; Kamalipour and 
Dovey, 2020).
Suburbanisation follows a gradual movement of citizens from high-density urban centres to the suburbs (Pieretti, 2014). The development 
of enclaves for higher-income people that appropriate resources and constrain access to those resources for disadvantaged populations 
has been recorded in places as distant as Santiago de Chile, People’s Republic of China, India, Indonesia and the Philippines (Calvet and

Castán Broto, 2016; Phelps, Miao and Zhang, 2020; Bulkeley, Castán Broto and Edwards, 2014; Buchori et al., 2021; Kleibert, 2018). The 
appropriation of land and resources in enclaves defends exclusive, privileged communities at the expense of everyone else. Enclaves 
exacerbate inequalities because those who cannot afford to live in the enclave suffer the fragmentation of public services, restrictions in 
access to resources, and greater exposure to climate risks (Hodson, 2010; Haase et al., 2017). Moreover, suburbanisation is linked to the 
privatisation of public spaces and the decline of public infrastructures, collective spaces and green projects (Long and Rice, 2019; North, 
Nurse and Barker, 2017). Climate gentrification, whereby vulnerable communities are displaced from urban areas with lower climate risks 
(UN-Habitat, 2020), reconfigures urban areas, for example, as higher-income populations move away from the city centres, as shown in

North American cities that have already suffered climate-related impacts such as Miami, Philadelphia and New Orleans (Keenan, Hill and 
Gumber, 2018; Shokry, Connolly and Anguelovski, 2020; De Koning and Filatova, 2020; Aune, Gesch and Smith, 2020).
Urbanisation leads to the spatial fragmentation of the hinterland, divided alongside functional units to serve the demands of the capitalist 
urban economy (Brenner and Schmid, 2017). Urbanisation is thus linked to new intensities of resource exploitation that threaten vulnerable 
land and ecosystems, as shown in the Amazon, and that extend across scales (Arboleda, 2016b; Wilson, 2018). The fragmentation of the 
hinterland for extractivist purposes depletes ecosystem services and further exacerbates cascading risks (high confidence) (Section 6.2.6).Box 6.1 (continued)

6
919Cities, Settlements and Key Infrastructure  Chapter 6
Adaptation and related concepts of urban climate resilience are also con-
cerns for the broader agenda of sustainable development (Wachsmuth, 
Cohen and Angelo, 2016). Urban areas can play a positive role in ad-
vancing sustainability, but the pace and scale of urban development 
can also undermine progress in SDGs (Barnett and Parnell, 2016; Maes  
et al. , 2019; Anarfi, Hill and Shiel, 2020) (high confidence). With careful 
planning, urbanisation can be a transformative force, enhancing equity 
and well-being through co-benefits and synergies between climate 
change adaptation, equitable urban development and mitigation (me-
dium evidence, medium agreement) (Parnell, 2016a; Solecki et al. , 2015; 
Sharifi, 2020). Cities can be effective change agents when supported by 
networked local and national institutions, including professional bodies 
(high confidence) (Andonova, Hale and Roger, 2017; Brandtner and

Suárez, 2021; Heidrich et  al. , 2016; Kern, 2019; Farzaneh and Wang, 
2020). Low Emission Development Strategies (LEDS) have developed 
effective science–policy interactions to support energy-system, environ-
mental and economic development planning strategies in the city of 
Shanghai, China (Farzaneh and Wang, 2020). New literature is emerging 
about how adaptive changes at the urban level could integrate both far 
reaching rapid emission reduction and community protection in trans-
formative ways (Wamsler and Raggers, 2018; Rosenzweig and Solecki, 
2018; UN-Habitat, 2020; Ziervogel, 2019a). There is an increasing con-
sensus about the need for integrated governance of urban areas with-
in and across regions, so that urban risk management and adaptation 
happen hand in hand with more general processes of transition toward 
more sustainable urban regions (Simon, 2016; UN-Habitat, 2020).
Since AR5, there has also been increasing recognition of the contribution

of diverse knowledges including local and Indigenous knowledge in 
contributing to the development and interpretation of urban relevant 
climate change data and policy for effective action (Klenk et al. , 2017; 
Hosen, Nakamura and Hamzah, 2020; Makondo and Thomas, 2018). 
Indigenous and local knowledge inform coping strategies in urban 
adaptation planning and new directions for action (Nakashima, 
Krupnik and Rubis, 2018; Abudu Kasei, Dalitso Kalanda-Joshua and Tutu 
Benefor, 2019). Indigenous and local knowledge is also found to shape 
perceptions about urban climate risk awareness, its acceptable limits, 
causation and preferences for adaptation (see also Pyhälä et al. , 2016 
for a review; see Jaakkola, Juntunen and Näkkäläjärvi, 2018 for impacts 
on Indigenous peoples in the EU; Saboohi et al. , 2019). Local perceptions 
about climate change in turn influence adaptation behaviours in 
settlements and urban communities (Lee et al. , 2015; Larcom, She and

van Gevelt, 2019). Engagement with Indigenous and local knowledge 
is an enabling condition for planning community-appropriate climate 
adaptation responses (Fernández-Llamazares et  al. , 2015). Urban 
decision making that includes Indigenous and local knowledge 
has co-benefits for addressing indigenous dispossession, historical 
inequities and marginalisation of indigenous values that occurred 
(Parsons et  al. , 2019; Carter, 2019; Maldonado et  al. , 2016; Orlove  
et al. , 2014; Pearce et al. , 2015). Indigenous and local knowledge can 
help deliver culturally appropriate strategies and local choices for urban 
risk management through, for example community-based observation 
networks (Alessa et al. , 2016), integrating ecosystem-based adaptation 
strategies in institutional structures (Nalau et al. , 2018), using multiple 
evidence-based approaches (Tengö et al. , 2014), and adopting forms 
of governance that centre Indigenous peoples in urban adaptation and

decision making (Horn, 2018; Parsons, Fisher and Nalau, 2016).6.1.5 Changes in the Global Enabling Environment
This section reports on changes in global enabling environment; the 
architecture of international agreements available to inform policy 
for national governments and others on urbanisation and climate 
adaptation, since the AR5.
Six new international agreements and initiatives have been achieved, 
each of which has far-reaching implications for the management 
of rapid urbanisation and climate change: the Paris Climate 
Agreement (United Nations, 2015b); the 2030 Agenda for Sustainable 
Development, including the SDGs (United Nations, 2015c); the Sendai 
Framework for Disaster Risk Reduction (UNISDR, 2015); the New 
Urban Agenda (United Nations, 2016a); Addis Ababa Action Agenda 
(July 2015); and the World Humanitarian Summit (May 2016). Table 6.2 
summarises these.
Alongside new international agreements are a series of new landmark

global stocktake reports: three IPCC special reports including the 
IPCC 1.5 report ( IPCC, 2019a; IPCC, 2019b Hoegh-Guldberg et  al., 
2018), the UN Environment GEO6 (UN Environment, 2019) and IPBES 
2019 (Brondizio et al., 2019), and UNDRR 2019 (UNDRR, 2019), each 
have argued for urgent action on climate mitigation and to invest in 
inclusive strategies for adaptation if the SDGs are to be met. These 
findings are comprehensively evidenced and do not need to be 
revisited here. Our starting point then is to assess the science on how 
inclusive, sustainable development can be delivered through enhanced 
adaptation to climate change risks.
As a blueprint for advancing human dignity, the SDGs emphasize the 
need to consider how to achieve a better and more sustainable future 
while ‘leaving no one behind.’ In doing so, they highlight an agenda 
focused on well-being, equality and justice. The objective for SDG11 is

defined as: ‘Make cities and human settlements inclusive, safe, resilient 
and sustainable’ with 10 associated targets including ensuring access 
for all to adequate, safe and affordable housing and basic services; 
participatory planning; safeguarding heritage features; reducing 
disasters, particularly water related disasters and economic impacts on 
the poor; and promoting resource efficiency, mitigation and adaptation 
to climate change, resilience to disasters, and develop and implement 
plans, in line with the Sendai Framework for Disaster Risk Reduction. 
Similarly, SDG9 aims to build resilient infrastructure, promote inclusive 
and sustainable industrialisation and foster innovation, with associated 
targets. The IPCC 1.5 special report emphasized that there are often 
co-benefits in pursuit of SDGs and adaptation strategies where 
‘well-designed mitigation and adaptation responses can support 
poverty alleviation, food security, healthy ecosystems, equality and

other dimensions of sustainable development’ (IPCC, 2018 FAQ5.1). 
However there may also be negative trade-offs, for example between 
pursuit of growth and reducing climate change risk (International 
Council for Science, 2017; IPCC, 2018 Executive Summary; Roy et al., 
2018a).
The Paris Agreement also envisioned a significantly more active role 
for cities and other non-state actors in facilitating policy change 
(Hale, 2016), including through participation in Nationally Determined 
Contributions (NDCs), although there is little systematic review of the

6
920Chapter 6 Cities, Settlements and Key Infrastructure
contributions made by cities to NDCs (Hsu et  al., 2020; Bäckstrand 
and Kuyper, 2017). Over two-thirds, 113 out of 164, of initial Intended 
Nationally Determined Contributions (INDCs), prior to ratification, had 
referenced urban responses in the context of sustainable development, 
climate mitigation and adaptation (UN-Habitat, 2016a). Analysis 
of those INDCs revealed 58 focused on urban climate adaptation, 
17 focused on both adaptation and mitigation, and 4 focused on 
mitigation (UN-Habitat, 2017). Simultaneously, multiple efforts have 
emerged to align the actions of nation states with those of other 
actors, including the UNFCC 2014 Global Climate Action Portal 
(Hsu, Weinfurter and Xu, 2017). While significant optimism has been 
gathered around the possibility to intervene at sub-national level,

the most difficult challenge has been to establish a coherent view of the overall contribution that cities and settlements are making (Hale, 
2016; Chan et al., 2015b). Although meeting the Paris goals will require 
staying within a ‘carbon budget’, supporting rapidly developing urban 
areas in the Global South to the same infrastructure level as developed 
cities may consume significant proportions of that budget (Bai et al., 
2018).
There is increasing international effort among non-Party stakeholders 
to the Paris Climate Agreement to collaborate to meet the Paris Climate 
goals (Data Driven Yale New Climate Institute PBL, 2018; Chan et al., 
2015a). A review of contributions by non-state actors in 2019 by the EU 
Covenant of Mayors identified 10427 cities with climate commitments, 
while the Global Covenant of Mayors included 10543 cities representing Table 6.2 |  International policy agreements with implications for urbanisation and climate adaptation
Agreement

(date of agreement)Scope of agreementRelevance for cities, settlements 
and infrastructureRelevance for addressing 
climate change risk
Sendai Framework for Disaster Risk 
Reduction
(March 2015)Global agreement for reducing disaster 
risks in all countries and at all levels.
Highlights urbanisation as a key driver 
of risk and resilience.Identifies rapid urbanisation as a key underlying 
risk factor for disasters and driver of resilience.
Promotes shift from disaster response to 
disaster risk management and reduction 
through cooperation between national and local 
governments. Limited focus on the role of civil 
society.Highlights the need to respond to systemic 
risk, including compound and cascading 
risks and impacts from natural, technological 
and biological hazards. Includes focus on 
chronic stressors and sudden shocks through 
governance, planning, disaster response, 
post-event recovery.
Addis Ababa Action Agenda
(July 2015)Global agreement arising from the

International Conference on Financing 
for Development (United Nations, 
2015a) emphasized the need for 
adequate financing at all levels of 
government, especially sub-national 
and local, to support sustainable 
development, infrastructure and climate 
mitigation (UN-Habitat, 2016b).Includes general comments on the importance 
of local actors and recognises the need for 
strengthening capacities of municipal and local 
governments.
Commits to ‘support’ local governments to 
‘mobilise revenues as appropriate’.
Offers little on how to get finance to 
support local governments addressing these 
commitments.Financing a critical element of risk reduction in 
cities and settlements (see Section 6.4).
Underlying variability of institutional 
arrangements inhibits development of universal 
framework.
Transforming our world: the 2030 
Agenda for Sustainable Development
(September 2015)Global agreement adopted by 193 
governments that includes the 17

Sustainable Development Goals (SDGs).SDG11 speaks explicitly to making cities 
‘inclusive, safe, resilient and sustainable’. 
Extensive reference to universal provision of 
basic services in other SDGs which will require 
substantial efforts in cities; equality and 
governance are also stressed.
Focuses on national goals and national 
monitoring with insufficient recognition of key 
roles of local and regional governments and 
urban civil society in addressing most of the SDGs.SDG13 on climate action requires action in cities 
and settlements.
Integrated approach can address underlying 
drivers of risk.
The Paris Agreement
(December 2015)Global agreement under UN Framework 
Convention on Climate Change: signed 
by 194 and ratified by 189 member 
states (05/01/21).References the role of the local or sub-national 
levels of government and cities as non-state 
actors.Encourages cities to develop specific agendas for 
climate action (mitigation and adaptation).

The World Humanitarian Summit
(May 2016)Not an agreement, but a summit of 180 
member states generating over 3500 
commitments to action and addressing 
the role of non-state actors in reducing 
risk of climate change related forced 
displacement of people.Includes five agreed ‘core responsibilities’ with 
relevance for urban areas, and commitments 
were made by professional associations, 
non-governmental organisations and networks 
of local authorities to address these in towns 
and cities.Climate change likely to shape flows of refugees 
and migrants who are likely to live in highly 
exposed areas, particularly in low-income cities. 
However ‘meagre funding for collaboration, poor 
data collection and sharing’ (Acuto, 2016) limits 
commitment effectiveness (Speckhard, 2016).
The New Urban Agenda 
(October 2016)Global agenda adopted at UN 
Conference on Housing and Sustainable 
Urban Development (Habitat III) 
envisioned national urban policies and

adaptation plans as a central device 
to inform sub-national governments 
addressing sustainable development.Intended as the global guideline for sustainable 
urban development for 20 years, seeking to 
provide coherence with other agreements. 
Focus on national policy and action. Limited 
recognition of urban governments or civil society 
as initiators and drivers of change.Clearly frames roles for cities within national 
and international systems in contributing 
to sustainability (including low-carbon 
development) and resilience (including 
adaptation). Frames the role for cities within 
national and international systems, including 
an ongoing assessment of their contribution 
to sustainability and resilience (Kaika, 2017; 
Valencia et al., 2019).

6
921Cities, Settlements and Key Infrastructure  Chapter 6
a population of 969  million citizens (Palermo et  al., 2020; Peduzzi 
et  al., 2020). International efforts also include the United Nations 
Framework Convention on Climate Change (UNFCCC) Non-State Actor 
Zone for Climate Action (Data Driven Yale New Climate Institute PBL, 
2018). There is also a proliferation of new non-governmental and 
public-private actors that address both adaptation and mitigation in 
cities and settlements, including: the C40 Cities Climate Leadership 
Group, 100 Resilient Cities; the Global Resilient Cities Network, We 
Mean Business, and We Are Still In (Ireland and Clausen, 2019) and 
the Global Alliance for Buildings and Construction (Dean et al., 2016). 
However, there is as yet limited research into the effectiveness of these 
initiatives in enhancing medium and small city adaptation and limited 
documentation of climate adaptation actions by non-traditional

agents, particularly in the Global South (Lamb et al., 2019).
New urban activists and stakeholders, including youth, and Indigenous 
and minority communities and NGOs alongside business groups have 
also been visible in the global urban climate debate, pressing for 
faster, more far-reaching change (Frantzeskaki et al., 2016; O’Brien, 
Selboe and Hayward, 2018; Alves, Campos and Penha-Lopes, 2019; 
Smith and Patterson, 2018; Crnogorcevic, 2019; Campos et al., 2016; 
Hayward, 2021). Emergent urban social movements for climate 
justice often build on established international networks including 
local activists such as Shack and Slum Dwellers International, while 
others are inspired by Indigenous movements and are focused on 
human rights, indigenous sovereignty and land claims, access to 
water, intergenerational justice, and gender and youth movements 
coordinated on social media (Agyeman et  al., 2016; Cohen, 2018;

Ulloa, 2017; Hayward, 2021; Prendergast et al., 2021). The emergence 
of climate justice movements in urban communities has the potential 
to reframe policy discussion in cities in ways that also bring inequality 
and climate justice to the fore (Sheller and Urry, 2016), underscoring 
growing public calls for more far-reaching, transformative changes 
toward socially just urban transformations (Akbulut et  al., 2019; 
Foran, 2019; Vandepitte, Vandermoere and Hustinx, 2019; Smith and 
Patterson, 2018).
This section demonstrates the consistency with which urban processes 
and places have been rising to the top of international agreements and 
agendas in the last 10 years (Bulkeley, 2015; van der Heijden et al., 
2018; Knieling, 2016). However, many cities, particularly smaller cities 
and informal settlements in the Global South where development is 
rapid, need greater support for local governance, more information, 
and more diverse sources of finance to meet the vision of global

climate agreements (Greenwalt, Raasakka and Alverson, 2018; Cohen, 
2019). Moreover, the response of many cities to climate change is 
often constrained by wider political, social and economic structures, 
development path dependences and high carbon lock in (Princeti, 
2016; Johnson, 2018; Jordan et al., 2015).
6.2 Impacts and Risks
This section assesses the impacts of hazards associated with climate 
change that will affect cities, settlements and key infrastructure, 
particularly how climate systems and urban systems interact to 
produce patterns of risk and loss. The conclusions of the IPCC Special Report on Global Warming of 1.5°C noted that ‘Global warming of 2°C 
is expected to pose greater risks to urban areas than global warming 
of 1.5°C (medium confidence)’.
This section commences with a review of scenarios and pathways 
linking urban and infrastructural development with climate change; 
then assesses the key risks (with a focus on those for which there is

a greater degree of evidence or confidence since AR5) and how these 
risks are created in urban settings. It then assesses evidence on the 
differentiated nature of human vulnerability and the risks affecting key 
infrastructure. Finally, this discussion reviews compound and cascading 
risks, and risks created by adaptation actions.
6.2.1 Risk Creation in Cities, Settlements and 
In addition to direct climate impacts, interactions among changing 
urban form, exposure and vulnerability can create climate 
change-induced risks and losses for cities and settlements. Climate 
change already interacts with ongoing global trends in urbanisation 
to create regionally specific impacts and risk profiles. Through 
demographic change and encroachment into natural and agricultural 
lands and coastal zones, rapidly expanding urban settlements can 
place new physical assets and people in locations with high exposure

(Tessler et al., 2015; Arnell and Gosling, 2016; Kundzewicz et al., 2014). 
Increasing rates of global urbanisation will pose additional challenges 
to areas that have high levels of poverty, unemployment, informality, 
and housing and service backlogs (Jiang and O’Neill, 2017; Williams 
et al., 2019). There is some evidence to suggest that climate change 
impacts themselves are increasing urbanisation rates, generating 
a challenging feedback loop. In sub-Saharan Africa, for example, 
manufacturing towns have experienced growth because of population 
movement following droughts in agricultural hinterlands (Henderson, 
Storeygard and Deichmann, 2017). The rapid rate of urbanisation 
therefore presents a time-limited opportunity to work toward risk 
reduction and transformational adaptation in towns and cities. The 
following sections explore these dynamic interactions between urban 
systems and climate change, and how these shape risk for people and 
for key infrastructures.

Examining projected climate change impacts and resulting risks in 
cities, settlements and key infrastructures requires the prerequisite 
development of scenarios which are plausible descriptions of how the 
future may develop based on a coherent and internally consistent set 
of assumptions about key driving forces, (e.g., rate of technological 
change, prices and relationships) and pathways or the temporal 
evolution of natural and/or human systems, such as demographic and 
urban land cover change, toward a future state or states (Gao and 
O’Neill, 2020; Gao and O’Neill, 2019; see also Section 6.1.5).
Climate change research creates scenarios integrating emissions and 
development pathways dimensions (Ebi et al., 2014; van Vuuren et al., 
2017b; van Vuuren et  al., 2017a) and Representative Concentration 
Pathways (RCPs) (Riahi et al., 2017). For risk reduction at regional scales, 
scenarios require urban-relevant climate projections, for example,

downscaling from global and regional climate models of variables such

6
922Chapter 6 Cities, Settlements and Key Infrastructure
as temperature, precipitation, air pollutants and sea level rise that are 
analysed usually for mid- or end-21st Century timeframes (e.g., Mika 
et al., 2018; Kusaka et al., 2016; Masson et al., 2014b). These data are 
needed to ascertain likely ranges of climate change impacts within city 
and settlement boundaries, and to quantify physical exposure when 
developing pathways for risk reduction. Consideration of current and 
projected future growth pathways of multiple urban sectors and key 
infrastructure, for example, transport, energy and buildings, are also 
needed to estimate probabilities of risk outcomes and damages within 
and across urban systems (O’Neill et al., 2015)(WGIII AR6 Section 8).
The challenges of managing these risks are amplified by the complex 
interactions between climate and urban scenarios, owing to the 
smaller spatial–temporal scales of urban areas in climate change

modelling relative to global climate models (GCM) and shared 
socioeconomic pathways (SSP); geographical or geomorphological 
variations in city location; uncertainties arising from incomplete 
assumptions about socio-economic pathways at urban scales affecting 
urban demographics, for example, fertility rates and life expectancies 
or increased rural–urban migration; and challenges in modelling the 
urban climate and in developing urban climate observational networks 
in cities (WGI Box 10.3; Kamei, Hanaki and Kurisu, 2016; Yu, Jiang and 
Zhai, 2016; Jiang and O’Neill, 2017; Baklanov et al., 2018). Additionally, 
carbon-intensive economic growth, increasing inequalities, global 
pandemics, and uncontrolled or unmanaged urbanisation will 
exacerbate the exposure and vulnerability of urban systems modelled 
in existing climate scenarios and pathways (high confidence) (Phillips 
et  al., 2020; Jackson, 2021; Raworth, 2017; Moraci et  al., 2020).

Mitigating these outcomes requires new forms of urban governance 
for climate adaptation, disaster risk reduction and building resilience 
(see Section 6.4).
Strong connections exist between climate change scenarios and urban 
climate-related risks. In some cases, the linkage is direct as climate 
change is associated with more frequent and more intense extreme 
weather and climate events, as assessed in Section  6.2.3. In other 
contexts, the connection is mediated by urban developmental pathways 
arising from local-scale environmental stresses and degradation, and 
access to adaptation options, as reviewed in Section 6.2.4.
6.2.2  Dynamic Interaction of Urban Systems with Climate
Urban systems interact with climate systems in multiple, dynamic and 
complex ways (Section  6.1.1, Doblas-Reyes et  al., 2021 Box  10.3). 
Climate change can have direct impacts on the functioning of urban 
systems, while the nature of those systems plays a substantial role

in modifying the effects of climate change (high confidence) (Frank, 
Delano and Caniglia, 2017; Smid and Costa, 2018). An example of this 
urban system climate nexus is the urban heat island effect (discussed 
in Section  6.2.3.1) (Susca and Pomponi, 2020). Assessing the 
inter-relationships between multiple systems and a range of hazards is 
particularly important as many cities are presently exposed to multiple 
climate-related hazards: more than 100 cities analysed as part of a 
571 city study in Europe were deemed vulnerable to two or more 
climate impacts (Guerreiro et  al., 2018). Rapid expansion of urban 
areas increases the exposure of urban populations to various hazards independent of global climate change. Huang et  al. (2019) project 
that urban land areas will expand by 0.6–1.3  million km2  between 
2015 and 2050, an increase of 78–171% over the urban footprint in 
2015. Specifically in relation to floods and droughts, Güneralp et al.

(2015) calculate that even without accounting for climate change, the 
extent of urban areas exposed to flood hazards will increase 2.7 times 
between 2000 and 2030, the extent exposed to drought hazards will 
approximately double during this period, and urban land exposed to 
both floods and droughts will increase more than 2.5 times.
This section assesses observed and expected impacts from the 
main hazards identified for cities, settlements and infrastructure; 
temperature extremes (and the urban heat island), flooding (including 
sea level rise), water scarcity and security, as well as other hazards that 
are either less well-studied and/or likely to affect only a limited number 
of locations. The data assessed in this section are limited by uneven 
coverage. Despite improvements since AR5, data continue to be more 
complete for extreme events than for chronic hazards and everyday 
risks, which may have high aggregate impacts and disproportionately

erode the well-being of urban poor households, especially for the most 
vulnerable, including women, children, the aged, disabled and homeless 
(van Wesenbeeck, Sonneveld and Voortman, 2016; Kinay et al., 2019; 
Connelly et al., 2018). Data coverage is also less comprehensive for 
smaller settlements in poorer countries, the locations where urban 
growth is often high and adaptive capacities are often low (e.g., Rufat 
et al., 2015). Thus, data gaps frequently coincide with highly vulnerable 
populations (Rufat et  al., 2015; Satterthwaite and Bartlett, 2017). 
Here, even small changes in livelihoods, health, or representation 
and voice can rapidly bring households into positions of risk, even 
when hazard conditions are relatively stable (Ziervogel et al., 2017). 
These structural limits in available data are discussed also in Section 7 
(Health, Well-being and the Changing Structure of Communities) and 
Section 8 (Poverty, Livelihoods and Sustainable Development), and in

Doblas-Reyes et  al. (2021) Box  10.3. There are implications also for 
adaptation (Section 6.3), where the greater availability of evidence on 
exposure-driven risk can limit resilience-building interventions that 
focus on the reduction of vulnerability.
6.2.2.1 Temperatures and the Urban Heat Island
Higher temperatures associated with climate change, through warmer 
global average temperatures and regional heatwave episodes, will 
interact with urban systems in a variety of ways (Doblas-Reyes et al., 
2021 Box  10.3). Future urbanisation will amplify projected local air 
temperature increase, particularly by strong influence on minimum 
temperatures, which is approximately comparable in magnitude to 
global warming (high confidence) (Arias et  al. In Press Box  TS14). 
Within cities, exposure to heat island effects is uneven, with some 
populations disproportionately exposed to risk including low income 
communities, children, the elderly, disabled, and ethnic minorities

(Quintana-Talvac et al., 2021; Sabrin et al., 2020; Chambers, 2020; and 
see later in this section).
The risks to cities, settlements and infrastructure from heatwaves 
will worsen (high confidence) (Leal Filho et  al., 2021; see also 
Sections 6.2.5; 6.3.3.1, Arias et al. In Press Box TS14). Depending on 
the RCP , between half (RCP2.6) to three-quarters (RCP8.5) of the

6
923Cities, Settlements and Key Infrastructure  Chapter 6
human population could be exposed to periods of life-threatening 
climatic conditions arising from coupled impacts of extreme heat and 
humidity by 2100 (Figure 6.3; Mora et al., 2017; Zhao et al., 2021). 
Cities in mid-latitudes are potentially subject to twice the levels of 
heat stress compared with their rural surroundings under all RCP 
scenarios by 2050, for example Belgian cities (Wouters et al., 2017). 
A disproportionate level of exposure exists in subtropical cities subject 
to year-round warm temperatures and higher humidity, requiring less 
warming to exceed ‘dangerous’ thresholds, for example Nairobi (Scott 
et al., 2017) and São Paulo (Diniz, Gonçalves and Sheridan, 2020). It 
is expected that more than 90% of the 300 million people who will 
be exposed to super- and ultra-extreme heatwaves in the Middle East

and North Africa will live in urban centres (Zittis et al., 2021), while the major driver for increased heat exposure is the combination of global 
warming and population growth in already-warm cities in regions 
including Africa, India and the Middle East (Klein and Anderegg, 2021).
Locally, the urban heat island also elevates temperatures within cities 
relative to their surroundings. It is caused by physical changes to 
the surface energy balance of the pre-urban site from urbanisation, 
resulting from the thermal characteristics and spatial arrangement 
of the built environment, and anthropogenic heat release (Oke et al., 
2017; Chow et  al., 2014; Susca and Pomponi, 2020; Doblas-Reyes 
et al., 2021 FAQ10.1). A considerable body of evidence exists on how 
the multi-scale impacts and consequent risks arise when local elevated 
temperatures within settlements are enhanced by climate change, with

Global distribution of population exposed to hyperthermia from extreme heat and humidity
(a)
Pressen t
2020
(b)
Mid-21st
Century
2050
(c) 
End-21st
Century
2100RCP2.6 RCP4.5 RCP8.5
RCP2.6 RCP4.5 RCP8.5
Low
0 daysHigh
365 days Projected 
number of days 
per year when air 
and humidity 
conditions turn 
deadly and pose 
a risk of death
Figure 6.3 |  Global distribution of population exposed to hyperthermia from extreme heat for (a) the present, and projections from selected Representative 
Concentration Pathways in (b) the mid-21st century and (c) the end of the 21st century. Shading indicates projected number of days in a year in which conditions of 
air temperature and humidity surpass a common threshold beyond which climate conditions turned deadly and pose a risk of death (Mora et al., 2017). Named cities are the top 
15 urban areas by population size during 2020, 2050 and 2100, respectively, as projected by Hoornweg and Pope (2017)

6
924Chapter 6 Cities, Settlements and Key Infrastructure
specific elements of this affecting megacities (Darmanto et al., 2019). 
The urban heat island itself is amplified during heatwaves (Founda 
and Santamouris, 2017), but the extent to which varies regionally 
and by time of day (Ward et al., 2016a; Zhao et al., 2018b; Eunice Lo 
et al., 2020). When combined with warming induced by urban growth, 
extreme heat risks are expected to affect half of the future urban 
population, with a particular impact in the tropical Global South and 
in coastal cities and settlements (Huang et al., 2019; Section CCP2.2.2; 
Table CCP2.A.1).
Heat risk is associated with a range of health issues for urban 
residents, with the consequences of higher urban temperatures being 
unevenly distributed across urban populations (high confidence). Clear 
evidence exists of increased health risks to elderly populations in 
settlements, especially higher levels of mortality in elderly populations

from urban heat islands during heatwave events (Fernandez Milan 
and Creutzig, 2015; Taylor et al., 2015; Ward et al., 2016a; Heaviside, 
Macintyre and Vardoulakis, 2017; Gough et al., 2019; Xu et al., 2020a), 
while health and fitness variables are also major determinants of the 
effects of heat stress (Schuster et al., 2017) (see also Table 7.2). Heat 
stress and dehydration are also related to behavioural and learning 
concerns, with dehydration impairing concentration and cognition 
for both adults and children (Merhej, 2019). Literature on paediatric 
heat exposure is associated with increases in emergency department 
visits for heat-related illnesses, electrolyte imbalances, fever, renal 
disease and respiratory disease in young children (Winquist et  al., 
2016), with less severe outcomes such as lethargy, headaches, rashes, 
cramps and exhaustion negatively affecting children in school and play 
environments (Vanos, 2015; Hyndman, 2017). Young children in cities

are particularly sensitive to heatwaves, and may have little experience 
or capacity to cope with heat extremes (Norwegian Red Cross, 2019). 
Such vulnerability of young children to heat is compounded with 
projected urbanisation rates and poor infrastructure, particularly 
in South Asian and in African cities (Smith, 2019). There is evidence 
that socioeconomically disadvantaged populations are more likely to 
live in hotter parts of cities associated with higher-density residential 
land use in dwellings with less effective insulation built with poorer 
or older construction materials (Inostroza, Palme and de la Barrera, 
2016; Tomlinson et al., 2011). Specific emerging risks for occupational 
and related heat illnesses are found in urban tropical or subtropical 
low- and middle-income countries (Andrews et al., 2018; Green et al., 
2019).
There is an emerging risk of diminished indoor thermal comfort due 
to climate change, evidenced by research into negatively affected

thermal comfort indices and/or increased number of overheating 
hours under future emissions scenarios (medium confidence) (e.g., 
Liu and Coley, 2015; van Hooff et al., 2014; Vardoulakis et al., 2015; 
Dodoo and Gustavsson, 2016; Invidiata and Ghisi, 2016; Makantasi 
and Mavrogianni, 2016; Mulville and Stravoravdis, 2016; Taylor et al., 
2016; Hamdy et al., 2017; Pérez-Andreu et al., 2018; Salthammer et al., 
2018; Dino and Meral Akgül, 2019; Osman and Sevinc, 2019; Roshan, 
Oji and Attia, 2019). Decreases in thermal comfort and increases in 
overheating risks depend on building characteristics, such as thermal 
2 Paper provides figures in Australian dollars: AUD 5.2–7.3 billion. Exchange rate correct July 2020.
3 Paper provides figures in yuan: 250 billion yuan yr−1 after 2030 (compared to 38.6 billion yuan yr−1 for 1979–2005). Exchange rate correct July 2020.resistance, presence of solar shading, thermal mass, ventilation,

orientation and geographical location (e.g., Liu and Coley, 2015; van 
Hooff et al., 2014; Vardoulakis et al., 2015; Dodoo and Gustavsson, 
2016; Invidiata and Ghisi, 2016; Makantasi and Mavrogianni, 2016; 
Mulville and Stravoravdis, 2016; Taylor et al., 2016; Hamdy et al., 2017; 
Pérez-Andreu et al., 2018; Salthammer et al., 2018; Dino and Meral 
Akgül, 2019; Osman and Sevinc, 2019; Roshan, Oji and Attia, 2019; 
Alves, Gonçalves and Duarte, 2021). Most of these studies employed 
numerical simulations in which different climate scenarios were used 
to construct future climate data. In hot climates, energy-efficient 
buildings with high insulation values and high airtightness, which have 
insufficient protection from solar heat gains and/or limited ventilation 
capabilities, are generally more vulnerable to overheating than older 
buildings with lower insulation levels (e.g., van Hooff et  al., 2014; 
Vardoulakis et al., 2015; Makantasi and Mavrogianni, 2016; Mulville

and Stravoravdis, 2016; Salthammer et al., 2018; Fisk, 2015; Hamdy 
et al., 2017; Fosas et al., 2018; Ozarisoy and Elsharkawy, 2019; see also 
Fox-Kemper et  al., 2021 9.7 for building heat mitigation/adaptation 
links).
Higher urban temperatures result in lower labour productivity levels 
and economic outputs (medium confidence) (Graff Zivin and Neidell, 
2014; Yi and Chan, 2017; Houser et  al., 2015; Stevens, 2017; see 
Section  8.2.1). Globally, urban heat stress is projected to reduce 
labour capacity by 20% in hot months by 2050 compared with a 
current 10% reduction (Dunne, Stouffer and John, 2013). Burke et al. 
(2015) demonstrate a nonlinear relationship between temperature 
and global economic productivity, with potential global losses of 23% 
by 2100 due to climate change alone. In specific cases, Zander et al. 
(2015) estimate heat-related reductions in urban labour productivity 
in Australia to cost USD 3.6–5.1  billion yr−1, based on self-reported

performance reduction and absenteeism among 1726 workers in 
2013–142; while the high-temperature subsidies given in China at 
outdoor air temperatures above 35°C are projected to increase to USD 
35.7  billion yr−1 after 2030 (compared with USD 5.5  billion yr−1 for 
1979–2005) (Zhao et al., 2016)3.
Higher urban temperatures place unequal economic stresses on 
residents and households through higher utilities demand during 
warm periods, for example, electricity in regions where air conditioning 
is predicted to become more prevalent, and due to medical costs 
associated with care for heat illnesses and related health effects, missed 
work and other related impacts (medium confidence) (Jovanović et al., 
2015; Liu et al., 2019; Schmeltz, Petkova and Gamble, 2016; Soebarto 
and Bennetts, 2014; Zander and Mathew, 2019; Zander et al., 2015). 
Such stresses are projected to increase in many regions associated 
with continuing global-scale climate change and urbanisation (e.g.,

Véliz et  al., 2017; Ang, Wang and Ma, 2017; Bezerra et  al., 2021), 
although some of these effects in cold-climate cities are offset by 
reduced stresses in winter associated with urban heat island or rising 
temperatures more generally (see Section 6.2.2.4).
Thermal inequity can also be seen as a distributive justice risk (Mitchell 
and Chakraborty, 2018). There are often disproportionate increases of

6
925Cities, Settlements and Key Infrastructure  Chapter 6
risk for individuals of lower socioeconomic status, especially migrants, 
from exposure to urban heat. These arise from inadequate housing, less 
access to air-conditioning, and occupations, such as manual labour and 
waste picking, that exacerbate heat exposure (Chu and Michael, 2018; 
Santha et al. , 2016). Research from South Africa has shown that housing 
occupied by poor communities regularly experience indoor temperature 
fluctuations that are between 4°C and 5°C warmer compared with 
outdoor temperatures (Naicker et  al. , 2017); while evidence from 
the USA indicates that historical housing policies, particularly the 
‘redlining’ of neighbourhoods based on racially motivated perceptions, 
are associated with areas that are exposed to elevated land surface 
temperatures (Hoffman, Shandas and Pendleton, 2020).
Social surveys from temperate and tropical cities highlight the risk of

reduced quality of life during heat events, including increased incidence 
of personal discomfort in indoor and outdoor settings, elevated 
anxiety, depression and other indicators of adverse psychological 
health, and reductions in physical activity, social interactions, work 
attendance, tourism and recreation (high confidence) (Chow et  al., 
2016; Elnabawi, Hamza and Dudek, 2016; Obradovich and Fowler, 
2017; Wang et al., 2017; Wong et al., 2017; Lam, Loughnan and Tapper, 
2018; Alves, Duarte and Gonçalves, 2016). Extreme heat may also 
have a cultural impact, for example affecting major sporting events, 
with negative impacts on the athletic performance (Brocherie, Girard 
and Millet, 2015; Casa et  al., 2015) and the experience and health 
of spectators (Hosokawa, Grundstein and Casa, 2018; Kosaka et al., 
2018; Matzarakis et al., 2018; Vanos et al., 2019).
6.2.2.2 Urban Flooding
Flood risks in settlements arise from hydrometeorological events

interacting with the urban system which exposes settlements to river 
(fluvial) floods, flash floods, pluvial (precipitation-driven) floods, sewer 
floods, coastal floods and glacial lake outburst floods (IPCC, 2012). 
Sea level increase and increases in tropical cyclone storm surge and 
rainfall intensity will increase the probability of coastal city flooding 
(high confidence) (WGI Box TS14). Globally, the increase in frequencies 
and intensities of extreme precipitation from global warming will 
likely4 expand the global land area affected by flood hazards (medium 
confidence) ((Alfieri et al., 2018; Alfieri et al., 2017; Hoegh-Guldberg 
et  al., 2018); Section  4.2.4.2). Mishra et  al. (2015) noted that out 
of 241 urban areas, only 17% of cities experienced statistically 
significant increases in frequencies of extreme precipitation events 
from 1973 to 2012. In the future, there is some evidence that changes

in high-intensity short duration (sub-daily) rainfall in urban areas will 
increase (limited evidence, medium agreement) (Kendon et al., 2014; 
Ban, Schmidli and Schär, 2015; Abiodun et al., 2017).
Flooding associated with sea level rise is addressed in more detail in 
Cross-Chapter Paper 2, with detailed regional examples from Africa 
discussed in Section  9.3. Coastal flooding associated with sea level 
rise is exacerbated due to the significant number of people living in 
subsiding areas. As a result of this, the average coastal resident is 
4 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about 
as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely: 95–100%, more likely than not >50–100% and extremely unlikely

0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, for example, very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an 
outcome lies within the 17–83% probability range.experiencing (over the last two decades) rates of relative sea level rise 
three to four times higher than typical estimates due to climate-induced 
changes (Nicholls et al., 2021). This process can also result in release of 
coastal waste into urban areas (Beaven et al., 2020).
Urban flooding risks are also increased by urban expansion and land 
use and land cover change which enlarges impermeable surface 
areas through soil sealing, impacting drainage of floodwaters with 
consequent sewer overflows (high confidence) (Arnbjerg-Nielsen 
et  al., 2013; Ziervogel et  al., 2016; Aroua, 2016; Kundzewicz et  al., 
2014). These risks are also driven by increasing societal complexity,

urban developmental policy on flood control and long-term economic 
growth (Berndtsson et al., 2019), including in mega-cities (Januriyadi 
et al., 2018). The increase in flood risk from urban development can be 
considerable; based on modelling of two RCP (4.5 and 8.5) scenarios, 
Kaspersen et al. (2017) noted flooding in four European cities could 
increase by up to 10% for every 1% increase in impervious surface area. 
Risks are also compounded by the location of settlements, with greater 
risks within cities located in low elevation coastal zones subject to sea 
level rise, potential land subsidence and exposure to tropical cyclones 
((Koop and van Leeuwen, 2017; Hoegh-Guldberg et al., 2018; see also 
Section CCP2.2) and within informal settlements, where generally little 
investment in drainage solutions exists and flooding regularly disrupts 
livelihoods and disproportionately undermines local food safety and 
security for the urban poor (Dodman, Colenbrander and Archer, 2017;

Dodman et al., 2017; Kundzewicz et al., 2014; Sections 5.4 and 5.8).
Future risks of urban flooding is increasing in conjunction with 
continued increases in global surface temperature (high confidence) 
(IPCC, 2019b; Winsemius et  al., 2015; Kulp and Strauss, 2019; 
Hoegh-Guldberg et  al., 2018). In particular, Asian cities are highly 
exposed to future flood risks arising from urbanisation processes. 
Between 2000 and 2030, rapid urbanisation in Indonesia will elevate 
flood risks by 76–120% for river and coastal floods, while sea level rise 
will further increase the exposure by 19–37% (Muis et al., 2015). In 
Can Tho, Vietnam, current urban development patterns put new assets 
and infrastructure at risk due to sea level rise and river flooding in 
the Mekong Delta (Chinh et al., 2017; Chinh et al., 2016). Flooding in 
urban areas is exacerbated both by the encroachment of urban areas 
into areas that retain water and by the lack of infrastructure such as

embankments and flood walls, as is the case for large areas of Dhaka 
East (Haque, Bithell and Richards, 2020). Zhou et al. (2019) have also 
shown that for the city of Hohhot, China, the increase in impervious 
surfaces contributes between 2–4  times more to modelled annual 
flood risk compared with risk induced by climate change.
Global trends in surface water flooding are increasing, which poses 
risks to vulnerable urban systems depending on current adaptation 
measures to manage flooding impacts, for example, stormwater 
management, green infrastructure and sustainable urban drainage 
systems (Molenaar et al., 2015). The economic risks associated with 
future surface water flooding in towns and cities are considerable. 
For example in the UK, expected annual damages from surface water

6
926Chapter 6 Cities, Settlements and Key Infrastructure
flooding may increase by £60–200 million for projected 2–4°C warming 
scenarios; enhanced adaptation actions could manage flooding up to a 
2°C scenario but will be insufficient beyond that (Sayers et al., 2015). 
Analyses conducted in South Korea suggests that future flood levels 
could exceed current flood protection design standards by as much 
as 70% by 2100, considerably increasing urban flood risk (Kang et al., 
2016). Modelling of urban flood damage in the Kelani River Basin in Sri 
Lanka showed increased frequency of flooding by 2030 could increase 
potential urban property damage by up to 10.2% (Komolafe Akinola, 
Herath and Avtar, 2018). Urban flood impacts may also exacerbate 
health burdens (including disease outbreaks of malaria, typhoid and 
cholera), which are compounded by damage to medical facilities (e.g., 
damage to hospitals and disruption of medicinal supply chains), as

observed in urban areas of Ghana (Gough et al., 2019). In addition, 
emerging research shows the cascading consequences of hazard 
events, in this case urban flooding, on other risks to well-being in 
ways that are particularly severe for the urban poor, including mental 
ill-health, incidents of domestic violence impacting children and 
women, chronic diseases and salinity of drinking water ((Matsuyama, 
Khan and Khalequzzaman, 2020); Section  4.2.4.5; Section  6.2.4.2; 
Box 7.2; Section 8.4.5.2).
6.2.2.3 Urban Water Scarcity and Security
Urban water scarcity occurs when gaps exist between supply and 
demand of available freshwater resources (Zhang et al., 2019). Urban 
water security requires a sustainable quantity and quality of water 
to meet community and ecosystem needs in a changing climate 
(Romero-Lankao and Gnatz, 2019; Allan, Kenway and Head, 2018; 
Huang, Xu and Yin, 2015; Chen and Shi, 2016). Risks arising from urban

water scarcity worldwide are very likely increasing due to climate drivers 
(e.g., warmer temperatures and droughts) and urbanisation processes 
(e.g., land use changes, migration to cities and changing patterns 
of water use including over extraction of surface and groundwater 
resources) affecting supply and demand (high confidence) (Allan, 
Kenway and Head, 2018; Crausbay et  al., 2020; Haddeland et  al., 
2014; Pickard et al., 2017; De Stefano et al., 2015; Sun et al., 2019; Van 
Loon et al., 2016; Zhang et al., 2019; Section 4.2.4.4; See Box 8.6 for 
case study on 2018 Cape Town drought). Flörke et al. (2018) estimates 
that nearly a third of all major cities worldwide may exhaust their 
current water resources by 2050. Globally, projections suggest that 
350 million (± 158.8 million) more people living in urban areas will 
be exposed to water scarcity from severe droughts at 1.5°C warming 
and 410.7 million (± 213.5 million) at 2°C warming (Liu et al., 2018).

Decreased regional precipitation and associated changes in runoff and 
storage from droughts is exacerbating urban scarcity by impairing the 
quality of water available for its resource management in cities (high 
confidence). For example, less runoff to freshwater rivers can increase 
salinity and concentrate pathogens and pollutants that increases risks 
of urban water scarcity (Hellwig, Stahl and Lange, 2017; Jones and van 
Vliet, 2018; Leddin and Macrae, 2020; Lorenzo and Kinzig, 2020; Ma 
et al., 2020; Mosley, 2015; Zhang et al., 2019; van Vliet, Flörke and 
Wada, 2017; see also Box 6.2). Drought also changes the dynamics of 
groundwater pollution, leading to increased environmental health risks 
when those sources are used for urban water supplies (Kubicz et al., 
2021; Moreira et al., 2020; Pincetl et al., 2019). Changes in the nature of droughts, for example, hotter droughts (Herrera and Ault, 2017), snow

droughts (Cooper, Nolin and Safeeq, 2016; Mote et al., 2016) or ‘flash’ 
droughts (Otkin et  al., 2016; Otkin et  al., 2018; Pendergrass et  al., 
2020) can exacerbate urban water scarcity, exposing the limitations of 
engineered water infrastructure designed to accommodate historical 
patterns of supply and demand (Gober et al., 2016; Ulibarri and Scott, 
2019; Zhao et al., 2018a).
Risks of urban water scarcity and security are compounded by 
vulnerabilities such as service availability and quality of infrastructure 
to supply water for increased urban demand from in-migration to 
cities (medium confidence) (Ahmadalipour et  al., 2019; Dong et  al., 
2020; Reynolds et al., 2019; Thomas et al., 2017; Mullin, 2020). Risks 
to local water security in cities are also exacerbated by drivers such 
as dependence on imported water resources from distant locales that 
may be exposed to additional drought risks (high confidence) (Ahams

et al., 2017; Li et al., 2019b; Marston et al., 2015; Zhao et al., 2020; 
Zhang et al., 2020); from considerable projected urban expansion in 
drought-stressed areas, for example, across drylands of Western Asia 
and North Africa (Güneralp et  al., (2015); and by export of virtual 
water (i.e., export of water embedded in food and energy) from local 
sources to distant trading partners (Djehdian et al., 2019; D’Odorico 
et al., 2018; Fulton and Cooley, 2015; Rushforth and Ruddell, 2016; 
Verdon-Kidd et al., 2017; Vora et al., 2017).
Droughts interact and manifest in complex ways in interconnected 
urban areas that likely increase risks of urban water scarcity (Tapia 
et al., 2017; Rushforth and Ruddell, 2015). Urban interdependencies 
mean droughts in one region can limit water resources availability 
in another (e.g., Macao and Zhuhai, Hong Kong, Shenzhen in China, 
Singapore and Johor, in cities in Pakistan and India, and in the west

and southwest USA) (Chuah, Ho and Chow, 2018; Gober et al., 2016; 
Srinivasan, Konar and Sivapalan, 2017; Zhang et al., 2019; Zhao et al., 
2020). Likewise, physical and social teleconnections mean decisions 
made about water resources in one region or location may impact 
another in unexpected ways (Moser and Hart, 2015; Liu et al., 2015).
Urban water security risks are confounded by inequities in economic 
opportunity, risk exposure and human well-being (medium evidence) 
(Sena et al., 2017; Stanke et al., 2013; Section 4.2.4.5). Water scarcity 
is felt more acutely among low-income compared with high-income 
populations (Nerkar et al., 2016), and scarcity on top of inequities and 
political instability can lead to security issues, for example, conflict 
between different water users (Cosic et al., 2019; von Uexkull et al., 
2016; Ahmadalipour et  al., 2019; Döring, 2020; Ide et  al., 2021), 
particularly when road infrastructures and access to water are

limited (Detges, 2016; Sena et al., 2017). Scarcity risks may also be 
exacerbated by human and ecosystem needs in water-short years 
(Srinivasan, Konar and Sivapalan, 2017). Finally, growing populations 
along with migration into water scarce regions can exacerbate water 
security issues (Akhtar and Shah, 2020; Singh and Sharma, 2019).
6.2.2.4 Other Dynamic Interactions
A range of other dynamic climate interactions are relevant for cities, 
settlements and infrastructure: cold spells, landslides, wind, fire and 
air pollution.

6
927Cities, Settlements and Key Infrastructure  Chapter 6
Cold spells. Although frequencies and intensities of cold spells/
cold waves are virtually certain to have decreased globally, and are 
projected to consistently decrease for most warming levels (high 
confidence; WGI Table  11.2), cold weather events can periodically 
occur and impact urban areas and their connected infrastructures. For 
cities in eastern Canada, the intra-annual distribution of freezing rain 
events may become more frequent from December to February, and less 
frequent in other months by 2100 (Cheng, Li and Auld, 2011). Freezing 
rain is also a risk to urban populations and infrastructure. In general, 
higher population mortality rates likely occur during the winter season, 
while more temperature-attributable deaths are caused by cold than 
by heat in cities located in temperate climates (Gasparrini et al. , 2015; 
Chen et al. , 2017; Ryti, Guo and Jaakkola, 2016). Winter mortality is

unlikely to significantly decrease due to warming trends, partly because 
a range of other medical factors (e.g., influenza seasons and elevations 
in cardiac risk factors) also drive this winter-excess mortality (Kinney  
et al. , 2015). However, the evidence is unclear whether mortality related 
to cold waves will decrease in coming decades in European (Smid  
et al. , 2019) or US cities (Wang et al. , 2016). While projected global 
cold extremes are expected to decrease in frequency and intensity, the 
higher regional variability of future climates means that cold waves 
may remain locally important threats, including in milder regions where 
there are larger temperature differences between ‘normal’ winter days 
and extreme cold events, and where there is less capacity to adapt (Ma, 
Chen and Kan, 2014; Ho et al. , 2019). This will be accentuated in many 
cities, particularly in Europe, by anticipated demographic changes that

result in a more elderly population susceptible to cold wave health risks 
(Smid et al. , 2019).
The effects of cold waves on the energy sector include breakdowns in 
power plants and reduced oil and gas production (Jendritzky, 1999), as 
well as failures in overhead power lines and towers leading to outages 
in Moscow and Bucharest (Panteli and Mancarella, 2015; Andrei et al. , 
2019). Six major power outages associated with cold shocks and ice 
storms have been recorded since 2010, the majority recorded from large 
cities in the USA (Añel et al. , 2017). Cold waves can also significantly 
increase energy demand. A cold wave that affected the Iberian Peninsula 
in January 2017 caused electricity prices to peak at a mean price of 
112.8 €/MWh, the highest ever recorded in Spain (AEMET, 2017).
Landslides. While geomorphological events (e.g., land subsidence from 
permafrost thaw at high latitudes or from groundwater extraction) and

factors associated with the built environment (e.g., settlement location 
adjacent to steep slopes and zonation laws for building construction) 
are major factors determining urban landslide risk, these can also 
be influenced by a range of climatic variables, namely precipitation 
(frequency, intensity and duration), snow melt and temperature 
change. Some 48 million people are exposed to landslide risk in Europe 
alone, with the majority in smaller urban centres (Mateos et al., 2020). 
Travassos et  al. (2020) also documented all landslide deaths in the 
São Paulo Macro Metropolis Region from 2016 to 2019 that occurred 
from extreme rainfall events in vulnerable areas prone to landslides. 
An increase in the number of people exposed to urban landslide risks is 
projected for landslide-prone settlements lying within regions projected 
to experience a corresponding increase in extreme rainfall (Gariano 
and Guzzetti, 2016). In addition, human factors such as expansion of

towns onto unstable land and land use changes within settlements (e.g., road building, deforestation) are increasing human exposure 
to landslides and the likelihood of landslides occurring (Kirschbaum, 
Stanley and Zhou, 2015). Rainfall triggered landslides kill at least 
5000 people per year, and at least 11.7% of these landslides occurred 
on road networks (Froude and Petley, 2018). Although the spatial 
footprint of an individual landslide might be small (i.e., < 1 km2), the 
‘vulnerability shadow’ cast over an area in terms of regional transport 
network disruptions can be a significant proportion of a region, and 
cascade to other infrastructures (Winter et al., 2016).
Landslides tend to occur on moderate to steep slopes, and are 
thus particularly prevalent in mountainous regions which are also 
characterised by low infrastructure redundancy (i.e., few alternative 
routes) and increased impacts from climate change (Schlögl et  al.,

2019). More robust forecasts of landslides driven by climate risk 
requires (a) more complete long-term records of previous landslides 
and (b) baseline studies of the Global South which are currently 
missing from the literature (Gariano et al., 2017).
Wind. Urban morphology alters wind conditions at multiple spatial 
scales; generally, increased surface roughness in settlements have 
resulted in declining trends in both measured wind speed and 
frequency of extremely windy days (Mishra et al., 2015; Peng et al., 
2018; Ahmed and Bharat, 2014; WGI Box 10.3).
Urban wind risks can also be affected by location adjacent to 
mountains, lakes or coasts with localised wind systems (WGI 10.3.3.4.2; 
WGI 10.3.3.4.3). In large cities with significant urban heat island, an 
urban-driven thermal circulation can enhance pollution dispersion 
under calm conditions (Fan, Li and Yin, 2018) or advect heat to areas 
downwind of the city (Bassett et al., 2016). Microscale wind conditions

within urban canyons also strongly affect ventilation of air pollution 
dispersion and thermal comfort at pedestrian level, especially in cities 
located in warm climates (Rajagopalan, Lim and Jamei, 2014; Middel 
et al., 2014; Lin and Ho, 2016).
In cities, wind risks from climate change hazards can arise from 
increased exposure from the expanding built environment. Very high 
wind speeds associated with severe weather systems, for example, 
tropical cyclones or derechos can cause significant structural damage 
to buildings and key infrastructure with insufficient wind load, as well 
as causing human injury through flying debris (Burgess et al., 2014). 
In particular, there is evidence from North American cities that tornado 
damage are likely fundamentally driven by growing built-environment 
exposure (medium confidence) (Ashley et al., 2014; Rosencrants and 
Ashley, 2015; Ashley and Strader, 2016).
Extreme winds in urban areas can have particularly damaging effects

on poorly constructed buildings, including low-income houses in 
African cities (Okunola, 2019), and on urban trees that may be 
uprooted by strong wind gusts from downbursts (Ordóñez and Duinker, 
2015; Pita and de Schwarzkopf, 2016; Brandt et al., 2016), as well as 
on disrupting transportation along urban road and railway networks 
(Koks et al., 2019; Pregnolato et al., 2016).
Fire. Hotter and drier climates in several regions, for example Australia, 
the Western USA, the Mediterranean and Russia (IPCC, 2018), likely

6
928Chapter 6 Cities, Settlements and Key Infrastructure
enable weather conditions driving fire events impacting cities within 
these regions (Section 2.4.4.2, 2.5.5.2). These include wildfires along 
the margins where cities are adjacent to wildlands, that is, the 
wildland-urban interface (WUI) (Bento-Gonçalves and Vieira, 2020; 
Radeloff et al., 2018), or fires in cities with a high degree of informal 
settlements having greater vulnerability to fire hazards (Kahanji, Walls 
and Cicione, 2019; Walls and Zweig, 2017; Sections  8.3.3.2). This 
vulnerability is considerable; over 95% of urban fire related deaths and 
injuries occur within informal settlements in low- and middle-income 
countries (Rush et al., 2020).
For wildfires at the WUI, anthropogenic climate change, natural 
weather variability, expansion of human settlement and a legacy of 
fire suppression are key factors in determining fire risk (Abatzoglou 
and Williams, 2016; Knorr, Arneth and Jiang, 2016; van Oldenborgh

et al., 2020). Recent wildfires in Australia and California both occurred 
under hot and dry weather conditions exacerbated by climate change, 
and resulted in substantial property damage along the WUI, ecosystem 
destruction and lives lost (Brown et al., 2020; Lewis et al., 2020; Yu 
et al., 2020). Future climate risk of fires at the WUI are likely (medium 
confidence), and are compounded by projected urban development 
along the WUI within several regions, such as in the Western USA 
(Syphard et al., 2019), Australia (Dowdy et al., 2019) and the Bolivian 
Chiquitania (Devisscher et al., 2016).
Air Pollution. Despite recent observed improvements in air quality 
arising from COVID-19 restrictions (Krecl et  al., 2020; Naik et  al., 
2021 Cross-Chapter Box  6.1), significant risks to human health in 
cities leading to premature mortality very likely arise from exposure 
to decreased outdoor air quality from a combination of biogenic

(e.g., wildfires at the WUI that advect into the urban atmosphere 
[Reddington et al., 2014; Naik et al., 2021 Chapter 12 Box 12.1]) and 
anthropogenic sources that are influenced by climate change (e.g., 
fine particulate matter such as PM 2.5, tropospheric ozone, oxides of 
nitrogen and volatile organic compounds [Burnett et al., 2018; Knight 
et al., 2016; Turner et al., 2016; West et al., 2016; Chang et al., 2019b; 
Li et al., 2019a; Alexader, Luisa and Molina, 2016; Naik et al., 2021 
Sections  6.5.1, 6.7.1.1, 6.7.1.2]). Risks of premature mortality from 
indoor air pollution in cities, arising from biomass burning for heating 
in winter or cooking, indoor pesticide use or exposure to volatile 
organic compounds from poor thermal insulation in buildings, are 
also likely to occur (Leung, 2015; Peduzzi et al., 2020; Cross-Chapter 
Box HEALTH in Chapter 7).
The mortality risk for several pollutants, for example PM 2.5, is

considerable (high confidence). Current estimates indicate that 95% 
of global population live in areas where ambient PM 2.5 exceeds the 
WHO guideline of annual average exposure of 10 µg m−3 (Shaddick 
et al., 2018a; Shaddick et al., 2018b; Chang et al., 2019b). Among the 
250 most populous urban areas, estimated PM 2.5 concentrations are 
generally highest in cities in Africa, South Asia, the Middle East and 
East Asia; PM 2.5 in many cities in North Africa and the Middle East is 
likely due mainly to wind-blown dust, whereas that in South Asia and 
East Asia are mainly anthropogenic in origin (Anenberg et al., 2019). 
However, data on PM 2.5 concentrations are unavailable in many cities 
in low- and middle-income countries owing to a lack of measurements 
(Martin et al., 2019).For some air pollutants, for example concentrations of PM 2.5 in several 
US, Western European and Chinese cities have recently decreased as

a result of clean air regulations that have controlled emissions from 
sources such as motor vehicles, fossil fuel power plants and major 
industries (Zheng et al., 2018a; Fleming et al., 2018). These decreases 
have brought substantial improvements in public health in settlements 
within these regions (Ciarelli et al., 2019; Zhang et al., 2018). In South 
Asia, Southeast Asia and Africa, however, concentrations of other 
air pollutants, for example tropospheric ozone, oxides of nitrogen 
and volatile organic compounds are likely to continue to grow and 
peak by mid-century before they subside due to global urbanisation 
assumptions embedded in the SSPs (Naik et al., 2021 Sections 6.2.1; 
6.7.1.2). Broadly, future air pollutant emissions are projected to decline 
globally by 2050 as societies become wealthier and more willing 
to invest in air pollution controls, but the trajectories vary among 
pollutants, world regions and scenarios (Silva et al., 2016b; Rao et al.,

2017; Silva et al., 2016c). Whereas cities in East Asia and South Asia 
currently have large exposure to anthropogenic air pollution, African 
cities may emerge by 2050 as the most polluted because of growing 
populations and demand for energy, increased urbanisation and 
relatively weak regulations to control emissions (Liousse et al., 2014).
Studies modelling climate change impacts on air quality find that the 
spatiotemporal patterns of concentration changes vary strongly at 
urban scales, and that often those patterns differ among the different 
years modelled due to internal variability (Saari et  al., 2019) and 
different models used (Weaver et al., 2009). Changes in PM 2.5 due to 
climate change are less clear than for ozone and may be relatively 
smaller (Westervelt et al., 2019) as climate change can affect PM 2.5 
species differently (Fiore, Naik and Leibensperger, 2015). For Beijing, 
climate change is expected to cause a 50% increase in the frequency

of meteorological conditions conducive to high PM 2.5 concentrations 
(Cai et al., 2017). The impacts of future climate change on air quality 
and consequent risks on human health have been studied at urban 
(Knowlton et  al., 2004; Physick, Cope and Lee, 2014) and national 
scales (Fann et al., 2015; Orru et al., 2013; Doherty, Heal and O’Connor, 
2017); globally, these studies have found a likely net increased risk 
of climate change on air pollution-related health (low confidence). 
They have focused mainly on the USA and Europe, with few studies 
elsewhere (Orru, Ebi and Forsberg, 2017), although the relationship 
between climate and air quality in megacities is particularly complex 
(Baklanov, Luisa and Molina, 2016). Silva et al. (2017) found that global 
premature mortality attributable to climate change (and not from 
urbanisation) from ozone and PM 2.5 will increase by about 260,000 
deaths per year in 2100 under RCP8.5, but substantial variance in

results exists between individual models.
6.2.3 Differentiated Human Vulnerability
Evidence from urban and rural settlements is unambiguous; climate 
impacts are felt unevenly, with differentiated human vulnerability 
leading to uneven social, spatial and temporal loss, risk and 
experiences of resilience, including capacity for transformation (high 
confidence) (Woroniecki et al., 2019; Tan, Xuchun and Graeme, 2015; 
Simon and Leck, 2015; Long and Rice, 2019; Chu, Anguelovski and 
Roberts, 2017; Borie et al., 2019). The evidence is also clear that for

6
929Cities, Settlements and Key Infrastructure  Chapter 6
those with fewest resources and already constrained life chances, 
losses from climate change associated events reduce well-being and 
exacerbate vulnerability (high confidence) (van den Berg and Keenan, 
2019; Kashem, Wilson and Van Zandt, 2016; Michael, Deshpande and 
Ziervogel, 2018). Human vulnerability is influenced by the adaptive 
capacity of physical (built) structures, social processes (economic, 
well-being and health) and institutional structures (organisations, laws, 
cultural and political systems/norms) (see Section  6.4). This section 
should be read in conjunction with Chapter 8 (Poverty, Livelihoods and 
Sustainable Development) and will emphasise urban processes that 
lead to the creation of differential vulnerability, risks and impacts.
6.2.3.1 Urban Poverty and Vulnerability
In both developed and less-developed regions, poverty in urban

areas is frequently associated with higher levels of vulnerability (Huq 
et al., 2020b). This is evident in both rural and urban settlements in a 
wide range of contexts, including the Philippines (Porio et al., 2019; 
Valenzuela, Esteban and Onuki, 2020); Bangladesh (Matsuyama, Khan 
and Khalequzzaman, 2020); Brazil (Lemos et  al., 2016), Santiago, 
Chile (Inostroza, Palme and de la Barrera, 2016); and New York City 
(Madrigano et al., 2015).
For individuals in urban communities, new literature highlights 
how differences in vulnerability established by social and economic 
processes are further differentiated by household and individual 
variability and intersectionality (Kaijser and Kronsell, 2014; Kuran et al., 
2020).This includes differences in wealth and capacity (Romero-Lankao, 
Gnatz and Sperling, 2016); gender and non-binary gender (Michael 
and Vakulabharanam, 2016; Sauer and Stieß, 2021; Mersha and van

Laerhoven, 2018); education, health, political power and social capital 
(Lemos et al., 2016); age, including young and elderly, low physical 
fitness, pre-existing disability, length of residence and social and ethnic 
marginalisation (Inostroza, Palme and de la Barrera, 2016; Schuster 
et al., 2017; Malakar and Mishra, 2017). An increasing proportion of 
refugees and displaced people now live in urban centres, and their 
characteristics also make them vulnerable to a range of shocks and 
stresses (Earle, 2016). While some individuals, including children, may 
be able to exercise agency to reduce their risk (Treichel, 2020), and 
some indicators are culturally specific, overall, poor, marginalised, 
socially isolated and informal urban households are particularly at risk 
(high confidence) (Brown and McGranahan, 2016; Kim et al., 2020b; 
Huq et al., 2020a; Huq et al., 2020b).
6.2.3.2 Informality, Planning and Vulnerability

Particularly in low- and middle-income countries, much urban building 
occurs outside formal parameters and entails a high degree of urban 
informality. According to the United Nations statistics, the proportion 
of urban populations living in slums and informal settlements increased 
from 23% in 2014 to 23.5% in 2018 (United Nations, 2018). Informality 
is one pathway through which urbanisation generates differentiated 
vulnerability, tending to increase exposure and susceptibility of physical 
structures and their occupants to climate-related risks (Dodman et al., 
2017; Dobson, 2017) in contexts including Guadalajara, Mexico (Gran 
Castro and Ramos De Robles, 2019), Kampala, Uganda (Richmond, 
Myers and Namuli, 2018), Bengaluru, India (Kumar, Geneletti and Nagendra, 2016), and Dar es Salaam, Tanzania (Yahia et al., 2018). In 
addition to facing emerging water- and heat-related risks, such areas 
are also more vulnerable to the health impacts of climate change

(Scovronick, Lloyd and Kovats, 2015).
Even where formal planning is the norm, this has often remained 
oriented toward enabling value by adding construction or the 
protection of existing high-value physical assets, for example 
infrastructure and built cultural heritage, private residential) rather 
than enabling disaster risk reduction for all (Long and Rice, 2019). 
This tendency has been widely documented, including from cases in 
Australia, Thailand, Indonesia (King et al., 2016), Canada (Stevens and 
Senbel, 2017), Amman, Moscow and Delhi (Jabareen, 2015), and South 
Africa (Arfvidsson et  al., 2017). Such inconsistencies between the 
delivery of land use planning and the aims of the SDGs combine with 
other social structures, economic pathways and governance systems to 
shape city risk profiles (Dodman et al., 2017).
6.2.3.3 Migration and Differentiated Vulnerability
Migration, displacement and resettlement each play a foundational

role in differentiated vulnerability (see Cross-Chapter Box  MIGRATE 
in Chapter 7). The relationship between migration and vulnerability 
is complex (robust evidence, high agreement), and is the first of the 
three components discussed within this section. Climate change, as a 
push factor, is only one among multiple drivers (political, economic and 
social) related to environmental migration (Heslin et al., 2019; Plänitz, 
2019; Luetz and Merson, 2019). There is consensus that it is difficult 
to pin climate change as the sole driver of internal (within national 
boundaries) rural to urban migration decisions owing to, among other 
factors, the disconnect between national and international policies 
(Wilkinson et al., 2016), the lack of unifying theoretical frameworks 
and the complex interactions between climatic and other drivers 
(social, demographic, economic and political) at multiple scales 
(Cattaneo et al., 2019; Borderon et al., 2019). Environmental migration,

including rural to urban migration, triggered by climate change may 
ensue from either slow- or rapid-onset climatic events and could be 
either temporary, cyclical or permanent movement that occurs within 
or beyond national boundaries (Heslin et al., 2019; Silja, 2017).
A range of specific studies highlight certain elements of vulnerability 
and migration, including the ways in which slow-onset events affect 
precarious, resource-dependent livelihoods (such as farming and 
fisheries) (Cai et al., 2016). In small town Pakistan and Colombia, heat 
stress increases long-term migration of men, driven by a negative 
effect on farm income (Mueller, Gray and Kosec, 2014; Tovar-Restrepo 
and Irazábal, 2013). A study from Mexico reveals that an increase 
in drought months led to increased rural to urban migration, while 
increased heat (temperature) led to a ‘nonlinear’ pattern of rural to 
urban migration that occurred only after extended periods of heat

(nearly 34  months) (Nawrotzki et  al., 2017). This aligns with other 
findings that a consistent increase in temperature between 2°C and 
4°C in some parts of the world renders involuntary, forced migration 
inevitable (Otto et al., 2017).
The complexity of migration drivers (as push or pull factors) explains 
why there is little agreement around quantitative estimates on

6
930Chapter 6 Cities, Settlements and Key Infrastructure
migration (especially international) triggered by climate change (Silja, 
2017; Otto et  al., 2017), and why estimates of future displacement 
attributed to climate change and other environmental causes 
vary between 25  million and 1  billion in 2050 (Heslin et  al., 2019). 
Many authors are critical of existing perspectives on climate-related 
migration, and argue for more nuanced research on the topic (Boas 
et al., 2019; Kaczan and Orgill-Meyer, 2020; Silja, 2017; Sakdapolrak 
et al., 2016; Singh and Basu, 2020; Luetz and Havea, 2018).
Climate-induced migration is not necessarily higher among poorer 
households whose mobility is more likely to be limited due to the 
poverty trap (i.e., lack of financial resources) (high confidence) 
(Cattaneo et  al., 2019; Kaczan and Orgill-Meyer, 2020; Silja, 2017). 
For example, in Bangladesh, vulnerability of rural populations is

increasing, so many of the poorest employ migration as a strategy of 
last resort (Paprocki, 2018; Penning-Rowsell, Sultana and Thompson, 
2013; Adri and Simon, 2018) that occurs as soil salinity (as opposed 
to inundation alone) increases and is paralleled by economic 
diversification (i.e., aquaculture) (Chen and Mueller, 2018). There is 
robust evidence and high agreement that rapid-onset climatic events 
trigger involuntary migration and short-term, short-distance mobilities 
(Cattaneo et  al., 2019). There is also robust evidence and high 
agreement that slow-onset climatic events (such as droughts and sea 
level rise) lead to long-distance internal displacement, more so than 
local or international migration (Kaczan and Orgill-Meyer, 2020; Silja, 
2017), while sea level rise is expected to lead to the displacement of 
communities along coastal zones, such as in Florida in the USA (Hauer, 
2017; Butler, Deyle and Mutnansky, 2016).

Migration, including rural–urban migration, is also recognised as an 
adaptation strategy in some circumstances, whether this is voluntary 
or planned (Jamero et al., 2019; Esteban et al., 2020a; Bettini, 2014). 
Voluntary migration can be an element of household strategies to 
diversify risk, depending on the nature of the climatic stress, and 
interacts with household composition, individual characteristics, social 
networks, and historical, political and economic contexts (Hunter, 
Luna and Norton, 2015; Carmin et al., 2015; Hayward et al., 2020). 
For example, in Colombia, rural to urban migration is differentiated 
across gender depending on the climatic stress whereby men migrate 
due to droughts, while women migrate due to excessive rain triggers 
(Tovar-Restrepo and Irazábal, 2013). Especially in Pacific small island 
developing states, migration can be a strategy for urban settlements 
or tribal communities to relocate in customary areas, as in the case

of Vunidogoloa in Fiji (McMichael, Katonivualiku and Powell, 2019; 
Hayward et al., 2020); it can be a livelihood strategy as shown in the 
Cataret Islands in Papua New Guinea (Connell, 2016); or it can be 
used to enhance education and international networks (i.e., voluntary 
‘migration with dignity’) as is the case in Kiribati (Heslin et al., 2019; 
Voigt-Graf and Kagan, 2017).
The second component, displacement, also plays a crucial role in 
differentiated vulnerability. The lack of resources and capacities 
to support mobility limits the effectiveness of migration as an 
adaptation strategy, therefore leading to both displacement and 
trapped populations in the future (Adger et al., 2015; Faist, 2018). For 
example, studies from Colombia (Tovar-Restrepo and Irazábal, 2013), 
India (Singh and Basu, 2020), Mekong Delta in Vietnam (Miller, 2019) and Pakistan (Islam and Khan, 2018) showed that migration as an 
adaptation strategy can be constrained due to resource barriers and

low mobility potential, and also, to high levels of place attachment 
such as in the Peruvian Highlands (Adams, 2016), Vanuatu (Perumal, 
2018) and the Tulun and Nissan Atolls of Bougainville, Papua New 
Guinea (Luetz and Havea, 2018). Migration can also be maladaptive 
for the receiving contexts, whether due to the pressure on and/or 
conflict over land and/or the urban resources (high confidence) (Faist, 
2018; Singh and Basu, 2020; Luetz and Havea, 2018). Other views 
maintain that migration as adaptation overlooks the agency of people 
and their resilience, that is the nuances of ‘translocal social resilience’ 
(Kelman, 2018; Silja, 2017; Sakdapolrak et al., 2016). For example, the 
ni-Vanuatu prioritise in situ adaptation measures and leave migration 
as a last resort (Perumal, 2018).
Regardless of the reasons and the initiators for migration, community 
control over resettlement both at the origin and destination leads to

more positive outcomes for both the communities being resettled and 
the receiving communities (high confidence) (Perumal, 2018; Ferris, 
2015; Price, 2019; Mortreux and Adams, 2015; Tadgell, Doberstein and 
Mortsch, 2018; Luetz and Havea, 2018). The protection of livelihoods 
contributes to ensuring the well-being (physical and mental) and the 
protection of the rights of communities (high confidence) (Ferris, 2015; 
Price, 2019). There is limited evidence but high agreement that the 
outcomes of resettlement initiatives are complex and multi-faceted 
(Ferris, 2015). For example, in Shangnan County, northwest China, the 
Massive Southern Shaanxi Migration Program, based on voluntary 
participation, reduced risk exposure and improved the quality of life 
in general, but also disproportionately increased the vulnerability 
of disadvantaged groups (the poor, migrants, and those left behind) 
(Lei et al., 2017). Similarly, vulnerability increased due to the loss of

connection to place and community bonds in Mekong Delta, Vietnam 
(Miller, 2019), and due to unsafe construction, poor infrastructure, 
institutional incapacity and general neglect in resettlement initiatives 
in Malawi, sub-Saharan Africa (Kita, 2017).
6.2.4 Risks to Key Infrastructures
Projected climatic changes, such as changing precipitation patterns, 
temperatures and sea levels, contribute to pressures on human 
well-being and the functioning of infrastructure systems (high 
confidence). Furthermore, risks evolve due to macro-scale drivers of 
change such as urbanisation, economic development, land use changes 
and other emergent factors (Adger, Brown and Surminski, 2018). 
Infrastructure networks are rapidly growing around the world (see 
Table 6.3). Since the quality and accessibility of infrastructure services 
are varied, it is important to understand how climate change poses 
different kinds of risk on them. Infrastructure can be broadly understood

to include social infrastructure (housing, health, education, livelihoods 
and social safety nets, security, cultural heritage/institutions, disaster 
risk management and urban planning), ecological infrastructure (clean 
air, flood protection, urban agriculture, temperature, green corridors, 
watercourses and riverways) and physical infrastructure (energy, 
transport, communications [including digital], built form, water and 
sanitation and solid waste management) (Thacker et al., 2019). This 
section focuses especially on physical infrastructure where the literature

6
931Cities, Settlements and Key Infrastructure  Chapter 6
provides discrete risk and impact assessments. Physical infrastructure 
systems are often immobile, indivisible, involve high fixed costs and 
have longer lifecycles. Social and ecological infrastructure elements 
are rarely assessed alone and instead tend to be included in wider 
assessments of event impacts.
Current climate variability is already causing impacts on infrastructure 
systems around the world (high confidence). For global physical 
infrastructure with a present value of USD 143 trillion, The Economist 
Intelligence Unit (2015) estimates present value losses of USD 4.2 
trillion by 2100 under a 2°C scenario. This estimation rises to USD 
13.8 trillion under a 6°C scenario. Extreme events are associated with 
disruption or complete loss of these infrastructure services, whilst 
gradual changes in mean conditions are altering physical infrastructure

performance. Physical infrastructure is usually costly to repair and also 
have significant impacts on people’s health and well-being.
This section synthesises and assesses the emerging literature on 
climate change risks to key physical infrastructure domains as 
listed in Table  6.3: energy/electricity infrastructure, transportation 
infrastructure and information and communication technology (ICT) 
(water infrastructure is discussed in Section 6.2.2). It draws on evidence 
from around the world, but the specific risks to infrastructure in 
different contexts are explained in more detail in the regional chapters 
(especially Section 9.8.4.1 for Africa, Section 10.4.6.3.8 for Asia and 
Section 13.6.1 for Europe). For cities and settlements, such risks are of 
particular concern owing to a lack of adaptive capacity across many 
economically important sectors and low levels of resource and capacity 
support to enhance adaptive capacity. Recent literature also illustrates

the interconnected and interdependent nature of infrastructure 
systems (see Box 6.2), which lead to uncertainties over how risks in Table 6.3 |  Selected indicators of global proliferation of infrastructure networks and their annual usage.
Infrastructure Scale Usage on annual basis Coverage/equity of access References
Electricity networks> 20 million km of power lines in 
Europe and USA25,721 TWh (2017)Global: 3130 kWh per person
Haiti: 39 kWh per person
Iceland: 53,832 kWh per personIEA (2019);
World Bank (2019);
ETSAP (2014)
Gas and LPG pipelinesWorldwide:
> 2.5 million km w−140,531 TWh (2017)Global: 4.96 MWh per person (2015)
South Africa: 0.96 MWh per person (2015)
Saudi Arabia: 34.65 MWh per person (2015)CIA (2015);
OWID (2020)
Railways 2.69 million km3835 billion passengers km−1 (2019)
9279.81 billion tonnes km−1 (2019)Switzerland: 0.7 m per person; 141 m km−2
Canada: 2.2 m per person; 8.6 m km−2
India: 0.06 m per person; 23 m km−2Koks et al. (2019);
Statista (2020)

Roads 63.46 million km12,148 billion passengers km−1 private 
vehicles (2015)
5713 billion passengers km−1 public 
vehicles, e.g., buses (2015)
302.5 billion passenger km−1 active 
modes, e.g., walking and bicycles (2015)Belgium: 15 m per person; 5 km km−2
Malawi: 1 m per person; 164 m km−2
Canada: 31 m per person; 115 m km−2Koks et al. (2019);
WorldByMap (2017);
ITF (2019)
Information and 
Communication TechnologyWorldwide:
91 million mobile phones in 1995;
8.2 billion in 2018 worldwideWorldwide:
43,000 PB in 2014
242,000 PB in 2018
(*1PB = 1 million GB)Europe: 85% of population are unique 
mobile subscribers; Asia Pacific: 66%; 
Sub-Saharan Africa: 45%ITU (2019);
Vodafone (2019);
GSMA (2019)
Water3.3 million km2 land equipped for 
irrigation
The Global Reservoir and Dam 
Database (conservatively records) 
at least 7100 damsThis irrigated land accounts for about 70% 
of total water withdrawals
These dams can retain over 7800 km3 water.Sub-Saharan Africa: 24% coverage of

safely managed drinking water services, 
28% safely managed sanitation services,
Europe and North America: 94% and 78%, 
respectively.Grigg (2019);
Lehner et al. (2011);
Lehner et al. (2019);
UN Water (2018)
one sector lead to cascading, compounding or knock-on effects across 
other sectors (Zscheischler and Seneviratne, 2017) (see Section 6.2.6 
for elaboration). Therefore, adaptation options should address climate 
risks to infrastructure in an integrated and co-beneficial manner 
(medium evidence, high confidence) (see Sections 6.3 and 6.4).
6.2.4.1 Energy Infrastructure
Energy infrastructure underpins modern economies and quality of 
life. Disruption to power or fuel supplies impacts upon all other 
infrastructure sectors, and affects businesses, industry, healthcare and 
other critical services both within and across jurisdictional boundaries 
(Groundstroem and Juhola, 2019). The economic impacts of climate 
change risks are significant, for example in the EU, the expected

annual damages to energy infrastructure, currently €0.5  billion yr−1, 
are projected to increase 1612% by the 2080s (Forzieri et al., 2018). 
In China, 33.9% of the population are vulnerable to electricity supply 
disruptions from a flood or drought (Hu et al., 2016), whilst in the USA, 
higher temperatures are projected to increase power system costs by 
about USD 50 billion by the year 2050 (Jaglom et al., 2014). In a study 
of 11 Central and Eastern European countries, researchers found that 
energy poverty is exacerbated by existing infrastructure deficits and 
energy efficient building stock, as well as income inequality, which can 
lead to reduced economic productivity (Karpinska and Śmiech, 2020). 
Climate change is expected to alter energy demand (Viguié et  al., 
2021), for example heatwaves increase spot market prices (Pechan 
and Eisenack, 2014), with a disproportionate impact on the poorest 
and most vulnerable populations. Energy infrastructure are susceptible

to a range of climate risks (Cronin, Anandarajah and Dessens, 2018), 
whilst issues pertaining to energy demand are considered by Working 
Group III.

6
932Chapter 6 Cities, Settlements and Key Infrastructure
Climate change can, for example, influence energy consumption 
patterns by changing how household and industrial consumers 
respond to short-term weather shocks, as well as how they adapt 
to long-term changes (Auffhammer and Mansur, 2014). Recent 
studies from Stockholm, Sweden, show that future heating demand 
will decrease while cooling demand will increase (Nik and Sasic 
Kalagasidis, 2013). A study from the USA showed that climate change 
will impact buildings by affecting peak and annual building energy 
consumption (Fri and Savitz, 2014). From an infrastructure standpoint, 
the vulnerability of current hydropower and thermoelectric power 
generation systems may change due to changes in climate and 
water systems and projected reduction of usable capacities (van Vliet 
et  al., 2016; Byers et  al., 2016). These examples show how energy 
infrastructure planning under climate change must take into account

a greater number of scenarios and investigate impacts on particular 
energy segments (Sharifi and Yamagata, 2016).
Electricity generation. Electricity generation infrastructure can be 
directly damaged by floods, storm and other severe weather events. 
Furthermore, the performance of renewables (solar, hydro-electric, 
wind) is affected by changes in climate.Most thermoelectric plants require water for cooling, many are therefore 
situated near rivers and coasts and thus vulnerable to flooding. Increases 
in water temperature or restrictions on cooling water availability 
affect hydroelectric and thermoelectric plants. A 1°C increase in the 
temperature of water used as coolant yields a decrease of 0.12–0.7% 
in power output (Mima and Criqui, 2015; Ibrahim, Ibrahim and Attia, 
2014). Excess biological growth, accelerated by warmer water, increases 
risk of clogging water intakes (Cruz and Krausmann, 2013). While some

regions are expected to experience increased capacity under climate 
change (namely India and Russia), global annual thermal power plant 
capacity is likely to be reduced by between 7% in a mid-century RCP2.6 
scenario and 12% in a mid-century RCP8.5 scenario (van Vliet et al., 
2016). Worldwide, hydroelectric capacity reductions are projected at 
0.4–6.1% (van Vliet et al., 2016). Analysis of the UK’s water for energy 
generation abstractions showed that an energy mix of high nuclear or 
carbon capture technologies could require as much as six times the 
current cooling water demands (Byers, Hall and Amezaga, 2014; Byers 
Increasing temperatures improve the efficiency of solar heating but 
decrease the efficiency of photovoltaic panels, and deposition and 
abrasive effects of wind-blown sand and dust on solar energy plants Box 6.2 | Infrastructure Interdependencies

Infrastructure networks are increasingly dependent on each other—for power, control (via ICT) and access for deliveries or servicing 
(Figure 6.2). Moreover, a range of other mechanisms can create interdependencies that impact upon climate risks by creating pathways 
for cascading failure (Undorf et al., 2020; Barabási, 2013). In the UK, for example, all infrastructures utilities identify failure of components 
in another utility as a risk to their systems (Dawson et al., 2018).
Key interdependencies include:
i. The use of ICT for data transfer, remote control of other systems, and clock synchronisation. Pant et al. (2016) show that ICT is 
crucial for the successful operation of the UK’s rail infrastructure. The study shows that flooding of the ICT assets in the1-in-200 year 
floodplain would disrupt 46% of passenger journeys across the whole network.

ii. Water to generate hydroelectricity and for cooling thermal power stations. Reductions in usable capacity for 61–74% of the hydropower 
plants and 81–86% of the thermoelectric power plants worldwide for 2040–2069 (van Vliet et al. , 2016), with some power generation 
technologies, including carbon capture and storage, requiring far higher volumes of water for cooling (Byers et al. , 2016).
iii. Energy to power other infrastructure systems. Failure of urban energy supply disrupts other infrastructure services, with disproportionate 
impacts on the urban poor (Silver, 2015).
iv. Transport systems that ensure access for resources such as fuel, personnel and emergency response. Pregnolato et al. (2017) show 
disruption across the city from a 1-in-10 year storm event could increase by 43% by the 2080s.
v. Green infrastructure can provide multiple services, creating interdependencies between multiple physical infrastructure systems. For

example, green space can support sustainable urban drainage, in situ wastewater treatment and urban cooling (Demuzere et al. , 2014).
vi. Geographical proximity of assets leads to multiple infrastructures being simultaneously exposed to the same climate hazard. 
Disruption is disproportionately larger for interconnected networks (Fu et al., 2014).
There is usually limited information on the risks between infrastructure sectors. Without frameworks for collaboration, and coupled 
with commercial and security sensitivities, this remains a barrier to routine sharing and cooperation between operators. Despite this, 
methods to tackle interdependence in climate risk analysis are emerging (Dawson, 2015). For example, Thacker et al. (2017) analysed the 
criticality of the UK’s infrastructure networks by integrating data on infrastructure location, connectivity, interdependence and usage. The

analysis showed that criticality hotspots are typically located around the periphery of urban areas where there are large facilities upon 
which many users depend or where several critical infrastructures are concentrated in one location. As infrastructure systems become 
increasingly interconnected, associated risks from climate change will increase and require a cross-sectoral approach to adaptation 
(Dawson et al., 2018).

6
933Cities, Settlements and Key Infrastructure  Chapter 6
can further reduce power output, and the need for cleaning (Patt, 
Pfenninger and Lilliestam, 2013). Projected changes in wind and 
solar potential are uncertain; the trends vary by region and season 
(Burnett, Barbour and Harrison, 2014; Cradden et  al., 2015; Fant, 
Schlosser and Strzepek, 2016). In an RCP8.5 scenario, Wild et al. (2015) 
conservatively calculate a global reduction of 1% per decade between 
2005 and 2049 for future solar power production changes due to 
changing solar resources as a result of global warming and decreasing 
all-sky radiation over the coming decades. However, positive trends are 
projected in large parts of Europe, the south-east of North America and 
the south-east of China.
Electricity Transmission and Distribution. Electricity transmission 
and distribution networks span large distances, with overhead power 
lines often traversing exposed areas. Power lines and other assets, such

as substations, are often located near population centres, including 
those in floodplains. Structural damage to overhead distribution lines 
will increase in areas projected to see more ice or freezing rain (e.g., 
most of Canada), snowfall (e.g., Japan) or wildfires (e.g., California, 
USA) (Bompard et al., 2013; Mitchell, 2013; Sathaye et al., 2013; Jeong 
et al., 2018; Ohba and Sugimoto, 2020). Electricity outages may last 
for prolonged periods of time and across vast areas, in addition to 
potentially disproportionately affecting poorer or more vulnerable 
communities. Increases in windstorm frequency and intensity increase 
the risk of direct damage to overhead lines and pylons, in many 
locations this is limited but Tyusov et al. (2017) calculate an increase as 
high as 30% in parts of Russia. Where the mode of failure is recorded, 
transmission pylons are seen to be more susceptible to wind damage, 
whilst distribution pylons are more likely to be affected by treefall and

debris (Karagiannis et al., 2019). Increased temperatures can lead to 
the de-rating (lower performance) of power lines, whose resistance 
increases with temperature with efficiency reductions of 2–14% being 
projected by 2100 (Cradden and Harrison, 2013; Bartos et al., 2016).
Fuels Extraction and Distribution. Non-electric energy infrastructure 
is susceptible to many of the same impacts as electric infrastructure. 
Extreme weather events impact extraction (onshore and offshore) and 
refining operations of petroleum, oil, coal, gas and biofuels. Disruption 
of road, rail and shipping routes (see Section 6.2.5.2) interrupts fuel 
supply chains. However, there are a number of risks that are specific 
to these sectors. Heat can lead to expansion in oil and gas pipes, 
increasing the risk of rupture (Sieber, 2013), whilst heatwaves and 
droughts can reduce the availability of biofuel (Moiseyev et al., 2011; 
Schaeffer et  al., 2012). Subsidence and shrinkage of soils damages

underground assets such as pipes intakes (Cruz and Krausmann, 
2013), while additional human activity such as extractive drilling may 
induce earthquakes, as observed in the northern Dutch province of 
Groningen (Van der Voort and Vanclay, 2015). In Alaska, USA, the thaw 
of permafrost and subsequent ground instability is estimated to lead to 
USD 33 million damages to fuel pipelines in an end-of-century RCP8.5 
scenario (Melvin et al., 2017), with low-lying coastal deltas particularly 
vulnerable (Schmidt, 2015).6.2.4.2 Transport
Since AR5, research has highlighted the implications for disruption to 
global supply chains (Becker et al., 2018; Shughrue and Seto, 2018; 
Pató, 2015), and has made advancements in quantifying costs of 
climate risks to transportation infrastructure. Climate risks to transport 
infrastructure (from heat- and cold waves, droughts, wildfires, 
river and coastal floods, and windstorms) in Europe could rise from

€0.5  billion to over €10  billion by the 2080s (Forzieri et  al., 2018). 
Across the Arctic, nearly four million people and 70% of all current 
infrastructure, including resource extraction and transportation routes, 
will be at risk by 2050 (Hjort et  al., 2018), although the design of 
specific infrastructure may also affect the degree of infrastructure 
damage, depending on local geological and ecological conditions. 
Globally, Koks et al. (2019) calculated that approximately 7.5% of road 
and railway assets are exposed to a 1-in-100 year flood events, and 
total global expected annual damages (EAD) of USD 3.1–22  billion 
(mean USD 14.6  billion) due to direct damage from cyclone winds, 
surface and river flooding, and coastal flooding. The majority of this is 
caused by surface water and fluvial flooding (mean USD 10.7 billion). 
Although twice as much infrastructure is exposed to cyclone winds 
compared with flooding, a mean EAD of USD 0.5 billion is significantly

less than for coastal flooding (USD 2.3 billion), as cyclone damages 
are largely limited to bridge damage and the cost of removing trees 
fallen on road carriageways and railway tracks. This is small relative 
to global gross domestic product (GDP; ~0.02%). However, in some 
countries EAD equates to 0.5–1% of GDP , which is the same order 
of magnitude as typical national transport infrastructure budgets, but 
especially significant for countries such as Fiji that already spend 30% 
of their government budget on transport (World Bank Group, 2017). 
Koks et al. (2019) did not assess future climate change impacts, but 
comparable studies calculating changes in EAD from flooding based 
upon land use show increases of 170–1370%, depending on global 
greenhouse gas emissions levels (Alfieri et al., 2017; Winsemius et al., 
2015). Moreover, Schweikert et  al., (2014) report that climate risks 
to transport infrastructure could cost as much as 5% of annual road

infrastructure budgets by 2100, with disproportionate impacts in some 
low and lower middle-income countries.
Changes in rainfall and temperature patterns are expected to increase 
geotechnical failures of embankments and earthworks (Briggs, 
Loveridge and Glendinning, 2017; Tang et  al., 2018; Powrie and 
Smethurst, 2018) from landslides, subsidence, sinkholes, desiccation 
and freeze-thaw action. For instance, Pk et al. (2018) show this could 
lead to a 30% reduction in the engineering factor of safety of earth 
embankments in Southern Ontario (Canada). Increased river flows in 
many catchments will also increase failures from bridge scours (Forzieri 
et al., 2018). HR Wallingford (2014) calculate that the projected 8% 
increase in scouring from high river flows in the UK will lead to 1 in 
20 bridges being at high risk of failure by the 2080s, whilst in the 
USA the 129,000 bridges currently deficient could increase by 100,000

(Wright et al., 2012). With respect to temperature, analysis by Forzieri 
et al. (2018) concludes that heatwaves will be the most significant risk 
to EU transport infrastructure in the 2080s, as a result of buckling of 
roads and railways due to thermal expansion, melting of road asphalt 
and softening of pavement material. In the USA, over 50% more roads 
will require rehabilitation (Mallick et al., 2018), whilst USD 596 million

6
934Chapter 6 Cities, Settlements and Key Infrastructure
will be required through 2050 to maintain and repair roads in Malawi, 
Mozambique and Zambia (Chinowsky, Price and Neumann, 2013).
In addition to direct damages from flooding and heatwaves, disruption 
caused by road blockages will be increased by more frequent flood 
events. For example, in the city of Newcastle upon Tyne (UK), road 
travel disruption across the city from a 1-in-50  year surface water 
flood event could increase by 66% by the 2080s (Pregnolato et al., 
2017), whilst heatwaves could treble railway speed restrictions in 
parts of the UK (Palin et al., 2013). Knott et al. (2017) highlighted risks 
to coastal infrastructure where ~30 cm sea level rise sea level rise 
would also push up groundwater and reduce design life by 5–17% 
in New Hampshire (USA). Heavy rain and flooding can also inundate 
underground transport systems (Forero-Ortiz, Martínez-Gomariz and 
Canas Porcuna, 2020).

Many airports, and by their nature ports, are in the low elevation 
coastal zone, making them especially vulnerable to flooding and 
sea level rise. Under a 2oC scenario, the number of airports at risk of 
storm surge flooding increases from 269 to 338 or as many as 572 
in an RCP8.5 scenario; these airports are disproportionately busy and 
account for up to 20% of the world’s passenger routes (Yesudian and 
Dawson, 2021). Airport and port operations could be disrupted by icing 
of aircraft wings, vessels, decks, riggings and docks (Doll, Klug and 
Enei, 2014; Chhetri et al., 2015). Warming will increase microbiological 
corrosion of steel marine structures (Chaves et al., 2016). Fog, high 
winds and waves can disrupt port and airport activity, but changes 
are uncertain and with regional variation (Mosvold Larsen, 2015; 
Izaguirre et al., 2021; Becker, 2020; León-Mateos et al., 2021; Taszarek, 
Kendzierski and Pilguj, 2020; Danielson, Zhang and Perrie, 2020; Kawai 

Waterways are still important transport routes for goods in many 
parts of the world, although they are mostly expected to benefit from 
reduced closure from ice (Jonkeren et al., 2014; Schweighofer, 2014), 
low flows will likely lead to reduced navigability and increased closures; 
van Slobbe et al. (2016) estimate the Rhine may reach a turning point 
for waterway transportation between 2070–2095. Obstruction due 
to debris and fallen vegetation of roads and rails and to inland and 
marine shipping from high winds are expected to increase (Koks et al., 
2019; Kawai et al., 2018; Karagiannis et al., 2019)..
6.2.4.3 Information and Communication Technology
Information and communication technology (ICT) comprises 
the integrated networks, systems and components enabling the 
transmission, receipt, capture, storage and manipulation of information 
by users on and across electronic devices (Fu, Horrocks and Winne, 
2016). ICT infrastructure faces a number of climate risks. Increased

frequency of coastal, fluvial or pluvial flooding will damage key ICT 
assets such as cables, masts, pylons, data centres, telephone exchanges, 
base stations or switching centres (Fu, Horrocks and Winne, 2016). This 
leads to loss of voice communications, inability to process financial 
transactions and interruption to control and clock synchronisation 
signals. Insufficient information about the location and nature of many 
ICT assets limits detailed quantitative assessment of climate change 
risks.Fixed-line ICT networks that sprawl over large areas are especially 
susceptible to increases in the frequency or intensity of storms that would 
increase the risk of wind, ice and snow damage to overhead cables and 
damage from wind-blown debris. More intense or longer droughts and 
heatwaves can cause ground shrinkage and damage underground ICT 
infrastructure (Fu, Horrocks and Winne, 2016). In mountain and northern 
permafrost regions, communications and other infrastructure networks

are subject to subsidence because of warming of ice-rich permafrost 
(Shiklomanov et al. , 2017; Li et al. , 2016; Melvin et al. , 2017).
6.2.4.4 Housing
For the urban housing sector, climate impacts such as flooding, heat, 
fire and wind assessed in Section  6.2.3 will likely have detrimental 
effects on housing stock (including physical damage and loss of 
property value) and on residents exposed to climate risks (robust 
In the USA, for example, 15.4  million housing units fall within a 
1-in-100-year floodplain (Wing et  al., 2018). Assessment of the 
Miami-Dade area in Florida noted that coastal inundation caused by 
tidal flooding (and to a lesser extent sea level rise) resulted in over 
USD 465 million in lost real-estate market value between 2005 and 
2016 (McAlpine and Porter, 2018), although property values have 
increased from high-end housing construction and climate adaptation

measures (Kim, 2020). Emergent risk reflecting novel research include 
aggravated moisture problems in buildings from wind driven rain (Nik 
et  al., 2015). Future risks from future sea level rise are elaborated 
in Section CCP2.2.1. Housing infrastructure are also susceptible 
to extreme heat and wind events (Stewart et al., 2018). These risks 
are further elaborated on in Section  6.2.3, although it is important 
to note that heat risks, in particular, tend to be concentrated within 
communities with a higher proportion of social housing (Mavrogianni 
et al., 2015; Sameni et al., 2015) or low-cost government-built houses 
and informal settlements.
6.2.4.5 Water and Sanitation
Apart from land subsidence from urbanisation (e.g., Case Study 6.2), 
substantial climate risks to urban sanitation arise from droughts, 
flooding and storm surges. Low flows from drought can lead to 
sedimentation, increase pollutant concentration and block sewer

infrastructure networks (Campos and Darch, 2015). Flooding poses a 
greater risk for urban sanitation in low- and middle-income settings 
(Burgin et  al., 2019) where onsite systems are more common. 
Floodwater may wash out pits and tanks, mobilising faecal sludges and 
other hazardous materials leading to both direct and indirect exposure 
via food and contaminated objects and surfaces, and pollute streams 
and waterbodies (Howard et al., 2016; Braks and de Roda Husman, 
2013; Bornemann et  al., 2019). Floods also damage infrastructure; 
toilets, pits, tanks and treatment systems are all vulnerable (Sherpa 
et al., 2014; UNICEF and WHO 2019).
Sanitation systems coupled with floodwater management are at risk 
of damage and capacity exceedance from high rainfall (Thakali, Kalra 
and Ahmad, 2016; Kirshen et al., 2015; Dong, Guo and Zeng, 2017). 
In England, the number of water and wastewater treatment plants at

6
935Cities, Settlements and Key Infrastructure  Chapter 6
risk of flooding is projected to increase by 33% under a 4oC scenario 
(Sayers et al., 2015), but risks are generally increasing for both formal 
and informal urban sanitation systems (Howard et al., 2016).
6.2.4.6 Natural and Ecological Infrastructure
Urban ecological infrastructure includes green (i.e., vegetated), blue 
(i.e., water-based) and grey (i.e., non-living) components of urban 
ecosystems (Li et al., 2017). While land cover change from urbanisation 
directly reduces the extent of natural and ecological infrastructure 
(e.g., Lin, Meyers and Barnett, 2015), notable risks arise from climate 
drivers. Recent research particularly highlights future climate impacts 
on coastal natural infrastructure, including beaches, wetlands and 
mangroves, which cause significant economic losses from property 
damage and decreasing tourism income, as well as loss of natural

capital and ecosystem services. Research on climate risks to urban 
trees and forests is comparatively limited. Instead, urban vegetation 
and green infrastructure are most often cast as adaptation strategies 
to reduce urban heat, mitigate drought and provide other ecosystem 
benefits (see Section 6.3.2).
Coastal natural infrastructure is exposed to sea level rise, wave action 
and inundation from increasing storm events (See also Section CCP 
2.2.1). Beaches, in particular, are highly exposed to climate-induced 
coastal erosion (Toimil et  al., 2018; Section CCP2). Research from 
settlements across coastal Southern California, USA, show that 67% 
of all beaches may completely erode by 2100 (Vitousek, Barnard and 
Limber, 2017). Coastal zones across Cancún, Mexico, are exposed 
to a combination of sea level rise and tropical hurricanes, further 
exacerbated by urban development patterns blocking natural sediment 
replenishment to beaches (Escudero-Castillo et al., 2018). In another

case, beach erosion along the heavily urbanised Valparaíso Bay, Chile, 
is heightened by El Niño Southern Oscillation (ENSO) events, which in 
the past have caused an additional 15–20 cm in mean sea level rise 
(Martínez et al., 2018).
Wetlands, mangroves and estuaries, which tend to be heavily urbanised 
areas, are highly at risk from sea level rise and changing precipitation 
(Green et al., 2017; Feller et al., 2017; Alongi, 2015; Osland et al., 2017; 
Chow, 2018; Godoy and Lacerda, 2015). Sea level rise is a concern 
for wetlands and mangroves across coastal urban Asia, the Mississippi 
Delta (US) and low lying small island states (Ward et  al., 2016b). 
Research on the highly urbanised Yangtze River estuary in China shows 
that soil submersion and erosion from sea level rise, compounded by 
land conversation to agriculture and urban development, will cause all 
tidal flats to disappear by 2100 (Wu, Zhou and Tian, 2017). In another

example, sea level rise and high rates of tidal inundation have increased 
overall salinity in the San Francisco Bay-Delta estuary, threatening the 
ecosystem’s ability to support biodiversity (Parker and Boyer, 2019).
Research on climate risks to urban trees and forests highlight direct 
impacts from extreme temperatures, precipitation, wind events and sea 
level rise, as well as exposure to other hazards such as air pollution, fires, 
invasive species and disease (Ordóñez and Duinker, 2014). Since the 
1960s, climate change has enabled growth of urban trees, supported 
by longer growing seasons, higher atmospheric CO 2 concentrations 
and reduced diurnal temperature range (Pretzsch et al., 2017), as well as increased fertilisation through urban-enhanced nitrogen deposition 
(Decina, Hutyra and Templer, 2020). However, these trends may change 
in the future as further warming and decreasing water supply may 
depress tree fitness, thus enabling more pests (Dale and Frank, 2017).

Climate risks to urban natural and ecosystem infrastructure entail 
significant economic costs. For example, in 2012, Hurricane Sandy led 
to total losses of up to USD 6.5 million to the New York City region’s 
low-lying salt marshes and beaches (Meixler, 2017). Research from 
coastal settlements across Catalonia, Spain, shows significant levels 
of tourism loss (which contribute to 11.1% of the region’s GDP), 
infrastructure damage and natural capital loss attributed to inundation 
and erosion of beaches, which are projected to retreat by −0.7 m yr−1 
given current sea level rise projections of 0.53–1.75 m by 2100 (Jiménez  
et al. , 2017).
6.2.4.7 Health Systems Infrastructure
Healthcare facilities (hospitals, clinics, residential homes) will suffer 
increasing shocks and stresses related to climate variability and 
change (Corvalan et  al., 2020). Some may be sudden shocks from 
extreme weather events, which both threaten the facility, staff and

patients and increase the number of people seeking health care. There 
are extensive reports of health facilities being damaged after major 
floods and windstorms (e.g., 2010 floods in Pakistan, Hurricane Sandy 
in the USA) which can be further exacerbated by power and water 
supply failures (Powell, Hanfling and Gostin, 2012). Disruption to 
services may persist for many months because of damage to buildings, 
loss of drugs and equipment, and damaged transport infrastructure 
significantly increasing travel time for patients (Hierink et al., 2020). 
The impacts of climate change on the health of residents of ‘slum’ 
settlements will also compound the existing health burdens faced by 
these individuals, including infectious disease and other environmental 
public health concerns (Lilford et al., 2016; Mberu et al., 2016).
6.2.5 Compound and Cascading Risks in Urban Areas
Compound events can be initiated via hazards such as single extreme

events, or multiple coincident events overlapping and interacting with 
exposed urban systems or sectors as compound climate risks (Leonard 
et al., 2014; IPCC, 2019b; Piontek et al., 2014). Hydrometeorological 
hazards, such as extreme precipitation from tropical cyclones, fronts 
and thunderstorms, often combine with storm surges and freshwater 
discharge leading to high compound risks at exposed settlements 
(Zheng, Westra and Sisson, 2013; Chen and Liu, 2014; Ourbak and 
Magnan, 2018; Dowdy and Catto, 2017). The compounding effect 
between these hydrometeorological hazards suggest that the combined 
impact of these events are greater than each of these variables on its 
own, and can amplify risks in affected settlements (Kew et al., 2013; 
Vitousek et  al., 2017). These risks are concentrated in coastal cities 
exposed to sea level rise and severe storms (van den Hurk et al., 2015; 
Wahl et al., 2015; Paprotny et al., 2018b; Lagmay et al., 2015), or in

settlements located in valleys prone to slope failure, such as the 2013 
Uttarakhand floods and landslides arising from extreme precipitation 
and glacial lake outbursts along the Mandakini river in India (Ziegler 
et al., 2016; Barata et al., 2018).

6
936Chapter 6 Cities, Settlements and Key Infrastructure
Cascading climate events occur when an extreme event triggers a 
sequence of secondary events within natural and human systems 
that causes additional physical, natural, social or economic disruption. 
The resulting impact can be significantly larger than the initial 
hazard (IPCC, 2019b). Each step in a risk cascade can generate direct 
(immediate impacts) and secondary (consequential impacts) losses. 
Risks from these cascading impacts are complex and multi-dimensional 
(Hao, Singh and Hao, 2018; Zscheischler and Seneviratne, 2017). For 
instance, combined droughts and heatwaves increases risks of urban 
water scarcity (Miralles et al., 2019; Gillner, Bräuning and Roloff, 2014; 
Gill et al., 2013), as well as increasing wildfire extent and lowering 
snowpack conditions that affected peri-urban settlements adjacent 
to forested areas, as observed in California during the 2014 drought

(AghaKouchak et al., 2014). Similarly, heatwaves can increase the risk 
of mortality associated with air pollution (see Section 7.2.2.5).
Urban areas and their infrastructure are susceptible to both 
compounding and cascading risks arising from interactions between 
severe weather from climate change and increasing urbanisation 
(medium evidence,  high agreement) (Moretti and Loprencipe, 2018; 
Markolf et  al. , 2019). Risks are complex and multi-dimensional, 
and can significantly amplify the impact of single events across 
space, scale and time. Impacts are determined by the magnitude 
of urban vulnerability and/or the interdependence of urban critical 
infrastructure (Pescaroli and Alexander, 2018; Zuccaro, De Gregorio 
and Leone, 2018). Poorer and wealthier settlements and cities 
are then both at risk from compound and cascading risks though 
potentially through contrasting mechanisms. For richer and poorer 
cities, managing climate risk as part of compound and cascading risks

that can also include technological, biological and political risks places 
renewed emphasis on investment in generic capabilities that reduce 
vulnerability and on risk monitoring capability to track and respond 
to impacts across infrastructures and places (limited evidence,  high 
agreement). Considering climate risk and managing such risk as part 
of complex, compounding and/or cascading risks is in its infancy but 
rapidly being accepted as necessary, especially when considering the 
wider poverty and justice implications of climate change arising from 
differentiated vulnerability in cities.
Compound risks to key infrastructure in cities have increased from 
extreme weather (medium evidence, high agreement), such as from 
urban flooding from extreme precipitation and storm surges disrupting 
transport infrastructure and networks, for example Mehrotra et  al. 
(2018), see also San Juan case study in this chapter), ICT networks, for

example underground cables or transmission towers (Schwarze et al., 
2018), and energy generation from power plants (Marcotullio et al., 
2018).
The increased risk arises not just from greater exposure from climate 
events impacting cities, but is also magnified by low adaptive capacity 
that can arise from intra-urban variations in infrastructure quality. 
For instance, infrastructure within expanding informal settlements is 
associated with deficiency in materials, structural safety and a lack 
of accessibility. These areas are often located in the most risk-prone 
urban areas in developing nations that are vulnerable to compound 
hazards (Dawson et al., 2018). Further, these risks can be exacerbated 
from complications arising from local versus national governance and/or regulations related to hazard management (Garschagen, 2016; 
Castán Broto, 2017).
Projected global compound risks will increase in the future, 
with significant risks across energy, food and water sectors that

likely overlap spatially and temporally while affecting increasing 
numbers of people and regions particularly in Africa and Asia (high 
confidence) (Hoegh-Guldberg et al., 2018). In cities, the prevalence of 
compounding risks therefore necessitates methodologies accounting 
for non-stationary risk factors.
Secondary impacts occurring sequentially after an extreme hazard 
can severely affect disaster management, especially in complex urban 
systems (robust evidence, high agreement). Over time, relatively small 
perturbations can cascade outward from a primary failure, triggering 
further failures in other dependent parts of the network some distance 
away from the primary failure (Penny et al., 2018). In some cities, such 
as those prone to compound flood hazards, these dependent network 
parts can be dams, levees or other critical flood protection infrastructure 
that are essential for managing these cascading risks (Serre and

Heinzlef, 2018; Fekete, 2019). Failure of these infrastructure systems 
can result in sequential failures in urban transport (Zaidi, 2018), energy 
networks (Sharifi and Yamagata, 2016), urban biodiversity (Solecki 
and Marcotullio, 2013) and so-called na-tech disasters; when natural 
hazards trigger technological disasters (Girgin, Necci and Krausmann, 
2019). This risk cascade can propagate more widely by stopping flows 
of people, goods and services, with economic consequences beyond 
urban areas (Wilbanks and Fernandez, 2014).
Compound and cascading climate risks require a different way of 
accounting for cumulative hazard impacts in urban areas (medium 
evidence, high agreement). There is emerging literature calling for 
analysis on interactions between individual and inter-related climate 
extremes with complex urban systems, so as to ascertain how urban 
and key infrastructural vulnerabilities can be identified and managed

in a warming world (Butler, Deyle and Mutnansky, 2016; Gallina et al., 
2016; Moftakhari et al., 2017; Zscheischler et al., 2018; Baldwin et al., 
2019; Pescaroli and Alexander, 2018; Yin et  al., 2017; AghaKouchak 
et al., 2020), as well as in managing adaptation for present and future 
pandemics, for example COVID-19 (Pelling et al., 2021; Phillips et al., 
2020).
In terms of policy, case studies from London’s resilience planning 
process stressed the need for intermodal coordination, hazard risk 
and infrastructure mapping, clarifying tipping points and acceptable 
levels of risk, training citizens, strengthening emergency preparedness, 
identifying relevant data sources, and developing scenarios and 
contingency plans (Pescaroli, 2018). Others also note the utility 
of a systems approach to analysing risks and benefits, including 
considerations of potential cascading ecological effects, full lifecycle 
environmental impacts, and unintended consequences, as well as

possible co-benefits of responses (Ingwersen et al., 2014). Lowering 
these risks requires urban stakeholders to reduce urban vulnerability 
by going beyond linear approaches to risk management (medium 

6
937Cities, Settlements and Key Infrastructure  Chapter 6
6.2.6 Impacts and Risks of Urban Adaptation Actions
Planning and implementing climate adaptation in cities and settlements 
can be hampered by incomplete scientific knowledge, a lack of 
awareness of cascading impacts (and residual risks), mismanagement of 
actions, human capacity and financing deficits, as well as opportunities 
for eroding long-term sustainable development priorities (Juhola et al. , 
2016). These tensions can become acute in fragile and conflict affected 
states (see Box 6.3). It is important to differentiate between the climatic 
drivers of risk and social drivers that may compound risk exposures 
and experiences (Brown, 2014; Nightingale et  al. , 2020), especially 
since technically- and scientifically-informed adaptation actions 
can be redirected depending on socioeconomic, political or cultural 
conditions on the ground (Eriksen, Nightingale and Eakin, 2015). The

implementation of adaptation, whether by government, private sector or 
civil society actors, can therefore lead to unanticipated and unintended 
amplification of political, economic and ecological risks (Swatuk et al. , 
2020). Many cities are still in the phase of piloting or testing out 
appropriate adaptation actions, although there is emerging consensus 
that adaptation plans and projects should acknowledge trade-offs, 
intentionally avoid past development mistakes, not lock-in detrimental 
impacts or further risks arising from implementation and explicitly 
anticipate the risks of maladaptation in decision making (Magnan  
et  al. , 2016; Gajjar, Singh and Deshpande, 2019). Maladaptation 
describes actions that lead to increased vulnerability or risk to 
climate impacts or diminish welfare. Urban examples include green 
gentrification which offers nature-based solutions to the few, social 
safety nets that promote risk inducing subsidies. Whether an action is

maladapted can depend on context, for example air conditioning can 
reduce risk for the individual but is maladaptive at a societal level 
(see Section 6.3.4.2). It is informed by process; corruption can distort processes and generate maladaptation (see Section 6.4.5.2). Climate 
resilient development raises the ambition for adaptation actions so that 
it is also possible to describe actions that do not also enhance climate 
mitigation and sustainable development outcomes as maladaptive (see 
Section  6.4.3.1). This section assesses three broad categories of risk 
arising from downstream adaptation actions, including interventions 
that transfer vulnerability across space and time, plans that yield 
socioeconomically exclusionary outcomes, and actions that undermine 
long-term sustainable and resilient development priorities.
Downstream impacts occur because adaptive capacity is often 
unequally distributed across sectors and communities (Matin, Forrester

and Ensor, 2018; Makondo and Thomas, 2018). In cities and settlements, 
adaptation interventions can displace ecological impacts to more 
vulnerable areas or directly lead to socioeconomically exclusionary 
outcomes (Anguelovski et al. , 2016), particularly when adaptation plans 
and actions are primarily assessed through the prism of economic and/
or financial viability (Shi et al. , 2016; Klein, Juhola and Landauer, 2017). 
As a result, adaptation actions make only minimal contributions to the 
reduction of vulnerability, as the increased vulnerability of excluded 
communities more than offsets the decreased vulnerability of more 
well-off communities. Numerous examples, ranging from the mega 
coastal planning in Jakarta, Indonesia (Salim, Bettinger and Fisher, 2019; 
Goh, 2019), fragmentation of urban infrastructure intended to promote 
climate resilience in Manila, Philippines (Meerow, 2017), exclusionary 
modes of flood control in São Paulo, Brazil (Henrique and Tschakert,

2019), strategies to reduce risks in the event of mudslides in Sarno, 
Italy (D’Alisa and Kallis, 2016), and involuntary community relocations 
in Vietnam (Lindegaard, 2020) and Mozambique (Arnall, 2019) all point 
to how an economic logic to adaptation can lead to exclusion of lower 
income, informal or minority communities in adaptation.
Box 6.3 | Climate Change Adaptation for Cities in Fragile and Conflict Affected States
Larger cities may be the most stable administrative entities in states affected by conflict. Even here, ability to plan and deliver adaptation 
can be hampered. Extending into urban areas within stable states, alienation and loss of trust between local populations and the 
state can be exacerbated by top-down adaptation planning and delivery; socially and spatially uneven adaptation investment; and in 
the economic and administrative limits of government that can lead to some places being excluded from formal planned investment

(high confidence) (see Sections 6.3 and 6.4). These pathways for exclusion can combine among already marginalised and low-income 
populations where trust in government agencies may already be low (Rodrigues, 2021).
Climate change can be a threat multiplier in cities and urban regions, exacerbating existing human security tension (limited evidence, 
medium agreement) (Froese and Schilling, 2019; Flörke, Schneider and McDonald, 2018; Rajsekhar and Gorelick, 2017). Where conflict 
or administrative tensions extend beyond cities, adapting regional infrastructure systems that underpin urban life is challenging, for 
example where elements of networked infrastructure are under the control of conflicting political interests. This has been noted for the 
water sector (Tänzler, Maas and Carius, 2010). Coordinating political processes is a major challenge even for industrialised countries

with adequate administrative capacity. In post-conflict societies, the difficulties of coordination for urban planning are disproportionately 
greater (Sovacool, Tan-Mullins and Abrahamse, 2018).
In planning adaptation measures in cities, conflict-sensitive approaches to ensure participatory methods (Bobylev et al., 2021) can avoid 
adaptation being a polarising activity (Tänzler, Maas and Carius, 2010; Tänzler, 2017). Adaptation can provide a common goal reaching 
across political differences and be a part of building political trust and local cooperation between alienated communities (Tänzler, Maas 
and Carius, 2010). Peacebuilding programmes led by government or civil society are typically concerned with the short-term and framed 
by socioeconomic policy, integrating the longer-term view and engineering-technical expertise for adaptation is a challenge (limited 
evidence, medium agreement) (Ishiwatari, 2021).

6
938Chapter 6 Cities, Settlements and Key InfrastructureTable 6.4 |  Key Risks to cities, settlements and infrastructure
Synthesis of key risks for cities, settlements and key infrastructure
Key riskGeographic 
regionConsequence that 
would be considered 
severe, and to whom.Hazard conditions that 
would contribute to 
this risk being severe.Exposure conditions that 
would contribute to this risk 
being severe.Vulnerability conditions 
that would contribute to 
this risk being severe.Adaptation options with 
highest potential for reducing 
risk.Confidence 
in key risk 
identification.Chapter 
and 
section
Risk to 
from increased 
heatGlobal but 
higher risk in 
temperate and 
tropical cities. 
(6.2.3.1)Increased heat stress, 
mortality and morbidity 
events from urbanisation 
and climate change.
Increased health risks 
and mortality in elderly 
population;
vulnerability of the young 
to heat. (6.2.3.1)Substantial increase in 
frequency and duration

of extreme heat events, 
exacerbated by urban heat 
island effects. (6.2.3.1)
Concentration of a mixture 
of extreme heat and 
humidity. (6.2.3.1)Large increases in exposure, 
particularly in urban areas, (6.2.3) 
driven by population growth, 
changing demographics, and 
projected urbanisation patterns.
Urbanisation increases annual mean 
surface air temperature by more 
than 1°C
Correlation between rising 
temperatures and increased heat 
capacity of urban structures, 
anthropogenic heat release and 
reduced urban evaporation. (6.2.3.1)Changing demographics from 
aging populations, potential 
for persistent poverty, slow 
penetration and increasing 
cost of air conditioning, and 
inadequate improvements in 
public health systems.
(6.2.3.1)
Inadequate housing and 
occupations with exposure to 
heat. (6.2.3.1)Nature-based solutions e.g., urban 
greenery at multiple spatial scales; 
vegetation; shading; lower energy 
costs; green roofs; community 
gardens; (6.3.3.1) enhanced space

conditioning in buildings; broader 
access to public health systems for 
most vulnerable populations.
Less economic stress on residents 
through utilities, especially electricity. 
(6.2.3.1)
Tree planting in communities that lack 
urban greening. (6.3.3.1)High confidence, 
robust evidence 
and high 
agreement.6.2, 6.3
Urban 
infrastructure 
at risk of 
damage from 
flooding and 
severe stormsGlobal, but 
coastal cities.Damage to key urban 
infrastructure (e.g., 
buildings, transport 
networks, and power 
plants) and services from 
flood events, particularly 
high risk within coastal 
cities, especially those 
located in low elevation 
coastal zones. (6.2.3.2)Substantial increase in 
frequency and intensity 
of extreme precipitation 
(6.2.3.2) from severe 
weather events and 
tropical cyclones 
contributing to pluvial and 
fluvial floods, which are 
exacerbated by long-term 
sea level rise and potential 
land subsidence. (6.2.3.2)Large increases in exposure,

particularly in urban areas, driven 
by population growth, changing 
demographics, and projected 
urbanisation patterns with a 
geographical focus in coastal regions. 
Flooding is exacerbated both by 
encroachment of urban areas into 
areas that retain water, and lack of 
infrastructure such as embankments 
and flood walls. (6.2.3.2)Costly maintenance of 
protective infrastructure, 
downstream levee effects, 
and increased concentrations 
of coastal urban population. 
Little investment in drainage 
solutions. (6.2.3.2)Early warning systems, Adaptive 
Social Protection (ASP) to reduce 
vulnerable populations, nature-based 
solutions e.g., in sponge cities to 
enhance flood protection and regulate 
storm- and floodwaters; this can be 
improved through reduced risk unto 
vulnerable urban systems such as 
stormwater management, sustainable 
urban drainage system, etc. (6.2.3.2)
Green infrastructure can be more 
flexible and cost effective for

providing flood risk reduction. (6.3.3)High confidence, 
and high 
agreement6.2, 6.3, 
CCP2
Population 
at risk from 
exposure 
to urban 
droughtsCities located 
in regions with 
high drought 
exposure 
(e.g., Europe, 
South Africa, 
Australia).Water shortages in urban 
areas, and restricted access 
to water resources to 
vulnerable populations and 
low-income settlements. 
People living in urban 
areas will be exposed 
to water scarcity from 
severe droughts. (6.2.3.3) 
Increased environmental 
health risks when using 
polluted groundwater. 
(6.2.3.3)Projections of more 
frequent and prolonged 
drought events potentially 
compounded with 
heatwave hazards, 
and land subsidence 
from coastal cities that 
extract groundwater. 
Climate drivers (warmer 
temperatures and 
droughts) along with 
urbanisation processes 
(land use changes, 
migration to cities and 
changing patterns of 
water use) contribute to 
additional risks. (6.2.3.3)Large increases in exposure,

urbanisation patterns. Limitations 
of engineered water infrastructure 
is also exposed by flash droughts. 
(6.2.3.3) Settlements are increasingly 
dependent on imported water 
resources by locales that may also be 
exposed to drought risk. (6.2.3.3)Greater water demand from 
urban populations from 
in-migration and key economic 
sectors, and inefficient or 
ineffective water resource 
management. (6.2.3.3)Demand and supply side management 
strategies that include incorporation 
of indigenous/local knowledge and 
practices, equitable access to water. 
Better water resource management 
will increase quality of water 
available. More beneficial physical 
and social teleconnections to bring 
mutual benefit of water resources 
between regions. (6.2.3.3)High confidence, 
and high 
agreement6.2, 6.3

6
939Cities, Settlements and Key Infrastructure  Chapter 6
in key risk 
and 
section
Health risks 
from air 
pollution 
exposure in 
citiesGlobal, in 
cities located 
in Africa, 
South Asia, 
the Middle 
East and East 
AsiaIncreased mortality and 
morbidity events from 
respiratory-related illnesses 
and co-morbidities 
toward vulnerable urban 
populations, arising from 
PM2.5 and tropospheric 
ozone exposure.Increased emissions 
of pollutants from 
anthropogenic (e.g., 
transportation, electric 
power generation, large

industries, indoor burning 
of fuel, and commercial 
and residential sources) 
and biogenic (e.g., 
forests, windblown dust, 
and biomass burning) 
emissions.
Potential for severe 
compound risks arising 
from droughts and wildfire.
Projections for frequency of 
meteorological conditions 
are expected to severe 
PM2.5 concentrations. 
(6.2.3.4)Large increases in exposure, 
demographics, projected urbanisation 
patterns and demand for energy 
combined with weak regulations for 
emissions control. (6.2.2.4)High proportion of young or 
aging populations vulnerable to 
respiratory illness, potential for 
persistent poverty, advection 
of pollutants from upwind, 
ex-urban areas, and stay in 
shelter policies from COVID-19. 
(Box 6.4; 6.2.5)Enhanced monitoring of air quality in 
rapidly developing cities,
investment in air pollution controls, 
e.g., stricter emissions. regulations, 
and
increased GHG emissions controls

resulting in co-benefits with air 
quality improvements.
Increase in trees or vegetated 
barriers with low VOC emissions, low 
allergen emissions, and high pollutant 
deposition potential to reduce 
particulate matter and maximise 
adaptation benefits. (6.3.3.2)High confidence, 
medium 
evidence and 
high agreement6.2, 6.3
from water 
pollution 
exposure and 
sanitation in 
citiesCities located 
exposure 
resulting in 
polluted water.Increased environmental 
(6.2.3.3)
Vulnerability of users such 
as women; children; the 
elderly; ill or disabled. 
(6.3.4.6)Decreased regional 
precipitation and changes 
in runoff and storage 
from droughts impairs the 
quality of water available. 
Less runoff to freshwater 
rivers can increase salinity, 
concentrate pathogens, 
and pollutants. (6.2.2.3)Large increases in exposure, 
by population growth, changing

urbanisation patterns. Low flows from 
drought can lead to sedimentation, 
increase pollutant concentration 
and blocking of sewer infrastructure 
networks. (6.2.4.8).Costly maintenance of 
protective infrastructure. 
Sanitation systems coupled 
with flood water management 
are at risk of damage and 
capacity exceedance from high 
rainfall. (6.2.4.8)Investment in well-regulated water 
sections; wastewater treatment 
plants; pumping stations. Reducing 
impacts of floods on sanitation 
infrastructure through active 
management such as reducing 
blockage in sewer infrastructure 
(6.3.4.6)
Adaptive planning; integration of 
measures of climate resilience; 
improved accounting and 
management of water resources. 
(6.3.4.6)High confidence, 
medium 
High agreement6.2, 6.3

6
940Chapter 6 Cities, Settlements and Key Infrastructure
A specific form of maladaptation is so-called green gentrification, this 
privileges wealthy urban residents in urban greening projects (Rice 
et  al., 2020; Shokry, Connolly and Anguelovski, 2020; Anguelovski, 
Irazábal-Zurita and Connolly, 2019; Blok, 2020). For example, in 
Miami-Dade County, Florida, USA, researchers found that adaptation 
functionality had a positive effect on property values (Keenan, Hill 
and Gumber, 2018). In New York City and Atlanta, Georgia, USA, 
research has shown that adaptation investments can increase 
property values and lead to neighbourhood change (Immergluck and 
Balan, 2018; Gould and Lewis, 2018). In the Gold Coast and Sunshine 
Coast, South East Queensland, Australia, where local communities 
have a strong preference for waterfront living, local governments 
are pressured by property developers to protect these coastal zones 
(Torabi, Dedekorkut-Howes and Howes, 2018). In Lagos, Nigeria,

efforts to achieve climate resilience and sustainability through future 
city practices risk perpetuating the enclosure and commodification of 
land (Ajibade, 2017). The exclusionary outcomes of some adaptation 
interventions can therefore further heighten the risk to communities 
that are socioeconomically more vulnerable. See Section  6.3 for 
further discussion of equity and justice considerations in local climate 
Human behaviour can exacerbate climate impacts, for example in the 
emergence of ‘last chance tourism’, Lemieux et al. (2018) focused on 
built cultural heritage at risk from climate change associated events, 
including from decay or even total loss generated by increased 
flooding and sea level rise (Camuffo, Bertolin and Schenal, 2017) and 
water infiltration from post-flood standing water (Camuffo, 2019). Last 
chance tourism can lead to increased touristic interest over a short 
time horizon and to precarious economic conditions, which can lead to

further accelerated degradation cultural heritage sites already at risk 
from climate change.
Finally, some adaptation policies or actions can erode the 
preconditions for sustainable and resilient development by indirectly 
increasing society’s vulnerability (Neset et  al., 2019; Juhola et  al., 
2016). Mandates to mainstream adaptation into existing development 
logics and structures perpetuates development-as-usual, reinforcing 
technocratic forms of local governance and locking in structural causes 
of marginalisation and differential vulnerability (Scoville-Simonds, 
Jamali and Hufty, 2020). Adaptation policy examples include: 
Australia’s adaptation policy focus on financial strategies, preference 
for business-as-usual scenarios and incremental change will not 
contribute to transformative change (Granberg and Glover, 2014); 
Surat, India, where a focus on adapting industries and economically 
important assets in the city can divert policy attention away from

general social equity and urban sustainability priorities (Chu, 2016; Blok, 
2020); Cambodia, where conflict between adaptation practitioners and 
local communities and non-compliance with regulatory safeguards 
led to conflict and potential for maladaptation (Work et  al., 2018). 
Finally, although insurance has the potential to incentivise practices 
to reduce risks, including through measures to reduce premiums (see 
Section  6.4.5 for additional details), researchers of insurance-led 
adaptation actions have argued that, since insurance regimes privilege 
normality, they tend to structurally embed risky behaviour and inhibit 
change (O’Hare, White and Connelly, 2016). All of these examples 
illustrate how incremental strategies that rely on business-as-usual actions can further entrench unequal and unsustainable development 
patterns in the long term. There are also significant limits to urban 
adaptation (see Section  6.4) with consequential impacts on human 

Table 6.4 lists a selection of key risks (broadly defined as have severe 
outcomes common to a majority of cities) identified in our assessment 
of urban impacts and risks in this section. It provides a description 
of the consequences of the risk that would constitute a severe 
outcome, as well as the hazard, exposure and vulnerability conditions 
contributing to its severity. It also provides adaptation options 
identified and elaborated on in Section  6.3 as having the highest 
potential for reducing the risk, and an assessment of the confidence 
in the judgement that this risk could become severe. This table is 
also reflected in Section 16.5.1, and the methodology is described in 
Table SM16.5.1.
Following Chapter 16, the severity of a risk or impact is a subjective 
judgment based on a number of criteria. Key risks are ‘potentially’ 
severe because, while some could already be severe now, more 
typically they may become so over time because of changes in the

nature of the climate-related hazards and/or of the exposure and/or 
vulnerability of societies or ecosystems to those hazards. They also may 
become severe owing to the adverse consequences of adaptation or 
mitigation responses to the risk.
6.3 Adaptation Pathways
6.3.1 Introduction
Adaptation pathways are composed of sequences of adaptation 
actions connected through collaborative learning with the possibility 
of enabling transformations in urban and infrastructure systems 
(Werners et  al., 2021). Individual adaptation actions co-evolve with 
risks (see Section  6.2) and development processes (Section  6.4) 
to compose more or less planned adaptation pathways that can 
include a range of unanticipated outcomes. This section engages 
with this complexity by approaching adaptation through the notion 
of infrastructure. The adaptation options for individual infrastructure 
systems are reviewed, and in Section  6.4 brought together through

assessment of cross-cutting enabling conditions. Interpreted broadly, 
infrastructure includes the social systems, ecological systems and grey/
physical systems that underpin safe, satisfying and productive life in 
the city and beyond (Grimm et al., 2016). Social infrastructure includes 
housing, health, education, livelihoods and social safety nets, cultural 
heritage/institutions, disaster risk management and security and urban 
planning. Ecological infrastructure includes nature-based services: 
temperature regulation, flood protection and urban agriculture. 
Grey, or physical infrastructure, includes energy, transport, water 
and sanitation, communications (digital), built form and solid waste 
management. Framing infrastructure in this way enables an assessment 
of adaptation that is not constrained to the administrative boundaries 
of urban settlements, but also includes the flows of material, people 
and money between urban, peri-urban and more rural places, and

can include adaptation actions deployed by government, individuals 
and the private sector. Recognising the complexity of adaptation and

6
941Cities, Settlements and Key Infrastructure  Chapter 6
the research literature that reaches beyond individual infrastructural 
domains, the section also reviews urban adaptation through the 
cross-cutting lenses of equity and mitigation. Section 6.4 assesses the 
enabling environment (political will, governance, knowledge, finance 
and social context) that shapes specific adaptation contexts and 
futures.
LowerHigher
LowerHigherLowerHigher
EuropeAfrica
Central
and South
America
North
America
Small
IslandsAsia
Central
and South
Asia
North
and EastAdaptation to
rain/river ﬂoodingAdaptation to 
storm/coastal ﬂoodingAdaptation to 
heatwavesAdaptation to 
water securityAdaptation to 
food security
All currently possible adaptation Current adaptation Planned adaptation
Higher = Exposed population with highest (20%) income
Lower = Exposed population with lowest (20%) incomeInequalityNo riskOccasional

discomfortFrequent
impacts
on
wellbeingExtreme
events
and/or
chronic riskOccasional
impacts
on
wellbeing
The urban adaptation gap
(residual risk after adaptation actions)
Adaptation actions deployedThe urban adaptation gap to current climate risks: inequality in all world regions  
Figure 6.4 |  The Urban Adaptation Gap. This is a qualitative assessment presenting individual, non-comparative data for world regions from 25 AR6 Coordinating Lead Authors 
(CLAs) and Lead Authors (LAs), the majority from regional chapters. Respondents were asked to make expert summary statements based on the data included within their chapters 
and across the AR6 report augmented by their expert knowledge. Multiple iterations allowed opportunity for individual and group judgement. Urban populations and risks are very 
diverse within regions making the presented results indicative only. Variability in data coverage leads to the overall analysis having medium agreement, medium evidence. Major

trends identified in 6.3.1 at least meet this level of confidence. Analysis is presented for current observed climate change associated hazards and for three adaptation scenarios: 
(1) current adaptation (based on current levels of risk management and climate adaptation), (2) planned adaptation (assessing the level of adaptation that could be realised if all 
national, city and neighbourhood plans and policies were fully enacted), (3) transformative adaptation (if all possible adaptation measures were to be enacted). Assessments were 
made for the lowest and highest quintile by income. Residual risk levels achieved for each income class under each adaptation scenario are indicated by five adaptation levels: 
no risk, occasional discomfort, occasional impacts on well-being, frequent impacts on well-being, extreme events and/or chronic risk. The urban adaptation gap is revealed when

levels of achieved adaptation fall short of delivering ‘no risk’. The graphic uses IPCC Regions, and has split Asia into two regions: North and East Asia, and Central and South Asia. 
Technical support is acknowledged from Greg Dodds and Sophie Wang6.3.2 The Adaptation Gap in Cities and Settlements
The adaptation gap is the difference between the ability to manage 
risk and loss and experienced risk and loss (Chen et al., 2016; UNEP , 
2021). It describes both levels of capacity and residual risk. Figure 6.4 
presents an analysis by IPCC World Region for urban populations and

6
942Chapter 6 Cities, Settlements and Key Infrastructure
current levels for risk and loss. The analysis seeks to draw out equity 
considerations by comparing the poorest and wealthiest quintiles for 
each region and for adaptation to the direct impacts of flooding and 
heatwave, as well as impacts felt in cities that include climate change 
impacts on supply chains; water and food security. Figure 6.4 should 
not be used to compare regions but can be used to contrast adaptation 
gaps by hazard type within regions.
The key finding from Figure 6.4 is that for all urban populations, both 
currently deployed and currently planned adaptations are not able to 
meet current levels of risk associated with climate change. Even if all 
conceivable adaptation was to be deployed, the majority of risks faced 
by the urban rich and poor today would not be fully resolved. This 
clearly emphasizes the fundamental importance of climate change 
mitigation to avoid urban risk and loss.

The urban adaptation gap is also found to be unequal. The poorest 
quintile has a larger adaptation gap than the richest quintile. Reported 
inequality in the application of urban adaptation is greatest in North, 
East and Southeast Asia, reflecting rapid urbanisation in this region. 
Reported inequality is lowest in Europe and Australasia. Observed 
inequalities indicate that the markets, government actions and civil 
society investments available to reduce vulnerability and risk among 
the poor have not been observed to offset inequalities based on 
individual and household capacities.
There is some catch-up as analysis moves through actually deployed 
to planned and all conceivable deployment, particularly for water and 
food security, but even here, inequality in risk is not fully resolved. 
Africa and South and Central Asia in particular show considerable 
disparity in adaptation to urban food security even with all conceivable

adaptation. This means that even if all available adaptation was to 
be deployed, inequality in ability to adapt to climate change would 
remain. This highlights the significance of addressing underlying 
inequalities in development that shape differential vulnerability (see 
Section 6.2.3.1, 6.2.3.3, 6.3.5.1 and 6.4) as part of vision and action on 
reducing risk to climate change so that no one is left behind.
Some hazard types and regions show strong capacity to close the 
adaptation gap if all planned adaptation was to be deployed: for 
example, Europe for heatwave and Europe and Central and South 
America for riverine and coastal flooding (particularly for wealthier 
populations). This reveals capacity within the current approaches 
to climate risk management, but also highlights the importance of 
resolving challenges that prevent planned adaptation from being 
deployed and deployed equitably.
6.3.3 Adaptation Through Social Infrastructure

Social infrastructure refers to social, cultural and financial activities 
and institutions as well as associated property, buildings and artefacts 
that can be deployed to reduce risk and recover from loss. This 
section examines land use planning, livelihoods and social protection, 
emergency and disaster risk management, health systems, education 
and communication, and cultural heritage.6.3.3.1 Land Use Planning
Land use planning plays a major role in the siting of settlements 
and infrastructure. In relation to climate change, it affects whether 
development takes place in locations that are exposed to hazards; 
similarly, it shapes the potential effects that the built environment 
can have on natural systems. Despite this, generally speaking, there is 
limited implementation of zoning and land use measures for climate 
adaptation from cities across diverse contexts (robust evidence, 
high agreement), see for example Maputo (Castán Broto, 2014),

sub-Saharan cities (Dodman et al., 2017) and Amman, Moscow and 
Delhi (Jabareen, 2015). Certain countries, such as South Korea, have, 
however, recently begun to address disaster risk reduction within their 
land use planning systems (Han et al., 2019).
Conventional zoning regulations (in which only one kind of use 
is permitted in a given area) and land use planning range in scale 
from the regional to the local and can be deployed to minimise risks 
through protection, accommodation or retreat. Protection entails, in 
addition to allocating zones for protective urban infrastructure (such 
as seawalls, levees and dykes, and slope revetments), avoidance 
measures that restrict or prevent urban development (e.g., through 
growth containment and/or no-build zones). Accommodation involves 
land use modifications and/or conversions while retreat requires either 
compulsory or voluntary relocations and may entail buyouts (Butler,

Deyle and Mutnansky, 2016; León and March, 2016; Lyles, Berke and 
Overstreet, 2018). Risk eliminating retreat measures are less widely 
adopted than other risk reducing zoning and land use measures 
(Anguelovski et al., 2016; Butler, Deyle and Mutnansky, 2016; Lyles, 
Berke and Overstreet, 2018). This is attributed to the controversies 
of relocation and to the complexities of buyouts (Butler, Deyle and 
Mutnansky, 2016; King et al., 2016).
Evidence from both richer countries and the Global South reveals 
that conventional zoning is more effective when governance 
systems facilitate the implementation of land use policies for climate 
adaptation that preclude negative human-nature interactions and that 
curb spatial inequity, both of which can trigger climate gentrification 
and increase the vulnerability of economically disadvantaged groups 
to climate-related risk (high confidence) (Marks, 2015; Liotta et  al., 
2020; Keenan, Hill and Gumber, 2018). Cascading benefits of zoning

and land use planning for climate adaptation are associated with 
the use of soft land cover, green infrastructure and improvement of 
livability through better conditions for walkability and cycling. This 
decreases auto-dependency and contributes to health and economic 
development (by attracting businesses and retail that stimulate 
economic prosperity and increase property values) (Larsen, 2015; 
Carter et al., 2015). Such increases in property values have also been 
observed in zones and areas protected from risks (such as flooding), 
where it may trigger spatial inequity leading to climate gentrification 
(Marks, 2015; Votsis, 2017; Votsis and Perrels, 2016; Keenan, Hill and 
Gumber, 2018).
Adaptation actions through zoning and land use are more effective 
when combined with other planning measures (high confidence), 
for example with ecosystem-based adaptations (e.g., for flood 
management and curbing the urban heat island effect) (Larsen, 2015;

6
943Cities, Settlements and Key Infrastructure  Chapter 6
Nalau and Becken, 2018; Perera and Emmanuel, 2018; Anguelovski 
et al., 2016; Carter et al., 2015; Tsuda and Duarte, 2018; Nolon, 2016); 
with community-based adaptations (trade-offs and valuations, i.e., 
which land uses are valued more) (Larsen, 2015; Nalau and Becken, 
2018; Perera and Emmanuel, 2018; Anguelovski et  al., 2016; Carter 
et al., 2015; McPhearson et al., 2018; Nolon, 2016); and with built form 
regulations and codes (León and March, 2016; Yiannakou and Salata, 
2017; Perera and Emmanuel, 2018; Straka and Sodoudi, 2019; Larsen, 
2015; Nolon, 2016). The imposition of planning-based tools such as 
scenario planning, flexible zoning and development incentivisation 
(among others) has the capacity to influence and encourage these 
adaptations (United States Environmental Protection Agency, 2017). 
Local risk-reduction inputs can inform land use adaptation policies

(accommodation and/or avoidance, specifically growth containment 
and no-build zones) that are better integrated within larger urban 
plans (Lyles, Berke and Overstreet, 2018; Nalau and Becken, 2018; 
Tsuda and Duarte, 2018) (limited evidence, high agreement).
Implementation of zoning and land use measures for climate adaptation 
from cities across diverse contexts remains limited (high agreement, 
robust evidence) owing to a range of challenges. A range of evidence 
from multiple locations indicates the challenges of mainstreaming 
land use planning for climate adaptation, including in Bangkok, 
Thailand (Marks, 2015), Legazpi City and Camalig Municipality in the 
Philippines (Cuevas et al., 2016; Cuevas, 2016), the USA (Cuevas et al., 
2016; Cuevas, 2016), British Columbia, Canada (Stevens and Senbel, 
2017), and Australia (Serrao-Neumann et al., 2017). Mainstreaming is 
hindered by a lack of clarity of implementation strategies for climate

adaptation, insufficient funding, competing priorities (especially 
among professional planners and politicians), institutional challenges 
(see Jabareen’s [2015] study of 20 cities globally) and the need to fill 
data gaps and continuously update weather statistics (Oberlack and 
Eisenack, 2018) (medium evidence, high agreement). At the same 
time, however, limited evidence from cities around the world such as 
the urban regions of Stuttgart and Berlin in Germany (Larsen, 2015), 
Greater Manchester in the UK (Carter et al., 2015), and Colombo in 
Sri Lanka (Perera and Emmanuel, 2018) reveals that risk reduction 
through zoning and land use can effectively protect and expand green 
infrastructure and soft land cover to alleviate pluvial flooding and 
decrease the urban heat island effect. This evidence points that one 
of the primary roles of land use planning is to guide the development 
of the urban form. As such, it underpins and establishes the basis

for other infrastructure systems such as physical infrastructure and 
nature-based solutions (Morrissey, Moloney and Moore, 2018).
6.3.3.2 Livelihoods and Social Protection
Understanding how livelihoods, particularly of the urban poor, are both 
impacted by climate risk and how they might be strengthened is central 
to understanding climate adaptation in cities and settlements (Dobson 
et al. 2015). Rapid urbanisation and expanding physical infrastructure 
do not have a clear relationship with improved outcomes for urban 
livelihoods of low-income residents (Soltesova et al., 2014). Municipal 
and national efforts need to be closely aligned with building adaptive 
capacity of residents themselves, often through community-based 
adaptation (Soltesova et al., 2014; Dobson, Nyamweru and Dodman, 
2015). Social safety nets protect individuals or households from falling below a defined standard of living by providing cash, in kind and

other social transfers to fight vulnerabilities (Islam and Hasan, 2019) 
including those associated with climate change impacts including food 
shocks. Strengthening the financial and social infrastructure of poor 
households is a critical component of adaptive and transformative 
capacity (Haque, Dodman and Hossain, 2014; Ziervogel, Cowen and 
Ziniades, 2016). Social safety nets are one mechanism for strengthening 
this capacity.
Social protection, or social security, is defined as the set of policies and 
programmes designed to reduce and prevent poverty and vulnerability 
throughout the lifecycle (ILO, 2017). Safety nets are intended to protect 
vulnerable households from impacts of economic shocks, natural 
hazards and disasters, and other crises. The UN policy frameworks for 
sustainable development, including the Sendai Framework for Disaster 
Risk Reduction 2015–2030, the new Strategic Framework 2018–2030 
of the United Nations Convention to Combat Desertification (UNCCD)

and UNFCCC, highlight the essential role of social protection in 
promoting comprehensive risk management (Aleksandrova, 2019). 
Since the term Adaptive Aocial Protection was introduced by the World 
Bank (2015) and the IPCC (2014), it has been an emerging strategic tool 
to integrate poverty reduction, disaster risk reduction and humanitarian 
development into adaptation to climate change (Béné, Cornelius and 
Howland, 2018; Aleksandrova, 2019; Watson et al. , 2016).
Adaptive social protection (ASP) is defined as a resilience-building 
approach by combining elements of social protection, disaster risk 
reduction and climate change adaptation, so as to break the cycle of 
poverty and vulnerability of household by investing in their capacity 
to prepare for, cope with and adapt to all types of shocks, especially 
under climate change and other global challenges (Bowen et  al., 
2020; Ivaschenko et al., 2018). ASP has been justified as an effective

instrument to build household and community resilience to climate 
extremes and slow-onset climate events such as sea level rise and 
environmental degradation (Schwan and Yu, 2018; Aleksandrova, 
2019). In contexts of extreme poverty or climatic extremes, international 
development organisations, national provisions and market charities 
are complementary where family and kinship networks are weak and 
inadequate. To deal with short-term vulnerability to climate shocks, ASP 
can act as a crucial complement to risk management tools provided by 
communities and markets, tools which tend to be insufficient in the 
face of large or systemic shocks, by providing predictable transfers, 
developing human capital and diversifying livelihoods (Hallegatte 
et al., 2016). ASP can also facilitate long-term change and adaptation 
by improving education and health levels, as well as providing a 
proactive approach to managing climate-induced migration in both

rural and urban areas (Schwan and Yu, 2018; Adger et al., 2014).
Many national ASP programmes are established to cover both rural and 
urban areas, however, only a small number of researchers pay attention 
to urban cases (Aleksandrova, 2019). ASP instruments can be classified 
into four major types as presented in Table 6.5 (Ivaschenko et al., 2018; 
ILO, 2017). ASP can contribute to both incremental and transformative 
interventions both at the system level (short-term and long-term 
coping strategies from communities) and at the beneficiaries’ level 
(vulnerable populations) (Béné, Cornelius and Howland, 2018; World 
Bank, 2015; Aleksandrova, 2019; Ivaschenko et al., 2018).

6
944Chapter 6 Cities, Settlements and Key Infrastructure
ASP may be very good at reducing extreme poverty by helping to meet 
individual or household needs but not collective needs to mitigate 
long-term climate shocks. For example, few programmes consider 
risk assessment and climate-proof infrastructures as anticipatory 
measures to foster early action and preparedness (Aleksandrova, 2019; 
Costella et al., 2017). They therefore need to enable the adoption of 
forward-looking strategies for long-lasting adaptation (Tenzing, 2020). 
Some examples from China show social protection can improve 
adaptive capacity of urban communities with social medical insurance, 
housing subsidies, weather-index insurance, post-disaster construction, 
relocation planning, livelihood shift strategies, and so on. (Pan et al., 
2015; Zheng et  al., 2018b; Rao and Li, 2019; Song, Zheng and Lin, 
2021). However, social protection may lead to maladaptation in urban

policy when social security, or similar tools (for example insurance) 
to compensate for exposure deincentivise risk reduction (Grove, 
2021). In many developing countries, high concentrations of poor 
and vulnerable groups living in disaster-prone zones of urban centres, 
new urban dwellers and informal residents are often excluded from 
community-based networks and social services (Aleksandrova, 2019). 
Risk transfer tools (such as insurance) and risk retention measures 
(such as social safety nets) can avoid and minimise the burden of loss 
and damage and limit secondary and indirect effects (Aleksandrova, 
2019; Roberts and Pelling, 2018).
Inclusive, targeted, responsive and equitable social protection can 
support long-term transition toward more sustainable, adaptive and 
resilient societies (Hallegatte et  al., 2016; Shi et  al., 2018; Béné, 
Cornelius and Howland, 2018; Carter and Janzen, 2018; Adger et al.,

2014). ASP systems can be cost effective and equitable when targeting 
accuracy, timely risk sharing (disaster assistance) and improved policy 
coherence. Carter and Janzen (2018) find that the long-term level 
and depth of poverty can be improved by incorporating vulnerability 
targeted social protection into a conventional social protection system. Table 6.5 |  Four categories and examples of adaptive social protection.
Category Example Urban cases Function
(or social assistance)Conditional and unconditional cash transfers, including 
non-contributory pensions and disability, birth and 
death allowances;
Food stamps, rations, emergency food distribution, 
school feeding and subsidies;
Cash or food for work programmes;
Free or subsidised health services;
Housing and utility subsidies;
Scholarships and fee waivers, etc. –A targeted asset transfer project for urban extreme poor in 
Dhaka city (Hossain and Rahman, 2018)

–Emergency food stockpiling in Japan; safety net food 
stocks in India, Indonesia and Malaysia (Lassa et al., 2019)
 –Household cash transfer programme in contingency 
planning in Mexico (Ivaschenko et al., 2018)
 –Governmental transfer to hurricane affected households in 
USA (Bowen et al., 2020)
 –Non-contributory disability cash benefits (ILO, 2017)Incremental adaptation; 
protective measures
Social insuranceOld age, survivor and disability contributory pensions;
Occupational injury benefit, sick or maternity leave;
Health insurance, etc.Old-age social pensions (Ivaschenko et al., 2018)Incremental adaptation and ex 
ante prevention
Labour market policiesUnemployment, severance and early retirement 
compensation;
Training, job sharing and labour market services;
Wage subsidises and other employment incentives, 
including for disabled people, etc.Public works and employment protection in Africa, Asia cases

(World Bank, 2015; ILO, 2017; Ivaschenko et al., 2018)Ex post protection and ex ante  
prevention measures, incremental 
Livelihood development 
measuresIncome diversification, employment support, 
weather-index insurance, housing subsidies, 
post-disaster construction, relocation planning, 
livelihood shift strategies, etc.Multiple programmes for differing household needs in 
Philippines (Bowen et al., 2020);
Weather-index insurance in Chinese coastal cities 
(Rao and Li, 2019);
Early warning forecast system and public meteorological 
service information in Beijing (Song, Zheng and Lin, 2021)Promotive and anticipatory 
measures; transformational 
Countries at all income levels can set up ASP systems that increase 
resilience to natural hazards, but the systems need to identify cost–
benefits and be scalable and flexible to adjust to future, increasing 
climate risk. Bastagli (2014) suggested a new design for effective social

protection including: (i) increasing the amount or value of transfer; (ii) 
extending the coverage of beneficiaries; and (iii) introducing payments 
or new programmes of social protections. For social protection 
programmes to contribute more effectively to adaptation, they need 
to be better coordinated across a range of agencies; better integrated 
with climate data to anticipate times of need for vulnerable groups; 
and better aligned with other risk management instruments such as 
insurance (Agrawal et al., 2019).
6.3.3.3 Emergency and Disaster Risk Management
There is growing evidence of the benefits of early warning systems 
for urban preparedness decision making and action for climate and 
weather-related hazards such as cyclones, hurricanes and floods 
(medium evidence; high agreement) (Lumbroso, Brown and Ranger, 
2016; Zia and Wagner, 2015; Marchezini et  al., 2017). Climate 
forecasting is constantly evolving and becoming increasingly accurate.

Global organisations such as the World Meteorological Organizations 
are increasingly focusing on new and emerging technologies such as 
crowdsourced data collection to support integrated city services and 
early warning systems (Baklanov et al., 2018). However, while climate 
forecasting is an increasingly central tool for risk management agencies, 
a focus on urban areas or key infrastructure is still considerably rare 
(Lourenço et  al., 2015; Nissan et  al., 2019; Harvey et  al., 2019). 
The significant rise in urban risks poses significant challenges to 
humanitarian agencies. Humanitarian responses and local emergency 
management are vital for disaster risk reduction yet are compromised 
in urban contexts where it is difficult to confirm property ownership 
and where renters and informal dwellers are often excluded from

6
945Cities, Settlements and Key Infrastructure  Chapter 6
decision-making and planning (Parker and Maynard, 2015; Maynard 
et al., 2017). Disaster survivors and growing urban refugee populations 
are often displaced across the city thereby complicating efforts to track 
and provide support (Maynard et al., 2017).
Existing early warning systems remain insufficient and the complexity 
of urban landforms makes accurate and detailed early warning 
difficult (medium evidence; high agreement) (Jones et al., 2015). This 
is particularly the case in low- and middle-income countries (LMICs) 
where urban centres are often characterised by rapid expansion 
of interlinked formal and informal human settlements and land use 
zones. In such contexts, early warning services vary in effectiveness 
within the same urban centre (Allen et  al., 2020c; Rangwala et  al., 
2018). Often, forecast-based action follows linear structures where 
forecast information is applied mainly for responding to negative

impacts rather than anticipatory decision-making and preparation to 
avoid such impacts (Marchezini et al., 2017). Early warning systems are 
effective for warning of threshold breaching events including cyclonic 
activity and riverine flooding but less able to provide localised warning, 
though capability is rapidly increasing. Probabilistic risk forecasting 
and forecast based early action are only beginning to be applied to 
urban contexts and often those that are most vulnerable do not receive 
warnings regarding hazardous events (Nissan et al., 2019). There is less 
capacity for early warning systems in LMICs with key challenges linked 
to a lack of well-established risk baseline information; accessibility, 
communication and understanding of forecast information, as well as 
political and institutional barriers and limited resources and capacities 
to act on such information (Jones et al., 2015; Mustafa et al., 2015;

Zia and Wagner, 2015; Marchezini et al., 2017; Gotgelf, Roggero and 
Eisenack, 2020). Political and institutional barriers to the incorporation 
of climate information to decision-making are not limited to LMICs 
(Harvey et al., 2019). For example, comprehensive studies on sectoral 
use of climate information in Europe revealed that, despite climate 
services becoming increasingly accessible and well resourced, there 
is limited organisational uptake of seasonal climate forecasts across 
key sectors (e.g., energy, transport, water and infrastructure) in 
informing their decision making processes (Soares and Dessai, 2016; 
Soares, Alexander and Dessai, 2018). This is due both to technical and 
non-technical barriers such as lack of awareness and knowledge of 
climate information and forecasting (Soares and Dessai, 2016; Soares, 
Alexander and Dessai, 2018).
Globally, a considerable diversity of tools and frameworks for urban

resilience assessments are being developed at multiple scales (Arup 
and Rockefeller, 2015; Elias-Trostmann et  al., 2018). These include 
hybrids such as ecosystem-based disaster risk reduction (Eco-DRR) 
(Begum et  al., 2014).While important advances have been made in 
assessing urban resilience, much debate remains around such tools and 
assessment approaches regarding issues such as validation, dynamics 
in exposure and vulnerability, and appropriateness of generic methods 
in high-density urban settlements (Leitner et al., 2018; Cardoso et al., 
2020; Rufat et al., 2019). Disaster impact and recovery time are strongly 
influenced by the behaviour and actions of individuals, communities, 
businesses, and government organisations (Meriläinen, 2020; Räsänen 
et al., 2020). For example, the review by Aaerts et al. (2018) shows how 
the limitations of existing flood risk assessment methods (which tend

to account for human behaviour in limited terms) can be addressed through innovative flood-risk assessments that integrate behavioural 
adaptation dynamics. The study by Moghadas et al. (2019) highlights 
the importance of hybrid multi-criteria approaches for assessing 
urban flood resilience in Tehran, Iran. A growing literature shows 
how multidisciplinary and inclusive approaches that include Local 
knowledge can achieve greater accuracy in risk characterisation 
and support lasting impact of investments into more robust climate 
services (Aerts et al., 2018; Lourenço et al., 2015; Sword-Daniels et al., 
2018; Singh et al., 2018; Nissan et al., 2019; Harvey et al., 2019; Simon 
and Palmer, 2020). This literature highlights the need for innovative 
approaches in urban contexts that transcend traditional approaches 
of local knowledge inclusion widely applied in rural contexts, such as 
participatory rural appraisal.
The inclusion of Local knowledge and Indigenous knowledge in urban

vulnerability and risk assessments can strongly enhance local resilience, 
but its effectiveness is constrained by wider decision making and policy 
contexts dominated by top-down approaches (medium evidence; high 
agreement) (Jones et  al., 2015; Sword-Daniels et  al., 2018; Nissan 
et  al., 2019). Established non-state actors such as Shack and Slum 
Dwellers International are particularly effective at implementing 
inclusive approaches for local knowledge incorporation into urban 
decision-making. Climate change and disaster risk exacerbate existing 
problems of economic development, yet macro-economic planning 
seldom incorporates adaptation. Recent evidence also confirms the 
role of Indigenous knowledge and local knowledge in management 
practices to reduce climate risks through early warning preparedness 
and response (see also Section 6.3.2.3). These practices are particularly 
important where alternative early warning methods are absent. For

instance, Abudu Kasei et al. (2019) show that Indigenous knowledge 
gathered through observations on changes in natural indicators 
(such as links between rainfall patterns, certain flora and fauna, and 
temperature changes) could be applied to develop early warning of 
climate hazards (floods and droughts) in informal urban settlements 
in African countries such as Ghana. Similarly, Hiwaski et  al. (2015) 
show that observations of changes in the environment and celestial 
bodies are used to predict climate-related hazards in Indonesia, the 
Philippines and Timor-Leste where communities in turn use local 
materials and methods, and customary practices to respond to the 
impacts of climate change.
Insurance is a risk transfer mechanism for middle- and high-income 
countries, yet is less widely available in LMICs (Surminski and 
Thieken, 2017). Additionally, where insurance options do exist in 
LMICs, these are not usually available to large populations living or

operating in the informal sector. Flood insurance is widely available 
in many Organisation for Economic Co-operation and Development 
(OECD) countries but the demand and uptake differ significantly 
across countries (Hanger et  al., 2018). This financial tool is subject 
to increasing pressure under the changing climate, with growing 
concerns around affordability and availability. More integrative 
approaches are required, such as where changes in the insurance 
industry are closely linked to adaptation strategies, building standards 
and land use planning and their application (Cremades et al., 2018). 
This is particularly important in LMICs and of central concern for all 
insurance schemes is ensuring access, fairness and affordability for the 
most poor and vulnerable. However, there are some notable examples

6
946Chapter 6 Cities, Settlements and Key Infrastructure
of low-income communities setting up their own disaster insurance 
mechanisms. For example, the Community Development Funds for the 
Baan Mankong upgrading programme in Thailand include disaster 
funds as insurance against housing damage (Archer, 2012). Such 
approaches also need to be more closely linked to existing urban risk 
management planning approaches where urban livelihoods are seldom 
integrated and informed by more dynamic risk reduction frameworks 
that consider adaptive cycles and how resilience changes over time 
(Beringer and Kaewsuk, 2018; Cremades et al., 2018).
Disaster risk management systems face increasing challenges in 
adapting to evolving risk profiles, shaped by expanding urban areas and 
changing environmental conditions associated with climate change. In 
addition to flooding, risk monitoring and management systems have 
recently shown considerable shortfalls in planning for and responding

to increased fire risk such as the devastating Californian wildfires 
in October 2019 (Morley, 2020) and Australia’s unprecedented and 
catastrophic 2019–2020 wildfire season. Risk management has also 
been challenged by new risk experiences including wild/bush fires 
encroaching on expanding urban areas and fire outbreaks in densely 
populated informal settlements pose increasing threats to livelihoods, 
human health and habitats globally (see also Sections  2.4.4.2 and 
2.5.5.2).
6.3.3.4 Climate Resilient Health Systems
Climate resilient health systems are a vital part of adaptation to 
protect the most vulnerable from climate change (WHO, 2020). 
Cardiovascular fitness for example is a root cause of morbidity and 
mortality form heat stress (Schuster et  al., 2017). The World Health 
Organization has developed a framework of climate-resilient health 
systems that addresses both mitigation and adaptation goals (WHO,

2015). Universal health coverage (UHC) is an essential component of 
climate-resilient health systems. In most countries, access to health 
services is better in urban than in rural areas. However, there remain 
large urban populations with insufficient coverage of health services 
(WHO and WB, 2015) and UHC tracking needs to take better account 
of inequalities in coverage, including differences in access within 
cities and further disaggregation of urban populations by income. 
Thus, health sector investment is an important tool in adaptive action 
and capacity. Analyses of health survey data shows that, globally, 
access to health care is increasing toward UHC targets (Lozano et al., 
2020). Financing for global health has increased steadily in the last 
two decades and modelling shows this trend is likely to continue 
to 2050, but at a slower pace of growth and the current disparities 
in per-capita health spending persist between high and low/middle

income countries, leading to insufficient health service coverage for 
the poorest populations (Chang et al., 2019a). Out-of-pocket spending 
is projected to remain substantial in LMIC and will remain the only 
means to access health care for many poor urban populations.
The WHO Operational Framework highlights the components that 
can be strengthened to adapt to extreme weather (e.g., health care 
workforce, information systems, etc.). The evidence is greatest for 
impacts on larger health facilities (such as hospitals) and there is 
less evidence regarding impacts on health service delivery outside 
these settings (smaller health facilities, pharmacies, first responders, public health inspectors, etc.). Improved building design and spatial 
urban planning (where facilities are located) are essential to increase 
resilience for higher temperature and flood risk (medium evidence; 
high agreement) (WHO, 2021; Codjoe et al., 2020; Korah and Cobbinah,

2017). Public health systems rely on information systems (including 
disease and vector surveillance and monitoring) to identify new and 
emergent public health risks. Improvements to health surveillance will 
increase resilience, particularly for populations in informal settlements 
that are absent from health and vital registration systems.
City-level and local government adaptation planning is facilitated 
by information on health impacts (Reckien et al., 2015), highlighting 
the need for monitoring and surveillance and the need for local 
evidence-based risk assessments. Adaptation in the health sector can 
be limited by lack of collaboration between health and other sectors, 
although this is often easier to facilitate at the local level (Woodhall, 
Landeg and Kovats, 2021).
6.3.3.5 Education and Communication
Since AR5, there has been significant growth in research about 
climate education and activism (Simpson, Napawan and Snyder,

2019; O’Brien, Selboe and Hayward, 2018; Hayward, 2021). Access 
to knowledge is an important determinant of well-being, inclusivity 
and livelihood mobility and of driving human behaviour. Knowledge 
systems include formal educational provision (capital assets, syllabus 
and human capital), informal learning based in social interaction and 
customary institutions (including through social media) and public 
communication (news media, government and other information 
systems including commercial messaging). There is a growing body 
of literature addressing the role of information and communication 
technology in shaping behaviour in disaster response and recovery and 
climate action, with particular focus on social media use and serious 
gaming (Houston et al., 2015; Carson et al., 2018) (see Section 6.3.4.3)
Given the amount of time that children spend in school settings, 
adapting educational infrastructure and programmes to climate

change is highly important. This includes not only making physical 
structures safe, but also providing students with the knowledge 
and confidence to support individual and family-based adaptation. 
Several UN agencies (e.g., UNICEF and UNDRR) and international 
non-governmental agencies (e.g., Plan International) have prioritised 
safer schools and child-centred risk management that often focus on 
schools as places that should be prioritised for retrofitting and safe 
construction, but also as focal points for knowledge dissemination and 
community organising where impacts can extend beyond the school 
to reduce risk among students’ families. Universities and think tanks, 
as well as the third and private sector are key support mechanisms, 
particularly at the local level and when working in collaboration with 
local government and communities. They can support the development 
of critical educational resources and innovative communication

methods, as well as facilitate the design and implementation of 
climate policies and related action plans.
Youth, adult communities, social media and the commercial media 
can have a significant impact on advancing climate awareness and 
the legitimacy of adaptive action, particularly in large urban areas

6
947Cities, Settlements and Key Infrastructure  Chapter 6
(medium evidence, high agreement). Climate change education in 
urban settlements has increasingly focused on enhancing children 
and young people’s political agency in schools, universities, and in 
formal and informal media settings (Cutter-Mackenzie and Rousell, 
2019). However, an ambiguous framing of climate impacts and 
adaptation, for example around the science of urban heat islands 
by the media, can also exacerbate local community confusion and 
uncertainty (Iping et  al., 2019) and further training and capacity 
building opportunities such as for vocational qualifications is still 
required across diverse settings (Simmons, 2021). Communication 
strategies deployed in formal education and social media can be highly 
influential in exchanging information and establishing narratives and 
viewpoints that frame what adaptive action is legitimate, especially

in large cities (Simpson, Napawan and Snyder, 2019). However, the 
effectiveness of communication strategies for change, for example 
from Mayoral offices, can also be influenced by wider political and 
structural drivers including community literacy or political partisanship 
(Boussalis, Coan and Holman, 2019). Recent research (e.g., Macintyre 
et  al., 2018) highlights the need for new learning approaches to 
climate education from school age to adult education. Emphasis is 
on inclusivity in learning and recognising diverse perspectives across 
multiple levels and settings, from formal and informal education to 
wider social learning. Informal learning that takes place outside of 
school settings, such as in libraries and botanical gardens, in everyday 
life is increasingly recognised as a key arena for climate education, 
life-long learning and nurturing environmental citizenship and activism 
(Paraskeva-Hadjichambi et al., 2020).
6.3.3.6 Cultural heritage/institutions

The integration of culture into urban policy and planning is increasingly 
recognised as critical to developing sustainable and resilient cities, 
and features in international agreements such as the SDGs (limited 
evidence; high agreement) (Sitas, 2020). However, urban cultural 
policies are still limited, for example, Cape Town is the only African 
city to have developed a city-level cultural policy (Sitas, 2020). Cultural 
heritage refers to both tangible (e.g., historic buildings and sites) and 
intangible (e.g., oral traditions and social practices) resources inherited 
from the past (Fatorić and Egberts, 2020; Jackson, Dugmore and Riede, 
2018). Learning about past societal and environment changes through 
heritage offers opportunity for reflection and transfer of knowledge and 
skills. This takes place in multiple contexts such as museums and cultural 
landscapes, and in everyday life (Fatorić and Egberts, 2020; Jackson,

Dugmore and Riede, 2018). Cultural heritage is primarily associated 
with identity and is closely intertwined with the complexities of history, 
politics, economics and memory. Climate change adds another layer 
of complexity to cultural heritage and resource management (Fatorić 
and Seekamp, 2017b). Changing climatic conditions are already 
negatively impacting World Heritage Sites such as the Cordilleras’ Rice 
Terraces of the Philippines and earthen architecture sites, for example 
the Djenné mosque in Mali, are particularly vulnerable to changes in 
temperature and water interactions (UNESCO, 2021). Climate change 
impacts intangible cultural heritage across diverse settings such as in 
the Caribbean and Pacific Small Island Developing States (SIDS) where 
traditional ways of life and related aspects such as oral traditions 
and performing arts are under threat from extreme weather events 
(UNESCO, 2021).The climate change adaptation options for built cultural heritage fall

into seven categories (Rockman et  al., 2016; Fatorić and Seekamp, 
2017b). Financial constraints are the primary barriers that underpin 
the first four adaptation options: no action at all, merely monitoring 
and/or documenting, or annual maintenance (Xiao et al., 2019; Sesana 
et al., 2019; Fatorić and Seekamp, 2017a; Fatorić and Seekamp, 2017b; 
Fatorić and Seekamp, 2018). Core and shell preservation, the fifth and 
sixth categories, are cost effective when they improve the condition of 
built cultural heritage (BCH) (Bertolin and Loli, 2018; Loli and Bertolin, 
2018a; Loli and Bertolin, 2018b), while elevation and/or relocation, the 
final adaptation options, are extremely costly and might jeopardise the 
historic value (Xiao et al., 2019). To date, however, evidence indicates 
that adaptation actions prioritise archaeological sites (Carmichael 
et al., 2017; Fatorić and Seekamp, 2018; Pollard et al., 2014; Dawson,

2013). The efficacy of adaptation of historic buildings can be increased 
through increased and stable funding, incentives, stakeholder 
engagement, and legal and political frameworks (Dutra et al., 2017; 
Fatorić and Seekamp, 2018; Fatorić and Seekamp, 2017b; Fatorić and 
Seekamp, 2017a; Leijonhufvud, 2016; Phillips, 2015; Sesana et  al., 
2019; Sesana et al., 2018; Sitas, 2020).
Other barriers to implementation include harnessing expert and 
local knowledge (of individuals and organisations) to identify both 
quantitative and qualitative methods and indicators that connect 
cultural significance and local values vis-à-vis climatic change over 
time and that move beyond the prevalent high-risk or high-vulnerability 
centred approaches (Carmichael et  al., 2017; Fatorić and Seekamp, 
2018; Haugen et al., 2018; Leijonhufvud, 2016; Pollard et al., 2014; 
Puente-Rodríguez et al., 2016; Richards et al., 2018; Dawson, 2013; 
Filipe, Renedo and Marston, 2017; Kotova et  al., 2019). This is

particularly important given that the significance of cultural heritage 
is often intangible, and its value cannot be determined solely through 
quantitative indicators. Accessing local resources (craftsmanship and 
materials compatible with the originals) can also improve built cultural 
heritage’s adaptation capacity (Phillips, 2015).
Effective decision-making and practice for adapting built and intangible 
cultural heritage requires open dialogue and exchange of cultural, 
historical and technical information between diverse stakeholders 
and decision makers (Fatorić and Seekamp, 2017b; Benson, Lorenzoni 
and Cook, 2016). As noted in Section  6.2.6, human behaviour can 
be a driving force for adaptation impacts on BCH at risk. Despite 
challenges associated with intangibility, socio-cultural heritage such 
as Indigenous knowledge (e.g., food security and water management 
practices) presents important opportunities for climate adaptation and

resilience building. More research is needed across diverse contexts 
to understand feasible climate adaptation measures, and barriers and 
opportunities for building the resilience of both built and intangible 
cultural heritage, as well as to increase awareness of cultural heritage 
benefits among climate change policymakers (Fatorić and Egberts, 
2020).

6
948Chapter 6 Cities, Settlements and Key Infrastructure
6.3.4 Adaptation Through Nature-Based Solutions
Well-functioning ecosystems can play a significant role in buffering 
cities, settlements and infrastructure from climate hazards at multiple 
scales (robust evidence,  high agreement). Nature-based solutions 
(NBS) are actions to protect, sustainably manage and restore natural 
or modified ecosystems that address societal challenges effectively 
and adaptively, simultaneously providing human well-being and 
biodiversity benefits (Cohen-Shacham et al. , 2016). Widely recognised 
as low-regret measures for disaster risk reduction and climate change 
adaptation, green and blue infrastructure investments and natural 
area conservation in cities can provide NBS across scales to reduce 
temperature shocks and provide natural flood defences among other 
adaptation and resilience benefits (McPhearson et al. , 2018; Andersson

et al. , 2019; Frantzeskaki et al. , 2019). Blue infrastructure, for example, 
provides ecological and hydrological functions (e.g., evaporation, 
transpiration, drainage, infiltration, detention) critical to sustainable 
urban water management (Iojă et al. , 2021). Public parks, urban forests, 
street trees and green roofs, as well as lakes, ponds and streams are 
widely documented for providing local cooling, grass and riparian 
buffers, forested watersheds can enhance flood and drought protection 
for cities and settlements, and mangrove stands and wetlands in coastal 
areas can reduce storm surges. Despite increasing knowledge about 
NBS (here encompassing literature on ecosystem services for climate 
change adaptation and resilience, ecosystem-based adaptation, and 
benefits of green and blue infrastructure for adaptation), recent studies 
indicate that nature-based approaches to adaptation and resilience 
are still under-recognised and under-invested in urban planning and

development (Matthews, Lo and Byrne, 2015; Geneletti and Zardo, 
2016; Frantzeskaki et al. , 2019), despite the potential scale of benefits, 
for example, a recent study covering 70 cities in Latin America calculated 
that 96 million people would benefit from improving main watersheds 
with green infrastructure (Tellman et al. , 2018).
Grey infrastructure often damages or eliminates biophysical processes 
(e.g., through soil sealing, stream burial or altered hydrology) 
necessary to sustain ecosystems, habitats and livelihoods, where urban 
ecological infrastructure (Childers et al., 2019) can be more flexible 
and cost effective for providing flood risk reduction and other benefits 
(Palmer et al., 2015). Hybrid approaches are emerging that integrate 
ecological and grey (engineered) infrastructure in adaptation planning 
and hazard protection (Grimm et al., 2016; Depietri and McPhearson, 
2017). Explicit policy uptake by city authorities is increasing (Hansen

et  al., 2015; Hölscher et  al., 2019), such as in New York where in 
2010 the city committed to a hybrid infrastructure plan for storm 
water management, investing USD 5.3 billion over 20 years, of which 
USD 2.4  billion was targeted for green infrastructure investments 
(NYC, 2010). A subset of services from urban ecosystems are being 
increasingly invested in as NBS for climate adaptation pathways 
(Keeler et al., 2019; Kabisch et al., 2016) and included as regulatory 
drivers through flood management, hazard mitigation and air pollution 
regulations that encourage or enforce the implementation of green 
infrastructure practices (Davis et al., 2020).
Development and climate mitigation co-benefits of NBS is an additional 
reason that NBS are being increasingly taken up by cities, including for 
improving health and livelihoods, particularly for poor, marginalised groups (Poulsen et al., 2015; Poulsen, Neff and Winch, 2017; Maughan,

Laycock Pedersen and Pitt, 2018; Simon-Rojo, 2019; Cederlöf, 2016). 
Co-benefits include a wide range of social and environmental benefits 
(Brink et al., 2016; Alves et al., 2019) for human physical and mental 
health (Kabisch, van den Bosch and Lafortezza, 2017; Sarkar, Webster 
and Gallacher, 2018; Engemann et al., 2019; Rojas-Rueda et al., 2019), 
climate mitigation (De la Sota et al., 2019) and as habitat for local 
biodiversity (Ziter, 2016; Knapp, Schmauck and Zehnsdorf, 2019). 
At the same time, concerns about the unintended consequences of 
investing in green infrastructure for NBS, such as how it may contribute 
to gentrification (Turkelboom et  al., 2018; Anguelovski et  al., 2018; 
Haase et al., 2017), create more public use, increase water demand 
(Nouri, Borujeni and Hoekstra, 2019) or contribute to criminal activity 
(Cilliers and Cilliers, 2015) underlines the challenges of investing in 
adaptation in complex urban systems (see Section 6.2.6). Additionally,

more place-based analyses of the efficacy of NBS for reducing climate 
impacts across varying urban contexts and future climate scenarios are 
needed to better understand the cost effectiveness of investing in NBS 
to provide disaster risk reduction and deliver critical co-benefits for 
human well-being. Cooperation between scientists, decision-makers 
and Indigenous knowledge-holders can supplement current efforts and 
ensure that investments in NBS do not negatively impact indigenous 
communities (Ban et al., 2018; Seddon et al., 2021; Townsend, Moola 
and Craig, 2020).
6.3.4.1 Temperature Regulation
Nature-based strategies, including street trees, green roofs, green walls 
and other urban vegetation, can reduce heat and extreme heat by 
cooling private and public spaces (robust evidence, high agreement). 
Shading and evapotranspiration are the primary mechanisms for 
vegetation-induced urban cooling (Coutts et  al., 2016). Shading

reduces mean radiant temperature, which is the dominant influence 
on outdoor human thermal comfort under warm, sunny conditions 
(Thorsson et al., 2014; Viguié et al., 2020). Outdoor green space and 
parks may also slightly reduce indoor heat hazard (Viguié et al., 2020). 
Apart from lowering temperature, NBS may also contribute to lower 
energy costs by reducing extra demand for conventional sources of 
cooling (e.g., air conditioning) (Viguié et al., 2020; Foustalieraki et al., 
2017), especially during peak demand periods. Homes with shade trees 
that are located in cities where air conditioning systems are common 
can save over 30% of residential peak cooling demand (Zardo et al., 
2017; Wang et al., 2015). Green roofs have been shown to significantly 
lower surface temperatures on buildings (Bevilacqua et al., 2017) and 
modelling suggests that green roofs, if employed widely throughout 
urban areas, have the potential to impact the regional heat profile of

cities (Bevilacqua et al., 2017; Rosenzweig, Gaffin and Parshall, 2006). 
Community or allotment gardens, backyard greening and other types 
of low vegetation, as well as lakes, ponds, rivers and streams, can 
also provide local cooling benefits to nearby residents (Gunawardena, 
Wells and Kershaw, 2017; Larondelle et al., 2014; Santamouris, 2020).
Urban climate models show that increased vegetation cover results 
in reducing both mean air temperatures and extreme temperatures 
during heatwaves (Heaviside, Cai and Vardoulakis, 2015; Ferreira and 
Duarte, 2019; Schubert and Grossman-Clarke, 2013). Greater density 
and more canopy coverage relative to other built and paved surfaces

6
949Cities, Settlements and Key Infrastructure  Chapter 6
increases shade provision and evapotranspiration (Hamstead et  al., 
2016; Grilo et  al., 2020; Herath, Halwatura and Jayasinghe, 2018; 
Knight et al., 2021). However, local cooling by vegetation depends on 
regional climate context, geographic setting of the city, urban form, 
the density and placement of the trees, in addition to a variety of other 
ecological, technical, and social factors, such as local stewardship 
(Salmond et al., 2016). Green spaces less than 0.5–2.0 ha may have 
negligible cooling effects at regional scales, but impacts of shading 
can have microscale cooling benefits (Gunawardena, Wells and 
Kershaw, 2017; Zardo et al., 2017). Vegetation impacts on day versus 
night-time cooling varies (Imran et al., 2019) as does cooling potential 
in temperate versus tropical climates. The supply of cooler air from 
surrounding peri-urban and rural areas can impact cooling in the urban

core suggesting that regional adaptation planning for NBS is important 
to maintain or extend ventilation paths from the urban fringe into the 
city centre (Schau-Noppel, Kossmann and Buchholz, 2020).
To maximize the adaptation benefits of NBS for regulating urban heat, 
it can be helpful to prioritise tree planting and other urban greening 
investments in areas where heat vulnerability and risk are the highest, 
especially communities that lack urban tree canopy or accessibility to 
parks to cool off during hot days or heatwaves (Ziter et  al., 2019). 
Planting trees closely together or in partly permeable vegetated 
barriers along streets can improve local cooling benefits. Additionally, 
choosing tree species with leaves that have the greatest leaf area 
index or the largest leaves can improve cooling performance, as those 
trees have the greatest shading and evapotranspiration benefits 
that, in turn, provide the greatest cooling effects (Keeler et al., 2019).

Drought-resistant trees, often native trees, are ideal to avoid high 
watering costs, though dry or water scarce areas may limit adoption of 
urban vegetation as an NBS strategy (Coutts et al., 2013). Native trees 
and permaculture can provide additional benefits for local biodiversity 
as shown in study in Melbourne, Australia which found that increasing 
vegetation from 10% to 30% increased occupancy of bats, birds, 
bees, beetles and bugs by up to 130% (Threlfall et  al., 2017), with 
particularly high impact on native species.. Additionally, planting fruit 
or nut trees can provide co-benefits for local food production, and yet 
choice of species and placement is important to consider with respect 
to local cultural needs and norms (Adegun, 2018; Adegun, 2017).
6.3.4.2 Air Quality Regulation
NBS in cities can help regulate air quality by absorbing air pollutants 
(medium evidence, medium agreement). For example, planting trees

or vegetated barriers along streets or in urban forests can reduce 
particulate matter, the ambient air pollutant with the largest global 
health burden (Janhäll, 2015; Tiwary, Reff and Colls, 2008; Matos 
et  al., 2019; McDonald et  al., 2016). However, findings show that 
trees can also positively affect ground-level ozone (Calfapietra et al., 
2013; Kroeger et al., 2014), airborne pollen concentrations (Willis and 
Petrokofsky, 2017) and indirectly affect air quality through reduced 
emissions from energy production offset by shade provision (Keeler 
et  al., 2019). Certain tree species however can also be detrimental 
to urban ozone formation by emitting significant amounts of reactive 
biogenic volatile organic compounds (VOCs). Decreasing urban 
emissions of VOCs is an increasingly important ozone mitigation 
strategy in urban areas (Fitzky et al., 2019).Trees can also have negative effects by increasing pedestrian exposure

to pollution if they are introduced in heavily travelled street canyons 
where air pollutants can be trapped (Vos et  al., 2013; Gromke and 
Blocken, 2015). To maximise the adaptation benefits of NBS for 
improving air quality, planners and managers can target tree selection 
for species with low VOC emissions, low allergen emissions and high 
pollutant deposition potential (Keeler et  al., 2019), and combine 
with low pollution transportation policies. Studies suggest sensitive 
planting of roadside tree canopies can have positive effects on air 
pollutants (Beckett, Freer Smith and Taylor, 2000; Yang, Chang and 
Yan, 2015). For example, Xue et  al. (2021) found that the PM2.5 
reduction between 2013 and 2017 in China was associated with a 
saving of approximately USD 111 billion yr-1 nationally. Tree planting 
near schools, nursing homes and hospitals can ensure that benefits 
provided by trees are delivered to the local populations that stand to

benefit the most from improved air quality, but species need to be 
adapted to regional climate to provide benefits over time (Donovan, 
2017; Nowak et al., 2018).
6.3.4.3 Stormwater Regulation and Sanitation
Urban parks and open spaces, forests, wetlands, green roofs and 
engineered stormwater treatment devices help manage stormwater 
and wastewater by reducing the volume of stormwater runoff, reducing 
surface flooding, and reducing contamination of runoff by pollutants 
(robust evidence, high agreement). Engineered devices include 
bioswales, rain gardens, and detention and retention ponds, and are 
becoming common and standard approaches to mitigate the negative 
effects of impervious surfaces on stormwater quality and surface 
flooding in cities (Zhou, 2014; McPhillips et  al., 2020). Allotment 
gardens, street trees, green roofs and urban forests may also help 
reduce runoff and provide a stormwater retention service (Pennino,

McDonald and Jaffe, 2016; Berland et al., 2017; Gittleman et al., 2017). 
Modelling and empirical studies show that NBS at small spatial scales 
lead to improvements in water quality and reduction of peak flows 
(Moore et  al., 2016; Keeler et  al., 2019; Webber et  al., 2020). Peak 
flow reductions are greatest for small rain events. For example, D-Ville 
et al. (2018) observed a 30–70% reduction in peak flow for the 1-in-
30 year storm, but performance reduces for more intense rainfall or if 
saturated (Garofalo et al., 2016). Employing NBS to reduce flooding 
on roads can be an important adaptation mechanism for reducing the 
impact of flooding events on traffic flows (Pregnolato et al., 2016).
During periods with intense precipitation, low-lying urban parks and 
open space, engineered devices and wetlands can play an important 
role in reducing stormwater runoff volumes by providing places for 
water to be stored and infiltrate during heavy storms (Moore et al.,

2016). However, the magnitude of the runoff reduction service will 
depend on the total area of green infrastructure, vegetation type and 
its position on the landscape. There is less evidence of the effectiveness 
of NBS at larger temporal and spatial scales (Pregnolato et al., 2017; 
Jefferson et al., 2017). The performance of NBS depends on the degree 
to which their extent and spatial configuration in the city are optimised 
to capture runoff (Fry and Maxwell, 2017). Investing in a diversity of 
NBS types may be important to maximise stormwater management 
and flood regulation, as different types of engineered NBS have 
different strengths and weaknesses.

6
950Chapter 6 Cities, Settlements and Key Infrastructure
Overall, NBS are attractive adaptation options for stormwater 
management and to reduce impacts of pluvial and fluvial flooding in 
cities (Rosenzweig et al., 2018a) compared, and in combination, with 
grey infrastructure. Cities with combined sewer infrastructure are likely 
to see benefits from NBS due to reductions in stormwater quantity 
and reduced sewage overflows. Cities where a large proportion 
of residents lack access to piped infrastructure and drink surface 
water may see large benefits, especially to human health, from NBS 
investments (Keeler et al., 2019). Where future large-scale upgrades or 
installation of grey infrastructure will be necessary, new and growing 
cities may have more opportunity to realise large net benefits from 
investments in NBS. Older cities, and new, rapidly urbanising areas 
that lack large-scale water infrastructure may see the greatest benefits

from enhanced NBS, relative to cities where heavy investments 
infrastructure upgrades have already been made. Cities facing climate 
changes that include more frequent or extreme precipitation may also 
see large water quality benefits from investment in NBS (Keeler et al., 
2019). Overall, there is increasing evidence that NBS for addressing 
stormwater is cost effective (Bixler et  al., 2020; Kozak et  al., 2020; 
Mguni, Herslund and Jensen, 2016), especially in cities facing a need 
to update current infrastructures.
6.3.4.4 Coastal Flood Protection
Coastal ecosystems including coral and oyster reefs, coastal forests 
including mangroves and other tree species, salt marshes and other 
types of wetland habitat, seagrass, dunes and barrier islands can 
reduce impacts of coastal flooding and storms (robust evidence, high 
agreement) (Zhao, Roberts and Ludy, 2014; Boutwell and Westra, 
2016; Narayan et al., 2017; Yang, Kerger and Nepf, 2015; Bridges et al.,

2015; World Bank, 2016) (see also Section CCP2 Cities and Settlements 
by the Sea). Recent literature highlights the value of nature-based 
approaches for coastal protection in terms of avoided damages and 
human well-being (Narayan et  al., 2017; Silva et  al., 2016a). NBS 
can protect coasts from flooding through reducing the wave energy 
by drag friction, reducing wave overtopping by eliminating vertical 
barriers, and absorbing floodwaters in soil (Arkema, Scyphers and 
Shepard, 2017; Dasgupta et al., 2019; Zhu et al., 2020). For example, 
coastal and marine vegetation and reefs can dissipate wave energy, 
attenuate wave heights and nearshore currents, decrease the extent 
of wave run-up on beaches, and trap sediments (Ferrario et al., 2014; 
Bridges et  al., 2015). These effects result in lower water levels and 
reduce shoreline erosion, which in turn has potential to save lives and 
prevent expensive property damages (Narayan et al., 2017).

Researchers, practitioners and policy-makers are increasingly calling 
for the use of nature-based approaches to protect urban shorelines 
from coastal hazards (Cunniff and Schwartz, 2015; Bilkovic et  al., 
2017). The expectation is that coastal ecosystems can help stabilise 
shorelines, protect communities against storm surge and from 
tidal-influenced flooding, while providing other co-benefits for people 
and ecosystems. However, vegetation along protected coastlines with 
higher frequency, lower intensity coastal hazards (National Research 
Council, 2014) may be more effective for stabilising shorelines and 
reducing risk to coastal communities and properties, and benefits will 
depend on local hydrology of the coastal region. Narayan et al. (2017) 
estimate that coastal wetlands alone reduced direct flood damages by USD 625 million during Hurricane Sandy in the USA in 2012. Similarly, 
researchers found that villages with wider mangroves between them

and the coast experienced significantly fewer deaths than villages with 
narrow or no mangroves during a 1999 cyclone in India (World Bank, 
2016). Recently, Arkema et al. (2017) noted that the number of people, 
poor families, elderly and total value of residential property most 
exposed to hazards along the entire coast of the USA can be reduced 
by half if existing coastal habitats remain fully intact.
Coastal habitats also have limitations in their ability to protect coasts 
from extreme events. Some studies suggest reduced effectiveness of 
vegetation and reefs for coastal protection from large storm waves 
and surge (Möller et al., 2014; Guannel et al., 2016) and there is active 
debate in the literature about the ability of ecosystems to mitigate 
the impact of tsunamis (Gillis et al., 2017). Further research is needed 
to understand and quantify coastal protection services provided by 
these hybrid green-grey solutions, especially in urban areas (Bilkovic

et al., 2017). Additionally, in some coastlines, water may be too deep 
or waves too high for some species such as mangroves to grow, thrive 
and provided needed NBS.
Maximising the adaptation benefits of NBS for improving coastal flood 
protection research requires that cities seek to restore and conserve 
the vegetation and reef types that are appropriate for the exposure 
setting and in sufficient abundance to be effective. In particular, 
planners and managers can use vegetation in protected bays as 
alternatives to hard infrastructure for shoreline stabilisation. However, 
the influence of ecosystems on flooding and erosion is variable and 
depends on a suite of social, ecological and infrastructural factors that 
vary within and among urban areas (Narayan et al., 2017; Ruckelshaus 
et al., 2016; Bridges et al., 2015). Additionally, long-term planning to 
restore or ensure resilience of individual species and ecosystems that

may themselves be damaged or destroyed during extreme events is 
needed in order for urban green and blue infrastructure to continue 
providing NBS over the longer term.
6.3.4.5 Riverine Flood Impact Reduction
NBS reduce both the volume of floodwater and the impact of floods 
(medium evidence, medium agreement). NBS reduce the volume of 
runoff by increasing infiltration and water storage (Shuster et  al., 
2005; Salvadore, Bronders and Batelaan, 2015), and affect the 
production and impact of flood waters through reducing river energy 
and flow speed through physical blockage, stabilising riverbanks 
during flood events, creating space for floodwaters to expand and 
combating land subsidence (Palmer, Filoso and Fanelli, 2014; Ahilan 
et al., 2018). Installing NBS to increase infiltration on low slopes and 
high-permeability soils can reduce the impacts of potential increases 
in urban flooding driven by climate change, especially for small- to

medium-scale flood events (lower than 20% mean annual flood) 
(Moftakhari et al., 2018).
Source reduction strategies include creating permeable areas such 
as parks and open spaces, as well as engineered devices such as 
raingardens, bioswales and retention ponds that help retain stormwater 
runoff from impervious areas. River restoration can reduce flood 
peak flow and provide space for floodwaters to expand. Planting and

6
951Cities, Settlements and Key Infrastructure  Chapter 6
maintaining vegetation along riverbanks, often in the form of parks 
or river restoration, maintains structural integrity during flood events. 
Wetland construction and improved connectivity to floodplains also 
reduces flood peaks. Efforts to restore floodplains are important to 
create space for floodwaters and reduce exposure by moving people out 
of the hazard zone. Floodplain restoration also provides access to the 
river that has multiple benefits including recreation, access to water for 
domestic use and other cultural ecosystem services. A key adaptation 
strategy is to reduce streambank erosion (a result of high peak flow) 
using riparian vegetation to stabilise riverbanks during flood events.
Cities manage flood risk using different types of adaptation and 
regulatory mechanisms (Naturally Resilient Communities, 2017). Built 
flood-control infrastructure, such as levees and stream channelisation,

reduces the demand for nature-based flood impact reduction. 
Cities facing flood risk that do not currently have extensive grey 
flood-mitigation infrastructure may find NBS to be an appealing, 
lower cost solution (Keeler et al., 2019). In cities where flood-control 
grey infrastructure already exists, there is less demand for NBS of 
flood protection, but NBS may provide important back up, especially 
in a changing climate that may increase flood hazards (City of Los 
Angeles, 2017; Elmqvist et al., 2019). Overall, city and basin-wide NBS 
for riverine flood impact reduction can reduce the generation of new 
hazards by making space for water which can reduce the potential for 
a false sense of security provided by traditional flood management 
approaches (Ruangpan et al., 2020; Turkelboom et al., 2021).
6.3.4.6 Water Provisioning and Management
The role of NBS has been increasingly recognised for improving urban 
water management, emphasising it’s contribution for climate-adapted

development and sustainable urbanisation (robust evidence, high 
agreement) (Wong and Brown, 2009). NBS that protect or restore the 
natural infiltration capacity of a watershed can increase the water 
supply service to various extents, improving drought protection and 
providing resilient water supply (Drosou et  al., 2019; Krauze and 
Wagner, 2019), although different forms of NBS (e.g., street trees, 
parks and open space, community gardens, and engineered devices 
such as rain gardens, bioswales or retention ponds) contribute in 
different ways to increasing stormwater infiltration. Additional sources 
of water may be available to replace the water supplied by NBS, 
such as rainwater harvesting, inter-basin transfers or desalination 
plants. Reliance on naturally sourced, locally available surface water 
and groundwater is more energy efficient and economical than 
desalination or water reuse for potable use (Boelee et al., 2017), while

rainwater harvesting is even more economical. Increasing the amount 
of green space in urban areas can secure and regulate water supplies, 
improving water security (Liu and Jensen, 2018; Bichai and Cabrera 
Flamini, 2018). However, Bhaskar et al. (2016) reviewed the effect of 
urbanisation and NBS on baseflow and suggest that the confounded 
effects of infiltration and evapotranspiration losses, combined with 
the subsurface infrastructure (sewer systems) and geology, makes it 
difficult to predict the magnitude of baseflow enhancement resulting 
from the implementation of NBS in cities.
To maximise the adaptation benefits of NBS for urban water supply 
research suggests that managers and planners consider NBS as alternatives to traditional stormwater management techniques, where 
possible, since these solutions can promote groundwater recharge. 
As green infrastructure is increasingly being used for stormwater

absorption in cities (McPhillips et al., 2020), rain gardens, wetlands, or 
engineered infiltration ponds and bioswales are the NBS most likely to 
promote recharge, reduce evapotranspiration and contribute to water 
provisioning.
6.3.4.7 Food Production and Security
Urban agriculture can serve as a NBS for food security (medium 
evidence, medium agreement) across a range of urban contexts 
(Lwasa and Dubbeling, 2015; Nogeire-McRae et  al., 2018; Pourias, 
Aubry and Duchemin, 2016) by contributing to food provisioning as 
well as providing co-benefits including for recreation, place making 
and mental health (Petrovic et al., 2019; Soga, Gaston and Yamaura, 
2017; Goldstein et al., 2016b).
Urban agriculture among poorer communities in lower income areas 
is already an important source of food supply for those communities, 
contributing to food security and health (Orsini et al., 2013). However, 
potential for expanding open air urban food production may be

practically constrained by land availability (Badami and Ramankutty, 
2015; Martellozzo et  al., 2014). This is particularly true in some 
lower-income countries where rapid urbanisation is occurring, which 
compounds existing food insecurity (Satterthwaite, McGranahan and 
Tacoli, 2010; Vermeiren et al., 2013). Land availability and suitability 
for gardens can be further constrained by land use history, including 
past industrial uses that can contaminate soils with pollutants such 
as lead.
At the same time, investments in vertical agriculture continue to expand, 
such as in Singapore where private investment in food production is 
occurring in high rise buildings (Wong, Wood and Paturi, 2020). Not 
all cities can benefit similarly from vertical agriculture since higher 
heating costs to produce vegetables indoors during northern winters 
consumes considerable amounts of energy and may generate fossil 
fuel emissions depending on the energy source (Goldstein et al., 2016a;

Mohareb et al., 2017). Some regions can benefit from more traditional 
outdoor urban farming, such as in South and Southeast Asia, which can 
support multiple growing cycles per year for some crops, particularly in 
tropical areas where irrigation is available. Light availability, soil health 
and water availability will impact food production in urban areas. For 
example, a study conducted in Vancouver, Canada, demonstrated that 
light attenuation from buildings and trees can reduce both crop yield 
and water demand for crop growth (Johnson et al., 2015).
Climate change may have important impacts on urban food production 
and food security. While urban agriculture may provide benefits in 
terms of stability of food access in low-income households in some 
regions of the Global South where the climate is warmer, the shorter 
growing seasons in colder climates will reduce the role of outdoor 
urban agriculture in year-round food supply and diets. Though urban

agriculture constitutes a small fraction of total food consumption in 
some urban areas, several studies have attempted to estimate the 
extent to which urban agriculture could theoretically meet urban 
total food or vegetable demand (Badami and Ramankutty, 2015;

6
952Chapter 6 Cities, Settlements and Key Infrastructure
McClintock, 2014; Hara et al., 2018). Maximising the adaptation and 
resilience benefits of NBS for food production and security suggests 
the need to embrace the multi-functionality of urban agriculture, 
rather than viewing it as solely concerning food production (Barthel, 
Parker and Ernstson, 2015).
6.3.5 Adaptation Through Grey/Physical Infrastructure
Globally, it is estimated that as much as USD 94 trillion of investment 
is required between 2016 and 2040 to replace, upgrade and extend 
the world’s physical infrastructure (Oxford Economics, 2017), much 
of which is ageing and will require replacement. Given the typical 
lifespan of infrastructure, this is both an opportunity and an imperative 
to ensure this investment is low carbon and resilient to climate change 
risks (Grafakos et  al., 2020). ‘Grey’ or physical infrastructure is a 
priority for adaptation because its performance is sensitive to climate

(particularly extreme events) and decisions on design and renovation 
have long-lasting implications and are hard to reverse (Ürge-Vorsatz 
et al., 2018). Avoiding longer-term impacts on society, the economy 
and the environment will require future investment and retrofitting 
of existing infrastructure, to be undertaken in the context of the risks 
of climate change (Dawson et  al., 2018; Rosenzweig et  al., 2018b). 
However, evidence from Africa shows that the benefits of pro-active 
adaptation measures and policies for infrastructure can result in net 
savings depending on the country context (Section 9.8.5).
Engineered measures for hazard mitigation such as seawalls, slope 
revetments and river levees, as well as air conditioning are increasingly 
implemented in urban centres, but many engineering interventions are 
less affordable and accessible in LMICs because of high construction 
and maintenance costs. These adaptive measures can also counter

mitigation objectives because of reliance on climate-polluting energy 
sources. Despite this, engineering measures such as seawalls for 
tsunami protection and cooling areas in cities provide critical hazard 
reduction functions in urban contexts (Depietri and McPhearson, 
2017). As Pelling et  al. (2018) highlight, sustainable risk reduction 
can be better achieved where these engineering measures include the 
at-risk poor majority and inclusive planning to support pro-poor risk 
reduction. Inclusive design and management of physical infrastructure 
can enhance contributions to climate resilient development (Table 6.6 
and Supplementary Material). This section covers urban morphology 
and built form, building design, information and communication 
technology, energy, transport, water and sanitation, and coastal 
management. All these domains of physical infrastructure will require 
adaptation to cope with a changing climate; many of them can also

contribute to broader adaptation for cities and settlements.
6.3.5.1 Urban Morphology and Built Form
Urban morphology describes the overall status of cities as physical, 
environmental and cultural entities. Cities interact with surrounding 
environmental processes, for example, as documented in Section 6.2 
by influencing urban temperature, but also precipitation and through 
coastal and riverine development fluvial and coastal sedimentary 
regimes of erosion and deposition that impact on flood risk. Rapid, 
increased urbanisation has contributed to observed flood risks in recent decades (see Section 5 4.2.4; Tramblay et al., 2019). The design 
process for physical infrastructure projects and significant construction 
(e.g., residential or industrial estates and large industrial development) 
typically includes risk assessments and social and environmental impact 
assessments that consider neighbouring land uses and connected

infrastructure. Land use planning can consider diverse land uses 
and their interactions at the neighbourhood level (Section  6.3.2.1). 
Resilience planning aims to bring together integrated, systemic views 
and enable joined-up planning at the city level (as well as lower scales) 
(Section  6.3.2.1). There is however a lack of long-term studies that 
assess the climate change impacts on urban form, including informal 
settlements (Bai et  al., 2018; Ramyar, Zarghami and Bryant, 2019), 
leading to impact assessments that often overlook urban form (Ramyar, 
Zarghami and Bryant, 2019). Additionally, context-specific spatial tools 
and community based approaches lack a precise connection to urban 
morphology. For example, there is a need for further studies that 
connect solar radiation, urban morphology (e.g., aspect and plot ratio), 
and the urban heat island spatio-temporal variability (Giridharan and 
Emmanuel, 2018; Li et al., 2019c).

Several tools and models have emerged in response to 
recommendations from AR5, including models that assess the impacts 
of urban heat island (Ramyar, Zarghami and Bryant, 2019), climatic 
uncertainty (Dhar and Khirfan, 2017), flood vulnerability (Abebe, Kabir 
and Tesfamariam, 2018) and inundation (Barau et al., 2015; Ford et al., 
2019). For example, findings from Kano, Nigeria, reveal that a lack of 
distribution of certain urban morphological features, including open 
spaces and streets (both pervious and impervious), roof and building 
materials (e.g., concrete and metallic) and urban ecological features 
(e.g., urban ponds and ecological basin), exacerbates inundations 
and their associated impacts (Barau et al., 2015). Also, findings about 
the urban forms of coastal settlements, particularly in small islands, 
reveal that they often experience severe beach erosion due to wave 
action, sea level rise and storm surge that leads to landward retreat

of coastline which threatens their social and economic activities (Dhar 
and Khirfan, 2016; Lane et  al., 2015; Khirfan and El-Shayeb, 2019). 
Despite these examples, very limited research is available to offer 
assessments of different urban scale morphologies and urban scale 
adaptation planning, including planning adaptation across supply 
chains and networked relationships with distant urban and rural 
places connected through trade and resource (financial, human and 
material) or waste flows.
Interventions in the morphology and built form of cities can contribute 
to the reduction of the urban heat island effect and reduce the 
consequences of urban heatwaves. These can include installing air 
conditioning, establishing public cooling centres (i.e., for use during 
heatwaves), pavement watering (Parison et al., 2020a) and increasing 
surface albedo through ‘cool roofs’ (i.e., with high-reflectance 
materials) and walls. Air conditioning can significantly increase the

local urban heat island (Salamanca et al., 2014; Wang et al., 2019a) 
and the choice of refrigerant has a significant impact on global 
warming potential (McLinden et  al., 2017). The relative efficiency 
of cool roofs compared with green roofs is variable, because while 
white roofs have similar potential to reduce the urban heat island (Li, 
Bou-Zeid and Oppenheimer, 2014), they can quickly turn grey due to 
dust and air pollution, losing their effectiveness (Gunawardena, Wells

6
953Cities, Settlements and Key Infrastructure Chapter 6
and Kershaw, 2017), although these effects are now well studied and 
newer performance standards should account for ageing and soiling effects on reflectivity (Paolini et al., 2014). Ageing of ‘cool pavements’ 
is more complex, which makes their long-term performance less reliable to predict (Lontorfos, Efthymiou and Santamouris, 2018). The cooling performance of green roofs is highly variable and depends on the actual water content of the green roof substrate, with dry vegetation performing poorly in terms of cooling (Parison et  al., 2020b). This 
holds true for regular vegetation and NBS in general (Daniel, Lemonsu and Viguie, 2018). For all built environment adaptations, changes are locked-in for a long time, and are likely to be expensive so that care is needed to avoid potential negative impacts on social equity (Cabrera and Najarian, 2015; Romero-Lankao et  al., 2018; Fried et  al., 2020;

Rode et al., 2017) and carbon-intensive construction (Bai et al., 2018; 
Seto et al., 2016).
6.3.5.2  Building Design and Construction
Architectural and urban design regulations at the single-building scale (building codes and guidelines) facilitate climate responsive buildings that adapt to a changing climate and have the potential to collectively change user behaviour during extreme weather events (Osman and Sevinc, 2019). They include buildings that are adaptive to ensure user comfort during extremes of hot and cold as well as to floods (e.g., building on stilts and amphibian architecture). Changes to design standards can scale quickly and widely, but retrofit of existing buildings is expensive, so care must be taken to avoid potential negative impacts on social equity (Schünemann et al.,

2020; Matopoulos, Kovács and Hayes, 2014; Ajibade and McBean, 2014; Bastidas-Arteaga and Stewart, 2019). Buildings can be adapted to the negative consequences of climate change by altering their characteristics, for example increasing the insulation values (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; Fisk, 2015; Fosas 
et al., 2018; Barbosa, Vicente and Santos, 2015; Invidiata and Ghisi, 2016; Pérez-Andreu et al., 2018; Taylor et al., 2018; Triana, Lamberts 
and Sassi, 2018), adding solar shading (e.g., van Hooff et al., 2014; 
Makantasi and Mavrogianni, 2016; Barbosa, Vicente and Santos, 2015; Invidiata and Ghisi, 2016; Pérez-Andreu et al., 2018; Taylor et al., 2018; 
Triana, Lamberts and Sassi, 2018; Dodoo and Gustavsson, 2016; Osman and Sevinc, 2019), increasing natural ventilation, preferably during the night (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; 
Pérez-Andreu et al., 2018; Triana, Lamberts and Sassi, 2018; Dodoo and

Gustavsson, 2016; Osman and Sevinc, 2019; Mulville and Stravoravdis, 2016; Cellura et al., 2017; Fosas et al., 2018; Dino and Meral Akgül, 
2019), solar orientation of bedroom windows (Schuster et al., 2017), 
applying high-albedo materials for the building envelope (van Hooff et al., 2014; Invidiata and Ghisi, 2016; Baniassadi et al., 2018; Triana, 
Lamberts and Sassi, 2018), altering the thermal mass (van Hooff et al., 
2014; Mulville and Stravoravdis, 2016; Din and Brotas, 2017), adding green roofs/facades to poorly insulated buildings (Geneletti and Zardo, 2016; Skelhorn, Lindley and Levermore, 2014; van Hooff et al., 2014; 
de Munck et  al., 2018; Feitosa and Wilkinson, 2018) and for water 
harvesting (Sepehri et al., 2018).
In general, the most promising adaptation measures are a combination

of solar shading with increased levels of insulation and ample possibilities to apply natural ventilation to cool down a building (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; Fosas et al., 
2018; Barbosa, Vicente and Santos, 2015; Taylor et al., 2018; Triana,

Lamberts and Sassi, 2018; Dodoo and Gustavsson, 2016). However, it must be noted that the cooling potential of natural ventilation will decrease in the future because of increasing outdoor air temperatures (Gilani and O’Brien, 2020). Increased insulation (including through green solutions) without shading and ventilation can also lead to adverse impacts through the lowering of nighttime cooling (Reder et  al., 2018). Similarly, air conditioning performance also decreases with increasing outdoor temperatures, in addition to being maladaptive where use increases anthropogenic heat emissions into the urban area, and global greenhouse gas emissions if powered by carbon intensive energy systems (Wang et al., 2018c).
Passive cooling is a design-based, widely used strategy to create

naturally ventilated buildings, making it an important alternative to address the urban heat island for residential and commercial buildings (Al-Obaidi, Ismail and Rahman, 2014). Generally, passive cooling is achieved by controlling the interactions between the building envelope and the natural elements. Façade fixes such as overhangs, louvres and insulated walls are effective at shading buildings from solar radiation, while complex ones such as texture walls, diode roofs and roof ponds are effective at minimising heat gains from solar radiation and ambient heat (Oropeza-Perez and Østergaard, 2018). Passive cooling is inspired also by traditional design forms, for example from Mediterranean, Islamic and Mughal architecture in the Indian sub-continent (Di Turi and Ruggiero, 2017; Izadpanahi, Farahani and Nikpey, 2021).
In addition, wind towers, solar chimneys and air vents are features that

facilitate cool air circulation within buildings while dissipating heat (Bhamare, Rathod and Banerjee, 2019). These features may be arranged to address hotspots or highly frequented spaces within buildings. Similar to NBS, the effectiveness of passive cooling to ameliorate the urban heat island varies widely depending on the location of the sun, wind direction and the type of strategy used. For instance, natural ventilation strategies (e.g., wind towers, solar chimneys, etc.) have shown temperature reductions of up to 14°C (Bhamare, Rathod and Banerjee, 2019; Calautit and Hughes, 2016; Rabani et al., 2014).

Shading strategies alone can reduce indoor temperatures by 3°C, while heat sinks (in which heat is directed at a medium such as water) may result in indoor temperatures up to 6°C lower than the outdoor temperature (Oropeza-Perez and Østergaard, 2018). More systemic interventions, such as altering urban form through urban planning, can mitigate the urban heat island across suburbs and cities (Lee and Levermore, 2019; Takkanon and Chantarangul, 2019; Yin et al., 2018; 
Liang and Keener, 2015; Emmanuel and Steemers, 2018). Experience in Kano (Nigeria) has shown that incorporating Indigenous knowledge into building design and urban planning can increase resilience to heat and flood risks (Barau et al., 2015). A review by Lemi (2019) suggests 
that traditional ecological knowledge can provide wider climate change adaptation benefits.
Limits on housing and building adaptation include failure of regulatory

systems so that formal design standards are not followed even when legally required (Arku et al., 2016; Durst and Wegmann, 2017; 
Pan and Garmston, 2012; Awuah and Hammond, 2014). This can be a result of pressures from clients for cheaper structures, developers

6
954Chapter 6 Cities, Settlements and Key Infrastructure
illegally cutting costs or regulators lacking capacity for enforcement. 
Technological innovation can also be slow to embed itself in building 
norms and standards. Innovation also lies outside the formal sector 
and can include artisanal building techniques that may have adaptive 
value. Examples from Latin America demonstrate how initiatives in 
informal settlement improvement associated with housing policy, 
guaranteeing access to land and decent housing, show the opportunity 
for overarching policies encompassing development, poverty reduction, 
disaster-risk reduction, climate-change adaptation and climate-change 
mitigation (see Section 12.5.5).
6.3.5.3 Information and Communication Technology
Information and communication technologies (ICTs) are deeply 
intertwined with the functioning of urban and infrastructure systems, 
and are at the core of the ‘smart city’ concept (Angelidou, 2015). ICT

is more flexible than other physical infrastructure, although as other 
sectors are increasingly reliant on ICT, it is creating new climate-related 
failure mechanisms (Norman, 2018; Maki et al. , 2019). ICT assets and 
networks in urban, national and international communications systems 
will need to be strengthened to enable ICT infrastructure to better cope 
with climate change, and to enable ICT infrastructure to support the 
resilience of cities, settlements and other infrastructure. The increased 
pervasiveness of ICT in smart cities, smart infrastructure and day-to-day 
living, will evidently have long-term implications for exposure to 
climate change risks and how cities manage those risks (Norman, 2018; 
Maki et  al. , 2019). For example, even if the ICT network is resilient 
to heatwaves, it is dependent on the electricity network to power it. 
Conversely, other networks are dependent upon ICT for control systems,

for example smart grids for energy. There is limited information on how 
these interdependencies, and associated risks, will evolve.
Although networked like many other infrastructure systems, ICT 
components have some distinctive properties. They are relatively 
cheap, and the advent of wireless communications has enabled ICT 
to have the widest reach of all infrastructures. Components can be 
rapidly deployed or repaired, and generally ICT networks are therefore 
built with inherent redundancy and flexibility (Sakano et  al., 2016). 
Components have a wide range of expected lifetimes which leads 
to faster cycles of innovation. There is therefore greater potential to 
accelerate uptake of climate resilience in this infrastructure sector, but 
conversely, this can increase waste and (energy intensive) resource 
consumption. For example, mobile phones and computers may last as 
little as a year, cables and switching units may be moved and upgraded

to improve bandwidth every few years, poles and masts are typically 
designed to last several decades, whilst exchanges and other critical 
nodes can be in use for over half a century.
ICTs are playing an increasing role in resilience building and enabling 
climate change adaptation. They are enabling access to information 
needed for decision making, facilitating learning and coordination 
among stakeholders, and building social capital, as well as helping to 
monitor, visualise and disseminate current and future climate impacts 
(Eakin et  al., 2015; Heeks and Ospina, 2019; Haworth et  al., 2018; 
Imam, Hossain and Saha, 2017). Advocacy and awareness raising 
through ICTs, such social media applications, can influence behaviours 
and attitudes in support of adaptive pathways (Laspidou, 2014).ICTs play a role in adaptive responses to both short-term shocks and 
long-term trends associated with climate change. Timely access to

information (e.g., early warning, temperature and rainfall, agricultural 
advice) through ICTs (e.g., mobile devices, SMS, radio, social media) 
can be crucial to respond and mitigate the impact of emergencies such 
as floods and drought, for identifying pest and disease prevalence, 
and for informing livelihood options, key in adaptation pathways of 
vulnerable communities (Devkota and Phuyal, 2018; Panda et  al., 
2019).
In addition to contributing to the robustness and stability of the 
critical infrastructure in the event of disasters, ICTs can strengthen 
other attributes of resilient urban systems by enabling learning and 
community self-organisation, cross-scale networks and flexibility, 
helping vulnerable stakeholders, in particular, to adjust to change and 
uncertainty (Heeks and Ospina, 2015; Heeks and Ospina, 2019). Big 
data is being used to inform responses to humanitarian emergencies 
(Pham et al., 2014; Ali et al., 2016), as well as to generate new forms

of citizen engagement and reporting (e.g., community-based maps 
of flood-prone areas) that can help to inform coping and adaptive 
responses (Ogie et al., 2019).
The selection and use of ICTs for adaptation needs to be fairly grounded 
in the broader socio-cultural, economic, political and institutional 
context, to ensure that these tools effectively help address existing, 
emerging and future adaptive needs. Typically, ICT is inadequate on its 
own to make a significant difference (Toya and Skidmore, 2015). The 
role of ICTs in adaptive pathways is influenced by the availability of 
locally relevant information (e.g., weather-based advisory messages, 
local market prices), the accessibility of information by all members 
of the community (e.g., using various text, audio and visual content, 
local languages, addressing gender-related exclusion, cost and digital 
competencies) and the applicability of information at the appropriate

scale (local, regional or national), including data quality and verification 
(Namukombo, 2016; Haworth et al., 2018).
Information privacy and security, as well as the unintended impacts of 
ICTs on inequality, spread of misinformation and on widening existing 
gaps (e.g., due to poverty, gender and power differentials), can also 
constrain the contribution of ICTs to urban adaptation (Haworth et al., 
2018; Coletta and Kitchin, 2017; Leszczynski, 2016) and are among the 
key challenges that need to be addressed in order to fully realise their 
potential.
6.3.5.4 Energy
A number of measures are available to adapt existing energy 
infrastructure to climate change. These typically involve changing 
engineering design codes and upgrading facilities to cope with new 
climatic conditions, building redundancy and robustness into systems, 
and preparation to ensure continued operation following extreme 
events. Adapting low carbon energy infrastructure improves its climate

resilience whilst simultaneously delivering mitigation goals (Kemp, 
2017; Feldpausch-Parker et  al., 2018), benefitting all other sectors 
(Dawson et al., 2018; Pescaroli and Alexander, 2018; Kong, Simonovic 
and Zhang, 2019).

6
955Cities, Settlements and Key Infrastructure  Chapter 6
Hall et al. (2019) identified 4223 GW of global power generation at risk 
of flooding. If these assets were protected by 0.5 m flood protection, 
~700 GW would be at risk from the 1-in-100 year flood. Many assets 
can be strengthened, relocated or replaced with new equipment built 
to higher standards. An example of this is in the UK where a total 
of £172  million is being invested between 2011 and 2023 to raise 
flood protection of substations to be resilient to the 1-in-1000 year 
flood (ENA, 2015). Electricity cables can be upgraded in anticipation of 
reduced efficiency in a warmer climate, although in many locations this 
may be achieved autonomously to meet growth in electricity demand 
(Fu et al., 2017).
Fuels, including oil, natural gas, hydrogen, biomass and CO 2 prior to 
sequestration are delivered and distributed by pipeline or transportation 
by road, rail and shipping. In addition to engineering improvements,

adaptation measures also include planning and preparation for 
service disruption by changing transport patterns, increasing local 
storage capacities and identifying and prioritising protection of critical 
transport nodes (Wang et al., 2019b; Panahi, Ng and Pang, 2020).
Several options are available to reduce the impacts of reduced 
cooling water for thermoelectric power generation, increases in water 
temperature and lower flows for hydropower generation. These include 
(i) switching from freshwater to seawater (if available) or air cooling; 
(ii) replacing once-through cooling systems with recirculation systems; 
(iii) replacing fuel sources for thermoelectric power generation; (iv) 
increasing the efficiency of hydro and thermoelectric power plants; (v) 
relaxing discharge temperature rules to allow warmer water to enter 
rivers; (vi) installation of screens to stop algae or jellyfish blooms clogging 
intakes; (vii) reducing power production and managing demand; and

(viii) changing reservoir operation rules (where available). Shreshta 
et al. (2021) show that changing reservoir operation rules can offset 
reduced water availability under RCP8.5 until 2050, but is insufficient 
by the 2080s. van Vliet et al., (2016) showed that a 10% increase in 
hydroelectric generation efficiency can compensate for reduced water 
availability in most regions. Higher efficiency thermoelectric plans 
offset impacts under lower climate change scenarios but are shown 
to be inadequate under RCP8.5 by the 2080s; whereas a switch 
to seawater and dry (air) cooling provides a net increase under this 
scenario. However, these technologies can increase costs. Increasing 
the temperature of water discharged from the power station can have 
negative environmental impacts (Thome et al. , 2016; Yang et al. , 2015).
Longer term systemic strategies could include a combination of 
increased network redundancy and decentralisation of generation

locations (Fu et al., 2017), or the use of ‘defensive islanding’ which 
involves splitting the network into stable islands to isolate components 
susceptible to failure and subsequently trigger a cascading event 
(Panteli et  al., 2016). Smart grids are being increasingly deployed 
within municipalities to provide more efficient management of supply 
and demand and mitigate greenhouse gas emissions, however, there 
is limited understanding of their performance and reliability during 
floods and other extreme weather events (Vasenev, Montoya and 
Ceccarelli, 2016; Feldpausch-Parker et al., 2018).
Adaptation and preparedness at the household level can minimise 
impacts during power outages, but neighbourhood-level assistance may be more appropriate to ensure support for vulnerable households 
and coordination of action and information (Ghanem, Mander 
and Gough, 2016). More generally, it is important for responder 
organisations to integrate energy needs in disaster preparedness and

response plans. Whilst over the longer term, reducing household and 
industrial demand for energy supply will reduce the need for capital 
investments and upgrades (Fu et al., 2017).
Providing a reliable and resilient power supply is crucial to economic 
and social development (Fankhauser and Stern, 2016). Furthermore, 
there are co-benefits from the use of low carbon energy systems 
(Chapter 8, WGIII AR6). For example, solar-charged street lamps and 
household lighting gives reliable nighttime lighting, providing safety, 
security and resilience to disruption of network power supplies (Burgess 
et al., 2017). At larger scales, deploying solar power on building roofs 
reduces energy demand for cooling by 12% and lowers the urban heat 
island, and thereby has health benefits (Masson et al., 2014a). In the 
USA, construction of solar panels over 200  million parking spaces 
would generate a quarter of the country’s electricity supply (Erickson 
and Jennings, 2017).

As presented in Table 6.3, access to energy supply varies considerably. 
In particular, many African countries require substantial energy 
infrastructure to support their economic development. The combination 
of smart technologies with solar and other renewable generation 
provides a huge opportunity (Anderson et al., 2017; Kolokotsa, 2017). 
However, care must be taken in rapidly developing cities, as failure 
to ensure energy access during urbanisation can reduce resilience 
(Ürge-Vorsatz et al., 2018).
6.3.5.5 Transport
A wide range of adaptation options are available for transport 
infrastructure and most provide a good benefit cost ratio (Doll, Klug 
and Enei, 2014; Forzieri et  al., 2018). Options include upgrading 
infrastructure (which can often be achieved autonomously as part 
of standard repair and replacement schedules) and strengthening 
or relocating (critical) assets. Adaptation of road and rail networks

in Australasia includes re-routing, coastal protection, improved 
drainage and upgrading of rails (Table 11.7.) In areas with substantial 
infrastructure deficits, such as much of Africa, investments in public 
transport and transit-oriented development are highlighted as desired 
mitigation-adaptation interventions within cities of South Africa, 
Ethiopia and Burkina Faso (Section  9.8.5.3). Adapting low carbon 
transport infrastructure will be crucial to ensure resilience to climate 
change impacts whilst simultaneously delivering mitigation goals 
(Shaheen, Martin and Hoffman-Stapleton, 2019; Costa et al., 2018).
Wright et al. (2012) calculated that strengthening bridges in the USA 
would cost USD 140–250  billion by 2090 (or several billion dollars 
a year), but costs are reduced by 30% if interventions are made 
proactively. Koks et al. (2019) calculate a benefit–cost ratio of greater 
than one for over 60% of the world’s roads exposed to flooding. The

greatest benefits from adaptation of the global road network are in 
LMICs where reductions in flood risk are typically between 40% and 
80%. Pregnolato et al. (2017) showed that in the city of Newcastle 
upon Tyne (UK), two carefully targeted interventions at key locations to

6
956Chapter 6 Cities, Settlements and Key Infrastructure
manage surface water flooding reduced the impacts of the 1-in-50 year 
event in 2050 by 32%. In permafrost regions, geo-reinforcement, 
foundation and piles can be strengthened (Trofimenko, Evgenev and 
Shashina, 2017), whilst passive cooling methods, including high-albedo 
surfacing, sun-sheds and heat drains can cool infrastructure (Doré, Niu 
and Brooks, 2016).
Hanson and Nicholls (2020) calculate the total global investment costs 
for port adaptation to sea level rise and provision of new areas at 
USD 223–768 billion by 2050. However, adaptation of existing ports 
is only 6% of this. Yesudian and Dawson (2021) estimate the cost 
of maintaining present levels of flood risk in 2100 for the global air 
network will cost up to USD 57 billion (Monioudi et al., 2018; Esteban 
et al., 2020b).
New technologies and design innovations can improve the resilience 
of cars, trains, boats and other vehicles to cope with more extreme

weather. Mobility transitions have the potential to improve mobility 
and accessibility, to influence urban form and to reduce vehicular 
use (and thereby infrastructure degradation), vehicle miles travelled 
and vehicle-based emissions (Sperling, Pike and Chase, 2018). For 
example, use of electric vehicles, hydrogen vehicles and greater uptake 
of public transport and other vehicles that reduce exhaust head 
emissions reduces the urban heat island (Kolbe, 2019). Carsharing 
can reduce carbon emissions by over 50% (Shaheen, Martin and 
Hoffman-Stapleton, 2019). Ride hailing, matching non-professional 
drivers of private vehicles with paying passengers, positively impacts 
low-income, low-car ownership households in Los Angeles (Brown, 
2018), and fills market gaps in cities where public transit infrastructure 
is inadequate, unreliable or unsafe (Suatmadi, Creutzig and Otto, 2019; 
Vanderschuren and Baufeldt, 2018), but can also create a precarious

and insecure job market that impacts well-being (Fleming, 2017). 
Whether the resulting impacts are positive or negative, largely depends 
on local, national and international policy and practices.
Safe and convenient walking and cycling (and public transport) 
infrastructure in cities reduces carbon emissions and urban heat 
island intensity, but also improve cardiovascular capacity which 
reduces heat stress (Schuster et al., 2017). In some regions, warmer 
weather may bring opportunities for increased uptake of cycling and 
walking, though precipitation or thermal discomfort caused by high 
temperature and humidity can reduce the use of active travel modes 
for commuting and recreation (Chapman, 2015). Shaded pavements 
and lanes, and measures to mitigate the urban heat island can reduce 
risks to disruption of active travel thereby also enhancing mitigation 
(Wong et al., 2017).
Full system re-design may enable the greatest resilience but it does

not usually have a good benefit–cost ratio (Doll, Klug and Enei, 2014). 
Moreover, Caparros-Midwood et  al. (2019) show that transport 
infrastructure planners will not always be able to resolve trade-offs 
between managing climate risks and mitigating greenhouse gases 
without tackling other sectors. However, infrastructure planners should 
continually seek opportunities for positive infrastructure lock in where 
available (Ürge-Vorsatz et al., 2018).6.3.5.6 Water and Sanitation
Adaptation to water scarcity can be through measures to increase 
supply (e.g., water storage, rainwater harvesting, desalination, river 
basin transfers, increased abstraction, reduced pollution of water 
sources), or manage demand (e.g., reduce leakage lower consumption, 
use of water efficiency devices, greywater reuse, behaviour change). A 
combination of these measures is usually required (e.g., Ives, Simpson 
and Hall, 2018; Dirwai et al., 2021; Wang et al., 2018a). Reliable and

well-adapted water and sanitation services support economic growth, 
public health, reduce marginalisation and poverty, and can lower 
energy use and improve water quality (Campos and Darch, 2015; 
Miller and Hutchins, 2017; Jeppesen et al., 2015; Hamiche, Stambouli 
and Flazi, 2016).
Globally, water sector adaptation costs are estimated to be USD 
20  billion yr-1 by 2050 (Fletcher, Lickley and Strzepek, 2019). 
Globally, the budget required by 2030 for water infrastructure (new 
and refurbishment) is more than half of the budget required for all 
infrastructure (Koop and van Leeuwen, 2017). For OECD countries, 
water adaptation increases costs by 2%, but this proportion is far 
higher for developing nations (Olmstead, 2014).
A number of adaptation actions are available to reduce the impacts 
of floods on water and sanitation infrastructure. Active management 
reduces blockages in water infrastructure and protects related

services such as roads and culverts which are essential to ensure the 
operation of onsite sanitation infrastructure (Capone et al., 2020). The 
impact of floods for onsite or sewerage systems can be lowered by 
reducing or eliminating excreta from the environment through regular 
maintenance, cleaning and clearing of blockages (O’Donnell and 
Thorne, 2020; Borges Pedro et al., 2020).
Infrastructure to protect key assets such as water and wastewater 
treatment plants or pumping stations has a high cost but benefits 
all connected households and reduces pollution from flood events. In 
well-regulated water sectors, there has been an increasing focus on 
such investments (Campos and Darch, 2015). Whereas more diffused 
cheaper interventions can reduce flood water ingress to domestic 
toilets (Irwin et al., 2018). Luh et al. (2017) found that protected dug 
wells were one of the least resilient technologies, whereas piped,

treated, utility managed surface water systems had higher resilience.
Protecting water sources from pollution is even more important 
in a warmer climate that increases the frequency of algal blooms. 
Individual assets such as water intake pipes can be protected using 
screens (Kim et al., 2020a), whereas basin-scale land management is 
required to reduce nutrient load from runoff (Me et al., 2018), whilst 
injecting water or installing barriers can protect coastal aquifers from 
salinisation (Siegel, 2020).
More radical structural interventions may be needed in the longer 
term, but would need to be planned and delivered in coordination 
with investments in other sectors, particularly housing (Lüthi, Willetts 
and Hoffmann, 2020). As an interim measure, sanitation services with 
a lower reliance on fixed infrastructure, or container-based sanitation

6
957Cities, Settlements and Key Infrastructure  Chapter 6
could be appropriate in many urban areas that are badly affected by 
flooding (Mills et al., 2020).
Other actions include use of adaptive planning (Evans, Rowell and 
Semazzi, 2020), integration of measures of climate resilience into 
water safety plans (Prats et al., 2017), as well as improved accounting 
and management of water resources (Lasage et  al., 2015). Policy 
prescriptions on technologies for service delivery and changes in 
management models offer potential to reduce risks, particularly in 
low-income settings (Howard et  al., 2016). Where formal sewerage 
provision is lacking, community based adaptation that incorporates 
both the function of the sanitation system and the vulnerability of 
users (e.g., women, children, elderly, ill or disabled) into the design is 
essential (Duncker, 2019).
6.3.5.7 Flood Management
Cities are deploying a broad range of strategies to adapt infrastructure

to flooding, with hard engineering approaches (e.g., dikes and seawalls) 
increasingly complementing soft approaches, including planning and 
use of nature-based solutions, that emphasise natural and social 
capital (Jongman, 2018; Sovacool, 2011). The infrastructure can alter 
downstream risks and lead to increased residual risk by encouraging 
more floodplain construction (Miller, Gabe and Sklarz, 2019; Ludy 
and Kondolf, 2012). Physical infrastructure is highly cost effective for 
large settlements, but not always for small settlements (Tiggeloven 
et al., 2020) and can be inaccessible to poorer communities (Sayers, 
Penning-Rowsell and Horritt, 2018; Van Bavel, Curtis and Soens, 2018). 
It is often inflexible once installed but new designs and adaptive 
pathways are emerging (Anvarifar et al., 2016; Kapetas and Fenner, 
2020).
As urban areas have expanded, so too have the number of vulnerable 
assets, and efforts may now emphasise reducing construction in

high-risk regions (Paprotny et  al., 2018a). The National Flood and 
Coastal Erosion Risk Management Strategy for England, for example, 
calls for reductions in inappropriate developments in floodplains 
(Kuklicke and Demeritt, 2016; UK Environment Agency, 2020). Because 
climate change increases the flood risk profile of certain regions, 
reconsideration of design criteria has become more common (Ayyub, 
2018). New York City now requires the sewer system currently designed 
for hydraulic capacity in 5-year design life should be designed for 50-
year design life, taking into account climate changes over that period 
(NYC, 2019).
Adaptation strategies are diverse and often involve hybrid physical and 
NBS, and increasingly integrated management plans that consider both 
flood prevention and designing infrastructure and supporting people 
to cope with floods when they occur. Adaptation typically focuses on (i) 
increasing the standard of protection to compensate for the increased

magnitude of extreme events; (ii) increased maintenance to cope with 
increased frequency of extremes and changes in ambient conditions; (iii) 
changed maintenance regimes from narrower maintenance windows, 
for example as assets are used more frequently (Sayers, Walsh and 
Dawson, 2015); (iv) land use planning and management to reduce 
exposure and manage hydrological flows; and (v) raising awareness, 
preparedness and incident management. In high population areas, hard interventions such as dikes and levees are generally cost effective 
(Jongman, 2018; Ward et al., 2017).
Prevention or attenuation solutions include: rooftop detention, 
reservoirs, bioretention, permeable paving, infiltration techniques, 
open drainage, floating structures, wet-proofing, raised structures, 
coastal defences, barriers and levees, and have been deployed in 
diverse configurations and environments around the world (Matos 
Silva and Costa, 2016). Barcelona (Spain) reached 90% impermeable

surface cover by the 1980s, and has recently begun implementing 
artificial detention, underground reservoirs and permeable pavement 
technologies (Favaro and Chelleri, 2018; Matos Silva and Costa, 2016). 
Florida Power and Light (USA), which provides service to approximately 
10 million people, is investing USD 3 billion in flood protection and 
the hardening of assets (for example, upgrading wooden poles to 
steel and concrete) (Brody, Rogers and Siccardo, 2019). The City of 
Seattle recommends increasing preventative maintenance activities, 
the regular review of appropriate pavement technologies and 
modifications to subgrades and drainage facilities for high-risk areas 
(City of Seattle, 2017), whilst also providing benefits to transport 
disruption (Arrighi et al., 2019). Adaptation in African cities is often 
dominated by informal responses (Owusu-Daaku and Diko, 2018). In 
the absence of centralised responses, low-income residents in Nairobi

(Kenya) dig trenches and construct temporary dikes to protect homes, 
and in Accra (Ghana) the community has developed a range of social 
responses, including communal drains and local evacuation teams, to 
help protect people and critical valuables, although these innovations 
require connection to city-wide infrastructure to effectively reduce 
widespread risk (Amoako, 2018).
More recent developments include sensor arrays to catalogue a river’s 
reach and how changing hydraulics interact with roadways (Forbes 
et  al., 2019). Kuala Lumpur’s (Malaysia) stormwater management 
and road tunnel (SMART) during extreme rain events transitions the 
motorway to a stormwater conduit, an example of multifunctionality 
enabling agility (Isah, 2016; Markolf et al., 2019). Smart stormwater 
control systems are starting to use real-time control to dynamically 
manage the retention and movement of water during storms, though 
uptake at large scales which provide the greatest improvements in

performance have been limited (Xu et al., 2020b).
In contrast to a ‘fail-safe’ approach to design which emphasises 
strengthening infrastructure against more intense environmental 
conditions, ‘safe-to-fail’ flood strategies allow infrastructure to fail in 
its ability to carry out its primary function but control the consequences 
of the failure. Examples include the use of a bioretention basin in 
Scottsdale (Arizona, USA) to accommodate excess runoff and help 
drain the city; a subsidy for affected farmers for lost crop production 
as part of the Netherlands’ Room for the River programme; targeted 
destruction of a levee to control flooding in the Mississippi River Valley 
in 2011 (Kim et al., 2019). Water-sensitive urban design, low-impact 
development, sponge cities, sustainable urban drainage and natural 
flood management involve deployment of systems and practices 
that use or mimic natural processes that result in the infiltration,

evapotranspiration or use of stormwater to protect water quality and 
associated aquatic habitat. These are being designed and implemented 
at increasingly ambitious scales. For example, China’s Sponge City

6
958Chapter 6 Cities, Settlements and Key Infrastructure
initiative sets a goal of 80% of urban land able to absorb or reuse 
70% of stormwater through underground storage tanks and tunnels, 
and use of pervious pavements, in addition to NBS (Chan et  al., 
2018; Muggah, 2019). Similarly, several thousand water-sensitive 
urban design interventions have been implemented across the city of 
Melbourne (Kuller et al., 2018).
6.3.5.8 Coastal Management
Physical coastal management infrastructure has significant benefits in 
reducing flood and erosion losses and damage from storms. Physical 
infrastructure includes seawalls, dikes, breakwaters, revetments, 
groynes and tidal barriers. Adapted infrastructure can alter risks in 
morphologically connected areas, and lead to increased residual risk 
by encouraging more construction in the coastal zone (Miller, Gabe 
and Sklarz, 2019; Ludy and Kondolf, 2012). The infrastructure is

highly cost effective for large settlements, but not always for small 
settlements (Tiggeloven et al., 2020) and can be inaccessible to poorer 
communities (Fletcher et al., 2016; Pelling and Garschagen, 2019).
Anticipated costs for this vary widely. For example, Hinkel et al. (2014) 
calculate that adaptation costs to maintain current global levels of 
coastal flood protection would be 1.2–9.3% of gross world product 
but protect assets in human settlements of USD 21–210  billion; 
Tiggeloven et  al. (2020) calculate the cost of adaptation to be USD 
176 billion (although this would provide a benefit–cost ratio of 106 
under RCP8.5); while Nicholls et al. (2019) estimate that global coastal 
protection would cost substantially more, up to USD 18.3 trillion 
between 2015 and 2100 for RCP8.5 (this includes ranges of unit costs 
and maintenance costs which have often been ignored).
Coastal protection infrastructure such as dikes and sluice gates can

inhibit salinity intrusion through careful management of water levels, 
this can provide co-benefits for flood risk reduction and agricultural 
productivity, but can also have negative impacts on ecosystems 
(Renaud et al., 2015). Managed aquifer recharge can be effective if 
the objective is to secure freshwater drinking supply (Hossain, Ludwig 
and Leemans, 2018).
Physical infrastructure can provide substantial benefits, be constructed 
quickly and has enabled coastal cities and settlements around the 
world to flourish and grow. Multifunctional physical infrastructure 
can also provide economic and social co-benefits. These include 
integration of transport, recreation, agriculture (e.g., cattle pasture), 
founding for wind turbines, housing, office or industry into the coastal 
management infrastructure (Anvarifar et al., 2017; Kothuis and Kok, 
2017). However, physical infrastructures can also disrupt natural 
processes, often leading to undesirable impacts such as pollution,

degradation of ecosystems and displacement of erosion and flood 
risk to other locations (Wang et al., 2018b; Dawson, 2015; Nicholls, 
Dawson and Day, 2015). Coastal management strategies that take 
a hybrid approach, integrating physical and natural infrastructure, 
provide the best opportunities for managing risk and achieving wider 
socioeconomic and environmental benefits (Depietri and McPhearson, 
2017; Morris et al., 2018; Schoonees et al., 2019; Powell et al., 2019).6.3.6 Cross-Cutting Themes
This section builds on 6.3.4 to offer two entry points for assessing 
urban adaptation that extend beyond individual infrastructure types 
and that demonstrate the interdependent and dynamic natures of 
urban systems.
6.3.6.1 Equity and Justice
Questions of equity and justice influence adaptation pathways for 
cities, settlements and infrastructure (see also Chapter 8). Although 
infrastructure, ranging from social to ecological and physical to

digital, can help to reduce the impacts of climate change (Stewart 
and Deng, 2014; Baró Porras et al., 2021), there is limited evidence of 
how infrastructures, implemented to reduce climate risk, also reduce 
inequality. Rather, there is more evidence to suggest that both adaptation 
plans and associated infrastructure implementation pathways are 
increasing inequality in cities and settlements (Chu, Anguelovski and 
Carmin, 2016; Anguelovski et  al., 2016; Romero-Lankao and Gnatz, 
2019). Social, economic and cultural structures that marginalise 
people by race, class, ethnicity and gender all contribute in complex 
ways to climate injustices and need to be urgently addressed for 
adaptation options to shift to benefit those most vulnerable, rather 
than mainly benefitting the already privileged and maintaining the 
status quo (Thomas et al., 2019; Porter et al., 2020; Ranganathan and 
Bratman, 2019). Innovation and imagination are needed in adaptation

responses to ensure that cities and settlements shift from perpetuating 
structural domination and inequality to fairer cities (Porter et  al., 
2020; Henrique and Tschakert, 2019; Parnell, 2016b). To support these 
possibilities, this section explores adaptation through the lens of 
distributive and procedural justice. Although not expanded on here, 
spatial and recognition injustices are equally important (Fisher, 2015; 
Chu and Michael, 2018; Campello Torres et  al., 2020). Recognition 
can be supported through a capabilities approach that helps to bring 
attention to past cultural domination and enable citizens to develop 
the functioning life they choose (Schlosberg, Collins and Niemeyer, 
2017). This brings a focus on local action, emphasising the relevance 
to vulnerability reduction and resilience building of individual and 
local/community capacities and supporting structures. This blurs 
the distinction between climate change adaptation and community

development, with the former firmly embedded in the latter. Struggles 
for recognition are deeply political and central to adaptation responses 
which requires increased focus on power to support more equitable 
and just adaptation (Nightingale, 2017). Justice questions are not 
static, Box  6.4 overviews the implications of COVID-19 for urban 
justice and vulnerability.
Distributive justice calls attention to unequal access to urban services, 
land, capital and technology. Related to this, exposure to health, 
flooding and drought risks of people living in low-income and informal 
settlements is a growing concern, as is disaster preparedness and the 
ability to support the needs of vulnerable groups such as the elderly, 
children and disabled, where data is often lacking (Lilford et al., 2016; 
Castro et al., 2017). There are also differences in who benefits from 
infrastructures, as they are inherently political, embedded in social

contexts, politics and cultural norms (McFarlane and Silver, 2017) and 
often tend to benefit those already privileged (Henrique and Tschakert,

6
959Cities, Settlements and Key Infrastructure  Chapter 6
2019). As an example, fixing water leaks can depend as much on the 
politics of who is involved and whose knowledge is prioritised as on 
the technical aspects (Anand, 2015).
The quality and maintenance of infrastructure is often unequal across 
cities, benefiting some and increasing vulnerability of others. Some 
property is seen as dangerous and of lower value if highly exposed to 
risk (Wamsley et al., 2015). Similarly, areas suffering from disinvestment 
in infrastructure might have a high risk of flooding (Haddock and 
Edwards, 2013). Zoning and land use trade-offs have been seen to 
be unequally skewed in favour of prime real estate and economically 
valuable assets (e.g., protecting factories and refineries from flooding) 
(Anguelovski et al., 2016; Carter et al., 2015). Urban planning reforms 
are therefore central to building a fairer urban adaptation response 
(Parnell, 2016b).

Infrastructure is often not adequately implemented in low-income 
urban areas and not equally accessible to all (Meller et al., 2017). For 
example, low-income neighbourhoods often have less green space 
and therefore less associated cooling benefits. Even in high-income areas, there is often unequal access to services. For example, an 
assessment of sustainable urban mobility plans in Portugal showed 
that some areas have considered equity in their plans and increased 
access for disadvantaged users including the elderly and disabled, but 
in other cities this is lacking (Arsenio, Martens and Di Ciommo, 2016). 
Understanding who has access to what infrastructure can help to 
redress the drivers of social vulnerability that are central to just urban 
adaptation (Michael, Deshpande and Ziervogel, 2018; Shi et al., 2016).
Changing land use and increasing green spaces to reduce climate 
risks and attract investments and job opportunities has increased real

estate values, triggered climate gentrification in some areas (Keenan, 
Hill and Gumber, 2018) and decreased access to affordable housing 
in other areas (Larsen, 2015; Carter et  al., 2015). Displacement 
through evictions and relocations linked to land use conversion and 
resettlement in the name of adaptation has also increased people’s 
vulnerability (Anguelovski et al., 2016; Henrique and Tschakert, 2019).
Understanding social and economic elites and their investment in 
infrastructure has implications for distributive justice, particularly when Box 6.4 | Adapting to Concurrent Risk: COVID-19 and Urban Climate Change
COVID-19 impacts have highlighted the depth and unevenness of systemic social vulnerability and the compounding characteristics of 
contemporary development models, with direct relevance to climate change risk accumulation and its reduction (Patel et al., 2020b;

Manzanedo and Manning, 2020; Bahadur and Dodman, 2020). This is plain at the global level: of the estimated 119–124 million additional 
people induced into poverty by COVID-19 in 2020, South Asia and sub-Saharan Africa each contribute two-fifths (Lakner et al., 2021). 
These are rapidly urbanising and highly climate-hazard-exposed world regions, indicating COVID-19 impacts may further concentrate 
risk in these regions. Within cities, COVID-19 and climate change risk and loss is concurrent by gender, race and income or livelihood, 
for example, when vulnerable elderly populations are simultaneously exposed to COVID-19 and heatwave risk. Globally, in 2020, about 
431.7 million vulnerable people were exposed to extreme heat during the COVID-19 pandemic, including about 75.5 million during the 
July and August 2020 European heatwave, with an excess mortality of over 9000 people arising from heat exposure (Walton and van 
Aalst, 2020).

The pandemic has demonstrated the multiple, often reinforcing, ways in which specific drivers of vulnerability interact both in generating 
urban risk and shaping who is more or less able to recover (Phillips et al., 2020; Honey-Rosés et al., 2020) (see Section 6.2). Again, this 
is not a new lesson for urban climate change adaptation, but it is a lesson that has not yet been seen to enter into routine practice for 
urban adaptation. Two key challenges for climate change adaptation are the associations between COVID-19 risk and urban connectivity 
and overcrowding. Connectivity has been presented in urban adaptation policy as a virtue, a means to share risk and diversity inputs (Ge 
et al., 2019; Kim and Bostwick, 2020), COVID-19 has surfaced the unevenness with which people and places are connected and also the 
need to balance connectivity against risk transfer, through the failure of food supply chains or remittance flows, as well as by the direct

transfer of disease (Challinor et al., 2018). High-density living has advantages for urban resource efficiency including benefiting climate 
change mitigation. When high-density living is not supported by adequate access to critical infrastructure (sufficient internal living space, 
access to potable water and sanitation, access to open green space), this exacerbates overcrowding and generates vulnerability to 
multiple risks, including climate change hazards and communicable disease (Bamweyana et al., 2020; Hamidi, Sabouri and Ewing, 2020; 
Peters, 2020; Satterthwaite et al., 2020). Where overcrowding coincides with precarious livelihoods, for example in informal settlements, 
risk is further elevated (Wilkinson, 2020). Neighbourhood associations (a benefit of high-density living) have been an important source 
of resilience through providing trusted information, access to food and water for washing during the pandemic and serving populations

unable to access government or market provision (Pelling et al., 2021). Here local organising has not only met gaps in service provision, 
but opened dialogue to vision and organise for alternative development futures. These distinctly urban challenges should be read as a 
sub-set of wider cross-cutting lessons for recovery from COVID-19 (see Cross-Chapter Box COVID in Chapter 7).
Where responses to COVID-19 include addressing inequities in social infrastructure, this opens a considerable and potentially society-
wide opportunity to reduce social vulnerability to climate change risks (see Cross-Chapter Box COVID in Chapter 7).

6
960Chapter 6 Cities, Settlements and Key Infrastructure
there is secession from public infrastructure services that has financial 
implications for viability (Romero-Lankao, Gnatz and Sperling, 2016). 
In the case of the 2015–17 Cape Town drought, wealthy households 
secured their water needs through off-grid technologies such as 
rainwater tanks and boreholes. Although this resulted in more water 
being available in the dams, it also led to less revenue being collected 
for municipal water and less ability to cross-subsidise water for poor 
households (Ziervogel, 2019b; Simpson, 2019; Bigger and Millington, 
2019). More attention needs to be paid to how shifts in infrastructure 
are serving the interests of urban elites, often driven by the state, 
and failing to adequately consider the needs of the disadvantaged 
(Bulkeley, Castán Broto and Edwards, 2014; Ajibade, 2017; Shi et al., 
2016). Equally, more risk-reducing infrastructure is needed across all

urban areas (Reckien et al., 2018a).
Procedural justice, which focuses on the institutional processes by 
which adaptation decisions are made, brings attention to the lack 
of opportunity for engaging in political decision making and limited 
representation of diverse voices in cities and settlements, and in 
relation to investment in infrastructure (Coates and Nygren, 2020; 
Henrique and Tschakert, 2019). Even when inclusive adaptation 
processes are run, they seldom produce procedurally just outcomes 
(Malloy and Ashcraft, 2020). Understanding who is excluded and 
included is important (Sara, Pfeffer and Baud, 2017). One example 
is the increasing numbers of migrants who are confronted with lack 
of access to citizenship rights and housing tenure (Romero-Lankao 
and Norton, 2018). Often, migrants are not allowed to formally claim 
public provisions in health, finance and shelter (Chu and Michael, 
2018). Further, migrants and their settlements are likely unrecognised

in spatial or infrastructure development plans. In this context, social 
infrastructure, zoning and land use planning for climate adaptation 
has triggered inequity through omission, as some planning process 
have been racialised and excluded groups such as migrants and ethnic 
minorities (Anguelovski et al., 2016). Urban adaptation policy-making 
processes that explicitly integrate multiple stakeholder interests can 
help to balance top-down solutions (Reckien et al., 2018a).
Identifying who is least able to adapt to climate risks sufficiently 
is important (Thomas et  al., 2019). Some people may have few 
opportunities to relocate away from flooded areas in the long term 
or to evacuate in the short term. It is also harder for many from 
low-income areas to rebuild after an extreme event. Lack of housing 
tenure and sub-standard housing has been shown to limit the ability 
of residents to improve and manage their landscapes and therefore

it is hard for them to enhance energy efficiency (Dempsey et  al., 
2011). Access to information is critical for adapting to climate risk 
and reducing vulnerability to hazards, yet access to this information is 
often not equally available (Ma et al., 2014). For example, low literacy 
can hamper ability to respond to early warning information (Dugan 
et  al., 2011). In other instances, racial violence has surfaced during 
disasters, with Black victims’ lives being seen as less important than 
others (Anderson et al., 2020).
When looking at justice issues in urban adaptation, it is important 
to recognise that the adaptation of one individual or household may 
lead to maladaptation and negative impacts elsewhere (Holland, 
2017; Limthongsakul, Nitivattananon and Arifwidodo, 2017; Atteridge and Remling, 2018). For example, the case of an area of peri-urban 
Bangkok experiencing localised flooding due to unregulated private

sector development saw households take both individual (building 
flood walls around homes, digging temporary drainage swales in the 
carriageway) and collective action (petitioning authorities, pumping 
water into vacant land). These actions, to a certain extent, merely 
displaced the flood water to other areas, or created new problems 
by damaging the carriageway, creating negative impacts on other 
households and the wider community. However, ultimately, it was the 
actions of improperly regulated private sector developers driving the 
need for this autonomous adaptation (Limthongsakul, Nitivattananon 
and Arifwidodo, 2017).
One of the tensions that emerge when addressing injustice is that 
the global provision of modern infrastructure is increasingly seen as 
unfeasible. It is unfeasible, both in terms of the current high emissions 
associated with infrastructure (World Bank, 2017) and the centralised, 
high standard ideal (Lawhon, Nilsson and Silver, 2018; Coutard and

Rutherford, 2015). Decentralisation is increasingly needed, which 
the urban poor already engage in through their use of ‘informal’ 
infrastructure technologies, given their limited access to infrastructure 
networks. Transformative adaptation pathways that reduce climate risk 
whilst reducing inequity require an approach that sees infrastructure 
as inherently social and political.
6.3.6.2 Mitigation and Adaptation
As analytical concepts, mitigation and adaptation have helped, over 
the years, to structure thinking and action around climate change. 
However, since AR5 there has been a growing debate about the 
adequacy of a neat separation between adaptation and mitigation 
(Castán Broto, 2017).
The delivery of climate change action has revealed numerous 
co-benefits between adaptation and mitigation, around diverse areas 
such as implementing NBS and delivering health and development 
benefits (Ürge-Vorsatz et  al., 2014; Suckall, Stringer and Tompkins,

2015; Puppim de Oliveira and Doll, 2016; Spencer et  al., 2017). 
There has been a strong interest in delivering development benefits 
alongside climate mitigation, thus benefiting the overall infrastructure 
base (Suckall, Stringer and Tompkins, 2015). Some of these co-benefits 
have also emerged in experiences of urban planning, pointing toward 
the dilemma of separating adaptation and mitigation in a context in 
which integration, rather than an analytical differentiation, was seen 
as being required to transcend work in silos (Aylett, 2015). Because 
urban planning needs to carefully consider long time scales, the 
neat separation between mitigation and adaptation runs counter to 
integrated forms of planning that can consider scales (time and space) 
carefully and that are aimed to deliver the sustainable city as a whole 
(Solecki et al., 2015; Grafakos et al., 2020).
For example, the ideas of climate resilient development and climate

compatible development help planners to consider the simultaneous 
wins that emerge between adaptation, mitigation and development, 
requiring institutional building and partnerships to deliver triple 
win solutions (Stringer et al., 2014; Seo, Jaber and Srinivasan, 2017; 
Mitchell and Maxwell, 2010). While the evidence base for the actual

6
961Cities, Settlements and Key Infrastructure  Chapter 6
possibility of achieving such triple wins remains scarce (Tompkins 
et  al., 2013; Sharifi, 2020), emerging examples show important 
developments. For example, establishing safe and convenient walking 
and cycling infrastructure can lead to improvements in population 
health, thereby highlighting the close interaction between urban land 
use, infrastructure and population health (Schuster et al., 2017), while 
clean cooking has the potential to deliver positive health outcomes 
alongside improvements in air quality and emissions reductions and 
through reducing pressure on woodland as a fuel source for expanding 
urban populations (Msoffe, 2017). Furthermore, active transport 
infrastructure reduces air pollution and related health risks, and helps 
to mitigate further climate change (Schuster et al., 2017). These are 
supported by city networks such as the C40 Clean Air Cities Declaration

and the Clean Air Coalition that complements WHO guidelines 
and standards, for example through the Breathe Life Campaign. In 
conclusion, in both urban environments and infrastructural sectors, 
triple wins are only realisable through broader perspectives that 
link climate compatible development to institutional change or the 
achievements of wider welfare objectives such as those enshrined in 
the United Nations 2030 Agenda of Development (Castán Broto et al., 
2015; England et al., 2018) (medium evidence, high agreement).
The aspiration to deliver climate change action within a broader 
agenda of transformative change, introduced in the SREX report, 
received renewed attention after the publication of IPCC Special 
Report on Global Warming of 1.5°C, which argues for a focus on 
urban transformations and highlighted that informal settlements 
were vital for understanding the delivery of these transformations. 
Deep decarbonisation has emerged as a new idea that regards the

development of low or zero carbon pathways as a condition for good 
adaptation in the long term. Decarbonisation becomes urgent in 
the face of growing impacts attributable to climate change (Ribera 
et al., 2015; Bataille et al., 2016; Wesseling et al., 2017). Urbanisation 
opens opportunities for deep mitigation in low-impact developments, 
and hence, it is imperative to understand the implications of those 
opportunities for climate action (Mulugetta and Broto, 2018). These 
gains are not limited to urban areas. The reliance on connected urban–
rural systems for water, food and fuel has led to city government and 
urban-based businesses supporting landscape adaptations in rural 
hinterlands with strong potential for mitigation and rural development 
co-benefits. Water Funds bring downstream urban public and private 
finance to support upstream rural residents to make land use and 
agricultural management decisions to avoid damaging runoff, soil

erosion and downstream sedimentation with reduction in water quality 
and increased flood risk. There are more than 30 Water Funds in Latin 
America and sub-Saharan Africa. These operate at landscape scale; 
the Upper Tana-Nairobi Water Fund, Kenya (Vogl et al., 2017), planned 
for a USD 10  million investment in Water Fund-led conservation 
interventions, with a projected return of USD 21.5 million in economic 
benefits over a 30-year timeframe (Apse and Bryant, 2015). However, 
these investments do not occur where communities lack funding or 
the institutions to direct funding from downstream beneficiaries to 
upstream residents (Brauman et al., 2019).6.3.7 Climate Resilient Development Pathways
Table  6.6 represents the contribution of 21 adaptation measures 
identified in this chapter to 17 components of climate resilient 
development (CRD). CRD brings together the aims of climate adaptation, 
climate mitigation, sustainable development and social justice (Singh

and Chudasama, 2021). This provides a first assessment of the viability 
of adaptation to cities, settlements and key infrastructure as a part of 
global transition to sustainability (see also Cross-Chapter Box FEASIB 
in Chapter 18).
Two overarching messages and one key consequence for planning 
arise from Figure  6.4. First, urban adaptation measures can offer a 
considerable contribution to CRD. Second, this potential is realised by 
adaptations that extend predominant physical infrastructure approaches 
to also deploy nature-based solutions and social interventions. The 
consequence for planning is support for comprehensive monitoring 
and joined-up evaluation across the multiple components of CRD, as 
well as between the sectors that contribute to adaptation.
Table 6.6 shows that adapting key grey/physical infrastructure (built 
form and design, ICT, energy, transport, water and sanitation) is 
fundamental to CRD. This provides resilience to a range of hazards,

with benefits to livelihoods, social capital and health, and provides 
benefits for the adaptation of other, connected infrastructure systems. 
Challenges to the contributions of grey/physical infrastructure, where 
adaptation through nature-based solutions and social policy offer 
alternatives are a lack of flexibility post-deployment constraining 
ability to flex as climate and vulnerability change; risk transferred to 
other people/places, not resolved; negative ecological consequences; 
and limited evidence of targeting marginality and inequality.
The significance of a CRD lens for the evaluation of adaptation 
strategy can be seen in approaches to riverine and coastal flooding. 
This viewpoint brings physical (e.g., embankments and defenses), 
nature-based (e.g., mangrove stands) and social policy (livelihood 
and social protection) options together. The benefits of physical 
infrastructure interventions for strengthening existing livelihoods

and protecting health, for being deployable at scale and supporting 
other infrastructures to adapt are recognised and set these against 
challenges including hazard generation and risk transfer, limited 
flexibility, ecological harm, carbon costs and an undermining of social 
inclusion and accountability. Final evaluations will be determined 
by individual contexts, raising the importance of comprehensive 
monitoring of existing urban systems adaptation interventions and 
their association with ongoing development processes and outcomes 
The most consistent limit for all urban systems infrastructure types is 
in risk transfer. Current adaptation approaches in cities, settlements 
and key infrastructure have a tendency to move risk from one sector 
or place to others. With the exception of social infrastructure, the 
observed contribution of adaptation to social transformation is also 
limited. There are consequences for equity and sustainability as the

impacts of climate change increase, and implications for evaluation 
and planning to work across adaptation interventions and connect 
with social and environmental policy and practice.

6
962Chapter 6 Cities, Settlements and Key Infrastructure
Table 6.6 |  Urban climate resilient development
Adaption Measure
Land-use planning 6.3.2.1
Livelihoods and social protection 6.3.2.2
Emergency management and security 6.3.2.3
Health 6.3.2.4
Education & Comms. 6.3.2.5
Cultural heritage & institutions 6.3.2.6
Temp. regulation 6.3.3.1
Air quality regulation 6.3.3.2
Stormwater and sanitation 6.3.3.3
Coastal flood protection 6.3.3.4
Riverine flood impact reduction 6.3.3.5
Water provisioning and management 6.3.3.6
Food production and security 6.3.3.7
Built form 6.3.4.1
Housing and building design 6.3.4.2
ICT 6.3.4.3
Energy Inf. 6.3.4.4
Transport 6.3.4.5
Water and sanitation 6.3.4.6
Flood management 6.3.4.7
Coastal management 6.3.4.8Inf. 
Systems
Social 
Inf.
Nature 
based 
solu-
tions
Grey/
Physical 
Inf.Transformation 
towards sustainable 
development  
(human systems 
fundamental change 
+ impact on wider 
system)
Eco-
logical 
transfor -
mation
HA-ME
LA-LE
MA-ME
LA-LE
MA-RE
HA-RE

HA-ME
MA-LE
MA-ME
MA-ME
MA-ME
HA-ME
MA-ME
MA-ME
MA-ME
LA-LE
LA-LE
LA-LE
HA-RE
MA-LE
MA-LESocial 
transfor -
mation
HA-ME
MA-ME
MA-ME
LA-LE
HA-ME
HA-RE
LA-LE
LA-LE
LA-LE
MA-LE
LA-LE
HA-LE
HA-ME
LA-LE
MA-ME
MA-ME
LA-LE
LA-LE
LA-LE
LA-LE
LA-LEEquity benefits
Inclusive 
and 
locally 
account-
able
HA-ME
MA-RE
MA-ME
MA-ME
MA-ME
HA-ME
MA-LE
MA-LE
MA-LE
LA-LE
MA-LE
MA-LE
MA-LE
LA-LE
MA-ME
LA-ME
MA-ME
MA-ME
MA-ME
LA-ME
LA-METargets 
pov-
erty and 
margin-
ality
MA-LE
HA-ME
MA-ME
HA-RE
MA-ME
MA-LE
MA-LE
LA-LE
MA-LE
MA-LE
LA-ME
LA-LE
MA-ME
LA-LE
MA-ME
LA-ME
MA-ME
MA-ME
MA-RE
MA-ME
MA-MECon-
tribution 
to GHG 
emission 
reduction
Mitiga-
tion co-
benefit
HA-RE
LA-LE
HA-ME
MA-ME
HA-ME
MA-LE
HA-RE
HA-ME
HA-ME
MA-ME
HA-RE
HA-ME
MA-ME
MA-ME
MA-ME
MA-ME
HA-RE
MA-LE
HA-ME
HA-LE
HA-LEPotential effectiveness
Eco-
nomic 
feasi-
bility
HA-RE
MA-ME
HA-ME
MA-ME
LA-LE
HA-ME
HA-ME
MA-ME
HA-ME
HA-ME
LA-ME
HA-RE
HA-ME
LA-LE
MA-ME
HA-ME
MA-ME
MA-ME
MA-ME
HA-RE
HA-REBenefit to 
other inf. 
systems 
adapta-
tion

HA-ME
HA-LE
HA-ME
MA-ME
LA-LE
MA-LE
MA-ME
MA-LE
HA-ME
HA-ME
MA-ME
MA-ME
MA-ME
LA-LE
MA-LE
HA-RE
HA-RE
MA-ME
HA-RE
HA-RE
HA-REDeploy 
at scale
HA-ME
HA-ME
MA-ME
HA-RE
MA-LE
LA-LE
HA-ME
MA-ME
MA-LE
MA-LE
MA-ME
HA-ME
HA-ME
HA-RE
HA-RE
HA-ME
MA-ME
MA-ME
MA-ME
HA-RE
HA-REFlexibility 
post de-
ployment
HA-RE
MA-ME
MA-ME
MA-ME
HA-ME
MA-ME
MA-LE
HA-ME
MA-ME
HA-LE
MA-LE
MA-ME
MA-ME
HA-RE
HA-RE
HA-RE
HA-ME
HA-ME
HA-ME
HA-ME
HA-MEBenefits 
to eco-
system 
services
Eco-
logical
HA-RE
LA-LE
HA-ME
MA-ME
HA-LE
MA-LE
HA-ME
MA-ME
HA-ME
HA-ME
HA-RE
HA-RE
HA-ME
MA-ME
LA-LE
LA-LE
LA-LE
LA-LE
HA-RE
MA-ME
MA-MEBenefits to Human
Health
HA-RE
HA-LE
HA-ME
HA-RE
HA-ME
HA-ME
HA-ME
HA-ME
HA-LE
HA-LE
HA-ME
HA-ME
MA-ME
HA-RE
HA-RE
LA-LE
LA-LE
LA-LE
HA-RE
HA-RE
HA-RELiveli-
hood
HA-RE
HA-ME
HA-ME
HA-ME
HA-ME
MA-RE
HA-ME
MA-ME
MA-LE
MA-RE
LA-ME
MA-ME
MA-ME
MA-ME
MA-ME
HA-ME
HA-RE
HA-RE
HA-RE
HA-RE
HA-RESocial 
capital
HA-RE
HA-LE
HA-ME
HA-ME
HA-RE
MA-ME
HA-ME
HA-ME
MA-ME
MA-LE
HA-ME
MA-LE
MA-ME
LA-LE
LA-LE
HA-RE

HA-LE
HA-LE
HA-LE
MA-ME
MA-MERisk coverage
Transfer 
risk or 
impact 
to other 
people or 
places
HA-RE
HA-LE
HA-ME
HA-ME
MA-ME
HA-ME
LA-LE
LA-LE
HA-ME
LA-LE
HA-ME
MA-LE
LA-LE
MA-LE
MA-ME
LA-LE
LA-LE
LA-LE
LA-ME
HA-RE
HA-REReduces 
new 
hazard 
exposure 
generated
HA-RE
HA-ME
MA-ME
HA-ME
MA-ME
MA-LE
LA-LE
MA-LE
MA-LE
HA-LE
MA-ME
MA-LE
MA-LE
HA-RE
HA-RE
LA-LE
LA-LE
LA-LE
MA-ME
HA-ME
HA-MESystemic 
vulner -
ability 
reduction
HA-RE
HA-RE
MA-ME
HA-RE
MA-ME
MA-ME
LA-ME
MA-LE
MA-ME
MA-ME
MA-ME
MA-LE
HA-ME
HA-RE
HA-RE
HA-ME
HA-ME
HA-ME
HA-LE
HA-ME
HA-MEMulti-
climate 
Hazard
HA-RE
HA-ME
HA-ME
HA-RE
HA-ME
HA-ME
HA-RE
HA-ME
HA-ME
HA-ME
HA-RE
HA-RE
HA-ME
HA-RE
HA-RE
HA-RE
HA-RE
HA-RE
HA-RE
HA-RE
HA-RE

6
963Cities, Settlements and Key Infrastructure  Chapter 6
Key:
Climate Resilient Development Contribution
Negative
HighNegative
ModerateNegative
SmallNeglible
negativeNilPositive
NegliblePositive
SmallPositive
ModeratePositive
HighNo
data
HA-
LEHigh
agreement –
limited
evidenceHA-
MEHigh
medium
REHigh
robust
evidenceMA-
LEMedium
limited
evidenceME-
MEMedium
medium
REMedium
robust
evidenceLA-
LELow
limited
MELow
medium
RELow
robust
evidence
Overall confidence: Medium agreement—medium evidence. Supplementary Material provides a detailed analysis including definitions for each component of climate resilient development and for each of the 357 entries an underlying 
explanatory statement linked to key evidence. Analysis was by Chapter 6 Lead and Contributing Authors.6.4 Enabling Conditions for Adaptation

Action in Urban Areas, Settlements and 
This section assesses the effectiveness of efforts to create enabling 
conditions for adaptation. New policy innovations such as National 
Urban Policies are emerging to address the multi-level governance 
demands of climate change (UN-Habitat, 2020; Kinyanjui, 2020). 
There is no one-size-fits-all approach to deliver adaptation that will 
fit every case, because the local conditions of implementation bear a 
strong influence on adaptation’s feasibility and impacts (Archer et al., 
2014). Ways to foster adequate enabling conditions for adaptation are 
well-documented (IPCC, 2018 Ch.4). These often include integrated 
planning, multi-agency working and multi-scale and sector action. 
Existing techniques can be shared and new innovations taken up 
(Maxwell et al., 2018).
Adaptation in urban areas and settlements can be incremental (when 
it addresses the causes of problems but without fundamentally

changing the social and political structures that drive it, for example 
through planning or new regulations), reformist (when it changes the 
features that cause problems but without fundamentally changing the 
structures) or transformative (when it addresses fundamental systems 
attributes and outcomes such as reducing inequality in political and 
socioeconomic structures or enhancing well-being [Mendizabal et al., 
2018; Rosenzweig and Solecki, 2018] which change the situation 
completely) (Heikkinen, Ylä-Anttila and Juhola, 2019; Roberts and 
Pelling, 2020; O’Brien, Selboe and Hayward, 2018). In the context of 
the SGDs mission to leave-no-one behind, transformative adaptation 
addresses fundamental systems’ functions to enable enhanced social 
justice and socio-ecological well-being. Incremental adaptation actions 
seek to maintain the essence and integrity of a system or process at 
a given scale (see Annex II: Glossary). Adaptation that seeks only to

defend existing development status will not contribute to enhanced 
well-being and is not transformative, even if fundamental infrastructure 
engineering or legislative systems are changed to maintain the status 
quo in the face of increasing risk (Mendizabal et al., 2018).
City populations and non-state actors, together with local and 
regional governments, can play an essential role in creating enabling 
conditions for action, including, for example, civil society mobilising 
concerns of marginalised voices and future generations, as indicated 
in the worldwide student mobilisations against climate change (Wood, 
2019; Maor, Tosun and Jordan, 2017; Cloutier, Papin and Bizier, 2018; 
Prendergast et al. , 2021), which may then be prioritised by local and 
regional governments. National governments also play a crucial role, 
for example in facilitating resources and finance for urban adaptation 
actions, alongside financial organisations and the business sector (see

Section 6.4.5). This section starts assessing adaptation experiences in 
cities, settlements and infrastructures since the AR5, before reviewing 
evidence of how to foster enabling conditions for adaptation through 
institutionalisation, governance capacity, finance, evaluation and social 
learning.

6
964Chapter 6 Cities, Settlements and Key Infrastructure
6.4.1 Adaptation Experiences in Cities, Settlements and 
Infrastructures
Since AR5, there is increasing evidence that successful adaptation to 
climate change is context-specific and responsive to the particular 
needs of urban locations. This section assesses the contributions of key 
urban actors, local government, civil society and the local private sector, 
in enabling adaptation. Wider influences from national government 
cross cut this and are discussed with the role of international agencies, 
and finance which is assessed in Section 6.4.5.
The literature on the governance of adaptation has grown since the 
AR5, though with few cases from cities and settlements in the Middle 
East, North Africa, Central Asia and former USSR countries. Potential 
reasons for the continued lack of studies in these areas include the 
centralised character of decision making systems in countries in these

regions and the early stage of adaptation planning in these urban 
areas (Clar, 2019; Mitchell and Laycock, 2019; Olazabal et al., 2019a).
Flexible institutions that allow for both top-down and bottom-up action 
can bring capacities together from across levels of government and 
actors within a settlement (Sharifi and Yamagata, 2017). Predominant 
planning and capacity-building strategies, however, lack the flexibility 
to address the needs of a rapidly changing environment (Carter et al., 
2015; Dhar and Khirfan, 2017; Juhola, 2016). Efforts to adapt to new 
challenges may have to speed up. This is especially true in urban areas 
and settlements with lower levels of development and experiencing 
rapid urbanisation, growing inequality and exposure to multiple 
hazards (Dulal, 2019; Grafakos et al., 2019; Solecki et al., 2018). Even 
within cities that share similar characteristics, there are considerable 
differences in the level of investment in adaptation (Georgeson et al.,

2016). There is also a danger that uncoordinated actions for climate 
change mitigation and adaptation may constrain future adaptation 
opportunities or create maladaptation (Juhola et  al., 2016). The 
evidence emerging since the AR5 suggests that institutional change 
can be accelerated by closer collaboration between the diverse actors 
and deployment of the diverse approaches that can deliver adaptation.
6.4.1.1 Experiences of Adaptation Action in Sub-national 
Governments
The assessment of cases of local adaptation demonstrates that most 
urban adaptation is led by local governments (although the local 
government is also a heterogeneous category and local governance 
arrangements may vary across administrative and political contexts) 
(high confidence) (Amundsen et al., 2018; Lesnikowski et al., 2021). 
Local government reform at different levels can improve local 
adaptation, whether this is by strengthening specific teams or building

cross-departmental linkages (high confidence) (Paterson et al., 2017; 
Shi, 2019; Wamsler and Raggers, 2018). Adaptation success often 
depends on having political champions driving the adaptation agenda 
alongside measures such as access to a knowledge base, resources at 
hand, political stability and the presence of dense social networks that 
can be supported through local government reform (Pasquini et al., 
2015). Aligning adaptation objectives with other potential benefits 
of sustainable development also supports adaptation. Specifically, 
policies and plans that link adaptation to the objectives of Agenda 2030 supports action at the local level (UN-Habitat, 2016b). Showing 
the economic benefits of adaptation is a strategy for local institutions 
to gain support for adaptation action. For example, local governments 
in Surat, Indore and Bhubaneswar in India linked adaptation to local 
development needs in experiments that facilitated accessing human

and finance resources, at the local, national and international levels 
(Chu, 2016). However, linking adaptation to co-benefits may also 
divide efforts and reduce the effectiveness of adaptation actions. For 
example, urban land use planning and management in Ambo town, 
Ethiopia, resulted in the implementation of urban greening projects, 
but these projects did not directly address the climate-related disaster 
risks affecting the settlement, including urban flooding, water stress, 
water shortages, increased urban heat, wind and dust storms (Ogato 
Multi-level governance measures that support local governments 
can foster robust adaptation approaches and address risks and 
vulnerabilities across scales (high confidence) (Westman, Broto and 
Huang, 2019; Hardoy et al., 2014; Romero-Lankao and Hardoy, 2015). 
Effective action by local government requires national government’s 
support (medium confidence). For example, Araos et  al. (2017)

documents the case of Dhaka, Bangladesh, where a national plan 
prioritises measures for protecting coasts and agricultural production. 
In this context, the local government has minimal access to human and 
financial resources. Without national support, the local government 
struggles to coordinate action among different stakeholders. National 
urban adaptation directives can influence municipal governments’ 
action and planning, but evidence suggests that national policy alone 
is not sufficient to deliver action on the ground without understanding 
local conditions (high confidence) (Archer et al., 2014; Lehmann et al., 
2015).
There are barriers for municipal adaptation plans to deliver effective 
adaptation outcomes and implemented actions often diverge from 
plans (see Section  6.4.6). For example, a comparison of adaptation 
plans and budget expenditures of six metropolitan cities in South 
Korea between 2012 and 2016 showed that the implementation of

adaptation programmes diverged substantially from the original plans, 
both in terms of total and sector-specific spending (Lee and Kim, 2018). 
Often, a focus on institutional change and reform limits attention 
to more practical aspects of adaptation that improve communities’ 
resilience (Castán Broto and Westman, 2020). Adaptation actions, even 
where financed effectively, do not always deliver positive outcomes 
(high confidence) (Reckien et  al., 2015; Woodruff and Stults, 2016; 
Uittenbroek, 2016; Aguiar et al., 2018; Reckien et al., 2018a; Olazabal 
et al., 2019b; Campello Torres et al., 2021) (see also Section 6.4.7).
6.4.1.2 The Role of Non-State Actors in Local Adaptation
There are multiple actors, other than local governments, that can deliver 
adaptation action, including businesses, not for profit organisations 
and trade unions (high confidence) (Giordano et  al., 2020; Eakin 
et  al., 2021). Empirical evidence since the AR5 highlights the role

of communities, universities, the private sector and transnational 
networks in adaptation (Hunter et al., 2020; Bäckstrand et al., 2017). 
Non-state actors are particularly important in enabling adaptation 
by linking government agencies with low-income and marginalised

6
965Cities, Settlements and Key Infrastructure  Chapter 6
communities, including those living in informal settlements (Kuyper, 
Linnér and Schroeder, 2018; Khosla and Bhardwaj, 2019).
Since AR5, civil society and private actors have emerged as core 
knowledge holders and drivers of experimentation, even succeeding 
in changing public policy in the process (Klein, Juhola and Landauer, 
2017; McKnight and Linnenluecke, 2016; Mees, 2017). Previous 
IPCC Assessment Reports noted that civil society actors enable local 
risk awareness, sensitisation and adaptive capacity, and generate 
locally based innovation (e.g., through community based adaptation 
programmes).
Community based adaptation includes a range of initiatives that put 
communities at the centre of planning for adaptation, often led by 
communities themselves (Reid, 2016). Community based adaptation is 
a comprehensive and effective strategy to deliver resilience at a human

scale (Trogal et al., 2018; Greenwalt et al., 2020). Many community 
based responses to climate impacts represent coping strategies 
developed within households with a small effect on adaptation 
capacities beyond incremental improvements. Residents adopt private 
coping strategies to reduce exposure to and the impacts of heat, floods, 
flash floods, landslides, storms and diseases on their lives (Hambati 
and Yengoh, 2018). These coping strategies include the construction 
of physical protection against flooding, reforestation, the construction 
of terraces, flood diversion measures and interventions to protect 
houses (such as raised doorsteps or use of sandbags and adoption 
of building techniques for making homes resilient to storms and 
landslides), ventilation of houses, urban agriculture and redefinition 
of daily practices and livelihoods (Navarro et al., 2020; Malabayabas 
and Baconguis, 2017; Apreda, 2016; de Andrade and Szlafsztein, 2020;

Sahay, 2018; Bausch, Eakin and Lerner, 2018).
Individual coping strategies are generally ineffective in reducing 
extreme risks and they rarely address the underlying structural 
causes of vulnerability (high confidence) (Sahay, 2018; Rözer et  al., 
2016; Jay et  al., 2021). Expending resources on private coping 
strategies in some cases may divert resources and capacity for wider 
community adaptation efforts (de Andrade and Szlafsztein, 2020). 
However, individual coping strategies can provide foundations for the 
implementation of collaborative action in communities, building on 
people’s experiences, in ways which may have a longer-term, durable 
impact on developing resilience (high confidence) (McEwen et  al., 
2018). Community based adaptation can be effective at different 
scales, whether this is to manage transboundary issues (Limthongsakul, 
Nitivattananon and Arifwidodo, 2017), support the replication of local

solutions (Danière et  al., 2016), increase the uptake of adaptation 
measures (Liang et al., 2017) or inform the design of more effective 
policies for resilience (Berquist, Daniere and Drummond, 2015; 
Odemerho, 2015). Community action may be mediated by NGOs or 
third sector organisations who play a coordinating or enabling role, 
particularly where other local government mechanisms are absent.
6.4.1.3 The Role of the Private Sector in Local Adaptation
There is weak evidence of private sector involvement in urban 
adaptation (Pauw, 2015; Heurkens, 2016). The absence of private 
sector investment in adaptation is particularly visible in rapidly urbanising countries (Nagendra et  al., 2018). Business continuity 
describing private sector preparedness notes that firms underestimate 
the impacts of climate risks on their business models (Goldstein et al., 
2019; Forino and von Meding, 2021; Korber and McNaughton, 2017;

Crick et  al., 2018b). There is little research on how businesses can 
play a leading role in urban adaptation (Klein et al., 2018). A global 
assessment of the private sector’s role in urban adaptation using 
data from 402 cities shows that most adaptation projects focus on 
the public sector and do not address private sector concerns or local 
people’s participation (Klein et  al., 2018). Recorded private sector 
action is recognised through partnerships and participation (Peterson 
and Hughes, 2017; Hughes and Peterson, 2018). There are a few 
examples of studies of private sector-led adaptation action which 
adopts a national focus (Crick et al., 2018a; Crick et al., 2018b). This 
lack of evidence contrasts with a well-developed body of literature on 
private sector-led mitigation (Averchenkova et al., 2016). 
Businesses have an essential role in urban adaptation actions, through 
the collective formulation of adaptation strategies, the provision

of critical adaptive interventions and collaboration in partnerships. 
Businesses in the property sector, such as real estate developers, 
are on the frontline of climate change impacts but display differing 
attitudes toward climate adaptation. A study of property businesses 
in cities in Australia (Taylor et  al., 2012) showed that speeding up 
planning approval processes facilitated adaptation actions, and joint 
private–public decision-making was the preferred mode of governance 
for responding to climate concerns. Property businesses in cities in 
Sweden had a limited and reactive engagement in climate issues and 
resisted regulation (Storbjörk et  al., 2018). Corporate, private sector 
interventions in urban risk reduction more broadly remain limited, with 
a mix of public and private responsibility for planning, implementing 
and maintaining adaptations in the built environment, and yet limited 
engagement of private sector actors in providing healthcare measures

for heat prevention (medium confidence) (Mees, 2017).
There is little published literature documenting the heterogeneity 
of business and the private sector’s responses to climate impacts 
(Linnenluecke, Birt and Griffiths, 2015; Doh, Tashman and Benischke, 
2019). Firms have varying abilities to introduce climate adaptation 
measures related to staff availability, levels of awareness, perceptions 
of responsibility and duration of contracts (short-term projects implies 
less interest in adaptation outcomes) (Shearer et al., 2016). The impact 
of COVID-19 has serious but uncertain implications for both access 
to finances for sustainable development by LMICs and sub-national 
governments, and the possibility of stimulating maladaptive 
infrastructure and policy responses (OECD, 2020; Sovacool, Del Rio and 
Griffiths, 2020). The response of businesses to disasters influences the 
resilience in the communities in which they operate (McKnight and

Linnenluecke, 2016; Linnenluecke and McKnight, 2017). However, 
at the same time there is a growing literature that warns against 
the conflict interests that businesses may have in their adaptation 
strategies. For example, real estate responses to flooding have led to 
processes of climate gentrification, whereby lower income populations 
are displaced toward higher risk areas which stablishes racialised 
and class-based patterns of inequality of exposure to risk, with hard 
evidence rapidly growing specially in US cities (Keenan, Hill and 
Gumber, 2018a; Shokry, Connolly and Anguelovski, 2020; De Koning

6
966Chapter 6 Cities, Settlements and Key Infrastructure
and Filatova, 2020; Aune, Gesch and Smith, 2020). Private-sector 
participation in adaptation solutions depend on having mechanisms 
to enable transparency and open reporting on the nature of support 
and the solutions proposed. For example, businesses adopting 
‘community-centric’ disaster management strategies can assist local 
recovery efforts by protecting employment, provision of emergency 
supplies and participation in reparations (McKnight and Linnenluecke, 
2016). Private sector actors engaged in community climate responses 
can play a role in funding and managing programmes that address 
public health and education concerns. The potential of ecopreneurship, 
social enterprises, cooperatives and other sustainability-oriented 
business models (Schaltegger, Hansen and Lüdeke-Freund, 2016; 
Lopes et al., 2020; Battaglia, Gragnani and Annesi, 2020) for urban

adaptation remains under-explored in the literature on urban climate 
The private sector also constitutes a key stakeholder group involved in 
collaborative processes to develop adaptation strategies. The inclusion 
of private sector actors in deliberative policy-making processes in 
urban adaptation can lead to higher procedural legitimacy levels, as 
witnessed in Rotterdam’s case (Mees, Driessen and Runhaar, 2014). 
Rotterdam has created an institutional environment that favours 
eco-innovation (Huang-Lachmann and Lovett, 2016). The municipal 
government works directly with the private sector to enhance 
protection against flooding constructing a marketing strategy around 
a ‘floating city’ concept. A ‘floating housing’ market has expanded, 
with benefits for the local real estate and construction industries and 
knowledge-exporting businesses that provide consultation expertise, 
delta technologies and architectural models. Nevertheless, these new

trends raise new governance challenges to deliver adaptation.
There are obstacles associated with reconciling private sector interests 
with public priorities and justice agendas in local climate programmes. 
The involvement of the private sector in adaptation actions may lead 
to the appropriation of land and natural resources, and to the exclusion 
of vulnerable populations (Anguelovski et al., 2016; Rumbach, 2017; 
Scoppetta, 2016) (see also Section 6.4.4.2). Navigating the inclusion 
of businesses in urban planning processes requires local authorities 
to engage in ongoing negotiations, to reflect on constantly shifting 
power balances and to move delicately between the role of regulator 
and facilitator in the process of defining and maintaining long-term 
objectives (Storbjörk, Hjerpe and Glaas, 2019b; Storbjörk, Hjerpe and 
Glaas, 2019a).
6.4.1.4 Partnerships for Adaptation
Multi-level governance remains an influential paradigm that

recognises government institutions’ influence at different scales and 
the diversification of actors intervening in public issues from the private 
sector and civil society (robust evidence, high agreement). Establishing 
linkages between multiple organisations can help deliver coordinated 
action. Multi-level governance includes mechanisms for multiple 
actors to engage in local adaptation strategies through collaborative 
processes of planning, learning, experimentation, capacity building, 
construction of coalitions and communication channels (Barton, 
2013; Jaglin, 2013; Reed et al., 2015; Restemeyer, van den Brink and 
Woltjer, 2017; Melica et  al., 2018). Many of these studies directly focus on institutional arrangements that facilitate interaction between 
communities and civil society, experts, government representatives, 
firms and international organisations. Box  6.5 demonstrates the 
decisive role that community activists can play in building resilience 
over long periods.

Institutional fragmentation reduces the capacity to deliver adaptation 
(Den Uyl and Russel, 2018) Multi-level governance shows a commitment 
to tackling fragmented and complex policy issues through collaboration 
between national governments and non-state actors, as explained in 
the 2030 Development Agenda, especially SDG17 (‘Revitalize the global 
partnership for sustainable development’). Multi-level governance 
is particularly important to deliver adaptation at the metropolitan 
scale, that require coordinating actions across different institutions 
in inter-municipal institutions (Lundqvist, 2016). Gaps in knowledge 
remain regarding the effectiveness of multi-level governance actions 
in different contexts and the extent to which multi-level governance 
strategies transfer the brunt of responsibility for adaptation action to 
less-resourced local governments (Hale et al. , 2021).
Public–private partnerships are increasingly relevant for collaborative

development of urban adaptation (Klein et  al., 2018). Partnerships 
can deliver infrastructure, coordinate policy and support learning. 
The main limitation of partnerships is scale, as partnership action is 
usually limited to discrete projects or objectives. Partnerships tend to 
be linked to reactive (rather than proactive) adaptation projects and 
the deviation of objectives away from adaptation concerns (Harman, 
Taylor and Lane, 2015). Partnerships can support capacity building in 
public and private organisations and facilitate networking efforts that 
extend beyond the private sector to communities and NGOs (Bauer 
and Steurer, 2014; Castán Broto et al., 2015). Public actors can benefit 
from the private sector’s innovation and implementation capacity, 
and businesses can de-risk investments. Still, partnerships can also 
strengthen the ideologies of growth and managerialism within the 
operations of the local government (Taylor et al., 2012). Reconciling

divergent norms and routines within public and private organisations 
remains one of the challenges to establishing successful public–private 
partnerships for adaptation (Lund, 2018). Administrative and political 
culture influences the nature of interactions between public and private 
sector actors in urban adaptation agendas (Bauer and Steurer, 2014), 
with negative consequences such as the imposition of vertical chains 
of commands on horizontal collaborations, and the need to formalise 
contractual relations (Klein and Juhola, 2018).
Local authorities are an important enabling actor that can guide 
the private sector and communities to take responsibility for 
creating policy and regulatory environments that encourage private 
sector participation aligned with the SDGs’ equity and ecological 
sustainability principles (high confidence). For example, Frantzeskaki 
et al. (2014) report a port relocation project in the Netherlands where

sustainability principles drove private sector participation. Klein et al. 
(2017) cite examples from two cities—Helsinki and Copenhagen, 
where local authorities have shifted adaptation responsibilities to 
private actors through regulation and public problem ownership. In 
Mombasa, private companies provide green infrastructure to match 
local government requirements, in what has frequently been cited as 
an example of NBS (Kithiia and Dowling, 2010; Kitha and Lyth, 2011).

6
967Cities, Settlements and Key Infrastructure  Chapter 6
6.4.1.5 Trans-national Municipal Networks
Since the late 1990s, transnational municipal networks (TMNs) have 
increased awareness of climate change and served as a bridge for cities 
to access critical financial resources from private and philanthropic 
sources (Rashidi and Patt, 2018; Fünfgeld, 2015). Recently, TMNs 
have taken on more programmatic functions, working with cities 
to strategise, plan and incrementally improve their organisation 
functions in the face of climate change. For example, the Rockefeller 
Foundation’s 100 Resilient Cities program (2014–2019) provided a 
two-year salary for a Chief Resilience Officer (CRO) to be situated in 
a municipal authority to bridge silos, incentivise change and develop 
development strategies for resilience (Bellinson and Chu, 2019; Spaans 
and Waterhout, 2017). In these cases, external actors have enabled 
broad organisation change, resource mobilisation pathways and

alternative forms of agenda-setting in cities (Chu, 2018; Hakelberg, 
2014) (see also Case Study 6.2, Semarang).
A range of TMNs also support and encourage cities and settlements 
to plan and implement adaptation actions. ICLEI-Local Governments 
for Sustainability has developed protocols and implemented projects 
for member cities. The C40 Climate Leadership Group has facilitated 
the coordination of both local governments and business actors at a 
global scale (Gordon, 2020). Policy coordination has been central to 
the signatories of the Covenant of Mayors (Domorenok et al., 2020). 
Such networks can encourage the sharing of information about 
appropriate practices between urban areas; contribute to goal setting; 
support experimentation and development of new policy instruments; 
enhance stakeholder engagement; institutionalise climate agendas; 
and encourage policy integration across governance levels and sectors (Bellinson and Chu, 2019; Busch, Bendlin and Fenton, 2018;

Fünfgeld, 2015; Busch, 2015; Papin, 2019; Rashidi and Patt, 2018). 
However, participation in TMNs is biased toward cities in the Global 
North (Bansard, Pattberg and Widerberg, 2017; Haupt and Coppola, 
2019). A recent comparative study of 337 cities found out that cities 
that participation in TNMs are more likely to take adaptation action 
and that being part of multiple networks leads to higher levels of 
adaptation planning (Heikkinen et al., 2020).
6.4.2 Institutional Change to Deliver Adaptation in 
Cities, Settlements and Infrastructure
The main barriers to urban climate adaptation, and strategies to 
address them, relate to institutional change (high confidence) (see 
Table 6.7). Institutions include legislative and policy frameworks and 
guidelines intended to direct the action of government, civil society and 
private sector organisations and extend into informal and customary 
practices that shape individual behaviour. Many of the barriers that

inhibit institutions acting in ways that can support action for inclusive 
and sustainable adaptation have historical roots, grounded in complex 
political and social relations and can be reinforcing (Table  6.7). 
Overcoming these barriers requires coordinating the activities of 
multiple actors who can facilitate institutional and political change 
(Eisenack et al., 2014).
Institutional change is needed to open new options for inclusive and 
sustainable adaptation and to integrate adaptation and mitigation 
(robust evidence, high agreement) (see also Section 6.3.5). Box 6.5 | Building Water Resilience in Urban Areas through Community Action and Activism
In Bengaluru, India, communities have traditionally managed a network of water tanks of immense ecological importance. However, 
in the last half-century, urban development has increasingly threatened this blue network (Unnikrishnan and Nagendra, 2015). Today’s

Bengaluru depends on long-distance water transfers that create political conflict and a dense network of private boreholes that are 
depleting the city’s water resources. The restoration of the existing community managed water tanks network offers a more sustainable 
and socially just alternative for managing water resources.
Unnikrishnan et  al. (2018) have documented how the colonial and postcolonial history of water management in Bengaluru shapes 
the water infrastructure and provision systems today. Water access inequalities can be traced to the patterns of spatial development 
developed by colonial policies. Records from the sixth century onwards show how city rulers invested in an interconnected, community 
managed network of tanks and open wells, regularly recharged through harvested rainwater. The water system was changed at the 
end of the 18th century, as first the colonial state, then the post-independence government of Karnataka took responsibility for water

management. Ideas of modernist planning influenced the development of new water infrastructure and piped networks, including the 
first piped infrastructure, bringing water from sources 30 km away, including the Hesaraghatta and then the TG Halli reservoirs. The old 
network of tanks gradually deteriorated as tanks became disused, polluted or built over. More prolonged and costly water transfers took 
place in the post-colonial period, delivering water from the Cauvery River in a massive engineering project with a high energetic cost and 
enmeshed in inter-state conflicts over water use (Castán Broto and Sudhira, 2019). Scarcity is still a problem in Bengaluru. The citizen 
response has been an activist movement to reclaim the city’s tanks, accompanied by a plea to reconsider current water uses within the 
city, including actions to protect and rejuvenate water wells (Nagendra, 2016). Unnikrishnan et al. (2018) document different actions

led by citizen-led collectives, including projects for lake rejuvenation, filtering technologies to treat sewage, recovering the value of lakes 
through a share of photos and art projects, and involvement of local knowledge in-tank restoration. Those efforts suggest an untapped 
potential to deliver adaptive green spaces through the recovery of Bengaluru’s tanks.

6
968Chapter 6 Cities, Settlements and Key Infrastructure
Institutional change refers to processes that aim to shift existing 
norms and practices within organisations to deliver more effective 
action for adaptation. Institutional change at the local level can be 
achieved with diverse strategies (Patterson, de Voogt and Sapiains, 
2019). Table  6.7 illustrates various instruments that enable the 
institutionalisation of climate adaptation concerns into policy and 
planning. As Table  6.7 shows, institutional change is often used as 
synonymous with mainstreaming. Both terms refer to the integration 
of climate adaptation concerns into other areas of work and as part of 
practical routines and arguments (Chu, Anguelovski and Carmin, 2016; 
Storbjörk and Uggla, 2015; Runhaar et al., 2018; Uittenbroek et al., 
2014). Early assessments understood mainstreaming as activities 
that integrate climate adaptation into long-range and sectoral plans

(Anguelovski and Carmin, 2011; Aylett, 2015). Since then, efforts to 
mainstream climate adaptation have grown into agendas around 
the community and economic development (Ayers et  al., 2014), 
climate mitigation (Göpfert, Wamsler and Lang, 2019), spatial and 
infrastructure planning (Anguelovski, Chu and Carmin, 2014), urban 
finance (Musah-Surugu et  al., 2018; Keenan, Chu and Peterson, 
2019), public health (Araos et al., 2015), environmental management 
(Wamsler, 2015; Kabisch et al., 2016) and multi-level decision making 
(Ojea, 2015; Visseren-Hamakers, 2015). Such efforts require various 
degrees of regulatory or programmatic action to integrate adaptation 
with other concerns (Wamsler and Pauleit, 2016). However, institutional 
change has a broader remit than mainstreaming adaptation, as it may 
include, for example, changing the organisations already dealing with 
climate adaptation and make them more effective including changes

in inputs, procedures and options (Patterson, de Voogt and Sapiains, 
2019).
6.4.2.1 Input-Driven Institutional Change
Input-driven institutional change creates incentives to deliver adaptation 
action. An input view focuses on the intrinsic capacities of a given 
organisation. Input indicators are often referred to as political capital 
(Rosenzweig and Solecki, 2018; Diederichs and Roberts, 2016), existing 
or endogenous resources (Moloney and Fünfgeld, 2015; Wamsler 
and Brink, 2014), or local drivers for adaptation (Dilling et al. , 2017). 
ReResearch conducted across two municipalities in Western Cape, 
South Africa, showed the importance of a dedicated environmental 
champion, access to a knowledge base, the availability of resources, 
political stability and the presence of dense social networks (Pasquini  
et  al. , 2015). Research from São Paulo, Brazil, showed how intrinsic 
political capacities and contextual factors, such as the political ideology

of elected officials, shaped opportunities for embedding adaptation into 
ongoing urban agendas (Di Giulio et al. , 2018).
Networks, interactions and actor coalitions shape options for institutional 
change. Aylett (2015) noted the importance of internal networks between 
municipal departments, including informal communication channels, 
cultivating personal contacts and trust between the person or team 
responsible for climate planning and staff within other local government 
agencies. Internal networks can facilitate the commitment of local 
elected officials (Hughes, 2015), support higher municipal expenditures 
per capita and foster perceptions that climate adaptation is needed (Shi, 
Chu and Debats, 2015). Collective decision-making can integrate multiple 
types of information with moral concerns and provide key rationales that enable adaptation action (Carlson and McCormick, 2015). In urban 
areas in Africa, research on internal networks has also investigated

how informal arrangements shape action possibilities (Satterthwaite  
et al. , 2020). For example, in Zimbabwe, informal, traditional and civil 
society institutions are core arenas for issue discussion because of lower 
public sector capacities (Mubaya and Mafongoya, 2017). In Durban, 
South Africa, local governments rely considerably on shadow systems 
and informal spaces of information and knowledge exchange across 
their operations to introduce and sustain new ideas (Leck and Roberts, 
2015). In the metropolitan area of Styria, Austria, informal cooperation 
has supported the development of rural–urban partnerships for the 
formulation of common goals (Oedl-Wieser et al. , 2020). In Arkansas, 
USA, informal governance structures support planning to manage 
wildfires (Miller, Vos and Lindquist, 2017).
Cities can leverage input-driven institutional change even without 
national support for climate change adaptation or mitigation. For

example, where cities have defined policymaking and budget raising 
powers, city level political leadership can support adaptation action 
going beyond national policy (Hamin, Gurran and Emlinger, 2014; 
Shi, Chu and Debats, 2015; Carlson and McCormick, 2015). Examples 
include the Surat Climate Change Trust in Surat, India (Chu, 2016) and 
Initiative for Urban Climate Change and Environment in Semarang, 
Indonesia (Taylor and Lassa, 2015). In Saint Louis, Senegal, support 
from national and state-level actors enabled local institutional change 
(Vedeld et  al., 2016). Processual levers may be also mobilised in 
situations of political instability (which disrupts patterns in champions 
and networks), clientelism (which can cause environmental projects 
to be discontinued) (Pasquini et al., 2015) or in contexts where there 
are high political and socioeconomic inequalities (Harris, Chu and 
Ziervogel, 2018; Chu, Anguelovski and Carmin, 2016).
6.4.2.2 Output-Driven Institutional Change

Output-driven institutional change is shaped by organisational 
products such as strategies, plans, policies and evaluative metrics 
(Patterson and Huitema, 2019; Bellinson and Chu, 2019) (See Table 
6.8). There are numerous examples of institutional change through 
planning outcomes. For example, Manizales, Colombia has included 
climate adaptation into long-established environmental policy 
(Biomanizales) and a local environmental action plan (Bioplan), which 
follows on from a long coherent trajectory of climate change policy 
(Hardoy and Velásquez Barrero, 2014). A significant number of North 
American cities have integrated adaptation into long-range plans, 
while fewer cities integrate adaptation in sustainable development 
plans or sectoral plans (Aylett, 2015). Canadian cities are more likely 
to have a plan specifically focused on adaptation rather than having 
adaptation integrated into municipal long-range planning (Aylett,

2015). In the European Union, adaptation plans depended on national 
climate legislation or, in fewer cases, the influence of an international 
climate network (Reckien et  al., 2018b). A comparative report from 
the Covenant of Mayors, however, suggests that the adaptation pillar 
needs development to demonstrate the effectiveness of adaptation 
responses and their integration with mitigation goals (Bertoldi et al., 
2020). Municipalities in Sweden have been called ‘pre-reactive’ because 
adequate strategic guidelines are in place to frame the accessibility, 
aesthetics and adaptability of waterfront developments (Storbjörk and

6
969Cities, Settlements and Key Infrastructure  Chapter 6
Uggla, 2015). Some Asian cities also report high output effectiveness, 
where they are more likely to indicate senior local government officials’ 
performance management contracts, the budgeting procedures of 
local government agencies and the procedures that local government 
agencies use for budgeting infrastructure spending (Aylett, 2015). 
Despite this evidence, there is a gap in understanding the general 
trends of planning and institutional change in Africa, Asia, East Europe 
and the Middle East.
Institutional change processes are complex, contested and sporadic 
(Patterson, de Voogt and Sapiains, 2019). Such processes are often 
inhibited by unclear planning mandates, conflicting development 
priorities, lack of leadership and resource and capacity shortfalls 
(Anguelovski et  al. 2014). There is no one size fits all approach to 
institutional change, which works in situ, and benefits from clearly

defined plans and an incremental approach to revising new elements 
and addressing gaps or failures (Beunen et al., 2017). A longitudinal 
view of institutional change allows for assessing actors and dynamics 
involved in integrating adaptation into the sectoral agendas or 
governance arrangements mentioned above (Patterson and Huitema, 
2019).6.4.3 Solution Spaces to Address the ‘Policy Action Gap’
A policy action gap arises when administrative, communication, 
financial and other organisational blockages and inertia interrupt 
implementation of policy, the intent of political leadership and 
delivery of adaptation interventions on the ground (Ampaire et  al., 
2017; Bell, 2018; Shi, 2019). Political and policy confidence are key 
enabling conditions for adaptation decision making. As the AR5 
already acknowledged, political inaction can arise where there is low 
confidence that adaptation actions can deliver a safer future for all

(Chan et al., 2015a). For example, in some administrative jurisdictions 
(most of them local governments), calls by social movements for 
the adoption of Climate Emergency Declarations were addressed, 
however, practical outcomes in terms of adaptation have been limited, 
and may have foreclosed other future local actions (Nissen et  al., 
2020; Ruiz Campillo, Castán Broto and Westman, 2020) and raised 
concerns about maladaptation (Long and Rice, 2019). Political inaction 
for climate justice is particularly visible in contexts of informality 
(Ziervogel, 2020). Studies of city and local authority decision-making 
in South America (Di Giulio et  al., 2019), Asia (Araos et  al., 2017) 
and Europe (Lesnikowski et  al., 2021) indicate that where there is Table 6.7 |  Barriers to climate adaptation
Examples of barriers 
to climate adaptationInstitutional changes to overcome 
those barriersExamples Evidence
Lack of financial resourcesStrategic combination of municipal, regional and

national level funds
Access to multiple financing mechanismsIn European countries, large cities tend to fund their own 
adaptation, while smaller settlements depend on regional 
or national fundingAguiar et al. (2018);
Moser et al. (2019)
Lack of human resources 
and capacitiesDevelopment of formal and informal partnerships, 
cooperative agreements and inter-agency 
arrangementsInternational cooperation programmes for adaptation in 
urban areas in the Global South are most likely to succeed 
if they can align their objectives with local priorities and 
capacitiesUN-Habitat (2016b)
Political commitment and 
willingness to actUse of policy windows and extreme events to 
generate interest and create lasting responsesIn Germany, responses to flooding were strongly shaped 
by public perceptions of safety during the electoral cycle, 
leading to inadequate responsesGawel et al. (2018); 
Di Giulio et al. (2018)
Uncertainty about future 
impacts and dynamic

interactionsDevelop institutional arrangements that 
acknowledge and reduce uncertainty
Facilitate the development of bottom-up initiatives 
that relate directly to the context of actionPower plant operators and the federal state of 
Baden-Württemberg negotiated the minimum power plant 
concept (‘Mindestkraftwerkskonzept’, MPP), a contract to 
establish more predictable and workable procedures for 
curtailment in the event of severe heatwavesEisenack (2016);
Thaler et al. (2019)
Institutional fragmentation 
and unclear responsibilitiesEvaluation of existing institutions to diagnose 
miscoordination
Creation of policy networks that address emerging 
interdependencesIn settlements in Languedoc, France, decentralisation adds 
complexity to the ongoing challenges of population growth 
and climate changeTherville et al. (2019)
Legal issues and 
regulationsAddress the legal hurdles to create frameworks

that allow for experimental actionPolicymakers in the San Francisco Bay Area, USA, reported 
that minor changes could have a definitive influence in 
delivering regulatory changes to support adaptation action
In the Netherlands, a lack of climate change adaptation 
policy for cultural heritage hamper adaptation of cultural 
heritage to current and projected climate risksEkstrom and Moser (2014);
Fatorić and Biesbroek (2020)
Competition of adaptation 
with other policy agendas 
and polarisationPrioritisation and development of synergies across 
sectors
Mainstreaming adaptation into other sectorsIn European cities, for example, urban planning is strongly 
correlated with water management strategiesAguiar et al. (2018);
Sieber, Biesbroek and de Block (2018)
Lack of data, knowledge 
generation capacity and 
knowledge exchangeMobilise multiple strategies for the use of climate 
information in local decision making
Involve a wide range of stakeholders, with different

values and knowledge, in decision makingIn Scotland, Hungary and Portugal, local decision makers 
use high-end climate change (HECC) scenarios, but most 
often as background data
Sharing knowledge alongside the supply chain favours 
adaptation for both multinationals and small and 
medium-sized enterprises (SMEs)Lourenço et al. (2019);
Herrmann and Guenther (2017);
Gotgelf, Roggero and Eisenack (2020);
Wamsler (2017)

6
970Chapter 6 Cities, Settlements and Key Infrastructure
Table 6.8 |  Examples of institutional and policy instruments to enable adaptation
ObjectiveType of in-
strumentDescription Examples Assessment
PolicyInformation 
instrumentsA diverse range of activities such as 
training, research and development, and 
awareness campaigns to produce and 
share informationUrban-LEDS II Capacity Building 
Workshop for cities in Laos arranged 
for local government by ICLEI 
Southeast Asia Secretariat and 
UN-Habitat (UN-Habitat, 2019)Information instruments tend to be low-cost and low-risk 
options, but their impact is unpredictable and the effects may be 
uneven (Henstra, 2016). In the example of the workshops in Laos 
(UN-Habitat, 2019), the result was to map vulnerable sectors and 
build capacity for mainstreaming
Voluntary 
instrumentsPractices such as codes, labelling, 
management standards or audits, 
voluntarily, that can provide incentives

for adaptationSingapore’s National Water Agency’s 
Voluntary Water Efficiency Labelling 
Scheme (Voluntary WELS) (Tortajada 
and Joshi, 2013)A problem with voluntary instruments is that implementation 
varies. Uptake is likely to be more common among organisations 
self-identifying as ‘champions’ and less effective among other actors 
to bring about far-reaching change (Haug et al., 2010)
Economic 
instrumentsTaxes or subsidies can be used to 
promote adaptive activitiesUS Office for Coastal Management 
NOAA Coastal Resilience Grants 
Program (NOOA, 2019)Economic incentives can be effective as they ‘engage local 
stakeholders and provide price signals that stimulate individual 
adaptation’ (Filatova, 2014). However, uptake of incentives may be 
low (Sadink, 2013; Henstra, 2016) and resource intensiveness and 
potential regressive effects (equity impacts) must be considered 
(Henstra, 2016)
Regulatory 
instrumentsThese include a range of mandatory 
requirements through controls, bans,

quotas, licensing, standards often applied 
when a specific outcome is requiredBuilding codes to enhance structural 
stability for storm resilience in Moore, 
Oklahoma (US) (Ramseyer, Holliday 
and Floyd, 2016)Regulatory instruments can be effective in changing and 
institutionalising adaptation behaviours (Nilsson, Gerger Swartling 
and Eckerberg, 2012; Henstra, 2016), but outcomes depend on 
the strength of implementation (e.g., monitoring, transparency, 
mechanisms for accountability)
ProcessVisioningEvents that bring together different 
stakeholders to produce a city visionRotterdam Resilient City participatory 
processes to create resilience 
strategies (Resilient Rotterdam, 2016)There may be challenges in translating complex climate science into 
understandable and meaningful forms (Sheppard et al., 2011) and 
creating inclusive processes that allow for co-creation of visions, for 
example, by involving new digital platforms (Baibarac and Petrescu, 
2019)
Baseline

studiesFocus on understanding the current 
conditions in a neighbourhood or city 
from an interdisciplinary perspectiveFlood Risks, Climate
Change Impacts
and Adaptation Benefits
in Mumbai, an OECD assessment 
study (Hallegatte, Ranger and 
Bhattacharya, 2010)Baseline studies can be mobilised to track the progress of adaptation 
actions in multiple sectors over time. In the example of the study in 
Mumbai (Hallegatte, Ranger and Bhattacharya, 2010), the analysis 
includes different climate scenarios and quantification of how 
adaptation could reduce economic loss
prioritiesSpecific methods to ensure an open 
definition of multiple priorities and 
contrasting values that will inform the 
planning processParticipatory housing upgrading 
through the Baan Mankong Program 
in Bangkok (Thailand) (Berquist, 
Daniere and Drummond, 2015)Participatory planning can help navigate which action to take to 
build resilience and, at the same time address prioritised social

concerns (Cloutier et al., 2015). As with all participatory processes, 
issues of recognition, access/inclusion and potential capture of the 
process by actors in power must be considered
PlanningProfilesDevelop a common understanding of 
how different sectors interact with 
adaptation and the governance capacityNew York City Panel on Climate 
Change 2019 Report (Nycpcc, 2019)As with baseline studies, the development of profiles can inform 
plans for adaptation action, which considers social priorities and 
synergies across various sectors. Multiple forms of knowledge should 
be considered in the development of profiles (Codjoe, Owusu and 
Burkett, 2014)
Risk 
assessmentThis includes a range of instruments to 
evaluate the impact of riskClimate risk assessment for Buenos 
Aires, conducted by the World Bank 
(Mehrota et al., 2009)Risk assessments can be a useful starting point for adaptation. 
However, assessments do not directly prescribe adaptation options

but must be seen as the basis for debate (Yuen, Jovicich and Preston, 
2013). A common challenge is a lack of data at the city level 
(Maragno, Dalla Fontana and Musco, 2020; Cloutier et al. , 2015)
Impact 
toolsTools such as strategic impact assessments 
or sustainability assessments provide a 
means to assess the impact of specific 
policies and programmes concerning 
adaptive capacityEconomic Impact Assessment
of Climate Change in Key
Sectors in Nepal (Government of 
Nepal, 2014)Embedding climate risks into impact assessment tools (either 
mandatory or voluntary) builds resilience by integrating climate 
objectives into plans and specific projects (Richardson and Otero, 
2012), and they are seen as a legitimate tool in many contexts 
(Runhaar, 2016)
Monitoring 
systems and 
indicatorsSystems to take measurements at 
regular intervals to specify progress 
against objectives and revise the 
planning processClimate Change Adaptation Indicators

for London (London climate change 
partnership, 2018)Monitoring systems are essential to make sure that formal objectives 
are met. However, many urban climate adaptations do not have 
monitoring and evaluation components (Woodruff and Stults, 2016) 
and there is no standard set of indicators to monitor adaptation or 
resilience (Brown, Shaker and Das, 2018; Ford and Berrang-Ford, 2016)
Man-
agementBudgets and 
auditsMethods for the periodic revision of 
adaptation plans and policiesHelsinki metropolitan area climate 
change adaptation monitoring 
strategy (HSY , 2018)As with monitoring, budgets and audits can be incorporated into the 
adaptation planning process to ensure reflexivity and accountability. 
Low levels of implementation and monitoring of adaptation plans 
suggest that the uptake may be low (although the evidence is limited)

6
971Cities, Settlements and Key Infrastructure  Chapter 6
insufficient political will (that is lack of prioritisation of the issue and 
inadequate allocation of resources including staffing and finance) and 
lack of inclusive, coordinated leadership, it can be difficult to overcome 
inaction, generating a policy action gap.
Multiple actors contribute to deliver climate change adaptation (Chan 
et al., 2015a; Bäckstrand et al., 2017). There are also multiple scales 
of action, from the provision of local services to large infrastructures 
of national or even international significance. Figure 6.5 provides an 
insight into the challenges that shape the policy action gap and a 
range of strategies that can help bridge policy action gaps. Effective 
adaptation governance will depend on the compound impact of the 
actions of multiple agents operating at different scales (medium 
confidence, medium agreement) (Di Giulio et  al., 2019; Hale et  al., 
2021; Zwierzchowska et al., 2019).

6.4.3.1 Delivers on the Needs of the Most Vulnerable
Success in urban adaptation is most often understood as requiring 
measurable outcomes and evaluation (see also Section 6.4.6). However, 
many adaptation outcomes are not measurable (medium evidence, 
medium agreement) (Béné et  al., 2018). Adaptation action solely 
focused on action tends to ignore areas in the city for which there is no 
existing data even though actions in these areas may play an essential 
role in shaping resilience and its limits. Informal settlements and 
informal economies, which are integral in managing urban resources 
for effective climate adaptation, are not routinely included in formal 
urban and national monitoring(Guibrunet and Castán Broto, 2016). The 
resulting understanding and monitoring of city needs, capacities and 
actions that feed into policy is incomplete. The innovation, as well as 
particular concerns and capacities of the informal sector, which is often

highly gendered, are not always measured (Brown and McGranahan, 
2016). An emphasis on measurable adaptation outcomes may lead 
to prioritising techno-economic measures to adaptation at the local 
level. Technocratic approaches to environmental policy continue to 
shape local sustainability politics (Bulkeley, 2015). The deployment 
of such technocratic approaches at the local scale is detrimental for 
democratic and collaborative practices (Metzger and Lindblad, 2020). 
For example, while China has received praise in terms of delivering 
urban policies that put climate change at its core, thus suggesting its 
role providing leadership in climate change debates (Liu et al., 2014; 
Wang and He, 2015; Fu and Zhang, 2017), other analyses suggest that 
processes of planning should take greater account of certain groups 
and interests (Westman and Broto, 2018). Urban sustainability policy 
may, as a result, fail to deliver collaborative social and environmental

objectives, and this is maladaptive in terms of CRD.
6.4.3.2 Moves from Mainstreaming to Transformative 
Two forms of mainstreaming are usually found in urban policy: 
incorporating climate adaptation into different sectors or incorporating 
climate adaptation in holistic sustainability or resilience plans, linking 
climate adaptation objectives with other social and development 
objectives (Reckien et  al., 2019; Fainstein, 2018). The integration 
of climate adaptation in local policies in cities and settlements has 
often been seen as maintaining business-as-usual and not always aligned with transformative efforts to address structural drivers of 
vulnerability (high confidence). For example, mainstream actions 
that seek to advance other development objectives, as explained 
above, may reduce adaptation to ‘low-hanging fruits’, which may 
maintain business-as-usual practices without any fundamental 
transformation of the social, institutional and economic systems that

drive vulnerabilities (Aylett, 2014). However, as explained above, 
mainstreaming can also generate wider processes of institutional 
change (Section 6.4.2). Mainstream strategies may help to demonstrate 
how policy and frameworks can produce practical outcomes on the 
ground (Biesbroek and Delaney, 2020). However, previous experiences 
in other sectors, such as gender mainstreaming, have shown the 
limitations of the mainstreaming approach, particularly in terms of 
addressing the structural drivers of inequality and vulnerability, and 
in achieving justice for those who suffer most (Moser, 2017). Local 
governments in particular, can link mainstreaming efforts with specific 
strategies that support justice in adaptation, including redistribution 
efforts to address vulnerabilities (see Section  6.3.2), representation 
in local institution and deliberative processes, and recognition of the 
conditions for self-realisation, including personal and collective safety

(Agyeman et al., 2016; Castán Broto and Westman, 2017; Castán Broto 
and Westman, 2019; Hess and McKane, 2021).
6.4.3.3 Facilitates Coordination Across Separate Actors and 
Interests
Coordination of adaptation policy goals cuts across cities to integrate 
them into international processes of climate policy formulation; 
coordination in cities produces effective collective outcomes, 
cementation of common standards and methodologies for climate 
action (e.g., emission inventories) (high agreement, medium evidence) 
(Gordon and Johnson, 2017; Hsu and Rauber, 2021). A collective 
global response has become a significant concern in international 
climate policy (Chan et al., 2015a). The UNFCCC has adopted a role as 
an orchestrator, including providing framework for city governments 
(Bäckstrand and Kuyper, 2017). Within cities, coordination can arise 
from active programming; for example, in Rotterdam and New York 
City, local authorities adopted long-term objectives and conditions for

action, bringing together a multiplicity of actors across sectors to orient 
contributions, share knowledge and coordinate actions (Hölscher 
et  al., 2019). Where national politics is supportive, coordination 
between city and national government is an asset (Chan and Amling, 
2019; Inch, 2019). The use of social media and digital mechanisms for 
coordination with public interest is ambiguous: in China, Weibo has 
facilitated an expansion of public engagement, although it remains top 
down and dominated by a few influencial actors (Liu and Zhao, 2017; 
Yang and Stoddart, 2021). The pilot project #OurChangingClimate 
is one example of engaging youth with an understanding of their 
communities and their resilience or vulnerability to climate change 
(Napawan, Simpson and Snyder, 2017).
6.4.3.4 Enables the Co-production of Adaptation Strategies 
with Citizens
Co-production can advance urban sustainability and social justice in

cities and settlements to provide infrastructure adapted to the human 
scale and advancing SDGs (medium confidence) (McGranahan, 2015;

6
972Chapter 6 Cities, Settlements and Key Infrastructure
McGranahan and Mitlin, 2016; Chowdhury, Jahan and Rahman, 
2017; Moretto and Ranzato, 2017; Nastiti et al., 2017). Co-production 
involves the active involvement of citizens and citizens’ organisation 
in iterative public service planning and delivery, and has become 
increasingly central in climate change responses alongside other 
bottom-up, community-led strategies (Bremer et al., 2019; Vasconcelos, 
Santos and Pacheco, 2013).
Co-production builds on public participation that brings together 
diverse sets of citizen interests, values and ideas to inform change and 
solve problems relating to a collective adaptation challenge (Archer 
et al., 2014; Bisaro, Roggero and Villamayor-Tomas, 2018; Sarzynski, 
2015), and is increasingly important in environmental policy more 
widely (McGranahan, 2015; Moretto and Ranzato, 2017). For example, 
Addresses inequalities 
through intersectional 
perspectives
Facilitates 
coordination

across separate 
actors and 
interestsEnables the 
coproduction of 
with citizens
Moves from mainstreaming 
to transformative adaptationSupports visionary 
and imaginative 
designDelivers on the 
needs of the  
most vulnerable
Place-boundActor-ledMulti-level
Multi-actorPlace-bound
Multi-actor
Multi-level
Actor-ledPolicy
action gapSolution spaces for the policy action gap
Figure 6.5 |  Solution spaces for the policy action gap. The categories in the outer circle represent the tension that shape the policy action gap. On the one hand, there is 
a tension between the need to deliver action at scale (multi-level) and the need to mobilise the capacities in a given place (place-bound). On the other hand, there is a tension 
between the need to facilitate collaborations among multiple actors (multi-actor) and the fundamental impact that leadership can have in actor-led initiatives (actor-led). These two

tensions interact creating different possibilities for transformative adaptation. The inner ring represents different areas of intervention that configure the solution space to tackle the 
policy action gap and that bridge these two tensions.
in three cities across the Czech Republic, stakeholder participation 
exercises were used to prioritise climate change risks, provide impetus 
and opportunity for knowledge co-production, and support adaptation 
planning (Krkoška Lorencová et  al., 2018). In municipalities in 
Malaysia, stakeholders and citizens are active in the adaptation policy 
cycle (Palermo and Hernandez, 2020). In Quebec, Canada, citizens 
collaborated with the municipal authority to bring together climate 
science and ‘ordinary’ urban management and design solutions 
(Cloutier et  al., 2015). Service co-production enables integrating 
multiple actors in the management and delivery of public services 
(Pestoff and Brandsen, 2013; Pestoff, Brandsen and Verschuere, 2013).

Civil society-driven, co-productive approaches can pioneer new forms 
of institutional relations and practices filling gaps where the public 
sector is absent or retreating (Frantzeskaki et al., 2016).

6
973Cities, Settlements and Key Infrastructure  Chapter 6
A co-production approach to climate change governance addresses 
the increasing public interest on climate change (Davies, Broto and 
Hügel, 2021). Youth movements such as Forum for Future have joined 
forces with other environmental and Indigenous organisations to lobby 
governments and institutions to action (Kenis, 2021; Fisher and Nasrin, 
2021; Davies and Hügel, 2021; Hayward, 2021). These movements have 
built momentum moving local governments and other institutions to 
declare a climate emergency and have supported the creation of new 
forums where climate change can be addressed collectively, such as 
citizens’ assemblies. In the UK, for example, initial scepticism has led to 
the proliferation of citizen-centric Climate Assemblies at the local level 
(Sandover, Moseley and Devine-Wright, 2021).
Cooperative governance models provide insights for designing forms

of participatory and collaborative planning through which communities 
and state actors can identify concrete actions and resources to improve 
services and mitigate structural vulnerabilities to disasters (Castán 
Broto et al., 2015). Experiences of co-production of sanitation services 
show how co-production may improve outcomes, while at the same 
time opening up avenues for grassroots organisations to claim political 
influence (McGranahan and Mitlin, 2016). Co-production may change 
institutions in response to external interventions (Das, 2016). Although 
there are drawbacks in terms of the extent to which co-production 
can be used to legitimise unfair interventions within a given context, 
co-production may also be a tool for improving the accountability of 
dominant groups to vulnerable sectors of the population (Nastiti et al., 
2017). There are limitations to co-production. The city of Barcelona, 
Spain, used co-production methodologies to develop the Barcelona

Climate Plan. However, policymakers and civil servants were reluctant 
to use lay knowledge from participants and political deadlines 
constrained the time dedicated to deliberation (Satorras et al., 2020).
6.4.3.5 Addresses Inequalities through Intersectional 
Perspectives
Inclusive and sustainable adaptation can address the causes of 
systemic vulnerability (medium evidence, high agreement). This points 
to the fundamental requirements of adaptation action in line with the 
Universal Declaration of Human Rights. Climate justice theories draw 
on the environmental justice movement experiences at the local level 
(Bickerstaff, 2012; Bickerstaff, Walker and Bulkeley, 2013; Perez et al. , 
2015; Hall, Hards and Bulkeley, 2013). Slogans such as ‘leave no one 
behind’ embedded in international policy for cities and settlements 
recognise the connection between systems of oppression and exclusion 
that reproduce and perpetuate urban inequality and the delivery of

urban services and security (Kabeer, 2016; Stuart and Woodroffe, 2016).
Intersectional strategies of action seek to consider the multiple forms 
of structural oppression experienced at the local level (Grunenfelder 
and Schurr, 2015) and, in the context of adaptation, explain how they 
produce or exacerbate vulnerabilities. For example, intersectionality 
ties with the idea of how multiple deprivations shape access to 
services (from sanitation to health and education) and the exposition 
to environmental risks (Sicotte, 2014; Lau and Scales, 2016; Van Aelst 
and Holvoet, 2016; Lievanos and Horne, 2017; Raza, 2017; Yon and 
Nadimpalli, 2017; European Environment Agency, 2020) (see Box 6.6 
on the participation of women in local decision making bodies). For example, fisherwomen in the western coast of India rely on a complex 
arrangement of relationships around categories of class, caste and 
gender that shapes their possibilities to draw political resources

to maintain their livelihoods and, hence, influence the dynamics of 
transformation (Thara, 2016). Intersectionality is central to build 
resilience across communities, rather than in particular areas (Khosla 
and Masaud, 2010; Reckien et  al., 2017). Including intersectionality 
deliberately in partnerships with communities can empower socially 
excluded groups and highlight justice issues while aligning agendas 
with local development priorities (Castán Broto et al., 2015a). Despite 
the high confidence on the growing importance of intersectionality 
concerns in the delivery of just environmental policies, there is limited 
evidence of its explicit inclusion in adaptation policies.
Box 6.6 | Invisible Women: Lack of Women’s 
Participation in Urban Authorities
Women are under-represented internationally in governance 
structures (Prihatini, 2019; Gonzalez-Eiras and Sanz, 2018; 
Rashkova and Zankina, 2017; Koyuncu and Sumbas). This

situation is reflected in urban authorities where participation 
by those who identify as women is low (Williams, Devika and 
Aandahl, 2015; Kivoi, 2014). Das (2014) reports deep-rooted 
economic inequalities are barriers for women’s participation 
in Indore, India, and that women’s collective empowerment 
could increase their bargaining power within households 
as well as in the community and state. Kivoi (2014) draws a 
similar conclusion presenting experiences from Kenya. The big 
question is how to make women more visible in the urban 
governance process?
What are the barriers women face and how do we increase 
their participation so that urban governance become more 
inclusive? Escalante and Valdivia (2015) show the participatory 
tools that can be used at different stages of planning for 
women’s empowerment using bottom-up planning models. 
Using these tools makes planning processes more inclusive. 
Araujo and Tejedo-Romero (2016) show from Spanish local

councils that women’s political representation in municipalities 
has a positive influence on the level of transparency, increasing 
information transparency and reducing information asymmetry. 
In Myanmar, Minoletti (2014) increased levels of women’s 
participation in urban authorities helped to improve the quality 
of governance such as reducing corruption and conflicts, and 
improving service delivery.
People traditionally excluded from climate change governance, such 
as children, are also more likely to have their needs and priorities 
considered in urban planning for adaptation where there are national 
advocacy bodies, for example, Commissions for Future, or Children’s 
commissions (Nordström and Wales, 2019; Watts et  al., 2019; 
Hayward, 2021). An emphasis on procedural justice in decision-making 
has potential to produce transformational outcomes where these are 
defined as significantly reducing inequality (Holland, 2017). In this light,

emerging evidence suggests transformative adaptation is more likely

6
974Chapter 6 Cities, Settlements and Key Infrastructure
to occur if people have the agency to influence decisions and enact 
change (Archer and Dodman, 2015). Cities are also more likely to build 
and develop infrastructure that serves the needs of disadvantaged 
groups when urban climate governance encourages wider community 
participation and inclusion (Ziervogel, 2019a; Hölscher et  al., 2019; 
Anguelovski et al., 2016). This can help to stimulate innovation, shift 
power relations and address diverse needs (Martel and Sutherland, 
2019; Chu, Schenk and Patterson, 2018). Experiments in including 
marginalised groups in adaptation planning are starting to emerge in 
places such as Quito (Ecuador), Lima (Peru), Manizales (Colombia) and 
Surat (India), where disadvantaged youth, informal settlers and other 
vulnerable communities are included in discussions of short-/long-term 
adaptation needs and fair distribution of adaptation resources (Chu,

Anguelovski and Carmin, 2016; Sara, Pfeffer and Baud, 2017; Hardoy 
and Velásquez Barrero, 2014). These processes can also support 
citizens to manage risks as they encounter them in their everyday life 
(Ziervogel et al., 2017).
To respond to urban injustices, attention needs to be paid to both the 
local level and to broader system-wide governance issues (that are 
unpacked further in Section 6.4). At the local level, it is important to 
understand who is most vulnerable to climate risk, which is likely to be 
related to class, race, gender, ability and age (Wilby and Keenan, 2012; 
Ranganathan and Bratman, 2019; Thomas, Cretney and Hayward, 
2019). Factors such as age and levels of ability, as well as those 
pursuing outdoor livelihoods, have a direct link to higher vulnerability 
to heat stress (Conry et al., 2015). In least-developed countries, less 
than 60% of the urban population have access to piped water which

impacts on health and well-being, and emphasises the importance of 
alternative resources for these households (World Health Organization, 
Nations and Fund, 2017).6.4.3.6 Supports Visionary and Imaginative Design
The failure to deliver inclusive and sustainable adaptation contributes 
to a collective inability to mobilise the power of creative community 
vision (medium evidence, high agreement). Urban design plays a 
central role to support creative adaptation strategies (Box 6.7). Much 
adaptation action repeats previous experiences. However, the potential 
for building resilience to deliver adaptation, especially transformative 
adaptation, requires an articulation of collective visions of the future 
and the imagination of new or alternative urban futures (Glaas et al., 
2018), including through design and deliberate engagement with 
cultural artefacts, technologies and performances (Jordan, 2020). Social 
movements can be powerful sources of such alternative visions of the

future, as exemplified by recent Youth Climate Strikes and Extinction 
Rebellion (limited evidence, medium agreement). Community 
protest such as Youth Climate Strikes have influenced urban climate 
policy agendas including the declaration of climate emergency in 
municipalities worldwide, fostering a new debate on climate change, 
although their impact on local policy is ambiguous (Davidson et al., 
2020; Thomas, Cretney and Hayward, 2019; Prendergast et al., 2021; 
Ruiz Campillo, Castán Broto and Westman, 2020). Social movements 
on climate mitigation, such as the Transition Movement and Transition 
Towns (Feola and Nunes, 2014), and school strikes may serve as an 
example for mobilisations more specifically about climate adaptation 
and the way new, networked, grassroots citizen activism and 
community organisations can encourage urban institutional change 
(Gunningham, 2019; Jordan et al., 2018; Wahlström et al., 2019). Other

strategies such as cultural production and exhibitions may also have 
an impact (Stripple, Nikoleris and Hildingsson, 2021).Box 6.7 | The Role of Urban Design in Local Adaptation
Since AR5, there has been a growing literature about the role of urban design, creating new opportunities for both incremental and 
transformative adaptive responses to climate change (medium evidence, high agreement). For example, some of these creative design 
approaches compliment and extend regulatory and land use planning approaches such as form-based codes and established certifications 
such as Leadership in Energy and Environmental Design–Neighbourhood Design (LEED-ND) (Garde, 2018; Garde and Hoff, 2017) and the 
USA’s Sustainable Sites Initiative (SITES) (Valente, 2014). Emphasis on sufficiency has also influenced urban design, for example, with the 
mobilisation of ‘doughnut’ economics that emphasise both a social foundation and an environmental ceiling, for example Amsterdam

(Raworth, 2017). However, such cases are rare, substantial public investment is often required (high confidence, high agreement) (see 
also Section  6.4.7 on finance and insurance). Other approaches underscore innovation and creativity, at the essence of which are 
context-specific interventions that draw on a compendium of urban design principles such as indeterminacy (to accommodate climate 
uncertainty), polyvalency and diversity, and harmony with nature (Dhar and Khirfan, 2017). Creative interventions include the daylighting 
of buried streams to create climate adaptive public realms (Khirfan et al., 2020; Khirfan, Mohtat and Peck, 2020). For example, the 
demolition of a major expressway and the restoration of the Cheonggyecheon Stream reorganised downtown Seoul, South Korea, and 
significantly contributed to climate change adaptation through stormwater management and reducing the urban heat island effect

(Kim and Jung, 2019). Biomimicry and ecological infrastructure are design features that governance bodies can use to reshape space 
and contribute to place making (Santos Nouri and Costa, 2017; Prior et al., 2018). For example, urban metabolism and local ecological 
knowledge has constituted the essence of urban design interventions on the Island of Tobago in ways that capitalise on the contiguous 
relationship between ecosystems (e.g., the mangrove forest) and human actions (rainwater harvesting and grey water management) 
(Khirfan and Zhang, 2016). While lack of funding or design capacity, restrictive planning regulations, inequality and competing urban 
agendas can create barriers for the implementation of creative design solutions. Transition architecture movements are also driving local 
urban adaptation experiments and exploring ways local learning can be scaled up (Tubridy, 2020; Irwin, 2019).

6
975Cities, Settlements and Key Infrastructure  Chapter 6
6.4.4 Limits of Adaptation Capacity at the Institutional 
Level
In delivering adaptation in cities, settlements and infrastructure, 
however, there is a need to understand and measure the adaptive 
capacity and limits to manage future risks in communities, institutions 
and organisations (Filho et al., 2019). However efforts to track urban 
adaptation lack consistent methods, metrics and data gathering 
(Olazabal et al., 2019b). The scale of complex, cascading challenges, 
limited finance and governance capacity, combined with the impacts 
of growing social inequality and sustainable development priorities 
can result in both soft and hard limits on cities government’s capacity 
to adapt to climate change (Chanza, 2018; Sanchez Rodriguez, 
Ürge-Vorsatz and Barau, 2018; Lehmann et al., 2015; Di Giulio et al., 
2018). Hard limits to adaptation are identified when it is unfeasible

to avoid severe risks, while soft limits exist when technological and 
socioeconomic options are not immediately deployable (IPCC, 2014). 
In urban contexts, soft limits may become hard limits when large 
numbers of people are unable to avoid severe climate-related risks 
of loss and damage (Mechler et  al., 2020). Climate change-related 
loss and damage that are intangible also require more caution in 
assessment processes (Roberts and Pelling, 2018; Andrei et al., 2015; 
Barnett et  al., 2016; Thomas and Benjamin, 2018). Incorporating 
Indigenous knowledge can identify people-oriented and place-specific 
scenarios, leading to development of urban adaptation policies that 
foster identity, dignity, self-determination and better collective decision 
making/capacity to act (McShane, 2017; Preston, 2017), and are also 
sensitive to the local context and limits of community adaptation 
(Makondo and Thomas, 2018).
Urban transformations represent forms of adaptation that challenge

the principles in which a society is established (Pelling, O’Brien and 
Matyas, 2015) and can be deployed to go beyond the existing limits 
of development justice and climate change adaptation capacity. While 
not all adaptation will be transformative, transformative capacities 
support both ongoing adaptation efforts and the broader systemic 
change processes that align adaptation efforts with decarbonisation 
requirements and the delivery of SDGs. ‘Urban transformative capacity’ 
focuses on understanding what elements of a system to respond to 
external changing conditions in a manner that transforms the system 
toward a more sustainable state (Ziervogel, Cowen and Ziniades, 
2016). The capacities required to deliver adaptation action in cities and 
settlements are ‘transformative capacities’, because they move away 
from thinking of adaptation as an adjustment to a changing external 
environment to think instead of it as a reconfiguration of infrastructures

and institutions to build resilience in the surrounding environment 
(Pelling, 2010; Matyas and Pelling, 2015). Reflective and iterative 
learning is integral to fostering transformative capacity (c.f. Luederitz 
et al., 2017). Transformative capacity extends across multiple agency 
levels or geographical locations, as well as various domains (Wilson 
et al., 2013; Olsson, Bodin and Folke, 2010; Keeler et al., 2019b). The 
components of transformative capacity in cities and settlements can be 
grouped into three categories (see Table 6.9): (1) agency and forms of 
interaction, (2) development processes and (3) relational dimensions 
(Wolfram, 2016). Alongside different forms of technical expertise, there 
is a need to broaden the interventions of disadvantaged populations in 
urban sustainability (Wolfram, Borgström and Farrelly, 2019).Table 6.9 presents a defined framework of ideas that local institutions, 
mostly local governments, can put into practice to improve their

adaptive capacity. Enabling transformative capacity requires novel 
governance arrangements based on broad participation, a diversity of 
actor networks, socially embedded leadership and empowerment of 
communities, alongside an understanding of the system dynamics, which 
refers to system awareness, collective visions, practical experimentation, 
reflexivity, capacity building, institutional mainstreaming and the 
multiple levels of agency or scales (Ziervogel, Cowen and Ziniades, 
2016; Ziervogel, 2019a; Wolfram, 2019; Hölscher and Frantzeskaki, 
2020; Castán Broto et al., 2018). Many of the transformative capacity 
components are already visible in local adaptation actions, but many 
efforts emphasise one element at the expense of others without 
delivering a systemic perspective. In particular, measures to facilitate 
the empowerment of communities, reflexivity and social learning are 
rare but often point toward heightened capacities for transformative,

alongside incremental, adaptation (Castán Broto et  al., 2018). 
Transformative capacity frameworks may foster inclusive governance 
to deliver risk management that works for the poor in countries such 
as South Africa (Ziervogel, 2019a).
6.4.5 Financing Adaptation in Cities, Settlements and 
The amount invested in urban adaptation is limited. The Cities Climate 
Finance Leadership Alliance tracked USD 3.7  billion of investments 
in adaptation projects in 2017–2018, of which only 3–5% had an 
urban component (Richmond et  al., 2021). Cities and settlements 
frequently face barriers of inadequate financing for climate adaptation 
and mitigation (Cook and Chu, 2018). Finance barriers interact 
with economic barriers and socioeconomic conflicts and need to be 
considered within an integrated perspective (Hinkel et al., 2018).
Many early leaders in climate adaptation are, therefore, perhaps 
unsurprisingly, political capitals or financial centres in the Global North

with much larger resource envelopes and well-developed fiscal and 
financing capacities (Westerhoff, Keskitalo and Juhola, 2011; Shi, Chu 
and Debats, 2015).
The funding required to deliver climate change adaptation will depend 
on choices made about climate mitigation (IPCC, 2018). Still, the cost 
of adapting to a global temperature increase of 1.5°C will be a fraction 
of the cost of adapting to a global temperature increase exceeding 3oC 
(IPCC, 2019a; IPCC, 2019b; Hoegh-Guldberg et al., 2018). It will also 
depend on selected adaptation options, as they have different capital 
requirements, operating costs and returns on investment (See 6.3). 
Finally, costs depend on financing sources and mechanisms selected.
Broadly, there are two options for adaptation investment: funding, 
direct expenditure in preparation for or response to climate change 
impacts, and financing, the deployment of market-based instruments 
to attract third-party resources to an adaptation action (Keenan,

2018; Banhalmi-Zakar et al., 2016). Using funding can be a lower-cost 
strategy, as there is no third party expecting a return on investment. 
However, using financing can expand the total resources available for 
adaptation (White and Wahba, 2019).

6
976Chapter 6 Cities, Settlements and Key Infrastructure
The choice of funding and financing mechanism is often based on 
implicit economic world views (Keenan, Chu and Peterson, 2019) 
or on the technical support available to sub-national governments, 
such as preparing municipal bonds or contracting for public–private 
partnerships (Bisaro and Hinkel, 2018). The urban finance literature has 
long called for critical interrogation of these choices, as adaptation 
finance has profound justice implications (Khan et al., 2020). However, 
the literature on adaptation investments is limited (Harman, Taylor and 
Lane, 2015; Keenan, Chu and Peterson, 2019). The use of municipal 
debt such as green bonds, for example, intensify the financial and 
environmental risks borne primarily by the poor, the working class or 
people discriminated against because of race, sexual orientation or 
ability (Bigger and Millington, 2019).
The climate imperative has not yet fundamentally changed urban

infrastructure investment (White and Wahba, 2019). Mobilising 
adaptation investment in urban areas continues to depend on 
strengthening public finance capacities (particularly evaluating and 
integrating climate risk into economic decisions) and meeting private 
investors and lenders’ expectations. Climate change creates new 
investment risks and physical risks (Martimort and Straub, 2016), 
and highlights the limitations of current models to account for risk 
and uncertainty when pricing investments (Keenan, 2018). Private investors and lenders do not seem ready to provide adaptation finance 
on significantly easier or cheaper terms than conventional finance 
(White and Wahba, 2019). However, a variety of means for financing 
climate change adaptation in urban areas exist (Table 6.10).
6.4.5.1 Urban Adaptation Financing Gap
Cities and settlements in higher-income countries typically have 
access to funding that could be used to enhance resilience and build

adaptive capacity; this includes both the private resources of individual 
households and firms (which varies significantly within and among 
cities) and public budgets of different government tiers (see Table 6.10).
Depending on fiscal devolution levels within a country, public 
revenues may be collected and managed primarily at the national, 
state, metropolitan or local level. In federal countries, sub-national 
governments collect an average of 49.4% of public revenues, 
compared with only 20.7% in unitary countries (OECD/UCLG, 2019). 
For example, sub-national revenues represent over a quarter of total 
public revenues in Belgium, Canada and Denmark, but less than 5% in 
Greece, Ireland and New Zealand (OECD/UCLG, 2019). The share of the 
national revenue transferred to sub-national governments also varies

significantly among countries: grants and subsidies account for over Table 6.9 |  Components of urban transformative capacity with broader relevance for multiple forms of adaptation (Wolfram, 2016).
Component Manifests in…
Agency and 
interactionInclusive, multiform urban governance (C1)
Participation/inclusiveness (C1.1)Citizens and/or civil society organisations participating directly in planning and/or decision making processes.
Diverse governance modes / Networks (C1.2)Different and various stakeholders working together and building connections between sectors in different 
manners.
Sustained intermediaries and hybridization (C1.3) An intermediary positioned between the stakeholders of a project.
Transformative leadership (C2) Leadership acting as a collaborative driving force in an initiative.
Empowered communities (C3)
Social needs (C3.1)Either analysing or addressing social needs.

Autonomous communities (C3.2) Integrating into the design of the project different aspects of community empowerment.
processesSystem awareness (C4)
Baseline analysis and system(s) awareness (C4.1)Agendas aiming to tackle sustainability challenges after deliberate analysis of urban systems.
Recognition of path dependencies (C4.2) Explicitly tackling systemic barriers to change.
Foresight (C5)
Co-production of knowledge (C5.1)Involvement of various and multiple stakeholders in knowledge production processes.
A collective vision for change (C5.2)An explicit future vision shared among stakeholders as a means for motivating partners and fostering 
commitments.
Alternative scenarios, future pathways (C5.3)Comparative scenarios that evaluate the mutual shaping of social, ecological, economic and technological 
dimensions.

Experimentation with disruptive solutions (C6)The deliberate use of experiments or ideas that seek to challenge the existing landscape of established policies, 
technologies or social practices.
Innovation embedding (C7)
Resources for capacity development (C7.1)Project stakeholders sharing resources for capacity development outside the project to disseminate and multiply 
results.
Mainstreaming transformative action (C7.2) Attempts to generalise the project operation or results beyond the initial context of an application.
Regulatory frameworks (C7.3) A new regulation was established as a result of the project or as part of the project activities.
Relational 
dimensionsReflexivity and social learning (C8) Stakeholders reflecting on learning and capacity building processes.
Working across human agency levels (C9) Project activities contributing to capacity development across human agency levels.

Working across levels and scales (C10) Project activities contributing to building capacity across geographical or political–administrative levels.

6
977Cities, Settlements and Key Infrastructure  Chapter 6
three-quarters of sub-national government revenue in Malta, but less 
than a quarter of sub-national revenue in Iceland (OECD/UCLG, 2019). 
A local government’s capacity to collect revenues is further mediated 
by incomes within a city (which dictates the prospective tax base) and 
the capacity of civil servants to administer taxes, fees and charges. 
The result is that metropolitan and local governments’ budgets vary 
dramatically, across and within countries. For example, per capita 
municipal budgets vary from USD 1114 in Saskatoon and USD 2682 in 
Peterborough (Canada), USD 2635 in Leipzig and USD 3638 in Freiburg 
(Germany), to USD 4907 in Bristol and USD 5612 in Aberdeen (UK) 
(Löffler, 2016).
Revenue streams are often insufficient relative to the scale of 
adaptation requirements. For example, Kano, Nigeria, is a large 
urban area that urgently needs investment in human development

and climate resilience but where a fragmented local government has 
little capacity to finance their climate plans (Mohammed, Hassan and 
Badamasi, 2019). Many local governments are unable to mobilise 
funds for adaptation as they face competing priorities, meaning that resources for resilience must be allocated by higher levels of 
government (Hughes, 2015), which also perceive opportunity costs to 
adaptation investments. Funding from non-state actors is, therefore, 
proving important. For example, in the USA, private foundations and 
non-profit organisations account for 17% and 16%, respectively, of 
adaptation support in urban areas (Carmin, Nadkarni and Rhie, 2012). 
However, tapping into these funding sources raises complex questions 
about accountability and ownership of urban adaptation (Chu, Schenk 
and Patterson, 2018). Land reclamation may foster real estate markets 
and mobilise finance for adaptation, as shown in Germany, the

Netherlands and the Maldives (Bisaro et al., 2019).
City governments need to anticipate climate shocks and stresses, and 
design their operating models and investment plans accordingly to 
ensure financial resilience (Clarvis et al, 2015). Climate risks threaten 
fiscal models, for example, a drought may disrupt water revenues by 
reducing total water consumption and incentivising households and 
firms to invest in independent water storage or supply infrastructure 
(Simpson et al., 2019). Storm surges and sea level rise may threaten Table 6.10 |  Finance instruments to deliver adaptation in urban areas. Source: adapted from Richmond et al. (2021) and UN-Habitat (2016b)
Type of 
financeFinance source Instruments Examples of specific instruments in urban settings
PublicMunicipal government Local revenue generationUtility fees
Open space funds/land value capture
General obligation bonds
Local property, income and sales taxes
State/Provincial government

National governmentGrants, incentives, technical assistance fundsInsurance
Tax advantages
Low-cost project debt
Infrastructure investment funds
Shared taxes
Intergovernmental funding transfers/revenue sharing
Public financeNational development finance institutions (DFIs)
Bilateral DFIs
Multilateral DFIsGrants, project debt (low-cost market rate), 
technical assistance, risk instrumentsRisk mitigation support of PPP
Project level debt
Project preparation facilities and other technical advisory
Insurance
Climate funds Grants, debt, equity, guarantees Dedicated climate fund
PrivateCommercial FIs Project debt and equity (market rate), guaranteesInternal climate risk mitigation
PPP financing
Climate loans
Private equity (PE)/infrastructure funds Project equity (market rate)Direct urban infrastructure investments
Corporate equity investment
Institutional investors Project debt and equity (market rate)Direct urban infrastructure investment
Corporate debt and equity investments

Private insurance InsurancePublic and private risk mitigation
Catastrophe bonds
Parametric insurance
Corporate actorsBalance sheet financing and project equity 
(market rate)Internal risk mitigation
Leasing
PPP
Household Balance sheet financing Internal climate risk mitigation
Non-profits, philanthropies and foundations Grants, technical assistance, donationsMicrofinance
Impact investment
Communities Grants and collective supportRisk sharing
Upgrading funds
Community development funds
Crowdfunding

6
978Chapter 6 Cities, Settlements and Key Infrastructure
sunk investments in revenue-generating infrastructures such as toll 
roads or electricity generation and transmission systems. .
6.4.5.2 Barriers to Adaptation Investments
Common sources of adaptation finance might include donor 
agencies including the Green Climate Fund, sovereign funds (e.g., the 
Bangladesh Climate Change Resilience Fund) and private finance from 
commercial banks, investment companies, pension funds and insurance 
companies (Floater et al., 2017). These capital sources have different 
risk–return expectations and investment horizons, so they will suit 
different types and stages of projects. Many sub-national governments 
in the Global North have access to well-developed domestic, if not 
global, capital markets to raise and steer finance for urban investment 
(Banhalmi-Zakar et al., 2016).
However, investments in ex ante urban climate adaptation may prove

less attractive to these financiers than other opportunities because of 
their long maturities and high risk (Keenan, Chu and Peterson, 2019) 
(see also Table  6.11). Many generate economic returns primarily 
through avoided losses from climate impacts, which are difficult 
to measure and are, in any case, more attractive to funders than 
financiers (Kaufman, 2014). Ex post, insurance already plays a critical 
role in protecting urban households, firms and other stakeholders 
from the full economic costs of high-severity, low-frequency events 
by sharing risk over time and space. Insurance can also be designed to 
incentivise risk-reducing behaviours and investments (Banhalmi-Zakar 
et  al., 2016; Paddam and Wong, 2017). Some researchers suggest 
that, in urban environments, insurance practices are helping to 
establish adaptation and risk as a new area of public health and 
public protection. For example, local governments are using new risk

transfer instruments, such as re-insurance and catastrophe bonds, to 
fund investments in resilience projects and disaster recovery (Collier 
and Cox, 2021). However, the commercial feasibility of private sector 
insurance depends on more robust estimates of current and future 
risks, and premiums commensurate with the ability and willingness of 
consumers to pay. Therefore, ex ante investments must complement 
insurance schemes to improve climate modelling and reduce climate 
risk (Surminski, Bouwer and Linnerooth-Bayer, 2016). The private sector 
also faces practical barriers to invest in adaptation.National governments typically determine the fiscal transfers that 
sub-national governments receive and the taxes, fees and charges 
they permit to collect (see for example CBO, 2016). Local governments 
may strengthen their own source revenue collection and management 
capacities to better exploit these funding streams and improve their

balance sheets, but their total budget will be limited to these funding 
sources (Ahmad et  al., 2019). The amount of local public funding 
available for urban adaptation depends on the relationships across 
different government levels.
Similarly, mobilising private finance for urban adaptation projects 
demands robust institutional, fiscal and regulatory frameworks, which 
are typically national authorities’ responsibility. For local governments 
to access private finance for adaptation may require national (or in 
federal countries, state) governments to reform policies and rules 
governing municipal borrowing, public–private partnerships, land 
value capture instruments and other financing mechanisms (Ware 
and Banhalmi-Zakar, 2017). Such fiscal reforms tap into fundamental 
political and policy issues, such as local governments’ autonomy or the 
tariff-setting powers of national ministries (Gorelick, 2018; White and 
Wahba, 2019).

Access to private finance can support infrastructure development 
through private provisioning, public–private partnerships (PPP) and 
public debt arrangements (high confidence) (see also Section 6.4.1.2). 
Private provisioning attracts coastal adaptation investment when 
returns are high (e.g., when there is a real estate market associated 
with it) (Bisaro and Hinkel, 2018). Public–private partnerships attract 
investments from dredging and construction companies that involve 
a large share of operational costs (Bisaro and Hinkel, 2018). Public 
debt instruments appear less successful in supporting investment 
in adaptation infrastructure. Real estate firms focus on adaptation 
actions if they perceive climate change impacts such as flooding may 
impact their activity, mostly focusing on adaptation action as a means 
to gain competitive advantage (Teicher, 2018).
There have been numerous attempts to innovate in climate finance,

for example, mobilising community and cooperative forms of finance, 
or crowdfunding, which have already proven effective in the context 
of mitigation (De Broeck, 2018). A well-studied instrument in urban Table 6.11 |  Barriers to finance adaptation in urban areas (Richmond et al., 2021)
Barrier application to urban adaptation
Barriers to adaptation finance
Poor policy environmentMunicipal policy environment lacks conditions supportive to private adaptation investment (e.g., lack of requirements that private 
sector organisations operating in cities implement climate risk mitigation strategies or invest in systemic resilience).
Poor institutional environmentLegal and regulatory infrastructure in the city lacks clarity of purpose toward addressing urban climate risks (e.g., no limitations on 
development in high climate risk areas).
Poor market environmentMarket environment is unsupportive toward adaptation investment (e.g., lack of creditworthy partner municipalities for private

sector engagement).
High cost of projects and unknown value addThe value or benefit of the technology is uncertain; private sector actors do not sufficiently consider climate risk in decisions; 
upfront costs of technology are high.
Lack of technical capacityProspective users of technology do not have technical capacity to implement (e.g., limited or siloed expertise in implementing 
resilient urban infrastructure solutions).
Limitations of private insuranceInsurance has to date largely not been engaged in cities to efficiently transfer risk or incentivise adaptive action and the private 
insurance industry is facing considerable risk associated with the accelerating impacts of climate change in.

6
979Cities, Settlements and Key Infrastructure  Chapter 6
environments is land-value capture. Land-value capture refers to 
communities’ ability to capture the benefit of increased land values 
that result from public investment or other government actions 
(Germán and Bernstein, 2020). There is considerable potential to 
mobilise land-value capture for adaptation (limited evidence, medium 
agreement), but its potential remains unexplored (Dunning and 
Lord, 2020). While there are numerous examples of the mobilisation of land-value capture to finance sustainable development action (Li 
and Love, 2019; Wang, Samsura and van der Krabben, 2019), there 
is limited evidence of its use in climate adaptation (see Case Study 
6.2). These innovations are particularly important in contexts where 
resources are very constrained, such as in the financing of adaptation 
in African cities (See Box 6.7).Box 6.8 | Challenges to Investment in Adaptation in African Cities

In Africa, new investment in institutions can support other enabling conditions for climate-resilient urban development (Robins, 2018). 
While several studies reveal the net economic benefit of climate-resilient, low-carbon African cities (Global Commission on Economy and 
Climate, 2017), structural impediments remain to the mobilisation of investment for the types of public good infrastructure that would 
unlock this benefit (Dodman et al., 2017).
Since the 1960s, gross capital formation (sometimes called Gross domestic investment) has been less than 22% in Africa, whilst in East 
Asian countries, it has risen to 42% (OECD, 2016). Africa faces an estimated 40% infrastructure financing gap, but this gap is almost 
certainly higher in the continent’s rapidly growing cities (Baker and McKenzie, 2015). Relative poverty, weak or absent local fiscal 
systems, and contested tenure that prevents land being used as collateral, have restricted investment in African cities (Berrisford, Cirolia

and Palmer, 2018; Dodman et al., 2017). Sub-Saharan African countries are reaching the 40% urban threshold at national per capita 
incomes of around USD 1000 per annum, significantly poorer than South-East Asian and Latin American cities at the same level of 
urbanisation (Freire, Lall and Leipziger, 2014). Absolute poverty, in conjunction with weak revenue collection and low levels of investment, 
render conventional infrastructure finance difficult (Smolka, 2013; Global Commission on Economy and Climate, 2017; Berrisford, Cirolia 
and Palmer, 2018; Cirolia and Mizes, 2019). Sprawled urban development in Africa might make the provision of public services both more 
energy intensive and three times more expensive than high-density developments (Collier and Venables, 2016).
Data on private finance in African cities are inadequate (OECD, 2017), but all of Africa secured just 3.5% (USD 46 billion) of global foreign

direct investment (FDI), despite a 10.9% increase in 2018 (UNCTAD, 2019). Mining and the extraction and processing of fossil fuels 
accounted for almost a third of greenfield FDI in Africa in 2018 (UNCTAD, 2019). The FDI secured by cities has tended to serve an urban 
elite and has been used to build shopping malls, housing settlements and airlines (Watson, 2015). It is also unevenly distributed across 
the continent and within cities. Five countries; Egypt, South Africa, Congo, Morocco and Ethiopia, accounted for more than half the total 
FDI in 2018 (UNCTAD, 2019), leaving large parts of Africa’s growing cities described by financiers as ‘high risk’ and their citizens deemed 
‘unbankable’ (UCLG, 2016).
Private financiers have begun entering public–private partnerships with African cities, often supported by bilateral agreements between 
the respective countries, including the growing number of Asian and Middle-Eastern countries contributing to infrastructure in African

cities (Cirolia and Rode, 2019). In the absence of enforceable spatial plans and strong urban governance, the risk remains that individual 
investment projects that are completed will aggregate to create urban systems that are at risk from climate change through the locking-
in of inequality, urban sprawl, flooding and greenhouse gas emissions (Dodman et al., 2017; Wachsmuth, Cohen and Angelo, 2016). 
These risks will constitute a future burden for asset owners, financiers and insurers, and cause a progressive hemorrhaging of economic 
opportunities in Africa’s urban centres (UCLG, 2016).
Securing climate finance for urban development is contingent upon robust multi-level governance arrangements (Tait and Euston-Brown, 
2017; OECD/UN-Habitat, 2018). Such investments are needed for cities that do not yet have the balance sheets or rate-paying citizens 
required to enter financial markets on favourable terms. Similarly, Central Banks have a crucial role in managing the transition risks

within cities and limiting the systemic impact of stranded urban assets due to technology shifts or sea level rise (Safarzyńska and van 
den Bergh, 2017).
New energy, water and sanitation technologies alter the public good nature of urban services and offer novel opportunities for private 
sector financiers and blended finance. Still, financial sector innovation remains necessary if technological innovation is to be scaled 
(Cities Climate Finance Leadership Alliance, 2015; European Environment Agency, 2020). UNEP has cited anecdotal evidence of a 
‘quiet revolution’ toward a more developmental and sustainable global finance sector, in part due to global environmental, social and 
governance requirements, and industry initiatives within the financial and insurance sectors (UNEP , 2015). Scope remains to strengthen 
Development Finance Institutions programmes, such as the World Bank’s City Creditworthiness Programme and the activities of China’s

6
980Chapter 6 Cities, Settlements and Key Infrastructure
Corruption in urban adaptation and disaster risk management finance 
is a considerable but little researched challenge observed from all world 
regions (Sanderson et al. , 2021). Corruption generates maladaptation, 
increasing risk, for example where infrastructure is constructed with 
faulty design, substandard materials and inadequate maintenance 
(Kabir et al. , 2021). More widely, corruption increases vulnerability 
and reduces capacity by damaging the body politic, distorting markets 
and reducing economic growth (Alexander, 2017). The construction 
and infrastructure industries are repeatedly identified as sources of 
corruption (GIACC, 2020; Chan and Owusu, 2017; Sanderson et al. , 
2021). Corruption and misuse of climate finance is exacerbated 
by limited public access to information, political considerations in 
finance decision making and lack of accountability for decisions and

actions (Kabir et al. , 2021). In construction, Owusu et al. (2019) found 
causes included too-close relationships, poor professional ethical 
standards, negative industrial and working conditions, negative 
role models and inadequate sanctions throughout the phases of 
construction. Post-disaster response and reconstruction, and periods 
of surge funding following international or national policy priorities 
are especially vulnerable to corruption, with increased funding and 
pressure to lower norms of financial management (Imperiale and 
Vanclay, 2021). Mixed delivery mechanisms have been shown to 
reduce corruption, for example where civil society organisations 
are involved in project approval stages, although there is also a risk 
that civil society organisations will themselves become entangled 
in corruption. International donors have a role to play in working 
with government and civil society to promote wider scrutiny and

transparency of financing processes and project delivery through 
promoting media and press freedom and legislation for access to 
information to reduce corruption by enhancing transparency and 
accountability (Kabir et al. , 2021).
Expanding the resource envelope available for adaptation investment 
is often beyond the authority or competency of city governments. 
Sovereign and state governments have critical roles to play in 
providing funding or securing finance for adaptation investments. Such 
a role is particularly important where the impacts of climate change 
are distributed inequitably across a country, so that the costs borne by 
a city may exceed local budgets.
6.4.6 Monitoring and Evaluation Frameworks for 
Adaptation Used in Cities, Settlements and 
Urban adaptation plans can focus attention on the needs of 
marginalised or vulnerable communities including the elderly, children 
and the disabled (Dahiya and Das, 2020; Yang, Lee and Juhola, 2021).

However, monitoring and evaluation (M&E) frameworks for adaptation 
are far from being fully developed and operationalised both in theory 
and in practice for cities, settlements and infrastructures. See also 
Section  17.5 for an assessment of monitoring and evaluation in 
climate adaptation. Despite significant experience on the application 
in other sectors (e.g., health, water, industry or business) or with other 
climate change objectives (e.g., emissions reduction), the assessment 
of adaptation efforts has been to date under-theorised in current urban 
adaptation literature (Berrang-Ford et  al., 2019; Leiter et  al., 2019; Olazabal et al., 2019b). There is also limited evaluation of new social 
innovations of the last two decades, including participatory budgeting, 
social financing, crowdfunding and low-cost urban infrastructure 
that can be enabling conditions for transformative urban adaptation 
(Dahiya and Das, 2020; Caprotti et al., 2017).

The challenges related to the evaluation of adaptation progress (lack of 
methods, agreed metrics, data and definitions, including the ambiguity 
of the concept of ‘adaptation’) have been widely recognised after 
the Paris Agreement by multiple organisations, including the OECD, 
the World Bank, the European Environment Agency and the Global 
Environment Facility (Ford et al. , 2015; Magnan, 2016; Bours, McGinn 
and Pringle, 2014). Monitoring and evaluation systems in urban areas 
will necessarily be incremental and additive, and will have to build on 
existing indicator systems (Solecki and Rosenzweig, 2020). There is a 
need to develop practical and efficient frameworks to assess adaptation 
progress across all levels of public and private decision making. 
This should include the assessment and consideration of top-down 
adaptations alongside informal, bottom-up community actions, or 
corporate-led programmes developed to reduce vulnerabilities and

climatic risks and increase resilience (high confidence, high agreement).
On the one hand, there is a need to guarantee that planned adaptation 
actions are efficient, just and equitable (Olazabal et al., 2019b), including 
being able to disaggregate, for example by gendered impacts. On the 
other hand, there is a need to observe if and how environmental, social 
and economic vulnerability and climatic risk conditions evolve with time. 
Surveillance, monitoring and evaluation facilitate adaptation decision 
making by linking three aspects (Berrang-Ford et al. , 2019): (1) changing 
vulnerabilities and risks, (2) established adaptation goals and targets, 
and (3) adaptation efforts put in place. The process will help evaluate 
whether current adaptation efforts are sufficient or adequate, thus 
enabling the learning process that adaptation action requires (Haasnoot, 
van’t Klooster and Van Alphen, 2018; Klostermann et al. , 2018).

Monitoring and evaluation of government-led urban adaptation in major 
cities around the globe is largely missing (Araos et al. , 2017; Olazabal  
et al. , 2019a). This reveals: (1) a lack of awareness by local adaptation 
managers about the critical importance of monitoring and evaluation 
systems in adaptation decision making, (2) inadequacy, irrelevancy 
or underuse of available monitoring and evaluation resources, or (3) 
a lack of knowledge, capacity and resources to make monitoring and 
evaluation work in practice at city scale.
Olazabal et al. (2019b) argue that six components are at least required 
to make monitoring and evaluation operational for urban adaptation 
planning: (1) the definition of a context-specific tailored system 
adapted to existing local institutions, (2) the definition of a responsible 
party (public authority, department, group or organisation) that will 
be in charge of monitoring and evaluation system management, (3)

the definition and assignation of the appropriate budget over time, 
(4) the identification of monitoring objectives and indicators, (5) 
the definition of a method and process to evaluate outcomes of the 
monitoring process and finally, (6) the reporting process (how and who 
the outputs will be reported to). Klostermann et al. (2018) emphasise 
the importance of learning through iterative cycles of selection of 
monitoring objectives, procedures, data collection and evaluation, and

6
981Cities, Settlements and Key Infrastructure  Chapter 6
inputs to adaptation policy and planning processes (see also discussion 
of evaluation and learning in Section 17.5.1.7). Yet practical exemplary 
approaches are still missing.
The IPCC’s Fifth Assessment Report acknowledged the lack of standard 
metrics to measure and monitor success in urban adaptation and 
suggested a list of indicators that could be developed, while also taking 
note of the localised nature of adaptation (see also (Rufat et al., 2015)). 
However, predominant approaches are typically not conducted at the 
appropriate scale to inform adaptation decision-making (Ford et al., 
2018). While some scholars advocate the use of a unifying indicator of 
social vulnerability (Spielman et al., 2020), others propose to develop 
flexible sets of comparable indicators that can be adjusted to different 
contexts (Leiter et al., 2019). Risk-based approaches are seen as an

alternative in a context where the monitoring of decision-relevant 
variables in urban climate adaptation planning is essential to link 
climatic risk assessment and action (Hallegatte and Engle, 2019; 
Kingsborough, Borgomeo and Hall, 2016; McDermott and Surminski, 
2018). Because of the need to define normative frameworks for risk 
evaluation, what is acceptable, for what purpose and for how long 
(Galarraga et al., 2018), these approaches may offer an opportunity 
for the generation of a shared understanding on goals and limitations 
of adaptation (McDermott and Surminski, 2018). However, risk-based 
indicators may also create a bias toward quantifiable variables that 
tend to be based on climatic modelling outputs, engineering or 
financial assessments. Based on this and various examples of urban 
development projects, Hallegatte and Engle (2019) claim it is important 
to consider output-based indicators and process-based indicators that

talk about government, voice and empowerment. Overall, dozens of 
indicator-based approaches to assess climate adaptation have been 
proposed across the scientific and policy literature, especially in the 
broader framework of (community) resilience assessment tools (Sharifi, 
2016; Feldmeyer et al., 2019), and in different sectors, for example the 
climate benefits of NBS (Kabisch et  al., 2016; Donatti et  al., 2020). 
Although these efforts may help to mainstream the evaluation of 
adaptation in current city evaluation initiatives, the development of 
comprehensive monitoring and evaluation systems is lacking.
There is little evidence on how best to make monitoring and evaluation 
approaches practical at the local scale. Cities worldwide face important 
social, environmental and economic conflicts related to resource 
inequality, poverty, environmental pollution and social tensions that 
coexist with climatic risks. It makes sense to integrate climate change

adaptation assessment goals and needs into existing frameworks for 
the sake of efficiency. This will benefit small urban areas and cities 
in developing regions that often face data scarcity and may also find 
available indicators irrelevant to their realities and, thus, be required 
to adjust them (Simon et al., 2016). Efforts to coordinate frameworks 
for the assessment of sustainability (e.g., Local Agenda, sustainability 
appraisals), resilience (e.g., 100 Resilient Cities, new standards for 
urban resilience), greenhouse gas (GHG) emissions reporting (e.g., 
Global Covenant of Mayors for Energy and Climate) can be deployed 
to learn about contexts. However, they need to be applied with caution 
as enforcing external requirements may lead to local tensions during 
their application (for example Roberts et al., 2020). In a context where 
adaptation efforts need to be aggregated and evaluated across nations

(Magnan, 2016) and their implications on wider objectives such as sustainable development and social justice need to be assessed 
(Long and Rice, 2019), urban adaptation monitoring and evaluation 
can inform national and international processes that enable a global 
stocktake of adaptation.
6.4.7 Enabling Transformations
Growing awareness of the interlocking of drivers of urban change and 
vulnerability has motivated an interest in transformational approaches 
to adaptation action in cities, settlements and infrastructure. While 
the idea of transformation has been adopted across the field, there 
is no consensus about what an urban transformation that addresses 
adaptation means. There is no one single transformative solution or 
approach relevant in every case (Chu, Schenk and Patterson, 2018; 
Shi, 2019; Goh, 2019). What constitutes ‘urgent’ and ‘far-reaching’ 
transformation depends on the local community’s expectations and 
ideas (Choko et al., 2019).

Transformation is often approached as a process of institutional 
transformation, akin to the process described in Section 6.4.2 (see, for 
example, Duijn and van Buuren, 2017). Transformation engages with 
critiques of adaptation or risk reduction as an individual responsibility 
(Sou, 2018). The idea is to use transformation to focus on coordinating 
collective efforts (Haque et al, 2014). The coordination of multiple 
actors is a condition to enable transformative institutions (Torabi et 
al, 2018) and link adaptation action to development efforts (Chu et 
al, 2017; Roberts and O’Donoghue, 2013). The role of communities 
and citizens in such an approach to transformation is ambiguous. 
Sometimes communities and citizens are presented as critical agents of 
transformation (Limthongsakul et al, 2017). Other times, however, they 
are simply situated within strong and durable networks that provide 
the institutional setting to build resilience (Danière et al. , 2016). Despite

the political nature of transformative approaches and the evidence that 
transformative approaches rely on protest and political activism, few 
authors recognise this strategy (but see Bahadur and Tanner, 2014; Chu, 
Anguelovski and Roberts, 2017; Dierwechter and Wessells, 2013).
Transformation is also more than a single instance of institutional 
change. Historical perspectives on transformation enable an 
understanding of the chain of institutional changes that ultimately 
lead to significant or far-reaching reconfiguration of infrastructure 
and service provision (Rojas et al, 2015). Paradigm changes, such as 
new engagements with nature and green infrastructure, will improve 
adaptation outcomes (Roberts et  al., 2012). Changes of paradigms, 
however, are not inherently positive and may clash with existing 
interests or involve trade-offs with other priorities. When care is taken 
to ensure greater inclusion in urban decision-making, disadvantaged,

vulnerable communities are less likely to be disadvantaged. For 
example, indigenous traditions of nature management provide entry 
points for the sustainable management of resources, such as seed 
banks, urban agriculture and the local management of watersheds 
and floods, may be at odds with conventional structures of expert 
knowledge (Cid-Aguayo, 2016; Chandra and Gaganis, 2016). These 
traditions are vital both because of the solution space that they open 
in the local context and how they serve to create resilience through 
collective and intergenerational learning (Chandra and Gaganis, 2016).

6
982Chapter 6 Cities, Settlements and Key Infrastructure
While aspects of transformative capacity identified in the literature 
may facilitate far-reaching change, there is limited evidence of actual 
transformations as an outcome of adaptation. While community led 
resilience agendas may tackle poverty related issues, they struggle 
to tackle city-wide structural forms of inequality (Chu, Schenk and 
Patterson, 2018). Processes of shared learning and co-production of 
knowledge can reinforce existing power dynamics and be limited by 
technical framings of vulnerability that marginalise political issues 
(Orleans Reed et al., 2013). These issues are especially acute in relation 
to land use decisions where short-term fiscal and commercial interests 
conflict with long-term vulnerability reduction objectives (Brown, 
Dayal and Rumbaitis Del Rio, 2012). It can be difficult for adaptation 
actions to target cities’ underlying political–economic structure,

such as entrenched political–economic interests, elite influence over 
decision making or neoliberal planning logics that maintain and 
reproduce inequality (Chu, Anguelovski and Roberts, 2017). Urban 
resilience plans may be formulated in disconnection from broader 
development strategies, which leads to a limited ability to tackle 
underlying structures of political power and urban development 
practices (Weinstein et al., 2019). Evidence from Kolkata demonstrates 
the limitations of resilience plans to address underlying conditions 
of vulnerability, including the commodification of hazardous land, 
under-provision of informal settlements and spatial segregation of the 
urban poor (Rumbach, 2017).
Planning for transformative adaptation is more likely where communities 
can learn collectively (medium evidence,  medium agreement) 
(Restemeyer, van den Brink and Woltjer, 2017; Kabisch et al., 2017; Fraser  
et al. , 2017; Putri, Dalimunthe and Prasojo). Greater citizen engagement

facilitates implementing specific measures for radical policymaking 
or the mainstreaming of environmental knowledge into adaptation 
practices (Reed et al, 2015). Do it yourself (DIY) planning, in which 
stakeholders focus on creating and improving specific urban spaces they 
inhabit, has led to urban greening experiments led by civil society that 
change paradigms of urban and environmental management (Cloutier, 
Papin and Bizier, 2018). Social learning may occur through combinations 
of activism and collaboration with and between informal settlement 
dwellers, as shown in adaptation experiences in informal settlements 
in Hanoi and Bangkok (Danière et al. , 2016).. The adaptation process 
can benefit from the inclusion of multiple sources of knowledge for 
social learning, including universities but also communities and citizens 
(Chu, Schenk and Patterson, 2018). Citizens assemblies are increasingly 
recognised as spaces for transformative adaptation (Muradova,

Walker and Colli, 2020), although their potential influence at different 
government levels is still not fully understood.
The integration of multiple forms of knowledge leads to social learning 
(medium evidence, high agreement). Indigenous knowledge and local 
knowledge can provide essential insights into community needs and 
experiences of housing and urban infrastructure to inform climate 
adaptation, including improper waste disposal, inadequate drainage 
and poor sanitation, but there is significant variation in community 
knowledge networks (Roy et al., 2018b; Douglas et al, 2018; Waters 
and Adger, 2017). It is important to identify and address barriers to 
the incorporation of Indigenous knowledge and local knowledge, such 
as the dominance of scientific knowledge, oppression and/or racism, 
and fragmentation of knowledge including gender and generational divides (see Burke and Heynen, 2014; Whyte, 2017; Victor, 2015;

Lövbrand et  al., 2015; Kelly, 2019).The incorporation of Indigenous 
knowledge in urban decision making requires a constructive dialogue 
with scientists and urban planners.
Indigenous knowledge and local knowledge have an important role 
to play in urban planning and management. They can support impact 
detection and evaluation in urban areas (Codjoe et al, 2014), weather 
forecasting in urban areas (Magee et al., 2016; Ebhuoma and Simatele, 
2019), climate change adaptation in urban agriculture (Wahab and 
Popoola, 2018; Solomon et al., 2016), urban food security (Simatele 
and Simatele, 2015), planning and managing  urban  solid  waste 
(Kosoe et al, 2019), urban flood management (Thorn et al, 2015; 
Jameson and Baud, 2016; Hooli, 2016), drought perception and 
coping strategies (Saboohi et  al., 2019), and ecological restoration 
and urban commons management (Nagendra, 2016; Nagendra and 
Mundoli, 2019). They can help define baselines for past climate and

ecological change, providing a historical perspective on changes in 
urban commons such as lakes and trees (Nagendra, 2016), as well 
as past climatic changes or climate baselines (Ajayi and Mafongoya, 
2017) and shifting baseline syndrome (Fernández-Llamazares et  al., 
2015; Soga and Gaston, 2018; see Businger et al., 2018 for a review 
of hurricane history in Hawaiian newspapers; also Wickman, 2018). 
Incorporating Indigenous knowledge and local knowledge can help 
generate more people-oriented and place-specific approaches, leading 
to adaptation policies that foster identity, dignity, self-determination 
and better collective decision making and capacity to act (Preston, 
2017; McShane, 2017) (see also Section 6.1).
Envisioning development alternatives through adaptation as a first 
step toward transformative adaptation can leverage social learning. 
Experiences of migration, length of residence and the density of local

social-networks impact social learning opportunities and underscore 
why context-specific social education is vital (Waters and Adger, 
2017; Karunarathne and Lee, 2020). Learning across and between 
communities can be enhanced when care is taken to understand local 
challenges. Given power relationships, cultural needs and community 
aspirations, a top-down approach to information sharing is generally 
less effective than community partnerships and co-created knowledge 
at surfacing visions and strategies for getting past baked-in, unequal 
and unsustainable development assumptions and practices (medium 
evidence, high agreement) (Clemens et al., 2016; Thi Hong Phuong, 
Biesbroek and Wals, 2017; Fitzgerald and Lenhart, 2016; Fisher and 
Dodman, 2019). Social learning in formal and informal urban contexts 
is also enhanced when care is taken to ensure multiple stakeholders 
have opportunities to understand a variety of viewpoints, values,

resources and ideals, and that these viewpoints are clearly identified 
in decision-making (Thi Hong Phuong, Biesbroek and Wals, 2017). 
However much social learning still happens only after a crisis, for 
example in urban water adaptation, and new knowledge is often 
frustrated by the lock in of powerful local institutions and groups 
(Johannessen et  al., 2019). Social learning is, however, only one 
component of the development of climate-resilient pathways. System 
perspectives theorise the possibility of tipping points, leverage points 
or disruptive technologies to challenge the stable regime to create a 
broader reconfiguration (Chapter 17; O’Neill et al., 2018).

6
983Cities, Settlements and Key Infrastructure  Chapter 6
Case Studies
Case Study 6.1: Urbanisation and Climate Change in the 
Himalayas: Increased Water Insecurity for the Poor
The Hindu Kush Himalayan region extends over roughly 3500 km 
covering eight countries: Afghanistan, Pakistan, Nepal, China, India, 
Bhutan, Bangladesh and Myanmar. Projections show that by 2050, 
more than 50% of the population in Hindu Kush countries will live 
in cities (UNDESA, 2014). The region is home to 10 major river basins 
that feed south and south-east Asia. In 2017, the total population in 
the 10 major river basins with their headwaters in the region was 
around 1.9 billion, including 240 million in the mountain and hills of 
the Hindu Kush (Wester et al., 2019). The region is characterised by 
unique mountain topography, climate, hydrology and hydrogeology. 
Each one of these factors plays an important role in determining 
the availability of water for people living in the Himalayas (Nepal,

Flügel and Shrestha, 2014; Scott et  al., 2019; Prakash and Molden, 
2020). The total landmass that can support physical infrastructure for 
towns to develop is much less in the Hindu Kush Himalayan region as 
compared with the plains. Due to this physical constraint, the process 
of urbanisation is slow in the region. Only 3% of the total population 
in the region live in larger cities and 8% in smaller towns (Singh et al., 
2019b). However, there has been an increase in urbanisation, largely 
due to regional imbalances in providing economic opportunities for 
the poor. People from rural areas are flocking to the nearest urban 
centres in search of employment and other economic opportunities 
(Singh and Pandey, 2019). As a result, the share of urban population is 
increasing in the region, while that of the rural population is declining.
One of the major challenges of urbanisation in the Himalayas is 
sprawling small towns with populations of under 100,000 (see

Figure 6.6). These towns are expected to become major urban centres within a decade because of high growth rate. A recent study by 
Maharjan et al. (2018) on migration documented that 39% of rural 
communities have at least one migrant, of whom 80% are internal and 
the remaining 20% are international. Around 10% of the migration 
is reported as environmental displacement. Males account for the 
majority of the migration, which forms an important aspect of gendered 
vulnerability (Sugden et al., 2014; Goodrich, Prakash and Udas, 2019). 
The ever-expanding urban population in the Himalayas generates many 
challenges, especially in the context of climate change adaptation. 
First, unplanned urbanisation is causing significant changes in land 
use and land cover, with recharge areas of springs being reduced. 
Most of the towns in Hindu Kush Himalayan region meet their water 
needs using supplies from springs, ponds and lakes which are largely

interlinked systems. Water insecurity in hill towns are becoming the 
order of the day (Virk et  al., 2019; Bharti et  al., 2019; Singh et  al., 
2019a; Sharma et al., 2019). Second, climate-induced changes in the 
physical environment include increased rainfall variability. Due to this, 
heavy rains are becoming frequent and are leading to more landslides. 
Third, global warming has increased the average temperature in the 
Himalayas which has caused glacier melt and subsequent change in 
hydrological regimes of the region. One of the contributing factors 
of glacial decline is also the deposition of black carbon (Gautam 
et al., 2020; Gul et al., 2021) which is contributed by burning of crop 
residue in Punjab (Kant et al., 2020). These critical stressors, climatic 
and non-climatic, are adversely affecting the socioecology of urban 
conglomerations in the region (Pervin et  al., 2019). Encroachment 
or degradation of natural water bodies and the disappearance of

traditional water systems such as springs are evident (Shah and 
Badiger, 2018; Sharma et al., 2019). While water availability in these 
towns has been adversely affected by the climatic and socioeconomic 
changes, demand for water has increased greatly (Molden, Khanal and 
Pradhan, 2018). Some of the towns are major tourist attractions that 
create a floating population in peak tourist seasons, challenging the 
500 kmPopulation of
urban centresUrbanisation
in Hindu-Kush
Himalayan
Region
KhuzdarPakistanIndia
Nepal
BangladeshKohatMardanSrinagarMingora
Anantnag
Jammu
Shimla
Dehradun
Kashipur
RudrapurHaldwaniDhangadhi
Butwal
BharatpuBirguniPokaraKathmandu
DharanDarjeeling
Gangtok
ShillongBiratnagar
AgartalaDimapur
Imphal
AizawlSilchar
Hindu Kush-Himalayan
Region
Bangladesh
MyanmarChina
Afghanistan
PakistanNepal
IndiaButhan< 50,000
50,000–100,000
100,001–500,000
500,001–1,000,000
> 1,000,000
Hindu
Kush-Himalayan
Region
Boundary

Figure 6.6 |  Urbanisation in Hindu Kush Himalayan Region.  Figure based on Singh et al. (2019b)

6
984Chapter 6 Cities, Settlements and Key Infrastructure
carrying capacities of the towns. The residents must cope with water 
scarcity as the demand for water increases in peak seasons and water 
distribution through the public water supply systems becomes highly 
inequitable (Raina, Gurung and Suwal, 2018). The usual challenges of 
utilities being inefficient also applies in these areas, though it becomes 
much more critical as the sources of water are limited and the local 
geology limits the ability to access groundwater. All these processes are 
resulting in increased water insecurity for the poor and marginalised 
in urban towns of Hindu Kush (Prakash and Molden, 2020). To cope 
with the scarcity situation, people are adapting through various means 
such as rationing of intra-household water access and groundwater 
extraction to access water supply (Virk et al., 2019; Bharti et al., 2019; 
Sharma et al., 2019). This is due to lack of long-term strategies and

options provided by utilities.
Case Study 6.2: Semarang, Indonesia
The City of Semarang, on the northern coast of Central Java in 
Indonesia, has a population of nearly 1.8 million (CBS, 2019). The city 
has experienced rapid urbanisation over last three decades, with the 
population almost doubling and density reaching 4650 people per 
square kilometre (Handayani and Rudiarto, 2014; Handayani et al. , 
2020b). Semarang is vulnerable to sea level rise, tidal flooding and 
inundation (Suhelmi and Triwibowo, 2018; Yuniartanti, Handayani and 
Waskitaningsih, 2016), risks which are worsened by land subsidence 
along the coast (Abidin et  al. , 2013). Globally, land subsidence is 
a notable compounder of climate change-induced sea level rise 
and coastal flooding (Bagheri et  al. , 2021). In Semarang, the land 
subsidence rate is projected to be up to 60 mm yr−1 (Abidin et al. , 
2013; Bott et  al. , 2021). Approximately 20% of the city’s coastline

is characterised as extremely vulnerable because of sea level rise 
and enhanced land subsidence (Husnayen et  al. , 2018), with the 
north-eastern portions of the city experiencing larger subsidence than 
the rest (Yastika, Shimizu and Abidin, 2019). Associated public health 
and sanitation risks are also evident, including increasing outbreaks 
of dengue fever and diarrhoea (Pratama et  al. , 2017; Indonesia 
Ministry of Health, 2020).
The City of Semarang first engaged with climate change in 2009, 
when the Rockefeller Foundation launched the Asian Cities Climate 
Change Resilience Network (ACCCRN), an initiative to develop 
resilience capacity across secondary and rapidly growing cities in 
South and Southeast Asia (Reed et al., 2015). Semarang was a pilot 
city for ACCCRN from 2009 to 2016, when it introduced a participatory 
approach to planning and decision making that challenged the 
government-dominated tradition in the city, and in turn played a

key role in Semarang’s climate adaptation and resilience planning 
process (Orleans Reed et  al., 2013; Moench, 2014; Kernaghan and 
Da Silva, 2014). A City Team was formed in 2010 consisting of City 
Environmental Agency (BLH; Badan Lingkungan Hidup), Regional 
Disaster Management Agency (BPBD; Badan Penanggulangan Bencana 
Daerah), Water Resources Management Office (PSDA; Kantor Dinas 
Pengelolaan Sumber Daya Air), Regional Planning and Development 
Agency (BAPPEDA; Badan Perencanaan Pembangunan Daerah), local 
universities and NGOs such as the Bintari Foundation, with technical 
support from Mercy Corps Indonesia (Nugraha and Lassa, 2018).The City Team was first established within the City Environment Agency 
(BLH) but was then transferred to the Development and Planning 
Agency (BAPPEDA) (Lassa, 2019). This corresponded to a shift in framing 
of climate change from an environmental priority to encompassing 
broader development issues such as economic development, housing

and infrastructure delivery. By asserting that climate change affects 
the operations of every critical sector across the city, the number of 
municipal agencies involved in climate change programming increased 
significantly (Setiadi, 2015). Most notably, this approach helped the 
municipal health agency to recognise the relationship between climate 
change and health (Setiadi, 2015), and helped to shift the emphasis of 
dengue fever management toward a more proactive community-based 
health early warning system (Pratama et  al., 2017). In 2017, these 
measures helped to reduced dengue fever infection rates by 56% 
compared with 2011–2016 levels (Indonesia Ministry of Health, 2020). 
ACCCRN also supported policy experimentation through implementing 
rainwater harvesting facilities and a community-based flood early 
warning system (Archer and Dodman, 2015; Yuniartanti, Handayani 
and Waskitaningsih, 2016; Sari and Prayoga, 2018). These projects

were designed in conjunction with national government investments 
in flood management infrastructure, which led to a reduction in the 
city’s inundated area by 24% or approximately 1% of the total urban 
area (Semarang City Government, 2016).
Building on Semarang’s ACCCRN experience, the city then became a 
member of the Rockefeller Foundation’s 100 Resilient Cities (100RC) 
programme between 2016 and 2018. As in ACCCRN, this new process 
emphasised stakeholder involvement, with the previous City Team 
recast as a team of City Resilience Officers (CRO), which was in turn led 
by the City Mayor and received strategic advisory support from the City 
Secretary. Semarang synthesised its experiences in climate adaptation 
planning through the Resilient Semarang Strategy published in May 
2016 (Semarang City Government, 2016). The Resilient Semarang 
Strategy (2016) acknowledged that urban resilience must be pursued 
in a comprehensive and inclusive manner and highlighted 18 strategies

across 6 themes: water and new energy, new economy, disaster and 
disease, integrated mobility, transparency of public information and 
competitive human resource, to be mainstreamed into the revision of the 
Mid-Term Regional Development Plan (RPJMD, Rencana Pembangunan 
Jangka Menengah Daerah) of 2016–2021. City Resilience Officers were 
formally appointed to serve on the RPJMD team, thereby formalising 
climate resilience as a critical item on the RPJMD programme list.
Engagement with 100RC allowed Semarang’s resilience programmes to 
appear on 100RC’s ‘marketplace’ of municipal projects, allowing them 
to be connected with bi-/multi-lateral donor resources, while continuing 
to align projects with goals articulated within the Mid-Term Regional 
Development Plan. The 100RC marketplace is a resilience platform that 
showcases particular initiatives of 100RC network cities to potential 
resilience partners, thereby attracting investment and donor support

to Semarang’s resilience programmes. Examples include the Water 
as Leverage (WaL) project that has been working to conserve urban 
water resources in the face of climate change since 2018 (Handayani 
et  al., 2020a; Laeni et  al., 2021) and the Transboundary Flood Risk 
Management through Governance and Innovative Information 
Technology Program (TRANSFORM) that has been helping Semarang 
tackle flood risks beyond city boundaries through reforestation and

6
985Cities, Settlements and Key Infrastructure  Chapter 6
development of dry wells and swales in upstream areas, as well as 
promoting cross-region dialogue (Global Resilience Partnership, 2018). 
Other collaborations focused on developing resilience indicators (ARUP , 
2018; Rangwala et al., 2018). For example, the Zurich Flood Resilience 
Program implemented resilience measurement tools in 16 sub-districts 
along the East Flood Canal. Results of the assessment were then used 
to develop local disaster contingency plans (Rangwala et al., 2018).
The conclusion of the Rockefeller Foundation’s formal engagement in 
Semarang in 2018 has brought forth questions about continued financial 
and institutional support for climate adaptation action in the city. 
Increasing land subsidence will also likely overwhelm current efforts to 
incrementally adapt to sea level rise and coastal flooding (Abidin et al. , 
2013). Still, the Semarang case study does highlight several key lessons

for urban climate governance in secondary rapidly urbanising cities in 
the Global South. First, transnational institutions and partnerships are 
critical enablers (Aisya, 2019; Setiadi, 2015; Chu, Hughes and Mason, 
2018; Handayani et  al. , 2020a). Institutions such as the Rockefeller 
Foundation foster programmes and investment in the city, leverage 
access to adaptation funding, accelerate climate mainstreaming into 
wider urban sectors, and promote better knowledge management 
(Setiadi, 2016). However, such opportunities are also supported by the 
city’s ability to further mobilise its own resources in the long term and 
remove its dependency on the national government and transnational 
supporters (Handayani et al. , 2020a). Second, scaling up of programmes 
and replication of adaptation actions are increasingly important to 
close the gap between planning and implementation (Setiadi, 2016). 
It is evident that increased community empowerment and participation

can help fill this gap (Hadi, 2018; Miladan, 2016), but this must also 
be evidence-based to ensure its applicability and effectiveness (Suarma  
et al. , 2018). Questions remain around how to determine and assess 
evidence-based participatory adaptation at the local level. Third, 
sustainable financing (from both external and internal sources) to 
support proposed adaptation strategies is essential as it allows for more 
capacity building, technology transfer and programme implementation 
in the long run (Handayani et al. , 2020a; Laeni et al. , 2021; Hadi, 2017). 
An example is the development of a water retention on the eastern 
coast of Semarang using a collaborative financing model, which helped 
further adaptation by protecting water resources for local industries as 
well as promote the idea of land value capture for community residents.
Case Study 6.3: Institutional Innovation to Improve Urban 
Resilience: Xi’xian New Area in China

Located in Northwest China and the Silk Road Economic Belt, Xi’Xian 
covers a total of 882 km2 of the border zone of two cities of Xi’an 
and Xianyang, Shaanxi province. Xi”xian accommodates a registered 
population of 1.06 million, with a planned area of 272 km2 reserved 
for urban development. As a new engine for promoting the West 
Development Strategy and people-centred urbanisation in the 
northwest China, Xi’xian has paved the way for China’s ecological city 
agenda since January 2014.
Xi’xian aimed to build a ‘modern garden city’ when it was selected 
as national demonstration sites for Sponge City (SC) during 2015–
2018 and Climate Resilient City (CRC) during 2017–2020. Under the changing climate, the old cities of Xi’xian suffers urban heat island, 
drying and water scarcity, heavy rains and waterlogging, thunderstorms 
and so on, which bring adverse effects to transportation, construction, 
cultural relics tourism resources and other industries (Ma, Yan and

Zeyu, 2021). SC status requires innovation to reduce flood risk through 
design to absorb, store and purify rainfall and storm water in an 
ecologically friendly way that reduces dangerous and polluted runoff. 
When required, the stored water is released and added to the urban 
water supply (MoHURD, 2014). As a CRC, the aim is to adapt to climate 
risk and environmental change by integrating climate resilience into 
urban renewal and revitalisation.
In practice, building ecological cities in China has primarily focused 
on hard measures (Li et al., 2020). Key areas of development include 
stakeholder engagement and horizontal coordination (Li et al., 2016). 
Among one of 19 national-level New Areas in China, Xi’xian enjoys 
special preferential policies in the fields of fiscal autonomy, investment 
and tax policy, and permission in land utility for industrial development 
purpose. These policy freedoms allow Xi’Xian to explore adaptation

options. This has opened engagement with business through an urban 
construction investment group sponsored and invested in jointly by 
Xi’Xian Management Committee (administrative authority) and local 
enterprises (Wei and Zhao, 2018). Second, the municipal government 
has simplified administrative systems to reduce the project waiting 
period from evaluation to approval to 50 d. Third, a green financial 
mechanism creates a leverage effect for national funding, including the 
first provincial Green Sponge Development Fund (RMB 1.2 billion) and 
in Shaanxi, special funding from the Urbanization Development Fund 
(RMB 2.64 billion). Furthermore, a public–private partnership model 
with a whole-lifecycle-management approach has been introduced, 
raising funding of RMB 1.24 billion with a packaged project including 
public pipelines and sewage water treatment facilities.
Such institutional and financial support have allowed Xi’xian to

implement a Pilot Construction Plan and Three-year Action Plan for 
Adapting to Climate Change. In 2020, Xixian formed an urban ecology 
system including 21 m2 of green space per capita. The old cities’ 
underground drainage pipe network has been replaced by sponge 
designs such as green corridors, grass ditches, water storage gardens 
and recessed green spaces. The 10 waterlogging prone points in Xi’xian 
New Area have been eliminated and the green area has alleviated urban 
heat, with average temperature about 1°C lower than the neighbouring 
densely populated mega-cities of Xi’an and Xianyang. Groundwater in 
the New Area has also risen by 3.43 m compared with 2015.
At the end of 2020, Xi’xian New Area has built 2.4  million m2 of 
modern garden cities, more than 50 km of sponge roads, 1.4 million 
m2 of resilient park green space and established a green coverage of 
more than 50% of the urban space. The target of becoming a green

city in which everyone can ‘see green in 100 meters, step into garden 
every 300 meter’ has been realised (Ma et al., 2021). The urban parks 
and green spaces play a role in regulating local microclimate and 
also improve the urban environmental amenities for residents. In a 
comprehensive performance assessment for the Climate Resilient 
Cities facilitated by the Climate Change Department of the Ministry of 
Ecology and Environment (MEE), the Xi’xian ranked number 9 among 
all of the 28 pilot cities.

6
986Chapter 6 Cities, Settlements and Key Infrastructure
Case Study 6.4: San Juan: Multi-Hazard Risk and Resilience 
in Puerto Rico and Its Urban Areas
This case study illustrates multi-hazard risk and reviews the formation 
of a multi-stakeholder adaptation governance regime as one response 
to this.
In two weeks in 2017, Puerto Rico experienced two powerful 
hurricanes, Irma (category 5) and María (category 4). The compound 
effects decimated the island’s power, water, communications and 
transportation infrastructure, and an estimated 2975 people lost 
their lives (Irvin-Barnwell et  al., 2020; Santos-Burgoa et  al., 2018). 
Soon after, while many homes still had no electricity or roofs and the 
tree canopy was still bare, Puerto Ricans were faced with cascading 
effects including environmental health impacts from air pollution, 
extreme heat and mosquitoes (Ortiz et al., 2020). In 2020, while still 
recovering, Puerto Ricans experienced earthquakes, extreme African

dust events, intense coastal and urban floods, and the COVID-19 
pandemic (Keck, 2020; NASA Explore Earth, 2020; NASA/JPL-Caltech, 
2020). These events continue to unveil unresolved conditions of social 
vulnerability and its root causes in economic poverty, social inequities, 
aged and deteriorating infrastructure, and population loss (Bonilla 
and LeBrón, 2019). Combined with limited past investment in climate 
change adaptation and underlying governance challenges including 
corruption, bankruptcy and political crisis (Holladay et al., 2019), this 
has constrained a more CRD for Puerto Rico.
It is in this context that government, academic institutions and local 
civil society have taken important steps and often joint action toward 
mitigation and adaptation. Federal funding included USD 20 billion of 
disaster recovery funding with USD 8 billion allocated for adaptation 
and resilience projects, such as flood risk mitigation. During the year

2020, the Federal Emergency Management Agency (FEMA) approved 
USD 13 billion to rebuild the power grid and education system (Delgado, 
2020). These programmes allow communities and local governments 
to plan and implement strategies and build new infrastructure that 
reduces risks and builds long-term adaptive capacities. The Government 
of Puerto Rico also approved two key climate adaptation policies in 
2019. The Puerto Rico Mitigation, Adaptation and Resilience to Climate 
Change Law (Law 33, Senate Bill PS 773) established, for the first time 
in the island’s history, a legal framework that acknowledges that the 
climate is changing and threatens the quality of life. The law recognises 
important scientific projections for the island, including an increase of 
0.5 to 1 m in sea levels by 2015, maximum temperatures of up 2.5°C 
and precipitation decrease of up to 50% by 2100 (Gould et al., 2018). 
The law generated the formation of an Expert and Advisory Committee

on Climate Change to develop the plan with specific recommendations 
and present it to the Legislature within a year of the passing of the 
law in 2020. Along with strategies to specifically protect and build 
the resilience of urban and rural communities to future climate 
disasters, the law establishes SDGs, including water and food security, 
urban planning and densification, and transition to renewable and 
alternative forms of energy. The energy target is reinforced by another 
key state policy approved in 2019 in response to the failed energy 
infrastructure during Hurricane María, the Puerto Rico Energy Public 
Policy Act (Senate Bill PS 1121). This law calls for a transition to 100% 
renewable and alternative energy by 2050.Puerto Rico has a strong science base that produced extensive 
knowledge on climate change and sustainability long before Hurricane 
María. The Puerto Rico Climate Change Council has collected and

synthesised scientific information for Puerto Rico since before its 
formation in 2009. Many Puerto Rican scientists were also editors 
and authors on Chapter 20: US Caribbean Region for 4th US National 
Climate Assessment (Gould et  al., 2018). The National Institute of 
Island Energy and Sustainability (INESI in Spanish) recently published 
a catalogue with more than 60 scientists and experts working on 
energy and sustainability innovations in the University of Puerto 
Rico (UPR) system. The scientific community became very active after 
the hurricane in efforts to empower local groups and communities 
to build more sustainable and resilient futures. UPR environmental 
health scientists worked with communities to design and implement 
risk reduction action plans, including NBS , through the Community 
Climate Actions Plans and the Puerto Rico Community Resiliency 
Initiative sponsored by Fundación Comunitaria de Puerto Rico and

Education Development Center-Regional Education Laboratories, 
Northeast and Islands. A successful example of these alliances is the 
development of the first solar power community in Toro Negro, Puerto 
Rico. These initiatives were inspired by principles of human-centred 
design, a problem-solving approach that starts with the people 
impacted the most by the problem to be solved. In San Juan, the 
capital and major urban centre of the island, scientists from UPR and 
the US Forest Service International Institute of Tropical Forestry worked 
with local stakeholders and communities to develop sustainable and 
transformative urban futures with the support of the Urban Resilience 
to Extreme Events Sustainability Research Network (UREx SRN). The 
UREx SRN is a knowledge network of 10 cities in the USA and Latin 
America and 20 other institutions building scientific knowledge, 
models and participatory tools to build resilience and transformative 
capacities for cities.

Perhaps the greatest source of adaptive capacity that emerged after 
the hurricane came from the civic sector and community-based 
organisations, and local residents. Hundreds of non-profit and 
grassroots organisations became active in disaster recovery and 
are now catalysing actions to advance social transformation and 
sustainable development. In the energy sector, numerous communities 
and NGOs developed new action plans to promote transitions to 
renewable energy and community-based microgrids, such as the 
Queremos Sol initiative (https://www.queremossolpr.com/), and the 
establishment of solar panels in community centres and residences by 
the Puerto Rico Community Foundation and Resilient Power Puerto 
Rico. The San Juan Bay Estuary Program, an NGO in the San Juan 
metropolitan area, launched alongside the Clinton Global Initiative 
the development of a watershed-based multi-jusrisdictional hazard 
mitigation plan, the first watershed-based plan for the metro region.

The organisation has established resilience hubs to support the 
community with critical resources, communications and energy supply 
during an emergency. In many of the most isolated areas across the 
island where government aid did not reach them for months, the 
communities that self-organised during recovery are also leading 
examples of community social–ecological resilience. In Utuado, one 
of the hardest hit areas by the hurricane, their main community 
organisation known as COSSAO (Corporación de Servicios de Salud 
y Desarrollo Socioeconómico, El Otoao) emerged from the hurricane

6
987Cities, Settlements and Key Infrastructure  Chapter 6
with a strong and holistic sustainable development vision, the Tetuan 
Reborn initiative, to improve the socioeconomic status and health of 
community members while building capacity for disaster resilience 
through various initiatives. The long-term outcome of this initiative is 
to support efforts toward self-empowerment within neighbourhoods 
by identifying and designing viable solutions to hurricane-related 
and economic development challenges specific to the local context, 
including constructing a primary health care clinic, a public health 
promoter programme, pursuing farms rehabilitation, and promoting 
agritourism, agrotherapy and education (Holladay et al., 2019).
Adaptation efforts, however, continue to face many governance hurdles. 
Up to 2020, only 2–3% of the USD 20  million Federal Government 
recovery funds had been spent with hundreds of families that lost

their homes or roofs in 2017 yet to receive the help they need (Colón 
Almenas, 2020). Lack of administrative capacities, coordination across 
sectors and efforts, transparency and accountability are some of the 
governance barriers that keep recovery and transformation efforts from 
materialising (Lamba Nieves and Marxuach, 2020). Puerto Ricans are 
now contending with the reality that the disaster they are experiencing 
is not an outcome of a singular event but of multiple hazards converging 
with pre-existing vulnerabilities and low adaptive capacities creating 
severe multi-hazard risk to the island (Eakin, Muñoz-Erickson and 
Lemos, 2018; Gould et al. , 2018). Many Puerto Ricans now question 
when the disaster began and when it ended because they have been 
living in a state of chronic crisis (Bonilla and LeBrón, 2019).
Case Study 6.5: Climate-Resilient Pathways in Informal 
Settlements in Cities in Sub-Saharan Africa

Informal settlements account for over three-quarters of residential 
areas in sub-Saharan Africa and have grown rapidly over the last 
three decades (Visagie and Turok, 2020). Informal settlements will 
remain home to a significant proportion of the urban population of 
this region which is projected to grow by 2.5 times between 2020 and 
2050 (UNDESA, 2018), driven by a complex set of underlying factors 
including socioeconomic conditions, inadequate planning systems, 
local and foreign investment patterns, and rural-to-urban migration 
(De Longueville et al., 2020). Yet residents of informal settlements are 
often excluded from macro-level visions and policies that seek to make 
cities safer and improve resilience (Adenle et al., 2017; Pelling et al., 
2018). This case study compares the experience of collective action to 
manage risk in the informal settlements of Freetown, Sierra Leone, with 
other cases in Sub-Saharan Africa. These examples show how local

knowledge and capacity, engagement of policymakers in meaningful 
ways with residents of informal communities, and institutional change, 
can combine to deliver adaptation outcomes at a city scale (Kareem 
Despite their diversity and differences across the continent 
(Kovacic et  al., 2019), informal settlements are frequently located 
in hazard-prone areas, with residents living in precarious housing 
conditions on marginal lands (Badmos et  al., 2020; Kironde, 2016), 
lacking essential services and risk-reducing infrastructure, and often 
developing outside the legal systems intended to record land tenure 
and ownership (Satterthwaite et  al., 2020; Adelekan et  al., 2015). Consequently, they are particularly vulnerable to climate change, and 
the urban poor residents suffer disproportionate burdens and losses 
from natural hazards, which undermines urban resilience (Williams 
et al., 2019). Recent impacts from flooding have brought wide-spread

devastation to urban poor residents in major coastal urban centres 
including Accra, Lagos, Freetown, Maputo and Dar es Salaam, 
resulting in injury and death, displacement of people, loss of assets, 
destruction of public infrastructure and disruption to livelihoods and 
economies (Douglas et al., 2008; Adelekan, 2010; Yankson et al., 2017; 
Allen et al., 2017). Flooding and long-term inundation also lead the 
spread of diseases and health risks (Sverdlik, 2011; Zerbo, Delgado 
and González, 2020). Climate change will also bring stresses such as 
city-wide reductions in freshwater availability, and heatwaves that 
have particularly severe consequences for residents of poorly built 
homes in informal settlements (Pasquini et  al., 2020; Kayaga et  al., 
2021; Wilby et al., 2021).
In response to these risks, a wide range of adaptation efforts have been 
implemented in cities across sub-Saharan Africa (Hunter et al., 2020).

In Freetown, informal settlement residents have led data generation 
efforts that capture the value of local knowledge in understanding 
climate risk. Through partnerships with NGOs and research institutions, 
informal settlement residents have mapped climate hazard hotspots 
using geo-referenced tools, producing both digital and hardcopy 
outputs that serve as a blueprint for climate-informed community 
development discourses (Allen et  al., 2020b; Visman et  al., 2020). 
Similarly, residents of informal settlements in Dar es Salaam, Tanzania, 
have profiled community climate and health risks by using an adaptation 
of the ‘Action at the Frontline’ methodology developed by the Global 
Network of Civil Society Organisations for Disaster Reduction (GNDR). 
Locally informed risk profiles support the development of community 
action plans based on prioritisation and ranking of scaled-down 
interventions that communities can collectively do on their own

(Osuteye et  al., 2020). This process highlights the lived experiences 
of climate change, and allows communities to develop deliberation 
spaces, communal solidarity and cohesion, and share adaptation 
strategies (Sakijege et  al., 2014). Such sharing and peer-to-peer 
learning is particularly useful because adaptive capacities are unevenly 
distributed among exposed populations (Ajibade and McBean, 2014). 
The community-generated assessments and data consider the range of 
environmental, socioeconomic and political factors that contribute to 
a better understanding of how climate change affects the vulnerability 
of low-income urban residents, and how this changes over time.
Data that is generated and owned by residents of informal settlements 
provides a basis for making the risks facing these neighbourhoods 
more visible to city planners, and for enabling collaboration between 
a range of urban stakeholders (Dobson, 2017). In Freetown, this

process has been led by the Federation of the Urban and Rural Poor 
(FEDURP) and the Centre for Dialogue on Human Settlement and 
Poverty Alleviation (CODOHSAPA). The FEDURP belongs to the global 
Slum Dwellers International network, committed to empowering 
poor residents in urban spaces, and has a presence in several other 
African cities (Macarthy et al., 2017). With the support of CODOHSAPA, 
FEDURP coordinates community development committees (CDC) and 
community disaster management committees (CDMC) in nearly all 
the informal settlements in the city. Both CODOHSAPA and FEDURP

6
988Chapter 6 Cities, Settlements and Key Infrastructure
work closely with the local research institution, the Sierra Leone 
Urban Research Centre (SLURC). SLURC has played an essential role 
in curating spaces for continuous learning and relationship-building 
between FEDURP and community residents, including the formation of 
‘Community Learning Platforms’ (CLP) for mixed groups of community 
actors (City Learning Platform, 2019) to build their capacities to address 
climate risk collectively. This is done by drawing on the data, agency 
and mobilisation potential of community organisations in informal 
settlements. In the coastal settlement of Cockle Bay at the western end 
of the city, uncontrolled traditional land reclamation (‘banking’) along 
the shores progressively exposed residents to perennial floods from 
tidal surges, and the settlement received regular threats of evictions 
from city authorities. However, residents have drawn on their climate

risk knowledge and hazard profiling to self-manage a process of action 
planning resulting in a decision to prohibit further land reclamation. It 
also identified and demarcated an exterior boundary of the settlement 
and planned and constructed new drainage channels to carry away 
runoff water within the community (Allen et al., 2017). The community 
organisations have subsequently successfully negotiated with the Ministry of Environment to formalise this new exterior boundary, 
which has led to the authorities dropping their threats of evictions.
The approach taken in Freetown demonstrates a pathway to adaptation 
that is based on a more people-centred approach to urban planning 
that understands the aspirations of urban residents, addresses climate 
risk and advances sustainable development (Woodcraft et al., 2020; 
Fraser et al., 2017). It further provides an example of the ways in which 
different sources and scales of data can be co-produced (Kovacic et al.,

2019) and targeted interventions can be co-designed with community 
residents (Musango et al., 2020). The community-generated data on 
climate and health risks and the subsequent strategic action plans 
developed through local community organisations’ work have been 
recognised and incorporated into a new city-wide initiative led by 
the Office of the Mayor, dubbed Transform Freetown (Allen et  al., 
2020a). The action has expanded the political space for the urban 
poor’s collectives to strategically engage in urban resilience planning, 
highlighting the value and potential of participatory processes and 
community-generated data.
Cross-Working-Group Box URBAN | Cities and Climate Change
Authors: Xuemei Bai (Australia), Vanesa Castán Broto (UK/Spain), Winston Chow (Singapore), Felix Creutzig (Germany), David Dodman 
(Jamaica/UK), Rafiq Hamdi (Belgium), Bronwyn Hayward (New Zealand), Şiir Kılkış (Turkey), Shuaib Lwasa (Uganda), Timon McPhearson

(USA), Minal Pathak (India), Mark Pelling (UK), Diana Reckien (Germany), Karen Seto (USA), Ayyoob Sharifi (Japan/Iran), Diána Ürge-
Vorsatz (Hungary)
This Cross-Working Group Box on Cities and Climate Change responds to the critical role of urbanisation as a megatrend impacting 
climate adaptation and mitigation. Issues associated with cities and urbanisation are covered in substantial depth within all three 
Working Groups (including WGI Box TS.14, WGII Chapter 6 ‘Cities, settlements and key infrastructure’; WGII regional chapters; WGII 
Cross-Chapter Paper ‘Cities and settlements by the sea’; WGIII Chapter 8 ‘Urban systems and other settlements’). This Box highlights 
key findings from Working Groups II and III and substantial gaps in literature where more research is urgently needed relating to policy 
action in cities. It describes methods of addressing mitigation and adaptation in an integrated way across sectors and cities to advance

sustainable development and equity outcomes; and assesses the governance and finance solutions required to support climate resilient 
responses.
Urbanisation: A Megatrend Driving Global Climate Risk and Potential for Low-Carbon and Resilient Futures
Severe weather events, exacerbated by anthropogenic emissions, are already having devastating impacts on people who live in urban 
areas, and on the infrastructure that supports these communities and those of many other distant places (high confidence) (Cai et al., 
2019; Folke et al., 2021). Between 2000 and 2015, the global population in locations that were affected by floods grew by 58–86 million 
(Tellman et al., 2021). The direct economic costs of all extreme events reached USD 210–268 billion in 2020 (Aon, 2021) or about USD 
0.7 billion d-1; this figure does not include knock-on costs in supply chains or days off work lost so that the actual economic costs could

be far higher. Depending on RCP , between half (RCP2.6) and three-quarters (RCP8.5) of the global population could be exposed to periods 
of life-threatening climatic conditions arising from coupled impacts of extreme heat and humidity by 2100 (see Section 6.2.2.1; WGII 
Figure 6.3; Mora et al., 2017; Zhao et al., 2021; Huang et al., 2019).
The interdependencies between infrastructure, services and networks driven by urban production and consumption mean that urban 
systems are now global; remittance flows and investments reach into rural places shaping natural resource use far from the city and 
bring risk to the city when these places are impacted by climate change. This urbanisation megatrend (Kourtit, Nijkamp and Scholten, 
2015) amplifies and shapes the potential impacts of climate events. It provides the economic and institutional framework for integrating

the aims and approaches that can deliver mitigation, adaptation and sustainable development (medium evidence, high agreement) 
(Zscheischler et al., 2018; Dawson et al., 2018; Tsavdaroglou et al., 2018). For cities facing flood damage, wide-ranging impacts have 
been recorded on other urban areas (Simpson et al., 2021; Carter et al., 2021) as production and trade is disrupted (Shughrue et al., 2020).

6
989Cities, Settlements and Key Infrastructure  Chapter 6
In the absence of integrated mitigation and adaptation across and between infrastructure systems and local places, impacts that bring 
urban economies to a standstill can extend into supply chains or across energy networks causing power outages.
Urban settlements are drivers of climate change, generating about 70% of global CO 2-eq emissions (high confidence) (WGI Box TS.14; 
WGIII 8 ES; WGII 6.1, WGII 6.2). This global impact feeds back to cities through the exposure of infrastructure, people and business to 
the impacts of climate-related hazards. Especially in the larger cities, this climate feedback is exacerbated by local choices in urban 
design, land use, building design and human behaviour (Viguié et al., 2020) that shape local environmental conditions. Local and global 
conditions influence the nature of hazards in urban centres: urban form can add up to 2°C to warming, concretisation of open space can

increase runoff and building height and orientation influences wind direction and strength (WGII 6.3).
Building today for resilience and lower emissions is far easier than retrofitting tomorrow. As urbanisation unfolds, its legacy continues 
to be the locking in of emissions and vulnerabilities (high confidence) (Ürge-Vorsatz et al., 2018; Seto et al., 2016). Retrofitting, disaster 
reconstruction and urban regeneration programmes offer scope for strategic direction changes to low-carbon and high-resilience urban 
form and function if they are inclusive in design and implementation. Rapid urban growth means new investment, new buildings and 
infrastructure, new demands for energy and transport and new questions about what a healthy and fulfilling urban life can be. The USD 
90 trillion expected to be invested in new urban development by 2030 (NCE, 2018), is a global opportunity to place adaptation and

mitigation directly into urban infrastructure and planning, and social policy including education and health care and environmental 
management (Ürge-Vorsatz et al., 2018). If this opportunity is missed, if business as usual urbanisation persists, then social and physical 
vulnerability will be not be so easily confronted.
The benefits of actions taken to reduce greenhouse gas (GHG) emissions and climate stressors diminish with delayed action, indicating 
the necessity for rapid responses. Delaying the same actions for increasing the resilience of infrastructure from 2020 to 2030 is estimated 
to have a median cost of at least USD 1 trillion (Hallegatte et al., 2019), while also missing the carbon emissions reductions required in 
the narrowing window of opportunity to limit global warming to 1.5°C (WGI). In contrast, taking integrated actions toward mitigation,

adaptation and sustainable development will provide multiple benefits for the health and well-being of urban inhabitants and avoid 
stranded assets (WGII 6.3, WGII 17; WGIII 5; WGIII 8.2; Cross-Chapter Box FEASIB in Chapter 18).
The Policy-Action Gap: Urban Low-Carbon and Climate Resilient Development
Cities are critical places to realise actions on both adaptation and mitigation simultaneously, with potential co-benefits that extend far 
beyond cities (medium evidence, high agreement) (Grafakos et al., 2020; Göpfert, Wamsler and Lang, 2019). Given rapid changes in the 
built environment, transforming the use of materials and the land intensiveness of urban development including in many parts of the 
Global South in the next decades will be critical, as well as mainstreaming low-carbon development principles in new urban development 
in all regions. Much of this development will be self-built and ‘informal’, and new modes of governance and planning will be required

to engage with this. Integrating mitigation and adaptation now rather than later, through reshaping patterns of urban development and 
associated decision making processes, is a prerequisite for attaining resilient and zero carbon cities.
While more cities have developed plans for climate adaptation and mitigation since AR5, many remain to be implemented (limited 
evidence, high agreement) (Araos et al., 2017; Olazabal and De Gopegui, 2021; Aguiar et al., 2018). A review of local climate mitigation 
and adaptation plans across 885 urban areas of the European Union suggests mitigation plans are more common than adaptation plans, 
and that city size, national legislation and international networks can influence the development of local climate plans with an estimated 
80% of cities with above 500,000 inhabitants having a mitigation and/or an adaptation plan (Reckien et al., 2018b).

Integrated approaches to tackle common drivers of emissions and cascading risks provide the basis for strengthening synergies across 
mitigation and adaptation, and managing possible trade-offs with sustainable development (limited evidence, medium agreement) 
(Grafakos et al., 2019; Landauer, Juhola and Klein, 2019). Analysis of 315 local authority emission reduction plans across the European 
Union reveals that the most common policies cover municipal assets and structures (Palermo et al. 2020). Estimates of emission reductions 
by non-state and sub-state actors in 10 high-emitting economies projected GHG emissions in 2030 would be 1.2–2.0 GtCO 2-eq per year 
or 3.8–5.5% lower compared to scenario projections for current national policies (31.6–36.8 GtCO 2-eq per year) if the policies are 
fully implemented and do not change the pace of action elsewhere (Kuramochi et al. 2020). The value of integrating mitigation and

adaptation is underscored in the opportunities for decarbonising existing urban areas, and investing in social, ecological and technological 
infrastructure resilience (WGII 6.4). Integrating mitigation and adaption is challenging (Landauer, Juhola and Klein, 2019) but can provide 
multiple benefits for the health and well-being of urban inhabitants (Sharifi, 2020).Cross-Working Group Box (continued)

6
990Chapter 6 Cities, Settlements and Key Infrastructure
Effective climate strategies combine mitigation and adaptation responses, including through linking adaptive urban land use with GHG 
emission reductions (medium evidence, high agreement) (Xu et al., 2019; Patterson et al., 2021). For example, urban green and blue 
infrastructure can provide co-benefits for mitigation and adaptation (Ürge-Vorsatz et al., 2018) and is an important entry point for 
integrating adaptation and mitigation at the urban level (Frantzeskaki et al., 2019). Grey and physical infrastructure such as sea defences 
can immediately reduce risk, but can also transfer risk and limit future options. Social policy interventions including social safety nets 
provide financial security for the most at risk and can manage vulnerability determined both by specific hazards and independently.

Hazard-independent mechanisms for vulnerability reduction, such as population-wide social security, provide resilience in the face of 
unanticipated cascading impacts or surprise and novel climate-related hazard exposure. Social interventions can also support, or be 
led by, ambitions to reach the Sustainable Development Goals (Archer, 2016). Climate resilient development invites planners to plan 
interventions and monitor the effectiveness of outcomes beyond individual projects and across wider remits that reach into sustainable 
development. Curbing the emission impacts of urban activities to reach net zero in the next decades while improving the resilience of 
urban areas necessitates an integrated response now.
Key gaps in knowledge include urban enabling environment; how smaller settlements, low-income communities living in slums and 
informal settlements, but also those in rental housing spread across the city, and actions to reduce supply chain risk can be supported to

accelerate equitable and sustainable adaptation in the face of financial and governance constraints (Birkmann et al., 2016; Shi et al., 
2016; Dulal, 2019; Rosenzweig et al., 2018b).
Enabling Action
Innovative governance and finance solutions are required to manage complex and interconnected risks across essential key infrastructures, 
networks and services and meet basic human needs in urban areas (medium confidence) (Moser et al., 2019; Colenbrander, Dodman and 
Mitlin, 2018). There are many examples of ‘ready-to-use’ policy tools, technologies and practical interventions for policymakers seeking 
to act on adaptation and mitigation (Keenan, Chu and Peterson, 2019; Bisaro and Hinkel, 2018; Chirambo, 2021). Tax and fiscal incentives 
for business and individuals can help support city-wide change behaviour toward low carbon and risk reducing choices. Change can start

where governments have most control; in public sector institutions and investment, but the challenge ahead requires partnership with 
private sector and community actors acting at scale and with accountability. Urban climate governance and finance needs to address 
urban inequalities at the forefront if the urban opportunity is to realise the ambition of the Sustainable Development Goals.
Increasing investment at pace will put pressure on governance capability and transparency and accountability of decision making 
(medium confidence) (WG II 6.6.4.5). Urban climate action that actively includes local actors and is built on an evidence base open to 
independent scrutiny is more likely to avoid unintended, negative maladaptive impacts and mobilise a wide range of local capacities. 
In the long run, this is also more likely to carry public support, even if some experiments and investments do not deliver the intended

social benefits. Legislation, technical capacity and governance capability is required to be able to absorb additional finance. About USD 
384 billion yr-1 of climate finance has been invested in urban areas in recent years. This remains at about 10% of the annual climate 
finance that would be necessary for low-carbon and resilient urban development (Negreiros et al., 2021). Rapid deployment of funds 
to stimulate economies in recovery from COVID-19 have highlighted the pitfalls of funding expansion ahead of policy innovation and 
capacity building. The result can be an intensification of existing urban forms, exactly the kinds of choices and preferences that contribute 
to risk creation and its concentration among those with little public voice or economic power.
Iterative and experimental approaches to climate adaptation and mitigation decision making co-generated in partnership with

communities, can advance climate-resilient decarbonisation (medium evidence, high agreement) (Caldarice, Tollin and Pizzorni, 2021; 
Culwick et al., 2019; van der Heijden and Hong, 2021). Conditions of complexity, uncertainty and constrained resources require innovative 
solutions which are both adaptive and anticipatory. Complex interactions among multiple agents in times of uncertainty makes decision 
making about social, economic, governance, and infrastructure choices challenges, and can lead decision makers to postpone action. 
This is the case for those balancing household budgets, residential investment portfolios and city-wide policy responsibilities. Living with 
climate change requires changes to business-as-usual design making. Co-design and collaboration with communities through iterative 
policy experimentation can point the way toward CRD pathways (Ataöv and Peker, 2021). Key to successful learning is transparency in

policymaking, inclusive policy processes and robust local modelling, monitoring and evaluation, which are not yet widely undertaken 
(Ford et al., 2019; Sanchez Rodriguez, Ürge-Vorsatz and Barau, 2018).
The diversity of cities’ experiences of climate mitigation and adaptation strategies brings an advantage for those city government and 
other actors willing to ‘learn together’ (limited evidence, high agreement) (Bellinson and Chu, 2019; Haupt and Coppola, 2019). While 
contexts are varied, policy options are often similar enough for the sharing of experiments and policy champions. Sharing expertise can Cross-Working Group Box (continued)

6
991Cities, Settlements and Key Infrastructure  Chapter 6
build on existing regional and global networks, many of which have already placed knowledge, learning and capacity building at the 
centre of their agendas. Learning from innovative forms of governance and financial investment, and strengthening co-production of 
policy through inclusive access to knowledge and resources, can help address mismatches in local capacities, strengthen wider Sustainable 
Development Goals and COVID-19 Recovery agendas (limited evidence, medium agreement). Perceptions of risk can greatly influence 
the reallocation of capital and shift financial resources (Battiston et al., 2021). Coupling mitigation and adaptation in an integrated 
approach offers opportunities to enhance efficiency, increases the coherence of urban climate action, generates cost savings and provides 
opportunities to reinvest the savings into new climate action projects to make all urban areas and regions more resilient.

Local governments play an important role in driving climate action across mitigation and adaptation as managers of assets, regulators, 
mobilisers and catalysts of action, but few cities are undertaking transformative climate adaptation or mitigation actions (limited 
evidence, medium agreement) (Heikkinen, Ylä-Anttila and Juhola, 2019). Local actors are providers of infrastructure and services, 
regulators of zoning, and can be conveners and champions of an integrated approach for mitigation and adaptation at multiple levels 
(limited evidence, high confidence). New opportunities in governance and finance can enable cities to pool resources together and 
aggregate interventions to innovate ways of mobilising urban climate finance at scale (White and Wahbah, 2019; Simpson et al., 2019; 
Colenbrander, Dodman and Mitlin, 2018). However, research increasingly points toward the difficulties faced during the implementation

of climate financing in situ, such as the fragmentation of structures of governance capable of managing large investments effectively 
(Mohammed et al., 2019).
Scaling up transformative place-based action for both adaptation and mitigation requires enabling conditions including land-based 
financing, intermediaries and local partnerships (medium evidence, high agreement) (Tirumala and Tiwari, 2021; ). Governance 
structures that combine actors working at different levels with different mixes of tools are effective in addressing challenges related 
to implementation of integrated action, while cross-sectoral coordination is necessary (Singh et al., 2020). Joint institutionalisation of 
mitigation and adaptation in local governance structures can also enable integrated action (Göpfert et al., 2020; Hurlimann et al., 2021). 
However, the proportion of international finance that reaches local recipients remains low, despite the repeated focus of climate policy

on place-based adaptation and mitigation (Manuamorn, 2019). Green financing instruments that enable local climate action without 
exacerbating current forms of inequality can jointly address mitigation, adaptation and sustainable development. Climate finance that 
also reaches beyond non-state enterprises, including SMEs, communities and NGOs, and is responsive to the needs of urban inhabitants, 
including disabled individuals and different races or ethnicities, is essential for inclusive and resilient urban development (Colenbrander, 
Dodman and Mitlin, 2018Frenova, 2020). Developing networks that can exert climate action at scale is another priority for climate 
finance.
The urbanisation megatrend is an opportunity to transition global society. Enabling urban governance to avert cascading risk and achieve 
low-carbon, resilient development will involve co-production of policy and planning, rapid implementation and greater cross-sector

coordination, monitoring and evaluation (limited evidence, medium agreement) (Grafakos et  al., 2019; Di Giulio et  al., 2018). New 
constellations of responsible actors are required to manage hybrid local-city or cross-city risk management and decarbonisation initiatives 
(limited evidence, medium agreement). These may increasingly benefit from linkages across more urban and more rural space as 
recognition of cascading and systemic risk brings recognition of supply chains, remittance flows and migration trends as vectors of risk 
and resilience. Urban governance will be better prepared in planning, prioritising and financing the kind of measures that can reduce GHG 
emissions and improve resilience at scale and pace when considering a view of cascading risks and carbon lock-ins globally, while acting 
locally to address local limitations and capacities, including the needs and priorities of urban citizens (Colenbrander, Dodman and Mitlin, 
2018).Cross-Working Group Box (continued)

6
992Chapter 6 Cities, Settlements and Key Infrastructure
FAQ 6.1 | Why and how are cities, settlements and different types of infrastructure especially vulnerable to the 
impacts of climate change?
Cities, settlements and infrastructure become vulnerable when investment decisions fail to take the risks of climate change fully into account. 
Such failures can result from a lack of understanding, competing priorities, a lack of finance or access to appropriate technology. Around the 
world, smaller cities and poorer populations are often most vulnerable and suffer the most over time, while large cities can register the greatest 
losses to individual events.
The world is urban. Billions of people live in towns and cities. Hardly anyone, even in remote rural locations, is 
separated from the flows of trade that connect the world and are held together by networks of transport and

communication infrastructure systems. Connected networks once broken can cascade out, multiplying impacts 
across urban and rural areas. When major manufacturing centres or regionally important ports are impacted, global 
trade suffers. For example, flooding in Bangkok in 2011 led to a global shortage in semiconductors and a slowdown 
in global computer manufacturing.
Despite cities generating wealth, additional vulnerability to climate change is being created in urban areas 
every day. Demographic change, social and economic pressures, and governance failures that drive inequality 
and marginality mean that increasing numbers of people who live in towns and cities are exposed to flooding, 
temperature extremes and water or food insecurity. This leads to an adaptation gap, where rich neighbourhoods 
can afford strategies to reduce vulnerability while poorer communities are unable to do the same. Although this

would be so even without a changing climate, climate change increases the variability and extremes of weather, 
exposing more people, businesses and buildings to floods and other events. The combination of rising vulnerability 
and increasing exposure translates to a growth in the number of people and properties at risk from climate change 
in cities worldwide.
Around the world, vulnerability is rising but differs considerably between and within urban areas. Settlements of 
up to 1 million people are the most rapidly expanding and also among the most vulnerable. These settlements 
often have limited community level organisation and might not have a dedicated local government. Coping with 
rapid population growth under conditions of climate change and constrained capacity is a major challenge. For 
large cities, multiple local governments and well-organised community-based organisations interact with large

businesses and national political parties in a complicated cocktail of interests that can interfere with planning and 
action to reduce vulnerability.
For the poorest living in urban slums, informal settlements or renting across the city, lack of secure tenure and 
inadequate access to basic services compound vulnerability. But even the wealthy in large cities are not fully protected 
from climate change-related shocks. Just like breaks in infrastructure between towns and rural settlements, big city 
infrastructure can be broken by even local landslides, floods or temperature events, with consequences cascading 
across the city. Electricity blackouts are the most common and can affect water pumping, traffic regulation and 
streetlights, as well as hospitals, schools and homes. Still, it is the urban poor and marginalised who experience the 
greatest exposure, most vulnerability and least capacity to cope.

Rounds of exposure and impact can reduce the capacity of survivors to cope with future events. As a result, the 
already vulnerable and exposed become more vulnerable over time, increasing urban inequalities. But this need 
not be the case. Focussing on vulnerability reduction is not easy, it requires joined-up action across social and 
economic development sectors, together with critical infrastructure planning. It often also means partnering local 
government with informal and community-based actors. But there is considerable experience globally on what 
works and how to deliver reduced vulnerability for the urban poor and for cities as a whole. The challenge is to scale 
up this experience and accelerate its application to keep pace with climate change and address the adaptation gap.

6
993Cities, Settlements and Key Infrastructure  Chapter 6
FAQ 6.2 | What are the key climate risks faced by cities, settlements and vulnerable populations today, and how 
will these risks change in a mid-century (2050) 2°C warmer world?
Climate change will interact with the changing physical environment in cities and settlements to create or exacerbate a range of risks. Rising 
temperatures and heatwaves will cause human illness and morbidity, as well as infrastructure degradation and failures, while heavy rainfall 
and sea level rise will worsen flooding. Low-income groups and other vulnerable populations will be affected most severely because of where 
they live and their limited ability to cope with these stresses.
Cities and settlements are constantly changing. Their populations grow and shrink, economic activities expand 
or decline, and political priorities shift. The risks that cities and their residents face are influenced by both urban

change and climate change. The seriousness of these risks into the 21st Century will be shaped by the interactions 
between drivers of change including population growth, economic development and land use change.
In a warming world, increasing air temperature makes the urban heat island effect in cities worse. One key risk 
is heatwaves in cities that are likely to affect half of the future global urban population, with negative impacts on 
human health and economic productivity. Heat and built infrastructure such as streets and houses interact with 
each other and magnify risks in cities. For instance, higher urban temperatures can cause infrastructure to overheat 
and fail, as well as increase the concentration of harmful air pollutants such as ozone.
The density of roads and buildings in urban areas increases the area of impermeable surfaces, which interact with 
more frequent heavy precipitation events to increase the risk of urban flooding. This risk of flooding is greater

for coastal settlements due to sea level rise and storm surges from tropical cyclones. Coastal inundation in the 
Miami-Dade region in Florida, USA, is estimated to have caused over USD 465  million in lost real estate value 
between 2005 and 2016, and it is likely that coastal flood risks in the region beyond 2050 will increase without 
adaptation to climate change.
Within cities, different groups of people can face different risks. Many low-income residents live in informal 
settlements alongside coasts or rivers, which greatly heightens exposure and vulnerability to climate-driven hazards. 
In urban areas in Ghana, for example, risks from urban flooding can compound health risks, and have resulted in 
outbreaks of malaria, typhoid and cholera. Those outbreaks have been shown to disproportionately affect poorer 
Severe risks in cities and settlements also arise from reduced water availability. As urban areas grow, the amount

of water required to meet basic needs of people and industries increases. When increased demand is combined 
with water scarcity from lower rainfall due to climate change, water resource management becomes a critical 
issue. Low-income groups already face major challenges in accessing water, and the situation is likely to worsen 
due to growing conflicts over scarce resources, increasing water prices and diminishing infrastructure provisions in 
ever-expanding informal settlements.
These key risks already differ greatly between cities, and between different groups of people in the same city. By 
2050, these discrepancies are likely to be even more apparent. Cities with limited financial resources, regulatory 
authority and technical capacities are less equipped to respond to climate change. People who already have fewer 
resources and constrained opportunities face higher levels of risk because of their vulnerability. As a result of this,

key risks vary not only over time as climate change is felt more strongly, but also over space, between cities exposed 
to different hazards and with different abilities to adapt, and between social groups, meaning between people 
who are more or less affected and able to cope.

6
994Chapter 6 Cities, Settlements and Key Infrastructure
FAQ 6.3 | What adaptation actions in human settlements can contribute to reducing climate risks and building 
resilience across building, neighbourhood, city and global scales?
Settlements bring together many activities, so climate action will be most effective if it is integrated and collaborative. This requires (i) embedding 
information on climate change risks into decisions; (ii) building capacity of communities and institutions; (iii) using both nature-based and 
traditional engineering approaches; (iv) working in partnership with diverse local planning and community organisations; and (v) sharing best 
practice with other settlements.
Settlements bring together people, buildings, economic activities and infrastructure services, and thus integrated, 
cross-sector, adaptation actions offer the best way to build resilience to climate change impacts. For example, actions

to manage flood risk include installing flood proofing measures within and outside properties, improving capacity 
of urban drainage along roads, incorporating nature-based solutions (NbS) within the urban areas, constructing 
flood defences and managing land upstream of settlements to reduce runoff.
Adaptation actions will be more effective if they are implemented in partnership with local communities, national 
governments, research institutions, and the private and third sector. Climate action should not be considered as an 
additional or side action to other activities. Rather, climate action should be mainstreamed into existing processes, 
including those that contribute to the UN Sustainable Development Goals (2015) and New Urban Agenda adopted 
at the UN Conference on Housing and Sustainable Urban Development (Habitat III) in 2016. Cities are already 
coming together through international networks to share good practice about adaptation actions, speeding up the

dissemination of knowledge.
This integrated approach to adaptation in human settlements needs to be supported by various other actions, 
including potential co-benefits with carbon emissions reductions, public health and ecosystem conservation goals. 
First, information on climate risks needs to be embedded into the architectural design, delivery and retrofitting 
of housing, transportation, spatial planning and infrastructure across neighbourhood and city scales. This includes 
making information on climate impacts widely available, updating design standards and strengthening regulation 
to avoid development in high-risk locations. Second, the capacity of communities needs to be strengthened, 
especially among those in informal settlements, the poorest and other vulnerable groups including minorities, 
migrants, women, children, elderly, disabled and people with serious health conditions such as obesity. This involves

raising awareness, incorporating communities into adaptation processes, and strengthening regulation, policies and 
provision of infrastructure services. Third, nature-based solutions should be integrated to work alongside traditional 
‘grey’ or engineered infrastructure. Vegetation corridors, greenspace, wetlands and other green infrastructure can 
be woven into the built environment to reduce heat and flood risks, whilst providing other benefits such as health 
and biodiversity.
Although even the largest city covers only a small area of the planet, all settlements are part of larger catchments 
from which people, water, food, energy, materials and other resources support them. Actions within cities should 
be mindful of wider impacts and avoid displacing issues elsewhere.

6
995Cities, Settlements and Key Infrastructure  Chapter 6
FAQ 6.4 | How can actions that reduce climate risks in cities and settlements also help to reduce urban poverty, 
enhance economic performance and contribute to climate mitigation?
If carefully planned, adaptation actions can reduce exposure to climate risk and reduce urban poverty, advance sustainable development and 
mitigate greenhouse gas emissions. When adaptation responses are equitable, and if a range of voices are heard in the planning process, the 
needs of the disadvantaged are more likely to be addressed and wider societal benefits can be maximised.
Urbanisation is a global trend which is interacting with climate change to create complex risks in cities and 
settlements, especially for those that already have high levels of poverty, unemployment, housing informality and 
backlogs of services. Many cities and settlements are seeing increasing action to manage climate risks. On top of

reducing communities’ exposure to climate risk, adaptation actions can have benefits for reducing urban poverty 
and enhancing economic performance in ways that reduce inequality and advance sustainability goals. Adaptation 
actions, however, can also have unintended consequences. That is why care needs to be taken to ensure climate 
adaptation planning and development of new infrastructure does not exacerbate inequality or negatively impact 
other sustainable development priorities. Climate adaptation planning is most effective when it is sensitive to the 
diverse ways that low-income and minority communities are more likely to experience climate risk, including women, 
children, migrants, refugees, internally displaced peoples and racial/ethnic minority groups, among others.
Adapting to climate change can have benefits for reducing greenhouse gas (GHG) emissions and urban inequalities.

In cities where growing numbers of people live in informal settlements, introducing risk-reducing physical 
infrastructure such as piped water, sanitation and drainage systems can enhance the quality of life of the community. 
At the same time, those measures can increase health outcomes and reduce urban inequalities by reducing exposure 
to flooding or heat impacts. In less developed countries, less than 60% of the urban population have access to piped 
water which, in turn, impacts their health and well-being. Increasingly, housing is being built better to manage 
heat risk through insulation or changing building orientation, or to flood risk by raising structures, which then 
contributes to well-being and ability to work. Improvements to early warning systems can help people evacuate 
rapidly in case of storm surges or flooding. Although the most vulnerable often do not get these warnings in time.

Carefully planned nature-based solutions (NbS), such as public green space, improved urban drainage systems and 
storm water management, can deliver both health and development benefits. When these adaptation actions 
succeed, water, waste and sanitation can be improved to better manage climate risk and provide households and 
cities with better services. Many nature-based solutions entail bringing back plants and trees into cities, which also 
helps to reduce the concentration of heat-trapping GHGs in the atmosphere.
When care is taken to ensure that adaptation responses are equitable, and that a range of voices are heard in 
planning, the needs of the disadvantaged are more likely to be addressed. For example, a study that looked 
at transport plans across 40 cities in Portugal saw that some urban communities have prioritised the needs of 
disadvantaged users such as the elderly and disabled, while at the same time reducing urban transport emissions

and enhancing public well-being and equity of transport. On the other hand, in some cities, there is evidence of 
emerging trade-offs associated with climate adaptation actions where sea walls and temporary flood barriers were 
erected in economically valuable areas and not is less well-off areas. Going forward, it is important to ensure that 
vulnerable groups’ needs are carefully considered, both in terms of climate and other risks, as this has not been 
sufficiently done in the past.

6
996Chapter 6 Cities, Settlements and Key Infrastructure
FAQ 6.5 | What policy tools, governance strategies and financing arrangements can enable more inclusive and 
effective climate adaptation in cities and settlements?
Inclusive and effective climate adaptation requires efforts at all levels of governance, including the public sector, the private sector, the third 
sector, communities and intermediaries such as universities or think tanks. Inclusive and effective adaptation requires action fit for the diverse 
conditions in which it is needed. Collaborative dialogues can help to map both adaptation opportunities and potential negative impacts.
There is no one-size-fits-all approach to ensure that climate adaptation efforts have positive results and include the 
concerns of everyone affected. Cities and local communities are diverse, and thus they have diverse perspectives on

what responses to prioritise. Moreover, adaptation efforts may impact people’s lives in very different ways. Policy 
tools, strategies and financial arrangements for adaptation can include all society sectors and address socioeconomic 
inequalities. Planning and decision making must respond to marginalised voices and future generations (including 
children and youth).
Efforts to adapt to climate change can be incremental, reformist or transformational, depending on the scale of 
the change required. Incremental action may address specific climate impacts in a given place, but do not challenge 
the social and political institutions that prevent people from bouncing back better. Reformist action may address 
some of the social and institutional drivers of exposure and vulnerability, but without addressing the underlying 
socioeconomic structures that drive differential forms of exposure. For example, social protection measures may

improve people’s capacity to cope with climate impacts, but that improved capacity will depend on maintaining 
such protection measures. Transformative action involves fundamental changes in political and socioeconomic 
systems, oriented toward addressing vulnerability drivers (e.g., socioeconomic inequalities, consumption cultures). 
All forms of adaptation are relevant to deliver resilient futures because of the variability of conditions in which 
adaptation action is needed.
Local and regional governments play an essential role in delivering planning and institutional action suited to local 
conditions in cities and settlements. Potential strategies can span multiple sectors and scales, ranging from land 
use management, building codes, critical infrastructure designs and community development actions, to different 
legal, financial, participatory decision making and robust monitoring and evaluation arrangements. NGOs or third

sector organisations can also play a coordinating role by building dialogues across governments, the private sectors 
and communities through effective communication and social learning. Local action tends to falter without the 
support of national governments as they are often facilitators of resources and finance. They can create institutional 
frameworks that facilitate (rather than impede) local action. National governments also play a crucial role in the 
development of large-scale infrastructures.
Private actors can also drive adaptation action. The evaluation of private-led infrastructure and housing projects 
suggests that the prioritisation of profit, however, may have a detrimental impact on the overall resilience of a 
place. New institutional models such as public–private partnerships respond to the shortcomings of both the public 
and private sectors. Still, the evidence of them facilitating the inclusion of multiple actors is mixed.

The private sector can mobilise finance. However, the forms of finance available for adaptation are limited and 
directed to huge projects that do not always address local adaptation needs. Private actors tend to join adaptation 
projects when there is an expectation of large profits, such as in interventions that increase real estate value. 
Private-led adaptation can lead to ‘gentrification’ whereby low-income populations are relocated from urban 
centres and safer settlements. Models that enable the collaboration between public, private and civil society sectors 
have greater potential to mobilise adaptation finance in inclusive ways.
Forms of collaborative planning and decision making can create dialogues for a sustainable future in cities, 
settlements and infrastructure systems. Adaptation action needs multiple approaches. For example, adaptation 
needs both actions that depend on dialogues between multiple actors (e.g., urban planning and zoning) and action

that follows strong determination and leadership (e.g., declarations of emergency and target commitments). 
There are adaptation actions that depend on place-based conditions (e.g., flood defences) and those that require 
considering interactions across scales (e.g., regulatory frameworks). The growth of adaptation capacities, fostering 
dialogues, empowered communities, multi-scalar assessments and foresight within current institutions can support 
effective and inclusive adaptation action that is also sustained in the long term.

7
1044Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Climate-related illnesses, premature deaths, malnutrition in all its 
forms, and threats to mental health and well-being are increasing 
(very high confidence1). Climate hazards are a growing driver of 
involuntary migration and displacement (high confidence) and are 
a contributing factor to violent conflict (high confidence). These 
impacts are often inter-connected, are unevenly distributed across and 
within societies, and will continue to be experienced inequitably due 
to differences in exposure and vulnerability (very high confidence). 
Cascading and compounding risks affecting health due to extreme 
weather events have been observed in all inhabited regions, and risks 
are expected to increase with further warming (very high confidence) 
{7.1.3, 7.1.4; Cross-Chapter Box COVID in Chapter 7; 7.2.1, 7.2.2, 7.2.3, 
7.2.4, 7.3.1, 7.3.2, 7.3.3, 7.4.1, 7.4.4; Cross-Chapter Box  HEALTH in

Chapter 7; Cross-Chapter Box ILLNESS in Chapter 2}.
Since AR5, new evidence and awareness of current impacts 
and projected risks of climate change on health, well-being, 
migration and conflict have emerged, including greater 
evidence of the detrimental impacts of climate change on 
mental health (very high confidence). New international agreements 
were reached on climate change (Paris Agreement), disaster risk 
reduction (DRR) (Sendai Agreement), sustainable development (the 
Sustainable Development Goals (SDGs)), urbanisation (The New 
Urban Agenda), migration (Global Compact for Safe, Orderly and 
Regular Migration) and refugees (Global Compact on Refugees) that, 
if achieved, would reduce the impacts of climate change on health, 
well-being, migration and conflict (very high confidence). However, the 
challenges with implementing these agreements are highlighted by 
the coronavirus disease 2019 (COVID-19) pandemic, which exposed

systemic weaknesses at community, national and international levels 
in the ability of societies to anticipate and respond to global risks 
(high confidence). Incremental changes in policies and strategies 
have proven insufficient to reduce climate-related risks to health, 
well-being, migration and conflict, highlighting the value of more 
integrated approaches and frameworks for solutions across systems 
and sectors that are embodied in these new international agreements 
(high confidence) {7.1.3, 7.2.1, 7.4.1, 7.4.2, 7.4.3, 7.4.6; Cross-Chapter 
Box COVID in Chapter 7}.
With proactive, timely and effective adaptation, many risks 
for human health and well-being could be reduced and some 
potentially avoided (very high confidence). A significant adaptation 
gap exists for human health and well-being and for responses to disaster 
risks (very high confidence). Nationally Determined Contributions 
(NDCs) to the Paris Agreement from low- and middle-income countries

identify health as a priority concern. National planning on health and 
climate change is advancing, but the comprehensiveness of strategies 
and plans need to be strengthened, and implementing action on 
key health and climate change priorities remains challenging (high 
confidence). Multi-sectoral collaboration on health and climate change 
policy is evident, with uneven progress, and financial support for health 
1 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is 
expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.adaptation is only 0.5% of dispersed multi-lateral climate finance 
projects (high confidence). This level of investment is insufficient to 
protect population health and health systems from most climate-
sensitive health risks (very high confidence) {7.4.1, 7.4.2, 7.4.3}.
Climate resilient development has a strong potential to 
generate substantial co-benefits for health and well-being and 
to reduce risks of involuntary displacement and conflict (very 
high confidence). Sustainable and climate resilient development 
that decreases exposure, vulnerability and societal inequity and 
that increases timely and effective adaptation and mitigation more 
broadly, has the potential to reduce but not necessarily eliminate 
climate change impacts on health, well-being, involuntary migration 
and conflict (high confidence). This development includes greenhouse

gas (GHG) emission reductions through clean energy and transport; 
climate-resilient urban planning; sustainable food systems that lead 
to healthier diets; universal access to healthcare and social protection 
systems; wide-scale, proactive adaptive capacity building for climate 
change; and achievement of the SDGs (very high confidence). Meeting 
the objectives of the Global Compact for Safe, Orderly, and Regular 
Migration and building inclusive and integrative approaches to 
climate-resilient peace would help prevent health risks related to 
migration and conflict (high agreement, medium evidence). The net 
global financial gains from these co-benefits to health and well-
being, including avoided hospitalisations, morbidity and premature 
deaths, exceed the financial costs of mitigation (high confidence). 
As an example of co-benefits, the financial value of health benefits 
from improved air quality alone is projected to be greater than the

costs of meeting the goals of the Paris Agreement (high confidence). 
All pathways to climate resilient development, including those for the 
health and healthcare systems, involve balancing complex synergies 
and trade-offs between development pathways and the options that 
underpin climate mitigation and adaptation pathways (very high 
confidence) {7.4.6; Cross-Chapter Box  HEALTH in Chapter 7; Cross-
Chapter Box MIGRATE in Chapter 7}.
Key transformations are needed to facilitate climate 
resilient development pathways (CRDPs) for health, well-
being, migration and conflict avoidance (high confidence). The 
transformational changes will be more effective if they are 
responsive to regional, local and Indigenous knowledge and 
consider the many dimensions of vulnerability, including those 
that are gender- and age-specific (high confidence). A key pathway 
towards climate resilience in the health sector is universal access to

primary healthcare, including mental healthcare (high confidence). 
Investments in other sectors and systems that improve upon the social 
determinants of health have the potential to reduce vulnerability to 
climate-related health risks (high confidence). Links between climate 
risks, adaptation, migration and labour markets highlight the value 
of providing better mobility options as part of transformative change 
(medium confidence). Strong governance and gender-sensitive 
approaches to natural resource management can reduce the risk of 
inter-group conflict in climate-disrupted areas (medium confidence)

7
1045Health, Wellbeing and the Changing Structure of Communities  Chapter 7
{7.4.6; Cross-Chapter Box  COVID in Chapter 7; Cross-Chapter 
Box HEALTH in Chapter 7; Cross-Chapter Box GENDER in Chapter 18; 
Cross-Chapter Box INDIG in Chapter 18; Cross-Chapter Box MIGRATE 
in Chapter 7}.
Climate hazards are increasingly contributing to a growing 
number of adverse health outcomes (including communicable 
and non-communicable diseases (NCDs)) in multiple geographical 
areas (very high confidence) . The net impacts are largely negative at 
all scales ( very high confidence), and there are very few examples 
of beneficial outcomes from climate change at any scale ( high 
confidence). While malaria incidence has declined globally due to non-
climatic socioeconomic factors and health system responses, a shift to 
higher altitudes has been observed as the climate warms (very high 
confidence). Climate variability and change (including temperature,

relative humidity and rainfall) and population mobility are significantly 
and positively associated with observed increases in dengue globally; 
chikungunya virus in Asia, Latin America, North America and Europe (high 
confidence); Lyme disease vector Ixodes scapularis in North America 
(high confidence); and Lyme disease and tick-borne encephalitis vector 
Ixodes ricinus in Europe (medium confidence). Higher temperatures (very 
high confidence), heavy rainfall events (high confidence) and flooding 
(medium confidence) are associated with an increase of diarrhoeal 
diseases in affected regions, including cholera (very high confidence), 
other gastrointestinal infections (high confidence) and food-borne 
diseases due to Salmonella and Campylobacter (medium confidence).  
Floods have led to increases in vector- and waterborne diseases and 
to disturbances of public health services (high confidence). Climate 
extremes increase the risks of several types of respiratory tract infections

(high confidence). Climate-related extreme events such as wildfires, 
storms and floods are followed by increased rates of mental illness in 
exposed populations (very high confidence) {7.2.1, 7.2.2, 7.2.3, 7.2.4, 
7.2.5, 7.2.6}.
Several chronic, non-communicable respiratory diseases are 
climate-sensitive based on their exposure pathways (e.g., 
heat, cold, dust, small particulates, ozone, fire smoke and 
allergens) (high confidence), although climate change is not the 
dominant driver in all cases. Worldwide, rates of adverse health 
impacts associated with small particulate matter (PM) exposure have 
decreased steadily due to decreasing primary emissions (very high 
confidence), while rates of adverse health impacts from ozone air 
pollution exposure have increased (very high confidence). Exposure to 
wildland fires and associated smoke has increased in several regions 
(very high confidence). Spring pollen season start dates in northern

mid-latitudes are occurring earlier due to climate change, increasing 
the risks of allergic respiratory diseases (high confidence) {7.2.3.2}.
Heat is a growing health risk due to burgeoning urbanisation, 
an increase in high temperature extremes and demographic 
changes in countries with aging populations (very high confidence). 
Potential hours of work lost due to heat has increased significantly 
over the past two decades (high confidence). Some regions are already 
experiencing heat stress conditions at or approaching the upper limits 
of labour productivity (high confidence). A significant proportion of warm-season heat-related mortality in temperate regions is linked 
to observed anthropogenic climate change (medium confidence) but 
greater evidence is required for tropical regions. For some heatwave 
events over the last two decades, associated health impacts can be at 
least partially attributed to observed climate change (high confidence).

Extreme heat has negative impacts on mental health, well-being, 
life satisfaction, happiness, cognitive performance and aggression 
(medium confidence) {7.2.4.1, 7.2.4.5}.
Climate variability and change contribute to food insecurity, 
which can lead to malnutrition, including undernutrition, 
overweight and obesity, and to disease susceptibility in low- and 
middle-income countries (high confidence). Populations exposed 
to extreme weather and climate events may consume inadequate or 
insufficient food, leading to malnutrition and increasing the risk of 
disease (high confidence). Children and pregnant women experience 
disproportionately greater adverse nutrition and health impacts (high 
confidence). Climatic influences on nutrition are strongly mediated by 
socioeconomic factors (very high confidence) {7.2.4.4, 7.3.1}.
Extreme climate events act as both direct drivers (e.g., 
destruction of homes by tropical cyclones) and as indirect

drivers (e.g., rural income losses during prolonged droughts) of 
involuntary migration and displacement (very high confidence). 
Most documented examples of climate-related displacement occur 
within national boundaries, with international movements occurring 
primarily within regions, particularly between countries with 
contiguous borders (high confidence). Global statistics collected since 
2008 by the Internal Displacement Monitoring Centre (IDMC) show 
an annual average of over 20 million people internally displaced by 
weather-related extreme events, with storms and floods the most 
common drivers (high confidence). The largest absolute number of 
people displaced by extreme weather each year occurs in Asia (South, 
Southeast and East), followed by sub-Saharan Africa, but small island 
states in the Caribbean and South Pacific are disproportionately affected 
relative to their small population size (high confidence). Immobility in

the context of climate risks can reflect vulnerability and lack of agency 
but can also be a deliberate choice of people to maintain livelihoods, 
economic considerations and social and cultural attachments to place 
(high confidence) {7.2.6; Cross-Chapter Box MIGRATE in Chapter 7}.
Climate hazards have affected armed conflict within countries 
(medium confidence), but the influence of climate is small 
compared to socioeconomic, political and cultural factors (high 
confidence). Climate increases conflict risk by undermining food 
and water security, income and livelihoods in situations where there 
are large populations, weather-sensitive economic activities, weak 
institutions and high levels of poverty and inequality (high confidence). 
In urban areas, food and water insecurity and inequitable access to 
services has been associated with civil unrest where there are weak 
institutions (medium confidence). Climate hazards are associated

with increased violence against women, girls and vulnerable 
groups, and the experience of armed conflict is gendered (medium 
confidence). Adaptation and mitigation projects implemented without 
consideration of local social dynamics have exacerbated non-violent 
conflict (medium confidence) {7.2.7}.

7
1046Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Projected Risks and Vulnerabilities
A significant increase in ill health and premature deaths from 
climate-sensitive diseases and conditions is projected due to 
climate change (high confidence). An excess of 250,000 deaths 
yr–1 by 2050 attributable to climate change is projected due to heat, 
undernutrition, malaria and diarrhoeal disease, with more than half of 
this excess mortality projected for Africa (compared to a 1961–1991 
baseline period for a mid-range emissions scenario) (high confidence). 
Risks for heat-related morbidity and mortality, ozone-related mortality, 
malaria, diseases carried by Aedes sp. mosquitoes, Lyme disease and 
West Nile fever, as well as the temperatures at which risk transitions 
occur (i.e. from moderate to high to very high), are contingent on 
future development pathways (high confidence) {7.3.1}.
Climate change is projected to significantly increase population

exposure to heatwaves (very high confidence) and heat-related 
morbidity and mortality (high confidence). Models suggest exposure 
increases 16  times under Representative Concentration Pathway 
(RCP)4.5 and 36  times under RCP8.5, with the impact of warming 
amplified under development pathways that do not foster sustainable 
development. Globally, the impact of projected climate change on 
temperature-related mortality is expected to be a net increase under 
RCP4.5 to RCP8.5, even with adaptation (high confidence). Heat 
related cardiovascular disease mortality is projected to increase by the 
end of this century (high confidence). Strong geographical differences 
in heat-related mortality are projected to emerge later this century, 
mainly driven by population growth and aging in regions with tropical 
and subtropical climates (very high confidence) {7.3.1}.
The burdens of several climate-sensitive food-borne, waterborne,

and vector-borne diseases (VBDs) are projected to increase under 
climate change, assuming no additional adaptation ( very high 
confidence) . The distribution and intensity of transmission of malaria is 
expected to decrease in some areas and increase in others, with increases 
projected mainly along the current edges of its geographic distribution 
in endemic areas of sub-Saharan Africa, Asia and South America (high 
confidence). Dengue risk will increase, with a larger spatio-temporal 
distribution in Asia, Europe and sub-Saharan Africa under RCP6.0 and 
RCP8.5, potentially putting another 2.25  billion people at risk (high 
confidence). Higher incidence rates are projected for Lyme disease in 
the Northern Hemisphere (high confidence) and for transmission of 
Schistosoma mansoni in eastern Africa (high confidence) {7.3.1; Cross-
Chapter Box ILLNESS in Chapter 2}.
Increasing atmospheric concentrations of carbon dioxide and

climate change are projected to increase diet-related risk 
factors and related non-communicable diseasess globally 
and increase undernutrition, stunting and related childhood 
mortality particularly in Africa and Asia, with outcomes 
depending on the extent of mitigation and adaptation (high 
confidence). These projected changes are expected to slow progress 
towards eradication of child undernutrition and malnutrition (high 
confidence). Higher atmospheric concentrations of carbon dioxide 
reduce the nutritional quality of wheat, rice and other major crops, 
potentially affecting millions of people at a doubling of carbon dioxide 
(very high confidence) {7.3.1}.Climate change is expected to have adverse impacts on 
well-being and to further threaten mental health (very high 
confidence). Children and adolescents, particularly girls, elderly people, 
and people with existing mental, physical and medical challenges are

particularly at risk. Mental health impacts are expected to arise from 
exposure to high temperatures, extreme weather events, displacement, 
malnutrition, conflict, climate-related economic and social losses, and 
anxiety and distress associated with worry about climate change (very 
high confidence) {7.3.1.11}.
Future climate-related migration is expected to vary by region 
and over time, according to future climatic drivers, patterns of 
population growth, adaptive capacity of exposed populations 
and international development and migration policies (high 
confidence). The wide range of potential outcomes is reflected in model 
projections of population displacements by 2050 in Latin America, sub-
Saharan Africa and south Asia due to climate change, which vary from 
31  million to 143  million people, depending on assumptions about 
future GHG emissions and socioeconomic development trajectories 
(high confidence). With every additional one degree Celsius of

warming, the global risks of involuntary displacement due to flood 
events are projected to rise by approximately 50% (high confidence). 
High emissions/low development scenarios raise the potential 
for higher levels of migration and involuntary displacement (high 
confidence) and increase the need for planned relocations and support 
for people exposed to climate extremes but lacking the means to move 
(high confidence) {7.3.2; Cross-Chapter Box MIGRATE in Chapter 7}.
Climate change may increase susceptibility to violent conflict, 
primarily intra-state conflicts, by strengthening climate-sensitive 
drivers of conflict (medium confidence). Future violent conflict risk 
is highly mediated by socioeconomic development trajectories (high 
confidence) and so trajectories that prioritise economic growth, 
political rights and sustainability are associated with lower conflict risk 
(medium confidence). Future climate change may exceed adaptation

limits and generate new causal pathways not observed under current 
climate variability (medium confidence). Economic shocks are not 
included in many models of conflict risks currently used, and some 
projections do not incorporate known socioeconomic predictors of 
conflict (medium confidence). As such, future increases in conflict-
related deaths with climate change have been estimated, but results 
are inconclusive (medium confidence).
Solutions
Since AR5, the value of cross-sectoral collaboration to advance 
sustainable development has been more widely recognised, 
but despite acknowledgement of the importance of health 
adaptation as a key component, action has been slow (high 
confidence). Building climate-resilient health systems will require 
multi-sectoral, multi-system and collaborative efforts at all governance 
scales (very high confidence) (Sections 7.4.1, 7.4.2). Globally, health 
systems are poorly resourced in general, and their capacity to

respond to climate change is weak, with mental health support being 
particularly inadequate (very high confidence). The health sectors of 
some countries have focused on implementing incremental changes to 
policies and measures to fill the adaptation gap (very high confidence).

7
1047Health, Wellbeing and the Changing Structure of Communities  Chapter 7
As the likelihood of dangerous risks to human health continue to 
increase, there is greater need for transformational changes to health 
and other systems (very high confidence). This highlights an urgent 
and immediate need to address the wider interactions between 
environmental change, socioeconomic development and human health 
and well-being (high confidence) {7.4.1, 7.4.2, 7.4.3}.
Targeted investments in health and other systems, including 
multi-sectoral, integrated approaches to protect against key 
health risks can effectively increase resilience (high confidence). 
Increased investment in strengthening general health systems, along 
with targeted investments to enhance protection against specific 
climate-sensitive exposures (e.g., hazard early warning and response 
systems, and integrated vector control programmes for VBDs) will

increase resilience if implemented to at least keep pace with climate 
• The future effects of climate change on VBDs can be significantly 
offset through enhanced commitment to and implementation 
of integrated vector control management approaches, disease 
surveillance, early warning systems and vaccine development (very 
high confidence) (Sections 7.4.1, 7.4.2).
• Adaptation options for future climate risks associated with 
waterborne and food-borne diseasess include improving access 
to potable water, reducing exposure of water and sanitation 
systems to flooding and extreme weather events, and improved 
(including expanded) early warning systems (very high confidence) 
(Sections 7.4.1, 7.4.2).
• Adaptation options for future extreme heat risks include heat 
action plans (HAPs) that incorporate early warning and response 
systems for urban and non-urban settings; tried, tested and 
iteratively updated response strategies targeting both the general

population and vulnerable groups such as older adults or outside 
workers; and effective stakeholder communication plans (high 
confidence). These short-term responses can be complemented by 
longer-term urban planning and design, including nature-based 
solutions (NbS) that mitigate urban heat island (UHI) effects (high 
confidence) (Sections 7.4.1, 7.4.2, 7.4.3).
• Adaptation options to reduce the future risks of malnutrition 
include access to healthy, affordable, diverse diets from sustainable 
food systems (high confidence); health services including maternal, 
child and reproductive health (high confidence); nutrition services, 
nutrition and shock sensitive social protection (high confidence); 
water, sanitation and early warning systems (high confidence); and 
risk reduction schemes such as insurance (medium confidence) 
(Section 7.4.2.1.3).
The COVID-19 pandemic has demonstrated the value of 
coordinated and multi-sectoral planning, social protection

systems, safety nets and other capacities in societies to cope 
with a range of shocks and stresses (high confidence). The 
pandemic has posed a severe shock to many socioeconomic systems, 
resulting in substantial changes in vulnerability and exposure of 
people to climate risks (high confidence). The pandemic emphasises 
the inter-connected and compound nature of risks, vulnerabilities, and 
responses to emergencies that are simultaneously local and global 
(high confidence). Pathways to climate resilient development can be pursued simultaneously with recovering from the COVID-19 pandemic 
(high confidence). The COVID-19 pandemic has aggravated climate 
risks, demonstrated the global and local vulnerability to cascading 
shocks and illustrated the importance of integrated solutions that 
tackle ecosystem degradation and structural vulnerabilities in human 
societies (high confidence) {Cross-Chapter Box COVID in Chapter 7}.
Transitioning towards equitable, low-carbon societies has

multiple benefits for health and well-being (very high confidence). 
Benefits for health and well-being can be gained from wide-spread, 
equitable access to affordable renewable energy (high confidence); 
active transport (e.g., walking and cycling) (high confidence); green 
buildings and nature-based solutions, such as green and blue urban 
infrastructure (high confidence); and by transitioning to a low-carbon, 
well-being-oriented and equity-oriented economy consistent with the 
aims of the SDGs (high confidence). Plant-rich diets consistent with 
international recommendations for healthy diets could contribute to 
lower GHG emissions while also generating health co-benefits, such 
as reducing ill health related to over-consumption of animal-based 
products (high confidence) {7.4.2; Cross-Chapter Box  HEALTH in 
Chapter 7; 7.4.4}.
Reducing future risks of involuntary migration and displacement 
due to climate change is possible through cooperative

international efforts to enhance institutional adaptive capacity 
and sustainable development (high confidence). Institutional and 
cross-sectoral efforts to build adaptive capacity, coupled with policies 
aimed at ensuring safe and orderly movements of people within and 
between states, can form part of the CRDPs that reduce future risks 
of climate-related involuntary migration, displacement and immobility 
(medium confidence). In locations where permanent, government-
assisted relocation becomes unavoidable, active involvement of local 
populations in planning and decision-making increases the likelihood 
of successful outcomes (medium confidence). People who live on small 
island states do not view relocation as an appropriate or desirable 
means of adapting to the impacts of climate change (high confidence) 
{7.4.3; Cross-Chapter Box MIGRATE in Chapter 7}.
Adaptation and sustainable development build peace in conflict-

prone regions by addressing the drivers of grievances that lead 
to conflict and vulnerability to climate change (high confidence).  
Environmental peacebuilding (EP) through natural resource sharing, 
conflict-sensitive adaptation and climate-resilient peacebuilding offer 
promising avenues for addressing conflict risk, but their efficacy is still 
to be demonstrated through effective monitoring and evaluation (high 
confidence). Formal institutional arrangements for natural resource 
management contribute to wider cooperation and peacebuilding 
(high confidence) and gender-based approaches provide under-utilised 
pathways to achieving sustainable peace (medium confidence). 
Inclusion, cross-issue and cross-sectoral integration in policy and 
programming, and approaches that incorporate different geographical 
scales and work across national boundaries can support climate-
resilient peace (high confidence) {7.4.5, 7.4.6}.

7
1048Chapter 7 Health, Wellbeing and the Changing Structure of Communities
7.1 Introduction
This chapter assesses peer-reviewed and selected grey literature 
published since the IPCC’s Fifth Assessment Report (AR5) on the 
impacts and projected future risks of climate change for health, well-
being, migration and conflict, taking into consideration determinants 
of vulnerability and the dynamic structure of human populations and 
communities. Particular attention is given to potential adaptation 
challenges and actions as well as potential co-benefits for health 
associated with mitigation actions. AR5 presented strong evidence-
based statements regarding the likely2 impacts of climate change on 
health, migration and conflict in two separate chapters on Human Health 
(Chapter 11) and Human Security (Chapter 12). The present chapter 
covers all topics found in AR5 Chapter 11 and Sections 12.4 (Migration 
and Mobility Dimensions of Human Security), 12.5 (Climate Change and

Armed Conflict) and 12.6 (State Integrity and Geopolitical Rivalry) and 
provides an additional, expanded assessment of mental health impacts, 
gender dimensions of climate risks and solution pathways.
7.1.1 Major Health-Related Statements in AR5
AR5 stated with very high confidence that the health of human 
populations is sensitive to climate change (Smith et  al., 2014). 
Specific observations of current impacts included the expansion of the 
geographical ranges of some diseases into previously unaffected areas 
and changes in the distributions of some food-, water- and vector-
borne diseases (high confidence). Increasing future health risks were 
projected from injury, disease and death due to more intense heatwaves 
and fires (very high confidence), undernutrition in poor regions (high 
confidence), food- and waterborne diseases (very high confidence) 
and VBDs (medium confidence). AR5 found that climate change is a

multiplier of existing health vulnerabilities, including food insecurity 
and limited access to safe water, improved sanitation, healthcare and 
education, and that the most effective measures to reduce vulnerability 
in the near term are programmes that implement and improve basic 
public health (very high confidence). Opportunities for co-benefits from 
mitigation actions were identified through such actions as reducing 
local emissions of short-lived climate pollutants from energy systems 
(very high confidence) and expanding transport systems that promote 
active travel (high confidence). The significant growth in peer-reviewed 
publications on links between climate change and human health and 
well-being since AR5 allowed for a more detailed and wider reaching 
assessment in the present chapter and stronger confidence statements 
for many climate-sensitive health outcomes.
7.1.2 Major Statements About Migration and Conflict in 
AR5

Key statements made in AR5 Chapter 12 (Human Security) about the 
impacts of climate change on migration were that climate change 
will have significant impacts on forms of migration that compromise 
likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely than not > 50–100%, and Extremely unlikely 
lies within the 17–83% probability range.human security and that mobility is a widely used strategy to maintain 
livelihoods in response to social and environmental changes (high

agreement, medium evidence). Research on the influence of climate 
change and climate extremes on multiple forms of migration (including 
voluntary migration, involuntary displacement and immobility) has 
expanded significantly since AR5, which has allowed for a more robust 
assessment in this chapter, with migration also featuring in most other 
sectoral and regional chapters of this report. With respect to violent 
conflict, AR5 Chapter 12 found that people living in places affected by 
violent conflict are particularly vulnerable to climate change (medium 
evidence, high agreement), that some of the factors increasing the risk 
of violent conflict within states are sensitive to climate change (medium 
evidence, medium agreement) and that climate change will lead to 
new challenges to states and will increasingly shape both conditions 
of security and national security policies (medium evidence, medium 
agreement). As with other subjects assessed in this chapter, there

has been significant growth in the number of assessable studies, but 
there remain shortcomings with respect to the availability of evidence 
regarding the specific nature of causal linkages and the attributability 
of particular outcomes to climate events or conditions.
7.1.3 Important Developments Since AR5
7.1.3.1 International Agreements
Since AR5, several new international agreements have come into 
effect that have implications for international responses to the 
climate risks assessed in this chapter. The 2015 Paris Agreement, 
which explicitly mentions health in three separate sections, set new 
goals for adaptation and established a working group to study the 
effects of climate change on population displacement. The 17 United 
Nations (UN) SDGs for 2030, adopted in 2015, are all important for 
building adaptive capacity in general, with goals 13 (‘Take urgent 
action to combat climate change and its impacts’) and 3 (‘Ensure

healthy lives and promote well-being for all at all ages’) being directly 
relevant to this chapter. Other SDGs contain specific targets that are 
also relevant for this chapter, including Target 10.7 (‘Well-managed 
migration policies’), Target 8.3 (‘Decent work for all’) and Target 5.4 
(‘Promotion of peaceful and inclusive societies’) (Piper, 2017). The 
2015 Sendai Framework for Disaster Risk Reduction puts an emphasis 
on health and well-being (Aitsi-Selmi and Murray, 2016) In 2018, UN 
member states negotiated Global Compacts for Safe, Orderly and 
Regular Migration and on Refugees that, taken together with the Paris 
Agreement, provide pathways for coordinated international responses 
to climate-related migration and displacement (Warner, 2018).
7.1.3.2 IPCC Special Reports
All three post-AR5 IPCC Special Reports considered some of the 
research that is assessed here in greater detail. The 2018 report on 
1.5°C (SR1.5) included a review of climate change and health literature

published since AR5 and called for further efforts for protecting health

7
1049Health, Wellbeing and the Changing Structure of Communities  Chapter 7
and well-being of vulnerable people and regions (Ebi et  al., 2018b) 
and highlighted links between climate change hazards, poverty, food 
security, migration and conflict. The 2019 Special Report on Climate 
Change and Land (SRCCL) (IPCC, 2019b) emphasised the impacts of 
climate change on food security; highlighted links between reduced 
resilience of dryland populations, land degradation, migration and 
conflict; and raised concerns about the impacts of climate extremes. 
The 2019 Special Report on the Ocean and Cryosphere in a Changing 
Climate (IPCC, 2019a) detailed how changes in the cryosphere 
and ocean systems have impacted people and ecosystem services, 
particularly food security, water resources, water quality, livelihoods, 
health and well-being, infrastructure, transportation, tourism and 
recreation as well as the culture of human societies, particularly for

Indigenous Peoples. It also noted the risks of future displacements due 
to rising sea levels and associated coastal hazards.
7.1.4 Interpretation of ‘Health and Well-Being’ Used in 
This Chapter
Assessing the links between human health, well-being and climate 
change is a new task for AR6, reflecting a broad perspective on health 
that increasingly acknowledges the importance of well-being and its 
interactions with individual and population health. The World Health 
Organization (WHO) defines health as ‘a state of complete physical, 
mental and social well-being and not merely the absence of disease or 
infirmity’ (WHO, 1946). Although this chapter assesses physical health, 
mental health and general well-being separately, they are inter-
connected; any type of health problem can reduce overall well-being 
and vice versa. For example, a child receiving inadequate nutrition may 
not be sick but is experiencing a clear threat to well-being that has

implications for future physical and mental health.
There is no consensus definition of well-being, but it is generally agreed 
that it includes a predominance of positive emotions and moods (e.g., 
happiness) compared with extreme negative emotions (e.g., anxiety), 
satisfaction with life, a sense of meaning and positive functioning, 
including the capacity for unimpaired cognitive functioning and 
economic productivity (Diener and Tay, 2015; Piekałkiewicz, 2017). 
A capabilities approach (Sen, 2001) focuses on the opportunity for 
people to achieve their goals in life (Vik and Carlquist, 2018) or the 
ability to take part in society in a meaningful way, and is reflected 
in personal freedoms, human agency, self-efficacy, ability to self-
actualise, dignity and relatedness to others (Markussen et al., 2018). 
An indigenous perspective on well-being is broad and typically 
incorporates a healthy relationship with the natural world (Sangha

et al., 2018); emotional and mental health have also been linked to 
a strong cultural identity (Butler et al., 2019; Dockery, 2020). ‘Health’ 
itself is sometimes described as including relationships between 
humans and nature as well as links to community and culture 
(Donatuto et al., 2020; Dudgeon et al., 2017).
Subjective well-being is consistently associated with personal indicators 
such as higher income, greater economic productivity, better physical 
health (Diener and Tay, 2015; Delhey and Dragolov, 2016; De Neve 
et al., 2013) and environmental health, and it is reflected in societal 
indicators such as social cohesion and equality (Delhey and Dragolov, 2016). In a global survey of over 1  million people taken between 
2004 and 2008 via the Gallup World Poll, annual income and access 
to food were strong predictors of subjective well-being and a healthy 
environment. In particular, access to clean water was important even

when household income was controlled (Diener and Tay, 2015). Access 
to green spaces was also closely associated with well-being (high 
confidence) (Lovell et al., 2018; Yuan et al., 2018).
7.1.5 Towards Socioecological Perspectives on Health, 
Well-Being, and Loss and Damage
Since AR5, more comprehensive frameworks for framing and studying 
global health issues, including planetary health, ‘One Health’ and eco-
health, have gained traction. These frameworks share an ecological 
perspective, emphasise the role of complex systems and highlight the 
need for inter-disciplinary approaches related to human health research 
and practice (Lerner and Berg, 2015; Zinsstag et al., 2018; Whitmee 
et  al., 2015; Steffen et  al., 2015). These frameworks increasingly 
shape the evidence related to climate change health impacts and 
response options, highlight the dynamics of complex systems in risk 
management and direct risk management efforts in new directions.

Building on these frameworks and perspectives, there is increasing 
overlap in the literature on global health, climate change impacts and 
estimates of loss and damage. The Global Burden of Disease study 
for 2019 for the first time included non-optimal temperature as a 
risk factor (Murray et  al., 2020). Work by social scientists continues 
to explore how climate change indirectly affects resource availability, 
productivity, migration and conflict (Burke et al., 2015a; Carleton and 
Hsiang, 2016; Hsiang et al., 2017), bringing multiple lines of inquiry 
together to study the associations between global environmental 
changes, socioeconomic dynamics and impacts on health and well-
being. Morbidity associated with migration and displacement, 
especially in the context of small island states, has been identified as 
a non-material form of loss and damage (Thomas and Benjamin, 2020; 
McNamara et al., 2021). Social costs of carbon estimates have been

updated to include excess mortality associated with climate change, 
increasing estimates substantially (Dressler, 2021).
7.1.6 Developments Relevant to Tracking and Assessing 
Climate Change Impacts on Health
Since AR5, there has been a steady increase in standardised, globally 
scoped, data-driven health impact assessments, such as the ongoing 
Global Burden of Disease study (James et al., 2018) that now includes 
scenario-based projections (Foreman et al., 2018), that make linkages 
with other global priorities, including the SDGs (Fullman et al., 2017). 
Attention has turned from prioritising specific diseases like HIV/
AIDS, malaria and tuberculosis, to strengthening health systems 
and providing universal health coverage (Chang et  al., 2019), with 
an ongoing emphasis on the social determinants of health. Several 
climate-sensitive health outcomes are now tracked in the annual 
Lancet Countdown reports (Watts et al., 2015; Watts et al., 2017; Watts

et al., 2018b; Watts et al., 2019; Watts et al., 2021). The Global Burden 
of Disease study is beginning to examine climate-sensitive disease

7
1050Chapter 7 Health, Wellbeing and the Changing Structure of Communities
burdens, incorporate temperature as a risk factor (Murray et al., 2020) 
and project future cause-specific disease burdens in a warming world 
(Burkart et  al., 2021). Although not assessed in this chapter, there 
are numerous ongoing assessments of climate change impacts on 
health and well-being being undertaken by national and local health 
authorities that continue to generate insights into climate-related 
health impacts and suggest response options relevant for decision 
makers.
While the knowledge base regarding global health has increased, a 
comprehensive framework is not in place that fully integrates health, 
well-being and environmental impacts from climate change allowing 
for the cumulative assessment of their impact. Moreover, significant 
cracks in the foundation of global health governance that affect 
preparedness and adaptive capacity for climate change, among other

threats, have been identified (Phelan et al., 2020; Defor and Oheneba-
Dornyo, 2020; Ostergard et al., 2020; Shaffie, 2021). While attention 
to climate change and health has increased and there is evidence of 
increasing adaptation activity in the health sector (Watts et al., 2019), 
there is also continued evidence of substantial adaptation gaps (UNEP , 
2018; UNEP , 2021) including gaps in humanitarian response capacity 
for climate-related disasters (Watts et  al., 2021) that appear to be 
widening as adverse climate change impacts on health and well-being 
accrue.
7.1.7 Hazards, Exposure and Vulnerability in the Context 
of Human Health, Well-Being and the Changing 
Structure of Communities
7.1.7.1 Possible Climate Futures and Hazards
This chapter uses the conceptual framing described in Chapter 1, in 
which risks emerging from climate change are described in terms 
of hazard, exposure and vulnerability, with adaptation and climate

resilient development being responses that have the potential to 
reduce or modify risk. The observed and projected future risks to 
health well-being, involuntary population displacements and conflict 
identified in this chapter are associated with a range of hazards that 
are manifested at a variety of geographical and temporal scales. These 
include observed and projected changes in climate normals; changes 
in the frequency, duration, and/or severity of extreme events; and 
hazards such as rising sea levels and extreme temperatures where the 
impacts have only begun to be widely experienced. The 2021 report 
of IPCC WGI (IPCC, 2021) provides an assessment of observed and 
projected changes in these hazards and is the backdrop against which 
assessments of future risks and adaptation options identified in the 
present chapter should be considered.
The exposure to hazards of populations, infrastructure, ecosystem

capital, socioeconomic systems and cultural assets critical to health 
and well-being varies considerably across and within regions (high 
confidence). Exposure is also projected to vary across and within 
regions over time, depending on future GHG emissions pathways, 
development trajectories and differential vulnerability, particularly for 
exposure to extreme events and conditions, such as floods and droughts 
(Figures 7.1a, 7.1b) (Winsemius et al. 2018). For this reason, region-specific assessments of climate-related risks for health, displacement 
and conflict are found in each of the regional chapters of this report in 
addition to the general assessment that appears in this chapter.
7.1.7.2 Differential Vulnerability and Cascading Effects
Vulnerability to climate change varies across time and location, 
across communities, and among individuals within communities; it 
reflects variations and changes in macro-scale non-climatic factors

(such as changes in population, economic development, education, 
infrastructure, behaviour, technology and ecosystems) and individual- 
or household-specific characteristics, such as age, socioeconomic 
status, access to livelihood assets, pre-existing health conditions and 
ability (US Global Change Research Program, 2016; Chapter 1).
Many direct and indirect effects of climate change pose multiple 
threats to human health and well-being and can occur simultaneously, 
resulting in compounding or cascading impacts for vulnerable 
populations. For example, many of the long-term impacts of climate 
change on NCDs and injury described in Sections  7.2 and 7.3 are 
associated with future increases in air temperature and levels of air 
pollution; in many regions, and especially in large urban centres in Asia 
and Africa, these particular hazards are already causing substantial 
increases in morbidity and mortality due to respiratory illnesses (Tong

et  al., 2016). Climate change can therefore be expected to magnify 
such health risks over the long term.
At the same time, urban populations will also be experiencing indirect 
risks through climate change impacts on food and potable water 
systems, variations in the distribution and seasonality of infectious 
diseases and growing demand for shelter due to increased in-
migration. The accumulation of these risks over time can be expected 
to generate accelerating declines in community resilience and health, 
with future vulnerability potentially expanding in a nonlinear fashion 
(Dilling et al., 2017; Liang and Gong, 2017; El-Zein and Tonmoy, 2017; 
see also Chapter 6). Further, although each individual risk in isolation 
may be transitory or temporary for the individuals or groups exposed, 
taken cumulatively, the impacts could create conditions of chronic 
lack of well-being, and early life experiences with specific illnesses

and conditions could have lifelong consequences (Watts et al., 2015; 
Otto et al., 2017; WHO, 2018a). In this context, there is a distinct need 
for greater longitudinal research on vulnerability to multiple climatic 
and non-climatic health and well-being hazards over time (Fawcett 
et al., 2017). There is also need for more research to identify critical 
thresholds in social vulnerability to climate change (Otto et al., 2017); 
these include rapid, stepwise changes in vulnerability that emerge 
from changes in exposure (for example, air temperatures above which 
mortality rates or impacts on pre-natal health accelerate (Arroyo 
et al., 2016; Ngo and Horton, 2016; Abiona, 2017; Auger et al., 2017; 
Molina and Saldarriaga, 2017; Zhang et al., 2017b)) and thresholds in 
adaptation processes (such as when rural out-migration rates grow 
due to climate-related crop failures (McLeman, 2017)).
In virtually all of the research identifying particular climate-related

risks to health, well-being, migration and conflict, specific types of 
individuals are identified as having higher levels of vulnerability 
and exposure to climate-related health hazards: people who are

7
1051Health, Wellbeing and the Changing Structure of Communities  Chapter 7
impoverished, undernourished, struggle with chronic or repeated 
illnesses, live in insecure housing in polluted or heavily degraded 
environments, work in unsafe conditions, are disabled, have limited 
education and/or have poor access to health and social infrastructure 
(WHO, 2018a). Their disproportionate exposure to ongoing climate 
hazards and their inability to recover from extreme events increase 
not only their own vulnerability but also that of the wider communities 
in which they live (US Global Change Research Program, 2016). 
Highly vulnerable populations are not evenly distributed across 
regions (Figure  7.2) nor within countries. Yet, even those fortunate 
enough to live in better neighbourhoods with greater financial means, 
higher-paying jobs and good access to resources and services, may 
experience adverse climate-related outcomes through community-level

interactions and linkages (Haines and Ebi, 2019). Increased inequity 
itself threatens well-being and an effective response to climate change 
should not only avoid increased inequity but identify ways in which to 
reduce existing inequity.7.1.7.3 Heightened Vulnerability to Climate-Related Impacts on 
Health and Well-Being Experienced by Specific Groups 
and Through Specific Pathways
7.1.7.3.1 Women and Girls
Climate change poses distinct risks to women’s health. Vulnerability 
to climate-related impacts on health and well-being shows notable 
differentiations according to gender, beyond implications for pregnant 
women. In many societies, differential exposure to such risks relate 
to gendered livelihood practices and mobility options. Pregnancy and 
maternal status heighten vulnerability to heat, infectious diseases, 
food-borne infections and air pollution (Arroyo et  al., 2016; Ngo 
and Horton, 2016; Zhang et  al., 2017b). Extreme heat events, high

ambient temperatures, high concentrations of airborne particulates, 
water-related illnesses and natural hazards are associated with higher 
rates of adverse pregnancy outcomes such as spontaneous abortion, 
stillbirth, low birth weight and pre-term birth (Arroyo et  al., 2016; 
Ngo and Horton, 2016; Abiona, 2017; Auger et  al., 2017; Molina 
and Saldarriaga, 2017; Zhang et al., 2017b). Women and girls are at 
greater risk of food insecurity (FAO, 2018; Alston and Akhter, 2016), 
which is particularly problematic in combination with the nutritional 
<-50%>50%
15–25%
-5– 5%
-25– -15%25–50%
5–15%
-15– -5%
-50– -25%(b)  Projected exposure of poor people to droughts.Exposure
increase
RCP8.5
2050Projected exposure of poor people to floods and droughts in selected regions
by 2050 under a high emissions scenario (RCP8.5)
(a) Projected exposure of poor people to floods.
Central and South America Asia
 Africa
Central and South America Asia Africa

Figure 7.1 |  Projected exposure of poor people to a) floods respectively b) droughts in selected regions by 2050 under a high emissions scenario (RCP8.5) 
(adapted from Winsemius et al., 2018).

7
1052Chapter 7 Health, Wellbeing and the Changing Structure of Communities
(a) Vulnerability at the national level v aries. Vulnerability also greatly differs within countries .
Countries with moderate or low average vulnerability have sub-populations with high vulnerability and vice versa.Relative vulnerability
MediumVery high
Low
Very low
Population density
LowHighObserved human vulnerability to climate change is a key risk factor and differs globally
High
Children in rural low-income communities | food insecurity,  sensitivity to undernutrition and
disease | 5.12.3
People uprooted by conﬂict in the Near East and Sahel  | prolonged temporary status, limited 
mobility | Box 8.1,  Box 8.4
Women & non-binary | limited access to & control over resource s, e.g. water, land, credit |
Box 9.1,  CCB-GENDER,  4.8.3,  5.4.2,  10.3.3
Migrants  | informal status, limited access to health services & shelter , exclusion from
decision-making processes | 6.3.6,  Box 10.2

Aboriginal and Torres Strait Islander Peoples | poverty,  food & housing insecurity,
dislocation from community | 11.4.1
People living in informal settlements | poverty,  limited basic services & often located in areas 
with high exposure to climate hazards | 6.2.3,  Box 9.1,  9.9, 10.4.6,  12.3.2,  12.3.5,  15.3.4Examples of
Indigenous Pe oples with 
high vulnerability to  
climate change and
climate change responses 
(4.3.8,  5.10.2,  5.13.5,  
Box7.1,  8.2.1,  15.6.4) and  
the importance of 
Indigenous Knowledge 
(Box9.2.1,  11.4,  14.4,  
Cross-Chapter Box INDIG) 
Indigenous P eoples of the Arctic  | health inequality,  limited access to subsistence resources and 
culture | CCP 6.2.3,  CCP 6.3.1
Urban ethnic minorities | structural inequality,  marginalisation,  exclusion from planning processes 
| 14.5.9,  14.5.5,  6.3.6
Smallholder coffee producer s | limited mark et access & stability,  single crop dependency,  limited 
institutional support | 5.4.2

Indigenous P eoples in the Amazon | land degradation,  deforestation,  poverty,  lack of support |
8.2.1,  Box 8.6
Older people, especially those poor & socially isolated | health issues, disability,  limited access 
to support | 8.2.1,  13.7.1,  6.2.3,  7.1.7
Island communities | limited land , population growth and coastal ecosystem degradation | 15.3.2Examples of vulnerable local groups across different contexts include the following:AfricaNorth America
Central &
AustralasiaSmall islandsEurope
AsiaSmall islands
1
3
42
5
67
8
9
10
11
1278 91291
10128
12 3436121215
3612
113612
Figure 7.2 |  Global distribution of vulnerable people from two indices, with examples (see also Technical Summary, this report).

7
1053Health, Wellbeing and the Changing Structure of Communities  Chapter 7
needs associated with pregnancy or breastfeeding. Women and girls 
are more likely to die in extreme weather events (Garcia and Sheehan, 
2016; Yang et al., 2019). Women are also expected to face a greater 
mental health burden in a changing climate (Manning and Clayton, 
2018). Further, climatic extremes and water scarcity are associated 
with increases in violence against girls and women (Anwar et al., 2019; 
Opondo et al., 2016; Le Masson et al., 2016; Udas et al., 2019).
7.1.7.3.2 Children
Children are particularly vulnerable to climate change impacts. 
Children often have unique pathways of exposure and sensitivity to 
climate hazards, given their immature physiology and metabolism 
and high intake of air, food and water relative to their body weight 
as compared with adults (US Global Change Research Group, 2016). 
Climate change is expected to increase childhood risks of malnutrition

and infectious disease for children in low-income countries through its 
impacts on household food access, dietary diversity, nutrient quality, 
water and changes in maternal and childcare access and breastfeeding 
(Tirado, 2017; FAO et  al., 2018; Perera, 2017). Children living in 
locations with poor sanitation are especially vulnerable to GI illnesses, 
with future rates of diarrhoeal diseases among children expected to 
rise under many climate change scenarios (Cissé et  al., 2018; WHO, 
2014). Outdoor recreational opportunities for children may be reduced 
by extreme weather events, heat and poor air quality (Evans, 2019). 
Children and adolescents are particularly vulnerable to post-traumatic 
stress after extreme weather events; the effects may even be long-
lasting, with impacts on their adult functioning (Brown et al., 2017; 
UNICEF , 2021; Thiery et al., 2021)
7.1.7.3.3 Elderly
Population age structures and changes over time have a significant

influence on vulnerability to the impacts of weather and climate. 
Older adults (generally defined as persons aged 65 and older) are 
disproportionately vulnerable to the health impacts associated with 
climate change and weather extremes, including a greater risk of 
succumbing to waterborne pathogens due to poorer functioning 
thermoregulatory mechanisms, greater sensitivity to dehydration, 
changes in their immune systems and greater likelihood of having pre-
existing chronic illnesses such as diabetes or respiratory, cardiovascular 
and pulmonary illnesses (Benmarhnia et al., 2016; Diaz et al., 2015; 
Mayrhuber et al., 2018; Paavola, 2017). Older adults may be less prompt 
in seeking medical attention when suffering from GI illnesses, which 
can lead to dehydration (Haq and Gutman, 2014). Åström et al. (2017) 
anticipate heat-related mortality among the elderly in Europe to rise 
in the 2050s under RCP4.5 and RCP8.5 in the absence of significant

preventative measures. In a study of the combined effects of warming 
temperatures and an aging population in Korea, Lee and Kim (2016) 
projected a four- to six-fold increase in heat-related mortality by the 
2090s when accounting for temperature and age structure.7.1.7.3.4 Socioeconomically Marginalised Populations and People 
with Disabilities
People living in poverty are more likely to be exposed to extreme 
heat and air pollution and have poorer access to clean water and 
sanitation, accentuating their exposure to climate change-associated 
health risks (UNEP , 2021; FAO et al., 2018). Poverty influences how 
people perceive the risks to which they are exposed, how they 
respond to evacuation orders and other emergency warnings and 
their ability to evacuate or relocate to a less risk-prone location (US 
Global Change Research Program, 2016). Poorer households, who 
often live in highly exposed locations, are more likely to be forced

into low-agency migration as a means of adapting to climate risks 
and at the same time are the most likely to be immobile or trapped 
in deteriorating circumstances where migration would be a preferred 
response (Leichenko and Silva, 2014; Fazey et al., 2016; Sheller, 2018). 
Climate emergencies disproportionally affect people with disabilities 
because of their inherent vulnerabilities, which may impair their 
ability to take protective action; they are also frequently excluded 
from adaptation planning (Gaskin et al., 2017).
7.1.7.3.5 Urban Compared with Rural Populations
Rural and urban populations are often exposed to different types of 
climate-related health risks. For example, because of the UHI effect 
and high concentrations of air pollution from motor vehicles and 
industrial activity, people who live in urban areas may have higher 
rates of extreme heat stress and respiratory illnesses than their rural 
counterparts (Hondula et al., 2014; Heaviside et al., 2016; Macintyre

et  al., 2018; Schinasi et  al., 2018). Conversely, rural populations, 
especially those dependent on resource-based livelihoods, may have 
a greater exposure to climate impacts on food production or natural 
hazard events, which have subsequent effects on household nutrition 
and food security (Springmann et al., 2016a; see also Chapters 5 and 
6 of this report).
7.1.7.3.6 Indigenous Peoples
Indigenous Peoples, especially those who live in geographically 
isolated, resource-dependent and/or impoverished communities, are 
often at greater risk of health impacts of climate change (Ford et al., 
2020) (US Global Change Research Program, 2016). The close inter-
connection of land-based livelihoods and cultural identity of many 
indigenous groups exposes them to multiple health- and nutrition-
related hazards (Durkalec et  al., 2015; Sioui, 2019) with potential 
implications for community social relations and individual mental

health (Cunsolo Willox et  al., 2013; Cunsolo Willox et  al., 2015). 
Climate change risk exposures may be complicated by changes in 
lifestyle, diet and morbidity driven by socioeconomic processes, further 
increasing health risks for Indigenous Peoples (Jaakkola et al., 2018). 
Environmental consequences of climate change can also affect social 
ties and spiritual well-being, in part because land is often an integral 
part of their culture and spiritual identity.

7
1054Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Box 7.1 | Indigenous Peoples’ Health and Well-Being in a Changing Climate
Contributing authors: Hannah Tait Neufeld (Canada), Lena Maria Nilsson (Sweden), Rhys Griffith Jones (New Zealand)
The indigenous population worldwide is estimated at 476 million people spread across all geographic regions of the world (FAO et al., 
2021). Indigenous Peoples globally represent a large heterogeneity of people in terms of living conditions and social determinants of 
health. There is no simple definition of who is indigenous. In this text, we refer to Indigenous Peoples as people self-identified and 
organised as indigenous, according to the principles of the International Work Group for Indigenous Affairs (IWGIA), an international non-
governmental organisation (NGO) with observer status at the United Nations (UN). Indigenous Peoples are described as ‘distinct social

and cultural groups that share collective ancestral ties to the lands and natural resources where they live, occupy or from which they have 
been displaced’ (World Bank, 2021). A common experience among Indigenous Peoples are historical traumas related to overseas and/or 
settler/industrial colonisation.
Studies on climate change as it affects the health of Indigenous Peoples generally focus on non-displaced indigenous groups; that 
is, Indigenous Peoples maintaining culturally important elements of a land-based traditional lifestyle. Here we use an eco-medicine 
perspective in which the impacts of climate change on health are divided into primary, secondary and tertiary effects, as discussed below 
(Butler and Harley, 2010). Many analyses of indigenous health in relation to climate change use the One Health concept (Mackenzie and 
Jeggo, 2019; see Section 7.1.5).
Current Impacts of Climate Change on Health and Well-Being of Indigenous Peoples

Primary health effects of climate change include the immediate physical effects on human health, such as health hazards due to high 
temperatures, extreme weather events or accidents from exposure to climate-related hazards. For example, in arid and semiarid areas, 
an increased frequency of severe droughts is associated with immediate health problems related to overheating and lack of water for 
drinking, sanitation and livestock (Hall and Crosby, 2020; Mamo, 2020; Rankoana, 2021). In many cases, the possibilities for Indigenous 
Peoples to apply traditional strategies to mitigate droughts by migration are limited by competing land use, environmental protection 
and national borders, with many examples across Africa (Mamo, 2020). In the Jordan River Valley, the second most water stressed area 
in the world, water resources are not equally distributed to Indigenous Bedouin people, amplifying their immediate health threat during

predictable as well as unpredictable droughts (Mamo, 2020).
In Arctic and sub-Arctic areas, higher temperatures with increased numbers of freeze–thaw cycles during the winter means increased 
occurrences of transport-related accidents in indigenous communities due to weaker ice on travel routes that cross lakes, rivers and the 
sea, along with changes in the snow cover and increased risk of avalanches (Durkalec et al., 2015; Jaakkola et al., 2018). Impeded access 
to healthcare during extreme weather conditions is a primary health risk for Indigenous Peoples living in remote areas (Amstislavski et al., 
2013; Hall and Crosby, 2020; Mamo, 2020).
Pastoralists in many regions may experience changes in livestock behaviour due to climate change, leading to increased mobility-related 
health hazards (Jaakkola et al., 2018; Mamo, 2020). Indigenous Peoples living in low-lying coastal areas and small island states face long-

term risk of flooding and the stresses of resettlement (Maldonado et al., 2021; McMichael and Powell, 2021).
Extreme rainfall, flooding, storms, heatwaves and wildfires lead to individual health hazards that may include injuries and thermal and 
respiratory traumas (Mamo, 2020) There are many examples when emergency responses to extreme events have ignored the needs 
of displaced Indigenous Peoples (Mendez et al., 2020; Maldonado et al., 2021). Population-based quantitative studies documenting 
the direct effects of these events on Indigenous Peoples are rare. In Mexico, respiratory diseases are almost twice as common among 
Indigenous Peoples compared to non-Indigenous Peoples (de Leon-Martinez et al., 2020). In Alaska and northern Canada, alarming levels 
of respiratory stress and disease have been reported among Inuit and First Nation communities in relation to wildfires (Howard et al.,

2021), as well as increased mould in houses due to flooding resulting from increased precipitation (Furgal and Seguin, 2006; Harper et al., 
2015; Norton-Smith et al., 2016). Climate- and housing-related respiratory stress is also a risk factor for severe COVID-19 infection, which 
has been highlighted in recent literature from an indigenous health perspective (de Leon-Martinez et al., 2020).
Secondary effects relate to ecosystem changes, for example, the increased risk of the acute spread of air-, soil-, vector-, food-, and 
waterborne infectious diseases (Hueffer et  al., 2019). Higher proportions of climate-related infectious diseases are reported among 
indigenous groups compared to their non-indigenous neighbours, with examples from Torres Strait, Australia, showing a greater proportion 
of tuberculosis, dengue, Ross River virus, melioidosis, and non-tuberculous mycobacterial infections (Hall et al., 2021) and in the Republic

of Sakha, Russia, high levels of zoonoses (Huber et al., 2020a). Increasing levels of livestock and canine diseases are also reported (Mamo, 
2020; Bogdanova et al., 2021; Hillier et al., 2021). Another secondary health effect is an increase in human–animal conflicts, for example

7
1055Health, Wellbeing and the Changing Structure of Communities  Chapter 7
human–elephant conflicts in Namibia due to plant food scarcity (Mamo, 2020), human–bear conflicts in Arctic regions within Canada 
(Wilder et al., 2017), human–tiger conflicts in Bangladesh (Haque et al., 2015) and increased predatory pressure on Indigenous Peoples’ 
livestock and game worldwide (Haque et al., 2015; Jaakkola et al., 2018; Mukeka et al., 2019; Mamo, 2020; Terekhina et al., 2021). 
Undernutrition and metabolic disturbances associated with overnutrition and obesity due to the decreased availability or safety of local 
and traditional foods and increased dependency on imported substitutes affect many Indigenous Peoples worldwide (Amstislavski et al., 
2013; Zavaleta et al., 2018; Houde et al., 2020; Jones et al., 2020; Akande et al., 2021; Bogdanova et al., 2021; Bryson et al., 2021) and are

especially severe for pregnant women and small children (Mamo, 2020; Olson and Metz, 2020; Bryson et al., 2021); these are amplified 
by the combination of warming and the COVID-19 situation (Zavaleta-Cortijo et al., 2020). Decreased access to wild plants and animals 
as food sources and medicine due to climate change is another threat to the health and wellness of indigenous communities (Greenwood 
and Lindsay, 2019; Mamo, 2020; CIAT and and, 2021; Rankoana, 2021; Teixidor-Toneu et al., 2021).
Tertiary effects relate to culture-wide changes, for example, all forms of malnutrition due to climate-driven changes in food systems 
and anxiety, mental illness and suicidal thoughts related to cultural and spiritual losses. A wide range of tertiary, culture-related effects 
of climate change have been documented for Indigenous Peoples. These include anxiety, distress and other mental health impacts due

to direct and indirect processes of dispossession of land and culture related to the combination of climate change and other factors 
(Richmond and Ross, 2009; Bowles, 2015; Norton-Smith et al., 2016; Jaakkola et al., 2018; Fuentes et al., 2020; Mamo, 2020; Middleton 
et al., 2020b; Middleton et al., 2020a; Olson and Metz, 2020; Timlin et al., 2021). Increased risks of conflict and abuse, including violence 
and homicide against females, and/or conflicts resulting from environmental activism, are other tertiary health threats for Indigenous 
Peoples (Mamo, 2020). Between 2017 and 2019, close to 500 indigenous people were killed for activism in 19 different countries (Mamo, 
2020). In Uganda, climate change drives indigenous men to increase their distance and time from home and their families in search of 
water and food, leading to an increase in sexual violence against indigenous women and girls in their communities (Mamo, 2020).

Gender inequities amplify the tertiary health effects of climate change (Williams, 2018; Garnier et al., 2020). In an Inuit community, for 
instance, women reported a higher level of mental stress related to climate change than men (Harper et al., 2015). Adverse pregnancy 
outcomes and altered developmental trajectories have also been associated with climate change (Hall et al., 2021). Indigenous Batwa 
women in Uganda reported experiencing more severe circumstances of food insecurity during pregnancy due to drought and unpredictable 
seasons negatively impacting agricultural practices (de Leon-Martinez et al., 2020). More studies with a gender perspective on climate 
change as a determinant of Indigenous Peoples’ health are needed, along with the perspectives of indigenous children and youth, 
displaced individuals and communities in urban settings (Kowalczewski and Klein, 2018).

Because cultural continuity is a recognised health factor (Lemelin et al., 2010; de Leon-Martinez et al., 2020; Middleton et al., 2020b), 
displaced Indigenous Peoples may suffer from climate change by worrying about impacts on non-displaced relatives and family and 
from traditional food staples turning into expensive commodified products. This is a knowledge gap with lasting implications not only 
on physical environments (Guo et al., 2018). Social connections and knowledge pathways are disrupted, leading to a decreased ability to 
share locally harvested and cultivated foods (King and Furgal, 2014; Neufeld et al., 2020).
Tertiary effects of climate change on Indigenous Peoples’ health are primarily described in smaller case studies and not designed in a 
way allowing for systematic international comparisons, which represents an important and significant gap in our understanding of these 
often-complex associations and impacts (Middleton et al., 2020b).

Future Risks for Indigenous People’s Health and Well-Being in a Changing Climate
Future risks for Indigenous Peoples’ health and well-being in a changing climate will result foremost from exacerbations of observed 
impacts. Primary and secondary health risks are expected to increase as the frequency and/or severity of climate hazards grow in many 
regions. As one example, melting permafrost in the Siberian Arctic is projected to lead to more outbreaks of anthrax (Bogdanova et al., 
2021). Tertiary health threats are expected to persist even with strong global initiatives to mitigate greenhouse gases (GHGs) (Butler and 
Harley, 2010). Climate change is expected to compound non-climatic processes that lead to social exclusion and land dispossession that 
underlay health inequalities experienced by Indigenous Peoples (Huber et al., 2020a).
Options and Opportunities for Reducing Future Risks and Building Capacity/Resilience for Indigenous Peoples’ Health and 

Indigenous organisations worldwide stress the importance of applying a rights-based approach in responding to climate change (Mamo, 
2020). Although Indigenous Peoples are often identified as being vulnerable to climate change, this framing does not always reflect the 
diverse responses and adaptations of Indigenous Peoples to these ongoing challenges (Nursey-Bray et al., 2020). An emerging body of Box 7.1 (continued)

7
1056Chapter 7 Health, Wellbeing and the Changing Structure of Communities
research is focusing on the strength and resilience of indigenous communities globally as they adapt to these complex changes (Whyte, 
2018; FAO et al., 2021).
During droughts and water shortages, for example, indigenous pastoralists may face additional challenges if water supply assistance 
provides only for human needs and neglects water requirements of livestock (Mamo, 2020). Indigenous knowledge on how to adapt to 
drought through storing and sharing strategies, for example, is valuable (Fatehpanah et al., 2020; Mamo, 2020).
Indigenous Peoples have been adapting to changes in their environments since time immemorial by developing new practices and 
techniques (FAO et al, 2021). Their beliefs, value systems and principles include core elements and common values such as reciprocity,

solidarity, co-responsibility and community that are expressed in the dynamism of their knowledge systems (Lewis et al., 2020; Schramm 
et al., 2020b). The relevance of these knowledge systems, which are holistic and tied to relationships between all living things, cannot be 
ignored at this critical time (Garnier et al., 2020).
The health and equity impacts of climate change for Indigenous Peoples make mitigation efforts critical (Jones et al., 2020), including 
policies and actions that consider the effects of colonisation. Colonisation constrains the design and diversity of potential climate 
and health responses through its historic and ongoing suppression of Indigenous knowledge systems that are critical in supporting 
community-led actions to reduce future risks (Billiot et al., 2019; Reid et al., 2019; Nursey-Bray et al., 2020).
Four Brief Case Studies to Illustrate the Innovativeness of Indigenous Peoples’ Adaptation to Climate Risks

Bedouin Pastoralists’ Grazing Practices Decrease the Risk of Wildfires in Israel and Increase Food Sovereignty
Wildfires are a main cause of deforestation in Israel, and in recent years climate stress has decreased the forest resilience to fires (Klein 
et al., 2019). The original landscape, a shrubland or maquis consisting mostly of oak and Pistacia, has been used since time immemorial 
as grazing land for goats, sheep and camels belonging to Indigenous Bedouin people (Degen and El-Meccawi, 2009). Competing land use 
has reshaped the landscape with pine monocultures and cattle farming, reducing the availability of land suitable for herding goats the 
indigenous way (Perevolotsky and Sheffer, 2011). In addition, since 1950, plant protection legislation has decreased Bedouin forest 
pastoralism in Israel by defining indigenous black goats as an environmental threat (FAOLEX, 2021). In nature reserves where no human

interference has been allowed, these areas have regenerated into herbaceous shrublands susceptible to wildfires (Turco et al., 2017). 
Meanwhile, urbanised Bedouin exist on lower incomes and experience higher levels of unemployment compared to other citizens, and 
some keep non-pastoralised livestock in cities as a strategy for food sovereignty (Degen and El-Meccawi, 2009). In 2019, many severe 
wildfires occurred in Israel due to extreme heatwaves and, in response, plant protection legislation was repealed, allowing Bedouin 
pastoralists to graze their goats in areas from which they had been excluded. The amount of combustible undergrowth subsequently 
decreased, reducing the risk for wildfire and their related impacts, while simultaneously facilitating indigenous food sovereignty among 
the Bedouin (Mamo, 2020).
Gardening in the Ashes of Wildfires in the Pacific Northwest as a Strategy to Decrease Food Insecurity and Increase Connections With the Land

In the central interior of what is now known as British Columbia (BC), 2017 was an especially severe wildfire season, with over 1.3 million 
hectares of land burned and 65,000 people displaced (Timler and Sandy, 2020). The unceded and ancestral lands of the Tsilhqot’in, 
Dakelh and Secwépemc were impacted by two of the largest fires (Verhaeghe et al., 2017). Communities affected by the BC wildfires 
subsequently started indigenous gardens closer to home, to protect medicine and food plants and thereby sustaining relationships with 
these plants, the land and the community (Timler and Sandy, 2020). As there are cultural teachings for fire to cleanse the territory and 
the land, community members and plants previously isolated became better connected because of the wildfires. The regrowth of plants is 
part of the healing relationship between plants, people and other animals (Timler and Sandy, 2020). The wildfires were seen as events to

catalyse action and emphasise the importance of relationships to support foodways and gardening as responsibility.
Widening our understanding of gardening in the face of climate change and colonialism can support health and healing for Indigenous 
and non-Indigenous Peoples. Gardening as a means of indigenous food sovereignty has long been utilised by a variety of indigenous 
groups within Canada and elsewhere to address circumstances of chronic food insecurity and support health and wellness (Johnson-
Jennings et al., 2020; Timler and Sandy, 2020). The concept of gardening as both a Euro-Western agricultural practice and indigenous 
practice encourages an increased reverence and connection with the land and wider engagement with the natural world (Whyte, 2018). 
Much of this is because Indigenous knowledge and land management practices encompass processes that are known to be synergistic Box 7.1 (continued)

7
1057Health, Wellbeing and the Changing Structure of Communities  Chapter 7
and sustainable (Ottenhoff, 2021). Indigenous worldviews offer a different perspective on social resilience to environmental change, one 
that is based on moral relationships of responsibility that connect humans to animals, plants and habitats (Grey and Patel, 2015). These 
responsible practices not only ensure ecosystems are maintained for future generations; they centre the moral qualities necessary to carry 
out the responsibilities of consent, reciprocity and trust. Moral qualities of responsibility are the foundation for relying on each other 
when facing environmental challenges (Whyte, 2018; Miltenburg et al., 2021).
To restore these sustainable relationships, a resurgence is needed of community roles and responsibilities (Cidro et al., 2015) as well as 
a reconsideration of the concept of food security and the role of gardening within diverse indigenous contexts. Offering individual or

community gardening as a solution to ‘food insecurity’, a Eurocentric measure of health, ignores colonial contexts and sovereignty 
(Borrows, 2019; Timler and Sandy, 2020). Indigenous communities have historic, ongoing and evolving gardening and food gathering 
practices, including a wide variety of land-based and aquatic foods (Turner and Turner, 2008; Mt. Pleasant, 2016). Euro-Western science 
is beginning to recognise these longstanding relationships (Kamal et al., 2015; Hatfield et al., 2018; Timler and Sandy, 2020). For many 
indigenous communities, reconnecting with ancestral foodways holds the potential not only to address food security but to provide the 
community cohesion, self-esteem and wellness (Gordon et al., 2018).
A New Food Composition Database in Uganda to Guide Local Policy in Healthy Eating Based on Indigenous Foods
In sub-Saharan Africa, climate change is an emerging risk factor for undernutrition, particularly in countries that rely on subsistence

agriculture (Sorgho et  al., 2020). In Uganda, negative health effects associated with climate change are being observed, including 
increased rates of food insecurity, with the highest rates recorded among the Batwa of Kanungu District, where 97% of households are 
severely food insecure (Patterson et al., 2017). For many Indigenous Peoples, food security in a changing climate is a growing concern 
(Guyot et al., 2006; Patterson et al., 2017). Locally harvested indigenous foods have been adversely impacted by climate change, while 
connection to land is being disrupted by the processes of colonisation, discrimination and lack of representation in decision-making 
groups, thereby restricting adaptive capacity for indigenous communities (Bryson et al., 2021). In Uganda, the Indigenous Batwa have 
experienced significant disparities resulting from the forced eviction from their territory, dispossessing them of their land and the ability

to provide indigenous foods to their families (Patterson et al., 2017; Scarpa et al., 2021).
Nutrient-specific knowledge of indigenous foods is limited among many communities in Africa. A new food composition database in 
Uganda was constructed in dialogue with knowledge keepers from the Batwa and Bakiga Peoples to assess the nutrient density of these 
locally harvested foods (Scarpa et al., 2021). As in other lower resource settings, no food composition tables are available for southwestern 
Uganda. The only existing food database was designed for central and eastern Uganda; it does not include common recipes and local 
foods consumed by Batwa and Bakiga communities (Scarpa et al., 2021). Using a community-based approach and collaboration with 
local nutritionists, a list of foods was collected through focus group discussions, an individual dietary survey and market assessments.

Including these locally familiar foods ultimately supports a focus on indigenous justice and the importance of valuing indigenous food 
systems and practices, which in many contexts have been found to have superior nutritional and environmental benefits for communities 
(Kuhnlein et al., 2013; Scarpa et al., 2021). This new and unique database including indigenous foods will not only guide local nutrition 
and health initiatives, but also contribute towards policies related to indigenous food sovereignty and resilience to climate change.
Decreased Fragmentation of Winter Grazing Increases Mental and Spiritual Well-Being in Reindeer Herding Sámi and Decreases their 
Dependency on Fossil Fuels
Sami are the Indigenous Peoples of northernmost Scandinavia and the Kola Peninsula of Russia, whose livelihoods have been traditionally 
sustained by reindeer herding, hunting, fishing and small-scale farming (Nilsson et al., 2011). Climate change is threatening core conditions

for reindeer herding, with Sami pastoralists describing the situation as ‘facing the limit of resilience’ (Furberg et al., 2011). Sami pastoralists 
stress that an ability to continue reindeer herding is a prerequisite for their mental and spiritual health (Jaakkola et al., 2018).
In a pilot project for climate adaptation of reindeer herding run by the Swedish Sami Parliament, reindeer herding management plans (in 
Swedish, renbruksplaner) were used as a tool to develop strategies for climate adaptation (Walkepää, 2019). Four Sami reindeer herding 
cooperatives participated in the pilot study. They all agreed that climate change means that grazing patterns need to change. Traditionally, 
mountain reindeer graze in the Scandinavian mountains close to Norway in summer and in the coastal areas close to the Gulf of Bothnia 
in winter, representing a total migration route of up to 400 kilometres one-way. Rising temperatures are causing spring to occur earlier

in the coastal winter grazing land before the calving areas in the summer land are suitable for grazing and free from snow. When the 
snow cover disappears, the herds are dispersed, so it is important to migrate while snow is still present (Walkepää, 2019). Migration Box 7.1 (continued)

7
1058Chapter 7 Health, Wellbeing and the Changing Structure of Communities
routes are being destabilised by weaker ice cover on water and by hazardous weather events. Competing land use due to infrastructure, 
extractive industries, tourism, and energy production makes it difficult to find alternative grazing land. Supplementary feeding and 
increased use of trucks to transport reindeer is one result. Herds that are dispersed due to bad snow conditions have an increased 
exposure to predators (Walkepää, 2019; Uboni et al., 2020). By working strategically to secure adequate winter grazing and reduce 
fragmentation of grazing areas more generally represents win-win strategies for achieving decreased mental stress levels while reducing 
herders’ consumption of fossil fuels (Walkepää, 2019).Box 7.1 (continued)
7.1.7.3.7 Vulnerability Experienced through Food Systems
Stresses and shocks associated with climate change are drivers of food

insecurity, particularly in sub-Saharan Africa, Asia and Latin America 
(Betts et  al., 2018). The most vulnerable groups include smallholder 
farmers, pastoralists, agricultural laborers, poorer households, refugees, 
indigenous groups, women, children, the elderly and those who are 
socioeconomically marginalised (FAO et al., 2018; IPCC, 2019b) (high 
confidence). Men, women, children, the elderly and the chronically 
ill have different nutritional needs and these vulnerabilities may be 
amplified by gendered norms and differential access to resources, 
information and power (IPCC, 2019b). Extreme climate events have 
immediate and long-term impacts on food insecurity and malnutrition in poor and vulnerable communities, including when women and girls 
need to undertake additional duties as laborers and caregivers (FAO 
7.1.7.3.8 Health Vulnerability Experienced through Water and 
Sanitation Systems
Water and sanitation systems are particularly vulnerable to extreme

weather events, and damage to such systems can lead to contamination 
of drinking water and subsequent adverse health impacts (Howard 
et al., 2016; Khan et al., 2015; Sherpa et al., 2014). In areas with only 
very simple traditional excreta disposal facilities (e.g., latrines) and 
traditional sources of water (e.g., unprotected wells), the repeated 
Structure of chapter 07
Section 7.2S ection 7.3ADAPTATION &
CLIMATE RESILIENT 
DEVELOPMENT PATHWAYS
(CRDP)
Health secto r
Other sectors
OBSERVED IMPACT S
/g122/g3Health
/g122/g3Wellbein g
/g122/g3Migration
/g122/g3Conflict PROJECTED
FUTURE RISK SSection 7.4
HazardSection 7.1FUTURE EMISSIONS
(RCPs)    
2.6
4.5
6
8.5
Challenges for adaptatio nFUTURE SOCIO-ECONOMIC
& DEMOGRAPHIC PATHWAY S
(SSPs)
SSP1SSP3
SSP2
SSP4SSP5Challenges for mitigation+
ExposureHazard Risk

Figure 7.3 |  Structure of the chapter following a pathway from hazard, exposure and vulnerability to the observed impacts, projected future risks, adaptation 
and climate resilient development pathways.

7
1059Health, Wellbeing and the Changing Structure of Communities  Chapter 7
occurrence of floods and other extreme events can negatively affect 
water quality at household and community levels and increase the 
burden of food- and waterborne diseases (Cissé et  al., 2016; Khan 
et al., 2015; Kostyla et al., 2015).
7.1.8  Visual Guide to this Chapter
Figure 7.3 provides a visual guide to this chapter. Section 7.1 has briefly 
summarised major global frameworks and highlights groups that exhibit 
heightened vulnerability and exposure to the climatic risks assessed in 
this chapter. Section 7.2 assesses observed impacts on health and well-
being, migration and conflicts that have emerged from interactions of 
climate and weather-related hazards, exposure to such hazards and 
vulnerability of communities and systems, while Section 7.3 assesses 
projected future risks. Section  7.4 assesses adaptation responses to 
climate risks, opportunities for transformative change, co-benefits

and how solutions for reducing climate impacts on health, well-being, 
migration and conflicts may form part of the wider CRDPs.7.2. Observed Impacts of Climate Change on 
Health, Well-Being, Migration and Conflict
7.2.1 Observed Impacts on Health and Well-Being
Eleven categories of diseases and health outcomes have been identified 
in this assessment as being climate-sensitive through direct pathways 
(e.g., heat and floods) and indirect pathways mediated through natural 
and human systems and economic and social disruptions (e.g., disease 
vectors, allergens, air and water pollution, and food system disruption) 
(high confidence). A key challenge in quantifying the specific 
relationship between climate and health outcomes is distinguishing 
the extent to which observed changes in prevalence of a climate-
sensitive disease or condition are attributable directly or indirectly to 
climatic factors as opposed to other non-climatic causal factors (Ebi

et al., 2020). A subsequent challenge is then determining the extent 
to which those observed changes in health outcomes associated 
with climate are attributable to events or conditions associated with 
natural climate variability compared to persistent human induced 
shifts in the mean and/or the variability characteristics of climate 
(i.e., anthropogenic climate change). The context within which the 
impacts of climate change affect health outcomes and health systems 
is described in this chapter as being a function of risk, which is in turn 
a product of interactions between hazard, exposure and vulnerability 
(Chapter 1), with the impacts in turn having the potential to reinforce 
vulnerability and/or exposure to risk (Figure 7.4).
Interactions between hazard, exposure and vulnerability
ExposureHazard RiskAge
Gender
Mobilit y
Access to care
Socio-economic statu s
Pre-existing conditions
Characteristics of health system
Outdoor employment
Housing quality

Location/local geograph y
Livelihood typeHeat, drought
Floods
Storms
Vector spreadCLIMATE SENSITIVE OUTCOMES
- Adverse health (VBD, WBD, FBD, infectious disease ,
 heat-related illness, mental health, under nutrition )
- Migration & displacement
- Conflicts
SYSTEM IMPACTS
Health system (patient loads, emergency responses, costs)
- Food systems
- Livelihood system sIMPACTS
Figure 7.4 |  Interactions between hazard, exposure and vulnerability that generate impacts on health systems and outcomes, with selected examples.  
WBD: waterborne disease, VBD: Vector-borne disease, and FBD: Food-borne disease.

7
1060Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Box 7.2 | The Global Burden of Climate-Sensitive Health Outcomes Assessed in this Chapter
Global statistics for death and loss of health are increasingly described in terms of burden, which describes gaps between a population’s 
actual health status and what its status would be if its members lived free of disease and disability to their collective life expectancy 
(Shaffer et al., 2019). Burden for each disease/health outcome is estimated by adding together the number of years of life lost (YLL) by 
a person because of early death and the number of years of life lived with disability (YLD) from the considered outcome. The resulting 
statistic, the disability-adjusted life year (DALY) represents the loss of one year of life lived in full health. The total global burden of 
disease (Vos et al., 2020), expressed in DALYs, is what the world’s health systems must manage and is reported annually in Global Burden

of Disease Study (Vos et al., 2020). The estimated current global burden of climate-sensitive diseases and conditions described in this 
chapter, and the geographical regions most affected, are summarised in Table Box 7.2.1. As was observed in Chapter 11 (‘Human Health’) 
of AR5, the ‘background climate-related disease burden of a population is often the best single indicator of vulnerability to climate 
change – doubling of risk of disease in a low disease population has much less absolute impact than doubling of the disease when the 
background rate is high.’
The global magnitude of climate-sensitive diseases was estimated in 2019 to be 39,503,684 deaths (69.9% of total annual deaths) and 
1,530,630,442 DALYs (Vos et al., 2020). Of these, cardiovascular diseases (CVDs) comprised the largest proportion of climate-sensitive 
diseases (32.8% of deaths and 15.5% DALYs). The next largest category consists of respiratory diseases – with chronic respiratory disease

contributing to 7% of deaths and 4.1% of DALYs and respiratory infection and tuberculosis contributing to 6.5% of deaths and 6% of 
DALYs. The observed trend of climate-sensitive disease deaths since 1990 is marked by increasing cardiovascular mortality and decreasing 
mortality from respiratory infections, enteric diseases and other infectious diseases (Vos et al., 2020). Figure Box 7.2.1 illustrates specific 
global trends between 1990 and 2017 of selected health outcomes estimated by GBDs (Ahmad Kiadaliri et al. 2018).
Table Box 7.2.1 |  Global burden of climate-sensitive health risks assessed in this chapter (in order of assessment) (Vos et al., 2020) and synthesis of major observed 
and projected impacts in most affected regions. Blue represents an increase in positive health impacts, green represents an increase in negative health impacts and yellow

represents an increase in both positive and negative impacts, but not necessarily in equal proportions. The confidence level refers to both the attributed observed and 
projected changes to climate change. No assessment means the evidence is insufficient for assessment.
Data from Global Burden of Disease 2019 
(Vos et al. 2020)Chapter 7 Assessment
Health outcome (disease/
condition)Global annual deathsRegions most 
affected (deaths)Climate change observed 
impactsClimate change 
projected impacts in 
most affected regionsSelected key 
references of the 
Malaria 643,381 Africa (92%) **** ***M’Bra et al. (2018); 
Caminade et al. (2019); 
Gibb et al. (2020); 
Tompkins and Caporaso 
(2016b); Ebi et al. 
(2021a)
Dengue 36,055 Asia (96%) *** ***Bhatt et al. (2013); 
Rocklöv & Dubrow 
(2020); Messina et al. 
(2019); Monaghan et al. 
(2018)
Diarrhoeal diseases 1,534,443 Asia (56%) *** **Cissé (2019); Levy et al. 
(2018); Lo Iacono et al. 
(2017); Carlton et al. 
(2016)

Salmonella 79,046 Africa (89%) *** **Cissé (2019); Smith and 
Fazil (2019); Lake (2017)
RTIs 2,493,200 Asia (47%) **Geier et al. (2018); 
Oluwole (2017)
Non-communicable 
respiratory illness3,741,705 Asia (74%) *** **Schweitzer et al. (2018); 
Hansel et al. (2016); 
Collaco et al. (2018); 
D’Amato et al. (2020); 
Silva et al. (2017); 
Doherty et al. (2017); 
Beggs (2021)

7
1061Health, Wellbeing and the Changing Structure of Communities  Chapter 7
CVD 18,562,510 Asia (58%) ** ***Stewart et al. (2017); 
Phung (2016); Sun 
(2018); Wang (2016); 
Tian (2019); Chen (2019); 
Zhang (2018)
Death from malignant 
neoplasms10,079,637 Asia (55%) ***Ahmed et al. (2014); 
Modenese et al. (2018); 
Prueksapanich et al. 
(2018)
Diabetes 1,551,170 Asia (56%) ** **Hajat et al. (2017); Xu 
et al. (2019b); Li et al. 
(2014);Yang et al. (2016); 
Velez-Valle et al. (2016); 
Quast and Feng (2019)
Environmental heat and 
cold exposure47,461 Asia (46%) *** ****Zhang et al. (2019b); 
Green et al. (2019); 
Murray et al. (2020); Ma 
and Yuan (2021); Jones 
et al. (2018); Russo et al. 
(2019); Gosling et al. 
(2017)
Nutritional deficiencies 251,577 Africa (43%) *** ***Mbow et al. (2019); Lloyd 
(2018); Springmann 
et al. (2016b); Zhu et al. 
(2018); Weyant et al. 
(2018)

Mental healtha n.a. n.a. **** ****Cianconi et al. (2020); 
Charlson et al. (2021); 
Hayes and Poland 
(2018); Hrabok et al. 
(2020); Obradovich et al. 
(2018)
Legend
Climate change impacts Confidence
Positive health impacts Very high ****
Negative health impacts High ***
Positive and negative impacts Medium **
No assessment Low *
Notes:
(a) Mental health data were not available (n.a.) due to lack of information in GBD 2019 related to annual deaths and the most affected regions.Box 7.2 (contnued)

7
1062Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Global trends of selected health outcomes estimated by the Global Burden of Disease Study
2.0
100
50
0
1990 1999 2008 2017
150
100
50
0Diarrhoeal disease
Malaria
40
30
20
010
10
5
0Forces of nature
Protein-energy malnutrition
1990 1999 2008 201710
5
0
2.0
1.5
1.0
0.1
0Dengue
Heat and cold exposureDeaths per 100,000 people Deaths per 100,000 peopleAll causes
1400
1200
1000
800
600
0
African region
Region of the Americas
Eastern Mediterranean region
European region
South-East Asia region
Western Pacific region
Figure Box 7.2.1 |  Global trends of selected health outcomes estimated by GBDs. Source: Ahmad Kiadaliri et al. (2018a).Box 7.2 (contnued)
7.2.2 Observed Impacts on Communicable Diseases
7.2.2.1  Observed Impacts on Vector-Borne Diseases
Climate-sensitive VBDs include mosquito-borne diseases, rodent-borne

diseases and tick-borne diseases. Many infectious agents, vectors, non-
human reservoir hosts, and pathogen replication rates can be sensitive to 
ambient climatic conditions. Elevated proliferation and reproduction rates 
at higher temperatures, longer transmission season, changes in ecology 
and climate-related migration of vectors, reservoir hosts or human 
populations contribute to this climate sensitivity (Rocklöv & Dubrow, 
2020; Semenza and Paz, 2021). Age-standardised DALY rates for many 
VBDs have decreased over the last decade due to factors unrelated to 
climate. Vulnerability to VBD is strongly determined by sociodemographic 
factors (e.g., children, the elderly and pregnant women are at greater 
risk) with exposure to vectors being strongly influenced by various 
factors including socioeconomic status, housing quality, healthcare 
access, susceptibility, occupational setting, recreational activity, conflicts

and displacement ( Rocklöv & Dubrow, 2020; Semenza and Paz, 2021). 
Figure 7.5 illustrates how climatic and non-climatic drivers and responses 
determine VBD outcomes.Evidence has increased since AR5 that the vectorial capacity has 
increased for dengue fever, malaria and other mosquito-borne 
diseases and that higher global average temperatures are making 
wider geographic areas more suitable for transmission (very high 
confidence). Transmission rates of malaria are directly influenced by 
climatic and weather variables such as temperature, with non-climatic 
socioeconomic factors and health system responses counteracting 
the climatic drivers (very high confidence). The burden of malaria is 
greatest in Africa, where more than 90% of all malaria-related deaths 
occur (M’Bra et al., 2018; Caminade et al., 2019). Between 2007 and 
2017, DALYs for malaria have decreased by 39% globally. Malaria 
is mainly caused by five distinct species of plasmodium parasite

(Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, 
Plasmodium ovale and Plasmodium knowlesi) and is transmitted by 
Anopheline mosquitoes. Evidence suggests that in highland areas of 
Colombia and Ethiopia, malaria has shifted in warmer years towards 
higher altitudes, indicating that, without intervention, malaria will 
increase at higher elevations as the climate warms (Siraj et al., 2014; 
Midekisa et al., 2015). Each year, local outbreaks of malaria occur due 
to importation in areas from which it was once eradicated, such as 
Europe, but the risk of re-establishment is considered low.

7
1063Health, Wellbeing and the Changing Structure of Communities  Chapter 7
The transmission of dengue fever is linked to climatic and weather 
variables such as temperature, relative humidity and rainfall (high 
confidence). The dengue virus is carried and spread by Aedes 
mosquitoes, primarily Aedes aegypti. Dengue has the second highest 
burden of VBDs, with the majority of deaths occurring in Asia (Bhatt 
et  al., 2013). Since 1950, global dengue burden has grown and is 
attributable to a combination of climate-associated expansion in the 
geographic range of the vector species and non-climatic factors such 
as globalised air traffic, urbanisation and ineffective vector abatement 
measures. Temperature, relative humidity and rainfall variables are 
significantly and positively associated with increased dengue case 
incidence and/or transmission rates globally, including in Vietnam 
(Phung et al., 2015; Xuan le et al., 2014), Thailand (Xu et al., 2019a),

India (Mutheneni et al., 2017; Rao et al., 2018; Mala and Jat, 2019), 
Indonesia (Kesetyaningsih et al., 2018), the Philippines (Carvajal et al., 
2018), the USA (Lopez et al., 2018; Pena-Garcia et al., 2017; Duarte 
et  al., 2019; Rivas et  al., 2018; Silva et  al., 2016a), Jordan (Obaidat 
and Roess, 2018) and Timor-Leste (Wangdi et al., 2018). Variation in 
winds, sea surface temperatures and rain over the tropical eastern 
Pacific Ocean (El Niño-Southern Oscillation; ENSO) have been linked to 
increased dengue incidence in Colombia (Quintero-Herrera et al., 2015; 
McGregor and Ebi, 2018; Pramanik et al., 2020) and its interannual 
variation successfully forecasted in Ecuador using ENSO indices 
as predictors (Petrova et  al., 2019). The observed time lag between 
climate exposures and increased dengue incidence is approximately 
1–2 months (Chuang et al., 2017; Lai, 2018; Chang et al., 2018).
Changing climatic patterns are facilitating the spread of CHIKV, Zika,

Japanese encephalitis and Rift Valley Fever in Asia, Latin America, 
North America and Europe ( high confidence). Climate change may have facilitated the emergence of CHIKV as a significant public health 
challenge in some Latin American and Caribbean countries (Yactayo et al., 
2016; Pineda et  al., 2016) and contributed to chikungunya outbreaks 
in Europe (Rocklöv et al., 2019; Mascarenhas et al., 2018; Morens and 
Fauci, 2014). The Zika virus outbreak in South America in 2016 was 
preceded by 2007 outbreaks on Pacific islands and followed a period 
of record high temperatures and severe drought conditions in 2015 
(Paz and Semenza, 2016; Tesla et al., 2018). Increased use of household 
water storage containers during the drought is correlated with a range 
expansion of Aedes aegypti during this period, increasing household 
exposure to the vector (Paz and Semenza, 2016). Changing climate also 
appears to be a risk factor for the spread of Japanese encephalitis to

higher altitudes in Nepal (Ghimire and Dhakal, 2015) and in southwest 
China (Zhao et al., 2014). In eastern Africa, climate change may be a risk 
factor in the spread of Rift Valley Fever (Taylor et al., 2016a).
Changes in temperature, precipitation, and relative humidity have 
been implicated as drivers of West Nile fever in southeastern Europe 
(medium confidence). The average temperature and precipitation prior 
to the exceptional 2018 West Nile outbreak in Europe was above the 
1981–2010 period average, which may have contributed to an early 
upsurge of the vector population (Marini et al., 2020; Haussig et al., 
2018; Semenza and Paz, 2021). In 2019 and 2020, West Nile fever was 
first detected in birds and subsequently in humans in Germany and the 
Netherlands (Ziegler et al., 2020; Vlaskamp et al., 2020).
Climate change has contributed to the spread of the Lyme disease 
vector Ixodes scapularis, a corresponding increase in cases of Lyme

disease in North America (high confidence) and the spread of the Lyme 
disease and tick-borne encephalitis vector Ixodes ricinus in Europe Analysis of the 
underlying drivers of 
infectious disease threat 
events (IDTE)
detected in Europe during 
2008–2013 by epidemic 
intelligence at the European 
Centre of Disease Prevention 
and Control
Surveillance and
reporting failureOccupational Prevention Lifestyle MigrationTravel and
tourism Global tradeNatural
environmentHuman-made
environment Climate
IDTE (epidemics or first
autochthonous cases) of 
vector-borne diseases
Globalization and
environmental drivers
Public health systems drivers
Sociodemographic drivers
Size is proportional to the overall 
requency of the driver
Figure 7.5 |  Analysis of the underlying drivers of infectious disease threat events (IDTEs) detected in Europe from 2008 to 2013 by epidemic intelligence

at the European Centre of Disease Prevention and Control. Seventeen drivers were identified and categorised into three groups: globalisation and environment (green), 
sociodemographic (red) and public health system (blue). The drivers are illustrated as diamond shapes and arranged in the top and bottom row; the sizes are proportional to the 
overall frequency of the driver. Here IDTEs (epidemics or first autochthonous cases) of VBDs are illustrated as a horizontal row of dots in the middle. These empirical data include 
the IDTEs of VBDs such as West Nile fever, malaria, dengue fever, chikungunya and Hantavirus infection. Source: Semenza et al. (2016).

7
1064Chapter 7 Health, Wellbeing and the Changing Structure of Communities
(medium confidence). In Canada, there has been a geographic range 
expansion of the black-legged tick I. scapularis, the main vector of 
Borrelia burgdorferi, the agent of Lyme disease. Vector surveillance of I. 
scapularis has identified strong correlation between temperatures and 
the emergence of tick populations, their range and recent geographic 
spread, with recent climate warming coinciding with a rapid increase 
in human Lyme disease cases (Clow et al., 2017; Cheng et al., 2017; 
Gasmi et al., 2017; Ebi et al., 2017). Ixodes ricinus, the primary vector 
in Europe for both Lyme borreliosis and tick-borne encephalitis is 
sensitive to humidity and temperature (Daniel et  al., 2018; Estrada-
Peña and Fernández-Ruiz, 2020) (high confidence). There has been an 
observed range expansion to higher latitudes in Sweden and to higher 
elevations in Austria and the Czech Republic.

Rodent-borne disease outbreaks have been linked to weather and 
climate conditions in a small number of studies published since AR5, 
but more research is needed in this area. In Kenya, a positive association 
exists between precipitation patterns and Theileria-infected rodents, 
but for Anaplasma, Theileria and Hepatozoon, the association between 
rainfall and pathogen varies according to rural land use types (Young 
et al., 2017). Weather variability plays a significant role in transmission 
rates of haemorrhagic fever with renal syndrome (HFRS) (Hansen et al., 
2015; Xiang et al., 2018; Liang et al., 2018; Fei et al., 2015; Xiao et al., 
2014; Vratnica et al., 2017; Roda Gracia et al., 2015; Monchatre-Leroy 
et al., 2017; Bai et al., 2019). In Chongqing, HFRS incidence has been 
positively associated with rodent density and rainfall (Bai et al., 2015).
7.2.2.2 Observed Impacts on Waterborne Diseases
Important waterborne diseases (WBDs) include diarrhoeal

diseases (such as cholera, shigella, cryptosporidiosis and typhoid), 
schistosomiasis, leptospirosis, hepatitis A and E and poliomyelitis (Cisse, 
2019; Houéménou et al., 2021; Hassan et al., 2021; Archer et al., 2020; 
Mbereko et al., 2020; Fan et al., 2021). The number of cases of WBDs 
is considerable, and even in high-income countries WBDs continue 
to be a concern (Cissé et al., 2018; Kirtman et al., 2014; Levy et al., 
2018; Murphy et al., 2014; Brubacher et al., 2020; Lee et al., 2021). 
Nevertheless, diarrhoea mortality has declined substantially since 
1990, although there are variations by country, and the global burden 
of WBDs has decreased in line with vaccination coverage of some 
WBDs (such as polio and cholera), poverty reduction and improved 
sanitation and hygiene (Jacob and Kazaura, 2021; Mutono et al., 2020; 
Lee et al., 2019; Semenza and Paz, 2021; Jacob and Kazaura, 2021; 
Mutono et al., 2020).
Drinking water containing pathogenic microorganisms is the main

driver of the burden of WBDs (Murphy et al., 2014; Lee et al., 2021; 
Chen et al., 2021b; Musacchio et al., 2021). WBD outbreaks, particularly 
intestinal diseases, are attributable to a combination of the presence 
of particular pathogens (bacteria, protozoa, viruses or parasites) and 
the characteristics of drinking water systems in a given location (Bless 
et al., 2016; Ligon and Bartram, 2016; Mutono et al., 2021; Ferreira 
Since AR5 there is a growing body of evidence that increases in 
temperature (very high confidence), heavy rainfall (high confidence), 
flooding (medium confidence) and drought (low confidence) are associated with an increase of diarrhoeal diseases. In the majority of 
studies there is a significant positive association observed between 
WBDs and elevated temperatures, especially in areas where water, 
sanitation and hygiene (WASH) deficiencies are significant (Levy 
et al., 2018; Carlton et al., 2016; Levy et al., 2018; Sherpa et al., 2014;

Guzman Herrador et al., 2015; Levy et al., 2016; Lo Iacono et al., 2017). 
In Ethiopia, South Africa and Senegal, increases in temperatures are 
associated with increases in diarrhoea, while in Ethiopia, Senegal and 
Mozambique, increases in monthly rainfall are associated with an 
increase in cases of childhood diarrhoea (Azage et  al., 2015; Thiam 
et al., 2017; Horn et al., 2018). Similar associations between weather 
and diarrhoea have been observed in Cambodia, China, Bangladesh, 
Pacific Island countries and the Philippines (McIver et  al., 2016a; 
McIver et al., 2016b; Liu et al., 2018; Wu et al., 2014; Matsushita et al., 
2018). Heavy precipitation events have been consistently associated 
with outbreaks of WBDs in Europe, USA, UK and Canada (Guzman 
Herrador et al., 2015; Levy et al., 2016; Lo Iacono et al., 2017; Curriero 
et al., 2001; Guzman Herrador et al., 2016; Levy et al., 2018; Semenza 
and Paz, 2021).
Impacts of floods include outbreaks of WBDs, with such events

disproportionately affecting the young, elderly and immunocompromised 
(Suk et al., 2020; Guzman Herrador et al., 2015; Levy et al., 2016; Lo 
Iacono et al., 2017; Zhang et al., 2019a). Water shortage and drought 
have been found associated with diarrhoeal disease peaks (Epstein 
et  al., 2020b; Subiros et  al., 2019; Boithias et  al., 2016), while some 
reviews found insufficient evidence of the effects of drought on 
diarrhoea (Levy et al, 2016 ; Asmall et al., 2021; Epstein et al., 2020b; 
Subiros et al., 2019; Boithias et al., 2016; Ramesh et al., 2016).
Heavy rainfall and higher than normal temperatures are associated 
with increased cholera risk in affected regions (very high confidence). 
Cholera is an acute diarrhoeal disease typically caused by the 
bacterium Vibrio cholerae that can result in severe morbidity and 
mortality. Maximum and minimum temperatures and precipitation 
have been negatively associated with cholera cases. Cholera outbreaks

have occurred in several regions after natural disasters, including 
cholera incidence increasing three-fold in El Niño-sensitive regions of 
Africa (Mpandeli et al., 2018; Amegah et al., 2016; Escobar et al., 2015; 
Jutla et al., 2017; Asadgol et al., 2019; Moore et al., 2018; Moore et al., 
2017; Camacho et al., 2018; IPCC, 2019a; Cross-Chapter Box ILLNESS 
in Chapter 2; Box 3.3).
Heavy rainfall, warmer weather and drought are linked to increased 
risks for other GI infections (high confidence). As temperature 
increases, bacterial causes of GI infection also appear to increase, 
and this association is variably influenced by humidity and rainfall 
(Ghazani et al., 2018; Levy, 2016). In New York it has been found that 
every 1°C increase in temperature was correlated with a 0.70–0.96% 
increase in daily hospitalisation for GI infections (Lin et al., 2016). In 
the Philippines, leptospirosis and typhoid fever showed an increase

in incidence following heavy rainfall and flooding events (Matsushita 

7
1065Health, Wellbeing and the Changing Structure of Communities  Chapter 7
Box 7.3 | Cascading Risk Pathways Linking Waterborne Disease to Climate Hazards
The causal linkages between climate variability and change and incidence of WBDs follow multiple direct and indirect pathways, often as 
part of a cascading series of risks (Semenza, 2020). For example, extreme precipitation can result in a cascading hazard or disease event 
with implications of greater magnitude than the initial hazard, especially if there are pre-existing vulnerabilities in critical infrastructure 
and human populations (Semenza and Paz, 2021). Intense or prolonged precipitation can flush pathogens in the environment from 
pastures and fields to groundwater, rivers and lakes, consequently infiltrating water treatment and distribution systems (Howard et al., 
2016; Khan et al., 2015; Sherpa et al., 2014; Cissé et al., 2016; Kostyla et al., 2015; Chapter 4). Table Box 7.3.1 shows the variety and

complexity of pathways between climate hazard and WBD outcomes (Semenza, 2020).
Table Box 7.3.1 |  Pathways between climate hazard and waterborne disease (WBD) outcomes. Source: Semenza (2020).
Cascading risk pathways from heavy rain and flooding
Storm runoff yields water turbidity, which compromises water treatment efficiency
Storm runoff and floods mobilise and transport pathogens
Overwhelmed or damaged infrastructure compromises water treatment efficiency
Floods overwhelm containment system and discharge untreated wastewater
Floods damage critical water supply and sanitation infrastructure
Floods displace populations towards inadequate sanitation infrastructure
Cascading risk pathways from drought
Low water availability augments travel distance to alternate (contaminated) sources
Intensified demand for and sharing (e.g., with livestock) of limited water resources decreases water availability and quality

Intermittent drinking water supply results in cross-connections with sewer lines and water contamination
Uncovered household water containers are a source of vector breeding
Poor hygiene due to decreased volume of source water and increased concentration of pathogens
Exposure to accumulated human excrements and animal manure
Cascading risk pathways from increasing temperature
Extended transmission season for opportunistic pathogens
Permissive temperature for the replication of marine bacteria
Enhanced pathogen load in animal reservoirs (e.g., chicken)
Pathogen survival and proliferation outside of host
Wildfires during heatwaves degrade water quality
Exposure to contaminated water due to higher water consumption
Behaviour change due to extended season (e.g., food spoilage during barbeque)
Cascading risk pathways from sea level rise
Population displacement due to powerful storm surges
Disruption of drinking water supply and sanitation infrastructure due to inundation

Decline in soil and water quality due to saline intrusion into coastal aquifers
Seawater infiltration into drinking water distribution and sewage lines
Notes:
Examples are purposely not exhaustive and should be considered illustrative.
7.2.2.3 Observed Impacts on Food-Borne Diseases
FBDs refer to any illness resulting from ingesting food that is spoiled 
or contaminated by pathogenic bacteria, viruses, parasites, toxins, 
pesticides and/or medicines (WHO, 2018b). FBD risks are present 
throughout the food chain, from production to consumption, and most 
often arise due to contamination at source and from improper food 
handling, preparation and/or storage (Smith and Fazil, 2019; Semenza 
and Paz, 2021). As with WBDs, FBD outbreaks can follow multiple 
causal pathways as climatic risk factors interact with food production 
and distribution systems, urbanisation and population growth, 
resource and energy scarcity, decreasing agricultural productivity,

price volatility, modification of diet trends, new technologies and the emergence of antimicrobial resistance (Lake, 2018; Yeni and Alpas, 
2017). The burden of FBDs is also linked to malnutrition as reduced 
immunity increases susceptibility to various food-borne pathogens and 
toxins (FAO, 2020).
A strong association exists between increases in FBDs and high air and 
water temperatures and longer summer seasons (very high confidence). 
The risks occur through complex transmission pathways throughout 
the food chain and the wide range of food-borne pathogens (Cisse, 
2019; Hellberg and Chu, 2016; Lake and Barker, 2018; Park et  al., 
2018b; Smith and Fazil, 2019). The food-borne pathogens of most 
concern are those having low infective doses, a significant persistence 
in the environment and high stress tolerance to temperature change

7
1066Chapter 7 Health, Wellbeing and the Changing Structure of Communities
(e.g., enteric viruses, Campylobacter spp., Shiga toxin-producing E. 
coli strains, Mycobacterium avium, tuberculosis complexes, parasitic 
protozoa and Salmonella) (Lake, 2018; Lake, 2017; Lake and Barker, 
2018; Smith and Fazil, 2019; European Food Safety Authority 2020; 
Semenza and Paz, 2021). Priority risks include marine biotoxins, 
mycotoxins, salmonellosis, vibriosis, transfer of contaminants due to 
extreme precipitation, floods, increased use of chemicals in the food 
chain (plant protection products, fertilizers, veterinary drugs) and 
potential residues in food (European Food Safety Authority 2020; 
World Health Organization 2018b).
There is a strong association observed between the increase in average 
ambient temperature and increases in Salmonella infections (high 
confidence). Most types of Salmonella infections lead to salmonellosis,

while some other types (Salmonella Typhi and Salmonella Paratyphi) 
can lead to typhoid fever or paratyphoid fever. The transmission to 
humans of the non-typhoidal Salmonella infection, one of the most 
widespread FBDs, usually occurs through eating foods contaminated 
with animal faeces. Studies conducted in Australia (Milazzo et  al., 
2016), New Zealand (Lal et al., 2016), the UK (Lake, 2017), South Korea 
(Park et al., 2018a; Park et al., 2018c; Park et al., 2018a), Singapore (Aik 
et al., 2018) and Hong Kong, SAR of China (Wang et al., 2018a; Wang 
et  al., 2018b), have shown that Salmonella outbreaks are strongly 
associated with temperature increases.
Significant associations exist between FBDs due to Campylobacter, 
precipitation and temperature (medium confidence). The timing of 
heat-associated Campylobacteriosis events varies across countries, 
with infection rates in the UK appearing to decline immediately

after periods of high rainfall (Djennad et al., 2019; Lake et al., 2019; 
Rosenberg et  al., 2018; Yun et  al., 2016; Weisent et  al., 2014). This 
suggests the association with climate may be indirect and due to 
weather conditions that encourage outdoor food preparation and 
recreational activities (Lake, 2017; Semenza and Paz, 2021).
Outbreaks of human and animal Cryptococcus have been reported as 
being associated with a combination of climatic factors and shifts in 
host and vector populations (Chang and Chen, 2015; Rickerts, 2019). The 
prevalence of childhood cryptosporidiosis, which is the second leading 
cause of moderate to severe diarrhoea among infants in the tropics 
and subtropics, shows associations with population density and rainfall, 
with contamination due to Cryptosporidium spp. being 2.61  times 
higher during and after heavy rain (Lal et al., 2019; Young et al., 2015; 
Khalil et al., 2018). Studies from Ghana, Guinea Bissau, Tanzania, Kenya

and Zambia show a higher prevalence of Cryptosporidium during high 
rainfall seasons, with some peaks observed before, at the onset or at 
the end of the rainy season (Squire and Ryan, 2017).
7.2.2.4 Respiratory Tract Infections
Climatic risk factors for respiratory tract infections (RTIs) due to 
multiple pathogens (bacteria, viruses and fungi) include temperature 
and humidity extremes, dust storms, extreme precipitation events and 
increased climate variability. Amongst a range of RTIs, pneumonia and 
influenza represent a significant disease burden (Ferreira-Coimbra 
et al., 2020; Lafond et al., 2021; McAllister et al., 2019; Wang et al., 
2020c). The drivers of pneumonia incidence are complex and include a range of possible non-climate as well as climate factors. For example, 
chronic diseases (e.g., lung disease, chronic obstructive pulmonary 
disease (COPD) and asthma), other comorbidities, a weak immune 
system, age, gender, community, passive smoking, air pollution and

childhood immunisation may confound the climate pneumonia 
relationship (Miyayo et al., 2021).
In temperate regions, the incidence of pneumonia is higher in winter 
months, but the exact causes of this seasonality remain debated 
(Mirsaeidi et al., 2016). With regards to temperature, various J-shaped, 
U-shaped or V-shaped temperature–pneumonia relationships 
have been reported in the literature (Huang et al., 2018; Kim et al., 
2016; Liu et al., 2014; Qiu et al., 2016; Sohn et al., 2019) with such 
relationships dependent on location. Humidity also appears important 
but, like temperature, its effect is not consistent across studies – low 
temperatures and low humidity (Davis et al., 2016), high temperatures 
and high humidity (Lam et al., 2020) and low temperatures and high 
humidity (Miyayo et al., 2021) have all been found to be associated 
with an increased incidence of pneumonia.
Day-to-day variations in temperature also appear important.

For Australia, increases in emergency room visits for childhood 
pneumonia are associated with sharp temperature drops (Xu et al., 
2014). Large inter-daily changes in temperature are important for 
respiratory disease incidence in Guangzhou, China (Lin et al., 2013) 
and Shanghai (Lei et al., 2021) while rapidly changing and extreme 
temperatures during pregnancy have been linked to childhood 
pneumonia (Miao et al., 2017; Zeng et al., 2017; Zheng et al., 2021). 
In tropical and subtropical areas of Africa and Asia, pneumonia 
incidence has been reported to be higher during the rainy season, 
pointing to a positive association between pneumonia patterns and 
temperature and precipitation (Chowdhury et  al., 2018a; Lim and 
Siow, 2018; Paynter et al., 2010).
The degree to which the timing, duration and magnitude of local 
influenza virus epidemics is dependent on climate factors is poorly 
understood (Lam et  al., 2020). Further, a host of non-climate

confounders are likely to influence the incidence of seasonal 
influenza (Caini et  al., 2018). This poses a number of challenges 
for making reliable climate-based epidemiological forecasts for 
influenza (Gandon et  al., 2016). Although no association between 
anomalous climate conditions and influenza have been reported in 
some locations (Lam et al., 2020), generally, low winter temperatures 
and humidity in temperate regions and periods of high humidity 
and precipitation in the tropical and subtropical regions have been 
linked to outbreaks of influenza (Deyle et al., 2016; Soebiyanto et al., 
2015; Tamerius et  al., 2013). However, the climate sensitivity of 
influenza may be more complex than this, with both high and low 
humidity; the amount and intensity of precipitation; solar activity 
and/or sunshine; and latitude also being important (Axelsen et al., 
2014; Chong et al., 2020b; Geier et al., 2018; Park et al., 2019; Qu,

2016; Smith et  al., 2017; Wang et  al., 2017c; Zhao et  al., 2018a). 
Moreover, the shape of the climate variable influenza relationship 
may be conditioned on influenza type (Chong et al., 2020a). Further, 
distinct periods of weather variability characterised by rapid inter-
daily changes in temperature may act as precursors to influenza 
epidemics as has been demonstrated for the marked 2017–2018

7
1067Health, Wellbeing and the Changing Structure of Communities  Chapter 7
influenza season and others across the USA (Liu et al., 2020a; Zhao 
et  al., 2018a). For the eastern Mediterranean, such rapid weather 
changes are associated with the ‘Cyprus Low’, with the timing and 
magnitude of seasonal influenza related to the interannual frequency 
of this particular weather regime (Hochman et al., 2021). Potentially, 
large-scale modes of climatic variability such as ENSO and the Indian 
Ocean Dipole, which strongly moderate the frequency of weather 
regimes in some parts of the world, could affect influenza pandemic 
dynamics. However, studies conducted to date report inconsistent 
results. Some point to an increased (decreased) severity of seasonal influenza during El Niño (La Niña) (Oluwole, 2015; Oluwole, 2017), 
while others find influenza to be more severe and frequent when 
coinciding with La Niña events (Chun et  al., 2019; Flahault et  al.,

2016; Shaman and Lipsitch, 2013). This raises the possibly of non-
stationary associations between large-scale modes of climatic 
variability and influenza dynamics (Onozuka and Hagihara, 2015) 
as found for other diseases (Kreppel et  al., 2014), something that 
might be expected given El Niño’s time-varying impact on global 
precipitation and temperature fields and associated impacts on 
health outcomes (McGregor and Ebi, 2018).
Cross-Chapter Box COVID | COVID-19
Authors: Maarten van Aalst (Netherlands, Chapter 16), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Ayansina Ayanlade 
(Nigeria, Chapter 9), Lea Berrang-Ford (United Kingdom/Canada, Chapter 16), Rachel Bezner Kerr (Canada/USA, Chapter 5), Robbert 
Biesbroek (Netherlands, Chapter 13), Kathryn Bowen (Australia, Chapter 7), Martina Angela Caretta (Sweden, Chapter 4), So-Min Cheong 
(Republic of Korea, Chapter 17), Winston Chow (Singapore, Chapter 6), Mark John Costello (New Zealand/Norway/Ireland, Chapter 11,

CCP1), Kristie Ebi (USA, Chapter 7), Elisabeth Gilmore (USA/Canada, Chapter 14), Bruce Glavovic (South Africa/New Zealand, Chapter 
18, CCP2), Walter Leal (Germany, Chapter 8), Stefanie Langsdorf (Germany, TSU), Elena Lopez-Gunn (Spain/United Kingdom, Chapter 4), 
Ruth Morgan (Australia, Chapter 4), Aditi Mukherji (India, Chapter 4), Camille Parmesan (France/ United Kingdom /USA, 2), Mark Pelling 
(United Kingdom, Chapter 6), Elvira Poloczanska (United Kingdom, TSU), Marie-Fanny Racault (United Kingdom/France, Chapter 3), Diana 
Reckien (Germany/Netherlands, Chapter 17), Jan C. Semenza (Sweden, Chapter 7), Pramod Kumar Singh (India, Chapter 18), Stavana E. 
Strutz (USA), Maria Cristina Tirado von der Pahlen (Spain/USA, Chapter 7), Corinne Schuster-Wallace (Canada), Alistair Woodward (New 
Zealand, Chapter 11), Zinta Zommers (Latvia, Chapter 17)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, emerged in late 2019, halfway through the 
preparation of the IPCC WGII Sixth Assessment Report. This Cross-Chapter Box assesses how the massive shock of the pandemic and 
response measures interact with climate-related impacts and risks as well as its significant implications for risk management and climate 
resilient development.
COVID-19 and environmental connections
Infectious diseases may emerge and spread through multiple climate-related avenues, including direct effects of climatic conditions 
on disease reproduction and transmission and various indirect effects, often interlinked with ecosystem degradation (high confidence). 
Climate change is affecting the risk of emerging infectious diseases by contributing to factors that drive the movements of species,

including vectors and reservoirs of diseases, into novel human populations and vice versa (high confidence) (Sections 2.4.2.7, 5.2.2.3; 
Cross-Chapter Box Illness in Chapter 2; IPCC, 2019b; IPBES 2020). The spillover of some emerging infectious diseases from wildlife into 
humans is associated with live animal–human markets, intensified livestock production and climate-related movements of humans 
and wild animals into new areas that alter human–animal interactions (Section 2.4.2.7; Chapter 3; Sections 5.2.2.3, 7.2; Cross-Chapter 
Box ILLNESS in Chapter 2; Cross-Chapter Box MOVING PLATE in Chapter 5).
Human-to-human transmission is the prominent driver in the spread of the COVID-19 pandemic, rather than climatic drivers (high 
confidence). There is emerging literature on the environmental determinants of COVID-19 transmission, incidence and mortality rates,

with initial evidence suggesting that temperature, humidity and air pollution contribute to these patterns (Brunekreef et al., 2021; Xiong 
et al., 2020; Zhang et al., 2020b; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and climate). Climate 
change is altering environmental factors like temperature and seasonality that affect COVID-19 transmission (Choi et al., 2021).
The impact of COVID-19 containment measures resulted in a temporary reduction in greenhouse gas (GHG) emissions and reduced air 
pollution (high confidence) (IPCC WGI TS; Arias et al., 2021; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air 
quality and climate). However, global and regional climate responses to the radiative effect were undetectable above internal climate 
variability due to the temporary nature of emission reductions. They therefore do not result in detectable changes in impacts or risks due

to changes in climate hazards (Arias et al 2021; AR6 WGI CCB 6.1: Implications of COVID-19 restrictions for emissions, air quality and 
climate; Naik et al., 2021).

7
1068Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Cascading and compounding risks and impacts
The COVID-19 pandemic posed a severe shock to many socioeconomic systems, resulting in substantial changes in vulnerability and 
exposure of people to climate risks (high confidence). The disease and response measures significantly affected human health, economic 
activity, food production and availability, health services, poverty, social and gender inequality, education, supply chains, infrastructure 
maintenance and the environment. These COVID-19 impacts interact with many risks associated with climate change (IMF , 2020), often 
through a cascade of impacts across numerous sectors (van den Hurk et al., 2020). Beyond COVID-19-related mortality and long-term 
COVID, mortality from other diseases (some of which may also have a climate-related component), as well as maternal and neonatal

mortality, increased because of disruption in health services (Barach et al., 2020; Maringe et al., 2020; Zadnik et al., 2020; Goyal et al., 
2021). In addition, a rapid rise in poverty has disproportionately affected poorer countries and people (Ferreira et al., 2021), and thus 
increased their vulnerability. After many years of steady declines, extreme poverty increased by about 100 million people in 2020 (World 
Bank, 2021). The effects of the pandemic increased food insecurity and malnourishment, which increased by 1.5 percentage points to 
around 9.9% in 2020 after being virtually unchanged for the previous five years (FAO et al., 2021).
During the pandemic, extreme weather and climate events such as droughts, storms, floods, wildfires and heatwaves continued, resulting 
in disastrous compounding impacts (high confidence). Between March and September 2020, 92 extreme weather events coincided with

the COVID-19 pandemic, affecting an estimated 51.6 million people; additionally, 431.7 million people were exposed to extreme heat, 
and 2.3 million people were affected by wildfires (Walton and van Aalst, 2020). The COVID-19 pandemic, in combination with extreme 
events, affected disaster preparedness, disaster response and safe evacuations, while physical distancing regulations reduced the capacity 
of temporary shelters (UN DRR Asia-Pacific, 2020; Tozier de la Poterie et al., 2020; Shumake-Guillemot, J, et al, 2020; Bose-O’Reilly et al., 
2021). Complex humanitarian emergencies were aggravated, with vulnerable populations facing the combined risks of conflict, 
displacement, COVID-19 and climate impacts (FSIN, 2020). Compounding events are not only found in low-income countries but also in 
medium- and high-income countries, for instance in the case of COVID-19 and heatwaves (Shumake-Guillemot et al., 2020; Bose-O’Reilly 

Responses and implications for adaptation and climate resilient development
The pandemic emphasises the inter-connected and compound nature of risks, vulnerabilities and responses to emergencies that 
are simultaneously local and global (high confidence). COVID-19 is often considered a more ‘explosive’ risk than the more gradual 
anthropogenic climate change. However, many climate-related risks do already appear as severe shocks at smaller scales, and infrequent 
or unprecedented extreme weather-related events often warrant similar rapid responses (Dodds et al., 2020; Gebreslassie, 2020; Hynes 
et  al., 2020; Phillips et  al., 2020; Schipper, 2020; Semenza et  al., 2021; illustrated in Figure  COVID.1). Individuals, households, sub-
national and national entities, and international organisations had generally delayed responses or denied the pandemic’s severity before

responding at the scale and urgency required, a pattern that resembles the international action required on climate change (Polyakova 
et al., 2020; Shrestha et al., 2020).
Improved contingency and recovery planning, including disease mitigation measures, were crucial in responding to the pandemic in 
similar ways to those seen in the aftermath of climate-related disasters (Guo et al., 2020; Ebrahim et al., 2020; Baidya et al., 2020; 
Shultz et al., 2020; Mukherjee et al., 2020). The pandemic highlighted the lack of global and country-specific capacity to respond to an 
unexpected and unplanned event and the need to implement more flexible detection and response systems (Ebi et al., 2021b).
It also exposed underlying vulnerabilities, such as the lack of water access and healthcare in select low- and middle-income countries 
and among indigenous and marginalised groups in high-income countries (Section 4.4.3; Box 4.3; 5.12.1). Increased risks of COVID-19

transmission emerged in crowded areas such as urban settings, refugee camps, detention centres and some workplaces, including in rural 
settings (Brauer et al., 2020; Ramos et al., 2020; Staddon et al., 2020; Haddout et al., 2020). Public health responses to the COVID-19 
pandemic, such as mandates for social distancing and advice for frequent handwashing, underlined the need for access to water and 
sanitation facilities and wastewater management. However, they also sometimes interfered with access, for example, in evacuation and 
shelter infrastructure during climate-related disasters (Armitage and Nellums, 2020; Adelodun et al., 2020; Poch et al., 2020; Hallema 
et al., 2020; Patel et al., 2020; Espejo et al., 2020).
The experience of COVID-19 demonstrates that many warnings about the risks of the emergence of zoonotic transmission (‘delay is 
costly’, ‘adapt early’ and ‘prevention pays’) did not result in sufficient political attention, funding and pandemic prevention. In some

countries, there has been an increased awareness of the risks and the real or perceived trade-offs associated with risk management (e.g., 
economy compared with health and impacts compared with adaptation). Building trust and participatory processes and establishing Cross-Chapter Box COVID (continued)

7
1069Health, Wellbeing and the Changing Structure of Communities  Chapter 7
stronger relationships with communities and other civic institutions may enable a recalibration of how the government responds to crises 
and society–government relationships more generally (Amat et al., 2020; Deslatte, 2020).
The management of the COVID-19 pandemic has highlighted the value of scientific (including medical and epidemiological) expertise and 
the importance of fast, accurate and comprehensive data to inform policy decisions and to anticipate and manage risk (high confidence). It 
emphasises the importance of effective communication of scientific knowledge (Semenza et al., 2021), decision-making under uncertainty 
and decision frameworks that navigate different values and priorities. Successful policy responses were based on the emerging data, 
medical advice and collaboration with a wider set of societal stakeholders beyond public health experts. For instance, experience in

Aotearoa, New Zealand, highlights the importance of pandemic responses attuned to the needs of different sociocultural groups and 
Indigenous Peoples in particular. Their strengths-based COVID-19 response goes beyond identifying vulnerabilities to unlocking the 
resources, capabilities and potential that might otherwise be latent in communities (McMeeking and Savage, 2020). As far as the value 
of information for risk management is concerned, compared to the initial uncertainties regarding COVID-19, data about near- and 
longer-term climate-related hazards is generally very good; however, high-quality and dense meteorological data are often still lacking in 
lower income countries (Otto et al., 2020). Health data are particularly difficult to obtain in real time, as is the case for biodiversity data, 
which has a time lag of years before being made available and for which there is no coordinated monitoring, hampering effective risk

management (Navarro et al., 2017). Therefore, both epidemiological and meteorological forecasts would benefit from more focus on (a) 
decision support, (b) conveying uncertainty and (c) capturing vulnerability (Coughlan de Perez et al., 2021).
There is a considerable evidence base of specific actions that have co-benefits for reducing pandemic and climate change risks while 
enhancing social justice and biodiversity conservation (high confidence). The pandemic highlighted aspects of risk management that have 
long been recognised but are often not reflected in national and international climate policy: the value of addressing structural vulnerability 
rather than taking specific measures to control single hazards and drivers of risk and the importance of decision-making capacities and 
transparency, the rule of law, accountability and addressing inequities (or social exclusion) (reviewed by Pelling et al. (2021); see also 
Figure COVID.1).

Comprehensive and integrated risk management strategies can enable countries to address both the current pandemic and increase 
resilience against climate change and other risks (Reckien, 2021; Semenza et al., 2021; Ebi et al., 2021b). In particular, given their immense 
scale, COVID-19 recovery investments may offer an opportunity to contribute to climate resilient development pathways (CRDPs) through 
a green, resilient, healthy and inclusive recovery (high confidence) (Sovacool et al., 2020; Rosenbloom and Markard, 2020; Lambert et al., 
2020; Boyle et al., 2020; Bouman et al., 2020; UN DRR Asia-Pacific, 2020; Brosemer et al., 2020; Dodds et al., 2020; Hynes et al., 2020; 
Markard and Rosenbloom, 2020; Phillips et al., 2020; Schipper, 2020; Willi et al., 2020; Semenza et al., 2021; Pasini and Mazzocchi, 2020; 
Meige et al., 2020; Pelling et al., 2021). However, windows of opportunity to enable such transitions are only open for a limited period

and need to be swiftly acted upon to effect change (high confidence) (Chapter 18; Weible et al., 2020; Reckien, 2021). Initial indications 
suggest that only USD 1.8 trillion of the greater than USD 17 trillion COVID-19-related stimulus financing by G20 countries and other major 
economies that was committed up until mid-2021 contributed to climate action and biodiversity objectives, with significant differences 
between countries and sectors (Vivideconomics, 2021). Moreover, responses to previous crises (e.g., the 2008–2011 global financial 
crisis) demonstrate that despite high ambitions during the response phase, opportunities for reform do not necessarily materialise (Bol 
et al., 2020; Boin et al., 2005). In addition, heightened societal and political attention to one crisis often comes at the cost of other policy 
priorities (high confidence) (Maor, 2018; Tosun et al., 2017), which could affect investments for climate resilient development (Hepburn

et al., 2020; WHO, 2020a; Bateman et al., 2020; Meige et al., 2020; Semenza et al., 2021).
In summary, the emerging literature suggests that the COVID-19 pandemic has aggravated climate-related health risks, demonstrated the 
global and local vulnerability to cascading shocks and illustrated the importance of integrated solutions that tackle ecosystem degradation 
and structural vulnerabilities in human societies. This highlights the potential and urgency of interventions that reduce pandemic and 
climate change risks while enhancing compound resilience, social justice and biodiversity conservation (see Figure COVID.1).Cross-Chapter Box COVID (continued)

7
1070Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Hazard
Deforestation; 
Society/nature alienationVulnerability
Fragile livelyhoods ;
Fragile health
Exposure
Overcrowded housing;
Inadequate sanitationInadequate
response
Exclusive planning;
Uninformed decisionsRisk:
Compound risk to 
pandemic and climate 
change
Hazard reduction
Conservation of biodiversityVulnerability
reduction
Livelihood security ;
Healthy lifestyles
Exposure reduction
Adaptive housing;
Adaptive sanitation;Response
Inclusive planning;
Informal decisionsResilience:
Compound resilience 
to pandemic and 
climate changeCompound risk and compound resilience to pandemic and climate change
Figure COVID.1 |  Compound risk and compound resilience to pandemic and climate change. Source: Pelling et al. (2021).Cross-Chapter Box COVID (continued)

7
1071Health, Wellbeing and the Changing Structure of Communities  Chapter 7
7.2.2.5 Other Water Shortage and Drought-Associated Diseases 
and Health Outcomes
Water shortage and drought are associated with skin diseases 
(Schachtel et  al., 2021; Lundgren, 2018; Andersen and Davis, 2017; 
Kaffenberger et  al., 2017; Andersen and Davis, 2017), trachoma 
(Ramesh et  al., 2016) and violence (Epstein et  al., 2020a); more 
research is warranted in these areas for future assessment.
7.2.3 Observed Impacts on Non-communicable Diseases
NCDs are those that are not directly transmitted from one person to 
another person; they impose the largest disease burden globally. NCDs 
constitute approximately 80% of the burden of disease in high-income 
countries; the NCD burden is lower in low- and middle-income countries 
but are expected to rise (Bollyky et al., 2017). NCDs constitute a large 
group of diseases driven principally by environmental, lifestyle and

other factors; those identified as being climate sensitive include non-
infectious respiratory disease, cardiovascular disease (CVD), cancer 
and endocrine diseases including diabetes. Additionally, there are 
potential interactions between multiple climate-sensitive NCDs and 
food security, nutrition and mental health. The literature on climate 
change and NCDs continues to develop. More recently, scientists have 
identified key gaps in the calculation of the global burden of disease 
due to environmental health factors (Shaffer et al., 2019).
7.2.3.1 Cardiovascular Diseases
CVDs are a group of disorders of the heart and blood vessels that 
include coronary heart disease, cerebrovascular disease, peripheral 
arterial disease, rheumatic heart disease, congenital heart disease, deep 
vein thrombosis and pulmonary embolism. CVDs are the leading cause 
of death globally and over three quarters of the world’s CVD deaths 
now occur in low- and middle-income countries (Roth et al., 2020).

Climate change affects the risk of CVD through high temperatures 
and extreme heat (assessed in Section  7.2.4.1) and through other 
mechanisms (medium confidence), though the degree to which non-
temperature risks may increase remains unclear. For example, exposure 
to air pollutants including PM, ozone (via its precursors), black carbon, 
oxides of nitrogen, oxides of sulphur, hydrocarbons and metals can invoke 
pro-inflammatory and prothrombotic states, endothelial dysfunction 
and hypertensive responses (Giorgini et al., 2017; Stewart et al., 2017). 
Winter peaks in CVD events, associated with greater concentrations of 
air pollutants, have been reported in a range of countries and climates 
(Claeys et  al., 2017; Stewart et  al., 2017); however, the association 
between air pollution, weather and CVD events is complex and seems 
to differ between cold and warm months, particularly for gaseous 
pollutants such as ozone (Shi et al., 2020).

Climate change is projected to increase the number and severity of 
wildfires (Liu et  al., 2015b; Youssouf et  al., 2014) and the evidence 
for wildfire smoke-related CVD morbidity and mortality is suggestive 
of increased CVD morbidity and mortality risk (Chen et  al., 2021a) 
including significant increases in certain cardiovascular outcomes (e.g., 
cardiac arrests) (Dennekamp et al., 2015). CVD risks to highly exposed populations, such as firefighters, are clearer (Navarro et al., 2019) and 
could increase with additional exposure driven by climate change.
Other climate-related mechanisms that may increase CVD risk include 
reductions in physical activity related to hot weather (Obradovich et al., 
2017), sleep disturbance (Obradovich et  al., 2017) and dehydration 
(Lim et al., 2015; Frumkin and Haines, 2019). There is little literature 
on how changes in winter weather may affect these risks. Saline 
intrusion of groundwater related to sea level rise (Taylor et al., 2012)

may increase the salt intake of affected populations, a risk factor for 
hypertension that has been observed to increase blood pressure in 
exposed populations (Talukder et al., 2017; Al Nahian et al., 2018).
7.2.3.2 Non-communicable Respiratory Diseases
Lung diseases, including asthma, COPD and lung cancer, comprise the 
largest subsets of non-communicable pulmonary disease (Ferkol and 
Schraufnagel, 2014). Overall, the global burden of non-communicable 
lung disease including all chronic lung disease and lung cancer is 
substantial, being responsible for 10.6% of deaths and 5.9% of DALYs 
globally in 2019 (Vos et al., 2020).
Several non-communicable respiratory diseases are climate sensitive 
based on their exposure pathways (very high confidence). Multiple 
exposure pathways contribute to non-communicable respiratory 
disease (Deng et al., 2020), some of which are climate-related (Rice 
et al., 2014), including mobilisation and transport of dust (Schweitzer

et al., 2018); changes in concentrations of air pollutants such as small 
particulates (PM2.5) and ozone formed by photochemical reactions 
sensitive to temperature (Hansel et  al., 2016); increased wildland 
fires and related smoke exposure (Johnston et al., 2002; Reid et al., 
2016); increased exposure to ambient heat driving reduced lung 
function and exacerbations of chronic lung disease (Collaco et al., 
2018; Jehn et al., 2013; McCormack et al., 2016; Witt et al., 2015) and 
modification of aeroallergen production and duration of exposure 
(Ziska et al., 2019).
Burdens of allergic disease, particularly allergic rhinitis and allergic 
asthma may be changing in response to climate change (medium 
confidence) (D’Amato et al., 2020; Eguiluz-Gracia et al., 2020; Deng 
et al., 2020; Demain, 2018). This is supported by evidence showing an 
increase in the length of the North American pollen season attributable 
to climate change (Ziska et al., 2019), an association between timing

of spring onset and higher asthma hospitalisations presumed to be due 
to higher pollen exposure (Sapkota et al., 2020) and other evidence 
linking aeroallergen exposure with a worsening burden of allergic 
disease (Demain, 2018; Poole et al., 2019).
7.2.3.3 Cancer
Climate change is likely to increase the risk of several malignancies 
(high confidence), though the degree to which risks may increase 
remains unclear. Cancers, also known as malignant neoplasms, 
include a heterogeneous collection of diseases with various causal 
pathways, many with environmental influences. Malignant neoplasms 
impose a substantial burden of disease globally and are responsible 
for slightly more than 10  million deaths and 251  million DALYs

7
1072Chapter 7 Health, Wellbeing and the Changing Structure of Communities
globally in 2019 (Vos et al., 2020). Climatic hazards affect exposure 
pathways for several different chemical hazards associated with 
carcinogenesis (Portier et  al., 2010). Most relevant literature has 
focused on elaborating potential pathways and producing qualitative 
or quantitative estimates of effect, though there is limited literature 
on current and projected impacts.
The vast majority of elaborated pathways point to increased risk; for 
example, there is concern that climate change may alter the fate and 
transport of carcinogenic polyaromatic hydrocarbons (Domínguez-
Morueco et al., 2019) and increase mobilisation of carcinogens such 
as bromide (Regli et  al., 2015), persistent organic pollutants (POPs) 
including polychlorinated-biphenyls that have accumulated in areas 
contaminated by industrial runoff (Miner et al., 2018) and radioactive

material (Evangeliou et al., 2014). Exposure to these known carcinogens 
can occur through multiple environmental media and can be increased 
by climate change, for example through increased flooding related 
to extreme precipitation events and mobilisation of sediment where 
carcinogens have accumulated (León et al., 2017; Santiago and Rivas, 
2012). In addition, there is concern that changes in ultraviolet light 
exposure related to shifts in precipitation may increase the incidence 
of malignant melanoma, particularly for outdoor workers (Modenese 
et  al., 2018). Other harmful pathways include migration of and 
increased exposure to liver flukes, which cause hepatobiliary cancer 
(Prueksapanich et al., 2018) and the introduction of infectious diseases 
such as schistosomiasis that increase cancer risk due to climate-related 
migration (Ahmed et  al., 2014). Increased exposure to carcinogenic 
toxins via multiple pathways is also a concern. Aflatoxin exposure, for

example, is expected to increase in Europe (Moretti et al., 2019), India 
(Shekhar et al., 2018), Africa (Gnonlonfin et al., 2013; Bandyopadhyay 
et al., 2016) and North America (Wu et al., 2011). Other carcinogenic 
toxins originate from cyanobacteria blooms (Lee et al., 2017a), which 
are projected to increase in frequency and distribution with climate 
change (Wells et al., 2015; Paerl et al., 2016; Chapra et al., 2017).
7.2.3.4 Diabetes
Individuals suffering from diabetes are at higher risk of heat-related 
illness and death (medium confidence). Extreme weather events 
and rising temperatures have been found to increase morbidity and 
mortality in patients living with diabetes, especially in those with 
cardiovascular complications (Méndez-Lázaro et al., 2018; Zilbermint, 
2020; Hajat et al., 2017). Evidence suggests that the local heat loss 
response of skin blood flow is affected by diabetes-related impairments,

resulting in lower elevations in skin blood flow in response to a heat 
or pharmacological stimulus. Thermoregulatory sweating may also be 
diminished by type-2 diabetes, impairing the body’s ability to transfer 
heat from its core to the environment (Xu et al., 2019b). Higher rates 
of doctor consultations by patients with type-2 diabetes and diabetics 
with cardiovascular comorbidities have been observed during hot days, 
but without evidence of heightened risk of renal failure or neuropathy 
comorbidities (Xu et al., 2019b).
People with chronic illnesses are at particular risk during and after 
extreme weather events due to treatment interruptions and lack of 
access to medication (medium confidence). The impacts of extreme 
weather events on the health of chronically ill people are due to a range of factors including disruption of transport, weakened health 
systems including drug supply chains, loss of power and evacuations

of populations (Ryan et  al., 2015a). Evacuations also pose specific 
health risks to older adults (especially those who are frail, medically 
incapacitated or residing in nursing or assisted living facilities) and 
may be complicated by the need for concurrent transfer of medical 
records, medications and medical equipment (Becquart et  al., 2018; 
Quast and Feng, 2019; US Global Change Research Program, 2016). 
Emergency room visits after Hurricane Sandy rose among individuals 
with type-2 diabetes (Velez-Valle et al., 2016).
7.2.4 Observed Impacts on Other Climate-Sensitive 
Health Outcomes
7.2.4.1 Heat- and Cold-Related Mortality and Morbidity
Extreme heat events and extreme temperature have well-documented, 
observed impacts on health, mortality (very high confidence) and 
morbidity (high confidence). AR5 described the thermoregulatory 
mechanisms and responses, including acclimatisation, linking heat, 
cold and health, and these have been further confirmed by recent

studies and reviews (e.g., Giorgini et  al., 2017; Ikaheimo, 2018; 
McGregor et  al., 2015; Stewart et  al., 2017; Schuster et  al., 2017; 
Zhang et al., 2018b). The health impacts of heat manifest clearly in 
periods of extreme heat often codified as heatwaves. For example, 
heatwaves across Europe (2003), Russia (2010), India (2015) and 
Japan (2018) resulted in significant death tolls and hospitalisations 
(McGregor et  al., 2017; Hayashida et  al., 2019). Heat continues to 
pose a significant health risk due to increases in exposure, changes in 
the size and spatial distribution of the human population, mounting 
vulnerability and an increase in extreme heat events (high confidence) 
(Harrington et al., 2017; Liu et al., 2017; Mishra et al., 2017; Rohat 
et al., 2019a; Rohat et al., 2019b; Rohat et al., 2019c; Watts et al., 
2019). Some regions are already experiencing heat stress conditions 
approaching the upper limits of labour productivity and human

survivability (high confidence). These include the Persian Gulf and 
adjacent land areas, parts of the Indus River Valley, eastern coastal 
India, Pakistan, northwestern India, the shores of the Red Sea, the 
Gulf of California, the southern Gulf of Mexico and coastal Venezuela 
and Guyana (Krakauer et al., 2020; Li et al., 2020; Raymond et al., 
2020; Saeed et al., 2021; Xu et al., 2020).
Under a variety of methods, estimates of the world’s population 
exposed to extreme heat indicate very large and growing numbers 
and an increase since pre-industrial times. For example, Li et al. (2020) 
estimate that globally, 1.28  billion people each year experience 
heatwave conditions similar to that of the lethal Chicago 1995 event, 
compared with 0.99  billion people that would be similarly exposed 
under a pre-industrial climate. Further, for the 150 most populated 
cities of the world, a 500% increase in the exposure to extreme heat

events occurred over the 1980–2017 period (Li et al., 2021), while for 
the 1986–2005 period, the total exposure to dangerous heat in Africa’s 
173 largest cities was 4.2 billion person-days yr–1 (Rohat et al., 2019a). 
Globally the present exposure to heatwave events is estimated to be 
14.8 billion person-days yr–1, with the greatest cumulative exposures 
measured in person-days occurring across southern Asia (7.19 billion),

7
1073Health, Wellbeing and the Changing Structure of Communities  Chapter 7
sub-Saharan Africa (1.43 billion), and north Africa and the Middle East 
(1.33 billion) (Jones et al., 2018).
The country level percentage of mortality attributable to non-
optimum temperature (heat and cold) has been found to range from 
3.4 to 11% (Gasparrini et al., 2015; Zhang et al., 2019b). Heat as a 
health risk factor has largely been overlooked in low- and middle-
income countries (Campbell et al., 2018; Green et al., 2019; Dimitrova 
et  al., 2021). For 2019, the GBD report estimates the burden of 
DALYs attributable to low temperature was 2.2  times greater than 
the burden attributable to high temperature. However, this global 
figure obscures important regional variations. Countries with a 
high sociodemographic index—mainly mid-latitude high-income 
temperate to cool climate countries— were found to have a cold-
related burden 15.4 times greater than the heat-related burden, while

for warm lower-income regions, such as south Asia and sub-Saharan 
Africa, the heat-related burden was estimated to be 1.7  times and 
3.6  times greater, respectively (Murray et  al., 2020). For countries 
where data availability permits, there is evidence that extreme 
heat (and extreme cold) leads to higher rates of premature deaths 
(Armstrong et al., 2017; Cheng et al., 2018; Costa et al., 2017).
Rapid changes and variability in temperatures are observed to increase 
heat-related health and mortality risks, the outcomes varying across 
temperate and tropical regions (Guo et al., 2016; Cheng et al., 2019; Kim 
et al., 2019a; Tian et al., 2019; Zhang et al., 2018b; Zhao et al., 2019).
Several lines of evidence point to a possible decrease in population 
sensitivity to heat, albeit mainly for high-income countries (high 
confidence), arising from the implementation of heat warning systems, 
increased awareness and improved quality of life. (Sheridan and

Allen, 2018). Evidence suggests a general decrease in the impact of 
heat on daily mortality (Diaz et al., 2018; Kinney, 2018; Miron et al., 
2015), a decline in the relative risk attributable to heat (Åström et al., 
2018; Barreca et al., 2016; Petkova et al., 2014) and an increase in the 
minimum mortality temperature (MMT) (Åström et al., 2018; Folkerts 
et al., 2020; Follos et al., 2021; Chung et al., 2018; Todd and Valleron, 
2015; Yin et  al., 2019). It is difficult to draw conclusions regarding 
trends in heat sensitivity for low- to middle-income countries and 
specific vulnerable groups as these are under-represented in the 
literature (Sheridan and Allen, 2018). Trends in heat sensitivity are 
generally believed to be scale and situation dependent, but there is 
considerable variability in changes in heat sensitivity as measured 
by trends in heat-related mortality or MMT (Follos et al., 2021; Kim 
et al., 2019a; Lee et al., 2021), with notable variability across different

population groups (Lu et al., 2021).
Temperature interacts with heat-sensitive physiological mechanisms 
via multiple pathways to affect health. In the worst cases, these lead 
to organ failure and death (Mora et  al., 2017a; Mora et  al., 2017b). 
Excess deaths during extreme heat events occur predominantly in older 
individuals and are overwhelmingly cardiovascular in origin (very high 
confidence). A higher occurrence of CVD mortality in association with 
prolonged period of low temperatures has been well documented 
globally (Giorgini et al., 2017; Stewart et al., 2017); however, there is 
growing evidence that cardiovascular deaths are more related to heat 
events than cold spells (Chen et al., 2019; Liu et al., 2015a; Bunker et al., 2016). While there is strong association between ambient temperature 
and cardiovascular events globally, there are complex interactions and 
modulators of individual response (Wang et al., 2017b). Further, some

CVD morbidity sub-groups such as myocardial infarction (MI) and stroke 
hospitalisation display temperature sensitivity while others do not 
(Bao et al., 2019; Sun et al., 2018; Wang et al., 2016). Although older 
adults have inherent sensitivities to temperature-related health impacts 
(Bunker et al., 2016; Phung et al., 2016), children can also be affected by 
extreme heat (Xu et al., 2014). Cardiovascular capacity or health is also a 
critical determinant of individual health outcomes (Schuster et al., 2017). 
Medications to treat CVDs, such as diuretics and beta-blockers, may 
impair resilience to heat stress (Stewart et al., 2017). Other mediating 
factors in the causal pathway range from alcohol consumption (Cusack 
et al., 2011; Epstein and Yanovich, 2019) and obesity (Speakman, 2018) 
to pre-existing conditions, such as diabetes and hyperlipidaemia, and 
urban characteristics (Chen et al., 2019; Sera et al., 2019).

Under extreme heat conditions, increases in hospitalisations have 
been observed for fluid disorders, renal failure, urinary tract infections, 
septicaemia, general heat stroke as well as unintentional injuries (Borg 
et  al., 2017; Phung et  al., 2017; Goggins and Chan, 2017; Hayashida 
et  al., 2019; Hopp et  al., 2018; Ito et  al., 2018; Kampe et  al., 2016; 
McTavish et al., 2018; Ponjoan et al., 2017; van Loenhout et al., 2018). 
Hospitalisations and mortality due to respiratory disorders also occur 
during heat events with the interactive role of air quality being important 
for some locations but not others (Krug et al., 2019; Pascal et al., 2021; 
Patel et al., 2019). Increased levels of heat-related hospitalisation also 
manifest in elevated levels of emergency service calls (Cheng et al., 2016; 
Guo, 2017; Papadakis et al., 2018; Williams et al., 2020).
Heat- and cold-related health outcomes vary by location (Dialesandro

et al., 2021; Hu et al., 2019; Phung et al., 2016), suggesting outcomes 
are highly moderated by socioeconomic, occupational and other 
non-climatic determinants of individual health and socioeconomic 
vulnerability (Åström et  al., 2020; McGregor et  al., 2017; McGregor 
et al., 2017; Schuster et al., 2017; Benmarhnia et al., 2015; Watts et al., 
2019) (high confidence). For example, access to air conditioning is an 
important determinant of heat-related health outcomes for some 
locations (Guirguis et al., 2018; Ostro et al., 2010). Although there is a 
paucity of global level studies of the effectiveness of air conditioning for 
reducing heat-related mortality, a recent assessment indicates increases 
in air conditioning explains only part of the observed reduction in heat-
related excess deaths, amounting to 16.7% in Canada, 20.0% in Japan, 
14.3% in Spain and 16.7% in the US (Sera et al., 2020).
Significant effects of heat exposure are evident in sport and work

settings with exertional heat illness leading to death and injury 
(Adams and Jardine, 2020). Although most studies of heat-related 
sports injuries refer to high-income countries, these point to an 
increasing number of heat injuries with widening participation in 
sport and an increasing frequency of extreme heat events. The highest 
rates of exertional heat illness are reported for endurance type events 
(running, cycling and adventure races), American football and athletics 
(Gamage et  al., 2020; Grundstein et  al., 2017; Kerr et  al., 2020; 
McMahon et al., 2021; Yeargin et al., 2019). The health, safety and 
productivity consequences of working in extreme heat are widespread 
(Ma et al., 2019; Morabito et al., 2021; Kjellstrom et al., 2019; Orlov

7
1074Chapter 7 Health, Wellbeing and the Changing Structure of Communities
et al., 2020; Smith et al., 2021; Vanos et al., 2019; Varghese et al., 2020; 
Williams et al., 2020). Occupational heat strain in outdoor workers 
manifests as dehydration, mild reduction in kidney function, fatigue, 
dizziness, confusion, reduced brain function, loss of concentration and 
discomfort (Al-Bouwarthan et al., 2020; Boonruksa et al., 2020; Habibi 
et al., 2021; Levi et al., 2018; Venugopal et al., 2021; Xiang et al., 2014). 
In the case of armed forces, a global review of the available literature 
points to a slightly higher incidence of heat stroke in men compared 
to women but a higher proportion of heat intolerance and greater 
risk of exertional heat illness amongst women (Alele et  al., 2020). 
There is also some evidence that for healthcare workers, the risk of 
occupational heat stress grew during the COVID-19 pandemic due to

the need to wear personal protective equipment (Foster et al., 2020; 
Lee et al., 2020; Messeri et al., 2021). Based on a systematic review of 
the literature, one study estimates global costs from heat-related lost 
work time were USD 280 billion in 1995 and USD 311 billion in 2010, 
with low- and middle-income countries and countries with warmer 
climates experiencing greater losses as a proportion of gross domestic 
project (GDP) (Borg et al., 2021). Other global level assessments note 
an increase in the potential hours of work lost due to heat over 
the 2000–2018 period; in 2018, 133.6  billion potential work hours 
were lost, amounting to 45 billion hours more than in 2000 (Watts 
et  al., 2019). For China, heat-related productivity losses have been 
estimated at 9.9 billion hours in 2019, equivalent to 0.5% of the total 
national work hours for that year, with Guangdong province, one of 
the warmest regions in China, accounting for almost a quarter of the

losses (Cai et al., 2021).
Wide ranging knowledge regarding the specific detection of heat- and 
cold-related mortality/morbidity and its attribution to observed climate 
change is lacking. Although there has been an observed increase in 
winter-season temperatures for a number of regions, to date there is 
variable evidence for a consequential reduction in winter mortality and 
susceptibility to cold over time due to milder winters; some countries 
demonstrate decreasing trends, while other countries show stable or 
even increasing trends in cold-attributable mortality fractions over time 
(e.g., Arbuthnott et al. (2020); Åström et al. (2013); Diaz et al. (2019); 
Hajat (2017); Hanigan et  al. (2021); Lee et  al. (2018b)). While there 
is a burgeoning literature on the attribution of extreme heat events 
to climate change (e.g., Vautard et al. (2020)), the number of studies 
that assess the extent to which observed changes in heat-related

mortality may be attributable to climate change is small (Ebi et  al., 
2020). During the 2003 European heatwave, anthropogenic climate 
change increased the risk of heat-related mortality by approximately 
70% and 20% for London and Paris, respectively (Mitchell et al., 2016). 
For the severe heat event across Egypt in 2015, the impact on human 
discomfort was 69% (±17%) more likely due to anthropogenic climate 
change (Mitchell, 2016), and for Stockholm, Sweden, it has been 
estimated that mortality due to temperature extremes for 1980 to 
2009 was double what would have occurred without climate change 
(Åström et al., 2013). To date there has only been one multi-country 
attempt to quantify the heat-related human health impacts that have 
already occurred due to climate change. Based on an analysis of 732 
locations spanning 43 countries for the 1991–2018 period, the study 
found that on average 37.0% (inter-quartile range 20.5–76.3%) of

warm-season heat-related deaths can be attributed to anthropogenic 
climate change, equivalent to an average mortality rate of 2.2/100,000 (median: 1.67/100,000; interquartile range: 1.08–2.34/100,000). 
Regions with a high attributed percentage (> 50%) include southern 
and western Asia (Iran and Kuwait), Southeast Asia (Philippines and 
Thailand) and several countries in Central and South America. Those 
with lower values (< 35%) include Western Europe (the Netherlands, 
Germany and Switzerland), eastern Europe (Moldova, the Czech 
Republic and Romania), southern Europe (Greece, Italy, Portugal 
and Spain), North America (USA) and eastern Asia (China, Japan and 
South Korea) (Vicedo-Cabrera et  al., 2021). Due to data restrictions, 
some of the poorest and most susceptible regions to climate change 
and increases in heat exposure, such as west and east Africa (Asefi-
Najafabady et al., 2018; Sylla et al., 2018) and south Asia, could not be 
included in the analysis (Mitchell, 2021).

7.2.4.2 Injuries Arising from Extreme Weather Events Other 
than Heat and Cold
Injuries comprise a substantial portion of the global burden of disease. 
In 2019, injuries comprised 9.82% of total global DALYs and 7.61% 
of deaths (Vos et  al., 2020). The causal pathways for many injuries, 
particularly those from heat and extreme weather events, flooding 
and fires, exhibit clear climate sensitivity (Roberts and Arnold, 
2007; Roberts and Hillman, 2005), as do some injuries occurring in 
occupational settings (Marinaccio et  al., 2019; Sheng et  al., 2018), 
but a comprehensive assessment of climate sensitivity in injury causal 
pathways has not been done. Certain groups, including Indigenous 
Peoples, children and the elderly (Ahmed et al., 2020) are at greater 
risk for a wide range of injuries. Extreme events impose substantial 
disease burden directly as a result of traumatic injuries, drowning and 
burns and large mental health burdens associated with displacement

(Fullilove, 1996), depression and post-traumatic stress disorder (PTSD), 
but the overall injury burden associated with extreme weather is not 
known. It is known that the Asia-Pacific region has experienced the 
highest relative burden of injuries from extreme weather in recent 
decades (Hashim and Hashim, 2016).
Extreme weather imposes a substantial morbidity and mortality burden 
that is quite variable by location and hazard. The proportion of this 
burden related specifically to injuries is not established. From 1998 to 
2017 there were 526,000 deaths from 11,500 extreme weather events, 
and the average annual attributable all-cause mortality incidence 
in the ten most affected countries was 3.5 per 100,000 population 
(Eckstein et al., 2017). Rates can be much higher; mortality incidence 
in Puerto Rico and Dominica from extreme weather were 90.2 and 
43.7 per 100,000 population in 2017, respectively (Eckstein et  al.,

2017). Not all of these deaths are from injuries, and the proportion 
of mortality and morbidity associated with injuries varies by location 
and hazard. One review found that one-year post-event prevalence 
rates for injuries associated with extreme events (floods, droughts, 
heatwaves and storms) in developing countries ranged from 1.4% to 
37.9% (Rataj et al., 2016). Other literature has documented an increase 
in the risk of motor vehicle accidents in association with extreme 
precipitation (Liu et al., 2017; Stevens et al., 2019), temperature (Leard 
and Roth, 2019) and sandstorms (Islam et al., 2019) and an increased 
risk of traumatic occupational injuries associated with temperature 
extremes, particularly extreme heat, likely from fatigue and decreased 
psychomotor performance (Varghese et al., 2019).

7
1075Health, Wellbeing and the Changing Structure of Communities  Chapter 7
There is clear evidence of climate sensitivity for multiple injuries from 
floods, fires and storms, but there is a need for additional evidence 
regarding the current injury burden attributable to climate change. It is 
as likely as not that climate change has increased the current burden 
of disease from injuries related to extreme weather, particularly in low-
income settings (low confidence). Approximately 120  million people 
are exposed to coastal flooding annually (Nicholls et al., 2007), causing 
an estimated 12,000 deaths (Shultz et al., 2005), and there is significant 
concern for worsening flooding associated with climate change (Shultz 
et al., 2018a; Shultz et al., 2018b; Woodward and Samet, 2018) but 
very limited quantification of attributable burden. A range of adverse 
health outcomes has been identified in a study of fires in sub-zero

temperatures that are thought to be increasing in frequency due to 
climate change (Metallinou and Log, 2017).
7.2.4.3 Observed Impacts on Maternal, Foetal and Neonatal 
Health
Maternal and neonatal disorders accounted for 3.7% of total global 
deaths and 7.8% of global DALYs in 2019 (Vos et al., 2020). Children 
and pregnant women have potentially higher rates of vulnerability 
and/or exposure to climatic hazards, extreme weather events and 
undernutrition (Garcia and Sheehan, 2016; Sorensen et  al., 2018; 
Chersich et  al., 2018). Available evidence suggests that heat is 
associated with higher rates of pre-term birth (Wang et  al., 2020), 
low birthweight, stillbirth, neonatal stress (Cil and Cameron, 2017; 
Kuehn and McCormick, 2017) and adverse child health (Kuehn and 
McCormick, 2017). Extreme weather events are associated with 
reduced access to prenatal care, unattended deliveries (Abdullah 
et al., 2019) and decreased paediatric healthcare access (Haque et al., 
2019).

7.2.4.4 Observed Impacts on Malnutrition
Climate variability and change contribute to food insecurity that can 
lead to malnutrition, including undernutrition, overweight and obesity, 
and to disease susceptibility, particularly in low- and middle-income 
countries (high confidence) . Since AR5, analyses of the links between 
climate change and food expanded beyond undernutrition to include 
the impacts of climate change on a wider set of diet- and weight-
related risk factors and their impacts on NCDs, along with the role 
of dietary choices for GHG emissions (IPCC, 2019b) including dietary 
inadequacy (deficiencies, excesses or imbalances in energy, protein 
and micronutrients), infections and sociocultural factors (Global 
Nutrition Report 2020). Undernutrition exists when a combination of 
insufficient food intake, health, and care conditions results in one or 
more of the following: underweight for age, short for age (stunted),

thin for height (wasted), or functionally deficient in vitamins and/
or minerals (micronutrient malnutrition or ‘hidden hunger’). Food 
insecurity and poor access to nutrient-dense food contribute not only 
to undernutrition but also to obesity and susceptibility to NCDs in 
low- and middle-income countries (FAO et al., 2018; Swinburn et al., 
2019).
Globally, more than 690 million people are undernourished, 144 million 
children are stunted (chronic undernutrition), 47 million children are 
wasted (acute undernutrition), and more than 2 billion people have micronutrient deficiencies (FAO, 2020). More than 135 million people 
across 55 countries experienced acute hunger requiring urgent food, 
nutrition and livelihood assistance in 2019 (FSIN/GNAFC, 2020). 
The COVID-19 pandemic is projected to increase the number of 
acutely food insecure people to 270 million people (FSIN, 2020) and 
worsen malnutrition levels (FAO et al., 2020; Rippin et al., 2020). The

relationships between climate change and obesity vary based on 
geography, population sub-groups and/or stages of economic growth 
and population growth (An et al., 2017). Increasing temperatures could 
contribute to obesity through reduced physical activity, increased 
prices of produce or shifts in eating patterns of populations towards 
more processed foods (An et  al., 2018). In the largest global study 
to date exploring the connections between child diet diversity and 
recent climate, data from 19  countries in six regions (Asia, Central 
America, South America, north Africa, southeast Africa and west Africa) 
indicated significant reductions in diet diversity associated with higher 
temperatures and significant increases in diet diversity associated with 
higher precipitation (Niles et al., 2021).
Climate change can affect the four aspects of food security: food 
production and availability, stability of food supplies, access to food

and food utilisation (IPCC, 2019b). Access to sufficient food does not 
guarantee nutrition security. Extreme weather and climate events 
can result in inadequate or insufficient food consumption, increasing 
susceptibility to infectious diseases (Rodriguez-Llanes et al., 2016; Gari 
et al., 2017; Kumar et al., 2016; Lazzaroni and Wagner, 2016) but also 
to being overweight or obese and increasing susceptibility to non-
communicable diseases in low- and middle-income countries (FAO, 
2018; Swinburn et al., 2019).
Nearly half of all deaths in children under five years of age are 
attributable to undernutrition, putting children at greater risk of dying 
from common infections. Undernutrition in the first 1,000 days of a 
child’s life can lead to stunted growth, which can result in impaired 
cognitive ability and reduced future school and work performance 
and the associated costs of stunting in terms of lost economic growth

can be of the order of 10% of GDP yr–1 in Africa (UNICEF/WHO/WBG, 
2019).
At the same time, diseases associated with high-calorie, unhealthy diets 
are increasing globally, with 38.3 million overweight children under 
five years of age (Global Nutrition Report, 2018), 2.1 billion overweight 
or obese adults and the global prevalence of diabetes almost doubling 
in the past 30 years (Swinburn et al., 2019). Unbalanced diets, such 
as diets low in fruits and vegetables and high in red and processed 
meat, are the number one risk factor for mortality globally and in most 
regions (Gakidou et al., 2018; Afshin et al., 2019).
Socioeconomic factors that mediate the influence of climate change on 
nutrition include cultural and societal norms; governance, institutions, 
policies and fragility; human capital and potential; and social position 
and access to healthcare, education and food aid (Rozenberg, 2017; 
Alkerwi et  al. 2015; Tirado, 2017; FAO et  al., 2018; Global Nutrition

Report 2020). Extreme events may affect access to adequate diets, 
leading to malnutrition and increasing the risk of disease (Beveridge 
et  al., 2019; Rodriguez-Llanes et  al., 2016; Gari et  al., 2017; Kumar 
et al., 2016; Lazzaroni and Wagner, 2016; Thiede and Gray, 2020).

7
1076Chapter 7 Health, Wellbeing and the Changing Structure of Communities
7.2.4.5 Observed Impacts on Exposure to Chemical 
Contaminants
Climate change in northern regions, including Arctic ecosystems, 
is causing permafrost to thaw, creating the potential for mercury 
(Hg) to enter the food chain (medium agreement, low evidence) as 
methyl mercury (MeHg), which is highly neurotoxic and nephrotoxic 
and bioaccumulates and biomagnifies throughout the food chain via 
dietary uptake of fish, seafood and mammals. Mercury methylation 
processes in aquatic environments have been found to be exacerbated 
by ocean warming, coupled with more acidic and anoxic sediments 
(FAO, 2020). Consumption of mercury-contaminated fish has been 
found to be linked to neurological disorders due to methyl mercury 
poisoning (i.e., Minamata disease) that is associated with climate 
change-contaminant interactions that alter the bioaccumulation and

biomagnification of toxic and fat-soluble persistent organic pollutants 
and polychlorinated biphenyls (PCBs) (Alava et al., 2017) in seafood 
and marine mammals (medium confidence). Indigenous Peoples have 
a higher exposure to such risks because of the accumulation of such 
toxins in traditional foods (J.J. et al., 2017). Contamination of food with 
PCBs and dioxins has a range of adverse health impacts (Lake et al., 
2015).
Chapter 5 (Sections  5.4.3, 5.5.2.3, 5.8.1, 5.8.2, 5.8.3, 5.9.1, 5.11.1, 
5.11.3, 5.12.3) discusses the possible impacts of climate change on 
food safety, including exposure to toxigenic fungi, PCBs and other 
POPs, mercury and harmful algal blooms.
Climate change may affect animal health management practices, 
potentially leading to an increased use of pesticides or veterinary drugs 
(such as preventive antimicrobials) that could result in increased levels 
of residues in foods (high agreement, medium/low evidence) (Beyene

et  al., 2015; FAO and WHO, 2018; European Food Safety Authority, 
2020; MacFadden et al., 2018).
7.2.5 Observed Impacts on Mental Health and Well-Being
7.2.5.1 Observed Impacts on Mental Disorders
A wide range of climatic events and conditions have observed and 
detrimental impacts on mental health (very high confidence). The 
pathways through which climatic events affect mental health are 
varied, complex and inter-connected with other non-climatic influences 
that create vulnerability (Figure  7.6). The climatic exposure may be 
direct, such as experiencing an extreme weather event or prolonged 
high temperatures, or indirect, such as mental health consequences 
of undernutrition or displacement. Exposure may also be vicarious, 
with people experiencing decreased mental health associated with 
observing the impact of climate change on others or simply by learning 
about climate change. Non-climatic moderating influences range

from an individual’s personality and pre-existing conditions, to social 
support, and to structural inequities (Gariepy et  al., 2016; Hrabok 
et al., 2020; Nagy et al., 2018; Silva et al., 2016b). Depending on these 
background and contextual factors, similar climatic events may result 
in a range of potential mental health outcomes, including anxiety, 
depression, acute traumatic stress, PTSD, suicide, substance abuse and sleep problems, with conditions ranging from being mild in nature to 
those that require hospitalisation (Berry et al., 2010; Cianconi et al., 
2020; Clayton et al., 2017; Ruszkiewicz et al., 2019; Bromet et al., 2017; 
Lowe, 2019). The line between mental health and more general well-
being is permeable, but in this section we refer to diagnosable mental 
disorders—conditions that disrupt or impair normal functioning 
through impacts on mood, thinking or behaviour.
There is an observable association between high temperatures and

mental health decrements (high confidence), with an additional 
possible influence of increased precipitation (medium agreement, 
medium evidence). Heat-associated mental health outcomes include 
suicide (Williams et al., 2015a; Carleton, 2017; Burke et al., 2018; Kim 
et  al., 2019b; Thompson et  al., 2018; Schneider et  al., 2020; Cheng 
et al., 2021; Baylis et al., 2018; Obradovich et al., 2018); psychiatric 
hospital admissions and emergency room visits for mental disorders 
(Hansen et al., 2008; Wang et al., 2014; Chan et al., 2018; Mullins and 
White, 2019; Yoo et al., 2021); experiences of anxiety, depression and 
acute stress (Obradovich et al., 2018; Mullins and White, 2019); and 
self-reported mental health (Li et  al., 2020). In Canada, Wang et  al. 
(2014) found an association between mean heat exposure of 28°C and 
greater hospital admissions within 0 to 4 days for mood and behavioural 
disorders (including schizophrenia, mood and neurotic disorders). A US

study found mental health problems increased by 0.5% when average 
temperatures exceed 30°C, compared to averages between 25°C 
and 30°C; a 1°C warming over five years was associated with a 2% 
increase in mental health problems (Obradovich et al., 2018). Another 
study found a 1°C rise in monthly average temperatures over several 
decades was associated with a 2.1% rise in suicide rates in Mexico and 
a 0.7% rise in suicide rates in the USA (Burke et al., 2018). A systematic 
review of published research using a variety of methodologies from 
19 countries (Thompson et al., 2018) found an increased risk of suicide 
associated with a 1°C rise in ambient temperature.
Discrete climate hazards including storms (Kessler et al., 2008; Boscarino 
et  al., 2013; Boscarino et  al., 2017; Obradovich et  al., 2018), floods 
(Baryshnikova and Pham, 2019), heatwaves, wildfires and drought 
(Hanigan et al., 2012; Carleton, 2017; Zhong et al., 2018; Charlson et al.,

2021) have significant negative consequences for mental health ( very 
high confidence). A large body of research identifies the impacts of 
extreme weather events on PTSD, anxiety and depression; much of the 
research has been done in the USA and the UK, but a growing number 
of studies find evidence for similar impacts on mental health in other 
countries, including Spain (Foudi et al., 2017), Brazil (Alpino et al., 2016), 
Chile (Navarro et  al., 2016), Small Island Developing States (Kelman 
et al., 2021) and Vietnam (Pollack et al., 2016). Approximately 20–30% 
of those who live through a hurricane develop depression and/or PTSD 
within the first few months following the event (Obradovich et al., 2018; 
Schwartz et al., 2015; Whaley, 2009), with similar rates for people who 
have experienced flooding (Waite et al., 2017; Fernandez et al., 2015). 
Studies conducted in South America and Asia indicate an increase in 
PTSD and depressive disorders after extreme weather events (Rataj

et al., 2016). Evidence is lacking for African countries (Otto et al., 2017). 
stress after extreme weather events (Brown et al., 2017; Hellden et al., 
2021; Kousky, 2016), and increased susceptibility to mental health 
problems may linger into adulthood (Maclean et al., 2016).

7
1077Health, Wellbeing and the Changing Structure of Communities  Chapter 7
Climate change impacts on mental health and adaptation responses
2 1
Hazard
(e.g. storms, floods, wildfires, 
extreme heat)
(e.g. drought, sea level rise, sea ice 
loss, changing climate normals)
   Vulnerability
Pre-existing health conditions
Gender
Age
OccupationExposure
(e.g. displacement, food systems  
disruption, occupational loss)
Vicarious exposure(s) 
(e.g. observed experiences of others,  
media depictions of climate change)Key adaptation responses
State and non-state actors: 
effective mental health systems, planning and 
preparedness, informed policies, early  
intervention
Local governments:
planning, design, green infrastructur e
Community
-  Supportive social networks, effective  
information channel s
- awareness, preparedness, mental health

support, nature-based therap y3 4
Risks to mental health and wellbeing 5
2. Vulnerability
3.Exposure
Community
Chronic changes5. Risks
[e.g., PTSD, depression, suicide]Diminished wellbeing
[e.g., stress, climate anxiety, cognitive impairment )Diminished social relations  
[e.g., loss of culture, interpersonal violence)
Figure 7.6 |  Climate change impacts on mental health and key adaptation responses. 
PTSD: Post traumatic stress disorder.

7
1078Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Wildfires have observed negative impacts on mental health (high 
confidence). This is due to the trauma of the immediate experience 
and/or subsequent displacement and evacuation (Dodd et  al., 2018; 
Brown et al., 2019; Psarros et al., 2017; Silveira et al., 2021b). Sub-
clinical outcomes, such as increases in anxiety, sleeplessness or 
substance abuse are reported in response to wildfires and extreme 
weather events, with impacts being pronounced among those who 
experience greater losses or are more directly exposed to the event; 
this may include first responders.
Mental health impacts can emerge as result of climate impacts on 
economic, social and food systems (high confidence). For example, 
malnutrition among children has been associated with a variety of 
mental health problems (Adhvaryu et  al., 2019; Hock et  al., 2018; 
Yan et  al., 2018), as has food insecurity among adults (Lund et  al.,

2018). The economic impacts of droughts have been associated with 
increases in suicide, particularly among farmers (Carleton, 2017; 
Edwards et al., 2015; Vins et al., 2015); those whose occupations are 
likely to be affected by climate change report that it is a source of 
stress that is linked to substance abuse and suicidal ideation (Kabir, 
2018). Studies of Indigenous Peoples often describe food insecurity or 
reduced access to traditional foods as a link between climate change 
and reduced mental health (Middleton et al., 2020b). The loss of family 
members, for example due to an extreme weather event, increases 
the risk of mental illness (Keyes et al., 2014). Individuals in low- and 
middle-income countries may be more severely impacted due to lesser 
access to mental health services and lower financial resources to help 
cope with impacts compared with high-income countries (Abramson 
Anxiety about the potential risks of climate change and awareness

of climate change itself can affect mental health even in the absence 
of direct impacts (low confidence). There is a need for more evidence 
about the prevalence or severity of climate change-related anxiety, 
sometimes called ecoanxiety, but national surveys in the USA, Europe 
and Australia show that people express high levels of concern and 
perceived harm associated with climate change (Steentjes et  al., 
2017; Clayton and Karazsia, 2020; Cunsolo and Ellis, 2018; Helm 
et al., 2018; Leiserowitz et al., 2017; Reser et al., 2012; Steentjes et al., 
2017). In a US sample, perceived ecological stress, defined as personal 
stress associated with environmental problems, predicted depressive 
symptoms (Helm et al., 2018); in a sample of Filipinos, climate anxiety 
was correlated with lower mental health (Reyes et  al., 2021) and a 
non-random study in 25  countries showed positive correlations 
between negative emotions about climate change and self-rated

mental health (Ogunbode et al., 2021). However, an earlier study found 
no correlation between climate change worry and mental health issues 
(Berry and Peel, 2015). Because the perceived threat of climate change 
is based on subjective perceptions of risk and coping ability as well 
as on experiences and knowledge (Bradley et al., 2014), even people 
who have not been directly affected may be stressed by a perception 
of looming danger (Clayton and Karazsia, 2020). Not surprisingly, 
those who have directly experienced some of the effects of climate 
change may be more likely to show such responses. Indigenous 
Peoples, whose culture and well-being tend to be strongly linked to 
local environments, may experience mental health effects associated 
with changes in environmental risks; studies suggest connections to an increase in depression, substance abuse or suicide in some Indigenous 
Peoples (Canu et al., 2017; Cunsolo Willox et al., 2013; Middleton et al.,

2020b; Jaakkola et al., 2018).
7.2.5.2 Observed Impacts on Well-Being
Overall, research suggests that climate change has already had 
negative effects on subjective well-being (medium confidence). 
Climate change can affect well-being through a number of pathways, 
including loss of access to green and blue spaces due to damage 
from storms, coastal erosion, drought or wildfires; heat; decreased air 
quality; and disruptions to one’s normal pattern of behaviour, residence, 
occupation or social interactions (Hayward and Ayeb-Karlsson, 2021). 
For example, substantial evidence shows a negative correlation 
between air pollution and subjective well-being or happiness (Apergis, 
2018; Cunado and de Gracia, 2013; Lu, 2020; Luechinger, 2010; 
Menz and Welsch, 2010; Orru et al., 2016; Yuan et al., 2018; Zhang 
et  al., 2017a); in the reverse direction, there is evidence not only 
that time in nature but more specifically a feeling of connectedness

to nature are both associated with well-being (Martin et  al., 2020) 
and healthy ecosystems offer opportunities for health improvements 
(Pretty and Barton, 2020). Negative emotions such as grief—often 
termed ‘solastalgia’ (Albrecht et al., 2007)—are associated with the 
degradation of local or valued landscapes (Eisenman et al., 2015; Ellis 
and Albrecht, 2017; Polain et al., 2011; Tschakert et al., 2017; Tschakert 
et  al., 2019), which may threaten cultural rituals, especially among 
Indigenous Peoples (Cunsolo and Ellis, 2018; Cunsolo et  al., 2020). 
Studies conducted in the Solomon Islands and Tuvalu found qualitative 
and quantitative evidence of experiences of climate change and worry 
about the future, with negative impacts on respondents’ well-being 
(Asugeni et al., 2015; Gibson et al., 2020).
Heat is one of the best-studied aspects of climate change observed 
to reduce well-being (high confidence). Higher summer temperatures

are associated with decreased happiness and ratings of well-being 
(Carleton and Hsiang, 2016; Miles-Novelo and Anderson, 2019; 
Connolly, 2013; Noelke et al., 2016; Baylis et al., 2018; Moore et al., 
2019; Wang et al., 2020b). A study of 1.9 million Americans (Noelke 
et al., 2016) found that exposure to one day averaging 21°C–27°C was 
associated with reduced well-being by 1.6% of a standard deviation 
and days above 32°C were associated with reduced well-being 
by 4.4% of a standard deviation relative to a reference interval of 
10°C–16°C.  A similar relationship between heat and mood has been 
observed in China, where expressed mood began to decrease when 
the average daily temperature was over 20°C (Wang et al., 2020b). The 
causal mechanism is unclear but could be due to impacts on health, 
economic costs or social interactions (Belkin and Kouchaki, 2017; 
Osberghaus and Kühling, 2016) or reduced quality or quantity of sleep

(Fujii et al., 2015; Obradovich et al., 2017; Obradovich and Migliorini, 
2018). Heat has also been associated with inter-personal and inter-
group aggression and increases in violent crime (Heilmann et al., 2021; 
Mapou et al., 2017; Tiihonen et al., 2017). For the most part, studies 
have measured daily response to average daily temperatures and are 
unable to predict whether the effect is cumulative in response to a 
sequence of unusually warm days. However, there is no evidence that 
adaptation occurs over time to eliminate the negative response to very 
warm temperatures (Moore et  al., 2019). Some research has found

7
1079Health, Wellbeing and the Changing Structure of Communities  Chapter 7
a negative effect of extreme cold on well-being (Yoo et  al., 2021); 
increasing winter temperatures associated with climate change could 
serve to compensate for the impact of increased summer temperatures. 
However, the effect of high temperatures is typically found to be 
stronger than the effect of low temperatures, and in some cases no 
detrimental impacts of cold weather are found (Almendra et al., 2019; 
Mullins and White, 2019).
Climate change also threatens well-being defined in terms of 
capabilities or the capacity to fulfil one’s potential and fully participate 
in society. Heat can limit labour capacity; one study estimated that 
45  billion hours of labour productivity were lost in 2018 compared 
to 2000 due to high temperatures (Watts et  al., 2019). Both heat 
and air pollution also impair human capabilities through a negative

effect on cognitive performance (Taylor et al., 2016b) and even impair 
skills acquisition, reducing the ability to learn (Park et  al., 2021) 
and affecting marginalised groups more strongly (Park et al., 2020), 
although findings are inconsistent and depend in part on the nature of 
the task (low confidence).
Systematic reviews have found an association between higher 
ambient levels of fine airborne particles with cognitive impairment 
in the elderly and with behavioural problems (related to impulsivity 
and attention problems) in children (Power et al., 2016; Yorifuji et al., 
2017; Younan et al., 2018; Zhao et al., 2018b) (medium confidence). 
Malnutrition has also been associated with reduced educational 
achievement and long-term decrements in cognitive function 
(Acharya et al., 2019; Asmare et al., 2018; Na et al., 2020; Kim et al., 
2017; Talhaoui et al., 2019).
7.2.6 Observed Impacts on Migration
Consistent with peer-reviewed scholarship and with the United

Nations Framework Convention on Climate Change (UNFCCC) Cancun 
Adaptation Framework section 14(f) and the Paris Agreement, this 
Chapter assesses the impacts of climate change on four types of 
migration: (a) adaptive migration (i.e., where migration is an outcome 
of individual or household choice), (b) involuntary migration and 
displacement (i.e., where people have few or no options except to 
move), (c) organised relocation of populations from sites highly exposed 
to climatic hazards and (d) immobility (i.e., an inability or unwillingness 
to move from areas of high exposure for cultural, economic or social 
reasons) (Cross-Chapter Box MIGRATE in Chapter 7).
A general theme across studies from all regions is that climate-related 
migration outcomes are diverse ( high confidence)  and may be manifest 
as decreases or increases in migration flows, and may lead to changes 
in the timing or duration of migration and to changes in migration

source locations and destinations. Multi-country studies of climatic 
impacts on migration patterns in Africa have found that migration 
exhibits weak, inconsistent associations with variations in temperature 
and precipitation and that migration responses differ significantly 
between countries and between rural and urban areas (Gray and Wise, 
2016; Mueller et al., 2020). Multi-directional findings such as these are 
also common in single-country studies from multiple regions (Call et al., 
2017; Nawrotzki et al., 2017; Cattaneo et al., 2019; Kaczan and Orgill-Meyer, 2020). The diversity of potential migration and displacement 
outcomes reflects (a) the variable nature of climate hazards in terms 
of the rate of onset, intensity, duration, spatial extent and severity of 
damage caused to housing, infrastructure and livelihoods and (b) the 
wide range of social, economic, cultural, political and other non-climatic 
factors that influence exposure, vulnerability, adaptation options

and the contexts in which migration decisions are made (Neumann 
and Hermans, 2015; McLeman, 2017; Barnett and McMichael, 2018; 
Cattaneo et al., 2019; Hoffmann et al., 2020) (high confidence).
Weather events and climate conditions can act as direct drivers of 
migration and displacement (e.g., destruction of homes by tropical 
cyclones) and as indirect drivers (e.g., rural income losses and/or 
food insecurity due to heat- or drought-related crop failures that 
in turn generate new population movements) (high confidence). 
Extreme storms, floods and wildfires are strongly associated with 
high levels of short- and long-term displacement, while droughts, 
extreme heat and precipitation anomalies are more likely to stimulate 
longer-term changes in migration patterns (Kaczan and Orgill-Meyer, 
2020; Hoffmann et  al., 2020). Longer-term environmental changes 
attributable to anthropogenic climate change—such as higher

average temperatures, desertification, land degradation, biodiversity 
loss and sea level rise—have had observed effects on migration 
and displacement in a limited number of locations in recent decades 
but are projected to have wider-scale impacts on future population 
patterns and migration, and are therefore assessed in Section  7.3.2 
(Projected Risks).
The diversity of potential migration and displacement outcomes 
reflects the scale and physical impacts of specific climate hazard 
events and the wide range of social, economic, cultural, political 
and other non-climatic factors that influence exposure, vulnerability, 
adaptation options and the contexts in which migration decisions are 
made (high confidence). The diversity in drivers, contexts and outcomes 
makes it difficult to offer simple generalisations about the relationship 
between climate change and migration. The characteristics of climatic 
drivers vary in terms of the rate of onset, intensity, duration, spatial

extent and severity of damage caused to housing, infrastructure and 
livelihoods; the potential migration responses to these are further 
mediated by cultural, demographic, economic, political, social and 
other non-climatic factors operating across multiple scales (Neumann 
Cattaneo et al., 2019; Hoffmann et al., 2020).
Climate-related migration and displacement outcomes display high 
variability in terms of migrant success, often reflecting pre-existing 
socioeconomic conditions and household wealth (high confidence). 
The decision to migrate or remain in place when confronted by 
climatic hazards is strongly influenced by the range and accessibility 
of alternative, in situ (i.e., non-migration) adaptation options that may 
be less costly or disruptive (Cattaneo et al., 2019). Migration decisions 
(whether climate-related or not) are typically made at the individual or

household level and are influenced by a household’s perceptions of risk, 
social networks, wealth, age structure, health and livelihood choices 
(Koubi et al., 2016b; Gemenne and Blocher, 2017). Households with 
greater financial resources and higher levels of educational attainment 
have greater capacity to adapt in situ (Cattaneo and Massetti, 2019;

7
1080Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Cross-Chapter Box MIGRATE | Climate-Related Migration
Authors: David Wrathall (USA, Chapter 8), Robert McLeman (Canada, Chapter 7), Helen Adams (United Kingdom, Chapter 7), Ibidun 
Adelekan (Nigeria, Chapter 9), Elisabeth Gilmore (USA/Canada, Chapter 14), François Gemenne (Belgium, Chapter 8), Nathalie Hilmi 
(Monaco, Chapter 18), Ben Orlove (USA, Chapter 17), Ritwika Basu (India/United Kingdom, Chapter 18), Halvard Buhaug (Norway, 
Chapter 16), Edwin Castellanos (Guatemala, Chapter 12), David Dodman (United Kingdom, Chapter 6), Felix Kanungwe Kalaba (Zambia, 
Chapter 9), Rupa Mukerji (Switzerland/India, Chapter 18), Karishma Patel (USA, Chapter 1), Chandni Singh (India, Chapter 10), Philip 
Thornton (United Kingdom, Chapter 5), Christopher Trisos (South Africa, Chapter 9), Olivia Warrick (New Zealand, Chapter 15), Vishnu 
Pandey (Nepal, Chapter 4)
Key messages on migration in this report

Migration is a universal strategy that individuals and households undertake to improve well-being and livelihoods in response to 
economic uncertainty, political instability and environmental change (high confidence). Migration, displacement and immobility that 
occur in response to climate hazards are assessed in general in Chapter 7, with specific sectoral and regional dimensions of climate-
related migration assessed in sectoral and regional Chapters 5 to 15 (Table MIGRATE.1 in Chapter 7) and involuntary immobility and 
displacement being identified as representative key risks in Chapter 16 (Sections  16.2.3.8, 16.5.2.3.8). Since AR5 there has been a 
considerable expansion in research on climate–migration linkages, with five key messages from the present assessment report warranting 
emphasis.
Climatic conditions, events and variability are important drivers of migration and displacement (high confidence) (Table MIGRATE.1 in

Chapter 7), with migration responses to specific climate hazards being strongly influenced by economic, social, political and demographic 
processes (high confidence) (Sections 7.2.6, 8.2.1.3). Migration is among a wider set of possible adaptation alternatives and often emerges 
when other forms of adaptation are insufficient (Sections 5.5.1.1, 5.5.3.5, 7.2.6, 8.2.1.3, 9.7.2). Involuntary displacement occurs when 
adaptation alternatives are exhausted or not viable and reflects non-climatic factors that constrain adaptive capacity and create high 
levels of exposure and vulnerability (high confidence) (Cross-Chapter Box SLR in Chapter 3; Sections 4.3.7, 7.2.6; Box 8.1; Section 10.3; 
Box 14.7). There is strong evidence that climatic disruptions to agricultural and other rural livelihoods can generate migration (high 
confidence) (Sections 5.5.4, 8.2.1.3, 9.8.3; Box 9.8).

Specific climate events and conditions may cause migration to increase, decrease or flow in new directions (high confidence), and the 
more agency migrants have (i.e., the degree of voluntarity and freedom of movement), the greater the potential benefits for sending and 
receiving areas (high agreement, medium evidence) (Sections 5.5.3.5, 7.2.6, 8.2.1.3; Box 12.2). Conversely, displacement or low-agency 
migration is associated with poor outcomes in terms of health, well-being and socioeconomic security for migrants and returns fewer 
benefits to sending or receiving communities (high agreement, medium evidence) (Sections 4.3.7, 4.5.7; Box 8.1; Sections 9.7.2, 10.3; 
Box 14.7).
Most climate-related migration and displacement observed currently takes place within countries ( high confidence)  (Sections 4.3.7, 
4.5.7, 5.12.2, 7.2.6). The climate hazards most commonly associated with displacement are tropical cyclones and flooding in most

regions, with droughts being an important driver in sub-Saharan Africa, parts of south Asia and South America (high confidence) 
(Sections  7.2.6.1, 9.7.2, 10.4.6.3, 11.4.1, 12.5.8.4, 13.8.1.3, 14.4.7.3). Currently, observed international migration associated with 
climatic hazards is considerably smaller relative to internal migration and is most often observed as flowing between states that are 
contiguous and have labour-migration agreements and/or longstanding cultural ties (high agreement, robust evidence) (Sections 4.3.7, 
4.5.7, 5.12.2, 7.2.6).
In many regions, the frequency and/or severity of floods, extreme storms and droughts is projected to increase in coming decades, 
especially under high-emissions scenarios (WGI AR6 Chapter 12 (Ranasinghe et al. 2021)), raising future risk of displacement in the most 
exposed areas (high confidence) (Section 7.3.2.1). The additional impacts of climate change anticipated to generate future migration and

displacement include mean sea level rise that increases flooding and saltwater contamination of soil and/or groundwater in low-lying 
coastal areas and small islands (high confidence) (Section 7.3.2.1; Cross-Chapter Box SLR in Chapter 3) and more frequent extreme heat Ocello et  al., 2015) but are also better able to migrate and with 
greater agency once such a decision is made (Kubik and Maurel, 2016; 
Koubi et al., 2016b; Riosmena et al., 2018; Adams and Kay, 2019). By 
contrast, poor households with limited physical, social and financial 
resources have less capacity to adapt in situ and are often limited in 
their migration options (Nawrotzki and DeWaard, 2018; Suckall et al., 2017; Zickgraf et al., 2016). Thus, when poorer households do migrate 
after an extreme climate event, it is often in reaction to lost income or 
livelihood and occurs with low voluntarity (Mallick et al., 2017; Bhatta 
et al., 2015) and may perpetuate or amplify migrants’ socioeconomic

precarity and/or their exposure to environmental hazards (Natarajan 
et al., 2019; see also Section 8.3.1).

7
1081Health, Wellbeing and the Changing Structure of Communities  Chapter 7
events that threaten the habitability of urban centres in the tropics and arid/semiarid regions (medium confidence), although the causal 
links between heat and migration are less clear (Section 7.3.2.1).
There is growing evidence about the future prospects of immobile populations: groups and individuals that are unable or unwilling to 
move away from areas highly exposed to climatic hazards (high confidence) (Sections 4.6.9, 7.2.6.2; Box 8.1; Box 10.2). Involuntarily 
immobile populations may be anticipated to require government interventions to continue living in exposed locations or to relocate 
elsewhere (high agreement, medium evidence) (Box 8.1). Managed retreat and organised relocations of people from hazardous areas 
in recent years have proven to be politically and emotionally charged, socially disruptive and costly (high confidence) (Section 7.4.5.4).
Climate-migration interactions and outcomes

Figure MIGRATE.1 in Chapter 7 presents a simplified framework for understanding how migration and displacement may emerge from 
the interactions of climatic and non-climatic factors, based on the characteristic risk framework introduced in Chapter 1 (Section 1.3). 
Voluntary migration can be used by households when adapting to climate hazards, while less voluntary forms of migration and displacement 
emerge when other forms of adaptation (referred to in Figure MIGRATE.1 in Chapter 7 as in situ adaptation) are inadequate. Migration 
outcomes—expressed in Figure MIGRATE.1 in Chapter 7 as changes in future risks to the well-being of migrants, sending communities 
and destination communities—are heavily influenced by the political, legal, cultural and socioeconomic conditions under which migration 
occurs. Groups and individuals that are involuntarily immobile may find that their exposure, vulnerability and risk increase over time.

Table MIGRATE.1 in Chapter 7 summarises the range of potential migration outcomes that may emerge from this dynamic and indicates 
specific sections in sectoral and regional chapters of the report that describe examples of each.
Climate-migration processes and outcomes
In SITU 
ExposureResponseHazard Risk
Risk
decreases
Risk
increasesClimate Factors
Non-Climate FactorsSuccessfulinSituAdaptationThreshold(s)MIGRATION OUTCOMES
See Table CCB 1
Immobility
Figure MIGRATE.1 |  General interactions between climatic and non-climatic processes, adaptation, potential migration outcomes and implications 
for future risk. Adapted from McLeman et al. (2021).Cross-Chapter Box MIGRATE (continued)

7
1082Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Table MIGRATE.1 |  Typology of climate-related migration and examples in sectoral and regional chapters of AR6.
Type of climate-re-
lated migrationCharacteristicsRecent or current 
examplesExamples in the literature References in AR6 WGII
Temporary and/or 
seasonal migrationFrequently used as a risk-reduction 
strategy by rural households in 
less-developed regions with highly 
seasonal precipitation; includes 
transhumancePastoralists in 
sub-Saharan Africa; 
seasonal farm workers in 
south Asia; rural–urban 
labour migration in 
Central AmericaAfifi et al. (2016); Call et al. 
(2017); Piguet et al. (2018); 
Borderon et al. (2019); Cattaneo 
et al. (2019); Hoffmann et al. 
(2020); Lopez-i-Gelats et al. 
(2015); Lu et al. (2016) Kaczan 
and Orgill-Meyer (2020)Sections 5.5.1.1, 5.5.3.5; 
Section 7.2.6; Section 8.2.1.3; 
Section 9.8.3; Box 13.2
Indefinite or permanent

migrationLess common than temporary or seasonal 
migration, particularly when the whole 
household permanently relocatesNumerous examples in all 
regionsSee reviews listed in cell aboveSection 7.2.6; Section 8.2.1.3; 
Box 10.2
Internal migrationMovements within state borders; most 
common form of climate-related migrationNumerous examples in all 
regionsSee reviews listed in cell aboveSection 4.3.7; Sections 5.5.4, 
5.10.1.1; Section 7.2.6; 
Sections 9.7.2, 9.11; Box 9.8; 
Sections 10.3.3, 10.4.6.3, 
Box 10.2; Section 11.4.1; 
Section 12.5.8.4; Section 13.8.1.3; 
Section 14.4.7.3; Section 15.3.4.6
International migrationLess common than internal migration; 
most often occurs between contiguous 
countries within the same region; often 
undertaken for purpose of earning wages 
to remit homeCross-border migration 
within south and 
Southeast Asia, 
sub-Saharan AfricaSee reviews listed in cell above; 
also Veronis et al. (2018); 
McLeman (2019); Cattaneo and

G. (2016); Missirian and Schlenker 
(2017); Schutte et al. (2021)Sections 4.3.7, 4.5.7; 
Section 5.12.2; Section 7.2.6
Rural–urban or rural–
ruralTypically internal but may also flow 
between contiguous states; may be 
for temporary or indefinite periods; 
migration may be undertaken by an 
individual household member or the 
entire household; may be followed by 
remittancesDrought migration in 
Mexico, east Africa and 
south AsiaSee reviews in the cell above; 
also Adger et al. (2015); Gautier 
et al. (2016); Nawrotzki et al. 
(2017); Wiederkehr et al. (2018); 
Robalino et al. (2015); Borderon 
et al. (2019); Murray-Tortarolo and 
Martnez (2021)Section 5.13.4; Section 7.2.6; 
Section 6.2.4.3; Section 8.2.1.3; 
Section 9.8.1.2; Section 12.5.8.4; 
Section 14.4.7.1
DisplacementHouseholds are forced to leave homes for 
temporary or indefinite period; typically 
occurs as a result of extreme events 
and starts with seemingly temporary 
evacuation; risk is expected to rise in most

regions due to sea level rise and changes 
in associated coastal hazardsTropical cyclones in the 
Caribbean, Southeast Asia 
and Bay of Bengal regionIslam and Shamsuddoha (2017); 
Desai et al. (2021); see Internal 
Displacement Monitoring Centre 
annual reports for global statisticsCross-Chapter Box SLR in 
Chapter 3; Section 4.3.7; 4.5.7; 
Cross-Chapter Box MOVING PLATE 
in Chapter 5; Section 7.2.6.1; 
Box 8.1; Section 9.7.2; 
Section 9.9.2; Section 10.3; 
Box 14.7; Sections 15.3.4.6;
CCP2.2.2
Planned and/or 
organised resettlementInitiated in areas where settlements 
become permanently uninhabitable; 
requires assistance from governments 
and/or institutions; government-sponsored 
sedentarisation of pastoral populationsFiji, Carteret Islands, 
Papua New Guinea, Gulf 
of Mexico coast and 
coastal Alaska, USAMarino and Lazrus (2015); 
Hino et al. (2017); McNamara 
et al. (2018); McMichael and 
Katonivualiku (2020); Tadgell et al. 
(2017); Arnall (2014); Wilmsen

and Webber (2015)Section 4.6.9; Sections 5.14.1, 
5.14.2; Section 7.4.4.4; 
Section 10.4.6; Section 15.5.3; 
CCP2.2.2; CCP6.3.2
ImmobilityAdverse weather or climatic conditions 
warrant moving, but households are 
unable to relocate because of lack of 
resources or choose to remain because 
of strong social, economic or cultural 
attachments to placeExamples in most regionsAdams (2016); Zickgraf (2018); 
Nawrotzki and DeWaard (2018); 
Farbotko et al. (2020)Section 4.6.9; Section 7.2.6.2; 
Box 8.1; Box 10.2
Policy implications
Future migration and displacement patterns in a changing climate will depend not only on the physical impacts of climate change, but 
also on future policies and planning at all scales of governance (high confidence) (4.6.9, 5.14.1, 5.14.1.2, 7.3.2, 7.4.4, 8.2.1.3; Box 8.1; 
CCP6.3.2). Policy interventions can remove barriers to and expand the alternatives for safe, orderly and regular migration that allows

vulnerable people to adapt to climate change (high confidence) (Section 7.2.6). With adequate policy support, migration in the context Cross-Chapter Box MIGRATE (continued)

7
1083Health, Wellbeing and the Changing Structure of Communities  Chapter 7
of climate change can result in synergies for both adaptation and development (Sections 5.12.2, 7.4.4, 8.2.1.3). Migration governance at 
local, national and international levels will influence the outcomes of climate-related migration for the migrants themselves as well as for 
receiving and origin communities (Sections 5.13.4, 7.4.4, 8.2.1.3). At the international level, a number of relevant policy initiatives and 
agreements, including Global Compacts for Safe, Orderly and Regular Migration and for the protection of Refugees; the Warsaw 
International Mechanism of the UNFCCC; the Sustainable Development Goals; the Sendai Framework for Disaster Risk Reduction; and the 
Platform on Disaster Displacement, have already been established, merit continued pursuit and provide potential migration governance

pathways (Section 7.4.4). Policy and planning decisions at regional, national and local scales that relate to housing, infrastructure, water 
provisioning, schools and healthcare are relevant for successful integration of migrants into receiving communities (Sections  5.5.4, 
5.10.1.1, 5.12.2, 9.8.3). Policies and practices on movements of people across international borders are also relevant to climate-related 
migration, with restrictions on movement having implications for the adaptive capacity of communities exposed to climate hazards 
(Section  7.4.4.2; Box  8.1). Perceptions of migrants and the framing of policy discussions in receiving communities and nations are 
important determinants of the future success of migration as an adaptive response to climate change (Section 7.4.4.3) (high agreement, 
medium evidence).
Reducing the future risk of large-scale population displacements, including those requiring active humanitarian interventions and

organised relocations of people, requires the international community to meet the requirements of the Paris Agreement and take further 
action to control future warming (high confidence) (Cross-Chapter Box SLR in Chapter 3; Section 7.3.1; Box 8.1). Current emissions 
pathways lead to scenarios for the period between 2050 and 2100 in which hundreds of millions of people will be at risk of displacement 
due to rising sea levels, floods, tropical cyclones, droughts, extreme heat, wildfires and other hazards, with land degradation exacerbating 
these risks in many regions (Section 7.3.2; IPCC 2019b; Cross-Chapter Box SLR in Chapter 3). At high levels of warming, tipping points may 
exist, particularly related to sea level rise, that, if crossed, would further increase the global population potentially at risk of displacement 
(Ranasinghe et al. 2021). Populations in low-income countries and small-island states that have historically had low greenhouse gas

(GHG) emissions are at particular risk of involuntary migration and displacement due to climate change, reinforcing the urgency for 
industrialised countries to continue lowering GHG emissions, to support adaptive capacity-building initiatives under the UNFCCC and to 
meet objectives expressed in the Global Compacts regarding safe, orderly and regular migration and the support and accommodation of 
displaced people (Sections 4.3.7, 4.5.7, 5.12.2, 7.4.5.5, 8.4.2; Box 8.1; Cross-Chapter Box SLR in Chapter 3).Cross-Chapter Box MIGRATE (continued)
Climate-related migration originates most often in rural areas in 
low- and middle-income countries, with migrant destinations usually 
being other rural areas or urban centres within their home countries 
(i.e., internal migration) ( medium confidence) . Rural livelihoods and 
incomes based on farming, livestock rearing and/or natural resource 
collection are inherently sensitive to climate variability and change,

creating greater potential for migration as a response (Bohra-Mishra 
et  al., 2017; Viswanathan and Kumar, 2015). Drought events have 
been associated with periods of higher rural to urban migration 
within Mexico (Chort and de la Rupelle, 2016; Leyk et  al., 2017; 
Nawrotzki et  al., 2017; Murray-Tortarolo and Martnez, 2021) and 
Senegal (Nawrotzki and Bakhtsiyarava, 2017). Extreme temperatures 
are associated with higher rates of temporary rural out-migration in 
South Africa and in Bangladesh (Mastrorillo et al., 2016; Call et al., 
2017). In rural Tanzania, weather-related shocks to crop production 
have been observed to increase the likelihood of migration but 
typically only for households in the middle of community wealth 
distribution (Kubik and Maurel, 2016). Weather-related losses in rice 
production have been associated with small-percentage increases 
in internal migration in India (Viswanathan and Kumar, 2015) and

the Philippines (Bohra-Mishra et al., 2017). In east Africa, temporary 
rural–urban labour migration does not show a strong response to 
climatic drivers (Mueller et  al., 2020). There is limited literature on 
mobility as adaptation in urban populations, with the focus being on 
resettlement of flood-prone informal settlements within cities (Kita, 
2017; Tadgell et al., 2017).Most documented examples of international climate-related migration 
are intra-regional movements of people between countries with shared 
borders (high agreement, medium evidence). Systematic reviews find 
few documented examples of long-distance, inter-regional migration 
driven by climate events (Veronis et  al., 2018; Kaczan and Orgill-
Meyer, 2020; Hoffmann et  al., 2020). One macro-economic analysis 
found a correlation between migrant flows from low- to high-income 
countries and adverse climatic events in the source country (Coniglio

and Pesce, 2015). Another study found that high heat stimulates higher 
rates of international migration from middle-income countries but 
typically not from low-income countries (Cattaneo and Peri., 2016), 
while other studies found international climate-related migration 
originates primarily from agriculture-dependent countries (Cai et al., 
2016; Nawrotzki and Bakhtsiyarava, 2017). Small-sample studies of 
migrants to Canada from Bangladesh, Haiti and sub-Saharan Africa 
suggest environmental factors in the source country can be a primary 
or secondary motivation for some migrants within larger flows of 
economic and family-reunification migrants (Veronis and McLeman, 
2014; Mezdour et  al., 2015; McLeman et  al., 2017). Research on 
the links between climate hazards and international movements of 
refugees and/or asylum seekers shows differing results. One study 
found that asylum applications in Europe increase during climate

fluctuations due to interactions with conflict (Missirian and Schlenker, 
2017), and another found links between heat, drought, conflict and 
asylum-seeking migration originating in the Middle East between 
2011 and 2015 (Abel et  al., 2019). Other studies have found that

7
1084Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Average annual weather-related displacements, 2010–2020
Wet mass movement
Mass movement
Extreme temperatures
Drought
Wildfire
Storm
Flood
C. & S.
IslandsEurope AustralasiaMillions
of people
displacedWeather-related hazards
Expanded inset 2 4 6 8 10 12 14 16 18
Asia Africa North
AmericaCentral
and South
IslandsEurope Australasia00.1
00.20.30.40.50.60.70.80.9
Figure 7.7 |  Average number of people displaced annually by selected weather-related events from 2010 to 2020 by region. See text for important notes 
regarding data collection and trends. Source statistics provided by the Internal Displacement Monitoring Centre (https://www.internal-displacement.org/).asylum claims in Europe correspond minimally with climatic hazards 
in source countries (Schutte et al., 2021), with choices in baseline data, 
timeframes for analysis and methodological approaches potentially

explaining the inconsistent results across studies (Boas et al., 2019). 
Media reports and other studies in recent years suggest that climate 
change has driven large numbers of migrants to the US from Central 
America and to Europe from the Middle East and Africa, but empirical 
studies were not identified for this assessment.
7.2.6.1 Relative Importance of Specific Climatic Drivers of 
Migration and Displacement
Reliable global estimates of voluntary climate-related migration 
within and between countries are not available due to a general 
absence of data of this specific nature, with existing national and 
global datasets often lacking information on migration causation or 
motivation. Better data are available for involuntary displacements 
within countries for reasons associated with weather-related hazards. 
Data collected annually since 2008 on internal displacements 
attributed to extreme weather events by the IDMC indicate that

extreme storms and floods are the two most significant weather-
related drivers of population displacements globally. Because of 
improvements in collection sources and methods since it first began 
reporting data in 2008, upward trends since that year in the total 
reported annual number of people displaced should be treated 
cautiously. However, it is reasonable to conclude that the average 
annual rate currently exceeds 20  million people globally, with 
considerable interannual variation due to the frequency and severity 
of extreme events in heavily populated areas. Regional distribution 
of displacement events has been consistent throughout the IDMC data collection period (high confidence), with displacement events 
occurring most often in East, Southeast and south Asia; sub-Saharan 
Africa; the USA; and the Caribbean region (Figure  7.7). Relative 
to their absolute population size, small island states experience a

disproportionate risk of climate-related population displacements 
(Desai et al., 2021) (high confidence).
Tropical cyclones and extreme storms are a particularly significant 
displacement risk in East and Southeast Asia, the Caribbean region, 
the Bay of Bengal region and southeast Africa (IDMC 2020) ( high 
confidence) . The scale of immediate displacement from any given 
storm and potential for post-event migration depend heavily on the 
extent of damage to housing and livelihood assets and the responsive 
capacity of governments and humanitarian relief agencies (Saha, 
2016; Islam et  al., 2018; Mahajan, 2020; Spencer and Urquhart, 
2018). In Bangladesh, the rural poor are most often displaced, with 
initial increases in short-term, labour-seeking migration followed by 
more permanent migration by some groups (Saha, 2016; Islam and 
Hasan, 2016; Islam and Shamsuddoha, 2017). Past hurricanes in the 
Caribbean basin have generated internal and inter-state migration

within the region, typically along pre-existing social networks, 
and to the USA (Loebach, 2016; Chort and de la Rupelle, 2016). In 
2017, Hurricanes Irma and Maria caused widespread damage to 
infrastructure and health services, and a slow recovery response by 
authorities was followed by the migration of tens of thousands of 
Puerto Ricans to Florida and New York (Zorrilla, 2017; Echenique and 
Melgar, 2018). In the US, coastal counties experience increased out-
migration after hurricanes that flows along existing social networks 
(Hauer, 2017), with post-disaster reconstruction employment 
opportunities potentially attracting new labour migrants to affected

7
1085Health, Wellbeing and the Changing Structure of Communities  Chapter 7
areas (Ouattara and Strobl, 2014; Curtis et al., 2015; DeWaard et al., 
2016; Fussell et al., 2018).
Riverine flood displacement can lead to increases or decreases in 
temporary or short-distance migration flows, depending on the local 
context (medium confidence) (Robalino et  al., 2015; Ocello et  al., 
2015; Afifi et al., 2016; Koubi et al., 2016b). Floods are a particularly 
important driver of displacement in river valleys and deltas in Asia and 
Africa, although large flood-related displacements have been recorded 
by the IDMC in all regions. In Africa, populations exposed to low flood 
risks, as compared with other regions, are observed to have a greater 
vulnerability to displacement due to limited economic resources and 
adaptive capacity (Kakinuma et al., 2020). In areas where flooding is 
especially frequent, in situ adaptations may be more common, and

out-migration may temporarily decline after a flood (Afifi et al., 2016; 
Chen et al., 2017; Call et al., 2017). Rates of indefinite or permanent 
migration tend not to change following riverine floods unless damage 
to homes and livelihood assets is especially severe and widespread, 
with household perceptions of short- and longer-term risks playing an 
important role (Koubi et al., 2016a).
Displacements due to droughts, extreme heat and associated impacts on 
food and water security are most frequent in east Africa and, to a lesser 
extent, south Asia and west and southern Africa (IDMC, 2020). Since 
droughts unfold progressively and typically do not cause permanent 
damage to housing or livelihood assets, there is greater opportunity for 
government and non-governmental organisation (NGO) interventions 
and greater use of in situ adaptation options (Koubi et  al., 2016b; 
Koubi et al., 2016a; Cattaneo et al., 2019). Drought-related population

movements are most common in dryland rural areas of low-income 
countries and occur after a threshold is crossed and in situ adaptation 
options are exhausted (Gautier et al., 2016; Wiederkehr et al., 2018; 
McLeman, 2017). Observed population movements may occur for an extended period after the event; one study of Mexican data found this 
lag to be up to 36 months (Nawrotzki et al., 2017). The most common 
response to drought is an increase in short-distance, rural–urban 
migration (medium confidence), with examples being documented 
in Bangladesh, Ethiopia, Pakistan, sub-Saharan Africa, Latin America 
and Brazil (Neumann and Hermans, 2015; Gautier et al., 2016; Gautier 
et al., 2016; Mastrorillo et al., 2016; Baez et al., 2017; Call et al., 2017; 
Nawrotzki et al., 2017; Jessoe et al., 2018; Carrico and Donato, 2019; 
Hermans and Garbe, 2019).
Few assessable studies were identified that examine links between 
wildfires and migration. Wildfire events are often associated with

urgent evacuations and temporary relocations, which place significant 
stress on receiving communities (Spearing and Faust., 2020), but 
research in the USA suggests fires have only a modest influence on 
future migration patterns in exposed areas (Winkler and Rouleau., 
2021). More research, particularly in other regions, is needed.
7.2.6.2 Immobility and Resettlement in the Context of Climatic 
Risks
Immobility in the context of climatic risks can reflect vulnerability and 
lack of agency (an inability to migrate), but can also be a deliberate 
choice (high confidence). Research since AR5 shows that immobility 
is best described as a continuum from people who are financially or 
physically unable to move away from hazards (involuntary immobility) 
to people who choose not to move (voluntary immobility) because 
of strong attachments to place, culture and people (Nawrotzki and 
DeWaard, 2018; Adams, 2016; Farbotko and McMichael, 2019; Zickgraf,

2019; Neef et al., 2018; Suckall et al., 2017; Ayeb-Karlsson et al., 2018; 
Zickgraf, 2018; Mallick and Schanze, 2020). Involuntary immobility is 
associated with individuals and households with low adaptive capacity 
and high exposure to hazard, and can exacerbate inequality and future 
Box 7.4 | Gender Dimensions of Climate-Related Migration
Migration decision-making and outcomes—in both general terms and in response to climatic risks—are strongly mediated by gender, 
social context, power dynamics and human capital (Bhagat, 2017; Singh and Basu, 2020; Rao et al., 2019a; Ravera et al., 2016). Women 
tend to suffer disproportionately from the negative impacts of extreme climate events for reasons ranging from caregiving responsibilities 
to lack of control over household resources to cultural norms for attire (Belay et al., 2017; Jost et al., 2016). In many cultures, migrants

are most often able-bodied, young men (Call et al., 2017; Heaney and Winter, 2016). Women wait longer to migrate because of higher 
social costs and risks (Evertsen and Van Der Geest, 2019) and barriers such as social structures, cultural practices, lack of education and 
reproductive roles (Belay et al., 2017; Afriyie et al., 2018; Evertsen and Van Der Geest, 2019).
Research critiques the tendency to portray women as victims of climate hazards rather than recognising differences between women and 
the potential for women to use their agency and informal networks to negotiate their situations (Eriksen et al., 2015; Ngigi et al., 2017; 
Pollard et al., 2015; Rao et al., 2019b; Ravera et al., 2016). Migration can change household composition and structure, which in turn 
affects the adaptive capacity and choices of those who do not move (Rao et al., 2019a; Rao et al., 2019b; Singh, 2019). For example, when

only male household members move, the remaining members of the now female-headed household must take on greater workloads 
(Goodrich et al., 2019; Rao et al., 2019b; Rigg and Salamanca, 2015), leading to increased workload and greater vulnerability for those 
left behind (Arora et al., 2017; Bhagat, 2017; Flatø et al., 2017; Lawson et al., 2019). It can, however, also increase women’s economic 
freedom and decision-making capacity, enhance their agency (Djoudi et al., 2016; Rao, 2019) and alter the gendered division of paid 
work, care and intra-household relations (Rigg et al., 2018; Singh and Basu, 2020), a process that may reduce household vulnerability to 
extreme climate events (Banerjee et al., 2019b).

7
1086Chapter 7 Health, Wellbeing and the Changing Structure of Communities
vulnerability to climate change (Sheller, 2018), including through 
impacts on health (Schwerdtle et  al., 2018). Voluntary immobility 
represents an assertion of the importance of culture, livelihoods and 
people to well-being, and is of particular relevance for Indigenous 
Peoples (Suliman et al., 2019).
Planned relocations by governments of settlements and populations 
exposed to climatic hazards are not presently commonplace, 
although the need is expected to grow in coming decades (Hino 
et al 2017). Examples include relocations of coastal settlements 
exposed to storm and erosion hazards as well as smaller numbers 
of cases of flood-prone settlements in river valleys; these examples 
suggest that organised relocations are expensive, contentious, create 
multiple challenges for governments and generate short- and longer-
term disruptions for the people involved (high agreement, medium

evidence) (Ajibade et al., 2020; Henrique and Tschakert, 2020; Desai 
Examples of relocations of small indigenous communities in coastal 
Alaska and villages in the Solomon Islands and Fiji suggest that 
relocated people experience significant financial and emotional distress 
as cultural and spiritual bonds to place and livelihoods are disrupted 
(Albert et al., 2018; Neef et al., 2018; McMichael and Katonivualiku, 
2020; McMichael and Katonivualiku, 2020; McMichael et  al., 2021; 
Piggott-McKellar et  al., 2019; Bertana, 2020). Voluntary relocation 
programmes offered by US state governments in communities damaged 
by Hurricane Sandy in 2012 have been subject to multiple studies, and 
these show longer-term economic outcomes, social connections and 
mental well-being vary for a range of reasons unrelated to the impacts 
of the hazard event itself (Bukvic and Owen, 2017; Binder et al., 2019; 
Koslov and Merdjanoff, 2021).

7.2.6.3  Connections Between Climate-Related Migration and 
Health
The number of assessable peer-reviewed studies that make connections 
between climate-related migration and health and well-being is small. 
The health outcomes of migrants generally, and of climate-migrants 
in particular, vary according to geographical context, country and the 
particular circumstances of migration or immobility (Hunter and Simon, 
2017; Hunter et  al., 2021; Schwerdtle et  al., 2020). Such linkages are 
‘multi-directional’, with studies suggesting that healthy individuals 
may be more likely to migrate internationally in search of economic 
opportunities than people in poorer health, except during adverse 
climatic conditions when migration rates may change across all groups, 
and that migrants may have different long-term health outcomes than 
people born in destination areas, potentially displaying a range of positive 
and negative health outcomes compared to non-migrants (Kennedy

et al., 2015; Dodd et al., 2017; Hunter and Simon, 2017; Riosmena et al., 
2017). Refugees and other involuntary migrants often experience higher 
exposure to disease and malnutrition, adverse indirect health effects of 
changes in diet or activity and increased rates of mental health concerns. 
These latter may be attributable to a sense of loss or fear (Schwerdtle 
et al., 2018; Torres and Casey, 2017) as well as due to the interruption 
of healthcare; occupational injuries; sleep deprivation; non-hygienic 
lodgings and insufficient sanitary facilities; heightened exposure to 
vector- and WBDs; vulnerability to psychosocial, sexual and reproductive issues; behavioural disorders; substance abuse; and violence (Farhat et al., 
2018; Wickramage et  al., 2018) (high confidence). Linkages between 
climate migration and the spread of infectious disease are bidirectional; 
migrants may be exposed to diseases at the destination to which they

have lower immunity than the host community; in other cases, migrants 
could introduce diseases to the receiving community (McMichael, 2015). 
Thus, receiving areas may have to pay greater attention to building 
migrant sensitive health systems and services (Hunter and Simon, 2017). 
The risk of migration leading to disease transmission is exacerbated by 
weak governance and lack of policy to support public health measures 
and access to medicines (Pottie et al., 2015).
7.2.7 Observed Impacts of Climate on Conflict
7.2.7.1 Introduction
In AR5, conflict was addressed in WGII Chapter 12 on human security. 
The chapter concluded that some of the factors that increase the risk of 
violent conflict within states are sensitive to climate change (medium 
evidence, medium agreement), that people living in places affected by 
confidence) and that climate change will lead to new challenges to

states and will increasingly shape both conditions of security and 
national security policies (medium evidence, medium agreement). AR5 
characterised a major debate within the field as: authors supporting 
an association between climate anomalies and conflict that can be 
extrapolated into the future (e.g., Hsiang et  al. (2013); Hsiang and 
Marshall (2014); Burke et al. (2015a)) and authors arguing that these 
associations are not universal and break down when contextual, scale 
and political factors are introduced (e.g., Buhaug et al. (2014); Buhaug 
(2016)).
Consistent with AR5 findings, there continues to be little observed 
evidence that climatic variability or change cause violent inter-state 
conflict. In intra-state settings, climate change has been associated 
with the onset of conflict, civil unrest or riots in urban settings (high 
agreement, medium evidence) (Ide (2020), and changes in the duration 
and severity of existing violent conflicts (Koubi, 2019). Climate change

is conceptualised as one of many factors that interact to raise tensions 
(Boas and Rothe, 2016) through diverse causal mechanisms (Mach 
et al., 2019; Ide et al., 2020) and as part of the peace-vulnerability-
development nexus (Barnett, 2019; Abrahams, 2020; Buhaug and von 
Uexkull, 2021).  New areas of the literature assessed in this report 
include the security implications of responses to climate change, the 
gendered dynamics of conflict and exposure to violence under climate 
change and civil unrest in urban settings. The impact of violent conflict 
on vulnerability is not addressed in this chapter but does arise in 
other chapters (Sections  8.3.2.3, 17.2.2.2). Other chapters address 
non-violent conflict over changing availability and distribution of 
resources, for example, competing land uses and fish stocks migrating 
to different territories (Sections  5.8.2.3; 5.8.3, 5.9.3, 5.13, 9.8.1.1, 
9.8.5.1). A commonly used definition of armed conflict is conflicts

involving greater than 25 battle-related deaths in a year; this number 
represents the Uppsala Conflict Data Program threshold for inclusion 
in their database, a core resource in this field.

7
1087Health, Wellbeing and the Changing Structure of Communities  Chapter 7
Climatic conditions have affected armed conflict within countries, but 
their influence has been small compared to socioeconomic, political 
and cultural factors (Mach et  al., 2019) (high agreement, medium 
evidence). Inter-group inequality, and consequent relative deprivation 
can lead to conflict and the negative impacts of climate change 
lower the opportunity cost of involvement in conflict (Buhaug et al., 
2020; Vestby, 2019). Potential pathways linking climate and conflict 
include direct impacts on physiology from heat or resource scarcity; 
indirect impacts of climatic variability on economic output, agricultural 
incomes, higher food prices and increasing migration flows; and the 
unintended effects of climate mitigation and adaptation policies (Koubi, 
2019; Busby, 2018; Sawas et al., 2018). Relative deprivation, political 
exclusion and ethnic fractionalisation and ethnic grievances are other

key variables (Schleussner et al., 2016; Theisen, 2017). Research shows 
that factors such as land tenure and competing land uses interacting 
with market-driven pressures and existing ethnic divisions produce 
conflict over land resources rather than a scarcity of natural resources 
caused by climate impacts such as drought (high agreement, medium 
evidence) (Theisen, 2017; Balestri and Maggioni, 2017; Kuusaana and 
Bukari, 2015; Box 8.3).
7.2.7.2 Impacts of Climate Change and Violent Conflict
Positive temperature anomalies and average increases in temperature 
over time have been associated with collective violent conflict in certain 
settings (medium agreement, low evidence) . Helman and Zaitchik 
(2020) find statistical associations between temperature and violent 
conflict in Africa and the Middle East that are stronger in warmer places 
and identify seasonal temperature effects on violence. However, they 
are unable to detect the impact of regional temperature increases on

violence. For Africa, Van Weezel (2019) found associations between 
average increases in temperature and conflict risk. Caruso et al. (2016) 
found an association between rises in minimum temperature and 
violence through the impact of temperature on rice yields (Box  9.4). 
However, the associations between temperature and violence are 
weak compared to those with political and social factors (e.g., Owain 
and Maslin (2018)) and research focuses on areas where conflict is 
already present and, as such, is sensitive to selection bias (Adams et al., 
2018). There is a body of literature that finds statistical associations 
between temperature anomalies and inter-personal violence, crime 
and aggression in the Global North, predominantly in the USA (e.g., 
Ranson (2014); Mares and Moffett (2019); Tiihonen et  al. (2017); 
Parks et al. (2020); Section 14.4.8). However, authors have cautioned 
against extrapolating seasonal associations into long-term trends and

against focusing on individual crimes rather than wider social injustices 
associated with climate change and its impacts (Lynch et al., 2020).
Variation in availability of water has been associated with international 
political tension and intra-national collective violence (low agreement, 
medium evidence). Drought conditions have been associated with 
violence due to impacts on income from agriculture and water and 
food security, with studies focusing predominantly on sub-Saharan 
Africa and the Middle East (Ide and Frohlich, 2015; De Juan, 2015; Von 
Uexkull et al., 2016; Waha et al., 2017; Abbott et al., 2017; D’Odorico 
et al., 2018). A small set of published studies has argued inconclusively 
over the role of drought in causing the Syrian civil war (Gleick, 2014; 
Kelley et al., 2015; Selby et al., 2017; 16.2.3.9). In general, research stresses the underlying economic, social and political drivers of 
conflict. For example, research on conflict in the Lake Chad region

has demonstrated that the lake drying was only one of many factors 
including lack of development and infrastructure (Okpara et al., 2016; 
Nagarajan et al., 2018; Tayimlong, 2020). Fewer studies examine the 
relationship between flooding (excess water) and violence and often 
rely on migration as the causal factor (see below). However, some 
studies have shown an association between flooding and civil unrest 
(Ide et al., 2020; Section 4.3.6; Section 12.5.3; Box 9.4).
Extreme weather events can be associated with increased conflict risk 
(low agreement, medium evidence). There is the potential for extreme 
weather events and disasters to cause political instability and increase 
the risk of violent conflict, although not conclusively (Brzoska, 2018). 
Post-disaster settings can be used to intensify state repression (Wood 
and Wright, 2016) and to alter insurgent groups’ behaviour (Walch, 
2018). Different stakeholders use disasters to establish new narratives

and alter public opinion (Venugopal and Yasir, 2017). Some research 
has demonstrated how post-disaster activities have had positive 
impacts on the social contract between people and the state, reducing 
the risk of conflict by strengthening relations between government and 
citizens and strengthening the citizenship of marginalised communities 
(Siddiqi, 2018; Pelling and Dill, 2010; Siddiqi, 2019). However, post-
disaster and disaster risk-related activities themselves have limited 
capacity to support diplomatic efforts to build peace (Kelman et al., 
2018).
7.2.7.3 Causal Pathways Between Climate Change Impacts and 
Violent Conflict
Increases in food prices due to reduced agricultural production and 
global food price shocks are associated with conflict risk and represent 
a key pathway linking climate variability and conflict (medium 
confidence). Increases in food prices are associated with civil unrest 
in urban areas among populations unable to afford or produce their

own food and in rural populations due to changes in availability of 
agricultural employment with shifting commodity prices (Martin-
Shields and Stojetz, 2019). Under such conditions, locally specific 
grievances, hunger and social inequalities can initiate or exacerbate 
conflicts. Food price volatility in general is not associated with violence, 
but sudden food price hikes have been linked to civil unrest in some 
circumstances (Bellemare, 2015; McGuirk and Burke, 2020; Winne and 
Peersman, 2019). In urban settings in Kenya, Koren et al. (2021) found 
an association between food and water insecurity that is mutually 
reinforcing and associated with social unrest (although insecurity in 
either food or water on its own was not). Analysing the global food 
riots in 2007/2008 and 2011, Heslin (2021) stresses the role of local 
politics and pre-existing grievances in determining whether people 
mobilise around food insecurity (Chapter 5).

Climate-related internal migration has been associated with the 
experience of violence by migrants, the prolongation of conflicts in migrant 
receiving areas and civil unrest in urban areas ( medium agreement, low 
evidence). Research points to the potential for conflict to serve as an 
intervening factor between climate and migration. However, the nature 
of the relationship is diverse and context specific. For example, displaced 
people and migrants may be associated with heightened social tensions

7
1088Chapter 7 Health, Wellbeing and the Changing Structure of Communities
in receiving areas through mechanisms such as ecological degradation, 
reduced access to services and a disturbed demographic balance in the 
host area (Rüegger and Bohnet, 2020). Ghimire et al. (2015) observed 
that an influx of flood-displaced people prolonged conflict by causing a 
lack of access to services for some of the host population and feelings of 
grievance. Further, migration from drought-stricken areas to local urban 
centres has been used to suggest a climate trigger for the Syrian conflict 
(e.g., Ash and Obradovich (2020)). However, this link has been strongly 
contested by research that contextualises the drought in wider political 
economic approaches and existing migration patterns (De Châtel, 2014; 
Fröhlich, 2016; Selby, 2019; 16.2.3.9).
There is some evidence of an association between climate-related 
rural-to-urban migration and the risk of civil unrest (medium

agreement, low evidence). Petrova (2021) found that while migration 
in general was associated with increased protests in urban receiving 
areas, the relationship did not hold for hazard-related migration. In 
other settings, the association of civil unrest with in-migration was 
found to depend on the political alignment of the host state with the 
capital (Bhavnani and Lacina, 2015), previous experience of extreme 
climate hazards (Koubi et  al., 2021) and previous experience of 
violence among migrants (Linke et al., 2018). Climate-related migrants 
have reported higher levels of perception and experience of violence 
in their destination (Linke et al., 2018; Koubi et al., 2018). There has 
been no association established between international migration and 
conflict. The literature highlights how unjust racial logics may generate 
spurious links between climate migration and security (Fröhlich, 2016; 
Telford, 2018).
7.2.7.4 Gendered Dimensions of Climate-Related Conflict

Structural inequalities play out at an individual level to create gendered 
experiences of violence ( high agreement, medium evidence) . Violent 
conflict is experienced differently by men and women because of 
gender norms that already exist in society and shape vulnerabilities. 
For example, conflict deepens gendered vulnerabilities to climate 
change related to unequal access to land and livelihood opportunities 
(Chandra et  al., 2017). Motivations for inter-group violence may 
be influenced by constructions of masculinity, for example the 
responsibility to secure their family’s survival or pay dowries 
(Myrttinen et  al., 2017), and gendered roles may incentivise young 
men to protest or to join non-state armed groups during periods of 
adverse climate (Myrttinen et al., 2015; Myrttinen et al., 2017; Anwar 
et al., 2019; Hendrix and Haggard, 2015; Koren and Bagozzi, 2017). 
Research has found a positive correlation between crop failures and

suicides by male farmers who could not adapt their livelihoods to 
rising temperatures (Bryant and Garnham 2015; Kennedy and King, 
2014; Carleton, 2017).
Extreme weather and climate impacts are associated with increased 
violence against women, girls and vulnerable groups ( high agreement, 
medium evidence) . During and after extreme weather events, women, 
girls and LGBTQI people are at increased risk of domestic violence, 
harassment, sexual violence and trafficking (Le Masson et al., 2019; 
Nguyen, 2019;Myrttinen et al., 2015; Chindarkar, 2012). For example, 
early marriage is used as a coping strategy for managing the effects 
of extreme weather events (Ahmed et  al., 2019) and women are exposed to increase risk of harassment and sexual assault as scarcity 
and gender-based roles cause them to walk longer distances to fetch 
water and fuel (Le Masson et al., 2019). Within the household, violent 
backlash or heightened tensions may arise from changing gender

norms as men migrate to find work in post-disaster settings (Stork 
et al., 2015) and men’s use of negative coping mechanisms, such as 
alcoholism, when unable to meet norms of providing for the household 
(Anwar et  al., 2019; Stork et  al., 2015). Rates of intimate partner 
violence have been found to increase with higher temperatures (Sanz-
Barbero et al., 2018).
7.2.7.5 Observed Impacts on Non-violent Conflict and Geopolitics
Climate adaptation and mitigation projects implemented without 
taking local interests and dynamics into account have the potential 
to cause conflict ( high agreement, medium evidence) . Reforestation 
or forest management programmes driven by reducing emissions 
through deforestation, land zoning and managed retreat due to sea 
level rise have been identified as having the potential to cause friction 
and conflict within and between groups and communities (de la Vega-
Leinert et  al., 2018; Froese and Schilling, 2019). Conflict may arise

when there is resistance to a proposed project, where interventions 
favour one group over another, or when projects undermine 
livelihoods or displace populations (e.g., Nightingale (2017); Sovacool 
et  al. (2015); Sovacool (2018); Corbera (2017); Hunsberger (2018); 
Sections  4.6.8, 5.13.4, 14.4.7.3). In addition to conflict generated 
by the poor implementation of land-based climate mitigation and 
adaptation projects, Gilmore and Buhaug (2021) highlight the links 
between climate policy and conflict through the potential effects of 
unequal distribution of economic burdens and fossil fuel markets on 
economic growth. There is a small literature that draws attention 
to the potential security of nuclear proliferation, if nuclear energy 
is increasingly employed as a low-carbon energy source (e.g., 
Parthemore et al. (2018); Bunn, (2019)).
Economic and social changes due to changes in sea ice extent in the 
Arctic are anticipated to be managed as part of existing governance

structures (high agreement, medium evidence). The opening-up of the 
Arctic and associated geopolitical manoeuvring for access to shipping 
routes and sub-sea hydrocarbons is often highlighted as a potential 
source of climate conflict (e.g., Koivurova (2009); Åtland (2013); 
Tamnes and Offerdal (2014)). Research assessed in AR5 focused 
on the potential for resource wars and Arctic land grabs. However, 
research since AR5 is less sensationalist in its approach to Arctic 
security, focusing instead on the practicalities of polycentric Arctic 
governance under climate change, the economic impacts of climate 
change, protecting the human security of Arctic populations whose 
autonomy is at risk (Heininen and Exner-Pirot, 2020), understanding 
how different regions (e.g., the EU) are positioning themselves more 
prominently in the Arctic space (Raspotnik and Østhagen, 2019) and 
Arctic Indigenous Peoples’ understanding of security (Hossain, 2016; 
Chapter 3; Chapter 14; CCP6).

7
1089Health, Wellbeing and the Changing Structure of Communities  Chapter 7
7.3 Projected Future Risks under Climate 
Change
7.3.1 Projected Future Risks for Health and Well-Being
7.3.1.1 Global Impacts
Climate change is expected to significantly increase the health risks 
resulting from a range of climate-sensitive diseases and conditions, 
with the scale of impacts depending on emissions and adaptation 
pathways in coming decades (very high confidence). Sections 7.3.1.2 to 7.3.1.11 assess the available studies on future projections for risks 
associated with specific climate-sensitive diseases and conditions 
previously described in Section 7.2.1. In the case of diabetes, cancer, 
injuries, mosquito-borne diseases other than dengue and malaria, 
rodent-borne diseases and most mental illnesses, insufficient literature 
was found to allow for assessment. Adaptation pathways and options 
for managing such risks are detailed in Section 7.4.

Even in the absence of further warming beyond current levels, the 
proportion of the overall global deaths caused by climate-sensitive 
diseases and conditions would increase marginally by mid-century 
Projected annual additional deaths attributable to climate change, in 2030 and 2050 compared to 1961–1990
/g132/g3Heat in elderly people /g132Diarrhoeal disease in children under 15 years /g132/g3Malaria /g132/g3Dengue /g132/g3Undernutrition (stunting)
Total deaths
2030 2050
2030 2050
Australasia & 
OceaniaSouthern AfricaCentral Latin America 
& CarribeanNorth Amercia
South-east Asia9280966114
South AsiaCause of death
61022992
77694083
Andean 
Latin 
America
Southern Latin AmericaTropical Latin America
Eastern AfricaDengue
Malaria
UndernutritionDiarrhoeal
Heat
Asia Pacific, high income
West AfricaAfrica and Middle East total
Asia total
Europe total127418162383
Oceania totalNorth America total
Central &
South Amercia total
351172
Central AsiaEast Asia
Central
Africa

Adapted from source: W orld Health Organization. 2014. Quantitative risk assessment of the effects of climate change on selecte d causes of death, 2030s and 2050s .104735485
Europe
North
Africa &
Middle
East
Figure 7.8 |  Projected additional annual deaths attributable to climate change in 2030 and 2050 compared to 1961–1990 (WHO, 2014).

7
1090Chapter 7 Health, Wellbeing and the Changing Structure of Communities
(high confidence) . Two global projections of climate change health 
impacts have been conducted since AR5. The first focused on cause-
specific mortality for eight exposures for 2030 and 2050 for a mid-
range emissions scenario (A1b) and three scenarios of economic growth 
(WHO, 2014). The study estimated that the climate change projected 
to occur by 2050 (compared to 1961–1990) could result in an excess 
of approximately 250,000 deaths yr–1, dominated by increases in 
deaths due to heat (94,000, mainly in Asia and high-income countries), 
childhood undernutrition (85,000, mainly in Africa but also in Asia), 
malaria (33,000, mainly in Africa) and diarrhoeal disease (33,000, mainly 
in Africa and Asia). Overall, more than half of this excess mortality is 
projected for Africa. Near-term projections (for 2030) are predominantly 
for childhood undernutrition (95,200 out of 241,000 total excess deaths)

(Figure  7.8). The second study (Carleton et  al. 2020) focused on all-
cause mortality associated with warming under both a high emissions 
scenario (RCP8.5) and a middle emissions scenario (RCP4.5). The 
analyses created a metric of death equivalents that accounted for hot 
and cold temperature-related mortality and the costs of individual level 
adaptation; no acclimatization or community-level adaptation, such as 
early warning systems, were incorporated. Average annual temperature-
mortality-income per capita relationships estimated from pooled data 
from 40 predominantly middle- and high-income countries (38% of the 
world population) were applied worldwide. Under the high emissions 
scenario, climate change was projected to result in approximately 85 
deaths equivalents per 100,000 population.Temperature increases are projected to exceed critical risk thresholds 
for six key climate-sensitive health outcomes, highlighting the criticality

of building adaptive capacity in health systems and in other sectors 
that influence health and well-being  (high confidence).  Recently 
reported research illustrates the temperature thresholds under three 
adaptation scenarios describing the effectiveness of health systems 
to manage additional risks from climate change  for  heat-related 
morbidity and mortality; ozone-related mortality; malaria incidence 
rates; incidence rates of Dengue and other diseases spread by Aedes 
sp. mosquitos; Lyme disease; and West Nile fever (Ebi et al., 2021a). As 
shown in Figure 7.9, these adaptation scenarios significantly alter the 
warming thresholds at which risks accelerate, with  the proactive 
adaptation scenario,  a  scenario that emphasises international 
cooperation towards achieving sustainable development, having 
the greatest potential to avoid significant increases in risks under 
all but the highest levels of warming.  The incomplete adaptation

scenario  describes a world with moderate challenges to adaptation 
and mitigation. The limited adaptation scenario describes a world with 
high challenges to adaptation and mitigation. In the figure, transitions 
are based on the peer-reviewed literature projecting risks for each of 
the health outcomes. Projections for time intervals were changed to 
temperature increase above pre-industrial levels based on the climate 
models and scenarios used in the projections.The assessed projections 
were based on a range of scenarios, including SRES, CMIP5, and ISIMIP , 
and, in some cases, demographic trends. The black dots are levels of 
Climate sensitive health outcomes under three adaptation scenarios
Scenario narratives Limited adaptation: Failure to proactively adapt; low investment in health systems.
Incomplete adaptation: Incomplete adaptation planning; moderate investment in health systems .
Proactive adaptation: Proactive adaptive management; higher investment in health system s

Historical average 
temperature increase
in 2011–2020 was
1.09°C (dashed line)
range 0.95–1.20°CConfidence level
assigned to 
transition
rangeRisk/impact
Low Very highVery high
High
Moderate
Undetectable•
•••••
••••Transition range
* Mortality projections 
include demographic 
trends but do not 
include future efforts 
to improve air quality 
that reduce ozone 
concentrations .
0234
1.5
1Global surface temperature change (°C)
Limited
Heat-related morbidity
and mortality
Proactive
Incomplete
•••• ••• ••••••
•••• •••
Limited
Ozone-related mortality *
Proactive
adaptationIncomplete
•••• ••• •••••
•••• •••
Limited
adaptationMalaria
Proactive
•••••• ••••• ••••••
0234
1.5
•••• ••• ••
•••• •••
••••Lyme disease
Limited
adaptationProactive
••• •• •
••• ••
•••West Nile fever
Limited

Limited
adaptationDengue and other diseases carried
by species of Aedes mosquitoes
Proactive
•••• •••
Figure 7.9  |  Climate-sensitive human health outcomes under three adaptation scenarios.

7
1091Health, Wellbeing and the Changing Structure of Communities  Chapter 7
confidence, from very high (four dots) to low (one dot). The diagrams 
for the proactive and incomplete adaptation scenarios are truncated at 
the nearest whole °C within the range of temperature change in 2100 
under three SSP scenarios used in panel (a) of SPM.3.
7.3.1.2 Projected Changes in Heat- and Cold-Related Exposure 
and Related Health Outcomes
This section considers the broad impacts of projected changes in heat- 
and cold-related exposure and related outcomes including mortality 
and work productivity. Several of the most common heat- and cold-
related specific health outcomes (e.g., CVD) are assessed individually 
in later sections of this chapter.
Population heat exposure will increase under climate change 
(very high confidence). Since AR5 there has been considerable 
progress with quantifying future human exposure to extreme heat

(Schwingshackl et  al., 2021), especially as determined by different 
combinations of SSPs and RCPs (Chambers, 2020; Cheng et  al., 
2020; Jones et al., 2018; Liu et al., 2017; Ma and Yuan, 2021; Russo 
et al., 2019). For example, Table 7.1 shows projections of population 
exposure to heatwaves, as expressed by the number of person-days, 
for the 2061–2080 period aggregated by geographical region and 
SSP/RCP . At the global level, projected future exposure increases 
from approximately 15 million person-days for the current period to 
535 billion person-days for high population growth under the high 
GHG emission SSP3-RCP8.5 scenario, while for the low population 
growth/high urbanisation and business as usual SSP5-RCP4.5 
scenario, the exposure is substantially lower at 170 billion person-
days. Spatial variations in future heatwave frequency and population 
growth play out in the form of significant geographical contrasts in

exposure, with the largest increases projected for low latitude regions 
such as India and significant portions of sub-Saharan Africa, where 
increases in heatwave frequency and population are expected. Over 
East Asia and especially eastern China, exposures are projected to 
rise, with the effect of increases in heatwave frequency exceeding 
the countering effect of projected reductions in population, especially 
in non-urban areas. Further, for North America and Europe, where 
rural depopulation is projected, the predominant driver of increases 
in exposure is urban growth (Jones et al., 2018).Comparisons of heatwave exposure for 1.5°C and 2.0°C warming 
for different SSPs indicate strong geographical contrasts in potential 
heatwave risk (high confidence). One global level assessment for a 
1.5°C warming projects that low human development index countries 
will experience exposure levels equal to or greater than the exposure

levels for very high human development index countries under a 2°C 
warming (Russo, 2019). The same assessment also finds that holding 
global warming below 1.5°C in tandem with achieving sustainable 
socioeconomic development (e.g., SSP1 as opposed to SSP4) yields 
reduced levels of heatwave exposure, especially for low human 
development index countries, particularly across sub-Saharan Africa. 
Similar findings were found in other global level assessments. Global 
exposure to extreme heat increases almost 30  times under a SSP3-
8.5 combination, with the average exposure for Africa 118  times 
greater than historical levels, in stark contrast to the four-fold increase 
projected for Europe. Compared to a SSP3-8.5 scenario, exposure was 
reduced by 65% and 85% under the SSP2-4.5 and SSP1-2.6 scenarios, 
respectively (Liu et al., 2017).
Regional level assessments of changes in population heat exposure for 
Africa, Europe, the USA, China and India corroborate general findings

at the global level, that the impact of warming is amplified under 
divergent regional development pathways (e.g., SSP4 – inequality) 
compared to those fostering sustainable development (e.g., SSP1 – 
sustainability) (high confidence) (Rohat et  al., 2019a; Weber et  al., 
2020; Broadbent et al., 2020; Dahl et al., 2019; Harrington and Otto, 
2018; Rohat et al., 2019b; Vahmani et al., 2019; Huang and et al., 2018; 
Zhang et al., 2020a; Liu et al., 2017). For some regions, such as Europe, 
changes in exposure are projected to be largely a consequence of 
climate change, while for others, such as Africa and to a lesser extent 
Asia, Oceania, North America and South America, the interactive effects 
of demographic and climate change are projected to be important 
(Jones et al., 2018; Liu et al., 2017; Russo et al., 2016; Ma and Yuan, 
Compared to research that estimates the temperature only impacts of

climate change on heat-related mortality (see below), the number of 
studies that explicitly model mortality responses considering various 
combinations of SSPs and RCPs is small and mostly restricted to the 
country or regional level. These studies point to increases in heat-
Table 7.1 |  Projected exposure to heatwaves in millions of person-days by region under different SSP/RCP combinations.
RegionExposure in millions of person-days
Current SSP3-4.5 SSP5-4.5 SSP3-8.5 SSP5-8.5
Global
USA
Europe
Latin America and 
Caribbean
North Africa and Middle 
East
Russia and Central Asia
South Asia
East Asia
Southeast Asia
Oceania14,811
375
376
191
803
1,335
1,427
272
7,194
977
711
37244,807
4,769
4,821
2,967
17,287
34,721
67,442
3,074
84,044
12,176
12, 452
247168,488
8,671
8,778
3,775
10,856
23,160
41,339
1,951
53,655
10,855
9,146
492534,848
10,802
10,990
7,326
45,612
65,072
158,290
6,554
146,709
35,381
60,909
822374,269
19,646
20,153
9,969
28,435
43,648
96,054

7
1092Chapter 7 Health, Wellbeing and the Changing Structure of Communities
related mortality especially amongst the elderly across a range of SSPs, 
with the greatest increases under SSP5 and RCP8.5 (Rail et al., 2019; 
Yang et al., 2021).
Estimates of heat-related mortality based solely on changes in 
temperature point to elevated levels of global and regional level 
mortality compared to the present, with the magnitude of this 
increasing from RCP4.5 through to RCP8.5 (high confidence) 
(Ahmadalipour and Moradkhani, 2018; Cheng et al., 2019; Kendrovski 
et al., 2017; Lee et al., 2020; Limaye et al., 2018; Morefield et al., 2018). 
Further support comes from the projection that heat-related health 
impacts for a 2°C increase in global temperatures will be greater than 
those for 1.5°C warming (very high confidence) (Dosio et al., 2018; 
Mitchell et  al., 2018; King and Karoly, 2017; Vicedo-Cabrera et  al., 
2018a).

Estimates of future mortality that incorporate adaptation in addition to 
temperature change point to increases in heat-related mortality under 
global warming, albeit at lower levels than the case of no adaptation 
(high confidence) (Anderson et  al., 2018; Gosling et  al., 2017; Guo 
et al., 2018; Honda and Onozuka, 2020; Vicedo-Cabrera et al., 2018b; 
Wang et  al., 2018b). Whether adaptation is considered or not, the 
consensus is Central and South America, southern Europe, southern 
and Southeast Asia and Africa will be the most affected by climate 
change in terms of heat-related mortality (high confidence). Similarly, 
projections of the impacts of future heat on occupational health, worker 
productivity and workability point to these regions as problematic 
under climate change (high confidence) (Andrews et al., 2018; de Lima 
et al., 2021; Dillender, 2021; Kjellstrom et al., 2018; Orlov et al., 2020; 
Rao et  al., 2020; Tigchelaar et  al., 2020), especially for occupations

with high exposure to heat, such as agriculture and construction. This 
accords with the findings from independent projections of population 
heat exposure as outlined above (high confidence).
The effect of climate change on productivity is projected to reduce 
GDP at a range of geographical scales ( high confidence) (Borg et al., 
2021; Oppermann et  al., 2021; Orlov et  al., 2020). For example, 
measuring economic costs using occupational health and safety 
recommendations, it was estimated that RCP8.5 would result in a 
2.4% reduction in global GDP compared to a 0.5% reduction under 
RCP2.6 (Orlov et al., 2020). For the USA, it was estimated that the 
total hours of labour supplied declined ∼ 0.11% (±0.004%) per degree 
Celsius increase in global mean surface temperature for low-risk 
workers and 0.53% (±0.01%) per degree Celsius increase for high-
risk workers exposed to outdoor temperatures (Hsiang et al., 2017).

Further, a systematic review of the literature indicates that extreme 
heat exacts a substantial economic burden on health systems, which 
bears implications for future heat-attributable healthcare costs 
(Wondmagegn et al., 2019).
Since AR5, there has been an increase in the understanding of the extent 
to which a warming world is likely to affect cold- or winter-related 
health impacts. Future increases in heat-related deaths are expected 
to outweigh those related to cold (high confidence) (Aboubakri et al., 
2020; Achebak et al., 2020; Burkart et al., 2021; Huber et al., 2020b; 
Martinez et al., 2018; Rodrigues et al., 2020; Vardoulakis et al., 2014; 
Weinberger et al., 2017; Weinberger et al., 2018a; Weitensfelder and Moshammer, 2020). However, strong regional contrasts in heat- and 
cold-related mortality trends are likely under a RCP8.5 scenario, with 
countries in the Global North experiencing minimal to moderate

decreases in cold-related mortality while warm climate countries in the 
Global South are projected to experience increases in heat-attributable 
deaths by the end of the century (Gasparrini et al., 2017; Burkart et al., 
2021). Projections of the magnitude of change in the temperature-
related burden of disease do, however, demonstrate great variability, 
due to the application of a wide range of climate change, adaptation 
and demographic scenarios (Cheng et al., 2019).
A particular focus since AR5 has been the impact of climate change 
on cities (see AR6 Chapter 6). Heat risks are expected to be greater 
in urban areas due to changes in regional heat exacerbated by ‘heat 
island’ effects (high confidence) (Doan and Kusaka, 2018; Heaviside 
et al., 2016; Li et al., 2021; Rohat et al., 2019a; Rohat et al., 2019c; 
Varquez et  al., 2020; Wouters et  al., 2017; Zhao et  al., 2021), with 
intra-urban scale variations in heat exposure attributable to land cover

contrasts and urban form and function (Avashia et  al., 2021; Jang 
et  al., 2020; Macintyre et  al., 2018; Schinasi et  al., 2018). However, 
further research is required to establish the health implications of 
increasing chronic slow-onset extreme heat (Oppermann et al., 2021) 
in addition to the acute health outcomes of UHI–heatwave synergies 
under climate change. The latter is particularly important as studies 
that address UHI–heatwave interactions have mainly focused on 
changes in UHI intensity (e.g., Ramamurthy and Bou-Zeid (2017); Scott 
et al. (2018)). Whether significant urban mortality anomalies arise from 
the interplay of heatwaves and UHIs largely remains an open question 
although at least one study demonstrated higher urban compared 
to rural mortality rates during heatwaves (Ruuhela et al., 2021). The 
benefits of the winter UHI effect for cold-related mortality remain 
largely unexplored, but one study for Birmingham, UK, indicates the

winter UHI will continue to have a protective effect in future climate 
(Macintyre et al., 2021).
7.3.1.3 Projected Impacts on Vector-Borne Diseases
The distribution and abundance of disease vectors, and the transmission 
of the infections that they carry, are influenced both by changes in 
climate and by trends such as human population growth and migration, 
urbanisation, land use change, biodiversity loss and public health 
measures. Each of these may increase or decrease risk, interact with 
climate effects and may contribute to the emergence of infectious 
disease, although there are few studies assessing future risk of 
emergence (Gibb et al., 2020). Unless stated otherwise, the assessments 
below are specifically for the effects of climate change on individual 
diseases, assuming other determinants remain constant.
There is a high likelihood that climate change will contribute to increased 
distributional range and vectorial capacity of malaria vectors in parts

of sub-Saharan Africa, Asia and South America (high confidence).  
In Nigeria, the range and abundance of Anopheles mosquitoes are 
projected to increase under both lower (RCP2.6) and especially under 
higher emissions scenarios (RCP8.5) due to increasing and fluctuating 
temperature, longer tropical rainfall seasons and rapid land use 
changes (Akpan et al., 2018). Similarly, vegetation acclimation due to 
elevated atmospheric CO 2 under climate change will likely increase the

7
1093Health, Wellbeing and the Changing Structure of Communities  Chapter 7
abundance of Anopheles vectors in Kenya (Le et al., 2019). Distribution 
of Anopheles may decrease in parts of India and Southeast Asia, but 
there is an expected increase in vectorial capacity in China (Khormi and 
Kumar, 2016). In South America, climate change is projected to expand 
the distributions of malaria vectors to 35–46% of the continent by 2070, 
particularly species of the Albitarsis complex (Laporta et al., 2015).
Malaria infections have significant potential to increase in parts 
of sub-Saharan Africa and Asia, with risk varying according to the 
warming scenario (medium confidence). In Africa, where most 
malaria is due to the more deadly Plasmodium falciparum parasite, 
climate change is likely to increase the overall transmission risk due 
to the likely expansion of vector distribution and increase in biting rates (Bouma et al., 2016; M’Bra et al., 2018; Nkumama et al., 2017;

Ryan et  al., 2015b; Tompkins and Caporaso, 2016a). The projected 
effect of climate change varies markedly by region, with projections 
for west Africa tending to indicate a shortening of transmission 
seasons and neutral or small net reductions in overall risk, whereas 
studies consistently project increases in southern and eastern Africa, 
with potentially an additional 76  million people at risk of endemic 
exposure (10–12 months yr–1) by the 2080s (Nkumama et al., 2017; 
Ryan et al., 2015b; Semakula et al., 2017; Zaitchik, 2019; Leedale et al., 
2016; Murdock et al., 2016; Yamana et al., 2016; Ryan et al., 2020). In 
sub-Saharan Africa, malaria case incidence associated with dams in 
malaria-endemic regions will likely be exacerbated by climate change, 
with significantly higher rates projected under RCP8.5 in comparison 
Projected change in the abundance of Aedes aegypti 
<-200 -100 0 >200Potential abundance change (2090–2099) - (1987–2016)
100
(a) RCP2.6
(b) RCP8.5

Figure 7.10 |  Projected change in the potential abundance of Aedes aegypti over the 21st century (2090–2099 relative to 1987–2016) (Liu-Helmersson 

7
1094Chapter 7 Health, Wellbeing and the Changing Structure of Communities
to lower-emission scenarios (Kibret et al., 2016). Incidence of malaria 
in Madagascar is projected to increase under RCP4.5 through RCP8.5 
(Rakotoarison et al., 2018). Distribution of P . vivax and P . falciparum 
malaria in China is likely to increase under RCPs higher than 2.6, 
especially RCP8.5 (Hundessa et al., 2018). In India, projected scenarios 
for the 2030s under RCP4.5 indicate changes in the spatial distribution 
of malaria, with new foci and potential outbreaks in the Himalayan 
region, southern and eastern states, and an overall increase in months 
suitable for transmission overall, with some other areas experiencing a 
reduction in transmission months (Sarkar et al., 2019).
Rising temperatures are likely to cause poleward shifts and overall 
expansion in the distribution of mosquitoes Aedes aegypti and Aedes 
albopictus, the principal vectors of dengue, yellow fever, chikungunya

and Zika (high confidence). Globally, the population exposed to 
disease transmission by one of these vectors is expected to increase 
significantly due to the combination of climate change and non-climatic 
processes including urbanisation and socioeconomic inter-connectivity, 
with exposure rates rising under higher warming scenarios (Kamal 
et al., 2018; Kraemer et al., 2019). For example, approximately 50% of 
the global population is projected to be exposed to these vectors by 
2050 under RCP6.0 (Kraemer et al., 2019). The effect of climate change 
alone is projected to increase the population exposed to Aedes aegypti 
by 8–12% by 2061–2080 (Monaghan et al., 2018), and its abundance 
is projected to increase by 20% under RCP2.6 and 30% under RCP8.5 
by the end of the century (Liu-Helmersson et al., 2019; Figure 7.10). 
Exposure to transmission by Aedes albopictus specifically would be 
highest at intermediate climate change scenarios and would decrease

in the warmest scenarios (Ryan et al., 2019). Under scenarios other 
than RCP2.6, most of Europe would experience significant increases in 
exposure to viruses transmitted by both vectors (Liu-Helmersson et al., 
2019).
Climate change is expected to increase dengue risk and facilitate 
its global spread, with the risk being greatest under high emissions 
scenarios (high confidence). Future exposure to risk will be influenced 
by the combined effects of climate change and non-climatic factors 
such as population density and economic development (Akter et al., 
2017). Overall, risk levels are expected to rise on all continents (Akter 
et  al., 2017; Messina et  al., 2015; Rogers, 2015; Liu-Helmersson 
et al., 2016; Messina et al., 2019). Compared to 2015, an additional 
1  billion people are projected to be at risk of dengue exposure by 
2080 under an SSP1-4.5 scenario, 2.25  billion under SSP2-6.0, and 
5 billion under SSP3-8.5 (Messina et al., 2019). In North America, risk

is projected to expand in north-central Mexico, with annual dengue 
incidence in Mexico increasing by up to 40% by 2080, and expand 
from US southern states to mid-western regions (Proestos et al., 2015; 
Colon-Gonzalez et al., 2013). In China, under RCP8.5, dengue exposure 
would increase from 168 million people in 142 counties to 490 million 
people in 456 counties by the late 2100s (Fan and Liu, 2019). In Nepal, 
dengue fever is expected to expand throughout the 2050s and 2070s 
under all RCPs (Acharya et al., 2018). In Tanzania, there is a projected 
shift in distribution towards central and northeastern areas and risk 
intensification in nearly all parts of the country by 2050 (Mweya et al., 
2016). Dengue vectorial capacity is projected to increase in Korea 
under higher RCP scenarios (Lee et al., 2018a).There are insufficient studies for assessment of projected effects of 
climate change on other arboviral diseases, such as chikungunya

and Zika. Zika virus transmits under different temperature optimums 
than does dengue, suggesting environmental suitability for Zika 
transmission could expand with future warming (low confidence) 
(Tesla et al., 2018).
Climate change can be expected to continue to contribute to the 
geographical spread of the Lyme disease vector Ixodes scapularis 
(high confidence) and the spread of tick-borne encephalitis and 
Lyme disease vector Ixodes ricinus in Europe (medium confidence). 
In Canada, vector surveillance of the black-legged tick I. scapularis 
identified strong temperature effects on the limits of their occurrence, 
on recent geographic spread, temporal coincidence in emergence of 
tick populations and acceleration of the speed of spread (Clow et al., 
2017; Cheng et al., 2017). In Europe, increasing temperatures over the 
1950–2018 period significantly accelerated the life cycle of Ixodes 
ricinus and contributed to its spread (Estrada-Peña and Fernández-

Ruiz, 2020). Under RCP4.5 and RCP8.5 scenarios, projections indicate a 
northward and eastward shift of the distribution of I. persulcatus and I. 
ricinus, vectors of Lyme disease and tick-borne encephalitis in northern 
Europe and Russia, with an overall large increase in distribution in 
the second half of the current century (Popov and Yasyukevich, 2014; 
Yasjukevich et  al., 2018) and increases in intensity of tick-borne 
encephalitis transmission in central Europe (Nah et al., 2020).
Climate change is projected to increase the incidence of Lyme 
disease and tick-borne encephalitis in the Northern Hemisphere (high 
confidence) (Figure  7.9). The basic reproduction number (R0) of I. 
scapularis in at least some regions of Canada is projected to increase 
under all RCP scenarios (McPherson et al., 2017). In the USA, a 2°C 
warming could increase the number of Lyme disease cases by over 
20% over the coming decades and lead to an earlier onset and longer

length of the annual Lyme disease season (Dumic and Severnini, 2018; 
Monaghan et al., 2015).
Climate change is projected to change the distribution of schistosomiasis 
in Africa and Asia (high confidence), with a possible increase in global 
land area suitable for transmission (medium confidence). A global 
increase in land area with temperatures suitable for transmission 
by the three main species of Schistosoma (S. japonicum, S. mansoni 
and S. haematobium) is projected under the RCP4.5 scenario for the 
2021–2050 and 2071–2100 periods (Yang and Bergquist, 2018), but 
regional outcomes are expected to vary. In Africa, shifting temperature 
regimes associated with climate change are expected to lead to 
reduced snail populations in areas with already high temperatures and 
higher populations in areas with currently low winter temperatures 
(Kalinda et al., 2017; McCreesh and Booth, 2014). Infection risk with 
Schistosoma mansoni may increase by up to 20% over most of eastern

Africa over the next 20–50 years but decrease by more than 50% in 
parts of north and east Kenya, southern South Sudan and eastern 
People’s Democratic Republic of Congo (PDRC) (McCreesh et  al., 
2015), with a possible overall net contraction (Stensgaard et al., 2013). 
In China, currently endemic areas in Sichuan Province may become 
unsuitable for snail habitats, but currently non-endemic areas in 
Sichuan and Hunan/Hubei provinces may see a new emergence (Yang 
and Bergquist, 2018). In addition to the projected effects of temperature

7
1095Health, Wellbeing and the Changing Structure of Communities  Chapter 7
described above, distribution and transmission of schistosomiasis will 
also be affected positively or negatively by changes in the availability 
of freshwater bodies, which were not included in these models.
7.3.1.4 Projected Impacts on Waterborne Diseases
Climate change will contribute to additional deaths and mortality 
due to diarrhoeal diseases in the absence of adaptation (medium 
confidence) (see Figure  7.8). Risk factors for future excess deaths 
due to diarrhoeal diseases are highly mediated by future levels of 
socioeconomic development and adaptation. An additional 1°C 
increase in mean average temperature is expected to result in a 7% 
(95% CI, 3–10%) increase in all-cause diarrhoea (Carlton et al., 2016), 
an 8% (95% CI, 5–11%) increase in the incidence of diarrheic E. coli 
(Philipsborn et al., 2016) and a 3–11% increase in deaths attributable

to diarrhoea (WHO, 2014). WHO Quantitative Risk Assessments for the 
effects of climate change on selected causes of death for the 2030s 
and 2050s project that overall deaths from diarrhoea should fall due 
to socioeconomic development but that the effect of climate change 
under higher emission scenarios could cause an additional 48,000 
deaths in children aged under 15  years in 2030 and 33,000 deaths 
for 2050, particularly in Africa and parts of Asia. In Ecuador, projected 
increases in rainfall variability and heavy rainfall events may increase 
diarrhoea burden in urban regions (Deshpande et al., 2020). A limit in 
the assessable literature is a lack of studies in the highest risk areas 
(Liang and Gong, 2017; UNEP , 2018).
Climate change is expected to increase future health risks associated 
with a range of other WBDs and parasites, with effects varying by 
region (medium confidence). WBDs attributable to protozoan parasites

including Cryptosporidium spp. and Giardia duodenalis (intestinalis) 
are expected to increase in Africa due to increasing temperatures 
and drought (Ahmed et al., 2018; Efstratiou et al., 2017). Recent data 
suggest a poleward expansion of Vibrios to areas with no previous 
incidence, particularly in mid- to high-latitude regions in areas where 
rapid warming is taking place (Baker-Austin et al., 2017). The number 
of Vibrio-induced diarrhoea cases yr–1 increased in past decades in the 
Baltic Sea region, and the projected risk of vibriosis will increase in 
northern areas, where waters are expected to become warmer and 
more saline due to reduced precipitation and have higher chlorophyll 
concentrations (Escobar et al., 2015; Semenza et al., 2017).
The risk of Campylobacteriosis and other enteric pathogens could rise 
in regions where heavy precipitation events or flooding are projected to 
increase (medium confidence). In Europe, the risk of Campylobacteriosis

and diseases caused by other enteric pathogens could rise in regions 
where precipitation or extreme flooding are projected to increase 
(European Environment Agency, 2017), although incidence rates may 
be further mediated by seasonal social activities (Rushton et al., 2019; 
Williams et  al., 2015b). Accelerated releases of dissolved organic 
matter to inland and coastal waters through increases in precipitation 
are expected to reduce the potential for solar ultraviolet inactivation of 
pathogens and increase risks for associated WBDs (Williamson et al., 
2017). The combined relative risk for waterborne campylobacteriosis, 
salmonellosis and diseases due to Verotoxin-producing Escherichia coli 
was estimated to be 1.1 (i.e., a 10% increase) for every 1°C in mean 
annual temperature, while by the 2080s, under RCP8.5, annual rates of cryptosporidiosis and giardiasis could rise by approximately 16% due 
to more severe precipitation events (Brubacher et  al., 2020; Chhetri

7.3.1.5 Projected Impacts on Food-Borne Diseases
The prevalence of Salmonella infections is expected to rise as higher 
temperatures enable more rapid replication ( medium confidence). 
Research from Canada finds a very strong association of salmonellosis 
and other FBDs with higher temperatures, suggesting that climate 
change could increase food safety risks ranging from increased public 
health burden to emergent risks not currently seen in the food chain 
(Smith and Fazil, 2019). In Europe, the average annual number of 
temperature-related cases of salmonellosis under high emissions 
scenarios could increase by up to 50% more than would be expected on 
the basis of on population change alone by 2100 (Lake, 2017; European 
Environment Agency, 2017). Warming trends in the southern USA may 
lead to increased rates of Salmonella infections (Akil et al., 2014).
7.3.1.6 Projected Impacts on Pollution- and Aeroallergens-
Related Health Outcomes

Global air pollution-related mortality attributable directly to climate 
change—the human health climate penalty associated with climate-
induced changes in air quality—is likely to increase and partially 
counteract any decreases in air pollution-related mortality achieved 
through ambitious emission reduction scenarios or stabilisation of 
global temperature change at 2°C (medium confidence). Demographic 
trends in aging and more vulnerable population are likely to be 
important determinants of future air quality—a human health climate 
penalty (high confidence).
Poor air quality contributes to a range of NCDs, including cardiovascular, 
respiratory and neurological, commonly resulting in hospitalisation or 
death. This section considers the possible risks for health of future 
climate-related changes in ozone and PM. The climate penalty, the 
degree to which global warming could affect future air quality, is better

understood for ozone than for PM (von Schneidemesser et al., 2020). 
This is because increases in air temperature enhance ozone formation 
via associated photochemical processes (Archibald et al., 2020; Fu and 
Tian, 2019). The association between climate and PM is complex and 
moderated by a diverse range of PM components as well as formation 
and removal mechanisms (von Schneidemesser et  al., 2020), added 
to which is uncertainty about future climate-related PM sources such 
as wildfires (Ford et al., 2018) and changes in aridity (Achakulwisut 
et al., 2019). As noted in AR6 WGI Chapter 6 (Naik et al 2021), future 
air quality will largely depend on precursor emissions, with climate 
change projected to have mixed effects. Because of the uncertainty 
in how natural processes will respond, there is low confidence in the 
projections of surface ozone and PM under climate change (Naik 
et al., 2021). This has implications on the levels of confidence in the

projections of the health climate penalty associated with climate-
induced changes in air quality (Orru et al., 2017; Orru et al., 2019; Silva 
There is a rich literature on global and regional level projections of 
air quality-related health effects arising from changes in emissions.

7
1096Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Comparatively few studies assess how changes in air pollution 
directly attributable to climate change are likely to affect future 
mortality levels. Projections indicate that emission reduction scenarios 
consistent with stabilisation of global temperature change at 2°C or 
below would yield substantial co-benefits for air quality-related health 
outcomes (Chowdhury et al., 2018b; von Schneidemesser et al., 2020; 
Silva et al., 2016c; Markandya et al., 2018; Orru et al., 2019; Shindell 
et  al., 2018) (high confidence). For example, by 2030, compared to 
2000, it was estimated that globally and annually 289,000 PM2.5-
related premature deaths could have been avoided under RCP4.5 
compared to 17,200 PM2.5-related excess premature deaths under 
RCP8.5 (Silva et al., 2016c). Further, and notwithstanding estimated 
reductions in global PM2.5 levels and an associated increase in the

number of avoidable deaths, the benefits of following a low emissions 
pathway are expected to be apparent by 2100, with avoidable deaths 
estimated at 2.39 million deaths yr–1 under RCP4.5. This contrasts with 
the 1.31  million deaths estimated under RCP8.5. A few projections 
of the health-related climate penalty indicate a possible increase in 
ozone and PM2.5-associated mortality under RCP8.5 (Doherty et al., 
2017; Orru et al., 2019; Silva et al., 2017).
At the global level for PM2.5, annual premature deaths due to 
climate change were projected to be 55,600 (−34,300 to 164,000) 
and 215,000 (−76,100 to 595,000) in 2030 and 2100, respectively, 
countering by 16% the projected decline in PM2.5-related mortality 
between 2000 and 2100 without climate change (Silva et al., 2017). 
Similarly for ozone, the number of annual premature ozone-related 
deaths due to climate change was projected to be 3,340 in 2030 and 
43,600 in 2050, with climate change accounting for 1.2% (14%) of

the annual premature deaths in 2030 (2100) (Silva et al., 2017). These 
global level projections average over considerable geographical 
variations (Silva et al., 2017). Projections of the climate change effect 
on ozone mortality in 2100 were greatest for East Asia (41 deaths 
yr–1 per million people), India (8 deaths yr–1 per million people) 
and North America (13 deaths yr–1 per million people). For PM2.5, 
mortality was projected to increase across all regions except Africa 
(−25,200 deaths yr–1 per million people) by 2100, with estimated 
increases greatest for India (40 deaths yr–1 per million people), the 
Middle East (45 deaths yr–1 per million people), East Asia (43 deaths 
yr–1 per million people) and the Former Soviet Union (57 deaths yr–1 
per million people). Overall, higher ozone-related health burdens 
were projected to occur in highly populated regions, and greater 
PM2.5 health burdens were projected in high PM emission regions 
(Doherty et al., 2017).

For central and southern Europe, climate change alone could result 
in an 11% increase in ozone-associated mortality by 2050. However, 
projected declines in ozone precursor emissions could reduce the 
EU-wide climate change effect on ozone-related mortality by up to 
30%; the reduction was projected to be approximately 24% if aging 
and an increasingly susceptible population were accounted for in 
projections to 2050 (Orru et al., 2019). For the USA in 2069, the impact 
of climate change alone on annual PM2.5- and ozone-related deaths 
was estimated to be 13,000 and 3,000 deaths, respectively, with heat-
driven adaptation of air conditioning accounting for 645 and 315 
of the PM2.5- and ozone-related annual excess deaths, respectively 
(Abel et al., 2018). An aging population is a determinant of future air quality-related mortality levels. An aging population along with an 
increase in the number of vulnerable people may work to offset the

decrease in deaths associated with a low emission pathway (RCP4.5) 
and possibly dominate the net increase in deaths under a business as 
usual pathway (RCP8.5) (Chen et al., 2020; Doherty et al., 2017; Hong 
et al., 2019; Schucht et al., 2015).
Complementing the longer-term changes in air quality arising from 
climate change are those associated with air pollution sensitive short-
term meteorological events, such as heatwaves. Studies of individual 
heat events (Garrido-Perez et al., 2019; Johansson et al., 2020; Kalisa 
et al., 2018; Pu et al., 2017; Pyrgou et al., 2018; Schnell and Prather, 
2017; Varotsos et al., 2019) and systematic reviews (Anenberg et al., 
2020) provide evidence for synergistic effects of heat and air pollution. 
However, the health consequences of a possible additive effect of air 
pollutants during heatwave events were heterogeneous, varying by 
location and moderated by socioeconomic factors at the intra-urban

scale (Analitis et al., 2014; Fenech et al., 2019; Krug et al., 2020; Pascal 
et al., 2021; Schwarz et al., 2021; Scortichini et al., 2018). This, combined 
with the challenges associated with projecting future concentrations 
of health-relevant pollutants during heatwave events (Jahn and Hertig, 
2021; Meehl et al., 2018), makes it difficult to say with any certainty 
that synergistic effects of heat and poor air quality will result in a 
heatwave–air pollution health penalty under climate change.
The burden of disease associated with aeroallergens is anticipated to 
grow due to climate change (high confidence). The incidence of pollen 
allergy and associated allergic disease increases with pollen exposure, 
and the timing of the pollen season and pollen concentrations are 
expected to change under climate change (Beggs, 2021; Ziska et al., 
2019; Ziska, 2020). The overall length of the pollen season and total 
seasonal pollen counts/concentrations for allergenic species such as

birch (Betula) and ragweed (Ambrosia) are expected to increase as a 
result of CO 2 fertilisation and warming, leading to greater sensitisation 
(Hamaoui-Laguel et al., 2015; Lake et al., 2017; Zhang et al., 2013). 
Changes in pollen levels for several species of trees and grasses are 
projected to increase annual emergency department visits in the USA 
by between 8% for RCP4.5 and 14% for RCP8.5 by the year 2090 
(Neumann et  al., 2019) with the exposure to some pollen types 
estimated to double beyond present levels in Europe by 2041–2060 
(Lake et al., 2017). The prospect of increases in summer thunderstorm 
events under climate change (Brooks, 2013) may hold implications 
for changes in the occurrence of epidemic thunderstorm asthma 
(Bannister et  al., 2021; Emmerson et  al., 2021; Price et  al., 2021). 
Similarly, projected alterations in hydroclimate under climate change 
may bear implications for increased exposure to mould allergens in

some climates (D’Amato et al., 2020; Paudel et al., 2021).
7.3.1.7 Future Risks Related to Cardiovascular Diseases
Climate change is expected to increase heat-related CVD mortality 
by the end of the 21st century, particularly under higher emission 
scenarios (high confidence) . Most modelling studies conducted since 
AR5 project higher rates of heat-related CVD mortality throughout 
the remainder of this century (Huang and et al., 2018; Li et al., 2015; 
Li et  al., 2018; Limaye et  al., 2018; Zhang et  al., 2018a; Silveira 
et  al., 2021a; Yang et  al., 2021). CVD mortality in Beijing, China,

7
1097Health, Wellbeing and the Changing Structure of Communities  Chapter 7
could increase by an average of 18.4%, 47.8% and 69.0% in the 
2020s, 2050s and 2080s, respectively, under RCP4.5 and by 16.6%, 
73.8% and 134%, respectively, under RCP8.5 relative to a 1980s 
baseline (Li et al., 2015). Projections of temperature-related mortality 
from CVD for Beijing in the 2080s vary depending on RCP and 
population assumptions (Zhang et al., 2018a). Projections for Ningo, 
China, suggest heat-related years of life lost (YLL) could increase 
significantly in the month of August by between 3 and 11.5 times 
over current baselines by the 2070s, even with adaptation (Huang 
and et  al., 2018). Yang and colleagues project that heat-related 
excess CVD mortality in China could increase to approximately 6% 
(from a 2010 baseline of under 2%) by the end of the century under 
RCP8.5 and to over 3% under RCP4.5 (Yang et al., 2021). The future

burden of temperature-related myocardial infarctions in Germany is 
projected to rise under high emissions scenarios (Chen et al., 2019), 
while in the eastern USA, Limaye et al. (2018) projected an additional 
11,562 annual deaths (95% CI: 2,641–20,095) by mid-century due to 
cardiovascular stress in the population 65 years of age and above. 
CVD mortality in Brazil is projected to increase up to 8.6% by the end 
of the century under RCP8.5, compared with an increase of 0.7% for 
RCP4.5 (Silveira et al., 2021a).
It is important to note that the assessed studies typically take an 
observed epidemiological relationship and apply future temperature 
projections (often derived from regional climate projections) to 
these relationships. Because the relationships between temperature 
and CVD deaths are influenced by both climatic and non-climatic 
factors (such as population fitness and aging), future projections are 
highly sensitive to assumptions about interactions between climate,

population characteristics and adaptation pathways. Changes in air 
quality because of climate change are an additional important factor. 
For example, an assessment of future annual and seasonal excess 
mortality from short-term exposure to higher levels of ambient 
ozone in Chinese cities under RCP8.5 projected approximately 1,500 
excess annual CVD deaths in 2050 (Chen et al., 2018). To the extent 
possible, the relationships reported above reflect changes derived from 
changes in heat exposure driven by climate change and not changes in 
population demographics or air pollution exposure.
Climate change could impact CVD through other pathways, including 
exposure to fine dust. For example, adult mortality attributable to fine 
dust exposure in the American southwest could increase by 750 deaths 
yr–1 (a 130% increase over baseline) by the end of the century under 
RCP8.5 (Achakulwisut et al., 2018).
7.3.1.8 Future Risks Related to Maternal, Foetal and Neonatal 
Health

Additional research is needed on future impacts of climate change 
on maternal, foetal and neonatal health. Maternal heat exposure is a 
risk factor for several adverse maternal, foetal and neonatal outcomes 
(Kuehn and McCormick, 2017), including foetal growth (Sun et  al., 
2019) and congenital anomalies (Haghighi et  al., 2021). There is 
very limited research on this subject, an exception being Zhang et al. 
(2020), which projected a 34% increase in congenital health disease 
risk in the USA in 2025 and 2035 based on increased maternal extreme 
heat exposure.7.3.1.9 Future Health Risks Related to Food, Diets and Nutrition
7.3.1.9.1 Malnutrition
Climate change is projected to exacerbate malnutrition (high 
confidence). Moderate and severe stunting in children less than five 
years of age was projected for 2030 across 44  countries to be an 
additional 570,000 cases under a prosperity and low climate change 
scenario (RCP2.6) to one million cases under a poverty and high climate

change scenario (RCP8.5), with the highest effects in rural areas 
(Lloyd, 2018). Future DALYs lost due to protein-energy undernutrition 
and micronutrient deficiencies without climate change have been 
projected to increase between 2010 and 2050 by over 30 million; with 
climate change (RCP8.5), DALYs were projected to increase by nearly 
10%, with the largest increases in Africa and Asia (Sulser et al., 2021).
The projected risks of hunger and childhood underweight vary under 
the five SSPs, with population growth, improvement in the equality of 
food distribution and income-related increases in food consumption 
influencing future risks (Ishida et al., 2014; Hasegawa et al., 2015). A 
review of 57 studies projecting global food security to 2050 under the 
SSPs concluded that global food demand was expected to increase by 
35–56% between 2010 and 2050 (van Dijk et al., 2021). In the same 
review, estimates of the change in population at risk of hunger by

2050 range between −91 to +8% if climate change is not considered 
and between -91 to +30% if climate change is considered, with the 
inclusion of climate change not leading to statistically significant 
differences in projections (van Dijk et al., 2021).
7.3.1.9.2 Climate Change, Carbon Dioxide, Diets and Health
Climate change could further limit equitable access to affordable, 
culturally acceptable, and healthy diets (high confidence). Climate 
impacts on agricultural production and regional food availability will 
affect the composition of diets, which can have major consequences for 
health. Variable by region and context, healthy diets are an outcome 
of the four inter-connected domains of sustainable food systems, 
namely ecosystems, society, economics and health (Drewnowski et al., 
2020; Fanzo et  al., 2020). Climate change limits the potential for 
healthy diets through adverse impacts on natural and human systems

that are disproportionately experienced by low-income countries 
and communities (FAO et al., 2021). Climate-driven droughts, floods, 
storms, wildfires and extreme temperatures reduce food production 
potential by diminishing soil health, water security and biological and 
genetic diversity (Macdiarmid and Whybrow, 2019). Models project 
that climate-related reductions in food availability, specifically fruit 
and vegetables, could result in an additional 529,000 deaths a year by 
2050 (Springmann et al., 2016b).
Diets reliant on marine fisheries and fish also face complex climate-
driven challenges (Hollowed et  al., 2013). Rapidly warming oceans 
(Cheng et al., 2020) limit the size of many fish and hamper their ability 
to relocate or adapt; many commonly consumed fish, like sardines, 
pilchards and herring, could face extinction due to these pressures 
(Avaria-Llautureo et  al., 2021). Other fisheries models project end-

of-century pollock and Pacific cod fisheries decreasing by > 70% and 
> 35% under RCP8.5 (Holsman et al., 2020). Climate-driven increases

7
1098Chapter 7 Health, Wellbeing and the Changing Structure of Communities
in marine mercury concentrations (Booth and Zeller, 2005) and harmful 
algal blooms (Jardine et  al., 2020) could impact dietary quality and 
human health.
Global crop and economic models project higher cereal prices of up 
to 29% by 2050 under RCP6.0, resulting in an additional 183 million 
people in low-income households at risk of hunger (Hasegawa et al., 
2018). Climate impacts on human health disrupt agricultural labour, 
food supply chain workers and ultimately regional food availability 
and affordability. A recent meta-analysis focused on sub-Saharan 
Africa and Southeast Asia combined metrics of heat stress and labour 
to project that a 3°C increase in global mean temperature, without 
adaptation or mechanisation, could reduce agricultural labour capacity 
by 30–50%, leading to 5% higher crop prices and a global welfare loss 
of USD 136 billion (de Lima et al., 2021).

The nutritional density, including protein content, micronutrients and 
B-vitamins, of wheat, rice, barley and other important food crops is 
negatively affected by higher CO 2 concentrations (very high confidence) 
(Mbow, 2019 ; Smith and Myers, 2018). Projections indicate negative 
impacts on human nutrition by rising CO 2 concentrations by mid- to 
late-century (Medek et al., 2017; Smith and Myers, 2018; Weyant et al., 
2018; Zhu et al., 2018; Beach et al., 2019). Staple crops are projected to 
have protein and mineral concentrations decreased by 5–15% and B 
vitamins up to 30% when the concentrations of CO 2 double above pre-
industrial levels (Ebi and Loladze, 2019; Beach et al., 2019; Smith and 
Myers, 2018). Without changes in diets and accounting for nutrient 
declines in staple crops, a projected additional 175  million people 
could be zinc deficient and an additional 122  million people could 
become protein deficient (Smith and Myers, 2018). Weyant et al. (2018)

projected that CO 2-related reductions in crop zinc and iron levels 
could result in 125.8 million DALYs lost globally, with Southeast Asian 
and sub-Saharan African countries most affected. Zhu et  al. (2018) 
estimated 600 million people at risk from reductions in the protein, 
micronutrient and B-vitamin content of widely grown rice cultivars in 
Southeast Asia.
The combined effect of CO 2 and rising temperatures because of climate 
change could result in a 2.4–4.3% penalty on expected gains by mid-
century in nutritional content because of technology change, market 
responses and the fertilisation effects of CO 2 on yield (Beach et al., 
2019). These penalties are expected to slow progress in achieving 
reductions in global nutrient deficiencies, disproportionately affecting 
countries with high levels of such deficiencies.
7.3.1.10 Projected Impacts on Harmful Algal Blooms, 
Mycotoxins, Aflatoxins and Chemical Contaminants

Harmful algal blooms are projected to increase globally, thus 
increasing the risk of seafood contamination with marine toxins (high 
confidence) (European Food Safety Authority et al., 2020; Gobler et al., 
2017; Barange et al., 2018; IPCC, 2019b; Wells et al., 2020). Climate 
change impacts on oceans could generate increased risks of ciguatera 
poisoning in some regions (medium confidence). Studies suggest 
that rising sea surface temperatures could increase rates of ciguatera 
poisoning in Spain (Botana, 2016) and other parts of Europe (European 
Food Safety Authority et al., 2020).Mycotoxins and aflatoxins may become more prevalent due to climate 
change (medium agreement, low evidence). Models of aflatoxin 
occurrence in maize under climate change scenarios of +2°C and 
+5°C in Europe over the next 100  years project that aflatoxin B1 
may become a major food safety issue in maize, especially in Eastern 
Europe, the Balkan Peninsula and the Mediterranean regions (Battilani,

2016). The occurrence of toxin-producing fungal phytopathogens has 
the potential to increase and expand from tropical and subtropical 
regions into regions where such contamination does not currently 
occur (Battilani, 2016).
Climate change may alter regional and local exposures to anthropogenic 
chemical contaminants (medium agreement, low evidence) . Changes 
in future occurrences of wildfires could lead to a 14% increase in 
global emissions of mercury by 2050, depending on the scenarios used 
(Kumar et al., 2018a). Mercury exposure via consumption of fish may 
be affected by warming waters. Warming trends in the Gulf of Maine 
could increase the methyl mercury levels in resident tuna by 30% 
between 2015 and 2030 (Schartup et al., 2019). An observed annual 
3.5% increase in mercury levels was attributed to fish having higher 
metabolism in warmer waters, leading them to consume more prey. 
The combined impacts of climate change and the presence of arsenic in

paddy fields are projected to potentially double the toxic heavy metal 
content of rice in some regions, potentially leading to a 39% reduction 
in overall production by 2100 under some models (Muehe et al., 2019).
7.3.1.11 Future Risks Related to Mental Health and Well-Being
Climate change is expected to have adverse impacts on well-being, 
some of which will become serious enough to threaten mental health 
(very high confidence). However, changes (Hayes and Poland, 2018) in 
extreme events due to climate change, including floods (Baryshnikova, 
2019), droughts (Carleton, 2017) and hurricanes (Kessler et al., 2008; 
Boscarino et al., 2013, Boscarino et al., 2017; Obradovich et al., 2018), 
which are projected to increase due to climate change, directly worsen 
mental health and well-being and increase anxiety (high confidence). 
Projections suggest that sub-Saharan African children and adolescents, 
particularly girls, are extremely vulnerable to negative direct and

indirect impacts on their mental health and well-being (Atkinson and 
Bruce, 2015; Owen et al., 2016). The direct risks are greatest for people 
with existing mental disorders, physical injuries, and compromised 
respiratory, cardiovascular and reproductive systems, with indirect 
impacts potentially arising from displacement, migration, famine and 
malnutrition, degradation or destruction of health and social care 
systems, conflict, and climate-related economic and social losses (high 
to very high confidence) (Burke et al., 2018; Curtis et al., 2017; Hayes 
et al., 2018; Serdeczny et al., 2017; Watts et al., 2019). Demographic 
factors increasing vulnerability include age, gender and low 
socioeconomic status, though the effect of these will vary depending 
on the specific manifestation of climate change; overall, climate change 
is predicted to increase inequality in mental health across the globe 
(Cianconi et  al., 2020). Based on evidence assessed in Section  7.2,

future direct impacts of increased heat risks and associated illnesses 
can be expected to have negative implications for mental health and 
well-being, with outcomes being highly mediated by adaptation, but 
there are no assessable studies that quantify such risks. There may 
be some benefits to mental health and well-being associated with

7
1099Health, Wellbeing and the Changing Structure of Communities  Chapter 7
fewer very cold days in the winter; however, research is inconsistent. 
Any positive effect associated with reduced low-temperature days is 
projected to be outweighed by the negative effects of increased high 
temperatures (Cianconi et al., 2020).
Human behaviours and systems will be disrupted by climate change 
in a myriad of ways, and the potential consequences for mental 
health and well-being are correspondingly large in number and 
complex in mechanism ( high confidence) . For example, climate 
change may alter human physical activity and mobility patterns, in 
turn producing alterations in the mental health statuses promoted by 
regular physical activity (Obradovich and Fowler, 2017; Obradovich 
and Rahwan, 2019). Climate change may affect labour capacity, 
because heat can compromise the ability to engage in manual labour 
as well as cognitive functioning, with impacts on the economic

status of individual households as well as societies (Kjellstrom 
et  al., 2016; Liu, 2020). Migrations and displacement caused by 
climate change may worsen the well-being of those affected (Vins 
et  al., 2015; Missirian and Schlenker, 2017). Climate change is 
expected to increase aggression through both direct and indirect 
mechanisms, with one study predicting a 6% increase in homicides 
globally for a 1°C temperature increase, although noting significant 
variability across countries (Mares and Moffett, 2016). Broad 
societal outcomes such as economic unrest, political conflict or 
governmental dysfunction assessed in Section 7.3.5 may undermine 
the mental health of populations in the future (medium confidence). 
Food insecurity presents its own severe risks for mental health and 
cognitive function (Jones, 2017).
7.3.2 Migration and Displacement in a Changing Climate
Future changes in climate-related migration and displacement are

expected to vary by region and over time according to: (a) region-specific 
changes in climatic drivers, (b) changes in the future adaptive capacity 
of exposed populations, (c) population growth in areas most exposed 
to climatic risks and (d) future changes in mediating factors such as 
international development and migration policies (high agreement, 
medium evidence) (Gemenne and Blocher, 2017; Cattaneo et al., 2019; 
McLeman, 2019). This section assesses future risks associated with 
changes in the frequency and/or severity of storms, floods, droughts, 
extreme heat, wildfires and other events assessed in Section 7.2 that 
currently affect migration and displacement patterns, as well as the 
impacts of emerging hazards, including average temperature increases 
that may affect the habitability of settlements in arid regions and the 
tropics, and sea level rise and associated hazards that threaten low-
lying coastal settlements. Studies assessed here consider projected

changes in future exposure to hazards over a variety of geographical 
and temporal periods, with some considering changes in population 
numbers in exposed areas. The uneven distribution of exposure of age 
cohorts is typically overlooked in existing research. For example, people 
younger than age 10 in the year 2020 are projected to experience a 
nearly four-fold increase in extreme events under 1.5°C of global 
warming and a five-fold increase under 3°C warming; such increases 
in exposure would not be experienced by a person of the age of 55 in 
2020 in their remaining lifetime under any warming scenario (Thiery 
et al., 2021).7.3.2.1 Region-Specific Changes in Climatic Risks
As outlined in 7.2, the most common drivers of observed climate-
related migration and displacement are extreme storms (particularly 
tropical cyclones), floods and droughts (high confidence). The future 
frequency and/or severity of such events due to anthropogenic climate

change are expected to vary by region according to future GHG 
emission pathways (Naik et al 2021; Regional Chapters, this report), 
with there being an increased potential for compound effects of 
successive or multiple hazards (e.g., tropical storms accompanied by 
extreme heat events (Matthews et al., 2019)). Table 7.2 summarises 
anticipated changes in future migration and displacement risks due to 
sudden-onset climate events by region (and by sub-regions for Africa 
and Asia, where climatic risks vary within the region).
In low-lying coastal areas of most regions, future increases in mean 
sea levels will amplify the impacts of coastal hazards on settlements, 
including erosion, inland penetration of storm surges and groundwater 
contamination by salt water, and eventually lead to inundation of very 
low-lying coastal settlements (high confidence) (Diaz, 2016; Hauer 
et al., 2016; Neumann et al., 2015; Rahman et al., 2019; IPCC, 2019a).

Projections of the number of people at risk of future displacement by 
sea level rise range from tens of millions to hundreds of millions by the 
end of this century, depending on (a) the sea level rise scenario or RCP 
selected, (b) projections of future population growth in exposed areas 
and (c) the criteria used for identifying exposure. These latter measures 
can include estimates of populations situated within selected elevations 
above sea level (with 1 m, 2 m and 10 m being common parameters), 
populations situated in 1-in-100  year floodplains or populations in 
areas likely to be entirely inundated under specific RCPs (Neumann 
et al., 2015; Hauer et al., 2016; Merkens et al., 2018; McMichael et al., 
2020; Hooijer and Vernimmen, 2021). As an illustrative example, an 
estimated 267 million people (error range = 197–347 million at 68% 
confidence level) worldwide lived within 2 m of sea level in 2020, 59% 
of whom reside in tropical regions of Asia (Hooijer and Vernimmen,

2021). At a 1 m increase in sea level and holding coastal population 
numbers constant, the number of people worldwide living within 2 m 
of sea level expands to 410 million (error range = 341–473 million). 
However, it is unlikely that coastal population growth rates will remain 
constant at global or regional scales in future decades. At present, 
coastal cities in many regions have relatively high rates of population 
growth due to the combined effects of in-migration from other regions 
and natural increase, with coastal areas of Africa having the highest 
projected future population growth rates (Neumann et  al., 2015; 
Hooijer and Vernimmen, 2021; Box 7.5). Further complicating future 
estimates is that many large coastal cities are situated in deltas with 
high rates of subsidence, meaning that locally experienced changes in 
relative sea level may be much greater than sea level rise attributable 
to climate change, thereby further increasing the number of people

exposed (Edmonds et al., 2020; Nicholls et al., 2021).
Sea level rise is not presently a significant driver of migration in 
comparison with hazards assessed in Section 7.2.6, but it has been 
attributed as a factor necessitating the near-term resettlement of small 
coastal settlements in Alaska, Louisiana, Fiji, Tuvalu and the Carteret 
Islands of Papua New Guinea (Marino and Lazrus, 2015; Connell, 2016; 
Hamilton et al., 2016; Nichols, 2019). In coastal Louisiana, communities

7
1100Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Table 7.2 |  Projected changes in sudden-onset climate events associated with migration and displacement by region.
RegionMain directions of current migration 
flows (from Abel and Sander (2014))Current climatic drivers of 
migration and displacement 
(Section 7.2.6.1)Expected changes in drivers (including confidence state-
ments) from IPCC WGI 2021 Technical Summary, 4.3.1–4.3.2
Asia –East and Southeast Asia: within countries and 
between countries within same region.
 –South and central Asia: within countries and 
between countries within same region; from 
south Asia to Middle East, North America, 
Europe.
 –West Asia: within countries and between 
countries within the same region; to EuropeFloods, extreme storms, extreme heatIncreased risk of flooding in East, north, south and Southeast Asia due 
to increases in annual mean precipitation (high confidence) and extreme

precipitation events in East, south, west central, north and Southeast Asia 
(medium confidence); uncertainty regarding future trends in cyclones 
(current trend = decreased frequency, increased intensity); higher average 
temperatures across region (high confidence)
Africa –Within countries and between countries 
within the same region; to Europe and the 
Middle EastFloods, droughts, extreme heatDecrease in total annual precipitation in northernmost and 
southernmost parts of Africa (high confidence); west-to-east pattern of 
decreasing-to-increasing annual precipitation in west Africa and east 
Africa (medium confidence); increased risk of heavy precipitation events 
that trigger flooding, across most parts of Africa (medium confidence); 
increased aridity and drought risks in north Africa, southern Africa and 
western parts of west Africa (medium-high confidence)
Europe –Within countries and between countries in

same regionFloodsIncreased risk of floods across all areas of Europe except Mediterranean 
areas (high confidence); higher risks of drought and fire weather in 
Mediterranean areas (high confidence)
North America –Within countries and between countries in 
same regionFloods, tropical cyclones (US Atlantic 
and Caribbean coast), tornadoes, 
wildfiresIncreased frequency of heavy precipitation events across most areas 
(high confidence); tropical cyclones to become more severe (medium 
confidence); increased risk of drought and fire weather in central and 
western North America
South America –Within countries and between countries in 
same region; to North America, EuropeFloods (Central and South America), 
extreme storms (Central America)Increases in mean annual precipitation and extreme precipitation events 
with higher risks of floods in most areas of South America (medium 
confidence); increased risk of droughts in northeastern and southern

South America and northern Central America (medium confidence); 
tropical cyclones becoming more extreme (medium confidence)
Australasia  –Displacement within countries WildfiresIncreases in fire weather across Australia and New Zealand (medium 
Small island 
states –Within and between countries in same region 
(e.g., Pacific Islands to Australia and New 
Zealand; Caribbean islands to USA)Extreme storms Potentially fewer but more extreme tropical cyclones (medium confidence)
tend to resist leaving exposed settlements until approximately 50% of 
available land has been lost (Hauer et al., 2019). Movements away from 
highly exposed areas may have longer-term demographic implications 
for inland settlements (Hauer, 2017), but this requires further study. 
Based on the available empirical evidence, sea level rise does not 
appear to currently be a primary motivation for international migration 
originating in small island states in the Indian and Pacific Oceans;

rather, economic considerations and family reunification appear to be 
the dominant drivers (McCubbin et al., 2015; Stojanov and Du, 2016; 
Heslin, 2019; Kelman et al., 2019). However, climatic drivers of migration 
are anticipated to take on a much greater causal role in migration 
decisions in coming decades (Thomas et al., 2020) and may discourage 
return migration to small island states (van der Geest et  al., 2020). 
Even under best-case sustainable development scenarios, rising sea 
levels and associated hazards create risks of involuntary displacement 
in low-lying coastal areas and should be expected to generate a need 
for organised relocation of populations where protective infrastructure 
cannot be constructed (Horton and de Sherbinin, 2021; Hamilton et al., 
2016). In high emissions scenarios, low-lying island states may face 
the long-term risk of becoming uninhabitable, creating the potential 
for a new phenomenon of climate-induced statelessness (Piguet, 2019;

Desai et al., 2021).Increased frequency of extreme heat events and long-term increases 
in average temperatures pose future risks to the habitability of 
settlements in tropical and subtropical regions, and may in the 
long term affect migration patterns in exposed areas, especially 
under high emissions scenarios (medium agreement, low evidence). 
Greater research into the specific dynamics between extreme heat 
and population movements is required in order to make an accurate 
assessment of this risk. Recent studies suggest that future increases 
in average temperatures could expose populations across wide areas 
of the tropics and subtropics to ambient temperatures for extended 
periods each year that are beyond the threshold for human habitability 
(Pal and Eltahir, 2016; Im et al., 2017; Xu et al., 2020). This effect would 
be amplified in urban settings where heat-island effects occur and 
create a heightened need for air conditioning and other adaptation

measures. In addition to risks associated with average temperature 
changes, Dosio et al. (2018) project that at 1.5°C warming, between 
9% and 18% of the global population will be regularly exposed to 
extreme heat events at least once in five years, with the exposure rate 
nearly tripling with 2°C warming. How these changes in exposure to 
high temperatures will affect future migration patterns, particularly 
among vulnerable groups, will depend heavily on future adaptation 
responses (Horton and de Sherbinin, 2021). Multiple country-level

7
1101Health, Wellbeing and the Changing Structure of Communities  Chapter 7
studies assessed in Section 7.2 observe existing associations between 
extreme heat, its impacts on agricultural livelihoods and changes in 
rural-to-urban migration flows in parts of south Asia and sub-Saharan 
Africa. A study conducted in Indonesia, Malaysia and the Philippines 
suggests that an increased risk of heat stress would likely influence 
migration intentions of significant numbers of people (Zander et al., 
2019).
7.3.2.2 Interactions with Non-climatic Determinants and 
Projections of Future Migration Flows
Only a very small number of studies have attempted to make systematic 
projections of future regional or global migration and displacement 
numbers under climate change. Key methodological challenges for 
making such projections include the availability of reliable data on 
migration within and between countries, definitional ambiguity in

distinguishing climate-related migration from migration undertaken for 
other reasons, and accounting for the future influence of non-climatic 
factors. The most reliable example of such studies to date is a World 
Bank report by Rigaud et al. (2018) that generated projections of future 
internal population displacements in south Asia, sub-Saharan Africa 
and Latin America by 2050 using multiple climate and development 
scenarios, resulting in a very large range of possible outcomes (from 
31 to 143 million people being displaced, depending on assumptions). 
An important outcome is the study’s emphasis on how the potential 
for future migration and displacement will be strongly mediated by 
socioeconomic development pathways in low- and middle-income countries. Hoffmann et al. (2020) used meta-regression-based analyses 
to project that future environmental influences on migration are likely 
to be greatest in low- and middle-income countries in Latin America

and the Caribbean, sub-Saharan Africa, the Middle East and most of 
continental Asia.
Research reviewed in AR4 and AR5 observed that at higher rates 
of socioeconomic development, the in situ adaptive capacity of 
households and institutions rises, and climatic influences on migration 
correspondingly decline. Recent evidence adds further support for such 
conclusions (high confidence) (Kumar et al., 2018b; Mallick, 2019; Gray 
et al., 2020; Box 7.5). Population growth rates are currently highest 
in low-income countries (UN DESA Population Division, 2019), many 
of which have high rates of exposure to climatic hazards associated 
with population displacement, further emphasising the importance of 
socioeconomic development and adaptive capacity-building. Although 
country-specific scenarios for socioeconomic development and 
population are embedded in SSPs, research into future migration flows 
under climate change has not made great use of these. One of the few

studies to do so found that safe and orderly international migration 
tends to increase wealth at regional and global scales in all SSP 
narratives, which in turn reduces income inequality between countries 
(Benveniste et  al., 2021). International barriers to safe and orderly 
migration may potentially impede progress towards attainment of the 
objectives described in the SDGs and increase exposure to climatic 
hazards in low- and middle-income countries (McLeman, 2019; 
Benveniste et al., 2020).
Box 7.5 | Uncertainties in projections of future demographic patterns at global, regional and 
national scales
Projections of future numbers of people exposed to climate change-related hazards described in this chapter and elsewhere in this 
report are heavily influenced by assumptions about population change over time at global, regional and national scales. One challenge

concerns global and regional variability of baseline data for current populations, which is typically aggregated from national censuses 
that vary considerably in terms of frequency, timing and reliability, especially in low-income countries. A number of gridded mapping 
dataset initiatives emerged in recent years to support population–environment modelling research at global and regional levels, common 
ones being the Gridded Population of the World, the Global Rural Urban Mapping Project, and LandScan Global Population dataset 
(McMichael et al., 2020). For future population projections at national levels, researchers commonly draw upon data generated by the 
Population Division of the United Nations Department of Economic and Social Affairs, which publishes periodic projections for future 
fertility, mortality, and international migration rates for over 200 countries, the most recent projections being for the 2020 to 2100

period (UN DESA Population Division, 2019). There have been debates among demographers regarding the precision of DESA projections, 
and whether these overestimate or underestimate future population growth in some regions (Ezeh et al., 2020). Population growth 
rates are highly influenced by socioeconomic conditions, meaning that future population levels at local, national and regional scales 
are likely to respond to relative rates of progress towards meeting the Sustainable Development Goals (Abel et al., 2016). The Shared 
Socioeconomic Pathways (SSPs) used in climate impacts and adaptation research include a variety of assumptions about future mortality, 
fertility and migration rates and provide a range of population growth scenarios that diverge after the year 2030 according to future 
development trajectories (Samir and Lutz, 2017) and are then further modified and downscaled by researchers for national-level studies.

Understanding future risks of climate change will benefit from continued efforts by the international community to collect and share 
data on observed population numbers and trends, and to work towards better projected data for population characteristics that strongly 
influence vulnerability to climate risks, such as gender, age and indigeneity.

7
1102Chapter 7 Health, Wellbeing and the Changing Structure of Communities
7.3.3 Climate Change and Future Risks of Conflict
Climate change may increase susceptibility to violent conflict, primarily 
intra-state conflicts, by strengthening climate-sensitive drivers of 
conflict (medium confidence). Section  7.2.7 described how climate 
variability and extremes affect violent conflict through food and water 
insecurity, loss of income and loss of livelihoods. Risks are amplified 
by insecure land tenure, competing land uses and weather-sensitive 
economic activities when they occur in the context of weak institutions 
and poor governance, poverty and inequality (Section  7.2.7). These 
known, climate-sensitive risk factors allow projections of where 
conflict is more likely to arise or worsen under climate change impacts 
(see Chapters 1, 4, 5, 6 and 16) (Mach et al., 2020). However, there 
is also the potential for new causal pathways to emerge as climate

changes beyond the variability observed in available datasets and 
adaptation limits are met (Theisen, 2017; Mach et  al., 2019; von 
Uexkull and Buhaug, 2021).
Future violent conflict risk is highly mediated by socioeconomic 
development trajectories (high confidence). Development trajectories 
that prioritise economic growth, political rights and sustainability are 
associated with lower conflict risk (medium confidence, low evidence). 
Hegre et al. (2016) forecast future conflict under the SSPs and found 
that SSP1, which prioritises sustainable development, is associated 
with lower risks of conflict. Using data from sub-Saharan Africa, 
Witmer et  al. (2017) forecast conflict along the SSPs and find that 
any increases in conflict that may be associated with climate change 
could be offset by increases in political rights. Strong predictors of 
future conflict are a recent history of conflict, large populations and

low levels of socioeconomic development (Hegre and Sambanis, 2006; 
Blattman and Miguel, 2010).
Increases in conflict-related deaths with climate change have been 
estimated but results are inconclusive (high agreement, medium 
evidence). Some studies attempted to attribute observed conflict 
outbreaks to changes in the physical environment and quantify future 
conflict risk associated with climate change (von Uexkull and Buhaug, 
2021; Theisen, 2017). Burke et al. (2015b) concluded that with each 
one standard deviation increase in temperature, inter-personal conflict 
increased by 2.4% and inter-group conflict by 11.3%. However, the 
statistical methods have been criticised for under-representing the 
known role that socioeconomic conditions and conflict history play 
in determining the prevalence of violence (Buhaug et al., 2014; van 
Weezel, 2019; Abel et  al., 2019). Forecasting armed conflict is used 
as a heuristic policy tool rather than a representation of the future

(Cederman and Weidmann, 2017) and forecasts have limitations. For 
example, what constitutes and is experienced as hazards and as drivers 
of conflict will shift over time as societies adapt to climate change 
(Roche et al., 2020). The SSPs assume economic convergence between 
countries and do not reflect growth disruptions (e.g., commodity price 
shocks) that are often a key conflict risk factor (Dellink et al., 2017; 
Buhaug and Vestby, 2019; Hegre et al., 2021).
Asia represents a key region where the peace, vulnerability and 
development nexus has been analysed. In central, south and Southeast 
Asia, there are large numbers of people exposed to changing climate 
(Busby et al., 2018; Vinke et al., 2017; Reyer et al., 2017). South Asia is one of the less peaceful regions in the world due to intra-state 
communal conflict, international military conflict and political tension 
(Wischnath and Buhaug, 2014; Huda, 2021), and many of the factors

that drive conflict risk (e.g. large populations with high levels of 
inequality) are present (Nordqvist and Krampe, 2018). Despite these 
risks, studies in this region also support the case for environmental 
peacebuilding and resource sharing, as it relates to transboundary 
water sharing (Berndtsson and Tussupova, 2020; Huda and Ali, 2018; 
Section 4.3.6; Section 7.4.5.2).
There is little evidence of weather-related impacts on conflict risk or 
prevalence, but the region is under-studied in general (Wischnath and 
Buhaug, 2014; Nordqvist and Krampe, 2018). Climate stressors may 
have contributed in part to local-level conflicts in Bangladesh and 
Nepal (Sultana et al., 2019) and intensified water use conflict in peri-
urban areas (Roth et al., 2019). In the future there is the potential 
for climate change to stretch the effectiveness of transboundary 
water agreements by raising regional geopolitical tensions (Atef

et  al., 2019; Scott et  al., 2019) or to generate water use conflicts 
between hydropower and irrigation within countries (Jalilov et al., 
2018). Climate change may have an impact on conflict by affecting 
food security (Caruso et  al., 2016; Raghavan et  al., 2019). There 
may be greater military involvement in humanitarian response to 
cyclones, flooding and to other impacts of climate change that might 
contribute to increased instability (Pai, 2008; Busby and Krishnan, 
2017).
7.4 Adaptation to Key Risks and Climate 
With proactive, timely and effective adaptation, many observed and 
projected risks for human health and well-being, health systems 
and those associated with migration and conflict can be reduced 
or potentially avoided (high confidence). Given the key health risks 
identified in this chapter, adaptation that increases resilience and 
sustainability will require moving beyond incremental adaptation to

sustained, adaptive management (Ebi, 2011; Hess et al., 2012) with 
the goal of transformative change for integrated protection of human, 
animal and ecosystem health. This includes differentiating adaptation 
to climate variability from adaptation to climate change (Ebi and Hess, 
2020). Health adaptation efforts are increasingly aiming to transition 
to building climate-resilient and environmentally sustainable health 
systems (WHO, 2015b; WHO, 2020a) and healthcare facilities, 
emphasising service delivery including climate-informed health policies 
and programmes; management of the environmental determinants of 
health; emergency preparedness and management; health information 
systems such as health and climate research, integrated risk monitoring 
and early warning systems; and vulnerability, capacity and adaptation 
assessments (Marinucci et al., 2014; Mousavi et al., 2020; WHO 2015a; 
Centres for Disease Control , 2019; WHO, 2020a).

Migration can contribute to or work against adaptation goals and 
progress, depending on the circumstances under which it occurs. 
Policies that support safe and orderly movements of people, protect 
migrant rights, and facilitate flows of financial and other resources 
between sending and receiving communities are consistent with

7
1103Health, Wellbeing and the Changing Structure of Communities  Chapter 7
adaptive capacity-building and building sustainability and are part of 
CRDPs (section 7.4.4).
Adaptation to prevent climate change from exacerbating conflict risk 
involves meeting development objectives encapsulated in the SDGs. 
Conflict-sensitive adaptation and climate-sensitive peacebuilding 
offer promising avenues to addressing conflict risk, but their efficacy 
is yet to be demonstrated through effective monitoring and evaluation 
(Gilmore et  al., 2018). Associations between environmental factors 
and conflict are weak in comparison to socioeconomic and political 
drivers. Therefore, meeting the SDGs, including Goal 16 on peace, 
justice and strong institutions represent unambiguous pathways to 
reducing conflict risk under climate change (Singh and Chudasama, 
2021). Actively pursuing peace rather than taking conflict for granted 
(Barnett, 2019), improving focus on gender within peacebuilding (Dunn

and Matthew, 2015; UNEP , 2021) and understanding how natural 
resources and their governance interact with peacebuilding (Krampe 
et al., 2021) present key elements of CRDPs for sustainable peace.
As documented across this chapter, there is a large adaptation deficit 
for health and well-being, with climate change causing avoidable 
injuries, illnesses, disabilities, diseases and deaths (high confidence). 
Implementation of health adaptation has been incremental because 
of significant constraints, primarily relating to financial and human 
resources and because of limited research funding on adaptation 
(Berrang-Ford et  al., 2021). Current global investments in health 
adaptation are insufficient to protect the health of populations and 
communities (high confidence) from most climate-sensitive risks, with 
large variability across and within countries and regions (UNEP , 2018). 
Climate change adaptation in health is < 1% of international climate

finance despite health being a priority sector in 54% of NDCs featuring 
adaptation (UNEP , 2018).
As climate change progresses and the likelihood of dangerous risks 
to human health continue to increase, there will be greater pressure 
for more transformational changes to health systems to reduce future 
vulnerabilities and limit further dangerous climate change (Ebi et al., 
2021a). Transformational resilience would need parallel investments 
in social and health protections, including achieving the SDGs, 
coupled with investments in mitigation (Ebi and Hess, 2020). Further, 
investments in mitigating GHG emissions will not only reduce risks 
associated with dangerous climate change but will improve population 
health and well-being through several salutary pathways.
This chapter section identifies and assesses specific elements in 
adapting to the risks identified in 7.2 and 7.3 and the opportunities for 
fostering sustainability and pursuing CRDPs.

7.4.1 Adaptation Solution Space for Health and Well-
Being
The solution space is the space within which opportunities and 
constraints determine why, how, when and who adapts to climate 
change (Chapter 1). There is increased understanding of exposure 
and vulnerabilities to climate variability and change, the capacities to 
manage the health risks, the effectiveness of adaptation (including a growing number of lessons learned and best practices), and the co-
benefits of mitigation policies and technologies (high confidence).
Effectively preparing for and managing the health risks of climate 
change requires considering the multiple interacting sectors that 
affect population health and the effective functioning of health 
systems (high confidence) . Given the wide range of causal pathways 
through which climate change affects environmental and social 
systems resulting in health impacts, a systems-based approach can 
promote identifying, implementing and evaluating solutions that

support population health and health systems in the short and 
longer term ( high agreement, medium evidence) . Such an approach 
provides insights into policies and programmes that promote health 
and well-being via multiple sectors (e.g., water and food safety and 
security) and can ensure that health policies do not have adverse 
consequences in other sectors (WHO, 2015b; Ebi and Otmani del 
Barrio, 2017; Wright et al., 2021).
Figure  7.11 illustrates the context within which risks to health 
outcomes and health systems emerge because of climate change. 
The figure presents the emergence of risk from interactions between 
specific types of climatic hazards, the exposure and vulnerability to 
those hazards, and the responses taken within the health sector. The 
figure also illustrates how health risks are situated within larger 
interactions between the health system and other sectors and 
systems, with underlying enabling conditions making adaptation

and transformation possible. Within this context, response options 
can decrease the impacts of climate change on human health, 
well-being and health systems by (a) reducing exposure to climate-
related hazards; (b) reducing vulnerability to such hazards and 
(c) strengthening health system responses to future risks. Such 
approaches are described as ‘Lateral Public Health’ and emphasise 
the importance of involving community members and stakeholders in 
the planning and coordination of activities (Semenza, 2021; Semenza, 
2011). Lateral public health strives for community engagement (e.g., 
through access to technology in decision-making, such as low-cost 
air sensors for wildfire smoke) in preparedness and response.
Effective health risk management incorporates the magnitude and 
pattern of future climate risks as well as potential changes in factors 
that determine vulnerability and exposure to climate hazards, such as

determinants of healthcare access, demographic shifts, urbanisation 
patterns and changes in ecosystems ( very high confidence) . Climate 
change is associated with shocks and stresses that can affect the capacity 
and resilience of health systems and healthcare facilities (WHO, 2020a). 
Figure 7.12 illustrates some possible extents to which the capacity of 
health systems could be reduced when exposed to a stress or shock, 
and possible pathways forward, from collapse to transformation. The 
subsequent sections of this chapter assess adaptation and mitigation 
options to  facilitate building the resilience of health systems and 
healthcare facilities to recover better than before or to transform.

7
1104Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Health systems capacity and resilience to climate change-related shocks and stresses
Above average
Normal
Below average
Low
Shock
StressTransform
Recover better than before
Recover to pre-event stat e
Recover but worse than befor e
Collapse
TimeClimate change adaptation
Prevention, preparednes sR espons eR ecoveryPerformance dropHealth system capacit y Health system resilience
Figure 7.12 |  Health systems capacity and resilience to climate change-related shocks and stresses. From WHO (2020).Adaptation responses to climatic risks
Adaptation in other sectors
Ch. 8
Ch. 2; CCP 1
Ocean &
coastal systems
Ch. 3
Urban
systems
Ch. 6
Water & food
Ch. 4, 5
Adaptation response sCan reduce vulnerabilit yCan reduce vulnerabilit y
Can reduce exposure
Health sector

Figure 7.11 |  Context within which adaptation responses to climatic risks to health are implemented in the frame of interactions between health and 
multiple other sectors.

7
1105Health, Wellbeing and the Changing Structure of Communities  Chapter 7
7.4.2 Adaptation Strategies, Policies and Interventions
7.4.2.1 Current State of Health Adaptation
Analysis of the NDCs to the Paris Agreement to determine how health 
was incorporated, including impacts, adaptation and co-benefits, 
concluded that most low- and middle-income countries referred to health 
in their NDC (Dasandi et  al., 2021). Figure  7.13 shows the degree of 
health engagement; this engagement is based on indicators measuring 
the specificity and detail of health references within a country’s NDC. 
Many vulnerable countries had high engagement of the health sector 
in the country NDC. However, this analysis did not determine whether 
the ambition expressed was sufficient to address the health adaptation 
needs.
The 2018 WHO Health and Climate Change Survey, a voluntary 
national survey sent to all 194 WHO member states, to which 101

responded, found that national planning on health and climate change 
is advancing, but the comprehensiveness of strategies and plans need 
to be strengthened. Implementing action on key health and climate 
change priorities remains challenging and multi-sectoral collaboration 
on health and climate change policy is evident, with uneven progress 
(Watts et al., 2021). Approximately 50% of respondent countries had 
developed national health and climate strategies, with over two-thirds 
doing so within the preceding five years, and 48 of 101 countries had 
conducted a health vulnerability and adaptation assessment (Watts 
et al., 2019). However, most countries reported only moderate or low 
levels of implementation, with financing cited as the most common 
barrier due to a lack of information on opportunities, in turn linked to a 
lack of connection by health actors to climate change policy processes 
and a lack of capacity to participate in national planning. A review of

public health systems in 34  countries found that only slightly more than half considered climate change impacts and adaptation needs 
(Berry et al., 2018). Because the health risks of climate change often 
vary within a country, sub-national assessments and plans are needed 
to help local authorities protect and promote population health in a 
changing climate (Aracena et al., 2021; Basel et al., 2020; Schramm 
7.4.2.2 Adaptation in Health Policies and Programmes
Health policies were historically not designed or implemented taking 
into consideration the risks of climate change and as currently 
structured are likely insufficient to manage the changing health 
burdens in coming decades (very high confidence). The magnitude 
and pattern of future health burdens attributable to climate change, 
at least until mid-century, will be determined primarily by adaptation 
and development choices. Current and future emissions will play an

increasing role in determining attributable burdens after mid-century. 
systems and integrated vector control programmes for VBDs) 
will increase resilience if implemented to at least keep pace with 
climate change (high confidence). Investments to address the social 
determinants of health can reduce inequities and increase resilience 
(high confidence) (Thornton et al., 2016; Marmot et al., 2020; Wallace 
et al., 2015; Semenza and Paz, 2021).
Peer-reviewed publications of health adaptation to climate change in 
low- and middle-income countries have typically focused on flooding, 
rainfall, drought and extreme heat through improving community 
resilience, DRR and policy, governance and finance (Berrang-Ford 
et al., 2021; Scheelbeek et al., 2021). Health outcomes of successful

Health engagement score in 
Nationally Determined Contributions (NDCs) 
across countries
0 1 2 4Health engagement score
3 5
Figure 7.13 |  Health engagement score in NDCs by country. Figure adapted from Dasandi et al. (2021).

7
1106Chapter 7 Health, Wellbeing and the Changing Structure of Communities
adaptation have included reductions in infectious disease incidence, 
improved access to water and sanitation and improved food security. 
Figure 7.14 shows a Sankey diagram of climate hazards, adaptation 
responses and health outcomes. The figure highlights the range of 
health adaptation responses that are discussed in more detail earlier 
in this chapter and demonstrates the potential health benefit of 
adaptation efforts that affect a broad range of health determinants.
Questions of the feasibility and effectiveness of health adaptation 
options differ from those in other sectors because public health is a 
societal enterprise that cuts across many different spheres of society. 
Consequently, there are dependencies that lie outside the jurisdiction 
of the health sector. All the health risks of a changing climate currently 
cause adverse outcomes, with policies and programmes implemented

in at least some health programmes in some places. Policies and 
programmes are continuously modified to increase effectiveness; 
this will need to accelerate in a changing climate. Improvements are 
needed as more is understood about disease aetiology, changing 
socioeconomic and environmental conditions, obstacles to uptake and 
other factors.
Policies and programmes for climate-sensitive health outcomes are 
only beginning to incorporate the challenges and opportunities of 
climate change, although this is critical for increasing resilience. The fundamentals of many policies and programmes in a changing 
climate will remain the same: implementing infectious disease control 
programmes, preventing heat-related mortality and morbidity and 
reducing the burden of other climate-related health endpoints, but 
activities will need to explicitly account for climate change to continue 
to protect health. Even with such attention to climate change, there

are limits to the feasibility and effectiveness of health adaptation 
options for extreme heat, controlling emerging infectious diseases and 
controlling cascading risk pathways.
As discussed in Sections  1.4.2 and 1.5, an adaptation option is 
feasible when it is capable of being implemented by one or more 
relevant actors. In the health sector, WHO, the United Nations 
Children’s Fund (UNICEF) and other organisations provide technical 
expertise to ministries of health, who then provide national to local 
healthcare and public health services. Generally, the question is 
less of overall feasibility, given the range of potential adaptation 
options that have yet to be fully explored and implemented, but 
more of readiness to buy into the adaptation efforts required 
from health and other sectors. In specific contexts, feasibility also 
depends on governance capacity, financial capacity, public opinion 
and the distribution of political and economic power (Chapter 17).

In other words, adaptation to climate change is broadly feasible 
with adequate investment and engagement, although this has yet pH
Extreme precipitation and flooding
Drought
Heat
Precipitation variability
Sea-level rise
Ocean acidificationOther climate hazardsBehaviour change
(Micro)FinancingGreen infrastructure
and climate-smart agriculture
Information and awarenessPhysical infrastructure
Policy optionsNew technologies
Early warning systems All cause mortalityFood security
WASH
Infectious diseaseMental health
Non-communicable diseaseHealth systemsOther health outcomesClimate hazards Adaptation  responses
Health (pathway)
outcomes
Figure 7.14 |  Sankey diagram of climate hazards, adaptation responses and health outcomes. CSA is climate-smart agriculture. Source: Scheelbeek et al. (2021).

7
1107Health, Wellbeing and the Changing Structure of Communities  Chapter 7
to materialise, and in specific contexts feasibility is contingent and 
time-varying, and needs to be assessed at national to sub-national 
scales. For example, a scoping review in the Pacific region noted the 
following areas where further and significant investment and support 
are needed to increase feasibility of climate and health action: (a) 
health workforce capacity development, (b) enhanced surveillance 
and monitoring systems and (c) research to address priorities and 
their subsequent translation into practice and policy (Bowen et al., 
2021). Vulnerability, adaptation and capacity assessments include 
consideration of the feasibility and effectiveness of priority health 
adaptation options and can help decision makers identify strategies 
for enhancing adaptation feasibility in specific contexts.7.4.2.3 Adaptation Options for Vector-borne, Water-borne and 
Food-Borne Diseases

Integrated vector control approaches are crucial to effectively manage 
the geographic spread, distribution and transmission of VBDs associated 
with climate change ( high confidence) . Some of the projected risks of 
climate change on VBDs can be offset through enhanced commitment 
to existing approaches to integrated case management and integrated 
vector control management (Cissé et al., 2018; Confalonieri et al., 2017; 
Semenza and Paz, 2021). Important components include enhanced 
disease surveillance and early warning and response systems that can 
identify potential outbreaks at sub-seasonal to decadal time scales 
Table 7.3 |  Summary of adaptation options for key risks associated with climate-sensitive vector-, water- and food-borne diseases (VBDs, WBDs, FBDs).
Key 
riskGeographic 
region(s) at 
higher riskConsequence that 
would be consid-
ered severe and to 
whomHazard conditions 
that would con-
tribute to this risk 
being severeExposure condi-
tions that would

contribute to this 
risk being severeVulnerability con-
ditions that would 
risk being severeAdaptation 
options with high 
potential for 
reducing riskSelected key 
references
VBDs GlobalIncrease in the 
incidence of some 
VBDs, such as malaria, 
dengue and other 
mosquito-borne 
diseases, in endemic 
areas and in new 
risk areas (e.g., 
cities, mountains and 
Northern Hemisphere)Increased climatic 
suitability for 
transmission (e.g., 
enhanced vectorial 
capacity through a 
temperature shift)Large increases in 
human exposure to 
vectors driven by 
growth in human and 
vector populations, 
globalisation, 
population mobility 
and urbanisationFew effective vaccines, 
weak health systems, 
ineffective personal 
and household 
protections, 
susceptibility to 
disease, poverty, poor 
hygiene conditions, 
insecticide resistance 
and behavioural 
factorsImproved housing, 
better sanitation 
conditions and 
self-protection 
awareness; 
insecticide-treated

bed nets and indoor 
spraying of insecticide; 
broader access to 
healthcare for the 
most vulnerable; 
establishment of 
disease surveillance 
and early warning 
systems for VBDs; 
cross-border joint 
control of outbreaks; 
effective vector 
control; targeted 
efforts to develop 
vaccinesCissé et al. (2018); 
Semenza (2021); 
Rocklöv and Dubrow. 
(2020)
WBDsMostly 
low- and 
middle-income 
countries 
(Africa and 
Asia); small 
islands; global 
for VibriosIncrease in the 
occurrence and 
intensity of WBDs 
such as Vibrios 
(particularly V. 
cholerae), diarrhoeal 
diseases and other 
waterborne GI 
illnessesSubstantial changes 
in temperature and 
precipitation patterns, 
increased frequency 
and intensity of 
events (e.g., droughts, 
storms and floods), 
ocean warming and 
acidificationLarge increases in 
exposure, particularly 
in flood-prone 
areas with poor 
sanitation and 
favourable ecological 
environments for WBD 
pathogensPoor hygiene 
conditions, lack of

clean drinking water 
and safe food, flood- 
and drought-prone 
areas and vulnerable 
water and sanitation 
systemsImproved WASH 
surveillance systems; 
improved personal 
drinking and eating 
habits; behaviour 
changeBrubacher et al. 
(2020); Ford and 
Hamner (2018); Lake 
(2018); Levy et al. 
(2018); Nichols et al. 
(2018); Rocklöv et al. 
(2021)
FBDs GlobalIncrease in the 
intensity of FBDs 
such as Salmonella 
and Campylobacter, 
including in 
high-income countriesSubstantial changes 
environments for FBD 
conditions; lack 
of clean drinking 
water and safe 
food; flood- and 
drought-prone areas; 
vulnerable water and

sanitation systems, 
food storage systems, 
food processes, food 
preservation and cold 
chain/storageImproved WASH 
change; improved 
food storage, food 
processing, food 
chain/storageBrubacher et al. 
(2021)

7
1108Chapter 7 Health, Wellbeing and the Changing Structure of Communities
(Rocklöv and Dubrow, 2020; Semenza and Zeller, 2014; Table 7.3). In 
many cases, the exposure dynamics of VBDs are strongly influenced by 
socioeconomic dynamics that should be considered when developing 
and deploying adaptation options (UNEP , 2018). This is especially 
the case in low-income countries. For example, insufficient access 
to sanitation and the presence of standing water are important 
determinants of the presence of Aedes aegypti populations and 
the pathogens that cause visceral leishmaniasis (L. donovani and L. 
infantum) in urban and peri-urban areas. Low housing quality and lack 
of refuse management are associated with higher rodent infestation. 
Strategies expected to have important health co-benefits include those 
that support health systems strengthening and ecosystem health, 
improve access to health coverage, increase awareness and education

and address the underlying conditions of uneven development and a 
lack of adequate housing and access to water and sanitation systems 
in areas endemic to mosquito-borne diseases (Semenza and Paz, 2021; 
Cross-Chapter Box ILLNESS in Chapter 2).
Adaptation options for climate-related risks for WBDs and FBDs 
are strongly associated with wider, multi-sectoral initiatives to 
improve sustainable development in low-income communities (high 
confidence). Effective measures include improving access to potable 
water and reducing exposure of water and sanitation systems to 
flooding and extreme weather events (Brubacher et al., 2020; Cisse, 
2019; Table 7.3). This requires focusing on farm-level interventions that 
limit the spread of pathogens into adjacent waterways, preventing 
the ongoing contamination of water and sanitation systems and the 
promotion of food-safe human behaviours (Levy et al., 2018; Nichols 
et  al., 2018). It is also important to implement well-targeted and

integrated WASH interventions, including at schools and ensuring 
proper disposal of excreta and wastewater. Cities can integrate regional 
climate projections into their engineering models to produce lower-
risk source waters and increase the resilience of water and sanitation 
technologies and management systems under a range of climate 
scenarios. Technologies can help abstract source waters from depth, 
introduce or increase secondary booster disinfection, design or modify 
systems to reduce residence times within pipes and/or coat exposed 
pipes (Levy et al., 2018). Other efficient interventions include source 
water protection, promoting water filtration, testing the presence of 
waterborne pathogens in shellfish, imposing trade restrictions where 
necessary and improving hygiene at all levels (Semenza and Paz, 
2021). Needed actions include early warning and response systems, 
strengthening the resilience of communities and health systems and

promoting ecosystem health, water safety plans and sanitation safety 
plans (Brubacher et  al., 2020; Cisse, 2019; Ford and Hamner, 2018; 
Lake and Barker, 2018; Levy et  al., 2018; Nichols et  al., 2018; WHO 
and International Water Association, 2009; WHO, 2016a; WHO, 2018b; 
Semenza, 2021; Rocklöv et al., 2021).
7.4.2.4 Adaptation Options for Heat-Related Morbidity and 
Mortality
Adaptations options for heat refer to strategies implemented at short 
time scales such as air conditioning and HAPs, including heat warning 
systems and longer-term solutions such as urban design and planning 
and NbS (Table 7.4).To date, air conditioning is the main adaptation approach for 
mitigating the health effects of high temperatures, especially in 
relation to cardiorespiratory health (Madureira et al., 2021). However, 
air conditioning may constitute a maladaptation because of its high 
demands on energy and associated heat emissions, especially in high-

density cities (Eriksen et  al., 2021; Magnan et  al., 2016; Schipper, 
2020), and also lead to ‘heat inequities’ as this is not an affordable or 
practical option for many (Jay et al., 2021; Turek-Hankins et al., 2021). 
HAPs link weather forecasts with alert and communication systems 
and response activities, including public cooling centres, enhanced 
heat-related disease surveillance and a range of individual actions 
designed to reduce the health effects of extreme heat events such 
as seeking shade and altering the pattern of work (McGregor et al., 
2015). While well-designed and operationalisable HAPs possess the 
potential to reduce the likelihood of mortality from extreme heat 
events (medium confidence) (Benmarhnia et  al., 2016; Heo et  al., 
2019b; Martinez-Solanas and Basagana, 2019; Martinez et al., 2019; 
De’Donato et al., 2018), full process and outcome-based evaluations 
of HAPs and their constituent components are lacking (Boeckmann

and Rohn, 2014; Chiabai et al., 2018b; Boeckmann and Rohn, 2014; 
Nitschke et al., 2016; Diaz et al., 2019; Benmarhnia et al., 2016; Heo 
et al., 2019a; Heo et al., 2019b; Ragettli and Roosli, 2019). Evaluations 
of heatwave early warning systems as a component within HAPs 
show inconsistent results in terms of their impact on predicting 
mortality rates (Nitschke et al., 2016; Benmarhnia et al., 2016; Heo 
et al., 2019a; Heo et al., 2019b; Ragettli and Roosli, 2019; Martinez 
et  al., 2019; De’Donato et  al., 2018; Weinberger et  al., 2018b), 
indicating climate-based heat warning systems, which use a range of 
heat stress metrics (Schwingshackl et al., 2021), are not sufficient as a 
stand-alone approach to heat risk management (high confidence). To 
support HAP and heat risk-related policy development, identification 
and mapping of heat vulnerability ‘hot spots’ within urban areas have 
been proposed (Chen et al., 2019; Hatvani-Kovacs et al., 2018)

A multi-sectoral approach, including the engagement of a range 
of stakeholders will likely benefit the response to longer-term heat 
risks through the implementation of measures such as climate-
sensitive urban design and planning that mitigates UHI effects ( high 
confidence) (Ebi, 2019; Jay et al., 2021; Alexander et al., 2016; Levy, 
2016; Masson et  al., 2018; McEvoy, 2019; Pisello et  al., 2018). In 
the shorter-term, potentially localised solutions can include awnings, 
louvers, directional reflective materials, altering roof albedo, mist 
sprays, evaporative materials, green roofs and building facades 
and cooling centres (Jay et  al., 2021; Macintyre and Heaviside, 
2019; Spentzou et  al., 2021; Takebayashi, 2018). NbS to reduce 
heat that offer co-benefits for ecological systems include green and 
blue infrastructure (e.g., urban greening/forestry and the creation 
of water bodies) (Koc et  al., 2018; Lai et  al., 2019; Shooshtarian

et  al., 2018; Ulpiani, 2019; Zuvela-Aloise et  al., 2016; Hobbie and 
Grimm, 2020). The implementation of climate-sensitive design and 
planning can be constrained by governance issues (Jim et al., 2018) 
and the benefits are not always evenly distributed among residents. 
Implementation of climate-sensitive design and NbS does, however, 
need to be carried out within the context of wider public health 
planning because water bodies and moist vegetated surfaces provide 
suitable habitats for a range of disease vectors (Nasir et al., 2017; 
Tian et  al., 2016; Trewin et  al., 2020). Solutions recommended for

7
1109Health, Wellbeing and the Changing Structure of Communities  Chapter 7
managing exposure to heat in outdoor workers include improved 
basic protection (including shade and planned rest breaks), heat-
appropriate personal protective equipment, work scheduling for 
cooler times of the day, heat acclimation, improved aerobic fitness, 
access to sufficient cold drinking water and on-site cooling facilities 
and mechanisation of work (Morabito et  al., 2021; Morris et  al., 
2020; Varghese et al., 2020; Williams et al., 2020).
Most adaptation options were developed in high- and middle-income 
countries and typically require significant financial resources for their 
planning and implementation. Studies are needed of the benefits of 
indigenous and non-Western approaches to managing and adapting 
to extreme heat risk. Recently published reviews of approaches 
to heat adaptation outline the nature and limitations of a range of

cooling strategies with optimal solutions for a number of settings 
recommended (Jay et al., 2021; Turek-Hankins et al., 2021).
7.4.2.5 Adaptation Options for Air Pollution-related Health 
Effects
As noted in Section  7.3.1.6, air pollution projections indicate 
ambitious emission reduction scenarios or stabilisation of global 
temperature change at 2°C or below would yield substantial co-benefits for air quality-related health outcomes. Improvements in 
air quality could be achieved by the deliberate adoption of a range 
of adaptation options to complement mitigation measures such 
as decarbonisation (e.g., renewable energy, fuel switching, energy 
efficiency gains and carbon capture storage and utilisation) and 
negative emissions technologies (e.g., bioenergy carbon capture and 
storage, soil carbon sequestration, afforestation and reforestation 
and wetland construction and restoration).
Adaptation options for air pollution include implementing ozone

precursor emission control programmes; developing mass transit/
efficient public transport systems in large cities; encouraging car-
pooling, cycling and walking (active transport); traffic congestion 
charges; low emission zones in cities; integrated urban planning 
implementing NbS such as green infrastructure for pollutant 
interception and removal; managing wildfire risk regionally and 
across jurisdictional boundaries; developing air quality warning 
systems; altering activity on high pollution days; effective air 
pollution risk communication and education; wearing protective 
equipment such as face masks; avoiding solid fuels for cooking and 
indoor heating; ventilating and isolating cooking areas; and using 
portable air cleaners fitted with high-efficiency particulate air filters 
(Abhijith et al., 2017; Carlsten et al., 2020; Cromar et al., 2020; Ding Table 7.4 |  Summary of adaptation options for key health risks associated with heat.
regionConsequence

that would 
be considered 
severe, and to 
whomHazard condi-
tions that would 
contribute to 
this risk being 
severeExposure condi-
severeVulnerability 
conditions that 
would contrib-
ute to this risk 
being severeAdaptation options 
with high potential 
for reducing riskSelected key 
Heat-related 
mortality, 
morbidity and 
mental illness –Global but 
especially where 
extremes 
beyond physical 
and mental 
health and 
thermal comfort 
threshold levels 
are expected to 
increase –Substantial 
heat-related 
mortality and 
morbidity rates, 
urban centres 
(heat island 
effect) and rural 
areas (outside 
workers), 
outdoors in 
general (sports 
and related 
activities) and 
for people 
suffering from 
obesity, weak 
cardiovascular 
capacity /physical 
fitness
 –Increased risk 
of respiratory 
disease and CVD 
mortality
 –Loss of economic 
 –Substantial 
increase in

mental illness 
compared to 
base rate –Substantial 
duration of 
extreme heat 
events, especially 
in cities where 
heat will be 
exacerbated by 
UHI effects
 –Unintended 
increases 
in urban 
from 
anthropogenic 
heat (vehicles, 
air conditioning, 
urban 
metabolism)
 –Increased number 
of days with high 
in non-urban 
settings such as 
agricultural areas –Large increases 
in urban heat and 
population heat 
exposure driven 
by demographic 
change (e.g., 
aging) and 
urbanisation
 –Exposure will 
increase amongst 
agricultural and 
construction 
workers –Mortality/
morbidity: 
Increases in 
the number of 
very young and 
elderly and of 
those with other 
health conditions 
such as lack of 
physical fitness, 
obesity, diabetes 
and associated 
comorbidities; 
lack of 
adaptation 
capacity
 –Mental illness: 
Lack of air 
conditioning; 
lack of access 
to healthcare 
services –Heat warning systems.

–Improved building 
and urban design 
(including green and 
blue infrastructure) 
and passive 
cooling systems, 
acknowledging 
that not all will 
have access to air 
conditioning
 –Broader 
understanding of heat 
hazard and better 
access to public health 
systems for the most 
vulnerable
 –Application where 
possible of renewable 
energy sources
 –Communication 
around drinking 
water; availability 
of clean water via 
simple effective water 
purification systems 
in low water quality 
settings; water spray 
cooling
 –Mental health supportBenmarhnia et al. 
(2016); Chen 
et al. (2019); Jay 
et al. (2021); Heo 
et al. (2019b); 
Martinez-Solanas 
and Basagana 
(2019); Morabito 
et al. (2021); 
Schwingshackl 

7
1110Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Table 7.5 |  Summary of adaptation options for key health risks associated with air pollution.
regionConsequence 
that would 
severe and to 
being severeAdaptation op-
tions with high 
Air 
pollution-related 
health effects –Global, but 
regions with 
existing poor 
air quality, 
particularly in 
relation to PM 
and ozone
 –Greatest climate 
change driven 
ozone-related 
mortality is 
expected for East 
Asia and North 
America
 –For PM the 
highest 
climate and air 
quality-related 
mortalities are 
projected for 
India, the Middle 
East, Former 
Soviet Union and 
East Asia –Substantial 
increase in air

urban centres, 
related to both 
severe pollution 
episodes and 
longer-term 
deterioration of 
air quality
 –People 
include those 
with RTIs and 
CVD
 –Increase in 
(depression) as 
a result of poor 
air quality and 
visibility –Non-achievement 
of emission 
reduction targets
 –Substantial increase 
in frequency 
and duration of 
meteorological 
conditions 
conducive to 
the buildup of 
both primary 
and secondary 
air pollutants 
(e.g., greater 
frequency of 
calm atmospheric 
‘blocking’ 
conditions) and 
no long-term 
improvement in air 
quality at a range 
of geographical 
scales (global to 
local)
intensity of 
wildfires and dust 
storms
 –Increase in the 
intensity of UHIs, 
especially in the 
summer, and the 
occurrence of ozone 
episodes due to 
anomalously high 
urban temperatures –Large increases 
in exposure to air 
pollutants driven

aging) and 
 –For arid regions 
increases in 
exposure to dust 
storms
 –Areas adjacent/
downwind of 
major wildfires
 –For urban 
populations 
intensifying UHIs 
and enhanced 
formation of 
secondary 
pollutants –Increases in 
elderly and those 
with respiratory 
or cardiovascular 
conditions, 
and lack of 
capacity (e.g., 
reduced reliance 
on solid fuel for 
cooking/heating)
Lack of access 
services –Air quality 
management 
policies, air 
quality warning 
systems, efficient 
and cheap mass 
transit systems, 
integrated 
urban planning 
(including NbS 
and green 
infrastructure)
 –Broader 
understanding 
of air pollution 
access to public 
health systems 
for the most 
 –Application 
where possible of 
renewable energy 
sources to reduce 
emissionsCarlsten et al. 
(2020); Doherty 
et al. (2017);

Jennings et al. 
(2021); Kumar et al. 
(2019); Orru et al. 
(2017); Orru et al. 
(2019); Schumacher 
and Shandas 
(2019); Silva et al. 
(2017); Voordeckers 
et al., 2021; Holman et al., 2015; Jennings et al., 2021; Kelly et al., 
2021; Kumar et  al., 2019; Masselot et  al., 2019; Ng et  al., 2021; 
Riley, 2021; Voordeckers et  al., 2021; Xu et  al., 2017; Table  7.5). 
While the range of air pollution adaptation options is potentially 
extensive, barriers may need to be overcome to achieve successful 
implementation, including financial, institutional, political (i.e. inter- 
and intra-governmental) and social barriers (Barnes et  al., 2014; 
Ekstrom and Bedsworth, 2018; Fogg-Rogers et al., 2021; Schumacher 
and Shandas, 2019).
7.4.2.6 Multi-sectoral Adaptation for Risks of Malnutrition
Adaptation to reduce the risk of malnutrition requires multi-sectoral, 
integrated approaches (very high confidence). Adaptation actions

include access to healthy, affordable diverse diets from sustainable 
food systems (high confidence); a combination of access to health—
including maternal, child and reproductive health— and nutrition 
services, water and sanitation (high confidence); access to nutrition-
sensitive and shock-responsive social protection (high confidence); and early warning systems (high agreement), risk sharing, transfer, 
and risk reduction schemes such as index-based weather insurance 
(medium confidence) (Mbow et  al., 2019; Swinburn et  al., 2019; 
UNICEF/WHO/WBG, 2019; FAO et  al., 2021; Macdiarmid and 
Whybrow, 2019; Liverpool-Tasie et al., 2021). Common enablers across 
adaptation actions that enhance the effectiveness and feasibility of 
the adaptation include: education, women’s and girls’ empowerment 
(high confidence), rights-based governance and peacebuilding social 
cohesion initiatives such as the framework of the Humanitarian 
Development and Peace Nexus (medium confidence).

Nutrition-sensitive and integrated agroecological farming systems 
offer opportunities to increase dietary diversity at household levels 
while building local resilience to climate-related food insecurity 
(high confidence) (Bezner Kerr et  al., 2021; IPES-Food, 2020; Altieri 
et  al., 2015) especially when gender equity, racial equity and 
social justice are integrated (Bezner Kerr et  al., 2021). Adaptation 
responses include a combination of healthy, culturally appropriate 
and sustainable food systems and diets; soil and water conservation;

7
1111Health, Wellbeing and the Changing Structure of Communities  Chapter 7
social protection schemes and safety nets; access to health services; 
nutrition-sensitive risk reduction; community-based development; 
women’s empowerment; nutrition-smart investments; increased policy 
coherence; and institutional and cross-sectoral collaboration (high 
agreement, medium evidence) (FAO et al., 2018; Mbow et al., 2019; 
Pozza and Field, 2020; FAO et al., 2021; Table 7.7). Nutrition security can 
be enhanced through consideration of nutrient flows in food systems 
(Harder et  al., 2021).This ‘circular nutrient economy’ perspective 
highlights the potential for adaptations throughout the food supply 
chain, including sustainable production practices that promote nutrient diversity and density, processing, storage, and distribution that 
conserves nutrition; equitable access and consumption of available, 
affordable, appropriate, and healthy foods; and waste management

that supports nutrient recovery (Harder et al., 2021; Boon and Anuga, 
2020; FAO et  al., 2021; Pozza and Field, 2020; Ritchie et  al., 2018). 
Traditional, indigenous and small-scale agroecology and regional food 
systems provide context-specific adaptations that promote food and 
nutrition security as well as principles of food sovereignty and food 
systems resilience (HLPE, 2020; Bezner Kerr et  al., 2021; IPES-Food, 
2020; IPES-Food, 2018).Table 7.6 |  Feasibility and effectiveness assessments of multi-sectoral adaptation for food security and nutrition.
Feasibility and effectiveness assessments 
of multisectoral adaptation for food security and nutrition
Climate change impacts on food security and nutrition
Key risk: Malnutrition in all its forms linked to decline in food availability and increased 
cost of healthy food
Adaptation option
Assessment resultsFeasibility dimensions
Climate-resilient, nutrition-sensitive and agroecological food production

Sustainable and healthy diets (local, equitable, diverse)
Access to health, nutrition services and healthy environments (water and sanitation)
Early warning systems to prevent adverse effects on nutrition
Nutrition-sensitive social protection
Nutrition-sensitive risk reduction, risk sharing and insurance
Technological
GeophysicalSocial
EnvironmentalAgreement
EconomicEvidence
EffectivenessEnabler relevance
Rights-based approach
and good governanceHumanitarian Development
and Peace nexusEducationWomen empowerment
= no data or not assessednana
Table 7.7 |  Summary of adaptation options for key risks associated with malnutrition.
being severeAdaptation options

due to 
decline 
in food 
availability 
and 
increased 
cost of 
healthy food –Global, with 
greater risks in 
Africa, south 
Asia, Southeast 
Asia, Latin 
America, the 
Caribbean and 
Oceania –Substantial number 
of additional 
people at risk of 
hunger, stunting, 
and diet-related 
mortality, including 
decreased mental 
health and cognitive 
function
 –Micro- and 
macronutrient 
deficiencies
 –Severe impacts 
on low-income 
populations from 
LIMICs
 –Risks especially 
high for groups 
that suffer greater 
inequality and 
marginalisation –Climate changes 
leading to 
reductions in 
crop, livestock 
or fisheries 
yields, including 
changes and 
extremes, 
drought, and 
ocean warming 
and acidification –Large numbers of 
people in areas 
and markets 
affected by 
on food security 
and nutrition –High levels 
of inequality

(including gender 
inequality) and 
substantial 
numbers of 
people subject 
to poverty or 
violent conflict, 
in marginalised 
groups or with 
low education 
levels
 –Slow economic 
 –Ineffective 
social protection 
systems, nutrition 
services, and 
health services –Multi-sectoral 
approach to 
nutrition-sensitive 
and disaster 
risk reduction/
management, 
including food, health 
and social protection 
systems
 –Inclusive governance 
involving 
marginalised groups
 –Improved education 
for girls and women
 –Maternal and child 
health, water and 
sanitation, gender 
equality, climate 
services and 
mechanismsGlover and Poole 
(2019); Mbow 
Swinburn et al. 
(2019)

7
1112Chapter 7 Health, Wellbeing and the Changing Structure of Communities
A feasibility and effectiveness assessment was conducted for six 
adaptation strategies often used and recommended by the UN to 
respond to malnutrition risks that combined a literature review and 
expert judgment assessment of 80 peer-reviewed studies (UNSCN, 
2010; Tirado et  al. 2013; methods adapted from de Coninck et  al. 
(2018) and Singh et al. (2020)). Nineteen indicators of six dimensions 
of feasibility (economic, technical, social, institutional, environmental 
and geophysical) were considered. The lead time to initiate and the 
expected longevity of each option were examined. Feasibility was 
defined as how significant the reported barriers were to implement a 
particular adaptation option. Highly feasible options were those where 
no or very few barriers were reported. Moderately feasible were those 
where barriers existed but did not have a strong negative effect on

the adaptation option (or evidence was mixed). Low feasibility options 
had multiple barriers reported that could block implementation. 
Effectiveness ratings were based on expert consultation and reflected 
the potential of the adaptation option to reduce risk. The final 
effectiveness and feasibility scores were categorised as high, medium 
or low and reflect the combined results of all studies for a given 
adaptation option (Table 7.6).
Adaptive social protection programmes and mechanisms that can 
support food insecure households and individuals include cash transfers 
or public work programmes, land reforms, and extension of credit and 
insurance services that reduce food insecurity and malnutrition during 
times of environmental stress (Carter and Janzen, 2018; Johnson 
et  al., 2013; Alderman, 2016). For example, children from families 
participating in Ethiopia’s Productive Safety Net Program experienced 
improved nutritional outcomes, partly due to better household food

consumption patterns and reduced child labour (Porter and Goyal, 
2016). School feeding programmes improve nutritional outcomes, especially among girls, by promoting education, and by reducing child 
pregnancy and fertility rates (Bukvic and Owen, 2017). Adaptive social 
protection is most effective when it combines climate risk assessment 
with DRR and wider socioeconomic development objectives (Davies 
et al., 2013).
Transformative approaches towards healthier, more sustainable, 
plant-based diets require integrated strategies, policies and 
measures, including economic incentives for the agroecological 
production and equitable access to and consumption of more 
fruits, vegetables and pulses; inclusion of sustainability criteria in 
dietary guidelines, labelling and public education programmes; and 
promoting collaboration, good governance and policy coherence 
(Glover, 2019).
7.4.2.7 Adaptation Options for Risks to Mental Health

Adaptation options for reducing mental health risks associated with 
extreme weather include preventive and post-event responses ( high 
confidence) (Brown et  al., 2017; Cohen, 2019; James et  al., 2020; 
Table 7.8). Responses include improving funding and access to mental 
healthcare, which is under-resourced (WHO, 2019a); surveillance 
and monitoring of psychosocial impacts of extreme weather events; 
community-level planning for mental health as part of climate-
resilience planning (Clayton et  al., 2017); and mental health and 
psychological first aid training for care providers and first responders 
(Hayes et al., 2018; O’Donnell et al., 2021; Hayes et al., 2018; Taylor, 
2020; Morgan et  al., 2018; Sijbrandij et  al., 2020). Legislation can 
ensure access to services as well as establish a regulatory framework 
(Ayano, 2018). Advanced disaster risk planning reduces post-event 
mental health challenges. One example is from China, where pre-

Table 7.8 |  Summary of adaptation options for key risks associated with mental health.
Key RiskGeographic 
that would 
Mental health 
impacts in 
floods, storms, 
and wildfires –Global; some 
areas at greater 
risk for storms, 
flooding, or 
wildfires –Substantial 
base rate –Increased 
major storms, 
weather-related 
flooding or 
wildfires –Low-lying areas, 
dry areas, urban 
areas –Physical 
that is vulnerable 
to extreme 
weather, 
inadequate 
emergency 
response and 
mental health 
services, social 
inequality –Improved urban 
infrastructure,

warning systems, and 
post-disaster social 
support
 –Improved funding 
and access to mental 
healthcare
 –Improved surveillance 
and monitoring of 
mental health impacts 
of extreme weather 
events
 –Climate change 
resilience planning 
in the mental health 
system (including at a 
community level
 –Mental health first 
aid training for care 
providers and first 
respondersAli et al. (2020); 
Ayano (2018); 
Buckley et al. 
(2019); Clayton 
et al. (2017); 
Hayes et al. (2019); 
James et al. (2020); 
Sijbrandij et al. 
(2020)

7
1113Health, Wellbeing and the Changing Structure of Communities  Chapter 7
planning of temporary shelters resulted in significantly lower rates 
of anxiety, depression and PTSD in the aftermath of flooding among 
displaced people who accessed them (Zhong et  al., 2020). Key 
elements of successful initiatives include coordinated planning and 
action between key regional agencies and governments with a focus 
on improving accountability and removing barriers to implementation 
and subsequent access to programmes (Ali et  al., 2020). As an 
example, following the 2019/2020 Australian bushfires, the federal 
government allocated funds to support mental health through 
free counselling for those affected, increased access to telehealth, 
extended hours for mental health services and programmes designed 
specifically for youth (Newnham et al., 2020).
Because mental health is fundamentally inter-twined with social and 
economic well-being, adaptation for climate-related mental health

risks benefits from wider multi-sectoral initiatives to enhance well-
being, with the potential for co-benefits to emerge ( high confidence) . 
Improvements in education, quality of housing, safety and social 
protection support enhance general well-being and make individuals 
more resilient to climate risks (Lund et al., 2018; Hayes et al., 2019). 
Among Indigenous Peoples, connections to traditional culture and 
to place are associated with health and well-being (Bourke et  al., 
2018) as well as with resilience to environmental change (Ford et al., 
2020). As an example of the connection between infrastructure 
improvements and mental health, a study of domestic rainwater 
harvesting initiatives to promote household water security also 
improved mental health in participating households (Mercer and 
Hanrahan, 2017). Adaptive urban design that provides access to 
healthy natural spaces—an option for reducing risks associated with

heat stress—also promotes social cohesion and mitigates mental 
health challenges (high confidence) (Buckley et  al., 2019; Clayton 
et al., 2017; Jennings and Bamkole, 2019; Liu et al., 2020b; Mygind 
et al., 2019; Marselle et al., 2020).
7.4.2.8 Adaptation Options to Facilitate Early-Warning and 
Response Systems
Early warning systems are a potentially valuable tool in adapting to 
climate-related risks associated with infectious diseases when based 
on forecasts with high skill and when there are effective responses 
within the time frame of the forecast ( high confidence) . Through 
advanced seasonal weather forecasting that draws upon established 
associations between weather/climate and infection/transmission 
conditions, conditions conducive to disease outbreaks can be identified 
months in advance, providing time to implement effective population 
health responses (Morin et  al., 2018). Most current early warning

systems are focused on malaria and dengue but there are examples 
for other diseases, such as an early warning system developed 
for Vibrios monitoring in the Baltic Sea (Semenza et  al., 2017). An 
early warning system for dengue outbreaks in Colombia based on 
temperature, precipitation and humidity successfully detected 75% 
of all outbreaks between one and five months in advance, detecting 
12.5% in the same month (Lee et  al., 2017b). Dengue warning 
systems in Brazil, Malaysia and Mexico have generated satisfactory 
results (Hussain-Alkhateeb et  al., 2018). An effective early warning 
system for malaria was implemented in the Amhara region of Ethiopia 
(Merkord et al., 2017).Early warning systems are effective at detecting and potentially 
reducing food security and nutrition risks (high confidence). Examples 
of proven systems include the United States Agency for International 
Development (USAID) Famine Early Warning System, the Food and

Agricultural Organization’s Global Information and Early Warning 
System and the World Food Programme’s Corporate Alert System. Such 
systems are fundamental for anticipating when a crisis might occur 
and setting priorities for interventions (Funk et  al., 2019). Financial 
investments to develop early warning systems are cost-effective and 
reduce human suffering (Choularton and Krishnamurthy, 2019) (high 
confidence). For instance, during the 2017 drought-induced food crisis 
in Kenya, 500,000 fewer people required humanitarian assistance 
than would have been expected based on past experiences; this was 
largely due to timely and effective interventions triggered by the early 
warning (Funk et al., 2018).
Early warning systems have been established for other climate-
sensitive health outcomes, such as respiratory diseases associated 
with air pollution (Shih et  al., 2019; Li and Zhu, 2018; Yang and 
Wang, 2017). Early warning systems for non-heat extreme weather

and climate events, such as storms and floods, are designed to 
protect human health and well-being; disaster risk management 
organisations and institutions typically communicate these warnings 
through their networks. Research is ongoing to extend the time 
period for warnings.
7.4.2.9 Incorporating Disaster Risk Reduction into Health 
Integrating health into national disaster risk management plans has 
wider benefits for resilience and adaptation to climate change risks 
(high confidence) (UNFCCC, 2017a; Watts et al., 2019). DRR, including 
disaster preparedness, management and response, is widely recognised 
as important for reducing health consequences of climate-related 
hazards and extreme weather events (Keim, 2008; Phalkey and Louis, 
2016). A systematic review by Islam et al. (2020) identified multiple, 
ongoing challenges to integrating climate adaptation and DRR at 
global and national levels, including a lack of capacity among key

actors and institutions, a lack of coordination and collaboration across 
scales of government and general lack of funding—challenges that 
are particularly relevant for the health sector. Global events, including 
climate-related extreme events and public health emergencies of 
international concern (for example, Ebola, Middle East respiratory 
syndrome (MERS) and COVID-19) have influenced the development 
of national public health preparedness and response systems and 
attracted significant investment over the last two decades (Khan et al., 
2015; Murthy et al., 2017; Watson et al., 2017). The Sendai Framework 
for Disaster Risk Reduction and the International Health Regulations 
establish important global and regional goals for increasing health 
system resilience and reducing health impacts from biological hazards 
and extreme climate events (Aitsi-Selmi et al., 2015; Maini et al., 2017; 
UNFCCC, 2017b; Wright et al., 2020). There are explicit links between

the health aspect of the Sendai Framework and UN SDGs 1, 2, 3, 4, 6, 9, 
11, 13, 14, 15 and 17 (Wright et al., 2020). More specifically, reducing 
the number of disaster-related deaths, illnesses and injuries, as well as 
damage to health facilities are key indicators for achieving the goals 
set out in the Sendai Framework (UNFCCC, 2017b).

7
1114Chapter 7 Health, Wellbeing and the Changing Structure of Communities
The intersection of health and multi-sectoral DRR and manage-
ment, generally described as health emergency and disaster risk 
management (health-EDRM), encompasses multi-sectoral approaches 
from epidemic preparedness and response including the capacities 
for implementing the International Health Regulations (IHR, 2005), 
health systems strengthening and health systems resilience (Lo Iacono 
et  al., 2017; WHO 2019; Wright et  al., 2020). Health-EDRM costs to 
governments are notably lower than the cost of inaction (Peters et al., 
2019). Additional per capita costs in low-income countries have been 
estimated to range from USD 4.33 (capital) and USD 4.16 (annual 
recurrent costs), and in upper middle-income countries to an additional 
USD 1.35 in capital costs and USD 1.41 in extra annual recurrent costs 
(Peters et  al., 2019). Adopting a health-EDRM approach supports the

systematic integration of health and multi-sectoral EDRM to ensure a 
holistic approach to health risks and assists in the alignment of action in 
health security, climate change and sustainable development (Chan and 
Peijun, 2017; Dar et al., 2014; WHO, 2019; Wright et al., 2020).
Climate-informed health-EDRM is crucial for the climate resilience of 
health systems (WHO, 2015a), particularly to account for additional 
risks and uncertainties associated with climate change and allow for 
well-planned, effective and appropriate EDRM and adaptation (Watts 
et al., 2018a; WHO, 2013; WHO, 2015a). Potential coherent approaches 
to addressing climate change and disaster risks to health include: 
strengthening health systems; vulnerability and risk assessments that 
incorporate disaster and climate change risk; building resilience of 
health systems and health infrastructure; and climate-informed EWSs 
(Banwell et al., 2018; Phalkey and Louis, 2016). However, a review of

DRR projects including climate change in south Asia found that the 
health sector was the least represented with only 2% of 371 projects 
relating to health (Mall et al., 2019), indicating a need to strengthen 
the incorporation of climate change in health-EDRM. Current tracking 
under the Sendai Framework of Disaster Risk Reduction 2015–2030 
shows that most countries (particularly low-income countries and 
lower middle-income countries) still lack robust systems for integrated 
risk monitoring and early warning (UNEP , 2018). The incorporation of 
DRR and management strategies into climate adaptation for health 
and health systems at local scales is particularly important, given that 
it is at local scales where health services are most often delivered 
and where knowledge of specific needs and challenges is often 
greatest (Amaratunga et al., 2018; Schramm et al., 2020a). Indigenous 
knowledge has been shown to be valuable in DRR, with particularly

strong evidence existing for drought risk reduction in sub-Saharan 
Africa (Fummi et al., 2017; Muyambo et al., 2017; Dube and Munsaka, 
2018; Macnight Ngwese et  al., 2018). In the USA, DRR strategies 
that draw upon traditional knowledge and local expertise are being 
incorporated into climate adaptation planning for health in a number 
of indigenous communities under the ‘Climate-ready Tribes Initiative’ 
(Schramm et al., 2020b).
7.4.2.10 Monitoring, Evaluation and Learning
Monitoring, evaluation and learning (MEL) can assess the ability of 
nations and communities to prepare for and adequately respond to the 
health risks of climate change over time ( high confidence) (Boyer et al., 
2020). MEL describes a process that includes baseline assessment, 
prioritising actions and activities, identifying key indicators to track, ongoing data collection and periodically considering new information 
(Kruk et  al., 2015). MEL determines whether adaptation options

achieved their goals and whether resources were used effectively and 
efficiently (Boyer et al., 2020). One of the challenges for MEL in the 
context of adaptation is that climate risks vary as a function of time, 
location, socioeconomic development, demographics and activities in 
other sectors (Ebi et al., 2018a). MEL indicators in the health sector 
need to account for factors related to governance, implementation 
and learning as well as for exposures, impacts and programmatic 
activities, all of which are context dependent and are often outside 
the health sector (Boyer et al., 2020; Ebi et al., 2018a; Fox et al., 2019).
No universal standardised approach exists for monitoring or evaluating 
adaptation activities in the health sector (high confidence). Candidate 
indicators of climate change health impacts and adaptation activity, 
typically at the national level, are available (Bowen and Ebi, 2017; 
Cheng and Berry, 2013; Kenney et al., 2016; Navi et al., 2017; WHO,

2015b). Indicators are best grouped by category of activity, that is, 
vulnerability, risk and exposure; impacts; and adaptation and resilience 
(Ebi et al., 2018a). As health adaptation expands, enhanced monitoring 
will be needed to ensure that scientific advances are translated into 
policy and practice. A promising initiative that emerged since the AR5 
is the Lancet Countdown, which represents a global effort at tracking 
various indicators of exposures, impacts, adaptation activities, finance 
and media activity related to climate change and health (Watts et al., 
2018a), although this effort is principally focused on monitoring and 
does not explicitly focus on evaluation adaptation efforts or learning 
from adaptation efforts.
Community-based monitoring of adaptation responses to health 
impacts, especially by Indigenous Peoples, has not been widely 
undertaken, despite its potential to improve monitoring of and local

adaptation to environmental change (Kipp et  al., 2019). The health 
sector has been particularly weak at recognising the climate impacts 
on and the adaptation needs of Indigenous Peoples and in engaging 
Indigenous Peoples in monitoring progress (Ford et al., 2018; David-
Chavez and Gavin, 2018; Ramos-Castillo et  al., 2017). Successful 
adaptation to the health impacts of climate change in Indigenous 
Peoples requires recognition of their rights to self-determination, 
focusing on indigenous conceptualisations of well-being, prioritising 
Indigenous knowledge and understanding the broader agenda of 
decolonisation, health and human rights (high confidence) (Ford and 
King, 2015; Green and Minchin, 2014; Hoy et al., 2014; Jones, 2019; 
Jones et al., 2014; Mugambiwa, 2018; Nursey-Bray and Palmer, 2018).
Indicators should capture measures of processes that drive adaptation 
readiness, including leadership, institutional learning and inter-

sectoral collaboration (Boyer et  al., 2020; Ford and King, 2015) as 
well as outcome measures such as the presence of programming 
known to reduce risks (Ebi et  al., 2018a). Additionally, indicators 
related to scaling up of effective interventions and relying on the 
implementation of science frameworks are important (Damschroder 
et al., 2009; Theobald et al., 2018, 2020; Ebi et al., 2018a; Fox et al., 
2019). Measuring impacts attributable to climate change could be 
addressed with a combination of indicators related to overall health 
system performance and population vulnerability (Ebi et al., 2017; Ebi 

7
1115Health, Wellbeing and the Changing Structure of Communities  Chapter 7
7.4.3 Enabling Conditions and Constraints for Health 
7.4.3.1 Governance, Collaboration and Coordination
Effective governance institutions, arrangements, funding and mandates 
are key for adaptation to climate-related health risks ( high confidence) . 
Without integration and collaboration across sectors, health adaptation 
can become siloed, leading to less effective adaptation or even 
maladaptation (Magnan et al., 2016; Fox et al., 2019). Integration and 
collaboration include working laterally across national government 
departments and agencies, as well as vertically from national 
agencies to local governments and with the private sector, academia, 
NGOs and civil society. In this context, top-down policy design and 
implementation are complemented by bottom-up approaches that 
engage community actors in programme design and draw upon their

local practices, perspectives, opinions and experiences. Opportunities 
exist to better integrate public health into climate change discourse and 
policymaking processes, and to strengthen public health partnerships 
and collaborations (Awuor et al., 2020). Creating networks, integration 
across organisations and jointly developed policies can facilitate cross-
sectoral collaboration (Bowen and Ebi, 2017).
7.4.3.2 Multi-sectoral Collaborations
Multi-sectoral collaborations aimed at strengthening the health sector 
can generate multiple co-benefits in other sectors (high agreement, 
medium evidence). Solutions for the health and well-being risks 
described in 7.2 and 7.3 often have their origins in sectors that include 
water, sanitation, agriculture, food systems, social protection systems, 
energy and key components of urban systems such as housing and 
employment (WHO, 2015a; Bowen et  al., 2014b; Machalaba et  al.,

2015; Confalonieri et al., 2015; Bowen et al., 2014a; Semenza, 2021). 
Climate resilient development pursued in these other sectors, and 
in cooperation with the health sector, simultaneously increases the 
potential for adaptation and climate resilience in terms of health and 
well-being (high confidence) (Ahmad et al., 2017; Watts et al., 2018b; 
Levy and Patz, 2015; WHO, 2018a; Chiabai et al., 2018a; Dudley et al., 
2015; Zinsstag et al., 2018; Sherpa et al., 2014).
7.4.3.3 Financial Constraints
Financial constraints are the most referenced barrier to health 
adaptation and so scaling up financial investments remains a key 
international priority ( very high confidence) (Wheeler and Watts, 2018; 
UNFCCC, 2017a). AR5 estimated the costs of adaptation in developing 
countries at between USD  70  billion and USD  100  billion annually in 
the year 2050, but these are likely to be a significant underestimate, 
particularly in the years 2030 and beyond (UNEP , 2014). National surveys

conducted by WHO identified financial constraints as a major barrier to 
the implementation of health adaptation priorities (WHO, 2019b; Watts 
et al., 2021). Novel research drawing on global financial transaction data 
suggests that in 2019, global financial transactions with the potential 
to deliver adaptation in the health and healthcare sector reached 
USD  18.4  billion, driven by transactions in high- and upper middle-
income countries, with investment in Africa, Southeast Asia and the 
eastern Mediterranean mostly stagnant (Watts et al., 2021).There has been limited participation of the health sector in 
international climate financing mechanisms (Martinez and Berry, 
2018). Of 149 projects listed in the Adaptation Fund database in 
October 2020, a large number were broad-based initiatives that 
may have considerable indirect benefits for health systems, such as 
enhanced disaster preparedness and food security, but none were

explicitly aimed at strengthening health systems or had directed funds 
through ministries of health. A review of projects funded by the major 
multi-lateral climate funds showed that less than 1.5% of dispersed 
adaptation funding and less than 0.5% of overall funding have been 
allocated to projects aimed at protecting health (WHO, 2015a). A 
survey of national public health organisation representatives from 
a mix of low-, middle- and high-income countries found that a lack 
of political commitment, insufficient coordination across sectors and 
inadequate funding for public health-specific adaptation initiatives 
were common barriers to building climate resilience (Marcus and 
Hanna, 2020). Under-investment in climate-specific initiatives in health 
systems coincides with persistent under-investment in healthcare more 
generally, especially in low- and middle-income countries (Schaferhoff 
Adaptation financing does not often reach places where the

climate-sensitivity of the health sector is greatest (Weiler, 2019). 
Financial constraints in Africa are one of the key reasons for slow 
implementation of health adaptation measures (Nhamo and Muchuru, 
2019). Strengthening health systems in vulnerable countries has the 
potential to reduce current and future economic costs related to 
environmental health risks, thus enabling reinvestment in the health 
system and sustainable development (WHO, 2020a; WHO, 2015a). 
Robust and comprehensive climate and health financing builds first 
on core health sector investments (WHO, 2015a). Other potential 
opportunities for resource mobilisation include health-specific funding 
mechanisms, climate change funding streams and investments from 
multi-sectoral actions and actions in health-determining sectors (WHO, 
2015a). Incorporating climate change and health considerations into 
disaster reduction and management strategies could improve funding

opportunities and increase potential funding streams (Aitsi-Selmi 
et  al., 2015). Reinforcing cross-sectoral governance mechanisms 
maximises health co-benefits and economic savings by allowing for 
multi-sectoral costs and benefits to be comprehensively considered 
in decision-making (Belesova et al., 2016; WHO, 2020a; WHO, 2015b). 
An additional financial need concerns health research, the existing 
funding for which does not match what is needed to support the 
implementation of the combined objectives of the UN 2030 Agenda 
for Sustainable Development, the Sendai Framework for Disaster Risk 
Reduction and the Paris Agreement (Green and Minchin, 2014; Ebi, 
2016; Green et al., 2017).
7.4.3.4 Perceptions of Climate Change Risks and Links to 
Adaptation decisions and responses to climate change can be 
influenced by perceptions of risks, which are shaped by individuals’ 
characteristics, knowledge and experience (medium agreement,

medium evidence). Institutional and governmental responses are 
critical for adapting to climate-related risks in health and other 
sectors, but individual responses also are relevant, such as choosing

7
1116Chapter 7 Health, Wellbeing and the Changing Structure of Communities
to implement adaptation measures. Individual responses are in turn 
affected not only by capabilities but also by perceptions that climate 
change is real and requires a response (Ogunbode et  al., 2019). 
Perceptions of climate risks are formed by experiences of changes in 
local weather and extreme weather events (Sattler et al., 2018; Sattler 
et  al., 2020; van der Linden, 2015), observations of environmental 
changes (Hornsey et al., 2016), experiences of and knowledge about 
climate change impacts (Ngo et al., 2020; van der Linden, 2015) and 
individual characteristics such as values and worldviews (Poortinga 
et  al., 2019) (high agreement, medium evidence). Risk perceptions 
include both logical assessments about the likelihood and severity of 
climate change impacts and affective feelings about those impacts. 
On average, affective measures of risk perception are more strongly

associated with disaster preparation than cognitive measures 
(Bamberg et al., 2017; van Valkengoed and Steg, 2019).
In addition to perceptions of risk, the likelihood that an individual will 
implement behavioural adaptations or support relevant public policy is 
affected by subjective assessments of the response options (Bamberg 
et  al., 2017; van Valkengoed and Steg, 2019; Akompab et  al., 2013; 
Carman and Zint, 2020; Hornsey et al., 2016; Brenkert-Smith et al., 2015).
Efficacy beliefs, social norms and subjective resilience also affect 
adaptation behaviour ( medium confidence) , which has implications 
for communication about the need for climate adaptation. Efficacy 
beliefs represent the belief in one’s ability to carry out particular 
action(s) and the belief that the action(s) will have the desired 
outcome. Belief that one is personally able to complete a behaviour 
is moderately associated with engaging in disaster preparations

(Navarro et  al., 2021; van Valkengoed and Steg, 2019) and with 
adaptation intentions (Burnham and Ma, 2017). Collective efficacy, 
the belief that a group of people working together can achieve 
a desired outcome, is important for participating in community 
adaptation behaviours (Bandura, 1982; Chen, 2015; Thaker et  al., 
2015). Related to this is response efficacy, a belief that a behaviour 
will achieve its desired outcome, which is also moderately associated 
with engaging in disaster preparations (van Valkengoed and 
Steg, 2019). Collective efficacy can potentially be developed by 
strengthening communication networks and social ties within a 
community (Haas et al., 2021; Jugert et al., 2016). Norms describing 
the adaptation strategies of others in a community, particularly 
those with high social status, can either facilitate or inhibit individual 
adaptation decisions (Neef et al., 2018; Smith et al., 2021).

Distinct from efficacy beliefs, subjective resilience is a more general 
optimism or belief about one’s ability (Jones, 2019; Khanian et  al., 
2019). Subjective resilience (Clare et al., 2017) can influence preferred 
responses to climate change via assessment of one’s ability to engage 
in specific response options. Identities can influence assessment of 
subjective resilience. Place attachment, having a strong emotional 
connection to a particular location, is weakly associated with disaster 
preparation (Brügger et  al., 2015). In some cases, place attachment 
may inhibit adaptive responses, either by reducing perceptions of risk 
or by making people reluctant to leave an area that is threatened 
(De Dominicis et  al., 2015; van Valkengoed and Steg, 2019). Place 
attachment can also contribute to enhanced community resilience 
(Khanian et al., 2019; Jones, 2019; Wang et al., 2021).7.4.4 Migration and Adaptation in the Context of 

7.4.4.1 Linkages between Migration, Adaptation and 
Household Resilience
AR5 (Chapter 17) concluded that migration is often, though not in 
all situations, a potential form of adaptation initiated by households. 
Subsequent research indicates that the circumstances under which 
migration occurs and the degree of agency under which household 
migration decisions are made are important determinants of whether 
migration outcomes are successful in terms of advancing the well-
being of the household and providing benefits to sending and 
receiving communities (high confidence) (Adger et al., 2015; Cattaneo 
et al., 2019; Cross-Chapter Box MIGRATE in Chapter 7). Evidence from 
refugee studies and general migration research indicates that higher 
agency migration, in which migrants have mobility options, allows 
migrants greater opportunities for integrating into labour markets at 
the destination, makes it easier to remit money home and generally

creates conditions for potential benefits for migrant households and 
for sending and receiving communities (International Organization for 
Migration, 2019). Bilateral agreements that facilitate labour migration 
have been identified as being especially urgently needed for Pacific 
small island states (Weber, 2017).
Adaptive migration and the implied assumption that people can 
or should simply move out of harm’s way is not a substitute for 
investment in adaptive capacity-building (high agreement) (Bettini 
and Gioli, 2016). Climate-related migration, and especially involuntary 
displacement, often occurs only after in situ adaptation options have 
been exhausted and/or where government actions are inadequate 
(Adger et al., 2015; Ocello et al., 2015; Cross-Chapter Box MIGRATE 
in Chapter 7). The threshold at which household adaptation transitions 
from in situ measures to migration is highly context specific and reflects

the degree of exposure to specific climate risks, mobility options 
and the socioeconomic circumstances of the household and local 
community (McLeman, 2017; Adams and Kay, 2019; Semenza and Ebi, 
2019; Cross-Chapter Box MIGRATE in Chapter 7). A consistent theme 
in the research literature reviewed for all sections of this chapter is 
that proactive investments in health, social and physical infrastructure, 
including those not aimed specifically at climate risks, build societal 
adaptive capacity and household resilience. In turn, expanding the 
range of adaptation options available to households increases the 
likelihood that, when migration does occur, it does so under conditions 
of high agency that lead to greater chances of success. In communities 
where climate-related migration and/or relocation is occurring or may 
occur, policymaking and planning benefits from understanding the 
cultural, social and economic needs of exposed populations and helps

in the identification of responses and policies that build resilience 
(Hino et al. 2017)
7.4.4.2 Climate, Migration and Linkages to Labour Markets and 
Social Networks
Adaptive climate-related migration is often closely related to 
wage-seeking labour migration (medium confidence). Due to the 
circumstances under which they move, climate-related migrants’

7
1117Health, Wellbeing and the Changing Structure of Communities  Chapter 7
destinations, labour market choices and returns from migration may be 
more heavily constrained than those of other labour migrants (Jessoe 
et al., 2018; Wrathall and Suckall, 2016).Within low- and middle-income 
countries, rural–urban migrant networks are important channels for 
remittances that may help build socioeconomic resilience to climate 
hazards in sending areas (Porst and Sakdapolrak, 2020), with higher 
levels of wage-seeking labour participation observed in climate-
sensitive locales in south Asia (Maharjan et  al., 2020). Local-level 
research in China and south Asia shows, however, that the potential 
for remittances to generate improvements in household level adaptive 
capacity is highly context specific, has significant gender dimensions 
and depends on such factors as the nature of the hazard, the distance 
migrated and the length of time over which remittances are received

(Banerjee et al., 2019a; Banerjee et al., 2019b). Social networks are a 
key asset in helping climate migrants overcome financial and structural 
impediments to their mobility, but these have their limits, particularly 
with respect to international migration (Semenza and Ebi, 2019). Since 
AR5, greater restrictions have emerged on movement between many 
low- and high-income countries (not including those necessitated by 
public health measures during the COVID-19 pandemic), a trend that, 
if it continues, would generate additional constraints on destination 
choices for future climate migrants (McLeman, 2019). Transnational 
diasporic connections are a potential asset for building resilience 
in migrant-sending communities highly exposed to climatic risks, 
with migrants’ remittances potentially providing resources for long-
term resilience building, recovery from extreme events and reducing 
income inequality (Bragg et al., 2018; Mosuela et al., 2015; Obokata

and Veronis, 2018; Shayegh, 2017; Semenza and Ebi, 2019). Safe 
and orderly labour migration is consequently a potentially beneficial 
component of wider cross-sectoral approaches to building adaptive 
capacity and supporting sustainable development in regions highly 
exposed to climate risks (McLeman, 2019).
7.4.4.3 Attitudes Towards Climate Migration
The success of climate-related migration as an adaptive response is 
shaped by how migrants are perceived and how policy discussions 
are framed (high agreement, medium evidence). The possibility that 
climate change may enlarge international migrant flows has in some 
policy discussions been interpreted as a potential threat to the security 
of destination countries (Sow et al., 2016; Telford, 2018), but there is 
little empirical evidence in peer-reviewed literature assessed for this 
chapter of climate migrants posing significant threats to security at 
state or international levels. There is also an inconsistency between

framing in some policy discussions of undocumented migration 
(climate-related and other forms) as being ‘illegal’ and the objectives 
of the Global Compact on Safe, Orderly and Regular Migration and 
the Global Compact on Refugees (McLeman, 2019). Although climate-
related migrants are not officially recognised as refugees under the 
1951 Convention relating to the Status of Refugees, terms such as 
‘climate refugees’ are common in popular media and some policy 
discussions (Høeg and Tulloch, 2018; Wiegel et al., 2019). The framing 
of migration policy discussions is relevant, for example, in discussing 
climate adaptation options for Pacific Island Countries, where there is 
considerable disagreement over policies that range from a ‘migration 
with dignity’ approach that would liberalise labour migration in the 
Pacific region to those that see migration as a last resort option to be avoided as much as possible (McNamara, 2015; Farbotko and

McMichael, 2019; Oakes, 2019; Remling, 2020). A more beneficial 
policy framing in terms of ensuring that future migration contributes to 
climate resilience and sustainable development has been established 
since AR5 within the framework of the Global Compact for Safe, 
Orderly and Regular Migration (see Section 7.4.7.7).
Attitudes of residents in migrant-receiving areas with respect to 
climate-related migration warrant consideration when formulating 
adaptation policy (medium confidence). Existing research is modest 
and difficult to generalise with respect to the impacts of climate-
related migration and displacement on social dynamics and stability in 
receiving destinations, with outcomes being tied to the attitudes and 
social acceptance of receiving communities and efforts to integrate 
migrant arrivals into the community (Koubi and Nguyen, 2020). 
Research from Kenya and Vietnam shows that residents of receiving

communities view environmental drivers as being legitimate reasons 
for people to move and consequently tend not to stigmatise such 
migrants (Spilker et  al., 2020). In these examples, urban residents 
viewed environmental motivations as being comparable to economic 
reasons for migrating and did not see climate-related migrants as 
posing any particular risks for receiving communities. However, 
case studies from India suggest that a lack of recognition by local 
authorities of climatic factors being legitimate drivers of rural–urban 
migration may lead to discrimination against migrants in terms of 
access to housing and other social protections, thereby undermining 
household resilience (Chu and Michael, 2018).
7.4.4.4 Planned Relocation and Managed Retreats
There is high agreement among existing studies that immobile 
populations often have high vulnerability and/or high long-term exposure 
to climate hazards, and that non-climatic political, economic and social

factors within countries may strongly constrain mobility (Zickgraf, 
2019; Ayeb-Karlsson et al., 2020; Cundill et al., 2021). Section 7.2.6.2 
highlighted the particular vulnerability of immobile populations in the 
face of growing climatic risks. However, research suggests governments 
should be slow to label such populations as being ‘trapped’ or to actively 
promote relocations in the absence of local agreement that in situ  
adaptation options have been exhausted (Adams, 2016; Farbotko and 
McMichael, 2019). In the case of indigenous settlements, efforts made 
to incorporate traditional knowledge in decision-making and planning 
increase the potential for longer-term success (Manrique, 2018). 
Considerable health implications can emerge within populations that 
are relocated as part of a planned retreat, and represent an important 
consideration for planners that requires greater research (Dannenberg 
et al., 2019). Organised relocations are not inherently transformative

in their outcomes but, depending on the circumstances under which 
they occur and on how issues of equity and respect for the rights of 
those affected are implemented, relocation could potentially represent 
a positive transformation (Siders et al., 2021).
Disruptive and expensive relocations of low-lying coastal settlements in 
many regions would become increasingly necessary in coming decades 
under high levels of warming ( high confidence) . Organised relocations 
require long-term innovation, planning and cooperation on the part of 
governments, institutions, affected populations and civil society (Hauer,

7
1118Chapter 7 Health, Wellbeing and the Changing Structure of Communities
2017; Hino et  al., 2017; Haasnoot et  al., 2021; Moss et  al., 2021). 
Recent examples illustrate the substantial financial costs of organised 
relocations, ranging from USD 10,000 per person in examples from Fiji 
to USD 100,000 per person in coastal Louisiana, USA (Hino et al., 2017). 
Organised relocations are politically and emotionally charged, may 
not necessarily be seen as desirable by exposed populations and are 
most successful when approached proactive and strategically to avoid 
increasing the socioeconomic vulnerability of those who are relocated 
(Jamero et al., 2017; Wilmsen and Webber, 2015; Chapin et al., 2016; 
McNamara et  al., 2018; Hauer et  al., 2019; Bertana, 2020). Key 
considerations for protecting the rights and well-being of people who 
might need to be resettled include proactive communication with and 
participation of the affected communities, availability of compensation,

livelihood protection and ensuring there is permanence and security of 
tenure at the relocation destination (Tadgell et al., 2018). Availability 
of funds for resettlement, how to manage relocation from communally 
owned lands, how to value privately owned land to be abandoned and 
the potential for loss and damage claims are just some of the many 
potential complications (Marino, 2018; McNamara et  al., 2018). As 
a proactive option, researchers in Bangladesh have suggested the 
creation of ‘migrant-friendly towns’ to provide options for autonomous 
relocation from hazardous areas (Khan and Huq, 2021).
7.4.5 Adaptation Solutions for Reducing Conflict Risks
There has been increased activity within the international community 
to understand and address climate–conflict linkages since AR5, with 
high level actions including the UN Climate Security Mechanism, 
launched in 2018 and tasked with providing integrated climate

risk assessments to the United Nations Security Council and other 
UN bodies in partnership with UN and external actors (DPPA et al., 
2020). G7 governments initiated an integrated agenda for resilience 
(Rüttinger et al., 2015) and the Berlin Call for Action in 2019 sought a 
foreign policy platform to address climate security concerns, focusing 
on risk-informed planning, enhanced capacity for action within the UN 
and improvements to operational response to climate security risks 
(Federal Foreign Office, 2019). The non-peer-reviewed literature that 
currently addresses these policy dimensions is often generated by a 
small number of consultancies funded by governments from the Global 
North and can lack diverse perspectives and priorities.
7.4.5.1 Environmental Cooperation and Peacebuilding
The environment can form the basis for active peacebuilding, and 
a sustainable natural environment is important for ongoing peace

(high agreement, medium evidence) . EP is a framework increasingly 
utilised to understand the diverse ways in which the natural 
environment supports peace and can be utilised in peacebuilding; 
key tenets include preserving the natural environment such that 
degradation does not contribute to violence, protecting natural 
resources during conflict and using natural resources to support 
post-conflict economic recovery (Kron, 2019). EP frames natural 
resources as facilitating peace rather than driving conflict (Dresse 
et  al., 2019) with emerging literature analysing what this means 
in practice (Kovach and Conca, 2016; Krampe, 2017; Ide, 2019; 
Ide et  al., 2021; Johnson, 2021; Kalilou, 2021). There is emergent evidence for the success of EP pathways. For example, a natural 
resource sharing agreement on the Kenya–Uganda border was able 
to reconcile spatial, logistical and conceptual barriers to addressing 
climate risks in development contexts (Abrahams, 2020). However,

the long-term impacts of EP approaches on sustaining peace are 
yet to be monitored and evaluated (Ide and Tubi, 2020). EP may be 
successful depending on the context and the element of peace being 
built (Johnson, 2021) or undermine processes when environmental 
arguments are co-opted for geopolitical purposes (Barquet, 2015) or 
to depoliticise conflict (Ide, 2020).
Formal institutional arrangements for natural resource management 
can contribute to transnational cooperation (high confidence) (see also 
Chapter 4). Evidence from transboundary water sharing agreements 
provides evidence for cooperation rather than conflict over resources 
(Timmerman et  al., 2017; Timmerman, 2020; Dinar et  al., 2015). 
Transboundary water agreements and river basin organisations help 
build robust institutions that facilitate trust and relationship building 
that have benefits in other domains (strong agreement, medium 
evidence) (Dombrowsky, 2010; Krampe and Gignoux, 2018; Barquet

et al., 2014; Ide and Detges 2018). However, outcomes can be mixed, 
and the international and top down nature of these approaches may 
limit their transferability to intra-state conflicts at local levels (Rigi and 
Warner, 2020; Ide et al., 2021; Krampe et al., 2021).
7.4.5.2 Adaptation in Fragile Settings
Climate-resilient peacebuilding has the potential to limit the impact 
of future climate change on peace efforts ( medium confidence) . 
Practical guidance has been developed, driven by policy concerns on 
climate–conflict links. The United Nations Environment Programme 
(UNEP), the European Union and Adelphi have developed a toolkit 
for addressing climate fragility risks in peacebuilding, adaptation 
and livelihoods support (UNEP et al., 2019). Crawford et al. (2015) 
provide recommendations for climate-resilient peacebuilding 
consistent with the UN Secretary General’s five peacebuilding 
principles, including integrating ex-combatants through the

construction of climate-resilient infrastructure, using climate impacts 
as a platform to engage previously conflicting groups, developing 
national DRR and management strategies, and climate-proofing 
economic development activities. The USAID, in a report prepared 
for the Adaptation Thought Leadership and Assessments (ATLAS) 
programme (Adelphi & Chemonics International, 2020) that drew 
upon resilience and peacebuilding programmes in the Horn of Africa, 
recommend two critical conditions to ensure activities address 
compound climate fragility risks. Firstly, conducting local analyses 
of the links between climate, conflict and fragility to identify specific 
risks to target and, secondly, ensuring long-term commitment with a 
focus on participation and flexibility.
Conflict-sensitive adaptation that focuses on institutional frameworks, 
conflict management and governance mechanisms has the potential 
to address complex interacting risks and emergencies over the long

term (medium agreement, limited evidence) (Scheffran et  al., 2012; 
Matthew, 2018; Okpara et  al., 2018). However, most adaptation 
activities are planned and implemented under development or climate 
finance funds without systematic integration of conflict sensitivity,

7
1119Health, Wellbeing and the Changing Structure of Communities  Chapter 7
and National Adaptation strategies rarely and only implicitly address 
conflict and potential changes to power relations (Tänzler et al., 2019). 
Practitioners and policy researchers have attempted to address this 
gap by developing guidance and delivering training (e.g., Tänzler et al. 
(2019); Bob and Bronkhorst (2014)). However, there are real challenges 
relating to discounting indirect impacts on conflict and maladaptation 
(Asplund and Hjerpe, 2020) and risks of unintended outcomes 
(Mirumachi et  al., 2020). Crawford and Church (2020) highlight the 
synergies between adaptation planning under the UNFCCC’s National 
Adaptation Plan process and conflict reduction. Discussing development 
more broadly, Abrahams (2020) suggests three barriers to development 
that incorporate conflict–climate risks: geographically disconnected 
impacts and outcomes, the discourse of climate as a threat multiplier

(rather than underlying peace) and teleconnected risks occurring at 
different scales. Effective approaches rely on understanding local power 
dynamics and social relations (Sovacool 2018; Roth et al. 2019; Sapiains 
et al. 2021) (high agreement, medium evidence).
7.4.5.3  Gender-Based Approaches to Peacebuilding
Gender-based approaches provide novel under-utilised pathways 
to achieving sustainable peace (high confidence). Security council 
resolutions have encouraged the incorporation of gender analysis into 
peacebuilding and research has shown that taking into account the 
gendered nature of networks and dialogues opens new avenues for 
cooperation that are conflict sensitive (Dunn and Matthew, 2015), 
creating potential for women’s rights and advocacy groups to be drivers 
of peace (Céspedes-Báez, 2018). For example, women are working to 
reduce climate vulnerability security risks in urban settings by entering

local politics and joining community-based organised and civil society 
networks (Kellog, 2020). The gendered nature of vulnerability and 
access to natural resources (Sections  4.6.4, 4.7.5.3, 5.4.2.3, 5.5.2.6, 
5.8.2.2; Cross-Chapter Box GENDER in Chapter 18) will influence the 
efficacy of interventions to prevent conflict or to build durable peace 
(Pearse, 2017; Chandra et al., 2017; Fröhlich et al., 2018). However, this 
understanding has not so far resulted in widespread employment of 
gender-led analyses (Fröhlich and Gioli, 2015). This represents a key 
opportunity for expansion of the solution space for climate-related 
conflict. Analysis of peace processes more generally demonstrates 
the benefits of women’s participation in peace processes for devising 
strategies for building peace (Paffenholz, 2018; Cárdenas and Olivius, 
2021) and for the durability of that peace (Shair-Rosenfield and Wood, 
2017; Krause et al., 2018).
7.4.6 Climate Resilient Development Pathways

Climate resilient development is a set of trajectories that strengthens 
inequalities while promoting fair and equitable reductions of GHG 
emissions. Climate resilient development also serves to steer societies 
towards low-carbon, prosperous and ecologically safer futures 
(Chapter 1). All pathways to pursue climate resilient development 
will involve balancing complex synergies and trade-offs (very high 
confidence; Chapter 18). Pathways to climate resilient development 
can be pursued simultaneously with recovering from the COVID-19 
pandemic (Cross-Chapter Box COVID in Chapter 7; Ebi et al., 2021).Meeting commitments against the following seven existing global 
priorities would facilitate CRDPs and transformational futures for 
health, well-being, conflict and migration (high agreement, medium 
evidence):
i) Fully implementing the WHO Operational Framework for building

climate-resilient health systems (WHO, 2015b)
ii) Achieving Universal Health Coverage (UHC) under SDG 3 (good 
health and well-being)
iii) Achieving net zero GHG emissions from healthcare systems and 
services
iv) Achieving the SDGs more generally
v) Adopting mitigation policies and technologies that have significant 
health co-benefits (see Cross-Chapter Box HEALTH)
vi) Meeting the objectives of the Global Compact for Safe, Orderly and 
Regular Migration
vii) Inclusive and integrative approaches to climate-resilient peace
These transformations map across all five of the system transitions 
identified in Chapter 18: energy systems; land, ocean, and ecosystems; 
urban and infrastructural systems; industrial systems; and societal 
systems.
7.4.7.1 Fully Implementing the World Health Organization 
Operational Framework
The WHO Operational Framework for building climate-resilient health 
systems was designed to increase the capacity of health systems

and public health programming to protect health in an unstable and 
changing climate (WHO, 2015b). The guidance defines a climate-
resilient health system as one that is capable to anticipate, respond 
to, cope with, recover from and adapt to climate-related shocks and 
stress, so as to bring sustained improvements in population health 
despite an unstable climate. Full implementation of this framework has 
the potential to achieve transformational adaptation; the fundamental 
attributes of health systems would change to anticipate and effectively 
manage the population health and healthcare risks of climate change. 
This includes having the knowledge, capacity, tools and human and 
financial resources for health systems to extend beyond soft limits to 
The WHO framework outlines 10 key components (Figure 7.15) that, 
when achieved, will:
• Guide professionals working in health systems and in health 
determining sectors (e.g., water and sanitation, food and

agriculture, energy, and urban planning) to understand and 
effectively prepare for the additional health risks posed by climate 
variability and change
• Identify the main health functions that need to be strengthened to 
build climate resilience, and to use these to develop comprehensive 
and practical plans (e.g., the health component of National 
Adaptation Plans (H-NAP))
• Support health decision makers to identify roles and responsibilities 
to implement this plan for actors within and outside the formal 
health sector

7
1120Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Achieving full implementation of the WHO Operational Framework 
requires determination and commitment—with associated funding—
from the health community specifically and health-determining sectors 
more generally. Identifying priority areas is an immediate step required 
to commence this implementation process, which will vary across 
different contexts. Active engagement with Communities of Practice to 
share lessons and experiences would be a useful approach to support 
national and sub-national efforts; examples of this already exist (e.g., the 
Climate Change Community of Practice in Canada and the ‘weADAPT’ 
initiative under the auspices of the Stockholm Environment Institute).
Table 7.9 summarises selected characteristics of health systems as they 
might be under SSP1 (a world aiming to sustainable development), 
SSP2 (a world continuing current trends) and SSP3 (a world with high

challenges to adaptation and mitigation), with systems under SSP1 being 
most consistent with climate resilient development. The table highlights 
the importance of investments that promote sustainable and resilient 
development to decrease vulnerability, no matter the magnitude and pattern of climate change. Adapting under SSP3 would be challenging 
even under pathways of limited additional climate change.
Stress testing is an approach for evaluating the extent to which health 
systems are prepared for a future different from today (Ebi et al., 2018a). 
These desk-based exercises identify a desirable future outcome, such 
as successfully managing an extreme heatwave, flood or storm with 
characteristics outside the range of recent experiences. The exercises 
move beyond identifying likely challenges from hazardous exposures 
to specifying policies and measures that could be successful under a 
different climate and development pathway. The exercises consider

socioeconomic and political factors that can influence the extent of 
health system vulnerability and other factors that can affect health 
system demands by impacting population health. Stress testing is 
designed to identify conditions under which it would be difficult for 
the health system to maintain its essential functions and to identify 
interventions that could maintain essential system functions despite 
climate-related shocks and stresses.Ten components of the WHO operational framework for building climate resilient health systems,
with links to the building blocks of health systems.
CLIMATERESILIENCE
FinancingLeadership & 
Health
workforce
Health 
systems
Essential 
medical 
products & 
technolologiesService
delivery
Health
workforce
C
Financin g
CLIMATE &
HEALTH FINANCINGLEADERSHIP  & 
GOVERNANCE
HEAL
TH WORKFORCE
VULNERADBILITY
, CAP
ACITY
 & ADAPT
ATION ASSESSMENT
Building blocks 
of health systems 
INTEGRATED RISK 
MONITORING & EARLY 
WARNING

HEALTH & CLIMATE 
RESEARCHCLIMA TE RESILIENT & 
SUST AINABLE 
TECHNOLOGIES AND 
INFRASTRUCTUREMANAGEMENT OF
 ENVIRONMENT
AL DETERMINANTS OF
 HEAL
THCLIMATE-INF
ORMED
 HEAL
TH PROGRAMMESEMERGENCY 
PREPAREDNESS & 
MANAGEMENT
Figure 7.15 |  Ten components of the WHO operational framework for building climate-resilient health systems with links to the building blocks of health 
systems. Source: WHO (2015b).

7
1121Health, Wellbeing and the Changing Structure of Communities  Chapter 7
Table 7.9 |  Characteristics of future health systems under three SSPs; modified from Sellers and Ebi (2017).
SSP3 SSP2 SSP1
Basic characteristicsReactive; failure to adapt; siloed information 
channels and national governance; limited 
partnershipsIncomplete planning; new information incorporated 
as convenient; occasional partnershipsProactive; adaptively managed; frequent 
partnerships; inter-disciplinary
Leadership and 
governanceLittle focus at national and international levels 
on climate change and health; minimal planning 
conductedPlanning for climate change and health, but not 
comprehensive and often side-tracked by other 
issuesStrong climate change and health planning 
apparatus, including health components of 
national adaptation plans; regional/international 
partnerships
Health workforceClimate change and health not often incorporated 
into training; few provisions for new training

programmes or funding for increase health worker 
positions in climate change-relevant specialties; 
health disparities not addressedClimate change and health not systematically 
incorporated into training; new training 
programmes insufficient to fill gaps in demand; 
limited attention to addressing health disparitiesSystematic inclusion of climate change and health 
in worker training; expansion of funding and 
training; financing and incentive mechanisms to 
address health disparities
Health information 
systemsAssessments of vulnerability and adaptation rarely 
conducted, if ever; information not useful for 
planning; minimal risk monitoring or researchVulnerability and adaptation assessments 
occasionally conducted, but generally of poor 
quality; early warnings incomplete; fiscal and 
political constraints on researchVulnerability and adaptation assessments regularly 
conducted and used in planning; robust early 
warning networks; research agenda focused on

vulnerable communities
Climate-resilient 
and sustainable 
technologies and 
infrastructureFacilities sited and constructed without climate 
consideration incorporated; medical supply chains 
not modifiedCapital cost serves as a key factor in siting and 
construction; increasing vulnerability of facilities 
to shocksHealth infrastructure designed to be robust to 
storms/floods, with redundant systems added to 
ensure continuity of care
Service deliveryPolicies to manage environmental health hazards 
generally not followed; care practices not modified 
to accommodate climate information; few changes 
to emergency management procedures; health 
inequities worsenEnvironmental health policies are not robust; 
marginal improvements in care practices; risk 
assessments and communication inadequate; no 
shift in health inequitiesPolicies to manage environmental health hazards 
regularly reviewed; practitioners review care 
practices and adjust as appropriate based on

local climate and health conditions; robust 
communication tools developed; health service 
improvements reduce health inequities
Climate and health 
financingFew funds devoted to climate change and health 
activities, particularly in low- and middle-income 
countries; few if any financing partnerships between 
high-, low- and middle-income countries; very weak 
regional and international coordinating bodies due 
to funding constraintsHigh-income countries generally form robust 
financing mechanisms; fiscal pressures in low- and 
middle-income countries constrain their financing 
abilities; financial partnerships formed across 
countries, but financing often not robust; regional 
and international coordinating bodies receive 
inadequate fundsRobust funding streams for climate change and 
health; climate change and health activities 
receive continuing financial support; effective 
financing partnerships; regional and international 
coordinating bodies effectively funded

7.4.6.2 Achieving Universal Health Coverage Under SDG 3 
(good health and well-being)
UHC is when all people have access to the health services they need, 
when and where they need them, without financial hardship (WHO, 
2021b). Achieving UHC is one of the targets in the SDGs. However, 
climate change is threatening to undermine the achievement of UHC 
through negative health outcomes and healthcare system disruptions 
(Salas and Jha, 2019; Phillips et  al., 2020; Kadandale et  al., 2020; 
Roa et  al., 2020). Climate change adaptation and UHC progress 
are closely linked to one another, as both may improve health and 
achieve health equity (Salas and Jha, 2019). Supporting UHC is key 
to securing population health under a changing climate as well as 
addressing structural inequalities (Roos et  al., 2021; Aleksandrova, 
2020; Phillips et al., 2020). Many regions of the world with the highest 
levels of vulnerability to the health impacts of climate change also

have low levels of UHC; an integrated approach to UHC planning that 
incorporates climate change will have great benefits particularly in 
improving health equity (Salas and Jha, 2019).
The COVID-19 pandemic has shown some countries taking positive steps 
to achieving UHC. For example, Ireland nationalised healthcare for the 
duration of the pandemic and many countries, including Australia, have enhanced their telehealth services, which has enabled specific groups 
to access health services, particularly those in rural and remote settings, 
and has allowed continuous care to the community (Monaghesh and 
Hajizadeh, 2020; Cross-Chapter Box COVID in Chapter 7).
7.4.6.3 Achieving Net Zero GHG Emissions from Healthcare 
Systems and Services
The healthcare system is a core component of UHC, supporting 
climate-resilient and environmentally sustainable healthcare facilities 
(Corvalan et  al., 2020). Health systems are large carbon polluters

and have the potential to look beyond traditional ‘green’ initiatives 
towards a more fundamental, longer-term redesign of current 
service models, with health practitioners participating actively in this 
process (Charlesworth and Jamieson, 2018). In the largest and most 
comprehensive accounting of national healthcare service emissions, 
the UK’s National Health Service (NHS) quantified its health services’ 
emissions and identified that 62% came from the supply chain, 24% 
from the direct delivery of care, 10% from staff commute and patient 
and visitor travel, and 4% from private health and care services 
commissioned by the NHS (Tennison et al., 2021).

7
1122Chapter 7 Health, Wellbeing and the Changing Structure of Communities
The health sector has considerable opportunity to reduce its own 
carbon footprint and by doing so would contribute to mitigation efforts 
and help reduce health burdens associated with GHG emissions (Vidal 
et  al., 2014; Duane et  al., 2019; Charlesworth and Jamieson, 2019; 
Charlesworth et al., 2018; Guetter et al., 2018; Bharara et al., 2018; 
Frumkin, 2018) (high confidence). The UK’s NHS has committed to 
becoming the world’s first net zero national healthcare system. Other 
examples of recent and ongoing initiatives include those undertaken 
by the Kaiser Permanente and the Gundersen Clinics in the USA, Health 
Care without Harm in the Asia Pacific region, and the Green Hospital 
Initiative in New Delhi (Frumkin, 2018; Bharara et al., 2018).
7.4.6.4 Achieving the SDGs Would Increase Resilience in 
Health-Determining Sectors and Contribute to Reducing 
the Risks of Involuntary Displacement and Conflict

The SDGs are globally agreed objectives that integrate the economic, 
environmental and social aspects of sustainable development to end 
poverty, protect nature and ensure that all people enjoy peace and 
prosperity. The SDGs were developed under the principle that the 
goals are integrated and indivisible, such that progress in one goal 
depends on progress in others (WHO, 2016b). Promoting health and 
well-being is not the sole responsibility of the health sector; it is also 
partially determined by strategies, policies and options such as poverty 
reduction, promoting gender equality, ensuring all people enjoy 
peace and prosperity, eliminating nutritional insecurity and ensuring 
availability and sustainable management of water and sanitation 
(Morton et  al., 2019; Bennett et  al., 2020). Unique themes in the 
SDGs for health policy and systems research include social protection, 
access to health services, stronger and more effective multi-sectoral

collaborations beyond the health sector to address the upstream 
drivers of health and well-being, and participatory and accountable 
institutions to strengthen civic engagement and local accountability 
within health systems (Bennett et al., 2020).
For example, clean water, sanitation and hygiene are essential to 
human health and well-being. Unsafe water and sanitation and a lack 
of hygiene caused an estimated 870,000 associated deaths in 2016 
(WHO, 2021c). Only 71% of the global population has access to safely 
managed drinking water services; only 45% of the global population 
has access to safely managed sanitation services; and 60% has basic 
handwashing facilities in their home. About 25% of healthcare facilities 
lack basic water services, exposing workers and patients to higher 
infection risks. More than 80% of countries reported in 2018 that they 
lacked sufficient funding to meet national WASH targets. As detailed in

Section 7.2.2.2, Box 7.3, Section 7.3.1.4 and Section 7.4.2.3, the burden 
of climate-sensitive WBDs would be reduced if WASH targets were met.
WHO developed a Global Action Plan for Healthy Lives and Well-Being 
for All that brings together multi-lateral health, development and 
humanitarian agencies to support countries in accelerating progress 
towards the health-related SDGs (WHO, 2021c). Themes include 
sustainable financing to reduce unmet needs for services, community 
and civil society engagement to generate knowledge to inform 
policymaking and health responses, addressing the socioenvironmental 
determinants of health, ensuring health and humanitarian services are 
available in fragile and vulnerable settings, research and development, and greater implementation of digital health delivery. In 2020, 
enhanced collaboration through the Global Action Plan provided 
support for an equitable recovery from the COVID-19 pandemic in,

for example, Lao People’s Democratic Republic, Pakistan, Tajikistan, 
Somalia, South Sudan, Malawi, Nepal and Columbia, highlighting the 
potential for multi-sectoral integration of economic, environmental 
and social aspects of sustainable development to maintain essential 
health services and core public health functions during shocks and 
stresses (WHO, 2021a).
Meeting the SDGs also contributes towards reducing involuntary 
displacement and conflict, as assessed in Sections 7.4.6.6 and 7.4.6.7.
7.4.6.5 Adopting Mitigation Policies and Technologies that 
Have Significant Health Co-benefits
Substantial co-benefits from climate action can result from investing 
in health systems, infrastructure, water and sanitation, clean energy, 
affordable healthy diets, low-carbon housing, public transport, 
improved air quality, and social protection. These benefits are in 
addition to the avoided health impacts associated with climate change 
(see Cross-Chapter Box HEALTH in Chapter 7).

7.4.6.6 International Policy Frameworks for Migration that 
Contribute to Climate Resilient Development
Climate-related migration, displacement and immobility in coming 
decades will coincide with global and regional demographic changes 
that will produce a widening distinction between high-income 
countries that have aging, slow-growing (or in some countries, 
shrinking) population numbers and low-income countries that have 
rapidly growing, youthful populations. Given this dynamic, coordinated 
national and international strategies that integrate migration and 
displacement considerations with wider adaptation and sustainable 
development policies may contribute to climate resilient development. 
Since AR5, the international community has established a number 
of agreements and initiatives that, with continued pursuit and 
implementation, would create potential for climate-related migration 
to be a positive contribution towards adaptive capacity-building and

sustainable development more broadly (Warner, 2018).
The 2018 Global Compact for Safe, Orderly and Regular Migration 
provides an important opportunity for planning for and responding 
to future climate-related migration and displacement (Kälin, 2018).  
Among its 23 objectives, the Compact explicitly encourages the 
international community to implement migration policies that facilitate 
voluntary migration and actively prepare for involuntary displacements 
due to climate change, especially in low- and middle-income countries. 
The Compact’s objectives include reducing barriers to legal and safe 
migration, and facilitating the freer flow of remittances between sending 
and receiving communities. By doing so the Compact aims to increase 
the potential for migration to make positive contributions to sustainable 
development and to adaptive capacity-building. It also contains specific 
provisions pertaining to climate- and disaster-related migration and

displacement. Objective 2 of the Compact aims at reducing drivers 
of involuntary or low-agency migration and recommends that states 
establish systems for sharing information on environmental migration,

7
1123Health, Wellbeing and the Changing Structure of Communities  Chapter 7
develop climate adaptation and resilience strategies harmonised 
at sub-regional and regional levels, and cooperate on disaster risk 
prevention and response. Other objectives in the Compact relevant 
to climate-related migration include Objective 5 (increasing pathways 
for regular migration) and Objective 19 (facilitating migrants’ ability 
to contribute to sustainable development). Objective 18, which links 
migration with skills development, is consistent with the ‘migration with 
dignity’ approach to displacement risks (McNamara, 2015; Kupferberg, 
2021). The 2018 Global Compact on Refugees observes that climate 
hazards increasingly interact with the drivers of refugee movements. 
The guidelines this Compact provides to governments regarding actions 
for addressing the causes of refugee movements and considerations for 
assisting and supporting refugees are useful for governments seeking

guidance for all forms of displacement more generally, including 
displacement linked to climate change.
Pursuant to the Paris Agreement, a task force was struck by the 
Warsaw International Mechanism to make recommendations to the 
Conference of the Parties to the UNFCCC on how to reduce the risks 
of climate-related displacement. Its 2018 report recommended that 
parties work towards development of national legislation, cooperate 
on research, strengthen preparedness, integrate mobility into wider 
adaptation plans, work towards safe and orderly migration, and 
provide assistance to people internally displaced for climate-related 
reasons. Such recommendations dovetail strongly with the objectives 
of the Compacts on Migration and Refugees as well as the Sendai 
Framework for DRR and the 2030 SDGs. The SDGs, which include 
multiple goals and targets in which migration plays an explicit role 
in fostering development (Nurse, 2019), may be seen as completing

the international policy arrangements necessary for addressing future 
climate-related migration and displacement.
7.4.6.7 Inclusive and Integrative Approaches to Climate-
Resilient Peace
CRDPs to reduce conflict risk rely on a shift in perspective from framings 
around resource scarcity and security to sustainable natural resource 
governance and peace (Brauch et  al., 2016; Barnett, 2018; Dresse 
et  al., 2018; Day and Caus, 2020). Recognising that conflict results 
from underlying vulnerabilities, development that reduces vulnerability 
offers the best win-win option for building sustainable, climate-
resilient peace rather than specific security-focused interventions (high 
confidence). To this end, meeting the SDGs represents an unambiguous 
path to reducing conflict risk in a climate-changed world (Singh and 
Chudasama, 2021). There is growing acceptance in the development 
community, despite reservations about the securitisation of climate,

that instability and conflict exacerbated by climate change has the 
potential to undermine development gains (Casado-Asensio et  al., 
2020; Day and Caus, 2020).
Core to achieving climate-resilient peace are new ways of working that 
involve cross-issue and cross-sectoral collaboration and integration as 
a default to policy and programming. The Security Council Resolution 
1325 Women and peace and security (S/RES/1325 (2000)) and the 
Sustaining Peace Agenda (A/RES/70/262 (2016)) are notable examples 
of this. The 2020 UNEP report on gender and security recommends 
integrated policy frameworks, better financing to strengthen women’s roles in peacebuilding, integrated programme design, and further 
research on gender, climate and security linkages. Inclusive approaches 
recognise that much of the vulnerability that drives conflict risk 
is generated by existing inequality and marginalisation of large 
proportions of the population—for example women and youth—and

that peace cannot be achieved without their needs being taken into 
account and without their participation in peace processes (Mosello 
et al., 2021). Diverse and inclusive partnerships also require ways to 
better engage local-level participation, and improve understanding of 
how to build consensus through human rights-based approaches that 
recognize non-violent conflict and protest to be potentially positive 
and constructive elements of transformational approaches to building 
resilience (Nursey-Bray, 2017; Ensor et al., 2018; Schipper et al., 2021). 
Addressing the lack of participation of researchers and experts from 
countries most at risk of conflict in many climate-related conflict and 
peacebuilding assessments and initiatives could also support this 
objective. There is an increasing focus on the role of environmental 
defenders in highlighting violations and gaps in state obligations 
through non-violent protest (Butt et al., 2019; Scheidel et al., 2020).

CRDPs for sustainable peace also require different ways of gathering 
intelligence and informing conflict risk. Dynamics that affect such 
risks exist across scales from the local to the regional, and require 
response in a transboundary manner. There is increasing emphasis on 
engaging local stakeholders and diverse partnerships to inform context 
appropriate measures and better policy coordination (Bremberg et al., 
2019; Tshimanga et  al., 2021; Abrahams, 2020). The UN’s Climate 
Security Mechanism, working across three UN departments, takes an 
integrated approach to analyse and support timely and appropriate 
responses to conflict risk, focusing on risk assessments and early 
warning systems to aid conflict prevention, climate-informed peace and 
security activities and conflict-sensitive development, and to promote 
inter-sectoral cooperation, partnership and information sharing (DPPA 
et al., 2020). There is already acknowledgement that adaptation needs

to be effectively monitored so that maladaptation can be avoided 
(Eriksen et al., 2021). Here, academic research, which until now has 
predominantly focused on understanding the causal relationship 
between conflict and climate, could contribute to advancing the 
monitoring and evaluation of climate-resilient peacebuilding initiatives 
(Mach et al., 2020; Gilmore et al., 2018).

7
1124Chapter 7 Health, Wellbeing and the Changing Structure of Communities
Cross-Chapter Box HEALTH | Co-benefits of Climate Actions for Human Health, Well-Being and 
Equity
Authors: Cristina Tirado (USA/Spain, Chapter 7); Robbert Biesbroek (Netherlands, Chapter 13); Mark Pelling (United Kingdom, Chapter 6); 
Jeremy Hess (USA, Chapter 7); Felix Creutzig (Germany, WGIII); Rachel Bezner Kerr (Canada/USA, Chapter 5); Siri Eriksen (Norway, Chapter 
18); Diarmid Campbell-Lendrum (United Kingdom, Chapter 7); Elisabeth Gilmore (USA/Canada, Chapter 14); Maria Figueroa (Denmark/
Venezuela, WGIII); Nathalie Hilmi (Monaco, Chapter 18); Peter Newman (Australia, WGIII); Sebastian Mirasgedis (Greece, WGIII); Sharma 
Rohit (India); Yamina Saheb (France/Algeria, WGIII); Gerardo Sanchez Martinez (Spain); Peter Smith (United Kingdom, WGIII); Adrian Leip 
(Italy, WGIII); Dhar Subash (Denmark/India, WGIII); Chris Trisos (South Africa, Chapter 9); Mercedes Bustamante (Brazil, WGIII); Luisa

Cabeza (Spain, WGIII); Diana Urge-Vorsatz (Hungary, WGIII)
Achieving the Paris Agreement and SDGs can result in low-carbon, healthy, resilient and equitable societies with high well-being for all (very 
high confidence) (Alfredsson et al., 2018; O’Neill et al., 2018). Given the overlap in sources of greenhouse gases (GHGs) and co-pollutants 
in energy systems, strategies that pursue GHG emission reductions and improvements in energy efficiency hold significant potential health 
co-benefits through air pollution emission reductions (high confidence) (Gao et al., 2018). Air quality improvements alone can substantially 
offset, or most likely exceed, mitigation costs at the societal level (Schucht et al., 2015; Chang et al., 2017; Markandya et al., 2018; Vandyck 
et al., 2018; Peng et al., 2017; Woodward et al., 2019; Sampedro et al., 2020; Xie et al., 2018). Pursuit of a mitigation pathway compatible

with warming of +1.5°C with associated cleaner air, avoided extreme events and improved food security and nutrition could result in 152 
± 43 million fewer premature deaths worldwide between 2020 and 2100 compared with a business-as-usual scenario (Shindell et al., 
2018). Reaching the Paris Agreement could result in an annual reduction of 1.18 million air pollution-related deaths, 5.86 million diet-
related deaths and 1.15 million deaths due to physical inactivity across nine major economies by 2040 (Hamilton et al., 2021). In Europe, 
a mitigation scenario compatible with RCP2.6 could reduce total pollution costs, mostly from PM2.5, by 84%, with human health benefits 
equal to more than Euro 1 trillion over five years (Scasny et al., 2015). In the EU, ambitious climate mitigation policies could reduce years 
of lost life due to fine particulate matter (PM) from over 4.6 million in 2005 to 1 million in 2050, reduce ozone-related premature deaths

from 48,000 to 7,000 and generate health benefits of Euro 62 billion yr–1 in 2050 (Schucht et al., 2015).
However, there may be significant trade-offs between mitigation and other societal goals (Dong et  al., 2019; Gao et  al., 2018). In 
some scenarios, mitigation policies consistent with the NDCs may slow poverty reduction efforts (Campagnolo and Davide, 2019) with 
implications for health. A framework of ‘co-impacts’ that assumes neither a general beneficial nature of all implications from mitigation 
policy nor a hierarchy between climate and other types of benefits, may be more appropriate (Ürge-Vorsatz et al., 2014; Cohen et al., 2017).
Transitioning to affordable clean energy sources for all presents opportunities for substantial well-being, health, and equity co-benefits 
(high confidence) (Gibon et al., 2017; Lacey et al., 2017; Peng et al., 2018; Vandyck et al., 2018; Williams et al., 2018). Residential solid fuel

use affects health and degrades indoor air quality for up to 3.1 billion people in low- and middle-income countries (WHO, 2016b; Wang 
et al., 2017a). Adherence to planned emission reductions from the Paris Agreement related to renewables could subsequently improve air 
quality and prevent 71,000–99,000 premature deaths annually by 2030 (Vandyck et al., 2018). This effect increases with a 2°C pathway, 
with 0.7–1.5 million premature deaths avoided annually by 2050 (Vandyck et al., 2018). Co-benefits are also observed at national and 
regional levels. For instance, China could expect 55,000–69,000 averted deaths in 2030 if it transitioned to a half-decarbonised power 
supply for its residential and vehicle sectors (Peng et al., 2018).
Investing in universal basic infrastructure, including sanitation, clean drinking water, drainage, electricity, and land-rights, can transform

development opportunities, increase adaptive capacity, and reduce vulnerability to climate-related risks (high agreement, high evidence).
Transformative approaches that reduce climate-related risks and deliver enhanced social inclusion and development opportunities for 
the urban poor are most likely where local governments act in partnership with local communities and other civil society actors (high 
confidence) (Chapter 6, sections 6.1, 6.3, 6.4).
Rapid urbanisation offers a time-limited opportunity to work at scale towards transformational adaptation and climate resilient 
development (medium evidence, high agreement). Multi-level leadership, institutional capacity and financial resources to support 
inclusive adaptation in the context of multiple pressures and inter-connected risks can help ensure that the additional 2.5 billion people 
projected to live in urban areas by 2050 are less exposed to climate-related hazards and contribute less to global warming (high

confidence) (Chapter 6, sections 6.1, 6.3, 6.4). Integrating low-carbon, inclusive adaptation into infrastructure investment driven by rapid 
urban population growth and COVID-19 recovery can accelerate co-benefits (Chapter 6).
Urban planning that combines clean, affordable public transportation, shared clean vehicles and accessible active transportation modes 
can improve air quality and contribute to healthy, equitable societies and higher well-being for all. Stimulating active mobility (walking 
and bicycling) can bring physical and mental health benefits (high confidence) (Chapter 6; Rojas-Rueda et al., 2016; Avila-Palencia et al.,

7
1125Health, Wellbeing and the Changing Structure of Communities  Chapter 7
2018; Gascon et al., 2019; Hamilton et al., 2021). The health gains from active mobility outweigh traffic-related injuries due to a decreased 
incidence of chronic diseases (Ahmad et al., 2017; Maizlish et al., 2017; Tainio et al., 2017; Woodcock et al., 2018).
Urban green and blue spaces contribute to climate change adaptation and mitigation and improve physical and mental health and 
well-being (high confidence) (Hansen 2017; EC, 2018; WHO, 2018a; Rojas-Rueda et al. 2019; 13.7.3, WGII; 6. WGII; 8.4 WGIII). Urban 
green infrastructure including urban gardens, can bring benefits to social cohesion, mental health and well-being and reduce the health 
impacts of heatwaves by decreasing temperatures, thus reducing inequities in exposure to heat stress for low income, marginalised

groups (Hoffman et al., 2020; Hoffmann et al., 2020; Chapter 5 section 5.12.5; Chapter 6; Chapter 7 section 7.4; Chapter 13 section 13.7). 
The trade-offs of increasing urban green and blue spaces include potential public health risks related to increased vectors or hosts for 
infectious diseases, toxic algal blooms, drowning and aeroallergens (Choi et al., 2021; Stewart-Sinclair et al., 2020; Chapter 6).
Climate adaptation and mitigation policies in the building sector offer multiple well-being and health co-benefits (high confidence) 
(Diaz-Mendez et al., 2018; Macnaughton et al., 2018; Chpater 3 section 3.6.2). Leadership in Energy and Environmental Design (LEED) 
certified buildings in the USA, Brazil, China, India, Germany and Turkey saved an estimated USD 7.5 billion in energy costs and averted 
33 Mt of CO 2 from 2000–2016 (Macnaughton et al., 2018). These measures can increase health benefits through better indoor air quality,

reduction of the heat island effect, improved social well-being through energy poverty alleviation, creation of new jobs, increased 
productive time and income, increased thermal comfort and lighting indoors and reduced noise impact (Smith et al., 2016; McCollum 
et al., 2018; Thema et al., 2017; Mirasgedis et al., 2014; Alawneh et al., 2019; Diaz-Mendez et al., 2018). The value of these multiple co-
benefits associated with climate actions in buildings is equal to or greater than the costs of energy savings (Ürge-Vorsatz et al., 2016; 
Payne et al., 2015; Chapter 14 section 14.4.5).
Shifting to sustainable food systems that provide affordable, diverse and plant-rich diets with moderate quantities of GHG-intensive 
animal protein can bring health co-benefits and substantially reduce GHG emissions, especially in high income countries and where ill 
health related to overconsumption of animal-based products is prevalent (very high confidence) (Chapter 5 section 5.12.6; Chapter 7

section 7.4, Chapter 13 section 13.5; Springmann et al., 2018c; IPCC, 2019b; Clark and Tilman, 2017; Poore and Nemecek, 2018; Hayek 
et al., 2021). Transforming the food system by limiting the demand for GHG-intensive animal foods, reducing food over-consumption and 
transitioning to nutritious, plant-rich diets can have significant co-benefits to health (high confidence) (Hedenus et al., 2014; Ripple et al., 
2014; Tirado, 2017; Springmann et al., 2018c; IPCC, 2018; IPCC, 2019a; IPCC, 2019b; Nelson et al., 2016; Willett et al., 2019; Tilman and 
Clark, 2014; Green et al., 2015; Springmann et al., 2016b; Springmann et al., 2018b; Springmann et al., 2018a; Springmann et al., 2018c; 
Milner et al., 2015; Milner et al., 2017; Farchi et al., 2017; Song et al., 2017; Willett et al., 2019). Reduction of red meat consumption 
reduces the risk of cardiovascular disease (CVD) and colorectal cancer; the consumption of more fruits and vegetables can reduce the risk

of CVD, type II diabetes, cancer and all causes of mortality (Tilman and Clark, 2014; Sabate and Soret, 2014; Willett et al., 2019; Chapter 
7 section 7.4; Chapter 5 section 5.12.5). Globally, it is estimated that transitioning to more plant-based diets—in line with World Health 
Organization (WHO) recommendations on healthy eating—could reduce global mortality by 6‒10% and food-related GHG emissions by 
29‒70% by 2050 (Springmann et al., 2016b). There are limitations in accessibility of affordable of healthy and diverse diets for all 
(Springmann et al., 2020) and trade-offs such as the potential increase of GHG emissions from producing healthy and diverse diets in 
low- and medium-income countries (Semba et  al., 2020). Agroecological approaches have mitigation and adaptation potential and 
deliver ecosystem services, biodiversity, livelihoods and benefits to nutrition, health and equity (Rosenstock et al., 2019; Bezner Kerr et al.,

2021; Chapter 5 sections 5.4.4, 5.14.1; Chapter 13 section 13.5; Chapter 14 section 14.4.4).Cross-Chapter Box HEALTH (continued)

7
1126Chapter 7 Health, Wellbeing and the Changing Structure of Communities
FAQ 7.1 | How will climate change affect physical and mental health and well-being?
Climate change will affect human health and well-being in a variety of direct and indirect ways that depend on 
exposure to hazards and vulnerabilities that are heterogeneous and vary within societies, and that are influenced 
by social, economic and geographical factors and individual differences (see Figure  FAQ7.1.1). Changes in the 
magnitude, frequency and intensity of extreme climate events (e.g., storms, floods, wildfires, heatwaves and dust 
storms) will expose people to increased risks of climate-sensitive illnesses and injuries and, in the worst cases, higher 
mortality rates. Increased risks for mental health and well-being are associated with changes caused by the impacts 
of climate change on climate-sensitive health outcomes and systems (see Figure FAQ7.1.2). Higher temperatures and

changing geographical and seasonal precipitation patterns will facilitate the spread of mosquito- and tick-borne 
diseases, such as Lyme disease and dengue fever, and water- and food-borne diseases. An increase in the frequency 
of extreme heat events will exacerbate health risks associated with cardiovascular disease and affect access to 
freshwater in multiple regions, impairing agricultural productivity and increasing food insecurity, undernutrition 
and poverty in low-income areas.
Pathways from hazards, exposure and vulnerabilities to climate change impacts on health outcomes
and health Systems
Gender
Mobilit y
Floods
Storms
- Adverse health (VBD, WBD, infectious disease,

heat-related illness, mental health, under nutrition)
- Livelihood systemsIMPACTS
Figure FAQ7.1.1 |  Pathways from hazards, exposure and vulnerabilities to climate change impacts on health outcomes and health systems. 

7
1127Health, Wellbeing and the Changing Structure of Communities  Chapter 7
2 1
Hazard
(e.g. drought, sea level rise, 
sea ice loss, changing climate 
normals)
Gender
Age
(e.g. displacement, food systems 
(e.g. observed experiences of others, 
effective mental health systems, 
planning and preparedness, informed 
policies, early intervention
planning, design, green infrastructure
Community
-  Supportive social networks, effective 
information channels
- awareness, preparedness, mental

health support, nature-based therapy3 4
Risks to mental health and wellbeing
[e.g., PTSD, depression, suicide ]Diminished wellbeing
[e.g., stress, climate anxiety, cognitive 
impairment )Diminished social relations  
[e.g., loss of culture, interpersonal violence)5
Community
Figure FAQ7.1.2 |  Climate change impacts on mental health and key adaptation responses. 
PTSD: Post traumatic stress disorder.Box FAQ 7.1 (continued)

7
1128Chapter 7 Health, Wellbeing and the Changing Structure of Communities
FAQ 7.2 | Will climate change lead to wide-scale forced migration and involuntary displacement?
Climate change will have impacts on future migration patterns that will vary by region and over time, depending 
on the types of climate risks people are exposed to, their vulnerability to those risks and their capacity—and the 
capacity of their governments—to adapt and respond. Depending on the range of adaptation options available, 
households may use migration as a strategy to adapt to climate risks, often through labour migration. The most 
common drivers of climate-related displacement are extreme weather events, floods and droughts, especially when 
these events cause severe damage to homes, livelihoods and food systems. Rising sea levels will present a new 
risk for communities situated in low-lying coastal areas and small island states. The greater the scale of future

warming and extreme events, the greater the potential scale of future, involuntary climate-related migration and 
displacement.Progress towards achieving the Sustainable Development Goals (SDGs) has strong potential to reduce 
future involuntary climate-related migration and displacement.
FAQ 7.4 | What solutions can effectively reduce climate change risks to health, well-being, forced migration and 
conflict?
The solution space includes policies, strategies and programmes that consider why, how, when and who should be 
involved to sustainably adapt to climate change. Effectively preparing for and managing the health risks of climate 
change requires considering the multiple interacting sectors that affect population health and effective functioning 
of health systems. Considering the close inter-connections between health, migration and conflict, interventions

that address climate risks in one area often have synergistic benefits in others. For example, conflicts often result 
in large numbers of people being involuntarily displaced and facilitate the spread of climate-sensitive diseases; 
tackling the underlying causes of vulnerability and exposure that generate conflict reduces risks across all areas. 
A key starting point for health and well-being is strengthening public health systems so that they become more 
climate resilient, which also requires cooperation with other sectors (water, food, sanitation, transportation, etc.) to 
ensure appropriate funding and progress on sustainable development goals. Interventions to enhance protection 
against specific climate-sensitive health risks could reduce morbidity and mortality and prevent many losses and 
damages (Figure FAQ7.4.1). These range from malaria net initiatives, vector control programmes, health hazard

(syndromic) surveillance and early warning systems, improving access to water, sanitation and hygiene (WASH), heat 
action plans (HAPs), behavioural changes and integration with disaster risk reduction (DRR) and response strategies. 
More importantly, climate resilient development pathways (CRDPs) are essential to improve overall health and 
well-being, reduce underlying causes of vulnerability and provide a framework for prioritising mitigation and Frequently Asked Questions
FAQ 7.3 | Will climate change increase the potential for violent conflict?
Climate hazards have affected armed conflict within countries but the observed influence of climate is small relative 
to socioeconomic, political and cultural factors. Adverse impacts of climate change threaten to increase poverty 
and inequality, undermine progress in meetings Sustainable Development Goals (SDGs) and place strain on civil

institutions—all of which are factors that contribute to the emergence or worsening of civil unrest and conflict. 
Climate change impacts on crop productivity and water availability can function as a ‘risk multiplier’ for conflict in 
areas that are already politically and/or socially fragile and, depending on circumstances, could increase the length 
or the nature of an existing conflict. Institutional initiatives within or between states to protect the environment 
and manage natural resources can serve simultaneously as mechanisms for engaging rival groups and adversaries 
to cooperate in policymaking and peacebuilding.

7
1129Health, Wellbeing and the Changing Structure of Communities Chapter 7
FAQ 7.5 | What are some specific examples of actions taken in other sectors that reduce climate change risks in the 
health sector?
Many actions taken in other sectors to address the risks of climate change can lead to benefits for health and

well-being. Adaptive urban design that provides greater access to green and natural spaces simultaneously enhances biodiversity, improves air quality and moderates the hydrological cycle; it also helps reduce health risks associated with heat stress and respiratory illnesses, and mitigates mental health challenges associated with congested urban living. Transitioning away from internal-combustion vehicles and fossil fuel-powered generating stations to renewable energy mitigates greenhouse gas emissions, improves air quality and lowers the risks of respiratory illnesses. Policies and designs that facilitate active urban transport (walking and bicycling) increase efficiency in that sector, reduce emissions, improve air quality and generate physical and mental health benefits for residents. Improved building and urban design that foster energy efficiency improve indoor air quality and reduce risks of heat stress and respiratory illness. Food systems that emphasise healthy, plant-centred

diets reduce emissions in the agricultural sector while helping in the fight against malnutrition.adaptation options that support sustainable development. Transformative changes in key sectors including water, food, energy, transportation and built environments offer significant co-benefits for health.

Adaptation responses to climatic risks
Adaptation in
other sectors
Ch. 8
Ch. 2; CCP 1
Ocean &
Ch. 3
Urban
systems
Ch. 6
Ch. 4, 5
Figure FAQ7.4.1 |  Solution space for adaptation to climate change in health and other sectors.FAQ 7.4 (continued)

8
1174Chapter 8 Poverty, Livelihoods and Sustainable Development
Adverse impacts of climate change, development deficits and 
inequality exacerbate each other. Existing vulnerabilities and 
inequalities intensify with adverse impacts of climate change 
(high confidence1). These impacts disproportionately affect margin-
alised groups, amplifying inequalities and undermining sustainable 
development across all regions (high confidence). Due to their socio-
economic conditions and the broader development context, many poor 
communities, especially in regions with high levels of vulnerability 
and inequality, are less resilient to diverse climate impacts (high con-
fidence). {8.2.1, 8.2.2, 8.3.2, 8.3.3}
Under all emissions scenarios, climate change reduces capacities 
for adaptive responses and limits choices and opportunities for 
sustainable development. Higher levels of global warming lead 
to greater constraints on societies. Climate change increases

the threat of chronic and sudden onset development challenges, 
such as poverty traps and food insecurity (high confidence). 
Adaptation interventions and transformative solutions that prioritise 
inclusive and wide-ranging climate resilient development and the 
reduction of poverty and inequality are increasingly seen as necessary 
to minimise loss and damage from climate change (high confidence). 
{8.2.1, 8.2.2, 8.3.1, 8.3.2, 8.3.3}
Observed societal impacts of climate change, such as mortality 
due to floods, droughts and storms, are much greater for regions 
with high vulnerability compared to regions with low vulner -
ability, which reveals the different starting points that regions 
have in their move towards climate resilient development (high 
confidence). Observed average mortality from floods, drought and 
storms is 15 times higher for regions and countries ranked as very high 
vulnerable, such as Mozambique, Somalia, Nigeria, Afghanistan and

Haiti compared to very low vulnerable regions and countries, such as 
UK, Australia, Canada and Sweden in the last decade (high confidence). 
Over 3.3 billion people are living in countries classified as very highly 
or highly vulnerable, while around 1.8 billion people live in countries 
with low or very low vulnerability (high confidence). Approximately 
3.6 billion people live in low and lower middle-income countries, which 
are most vulnerable and disproportionally bear the human costs of dis-
asters due to extreme weather events and hazards (high confidence). 
The population in most vulnerable countries is projected to increase sig-
nificantly by 2050 and 2100, while the population in countries with low 
vulnerability is projected to decrease or grow only slightly. Vulnerability 
is a result of many interlinked issues concerning poverty, migration, in-
equality, access to basic services, education, institutions and govern-

ance capacities, often made more complex by past developments, such 
as histories of colonialism (high confidence). {8.3.2, 8.3.3}
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as

3 Meaning low or moderate emission scenarios.A growing range of economic and non-economic losses have 
been detected and attributed to climate extremes and slow-
onset events under observed increases in global temperatures 
(medium evidence, high agreement). If future climate change under 
high emissions scenarios continues and increases risks, without strong 
adaptation measures, losses and damages will likely2 be concentrated 
among the poorest vulnerable populations (high confidence). The

intersection of inequality and poverty presents significant adaptation 
limits, resulting in residual risks for people and groups in vulnerable 
situations, including women, youth, elderly, ethnic and religious 
minorities, Indigenous People and refugees. Climate change is likely 
to force economic transitions among the poorest groups, accelerating 
the switch from agriculture to other forms of wage labour, with 
implications for labour migration and urbanisation (medium evidence, 
high agreement). Under an inequality scenario (Shared Socioeconomic 
Pathway (SSP) 4) the projected number of people living in extreme 
poverty may increase by 122  million by 2030 (medium confidence). 
{8.2, 8.3.4, 8.4.1, 8.4.5, Figure 8.6, Box 8.5, 16.5.2.3.4}
Both climate change and vulnerability threaten the achievement 
of the UN Sustainable Development Goals (SDGs) (medium 
confidence). This undermines progress toward various goals such as

no poverty (SDG1), zero hunger (SDG2), gender equality (SDG5) and 
reducing inequality (SDG10), among others (medium evidence, high 
agreement). Gender inequality and discrimination are among the barriers 
to adaptation (high confidence). {8.2.1¸8.4.5} Also maladaptation can 
lead to additional complex and compounding future risks and threaten 
sustainable development (high confidence). {8.4.5.5, 8.2.1.7}
Under higher emissions scenarios and increasing climate haz-
ards, the potential for social tipping points increases (medium 
confidence). Even with moderate climate change3 people in vulner -
able regions will experience a further erosion of livelihood security that 
can interact with humanitarian crises, such as displacement and forced 
migration (high confidence) and violent conflict, and lead to social 
tipping points (medium confidence). Social tipping points can also be 
coupled with environmental tipping points. {8.3, 8.4.4}

Vulnerable population groups in most vulnerable regions have 
the most urgent need for adaptation (high confidence). The 
most vulnerable regions are particularly located in East, Central 
and West Africa, South Asia, Micronesia and Melanesia and in 
Central America (high confidence). These regions are characterised 
by compound challenges of high levels of poverty, a significant number 
of people without access to basic services, such as water and sanitation, 
and wealth and gender inequalities, as well as governance challenges. 
Areas of high human vulnerability are characterised by larger 
transboundary regional clusters (high confidence). Additional support

8
1175Poverty, Livelihoods and Sustainable Development  Chapter 8
and structures are needed to reduce the existing gaps between future 
adaptation needs and current capacities, and to support transitions 
from vulnerable livelihoods with adequate integration of the 
Indigenous knowledge and local knowledge (IKLK) systems. Greater 
investments are required under higher levels of global warming and 
of inequality (Relative Concentration Pathway (RCP) 4.5; RCP8.5 and 
SSP4) (high confidence). {8.3, 8.4, Box 8.6}
The direct and indirect consequences of the COVID-19 pandemic 
have worsened inequalities within societies, thereby increasing 
existing vulnerabilities to climate change and further limiting 
the ability of marginalised communities to adapt (medium 
confidence). The COVID-19 pandemic is expected to increase the adverse 
consequences of climate change since the financial consequences have 
led to a shift in priorities and constrain vulnerability reduction (medium

confidence). Moreover, the COVID-19 pandemic is also influencing 
the capacities of governmental institutions in developing nations to 
support planned adaptation and poverty reduction of most vulnerable 
people/groups, since the crisis also means significant reductions in tax 
revenues (high confidence). {8.3, 8.4, 8.4.5.5}
Those with climate-sensitive livelihoods and precarious liveli-
hood conditions are often least able to adapt, afforded limited 
adaptation opportunities and have little influence on decision 
making (high confidence). Enabling environments that sup-
port sustainable development are essential for adaptation and 
climate resilient development (high confidence). Enabling and 
supportive environments for adaptation share common governance 
characteristics, including multiple actors and assets, multiple centres 
of power at different levels and an effective vertical and horizontal 
integration between levels (high confidence). Enabling conditions can

support livelihood strategies that do not undermine human well-being 
(medium confidence). {8.5.1, 8.5.2, 8.6.3, 5.13}
Mitigation and adaptation responses to climate change influence 
inequalities, poverty and livelihood security and thereby aspects 
of climate justice (medium confidence). Improving coherence 
between adaptations of different social groups and sectors at 
different scales can reduce maladaptation, enable mitigation 
and advance progress towards climate resilience (medium 
confidence). The poor typically have low carbon footprints but are 
disproportionately affected by adverse consequences of climate change 
and also lack access to adaptation options. In many cases, the poor 
and most vulnerable people and groups are most adversely affected by 
maladaptation (medium evidence, high agreement). Climate justice and 
rights-based approaches are increasingly recognised as key principles 
within mitigation and adaptation strategies and projects (medium

confidence). Narrowing gender gaps can play a transformative role 
in pursuing climate justice (medium confidence). Climate resilient 
development is therefore closely coupled with issues of climate justice. 
Synergies between adaptation and mitigation exist, and these can 
have benefits for the poor (medium confidence). {8.4, 8.4.5.5, 8.6}
There is increasing evidence that nature-based solutions (e.g., 
urban green infrastructure, ecosystem-based management) can 
provide important livelihood options and reduce poverty while 
also supporting mitigation and adaptation (medium confidence).  However, the trade-offs over time between nature-based solutions and 
their dynamics are insufficiently understood. Appropriate governance, in-
cluding mainstreaming and policy coherence, supported by adaptation 
finance that targets the poor and marginalised, is essential for adaptation 
and climate compatible development (medium confidence). {8.5.2, 8.6.3, 
5.14}

8
1176Chapter 8 Poverty, Livelihoods and Sustainable Development
8.1 Introduction
The impacts of climate change have already significantly affected 
livelihoods and living conditions, especially of the poorest and most 
vulnerable, and will continue to undermine development during the 
coming century. This chapter assesses the societal consequences of 
climate change and related hazards in terms of adverse and irreversible 
consequences for the most vulnerable. To understand societal conse-
quences of climate change, we assess impacts through the perspective 
of vulnerability, poverty and livelihoods of people. We identify why cli-
mate events trigger sudden and slow-onset disasters, and how the most 
severe, acute and chronic impacts cause and deepen human suffering. 
We also examine issues of climate justice. Understanding and engaging 
with climate justice requires a plural focus on the historical social and 
institutional relations and inequalities that produce climate change,

cause people to be vulnerable to climate hazards and shape responses 
to them (Newell et al., 2021). An assessment of observed impacts on the 
poorest and their strategies for adaptation carries important lessons for 
inclusive, broad-based solutions to climate change.
As a starting point, this chapter examines linkages between climate 
change, specific climate-related hazards and impacts on multidimen-
sional poverty, vulnerability and livelihoods. Past assessments have 
identified the linkages between climate change, poverty, livelihoods 
and human vulnerability, and shown how climate change leads to 
differential consequences for different communities and populations. 
The IPCC Fifth Assessment Report (AR5) identified socially and geo-
graphically disadvantaged people exposed to persistent inequalities 
at the intersection of various dimensions of discrimination based on 
gender, age, ethnicity, class and caste (IPCC, 2014a). AR5 also showed

evidence that climate change is a universal driver and multiplier of 
risk that shapes dynamic interactions between these factors. Climate 
change is one stressor that shapes dynamic and differential livelihood 
trajectories. Also, the IPCC Special 1.5°C Report (IPCC SR 1.5°C) under -
scored with very high confidence that global mean temperature, harm 
and human well-being losses are increasing substantially (Hoegh-
Guldberg et al., 2018; Roy et al., 2018).
This chapter builds on this, examining equitable development, robust 
institutions and poverty reduction as essential inputs to societies’ 
capacity for adaptation (i.e., closes the adaptation gap) in order to avoid 
losses and damages (L&Ds) from climate change. It assesses quantitative 
spatio-temporal information on human vulnerability at a global scale 
and for specific sub-regions, livelihood groups and communities at 
the local level. The chapter assesses the newest literature on how

multidimensional poverty and human vulnerability to climate change is 
measured and also examined the agreement of different index systems 
in terms of global hotspots of human vulnerability.
In addition, the chapter explores how climate change affects different 
livelihoods and livelihood assets and also examines factors that 
characterise vulnerability to climate change, focusing on different 
dimensions of human vulnerability and its subsystems (e.g., access 
to infrastructure services). In this context the chapter also assesses 
quantitative data to map human vulnerability as well as economic 
and non-economic losses that are highly relevant for understanding 
adverse impacts of climate change.The chapter assesses the newest scientific knowledge on how the 
most vulnerable and marginalised people are experiencing different 
climate-influenced hazards and changes, how these groups prepare for 
and adapt to these changes. Hence, it examines how climate change

intersects with broader processes of development. It also considers the 
various impacts of climate change on the livelihoods of the poorest, 
the capabilities, assets and activities required for a means of living. It 
examines the institutional conditions that promote livelihood resilience 
in the face of climate change. Quantitative analysis and qualitative data 
on observed adverse climate change impacts and future projections and 
trends in vulnerability show that societal impacts of climate change 
cannot solely be explained by looking at temperature changes or 
climatic hazards alone.
The chapter provides due consideration of how societal impacts 
of climate change are emerging as a result of climatic changes, 
development and vulnerability. In this regard, it also explores how 
past and present conditions of poverty, inequality and vulnerability 
determine observed and future societal impacts of climate change,

including future adaptive capacities of societies exposed to climate 
change. It highlights new entry points to address climate risks and 
adaptation needs through the targeted reduction of poverty, inequity 
and vulnerability, linking particularly global quantitative information 
with local livelihood-orientated qualitative information.
The chapter outlines new approaches for identifying social tipping 
points, meaning moments of rapid, destabilising change across scales 
that can complement the discussion about physical tipping points in 
the climate system. It also addresses new perspectives on the baselines 
for assessing future vulnerabilities, and the potential for irreversible 
losses, emphasising not only economic but also non-economic losses, 
which are linked to past and present development trajectories. There 
is robust evidence on non-economic losses, including the loss of 
land, livelihoods, social networks, cultural values and the irreversible

degradation of ecosystem functions, as observed, for example, in parts 
of the Amazon. Non-economic losses are intertwined with economic 
losses to influence human health, nutrition, well-being and social 
stability, and therefore also influence present and future vulnerabilities 
and adaptive capacities. Non-economic losses from climate change 
disproportionately affect the poor. People in vulnerable situations are 
often disproportionately affected as they are less resilient and have 
less access to institutional support (including protection mechanisms) 
and coping strategies. This knowledge is key for informing integrated 
strategies for sustainable livelihood transitions and adaptation.
The chapter assesses newer literature about the synergies and trade-
offs for the poorest and most vulnerable people and groups between 
adaptation–mitigation and sustainable development strategies, which 
societies must negotiate in order to pursue climate resilient develop-

ment. It explores synergies and mismatches in key development sectors 
that the poorest rely on, including agriculture, forestry and energy. It 
identifies the development strategies, elements of institutional design 
and financial mechanisms that could support risk reduction and adap-
tation. Our assessment reveals that successful adaptation is not solely 
a question of levels of funding, but depends on broader institutional 
design that determine societal development and enabling conditions 
for adaptation to and mitigation of climate change. An assessment

8
1177Poverty, Livelihoods and Sustainable Development  Chapter 8
of enabling conditions for adaptation supports the finding that more 
convergent, integrated and comprehensive approaches to adaptation 
are needed. The chapter concludes that climate justice requires con-
sideration of the legal, institutional and governance frameworks that 
significantly determine whether adaptation is successful in addressing 
the needs of the poor.
Thus, intersections between climate hazards and socioeconomic 
development are assessed from the point of view of vulnerability, 
poverty, livelihoods and inequality (see Figure 8.1). Chapter 8 adopts 
this wider perspective to examine the differential nature of observed 
and future disproportionate vulnerabilities (i.e., who is most susceptible 
to climate hazards and events, where, at the core to understanding of 
what scale and why?), as well as the inequalities inherent in adaptation

and mitigation solutions as part of a wider climate justice perspective 
adopted in Chapter 8, and challenges for climate resilient development.
Finally, our assessment points towards the fact that human vulnerability 
to climate change is a complex and multifaceted phenomenon that is 
often influenced by historic development processes, such as structures 
that originated with colonisation. Also, recent global shocks not 
directly related to climate change, such as the COVID-19 pandemic 
and its socioeconomic consequences, impact climate vulnerability and 
inequitable impacts occurring between countries and within countries. 
Recent studies show that COVID-19, and other social, economic and 
political crises, have worsened the circumstances of the poor and 
further marginalised them.Overall, the chapter is key in terms of understanding societal impacts 
of climate change and factors that determine the various differential

adverse consequences of climate change on societies. The information 
presented and assessed is fundamental for informing adaptation and 
risk reduction strategies, since climatic information alone cannot 
explain sufficiently why some regions, societies or groups are 
suffering significantly more under climate change compared to others. 
Concepts such as vulnerability, intersectionality and climate justice 
provide important insights on how societal impacts of climate change 
are influenced and determined by broader societal development 
contexts.
8.2 Detection and Attribution of Observed 
Impacts and Responses
8.2.1 Observed Impacts of Climate Change with 
Implications for Poverty, Livelihoods and 
Sustainable Development
This section reports on new evidence on the observed impacts 
of climate change to livelihoods and the poor since the previous 
assessment (IPCC, 2014a). New evidence provides additional insight

into the interlinkages between climate change, poverty and livelihoods. 
New evidence has been evaluated according to climate change hazard 
categories developed for the AR6 (IPCC, 2021), and summarised in 
Figure 8.2.Human dimension of climate change at the nexus of climate change,
climate hazards and socio-economic development
 and socio-economic  devel opment
LIVELIHOODS
INEQUALITYVULNERABILITY
POVER TYCLIMA TE HAZARDS
SOCIO-ECONOMIC 
DEVELOPMENTMITIGA TION & ADAPT ATION 
ADAPT ATION 
& MITIGA TION
CLIMA TE JUSTICE
& GOVERNANCE
Figure 8.1 |  The lens of Chapter 8 to better understand the human dimension of climate change at the nexus of climate change, climate hazards and socio-
economic development.

8
1178Chapter 8 Poverty, Livelihoods and Sustainable Development
8.2.1.1 Interactions Between Climate Hazards and Non-climatic 
Stressors Affecting Livelihoods
New evidence highlights the potential for multi-hazard risks to push 
the poor into persistent traps of extreme poverty (Räsänen et  al., 
2016). Risk of extreme impoverishment increases for low-income 
people experiencing repeated and successive climatic events, whereby 
before they have recovered from one disaster, they face another impact 
(Forzieri et  al., 2016). Cascading and compounding risks arise from 
multiple climate hazards coinciding to produce impacts, for example, 
in mountainous regions, where the combination of glacier recession 
and extreme rainfall result in landslides (Martha et al., 2015). There is 
robust evidence that this effect has been observed around slow- and 
rapid-onset climate events related to drought (i.e., rising temperatures, 
heatwaves and rainfall scarcity), with devastating consequences for

agriculture (Vogt et al., 2018; Bouwer, 2019). In particular, the urban 
and rural landless poor face difficulties rebuilding assets following 
one-off disasters or a series of shocks (Garcia-Aristizabal et al., 2015).
Climate change is one driver among many that challenges livelihoods 
of the rural poor, including economic transitions associated with 
industrialisation and urbanisation, and also governance failures such 
as unclear property rights and civil conflict (e.g., Nyantakyi-Frimpong 
and Bezner-Kerr, 2015). Recent research adds evidence about the ways 
that climate hazards impact non-climatic stressors with implications for 
poverty reduction (Nelson et al., 2016). The risk that climate hazards may 
push the poor into persistent extreme poverty intensifies with stagnant 
wages, rising costs of living, mobility traps, and ethnic or religious 
discrimination (Cramer et  al., 2014; Carter et  al., 2016). Likewise in

both urban and rural environments, non-climatic factors related to 
governance exacerbate the impacts of climate events among the 
poorest, including poor service provisioning (e.g., waste collection), poor 
urban planning (e.g., waste water drainage) and water management 
failures (Di Baldassarre et al., 2010; Leal Filho et al., 2018), as well as 
poor rangeland management, intensification of farming land uses (i.e., 
overgrazing, deforestation), degradation of wetlands, shortage of water 
and soil erosion in rural areas (Olsson et al., 2019).
A key risk for the poor is shocks to specific livelihood assets that may 
force low-income groups into persistent poverty traps (Figure  8.4; 
Chambers and Conway, 1992; Cinner et al., 2018) but research also 
suggests that climate change impacts are also driving transient forms 
of poverty, a modality of poverty which is recurring (Angelsen et al., 
2014). Recurrent poverty is, for instance, seen in relation to crop losses

and decreasing agricultural production when income losses worsen 
living conditions (Ward, 2016; Kihara et  al., 2020). Recent research 
shows that climate change impacts may exacerbate poverty indirectly 
through increasing cost of food, housing and healthcare, among other 
rising costs borne by the poor (Islam et  al., 2014; Ebi et  al., 2017; 
Hallegatte et al., 2018) (high confidence). Severe adverse impacts of 
climate change at present and future risks may result from permanent, 
sudden, destabilising changes accompanying climate events such as 
decreases in food security, large-scale migration, changes in labour 
capacity or conflict (Bentley et al., 2014). Overall, there is more evidence 
that even under medium warming pathways, climate change risks to 
poverty would become severe if vulnerability is high and adaptation is 
low (limited evidence, high agreement) (see Section 16.5.2.3.4)Reliable and precise estimates of the impacts of climate change on

persistent poverty are difficult to generate, for example, due to data 
scarcity and data gaps (Hallegatte et  al., 2015; Hallegatte et  al., 
2018; Kugler et al., 2019). However, progress has been made towards 
detection and attribution of climate change impacts on the poorest 
by linking standard climate observations in low-income countries with 
new non-traditional forms of data (including Indigenous knowledge, 
historical archival data, satellite imagery, and data from digital 
devices) (Kuffer et al., 2016; Lu et al., 2016; Bennett and Smith, 2017; 
Steele et al., 2017).
8.2.1.2 Links Between Climate-related Hazards, Observed 
Losses, Poverty and Inequality Globally
There is high confidence that climate-related hazards, including both 
slow-onset shifts and extreme events, directly affect the poor through 
adverse impacts on livelihoods (see Figure 8.2), including reductions 
and losses of agricultural yields, impacts on human health and food

security, destruction of homes, and loss of income (Hallegatte et al., 
2015; Connolly-Boutin and Smit, 2016). One of the key factors that 
drives disproportionate impacts among poor households globally is 
lost agricultural income (high confidence) (Hallegatte et  al., 2015; 
Islam and Winkel, 2017). Also of concern are the impacts of climate 
hazards to human health, which is a primary resource that the poor 
rely on (Figure  8.2). There are only few robust global estimates of 
observed income losses to the poor that comprehensively account for 
all climate hazards; nevertheless, (Hallegatte and Rozenberg, 2017), 
estimating average impacts of climate change on incomes of the poor, 
found that across 92 developing countries, the poorest 40% of the 
population experienced losses that were 70% greater than the losses 
of people with average wealth.
Overall, our assessment shows (see Figure 8.2) high confidence that 
two categories of climate hazards pose high risk to a broad range of

livelihood resources that the poor rely on: warming trends and droughts 
(Figure  8.2b). Two key livelihood resource categories—life, bodily 
health and food security, and crop yield (representing agricultural 
productivity) are most at risk to a broad range of climate hazards (high 
confidence, Figure  8.2b). In addition to warming and drought, both 
pluvial and fluvial flooding, severe storms and sea level rise represent 
a high-risk cluster for livelihood impacts (high confidence, Figure 8.2b).
Figure 8.2 reflects the fundamental threat that climate hazards pose 
to the survival of plants, livestock and fish, as well as the people on 
which livelihoods depend (high confidence) (see Horton et al., 2021). 
The dependence of livelihoods on biological, ecological and human 
survival depicted in Figure 8.2 is also treated in Chapter 5. Likewise, 
impacts to livelihood resources can be compared to impacts to other 
key assets (see Working Group I (WGI) Section 12.3; WGI Table 12.2,

Ranasinghe et al., 2021).
It is revealed that warming trends and droughts pose greatest risks to 
the widest array of livelihood resources, and are particularly detrimental 
to crops and human health, a long-term requirement for livelihoods 
and well-being (high confidence) (see Figure  8.2B; Section  8.4.5.3; 
Section  16.5.2.3.4; Campbell et  al., 2018). A wide range of hazards 
also threaten the survival of fish and livestock that livelihoods depend

8
1179Poverty, Livelihoods and Sustainable Development  Chapter 8
on (high confidence, Figure 8.2b), as well as other sources of income 
for the poor. Salinity is a secondary hazard related to droughts, coastal 
flooding and sea level rise, and poses a fundamental risk to agriculture (high confidence). There is also robust evidence for rainfall variability 
driving short-term impacts to agricultural productivity as well as 
permanent loss of agriculture (high confidence).
Summary of confidence on the observed impacts of 23 climate hazards on nine key livelihood resources 
on which the poor depend most
Warming trend
Frost
Wet trend
DroughtCold spell
LandslidesClimate Hazards
Livelíhood resources
Crop yield
Pluvial floodHeatSnow/
ice CoastalWind/
Storm Other Wet Cold Dry
Dry trend
Wildfire
Hail
Heavy snowSevere storms
Lake/sea ice reduction
Snow reduction
Ocean/lake acidificationAverage
confidenceCoastal flood

SalinityTotal risk
from all
hazards
Housing stockFarmland/arable cropland
Fisheries and aquaculture
Forest products
Income/financial assets
Life/bodily health/food security
Pasture/rangeland/livestock
Crop variety
Heatwave
Crop yield
DroughtPluvial flood
Severe storms
Average confidence
Total risk to livelihoodsAverage confidence
Total risk to livelihoods
Confidence on the observed impacts2.1
1.61.5
1.7
1.4
1.8
2.2
1.8
1.4
2.1 2.0 1.3 1.7 1.3 1.0 1.1 2.3 1.3 1.4 2.3 2.6 1.2 2.0 2.4 1.6 1.4 1.1 2.6 1.2 1.8 2.31 .4
3.0 2.8 2.2 3.0 2.8 2.22.33.02.72.52.8Average
confidenceTotal risk
from all
hazards(a) Display of 207 
confidence statements 
on the total set of 
livelihood impacts
Confidence on the observed impacts
Lower Higher(b) High risk cluster (c) Spatial distribution of relative confidence

Figure 8.2 |  Summary of confidence on the observed impacts of 23 climate hazards on nine key livelihood resources on which the poor depend most. 
(a) A total of 207 confidence statements on the total set of livelihood impacts. Based on a standardised assessment of available literature since the AR5 (IPCC, 2014a), each impact 
category was assigned a confidence statement based on weight of evidence; high confidence is represented with HC, medium confidence with MC and low confidence with LC. An 
average numerical confidence score is assigned for impacts from each climate hazard, and for each livelihood resource category, representing total risk.
(b) The ‘high-risk’ cluster of livelihood impacts, where confidence is highest. (c) The spatial distribution of relative confidence. Hotspots represent highest confidence of observed

livelihood impacts; however, the absence of spatial information reflects not an absence of observed livelihood risk, but the relative weight of evidence sampled in this assessment 
exercise.

8
1180Chapter 8 Poverty, Livelihoods and Sustainable Development
While severe storms, pluvial and riverine floods, and coastal floods 
primarily impact private livelihood resources, such as homes and 
income (high confidence, Figure  8.2b), warming and droughts also 
affect common pool resources, such as rangeland, fisheries and forests 
(high confidence, Figure 8.2b). Multiple hazards undermine ecosystems 
that Indigenous Peoples and poor communities depend on for food 
security and income and have sustainably managed over the long 
term, such as forests, grazing land and marine fisheries (Barange et al., 
2014; Leichenko and Silva, 2014; Béné et al., 2016; Jantarasami et al., 
2018).
High confidence for observed livelihood impacts is spatially 
concentrated in South Asia, Africa, North America, and to a lesser extent 
Small Island Developing States (SIDS) (Figure 8.2c). The hazards most 
prevalent in all regions include warming trends, droughts and sea level

rise (Figure 8.2c), and undermine crop productivity, crop varieties, and 
cropland in most regions (high confidence). Along coastlines, climate 
hazards threaten livelihoods particularly exposed to extreme weather, 
flooding and sea level rise, and where poor populations are heavily 
dependent on agriculture and fisheries (high confidence). One third of 
total sampled evidence on livelihood impacts was observed in just three 
countries—Bangladesh, India and Nepal—indicating accumulating 
experience with livelihood impacts in South Asia (Figure 8.2c). However, 
this spatial representation of confidence does not mean that observed 
livelihood impacts are not occurring in other regions as well. Relative to 
South Asia, in Central Asia and the Caribbean, for example, the weight 
of evidence of livelihood impacts though lighter is still robust. Among 
industrialised nations, there is high confidence that climate change has 
impacted livelihood resources in the USA.

8.2.1.3.  Observed Differential Vulnerability to Climate Change, 
and Loss and Damage
The negative impacts of climate change on groups of vulnerable or 
marginalised communities generate so-called ‘residual impacts’ and 
residual risks that can remain a challenge in their lives (Warner and 
Van der Geest, 2013; James et al., 2014; Klein et al., 2014; Boyd et al., 
2017). Such ‘unacceptable’ L&Ds include the loss of income sources, 
food insecurity, malnutrition, permanent impacts to health and 
labour productivity, loss of life and loss of homelands, among others 
(McNamara and Jackson, 2019; Schwerdtle et al., 2020). The literature 
on L&D provides robust evidence not only on economic dimensions 
of global L&Ds, but also experiences of non-economic losses from the 
impacts of climate change (see detail in Section  8.3; Barnett et  al., 
2016; Roy et al., 2018; McNamara and Jackson, 2019). The extreme 
events that have occurred in recent years highlight the potential for

L&D, including 2019’s Cyclone Kenneth, the strongest in the recorded 
history of the African continent, which made landfall in northern 
Mozambique causing 45 deaths and destroying approximately 40,000 
houses, leaving hundreds of thousands at risk of acquiring waterborne 
diseases such as cholera during a prolonged recovery period (Cambaza 
In parallel to evidence on L&D, the science of climate event attribution 
has evolved from a theoretical possibility into a subfield of climate 
science. As attribution science strengthens, with it the evidence base 
linking greenhouse gas (GHG) emissions to extreme heat events, heavy rainfall and wind storms grows and becomes more robust (Otto et al., 
2016; Stott et al., 2016; Otto et al., 2018; Otto, 2020; Clarke et al., 2021; 
van Oldenborgh et al., 2021a; van Oldenborgh et al., 2021b; Verschuur 
Climate justice questions arise about the observed differential L&Ds

due to climatic hazards to affected populations in close connection with 
their vulnerability (Wrathall et al., 2015). Individual extreme weather 
events attributable to climate change result in L&Ds in communities 
and societies, which allow a quantification of the differential impacts 
of such events on different groups (Hoegh-Guldberg et  al., 2019a). 
Considering the disproportionately adverse impacts of climatic hazard 
on most vulnerable groups and regions and their relatively minor 
contribution to anthropogenic climate change (Mora et  al., 2018; 
Robinson and Shine, 2018), it is evident that vulnerability reduction 
and adaptation to climate change have also to be seen as an issue of 
climate justice and climate just development (Byers et al., 2018).
Probabilistic attribution allows an assessment of people’s future 
climate risks and estimates about the costs of successfully adapting 
to them (James et al., 2014; James et al., 2019). To answer questions

about impacts on people, the vulnerable and poor in particular, 
requires attribution, vulnerability and adaptation science need to move 
far beyond understanding physical events and incorporate information 
(including Indigenous knowledge and local knowledge (IKLK)) 
on people’s vulnerability and capacities, and exposure and losses 
resulting from discrete events (Bellprat et al., 2019). Attribution science 
is therefore highly compatible with risk management tools (i.e., risk 
reduction, risk transfer, insurance, risk pooling, recovery, rehabilitation 
and compensation) suggested in policy (James et al., 2019).
New observations provide greater evidence on the role of extreme 
poverty and global inequality, most of the detrimental direct impacts of 
climate change (e.g., rising food insecurity) disproportionately affecting 
the Global South (Hasegawa et al., 2018; Mbow et al., 2019; Khan and 
Zhang, 2021) compared with the Global North. Poor populations in

many countries are also disproportionately facing extreme L&D from 
heatwaves, flooding and tropical weather extremes (Gamble et  al., 
2016). New case studies, such as the European heatwave of 2018, 
illustrate significant negative impacts across crop production in the 
Global North (Beillouin et al., 2020), livestock value chain (FAO, 2018; 
Godde et  al., 2021) and fishing (Plagányi, 2019). Heatwave-induced 
intense fires can cause property damage, physical injury and death, as 
well as health and psychological harm of the victims. Heatwaves also 
create ideal conditions for the prevalence of certain pathogens, increase 
the risk of temperature-related health problems and exacerbate many 
pre-existing diseases (Rossiello and Szema, 2019).
A focus in the chapter is on the intersections between climate 
hazards and differential vulnerability resulting in actual and potential 
economic and non-economic losses (Section  8.3, 8.4; Thomas et  al.,

2019). Increasingly, intersections of age, gender, socioeconomic class, 
ethnicity and race are recognised as important to the climate risks and 
differential impacts and losses experienced by vulnerable, marginal and 
poor in societies (high confidence).(Section  8.2,2.3; CCB GENDER in 
Chapter 18; Nyantakyi-Frimpong and Bezner-Kerr, 2015). For example, 
linkages between wildfires and gendered norms and values are real-

8
1181Poverty, Livelihoods and Sustainable Development  Chapter 8
world examples (Walker et al., 2021). A broader climate agenda which 
considers social structures and power relations intersecting with climate 
change extremes is important (Versey, 2021), in order to understand 
disproportionate impacts of climate hazards, observed and future losses 
and vulnerability (see Figure 8.3).
Extreme events (e.g., heatwaves, cold periods, icy conditions) occurring 
in the Global North illustrate that such events cause disproportionate 
impacts among ageing populations, due to their immobility, isolation, 
infrastructure deficiencies and poor health assistance (Carter et  al., 
2016; Reckien et al., 2018). A well-known example is the heatwave 
in 2003 that killed thousands of elderly citizens across Europe 
(Poumadere et  al., 2005; García-Herrera et  al., 2010; Laaidi et  al., 
2011). More recently, in the Nordic region, elderly populations have

been experiencing distress associated with heatwaves and extreme 
cold events, with significant increases in morbidity and mortality due 
to cardiovascular and respiratory failure, showing that both age and 
underlying health issues intersect with climate change impacts (Carter 
et al., 2016; Li et al., 2016). The elderly also experience severe impacts 
from extreme winter seasons, such as in Finland, where of the from 
3000 deaths associated with extreme winter weather and 50,000 
injuries associated with slippery pavement conditions, the majority 
were people over 65  years old (Carter et  al., 2016). Adaptation to 
extreme events including heatwaves, cold periods and icy conditions 
in the Global South and North will increase energy demand and the 
individuals’ carbon footprint across all income levels (van Ruijven 
The 2018 US National Climate Assessment has identified that 
southeastern USA is already experiencing more frequent and longer

summer heatwaves and, by 2050, rising global temperatures are expected 
to mean that cities in southeastern USA may experience extreme 
heat (USGCRP , 2018). This includes disadvantaged African American 
communities, who are more exposed and hence disproportionately 
experience the impacts of climate change (Shepherd and KC, 2015; 
Marsha et  al., 2018). The historically discriminated Sami in northern Sweden and Maasai in Africa are examples of Indigenous People who 
also face climate risks and have limited resources, capacity or power to 
respond (Leal Filho et al., 2017; Persson et al., 2017).
8.2.1.4 Climate-related Hazards, Livelihood Transitions and 
Migration
Agricultural livelihoods of the rural poor, especially in Africa, Asia 
and Latin America, are already in transition due to the forces of 
industrialisation, urbanisation and economic globalisation (De Brauw 
et al., 2014; Tacoli et al., 2015). Scientific evidence shows that climate

change is accelerating livelihood transitions from rural agricultural 
production to urban wages (Cai et al., 2016; Cattaneo and Peri, 2016; 
Kaczan and Orgill-Meyer, 2020).
There is now robust evidence from virtually every region on Earth 
showing that the livelihood impacts from a multitude of climate hazards 
are driving people to diversify rural income sources (Figure 8.2; Cross-
Chapter Box  MIGRATE in Chapter 7). Rural households frequently 
accomplish the goal of livelihood diversification with an increasing 
reliance on migration, urban wage labour and remittances (Marchiori 
et al., 2012; Bohra-Mishra et al., 2014; Gray and Wise, 2016; Nawrotzki 
and DeWaard, 2016; Banerjee et al., 2019a). What is different about 
rural-to-urban livelihood transitions under climate change impacts 
is that they accelerate both rural and urban stratification of wealth 
(Barrett and Santos, 2014; Thiede et al., 2016). On the one hand, climate

change impacts on rural livelihoods increase the necessity of migration 
as an income strategy, accelerating migration (Cai et al., 2016) even 
while households that cannot select individuals for migration become 
more impoverished (Suckall et  al., 2017; Nawrotzki and DeWaard, 
2018).
On the other hand, climate change impacts widen the range of 
households willing or needing to engage in migration to include those 
less able to bear the costs of urban migration (Afifi et al., 2016; Hunter 
and Simon, 2017). The effect is also greater urban poverty, and a The interface between climate hazards and factors of human vulnerability
CLIMA TE HAZARDS HUMAN SYSTEMWildfires
Floods
Sea level riseFast
Slow
Intersectionalit y
DroughtsAge
Ethnicity
Actual and potential economic and non-economic losse sClassGenderHAZARD-VULNERABILITY  INTERF ACEExamples
Examples

Figure 8.3 |  Illustration of the relationship between climate hazards, their impacts (including economic and non-economic losses and damages) and human 
systems leading to systemic vulnerability. We need to understand who is vulnerable, where, at what scale and why. We cannot just look at the climate hazard (e.g., wild 
fires, floods, droughts, sea level rise, etc.) but must also look at who is being affected by these hazards and factors that make people and groups vulnerable (e.g., poverty, uneven 
power structures, disadvantage and discrimination due to, for example, social location and the intersectionality or the overlapping and compounding risks from ethnicity or racial 
discrimination, gender, age, or disability, etc.) (see also Cross-Chapter Box GENDER in Chapter 18; Section 5.12).

8
1182Chapter 8 Poverty, Livelihoods and Sustainable Development
higher social burden of migrants seeking urban wages (Singh, 2019). 
Evidence suggests that poor households often move in desperation 
to make ends meet. In the context of climate hazards, such as coastal 
inundation and salinity, economic necessity often drives working-age 
adults in poor households to seek outside earnings (Dasgupta et al., 
2016). Labour migration in the context of climate change is also 
gendered, and as more men seek employment opportunities away 
from home, women are required to acquire new capacities to manage 
new challenges, including increasing vulnerability to climate change 
(Banerjee et al., 2019b).
Migration and displacement are directly induced by the impacts of 
climate change (high confidence) (Cross-Chapter Box  MIGRATE in 
Chapter 7), however, migration responses to climate change are 
differentiated across the spectrum of households’ wealth. In well-

off households, migration can be used as a way to support income 
diversification through remittances (Gemenne and Blocher, 2017). High 
levels of poverty mean that a large part of the African population does 
not have sufficient resources to be mobile (Borderon et al., 2019; Leal 
Filho et al., 2020c). The poorest households, conversely, will typically 
lack the resources that would allow them to migrate in ways that 
maintain an acceptable standard of living, and may find themselves 
unable or unwilling to move in the face of climate change impacts 
(Sam et al., 2021).
There is high agreement and robust evidence that climate change 
impacts also have a major influence on key enabling conditions for 
migration, such as sociodemographic, economic and political factors 
(Abel et  al., 2019; Borderon et  al., 2019), and that climate change 
impacts to development and governance may affect how people migrate 
(Wrathall et al., 2019; CCB MIGRATE in Chapter 7). Mobility, which

was considered the most viable climate change adaptation strategy 
to poor pastoralists, is restricted due to the political marginalisation 
of pastoral groups, land privatisation, governments’ decentralisation 
policies and plantation investment (Blench, 2001; Randall, 2015; Leal 
Filho et al., 2020c). While migration can be an adaptation response 
to climate change impacts (Black et al., 2011; Gemenne and Blocher, 
2017), climate change impacts can also act as a direct driver of forced 
displacement (Marchiori et al., 2012). Societal groups that are forced 
to involuntarily migrate in response to climate change impacts may 
lack resources to invest in planned relocation mainly due to lack of 
good governance systems (Reckien et al., 2018). For people displaced 
by climate change impacts, policy interventions have a determining 
influence on migration outcomes, such as the numbers of migrants, 
the timing of migration and destinations (Gemenne and Blocher,

2017; Wrathall et al., 2019).The process of displacement and forced 
migration leaves people more exposed to climate change-related 
extreme weather events, particularly in low-income countries which 
often host the highest number of displaced people (Adger et al., 2018).
Climate change may be accelerating livelihood transitions and 
migration in ways that accelerate urbanisation (Adger et  al., 2020). 
Although a range of climate hazards are noted for accelerating rural-
to-urban livelihood transitions (see Cross-Chapter Box  MIGRATE in 
Chapter 7), a key theme to emerge across many case studies is the 
impact of rising temperatures on agricultural productivity (Mueller 
et  al., 2014; Cattaneo and Peri, 2016; Call et  al., 2017; Wrathall et al., 2018). In other words, when people cannot farm due to rising 
temperatures (and related stressors), they migrate. In this context, 
migration as a livelihood diversification strategy may evolve and

take multiple forms over time (Bell et al., 2019), such as temporary 
migration (Mueller et al., 2020), seasonal migration (Gautam, 2017) or 
permanent migration (Nawrotzki et al., 2017), but generally conforms 
to existing patterns of migration (Curtis et al., 2015).
A key concern for the poor is climate change impacts that undermine 
livelihood diversification and resilience, narrowing the set of available 
livelihood alternatives (Tanner et  al., 2015; Bailey and Buck, 2016; 
Perfecto et al., 2019).
8.2.1.5 The Long-lasting Effects of Climate Change on Poverty 
and Inequality
New studies document the long-term effects of climate change 
impacts on people’s livelihoods that persist long after a hazard event. 
For example, the impact of drought on livelihoods and food security 
is still recognisable in Mali, 30 years after 1982–1984, the period of 
most intense drought during the protracted late 20th century drying of

the Sahel. The most food secure households associated with persistent 
drought-induced famine were those that diversified livelihoods away 
from subsistence agriculture during and after the famine (Giannini 
et al., 2017). Meanwhile, a larger fraction of households with fewer 
livelihood activities, lower food security with higher reliance on 
detrimental nutrition-based coping strategies (such as reducing the 
quantity or quality of meals) were those unable to diversify livelihoods 
30 years previously. Sufficient time has passed to consider the long-
term outcomes for the poor in extreme cases featured in previous 
IPCC assessments, including Hurricane Katrina (2005) (e.g., Fussell, 
2015; Raker et  al., 2019) and Hurricane Mitch (1998) (e.g., Alaniz, 
2017), forewarning that recovery is complex and requires significant 
sustained long-term investment in ‘soft’ aspects of development, 
including community organisation and mental health (O’Neill et  al., 
2020; Fraser et al., 2021).

The IPCC Special Report on 1.5°C concluded that climate change has 
already increased the probability and intensity of individual extreme 
weather events occurring (Roy et  al., 2018), and our new baseline 
consideration should be that serious climate change impacts are 
already being experienced by the most vulnerable, with long-term 
implications for development (Box  8.1; Roy et  al., 2018). In both 
developing and developed countries the disproportionate impacts 
of the compounding effects of climate change on development are 
felt by the most disadvantaged. For example, the residual impacts of 
storms like Hurricane Maria (see Section 8.2.1.1) illustrate how rising 
temperatures, extreme weather events, coral bleaching and sea level 
rise come together and create compounding hazard-cascades to leave 
long-lasting effects on the lives of the poor, as well as their food 
and water security, health, livelihoods and prospects for sustainable

development—not only in developing countries (Adger et  al., 2014; 
Olsson et  al., 2014; Hoegh-Guldberg et  al., 2018; Roy et  al., 2018), 
but also in highly inequitable industrialised countries within the same 
region (Gamble et  al., 2016). According to the US National Climate 
Assessment (USGCRP , 2018), damages caused to communities 
by Hurricanes Irma and Maria in 2017 sparked unprecedented

8
1183Poverty, Livelihoods and Sustainable Development  Chapter 8
humanitarian crises. Hurricane Maria, a category 5 hurricane, passed 
through Dominica, St Croix and Puerto Rico and is considered the worst 
climate disaster in recorded history to affect those islands (Rodríguez-
Díaz, 2018). Approximately 200,000 people migrated from Puerto Rico 
to the mainland USA in the weeks following the storm (Alexander et al., 
2019). Estimates for direct and indirect casualties in Puerto Rico point 
out a total of 4645 excess deaths, equivalent to a 62% increase in the 
mortality rate (Kishore et al., 2018). The example of Hurricane Maria 
and Puerto Rico illustrates that vulnerability is part of a long history of 
discrimination and colonial governance, which led to greater impacts 
on the island (Moleti et al., 2020). In Puerto Rico, the economic costs of 
the collapse of the island’s energy, water, transport, and communication

infrastructures are estimated to range from USD 25 to USD 43 billion 
(USD in 2017), further indebting the island and putting its long-term 
development at risk. Meanwhile the economic impacts of Hurricanes 
Irma and Maria on the Caribbean region are estimated between USD 27 
and USD 48 billion, and have long-term implications for state budgets 
for infrastructure supporting development of the poorest.
New evidence provides little expectation of net positive impacts of 
climate change for the poor (Hallegatte et  al., 2015). Nevertheless, 
some benefits of climate change adaptation include improved disaster 
preparedness, the accumulation of social assets, economic benefits of 
agricultural diversification and benefits associated with migration, as well as the political benefits of collective action (Pelling et al., 2018). 
In contrast, wealthier tiers of society facing climate change impacts 
are more able to liquidate assets to avoid losses from climate change,

to be formally compensated for losses (Fang et al., 2019) and employ 
social positions to leverage gains from adaptation (Nadiruzzaman and 
Wrathall, 2015).
The poor frequently suffer the direct and indirect impacts of climate 
change, including the cost of adopting adaptive measures (Atteridge 
and Remling, 2018; Bro et al., 2020). Costs to the poor may also include 
the secondary impacts of first-order adaptation activities, including the 
livelihood consequences to people migrating due to climate change 
impacts. The poor frequently bear indirect impacts of adaptation 
interventions, such as flood protection barriers, which may displace 
flood waters away from high-income populations toward poorer 
communities (Mustafa and Wrathall, 2011). Adaptation programming 
may also indirectly affect the poor as public resources are drawn into 
risk reduction interventions, and away from spending on social welfare 
and safety nets (Eriksen et  al., 2015). Measures to enhance social

welfare and safety nets themselves help enhance the poor’s resilience 
to climate impacts because they focus on non-climatic stressors 
affecting livelihoods, which interact with climate hazards. Therefore, 
diverting attention away from safety nets may in fact undermine 
adaptation efforts (Leichenko and O’Brien, 2019; Tenzing, 2020).
Box 8.1 | Climate traps: A focus on refugees and internally displaced people
Populations of concern, who are extremely vulnerable to climate change impacts with limited capacity to adapt, are those displaced 
and resettled in the course of conflict or disaster, either internally or across borders (Burrows and Kinney, 2016). The risk for refugees 
and internally displaced people (IDPs) is two-fold: on the one hand, refugee and IDP settlements are disproportionately concentrated 
in regions (e.g., Central Africa and the Near East) that are exposed to higher-than-average warming levels and specific climate hazards,

including temperature extremes and drought. On the other, these populations frequently inhabit settlements and legal circumstances that 
are intended to be temporary but are protracted across generations, and at the same time, face legal and economic barriers on their ability 
to migrate away from climate impacts. (Adams, 2016; Devictor and Do, 2016). Large concentrations of these settlements are located in 
the Sahel, the Near East and Central Asia, where temperatures will rise higher than the global average, and extreme temperatures 
will exceed thresholds for safe habitation (Figure Box 8.1.1). Already largely dependent on state and humanitarian intervention, these 
immobile populations will require interventions to safely maintain residence in areas exposed to climate hazards. Adaptation planning 
should prioritise immobile populations living in an already destabilised development context, on improving their capacities to deal with 
the further consequences of climate change.

Refugees and IDPs fit into a global category of extremely structurally vulnerable people that are missing from standard poverty 
assessments, officially uncounted or uncountable using traditional census and survey methods (Carr-Hill, 2013). These include highly 
mobile populations, internally displaced by war and environmental hazards (UNHCR, 2020; IDMC, 2021); itinerant labourers; urban poor 
in informal settlements (Lucci et al., 2018); unauthorised migrants living in countries where they do not hold citizenship (Passel, 2006); 
guest workers (Reichel and Morales, 2017); the homeless and institutionalised (Caton et al., 2007); rural nomadic, pastoralist or landless 
populations (Randall, 2015); and Indigenous Peoples and forest-dwelling communities (Galappaththi et  al., 2020). Frequently living 
without social safety nets, such as health care and formal education, these uncounted or ‘missing millions’ are vulnerable to problems

associated with acute and chronic poverty, such as the spread of infectious disease and malnutrition (Ezeh et al., 2017). Because these 
‘missing’ populations are not counted, they are frequently not a part of planning (Carr-Hill, 2013), including adaptation planning. In 
any particular national context, these missing populations may represent a small fraction of the population (about 5% in South Asian 
countries), however cumulatively hundreds of millions of people may be missing from official estimates (Carr-Hill, 2013). Over the last 
decade, techniques for estimating the locations, numbers and socioeconomic status of missing populations have moved beyond census 
and nationally representative household surveys, leveraging advances in satellite imagery (Kuffer et al., 2016; Bennett and Smith, 2017) 
and data from mobile digital devices (Jean et al., 2016; Xie et al., 2016; Steele et al., 2017).

8
1184Chapter 8 Poverty, Livelihoods and Sustainable Development
Present-day global distribution of camps for refugees and internally displaced people
Background of days with temperature exceeding 35°C in 2041–2060
Distribution of 
3,741 camps registered 
to the United Nations High 
Commissioner for Refugees 
(UNHCR) and 4,012 camps for 
Internally Displaced People (IDP).
UNHCR
campsIDP
camps> 500
100
50
25
< 5Number
of camps
60 70 < 0 10 50 30 40 20
Days with  temperature exceeding 35°C in 2041–2060, relative to 1850–1900
CMIP6, SSP2-8.580 90 >100
Figure Box 8.1.1 |  The global distribution of the United Nations High Commissioner for Refugees (UNHCR) refugee and internally displaced people 
(IDP) settlements (as of 2018) overlaid on a gridded map of the days predicted to exceed safe temperature thresholds for human health in the

coming decades (2041–2060 under SSP2 8.5). Semi-circles indicate the presence of refugee and IDP camps in grid cells, with darker semi-circles depicting increasingly 
dense concentrations of settlements. Darker background colors indicate increasingly unsafe conditions. Regions of concern include the southern edge of the Sahel, and the 
northern edge of the LevantBox 8.1 (continued)

8
1185Poverty, Livelihoods and Sustainable Development Chapter 8
8.2.1.6  Interactions Between Climate Hazards and Social-
ecological T
hresholds
Climate change threatens to rapidly transform unique and threatened

ecosystems (Reasons for Concern RFC1), such as tropical rain forests, coral reefs, arctic and high-mountain ecosystems, as well as the indigenous and forest-dwelling people whose livelihoods, cultures and identities are dependent on these ecosystems. In recent years, the case of Amazonia has illustrated how such systems are transforming, with detrimental consequences for Indigenous Peoples, and the vital role that Indigenous Peoples serve in protecting vulnerable ecosystems (Ricketts et  al., 2010; Box  8.6). Globally, indigenous territories cover the greatest area of remaining tropical forest in comparison to other protected areas. They encompass the bulk of Earth’s biodiversity and are the locus for a number of key ecosystem services across spatial and temporal scales (Walker et al., 2020). Specifically, in 2014 indigenous territories and other protected areas represented the equivalent of 58.5% of all the carbon stored in the Brazilian Amazon biome and had the lowest deforestation

rate (2.1%) and fire incidences, evidencing the effectiveness in safeguarding important ecosystems services and well-being (Nogueira et al., 2018). It is estimated that indigenous territories in the Brazilian Amazon contribute at least USD 5 billion each year to the global economy through food and energy production, GHG emissions offsets, and climate regulation and stability (Siqueira-Gay et al., 2020). Given the high incidence of poverty of Amazonian countries and high proportion of traditional and Indigenous Peoples, remoteness and neglected governance place these unique ecosystems and indigenous populations as highly vulnerable to climate change impacts (Pinho et al., 2014; Brondízio et al., 2016; Mansur et al., 2016; Kasecker et al., 2018). Despite their importance, the survival of Indigenous Peoples in the Amazon is on the brink in the wake of increasing deforestation, land conflicts and invasions, cattle ranching, mining, fire incidence, health problems and human rights

violation (Ferrante and Fearnside, 2019). There is robust evidence that both economic and non-economic

L&Ds are currently, and will be, unevenly experienced by populations in vulnerable conditions, such as children, women, Indigenous Peoples and traditional communities (Pinho, 2016; Lapola et al., 2018; Roy et al., 2018; Eloy et al., 2019; Machado-Silva et al., 2020). Increasing wildfires inside protected areas, in particular, territories of Indigenous Peoples and traditional communities, is worrisome and presents challenges for the future of unique and threatened socio-ecological systems, and the ecosystem services they provide. The Amazonian indigenous territories and protected areas can deliver protection of biodiversity and important ecosystem services if appropriate governance mechanisms are in place and their land tenure rights and livelihoods are secured (Steege et al., 2015). The role of enabling environments is discussed in Section 8.5.
8.2.1.7  Linkages Between Climate Change Impacts and 

migration, are also outcomes of multidimensional poverty in low-income countries (Burrows and Kinney, 2016). Future impacts may be better understood if the vulnerability and the capacity for adaptation is understood to be rooted in a sustainable development context (see Box 8.2). The UN Sustainable Development Goals (SDGs), which aim to reduce poverty and inequality, and identify options for achieving development progress, also provide insight on reducing climate vulnerability (United Nations, 2015). First, climate change impacts may undermine progress toward various SDGs (medium confidence), primarily poverty reduction (SDG1), zero hunger (SDG2), gender equality (SDG5) and reducing inequality (SDG10), among others (medium evidence, high agreement). In both developing and high-income countries, climate change hazards in connection with other non-climatic drivers already accelerate trends of wealth inequality (SDG 1) (Leal Filho et al., 2020b). Climate impacts on SDGs illustrate the

complex interrelations in development. For example, in regions encountering obstacles to SDGs, characterised by high levels of inequality and poverty, such as in Africa, Central Asia and Central America, climate change is exacerbating water insecurity (SDG 6), which may then also drive food insecurity (SDG 2), impacting the poor directly (e.g., via crop failure), or indirectly (e.g., via rising food prices) (Conway et al., 2015; Hertel, 2015; Cheeseman, 2016; Rasul and Sharma, 2016). There is a pressing need to address poverty issues, since these may negatively influence the implementation of all SDGs (Leal Filho et al., 2021a).

At the same time, there is increasing evidence that successful adapta-
tion depends on equitable development and climate justice; for exam-ple, gender inequality (SDG 5) and discrimination (SDG 16) are among the barriers to effective adaptation (high confidence) (Bryan et  al., 
Box 8.2 | Livelihood strategies of internally displaced atoll communities in Yap

On Yap Island in the Federated States of Micronesia, displaced atoll communities have been under considerable pressure due to climate change. This is because of the island’s vulnerability, as a result of its weak economic status, and the little access it has to technologies that may support adaptation efforts. This trend is seen in many SIDS (see also Chapter 15). On small islands and remote atolls where resources are often limited, recognising the starting point for action is critical to maximising benefits from adaptation. They do not have uniform climate risk profiles, and not all adaptations are equally appropriate in all contexts (Nurse et al., 2014) (high confidence).
The recurrences of natural hazards (e.g., El Niño-driven tropical storms, associated coastal erosion and saltwater or seasonal droughts

leading to water scarcity) and crises threaten food and nutrition security through impacts on traditional agriculture, leading to income losses and causing the forced migration of coastal communities to highlands in search of better living conditions. As many of the projected climate change impacts are unavoidable, implementing some degree of adaptation becomes crucial for enhancing food and nutrition security, strengthening livelihoods, preventing poverty traps and increasing the resilience of coastal communities to future climate risks (Krishnapillai, 2018).

8
1186Chapter 8 Poverty, Livelihoods and Sustainable Development
With support from the US Department of Agriculture and the US agency for International Development, the Cooperative Research 
and Extension wing of the College of Micronesia- Federated States of Micronesia Yap Campus has been providing outreach, technical 
assistance and extension education to regain food and nutrition security and stability. They have done this by improving the soil and 
cultivating community vegetable gardens, as well as indigenous trees and traditional crops. This programme implemented a three-
pronged adaptation model to boost household and community resilience under harsh conditions on a degraded landscape, hence 
addressing poverty risks and promoting more sustainable livelihoods (Meyer and Jose, 2017).
The following three strategies: (a) gender-focused capacity development on soil health management, (b) good practices in sustainable

land management (SLM) and (c) income-generation activities were employed to mitigate crop production losses and increase resilience 
to climate-influenced hazard events within the 258 ha of degraded lands in Gargey Village.
The project first focused on increasing the capacity development for 1100 residents of Gargey Village, including women and youth, in order 
to create a base of community knowledge for soil health management. Training on soil health management including the following: use of 
cover crops and improved fallow, legumes, composting and agroforestry systems, mulching, minimum tillage and contour farming, as well as 
altering production practices (planting time, spacing, pest and disease treatment, harvesting time), alternative crop production methods 
(container gardening, raised-bed gardening, small-plot intensive farming), hands-on training on compost preparation and seed germination.
Dissemination and use of good practices in sustainable land management

Following capacity building, the project trained villagers in the use of SLM practices to further soil resilience during ongoing and acute 
precipitation events. The SLM practices focused on volcanic soil management and compost preparation and use, along with the planting 
of native trees and crops. The protective soil cover was improved through cover crops, crop residues or mulch, and crop diversification 
through rotations. Local salt-tolerant crop varieties were introduced. Seed packets and seedlings were distributed to ensure a continuous 
supply of resilient traditional plants and to provide for sustainable post-disaster recovery.
Income-generation activities
The project also included training to increase the incomes of households by training household members in the cultivation of vegetables 
using various alternative crop production methods. Households were then able to sell their vegetables in the local markets.

Less hunger and more cash from leafy vegetables is a concept adopted at the household level to not only reduce poverty, but also to 
empower displaced communities to address the issue of malnutrition. Practices include growing a variety of nutritious vegetables as part 
of a large crop portfolio and using alternative crop production methods, such as small-plot intensive farming using container gardening or 
raised-bed gardening (Krishnapillai and Gavenda, 2014). In addition, focusing efforts on increasing the sustainable production of staple 
crops confers significant nutritional benefits.
More households in the settlements are consuming vegetables since home gardeners started harvesting regularly and sharing their 
produce with extended families or selling them to generate income. The location-specific, community-based adaptation model improved

food and nutrition security and livelihoods (Krishnapillai, 2017). People can access more nutritious and reliable food sources, and they are 
growing their own food and selling their surplus, creating new optimism about their future.
The climate-smart agriculture (CSA) package increased land cover by more than 50% within Gargey Village. This includes the planting of 
42 varieties of native trees and crops. Current major crops that are being successfully grown at this location include coconut, breadfruit, 
mango, noni, chestnut, pineapple, sugarcane, land taro, tapioca and sweet potato. There have been additional benefits in terms of 
improvement in water availability. These activities have directly benefited the resilience and food security of more than 1000 residents 
in Gargey Village, and lessons learnt from this project have helped to scale up similar projects at three locations in Yap that have 
experienced equivalent climate-damaging processes.

Overall, this case study illustrates the benefits of promoting resilient crop production in Gargey Village, as an example of displaced atoll 
communities. Innovative and sustainable CSA strategies have offered broader insights and lessons for enhancing adaptive capacity 
and resilience, on a degraded landscape. The coherent strategies and methods employed have strengthened livelihood opportunities by 
improving access to services, knowledge and resources. By its concurrent focus on enhancing food security through traditional crops, 
coupled with nutrient-rich vegetables, promoting rainwater harvesting systems and water conservation, and promoting resilient household 
livelihood opportunities, atoll communities brought together crucial elements needed to reduce vulnerabilities and to better cope with 
disasters and climate extremes, while embracing the traditional culture. The location-specific yet knowledge-intensive CSA methods

deployed, offered opportunities for atoll communities to revitalise themselves, overcoming barriers while adjusting to new landscapes.Box 8.2 (continued)

8
1187Poverty, Livelihoods and Sustainable Development  Chapter 8
2018; Onwutuebe, 2019; Garcia et al., 2020). Likewise, both climatic 
and non-climatic threats to development, such as conflict (SDG 16), 
may seriously undermine capacity to formulate and implement adap-
tation policies, and design planning pathways (Hinkel et  al., 2018). 
The risk of conflict associated with climate change has great potential 
to undermine other development goals (Box 8.4). Where sustainable 
development lags and human vulnerability is high, there is also often 
also a severe adaptation gap (Figure 8.12; Birkmann et al., 2021a). The 
SDGs may provide important cues on how to close the adaptation gap: 
climate action needs to be prioritised where past and future climate 
change impacts threaten SDGs, and where investment in SDGs im-
prove capacity for adaptation (see Section 8.6).
8.2.2  Poverty–Environment Traps and Observed 
Responses to Climate Change with Implications for

Poverty, Livelihoods and Sustainable Development
Across all geographical regions, there is evidence that anthropogenic 
climate change is hindering poverty alleviation and thereby constraining 
responses to climate change in five main ways:
• By worsening living conditions (Hallegatte et  al., 2017; Hsiang 
• By threatening food and nutrition security due to undernutrition and 
reduced opportunities for income generation (Burke et al., 2015)• By disrupting access to basic ecosystems services such as rainwater, 
soil moisture (reducing the productivity of agricultural land) or via 
the depletion of habitats (e.g., mangroves, fishing grounds) that 
particularly vulnerable and poor people are depending on (Malhi 
• By creating favourable conditions for the spread of vector-
transmitted diseases (Liang and Gong, 2017)
• By threatening underlying gender inequalities exacerbated by 
climate impacts, such as access and control to productive inputs

and reinforcing social-cultural norms that discriminate against 
gender, age groups, social classes and race (Singh et al., 2019b).
Responses to observed impacts such as glacier melt, sea level rise 
and increases in the frequency of extreme weather events such as 
droughts, hurricanes and floods need to take into account how they 
influence other policy issues and sectors, including poverty alleviation, 
human health and well-being (Orimoloye et al., 2019), water/energy 
and the built environment (Andrić et  al., 2018), transportation and 
mobility (Markolf et  al., 2019), agriculture (Hertel and Lobell, 2014) 
and biodiversity/ecosystems (Nogués-Bravo et  al., 2019), only to 
mention a few. Recent literature provides evidence that impacts of 
climate change together with non-climatic drivers can create poverty–
environment traps that may increase the probability of long-term and 
chronic poverty (Figure  8.4; Hallegatte et  al., 2015; Djalante et  al.,

2020; Malhi et al., 2020; McCloskey et al., 2020) (high confidence).
Schematic representation of a poverty-environment trap that can increase chronic poverty
POVER TY TRAP POOR NUTRITIONLIMITED INCOME 
GENERA TION 
STREAM SLIVEST OCK AND 
CROP LOSSESLOW 
PRODUCTIVITY  
AND OUTPUT
LOW ASSET  AND 
SECURITYLOW LEVELS OF 
HUMAN CAPI TALLIMITED OPPORTUNIT Y 
TO ADAP T
Figure 8.4 |  Schematic representation of a poverty–environment trap that can increase chronic poverty.

8
1188Chapter 8 Poverty, Livelihoods and Sustainable Development
In addition, observed climate change responses, including autonomous 
and planned adaptation, can exacerbate poverty and vulnerability 
(Eriksen et al., 2021). There is robust evidence that planned responses 
to climate change, such as large-scale adaptation projects, in some 
context can also increase vulnerability due to the reinforcement of 
inequalities and the effects of further marginalisation (Fritzell et al., 
2015; Eriksen et  al., 2021). There is increasing evidence that the 
responses to indirect impacts of climate change, such as to shifts in 
marine or terrestrial ecosystems due to climate change (Seddon et al., 
2016) also affect different groups differently and impact poverty 
and livelihood security. Apart from influences on agriculture trends 
(Reichstein et al., 2014) and changes in yields (Reyes-Fox et al., 2014; 
Craparo et al., 2015), climate change has significant (direct and indirect)

impacts on livelihood assets and resources such as forests, livestock 
production and fisheries, which may undermine the livelihoods security 
in the medium and long run.8.2.2.1  Characteristics of Responses
Many of the observed responses to climate change aim to reduce 
exposure of people to climate-related hazards, such as flood defences, 
sea walls and embankments (Gralepois et  al., 2016), rather than 
aiming specifically to address structural vulnerability to climate 
change, which means the root causes of vulnerability (e.g., Mikulewicz, 
2020; McNamara et  al., 2021a). Evidence suggests that responses 
to the impacts of climate change should consider the physical 
climate event, and also historical and institutional root causes that 
make people or systems vulnerable. However, addressing structural 
vulnerability must be balanced with the political context and the 
range of options available to people, communities or countries (see

Section  8.3). Political frameworks need to consider both types of 
responses, to revive democratic debate and citizenship (Pepermans 
et  al., 2016). In addition to reducing poverty and vulnerability, 
planned climate change responses must also be intersectoral, in order Box 8.3 | COVID-19 pandemic
During the COVID-19 pandemic, countries such as India were affected by hydro-meteorological hazards (Raju, 2020) making it extremely 
difficult to handle a public health crisis in the context of compounding risks and cascading hazards (Phillips et al., 2020). The COVID-19 
pandemic can increase the adverse consequences of climate change since it has the potential to delay some key adaptation actions. On 
the other hand, the pandemic also highlights the importance of better preparedness to the impacts of climate change (Djalante et al., 
2020). Overall, the COVID-19 pandemic has worsened the economic situation within many countries and local communities particularly

for already marginalised groups (Gupta et al., 2021). The accumulation of crises, such as the COVID-19 pandemic alongside climate 
change impacts, underscore the fact that stressors do not occur in isolation, but are interlinked, with clear implications for structural 
vulnerability and adaptation options available to the poorest (Sultana, 2021). Responses to COVID-19 have led to significant economic 
and social distress within and across societies and local communities, especially in poorer countries. The direct health and economic 
impacts of the lockdowns have further limited the ability of many people across the developing world to pursue income-generating 
activities, and sustain livelihoods that are already affected by climate hazards. In addition, poor or most vulnerable groups face further 
marginalisation due to misinformation that these groups transmit the virus to other wealthier groups and areas. The pandemic has

intensified inequalities in both developing countries (FAO, 2020) and in industrialised nations (Anderson et al., 2020; McCloskey et al., 
2020), whereby vulnerable groups are especially affected (Raju et al., 2021). Whereas different models and scenarios contain different 
data and figures, there is high agreement that it is likely that socioeconomic impacts are particularly severe within selected global regions 
and areas that are already characterised by a rather high level of human vulnerability (see also Section 8.3). This also implies that the 
capacity of people to prepare for present and future climate change impacts will further decrease within these countries and population 
groups under the direct and indirect consequences of the COVID-19 pandemic.
Moreover, the COVID-19 pandemic is not only influencing climate change research (Leal Filho et al., 2021b) but is also influencing the

capacities of governmental institutions and nations to support planned adaptation and poverty reduction favouring the most vulnerable 
groups, since the crisis also means among other issues a significant reductions in tax revenues (Clemens and Veuger, 2020). COVID-19 
may also force people to seek alternative sources of income that can lead to the further erosion of long-term adaptive capacities. In many 
settings, the pandemic has had significant impact on businesses and SMEs (Schmid et al., 2021). The important role of governmental 
support for buffering crises and periods of income loss of individual households (e.g., unemployment) and private businesses (e.g., 
SMEs) has also been demonstrated during the COVID-19 pandemic in Organisation for Economic Co-operation and Development (OECD) 
countries (OECD, 2020b).
Livelihood disruptions and an increasing probability of higher levels of poverty and of structural vulnerability in various countries have

already been observed (Laborde et al., 2020b). These vulnerabilities and the new layers created by the pandemic must be seen with an 
intersectional lens (Raju, 2019; Sultana, 2021).
In addition, the COVID-19 pandemic has also revealed the unequal access to vaccine and the importance of national state institutions to 
buffer negative impacts, for example, of the lock downs or in terms of unemployment. The COVID-19 pandemic recovery also sets some 
basis for a stronger narrative towards a green recovery approach (Djalante et al., 2020; Forster et al., 2020).

8
1189Poverty, Livelihoods and Sustainable Development  Chapter 8
to increase their effectiveness. This requires higher levels of vertical 
and horizontal coordination and integration (GIZ, 2019). Horizontal 
coordination encompasses, for example, the integrated coordination 
of responses to climate change across different sectors, which requires 
suitable governance structures and processes that allow for such a 
coordination (Di Gregorio et  al., 2017; Burch et  al., 2019). Vertical 
integration is needed in order to ensure that effective responses also 
include different levels of governance and benefit from knowledge at 
different scales. The inclusion of local knowledge within national or 
provincial adaptation strategies requires such linkages and vertical 
coordination. Overall, there is an increasing body of literature that 
highlights the importance of improved integration and coordination 
also in order to promote a higher effectiveness of strategies and an

improved consideration of social justice and climate justice when 
designing and implementing responses (Levy and Patz, 2015).
However, evaluating the effectiveness, social impacts and social justice 
of climate change responses is not uniform across locations, nations 
and regions for three principal reasons:
• Temporal dimensions of responses: effective and appropriate 
climate change responses require that strategies and responses 
are tested in a specific context and that ongoing learning and 
adaptive management is a necessary to avoid maladaptation or 
other unintended consequences (Eriksen et al., 2021),
• Goal of responses: responses may have distinct and locally specific 
goals, such as reducing vulnerability (Sarker et al., 2019), which is 
distinct from increasing resilience (Alam et al., 2018). Vulnerability 
reduction and the increase of resilience (i.e., raising the ability to 
cope) are two different goals and often involve different processes.

• Level of responses: there is a need to ascertain the relevant level 
at which the responses are needed or expected (e.g., the individual 
level, community level, regional level). This analysis, however, also 
needs to consider the differential capacities of people, for example, 
the limited capacities of poor people or constrained capacities of 
most vulnerable countries (see also Section 8.3).
Effective responses to climate change impacts for one group could 
impose higher costs and negative consequences for other groups, in 
terms of shifts in exposure and vulnerability. This category of response 
is known as maladaptation. Maladaptation actions defined in the IPCC 
SR1.5°C (IPCC, 2018b) and in the Land Report (IPCC, 2019a) are the 
ones that usually have unintended consequences, and can lead to 
increased negative risk to poor population mostly in the Global South 
to climate hazards by either increasing GHG emissions or by increasing

the vulnerabilities to climate change with diminished welfare, now 
and in the near future (Roy et al., 2018). For example, migration to 
urban centres can represent a significant adaptation opportunity for 
the migrants themselves, but can also increase the vulnerability of 
their community of origin or destination (e.g., through a depletion 
of the workforce or an addition pressure on environmental resources 
and infrastructure respectively) (Gemenne and Blocher, 2017). Some 
types of observed responses to climate change may not yield long-term 
benefits. For example, food imports during droughts or adverse climate 
conditions are not a fully adequate response, since they may alleviate 
a problem on the one hand (i.e., an imminent food shortage due to 
crop failure) but, on the other, lead to no long-lasting improvements in physical conditions and create new dependencies that can increase 
vulnerability in the long run (Zimmermann et al., 2018).

In the AR5, the maladaptation outcomes emerge when climate change 
impacts and risks are disproportionately born by the poorest populations 
(Olsson et al., 2014). Since then, most maladaptation evidence emerges 
as a consequence of failure to address root causes of vulnerabilities 
that emerge under high and multiple forms of inequalities. In fact, 
the literature shows that adaptation practices can indeed redistribute 
vulnerabilities and increase risks to already poor and marginalised 
people with risk to maladaptation outcomes mainly in the Global South 
countries (Atteridge and Remling, 2018).
The maladaptation outcomes also emerge when responses are not 
equitable at the policy level, and exacerbate the precarity of vulnerable 
populations by excluding them from benefits and support, while 
attending to the needs of people of the most enfranchised segments 
of society (Thomas and Warner, 2019; Asplund and Hjerpe 2020). In

Tanzania, the political marginalisation of pastoralist access to critical 
riparian wetlands and increasing expansion of agriculture may result 
in adaptation pathways that heighten risk for these groups, while 
reducing risk for others (Smucker et  al., 2015). Salim et  al. (2019) 
found that adaptation to flooding in Jakarta privileges political 
economic elites, while poor infrastructure in poorest neighbourhoods 
exacerbates loss of assets, housing and displacements (Salim et  al., 
2019). In Bangladesh, intense and consecutive flooding led to national 
and regional adaptation plans, that resulted in maladaptive trajectories 
as local poverty context and precarities of properties were not carefully 
considered and disconnected from local autonomous practice (Rahman 
and Hickey, 2019).
Overall, the assessment shows that understanding impacts of climate 
change should not be limited to the analysis of direct impacts or

physical changes under different climatic conditions, but needs also 
account for the distributional effects that responses to climate change 
may imply. For example, responses implemented in order to benefit one 
sector or social group (e.g., farmers), should not undermine the well-
being of others (e.g., pastoralists). Documented cases of maladaptation 
(see Eriksen et al., 2021) hint that responses to climate change can 
exacerbate existing inequality in some cases and may discourage other 
types of responses (see also Sections 8.5; 8.6). Furthermore, responses 
to similar climate change impacts and hazards may be extremely 
differentiated according to various social contexts (see Section 8.3). In 
some cases, responses to climate change (e.g., relocation programmes) 
can even trigger social tipping points when climate change responses 
lead to major social transformations, such as forced displacement (see 
Section 8.4).

Also the influence of new global phenomena, such as urbanisation, 
issues of urban health (Schmid and Raju, 2020) and the consequences 
of the COVID-19 pandemic need to be considered when assessing 
actual and potential consequences of different responses to climate 
change. For example, inequalities, vulnerabilities and poverty pockets 
are expected to change and increase, particularly in urban areas in 
countries with rapid urbanisation processes and high levels of poverty 
(Djalante et al., 2020), hence urban and urbanisation trends need more 
attention. Urbanisation processes add another level of complexity (Raju

8
1190Chapter 8 Poverty, Livelihoods and Sustainable Development
Box 8.4 | Conflict and governance
Climate change impacts carry the risk of amplifying or aggravating existing tensions within and between communities or countries 
(Sakaguchi et al., 2017). There is, however, limited evidence for a universal direct causal linkage between climate change and violent 
conflicts (Mach et al., 2019). The triggering of conflicts related to climate impacts is strongly determined by contextual factors, such as 
the type of government or the level of development (Mach et al., 2019). A study of 156 countries (Abel et al., 2019) showed that an 
increase in periods of drought exacerbate the risk of conflict, especially in democratic countries. This influence was particularly marked 
during the period 2010–2012 in countries of western Asia and northern Africa that were undergoing political transformations, such as the

Arab Spring. Conflict can then represent people’s discontent in governments’ inefficient responses to climate impacts (Abel et al., 2019). 
Research has noted conditions under which climate change can increase the risk of armed conflict, which includes ethnic exclusion, 
agricultural dependence, large populations, insufficient infrastructure, dysfunctional local institutions and low levels of development (von 
Uexkull et al., 2016; Ide et al., 2020).
Since the AR5, there is robust evidence of the socially destabilising measures and high-risk income alternatives that the world’s poorest 
commonly take to cope with the impacts of climate change on livelihoods (Blattman and Annan, 2016). To avoid impoverishment, 
households often pursue risky livelihood alternatives, with high potential for return on investment (Sovacool et al., 2018), but which in 
some cases undermine environmental quality (Bolognesi et al., 2015), violate laws (Ahmed et al., 2019), contradict social norms (Hagerman

and Satterfield, 2014), erode institutions (Sovacool et al., 2018), or affect intra- and inter-community cooperation (Nadiruzzaman and 
Wrathall, 2015). At the same time, a narrowing of livelihood options carries a strong potential for participation and association with 
violent non-state organisations and movements, either criminal or ideological (Nett and Rüttinger, 2016). In order to reduce the risk of 
instability and violence associated with climate change, a broadening of livelihood options among the most vulnerable people appears 
to be an effective policy approach (Miguel et al., 2004).
The determinants of violence in the context of climate shocks are primarily poor institutional planning and response to impacts, such as 
the capacity of a government to respond to and manage environmental risk (Selby et al., 2017). In Latin America, for example, evidence on

social conflicts related to disputes over access to water in the context of drought and decreasing water availability point to institutional 
failures, such as poor, inequitable or corrupt water governance (Poupeau et al., 2017). Such observations are not confined to low-income 
countries. In industrialised countries, failure of governments to address climate change is likely to fuel discontent, a condition in which 
violent outcomes are possible (Ide et al., 2020).
In this regard, specific attention ought to be paid to how responses to climate change exacerbate inequalities within societies and create 
tensions between different groups—typically between those who are able to protect themselves from climate change impacts and those 
who do not have sufficient resources or are not prioritised in the responses to climate change. Frequently the possibility of migration

from climate change is conflated with conflict outcomes from climate change; however, there is limited evidence and low agreement 
that climate change and migration will result in increased conflict (Okpara et al., 2016b), while there is robust evidence and medium 
agreement that climate change can exacerbate existing tensions, which can in turn result in political violence and an increase in asylum-
seeking (Marchiori et al., 2012). In the future, conflict in the context of climate change impacts may increase the number of migrants 
seeking asylum, although at present there is scant empirical evidence for this (Schutte et al., 2021). Recent evidence also provides 
support for social conflict around inequitable climate mitigation policy as well (e.g., fossil fuel subsidies and emissions reductions targets) 
(Rentschler, 2016).
In recent years, research on the climate–security nexus has developed considerably, and has highlighted risks pertaining to conflicts,

geo-political rivalries, critical infrastructure, terrorism or human security (Gemenne et al., 2014). While different studies have identified 
strong past correlations between climatic variations (of temperature and rainfall in particular) and the occurrence of violent conflicts 
(Hsiang et al., 2013), others have stressed the need for stronger explanatory models or the risk of a selection bias (Benjaminsen et al., 
2012; Solow, 2013; Buhaug et al., 2014).
While climate change may increase armed conflict risks in certain contexts (Mach et al., 2019), responses to climate change will be crucial 
to mitigate these risks. Poor institutional responses can directly drive violence, and there is robust evidence that inequitable responses 
further exacerbate marginalisation, exclusion or disenfranchisement of some populations, which are commonly recognised drivers of 
violent conflict.

Robust evidence suggests environmental problems (related to climate change) can be dealt with cooperatively, hence leading to more 
positive and peaceful relations between groups (Wolf et al., 2003; Ide, 2019). To avert violent outcomes induced by climate change, 
stronger local and national climate adaptation institutions within vulnerable societies, and stronger cooperative resource governance 
mechanisms between vulnerable countries (such as transboundary water governance agreements) are needed.

8
1191Poverty, Livelihoods and Sustainable Development  Chapter 8
et al., 2021). This is particularly the case in rapidly growing medium-
sized cities in Africa that at present do not have sufficient resources to 
cope and adapt, and to implement climate-sensitive land use planning 
(Birkmann et al., 2016).
Tables 8.1 and 8.2 present a summary of a set of common climate change 
responses observed, classified according to their main approach. All 
these responses demand a certain level of commitment, the support of 
adequate policies and enough budget for their implementation (Archie 
et  al., 2018). The observed climate change adaptation responses—
differentiated along urban and rural settings—underscore the very 
different nature of various responses and the need for cross-sectoral 
approaches.
While Table 8.1 shows selected adaptation responses, Table 8.2 shows 
selected mitigation responses that highlight that some mitigation

responses (e.g., increasing energy efficiency) also have a potential 
benefit for the poor or more vulnerable groups, for example, through 
the reduction of costs for electricity. Both tables underscore that 
climate change mitigation and adaptation responses are strongly interlinked with broader development issues (industrial production, 
land use planning, education, etc.) at different scales.
8.2.2.2 Observed Impacts and Implications for Structural 
Inequalities, Gender and Access to Resources
This section examines the mutual reinforcement of climate change 
impacts and structural inequalities. There is robust evidence that 
negative impacts and harm posed by climate change are also a result 
of social and political processes and existing structural inequalities 
(Sealey-Huggins, 2018). Climate change encompasses unevenly 
distributed impacts on women, youth, elderly, Indigenous Peoples, 
communities of colour, urban poor and socially excluded groups,

exacerbated by unequal distribution of resources and poor access 
for some (Rufat et al., 2015; McNeeley, 2017; Sealey-Huggins, 2018). 
Structurally disadvantaged people, who are subject to social, economic 
and political inequalities resulting historically from discrimination, 
marginality or disenfranchisement because of gender, age, ethnicity, 
class, language, ability and/or sexual orientation, are disproportionately 
vulnerable to the negative impacts of climate change hazards (Kaijser Table 8.2 |  Selected climate change mitigation responses.
Modality of response Impacts on urban communities Impacts on rural communities (e.g., farmers, pastoralists)
Land use planning
(e.g., Frose and Schiling, 2019)Helps to reduce GHG emissions and support environmental 
conservation, preventing urban heat islandsHelps to reduce pressure on the natural resources (deforestation, land 
filling, damaging wetland) and promotes carbon sequestration
Improving industrial processes

(e.g., van Vuuren et al., 2018)Unlocks many opportunities for improvement, including the 
optimised use of energy, reuse of waste in production, reducing 
GHG emissions, use of biomass and more efficient equipmentIn rural settings, industrialisation and technological innovation may 
directly assist vulnerable communities through provision of inputs 
(e.g., water storage, drip irrigation, forecast information), or reuse of 
biowaste in agriculture or energy production, hence reducing costs 
and pollution levels
Renewable energy
(e.g., Cronin et al., 2018)Reduction of GHG emissions and reduction of the cost of electricitySome options (e.g., solar, wind) may help to reduce deforestation, 
reduce GHG emissions and promote healthier air within households
(e.g., Abrahamse and Shwom, 2018)Efficient end-users’ energy utilisation reduces energy wastage, 
reduces costs and lowers carbon emissionsEfficient end-users’ energy utilisation leads to natural resource

conservation and a reduction of GHG emissions
Local/individual actions
(e.g., Shaffril et al., 2018; Tvinnereim 
et al., 2018)Can contribute to reduce carbon footprintsFosters personal and community motivation to manage individually 
and communally owned resources, helps to reduce GHG emissions 
and foster resources conservationTable 8.1 |  Selected observed climate change adaptation responses in urban and rural areas commonly associated with positive implications for poverty, livelihoods and sustainable 
Modality of response Impacts to urban communities Impacts to rural communities (e.g., farmers, pastoralists)
Integrated natural resource management
(e.g., van Noordwijk, 2019)Better conservation of green areas and reduced exposure to floodsConservation of natural resources (e.g., water, soil, pasture, forest, 
wildlife, biodiversity, aquatic life)
Disaster risk management
(e.g., Mall et al., 2019)Pre-disaster risk management and post-disaster risk management

measures reduce loss of life and damage to propertyDisaster risk management may play an important role in avoiding or 
limiting the impacts of floods, droughts and other extreme events
Physical/structural improvements
(e.g., Vallejo and Mullan, 2017)Improving physical/structural measures to prevent property damage 
and foster ecosystems integrityFlood defences may help to prevent property losses, planting of trees 
may stabilise slopes, reduce soil erosion and siltation, rainwater 
harvesting increases water availability, protection of biotopes 
supports biodiversity
Relocation of vulnerable communities
(e.g., McNamara and Des Combes, 2015)Moving vulnerable communities before and during climate-induced 
hazards may reduce loss of lifeReduces the exposure of vulnerable communities to climate change 
and extremes hazards (e.g., floods and droughts), lessens their 
vulnerability, improves access to better resources and builds their 
capacity to adjust to a new context

Education and communication
(e.g., Monroe et al., 2017)Public education and awareness, improved communication may 
reduce the damages and losses from adverse impacts of climate 
change and from extreme eventsFosters awareness creation, reducing the degree of vulnerability to 
certain climate-induced hazards and help build the capacity to adapt

8
1192Chapter 8 Poverty, Livelihoods and Sustainable Development
and Kronsell, 2014; Otto et al., 2016). High levels of vulnerability at 
national scale (see Section 8.3) are often linked to complex histories, 
including long-term economic dependencies established and reinforced 
in the context of colonisation.
Links between climate change, structural racism and development 
are less well established as an element of disproportionate impacts 
of climate change (Sealey-Huggins, 2018). Discrimination is not 
restricted to structural racism and includes discrimination of all kinds, 
including that of gender and caste, because of which a considerable 
population is directly bound to suffer the harsh impacts of the 
climate change. The climate change and gender literature has come 
a long way in demonstrating concrete examples of how structural 
inequalities operate. The political and micro-political aspects and 
how they interact with structural inequalities are also important to

understand vulnerability. Henrique and Tschakert (2020) shows how 
the many adaptation efforts benefit powerful actors, while further 
entrenching the poor and disadvantaged in cycles of dispossession. 
This critical analysis recommends acknowledging injustices, embracing 
deliberation and nurturing responsibility for human and more-than-
human others. Garcia et al. (2020) describes the socio-political drivers 
of gendered inequalities that produce discriminatory opportunities for 
adaptation. They use an intersectional subjectivities lens to examine 
how entrenched power dynamics and social norms related to gender 
create barriers to adaptation, such as lack of resources and agency. 
The analysis shows a pronounced dichotomy as women experience the 
brunt of these barriers and a persistent power imbalance that positions 
them as ‘less able’ to adapt than men.
Historical marginality and exclusion are context-specific conditions that

shape vulnerability (Leichenko and Silva, 2014). There is also robust 
evidence that gender inequalities contribute to climate vulnerability, 
and that consideration of gender is a key approach to climate justice 
(see Cross-Chapter Box  GENDER in Chapter 18). There is robust 
evidence for the differentiated impacts of climate change and climate-
orientated policies on women (McOmber, 2020). For example, Friedman 
et  al. (2019) show that, in Ghana, homogeneous representations of 
women farmers and a technical focus of climate-orientated policy 
interventions may threaten to further marginalise the most vulnerable 
and exacerbate existing inequalities. Climate change impacts can also 
heighten existing gender inequalities (Jost et  al., 2016; Glazebrook 
et al., 2020). On the one hand, climate change impacts can be gendered 
as a result of customary roles in society, such as triple workloads for 
women (i.e., economic labour, household and family labour, and duties

of community participation), and occupational hazards from gendered 
work indoors and outdoors (Murray et al., 2016). On the other, climate 
change hazards interact with changing gender roles in society, such 
as urban migration of both men and women in ways that break with 
tradition (Bhatta et al., 2016).
Gender influences the way that people also experience loss and 
process psychological and emotional distress of losses, such as 
mortality of children and other relatives in climate-related disasters 
(Chandra et al., 2017).Women’s capacities are often constrained due 
to their roles in their household and society, institutional barriers 
and social norms. These constraints result in low adaptive capacity of 
women, which make them more vulnerable to hazards. As more men seek employment opportunities away from home, women are required 
to acquire new capacities to manage new challenges, including risks 
from climate change. Banerjee et  al. (2019b) finds that capacity-

building interventions for women staying behind, which aimed to 
strengthen autonomous adaptation measures (e.g. precautionary 
savings and flood preparedness), also positively influenced women 
to approach formal institutions. Besides, the intervention households 
were more likely to invest a part of the precautionary savings in flood 
preparedness measures than control households.
Next to the direct differential impacts of climate change on different 
social groups, the impacts of climate change can also exacerbate 
inequality due to the lower access and limited ability to benefit from 
services provided by ecosystems. Marginalised poor people often 
significantly depend on the access to surrounding environments, 
natural resources and ecosystem services for their livelihoods, for 
leisure or cultural practices. Thus shifts in such resources, for example, 
due to the bleaching of coral reefs or shifts in fish stock, also cause

severe challenges and risks to these communities (Leal Filho, 2018; Le, 
2019; UNTTSDCC, 2014).
Overall, the assessed literature highlights that climate change 
impacts are not emerging in isolation from development context and 
development pathways. Economic and social ramifications mean that 
they may exacerbate poverty and marginalisation (Finkbeiner et  al., 
2018; Dogru et al., 2019). Choudhary et al. (2019) and Orimoloye et al. 
(2019) highlight how the effects of climate change can be even more 
prejudicial to poor countries, which, in most cases, already suffer from 
weak governance, high prevalence of informal settlements and lack 
of resources. Health, livelihood assets and economy are examples of 
aspects that will worsen as a result of the negative impacts of climate 
change and failure to provide opportunities for sustainable adaptation 
(United Nations, 2015). These facts highlight the importance of 
mitigation and adaptation measures especially in these regions

characterised by high levels of vulnerability (see also Section 8.3).
8.2.3 Observed Impacts and Responses and their 
Relevance for Decision Making
Many countries base their adaptation strategies on National Adaptation 
Programmes of Action (NAPAs), which often correlate different levels 
of decision making and governance (Golrokhian et al., 2016). Whereas 
the involvement of national governments is needed for designing 
appropriate responses to climate change, recent studies underscore 
the need to also consider IKLK within adaptation and risk reduction 
strategies, thus fostering stronger linkages with local communities, 
leading to improved vertical integration between different strategies, 
programmes and actors (Ford et al., 2016; Vij et al., 2017; Singh et al., 
2020). The relevance of addressing the issue of vulnerability and poverty 
to reduce the climate change risks has been demonstrated within the 
assessed literature on the impact of climate change (Hallegatte et al.,

2017). In this regard, it is noticeable that not many NAPAs explicitly aim 
to reduce poverty, even though poverty reduction is associated with 
vulnerability reduction to climate change (Demski et al., 2017).

8
1193Poverty, Livelihoods and Sustainable Development  Chapter 8
Next to issues of observed impacts and responses to climate change, 
it is important to assess observed barriers in implementing climate 
change responses. The discussion of barriers is complemented later 
in the chapter with an assessment of the enabling environments for 
adaptation (see Section  8.5.1). Some of the most common barriers 
outlined in the scientific literature are summarised in Table 8.3.
There are various characteristics of responses to climate change, 
which aim to protect livelihoods and prevent poverty expansion (i.e., 
an enlargement of the group of people already affected by poverty). 
Some of them are:
• Timely: meaning that responses need to take place within a matter 
of weeks or months and not over years (Wise et al., 2014).
• Targeted: with a focus on the affected communities and groups, 
to help alleviate the pressures they are under (e.g., Aleksandrova, 
2020).

• Sustainable: with long-lasting results leading to self-sufficiency of 
the affected communities and their resource base, as opposed to 
short-term ones relying on external support (e.g., Caetano et al., 
2020).
• Integrated: the impact of climate change is multifaceted and far 
reaching and requires the engagement of various actors (e.g., the 
vulnerable community, government agencies, local and international 
nongovernmental organisations, civil societies, media) (Ayal et al., 
2020).
Finally, responses such as those outlined in Table 8.1 and Table 8.2, 
need to ensure the active participation of local stakeholders considering 
their diverse interests, so that they are grounded in reality. In addition, 
responses need to be complemented with operational procedures 
and time frames so that they can be more systematically pursued and 
implemented (Alves et al., 2020).8.3 Human Vulnerability, Spatial Hotspots, 
Observed Loss and Damage, and 
Livelihood Challenges

This section assesses the literature on vulnerability—the assessment 
of vulnerability at global and national scales—and explores economic 
and non-economic losses of people and livelihoods exposed to and 
impacted by climate change. The section examines how climate change 
threatens livelihoods and juxtaposes global and local level assessments 
of vulnerability based on empirical data at different scales. The analysis 
of recent literature underscores that climate change impacts and 
adaptation needs cannot be understood by looking at climate change 
only. Vulnerability and livelihood security are seen as an important 
component for understanding the human dimension of climate change 
(Rhiney et al., 2016; Cardona, 2017; Byers et al., 2018; Eriksen et al., 
2020; Wisner, 2020; Birkmann et al., 2021a; Cole et al., 2021).
Linkages between global and individual vulnerability and livelihood 
security, including aspects of intersectionality are also assessed.

Overall, this Section 8.3 reveals that different countries, societies and 
specific groups within a society have very different starting points on 
their move towards climate resilience.
8.3.1 Assessments of Risk and Vulnerability
Conventional assessments of risks and the benefits of adaptation and 
risk reduction measures in the context of climate change primarily 
focus on the financial value of the avoided losses (in USD) and the 
assets that are going to be protected from adverse consequences 
of climate change or extreme events due to specific measures (e.g., 
dyke construction). Even though these assessments fall short of 
measuring the real costs of addressing climate change impacts (see 
DeFries et  al., 2019), they often support the definition of priorities 
in terms of protecting economic values and assets. However, these Table 8.3 |  Some common barriers in implementing climate change responses and their implications.
DimensionsBarriers in implementing effective

climate change responsesImplications
Governance Unfavourable political frameworks (Gupta, 2016)Governance structures can undermine autonomous adaptation (Section 8.4; Table 8.6); inability to 
include gender differentiated vulnerabilities in governance schemes (Bryan et al., 2017)
SocialAttitudes to risks and cultural values may hamper responses 
(Billi et al., 2019)Social norms of reciprocity and cohesion may erode as a consequence of climate change 
responses (Volpato and King, 2019); socio-cultural conditions as key barriers to gender 
differentiated support to impact reduction (Bryan et al., 2017)
InstitutionalLimited availability coordination and prioritisation processes 
(Patterson and Huitema, 2019)Lack of anticipatory risks undermining local efforts to cope with hazards (Singh et al., 2019a)
BehaviouralPsychological distress may cause insecurity and behaviour of

some groups may increase vulnerability (Van Lange et al., 2018)Psychological distress associated with loss of attachment to a place has also been observed 
among vulnerable communities in regions such as South Asia (Maharjan et al., 2020)
FinancialLimited financial resources to support adaptation projects 
(Khan et al., 2019)Lack of financial resources and assets among urban poor increase their exposure and 
vulnerabilities to the increasing climate hazards (Salim et al., 2019)
StructuralUnsuitable infrastructure may increase exposure 
(Chinowsky et al., 2015; Vallejo and Mullan, 2017)Structural marginalisation of Indigenous Peoples and their IKLK can exacerbate risks of 
maladaptation among SIDS countries (McNamara and Prasad, 2014; Aipira et al., 2017; 
Granderson, 2017); infrastructure projects to adapt to climate change impacts may increase the 
vulnerability of poor slum people
TechnicalLack of access to technologies which may support adaptation

(e.g., climate services) (Bel and Joseph, 2018)The highest level of illiteracy among women prevent their engagement to access technology and 
risk reductions in vulnerable communities (Balehey et al., 2018)

8
1194Chapter 8 Poverty, Livelihoods and Sustainable Development
assessments do not sufficiently account for how climate change 
impacts and imposes risks on poor people, nor do they capture issues 
of climate justice and more complex societal impacts and future 
risks. For example, various observed losses in the context of climate 
change cannot be sufficiently expressed in terms of an economic value 
(see Section 8.3.5), but these items or assets are highly relevant for 
various people with limited economic resources (Hallegatte et  al., 
2017). Consequently, the assessment of risks from climate change 
facing particularly poor people requires comprehensive assessments 
of human vulnerability, resilience and the impacts of climate change 
on human well-being going beyond a simple temperature–societal-
impact understanding. Knowledge about methods and approaches to 
assess human or human–environmental vulnerability and livelihood

security, including aspects of intersectionality, is important in order 
to explore whether or not adaptation and development programmes 
are able to reduce vulnerability. The body of literature on these issues 
has grown significantly since the AR5 publication (IPCC, 2014a; Moser, 
2014).
This literature underscores that approaches to assess resilience, 
vulnerability and human well-being include global assessments that 
can inform strategies and priority settings for adaptation and risk 
reduction in the context of climate change (high confidence) (WHO, 
2014b; Young et al., 2015; Feldmeyer et al., 2017; GIZ and BMZ, 2017; 
Hallegatte et al., 2017; Birkmann et al., 2021a; Garschagen et al., 2021; 
Toolkit, 2021).
These quantitative global assessments that have emerged within 
the last decades have not been sufficiently assessed in former IPCC 
reports, for example, in terms of the agreement on spatial hotspots or

in terms of regional clusters of vulnerability and the linkages between 
past societal impacts and levels of vulnerability. The assessed literature 
shows that conditions and phenomena that characterise systemic 
vulnerability (hazard independent vulnerability), such as high levels of 
poverty and gender inequality, limited access to basic infrastructure 
services or state fragility are highly relevant for understanding societal 
impacts of climatic hazards and future risks of climate change (e.g., 
Cutter et al., 2003; ADB, 2005; Cutter and Finch, 2008; World Bank, 
2008; UNISDR, 2009; Crawford et al., 2015; Rufat et al., 2015; Carrao 
et  al., 2016; Gupta, 2016; Rahman, 2018; Andrijevic et  al., 2020; 
Jamshed et  al., 2020a; Feldmeyer et  al., 2021; Garschagen et  al., 
2021). These factors and context conditions also influence individual 
vulnerability at household or community level. Access to basic services, 
such as water and sanitation, are linked to human rights and if not

granted increase the likelihood that people disproportionately suffer 
from climate-induced hazards, due to their pre-existing lack of access 
to such services. In addition, increasing climate hazards further 
constrain the access to such services (United Nations, 2018; Kohlitz 
et al., 2019; Gupta et al., 2020).
There is an increasing evidence base that successful adaptation and risk 
reduction strategies need to acknowledge not only climate change and/
or specific climate hazards (sea level rise, flooding, droughts, etc.), but 
also human vulnerability and existing adaptation gaps and thereby the 
different starting points that societies or different groups have towards 
climate resilience (see UNEP , 2016; Birkmann et  al., 2021a). Recent 
reports underscore that development and capacity indicators are useful to assess the broader adaptation challenges and adaptive capacities 
at global scale independent of a specific climatic hazard. Examples

include the percentage of population with access to improved water 
sources and improved sanitation, the number of physicians per 1000 
people or the dependency ratio (UNEP , 2018). These indicators are also 
part of more comprehensive vulnerability assessments, such as those 
assessed within this section namely the vulnerability components of 
the INFORM risk index (e.g., INFORM, 2019) and of the WorldRiskIndex 
(e.g., Birkmann and Welle, 2016; Birkmann et  al., 2021a; Feldmeyer 
et al., 2021). Recent literature underscores that measuring vulnerability 
is key for assessing factors that significantly determine actual and 
future adverse consequences of climate change and complex risks 
(Cutter and Finch, 2008; Cardona et al., 2012; de Sherbinin et al., 2019; 
Peters et al., 2019; Jamshed et al., 2020c; Visser et al., 2020; Feldmeyer 
et al., 2021). However, there is also important critique on indicator-
based assessments of vulnerability (see de Sherbinin et al., 2019; Rufat

et al., 2019; Visser et al., 2020), particularly with regard to issues of 
validation and its use in decision-making processes. Nevertheless, 
we observe an emerging agreement in the literature that resilience 
building and adaptation to climate change has to be informed by 
climate and multidimensional assessment of the vulnerability of 
people, different groups and coupled human–environmental systems, 
including both quantitative and qualitative assessment approaches 
(IPCC, 2014b; UNEP , 2018; Singleton et  al., 2021; Birkmann et  al., 
2022). Since, interdependencies between regional (supranational/sub-
continental), national, community and individual vulnerability have 
often been overlooked, this chapter assesses both global and regional 
vulnerability, as well as local livelihood vulnerabilities.
While past research regarding the nexus between climate change and 
poverty often focused on vulnerable groups in rural areas of low-income

countries (de Sherbinin, 2014; IPCC, 2014a; Barbier and Hochard, 2018), 
new global mega-trends, such as urbanisation, underscore the need to 
assess both rural and urban communities and their vulnerability. In 
many rapidly growing cities in the Global South, access to land and 
to housing is a challenge, particularly for the poor and marginalised, 
contributing to a further increase in informal settlements that often 
emerge in highly hazard-exposed areas (Jeschonnek et al., 2014; Rana 
et al., 2021). In addition, migration from rural areas to urban centres, 
also due to increasing adverse impacts of climate change on rural 
livelihoods, can add another level of complexity (Flavell et al., 2020). 
Moreover, the context in which such urbanisation processes take place 
is key. For example, rapidly growing medium-sized cities, for example 
in West Africa, often do not have sufficient financial, technical and 
institutional resources to adapt urban structures to climate change

(Birkmann and Welle, 2016; Birkmann et al., 2016; de Sherbinin et al., 
2017). Hence, vulnerability in urban contexts is an emerging issue for 
international, national and local adaptation programmes. Rather than 
focusing on mega-cities and their exposure as primary hotspots, more 
attention has to be given to rapidly growing small- and medium-sized 
cities and their adaptation needs from the perspective of vulnerability 
reduction and poverty.

8
1195Poverty, Livelihoods and Sustainable Development  Chapter 8
8.3.2 Global Hotspots of Human Vulnerability to Climate 
Change
8.3.2.1 Hotspots and Spatial Patterns of Multidimensional 
The assessment of literature published since the AR5 suggests that 
alongside already deteriorated specific conditions that determine 
individual vulnerability and livelihood security to climate change (see 
Section  8.2), high levels of poverty, lack of access to basic services 
(human rights to water and sanitation), poor governance and conflicts 
are important factors that characterise vulnerability and systemic 
human vulnerability in particular (EC-DRMKC, 2020; Wisner, 2020; 
Feldmeyer et  al., 2021; Garschagen et  al., 2021; GIZ, 2021). These 
context conditions within a country or region limit the access to 
effective adaptation options particularly for the poor and marginalised 
groups.
Recent studies underscore that human vulnerability—thus the

predisposition to be adversely affected—is largely determined by past 
and present development processes, rather than by the occurrence of 
individual events (Wisner, 2016; Cutter, 2018; Birkmann et al., 2020). 
Also the consequences of the COVID-19 pandemic will create newly 
poor, particularly in countries that are already characterised by high 
levels of vulnerability (see Box 8.3; Laborde et al., 2020b; Lakner et al., 
2020).
Quantitative studies and assessments published since AR5 provide 
additional insights about human vulnerability to climate change and 
resilience of societies at different scales using different indicator sets and 
approaches (Feldmeyer et al., 2017; Hallegatte et al., 2017; EC-DRMKC, 
2020; Birkmann et al., 2021a; Feldmeyer et al., 2021; Garschagen et al., 
2021).
While quantitative measures of vulnerability are widely used at 
different scales (Cutter et  al., 2016; Garschagen et  al., 2021), there

are also studies that caution the use of such indices in policy making 
or risk reduction efforts (Rufat et  al., 2019; Spielman et  al., 2020). 
Such assessments of vulnerability have to be internally and externally 
validated and handled with care when applied in decision-making 
processes in terms of their options and limits. At the same time, these 
assessments capture important conditions and structures that make 
people more susceptible to various climate hazards and climate change 
impacts. The relevance of these conditions is confirmed by quantitative 
impact assessments as well as many specific case study assessments 
(Welle and Birkmann, 2015; Feldmeyer et al., 2021; Birkmann et al., 
2022). For example, the access to basic services (e.g., water and 
sanitation) (Bollin and Hidajat, 2013; Pandey et  al., 2017b; UNEP , 
2018; United Nations, 2018; Gupta et al., 2020; Jamshed et al., 2020a) 
and broader modes of engagement in governance and governance

fragility (Crawford et al., 2015; Rahman, 2018; Andrijevic et al., 2020) 
significantly influence how climatic hazards translate into severe or 
non-severe losses and harm (see Section 8.5.2).
The lack of such support structures and resources can severely constrain 
opportunities of people to cope with and adapt to climate change, since 
it is not only the climate hazard, but also exposure and particularly the vulnerability of a society, a specific community or an individual 
household that determine adverse societal consequences of climatic 
hazards. International vulnerability and resilience assessments show 
that vulnerability varies across countries of similar wealth or income 
because multidimensional vulnerability, well-being and resilience 
depend on a larger set of factors (Birkmann and Welle, 2016; Hallegatte 
et al., 2017; INFORM, 2019). In this regard, vulnerability assessment is 
significantly different from climate exposure mapping.

The findings of these global assessments suggest, among other 
issues, that options to reduce vulnerability and enhance resilience 
do exist in various countries at different levels, in part irrespective 
of their income level (Feldmeyer et al., 2017; Hallegatte et al., 2017). 
Vulnerabilities at national and regional-level influence community and 
individual vulnerability, particularly through structures that determine 
entitlements, the access to resources and processes of marginalisation 
(Watts and Bohle, 1993; Thomas and Warner, 2019).
While different assessments use different sets of indicators, most 
of the global assessments with national-scale resolution (Birkmann 
and Welle, 2016; Kreft et al., 2016; Feldmeyer et al., 2017; Hallegatte 
et  al., 2017; Eckstein et  al., 2019; INFORM, 2019; ND-GAIN, 2019; 
Garschagen et  al., 2021), contain indicators that cover different 
aspects of economic poverty, inequality, access to basic infrastructure

services, education and human capital (e.g., adult literacy rate) and 
some also include issues of gender inequality, specific vulnerable 
groups or insurance against extreme events. The assessments also 
differ, for example, in terms of their consideration of aspects of 
governance, such as corruption and conflict, or the consideration 
of social safety nets, such as insurance coverage, or the number of 
people affected by hazards (Feldmeyer et al., 2017; INFORM, 2019), 
as well as in terms of the consideration of losses experienced in the 
past or issues such as biodiversity as an aspect of adaptive capacity 
(Hallegatte et  al., 2017; Birkmann et  al., 2022). Moreover, the 
assessments differ in terms of the consideration of specific indicators 
and the inclusion or non-inclusion of specific hazard exposure (Welle 
and Birkmann, 2015; Hallegatte et al., 2017; INFORM, 2019; ND-GAIN, 
2019; Birkmann et al., 2022).
Recent comparative studies of global assessments of vulnerability

show high agreement on the spatial clusters that have very high 
or very low vulnerability to climate change, compared to larger 
differences in terms of exposure and risk (Birkmann and Welle, 
2016; Hallegatte et al., 2017; INFORM, 2019; Feldmeyer et al., 2021; 
Garschagen et al., 2021; Schleussner et al., 2021). The comparison of 
the averaged ranking results at the scale of ‘climate regions’ using the 
vulnerability components of INFORM and the WorldRiskIndex—as two 
comprehensive global assessment approaches of systemic vulnerability 
(hazard independent vulnerability) (see Figures 8.5; 8.6)—also finds a 
high agreement in terms of most vulnerable regions and regions with 
low vulnerability (Figure 8.5; Feldmeyer et al., 2021). The assessment 
at this scale reveals that global hotspots of human vulnerability can be 
found in climate regions in East Africa, Central Africa and West Africa,  
followed by high vulnerability in Central America, South Asia and

Southeast Asia, for example. Garschagen et al. (2021) in a comparison 
of further risk indices also found that there is high agreement on global 
assessments of vulnerability compared to exposure or overall risk.

8
1196Chapter 8 Poverty, Livelihoods and Sustainable Development
The analysis of vulnerability assessment results of the INFORM Risk 
Index and WorldRiskIndex4 at the level of countries coupled with 
population data confirms a high agreement on most vulnerable 
countries. It also shows that global hotspots of human vulnerability 
are not just single countries, but often emerge within regional 
clusters, particularly in Africa, but also in Asia and Central America 
(see Figure  8.6 and Birkmann et  al., 2021a). These regional clusters 
(Figure 8.6) are characterised by high levels of vulnerability in terms 
of socioeconomic, demographic, environmental and governance 
conditions that make people more likely to face adverse consequences 
once a climate hazard occurs. The internal and external validation of 
these index systems shows its statistical validity and robustness (Welle 
and Birkmann, 2015; Marin-Ferrer et al., 2017; Birkmann et al., 2022).

It also confirms a quantitative relationship between most vulnerable 
regions and fatalities and severely affected people due to climate-
influenced hazards (Birkmann et al., 2022). The vulnerability map in 
Figure 8.5 shows the vulnerability level (systemic societal vulnerability) 
linked to national scale and provides additional information about the 
population density within these countries. The background map does 
not show specific vulnerable populations within countries. Selected 
examples of sub-national human vulnerabilities have been added as 
additional information in terms of case studies based on information 
from other chapters within this report (see, for example, Box  8.7; 
Sections  5.12; 10.3.3; 10.5.1; 13.8.1; 14.4.7; 15.3.4; Cross-Chapter 
Paper 6.2.7).
4 Both index system analyse risk and vulnerability at the country level and are updated yearly. The WorldRiskIndex (WRI) conceptualizes vulnerability as having susceptibility, lack of coping capacity and

lack of adaptive capacity components. It is based on 28 indicators (23 vulnerability indicators) for 171 countries. It uses different weights based on statistical tools complemented by expert judgements 
and equal weights for the three components. The index is composed of additive functions for vulnerability components (Welle and Birkmann_2015). The INFORM Risk Index conceptualizes vulnerability 
as having two components namely socioeconomic vulnerability and vulnerable groups while lack of coping capacity is considered as a separate component. The INFORM index consists of 18 indicators 
to assess vulnerability and 14 indicators for measuring lack of coping capacity. It analyses risk and vulnerability for 191 countries. It uses equal weights for indicators and components and uses a 
multiplicative function for aggregating components to compose the final index (Marin-Ferrer et al., 2017). In this chapter, the lack of coping capacity component of INFORM is included in vulnerability

calculations in line with the IPCC framing of vulnerability. The vulnerability map presented in this report is based on both WRI and INFORM indices (see Birkmann et al. (2022), Feldmeyer et al. (2021), 
Garschagen et al. (2021) for agreement between the WRI and INFORM indices)Figure  8.7 provides an aggregated regional overview of selected 
indicators used within the vulnerability index mapped in Figure 8.6. 
The overview shows that the many compounded challenges faced by 
African countries are starkly pronounced, but also in other regions, 
especially Asia, Central and South America, and among SIDS, there 
are several challenges such as inequality, governance issues and 
displacement, which all increase the vulnerability and constrain 
adaptive capacities of these regions to climate change.
However, it is also important to note that vulnerability assessments 
do have their limitations (Heesen et  al., 2014; Rufat et  al., 2019).

For example, in high-income countries, specific groups can be highly 
vulnerable to climate change due to marginalisation and discrimination 
due to ethnicity or gender. Gender inequality, for example, is also high 
in some countries classified in the literature as having low vulnerability 
(see Birkmann et al., 2021a; Birkmann et al., 2022). Nevertheless, these 
countries have, in theory, sufficient financial resources and governance 
capacities to deal with these challenges, while this is different for 
many country clusters classified as highly vulnerable.
Countries and regional clusters with low vulnerability (see Figures 8.5; 
Figure 8.6), such as Australia and New Zealand or Iceland and North 
Europe, encompass population groups that are exposed and vulnerable 
to climate hazards, such as sea level rise or droughts but, within these 
regions’ context, conditions exist that allow the negative impacts Agreement between global vulnerability indices
World Risk Index

Vulnerability rank
10 20 3030
20
10
0
INFORM Risk Index
Vulnerability rank 35High
Low35
HighClimate regions in the map are each represented with one circle in the diagram.
10
 20
 30
 3
Climate regions with very high agreement fall within the center line area.
Figure 8.5 |  Aggregated vulnerability map at the scale of climate regions based on the averaged ranking of the INFORM Index’s vulnerability component 
and the averaged ranking of the vulnerability component of the WorldRiskIndex. Based on the rankings of the INFORM index (INFORM, 2019) and the WorldRiskIndex 
(Birkmann and Welle, 2016; Feldmeyer et al., 2017). The map and diagram show agreement between the two global vulnerability indices when ranking climate regions according 
to their vulnerability—darker colours show regions of higher vulnerability. The diagram shows how the 35 climate regions are ranked by each index and also serves as a legend 
for the map above.

8
1197Poverty, Livelihoods and Sustainable Development  Chapter 8
and losses to be buffered (also for most vulnerable groups). These 
regions have higher financial and institutional capacities to support 
people at risk and planned adaptation at a different magnitude 
within their region, for example, as seen in compensation payments 
for drought exposed farmers (Hochrainer-Stigler and Hanger-Kopp, 
2017; Australian-Government, 2021) or flood affected households in 
Germany in 2021. Also, the percentage of households insured against 
climate-influenced hazards, such as floods or storms, is significantly 
higher in these regions (North America, Western Europe) compared to 
regions such as Western Africa or Micronesia (Welle and Birkmann, 
2015; Feldmeyer et al., 2021; Birkmann et al., 2022).
While climate change differentially impacts people in vulnerable 
situations within countries, including the poor, children, women,

marginalised Indigenous or other ethnic minority people (Rhiney et al., 
2016; Méndez et al., 2020), the global assessment results underscore 
that, in most vulnerable regions and countries, very limited resources 
and structures exist to support these groups when droughts, floods or 
storms occur and place an additional burden on these groups.
The assessments of human vulnerability also point towards important 
adaptation options that are not visible if one focuses on climatic 
hazards or temperature changes alone (Figure  8.9; Dückers et  al., 
2015; Cutter et  al., 2016; Birkmann et  al., 2021a). Fundamental for 
vulnerability reduction and adaptation are social insurances and 
infrastructure programmes, as well as legislation that improves the 
access of poor and marginalised groups to basic infrastructure services 
and security. For example, the ‘free basic service programme’ of the 
national government of South Africa (GovSA, 2021) is one example

where a national government (Government of South Africa) has 
committed itself to providing a basic amount of free water, electricity 
and sanitation to low-income households, particularly indigent 
people, such as those living in informal settlements or remote rural 
areas. Coupled with incentives, for example in terms of a higher use 
of renewable energy (e.g., solar home systems in rural areas) (see 
GovSA, 2021), these investments can support vulnerability reduction 
and mitigation of GHG emissions. However, the programme design and 
implementation has also been criticised (see Nel and Rogerson, 2005; 
Muller, 2008), as is witnessed by ongoing service delivery protests 
(Mutyambizi et al., 2020). This example shows that current national 
programmes can—even if they are not classified as adaptation 
measures—provide important entry points to reduce human 
vulnerability to climate change.
The relevance of human vulnerability has also been confirmed by

recent assessments. The assessment of vulnerability studies and 
mortality data found that the average mortality5 from floods, storms 
and droughts is 15  times higher in regions and countries ranked 
as very highly vulnerable (e.g., Afghanistan, Haiti, Mozambique, 
Nigeria, Somalia) compared to regions and countries with very low 
vulnerability (e.g., Canada, Italy, Sweden, UK) (Birkmann et al., 2022). 
These patterns are confirmed by other studies (e.g., CRED and UNDRR, 
2015; CRED and UNDRR, 2016; CRED and UNDRR, 2020b) that 
examined disaster mortality per hazard event in low and lower middle 
income countries compared to high income countries and therewith 
5 Measured as death per hazard event and calculated by averaging the country values of mortality per event falling in different vulnerability categories.also point towards major differences between countries with high and 
low vulnerability (Pelling et al., 2004; CRED and UNDRR, 2015; CRED

and UNDRR, 2016; CRED and UNDRR, 2020b). Even if one takes solely 
‘highly vulnerable countries’ such as India, Pakistan and the Philippines 
(and not ‘very highly’ vulnerable countries), mortality is still nine times 
higher compared to very low vulnerability countries. Similarly, studies 
further revealed that average number of adversely affected people per 
hazard event (e.g., loss of the house) is 11 times higher in regions and 
countries categorised as having very high vulnerability compared to 
very low vulnerability (Birkmann et  al., 2022). In addition to floods, 
droughts and storms, published EM-DAT data for wildfires and heat 
stress, confirmed higher suffering (higher average mortality) in more 
vulnerable regions compared to less vulnerable regions, particularly 
when excluding extreme outliers (CRED, 2020). These findings point 
towards the fact that in regions identified as highly vulnerable in the 
assessments even moderate future climate change and future climate

hazards are likely to push people further into poverty and lead to 
significant destabilisation processes in terms of livelihoods security 
(Wallemacq and House, 2018; Birkmann et al., 2022).
8.3.2.1.1 Historic roots of vulnerability in regions classified as highly 
While increasing attention is given to issues of human vulnerability, 
less attention has been given to the historical conditions that foster 
systemic vulnerability of societies. It is important to acknowledge 
that drivers and root causes of systemic human vulnerabilities and 
development challenges are not always new, and sometimes—for 
example in various countries in Africa, Asia and the Caribbean—can 
be linked to histories of imperialism, colonial structures (Grasham 
et al., 2019), and subsequent development and governance contexts 
(Southard, 2017; Zhukova, 2020). Thus, root causes of present 
structures of human and human–environmental vulnerability often

have historic dimensions, for example, chronic poverty and structural 
inequality in Africa (Grasham et al., 2019) or the Caribbean are still 
influenced by the colonial power relations outside of these countries 
making solutions for vulnerability reduction more difficult (see e.g., 
Douglass and Cooper, 2020). In addition, national borders, such as 
in many regions in Africa, sometimes cut through ethnic groups and 
therewith ignore important interrelations between communities on 
both sides of the border.
8.3.2.1.2 People residing in most vulnerable versus least vulnerable 
regions
While global assessments often allow for country rankings, it is 
similarly important to better understand how many people are living 
in these different levels of vulnerability. The quantitative assessments 
underscore that a significantly higher number of people live in countries 
with very high and high vulnerability compared to the population

living in countries classified as having low and very low vulnerability. 
An analysis that measured the vulnerability of countries according 
to the INFORM Risk Index and the WorldRiskIndex vulnerability 
index components, differentiating vulnerability values into seven 
vulnerability classes found that nearly twice as many people are living

8
1198Chapter 8 Poverty, Livelihoods and Sustainable Development
Vulnerability at the national level v aries. Vulnerability also greatly differs within countries .
Countries with moderate or low average vulnerability have sub-populations with high vulnerability and vice versa.
LowHighObserved human vulnerability to climate change is a key risk factor and differs globally Relative vulnerability
Low
Very lowHigh
Children in rural low-income communities | food insecurity , sensitivity to undernutrition and
People uprooted by con/f_lict in the Near East and Sahel  | prolonged temporary status, limited 

Aboriginal and Torres Strait Islander Peoples | poverty , food & housing insecurity ,
People living in informal settlements | poverty , limited basic services & often located in areas 
Indigenous P eoples of the Arctic  | health inequality , limited access to subsistence resources and 
Urban ethnic minorities | structural inequality , marginalisation,  exclusion from planning processes | 
14.5.9,  14.5.5,  6.3.6
Smallholder coffee producer s | limited mark et access & stability,  single crop dependency , limited 

Indigenous P eoples in the Amazon | land degradation,  deforestation,  poverty , lack of support |
Central &
1
3
42
5
67
8
9
10
11
1278 91291
10128
3612
113612
Pie charts
Storm Flood Drought Wild Fires HeatThe size of the pie charts show average mortality per hazard event per region between 2010 and 2020.  
The slices of pie charts show the distribution of deaths from a particular hazard.

8
1199Poverty, Livelihoods and Sustainable Development  Chapter 8
in most vulnerable countries compared to the number living in less 
vulnerable countries (Birkmann et al., 2021a). Another study that uses 
the same data and differentiates vulnerability into five classes (also 
considering the lack of coping capacity within the INFORM index, see 
(Marin-Ferrer et al., 2017)) concludes that about 3.3 billion people are 
living in countries classified as highly vulnerable, while approximately 
1.8 billion people live in countries with low vulnerability (Birkmann 
et  al., 2022). Additional assessments based on the classification of 
Africa
 Australasia
 AsiaDifferent aspects and dimensions of vulnerability (regional averages of selected vulnerability indicators)
Central & South America
Europe
 Small Islands
Relatively moderate challenges
Health statusAccess to health careRelatively severe challenges
Relatively mild challengesGovernance Gender inequality

Food securityExtreme poverty Adult literacy rate
Dependency ratioAccess to basic infrastructure
Figure 8.7 |  The figure shows selected aspects of human vulnerability, such as extreme poverty and inequality, and access to health care and basic infra-
structure as regional averages. These vulnerability aspects are a selection of indicators from the indicator systems (the INFORM Risk Index and WorldRiskIndex 2019) used 
for the global vulnerability map (Figure 8.6). These normalized indicator scores were averaged for each region and classified into three levels of severity using the natural breaks 
method. This figure provides a more differentiated picture about the various dimensions of vulnerability that different regions and countries face and the severity of such challenges 
in each region. Such vulnerability challenges increase the risk of severe adverse impacts of climate change and related hazards (Birkmann et al., 2022).

income groups of countries reveal that approximately 3.6  billion 
people live in low and lower middle-income countries, which are most 
vulnerable and disproportionally bear the human costs of disasters due 
to extreme weather events and hazards (World Bank, 2019b; CRED 
and UNDRR, 2020b; EC-DRMKC, 2020; UN-DESA, 2020a; UN-DESA, 
2021; Birkmann et al., 2022). While these numbers are different, both 
results underscore that the absolute and relative number of people 
living in most vulnerable contexts is significantly higher compared to Figure 8.6 |  Global map of vulnerability. This map shows the relative level of average national vulnerability as calculated by global indices (INFORM and WRI see details 
in 8.3.2). Areas shaded light yellow are on average the least vulnerable and those shaded darker red are the most vulnerable. The map combines information about the level of

vulnerability (independent of the population size) with the population density (see legend) to show where both high vulnerability and high population density coincide. The map 
reveals that there are densely populated areas of the world that are highly vulnerable, but also highly vulnerable populations in more sparsely populated areas. There are also 
highly vulnerable communities and populations in countries with overall low vulnerability as shown with local case studies alongside the map. The pie charts show the number 
of deaths (mortality) per hazard (storm, flood, drought, heatwaves and wildfires) event per continental region based on EM-DAT Data (CRED, 2020). The size of the pie chart 
represents the average mortality per hazard event while slices of each pie chart show the absolute number of deaths from each hazard. This reveals that over the past decade, there

were significantly more fatalities per hazard in the more vulnerable regions, e.g., Africa and Asia. The analysis of the data shown in this map revealed that over 3.3 billion people 
are living in countries classified as very highly and highly vulnerable, while approximately 1.8 billion people live in countries with low and very low vulnerability (Birkmann et al., 
2022). These vulnerability values are based on the average of the vulnerability components of the INFORM Index (INFORM, 2019) and WorldRiskIndex (Birkmann and Welle, 2016; 
Feldmeyer et al., 2017) with updated data from 2019 classified into five classes using the quantile method. Other studies applied more vulnerability classes within their assessment 
and therefore provide slightly different numbers (Birkmann et al., 2021a). However, despite different calculation methods, the conclusion remains that there are significantly more

people residing in countries with very high and highly vulnerability compared to those living in countries classified as having low or very low vulnerability.

8
1200Chapter 8 Poverty, Livelihoods and Sustainable Development
those that live in a country with a low vulnerability status (Birkmann 
et al., 2021a; Birkmann et al., 2022). These differences have also been 
observed in former years (Welle and Birkmann, 2015; Feldmeyer et al., 
2017).
That means, even moderate changes in the global mean temperature, 
as identified in the recent IPCC report SR1.5°C (IPCC, 2018c) and 
in scientific literature (Hoegh-Guldberg et  al., 2019a), can mean 
substantial increases in risks for more than 3 billion people due to high 
levels of vulnerability.
Overall, there is robust evidence and high agreement in the recent 
literature that countries and regions classified as highly vulnerable 
face multiple development challenges at once, in which high levels of 
poverty interact with limited access to water and sanitation or with 
high levels of forced migration and, in some cases, with state fragility

making solutions difficult (Hallegatte et al., 2017; Marin-Ferrer et al., 
2017; Feldmeyer et al., 2021; Garschagen et al., 2021; Birkmann et al., 
2022). High levels of vulnerability within these regional clusters are 
the product of current development challenges, but are often caused 
by long and complex histories, including issues of colonisation and 
marginalisation, for example, in hotspots in Africa (Birkmann et  al., 
2021a).
8.3.2.2 Transboundary Vulnerability and Adaptation
Next to the identification of the level of agreement between different 
vulnerability assessments (Garschagen et  al., 2021) and the spatial 
hotspots, global assessments of vulnerability and adaptation readiness 
also point towards the need for a transboundary perspective and 
transboundary cooperation in terms of vulnerability reduction and 
adaptation (Tilleard and Ford, 2016; Birkmann et  al., 2021a). Newer 
research points towards the fact that various phenomena of vulnerability,

particularly in highly vulnerable regions, spill over national borders and 
emerge in rather regional clusters, such as forced migration and poverty 
in West and Central Africa, as well as conflicts in the Near East and Asia 
(IDMC, 2020). This means that regional and transboundary challenges 
contribute to the formation of systemic human vulnerability, for 
example, forced migration that is occurring within countries, but also 
across international borders that is also influenced by climate change 
(Kaczan and Orgill-Meyer, 2020). In summary, these findings point 
towards the need for more transboundary approaches in vulnerability 
and risk reduction, adaptation and development. Recent literature and 
data presented in Figure 8.6 and (Birkmann and Welle, 2016; Feldmeyer 
et  al., 2017; Hallegatte et  al., 2017; INFORM, 2019; Birkmann et  al., 
2021a) demonstrate the need to strengthen approaches to monitor 
the regional dimensions of vulnerability and to develop strategies

and programmes that consider transboundary vulnerability in risk 
reduction and cooperation at different scales. This includes, for example, 
cooperation between national-level institutions, but also transboundary 
networks of cities or communities (Tilleard and Ford, 2016; Benzie and 
Persson, 2019; Birkmann et  al., 2021a). The transnational nature of 
climate change impacts means that addressing them requires concerted 
efforts among nations (IPCC, 2014b; Dzebo, 2019).
In addition, national response strategies for specific transboundary 
climate-influenced hazards, such as river flooding, droughts or coastal flooding can also significantly influence neighbouring countries and 
can affect exposure and vulnerability of the respective country (Nadin 
and Roberts, 2018; Booth et al., 2020). Likewise, climate change may 
affect transboundary resources (e.g., underground water reserves) and 
transboundary ecosystems (e.g., in terms of the migration of species)

(Vij et al., 2017) and thereby further reduce the capacity of vulnerable 
groups to cope and adapt. In addition, recent research indicates 
that social inequities are also coupled with access to and quality of 
environmental resources in urban environments—meaning social and 
environmental justices are interconnected (see Schell et al., 2020).
Individual adaptation projects to specific climate hazards in regions 
classified as highly vulnerable are needed. However, recent studies 
underscore that deeper development challenges need to be addressed 
in order to make progress towards adaptation and vulnerability 
reduction and to avoid maladaptation (Eriksen et al., 2021). Adaptation 
and development projects, such as the construction of a dam as a 
response to water shortages in one country can significantly influence 
the exposure to water shortages and the response capacities of 
another country downstream. Often, transboundary challenges are a

result of policy and resource management choices or uncertainty, and 
addressing them requires a greater engagement between governing 
bodies, which may also guide more suitable responses in the context 
of climate change adaptation and vulnerability reduction (Earle et al., 
2015; Tilleard and Ford, 2016; McLeman, 2018; Birkmann et al., 2021a).
Most of those countries and regional clusters identified as highly 
vulnerable have contributed little to the overall amount of GHG 
emissions and therefore support for (transboundary) adaptation from 
the international community is required in these places and for those 
living under these conditions in order to support and achieve climate 
justice.
8.3.2.3 The Effect of Higher Levels of Global Warming for Most 
Vulnerable Regions and Specific Livelihoods
Evidence exists that threats to land-based livelihoods and risks of 
undernutrition increase significantly with higher levels of global

warming (Hoegh-Guldberg et al., 2019a). With global warming of 1.5°C 
or less, impacts of climate change on livelihoods are still significant, 
for example, for West Africa and the Sahel there will be an estimated 
reduction of the area suitable for maize production of about 40%. The 
consequences of global warming of up to 3°C would mean a high 
risk of undernutrition for entire regions (see Hoegh-Guldberg et  al., 
2019a) that are already classified as most vulnerable (see Figure 8.6). 
That means the consequences of significant warming are a particular 
challenge for regional hotspots of vulnerability. Small changes in crop 
productivity, already observed due to increasing droughts, floods or 
changes in rainfall patterns, could lead to severe health risks and 
undernutrition. This is because of existing precarious living conditions 
and the limited capacities that people and institutions have to build 
and enhance coping and adaptive capacities at the level of individual

households, communities and state institutions (see UNEP , 2018; 
Birkmann et  al., 2021a). The risk of loss of life, displacement and 
adverse health consequences due to climate change in these most 
vulnerable regions (such as Micronesia, South Asia, West Africa—see 
Figures  8.5; 8.6) is higher compared to regions classified as having

8
1201Poverty, Livelihoods and Sustainable Development  Chapter 8
medium or low vulnerability (Birkmann et  al., 2022). Nevertheless, 
other regions and countries classified as less vulnerable, for example 
in Asia, are experiencing disasters and have a relative high share of 
the observed global fatalities or losses, when considering non-climatic 
natural hazards (CRED and UNDRR, 2020a; see also Section 8.3.2.1). In 
addition, changing climatic hazard and exposure patterns have to be 
considered. However, the agreement of major global index systems on 
exposure is significantly lower compared to vulnerability (Garschagen 
Moreover, the assessment reveals that in most vulnerable regions 
a double burden of existing destabilised livelihood conditions and 
additional climatic hazards is already visible and largely influences 
societal impacts of climate change. For example, flooding along the 
White Nile in Uganda and South Sudan hit vulnerable communities

that were displaced due to conflicts and were thus uprooted again 
by flooding (IDMC, 2020). Societal impacts and future risks of climate 
change to societies need to incorporate information about vulnerability 
and exposure—including capacities of people to cope and adapt 
(Wisner, 2016; Cardona et al., 2020). There is increasing evidence that 
individual and societal capacities to cope and adapt also depend on 
how governmental and national institutions can support people at risk 
(see Section  8.6). For example, climate information services depend 
on a functioning weather service. Likewise, social safety nets as an 
adaptation strategy require financial resources, which are often absent 
for most people in highly vulnerable regions. In addition, examples of 
national programmes that target most vulnerable groups, such as the 
free basic service programme in South Africa, show that next to the 
adaptation to individual hazards, strategies exist that aim to reduce

systemic human vulnerability (see GovSA, 2021).
At the same time, there is scientific evidence that more intense and 
frequent climate-influenced hazards (e.g., storms, flooding, droughts, 
heat stress) can undermine decade-long poverty reduction efforts, 
particularly in most vulnerable regions (Mysiak et al., 2016; Formetta 
and Feyen, 2019; Laborde et  al., 2020b; Lakner et  al., 2020). There 
is high agreement that, with global warming of about 3°C, such 
undermining of poverty reduction efforts will intensify and more 
regions will face development setbacks due to the spatial and temporal 
expansion of climate hazards, including the further erosion of capital 
that enables people to develop adaptive capacities (high confidence) 
(see Section  8.5). Such trends can further exacerbate poverty traps 
(see Section 8.2.2). According to a World Bank report, between 32 and 
132 million people could fall into extreme poverty by 2030 due to the

impacts of climate change (Jafino et al., 2020). Models estimate that at 
3°C warming and under Shared Socioeconomic Pathway (SSP) 1, there 
would be an additional 245 million people exposed to poverty. Under 
SSP2 this number would increase to 904  million additional people 
exposed to poverty (SSP2) and under SSP3 (with significant challenges 
for equity) about 1918 million additional people could be exposed to 
poverty in the year 2050 (Byers et al., 2018).
Overall, the assessments above underscore that adaptation and 
risk reduction require not only information about changing climatic 
conditions, but also assessments that capture the development 
contexts and structural inequality that determine and influence human 
vulnerability. Strategies that reduce poverty and inequality and that improve the access of people to basic services need to become a 
higher priority in adaptation and development planning in order to

avoid more than 3 billion people currently and even more in the future 
being exposed to severe adverse consequences of climate change. 
Reducing vulnerability to climate change is therefore indispensable for 
climate justice and just transitions (high confidence).
8.3.2.4 Compound Challenges: Vulnerability and State Fragility
Literature in the area of climate change risk management and 
adaptation highlights the importance of overall governance systems 
and their functioning and inclusiveness in terms of vulnerability and 
risk reduction (Burch et  al., 2019). Empirical evidence and scientific 
studies show linkages between issues of governance, conflicts and 
high levels of state fragility and systemic human vulnerability (see 
Figure 8.8; Section 8.5.2; Eklöw and Krampe, 2019; Peters et al., 2019; 
Mawejje and Finn, 2020)
The comparison of state fragility and vulnerability at the level of 
regions (United Nations Statistics Division regions) based on the

vulnerability information of the INFORM and WorldRiskIndex systems 
and information from the Failed State Index indicates clear linkages 
(see Figure 8.8), meaning that societal development and governance 
challenges often interact and, in many cases, are influenced by complex 
histories (see FFP , 2020; Birkmann et al., 2021a; Feldmeyer et al., 2021). 
Strategies to reduce systemic vulnerability and multidimensional 
poverty have to account for these broader governance challenges that 
hamper resilience building and the development of adaptive capacities 
to climate change at various levels.
Strategies to strengthen adaptation to climate change have therefore 
to acknowledge these interdependencies between climate change, 
vulnerability, development and governance (see Section  8.6.5). The 
results of different global vulnerability assessments and the role of 
governance conditions underscore that next to individual adaptation

projects in specific sectors, integrated strategies and programmes 
are needed that reduce systemic vulnerability and support 
enabling conditions for adaptation for most vulnerable groups (see 
Section 8.6.5).
8.3.2.5 Trends in Vulnerability and Poverty in Light of Climate 
Change and the COVID-19 Pandemic
Literature that assesses trends of poverty and vulnerability, as well as 
exposure to climate change, reveals that geographic patterns of poverty 
and vulnerability are uneven and changing over time (Feldmeyer et al., 
2017). However, a robust finding of different studies is that population 
growth in most vulnerable country groups and regions ‘is’ and ‘will be’ 
significantly higher in the future compared to population growth in 
countries classified as having low vulnerability (see Section 8.4.5.2). 
In summary, a significant increase of population is expected in 
highly vulnerable countries in the future. In addition, global studies

predict that, by 2030, almost 50% of the world’s poor will be living 
in countries affected by state fragility, conflict and violence (UNISDR, 
2009; Hallegatte et al., 2017).

8
1202Chapter 8 Poverty, Livelihoods and Sustainable Development
Another important phenomenon that modifies trends in vulnerability to 
climate change and poverty is the COVID-19 pandemic (see Box 8.3). It 
is likely that the COVID-19 pandemic with its global repercussions will 
continue to modify and, in many cases, intensify poverty and human 
vulnerability (Laborde et al., 2020a; Sumner et al., 2020). Recent studies 
that estimate the impact of COVID-19 on global poverty agree that 
a significant increase of poverty due to COVID-19 and the respective 
lockdown of countries is already observed or expected in the near future 
(Laborde et al., 2020b; Sumner et al., 2020). These studies underscore 
that 80% of those newly living in extreme poverty (living on under 1.9 
USD d−1) due to COVID-19 would be mainly located in two global regions: 
sub-Saharan Africa and South Asia (Sumner et al., 2020). Consequently, 
the COVID-19 pandemic is likely to further increase inequality at different

scales and increase the burden within regions already characterised by 
a significant adaptation gap in terms of high vulnerability (see also 
Figure 8.6). This implies that the capacity of people to prepare for present Comparison of vulnerability and state fragility of global regions
010203040506070
01 02 03 04 05 06 07 08 09 01 00
StateFragility Most fragil e Least fragi leVulnerabilit y Most vulnerable Least vulnerabl eSouth
America
Australia
& New Zealand North
EuropeWest EuropeSouth EuropeNorth Americ aCentral AsiaWest
AsiaSouthNorth AfricaMelanesi a
South AfricaSouth AsiaWest AfricaMiddle Africa  
East Africa
East Asia
East EuropeCaribbeanCentral
AmericaMicronesia
Polynesi a East
Asia
Figure 8.8 |  Comparison of the vulnerability and state fragility of global regions. The vulnerability values are the average of the vulnerability component of the

WorldRiskIndex 2019 (Birkmann et al., 2021a; Feldmeyer et al., 2021) and the vulnerability and lack of coping capacity components of the INFORM Risk Index 2019 (Marin-Ferrer 
et al., 2017) classified into five classes using the equal count method (Birkmann et al., 2022). The state fragility values are based on the Fragile States Index 2019 (FFP , 2020) and 
regions are based on the intermediate and sub-regions of the United Nations Statistical Division. The size of each circle is proportional to the population (World Bank, 2019b) in 
the respective region.
and future climate change impacts will further decrease within these 
countries and for specific vulnerable people or groups in these regions.
Recent scientific studies in the context of climate-influenced hazards 
and disasters also underscore that various regions and countries 
classified as highly vulnerable are characterised by a high persistence 
of human vulnerability and chronic poverty (Feldmeyer et al., 2017; UN-

DESA, 2020b; World Bank, 2020). For example, various highly vulnerable 
regions in Central, West and East Africa, countries such as Afghanistan, 
Democratic Republic of Congo and Haiti, and also SIDS in Melanesia 
and Micronesia have been characterised by high levels of poverty for 
decades (World Bank, 2020). Several of these highly vulnerable regions 
are also likely to experience a further increase in climate hazards such 
as sea level rise in Melanesia and Micronesia and in coastal zones of 
West Africa and more severe droughts in Africa (IPCC, 2021).

8
1203Poverty, Livelihoods and Sustainable Development  Chapter 8
There is robust evidence that in many world regions the exposure to 
climatic hazards is increasing with additional global warming (Chin-
Yee, 2019; Hoegh-Guldberg et  al., 2019a; IPCC, 2021). In addition, 
development patterns and practices such as urbanisation and migration 
to exposed areas, for example, to coastal zones in West Africa or South 
Asia is increasing exposure. While the spatial and temporal exposure 
to impacts from climate change and extreme events increases with 
higher levels of global warming (Hoegh-Guldberg et al., 2019a), in all 
global regions and various climate zones (IPCC, 2021), the burden is 
greater for the most vulnerable regions where people have limited 
support and capacities to build adaptive capacities for future impacts 
of climate change.
In this regard, vulnerability assessment results provide an important 
additional layer of information for decision making in terms of

defining adaptation and risk reduction needs and priorities, as shown 
in Figure  8.9. The figure shows the published climatic information 
regarding observed changes in agricultural and ecological droughts 
(IPCC, 2021) combined with a background map of vulnerability. For 
example, the combined information reveals that even if the agreement 
Observed change sinagricultura landecological drought s
Increase
Decreas e
Low agreement onthetypeofchang e
Limite ddataand/or literatur ePopulatio ndensity Relative vulnerabilityMap with observed changes in agricultural and ecological droughts (IPCC , 2021)
overlaid over human vulnerability
Low
Very lowHigh LowHigh
Figure 8.9 |  Map with observed changes in agricultural and ecological droughts (IPCC, 2021) overlaid over human vulnerability (see Figure 8.6) provides a 
more comprehensive overview for defining adaptation priorities.
on the type of changes observed in droughts is low for North and

southeast Africa, it is the high vulnerability in this region that requires 
urgent attention (see Figure 8.9).
Recent reports on extreme poverty and human rights (Alston, 2019) 
show that millions already face malnutrition due to devastating 
drought. In addition, the linkages between ecosystem vulnerability 
and human vulnerability and human well-being are important aspects 
that need more attention, since, for example, the degradation of 
marine ecosystems that support food systems for hundreds of millions 
of people will threaten food security (see for details Cross-Chapter 
Box MOVING PLATE in Chapter 5).
While the findings of the Alston report underscore the urgency to act 
in order to protect people’s livelihoods, particularly in low-income 
countries, it also shows that extreme poverty (Alston, 2019) and 
different dimensions of poverty are found in middle- and high-income 
countries.

8
1204Chapter 8 Poverty, Livelihoods and Sustainable Development
A study of the World Bank (Hallegatte et  al., 2017) estimates that 
losses in terms of well-being are significantly higher than actual asset 
losses experienced (Hallegatte et al., 2017). A higher proportion of the 
global absolute economic losses occurred in high-income countries. 
About 56% of all disasters reported occurred in high-income countries, 
while the low-income countries account for 44% of the recorded 
disasters. However, low-income countries account for about 68% 
of the total deaths reported, high-income countries for about 32% 
(CRED and UNDRR, 2020b). In contrast, average absolute economic 
losses6 were significantly lower in most vulnerable countries compared 
to low vulnerable countries (Birkmann et  al., 2022). Economic loss 
trends from EM-DAT database (CRED, 2020) must be interpreted 
with caution. Economic loss data is often incomplete and needs to

be improved. However, these differences in terms of economic losses 
can also be explained in part by the significant wealth differences and 
the monetary value of assets exposed. Consequently, there is a need 
to critically reflect on the measures used to assess L&D from climate 
change. Interestingly, the number of people affected by droughts, 
floods and storms as a percentage of the total population and per 
hazard event again points to the disproportionate suffering of most 
vulnerable countries (Birkmann et al., 2022).
Overall, there is robust evidence that at the global scale poor and most 
vulnerable people, particularly in regions classified as highly vulnerable, 
are disproportionately affected by well-being losses and loss of life in 
the context of climate change and climate-influenced natural hazards 
(CRED and UNDRR, 2015; Hallegatte et  al., 2017; Birkmann et  al., 
2022) (high agreement). In this context, non-economic losses also

need to receive more attention (see Section 8.3.3.2). While there is an 
emerging understanding that inequality and multidimensional poverty 
are important determinants of systemic vulnerability to climate change 
(Dennig et  al., 2015; Hallegatte and Rozenberg, 2017; Islam and 
Winkel, 2017) that affects more than 3 billion people today, only very 
few countries explicitly aim to reduce poverty and income inequality as 
an adaptation measure (see e.g., Brazil Ministry of Environment, 2016) 
(high agreement). Reducing vulnerability is a prerequisite for climate 
justice and just transitions.
8.3.3 Livelihood Impacts, Shifting Livelihoods and the 
Challenges for Equity and Sustainability in the 
Context of Climate Change
This section complements the global and regional assessment of 
vulnerability in the previous section with a more precise assessment of 
observed local conditions and livelihood impacts and shifts. First, the

section reviews linkages between vulnerability and livelihood impacts 
of climate change broadly. Second, it examines the range of observed 
disproportionate impacts according to economic (e.g., income) and 
non-economic (e.g., cultural) impacts of climate change. Third, it 
examines current risks of adaptation limits and compounding effects 
across social groups and associated livelihood shifts.
6 Calculated by averaging the country values of economic losses per event falling in different vulnerability categories.8.3.3.1 The Implications of Vulnerability for Past and Present 
Livelihood Impacts of Climate Change
Climate change impacts add to livelihood challenges and can further 
increase inequality and poverty (see Section 8.2.1), whose root causes 
are social, institutional and governance related. Various regional 
clusters of high vulnerability (see Figure  8.6) are also influenced by 
historical processes, such as colonialism and power relations that made

people and countries vulnerable (Schell et al., 2020). Thus, vulnerability 
to climate change is not primarily linked to the degree of exposure 
to climate change impacts, but determined by societal structures and 
development processes that shape context and individual vulnerability 
(see Section 8.3.2), and values and lived experiences of climate hazards 
(Djoudi et al., 2016; Walker et al., 2021). Intersectionality approaches 
are central to grasping differential vulnerability (Thomas et  al., 
2019) for past and present livelihood impacts of climate change (see 
Figure 8.3; Section 8.2.2.2). Assessing observed local conditions and 
livelihood impacts and shifts requires us to consider reinforcing social 
phenomena such as age, gender, class, race and ethnicity, which shape 
social inequalities and experiences of the world and also intersect with 
climate hazards and vulnerability (Walker et al., 2021).
This understanding helps to clarify how social structures, institutions

and governance mechanisms matter to address social causes in 
addition to climate magnifiers while holding them accountable (see 
Section  8.5). For example, low-elevation coastal zones concentrate 
high levels of poverty in some specific areas: 90% of the world’s 
rural poor are concentrated in the low-elevation coastal zones of just 
15 countries, and this population keeps growing (Barbier, 2015). Yet 
studies on the economic impacts of climate change and also integrated 
assessment models typically overlook the distributional effects of 
these impacts according to vulnerability and exposure and do not 
sufficiently account for agent and societal heterogeneity (Balint et al., 
2017; Sovacool et al., 2021).
Since the AR5, high confidence is attributed to the fact that the, mostly 
detrimental, climate change impacts and risks are experienced mainly 
by the poorest people around the world (Olsson et al., 2014; Roy et al.,

2018). There is high confidence that climate change impacts will put a 
disproportionate burden on low-income households and thus increase 
poverty levels (IPCC, 2014a; Hallegatte and Rozenberg, 2017).
There is robust evidence that economic development based on 
the exploitation of natural resources can significantly increase the 
vulnerability of communities at the local level. For example, there is 
a correlation between political arrangements and environmental 
degradation that brings about both disasters and an increase in disaster 
risk (Cannon and Müller-Mahn, 2010; Pereira et  al., 2020), while 
development is recognised by some as a key element for adaptation 
(Cannon and Müller-Mahn, 2010).
Maladaptation is an important thread given its relevance to assess 
ways that well-intentioned development can exacerbate past and 
existing vulnerabilities and undermine livelihoods (see Section 8.2.2.1).  
Evidence shows that some local development projects can undermine

8
1205Poverty, Livelihoods and Sustainable Development  Chapter 8
resilience and increase the vulnerability of neighbouring communities, 
leading to maladaptation (Magnan et al., 2016; Schipper, 2020; Eriksen 
et  al., 2021). Development projects can also negatively affect the 
vulnerability and create new ones of the very community where they are 
implemented (Burby, 2006; Magnan et al., 2016; Atteridge and Remling, 
2018; Thomas and Warner, 2019; Work et  al., 2019). Maladaptation  
has also received growing attention since AR5 as a projected future 
climate risk for vulnerable social groups (see Section  8.4.5.5) and in 
the context of adaptation constraints and trade-offs in climate resilient 
development (see Sections 8.5.1; 8.6.1),  Despite maladaptation, there 
is however robust evidence that inclusive and sustainable development 
at the local level, can reduce vulnerability (Cannon and Müller-Mahn, 
2010; Patnaik et al., 2019).

8.3.3.2 Economic and Non-economic Losses and their 
Relevance for Poverty and Livelihoods
Economic losses include income and physical assets and non-economic 
losses include mortality, mobility and mental well-being losses from 
climate change (see Section 8.3.4). The IPCC WGII AR5 (IPCC, 2014a) 
primarily associated L&Ds with extreme weather events and economic 
impacts, and treated it primarily as a future risk. New evidence 
provides insights into present-day L&Ds from slow-onset impacts (e.g., 
sea level rise) (Adamo et  al., 2021) and non-economic losses (e.g., 
cultural impacts, emotional and psychological distress) (McNamara 
et al., 2021b) which previously received much less attention. AR5 had 
more focus on L&Ds in high-income regions than in regions most at 
risk, such as SIDS and least developed countries (LDCs) (van der Geest 
and Warner, 2020).
Impacts of climate change are affecting the economic and non-

economic dimensions of people’s lives, including subsistence practices 
of communities that are experiencing decreases in agriculture 
productivity and quality, water stress, increases in pests and diseases, 
disruption to culture, and emotional and psychological distress, to cite 
just a few (Savo et al., 2016). For example, the cumulative effects of 
slow-onset events threaten food security especially among the poor 
in Latin America and the Caribbean—regions which face the largest 
gender gap in terms of food security globally (Zuñiga et al., 2021). In 
general for Global South countries, the global average temperature 
warming (including the Paris target of 1.5°C) means substantially 
higher warming and including higher frequency and magnitude 
of extreme events, that will result in significant impacts on societal 
vulnerability (Aitsi-Selmi and Murray, 2016; Djalante, 2019).
Measuring losses from climate change impacts in terms of poverty and

inequality can be difficult, and part of the lack of assessments of non-
economic L&D can be attributed to the limited observational climate 
data on poor countries and population impacted, which are mostly 
concentrated in the Southern Hemisphere (Roy et al., 2018).This is also 
due to the challenges posed by the limited data available for assessing 
attribution (Cramer et al., 2014; Harrington and Otto, 2020; Otto et al., 
2020a) and lack of a comprehensive set of adaptation metrics (Otto 
et al., 2020b). Economic L&Ds from climate change are often assessed 
and reported after disasters or within crises, however, non-economic 
losses from climate change are often overlooked as is their relevance 
for poverty and livelihoods. For those who experience both economic and non-economic losses, the impacts of climate change are very real 
and profound (Tschakert et  al., 2017; Roy et  al., 2018) Particularly 
in low-income and most vulnerable regions, it is not the absolute

economic loss, but the combination of economic and especially non-
economic losses that need to receive higher attention and need to 
inform adaptation strategies.
8.3.4 Observed Disproportionate Impacts According to 
Economic and Non-economic Losses and Damages 
Due to Climate Change
Since AR5 a new discourse on L&D has emerged with new typology 
and elaboration of a definition. L&D has a long and contentious 
history and is enshrined in the Paris Agreement (see Cross-Chapter 
Box LOSS in Chapter 17). Despite ambiguity about what constitutes 
L&D (Boyd et al., 2017), it focuses on how to avert, minimise, and 
address the negative impacts of climate change, including those 
that cannot be avoided through adaptation. It can also be thought 
of as the observed residual risk (and potentially irreversible losses) 
from climate change when adaptation limits are encountered and 
mitigation has failed (Boda et al., 2020). L&D is considered a policy

mechanism (see Cross-Chapter Box LOSS in Chapter 17). There is also 
a burgeoning science for L&D (Mechler et al., 2019b) which advances 
the breakdown on compounding vulnerabilities and highlights the 
disproportionate effects of climate change on the vulnerable and 
marginal (see Box 8.5 for illustration of distributional effect of both 
the drought and responses in the Cape region in South Africa). New 
evidence provides additional insight into L&D from slow-onset events 
related to climate change (sea level rise, drought) (see Anjum and 
Fraser, 2021; Lund, 2021) For example, (Singh et  al., 2021) found 
growing evidence of urban droughts leading to economic losses (e.g., 
groundwater overextraction, financial impacts) and non-economic 
losses (e.g., conflict, increased drudgery).
The literature is assessed according to this new L&D typology, which 
includes both extreme and slow-onset events and has a strong emphasis

on climate justice and disproportionate impacts of climate hazards (see 
Figure 8.3), with a new focus non-economic L&D.
8.3.4.1 Economic (e.g., Income, Assets) Impacts of Climate 
Change and Vulnerability
While extreme events are not new, the intensity and frequency of 
extreme events are stacking, leading to additional increase in poverty 
or vulnerability in some regions, exacerbated by COVID-19, and up 
against existing development pathways leading to significant impact 
on economic losses globally (high confidence). There is robust evidence  
that many African countries experience climate-related losses in terms of 
loss of crop yields, destroyed homes, food insecurity through increased 
food prices, and displacement (Box 8.5; Olsson et al., 2014). Attention 
has been focused on low-income groups, women and children, poor 
rural communities, and Indigenous Peoples such as the example of the 
Dupong, an Indigenous Peoples in Ghana using Indigenous strategies

to limit adverse impacts of climate change-induced water shortages 
(Opare, 2018). In Kenya, economic L&D during droughts between 2009 
and 2011 incurred costs that included trucking emergency water and

8
1206Chapter 8 Poverty, Livelihoods and Sustainable Development
food supplies, and loss of livestock and livelihoods. Cross-sectoral 
economic effects were estimated to reduce gross domestic product 
(GDP) by 2.8% yr−1 (King-Okumu et  al., 2021). Past studies have 
similarly shown that in the context of extreme events, such as floods 
or droughts, the most commonly sold assets are livestock and land. The 
sale of property particularly reduces the asset base, creates long-term 
vulnerabilities to future events and can trigger chronic poverty (high 
confidence). People may face food shortages in the future from lack of 
crop production (Opondo, 2013).The sale of cattle affects the household 
asset base, as well as important access to animal traction power for 
farming.
In South Asia, there is robust evidence of economic impacts of climate 
change (Cao et  al., 2021), for example in the Sundarbans (a transboundary 
ecosystem with components in both India and Bangladesh, with the

problem of unproductive livelihoods being common across residents 
of both countries) observations show local livelihoods are rapidly 
becoming unproductive (loss of fish, and increasing salination making 
agriculture increasingly difficult) (Ghosh, 2018); conditions that are 
exacerbated by climate change impacts (high confidence). Cyclone 
and storm surges induced by climate change force saline water into 
agricultural lands along the coast, which damages crops not only in 
the year the cyclone hits, but for several years afterwards (Rabbani 
et al., 2013). They showed in Shyamnagar Upazilla in Satkhira district 
the proportion of salinity-free farmland has gone down over the past 
20 years, from more than 60% to nil (Rabbani et al., 2013). Vietnam 
has also experienced effects of flooding and salinisation in the Mekong 
delta, coupled with rapid social development. Intensified floods and 
droughts have dramatically resulted in loss of livelihoods in agriculture

and fisheries in some areas of the basin (Evers and Pathirana, 2018). In 
Vietnam, the expected salinisation increases livelihood shifts into areas 
that are riskier, such as shrimp farming. Furthermore, the Vietnamese 
Mekong Delta is characterised by strong migration processes towards 
cities, particularly Ho Chi Min, meaning that abrupt livelihood shifts 
are already happening. There are emerging examples of Indigenous 
Peoples affected by climate change in indigenous farming mountain 
communities of the Nepal Himalaya. (Sujakhu et  al., 2019). The 
Philippines has experienced extreme events, such as Typhoon Haiyan 
in 2013, which left more than 7353 people reported dead or missing, 
damaged or swept away more than 1.1 million houses and injured more 
than 27,000 people (McPherson et al., 2015). More than 4 million were 
displaced. The cost of damages has been estimated at USD 864 million 
with USD  435  million for infrastructure and USD  440  million for

agriculture in affected regions (McPherson et al., 2015).
Sea level rise, coastal flooding and surge inundation are increasingly 
pressing problems across the urban Pacific, including the urban and 
coastal population of Vanuatu (McDonnell, 2021). Pacific region islands, 
such as Vanuatu (Handmer and Nalau, 2019), are particularly vulnerable 
to climate change. Kiribati and Tuvalu are impacted by exceptionally 
high tides that affect the urban atolls of South Tarawa and Funafuti, and 
cyclonic activity causing extensive economic damage in Tuvalu (Curtain 
and Dornan, 2019). Limited migration opportunities for low-income 
households can result in forced immobility, and high tides, sea level rise 
and cyclonic damages could result in relocation of significant groups of 
the population.A pertinent example of economic losses is the example of the Torres 
Strait in Australia. This example shows evidence of communities living

on remote islands. Boigu is a low-lying mud island inundated by the 
sea during high tides and storm surges. Those most exposed and 
vulnerable to climate change have limited livelihood assets and face 
challenges to secure external support with government and others. 
Place-based values evoke a reluctance to relocate or retreat with 
economic losses such as community infrastructure, housing and cultural 
sites (McNamara et al., 2017). In the Great Barrier Reef, Australia sea 
level rise and sea level global temperature warming affects fisheries’ 
productivity and tourism (Evans et al., 2016). Unprecedented burn area 
of wild forest fires in Australia between September 2019 and January 
2020 (Boer et  al., 2020) extended over almost 19  million hectares, 
destroyed over 3000 houses and killed 33 people (Filkov et al., 2020).
The 2018 European heatwave in Northern and Eastern Europe caused 
multiple and simultaneous crop failures—among the highest observed

in recent decades (high agreement). These yield losses were associated 
with extremely low rainfall in combination with high temperatures 
between March and August 2018 (Beillouin et al., 2020). Across Europe, 
in 2018, people experienced one of the worst harvests in a generation. 
Northern and Eastern Europe experienced multiple and simultaneous 
crop failures—among the highest observed in recent decades. These 
yield losses were associated with extremely low rainfalls in combination 
with high temperatures between March and August 2018. This 
compounding of extreme conditions in 2018 led to one of the highest 
negative relative yield anomalies at the scale of Eastern and Northern 
Europe, across a large array of crop species (Beillouin et al., 2020).
Extreme climate events are disproportionately impacting economies of 
the most vulnerable everywhere (medium evidence, high agreement). 
In the Caribbean, Central America and USA, Hurricanes Katrina, Harvey,

Irma, Maria and Michael are examples of extreme climate events that 
have displaced households, destroyed homes, and led to loss of income 
among the poor and marginalised (Klinenberg et al., 2020). Puerto Rico 
was devastated by Hurricane Maria but received less support from the 
Federal Emergency Management Agency (García, 2021). Evidence is 
emerging on unequal governance responses in the USA versus Puerto 
Rico (Joseph et al., 2020). Floods, storms and heatwaves have impacted 
poorer communities and wildfires in California have impacted many 
wealthy groups, and also infrastructure used by all, for example, with 
lengthy electrical power blackouts. However, they have particularly 
impacted those vulnerable to disasters, such as undocumented Latino/a 
and Indigenous immigrants in the case of the Thomas Fire in California’s 
Ventura and Santa Barbara counties (Méndez et  al., 2020). In 2017, 
Hurricane Irma hit Ragged Island in the Bahamas as a category 5 storm,

leaving the island in ruins and deemed ‘unliveable’ by its authorities, 
with most infrastructure left as rubble, no essential utilities remained, 
schools and health clinics were in ruins and the stench of dead animals 
was overwhelming. This storm resulted in significant economic L&D 
by the community through loss of their homes, churches, schools, 
agricultural land and infrastructure (Thomas and Benjamin, 2020).
Across South America, groups of farmers, children, elderly, Indigenous 
Peoples and traditional communities are increasingly exposed to 
floods, droughts, wild forest fires and losses in crop yields, resulting 
in significant economic costs (medium evidence, high agreement) (see

8
1207Poverty, Livelihoods and Sustainable Development  Chapter 8
Box  8.6). Urban communities, in particular those living in informal 
settlements, are exposed to heatwaves. In Peru, analysis of water risks 
posed by climate change in the Vilcanota-Urubamba basin, Southern 
Peru, revealed seasonal water scarcity and glacial lake outburst floods 
(GLOFs), pose a serious threat for highly exposed and vulnerable 
people. It showed that very high-risk potentials of 134 current and 
another 6 out of 20 future glacier lakes as potentially highly susceptible 
to outburst floods. A total of eight existing and one possible future 
lakes indicate future river discharge could be reduced by some 2–11% 
(7–14%) until 2050 (2100). Farmers, in particular smallholders, in 
some regions face losses to irrigated agriculture and hydropower 
capacity with effects on water scarcity and food security (Drenkhan 
et al., 2019). However, other assessments also point towards positive

effects of water reservoirs and hydropower in terms of water storage, 
flood management and irrigation (Ahmad et al., 2014; Liu et al., 2015; 
Kuraku et al., 2019)
There are additional dimensions of economic losses that are of a 
more diffuse nature. In particular, climate change is also expected to 
negatively affect labour supply, particularly in temperature exposed 
industries (agriculture, mining, manufacturing, construction), due to 
increases in the number of extreme hot days (Graff Zivin and Neidell, 
2014; Garg et  al., 2020). Low-income countries have on average a 
large share of workers in such industries and will thus be especially 
hard hit. Aside from labour supply, a number of studies also document 
negative impacts to manufacturing productivity (Acharya et al., 2018; 
Pogacar et al., 2018; Somanathan et al., 2021). These findings provide 
a channel to explain macroeconomic consequences of climate change

(Burke et  al., 2015). However, there are also non-economic costs in 
that extreme heat will cause increased discomfort to workers, such 
as psychological stress, disease and in extreme cases, death among 
the workforce in developing economies, as well as tropical and sub-
tropical countries (Ansah et al., 2021).
8.3.4.2 Non-economic loss and damage 
(e.g., Mobility, Well-being)
Climate change L&D presents an existential threat to some (Boyd et al., 
2017). For example, Pacific Island countries have contributed least to 
total GHG emissions, but the nations of the South Pacific are highly 
vulnerable to rising sea levels, tropical cyclones and other climate-
related risks (Nand and Bardsley, 2020). For example, across Oceania 
there is significant risk that sea level rise will lead to forced relocation. 
Pacific leaders underscore the importance of losses, including deep 
connections between their world views and their land, and that

leaving their islands can only be considered an option of ‘last resort’ 
(McDonnell, 2021).
Non-economic loss and damage (NELD) is values based (subjective 
and intangible) and relates to norms, social values and highlights 
intersectional experiences and perspectives on climate risk. The 
discourse on L&D includes a framing of NELD as loss of human and 
non-human life, and mental and physical health that is experienced 
widely across the world in vastly different ways associated with social 
values (Tschakert et al., 2019). There are respectable arguments for the 
case that all life has intrinsic value (Vetlesen, 2019). The NELD framing 
of climate impacts highlights that not all risks are measurable. While difficult to measure, there are a growing number of cases of NELD 
globally (medium evidence, high agreement). Illustrative examples 
of NELD from climate change include the Pacific (McNamara et  al., 
2021b) and SIDS in the Caribbean. (Martyr-Koller et  al., 2021). For

example, the hurricane season in 2017 was particularly extreme 
resulting in climate-induced displacement with direct implications for 
NELD, including threats to health and well-being and loss of culture 
and agency (Thomas and Benjamin, 2020).
In the context of the Pacific Islands, NELDs are thought of as 
interconnected and span human mobility and territory, cultural heritage 
and Indigenous knowledge, life and health, biodiversity and ecosystem 
services, and sense of place and social cohesion (Carmona et al., 2017; 
Ojwang et al., 2017; McNamara et al., 2021b). There are gaps in our 
understanding of NELD, much of the evidence is from the Global 
South and at smaller scales (high agreement), NELD is not explicitly 
linked to attribution science yet and evidence often lacks coverage on 
certain groups (Boyd et al., 2017; Carmona et al., 2017; Ojwang et al., 
2017). Non-economic losses are often associated with displacements

and migration in terms of climate change and human vulnerability 
(Section 8.2.1.4), studies show that the impacts of extreme flooding, 
droughts and/or hurricanes and cyclones that can lead to a sense of 
lost identity and place, and emotional distress, that are hardly assessed 
dimensions of impacts and risks (Adger et al., 2014; Barnett et al., 2016; 
Tschakert et al., 2017; Serdeczny et al., 2018). Non-economic losses are 
particularly relevant for understanding adverse consequences of climate 
change on the poor and most vulnerable population groups (high 
confidence). These NELD categories are still overlooked in vulnerability 
assessments and adaptation planning. A novel way to consider NELD 
in assessments is to interconnect with a sustainable development 
perspective (Boyd et al., 2017; Boda et al., 2020).
In order to categorise the different types of NELD that exist, (Serdeczny 
et  al., 2016), reviewed the literature and came up with a set of

systematic categories that capture what is usually thought about as 
having intrinsic value and according this framing of NELD this includes: 
human life, sense of place and mobility, cultural artefacts, biodiversity 
and ecosystems, communal and production sites and agency and 
identity (Serdeczny et al., 2016; Serdeczny, 2019). For example, there 
is emerging evidence on linkages between slow-onset events and 
mobility decisions, trajectories and outcomes (Zickgraf, 2021). In 
addition, categories include psychosocial and emotional distress (van 
Der Geest and Schindler, 2016). For example, research shows potential 
increased risk of intimate partner violence following disasters, noting 
that societies that are vulnerable to climate change may need to 
prepare for the social disasters that can accompany disasters revealed 
by natural hazards (Malik and Stolove, 2017; Rai et al., 2021).
Geographical focus on non-economic losses in the literature is largely on

the Global South with studies mainly smaller in scale (high agreement). 
Many events studied include severe storms, floods and landslides. Key 
groups affected include low-income groups, agro-pastoralists, women 
and girls, children and youth, Indigenous Peoples, ethnic and religious 
minorities. In Europe, the Samis face significant challenges to health as 
ecosystems deteriorate (Jaakkola et al., 2018). In Zimbabwe, Storm Idai 
affected 270,000 people and subsequent flooding and landslides left 
340 people dead and many others missing (Chanza et al., 2020). There

8
1208Chapter 8 Poverty, Livelihoods and Sustainable Development
is evidence of loss of cultural heritage sites due to sea level rise and 
coastal erosion as well as other climate variability (Brooks et al., 2020). 
Haile et al. (2013) show flood casualties in Ethiopia include children 
drowned while playing outside during the 2007 flood period although 
official data is hard to come by (p. 489). Moreover, loss of place was 
experienced in Itang, where many of the local houses are built from 
wood, grasses and mud walls, which are easy to reconstruct, but are 
not strong enough to withstand an extreme flood. Here, 38% of the 
surveyed houses were severely damaged by the 2007 flood. These 
houses were constructed as an adaptation strategy but could not 
withstand the floods. In Kenya, Opondo (2013) shows loss of human 
life was the most severe impact of floods. For example, in the focus 
group discussion with men, ‘it was reported that a boat capsized on

River Nzoia at Siginga and ten people died’ (p. 457). In Mozambique, 
Brida et al. (2013) show loss of sense of place occurred after flooding 
in the central districts of Caia and Mopeia, which had a devastating 
impact on homes and livestock (Brida et  al., 2013). Health impacts 
of the forest fires in Amazon basin countries have disproportionately 
affected vulnerable people and social groups (see Box 8.6).
In the literature on NELD, there are many examples of loss of life (high 
agreement). In Nepal, one of the deadliest landslides in Nepal history 
resulted in the death of 156 people (van der Geest, 2018). Evidence 
from Landslide Jure and consecutive rainfall in Sindhupalchok in Nepal 
showed the experience led to mental stress, such as fear of new landslides, 
in about 68.4% of people interviewed (van Der Geest and Schindler, 
2016). One study in Nepal showed that almost a quarter (23%) of the 
households interviewed had sold property, including homes, livestock

and heirlooms in response to flooding (Bauer, 2013). Human deaths are 
increasingly associated with L&Ds from tropical cyclones and typhoons, 
such as in the southern coastal districts of Bangladesh, in particular 
Khulna and Satkhira (Chiba et al., 2017). A case study from Mindanao, 
Philippines, by Chandra et al. (2017) also reported physical injuries and 
loss of life from the most powerful typhoon for over a century in 2012, 
affecting more than 6 million people and killing at least 1000 people 
(Eugenio et al., 2016). Beckman and Nguyen (2016) reported that in 
Vietnam floods in 2004 washed away 24 houses in the commune, with 
the loss of families when their houses were washed away.
An illustrative example is climate-induced loss of well-being and (im)
mobility in Bhola Slum, an informal settlement in Dhaka, Bangladesh. 
Research revealed that IDPs from the southern coast experienced 
loss of belonging, identity, quality of life and social value produced

in people a nostalgia and desire to return home (Ayeb-Karlsson 
et  al., 2020). Another example is of urban climate change justice 
experienced by migrants in the Indian cities of Bengaluru and Surat, 
where environmental marginalisation can be attributed to a lack of 
recognition of citizenship rights and informal livelihood strategies 
driven by broken social networks and a lack of political voice, as 
well as heightened exposure to emerging climate risks and economic 
precariousness. In this case, migrants experience extreme forms of 
climate injustice in their invisibility to formal government and are even 
actively erased from cities through force or discriminatory development 
policies (Chu and Michael, 2019).
NELD also includes the loss of social networks. This has lasting 
implications for psychological health as well as for coping with crises following disasters or challenges posed by adverse climate change

impacts. For example, many households in villages affected by Cyclone 
Aila in Dacope and Koyra upazilas of Khulna district in Bangladesh 
migrated to other places permanently after the cyclone, as these 
affected villages were subject to long-term flooding (e.g., 2–3 years) 
following the cyclone. They migrated as they were unable to restore 
their livelihoods and, thus, were unable to secure necessary income for 
survival (Saha, 2017).
The examples show the multifaceted nature of intangible and non-
economic losses that people experience in the context of climate 
change and the daily risks they are exposed to. Conventional 
vulnerability assessments cover some aspects that are linked to the 
likelihood of experiencing non-economic losses, such as aspects of 
health, governance, education and in some cases also forced migration 
and the role of social networks. Overall, the elements of this assessment 
here underscore that it is not just the climatic stressor, but rather the

underlying context conditions that decide whether an extreme event 
translates into a disaster.
8.3.5 Economic and Non-economic Losses and Damages 
Due to Climate Change and their Implications for 
Livelihoods and Livelihood Shifts
This section examines the intersections between L&Ds and livelihood 
shifts. This requires an examination of the differentiated aspects 
of livelihoods. Understanding economic (e.g., loss of food crops, 
infrastructure, assets etc.) and non-economic losses (e.g., health, well-
being, loss of place, agency) and their consequences for livelihoods is 
important that the intangible aspects become clearly visible and receive 
greater attention in loss assessments and in designing adaptation 
strategies and programmes. Figure  8.10 provides a summary of 
examples of observed impacts of climate hazards on economic and 
non-economic capital and the section assesses livelihood implications 
across regions. It shows examples of climate hazards attributed to

climate change in studies since AR5, across a range of geographical 
sites for heatwaves, drought, hurricanes, and floods and non-economic 
L&Ds. Figure 8.10 reveals examples of climate hazards attributed to 
climate change in studies since AR5 across a range of geographical 
sites for extreme and slow-onset events, such as heatwaves, drought, 
hurricanes and sea level rise. These are associated with non-economic 
L&Ds. The figure underscores that non-economic L&Ds lead to 
significant livelihood threats and livelihood changes. In addition, the 
limits of adaptation become evident (Chapter 16).
8.3.5.1 Livelihood Shifts Resulting from L&D from Climate 
Change
While there are limited studies that view economic and NELD from 
climate change at a global scale of livelihood transformations there is 
robust evidence on the granular linkages, at community, national and 
regional levels, between losses, coping strategies and livelihood shifts.

Across Africa, climate change is impacting crop yields and destroying 
homes, resulting in loss of infrastructure and leading to non-economic 
losses associated with involuntary migration and displacement (Olsson 
et al., 2014), and loss of livestock and assets (see IPCC SR 1.5°C, Chapter

8
1209Poverty, Livelihoods and Sustainable Development  Chapter 8
3, (Hoegh-Guldberg et al., 2018), resulting in long-term reduction in the 
capacity for agriculture and land management. For example, in March 
2019 Tropical Cyclone Idai in Mozambique, Zimbabwe and Malawi 
led to substantial losses of agriculture, infrastructure, and access to 
aid and support, all of which contributed to significant displacement 
in each country (Fischel de Andrade and de Lima Madureira, 2021). 
Examples of livelihood impacts include livelihood shifts among Kenyan 
pastoralists to camel husbandry, resulting from household inequalities 
in assets and changes in relation to weakening of social norms of 
reciprocity and social cohesion (Volpato and King, 2019).
Extreme climatic events pose serious disruptions to local livelihoods 
and asset bases, requiring people to reconstruct, transform and 
diversify livelihoods (Uddin et al., 2021). Examples of livelihood shifts

across Asia and Southeast Asia (e.g., Bangladesh, India, Philippines, 
Vietnam) include rural communities in coastal areas, urban settlements 
that are experiencing economic losses (high confidence) from, for 
example, crop failure and reduced access to fish, which contribute to 
non-economic losses associated with involuntary migration (Ghosh, 
2018) and the malnutrition of children (Siddiqi et  al., 2011). For 
Bangladesh, Chiba et al. (2017) show a connection between mental 
stress and impacts to the fundamental capacity to sustain livelihoods, 
such as food and a place to live, due to severe damage to houses, 
homesteads, properties, livestock and crops, loss of family members 
and relatives, and anxiousness about securing employment and 
income in the future. In Bangladesh coastal communities experienced 
losses in livelihood assets due to Cyclones Sidr and Aila (Uddin et al., 
2021) and a significant number of cyclone victims were displaced

from their homes by severe cyclones. People have had to change their 
occupations—both intra- and intersectorally—and are confronted 
by increased consumption and social costs. The study uncovered 
differences in impacts between occupations, such as farming and 
fishing; fishers changed their occupation post-disaster. The study also 
showed evidence that local people are learning to live with change and 
uncertainty by nurturing and combining various types of knowledge 
and social memory, generating diversified livelihood options and 
self-organising to enhance their resilience to future extreme weather 
events. In Bangladesh, Ahmed et al. (2019) found cyclones, riverbank 
erosion, salinity intrusion and floods negatively impacted people’s 
lives by reducing their livelihood options. Their study found that when 
there are limited adaptation strategies, many people turn to ‘illegal 
livelihoods’ included using fine mesh nets to collect shrimp fry in the

rivers, as well as logging in the Sundarbans. These people include the 
poorest and vulnerable, and law enforcement only exacerbates their 
vulnerability. Escarcha et  al. (2020), studied impacts of typhoons, 
floods and droughts on crop production and effects on livelihoods of 
cash crop focused on rural villages in the Philippines. Their preliminary 
observations show a shift from crop to livestock production as a buffer 
activity to recover from crop losses. Farmers changed their farming 
activities as a multi-adaptive response driven by past experiences of 
climatic changes, farmers’ social relations, household capacity and 
resources available.
In Central Asia, the Sahel and South Asia, three global poverty hotspots, 
change impacts were shown to undermine traditional knowledge about 
livelihoods in ways that jeopardise future culture cohesion and sense of place (Tucker et  al., 2015). Acosta et  al. (2016) identified loss to

productive sites in the Philippines with landslides destroying agriculture, 
leaving many farmers without livelihoods. Similarly, Beckman and 
Nguyen (2016) in Vietnam identified an example where communal 
dams had been destroyed in floods leading to lack of irrigation for 
communal sites and local loss of farmland for farming communities. 
Chandra et  al. (2017) identified the vicious cycle between declining 
agricultural production and conditions of soil erosion due to floods and 
droughts resulting in decreased crop fertility to productive sites with 
implications for decline in crop yields, loss of crops and of livelihood 
assets. Climate change-related extreme weather events, such as 
typhoons, floods, and droughts, can have detrimental impacts on crop 
production (high confidence) and in the Philippines and Pakistan have 
significantly affected the livelihoods of cash crop-focused rural villages 
(Escarcha et al., 2020; Jamshed et al., 2020b). There is an emerging shift

from crop to livestock production as a buffer activity to recover from 
crop losses (Section 5.10.4; Jamshed et al., 2017; Escarcha et al., 2020). 
As with many examples of livelihood shifts, the viability of the shifts in 
the long term under climate change have yet to be assessed.
In Africa, many communities already experience drought- and flood-
related disasters (high confidence) such as those that negatively 
impact livelihoods and assets in the Muzarabani district of Zimbabwe 
(Mavhura, 2017). In Muzarabani community has revived and 
developed new livelihood strategies to manage risks, including local 
informal safety nets, local farming practices and the traditional flood-
proofing structures. Food security and agriculture productivity are 
examples of livelihood resources most at risk to climate hazards (see 
Figure  8.2) (high confidence). An illustration of such risks to cocoa 
farmers in Ghana includes increased incidences of crop pests and

diseases, wilting of cocoa leaves, high mortality of cocoa seedlings 
which affected expansion and farm rehabilitation, and wilting of 
cherelles resulting in losses of crop yield. An illustration of livelihood 
shifts resulting from losses is of farmers shifting to cereals due to the 
unpredictable climatic patterns and the shortened duration of rainfall. 
Yet, insecurity with storage, supply chains and low returns from cereal 
production, coupled with land scarcity in the Western region, have 
resulted in a return to cocoa production (Asante et al., 2017).
Research from Australia shows complex linkages between the impacts 
of drought on livelihood income, health and cultural heritage, increasing 
risk of heat stroke, and possibly a link to suicide among male farmers 
(Alston, 2012; Hanigan et  al., 2012; Marshall et  al., 2019). The link 
between agricultural losses and suicides has also been noted in South 
Asia, including India (Carleton, 2017). Livelihoods are shifting with

impacts to well-being, as noted by (Evans et al., 2016), who showed 
connections between loss of fishery productivity and impact on tourism 
sector livelihoods in the Great Barrier Reef region. In Europe, losses 
to Indigenous Peoples are associated with loss of well-being of Sami 
communities and has forced livelihood shifts from reindeer herding 
due to loss of ecosystems to support the animals (Persson et al., 2017; 
Jaakkola et al., 2018). Traditional pastoralist systems are also greatly 
impacted by cumulative dual challenges of encroachment of other 
land users and by climate change. Traditional Sami reindeer herding 
strategies are still practiced, but the rapidly changing environmental 
circumstances are forcing herders into uncharted territories where 
traditional strategies and the transmission of knowledge between

8
1210Chapter 8 Poverty, Livelihoods and Sustainable Development
Non-economic loss and damage associated with climate hazards attributed to climate change
with background map of global vulnerability
Sahel drought
Urban drought Dhaka, Bangladesh2015 Amazon forest fire in Brazil AR5 WGII Chapter 8, Box 8.6; Li et al., 2021
AR6 WGII Chapter 8;  Semde et al. 2021 
Alaska wildfires, July 2019 Yu et al., 2021; Hahn et al., 2021
Singh et al., 2021; Ray and Shaw , 2019; Amjad 2019 
Wang et al., 2016; Zhang et al., 2021; Boullion et al.,  
2020; Keegan et al., 2018
Funk, C. et al., 2018; Feeny and Chagutah, 2016; 
King-Okumu et al., 2021The 2011 –2017 California droughtSeager et al.2015;  Murray et al. 2021;.Greene 2021, 
Greene, 2018;  Barreau et al., 2017. Hung et al, 
2021; Garwood, et al. (n.d.)
Flooding on the Lancang-Mekong River Basin, 
2008–16 Wildfires Sweden 2018 Krikken et al. 2021; Jaakkola et al., 2018

Australia bushfires, 2019-20 van Oldenborgh et al. 2020;  Celermajer et al., 2021; 
Nguyen et al., 2021; Godfree et al., 2021
Hurricane Maria "extreme rainfall" over
Puerto Rico, 2017Patricola et al., 2018; Keellings  and Hernández Ayala, 
2019; Moleti et al., 2020; Salas-Wr ight et al., 2021
Increased outburst flood hazard from Lake  
Palcacocha due to human-induced glacier retreatStuart-Smith et al., 2021;  Bergmann et al., 2020;  
Drenkhan, 2019
Louisiana floods, August 2016
Severe drought and poor harvests over
southern Africa, 2016
Yun, X. et al. 2020; Evers and Pathirana, 2018Pacific sea level rise Herring et al., 2014; Herring et al., 2015; Herring et  
al., 2015; McNamara et al., 2021
Great Barrier Reef mass bleaching, 2016 Lewis and Mallela, 2018; Curnock et al., 2019
East China's hottest spring, 2018 Lu et al., 2021; Kinay et al. 2019 
Storm Desmond, 2018 UK Howard et al., 2017; Otto et al., 2017

Unprecedented Europe heat, June-July 2019 Ma et al., 2020; Kandic, 2020; Schuldt et al., 2020Non-economic Loss and Damag e
Loss of quality of lif e
Loss of quality of lif eLoss of quality of lif e
Loss of agency  Loss of life, loss of quality of life, loss of 
culturally meaningful places and biodiversity
Loss of cultural way of lif e
Loss of life, loss of quality of life, loss of  
Loss of safety networks and displacement
Loss of livelihoods
Loss of livelihoodsLoss of Indigenous and local knowledge 
Loss of culturally meaningful places
Loss of cultural heritage
Loss of ecosystem services and
loss of quality of lif eLoss of quality of lif e
Loss of lives NELD Climate hazar dR eferences
Western Cape region in South Africa
drought "Day Zero" Pascale et al., 2020; see also AR6 WGI Chapter 10, 
Section 10.6.2; M. Fanadzo,2021
East Africa drought 2017

(Tanzania, Ethopia, Keny and Somalia) Funk, C. et al. 2018: Haile et al., 2020
Agency
Culturally meaningful places
Ecosystem service s
Health
Human life
Identity
Livelihood sNELD T ypes
Figure 8.10 |  Examples of non-economic loss and damage associated with climate hazards attributed to climate change against a background of global 
vulnerability. Symbols with corresponding detail in the table show examples where non-economic losses have been documented. The figure is not exhaustive in terms of examples 
of extreme or slow-onset events or losses. It does not capture undocumented cases. It is an illustration of the relationship between unequivocal human-induced climate change and 
intangible losses (Adapted from Boyd et al., 2021).

8
1211Poverty, Livelihoods and Sustainable Development  Chapter 8
generations may be of limited use. For example, rotational grazing 
is no longer possible as all pastures are being used, and changes in 
climate result in unpredictable weather patterns unknown to earlier 
generations (Axelsson-Linkowski et al., 2020). These examples show 
that there are complex factors underpinning the linking L&D and 
shifting livelihoods. Moreover, there are significant challenges to 
undertaking a shift to secure alternative livelihoods.
Linkages between losses, coping strategies and livelihood shifts in 
small islands (e.g., in the Pacific region, Kiribati and Tuvalu, and in 
the Caribbean, the Bahamas) shed light on impacted low-income 
households. For example, farmers have experienced extensive damage 
to homes and loss of infrastructure, and experience lack of migration 
opportunities (Curtain and Dornan, 2019). Evidence is growing that

there is also significant loss of cultural heritage in resettlement 
(Barnett and O’neill, 2012), evidence from small islands’ displaced 
communities suggests that resettlement can have impacts on sense of 
place, identity and social fabric, a theme highly relevant to loss, coping 
and adapting livelihoods, and not only restricted to small islands 
(McNamara et al., 2021b). Roberts (2015) identified loss of communal 
sites in Kiribati. It is predicted that, by 2050, up to 80% of the land 
on the island of Buariki and 50% of the land on Bikenibeu may be 
completely inundated and these effects will result in significant loss 
of livelihoods and displacement. Throughout the Caribbean, evidence 
indicates that there will be an overall reduction in the area of land 
suitable for crop cultivation, as the region’s climate gets progressively 
warmer and as rainfall becomes more variable (Rhiney et al., 2016).
The multiple shocks of extreme events reduce crop yields, destroy homes,

and lead to loss of infrastructure and displacement (high confidence). 
These are experienced in South and North America. For example, in Peru, 
glacial outbursts have led to loss of livelihoods (Drenkhan et al., 2019). 
People use a range of coping and adaptation strategies to deal with 
hazards where they live, such as shifting livelihood activities, inputs or 
production areas. However, traditional techniques are increasingly failing 
due to changing weather patterns. Across Peru, findings demonstrate 
that people use temporary and permanent migration among their many 
coping and adaptation strategies. Hazards related to water excess have 
been the key force in destroying homes and driving displacement in 
Peru. In contrast, studies demonstrate that water scarcity also threatens 
livelihoods and thereby influences migration in Peru. While non-
climatic reasons for moving dominate migrants’ motivations in many 
areas of Peru, water-related climatic drivers of migration are becoming

increasingly relevant (Wrathall et al., 2014). Peru’s smallholder farmers 
and urban poor are not responsible for the climate crisis, yet their lives 
and cultural heritage are being increasingly jeopardised by its effects, 
making improvements in governance an imperative for Peru (Bergmann 
et  al., 2021). Another area of significance is coffee production in 
Brazil, where the majority of Brazilian coffee farms are operated by 
smallholders, producers with relatively small properties, who are 
mostly reliant on family labour (Koh et al., 2020). In the USA (e.g., New 
Orleans and Puerto Rico), people have lost livelihoods due to displaced 
households and destroyed homes, leading to loss of income, as well 
as loss of social networks and family networks and loss of cultural 
heritage. For example, impacts of Hurricane Katrina have led to people 
being displaced from their employment, many evacuees had to relocate

to new areas, which disrupted their social networks and placed them in unfamiliar labour markets, resulting in mental health challenges 
(Palinkas, 2020). There has also been a ‘climate gentrification’ in parts 
of New Orleans (Aune et al., 2020). Many of those who returned to their 
pre-Katrina areas had to deal with extensive damage to their homes 
and to public infrastructure.
In summary, across regions there is an increasing number of examples 
of observed economic and NELD from climate change. Adaptation 
measures need to better incorporate actions to tackle the burgeoning 
negative social, psychological and well-being impacts of climate 
change (Barnett et al., 2016; Box 8.5). At present, losses from climate 
change are potentially growing faster than adaptation measures 
across the globe. It is still uncertain how economic and non-economic 
losses trigger successful or viable new climate-related livelihood 
transitions for the poor and people or groups in vulnerable situations

in the future (see Sections  8.4.4; 8.4.5). In all likelihoods, economic 
losses from climate hazards (e.g., drought) will be compounded by 
many factors including COVID-19 and other vulnerability drivers. For 
instance, globally, small-scale coffee producers have been destabilised 
by COVID-19, but also because of a history of recurrent (climate) shocks 
and structural inequalities, and may have to shift into alternative 
livelihoods (Guido et al., 2020). Coastal communities in Vanuatu have 
been impacted in the immediate period after COVID-19 showing 
changes in village populations, loss of cash income and difficulties in 
accessing food, and have experienced shifting pressures on particular 
resources and habitats (Steenbergen et al., 2020). This trend poses real 
challenges to equity and sustainability.
In summary, this section has moved beyond the IPCC WGII AR5 in 
laying out structural elements of vulnerability and climate-related

vulnerability hotspots globally, such as poverty, lack of access to basic 
services, gender inequality and undernourishment. The assessment 
provides new quantitative evidence about the global spatial 
distribution of systemic human vulnerability and therewith underscores 
that various hotspots of countries classified as having very high or 
high vulnerability emerge in regional clusters. In addition, the number 
of people living in very highly and highly vulnerable country contexts 
is significantly higher in some assessments, with even twice as many 
as the number of people living in countries classified as having low 
and very low vulnerability. The evidence suggests that statistically 
relevant differences in fatalities per hazard event are not just a 
product of the hazard event, but also strongly linked with the level 
of vulnerability of the region or community exposed. The assessment 
of non-economic losses has also received little attention in past IPCC

Assessment Reports, therefore this section has provided new insights 
on how (next to measurable economic losses) non-economic losses 
and intangible losses emerge. These non-economic losses represent 
an important dimension of societal impacts of climate change that 
has not sufficiently captured so far within standard damage or post-
disaster assessments. Finally, the section provides evidence about the 
existing adaptation gap in terms of differential vulnerabilities and 
various non-economic losses already experienced.

8
1212Chapter 8 Poverty, Livelihoods and Sustainable Development
Box 8.5 | Western Cape Region in South Africa: drought challenges to equity and sustainability
Nature of the drought
Between 2015 and 2017, the Western Cape region experienced an unprecedented three consecutive years of below average rainfall, 
leading to acute water shortages, most prominently in the city of Cape Town (Sousa et al., 2018). Anthropogenic climate change made 
the drought five to six times more likely (Pascale et al., 2020; see also AR6 WGI Chapter 10, Section 10.6.2). The severity of the drought 
presented new challenges to the existing management and governance capacity to ensure equitable and sustainable water service 
delivery. The city’s water supply infrastructure and demand management practice were unprepared for the ‘rare and severe’ event of three 
consecutive years of below average rainfall (Wolski, 2018; Muller, 2019). Despite a potential total storage volume of about 900,000 Ml

of water (enough water for around a year and a half of normal usage, after taking evaporation into account), Cape Town’s reservoirs fell 
from 97% full in 2014 to less than 20% in May 2018 (Ouweneel et al., 2020; Cole et al., 2021). The drought saw residents queue for water 
as restrictions were imposed together with threats of closure of water provision to households (Sorensen, 2017; Scheba and Millington, 
2018). Poor communication in the early stages of the drought (Hellberg, 2020) and a lack of trust in the administration contributed to 
a near-panic situation at the threat of ‘Day Zero’ as dams almost ran dry in the first half of 2018 (Enqvist and Ziervogel, 2019; Simpson 
et al., 2020c). ‘Day Zero’ was avoided largely through public response, water demand management and the 2018 winter rains (Sorensen, 
2017; Booysen et al., 2019a; Muller, 2019; Rodina, 2019b; Matikinca et al., 2020). At a household level, responses to the drought showed

everyday residents can display unprecedented degrees of resilience (Sorensen, 2017), including behavioural and attitudinal shifts and 
technological innovation across the full socioeconomic spectrum (Ouweneel et  al., 2020). But the private nature of some of these 
responses extended existing inequality in water access through privileged forms of ‘gated adaptation’ by elites which conventional water 
governance arrangements were unprepared for (Simpson et al., 2019b; Simpson et al., 2020a).
These ‘climate gating’ actions, such as drilling boreholes, secured water access for high-income households and companies, but excluded 
a large proportion of Cape Town’s population who could not afford such private technologies (Simpson et al., 2019a; Simpson et al., 
2020b). These responses were unanticipated by the city administration and compounded fiscal challenges faced by the municipality

which could no longer use revenues from high-consumption households to cross-subsidise water for low-income households (Simpson 
et al., 2020a). This shift threatened to undermine the sustainability of the municipal fiscus and general water access ( Box 9.8; Simpson 
et al., 2019a; Simpson et al., 2020a). In order to recover losses, municipal water tariffs for consumers were raised by 26% in 2018 (Muller, 
2018; Simpson et al., 2019a). In addition to a decline in tourism, median estimations of the overall economic impact of the drought 
indicate loss of 27.6 billion South African Rand (USD 1.7 billion) translating into 64,810 job losses in the Western Cape, with Cape Town 
accounting for approximately half of those job losses (DEDAT, 2018). This had a disproportionate impact on unskilled and semi-skilled 
workers, particularly for those from low- and middle-income households (DEDAT, 2018). The drought also exacerbated the potential for

sanitation health risks of the urban poor where tens of thousands of people lack access to safely managed sanitation facilities (Enqvist 
and Ziervogel, 2019).
The Day Zero Disaster Plan included prioritising and protecting the poor and most vulnerable communities where critical infrastructure 
and facilities and vulnerable and informal residential areas would remain connected while higher-income residential areas would be 
cut off (Cole et  al., 2021). Yet it is important to recognise that pre-existing deficiencies in service delivery meant water access for 
the urban poor did not change as significantly during the drought, particularly those in informal settlements who collect water from 
standpipes (Enqvist and Ziervogel, 2019; Matikinca et al., 2020). For these communities, the negative economic impact of the drought was 
compounded by the unintended consequences of demand management regulation emanating from the drought response. South Africa

ostensibly ensures a constitutional right to water, regardless of ability to pay (Rodina, 2016), 58). Since 2018, however, as a consequence 
of new water tariffs instituted during the drought, Cape Town residents now have had to ‘prove their poverty’ in order to register as 
indigent households and access their water right (Scheba and Millington, 2018). Further, since 2007 and with increasing effect during the 
drought, the municipality has installed approximately 250,000 water management devices as a credit control and, during the drought, 
also a consumption control measure. As these have been largely installed in low-income homes, this control measure disproportionately 
affected poor households (Scheba and Millington, 2018; Enqvist and Ziervogel, 2019).
Lessons from the drought
The effect of communication at different stages in the drought highlights how critical information needs to be provided in a format

and language that empowers people to act appropriately and collaboratively (Muller, 2019; Rodina, 2019b; Rodina, 2019a). Getting 
political decisions made in a timely fashion and with public support is a long-standing challenge for managers of urban water supplies 
(Muller, 2017; Muller, 2019). In Cape Town this was further challenged by dependence on a malfunctioning national department for 
water supply planning, poor coordination between the spheres of government—city, provincial and national governments—and poor 
collaboration between political representatives, technical experts and strategic managers (Madonsela et al., 2019; Nhamo and Agyepong, 
2019; Rodina, 2019a; Ziervogel, 2019b). This highlights the need to strengthen partnerships and collaboration across sectors and scales

8
1213Poverty, Livelihoods and Sustainable Development  Chapter 8
8.4 Future Vulnerabilities, Risks and 
Livelihood Challenges and Consequences 
for Equity and Sustainability
Future climate vulnerability and risks to livelihood security are 
significantly influenced by present and past development trends, equity 
and sustainability. Consequently, observed impacts covered in previous 
sections provide essential insight for enhancing future adaptation and 
risk reduction. Since the AR5, new research approaches incorporate 
past lessons to project and assess climate change vulnerability and 
socioeconomic conditions into the future. Scenario tools and methods 
are a powerful approach for integrated assessments of emissions 
pathways, associated warming and development contexts, helpful in 
guiding analysis of adaptation policy and planning (Berkhout et al., 
2014; Birkmann et  al., 2021a). Both quantitative and qualitative

scenario approaches that assess future vulnerability and risks, as well 
as livelihood challenges at global, national and local scales, allow 
experts, planners, decision makers and affected people to articulate 
and visualise development futures. These approaches can complement 
emissions pathway scenarios.
8.4.1 Future Exposure, Climate Change Vulnerability and 
Poverty at the Global Scale
The SSPs scenarios orient climate models around possible development 
pathways that produce future exposure patterns, risk probabilities and 
vulnerability for future populations (O’Neill et al., 2014; O’Neill et al., 
2017a). While the likelihood of any given scenario actually occurring is 
highly uncertain, they have the advantage of pairing with computational 
models to generate robust projections about risk profiles in possible 
futures, and therefore assess the relative influence of different drivers 
of change. In this way, scenario tools generate pictures of future

vulnerability and adaptation pathways, and often have both an analytic 
and normative function. The decision-making context will determine 
which specific scenario approach is most appropriate (Rozenberg et al., 2014). Scenarios are limited by stakeholders’ imaginations and, 
as such, new emergent challenges, such as the COVID-19 pandemic, 
are difficult to anticipate in scenario planning. Nevertheless, recent 
studies and forecasts of the impact of COVID-19 on poverty conclude 
that in the near- and medium-term future major portions of the newly 
poor will emerge in sub-Saharan Africa and South Asia (Laborde 
et al., 2020b; Sumner et al., 2020). Since these countries are already 
characterised by high levels of absolute poverty and vulnerability to 
climate change, it is likely that these regions will face more severe 
challenges in overcoming vulnerability and will be confronted with a 
growing adaptation gap. Thus, the implication for scenario planning is

that single crises or events, such as the COVID-19 pandemic, might not 
significantly alter existing vulnerabilities, but rather reinforce them.
8.4.1.1 Exposure and Vulnerability under Different Scenarios 
and Alternative Development Pathways
At the international and national level, the SSPs (O’Neill et al., 2017a) 
have been developed to outline various development pathways, 
associated emissions and levels of warming, but also different possible 
development profiles (i.e., levels of economic growth, poverty, inequality, 
demographic change, etc.) that are highly relevant for adaptation.
Studies using the SSPs to understand multidimensional poverty are 
few but growing, and underscore that the impacts of climate change 
on poverty are extremely sensitive to different levels of warming (Byers 
et al., 2018). Multi-sector risks approximately double between 1.5°C 
and 2°C global mean temperature (GMT) change, and double again

in a +3°C world. Comparing a +1.5°C world pursuing sustainable 
development (SSP1) to a high-poverty and high-inequality +3°C world 
(SSP3), Byers et al. (2018) project substantial increases in populations 
exposed to drought, water stress, heat stress and habitat degradation 
(see in detail Byers et  al., 2018). While in a +1.5°C world exposed 
populations increase by 7–17%, the increase within a +3°C plus 
world is 27–51% (Byers et al., 2018; Frame et al., 2018). Populations 
in Asia and Africa account for more than 80% of the global population of governance (Ziervogel, 2019a), including the adoption of a ‘whole-of-society’ approach that recognises the contributions of non-state 
actors as adopted in the Cape Town Resilience Strategy (CoCT, 2019; Simpson et al., 2020a). Experienced yet inflexible water management 
initially operated at a distance from politicians and their citizens. There was limited knowledge and capacity in how various municipal

departments thought about risk, exposure and vulnerability of Cape Town’s highly differentiated population (Mukheibir and Ziervogel, 
2007; Pasquini et al., 2015; Madonsela et al., 2019). In the later stages of the drought, Cape Town’s water management department was 
able to work collaboratively across different departments and with politicians to implement responses.
The Cape Town case highlights how disaster planning for slow-onset city-wide shocks will be become increasingly important to safeguard 
equity and sustainability across African cities (Cole et al., 2021). It demonstrates the importance of integrating state and non-state 
responses to climate change in municipal adaptation and disaster planning (Booysen et al., 2019a; Booysen et al., 2019b; Simpson et al., 
2020a), particularly for responses with unintended consequences. Further, water tariff models need to be flexible enough and have built-

in redundancies in order to prioritise the needs of the urban poor and ensure climate responses do not disproportionately affect low-
income groups and deepen existing inequalities (Scheba and Millington, 2018; Enqvist and Ziervogel, 2019; Simpson et al., 2019b). 
Systems and relationships of mutual accountability can also build more effective water management between spheres of government and 
enhance horizontal collaboration between municipal departments and non-state entities (Ziervogel, 2019b; Ziervogel, 2019a).Box 8.5 (continued)

8
1214Chapter 8 Poverty, Livelihoods and Sustainable Development
exposed to these phenomena, and within South Asia and the Sahel, 
up to 90% of populations are exposed. Scenario tools help us to 
understand the burden of increasing multidimensional poverty, and 
potential for poverty traps, if mitigation and adaptation measures are 
not taken rapidly and effectively implemented.
At the national and sub-national levels, studies on development and 
risk scenarios capture specific challenges, for example, urban growth, 
demographic change, human health and ageing (e.g., Dong et  al., 
2015; Chapman et al., 2019). In this regard, local scenarios of human 
vulnerability can inform future strategies for adapting to hazards such 
as heatwaves in cities under different socioeconomic development 
strategies. These scenario approaches allow us to focus on changes 
in climatic and societal conditions as well as urban transformations.

This provides a more comprehensive basis for defining adaptation 
goals (see Fekete, 2019; Birkmann et al., 2021b). Costs and benefits of 
different adaptation measures can be assessed against different future 
scenarios of climatic and societal change.
Contrasting with ‘top-down’ SSP scenarios, (Berkhout et  al., 2014) 
outline how mesoscale and ‘bottom-up’ scenarios have been developed 
to inform spatial planning, for example, in the Netherlands. Increasing 
computational power has opened possibilities for large-scale ‘bottom-
up’ simulations of people’s livelihoods in the context of evolving 
climate change impacts, such as the migration decisions of farmers 
facing drought in Mexico over the coming century (Bell et al., 2019) and 
livelihood decisions of people facing coastal flooding in Bangladesh 
to the year 2100 (Bell et al., 2021). Such ‘bottom-up’ scenarios can 
generate projections about future outcomes, inform mapping and

assess future vulnerability, with special emphasis on livelihoods of the 
poor. Researchers conclude that results of respective scenarios that 
aim to inform adaptation and risk reduction policies in the context of 
climate change have to match the frames of the stakeholder (Berkhout 
et  al., 2014; Conway et  al., 2019). Scenarios that assess potential 
future vulnerabilities and future capacities for adaptation require 
more attention, since many approaches for projecting future climate 
risk still largely overlook non-climatic drivers that determine future 
vulnerability and exposure (Windfeld et al., 2019).
8.4.2 The Influence of Future Climate Change Impacts 
on Future Response Capacities
The influence of climate change also impacts the future response 
capacities of people and nations to deal with future climate change 
and climate hazards. Recent studies (e.g., Mysiak et al., 2016) conclude 
that climate change can increase the severity and intensity of crises or

even trigger disasters, particularly floods, storms, forest and wildfires, 
and droughts. These have undermined decade-long poverty reduction 
efforts, particularly in low-income and at-risk countries (Djalante, 
2019). Climate-influenced (disaster) risks are getting more complex 
and systemic (UNDRR, 2019). The magnitude of global annual average 
economic losses from natural and climate-induced hazards to the built 
environment alone are estimated in the United Nations Office for 
Disaster Risk Reduction (UNDRR) Global Assessment Report (2015) as 
being comparable with the GDP of the 36th largest economy in the 
world at that time—the Philippines (in 2015) (UNISDR, 2015; Mysiak et al., 2016). In addition, a World Bank study concludes that losses of 
human well-being are higher than the overserved economic losses from 
natural hazards (Hallegatte et al., 2017). In this regard, it is likely that 
future impacts of climate change, particularly under increasing levels

of global warming (above 1.5°C) will also increase non-economic 
losses (see Section 8.3.2.3) and losses of human well-being that are 
particularly relevant to most vulnerable groups and the poor.
Furthermore, the expected future increase in the number of people 
exposed to climate hazards, such as sea level rise and coastal flooding, 
is not only determined by changing hazard patterns, but also by regional 
processes of migration and urbanisation for example in Asia and Africa, 
including an increasing number of urban poor living in low-elevation 
coastal zones (United Nations, 2018). This can increase again the 
probability that more people require assistance and support for buffering 
these effects of climate-related hazards, for example, in coastal zones. 
Historical urbanisation processes, in coastal cities in Asia (e.g., in China, 
Vietnam, etc.) and Africa (e.g., in Nigeria) have increased the exposure 
of people to climate hazards, such as sea level rise, which by 2100

under Relative Concentration Pathway (RCP) 8.5 will globally threaten 
630 million people, largely in coastal cities (Kulp and Strauss, 2019).
In addition, Smirnov et al. (2016) conclude that worldwide the number 
of people exposed to extreme droughts will increase under both the 
RCP4.5 and the RCP8.5 particularly at the end of the century. The authors 
assess that under RCP4.5 the average monthly global population 
exposed to drought will increase between the periods 2008–2017 
and 2081–2100 from a mean of 80 million to 212 million, and under 
RCP8.5 from about 90  million to approximately 472  million people. 
The research findings underscore that there is a high probability that 
exposure to extreme droughts will increase, particularly in regions and 
countries already classified as highly vulnerable (e.g., Nigeria, Sudan, 
etc.) (Smirnov et al., 2016). Extreme droughts are expected to further 
erode coping and adaptive capacities of those already characterised

by high levels of vulnerability (see Section  8.3.1). Building adaptive 
capacities for the most vulnerable groups in the future in these areas 
will be a challenge, since high levels of livelihood insecurity are coupled 
with high levels of structural vulnerability at national and regional 
scale (poverty, state fragility, etc.) making planned adaptation support 
very complex and difficult. Therefore, increasing adaptation gaps at 
different scales are anticipated in the future.
Increasing population exposure (e.g., due to urbanisation of coastal 
zones, etc.), coupled with higher frequencies and intensities of specific 
climate hazards are likely in connection with the existing adaptation 
gap (e.g., high levels of vulnerability) to compromise development 
and human security. Recent studies, such as that by Harrington 
(2018), conclude that the actual exposure and the physical individual 
recognition of some climate hazards, will be higher in low-income

countries. The study of Harrington (2018) underscores that changes 
in extreme heat, for example, will be felt by the average citizen of 
a low-income country after 1.5°C of global warming and will not 
be felt by about 40% of people living in high-income nations until 
well after double the amount of global warming is reached (3°C 
increase). In this context, even if a city or place is exposed to heat 
stress, people experience it quite differently due to different levels of 
vulnerability and adaptive capacities, such as the ability to afford air

8
1215Poverty, Livelihoods and Sustainable Development  Chapter 8
conditioning (Barreca et  al., 2016). That means well-off populations 
are better insulated from effects of global warming than poorer or 
more vulnerable groups, even if they are geographically living in the 
same exposure zone. These findings underscore that issues of climate 
justice need to be considered within the problem definition when 
designing adaptation strategies, and not solely at the end. Impacts 
of future climate hazards (heat stress, flooding, etc.) differ not only 
due to changes in frequency and intensity of the hazard itself, but 
also significantly in terms of the opportunities people have to respond 
and prepare for these hazards and climatic changes at present and in 
the future. However, extreme heat stress has also caused significant 
fatalities in countries classified as having low vulnerability, such as 
seen within the heat wave in Europe in 2003.

8.4.3 The Influence of Climate Change Responses on 
Projected Development Pathways
Responses to climate change can have dual effects on development 
pathways. On the one hand, mitigation and adaptation processes 
can create significant development opportunities. The potential of 
mitigation policies for job creation, in particular, has been highlighted 
(Healy and Barry, 2017). However, responses to climate change can also 
have detrimental effects on future development: mitigation policies, 
such as the building of hydro-electrical dams or the culture of biofuels 
can lead to communities’ dislocation and populations’ resettlement, 
particularly of disadvantaged groups within a society (de Sherbinin 
et al., 2011; Eriksen et al., 2021). Adaptation policies can also hinder 
some development processes: for example, the promotion of migration 
as an adaptation strategy can lead to communities being deprived of 
their workforce and resenting the departure of some of their members

(Gemenne and Blocher, 2017), even though this may offer new 
livelihood opportunities. However, the migration consequences in the 
context of climate change are often more nuanced and different trade-
offs and benefits occur (see Porst and Sakdapolrak, 2020). For example, 
remittances support family members but, at the same time, can also 
create imbalances in local markets (Melde et  al., 2017). Evidence 
exists that some climate responses, such as small-scale agricultural 
livelihood adaptation strategies, have improved the ability of people 
to sustain their livelihood and to reduce poverty (Osbahr et al., 2010).
8.4.4 Social Tipping Points in the Context of Future 
Climate change has the potential to trigger major, sudden social 
transformations, yet there are no clear linear relationships between 
the magnitude of climate change impacts and the social changes they 
induce (Steffen et al., 2018). Evidence shows that major destabilising

social transformations (e.g., forced migration) can occur in response 
to limited climate change impacts, even while major climate change 
impacts can be mitigated through the resilience of social, political and 
economic systems, and thus yield only minor social impacts.
In the context of climate change, ‘tipping points’ have been identified 
as critical thresholds at which a tiny perturbation can qualitatively 
alter the state or development of a system (Lenton et al., 2008; Lenton et al., 2019). The concept of tipping points is usually associated with 
large-scale components of the climate system that could be pushed 
past an irretrievable threshold as a result of human-induced climate 
change (Lenton et  al., 2008), such as the deterioration of Antarctic 
ice sheets (Pattyn and Morlighem, 2020). Social tipping points refer 
to similar mechanisms of destabilisation resulting from impacts of 
climate change on human societies at multiple scales and the societal

context conditions in which these impacts occur. They are reached 
when climate change impacts force destabilising social transformations 
from one state to another (Lenton et al., 2019): from sporadic losses 
due to climate change to chronic losses and impoverishment, from 
peace to violence, from a democracy to an authoritarian regime, from 
adequate food provisioning to famine, or into forced migration. For 
example, small variations in the rainfall or temperature can jeopardise 
livelihoods that are dependent upon subsistence agriculture, which can 
lead to migration or tensions around resources (see Figure 8.11). Social 
tipping points can also occur when intangible elements that ensure the 
survival of individuals and communities are eroded or removed. This 
is the case, for example, when the social fabric of a community falls 
apart. The Millennium drought in Australia led to higher rates of male 
suicide, especially among farmers, and droughts in Ghana led to similar

outcome when people were forced to drink from the same water source 
as their animals, which they perceived as robbing them off their human 
dignity (Bryant and Garnham, 2015; Tschakert et al., 2019).
In socio-ecological systems, tipping points occur when a (small 
quantitative) change inevitably triggers a nonlinear change in the 
corresponding social component of the socio-ecological systems, driven 
by a self-reinforcing positive feedback mechanism, that inevitably and 
often irreversibly leads to a qualitatively different state of the social 
system (Milkoreit et al., 2018).
In recent years, significant research efforts have been made to identify 
early warning signals for social tipping points (Barrett and Dannenberg, 
2014; Bentley et al., 2014; Lenton et al., 2019). While some identify 
early warning signals through time series (Scheffer et al., 2012), others 
see them in interaction networks and individual thresholds (Barrett and

Dannenberg, 2014; McLeman, 2018). Empirical research conducted 
in a transboundary contentious region—the Jordan river valley—
showed that there were significant local and regional differences in 
the identification of social tipping points (Rodriguez Lopez et al., 2019).
Empirical evidence shows that social tipping points can be triggered 
long before climate tipping points are reached. For example, recent 
research in West Africa shows that migration decisions are often based 
on the perceptions of environmental changes by local populations 
rather than on the actual observed changes (De Longueville et  al., 
2020). The migration of some members of a community can also 
trigger the migration of the whole group, as the migration of some 
members can have a strong impact on the community (Gemenne and 
Blocher, 2017). In other contexts, the expectation of a climate impact 
can trigger social or political shifts: for example, the expectation of

lower snow cover levers can reduce or stop investments in ski resorts. 
Some planned relocations of populations are already underway in 
anticipation of future climate impacts (de Sherbinin et al., 2011), while 
the government of Indonesia decided in 2019 to move its capital city, 
Jakarta, in anticipation of future floods.

8
1216Chapter 8 Poverty, Livelihoods and Sustainable Development
Shifting livelihoods is a typical adaptation strategy but can also 
reflect a social tipping point if this shift affects the community as a 
whole. Therefore, social tipping points should not be confused with 
the carrying capacity of a community. While the carrying capacity 
of a community is a fixed, predetermined limit, social tipping points 
are dynamic and constantly evolving under the influence of different 
social and political factors, such as solidarity networks or governance 
mechanisms. The carrying capacity of a community can evolve over time, 
but remains a static concept, unlike social tipping points. Social tipping 
points have also been applied to adaptation, through the concept of 
adaptation tipping points, which indicate how much pressure a socio-
environmental system is able to absorb (Ahmed et al., 2018). Beyond 
the adaptation tipping point, the efficiency of adaptation responses

will be limited, and can even transform into maladaptive options.
8.4.5 Projected Risks for Livelihoods and Consequences 
8.4.5.1 Projected Risks for Livelihoods
There is robust evidence with high agreement that future climate 
change impacts will have severe consequences for poor households, 
particularly those situated in areas highly exposed to actual or future 
climate hazards, such as low-lying coastal communities (see also 
Cross-Chapter Paper 1), drylands (see also Cross-Chapter Paper 3) or 
remote mountain (see also Cross-Chapter Paper 5) settlements with 
low levels of connectivity to markets, poor infrastructure and high 
dependence upon poor quality natural capital (Barbier and Hochard, 
2018; Gioli et al., 2019). While livelihoods operate in a dynamic context 
characterised by multiple interacting structures and processes, climate 
change can act as a risk multiplier. When current livelihood activities

become untenable as a result of both long trends and short-term A social tipping point is reached when climate impacts push a society towards a state of instability
PRECARIT YSTRESSORS :
-Resources
-Population
-Political regimeRESILIENCE
STABILITYADAPT
ATION CAP
ACITIESAggravating factors
Climate impactsSOCIAL  
TIPPING POINT
Economic 
instability
Social 
instabilityPolitical
Figure 8.11 |  A social tipping point is reached when climate impacts push a society towards a state of instability. Those climate impacts are typically aggravated by 
economic, social and political stressors that reduce adaptive capacity and overwhelm its resilience. Once a social tipping point is reached, a society may experience mutually reinforcing 
states of economic, social and political instability, leading to cascading disruptions such as livelihood insecurity, migration and displacement, food insecurity, impoverishment, civil 
and political conflict, and change of political regimes.

shocks and climate hazards (e.g., droughts, floods), shifting livelihoods 
is a common response and, in many cases, can be unavoidable due 
to the negative consequences of these climate hazards on specific 
livelihood capitals (see Section 8.5). Such shifts can involve a change 
in livelihood activities (e.g., continuing in agriculture but growing 
different kinds of crops), or a change to broader livelihood strategies 
(e.g., diversifying into handicrafts or paid employment, specialising 
in one particular activity, or migrating, seasonally or permanently, in 
search of other livelihood opportunities) or even an entire change of 
the livelihood activity, for example, abandoning agriculture altogether 
(McLeman and Smit, 2006; Black et al., 2011). Shifting livelihoods can 
therefore involve mobility or take place in situ. Some of these shifts 
also lead to social tipping points.
8.4.5.1.1 Proactive and reactive livelihood shifts and their relevance

for future risks due to climate change
Livelihood shifts may also take place proactively as new opportunities 
emerge and reduce climate impacts by providing buffers of financial 
capital. For example, Hirons (2014) assesses artisanal and small-scale 
mining as an emerging livelihood opportunity in Ghana. Evidence 
challenges the popular assertion around the idea of wealth seeking for 
short-term profit and reveals an alternative scenario whereby artisanal 
and small-scale mining can be a poverty-driven activity, particularly in 
areas in which agricultural employment has not delivered sufficient 
income or where crops are highly exposed and sensitive to climate 
change impacts. Income from new livelihood activities can support 
recovery following specific events (major flooding or drought) linked 
to climate hazards and climate change. Livelihood shifts therefore 
take place in a highly dynamic and heterogeneous context. Another

example comes from the Small Lake Chad, Republic of Chad studied by 
(Okpara et al., 2016a). Fluctuating water levels linked to seasonal flood 
pulses and droughts were shown to link closely to livelihood dynamics.

8
1217Poverty, Livelihoods and Sustainable Development  Chapter 8
Lake drying led to new adaptive behaviours based on seasonality (e.g., 
migration of herders to different areas of the lake shore to access 
water resources, in line with more predictable seasonal changes), as 
well as linking to opportunism supported by climate change impacts. 
For example, during times of lake flooding, new opportunities for 
fishing opened for people that were otherwise operating primarily as 
pastoral or agricultural households. However, these kinds of livelihood 
shifts remain largely reactive and can bring negative as well as positive 
impacts. In the Lake Chad case, it resulted in social clashes between 
different groups, while in other examples from Tanzania, livelihood 
shifts towards extensification of farming led to deforestation (Suckall 
et  al., 2014), which could constitute a maladaptive shift. Such 
findings have important implications for the types of government and

institutional support that can enable livelihood shifts and highlight the 
need to consider trade-offs for climate change mitigation, as well as 
with other adaptation options (see Section 8.6).
8.4.5.2 Future risks, vulnerabilities, differentiated inequalities 
and livelihood shifts
Overall, there is high agreement that future climate change impacts 
are going to worsen poverty and exacerbate inequalities within and 
between nations, with projections that by 2030 these will increase 
significantly (Olsson et al., 2014; Hallegatte and Rozenberg, 2017; Roy et al., 2018). In addition, the COVID-19 pandemic and consequences 
linked to measures to reduce the spreading of the virus are likely to 
increase poverty, particularly in regions already facing high levels of 
vulnerability and poverty (Laborde et al., 2020b; Sumner et al., 2020).
Key risks due to future climate change, exposure and vulnerability 
are difficult to assess and are based on evidence from the past and

likely future vulnerabilities and livelihood challenges. The assessment 
of Representative Key Risks (see Section 16.5.2.3.4) underscores that 
risks to living standards are potentially severe as measured by the 
magnitude of impacts in comparison to historical events or as inferred 
from the number of people currently vulnerable (see in detail Chapter 
16). Table 8.4 provides an overview of what is known in the literature 
assessed about future risks, inequalities and particularly future 
vulnerabilities, including potential challenges for climate justice and 
adaptation barriers. For example, barriers for gender, ethnicity and class 
have been addressed for a long time yet need substantive intervention. 
Gender, along with many other structural inequalities (Table  8.4) 
that are deeply rooted, pose future threats to people and groups in 
vulnerable situations from, for example, the loss of land or assets, 
exposure to extreme events and so on. These people will also likely be

highly exposed to future climate risks unless there are significant and 
new avenues for action on climate change now. For example, recent 
studies suggest that the total population of all countries classified 
Table 8.4 |  Summary of interlocking categories differentiation future risks, vulnerabilities, inequality and adaptation
Future risks InequalitiesFuture vulnerabilities, future livelihood, future 
exposure (examples)References
Increasing risk of 
displacement and damage 
to women and girls in 
floodsGender inequality leaves women and girls hidden, forgotten 
and exposed, resulting in displacement impacts and limited 
resources, including social capital and increasing risk of 
human trafficking.Increasing future vulnerability of women and girls due to 
high hazard exposure; gender differentiated vulnerability to 
urban flooding (in India); increasing risk of human trafficking 
associated with exposure to future extreme events.(Singh, 2020; CCB GENDER 
in Chapter 18)

Increasing risks of 
exacerbating inequalities 
and tensionsDifferentiation based on ethnicity and race leads to groups 
in society being less visible, with fewer rights, particularly for 
livelihoods that expose them to extremes. Unequal access to 
adaptation opportunities and benefits.Increasing future vulnerability of Indigenous Peoples due 
to exposure to extreme events. Communities of colour are 
likely to be exposed to increased climate change impacts, 
e.g., differentiated health impacts on black and Hispanic 
communities heat-related mortality rates and poverty for 
neighbourhoods in New York City.Section 8.3; (Hsu et al., 
2021; Section 8.3)
Increasing risk of loss of 
homes and assets in the 
case of floodsClass differences in exposure and awareness of flood risks. 
Lower caste disproportionately impacted by climate change.Increasing differentiated exposure among classes to events 
such as flooding.(Jones and Boyd, 2011; 
Fielding, 2018)
Risks to loss of lives in

cases where there is no 
agencyReligious beliefs impact experience of climate change.Increasing vulnerability to climate change among different 
religious groups.(Schuman et al., 2018)
Risk of premature 
mortality, risk of loss of 
livelihoods in employmentAge and ageing populations. Elderly and young are 
disproportionately impacted by climate change, e.g., 
heatwave in France 2003 and Japan 2018. Youth 
underemployed or in vulnerable livelihoods could be 
vulnerable to climate-related risks, which adversely affects 
the economy.Increasing future vulnerability among elderly, underage 
youth and children vulnerable to increasing risks of health 
impacts of pollutants, floods or heatwaves.(Hsu et al., 2021; 
Section 8.3)
Risks to mobility in a 
climate extremePeople with disabilities, for instance; evidence emerging in 
the disaster risk reduction and humanitarian sector.Increasing risks to people with disabilities, who are

disadvantaged when exposed to extreme events.(King et al., 2019)
Risks of isolation for 
communities remote from 
centres of powerGeographical exposure. The location of people and societies 
within a particular territory is a determinant of inequality 
e.g., disruptions to food supplies to the Caribbean when 
there are climate extreme events.Increasing risk and exposure among communities remote 
from urban centres, far from resources and exposed to 
climate impacts.Section 8.3; Cross-Chapter 
Box GENDER in Chapter 18
Risks of food insecurityDifferentiation of asset/ownership/access among groups 
where status is unclear.Increasing risks to tenurial landless. If tenurial status 
is unclear, groups may experience loss of land and 
displacement.Section 8.2; Cross-Chapter 
Box GENDER in Chapter 18.

8
1218Chapter 8 Poverty, Livelihoods and Sustainable Development
as most highly vulnerable is projected to grow significantly. A study 
using five vulnerability categories globally concludes that the total 
population of all countries with very high vulnerability (see Figure 8.6) 
is projected to increase from 2019 numbers approximately by 102% 
by 2050 (i.e., roughly double) and 257% by 2100, while the population 
of all countries with very low vulnerability is projected to decrease by 
9% by 2050 and 17% by 2100 (based on UN medium probabilistic 
projections). Another study estimates that the total population of 
all countries classified at most vulnerable (top two categories; using 
seven vulnerability categories globally) is predicted to increase by 82% 
by 2050 and 192% by 2100. In contrast the population of all countries 
classified as least vulnerable (bottom two categories) is projected to 
only increase by 9% by 2050 and 1% by 2100 (see in detail UN-DESA,

2019; Birkmann et al., 2021a; Birkmann et al., 2022).
That means that, based on current population growth estimates and if 
vulnerability levels are not reduced significantly, more people will be 
living in more vulnerable context conditions in the future compared 
to those living in less vulnerable contexts. This is independent of the 
development of climatic hazard exposure. If significant reductions 
of vulnerability are achieved, this projection will change. However, 
the vulnerability and poverty of some regions and countries, such 
as Afghanistan or Haiti, has proved over decades to be persistent. 
Consequently, the estimated future population growth is another 
factor that points towards the urgent need to reduce vulnerability and 
to narrow the adaptation gap.
While future adaptation options can also encompass measures or tools 
that emerge in future, most of the future adaptation options and their 
relevance for reducing vulnerability, poverty and inequality are known.

Evidence exists that the importance of social networks that organise 
social protection and leverage resources in terms of reducing risks to 
climate change is increasing, particularly for most vulnerable people or 
groups in countries that have limited social security measures in place.
8.4.5.3 Future Limits to Adaptation
Local perceptions of losses from adverse effects of climate variability 
and change can help to assess the magnitude of impacts that 
individuals and communities have not been able to cope with or adapt 
to (James et al., 2014; Barnett et al., 2016; McNamara and Jackson, 
2019 McNamara et al. 2021, Mecheler et al. 2020).
The IPCC Special Report on a 1.5°C warming world shows with high 
confidence that for the Arctic systems, if average temperature increase 
exceeds 1.5°C by the end of the century limits to adaptation and 
residual impacts will be exceeded, compromising people’s livelihoods 
(Ford et  al., 2015; O’Neill et  al., 2017b; Roy et  al., 2018; Hoegh-

Guldberg et  al., 2019a). The loss and degradation of the Amazon 
forest with global warming temperatures beyond 1.5°C is another 
clear example of irreversible loss, with significant impact to people’s 
livelihoods today and in the future (Hoegh-Guldberg et al., 2018; Roy 
et al., 2018). Moreover, the L&D from climate change impacts are also 
felt heavily by women, children and elderly given the intersectionality 
with socioeconomic and gender inequalities (Li et al., 2016; Roy et al., 
2018). For instance, gender and wealth inequality offers challenges 
to scale up the Maasai pastoralist community autonomous adaptive practices (Wangui and Smucker, 2018). This study found that most 
female-headed and poorest households could not access the land, 
water for irrigation and financial assets required to access adaptive 
practices that are available in the wider community. Consequently, 
future impacts of climate change are likely to increase rather than

decrease inequality based on already observed impacts on adaptive 
capacities that constrain future adaptation options, particularly for the 
poor (Roy et al., 2018).
8.4.5.4 Future Livelihood Challenges in the Context of Risks 
and Adaptation Limits
The climate change risks in this section are addressed through the lens 
of livelihoods, human, food, water and ecosystem security, building 
on key impacts and risks since AR5 (Oppenheimer et al., 2014) and 
key findings from SR1.5°C (Hoegh-Guldberg et  al., 2018; Roy et  al., 
2018), SROCC (IPCC, 2019b), and SRCCL (IPCC, 2019a). The AR5 
WGII risk tables (IPCC, 2014b), updated in SR1.5°C (Roy et al., 2018) 
offer an interesting entry point as they show high confidence on key 
observed impacts and limits to the adaptation of natural and social 
systems that are compounded by the effects of poverty and inequality 
on water scarcity, ecosystem alteration and degradation, coastal

cities in relation to sea level rise, cyclones and coastal erosion, food 
systems and human health (Hoegh-Guldberg et al., 2018; Roy et al., 
2018). As a consequence, climate change risks pose substantially 
negative impacts on climate-sensitive livelihoods of smallholder 
farmers, fisheries communities, urban poor, Indigenous Peoples and 
informal settlements, with limits to adaptation evidenced by the loss 
of income, ecosystems and health, and increasing migration (Roy 
et al., 2018). The compounded effects of socioeconomic development 
patterns and climate change impacts are worst in climate-sensitive 
ecosystems in the Arctic and SIDS (Roy et al., 2018). The future risks 
to these climate-sensitive ecosystems and livelihoods are potentially 
severe given their current high exposure to climate hazards, and high 
number of vulnerable of people exposed for example in the SIDS (see 
also Chapter 16; Ahmadalipour et  al., 2019; Liu and Chen, 2021).

Residual losses then may be unavoidable for some ecosystems and 
livelihoods affecting the vulnerable groups of people and countries as 
consequences of structural poverty, socioeconomic, gender and ethnic 
inequalities, that marginalise and exclude and limit the development 
of adaptive capacity for future changes (Olsson et al., 2014; Roy et al., 
2018).
In SIDS, key risks are represented by losses of livelihoods of coastal 
settlements, ecosystem services, infrastructure and economic stability, 
exhibiting limits to adaptation in the face of local people’s coping 
strategies capacity (Hoegh-Guldberg et  al., 2019a). There is high 
confidence that sea level rise in SIDS combined with extreme flooding 
events will threaten the future livelihoods of coastal communities 
(Hoegh-Guldberg et al., 2018; Roy et al., 2018).
In the Global South, the increasing heat associated with warming 
global temperature represents an important risk due to losses in

labour productivity, crop failures and livelihood security. These involve 
economic losses and health effects, as well as increasing deaths 
that are anticipated to have significant implications for poverty, 
inequality and equity (Carleton, 2017; Roy et al., 2018). The increasing

8
1219Poverty, Livelihoods and Sustainable Development  Chapter 8
temperature, droughts and excessive rain lead to successive crop 
failures and reduced productivity that are affecting children’s growth 
and health in developing countries (Hanna and Oliva, 2016). Likewise, 
the expected global temperature increase by the end of the century 
will have devastating health consequences for children, associated 
with sea level rise, heatwaves, affecting the incidence of malaria and 
dengue, and malnutrition, especially in Asian (Ghosh et al., 2018) and 
African countries, such as Chad, Mali, Niger and Somalia (Hanna and 
Oliva, 2016; Ghosh et al., 2018; Clark et al., 2020).
The incidence of floods also increases the occurrence of diseases (e.g., 
diarrhoea and respiratory infections) and undernutrition in children 
living in informal settlements and slums in Asia (Ghosh, 2018) and 
Africa (Clark et al., 2020). Women and children are currently bearing

the worst impacts of climate hazards, and are unable to move due 
to assigned gender roles to avoid flooding risks in highly vulnerable 
slums in Bangladesh. This results in poor living conditions and causes 
the women emotional distress (Ayeb-Karlsson et al., 2020). This region 
experienced severe floods associated with death, injury, infectious 
disease, mental and emotional stress and cultural disruptions—
dimensions of non-economic losses that are often not accounted for in 
disaster relief policies (Chiba et al., 2017) and these greatly influence 
the ability to build adaptive capacities for future hazards (Roy et al., 
2018). In the same way, risks to female-headed households that have 
insecure tenure rights are greater. This group was the most affected 
by flooding in 2018 in Dar es Salaam, Tanzania, costing 3–4% of the 
country’s GDP and affecting 4.5 million people (Erman et al., 2019).
In the Himalayas (part of the Hindu Kush Himalaya, HKH) temperature

warming is expected to increase up to 2°C by 2050 (high confidence), 
increasing flooding and bringing larger risks to food and water security 
for mountain communities that are already highly vulnerable given 
limited livelihood options and supporting infrastructure in these regions 
(Mishra et al., 2017). In Nepal, agriculture-orientated livelihoods are 
reported to be negatively affected by an increase in landslide frequency 
(92.6%) and intensity (97.3%) over a 20  years period (1996–2016) 
(van Der Geest and Schindler, 2016). The catastrophic landslide in 2014 
caused material losses associated with loss of crops and land to poor 
households that were 14 times greater than their annual gains. The 
NELD losses were emotional distress and fear of new event occurrence, 
showing that poorest households may not fully recover following 
an extreme event. This example is indicative of the representative 
future climate risks to these populations. Livelihood diversification is

commonly adopted by poor households and smallholders in Nepal to 
reduce the impacts of extreme rainfall and landslides. However, there 
are limits to these strategies given poor household infrastructure that 
challenge risk reduction, as a result, it is expected that migration to 
neighbouring countries as Bhutan or India will increase (van Der Geest 
and Schindler, 2016).
Expected future risks to vulnerable communities and Indigenous 
Peoples include losses across a range of impacts. A larger household 
comparative analysis across mountain regions in Africa, Asia and 
Southeast Asia shows that more than 60% of the population reported 
losses from residual impacts concerning droughts, floods, cyclones, 
sea level rise, glacier retreat and desertification, despite autonomous 
adaptation involving changing food consumption and formal aid from the government (Warner and Van der Geest, 2013). Among Indigenous 
Peoples in the Global South, for example in the Brazilian Amazon,

Australia and Botswana, locally autonomous adaptive measures were 
not sufficient to avoid significant losses (some irreversible in case of 
lost habitats). The barriers and insufficient adaptive capacities are also 
intrinsically linked to historical marginalisation and vulnerability of the 
population in these countries (Maru et al., 2014).
In the Arctic, warming temperature and sea level rise constitute key 
risks to the loss of identity and culture of Indigenous People. This is 
associated with migration and relocation due to livelihood deterioration 
resulting from coastal erosion, permafrost thaw and reduced fisheries 
productivity (Roberts and Andrei, 2015; Roy et al., 2018). These risks 
and losses often encompass various non-economic losses, such as the 
loss of identity, that cannot be replaced or economically compensated 
(see also Section 8.3.5).
Likewise, in the Amazon basin, climate change hazards of severe

droughts and floods (high confidence) (Cox et  al., 2004; IPCC, 
2019a) are revealing limits to adaptation among the majority 
of riverine communities and smallholder farmers with residual 
impacts associated with losses of income, fisheries and agricultural 
productivity, as well as affecting non-economic livelihood dimensions, 
such as the ability to attend school and losses of place and identity 
through forced migration (Maru et al., 2014; Pinho et al., 2015; Lapola 
et  al., 2018). Furthermore, the expansion of the agricultural frontier 
and construction of large dams to supply energy needs in the Amazon 
basin are amplifying the vulnerabilities and reducing future adaptive 
capacities of smallholders and the fisheries communities to climate 
risk (Bro et al., 2018; Castro-Diaz et al., 2018). It is expected that a 
global temperature warming level of 2°C by 2050 in the Amazon will 
lead to a significant reduction of water flow in major rivers leading to

further food and water insecurity (Betts et al., 2018). This is affecting 
forest- and river-dependent livelihoods in the region (Box 8.6; Lapola 
The glacier retreat associated with the increase in global warming 
temperature has also shown losses that are permanent and related 
to a sense of belonging and cultural heritage for glacier countries. The 
most negative livelihood impacts are experienced by poor households 
in the Peruvian Andes and Himalayas (Jurt et al., 2015). The risks for 
smallholder livelihoods in glaciated regions are expected to increase 
as the shrinking glaciers result in increased water competition, crop 
failure and extreme flooding (Kraaijenbrink et al., 2017). For example, 
in Bhutan adaptive measures such as changing crops, developing 
irrigation channels and sharing water among community members 
are still insufficient to avoid L&D associated with the dramatically 
reduced water availability (Kusters and Wangdi, 2013; Warner and

Van der Geest, 2013). In high-mountain regions, the intersections of 
agro-pastoralist marginalisation, difficulty of access and ecological 
sensitivity contribute to residual impacts associated with extreme 
climate hazards, which can lead to irreversible losses and challenge 
poverty reduction efforts (Mishra et al., 2019).
In semiarid West Africa, longer-term local adaptation is in place to help 
poor households deal with severe droughts. This involves reducing 
household and cattle water consumption, planting drought-tolerant

8
1220Chapter 8 Poverty, Livelihoods and Sustainable Development
crops and adopting integrated crop–livestock production for efficiency, 
with migration being either seasonal and or permanent. These measures 
are mostly effective (van der Geest et al., 2019). Likewise, in Ethiopia, 
Northern Kenya and Senegal, adaptation has advanced with external 
government and non-government organisation (NGO) support (Schäfer 
et al., 2019). This includes technological innovations and insurance for 
households (Schäfer et al., 2019), but is not enough to prevent losses in 
already impoverished households (Schäfer et al., 2019).
There is robust evidence that future risks to climate-sensitive livelihoods, 
such as agriculture, livestock and fisheries are amplified by gender, age, 
wealth inequalities (Wangui and Smucker, 2018), ethical background 
and geography (Piggott-McKellar et al., 2020; Thomas and Benjamin, 
2020), as well as by ecological thresholds that challenge autonomous

adaptation among vulnerable disadvantaged communities mostly in 
the Global South (Roy et al., 2018; Mechler et al., 2020).
The assessment also points towards the fact that there are strong linkages 
between national-level vulnerability (e.g., Figure  8.6) and individual 
vulnerability at household or livelihood scale. Various disadvantaged and 
marginalised groups or communities within a society are significantly 
constrained in terms of the ability to build adaptive capacities for future 
climate change threats due to limited access to resources or government 
support for planned adaptation. Consequently, these linkages between 
regional, national and local vulnerability need more attention in research 
and practical adaptation strategies (vertical integration).The next section discusses how risks emerge as a result of the failure in 
adaptation or failure to implement it, with particular attention to risks 
that are impossible to adapt to and lead to inevitable L&D among poor

households, livelihoods and countries.
8.4.5.5 Maladaptation as a Projected Future Risk Particularly 
for the Poor and Marginalised
There is increasing evidence that maladaptation can lead to future 
risks to socio-ecological security. Adaptation measures focusing 
on short-term action can lead to adverse longer-term impacts to 
livelihoods and failures to address transboundary scales to avoid 
negative consequences for social and ecological systems (Warner 
and Van der Geest, 2013; Roy et al., 2018; Mechler et al., 2019a; see 
also Section  5.13.3). Hence, maladaptation can intensify and even 
accelerate future risks as a result of climate change mitigation and 
adaptation policies when responses to climate change hazards are 
embedded within business-as-usual development approaches (Work 
et al., 2019). For instance, in Cambodia, the conventional development 
strategies intertwined with climate change mitigation and adaptation

initiatives are increasing the probability of maladaptive outcomes in 
the context of high informality, and conflicts among poor farmers 
exposed and vulnerable to flooding (Work et al., 2019). The potential 
for maladaptation emerges from the vulnerability of precarious living 
conditions of informal poor farmers, not accounted for in climate 
mitigation and adaptation strategies for irrigation, protected areas 
Box 8.6 | Social dimensions of the Amazonia forest fires and future risks
The Amazon ecosystem, together with the Arctic, is listed as the first of five IPCC Reasons for Concern due to climate change, given 
the high confidence level that different temperature warming and GHG emissions will pose significant risks that threaten these unique 
ecosystems (O’Neill et  al., 2017b; Roy et  al., 2018). In addition to the scientific evidence, a resurgence of cross-national collective

expressions about the fate of the Amazon forest, Indigenous Peoples and traditional communities, in the context of an unprecedented 
climate crisis and sustainable future, have gained pronounced importance. On 19 August 2019, the skies of Sao Paulo State were dark 
by 3 pm due to the formation of a ‘smoke corridor’ associated with the extensive burning of the Amazon forest (Seymour and Harris, 
2019). The fire outbreaks were a consequence of multiple factors related to political, social, economic and environmental scenarios 
concomitant with the weakening of environmental governance, such as control and monitoring of deforestation and fire incidences 
programmes (Escobar, 2019; Seymour and Harris, 2019). The deforestation rate and incidences of fire are both increasing in the Amazon 
of Brazil, Colombia and Peru (Seymour and Harris, 2019). Accordingly, 2019 registered an increase of 60% in the cumulative fire count

in Brazil, Bolivia and Peru in comparison with the same period in 2018, and a 12% increase in comparison with the same period in an 
extremely dry year in 2016 (GFED, 2019). In this context, looking at this case study through the lenses of poverty, inequality and the SDGs, 
it addresses the compound effect of climate and land use change in the Amazon forest fires and its cascading impacts and risks on the 
social domain in the region. There is evidence that both climate and land use change impacts and risks are disproportionately borne by 
poor and vulnerable ethnic groups, remote rural communities and poor urban households in the Amazon (Pinho et al., 2015; Brondízio 
et al., 2016; Mansur et al., 2016; Pinho, 2016).
Fires are not a natural phenomenon in the Amazon region (Bush et al., 2004; McMichael et al., 2012); they are used for food security, 
hunting and religious rituals by Indigenous Peoples and traditional communities (Hecht, 2006; Carmenta et al., 2019; da Cunha, 2020),

and also as a widespread technique for land clearing for small- and large-scale farms for agriculture (Morello et al., 2019). The dramatically 
increased forest burning observed in the Amazon recently are the result of illegal land grabbing, the small-a and large-scale cattle 
ranching sector and agribusiness practices coupled with loosening of land tenure policies and decision makers’ neglect of deforestation 
and burning monitoring data (Nobre et  al., 2016; Lovejoy and Nobre, 2018; Leal Filho et  al., 2020a). The fire outbreaks intensified 
substantially to the point that, in August 2019, there were approximately 3500 fires in 148 Indigenous territories (DETER and INPE, 2019; 
ISA, 2019). Although most of the burning in the Legal Amazon in Brazil occurred on private land of medium and larger sizes (about 67%),

8
1221Poverty, Livelihoods and Sustainable Development  Chapter 8
around 33% was observed within Indigenous territories and protected areas called conservation units (UCs) (DETER and INPE, 2019; ISA, 
2019). In 2019, 40% of the deforestation occurred in public forests, which encompasses undesignated forest lands, Indigenous territories 
and UCs. This deforestation came accompanied by fires: 18% of the 2019 fires occurred on undesignated lands, 7% on Indigenous 
territories and 6% on UCs, where many traditional populations live (Alencar et al., 2020). During 2019, 46% of the deforestation and 
52% of the fires occurred on private rural properties and settlements, respectively, where legal accountability for these crimes is possible. 
The 2020 deforestation rate increased by 47% and 9.5% compared to 2018 and 2019, respectively, and was the highest in the decade

(Silveira et al., 2020). The clear-cut inside indigenous territories more than doubled from 2018 to 2019 (Brasilis, 2021) and, despite it 
decreasing from the 2019 rate, during 2020 it was the highest since 2008. On average, at least 50% of yearly active fires were within 5 km 
of deforested areas in the same year, reaching 74% during 2019 (Silveira et al., 2020). This means, that fires and deforestation have an 
increased threat to Indigenous populations (Oliveira et al., 2020), particularly during the year 2020 and currently in 2021, since COVID-19 
and air pollution from agricultural burning greatly impacts respiratory health in the Amazon (Morello, 2021).
Health impacts, economic and non-economic losses
The health impacts and economic losses estimates are not homogeneously gathered for the entire Amazon basin countries, but some 
recent evidence associated with this knowledge gap shows the magnitude of the forest fire impacts, as well as where they spatially occur

and who are the most affected by it. Fires associated with deforestation in the Amazon have been related to 1065–4714 deaths annually 
in South America (Reddington et al., 2015). The recent fires in the Amazon basin are directly affecting 24 million Amazonians with the 
worst impacts felt by children and the elderly (Machado-Silva et al., 2020), Indigenous Peoples and traditional communities (Fellows 
et al., 2020). Children under 5 years old and the elderly in rural areas are respectively 11 and 22 times more affected by the smoke from 
fire outbreaks and temperature increase in the Amazon (Machado-Silva et al., 2020).
In Acre State, the fire incidence coupled with extreme droughts in 2005 and 2010 led to an increase—from 1.2% to 27%—in 
hospitalisations of children (under 5 years) due to respiratory diseases (Smith et al., 2015). The same evidence was found among the

rapidly deforested areas known as the ‘Arc of Deforestation’, with a dramatically higher number of respiratory diseases recorded, mainly 
in children under 5 years (do Carmo et al., 2013). There is also evidence for interlinked dynamics between deforestation, urbanisation and 
incidence of fire episodes providing an appropriate environment for Anopheles darlingi vector propagation and the increased incidence 
of malaria in the region (Hahn et al., 2014). In the 2005 drought, burning in Acre alone recorded 400,000 people affected and the loss of 
300,000 ha of forest with direct costs of USD 50 million (Brown et al., 2006). In 2010, the fires during the drought were approximately 
16 times larger than those in the meteorologically normal years (Campanharo et al., 2019). The estimated total economic loss in 2010 
was about USD 243.36 ± 85.05 million, representing 9.07 ± 2.46% of Acre’s GDP (Campanharo et al., 2019). The economic and non-

economic losses associated with the impacts of climate change and future risks of fire outbreaks on native food crops (açai, guaraná), 
livelihoods, tourism, medicinal and spiritual sites, culture, migration patterns, place-based attachments, emotional and mental distress 
among the most affected and vulnerable population as Indigenous Peoples and traditional communities are still to be fully estimated 
for the region (Pinho et al., 2015; Brondízio et al., 2016). Also relevant is a trend of Amazonian forest fires spreading from the southern 
Brazilian Amazon to Bolivia and Peru, indicating that transboundary burning increases are systemic and will lead to extensive economic 
losses of wild crops, infrastructure and livelihoods, requiring a landscape level approach for deforestation and fire management and 
control (Kalamandeen et al., 2018).
Future vulnerabilities and risks for Indigenous Peoples and traditional communities

It is expected that by 2030 the incidence of extreme droughts in the Amazon will increase the costs of the health sector associated with 
treatment costs of respiratory diseases (20–50%) and malaria incidence (5–10%). This will also incur a high social cost as people will less 
able to carry out their livelihoods (Lapola et al., 2018). It is also expected that the droughts will accelerate and intensify rural (traditional 
communities and Indigenous Peoples) migration to urban centres where migrants living standards are expected to decrease once they 
will occupy marginal areas within larger urban centres (Lapola et al., 2018).
In terms of adaptation and risk reduction, priority should be given to strengthening multi-scale governance and partnerships among 
different private and public actors. Policies at national and sub-national levels are needed, such as control strategies to reduce deforestation

and fire incidence, demarcating new Indigenous territories, payment for ecosystem services (REDD+) and investment in traceability for 
commodity production chains are needed (Morello et al., 2017; Scarano, 2017; Carmenta et al., 2019; Seymour and Harris, 2019). The 
increase in global temperature level up to 2°C will exacerbate food and water insecurity in the Amazon (Betts et al., 2018; Hoegh-
Guldberg et al., 2018) (medium confidence). Thus, curbing fire incidence and deforestation rate will make it easier for Indigenous Peoples, 
traditional and vulnerable populations to reach the SDGs, especially in terms of reducing poverty (SDG1), improving food security (SDG2), 
improving well-being and health (SDG3) and protecting terrestrial ecosystem (SDG15) (Roy et al., 2018).Box 8.6 (continued)

8
1222Chapter 8 Poverty, Livelihoods and Sustainable Development
management and reforestation projects funded by multilateral donors 
(Work et al., 2019). As a consequence, losses emerge despite actions 
to prevent adverse impacts, and maladaptation instead becomes a 
vector of increased vulnerability for poor and vulnerable communities 
(Mechler et al., 2019a).
The maladaptation outcome also emerges as a failure of adaptation. In 
Ghana, poor farmers, facing crop yield failure during severe droughts 
further exacerbated by water use for irrigation have diversified their 
livelihoods (e.g., selling firewood for charcoal production).This is a 
form of maladaptation that can further increase their vulnerability to 
climate risks, compromising food production, income generation and 
sustainability (Antwi-Agyei et al., 2018b). In Cambodia, governmental 
adaptation strategies focusing on reforestation and conservation 
measures are eroding local biodiversity, and crop irrigation strategies

are compromising scarce water resources and also excluding poor 
farmers, who are susceptible to flooding, from decision making and 
benefits (Work et al., 2019). Likewise, in Ethiopia, efforts of adaptation 
programmes to address droughts contribute to current unsustainable 
development trajectories among pastoralist communities, resulting in 
charcoal production, overgrazing, migration, conflict with other groups 
and marginalisation of livelihoods (Magnan et al., 2016). In the Sudan, 
maladaptation outcomes for the poor population are linked to a 
dependency on a war economy and post-conflict power dynamics that 
are and will continue to affect sustainability and equity in the context 
of drought incidence (Young and Ismail, 2019).
In Bangladesh, an expensive coastal climate-resilient infrastructure 
project could potentially increase the vulnerability of urban poor as 
they will remain in areas that are highly susceptible to flooding brought

by sea level rise (Magnan et  al., 2016). In Central America, the lack 
of assessments of future climate variability on crop yield scenarios, 
coupled with lack of policymakers to incorporate autonomous local 
adaptation practices, could lead to an unsustainable trajectory for local 
communities and risk of maladaptation (Beveridge et  al., 2018). In 
Bhutan, small-scale rice farmers have adopted water-sharing measures 
to avoid the impacts of reduced and uncertain precipitation levels associated with monsoons. However, these measures led to disruptions 
in social cohesion as conflicts over water sharing escalated (Mathew 
and Akter, 2015). In the same region, local governments prioritise the 
glacier retreat as a perceived risk to flooding from dams, but overlook 
the slow and gradual impact of the deficit in precipitation that is 
negatively affecting rice productivity (Mathew and Akter, 2015). In 
Burkina Faso, a region highly impacted by severe droughts, local

communities have become less able to cope with droughts given a 
decline in cultural pastoralism and increased dependence on crops (van 
As seen, maladaptive responses to droughts, sea level rise and 
flooding are negatively affecting poor farmers, pastoralists, and rural 
and urban informal workers, increasing loss of crops, infrastructure, 
income, conflict and migration. Given the high risks of maladaptation 
to poor people this agenda should be given priority by development 
and planning sectors (Magnan et al., 2016). The categories in Table 8.5 
also represent important future compounding and complex risks that 
can emerge due to maladaptation (high confidence).
8.4.5.6 Future Challenges for Vulnerability and Livelihood 
Security due to Adaptation Limits of People and 
Communities and livelihoods with higher exposure to the risks posed 
by climate change and with lower adaptive capacity will experience a

higher burden of L&D in comparison to others (Tschakert et al., 2017). 
In Asia (Indonesia) and the Arctic region, a decline in marine fisheries 
by approximately 3 million tonnes per degree of warming is expected 
to have severe negative regional impacts, especially on Indigenous 
People (Cheung et al., 2016).
It is projected that climate change impacts on the incidence of disasters 
will push 122  million additional people into extreme poverty with 
global temperature increase by 2030 (Hallegatte and Rozenberg, 2017; 
Hoegh-Guldberg et al., 2018; Jafino et al., 2020). It is also expected 
that around 330–396 million people will experience lower agricultural Table 8.5 |  Categories of maladaptation as future risk and examples of outcomes and world regions based on literature assessment evidence.
Categories of risks to maladaptation Examples of outcomes
Uncertainty (climate events) Lack of knowledge of future climate extreme events hinder adaptation actions for the poor

Inequalities Exclusion of rights and access, and benefits of adaptation
Sustainability Further ecological degradation and biodiversity loss
Informality Reinforced vulnerabilities of the poor and marginalised populations
Poverty Increased vulnerabilities and risks of maladaptation
Scales (temporal and spatial) Negative trade-offs across short- and longer-term decisions, as well as transboundary issues resulting in increased likelihood of maladaptation
Regional evidenceSouth Asia and Southeast Asia (Bangladesh, India, Indonesia, Maldives, Nepal and Thailand) (6) **
Africa (Ethiopia, Ghana, Malawi ) (3)
Central America (1)
Global South (2)
Global (1)
Notes:
Confidence level **medium (5–9 papers).

8
1223Poverty, Livelihoods and Sustainable Development Chapter 8
yields at warming beyond 1.5°C (Hoegh-Guldberg et al., 2018), most 
of them in South Asia and sub-Saharan Africa (Chapter 16; Roy et al., 2018; World Bank, 2019a). There is also medium evidence that tens to hundreds of millions of people that are dependent upon climate-sensitive livelihoods could out-migrate as a consequence of global temperature increasing, mostly in Africa, Asia and Latin America—posing additional risks to unsustainable urbanisation and group conflict (Chapter 16; Hoegh-Guldberg et al., 2018; Roy et al., 2018).
The multi-intersectionality of inequalities (socioeconomic, caste,

ethnicity, among others) and marginalisation, result in differential capacity to avoid risks, which is particularly limited amongst the most vulnerable communities who are in, or at the brick of falling into, poverty traps, which then also affects future generations (Hallegatte and Rozenberg, 2017; Roy et  al., 2018; Tschakert et  al., 2019). For instance, the poorest communities in the Global South, who are dependent upon thriving ecosystems for health, food, water and energy, are disproportionately more exposed to temperature extremes and droughts, compromising food and water security (Byers et  al., 2018). There are also inequalities associated with opportunities to adapt to risks that are unevenly distributed among global regions, with richer and more equal societies in the Global North presenting superior capacities than Global South communities, sectors, ecological systems and species, where the most detrimental climate change impacts are experienced (Hoegh-Guldberg et al.,

2018; Roy et al., 2018). The climate-sensitive livelihoods of poor and vulnerable communities in the Global South, and the unprecedented ecosystems losses are examples of multiple limits of adaptation that emerge simultaneously and are also linked to the differential access to assets and resources, such as physical (propriety, income), social (health, age, education) cultural (shared community values and norms, ethnicity), ecological (linked to land use change and productivity) and institutional (market, policies and governance) (Roy et  al., 2018; Hoegh-Guldberg et  al., 2019a; Olsson et al., 2019). The adaptation limits emerge mostly in countries in Global South, and disproportionately affect specific groups, with high poverty incidence, that are constrained by inadequate financial resources and institutional instruments (Tian and Lemos, 2018; Volpato and King, 2019), including lack of understanding and preparedness of the risks posed by climate change (Ayeb-Karlsson et  al., 2016;

In other situations, adaptation limits to household livelihoods emerge 
from ecological thresholds associated with global warming temper -
atures, such as deterioration of land and water resources, extinction of species and biodiversity that can lead to systemic crop failures, declin-ing fisheries productivity and water availability and substantial risks to households’ livelihoods (Roy et al., 2018). However, it is also important to note that limits are associated with development, technology and cultural norms and values that can change over time to enhance or reduce the capacity of systems to avoid limits (Adger et al., 2014; Roy et al., 2018). It could also include aspects of maintaining security of air or water quality, as well as equity, cultural cohesion and preser -

vation of livelihoods (Adger et al., 2014; Tschakert et al., 2019). For soft limits, however, adaptation options could become available in the future through changing attitudes or values or as a result of innovation or other resources becoming available to most vulnerable and poor actors, households and countries. However, when compounded with lack of finance, and high costs associated with disasters, poverty and environmental degradation, soft limits could become hard ones in the future (see Figure 8.5; Gracia et al., 2018).
Table  8.6, built from SR1.5°C (Roy et  al., 2018), illustrates how

ecological thresholds and socioeconomic determinants are linked to soft and hard adaptation limits and what the potential and magnitude of livelihoods risks will be in the future. For instance, in the SR1.5°C (IPCC, 2018b) and Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC, 2019b), hard limits are expected with global warming beyond 1.5°C associated with the loss of coral reefs, that will lead to substantial loss of income and livelihoods for coastal communities (Roy et  al., 2018; Mechler et  al., 2019b; Oppenheimer et  al., 2019). The loss of coral reefs around the remote islands of Boigu in Australia is affecting low-lying communities facing financial, institutional (Evans et al., 2016) and cultural place-based attachment adaptation limits (McNamara et  al., 2017). Another hard limit to adaptation with implications for income, and culture- and place-based livelihoods is related to the sensitivity of fish to global temperature increase, with losses in

fish reproduction expected to be 10% (SSP1–1.9) to about 60% (SSP5–8.5), potentially cascading into severe risks for fisheries livelihoods (Dahlke et al., 2020). In West African fisheries, the loss of coastal ecosystems and productivity are estimated to require 5–10% of countries’ GDP in adaptation costs (Zougmoré et al., 2016), incurring financial limits in poor countries to avoid socioeconomic risks. The SROCC (IPCC, 2019b) showed that scientific knowledge limitations can constrain management of coastlines, mainly in the context of lack of data, affecting most of the vulnerable and poor communities in the Global South (Perkins et al., 2015; Sutton-Grier et al., 2015; Wigand et al., 2017; Romañach et al., 2018). Hard and soft adaptation limits are challenging to define, given the rate and intensity of climate change hazards and the mitigation and adaptation options available, but also the level and rate of non-climatic stresses increasing vulnerabilities and undermining adaptive

capacity of poorest members of society and sensitive ecosystems (medium evidence, high agreement) (Klein et al., 2014; Roy et al., 2018).

associated with financial and institutional mechanisms, and related to structural poverty and inequalities among rural farmers in India (Singh et  al., 2019a) and among low-income countries (Tenzing, 2020), agro-pastoralist communities (Volpato and King, 2019), women (Balehey et  al., 2018), informal slum settlements in Latin America (Núñez Collado and Wang, 2020) and informal workers in Southeast Asia (Balehey et al., 2018). For SIDS, multiple adaptation limits also emerge as a combination of political–institutional and cultural aspects (Robinson and Wren, 2020), such as preserving national identity and sovereignty in the context of migration in the Marshall Islands (Bordnera et al., 2020). A widespread narrative is that an increase in migration in SIDS, given sea level rise and global temperature increase by 2050, is inevitable, desirable and economically necessary. Many more people will be exposed to migration and affected by multiple forms of physiological and emotional stress

(Bordnera et  al., 2020). In the same way, the Mohawk community of Kanesatake, Canada, is faced with institutional and socio-political adaptation limits such as lack of land ownership rights, insurance and social institutions (Fayazi et al., 2020).

8
1224Chapter 8 Poverty, Livelihoods and Sustainable Development
Table 8.6 |  Synthesis of hard and soft limits to adaptation and risks to livelihoods, equity and sustainability adapted from Chapter 5 of SR1.5°C (Roy et al., 2018).
DeterminantNature of barrier to livelihood 
adaptationMagnitude + Indicator Soft limit Hard limitConfidence level
based on number 
of papers
Socioeconomic and human-geographical determinants
Gender-based 
inequality or 
discriminationGender-based inequalities constrain women’s 
access to resources, thus limiting ability to 
invest in adaptive capacity and heightening 
vulnerability.World Bank: 62.151% [Employment in agriculture, female 
(% of female employment) (modelled International 
Labour Organization (ILO) estimate) – Low income, 2020]; 
25.409% [Employment in agriculture, female (% of female 
employment) (modelled ILO estimate)].X***high 
(≥ 10 papers)
Poverty and 
socioeconomic 
inequalityPoverty and lack of financial resources constrain

ability to invest in livelihood diversification, 
resilience and adaptive capacity.World Bank: 10% [Poverty headcount ratio at USD 1.90 d−1 
(2011 PPP) (% of population)]; 26.498% [Employment 
in agriculture (% of total employment) (modelled ILO 
estimate)]; 58.783% [Employment in agriculture (% of 
total employment) (modelled ILO estimate) – Low income], 
Low-income countries, 2020.X***high 
Indigeneity and 
other cultural 
place-based 
attachmentsIndigenous and other populations with strong 
cultural or economic attachments to place face 
barriers to adaptation due to non-economic 
losses associated with migration, urbanisation 
and some forms of livelihood transformation.SIDS total population of around 65 million (UN-OHRLLS, 
2015); 476 million indigenous people worldwide (World 
Bank, 2016).X***high 
Arctic hunting 
and fishing 
communitiesResidents of arctic regions dependent on hunting 
and fishing livelihoods interrelated cultural and

economic vulnerability due to risk crossing arctic 
ecosystem thresholds and tipping points.Global arctic population, around 4 million (Larsen, 2015). X X***high 
Urban slum 
and informal 
settlement 
populationsResidents of slums and informal urban 
settlements are particularly vulnerable due to 
limited infrastructure and limited employment 
opportunities.33.331% [Population living in slums (% of urban 
population)], World, 2009; It is estimated that 50–
57 million urban Africans (47% (44–50%) of the urban 
population analysed) were living in unimproved housing in 
2015, mostly in sub-Saharan Africa (Tusting et al., 2019).X***high 
Ecological determinants
Glacier retreatSeasonal water scarcity and/or glacial lake 
outburst floods pose a serious threat for highly 
exposed and vulnerable smallholders in the 
Peruvian Andes (Drenkhan et al., 2019). Tibetan 
Plateau region will reach peak water between

2030 and 2050 (Yao et al., 2020).The flow decrease of the Tibetan Plateau region will 
affect water availability for several countries, affecting a 
population of 1.7 billion people and a GDP of USD 12.7 
trillion (Yao et al. 2019). In 2050, the number of people 
that will be living in water-scarce regions will increase to 
2.7–3.2 billion (Luterbacher et al., 2020). As of 2010, 27% 
of global population (~1.9 billion people) lived in severely 
water-scarce areas (Luterbacher et al., 2020).X X***high 
Loss of coral 
reefsLoss of 70–90% of tropical coral reefs by 
mid-century under 1.5°C scenario (total 
loss under 2°C scenario) (see SR1.5°C, 
Hoegh-Guldberg et al., 2018, Sections 3.4.4; 
3.5.2.1; Box 3.4; (Magnan et al., 2019); Roy 
et al., 2018, Section 5.2).Coral reef fisheries-dependent and coastal livelihoods, 
sustain 6 million direct fishing jobs and more than 
USD 6 billion in revenues globally (Teh et al., 2013), often

among disadvantaged populations (Hoegh-Guldberg et al., 
2018). In tropical regions, there are 1.3 billion people 
living by coast and depending upon fisheries for food 
and livelihoods (Sale et al., 2014). In Africa and Asia over 
400 million people are dependent upon protein intake from 
fisheries (Hoegh-Guldberg et al., 2019b). Approximately 
850 million people live within 100 km of reefs and more 
than 275 million reside within 30 km, many of whom are 
likely to be highly dependent on coral reefs, especially 
those who look to these marine ecosystems for food and 
livelihoods (Burke et al., 2011).X***high 
Biodiversity 
lossTerrestrial species on average lose 20–27% 
of their range at 1.5°C (significantly higher 
range losses projected for some species at 
2°C) (see SR1.5°C, Hoegh-Guldberg et al., 
2018, Section 3.4.3.2; de Coninck et al., 2018, 
Section 4.3.2).
Tropical forests (vegetation shifts due mainly to 
drying), high-latitude and altitude ecosystems

and Mediterranean-climate ecosystems (high 
vulnerability).Forest-dependent livelihoods of 1.6 billion rural people (in 
2012) are likely to be affected to risks of terrestrial forest 
and biodiversity loss (Newton et al., 2020).X**medium 
(5–9 papers)

8
1225Poverty, Livelihoods and Sustainable Development  Chapter 8
New emerging considerations to ecological limits to adaptation 
associated with severe glacier retreat in the Peruvian Andes, is expected 
to reduce lake discharge by 2–11% (7–14%) by 2050 (2100). This will 
affect smallholders farmers, through crop yield failures and severely 
reduced hydropower capacity (Drenkhan et al., 2019). In addition, the 
study showed a very high risk of glacier lakes being affected by GLOFs 
under RCP8.5, posing serious threat to rural people’s livelihoods 
(Drenkhan et al., 2019).
Table 8.6 represents different types of adaptation limits (soft or hard) 
that emerge over time, sometimes concomitantly, that are leading to 
severe risks to livelihoods in a high poverty, unequal and hotter future, 
especially among poor and vulnerable populations, and within those 
Indigenous People, women and children (see Section 16.5.2.3.4). The

confidence statements are assessed through the evidence on papers 
as high (≥10 papers), medium (5–9 papers) and low (≤ 4 papers) to 
ensure traceability on the nature of livelihoods barriers and ecological 
thresholds associated with ‘soft’ or ‘hard’ limits to adaptation under 
a warming global world. The determinants of livelihood barriers are 
linked to gender-based inequality or discrimination, poverty and 
inequality, indigeneity and cultural place attachment, artic hunting 
and fishing, and urban slum and informal settlements incurring soft 
and hard limits to adaptation. The ecological thresholds assessed are 
associated with glacier retreat, loss of coral reefs, biodiversity loss, 
ocean acidification and warming, sea level rise and heat stress incurring 
hard limits to adaptation and severe risks to people’s livelihoods. The 
severity of risks to livelihoods is assessed using a magnitude indicator of the current number of people exposed and vulnerable to climate-

sensitive livelihoods. The supporting literature is listed in Table SM8.1.
8.4.5.7 Compounding Future Risks on Equity and Sustainability
The compounding future effects on equity and sustainability emerge 
when multiple stressors linked to environmental and/or climate 
change, together with underlying structural poverty, exclusion, 
marginalisation, and conflicts creating risks that need to be addressed 
simultaneously. Compounding risks of climate change received 
attention in AR5 (Oppenheimer et  al., 2014). This included risks 
associated with compound hazards (O’Neill et  al., 2017b) and their 
implications for future risk when repeated impacts erode human and 
ecosystem capacity, including through transboundary effects. In SRCCL 
(IPCC, 2019a), land degradation and climate change compounded 
to highly expose the livelihoods of the poor to climate hazards and 
caused food insecurity (high confidence), migration, conflict and loss

of cultural heritage (low confidence) (Olsson et al., 2019).
The evidence of compounded risks emerges from specific climate and 
environmental hazards, as in relation to heatwaves, droughts, altered 
precipitation regimes and increasing aridity, cyclones, floods, hurricanes 
and wildfires (Table  8.7). Other evidence shows that the structural 
poverty and socioeconomic inequalities (Lusseau and Mancini, 2019), 
disability (Sun et al., 2017), corruption (Markkanen, 2019) and isolation 
(Reyer et  al., 2017) (Table  8.7) compound to amplify climate risks 
among rural and urban poor, smallholder farms, coastal settlements, DeterminantNature of barrier to livelihood 
of papers
Ocean 
acidification 
and warmingLarge-scale changes in oceanic systems 
(temperature, acidification) inflict damage and 
losses on livelihoods, income, cultural identity 
and health for island and coastal-dependent

communities at 1.5°C (potential for higher 
losses increases from 1.5°C to 2°C and above) 
(see SR1.5°C, (Hoegh-Guldberg et al., 2018); (de 
Coninck et al., 2018); (Roy et al., 2018).500 million people who derive food, income, coastal 
protection and a range of other services from coral reefs 
(Hoegh-Guldberg et al., 2017).X X**medium 
Sea level rise 
(SLR)SLR and increased wave run up, combined with 
increased aridity and decreased freshwater 
availability, at 1.5°C warming potentially 
leaving several atoll islands uninhabitable (see 
IPCC SR1.5°C Hoegh-Guldberg et al., 2018, 
Box 3.5; de Coninck et al., 2018, Cross-Chapter 
Box 4.1). SLR is projected to affect human 
health and well-being, cultural and natural 
heritage, freshwater, biodiversity, agriculture and 
fisheries (IPCC, 2018b; WHO, 2018; IDMC, 2019; 
McMichael et al., 2020).It is projected that ~316–411 million people in 2060 will 
be living in areas affected by SLR, with most in South

and Southeast Asia and in Africa (Neumann et al., 2015; 
Oppenheimer et al., 2019). The number of people at risk of 
floods will increase from its current level of 1.2 billion to 
1.6 billion by 2050 (Luterbacher et al., 2020).
It is estimated that 6–8% of Latin America and the 
Caribbean’s population, face high risk associated with 
coastal hazards (Oppenheimer et al., 2019).X***high 
Heat stressIt is expected that by 2070 over 30% of global 
poor population will be living outside the human 
thermal comfort, beyond adaptive capacity. This 
will also affect crop and livestock productivity 
(Xu et al., 2020).Currently 30% of the global population is exposed 
to deadly heat waves and this percentage by 2100 is 
projected to increase to ~ 48% under a drastic mitigation 
scenario to ~ 74% under a scenario of growing emissions. 
(Mora et al., 2017).
Heat stress contributes to deaths and health problems 
among the elderly and children. Specifically, heat stress

is currently responsible for 38,000 annual deaths mostly 
among the elderly, and 48,000 from diarrhoea, 60,000 
from malaria and 95,000 from childhood undernutrition 
(WHO, 2014a; Roy et al., 2018).X**medium 

8
1226Chapter 8 Poverty, Livelihoods and Sustainable Development
with health impacts on children’s development (Perera, 2017) and 
urban elderly (Sun et  al., 2017). In Tanzania, a greater exposure of 
households to climate change impacts and risks is associated with 
increasing land value and variable tenure, compounded by declining 
farm yields, accelerating the negative effects among the population 
(Röschel et  al., 2018). In India, extreme droughts and heatwaves 
compound extreme poverty and high dependence on agriculture for 
income and food production will affect crop productivity, income 
and food prices among smallholder farms (Singh and Leua, 2017). In 
Mozambique, soil degradation and fertility, compounded by incidence 
of droughts, increase the vulnerability of already poor smallholders 
who lack access to technological advances for crop yield management 
and drought-resistant crops (Kidane et al., 2019).

In the context of urbanisation, in fast growing cities in Asia, Africa and 
Latin America that are highly socially and economically unequal, the 
climate change impacts from events such as flooding and droughts, 
are amplified as water crises, mostly among the poor and marginalised 
population, challenging governance for risk reduction (Gore, 2015; 
Dodman et al., 2017; Jiang and O’Neill, 2017; Pelling et al., 2018; Solecki 
et al., 2018). In the Global South, over 880 million people are living 
in precarious and informal conditions without access to water and 
sanitation, mostly in sub-Saharan Africa and South Asia (see Chapter 
6; Rosenzweig et al., 2018; Satterthwaite et al., 2018; Tusting et al., 
2019). In rapidly urbanising sub-Saharan African countries, around 53 
(50–57) million urban inhabitants (50% of urban population ) and 595 
(585–607) million rural inhabitants (82% of the rural population) were 
still living in unimproved housing in 2015 (Tusting et al., 2019).

L&D from climate extremes, such as fatalities or economic losses 
due to droughts or floods (see also Figure 8.6) also matter for future 
vulnerability and risk, since the poorest segments of society take 
longer to recover after shocks (Gupta and Sharma, 2006; van der 
Geest, 2018). In some cases, poor households might never be able to fully recover post-disaster, especially in the context of increasing 
global temperature increase (van der Geest, 2018). Another example of 
compounding effects of climate change to equity and sustainability is 
migration, which is underpinned by the underlying socioeconomic and 
political context of vulnerability (see Section 8.2).
In Latin America, compounding effects of climate change impacts 
(disasters) and armed conflict has contributed to forced migration 
to the point that in 2018 alone, 1.7 million people migrated due to 
extreme events, four times as many as the number of people leaving

their homeland due to armed conflict (Serraglio and Schraven, 2019). 
In South America, migration within and between countries can stem 
from climate extremes, primarily felt by the poorest and marginalised 
(by gender, age, ethnicity) populations that might not be able to adapt 
to the fast pace and scale of changes at the local level (Maru et al., 
2014; Pinho et al., 2015; Serraglio and Schraven, 2019). In mountain 
regions, intersections of people’s marginalisation, difficulty in access 
and environmental sensitivity in the context of incidence of climate 
extremes have combined to reduce the ability of mountain agro-
pastoralists to cope with climate extremes (Mishra et  al., 2019). 
Mountain ecosystems are also highly susceptible to disasters and 
disturbances, which can lead to irreversible loss and challenge poverty 
reduction efforts (Mishra et al., 2019) Some risks associated with the 
degradation and loss of habitats and ecosystem services associated

with land use changes and commodities in many countries have 
compounding impacts on equity and sustainability, associated with 
permanent losses to the livelihoods of poor and marginalised groups, 
such as Indigenous Peoples and traditional communities around the 
world (Roy et  al., 2018). For instance, high deforestation rates and 
increased forest burning in many Amazonian countries are further 
exposing vulnerable Indigenous Peoples and traditional populations 
to health problems, crop failures and shortages of freshwater supply, 
especially in the context of extreme droughts and non-supportive 
governance (Leal Filho et al., 2020a; Walker et al., 2020).Table 8.7 |  Effects of compounded risks on the poor. Climate hazards: flooding, hurricanes, drought and heatwaves.
Dimensions of compounded risk effects on the poor Equity Sustainability
Poverty (9)** ✓ ✓
Environmental (ecological change, soil degradation, fertility and aridity) and socioeconomic changes (8)** ✓ ✓

Inequalities (4)* ✓
Governance (3)* ✓ ✓
Geographical (isolation) (1) ✓ ✓
Population growth (3)* ✓
Diseases (3)* ✓ ✓
Uncertainty (1)*
Finance (1)*
Informality urban (2)* ✓ ✓
Disability (1)* ✓
Climate-sensitive livelihoods (1)* ✓
Infrastructure (1)* ✓
Notes:
Confidence level: ***high (≥10 papers); **medium (5–9 papers); *low (≤4 papers).

8
1227Poverty, Livelihoods and Sustainable Development  Chapter 8
Overall, there is increasing evidence that the compounding effects of 
climate hazards intertwined with dimensions of poverty, environmental 
degradation and inequalities, represent a key risk to equity and 
sustainability among poor and vulnerable populations (medium evidence 
and high agreement). Compounding risks—compared to compounding 
hazards—can also be significantly influenced by societal tipping points 
and by different factors of human vulnerability that determine underlying 
destabilisation processes of societies and communities exposed to 
climate change, including issues of governance.
8.5 Adaptation Options and Enabling 
Environments for Adaptation with a 
Particular Focus on the Poor, Different 
Livelihood Capitals and Vulnerable Groups
This section focuses on adaptation at household and community scales, 
including options, capacity and enabling environment, which include

actions required towards building resilience. The emphasis is on the 
decision-making space and governance including the role of the state, 
private sector and other actors. Successful adaptation requires not only 
identifying adaptation options and assessing their costs and benefits, 
but also exploiting available mechanisms for expanding the adaptive 
capacity of human and natural systems (Klein et  al., 2014). At the 
same time, developing suitable responses to hazards for communities 
and users of climate services is important in ensuring the success of 
adaptation measures. But despite this, knowledge about adaptation 
options, including possible actions that can be implemented to improve 
adaptation and reduce the impacts of climate change hazards, is still 
limited.
8.5.1 Adaptation Options to Climate Change Hazards 
Focusing on Vulnerable Groups
In light of the severe adverse consequences of climate change for the

poorest populations, whose livelihoods are frequently dependent on 
vulnerable ecosystems, it is essential to enhance knowledge about 
sustainable and appropriate adaptation strategies and measures, as 
well as recognise and respond to limits to adaptation as reported in 
AR5 (Somorin, 2010; Noble et  al., 2014; Connolly-Boutin and Smit, 
2016). There is increasing evidence on the adaptation options that 
enhance the ability of different socio-ecological systems to become 
resilient in the long term in ways that do not exacerbate poverty and 
inequality, and on which adaptations may have little or no impact, or 
even adverse effects (maladaptation). Analysis of climate hazards can 
provide an indication of required adaptation strategies, however, most 
important is the focus on exposure and vulnerability. The novelty of 
the AR6 is the assessment of existing response capacities to cope and 
adapt to climate changes and associated hazards. There is increasing

knowledge about the differential adaptation options within and across 
social groups and the influence of (enabling) conditions that enhance 
or limit these options.
From the analysis in the IPCC  AR5, there is high agreement that 
engineered and technological adaptation options are still the most 
common adaptation responses. However, there is increased recognition of the value of ecosystem-based, institutional and social measures, 
including the provision of climate-linked safety nets for those who 
are most vulnerable (IPCC, 2014a). Climate adaptation measures are 
increasingly integrated within wider policy, development strategies 
and spatial planning frameworks. Such integration streamlines the 
adaptation planning and decision-making process and embeds climate-
sensitive thinking in existing and new institutions and organisations 
across scales and levels.
In past decades, a number of categories of adaptation options have

been identified and are discussed in Section 8.5. Adaptation options 
are categorised in various ways, such as in terms of grey and green 
adaptation or hard and soft measures (Depietri et al., 2013; Chambwera 
et  al., 2014; Grimm et  al., 2015). Grey measures refer, for example, 
to technological and engineering solutions to improve adaptation 
of infrastructures or to protect a specific land use or city from 
adverse consequences of climate hazards (OECD, 2018). Accordingly, 
ecosystem-based approaches, including natural infrastructure, can 
provide an effective complement or substitute for traditional built 
(or ‘grey’) infrastructure. For example, watershed restoration can 
protect sources of drinking water and reduce the need for subsequent 
treatment. Green measures often encompass ecosystem-based (or 
nature-based) approaches. These make use of the multiple services 
provided by ecosystems to improve resilience and adaptive capacity

or to reduce risk. Soft adaptation measures include policy, legal, social, 
management and financial measures that can alter human behaviour 
and support adaptive governance, contributing to improved adaptation 
capacity, increased awareness, and change in values and actions on 
climate change issues.
Adaptation actions frequently include deliberate, coordinated, proactive 
policy decisions based on the awareness that conditions have changed 
or will change and that action is required to avert impacts or return to, 
maintain or achieve a desired state (Carter et al., 1994). Governance 
provides an important contextual framing, particularly in contexts 
where it is weak or contested (e.g., some of the Sahel zone). In these 
cases, it can mean that adaptation options stem largely from the local 
level. Adaptation processes can be categorised as individual, collective, 
proactive, reactive, autonomous, coordinated or natural (Chambwera

et al., 2014). Apart from governments, other actors, organisations and 
institutions (including non-state agencies and private industry actors) 
also play an important part in adaptation processes, and consequently 
the discussion of enabling environments for sustainable or successful 
adaptation has to consider these different scales and actors. For 
example, while autonomous adaptations are mainly undertaken by 
private actors, triggered by climate change-induced market or welfare 
changes, planned adaptations can be carried out by both private and 
public actors. Natural adaptations appear within ecosystems as a 
reaction to climate change, as well as other factors, and incorporate 
innumerable possible actions that are context specific, ranging from 
managerial approaches to technological innovations and ecosystem-
based approaches (Huq et  al., 2004). Sanchez et  al. (2017) draws 
attention to preconceived ideas about some adaptation measures

that are either considered good or bad without proper evaluation. It is 
argued that the association ‘hard-bad’ and ‘soft-good’ is not necessarily 
true; the impacts of adaptation can only be established through 
a case-by-case assessment. The decision to select a more or less

8
1228Chapter 8 Poverty, Livelihoods and Sustainable Development
intensive adaptation measure should integrate all approaches, social, 
environmental, technical and economic, in a multi-criteria analysis. This 
analysis should value, inter alia, social and environmental sensitivity, 
benefits and drawbacks or trade-offs with climate, including all the 
adaptation options, among them the ‘no action’ alternative.
Adaptation frequently responds to an observed or anticipated ‘trigger’ 
for response, such as the looming loss of land to sea level rise (Barnett 
et al., 2014). Identifying adaptation needs stemming from climate risks 
and vulnerabilities provides a foundation for selecting a sequence of 
adaptation options that connect through time, a long-term adaptation 
pathway (Wise et al., 2014; Turnheim et al., 2015). National, sectoral 
or local adaptation plans are likely to include a number of measures 
that are implemented jointly from across various categories, including

structural, institutional and social options. While structural or 
physical adaptation encompasses measures for the engineered built 
environment it also can encompass nature-based solutions, which 
include ecosystem-based protection measures, for example to buffer 
risks and hazard exposure to extreme weather events. The category of 
‘soft’ adaptation measures—changes in societal values or practices—
is often linked to issues of education, information and behavioural 
changes to support communities within specific adaptation processes 
to climate change and climate hazards. Institutional adaptation deals 
with adaptation actions and measures introduced through new legal 
frameworks, laws and regulations for new institutions or policies for 
risk reduction and adaptation. This category can also encompass the 
development of new organisations that have a mandate to support 
adaptation (Noble et al., 2014). The appropriateness and accessibility

of adaptation options under these categories for supporting the poor 
and most vulnerable groups differs. In many cases large-scale structural 
measures are not affordable for many poor communities. Despite this 
important potential of Indigenous knowledge for disaster risk reduction 
of communities, it is often shunned by practitioners (Dube and Munsaka, 
2018). It is further argued by practitioners that Indigenous knowledge 
lacks documentation, it is not found in all generational classes, it is 
contextualised to particular communities and the knowledge cannot be 
scientifically validated. However, there is also evidence that both local 
communities and disaster risk reduction practitioners can benefit from 
the Indigenous knowledge of communities (Dube and Munsaka, 2018).
In practice, adaptation refers to initiatives such as a policy, plan, project 
or decision that are designed to change and/or respond to something in

the context of existing risks and hazards. For example, a farmer might 
adapt to drought by deciding to harvest their crop earlier; a municipality 
can decide to build a sea wall to adapt to increased flood risk.
The increasing complexity of adaptation practice means that institutional 
learning is an important component of effective adaptation (Noble et al., 
2014). It is paramount that approaches to selecting adaptation options 
continue to emphasise incremental change to reduce impacts while 
achieving co-benefits. There is increasing evidence that transformative 
changes may be necessary in order to prepare for climate change impacts 
and adaptation options in the context of climate hazards (Noble et al., 
2014). Transformation for some actors at some levels may equate with 
incremental change and transitions for other actors and scales. While 
attention to flexibility and safety margins is becoming more common

in selecting adaptation options, many see the need for more urgent and transformative changes in our perception and paradigms about the 
nature of climate change, adaptation and their relationship to other 
natural and human systems.
In this context, there are many potential adaptation options available 
for a marginal change of existing agricultural and other livelihood 
systems, often variations of existing climate risk management. 
According to Howden et al. (2007), implementation of these options 
is likely to have substantial benefits under moderate climate change 
for some existing cropping systems. Apparently, there are limits to 
their effectiveness under more severe climate changes. Hence, more 
systemic changes in resource allocation need to be considered, such as 
targeted diversification of production systems and livelihoods. Howden 
et al. (2007) further argue that achieving increased adaptation action 
will necessitate integration of climate change-related issues with

other risk factors, which implies integrating non-climatic factors, such 
as climate variability and market risk, and with other policy domains, 
such as sustainable development. An increasing number of research 
programmes seek to support adaptation to climate change through 
the engagement of large-scale transdisciplinary networks that span 
countries and continents (Cundill et al., 2019).
Based on analysis of different adaptation options, there is high 
agreement that the many barriers to effective adaptation will require 
a comprehensive and dynamic policy approach covering a range of 
geographical scales and multiple actors across scales, taking into 
consideration both climatic and non-climatic stress factors (Eriksen 
et al., 2015). For instance, from the agricultural perspective, this could 
imply the understanding by farmers of change in risk profiles to the 
establishment of efficient markets that facilitate response strategies.

It is also important to note that science, too, has to adapt employing a 
range of approaches, based on the fact that multidisciplinary problems 
require multidisciplinary solutions. Towards enhancing resilience, a 
focus on integrated rather than disciplinary science alone could be of 
utmost importance as well as strengthening of the interface with key 
stakeholders, ranging from decision makers, practitioners, policymakers 
and scientists.
8.5.2 Enabling Environments for Adaptation in Different 
Socioeconomic Contexts
8.5.2.1 Factors that Support Enabling Environments for 
This section assesses the literature on components of the enabling 
environment for adaptation. The point of departure considers findings 
in both the SR1.5°C report, which notes that adaptation becomes 
increasingly difficult (and expensive) at temperatures that are more than 
1.5°C warmer (IPCC, 2018a). In addition, (IPCC, 2014a) underscores

that there is no one-size-fits-all approach to adaptation for all contexts, 
and that mitigation and adaptation must be pursued in tandem.
Climate change affects people inequitably, and everyone does not 
contribute equally to climate change. A range of economic and non-
economic impacts can be experienced. This has led some researchers to 
call for a more central role for rights-based approaches to adaptation

8
1229Poverty, Livelihoods and Sustainable Development  Chapter 8
to help secure space for those marginalised from adaptation decision 
making and to prioritise access to resources and information for those 
most vulnerable to, or affected by, the social, cultural or economic 
consequences of climate change (Bee et  al., 2013; Da Costa, 2014; 
Toussaint and Martinez Blanco, 2020; Box 8.7; Section 5.12). In terms 
of international law, the human rights obligations of states have been 
subject to multiple recommendations relating to climate change by United Nations treaty bodies in the reporting period. More broadly, 
rights-based approaches rely on the normative framework of human 
rights, requiring adaptation to be non-discriminatory, participatory, 
transparent and accountable in both formal (e.g., legal and regulatory) 
and informal (e.g., social or cultural norms) settings and at international, 
national and sub-national scales (Ensor et  al., 2015; Arts, 2017).

Sovacool et al. (2015) note that unless critical competing interests are 
Box 8.7 | Addressing inequalities in national capabilities: common but differentiated 
responsibilities and respective capabilities relating to adaptation and the Paris Agreement
Common but differentiated responsibilities and respective capabilities (CBDR-RC) is a key principle within the United Nations Framework 
Convention on Climate Change (UNFCCC), and attempts to acknowledge countries’ diverse development situations. The Convention and 
its Kyoto Protocol operationalised the principle by committing developed (Annex I) countries to absolute emission reduction or limitation 
targets and exempting developing countries from any binding reductions in emissions (Huggins and Karim, 2016; Pauw et al., 2019). In 
contrast, the Paris Agreement distinguishes between ‘developed’ and ‘developing’ countries instead of Annex I and non-Annex I countries

and acknowledges significant asymmetries and inequalities, not only between developed and developing countries, but also between 
developed and developing countries themselves, both in terms of vulnerability to climate change impacts and capacity to mitigate the 
problems. The literature contains extensive analyses of CBDR-RC in relation to equity in mitigation efforts in the post-2020 regime (e.g., 
Michaelowa and Michaelowa, 2015; du Pont et al., 2017; Liu et al., 2017; Holz et al., 2018; Sælen et al., 2019), but little in relation to 
adaptation, particularly relating to how it plays out in the Paris Agreement.
The somewhat static interpretation of CBDR-RC prior to the Paris Conference of the Parties was overcome through the introduction 
of a qualification to the CBDR-RC principle: the phrase ‘in the light of different national circumstances’. Without changing the original

principle, the qualifier adds a dynamic element (Rajamani, 2016). Common but differentiated responsibilities and respective capabilities 
of parties are therefore recognised not to be ‘tied to the annexes’, but instead evolve alongside national circumstances (Maljean-Dubois, 
2016; Voigt and Ferreira, 2016 p.301). The Paris Agreement also recognises context, considering differentiation in relation to each of the 
Durban pillars: mitigation, adaptation, finance, technology, capacity building and transparency (Rajamani and Guérin, 2017).
Article 7 of the Paris Agreement acknowledges adaptation as a ‘global challenge faced by all’, recognising, for the first time, a global 
aspiration of ‘enhancing adaptive capacity, strengthening resilience and reducing vulnerability to climate change’. It calls for a balance 
between mitigation and adaptation funding and emphasises the need to provide developing country parties, especially the most

vulnerable, with ‘[c]ontinuous and enhanced international support’ for adaptation. The basis for differentiation under Article 7 therefore 
relies mostly on diverse national circumstances, capabilities and vulnerabilities. LDCs, as well as SIDS, are assumed by the literature, to be 
part of this category (Maljean-Dubois, 2016).
The literature offers two main perspectives when evaluating the effectiveness of these provisions on adaptation in the context of the 
post-Paris climate change regime. One argument follows that the Paris Agreement gives priority attention to the most vulnerable parties 
and, unlike previous international agreements in the climate change regime, places adaptation on equal footing to mitigation (Magnan 
and Ribera, 2016; Pérez and Kallhauge, 2017; Morgan, 2018). Article 7 is interpreted here as a breakthrough, containing unprecedented

provisions that give adaptation prominence and which elevate the importance of undertaking adequate action to cope with current and 
future climate change impacts. A second view argues that the Article 7 marks little departure from previous efforts to support adaptation 
efforts in developing countries (Doelle, 2016) or that it could have included stronger provisions, such as a quantitative goal with respect 
to adaptation needs and costs (Bodansky, 2016).
The literature nevertheless shows high agreement that other parts of the Paris Agreement do contain consequential provisions on 
adaptation and the operationalisation of the CBDR-RC principle. Those provisions covering financial support are arguably the most 
pertinent, as they replace the dichotomy between developing countries and developed countries with a trichotomy which also includes 
‘other Parties’ (Maljean-Dubois, 2016). While provision of support from developed parties continues to be mandatory, these ‘other parties’,

apparently developing country parties, are ‘encouraged to provide or continue to provide such support voluntarily’ (Article 9.2). Parties 
themselves determine whether they belong to this category. So far, several developing countries have made contributions to the Green 
Climate Fund, ranging from Indonesia and Mexico to Mongolia and Panama (Green Climate Fund, 2017). Expanding the donor base to 
these ‘other parties’ and breaking down the wall between donor and recipient countries marks a departure from previous practice, under 
which developing countries had no formal role in climate finance and support (Bodansky, 2016; Voigt and Ferreira, 2016).

8
1230Chapter 8 Poverty, Livelihoods and Sustainable Development
addressed during planning, adaptations may fail to achieve the desired 
outcomes. This is increasingly seen at a political level within efforts to 
implement the Paris Agreement, in relation to the principle of ‘common 
but differentiated responsibilities and respective capacities’ (CBDR-RC) 
(Box 8.7).
The scale of analysis, baseline conditions prior to adaptation and scale 
of action matter too when assessing the key components of an enabling 
environment for adaptation. At a national scale, it is well established 
that low-income countries are less well positioned to manage climate 
change impacts, being variously attributed to a lack of institutional, 
economic or financial capacity to adapt effectively (Tol and Yohe, 2007; 
Barr et al., 2010). It can be particularly difficult to adapt to drought, 
for example, when it occurs in the pre-conditions of poor water

supplies and sanitation (see Box 8.5; Section 8.3.2), and in a context of 
corruption, governance failure and a lack of accountability. Adaptation 
productivity in higher-income countries is further supported by better 
infrastructure and stronger institutions—low adaptation efficiency is 
linked to lower government spending, higher inequalities in income 
distribution and poor governance (Fankhauser and McDermott, 2014). 
At smaller scales, even within a single socioeconomic setting, different 
groups require different kinds of adaptation support and exhibit 
different vulnerabilities to climate change impacts. Huynh and Stringer 
(2018) found that households vulnerable to climate change impacts 
linked to sea level rise and flooding in Da Nang City and Ngu Hanh 
Son district, Vietnam, had limited access to human, natural, physical, 
financial and social assets, and lacked a diversified livelihood portfolio. 
An enabling environment for household-level adaptation would need

to address these factors in this context. However, the same authors 
found that at district scale, different challenges persisted, including 
obstacles to multidirectional flows of climate information, poor 
vertical interplay both upward and downward, and a lack of citizen 
participation in the governance of climate change.
Acknowledging that context and scale matter, it is nevertheless possible 
to set out the core components of a generic enabling environment 
(Figure  8.12), linking them to the literature on climate change and 
recognising how they can support adaptation in different socioeconomic 
and environmental settings in which different emphases are required. 
This broad set of enablers requires different emphases according to the 
specific context, yet the interdependence between them is universally 
applicable.
The specific political economy of each country and its underpinning 
philosophies shape the national political context in which public policy

supporting adaptation is developed and implemented. It further shapes 
the context for private adaptation. Public policy targeting climate 
change seeks to address market failures, amend policy distortions 
and offer incentives for private adaptation, as well as provide climate-
resilient public goods, climate services and safety nets for the poor and 
vulnerable (Fankhauser, 2017). In some countries that have a more 
stable institutional context, such policies are more straightforward to 
develop and implement; while in countries with weaker institutions 
(e.g., those emerging from conflict), a larger role may be needed for 
regional economic commissions and transnational networks to support 
the governance of ‘borderless climate risks’ (Benzie and Persson, 2019), 
particularly where these countries also are most vulnerable to climate change (see also Figure 8.6). To support enabling conditions in highly 
vulnerable countries that are also characterised by state fragility (see

Figure 8.8), funding and projects designed to support adaptation may 
need to be modified to effectively promote regional cooperation and 
transboundary adaptation. Nevertheless, such interventions can also 
reinforce particularly powerful agendas and fail to assist and empower 
those with the greatest need to adapt (Biermann et al., 2010; Burch 
et al., 2019) neglecting community voices and sovereignty (Schlosberg 
and Collins, 2014). It is therefore important that the relevance of people 
and community empowerment to effectively achieve vulnerability 
reduction and climate change adaptation is recognised.
It is also insufficient to consider countries as stand-alone entities, due 
to links such as those provided by international trade. Taking Europe as 
an example, the continent has strong links to major trade partners such 
as India, Indonesia, Nigeria and Vietnam, so failure to assist adaptation 
in other locations opens up important vulnerabilities through supply

chains (Lung et al., 2017). Policies seeking to protect national interests 
alone (e.g., in terms of food security) are seen as causes of negative 
impacts at a global scale (Puma et al., 2015; Challinor et al., 2017), 
with those nations and individuals least able to adapt to evolving 
climate changes experiencing exacerbation of existing imbalances 
(Elbehri et al., 2015). LDCs are projected to suffer greater import losses 
in more connected networks (Puma et al., 2015). In the food sector, 
poorer net food buyers are anticipated to experience the worst impacts 
of climate change (Gitz et al., 2015).
Behind each policy are decisions about the magnitude of financial 
resource investments in specific adaptation actions, and their 
allocation between different sectors and groups in society, both 
spatially and temporally. The IPCC has estimated that limiting the 
rise in global average surface temperatures to 1.5°C would require

between USD 1.6 trillion to USD 3.8 trillion of annual investment in 
supply-side energy systems (those that generate energy) between 
2016 and 2050 (IPCC, 2018b). Resource allocations, however, are 
shaped by perceptions of the risks of climate change and the urgency 
of actions, as well as other motivational factors such as descriptive 
norms and perceived self-efficacy (van Valkengoed and Steg, 2019) 
and the underlying approaches taken to valuing human well-being 
(e.g., see work from Bhutan on Gross National Happiness and climate 
change actions (Kamei et al., 2021)).
An increase in finance mobilised, however, does not automatically 
equate to adaptation interventions on the ground, nor does it 
guarantee the effectiveness of those adaptations deployed (Berrang-
Ford et al., 2021). Unintended negative consequences may arise due 
to lack of understanding of the drivers of vulnerability (such as gender 
inequality or inequitable access to natural resources), non-involvement

of marginalised local groups, retrofitting adaptation into existing 
development agendas, and insufficiently defining adaptation success 
(Eriksen et al., 2021). A 2017 study estimated that less than 10% of 
climate finance committed from international, regional and national 
climate funds to developing countries between 2003 and 2016 went to 
locally focused projects, suggesting a need to rethink approaches if the 
most affected groups are to build sufficient resilience to the impacts of 
climate change (Soanes et al., 2017).

8
1231Poverty, Livelihoods and Sustainable Development  Chapter 8
Risk perceptions
Economic resources &
financial investmentsNatural capital
Physical capital Climate
service sPolitical economy
Political and
institutional context
Legal and
regulatory frameworksSocio-cultural
factors
ClClCllCimimimimmaat
servic e
National
RegionalLocal
Global
Phy
Human capital
Core components of an enabling environment for adaptation to climate change
Climate hazard context
WaterLiving organisms
AirSoil
Heath
Infrastructur eIndigenous
knowledge 
Education
N
onn
o
onn
e
s
ess
te
e
P
 ee
s
ss
Figure 8.12 |  Core components of the enabling environment for adaptation to climate change (key interactions are illustrated but there are overlaps, interactions 
and feedbacks both within and between each item; and different countries have different capacities and starting points in addressing these enablers and the interlinkages between 
them).

The literature shows with high confidence that the poorest groups in 
society often lose out, and require greater planned adaptation support, 
having less capacity to adapt than better off groups with easy access 
to assets (Barbier and Hochard, 2018; Ziervogel, 2019b; Box  8.5). 
Developing countries such as Burkina Faso, Mali and Zambia are not 
only among the most vulnerable to climate change, they are also the 
least able to mobilise the finance needed to adapt to its impacts (ND-
GAIN, 2019). Women and girls are often most heavily burdened. When 
building adaptive capacity, these groups can require different support 
such that their knowledge, capacities and skills can be harnessed, 
in such a way that does not feminise responsibility and add to their 
burdens (Clissold et al., 2020; McNamara et al., 2021a).
There is broad support for the notion, enshrined in the Paris Agreement, 
that adaptation finance flowing to developing countries of the Global

South should primarily benefit the most climate-vulnerable among them 
due to their limited technical capacity and financial capabilities, yet 
such countries are often insufficiently considered in funding decisions. There are nevertheless concerns regarding institutional fit: that 
foreign funding regimes may not map onto more recently developed 
administrative traditions, leading to dominance of governance models 
emanating from donors (Vink and Schouten, 2018). Research has 
found multilateral donors do not prioritise vulnerable developing 
countries at the project selection stage and they have received smaller 
allocations of adaptation finance from bilateral donors than less 
vulnerable countries (Saunders, 2019), leaving the poor vulnerable 
to climate impacts. The lack of climate finance flowing to LDCs and 
SIDs (currently 14% and 2% of the total, respectively) is compounded 
by access issues due to the inability of domestic institutions to meet

specific fiduciary standards and other access requirements, insufficient 
human resource support and the inflexibility of current approaches, 
which are biased in favour of governments and against non-traditional 
actors, such as local enterprise and grassroots organisations (Shakya 
et  al., 2021). Further, vulnerable developing countries shoulder 
additional financial burden, embodied in higher interest payments to 
service public and private debt, due to the increased cost of capital

8
1232Chapter 8 Poverty, Livelihoods and Sustainable Development
brought about by greater exposure to climate risks (Buhr et al., 2018). 
This has been further exacerbated by the recession and debt distress 
accompanying the COVID-19 pandemic (Kose et  al., 2021). A range 
of reforms, including comprehensive debt relief by public creditors, 
green recovery bonds, debt-for-climate swaps and new SDG-aligned 
debt instruments may address unsustainable debt burdens, freeing up 
investment in climate adaptation and a green economic recovery (Volz 
et al., 2020; see Section 8.6.3.1).
Greater investment is also needed in the developed countries of 
the Global North. For example, the 2018 forest fires in Sweden, the 
2019–2020 Australian bushfire season and the 2020 forest fire season 
along the US West Coast were unusually long and severe, resulting in 
unprecedented damage to natural habitats and human livelihoods and, 
relatedly, significant economic cost, particularly given interlinkages

with other stressors such as COVID-19. While a range of drivers 
underpin annual fire seasons, including greater water withdrawal and 
years of fire suppression, early research indicates that climate change 
increases their likelihood due to long-term warming trends (van 
Oldenborgh et al., 2021a).
However, investing in poverty reduction does not necessarily lead 
to climate change adaptation and where adaptation does result, it 
does not always reduce vulnerability of the most marginalised, as 
documented in case studies from northeast Brazil (Nelson et al., 2016). 
Poverty also affects private adaptation options. For example, research 
from Portugal highlights the importance of private financial assets in 
helping older adults to adapt to extreme temperatures (Nunes, 2018).
Policies and investments that are adopted are embedded within 
the relevant legal and regulatory frameworks, which extend beyond 
national jurisdictions upward to the regional scale (such as the

Southern Africa Development Community’s Southern Africa Regional 
Framework of Climate Change Programmes, 2010) and international 
scale, for example, UNFCCC, the 2015 Paris Agreement, the Sendai 
Framework for Disaster Risk Reduction, the New Urban Agenda and the 
SDGs. Legal and regulatory concerns also extend downward to shape 
local- and city-scale adaptation efforts (e.g., Sao Paulo’s municipal 
policy and new master plan). Nevertheless, only a minority of countries 
have dedicated legal frameworks supporting adaptation (Lesnikowski 
et al., 2017) and these often lack in both precision and obligation—
largely because adaptation is a contested global public good but 
also because adaptation is commonly bundled in with mitigation 
commitments (Hall and Persson, 2018). Coherence, horizontally and 
vertically in both policy and law is often lacking. At the same time, 
bottom-up, private, autonomous adaptation efforts are being better

tracked, with different actors motivated by growing experiences of 
local climate change impacts (Berrang-Ford et  al., 2014). While the 
emergent polycentricity of adaptation governance is beginning to 
take shape, wherein both state and non-state actors share a common 
adaptation goal and interact coherently, yet often independently, to 
advance progress towards it (Morrison et al., 2019), understandings 
of how various centres of decision making with different degrees of 
autonomy support an enabling environment for adaptation, remain 
at a nascent stage. Multiple scales and forms of adaptation occur, 
with attributes such as self-organisation, appreciation of site-specific 
conditions, and the need for learning and experimentation, alongside building of trust, increasingly shown to be vital (Dorsch and Flachsland, 
2017). Literature indicates that professional and learning networks are 
important groups supporting adaptation in cities and can help harness

resources (Woodruff, 2018); while the research of (Hauge et al., 2019) 
in Norway underscores the importance of working across multiple 
disciplines and the inclusion of actors from different levels of authority 
in multi-level municipal networks. They found that these factors can 
help to identify specific adaptation actions as well support knowledge 
sharing within participating organisations, which in turn helps garner 
commitment to adaptation and its implementation. They also found 
that it is important to involve local leaders in polycentric adaptation 
networks.
Among the many institutions, actors and roles associated with 
successful adaptation, two play an increasingly important role: local 
governments and the private sector (Noble et al., 2014). These groups 
often define the flows of information and finance from the top down, 
as well as supporting the scaling up of community and household 
adaptation. In some countries, for example, in South America

(Argentina, Brazil, Paraguay) vocational agricultural schools, often in 
remote rural locations, play a key part in knowledge-sharing activities 
that support adaptation. Similar valuable contributions are made 
by universities through their outreach activities, particularly those 
offering programmes in environmental and agricultural fields. Many 
actors face a lack of resources and capacity, particularly at the local 
level. Local institutions, including local governments, NGOs and civil 
society organisations, are hampered by ongoing challenges in gaining 
support from higher governance levels—from national government or 
the international community—particularly in developing countries. At 
the same time, private sector actors, from individual farmers and small/
medium enterprises (SMEs) as well as large multinational businesses, 
will seek to protect and enhance their production systems, supply 
chains and markets by pursuing adaptation-related opportunities. Yet,

while these goals will help expand adaptation activities, they may 
not align with government or community objectives and priorities 
without coordination and incentives, and in the process, can reinforce 
existing capacities, inequalities and power relations (Sovacool et al., 
2015). Similarly, an enabling environment for businesses’ adaptation 
is highly differentiated and often requires structural deficits (such as 
limited market access, finance and transport and communications 
infrastructure) to be tackled (Gannon et al., 2020).
The challenges of climate change have driven governments around the 
world to emphasise climate services as a route to enhance decision 
making and reduce climate-related risks, as well as inform adaptation, 
supporting calls for the right to information (Tall and Njinga, 2013). 
While there have been some efforts to evaluate the economic impact of 
climate services alongside other impacts (e.g, Tall et al., 2018), little is

known about the institutional contexts in which investments in climate 
services have taken place, nor those groups that are most vulnerable or 
marginalised in relation to specific climate risks. Vincent et al. (2017) 
offer preliminary insights from Malawi, identifying that barriers to 
improved integration of climate services in national policy planning 
include factors relating to spatial and temporal scale, accessibility 
and timing of information provision, credibility and mismatches in 
time frames between planning cycles and climate projections. An 
understanding of the factors that enable climate service investment is

8
1233Poverty, Livelihoods and Sustainable Development  Chapter 8
important for the development of climate services at local, national and 
international levels (Vaughan et al., 2017) but this area of literature is 
not yet well developed.
Overall, adaptation entails financial (and non-financial) costs not just 
in implementing adaptation actions, but also in designing, facilitating 
and preparing for actions—costs to create and maintain an enabling 
environment (see also Section  8.2.2.3; Cross-Chapter Box  LOSS in 
Chapter 17). Financial and economic investments target the whole 
range of other types of asset (natural capital, physical capital, human 
capital, social capital). AR5 reports that aggregate economic losses 
accelerate with increasing temperatures (IPCC, 2014a). Costs may 
be borne when gaining information (e.g., investments in climate 
services), while adjustment costs are incurred as adaptations take 
place. Nevertheless, to enable adaptation, investment is needed

in various natural, human, physical and social assets, as considered 
below. The importance of investment in each of these different types 
of asset varies according to the scale and livelihood system in need of 
adaptation and the ways in which livelihood resilience is framed and 
power is distributed, within each specific setting (Carr, 2020).
8.5.2.2 Natural Capital
It is well established that climate change compounds the impacts of 
pressures that humans place on the environment (high confidence) 
and that environmental degradation can undermine options for 
adaptation and an enabling environment, with poor and natural 
resource-dependent groups most acutely affected (see e.g., Cross-
Chapter Paper 3 for insights from deserts and semiarid areas). 
Sustainable management of natural capital contributes to building 
resilience and the natural ability of ecosystems to adapt to climate 
change (IPCC, 2014a; see also IPCC SROCC, Section  5.3.2, Bindoff

et al., 2019). Some systems like mangroves (found in 123 countries, 
many of which are in the Developing World) offer a broad range of 
vital ecosystem services (Hamza et  al., 2020). Mangroves provide 
regulating services by acting as a natural defence against sea level rise 
and storm surges; and by sequestering carbon in both the trees and 
sediments they capture. Provisioning services (e.g., fish, crabs, timber 
and fuelwood) from mangroves support livelihoods and livelihood 
adaptation options, especially for those with few other livelihood 
opportunities, while these systems also provide important habitat 
(breeding, spawning and nursery grounds for fish) and biodiversity, 
and offer cultural services in the forms of education, recreation and 
spiritual benefits (Quinn et al., 2017). As the frequency of events such 
as hurricanes, storms and typhoons rises with climate change, natural 
capital assets like mangroves become increasingly important in

protecting coastlines and supporting adaptation. While not reducing 
the hazard itself, the mangroves reduce exposure and, in some cases, 
also vulnerability. The literature shows with high confidence that 
environmental assets support both climate change mitigation (at a 
large scale) and adaptation (at a smaller scale), particularly for the 
poorest groups in society, who directly depend upon natural capital 
for their subsistence (e.g., Angelsen et al., 2014). In turn, the legal and 
regulatory context and institutional set up determines who has access 
rights to different aspects of the natural resource base. This shows 
how different aspects of the enabling environment work in tandem to 
constitute one another.In a market economy, human activities tend to exacerbate degradation 
of natural capital, despite its role in buffering climate change impacts, 
supporting mitigation and providing adaptation options. Economic

agents base their decisions on market prices, even though market prices 
do not incorporate the costs of deteriorating natural capital because 
of externalities and other market failures, that is environmental 
degradation is not internalised (Bowen et al., 2012). At the same time, 
expanding populations, capitalism and consumption choices affect the 
condition of natural capital, alongside short-termism stemming from 
poverty, linked to the need for survival. All these factors therefore 
interact, with the aggregate effect of worsening the impacts of climate 
change, while also undermining future adaptation options, particularly 
for the poor. Adaptation policies should, but do not always, compensate 
for the prevalent market failures. For example, in Melanesia, sea 
walls have been built out of coral by local people in an attempt to 
reduce the impacts of rising sea levels, leading to outright destruction 
of some of the world’s most productive and biodiverse coral reefs

(Martin and Watson, 2016). Similarly, in the Congo Basin, farmers are 
adapting to increasingly variable rainfall by expanding their cropping 
activities into forested areas, releasing carbon into the atmosphere 
through forest clearance activities and threatening biodiversity. 
Agricultural land is also being degraded globally (see IPCC, 2019a), 
and this too closes down adaptation and livelihood options for the 
poorest, natural resource-dependent populations, while jeopardising 
food security, biodiversity and human health at wider scales. An 
enabling environment for adaptation therefore demands investment in 
sustaining natural capital at multiple scales, internalising the costs of 
degradation, as well as establishing the necessary legal and regulatory 
frameworks (and associated enforcement) to reduce its degradation 
(IPBES, 2018).
The literature increasingly shows that approaches such as nature-
based solutions (NBS) and ecosystem-based adaptation (see Chapters

2; 6) can offer value for money in tackling climate change from both a 
mitigation and adaptation standpoint (Seddon et al., 2020). According 
to the Global Commission on Adaptation, a global investment of 
USD 1.8 trillion between 2020 and 2030 into adaptation measures such 
as early warning systems, climate-resilient infrastructure, improved 
dryland agriculture, mangrove protection, and resilient water resources 
can yield USD 7.1 trillion in total net benefits (Global Commission on 
Adaptation, 2019). NBS operate by harnessing natural processes, 
sometimes in combination with technological or engineered solutions. 
Examples encompass green public spaces and parks (Sahakian and 
Anantharaman, 2020), green infrastructure, such as urban forests and 
street trees (Richards and Edwards, 2017), which create shade and 
reduce urban heat island effects whereby urban areas are warmer than 
their surroundings (Depietri et al., 2013), and support human health

and well-being by keeping people in cities more closely linked with 
nature (Gulsrud et al., 2018). NBS also encompasses blue infrastructure 
including constructed wetlands, bioswales, rain gardens and so forth, 
which can reduce flood risks (Haase, 2015). While the literature is 
generally positive about the ability of NBS to support climate risk 
reduction and deliver multiple other benefits (Connop et al., 2016), such 
as green job opportunities, improved provision of recreational space, 
cleaner air, habitat provision and increased property values (Emmanuel 
and Loconsole, 2015), more research is required to specifically assess 
and evaluate the conditions and contexts in which these kinds of

8
1234Chapter 8 Poverty, Livelihoods and Sustainable Development
potential benefits are realised and how they can be mainstreamed into 
policy (Frantzeskaki et al., 2019). Similarly, there is limited evidence 
on unintended consequences (e.g., methane production, creation of 
habitat for disease vectors, increased human–wildlife conflict) and 
how these can be avoided (Wolch et al., 2014).
8.5.2.3 Human Capital
Successful adaptation requires support to be directed towards human 
capital and socioeconomic capabilities and competences, in terms of 
education, knowledge, experience, health and well-being, and migration, 
enabling people to contribute meaningfully towards development 
(Bowen et  al., 2012). At the same time, strong human capital and 
investment in actions that build human capacities to deal with climate 
change, can further enhance adaptation activities linked to other capitals, 
and contribute positively to overall disaster risk reduction.

Analyses of educational attainment distributions with datasets reaching 
back as far as 1970 show that improving educational attainment in 
people of working age has been the most consistent and significant 
driver of economic growth globally (Lutz et  al., 2008), showing the 
importance of the right to education. Education has further supported 
sustainable development by fostering empowerment, yielding access 
to information (including on climate change) and has clear links to 
other aspects of human capital, including health and mortality (Samir 
and Lutz, 2017). There is medium evidence and high agreement that 
education reduces vulnerability and enhances adaptive capacity 
(Frankenberg et al., 2013; Sharma et al., 2013), with high agreement 
that climate change impacts can have negative effects on existing 
levels of human capital, with some development pathways affected 
more than others (Samir and Lutz, 2017). Education can help to

shape people’s risk perception and assessment, as well as affecting 
knowledge sharing and the development of problem-solving abilities 
(Striessnig et al., 2013).
At the same time, IKLK can inform adaptation actions (Apgar et al., 
2018), but is poorly integrated into formal educational systems and, in 
some cases, is insufficient to adapt to new hazards that are emerging 
as a consequence of climate change. Education further feeds into 
livelihood options, with close relationships between people’s earning 
capacities, the livelihood choices they can make and their levels of 
financial capital. It also supports food security (Lutz et  al., 2004). 
There is medium evidence that climate change can undermine human 
capital and education. For example, studies have shown that higher 
temperatures reduce exam educational performance (Park, 2020), 
while extreme weather events such as snowstorms disrupt learning, 
yielding long-lasting and multidimensional effects (Maccini and Yang,

2009; Cho, 2017; Graff Zivin et al., 2018).
As well as studies examining formal education, a large body of re-
search has focused on social learning and its role in building adaptive 
capacity through joint knowledge production and reflexivity. Fore-
grounding the need for continuous changes in response to emerging 
conditions, this literature identifies the potential of shared learning for 
co-constructing policy and practice responses to complex, multi-stake-
holder environmental problems, and highlights both the necessity and 
challenge of including non-dominant values, knowledge and expertise in adaptation decision making, considering the role of power dynamics 
therein (Collins and Ison, 2009; Ensor and Harvey, 2015; Phuong et al., 
2017; Apgar et al., 2018; Brymer et al., 2018; Fisher and Dodman, 2019). 
A growing body of evidence also links to organisational learning and 
adaptation. Organisations’ adaptive behaviours, like those of house-

holds and individuals, do not operate in a vacuum, with organisations’ 
behaviours shaped by policy and market conditions amongst other 
factors. Mudombi et al. (2017) highlight further barriers in their study 
in South Africa, linked to inadequate resourcing, political interference, 
governance shortcomings and knowledge/expertise gaps within or -
ganisations, alongside short time frames for implementing projects.
Adaptations that support human health and well-being require 
investments in physical assets and infrastructure linked to water and 
sanitation (see Chapter 4), particularly in rapidly urbanising areas in 
the Global South, alongside specific pro-poor investment strategies 
given disproportionate climate change impacts on women (see Cross-
Chapter Box GENDER in Chapter 18), other marginalised groups and 
low-income households who lack access to healthcare. Climate change 
facilitates the spread of vector-borne diseases such as malaria, as well

as illnesses such as meningitis (Rocklöv and Dubrow, 2020). Impacts 
on health are also experienced, through food insecurity resulting 
from climate change, including malnutrition, as well as through loss 
of livelihoods, making it more difficult to afford and to access health 
services. Health aspects are considered in-depth in Chapter 7, but we 
underscore the importance of a rights-based approach to adaptation 
in supporting the right to health and food in the context of inequality.
A key dimension of human capital is local understanding of climate risk, 
which includes knowledge systems outside Western scientific approach-
es. For millennia, local communities have relied heavily upon culturally 
accumulated Indigenous knowledge, participating in landscapes as 
stewards of their environment, engaged in profoundly detailed liveli-
hood strategies that deal with natural hazards (Ajayi and Mafongoya, 
2017). Indigenous knowledge systems are embedded in culture, and are

passed from generation to generation in various ways: livelihoods, tradi-
tions, spiritual practices and oral tradition, cultural identity and historical 
memory. Indigenous knowledge is known or learnt from experience, or 
acquired through observation and practice, and handed down from gen-
eration to generation. It is acknowledged that Indigenous communities, 
particularly those in hazard-prone areas, have developed a profound un-
derstanding and knowledge of disaster prevention and mitigation, early 
warning, preparedness and response, and post-disaster recovery. Indige-
nous knowledge systems, themselves, are an indispensable dimension 
of capacity for adaptation, and where threatened represent a major risk 
to Indigenous communities. While still robust among Indigenous Peoples 
in many parts of Africa, Asia and Latin America, Indigenous knowledge 
is not well reflected or incorporated in assessments such as this, and

stands in danger of being lost as its custodians are passing away.
Indigenous knowledge about natural hazards enables communities 
at risk to take steps to reduce climate risk. Indigenous knowledge 
systems are locally indispensable resources for adaptation to climate 
change, yet are often misunderstood and undervalued. Generally, 
Indigenous Peoples and other local groups hold relevant local-
scale knowledge about environmental change, the impacts of those 
changes on ecosystems and livelihoods at local scales, and possible

8
1235Poverty, Livelihoods and Sustainable Development  Chapter 8
locally effective adaptive responses. However, it is important that IKLK 
is situated within knowledge from other scales in order to assess its 
broader relevance and applicability (Ahlborg and Nightingale, 2012). 
Some authors suggest including Indigenous knowledge in the IPCC 
assessment process should be of high priority, as it is becoming 
increasingly relevant for climate services (high confidence) (Strauss and 
Orlove, 2003; Crate and Nuttall, 2009; Crate, 2011). Their knowledge 
can draw attention to climate baselines and change, and identify 
adaptation priorities, such as plant and animal species that should 
be protected given local contextual environmental considerations. 
For example, using Indigenous knowledge in weather and climate 
prediction, local communities in different parts of Tanzania have been 
coping with, and adapting to, increased climate variability normally

manifested in the form of increased frequency and magnitude of 
various exigencies, including droughts and floods, and outbreak of 
pests and diseases (Kijazi et al., 2013). Prediction of impending hazards 
has been an integral part of Indigenous Peoples’ adaptation strategies. 
Various environmental and astronomical indicators are used to predict 
rainfall, including plant phenology, behaviour and movement of birds, 
animal and insects, in many parts of Tanzania (Kijazi et al., 2013).
There are efforts in developing adaptation plans that utilise local 
knowledge. Local knowledge-based adaptation is focused primarily on 
the use of traditional knowledge to increase adaptive capacity at the 
community level and less on integration (Mimura et al., 2014). Hence, 
there is need to increase effectiveness of policy processes that work 
towards integration of local and scientific knowledge (Nakashima 
et al., 2013; IPCC, 2014a).
8.5.2.4 Physical Capital

Ensuring sufficient investment in physical capital is vital to support 
development pathways at the national level, but for the poorest and 
most marginalised in society, physical capital represents an invaluable 
source of adaptation options (Hallegatte et al., 2019). Physical capital 
constitutes assets such as land, roads and other infrastructure (e.g., 
water supplies, electricity, mobile phone connectivity), housing and 
other buildings, as well as the materials and tools needed to make 
a living (e.g., farming, forestry and fishing equipment, transportation 
vehicles, technology). It can also help to foster a sense of place, and 
can support well-being. Climate change impacts on physical capital 
are often widespread, as well as economically and emotionally costly, 
particularly when communities are afflicted by hardship (inadequate 
levels of sustainable human development through access to essential 
public goods and services and access to income opportunities) (Abbott

and Pollard, 2004).
Given the massive scale of investments required to build and sustain 
physical capital at the state level, it is imperative to ensure physical 
capital decisions consider climate resilience; not least because 
retrofitting and replacing are both highly costly. The World Bank 
estimates that adapting over the period 2010–2050 to a world that 
is 2°C warmer by 2050 will cost USD  70  billion to USD  100  billion 
per annum, with the infrastructure sector accounting for the largest 
share of costs (World Bank, 2010). At the same time, every USD  1 
invested in preventive measures can save USD  5 of repairs (PRIF , 
2013). While adequate financing and technical expertise are required, as well as foresight in planning and design and climate risk screening, 
successful adaptation relating to physical capital also demands legal 
and institutional enablers (e.g., development and enforcement of 
building codes and regulations; roll out of insurance options; planning

restrictions to reduce construction in locations that are highly exposed 
to climate hazards, etc). In some situations, these are lacking. For 
example, low-lying LDCs, such as Bangladesh, as well as SIDS, regularly 
suffer from climate events such as floods, typhoons, cyclones, hurricanes 
and saline intrusion (see Chapter 15 on small islands). Hazards such as 
typhoons cause substantial damage and destruction, impede mobility, 
reduce connectivity, disrupt communications, food, water and energy 
supplies, and render people homeless and without the assets they rely 
on to make a living. In the absence of adequate legal and institutional 
enablers, as well as livelihood assets, the maintenance of physical 
capital is far more challenging, as the case of Cyclone Aila in Box 8.8 
demonstrates.
Physical capital in the form of technology is increasingly supporting 
climate change adaptation, despite that innovations can be rolled

out under high uncertainty, opening up new risks (e.g., hacking). 
Moreover, deployment of technology is closely tied to other forms 
of capital, especially human capital, and innovations cannot just be 
rolled out in the absence of suitable institutional and technical support 
and training. Similarly, access to finance is vital. Some technological 
adaptations require a pre-existing level of infrastructure and literacy, 
raising important questions about inequality (Taylor, 2018). Rotz et al. 
(2019) warn of automation impacts on rural labour, especially in 
places with high youth unemployment, while Taylor (2018) notes that 
social classes and gender are impacted differently by technological 
change, and failure to address underlying inequalities will shape who 
becomes vulnerable. Adequate testing of technologies in terms of their 
applicability to different contexts is also required, ensuring they do not 
become maladaptive when applied at scale.

Similarly, technology must always be grounded in an appreciation 
of the cultural context. Research in the European Arctic with the 
Indigenous Sami Peoples found that use of GPS technology on reindeer, 
together with supplementary feeding, offered useful adaptations for 
some herders. However, there are fears such technologies may, over 
time, reduce the skills, cultural knowledge and Indigenous adaptations 
of the Sami (Andersson and Keskitalo, 2017), as, for example, reindeer 
become tamer through supplementary feeding, affecting their range 
selection. Overall, technology and other adaptations should seek not 
to erode Sami culture’s adaptive capacity (Vuojala-Magga et al., 2011; 
Risvoll and Hovelsrud, 2016), particularly because reindeer grazing 
as a land management practice can play a useful climate change 
mitigation role too. Reindeer grazing protects tundra from tree line and 
bush encroachment, while summer grazing increases surface albedo

by delaying snowmelt (Jaakkola et al., 2018).
8.5.2.4.1 Socio-cultural Factors
Social and cultural factors are closely linked to values, beliefs and 
identities (Heimann and Mallick, 2016) and mediate the ways in which 
people respond to climate variability and change (Adger et al., 2013). 
There is limited evidence but medium agreement about the importance 
and role of social and cultural factors in shaping adaptation, in terms of

8
1236Chapter 8 Poverty, Livelihoods and Sustainable Development
Box 8.8 | Cyclone Aila in Bangladesh: impact, adaptation and way forward
Historically, southern coastal Bangladesh, where the 1970 Bhola Cyclone killed 500,000 people, has been considered among the most 
climate-vulnerable environments on Earth. However, in recent decades, extreme weather events, like Cyclone Aila, though still destructive 
and destabilising, have resulted in lower death tolls thanks to a concerted investment in flood mitigation infrastructure, a dense network 
of cyclone shelters and a robust early warning system (Chowdhury et al., 1993; Paul, 2009). Cyclone Aila struck the southwest coast of 
Bangladesh on 25 May 2009 with a wind speed of 120 km hour–1 (Islam and Hasan, 2016). With tidal surges of up to 6.5 m, occurring 
over dry pre-monsoon soils, 11 coastal districts and more than 3.9 million people were affected (United Nations, 2010), 190 people died 
and 7100 people suffered injuries (Saha, 2017).

Aila greatly damaged the region’s physical capital, including 6000 km of roads and 17,000 km of embankments. The cyclone polluted 
and damaged sources of drinking water and destroyed 243,000 houses and thousands of schools (Mallick et  al., 2017; Paul and 
Chatterjee, 2019). In Satkhira and Khulna districts alone, 165,000 houses were destroyed and households were forced to live on damaged 
embankments in makeshift shanties (UNDP , 2015). Many people had to live in these temporary shelters for years (Saha, 2017). Aila 
occurred during a high tide and the surge of saline water inundated not only the roads, embankments and houses but also vast areas 
of agricultural field and shrimp farms (Paul and Chatterjee, 2019) leaving many areas waterlogged for months (Abdullah et al., 2016; 
Mallick et al., 2017). The effect of saline water logging inside embankments caused further harm to houses, roads and culverts, adding

more barriers to the post-disaster reconstruction activities (Roy, 2020). In the same area, tube-wells were damaged. Women had to travel 
up to 2 km every day to collect safe water, spending 30–90 minutes on this activity daily (Alam and Rahman, 2019). The distribution 
of costs across different socioeconomic groups was not always as expected. A study in Aila-affected Koyra sub-district of Khulna found 
that households with higher incomes were more vulnerable to Aila in both relative and absolute terms compared to middle- and low-
income groups mainly due to damage to shrimp farming, which underpinned their livelihoods (Abdullah et al., 2016). This highlights how 
specialised livelihoods can leave people more vulnerable as they have fewer options. However, the same study found that the damage 
to physical capital such as fishing nets and boats was statistically significantly greater for middle- and low-income groups. Damage to

houses was statistically significantly more among poorer households followed by middle- and higher-income groups.
A range of coping and adaptation actions were enacted in response to losses of and damage to physical capital (Table Box 8.8.1). Actions 
varied across the different affected areas and were taken by the households themselves, by the government and by NGOs.
Table Box 8.8.1 |  Coping and adaptation actions enacted in the Cyclone Aila-affected area in response to losses of and damage to physical capital.
Coping and adaptation actions Action group References
Human migration—mostly forced due to loss of houses as well as other resources and livelihood activities Households(Abdullah et al., 2016; Mallick et al., 
2017; Paul and Chatterjee, 2019)
Alternative livelihood activities such as crafts, and honey and wood collection from the Sundarbans, due 
to irreparable damage to fishing gearHouseholds (Alam et al., 2015)

Saving money for house repairs or construction Households (Alam et al., 2015)
Underground storage of emergency items such as foods, matchbox, cooker and cooking fuel Households (Alam et al., 2015)
Selection of high land to build shelter along both sides of the embankments Households (Alam et al., 2015)
Tree plantation in the homestead periphery to protect the house from gusty winds and to use as a 
source of wood for house repair/constructionHouseholds (Alam et al., 2015)
Increasing height of the house plinth Households (Alam et al., 2015)
Changing of house roofing material from thatched to corrugated iron sheet or asbestos Households (Alam et al., 2015)
Informally allowing people to harvest Sundarbans forest wood without any charge so they could make 
makeshift housesForest Department (Abdullah et al., 2016)
Rainwater harvesting using plastic or clay pots and artificial aquifer tube-wells for securing drinking water. NGOs and households (Sultana and Mallick, 2015)

Replacement of mud walls of houses with wood or bamboo sticks to enhance durability NGOs and households (Sultana and Mallick, 2015)
Making thick shelterbelts along coastal embankments NGOs and households (Rahman and Rahman, 2015)
The impacts of some of these adaptations, particularly engagement in new livelihood activities after Aila, were varied, with income of 
the affected households increasing in some cases and decreasing in others. In Koyra, the income of the poorest and middle-income 
households increased by 16% and 4%, respectively, while the income of richer households (many of whom lost physical capital assets 
that they used to pursue their livelihoods) decreased by 50% (Abdullah et al., 2016).

8
1237Poverty, Livelihoods and Sustainable Development  Chapter 8
both the need to adapt and the way it is presented and communicated, 
although evidence is somewhat mixed in terms of how experiences 
of weather affect opinions and perceptions of climate change (Howe 
et  al., 2019). Research also highlights the importance of context in 
understanding relations between perceptions of risks and behaviour, 
arguing that power relations and other obstacles and opportunities 
play a vital role in shaping actions (Rufat et  al., 2020). In general, 
nonetheless, adaptation is spurred when people perceive that there is 
an action they can take to make a difference (Kuruppu and Liverman, 
2011; Mayer and Smith, 2019), although it cannot be assumed that 
action will be taken if the socio-cultural setting is not amenable and it 
contravenes the values underlying people’s perceptions (Kwon et al., 
2019). Research testing for the effect of beliefs on behavioural change

from 48 countries highlighted the need for policy leaders to present 
climate change as solvable yet challenging, if fatalistic beliefs that act 
as barriers to adaptation were to be reduced (Mayer and Smith, 2019). 
This demonstrates how beliefs do not always reinforce actions, even 
when risks are perceived. Similarly, research from Burkina Faso working 
with the Fulbe ethnic group found that cultural norms restricted 
engagement in four of the most successful livelihood strategies that 
support adaptation to climate change (labour migration, working for 
development projects, gardening and female engagement in economic 
activities) (Nielsen and Reenberg, 2010). Cultural factors therefore 
play an important but under-researched role in adaptation.
Social factors in the context of adaptation, by contrast, are more widely 
studied. The literature on adaptation and the role of social capital as an 
enabler is diverse. There is high confidence that during disasters, social

capital plays an important role in linking those who are affected to 
external supports and resources. On small islands, social networks can be 
dense and support adaptation (Petzold and Ratter, 2015), with traditional 
knowledge and societal cohesion helping small island communities to 
have self-belief and build resilience even in the absence of external 
interventions (Nunn and Kumar, 2018). Even the development of weak ties (e.g., one-way information transfer) can lead to the establishment 
of mutual collaboration relations that can be more easily drawn on in 
times of climate change-related shocks and stresses (Ingold, 2017), 
while collective shared disaster experiences can cause new social groups 
to emerge and spur action, linked to a perceived common fate (Ntontis 
et al., 2020). However, this can exacerbate inequalities and create new 
ones, with those who are more connected having enhanced access to, 
for example, shelters following storm evacuations or earthquakes (Rahill

et al., 2014). In adapting to more incremental changes, social capital has 
been shown to increase shared local knowledge and awareness, support 
participatory processes and strengthen ties to corporate and political 
institutions, increasing their responsiveness to local concerns, as shown 
by examples from Aldrich et al. (2016). They describe how in Houma, 
Louisiana, located west of New Orleans, rising sea levels and hurricane 
risks have drawn on and built social capital at the community level. Having 
what was perceived locally as insufficient federal government support, 
residents, church groups and town council members collaborated to spur 
adaptation. Community mobilisation led to construction of self-funded 
levees and water projects to protect 200,000 residents from storm surges. 
Projects include marshland restoration, the elevation of existing housing, 
improved pumping systems and canal drainage, as well as buyouts

and relocations of businesses and housing that has been repetitively 
damaged. Funds were raised from households through donations via 
a self-imposed sales tax. While this example paints a positive picture of 
the role of social capital and collective action in adaptation activities, it 
also raises questions about the coherence of actions across levels, again, 
highlighting a role for polycentric governance if risks of maladaptation 
are to be reduced. The danger in the example presented here is that 
should federal plans conflict with the community level work in the future, 
local efforts may have been in vain if installations have to be removed. 
This highlights the importance of careful evaluation of all adaptation 
options on an ongoing basis.Research into adaptation projects led by various actors has shown that adaptations taken by the households and community themselves

are effective only to address typical challenges (such as seasonal shifts in temperature or rainfall) but are less effective in addressing 
extreme events that have long-lasting impacts. This is mainly due to lack of adequate resources and institutional support (Alam et al., 
2015). At the same time, some coping mechanisms are harmful in the longer term, for example, harvesting Sundarbans forest wood after 
Aila for reconstruction could have negative impacts on the forest.
As of 2017, many of the affected areas had not yet been able to recover from the effects of Aila (Paul and Chatterjee, 2019). A transformative 
approach needs to be taken not only to help them recover in livelihood terms, but also to support people’s well-being. Suggestions of 
physical interventions that are needed include higher and stronger dykes, cyclone-resistant housing, active maintenance and strict

policing of embankment use and good governance (Abdullah et  al., 2016). Enabling formal institutions could help, for instance, by 
improving the climate resilience of physical capital (e.g., by developing and enforcing building codes for houses). Other institutional 
mechanisms could help to improve access to low interest credit, prevent maladaptation, improve enforcement of laws, and provide 
insurance. However, such institutional reforms need to be co-developed with local people and incorporate local cultural mechanisms 
(Islam and Nursey-Bray, 2017). Future adaptation strategies also need to consider the limits to autonomous adaptation (i.e. that without 
external intervention) and differential level of impacts and adaptive capacities among different groups of households in the Aila-affected 
areas. This example illustrates the importance of a more comprehensive approach to resilience building, and the need to better understand

the interlinkages between the core components of an enabling environment for adaptation (see Figure 8.12).Box 8.8 (continued)

8
1238Chapter 8 Poverty, Livelihoods and Sustainable Development
Further warnings about social capital as an adaptation enabler come 
from Acosta et al. (2016) who recognise that it may be detrimental to 
private adaptation in some cases. Their research in rural Ethiopia found 
that qualitative measures of trust predict contributions to public goods, 
supporting theories about collective action, but that the effects of social 
capital are not homogenous: it can be helpful in some contexts, but 
unhelpful, or even detrimental in others. This led them to highlight the 
need for policymakers to consider these potentially different outcomes. 
Other research, also from Ethiopia, suggested that households with 
more social capital are more specialised in their livelihood strategies. 
This could leave them more vulnerable to climate change impacts (as 
per the Cyclone Aila example where shrimp farmers were specialised 
and hit hardest by the cyclone’s impacts), though social capital acts as

a kind of informal insurance (Wuepper et al., 2018).
8.6 Climate Resilient Development for the 
Poor and Pro-poor Adaptation Finance: 
Ensuring Climate Justice and Sustainable 
This section evaluates climate resilient development (CRD) focusing 
on potential synergies between adaptation and mitigation in different 
sectors, decision-making approaches and adaptation finance, especially for the poor. It examines whether climate change response options, 
meaning mitigation and adaptation, in different development sectors, 
create development synergies or trade-offs for low-income households 
and people living in poverty.
The link between development and climate change was not 
evaluated comprehensively until the first decades of the 21st century 
(Figure  8.13; Klein et  al., 2005; Tol, 2005). Until recently mitigation 
and adaptation, the two primary approaches to climate action, have 
been dealt with separately in climate change science and policy

(Landauer et al., 2015). Nevertheless, synergistic ‘co-benefits’ between 
mitigation and adaptation may be enhanced, and trade-offs reduced, 
through the holistic empirical evaluation of actions for climate change 
response (Runhaar et al., 2018). The synergistic effect of mitigation and 
adaptation has been documented for a few interventions across the 
globe, however, evidence-based quantification of the synergies and 
trade-offs are rare.
Where co-benefits have emphasised identifying mitigation–adaptation 
synergies, a key turn has been evaluating climate compatible 
development (CCD), ‘development that minimises the harm caused 
by climate change impacts, while maximising the many human 
development opportunities presented by a low emission, more resilient 
future’ (Mitchell and Maxwell, 2010). CCD calls for triple wins, resulting 
Climate Resilient Development (CRD)
Actions and strategies consider both Climate Compatible Development and Climate Action
Strategies

Climate
Resilient
DevelopmentNarrowing
Adaptation 
GapAdaptation 
StrategiesCo-BenefitsLow Carbon
Climate actionClimate compatible
develpment
Figure 8.13 |  Climate resilient development (CRD). Actions and strategies consider both climate compatible development (CCD) and climate action.

8
1239Poverty, Livelihoods and Sustainable Development  Chapter 8
in synergies between mitigation–adaptation–development through 
single interventions (Figure  8.13; Ellis and Tschakert, 2019). CCD 
offers specific entry points for identifying ways on how to strengthen 
synergies between mitigation and adaptation, particularly within the 
context of low-income countries. Effective integration of emission 
reductions and accommodation actions for mitigation and adaptation 
can be win–win strategies, may be cost-efficient (Runhaar et al., 2018) 
and have the potential to create opportunities to foster sustainable 
development (Denton et al., 2014).
This assessment identifies and evaluates approaches to CRD ‘that 
deliberately adopt mitigation and adaptation measures to secure a safe 
climate, meet basic needs, eliminate poverty and enable equitable, just 
and sustainable development’. The body of literature on the synergies

and trade-offs between adaptation, mitigation, poverty, equity and 
sustainable development has grown steadily since the AR5 (IPCC, 
2014a). The IPCC Special Report on the impacts of global warming of 
1.5°C (IPCC, 2018c), suggests that ‘Limiting warming to 1.5°C can be 
achieved synergistically with poverty alleviation and improved energy 
security and can provide large public health benefits through improved 
air quality, preventing millions of premature deaths.’
Implementing the integrative concept of CRD is expected to produce 
transformative benefits affecting the poorest populations primarily 
(Roy et al., 2018; Leal Filho et al., 2019). The risks of transformative 
actions to the poor are diminished when undertaken in the context 
of good governance at multiple levels, within existing top-down and bottom-up processes, and making use of available levers of policy, 
technology, education and financial/economic systems (Stringer et al., 
2020).

8.6.1  Synergies and Trade-offs Between Adaptation and 
Mitigation in Different Sectors with Implications for 
8.6.1.1 Climate Resilient Development
CRD relies on identifying synergies between different strategies and 
actions in the field of climate change, primarily between mitigation 
actions with adaptation benefits (Locatelli et  al., 2015), adaptation 
actions with mitigation benefits (Denton et  al., 2014; Sánchez and 
Izzo, 2017), processes that promote both mitigation and adaptation 
measures, and policies and strategies that promote integrated 
mitigation and adaptation measures (Zhao et al., 2018). At the same 
time, adaptation and mitigation actions can be evaluated in terms of 
their co-benefits, the social, economic or other benefits of actions in 
addition to avoiding climate change impacts (Karlsson et al., 2020). 
The clearest co-benefits of mitigation are associated with economic

development through low-carbon industrialisation (IPCC, 2014c; Jakob 
et al., 2014; Lu, 2017). Co-benefits can include contributing to economic 
growth, reducing competition for resources, improved integration 
of scientific input to policy development and implementation, or 
improving political participation and social licensing in large-scale 
Sustainable development and the adaptation gap
DevelopmentHigh poverty,
high inequality
Low poverty,
low inequalityAdaptation Gap
Emissions
PathwaysHigh
Low4°C
2°C
1.5°C
RiskUnequal Distribution
of Loss and Damage
Climate
Resilient
DevelopmentUniversal Loss
and Damage
Future WorldsDevelopment
Context
Current communities and
countries at different levels
of developmen t
Sustainability Middle of the RoadInequality Regional Rivalry
Fossil-fueled Development
limits
Low intensity
and frequenc y
of impact sHigh intensity
and frequency
of impacts
Sustainable Developmen t
and the Adaptation Ga p

Figure 8.14 |  Conceptual figure illustrating the link between sustainable development and the adaptation gap. Even if emissions are kept low, if poverty and 
inequality remain high, then impacts are expected to remain high and may overwhelm capacity for adaptation.

8
1240Chapter 8 Poverty, Livelihoods and Sustainable Development
projects (e.g., hydropower) (Hennessey et al., 2017). Adaptation can 
support mitigation and contribute to co-benefits in various ways: 
ensuring development-based natural resource management (Denton 
et al., 2014; Suckall et al., 2015; Reang et al., 2021), integrating water 
resources management (Liang et  al., 2016; Sharifi, 2021), practicing 
sustainable agriculture (Bustamante et al., 2014; Duguma et al., 2014a; 
Di Gregorio et al., 2017; Reang et al., 2021), ensuring the protection 
of ecosystem services (Pandey et  al., 2017a; Baumber et  al., 2019), 
conserving biodiversity (Di Gregorio et al., 2017; Loboguerrero et al., 
2019; Smith et  al., 2019) and managing bioenergy resource (Dovie, 
2019).
The key challenge for CRD is addressing climate change from the 
perspective of development: addressing the fundamental development 
obstacles that limit capacity for adaptation. Where development is

not sustainable, especially if it is not equitable, capacity for adapting 
is greatly reduced—a phenomenon known as the adaptation gap 
(Figure 8.14; Birkmann et al., 2021a; UNEP , 2021). Figure 8.14 depicts 
the effect of development trajectories (as described in the SSPs 
framework) on capacity for adaptation, a key determinant of eventual 
outcomes. Achieving CRD through coupling adaptation with equitable 
sustainable development under and low emissions profiles that limit 
warming to 1.5°C (i.e., sustainability scenario) is necessary to close the 
adaptation gap. Even if emissions are kept low and 1.5°C emissions 
targets are achieved, if poverty and inequality remain high, then 
impacts are expected to remain high and may overwhelm capacity 
for adaptation. High poverty and high inequality in a society (i.e., 
inequality scenario) reduce the likelihood that countries are able to 
manage risk and avoid residual impacts, such as also documented

in the assessment above (see Sections  8.2; 8.3; 8.4). Unsustainable 
development trajectories reduce capacity for adaptation and may 
result in highly unequally distributed residual impacts from climate 
change. Even despite rapid, equitable development and modest 
emissions reductions efforts necessary to limit warming to 2°C (i.e., the 
middle of the road scenario), there is still risk of unequal distribution of 
impacts. Under all high emissions scenarios (>3°C warming), universal 
residual impacts are unavoidable.
Mitigation planning has not sufficiently considered poverty reduction 
policies, the basis for narrowing the adaptation gap (see also 
Figure 8.14). Many synergies between climate change mitigation and 
poverty reduction have been identified, although sometimes with limited 
evidence. The mitigation measures that have been most evaluated 
include clean development mechanisms (CDM), programmes aimed 
at reduction of emissions from deforestation and forest degradation

(REDD+), voluntary carbon offsets and biofuel production. However, 
while these mitigation programmes stimulate economic growth, 
they may contribute to processes that trade-off against equitable 
development and threaten to further impoverish forest communities, 
such as large-scale land acquisitions (Carter et  al., 2017; Schaafsma 
et al., 2021) and fortress conservation (see IPCC SR 1.5°C, Chapter 5 
(Roy et al., 2018); and see also Chapter 6 of this report).
The IPCC Special Report on Climate Change and Land (IPCC, 2019a) 
states that agriculture, food production and deforestation are 
major drivers of climate change and calls for coordinated action to 
tackle climate change that can simultaneously improve land, food security and nutrition, and help to end hunger. There are five land 
challenges identified including climate change mitigation, adaptation, 
desertification, land degradation and food security. This report identified

three major categories of climate response options that show promise 
for achieving mitigation and increasing capacity for adaptation while 
addressing poverty: SLM options, value chain management and 
risk management options (IPCC, 2019a). For example, programmes 
supporting no-till agriculture and residue retention allow small-scale 
farmers to participate in mitigation and adaptation activities, with 
long-term benefits to soil health and food, energy and water security 
(Wright et al., 2014). Likewise, the installation of a solar powered drip 
irrigation system simultaneously reduces emission, improves water 
security and increases farmers’ income (Locatelli et al., 2015). Response 
options in terms of SLM options, and value chain and risk management 
involve interlinkages between land-based climate strategies, synergies 
and trade-offs (see Chapter 6). On the other hand, a key trade-off is 
the potential for maladaptation, where one adaptation intervention at

one time, location or sector could increase the vulnerability at another 
time, location or sector, or increase the vulnerability of the target group 
to future climate change (medium evidence, high agreement) (Eriksen 
et al., 2011). A cause of increasing concern to adaptation planners is 
the understanding of maladaptation has changed subtly to recognise 
that it arises inadvertently, from poorly planned adaptation actions, but 
also from carefully deliberated decisions where wider considerations 
place greater emphasis on singular or short-term outcomes ahead of 
broader, longer-term threats, or discount, or fail to consider, the full 
range of interactions arising from the planned actions across scales 
(Eriksen et  al., 2021). Research identifies the challenge of avoiding 
maladaptation as one of reducing long-term structural vulnerability. 
Accordingly, one can consider CCD and maladaptation as two sides 
of the same coin. Scholars of ‘sustainable adaptation’ define it as

adaptation that contributes to socially and environmentally sustainable 
development pathways, which takes into account both social justice 
and environmental integrity (Eriksen et  al., 2011). The parallels in 
maladaptation include the underlying drivers of vulnerability, namely 
socio-environmental processes such as conflict, marginalisation, 
economic restructuring, exploitation, institutional fragility and so forth 
(Antwi-Agyei et al., 2018b; Neef et al., 2018).
Harnessing opportunities for mitigation, adaptation and development 
in an effective manner may lead to ‘triple wins’ under CRD, though 
empirical evidence is extremely rare for such triple win strategies that 
address mitigation, adaptation and development in an effective manner 
(Tompkins et  al., 2013). Integration of mitigation, adaptation and 
development is being initiated and operationalised through projects 
by several developing countries to achieve main national development

priorities, such as poverty reduction, increased employment 
opportunities, energy security and transportation (Denton et al., 2014; 
Stringer et al., 2014). Important follow-on questions are pressing social 
questions about how trade-offs are deliberated, who wins and loses and 
who decides (see Section 8.4 and Ellis and Tschakert, 2019). Likewise, 
the efficiency, effectiveness and feasibility trade-offs of climate policies 
must be considered (i.e., can programmes in developing countries be 
economically efficient and provide opportunities to achieve sustainable 
development targets for developing countries?) (Dang et  al., 2003). 
Moreover, questions about co-benefits must consider the benefit–
cost ratio of mitigative versus adaptive action for assets saved from

8
1241Poverty, Livelihoods and Sustainable Development  Chapter 8
destruction by climate impacts, for example (Stadelmann et al., 2014). 
Implementing a mitigation or adaptation option may positively or 
negatively, directly or indirectly, affect the feasibility and effectiveness 
of other options, such as soil management leading to soil organic 
carbon (Locatelli et al., 2015; de Coninck et al., 2018). Farmers and local 
people are often also being encouraged to undertake mitigation and 
adaptation activities leading to long-term benefits, such as cultivation 
of no-till wheat with residue retention leading to low emissions along 
with saving energy and water (Wright et al., 2014).
Moreover, a regulatory structure for evaluation of mitigation and 
adaptation actions is required to understand the co-benefits of these 
two actions. For example, the choice of adaptation actions can be 
made according to their effectiveness per unit of money invested such

as economic assets saved from destruction of climate change impacts 
and benefits can be evaluated in terms of economies, people and the 
environment, such as human lives and health protected rather than the 
emission reduction by mitigation strategies (Stadelmann et al., 2014).
8.6.1.2.  Climate Resilient Development Synergies and Trade-offs 
by Sector
Some sectors—such as agriculture, forestry, energy—are found to 
have more potential for CRD synergies than others, although trade-
offs are also identified. CSA, carbon-forestry programmes and the 
water–energy–climate nexus show trade-offs across levels and sectors 
with identified winners and losers (high confidence) (IPCC, 2018a). 
Mitigation can be designed to provide opportunities for enhanced 
adaptation with comparable co-benefits, even while adaptation 
portfolios can maximise co-benefits around sustainable resource 
management that reduce emissions (Dovie, 2019). Climate policy

integration can be considered as the integration of multiple policy 
objectives, governance arrangements and policy processes of climate 
change mitigation and adaptation along with other policy domains 
(Di Gregorio et al., 2017), as well as sector policies integrating climate 
change adaptation and mitigation (England et al., 2018). Integrating 
climate policies may require balancing multiple sectoral goals, such 
as REDD+ projects, CSA, water sector strategies, national policies 
on climate change and national conservation plans (Duguma et  al., 
2014a). Within the scientific discourse, increasing attention is given to 
the question of the synergies and mismatches between mitigation and 
adaptation policies.
The assessed literature underscores that for synergies to be realised, 
mitigation and adaptation policies must be institutionally supported 
within a multi-level governance architecture (national to sub-national

to municipal levels) with other priorities, and sustainable financing 
mechanisms identified within the country or via the international 
community (Dovie and Lwasa, 2017). Integrating and mainstreaming 
adaptation and mitigation across agencies within countries can bridge 
the divide between climate policy and sustainable development 
(Venema and Rehman, 2007).
The Paris Agreement recognised that the agreement will reflect equity 
and CBDR-RC of national circumstances, (Voigt and Ferreira, 2016) and 
should be broadened to include mitigation co-benefits (Dovie, 2019). 
Integrating adaptation with mitigation may possibly contribute to amending or reducing the discursive rift between climate policy and 
sustainable development (Venema and Rehman, 2007).
Integrated climate change actions or responses can be inefficient 
and infeasible in the absence of enabling conditions, including the 
policy conditions that reinforce unified climate action, and sustainable

financial mechanisms for implementation of the programmes and 
policies (Duguma et al., 2014b). In the absence of strong coordination, 
integrating mitigation and adaptation may undermine the overall or 
individual objectives of either climate response (Kongsager, 2018). A 
lack of coordination in mitigation and adaptation may also exacerbate 
the threats of climate change to sustainable development (Ayers and 
Huq, 2009; Kongsager, 2018). Therefore, for successful integration of 
CRD, it is necessary to move beyond considering either adaptation 
or mitigation towards better understanding the linkages between 
adaptation and mitigation projects and policies at multiple levels of 
governance to identify potential trade-offs in projects and policies 
(Suckall et  al., 2015) and to identify the enabling conditions for 
designing and implementing action leading to synergies (Denton et al., 
2014; Kongsager, 2018).
Despite the potential effectiveness and efficiency of integrating

mitigation and adaptation under a common CRD framework, gaps 
persist in our knowledge about the enabling conditions for synergies, 
due to the limited number of examples and even fewer evaluations. 
Potential benefits may be achieved by pursuing multi-level governance 
approaches, that means integrating decision making at the local level 
with coordination at other levels, by actors and agencies simultaneously 
pursuing multiple other priorities (see Section 8.5.2 Shaw et al., 2014). 
For example, pursuing climate-resilient land use pathways integrating 
climate policy within the land use sector requires a governance policy 
environment that combines multiple policy aims, including urban 
growth, soil conservation and water management alongside mitigation 
and adaptation. Facilitating climate-resilient land use pathways 
combining the aims of climate change adaptation, mitigation and 
sustainable development requires a governance environment with: (a)

internal climate policy coherence between mitigation and adaptation 
objectives and policies, (b) external climate policy coherence between 
climate change and development objectives; (c) vertical policy 
integration that mainstreams climate change into sectoral policies and 
(d) overarching governance structures that facilitate horizontal policy 
integration for cross-sectoral coordination (Di Gregorio et  al., 2017) 
as well as sector policies integrating climate change adaptation and 
mitigation (England et al., 2018).
Within sector policies and economic sectors (such as land use, 
transportation and technology), mitigation and adaptation have many 
positive, negative, direct and indirect linkages within and beyond the 
sector (Locatelli et al., 2015). The land use sector, for example, includes 
agriculture and forestry, and encompasses the management of a mosaic 
of interacting urban environments and ecosystems with a diversity of

cultural and institutional attributes (Locatelli et  al., 2015). The land 
use sector is key to climate adaptation, where policy coordination can 
enhance food production, regulate urban microclimates, affect water 
security and, in the case of mangroves, buffer the impacts of extreme 
climate events in coastal areas (Locatelli et al., 2015). City-level actions, 
such as zoning and planning that promotes green development and

8
1242Chapter 8 Poverty, Livelihoods and Sustainable Development
green and efficient energy use, can also be pivotal for reduction in 
emissions and improvement in resilience (UCLG, 2015). Urban planning 
and transport policies, such as means of transportation, are crucial to 
support a transition towards a low-carbon and resilient future (Ford 
et al., 2018), as public and private transport facilities are crucial for 
emission reduction.
CRD may require multi-sectoral coordination, including public–private 
partnerships (Campbell et al., 2018). In the food system, for example, 
under a CRD framework transformative actions may require (a) 
incentives for expanded private sector activities and/or public–private 
partnerships, (b) publicly backed credit and/or insurance, (c) public 
institutional support for strong local organisations and networking, (d) 
climate-informed weather advisories and early warning systems, (e)

digital investments in technological transformation for agriculture (e.g., 
‘digital agriculture’ and virtual markets), (f) investments in climate-
resilient and low-emission practices and technologies (Duguma et al., 
2014b), (g) prioritisation and pathways of change, (h) capacity and 
enabling policy and institutions are crucial with careful consideration of 
trade-offs between adaptation and mitigation, and amongst other SDGs 
for achieving SDG13 ‘urgent action to combat climate change and its 
impacts’ (Campbell et al., 2018). Moreover, the risks of transformative 
actions to the farmers is addressed by strong good governance at 
multiple levels, combining top-down and bottom-up processes along 
with by a mix of levers that combine policy, technology, education and 
awareness raising, dietary shifts and financial/economic mechanisms, 
attending to multiple time dimensions (Stringer et al., 2020).
8.6.1.2.1 Agriculture and food production

Integrated CRD approaches in agriculture, such as CSA, can reduce trade-
offs and exploit synergies with biodiversity and food security to reduce 
the risk of climate change (Di Gregorio et al., 2017; Loboguerrero et al., 
2019). There are many technologies and approaches in agriculture that 
leverage synergies relevant for CRD, including agroecology (Pandey 
et al., 2017a; Saj et al., 2017), CSA, climate-smart landscapes, organic 
agriculture mitigating climate change, conservation agriculture, 
ecological intensification and sustainable intensifications, which in 
many cases aim to address both adaptation and mitigation to climate 
change simultaneously (Kongsager, 2018). From these approaches, 
a number of scalable agriculture technologies have emerged that 
simultaneously achieve mitigation and adaptation goals, such as 
reducing water consumption while maintaining grain yield, including 
alternate wetting and drying irrigation technology (Liang et al., 2016)

and aerobic rice production (Wichelns, 2016). Likewise, a number 
of these approaches have been supported within international and 
national institutional frameworks (e.g., through incentives) to harness 
synergies (Kongsager et al., 2016).
CSA is discussed in the scientific literature as an approach that could 
transform agricultural production systems and food value chains in 
line with sustainable development and food security under climate 
change. However, concerns and criticisms have been raised, such as 
the insufficient consideration of access to entitlements within CSA 
and the question who wins and loses when applying CSA in different 
country contexts (see Karlsson et al., 2017; Sain et al., 2017). CSA has 
three main objectives: sustainably increase agricultural productivity and incomes, adapt and build resilience to climate change, and reduce 
and/or remove GHG emissions (FAO, 2017). Various CSA technologies

are capable of improving crop yields, increasing net income, increasing 
input-use efficiencies and reducing emissions (Khatri-Chhetri et  al., 
2017). However, uptake and adoption of CSA by local farmers in poor 
developing countries remains a challenge (Palanisami et al., 2015) due 
to the difficulty of identifying and prioritising of technologies suiting 
local climate risks and accommodating the farming practices of locals 
(Dougill et al., 2017; Khatri-Chhetri et al., 2017). An analysis of CSA 
implementation in Mali, for example, identified major challenges to 
policymakers’ efforts to adopt CSA, including difficulties identifying 
CSA options and portfolios, valuing them and prioritising investments 
(Andrieu et al., 2017).
Potential opportunities from CSA may also result from integration 
of ‘technological packages’ (Totin et  al., 2018), which include new 
market structures, knowledge infrastructure and agriculture extension

services, capacity-building programmes (Dougill et  al., 2017; Totin 
et al., 2018) and institutional support for key enabling programmes, 
such as crop insurance, agro-advisories and rainwater harvesting 
(Khatri-Chhetri et  al., 2017). CSA is able—if carefully designed—to 
achieve transformative ‘triple wins’ for climate and development when 
it is accompanied by new governance architectures that are socially 
inclusive and respectful of traditions and livelihoods, and accommodate 
traditional institutions that underpin the bargaining power of the 
poorest and most vulnerable groups (Karlsson et al., 2017).
Conservation agriculture (CA), another framework for achieving CRD, is 
based on three synergistic principles: (a) soil management to reduce soil 
physical disturbance and reduce its degradation, (b) crop management 
such as residue management to protect the soil top layers and (c) 
genetic management to increase agricultural systems’ biodiversity and

therefore their resilience (DeLonge and Basche, 2017). In the cereal 
systems of the Indo-Gangetic Plains, India, CA has increased crop yields, 
returns from crop cultivation and input-use efficiency, in spite of heat 
stress, while reducing GHGs emissions (Sapkota et al., 2015). However, 
challenges with CA are also documented in the scientific literature. For 
example, an evaluation of CA in Malawi noted that adoption of CA 
was challenged by weak integration of CA in agricultural policies, lack 
of institutional arrangements of promoters and farmers’ experiences 
(Chinseu et al., 2019).
Locally appropriate agro-ecological practices have clear potential 
to increase the resilience of livelihoods and enhance adaptation 
to climate change at field and farm levels across a wide range of 
contexts, often with significant mitigation co-benefits (Sinclair et al., 
2019). Relatedly, agroforestry systems are the intentional integration

of trees and shrubs into crop and animal production systems to solve 
societal challenges including climate change (Raymond et al., 2017). 
For example, in the tropics, such systems offer viable opportunities to 
mitigate and adapt to climate change for farmers by transitioning to 
resilient farming systems and improving farm economy while securing 
environmental benefits for local and global communities (Swamy and 
Tewari, 2017). In Western Africa, the high plant functional diversity of 
agroforestry systems with a mix of trees and crops having different 
roles, such as shade provision, soil fertilization, fruit production or 
timber value, maximises benefits and allows alternative adaptation

8
1243Poverty, Livelihoods and Sustainable Development  Chapter 8
strategies (Tschora and Cherubini, 2020). In spite of various benefits 
of agroforestry, the expansion of existing areas of agroforestry and 
the establishment of new agroforestry systems has remained limited 
(Martineau et al., 2016), mainly due to a lack of institutional support, 
a lack of expert support to ensure adequate management, weak 
capacity for monitoring and regulation, and a lack of financial support 
(Hernández-Morcillo et al., 2018).
The enabling conditions for the expansion of agroforestry include 
training and expert support programmes for managers and sharing 
of best practices (Ashraf et al., 2015; Hernández-Morcillo et al., 2018; 
Tschora and Cherubini, 2020). Other scalable frameworks integrating 
food and agriculture within CRD include sustainable intensification 
(SI), which emphasises sustainable practices to safeguard sustainable

use of natural resources and meet the growing demand for agricultural 
production, while building resilience (Thierfelder et al., 2018). Integrated 
agricultural systems aim to increase farm diversity and lower reliance 
on external inputs, enhancing nutrient cycling and increasing natural 
resource use efficiency (Smith et al., 2017), and may have the potential 
to enhance resilience against climate change impacts and risks (Gil 
et  al., 2017). Policy frameworks that aim to integrate any of these 
approaches for climate action must account for the costs associated 
throughout the uptake and adoption process (Gil et al., 2017).
8.6.1.2.2 Livestock
As the consumption of animal protein and products rises along 
with global standards of living, CRD will require transformations in 
livestock-centred livelihoods. Livestock are a key contributor to global 
food security, especially in marginal lands where animal products are

a unique source of energy, protein and micronutrients (FAO, 2017; 
IPCC, 2019a). However, they also contribute disproportionately to total 
annual anthropogenic GHG emissions globally and influence climate 
through land use change, processing and transport through emitting 
CO 2, animal production by increasing methane emissions, and feed 
and manure production by emitting CO 2, nitrous oxide, and methane, 
(Rojas-Downing et  al., 2017). Mitigation of livestock emissions can 
be achieved by implementation of various technologies and practices 
such as improving diets to reduce enteric fermentation, improving 
manure management and improving animal nutrition and genetics 
(Rojas-Downing et al., 2017); altering land use for grazing and feed 
production, altering feeding practices, improving manure treatment 
and reducing herd size (Zhang et  al., 2017). Adaptation strategies 
in the livestock sector include changes in animal feeding, genetic

manipulation, alterations in species and/or breeds (Zhang et  al., 
2017), shifting to mixed crop–livestock systems (Rojas-Downing et al., 
2017), production and management system modifications, breeding 
strategies, institutional and policy changes, science and technology 
advances, and changing farmers’ perceptions and adaptive capacity 
(USDA, 2013).
Policies supporting sustainable rangeland management and the 
livelihood strategies of rangeland users have an outsized influence on 
both development and climate action (Gharibvand et al., 2015). Climate 
change adaptation, mitigation practices and livestock production can 
be supported by policies that encourage diversification of livestock 
animals (within species), support sustainable foraging and feed varieties (Rivera-Ferre et al., 2016) and strengthen institutions such as agricultural 
support programmes, markets and intra- and inter-regional trade (Zhang 
et  al., 2017). For example, sustainable pastoralism can contribute to

mitigation both by increasing carbon sequestration through improved 
soil management and by reducing methane emissions through changing 
the mix and distribution of the herd. Likewise sustainable pastoralism 
can also contribute to adaptation by changing grazing management, 
introducing alternative livestock breeds, improving pest management 
and modifying production structures (Joyce et  al., 2013). Another 
example of rangeland adaptation is diversifying the use of rangelands, 
such as supplementing with payments for ecosystem services, carbon 
sequestration, tourism or supplementary assistance for all land-based 
activities (Gharibvand et  al., 2015). However, challenges for climate-
smart livestock production systems remain due to a lack of information, 
limited access to technology and insufficient capital (FAO, 2017). 
Smallholders in cropping and livestock systems in sub-Saharan Africa 
and South Asia, for example, face obstacles obtaining climate change

mitigation and adaptation synergies due to poor access to markets 
and relevant knowledge, land tenure insecurity and the common 
property status of most grazing resources (Descheemaeker et al., 2016). 
Consequently, the appropriateness of these strategies and measures 
needs to be further evaluated, particularly in terms of their usefulness 
for the poor and most vulnerable.
Overall, different farming and pastoral systems can achieve reductions 
in the emissions intensity of livestock products. Depending on the 
farming and pastoral systems and level of development, reductions 
in the emissions intensity of livestock products may lead to absolute 
reductions in GHG emissions (IPCC, 2019a) (medium confidence). 
Significant synergies exist between adaptation and mitigation, for 
example, through SLM approaches (high confidence).
8.6.1.2.3 Forestry
Forests can support CRD in rural communities and households: they 
support consumption of energy, food and fibre, provide a safety net in

cases of shocks, fill gaps during seasonal shortfalls and are a means 
to accumulate assets and provide support to emerge out of poverty 
(Angelsen et  al., 2014; Adams et  al., 2020). Forest ecosystems are 
an essential element of climate change mitigation and adaptation, 
with the potential for synergy and conflict between the two climate 
action objectives (Morecroft et al., 2019). However, there are varied 
perspectives on the role of the forests, with some treating conservation 
and forest management practices as a barrier to livelihood resilience 
(Few et al., 2017) despite the broader role of forest management in 
climate mitigation (Houghton, 2012).
Forestry mitigation projects such as forest conservation, reduced de-
forestation, protected area management and sustainable forest man-
agement, can promote adaptation and can also have consequences 
for the development objectives of other sectors (e.g., expansion of

farmland) (Smith et al., 2014). REDD+ (reducing emissions from de-
forestation and forest degradation, fostering conservation and sus-
tainable management of forest and enhancement of carbon stocks) 
is a payment programme that may provide adaptation benefits by 
enhancing households’ economic resilience (Sills et al., 2014; Duch-
elle et al., 2018) and also produce positive livelihood impacts through

8
1244Chapter 8 Poverty, Livelihoods and Sustainable Development
the employment benefits of supporting conservation and sustainable 
management of forests (Caplow et al., 2011). Furthermore, the man-
agement of ecosystem services may contribute to both mitigation and 
adaptation. For example, REDD+ projects, such as mangrove conser -
vation and restoration, simultaneously contribute to carbon storage 
and diversification of incomes and economic activities. At the same 
time, mangroves protect coastal areas against flooding and hydrolog-
ical variations, improving capacity for adaptation in local livelihoods 
(Locatelli et al., 2016).
However, while studies of existing REDD+ programmes noted 
the moderately encouraging impacts for mitigation and small or 
insignificant impacts for adaptation options (especially well-being), 
they underscored the potentially damaging impacts to local livelihoods 
(Milne et  al., 2019; Skutsch and Turnhout, 2020). They suggested

improved engagement with local communities, increased funding 
to strengthen the interventions on the ground, and more attention 
to both mitigation and adaptation outcomes in implementation for 
achieving the benefits of REDD+ programme (Duchelle et al., 2018). 
Moreover, to effectively counter local threats to forests and biodiversity 
and attain positive biodiversity and development outcomes, REDD+ 
programmes must be focused on better institutional support for 
governance, coordinating interventions and monitoring of plans, 
as well as making explicit linkages between REDD+ activities and 
national biodiversity conservation efforts (Panfil and Harvey, 2016) 
and assuring a fair distribution of benefits to local communities (Myers 
et  al., 2018). An analysis of country-specific REDD+ programmes in 
Cameroon looking at synergies of REDD+ with other national goals, 
such as poverty reduction, identified two principal modes of strategic

interaction management among actors. The first priority relates to 
specific structures for designing REDD+ giving high priority to social 
safeguards. The second relates to programming that builds trust, 
communication and confidence of participants creating an environment 
for enabling management through commitment and behavioural 
interaction by creating an overarching institutional framework and 
unilateral management (Somorin et al., 2016).
To achieve CRD, forestry conservation strategies need to be driven 
by climate action and forest management policies that benefit 
both ecological and human systems, and, above all, involve forest 
communities in programme and project implementation (Cordeiro-
Beduschi, 2020). Synergies between mitigation and adaptation of the 
forestry sector can be enhanced by considering on-the-ground contexts 
of constraints and social trade-offs that may undermine implemented 
actions (Few et al., 2017). However, the lack of knowledge about trade-

offs and synergies at the local level and between local and global 
scales makes this challenging.
Despite these constraints, forestry can serve as a foundation for CRD 
when adaptation and mitigation activities are effectively integrated 
from the stage of policy formulation with consideration of specific 
institutional structures and procedures that can help to facilitate such 
integration (Locatelli et  al., 2015). Effectively integrated adaptation 
and mitigation activities can be achieved by encouraging collaboration 
between the two activities, promoting research on the impacts of 
the integrated activities, their cost-effectiveness and their synergies 
within the complex setting of risks and uncertainty concerning the magnitude of climate change impacts (Bakkegaard et al., 2016), along 
with facilitating participation of communities in the two activities and 
defining forest policies (Ngum et  al., 2019). Moreover, international

donors and funds are also critical to guide countries to identify 
adaptation–mitigation synergies, through consultation processes, 
dialogue and awareness raising (Locatelli et al., 2016). Moreover, in 
order to be effective, nature-based climate solutions such as mixed 
species plantation, forest expansion and REDD+, must be people-
centric and respond to the needs of the rural and Indigenous Peoples 
who manage ecosystems for their livelihoods, while at the same time 
supporting the biodiversity of the ecosystems (Temperton et al., 2019; 
Fleischman et al., 2020).
8.6.1.2.4 Energy
The continued dependence on fossil energy sources for economic 
development is the primary source of increasing GHGs (Hansen et al., 
2017). There is emerging agreement in terms of the importance of the 
bioenergy sector for climate change mitigation (Jackson et al., 2016; 
Hansen et  al., 2017), however, the options and limitations in terms

of transforming the energy systems to support both mitigation and 
adaptation are still contested.
About 1 billion people globally (12.5% of the world’s population) do 
not have access to electricity (World Bank, 2021), and yet access to 
electricity is required for basic adaptation strategies, such as the use 
of air conditioning and fans in homes and working spaces to mitigate 
heat stress and enable healthier lives, daytime activities and night-
time sleep quality. Electrification enables farmers to mechanically 
pump water from the underground to boost agricultural productivity, 
stabilise yields and make food security less reliant on erratic rainfall 
patterns and less vulnerable to dry spells. Access to electricity enables 
the spread of valuable information through television, radio, computers 
and smartphones, including weather forecasts and disaster prevention 
and response (Dagnachew et  al., 2018). The increasing access to

electricity facilitates SDG 7 coupled with other SDGs and societal 
goals, including mitigation of climate change (van Vuuren et  al., 
2018) through reducing energy consumption by the use of efficient 
technology and appliances. Electricity access can be an important 
enabler of adaptation action for different purposes in different sectors 
(Mastrucci et al., 2019).
Low-carbon development strategies can also be compatible with 
ecological sustainability, as proponents of bioenergy have claimed. 
Bioenergy can contribute to reducing emissions and energy inefficiencies 
in agricultural food and bioenergy sectors, while safeguarding food 
and energy security. However, recent literature also points towards 
significant tensions and mismatches between increasing bioenergy on 
agricultural land and local livelihoods and food security (Yildiz, 2019). 
A growing list of studies have documented the detrimental trade-offs 
between smallholder food systems and large-scale biofuel production,

which include dispossession and impoverishment of smallholder farmers, 
food insecurity, food shortages and social instability (Hunsberger et al., 
2017). Nevertheless, synergies between bioenergy and food security can 
be promoted by integrated resource management designed to improve 
both food and water security and access to bioenergy; investments in 
technology, rural extension, promotion of stable prices to incentivise

8
1245Poverty, Livelihoods and Sustainable Development  Chapter 8
local production; and use of double cropping and flex crops to provide 
food and energy (Souza et al., 2017).
Trade-offs of bioenergy can be minimised by replacing land-intensive 
first-generation biofuels (e.g., oil palm) with second and subsequent 
generations (e.g., microalgae). However, there are costs of relying on 
‘sustainable biofuels’ as most of the agricultural and non-agricultural 
land would be needed for cultivation of biofuels along with reduction 
in patterns of energy consumption a significant reduction in population 
(Gomiero, 2015). Contrasting impacts on environmental, economic and 
social sustainability are reported for production and use of biofuels 
(Azapagic and Perdan, 2011), ranging from positive impacts, such as 
reduction in GHG emissions, energy security and rural development, 
to negative impacts, such as risks of increasing food prices, increasing

GHG emissions through direct and indirect land use change from 
production of biofuel feedstocks, and degradation of land, forests, 
water resources and ecosystems (UNEP , 2009). Biofuel production 
may cause loss of biodiversity (Jeswani et  al., 2020) and may also 
impact various ecosystem services, such as land, water and food, and 
may pollute air, water and soil (Scovronick and Wilkinson, 2014). The 
collective benefits of biofuels could be realised by developing future 
policies based on integrated systems with a clear understanding about 
the interactions across sectors and land uses gained by analysing 
complete value chains (Jeswani et al., 2020).
Clean sources of energy, such as solar and wind, can facilitate both 
mitigation and adaptation. For example, in South Africa, clean sources 
of energy provide energy security with huge water savings along with 
creation of employment, proximity to point of use and, in many cases,

less reliance on concentrated sources of energy (Mpandeli et al., 2018). 
Overall, the increased use of thermal solar panels contributes to reducing 
GHG emissions and improves air quality, as well as providing benefits 
to the community and the environment. The differential adoption of 
solar panels can be managed by simultaneous investment in other 
technologies that utilise renewable energy along with investment in 
solar panels (Kaya et al., 2019). Development of a smart electricity grid 
connected to a renewable energy source reduces GHG emissions and 
decreases vulnerability to climate change by enhancing the response 
to changing conditions and providing a more reliable service to the 
population (Hennessey et al., 2017). Moreover, development of policies 
for a low-carbon and climate-resilient power system, a local nexus 
between mitigation and adaptation could be explored (Handayani 
et al., 2020). For example, use of efficient fuel in urban areas facilitates

air pollution reduction and also provides health benefits for urban 
populations (Ramaswami et al., 2017). Green buildings substantially 
reduce energy consumption and also improve indoor environmental 
quality and thus contribute to mitigation and provide societal value in 
terms of health (MacNaughton et al., 2018). In addition, green-roofed 
buildings contribute to keeping local temperatures cooler during hot 
days and thereby reducing energy use for air conditioning and thus 
contributing to both mitigation and adaptation (Sharma et al., 2016).
Positive synergies between adaptation and mitigation in the energy 
sector can include changes in production technologies and utilisation 
of technologies by various industries, changes in consumer or corporate 
behaviour, and the development of policies that alter the energy 
sector activities sufficiently to achieve a combination of reduced GHGs emissions and increased benefits for communities (Morand et  al.,

2015). However, the policy perspective must be based on the country 
circumstances, especially urbanisation, economic growth and energy 
consumption matching with the income level of the country (Wang 
8.6.2 Decision-making Approaches for Climate Resilient 
A range of different traditional economic decision support tools can 
be used to help guide resource allocation in relation to climate change 
adaptation (e.g., cost–benefit analysis, cost-effectiveness analysis, 
multi-criteria analysis) (Watkiss et al., 2016), with a strong focus on 
monetary values and the present and near-term. There are also tools 
to assess uncertainty (e.g., iterative risk management) and to guide 
decision making under uncertainty over longer time frames (through, 
e.g., real options analysis, robust decision making involving substantial 
numbers of scenarios, portfolio analysis and rule-based decision 
support for uncertainty where maximum regrets are minimised). Use

of these tools nevertheless requires human capital and skills, and more 
commonly they are applied to public rather than private (individual/ 
household) adaptation decision processes. Tools grounded in 
economics can lack sufficient consideration of which groups in society 
might gain and lose out from particular options (Sovacool et al., 2015; 
Stringer et al., 2019), neglecting to appreciate non-monetary factors 
(like well-being) which are non-economic, less tangible and harder to 
put a value on (see Section 8.3).
This section lists several groups of strategies, including mainstreaming 
and coherence; dealing with complexities through broader and 
innovative governance; provision of funding and the associated cost 
and benefit analysis; and focusing on the community and addressing 
underlying equity through transformational adaptation.
8.6.2.1 Policy Coherence, Policy Integration and Broader 
Governance Approaches
Mainstreaming and policy coherence is one of the most proposed

strategies for dealing with adaptation and mitigation as a coherent 
approach, in the context of good governance. Politics, power and 
interests influence the prospects of achieving integrated climate policy 
and development goals in practice (Naess et al., 2015). Institutional 
incoherence has led to inefficiency and ineffectiveness (Di Gregorio 
et  al., 2017). To achieve more coherent institutions and synergies, 
four major enabling conditions have been identified: (a) planned and/
or existing national laws, policies and strategies, (b) existing and 
planned financial means and measures, (c) institutional arrangements 
in the country with specific reference to climate change issues and 
(d) planned and/or existing programmes and initiatives in the country 
(Kabisch et al., 2016). Another strategy offered is to develop a ‘dual 
track approach’ at local/municipality/city level by having a local 
climate plan and/or mainstreaming plan (Duguma et al., 2014b). This

can lead to effective implementation of climate actions and diffusion of 
climate issues into local sector policies (Reckien et al., 2019). Effective 
climate policy integration (CPI) calls for four levels of coherence (Di 
Gregorio et al., 2017), namely between internal coherence (mitigation

8
1246Chapter 8 Poverty, Livelihoods and Sustainable Development
and adaptation policies objectives and policies), external coherence 
(climate change and development objectives), vertical integration 
(mainstream climate change into sectoral policies) and horizontal 
integration (overarching governance structures for cross-sectoral 
coordination).
Progress of policy integration varies from the global to local level. 
Progress in mainstreaming and coherence is emerging globally and 
has slowly made it down to the national level (Di Gregorio et al., 2017). 
Adaptation and mitigation should be mainstreamed into planning 
and implementation on food security programmes, and cross-cutting 
oversights are required to integrate land restoration, climate policy, 
food security and disaster risk management into a coherent policy 
framework (Woolf et al., 2015).
There has been an increase in the literature examining adaptation 
and mitigation synergy in the Nationally Determined Contributions

submitted by countries to the UNFCCC. Agriculture and energy 
are the two priority sectors for which there have been significant 
pledges and commitments from countries, with, to some extent, good 
alignment between adaptation and mitigation. This alignment can 
provide good opportunities to integrate both into national sectoral 
policies (Antwi-Agyei et al., 2018a). This suggests that inclusive and 
sustainable economic and social development can be achieved if 
national governments focus on developing coherent, cross-sector 
approaches that deliver potential triple wins of mitigation, adaptation 
and development.
Different governance approaches, such as polycentric governance, 
adaptive governance, multi-level governance, collaborative governance 
or network governance, are increasingly utilised to understand the 
processes of transitioning towards CRD. The potential of polycentric 
governance approaches for promoting both climate mitigation and

adaptation is well established (Cole, 2015; Abbott, 2017; Morrison 
et al., 2017a; Warner et al., 2018). Polycentric governance deals with 
active steering of local, regional, national and international actors, and 
instigates learning from experience across multiple actors, levels of 
decision making and temporal scales (Ostrom, 2010). It is the source of 
power to achieve collective goals. Polycentric actors have the framing 
power, power by design and pragmatic power (Morrison et al., 2017b). 
Polycentric governance offers new opportunities for climate action 
through more opportunities for communication, trust-building, policy 
experimentation and learning (Cole, 2015). Adaptive governance 
is understood as various interactions between actors, networks, 
organisations and institutions towards achieving a desired state of 
social-ecological systems (Chaffin et al., 2014). It requires a structure of 
nested institutions, diversity at different levels, connected by formal and

informal social networks (Dietz et al., 2003). As Brunner and Lynch (2010) 
observe, the emergence of community-based initiatives in addressing 
climate change marks the emergence of adaptive governance.
8.6.2.2 The Water–Energy–Food–Nexus Approach
Increasing demands for water, energy, food and materials are putting 
pressure on resource supply, and hence the nexus approach can inform 
transition pathways for interlinked resource systems (Johnson et al., 
2019). The nexus approach, especially the water–energy–food nexus, is used to examine synergies and trade-offs between adaptation and 
mitigation (Howells and Rogner, 2014). As reviewed by Wiegleb and 
Bruns (2018), early use of the concept was by the World Economic 
Forum in 2008 where it was emphasised that issues of economic 
growth need to be considered within water, energy and food resource 
systems. This was later published as Water Security: The Water–Food–
Energy–Climate Nexus. Another key activity was the Bonn2011 Nexus

conference. Then, in 2015, The Nexus Dialogue Programme was 
held by the UN and EU Commissions as an approach to implement 
the SDGs. UN Water underscores the water–energy–food nexus as 
central to development (Newell et al., 2019). It notes that demand for 
water, food and energy are rising due to a growing population, rapid 
urbanisation, changing diets and economic growth, and in most cases, 
the lack of knowledge on the water–energy–food nexus has often led 
to mismatches in prioritisation and decision making which hinders 
sustainable development (Mitra et al., 2020). However, the benefits of 
nexus approach are not always easily quantified and often accrue to 
local communities over time (Amjath-Babu et al., 2019).
A well-coordinated and integrated nexus approach offers opportunities 
to build resilient systems while harmonising interventions, mitigating 
trade-offs and hence improving sustainability (Biggs et  al., 2015).

This can be achieved through greater resource mobilisation and 
coordination, policy convergence across sectors and targeting nexus 
points in the broader landscape (Mpandeli et  al., 2018). Studies 
utilising the nexus approach to climate change in different places 
show considerably different results. In the southern African region, 
climate change is already affecting water–energy–food resources and 
exerting further pressure on already scarce resources. It is proposed 
that adaptation can be achieved through cross-sectoral management 
of resources, by adopting water management practices, aiming to 
produce more food and energy with less water resources and adopting 
cleaner and renewable sources of energy. This will result in saving 
water and ensuring energy security in a region that depends on 
hydro and coal energy sources (Mpandeli et al., 2018). Applying the 
nexus approach to the Hindu Kush Himalayan region identified three

challenges: increasing population and declining agricultural land, 
stagnating or declining food production and increasingly water- and 
energy-intensive food production despite water and energy scarcity 
(Rasul and Sharma, 2016). Nexus smart adaptation policies need to 
be complemented with system-wide adaptation, policy coherence and 
sectoral coordination that targets poverty and vulnerability linkages 
(Rasul and Sharma, 2016).
8.6.2.3 Community-based Approach
Another important strategy to better determine impacts of 
adaptation and mitigation and to promote inclusivity, transparency 
and accountability is the community-based approach. This approach 
also supports adaptation and mitigation indirectly through the 
strengthening of capacity and social capital. For example, in Bangkalan, 
Indonesia, the presence of high social capacity and readily available 
free agricultural inputs are two decisive factors for effective climate

change mitigation and adaptation, as well as for enhancing community 
livelihood (Sunkar and Santosa, 2018). The calls to consider Indigenous 
knowledge and Indigenous People to support integrated strategies in 
adaptation and mitigation are increasing (Ford et  al., 2016; Altieri

8
1247Poverty, Livelihoods and Sustainable Development  Chapter 8
and Nicholls, 2017; Brugnach et  al., 2017). Detailed knowledge of 
local socio-ecological contexts may offer transformational processes 
to harness synergies (Thornton and Comberti, 2017). A study in the 
Ukraine on cooperatives shows that it offers a well-established 
livelihood strategy and means to support agriculture smallholders. 
Moreover, social capital fulfils key roles in the process of capacity 
building and implementation of sustainable measures (Kopytko, 2018). 
In Indonesia, a well-known programme focusing on community-
led adaptation and mitigation activities is Proklim. It empowers 
communities to learn about climate change impacts, record data 
and plan actions for climate change (Muttaqin and Yulianti, 2019). 
Multi-stakeholder, participatory planning processes are beneficial 
to help farmers to screen and prioritise rural livelihood strategies in

Indonesia. The necessity of CRD is reflected in standard development 
interventions: water management, intensification and diversification 
of agriculture and aquaculture, education, health, food security and 
skill building for farmers (Wise et al., 2016).
8.6.3.  Future Adaptation Finance and Social and 
Economic Changes within the Context of Poverty, 
Livelihoods, Equity, Equality and Justice
8.6.3.1 Coverage of Adaptation Finance
There is still some debate on what qualifies as adaptation finance and 
how such finance should be measured (UNFCCC, 2016). According to 
the Climate Policy Initiative, adaptation finance is ‘finance with the 
aim of improving preparation and reducing climate-related risk and 
damage, for both human and natural systems, as short-term climate 
impacts will continue to exact economic, social, and environmental 
costs even if appropriate mitigation actions are taken’ (CPI, 2019). 
According to UNEP , the annual costs of adaptation in developing

countries could range from USD  140  billion to USD  300  billion by 
2030. Globally, adaptation costs are estimated to be even greater, 
with up to USD 500 billion yr−1 by 2050 under a Business-As-Usual 
scenario (UNEP , 2021). While global climate finance flows reached 
USD 579 billion on average over the 2017/18 period, there has been 
a continued heavy imbalance in favour of mitigation finance, with 
adaptation finance totalling around USD  30  billion (compared to 
USD 532 billion for mitigation), or 5% of tracked climate finance. The 
World Bank has, however, committed to increase direct adaptation 
finance to USD 50 billion over the 2020–25 period, putting the Bank’s 
adaptation finance in developing countries on par with its mitigation 
investments (World Bank, 2019a). Adaptation finance is also growing 
alongside finance for actions with both mitigation and adaptation 
benefits, for example in forestry or agriculture, which rose to just over

USD 12 billion (CPI, 2019), as well as increasing focus on adaptation 
and cross-sectoral projects. Looking only at climate finance flows 
from developed to developing countries, the OECD estimates a total 
of USD 78.9 billion mobilised in 2018, with mitigation accounting for 
70% (USD 55 billion) of the total, adaptation 21% (USD 16.8 billion) 
and cross-cutting finance making up the remainder (OECD, 2020a).
Adaptation finance funds actions to adapt to the impacts of climate 
change, yet such actions are heavily context, scale and time specific. 
Many mitigation actions in the energy sector can be easily quantified and employed across different jurisdictions. For example, solar 
photovoltaic (PV) presents an established way across a multitude 
of countries to produce low-carbon energy at a profit and reduce 
global GHG emissions. Adaptation needs, however, vary greatly from 
location to location and short-term solutions, for example investments

in irrigation technologies to improve water availability for specific 
crops in a growing season, may differ from longer-term solutions, 
for example, switching to different crops altogether. Benefits are not 
always easily quantified and often accrue to local communities over 
time rather than to investors looking for the kind of returns realised in 
mitigation actions.
Development finance institutions mainly draw on market-rate loans 
and, to a lesser extent, concessional lending and grants to finance 
adaptation actions. There are regional differences in the choice of 
instruments, too, owing to the degree of economic development: while 
most of the adaptation finance flowing to the Asia-Pacific is market-
rate debt, the vast majority of adaptation finance flowing to sub-
Saharan Africa is in the form of concessional debt or grants (Richmond 
Globally, the main sectors benefiting from adaptation finance to date

include water and waste water management; agriculture, forestry, land 
use and natural resource management; disaster risk management; and 
infrastructure, energy, and other built environment (Oliver et al., 2018). 
In recent years, this finance has moved away from concentrating on 
water and wastewater management to spread out more evenly across 
the sectors. Between 2015/16 and 2017/18, investment in water and 
wastewater management dropped from USD 11 billion to USD 9 billion, 
while investment in agriculture, forestry, land use and natural resource 
management grew from USD 5 billion to USD 7 billion, and investment 
in disaster risk management more than doubled from USD 3 billion 
to USD 7 billion (CPI, 2019). In addition, while mitigation actions are 
more easily delineated, for example wind farms in the energy sector, 
adaptation measures often need to be mainstreamed across a number 
of sectors and investment decisions.

There are strong interconnections between NBS, climate adaptation 
and mitigation actions. Ecosystem-based adaptation is a nature-
based solution that uses ecosystem services to help communities 
adapt to climate change. Examples of such approaches were covered 
in Section  8.5.2.2. For example, mangrove restoration provides 
both climate mitigation (as carbon sinks) and adaptation to climate 
change (increasing the resilience of coastal communities), while also 
supporting the implementation of a range of other SDGs (e.g., through 
increased food security). Research has found that without mangroves, 
global flood damage costs would increase by more than USD 65 billion 
a year (Menéndez et al., 2020). There is, therefore, an urgent need to 
invest in a range of NBS.

8
1248Chapter 8 Poverty, Livelihoods and Sustainable Development
Box 8.9 | Adaptation financing for the poor 
and the need for systems transition: Eastern 
Indonesian Islands
Summary
A 4-year project in Nusa Tenggara Barat Province, Indonesia, 
aimed to stimulate an adaptation pathways process. The goal 
was to support CRD in a context with low stakeholder capacity, 
high poverty, and rapid environmental and social change. On 
these archipelagic islands, livelihoods are predominantly rural, 
far from political and urban centres. The project focused on 
integrated top-down and bottom-up development planning 
that could enable CRD at the local level, linked to provincial 
and national plans.
Lessons learnt
• Substantial gradients in both climate and livelihoods in 
the island geographies necessitate fine-scale planning and 
make it difficult to scale up.
• Infrastructural investments, including roads, ports and 
irrigation, are crucial to CRD. If not well designed, such

investments are prone to maladaptation, such as exposure 
to sea level rise.
• Although some development interventions are delivering 
climate resilience, such outcomes are often haphazard, 
rather than strategically conceived, coordinated and 
delivered (Butler et al., 2016).
New financial instruments can help to support investment in, for 
example, ecosystem-based adaptation. For example, green bonds 
can raise significant amounts of capital in support of projects with 
environmental/climate benefits. The green bond market has quickly 
developed since the European Investment Bank launched the first 
green bond in 2007, with issuance growing to USD 257.7 billion in 
2019, up more than 50% on the previous year (CPI, 2019). Most green 
bonds focus on energy, buildings and transport infrastructure but 
green bond issuance to support sustainable agriculture and forestry 
has grown from USD 208 million in 2013 to USD 7.4 billion in 2018

(Wilkins, 2019). The Seychelles issued the world’s first ‘blue’ bond in 
2018 with the support of the World Bank. Similar to green bonds, blue 
bonds earmark the use of bond proceeds for specific purposes, here the 
sustainable use of marine resources (World Bank, 2018). In 2019, the 
European Bank for Reconstruction and Development issued the world’s 
first ever dedicated climate resilience bond, raising USD 700 million. 
The 5-year bond will be used to finance the Bank’s projects in climate-
resilient infrastructure (e.g., water, energy and transport), climate-
resilient business, commercial operations, climate-resilient agriculture 
and ecological systems (Bennett, 2019). While these issuances are still 
small compared to the overall green bond market, their rapid growth 
points to enormous opportunities for ecosystem-based adaptation.
Despite the growth of official adaptation funding at international and 
national levels, for the world’s poorest, adaptation to the impacts and

opportunities of climate change frequently occurs in response to L&Ds at the individual or household scale, without coordination at larger 
institutional scales (Section  8.3, 8.4; Barrett, 2014). Discussions of 
adaptation finance often occur in the context of dwindling resources 
and trade-offs: triage decisions about other investments that societies 
can tolerate suspending (Warner and Van der Geest, 2013; Tanner 
et al., 2015). In many poor, vulnerable countries, complex governance 
challenges, such as budget austerity or corruption, hamper the provision 
of such support. In the absence of adaptation funding for the poor, 
coordinated at higher scales, the costs of adaptation are borne by the 
poor at community, kin-group and household scales. Bearing the cost 
of adaptation, thus, can become, in the short term, an erosive process 
of coping that ultimately increases the likelihood that communities and 
households will remain trapped in poverty (Antwi-Agyei et al., 2018b).

In the long term, measures financing adaptation may be maladaptive, 
meaning they ultimately leave the poor at greater risk of experiencing 
climate change impacts (Section 8.4.5; Rahman and Hickey, 2019). Such 
circumstances highlight the governance gap that drives the poorest to 
rely on extreme measures to finance adaptation.
Since the AR5, there is greater documentation of the extreme measures 
and high-risk income alternatives that the world’s poorest commonly 
take to finance adaptation (Dawson, 2017; Ahmed et al., 2019). While 
still a controversial topic, clear examples of extreme adaptation 
finance measures include:
• Unauthorised international migration (McLeman, 2018)
• Informal small-scale mining of precious metals and minerals 
(Hilson and Van Bockstael, 2012; Osumanu, 2020)
• Illegal poaching of flora and fauna, including participation in 
illegal timber harvesting (Bolognesi et al., 2015)
• Illegal, unregulated or unreported fishing, including within marine

protected areas, or the coastal zones of neighbouring countries 
(Tanner et al., 2014)
• Utilisation of livelihood resources, such as boats, in smuggling 
activities, including drug and arms trafficking (Belhabib et  al., 
2020)
• Participation in piracy, extortion or kidnapping economies (Staff, 
2017).
Enabling conditions for formal adaptation finance for the poorest are 
needed to reduce reliance on high-risk, extra-legal sources of income (see 
Section 8.5.2). In general, the antidote to this emerging problem is access 
to living wages that the poor can rely on to finance adaptation. There are 
few examples of pro-poor mechanisms, programmes or institutions that 
prioritise coordinated, access to credit for proactively adapting livelihoods 
of the poor (Agrawal and Perrin, 2009). Institutions can reduce incentives 
for vulnerable people to engage in high-risk activities by including them 
in the process of adaptation governance, which aims not only to support

sustainable livelihood practices (such as farming, fishing and forestry), 
but also to guarantee land tenure (Wrathall et al., 2019). Also critical 
for risk reduction to the poor is the ability of authorities across multiple 
spatial and temporal scales to maintain social protection to reduce 
the dependency of illegal sources of income and facilitate adaptation 
(Tenzing, 2020). A range of tools exists for opening access to credit to 
poor and marginalised people whose livelihoods are most vulnerable 
(Ribot, 2013): climate insurance tools that are designed and targeted 
at the poorest and which have been properly assessed to ensure that

8
1249Poverty, Livelihoods and Sustainable Development  Chapter 8
they do not undermine other coping strategies such as risk spreading, 
programmes that ease access or subsidise loans for adaptation, mobile 
banking and mobile-based financial and risk management tools, impact 
pay-outs in the form of direct transfers and institutional support for 
hometown associations. International governance arrangements, such 
as the Warsaw International Mechanism on Loss and Damage, might 
aim primarily to clear the financing gap between global financial and 
risk management institutions and the pocketbooks of the poorest 
(Wrathall et al., 2015).
8.7 Conclusion
The chapter has moved beyond the IPCC WGII AR5 in that it lays 
out structural elements of vulnerability and provides quantitative 
information about climate-related vulnerability hotspots globally 
complemented by the assessment of poverty, local livelihood 
vulnerability and sustainable development. The assessment of non-

economic losses, and enabling and supportive environments for 
adaptation are also new aspects.
The chapter provides additional evidence on livelihood resources at 
local levels that have been impacted by different climate hazards and, 
globally, that specific hazards (namely, drought and rising temperatures) 
are more threatening and destabilising to livelihoods than others. 
There is robust evidence that coping and adaptive capacities erode 
with increasing GMT—substantial differences are expected between 
a GMT increase of less than 1.5°C compared to an increase of more 
than 3°C—and the frequencies of climate hazards, such as heat waves, 
droughts or floods is likely to increase substantially. Nevertheless, this 
assessment also revealed that the adverse impacts of climate change 
for livelihoods and multidimensional poverty differ substantially 
between different population groups exposed to climate hazards, 
based on the socioeconomic and governance context. Consequently,

societal impacts of climate change need to be understood in the 
broader context of development and the development challenges that 
influence exposure, vulnerability and adaptation.
There is robust evidence of the impacts of all climate hazards on 
the key livelihood resources that the poor depend on. There is high 
confidence that two climate hazards pose high risk to a broad range 
of livelihood resources: warming trends and droughts. Meanwhile, 
the livelihood resources that are globally at greatest risk include 
people’s bodily health, food security and agricultural productivity (high 
confidence). Evidence suggests that the fundamental challenge of 
climate change to livelihoods is that rising temperatures, drought and 
other hazards endanger human life, and the lives of plants and animals 
that humans rely on to survive (high confidence). There is now robust 
evidence that the impacts of climate change on livelihoods are driving

people to migrate in search of alternative incomes, and this tendency 
will increase with rising temperatures. Of greatest concern are people 
whose development context is compromised by war, conflict and 
extreme poverty and inequality, such as refugee populations and 
displaced people.
This chapter reports quantitative evidence of human vulnerability and 
therefore identifies various spatial hotspots of vulnerability emerging in regional clusters. It reports that significantly more people are living 
in highly vulnerable context conditions compared to those living in 
low vulnerability contexts. The assessment revealed that more than 
3 million people are living in countries classified as very highly or highly 
vulnerable (depending on the assessment method and the number 
of classes used, and countries included). In contrast, approximately 
1.8 billion people reside in low or very low vulnerable country contexts. 
Studies estimate the population in the most vulnerable regions to

almost double by the year 2100 (Section  8.4.5.2). When near-term 
estimates are used, the population growth in highly vulnerable 
countries is still significantly higher compared to less vulnerable 
countries. Consequently, this assessment points towards the fact 
that even if we do not know how societal or community vulnerability 
will develop in specific areas, it is very likely that in the future, 
more people will live in destabilised and highly vulnerable country 
contexts compared to the population today. However, it is important 
to note that the scientific literature also underscores that trends in 
vulnerability differ significantly between different world regions and 
within countries.
The chapter also advances knowledge in terms of the interconnections 
between human vulnerability, observed losses and adverse 
consequences. The assessment shows that statistically relevant 
differences in observed fatalities per hazard event can be explained

by hazard intensity and frequency, and are also linked to different 
levels of vulnerability of a region exposed. Despite all uncertainties 
about future change, the assessed literature clearly provides an 
accurate picture of the expected societal impacts of climate change, 
the requirements for successful adaptation and the need to address 
the adaptation gap from the perspective of vulnerability.
The chapter shows that intersectionality approaches are becoming 
increasingly central to grasping how differential vulnerability to climate 
hazards is experienced by different social groups. Intersectionality 
recognises that age, gender, class, race and ethnicity are reinforcing 
social phenomena, shaping social inequalities and experiences of the 
world, and also intersect with climate hazards and vulnerability. Our 
assessment reveals the central role of maladaptation with robust new 
evidence on negative consequences of interventions on different social

groups. Well-intentioned adaptation can exacerbate past and existing 
vulnerabilities and undermine livelihoods. There is also evidence that, 
despite maladaptation, inclusive and sustainable development at the 
local level can reduce vulnerability.
Since AR5 L&D has taken much more central stage in sustainable 
development, policy, and poverty and livelihoods discourse. While 
there is ambiguity about what constitutes L&D, the chapter highlights 
new evidence of observed L&Ds, including slow-onset impacts (e.g., 
sea level rise and drought). Our assessment reveals that there is a 
body of literature that explicitly addresses non-economic losses and 
that these are experienced everywhere now due to human-induced 
climate change. These are coupled with advancements in the science 
of extreme event attribution with new focus on adaptation metrics and 
vulnerability assessments.
This assessment also identifies emerging evidence of linkages between

8
1250Chapter 8 Poverty, Livelihoods and Sustainable Development
suggests that losses are leading to a range of shifts in livelihoods, 
which may be easier for some social groups than others, and which 
have implications for livelihoods security across transboundary 
scales. Yet, climate change is only one driver. Untangling the drivers 
of vulnerability using intersectionality approaches is also critical. Our 
quantification of vulnerability hotspots supports this concern. It is 
critical to seek further knowledge on the extent of livelihood shifts 
among the most vulnerable resulting from specific NELD, for whom, 
where and at what scale. Gaps in knowledge highlight this as an area 
that needs further work in order to develop and understand further the 
full extent and reach of the relationships between extreme and slow-
onset climate events, non-economic losses and shifting livelihoods.
This chapter builds on AR5 and the IPCC SR 1.5°C on key limits to the

adaptation of natural and social systems that are compounded by the 
effects of poverty and inequality, such as on water scarcity, ecosystems 
alteration and degradation, coastal cities in relation to sea level rise, 
cyclones and coastal erosion, food systems and human health (high 
confidence). Climate change risks could have substantial negative 
impacts on climate-sensitive livelihoods of smallholder farmers, 
fisheries communities, Indigenous People, urban poor and informal 
settlements, with limits to adaptation evidenced in the loss income, 
ecosystems, health and increasing migration (high confidence). The 
chapter also addresses how ecological thresholds and socioeconomic 
determinants of vulnerabilities are linked to soft and hard adaptation 
limits, including the potential and magnitude of livelihood risks in the 
future. For instance, a hard limit associated with losses of coral reefs 
in a 1.5°C warmer world will lead to substantial loss of income and

livelihoods for coastal communities (high confidence), including loss 
of culture- and place-based attachment (medium confidence). Hard 
adaptation limits are expected for the Arctic ecosystem. Their threshold 
will affect residents of Arctic regions dependent on hunting and 
fishing livelihoods (high confidence). New emerging considerations 
to ecological limits to adaptation, such as severe glacier retreat 
and Amazon forest dieback, is expected to affect the livelihoods of 
smallholder farmers and Indigenous People through crop yield failures, 
biodiversity loss, reduced hydropower capacity and health (medium 
evidence). While a knowledge gap remains on the projected risks 
of increasing global temperature to climate-sensitive livelihoods 
among Global South countries and specific groups of people, current 
observations show negative impacts to livelihoods for tens to hundreds 
of millions of people. Thus, without sustainable, equitable and urgent

adaptation measures, maladaptation risks are likely to further increase 
vulnerability, marginalisation and ecological tipping points among the 
poor within countries (medium confidence).
Evidence on the kinds of enabling environment required paints a com-
plex picture. The assessment highlights the interaction of different 
capital assets within the broader context of key enablers in shaping 
the overall enabling environment for adaptation, which itself is highly 
context dependent. In this regard, countries present different starting 
points for adaptation, with some requiring, for example, more of an 
emphasis on institutional capacity building; others requiring transfor -
mation to the broader legal and political conditions. Capitals are not 
necessarily substitutable but rather act as an assemblage in shaping 
both perceptions of climate risk and the necessity and appropriateness

of actions. At the same time there is robust evidence that livelihoods that depend strongly on natural capital for both subsistence and as 
a source of income are particularly sensitive to climate risks; and are 
where perhaps adaptive actions are most urgently needed, even with 
smaller rises in temperature under the most optimistic scenarios. This 
applies to both the Global South and the Global North. Investments in 
any form of capital asset to support adaptation need to be mindful of 
reinforcing existing inequalities and introducing new ones, particularly 
if transformation takes place. This also underscores the importance of 
inclusive, polycentric governance in ensuring the voices of all groups 
are heard and that wide-ranging knowledge types are incorporated 
in decision making, nevertheless recognising that trade-offs are in-
evitable.
The chapter also highlights and provides quantitative evidence that 
adaptation strategies need to go beyond the idea of adapting to warming

levels only. Adaptation strategies have to reduce the adaptation gap 
and therewith reduce human vulnerability independent of a specific 
climatic hazard. It has been shown that adaptation strategies that 
explicitly address poverty and inequities, and also consider rights-
based approaches can generate co-benefits for resilience building of 
most vulnerable groups and for sustainable development.

8
1251Poverty, Livelihoods and Sustainable Development  Chapter 8
FAQ 8.2 | Which world regions are highly vulnerable and how many people live there?
A mix of multiple development challenges, such as poverty, hunger, conflict and environmental degradation, make countries and whole regions 
vulnerable to climate change. Many of the people in the most vulnerable situations and in the most vulnerable regions are also highly exposed 
to climate hazards, such as droughts, floods or sea level rise at present and will become increasingly so in the future. Studies estimate that 
around 3.3 to 3.6 billion people are living in regions classified as highly vulnerable to climate change impacts, which is significantly higher than 
the number of people who reside in regions classified as least vulnerable. The most vulnerable regions include East, Central and West Africa, 
South Asia, Micronesia and Melanesia, and Central America.

When a country or region is considered ‘vulnerable’ to climate change this means that climate hazards (e.g., drought, 
flood, heatwaves) have a very negative impact because there is a high number of people in these areas that lack 
the ability or opportunity to cope and adapt to such events, due to, for example, high average poverty, inequality 
and lack of institutional support. This vulnerability could be due to many different development challenges that 
all come together and influence each other, such as poverty, lack of access to basic infrastructure services, high 
numbers of uprooted people, state fragility, low or below average life expectancy and biodiversity degradation. 
These structural social issues often affect regions for many decades and make it difficult for the state and for 
individuals to respond to climate change and climate-related hazards.
For example, if a region is already characterised by poverty and struggling to feed its population and provide

adequate access to basic infrastructure services, such as water and sanitation, this makes them vulnerable. If this 
region is then faced with an increased number of extremely dry years, this exposes them to drought and will make 
things even harder causing more hunger, poverty and worsened health—these are climate impacts.
Most vulnerable regions are in Africa, as well as in South Asia, the Pacific and the Caribbean. In these regions, there 
are often multiple neighbouring countries that all are highly vulnerable, for example in Central and West Africa. 
These regional clusters require special attention.Frequently Asked Questions
FAQ 8.1 | Why are people who are poor and disadvantaged especially vulnerable to climate change and why do 
climate change impacts worsen inequality?
Poor people and their livelihoods are especially vulnerable to climate change because they usually have fewer assets and less access to

funding, technologies and political influence. Combined, these constraints mean they have fewer resources to adapt to climate change impacts. 
Climate change impacts tend to worsen inequalities because they disproportionately affect disadvantaged groups. This in turn further increases 
their vulnerability to climate change impacts and reduces their ability to cope and recover.
Climate change and related hazards (e.g., droughts, floods, heat stress, etc.) affect many aspects of people’s lives—
such as their health, access to food and housing, or their source of income such as crops or fish stocks—and many 
will have to adapt their way of life in order to deal with these impacts. People who are poor and have few resources 
with which to adapt are thus much more seriously negatively affected by climate-related hazards. ‘Vulnerability’ 
is when a person or community is not able to cope and adapt to climate-related hazards. For example, if someone

who is very rich has their house washed away in a flood, this is terrible, but they often have more resources to 
rebuild, have insurances that support recovery and maybe even build a house that is not in a flood-prone area. 
Whereas for someone who is very poor and who does not live in a state that provides support, the loss of their 
house in a flood could mean homelessness. This example shows that the same climate hazard (flood) can have a very 
different impact on people depending on their vulnerability (their capacity to cope and adapt to hazards).
It is not just poverty that can make people more vulnerable to climate change and climate-related hazards. 
Disadvantage due to discrimination, gender and income inequalities and lack of access to resources (e.g., those 
with disabilities or of minority groups) can mean these groups have fewer resources with which to prepare and

react to climate change and to cope with and recover from its adverse effects. They are therefore more vulnerable. 
This vulnerability can then increase due to climate change impacts in a vicious cycle unless adaptation measures are 
supported and made possible.

8
1252Chapter 8 Poverty, Livelihoods and Sustainable Development
FAQ 8.3 | How does and will climate change interact with other global trends (e.g., urbanisation, economic 
globalisation) and shocks (e.g., COVID-19) to influence livelihoods of the poor?
A range of local, regional and global economic and political processes already underway have put the livelihoods of the poor at risk. These 
processes include urbanisation, industrialisation, technological transformation, monetisation of rural economies, increasing reliance on wages, 
and inequality at national and international levels. Climate change intersects with these processes.
The world’s poorest already struggle to provide for themselves and their families in their pursuit of livelihoods. 
Despite hard work there are many factors beyond an individual’s control that can make earning a living very

difficult. Climate change is one problem among many that puts stress on livelihoods. Poor and marginal groups 
disproportionately bear impacts of climate change, in ways that accelerate transitions from traditional livelihoods, 
such as rural farming, to wage jobs in urban areas. Where adaptation measures are insufficient and where the poor 
are excluded from decision making, these livelihood transitions can be severely destabilising.
For example, climate change may alter the frequency or intensity of hazards that threaten the viability of a 
community’s traditional farming or fishing livelihoods. Local farmers or fishers are then forced to adapt how they 
farm or fish or abandon livelihood practices entirely. The latter may mean migrating to a city to find work. As 
many communities face the same challenge, this intersects with a global trend that is affecting billions of lives and

livelihoods—urbanisation—as seen in the rapid growth of informal settlements at the peripheries of cities around 
the world, particularly rapidly growing mega-cities in Africa, Asia and Latin America. These developments will be 
accelerated by negative impacts of climate change and increase risks that larger segments of the population enter 
conditions of persistent poverty.
At the same time, people whose livelihoods have been upended by climate change are subject to new threats, 
such as the global COVID-19 pandemic, which has shone a light on the plight of the most vulnerable people. For 
example, the elderly, Indigenous Peoples and Communities of Colour were disproportionately severely impacted by 
COVID-19; also the indirect economic consequences particularly hit the poor. Hence, COVID-19 demonstrates that 
the livelihoods of the poorest and most marginalised are vulnerable to other global trends beyond climate change.

Also, most severe impacts are expected in regions that are already characterised by high levels of systemic human 
vulnerability.There are also highly vulnerable groups and individuals within less vulnerable regions. For example, marginalised, 
disadvantaged and poor minorities within highly affluent cities. Programmes that aim to support adaptation to 
climate change need to focus on reducing the vulnerability of individuals, groups, countries and regions.BOX FAQ 8.2 (continued)

8
1253Poverty, Livelihoods and Sustainable Development Chapter 8
FAQ 8.4 | What can be done to help reduce the risks from climate change, especially for the poor?
Public and private investment in different types of assets can help reduce risks from climate change. Exactly which assets require investment 
depends on the specific situation. However, the provision of access to basic services, such as water and sanitation, education and health care as well as the importance of reducing inequity is shown within the assessment for many regions. The poor have fewer resources to invest, so in poorer countries greater public investment is needed. Legal, social, political, institution and economic interventions can alter human behaviour, though care must be taken that these do not amplify existing inequalities, create new inequalities or reduce future adaptation options.

Adaptation can help to reduce risks for the poor and requires both public and private investment in various natural

assets (e.g., mangroves, farmland, wetlands), human assets (e.g., health, skills, Indigenous knowledge), physical assets (e.g., mobile phone connectivity, housing, electricity, technology), financial assets (e.g., savings, credit) and social assets (e.g., social networks, membership of organisations such as farmer cooperatives). Often, the poor have the least to invest, so poverty can reduce adaptation options. Sometimes people migrate as a reaction to floods or droughts, though the poorest groups often lack the resources to move. Exactly what needs investing in to reduce risks varies according to the scale and livelihood system in need of adaptation. In general, risks can be reduced through a range of different technological and engineering approaches (for example, building sea defences to reduce storm surge impacts), as well as ecosystem-based approaches (such as replanting mangroves, altering the types of crops grown, changing the timing of farming activities, or using

At the same time, legal, social, political, institutional and economic solutions can alter human behaviour (e.g., 
through enforcement of building codes to prevent construction on low-lying land prone to flooding, timely provision of weather information and early warning systems, knowledge-sharing activities, including adaptation strategies grounded in Indigenous knowledge, crop insurance schemes, incentives such as payments to stop people cutting down trees or to enable them to plant them and social protection to provide a safety net in times of crisis).
The poorest groups often require greater public adaptation investments. Efforts to support adaptation need to be

mindful of reinforcing existing inequalities and introducing new ones, making sure they are inclusive, culturally sensitive and that the voices of all groups of people are heard. It is also important that adaptations which reduce immediate risks for the poor do not rule out adaptation options that could help them later on or which could cause them to increase their emissions. Political will is needed to put people at the centre of climate change risk reduction efforts, including support for their livelihoods.
FAQ 8.5 | How do present adaptation and future responses to climate change affect poverty and inequality?

Present adaptation can help to reduce the current and possibly future impacts of climate change. Future responses to climate change can reduce poverty and inequality, and even help transition toward climate-resilient livelihoods and climate resilient development. Pro-poor adaptation planning is necessary to ensure future risks for the poor are being accounted for and the inequality underlying the poverty is being addressed.
There are many ways in which poverty and inequality are influenced by climate change. The livelihood sources of the poor are likely to be affected and cumulative effects of losses and damages, and may influence future poverty. There are cases when present adaptation worsens future poverty and exacerbates inequality—this is called maladaptation. The risks of maladaptation are greater in societies characterised by high inequality, and in many cases the poor and most vulnerable groups are the ones most adversely affected.

Effective decision making in adaptation should be informed by past, present and future climate data, information 
and scenarios to cater for reliable plans and actions for climate-resilient livelihoods. Adaptation lessons from the past play an important role in decision making regarding responses to climate change. There is an emerging debate on the role of learning, particularly forward-looking (anticipatory) learning, as a key element or important aspect for adaptation and resilience in the context of climate change. Memory, monitoring of key drivers of change, scenario planning and measuring anticipatory capacity are seen as crucial ingredients for future adaptation and resilience pathways, and, hence overcoming maladaptation. Moreover, climate resilient development calls for ensuring synergies between adaptation, mitigation and development are maximised, while trade-offs, especially those affecting the poor, are minimised.

9
1289Africa Chapter 9
Overall Key Messages
Africa is one of the lowest contributors to greenhouse gas 
emissions causing climate change, yet key development sectors have already experienced widespread losses and damages attributable to human-induced climate change, including biodiversity loss, water shortages, reduced food production, loss of lives and reduced economic growth (high confidence
1). 
{9.1.1, 9.1.6, 9.2, 9.6.1, 9.8.2, 9.10.2, 9.11.1, Box 9.4}
Between 1.5°C and 2°C global warming—assuming localised

and incremental adaptation—negative impacts are projected to become widespread and severe with reduced food production, reduced economic growth, increased inequality and poverty, biodiversity loss, increased human morbidity and mortality (high confidence). Limiting global warming to 1.5°C is expected to substantially reduce damages to African economies, agriculture, human health, and ecosystems compared to higher levels of global warming (high confidence). {9.2, 9.6.2, 9.8.2, 9.8.5, 9.10.2, 9.11.2}
Exposure and vulnerability to climate change in Africa

are multi-dimensional with socioeconomic, political and environmental factors intersecting (very high confidence). Africans are disproportionately employed in climate-exposed sectors: 55–62% of the sub-Saharan workforce is employed in agriculture and 95% of cropland is rainfed. In rural Africa, poor and female-headed households face greater livelihood risks from climate hazards. In urban areas, growing informal settlements without basic services increase the vulnerability of large populations to climate hazards, especially women, children and the elderly. {9.8.1, 9.9.1, 9.9.3, 9.11.4, Box 9.1}
Adaptation in Africa has multiple benefits, and most assessed 
adaptation options have medium effectiveness at reducing risks for present-day global warming, but their efficacy at future warming levels is largely unknown (high confidence). {9.3, 9.6.4, 9.8.3, 9.11.4}
Enabling Climate Resilient DevelopmentClimate-related research in Africa faces severe data constraints,

as well as inequities in funding and research leadership that reduces adaptive capacity (very high confidence). Many countries 
lack regularly reporting weather stations, and data access is often limited. From 1990–2019, research on Africa received just 3.8% of climate-related research funding globally: 78% of this funding for Africa went to EU and north American institutions and only 14.5% to African institutions. The number of climate research publications with locally based authors are among the lowest globally and research led by external researchers may focus less on local priorities. Increased funding for African partners, and direct control of research design and 

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.resources can provide more actionable insights on climate risks and 
adaptation options in Africa. {9.1,5 9.4.5, 9.5.2}
Adaptation generally is cost-effective, but annual finance 
flows targeting adaptation for Africa are billions of US dollars less than the lowest adaptation cost estimates for near-term climate change (high confidence). Finance has not targeted more 
vulnerable countries (high confidence). From 2014–2018 more finance commitments were debt than grants and—excluding multilateral development banks—only 46% of commitments were disbursed (compared to 96% for other development projects). {9.4.1}
Adaptation costs will rise rapidly with global warming (very

high confidence). Increasing public and private finance flows by billions of dollars per year, increasing direct access to multilateral funds, strengthening project pipeline development and shifting more finance to project implementation would help realise transformative adaptation in Africa (high confidence). Concessional finance will be required for adaptation in low-income settings (high confidence). Aligning sovereign debt relief with climate goals could increase finance by redirecting debt-servicing payments to climate resilience. {9.4.1}
Governance for climate resilient development includes long-
term planning, all-of-government approaches, transboundary cooperation and benefit-sharing, development pathways that increase adaptation and mitigation and reduce inequality, and implementation of Nationally Determined Contributions (NDCs) (high confidence). {9.3.2, 9.4.2, 9.4.3}
Cross-sectoral ‘nexus’ approaches provide significant oppor -

tunities for large co-benefits and/or avoided damages (very 
high confidence). For example, climate change adaptation benefits pandemic preparedness, ‘One Health’ approaches benefit human and ecosystem health, and ecosystem-based adaptation can deliver adap-tation and emissions mitigation (high confidence). {9.4.3, 9.6.4, 9.11.5; Box 9.6}
Without cross-sectoral, transboundary and long-term planning,

adaptation and mitigation response options in one sector can become response risks, exacerbating impacts in other sectors and causing maladaptation (very high confidence). For example, maintaining indigenous forest benefits biodiversity and reduces greenhouse gas emissions, but afforestation—or wrongly targeting ancient grasslands and savannas for reforestation—harms water security and biodiversity, and can increase carbon loss to fire and drought. Planned hydropower projects may increase risk as rainfall changes impact water, energy and food security, exacerbating trade-offs between users, including across countries. {9.4.3, Boxes 9.3, 9.5}
Robust legislative frameworks that develop or amend laws to 
mainstream climate change into their empowerment and plan-ning provisions will facilitate effective design and implemen-

9
1290Chapter 9 Africa
tation of climate change response options (high confidence). 
{9.4.4}
Climate information services that are demand driven and 
context specific (e.g., for agriculture or health) combined with 
climate change literacy can be the difference between coping 
and informed adaptation responses (high confidence). Across 
33 African countries, 23–66% of people are aware of human-caused 
climate change—with larger variation at sub-national scales (e.g., 
5–71% among states in Nigeria). Climate change literacy increases 
with education level but is undermined by poverty, and literacy rates 
average 12.8% lower for women than men. Around 71% of Africans 
that are aware of climate change agree it should be stopped. Production 
of salient climate information in Africa is hindered by limited availability 
of and access to weather and climate data. {9.4.5, 9.5.1, 9.8.4, 9.10.3}
Ecosystem-based adaptation can reduce climate risk while

providing social, economic and environmental benefits (high 
confidence). Direct human dependence on ecosystem services in 
Africa is high. Ecosystem protection and restoration, conservation 
agriculture practices, sustainable land management, and integrated 
catchment management can support climate resilience. Ecosystem-
based adaptation can cost less than grey infrastructure in human 
settlements (e.g., using wetlands and mangroves as coastal protection). 
{9.6.4, 9.7.3, 9.8.3, 9.9.5, 9.12.3, Box 9.7}
Observed Impacts and Projected Risks
Climate
Increasing mean and extreme temperature trends across 
Africa are attributable to human-caused climate change (high 
confidence). {9.5.1, 9.5.2}
Climate change has increased heat waves (high confidence) 
and drought (medium confidence) on land, and doubled the 
probability of marine heatwaves around most of Africa (high 
confidence). Multi-year droughts have become more frequent in west

Africa, and the 2015–2017 Cape Town drought was three times more 
likely2 due to human-caused climate change. {9.5.3–7, 9.5.10}
Increases in drought frequency and duration are projected over 
large parts of southern Africa above 1.5°C global warming 
(high confidence), with decreased precipitation in North Africa 
at 2°C global warming (high confidence), and above 3°C global 
warming, meteorological drought frequency will increase, and 
duration will double from approximately 2 months to 4 months 
in parts of North Africa, the western Sahel and southern Africa 
(medium confidence). {9.5.2, 9.5.3, 9.5.6.}
Frequency and intensity of heavy rainfall events will increase at 
all levels of global warming (except in north and southwestern 

lies within the 17–83% probability range.Africa), increasing exposure to pluvial and riverine flooding 
(high confidence). {9.5.3–7, 9.7}
Glaciers on the Rwenzoris and Mt Kenya are projected to disappear 
by 2030, and by 2040 on Kilimanjaro (medium confidence). {9.5.8}
In east and southern Africa, tropical cyclones making landfall 
are projected to become less frequent but have more intense 
rainfall and higher wind speeds at increasing global warming 
(medium confidence). {9.5.7}
Heat waves on land, in lakes and in the ocean will increase

considerably in magnitude and duration with increasing global 
warming (very high confidence). Under a 1.5°C-compatible 
scenario, children born in Africa in 2020 are likely to be exposed 
to 4–8  times more heat waves compared to people born in 1960, 
increasing to 5–10  times for 2.4°C global warming. The annual 
number of days above potentially lethal heat thresholds reaches 50–
150 in west Africa at 1.6°C global warming, 100–150 in central Africa 
at 2.5°C, and 200–300 over tropical Africa for >4°C. {9.5.2, 9.5.3, 
9.5.4, 9.5.5, 9.5.6, 9.7.2.1}
Most African countries will enter unprecedented high temperature 
climates earlier in this century than generally wealthier, higher 
latitude countries, emphasising the urgency of adaptation 
measures in Africa (high confidence). {9.5.1}
Multiple African countries are projected to face compounding 
risks from reduced food production across crops, livestock 
and fisheries, increased heat-related mortality, heat-related

loss of labour productivity and flooding from sea level rise, 
especially in west Africa (high confidence). {9.8.2, 9.8.5, 9.9.4, 
9.10.2, 9.11.2}
Water
Recent extreme variability in rainfall and river discharge 
(around −50% to +50% relative to long-term historical means) 
across Africa have had largely negative and multi-sector 
impacts across water-dependent sectors (high confidence).  
{9.7.2, 9.10.2} Hydrological variability and  water scarcity have induced 
cascading impacts from water supply provision and/or hydroelectric 
power production to health, economies, tourism, food, disaster risk 
response capacity and increased inequality of water access. {Box 9.4}
Extreme hydrological variability is projected to progressively 
amplify under all future climate change scenarios relative to 
the current baseline, depending on region (high confidence). 
Projections of numbers of people exposed to water stress by the 2050s

vary widely—decreases/increases by hundreds of millions, with higher 
numbers for increases—with disagreement among global climate

9
1291Africa  Chapter 9
models on the major factor driving these large ranges. Populations in 
drylands are projected to double by 2050. Projected changes present 
heightened cross-cutting risks to water-dependent sectors, and require 
planning under deep uncertainty for the wide range of extremes 
expected in future. {9.7.1, 9.7.2, 9.9.4}
Economy and livelihoods
Climate change has reduced economic growth across Africa, 
increasing income inequality between African countries 
and those in temperate northern hemisphere climates (high 
confidence). One estimate suggests gross domestic product (GDP) 
per capita for 1991–2010 in Africa was on average 13.6% lower 
than if climate change had not occurred. Impacts manifest largely 
through losses in agriculture, as well as tourism, manufacturing and 
infrastructure. {9.6.3, 9.11.1}
Climate variability and change undermine educational attainment 
(high agreement, medium evidence). High temperatures, low

rainfall and flooding, especially in the growing season, may mean 
children are removed from school to assist income generation. Early life 
undernutrition associated with low harvests or weather-related food 
supply interruptions can impair cognitive development. {9.11.1.2}
Limiting global warming to 1.5°C is very likely to positively 
impact GDP per capita across Africa. Increasing economic damage 
forecasts under high emissions diverge from low emission pathways 
by 2030. Inequalities between African countries are projected to widen 
with increased warming. Across nearly all African countries, GDP per 
capita is projected to be at least 5% higher by 2050 and 10–20% higher 
by 2100 if global warming is held to 1.5°C compared with 2°C. {9.11.2}
Food systems
In Africa, climate change is reducing crop yields and productivity 
(high confidence). Agricultural productivity growth has been reduced 
by 34% since 1961 due to climate change, more than any other

region. Maize and wheat yields decreased on average 5.8% and 2.3%, 
respectively in sub-Saharan Africa due to climate change in the period 
1974–2008. Farmers and pastoralists perceive the climate to have 
changed and over two-thirds of Africans perceive climate conditions for 
agricultural production have worsened over the past 10 years. Woody 
plant encroachment has reduced fodder availability. {9.4.5, 9.6.1, 9.8.2}
Future warming will negatively affect food systems in Africa 
by shortening growing seasons and increasing water stress 
(high confidence). By 1.5°C global warming, yields are projected 
to decline for olives (north Africa) and sorghum (west Africa) with a 
decline in suitable areas for coffee and tea (east Africa). Although yield 
declines for some crops may be partially compensated by increasing 
atmospheric CO 2 concentrations, global warming above 2°C will result 
in yield reductions for staple crops across most of Africa compared

to 2005 yields (e.g., 20–40% decline in west African maize yields), 
even when considering adaptation options and increasing CO 2 
(medium confidence). Relative to 1986–2005, global warming of 3°C 
is projected to reduce labour capacity in agriculture by 30–50% in sub-
Saharan Africa. {9.8.2, 9.8.3, 9.11.2}Climate change threatens livestock production across 
Africa (high agreement, low evidence). Rangeland net primary 
productivity is projected to decline 42% for west Africa by 2050 at 2°C 
global warming. Vector-borne livestock diseases and the duration of 
severe heat stress are both projected to become more prevalent under 
warming. {9.8.2}
   Climate change poses a significant threat to African marine and 
freshwater fisheries (high confidence). Fisheries provide the main 
source of protein for approximately 200  million people in Africa and 
support the livelihoods of 12.3 million people. At 1.5°C global warming,

marine fish catch potential decreases 3–41%, and decreases by 12–69% 
at 4.3°C by 2081–2100 relative to 1986–2005 levels, with the highest 
declines for tropical countries. Under 1.7°C global warming, reduced fish 
harvests could leave 1.2–70 million people in Africa vulnerable to iron 
deficiencies, up to 188 million for vitamin A deficiencies, and 285 million 
for vitamin B 12 and omega-3 fatty acids by mid-century. For inland 
fisheries, 55–68% of commercially harvested fish species are vulnerable 
to extinction under 2.5°C global warming by 2071–2100. {9.8.5}
Health
Climate variability and change already negatively impacts the 
health of tens of millions of Africans through exposure to non-
optimal temperatures and extreme weather, and increased 
range and transmission of infectious diseases (high confidence). 
{9.10.1}
Mortality and morbidity will escalate with further global 
warming, placing additional strain on health and economic

systems (high confidence). Above 2°C of global warming, distribution 
and seasonal transmission of vector-borne diseases is expected to 
increase, exposing tens of millions more people, mostly in west, east 
and southern Africa (high confidence). Above 1.5°C risk of heat-related 
deaths rises sharply (medium confidence), with at least 15 additional 
deaths per 100,000 annually across large parts of Africa, reaching 
50–180 additional deaths per 100,000 people annually in regions 
of North, West, and East Africa for 2.5°C, and increasing to 200–600 
per 100,000 people annually for 4.4°C. Above 2°C global warming, 
thousands to tens of thousands of additional cases of diarrhoeal 
disease are projected, mainly in west, central and east Africa (medium 
confidence). These changes risk undermining improvements in health 
from future socioeconomic development (high agreement, medium 
evidence). {9.10.2, Fig. 9.35}
Human settlements

Exposure of people, assets and infrastructure to climate hazards 
is increasing in Africa compounded by rapid urbanisation, 
infrastructure deficit, and growing population in informal 
settlements (high confidence).
High population growth and urbanisation in low-elevation 
coastal zones will be a major driver of exposure to sea level rise 
in the next 50 years (high confidence). By 2030, 108–116 million 
people in Africa will be exposed to sea level rise (compared to

9
1292Chapter 9 Africa
54 million in 2000), increasing to 190–245 million by 2060 (medium 
confidence). {9.9.1, 9.9.4}
Africa’s rapidly growing cities will be hotspots of risks from 
climate change and climate-induced in-migration, which could 
amplify pre-existing stresses related to poverty, informality, 
social and economic exclusion, and governance (high confidence). 
Urban population exposure to extreme heat is projected to increase from 
2 billion person-days per year in 1985–2005 to 45 billion person-days 
by the 2060s (1.7°C global warming with low population growth) and 
to 95  billion person-days (2.8°C global warming with medium-high 
population growth), with greatest exposure in west Africa. Under relatively 
low population growth scenarios, the sensitive populations (people under 
5 or over 64 years old) in African cities exposed to heat waves of at least 
15 days above 42°C in African cities is projected to increase from around

27  million in 2010 to 360  million by 2100 for 1.8°C global warming 
(Shared Socioeconomic Pathway 1 (SSP1)) and 440  million (SSP5) for 
>4°C global warming. Compared to 2000, urbanisation is projected to 
increase urban land extent exposed to arid conditions by around 700% 
and exposure to high-frequency flooding by 2600% across west, central 
and east Africa by 2030. {9.9.1, 9.9.2, 9.9.4, Box 9.8}
Migration
Most climate-related migration observed currently is within 
countries or between neighbouring countries, rather than to 
distant high-income countries (high confidence). Urbanisation has 
increased when rural livelihoods were negatively impacted by low rainfall. 
Over 2.6  million and 3.4  million new weather-related displacements 
occurred in sub-Saharan Africa in 2018 and 2019. {Box 9.8}
Climate change is projected to increase migration, especially 
internal and rural to urban migration (high agreement, medium

evidence). With 1.7°C global warming by 2050, 17–40 million people 
could migrate internally in sub-Saharan Africa, increasing to 56–
86 million for 2.5°C (>60% in west Africa) due to water stress, reduced 
crop productivity and sea level rise. This is a lower-bound estimate 
excluding rapid-onset hazards such as floods and tropical cyclones. 
{Box 9.8}
Climate-related infrastructure damage and repairs will be a 
financially significant burden to countries (high confidence). 
Without adaptation, aggregate damages from sea level rise and coastal 
extremes to 12 major African coastal cities in 2050 under medium and 
high emissions scenarios will be USD 65 billion and USD 86.5 billion, 
respectively. Potential costs of up to USD  183.6  billion may be 
incurred through 2100 to maintain existing road networks damaged 
from temperature and precipitation changes due to climate change. 
Increased rainfall variability is expected to affect electricity prices in

countries highly dependent on hydropower. {9.9.4, Boxes 9.4, 9.5}
Increasing CO 2 levels and climate change are destroying marine 
biodiversity, reducing lake productivity, and changing animal and vegetation distributions (high confidence). Impacts include 
repeated mass coral bleaching events in east Africa, and uphill (birds) 
or poleward (marine species) shifts in geographic distributions. For 
vegetation, the overall observed trend is woody plant expansion, 
particularly into grasslands and savannas, reducing grazing land and 
water supplies. {9.6.1, 9.6.2, 9.8.2}
The outcome of the effect of the interaction of increasing 
CO 2 and aridity that operate in opposing directions on future 
biome distributions is highly uncertain. Further increasing CO 2 
concentrations could increase woody plant cover, but increasing aridity 
could counteract this, destabilising forest and peatland carbon stores 
in central Africa (low confidence). Changes in vegetation cover could

occur rapidly if tipping points are crossed {9.6.1, 9.6.2, 9.8.2}
African biodiversity loss is projected to be widespread and 
escalating with every 0.5°C increase above present-day global 
warming (high confidence). Above 1.5°C, half of assessed species 
are projected to lose over 30% of their population or area of suitable 
habitat. At 2°C, 36% of freshwater fish species are vulnerable to 
local extinction, 7–18% of terrestrial species assessed are at risk of 
extinction, and over 90% of east African coral reefs are projected 
to be destroyed by bleaching. Above 2°C, risk of sudden and severe 
biodiversity losses becomes widespread in west, central and east 
Africa. Climate change is also projected to change patterns of invasive 
species spread. {9.6.2, Figure 9.19}
Climate security
There is increasing evidence linking increased temperatures 
and drought to conflict risk in Africa ( high confidence) . 
Agriculturally dependent and politically excluded groups are

especially vulnerable to drought-associated conflict risk. However, 
climate is one of many interacting risk factors, and may explain 
a small share of total variation in conflict incidence. Ameliorating 
ethnic tensions, strengthening political institutions and investing in 
economic diversification could mitigate future impacts of climate 
change on conflict. {Box 9.9}
Heritage
African cultural heritage is already at risk from climate hazards, 
including sea level rise and coastal erosion. Most African 
heritage sites are neither prepared for, nor adapted to, future 
climate change (high confidence). {9.12}
With global warming increasing above present-day levels, the 
ability of adaptation responses to offset risk is substantially 
reduced (high confidence). Crop yield losses, even after adaptation, 
are projected to rise rapidly above 2°C global warming. Limits to 
adaptation are already being reached in coral reef ecosystems.

Immigration of species from elsewhere may partly compensate for 
local extinctions and/or lead to local biodiversity gains in some regions. 
However, more African regions face net losses than net gains. At 1.5°C

9
1293Africa  Chapter 9
global warming, over 46% of localities face net losses in terrestrial 
vertebrate species richness with net increases projected for under 15% 
of localities. {9.6.1.4, 9.6.2.2, 9.8.2.1, 9.8.2.2, 9.8.4}
Technological, institutional and financing factors are major 
barriers to climate adaptation feasibility in Africa (high con-
fidence). {9.3, 9.4.1}
There is limited evidence for economic growth alone reducing 
climate damages, but under scenarios of inclusive and sustainable 
development, millions fewer people in Africa will be pushed 
into extreme poverty by climate change and negative impacts 
to health and livelihoods can be reduced by 2030 (medium 
confidence). {9.10.3, 9.11.4}
Gender-sensitive and equity-based adaptation approaches reduce 
vulnerability for marginalised groups across multiple sectors 
in Africa, including water, health, food systems and livelihoods 
(high confidence). {9.7.3, 9.8.3, 9.9.5, 9.10.3, 9.11.4, Boxes 9.1, 9.2}

Integrating climate adaptation into social protection pro-
grammes, such as cash transfers, public works programmes and 
healthcare access, can increase resilience to climate change 
(high confidence). Nevertheless, social protection programmes may 
increase resilience to climate-related shocks, even if they do not spe-
cifically address climate risks. {9.4.2, 9.10.3, 9.11.4}
The diversity of African Indigenous Knowledge and local knowl-
edge systems provide a rich foundation for adaptation actions 
at local scales (high confidence). African Indigenous Knowledge 
systems are exceptionally rich in ecosystem-specific knowledge used 
for management of climate variability. Integration of Indigenous 
Knowledge systems within legal frameworks, and promotion of In-
digenous land tenure rights can reduce vulnerability. {9.4.4, Boxes 
9.1, 9.2}
Early warning systems based on targeted climate services 
can be effective for disaster risk reduction, social protection

programmes, and managing risks to health and food systems 
(e.g., vector-borne disease and crops) (high confidence). {9.4.5, 
9.5.1, Box 9.2, 9.8.4, 9.8.5, 9.10.3, 9.11.4}
Risk-sensitive infrastructure delivery and equitable provision 
of basic services can reduce climate risks and provide net 
financial savings (high confidence). However, there is limited 
evidence of proactive climate adaptation in African cities. Proactive 
adaptation policy could reduce road repair and maintenance costs 
by 74% compared to a reactive policy. Adapting roads for increased 
temperatures and investment in public transport are assessed as ‘no 
regret’ options. In contrast, hydropower development carries risk of 
regrets due to damages when a different climate than was expected 
materialises. Energy costs for cooling demands are projected to 
accumulate to USD 51.3 billion by 2035 at 2°C global warming and to 
USD 486.5 billion by 2076 at 4°C. {9.8.5}

Reduced drought and flood risk, and improved water and 
sanitation access, can be delivered by water sensitive and climate scenario planning, monitored groundwater use, waterless on-
site sanitation, rainwater harvesting and water re-use, reducing 
risk to human settlements, food systems, economies and human 
health (high confidence). {9.8, 9.9, 9.10, 9.11}
Water sector adaptation measures show medium social and 
economic feasibility but low feasibility for most African cities 
due to technical and institutional restrictions, particularly for 
large supply dams and centralised distribution systems (medium 
confidence). {9.3.1, 9.7.3} Use of integrated water management, 
water supply augmentation and establishment of decentralised water 
management systems can reduce risk. Integrated water management 
measures including sub-national financing, demand management 
through subsidies, rates and taxes, and sustainable water technologies

can reduce water insecurity caused by either drought or floods (medium 
confidence). {9.7.3, Box 9.4}
Agricultural and livelihood diversification, agroecological 
and conservation agriculture practices, aquaculture, on-farm 
engineering and agroforestry can increase resilience and 
sustainability of food systems in Africa under climate change 
(medium confidence). However, smallholder farmers tend to address 
short-term shocks or stresses by deploying coping responses rather 
than transformative adaptations. Climate information services, 
institutional capacity building, secure land tenure, and strategic 
financial investment can help overcome these barriers to adaptation 
(medium confidence). {9.3.1, 9.4.5, 9.8.3, 9.8.5}
African countries and communities are inadequately insured 
against climate risk, but innovative index-based insurance 
schemes can help transfer risk and aid recovery, including 
in food systems (medium confidence). Despite their potential,

uptake of climate insurance products remains constrained by lack of 
affordability, awareness and product diversity. {9.4.5, 9.8.4, 9.11.4.1}
Human migration is a potentially effective adaptation strategy 
across food systems, water, livelihoods and in climate-induced 
conflict areas, but can also be maladaptive if vulnerability is 
increased, particularly for health and human settlements (high 
confidence). Migration of men from rural areas can aggravate the 
work burden faced by women. The more agency migrants have (i.e., 
degree of voluntarity and freedom of movement) the greater the 
potential benefits for sending and receiving areas (high agreement, 
medium evidence).{9.3, 9.8.3, 9.9.1–3, 9.10.2.2.2, Boxes 9.8, 9.9, 
Cross-Chapter Box MIGRATE in Chapter 7}

9
1294Chapter 9 Africa
9.1.1 Point of Departure
This chapter assesses the scientific evidence on observed and projected 
climate change impacts, vulnerability and adaptation options in Africa. 
The assessment refers to five African sub-regions—north, west, 
central, east and southern—closely following the African Union (AU), 
but including Mauritania in west Africa and Sudan in north Africa 
because much of the literature assessed places these countries in these 
regions (Figure 9.1). Madagascar and other island states are addressed 
in Chapter 15.
The contribution of Africa is among the lowest of historical greenhouse 
gas (GHG) emissions responsible for human-induced climate change 
and it has the lowest per capita GHG emissions of all regions currently 
(high confidence) (Figure  9.2). Yet Africa has already experienced 
widespread impacts from human-induced climate change (high 
confidence) (Figure 9.2; see Table 9.1).

Since AR5 (Assessment Report 5), there have been notable policy 
changes in Africa and globally. The Paris Agreement, 2030 Sustainable 
Development Goals (SDGs), the Sendai Framework and Agenda 2063 
emphasise interlinked aims to protect the planet, reduce disaster risk, 
end poverty and ensure all people enjoy peace and prosperity (AU, 
2015; UNFCCC Paris Agreement, 2015; UNISDR Sendai Framework, 
2015; United Nations General Assembly, 2015). To match these 
interlinked ambitions, this chapter assesses risks and response options 
both for individual sectors and cross-sectorally to assess how risks 
can compound and cascade across sectors, as well as the potential 
feasibility and effectiveness, co-benefits and trade-offs and potential for 
maladaptation from response options (Simpson et al., 2021b; Williams 
9.1.2 Major Conclusions from Previous Assessments
Based on an analysis of 1022 mentions of Africa or African countries

across the three AR6 Special Reports, the following main conclusions 
emerged.
• Hot days, hot nights and heatwaves have become more frequent; 
heatwaves have also become longer (high confidence). Drying 
is projected particularly for west and southwestern Africa (high 
confidence) (IPCC, 2018c; Shukla et al., 2019).
• Climate change is contributing to land degradation, loss of 
biodiversity, bush encroachment and spread of pests and invasive 
species (IPCC, 2018b; IPCC, 2019a; IPCC, 2019b).
• Climate change has already reduced food security through losses 
in crop yields, rangelands, livestock and fisheries, deterioration in 
food nutritional quality, access and distribution, and price spikes. 
Risks to crop yields are substantially less at 1.5°C compared with 
2°C of global warming, with a large reduction in maize cropping 
areas projected even for 1.5°C, as well as reduced fisheries catch 
potential (IPCC, 2018b; IPCC, 2019b; IPCC, 2019a).

• Increased deaths from undernutrition, malaria, diarrhoea, heat stress 
and diseases related to exposure to dust, fire smoke and other air pollutants are projected from further warming (IPCC, 2018c; Shukla 
• The largest reductions in economic growth for an increase from 
1.5°C to 2°C of global warming are projected for low- and middle-
income countries, including in Africa (IPCC, 2018c).
• Climate change interacts with multi-dimensional poverty, among 
other vulnerabilities. Africa is projected to bear an increasing 
proportion of the global exposed and vulnerable population at 2°C 
and 3°C of global warming (IPCC, 2018c).
• Poverty and limited financing continue to undermine adaptive 
capacity, particularly in rapidly growing African cities (Shukla et al., 
2019).
• Large-scale afforestation and bioenergy can reduce food 
availability and ecosystem health (IPCC, 2018c; IPCC, 2019a).
• Transitioning to renewable energy would reduce reliance on wood

fuel and charcoal, especially in urban areas, with co-benefits 
including reduced deforestation, desertification, fire risk and 
improved indoor air quality, local development and agricultural 
yield (Shukla et al., 2019).
5,001–10,000
2,501–5,000
1,001–12,500
501–1,000
101–500
51–100
21–50
11–20>10,000
5–10
<5The five regions of Africa used in Chapter 9
Estimated population
density in 2019
Number of people per km2
Figure 9.1 |  The five regions of Africa used in this chapter, also showing 
estimated population density in 2019. The population of Africa was estimated 
at 1.312 billion for 2020, which is about 17% of the world’s population but this is 
projected to grow to around 40% of the world’s population by 2100 (UNDESA, 2019a). 
Although 57% of the African population currently live in rural areas (43% urban), Africa 
is the most rapidly urbanising region globally and is projected to transition to a majority

urban population in the 2030s, with a 60% urban population by 2050 (UNDESA, 
2019b). The 2019 gross domestic product (GDP) per capita in constant 2010 USD 
averaged USD  2250 across the 43  countries reporting data, ranging from USD  202 
(Burundi) to USD 8840 (Gabon), with 40% of the population of sub-Saharan Africa 
living below the international poverty line of USD 1.90 per day in 2018 (World Bank, 
2019). The highest life expectancy at birth is 67 (Botswana and Senegal) and the lowest 
is 52 (Central African Republic) World Bank (2019). Grid-cell population density data for 
mapping are from Tatem (2017); WorldPop (2021).

9
1295Africa  Chapter 9
Historical greenhouse gas (GHG) emission trends for Africa compared to other world regions
Smal lIslandsNorthAmericaEuropeCentraland
S.Amer icaAustralasiaAsiaAfrica
01 02 03 04 05 0
GHG Emissions per capita (tCO ₂eq/capita)01020
525
15
19902 000 20102 019GHG Emissions (Gt CO ₂eq)(b) Regional GHG emission trends
TunisiaCong o,Dem. Rep.UgandaChadTanzani aLibyaAngolaKenyaMoroccoSudanEthiopiaAlgeriaEgyp t,ArabRep.NigeriaSouthAfrica
0 200 4006 00
GHG Emissions (MtCO ₂eq)1990
20190123
1990 2000 2010 2019GHG Emissions (Gt CO ₂eq/year)CH₄
CO₂
N₂O
1990 2000 2010 2019Agricultur e
Buildings
Indust ry
Transpor tEnergy
systems(a) Regional per capita GHG emissions
(c) Country GHG emissions (Africa) (d) Total GHG emissions by gas and sector (Africa)1990
2019
Region
average
Single
countriesAfricaAsia
AustralasiaEuropeNorth America
Figure 9.2 |  Historical greenhouse gas (GHG) emission trends for Africa compared to other world regions:

(a) Per person GHG emissions by region and their change from 1990 to 2019 (circles represent countries, diamonds represent the region average).
(b) Total GHG emissions by region since 1990.
(c) The total GHG emissions in 1990 and 2019 for the 15 highest emitting countries within Africa.
(d) Total emissions in Africa since 1990, broken down by GHG (left) and sector (right). Methane and CO 2 emissions comprise an almost equal share of GHG emissions in Africa, 
with the largest emissions sectors being energy and agriculture (Crippa et al., 2021). Agriculture emissions in panel (d) do not include land use, land use change and forestry 
(LULUCF CO 2). One-hundred-year global warming potentials consistent with WGI estimates are used. Emissions data are from Crippa et al. (2021), compiled in Working Group III 
(WGIII) Chapter 2.
• Sustainable use of biodiversity, conservation agriculture, reduced 
deforestation, land and watershed restoration, rainwater

harvesting and well-planned reforestation can have multiple 
benefits for adaptation and mitigation, including water security, 
food security, biodiversity, soil conservation and local surface 
cooling (IPBES, 2018; Shukla et al., 2019).
• Climate resilience can be enhanced through improvements to 
early warning systems, insurance, investment in safety nets, secure 
land tenure, transport infrastructure, communication, access to 
information and investments in education and strengthened local 
governance (Shukla et al., 2019).
• Scenarios of socio-environmental change are under-used in 
decision making in Africa (IPBES, 2018).
• Africa’s rich biodiversity together with a wealth of Indigenous 
Knowledge and Local Knowledge (IKLK) is a key strategic asset for 
sustainable development (IPBES, 2018).9.1.3 What’s New on Africa in AR6?
• Increased confidence in observed and projected changes in climate 
hazards, including heat and precipitation

• Increased regional, national and sub-national observed impacts 
and projected risks
• Loss and damage assessment
• Increased quantification of projected risks at 1.5°C, 2°C, 3°C and 
4°C of global warming (see Section 9.2; Figure 9.6)
• Improved assessment of sea level rise risk (Sections 9.9; 9.12)
• Increased quantification of risk across all sectors assessed
• Expanded assessment of adaptation feasibility and effectiveness 
and limits to adaptation (see Figure 9.7)
• Assessment of adaptation finance (Section 9.4.1)
• Increased assessment of how climate risk and adaptation and 
mitigation response options are interlinked across multiple key 
development sectors (Section 9.4.3; Boxes 9.4; 9.5).

9
1296Chapter 9 Africa
Funding for climate-related research on Africa is a very small proportion
of global climate-related research funding
020406080100120140(e) Distribution of funding across risk categories, 1990–2020(c) Countries financing Africa-related climate research
before and after the Paris Agreement, million 2010 USD
JapanPolandChinaSwitzerlandFinlandCanadaFranceNorwaySwedenGermanyUSAEUUK
05 0 100 150
2016–2020, Total 1990–2015, Total02 04 06 08 0 100 120 140South AfricaKenyaItalyNorwayNetherlandsFranceSwedenGermanyUnited KingdomUnited States(d) Top 10 country locations of institutions receiving funding for
climate change research on Africa, 1990–2020, million 2010 USD
17%Other climate change research (3%)
40%Impact 
40%(f) Funding for climate impact, mitigation and adaptation research on Africa1990 2000 2010 2020Million
2015
USD(a) Funding for climate research on Africa and on whole world
3500
3000
2500
2000
1500
1000
500
0WorldAfrica
02%4%6%8%10%12%

1990 2000 2010 2020Climate
research
funding as
percentage
of general
research
funding(b) Percentage of total research funding spent on climate research
WorldAfrica
05 0 100 150
Million
2010
USD
Food
systemsEco-
systemsFresh-
waterHealth Poverty
and
livelihoodsCities/
urban
areasSecurity
and
conflict
Figure 9.3 |  Climate-related research on Africa has received a very small percentage (around 4%) of global climate research funding (a).
(b) As a percentage of all research funding allocated to a region, climate research has, since 2010, made up 5% of Africa-related research funding compared to a 3% share for 
climate research in global research funding. 
(c) Major funders are the UK, EU, USA, Germany and Sweden. 
(d) Most funding for climate-related research on Africa flows to institutions based in Europe and the USA. Funding comes mainly from government organisations with private 
philanthropy providing only around 1% (Overland et al., 2021).

(e) Africa-related climate research funding focuses mostly on food systems, ecosystems and freshwater, while health, poverty, security and conflict, and urban areas have received 
the least.
(f) Research on climate mitigation received only 17% of funding while climate impacts and adaptation each received 40%. A greater proportion of Africa-focused climate funding 
has gone to social sciences and humanities (28%) than is the case globally (12%) (Overland et al., 2021). Data are from an analysis of 4,458,719 research grants in the Dimensions 
database with a combined value of USD 1.51 trillion awarded by 521 funding organisations globally (Overland et al. 2021). The Dimensions database is the world’s largest database 
on research funding flows (Overland et al. 2021). It draws on official data from all major funding organisations in the world, mainly government research councils or similar

institutions. Note: The South African National Research Foundation is the only African research funding body that is sufficiently large to be included in Dimensions.

9
1297Africa  Chapter 9
Major gaps in climate change research funding, participation and publication exist within Africa, 
and for Africa compared to the rest of the world
(a) Climate change research funding focused on African countries (b) Climate change studies with locally-based authors
(c) Climate change adaptation research focused on individual countries
Funding amount
in USD millions
(1990–2020)Percentage
of studies with
locally-based authors
41–50
31–4051
11–20
1–1021–30
research on
individual countries
(number of papers)
401–600
201–400601–1,000>1,000
1–100
No data101–20041–60%
21–40%61%
0
No data1–20%
Figure 9.4 |  Major gaps in climate change research funding, participation and publication exist within Africa, and for Africa compared to the rest of the 
world. 
(a) Funding: Amount of climate change research funding focused on African countries 1990–2020 (Overland et al., 2021). Considering population size, research on Egypt and

Nigeria stands out as particularly underfinanced.
(b) Participation: Percentage of peer-reviewed climate change papers on impacts and adaptation published on a given country that also include at least one author based in that 
country (Pasgaard et al. 2015).
(c) Number of publications of climate change adaptation research focused on individual countries identified from a global sample of 62,191 adaptation-relevant peer-reviewed 
articles published from 1988–2020 (Sietsma et  al., 2021). There is a general lack of adaptation-related research on many vulnerable countries in Africa. Topic biases in 
adaptation-relevant research also exist where research focuses more on disaster and development-related topics in global south countries (but published by authors from the global 
north), while research on global north countries focuses more on governance topics (Sietsma et al., 2021).

9
1298Chapter 9 Africa
9.1.4 Climate Change Impacts Across Africa
In many parts of southern, east and west Africa, temperature or 
precipitation trends since the 1950s are attributable to human-
caused climate change and several studies document the impacts of 
these climate trends on human and natural systems (high confidence) 
(Figure  9.5; Sections  9.5.6; 9.5.7). Nevertheless, research into 
attribution of trends to human-caused climate change or climate 
impacts remains scarce for multiple regions, especially in north and 
central Africa. This illustrates an ‘attribution gap’ where robust evidence 
for attributable impacts is twice as prevalent in high- compared to 
low-income countries globally (Callaghan et al., 2021). Most studies 
on climate impacts in Africa have focused on terrestrial ecosystems or 
water, with fewer on marine ecosystems, agriculture, migration, and 
health and well-being (Callaghan et al., 2021). Specific factors driving

these knowledge gaps include limited data collection, data access and 
research funding for African researchers (see next section).
9.1.5 Climate Data and Research Gaps Across Africa
Since AR5, there have been rapid advances in climate impact research 
due to increased computing power, data access and new developments 
in statistical analysis (Carleton and Hsiang, 2016). However, sparse and 
intermittent weather station data limit attribution of climate trends to 
human-caused climate change for large areas of Africa, especially for 
precipitation and extreme events, and hinder more accurate climate 
change projections (Section 9.5.2; Figure 9.5; Otto et al., 2020). Outside 
of South Africa and Kenya, digitally accessible data on biodiversity is 
limited (Meyer et al., 2015). Lack of comprehensive socioeconomic data 
also limits researchers’ ability to predict climate change impacts. Ideally, 
multiple surveys over time are needed to identify effects of a location’s

changing climate on changing socioeconomic conditions. Twenty-five 
African countries conducted only one nationally representative survey 
that could be used to construct measures of poverty during 2000–2010 
and 14 conducted none over this period (Jean et al., 2016). Because 
of these challenges, much of what is known about climate impacts 
and risks in Africa relies on evidence from global studies that use 
data largely from outside Africa (e.g., Zhao et al., 2021). These studies 
generate estimates of average impacts across the globe, but may not 
have the statistical power to distinguish whether African nations display 
differential vulnerability, exposure or adaptive capacity. In sections 
of this chapter, we have relied, when necessary, on such studies, as 
they often provide best available evidence for Africa. Increasing data 
coverage and availability would increase the ability to discern important 
differences in risk both among and within African countries.

Climate-related research in Africa faces severe funding constraints 
with unequal funding relationships between countries and with 
research partners in Europe and North America (high confidence). 
Based on analysis of over 4  million research grants from 521 
funding organisations globally, it is estimated that, from 1990–2020, 
USD  1.26  billion funded Africa-related research on climate impacts, 
mitigation and adaptation. This represents only 3.8% of global funding 
for climate-related research—a figure incommensurate with Africa’s 
high vulnerability to climate change (see Figure 9.3; Box 9.1; Chapter 8 
Figure 8.6; Overland et al., 2021). Almost all funding for Africa-related Climate impacts on human and natural systems
are widespread across Africa, as are climate trends
attributable to human-caused climate change
Temperature or precipitation Temperature and precipitationEvidence for climate impactsAttributable to Human
induced climate change
Attributable to other causes

Low Robust HighClimate
trends
Figure  9.5 |   Observed climate change impacts on human and natural 
systems are widespread across Africa, as are climate trends attributable 
to human-induced climate change. This machine-learning-assisted evidence map 
shows the presence of historical trends in temperature and precipitation attributable to 
human-induced climate change (pinks compared to greys) and the amount of evidence 
(shown by intensity of colours) documenting the impacts of these climate trends on 
human and natural systems (e.g., ecosystems, agriculture, health) across Africa. ‘Robust’ 
indicates more than five studies documented impacts per grid cell. A ‘High’ amount of 
evidence indicates more than 20 studies documented impacts for a grid cell. Climate 
impact studies from the literature were identified and categorised using machine 
learning. A language representation model was trained on a set of 2373 climate impact

studies coded by hand. This supervised machine learning model identified 102,160 
published studies predicted to be relevant for climate impacts globally; references to 
places in Africa were found in 5081 studies (5% of global studies). Temperature trends 
were calculated from 1951–2018 and precipitation from 1951–2016. Attribution of 
climate trends to human induced climate change is limited in some regions of Africa 
due to insufficient data (see Section 9.5.1, Figure 9.15). Hatching shows regions where 
trends in both temperature and precipitation are attributable to human-induced climate 
change. Data from Callaghan et al. (2021).
climate research originates outside Africa and goes to research 
institutions outside Africa (Blicharska et  al., 2017; Bendana, 2019; 
Siders, 2019; Overland et al., 2021). From 1990–2020, 78% of funding 
for Africa-related climate research flowed to institutions in Europe

9
1299Africa  Chapter 9
and the USA—only 14.5% flowed to institutions in Africa (Figure 9.3; 
Overland et al., 2021). Kenya (2.3% of total funding) and South Africa 
(2.2%) are the only African countries among the top 10 countries in 
the world in terms of hosting institutions receiving funding for climate-
related research on Africa (Overland et al., 2021).
These unequal funding relations influence inequalities in climate-related 
research design, participation and dissemination between African 
researchers and researchers from high-income countries outside Africa, in 
ways that can reduce adaptive capacity in Africa (very high confidence). 
Those empowered to shape research agendas can shape research 
answers: climate research agendas, skills gaps and eligible researchers 
are frequently defined by funding agencies, often from a global north 
perspective (Vincent et  al., 2020a). Larger funding allocations for 
research focused on Ghana, South Africa, Kenya, Tanzania and Ethiopia

are reflected in higher concentrations of empirical research on impacts 
and adaptation options in these countries, and there is a general lack 
of adaptation research for multiple of the most vulnerable countries in 
Africa (Figure 9.4) (Callaghan et al., 2021; Overland et al., 2021; Sietsma 
et al., 2021; Vincent and Cundill, 2021). The combination of northern-led 
identification of both knowledge and skills gaps can result in projects 
where African partners are positioned primarily as recipients engaged 
to support research and/or have their ‘capacity built’ rather than also 
leading research projects on an equal basis (Vincent et al., 2020a; Trisos 
et al., 2021). Analysis of >15,000 climate change publications found for 
over 75% of African countries 60–100% of climate change publications 
on these countries did not include a single local author, with authorship 
dominated by researchers from richer countries outside Africa (Pasgaard

et al., 2015). This can reduce adaptive capacity in Africa as researchers 
at global north institutions may shape research questions and outputs 
for a northern audience rather than providing actionable insights on 
priority issues for African partners (Pasgaard et  al., 2015; Nago and 
Krott, 2020). Moreover, in order to access research publications in a 
timely manner, many researchers in Africa are forced to use shadow 
websites bypassing journal paywalls (Bohannon, 2016). Ways to 
enhance research partnerships to produce actionable insights on climate 
impacts and solutions in Africa include: increased funding from African 
and non-African sources, increasing direct control of resources for 
African partners, having African research and user priorities set research 
questions, identify skills gaps, and lead research, and having open access 
policies for research outputs (ESPA Directorate, 2018; Vogel et al., 2019; 
Vincent et al., 2020a; IDRC, 2021; Trisos et al., 2021).

9.1.6 Loss and Damage from Climate Change
Assessment of impacts, vulnerability, and adaptation highlights climate 
change is leading to loss and damage across Africa, that breach current 
and projected adaptation limits (Table 9.1; Cross-Chapter Box LOSS in 
Chapter 17).
9.2 Key Risks for Africa
A key risk is defined as a potentially severe risk. In line with AR5, 
‘severity’ relates to dangerous anthropogenic interference with the 
climate system, the prevention of which is the ultimate objective of the United Nations Framework Convention on Climate Change (UNFCCC) 
as stated in its Article 2 (Oppenheimer et al., 2014). The process for 
identifying key risks for Africa included reviewing risks from Niang 
et  al. (2014) and assessing new evidence on observed impacts and 
projected risks in this chapter.
Several key risks were identified for both ecosystems and people 
including species extinction and ecosystem disruption, loss of food

production, reduced economic output and increased poverty, increased 
disease and loss of human life, increased water and energy insecurity, 
loss of natural and cultural heritage and compound extreme events 
harming human settlements and critical infrastructure (Table 9.2). In 
order to provide a sector- and continent-level perspective, the key risks 
aggregate across different regions and combine multiple risks within 
sectors. For detailed assessments of observed impacts and future risks 
within each sector and each sub-region of Africa, see the sector-specific 
sections of this chapter (Sections 9.6 to 9.12).
Several expert elicitation workshops of lead and contributing authors 
were held to develop ‘burning embers’ assessing how risk increases 
with further global warming for a subset of key risks, specifically risk 
of food production losses, risk of biodiversity loss and risk of mortality Key risks for Africa
increase with increasing global warming
Very high
High
Moderate

UndetectableLevel of
impact or riskConfidence level
••
•••
••••
rangeGlobal mean temperatur e
1.5 °C
Recent
climat e
(2010–2020)
Reduced 
food production
from crops
fisheries
and livestock••••••••
loss and
ecosystem
disruption•••••••••
Mortality and
morbidity from
heat and
infectious
disease••••••••
Figure  9.6 |   Risks increase with increasing levels of global warming, as 
shown by this Burning Embers figure for selected key risks from climate 
change in Africa. Increases in risk are assessed for the levels of global warming above 
pre-industrial (1850–1900). All three risks are assessed to have already transitioned to 
moderate risk by the recent level of global warming 2010–2020 (1.09°C). Risks are 
characterised as undetectable, moderate, high, or very high, and the transition between 
risk levels as a function of global warming is represented by the colour change of each

bar (IPCC, 2021). Vertical lines show the range of global warming for a change in the 
risk level. The dots indicate the confidence level for a given transition in risk and are 
placed at the level of global warming that is the assessed best estimate for that increase 
in the risk level. For the range of global warming levels for each risk transition used to 
make this figure see Supplementary Material Table SM 9.1.

9
1300Chapter 9 Africa
and morbidity from heat and infectious disease (Figure 9.6). These key 
risks were selected in part because of underlying assessment work in 
the chapter to connect multiple studies to observed impacts and/or 
risk at increasing global warming levels (Sections 9.6.2; 9.8.2; 9.8.5.2; 
9.10.2).
All three of these key risks are assessed to have already transitioned 
completely into moderate risk—that is, negative impacts have been 
detected and attributed to climate change—before the 2010–2020 
level of global warming (1.09°C) above pre-industrial times (IPCC, 
2021), with medium confidence for increased mortality and morbidity 
and high confidence for losses of food productivity and biodiversity 
(Figure  9.6). For biodiversity, these impacts include repeated mass 
die-offs of coral reefs due to marine heat (Section 9.6.1.4), reductions 
in lake productivity due to warming (Section  9.6.1.3), and woody

encroachment of grasslands and savannas due to increased atmospheric 
CO 2 concentrations (Section  9.6.1.1), with negative impacts on 
livelihoods (Section  9.6.2). For food production, climate change 
impacts include up to 5.8% mean reduction in maize productivity due 
to increased temperatures in sub-Saharan Africa (Sections  9.8.2.1; 9.8.2.2) and reduced fisheries catches due to increased temperatures, 
especially in tropical regions (Section 9.8.2). For health, climate change 
impacts include increased mortality and morbidity from changes in the 
distribution and incidence of malaria and cholera and the direct effects 
of increasing temperatures (Section 9.10.2).
In scenarios with low adaptation (that is largely localised and 
incremental), the transition to high risk—widespread and severe 
impacts—has already begun at the current level of global warming 
for biodiversity loss (high confidence), and begins below 1.5°C global

warming for both food production (medium confidence) and mortality 
and morbidity from heat and infectious disease (high confidence). 
Across all risks, the best estimate for the transition to high risk is at 
1.5°C of global warming, with transition to high risk completing before 
2°C (Figure 9.6). Projected impacts considered high risk around 1.5°C 
include: across more than 90% of Africa, more than 10% of species are 
at risk of local extinction (Figure 9.6; Table 9.1); the further expansion 
of woody plants into grass-dominated biomes (Section 9.6.2.1); 9% 
declines in maize yield for west Africa and 20–60% decline in wheat 
yield for southern and northern Africa, as well as declines in coffee Table 9.1 |  Loss and damage from climate change across sectors covered in this report. Loss and damage arise from adverse climate-related impacts and risks from both

sudden-onset events, such as floods and cyclones, and slower-onset processes, including droughts, sea level rise, glacial retreat and desertification and include both include both 
economic (e.g., loss of assets and crops) and non-economic types (e.g., loss of biodiversity, heritage and health) (UNFCCC Paris Agreement, 2015; IPCC, 2018a; Mechler et al., 
2020). Sections marked with * and in bold highlight Loss and Damage attributed to human-induced climate change (16.1.3).
Sector Loss and damage from climate change Observed Projected
EcosystemsLocal, regional and global extinction
Reduced ecosystem goods and services
Declining natural coastal protection and habitats
Altered ecosystem structure and declining ecosystem functioning
Nature-based tourism
Biodiversity loss9.6.2
9.6.1; 9.6.2
9.6.1
9.6.3
9.6.2*9.6.2
9.6.2
9.6.2
9.6.2
9.6.3
WaterDeclining lake and river resources
Reduced hydroelectricity and irrigation
Disappearing glaciers

Reduced groundwater recharge and salinisation
Drought9.7.1
9.7.2; 9.9.1
9.5.9*; 9.7.1
–
Box 9.4*9.7.2
9.7.2; 9.9.3; Box 9.5
9.5.9
9.7.2
Food systemsReduced crop productivity and revenues
Increased livestock mortality and price shocks
Decreased fodder and pasture availability
Reduced fisheries catch and fisher livelihoods9.7.2*, 9.8.1; 9.8.2; 9.11.1; Box 9.5
9.8.2
9.8.2
9.6.1; 9.8.59.8.2; 9.8.3; Box 9.5
9.8.2
9.8.2
9.8.5
Human settlements and 
infrastructureLoss or damage to formal and informal dwellings
Damage to transport systems
Damage to energy systems
Water supply, sanitation, education and health infrastructure
Migration9.9.2
9.9.2
9.9.2
9.9.2; 9.10; 9.11.1
9.9.1; Box 9.89.9.4
9.9.4
9.7.2; 9.9.4
9.7.3; 9.9.4; 9.10; 9.11.1
9.9.4; Box 9.8
HealthLoss of life
Loss of productivity
Reduced nutrition9.9.2*; 9.10.2; Box 9.9
9.10.3; 9.11.1
9.8.1; 9.10.29.9.4; 9.10.2
9.10.2; 9.11.2
9.10.2
Economy, poverty and 
livelihoodsLoss of livelihoods, jobs and income
Reduced productive land

Reduced economic growth and increased inequality
Community and involuntary displacement
Reduced labour productivity and earning potential
Delayed and poorer education progress
Reduced tourism
Increased urban in-migration9.9.2; 9.10.2; 9.11.1
9.8.2
9.11.1*; Box 9.5
9.9.3; Box 9.8
9.11.1
9.11.1
9.6.3
9.8.1; 9.9.1; Table Box 9.89.10.2; 9.11.2
9.8.2
9.11.2
9.11.2
9.11.1
9.5.9, 9.6.3, 9.12.2
9.9.4; Table Box 9.8
HeritageLoss of traditional cultures and ways of life
Loss of language and knowledge systems
Damage to heritage sitesBox 9.2; 9.12.1
–
9.12.19.12.2
9.12.1
9.12.2

9
1301Africa  Chapter 9
and tea in east Africa and sorghum in west Africa (Figures 9.22; 9.23; 
Sections 9.8.2.1; 9.8.2.2), and >12% decline in marine fisheries catch 
potential for multiple west African countries, potentially leaving 
millions at risk of nutritional deficiencies (Figure 9.25; Section 9.8.5); 
tens of millions more people exposed to vector-borne diseases in east 
and southern Africa (malaria), and north, east and southern Africa 
(dengue, zika), increased risk of malnutrition in central, east and west 
Africa, and more than 15 additional deaths per 100,000 annually due 
to heat in parts of west, east and north Africa (Figures  9.32; 9.35; 
Sections 9.10.2; 9.9.4.1).
The transition from high to very high risk—that is severe and 
widespread impacts with limited ability to adapt—begins either at or 
just below 2°C for all three risks (Figure 9.6). The assessed temperature 
range for the transition to very high risk is wider for food production

than for biodiversity and health. Projected impacts for food include: 
10–30% decline in marine fisheries catch potential for the Horn of 
Africa region and southern Africa and more than 30% decline for west 
Africa at 2°C global warming, with greater declines at higher levels 
of warming (Section  9.8.2). Beyond 2°C global warming, over 50% 
of commercially important freshwater fish species across Africa are 
projected to be vulnerable to extinction (Figure  9.26). Between 2°C 
and 4°C, wheat, maize and rice yields are projected, on average, to be 
lower than 2005 yields across all regions of Africa. From 2°C global 
warming, over 40% losses in rangeland productivity are projected for 
western Africa. By 3.75°C, severe heat stress may be near year-round 
for cattle across tropical Africa (Figure 9.24). Multiple countries in west, 
central and east Africa are projected to be at risk from simultaneous 
negative impacts on crops, fisheries and livestock (Sections  9.8.2;

9.8.5; Thiault et al., 2019).
The best estimate for the onset of very high risk for biodiversity and 
health is at 2.1°C. Projected impacts considered very high risk for 
biodiversity include potential destabilisation of the African tropical 
forest carbon sink, risk of local extinction of more than 50% of plants, 
vertebrate and insect species across one-fifth of Africa, 7–18% of 
African species at risk of total extinction including, a third of freshwater 
fish, and more than 90% warm-water coral reefs lost (Section 9.6.2). 
For health, projected impacts considered high risk include potentially 
lethal heat exposure for more than 100 days per year in west, central 
and east Africa, with more than 50 additional heat-related deaths per 
100,000 annually across large parts of Africa, and hundreds of millions 
more people exposed to extreme heat in cities (Section  9.5; 9.10.2; 
9.9.4.1; Figure  9.35), tens to hundreds of thousands of additional

cases of diarrhoeal disease in east, central and west Africa, and tens 
of millions more people exposed to mosquito-borne arboviruses like 
dengue or zika in north, east and southern Africa (Section 9.10.2).
The feasibility and effectiveness of existing adaptation options 
under current levels of warming are assessed in Section 9.10.2 and 
adaptation options considering future levels of warming are assessed 
in the chapter section for each sector.9.3 Climate Adaptation Options
9.3.1 Adaptation Feasibility and Effectiveness
Based on a systematic assessment of observed climate adaptation 
responses in the scientific literature covering 827 adaptation response 
types in 553 studies (2013–2021), and expert elicitation process, 24 
categories of adaptation responses in Africa were identified (Williams 
et  al., 2021; Figure  9.7). This assessment excluded autonomous 
adaptation in ecosystems, such as migration and evolution of animal 
and plant species.

At the current global warming level, 83% of adaptation response 
categories assessed showed medium potential for risk reduction 
(that is, mixed evidence of effectiveness). Bulk water infrastructure 
(including managed aquifer recharge, dams, pipelines, pump stations, 
water treatment plants and distribution networks), human migration, 
financial investment for sustainable agriculture, and social infrastructure 
(including decentralised management, strong community structures 
and informal support networks) show high potential for risk reduction 
(high evidence of option’s effectiveness)  (Sections  9.6.4; 9.7.3; Boxes 
9.8; 9.9; 9.10; 9.11). However, there was limited evidence to assess 
the continued effectiveness of these options at higher global warming 
levels (Williams et  al., 2021) with some options, such as bulk water 
infrastructure (particularly large dams), expected to face increasing risk 
with continued warming with damages cascading to other sectors (see

Box 9.5), while others, such as crop irrigation and adjusting planting 
times, may increasingly reach adaptation limits above 1.5°C and 2°C 
global warming (Sections 9.8.3; 9.8.4).
The majority of adaptation studies were in west and east Africa 
(Ethiopia, Ghana, Kenya and Tanzania), followed by southern Africa, 
with the least coming from central and north Africa (Figure  9.7; 
Williams et al., 2021). Most studies were on adaptation actions in the 
food sector, with the least on health (Figure 9.7). The five adaptation 
response categories with the highest number of reported actions were 
sustainable water management (food sector), resilient infrastructure 
and technologies (health sector), agricultural intensification (food 
sector), human migration (poverty and livelihoods) and crop 
management (food sector).
No adaptation response categories were assessed to have high 
feasibility of implementation. Technological barriers dominate factors

limiting implementation (92% of adaptation categories have low 
technological feasibility) followed by institutional barriers (71% 
of adaptation categories have low institutional feasibility). This 
assessment matches review studies finding institutional responses 
to be least common in Africa and highlight inadequate institutional 
capacities as key limits to human adaptation (Berrang-Ford et  al., 
2021; Thomas et al., 2021) (Cross-Chapter Box FEASIB in Chapter 18). 
Feasibility is higher for the social dimension of adaptation responses 
(with moderate feasibility for 88% of categories). The largest evidence 
gap is for environmental feasibility for which 67% could not be 
assessed due to insufficient evidence (Figure 9.7).
Sustainable Water Management (SWM) includes rainwater harvesting 
for irrigation, watershed restoration, water conservation practices

9
1302Chapter 9 Africa
(e.g., efficient irrigation) and less water-intensive cropping (also see 
Section 9.8.3), and was the most reported adaptation response in the 
food sector. SWM was assessed with medium economic and social 
feasibility and low environmental, institutional and technological 
feasibility. The feasibility of this adaptation category may depend 
largely on socioeconomic conditions (Amamou et al., 2018; Harmanny 
and Malek, 2019; Schilling et  al., 2020), as many African farmers 
cannot afford the cost of SWM facilities (Section 9.8.4).
Resilient Infrastructure and Technologies (RIT) for health include improved 
housing to limit exposure to climate hazards (Stringer et al., 2020), and 
improved water quality, sanitation and hygiene infrastructure (e.g., 
technology across all sectors to prevent contamination and pollution 
of water, improved water, sanitation and hygiene (WASH) approaches 
such as promotion of diverse water sources for water supply, improving

health infrastructure) (Section 9.10.3). Overall, RIT had medium social feasibility and low institutional and technological feasibility. Bulk 
water infrastructure was assessed to have high effectiveness, but low 
institutional and technological feasibility. Increasing variability in climate 
and environmental challenges has made sustainable and resilient 
infrastructure design a key priority (Minsker et al., 2015). RIT is, however, 
generally new in the African context (Cumming et al., 2017) and that 
may be why there is limited evidence to assess some of its dimensions 
(economic and environmental feasibility). Construction of resilient public 
water infrastructures that include safeguards for sanitation and hygiene 
are expensive and, across national and local levels, planning for its 
construction poses multiple challenges (Choko et al., 2019).
Sustainable agricultural intensification in smallholder farming systems

(especially agroecological approaches, such as mixed cropping, mixed 
farming, no soil disturbance, and mulching) and agroforestry are key 
response options to secure food for the growing African population Table 9.2 |  Key risks from climate change in Africa
Key climate change risk Climate impact driver Vulnerability Section
Local or global extinction of species and 
reduction or irreversible loss of ecosystems 
and their services, including freshwater, land 
and ocean ecosystemsIncreasing temperatures of freshwaters, 
ocean and on land; heatwaves; precipitation 
changes (both increases and decreases); 
increased atmospheric CO 2 concentrations; 
sea level rise; ocean acidificationVulnerability highest among poorly dispersing organisms (plants) and 
species with narrow and disappearing niches (e.g., mountain endemics), 
and is exacerbated by non-climate hazards (e.g., habitat loss for 
agriculture or afforestation projects); vulnerability is high for Protected

Areas surrounded by transformed land preventing species’ dispersal and 
areas with limited elevational gradients that reduce their potential to act 
as climate refugia.9.6
Risks to marine ecosystem health and to 
livelihoods in coastal communitiesmarine heatwaves, increased acidification 
and sedimentation/turbiditylow-income coastal communities (e.g., artisanal fisherfolk, fishmongers) 
whose livelihood depends on healthy coral reefs, seagrass beds and 
mangroves9.6, 9.8
Loss of food production from crops, 
livestock and fisheriesIncreasing temperatures and heat waves 
for freshwaters, ocean and on land; 
precipitation changes; drought; increased 
atmospheric CO 2 concentrationsHigh for low-income coastal and riparian communities whose livelihood 
depends on healthy ocean and freshwater ecosystems, and for populations 
reliant on fish for protein and micronutrients. Vulnerability is high for 
many food producers dependent on rainfall and temperature conditions,

including subsistence farmers, the rural poor, and pastoralists. Lack of 
access to climate information and services increases vulnerability.9.8
Mortality and morbidity from increased 
heat and infectious diseases (including 
vector-borne and diarrhoeal diseases)Increasing temperatures; heatwaves; 
precipitation change (both increases and 
decreases)Vulnerability is highest for the elderly, pregnant women, individuals with 
underlying conditions, immune-compromised individuals (e.g., from HIV) 
and young children.
Regions without vector control programmes in place or without detection 
and treatment regimens.
Inadequate insulation in housing in informal settlements in urban heat 
islands. Inadequate improvements in public health systems.
Inadequate water and sanitation infrastructure, especially in rapidly 
expanding urban areas and informal settlements.9.10
Reduced economic output and growth, and 
increased inequality and poverty ratesIncreased temperatures; reduced rainfall;

drought; extreme weather eventsConditions underlying severe risk are lower income growth, higher 
population levels, low rates of structural economic change with more of 
the labour force engaged in agriculture and other more climate-exposed 
sectors due in part to physical labour outdoors.9.11
Water and energy insecurity due to 
shortage of irrigation and hydropowerHeat and droughtHigh reliance on hydropower for national electricity generation, especially 
east and southern African countries. Planned for high reliance on irrigated 
food production. Concentrations of hydropower plants within river basins 
experiencing similar rainfall and runoff patterns. Limited electricity trade 
between major river basins.9.7; 9.9; 
Box 9.5
Cascading and compounding risks of loss of 
life, livelihoods and infrastructure in human 
settlementsExtreme heat; floods; drought; sea level rise 
and associated coastal hazards; compound 
climate hazards (e.g., coinciding heat and

drought)Coastal and low-lying urban areas and those in dryland regions with 
rapidly growing populations. People living in informal settlements. 
Increased magnitude of heat waves due to urban heat island effects. 
Climate shocks to municipal revenues (e.g., from water). Unaffordable 
maintenance of transport and protective infrastructure with increasing 
climate impacts. Greater water resource demand from urban and 
non-urban populations and key economic sectors.9.9

9
1303Africa  Chapter 9
Northern
CentralEasternWestern
Southern
na
Evidence
* water conservation
  and efficiencySustainable water management *Food fibre
and other ecosystem
products
Health, well-being
and communities
Poverty, livelihoods and
Terrestrial,  freshwater,
ocean and coastal
Water and sanitation
Cities, settlements and
key infrastructureSynthesis of adaptation options for AfricaFeasibility Dimensions
Environmental
TechnologicalInstitutionalSocialEconomic
EffectivenessObservations
per region
Sectors Adaptation options
//
/
/
//
/
/
/
/
/
/
/
/
//
/
/
/
/
/
/
//
//
//
/
/
/
/
/
/
///
//
/
/
/ /
//
//
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
//
= insufficient evidence/Assessment score Agroforestry
Sustainable agricultural practices
Sustainable agricultural intensification
Climate information services
Financial investment
Crop management
Livestock management
Fisheries management
Health governance and planning
Health advisory services and education

Resilient infrastructure and technologies
Risk spreading and sharing
Human migration
Ecosystem restoration and conservation
Ecosystem governance and planning
Alternative water supply
Bulk water infrastructure
Integrated water management
Water governance and planning
Urban governance and planning
Infrastructure and built environment/Medium Low High
= not applicabl e na
Figure 9.7 |  Assessment of the feasibility and effectiveness of observed climate adaptation responses under current climate conditions for 24 categories of 
adaptation responses across regions of Africa. The assessment comprised evaluation of each adaptation category along six dimensions: for feasibility these were economic 
viability, environmental sustainability, social validity, institutional relevance, and technological availability; and for effectiveness this was potential for risk reduction (considering current

climate conditions) (Williams et al., 2021). Fifty-six experts on the African region were consulted using a structured, expert-driven elicitation process to increase the coverage and 
robustness of the continent-wide adaptation feasibility and effectiveness assessment in Williams et al. (2021). Assessment included both peer-reviewed articles and grey literature.
(Nziguheba et  al., 2015; Ritzema et  al., 2017). Yet many of these 
options currently face low institutional and technological feasibility 
(Figure  9.7). Social and economic feasibility is higher, but barriers 
include high cost of farm inputs (land, capital and labour), lack of 
access to timely weather information and lack of water resources can 
make this option quite challenging for African smallholder farmers 
(Sections 9.8.1; 9.11.4; Kihila, 2017; Williams et al., 2019b).
Crop management includes adjusting crop choices, planting times, or the 
size, type and location of planted areas (Altieri et al., 2015; Nyagumbo

et  al., 2017; Dayamba et  al., 2018). This option faces environmental, 
institutional and technological barriers to feasibility. Social and 
economic barriers to implementation are fewer. Factors such as tenure 
and ownership rights, labour requirements, high investment costs and 
lack of skills and knowledge on how to use the practices are reported 
to hinder implementation of crop management options by smallholder 
farmers (Muller and Shackleton, 2013; Nyasimi et al., 2017). For instance, when improved seed varieties are available, high price limits access for 
rural households (Amare et al., 2018; see Sections 9.8.3; 9.8.4).
Human migration was assessed to have high potential for risk 
reduction (Cross-Chapter Box  MIGRATE in Chapter 7, Box  9.8; Rao 
et  al., 2019; Sitati et  al., 2021). However, it had low feasibility for 
economic, institutional and technological dimensions, with limited 
evidence on environmental feasibility. Institutional factors such

as the implementation of top-down policies have been reported as 
limiting options for coping locally, resulting in migration (Brockhaus 
et  al., 2013). Limited financial and technical support for migration 
limits the extent to which it can make meaningful contributions to 
climate resilience (Djalante et al., 2013; Trabacchi and Mazza, 2015). 
International and domestic remittances are an important resource that 
can help aid recovery from climate shocks, but inadequate finance 
and banking infrastructure can limit cash transfers (Box  9.8). Male 
migration can increase burdens of household and agricultural work,

9
1304Chapter 9 Africa
especially for women (Poudel et al., 2020; Rao et al., 2020; Zhou et al., 
2020). The more agency migrants have (that is, degree of voluntarity 
and freedom of movement), the greater the potential benefits for 
sending and receiving areas (high agreement, medium evidence) 
(Cross-Chapter Box MIGRATE in Chapter 7; Box 9.8)
Adaptation options within a number of categories, including sustainable 
agriculture practices, agricultural intensification, fisheries management, 
health advisory services and education, social infrastructure, 
infrastructure and built environment, and livelihood diversification, 
were observed to reduce socioeconomic inequalities (Williams et  al., 
2021). Whether adaptation options reduce inequality can be a key 
consideration enhancing acceptability of policies and adaptation 
implementation (Box 9.1; Section 9.11.4; Islam and Winkel, 2017).
9.3.2 Adaptation Co-Benefits and Trade-Offs with 
Mitigation and SDGs

Synergies between the adaptation to climate change and progress 
towards the SDGs present potential co-benefits for realising multiple 
objectives towards climate resilient development in Africa, increasing 
the efficiency and cost-effectiveness of climate actions (Cohen et al., 
2021). However, designing adaptation policy under conditions of 
scarcity, common to many African countries, can inadvertently lead to 
trade-offs between adaptation options, as well as between adaptation 
and mitigation options, can reinforce inequality, and fail to address 
underlying social vulnerabilities (Kuhl, 2021).
Adaptation options, such as access to climate information, provision 
of climate information services, growing of early maturing varieties, 
agroforestry systems, agricultural diversification and growing of 
drought-resistant varieties of crops may deliver co-benefits, providing 
synergies that result in positive outcomes. For instance, in sub-

Saharan African drylands including northern Ghana and Burkina Faso 
and large parts of the Sahel, migration as a result of unfavourable 
environmental conditions closely linked to climate change has 
often provided opportunities for farmers to earn income (SDG 1) 
and mitigate the effects of climate-related fluctuations in crop and 
livestock productivity (SDG 2) (Zampaligré et al., 2014; Antwi-Agyei 
et al., 2018; Wiederkehr et al., 2018). Renewable energy can mitigate 
climate effects (SDG 13), improve air quality (SDG 3), wealth and 
development (SDGs 1, 2).
Different types of irrigation including drip and small-scale irrigation can 
contribute towards increased agricultural productivity (SDG 2), improved 
income (SDG 1) and food security (SDG 2) and increase resilience to 
long-term changes in precipitation (SDG 13) (Bjornlund et al., 2020). 
In Kenya and Tanzania, small-scale irrigation provides employment 
opportunities and income to both farmers and private businesses (SDGs

8 and 9) (Lefore et al., 2021; Simpson et al., 2021c). Land management 
practices including the use of fertilizers and mulching have also been 
highlighted as adaptation options improving soil fertility for better 
yields (SDG 2) and delivering opportunities to reduce the climate 
change effects (SDG 13) (Muchuru and Nhamo, 2019).Climate-smart agriculture (CSA) offers opportunities for smallholder 
farmers to increase productivity (SDG 2), build adaptive capacity while 
reducing the emission of GHGs (SDG 13) from agricultural systems 
(Lipper et  al., 2014; Mutenje et  al., 2019). CSA practices including 
conservation agriculture, access to climate information, agroforestry 
systems, drip irrigation, planting pits and erosion control techniques 
(Partey et al., 2018; Antwi-Agyei et al., 2021) can improve soil fertility, 
increase yield and household food security (Zougmoré et  al., 2016; 
Zougmoré et al., 2018), thereby contributing to the realisation of SDG

2 in Africa (Mbow et al., 2014).
In contrast, adaptation actions may induce trade-offs with mitigation 
objectives, as well as other adaptation and developmental outcomes, 
delivering negative impacts and compromising the attainment of 
the SDGs. For example, increased deployment of renewable energy 
technologies can drive future land use changes (Frank et al., 2021) and 
threaten important biodiversity areas if poorly deployed (Rehbein et al., 
2020). The use of early maturing or drought-tolerant crop varieties may 
increase resilience (SDGs 1, 2), but adoption by smallholder farmers 
can also be hindered by affordability of seed. Cultivation of biodiesel 
crops also can hinder food security (SDG 2) at local and national 
levels (Tankari, 2017; Brinkman et  al., 2020). Additionally, the use 
of fertilizers in intense systems can result in increased environmental 
degradation (Akinyi et  al., 2021). When farmers migrate, it puts

pressure on inadequate social services provision and facilities at 
their destination (SDG 8) and leads to reduced farm labour and a 
deterioration of the workforce and assets (SDG 2) (Gemenne and 
Blocher, 2017a), which negatively affects farm operations and non-
migrants, particularly women, elderly and children, at the point of 
origin (Nyantakyi-Frimpong and Bezner-Kerr, 2015; Ahmed et al., 2016; 
Otto et al., 2017; Eastin, 2018). Farmers may also miss critical periods 
during the farming season that eventually makes them food insecure 
(SDG 2) and vulnerable to climate change (SDG 13) (Antwi-Agyei et al., 
2018). Migrants should be supported to reduce their overall shocks 
to climate vulnerability at the points of origin and destination. Small-
scale irrigation infrastructure if not managed properly, may lead to 
negative environmental effects and compromise the integrity of 
riparian ecosystems (SDG 15) (Loucks and van Beek, 2017) and serve

as breeding grounds for malaria-causing mosquitoes (SDG 3) (Attu and 
Adjei, 2018).
9.4 Climate Resilient Development
Climate resilient development (CRD) is a process of implementing 
GHG mitigation and adaptation measures to support sustainable 
development for all (Denton et  al., 2014; Andrijevic et  al., 2020; 
Owen, 2020; Cornforth et al., 2021). It emphasises equity as a core 
element of sustainable development as well as conditions for inclusive 
and sustained economic growth, shared prosperity and decent work 
for all, taking into account different levels of national development 
and capacities as encoded in the SDGs (Section  9.3.2; Chapter 18 
Section 18.1). This section identifies five key dimensions of CRD for 
Africa: climate finance, governance, cross-sectoral and transboundary 
solutions, adaptation law, and climate services and literacy.

9
1305Africa  Chapter 9
9.4.1 Climate Finance
Access to adequate financial resources is crucial for climate change 
adaptation (Cross-Chapter Box  FINANCE in Chapter 17). Since the 
Copenhagen Accord (UNFCCC, 2009), and then extended by the Paris 
Agreement (UNFCCC Paris Agreement, 2015 see Article 4.4, and also 4.8, 
4.9), developed countries are expected to scale up climate finance for 
developing countries toward a collective goal of USD 100 billion per year 
by 2020, with a balanced allocation between adaptation and mitigation.
9.4.1.1 How Much Adaptation Finance is Needed?
There is limited research providing quantitative estimates of 
adaptation costs across Africa. Adaptation costs in Africa have been 
estimated at USD  7–15  billion per year by 2020 (Schaeffer et  al., 
2013), corresponding to USD  5–11 per capita per year. The African 
Development Bank estimates costs of near-term adaptation needs 
identified in the Intended NDCs (INDCs) of African countries as

USD 7.4 billion per year from 2020, recognising INDCs describe only 
a limited subset of adaptation needs (AfDB, 2019). Many African 
countries, particularly Least Developed Countries (LDCs), express 
a stronger demand for adaptation finance—a study of financial 
demands in INDCs for 16 African countries suggests a ratio around 
2:1 for adaptation to mitigation finance with demand for Eritrea and 
Uganda approximately 80% for adaptation (Zhang and Pan, 2016).
Adaptation costs in Africa are expected to rise rapidly as global 
warming increases (high confidence). A meta-analysis of adaptation 
costs identified in 44 NDCs and National Adaptation Plans (NAPs) 
from developing countries estimated a median adaptation cost around 
USD 17 per capita per year for 2020–2030 (Chapagain et  al., 2020). 
Adaptation cost estimates for Africa increase from USD  20–50  billion 
per year for Representative Concentration Pathway (RCP) 2.6 in 2050

(around 1.5°C of warming), to USD 18–60 billion per year for just over 
2°C, to USD 100–437 billion per year for 4°C of global warming above 
pre-industrial levels (Schaeffer et al., 2013; UNEP , 2015; Chapagain et al., 
2020). Focusing on individual sectors, the average country-level cost is 
projected to be USD 0.8 billion per year for adapting to temperature-
related mortality under 4°C global warming (Carleton et al., 2018), with 
cumulative energy costs for cooling demand projected to reach USD 
51 billion by 2°C and USD 486 billion by 4°C global warming (Parkes 
et  al., 2019). Transport infrastructure repair costs are also projected 
to be substantial (Section  9.8.2) More precise estimates are limited 
by methodological difficulties and data gaps for costing adaptation, 
uncertainties about future levels of global warming and associated 
climate hazards, and ethical choices such as the desired level of 
protection achieved (Fankhauser, 2010; Hallegatte et al., 2018; UNFCCC,

2018) (Cross-Chapter Box  FINANCE in Chapter 17). As such, existing 
estimates are expected to substantially underestimate eventual costs 
with adaptation costs possibly 2–3  times higher than current global 
estimates by 2030, and 4–5 times higher by 2050 (UNEP , 2016a).
9.4.1.2 Benefit–Cost Ratios in Adaptation
Although analysts face challenges related to the nature of climate 
change impacts (Sussman et  al., 2014) and data limitations (Li et  al., 
2014) when estimating all costs and benefits for adaptation measures in specific contexts, adaptation generally is cost-effective (high confidence). 
The Global Commission on Adaptation estimated the benefits and costs 
of five illustrative investments and found benefit–cost ratios ranging 
from 2:1 to 10:1. However, it also noted that ‘actual returns depend on 
many factors, such as economic growth and demand, policy context, 
institutional capacities and condition of assets’ (The Global Commission

on Adaptation, 2019). A review of ex-ante cost–benefit analyses for 19 
adaptation-focused projects in Africa financed by the Green Climate 
Fund (GCF) shows benefit–cost ratios in a similar range. Using a 
10% discount rate, as used by many of GCF’s accredited entities, the 
benefit–cost ratio for individual projects ranges from 0.9:1 to 7.3:1, the 
median benefit–cost ratio is 1.8:1 and total ratio across all 19 projects is 
2.6:1. When using lower discount rates, as some entities do for climate 
projects, the benefit–cost ratio is even higher, reflecting the front-loaded 
costs and back-loaded benefits of many adaptation investments. Using 
a 5% discount rate, the overall benefit–cost ratio of the GCF projects 
is 3.5:1, with a range from 1:1 to 11.5:1 and a median ratio of 2.4:1 
(Breitbarth, 2020). In addition, many proposals have activities for which 
further benefits were not estimated due to the difficulty of attributing

benefits directly to the intervention. The benefits of adaptation measures 
for infrastructure and others with clear market impacts are often easier 
to estimate than for policy interventions and where markets may not 
exist, such as ecosystem services (Li et al., 2014).
9.4.1.3 How Much Finance is Being Mobilised?
The amounts of finance being mobilised internationally to support 
adaptation in African countries are billions of US dollars less than 
adaptation cost estimates, and finance has targeted mitigation more 
than adaptation (high confidence). The Organisation for Economic Co-
operation and Development (OECD (2020) estimates an average of 
USD  17.3  billion per year in public finance targeting mitigation and 
adaptation from developed countries to Africa in 2016–2018, with 
adaptation expected to be a small share of this amount. Of the global 
total, only 21% in 2018 targeted adaptation (there is no breakdown

provided for Africa). Analysis of OECD data that is reported by the 
funders, covering bilateral and multilateral funding sources, estimated 
international public finance (grants and concessional lending) committed 
to Africa for climate change for 2014–2018 at USD 49.9 billion: 61% 
(30.6 billion) for mitigation, 33% (16.5 billion) for adaptation and 5% 
(2.7 billion) for both objectives simultaneously (Figure 9.8a; Savvidou 
et  al., 2021). This equates to an average of USD  3.8  billion per year 
targeting adaptation (Savvidou et al., 2021). In per capita terms, only 
two countries (Djibouti and Gabon) were supported with more than 
USD 15 per person per year, most were supported with less than USD 5 
per person per year (Savvidou et al., 2021).
The multilateral development banks (MDBs) report 43% of their climate-
related commitments to sub-Saharan Africa in 2018 targeted adaptation 
(EBRD et al., 2021). Sources other than international public finance are

more difficult to track and there is limited data on Africa (Cross-Chapter 
Box  FINANCE in Chapter 17). Considering a wider range of finance 
types (including private flows and domestic mobilisation), an estimated 
annual average of roughly USD 19 billion in climate finance for 2017–
2018 went to sub-Saharan Africa, of which only 5% was for adaptation 
(CPI, 2019; Adhikari and Safaee Chalkasra, 2021). The mobilisation of 
private finance by developed country governments, through bilateral and

9
1306Chapter 9 Africa
Climate finance commitments targeting African countries and regions
Multilateral
banks
Climate funds
Other
multilateralsMultilateral sources
Bilateral sources
Adaptation and mitigation 
simultaneously
2,742Adaptation
16,489
World Bank
IrelandCanadaSweden
NorwayGermanyUnited StatesUnited KingdomFranceEU Institutions (excl. EIB)European Bank for
Reconstruction and Development
OtherNetherlandsAfrican Development Bank
European Investment Ban k
Green Climate Fund
Global Environment Facility
Climate Investment Funds
International Fund fo r
Agricultural Developmen t5,825
153231308
2736979001,3131,5721,466441
6241502,612
188
1,041
376
178
127
754Eastern
Africa 5,630
Western
Africa 4,816
Regional
allocations  2,663Central
Africa 1,443Southern
Africa 2,059Northern
Africa 2,619(a) Total adaptation-related finance (commitments) to African countries and regions, by source and recipient regions, 2014-2018
(b) Trend of adaptation-related finance

commitments to African regions over time
Adaptation-related
finance
01,0002,0003,0004,0005,000(c) Total African adaptation- and mitigation-related finance
commitments by country, 2014–2018
KenyaEthiopia3,323
3 216Morocco5,916Tunisia 1,871
Nigeria1,897
Uganda1,650
Mozambique 932Algeria530Libya
Sudan135Egypt6,906
Mauritani a189
Niger666
Chad193Mali775
Democr atic Republic of the Congo843
Namibia221
South A frica1,81498 Lesotho64EswatiniAngola215Tanzani a1,775
Malaw i 759
671
Zambia
Botswana164Zimbabw e121Somalia 166Cameroon850101
South SudanSenegal 2,008
966 Rwanda
Burundi 426158
Togo801
Ghana
Benin588
825
Côte d’Iv oireBurkina Faso732
Equatorial Guinea6Congo118
Gabo n208Centr al African Republic43
Liberia 330Sierra Leone 136Guinea 311Guinea-Bissau 157Eritre a 41
Djibout i157 Gambia 346
including financial commitments
that were reported separately from
allocations to individual countriesRegional allocations5,936Millions of USD 
(constant prices)Millions of USD (constant prices)

2018 2017 2016 2015 2014Western
Africa
Southern
Africa
Northern
Africa
Eastern
AfricaRegional
allocations
Central AfricaTotal allocations49,876Adaptation
(33.1%)Mitigation
(61.4%)Adaptation
and mitigation
(5.5%)
Millions of USD 
(constant prices)Total
allocation s
Figure 9.8 |  Total adaptation-related finance (commitments) to African countries and regions from 2014–2018 (USD millions, constant prices) as reported 
to OECD. 
(a) Flows of committed finance targeting adaptation by source and recipient region; 
(b) trend over time in international development finance commitments targeting adaptation in Africa; and 
(c) country-level shares of total climate finance commitments that targeted adaptation or mitigation or both simultaneously. Source: Savvidou et al. (2021).

9
1307Africa  Chapter 9
multilateral financial support, is lower in Africa relative to other world 
regions. Globally, in 2016–2018, Africa made up only 17% of mobilised 
private finance relevant for climate change (OECD, 2020).
Strong differences exist among African sub-regions. Finance commitments 
targeting adaptation increased from 2014–2018 for east and west Africa but decreased in central Africa (Savvidou et  al., 2021) (Figure  9.8b). 
Climate-related finance was >50% for adaptation in 19 countries, while 
26 received >50% for mitigation (Savvidou et al., 2021).
African countries expect grants to play a crucial role in supporting 
adaptation efforts because loans add to already high debt levels that 
(a) Sectoral distribution of adaptation finance commitments to Africa 2014–2018 
(b) Disbursement ratios for Africa compared to global average (c) Disbursement ratios for adaptation finance broken down by sub- regionAgriculture  (30%) 5,715 Water supply and sanitation (20%) 3,770

Other multisector (12%) 2,221 General environment 
protection (9%) 1,818Agricultural
1,450Agricultural policy
and administrative
1,401
Agricultural
land resources
472Livestock
269Agri-
cultural
research
226Water supply - large systems
996
Water sector policy and
administrative management
976Sanitation - large systems
868
Water supply
and sanitation -
large systems
494Other
435
Rural development
903
Urban
and management
503Disaster
Risk
Reduction
489
Other 325Environmental policy
1283
Bio-diversity
239Agricultural
water resources
1,082Other
813Other
296Other social
and services (4%)
Disaster
preparednes (4%)Transport 
and storage (3%)
Government and
civil society (2%)Development
food 
assistance (2%)
Other sectors (15%)
80% 60% 40% 20%
Ratio of disbursements to commitments100% 80% 60% 40% 20% 0
Ratio of disbursements to commitments0Mitigation
56%64%Adaptation
46%51%Total
96%
33%
67%71%
15%
48%

33%85%
Western Africa
Southern AfricaRegional allocations
Northern Africa
Eastern Africa
Central Africa(% of amounts committed)
Values in millions of US DAdaptation finance commitments for Africa focused most on agriculture and water,
and disbursement ratios for climate-related finance were very low
World
Africa
Figure 9.9 |  Adaptation finance for Africa has focused most on agriculture and water, and disbursement ratios for climate-related finance are very low  
(a) The amounts of finance targeting adaptation committed to different sectors across Africa from 2014–2018 in millions of USD as reported to OECD and including multilateral 
development banks (Savvidou et al., 2021). 
(b) Disbursement ratios (disbursements expressed as percentage of commitments) for finance targeting mitigation and adaptation, and for total development finance; showing 
disbursement ratios for Africa compared to global average; and

(c) disbursement ratios for adaptation finance broken down by each African sub-region for 2014–2018 (for all funders reporting to OECD except multilateral development banks). 
Source: Savvidou et al. (2021).

9
1308Chapter 9 Africa
exacerbate fiscal challenges, especially in light of high sovereign debt 
levels from the COVID-19 pandemic (Bulow et al., 2020; Estevão, 2020). 
From 2014–2018, more finance commitments targeting adaptation 
in Africa were debt instruments (57%) than grants (42%) (Savvidou 
For Africa combined, the sectors targeted with most support for 
adaptation are agriculture and water supply and sanitation, which 
account for half of total adaptation finance from 2014–2018 
(Figure 9.9a). The sectoral distribution has changed little over these 
years, suggesting adaptation planners and funders are maintaining a 
relatively narrow view of where support is needed and how to build 
climate resilience (Savvidou et al., 2021).
However, to understand actual expenditure on adaptation, it is necessary 
to look at disbursements (that is, the amounts paid out compared to 
committed amounts). Low ratios of disbursements to commitments

suggest difficulties in project implementation. Disbursement ratios for 
climate-related finance from all funders other than MDBs (for which 
data is not published) in Africa are very low (Figure 9.9b; Savvidou et al., 
2021). Only 46% of 2014–2018 commitments targeting adaptation 
were dispersed (Savvidou et al., 2021). Regions faring worst are north 
Africa (15%), central Africa (33%) and west Africa (33%) (Figure 9.9c). 
These disbursement ratios for adaptation and mitigation finance in 
Africa are lower than the global average (Savvidou et al., 2021), which 
suggests greater capacity problems in implementing climate-related 
projects and, in turn, means lost opportunities to build resilience and 
adaptive capacity and a wider gap in adaptation finance for Africa 
9.4.1.4 What Are the Barriers and Enabling Conditions for 
Adaptation Finance?
The present situation reflects not only an insufficient level of finance

being mobilised to support African adaptation needs (Section 9.4.1) 
but also problems in accessing and using funding that is available. 
The direct-access modality introduced by the Adaptation Fund 
and GCF , whereby national and regional entities from developing 
countries can be accredited to access funds directly, is aimed at 
reducing transaction costs for recipient countries, increasing national 
ownership and agency for adaptation actions, and enhancing decision-
making responsibilities by national actors, thereby contributing to 
strengthening local capacity for sustained and transformational 
adaptation (CDKN, 2013; Masullo et al., 2015). Indeed, direct-access 
projects from the Adaptation Fund tend to be more community 
focused than indirect-access projects (Manuamorn and Biesbroek, 
2020). Country institutions in Africa, however, are struggling to be 
accredited for direct access because of the complicated, lengthy

and bureaucratic processes of accreditation, which requires, for 
example, strong institutional and fiduciary standards and capacity 
to be in place (Brown et al., 2013; Omari-Motsumi et al., 2019). As 
of December 2019, over 80% of all developing countries had no 
national direct access entities (DAEs) (Asfaw et al., 2019). Capacity 
to develop fundable projects in Africa is also inadequate. An analysis 
of proposals submitted to the GCF up to 2017 revealed that, while 
African countries were able to submit proposals to the GCF , they 
had the lowest percentage of approvals (39%) compared to all other regions (Fonta et  al., 2018). This suggests the quality of proposals 
and therefore the capacity to develop fundable proposals remains 
inadequate in the region.
Even when accredited, some countries experience significant institutional 
and financial challenges in programming and implementing activities to 
support concrete adaptation measures (Omari-Motsumi et  al., 2019). Low

disbursement ratios suggest inadequate capacity to implement projects 
once they are approved (Savvidou et al., 2021). Systemic barriers have 
been highlighted in relation to the multilateral climate funds, including 
funds not providing full-cost adaptation funding, capacity barriers in 
the design and implementation of adaptation actions (including the 
development of fundable project proposals) and barriers in recognising 
and enabling the involvement of sub-national actors in the delivery and 
implementation of adaptation action (Omari-Motsumi et al., 2019). As 
of 2017, most GCF disbursements to Africa (61.9%) were directed to 
support national stakeholders’ engagement with regards to readiness 
activities, with only 11% directed to support DAEs in implementation 
of concrete projects/pipeline development (Fonta et  al., 2018). While 
supporting readiness activities is important for strengthening country 
ownership and institutional development, research suggests adaptation

finance needs to shift towards implementation of concrete projects and 
more pipeline development if the goal of transformative and sustained 
adaptation in Africa is to be realised (Fonta et  al., 2018; Omari-
Motsumi et al., 2019). The source of these problems needs to be better 
understood so that the prospects for future climate-related investments 
can be improved and institutional strengthening and targeted project 
preparation can be supported (Omari-Motsumi et al., 2019; Doshi and 
Garschagen, 2020; Savvidou et al., 2021).
Some progress has been made in supporting developing countries 
to enhance their adaptation actions. The process to formulate and 
implement NAPs was established by parties under the UNFCCC to 
support developing countries in identifying their vulnerabilities, and 
determine their medium- and long-term adaptation needs (UNFCCC 
Paris Agreement, 2015). NAPs provide a means of developing and 
implementing strategies and programmes to address those needs. In

2016, the parties agreed the GCF would fund up to USD 3 million per 
country for adaptation planning instruments, including NAPs. However, 
accessing funding through the GCF for NAP formulation is challenging 
(Fonta et al., 2018) and, as of October 2020, 4 years after the decision 
to fund NAPs, only six African countries had completed their NAPs 
(UNFCCC NAP central). The next step is to convert adaptation planning 
documents into programming pipeline projects that are fundable 
and implementable, which presents a significant barrier to enhanced 
adaptation action (Omari-Motsumi et al., 2019).
Adaptation finance has not been targeted more towards more 
vulnerable countries (Barrett, 2014; Weiler and Sanubi, 2019; Doshi 
and Garschagen, 2020; Savvidou et al., 2021). Reasons for this include 
fast-growing middle-income countries offering larger gains in emission 
reductions, so finance has favoured mitigation in these economies,

even within sub-Saharan Africa, and as more climate finance uses debt 
instruments, mitigation projects are further preferred because returns 
are perceived to be more certain (Rai et al., 2016; Lee and Hong, 2018; 
Carty et al., 2020; Simpson et al., 2021c).

9
1309Africa  Chapter 9
Many adaptation interventions for most vulnerable countries and 
communities provide no adequate financial return on investments 
and can therefore only be funded with concessional public finance 
(Cross-Chapter Box FINANCE in Chapter 17). Yet, public funds alone 
are insufficient to meet rapidly growing adaptation needs. Public 
mechanisms can help leverage private sector finance for adaptation by 
reducing regulatory, cost and market barriers through blended finance 
approaches, public–private partnerships, or innovative financial 
instruments and structuring in support of private sector requirements 
for risk and investment returns, such as green bonds (Cross-Chapter 
Box FINANCE in Chapter 17). Sub-national actors can be core agents 
to conceptualise, drive and deliver adaptation responses, and unlock 
domestic resources in the implementation of adaptation action (CoM 
SSA, 2019; Omari-Motsumi et al., 2019), provided they are sufficiently

resourced and their participation and agency are supported.
Many African countries are at high risk of debt distress, especially due 
to the COVID-19 pandemic, and will need to decrease their debt levels 
to have the fiscal space to invest in climate resilience (Estevão, 2020; 
Dibley et al., 2021). As of mid-2021, the G20’s Debt Service Suspension 
Initiative is providing temporary relief for repayment of bilateral credit, 
but this has largely not been taken up by private lenders (Dibley et al., 
2021; World Bank, 2021). The total external debt-servicing payments 
combined for 44 African countries in 2019 were USD 75 billion (World 
Bank, 2019), far exceeding discussed levels of near-term climate 
finance. Aligning debt relief with Paris Agreement goals could provide 
an important channel for increased financing for climate action, for 
example, by allowing African countries to use their debt-servicing 
payments to finance climate change mitigation and adaptation (Fenton

et al., 2014). Governments can disclose climate risks when taking on 
sovereign debt, and debt-for-climate resilience swaps could be used 
to reduce debt burdens for low-income countries while supporting 
adaptation and mitigation (Dibley et al., 2021).
9.4.2 Governance
9.4.2.1 Governance Barriers
Overcoming governance barriers is a precondition to ensure successful 
adaptation and CRD (Pasquini et  al., 2015; Owen, 2020).  Despite 
the ambitious climate targets across African countries and renewed 
commitments in recent years (Zheng et al., 2019; Ozor and Nyambane, 
2020), governance barriers include, among others, slow policy 
implementation progress (Shackleton et  al., 2015; Taylor, 2016), 
incoherent and fragmented approaches (Zinngrebe et  al., 2020; 
Nemakonde et al., 2021), inadequate governance systems to manage 
climate finance (Granoff et al., 2016; Banga, 2019), poor stakeholder 
participation (Sherman and Ford, 2014), gender inequalities (Andrijevic

et  al., 2020), unaligned development and climate agendas (Musah-
Surugu et al., 2019; Robinson, 2020), elite capture of climate governance 
systems (Kita, 2019), hierarchical and complex state bureaucracy 
(Meissner and Jacobs, 2016; Biesbroek et  al., 2018) and weak, non-
existent or fragmented sub-national institutions (Paterson et al., 2017; 
Musah-Surugu et  al., 2019). Further, adaptation planning involves 
cross-cutting themes, multiple actors and institutions with different 
objectives, jurisdictional authority and levels of power and resources, yet there is often a lack of coordination, clear leadership or governance 
mandates (Shackleton et al., 2015; Leck and Simon, 2018) and unequal 
power relations between African countries and developed countries can 
hinder progress on governance of financial markets, budget allocations 
and technology transfer to address addressing climate technology gaps 
in Africa (Rennkamp and Boyd, 2015; Olawuyi, 2018).

Policy implementation can be slow due to the absence of support 
mechanisms and dependency on funding by international partners 
(Leck and Roberts, 2015; Ozor and Nyambane, 2020). In many countries, 
commitment to climate policy objectives is low (Naess et al., 2015), 
particularly in light of competing development imperatives and post-
COVID-19 recovery efforts (Caetano et al., 2020), although COVID-19 
recovery efforts offer significant opportunities for health, economic 
and climate resilience co-benefits (Sections  9.4.3; 9.11.5; Cross-
Chapter Box COVID in Chapter 7). Another challenge relates to long-
term planning and decision making which is hampered by uncertainty 
related to future socioeconomic and GHG emissions scenarios (Coen, 
2021), political cycles and short-term political appointment terms 
(Pasquini et al., 2015).
Lack of community agency in climate governance affects the capacity 
for citizen-led climate interventions in Africa (Antwi-Agyei et al., 2015;

Mersha and Van Laerhoven, 2016). This is attributed partly to low civic 
education, limited participation power of citizens and tokenism due 
to perceived lack of immediate benefits (Odei Erdiaw-Kwasie et  al., 
2020), as well as low rates of climate change literacy in many regions 
(Section 9.4.3; Simpson et al., 2021a). Participation in climate policy 
also extends to the private sector, which has been relatively uninvolved 
in adaptation discussions to date (Crick et al., 2018).
Africa requires substantial resources and support to adapt to the 
unavoidable consequences of climate change, a pertinent climate 
justice concern for governments. However, the mechanisms needed to 
redress current power imbalances, structural and systemic inequality 
are often absent (Saraswat and Kumar, 2016; see Section  9.11.4) 
and policies that underpin environmental justice concerns, including 
distributive justice, participation, recognition and capability (Shi et al.,

2016; Chu et al., 2017) are also needed.
9.4.2.2 Good Governance
Good governance can contribute to positive climate outcomes and 
CRD in Africa through long-term planning, development-focused policy 
environments, the development of robust and transformational policy 
architecture, inclusive participation and timely implementation of NDCs 
(Bataille et al., 2016; Werners et al., 2021; see Table 9.3 for examples).
African governments are developing and revising ambitious adaptation 
policies that are enforceable and aligned with wider societal 
development goals, including an enabling environment for finance and 
investment in the jobs and skills development necessary to support 
a just transition (Section 9.4.5; ILO, 2019). If appropriately designed, 
such institutions offer the opportunity to foster adaptive governance 
that is collaborative, multi-level and decentralised, offering integration 
of policy domains, flexibility and an emphasis on non-coerciveness and

9
1310Chapter 9 Africa
Coordination across multiple sectors, supported with leadership from 
the highest levels of government, has shown to improve implementation 
effectiveness and anticipated scaling up (Rigaud et al., 2018). This high-
level engagement promotes the inclusion of climate resilience and 
adaptation targets in national planning and budgeting. Financial and 
capacity support is essential (Adenle et  al., 2017; UNEP , 2021), as is 
the tracking of national progress towards development goals (Box 9.6).
In Africa, climate governance occurs in a context of deep inequality 
and asymmetric power relations—both within countries and between 
countries—making adequate mechanisms for multi-stakeholder 
participation essential (Sapiains et al., 2021). This requires the creation 
of avenues for the voices of marginalised groups in policy processes 
and enabling policy environments that can catalyse inclusive action

and transformational responses to climate change (Totin et al., 2018; 
Revi et  al., 2020; Ziervogel et  al., 2021), safeguarding protection 
against the climate harms of the most vulnerable in society, particularly 
of women and children (see also Box 9.1). Community-based natural 
resource management in pastoral communities was observed to 
improve institutional governance outcomes through involving community members in decision making, increasing the capacity of 
these communities to respond to climate change (Reid, 2014).
Specific indicators can be included in the performance metrics and 
monitoring frameworks for each sector, policy intervention and budget 
planning cycle (Wojewska et al., 2021). Many countries in Africa are also 
revamping their institutional coordination mechanisms to reflect an all-
of-government approach and partnership with non-state stakeholders 
with diverse capabilities and expertise (see examples from Rwanda

and Zambia in Table 9.3). This includes Cape Town’s drought response 
in 2017/2018 where non-state actors actively partnered with the state 
response around water management/savings practices (Simpson et al., 
2020a; 2020b; Cole et al., 2021b).
9.4.3 Cross-sectoral and Transboundary Solutions
Climate change does not present its problems and opportunities 
conveniently aligned with traditional sectors, so mechanisms are 
needed to facilitate interactions and collaborations between people Table 9.3 |  Characteristics and examples of governance that contribute towards CRD in Africa.
Governance characteristic Example
Long-term development planningCountries are mainstreaming adaptation into their long-term development cycles (UNFCCC Adaptation Committee, 2019). For example, Burkina Faso’s 
National Adaptation Plan elaborates its perspective to 2050 and links to its development pathways (Government of Burkina Faso, 2015). Many African

countries are also enhancing the adaptation components of their long-term low emissions strategies.
Climate justice and 
inequality-focused policiesClimate policies can be designed to include specific policy mechanisms (e.g., carbon taxes, renewable energy subsidies) to maximise developmental 
gains while reducing inequality (Andrijevic et al., 2020). For example, revenues from a carbon tax can be used to increase social assistance 
programmes that benefit poor people and reduce their vulnerability to climate change (Hallegatte et al., 2016). Climate risk management can be 
integrated into social protection and assistance programmes, such as public works programmes that increase climate resilience (Section 9.11).
Interlinkages between adaptation 
and development pathwaysCross-sectoral and multi-level governance approaches can harness synergies with the SDGs, Paris Agreement and Agenda 2063 aspirations, helping

to counter the adaptation deficit, promote sustainable resource use and contribute to poverty reduction (Niang et al., 2014; IPBES, 2018; Roy et al., 
2018b). Ghana, Namibia, Rwanda and Uganda all link adaptation with disaster risk reduction in their NDCs (UNFCCC Adaptation Committee, 2019).
High-level engagementClimate policies, traditionally overseen by environment ministries, are increasingly receiving priority from finance and planning ministries. Zambia’s 
Climate Change Secretariat is currently led by the Ministry of Finance (Government of the Republic of Zambia, 2010), while Tanzania’s environmental 
division sits in the office of the Vice-President (Governmet of the United Republic of Tanzania, 2011).
All-of-government approachIn Kenya, the Climate Change Directorate is the secretariat for the National Climate Change Commission, serving as an overarching mechanism

to coordinate sectoral and county-level action (Government of the Republic of Kenya, 2018). In South Africa, the National Committee on Climate 
Change, the Intergovernmental Committee on Climate Change and the Presidential Climate Change Commission have been established to enhance 
intergovernmental and multi-sectoral coordination on climate action (Climate Action Tracker, 2021).
Participatory engagementPolycentric, bottom-up and locally implemented approaches are more able to include the emergence of new actors (e.g., city networks, multinational 
companies and sub-state entities), new instruments and levels (soft law instruments or transnational dynamics) and new guiding principles and 
values (fairness, transparency and co-participation) (Leal Filho et al., 2018; Sapiains et al., 2021). Case studies include the community-based, 
participatory scenario planning approach used in Malawi to generate information for farmers from seasonal forecasts, as well as the integration

of climate risk into Lusaka’s Strategic Plan through engagement with city planners (Conway and Vincent, 2021; Vincent and Conway, 2021). Many 
innovative solutions have been designed to promote participation, such as Pamoja Voices toolkits in pastoralist communities in northern Tanzania 
(Greene et al., 2020).
Inclusive and diverse stakeholdersKenya’s Climate Change Directorate has a designated team to integrate gender into its national climate policies (Murray, 2019), while Seychelles’ 
National Climate Change Council has allocated a seat exclusively for a youth candidate (Government of The Seychelles, 2020). Tanzanian 
Climate-Smart Agriculture Alliance supports the integration of farmers and builds strategic alliances to support climate processes (Nyasimi et al., 
2017).
PartnershipsGhana, Kenya, Uganda and Zambia are developing anticipatory scenarios for low-carbon CRD pathways for the agricultural sector, aimed at informing

input into national climate policy (Balié et al., 2019). This science to policy to practice interface is bridged through the inclusion of policymakers, 
practitioners and academics (Dinesh et al., 2018). In Lusaka, Durban and other African cities, processes of engagement and learning have built the 
trust and capacities needed to inform city-scale, climate-resilient decisions and associated actions (Taylor et al., 2021a; Taylor et al., 2021b).
Nationally Determined 
Contributions (NDC) 
implementationRwanda has developed an indicator-based monitoring, reporting and verification (MRV) framework for tracking its NDC implementation and 
associated financial flows (Government of Republic of Rwanda, 2020). Zambia has also integrated gender indicators into its NDC implementation 
plan and is incorporating gender considerations into its MRV framework (Murray, 2019).

9
1311Africa  Chapter 9
working in widely different sectors (Simpson et al., 2021b). Traditional 
risk assessments typically only consider one climate hazard and one 
sector at a time, but this can lead to substantial misestimation of risk 
because multiple climate risks can interact to cause extreme impacts 
(Zscheischler et al., 2018; Simpson et al., 2021b).
Because multiple risks are interlinked and can cascade and amplify 
risk across sectors, cross-sectoral approaches that consider these 
interlinkages are essential for CRD, especially for managing trade-offs 
and co-benefits among SDGs, mitigation and adaptation responses 
(Liu et al., 2018a).
In Africa, placing cross-sectoral approaches at the core of CRD provides 
significant opportunities to deliver large benefits and/or avoided 
damages across multiple sectors including water, health, ecosystems 
and economies (very high confidence) (Boxes 9.5; 9.6; 9.7). They can

also prevent adaptation or mitigation action in one sector exacerbating 
risks in other sectors and resulting in maladaptation, for example, from 
large-scale dam construction or large-scale afforestation (e.g., water–
energy–food nexus and large-scale tree planting efforts) (Boxes 9.3; 
9.5).
Cross-sectoral or ‘nexus’ approaches can improve the ability of 
decision makers to foresee and prevent major climate impacts. Barriers 
to developing nexus approaches arise from rigid sectoral planning, 
regulatory and implementation procedures, entrenched interests, and 
power structures and established sectoral communication structures. 
Opportunities for overcoming these barriers include creating a 
dedicated home for co-development of nexus risk assessment 
and solutions, promoting co-leadership of projects by multiple 
sectors, specific budget allocations for nexus projects, facilitating 
and coordinating services, compiling useful strategies into toolkits,

ameliorating inequitable power relations among participants and 
measuring progress on nexus approaches through metrics (Palmer 
et al., 2016; Baron et al., 2017).
Beyond cross-sectoral collaboration, international cooperation is vital 
to avert dangerous climate change as its impacts reach beyond the 
jurisdiction of individual states. International good practice and regional 
agreements, protocols and policies together recognise that regional 
integration, cooperative governance and benefit-sharing approaches 
are cornerstones of effective resource security and climate change 
responses in Africa (Jensen and Lange, 2013; World Bank, 2017a; 
Dombrowsky and Hensengerth, 2018). Natural resource development, 
particularly governance of shared river basins, exemplifies opportunities 
for governance responses for African nations that can be cooperative, 
regionally integrated and climate resilient.
In Africa, climate vulnerability crosses geopolitical divides as regional

clusters of fragile and high vulnerability countries exist, emphasising 
the need for transboundary cooperation (Birkmann et al., 2021; Buhaug 
and von Uexkull, 2021). Natural resource security is increasingly reliant 
on transboundary governance, regional integration and cooperation 
(Namara and Giordano, 2017). There are 60 international or shared river 
basins on the continent, a function of colonial divides and topography, 
with some basins shared by four or more countries (UNECA, 2016; 
Popelka and Smith, 2020). Climate changes which result in impact and risk pathways across country boundaries and regions (although 
with different levels of impact) accelerate the urgency for integrated 
approaches to manage and benefit from shared resources and promote 
their security for populations and economies (Namara and Giordano, 
2017; Frame et al., 2018; Carter et al., 2021). At the same time, natural 
resources such as water generate economic benefits shared across

boundaries, such as hydroelectric power generation and regional food 
security (Dombrowsky and Hensengerth, 2018).
Poor governance, particularly at the transboundary level, can undermine 
water security and climate change is likely to add new challenges to pre-
existing dynamics, emphasising the necessity of formal transboundary 
arrangements (Jensen and Lange, 2013; UNECA, 2016). Further, it can 
constrain access to critical financial resources at a time when it is 
needed most. This is particularly the case when climate impact pathways 
manifest at the transboundary level (Challinor et  al., 2018; Simpson 
et al., 2021b), but where the need to protect sovereign interests can 
block regionally integrated institutional arrangements that are pivotal 
for accessing the multilateral climate funds for transboundary climate 
investments that include resilient infrastructure and greater water 
benefits across Africa’s shared river basins (Cross-Chapter Box INTEREG

in Chapter 16; Carter et al., 2021).
In response, the African Development Bank is supporting two of the 
most climate-vulnerable and larger African river basins to leverage GCF 
and Global Environment Facility (GEF) funds to finance Programmes for 
Integrated Development and Adaptation to Climate Change (PIDACC). 
PIDACC finance is approved at the multinational level in the Niger basin 
which is shared by nine west and central African States (AfDB, 2018c; 
GCF , 2018a), while a PIDACC proposal is currently under development 
for the Zambezi basin (Zambezi Watercourse Commission, 2021).
Stakeholders across Africa are recognising the scale and severity of 
transboundary risks to water. Such risks are two-fold in nature, arising 
both from potential impacts due to climate change and from responses 
to climate change (Simpson et  al., 2021b). This awareness among 
stakeholders is leading to increasingly progressive approaches to natural

resource development that can also reduce risk across boundaries 
within regions. For example, river basin organisations in Southern Africa 
such as the Orange-Senqu and the Okavango River Basin Commissions 
are revising treaties considered to pre-date the interrelated issues of 
climate change, growing populations and water scarcity (OKACOM, 
2020). In parts of west Africa, where climate change is characterised by 
reduction of precipitation (Barry et al., 2018), regionally integrated and 
climate-resilient economic investments for water resource development 
are enabled by the Senegal River Basin Organisation (OMVS) which 
emphasises programme and project development, financing and 
implementation in ensuing work plans (World Bank, 2020e), as does 
the Nile Basin Initiative (NBI) in north and east Africa (Schmeier, 2017; 
Blumstein and Petersen-Perlman, 2021).
Enhanced transboundary governance arrangements suggest that

countries are joining forces to coherently manage and protect natural 
resources (Spalding-Fecher et  al., 2014; AfDB, 2021). Underlying 
governance issues and political economy interests block or advance 
such transitions to regionally integrated resource management and 
benefit-sharing, the market drivers of water security (AMCOW, 2012;

9
1312Chapter 9 Africa
Soliev et al., 2015). Angola, for example, outlines regional adaptation 
as a priority and one of its unconditional adaptation strategies (which 
is already funded) is enhancing resilience in the Benguela fisheries 
system, a project shared with Namibia and South Africa (GEF and FAO, 
2021). Another example is The Great Green Wall for the Sahara and 
Sahel Initiative, which was launched in 2007 with the aim of tackling 
land degradation in Africa (UNCCD, 2020). This transboundary project, 
led by the African Union Commission, is being implemented in more 
than 20  countries across Africa’s Sahel region, in cooperation with 
international partners including UNCCD, GEF and the World Bank. 
Approximately USD 10 billion have been mobilised and/or promised 
for this initiative (UNCCD, 2020). Such statements demonstrate 
the increasing identification of transboundary risks and approaches 
to manage and adapt to them as areas of ‘common concern’ that

require cooperative adaptation actions. Accelerating strengthened 
transboundary water and climate governance needs to integrate these 
climate drivers of compromised water security. The role of institutions 
such as OMVS and the NBI have demonstrated they can influence 
economic behaviour among riparian countries of shared river basins 
highlighting that institutions are an integral part of climate governance 
in evolving economic systems (Hodgson, 2000).
9.4.4 Climate Change Adaptation Law in Africa
9.4.4.1 The Rise of Climate Change Adaptation Law
Robust legislative frameworks, both climate change specific and non-
specific, can foster adaptive responses to climate change, particularly 
in LDCs (Nachmany et  al., 2017). As discussed in Chapter 17, there 
are many reasons for this. The successful implementation of policy 
objectives across the continent is often contingent upon or at least 
supported by an underlying legislative framework (Averchenkova and

Matikainen, 2017; Scotford et al., 2017). There are also wider systemic 
and structural reasons for developing climate change legislation, 
including the promotion of coordination within government, its policy 
entrenching role, its symbolic value and its potential to support climate 
finance flows (Nachmany et al., 2017; Scotford and Minas, 2019).
Legal systems, however, also have the potential to be maladaptive. 
Laws may be brittle, often assuming and reinforcing a static state, and 
the boundary of the law may not align to the relevant location, scale or 
impact (Craig, 2010; Arnold and Gunderson, 2013; Wenta et al., 2019). 
This necessitates the review and revision of existing laws to remove 
such barriers and foster adaptive management (Craig, 2010; Ruhl, 
2010; Cosens et al., 2017) and, where necessary, the promulgation of 
new laws.
There has been a rise in framework and sectoral climate change laws 
across Africa, as illustrated in Figure  9.10. The map illustrates the

two framework statutes which have been promulgated in Benin and 
Kenya, as well as the three framework bills which have been drafted 
in Nigeria, South Africa and Uganda. There are also discussions taking 
place in Zimbabwe and Ghana regarding the potential development of 
a draft framework climate change bill. A review of the climate change 
framework laws indicates evidence of cross-pollination in design across 
African jurisdictions, creating the potential for a unique and regionally Progress in development of climate change 
framework law in Africa
Framework Bill under discussionFramework Bill in draft form Framework Law enactedLaw contains dedicated
climate change references
or considerations
No or limited information availableCape Ve rde
São Tome and Principe
St. Helena
Mauritius
Réunion
Comoros
MayotteSeychelles
Figure 9.10 |  Progress in development of climate change framework law 
in Africa derived from an analysis of public databases of African laws,

data drawn from Government of Niger (1998); Government of Liberia (2002); 
Government of Algeria (2004); Government of Tanzania (2004); Government of 
Central African Republic (2008); Government of Lesotho (2008); Government of Togo 
(2008); Government of Guinea Bissau (2011); Government of Ivory Coast (2012); 
Government of Rwanda (2012); Government of Sierra Leone (2012); Government of 
Cape Verde (2014); Government of Morocco (2014); Government of Mozambique 
(2014); Government of Madagascar (2015); Government of the Seychelles (2015); 
Government of Gabon (2016); Government of Kenya (2016); Government of Mali 
(2016); Government of Zambia (2016); Government of Malawi (2017); Government 
of Nigeria (2017); Government of Benin (2018); Government of Ghana (2018); 
Government of South Africa (2018); Government of Uganda (2018); Government of 
Zimbabwe (2019) sources quoted as of September 2019.
appropriate body of law with a strong focus on adaptation responses

(Rumble, 2019). As discussed in Chapter 17, however, there remains the 
need for in-country expert input on how the domestic legal landscape 
may influence their operation, and for each jurisdiction to independently 
interrogate its adaptation needs and objectives (Scotford et al., 2017).
Numerous African states have also included dedicated climate change-
related provisions within various existing statutes that regulate 
the environment or disaster management. For example, Tanzania’s 
Environmental Management Act 20 of 2004 contains dedicated 
provisions to address climate change. Rwanda’s Law on Environment 
48/2018 also contains detailed provisions on mainstreaming climate 
change into development planning processes, education on climate 
change, vulnerability assessments and the promotion of measures 
to enhance adaptive capacity. Some countries have also developed 
laws dedicated to a specific aspect of adaptation. For example, the

Conservation and Climate Adaptation Trust of Seychelles Act 18 of 2015 
establishes a trust fund to finance climate change adaptation responses 
in Seychelles. Similarly, many countries including Algeria, Burkina Faso,

9
1313Africa  Chapter 9
Djibouti, Ghana, Namibia, Malawi, Mauritius, Madagascar, Mozambique, 
Tanzania and South Africa have dedicated disaster management laws. 
At this stage, it is still too early to determine whether these laws 
are having any substantive influence in strengthening resilience and 
reducing vulnerability and, as discussed in Chapter 17, this is identified 
as a knowledge gap requiring further research.
9.4.4.2 Common Themes in Framework Laws
Laws are now being developed to formalise and entrench institutional 
structures, specifying their mandate, function, membership and related 
procedures. A useful example of such an approach can be found in the 
Nigerian Climate Change Bill which establishes the National Climate 
Council on Climate Change headed and chaired by the Vice-President, 
with a wide membership of ministers, the Chairmen of the Governors’ 
Forum and Association of Local Governments, as well as the private

sector and non-governmental organisation (NGO) representatives.
Climate change framework laws can play an instrumental role in 
achieving mainstreaming by directing relevant actors to integrate 
adaptation considerations into existing mandates, operations and 
planning instruments (Rumble, 2019). By way of example, the 
South African Draft Climate Change Bill contains a general duty to 
‘coordinate and harmonise the policies, plans, programmes and 
decisions of the national, provincial and local spheres of government’ 
to achieve, among other things, the climate change objectives of the 
Bill and national adaptation objectives.
Another common theme is the requirement to develop national climate 
change adaptation strategies and plans. Many laws further entrench 
their longevity by requiring them to be subject to strong community 
participation and consultation, as demonstrated by the Kenyan Climate 
Change Act and the Nigerian Climate Change Bill.

9.4.4.3 Local Climate Change Laws and Indigenous Knowledge 
Systems
The Paris Agreement acknowledges, in Article 7.5, that adaptation 
should be based on and guided by, among other things, ‘traditional 
knowledge, knowledge of indigenous peoples and local knowledge 
systems’. The accumulated knowledge within Indigenous knowledge 
systems is particularly important as it can assist governments in 
determining how the climate is changing, how to characterise these 
impacts and provide lessons for adaptation (Salick and Ross, 2009). 
In this context, Indigenous knowledge systems can play an important 
role in the effective design of local laws (Mwanga, 2019), as well 
as national laws. Doing so can contribute to the success of climate 
change response strategies, including enhancing local participation 
and the implementation of community-based and ecosystem-based 
adaptations (Chanza and de Wit, 2016; Mwanga, 2019). For example,

the Makorongo Village Forest Management By-Law in Tanzania codifies 
local customary practices relating to forest management and sustainable 
harvesting with associated dual adaptation and mitigation benefits and 
includes all villagers in the decision-making processes relating to forest 
management (Mwanga, 2019). The inclusion of beneficial Indigenous 
knowledge systems within local by-laws is contingent on the active 
involvement of members of the Indigenous community and awareness of climate change considerations within the local sphere of government, 
and a willingness to foster such practices (Mwanga, 2019).
In addition to the advancement of Indigenous knowledge in adaptive 
responses, it has been suggested that the protection of the rights of 
Indigenous Peoples can have adaptive benefits, in particular through 
the protection of land tenure rights (Ayanlade and Jegede, 2016). It 
has been argued that doing so will protect Indigenous Peoples’ lands

and resources from overconsumption, secure the recognition of their 
cultural stewardship over the environment, provide the financial 
incentive for land stewardship and promote the application of their 
unique knowledge on the sustainable development of that land 
and its preservation (Jaksa, 2006; Ayanlade and Jegede, 2016). Not 
only can a lack of protection of Indigenous legal tenure undermine 
these objectives, but a number of African laws may actively work 
against them. For example, a review of Tanzanian and Zambian laws 
highlighted existing provisions that empowered the state to terminate 
or criminalise the occupation of vacant, undeveloped or fallow lands, 
which undermined the occupation by Indigenous peoples of forests 
and other uncultivated lands (Ayanlade and Jegede, 2016).
9.4.5 Climate Services, Perception and Literacy
Policy actors across Africa perceive that human-caused climate 
change is already impacting their locales through a range of negative

socioeconomic and environmental effects (Pasquini, 2020; Steynor and 
Pasquini, 2020). They are highly concerned about and motivated to 
address these impacts (Hambira and Saarinen, 2015; Pasquini, 2020). 
Transformative responses to the impacts of climate change facilitate 
CRD and are informed by perceptions of climate variability and change 
and climate change literacy (Figure 9.11).
9.4.5.1 Climate Information and Services
Climate services (CS) broadly include the generation, tailoring and 
provision of climate information for use in decision making at all levels 
of society (Street, 2016; Vaughan et al., 2018). There is a range of climate 
service providers in Africa, including primarily National Meteorological 
and Hydrological Services (NMHS) and partner institutions, 
complemented by NGOs, the private sector and research institutions 
(Snow et al., 2016; Harvey et al., 2019), which offer the potential for 
public–private partnerships (Winrock, 2018; Harvey et al., 2019).

International development funding has progressed the provision of 
CS and, together with technological advances and capacity-building 
initiatives, has increased the reliability of CS across Africa (Vogel 
et al., 2019). Most CS investments have been towards the agricultural 
sector, with other focal sectors, including pastoralism, health, water, 
energy and disaster risk reduction, having only small CS initiatives 
directed towards them (Nkiaka et al., 2019; Carr et al., 2020). Despite 
this focus and investment, however, there remains a mismatch 
between the supply and uptake of CS in Africa as information is 
often inaccessible, unaffordable, not relevant to context or scale, 
and is poorly communicated (Singh et  al., 2018; Antwi-Agyei et  al., 
2021) (Table 9.4; Sections 9.4.1.5.1 and 9.13.4.1). Observational data 
required for effective regional CS, including trend analyses, seasonal

9
1314Chapter 9 Africa
climate assessment, modelling and model evaluation, is sparse and 
often of poor quality (Figure 9.11) and usually requires payment which 
renders it unaffordable (Winrock, 2018).
A number of these challenges can be addressed through the transdis-
ciplinary co-production of CS (Alexander and Dessai, 2019; Vogel et al., 2019; Carter et  al., 2020). Co-production of CS involves climate in-
formation producers, practitioners and stakeholders, and other knowl-
edge holders participating in equitable partnerships and dialogues to 
collaboratively identify climate-based risk and develop scale-relevant 
climate information to address this risk (Table  9.4) (Vincent et  al., 
2018; Carter et al., 2020).The importance of climate services and climate change literacy
for more transformative responses to climate change in Africa
(a)
Agreement/
no agreement of 
perception and local 
temperature records
No studiesNo dataAgreementNo agreementAgreement/
no agreement of

precipitation recordsClimate Change 
Literacy Rate
No data20–29%30–39%40–49%50–59%60–69%
No studiesNo dataAgreementNo agreement(b)Perception of change 
affects risk perception 
and urgencyTransformative
Climate ResponseClimate services 
provide information that 
informs response
Experience and
perception of changesClimate change 
literacy
Figure 9.11 |  Climate services and climate literacy are important for informing transformative responses to climate change (including adaptation and 
mitigation responses) 
(a) The importance of climate services and climate change literacy for more transformative responses to climate change in Africa adapted from Simpson et al. (2021a). Climate 
services promote climate resilient development by providing climate information for adaptation decision making (Street, 2016; Vaughan et al., 2018). Scalable uptake of climate

services relies partly on climate risk perception of users, which is largely driven in Africa by experience and perception of local climate changes (Jacobs and Street, 2020; Steynor 
et al., 2020b; Steynor and Pasquini, 2020). Perception of climate change can occur without knowledge of its human-induced causes and its effects (Lee et al., 2015; Alemayehu 
and Bewket, 2017; Andrews and Smirnov, 2020). This can lead to coping responses to climate change which fall short of adaptation. Climate change literacy encompasses being 
aware of climate change and its anthropogenic causes and, together with climate services, can strengthen responses to climate change through better understanding of future risk 
(IPCC, 2019b; Simpson et al., 2021a).
(b, c)  Percentage of studies that have recorded that perception of temperature changes and precipitation changes agreed with local meteorological or climate records across 33

African countries (size of bubble indicates number of studies per country for both b and c. In b, agreement with temperature changes is indicated for all studies within a country in 
red, and articles indicating no agreement in orange; while in c, agreement with precipitation changes is indicated per country in dark blue and articles indicating no agreement in 
light blue. A total of 144 studies assessed across the 33 countries).
(d) Country-level rates of climate change literacy for 33 African countries (i.e., percentage of the population that have heard about climate change and think that human activity 
is wholly or partly the cause of climate change) Simpson et al. (2021a).

9
1315Africa  Chapter 9
Table 9.4 |  Challenges and opportunities for Climate Services in Africa for the supply and uptake of climate services.
Challenges Opportunities/solutions ReferencesExamples of programmes that address these 
challengesa
Supply of Climate Services
Poor infrastructure (e.g., 
non-functioning observational networks; 
limited Internet bandwidth; lack of 
climate modelling capacity; issues of 
keeping pace with changing technology) –International funding for observation 
networks, data rescue and data sharing
 –Regular NMHS budgets from 
governments
 –Public–private partnershipsSnow et al. (2016); World Bank 
Group (2016); Winrock (2018); 
Cullmann et al. (2020); Meque 
et al. (2021)East Africa and the West African Sahel (ENACTS 
programme)
Working with NMHS to provide enhanced services by 
overcoming the challenges of data quality, availability and 
access.
Creating of reliable climate information suitable for national

and local decision-making using station observations and 
satellite data to provide greater accuracy in smaller space 
and time scales.
Fragmented delivery of Climate Services –Greater collaboration between the NMHS 
and sector-specific specialists to create a 
central database of sector-based climate 
servicesWinrock (2018); Hansen et al. 
(2019a)Rwanda (RCSA programme)
Improving CS and agricultural risk management at local and 
national government levels in the face of a variable and 
changing climate.
Mismatch in time scales: short-term 
information more desirable (e.g., 
seasonal predictions as opposed to 
decadal or end of century projections) –Co-production of climate service productsJones et al. (2015); Vincent 
et al. (2018); Hansen et al. 
(2019a); Carr et al. (2020); 
Sultan et al. (2020)Burkina Faso (BRACED project)
Strengthening technical and communication capacities 
of national meteorological services to enable partners 
to jointly develop forecasts tailored to support

agro-pastoralists.
Development funding interventions 
operate on time scales that inhibit 
or restrict effective adaptation and 
neglect to build in considerations 
for sustainability post the funded 
intervention –Co-production of climate service products
 –Endogenously driven climate services 
(services that are developed by regional 
actors, not by remote, usually developed 
nation actors)Vincent et al. (2018); Vogel 
et al. (2019) Vincent et al. 
(2020a)Burkina Faso (BRACED project)
Actors recognised the need to ensure continuation of CS 
post-project. Burkina Faso NMHS (ANAM) and National 
Council for Emergency Assistance and Rehabilitation 
(CONASUR) budgeted for the continued communication of 
CS and training of focal weather intermediaries. Local radio 
stations agreed to continue transmitting CS.
Use of Climate Services
Insufficient access to usable data, 
including station data, and information 
suited to the decision context (including

accessibility limitations based on gender 
and social inequalities) –Capacity development initiatives for 
Climate Services providers, intermediaries 
(including extension agents, NGO workers 
and others) and users
 –User needs assessments
 –Consistent monitoring and evaluation of 
Climate Services interventionsJones et al. (2015); Winrock 
(2018); Hansen et al. (2019a); 
Hansen et al. (2019c); Mercy 
Corps (2019); Nkiaka et al. 
(2019); Carr et al. (2020); 
Cullmann et al. (2020); 
Gumucio et al. (2020); Sultan 
Figure 9.11Kenya, Ethiopia, Ghana, Niger and Malawi (ALP 
Programme)
Co-production of relevant information for decision making 
and planning at seasonal time scales. The methods and 
media for communication and messages differ between 
different users. Strong emphasis on participation by women.
Limited capacity of users to understand 
or request appropriate Climate Services 
products –Co-production of climate service products

–Capacity developmentSnow et al. (2016); Singh et al. 
(2018); Vincent et al. (2018); 
Nkiaka et al. (2019); Daniels 
et al. (2020)Cities in Zambia, Namibia, Mozambique, Zimbabwe, 
Botswana, Malawi and South Africa (FRACTAL programme)
Repeated interactions between each represented sector 
to learn and more completely understand the different 
contexts of each represented party and build understanding 
through an ethic of collaboration for solving climate-related 
problems in each unique city.
Lack of user trust in the information –Co-production of climate service products
 –Combine scientific and Indigenous 
forecasts
 –Demonstrate added value of the climate 
serviceVincent et al. (2018); Nkiaka 
et al. (2019); Vaughan et al. 
(2019); Vogel et al. (2019); 
Nyadzi et al. (2021)Tanzania (ENACTS programme)
Co-production to inform malaria decisions systematically 
and change relationships, trust, and demand in a manner 
that had not been realised through previous singular and

siloed approaches.
Socioeconomic, and institutional 
barriers (limited professional mandates, 
financing limitations, institutional 
cooperation) –Regular NMHS budgets from 
 –Public–private partnerships
 –Supportive institutions, policy frameworks 
and individual capacity and agencySnow et al. (2016); World Bank 
Harvey et al. (2019); Vincent 
et al. (2020b)
Notes:
(a) Reproduced from Carter et al. (2020) with permission.

9
1316Chapter 9 Africa
However, the effectiveness of co-production processes are hindered 
by aspects such as inequitable power relationships between different 
types of knowledge holders (e.g., scientists and practitioners), 
inequitable distribution of funding between developed country and 
African partners that favours developed country partners, an inability 
to develop sustained trust relationships as a result of short-funding 
cycles, a lack of flexibility due to product-focused engagements and the 
scalability of co-production to enable widespread reach across Africa 
as the process is usually context specific (high confidence) (Vincent 
et al., 2018; Vogel et al., 2019; 2020a).
Despite these challenges, the inclusive nature of co-production has 
had a positive influence on the uptake of CS into decision making 
where it has been applied (Table 9.4; Figure 9.12; Vincent et al., 2018; 
Vogel et al., 2019; Carter et al., 2020; Chiputwa et al., 2020) (medium

confidence), through sustained inter/transdisciplinary relationships 
and capacity development (Norström et al., 2020), strategic financial 
investment, fostering of ownership of resulting products and the 
combining of scientific and other knowledge systems (Carter et  al., 
2020; Steynor et  al., 2020a). There is high confidence that together 
with improved institutional capacity building and strategic financial 
investment, CS can help African stakeholders adapt to projected 
climate risks (Figure 9.11).
9.4.5.2 Community Perceptions of Climate Variability and 
Change
Perceptions of climate variability and change affect whether and how 
individuals and institutions act, and thus contribute to the success or 
failure of adaptation policies related to weather and climate (Silvestri 
et al., 2012; Arbuckle et al., 2015; Simpson et al., 2021a).
A recent Afrobarometer study covering 34 African countries found 67% 
of Africans perceive climate conditions for agricultural production to

have worsened over time, and report drought as the main extreme 
weather event to have worsened in the past decade (Selormey et al., 
2019). Of these participants, across all socioeconomic strata, 71% of 
those who were aware of the concept of climate change agreed that it 
needs to be stopped, but only 51% expressed confidence about their 
ability to make a difference. East Africans (63%) were almost twice as 
likely as north Africans (35%) to report that the weather for growing 
crops had worsened. Additionally, people engaged in occupations 
related to agriculture (farming, fishing or forestry) were more likely to 
report negative weather effects (59%) than those with other livelihoods 
(45%) (Selormey et al., 2019). Similar perceptions have been reported 
among a diversity of rural communities in many sub-Saharan African 
countries (Mahl et al., 2020; Simpson et al., 2021a).
Rural communities, particularly farmers, are the most studied

groups for climate change perception. They perceive the climate 
to be changing, most often reporting changes in rainfall variability, 
increased dry spells, decreases in rainfall and increased temperatures 
or temperature extremes. They perceive these changes to bring a range 
of negative socioeconomic and environmental effects (Alemayehu and 
Bewket, 2017; Liverpool-Tasie et al., 2020; Simpson et al., 2021a). In 
some cases, farmers’ perceptions of changes in weather and climate 
frequently match climate records for decreased precipitation totals, increased drought frequency, shorter rainy season and rainy season 
delay, and increased temperatures (Figure 9.11; Rurinda et al., 2014; 
Boansi et al., 2017; Ayanlade et al., 2018), but not in all cases or not for 
all perceived changes, with common discrepancies in perceived lower 
rainfall totals (Alemayehu and Bewket, 2017; Ayal and Leal Filho, 2017; 
Simpson et al., 2021a).

Farming experience, access to extension services and increasing 
age are the most frequently cited factors positively influencing the 
perceptions of climate changes (Alemayehu and Bewket, 2017; Oduniyi 
and Tekana, 2019). Personal experience of climate-related changes 
and their impacts appears to be an important factor influencing 
perceptions through shaping negative associations, for example, 
experience of flash floods (Elshirbiny and Abrahamse, 2020) or direct 
effect on economic activity, indicating that perception is not restricted 
to crop farmers (Liverpool-Tasie et  al., 2020). However, perceptions 
show common misconceptions about the causes of climate change, 
which has implications for climate action (Elshirbiny and Abrahamse, 
2020), highlighting the importance of climate change literacy.
9.4.5.3 Climate Change Literacy
Understanding the human cause of climate change is a strong 
predictor of climate change risk perception (Lee et  al., 2015) and a

critical knowledge foundation that can affect the difference between 
coping responses and more informed and transformative adaptation 
(Figure 9.11; Oladipo, 2015; Mutandwa et al., 2019). At a minimum, 
climate change literacy includes both having heard of climate change 
and understanding it is, at least in part, caused by people (Simpson 
et al., 2021a). However, large inequalities in climate change literacy 
exist between and within countries and communities across Africa.
The average national climate change literacy rate in Africa is only 39% 
(country rates range from 23–66%) (Figure 9.11). Of 394 sub-national 
regions surveyed by Afrobarometer, 8% (37 regions in 16 countries) 
have a climate change literacy rate lower than 20%, while only 2% 
(8 regions) score higher than 80%, which is common across European 
countries (Simpson et  al., 2021a). Striking differences exist when 
comparing sub-national units within countries. Climate change literacy

rates in Nigeria range from 71% in Kwara to 5% in Kano, and within 
Botswana from 69% in Lobatse to only 6% in Kweneng West (Simpson 
et al., 2021a). Education is the strongest positive predictor of climate 
change literacy, particularly tertiary education, but poverty decreases 
climate change literacy and climate change literacy rates average 
12.8% lower for women than men (Simpson et al., 2021a).
As the identified factors driving climate change literacy overlap 
with broader developmental challenges on the continent, policies 
targeting these factors (e.g., increased education) can potentially yield 
co-benefits for both climate change adaptation as well as progress 
towards SDGs, particularly education and gender equality (Simpson 
et al., 2021a). Progress towards greater climate change literacy affords 
a concrete opportunity to mainstream climate change within core 
national and sub-national developmental agendas in Africa towards

more CRD pathways. Synergies with CS can also overcome gendered 
deficits, for example, although women are generally less climate 
change aware and more vulnerable to climate change than men in

9
1317Africa  Chapter 9
Selected examples of the co-production of climate services and the sectors involved
Agriculture Flood risk
Cities Gender
Health
Disaster Risk 
ManagementLivestock
Energy  Transport
Fishing WaterClimate finance
 Flood risk
Cities
 Gender
Health
Disaster Risk
Managemen t
Livestock
Ener
g
y 
 T
ransp o
r
t
F
ishing
 W
ater
W
W
BURKINAF ASO
123
BOTSWANA
69
ZIMBABWE
69
MOZAMBIQUE
89
SOUTH AFRI CA
89
ETHIOPIA
51317
UGAN DA
28
212224
ZAMBIA
9
NAMIBI A
9
RWANDA
21 410
KENY A
25131618
2021 19 2324
CHAD
2
SUDAN
2
SENEGA L
1
TANZANI A
242119 711 1415 12
MALA WI
6914
Figure 9.12 |  The inclusive nature of co-production has had a positive influence on the uptake of climate services into decision making in Africa. Selected 
examples of the co-production of climate services and the sectors involved. Icons indicate sectors and numbers show the programmes under which the co-production engagements

occurred. Programmes listed are (1) AMMA-2050: Combining Scenario Games, Participatory Modelling and Theatre Forums to Co-produce Climate Information for Medium-term 
Planning, (2,3) BRACED: Sharing Lessons on Promoting Gender Equality through a ‘Writeshop’, (4) RCSA: Bringing Climate Services to People Living in Rwanda’s Rural Areas, (5) 
ALP: Participatory Scenario Planning for Local Seasonal Climate Forecasts and Advisories, (6) Climate Risk Narratives: Co-producing Stories of the Future, (7) ENACTS: Developing 
Climate Services for Malaria Surveillance and Control in Tanzania, (8) FATHUM: Forecast for Anticipatory Humanitarian Action, (9) FRACTAL: Learning Labs, Dialogues and Embedded 
Researchers in Southern African Cities, (10) FONERWA: Climate Risk Screening Tool, (11) MHEWS: Multi-hazard Early Warning System for Coastal Tanzania, (12) Resilient Transport

Strategic Assessment for Dar es Salaam, (13) RRA: Climate Attribution for Extreme Weather Events in Ethiopia and Kenya, (14) UMFULA: Co-producing Climate Information for 
Medium-term Planning in the Water-Energy-Food Nexus, (15) IRRP: Building Resilience in Tanzania’s Energy Sector Planning, (16) PRISE: Co-exploring Relevant Evidence for Policy 
Change in Kenya, (17) NMA ENACTS: An Example of a Co-produced Climate Service Fit for Purpose, (18) REACH: Improving Water Security for the Poor in Turkana County, Kenya, 
(19) DARAJA: Co-designing Weather and Climate Information Services for and with Urban Informal Settlements in Nairobi and Dar es Salaam, (20) ForPAc: Co-producing Approaches 
to Forecast-based Early Action for Drought and Floods in Kenya, (21) HIGHWAY: Co-produced Impact-based Early Warnings and Forecasts to Support Fishing Communities on Lake

Victoria, (22) HyCRISTAL: Using Video to Initiate Farmer Dialogue with Local Government in Mukono, Uganda, (23) SCIPEA: Co-produced Seasonal Forecasts for More Effective 
Management of Hydropower Supply in Kenya, (24) Weather Wise: Co-producing Weather and Climate Radio Content for Farmers, Fishermen and Pastoralists in East Africa. See 
Carter et al. (2020) for details and outcomes of each engagement. Source: Carter et al. (2020).

9
1318Chapter 9 Africa
Box 9.1 | Vulnerability Synthesis
Vulnerability in Africa is socially, culturally and geographically differentiated among climatic regions, countries and local communities, with 
climate change impacting the health, livelihoods and food security of different groups to different extents (Gan et al., 2016; Onyango et al., 
2016a; Gumucio et al., 2020). This synthesis emphasises intersectionality within vulnerable groups as well as their position within dynamic 
social and cultural contexts (Wisner, 2016; Kuran et al., 2020), and highlights the differential impacts of climate change and restricted 
adaptation options available to vulnerable groups across African countries (see also Cross-Chapter Box GENDER in Chapter 18).
Vulnerability and exposure to the impacts of climate change are complex and affected by multiple, interacting non-climatic processes,

which together influence risk, including socioeconomic processes (Lwasa et al., 2018; UNCTAD, 2020), resource access and livelihood 
changes (Jayne et al., 2019b) and intersectional vulnerability among social groups (Figure Box 9.1.1; Rao et al., 2020). Socioeconomic 
processes encompass broader social, economic and governance trends, such as expanded investment in large energy and transportation 
infrastructure projects (Adeniran and Daniell, 2020), rising external debt (Edo et al., 2020), changing role of the state in social development 
(Wunsch, 2014), environmental management (Ramutsindela and Büscher, 2019) and conflict, as well as those emanating from climate 
change mitigation and adaptation projects (Beymer-Farris and Bassett, 2012; van Baalen and Mobjörk, 2018; Simpson et al., 2021b). 
These macro trends shape both urban and rural livelihoods, including the growing diversification of rural livelihoods through engagement

in the informal sector and other non-farm activities, and are mediated by complex and intersecting factors like gender, ethnicity, class, 
age, disability and other dimensions of social status that influence access to resources (Luo et al., 2019). Research increasingly highlights 
the intersectionality of multiple dimensions of social identity and status that are associated with greater susceptibility to loss and damage 
(Caparoci Nogueira et al., 2018; Li et al., 2018).
Arid and semi-arid countries in the Sahelian belt and the greater Horn of Africa are often identified as the most vulnerable regions on the 
continent (Closset et al., 2017; Serdeczny et al., 2017). Particularly vulnerable groups include pastoralists (Wangui, 2018; Ayanlade and 
Ojebisi, 2019), fishing communities (Belhabib et al., 2016; Muringai et al., 2019a), small-scale farmers (Ayanlade et al., 2017; Mogomotsi

et al., 2020; see Section 9.8.1) and residents of formal and informal urban settlements (see Section 9.9). Research has identified key 
macro drivers, as well as multiple dimensions of social status that mediate differential vulnerability in different African contexts. For 
example, the contemporary vulnerability of small-scale rural producers in semi-arid northern Ghana has been shaped by colonial economic 
transformations (Ahmed et al., 2016), more recent neoliberal reforms reducing state support (Fieldman, 2011) and the disruption of local 
food systems due to increasing grain imports (Nyantakyi-Frimpong and Bezner-Kerr, 2015). Age interacts with other dimensions of social 
status, shaping differential vulnerability in several ways. Projected increases in mean temperatures and longer and more intense heat 
waves (Figure Box 9.1.1) may increase health risks for children and elderly populations by increasing risks associated with heat stress

(Bangira et al., 2015; Cairncross et al., 2018). Temperature extremes are associated with increased risk of mortality in Ghana, Burkina 
Faso, Kenya and South Africa, with greatest increases among children and the elderly (Bangira et al., 2015; Amegah et al., 2016; Omonijo, 
2017; Wiru et al., 2019; see Section 9.10.2.3.1).
Rural African women are often disadvantaged by traditional, patriarchal decision-making processes and lack of access to land—issues 
compounded by kinship systems (that, is matrilineal or patrilineal), migrant status, age, type of household, livelihood orientation and 
disability in determining their adaptive options (Ahmed et  al., 2016; see Section  9.8.1; 9.11.1.2; Box  9.8). Differential agricultural 
productivity between men and women is about 20–30% or more in dryland regions of Ethiopia and Nigeria (Ghanem, 2011) and 
challenges women’s ability to adapt to climate change. Limited access to agricultural resources and limited benefits from agricultural

policies, compounded by other social and cultural factors, make women more vulnerable to climatic risks (Shukla et al., 2021). Kinship 
systems can contribute to their vulnerability and capacity to adapt. Women in matrilineal systems have greater bargaining power and 
have access to more resources than those in patrilineal systems (Chigbu, 2019; Robinson and Gottlieb, 2021; See section 9.8.1; 9.11.1.2).

9
1319Africa  Chapter 9
Changing Patterns of Resource Access and 
Ownership
/g122 Large-scale land acquisitions and transformation
 (Hufe and Heuermann 2017) (9.6; 9.8) .
/g122 Growing inequality in rural land distribution and
 declining land availability within smallholder systems  
 (Jayne et al. 2019) (9.8) .
/g122 Land fragmentation and land use intensification
 among smallholder farmers (Cholo et al 2018;
 Clay and Zimmerer 2020; Rasmussen et al. 2018)
 (9.6; 9.8).
/g122  Fragmentation of dryland landscapes, constricted   
 livestock mobility , and sedentarization among   
 pastoralists (Mabhuye 2018; Suleiman and Young   
 2013) (9.6; 9.8) .
Livelihood Diversification and Change
/g122 Growing engagement in rural, peri-urban and urban   
 informal sector activities (Adom 2014; Allard 2017;   
 Potts 2008; Chihambakwe et al. 2019; Dolislager et   
 al. 2021) (9.9)).
/g122 Rural deagrarianization with landless and land poor

entering low return non-farm activities (Asfaw et al.   
 2017; Bryceson 2019; Headey and Jayne 2014)   
 (9.8; 9.11 ).
/g122 Stress-related and opportunistic rural out-migration   
 and mobility (see Migration CCB) (Kaczan and    
 Orgill-Meyer , 2020; Tierney et al. 2017; W aha et al.   
 2017; Serdeczny eta l 2017; Lassailly-Jacob and   
 Peyraut 2016) (Box 9.8) .
/g122 Livelihood diversification among smallholder farmers  
 and fishing communities (9.8)
/g122 Increasing variability and overall decline in catches   
 in marine and inland fisheries, eroding rural   
 diversification options for some (Lammers et al.   
 2020; Lowe et al. 2019) (9.8)
Human dimensions of climate change vulnerability in Africa
Colonial Legacies and Postcolonial 
Development Pathways 
/g122 Dependency on commodity exports and  
 volatility of extractive economies
 (UNCT AD 2019).
/g122Unintended consequences of investments in  
 large-scale energy , water,  and infrastructure

projects (Adeniran and Daniell 2020;   
 Higginbottom et al. 2021) .
/g122Rising external debt and debt service costs  
 (Edo et al. 2020) (9.1 1)
/g122Rapid urbanization (9.9; Box 9.8) .
Governance 
/g122Uneven progress toward democratic   
 decentralization and civil society   
 development (Dickovick and W unsch, 2014;  
 Makara, 2018) (9.4.2).  
/g122Securitization of environmental governance  
 (Ramutsindela and Büscher 2019) (9.4.2).
/g122Civil conflict, inadequate peacebuilding and  
 conflict resolution structures (Adetula et al.  
 2016; van Baalen and Mobjörk, 2018;
 Box 9.9).
/g122Corruption and ‘illicit’  financial flows   
 (UNCT AD 2020) (9.4.2)
Adaptation and Mitigation Actions
/g122Top down and exclusionary mitigation   
 strategies (Beymer-Ferris and Bassett 2012)
/g122 Pathways of urban growth (Lwasa et al.  
 2018; van der Zwaan et al. 2018) (9.4.2)
/g122Social protection (9.1 1) 
/g122 Unequal access to coping mechanisms

bolstered by locally-driven, inclusive and  
 gender responsive adaptation (Eriksen et al.  
 2011; Ng’ang’a and Crane 2020)
CCC
OOO
/g122/g122/g122
LL
Age: Elderly populations and young children are most vulnerable to health consequences of heat  
waves, poor air qualit y, and climate disasters (Cairncross et al. 2018 (Drivdal 2016; Buyana et al. 
2019) (Box 9.1). These groups might not get appropriate food, their mobility might be reduced,  
education options impaired, and their dependence on others, especially women caregivers may  
increase (Popoola, 2021) (Box 9.1; 9.8)
Gender: Wo men farmers have limited access to state agro-advisory extension services and 
financial resources, and experience fewer benefits from technology adoption (Cundill et al. 2021;

Theis et al. 2017) (9.3; 9.8). Discriminatory health policies, povert y, and cultural norms including 
employment and household roles increase the vulnerability of women to extreme weather events 
and impair their adaptive capacity (Ajibade et al. 2013; Djoudi and Brockhaus 20 11;
Frick-Tr zebitzky et al. 2017) (9.7; 9.8; 9.10; 9.1 1).
Ethnicity: Ethnicity may be a factor that limits the range of adaptation options of some groups,  
either due to historical marginalization or cultural preference for specific livelihood orientations 
(Nielsen and Reenberg 2010; Azong and Kelso 2021; Tesfamariam and Zinyengere 2017).
Physical ability: People with disabilities are more likely to be excluded from provision of  
agricultural, health and education services, and livelihood options that could reduce vulnerability 
(Lunga et al. 2019; Alexander 2020; Kuper et al. 2016).
Migrant status: Many international migrants in the region experience greater cultural and economic

barriers to more resilient livelihoods (Anderson 2017; Adepoju 2019; Anderson et al. 2017), and  
frequently reside in poorly serviced areas that are more exposed to climate hazards (see Migration  
CCB; Box 9.8).
Wealth: Poor households are less capable of coping with climate shocks (Drivdal 2016; Buyana et  
al. 2019; Grasham et al. 2019) and frequently are more exposed to hazards through inadequate 
infrastructure, service provision, and dwelling in high-risk areas (Box 9.8; 9. 11).
Examples of intersectional vulnerability:
Age-wealth intersection: many children in poor households in urban informal settlements face  
severe health and educational consequences when flooding halts education and produces acute 
infectious disease risks (Drivdal 2016).
Age-gender-ethnicity intersection: elder women experienced heightened vulnerability under 
patriarchal cultural conditions (Azong and Kelso 2021).
Gender-wealth intersection: women from poor households were denied access to healthcare

unless accompanied by a man willing to donate blood (Ajibade et al. 2013) .Socio-Economic Processes Intersectional and Compounding V ulnerabilities Among Social Groups
Effect of driver on vulnerabilit y increases vulnerabilit y decreases vulnerabilit yVulnerability
Exposure
Risk
EEbi-directional uni-directionalaggregate
Ha
bi-d
Ability
Migrant status
Wealth
EthnicityGender
AgeResource Access and Livelihood Changes 
Figure  Box  9.1.1 |   Factors contributing to the progression of vulnerability to climate change in African contexts considering socioeconomic 
processes, resource access, livelihood changes, and intersectional vulnerability among social groups. This figure reflects a synthesis of vulnerability across 
sections of this chapter and highlights how the interactions of multiple dimensions of vulnerability compound each other to increase overall vulnerability (Potts, 2008; Box 9.1 (continued)

9
1320Chapter 9 Africa
Nielsen and Reenberg, 2010; Akresh et al., 2011; Eriksen et al., 2011; Beymer-Farris and Bassett, 2012; Davis et al., 2012; Adom, 2014; Akello, 2014; Headey and Jayne, 
2014; Otzelberger, 2014; Wunsch, 2014; Conteh, 2015; Huntjens and Nachbar, 2015; Spencer, 2015; Adetula et al., 2016; Djoudi et al., 2016; Kuper et al., 2016; Stark 
and Landis, 2016; Allard, 2017; Anderson, 2017; Asfaw et al., 2017; Hufe and Heuermann, 2017; Hulme, 2017; Paul and wa Gĩ thĩnji, 2017; Rao et al., 2017; Serdeczny 
et al., 2017; Tesfamariam and Zinyengere, 2017; Tierney et al., 2017; Waha et al., 2017; Chihambakwe et al., 2018; Cholo et al., 2018; Jenkins et al., 2018; Keahey, 2018; 
Lwasa et al., 2018; Makara, 2018; Nyasimi et al., 2018; Petesch et al., 2018; Schuman et al., 2018; Theis et al., 2018; van Baalen and Mobjörk, 2018; van der Zwaan et al.,

2018; Adepoju, 2019; Adzawla et al., 2019b; Bryceson, 2019; Grasham et al., 2019; Jayne et al., 2019a; Lowe et al., 2019; Lunga et al., 2019; OGAR and Bassey, 2019; 
Onwutuebe, 2019; Ramutsindela and Büscher, 2019; Sulieman and Young, 2019; Torabi and Noori, 2019; Adeniran and Daniell, 2020; Alexander, 2020; Clay and Zimmerer, 
2020; Devonald et al., 2020; Dolislager et al., 2020; Edo et al., 2020; Kaczan and Orgill-Meyer, 2020; Lammers et al., 2020; World Bank, 2020b; Asiama et al., 2021; Azong 
and Kelso, 2021; Birgen, 2021; Paalo and Issifu, 2021; Simpson et al., 2021b).
Knowledge gaps on  Vulnerability in Africa and Uneven Acces to Resources
The differential impacts of climate change and adaptation options available to vulnerable groups in Africa are a critical knowledge gap. 
More research is needed to examine the intersection of different dimensions of social status on climate change vulnerability in Africa

(Thompson-Hall et al., 2016; Oluwatimilehin and Ayanlade, 2021). More analysis of vulnerability based on gender and other social and 
cultural factors is needed to fully understand the impacts of climate change, the interaction of divergent adaptive strategies, as well as 
the development of targeted adaptation and mitigation strategies, for example, for women in patrilineal kinship systems, people living 
with disabilities, youth, girls and the elderly. Finally, there is an urgent need to build capacity among those conducting vulnerability 
assessments, so that they are familiar with this intersectionality lens.
Additional information and capacity development through education and early warning systems could enhance vulnerable groups’ ability 
to cope and adapt their livelihoods (Jaka and Shava, 2018). However, some groups of people may struggle to translate information into

actual changes (Makate et al., 2019; McOmber et al., 2019). Lack of access to assets and social networks, for example, among older 
populations, are critical limitations to locally driven or autonomous adaptation and limit potential benefits from planned adaptation 
actions (e.g., adoption of agricultural technologies or effective use of early warning systems).
There is an urgent need for societal and political change to realise potential benefits for these vulnerable groups in the long term (Nyasimi 
et al., 2018). There is a need for gender-sensitive climate change policies in many African countries and gender-responsive policies, 
implementation plans and budgets for all local-level initiatives (Wrigley-Asante et al., 2019).Box 9.1 (continued)
Africa, they are generally more likely to adopt climate-resilient crops 
when they are climate change aware and have exposure to extension 
services (Acevedo et al., 2020; Simpson et al., 2021a).
9.5 Observed and Projected Climate Change

This section assesses observed and projected climate change over Africa. 
In Working Group I of the IPCC AR6 (WGI), four chapters make regional 
assessments of observed and projected climate change (Doblas-Reyes 
et al., 2021; Gutiérrez et al., 2021; Ranasinghe et al., 2021; Seneviratne 
et  al., 2021), which facilitates a more nuanced assessment in this 
section of climate and ocean phenomena that impact African systems.
9.5.1 Climate Hazards in Africa
Temperature increases due to human-caused climate change are 
detected across Africa and many regions have warmed more rapidly 
than the global average (Figure  9.13a; Ranasinghe et  al., 2021). A 
signal of increased annual heatwave frequency has already emerged 
from the background natural climate variability over the whole 
continent (Figure  9.14; Engdaw et  al., 2021). However, detection of

statistically significant rainfall trends is evident in only a few regions (Figure  9.13b), and in some regions different observed precipitation 
datasets disagree on the direction of rainfall trends (Panitz et al., 2013; 
Sylla et al., 2013; Contractor et al., 2020). The uncertainty of observed 
rainfall trends results from a number of sources, including high 
interannual and decadal rainfall variability, different methodologies 
used in developing rainfall products, and the lack of and poor quality 
of rainfall station data (Figure 9.15; Gutiérrez et al., 2021).
With increased GHG emissions, mean temperature is projected to 
increase over the whole continent, as are temperature extremes 
over most of the continent (Figure 9.16a, b). Increased mean annual 
rainfall is projected over the eastern Sahel, eastern east Africa and 
central Africa (Figures 9.14; 9.16c). In contrast, reduced mean annual 
rainfall and increased drought (meteorological and agricultural) are

projected over southwestern southern Africa and coastal north Africa, 
with drought in part as a result of increasing atmospheric evaporative 
demand due to higher temperatures (Figure 9.16e; Ukkola et al., 2020; 
Ranasinghe et al., 2021; Seneviratne et al., 2021). The frequency and 
intensity of heavy precipitation are projected to increase across most of 
Africa, except northern and southwestern Africa (Figures 9.14; 9.16d).
Most African countries are expected to experience high temperatures 
unprecedented in their recent history earlier in this century than generally 
wealthier, higher latitude countries (high confidence). As low latitudes

9
1321Africa  Chapter 9
have lower internal climate variability (e.g. low seasonality), the low-
latitude African countries are projected to be exposed to large increases 
in frequency of daily temperature extremes (hotter than 99.9% of their 
historical records) earlier in the 21st century compared to generally 
wealthier nations at higher latitudes (Harrington et  al., 2016; Chen 
et al., 2021; Doblas-Reyes et al., 2021; Gutiérrez et al., 2021). Although 
higher warming rates are projected over high latitudes during the first 
half of this century, societies and environments in low-latitude, low-
income countries are projected to become exposed to unprecedented 
climates before those in high latitude, developed countries (Frame 
et al., 2017; Harrington et al., 2017; Gutiérrez et al., 2021). For example, 
beyond 2050, in central Africa and coastal west Africa, 10 months of 
every year will be hotter than any month in the period 1950–2000

under a high emissions scenario (RCP8.5) (Harrington et  al., 2017; 
Gutiérrez et al., 2021). Ambitious, near-term mitigation will provide the 
largest reductions in exposure to unprecedented high temperatures 
for populations in low-latitude regions, such as across tropical Africa 
(Harrington et al., 2016; Frame et al., 2017).
9.5.1.1 Station Data Limitations
Sustained station observation networks (Figure  9.15) are essential 
for the long-term analysis of local and regional climate trends, 
including for temperature and rainfall, as well as: the calibration of 
satellite-derived climate products; development of gridded climate 
datasets using interpolated and blended station–satellite products 
that form the baseline from which climate change departures are measured; development and running of early warning systems; 
climate projection and impact studies; and extreme event attribution 
studies (Harrison et al., 2019; Otto et al., 2020).

However, production of salient climate information in Africa is 
hindered by limited availability of and access to weather and climate 
data, especially in central and north Africa (Figure  9.15; Coulibaly 
et  al., 2017; Hansen et  al., 2019a). Existing weather infrastructure 
remains suboptimal for development of reliable early warning 
systems (Africa Adaptation Initiative, 2018; Krell et  al., 2021). For 
example, it is estimated only 10% of the world’s ground-based 
observation networks are in Africa, and that 54% of Africa’s surface 
weather stations cannot capture data accurately (Africa Adaptation 
Initiative, 2018; World Bank, 2020d). Some programmes are trying to 
address this issue, including the trans-African hydro-meteorological 
observatory (van de Giesen et al., 2014), the West African Science 
Service Centre on Climate Change and Adaptive Land Management 
(WASCAL) (Salack et al., 2019), the Southern African Science Service

Centre for Climate Change, Adaptive Land Management (SASSCAL) 
(Kaspar et  al., 2015) and the AMMA-CATCH National Observation 
Service and Critical Zone Exploration Network (Galle et al., 2018). 
However, the sustainability of observation networks beyond the 
life of these programmes is uncertain as many African National 
Meteorological and Hydrology Services experience structural, financial 
and technical barriers to maintaining these systems (Section 9.4.5).Observed climate trends calculated for 1980–2015
(a) Temperature trend (b) Precipitation trend
Figure 9.13 |  Temperature increases due to human-caused climate change are detected across Africa and many regions have warmed more rapidly than the 
global average. Mean observed trends in (a) average temperature (°C per decade) and (b) average precipitation in (mm per decade) for 1980–2015. Trends were calculated with

respect to the climatological mean over 1980–2015. The Climate Research Unit Time Series data (CRU TS) are used to compute temperature trends using 2-m temperature and the 
Global Precipitation Climatology Centre data (GPCC) precipitation trends. Regions with no cross-hatching indicate statistically significant trends over this period and regions in grey 
indicate insufficient data. The figures are derived from Gutiérrez et al. (2021).

9
1322Chapter 9 Africa
Summary of confidence in direction of projected change in climate impact drivers in Africa
1 = Contrasted regional signal: drying in western portions and wettening in eastern portions
2 = Likely increase over the Ethiopian Highlands
3 = Medium confidence of decrease in frequency and increase in intensity
4 = Along sandy coasts and in the absence of additional sediment sinks/sources or any physical barriers to shoreline retreat
5 = Substantial parts of the ESAF and MDG coasts are projected to prograde if present-day ambient shoreline change rates contin ueHigh confidence of increas e
Medium confidence of increas e
Low confidence in direction of change
Medium confidence of decreas e
High confidence of decreas e
– = Not broadly relevantAlready emerged in the historical perio d
Emerging by 2050 at least in scenarios RCP8.5/SSP5-8.5Climate impact drivers
Heat
and dryMean air temperatur e
Cold spell

Frost
Heavy precipitation & pluvial flood
Landslide
Aridity
Agricultural & ecological drought
Hail
Ocean acidit y
Air pollution weatherRegions
* North Africa is not an 
official region of IPCC 
AR6, but assessment 
here is based upon the 
African portions of the 
Mediterranean region.MED*
SAH
WAF
CAFNEAF
SEAF
WSAF ESAFMDG
Radiation at surfaceAtmospheric CO2 at surface
  North Eastern Africa (NEAF) – 1, 21 11 4
  South Eastern Africa (SEAF) 11 11 3 4  Western Africa (WAF) – – 1 11 14
  East Southern Africa (ESAF) – 3 4, 5
  Madagascar (MDG) – 4, 5  North Africa (MED)* – 34
  West Southern Africa (WSAF) – – 4  Central Africa (CAF) – – 4Sahara (SAH) – – 4
3

Figure 9.14 |  Summary of confidence in the direction of projected change in climate impact drivers (CIDs) in Africa. Projected changes represent the aggregate 
changes characteristic for mid-century for a range of scenarios, including: medium emission scenarios RCP4.5, SSP3-4.5, Scenario A1B from Special Report on Emissions Scenarios 
(SRES), or higher emissions scenarios (e.g., RCP8.5, SSP5-RCP8.5), within each AR6 WGI region (inset map) approximately corresponding to global warming levels between 2°C 
and 2.4°C (for CIDs that are independent of sea level rise). CIDs are drivers of impacts that are of climatic origin (that is, physical climate system conditions including means and 
extremes) that affect an element of society or ecosystems. The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the

natural interannual variability if found with at least medium confidence (dots). Emergence of a climate change signal or trend refers to when a change in climate (the ‘signal’) 
becomes larger than the amplitude of natural or internal variations (the ‘noise’). The figure is a modified version of Table 12.3 in Chapter 12 of WGI (Ranasinghe et al., 2021), please 
see this chapter for definitions of the various climate impact drivers and the basis for confidence levels of the assessment. Please note these WGI regions do not directly correspond 
to the regionalisation in this chapter nor do we assess climate risks for Madagascar.
9.5.2 North Africa
9.5.2.1 Temperature
9.5.2.1.1 Observations
Mean and seasonal temperatures have increased at twice the global rate 
over most regions in north Africa due to human-induced climate change 
(Ranasinghe et  al., 2021; Figures  9.13a and; 9.14) (high confidence).  
Increasing temperature trends have been particularly strong since the

1970s (between 0.2°C per decade and 0.4°C per decade), especially in 
the summer (Tanarhte et al., 2012; Donat et al., 2014a; Lelieveld et al., 
2016). Similar warming signals have been observed since the mid-1960s over the Sahara and the Sahel (Fontaine et  al., 2013; Moron et  al., 
2016). Trends in mean maximum (TX) and minimum (TN) temperatures 
range between +2°C and +3°C per century over north Africa, and the 
frequencies of hot days (TX > 90th percentile, TX90p) and tropical nights 
(TN > 20°C), as well as the frequencies of warm days and nights, roughly 
follow these mean TX and TN trends (Fontaine et al., 2013; Moron et al., 
2016; Ranasinghe et  al., 2021; Seneviratne et  al., 2021). Warm spell 
duration has increased in many north African countries (Donat et  al., 
2014a; Filahi et al., 2016; Lelieveld et al., 2016; Nashwan et al., 2018) 
and heatwave magnitude and spatial extent have increased across north

Africa since 1980, with an increase in the number of events since 2000 
that is beyond the level of natural climate variability (Russo et al., 2016; 
Ceccherini et al., 2017; Engdaw et al., 2021).

9
1323Africa  Chapter 9
9.5.2.1.2 Projections
At 1.5°C, 2°C and 3°C of global warming above pre-industrial levels, 
mean annual temperatures in north Africa are projected to be on 
average, 0.9°C, 1.5°C and 2.6°C warmer than the 1994–2005 average, 
respectively (Figure  9.16a). Warming is projected to be stronger in 
summer than winter (Lelieveld et al., 2016; Dosio, 2017). The number 
of hot days is likely to increase by up to 90% by the end of the century 
under RCP8.5 (global warming level [GWL] 4.4°C) (Gutiérrez et  al., 
2021; Ranasinghe et  al., 2021) and hot nights and the duration of 
warm spells to increase in the first half of the 21st century in both 
intermediate and high-emission scenarios (Patricola and Cook, 2010; 
Vizy and Cook, 2012; Lelieveld et al., 2016; Dosio, 2017; Filahi et al., 
2017). Heatwaves are projected to become more frequent and intense 
even at 1.5°C of global warming (Gutiérrez et al., 2021; Ranasinghe

et  al., 2021). Children born in 2020, under a 1.5°C-compatible 
scenario will be exposed to 4–6  times more heatwaves in their 
lifetimes compared to people born in 1960; this exposure increases to 
9–10 times more heatwaves for emission reduction pledges, limiting 
global warming to 2.4°C (Thiery et al., 2021).9.5.2.2 Precipitation
9.5.2.2.1 Observations
Mean annual precipitation decreased over most of north Africa 
between 1971–2000 (Donat et al., 2014a; Hertig et al., 2014; Nicholson 
et al., 2018; Zittis, 2018), with a gradual recovery to normal or wetter 
conditions in Algeria and Tunisia since 2000 and over Morocco since 
2008 (Nouaceur and Murărescu, 2016). Since the 1960s days with more 
than 10 mm of rainfall have decreased and the number of consecutive 
dry days have increased in the eastern parts of north Africa, while in the 
western parts of north Africa heavy rainfall and flooding has increased 
(Donat et  al., 2014a). Aridity, the ratio of potential evaporation to

precipitation, has increased over the Mediterranean and north Africa 
due to significant decreases in precipitation (Greve et al., 2019).
9.5.2.2.2 Projections
Mean annual precipitation is projected to decrease in north Africa at 
warming levels of 2°C and higher (high confidence) with the most 
pronounced decreases in the northwestern parts (Figures 9.13a and; 
9.14; Schilling et al., 2012; Filahi et al., 2017; Barcikowska et al., 2018; 
Ranasinghe et al., 2021). Meteorological drought over Mediterranean 
north Africa in CMIP5 and CMIP6 models are projected to increase 
in duration from approximately 2  months during 1950–2014 to 
approximately 4 months in the period 2050–2100 under RCP8.5 and 
Large regions of Africa lack regularly reporting and quality-controlled weather station data
(a) Distribution of weather stations since 1950
Stations that were 
still active in 2017x(b) Number of weather stations since 1950
1990 1980 1970 1960 1950 2020 2010 20001,800
1,600
1,400
1,200
1,000

Figure 9.15 |  Large regions of Africa lack regularly reporting and quality-controlled weather station data. This figure shows stations in Africa with quality-controlled 
station data used in developing the Rainfall Estimates on a Gridded Network (REGEN) interpolated rainfall product (Harrison et al., 2019). (a) A spatial representation of stations 
across the continent since 1950 shown as black dots and red crosses, where red crosses represent stations that were still active in 2017. (b) The decline in operational stations or 
stations with quality-controlled data since circa 1998, which is largely a function of declining networks in a subset of countries. Figure is derived from Carter et al. (2020).

9
1324Chapter 9 Africa
Projected changes 
of climate variables and hazards
(relative to 1995–2014 average)
at 1.5°C, 2°C and 3°C of global warming
above pre-industrial (1850–1900)
16 46 75 90 105(b)
Change in the number of days
per year above 35°C
13 16 0+ 1.5°C + 3.0°C + 2.0°C
-20 10 50 7080(c)
Mean annual precipitation change (%)
-30 -10 30 04 0 60 20
-5 25 55 70(d)
Change in heavy precipitation represented by
annual maximum 5-day precipitation change (%)
-20 10 40
-50 -20 40 70 85(e)
Change in drought represented by six-month
standardised precipitation index change (%)
-65 -35 10 25 55 -51.0 2.0 2.5 3.0(a)
Mean temperature change (°C)
0 0.5 1.5
0.8 1.4 1.9 2.2 > 2.5(f)
Mean sea surface temperature change (°C)
0.5 1.1 1.6
Figure 9.16 |  Projected changes of climate variables and hazards at 1.5°, 2° and 3° of global warming above the pre-industrial period (1850–1900).

9
1325Africa  Chapter 9
SSP5-85 (Ukkola et  al., 2020). Extreme rainfall (monthly maximum 
1-day rainfall – RX1 day) in the region is projected to decrease (Donat 
During 1984–2012, north Africa experienced a decreasing dust trend 
with north African dust explaining more than 60% of global dust 
variations (Shao et al., 2013). Dust loadings and related air pollution 
hazards (from fine particles that affect health) are projected to 
decrease in many regions of the Sahara as a result of decreased wind 
speeds (Evan et al., 2016; Ranasinghe et al., 2021).
9.5.3 West Africa
9.5.3.1 Temperature
9.5.3.1.1 Observations
Observed mean annual and seasonal temperatures have increased 
1–3°C since the mid-1970s with the highest increases in the Sahara and 
Sahel (Figures 9.13a; Cook and Vizy, 2015; Lelieveld et al., 2016; Dosio, 
2017; Nikiema et  al., 2017; Gutiérrez et  al., 2021; Ranasinghe et  al., 
2021) and positive trends in mean annual maximum (TX) and minimum

(TN) of 0.16°C and 0.28°C per decade, respectively (Mouhamed et al., 
2013; Moron et al., 2016; Russo et al., 2016; Barry et al., 2018). The 
frequency of very hot days (TX > 35°C) and tropical nights has increased 
by 1–9 days and 4–13 nights per decade between 1961–2014 (Moron 
et al 2016), and cold nights have become less frequent (Fontaine et al., 
2013; Mouhamed et al., 2013; Barry et al., 2018). In the 21st century, 
heatwaves have become hotter, longer and more extended compared 
to the last two decades of the 20th century (Mouhamed et al., 2013; 
Moron et al., 2016; Russo et al., 2016; Barbier et al., 2018).
9.5.3.1.2 Projections
mean annual temperatures in west Africa are projected to be on 
average, 0.6°C, 1.1°C and 2.1°C warmer than the 1994–2005 average, 
respectively (Figure  9.16a). Under mid- and high-emission scenarios 
end of century summer temperatures are projected to increase by 2°C

and 5°C, respectively (Sylla et  al., 2015a; Russo et  al., 2016; Dosio, 
2017). The annual number of hot days is projected to increase at all 
global warming levels with larger increases at higher warming levels 
(Figure  9.16b). By 2060 the frequency of hot nights is projected to be almost double the 1981–2010 average at GWL 2°C (Dosio, 2017; 
Bathiany et al., 2018; Gutiérrez et al., 2021; Ranasinghe et al., 2021). 
Heatwave frequency and intensity are projected to increase under all 
scenarios, but limiting global warming to 1.5°C leads to a decreased 
heatwave magnitude (–35%) and frequency (–37%) compared to 2°C 
global warming (Dosio, 2017; Weber et  al., 2018; Nangombe et  al., 
2019). Children born in 2020, under a 1.5°C-compatible scenario will 
be exposed to 4–6 times more heatwaves in their lifetimes compared 
to people born in 1960; this exposure increases to 7–9  times more 
heatwaves at GWL 2.4°C (Thiery et al., 2021).

The number of dangerous heat days (TX >40.6°C) is projected to 
increase from approximately 60 per year in 1985–2005 to approximately 
110, 130 and 140 under RCP2.6, RCP4.5 and RCP8.5, respectively, in 
the 2060s and to 105, 145 and 196 in the 2090s (Rohat et al., 2019). 
Over tropical west Africa, heat-related mortality risk through increased 
heat and humidity is 6–9 times higher than the 1950–2005 average at 
GWL 2°C, 8–15 times at GWL 2.65°C and 15–30 times at GWL 4.12°C 
(Ahmadalipour and Moradkhani, 2018) (Coffel et al., 2018). The number 
of potentially lethal heat days per year is projected to increase from <50 
during 1995–2005 to 50–150 at GWL 1.6°C, 100–250 at GWL 2.5°C 
and 250–350 at GWL 4.4°C, with highest increases in coastal regions 
(Mora et al., 2017). Increasing urbanisation concentrates this exposure 
in cities, such as Lagos, Niamey, Kano and Dakar (Section 9.9.3.1; Coffel 
et al., 2018; Rohat et al., 2019).
9.5.3.2 Precipitation
9.5.3.2.1 Observations

Negative trends in rainfall accompanied by increased rainfall variability 
were observed between 1960s–1980s over west Africa (Nicholson 
et  al., 2018; Thomas and Nigam, 2018), caused by a combination 
of anthropogenic aerosols and GHGs emitted between the 1950s 
and1980s (Booth et al., 2012; Wang et al., 2016; Giannini and Kaplan, 
2019; Douville et al., 2021). Declining rainfall trends ended by 1990 
due to the growing influence of GHGs and reduced cooling effect 
of aerosol emissions, with a trend to wetter conditions emerging in 
the mid-1990s accompanied by more intense, but fewer precipitation 
events (Sanogo et al., 2015; Sylla et al., 2016; Kennedy et al., 2017; 
Barry et  al., 2018; Bichet and Diedhiou, 2018a; 2018b; Thomas and 
Nigam, 2018). A shift to a later onset and end of the west African 
monsoon is also reported in west Africa and Sahel (low confidence)

(Chen et  al., 2021; Ranasinghe et  al., 2021). Between 1981–2014 Changes shown here are relative to the 1995–2014 period. Rows are 
(a) Mean temperature change (°C); 
(b) Change in the number of days per year above 35°C (days); 
(c) Mean annual rainfall change (%); 
(d) Heavy precipitation change represented by annual maximum 5-day precipitation (%); 
(e) Change in drought represented by the six-month standardised precipitation index (SPI) (%) – negative changes indicate areas where drought frequency, intensity and/or 
duration is projected to increase and positive changes show the opposite; 
(f) Mean sea surface temperature change (°C). All figures are derived from the WGI Interactive Atlas and show results from between 26 to 33 CMIP6 (Coupled Model Intercomparison 
Project) global climate models depending on the climate variable. CMIP6 models include improved representations of physical, biological, and chemical processes as well as higher

spatial resolutions compared to previous CMIP5 models (Eyring et al., 2021). Robustness of the projected change signal is indicated by hatching – no overlay indicates high model 
agreement, where at least 80% of models agree on sign of change; diagonal lines (/) indicate low model agreement, where fewer than 80% of models agree on sign of change. 
NOTE: Model agreement is computed at a gridbox level and is not representative of regionally aggregated results over larger regions (Gutiérrez et al., 2021).

9
1326Chapter 9 Africa
the Gulf of Guinea and the Sahel have experienced more intense 
precipitation events (Panthou et al., 2014; Bichet and Diedhiou, 2018a; 
Panthou et al., 2018) and the frequency of mesoscale storms has tripled 
(Taylor et al., 2017; Callo-Concha, 2018). Extreme heavy precipitation 
indices show increasing trends from 1981–2010 (Barry et al., 2018), 
increasing high flow events in large Sahelian rivers as well as small 
to mesoscale catchments leading to pluvial and riverine flooding 
(Douville et  al., 2021). Meteorological, agricultural and hydrological 
drought in the region has increased in frequency since the 1950s 
(medium confidence) (Seneviratne et al., 2021).
9.5.3.2.1 Projections
West African rainfall projections show a gradient of precipitation decrease 
in the west and increase in the east (medium confidence) (Figure 9.14; 
Dosio et al., 2021; Gutiérrez et al., 2021; Ranasinghe et al., 2021). This

pattern is evident at 1.5°C of global warming and the magnitude of 
change increases at higher warming levels (Figure  9.16c; Schleussner 
et al., 2016b; Kumi and Abiodun, 2018; Sylla et al., 2018). A reduction in 
length of the rainy season is projected over the western Sahel through 
delayed rainfall onset by 4–6 days at global warming levels of 1.5°C 
and 2°C (Kumi and Abiodun, 2018; Douville et al., 2021; Gutiérrez et al., 
2021). Although there are uncertainties in rainfall projections over the 
Sahel (Klutse et al., 2018; Gutiérrez et al., 2021), CMIP6 models project 
monsoon rainfall amounts to increase by approximately 2.9% per 
degree of warming (Jin et al., 2020; Wang et al., 2020a), therefore, at 
higher levels of warming and towards the end of the century, a wetter 
monsoon is projected in the eastern Sahel (medium confidence).
The frequency and intensity of extremely heavy precipitation are projected

to increase under mid- and high-emission scenarios (Figures 9.13a; 9.14; 
Sylla et al., 2015b; Diallo et al., 2016; Akinsanola and Zhou, 2019; Giorgi 
et al., 2019; Dosio et al., 2021; Li et al., 2021; Seneviratne et al., 2021). 
However, heavy rainfall statistics from global and regional climate models 
may be conservative as very-high-resolution, convection-permitting 
climate models simulate more intense rainfall than these models (Stratton 
et al., 2018; Berthou et al., 2019; Han et al., 2019; Kendon et al., 2019).
At 2°C global warming, west Africa is projected to experience a drier, 
more drought-prone and arid climate, especially in the last decades of 
the 21st century (Sylla et al., 2016; Zhao and Dai, 2016; Klutse et al., 
2018). The duration of meteorological drought in the western parts of 
West Africa is projected to increase from approximately 2 months during 
1950–2014 to approximately 4 months in the period 2050–2100 under

RCP8.5 and SSP5-8.5 (Ukkola et al., 2020). Increased intensity of heavy 
precipitation events combined with increasing drought occurrences will 
substantially increase the cumulative hydroclimatic stress on populations 
in west Africa during the late 21st century (Giorgi et al., 2019).9.5.4 Central Africa
9.5.4.1 Temperature
9.5.4.1.1 Observations
Mean annual temperature across central Africa has increased by 
0.75°C–1.2°C since 1960 (Aloysius et al., 2016; Gutiérrez et al., 2021). 
The number of hot days, heatwaves and heatwave days increased 
between 1979–2016 (Hu et  al., 2019) and cold extremes have 
decreased (Figure 9.14; Aguilar et al., 2009; Seneviratne et al., 2021). 
Uncertainties associated with the poor ground-based observation 
networks in the region and associated observational uncertainties 
(Section 9.5.1.1) result in an assessment of medium confidence in an 
increase in the number of heat extremes over the region.
9.5.4.2 Projections

mean annual temperatures in central Africa are projected to be on 
respectively (Figure  9.16a). By the end of the century (2070–2099), 
warming of 2°C (RCP4.5 ) to 4°C (RCP8.5) is projected over the region 
(Aloysius et al., 2016; Fotso-Nguemo et al., 2017; Diedhiou et al., 2018; 
Mba et al., 2018; Tamoffo et al., 2019) and the number of days with 
maximum temperature exceeding 35°C is projected to increase by 
150 days or more at GWL 4.4°C (Gutiérrez et al., 2021; Ranasinghe 
et al., 2021). According to CMIP6 and CORDEX (Coordinated Regional 
Climate Downscaling Experiment) models, the annual average number 
of days with maximum temperature exceeding 35°C will increase 
between 14–27 days at GWL 2°C and 33–59 days at GWL 3°C above 
the 61–63  days for 1995 –2014 (Gutiérrez et  al., 2021; Ranasinghe

et  al., 2021) (high confidence). The number of heatwave days is 
projected to increase and extreme heatwave events may last longer 
than 180 days at GWL 4.1°C (Dosio, 2017; Weber et al., 2018; Spinoni 
et al., 2019). Children born in 2020, under a 1.5°C-compatible scenario 
will be exposed to 6–8  times more heatwaves in their lifetimes 
compared to people born in 1960; this exposure increases to 7–9 times 
more heatwaves at GWL 2.4°C (Thiery et  al., 2021). The number of 
potentially lethal heat days per year is projected to increase from <50 
during 1995–2005 to 50–75 at GWL 1.6°C, 100–150 at GWL 2.5°C 
and 200–350 at GWL 4.4°C (Mora et al., 2017).
9.5.4.2 Precipitation
9.5.4.2.1 Observations
The severe lack of station data over the region leads to large uncertainty 
in the estimation of observed rainfall trends and low confidence in 
changes in extreme rainfall (Figure  9.13b; Creese and Washington, 
2018; Gutiérrez et al., 2021; Ranasinghe et al., 2021). There is some

evidence of drying since the mid-20th century through decreased mean 
rainfall and increased precipitation deficits (Gutiérrez et  al., 2021), 
as well as increases in meteorological, agricultural and ecological 
drought (medium confidence) (Seneviratne et  al., 2021). However, 
there is spatial heterogeneity in annual rainfall trends between 1983–
2010 ranging from −10 to +39 mm per year (Maidment et al., 2015),

9
1327Africa  Chapter 9
with a decline in mean seasonal April–June precipitation of −69 mm 
per year in most regions except in the northwest (Zhou et al., 2014; 
Hua et al., 2016; Klotter et al., 2018; Hu et al., 2019). Southern and 
eastern central Africa were identified as drought hotspots between 
1991–2010 (Spinoni et al., 2014).
9.5.4.2.2 Projections
Under low emission scenarios and at GWL 1.5°C and GWL 2°C there 
is low confidence in projected mean rainfall change over the region 
(Figure 9.16c). At GWL 3°C and GWL 4.4°C, an increased mean annual 
rainfall of 10–25% is projected by regional climate models (Coppola 
et al., 2014; Pinto et al., 2015) and the intensity of extreme precipitation 
will increase (high confidence) (Figure  9.16c, d; Sylla et  al., 2015a; 
Diallo et al., 2016; Dosio et al., 2019; Gutiérrez et al., 2021; Ranasinghe 
et al., 2021; Seneviratne et al., 2021). This is projected to increase the

likelihood of widespread flood occurrences before, during and after the 
mature monsoon season (Figure 9.14).
Convection-permitting simulations (4.5 km spatial resolution) simulate 
increased dry spell length not apparent at coarser resolutions, suggesting 
drying in addition to more intense extreme rainfall (Stratton et  al., 
2018). Although reduced drought frequency is indicated in Figure 9.16e, 
the SPI metric does not account for the effect of increased temperature 
on drought (increased moisture deficit), and metrics that account for 
this indicate slightly increased drought frequency or no change (Spinoni 
et al., 2020). Therefore, there is low confidence in projected changes of 
drought frequency over the region (Figure 9.14).
9.5.5 East Africa
9.5.5.1 Temperature
9.5.5.1.1 Observations
Mean temperatures over the region have increased by 0.7°C–1°C 
from 1973 to 2013, depending on the season (Ayugi and Tan, 2018;

Camberlin, 2018). Increases in TX and TN are evident across the region 
accompanied by significantly increasing trends of warm nights, warm 
days and warm spells (Russo et al., 2016; Gebrechorkos et al., 2019; 
Nashwan and Shahid, 2019). The greatest increases are found in 
northern and central regions.
9.5.5.1.2 Projections
mean annual temperatures in east Africa are projected to be on 
respectively (Figure 9.16a). Highest increases are projected over the 
northern and central parts of the region and the lowest increase 
over the coastal regions (Otieno and Anyah, 2013; Dosio, 2017). The 
magnitude and frequency of hot days are projected to increase from 
GWL 2°C and above with larger increases at higher GWLs (Figure 9.16a, 
b; Dosio, 2017; Bathiany et al., 2018; Dosio et al., 2018; Kharin et al.,

2018). At GWL 4.6°C a number of east African cities are projected to 
have an up to 2000-fold increase in exposure to dangerous heat (days > 40.6 °C) compared to 1985–2005 including Blantyre-Limbe, Lusaka 
and Kampala (Mora et al., 2017; Rohat et al., 2019). Children born in 
2020, under a 1.5°C-compatible scenario will be exposed to 3–5 times 
more heatwaves in their lifetimes compared to people born in 1960; 
this exposure increases to 4–9 times more heatwaves at GWL 2.4°C 
(Thiery et  al., 2021). The number of potentially lethal heat days per 
year is projected to increase from <50 during 1995–2005 to <50 at 
GWL 1.6°C, 50–120 at GWL 2.5°C and 150–350 at GWL 4.4°C with 
largest increases at the coast (Mora et al., 2017), highlighting the new 
emergence of dangerous heat conditions in these areas.
9.5.5.2 Precipitation
9.5.5.2.1 Observations
Over equatorial east Africa the short rains (October–November–
December) have shown a long-term wetting trend from the 1960s until

present (Manatsa and Behera, 2013; Nicholson, 2015; 2017), which is 
linked with western Indian Ocean warming and a steady intensification 
of Indian Ocean Walker Cell (Liebmann et al., 2014; Nicholson, 2015).
In contrast, the long rainfall season (March–April–May) has experienced 
a long-term drying trend between 1986 and 2007, with rainfall declines 
in each of these months and a shortening of the wet season (Rowell et al., 
2015; Wainwright et  al., 2019). Unlike previous decades, since around 
2000, the long rains have exhibited a significant relationship with the El 
Niño-Southern Oscillation (ENSO) (Park et al., 2020), as multiple droughts 
have occurred during recent La Niña events and when the western to 
central Pacific sea surface temperature gradient was La Niña-like (Funk 
et al., 2015; Funk et al., 2018a). Wetter-than-average rainfall years within 
this long-term drying trend are often associated with a stronger amplitude

of the Madden–Julian Oscillation (Vellinga and Milton, 2018).
In the northern, summer rainfall region (June–September), a decline 
in rainfall occurred in the 1960s and rainfall has remained relatively 
low, while interannual variability has increased since the late 1980s 
(Nicholson, 2017); the cause of this drying trend is uncertain.
Since 2005, drought frequency has doubled from once every 6 to 
once every 3  years and has become more severe during the long 
and summer rainfall seasons than during the short rainfall season 
(Ayana et al., 2016; Gebremeskel Haile et al., 2019). Several prolonged 
droughts have occurred predominantly within the arid and semi-arid 
parts of the region over the past three decades (Nicholson, 2017).
9.5.5.2.2 Projections
Higher mean annual rainfall, particularly in the eastern parts of east 
Africa are projected at GWL 1.5°C and 2°C by 25 CORDEX models 
(Nikulin et  al., 2018; Osima et  al., 2018). The additional 0.5°C of

warming from 1.5°C increases average dry spell duration by between 
two and four days, except over southern Somalia where this is reduced 
by between 2–3  days (Hoegh-Guldberg et  al., 2018; Nikulin et  al., 
2018; Osima et al., 2018; Weber et al., 2018).
During the short rainy season, a longer rainfall season (Gudoshava 
et  al., 2020) and increased rainfall of over 100 mm on average is

9
1328Chapter 9 Africa
projected over the eastern Horn of Africa and regions of high/complex 
topography at GWL 4.5°C (Dunning et al., 2018; Endris et al., 2019; 
Ogega et al., 2020).
During the long rainy season, there is low confidence in projected 
mean rainfall change (Gutiérrez et al., 2021). Although some studies 
report projected increased end of century rainfall (Otieno and Anyah, 
2013; Kent et al., 2015), the mechanisms responsible for this are not 
well-understood and a recent regional model study has detected no 
significant change (Cook et al., 2020b). Projected wetting is opposite 
to the observed drying trends, giving rise to the ‘east African rainfall 
paradox’ (Rowell et al., 2015; Wainwright et al., 2019). In other parts 
of east Africa, no significant trend is evident (Ogega et  al., 2020), 
agreement on the sign of change is low, and in some regions, CMIP5 
and CORDEX data show opposite signs of change (Lyon et al., 2017;

Lyon and Vigaud, 2017; Osima et al., 2018; Kendon et al., 2019; Ogega 
Heavy rainfall events are projected to increase over the region at 
global warming of 2°C and higher (high confidence) (Nikulin et  al., 
2018; Finney et al., 2020; Ogega et al., 2020; Li et al., 2021). Drought 
frequency, duration and intensity are projected to increase in Sudan, 
South Sudan, Somalia and Tanzania but decrease or not change over 
Kenya, Uganda and the Ethiopian Highlands (Liu et al., 2018c; Nguvava 
et al., 2019; Haile et al., 2020; Spinoni et al., 2020).
9.5.6 Southern Africa
9.5.6.1 Temperature
9.5.6.1.1 Observations
Mean annual temperatures over the region increased by between 
1.04°C and 1.44°C over the period 1961–2015 depending on the 
observational dataset (Gutiérrez et  al., 2021) and, in northern 
Botswana and Zimbabwe, they have increased by 1.6°C–1.8°C 
between 1961–2010 (Engelbrecht et al 2015). The annual number of

hot days have increased in southern Africa over the last four decades 
(Ceccherini et al., 2017; Kruger and Nxumalo, 2017a; 2017b) and there 
is increasing evidence of increased heat stress impacting agriculture 
and human health (Section 9.10.2). The occurrence of cold extremes, 
including frost days, have decreased (Figure 9.14; Kruger and Nxumalo, 
2017b).
9.5.6.1.2 Projections
mean annual temperatures in southern Africa are projected to be 
on average, 1.2°C, 2.3°C and 3.3°C warmer than the 1994–2005 
average respectively (Figure 9.16a). The annual number of heatwaves 
is projected to increase by between 2–4 (GWL 1.5°C), 4–8 (GWL 2°C) 
and 8–12 (GWL 3°C) and hot and very hot days are virtually certain to 
increase under 1.5°C and 2°C of global warming (Engelbrecht et al., 
2015; Russo et al., 2016; Dosio, 2017; Weber et al., 2018; Seneviratne 
et al., 2021). Cold days and cold extremes are projected to decrease

under all emission scenarios with the strongest decreases associated with low mitigation (Iyakaremye et al., 2021). Children born in 2020, 
under a 1.5°C-compatible scenario will be exposed to 3–4  times 
more heatwaves in their lifetimes compared to people born in 1960, 
although in Angola this is 7–8  times; at GWL 2.4°C this exposure 
increases to 5–9 times more heatwaves (>10 times in Angola) (Thiery 
9.5.6.2 Precipitation
9.5.6.2.1 Observations
Mean annual rainfall increased over parts of Namibia, Botswana and 
southern Angola during 1980–2015 by between 128 and 256 mm 
(Figure 9.13b). Since the 1960s, decreasing precipitation trends have 
been detected over the South African winter rainfall region (high 
confidence) and the far eastern parts of South Africa (low confidence) 
(Engelbrecht et  al., 2009; Kruger and Nxumalo, 2017b; Burls et  al., 
2019; Lakhraj-Govender and Grab, 2019; Gutiérrez et  al., 2021;

Ranasinghe et al., 2021). The frequency of dry spells and agricultural 
drought in the region has increased over the period 1961–2016 (Yuan 
et al., 2018; Seneviratne et al., 2021), the frequency of meteorological 
drought increased by between 2.5–3 events per decade since 1961 
(Spinoni et al 2019) and the probability of the multi-year drought over 
the southwestern cape of South Africa increased by a factor of three 
(95% confidence interval 1.5–6) in response to global warming (Otto 
et al., 2018). The number and intensity of extreme precipitation events 
have increased over the last century (Kruger and Nxumalo, 2017b; 
Ranasinghe et  al., 2021; Sun et  al., 2021), and in the Karoo region 
of southern South Africa, long-term station data show an increasing 
trend in annual rainfall of greater than 5 mm per decade over the 
period 1921–2015 (Kruger and Nxumalo, 2017b).
9.5.6.2.2 Projections
Mean annual rainfall in the summer rainfall region is projected to

decrease by 10–20%, accompanied by an increase in the number of 
consecutive dry days during the rainy season under RCP8.5 (Kusangaya 
et  al., 2014; Engelbrecht et  al., 2015; Lazenby et  al., 2018; Maúre 
et al., 2018; Spinoni et al., 2019). The western parts of the region are 
projected to become drier, with increasing drought frequency, intensity 
and duration likely under RCP8.5 (high confidence) (Figures 9.16c, e; 
9.14; Engelbrecht et al., 2015; Liu et al., 2018b; 2018c; Ukkola et al., 
2020), including multi-year droughts (Zhao and Dai, 2016; Dosio, 
2017).
Dryness in the summer rainfall region is expected to increase at 1.5°C 
and higher levels of global warming (Hoegh-Guldberg et al., 2018) and 
together with higher temperatures will enhance evaporation from the 
region’s mega-dams and reduce soil-moisture content (Section 9.7.1; 
Engelbrecht et al., 2015). Increases in drought frequency and duration 
are projected over large parts of southern Africa at GWL 1.5°C (Liu

et  al., 2018b; 2018c; Seneviratne et  al., 2021) and unprecedented 
extreme droughts (compared to the 1981–2010 period) emerge at 
GWL 2°C (Spinoni et  al., 2021). Meteorological drought duration is 
projected to increase from approximately 2 months during 1950–2014 
to approximately 4 months in the mid-to-late-21st century future under 
RCP8.5 (Ukkola et al., 2020). Heavy precipitation in the southwestern

9
1329Africa  Chapter 9
region is projected to decrease (Donat et al., 2019) and increase in the 
eastern parts of southern Africa at all warming levels (Li et al., 2021; 
9.5.7 Tropical Cyclones
There is limited evidence of an increased frequency of Category 5 
tropical cyclones in the southwestern Indian Ocean (Fitchett et al., 2016; 
Ranasinghe et al., 2021; Seneviratne et al., 2021) and more frequent 
landfall of tropical cyclones over central to northern Mozambique 
(Malherbe et  al., 2013; Muthige et  al., 2018). There is a projected 
decrease in the number of tropical cyclones making landfall in the 
region at 1°C, 2°C and 3°C of global warming, however, they are 
projected to become more intense with higher wind speeds so when 
they do make landfall the impacts are expected to be high (medium 
confidence) (Malherbe et al., 2013; Muthige et al., 2018; Ranasinghe 
9.5.8 Glaciers

Total glacial area on Mount Kenya decreased by 121 × 103 m2 (44%) 
during 2004–2016 (Prinz et al., 2016), Kilimanjaro from 4.8 km2 in 1984 
to 1.7 km2 in 2011 (Cullen et al., 2013) and in the Rwenzori Mountains 
from ~2 km2 in 1987 to ~1 km2 in 2003 (Taylor et al., 2006). Declining 
glacial areas in east Africa are linked to rising air temperatures (Taylor 
et  al., 2006; Hastenrath, 2010; Veettil and Kamp, 2019), and in the 
case of Kilimanjaro and Mount Kenya, declining precipitation and 
atmospheric moisture (Mölg et al., 2009a; 2009b; Prinz et al., 2016; 
Veettil and Kamp, 2019).
Glacial ice cover is projected to disappear before 2030 on the Rwenzori 
Mountains (Taylor et al., 2006) and Mount Kenya (Prinz et al., 2018) 
and by 2040 on Kilimanjaro (Cullen et al., 2013). The loss of glaciers is 
expected to result in a loss in tourism revenues, especially in mountain 
tourism (Wang and Zhou, 2019).
9.5.9 Teleconnections and Large-Scale Drivers of African 
Climate Variability

The ENSO, Indian Ocean Dipole (IOD) and Southern Annular Mode 
(SAM) are the primary large-scale drivers of African seasonal and 
interannual climate variability. The diurnal temperature range tends to 
be greater during La Niña than El Niño in northeastern Africa (Hurrell 
et al., 2003; Donat et al., 2014a), and in southern Africa, the El Niño 
warming effect has been stronger for more recent times (1979–2016) 
compared to earlier period (1940–1978) (Lakhraj-Govender and Grab, 
2019). In east Africa, ENSO and IOD exert an interannual control 
on particularly October–November–December (short rains) and 
June–July–August–September seasons. In southern Africa, El Niño is 
associated with negative rainfall and positive temperature anomalies 
with the opposite true for La Niña. The SAM exerts control on rainfall 
in the southwestern parts of the region and a positive SAM mode is

often associated with lower seasonal rainfall in the region (Reason and Rouault, 2005). The SAM shows a systematic positive trend over the 
last five decades (Niang et al., 2014).
There is no clear indication that climate change will impact the 
frequencies of ENSO and IOD (Stevenson et  al., 2012; Endris et  al., 
2019), although there is some indication that extreme ENSO events 
and extreme phases of the IOD, particularly the positive phase, may 
become more frequent with implications for extreme events associated 
with these features, such as drought (Collins et al., 2019; Cai et al., 
2021; Seneviratne et  al., 2021). Under high-emission scenarios, a 
positive trend in SAM is projected to continue through the 21st century, 
however, under low emission scenarios, this trend is projected to be 
weak or even negative given the potential for ozone hole recovery 
(Arblaster et al., 2011).
9.5.10 African Marine Heatwaves
Marine heatwaves are periods of extreme warm sea surface

temperature that persist for days to months and can extend up to 
thousands of kilometres (Hobday et al., 2016; Scannell et al., 2016), 
negatively impacting marine ecosystems (Section 9.6.1.4).
The number of marine heatwaves doubled in mediterranean north 
Africa and along the Somalian and southern African coastlines from 
1982–2016 (Frölicher et  al., 2018; Oliver et  al., 2018; Laufkötter 
et  al., 2020), very likely as a result of human-caused climate 
change (Seneviratne et  al., 2021). Marine heatwave intensity has 
increased along the southern African coastline (Oliver et al., 2018). 
In the ecologically sensitive region west of southern Madagascar, the 
longest and most intense marine heatwave in the past 35 years was 
recorded during the austral summer of 2017 in the region, it lasted 
48 days and reached a maximum intensity of 3.44°C above the 35-
year average (Mawren et al., 2021). Satellite-derived measurements

of coastal marine heatwaves may under-report their intensity as 
measured against coastal in situ measurements (Schlegel et  al., 
2017).
Sea surface temperatures around Africa are projected to increase by 
0.5°C–1.3°C for 1.5°C global warming and increase by 1.3°C–2.0°C 
for 3°C global warming (Figure  9.16 f). Globally, 87% of observed 
marine heatwaves have been attributed to human-caused global 
warming, and at 2°C of global warming, nearly all marine heatwaves 
would be attributable to heating of the climate caused by human 
activities (Frölicher et al., 2018; Laufkötter et al., 2020). Increases in 
frequency, intensity, spatial extent and duration of marine heatwaves 
are projected for all coastal zones of Africa. At 1°C and 3.5°C of 
global warming, the probability of marine heatwave days is between 
4–15  times and 30–60  times higher compared to the pre-industrial 
(1861–1880) 99th percentile probability, with highest increases over

equatorial and sub-tropical coastal regions (Figure 9.16; Frölicher et al., 
2018). These events are expected to overwhelm the ability of marine 
organisms and ecosystems to adapt to these changes (Section 9.6.1; 
Frölicher et  al., 2018). Reducing emissions and limiting warming to 
lower levels reduces risk to these systems (high confidence) (Hoegh-
Guldberg et al., 2018).

9
1330Chapter 9 Africa
Box 9.2 | Indigenous knowledge and local knowledge
This box aims to map the diversity of Indigenous Knowledge and local knowledge systems in Africa and highlights the potential of 
this knowledge to enable sustainability and effective climate adaptation. This box builds on the framing of the IPCC system for which 
‘indigenous knowledge (IK) refers to the understandings, skills and philosophies developed by societies with long histories of interaction 
with their natural surroundings’ (IPCC, 2019b), while ‘local knowledge (LK) refers to the understandings and skills developed by individuals 
and populations, specific to the place where they live’ (Cross-Chapter Box INDIG in Chapter 18; IPCC, 2019b).
Early warning systems and indicators of climate variability
In most African Indigenous agrarian systems, local communities integrate IK to anticipate or respond to climate variability (Mafongoya

et al., 2017). This holds potential for a more holistic response to climate change, as Indigenous Knowledge and local knowledge (IKLK) 
approaches seek solutions that increase resilience to a wide range of shocks and community stresses (IPCC, 2019b). In Africa, IKLK are 
exceptionally rich in ecosystem-specific knowledge, with the potential to enhance the management of natural hazards and climate 
variability (high confidence), but there is uncertainty about IKLK for adaptation under future climate conditions.
Common indicators for the quality of the rain season for local communities in Africa include flower and fruit production of local trees 
(Nkomwa et al., 2014; Jiri et al., 2015; Kagunyu et al., 2016), insect, bird and animal behaviour and occurrence (Jiri et al., 2016; Mwaniki 
and Stevenson, 2017; Ebhuoma, 2020) and dry season temperatures (Kolawole et  al., 2016; Okonya et  al., 2017). Fulani herders in

west Africa believe that when ‘nests hang high on trees, then rains will be heavy; when nests hang low, rains will be scarce’ (Roncoli 
et al., 2002). In South Africa, LK on weather forecasting is based on the hatching of insects, locust swarm movements and the arrival 
of migratory birds, which has enabled farmers to make adjustments to cropping practices (Muyambo et al., 2017; Tume et al., 2019). 
Most of these IK indicators apply to specific communities, and are used for short-term forecasting (e.g., event-specific predictions, such 
as a violent storm, and onset rain predictions) (Zuma-Netshiukhwi et al., 2013; Mutula et al., 2014). There is evidence of communities 
that rely heavily on IKLK indicators to forecast seasonal variability across the continent (Kagunyu et al., 2016; Mwaniki and Stevenson, 
2017; Tume et al., 2019). However, their accuracy is debatable, due to age-old knowledge losing accuracy because of recent changes

in weather conditions (Shaffer, 2014; Adjei and Kyerematen, 2018). There are also some limitations in the transferability of IK across 
geographical scales, as its understanding is framed by traditional beliefs and cultural practices, and historical and social conditions of 
each community, which vary significantly across communities. This has direct implications for the adoption of IKLK in national policy and 
planned adaptation by governments. However, in some parts of Africa, evidence of the integration of IKLK and scientific-based weather 
forecasting is increasing (Jiri et al., 2016; Mapfumo et al., 2017; Williams et al., 2020).
Indigenous Knowledge and Local Knowledge and climate adaptation
Communities across Africa have long histories of using IKLK to cope with climate variability, reduce vulnerability and improve the capacity 
to cope with climate variability (Iloka Nnamdi, 2016; Mapfumo et al., 2017). The adaptation is mostly incremental, such as customary

rainwater harvesting practices and planting ahead of rains (Ajibade and Eche, 2017; Makate, 2019), which are used to address the 
late-onset rains and rainfall variability. Although IKLK adaptation practices implemented by African communities are incremental, such 
practices record higher evidence of climate risk reduction compared to practices influenced by other knowledge types (Williams et al., 
2020). African communities have used IKLK to cope, adapt to and manage climate hazards, mainly floods, wildfires, rainfall variability and 
droughts (see Box Table 9.2.1; IPCC, 2018b; IPCC, 2019b).
Table Box 9.2.1 |  Selected studies where Indigenous knowledge and local knowledge have been used to cope with climate variability and climate change impacts in 
Africa.
Climate 
hazardAdaptation/coping strategyIndigenous group, community, 
countryEvidence
FloodsUse IK to predict floods (village
elders acted as meteorologists) and use LK to prepare

coping mechanisms (social capital); place valuable goods 
on higher ground, raise the floor level, leave the fields 
uncultivated when facing flood/drought, Indigenous 
earthen walls used to protect homesteads from flooding, 
planting of culturally flood-immunising Indigenous plantsCoastal communities in Nigeria; Oshiwambo 
communities in the northern region of 
Namibia; Matabeleland and Mashonaland 
provinces in Zimbabwe; communities in 
Nyamwamba watershed, Uganda; subsistent 
farmers in Mount Oku and Mbaw, Cameroon; 
Akobo in South SudanFabiyi and Oloukoi (2013); Hooli (2016); 
Lunga and Musarurwa (2016); Bwambale 
et al. (2018); Tume et al. (2019)

9
1331Africa  Chapter 9
Climate 
WildfiresEarly burning to prevent the intensity of the late-season 
firesSmallholders in Mutoko, Zimbabwe; Khwe and
Mbukushu communities in NamibiaMugambiwa (2018); Humphrey et al. (2021)
Rainfall 
variabilityChange crop type (from maize to traditional millet and 
sorghum); no weeding; forecasting, rainwater harvesting; 
women perform rainmaking rituals, seed dressing and crop 
maintenance as adaptation measures; mulchingCommunities in Accra, Ghana; small-scale 
farmers in Ngamiland in Botswana; Malawi; 
Zimbabwe; women in Dikgale, South Africa, 
agro-pastoral smallholders in Ntungamo, 
Kamuli and Sembabule in UgandaCodjoe et al. (2014); Nkomwa et al. (2014); 
Lunga and Musarurwa (2016); Rankoana 
(2016b); Mugambiwa (2018); Mfitumukiza 
et al. (2020); Mogomotsi et al. (2020)
DroughtsTraditional drying of food for preservation (to consume

during short-term droughts); harvesting wild fruits and 
vegetables; herd splitting by pastoralsCommunities in Accra, Ghana; Malawi; 
South Africa, Uganda; smallholder farmers 
in Mutoko, Zimbabwe; agro-pastoralists in 
Makueni, Kenya; pastoralists in South Omo, 
EthiopiaEgeru (2012); Gebresenbet and Kefale 
(2012); Codjoe et al. (2014); Kamwendo and 
Kamwendo (2014); Okoye and Oni (2017); 
Mugambiwa (2018)
Drought-related 
water scarcityTraditional rainwater harvesting to supplement both 
irrigation and domestic water; Indigenous water bottle 
technology for irrigationSmallholder farmers in Beaufort, South Africa Ncube (2018)
IKLK and adaptation/coping strategies in Table Box 9.2.1 are supportive measures that communities cannot solely rely upon, but which 
can be used to complement other adaptation options to increase community resilience.
African Indigenous language and climate change adaptation

The diversity of African languages is crucial for climate adaptation. Africa has over 30% of the world’s Indigenous languages (Seti et al., 
2016), which are exceptionally rich in ecosystem-specific knowledge on biodiversity, soil systems and water (Oyero, 2007; Mugambiwa, 
2018). Taking into consideration the low level of literacy in Africa, especially among women and girls, Indigenous languages hold great 
potential for more effective climate change communication and services that enable climate adaptation (Brooks et al., 2005; Ologeh 
et al., 2018; IPCC, 2019b). African traditional beliefs and cultural practices place great value on the natural environment, especially land 
as the dwelling place of the ancestors and source of livelihoods (Tarusarira, 2017; see Section 9.12).
Limitations of African Indigenous Knowledge and Local Knowledge in climate adaptation

Studies on IKLK and climate change adaptation conducted in various African countries and across ecosystems indicate that Indigenous 
environmental knowledge is negatively affected by several factors. Local farmers who depend on this knowledge system for their 
livelihoods hold the view that African governments do not support and promote it in policy development. Most government agricultural 
extension workers still consider IK to be unscientific and unreliable (Seaman et al., 2014; Mafongoya et al., 2017). At the national level, 
there is a lack of recognition and inclusion of IKLK in adaptation planning by African governments, partly because most of the IK and LK 
in African local communities remains undocumented, but also because IKLK are inadequately captured in the literature (Ford et al., 2016; 
IPCC, 2019b). This knowledge is predominantly preserved in the memories of the elderly and is handed down orally or by demonstration

from generation to generation. It gradually disappears due to memory gaps, and when those holding the knowledge die or refuse to pass 
it to another generation, the knowledge becomes extinct (Rankoana, 2016a). The way in which IK is transmitted, accessed and shared in 
most African societies is not smooth (IIED, 2015). IK is also threatened by urbanisation, which attracts rural migrants to urban areas where 
IKLK use may be more limited (Fernández-Llamazares et al., 2015). Further, most African societies that use IK were once colonised, 
whereby the African Indigenous ways of knowing were devalued and marginalised (Bolden et al., 2018). There are concerns about the 
effectiveness of both IK indicators and related adaptation responses by communities in predicting and adapting to weather events under 
future climate conditions (Speranza et al., 2009; Shaffer, 2014; Hooli, 2016).Box 9.2 (continued)Box 9.2 (continued)

9
1332Chapter 9 Africa
9.6 Ecosystems
9.6.1 Observed Impacts of Climate Change on African 
Biodiversity and Ecosystem Services
9.6.1.1 Terrestrial Ecosystems
The overall continental trend is woody plant expansion, particularly 
in grasslands and savannas, with woody plant cover increasing at 
a rate of 2.4% per decade (see Figure 9.17; Stevens et al., 2017; 
Axelsson and Hanan, 2018). There is also increased grass cover in 
arid regions in southwestern Africa (Masubelele et al., 2014). There 
is high agreement that this is attributable to increased CO 2, warmer 
and wetter climates, declines in burned area and release from 
herbivore browsing pressure, but the relative importance of these 
interacting drivers remains uncertain (O’Connor et al., 2014; Stevens 
et al., 2016; García Criado et al., 2020). Woody encroachment is the 
dominant trend in the western and central Sahel, occurring over 
24% of the region, driven primarily by shifts in rainfall timing and

recovery from drought (Anchang et al., 2019; Brandt et al., 2019). 
Remote sensing studies demonstrate greening in southern Africa 
and forest expansion into water-limited savannas in central and 
west Africa (Baccini et  al., 2017; Aleman et  al., 2018; Piao et  al., 
2020), with increases in precipitation and atmospheric CO 2 the 
probable determinants of change (Venter et al., 2018; Brandt et al., 
2019; Zhang et al., 2019). These trends of greening and woody plant 
expansion stand in contrast to the desertification and contraction 
of vegetated areas highlighted in AR5 (Niang et al., 2014), but are 
based on multiple studies and longer time series of observations. 
Reported cases of desertification and vegetation loss, for example, in the Sahel, appear transitory and localised rather than widespread 
and permanent (Dardel et al., 2014; Pandit et al., 2018; Sterk and 
Stoorvogel, 2020).
Shifts in demography, geographic ranges, and abundance of plants

and animals consistent with expected impacts of climate change are 
evident across Africa. These include uphill contractions of elevational 
range limits of birds (Neate-Clegg et  al., 2021), changes in species 
distributions previously reported in AR5 (Niang et al., 2014) and the 
death of many of the oldest and largest African baobabs (Patrut et al., 
2018). An increase in frequency and intensity of hot, dry weather 
after wildfires has led to a long-term decline in plant biodiversity in 
Fynbos since the 1960s (Slingsby et al., 2017). Increasing temperatures 
may have contributed to the declining abundance and range size of 
South African birds (Milne et  al., 2015), including Cape Rockjumper 
(Chaetops frenatus) and protea canary (Serinus leucopterus), from 
increased risk of reproductive failure (Lee and Barnard, 2016; Oswald 
et al., 2020). For hot and dry regions (e.g., Kalahari), there is strong 
evidence that increased temperatures are having chronic sublethal

impacts, including reduced foraging efficiency and loss of body mass 
(du Plessis et al., 2012; Conradie et al., 2019), and are approaching 
species physiological limits, with heat extremes driving mass mortality 
events in birds and bats (McKechnie et al., 2021). Vegetation change 
linked to climate change and increasing atmospheric CO 2 has had an 
indirect impact on animals. Increased woody cover has decreased the 
occurrence of bird, reptile and mammal species that require grassy 
habitats (Péron and Altwegg, 2015; McCleery et al., 2018). Decreased 
fruit production linked to rising temperatures has decreased the body 
condition of fruit-dependent forest elephants by 11% from 2008–2018 
(Bush et al., 2020).
Indigenous earth wall
Figure Box 9.2.1 |  Indigenous earth walls (hayit) built by Indigenous people in Akobo, Jonglei Region, South Sudan, to protect their houses and

infrastructure from the worst flood in 25 years that occurred in 2019. The wall is 1–2 m high. Photo credit: Laurent-Charles Tremblay-Levesque.Box 9.2 (continued)

9
1333Africa  Chapter 9
There is high agreement that land use activities counteract or exacerbate 
climate-driven vegetation change (Aleman et al., 2017; Timm Hoffman 
et al., 2019). Decreased woody plant biomass in 11% of sub-Saharan 
Africa was attributed to land clearing for agriculture (Brandt et al., 2017; 
Ordway et al., 2017). Localised loss of tree cover in Miombo woodlands 
and 16.6±0.5 Mha of forest loss in the Congo Basin between 2000–2014 
was driven largely by forest clearing and drought mortality (McNicol 
et al., 2018; Tyukavina et al., 2018).
Vegetation changes interacting with climate and land use change 
have impacted fire regimes across Africa. The frequency of weather 
conducive for fire has increased in southern and west Africa and is 
expected to continue increasing in the 21st century under both RCP2.6 
and RCP8.5 (Betts et  al., 2015; Abatzoglou et  al., 2019). Increased 
grass cover in arid regions introduced fire into regions where fuel

was previously insufficient to allow fire spread, such as the arid Karoo 
in South Africa (du Toit et  al., 2015; Strydom and Savage, 2016). In 
contrast, shrub encroachment, increased precipitation (Zubkova et al., 
2019), vegetation fragmentation and cropland expansion have reduced 
fire activity in many African grasslands and savannas (Andela and van 
der Werf, 2014; Probert et  al., 2019). These drivers are expected to 
negate the effect of increasing fire weather and ultimately lead to a 
reduction in the total burned area under RCP4.5 and RCP8.5 (Knorr 
et al., 2016; Moncrieff et al., 2016; Wu et al., 2016).
9.6.1.2 Vegetation Resilience
African ecosystems have a long evolutionary association with fire, 
large mammal herbivory and drought (Maurin et  al., 2014; Charles-
Dominique et  al., 2016). The maintenance of biodiversity depends 
on natural disturbance regimes. Natural regrowth of savanna plant 
biomass in southern Africa compensated for biomass removal through

human activities (McNicol et al., 2018), and rapid recovery occurred 
after the 2014–2016 extreme drought (Abbas et  al., 2019). During 
the same drought event, browsing and mixed feeder herbivores 
were resilient, but grazers declined by approximately 60% and were 
highly dependent on drought refugia (Abraham et al., 2019). African 
tropical forests remained a carbon sink through the record drought 
and temperature experienced in the 2015–2016 El Niño, indicating 
resilience in the face of extreme environmental conditions (Bennett 
et  al., 2021). This is likely due to the presence of drought-tolerant 
species and floristic and functional shifts in tree species assemblages 
(Fauset et  al., 2012; Aguirre-Gutiérrez et  al., 2019). This resilience 
indicates that there is the capacity to recover from disturbances 
and short-term change. However, resilience has limits and beyond 
certain points, change can lead to irreversible shifts to different states 
(Figure 9.18).

9.6.1.3 Freshwater Ecosystems
Small climatic variations have large impacts on ecosystem function 
in Africa’s freshwaters (Ndebele-Murisa, 2014; Ogutu-Ohwayo et  al., 
2016). Warming of water temperatures from 0.2°C to 3.2°C occurred in 
several lakes over 1927–2014 and has been attributed to human-caused 
climate change (Figure  9.17; Ogutu-Ohwayo et  al., 2016). Increased 
temperature, changes in rainfall and reduced wind speed altered the 
physical and chemical properties of inland water bodies, affecting water quality and productivity of algae, invertebrates and fish (high 
confidence). In deeper lakes, warmer surface waters and decreasing 
wind speeds reduced shallow waters mixing with nutrient-rich deeper 
waters, reducing biological productivity in the upper sunlit zone 
(Ndebele-Murisa, 2014; Saulnier-Talbot et  al., 2014). In several lakes, 
climate change was identified as causing changes in insect emergence 
time (Dallas and Rivers-Moore, 2014) and in loss of fish habitats

(Natugonza et  al., 2015; Gownaris et  al., 2016). This set of changes 
can harm human livelihoods, for example, from reduced fisheries 
productivity (see Section 9.8.5; Ndebele-Murisa, 2014; Ogutu-Ohwayo 
et al., 2016) and reduced water supply and quality (Section 9.7.1).
9.6.1.4 Marine Ecosystems
Anthropogenic climate change is already negatively impacting Africa’s 
marine biodiversity, ecosystem functioning and services by changing 
physical and chemical properties of seawater (increased temperature, 
salinity and acidification, and changes in oxygen concentration, ocean 
currents and vertical stratification) (high confidence) (Hoegh-Guldberg 
et al., 2014; 2018). Coastal ecosystems in west Africa are among the 
most vulnerable because of extensive low-lying deltas exposed to sea 
level rise, erosion, saltwater intrusion and flooding (Belhabib et  al., 
2016; UNEP , 2016b; Kifani et  al., 2018).  In southern Africa, shifting

distributions of anchovy, sardine, hake, rock lobster and seabirds have 
been partly attributed to climate change (Crawford et al., 2015; van 
der Lingen and Hampton, 2018; Vizy et al., 2018), including southern 
shifts of 30 estuarine and marine fish species attributed to increased 
temperature and changes in water circulation from decreased river 
inflow (Augustyn et  al., 2018). Warming sea surface temperatures 
inhibiting nutrient mixing have reduced phytoplankton biomass in the 
western Indian Ocean by 20% since the 1960s, potentially reducing 
tuna catches (Roxy et al., 2016).
Mangroves, seagrasses and coral reefs support nursery habitats for 
fish, sequester carbon, trap sediment and provide shoreline protection 
(Ghermandi et  al., 2019). Climate change is compromising these 
ecosystem services (medium confidence). Marine heatwaves associated 
with ENSO events have triggered mass coral bleaching and mortality

over the past 20 years (Oliver et al., 2018). Mass coral bleaching in the 
western Indian Ocean occurred in 1998, 2005, 2010 and 2015/2016 
with coral cover just 30–40% of 1998 levels by 2016 (Obura et al., 
2017; Moustahfid et  al., 2018). The northern Mozambique Channel 
has served as a refuge from climate change and biological reservoir 
for the entire coastal east African region (McClanahan et  al., 2014; 
Hoegh-Guldberg et  al., 2018). A southern shift of mangrove species 
has been observed in south Africa (Peer et al., 2018) with loss in total 
suitable coastal habitats for mangroves and shifts in the distribution of 
some species of mangroves and a gain for others (Record et al., 2013). 
Mangrove cover was reduced 48% in Mozambique in 2000 from 
Tropical Cyclone Eline, with 100% mortality of seaward mangroves 
dominated by Rhizophora mucronata (Macamo et al., 2016). Recovery 
of mangrove species was observed 14 years later in sheltered sites.

There is low confidence these cyclone-induced impacts are attributable 
to climate change owing, in part, to a lack of reliable long-term data 
sets (Macamo et  al., 2016). In west Africa, oil and gas extraction, 
deforestation, canalisation and de-silting of waterways have been the 
largest factors in mangrove destruction (Numbere, 2019).

9
1334Chapter 9 Africa
9.6.2 Projected Risks of Climate Change for African 
9.6.2.1 Projected Biome Distribution
The geography of African biomes is projected to shift due to changes in 
atmospheric CO 2 concentrations and aridity (Figure 9.18). Grassland 
expansion into the desert, woody expansion into grasslands and 
forest expansion into savannas are projected for areas of reduced 
aridity, caused by reduced moisture stress from CO 2 fertilisation 
under medium (RCP4.5) and high (SRES A2) emissions scenarios 
(Heubes et  al., 2011; Moncrieff et  al., 2016). This greening trend 
may slow or reverse with continued temperature increase and/or in 
areas of increased aridity (Berdugo et al., 2020). The net impact of 
these effects on vegetation is highly uncertain (Trugman et al., 2018; 
Cook et  al., 2020a; Martens et  al., 2021). The maintenance or re-
establishment of natural fire and large mammal herbivory processes

can mitigate projected CO 2 and climate-driven changes (Scheiter and 
Savadogo, 2016; Stevens et al., 2016). Expansion of croplands and 
pastures will reduce ecosystem carbon storage in Africa, potentially reversing climate- and CO 2-driven greening in savannas (Aleman 
et al., 2018; Quesada et al., 2018).
Vegetation growth simulated by dynamic vegetation models is often 
highly sensitive to CO 2 fertilisation. These models project the African 
tropical forest carbon sink to be stable or strengthened under scenarios 
of future climate change (Huntingford et al., 2013; Martens et al., 2021). 
In contrast, statistical modelling suggests it has begun to decline and 
will weaken further, decreasing from current estimates of 0.66 tonnes 
of carbon removed from the atmosphere per hectare per year to 0.55 
tonnes of carbon (Hubau et al., 2020). Increasing rainfall seasonality 
and aridity over central Africa (Haensler et  al., 2013) threatens the

massive carbon store in the Congo Basin’s Cuvette Centrale peatlands, 
estimated at 30.6 billion tonnes (Dargie et al., 2019).
9.6.2.2 Terrestrial Biodiversity
Local extinction is when a species is extirpated from a local site. 
The magnitude and extent of local extinctions predicted across 
Africa increase substantially under all future GWLs (high confidence) (a) Terrestrial vegetation (b) Freshwater ecosystems 
Functional type switch
Forest/woodland/shrub decline
Grass cover gain
Grass cover loss
Shrub/woodland cover gain
Climate driver
Correlated with
climate change driver
Proposed climate change driver
Not reported
Not climate change related
Surface water
temperature change (°C/decade)
-0.2– -0.1-0.1–00–0.10.1–0.20.2–0.30.3–0.40.4–0.5
Lake temperature change  (°C/decade)
0.05–0.20.2–0.40.4–0.60.6–0.76
Observation type
Satellite
in−situObserved changes in vegetation and freshwater ecosystems

Figure 9.17 |  Widespread changes to African vegetation have been reported, especially increasing woody plant cover in many savannas and grasslands, 
with 37% of these changes proposed to be driven by human-caused climate change and increased CO 2 (a). The warming of lakes and rivers has been detected across 
Africa and is attributed to climate change. Data on vegetation change was gathered from 156 studies published between 1989 and 2021(b). Climatic changes, mostly associated 
with changes in rainfall, are enhancing grass production in arid grasslands and savannas, and causing grass expansion into semi-desert regions with notable increases in the Sahel 
and southern Africa. Tropical forest expansion into mesic savannas is occurring on the fringes of the central African tropical forest. Interactions between land use, climate change

and increasing atmospheric CO 2 concentrations are causing a widespread increase in woody plant cover encroachment in tropical savannas and grasslands. Some tree death and 
woody cover decline associated with climate and land use change have also been recorded across biomes. Of the reported changes to terrestrial vegetation, 24% were explicitly 
linked to climate change and a further 13% were proposed to be driven by climate change. In 48% of studies, no climate driver was mentioned and in 15% climate change was 
ruled out as the driver of change. Annual surface water temperatures in African lakes have warmed at a rate of 0.05°C–0.76°C per decade. Both satellite-based measures spanning 
1985–2011 and in situ measurements spanning 1927–2014 agree on this warming trend. Other surface waters across Africa warmed from 1979–2018 at a rate of between

0.05°C and 0.5°C per decade (Woolway and Maberly, 2020). Vegetation change data were taken from a larger, global literature survey of existing databases supplemented with 
newer studies documenting changes in tree, shrub and grass cover linked to climate and land use change in natural and semi-natural areas (for further details see Section 2.4.3.5; 
Table SM2.1; Table SM9.2 for Africa vegetation change data and Table SM9.3 for studies reporting lake warming data).

9
1335Africa  Chapter 9
A schematic representing biome distribution across an aridity gradient in Africa.
Aridity is an important determinant of these biomes, however the distribution of 
grasslands, savannas and forests are also strongly shaped by interactions between 
disturbance and climate and as such changes in disturbances are also important 
determinants of biomes boundaries. Multiple stable biome states are often possible at 
the transition between savanna and forest. Shifts in rainfall seasonality are not 
depicted here, though through altering disturbance regimes and drought intensity , this 
is also expected to be an important factor .Increases in atmospheric CO2 and changes in aridity
are projected to shift the geographic distribution of major biomes across Africa
Savanna Grassland Shrubland Desert Forest
Forests expand into savannas;
savannas experience densification and tending to 
become closed canopy systems.
As CO2 increases the water use ef ficiency of

trees and grasses increases causing woody 
proliferation in arid savannas. This may also 
induce expansion of grasses into arid shrublands.
Increasing Aridity Decreasing AridityCO2
In areas with increased aridity in addition to rising CO2 an increase in forest die-of f 
is likely and a slowing in the rate of woody plant encroachment may occur . Arid 
shrublands may experience increases in plant die off. Further expansion of forest into savanna and a significant loss of savanna 
grassland to closed canopied forests and thickets. Expansion of savannas into 
cooler grasslands. Higher CO2 and decreased aridity will promote grass expansion 
into arid shrublands. 
ecreasin g Aridit y
Figure 9.18 |  Increases in atmospheric CO 2 and changes in aridity are projected to shift the geographic distribution of major biomes across Africa (high

confidence). Arrows in the diagram indicate possible pathways of biome change from current conditions resulting from changes in CO 2 and aridity. Changes need not be gradual 
or linear and may occur rapidly if tipping points are crossed. Currently, widespread greening observed in Africa has been at least partially attributed to increasing atmospheric CO 2 
concentrations. Future projected increases in aridity are expected to cause desertification in many regions, but it is highly uncertain how this will interact with the greening effect 
of CO 2. Inset maps show the projected geographical extent of changes in CO 2 concentrations and aridity. CO 2 is projected to increase globally under all future emission scenarios. 
Aridity index maps show projected change in aridity (calculated as annual precipitation/annual potential evapotranspiration) at around 4°C global warming relative to 1850–1900

(RCP8.5 in 2070–2099) from 34 CMIP5 models (Scheff et al., 2017). Shaded areas indicate regions where >75% of models agree on the direction of change.
(Table 9.5; Figure 9.19). Above 2°C, the risk of sudden disruption or 
loss of local biodiversity increases and becomes more widespread, 
especially in central, west and east Africa (Trisos et al., 2020).Global extinction is when a species is extirpated from all areas. At 2°C 
global warming, 11.6% of African species (mean 11.6%, 95% CI 6.8–
18.2%) assessed are at risk of global extinction, placing Africa second 
only to South America in the magnitude of projected biodiversity losses 
(Urban, 2015). At >2°C, 20% of north African mammals may lose all

9
1336Chapter 9 Africa
suitable climates (Soultan et  al., 2019), and over half of the dwarf 
succulents in South African Karoo may lose >90% of their suitable 
habitat (Young et al., 2016). Among the thousands of species at risk, 
many are species of ecological, cultural and economic importance such 
as African wild dogs (Woodroffe et al., 2017) and Arabica coffee (Moat 
With increasing warming, there is a lower likelihood species can 
migrate rapidly enough to track shifting climates, increasing global 
extinction risk and biodiversity loss across more of Africa (high 
confidence). Immigration of species from elsewhere may partly 
compensate for local extinctions and lead to local biodiversity gains 
in some regions (Newbold, 2018; Warren et al., 2018). However, more 
regions face net losses than net gains. At 1.5°C global warming, 
>46% of localities face net declines in vertebrate species richness

of >10%, with net increases projected for less than 15% of localities 
(Barbet-Massin and Jetz, 2015; Newbold, 2018). At >2°C, 9% of 
species face complete range loss by 2100, regardless of their dispersal 
ability (Urban, 2015). With >4°C global warming, a net loss of >10% 
of vertebrate species richness is projected across 85% of Africa 
(Barbet-Massin and Jetz, 2015; Mokhatla et al., 2015; Newbold, 2018; 
Warren et al., 2018). Mountain top endemics and species in north and 
southern Africa are at risk due to disappearing cold climates (Milne 
et al., 2015; Garcia et al., 2016; Bentley et al., 2018; Soultan et al., 
2019). For hot regions such as the Sahara, Congo Basin and Kalahari, 
no warmer-adapted species are available elsewhere to compensate 
for local extinctions, so the resilience of local biodiversity will depend 
entirely on the persistence of species (Burrows et  al., 2014; Garcia 
et  al., 2014). The capacity for species to avoid extinction through

behavioural thermoregulation, plasticity or evolution is uncertain but 
will become increasingly unlikely under higher warming scenarios 
(Conradie et al., 2019).
9.6.2.3 Marine Ecosystems
African coastal and marine ecosystems are highly vulnerable to 
climate change (high confidence). At 1.5°C of global warming, 
mangroves will be exposed to sedimentation and sea level rise, while 
seagrass ecosystems will be most affected by heat extremes (high 
confidence) (Hoegh-Guldberg et al., 2018) and turbidity (Wong et al., 
2014). These risks will be amplified at 2°C and 3°C (virtually certain) 
(Hoegh-Guldberg et  al., 2018). Over 90% of east African coral 
reefs are projected to be destroyed by bleaching at 2°C of global warming (very high confidence) (Hoegh-Guldberg et  al., 2018). At 
around 2.5°C global warming, an important reef-building coral 
(Diploastrea heliopora) in the central Red Sea is projected to stop 
growing altogether (Cantin et al., 2010). By 2.5°C, suitable habitat

of >50% of species are projected to decline for coastal lobster 
in east and north Africa, with large declines for the commercially 
important lobster species Jasus lalandii in southern Africa (Boavida-
Portugal et  al., 2018). More generally, tropical regions, especially 
exclusive economic zones in west Africa, are projected to lose large 
numbers of marine species and may experience sudden declines with 
extratropical regions having potential net increases as species track 
shifting temperatures poleward (García Molinos et al., 2016; Trisos 
9.6.2.4 Freshwater Ecosystems
Above 2°C global warming, the proportion of freshwater fish species 
vulnerable to climate change increases substantially (high confidence) 
(Figure 9.19). At 2°C, 36.4% of fish species are projected to be vulnerable 
to local or global extinction by 2100, increasing to 56.4% under 4°C 
warming (average of values from (Nyboer et  al., 2019; Barbarossa

et al., 2021) (Figure 9.19). Global warming reduces available habitat for 
freshwater species due to reduced precipitation and increased drought 
leading to increasing water temperatures above optimal physiological 
limits in floodplains, estuaries, wetlands, ephemeral pools, rivers and 
lakes (Dalu et al., 2017; Kalacska et al., 2017; Nyboer and Chapman, 
2018). Along the Zambezi River, projected flow reductions could cause 
a 22% reduction in annual spawning habitat and depletion of food 
resources for fry and juvenile fish that could impede fish migration 
and reduce stocks (Kangalawe, 2017; Martínez-Capel et  al., 2017; 
Tamatamah and Mwedzi, 2020). More aquatic species will have the 
capacity to cope with 2°C compared to 4°C global warming, with more 
negative effects on physiological performance at 4°C (Dallas, 2016; 
Pinceel et  al., 2016; Zougmoré et  al., 2016; Nyboer and Chapman, 
2017; Ross-Gillespie et  al., 2018). Endemic, specialised fish species

will have a lower capacity to adjust to elevated water temperatures 
compared to hardier generalist fishes (McDonnell and Chapman, 2015; 
Nyboer and Chapman, 2017; Lapointe et al., 2018; Reizenberg et al., 
2019). More work is needed to understand the risk for invertebrates 
(Dallas and Rivers-Moore, 2014; Cohen et al., 2016), and to understand 
the potential effects of reduced mixing of water and other climate risks 
on freshwater biodiversity.Table 9.5 |  Risk of local extinction increases across Africa with increasing global warming.
Global warming level 
(relative to 1850–
1900)TaxaPercentage of species 
at a site at risk of local 
extinctionExtent across Africa 
(percentage of the 
land area of Africa)Areas at risk References
1.5°CPlants, insects, 
vertebrates>10% >90%Widespread. Hot and/or arid regions 
especially at risk, including Sahara, 
Sahel and KalahariFigure 9.29b; Newbold (2018); 
Warren et al. (2018)
>2°CPlants, insects,

vertebrates>50% 18% WidespreadNewbold (2018); Warren et al. 
(2018)
>4°CPlants, insects, 
vertebrates>50% 45–73%Widespread. Higher uncertainty 
for central African tropical forests 
due to lower agreement between 
biodiversity modelsFig. 9.29c; Newbold (2018); 

9
1337Africa  Chapter 9
(a) The loss of African biodiversity 
under future climate change 
is projected to be widespread and increasing 
substantially with every 0.5°C above the 
current (2001–2020) level of global warming
(c) >4.0°C (b) >1.5°C
(e) 2.6–3.2°C (d) 1.5–2°C
Species
locally
extinct100%
75%
50%
25%
0 100% 75% 50% 25%
Vulnerable
fish
species100%
75%
50%
25%
Model agreement>3.0 >1.5 >2.0 >1.0
change100%
0
-20%
-40%
-60%
-80%
-100%200%
Global warming (°C)
Figure 9.19 |  The loss of African biodiversity under future climate change is projected to be widespread and increasing substantially with every 0.5° above 
the current (2001–2020) level of global warming (high confidence). 
(a) Projected biodiversity loss, quantified as percentage change in species abundance, range size or area of suitable habitat increases with increasing global warming levels

(relative to 1850–1900). Above 1.5°C global warming, half of all assessed species are projected to lose >30% of their population, range size or area of suitable habitat, with losses 
increasing to >40% for >2°C. The 2001–2020 level of global warming is around 1°C higher than 1850 –1900 (IPCC, 2021). Boxplots show the median (horizontal line), 50% 
quantiles (box), and points are studies of individual species or of multiple species (symbol size indicates the number of species in a study).
(b–c) The mean projected local extinction of vertebrates, plants and insects within 100 km grid cells increases in severity and extent under increased global warming (relative 
to 1850–1900). Local extinction >10% is widespread by 1.5°C. Pixel colour shows the projected percentage of species undergoing local extinction and the agreement between 
multiple biodiversity models.

9
1338Chapter 9 Africa
9.6.2.5 Climate Change and Ecosystem Services
Direct human dependence on provisioning ecosystem services in 
Africa is high (Egoh et al., 2012; IPBES, 2018). For example, natural 
forests provided 21% of rural household income across 11 African 
countries (Angelsen et al., 2014) and wild-harvested foods (including 
fisheries) provide important nutrition to millions of Africans, including 
through important micronutrients and increased dietary diversity 
(Sections 9.8.2.3; 9.8.5; Powell et al., 2013; Baudron et al., 2019a).
Climate change has affected ecosystem services in Africa by reducing 
fish stocks, crop and livestock productivity, and water provisioning due 
to heat and drought (see Sections  9.8.2.1; 9.8.2.2; 9.8.2.4; 9.8.5.1). 
Woody encroachment is decreasing cattle production and water 
supply (Smit and Prins, 2015; Stafford et  al., 2017), but can also 
provide forage for goat production, as well as resins, fuelwood and

charcoal (Reed et al., 2015; Stafford et al., 2017; Charis et al., 2019). 
Local communities perceive climate change to have decreased crop 
and livestock productivity, reduced wild food availability and reduced 
forest resources across Africa (see Sections  9.8.2.1; 9.8.2.2; 9.8.2.4; 
9.8.2.3; Onyekuru and Marchant, 2014).
With global warming >3°C, and with high population growth and 
agricultural expansion (SSP3, 2081–2100), 1.2  billion Africans are 
projected to be negatively affected by pollution of drinking water from 
reduced water quality regulation by ecosystems and 27 million people 
affected by reduced coastal protection by ecosystems (Chaplin-Kramer 
et al., 2019). The number of people affected reduces to 0.4 billion and 
22  million, respectively, under a sustainable development scenario 
with global warming below 2°C (SSP1, 2081–2100). The African 
tropical forest carbon sink has been more resilient than Amazonia

to recent warming but may already have peaked, and this service 
is predicted to decline with further warming, reducing 14% by the 
2030s (Hubau et al., 2020; Sullivan et al., 2020). This declining carbon 
storage may be offset by CO 2 fertilization (low confidence) (Martens 
et  al., 2021). Climate change is projected to shift the geographic 
distribution of important human and livestock disease vectors (see 
Sections 9.8.2.4; 9.10.2). Changes in rainfall seasonality compounded 
with land privatisation and population growth may adversely impact 
nomadic and semi-nomadic pastoralists who follow shifting patterns 
of greening vegetation (Van Der Ree et al., 2015).
9.6.2.6 Invasive Species
Invasive species threaten African ecosystems and livelihoods (Ranasinghe 
et  al., 2021). For instance, economic impacts were estimated at 
USD 1 billion per year for smallholder maize farmers in east Africa (Pratt et al., 2017). Climate change is projected to change patterns of invasive

species spread (high confidence). The area of suitable climate for Lantana 
camara is projected to contract (Taylor et  al., 2012) and to expand for 
Prosopis juliflora (Sintayehu et  al., 2020). Bioclimatic suitability for fall 
armyworm, a major threat to maize, is projected to decrease in central 
Africa but expand in southern and west Africa (Zacarias, 2020), and to 
expand for coffee berry borer (Hypothenemus hampei) in Uganda and 
around Mount Kenya (Jaramillo et  al., 2011). Climate suitability for 
tephritid fruit flies is projected to decrease in central Africa (Hill et  al., 
2016). Increased water temperature is projected to favour invasive over 
local freshwater fish populations and shift the range of invasive aquatic 
plants in South Africa (Hoveka et al., 2016; Shelton et al., 2018). Alterations 
to lake and river connectivity are predicted to modify invasion pathways in 
Lake Tanganyika and water hyacinth coverage may increase with warmer

waters in Lake Victoria (Masters and Norgrove, 2010; Plisnier et al., 2018).
9.6.3 Nature-based Tourism in Africa
Nature-based tourism is important for African economies and jobs. 
Tourism contributed 8.5% of Africa’s 2018 gross domestic product 
(GDP) (World Travel and Tourism Council, 2019a) with wildlife tourism 
contributing a third of tourism revenue (USD 70.6 billion), supporting 
8.8 million jobs (World Travel and Tourism Council, 2019b).
Climate change is already negatively affecting tourism in Africa (high 
confidence). The 2015–2018 Cape Town drought caused severe water 
restrictions, reducing tourist arrivals and spending with associated job 
losses (Dube et al., 2020). Human-caused climate change increased the 
likelihood of the reduced rainfall that caused the drought by a factor 
of three (Otto et al., 2018)(Pascale et al., 2020). Extreme heat days 
have increased across South African national parks since the 1990s

(van Wilgen et  al., 2016). This reduces animal mobility, decreasing 
animal viewing opportunities (Dube and Nhamo, 2020). Tourists and 
employees also fear heat stress (Dube and Nhamo, 2020). Visitors 
to South Africa’s national parks preferred to visit in cool-to-mild 
temperatures (Coldrey and Turpie, 2020). Extreme weather conditions 
disrupted tourist activities and damaged infrastructure at Victoria 
Falls, Hwange National Park, Kruger National Park and the Okavango 
Delta (Dube et al., 2018; Dube and Nhamo, 2018; Mushawemhuka 
et al., 2018; Dube and Nhamo, 2020). Rainfall variability and drought 
alter wildlife migrations, affecting tourist visits to the Serengeti 
(Kilungu et al., 2017). Reduced tourism decreases revenue for national 
park management (van Wilgen et al., 2016).
Future climate change is projected to further negatively affect nature-

based tourism. Decreased snow and forest cover may reduce visits to (d–e) The mean projected increase in species of freshwater fish vulnerable to local extinction within 10 km grid cells for future global warming. Around a third of fish species are 
projected to be vulnerable to extinction by 2°C global warming. Pixel colour shows the projected percentage of species vulnerable to extinction and agreement between multiple 
vulnerability models. In (a), data were obtained from 22 peer-reviewed papers published since 2012 investigating the impacts of projected climate change on African biodiversity. 
When a paper provided impact projections for several time periods, climate change scenarios or for more than one species, each impact was recorded as an individual biodiversity 
impact projection, resulting in a database of 1165 biodiversity impact projections. Data were initially collected by Manes et al. (2021) as part of a larger literature review for

Cross-Chapter Paper 1 on Biodiversity Hotspots and then expanded to include areas outside of African priority conservation areas (see Table SM 9.4). The literature review was 
limited to peer-reviewed publications that reported quantifiable risks to biodiversity, eliminating non-empirical studies. In (b–c), projections are based on intersecting current 
and future modelled species distributions at ~10 km spatial resolution from two recent global assessments of climate change impacts on terrestrial vertebrates (Newbold, 2018; 
Warren et al., 2018). In (d-e) projections are based on intersecting future species vulnerabilities from two recent assessments of climate change vulnerability of freshwater fish 
species (Nyboer et al., 2019; Barbarossa et al., 2021).

9
1339Africa  Chapter 9
Kilimanjaro National Park (Kilungu et al., 2019). Woody plant expansion 
in savanna and grasslands reduce tourist’s game viewing experience and 
negatively impact conservation revenues (Gray Emma and Bond William, 
2013; Arbieu et  al., 2017). Visitation rates to South African national 
parks, based on mean monthly temperatures, are projected to decline 
4% with 2°C global warming (Coldrey and Turpie, 2020). Sea level rise 
and increased intensity of storms is projected to reduce beach tourism 
due to beach erosion (Grant, 2015; Amusan and Olutola, 2017). Tourism 
in the Victoria Falls, Okavango and Chobe hydrological systems may be 
negatively affected by heat and increased variability of rainfall and river 
flow (Saarinen et al., 2012; Dube and Nhamo, 2019). Increased extreme 
heat will increase air turbulence and weight restrictions on aircraft, 
which could make air travel more uncomfortable and expensive to

African destinations (Coffel and Horton, 2015; Dube and Nhamo, 2019).
9.6.3.1 Protected Areas and Climate Change
African protected areas store around 1.5% of global land ecosystem 
carbon stocks and support biodiversity (Gray et al., 2016; Melillo et al., 
2016; Sala et al., 2018). They also support livelihoods and economies, 
such as through nature-based tourism and improved fisheries 
(Brockington and Wilkie, 2015; Mavah et al., 2018; Ban et al., 2019).
Climate change and land use change will interact to influence the 
effectiveness of African protected areas (high confidence). Species 
representation in the existing African protected area network is 
projected to decrease due to species range shifts for mammals, bats, 
birds and amphibians (Hole et  al., 2009; Baker et  al., 2015; Payne 713 Adaptation actions were identified in
52 Nationally Determined Contributions (NDCs)
from African countries as of early 2020
Coastal zone
Cross-cutting areaDisaster Risk Management (DRM)

EducationEnergy
Social development
TourismUrban(7)   Afforestation
(5)   Agroecology(8)   Agroforestry(12) Climate smart agriculture(18) Coastal zone
(17) Crops
(3)   Cross-cutting area(5)   Disaster Risk Management (DRM)(28) Ecosystem and biodiversity
(2)   Education(10) Energy
(7)   Fisheries and aquaculture
(5)   Food security(6)   Irrigation(22) Land and soil management
(5)   Land degradation
(5)   Livestock(9)   Reforestation
(1)   Social development(20) Sustainable forest management
(14) Sustainable land management
(1)   Tourism(5)   Urban
(5)   Water conservation and reuse
(3)   Water infrastructure(15) Water mmanagement
(7)   Water supply(12) Watershed and river basin management
(1)   WetlandsSectors planning strategies linked to ecosystem-based adaptation
258 or 36% of these
are Ecosystem-based 
adaptation (EbA)
actionsSub-sectors
(number of EbA actions)
EnvironmentForestry and 
other land use
WaterMore than 80% 
are in these 
four sectorsSectors

Figure 9.20 |  Over a third (36%) of all adaptation actions identified in the NDCs of 52 African countries as of early 2020 were ecosystem-based adaptations 
(EbA). Of these actions ±83% fall within the agriculture, land use/forestry, environment and water sectors. The EbA actions identified from the NDCs span 12 primary sectors and 
29 sub-sectors.
and Bro-Jørgensen, 2016; Smith et  al., 2016; Phipps et  al., 2017). 
Species ability to disperse between areas to track shifting climates is 
increasingly impaired by land transformation and fencing, which also 
impact seasonal wildlife migrations (Lovschal et al., 2017; Sloan et al., 
2017). On land, only 0.5% of the African protected area network is 
connected through low-impact landscapes (Ward et al., 2020). Linear 
transport infrastructure (e.g., roads, railways, pipelines) and fencing 
from proposed ‘development corridors’ are projected to bisect over 400 
protected areas and degrade around 1800 more (Laurance et al., 2015).

Climate change could increase human–wildlife conflict as resultant 
resource shortages cause communities to move into protected areas 
for harvesting or livestock grazing, or wildlife to move out of protected 
areas and into contact with people (Mukeka et al., 2018; Kupika et al., 
2019; Hambira et al., 2020). See Section 9.6.4 for the role of land and 
ocean protected areas in climate change adaptation.
9.6.4 Ecosystem-based Adaptation in Africa
Ecosystem-based adaptation (EbA) uses biodiversity and ecosystem 
services to assist people to adapt to climate change (Swanepoel and 
Sauka, 2019). Africa’s Nationally Determined Contributions (NDCs) 
show 36% of adaptation actions identified by 52  countries are 
considered to be EbA (Figure 9.20).
EbA can reduce climate impacts and there is high agreement EbA 
can be more cost-effective than traditional grey infrastructure 
when a range of economic, social and environmental benefits are

9
1340Chapter 9 Africa
Table 9.6 |  The beneficial outcomes of ecosystem-based adaptation (EbA) actions and assessed confidence in these outcomes. Assessment is provided for EbA options in the 
four most prevalent EbA sectors identified in the Nationally Determined Contributions of 52 African countries (Figure 9.20). See Chapter 2.6.3 and 3.6.2 of this report for further 
assessment of EbA approaches in terrestrial, freshwater and marine systems.
Sector EbA Action(s) Outcome(s) Confidence Source(s)
AgricultureConservation agricultureImproved soil and water conservation High Thierfelder et al. (2017)
Improved agricultural productivity and drought 
resilienceMediumPittelkow et al. (2015); Thierfelder et al. (2017); Adenle 
Diversified crop varietiesImproved agricultural productivity and drought 
resilienceHighShiferaw et al. (2014); Tesfaye et al. (2016); Thierfelder 
EnvironmentEcosystem protection and

restorationCarbon sequestration and storage High Melillo et al. (2016); Griscom et al. (2017); FAO (2018a)
Stepping stones for species migrating due to climate 
changeMedium Beale et al. (2013); Roberts et al. (2020)
Increased ecosystem resilience to disturbance HighAnthony et al. (2015); Sierra-Correa and Cantera Kintz 
(2015); Kroon et al. (2016); Roberts et al. (2017)
Livelihood diversification opportunities from 
ecotourism, resource harvesting and rangelands 
(among others)MediumLunga and Musarurwa (2016); Bedelian and Ogutu (2017); 
Agyeman (2019); Kupika et al. (2019); Naidoo et al. (2019)
Forestry and 
other land useRestoration/ reforestation
Sustainable forestry and land 
managementRestoration of degraded ecosystems and enhanced 
carbon sequestrationHigh Mugwedi et al. (2018)
Reducing pressure on forests for food and energy 
needsMedium Peprah (2017); Zegeye (2018)
WaterIntegrated catchment

managementImproved flood attenuation capacity HighBradshaw et al. (2007); Mwenge Kahinda et al. (2016); 
Rawlins et al. (2018)
Improved resilience of freshwater ecosystems HighNdebele-Murisa (2014); Natugonza et al. (2015); (2019); 
Tamatamah and Mwedzi (2020)
also accounted for (Table  9.6; Baig et  al., 2016; Emerton, 2017; 
Chausson et  al., 2020). This is particularly relevant in Africa where 
climate vulnerabilities are strongly linked to natural resource-based 
livelihood practices and existing grey infrastructure levels are low in 
many regions (Dube et al., 2016; Reid et al., 2019). However, financial 
constraints limit EbA project implementation (Mumba et  al., 2016; 
Swanepoel and Sauka, 2019).
Evidence for EbA in Africa is largely case study based and often anecdotal 
(Reid et  al., 2018). There is high agreement that costs, challenges and 
negative outcomes of EbA interventions are still poorly understood (Reid,

2016; Chaplin-Kramer et al., 2019), despite limited evidence for the efficacy 
of context-specific applications at different scales (Doswald et al., 2014).
9.6.4.1 Terrestrial Ecosystems
Improved ecosystem care and restoration are cost-effective for 
carbon sequestration while providing multiple environmental, social 
and economic co-benefits (Griscom et al., 2017; Shukla et al., 2019). 
Protecting and restoring natural forests and wetlands reduces flood 
risk across multiple African countries (Bradshaw et al., 2007). In Kenya, 
enclosures for rangeland regeneration diversified income sources, 
which could increase the adaptive capacity of local people (Mureithi 
et  al., 2016; Wairore et  al., 2016). Sustainable agroforestry in semi-
arid regions provides income sources from fuelwood, fruit and timber 
and reduces exposure to drought, floods and erosion (Quandt et al., 
2017). Forest protection in Zimbabwe maintains honey production

during droughts, providing food supply options if crops fail (Lunga and 
Musarurwa, 2016). Community-based natural resource management 
in pastoral communities improved institutional governance outcomes through involving community members in decision making, increasing 
the capacity of these communities to respond to climate change (Reid, 
2014).
EbA can also increase ecological resilience. Re-introduction of fire and 
large mammals can restore ecosystem services, enhance adaptive 
capacity and benefit people by combatting woody encroachment, 
restoring grazing and increasing streamflow (Asner et al., 2016; Stafford 
et al., 2017; Cromsigt et al., 2018). Herbivores can also reduce fuel loads 
in areas facing increased fire risk (Hempson et al., 2017).
Protected areas can be ‘stepping stones’ that facilitate climate-induced 
species range shifts (Roberts et  al., 2020), preserve medicinal plant 
diversity despite climate change (Kaky and Gilbert, 2017) and provide

livelihood diversification opportunities (Table 9.6). Protecting 30% of 
sub-Saharan Africa’s land area could reduce the proportion of species 
at risk of extinction by around 60% in both low and high warming 
scenarios (Hannah et al., 2020). The role of protected areas in EbA can be 
strengthened by: (a) increasing coverage of diverse environments and 
high carbon storage ecosystems, (b) restoring habitat, (c) maintaining 
intact habitat, (d) participatory, equitable conservation and adaptation 
strategies; (e) cooperating across borders and (f) adequate monitoring 
(Gillson et al., 2013; Rannow et al., 2014; Midgley and Bond, 2015; Pecl 
et al., 2017; Dinerstein et al., 2019; Roberts et al., 2020).
9.6.4.2 Freshwater Ecosystems
EbA can mitigate flooding and increase the resilience of freshwater 
ecosystems (Table 9.6). Adaptation in African freshwater ecosystems is 
heavily influenced by non-climate anthropogenic factors, including land

9
1341Africa  Chapter 9
use change, water abstraction and diversion, damming and overfishing 
(Dodds et al., 2013; Kimirei et al., 2020; UNESCO and UN-Water, 2020). 
Wetlands and riparian areas support biodiversity, act as natural filtration 
systems and serve as buffers to changes in the hydrological cycle, thereby 
increasing the resilience of freshwater ecosystems and the people that 
rely on them (Ndebele-Murisa, 2014; Musinguzi et al., 2015; Lowe et al., 
2019). However, national adaptation programmes of action, NAPs and 
national communications rarely consider the ecological stability of ecosystems safeguarding the very water resources they seek to preserve 
(Kolding et  al., 2016). Some countries have mandated the protection 
of riparian zones, but implementation is low (Musinguzi et  al., 2015; 
Muchuru and Nhamo, 2018). Protecting terrestrial areas surrounding Lake 
Tanganyika benefited fish diversity (Britton et al., 2017). Afforestation

reduces water availability but forest restoration and removing invasive 
plant species can increase water flows in regions facing water insecurity 
from climate change (Chausson et  al., 2020; Le Maitre et  al., 2020). Box 9.3 | Tree planting in Africa
Due to widespread deforestation and forest degradation (Malhi et al., 2014), future scenarios to limit global warming include large-scale 
reforestation and afforestation (Griscom et al., 2017; Bastin et al., 2019). Africa has been targeted through the AFR100 (https://afr100.
org) to plant ~1 million km2 of trees by 2030 (Bond et al 2019). Maintaining existing indigenous forest and indigenous forest restoration 
is a win–win, maximising benefits to biodiversity, adaptation and mitigation (Griscom et al., 2017; Watson et al., 2018; Lewis et al., 2019) 
Yet many areas targeted by AFR100 erroneously mark Africa’s open ecosystems (grasslands, savannas, shrublands) as degraded and

suitable for afforestation (Figure Box 9.3.1; (Veldman et al., 2015; Bond et al., 2019) (high confidence). These ecosystems are not degraded, 
they are ancient ecosystems that evolved in the presence of disturbances (fire/herbivory) (Maurin et al., 2014; Bond and Zaloumis, 2016; 
Charles-Dominique et al., 2016). Afforestation prioritises carbon sequestration at the cost of biodiversity and other ecosystem services 
(Veldman et al., 2015; Bond et al., 2019). Furthermore, it remains uncertain how much carbon can be sequestered as, compared to grassy 
ecosystems, afforestation can reduce belowground carbon stores and increase aboveground carbon loss to fire and drought (Yang et al., 
2019; Wigley et al., 2020b; Nuñez et al., 2021). Thus, afforested areas may store less carbon than ecosystems they replace (Dass et al., 
2018; Heilmayr et al., 2020). Afforestation would reduce livestock forage, ecotourism potential and water availability (Gray Emma and

Bond William, 2013; Anadón et al., 2014; Cao et al., 2016; Stafford et al., 2017; Du et al., 2021), and may reduce albedo thereby increasing 
warming (Bright et al., 2015; Baldocchi and Penuelas, 2019).
Exotic tree species are often selected for planting (e.g., Pinus spp. or Eucalyptus spp.), but in parts of Africa, they have become invasive 
(Zengeya, 2017; Witt et al., 2018), increasing fire hazards and decreasing biodiversity and water resources (Nuñez et al., 2021) (high 
confidence). Negative impacts of afforestation on ecosystems are not restricted to plantations of exotic species; they extend to 
inappropriate planting of native forest species (Slingsby et al., 2020).
(a)
Savannas at potential risk from afforestation(b)
Antelope species diversit y
Grassy biomes
at risk of
afforestation an d
forest expansion(c)
Cattle distribution
Antelope
species
richness
15–18
10–15
5–1018–25
0–5Cattle/km2
12
8
422
118

Figure Box 9.3.1 |  Many proposed tree planting plans in Africa present risks to biodiversity and livelihoods, because they are focused on 
(a) naturally non-forested ecosystems like savannas, grasslands and shrublands which 
(b) host uniquely adapted biodiversity and 
(c) offer important ecosystem services like grazing which supports subsistence and commercial agriculture. Figure adapted from Veldman et al. (2015); Bond et al. (2019).

9
1342Chapter 9 Africa
Regular, long-term monitoring of African freshwaters would improve 
understanding of responses to climate change. General principles for this 
type of monitoring were developed for Lake Tanganyika (Plisnier et al., 
2018) and could be applied to develop harmonised, regional monitoring 
of African lakes, rivers and wetlands (Tamatamah and Mwedzi, 2020)
9.6.4.3 Marine and Coastal Ecosystems
Marine and coastal ecosystems such as mangroves, seagrass and 
coral reefs provide storm protection and food security for coastal 
communities (high confidence) (IPCC, 2019d). Restoring reef systems 
reduced wave height in Madagascar (Narayan et al., 2016), but there is 
limited evidence for the efficacy of coral reef restoration at large scales 
with increased warming (Chapter 3 Section 3.6.3). Populations at risk 
from storm surge and/or sea level rise coincide with areas of high 
coastal EbA potential from Mozambique to Somalia, and coastlines

of the Gulf of Guinea, Gambia, Guinea-Bissau and Sierra Leone (Jones 
et al., 2020). Understanding hotspots of EbA potential is particularly 
important for west Africa with some of the highest levels of human 
dependence on marine ecosystems at high risk from climate change 
and large populations vulnerable to sea level rise (Sections  9.9.3.1; 
9.8.5.2; Selig et al., 2018; Trisos et al., 2020).
Marine protected areas (MPAs) can yield multiple adaptation benefits, 
such as buffering species from extinction and increasing fish stocks, as 
well as storing large amounts of carbon (Edgar et al., 2014; Roberts 
et  al., 2017; Lovelock and Duarte, 2019). However, this potential of 
MPAs will reach limits with increased warming (Roberts et al., 2017). 
For example, MPAs cannot prevent coral bleaching at scale and mass 
die-offs are well-described from MPAs following climate shocks (Bates 
et al., 2019; Bruno et al., 2019). However, prioritising MPA coverage of

climate refugia, such as the Northern Mozambique Channel, may offer 
some increased resilience (McClanahan et al., 2014).
9.7 Water
Much of Africa experiences very high hydrological variability in all 
components of the water cycle, with important implications for people 
and ecosystems. Most of the continent’s water is stored in groundwater 
(660,000 km3), which is 20  times more than the water stored in the 
lakes and 100 times more than the annual renewable water resources 
(MacDonald et  al., 2012). The accessible volume of groundwater via 
wells and springs is smaller than these estimates (Xu et  al., 2019). 
Africa has 63 transboundary river basins (UNEP , 2010), 72 mapped 
transboundary aquifers (Nijsten et  al., 2018) and 33 transboundary 
lakes (ILEC and UNEP , 2016), reflecting a highly water-connected and 
interdependent socio-ecological system across countries, also extending 
to the coastal areas of the continent (see Chapter 4 Section 4.1).

9.7.1 Observed Impacts from Climate Variability and 
Climate impacts on water are occurring against a backdrop of 
increasing temperatures and changes in rainfall, with increased 
seasonal and interannual variability, droughts in some regions, and increased frequency of heavy rainfall events (see Section 9.5). In west 
Africa, declines in river flows have been attributed to declining rainfall 
and increasing temperature, drought frequency and water demand 
(Biao, 2017; Thompson et al., 2017; Descroix et al., 2018). In central 
Africa, the Congo river demonstrates inter-decadal shifts but no long-
term trend (Mahe et al., 2013; Alsdorf et al., 2016). However, recently 
observed falling water levels in its upper and middle reaches are 
attributed to climate change (von Lossow, 2017).
A review of river flow and lake level changes in 82 basins in eastern and 
southern Africa regions for 1970–2010 showed mixed trends: 51% had

decreasing trends ranging from 10–49% and 11% increasing trends 
ranging from 7–60% (Schäfer et al., 2015). However, in southern Africa 
as a whole, river flows have mostly decreased (high confidence) (Dallas 
and Rivers-Moore, 2014). In east Africa, large rivers such as the Tana 
show increasing flow (1941–2016) related to increased rainfall in the 
highlands, with little influence of flow regulation by a series of dams 
(Langat et al., 2017). The Nile river basin has been experiencing a mainly 
increasing rainfall trend upstream and decreasing trend downstream 
(Onyutha et al., 2016). The observed changes are driven by a complex 
coupling of changes in climate, land use and water demand.
Observed climate changes in Africa (see Section 9.5) have led to changes 
in river flow and runoff (Dallas and Rivers-Moore, 2014; Wolski et al., 
2014) and high fluctuations in lake levels (high confidence) (Natugonza 
et al., 2016; Ogutu-Ohwayo et al., 2016; Gownaris et al., 2018). Shallow

lakes respond dramatically to hydrological changes, for example, Lake 
Chilwa has dried up completely nine times in the last century (Wilson, 
2014), while Lake Chad shrunk by 90% between 1963 and 2000 (Gao 
et al., 2011). However, recent analyses indicate that Lake Chad’s water 
levels have been stable since 2000 due to infilling from groundwater 
resources (Buma et  al., 2018; Pham-Duc et  al., 2020). Other factors 
such as deforestation and increased water use in upstream tributaries 
also contribute to lake shrinking (Mvula et  al., 2014). Water levels in 
Kenya’s mostly shallow rift lakes have been rising since 2010, with some 
exceeding historical record high levels (Schagerl and Renaut, 2016; 
Olago et al., 2021). The recent 10-year rising trend is partly attributed to 
increased rainfall and changing land uses (Onywere et al., 2012; Olago 
et  al., 2021). Changes in water level fluctuations of 13 African lakes

have been positively correlated with primary and overall production 
(Gownaris et  al., 2018), and will have important consequences for 
freshwater ecosystems and related ecosystem goods and services (see 
Sections  9.6.1.3; 9.8.5). Other effects of observed climate changes in 
Africa include higher episodic groundwater recharge, particularly in 
drylands, from heavy rainfall events that are in some cases related to 
ENSO and the IOD (Taylor et al., 2013; Fischer and Knutti, 2016; Cuthbert 
et  al., 2019; Kotchoni et  al., 2019; Myhre et  al., 2019), reduced soil 
moisture, more frequent and intense floods, more persistent and frequent 
droughts (Douville et  al., 2021) and the steady decline and projected 
disappearance by 2040 of African tropical glaciers (see Section 9.5.9).
The mixed signal in river flow trends (increase/decrease/no change) 
across Africa mirrors the results seen globally for runoff and streamflow

(see Chapter 4 Section 4.2.3). Hydrological extremes are, however, of 
increasing concern. There has been an increase in drought frequency, 
severity and spatial extent in recent decades. From 1900–2013, Africa 
suffered the largest number of drought events globally and registered

9
1343Africa  Chapter 9
Box 9.4 | African cities facing water scarcity
Many African cities will face increasing water scarcity under climate change (Grasham et al., 2019). The Cape Town and Dodoma cases 
illustrate challenges for both surface and groundwater supply and what adaptation responses have been employed.
The Cape Town drought (2015–2018)
The Cape Town drought illustrates how a highly diverse African city and its citizens responded to protracted and unanticipated water 
scarcity. Human-caused climate change made the reduced rainfall that caused the drought three times more likely (95% confidence 
interval 1.5–6) (Otto et al., 2018; Pascale et al., 2020; Doblas-Reyes et al., 2021). After three consecutive years of low precipitation, 
Cape Town braced for a ‘Day Zero’ where large portions of the city would lose water supply (Cole et al., 2021a). The risk of Day Zero

was anticipated to cascade to affect risks to health, economic output and security (Simpson et al., 2021b). The case study highlights 
the importance of communication, budgetary flexibility, robust financial buffers and insurance mechanisms, disaster planning, 
intergovernmental cooperation, nature-based solutions, infrastructure transformations and equitable access for climate adaptation in 
African cities facing water scarcity.
A substantial media campaign was launched to inform residents about the severity of the drought and urge water conservation (Booysen 
et al., 2019; Hellberg, 2019; Ouweneel et al., 2020). Together with stringent demand management through higher water tariffs, this 
communication campaign played an important role in reducing consumption from 540 to 280 litres per household per day (Booysen et al., 
2019; Simpson et al., 2019a). Revenue from water sales contributes 14% of Cape Town’s total revenue, making it the third-largest source

of ‘own’ revenue for the city (Simpson et al., 2019b). However, with an unprecedented reduction in water use, the municipal budget was 
undermined (Simpson et al., 2020b). Collecting less revenue created a financial shock as the city struggled to recover operating finance, 
even while new capital requirements were needed for the development of expensive new water supply projects (Simpson et al., 2019b). 
This financial shock was compounded by the economic stress of poor agricultural and tourism performance brought about by the drought 
(Shepherd, 2019; Simpson et al., 2021b). As wealthy residents invested in private, off-grid water supplies, the risk of reduced municipal 
revenue collections from newly off-grid households aggregated with the risk of reduced tourism, increasing the risk to the reputation 
of the incumbent administration (Simpson et al., 2021b). This demonstrates how a population cohort with a high response capability to

water scarcity can reduce risk while simultaneously increasing risks to the municipality and its capacity to provide water to vulnerable 
residents (Simpson et al., 2020b). Given that city populations in Africa pay 5–7 times more for water than the average price paid in the 
USA or Europe (Adamu and Ndi, 2017; Lwasa et al., 2018), municipal finance needs to delink operating revenue from potential climate 
shocks (see Box 8.6).
The drought led the municipality to consider a broader diversity of water supply options, including groundwater (CoCT, 2019), developing 
city-scale, slow-onset disaster planning (Cole et al., 2021a) and building an enhanced ‘relationship with water’ (CoCT, 2019; Madonsela 
et al., 2019). This shift in approach is displayed in the recognition of nature-based solutions as a priority in water resilience-building 
efforts (Rodina, 2019) and is signalled in Cape Town’s Water Strategy which aims to become a ‘water sensitive city’ that makes ‘optimal

use of stormwater and urban waterways for flood control, aquifer recharge, water re-use and recreation’ (CoCT, 2019).
The drought required cooperation between multiple spheres of government, and the management of a broad range of stakeholders and 
political entities (Nhamo and Agyepong Adelaide, 2019; Cole et al., 2021a). The case highlights how a lack of coordination between 
essential organs of state and political entities can reduce response efficacy (Rodina, 2019). Despite significant investments in water 
security by public and private entities, one-quarter of Cape Town’s population remains in persistent conditions of water stress, emphasising 
the challenge and importance of inclusive solutions that address the persistent social and economic stressors which affect vulnerability to 
water scarcity (Enqvist and Ziervogel, 2019).
Sustaining intensive groundwater use in a dryland city under climate change: Dodoma, Tanzania

Since 1954, the Makutapora wellfield in semi-arid, central Tanzania has supplied safe water to the city of Dodoma. Substantial rises 
in wellfield pumping and population growth have increased freshwater demand in Dodoma and dependence upon the Makutapora 
wellfield, currently the sole perennial source of piped water to the city. Yet, there is high uncertainty of groundwater recharge rates 
(Nkotagu, 1996; Taylor et al., 2013) which rely on intense seasonal rainfall associated with the ENSO and the IOD modes of climate 
variability (e.g., 2 to 7 years) to contribute disproportionately to recharge (Taylor et al., 2013; Kolusu et al., 2019).

9
1344Chapter 9 Africa
Defining a sustainable pumping rate for the Makutapora wellfield is complicated by the variable and episodic nature of groundwater 
replenishment in this dryland environment. For example, groundwater recharge during the 1997/1998 El Niño event, the strongest El Niño 
event of the 20th century, accounted for nearly 20% of all of the recharge received from 1955–2010 (Taylor et al., 2013), highlighting the 
vital role interannual groundwater storage plays in enabling adaptation to climate variability and change in drylands. The disproportionate 
contribution of intense seasonal rainfalls to the replenishment of the Makutapora wellfield, consistent with observations from across 
sub-Saharan Africa (Cuthbert et al., 2019), suggests that groundwater in drylands are currently naturally resilient to climate change. 
However, it remains unclear whether climate change will strengthen or weaken the influence of ENSO and IOD on rainfall (Brown et al.,

2020) and thereby affect the predictability of groundwater recharge.
As freshwater demand in Tanzania’s rapidly growing capital is projected to increase substantially in the coming decades, questions 
remain as to whether the capacity of the Makutapora wellfield can meet some or all of this demand. Nature-based solutions to improve 
the resilience of wellfield abstraction to increased pumpage and climate change include managed aquifer recharge (MAR). The sharing 
of general lessons learned from other cities in dryland Africa employing MAR, such as Windhoek in Namibia (Murray et al., 2018), could 
prove invaluable.Box 9.4 (continued)
the second largest number of people affected after Asia (Masih et al., 
2014). The likelihood of recent severe climate conditions such as the 
multi-year Cape Town drought has increased due to human-caused 
climate change (Otto et al., 2018; Pascale et al., 2020; see Box 9.4), 
and regional and urban floods (Yuan et al., 2018; Tiitmamer, 2020) and

droughts (Funk et al., 2018b; Siderius et al., 2018; Uhe et al., 2018) are 
expected to increase.
However, between 2010–2020 more people across Africa have been 
impacted by floods (e.g., related to Cyclone Idai in March 2019) 
compared to droughts (Lumbroso, 2020). Coastal cities are vulnerable 
to floods related to rainfall and sea level rise (Musa et al., 2014), as 
exemplified by the flood disasters experienced in the Niger delta in 2012 
which displaced more than 3  million people and destroyed schools, 
clinics, markets and electricity installations (Amadi and Ogonor, 2015). 
From 2000–2015, the proportion of people exposed to floods grew by 
20–24%, mostly in Africa and Asia, with Mozambique and multiple 
countries in West Africa estimated to have had the proportion of their 
populations exposed to flooding increase by more than 50% (Tellman 
et  al., 2021) and these numbers will increase under climate change.

Sectoral impacts from flooding within Africa and globally are further 
elaborated on in Sections 9.8.2 and 9.8.5.1, Table 9.3 and Chapter 4 
Section 4.3.
9.7.2 Projected Risks and Vulnerability
9.7.2.1 Projected Risks
By 2050, up to 921 million additional people in sub-Saharan Africa 
could be exposed to climate change-related water stress, while up 
to 459 million could experience reduced exposure (Dickerson et al., 
2021). This large variance in numbers and direction of change is 
related to uncertainties in climate models and non-climate factors 
like population growth and water withdrawals (Dickerson et  al., 
2021). The baseline for most of the projected risks presented here 
is 1971–2000.In west Africa, significant spatial variability in river flow is projected in 
the upper reaches of several rivers, with no clear pattern overall (Roudier 
et al., 2014) and large uncertainties in estimations of change in runoff

(Roudier et al., 2014; Bodian et al., 2018). In some higher altitude regions, 
like the Niger Inland Delta in west Africa, river flows and water levels are 
expected to increase (medium confidence) (Aich et al., 2014; Thompson 
et al., 2017). In the Lower Niger Basin, combined average annual rainfall 
and erosivity for all the climatic models in all scenarios shows increasing 
rainfall amounts are projected to result in an increasing average change 
in rainfall-runoff erosivity of about 14%, 19% and 24% for the 2030s, 
2050s and 2070s, with concomitant increase in soil loss of 12%, 19% 
and 21% (Amanambu et al., 2019). In the Volta River system, increasing 
wet season river flows (+36% by 2090s) and Volta lake outflow (+5% 
by 2090s) are anticipated under RCP8.5 (medium confidence) (Awotwi 
A et al., 2015; Jin et al., 2018). In the Volta River basin, compared to 
1976–2005, drought events are projected to increase by 1.2 events per

decade at around 2°C to 1.6 events per decade at around 2.5°C global 
warming, and drought area extent is projected to increase by 24% to 
34% (Oguntunde et  al., 2017). In central Africa, runoff in the Congo 
river system may increase by up to 50% (RCP8.5), especially in the wet 
season, enhancing flood risks in the entire Congo Basin, particularly 
in the central and western parts (CSC, 2013). Average river flows are 
expected to increase in most parts of central Africa, with expected 
increases in total potential hydropower production (Ludwig et al., 2013), 
but see Box 9.5.
In north Africa, in the upper White Nile basin, Olaka et  al. (2019) 
project a 25% and 5–10% (RCP4.5) increase in the intensification of 
future annual rainfall in the eastern and western parts of the Lake 
Victoria Basin, respectively, with corresponding variability in future 
river discharge ranging from 5% to 26%. In the upper Blue Nile basin,

models also indicate up to 15% increase in runoffs in wet season and 
up to −24% decrease in dry season during 2021–2040 (RCP8.5) (Ayele 
et al., 2016; Siam and Eltahir, 2017; Meresa and Gatachew, 2018). The 
increase of precipitation in the wet season indicates a higher possibility 
of flash floods, while decreased runoffs in dry season further intensify 
existing shortage of irrigation water demand (Ayele et al., 2016; Siam

9
1345Africa  Chapter 9
and Eltahir, 2017; Meresa and Gatachew, 2018). The annual flow and 
revenues from hydropower production and irrigated agriculture of the 
Blue Nile River at Khartoum are projected to increase under maximum 
but are expected to decrease under minimum and median projected 
changes in streamflow for 2041–2070 and 2071–2100, respectively 
(Tariku et al., 2021). The Middle Draa valley in Morocco is expected 
to experience more severe droughts and the estimation of the water 
balance suggests a lack of supply in the future (Karmaoui et al., 2016).
In east Africa, Liwenga et  al. (2015) project warmer and wetter 
conditions in the Great Ruaha River region and with increasing 
seasonal variation and extremes towards the end of the century. A 
similar observation is made for the River Pangani, with mean river 
flow being about 10% higher in the 2050s relative to the 1980–1999 
period, associated with a 16–18% increase in rainfall in its upper

catchment (Kishiwa et al., 2018). However, at more local scales, the 
projections cover a range of slight declines to significant increases 
in mean annual rainfall amounts (Gulacha and Mulungu, 2017). In 
the Tana River basin in Kenya, water yield is projected to increase 
progressively under RCP4.5 and RCP8.5 relative to the baseline period 
1983–2011 but is characterised by distinct spatial heterogeneity 
(Muthuwatta et al., 2018).
In southern Africa, reductions in rainfall over the Limpopo and Zambezi 
river basins under 1.5°C and 2°C global warming could have adverse 
impacts on hydropower generation, irrigation, tourism, agriculture and 
ecosystems (Figure Box  9.5.1) (Maúre et  al., 2018), although model projections of strong early summer drying trends remain uncertain 
(Munday and Washington, 2019).
Changes in the amplitude, timing and frequency of extreme events 
such as droughts and floods will continue to affect lake levels, rates of

river discharge and runoff and groundwater recharge (high confidence) 
(Gownaris et al., 2016; Darko et al., 2019), but with disparate effects 
at regional, basin and sub-basin scales, and at seasonal, annual and 
longer timescales. The increased frequency of extreme rainfall events 
under climate change (Myhre et  al., 2019) is projected to amplify 
groundwater recharge in drylands (Jasechko and Taylor, 2015; Cuthbert 
et al., 2019). However, declining trends in rainfall and snowfall in some 
areas of north Africa (Donat et al., 2014b) are projected to continue 
in a warming world (Seif-Ennasr et al., 2016), restricting groundwater 
recharge from meltwater flows, exacerbating the salinisation and 
depletion of groundwater (Hamed et al., 2018) and increasing the risk 
of reduced soil moisture (Petrova et  al., 2018) in this region where 
groundwater abstraction is greatest (Wada et al., 2014).
Lake surface temperatures across Africa are expected to rise in tandem

with increasing global warming. Lake heatwaves, periods of extreme 
warm lake surface water temperature, are projected to become hotter 
and longer (Figure 9.21), with heatwaves more than 300 days per year 
in many lakes for global warming of 4.2°C (Woolway et  al., 2021). 
Lake warming is expected to have adverse consequences for aquatic 
biodiversity, habitats, water quality and disruption of current lake physical 
processes and circulation patterns (Kraemer et al., 2021).Climate change is projected to increase the intensity of lake heatwaves across Africa
°C per decade(a) Under 1.8°C global warming (RCP2.6 in 2070–2099) (b) Under 4.2°C global warming (RCP8.5 in 2070–2099)
0.5
0.25
0.1
0
No data
Not significant 
Average intensity
of future lake heatwaves
1.89–2.27°C2.27–2.69°C2.69–2.92°C2.92–3.21°C3.21–3.69°C3.69–3.97°C3.97–4.54°C

Figure 9.21 |  Climate change is projected to increase the intensity of lake heatwaves across Africa. Projected increases in average intensity of lake heatwaves (°C) 
under 
(a) 1.8°C global warming (RCP2.6 in 2070–2099) and 
(b) 4.2°C global warming (RCP8.5 in 2070–2099). Each lake is represented by a point. Data were extracted from Woolway et al. (2021).

9
1346Chapter 9 Africa
9.7.2.2 Vulnerability
Climate change is projected to reduce water availability and increase the 
extent of water scarcity (Mekonnen and Hoekstra, 2016), particularly in 
southern and north Africa, while other regions will be more affected by 
increased hydrological variability over temporally short to interannual 
time scales (see Section 9.6.2). African countries are considered to be 
particularly at risk due to their underlying vulnerabilities (IPCC, 2014b; 
UNESCO and UN-Water, 2020), yet the continents’ water resources 
are still inadequately quantified and modelled (Müller Schmied et al., 
2016; Reinecke et  al., 2019), constraining sustainable management 
practices (Cuthbert et al., 2019; Hughes, 2019).
Hydrological fluctuations are associated with drought, flood and 
cyclone events which have had multi-sector impacts across Africa 
(Siderius et  al., 2021; see Chapter  4 Sections  4.3; 4.5), including:

reduced crop production (D’Odorico et  al., 2018), migration and 
displacement (Siam and Eltahir, 2017; IDMC, 2018), food insecurity 
and extensive livestock deaths (Nhamo et al., 2018), electricity outages 
(Gannon et  al., 2018), increased incidence of cholera (Olago et  al., 
2007; Sorensen et al., 2015; Houéménou et al., 2020) and increased 
groundwater abstraction amplifying the risk of saline intrusion from 
sea level rise (Hamed et al., 2018; Ouhamdouch et al., 2019).
The literature shows significant gender-differentiated vulnerability and 
intersectional vulnerability to climate change impacts on water in Africa 
(Fleifel et al., 2019; Grasham et al., 2019; Mackinnon et al., 2019; Dickin 
et al., 2020; Lund Schlamovitz and Becker, 2020), although studies are 
generally lacking in northern Africa (Daoud, 2021). Women and girls 
are, in most cases, more impacted than men and boys by customary 
water practices, as adult females are the primary water collectors

(46% in Liberia to 90% in Cote d’Ivoire), while more female than male 
children are associated with water collection (62% compared with 
38%, respectively) (Graham et al., 2016). Women and girls face barriers 
toward accessing basic sanitation and hygiene resources, and 71% of 
studies reported a negative health outcome, reflecting a water–gender–
health nexus (Pouramin et al., 2020). These differential vulnerabilities are 
crucial for informing adaptation, but are still relatively under-researched, 
more so for the urban poor than rural communities (Grasham et  al., 
2019; Mackinnon et al., 2019; Lund Schlamovitz and Becker, 2020).
9.7.3 Water Adaptation Options and Their Feasibility
9.7.3.1 Reducing Risk Through a Systems Approach to Water 
Resources Planning and Management
An integrated systems and risk-based approach to the design and 
management of water resources at scale is generally accepted as a 
practical and viable way of underpinning the resilience of water systems

to climate change and human pressures (Duffy, 2012; García et al., 2014). 
Such approaches confer multiple benefits to nature and society at scale 
and enhance efficiency gains through technology and management 
improvements, but their full implementation has not yet been realised 
(Weinzierl and Schilling, 2013; McDonald et al., 2014; UN Environment, 
2019). Drylands are particularly singled out as ignored areas that require 
integrated water resource management approaches (Section  9.3.1; Stringer et al., 2021). Appropriate ecosystem-based adaptations that are 
applicable at scale should be identified and strongly embedded in these 
approaches to deliver multiple benefits while maintaining the integrity of 
ecosystems and biodiversity (UN Environment, 2019; see Sections 9.6.4; 
9.8.5; Box 4.6). Furthermore, adaptation options are often influenced or 
constrained by institutions, regulation, availability, distribution, price and

technologies (McCarl et al., 2016). Thus, institutional capacity to manage 
complex water supply systems under rapidly increasing demand and 
climate change stress is critical in achieving water security for African 
cities, particularly as cities become more dependent on alternative and 
distant water sources (Padowski et al., 2016).
9.7.3.2 Adopting Nexus Lenses
The water–energy–food (WEF) nexus explicitly recognises the strong 
interdependencies of these three sectors and their high levels of 
exposure to climate change (Zografos et al., 2014; Dottori et al., 2018; 
see Box 9.5). With increasing societal demands on more variable water 
resources under climate change, an intensification of WEF competition 
and trade-offs are projected (D’Odorico et  al., 2018; Dottori et  al., 
2018). Other interacting factors, for example, the increasing number 
of transnational investments in land resources can lead to localised 
increased competition for water resources (Messerli et  al., 2014;

Breu et  al., 2016; Chiarelli et  al., 2016). Understanding such nexus 
interlinkages can help characterise risks to water resource security, 
identify co-benefits and clarify the range of multi-sectoral actors 
involved in and affected by development decisions (Kyriakarakos et al., 
2020). Major barriers and entry points for greater integration include 
coordination of horizontal policy and integration of climate change 
adaptation actions (England et al., 2018), capturing the scarcity values 
of water and energy embedded in food/energy products (Allan et al., 
2015), and inclusion of community-based organisations such as 
water resource user associations (Villamayor-Tomas et al., 2015) and 
agricultural cooperatives (Kyriakarakos et al., 2020).
9.7.3.3 Climate-proofing Water Infrastructure
While natural variability in the hydrological cycle has always been 
considered by water resources planners and engineers (Müller

Schmied et al., 2016; Muller, 2018), many countries will have to take 
into consideration the range of historically unprecedented extremes 
expected in the future. Increasingly, the provision of urban water 
security is dependent on the functioning of complex bulk water 
infrastructure systems consisting of dams, inter-basin transfers, 
pipelines, pump stations, water treatment plants and distribution 
networks (McDonald et al., 2014). Risk-based studies on the potential 
climate change risks for water security show that there are benefits 
when risks are reduced at the tails of the distribution—floods and 
droughts—even if there is little benefit in terms of changes in the 
mean (Arndt et  al., 2019). When risk is taken into account in an 
integrated (national) bulk water infrastructure supply system, the 
overall impact of climate change on the average availability of water to 
meet current and future demands is significantly reduced (Cullis et al.,

2015). Further, stemming leakages and enhancing efficiency through 
technology and management improvements is important in building 
climate-resilient water conveyance systems (UN Environment, 2019). 
African cities could leap-frog through the development phases to

9
1347Africa  Chapter 9
Box 9.5 | Water–energy–food nexus
The interdependencies in the water-energy-food (WEF) nexus, coupled with its high exposure to climate change, amplify WEF risks. Risks 
can be transmitted from one WEF sector to the other two with cascading risks to human health, cities and infrastructure (Conway et al., 
2015; Mpandeli et al., 2018; Nhamo et al., 2018; Yang and Wi, 2018; Ding et al., 2019; Simpson et al., 2021b). For example, increasing 
demand for water for agricultural and energy production is driving an increasing competition over water resources between food and 
energy industries which, among other effects, compromises the nutritional needs of local populations (Zografos et al., 2014; Dottori et al., 
2018). Drought events, such as in southern Africa during the 2015/16 El Niño, have been associated with major multi-sector impacts on

food security (over 40 million food-insecure people and extensive livestock deaths) and reduced energy security through disruption to 
hydropower generation (associated in Zambia with the lowest rate of real economic growth in over 15 years) (Nhamo et al., 2018). The 
WEF nexus of the Nile and Zambezi river basins, which include many of Africa’s largest existing hydropower dams, have received the 
most attention. In these two regions, where socioeconomic development is already driving up demand, projections indicate that water 
scarcity may be exacerbated by drying (Munday and Washington, 2019) and increased flow variability (Siam and Eltahir, 2017). However, 
for Africa more widely, very few studies fully integrate all three WEF nexus sectors and rarely include an explicit focus on climate change.
In Africa, the climate risks that the water, energy and food sectors will face in the future are heavily influenced by the infrastructure

decisions that governments make in the near term. The AU’s Programme for Infrastructure Development (PIDA), along with other national 
energy plans (jointly referred to as PIDA+), aim to increase hydropower capacity nearly six-fold, irrigation capacity by over 60% and 
hydropower storage capacity by over 80% in major African river basins (Cervigni et al., 2015). The vast majority of hydropower additions 
would occur in the Congo, Niger, Nile and Zambezi river basins, and the majority of the irrigation capacity additions would occur in the 
Niger, Nile and Zambezi River basins (Figure Box 9.5.1; Huber-Lee et al., 2015).
Climate change risk to the productivity of this rapidly expanding hydropower and irrigation infrastructure compound the overall WEF 
nexus risk. Future levels of rainfall, evaporation and runoff will have a substantial impact on hydropower and irrigation production.

Climate models disagree on whether climates will become wetter or dryer in each river basin. Cervigni et al. (2015) modelled revenues 
from the sale of hydroelectricity and irrigated crops in major African river basins under different climate scenarios between 2015 and 
2050 (Figure Box 9.5.1). The study found that hydropower revenues in the driest climate scenarios could be 58% lower in the Zambezi 
River basin, 30% lower in the Orange basin and 7% lower in the Congo basin relative to a scenario with current climate conditions. 
Hydropower revenues in the wettest climate scenario could be more than 20% higher in the Zambezi river basin and 50% higher in the 
Orange basin. The biggest risk to the production of irrigated crops is in the eastern Nile where irrigation revenue could be 34% lower in 
the driest scenario and 11% higher in the wettest than in a scenario without climate change (Cervigni et al., 2015).

Studies have used the river basin as a unit of analysis and adopted sophisticated techniques to assess and present trade-offs between 
competing uses. For example, Yang and Wi (2018) consider the WEF nexus in the Great Ruaha tributary of the Rufiji River in Tanzania 
motivated by an observed decrease in streamflow during the dry season in the 1990s, but without an explicit focus on climate. Yang and 
Wi (2018) show sensitivity of water availability for irrigated crop production to warming, and sensitivity of hydropower generation and 
ecosystem health to changes in precipitation and dam development. Understanding of WEF nexus interlinkages can help characterise 
risks and identify entry points and the relevant institutional levels for cross-sectoral climate change adaptation actions (England et al., 
2018). An integrated response can be enhanced through the inclusion of community-based organisations, such as water resource user

associations and the wide range of other multi-sectoral actors involved in and affected by development decisions. Capturing the scarcity 
values of water and energy embedded in food and other products can help identify the co-benefits and costs of integrated adaptation 
(Allan et al., 2015).achieve a water sensitive city ideal, reaping benefits such as improved 
liveability, reduced flooding impacts, safe water and overall lower 
net energy requirements and avoid making the mistakes developed 
countries’ cities have made (Fisher-Jeffes et al., 2017) (Brodnik et al., 
2018). However, the challenge of large proportions of the population 
lacking access to even basic water supply and sanitation infrastructure 
(Armitage et  al., 2014) must be simultaneously and effectively 
addressed, particularly in light of other major exacerbating factors, like 
the COVID-19 pandemic (Section 9.11.5).9.7.3.4 Decision Support Tools for Managing Complex Water 
Systems

Many studies in Africa use the river basin as a unit of analysis at scale 
and adopt sophisticated model-based techniques to assess climate 
change impacts on hydrology under different climate and development 
scenarios, thereby presenting trade-offs between competing uses such 
as hydropower generation, irrigation and ecosystem requirements 
(Section  9.12.1; Yang and Wi, 2018; Ahmed, 2020). However, longer

9
1348Chapter 9 Africa
Existing +
planned
hydropower
93,848
Megawatts
Existing +
planned
12,620,558
HectarsNile (Equatorial)Nile (Eastern)
Zambezi
Congo
NigerNile
(Equatorial) Zambezi Congo Niger Senegal
0.29
0.030.25
0.430.34
-0.180.52
-0.25
0.46 0.52 0.38
0.43 0.31
0.74-0.28weak strong negative
63 existing
79 planned (2015–2050)Hydropower 
3,052–39,000
5–255Capacity (Megawatts)
256–750751–1,6001,601–3,050
150200
100
50
0
150200
100
50
0Existing hydropower
13,774 MWPlanned hydropower
80,074 MW
Existing irrigation
7,765,688 HaPlanned irrigatio n
4,854,870 HaCongo
44,402Nile
21,392
Niger
4,667Zambezi
8,204Zambezi
4,827
Nile
6,220,270Senegal
754,460Niger
1,791,457
Nile
772,350Zambezi
668,542
Volta
177,389Senegal
255,327Niger
738,011
Billions US$ Billions US$Hydropower plants clustered within the same areas, are likely to 
experience similar rainfall and run-off patterns, increasing the risk 
that neighbouring states will experience concurrent drought-induced

hydropower shortages.
When historical annual river flows are weakly or negatively 
correlated, power trade between basins will be more effective in 
managing the risk of shortages than power trade between those 
experiencing similar patterns.  Approximate areas  
of shared rainfall  
variability & 
concentrated 
hydropower 
capacity
ZambeziNiger
CongoNile
(Eastern)
VoltaSenegal
OrangeNile
(Equatorial)
Zambezi Niger Congo Nile
(Eastern)Volta Senegal Nile
(Equatorial)Min. revenueMax. revenue
Net present value in 2012
(without climate change)Forecast revenues from planned hydropower 
under different climate scenarios (2015–2050)
Forecast revenues from planned irrigation
under different climate scenarios (2015–2050)Highest risk to 
hydropower output is 
in the Zambezi, where 
the driest scenarios 
would see a 58%
reduction in revenues 
relative to a scenario 
without climate change
Highest risk to 
production of irrigated

crops is in the 
Eastern Nile, where 
irrigation revenue 
could be 34% lower in 
the driest scenario 
than the baseline 
scenarioCorrelation of historical
annual river flows
(d) (c)
(f) (e)Climate risks to hydropower and irrigation in Africa
(b) (a) Distribution of hydropower plants within 7 major river basins
Box 9.5 (continued)

9
1349Africa Chapter 9
Figure Box 9.5.1 |  Climate risks to hydropower and irrigation in Africa. 
(a)T
he map shows the location and size of existing (blue) and planned (orange) hydropower plants in African governments’ infrastructure expansion plans, 2015–2050. 
(b)Matrix shows historical correlations in annual river flows between some of the major river basins indicating risk of hydropower shortages where correlations are higher. 
(c, e) Existing and planned hydropower and irrigation are indicated in charts. Dark blue shows forecasted revenues from 2015–2050 of existing hydropower and irrigation in 
major African river basins in a scenario without further climate change (i.e., based on historical data). Orange in charts (c, e) shows the expected increase in hydropower and irrigation revenues as new hydropower and irrigation infrastructure is added based on planned infrastructure development (PIDA+) in a scenario without climate change.

(d, f)  T he bar graphs show the forecast revenues for hydropower and irrigation infrastructure in each river basin under 121 different climate scenarios from 2015–2050, highlighting risk to revenues from high variability in river discharge due to climate change. In river basins with a wide range of potential river flow outcomes due to climate change, such as the eastern Nile and Zambezi, there is substantial uncertainty around revenue forecasts and potential for large reductions in future revenue. Hydropower revenues refer to net present value of hydroelectricity produced in each river basin over the period 2015–2050, and irrigation revenues refer to the crop revenues per hectare for each crop multiplied by the number of hectares of each crop across the basin. All figures are estimates of the net present value of revenues, using a discount rate of 3%, and are in 2012 USD billions. The 121 potential climate futures were derived using different General Circulation Models (GCMs),

Representative Concentration Pathways (RCPs), and downscaling methods. IPCC AR4 and AR5 provided data from 22 and 23 GCMs, respectively. These were evaluated across two or three emissions pathways, including RCP4.5 and RCP8.5. The Bias Corrected Spatial Disaggregation method of downscaling was then used to derive 99 potential climate futures. An additional 22 climate futures (11 GCMs driven by the RCP4.5 and RCP8.5 emissions pathways) were produced using the Empirical Statistical Downscaling Methods developed at the Climate Systems Analysis Group at the University of Cape Town. Data sourced from Cervigni et al. (2015).Box 9.5 (continued)

(multi-decadal) hydrological datasets and model improvements 
are required (Taye et  al., 2015), and models should incorporate the quantification of the wider benefits, risks and political opportunities arising from reservoir development to better inform decision makers to achieve a higher level of (transboundary) cooperation (Digna et al., 2016; Nijsten et  al., 2018). Collaboration between scientists and policymakers to address the complexity of decision making under uncertainty (Steynor et al., 2016) (Pienaar and Hughes, 2017), coupled with community involvement in participatory scenario development and participatory GIS to aid in collaborative planning that is context specific (Muhati et al., 2018; Álvarez Larrain and McCall, 2019) are powerful tools for more beneficial adaptive and resilience-building actions.
9.7.3.5  Other Adaptation Options

Climate change is projected to increase dependence upon groundwater withdrawals in most parts of Africa as an adaptive strategy to amplified variability in precipitation and surface water resources, highlighting the need for conjunctive surface-groundwater management and rainwater harvesting (Cobbing and Hiller, 2019; Taylor et  al., 2019). Alternative water supply options such as desalination, managed aquifer recharge, stormwater harvesting and re-use (direct and indirect, potable and non-potable), all require significant amounts of energy and are complex to operate and maintain. A failure to provide a source of reliable energy and the capacity to implement, maintain and operate these systems is a significant contributor to water scarcity risks in Africa (Muller and Wright, 2016). Soft adaptation options include increasing water use efficiency, changing agricultural practices, more appropriate water pricing (Olmstead, 2014) and enhancing capacity to tackle groundwater

overexploitation (Kuper et  al., 2016), among others (see Section 9.10.2.4 and Chapter 4 Sections 4.6 and 4.7).

Gender is important in building resilience and adaptation pathways to global environmental change (Ravera et al., 2016). It is well-established that women, in most societies, have accumulated considerable knowledge about water resources, including location, quality and storage methods because they are primarily responsible for the management of water for household water supply, sanitation and health, and for productive uses in subsistence agriculture (UN-Water, 2006). As gender-differentiated relationships are complex, adaptation should take into account intersectional differences such as homeownership, employment and age (Harris et al., 2016), educational, infrastructural and programmatic interventions (Pouramin et al., 2020), aspects of protection and safety (Mackinnon et al., 2019), barriers to adaptation and gendered differences in the choice of adaptation measures (Mersha and Van Laerhoven, 2016), the complex power dynamics of existing social and political relations (Djoudi

et al., 2016; Rao et al., 2017), and inclusion and empowerment of women in the management of environmental resources (Makina and Moyo, 2016). Incorporation of gender and water inequities into climate change adaptation would have a significant impact on achieving the SDGs (particularly 1, 3, 4, 5 and 6), while failure to incorporate gender will undermine adaptation efforts (Bunce and Ford, 2015; Fleifel et al., 2019; Pouramin et al., 2020).

Ideally, a systems approach (Ericksen, 2008; Rosenzweig et al., 2020) could be used to assess how global environmental changes affect the food sector in Africa, emphasising the complex interactions that exist within the components of the food supply system, including its enabling socioeconomic and biophysical environment (Ingram, 2011; Foran et  al., 2014; Tendall et  al., 2015), and how food is connected to other critical systems such as energy, water and transportation (Albrecht et al., 2018; see Box 9.5). Production will not be the only aspect of food security that is impacted by climate change. Processing, storage, distribution and consumption will also be affected. Access to healthy and adequate food in the face of climate change requires resilience across these components of the food system (Adenle et al., 2017). However, most studies on climate change impacts on food in Africa are heavily focused on production only. A significant knowledge gap, therefore, exists around the

9
1350Chapter 9 Africa
change will interact with broader components of African food systems, 
and strategies for making these systems more resilient, particularly 
in a context of rapid population growth and urbanisation across the 
continent (Adenle et al., 2017; Schmitt Olabisi et al., 2018).
9.8.1 Vulnerability to Observed and Projected Impacts 
from Climate Change
Agricultural activities are mainly rainfed and subsistence across Africa. 
The dominant farming system is mixed cereal–livestock (Thornton and 
Herrero, 2015; Nematchoua et al., 2019), with pastoral systems in east 
Africa, and commercial livestock and crop systems also representing a 
significant proportion of the food system in southern Africa (Thornton 
and Herrero, 2015). Many African regions are vulnerable to food 
insecurity, facing dwindling food production, food access, stocks and 
income due to low adaptive capacity (Evariste et al., 2018; Fuller et al., 
2018; Bang et al., 2019; Gebre and Rahut, 2021).

Across regions with food systems highly vulnerable to climate change, 
female farmers, cocoa farmers, pastoralists, plantain farmers, coastal 
zone communities, rural households and forest communities in central 
Africa indicate higher vulnerability (Chia et al., 2016; Schut et al., 2016; 
Nematchoua et  al., 2019). Their vulnerability is multi-dimensional 
and affected by low adaptive capacity, location, livelihood system, 
socioeconomic status, gender, age and ethnicity (Perez et  al., 2015; 
Weston et al., 2015; Gebre and Rahut, 2021; see also Box 9.1).
Across Africa, including west Africa, adverse climate conditions for 
agricultural and pastoral livelihoods have contributed to rural to urban 
migration patterns and migration among African regions (see Box 9.8; 
Baudoin et  al., 2014; Abbas, 2017; Gemenne and Blocher, 2017b). 
Rural to urban migration may increase vulnerability of migrants 
through exposure to additional risks, including food insecurity (Amadi

and Ogonor, 2015; Abbas, 2017). In general, west African countries 
are characterised by the poor adaptive capacity of rural households 
(Douxchamps et al., 2015; Dumenu and Obeng, 2016).
In north Africa, livelihoods and economies are strongly dependent 
on agriculture. Pressure on water demand due to climate change 
and variability is threatening income, development processes and 
food security in the region (high confidence) (Mohmmed et al., 2018; 
Khedr, 2019). Increased temperatures and droughts have enhanced the 
vulnerability of the irrigation sector (Verner et al., 2018; İlseven et al., 
2019), and the combined effect of these hazards negatively affects 
crop and animal production (Mohmmed et  al., 2018; Verner et  al., 
2018). For example, dairy farms in Tunisia are experiencing warmer 
temperatures above the thermoneutral zone of cows for more than 
5  months each year, reducing production efficiency and resulting in 
significant economic losses (Amamou et al., 2018).

Non-climatic stressors aggravate food insecurity in many parts of 
the continent, including lack of access to production inputs and land, 
lack of education and limited income sources, with adverse climate 
impacts on agriculture reducing education attainment for children 
(Section 9.11.1.2; Evariste et al., 2018; Fuller et al., 2018). Geographic 
and social isolation is another type of social vulnerability, especially for pastoralist communities in east and southern Africa (Sonwa et al., 
2017; Basupi et al., 2019). Rural communities often have poor transport 
networks, limited access to markets or information and fewer livelihood 
alternatives, and are less able to be informed of risks or be assisted in the 
event of extreme climate events (Sonwa et al., 2017; Basupi et al., 2019).
Extreme climate events have been key drivers in rising acute food 
insecurity and malnutrition of millions of people requiring humanitarian

assistance in Africa (high confidence). Between 2015 and 2019, an 
estimated 45.1  million people in the Horn of Africa and 62  million 
people in eastern and southern Africa required humanitarian assistance 
due to climate-related food emergencies. Children and pregnant women 
experience disproportionately greater adverse health and nutrition 
impacts (very high confidence) (Gebremeskel Haile et  al., 2019; see 
Chapter 7 Section 7.2.4).
Future climate warming is projected to have a substantial adverse 
impact on food security in Africa and is anticipated to coincide with low 
adaptive capacity as climate change intensifies other anthropogenic 
stressors, as 85% of Africa’s poor live in rural areas and mostly depend 
on agriculture for their livelihoods (Adams, 2018; Mahmood et  al., 
2019). This highlights the need to prioritise innovative measures for 
reducing vulnerabilities in African food systems (Fuller et  al., 2018; 
Mahmood et al., 2019).

Climate change impacts could increase the global number of people 
at risk of hunger in 2050 by 8 million under a scenario of sustainable 
development (SSP1) and 80  million under a scenario of reduced 
international cooperation and low environmental protection (SSP3), 
with populations concentrated in sub-Saharan Africa, south Asia and 
central America (see Chapter  5 Sections  5.2.2; 5.4.2; 5.4.3). Global 
climate impacts on food availability are expected to lead to higher food 
prices, increasing the risk of hunger for people in African countries, 
and slowing progress towards eradicating child undernutrition and 
malnutrition in all its forms (see Chapter 7 Section 7.4).
9.8.2 Observed Impacts and Projected Risks to Crops 
and Livestock
9.8.2.1 Observed Impacts and Projected Risks for Staple Crops
Climate change is already negatively impacting crop production and 
slowing productivity growth in Africa (high confidence) (Iizumi et al.,

2018; Ray et al., 2019; Sultan et al., 2019; Ortiz-Bobea et al., 2021). 
Climate change has reduced total agricultural productivity growth in 
Africa by 34% since 1961, more than in any other region (Ortiz-Bobea 
et al., 2021). Maize yields have decreased 5.8% and wheat yields 2.3%, 
on average, in sub-Saharan Africa due to climate change in the period 
1974–2008 (Ray et al., 2019). Overall, climate change has decreased 
total food calories across all crops in sub-Saharan Africa by 1.4% on 
average compared to a no climate change counterfactual since 1970, 
with up to 10% reductions in Ghana and Zimbabwe (Ray et al., 2019).
Farmers perceive a wide variety of climate threats to crop production 
including droughts, precipitation variability, a delayed onset and overall 
reductions in early growing season rainfall and excess heat (Rankoana,

9
1351Africa  Chapter 9
2016a; Elum et  al., 2017; Kichamu et  al., 2017; Alvar-Beltrán et  al., 
2020). Farmers attribute these perceived changes as a major driver 
of yield losses (Ayanlade and Jegede, 2016; see Section 9.4.5). Over 
half of surveyed farmers in west Africa perceive increases in crop pests 
and diseases as due to climate change as the range and seasonality of 
many pests and diseases change under warming (Callo-Concha, 2018). 
Pests and diseases contribute between 10–35% yield losses for wheat, 
rice, maize, potato and soybean in sub-Saharan Africa (Savary et al., 
2019). Recent locust outbreaks in 2019 in east Africa have been linked 
to climate conditions caused in part by ocean warming (Wang et al., 
2020b; see Box 5.8).
Future climate change may increase insect pest-driven losses in Africa 
for maize, rice and wheat. Compared to 1950–2000, losses may 
increase by up to 50% at 2°C of global warming (Deutsch et al., 2018).

However, many challenges remain in modelling pest and disease under 
climate change with additional research needed expanding the range 
of crops and diseases studied (Newbery et al., 2016).
Agriculture in Africa is especially vulnerable to future climate change 
in part because 90–95% of African food production is rainfed (Adams, 
2018). Maize, rice, wheat and soybean yields in tropical regions 
(20°S–20°N) are projected to decrease approximately 5% per degree 
Celsius of global warming in a multi-model ensemble (Rosenzweig 
et  al., 2014; Franke et  al., 2020). Dryland agricultural areas are 
especially sensitive to changes in rainfall. For example, without 
adaptation, substantial yield declines are projected for staple crops 
in north Africa. A recent meta-analysis of 56  studies indicates that, 
compared to 1995–2005, economic welfare in the agriculture sector 
in north Africa is projected to decline 5% for 2°C global warming and

20% for 3°C global warming, and in sub-Saharan Africa by 5% (2°C) 
and 10% (3°C) (Moore et  al., 2017a), both more pessimistic than 
previous economic estimates.
A synthesis of projected staple crop impacts across 35  studies for 
nearly 1040 locations and cases shows, on average, decreases in 
crop yields with increasing global warming across staple crops in 
Africa, including when accounting for CO 2 increases and adaptation 
measures. For example, for maize in west Africa, compared to 2005 
yield levels, median projected yields decrease 9% at 1.5°C global 
warming and 41% at 4°C, without adaptation (Figure 9.22). However, 
uncertainties in projected impacts across crops and regions are driven 
by uncertainties in crop responses to increasing CO 2 and adaptation 
response, especially for maize in east Africa and wheat in north Africa 
and east Africa (Figure 9.22; Hasegawa et al., 2021).
There is also growing evidence that climate change is likely beginning

to outpace adaptation in agricultural systems in parts of Africa (Rippke 
et al., 2016). For example, despite the use of adjusted sowing dates and 
existing heat-tolerant varieties, Sudan’s domestic production share of 
wheat may decrease from 16.0% to 4.5–12.2% by 2050 under RCP8.5 
(2.4°C global warming) (Iizumi et al., 2021).
Elevated CO 2 concentrations in the atmosphere might mitigate some or 
all climate-driven losses (Swann et al., 2016; Durand et al., 2018), but 
there is considerable uncertainty around the CO 2 response (Deryng et al., 
2016; Toreti et al., 2020), especially when nutrients such as nitrogen and phosphorus are limiting crop growth. Additional Free-Air Carbon dioxide 
Enrichment (FACE) experiments are needed in the tropics, particularly 
on the African continent, to better understand the impacts of increased 
CO 2 concentrations on the productivity of crops and cultivars grown in 
Africa under additional temperature impacts and water and nutrient

limitations (Ainsworth and Long, 2021). Warming and elevated CO 2 
may also change the nutritional content of some crops. By 2050 under 
RCP8.5 (2.4°C global warming), overall wheat yields and grain protein 
content may decrease by 10% and 15%, respectively, in north and east 
Africa, and by over 15% in southern Africa (Asseng et al., 2019). See 
Chapter 5 for more details on CO 2 impacts and uncertainties.
9.8.2.2 Observed Impacts and Projected Risks on Regional Cash 
Crops and Food Crops
Few studies have attributed changes in yields of cash crops and other 
regionally important food crops in Africa to human-caused climate 
change, but recent research suggests yields of cash crops in Africa 
have already been impacted by climate change, in both a negative 
and positive manner (Falco et al., 2012; Traore et al., 2013; Ray et al., 
2019). For example, between the period 1974–2008, sugarcane yields 
decreased on average by 3.9% and 5.1% in sub-Saharan Africa and

north Africa, respectively, due to climate change, while sorghum yields 
increased 0.7%, and cassava yield increased 1.7% in sub-Saharan 
Africa and 18% in north Africa (Ray et al., 2019).
There are also limited studies assessing projected climate change impacts 
on important cash crops and food crops other than maize, rice and wheat 
(Jarvis et al., 2012; Schroth et al., 2016; Awoye et al., 2017). These studies 
often represent changes at specific sites in a country or assess changes 
in the yield and/or suitability for cultivating a specific crop across a larger 
geographic area. Climate change is projected to have overall positive 
impacts on sugarcane and Bambara nuts in southern Africa, oil palm in 
Nigeria and chickpea in Ethiopia (low confidence) (Figure 9.23).
Climate change is projected to reduce sorghum yields in west Africa 
(Figure  9.23). For example, across the west African Sahel savanna 
sorghum yields are projected to decline on average 2% at 1.5°C and 5%

at 2°C global warming (Faye et al., 2018). For coffee and tea in eastern 
Africa, olives in Algeria and sunflower in Botswana and Morocco, 
studies indicate mostly negative impacts on production systems. 
For example, in Kenya, compared to 2000, optimal habitat for tea 
production is projected to decrease in area by 27% with yields declining 
10% for global warming of 1.8–1.9°C, although yield declines may be 
reduced at higher levels of warming (Beringer et al., 2020; Jayasinghe 
and Kumar, 2020; Rigden et al., 2020). Suitable area for tea production 
may reduce by half in Uganda (Eitzinger et al., 2011; Läderach et al., 
2013). In east Africa, the coffee-growing area is projected to shift up 
in elevation with suitability decreasing 10–30% between 1.5–2°C of 
global warming (Bunn et al., 2015; Ovalle-Rivera et al., 2015).
For all other crops, there is at least one study that finds low to highly 
negative impacts for one or several warming levels (Figure  9.23).

Mixed results on the direction of change often occur when several 
contrasting sites with varying baseline climates are studied, and when 
a study considers the full range of climate scenarios. For example, 
there are mixed results on the direction of change for impacts of 1.5°C

9
1352Chapter 9 Africa
global warming on cassava, cotton, cocoa and millet in west Africa 
(low confidence) (Figure 9.23). In general, there is limited evidence in 
the direction of change, due to single studies being available for most 
crop-country combinations (Knox et al., 2010; Chemura et al., 2013; Asaminew et  al., 2017; Bouregaa, 2019). Occasionally, two studies 
agree on the direction and magnitude of change, for example, for 
potatoes in east Africa, yields are projected to decrease by 11–17% 
with 3°C of warming (Fleisher et al., 2010; Tatsumi et al., 2011).
Projected yield changes for major crops in Africa due to climate change
Compared to 2005 yield levels
Maize Wheat Rice
Yield impact (%)  
Southern AfricaNorthern Africa
Yield impact (%)-100-50050
Yield impact (%)Without
adaptationWith
>1.5 >2.0 >3.0 >4.0Global warming level

>1.5 >2.0 >3.0 >4.0
Figure 9.22 |  Projected yield changes for major staple crops in Africa due to climate change (compared to 2005 yield levels). Projected impacts are grouped 
by projected global warming levels. Boxplots show a synthesis of projected staple crop impacts, with and without adaptation measures (e.g., planting date, cultivar, tillage or 
irrigation). On average crop yields are projected to decrease with increasing global warming across staple crops in Africa. The overall adaptation potential to offset yield losses 
across Africa for rice, maize and wheat reduces with increasing global warming. On average, in projections including adaptation options, yield losses in the median case are reduced 
from −33% to −10% of 2005 levels at 2°C of global warming and from −46% to −23% at 4°C. Global warming levels were calculated using a baseline for pre-industrial global

mean temperature of 1850–1900  . Data are a synthesis across 35 studies for nearly 1040 locations and cases of projected impacts for regions of Africa for maize, rice and wheat 
(Hasegawa et al., 2021; Table SM9.5).

9
1353Africa  Chapter 9
9.8.2.3 Observed Impacts and Projected Risks for Wild-
Harvested Food
Wild-harvested foods (e.g., fruits, vegetables and insects) provide 
dietary diversification and for many people in Africa, wild-harvested 
food plants may provide a livelihood and/or nutritional safety net 
when other sources of food fail, such as during drought (Sunderland et al., 2013; Shumsky et al., 2014; Wunder et al., 2014; Baudron et al., 
2019b). In Zimbabwe, during lean times, consumption of wild fruits 
increases, as does their sale to generate income for additional food 
expenses in poor, rural households (Mithöfer and Waibel, 2004). In Mali, 
Tanzania and Zambia, household surveys indicate that forest products 
including wild foods can play an important role in reducing household 
vulnerability to climate shocks by providing alternative sources of food 
Projected risks at increasing global warming levels for regionally important cash and food crops in Africa
Cassava
Sugarcane

Cotton
Oil Palm
Tobacco
Cocoa
Coffee
Tea
Groundnut 
Bambara nut
Chickpea
Olive
Millet 
Sorguhm
Potato
Sunflower
CowpeaEast Africa
West Africa
Central Africa
Southern Africa
Sahel
Southern Africa*
West Africa (Benin and Cameroon)
East Africa (Ethiopia)
North AFrica (Sudan)
West Africa (Nigeria)
Southern Africa (Zimbabwe)
East Africa (Kenya and Uganda)
West Africa (Benin)
North Africa (Sudan)
North Africa (Algeria)
Africa
Sahel
Southern Africa (Botswana)
North Africa (Morocco)
West Africa (Benin)>3.0°C Crop Region (country) >2.0°C >1.5°C >4.0°C
= insufficient data (empty)
*Southern Africa (South Africa and Swaziland)
**West Africa (Ghana and Côte d’Ivoire)A = Late planting can reduce the impact of climate change.

B= Crop modelling suggests that shifts in sowing date and 
fertilizer rate can be effective in reducing negative 
impacts on soghum yield in Southern Africa.Adaptation
options
A
B= negligible//
/
-
++
/
/---
--
--/+
+
West Africa**Benificial
outcome
Detrimental
outcomeMagnitude of
projected outcome% yield 
change
(biomass,
sucrose) % change
in current
real GDP
(due cost of
inaction on
adaptation)% change
in climate
suitability
(area)
Very positive >40% >4% >40%
Highly positive >20% >2% >20%
Moderately positive >10% >1% >10%
Low positive >5% + >0.5% >5%
>20% >2% >20% Highly negative
>40% >4% >40% Very negative>10% >1% >10% Moderately negative>5% >0.5% >5% Low negative-Projected changes
per global warming level
Level of confidence
High
LowMedium
Figure 9.23 |  Projected risks at increasing global warming levels for regionally important cash and food crops in Africa. Insufficient data indicates there were

limited to no published studies that have quantified projected climate change impacts or adaptation options for specific crops under different warming levels (see Table SM9.6). 
Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900  .

9
1354Chapter 9 Africa
and income during droughts and floods (Robledo et al., 2012). In the 
parklands of west Africa, wild trees are a significant source of wild 
foods and are thus a place where one might expect wild plant foods 
to make an important contribution to diets and nutrition (Boedecker 
et al., 2014; Leßmeister et al., 2015). Non-timber forest products are 
consumed by an estimated 43% of all households in Burkina Faso (FAO, 
2019), and wild vegetables accounted for about 50% of total vegetable 
consumption in southeastern Burkina Faso (Mertz et al., 2001).
The focus of projected climate change impacts has been almost 
exclusively on agricultural production, yet climate change could 
have substantial impacts on the distribution and availability of wild-
harvested food plants in Africa (Wessels et al., 2021). Non-cultivated 
species in Africa are vulnerable to current and future climate changes, 
with widespread changes in woody plant cover already observed

(see Section  9.6.1.1). Evidence about the impacts of climate change 
on individual wild food species is less consistent. Communities in the 
Kalahari (Crate and Nuttall, 2016) and Zimbabwe (Sango and Godwell, 
2015) report growing scarcity of wild foods (such as wild meat and fruit) 
perceived to be, at least in part, due to drought and climate change. 
Shea tree (Vitellaria paradoxa) nuts provide fats and oils for the diets of 
many rural populations in west Africa. In Burkina Faso, global warming 
of 3°C is projected to reduce area of suitable habitat for the shea tree 
by 14% (Dimobe et al., 2020). In southern Africa, 40% of native, wild-
harvested food plant species are projected to decrease in geographic 
range extent at 1.7°C global warming with range reductions for 66% of 
species projected for 3.5°C (Wessels et al., 2021).
9.8.2.4 Observed Impacts and Projected Risks on Livestock
Livestock systems in Africa are already being affected by changes in climate

through increased precipitation variability leading to decreasing fodder 
availability (Sloat et al., 2018; Stanimirova et al., 2019). More than twice 
as many countries in Africa have experienced increases in precipitation 
variability in the last century than decreases (Sloat et al., 2018). Fodder 
availability is also being impacted by woody plant encroachment—
the increase in shrub and tree cover—which has increased by 10% 
on subsistence grazing lands and 20% on economically important 
grazing lands in south Africa in the last 60 years (Stevens et al., 2016), 
and is driven in part by climatic factors (see Section 9.6.1.1). Increased 
temperature and precipitation have contributed to the expanding range, 
especially in east and southern Africa, of several ixodid tick species which 
carry economically important livestock diseases (Nyangiwe et al., 2018).
Pastoralists in Africa perceive the climate as already changing and

report more erratic and reduced rainfall, prolonged and more frequent 
droughts and a rise in temperature (Sanogo et al., 2017; Kimaro et al., 
2018). They also report reduced milk production, increased deaths and 
disease outbreaks in their herds due to malnutrition and starvation 
resulting from the shortages in forage and water (Kimaro et al., 2018). 
Additional research is required to attribute precipitation variability to 
human-induced climate change (see Section 9.5), and to evaluate the 
relative contributions of climate change and management to disease 
vector extent.
Future climate change will have compounding impacts on livestock, 
including negative impacts on fodder availability and quality, availability of drinking water, direct heat stress and the prevalence of livestock 
diseases (Nardone et al., 2010; Rojas-Downing et al., 2017; Godde et al., 
2021). Climate change is projected to negatively affect fodder availability

(Briske, 2017) because overall rangeland net primary productivity (NPP) 
by 2050 is projected to decrease 42% under RCP4.5 (2°C global warming) 
and 46% under RCP8.5 (2.4°C global warming) for western sub-Saharan 
Africa, compared to a 2000 baseline (Boone et al., 2018). NPP is also 
projected to decline by 37% in southern Africa, 32% in north Africa 
and 5% in both east Africa and central Africa by 2050 under RCP8.5 
(2.4°C global warming) (Boone et al., 2018). For example, in Zimbabwe 
by 2040–2070, net revenues from livestock production, compared to a 
2011 survey, are projected to decline by 8–32% under RCP4.5 for 2°C 
and 11–43% under RCP8.5 for 2.7°C global warming due to a decline in 
fodder availability (Descheemaeker et al., 2018). The available literature 
does not comprehensively capture the economic implications of climate-
related impacts on livestock production across Africa.
Fodder quality, critical for animal health and weight gain, is at risk

from climate change as increases in temperature, elevated CO 2 and 
water stress have been shown to reduce dry matter digestibility and 
nitrogen content for C 3 grasses (Augustine et  al., 2018), tropical C 4 
grasses (Habermann et al., 2019) and fodder crops such as Lucerne/
Alfalfa (Polley et al., 2013; Thivierge et al., 2016).
Climate change is projected to threaten water availability for 
livestock. Droughts in Africa have become more intense, frequent and 
widespread in the last 50 years (Masih et al., 2014), and progressive 
increase in droughts between 3- and 20-fold under climate change 
up to 3°C of warming are projected for most of Africa (Section 9.5). 
In the Klela basin in Mali by 2050, groundwater recharge is projected 
to decline by 49% and groundwater storage by 24% under RCP8.5 
(2.4°C global warming) compared to the 2006 baseline (Toure et al., 
2017). Water availability for livestock during drought is a major

concern for many African pastoralists including but not limited to those 
in Zimbabwe (Dzavo et al., 2019) and Nigeria (Ayanlade and Ojebisi, 
2019). Increased livestock mortality and livestock price shocks have 
been associated with droughts in Africa, as well as being a potential 
pathway for climate-related conflict (Catley et al., 2014; see Box 9.9; 
Maystadt and Ecker, 2014).
Heat stress may already be the largest factor impacting livestock 
production in many regions in Africa (El-Tarabany et al., 2017; Pragna 
et al., 2018), as the combination of high temperatures and high relative 
humidity can be dangerous for livestock and has already decreased 
dairy production in Tunisia (Amamou et al., 2018). Climate change is 
projected to increase heat stress for all types of livestock, especially in 
the tropics (Figure 9.24; Lallo et al., 2018). More studies quantifying 
the impact of heat stress on other types of livestock production loss 
are needed in Africa (Rahimi et al., 2021).

Climate change will impact livestock disease prevalence primarily 
through changes in vector dynamics or range (Abdela and Jilo, 
2016; Semenza and Suk, 2018). African Rift Valley Fever (RVF) and 
trypanosomiasis are positively associated with extreme climate events 
(droughts and ENSO) (Bett et al., 2017) and are projected to expand in 
range under climate change (Kimaro et al., 2017; Mweya et al., 2017). 
More quantitative estimates of projected risk from diseases are needed.

9
1355Africa  Chapter 9
Severe heat stress duration for cattle in Africa is projected to increase with increased global warming
(a) Historical risk (1985–2014) (b) Historical exposure (1985–2014)
(c) Global warming 1.5°C (d) Global warming 3.75°C Annual number
of days over
threshold
300
240
180
120
60
0
Increase in
annual number
threshold
300
240
180
120
60
0
More
cattle
More severe heat
Figure 9.24 |  Severe heat stress duration for cattle in Africa is projected to increase with increasing global warming. 
(a) Number of days per year with severe heat stress in the historical climate (1985–2014).
(b) Historical cattle exposure to severe heat. Cattle density data from Gilbert et al. (2018).
(c, d) Projected increase in the number of days per year with severe heat stress for a global warming level of 1.5°C and 3.75°C. Severe heat stress for cattle is projected to become

much more extensive in the future in Africa at increased global warming levels. Strong mitigation would substantially limit the spatial extent and the duration of cattle heat stress 
across Africa. Heat stress is estimated using the Temperature Humidity Index with a value greater than 79 considered the onset of severe heat stress (Livestock Weather Safety Index) 
(Lallo et al., 2018). Global warming of 1.5°C used scenario SSP1–2.6 and global warming of 3.75°C used SSP5-8.5, both for 2070–2099 (12 climate models from O’Neill et al., 
2016; Tebaldi et al., 2021). Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900  .

9
1356Chapter 9 Africa
9.8.3 Adapting to Climate Variability and Change in 
Agricultural and livelihood diversification are strategies used by 
African households to cope with climate change, enabling them to 
spread risks and adjust to shifting climate conditions (Thierfelder et al., 
2017; Thornton et al., 2018). This includes adjusting cropping choices, 
planting times, or size, type and location of planted areas (Altieri et al., 
2015; Nyagumbo et  al., 2017; Dayamba et  al., 2018). In southern 
Africa, changes in planting dates provide farmers with greater yield 
stability in uncertain climate conditions (Nyagumbo et  al., 2017). 
In Ghana, farmers are changing planting schedules and using early 
maturing varieties to cope with late-onset and early cessation of the 
rainy season (Antwi-Agyei et al., 2014; Bawakyillenuo et al., 2016).
The use of drought-tolerant crop varieties is another adaptation 
available to African farmers (Hove and Gweme, 2018; Choko et  al.,

2019). Adoption, however, is hindered by lack of information and 
training, availability or affordability of seed, inadequate labelling and 
packaging size for seed supplies and financial constraints (Fisher et al., 
2015). Moreover, drought-tolerant varieties do not address changing 
temperature regimes (Guan et al., 2017).
Crop diversification enhances crop productivity and resilience and 
reduces vulnerability in smallholder farming systems (McCord et al., 
2015; Mulwa and Visser, 2020). In Tanzania, diversified crop portfolios 
are associated with greater food security and dietary quality (Brüssow 
et al., 2017). In Kenya, levels of crop diversity are higher in villages 
affected by frequent droughts, which are the main cause of crop failure 
(Bozzola and Smale, 2020). Crop diversification also helps control 
pest outbreaks, which may become more frequent and severe under 
increased climate variability and extreme events (Schroth and Ruf,

2014). High farming diversity enables households to better meet food 
needs, but only up to a certain level of diversity (Waha et al., 2018), 
and the viability of and benefits from mixed farming are highly context 
dependent (Thornton and Herrero, 2015; Weindl et al., 2015).
Agroecological and conservation agriculture practices, such as 
intercropping, integration of legumes, mulching and incorporation of 
crop residues, are associated with household food security and improved 
health status (Nyantakyi-Frimpong et al., 2017; Shikuku et al., 2017). 
These practices can enhance the benefits of other adaptations, such as 
planting drought- and heat-tolerant or improved varieties, although 
effects vary across soil types, geographical zones and social groups 
(Makate et al., 2019; Mutenje et al., 2019). Non-climatic variables, such 
as financial resources, access to information and technology, level of 
education, land security and gender dynamics affect feasibility and

adoption (Makate et al., 2019; Mutenje et al., 2019).
To mitigate growing water stress, countries like Ethiopia, Rwanda, 
Tanzania and Uganda are striving to improve irrigation efficiency 
(McCarl et al., 2015; Connolly-Boutin and Smit, 2016; Herrero et al., 
2016). The feasibility and effectiveness of this adaptation depend 
on biophysical and socioeconomic conditions (Amamou et  al., 
2018; Harmanny and Malek, 2019; Schilling et  al., 2020). Irrigation 
is unaffordable for many smallholder farmers and only covers a 
negligible proportion of the total cultivated area. Nonetheless, in some regions of west Africa, small-scale irrigation, including the digging 
of ditches, holes and depressions to collect rainwater (Makondo and 
Thomas, 2018), is widely adopted and promoted to support national 
food security (Dowd-Uribe et al., 2018).
African farmers are also diversifying their income sources to offset 
reduced yields or crop losses by shifting labour resources to off-farm work,

or by migrating seasonally or longer term (Kangalawe et al., 2017; Hove 
and Gweme, 2018). Off-farm activities provide financial resources that 
rural households need to cope with extreme climate variability (Hamed 
et al., 2018; Rouabhi et al., 2019). However, in some cases, these off-farm 
activities can be maladaptive at larger scales, such as when households 
turn to charcoal production, which contributes to deforestation (Egeru, 
2016). Whether off-farm activities constitute maladaptation depends on 
whether resources are available to upgrade skills or support investments 
that make a new business more lucrative. Without such resources, this 
option may lead to impoverishment (see Box 5.8).
Smallholder farmers’ responses tend to address short-term shocks 
or stresses by deploying coping responses (e.g., selling labour, 
reducing consumption and temporary migration), rather than longer-
term sustainable adaptations (Ziervogel and Parnell, 2014; Jiri et al.,

2017). This is partly due to institutional barriers (e.g., markets, credit, 
infrastructure and information) and resource requirements that are 
unaffordable to smallholder farmers (Pauline et al., 2017). There is a 
need for policies that strengthen natural, financial, human and social 
capitals, the latter being key to household and community resilience, 
especially where government services may be limited (Mutabazi et al., 
2015; Alemayehu and Bewket, 2017). There is evidence that collective 
action, local organisations and climate information are associated with 
higher food security, and that institutional interventions are needed to 
ensure scaling up of adaptations (Thornton et al., 2018).
A range of options is considered potentially effective in reducing future 
climate change risk, including plant breeding, crop diversification 
alongside livestock, mixed planting, intercrops, crop rotation and 
integrated crop–livestock systems (see Chapter 5 Sections 5.4.4; 5.14.1;

Thornton and Herrero, 2014; Cunningham et al., 2015; Himanen et al., 
2016; Farrell et al., 2018; Snowdon et al., 2021). However, adaptation 
limits for crops in Africa are increasingly reached for global warming 
above 2°C (high confidence), and in tropical Africa may already be 
reached at current levels of global warming (low confidence).
Global warming beyond 2°C will place nearly all of sub-Saharan 
Africa cropland substantially outside of its historical safe climate 
zone (Kummu et al., 2021) and may exponentially increase the cost of 
adaptation and residual damage for major crops (Iizumi et al., 2020). 
Without accounting for CO 2 increases, global-scale studies employing 
ensembles of gridded crop models for 2°C of global warming find 
that for adaptation using genetic cultivar change in most of Africa net 
losses are projected, even with adaptation up to 2°C of global warming 
for rice, maize, soybean and wheat (Minoli et  al., 2019; Zabel et  al.,

2021), although model uncertainty is still high (Müller et al., 2021). In 
contrast, when accounting for CO 2 increases, applying new genetics for 
rice under warming is projected to fully counteract all climate change-
induced losses in Africa up to 3.5°C of global warming, except in west 
Africa (van Oort and Zwart, 2018).

9
1357Africa  Chapter 9
However, compared to temperate regions, risks of adaptation shortfalls—
that is climate change impacts even after adaptation—are generally 
greater for current agricultural conditions across much of Africa (tropical, 
arid and semi-arid) (Sun et al., 2019). The overall adaptation potential 
to offset yield losses across Africa for rice, maize and wheat reduces 
with increasing global warming. On average, in projections including 
adaptation options, yield losses, in the median case, are reduced from 
−33% to −10% of 2005 levels at 2°C of global warming and from −46% 
to −23% at 4°C, but estimates vary widely (Figure 9.22; Hasegawa et al., 
2021). Across Africa, the risks of no available genetic varieties of maize 
for growing season adaptation are higher for east Africa and southern 
Africa than for central or west Africa (Zabel et al., 2021). To keep pace 
with expected rates of climate change, crop breeding, development

and adoption must accelerate to meet the challenge (Challinor et  al., 
2016). Regional modelling has shown very little efficacy for late sowing, 
intensification of seeding density and fertilizers, water harvesting and 
other measures for cereals in west Africa at 2°C of global warming 
(Sultan and Gaetani, 2016; Guan et al., 2017). Historical climate change 
adaptation by crop migration has been shown in some cases (Sloat et al., 
2020) but poses risks to biodiversity and water resources, and this option 
may be limited for maize in Africa by suitable climate shifting completely 
across national borders and available land at the edges of the continent 
(Franke et al., 2021). More research is required to evaluate the potential 
effectiveness and limits of adaptation options in African agriculture under 
future climate change (see Chapter 5 Section 5.4.4 for more details).
9.8.4 Climate Information Services and Insurance for 
Agriculture Adaptation

In addition to adaptation in crop, soil and water management, the 
combination of (a) Climate Information Services, (b) institutional 
capacity building and (c) strategic financial investment can help 
African food producers adapt to projected climate risks (Carter et al., 
2015; Surminski et  al., 2016; Scott et  al., 2017; Cinner et  al., 2018; 
Diouf et al., 2019; Hansen et al., 2019a). There is growing evidence 
of farmers’ use of weather and climate information, especially at the 
short- and medium-time horizon (Carr et al., 2016; Singh et al., 2018). 
Digital services can contribute to the sustainable intensification of food 
production globally (Duncombe, 2018; Klerkx et al., 2019). This points 
to the need for the scientific and development communities to better 
understand the conditions that enable widespread adoption in Africa.
Although climate information services have the potential to strengthen

farmers’ resilience, barriers to accessibility, affordability and utilisation 
remain (Krell et  al., 2021). Often the information offered is not 
consistent with what farmers need to know and how they access 
and process information (Meadow et  al., 2015; Singh et  al., 2018). 
Production of salient and credible climate information is hindered 
by the limited availability of and access to weather and climate data 
(Coulibaly et  al., 2017; Hansen et  al., 2019a). The existing weather 
infrastructure remains suboptimal to enable the development of 
reliable early warning systems (Africa Adaptation Initiative, 2018; Krell 
et al., 2021). Of the 1017 land-based observational networks in the 
world, only 10% are in Africa, and 54% of Africa’s surface weather 
stations cannot capture data accurately (Africa Adaptation Initiative, 
2018; World Bank, 2020d).Advances in remote sensing and climate analysis tools have allowed 
the development of weather index insurance products as a potential

adaptation option, with Malawi and Ethiopia being early testbeds 
(Tadesse et al., 2015, Section 9.11.4). These pilot projects were initially 
sponsored by NGOs, but in the last decade, the private sector has 
become more active in this sector. The Ghana Agricultural Insurance 
Pool and Agriculture and Climate Risk Enterprise (ACRE) in Kenya, 
Tanzania and Rwanda are examples. Despite the potential for weather 
index insurance, uptake by smallholder farmers in Africa remains 
constrained by several factors. These include the failure to capture 
actual crop loss as in traditional crop insurance products, as well 
as the inability of poor farmers to pay premiums (Elum et al., 2017; 
Weber, 2019). Weather index insurance could be part of a wider 
portfolio of risk mitigation services offered to farmers (Tadesse et al., 
2015; Weber, 2019). Strategic partnerships between key players (e.g., 
credit institutions, policymakers, meteorologists, farmer associations,

extension services, NGOs) are needed to develop better products and 
build capacity among smallholder farmers to engage more beneficially 
with weather index insurance (Singh et al., 2018; Tesfaye et al., 2019).
9.8.5 Marine and Inland Fisheries
9.8.5.1 Observed Impacts of Climate Variability and Change on 
Marine and Inland Fisheries
Marine and freshwater fisheries provide 19.3% of animal protein intake 
(Chan et al., 2019) and support the livelihoods of 12.3 million people 
(de Graaf and Garibaldi, 2015) across Africa. Estimates suggest that fish 
provides approximately 200 million people in Africa with their main 
source of animal protein and key micronutrients (Obiero et al., 2019). 
Although marine fisheries account for >50% of total capture fishery 
production (Obiero et al., 2019), 2.9 million tonnes of fish are harvested 
annually from inland water bodies constituting the highest per capita 
inland fishery production of any continent (2.56 kg per person per year)

(Harrod et al., 2018a; Funge-Smith and Bennett, 2019).
Climate change already poses a significant threat to marine and 
freshwater fisheries and aquaculture in Africa (Blasiak et  al., 2017; 
Harrod et  al., 2018a). Severe (>30%) coral bleaching has impacted 
~80% of major reef areas in the western Indian Ocean and Red Sea 
along Africa’s eastern coast (Hughes et  al., 2018). Biological effects 
(e.g., changes in primary production, fish distribution) have also 
occurred (Hidalgo et  al., 2018). Range shifts in marine fish species 
can exacerbate boundary conflicts among fisher communities (Penney 
et al., 2017; Belhabib et al., 2019). Changes in fish distribution and 
reductions in catch across inland fisheries are associated with climatic 
variability by fishing communities (Okpara et al., 2017b; Lowe et al., 
2019; Muringai et al., 2019b). Floods and reduced river flow reduces 
fish catches (Kolding et al., 2019), which scale positively with discharge

rates in rivers across Africa (McIntyre et al., 2016). Warming air and 
water temperatures have altered water stratification patterns in African 
lakes causing reductions in or redistributions of primary productivity 
and leading to reduced fish biomass (Section 9.6.1.3). Such changes, 
partially explain reduced fish catches in Lake Tanganyika (Cohen et al., 
2016). In some regions, water scarcity has resulted in conflict within 
and among food production sectors (pastoralists, fishers and farmers)

9
1358Chapter 9 Africa
in this region (Okpara et al., 2017b). Small-scale and artisanal fisher 
communities are ill-equipped to adapt to climate impacts because 
there are few financially accessible alternative livelihoods (Belhabib 
et al., 2016; Ndhlovu and Saito, 2017).
9.8.5.2 Projected Risks of Climate Change to Fisheries
At 4.3°C global warming, maximum catch potential (MCP) from marine 
fisheries in African Exclusive Economic Zones (EEZs) would decrease by 
12–69% by the end of the 21st century relative to recent decades (1986–
2005), whereas global warming of 1.6°C would limit the MCP decrease to 
3–41% (Cheung William et al., 2016; IPCC, 2019c). By mid-century under 
2°C global warming, MCP would decrease by 10 to >30% on the western 
coast of South Africa, the Horn of Africa and west Africa, indicating these 
regions could be at risk to declines in MCP earlier in the century than 
other parts of Africa (Cheung et al., 2016). Declining fish harvests due to

sea temperature rise could leave 1.2–70 (median 11.1) million people in 
Africa vulnerable to deficiencies in iron, and up to 188 million to vitamin A and 285 million to vitamin B 12 and omega-3 fatty acids by mid-century 
under 1.7°C global warming (Golden et al., 2016). Maire et al. (2021) 
assessed the nutritional vulnerabilities of African countries to climate 
change and overfishing, and found that the four most vulnerable countries 
ranked on a scale from 0 (low vulnerability) to 100 (high vulnerability) 
were Mozambique (87), Madagascar (76), Tanzania (61) and Sierra Leone 
(58). Coral reef habitat in east Africa is projected to decrease, resulting 
in negative impacts on demersal fish stocks and invertebrates (Hoegh-
Guldberg et al., 2018). Central, west and east Africa are projected to be at 
the greatest nutritional risk from sea temperature rise, leading to reduced 
catch in coastal waters (Figure 9.25; Golden et al., 2016). In north Africa,

a rise in water temperatures is expected to impact the phenology and 
migratory patterns of large pelagic species (e.g., bluefin tuna, Thunnus 
thynnus) (Hidalgo et  al., 2018). Increased sea surface temperatures 
have been associated with increases in spring and summer upwelling 
intensity reducing the abundance and larval survival of small pelagic 
fishes and shellfish in west Africa (Bakun et al., 2015; Tiedemann et al., 
2017; Atindana et al., 2020). Ocean warming, acidification and hypoxia 
(a)Climate change risk to marine fisheries in Africa
Countries with 
high overlap of 
dependence and future threat
to fisheries from climate changeGlobal
warming
1.6°C
Present
Global
warming
>4°C
Dependence on
marine foods
for nutrition>23%
18–23%
7–17%
3–7%
<3%
No data> 13%
Projected decrease in
maximum catch potential (MCP)
of marine fisheriesNo data13–27%51–60%
41–50%
28–40%(b)
(c)(d)
(e)
60–70%

Figure 9.25 |  Climate change increases risks to the catch potential and nutrition from marine fisheries. 
(a) The percentage of animal source foods consumed that originate from a marine environment. Countries with higher dependence are indicated by darker shades of green (Golden 
(b–c) Projected percentage change in maximum catch potential of marine fisheries compared to the recent past (1986–2005) under 1.6°C global warming and >4°C global 
warming by end of 21st century (2081–2100) in countries’ Exclusive Economic Zones (EEZs) (Cheung William et al., 2016). Darker red indicates greater percentage reduction 
(negative values).
(d–e) Countries (in purple) that have overlap between high nutritional dependence on marine fisheries and high risk of reduction in maximum catch potential under the two global 
warming scenarios. Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900  .

9
1359Africa  Chapter 9
are predicted to affect the early life history stages of several marine 
food species, including fish and crustaceans (Kifani et al., 2018). Climate 
warming is projected to impact water temperature and horizontal and 
vertical mixing on the southern Benguela ecosystem, with marked 
negative effects on the biomass of several important fishery resources by 
2050 amplified under 2.5°C compared to 1.7°C global warming (Ortega-
Cisneros et al., 2018).
For inland fisheries, 55–68% of commercially harvested fish species will 
be vulnerable to extinction under 2.5°C global warming by the end of 
the 21st century (2071–2100) compared to 77–97% under 4.4°C global 
warming (Figure 9.26). This will increase the number of countries that 
are at food security risk due to fishery species declines from 10 to 13 
(Figure 9.26). Other recent analyses suggest that African countries with 
the highest inland fisheries production have low- to mid-range projected

climate risk (2.4°C–2.6°C local temperature increase compared to other regions with 2.7°C–3.3°C increase by end of century) based on a 
3.9°C global warming scenario (Harrod et al., 2018b). In regions where 
inland fishery production is derived primarily from lakes, there is a lower 
likelihood of reduced catch, especially where precipitation is projected 
to increase (e.g., African Great Lakes region) (Harrod et  al., 2018b). 
Regions reliant on rivers and floodplains (e.g., Zambezi and Niger 
basins) are more likely to experience downturns in catch, as hydrological 
dynamics may be altered (Harrod et al., 2018b). Projections suggest that 
opportunistic species that do well in modified systems (Escalera-vázquez 
et al., 2017) and small pelagic fishes will remain important components 
of inland fishery food systems (Kolding et al., 2016; Gownaris et al., 2018; 
2019). Climate adaptation responses that rely on freshwater resources

(e.g., hydroelectric power generation, agricultural irrigation) represent 
threats to inland fisheries (Cowx et al., 2018; Harrod et al., 2018c), by 
changing flow regimes, reducing water levels, and increasing runoff of 
pesticides and nutrients (Harrod et al., 2018c).
Climate change risk to freshwater fisheries in Africa
warming
2.4°C
<0.3
No data
Average number of
climate-change vulnerable, 
commercially harvested, freshwater fishes species>11.2
7.8–11.2
3.3–7.8
0.3–3.2
Index of current
dependence on 
inland fisheries<0.04 (Low)0.05–0.210.26–0.550.57–1.05>1.05  (High)
No dataGlobal
warming
>4°CPresent(a) (b)
(c)(d)
(e)
Figure 9.26 |  Climate change risk to freshwater fisheries. 
(a) Countries’ dependence on inland fisheries for nutrition; darker green shows higher dependence on inland fisheries.

(b–c) Projected numbers of freshwater fishery species vulnerable to climate change within freshwater ecoregions under >2°C global warming and >4°C global warming estimated 
by the end of the 21st century (2071 to 2100). Numbers of vulnerable fish species translate to an average of 55–68% vulnerable at >2°C and 77–97% vulnerable at >4°C global 
warming. Darker reds indicate higher concentrations of vulnerable fish species.
(d–e) Countries (in purple) that have an overlap between high dependence on freshwater fish and high concentrations of fishery species that are vulnerable to climate change 
under two warming scenarios. Countries’ dependence on inland fisheries for nutrition was estimated by catch (total, tonnes) (FAO, 2018b; Fluet-Chouinard et al., 2018), per capita 
catch (kg per person per year) (FAO, 2018b), percentage reliance on fish for micronutrients, and percentage consumption per household (Golden et al., 2016). Z-scores of each

metric were averaged for each country to create a composite index describing ‘current dependence on freshwater fish’ for each country with darker blue colours indicating higher 
dependence. Data on vulnerable fish species was from (Nyboer et al., 2019).

9
1360Chapter 9 Africa
For both marine and freshwater fisheries, climate-related extreme 
weather events and flooding may drive the loss of fishing days, cause 
damage and loss to fishing gear, endanger the lives of fishers and 
block transportation from damaged roads (Muringai et al., 2021). Fish 
processing via weather-dependent techniques such as sun drying may 
be hampered, causing post-harvest losses (Akintola and Fakoya, 2017; 
Chan et al., 2019).
9.8.5.3 Current and Future Adaptation Responses for Fisheries
Patterns of vulnerability and adaptive capacity are highly context 
dependent and vary within and among fishing communities in 
coastal and riparian areas (Ndhlovu and Saito, 2017; Lowe et  al., 
2019; D’agata et  al., 2020). Interventions that integrate scientific 
knowledge and fishers’ local knowledge while focusing on vulnerable 
groups are expected to be more successful (Musinguzi et al., 2018; 
Muringai et al., 2019b). Infrastructure improvements (e.g., storage

facilities, processing technologies, transport systems) could reduce 
post-harvest losses and improve food safety (Chan et al., 2019). Fisher 
safety can be aided by early warning of severe weather conditions 
(Thiery et  al., 2017), enhanced through communication via mass 
media and mobile phones (Thiery et al., 2017; Kiwanuka-Tondo et al., 
2019). Although changing fishing gears and shifting target species 
are important adaptation options for artisanal fishers, many have 
instead expanded their fishing range or increased effort (Musinguzi 
et al., 2015; Belhabib et al., 2016). Adapting to the impacts of climate 
change on marine fisheries productivity requires management 
reforms accounting for shifting productivity and species distributions, 
such as increasing marine protected areas, strengthening regional 
trade networks, and increasing the investment and innovation in 
climate-resilient aquaculture production (Golden et al., 2021). This

could yield higher catch and profits in the future relative to today 
in 50% of African countries with marine territories under 2°C global 
warming and in 35% under 4.3°C global warming (Free et al., 2020). 
For inland fisheries, opportunities for adaptation include better 
integration of inland fisheries into management plans from other 
sectors (e.g., hydropower and irrigation) (Harrod et al., 2018c; Cowx 
and Ogutu-Ohwayo, 2019; McCartney et al., 2019). There is growing 
interest in enhancing the supply of freshwater fishery production 
from small water bodies and reservoirs in dryland regions of sub-
Saharan Africa (Kolding et al., 2016).
9.9 Human Settlements and Infrastructure
This section assesses climate impacts, risks and adaptation options for 
human settlements comprising human populations and infrastructure 
such as buildings, roads and energy across Africa.
9.9.1 Urbanisation, Population and Development Trends

Africa is the most rapidly urbanising region in the world, with an 
annual urban population growth rate of 3.6% for 2005–2015 (UN-
Habitat, 2016). About 57% of the population currently lives in rural 
areas, but the proportion of the population living in urban areas is 
projected to exceed 60% by 2050 (UNDESA, 2019b) (UN-Habitat, 2016). Much of the rapid rate of urbanisation has resulted from the 
growth of small towns and intermediary cities (African Development 
Bank et al., 2016).
Approximately 59% of sub-Saharan Africa’s urban population resides 
in informal settlements (in some cities up to 80%), and the population 
in informal settlements is expected to increase (very high confidence) 
(Taylor and Peter, 2014; UN-Habitat, 2014; 2016; UNDP , 2019). These 
urbanisation trends are compounding increasing exposure to climate 
hazards, particularly floods and heatwaves (high confidence) (Dodman 
Globally, the highest rates of population growth and urbanisation are

taking place in Africa’s coastal zones (high confidence) (Merkens et al., 
2016). Coastal urban populations account for 25–29% of the total 
population in west, north and southern Africa (OECD/SWAC, 2020). 
Accounting for a continuing young population, stagnant economies 
and migration to regional growth centres, projections indicate that the 
low-lying coastal zone population of Africa could increase to over 100 
million people by 2030 and over 200 million people by 2060 relative to 
54 million in 2000 (Neumann et al., 2015; see Figure 9.28).
Climate-related displacement is widespread in Africa, with increased 
migration to urban areas in sub-Saharan Africa linked to decreased 
rainfall in rural areas, increasing urbanisation and affecting household 
vulnerability (see Box 9.9). Much of this growth can occur in informal 
settlements which are growing due to both climatic and non-climatic 
drivers, and which often house temporary migrants, including internally

displaced people. Such informal settlements are located in areas 
exposed to climate change and variability and are exposed to floods, 
landslides, sea level rise and storm surges in low-lying coastal areas, 
or alongside rivers that frequently overflow, thereby exacerbating 
existing vulnerabilities (Satterthwaite et al., 2020).
Sub-Saharan Africa’s large infrastructure deficit (quantity, quality and 
access) with respect to road transport, electricity, water supply and 
sanitation places the region at the lowest of all developing regions 
(AfDB, 2018a; Calderon et  al., 2018). Adequate infrastructure to 
support Africa’s rapidly growing population is important to raise living 
standards and productivity in informal settlements (AfDB, 2018b; 
UN Environment, 2019). Yet planned infrastructure developments, 
including those related to the AU’s PIDA, along with other energy 
plans, and China’s Belt and Road Initiative, may increase or decrease

both climate change mitigation and adaptation depending on whether 
infrastructure planning integrates current and future climate change 
risks (Cervigni et al., 2015; Addaney, 2020; see Box 9.5).
9.9.2 Observed Impacts on Human Settlements and 
African human settlements are particularly exposed to floods (pluvial 
and fluvial), droughts and heat waves. Other climate hazards are 
sea level rise and storm surges in coastal areas, tropical cyclones 
and convective storms. This sub-section provides an assessment of 
observed impacts and risks from climate hazards in different sub-
regions to underscore the relevance of climate-sensitive planning and

9
1361Africa  Chapter 9
actions to advance social and economic development, and reduce the 
loss and damage of property, assets and critical infrastructure.
9.9.2.1 Observed Impacts on Human Settlements
The spatial distribution of climate hazards and observed impacts in 
terms of total people affected (displaced persons and deaths) during 
2010–2020 is shown in Figure  9.27. From 2000–2019, floods and 
droughts accounted for 80% and 16%, respectively, of the 337 million 
affected persons, and a further 32% and 46%, respectively, of 46,078 
deaths from natural disasters in Africa (CRED, 2019). Flooding is a major hazard across Africa (Kundzewicz et al., 2014; Douglas, 2017) 
and is increasing (Zevenbergen et al., 2016; Elboshy et al., 2019). An 
increase in extreme poverty and up to a 35% decrease in consumption 
has been associated with exposure to flood shocks (Azzarri and 
Signorelli, 2020). Sub-Saharan Africa is the only region globally that did

not show decreasing rates of flood mortality since the 1990s (Tanoue 
et  al., 2016). Economic opportunities, transportation of goods and 
services, and mobility and access to essential services, including health 
and education, are greatly hindered by flooding (Gannon et al., 2018). 
Severe impacts from tropical cyclone landfalls have been recorded in 
east and southeastern Africa (Rapolaki and Reason, 2018; Cambaza 
Total people affected by climate hazards across Africa, 2010–2020
(a) Climate hazards between 2010–2020
(b) Total people affected by droughts(c) Total people affected
by convective storms(d) Total people affected
by floods
(e) Total deaths from
tropical cyclones(f) Total deaths
from heat waves
Floods
Heat wave
Convective storm
DroughtsClimate hazards
12,000–1,000,0001,000,001–2,000,0002,000,001–3,500,0003,500,001–5,000,0005,000,001–10,200,000Total people
affected12–1,5001,501–5,0005,001–15,00015,001–35,00035,001–120,000Total
people
affected

14–50,00050,001–150,000150,001–450,000450,001–1,000,0001,000,001–7,100,000Total
people
affected
2–2021–5051–100101–250251–700Total
deaths
11–1516–5051–150Total
deaths
Figure 9.27 |  From 2010–2020, over 166 million people were reported to be affected by climate hazards across Africa. Maps show 
(a) location of all reported climate hazards; 
(b) people affected by droughts; 
(c) people affected by convective storms; 
(d) people affected by floods, 
(e) total deaths from tropical cyclones, and 
(f) total deaths from heat waves. Source: EMDAT and CRED (2020). Note: Although extreme weather damage databases under-report heatwaves (which is indicated in panel (f) by 
very few deaths), the region has experienced a number of heatwaves and will be affected disproportionately by them in the future under climate change (Harrington and Otto, 2020).

9
1362Chapter 9 Africa
Table 9.7 |  Case studies of climate hazard impacts and risks to selected human settlements in Africa
Hazard Country/City Impact on Human Settlement and Infrastructure Source
Sea level rise and 
storm surgeEgypt
(North Africa)December 2010, January 2011 and October 2015: Storm surge of 1.2 m.a.s.  l. (metres above sea 
level) (typical of the Nile Delta coast: 0.4–0.5 m). Coastal flooding and damage to some coastal structures. 
Moderate flooding of the Nile Delta lowlands.
Alexandria city: Flooding generated by heavy rainfall (2015). Increased turbidity of water sources affected 
efficiency of water treatment plants leading to reduction of water supplies affecting public health systems. 
Potable water supply affected by saltwater intrusion. Coastal erosion and property damage.Kloos and Baumert (2015); 
Abutaleb et al. (2018)
Eldeberky Y (2015); Yehia et al. 
(2017)

DroughtSouthern AfricaEl Niño drought, 2015–2016: Western Cape Region affected 8.6 million people. Losses: 
>USD 2.2 billion. Power generation reduced by 75% at Kariba dam (Zambia) in 2016, and the Cahora 
Bassa dam (Mozambique) reduced to 34% of its capacity with widespread impact on electricity supplies 
across southern Africa.Davis-Reddy et al. (2017); 
Spalding-Fecher et al. (2017)
Brooks (2019)
Somalia
(East Africa)Somalia drought, 2016–2017: 926,000 newly displaced people reported (November 2016–October 
2017). Around 40% of total drought-related displacements accommodated in Mogadishu, Baidoa, 
Kismayo; 60% hosted in other secondary cities. Increased population density and overcrowding in 
Somalia’s urban areas. Explosion of new shelters and tents for displaced persons within and in outskirts of 
cities. In Mogadishu, 34% of new settlements developed within 6 months.Government of Somalia (2018)
FloodingMalawi

(East Africa)Floods, 2019: Approximately 975,600 people affected, 672 injured, 60 persons killed and 86,976 people 
displaced. 288,371 houses damaged. 129 bridges and 68 culverts destroyed. Around 1841 km of road 
network estimated at USD 36.1 million destroyed. Total cost of damage and losses: housing sector, USD 
106.9 million; energy, USD 3.1 million; water and sanitation, USD 6.4 million; transport, USD 37.0 million. 
Total cost of destroyed physical assets, USD 157.7 million. Damage and losses in Blantyre city: housing 
sector, USD 29.87 million; energy sector, USD 0.38 million; transport sector, USD 1.72 million.Government of Malawi (2019)
Tropical cycloneMozambique, 
Zimbabwe and 
Malawi (southern 
Africa)Cyclones Idai and Kenneth, 2019: Severe flooding of districts in Mozambique, Zimbabwe, and Malawi; 
233,900 houses completely destroyed or damaged in Mozambique.
Cyclone Kenneth: about 40,000 houses and 19 health facilities destroyed.

Cyclone Idai: destroyed or damaged 1345 km of transmission lines, 10,216 km of distribution lines, two 
90 MW generation plants, 30 sub-stations and 4000 transformers, resulting in estimated damage of 
USD 133.5 million and loss of USD 47.9 million in the energy sector in Mozambique. 602 and 299 people 
killed in Mozambique and Zimbabwe, respectively; about 1.5 million people affected in Mozambique and 
270,000 in Zimbabwe.
In Beira (Mozambique), 60% of city was inundated, 70% of houses damaged or totally destroyed, mostly 
in the poorest neighbourhood, and 90% of the city’s power grid affected. Huge losses and damages 
to infrastructures in the energy, transport, water supply, communication services, housing, health and 
education sectors were also recorded.(Cambaza et al., 2019; 
Chatiza, 2019; Government of 
Mozambique, 2019; Hope, 2019; 
Lequechane et al., 2020; Phiri 
(Enenkel et al., 2020)
LandslideFreetown

(West Africa)August, 2017: At least 500 people killed and over 600 people declared missing, >3000 residents 
rendered homeless; 349 houses destroyed. Damage to health facilities and educational buildings. 
Economic cost of landslide and flood, USD 31.6 million.(Cui et al., 2019)
(World Bank, 2017b)
Uganda
(East Africa)Slopes of Mt Elgon, 2010: More than 350 deaths and 500,000 people needed to be relocated. (Croitoru et al., 2019)
et al., 2019; Chatiza, 2019; Hope, 2019). Cyclones Idai and Kenneth 
in early 2019 caused flooding of districts in Malawi, Mozambique and 
Zimbabwe, with substantial loss and damage to infrastructure in the 
energy, transport, water supply, communication services, housing, 
health and education sectors, particularly in Mozambique (Figure 9.27; 
see also Cross-Chapter Box  DISASTER in Chapter 4; Warren, 2019; 
Dube et al., 2021; Phiri et al., 2021).
From 2005–2020, flood-induced damage over Africa was estimated at

over USD 4.4 billion, with eastern and western Africa being the most 
affected regions (EMDAT and CRED, 2020). Total damages in four west 
African countries (Benin, Cote d’Ivoire, Senegal and Togo) in 2017 were 
estimated at USD 850 million for pluvial floods and USD 555 million 
for fluvial floods (Croitoru et al., 2019). Unprecedented economic loss, 
in terms of goods and properties, estimated by the Nigerian insurance 
industry at USD  200  million resulted from floods in Lagos in 2011 
(Adelekan, 2016). In southern Africa, the highest costs were incurred from flood losses during the period 2000–2015 (UNEP-FI, 2019b; 
Simpson, 2020).
Business disruptions from climate impacts have implications for 
deepening poverty (Adelekan and Fregene, 2015). Small and medium 
enterprises (SMEs) employ 60–90% of workers in many African 
countries and contribute 40% or more to the GDP in Ghana, Kenya, 
Nigeria, South Africa, Tanzania and Zimbabwe (Muriithi, 2017). The

viability of businesses and economic well-being of large populations 
employed in SMEs is severely affected by climate hazards as reported for 
local wind storms in Ibadan (Adelekan, 2012), El Niño-related flooding 
(Nairobi), drought-induced water supply disruption (Gaborone) and 
power outages (Lusaka) (Gannon et  al., 2018). High water demand 
due to high rates of urbanisation and population growth, coupled with 
drought, reduce groundwater levels in cities (e.g., Bouake, Harare, 
Tripoli, Niamey) and increase saltwater intrusion into groundwater 
in coastal areas, reducing water availability and water security,

9
1363Africa  Chapter 9
particularly for poorer populations not connected to municipal water 
networks (Aswad et al., 2019; Claon et al., 2020).
Evidence of the impact of heat waves in urban Africa in the current 
climate is sparse, due in part to low reporting and monitoring 
(Engelbrecht et al., 2015; Harrington and Otto, 2020). Knowledge is 
also limited on the interaction of climate change, urban growth and 
the urban heat island effect in Africa (Chapman et al., 2017). In north 
Africa, the present-day number of high heat stress nights is around 
10 times larger in urban than rural areas (Fischer et al., 2012).
9.9.2.2 Observed Impacts to Road and Energy Infrastructure
The highest transport infrastructure exposures are from floods (Koks 
et al., 2019), with potentially severe consequences for food security 
(Fanzo et  al., 2018), communication and the economy of affected 
regions (high confidence) (Koks et al., 2019). Eight of the 20 countries

with the highest expected annual damages to road and rail assets, 
relative to the country’s GDP , are located in east, west and central 
Africa (Koks et al., 2019). Transport impacts compound climate impacts, 
such as heat stress and air pollution linked to vehicle emissions in Dar 
es Salaam (Ndetto and Matzarakis, 2014).
African economies that rely primarily on hydropower for electricity 
generation are particularly sensitive to climate variability (Brooks, 
2019). This sensitivity was already felt during the 2015/16 El Niño, 
in which Malawi, Tanzania, Zambia and Zimbabwe all experienced 
widespread and prolonged load shedding due to low rainfall. The 
impact was felt throughout the economy and reflected in reduced GDP 
growth in Zambia (Conway et al., 2017).
9.9.3 Observed Vulnerabilities of Human Settlements to 
Climate Risks
Urban vulnerabilities and exposure to climate change are increasing 
(medium to high confidence) and are influenced by patterns of urban

settlement and housing characteristics (Satterthwaite, 2017; Godsmark 
et al., 2019; Williams et al., 2019a). About 70% of African cities are 
highly vulnerable to climate shocks of which small- and medium-sized 
towns and cities are more at risk (Verisk Maplecroft, 2018). Flooding 
was perceived as the most prominent water risk in 75% of 36 sampled 
cities across African sub-regions, while drought-related water scarcity 
was indicated as very important/important in 66.7% of cities (OECD, 
2021). Almost one-third of African cities with populations of 300,000 
or more are located in areas of high exposure to at least one natural 
hazard, including floods (11%) and droughts (20–25%) using natural 
hazard data for the period 1970s to early 2000s (Gu et al., 2015). The 
coastal cities of east, west and north Africa are particularly vulnerable 
to the effects of rising sea levels (Abutaleb et al., 2018; IPCC, 2019a). 
From 2000–2015, the proportion of people exposed to floods increased

for most African countries, with Mozambique and multiple countries in 
West Africa estimated to have had the proportion of their populations 
exposed to flooding increase more than 50% (Tellman et al., 2021).
Globally, sub-Saharan Africa has the largest population living in 
extreme poverty that are exposed to high flood risk (~71  million people or 55% of global total) (Rentschler and Salhab, 2020). Poverty 
is a significant factor of flood-induced displacement in Africa, where 
even small flood exposure can lead to high numbers being displaced 
(Kakinuma et al., 2020). Africa’s large population of urban poor and 
marginalised groups and informal sector workers, further contribute 
to high vulnerability to extreme weather and climate change in many 
settlements (high confidence) (Adelekan and Fregene, 2015; IPCC, 
2019a; UNDP , 2019).
Other non-climatic stressors which exacerbate vulnerabilities, especially 
in urban areas, include poor socioeconomic development, weak

municipal governance, poor resource and institutional capacities, 
together with multi-dimensional, location-specific inequalities (high 
confidence) (Dodman et al., 2017; Satterthwaite, 2017).
9.9.4 Projected Risks for Human Settlements and 
9.9.4.1 Projected Risks for Human Settlements
The extent of urban areas in Africa exposed to climate hazards will 
increase considerably and cities will be hotspots of climate risks, which 
could amplify pre-existing stresses related to poverty, exclusion and 
governance (high confidence) (IPCC, 2018b).
9.9.4.1.1 Flooding
Continuing current population and GDP growth trends, the extent of 
urban land exposed to high-frequency flooding is projected to increase 
around 270% in north Africa, 800% in southern Africa, and 2600% 
in mid-latitude Africa by 2030 when compared to 2000, without 
considering climate change (Güneralp et al., 2015). In addition, global 
warming is projected to increase frequency and magnitude of river

floods in east, central and west Africa (Alfieri et al., 2017; Gu et al., 
2020; Kam et al., 2021). On average, across large African river basins, 
the frequency of flood events with a current return period of 100 years 
is projected to increase to 1 in 40  years at 1.5°C and 2°C global 
warming, and 1 in 21 years at 4°C warming, with Egypt, Nigeria, Sudan 
and the Democratic Republic of Congo in the top 20 countries globally 
for projected damages (Alfieri et al., 2017). Compared to population in 
2000, human displacement due to river flooding in sub-Saharan Africa 
is projected to increase 600% by 2066–2096 with moderate-to-high 
population growth and 2.6°C global warming, with risk reducing to a 
200% increase for low population growth and 1.6°C global warming 
(Kam et al., 2021).
Urban population exposure to tropical cyclone hazards in southeastern 
Africa, in particular Mozambique, is projected to increase due to the

intensification of cyclones and their extended duration associated 
with warmer sea surface temperatures (Fitchett, 2018; Vidya et  al., 
2020). Urban damage assessment based on a 10-year flood protection 
level for Accra, Ghana, shows that without flood protection, there is 
a 10% probability of a flood occurring annually which could cause 
USD 98.5 million urban damage, affect GDP by USD 50.3 million and 
affect 34,000 people (Asumadu-Sarkodie et  al., 2015). Many urban

9
1364Chapter 9 Africa
Current and future population exposed to sea level rise in low elevation coastal zone in Africa
Africa
Southern AfricaYear  2000 Year 2030 (+10 cm sea level rise) Year 2060 (+21 cm sea level rise)
A
117.6
47.1
52.3
15.1
2.2
0.9A
229.3
111.7
72.4
39.9
3.8
1.5D
108.9
43.6
48.6
13.8
2.0
0.9B
190.0
95.0
56.3
34.8
3.0
0.9D
185.6
88.9
61.4
31.1
3.0
1.1C
116.8
47.2
52.3
14.1
2.2
1.0C
245.2
122.3
74.8
42.5
4.1
1.7B
108.5
45.3
46.6
13.8
2.0
0.8Baseline
54.2
17.1
30.3
5.2
1.1
0.5
Population growth scenarios:
A  = growth at high end of forecasts
B  = growth at lowest end of forecasts
C  = growth at highest end of forecasts
D  = growth at low end of forecastsPopulation exposed to sea level rise
in low-elevation coastal zones (LECZ)
1 million 
people>200 million
people
Egypt
Nigeria
Senegal
Benin
Tanzania
Somalia
Cote d'Ivoire
Mozambique(a) Population exposed to sea level rise in low elevation coastal zone (LECZ)

(b) African countries in the global top 25 with highest populations within LECZ and in the 100-year floodplains, under growth scenario C
Populations within LECZ
Growth
2000–2060
0.25
0.79
0.66
1.06
2.2
1.68
0.64
0.33Year 
2030
45.0
19.8
8.5
5.4
2.8
2.2
3.0
4.4Year
2060
63.5
57.7
19.2
15.0
14.0
9.8
7.6
7.5Baseline
2000
25.5
7.4
2.9
1.4
0.6
0.6
1.2
2.3Populations within 100-year floodplains
Year 
2030
13.8
0.3
1.1
0.6
0.9
0.6
0.3
1.4Baseline
2000
7.4
0.1
0.4
0.1
0.2
0.2
0.1
0.7Growth
2000–2060
0.28
0.84
0.76
1.12
2.3
1.7
0.65
0.36Year 
2060
20.7
0.9
2.7
1.6
4.3
2.7
0.7
2.5
Once in 100-years floodplain
Figure 9.28 |  Tens to hundreds of millions of people in Africa are projected to be exposed to sea level rise, with a major risk driver being increased exposure 
due to population increase in low-lying areas.

(a) Population in the low-elevation coastal zone (LECZ) projected to be exposed to mean sea level rise (SLR) for 2030 (+10 cm SLR) and 2060 (+21 cm SLR). Scenarios A, C have 
exclusive social, political and  economic governance whereas scenarios B and D have inclusive social, political  and economic governance.
(b) African countries with the highest projected population numbers in the LECZ, and also the additional population projected to be exposed in these countries due to a 1-in-100 year 
storm surge event. For panel b projections of population exposure used the high population growth socio-economic scenario (scenario C). Data sourced from Neumann et al. (2015).
households and Africa’s growing assets could therefore be exposed to 
increased flooding (IPCC, 2018b).
9.9.4.1.2 Sea level rise and coastal flooding
Africa’s low-lying coastal zone population is expected to grow more 
than any other region from 2000 to 2060 (see Figure 9.28; Neumann

et al., 2015). Future rapid coastal development is expected to increase 
existing high vulnerabilities to sea level rise (SLR) and coastal hazards, 
particularly in east Africa (high confidence) (Figure 9.29; Hinkel et al., 
2012; Kulp and Strauss, 2019). By 2100, sea levels are projected to 
rise at least 40 cm above those in 2000 in a below 2°C scenario, and 
possibly up to 1 m by the end of the century under a 4°C warming 
scenario (Serdeczny et  al., 2017; see also Cross-Chapter Box  SLR in 
Chapter 3).In the absence of any adaptation, Egypt, Mozambique, and Nigeria 
are projected to be worst affected by SLR in terms of the number of 
people at risk of flooding annually in a 4°C warming scenario (Hinkel 
et al., 2012). Recent estimates have explored the potential damages 
due to SLR and coastal extreme events in 12 major African cities using 
a stochastic approach to account for uncertainty (Abadie et al., 2020). 
The aggreate of expected average damages to these cities in 2050

is USD  65  billion for RCP4.5 and USD  86.5  billion for RCP8.5, and 
USD 137.5 billion under a high-end scenario that incorporates expert 
opinion on additional ice sheet melting with damages up to (Table 9.8). 
When considering low-probability, high-damage events, aggregate 
damage risks can be more than twice as high, reaching USD 187 billion 
and USD 206 billion under RCP4.5 and RCP8.5 scenarios, respectively, 
and USD 397 billion under the high-end scenario. City characteristics 
and exposure play a larger role in expected damages and risk than 
changes in sea level. The city of Alexandria in north Africa leads the

9
1365Africa  Chapter 9
Selected regions at risk
of projected sea level rise(a) Dar es Salaam, Bagamoyo and Stonetown (Tanzania)
(b) Lagos (Nigeria) and Cotonou and Porto-Novo (Benin) (c) Cairo and Alexandria (Egypt)
Presentday
2050
2100
50 km
50 km
Present day 2050 2100
50 km
(c)
(b) (a)
RCP2.6RCP4.5
RCP8.5Permanent
flooding
due to
sea level
rise
Present
sea levelArea built-up by 2014
Figure 9.29 |  Multiple large African cities will be exposed to sea level rise (SLR), these include the selected examples: (a) Dar es Salaam, Bagamoyo, and Stone 
Town in Tanzania (east Africa), (b) Lagos in Nigeria, and Cotonou and Porto-Novo in Benin (west Africa) and (c) Cairo and Alexandria in Egypt (north Africa). Orange shows built-up 
area in 2014. Shades of blue show permanent flooding due to SLR by 2050 and 2100 under low (RCP2.6), intermediate (RCP4.5) and high (RCP8.5) greenhouse gas emissions

scenarios. Darker colours for higher emissions scenarios show areas projected to be flooded in addition to those for lower emissions scenarios. The figure assumes failure of coastal 
defences in 2050 and 2100. Some areas are already below current SLR and coastal defences need to be upgraded as SLRs (e.g., in Egypt), others are just above mean sea levels 
and they do not necessarily have high protection levels, so these defences need to be built (e.g., Dar es Salaam and Lagos). Blue shading shows permanent inundation surfaces 
predicted by Coastal Digital Elevation Model (DEM) and Shuttle Radar Topography Mission (SRTM) given the 95th percentile K14/RCP2.6, RCP4.5 and RCP8.5, for present day, 
2050, and 2100 sea level projection for permanent inundation (inundation without a storm surge event), and RL10 (10-year return level storm) (Kulp and Strauss, 2019). Low-lying

areas isolated from the ocean are removed from the inundation surface using connected components analysis. Current water bodies are derived from the SRTM Water Body Dataset. 
Orange areas represent the extent of coastal human settlements in 2014 (Corbane et al., 2018). See Figure CCP4.7 for projections including subsidence and worst-case scenario 
projections for 2100.

9
1366Chapter 9 Africa
Table 9.8 |  Regional relative sea level rise (SLR) for 2050 and 2100, and associated aggregated expected damage risks over the period 2020 to 2050 in 12 major African 
coastal cities under four SLR scenarios. (a) Regional relative SLR by 2050 and 2100. For SLR, median and 95th percentiles are presented, in centimetres. (b) Probabilistic damage 
estimations by 2050 include expected average damages (EAD), damages at the 95th percentile (value at risk; VaR) and the expected shortfall (ES), which represents the average 
damages of the 5% worst cases. Four relative sea level projections were considered under no adaptation: the RCP2.6, 4.5 and 8.5 scenarios from the (IPCC, 2014a), and a high-end 
RCP8.5 scenario that incorporates expert opinion on additional ice sheet melting. Note that figures are provided in undiscounted millions of US dollars (2005) and have been 
rounded off to avoid a false sense of precision (Abadie et al., 2020; Abadie et al., 2021).

(a) Regional relative sea level rise (cm)
City YearRCP2.6 RCP4.5 RCP8.5 High-end
Median P95 Median P95 Median P95 Median P95
Abidjan2050 21 30 22 32 24 34 28 48
2100 44 69 53 86 75 114 86 206
Alexandria2050 18 26 18 28 21 30 25 43
2100 36 58 46 73 67 102 78 186
Algiers2050 19 27 19 29 22 31 25 45
2100 39 62 47 76 66 98 78 192
Cape Town2050 20 30 21 31 23 33 27 48
2100 44 69 53 87 75 117 86 199
Casablanca2050 19 27 20 29 22 31 26 46
2100 39 63 47 78 65 99 77 198
Dakar2050 21 31 21 31 23 33 27 48
2100 43 69 53 86 73 111 85 209
Dar es 
Salaam2050 20 29 21 31 24 33 27 47
2100 45 70 54 86 76 117 87 206
Durban2050 20 30 22 32 25 34 28 49
2100 46 72 55 90 78 119 89 207
Lagos2050 21 30 22 32 24 34 28 48
2100 44 69 54 86 75 113 86 205
Lome2050 21 30 22 32 24 34 28 48
2100 44 69 53 86 76 115 87 205
Luanda2050 21 30 23 32 25 35 29 49
2100 45 70 55 88 78 119 90 205
Maputo2050 21 31 22 32 24 34 28 49
2100 45 71 55 89 78 120 89 209
(b) Expected average damages and risk measures (USD millions)

CityRCP2.6 RCP4.5 RCP8.5 High-end scenario
EAD VaR(95%) ES(95%) EAD VaR(95%) ES(95%) EAD VaR(95%) ES(95%) EAD VaR(95%) ES(95%)
Abidjan 14,290 33,910 41,690 16,730 38,230 46,390 20,910 42,140 49,550 32,670 77,750 96,570
Alexandria 32,840 74,100 92,470 36,220 83,700 104,270 49,990 99,500 117,580 79,360 180,090 221,390
Algiers 270 620 760 300 700 870 390 810 960 640 1,540 1,920
Cape Town 110 310 400 130 360 450 170 410 490 300 800 1,010
Casablanca 350 1,150 1,520 420 1,340 1,740 610 1,570 1,930 1,230 3,590 4,630
Dakar 590 1,310 1,590 620 1,390 1,690 760 1,530 1,800 1,180 2,880 3,610
Dar es 
Salaam880 2,100 2,600 1,050 2,440 2,970 1,360 2,760 3,250 2,140 5,120 6,360
Durban 110 370 470 150 420 530 210 490 590 370 970 1,230
Lagos 3,680 6,790 7,950 4,200 7,660 8,930 4,920 8,270 9,420 6,750 13,820 16,730
Lome 3,230 10,480 13,460 4,280 12,580 15,780 5,980 14,430 17,380 10,720 28,580 36,010
Luanda 160 380 470 200 440 530 260 510 600 400 910 1,130

Maputo 650 1,990 2,530 700 2,080 2,620 980 2,410 2,910 1,790 4,830 6,110
Aggregate 
damage
and risk57,160 133,510 165,910 65,000 151,340 186,770 86,540 174,830 206,460 137,550 320,880 396,700

9
1367Africa  Chapter 9
ranking, with aggregate expected damage of USD  36  billion and 
USD 50 billion under RCP4.5 and RCP8.5 scenarios, respectively, and 
USD 79.4 billion under a high-end scenario.
Sea level rise and associated episodic flooding are identified as key 
drivers of projected net migration of 750,000 people out of the east 
African coastal zone between 2020 and 2050 (IPCC, 2019a). These 
trends, alongside the emergence of ‘hotspots’ of climate in- and 
out-migration (Box  9.8), will have major implications for climate-
sensitive sectors and the adequacy of human settlements, including 
urban infrastructure and social support systems. Actions which could 
help reduce the number of people being forced to move in distress, 
include adoption of inclusive and CRD policies, together with 
targeted investments to manage the reality of climate migration; and 
mainstreaming climate migration in development planning (Box 9.8).
9.9.4.1.3 Drought

Although an increase in drought hazard is projected for north and 
southwest southern Africa with increased global warming (Figure 9.15), 
central African countries may have the highest drought risk because of 
high vulnerability and high population growth (Ahmadalipour et  al., 
2019). Among continents, Africa contains the second largest population 
of people living in drylands, which is expected to double by 2050 
(IPCC, 2019a). Continuing current population and GDP growth trends, 
the extent of urban land in arid zones is projected to increase around 
180% in southern Africa, 300% in north Africa and 700% in mid-latitude 
Africa by 2030 when compared to 2000, without considering climate 
change (Güneralp et  al., 2015). At 1.5°C warming, urban populations 
exposed to severe droughts in west Africa are projected to increase 
(65±34  million) and increase further at 2°C (IPCC, 2018b; Liu et  al., 
2018b). Risks associated with increases in drought frequency and

magnitudes are projected to be substantially larger at 2°C than at 1.5°C 
for north Africa and southern Africa (IPCC, 2018b; Oppenheimer et al., 
2019). Dryland populations exposed (vulnerable) to water stress, heat 
stress, and desertification are projected to reach 951 (178) million at 
1.5°C, 1152 (220) million at 2°C, and 1285 (277) million at 3°C of global 
warming (IPCC, 2019a). At global warming of 2°C under a scenario 
of low population growth and sustainable development (SSP1), the 
exposed (vulnerable) dryland population is 974 (35) million and for 
higher population growth and low environmental protections (SSP3) it 
is 1.27 billion (522 million), a majority of which is in west Africa (IPCC, 
2019a).
9.9.4.1.4 Extreme heat
Projections for 173 African cities show that around 25 cities will have 
over 150 days per year with an apparent temperature above 40.6°C for 
1.7°C global warming, increasing to 35 cities for 2.1°C and 65 cities

for 4.4°C warming, with west African cities most affected (Rohat et al., 
2019). Across Africa, urban population exposure to extreme heat was 
estimated to be 2 billion person-days per year above 40°C for 1985–
2005 (that is the annual average number of days with a maximum 
temperature above 40.6°C multiplied by the number of people exposed 
to that temperature), but this is expected to increase to 45  billion 
person-days for 1.7°C global warming with low population growth 
(SSP1), and to 95  billion person-days for 2.8°C and medium-high population growth (SSP4) by the 2060s, with increases of 20–52 times 
1985–2005 levels by 2080–2100, depending on the scenario (Rohat 
et al., 2019). West Africa (especially Nigeria) has the highest absolute 
exposure and southern Africa the least. Considering the urban heat 
island effect, the more vulnerable populations under 5 and over 64 
exposed to heat waves of >15 days over 42°C are projected to increase

from 27  million in 2010 to 360  million by 2100 for low population 
growth (SSP1) with 1.8°C global warming, increasing to 440 million for 
low population growth (SSP5) with >4°C global warming, with west 
Africa most affected (Marcotullio et al., 2021). This portends increased 
vulnerability to risk of heat stress in big cities of central, east and west 
Africa (very high confidence) (Gasparrini et al., 2015; Liu et al., 2017; 
Rohat et al., 2019). Shifting to a low urban population growth pathway 
is projected to achieve a greater reduction in aggregate exposure to 
extreme heat for most cities in west Africa, whereas limiting warming 
through lower emissions pathways achieves greater reductions in 
exposure in central and east Africa (Rohat et al., 2019).
The African population exposed to compound climate extremes, 
such as coincident heat waves and droughts or drought followed 
immediately by extreme rainfall, is projected to increase 47-fold

by 2070–2099 compared to 1981–2010 for a scenario with high 
population growth and 4°C global warming (SSP3/RCP8.5) and only 
12-fold for low population growth and 1.6°C global warming (SSP1/
RCP2.6), with west, central-east, northeastern and southeastern Africa 
especially exposed (Weber et al., 2020). Coincident heat waves and 
drought is the compound event to which the most people are projected 
to be exposed: ~1.9 billion person-events (a 14-fold increase) for SSP1/
RCP2.6 and ~7.3  billion person-events (52-fold increase) for SSP3/
RCP8.5 (Weber et al., 2020).
9.9.4.2 Projected Risks to Electricity Generation and Transmission
Climate change poses an increased risk to energy security for human 
settlements in Africa (high confidence). With burgeoning urban 
populations and growing economies, sub-Saharan Africa’s electricity 
needs are growing. The International Energy Agency (IEA) projects 
total generation capacity in Africa must grow 2.5 times from 244 GW

in 2018 to 614 GW by 2040 (IEA, 2019). African nations plan to add 
significant generation capacity from natural gas, hydropower, wind 
and solar power. Each of these technologies is associated to a varying 
degree with climate risk.
The long lifespan of hydropower dams exposes them to decades of 
climatic change risk. There is a wide range of uncertainty around the 
future climate of Africa’s major river basins, but in several basins, 
there is the likelihood of increased rainfall variability and a drier 
climate (see Box 9.5). In countries that rely primarily on hydropower, 
climate change could have considerable impacts on electricity prices 
and as a result, consumers’ expenditure (Sridharan et al., 2019). With 
increasing societal demands on limited water resources and future 
climate change, it is expected that there will be an intensification 
of WEF competition and trade-offs (high confidence) (Section  9.7; 
Box 9.5).

9
1368Chapter 9 Africa
9.9.4.3 Projected Risks to Road Infrastructure
Climate change and SLR will result in high economic costs for road 
infrastructure in sub-Saharan Africa (medium confidence) (Chinowsky 
et  al., 2015). Across Africa as a whole, potential cumulative costs 
estimates through 2100 range from USD  183.6  billion (with 
adaptation) to USD  248.3  billion (no adaptation) to repair and 
maintain existing roads damaged by temperature and precipitation 
changes directly related to projected climate change (see Figure 9.30) 
(Chinowsky et al., 2013). Climate-related road damage and associated 
repairs will be a significant financial burden to countries, but to 
varying degrees according to flood risk, existing road asset liability, 
topography and rural connectivity, among other factors (Chinowsky 
et al., 2015; Cervigni et al., 2017; Koks et al., 2019). For example, 
Mozambique is projected to face estimated annual average costs

of USD  123  million for maintaining and repairing roads damaged 
directly by precipitation and temperature changes from climate 
change through 2050 in a median climate change scenario for a 
policy that does not consider climate impacts during road design 
and construction (Chinowsky et al., 2015). Risk of river flooding to 
bridges in Mozambique under current conditions is estimated to be 
USD 200 million, equal to 1.5% of its GDP per year, and could rise to 
USD 400 million per year in the worst-case climate change scenario 
by 2050 (Schweikert et al., 2015).9.9.5 Adaptation in Human Settlements and for 
9.9.5.1 Solutions and Residual Risk Observed in Human 
Autonomous responses to climate impacts in 40 African cities show 
that excess rainfall is the primary climate driver of adaptation, followed 
by multi-hazard impacts, with 72% of responses focused on excess 
rainfall (Hunter et al., 2020). Innovation for adaptation in areas such

as home design, social networks, organisations and infrastructure, is 
evident (Swanepoel and Sauka, 2019). Social learning platforms also 
increase communities’ adaptive capacities and resilience to risk (Thorn 
There is limited evidence of successful, proactively planned climate 
change adaptation in African cities (Simon and Leck, 2015), particularly 
for those countries highly vulnerable to climate change (Ford et  al., 
2014). Planned adaptation initiatives in African cities since 2006 have 
been predominantly determined at the national level with negligible 
participation of lower levels of government (Ford et  al., 2014). 
Adaptation action directed at vulnerable populations is also rare (Ford 
et  al., 2014). There are emerging examples of cities planned climate 
adaptation measures, such as those advanced by Durban (Roberts, 
2010), Cape Town (Taylor et al., 2016) and Lagos (Adelekan, 2016). There

are also examples of community-led projects such as those in Maputo 
(Broto et al., 2015), which have seen meaningful help from a range of 
policy networks, dialogue forums and urban learning labs (Pasquini and 
Cowling, 2014; Shackleton et al., 2015). These researched cities can be 
lighthouses for wider exchange and the basis for a deeper synthesis of 
evidence (Lindley et al., 2019). However, planned adaptation progress is 
slow, especially in west and central Africa (Tiepolo, 2014).
Ecosystem-based approaches are also being deployed in mitigating 
and adapting to climate change, with demonstrated long-term health, 
ecological and social co-benefits (Section 9.6.4; Swanepoel and Sauka, 
2019). The cost–benefit analysis of nature-based solutions, compared 
to purely grey infrastructure initiatives, is discussed in Chapter 6 
(Section  6.3.3). Nature-based solutions can also lengthen the life of 
existing built infrastructure (du Toit et al., 2018). Since 2014, an increasing

number of EbA projects involving the restoration of mangrove, wetland 
and riparian ecosystems have been initiated across Africa, a majority of 
which address water-related climate risks (Table 9.9).
For green infrastructure to be successful, however, sustainable landscapes 
and regions require both stewardship and management at multiple 
levels of governance and social scales (Brink et al., 2016).
Currently, planned climate change adaptation to coastal hazards 
in Africa’s large coastal cities has mainly been achieved through 
expensive coastal engineering efforts such as sea walls, revetments, 
breakwaters, spillways, dikes and groynes. Examples are found in 
west Africa (Adelekan, 2016; Alves et al., 2020). Beach nourishment 
efforts have also been undertaken in Egypt, Banjul and Lagos (Frihy 
et al., 2016; Alves et al., 2020). However, the use of vegetated coastal 
ecosystems presents greater opportunities for African cities because of

the lower costs (Duarte et al., 2013).Projected costs for repair and maintenance of 
pre-2011 road infrastructure as a result of projected 
climate-change-related damages
05 % 10% 15% 20% 25% 30%
Proportion of 201 1 GDPMax of
22 SRES
scenariosSub-Saharan Africa
Mozambique
Tanzania
Ethiopia
 Côte d'Ivoire
Morocco
Democratic Republic
of Congo
Niger
Algeria
Mali
Namibia87%Median
Figure 9.30 |  Projected costs for repair and maintenance of pre-2011 road 
infrastructure in selected African countries as a result of projected climate-
change-related damages due directly to precipitation and temperature 
changes through to 2100. Data sources: Chinowsky et al. (2013). The analysis was 
run for 22 SRES climate scenarios and the median, and maximum results of the analyses 
are represented as proportions of the 2011 GDP of each country.

9
1369Africa  Chapter 9
Table 9.9 |  Examples of ecosystem-based adaptations to climate impacts in African cities.
Project City Ecosystem-based Adaptation Reference
Green Urban Infrastructure Beira (Mozambique)Mitigating against increased flood risks through restoration of mangrove and 
other natural habitats along the Chiveve river and the development of urban 
green spaces.IPCC (2019a); CES Consulting 
Engineers Salzgitter GmbH 
and Inros Lackner SE (2020)
The Msimbazi Opportunity Plan (MOP) 
2019–2024Dar es Salaam, TanzaniaEnhancing urban resilience to flood risk by reducing flood hazard, and 
reducing people, properties and critical infrastructure exposed to flood hazard.Croitoru et al. (2019)
Tanzania Ecosystem-based AdaptationDar es Salaam and five 
coastal districts, TanzaniaRehabilitation of over 3000 ha of climate-resilient mangrove species. UNEP (2019)
Building Resilience in the Coastal Zone 
through Ecosystem-based Approaches

to AdaptationMaputo, MozambiqueRestoration of mangrove and riparian ecosystems for flood control and 
protection from coastal flooding enhanced water supply.GEF (2019)
Addressing Urgent Coastal Adaptation 
Needs and Capacity Gaps in AngolaFive coastal communities 
in AngolaRestoration of 561 ha of wetland, mangroves and other ecological habitats to 
promote flood defence and mitigate the threat of drought.UNEP (2020)
Green City Kigali
2016Kigali, RwandaPlanned neighbourhood of 600 ha, integrating green building and design, 
efficient and renewable energy, recycling and inclusive living.SWECO (2019)
Urban Natural Assets for Africa—Rivers 
for LifeKampala, UgandaPreservation of natural buffers to enhance the protective functions offered by 
natural ecosystems that support disaster resilience benefit.World Bank (2015)
Most (>80%) of Africa’s large coastal cities have no adaptation 
policies and, where available, these are mostly, except for South

Africa, dominated by national plans (Olazabal et  al., 2019). Coastal 
adaptation actions minimally consider socioeconomic projections and 
are not at all aligned with future climate scenarios and risks, which is 
highly limiting for adaptation planning (Olazabal et al., 2019).
9.9.5.2 Anticipated Adaptation and Residual Risk for Human 
Africa’s smaller towns and cities have received far less scholarly and 
policy development attention for adaptation (Clapp and Pillay, 2017; 
White and Wahba, 2019). Smaller towns also have less ability to partner 
effectively with private entities for adaptation initiatives (Wisner et al., 
2015). Political will to address climate change and information flows 
between key stakeholders, professional and political decision makers 
may be easier to establish in smaller cities than in the megacity context 
(Wisner et al., 2015).
Exposure and vulnerability are particularly acute in informal areas,

making coordinated adaptation challenging. Yet, there is growing 
recognition of the potential for bottom-up adaptation that embraces 
informality in order to more effectively reduce risk (Figure 9.31; Taylor 
et al., 2021a). This can provide an opportunity for change towards more 
risk-sensitive urban development and transformative climate adaptation 
(Leck et al., 2018). Addressing social vulnerability is particularly important 
for ensuring the resilience of populations at risk. Improved monitoring, 
modelling and communication of climate risks is needed to reduce the 
impacts of climate hazards (Tramblay et al., 2020; Cole et al., 2021a).
9.9.5.3 Anticipated Adaptation for Transport Systems in Africa
Higher costs will be incurred to maintain and repair damages caused 
to existing roads as a result of climate change for countries with no 
adaptation policy for transport infrastructure (very high confidence) 
(Chinowsky et  al., 2013; Cervigni et  al., 2017; Koks et  al., 2019).

Countries with a greater percentage of unpaved roads will, however, incur higher economic costs through adaptation policy when compared 
to no adaptation policy (Cervigni et al., 2017).
Adaptation measures in the transport sector have focused on the climate 
resilience of road infrastructure. Modelling suggests that proactive 
adaptation of road designs to account for temperature increases is a 
‘no regret’ option in all cases, but accounting for precipitation increases 
should be assessed on a case-by-case basis (medium confidence) 
(Cervigni et al., 2017). African governments will need climate adaptation 
financing options to meet the higher capital requirements of resilient 
road infrastructure interventions (Hearn, 2016).
Under the Nationally Appropriate Mitigation Action programme, 
investments in public transport and transit-oriented development are 
highlighted as desired mitigation–adaptation interventions within

cities of South Africa, Ethiopia and Burkina Faso (UNFCCC, 2020). 
These interventions simultaneously reduce the vulnerability of low-
income residents to climate shocks, prevent lock-ins into carbon-
intensive development pathways and reduce poverty (high confidence) 
(Hallegatte et  al., 2016; Rozenberg et  al., 2019). The combined 
mitigation–adaptation interventions in the land use transport systems 
of African cities are also expected to have sufficient short-term co-
benefits (reducing air pollution, congestion and traffic fatalities) to be 
‘no regret’ investments (very high confidence) (Hallegatte et al., 2016; 
Rozenberg et al., 2019). Only eight African countries have transport-
specific adaptation measures in their NDCs (Nwamarah, 2018). Five 
African countries have submitted NAPs (Table 9.10).
9.9.5.4 Projected Adaptation for Electricity Generation and 
Transmission in Africa
Most electricity infrastructure in Africa has been designed to account

for historical climatic patterns. Failure to consider future climate 
scenarios in power system planning increases the climate risk facing 
infrastructure and supplies. Yet, energy demand for cooling over Africa, 
for example, is expected to increase, with a potential increase in heat 
stress, population growth and rapid urbanisation to 1.2% of total final

9
1370Chapter 9 Africa
Table 9.10 |  Transport sector references in the National Adaptation Plans (NAPs) of five African countries. Source: Government of Burkina Faso (2015); Government of Cameroon 
(2015); Government of Togo (2016); Government of Kenya (2017); Government of Ethiopia (2019).
CountryIdentify climate 
change impactsPromote transport 
as a disaster risk 
reduction measureTransport-specific adaptation measures
design standardsPromote public 
transportPromote non-mo-
torised transportUrban land use 
planning
Burkina Faso X X X X
Cameroon X X
Ethiopia X X X X
Kenya X
Togo X XKey elements of adaptation in informal settlements in Africa
Hazard
Climate hazard affecting the highest
number of people per country (2000–2019)Vulnerability context of
informal settlements in Africa
Currently:
• 189 million people (59%) of urban population live in 
informal settlements
• 72% of non-agricultural employment in the informal sector

• 78% of residential areas developed between 1990 and 2014
Projected:
•Cost of water, electricity and transport delays USD300 
million per year
• 3x urban population by 2050
• 4x physical footprint by 2050
•Africa needs to spend USD 130–170 million per year on 
basic infrastructure delivery
• 1.2 billion people will live in informal settlements by 2050Vulnerability
•Ecosystem-based 
measures to reduce 
riparian and coastal 
flooding
•Mangroves alleviate 
coastal storm energy
•Water reservoirs to 
buffer low-flows and 
water scarcity•Comprehensive in situ 
community upgrading
•Ameliorate flood desert 
•Actions to reduce 
discrimination
•High quality health care
•Insurance
•Clarification of land 
tenure•Risk sensitive land use 
planning
•Early warning systems
•Evacuation routes
•Maintained storm 
surface drains
•Hazard proof housing 
•Transport 
infrastructureAdaptation
pathways
Such as,
•Pooling (e.g. 
knowledge, care, 
space, income and 
labour

•Diversification of 
•Mobility/migration
•Rationing over 
time/community saving 
schemes
•Intensification (e.g. 
crop production)
•Material and symbolic 
exchange
•Innovation and 
reconfiguration of 
ideologies•Cooling spaces during 
heatwaves
•Access to clean 
drinking water and 
sanitation
•Effective Disaster 
Risk Reduction
•Access to clean, 
reliable renewable 
energy
•Watershed 
management•Physical
•Ecological
•Technological
•Economic
•Political
•Institutional
•Psychological and/or 
socio-culturalDrought
Extreme temperature
Flood
StormVulnerability
Exposure
ResponseRiskHazard
AdAd tttiti Actions to
reduce HazardsActions  to 
reduce ExposureActions  to
reduce VulnerabilityLimits to
adaptationActions  to
reduce Risks
Figure 9.31 |  Key elements of adaptation in informal settlements in Africa. Adapted from Thorn et al. (2015); Fedele et al. (2019); Satterthwaite et al. (2020).
energy demand by 2100 compared to 0.4% in 2005 (Parkes et  al.,

2019). Integrated energy system costs from increased demand for 
cooling to mitigate heat stress are projected to accumulate from 2005 
to USD 51.3 billion by 2035 at 2°C and to USD 486.5 billion by 2076 at 
4°C global warming (Parkes et al., 2019).For hydropower, adaptations to different climate conditions can 
be made at the level of the power plant, turbine size and reservoir 
storage capacities, and can be adjusted to projected hydrological 
patterns (Lempert et  al., 2015). At the river basin level, integrated 
water resource management practices can be implemented across

9
1371Africa  Chapter 9
sectors that compete for the same water resources (Howells et al., 
2013). At the power system level, the energy mix and the protocol 
through which different power plants are dispatched can be adapted 
to different climate scenarios (Spalding-Fecher et al., 2017; Sridharan 
Given the uncertainty around future hydroclimate conditions, 
hydropower development decisions carry risk of ‘regrets’ (that is, 
damages or missed opportunities) when a different climate than was 
expected materialises. ‘Robust adaptation’ refers to an adaptation 
strategy that balances risks across different climate scenarios (Cross-
Chapter Box DEEP in Chapter 17; Cervigni et al., 2015). Development 
bank lending principles require consideration of the regional picture 
and interactions with other developments along a river when they 
determine the social and environmental impacts of the proposed 
hydropower project. However, these principles often do not explicitly

consider climate change, so the risk of recurring drought-induced 
hydropower shortages could be missed (Box 9.5).
Lastly, given the degree to which hydropower competes with other 
sectors and ecosystems for the same water resources, it is critical 
that hydropower planning and adaptation does not occur in isolation. 
As discussed in Section  9.7, it must be part of an integrated water 
management system that balances the needs of different water-reliant sectors with other societal and ecological demands under increasingly 
variable climate and hydrological conditions (Section 9.7.3).
9.10 Health
The health section is organised by disease or health outcome, with 
observed impacts and projected risks described for each condition. All 
adaptation options are presented at the end of the section, highlighting 
prevention and preparedness, community engagement and disease-
specific adaptation options.
9.10.1 The Influence of Social Determinants of Health on 
the Impacts of Climate Change

The social determinants of health are ‘the conditions in which people 
are born, grow, live, work and age’ as well as the drivers of these, 
including the social circumstances which profoundly affect health 
and drive health disparities (Commission of Social Determinants of 
Health, 2008; Gurewich et  al., 2020). Social features (e.g., health-
related behaviours), socioeconomic factors (e.g., income, wealth and 
education) and environmental determinants (e.g., air or water quality) 
are critical for shaping health outcomes. These factors are inextricably 
Observed climate change impacts and projected risks across African regions for eight key health outcome s
Impact 
or riskPeople
exposedNumber
of casesNumber
of deathsIncrease in
incidence
(cases/
deaths)Increase in
at risk
Very high >10 million >100,000 >3,000 >10% 31–50% >100
High >1 million >10,000 >1,000 >7% 21–30% >50
Moderate >100,000 >1,000 >500 >5% 11–20% >10Cost
(million USD)Health outcomes
per region
Northern

Central
Eastern
Western
SouthernAir
pollution-
related
Low >1,000 >100 >100 >2% 5–10% >1
Reduced >1,000 >100 >100 >2% 5–10% >1Negligible –– –– –– –
Northern
Central
Eastern
Western
SouthernMalaria
Northern
Central
Eastern
Western
SouthernDengue
and Zika
Northern
Central
Eastern
Western
SouthernCholera
Northern
Central
Eastern
Western
SouthernDiarrhoeal
disease
Northern
Central
Eastern
Western
SouthernHIV
Northern
Central
Eastern
Western
SouthernAll-cause mortality
attributed to non-
optimal temperaturesNorthern
Central
Eastern
Western
SouthernHeat-related
illness and
mortality
Key for criteria used to define the severity of observed impact or projected risk for each health outcome
= Insufficient evidence available EmptyConfidence level
Very
highProjected risk
Medium
= Conflicting resultLow High>1°C>1.5°C>3°C>4°C
>2°C
–––
Observed
Observed impact

Figure 9.32 |  Risks to health in Africa increase with increasing global warming. Observed climate impacts and projected climate change risks across African regions 
for eight key health outcomes. Global warming levels shown refer to increases relative to pre-industrial values (1850–1900). This list of health impacts and risks is not intended 
to be exhaustive, but instead focuses on well-documented conditions. This assessment is a synthesis across 58 studies on observed impacts and 29 studies on projected risks for 
health (see Table SM 9.7). The category of air pollution-related health outcomes includes health impacts from changing particulate matter concentrations due to climate change.

9
1372Chapter 9 Africa
linked (Schulz and Northridge, 2004; Moore and Diaz, 2015) and are 
largely outside the domain of the health sector. Climate change is 
already challenging the health and well-being of African communities, 
compounding the effects of underlying inequalities (high confidence). 
The interlinkage between climate change and social determinants of 
health are largely discussed at a global level (Commission of Social 
Determinants of Health, 2008), or for developed countries (Ahdoot 
et  al., 2015; Levy and Patz, 2015; Department of Economic and 
Social Affairs, 2016), with scant evidence for Africa. Nevertheless, 
there is robust evidence that the health impacts of climate change 
disproportionately affect the poorest people and children and, in some 
situations, can differ by gender and age (St Louis and Hess, 2008; 
Nyahunda et al., 2020; Ragavan et al., 2020; see Box 9.1). Unequal 
access to health care particularly affects rural communities (Falchetta

et  al., 2020), vulnerable women and children (Wigley et  al., 2020a) 
and challenges the achievement of development priorities such as 
universal health care access (SDG 3) (Weiss et al., 2020).
9.10.2 Observed Impacts and Projected Risks
Climate change is already impacting certain health outcomes in 
Africa (e.g. temperature-related mortality) and risks for most (but 
not all) health outcomes are projected to increase with increasing 
global warming (Figure 9.32), with young children (<5 years old), the 
elderly (>65 years old), pregnant women, individuals with pre-existing 
morbidities, physical labourers and people living in poverty or affected by 
other socioeconomic determinants of health being the most vulnerable 
(high confidence). Women may be more vulnerable to climate change 
impacts than men (Chersich et al., 2018; Jaka and Shava, 2018; Adzawla 
et al., 2019a). Contextualising projected impacts of climate change on

health requires an understanding of observed impacts (Figure  9.32). 
Without management and mitigation, current and projected morbidities 
and mortalities will put additional strain on health, social and economic 
systems (Hendrix, 2017; Alonso et al., 2019).
9.10.2.1 Vector-borne Diseases
9.10.2.1.1 Malaria
Observed impacts
Higher temperatures and shifting patterns of rainfall influence the 
distribution and incidence of malaria in sub-Saharan Africa (high 
confidence) (Agusto et  al., 2015; Beck-Johnson et  al., 2017). Up to 
10.9 million km2 of sub-Saharan Africa is optimally suitable for year-round 
malaria transmission (Mordecai et al., 2013; Ryan et al., 2015). Current 
climate suitability for endemic malaria transmission is concentrated in 
the central African region, some areas along the southern coast of west 
Africa and the east African coast (Ryan et al., 2020).
In east Africa, there has been an expansion of the Anopheles vector

into higher altitudes (Gone et  al., 2014; Carlson et  al., 2019) and 
increasing incidence of infection with Plasmodium falciparum with 
higher temperatures (high confidence) (Alemu et al., 2014; Lyon et al., 
2017). Over southern Africa, changes in temperature and rainfall are 
increasing malaria transmission (Abiodun et al., 2018). In west Africa, studies show both positive (Adu-Prah and Kofi Tetteh, 2015; Darkoh 
et al., 2017) and negative (M’Bra et al., 2018) correlations of malaria 
incidence with increases in mean monthly temperatures, and an 
abundance of Anopheles gambiae s.s. associated with mean diurnal 
temperature (Akpan et al., 2018).
Malaria incidence and outbreaks in east Africa were linked with both 
moderate monthly rainfall and extreme flooding (Boyce et al., 2016; 
Amadi et  al., 2018; Simple et  al., 2018), and increase 1–2  months 
after periods of rainfall in southern and west Africa (Diouf et  al.,

2017; Ferrão et al., 2017; Adeola et al., 2019). The years following La 
Niña events (southern Africa) (Adeola et al., 2017)) and high relative 
humidity (west Africa) (Adu-Prah and Kofi Tetteh, 2015; Darkoh et al., 
2017) have been positively linked with malaria incidence.
Projected risks
Since AR5, significant progress has been made in understanding how 
changes in climate influence the seasonal and geographical range 
of malaria vectors, transmission intensity and burden of disease of 
malaria across Africa. Yet projecting changes remains challenging given 
the range of factors that influence transmission and disease patterns, 
and model outputs contain high degrees of uncertainty (Zermoglio 
et al., 2019; Giesen et al., 2020). Models have limited ability to account 
for population changes and development trends (Kibret et al., 2015; 
2017), investments in health sectors and interventions (McCord, 2016; 
Colborn et al., 2018; Caminade et al., 2019), and confounders such as

age, socioeconomic status, employment, labour migration and climate 
variability (Bennett et al., 2016; Karuri and Snow, 2016; Byass et al., 
2017; Chuang et al., 2017; Colborn et al., 2018). Nevertheless, available 
models do allow for projections of malaria transmission under different 
climate change scenarios to be made with high levels of certainty.
In east and southern Africa and the Sahel, malaria vector hotspots 
and prevalence are projected to increase under RCP4.5 and RCP8.5 by 
2030 (1.5°C–1.7°C global warming) (high confidence) (Leedale et  al., 
2016; Semakula et al., 2017b; Zermoglio et al., 2019), becoming more 
pronounced later in the century (2.4°C–3.9°C global warming) (Ryan 
et  al., 2020). Under RCP4.5, 50.6–62.1  million people in east and 
southern Africa will be at risk of malaria by the 2030s (1.5°C global 
warming), and 196–198 million by the 2080s (2.4°C global warming) 
(Ryan et al., 2020). Northern Angola, southern DRC, western Tanzania and

central Uganda are predicted to be worst impacted in 2030, extending 
to western Angola, upper Zambezi River basin, northeastern Zambia 
and the east African Highlands by 2080 (Ryan et al., 2020). Under rising 
temperatures, by the 2050s, the greatest shifts in suitability for malaria 
transmission will be seen in east, southern and central Africa (2°C global 
warming) (Tonnang et al., 2014; Zermoglio et al., 2019; Ryan et al., 2020).
Conversely, in some regions, changing climatic conditions are projected 
to reduce malaria hotspots and prevalence. With continued GHG 
emissions, these include: west Africa by 2030 (1.7°C global warming) 
(high confidence) (Yamana et al., 2016; Semakula et al., 2017b; Ryan 
et al., 2020), parts of southern central Africa and dryland regions in east 
Africa by 2050 (2.5°C global warming) (high confidence) (Semakula 
et  al., 2017b; Ryan et  al., 2020) and large areas of southern central

Africa and the western Sahel by 2100 (>4°C global warming) (Yu et al.,

9
1373Africa  Chapter 9
2015; Tourre et al., 2019). These reductions in transmission correspond 
with decreasing environmental suitability for the malaria vector and 
parasite in these regions (Ryan et al., 2015; Mordecai et al., 2020). Most 
areas in Burkina Faso, Cameroon, Ivory Coast, Ghana, Niger, Nigeria, 
Sierra Leone, Zambia and Zimbabwe will have almost zero malaria 
transmission under RCP8.5 (Semakula et al., 2017b; Tourre et al., 2019).
The ENSO cycle currently contributes to seasonal epidemic malaria in 
epidemic-prone areas (high confidence), and is projected to shift the 
malaria epidemic fringe southward and into higher altitudes by mid- 
to end-century (high confidence) (Bouma et al., 2016; Semakula et al., 
2017b; Caminade et al., 2019). More evidence is needed, however, of 
climate variability impacts through ENSO cycles in future risk projections, 
as well as a deeper understanding of how climate change will impact

the length of transmission season for mosquitoes, particularly in areas 
where increases in spring and autumn temperatures may increase 
suitability for the reproduction of malaria vectors (Ryan et al., 2020). 
Other gaps in knowledge include a better understanding of mosquito 
thermal biology and thermal limits for a variety of species, potential 
adaptations to extreme temperatures and how landscape changes 
contribute to malaria transmission (Tompkins and Caporaso, 2016).
9.10.2.1.2 Mosquito-borne viruses
Climate variability has driven a global intensification of mosquito-
borne viruses (e.g., dengue, Zika and RVF), including expansion into 
areas with higher altitudes (Leedale et al., 2016; Mweya et al., 2016; 
Messina et  al., 2019). Concerns centre on diseases vectored by the 
yellow fever mosquito (Aedes aegypti), common throughout most 
of sub-Saharan Africa, and the tiger mosquito (Aedes albopictus),

currently largely confined to western central Africa (Kraemer et  al., 
2019; Mordecai et al., 2020).
Although warming temperatures are largely responsible for increasing 
environmental suitability for mosquito vectors (Mordecai et  al., 
2019), droughts can augment transmission when open water storage 
provides breeding sites near human settlements, and when flooding 
enables mosquitoes to proliferate and spread viruses further (Mweya 
et al., 2017; Bashir and Hassan, 2019). Within Africa’s rapidly growing 
cities, diseases vectored by urban-adapted Aedes mosquitoes pose a 
major threat, especially in west Africa (Zahouli et al., 2017; Weetman 
et al., 2018; Messina et al., 2019). Dengue virus expansion may cause 
explosive outbreaks but the burden of dengue haemorrhagic fever and 
associated mortality is higher in areas where transmission is already 
endemic (Murray et al., 2013).
Populations of Aedes aegypti and Aedes albopictus mosquitoes and

epidemics of dengue and yellow fever and other Aedes-borne viruses 
are expected to increase, including at high altitudes (Weetman et al., 
2018; Messina et al., 2019; Ryan et al., 2019; Gaythorpe et al., 2020; 
Mordecai et al., 2020). Aedes albopictus may expand beyond western 
central Africa into Chad, Mali and Burkina Faso by mid-century at 
>2°C global warming (Kraemer et al., 2019). Shifts projected in Aedes  range due to changing environmental suitability, combined with rapid 
urbanisation and population growth, suggest that by 2050 populations 
exposed to these vectors in Africa may double, and by 2080 nearly 
triple at >2°C global warming (Kraemer et al., 2019). Southern limits 
of dengue transmission in Namibia and Botswana, and the western 
Sahel, may show the greatest expansions in environmental suitability 
under 1.8°C–2.6°C global warming (Messina et  al., 2019). In the 
warmest scenarios (RCP8.5), however, some parts of central Africa

may become too hot for mosquitoes to transmit dengue, and thus 
at-risk populations may peak at intermediate warming levels (Ryan 
et al., 2019). Climatic conditions favourable for mosquitoes, combined 
with the increase of animal trade, may result in the expansion of the 
geographic range of zoonotic diseases like RVF (Martin et al., 2008), 
a threat for human and animal health with strong socioeconomic 
impacts (Peyre et al., 2015).
9.10.2.2 Diarrhoeal Diseases, HIV and Other Infectious Diseases
9.10.2.2.1 Diarrhoeal diseases
Africa has the highest rates of death due to diarrhoeal diseases in the 
world (Havelaar et al., 2015; Troeger et al., 2018) and many children have 
repeated diarrhoeal episodes with impaired growth, stunting, immune 
dysfunction and reduced cognitive performance (Squire and Ryan, 
2017). High land and sea temperatures (Paz, 2009; Musengimana et al., 
2016) and precipitation extremes increase transmission of bacterial and

protozoal diarrhoeal disease agents (Boeckmann et al., 2019) through 
contamination of drinking water and food preparation and preservation 
practices (Figure 9.33; Levy et al., 2016; Soneja et al., 2016; Walker, 2018).
Cholera incidence has been shown to increase with temperature 
(Trærup et al., 2011). Outbreaks, however, are most frequent in east 
and southern Africa following tropical cyclones (Moore et al., 2017b; 
Troeger et al., 2018; Ajayi and Smith, 2019; Cambaza et al., 2019).
Africa’s rapidly urbanising population increases the demand for 
freshwater and is occurring in places that already have stretched water 
and sanitation infrastructure (Howard et  al., 2016). These conditions, 
especially during periods of water scarcity, can reduce the frequency and 
adequacy of hand washing and thereby increase disease transmission.
Disruptions in water availability, such as during droughts or infrastructure

breakdown, will jeopardise access to safe water and adequate sanitation, 
undermine hygiene practices and increase environmental contamination 
with toxins (Howard et al., 2016; WWF-SA, 2016; Miller and Hutchins, 
2017).
Climate change is projected to cause 20,000–30,000 additional 
diarrhoeal deaths in children (<15  years old) by mid-century under 
1.5°C–2.1°C global warming (WHO, 2014), with west Africa most 
affected, followed by east, central and southern Africa. Cholera 
outbreaks are anticipated to impact east Africa most severely during 
and particularly after ENSO events (Moore et al., 2017b).

9
1374Chapter 9 AfricaClimate hazard Pathways to impact Vulnerable population groups Health outcomes Pathogen exposure
Increased exposure, to bacterial 
(e.g. E. coli, Campylobacter , 
Salmonella & Shigella, Listeria, 
V. Cholera), protozoal (e.g. 
Cryptosporidium & Giardia) & 
other pathogens (2.4.2.9; 5.1 1; 
7.2.2.2; 7.2.2.3)/g122/g3Increased reproduction & survival of  
 pathogens in water (Box 3.3) & food  
 sources (5.1 1; 5.12)
/g122/g3Contamination of food & drinking sources  
(4.2.6; 4.3.3) with human & animal faeces  
(7.2.2.3) /g122/g3Increased phytoplankton, copepods & V.   
 Cholera (Box 3.3; 3.5.5)
/g122/g3Damage or disruption of water and  
 sanitation systems, reducing the quality and  
 quantity of water for drinking and hygiene  
(4.3.3)/g122/g3Use of unsafe sources of drinking water (4.2.6)
/g122/g3Reduced hygiene & food safety (cleaning   
 & processing food (5.11; 7.2.2.3)
/g122/g3Use of rainwater tanks for irrigation of vegetablesDecreased precipitation
(Box 7.2)

Increased sea temperature
(Box 7.2)
Increased precipitation & flooding
(Box 7.2)
Storms & extreme weather events
(Box 7.2) Increased heatDiarrhoeal disease/g122Disease, including dehydration
 with hospital admission, loss of
 weight, stunting & death (4.3.3;
 7.2.2.2)
/g122Asymptomatic infection
Shedding of 
pathogens/g122General population
/g122Infants & children (<5 years)
/g122Elderly (>65 years)
/g122Individuals with co-morbidities
/g122Undernourished individuals (5.12; 9.6.1)
/g122Urban residents in overcrowded informal  
 settlements (3.4.8; 4.4.1.3; 6)
/g122Resource-poor segment of the population  
 with no or limited access to potable water
/g122Displaced people settled in informal  
 settlements (Box 9.7)Pathways to impact: diarrhoeal disease
Figure 9.33 |  Schematic showing the pathways to diarrhoeal disease impacts in Africa as a result of exposure to climate hazards. Numbers in the figure refer to chapter sections of this report.

9
1375Africa  Chapter 9
9.10.2.2.2 HIV
Although levels of new HIV infections declined sharply during the 
last decade, still more than a million adults and children become 
infected each year (UNAIDS, 2020). Climate influences on HIV are 
predominately indirect such as through heightened migration due to 
climate variability, or extreme weather events leading to increased 
transactional sex to replace lost sources of income. Changes in 
climate affect each of the main drivers of HIV transmission in women, 
including poverty, inequity and gender-based violence (Burke et  al., 
2015a; Loevinsohn, 2015; Fiorella et al., 2019).
‘Oscillating’ or ‘circular’ migration for migrant workers in urban and 
mining centres drove HIV transmission in the 1990s and 2000s (Lurie, 
2006), and climate-related displacement may have similar effects (See 
Box 9.7; Gray and Mueller, 2012; Loevinsohn, 2015; Low et al., 2019).

Food insecurity and nutritional deficiencies, projected to increase with 
increasingly variable climates, has been shown to increase sexual risk-
taking and migration, as well as increase susceptibility to other infections 
(Lieber et  al., 2021). Projected increases in exposure to infectious 
diseases pose considerable threats to HIV-infected people who may 
already have compromised immune function. Additionally, reduced lung 
function in people with HIV from previous tuberculosis infection may put them at high risk for morbidity and death during extreme heat (Abayomi 
and Cowan, 2014). Moreover, extreme weather events accompanied by 
damage to health system infrastructure could compromise the continuity 
of antiretroviral treatment (Weiser et al., 2010; Pozniak et al., 2020).
9.10.2.2.3 Other infectious diseases
Poor populations in the western Sahel have the highest burden of 
bacterial meningitis worldwide, with seasonal dynamics driven by the

dry Harmattan winds that transport dust long distances across the 
continent (Agier et al., 2013; García-Pando et al., 2014). In Nigeria, rising 
temperatures are projected to increase meningitis cases by about 50% for 
1.8°C global warming (RCP2.6 in 2060–2075), and by almost double for 
3.4°C global warming (RCP8.5 in 2060–2075) (Abdussalam et al., 2014). 
Bilharzia is also highly climate sensitive, with its distribution influenced 
by changes in temperature and precipitation, as well as development, 
such as the introduction of freshwater projects (e.g., canals, hydroelectric 
dams and irrigation schemes) (Adekiya et al., 2019).
9.10.2.3 Temperature-related Impacts
9.10.2.3.1 Mortality and morbidity
Emergency department visits and hospital admissions have been 
shown to increase at moderate-to-high temperatures (Bishop-Williams Box 9.6 | Pandemic risk in Africa: COVID-19 and future threats

Rapid advances in vaccination and other control measures in high-income countries means that the burden of COVID-19 is increasingly 
concentrated in low- and middle-income countries, including those in Africa. The extent to which the COVID-19 pandemic is influenced 
by weather or by future changes in climate remains contested (WMO, 2021). In time, COVID-19 may develop seasonal dynamics (Baker 
et al., 2020; Kissler et al., 2020) similar to other respiratory infections (Carlson et al., 2020b).
Early work interpreted low-reported cases of COVID-19 in Africa as suggesting evidence of a protective climatic effect, but increasing 
evidence indicates the role of climate is secondary to the timing of disease introduction, the pace of implementation of non-pharmaceutical 
interventions and surveillance gaps (Evans et  al., 2020; WMO, 2021). Going forward, testing coverage, reporting, governance, non-

pharmaceutical interventions and vaccine distribution and uptake are likely to be far more significant for Africa’s COVID-19 trajectory than 
climate change. Compounding risks, where climate hazards and natural disasters impair outbreak responses, may disrupt interventions 
or cause additional deaths (Phillips et al., 2020; Salas et al., 2020).
Emerging and future pandemic threats
Future influenza pandemics are highly likely, as are regional epidemics and pandemics of novel zoonotic viruses (including coronaviruses 
and flaviviruses) (high confidence). In the next decades, climate change will reshape the risk landscape for emerging zoonotic threats as 
wildlife-livestock-human interfaces shift, facilitating the emergence of novel zoonotic threats and spillover of known zoonoses into novel 
geographies (Carlson et al., 2020a; Mordecai et al., 2020). Characteristics of urban development and level of service provision, for example,

crowded living spaces and transport facilities, and access to water and sanitation will influence the transmission of COVID-19 and future 
disease outbreaks (Wilkinson, 2020). Historically, west and central Africa were considered especially at risk of outbreaks given their high 
biodiversity, high intensity of human–wildlife contact including wild meat trade, vulnerable health systems and history of Ebola virus 
disease outbreaks (Paige et al., 2014; Allen et al., 2017; Pigott et al., 2017). However, as the Middle East respiratory syndrome coronavirus 
(MERS-CoV) and COVID-19 pandemics have shown, there are multiple hotspots of viruses with pandemic potential globally, many of which 
are not in Africa. Thus, labelling African rainforests as unique ‘hotspots’ undermines global health work and pandemic preparedness.

9
1376Chapter 9 AfricaClimate hazard Pathways to impact Vulnerable population groups Health outcomes
/g122General population
/g122Undernourished individuals  (5.12; 9.12)
/g122Outdoor workers
/g122Resource-poor segment of the   
 population with no or limited cooling  
 systems  
/g122Urban residents in overcrowded   
 informal settlements  (3.4.8; 4.4.1.3; 6)/g122/g3Reduced crop yields, (5; 9.12)   
 including from increased   
 evaporation & soil drying (4.2; 9.7.2)
/g122/g3Increased energy use from sweating  
 & higher metabolic rate
/g122/g3Increased outdoor activities &   
 environmental exposures  (7.2)Increased heatHeat-related mortality & morbidity/g122Heat stress
/g122Heat exhaustion
/g122Heat stroke
/g122Dehydration
/g122Cardio-respiratory compromise
/g122Heat-related symptoms e.g. headache
/g122Drowsiness, poor concentration, fatigue
/g122Reduced work performance

/g122Poor educational performance
/g122Accidents 
/g122Mortality
/g122Anxiety , anxiety disorders, depression
/g122Increased aggression
/g122Interpersonal violence including 
 homicide
/g122Suicide
/g122Collective violence
/g122Maternal morbidity , prolonged labour
/g122Preterm birth, stillbirth
/g122Adverse long-term outcomes
/g122Heat stress exacerbates heat production
 by placenta, developing foetus & from 
 additional maternal weight, & uterine 
 contractions during labourPathways to impact: diarrhoeal disease
Mental health
(7.2.4; 9.6.1)
Poor pregnancy outcomes
(7.2.4; 9.6.1)Direct heat-related 
morbidity & mortality
Figure 9.34 |  Schematic showing the pathways of impact for heat-related morbidities in Africa as a result of exposure to climate hazards. Numbers in the figure refer to chapter sections of this report. Indirect health

impacts of heat are not shown. For example, risk of malnutrition from reduced crop yields or reduced fisheries catches (see Section 9.8.5).

9
1377Africa  Chapter 9
et  al., 2018; van der Linden et  al., 2019), with increased levels of 
mortality recorded on days with raised temperatures in Burkina Faso 
(Kynast-Wolf et  al., 2010; Diboulo et  al., 2012; Bunker et  al., 2017), 
Ghana (Azongo et  al., 2012), Kenya (Egondi et  al., 2012; Egondi 
et al., 2015), South Africa (Wichmann, 2017; Scovronick et al., 2018), 
Tanzania (Mrema et al., 2012) and Tunisia (Bettaieb et al., 2010; Leone 
et al., 2013). Cause of death most commonly involves cardiovascular 
diseases (Kynast-Wolf et  al., 2010; Scovronick et  al., 2018), but 
increased incidences of respiratory (Scovronick et  al., 2018), stroke 
(Longo-Mbenza et al., 1999) and non-communicable diseases (Bunker 
et al., 2017) have also been linked with heat.
Excess death rates from non-optimal temperature in sub-Saharan 
Africa are estimated to be nearly double the global average, with 24% 
of the more than 5 million annual deaths globally associated with non-

optimal temperature occurring in Africa (Zhao et al., 2021). The region 
had the world’s highest cold-related excess death ratio and lowest 
heat-related excess death ratio over the period 2000–2019. However, 
during this time, cold-related excess deaths declined more rapidly than 
the increase in heat-related excess deaths, resulting in a net decrease 
in the excess death ratio from temperature.
Recent estimates of the burden of mortality associated with the 
additional heat exposure from recent human-caused global warming 
suggest approximately 43.8% of heat-related mortality in South Africa 
was attributable to human-caused climate change from 1991–2018 
(Vicedo-Cabrera et  al., 2021). In many of South Africa’s 52 districts, 
this equates to dozens of deaths per year. The elderly and children 
under 5 years are most vulnerable to heat exposure (Sewe et al., 2015; 
Scovronick et al., 2018).
Globally, Africa is predicted to suffer disproportionately from higher

temperature-related all-cause mortality from global warming, compared 
to temperate northern hemisphere countries (Carleton et al., 2018). The 
number of days projected to exceed potentially lethal heat thresholds 
per year reaches 50–150 days in west Africa at 1.6°C global warming, 
up to 200 days in west Africa and 100–150 days in central Africa and 
parts of coastal east Africa at 2.5°C, and over 200 days for parts of west, 
central and east Africa for >4°C global warming (Mora et al., 2017; see 
Sections 9.5.3–7; Figure 9.15). Projected rates of heat-related mortality 
among people in the Middle East and north Africa who are older than 
65 years increase by 8–20 fold in 2070–2099, compared with 1951–
2005, based on RCP4.5 and RCP8.5 (both at >2°C global warming) 
(Ahmadalipour and Moradkhani, 2018).
Temperature-related mortality across Africa is projected to escalate 
with global warming. Above 1.5°C the risk of heat-related deaths

rises sharply, with at least 15 additional deaths per 100,000 annually 
across large parts of Africa, reaching 50–180 additional deaths per 
100,000 people annually in regions of north, west, and east Africa 
for 2.5°C global warming, and increasing to 200–600 per 100,000 
people annually for 4.4°C global warming (Figure 9.35; Carleton et al., 
2018). However, some regions that currently experience cold-related 
mortality (e.g., Lesotho and Ethiopian Highlands) are projected to 
have reduced temperature-related mortality risk from warming. GHG mitigation is projected to save tens of thousands of lives: 
limiting warming to RCP4.5 (2.5°C) rather than RCP8.5 (4.4°C) at the 
end of the century is projected to avoid on average 71 deaths per 
100,000 people annually across Africa with larger reductions in risk 
in north, west, central and parts of east Africa (Figure 9.35). The cost 
of mitigating heat stress using energy-intensive cooling methods is

expected to be unachievable for many African countries (Parkes et al., 
2019; see Section 9.9.4).
9.10.2.3.2 Heat stress in specific settings
Heat stress symptoms are prevalent among people in buildings that 
are poorly ventilated or insulated, or constructed with unsuitable 
materials (e.g., corrugated metal sheeting). These features are 
common to many structures in Africa, including slums, informal and 
low-income settlements, as well as schools and healthcare facilities 
(Bidassey-Manilal et al., 2016; Naicker et al., 2017; Wright et al., 2019). 
Temperatures inside these structures can exceed outdoor temperatures 
by 4°C or more and have large diurnal fluctuations (Mabuya and 
Scholes, 2020). Daily wage labourers and residents of urban informal 
settlements are among the most vulnerable to heat stress because 
of the urban heat island effect, with congestion, and inadequate 
ventilation, shade, open space and vegetation (Bartlett, 2008) being

associated with impacts of both hot and cold conditions on public 
health (Ramin, 2009). Temperature extremes are expected to result in 
relatively more deaths in informal settlements than in other settlement 
types (Scovronick and Armstrong, 2012).
The urban heat island effect exacerbates current and projected heat 
stress in Africa’s rapidly growing cities (Mitchell, 2016) and is discussed 
in more detail in Section 9.9.3.
Escalating temperatures and longer-duration heatwaves are expected 
to heavily affect workers already exposed to extreme temperatures, 
for example, outdoor workers (Kjellstrom et  al., 2018) and miners 
(El-Shafei et al., 2018; Nunfam et al., 2019a; Nunfam et al., 2019b). 
Vulnerability may also be high for women who cook food for a living, 
and children who accompany them, due to prolonged exposure to 
high temperatures (Parmar et  al., 2019). Prisons, commonly poorly 
ventilated and overcrowded, are also high-risk settings (Van Hout and 
Mhlanga-Gunda, 2019).

9.10.2.3.3 Maternal and child health
Exposure to high temperatures during pregnancy has been linked with 
adverse birth outcomes, including stillbirths or miscarriages (Asamoah 
et al., 2018) and long-term behavioural and developmental deficiencies 
(Duchoslav, 2017; MacVicar et al., 2017).
9.10.2.4 Impacts of Extreme Weather
During extreme conditions, for example, Cyclone Kenneth (Codjoe 
et  al., 2020) and El Niño 2015–2016 (WHO, 2016; Pozniak et  al., 
2020), direct physical injury, loss of life, destruction of property and 
population displacement can occur. Flooding and landslides are 
common after extreme rainfall and are the most frequently described 
impact of climate variability in Africa’s cities currently, with residents

9
1378Chapter 9 Africa
of poorly serviced or informal settlements most vulnerable (Hunter 
et  al., 2020). Post-traumatic stress disorders in affected individuals 
are common, including in children (Rother, 2020). In rural areas, the 
resulting damage to health facilities, access routes and transport 
services can severely compromise health service delivery (WHO, 2016). The effects of extreme weather on urban health infrastructure depends 
on the characteristics, location and adaptive capacity of cities (see 
Section 9.9.4).
(c) Change in risk from stronger mitigation efforts (RCP4.5 - RCP8.5)
16–45
311–419Additional 
deaths 
(increased risk)
Avoided 
deaths
(reduced risk)
176–31046–1756–1011–15
1–501–5
6–10
11–1516–4546–80
1.6°C 2.0°C 2.5°C
(b) High emissions (RCP8.5)
1.7°C 2.5°C 4.4°CTemperature-related mortality risk in Africa with increased global warming
(a) Intermediate emissions (RCP4.5)Period 2020–2039 Period 2040–2059 Period 2080–2099 Changes in temperature-related

annual mortality rates per 100,000
1–5
16–45
46–149Additional 
deaths
Avoided 
deaths11–156–1046–175176–310
6–1011–1516–45
1–5
0311–450451–609
Figure 9.35 |  Projected temperature-related mortality risk in Africa with increasing global warming. Maps show changes in mortality rates in deaths per 100,000 
for global warming in the years 2020–2039, 2040–2059 and 2080–2099 for 
(a) intermediate emissions scenario (RCP 4.5); 
(b) a high emissions scenario (RCP 8.5); and 
(c) showing avoidable deaths due to increased emissions mitigation efforts to achieve a lower global warming level (RCP4.5 rather than RCP8.5). These estimates of climate 
change impacts on mortality rates include temperature-related impacts only. They account for the benefits of income growth and incremental adaptation to climate change, both 
of which reduce mortality sensitivity to extreme temperatures. Projections were based on income and demographics from Shared Socioeconomic Pathway 3 (SSP3), with future

adaptation based on adaptation actions observed in the global historical record. The estimates do not include the costs of the behaviours and investments required to achieve such 
adaptation (Carleton et al., 2018). Areas shown in burgundy in (c) have fewer deaths due to temperature under RCP8.5 than RCP4.5. This is because cold is currently the greatest 
driver of temperature-related deaths in these areas, which is projected to be alleviated with increasing levels of global warming (Zhao et al., 2021).

9
1379Africa  Chapter 9
9.10.2.5 Malnutrition
9.10.2.5.1 Observed impacts
Africa has experienced the greatest impacts of climate change on acute 
food insecurity and malnutrition (FAO and ECA, 2018). Adverse climatic 
conditions exacerbate the impacts of an unstable global economy, 
conflict and pandemics on food insecurity (AfDB, 2018b; Food Security 
Information Network (FSIN), 2019; Fore et  al., 2020; see Chapter  5 
Section 5.12.4).
More than 250 million Africans are undernourished, mostly in central 
and east Africa (FAO et al., 2020), which increases childhood stunting, 
affects cognition and has trans-generational sequelae (IFPRI, 2016; 
UNICEF et al., 2019). Undernutrition is strongly linked with hot climates 
(Hagos et  al., 2014; Tusting et  al., 2020). In Burkina Faso, low crop 
yields resulted in around 110 deaths per 10,000 children under 5 years, 
with 72% of this impact attributable to adverse climate conditions in 
the growing season (Belesova et al., 2019).

Increasing incidence and expanded distributions of vector-borne livestock 
diseases (e.g., bluetongue, trypanosomiasis and RVF) in response to 
changes in rainfall and increasing temperatures, undermine food security, 
especially among subsistence farmers (Samy and Peterson, 2016; 
Caminade et al., 2019). Locust infestations linked with changes in climate 
(Salih et al., 2020) are a major risk for food security in Africa.
9.10.2.5.2 Projected risks
Projected risks for malnutrition in Africa are high (FAO, 2016; see 
Section  9.8.1): 433  million people in Africa are anticipated to be 
undernourished by 2030 (FAO et al., 2020) and, compared to 1961–
1990, 1.4  million additional African children will suffer from severe 
stunting by 2050 under 2.1°C global warming (WHO, 2014). In Burkina 
Faso, the mortality burden due to low crop yields could double by 2100 
with 1.5°C of global warming (Belesova et al., 2019). Drought risks

will include crop and livestock failures (Ahmadalipour et  al., 2019). 
Additionally, increasing concentrations of atmospheric CO 2 will affect 
the nutritional quality of C 3 plant staples, lowering levels of protein 
and minerals like zinc and iron (Myers et al., 2014; Weyant et al., 2018). 
Declining fish catches due to ocean warming, illegal fishing and poor 
stock management are projected to increase deficiencies of zinc, iron 
and vitamin A for millions of people across Mozambique, Angola and 
multiple west African countries (see Section 9.8.5; Golden et al., 2016).
9.10.2.6 Non-communicable Diseases and Mental Health
Links between climate change and the environmental risk factors for 
non-communicable diseases (NCDs) may be direct (e.g., extreme heat 
exposure in people with cardiovascular disease) or indirect, such as 
via the global agriculture and food industry (Landrigan et al., 2018). 
These effects are largely unreported for Africa (Amegah et al., 2016),

where the burden of many NCDs is growing rapidly with increasing 
urbanisation and pollution (Rother, 2020).
Many urban poor populations have unhealthy dietary practices, which 
present major risks for obesity, type II diabetes and hypertension. Paradoxically, despite growing levels of undernutrition, the incidence 
of overweight and obesity continues to rise in Africa, particularly in 
children under 5 years from the northern and southern parts of the 
continent (FAO and ECA, 2018). Diabetes is increasingly prevalent 
and outcomes may worsen if climate change undermines health 
infrastructure and the range of available foods (Keeling et al., 2012; 
Kula et al., 2013; Chersich and Wright, 2019).
The relationship between cancer and climate change is complex 
and indirect. Changing temperature and humidity may alter the 
distribution of aflatoxin-producing fungi, contaminating food (grains, 
maize) and causing cancer (see Box 5.9 in Chapter 5; Sserumaga et al.,

2020; Valencia-Quintana et al., 2020). Severe storms and flooding may 
disrupt wastewater treatment or disposal, potentially contaminating 
drinking water with carcinogenic substances.
Areas with low service provision (e.g., informal settlements in Africa) 
suffer from increased infestations of pests such as flies, cockroaches, 
rats, bedbugs and lice, which may be controlled by chemical pesticides 
(Rother et al., 2020) and may become more prevalent with a changing 
climate (Mafongoya et  al., 2019). Inappropriate pesticide use and 
disposal cause endocrine disruption and increased incidences of some 
cancers (Rother et al., 2020).
9.10.2.6.1 Mental health and well-being
Mental health and well-being are affected by local climate conditions 
and are therefore sensitive to climate change (Burke et  al., 2018b; 
Obradovich et  al., 2018). High temperatures are strongly associated 
with poor mental health and suicide in South Africa (Kim et al., 2019).

Exposure to extreme heat directly influences emotional control, 
aggression and violent behaviour, escalating rates of interpersonal 
violence, with homicides rising by as much as 18% in South Africa when 
temperatures are above 30°C compared with temperatures below 20°C 
(Burke et al., 2015a; Chersich et al., 2019b; Gates et al., 2019).
Extreme weather events are often severely detrimental to mental health 
(Scheerens et al., 2020), with elevated rates of anxiety, post-traumatic 
stress disorder and depression in impacted individuals (Schlenker and 
Lobell, 2010; Nuvey et al., 2020). Youths may be at especially high risk 
(Barkin et al., 2021).
Loss of livestock from disease or lack of pastures is strongly linked 
with poor mental health among farmers (Nuvey et al., 2020). Climate 
change impacts on mental health among refugees is concerning but 
remains under-researched (Matlin et al., 2018).
9.10.2.7 Air Quality-related Health Impacts

Links between air quality and climate change are complex (Smith 
et  al., 2014; Szopa et  al., 2021). Increases in particulate matter 
concentrations are driven more by vehicle emissions, solid waste, 
biomass burning and development (Abera et  al., 2021) than by 
climate change, and these factors vary widely across regions of the 
continent (West et al., 2013). Women and children who are exposed 
to high particulate matter concentrations when cooking indoors and 
HIV-infected people are more vulnerable to the health impacts of air

9
1380Chapter 9 Africa
Box 9.7 | The health–climate change nexus in Africa
The intersections between climate change and human health involve interactions of numerous systems and sectors (Lindley et al., 2019; 
Yokohata et al., 2019). This complexity means that holistic, transdisciplinary and cross-sectoral (systems) approaches like One Health, 
EcoHealth and Planetary Health can improve the long-term effectiveness of responses to health risks (Zinsstag, 2012; Whitmee et al., 
2015; Nantima et al., 2019). More research is needed to identify sustainable solutions (Rother et al., 2020), as recently re-emphasised 
by the Intergovernmental Panel on Biodiversity in its report on the COVID-19 pandemic (IPBES, 2020). The close dependency of many 
Africans on their livestock and surrounding ecosystems forms a context where integrated health approaches are especially critical for 
addressing climate change risks to health (Figure Box 9.7.1; Watts et al., 2015; Cissé, 2019).

Integrated approaches to health in Africa can deliver multiple benefits for humans and ecosystems For example, rather than addressing 
micronutrient deficiencies with supplements, which may not be accepted culturally and can be disrupted by stockouts or similar, addressing 
nutrient deficiencies in staple crops by selecting or breeding more nutritious varieties (e.g., orange-fleshed sweet potatoes or ‘golden rice’ 
for vitamin A deficiency) may prove to be more sustainable options (Datta et al., 2003; Nair et al., 2016; Laurie et al., 2018; Oduor et al., 
2019; Stokstad, 2019). Additionally, some micro- or macronutrient deficiencies and food insecurities may be improved by addressing the 
depletion of soils through better management, including the incorporation of holistic, sustainable principles, such as those promoted by

agroforestry or regenerative agriculture (Rhodes, 2017; Elevitch et al., 2018; LaCanne and Lundgren, 2018; Chapter 5 Section 5.12.4).pollution (Abera et al., 2021). Information on the direction of change of 
air quality in different African regions attributable to climate change are 
contradictory (Westervelt et al., 2016; Silva et al., 2017). Additionally, 
much uncertainty remains about interactions between air quality and 
climate change and relative impacts of different modes of development 
and climate change on pollutants. However, increasing temperatures 
combined with a reduction in rainfall are likely to increase particulate 
matter concentrations (Abera et al., 2021), particularly in north Africa 
(Westervelt et al., 2016; Silva et al., 2017).
Nevertheless, continued dependence on fossil-fuelled power plants will 
result in tens of thousands of avoidable deaths due to air pollution by 
2030 (Marais and Wiedinmyer, 2016), and accelerate climate change.

Actions to reduce air pollution can both mitigate climate change and 
have major co-benefits for health (West et al., 2013; Rao et al., 2016; 
Markandya et al., 2018; Rauner et al., 2020a; Rauner et al., 2020b) see 
also AR6 WGIII, Chapters 3, 4, 8 and 10). Investing in renewable energy 
resources rather than reliance on the combustion of fossil fuels would 
mark an important step forward for African population health (Marais 
et al., 2019). This is especially important in South Africa which emits 
approximately half the total carbon emissions for Africa, ranking 12th 
in the world for carbon emissions (Mohsin et al., 2019).
Dust events in west Africa have severe health impacts (cardiorespiratory 
and infectious diseases, including meningitis) (Ayanlade et al., 2020) 
given the proximity of the Sahara, which produces about half of the 
yearly global mineral dust (de Longueville et al., 2013). Wildfires are 
projected to become the main source of particulate matter in west,

central and southern Africa under both the lowest and highest future 
emissions scenarios, whereas, under intermediate scenarios (i.e., SSP3/RCP4.5), anthropogenic sources of particulate matter are projected to 
exceed that produced by wildfires (Knorr et al., 2017).
9.10.3 Adaptation for Health and Well-being in Africa
In this section, we focus on adaptation actions that are well-documented 
or shown to have the potential for substantially improving health or 
well-being. These adaptation options are assessed in Figure 9.36 and 
Table 9.11.
In Africa, adaptive responses have begun to be implemented by 
local, national and international entities (Ebi and Otmani Del Barrio, 
2017). With strong leadership, these initiatives can be used as an 
opportunity for comprehensive, transformative change rather than 
incremental improvements to existing systems. Adaptation responses 
are necessarily context specific and can focus on providing services

for vulnerable and high-risk populations (Dumenu and Obeng, 2016; 
Herslund et al., 2016).
Adaptation actions in the health sector range from building resilient 
health systems to preparing responses to health impacts of extreme 
weather events to reducing effects of increasing temperatures in 
residential and occupational settings (Kjellstrom et al., 2016; Chersich 
and Wright, 2019). A climate-resilient health system involves functional 
and effective health systems (WHO, 2015), national and local policy 
plans with resources for implementation, and long- and short-term 
communication strategies to raise awareness around climate change 
(Nhamo and Muchuru, 2019).

9
1381Africa  Chapter 9
Human HealthEcosystem Health
Animal HealthDeforestation5, 6Loss of ecosystem
services1, 2, 3
Changing disease 
distributions
(incl. emerging
& zoonotic
diseases)3Reduction in quantity
and quality of feed
and forage4, 7Animal starvationFood insecurity , malnutrition
Reduced provisioning,
pollination,
seed dispersal
Sections 9.6
and Chapters 2, 3, 5
Section 9.10 and Chapter 7 Section 9.8 and Chapter 5Reduced access to phytomedicines /
traditional remedies
Reduced regulation
of air and 
water quality
Cardio-respiratory and
diarrhoeal diseasesIncreased
human-wildlife-livestock
interaction
Pandemics
Worsening livestock health4, 7Increased
antimicrobial us e
Antimicrobial resistance
Reduced livestock products & services7
Food insecurity ,
malnutritionReduced income
Impaired mental health
& wellbeing
Synergy ecosystem
and human heathSynergy ecosystem
and animal heathSynergy human
and animal heathSynergy ecosystem,
human, animal heathIncreased
chance of spillover

diseases in people and
livestoc kInterlinkage between human, ecosystem and animal health
Figure Box 9.7.1 |  Human, ecosystem and animal health are intimately interlinked, and require transdisciplinary approaches such as One Health, 
EcoHealth and Planetary Health for effective, sustainable, long-term management. This schematic shows some examples of these interlinkages, and how 
they impact human health, highlighting the complex interactions and the importance of holistic, systems approaches to health interventions, including for climate change 
adaptation. Supporting literature: (1) (Egoh et al., 2012); (2) (Wangai et al., 2016); (3) (Failler et al., 2018); (4) (Ifejika Speranza, 2010); (5) (Brancalion et al., 2020); (6) 
(Bloomfield et al., 2020); (7) (Rojas-Downing et al., 2017).Box 9.7 (continued)

9
1382Chapter 9 Africa
Many health conditions associated with climate change are not new, 
and existing evidence-based interventions can be modified to address 
shifting disease patterns (Ebi and Otmani Del Barrio, 2017). Adaptation 
options can build on a long tradition of community-based services in 
Africa (Ebi and Otmani Del Barrio, 2017). Indeed, strengthening many 
of the services already provided (e.g., childhood vaccinations and 
vector control) will help curtail emerging burdens of climate-sensitive 
conditions. However, a disproportionate focus on emerging zoonotic 
and vector-borne viruses could undermine climate change adaptation 
efforts in Africa if it shifts the focus away from health system 
strengthening and leaves few resources for addressing other health 
Core components of an adaptation response include rapid impact 
packages (e.g., mass drug administration for schistosomiasis), education

of women and direct poverty alleviation (Bailey et  al., 2019). Where 
droughts are more frequent and rainfall patterns have shifted, adaptation 
support can be provided for strategies developed by communities, 
including the adaptation of livelihoods and diversification of crops and 
livestock (Mbereko et al., 2018; Bailey et al., 2019). Continued efforts 
through partnerships, blending adaptation and disaster risk reduction, 
and long-term international financing are needed to bridge humanitarian 
and sustainable development priorities (Lindley et  al., 2019; Cross-
Chapter Box HEALTH in Chapter 7).
9.10.3.1 Risk Assessment and Warning Systems
Improved institutional capacity for risk monitoring and early warning 
systems is key to support emergency preparedness and responsiveness 
in Africa, as well as shock-responsive and long-term social protection 
(FAO and ECA, 2018). Climate risk assessments grounded in evidence

and locally appropriate technologies are important for identifying 
priority actions, the scale of intervention needed and high-risk 
geographical areas and populations. Potential tools include those 
developed by WHO (Ceccato et al., 2018) and the Strategic Tool for 
Analysis of Risk (Ario et al., 2019).
Warning systems that predict seasonal to intra-seasonal climate risks 
could assist in improving response times to extreme weather events 
(such as droughts, flooding or heat waves) and shifts in infectious 
diseases. Weather and other types of forecasting provide an advanced 
warning—a central tenet of disaster risk reduction (Funk et al., 2017; 
Okpara et  al., 2017a; Lumbroso, 2018). Models encompassing each 
component of the human–animal–environmental interface, including 
disease surveillance in humans and animals and remote sensing of 
vegetation indexes, water and soil can be used to project patterns of 
zoonose outbreaks (UNDP , 2016; Bashir and Hassan, 2019; Durand

et al., 2019). Early warning systems may help better prepare for these 
and other forms of infectious disease outbreaks (Thomson et al., 2006) 
but adaptation is possible in the absence of statistical tools through 
vaccination and surveillance, for example.
Surveillance systems for diseases and vectors are well-established in 
many parts of Africa (Ogden, 2017). However, many data gaps remain, 
especially in monitoring climate-sensitive conditions such as diarrheal- 
and arbovirus-related diseases, and morbidity and mortality stemming 
from heat exposure (Ogden, 2017; Buchwald et al., 2020).Climate and health adaptation indicators are required for Africa 
to strengthen institutional capacity for risk monitoring and early 
warning systems, emergency preparedness and response, vulnerability 
reduction measures, shock-responsive and long-term social protection, 
and planning and implementing resilience-building measures (FAO

and ECA, 2018). National-level progress is assessed through the 
Lancet Countdown indicators (Watts et  al., 2018), however, district- 
and local-level indicators are needed to measure levels of vulnerability 
and response effectiveness at a local level, and for informing planning 
local service delivery. Potential indicators include monitoring the 
number of excess health conditions during extreme heat events. 
Indoor temperature monitoring in sentinel houses and health facilities 
is a related indicator (Ebi and Otmani Del Barrio, 2017), linked with 
threshold temperature levels at which health impacts occur, and the 
ability of the built environment to protect against these impacts (e.g., 
for heatwaves).
Measuring climate-health linkages is challenging due to the considerable 
diversity of the exposures, impacts and outcomes, as well as constraints 
in key technical areas. Increasing our understanding of this diversity

and how this is influenced by adaptative changes is a major knowledge 
gap. This could be facilitated through a pan-African database of climate 
and other environmental exposures, together with real-time statistical 
support for analyses of climate and health associations.
9.10.3.2 Community Engagement
Increased awareness can facilitate community engagement and 
action (see Section 9.4.3). In Ghana, for example, local communities 
understand the climate hazards that drive outbreaks of meningitis 
and adapt accordingly by improving housing to limit heat and 
exposure, changing funeral practices during outbreaks, increased 
vaccination uptake and afforestation (Codjoe and Nabie, 2014). 
Similarly, participation in community organisations improved child 
nutrition in vulnerable rural households in Eswatini (Anchang et al., 
2019). Interventions specifically targeting women are beneficial for 
food security, although they may be undermined by harmful gender

norms in communities that are patriarchal, led by chiefs or have high 
rates of gender-based violence (Jaka and Shava, 2018; Kita, 2019; 
Masson et al., 2019). The BRACED project in Burkina Faso and Ethiopia 
specifically adopted a gender-transformative approach as an integral 
part of resilience building (McOmber et al., 2019). Improving ‘climate 
literacy’ could empower youth, women and men to be active citizens in 
promoting adherence of governments to international agreements in 
climate change (Mudombi et al., 2017; Chersich et al., 2019a).
9.10.3.3 Health Financing
Poor and low-income households often are not able to afford high 
out-of-pocket costs for medical care, or it consumes a large portion of 
their income. As a result, without financial aid, peoples’ health needs 
may not be met after a climate shock (Hallegatte and Rozenberg, 
2017). Microfinance (the provision of small-scale financial products 
to low income and otherwise disadvantaged groups by financial

institutions) and disaster contingency funds can serve to reduce health 
risks of climate change for low-income communities (Agrawala and 
Carraro, 2010; Ozaki, 2016), as can different forms of insurance and

9
1383Africa  Chapter 9
disaster relief (Fenton et al., 2015; Dowla, 2018). Unconditional cash 
transfers in Kenya, Uganda and Zambia assisted vulnerable groups to 
absorb the negative impacts of climate-related shocks or stress and to 
prepare for these (Lawlor et al., 2019; Ulrichs et al., 2019). Based on 
several case studies in Africa, the Food and Agriculture Organization 
recommends a ‘Cash+’ approach which combines cash transfers with 
productive assets, inputs or technical training to address the needs 
of vulnerable households in emergency situations, and enhance 
livelihoods potential, income generation and food security (FAO, 
2017). New economic models have been implemented in north Africa, 
focused on poor households, youth and women that enable access to 
credit and support the implementation of policies that balance cash 
and food crops, social safety nets and social protection (Mumtaz and 
Whiteford, 2017; Narayanan and Gerber, 2017; see also Sections 9.4; 
9.8; 9.11).

9.10.3.4 Disease-specific Adaptations
9.10.3.4.1 Adaptation to prevent malaria
Increasing distribution and coverage of long-lasting insecticide-treated 
bed nets, improved diagnostic tests and increasing health service 
access could mitigate the impacts of climate change on malaria if 
aligned with the predicted or actual burden of malaria (medium 
confidence) (Kienberger and Hagenlocher, 2014; Thwing et al., 2017). 
Understanding seasonal shifts in malaria transmission suitability as a 
result of climate change can guide more targeted seasonal public health 
responses and better planning for different types of management and 
control interventions based on the impact. For example, an increase 
in the number of months where climate conditions are suitable for 
mosquito survival will require public health responses for an extended 
period of time (Ryan et al., 2020).
In malaria-endemic areas, repeated malaria infections can provide

temporary immunity, which reduces new clinical cases (Laneri et al., 
2015; Yamana et  al., 2016). Conversely, where people have little or 
no immunity, exposure to malaria can lead to epidemics (Semakula 
et al., 2017a; Ryan et al., 2020). Pregnant women and infants remain 
at risk of severe malaria, regardless of immunity status. Vector control 
and case management capacity should be rapidly scaled up in newly 
affected areas where risks for epidemics are high and populations are 
especially vulnerable. Poverty-alleviation initiatives underpin malaria 
control as the malaria burden is strongly tied to socioeconomic status 
(Huldén et al., 2014; Degarege et al., 2019).
Contextualised risk studies on local drivers of transmission are still 
lacking and present a major gap in developing appropriate adaptation 
strategies (high confidence). Progress has been made identifying and 
ranking vulnerability and exposure indicators (Protopopoff et  al.,

2009; Onyango et al., 2016a), however, better linking of biophysical 
and socioeconomic determinants of risk in integrated assessment 
models is needed (Caminade et  al., 2019; Zermoglio et  al., 2019), 
as are applied approaches to develop adaptation strategies for risk 
management (Leedale et al., 2016; Onyango et al., 2016b; Sadoine 
et al., 2018).9.10.3.4.2 Adaptation to reduce diarrhoeal disease
Reducing pathogen concentrations in water and across food chains is 
fundamental for controlling diarrhoeal diseases (van den Berg et al., 
2019). Diarrhoea prevention and treatment post-disaster, encompass 
social mobilisation campaigns, water treatment, enhanced surveillance, 
and vaccination and treatment centres for cholera (Cambaza et  al., 
2019) and typhoid (Neuzil et al., 2019).
Improved WASH requires robust water and sanitation infrastructure 
(Duncker, 2017; Kohlitz et  al., 2017; Venema and Temmer, 2017) and

technological adaptations (Gabert, 2016; van Wyk et al., 2017), such as 
waterless on-site sanitation (Sutherland et al., 2021), diversification of 
water sources (e.g., rainwater harvesting (Lasage and Verburg, 2015) 
and groundwater abstraction (MacDonald et al., 2012)), and sharing of 
best practices across the continent (WASH Alliance International, 2015; 
Jack et al., 2016; see also Section 9.7.3; Chapter 4 Section 4.6.4). Hand 
hygiene can be improved through the creation of handwashing stations, 
increased access to soap and simple technologies such as the foot-
operated Tippy taps (Coultas and Iyer, 2020; Mbakaya et al., 2020).
9.10.3.4.3 Adaptation to reduce conditions related to heat exposure
Reducing morbidity and mortality during extreme heat events 
requires changes in behaviour and health promotion initiatives, 
health system interventions and modifications to the built and 
natural environment. Health promotion initiatives include promoting

adequate hydration and simple cooling measures, such as drinking 
cold liquids, water sprays and raising awareness of the symptoms 
and importance of heat stress, including heatstroke (Aljawabra and 
Nikolopoulou, 2018). Adaptive measures are especially important 
for high-risk groups such as outdoor workers, the elderly, pregnant 
women and infants. Health systems interventions may include 
early warning systems, heat health regulation and health workers 
providing cooling interventions, such as supplying cool water or 
fans, during heat waves. Changes to the built environment include 
painting the roofs of houses white and improving ventilation during 
extreme heat (Codjoe et al., 2020), the use of insulation materials or 
altering the building materials used for the construction of housing 
to improve their ability to moderate indoor temperatures (Mathews 
et al., 1995; Makaka and Meyer, 2006).
9.10.3.4.4 Adaptation to prevent malnutrition

Transformative adaptation requires integration of resilience and 
mitigation across all parts of the food system including production, supply 
chains, social aspects and dietary choices (IPCC, 2019a). Adaptation to 
prevent malnutrition goes hand-in-hand with adaptation to prevent 
food insecurity, as is discussed in Section 9.8.3; Chapter 5 Section 5.12.5.
Urban agriculture and forestry can improve nutrition and food 
security, household income and mental health of urban farmers 
while mitigating against some of the impacts of climate change, like 
flooding and landslides (by stabilising the soil and reducing runoff, for 
example), heat (by providing shade and through evapotranspiration) 
and diversifying food sources in case of drought (Zezza and Tasciotti, 
2010; Lwasa et al., 2014; Battersby and Hunter-Adams, 2020).

9
1384Chapter 9 Africa
Adaptation options across multiple sectors have potential for reducing risk across multiple health outcomes, 
considering their potential to reduce vulnerability, and potential barriers to implementation
Health outcome/benefit
Non-
communicable
diseases
(NCDs)Heat-
related
illnessesInfectious
diseasesVector-
borne
diseasesFood- and
water-
borne
diseases Nutrition
Adaptation optionsPotential for
risk reductionPositive
outcomes
populationsRequires
sensitivity and
of cultural and
traditional
practices 
Mainstreaming climate change into all health 
policiesxx xxx
Occupational setting interventions (labour laws;  
avoiding heat during the day; education re  
adaptations )xx
Local knowledge strengthening and education xxxx
Community, community health workers, and 
leadership resiliencexx
Teaching of climate change risks and 
behavioural changes in schools and universities xx
Access to healthcar e x xxxx x

Universal Health Coverage, including of 
services for climate-related diseasesx xxxx x
Infectious disease surveillance, early warning, 
outbreak response and controlxx
Heat health plan s xx
Vulnerability assessments x xxx x
Intervention studie s xx x
Risk assessments x xxxx x
Early warning systems forecasting/disaster 
management for smallholder farmer sxx xx
Disaster Preparednes s x xxxx x
Health information systems for climate-related  
diseasesx xxxx x
Surveillance of health and environmental 
factors x xxxx x
Improved management of environmental  
determinants of health (water quality; waste 
and sanitation; air quality)x xxx x
Strengthening of health systems and  
infrastructure against threat of extreme weather 
events, and for post-disaster recoveryx xxxx x
Transport (sustainable; public) (infrastructure) xx
Sustainable land use, forestry, water 
managementxx xxx
Sustainable farmin g xx xxx
Solar power/biogas for electricit y x
Tree and seed planting xx xx

Improved housing, including painting roofs  
whitexx xx
Insecticide-treated bed nets x
Indoor residual sprayin g x
Genetic modification xResponse
category
Policy 
development 
Education and 
awareness 
Health systems 
and primary 
healthcare 
services 
Surveillance, 
risk 
assessments, 
monitoring,
and research 
Resource 
Vector control
and disease 
prevention 
Key for sectors involved in each response category, and level of confidence (based on the literature)
Policy, governments, environmental health practitioners, communit y
Forestry
Agriculture, terrestrial
Indigenous and local knowledg e
Weather and climate services
Research, innovation and developmentConfidence
High
Medium
Low
Figure 9.36 |  Adaptation options across multiple non-health sectors have potential for reducing risk for multiple health outcomes, considering their

potential to reduce vulnerability. Reduced risk for health may result from targeted actions or as a result of co-benefits (see Table SM9.8 for a full list of references).

9
1385Africa  Chapter 9
The health sector needs to collaborate and coordinate adaptation 
activities with other sectors, as well as civil society and international 
agencies, to engage communities in health promotion (Irwin et al., 
2006; Commission of Social Determinants of Health, 2008; Braveman 
and Gottlieb, 2014). The importance of social determinants of 
health, such as socioeconomic status, education and the physical 
environment in which people live and work and their consideration 
during development are highlighted in Chapter 7 (see Sections 7.1.6; 
7.4.2)
9.11 Economy, Poverty and Livelihoods
9.11.1 Observed Impacts of Climate Change on African 
Economies and Livelihoods
9.11.1.1 Economic Output and Growth
Increased average temperatures and lower rainfall have reduced 
economic output and growth in Africa, with larger negative impacts 
than other regions of the world (Abidoye and Odusola, 2015; Burke 
et al., 2015a; Acevedo et al., 2017; Kalkuhl and Wenz, 2020). In one

estimate, GDP per capita is on average 13.6% lower for African 
countries than it would be if human-caused global warming since 1991 had not occurred (Diffenbaugh and Burke, 2019), although impacts 
vary substantially across countries (see Figure 9.37). As such, global 
warming has increased economic inequality between temperate, 
northern Hemisphere countries and those in Africa (Diffenbaugh and 
Burke, 2019). Warming also leads to differential economic damages 
within Africa (Baarsch et al., 2020). One estimate found a 1°C increase in 
20-year average temperature reduced GDP growth by 0.67 percentage 
points, with the greatest impacts in Central African Republic, DRC and 
Zimbabwe (Abidoye and Odusola, 2015). Changes in rainfall patterns 
also influence individual and national incomes. Had total rainfall not 
declined between 1960 and 2000, the gap between African GDP and 
that of the rest of the developing world would be 15–40% smaller

than today, with the largest impacts in countries heavily dependent on 
agriculture and hydropower (Barrios et al., 2010).
Aggregate macroeconomic impacts manifest through many channels 
(Carleton et  al., 2016). Macroeconomic evidence suggests aggregate 
impacts occurred largely through losses in agriculture with a smaller role 
for manufacturing (Barrios et al., 2010; Burke et al., 2015b; Acevedo et al., 
2017). Sector-specific analyses confirm that declines in productivity of 
food crops, commodity crops and overall land productivity contribute to 
lower macroeconomic performance under rising temperatures (Schlenker 
and Lobell, 2010; Bezabih et al., 2011; Jaramillo et al., 2011; Lobell et al., 
2011; Adhikari et  al., 2015). Labour supply and productivity declines Table 9.11 |  Co-benefits, barriers and enablers of adaptation responses to climate change impacts on human health in Africa (see Table SM9.9 for a full list of references).

Response category Co-benefitsInter-sectoral trade-offs and/
or drawbacksEnablers Barriers
Policy developmentPolicies and plans that facilitate service delivery 
and guide national and international funding; 
decreased number of work hours lost; improved 
work performance, increased productivityWillingness of policymakers; 
political support; politically 
willing environment; inter-sectoral 
collaborationLack of implementation; 
poor governance
awarenessPromotion of sustainable living and circular 
economyGuarantee of sustained funding; 
willing environment; increased 
accessibility of learning 
institutionsLack of implementation; 
historical and 
colonisation-related 
insensitivities
Health systems and 
primary healthcare 
servicesCapacity building in communities; buffered 
economic impact of outbreaks/disasters; job 
creationIncreased greenhouse gas emissions 
from building health infrastructure; 
increased energy demand; decreased

productivity and increased work 
hours lost due to waiting timesGuarantee of sustained funding; 
political support; politically willing 
environmentCorruption and fraudulent 
activities around resource 
allocation
Surveillance, risk 
monitoring and 
researchEvidence to improve adaptation response; fast 
post-disaster recovery; increased awareness 
and disease prevention; improved health system 
functioning post-disastersRequires effective institutional 
arrangements and inter-sectoral 
collaboration; guarantee to 
sustained funding; requires skills 
developmentMay be limited by 
uncertainty in modelled 
predictions and thresholds
Resource managementImproved health system functioning 
post-disasters; capacity building in communities; 
promotes economic growth/stability; increases 
the tourism potential of the area; increased 
accessibility/mobility of the community; 
reduced land degradation, desertification and 
bush encroachment; food security; decreased

emissionsPotential to increase energy demand; 
increased crowding/ population 
density; land use; microclimate and 
ecosystem disruptionGuarantee of sustained funding; 
environment; requires effective 
institutional arrangements and 
inter-sectoral collaboration; 
requires skills developmentCorruption and fraudulent 
Vector control and 
disease preventionDecreased mortality; improved work 
performance; increased productivity; improved 
mental healthIncreased GHG; decreased 
biodiversity; environmental impacts 
of production, packaging, and 
delivery; potentially detrimental to 
healthGuarantee to sustained funding; 
funding and resources; future 
planning or retrofit requiredLast-mile access; cost per 
capita and capacity for 
service delivery

9
1386Chapter 9  Africa
in manufacturing, industry, services and daily wage labour have been 
observed in other regions (Graff Zivin and Neidell, 2014; Somanathan et al., 2015; Day et al., 2019; Nath, 2020) and contribute to aggregate economic declines, countering aggregate poverty reduction strategies and other SDGs (Satterthwaite and Bartlett, 2017; Day et al., 2019). In a case study of a rural town in South Africa, over 80% of businesses (both formal and informal) lost over 50% of employees and revenue due to 
Observed aggregate economic impacts and projected risks from climate change in Africa
(a) Percentage change in GDP per capit a
due to observed climate change (1991–2010)
-30% -20% -10% 0 10%Mauritania
Mali
Niger
Sudan
Chad
Burkina Faso
Djibouti
DRC
Côte d'Ivoire
Guinea
Nigeria
Republic of Congo
Sierra LeoneSenegal
GambiaGhana
CameroonBenin
Guinea-Bissau
MozambiqueTogo
Liberia
Comoros
MalawiCentral African Republic
Namibia
Angola
Botswana
Uganda
ZambiaGabon
Tanzania
Rwanda

Burundi
Ethiopia
Cape Verde
South AfricaEgypt
KenyaEquatorial Guinea
Zimbabwe
Algeria
MoroccoMauritius
Madagascar
Swaziland
Tunisia
Lesotho
(b) Projected percentage
decrease in GDP per capitafor 4°C global warmingcompared to a scenariowith no global warmingafter 2010
(c) Percentage change in
GDP per capita from limitingglobal warming to 1.5°Crather than 2°C
(d) Probability of economic
benefits from limiting globalwarming to 1.5°Crather than 2°C80–89%
70–79%60–69%>5%90–92%
No dataPercentage
decrease
in GDP
per capita
No dataProbability
50
02575100No dataPercentage
020%
10%
-15%
-30%
Figure 9.37 |  Observed aggregate economic impacts and projected risks from climate change in Africa. 
(a)Estimated effect of human-caused climate change on GDP per capita for 48 
African countries between 1991 and 2010.
(b)Projected effect on GDP per capita of global w
arming of ~4°C by 2100 compared to economic growth with no further global warming after 2010.

(c)Projected percentage increase in GDP per capita of holding global w
arming to 1.5°C rather than 2°C above pre-industrial level.
(d)Probability of realising any economic benefits by holding w
arming to 1.5°C versus 2°C. Data sources: Burke et al. (2015b); (2018a); Diffenbaugh and Burke (2019).
agricultural drought (Hlalele et al., 2016). Drought and extreme heat 
events have also reduced tourism revenues in Africa (Section  9.6.3). Infrastructure damage and transport disruptions from adverse climate events reduce access to services and growth opportunities (Chinowsky et al., 2014). In global data sets including Africa, tropical cyclones have been shown to have large and long-lasting negative impacts on GDP growth (Hsiang and Jina, 2014).

9
1387Africa  Chapter 9
9.11.1.2 Human Capital Development and Education
Investments in human capital, particularly education, are critical for 
socioeconomic development and poverty reduction providing valuable 
skills and expanding labour market opportunities. Much progress has 
been made in improving education access, however, in sub-Saharan 
Africa, 32% of children, adolescents and youth (~97 million people) 
remain out of school (UNESCO Institute of Statistics, 2018). Climate 
variability and change can undermine educational attainment with 
negative impacts on later life earning potential and adaptive capacity 
to future climate change (Figure 9.11; Lutz et al., 2014).
Several studies indicate that experiencing low rainfall, warming 
temperatures or extreme weather events reduce education attainment 
and that future climate change may reduce children’s school participation, 
particularly for agriculturally dependent and poor urban households. In

west and central Africa, experiencing lower-than-average rainfall during 
early life is associated with up to 1.8 fewer years of completed schooling 
in adolescence, while more rainfall and milder temperatures during the 
main agricultural season are positively associated with educational 
attainment for boys and girls in rural Ethiopia (Randell and Gray, 2016; 
Randell and Gray, 2019). In Uganda, low rainfall reduced primary 
school enrolment by 5% for girls (Björkman-Nyqvist, 2013), and in 
Malawi, in utero drought exposure was associated with delayed school 
entry among boys (Abiona, 2017). In rural Zimbabwe, experiencing 
drought conditions during the first few years of life was associated with 
fewer grades of completed schooling in adolescence, which translates 
into a 14% reduction in lifetime earnings (Alderman et al., 2006). In 
Cameroon, warming temperatures have negatively affected plantain 
yields, which in turn is linked to lower educational attainment (Fuller

et  al., 2018). One suggested mechanism underlying the relationship 
between climate and schooling is that adverse climatic conditions 
can reduce income among farming households, leading them to pull 
children out of school (Randell and Gray, 2016; Marchetta et al., 2019). 
Other potential mechanisms are poor harvests from droughts or supply 
interruptions from extreme weather events leading to undernutrition 
among young children, negatively affecting cognitive development and 
schooling potential (Alderman et al., 2006; Bartlett, 2008).
More research is needed on climate change impacts on education 
in Africa. This information can help ensure families keep children in 
school amid climate-related income shocks. For example, in Mexico, a 
conditional cash transfer programme mitigated the negative effect of 
natural disasters on school attendance (de Janvry et al., 2006).
9.11.2 Projected Risks of Climate Change for African 

Future warming will have negative consequences for economic 
growth in Africa, relative to a future without additional climate 
change and assuming current levels of adaptation (high confidence) 
(Dell et al., 2012; 2015a; Burke et al., 2015b; Acevedo et al., 2017; 
Baarsch et  al., 2020). Statistically based empirical analyses project 
that global warming of 2.3°C by 2050 could lower GDP per capita 
across sub-Saharan Africa by 12% (SSP2) (Baarsch et al., 2020) and 
80% for warming >4°C by 2100 (SSP5, 75% for MENA) (Burke et al., 2015b). Depending on the future socioeconomic scenario, this could 
increase global inequality and leave some African countries poorer 
than at present (Burke et  al., 2015b). Inequalities between African 
countries are projected to widen under climate change, with negative 
impacts estimated to be largest in west and east Africa (Baarsch et al., 
2020). While negative impacts across African economies are highly

likely under climate change, precise magnitudes are debated in the 
literature. Alternative statistical analyses suggest a 12% reduction 
of GDP per capita by 2100 under RCP8.5 across African countries 
relative to a future without climate change (Kahn et al., 2021), while 
computable general equilibrium models generate smaller damages as 
well, ranging from 3.8% reduction across sub-Saharan Africa in 2060 
under warming of 2.5°C (Dellink et al., 2019) to 12% across all of 
Africa in 2100 under warming of 5°C (SSP4) (Takakura et al., 2019).
Substantial avoided economic damages to African countries are 
projected from ambitious, near-term global mitigation limiting global 
warming well below 2°C above pre-industrial levels (high confidence). 
Increased economic damage forecasts for Africa under high emissions 
scenarios start diverging rapidly from low emissions scenarios by the 
2030s (Baarsch et al., 2020). Across nearly all African countries, GDP

per capita is projected to be at least 5% higher by 2050 and 10–20% 
higher by 2100 if global warming is held to 1.5°C versus 2°C (Burke 
et al., 2018a; Baarsch et al., 2020) (Figure 9.37). The probability of this 
positive gain to GDP per capita from achieving 1.5°C versus 2°C is 
reported as close to 100% (Burke et al., 2018a). While these estimates 
rely on temperature and rainfall-driven damages, SLR also poses a risk 
for Africa. By 2050, damages from SLR across sub-Saharan Africa could 
reach 2–4% of GDP , depending on the socioeconomic, adaptation and 
emissions scenario (Parrado et al., 2020).
Heat stress is projected to reduce working hours and work capacity 
under climate change, with among the largest declines in sub-Saharan 
Africa and for workers in vulnerable occupation groups, such as those 
working outdoors (Kjellstrom et al., 2014; 2016; de Lima et al., 2021; 
Chapter 5). Global warming of 3°C is projected to reduce labour

capacity in agriculture by 30–50% in sub-Saharan Africa (relative to 
the baseline in 1986–2005) (de Lima et al., 2021). These effects lead 
to substantial aggregate losses, for example, in west Africa, labour 
productivity impacts under a 3°C temperature increase are estimated 
to cost up to 8% of GDP (Roson and Sartori, 2016). Manufacturing 
productivity across Africa is projected to decline under RCP8.5 by 
0–15% by 2080–2099, with the largest effects in the DRC, Ethiopia, 
Somalia, Mozambique and Malawi (Nath, 2020).
Large risks to road, rail and water infrastructure are projected from 
climate change with substantial economic cost implications (see 
Section 9.9.3; Box 9.5).
9.11.3 Informality
Aggregate GDP data capture formal economic activity but informal 
employment is the main source of employment in Africa, accounting 
for 85.8% of all employment (71.9%, excluding agriculture), which 
is 21.4% higher than the global average (ILO, 2018b). Estimates of

9
1388Chapter 9 Africa
Africa, to 94.6% in Burkina Faso (ILO, 2018b), with high variability 
within countries such as South Africa and Nigeria (Etim and Daramola, 
2020). Informal employment is a greater source of employment for 
women than for men in sub-Saharan Africa and young and old have 
especially high rates of informal employment: 94.9% of persons 
between ages 15 and 24 in employment and 96% of persons aged 65 
and older (ILO, 2018b).
Informal sector impacts are omitted from GDP-based impacts 
projections. Yet, informal sector activity and small to medium-sized 
enterprises can be highly exposed to climate extremes, as they are often 
located in low-lying areas, coastal areas, sloped or other hazardous 
zones (Thorn et al., 2015; Satterthwaite et al., 2020). Businesses and 
individuals in the informal sector, including construction workers, 
domestic workers, street vendors and transport workers, often cannot

operate during climate shocks due to interruptions in transportation 
and commodity flows and, without the ability to insure against risk, 
struggle to recover assets from extreme events such as flooding, 
landslides and waterlogging (Chen, 2014; Thorn et al., 2015; Roy et al., 
2018a). Women are overrepresented in the more poorly remunerated 
sections of the informal economy (Satterthwaite et al., 2020).
There is scope for governments to better harness the role of the 
informal sector in mitigation and adaptation (Douxchamps et al., 2015; 
Satterthwaite et al., 2020). Multi-level governance that includes informal 
service providers, such as informal water and sanitation networks, into 
planned adaptation programmes can increase climate resilience, in part 
because these networks can respond with more flexibility than hard 
infrastructure projects (Satterthwaite et al., 2020; Peirson and Ziervogel, 
2021). Climate risk is often concentrated in urban informal settlements

(Section  9.9.4). Here, informal land markets influence development 
patterns and can help ensure adherence to building codes to ensure 
safety of informally built structures at high risks of landslides and floods 
and enforce compliance with regulations relating to planning and land 
use (Thorn et  al., 2015; Satterthwaite et  al., 2020). Improving land 
management practices of charcoal producers and artisanal gold miners, 
combined with appropriate alternative livelihood and energy sources, 
can reduce emissions and increase resilience (e.g., reduce erosion and 
sedimentation, increase water infiltration) and benefit health (Atteridge, 
2013; Paz et al., 2015; Macháček, 2019; Barenblitt et al., 2021; Eniola, 
2021). Providing concessional loans, commercial financing or equity 
investment to informal brick makers can boost delivery of low emission 
social housing while the use of crop residues or renewable energy for

brick making can replace wood biomass and reduce pressure on forests 
(Alam, 2006; Paz et al., 2015).
9.11.4 Climate Change Adaptation to Reduce 
Vulnerability, Poverty and Inequality
High temperature-related income losses have been observed in both 
low- and high-income countries, suggesting optimistic economic 
development trajectories may not substantially reduce climate change 
impacts on aggregate economic performance in Africa (low confidence) 
(Burke et  al., 2015b; Deryugina and Hsiang, 2017; Henseler and 
3 Extreme poverty is defined using a consumption poverty line at USD 1.25 per day, using 2005 purchasing power parity exchange rates.Schumacher, 2019). Nevertheless, climate change impacts on poverty 
in Africa will depend on how socioeconomic development unfolds over 
the coming decades (medium confidence) (Rozenberg and Hallegatte, 
2015; Hallegatte and Rozenberg, 2017; Henseler and Schumacher, 
2019). Climate change by 2030 is projected to push 39.7  million

Africans into extreme poverty3 under a baseline scenario of delayed 
and non-inclusive growth, with food prices acting as the dominant 
channel of impact, but this number is cut roughly in half under an 
inclusive economic growth scenario (Rozenberg and Hallegatte, 2015; 
Hallegatte and Rozenberg, 2017; Jafino et al., 2020).
People in Africa are disproportionately employed in highly climate-
sensitive sectors: 55–62% of the sub-Saharan African workforce is 
employed in agriculture and, although between 90–95% of cropland 
is rainfed (Woodhouse et al., 2017; ILO, 2018a; International Institute 
of Water Management, 2019; World Bank, 2020c), there has been an 
expansion of small-scale ‘farmer-led irrigation’ (Woodhouse et  al., 
2017). Agricultural GDP also appears more strongly affected by 
increasing temperatures than non-agricultural GDP , implying livelihood 
diversification out of agriculture may help minimise future economic

damage (Bezabih et al., 2011; Burke et al., 2015b; Acevedo et al., 2017; 
Deryugina and Hsiang, 2017), although such workforce reallocation 
requires careful management and planning depending on the overall 
livelihood portfolios, type of farmer and profitability (Stringer et  al., 
2020). De-agrarianisation can feed urbanisation, which may exacerbate 
inequality within and between countries (Stringer et al., 2020).
Changes in trade patterns may help mitigate projected aggregate 
economic losses by reallocating agricultural production abroad and 
encouraging economic diversification toward less affected sectors. 
Temperature increases have been shown to lower agriculture and 
manufacturing exports with especially large declines in poor countries 
(Jones and Olken, 2010; Roberts and Schlenker, 2013). Further, imports 
of agricultural products are projected to rise across most of Africa by 
2080–2099 under a high emissions scenario (RCP8.5), with increases

ranging from ~30% of GDP in the Central African Republic to ~5% 
of GDP in South Africa and Nigeria, although some countries will 
experience increases in net agricultural exports (Nath, 2020). While 
these reallocation effects may be large, current evidence is mixed 
regarding whether such adjustment of production will dampen or 
amplify overall social costs of climate change in Africa (Costinot et al., 
2012; Bren d’Amour et al., 2016; Wenz and Levermann, 2016; Nath, 
2020), as food prices are projected to rise by 2080–2099 across all 
African countries under a scenario with high challenges to mitigation 
and adaptation (SSP3 and RCP8.5), with the largest price effects (up to 
120%) experienced in Chad, Niger and Sudan (Nath, 2020). Moreover, 
reallocating production of agriculture abroad could be maladaptive if 
it leads to decline or replacement of traditional sectors by industrial 
and service sectors, which could lead to land abandonment, food

insecurity and loss of traditional practices and cultural heritage (Thorn 
et al., 2020; Gebre and Rahut, 2021; Nyiwul, 2021).
African countries have high inequality: the average within-country 
share of income accruing to the top 10% of households was estimated 
at 50% for 2019 (Robilliard, 2020). However, analysis of INDCs across 54

9
1389Africa  Chapter 9
African countries suggests current climate policies do not, on average, 
target social inequality in energy, water and food security; proposed 
mitigation and adaptation actions fell about 23% for every 1% rise 
in social inequality across these sectors (Nyiwul, 2021). In contrast, 
adaptation actions can be designed in ways that actively work towards 
reducing inequality, whether gender, income, employment, education 
or otherwise (Andrijevic et al., 2020).
In rural Africa, poor and female-headed households face greater 
livelihood risks from climate hazards (high confidence). Women 
often constitute a high proportion of the informal workforce and 
are also more likely to be unemployed than men (ILO, 2018a). 
These factors leave women, and particularly female-headed 
households, at greater risk of poverty and food insecurity from 
climate hazards. Controlling for multiple factors, income of female-

headed households in agricultural districts in South Africa is more 
vulnerable to precipitation variability than those headed by men 
(Davidson, 2016; Flatø et  al., 2017). Across nine countries in east 
and west Africa women tend to control smaller plots of land that is 
often of poorer quality, have less access to inputs such as fertilizer, 
tools and improved seeds, have lower educational attainment and 
benefit less from extension services, government agencies and non-
governmental organisations (Perez et al., 2015). Gender assessments 
prior to adaptation programmes can identify disparities in division 
of labour and income and socio-cultural norms, hindering women 
from holding leadership positions or determining livelihood and 
resource-use activities, thereby helping ensure equitable benefits 
from livelihood diversification and improving women’s working 
conditions (ILO, 2018a). Gender-responsive policy instruments can

measure success using sex-disaggregated data to monitor impact 
and meaningful participation in decision making (GCF , 2018b).
Exposure to climate hazards can trap poorer households in a cycle of 
poverty (Dercon and Christiaensen, 2011; Sesmero et al., 2018) and 
poor people in Africa are often more exposed to climate hazards 
than non-poor people. For example, poor people live in hotter areas 
in Nigeria and in multiple African countries, poor households are 
more exposed to flooding (Section  9.9.2; Hallegatte et  al., 2016). 
Daily wage labourers and residents of urban informal settlements 
are vulnerable to heat stress because of the urban heat island effect 
combined with congestion, little shade and ventilation (Bartlett, 2008). 
Climate change can negatively affect household poverty through 
price spikes, destroying assets or ability to invest in new assets and 
reducing productivity (Hallegatte et al., 2016) with important impact

pathways operating through agriculture, ecosystem functioning and 
health (Sections 9.6; 9.8; 9.10; Chapters 5; 7; 8). Non-poor people can 
lose more in absolute terms from climate shocks because of having 
more assets and higher incomes, but in relative terms, poor people 
often lose more than the non-poor. These relative losses matter most 
for livelihoods and welfare (Hallegatte et al., 2016).
In Malawi, wealthier households were able to maintain more diversified 
livelihoods, buffering them from extreme weather-related income 
losses (Sesmero et al., 2018). Poorer households have limited access to 
resources such as savings, credit, irrigation technologies and insurance, 
which can lead to larger crop and other income losses from climate 
hazards, preventing investments to improve resilience to future climate shocks (Castells-Quintana et al., 2018). Poor households may reduce risk 
or aid recovery by cooperating with other households in their community

to adapt collectively to climate change, for example, through informal 
insurance networks (Paul et al., 2016; Wuepper et al., 2018). Prioritising 
poor households for interventions including social protection, EbA, 
universal healthcare, climate-smart buildings and agriculture, flexible 
work hours under extreme heat and early warning systems will increase 
adaptation to climate shocks (Section  9.6.4; Chapter 6; Angula and 
Menjono, 2014; Moosa and Tuana, 2014; Hallegatte et al., 2016; Day 
et al., 2019). Pro-poor policies that link mitigation and adaptation, such 
as using renewable energy to increase rural electrification or using 
revenues from a carbon tax, combined with international financial 
support to increase social assistance, could support sustainable 
eradication of poverty under near-term climate change (Hallegatte 
et al., 2016; Aklin et al., 2018; Simpson et al., 2021c). Integrating urban 
green infrastructure into adaptation planning in informal settlements

can simultaneously unlock pathways for inclusivity and social justice 
(Section 9.9.5; Tozer et al., 2020; Wijesinghe and Thorn, 2021).
Social protection has been used for decades, particularly in eastern and 
southern Africa, to safeguard poor and vulnerable populations from 
poverty and food insecurity (Niño-Zarazúa et al., 2012). Instruments of 
social protection include public works programmes, cash transfers, in-
kind transfers, social insurance and microinsurance schemes that assist 
individuals and households to cope during times of crisis and minimise 
social inequality. Evidence from Ethiopia, Kenya and Uganda indicates 
national social protection programmes are effective in improving 
individual and household resilience to climate-related shocks, regardless 
of whether they aim specifically to address climate risks (Ulrichs et al., 
2019). Strengthening social protection and better integrating climate 
risk management into design of social protection programmes can help

build long-term resilience to climate change (Hallegatte et  al., 2016; 
Agrawal et al., 2019). For example, public works programmes can build 
climate resilience by targeting soil, water and ecosystem conservation 
and carbon sequestration, such as South Africa’s Working for Water 
Programme that restores river catchments to reduce fire risk and 
increase water supplies (Turpie et al., 2008; Norton et al., 2020).
9.11.4.1 Climate Insurance
African countries and communities are inadequately insured against 
climate risk. Insurance penetration is less than 2% of GDP (Swis Re, 
2019) and 90% of natural catastrophe losses were uninsured in Africa 
in 2018 (Swis Re, 2019) leaving a large risk protection gap. The cost of 
reinsurance in Africa’s most mature insurance market—South Africa—
has increased since 2017 due to climate-related payouts (SAIA, 2018; 
Simpson, 2020), which is expected to further reduce the extent of 
insurance coverage. Emerging trends that seek to address this gap

include innovative weather and drought index-based insurance 
schemes to transfer risk, forward-looking climate data and models to 
manage risk and insurers transitioning from risk transfer providers to 
proactive risk managers.
The most significant area of climate risk insurance innovation has 
occurred in weather and drought index-based insurance schemes that 
pay out fixed amounts based on the occurrence of an event instead 
of full indemnification against assessed losses (Table 9.12). However,

9
1390Chapter 9 Africa
despite the relatively low cost, uptake remains low due to affordability 
constraints, lack of awareness, access to and trust in products, 
distribution challenges, basis risk, poor transparency, challenges 
regarding the integration of complementary interventions (e.g., access 
to improved inputs or informal savings/credit) and poor perceptions/
norms of insurance and risk transfer. Lack of data and models further 
hinders insurers’ ability to price risk correctly, which reduces value to 
clients (Greatrex et  al., 2015; Di Marcantonio and Kayitakire, 2017; 
WEF , 2021). Impact assessments point to potential but remain context-
specific (Awondo, 2019; Hansen et al., 2019b; Noritomo and Takahashi, 
2020). In addition, there is no comprehensive overview of the number 
of people covered by such schemes, nor of the value they provide in 
terms of actual claims payouts. Lastly, donor and/or public funds still

play an outsized role in launching and/or sustaining these schemes 
and schemes beyond weather and drought remain limited (Table 9.12).
Insurers and their clients are often unaware of their risk exposure, 
partly due to data and modelling gaps. Climate information services 
and related collaborations are increasingly helping to address this 
problem (see Section  9.4.5). Climate change attribution methods to 
estimate the contribution of human-casued climate change to the 
cost of parametric insurance offers possibilities for a sharing of the 
premium between the impacted African country and a global climate 
fund, such as the GCF (New et al., 2020). Technology companies and 
start-ups (including FinTechs) are also emerging as solutions to fill risk 
gaps, leveraging new approaches to data and technology through 
the use of sensors, drones and satellite imaging to speak to mainly 
agricultural risks, but also urban risks such as informal settlement fires,

exacerbated by heat and drought (Table 9.12).
Ten African insurers formally committed to help manage climate risk on 
the continent through the Nairobi Declaration of the UNEP Principles 
for Sustainable Insurance (PSI) in 2021 (UNEP PSI, 2021). Some 
early examples of public–private partnerships with municipalities 
and governments to better manage climate risk are also emerging 
(Table 9.12).9.11.5 COVID-19 Recovery Stimulus Packages for Climate 
Action
The COVID-19 pandemic recovery effort includes significant opportunities 
for African countries to reduce future vulnerability to compound climate, 
economic and health risks. Fiscal recovery packages could set economies 
on a pathway towards net-zero emissions, reducing future climate risk or 
entrench fossil-fuel intensive systems, exacerbating risk (Hepburn et al., 
2020; Dibley et al., 2021; IEA, 2021). Investments in renewable energy, 
building efficiency retrofits, education and training, natural capital (i.e.,

ecosystem restoration and EbA), R&D, connectivity infrastructure and 
sustainable agriculture can help meet both economic recovery and 
climate goals (Hepburn et al., 2020; Dibley et al., 2021).
The impacts of the COVID-19 pandemic have been substantially 
worsened by climate hazards in many places. In others, outbreak 
response has been disrupted (Phillips et  al., 2020; Kruczkiewicz 
et  al., 2021). These vulnerabilities are rooted in insufficient disaster 
preparedness infrastructure but are almost always worsened by social 
and economic inequality. Ensuring the most vulnerable populations 
are properly protected from climate change has co-benefits for 
recovery from the COVID-19 pandemic (Manzanedo and Manning, 
2020). In particular, efforts to reduce syndemic vulnerabilities across 
key sectors (especially health, livelihoods and food security) will lessen 
climate change impacts and will also reduce the risk and impacts of

future epidemics and pandemics, for example, during the pandemic, 
water scarcity has been a barrier to a key risk mitigation behaviour 
(hand washing). In the long-term, development efforts focused on 
WASH will reduce this vulnerability and also reduce the health toll of 
diarrheal disease linked to climate change (Anim and Ofori-Asenso, 
2020; Zvobgo and Do, 2020). Spending recovery funds on social 
safety nets will reduce inequality and protect the most vulnerable 
communities (especially women and low-income and marginalised 
communities) from the social and economic impacts of disasters. Key 
among these safety nets is universal health coverage, including low- or 
Table 9.12 |  Insurance opportunities to mitigate climate risk.
InitiativesDrought/
heatwaveFlood Cyclone Fire ExamplePolicyholders/ 
beneficiariesReference
Index and 
parametric 
schemes— 
smallholder farmerX XACRE Africa, Pula, R4 Rural 
Resilience Initiative, KLIP , FISP , 
Ghana Agricultural Insurance

Pool, Oko Crop AssuranceSmallholder 
farmersGreatrex et al. (2015); CTA (2019); Global 
Index Insurance Facility (2019); WFP 
(2020); Fava et al. (2021); OKO Finance 
(2021); Pula (2021); Tsan et al. (2021)
Index and 
parametric schemes 
– sovereign and 
sub-sovereignX X X African Risk Capacity Governments ARC (2019)
Index and 
– globalX XAfrican and Asian Resilience 
in Disaster Insurance Scheme 
(ARDIS)Individuals and 
smallholder farmersGlobal Parametrics (2018)
Risk management 
and data 
collaborationX X X XUNEP PSI
Santam
Tripartite AgreementInsurers and 
reinsurers, local 
municipalities, 
governmentsSantam (2018); Forsyth et al. (2019); 
UNEP-FI (2019a); InsurResilience (2020); 
Simpson (2020)
FinTech X X XLumkani, WorldCover, Econet, 
PlaNet GuaranteeIndividuals, 
smallholder farmersGreatrex et al. (2015); Hunter et al. (2018); 
CTA (2019); UK Space Agency (2020); Tsan 

9
1391Africa  Chapter 9
Box 9.8 | Climate change, migration and displacement in Africa
Climatic conditions are important drivers of migration and displacement with migration responses to climate hazards strongly influenced 
by economic, social, political and demographic processes (Cross-Chapter Box MIGRATE in Chapter 7).  
Most climate-related migration and displacement observed currently is within countries or between neighbouring countries, rather than 
to more geographically distant high-income countries (Hoffmann et al., 2020; Kaczan and Orgill-Meyer, 2020). Natural disaster-related 
displacements in sub-Saharan Africa were over 2.6 million in 2018 and 3.4 million in 2019 (13.9% of the global total and one of the 
highest historical figures for the region), with east (1,437,7000) and west Africa (798,000) being hotspots in 2018 (Table Box 9.8.1;

Mastrorillo et al., 2016; IDMC, 2019; IDMC, 2020). Estimates indicate future climate change effects on internal migration in Africa will be 
considerable (Table Box 9.8.1;Rigaud et al., 2018).
Internal migration, displacement and urbanisation
Climate change can have opposing influences on migration flows. Deteriorating economic conditions caused by climate hazards can 
encourage out-migration (Wiederkehr et al., 2018). However, these same economic losses undermine household resources needed to 
migrate (Cattaneo and Peri, 2016). The net effect of these two forces leads to mixed results across study methodologies and contexts 
(Carleton and Hsiang, 2016; Borderon et al., 2019; Cattaneo et al., 2019; Hoffmann et al., 2020).
Urbanisation in Africa is affected by climate conditions in rural agricultural areas (high confidence). Urbanisation can increase when

reduced moisture availability depresses farm incomes or pastoral livelihoods become unviable (Marchiori et al., 2012; Henderson et al., 
2014; Mastrorillo et al., 2016). The influence of rainfall on rural–urban migration increased since decolonisation, possibly due to more 
lenient legislation on internal mobility, with each 1% reduction in precipitation below a long-term average associated with a 0.45% 
increase in urbanisation (Barrios et  al., 2006). The rate of rural–urban migration is anticipated to increase (Neumann et  al., 2015) 
as a result of increasing vulnerability of agricultural livelihoods to climate change (Serdeczny et al., 2017). Nevertheless, rural–urban 
migration is not a simple one-way process. Peri-urban and rural areas provide developmental feedback loops, helping create a ‘regional 
agglomeration’ effect, for instance, through growing food demand, family and social connections, and flows back to rural areas of goods

and services and financial investments (UN-Habitat, 2016; Dodman et al., 2017).
Migration is an important and potentially effective climate change adaptation strategy in Africa and must be considered in adaptation 
planning (high confidence) (Williams et al., 2021). The more agency migrants have (that is, degree of voluntarity and freedom of movement), 
the greater the potential benefits for sending and receiving areas (high agreement, medium evidence) (Cross-Chapter Box MIGRATE in 
Chapter 7). In a synthesis of 63 studies covering over 9700 rural households in dryland sub-Saharan Africa, 23% of households employed 
migration (primarily temporary economic) to adapt to changes in rainfed agriculture (Wiederkehr et al., 2018). Migration responses 
to climate change tend to be stronger among wealthier households, as poorer households often lack financial resources necessary to 
migrate (Kaczan and Orgill-Meyer, 2020).
International migration

Studies on propensity to emigrate have uncovered conflicting results. Some findings suggest in low-income countries high temperatures 
‘trap’ people at home and lower migration rates abroad, but in middle-income countries, these same high temperatures encourage 
emigration (Cattaneo and Peri, 2016). However, other research finds in poor and agriculturally dependent countries, high temperatures 
encourage international out-migration, particularly to the OECD (Cai et al., 2016). Some evidence indicates people who leave tend to 
be more educated, possibly leading to ‘brain drain’ (Mbaye, 2017). Recent evidence suggests hotter-than-normal temperatures across 
103 countries, including many in Africa, increased asylum applications to the European Union (Missirian and Schlenker, 2017). Assuming 
no change in present-day vulnerability, asylum applications are projected to increase 34% across Africa (relative to 2000–2014) at 2.2°C

global warming (Missirian and Schlenker, 2017), although this finding has been challenged in the literature (Abel et al., 2019; Boas et al., 
2019).
International remittances are a vital resource for developing countries that can help aid recovery from climate shocks (Hallegatte et al. 
2016). Estimated at USD 48 billion in 2019 their importance is expected to grow further due to foreign direct investment declines during 
the COVID-19 pandemic (World Bank, 2020a). Furthermore, domestic remittances from rural–urban migration can help rural households 
respond to climate risks (KNOMAD, 2016). However, adequate finance and banking infrastructure are essential for remittances and, on 
average, cash transfer costs for sub-Saharan African countries remain the highest globally (World Bank, 2020a). Mobile money technologies

9
1392Chapter 9 Africa
and regulation that promotes competition in the remittances market can reduce transaction costs (World Bank, 2020a). Governments can 
further address challenges facing internal and international migrants by including them in health services and other social programmes 
and protecting them from discrimination (World Bank, 2020a).
Table Box 9.8.1 |  Reported impacts of climate on migration in Africa. (Findings on the linkages between climatic conditions and migration vary greatly across countries 
in Africa.)
Climate driver Country Climate – Migration linkages Reference
TemperatureKenya Cool temperatures linked to internal labour migration among males. Gray and Wise (2016)
UgandaHigh temperatures linked to increased non-labour migration among females.
Short hot spells linked to increased temporary migration. Long-term heat stress linked to 
permanent migration through an agricultural livelihoods pathway.Gray and Wise (2016); Call and Gray (2020)

TanzaniaTemperature-induced income shocks linked to decreased long-term rural–urban migration 
among men.Hirvonen (2016)
PrecipitationKenya Increased precipitation linked to decreased rural–urban migration. Mueller et al. (2020)
Zambia Increased precipitation linked to increased internal migration. Mueller et al. (2020)
Burkina FasoDrier regions linked to increased temporary and permanent migrations to other rural 
areas. Short-term precipitation deficits linked to increased long-term migration to rural 
areas and decreased risk of short-term migration to distant destinations.Henry et al. (2004)
EthiopiaDrought linked to men’s rural–urban labour migration, especially in land-poor households.
Drought linked to decreased marriage-related migration by women.
Precipitation variability and drought linked to rural–urban labour migration.
Precipitation variability and drought linked to out-migration to communities where 
precipitation variability and drought probability are lower.

High precipitation variability linked to increased migration, either through increased 
non-farm activities, which enable migration through economic resources or through 
insufficient agricultural production, which increase migration needs.Gray and Mueller (2012); Morrissey (2013); 
Hermans-Neumann et al. (2017); Groth 
GhanaIncreased severity of drought and household insecurity linked to reduced future migration 
intentions of households.Adger et al. (2021)
MalawiPrecipitation shocks linked to rural out-migration to communities where precipitation 
variability and drought probability are lower.
Precipitation shocks (flood and droughts) linked to longer-term urban migration and/or 
reverse (i.e., urban–rural) migration.Lewin et al. (2012); Suckall et al. (2015)
MaliDecreased precipitation linked to overall increase in out-migration—where farming 
families or individuals from farming communities will leave their origin community—and

some changes in duration and destination of trips. These moves can be either permanent 
or short-term, domestic or international.Grace et al. (2018)
NigerDrought linked to economically induced migration of households from rural areas to 
cities. Drought also linked to temporary international migration.Afifi (2011)
Temperature and 
precipitationBurkina FasoHigh temperatures linked to negative effects on all migration streams including 
international migration, much of which is to neighbouring countries. International 
migration also declines with precipitation.Gray and Wise (2016)
Senegal No detected linkages between climate and migration. Gray and Wise (2016)
Nigeria No detected linkages between climate and migration. Gray and Wise (2016)
Botswana Increased temperatures and precipitation linked to decreased internal migration. Mueller et al. (2020)
South AfricaHigher temperatures and precipitation extremes linked to increased rural out-migration,

especially among black and low-income South Africans.Mastrorillo et al. (2016)
SenegalPrecipitation variability, drought and increased temperatures linked to seasonal migration 
from rural to urban areas.Hummel (2016)
ZambiaHotter and drier climate linked to inter-district migration of wealthy districts. Poor districts 
characterised by climate-related immobility.Nawrotzki and DeWaard (2018)Box 9.8 (continued)

9
1393Africa  Chapter 9
Table Box 9.8.2 |  Projected numbers and shares of internal climate migrants in 2050 by sub-regions of sub-Saharan Africa. Projections are for internal migration 
driven by three slow-onset climate hazards (water stress, crop failure and SLR), and excluding rapid-onset hazards such as floods and tropical cyclones. As such, they 
present a lower-bound estimate of potential climate change impacts on internal migration. Projections are for two warming scenarios: low emissions (RCP2.6) and high 
emissions (RCP8.5), both coupled with a socioeconomic pathway (SSP4) in which low-income countries have high population growth, high rates of urbanisation, and 
increasing inequality within and among countries. By 2050, between 17.4 million (RCP2.6) and 85 million (RCP8.5) people (up to 4% of the region’s total population)

could be moving as a consequence of climate impacts on water stress, crop productivity and SLR. More inclusive socioeconomic pathways with lower population growth 
are projected to reduce these risks. West Africa has the highest levels of climate migrants, potentially reaching more than 50 million, suggesting that climate impacts will 
have a particularly pronounced impact on future migration in the region. In east Africa, out-migration hotspots include coastal regions of Kenya and Tanzania, western 
Uganda and parts of the northern highlands of Ethiopia. Kampala, Nairobi and Lilongwe may become hotspots of climate in-migration, coupled with existing rural to urban 
migration trends, and a high likelihood of movement toward non-climate-related sources of income in cities. Source: (Rigaud et al., 2018).
RegionGlobal warming around 2.5°C 
above pre-industrial by 2050 
(RCP8.5)Global warming around 1.7°C 
(RCP2.6)

East AfricaAverage number of internal migrants by 2050 (million) 10.1 6.9
Internal climate migrants as percent of population 1.28% 0.87%
West AfricaAverage number of internal migrants by 2050 (million) 54.4 17.9
Internal climate migrants as percent of population 6.87% 2.27%
Central AfricaAverage number of internal migrants by 2050 (million) 5.1 2.6
Internal climate migrants as percent of population 1.31% 0.66%
Southern AfricaAverage number of internal migrants by 2050 (million) 1.5 0.9
Internal climate migrants as percent of population 2.31% 1.40%
Sub-Saharan AfricaAverage number of internal migrants by 2050 (million) 71.1 28.3
Minimum (left) and maximum (right) million 56.6 85.7 17.4 39.9
Internal climate migrants as percent of population 3.49% 1.39%
Minimum (left) and maximum (right) percent 2.71% 4.03% 0.91% 2.04%
no-cost access to essential medicine, high-quality preventative care, 
financial protections against medical debt and increased geographic

and population coverage for all services (Hallegatte et al., 2016). All of 
these are key components of climate change adaptation for health and 
will reduce both the rate at which future outbreaks start and their total 
scope and impact (Carlson et al., 2021). The co-benefits of multilateral 
cooperation on the attainment of universal health coverage will be a 
key determinant of success or failure in both climate change adaptation 
and pandemic preparedness.
9.12 Heritage
Africa is a rich reservoir of heritage resources and Indigenous 
Knowledge, showcased by about 96 sites inscribed by the United 
Nations Educational, Scientific and Cultural Organization (UNESCO) 
as World Heritage Sites (UNESCO, 2018b). These include 53 sites 
specifically denoted as having great cultural importance and five sites 
with mixed heritage values. Unfortunately, valuable cultural heritage 
in forms of tangible evidence of past human endeavour, and the

intangible heritage encapsulated by diverse cultural practices of many 
communities (Feary et al., 2016), is under great threat from climate 
change.9.12.1 Observed Impacts on Cultural Heritage.
For more than 10,000 years, Africans recorded over 8000 painted and 
engraved images on rock shelters and rock outcroppings across 800 
known exceptional rock art sites of incalculable value (Hall et al., 2007; 
di Lernia and Gallinaro, 2011; di Lernia, 2017; Clarke and Brooks, 2018; 
Barnett, 2019), but which are exceptionally fragile to the elements. 
Unfortunately, there has been a poor study of direct climate change 
impacts on rock art across Africa.
Underwater heritage includes shipwrecks and artefacts lost at sea 
and extends to prehistoric sites, sunken towns and ancient ports that 
are now submerged due to climatic or geological changes (Spalding, 
2011). Off the shores of Africa, about 111 shipwrecks have been 
documented, with South Africa having a major share of about 41

sites. The sunken Egyptian city of Thonis-Heracleion and its associated 
60+ shipwrecks reflect the richness of Africa’s waters. Unfortunately, 
increased storm surges and violent weather currently threaten the 
integrity of shipwrecks by accelerating the destruction of wooden 
parts and other features (Harkin et al., 2020). However, climate change 
impacts on underwater cultural heritage sites are poorly studied, as 
it requires specialist assessment techniques (Feary et al., 2016), and 
marine archaeology studies are not well established in Africa.Box 9.8 (continued)

9
1394Chapter 9 Africa
Box 9.9 | Climate Change and Security: Interpersonal Violence and Large-scale Civil Conflict
There is substantial evidence that climate variability influences human security across Africa (see Chapter 7 Sections 7.2.7; 7.3.3 7). 
However, the strength and nature of this link depend on socioeconomic and institutional conditions, and climate is just one of many 
factors influencing violence and civil conflict (Schleussner et al., 2016a; von Uexkull et al., 2016; Linke et al., 2018; Mach et al., 2019; van 
Weezel, 2019; Ide et al., 2020).
Projections of security implications of long-run climate change in Africa are uncertain, as they rely on extrapolating observed effects of 
short-run climate variability (Burke et al., 2014). Lack of detection and attribution studies limit assessment of the impacts of observed 
human-caused climate change on security.
Interpersonal violent crime

Evidence from across the globe finds that interpersonal violence, ranging from use of profanity to violent crime, increases with temperature 
and sometimes low rainfall (Hsiang et al., 2013a; Burke et al., 2014; Gates et al., 2019). The effect of temperature may be driven by a 
physiological mechanism (Morrison et al., 2008; Seo et al., 2008; Ray et al., 2011), while effects of rainfall may operate through an 
agricultural yield impacts channel (Burke et al., 2014). While few studies link interpersonal violence to climate in Africa, Gates et al. 
(2019) documents homicide risks increasing under high temperatures in South Africa, and similarity across diverse study settings suggests 
temperature-induced violent crime likely generalises to Africa (Burke et al., 2014).
Large-scale intergroup conflict
Climatic conditions also change the risk of large-scale conflicts such as riots, ethnic conflicts and civil war (Burke et al., 2014; Koubi, 2019).

The effects of temperature are particularly well-studied in Africa. Risk of violent conflict rises with temperature in Sudan and South Sudan 
(Maystadt and Ecker, 2014; Maystadt et al., 2014; Scheffran et al., 2014), Kenya (Hsiang et al., 2013b; Scheffran et al., 2014), the east 
African region (O’Loughlin et al., 2012) and across sub-Saharan Africa (Burke et al., 2009; O’Loughlin et al., 2014; Witmer et al., 2017). 
Estimates indicate that warming trends since 1980 have elevated conflict risk across sub-Saharan Africa by 11% (Burke et al., 2009; 
Carleton et al., 2016).
Periods of low rainfall or flooding also contribute to social instability and upheaval across Africa (Miguel et al., 2004; Ralston, 2015; von 
Uexkull et al., 2016; Harari and Ferrara, 2018; van Weezel, 2019; Ide et al., 2020). The link between rainfall and conflict appears likely 
due to crop losses and declines in economic opportunity. One study found that dry growing seasons increase conflict incidence across

36 African nations, with spillover effects from the location of climate shock to neighbouring communities (Harari and Ferrara, 2018). 
Conflict-inducing impacts of drought have also been uncovered in Somalia (Maystadt and Ecker, 2014), Uganda, Sudan, Ethiopia and 
Kenya (Fjelde and von Uexkull, 2012; Hendrix and Salehyan, 2012; Couttenier and Soubeyran, 2014; Ralston, 2015; Linke et al., 2018; 
van Weezel, 2019), the DRC (von Uexkull et al., 2020) and in a pooled sample of African and Asian countries (von Uexkull et al., 2016). 
Extremely high rainfall may also incite conflict risk, although results are mixed (Hendrix and Salehyan, 2012; Raleigh and Kniveton, 2012). 
This uncertainty, combined with large uncertainties in rainfall projections under climate change, render future impacts of human-caused 
greenhouse gas emissions on rainfall-induced conflict in Africa highly uncertain.

While conflict–climate links have been repeatedly identified in Africa, climate is one of many interacting conflict risk factors and appears 
to explain only a small share of total variation in conflict incidence (von Uexkull et al., 2016; Mach et al., 2019; van Weezel, 2019).
Opportunities for adaptation
Adaptive capacity with respect to climate and conflict remains low in Africa (Sitati et al., 2021). For example, one study found that, 
relative to each country’s optimal annual temperature, realised temperatures across sub-Saharan Africa increase the annual incidence 
of war by 29.3% on average (Carleton et al., 2016). Another finds that rising temperatures due to climate change may lead to higher 
levels of violence in sub-Saharan Africa if political rights do not improve from current conditions (Witmer et al., 2017). Available studies 
on adaptation in conflict-affected areas tend to have a narrow focus, particularly on agriculture-related adaptation in rural contexts and

adaptation by low-income actors, with little known beyond these contexts (Sitati et al., 2021). Literature on the gender dimension of 
climate adaptation in conflict-affected countries is also limited (Sitati et al., 2021).

9
1395Africa  Chapter 9
Migration is a common response (Sitati et al., 2021) and may be an effective adaptive response to climate-induced conflict. Bosetti et al. 
(2018) find that countries with high emigration propensity display lower sensitivity of conflict to temperature, with no evidence of 
detrimental impacts on the destination countries. IK has also been applied to enable adaptation amidst conflict, for example, in Libya, to 
deal with erratic rainfall (Biagetti, 2017).
Other socioeconomic factors have been identified as adaptive opportunities. Rising incomes may mitigate conflict–climate relationships 
(Carleton et al., 2016), while weak institutions, lack of political freedom, agricultural dependence and exclusion of ethnic groups increase 
their strength (Schleussner et al., 2016a; von Uexkull et al., 2016; Witmer et al., 2017; Ide et al., 2020). In particular, agriculturally dependent

and politically excluded groups in Africa are especially vulnerable to the impact of drought on conflict (von Uexkull et al., 2016; Koubi, 
2019). Household-level resilience to economic shocks has been shown to lower support for violence after drought (von Uexkull et al., 2020). 
Local-level institutions have also been shown to support non-violence under adverse climate conditions (Bogale and Korf, 2007).
These findings suggest that ameliorating ethnic tensions, improving political institutions and investing in economic diversification and 
household resilience could mitigate future impacts of climate change on conflict.Box 9.9 (continued)
Intangible heritage includes instruments, objects, artefacts and cultural 
spaces associated with communities, and are almost always held orally 
(UNESCO, 2003). Loss of heritage assets may be a direct consequence 
of climate change/variability (Markham et al., 2016), or a consequence

of indirect factors resulting from climate change, for example, economic 
instability and poor decision making in areas of governance. In northern 
Nigeria, climate change exacerbates the impact of poor land use 
decisions, reducing the flow of the Yobe River and negatively impacting 
the Bade fishing festival because the available fish species continue 
to decline (Oruonye, 2010). Similarly, Lake Sanké in Mali has been 
degraded by a combination of urban development and poor rainfall, 
threatening the Sanké mon collective fishing rite (UNESCO, 2018b).
Migration related to climate change and climatic events could offer 
openings to women and young people to become de facto family 
heads (Kaag et al., 2019). However, such societal changes also increase 
community vulnerability to the loss of cultural knowledge held by village 
elders. For example, in Mauritius, the Sega tambour Chagos music is at 
risk, as elders familiar with the landscape pass on (Boswell, 2008).

9.12.1.1 Case Study: Traditional Earthen ‘Green Energy’ Buildings
Historically, Africa has had a unique and sustainable architecture (Diop, 
2018) characterised by area-specific, traditional earthen materials and 
associated Indigenous technology. Key examples include Tiébélé in 
Burkina Faso, Walata in Mauritania, Akan in Ghana, Ghadames in Libya, 
Old Towns of Djenné in Mali (World Heritage Site) and other diverse 
earthen architecture across sub-Saharan Africa. Adegun and Adedeji 
(2017) indicate that earthen materials provide advantages in thermal 
conductivity, resistivity and diffusivity, indoor and outdoor temperature, 
as well as cooling and heating capacities. Moreover, earthen materials 
are recyclable and environmentally ‘cleaner’ (Sanya, 2012) because of 
the absence or small quantity of cement in production, thus reducing 
carbon emissions. Despite these advantages, the expertise and socio-
cultural ceremonies that accompany building and renewal of earthen

architecture are disappearing fast (Adegun and Adedeji, 2017). Further, 
earthen construction is being threatened by extreme climatic variability and changing climate that exacerbates decay (Brimblecombe et  al., 
2011; Bosman and Van der Westhuizen, 2014; Brooks et al., 2020).
9.12.2 Projected Risks
Sea level rise (SLR) and its associated hazards will present increasing 
climate risk to African heritage in the coming decades (Figure 9.38; 
Marzeion and Levermann, 2014; Reimann et al., 2018; Brito and Naia, 
2020). Although no continental assessment has quantified climate 
risk to African heritage and little is known of near-term exposure to 
hazards such as SLR and erosion, for a handful of coastal heritage 
sites included in global or Mediterranean studies, 10 cultural sites are 
identified to be physically exposed to SLR by 2100 at high emissions 
scenarios (RCP8.5) (Marzeion and Levermann, 2014; Reimann et al., 
2018), of which, seven World Heritage Sites in the Mediterranean are

also projected to face medium or high risk of erosion (Figure 9.38; 
Reimann et al., 2018). Further, Brito and Naia (2020) identify natural 
heritage sites across 27 African countries that will be affected by 
SLR by 2100 (RCP8.5), of which 15 sites covering eight countries 
demonstrated a high need for proactive management actions because 
of high levels of biodiversity, international conservation relevance and 
exposure to SLR (Figure  9.38). These nascent studies highlight the 
potential severity of risk and loss and damage from climate change 
to African heritage, as well as gaps in knowledge of climate risk to 
African cultural and natural, particularly concerning bio-cultural 
heritage.
Although climate change is a significant risk to heritage sites (Brito 
and Naia, 2020), there is little research on how heritage management 
is adapting to climate change, and particularly, whether the capacity 
of current heritage management systems can prepare for and deal with

consequences of climate change (Phillips, 2015; see also Cross-Chapter 
Box SLR in Chapter 3).
Worsening climate impacts are cumulative and often exacerbate 
the vulnerability of cultural heritage sites to other existing risks,

9
1396Chapter 9 Africa
including conflict, terrorism, poverty, invasive species, competition 
for natural resources and pollution (Markham et  al., 2016). These 
issues may affect a broad range of tourism segments, including beach 
vacation sites, safari tourism, cultural tourism and visits to historic 
cities (UNWTO, 2008). Climate change impacts have the potential to 
increase tourist safety concerns, especially at sites where increased 
intensity of extreme weather events or vulnerability to floods and 
landslides are projected (Markham et al., 2016) (see also Cross-Chapter 
Box EXTREMES in Chapter 2). There may also be circumstances where 
interventions required to preserve and protect the resource alter its 
cultural significance (van Wyk, 2017).9.12.3 Adaptation
Research highlights potential in integrating Indigenous Knowledge, 
land use practices, scientific knowledge and heritage values to co-
produce tools that refine our understanding of climate change and

variability and develop comprehensive heritage adaptation policy 
(Table 9.13; Ekblom et al., 2019).
Conservation of heritage may require offsetting the impact of loss 
through partial or total excavation under certain circumstances, like 
environment instability, or where in situ heritage preservation is 
exorbitant in cost (Maarleveld and Guérin, 2013).
Risk to Africa’s cultural and natural coastal heritage sites from sea level rise and erosion by 2100 (RCP8.5)
(a) Cultural sites exposed to sea level rise and erosion (b) 15 natural sites of conservation priority exposed to sea level rise
* = Cultural sites exposed to sea level rise and facing
       medium and high risk of erosion*
*
*
*
*
*
*
Figure 9.38 |  Risk to Africa’s cultural and natural coastal heritage sites from see level rise (SLR) and erosion by 2100.

(a) World Heritage Sites projected to be exposed to flooding from SLR under a high emission scenario (RCP8.5) by 2100 (Marzeion and Levermann, 2014; Reimann et al., 2018). 
For north Africa, multiple sites are already identified to be at medium or high risk from erosion under both current and future SLR conditions (Reimann et al., 2018). At the time of 
assessment erosion risk had not been assessed for other African regions 
(b) The 15 African natural sites (coastal protected areas) projected to be most exposed to negative impacts from SLR and thus as priority sites for adaptation (Brito and Naia, 2020).

9
1397Africa  Chapter 9
Table 9.13 |  Examples of responses to climate change impacts to heritage sites.
Heritage Type ExampleType of 
climate 
impactIntervention 
focus or 
activityMain 
intervention 
activityState of materialsFinal state 
of heritageLiterature
TangibleAncientHistoric 
buildingsOunga 
Byzantine Fort 
archaeological 
remains, TunisiaCoastal 
erosionArchaeological 
of fortBuilding repairs 
to outer walls 
of fort but other 
areas no 
interventionMixed. Fort is in 
good condition, 
but other parts of 
the site are under 
threat of coastal 
erosion, particularly 
lesser archaeological 
remains of other 
periods.Some aspects 
of site well 
preserved, 
other parts 
damaged.Slim et al. 
(2004)
Archaeological 
sitesSabratha, 
Roman City, 
Libyan coastSLR, local 
flooding 
and coastal 
erosionMonitoring of 
conditionNoneLoss of 
remains into the sea.Some aspects 
other parts

damaged.Abdalahh 
(2011)
LivingCities/townsLamu Old Town 
and archipelago, 
KenyaSLR 
impacting 
low-lying 
areas and 
climate 
impacting 
protective 
mangrovesLamu Old 
Town managed 
by National 
Museums of 
Kenya the 
mangrove forests 
by Community 
Forest 
Associations 
and Forest 
Conservation and 
Management Act 
of 2016
Changes in 
biodiversity and 
cultural resilience 
to climate shocksDraft for 
National Policy 
for Disaster 
Management in 
KenyaMangrove forests 
provide protection 
from storm surges 
and coastal erosion. 
Changing biodiversity 
of mangroves 
is threatening 
mangroves which 
threaten Lamu Old 
Town.Continuing 
deterioration.Wanderi 
(2019)
Mud buildingsTiébélé, Burkina 
FasoClimate 
causing 
flooding, 
erosion.Local community 
conservationImprovements 
to drainage and 
land security, 
development of 
conservation and 
plansCurrent and ongoing 
conservation.Stable.Birabi and 
Nawangwe 
(2011)

Bio-cultural Rock artGolden Gate 
Highlands, 
South AfricaPrecipitation 
and 
atmospheric 
changes 
causing 
luxuriant 
lichen 
growthMonitoring of 
conditionNo known 
interventionBiodeterioration of 
condition of rock 
surface.Increasing 
loss of rock 
surfaces and 
images on the 
rock surfaces.Viles and 
Cutler 
(2012)Although many underwater shipwrecks and ruins of cities are currently 
preserved better in situ than similar sites on land (Feary et al., 2016), 
preserving such heritage is often financially prohibitive with many 
physical and technical challenges. Further, skill capacities of heritage 
agencies are limited to a few qualified archaeologists in Africa 
(Maarleveld and Guérin, 2013).
For centuries, Africans have drawn on intangible heritage to enhance 
their resilience to climatic variability and support adaptation practices. 
For example, pastoralist communities have historically translated their experiences into memories that can be ‘translated’ into diverse adaptive

practices (Oba, 2014). In coastal Kenya, Mijikenda communities rely 
on Indigenous Knowledge and practices used in the management of 
the sacred Kaya Forests to adapt their farming to a changing climate 
(Wekesa et al., 2015).
Hence, preservation measures for transforming oral information into 
written records should ensure viability of intangible cultural heritage by 
giving due consideration to the confidentiality of culturally sensitive 
information and intellectual property rights (Feary et al., 2016).

9
1398Chapter 9 Africa
climate 
focus or 
Intangible 
(Indigenous)Language!Xun and Khwe 
Youth of South 
AfricaClimate 
causing 
drought 
and loss of 
plantsGroups (youth) Documentation Non-formal, local.Enhancement, 
promotion.Bodunrin 
(2019)
Language Use 
in Agricultural 
Radio 
Programming in 
NigeriaClimate 
droughtFarmer groups, 
communitiesResearch, 
documentationFormal, localPromotion, 
transmission.Adeyeye 
et al. 
(2020)
RitualsEnkipaata, 
Eunoto and 
Olng’esherr 
Maasai male 
rites of passageClimate 
causing 
droughtMaasai 
community 
groupsIdentification, 
documentation, 
researchFormal, non-formal, 
local, foreign.Promotion.UNESCO 
(2018a)
Customs & 
beliefsSanké mon 
fishing festival 
in MaliClimate 
reducing 
rainfallMalinkés,

Bambara 
and Buwa 
communitiesIdentification, 
preservationFormal, non-formal, 
local.Promotion.UNESCO 
(2009)
engineering 
systemsWater 
measurers of 
the Foggara 
irrigation system 
in AlgeriaIncreased 
siltation and 
sandstorms
Climate 
causing 
floodingTouat and Tidikelt 
identification, 
documentationFormal, local. Transmission.Mokadem 
et al. 
(2018)
Arts and craftsTraditional crafts 
made from 
various parts 
of the Date 
Palm in Egypt, 
Mauritania, 
Morocco, Sudan, 
Tunisia and 
other countries 
outside AfricaClimate 
causing 
shift in plant 
habitatsResidents of 
oases, groups, 
communities, 
agricultural 
cooperative 
societiesResearch, 
preservation, 
protectionFormal, non-formal, 
local, foreign.Transmission, 
promotion, 
enhancement, 
revitalisation.UNESCO 
(2003)
Shabani 
et al. 
(2012)
Inclusion of cultural landscapes and intangible heritage in the landscape

approach at the regional scale development planning processes may 
have significant impacts on protected area management (Feary et al., 
2016). For example, at the Domboshava rock art site in Zimbabwe, all 
management decisions are taken in direct consultation with traditional 
leaders and other stakeholders from surrounding communities (Chirikure 
et al., 2010). Such adaptation strategies promote a more open-minded 
approach to heritage by leveraging local development (UNESCO, 2018b).
Lack of expertise and resources, together with legislation that privileges 
certain typologies of heritage, seem to limit implementation of approved 
policies (Ndoro, 2015). Additionally, cultural heritage has least priority 
in terms of budgetary allocation, capacity building and inclusion into 
school curricula. Failure to consider the views of people who attach 
spiritual significance to places is detrimental to the conservation of

heritage places (Bwasiri, 2011). In particular, documented cases of local 
people having to pay an entrance fee, like tourists, to access burial grounds and places of pilgrimage negate local participation in cultural 
site management (Ndoro, 2015).
In the long term, heritage managers and local authorities could shift 
from planning primarily for disaster response and recovery to strategies 
that focus on disaster preparedness, reducing the vulnerability of 
sites and strengthening resilience of local communities (UNFCCC, 
2007; Domke and Pretzsch, 2016). This could evolve into innovative 
approaches that integrate community, government and the research 
sector in productive cultural heritage management partnerships.
There is a need for institutions to establish, maintain and update a 
comprehensive inventory of underwater cultural heritage. This can be 
done using non-intrusive, detailed mapping of the wreck site and a

three-dimensional model from which scientists can reconstruct the site 
in detail (Maarleveld and Guérin, 2013).

9
1399Africa  Chapter 9
FAQ 9.1 | Which climate hazards impact African livelihoods, economies, health and well-being the most?
Climate extremes, particularly extreme heat, drought and heavy rainfall events, impact the livelihoods, health, and well-being of millions of 
Africans. They will also continue to impact African economies, limiting adaptation capacity. Interventions based on resilient infrastructure and 
technologies can achieve numerous developmental and adaptation co-benefits.
Multi-year droughts have become more frequent in west Africa, and the 2015–2017 Cape Town drought was three 
times more likely due to human-caused climate change. Above 2°C global warming, drought frequency is projected 
to increase, and duration will double from approximately 2 to 4 months over north Africa, the western Sahel and 
southern Africa. Estimates of increased exposure to water stress are higher than those for decreases. By 2050,

climate change could expose an additional 951 million people in sub-Saharan Africa to water stress while also 
reducing exposure to water stress by 459 million people. Compared to population in 2000, human displacement 
due to river flooding in sub-Saharan Africa is projected to triple for a scenario of low population growth and 1.6°C 
global warming. Changing rainfall distributions together with warming temperatures will alter the distributions 
of disease vectors like mosquitoes and midges. Malaria vector hotspots and prevalence are projected to increase 
in east and southern Africa and the Sahel under even moderate greenhouse gas emissions scenarios by the 2030s, 
exposing an additional 50.6–62.1 million people to malaria risk.
Increases in the number of hot days and nights, as well as in heatwave intensity and duration, have had negative 
impacts on agriculture, human health, water availability, energy demand and livelihoods. By some estimates, African

countries’ Gross Domestic Product per capita is on average 13.6% lower since 1991 than if human-caused global warm-
ing had not occurred. In the future, high temperatures combined with high humidity exceed the threshold for human 
and livestock tolerance over larger parts of Africa and with greater frequency. Increased average temperatures and 
lower rainfall will further reduce economic output and growth in Africa, with larger negative impacts than on other 
regions of the world.
Resilient infrastructure and technologies are required to cope with the increasing climate variability and change 
(Figure FAQ9.1.1). These include improving housing to limit heat and exposure, along with improving water and 
sanitation infrastructure. Such interventions to ensure that the most vulnerable are properly protected from climate 
change have many co-benefits, including for pandemic recovery and prevention.

9
1400Chapter 9 Africa
Climate hazards
Extreme weather
Extreme temperatures, heat
Reduced precipitation, droughtDirect influences on            Health & wellbeing
Food
Water
EcosystemShelter/housing
Poverty
Cities/communityWiderenvironment
Personalenvironment
Internal
A schematic illustration of the interconnectedness of different sectors and impacts
Figure FAQ9.1.1 |  A schematic illustration of the interconnectedness of different sectors and impacts that spillover to affect the health and 
well-being of African people.Box FAQ 9.1 (continued)

9
1401Africa  Chapter 9
FAQ 9.2 | What are the limits and benefits of climate change adaptation in Africa?
The capacity for African ecosystems to adapt to changing environmental conditions is limited by a range of factors, from heat tolerance to 
land availability. Adaptation across human settlements and food systems are further constrained by insufficient planning and affordability. 
Integrated development planning and increasing finance flows can improve African climate change adaptation.
With increasing warming, there is a lower likelihood species can migrate rapidly enough to track shifting climates, 
increasing extinction risk across more of Africa. At 2°C global warming more than 10% of African species are at 
risk of extinction. Species ability to disperse between areas to track shifting climates is limited by fencing, transport 
infrastructure, and the transformation of landscapes to agriculture and urban areas. Many species will lose large

portions of their suitable habitats due to increases in temperature by 2100. Coupled with projected losses of Africa’s 
protected areas, higher temperatures will also reduce carbon sinks and other ecosystem services. Many nature-based 
adaptation measures (e.g., for coral reefs, mangroves, marshes) are less effective or no longer effective above 
1.5°C of global warming. Human-based adaptation strategies for ecosystems reach their limits as availability and 
affordability of land decreases, resulting in migration, displacement and relocation.
The limits to adaptation for human settlements arise largely from developmental challenges associated with 
Africa’s rapid urbanisation, poor development planning, and increasing numbers of urban poor residing in informal 
settlements. Further limits arise from insufficient consideration of climate change in adaptation planning and

infrastructure investment and insufficient financial resources. There are also limits to adaptation for food production 
strategies. Increasing climate extreme events—droughts and floods—impose specific adaptation responses which 
poorer households cannot afford. For instance, the use of early maturing or drought-tolerant crop varieties may 
increase resilience, but adoption by smallholder farmers is hindered by the unavailability or unaffordability of seed.
Adaptation in Africa can reduce risks at current levels of global warming. However, there is very limited evidence 
for the effectiveness of current adaptation at increased global warming levels. Ambitious, near-term mitigation 
would yield the largest single contribution to successful adaptation in Africa.
Current adaptation finance flows are billions of USD less than the needs of African countries and around half 
of finance commitments to Africa reported by developed countries remain undisbursed. Increasing adaptation

finance flows by billions of dollars (including public and private sources), removing barriers to accessing finance and 
providing targeted country support can improve climate change adaptation across Africa.
FAQ 9.3 | How can African countries secure enough food in changing climate conditions for their growing 
populations?
Climate change is already impacting African food systems and will worsen food insecurity in sub-Saharan Africa in the future. An integrated 
approach to adaptation planning can serve as a flexible and cost-effective solution for addressing African food security challenges.
Maize and wheat yields have decreased an average of 5.8% and 2.3%, respectively, in sub-Saharan Africa due to 
climate change. Among the 135 million acutely food-insecure people in crisis globally, more than half (73 million) 
are in Africa. This is partly due to the growing severity of drought with increasing temperatures also a severe risk

factor. Adding to these challenges, Africa has the fastest-growing population in the world that is projected to grow 
to around 40% of the world’s population by 2100.
Sustainable agricultural development combined with enabling institutional conditions, such as supportive governance 
systems and policy, can provide farmers with greater yield stability in uncertain climate conditions. It is also widely 
acknowledged that an integrated approach for adaptation planning that combines (a) climate information services, 
(b) capacity building, (c) Indigenous and local knowledge systems and (d) strategic financial investment can serve as 
a flexible and cost-effective solution for addressing African food security challenges.

9
1402Chapter 9 Africa
FAQ 9.4 | How can African local knowledge serve climate adaptation planning more effectively?
A strong relationship between scientific knowledge and local knowledge is desirable, especially in developing contexts where technology for 
prediction and modelling is least accessible.
In many African settings, farmers use the local knowledge gained over time—through experience and passed on 
orally from generation to generation—to cope with climate challenges. Indigenous Knowledge systems of weather 
and climate patterns include early warning systems, agroecological farming systems and observation of natural 
or non-natural climate indicators. For instance, biodiversity and crop diversification are used as a buffer against 
environmental challenges: if one crop fails, another could survive. Local knowledge of seasons, storms and wind 
patterns is used to guide and plan farming and other activities.

Collaborative partnerships between research, agricultural extension services and local communities would create 
new avenues for the co-production of knowledge in climate change adaptation to better inform adaptation policies 
and practices across Africa.

10
1459Asia Chapter 10
Observed surface air temperature has been increasing since the 
20th century all over Asia (high confidence1). Significant warming 
has intensified the threat to social and economic sustainability (medium confidence). Rising temperatures increase the likelihood of the threat of heatwaves across Asia, droughts in arid and semiarid areas of West, Central and South Asia, delays and weakening of the monsoon circulation in South Asia, floods in monsoon regions in South, Southeast and East Asia, and glacier melting in the Hindu Kush Himalaya region (medium confidence). {10.3.1, 10.3.3}
Asian countries are experiencing a hotter summer climate,

resulting in an increase in energy demand for cooling at a rapid rate, together with the population growth (high confidence). Decrease in precipitation influences energy demand as well as desalination, underground water pumping and other energy-intensive methods are increasingly used for water supply (high confidence). More energy demands in summer seasons will exceed any energy savings from relatively lower heating demand due to warmer winter. Among 13 developing countries with large energy consumption in Asia, 11 are exposed to high-energy insecurity and industrial-systems risk (high confidence). {10.4.1}
Asian terrestrial-ecosystems change is driven by global 
warming, precipitation and Asian monsoon alteration, permafrost thawing and extreme events like dust storms, along with natural and human-related factors which are in interplay (high confidence). Treeline position in North Asian mountains moves

upwards after the 1990s, while in Himalaya treeline demonstrates a multi-directional shift, either moves upwards or does not show upslope advance, or moves downwards. This can be explained by site-specific complex interaction of positive effect of warming on tree growth, drought stress, change in snow precipitation, land-use change (especially grazing) and other factors (high confidence). The increased considerable changes in biomes in Asia are a response to warming (medium confidence). Terrestrial and freshwater species, populations and communities are altered in line with climate change across Asia (medium to high confidence). Climate change, human activity and lightning have caused the increase in wildfire severity and area burned in North Asia after the 1990s (medium confidence). Length of plant growth season has increased in some parts of East and North Asia, while the opposite trend, or no change at all, has been observed in other parts (high confidence). Observed biodiversity

of animals plants have been linked to climate change in some parts of Asia (high confidence). There is evidence that climate change can 
alter species interaction or spatial distribution of invasive species in Asia (high confidence). Changes in ecosystems in Asia during the 21st century are expected to be driven by projected climatic, natural and socioeconomic changes. Across Asia, under a range of representative concentration pathways and other scenarios, rising temperatures are expected to contribute to a northward shift of biome boundaries and an upwards shift of mountain treeline (medium confidence). {10.4.2}
1 In this Report,  the following summary terms are used to describe the available evidence: limited, medium or robust; and for the degree of agreement: low, medium or high. A level of confidence is

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.Coastal habitats of Asia are diverse, and the impacts of climate 
change including rising temperatures, ocean acidification and sea level rise (SLR) has brought negative effects to the services and the livelihoods of people depending on it (high confidence). The 
degree of bleaching of coral reefs is diverse among different presences of stress-tolerant symbionts and higher thermal thresholds. The risk of irreversible loss of coral reefs, tidal marshes, seagrass meadows, plankton community and other marine and coastal ecosystems increases with global warming, especially at 2°C temperature rise or more (high

confidence). Mangroves in the region continue to face threats due to pollution, conversion for aquaculture, agriculture and climate-based threats like SLR and coastal erosion. {10.4.3}
Both climatic and non-climatic drivers such as socio-economic 
changes have created water stress conditions in both water supply and demand in all sub-regions of Asia, except for North Asia (medium confidence). These changes in space and time directly or indirectly have affected water-use sectors and services. By mid-21st Century, the international transboundary river basins of Amu Darya, Indus, Ganges could face severe water scarcity challenges due to climatic variability and changes acting as stress multipliers (high confidence). Due to global warming, Asian countries could experience an increase in drought conditions (5–20%) by the end of this century (high confidence). {10.4.4}
The Asian glaciers were in minor-area shrinkage and mass loss

during 2006–2016, resulting in the instability of water resource supply (high confidence). Glaciers in Asia are the water resources of about 220 million people in the downstream areas. The glacier melt water in the southern Tibetan Plateau increased during 1998–2007, and will further increase till 2050. The total amount and area of glacier lakes have increased during the past decade (high confidence). More glacier collapses and surges were found in western Tibetan Plateau.Glacier lake outburst flood (GLOF) will threaten the securities of the local and downstream communities (high confidence). Snowmelt water contributed 19% of the increase change in runoff of arid regions’ rivers in Xinjiang, China, and 10.6% of the upper Brahmaputra River during 2003–2014 (medium confidence). {10.4.4, Box 10.4}
Since IPCC AR5, more studies have reinforced the earlier findings

on the spatio-temporal diversity of climate-change impacts on food production in Asia depending on the geographic location, agroecology and crops grown, recognising that there are winners and losers associated with the changing climate across scales (high confidence). Most of these impacts have been 
associated with drought, monsoon rain and oceanic oscillations, the frequency and severity of which have been linked with the changing climate. Climate-related risks to agriculture and food systems in Asia will progressively escalate with the changing climate, with differentiated impacts across the region (medium confidence). Major projected impacts of climate change in the agriculture and food sectors include decline in fisheries, aquaculture, crop production (particularly in South and Southeast Asia), reduction in livestock production in

10
1460Chapter 10 Asia
Mongolia and changes in crop, farming systems and crop areas in 
almost all regions, with negative implications to food security (medium 
confidence). In India, rice production can decrease from 10 to 30%, 
whereas maize production can decrease from 25 to 70% assuming 
a range of temperature increase from 1°C to 4°C. Similarly, rice 
production in Cambodia can decrease by 45% by 2080 under the 
high-emissions scenario. Occurrence of pests, such as the golden apple 
snail (Pomacea canaliculate), associated with the predicted increase 
in climatically suitable habitats in 2080, threatens the top Asian rice-
producing countries including China, India, Indonesia, Bangladesh, 
Vietnam, Thailand, Myanmar, the Philippines and Japan. Increasing 
temperatures, changing precipitation levels and extreme climate 
events, such as heatwaves, droughts and typhoons, will persist in being 
important vulnerability drivers that will shape agricultural productivity

particularly in South, Southeast and Central Asia. {10.4.5, Figure 10.6}
Asian urban areas are considered high-risk locations from 
projected climate change, extreme events, unplanned urbanisation 
and rapid land-use change (high confidence) but also sites of 
ongoing adaptation (medium confidence). Asia is home to the 
largest share of people living in informal settlements, with 332 million 
in Eastern and Southeast Asia, and 197 million in Central and Southern 
Asia. By 2050, 64% of Asia’s population will be urban. Coastal cities, 
especially in South and Southeast Asia, are expected to see significant 
increases in average annual economic losses between 2005 and 2050 
due to flooding, with very high losses in East Asian cities under the high-
emissions scenario (high confidence). Climate change will amplify the 
urban heat-island effect across Asian cities (especially South and East 
Asia) at 1.5°C and 2°C temperature rise, both substantially larger than

under the present climate (medium evidence, high agreement). Under 
the high-emissions scenario, higher risks from extreme temperature and 
precipitation are projected for almost all cities (medium confidence), 
with impacts on freshwater availability, regional food security, human 
health and industrial outputs. By 2080, 940 million to 1.1 billion urban 
dwellers in South and Southeast Asia could be affected by extreme heat 
lasting more than 30 d yr–1 (high confidence), with poorer populations 
affected the most. {10.4.6, Cross-Chapter Box URBAN in Chapter 6}
Climate change has caused direct losses due to the damage in 
infrastructure, disruption in services and affected supply chains in 
Asia (medium confidence) and will increase risk to infrastructure 
as well as provide opportunities to invest in climate-resilient 
infrastructure and green jobs (medium confidence). At higher 
warming, key infrastructures, such as power lines, transport by roads

and railways, and built infrastructures, such as airports and harbours, 
are more exposed to climate-induced extreme events, especially in 
coastal cities (medium confidence). Evidence on urban adaptation 
across Asia is growing with examples of infrastructural adaptation 
(e.g., flood protection measures, and climate-resilient highways and 
power infrastructure), institutional adaptation (e.g., sustainable land-
use planning, zoning plans), nature ecosystem-based solutions (e.g., 
mangrove restoration, restoring and managing urban green spaces, 
urban farming), technological solutions (e.g., smart cities, early warning 
systems) and behavioural adaptation (e.g., improved awareness and 
preparedness measures). However, adaptation actions tend to be in the 
initial stages and more reactive (57% of urban adaptations focus on 
preparatory interventions, such as capacity building, and 43% of cities report implemented adaptation interventions) (medium confidence). The

degree of implementation of urban adaptation is uneven with large cities 
receiving more funding and priority, and smaller cities and towns, and 
peri-urban spaces, seeing relatively lower adaptation action (medium 
confidence). {10.4.6}
Climate change is increasing vector-borne and water-borne 
diseases, undernutrition, mental disorders and allergy-related 
illnesses in Asia by increasing hazards such as heatwaves, 
flooding and drought, and air pollutants, in combination with 
more exposure and vulnerability (high confidence). Sub-regional 
diversity in socioeconomic and demographic contexts (e.g., ageing, 
urban compared with agrarian society, increasing population compared 
with reduced birth rate, high income compared with low to middle 
income) and geographic characteristics largely define the differential 
vulnerabilities and impacts within countries in Asia. Under the 
medium-to-high emissions scenario, rising temperatures and extreme

climate events will have an increasing impact on human health and 
well-being with varying types and magnitudes of impact across Asia 
(high confidence). More frequent hot days and intense heatwaves will 
increase heat-related deaths. Increased floods and droughts will have 
adverse impacts on food availability and the prices of food, resulting 
in increased undernourishment in South and Southeast Asia. Increases 
in heavy rain and temperature will increase the risk of diarrhoeal 
diseases, dengue fever and malaria in tropical and subtropical Asia. 
{10.4.7}
Increased climate variability and extreme events are already 
driving migration (robust evidence, medium agreement) and 
projecting that longer-term climate change will increase 
migration flows across Asia (medium confidence). One in three 
migrants comes from Asia, and the highest ratio of outward migrants 
is seen from hazard-exposed Pacific countries. In 2019, Bangladesh,

China, India and the Philippines each recorded more than 4  million 
disaster displacements. In Southeast Asia and East Asia, cyclones, 
floods and typhoons triggered internal displacement of 9.6  million 
people, almost 30% of total global displacements in 2019. {Box 10.2}
There is a small but growing literature highlighting the impor -
tance of behavioural aspects of adaptation in Asia (high con-
fidence), but this is restricted primarily to agriculture and dis-
aster risk reduction (DRR). Factors motivating adaptation actions 
include risk perception, perceived self-efficacy, sociocultural norms 
and beliefs, previous experiences of impacts, levels of education and 
awareness (high confidence). There is growing evidence on behaviou-
ral aspects of individual adaptation but lesser evidence on the socio-
cognitive factors motivating governments and private-sector actors to 
adapt. {10.5.3}
Climate change is already causing economic loss and damage

across Asian regions, and this will increase under higher 
warming (medium confidence). Non-material losses and damages 
are reported to a lesser degree, but this is due to under-reporting and 
methodological issues with detection and attribution to climate change 
(high confidence). Loss and damage represents a key knowledge 
gap, especially in West, Central and North Asia. Insufficient literature 
differentiating loss and damage under future adaptation scenarios

10
1461Asia  Chapter 10
renders a comprehensive assessment of residual damages, along with 
future loss and damage, difficult. {Box 10.6}
Options such as climate-smart agriculture, ecosystem-based 
DRR and investing in urban blue–green infrastructure meet 
adaptation, mitigation and Sustainable Development Goals 
simultaneously, presenting opportunities for climate resilient 
development (CRD) pathways in Asia (high confidence). Climate 
risks, vulnerability and adaptation measures need to be factored into 
decision making across all levels of governance (high confidence). To 
help achieve this, there is a need to advance the current understanding 
of climate impacts across sectors and spatio-temporal scales, and 
improve on the current strategies in planning and budget allocation. 
More accurate forecasting of extreme events, risk awareness and 
prioritising individual and collective decision making also need to 
be addressed (high confidence). Options for Asian countries are

transforming the risks of climate change into opportunities for the 
advancement of projects in the energy sector, including promoting 
investment in non-fossil energies, securing local natural gas resources, 
enhancing water harvesting, adopting green building technologies 
and encouraging multi-stakeholder partnerships. However, there are 
significant barriers to CRD such as fragmented, reactive governance; 
inadequate evidence on which actions to prioritise and how to 
sequence them; and finance deficits. Some Asian countries and regions 
offer solutions to overcome these barriers: through use of advanced 
technologies (in situ observation and remote sensing, a variety of 
new sensor technologies, citizen science, artificial intelligence and 
machine learning tools); regional partnerships and learning; improved 
forecasting capabilities; and better risk awareness (high confidence). 
{10.5, 10.6}

10
1462Chapter 10 Asia
10.1 Introduction
Asia is defined here as the land and territories of 51 countries/regions 
(Figure 10.1). It can be broadly divided into six sub-regions based on 
geographic position and coastal peripheries (IPCC, 2014b). These are, 
in alphabetical order, Central Asia (5 countries), East Asia (7 countries/
regions), North Asia (2 countries), South Asia (8 countries), Southeast 
Asia (12 countries) and West Asia (17 countries). The population of 
Asia was reported to be over 4.46 billion in 2016, which is about 60% 
of the world’s population with an estimated density of 100 people 
per square kilometre (UNDESA, 2017). The highest life expectancy at 
birth is 84  years (Japan) and the lowest is 52  years (Afghanistan) 
(CIA, 2017). The gross domestic product (GDP) per capita ranged from 
US$587 (Afghanistan) to US$81,585 (Macao, SAR, PRC) (IMF , 2018).
Box 10.1 | What Is New on Asia in AR6?
• Adaptation in energy sector is becoming increasingly

crucial in the Asian region, which has been assessed in a 
new subsection.
• Adaptation technology and innovations are also of high 
importance for the region. Classification of adaptation 
technology and its use in different systems are assessed.
• On the governance side, the nexus approach among 
several systems like food, energy and water is highlighted, 
and its importance is assessed.
• New concepts on decentralised and self-reliant society, 
such as the Circulating and Ecological Sphere (CES), are 
emerging for integrated adaptive governance.
• As a part of a sustainable development pathway, inter -
linkages of climate-change adaptation (CCA) and disaster 
risk reduction (DRR) are highlighted.
10.2 Major Conclusions from Previous 
Assessments
As the most populous continent, Asia is faced with a unique set of 
challenges that vary across its climatic zones:
• The most perceptible change in climatic trends is observable in

the increasing surface air temperature and rise in nighttime 
temperature, particularly during winter. This is accompanied by 
monsoon rainfall variability, which is observable interseasonally, 
interannually and spatially.
• There is increasing evidence of an upwards trend in the intensity 
and frequency of extreme weather events in Asia.
• The predictions for future climatic trends suggest an increase in 
warming along the higher latitudes of North Asia.
• Projections show that agricultural and food security will be 
impacted substantially, particularly in the area of cereal production, 
by the end of the 21st century.
• Malnutrition among the poor and marginalised sections of the 
population in Asia remains a major concern that is further rendered 
complex by climate change.• Projections of an increase in the incidence of pests and diseases 
impact directly on the food security and health of vulnerable 
populations.
• Erosion will occur simultaneously with SLR. The projected rise

could lead to large-scale flooding in low-lying areas, particularly 
South, Southeast and East Asia.
• The erosion of the major deltas of Asia may take place through 
a rise in sea levels, an increased frequency of extreme weather 
events and the excessive withdrawal of groundwater.
• The priority areas for Asia include an enhancement of capabilities 
to collect social and biophysical data, information sharing, sectoral 
interactions, a mainstreaming of science and the identification of 
critical climate thresholds across regions and sectors.
Drawing upon a greater number of studies made possible by greater 
use of advanced research tools, such as remote sensing as well as 
meticulous modelling of impacts, the Fifth Assessment Report could 
significantly expand its coverage of pertinent issues (IPCC, 2014c). 
For example, the discussion on the Himalayas was expanded to 
cover observed and projected impacts of climate change on tourism

(WGII AR5 Section 10.6.2); livelihood assets such as water and food 
(WGII AR5 Sections 9.3.3.1, 13.3.1.1, 18.5.3, 19.6.3); poverty (WGII 
AR5 Section  13.3.2.3); culture (WGII AR5 Section  12.3.2); flood 
risks (WGII AR5 Sections  18.3.1.1, 24.2.1); health risks (WGII AR5 
Section 24.4.6.2); and ecosystems (WGII AR5 Section 24.4.2.2; IPCC, 
2014b, 2014c).
• Over the past century, and across most of Asia, warming trends 
and increasing temperature extremes have been observed.
• Adequate supplies of freshwater resources are under considerable 
threat due to both the existing pattern of socioeconomic growth 
• With a number of regions already close to the heat stress limits, 
most models, using a range of general circulation models (GCMs) 
and Special Report on Emission Scenarios (SRES) scenarios, suggest 
that higher temperatures will lead to shorter growing periods of 
rice cultivation, resulting in lower rice yields.

• Climate-change impacts have led to visible shifts on the terrestrial 
systems in many parts of Asia in the phenologies, growth rates and 
distributions of plant species.
• Coastal and marine systems in Asia are under increasing stress 
from both climate-impact drivers (CIDs) and non-climate drivers, 
and mean SLR will contribute to upwards trends in extreme coastal 
high water levels (WGI AR5 Section  3.7.6) (Rhein, et  al., 2013). 
Mangroves, salt marshes and seagrass beds may decline unless 
they can move inland, while coastal freshwater swamps and 
marshes will be vulnerable to saltwater intrusion with rising sea 
levels. Damage to coral reefs will increase during the 21st century 
because of both warming and ocean acidification.
• Climate change will further compound multiple stresses caused 
due to rapid urbanisation, industrialisation and economic growth. 
Development of sustainable cities in Asia with fewer fossil-fuel-

driven vehicles and with more trees and greenery would have a 
number of co-benefits, including improved public health.
• Extreme climate events will have an increasing impact on human 
health, security, livelihoods and poverty, with the type and 
magnitude of impact varying across Asia.

10
1463Asia  Chapter 10
Countries and regions in Asi a
Cent ral Asia
(5)North Asia
(2)
East Asia
(7)
Southeast
Asia
(12)South Asia
(8)West Asia
(17)•Mongolia
•Russia (East of Ur als)
•Armenia •Iran •Kuwait• Oman
•Georgia •Iraq• Lebanon• Qatar
•Bahrain •Israel •Palestine •Jordan
•Afghanist an •Bangladesh 
•Bhutan• India 
•Maldives •Nepal 
•Pakistan •Sri Lanka•Myanma r •Malaysia •Timor-Leste
•Thailand •Singapore •Vietnam
•Indonesia •The Philippines •Brune i•Kazakhst an •Kyrgyzs tan
•Tajikistan •Turkm enistan
•Uzbekista n
•South Korea •Japan •North Kore a 
•Azerbaija n •Syria •Yemen
•China, Hong Kong Special Adm inistrative 
Region (Hong Kong SAR)  
•People's Republic of Cambodia •United Ar ab Em irates •Saudi Ar abia 
•Lao People's De mocratic Republ ic
•Papua New Gu inea•China, Macao Special  Adm inistr ative Region •People's Republic of China (C hina)
•Taiwan, Pr ovince of Chin a
Figure 10.1 |  Countries and regions in Asia

• Indigenous knowledge and local knowledge (IKLK) play an important 
role in the formulation of adaptation governance and related 
strategies (IPCC, 2007), and best quality, locality-specific knowledge 
can help address serious lack of education on climate change and 
uncertainties surrounding quality, salience, credibility and legitimacy 
of the available knowledge base.
Knowledge and research gaps identified in AR5 include, but are not 
limited to, an insufficient understanding of the impacts, vulnerability 
and adaptation in urban settlements, under-researched linkages 
between local livelihoods, ecosystem functions and land resources, and 
a poor understanding of the impacts of projected climate changes on 
the vegetation of the lowland tropics.
10.3 Regional and Sub-regional Characteristics
10.3.1 Climatic Characteristics
Climate characteristics in Asia are diverse covering all climate zones 
from tropical to polar, including mountain climate. Monsoonal winds

and associated precipitation are dominant in South, Southeast and East 
Asia. Annual mean surface air temperature averaged over the sub-region 
ranges from coldest in North Asia (–3°C) to warmest in Southeast Asia 
(25°C) based on JRA-55 (Kobayashi et al., 2015) climatology for 1981–
2010. Most of North Asia and higher altitude is underlain by permafrost. 
West Asia is the driest and Southeast Asia is the wettest, with the annual 
precipitation averaged over the sub-region ranging about ten times 
from 220 mm in West Asia to 2570 mm in Southeast Asia based on 
GPCC (Schamm et al., 2014) climatology for 1981–2010. Indonesia in 
Southeast Asia has the longest coastline in the world, causing this area (maritime continent) to be the wettest region (Yamanaka et al., 2018). 
The Hindu Kush Himalaya (HKH) region is a biodiversity hotspot (Wester 
et  al., 2019) and also has significant impacts on the Asian climate 
because of its orographic and thermodynamic effects (Wu et al., 2012).

Extreme precipitation events and related flooding occur frequently in 
monsoon Asia (i.e., Southeast, South and East Asia) (Mori et al., 2021b). 
Tropical cyclones also affect East and South Asia with torrential rain, 
strong winds and storm surge. Floods and other weather-related 
hazards are causing thousands of casualties and millions of people are 
affected each year (CRED/UNISDR, 2019). On the other hand, droughts 
have long-lasting effects on agriculture and livestock threatening water 
security in West Asia, Central Asia and northern China (Ranasinghe 
et  al., 2021; Seneviratne et  al., 2021). Adaptation to such extreme 
events has been limited in Asia.
10.3.1.1 Observed Climate Change
Observations of past and current climate in Asia are assessed in IPCC 
WGI AR6 (IPCC, 2021). Examples of observed impacts in Asia with 
attributed CIDs are shown in Figure  10.2. Surface temperature has 
increased in the past century all over Asia (very high confidence).

Elevation-dependent warming (i.e., the warming rate is different across 
elevation bands is observed in HMA) (medium confidence) (Hock 
et al., 2019; Krishnan et al., 2019). While there is an overall trend of 
decreasing glacier mass in HMA, there are some regional differences 
and even areas with a positive mass balance due to increased 
precipitation (Wester et al., 2019). Rising temperatures have resulted in 
an increasing trend of growing-season length. The number of hot days 
and warm nights continues to increase in all of Asia (high confidence), 
while cold days and nights are decreasing except in the southern part of

10
1464Chapter 10 Asia
Siberia (Gutiérrez et al., 2021). Large increases in temperature extremes 
are observed in West and Central Asia (high confidence). Temperature 
increase is causing strong, more frequent and longer heatwaves in 
South and East Asia. The 2013 East China heatwaves case is such an 
example (Xia et  al., 2016). In 2016 and 2018, extreme warmth was 
observed in Asia for which an event-attribution study revealed that this 
would not have been possible without anthropogenic global warming 
(medium confidence) (Imada et al., 2018; Imada et al., 2019).
There are considerable regional differences in observed annual pre-
cipitation trend (medium confidence). Observations show a decreas-
ing trend of the South Asian summer monsoon precipitation during the second half of the 20th century (high confidence) (Douville et al., 
2021). No clear trend in precipitation is observed in high-mountain 
Asia (Nepal and Shrestha, 2015), while a continuous shift towards a

drier condition has been observed since the early 1980s in spring over 
the central Himalaya (Panthi et al., 2017). Increase in heavy precipita-
tion occurred recently in South Asia (high confidence), and in South-
east and East Asia (medium confidence) (Seneviratne et  al., 2021). 
In Japan, there is no significant long-term trend in the annual pre-
cipitation, while significant increasing trend is observed in the annual 
number of events of heavy precipitation (daily precipitation ≥400 mm) 
and intense precipitation (hourly precipitation ≥50 mm) (JMA, 2018). 
Decreased precipitation and increased evapotranspiration are ob-Detection and attribution
of observed changes in Asia
High Medium LowEvidence, Agreement, ConfidenceClimate impact driversEvidence
Agreement
ConfidenceTemperature
increase
1. Heat wave
India, Pakistan, Central Eastern China | 1941–2015
4. Dust storms | West Asia, Iran, Persian Gulf 
countries | Multiple duration
5. Sea level rise only for coastal cities | Vietman

1993–2014, Bangladesh 1974–2004
6. Urban heat island effect South Asia (IND, PAK, 
LKA), East Asia (JPN, HKG, KOR), East Asia (THA, 
IDN, PHL), North Asia (RUS) | Multiple duration
7. Permafrost thawing | North Asia | 2007–2009Annual
precipitatio n
increasePrecipitatio n
increase
9. Extreme rainfall events in urban areas | India, 
Philippines | 1901–2010
12. Flood induced damages
Northwest China (Xinjiang) | 1980–2001Precipitatio n
regime Droughts
13. Agriculture and food systems | South and 
Southeast Asia | Multiple duration8. Wildfire | North Asia | 1970–1990
10. Urban drought | South Asia | Multiple durationOcean
warming/
stratification
11. Primary production in ocean | Western Indian  
Ocean | 1950–2012Climate 
change and 
interaction 
with human 
distrubance
3. Biodiversity and habitat loss East Asia | 
1700–2000Sea level
rise
2. Coastal urban flooding Across Asia, specifically 
Southeast Asia | Multiple duration

Figure 10.2 |  Detection and attribution of observed changes in Asia. Levels of Evidence (E), Agreement (A) and Confidence (C) are ranked by High (H), Medium (M) or 
Low (L). CID: climate-impact driver. References: (1) Heatwaves (Mishra et al., 2015; Rohini et al., 2016; Chen and Li, 2017; Panda et al., 2017; Ross et al., 2018); (2) Coastal urban 
flooding (Dulal, 2019); (3) Biodiversity and habitat loss (Wan et al., 2019); (4) Dust storms (Kelley et al., 2015; Yu et al., 2015; Alizadeh-Choobari et al., 2016; Nabavi et al., 2016); 
(5) Sea level rise (only for coastal cities) (Brammer, 2014; Shahid et al., 2016; Hens et al., 2018); (6) Urban heat island (UHI) effect (Choi et al., 2014; Santamouris, 2015; Estoque 
et al., 2017; Ranagalage et al., 2017; Kotharkar et al., 2018; Li et al., 2018a; Hong et al., 2019c); (7) Permafrost thawing (Shiklomanov et al., 2017a; Biskaborn et al., 2019); (8)

Wildfire (Schaphoff et al., 2016; Brazhnik et al., 2017); (9) Extreme rainfall events (in urban areas) (Ali et al., 2014); (10) Urban drought (Gu et al., 2015; Pervin et al., 2020); (11) 
Primary production in ocean (Roxy et al., 2016); (12) Flood-induced damages (Fengqing et al., 2005); (13) Agriculture and food systems (Heino et al., 2018; Prabnakorn et al., 2018).

10
1465Asia  Chapter 10
served in West and Central Asia, contributing to drought conditions 
and decreased surface runoff.
Annual surface wind speeds have been decreasing in Asia since the 
1950s (high confidence) (Ranasinghe et  al., 2021). The observed 
changes in the frequency of sand and dust storms vary from region 
to region in Asia (medium confidence). The frequency and intensity of 
dust storms are increasing in some regions, such as West and Central 
Asia, due to land use and climate change (Mirzabaev et  al., 2019). 
Significant decreasing trends of dust storms are observed in some 
parts of Inner Mongolia and over the Tibetan Plateau (Ranasinghe 
et al., 2021). In contrast, West Asia has witnessed more frequent and 
intensified dust storms affecting Iran and Persian Gulf countries in 
recent decades (medium confidence) (Nabavi et al., 2016).
There is no significant long-term trend during 1951–2017 in the numbers

of tropical cyclones (TCs) with maximum winds of 66.37 km  h–1 or 
higher forming in the western North Pacific and the South China Sea 
(medium confidence). There are substantial inter-decadal variations in 
basin-wide TC frequency and intensity in the western North Pacific (Lee 
et al., 2020a). Numbers of strong TCs (maximum winds of 124.93 km h–1 
or higher) also show no discernible trend since 1977 when complete 
wind-speed data near TC centre become available (JMA, 2018). 
According to Cinco et al. (2016), for TCs in the Philippines there are 
no significant trends in the annual number of TCs during 1951–2013. 
Their analysis showed that the Philippines have been affected by fewer 
TCs above 124.93 km h–1, but affected more by extreme TCs (above 
158.11 km h–1). There has been a significant northwestward shift in 
TC tracks since the 1980s, and a detectable poleward shift since the 
1940s in the average latitude where TCs reach their peak intensity in

the western North Pacific (medium confidence) (Lee et al., 2020a).
According to Bindoff et  al. (2019), the oceans have warmed 
unabatedly since 2004, continuing the multi-decadal ocean-
warming trends. Their report also summarised that there is increased 
agreement between coupled model simulations of anthropogenic 
climate change and observations of changes in ocean heat content 
(high confidence). Observed SLR around Asia over 1900–2018 is 
similar to the global mean sea level change of 1.7 mm yr–1, but for 
the period 1993–2018, the SLR rate increased to 3.65 mm yr–1 in 
the Indo-Pacific region and 3.53 m yr–1 in the Northwest Pacific, 
compared with the global value of 3.25 mm yr–1 (Ranasinghe et al., 
2021). The extreme SLR has occurred since the 1980s along the coast 
of China (Feng et al., 2018b).
Ocean acidification continues with surface seawater pH values having 
shown a clear decrease by 0.01–0.09 from 1981–2011 along the Pacific

coasts of Asia (high confidence) (Lauvset et al., 2015). For the western 
North Pacific along the 137°E line, the trend varies from −0.013 at 3°N 
to −0.021 at 30°N per decade during 1985–2017 (JMA, 2018). Ocean 
interior (about 150–800 m) pH also shows a decreasing trend with 
higher rates in the northern than the southern subtropics, which may be 
due to greater loading of atmospheric CO 2 in the former (JMA, 2018).
2 In this Report, the following terms are used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as likely 
as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100% and extremely unlikely 0–5%)

may also be used when appropriate. Assessed likelihood is typeset in italics (e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies 
within the 17–83% probability range.10.3.1.2 Projected Climate Change
Rising temperatures increase the likelihood of the threat of heatwaves 
across Asia, droughts in arid and semiarid areas of West, Central and 
South Asia, floods in monsoon regions in South, Southeast and East 
Asia, and glacier melting in the HKH region (high confidence) (Doblas-
Reyes et al., 2021; Ranasinghe et al., 2021; Seneviratne et al., 2021). 
Confidence in the direction of the projected change in CIDs in Asia are 
summarised in Table 12.4 of WGI AR6 Chapter 12 (Ranasinghe et al., 
2021).
Projections of future changes in annual mean surface air temperature 
in Asia are qualitatively similar to those in the previous assessments 
with greater warming at higher latitudes (i.e., North Asia) (high

confidence) (Gutiérrez et al., 2021). Projected surface air temperature 
changes in the Tibetan Plateau, Central Asia and West Asia are also 
significant (high confidence) (Gutiérrez et al., 2021). The highest levels 
of warming for extremely hot days are expected to occur in West and 
Central Asia with increased dryness of land (high confidence) (SR1.5). 
Over mountainous regions, elevation-dependent warming will continue 
(medium confidence) (Hock et al., 2019). Glaciers will generally shrink, 
but rates will vary among regions (high confidence) (Wester et  al., 
2019). Thawing permafrost presents a problem in northern areas of 
Asia, particularly Siberia (Parazoo et al., 2018). Temperature rise will 
be strongest in winter in most regions, while it will be the strongest 
on summer in the northern part of West Asia and some parts of South 
Asia where a desert climate prevails (high confidence) (Gutiérrez et al., 
2021). The wet-bulb globe temperature, which is a measure of heat

stress, is likely2 to approach critical health thresholds in West and 
South Asia under the RCP4.5 scenario, and in some other regions, such 
as East Asia, under the RCP8.5 scenario (high confidence) (Lee et al., 
2021a; Seneviratne et al., 2021). The occurrence of extreme heatwaves 
will very likely increase in Asia. Projections show that a sizeable part 
of South Asia will experience heat stress conditions in the future (high 
confidence). It is virtually certain that cold days and nights will become 
fewer (Ranasinghe et al., 2021).
Projections of future annual precipitation change are qualitatively 
similar to those in the previous SREX and AR5 assessments (IPCC, 
2021). A very likely large percentage increase in annual precipitation 
is projected in South and North Asia (high confidence) (Douville 
et al., 2021; Lee et al., 2021a). Precipitation is projected to decrease 
over the northwest part of the Arabian Peninsula and increase over

its southern part (medium confidence) (Gutiérrez et al., 2021). Both 
heavy and intense precipitation are projected to intensify and become 
more frequent in South, Southeast and East Asia (high confidence) 
(Seneviratne et  al., 2021). There will be a large increase in flood 
frequency in these monsoon regions (Oppenheimer et  al., 2019). 
Without further mitigation efforts, this will lead to continued loss of lives 
and infrastructure. SR1.5 assessed higher risk from heavy precipitation 
events at 2°C compared with 1.5°C of global warming in East Asia. A 
large ensemble-modelling study shows that future warming is expected 
to further increase winter precipitation, and extreme weather events,

10
1466Chapter 10 Asia
such as rain-on-snow, will result in an increase in extreme runoff in 
Japan (low confidence) (Ohba and Kawase, 2020). Furthermore, the 
earlier snowmelt will affect energy supply by hydropower.
Monsoon land precipitation likely will increase in East, Southeast and 
South Asia mainly due to increasing moisture convergence by elevated 
temperature (high confidence); however, there is low confidence in the 
magnitude and detailed spatial patterns of precipitation changes at the 
sub-regional scale in East Asia (Doblas-Reyes et al., 2021). Increasing 
land–sea thermal contrast and resultant lower tropospheric circulation 
changes, together with increasing moisture, are projected to intensify 
the South Asian summer monsoon precipitation (medium confidence). 
Anthropogenic aerosols greatly modify sub-regional precipitation 
changes, and their spatio-temporal changes are uncertain (Douville 
et al., 2021). Monsoonal winds will generally become weaker in a future

warming world with different magnitudes across regions (medium 
confidence). Future changes in sand and dust storms are uncertain.
The global proportion of very intense TCs (category 4–5) will increase 
under higher levels of global warming (medium to high confidence). 
Mean global TC precipitation rate will increase (medium to high 
confidence). Models suggest a reduction in TC frequency but an 
increase in the proportion of very intense TCs over the western North 
Pacific in the future; however, some individual studies project an 
increase in western North Pacific TC frequency (medium confidence) 
(Cha et al., 2020). In the western North Pacific, some models project a 
poleward expansion of the latitude of maximum TC intensity, leading 
to a future increase in intense TC frequency south of Japan (medium 
confidence) (Yoshida et al., 2017).
Relative SLR associated with climate change in Asia will range from 
0.3–0.5 m in SSP1-2.6 to 0.7–0.8 m in SSP5-8.5 for 2081–2100 relative

to 1995–2014 (Ranasinghe et al., 2021). In coastal regions, evaluation 
of SLR is necessary at the regional scale to assess the impacts on 
coastal sectors. Liu et  al. (2016c) investigated the regional-scale SLR 
using dynamic downscaling from the three global-climate models in the 
western North Pacific. In their projection in the case following the RCP8.5 
scenario, the regional sea level rises along Honshu Island in Japan during 
2081–2100 relative to 1981–2000 are 6–25 cm higher than the global 
mean SLR due to the dynamic response of the ocean circulation. For the 
impact assessment of coastal hazards, the total SLR included extreme 
events due to storm surge and high ocean waves, which are influenced 
by the changes in TCs (Seneviratne et al., 2021). Mori and Takemi (2016) 
summarised the characteristics of TCs in the western Pacific in the past 
and in the future, and the extreme value of significant wave height

increased in several regions. There is considerable increase in the return 
levels along the China coast under 2.0°C warming compared with that 
under the 1.5°C warming scenario (Feng et al., 2018b).
Ocean acidification will continue over the 21st century (virtually 
certain) (SROCC). Projected decrease in global surface ocean pH from 
1986–2005 to 2081–2100 is about 0.145 under RCP4.5 (Lee et  al., 
2021a).
Diverse and complex climate characteristics in Asia limit climate 
models’ ability to reasonably simulate the current climate and project 
its future change (Gutiérrez et al., 2021).10.3.2 Ecological Characteristics
Ecosystems in Asia are characterised by a variety of climate and 
topographic effects, and can be divided into several distinct areas 
(Figure  10.3). In addition, valuable ecosystem services provide vital 
support for human well-being and sustainable development (IPBES, 
2018).
Boreal forests and tundra dominate in North Asia: deserts and xeric

shrublands in Central and West Asia; and alpine ecosystems in the HKH, 
Tian Shan, Altai-Sayan, Ural and Caucasus mountain regions. Human-
transformed landscapes occupy most parts of other sub-regions. The 
remaining natural ecosystems in East Asia are temperate broadleaf 
and mixed forests, and there are subtropical evergreen forests, deserts 
and grasslands in the West Asia. South Asia has tropical forests and 
semi-deserts in the northwest, and Southeast Asia is covered mainly 
by tropical forests (Figure 10.3).
Ocean and coastal regions in Asia have various ecological characteristics, 
such as high productivity in arctic and subpolar regions, large biodiversity 
in tropical regions and unique systems in marginal seas. In the atlas of 
WGI AR6, the ocean biomes in Asia are divided into six sub-regions 
(WGI AR6 Figure Atlas.4) (Gutiérrez et al., 2021). For the coastal region, 
the concept of the large marine ecosystem (e.g., see Sherman, 1994)

provides the biological characteristics of each marginal/semi-enclosed 
region and the regions characterised by boundary current system.
Biodiversity and ecosystem services play a critical role in socioeconomic 
development as well as the cultural and spiritual fulfilment of the Asian 
population (IPBES, 2018). For example, species richness reaches its 
maximum in the ‘coral triangle’ of Southeast Asia (central Philippines 
and central Indonesia) (IPCA, 2017), and the extent of mangrove 
forests in Asia is about 38.7% of the global total (Bunting et al., 2018). 
These coastal ecosystems provide multiple ecosystem services related 
to food production by fisheries/aquaculture, carbon sequestration, 
coastal protection and tourism/recreation (Ruckelshaus et al., 2013).
10.3.3 Demographics and Socioeconomic Characteristics
In the six sub-regions of Asia, nature and biophysical impacts of 
climate change are observed in three climate-change hotspots

where strong climate signals and high concentrations of vulnerable 
people are present, namely in semiarid, glacial-fed river basins and 
mega deltas (De Souza et al., 2015; Kilroy, 2015; Szabo et al., 2016b). 
The impacts of global climate events also have profound social 
implications, threatening human health and well-being, destabilising 
assets, weakening coping capacities and response infrastructures 
and substantially increasing the number of socially, economically and 
psychologically vulnerable individuals and communities (Ford et  al., 
2015).
Vulnerability to climate change varies by geography and by the 
economic circumstances of the exposed population (Sovacool et al., 
2017). The concentration of population growth in less developed 
regions means that an increasing number of people live in countries 
with the least ability to adapt to climate change (Auffhammer and

10
1467Asia  Chapter 10
Asia Sub Region Boundary
FAO Major Fishing Area Boundary
Large Marine Ecosystem Boundary3232
3434
35353636
3737
38384848
474749495050515152525353545456565757
333326265858
6262
61. Pacific, 61. Pacific, 
NorthwestNorthwest
71. Pacific, 71. Pacific, 
Western CentralWestern Central
57. Indian Ocean, 57. Indian Ocean, 
EasternEastern51. Indian Ocean, 51. Indian Ocean, 
WesternWestern373718. Arctic Sea18. Arctic Sea
67.67.
Pacific, Pacific, 
NortheastNortheast
WestWest  AsiAsiaa SoutSout h h AsiAsiaa
SoutSout heastheast  AsiAsiaaCaspian SeaCaspian SeaAral SeaAral Sea37. Mediterranean and Black Sea
37. Mediterranean and Black Sea
Central AsiaCentral AsiaCentral AsiaCentral AsiaNorth AsiaNorth Asia
East AsiaEast Asia
32
34
3536
37
3848
474950515253545657
332658
62
61. Pacific, 
Northwest
71. Pacific, 
Western Central
57. Indian Ocean, 
Eastern51. Indian Ocean, 
Western3718. Arctic Sea
67.
Pacific, 
Northeast
West Asia Sout h Asia

Sout heast AsiaCaspian SeaAral Sea37. Mediterranean and Black Sea
Central AsiaCentral AsiaNorth Asia
East AsiaTerrestrial ecoregions, large marine ecosystems and major fishing areas of Asia
Mediterranean Forests, Woodlands, and ScrubMontane Grasslands and ShrublandsFlooded Grasslands and Savannas
Deserts and Xeric ShrublandsTundra
Tropical and subtropical grasslands, savannas, and shrublands
Temperate Grasslands, Savannas, and ShrublandsTropical and Subtropical Moist Broadleaf ForestsTropical and Subtropical Dry Broadleaf Forests
Tropical and Subtropical Coniferous Forests
Temperate Broadleaf and Mixed Forests
Temperate Coniferous Forests
Boreal Forests/Taiga Mangroves
Rock and Ice
Lakes
Figure 10.3 |  Terrestrial ecoregions, large marine ecosystems and major fishing areas of Asia. Large marine ecosystems: 26 Mediterranean Sea; 32 Arabian Sea;

33 Red Sea; 34 Bay of Bengal; 35 Gulf of Thailand; 36 South China Sea; 37 Sulu-Celebes Sea; 38 Indonesian Sea; 47 East China Sea; 48 Yellow Sea; 49 Kuroshio Current; 50 Sea 
of Japan; 51 Oyashio Current; 52 Sea of Okhotsk; 53 West Bering Sea; 54 Northern Bering and Chukchi seas; 56 East Siberian Sea; 57 Laptev Sea; 58 Kara Sea; 62 Black Sea. 
Developed after Olson et al. (2001), NOAA (2010) and FAO (2019), and made with Natural Earth. Natural Earth is a public domain map dataset archive available at https://www.
naturalearthdata.com/. The software used for generating the map was ArcGIS 10.0. Note that the map is for illustrative purposes only.
Kahn, 2018). Bangladesh with 163 million people, an example, is one of 
the most vulnerable countries in the world to climate risks and natural 
hazards, and faces severe floods, cyclones, droughts, heatwaves and 
storm surges on a regular basis (Dastagir, 2015; Hossain et al., 2018; 
Roy and Haider, 2019).

Differential human vulnerability to environmental hazards results 
from a range of social, economic, historical and political factors, all of 
which operate at multiple scales (De Souza et al., 2015; Thomas, 2019). 
Climate change is expected to have serious impacts on people living 
within these hotspot areas, as observed from loss of food crop yields 
to disasters such as floods, fluctuations in seasonal water availability 
or other systemic effects (De Souza et al., 2015). For instance, in South 
Asia, extreme climatic conditions are threatening food security; thus, 
agro-based economies, such as those of India and Pakistan, are the most vulnerable to climate change in this regard (Mendelsohn, 2014; 
Ahmad, 2015; Kirby et al., 2016; Ali et al., 2017).
A broad-based understanding of gender vulnerability in the context of 
poverty and social discrimination, as well as diverse social and cultural 
practices in different political, geographic and historical settings, apart

from climate variability along with environmental and natural risks, is 
central to understanding people’s capacities to cope with, and adapt to 
change (Morchain et al., 2015; Yadav and Lal, 2018; Rao et al., 2019). 
Studies highlight the fact that disasters do not affect people equally; 
mostly findings show that insufficient disaster education, inadequate 
protection measures and powerful cultural issues, both pre- and post-
disaster, increase women’s vulnerability during and after disasters (Isik 
et al., 2015; Reyes and Lu, 2016; Hamidazada et al., 2019). In particular, 
cultural issues play a role after disasters by affecting women’s security,

10
1468Chapter 10 Asia
access to disaster aid and health care (Raju, 2019). There must be 
more nuanced understanding and examination of gender, as well as 
poor, disadvantaged and vulnerable groups, in vulnerability and risk 
assessments (Reyes and Lu, 2016; Reyer, 2017; Xenarios et al., 2019).
Based on the ‘World Economic Situation and Prospects as of mid-
2019 Report’, the region has an estimated 400 million people living 
in extreme poverty below the threshold of 1.90 USD d–1. At the higher 
international poverty line of 3.20 USD d–1, the number of poor rises 
to 1.2  billion people, accounting for more than a quarter of the 
region’s total population (Holland, 2019). Beyond monetary measures, 
indicators of multi-dimensional aspects of poverty, most notably in 
Southern Asia, indicate that a large share of the population still lacks 
access to basic infrastructure and services (Bank, 2017b).
For instance, South Asia illustrates that on average it could lose nearly

2% of its GDP by 2050, rising to a loss of nearly 9% by 2100 under 
a business-as-usual scenario (Ahmed and Suphachalasai, 2014). The 
relationship between economic outcomes and cross-sectional climate 
variation is confounded by regional heterogeneity, including historical 
effects of settlement and colonisation (Dell et al., 2014; Newell et al., 
2018). Climate change vulnerability may also depend on sufficient 
employment opportunities in the risk-prone areas, land-holding size, 
gender, education level, and family and community size, as observed 
in Nepal, Thailand and Vietnam (Baul and McDonald, 2015; Lebel L., 
2015; Phuong et al., 2018a).
As poor households are constrained in their ability to receive nutrition, 
schooling and health care for their children, this is greatly dampening 
progress in human capital development and productivity growth, 
both of which are critical imperatives for sustainable development

(Carleton and Hsiang, 2016; Schlenker and Auffhammer, 2018). Studies 
also have shown negative impacts of climate change on several 
essential components of people’s livelihoods and well-being, such as 
water supply, food production, human health, availability of land and 
ecosystems (Alauddin and Rahman, 2013; Arnell et al., 2016; Roy and 
Haider, 2019).
Major population trends of urbanisation and urban area expansion are 
forecast to take place in Asia. It has been mentioned that demographic 
change will make humanity more vulnerable to climate change, 
particularly in places with high poverty rates and potentially prone to 
systemic disruptions in the food system (Puma et al., 2015; d’Amour 
et al., 2016; d’Amour et al., 2017).
The urban population of the world has grown rapidly from 751 million 
in 1950 to 4.2 billion in 2018. Asia, despite its relatively lower level 
of urbanisation, is home to 54% of the world’s urban population

(United Nations, 2019). Some cities have experienced population 
decline in recent years. Most of these cities are located in the low-
fertility countries of Asia, where overall population sizes are stagnant 
or declining, as observed in a few cities in Japan and the Republic 
of Korea (e.g., Nagasaki and Busan), which experienced a population 
decline between 2000 and 2018 (United Nations, 2019). By 2030, the 
world is projected to have 43 megacities with more than 10 million 
inhabitants, most of them in developing regions. However, some of 
the fastest-growing urban agglomerations are cities with fewer than 1 million inhabitants, many of them located in Asia and Africa (United 
Nations, 2019). Challenges with water supply, in many cases, have 
existed for decades (Dasgupta, 2015). Climate change increases these 
challenges (Hoque et al., 2016). As more people inhabit urban areas, 
the number of people vulnerable to heat stress is thus likely to rise,

a problem that will be compounded by rising temperatures due to 
climate change (Acharya et al., 2018). Compared with rural areas, hot 
temperature risk is even higher in urban regions (Luo, 2018a; Ye, 2018; 
Setiawati Martiwi Diah, 2021).
The impact of heat in rural areas has been a blind spot so far, particularly 
for farmers and outdoor labourers who are increasingly exposed to 
high outdoor temperatures due to increased intensity in agriculture 
combined with changes in working hours (Tasgaonkar, 2018).
Farmers as a group have shown an increasing number of females 
over the years due to migration of male members into urban areas 
for employment, which is putting women at more severe risk in the 
context of climate variability (Singh, 2019). Women are required to 
acquire new capacities to manage new challenges, including risks from 
climate change, through capacity-building interventions to strengthen 
autonomous-adaptation measures (Banerjee et al., 2019; James, 2019;

Mishra, 2019). However, the overlapping crises of climate change and 
the global public health crisis of COVID-19 represent a major challenge 
to gender equality and sustainable development (Katherine Brickell, 
2020; Sultana, 2021).
For vulnerable populations, such as Indigenous Peoples, older and 
low-income groups, women, children, people with disabilities and 
minorities, the health effects of climate-change-related extreme 
weather events can be especially devastating (McGill, 2016). Such 
populations may be more susceptible to disease, have pre-existing 
health conditions or live in areas that do not promote good health 
or well-being; for instance, loss of income and food supply shortages 
could lead children in rural households to nutritional deprivations that 
can have both immediate and lifelong impacts (Gleick, 2014; UNICEF , 
2015). Children, already susceptible to age-related insecurities, face 
additional destabilising insecurities from questions about how they

will cope with future climate change (Hansen et al., 2013).
10.4 Key Systems and Associated Impacts, 
Adaptation and Vulnerabilities
10.4.1 Energy Systems
10.4.1.1 Regional Diversity
Energy consumption of Asia accounts for 36% of the global total at 
present. China, India and the ASEAN countries have largely contributed 
to the ever-growing global energy consumption. Asia is predicted to 
account for 80% of coal, 26% of natural gas and 52% of electricity 
consumption of the world by 2040 (IEA, 2018). The share of Asia in 
the global primary energy consumption will increase to 48% by 2050. 
China continues to be the world’s largest energy consumer, and the 
combined consumption of India and ASEAN will be similar to that of 
China by that time (IEEJ, 2018).

10
1469Asia  Chapter 10
Box 10.2 | Migration and Displacement in Asia
Migration is a key livelihood strategy across Asia and is driven by multiple factors such as socioeconomic changes, increasing climate 
variability and disaster incidence, and changing aspirations. Displacement denotes a more involuntary movement in reaction to climatic 
or non-climatic factors. There is robust evidence, medium agreement that increased climate variability and extreme events are already 
driving migration (Gemenne et al., 2015; Rigaud et al., 2018; IDMC, 2019; Jacobson et al., 2019; Siddiqui et al., 2019; IDMC, 2020; 
Maharjan et al., 2020) and medium evidence, medium agreement projecting that longer-term climate change will increase migration 
flows across Asia (Abubakar et al., 2018; Rigaud et al., 2018; Hauer et al., 2020; Bell et al., 2021).
Detection and attribution: Does climate change drive migration? Ascertaining the role of climate change in migration is difficult

and contested (see Cross-Chapter Box MIGRATE in Chapter 7 and RKR-H in Chapter 16), with observation-based studies either linking 
extreme event incidence, weather anomalies and environmental change with migration numbers or drivers (McLeman, 2014; Singh et al., 
2019a; Kaczan and Orgill-Meyer, 2020), and projection studies looking at particular risks such as SLR or drought by linking increasing 
warming (often through representative concentration pathways, RCPs) and population growth. Despite methodological disagreement on 
detection and attribution of migration due to climate change, there is medium confidence that higher warming and associated changes in 
frequency and intensity of slow-onset events (such as drought and sea level rise) and rapid-onset events (such as cyclones and flooding) 
will increase involuntary displacement in the future, especially under SSP3 and SSP4 pathways (Dasgupta et al., 2014a; Davis et al., 2018;

Rigaud et al., 2018; Hauer et al., 2020). But its role is smaller than non-climatic socio-economic drivers of migration (Wodon et al., 2014; 
Adger et al., 2021).
Current migration and displacement. One in three migrants comes from Asia and the highest ratio of outward migrants is seen from 
hazard-exposed Pacific countries (Ober, 2019). In 2019, approximately 1900 disasters triggered 24.9 million new displacements across 
140 countries; in particular, Bangladesh, China, India and the Philippines each recorded more than 4 million disaster displacements (IDMC, 
2019). Tajikistan, Kyrgyzstan and Russia see significant disaster-associated displacements: for example, heavy rain-induced flooding in 
Khatlon (Tajikistan) triggered 5400 new displacements; landslides in the Jalal-Abad (Kyrgyzstan) saw 4700 new displacements; and 
floods in Altai, Tuva and Khakassia (Russia) displaced 1500 people. Iran reported the highest sub-regional figures with >520,000 new

disaster-related displacements in 2019 (IDMC, 2019). In Southeast and East Asia, cyclones, floods and typhoons triggered internal 
displacement of 9.6 million people in 2019, almost 30% of total global displacements (IDMC, 2019). With most migrants in the region 
being temporary migrant workers, loss of jobs and wages among them have been particularly severe due to adverse economic climate 
triggered by COVID-19 (ESCWA, 2020). It has also resulted in large-scale returns of migrant workers, and remittances have declined 
drastically (Khanna, 2020; Li et al., 2021). Remittances to Eastern Europe and Central Asia are expected to decline 16.1% from 57 billion 
USD in 2019 to 48 billion USD in 2020. Remittances in East Asia and the Pacific are estimated to fall 10.5% over the same period, from 
147 billion to 131 billion USD (United Nations, 2020). The COVID-19 pandemic has had significant impacts on migrants (Rajan, 2020) in

the region, and some countries have targeted migrants in economic stimulus packages or income-support programmes; however, access 
to such support has been heterogeneous.
Projected migration. Regional variation is significant across Asia. By one estimate, in South Asia, internal climate migrants (i.e., those 
migrating due to climate change and associated impacts such as water scarcity, crop failure, SLR and storm surges) are projected to be 
40 million by 2050 (1.8% of regional population) under high warming (Rigaud et al., 2018). While methodological critiques remain on 
projected migration estimates, what is certain is that some countries will be more affected that others; it is estimated that in southern 
Bangladesh, SLR could displace 0.9–2.1 million people by direct inundation by 2050 (Jevrejeva et al., 2016; Davis et al., 2018). In South 
Asia, migration hotspots include the Gangetic Plain and the Delhi–Lahore corridor, and coastal cities such as Chennai, Chittagong, Dhaka

and Mumbai, which will be simultaneously exposed to climate-change impacts, major migration destinations and amplified rural–urban 
migration (Ober, 2019). Importantly, there is low agreement on projected numbers (see Boas et al., 2019) with uncertainties around how 
local policies and individual behaviours will shape migration choices. Even in high-risk places, people might choose to stay or be unable 
to move, resulting in ‘trapped’ populations (Zickgraf, 2019; Ayeb-Karlsson et al., 2020). There is currently inadequate evidence to ascertain 
the nature and numbers of trapped populations currently or in the future.
Implications of migration for adaptation. The evidence on migration and its impacts on adaptive capacity and risk reduction are 
mixed (Upadhyay, 2014; Banerjee et al., 2018; Szabo et al., 2018; Maharjan et al., 2020; Singh and Basu, 2020). Financial remittances help

vulnerable households spread risk through better incomes, expanded networks and improved assets such as housing, education and 
communication technology (Jha et al., 2018; Szabo et al., 2018; Ober, 2019; Maharjan et al., 2020). Benefits from international remittances 
across the Asia Pacific region were approximately 276 billion USD in 2017 (UN, 2018), and in countries such as Kyrgyzstan, Tajikistan and 
Nepal remittances were ~25% of the national GDP in 2015. However, migration requires a minimum level of resources, and liquidity 
constraints impede internal migration by the poorest households often rendering them immobile (Ayeb-Karlsson et al., 2020; Maharjan

10
1470Chapter 10 Asia
et al., 2020). Furthermore, migration does not necessarily mean that people move out of risk; in fact, often they might be subjected to 
new risks. Notably, migrants in South and Southeast Asia have been severely affected by the compounding crises of disasters and the 
COVID-19 pandemic, and there is emerging evidence that inclusion of universal safety-net provisions that embed adaptation planning 
can reduce vulnerabilities of migrants (Sengupta and Jha, 2020; Cundill et al., 2021; Sultana, 2021).
While there is robust evidence (medium agreement) that migration exacerbates gendered vulnerability and work burdens (Banerjee et al., 
2019; Singh, 2019; Rao et al., 2020), it is well established that differential vulnerability of migrants intersects with ethnicity, age and 
gender; political networks and social capital; and livelihoods in destination areas (Maharjan et al., 2020; Cundill et al., 2021). Across Asia,

international and internal migration are changing social norms and household structures, with significant implications for local adaptive 
capacity (Singh, 2019; Evertsen and van der Geest, 2020; Porst and Sakdapolrak, 2020; Rao et al., 2020).Box 10.2 (continued)
The current energy structure of Asia is dominated by fossil fuels. As the 
trend indicates, the share of coal in China’s primary energy consumption 
is forecasted to sharply decline from 60% in 2017 to around 35% in 
2040 (BP , 2019). In contrast, India and ASEAN rely more on coal since 
coal may meet their soaring energy demand. Accordingly, more than 
80% of the global coal will be consumed in Asia by 2050. China will 
surpass the USA in about 10 years to become the world’s largest oil 
consumer. India will then replace the USA to be the second largest by 
the late 2040s (IEEJ, 2018).
Around 60% of the incremental electricity demand globally, predicted

to double by 2050, will occur in Asia. By that time, the electrification 
rate will increase to 30%, but 40% of electricity demand will be still 
covered by coal (IEEJ, 2018). Asia accounts for almost half of the 
growth in global renewable power generation. It is hardly possible 
for Japan and Republic of Korea to develop additional nuclear power 
plants as planned, whereas nuclear generation continues to increase 
quickly in China and the scale will be similar to the entire generation of 
OECD by 2040 (BP , 2019). India and Russia’s nuclear power sectors are 
also growing fast (e.g., the recent launch of the Akademik Lomonosov 
offshore nuclear power plant in Russia).
The rapid growth of energy demand in Asia reinforces the region’s 
position as the largest energy importer (BP , 2019). Around 80% of 
energy traded globally will be consumed in Asia, and the rate of self-
sufficiency will decrease from 72 to 63% by 2050. This tendency is

especially remarkable for ASEAN, which will become a net importer in 
the early 2020s. The self-sufficiency rate of coal will be maintained at a 
level of 80%, while that of oil and natural gas will decline significantly. 
The additional oil imports of the emerging Asian economies will be 
from North America, the Middle East and North Africa. The main 
players in Asia for the liquefied natural gas imports will extend from 
Japan and Republic of Korea to China and India. ASEAN has been a net 
exporter of natural gas but starts to expand its importation due to the 
increased consumption and resource depletion (IEEJ, 2018).
The increase in energy demand at a rapid rate in these countries 
thus cannot be attributed only to population growth and rising living 
standards, but also to increasingly extreme temperature variations. 
The decrease in precipitation influences energy demand as well, as

countries are becoming more dependent on energy-intensive methods (e.g., desalination, underground water pumping) to supply water. 
Similarly, energy systems are influenced by the way the agriculture 
sector, mainly in Al Mashrek, relies increasingly on energy-intensive 
methods (e.g., more fertilisers, different irrigation and harvesting 
patterns) (Farajalla, 2013).
Climate change has direct and indirect impacts on energy and 
industrial systems. It has a particularly wide and profound impact on 
energy systems (energy development, transportation, supply, etc.). 
With global warming, the energy consumption for heating in winter 
decreases, while the energy consumption for cooling in summer 
significantly increases, but the overall energy demand shows an 
upwards trend (high confidence) (Sailor, 2001; Szabo et al., 2018). Such 
demands in summer seasons will by far exceed any energy savings 
from the decrease in heating demand due to warmer winters. Higher

demand for cooling due to hotter temperatures has become a major 
challenge in the energy sector in all countries. Furthermore, decreased 
water levels due to lower precipitation reduces hydroelectric output. 
This is particularly the case for countries such as Syria and Iraq with 
large hydroelectric capacity (Hamid and Raouf, 2009). Additionally, the 
decrease in water levels negatively affects low-carbon energy systems 
such as concentrated solar power and thermal-generation plants that 
require regular cooling and cleaning.
Climate change adds extra pressure to current energy infrastructures 
in most countries where systems failures and blackouts are already 
common (Assaf, 2009). In the wake of extreme weather events (e.g., 
heatwaves), energy infrastructures remain inadequate to cope. This is 
particularly the case for countries such as Lebanon, Syria, Jordan and 
Palestine, with poor electricity infrastructures (Jordan, 2015). Extreme

weather events could generate grave damage to power plants, most 
being located only a few metres above sea level, as well as power-
transmission towers and lines. In Lebanon, a small country where there 
are no Indigenous energy resources, the disruption of shipping of fuel 
supplies due to extreme weather events is a major risk. Other extreme 
weather events, such as floods and sandstorms, expose energy and 
industrial systems in the coastal areas due to a rise in sea level. Countries 
of the Arabian Peninsula are projected to experience significant inland 
flooding as sea levels rise (Hamid and Raouf, 2009). In East Asia wet 
snow accretion enhanced by global warming often causes damage to 
electric power lines (Sakamoto, 2000; Ohba and Sugimoto, 2020).

10
1471Asia  Chapter 10
10.4.1.2 Key Drivers to Vulnerability, with Observed and 
Projected Impacts
Universal energy access is a big challenge for Asia (IEA, 2018). About 
230  million Indian people lack access to electricity, and around 
800  million still use solid fuels for cooking (Sharma, 2019). The 
average electricity access rate in South Asia was 74%, the equivalent 
of 417  million people without electricity and accounting for more 
than a third of the global 1.2 billion lacking the access (Shukla et al., 
2017). With a total population of nearly 640  million in ASEAN, an 
estimated 65 million people remain without electricity and 250 million 
rely on solid biomass for cooking fuel (IEA, 2017). Universal access to 
electricity is expected to be achieved by 2030, while 1.6 billion people 
in Asia will still lack clean energy for cooking (UNESCAP , 2018b).
Asia faces an energy security problem even with the rapid growth in

production and trade (IEEJ, 2018). Among 13 developing countries with 
large energy consumption in Asia, 11 are exposed to high energy security 
risk (WEC, 2018). This will be a major challenge for the sustainable 
development of Asia due to the vulnerability to global energy supplies 
and price volatility (Nangia, 2019). Asia lacks natural energy resources 
and has the smallest oil reserve but largely relies on fossil fuels. The 
dependency on fossil fuels was as high as 88.3% in China, 72.3% in 
India, 89.6% in Japan and 82.8% in Republic of Korea in 2013 (BP , 
2014). Many countries in South Asia rely on a single source to supply 
more than half of the electricity (i.e., 67.9% from coal for India, 99.9% 
from hydropower for Nepal, 91.5% from natural gas for Bangladesh 
and 50.2% from oil for Sri Lanka) (Shukla et al., 2017). Additionally, 
cooperation in Asia to create the integrated energy systems needed 
for enhancing overall security is still at a very preliminary stage due

to countries having different strategic plans and lack of cooperation 
among them on the common concerns (Kimura and Phoumin, 2013).
Even though energy efficiency is improving, the deployment of low-
carbon energy, such as renewables, is not sufficient in Asia. To be 
consistent with the temperature goal of the Paris Agreement, the share 
of renewables in total energy consumption needs to reach 35% in 
Asia by 2030. Moreover, the financing to deploy renewables presents 
another considerable challenge (UNESCAP , 2018b).
In order to cope with climate change, renewable energy has become 
the core of energy development and transformation. Since the 1960s, 
the total solar radiation on the ground in Asia has shown a downwards 
trend as a whole, which is consistent with the change in global total 
solar radiation on the ground, and has experienced a phased change 
process of ‘first darkening and then brightening’ (high confidence). This

conclusion has been further confirmed by ground station observations, 
satellite remote sensing inversion data and model simulation research 
(Wang and Wild, 2016; Qin et al., 2018; Yang et al., 2018a).
However, wind speed over most Asian regions is obviously decreasing 
(high confidence). Based on meteorological observation records or 
reanalysis data, many studies have analysed the variation of near-
surface average wind speed in Asia. It is generally found that wind 
speed has declined since the 1970s, although the declining trend is 
different in different subregions. (Yang et al., 2012c; Lin et al., 2013; Liu 
et al., 2014b; Zha et al., 2016; Guo et al., 2017a; Torralba et al., 2017; Wu et al., 2017a; Ohba, 2019). The decline of near-surface wind speed in 
Asia is consistent with the general decline of global land-surface wind 
speeds, among which the frequency of strong winds and the decline of 
wind speed are more prominent (McVicar et al., 2012; Jiang et al., 2013;

Blunden and Arndt, 2017; Wu et al., 2018c). Since the early 2010s, the 
average wind speed in the world and some parts of Asia has shown 
signs of increasing (Li et al., 2018d; Wu et al., 2018c; Zeng et al., 2019), 
which seems to be an inter-decadal variability. Whether this means a 
change in its trend needs the support of longer observation data.
At the same time, with the increase in the proportion of renewable 
energy in the power system, the power system will be more vulnerable 
to climate change and extreme weather and climate events, and 
the vulnerability and risk of the power system will greatly increase 
10.4.1.3 Adaptation Options
The overall solution would be to develop a resilient energy system 
and avoid the risk of unsustainable energy growth in developing 
Asia. This requires that strategic planning be consistent with the long-
term climate projection, impact and adaptation (EUEI-PDF , 2017).

Although no single policy package would be applicable for all the 
countries across the region, several measures could be addressed as 
the common options, including fortification of energy infrastructure 
and diversification of the sources by sufficient investment, 
improvement of energy efficiency for sector flexibility, and promotion 
of regional cooperation and integration for increasing energy security 
(UNESCAP , 2018b). Adaptation also includes promoting renewable 
energy resources, securing local natural gas resources, enhancing 
water production and adopting green-building technologies. These 
adaptation measures may help increase readiness for the anticipated 
impact of climate change.
The improvement of energy efficiency and demand-side management 
can alleviate supply constraints and thus lower overall required-energy 
capacity. Energy storage, smart grids for the electricity network as well 
as other flexible management measures enable this energy demand

shifting. Regional integration of energy markets drives productivity 
increase, cost reduction, new investment, human capability and 
diversity of energy sources (WEC, 2018). For example, better 
interconnection of natural gas supply networks among the ASEAN 
countries enhances gas security in the region. The development of the 
long-planned regional power grid would make large-scale renewable 
projects more viable and aid the integration of rising shares of wind 
and solar power (IEA, 2017).
Providing enough investment in energy supply is a top priority to 
extend the connections to those without access to electricity and 
satisfy the soaring demand (IEA, 2017). The investment in non-fossil 
energies like renewables has been expanding to leverage the economic 
growth in China, India and Republic of Korea. According to the updated 
estimate of ADB, 14.7 trillion USD will be needed for the infrastructure 
development in the power sector of developing Asia over the 15 years

from 2016 to 2030 (ADB, 2017a). The cumulative investment needs 
of ASEAN for energy supply and efficiency up to 2040 is estimated at 
2.7 to 2.9 trillion USD (IEA, 2017). Mobilising investment to such a

10
1472Chapter 10 Asia
scale will require significant participation from the private sector and 
international financial institutions.
Diversifying energy sources increases energy security and thus the 
resilience of the whole system. The deployment of renewable energy 
is widely recognised as a crucial measure for enhancing energy access 
and diversity. There remains huge potential for renewable sources in 
Asia (i.e., India has massive solar power potential) (Shukla et al., 2017). 
Many renewable technologies (i.e., hydro- and wind power as well as 
solar photovoltaics) are becoming competitive, and their life-cycle 
costs may fall below those of coal and natural gas in the near term. 
Great progress has been made in enhanced geothermal systems (EGS), 
and in the conventional and unconventional fusion power that China 
is promoting. Conventional and underground pumped hydropower will 
level out supplies for intermittent renewable energy generation.

Substantial progress may be fulfilled by increasing the share of 
renewable energy in the overall energy consumption of Asia (ADB, 
2017a). Access to energy, particularly in rural areas, can reduce climate 
vulnerability of developing Asia. Due to the high cost of extending the 
electricity network to rural regions, an alternative way is to develop 
the off-grid renewable energy systems in these areas. The distributed, 
instead of centralised, energy systems can increase energy access and 
resilience (EUEI-PDF , 2017).
Some countries in the Arabian Peninsula, such as the United Arab 
Emirates (UAE), are adopting an array of approaches to enhance the 
adaptive capacity of the energy infrastructure and diffuse the risk of 
climate change over a larger area (e.g., energy efficiency, demand 
management, storm planning for power plants). In Al Mashrek, 
building institutional capacity in the energy sector is a necessary first

step to mainstream climate-change adaptation (CCA). Countries such 
as Lebanon and Jordan have already made progress in mainstreaming 
CCA into electricity infrastructure. In the UAE, buildings account 
for more than 80% of the total electricity consumption. There are 
currently a set of measures and regulations on building conditions and 
specifications that are being applied to increase energy efficiency in 
buildings, but the rehabilitation and upgrading of old buildings still 
require further efforts (Environment, 2015). In Kuwait, one adaptation 
measure to dust storms is through the reduction of the proportion 
of open-desert land from 75 to 51%, the increase in protected areas 
from 8 to 18% and greenbelt projects in desert areas (Kuwait, 2015). 
Addressing climate-change impact on energy systems in Lebanon, 
Jordan, Syria, Iraq and Palestine needs to simultaneously consider 
other interlinked challenges of population growth, rapid urbanisation,

refugee influx, conflict and geopolitical location. To address these 
challenges and provide solutions for CCA, the promotion of multi-
stakeholder partnerships is key to breaking the silo approach.
These CCA measures need to be broadened to fit the scope and 
depth of mitigation efforts by each country. Risk assessments and 
vulnerability assessments are in their early stages in the energy and 
industrial sectors, and are not currently based on a comprehensive 
plan of action. The first step is to undertake comprehensive national 
assessments of the risks associated with climate change based on 
existing studies on climate impacts and risks, and by making evidence-
based decisions on adaptation actions.10.4.2 Terrestrial and Freshwater Ecosystems
Sub-regional diversity of ecosystems is high in Asia (Section 10.2.2). 
Climate-impact drivers of Asian terrestrial ecosystems (ATS) change 
are global warming, precipitation and Asian monsoon alteration,

permafrost thawing and extreme events like dust storms. Observed and 
projected changes in ATS are affected by several interacting factors. 
Non-climatic human-related drivers are change of land use, change 
of human use of natural resources, including species and ecosystems 
overexploitation as well as other non-sustainable use, socioeconomic 
changes and direct impacts of rising greenhouse gases (GHGs). 
Ecosystem vulnerability has resulted from complex interactions of CIDs 
and non-climate drivers. Species interaction and natural variability of 
organisms, species and ecosystems is currently poorly understood, 
and much more work still needs to be done to unravel these multiple 
stressors (i.e., Berner et al., 2013; Brazhnik and Shugart, 2015).
10.4.2.1 Observed Impacts
10.4.2.1.1 Biomes and mountain treeline
Changes in biomes in Asia are compatible with a response to 
regional surface air temperature increase (Arias et al., 2021) (medium

agreement, medium evidence). Expansion of the boreal forest and 
reduction of the tundra area is observed for about 60% of latitudinal 
and altitudinal sites in Siberia (Rees et al., 2020). In Central Siberia, the 
changes in climate and disturbance regimes are shifting the southern 
taiga ecotone northward (Brazhnik et al., 2017). In Taimyr, no significant 
changes in the forest boundary have been observed during the past 
three decades (Pospelova et al., 2017). For the Japanese archipelago, 
it is suggested that the change in tree community composition along 
the temperature gradient is a response to past and/or current climate 
changes (Suzuki et al., 2015).
Alpine treeline position in Asian mountains in recent decades either 
moves upwards in North Asia or demonstrates multi-directional shifts 
in Himalaya (high confidence). Since AR5, in North Asia new evidence 
has appeared of tree expansion into mountain tundra and steppe,

of intensive reproduction and increase in tree stands productivity in 
the past 30–100  years at the upper treeline in the Ural Mountains 
(Shiyatov and Mazepa, 2015; Zolotareva and Zolotarev, 2017; Moiseev 
et  al., 2018; Sannikov et  al., 2018; Fomin et  al., 2020; Gaisin et  al., 
2020), in the Russian Altai Mountains (Kharuk et al., 2017a; Cazzolla 
Gatti et al., 2019) and in the Putorana Mountains (Kirdyanov et al., 
2012; Pospelova et al., 2017; Grigor’ev et al., 2019). Lower treelines 
in the southernmost Larix sibirica forests in the Saur Mountains, 
eastern Kazakhstan, have suffered from increased drought stress in 
recent decades causing forest regeneration and tree growth decrease, 
and tree mortality increase (Dulamsuren et al., 2013). In Jeju Island, 
Republic of Korea, recent warming has enhanced Quercus mongolica 
growth at its higher distribution and has led to Abies koreana (ABKO) 
growth reduction at all elevations, except the highest locality. Thus,

the combination of warming, increasing competition and frequent 
tropical cyclone disturbances could lead to population decline or even 
extinction of ABKO at Jeju Island (Altman et al., 2020). In the Himalaya, 
the treeline over recent decades either moves upwards (Schickhoff 
et al., 2015; Suwal et al., 2016; Sigdel et al., 2018; Tiwari and Jha, 2018)

10
1473Asia  Chapter 10
or does not show upslope advance (Schickhoff et al., 2015; Gaire et al., 
2017; Singh et al., 2018c), or moves downwards (Bhatta et al., 2018). 
In the Tibetan Plateau, the treeline either shifted upwards or showed 
no significant upwards shift (Wang et al., 2019c). This can be explained 
by site-specific complex interaction of positive effect of warming on 
tree growth, and negative effects of drought stress, change in snow 
precipitation, inter- and intraspecific interactions of trees and shrubs, 
land-use change (especially grazing) and other factors (Liang et  al., 
2014; Lenoir and Svenning, 2015; Tiwari et  al., 2017; Sigdel et  al., 
2018; Tiwari and Jha, 2018; Sigdel et al., 2020). It is largely unknown 
how broader-scale climate inputs, such as pre-monsoon droughts, 
interact with local-scale factors to govern treeline response patterns 
(Schickhoff et al., 2015; Müller et al., 2016; Bhatta et al., 2018; Singh 

10.4.2.1.2 Species ranges and biodiversity
Since AR5, new evidence has appeared of alterations in terrestrial and 
freshwater species, populations and communities in line with climate 
change across Asia (medium to high confidence) (Arias et al., 2021). In 
North Asia, temperature increase and droughts have promoted spread 
northward of the current silk moth outbreak (has affected nearly 
2.5 × 106 ha) in Central Siberia dark taiga since 2014 (Kharuk et al., 
2017b; Kharuk et al., 2020). The climatic range of the Colorado potato 
beetle (Leptinotarsa decemlineata) in 1991–2010 expanded east- and 
northward in Siberia and the Russian Far East compared with the 
1951–1970 range (Popova, 2014). The climatic range of Ixodes ricinus, 
a vector of dangerous human diseases, expanded into Central Asia 
and south of the Russian Far East (Semenov et al., 2020). A butterfly 
(Melanargia russiae) in the Middle Urals moved northward (Zakharova

et al., 2017). Thrush birds in West Siberia penetrated northward up to 
the limits of the sparse woodlands (Ryzhanovskiy, 2019a). The increase 
in the length of frost-free period observed in the Ilmen Nature Reserve, 
Middle Urals, during recent decades is supposed to be interlinked with 
changes in the amplitude and frequency of population waves of bank 
vole (Kiseleva, 2020). In Katunskiy Biosphere Reserve, Russian Altai, 
in the period 2005–2015, alpine plant species have shifted towards 
higher altitudes by 5.3 m on average (Artemov, 2018). Wild reindeer 
herds in Taimyr, north of Central Siberia, migrated northward to the 
Arctic Sea coast in hot summers between 1999–2003 and 2009–2016 
because of an earlier massive emergence of bloodsucking insects 
(Pospelova et al., 2017). In Yakutia, the ranges of red deer, elk and the 
northern pika are expanding, and the winter survival of the mouse-like 
rodents has increased (Safronov, 2016). In the Chukchi Sea, in recent

decades the average duration polar bears spent onshore increased by 
30 d (Rode et al., 2015b) in line with global warming and the rapid 
decline of their sea ice habitat (Derocher et al., 2013; Jenssen et al., 
2015; Rode et al., 2015a).
In Central Kazakh Steppe, in line with warming, in 2018 there were 
more ‘southern’ sub-arid species in the communities and fewer 
relatively ‘northern’ boreal and polyzonal species of ground beetles 
(Carabidae) and black beetles (Tenebrionidae) than in 1976–1978 
(Mordkovich et al., 2020). The present distribution of Asian black birch 
(Betula davurica Pall.) in East and North Asia was formed as a result 
of northward expansion during post-Last Glacial Maximum global 
warming (Shitara et al., 2018). Both upper and lower limits of avifauna of two New Guinean mountains, Mt. Karimui and Karkar Island, have 
been shifting upslope since 1965 (Freeman and Freeman, 2014). In 
Republic of Korea, for the past 60 years, the northern boundary line

of 63 southern butterfly species has moved further north (Bae et al., 
2020). The change in the butterflies’ occurrence in this period has been 
influenced mostly by large-scale reforestation, not by climate change 
(Kwon et  al., 2021). Warming-driven geographic range shift was 
recorded in 87% of 124 endemic plant species studied in the Sikkim 
Himalaya in the periods 1849–1850 and 2007–2010 (Telwala et al., 
2013). In Darjeeling, India, significant change in lichen community 
structure was shown in response to climate change and anthropogenic 
pollution (Bajpai et al., 2016).
The observed loss of biodiversity and habitat of animals and plants has 
been linked to climate change in some parts of Asia (high confidence). 
Climate change, together with human disturbances, have caused local 
extinction of some large and medium-sized mammals during the 
past three centuries in China (Wan et al., 2019). Climate change has

shown significant impacts on subalpine plant species at low altitudes 
and latitudes in Republic of Korea and may impose a big threat to 
these plant species (Adhikari et al., 2018; Kim et al., 2019c). Climate 
change has caused habitat loss of amphibians (Surasinghe, 2011) and 
extinction of some endemic species in Sri Lanka (Kottawa-Arachchi 
and Wijeratne, 2017).
There is evidence that climate change can alter species interaction 
or spatial distribution of invasive species in Asia (high confidence). 
Climate warming has enhanced the competitive ability of the native 
species (Sparganium angustifolium) against the invasive species 
(Egeria densa) in China under a mesocosm experiment in a greenhouse 
(Yu et al., 2018e). It has also increased the non-target effect on a native 
plant (Alternanthera sessilis) by a biological control beetle (Agasicles 
hygrophila) in China due to range expansion of the beetle and change 
of phenology of the plant (Lu et  al., 2015). Climate warming has

expanded the distribution of invasive bamboos (Phyllostachys edulis 
and P . bambusoides) northward and upslope in Japan (Takano et al., 
2017), while soil dry-down rates have been a key driver of invasion of 
dwarf bamboo (Sasa kurilensis) in central Hokkaido above and below 
the treeline (Winkler et al., 2016).
Climate change along with land-use and land-cover change influences 
soil organic carbon content, microbial biomass C, microbial respiration 
and the soil carbon cycle in the Hyrcanian forests of Iran (Soleimani 
et al., 2019; Francaviglia et al., 2020). In the fir forest ecosystems of 
the Tibetan Plateau, winter warming affects the ammonia-oxidising 
bacteria and archaea, thus altering the nitrogen cycle (Huang et al., 
2016). Ecosystem carbon pool in the spruce forests of the northeast 
Tibetan Plateau was reduced by about 25% by deforestation due 
to recent decades of climate warming as well as wood pasture and

logging (Wagner et  al., 2015). In Mongolia’s forest steppe, recent 
decades of drought- and land-use-induced deforestation has reduced 
the ecosystem carbon stock density by about 40% (Dulamsuren et al., 
2016). In Inner Mongolia, the predicted decreases in precipitation and 
warming for most of the temperate grassland region could lead to a 
pH change, which would contribute to a soil C-N-P decoupling that 
could reduce plant growth and production in arid ecosystems (Jiao 

10
1474Chapter 10 Asia
In Central Asia, in the Vakhsh, Kafirnigan and Kyzylsu river basins, 
Tajikistan, it has been shown that temperature stimulates algal species 
diversity, while precipitation and altitude suppress it (Barinova et al., 
2015). In line with the warming of Lake Baikal, Russia, since the 1990s 
in the lake’s south basin, there have been shifts in diatom community 
composition towards higher abundances of the cosmopolitan Synedra 
acus and a decline in endemic species, mainly Cyclotella minuta and 
Stephanodiscus meyerii, and to a lesser extent Aulacoseira baicalensis 
and A. skvortzowii (Roberts et al., 2018). In Gonghai Lake, North China, 
diatom biodiversity has increased remarkably from 1966, but began to 
decline after 1990 presumably in response to rapid climate warming 
(Yan et al., 2018).
10.4.2.1.3 Wildfires
Climate change, human activity and lightning determine increases in 
wildfire severity and area burned in North Asia (high detection with

medium-to-low attribution to climate change). In North Asia, the 
extent of fire-affected areas in boreal forest can be millions of hectares 
in a single extreme fire year (Duane et al., 2021) and nearly doubled 
between 1970 and 1990 (Brazhnik et al., 2017). During recent decades, 
the number, area and frequency of forest fires increased in Putorana 
Plateau (north of Central Siberia), in larch-dominated forests of Central 
Siberia and in Siberian forests as a whole. This increase is in line with 
an increase in the average annual air temperature, air temperature 
anomalies, droughts and the length of fire season (Ponomarev et al., 
2016; Kharuk and Ponomarev, 2017; Pospelova et  al., 2017). The 
number of forest fires and damaged areas in Gangwon Province and 
the Yeongdong area in the 2000s increased by factors of 1.7 and 5.6, 
respectively, compared with the 1990s (Bae et  al., 2020). Climate 
change is not the sole cause of the increase in forest fire severity

(Wu et  al., 2014; Wu et  al., 2018d). Ignition is often facilitated by 
lightning (Canadell et al., 2021), and over 80% of fires in Siberia are 
likely anthropogenic in origin (e.g., (Brazhnik et al., 2017). Gas field 
development and Indigenous tundra burning practices that may get 
out of control contribute to fire frequency in the forest–tundra of West 
Siberia (Adaev, 2018; Moskovchenko et al., 2020). Climate change in 
combination with socioeconomic changes has resulted in an increase 
in fire severity and area burned in South Siberia, and illegal logging 
increases fire danger in forest–steppe Scots pine stands (Ivanova et al., 
2010; Schaphoff et al., 2016).
10.4.2.1.4 Phenology, growth rate and productivity
In East and North Asia, satellite measurements and ground-based 
observations in recent decades demonstrate either an increase in the 
length of plant growth season over sub-regions or in some territories

in line with climate warming, or do not show any significant trend in 
other territories (high confidence). In recent decades in China, there 
has been an increasing trend in annual mean grassland net primary 
production (NPP), average leaf area index and lengthening of the local 
growing season (Piao et al., 2015; Zhang et al., 2017b; Xia et al., 2019). 
Nevertheless, phenology patterns vary across different studies, species 
and parts of China. In most regions of Northeast China, start date and 
length of land surface phenology from 2000 to 2015 had advanced 
by approximately 1 d yr−1, except in the needle-leaf and cropland 
areas (Zhang et  al., 2017d). For Inner Mongolia, it has been shown that neither the start of growing season (SOS) nor the end of growing 
season (EOS) presented detectable progressive patterns at the regional 
level in 1998–2012, except for the steppe–desert (6% of the total 
area) (Sha et al., 2016). In the Tianshan Mountains in China, the NPP of

only 2 out of 12 types of vegetation increased in spring, and the NPP 
of only one type increased in autumn from 2000–2003 to 2012–2016 
(Hao et al., 2019). In Republic of Korea, from 1970 to 2013, the SOS has 
advanced by 2.7 d per decade, and the EOS has been delayed by 1.4 d 
per decade (Jung et al., 2015). During the past decade, leaf unfolding 
has accelerated at a rate of 1.37 d yr−1, and the timing of leaf fall has 
been delayed at a rate of 0.34 d yr−1 (Kim et al., 2019d). Cherry blossoms 
are predicted to flower 6.3 and 11.2 d earlier after 2090 according 
to scenarios RCP4.5 and RCP8.5, respectively (Bae et  al., 2020). On 
the Tibetan Plateau, it was found that the SOS has advanced and the 
EOS has been delayed over the past 30–40 years (Yang et al., 2017). 
Using normalised difference vegetation index (NDVI) datasets and 
ground-based Budburst data (Wang et al., 2017c) found no consistent 
evidence that the SOS has been advancing or delaying over the Tibetan

Plateau during the past two to three decades. The discrepancies among 
different studies in the trends of spring phenology over the Tibetan 
Plateau could be largely attributed to the use of different phenology 
retrieval methods. An uncertainty exists with the relationship between 
land-surface phenology and climate change estimated by satellite-
derived NDVI because these indices are usually composite products of 
a number of days (e.g., 16 d) that could fail to capture more details. 
Besides, due to lack of in situ observations, the SOS and EOS at large 
areas cannot be easy defined (Zhang et al., 2017d).
In North Asia, in Central Siberia and south of West Siberia, the growth 
index of Siberian larch based on tree-ring width increased with the 
onset of warming and changed in antiphase with aridity in the 1980s 
(Kharuk et al., 2018). In Mongolia and Kazakhstan, the temperature 
increase over the previous decade promoted radial stem increment of

the Siberian larch. However, the simultaneous influence of increased 
temperature, decreased precipitation and increased anthropogenic 
pressure resulted in widespread declines in forest productivity and 
reduced forest regeneration, and increased tree mortality (Dulamsuren 
et  al., 2013; Lkhagvadorj et  al., 2013a; Lkhagvadorj et  al., 2013b; 
Dulamsuren et al., 2014; Khansaritoreh et al., 2017). In Eastern Taimyr, 
growing season, the number of flowering shoots, annual increment, 
success of seed ripening and vegetation biomass have increased 
considerably in recent decades (Pospelova et  al., 2017). In Vishera 
Nature Reserve, northern Ural Mountains, annual temperature has 
increased in recent decades in parallel with a summer temperature 
drop and an increase in summer frost numbers. As a result, trends in 
vegetation change are mostly unreliable (Prokosheva, 2017).
In Asia, the date of arrival of migrant birds to nesting areas and the date

of departure from winter areas are changing consistently with climate 
change (medium confidence). Time of arrival of the grey crow to the 
Lower Ob river region, northwest Siberia, shifted to earlier dates in the 
period 1970–2017, which is consistent with an increase in the daily 
average temperatures on the day of arrival (Ryzhanovskiy, 2019b). In 
Ilmen Nature Reserve, Urals, an earlier arrival of the majority of nesting 
bird species has not been observed in recent decades. This is explained 
by the fact that other factors, such as the weather of each spring month 
of particular years, population density in the previous nesting period,

10
1475Asia  Chapter 10
the seed yield of the main feeding plants and migration of wintering 
species from adjacent areas, determinate the long-term dynamics of 
bird arrival (Zakharov, 2016; Zakharov, 2018). In Yokohama, Japan, 
observations since 1986 have revealed that the arrival of six winter bird 
species came later and the departure earlier than in the past, due to 
warmer temperatures (Kobori et al., 2012; Cohen et al., 2018). Some 
papers corroborate that earlier start and later end of phenological 
events in Asia are associated with global warming; however, other 
papers do not confirm such a connection. Comparison and synthesis of 
results is impeded by usage of different metrics, measurement methods 
and models (e.g., Hao et  al., 2019). Relative contribution of climatic 
stress and other factors to phenology and plant growth trends are 
poorly understood (e.g., Andreeva et al., 2019).
10.4.2.2 Projected Impacts
10.4.2.2.1 Biomes and mountain treeline

Across Asia, under a range of representative concentration pathways 
(RCPs) and other scenarios, rising temperatures are expected to 
contribute to a northward shift of biome boundaries and an upwards 
shift of mountain treeline (medium confidence). Northward shift and 
area change of bioclimatic zones in Siberia (Anisimov et  al., 2017; 
Torzhkov et  al., 2019) and northeast Asia (Choi et  al., 2019) are 
projected. Projected changes in vegetation in China at the end of 
the 21st century reveal that the area covered by cold–dry potential 
vegetation decreases as the area covered by warm–humid potential 
vegetation increases (Zhao et  al., 2017a). Forest expansion into 
mountain tundra of the northern Urals is expected (Sannikov et  al., 
2018). In Republic of Korea, projected under RCP4.5 and RCP8.5 in the 
2070s, suitable area loss of six subalpine tree species, namely, Korean 
fir, Khingan fir, Sargent juniper, Yeddo spruce, Korean yew and Korean

arborvitae, range from 17.7 ± 20.1% to 65.2 ± 34.7%, respectively 
(Lee et al., 2021b). Korean fir forests would be replaced by temperate 
forests at lower elevations, while they would continuously persist at 
the highest elevations on Mt. Halla, Jeju Island and Republic of Korea 
(Lim et al., 2018). Himalayan birch at its upper distribution boundary 
either is projected to move upwards (Schickhoff et al., 2015; Bobrowski 
et  al., 2018) or considered to downslope as a response to global-
change-type droughts (Liang et al., 2014). Upwards shift in elevation 
of bioclimatic zones, decreases in area of the highest elevation zones 
and large expansion of the lower tropical and sub-tropical zones can 
be expected by the year 2050 throughout the transboundary Kailash 
Sacred Landscape of China, India and Nepal, and likely within the 
Himalayan region more generally (Zomer et al., 2014).
In North Asia, a shift is projected in the dominant biomes from conifers

to deciduous species across Russia after 20 years of altered climate 
conditions (Shuman et  al., 2015). In South Siberia, Brazhnik and 
Shugart (2015) projected a shift from the boreal forest to the steppe 
biome. Rumiantsev et al. (2013) also project a positive northward shift 
of vegetation boundaries for the greater part of West Siberia in line with 
warming; however, no shift for the north of West Siberia and negative 
shift for the southern Urals and northwest Kazakhstan are projected 
for 2046–2065. The replacement of forest–steppe with steppe at the 
lower treeline in South Siberia is projected (Brazhnik and Shugart, 
2015), and retreat of larch forests from the southernmost strongholds of boreal forest in eastern Kazakhstan is expected as part of a global 
process of forest dieback in semiarid regions (Dulamsuren et al., 2013). 
In North Asia, tree growth is intertwined with permafrost, snowpack, 
insect outbreaks, wildfires, seed dispersal and climate (e.g., Klinge

et al., 2018). It is challenging to isolate the affects of individual factors, 
particularly since they can interact on one another in unanticipated 
ways because the underlying mechanisms are not well understood 
(Berner et  al., 2013; Brazhnik and Shugart, 2015). The accuracy of 
treeline-shift projections is limited because projections are based on 
vegetation models which do not consider all the factors (Tishkov et al., 
2020). The regional vegetation model structure and parameterisation 
can affect model performance, and the corresponding projections can 
differ significantly (Shuman et al., 2015).
10.4.2.2.2  Species ranges and biodiversity
Considerable changes in plant and animal species distribution under 
warming stress are expected in Asia until 2100 (high confidence). 
In East Asia, Cunninghamia lanceolate, a fast-growing and widely 
distributed coniferous timber species in China, is projected to increase

distribution, to decrease the establishment probability and to reduce 
total NPP by the 2050s (Liu et  al., 2014c). In the monsoon regions 
of Asia, by the end of the 21st century, NPP is projected to increase 
by 9–45% (Ito et  al., 2016). Under climate change on the Korean 
Peninsula (KP), the potential habitat for Abies nephrolepis is the 
northern part of KP , and A. koreana will disappear from Jeju Island 
and shrink significantly in the KP (Yun et al., 2018), while evergreen 
forests will expand to the northern part of KP (Koo et al., 2018; Lim 
et al., 2018). It is expected that under projected warming, fig species 
in China will expand to higher latitudes and altitudes (Chen et  al., 
2018c). In Japan, under the A1B scenario, 89% of the area currently 
covered by the Fagus crenata-dominant forest type will be replaced 
by Quercus spp.-dominant forest types (Matsui et al., 2018). Current 
trends of climate change will reduce distribution of tall (2–2.5 m

high) herb communities in Japan, and will increase suitably for them 
in the Russian Far East (Korznikov et al., 2019). A range expansion of 
Lobaria pindarensis, an endemic epiphytic lichen in the HKH region, is 
projected to move to the northeast and to higher altitudes in response 
to climate change, although the species’ low dispersal abilities and 
the local availability of trees as a substratum will considerably limit 
latitudinal and altitudinal shifts (Devkota et al., 2019).
The climatic range of Italian locust (Calliptamus italicus L.) under RCP4.5 
will expand north- and east-ward to Siberia, the Russian Far East and 
Central Asia (Popova et  al., 2016). In Krasnoyarsk Krai, Siberia, it is 
projected that the needle cast disease caused by fungi from the genus 
Lophodermium Chevall. in the Scots pine nurseries would shift northward 
up to 2080 under A2 and B1 scenarios (Tchebakova et al., 2016). All 
four RCP scenarios showed north-ward expansion of vulnerable regions

to pine wilt disease in China, Republic of Korea, the Russian Far East 
and Japan under climate conditions in 2070 (Hirata et al., 2017), and 
during 2026–2050 in Japan (Matsuhashi et al., 2020). It is noteworthy 
that disease expansion depends not only on climatic factors but also on 
the dispersal capacity of insect vectors, the transportation of infected 
logs to non-infected regions and the susceptibility of host trees (e.g., 
Gruffudd et al., 2016). The suitable habitat area of the snow leopard 
Panthera uncia is projected to increase by 20% under the IPCC Scenario

10
1476Chapter 10 Asia
A1B by 2080: for the seven northernmost snow leopard range states 
(Afghanistan, Tajikistan, Uzbekistan, Kyrgyzstan, Kazakhstan, Russia 
and Mongolia) the suitable habitat area will increase, while habitat loss 
is expected on the southern slope of the Himalaya and the southeast 
Tibetan Plateau (Farrington and Li, 2016). Climate change projected 
under four RCP scenarios will not affect the distribution patterns of 
Turkestan Rock Agama Paralaudakia lehmanni (Nikolsky 1896; Sancholi, 
2018). In Iran, among 37 studied species of plants and animals, the 
ranges of 30 species are expected to shrink and ranges of 7 species are 
expected to increase between 2030 and 2099 under climate-change 
stress (Yousefi et al., 2019).
Future climate change would cause biodiversity and habitat loss in 
many parts of Asia using modelling approaches (high confidence). 
Warren et al. (2018) projected that extirpation risks to terrestrial taxa

(plants, amphibians, reptiles, birds and mammals) from 2°C to 4.5°C 
global warming in 12 ‘priority places’ in Asia, under the assumption of 
no adaptation (i.e., dispersal) by the 2080s, is from 12.2–26.4% to 29–
56% (Table 10.1; Figure 10.4). Under different scenarios, future climate 
change could reduce the extent of a suitable habitat for giant pandas 
(Fan et al., 2014), moose (Alces alces) (Huang et al., 2016), black muntjac 
(Muntiacus crinifrons) (Lei et  al., 2016) and the Sichuan snub-nosed 
monkey (Rhinopithecus roxellana) (Zhang et al., 2019d) in China; the 
Persian leopard (Panthera pardus saxicolor) in Iran (Ashrafzadeh et al., 
2019a); the Bengal tiger (Mukul et al., 2019) in India; and four tree-
snail species (Amphidromus) in Thailand (Klorvuttimontara et al., 2017). 
However, climate change would have little impact on the habitats of

the Asian elephant, but would cause extinction of the Hoolock gibbon in Bangladesh by 2070 (Alamgir et al., 2015). Climate change would 
increase the distribution of the Mesopotamian spiny-tailed lizard 
(Saara loricate) in Iran (Kafash et  al., 2016). Future climate change 
would reduce the suitable habitat of certain protected plants (Zhang 
et al., 2014) including Polygala tenuifolia Wild (Lei et al., 2016); relict 
species in East Asia (Tang et al., 2018); tree Abies (Ran et al., 2018) in 
China; two threatened medicinal plants (Fritillaria cirrhosa and Lilium 
nepalense) in Nepal (Rana et  al., 2017); a medicinal and vulnerable 
plant species Daphne mucronata (Abolmaali et al., 2018) and Bromus 
tomentellus in Iran (Sangoony et al., 2016); a valuable threatened tree 
species, Dysoxylum binectariferum, in Bangladesh (Sohel et al., 2016); 
and plant diversity in Republic of Korea (Lim et al., 2018).
The impact of future climate change on invasive species may be species-

or region specific (medium confidence). Climate change would promote 
invasion of a highly invasive aquatic plant Eichhornia crassipes (You 
et  al., 2014), Ambrosia artemisiifolia (Qin et  al., 2014), alligator weed 
(Alternanthera philoxeroides) (Wu et  al., 2016), invasive alien plant 
Solidago canadensis (Xu et  al., 2014), three invasive woody oil-plant 
species (Jatropha curcas, Ricinus communis and Aleurites moluccana) 
(Dai et  al., 2018), and 90 of ~150 poisonous plant species (Zhang 
et  al., 2017a) in China; six mostly highly invasive species (Ageratum 
houstonianum Mill., Chromolaena odorata (L.) R.M. King & H. Rob., Hyptis 
suaveolens (L.) Poit., Lantana camara L., Mikania micrantha Kunth and 
Parthenium hysterophorus L.) in Nepal (Shrestha et al. 2018); 11 invasive 
plant species in the western Himalaya (Thapa et al., 2018); alien plants 
in Georgia (Slodowicz et  al., 2018); the invasive green anole (Anolis

carolinensis) in Japan (Suzuki-Ohno et al., 2017); the Giant African Snail 
Figure 10.4 |  Location of ‘priority places’ in Asia. (Modified from Warren et al., 2018).

10
1477Asia  Chapter 10
in India (Sarma et al., 2015); and a major insect vector (Monochamus 
alternatus) of the pine wilt disease (Kim et al., 2016b) and melon thrips 
(Thrips palmi Karny) (Park et al., 2014) in Republic of Korea. In contrast, a 
few studies have projected that climate change would inhibit the invasion 
of one exotic species (Spartina alterniflora) (Ge et al., 2015), alien invasive 
weeds (Wan et  al., 2017), an invasive plant (Galinsoga parviflora) (Bi 
et  al., 2019) and an invasive species (Galinsoga quadriradiata)  (Yang 
et al., 2018b) in China; and two invasive plants (Chromolaena odorata 
and Tridax procumbens) in India (Panda and Behera, 2019).
Five of 15 endemic freshwater fish species in Iran will lose some parts 
of their current suitable range under climate change by 2070 (Yousefi  
et  al., 2020). In line with projected large increases in mean water 
temperature, the strongest increase is projected in exceeded frequency

and magnitude of maximum temperature tolerance values for 
freshwater minnow (Zacco platypus) in East Asia for 2031–2100 (Van 
Vliet et al., 2013). Climate change under the A1B scenario is projected to 
decrease diversity ( –0.1%) along with increased local richness (+15%) 
and range size (+19%) of stream macroinvertebrates in the Changjiang 
River catchment, southeast China, for the period 2021–2050, while 
land-use change is predicted to have the strongest negative impact 
(Kuemmerlen et al., 2015). The Asian clam Corbicula fluminea Müller, an 
invasive species native to southeast China, the Republic of Korea and 
southeast Russia, is projected to invade Southeast Asia under all four 
RCP scenarios for the 2041–2060 and 2061–2080 periods (Gama et al., 
2017). Projected SLR, related aquatic salinisation and alteration in fish 
species composition may have a negative impact on poor households in 
southwest coastal Bangladesh (Dasgupta et al., 2017a).
10.4.2.2.3 Wildfires

Under regional projections for North Asia, warmer climate will increase 
forest fire severity by the late 21st century (medium confidence). For the 
southern taiga in Tuva Republic, Central Siberia, in a warmer climate, 
both the annual area burned and fire intensity will increase by 2100. 
For the central taiga in the Irkutsk region, the annual area burned as 
well as crown fire-to-ground fire ratiowill increase by the late 21st century compared with the historical (1960–1990) estimate. This 
moves forest composition towards greater contribution of hardwoods 
(e.g., Betula spp., Populus spp.) (Brazhnik et  al., 2017). This shifting 
was also proved by observations in northern Mongolia, where boreal 
forest fires likely promote the relative dominance of B. platyphylla and 
threaten the existence of the evergreen conifers, Picea obovata and 
Pinus sibirica (Otoda et al., 2013). For Tuva Republic, warming ambient 
temperatures increase the potential evapotranspiration demands

on vegetation, but if no concurrent increase in precipitation occurs, 
vegetation becomes stressed and either dies from temperature-based 
drought stress or more easily succumbs to insects, fire, pathogens or 
wind throw (Brazhnik et  al., 2017). Although Torzhkov et  al. (2019) 
also projected fire risk (FR) increase in Tuva Republic, they expect 
FR decrease in the Irkutsk region and Yakutia under RCP8.5, and FR 
decrease in major parts of Central and East Siberia under RCP4.5 for 
2090–2099. This discrepancy is due to differences in models, climate 
projections, fire severity metrics and other assumptions. According to 
global projections, FR will increase in Central Asia, Russia, China and 
India under a range of scenarios (Sun et al., 2019).
10.4.2.3 Vulnerabilities to Key Drivers
Both natural and managed ecosystems, ecosystem services and 
livelihoods in Asia will potentially be substantially impacted by changing

climate (Wu et al., 2018d). There will be increased risk for biodiversity, 
particularly many endemic and threatened species of fauna and flora 
already under environmental pressure from land-use change and other 
regional and global processes (Zomer et al., 2014; Rashid et al., 2015; Choi 
et al., 2019). Biomes shift not only serves as a signal of climate change 
but also provides important information for resources management and 
ecotone ecosystem conservation. A widespread upwards encroachment 
of subalpine forests would displace regionally unique alpine tundra 
habitats and possibly cause the loss of alpine species (Schickhoff et al., 
2015). In North Asia, emissions from fires reduce forests’ ability to 
regulate climate. A warmer and longer growing season will increase 
vulnerability to fires, although fires can be attributed both to climate 
warming and to other human and natural influences. Recent field-based

observations revealed that the forests in South Siberia are losing their 
ability to regenerate after fire and other landscape disturbances under 
a warming climate (Brazhnik et al., 2017). Data support the hypothesis 
of a climate-driven increase in fire frequency in boreal forests with the 
possible turning of boreal forests from a carbon sink to a carbon source 
(Ponomarev et al., 2016; Schaphoff et al., 2016; Brazhnik et al., 2017; 
Ponomarev et  al., 2018); however, warming resulting from forest fire 
is partly offset by cooling in response to increased surface albedo of 
burned areas in a snow-on period (Chen and Loboda, 2018; Chen et al., 
2018a; Jia et al., 2019; Lasslop et al., 2019).
10.4.2.4 Adaptation Options
Modelling of the interactions between climate-induced vegetation shifts, 
wildfire and human activities can provide keys to how people in Asia 
may be able to adapt to climate change (Kicklighter et al., 2014; Tian

et al., 2020). Conservation and sustainable development would benefit 
from being tailored and modified considering the changing climatic 
conditions and shifting biomes, mountain belts and species ranges 
(Pörtner et al., 2021). Expanding the nature reserves would help species Table  10.1 |   Projected extirpation risks: percentage of taxa (plants, amphibians, 
reptiles, birds and mammals) for 2°C and 4.5°C global warming in ‘priority places’ in 
Asia, without adaptation by the 2080s. (From Warren et al., 2018).
Priority places At 2°C (%) At 4.5°C (%)
Mekong 26.4 55.2
Baikal 22.8 49.5
Yangtze 20 42.6
Coral Triangle 19.2 41.8
Western Ghats 18.8 41.67
New Guinea 19.8 41.2
Atlai-Syan 18.6 37
Sumatra 16.8 37
Borneo 17.6 36.8
Amur 14.2 35.6
Eastern Himalayas 12.2 29
Black sea 26.2 56

10
1478Chapter 10 Asia
conservation; to facilitate species movements across climatic gradients, 
an increase in landscape connectivity can be elaborated by setting up 
habitat corridors between nature reserves and along elevational and 
other climatic gradients (Brito-Morales et al., 2018; D’Aloia et al., 2019; 
United Nations Climate Change Secretariat, 2019). Assisted migration 
of species should be considered for isolated habitats as mountain 
summits or where movements are constrained by poor dispersal ability. 
Introducing seeds of the species to new regions will help to protect them 
from the extinction risk caused by climate change (Mazangi et al., 2016). 
In Asian boreal forests, a strategy and integrated programmes should 
be developed for adaptation of the forests to global climate change, 
including sustainable forest management, firefighting infrastructure and 
forest fuel management, afforestation, as well as institutional, social

and other measures in line with Sustainable Development Goal (SDG) 
15 ‘Life on Land’ (Isaev and Korovin, 2013; Kattsov and Semenov, 2014; 
Bae et  al., 2020). Improvements in forest habitat quality can reduce 
the negative impacts of climate change on biodiversity and ecosystem 
services (Choi et al., 2021). Adaptation options for freshwater ecosystems 
in Asia include increasing connectivity in river networks, expanding 
protected areas, restoring hydrological processes of wetlands and rivers, 
creating shade to lower temperatures for vulnerable species, assisted 
translocation and migration of species (Hassan et  al., 2020; Chapter 
2). Reduction of non-climate anthropogenic impacts can enhance the 
adaptive capacity of ecosystems (Tchebakova et al., 2016).
10.4.3 Ocean and Coastal Ecosystems
Coastal habitats of Asia are diverse, and the impacts of climate change, 
including rising temperatures, ocean acidification and SLR, are known

to affect the services and livelihoods of the people depending on them. The risk of irreversible loss of many marine and coastal ecosystems 
increases with global warming, especially at 2°C or more (high 
confidence) (IPCC, 2018b). In the South China Sea, coral growth and 
sea surface temperature (SST) have shown regional long-term trends 
and inter-decadal variations, while coral growth is predicted to decline 
by the end of this century (Yan et al., 2019). Increasing human impacts 
have also been found to reduce coral growth (Yan et al., 2019). In the 
South China Sea, nearly 571 coral species have been severely impacted 
by global climate changes and anthropogenic activities (Huang et al., 
2015a).
The 2014–2017 global-scale coral bleaching event (GCBE) resulted 
in very high coral mortality on many reefs, rapid deterioration of reef 
structures and far-reaching environmental impacts (Eakin et al., 2019). 
The thermally tolerant Persian Gulf corals (Coles and Riegl, 2013)

are facing an increasing frequency of mass bleaching (Riegl et  al., 
2018) and each event leaves a substantial long-term impact on coral 
communities (Burt, 2014) with low capacity for recovery indicating a 
bleak future for Persian Gulf reefs (Burt et al., 2019).
One of the probable results of global warming is high SLR. Scientists 
believe that increasing GHGs (the Earth’s temperature controllers) is 
the cause of this global warming, and by using satellite measurements, 
these scientists have forecasted on average 1–2 mm for SLR (Jafari 
et al., 2016). The level of thermal stress (based on a degree heating 
month index, DHMI) at these locations during the 2015–2016 El Niño 
was unprecedented and stronger than previous ones (Lough et  al., 
2018). In the Persian Gulf, the reef-bottom temperatures in 2017 were 
among the hottest on record, with mean daily maxima averaging 35.9 
± 0.10°C across sites, with hourly temperatures reaching as high as

37.7°C (Riegl et al., 2018). About 94.3% of corals were bleached, and Box 10.3 | Case Study on Sand and Dust Storm, Climate Change in West Asia’s Iranian Region
The West Asia region, especially the Tigris–Euphrates alluvial plain, has been recognised as one of the most important dust-source areas 
in the world (Cao et al., 2015). The inhabitants of each of these settlements have experienced a decline in dust storms in recent decades, 
since the late 1980s at Nouakchott, since 2004 at Zabol and since the late 1970s at Minqin. Iran is mostly arid or semiarid, with deserts 
making up at least 25 million hectares of the country (NASA, 2018). Iran is experiencing unprecedented climate-related problems such 
as drying of lakes and rivers, dust storms, record-breaking temperatures, droughts and floods (Vaghefi et al., 2019). There are three 
key factors responsible for the generation of sand and dust storms: strong wind, lack of vegetation and absence of rainfall (EcoMENA,

2020). It seems that all of this is closely related to the heating surface and the occurrence of local dry instabilities (Ghasem et al., 2012). 
According to EcoMENA (2020), sand and dust storms cause significant negative impacts on society, the economy and the environment 
at the local, regional and global levels. The seasonality of the numbers of dusty days (NDD) in Iran shows the highest frequency in 
summer followed by spring and autumn (Modarres and Sadeghi, 2018). In the past decade, West Asia has witnessed more frequent and 
intensified dust storms affecting Iran and other Persian Gulf countries (Nabavi et al., 2016). In terms of long-term frequency of dust 
events, observational analyses show an overall rising trend of the frequency of Iran’s dust events in recent years (Alizadeh-Choobari et al., 
2016). Results show that there has been a direct relationship between dust event, drought and years of intensive drought (Dastorani

and Jafari, 2019). Compared with the period 1980–2004, in the period 2025–2049, Iran is likely to experience more extended periods 
of extreme maximum temperatures in the southern part of the country, more extended periods of dry (for ≥120 d: precipitation <2 mm, 
Tmax ≥30°C) as well as wet (for ≤3 d: total precipitation ≥110 mm) conditions and a higher frequency of floods (Vaghefi et al., 2019). The 
slope of precipitation in West Asia shows that during the period 2016–2045 in January, February, July and August, precipitation would 
increase and decrease in other months of the year (Ahmadi et al., 2018). Temperatures in Central Asia have risen significantly within 
recent decades, whereas mean precipitation remains almost unchanged (Haag et al., 2019); however, climatic trends can vary greatly 
between different sub-regions, across altitudinal levels and within seasons (Haag et al., 2019).

10
1479Asia  Chapter 10
about 66% perished, in 2017 (Burt et al., 2019). In 2018 coral cover 
averaged just 7.5% across the southern basin of the Persian Gulf. This 
mass mortality did not cause dramatic shifts in community composition 
as earlier bleaching events had removed the most sensitive taxa. 
An exception was the already rare Acropora spp. which were locally 
extirpated in summer 2017 (Burt et  al., 2019). During 2008–2011 
also the coral communities of Musandam and Oman showed changes 
depending on the stress-tolerance levels of the species and the local 
environmental disturbance level (Bento et al., 2016).
The health and resilience of corals have been found to be associated 
with beneficial microorganisms of coral (BMC) which alter during 
environmental stress. Increasing seawater temperatures have been 
found to affect the functioning of the symbiotic algae of corals (Lough 
et al., 2018; Gong et al., 2019) and its bacterial consortia leading to

coral bleaching and mortality (Bourne et al., 2016; Peixoto et al., 2017; 
Bernasconi et al., 2019; (Motone et al., 2020).
Coral reefs were found to be affected differentially during bleaching 
episodes, and those species which survived had more stress-tolerant 
symbionts and higher tolerance to thermal changes (Majumdar et al., 
2018; Thinesh et al., 2019; van der Zande et al., 2020). Rare thermally 
tolerant algae and host species-specific algae may play important 
roles in coral bleaching (van der Zande et al., 2020). Along the Indian 
coast, in the coral reefs of Palk Bay (Bay of Bengal), varied bleaching 
and recovery patterns among coral genera was observed during the 
2016 bleaching episode (Thinesh et al., 2019). Bleaching was high in 
Acropora spp. (86.36%), followed by Porites (65.45%), while moderate 
to no bleaching was observed in Favites Symphyllia, Favia, Platygyra 
and Goniastrea.
The presence of stress-tolerant symbiont Durusdinium (Clade D)

during the post-bleach period indicated the high adaptive capacity 
of Acropora spp. in tropical waters (Thinesh et al., 2019). Also Porites  
spp. were found to have higher thermal thresholds and showed better 
resilience to bleaching than species like Fungiid spp. (Majumdar et al., 
2018). In the Philippines, during the 2010 bleaching event, the size 
structure of the mushroom coral was found to be affected (Feliciano 
et al., 2018). In Indonesia, it was found that branching coral diversity 
may decrease relative to massive, more resilient corals (Hennige et al., 
2010). This would have large-scale impacts upon reef biodiversity 
and ecosystem services, and reef metabolism and net reef accretion 
rates, since massive species are typically slow growers (Hennige et al., 
2010).
Macro-tidal coral reefs are particularly sensitive to medium- to long-
term changes in sea-level Andaman trenches (Simons et  al., 2019). 
Data compiled from 11 cities throughout East and Southeast Asia,

with particular focus on Singapore, Jakarta, Hong Kong and Naha 
(Okinawa), highlights several key characteristics of urban coral 
reefs, including ‘reef compression’ (a decline in bathymetric range 
with increasing turbidity and decreasing water clarity over time and 
relative to shore), dominance by domed coral growth forms and low 
reef complexity, variable city-specific inshore–offshore gradients, early 
declines in coral cover with recent fluctuating periods of acute impacts 
and rapid recovery, and colonisation of urban infrastructure by hard 
corals (Heery et al., 2018).In Taiwan, Province of China, the calcification rate of the model reef 
coral Pocillopora damicornis was higher in coral reef mesocosms 
featuring seagrasses under ocean acidification conditions at 25°C and 
28°C. The presence of seagrass in the mesocosms helped to stabilise 
the metabolism of the system in response to simulated climate change 
(Liu et al., 2020a).

An increase in host susceptibility, pathogen abundance or virulence 
has led to higher prevalence and severity of coral diseases and to 
decline and changes in coral reef community composition (Maynard 
et al., 2015). Relative risk has been found to be high in the province 
of Papua in Indonesia, the Philippines, Japan, India, northern Maldives, 
the Persian Gulf and the Red Sea. For the combined disease-risk metric, 
relative risk was considered lower for locations where anthropogenic 
stress was low or medium, a condition found for some locations in 
Thailand (Maynard et al., 2015).
Degradation and loss of coral reefs can affect about 4.5 million people 
in Southeast Asia and the Indian Ocean (Lam et al., 2019). In the coral 
reef fisheries sector, there are about 3.35 million fishers in Southeast 
Asia and 1.5 million fishers in the Indian Ocean (Teh et al., 2013). The 
economic loss under different climate-change scenarios and fishing

efforts were estimated to range from 27.78 to 31.72  million USD 
annually in Nha rang Bay, Vietnam. A survey conducted in Taiwan, 
Province of China, showed that the average annual amount that people 
were personally willing to pay was 35.75 USD and the total amount 
was 0.43 billion USD. These high values indicate the need to preserve 
these coral reef ecosystems (Tseng et al., 2015). In Bangladesh, the 
coral reef of St. Martin’s Island contributes 33.6 million USD yr–1 to the 
local economy, but climate change, along with other anthropogenic 
activities, has been identified as a threat these habitats (Rani et al., 
2020a).
Mitigation of global warming has been identified to be essential to 
maintain healthy coral reef ecosystems in Asia (Comte and Pendleton, 
2018; Heery et al., 2018; Lam et al., 2019; Yan et al., 2019). Restoration 
of reefs (Nanajkar et al., 2019) and building resilience through multiple 
mechanisms, such as innovative policy combinations, complemented

by environmental technology innovations and sustained investment 
(Hilmi et al., 2019; McLeod et al., 2019), are suggested.
An ecosystem-based approach to managing coral reefs in the Gulf 
of Thailand is needed to identify appropriate marine protected area 
(MPA) networks and to strengthen marine and coastal resource 
policies in order to build coral reef resilience (Sutthacheep et  al., 
2013). Broadening the scope to develop novel mitigation approaches 
towards coral protection through the use of symbiotic bacteria and 
their metabolites (Motone et al., 2018; Motone et al., 2020) has been 
suggested. Coral culture and transplantation within the Gulf are 
feasible for helping maintain coral species populations and preserving 
genomes and adaptive capacities of Gulf corals that are endangered 
by future thermal-stress events (Coles and Riegl, 2013). Greater focus 
on understanding the flexibility and adaptability of people associated

with coral reefs, especially in a time of rapid global change (Hoegh-
Guldberg et al., 2019), and a well-designed research programme for 
developing a more targeted policy agenda (Lam et al., 2019), is also 
recommended. Cutting carbon emissions (Bruno and Valdivia, 2016)

10
1480Chapter 10 Asia
and limiting warming to below 1.5°C is essential to preserving coral 
reefs worldwide and protecting millions of people (Frieler et al., 2013; 
Hoegh-Guldberg et al., 2017). Many visitors to coral reefs have high 
environmental awareness, and reef visitation can both help to fund 
and encourage coral reef conservation (Spalding et al., 2017).
The largest mangrove forests are in Asia contributing to about 42% 
of the world’s mangroves. This includes Sundarbans, the world’s 
largest remaining contiguous mangrove forest (Dasgupta et al., 2020). 
Mangrove ecosystems are rich in biodiversity. The ecosystems are 
supported and maintained by both flora and a large array of living 
things, which include mammals, birds, fish, crustaceans, shrimps, 
insects and microbes3. Contemporary rates of mangrove deforestation 
are lower than in the late 20th century (Gandhi and Jones, 2019; Friess 
et al., 2019); however, some areas in Asia continue the trend. Myanmar

is the primary mangrove-loss hotspot in Asia, exhibiting 35% loss from 
1975 to 2005 and 28% from 2000 to 2014. Rates of loss in Myanmar 
were four times the global average from 2000 to 2012. The Philippines 
is additionally identified as a loss hotspot, with secondary hotspots 
including Malaysia, Cambodia and Indonesia (Gandhi and Jones, 
2019).
Mangrove deforestation is expected to increase as many tropical 
nations utilise mangrove areas for economic security. Increased river 
damming would reduce fluvial sediment sources to the coast making 
mangroves more vulnerable to SLR, and uncertain climate with 
extreme oscillations can create unstable conditions for survival and 
propagation of mangrove (Friess et al., 2019).
Valuation of ecosystem services of mangroves have indicated that they 
prevent more than 1.7 billion USD in damages for extreme events (i.e., 
one event in 50 years) in the Philippines (Menéndez et al., 2018). They

reduce flooding to 613,500 people yr–1, 23% of whom live below the 
poverty line and avert damages up to 1 billion USD yr–1 in residential 
and industrial property. Mangroves have also become a very popular 
source of livelihood in Asia through tourism (Dehghani et al., 2010; 
Kuenzer and Tuan, 2013; Spalding and Parret, 2019; Dasgupta 
et al., 2020) and they also support fisheries (Hutchison et al., 2014).
Mangroves, tidal marshes and seagrass meadows (collectively called 
coastal blue carbon ecosystems) have sequestered carbon dioxide 
from the atmosphere continuously over thousands of years, building 
stocks of carbon in biomass and organic rich soils. Carbon dynamics 
in mangrove-converted aquaculture in Indonesia indicate that the 
mean ecosystem carbon stocks in shrimp ponds are less than half of 
the relatively intact mangroves (Arifanti et al., 2019). Conversion of 
mangroves into shrimp ponds in the Mahakam Delta have resulted in

a carbon loss equivalent to 226 years of soil carbon accumulation in 
natural mangroves. In the Philippines, abandoned fishpond reversion 
to former mangrove has been found to be favourable for enhancing 
climate change mitigation and adaptation (Duncan et  al., 2016). 
Integrated mangrove-shrimp farming, with deforested areas not 
exceeding 50% of the total farm area, has been suggested to support 
both carbon sequestration as well as livelihood (Ahmed et al., 2018).
3 Tropical Coastal Ecosystems Portal. Available at www.nies.go.jp/TroCEP/index.html, accessed 8 October 2020.Globally, the extent of the blue carbon ecosystem has been estimated 
at 120,380 km2, with the highest spread by mangroves at 114,669 km2 
(95.3%), followed by seagrass meadows at 2,201 km2 (1.8%) and salt 
marshes at 3510 km2 (2.9%) (Himes-Cornell et al., 2018). In Asia, the 
total extent of these three ecosystems is 33,224 km2, forming 27.6%

of the global total with the highest spread of mangrove at 32,767 km2, 
which forms 28.6% of the global mangrove coverage. The area of 
seagrass meadows spread in Asia has been estimated as 236 km2 and 
salt marsh 220 km2,which forms 10.8 and 6.03% of the respective 
ecosystems globally (Himes-Cornell et al., 2018). Found at the land–
sea interface, seagrasses provide varied services apart from acting as 
ecosystem engineers providing shelter and habitat for several marine 
fauna which are fished in several Asian countries (Jeyabaskaran et al., 
2018; Nordlund et al., 2018; Unsworth et al., 2019b) thereby providing 
livelihood to millions across the continent (UNEP , 2020).
The seagrass meadows are also good sinks of carbon (Fourqurean 
et al., 2012) capable of storing 19.9 petagrams (pg) of organic carbon, 
but with very high regional and site- and species variability (Ganguly 
et al., 2017; Stankovic et al., 2018; Gallagher et al., 2019; Ricart et al.,

2020). As highly efficient carbon sinks, they store up to 18% of the 
world’s oceanic carbon, and they also reduce the impacts of ocean 
acidification (UNEP , 2020).
The deterioration of this ecosystem is fast, 7% yr–1 since 1990 (Waycott 
et  al., 2009), which has led to development of restoration protocols 
across Asia (Paling, 2009; van Katwijk et  al., 2016). In Vietnam, the 
loss of seagrass has been estimated as above 50% and in some 
regions complete loss has been observed (Van Luong et al., 2012). The 
seagrass meadows of Indonesia are fast deteriorating, and the need for 
increased local autonomy for the management of marine resources and 
restoration has been highlighted (Unsworth et al., 2018). Development 
of science-based policies for conservation, including participatory 
methods (Fortes, 2018; Ramesh et al., 2019; Unsworth et al., 2019a) and 
large-scale planting (van Katwijk et al., 2016), has been recommended 
to preserve the ecosystem services of these habitats.

Globally, the diversity of the plankton community has been predicted 
to be affected by warming and related changes (Ibarbalz et al., 2019), 
and these changes are expected in Asia also. Combined effects of high 
temperature, ocean acidification and high light exposure would affect 
important phytoplankton species in the SCS, Thalassiosira pseudonana 
(Yuan et al., 2018) and Thalassiosira weissflogii (Gao et al., 2018b). 
Also in the SCS the phytoplankton-assemblage responses to rising 
temperatures and CO 2 levels were found to differ between coastal and 
offshore waters and the predicted increases in temperature and pCO 2 
may not boost surface-phytoplankton primary productivity (Zhang 
Ocean warming and acidification can affect the functioning and 
ecological services of sedentary molluscs like the bivalves (Guo et al., 
2016; Zhao et al., 2017b; Cao et al., 2018; Zhang et al., 2019c; Liu et al.,

2020b) and gastropods (Leung et al., 2020), and also sea urchins (Zhan 
et al., 2020). The oyster Crassostrea gigas becomes more vulnerable

10
1481Asia  Chapter 10
to disease when exposed to acidification conditions and pathogen 
challenge indicating incapability for supporting long-term viability of 
the population (Cao et al., 2018). More tolerance and benefits to rising 
pCO 2 was observed in clam species like Paphia undulate which has 
been attributed to adaptation to its acidified sediment habitat (Guo 
et al., 2016). Warming boosted the energy budget of marine calcifiers 
like the gastropod Austrocochlea concamerata, by faster shell growth 
and greater shell strength, making them more mechanically resilient 
while acidification negatively affected shell building thereby impacting 
the physiological adaptability (Leung et al., 2020). It is expected that 
there will be transgenerational acclimation to changes in ocean 
acidification in marine invertebrates (Lee et al., 2020b).
Assessment of the potential impacts and the vulnerability in marine 
biodiversity in the Persian Gulf under climate change has suggested a

reduction of up to 35% of initial species richness and habitat loss for 
hawksbill turtles in the southern and southwest parts of the Persian 
Gulf (Wabnitz et al., 2018).
Seaweeds are an important biotic resource capable of capturing carbon 
and used widely as food, medicine and as raw material for industrial 
purposes. Warming and altered pH can affect seaweeds in different 
ways (Gao et al., 2016; Gao et al., 2017; Gao et al., 2018a; Wu et al., 
2019b). Outbreak of intense blooms of species like Ulva rigida (Gao 
et al., 2017) and Ulva prolifera (Zhang et al., 2019 f) have increased due 
to varied factors including climate change. These blooms have created 
huge economic losses in the Yellow Sea affecting local mariculture, 
tourism and the functioning of the coastal and marine ecosystems 
(Zhang et  al., 2019 f). Increased temperature was found to enhance 
the dark respiration and light compensation point of Ulva conglobate,

which thrives in the mid-intertidal to upper subtidal zones, while the 
altered pH showed a limited effect (Li et al., 2020). Elevated temperature 
significantly enhanced growth, photosynthetic performances and 
carbon-use efficiency of Sargassum horneri in both elevated and ambient 
CO 2 levels suggesting that the present greenhouse effect would benefit 
the golden tide blooming macroalgae Sargassum horneri, which might 
enhance both the frequency and scale of golden tide (Wu et al., 2019b).
10.4.3.1 Key Drivers to Vulnerability
The vulnerabilities to disaster in coastal regions with high population 
densities are reported in several studies (Sajjad et al., 2018) that have 
assessed the vulnerabilities of coastal communities along the Chinese 
coast and shown that roughly 25% of the coastline, and more than 
5 million residents, are in highly vulnerable coastal areas of mainland 
China, and these numbers are expected to double by 2100. Husnayaen

et al. (2018) assessed the Semarang coast in Indonesia and showed 
that 20% of the total coastline (48.7 km) is very highly vulnerable. 
Mangroves continue to face threats due to pollution, conversion for 
aquaculture, agriculture, apart from climate-based threats like SLR 
and sea erosion (Richards and Friess, 2016; Romañach et  al., 2018; 
Wang et  al., 2018b; Friess et  al., 2019). Hypersalinity, storm effects 
on sediment deposition, fishery development and land erosion are 
responsible for most of the Sunderban mangrove degradations leading 
to loss of livelihood (Uddin, 2014; Paul, 2017). In the Sunderbans of 
Asia, climate change is expected to increase river salinisation, which 
in turn could significantly negatively impact the valued timber species, Heritiera fomes (Dasgupta et  al., 2017b). Augmented potential for 
honey production is also predicted, which could increase the conflict 
between humans and wildlife (Dasgupta et al., 2017b).

Destruction by natural hazards was found to remove the above-
ground C pool, but the sediment C pool was found to be maintained 
(Chen et al., 2018b). In the Andaman and Nicobar Islands, the 2004 
Indian Ocean tsunami severely impacted mangrove habitats at the 
Nicobar Islands (Nehru and Balasubramanian, 2018), although new 
inter-tidal habitats suitable for mangrove colonisation did develop. 
Mangrove species with a wide distribution and larger propagules 
showed high colonisation potential in the new habitats compared 
with other species (Nehru and Balasubramanian, 2018). Mangrove 
sites in Asia are predominantly minerogenic, so continued sediment 
supply is essential for the long-term resilience of Asia’s mangroves to 
SLR (Lovelock et al., 2015; Balke and Friess, 2016; Ward et al., 2016a; 
Ward et al., 2016b).
10.4.3.2 Observed Impacts
Primary production in the western Indian Ocean showed a reduction 
by 20% during the past six decades, attributed to rapid warming and

ocean stratification which restricted nutrient mixing (Roxy et al., 2016). 
Variation in secondary-production zooplankton densities and biomass 
in the East Asian Marginal Seas affected the recruitment of fishes due 
to mismatch in spawning period and larval-feed availability during the 
last three climate regime shifts (CRS) in the mid-1970s, late 1980s and 
late 1990s, which were characterised by the North Pacific index and 
the Pacific Decadal Oscillation index (Kun Jung et  al., 2017). In the 
western North Pacific, climate change has affected recruitment and 
the population dynamics of pelagic fishes, such as sardine and anchovy 
(Nakayama et al., 2018), and also shifts in the spawning ground and 
extension of the spawning period of the chub mackerel Scomber 
japonicas (Kanamori et al., 2019).
Varied responses to CRS in the China seas have been observed for 
small pelagic fishes (Ma et  al., 2019) and cephalopods (Ichii et  al.,

2017). The winter and summer SSTs have shown evidence of decadal 
variability with abrupt changes from cold to warm in substantial 
association with climate indices to which coastal cephalopods in the 
China seas respond differentially, with some benefiting from warmer 
environments while others respond negatively (Pang et  al., 2018). 
In the western and eastern North Pacific marine ecosystem, it is 
indicated that groundfish may suffer more than pelagic fish (Yati et al., 
2020). Habitat Suitability index models using SST, chlorophyll-a, sea 
surface height anomaly (SSHA) and sea surface salinity (SSS), as well 
as fishing effort, strongly indicate that Neon flying squid is affected 
by interannual environmental variations and undertakes short-term 
migrations to suitable habitat, affecting the fisheries (Yu et al., 2015). 
The 2015–2016 El Niño was found to impact coral reefs of shallower 
regions (depth of 5–15 m) in South Andaman, India, more than those

beyond 20 m (Majumdar et al., 2018). On the southeast coast of India, 
with bleaching largely mediated by the SST anomaly and during the 
recovery period, macroalgae outgrowth has been observed (2.75%) 
indicating impacts on the benthic community (Ranith and Kripa, 2019). 
In the South China Sea, the increase in SST was found to be higher 
than predicted in recent decades, while the pH decreased at a rate of

10
1482Chapter 10 Asia
0.012–0.014 yr–1, more than the predicted level, due to high microbial 
respiratory processes releasing CO 2 (Yuan et  al., 2019). Simulation 
experiments have shown differential adaptation capacity of common 
species (Zheng, 2019; Yuan et al., 2019).
The UN’s (2019) report on climate action and support trends highlights 
that the impacts of climate change on coastal ecosystems are mainly 
increased risks due to flooding, inundation due to extreme events, 
coastal erosions, ecosystem processes and, in the case of fisheries, 
variations in population or stock structures due to ocean circulation 
pattern, habitat loss degradation and ocean acidification. Analysis of 
data on the occurrence of varied natural hazards from 1900 to 2019 
has shown that tropical cyclones, riverine floods and droughts have 
increased significantly, and the impacts of these events on coastal 
communities are also severe and destructive. The UN’s average score

for SDG Goal 14 (Life Under Water) for Asia was estimated as 46 
among the scores of 40 nations, and the Ocean Health Biodiversity 
index was comparatively high (average 87.9); however, the indices 
show that more region-specific action plans are required to achieve 
the UN 2030 goal for Life Under Water.
Apart from the human impacts, the ecology and resource abundance 
of coastal waters have been found to be impacted by extreme events. 
During tropical cyclones ecological variations, like lowering of SST, an 
increase in chlorophyll-a  and a decrease in oxygen (Chacko, 2019; 
Girishkumar et al., 2019) have been observed. Global analyses of such 
events have indicated that they may have an impact on the fishery directly 
by creating unfavourable ecological conditions and destruction of critical 
habitats indirectly by affecting the eggs and larvae as well as subsequent 
fishery recruitment (McKinnon et al., 2003; Bailey and Secor, 2016). In

the South China Sea in July 2000, during a 3-day cyclone period, an 
estimated thirtyfold increase in surface chlorophyll-a  concentration was 
observed (Lin et al., 2003). The estimated carbon fixation resulting from 
this event alone is 0.8 Mt, or 2–4% of the SCS’s annual new production 
(Lin et al., 2003). Since an average of 14 cyclones pass over this region 
annually, the contribution of cyclones to the annual new production has 
been estimated to be as high as 20–30% (Lin et al., 2003).
10.4.3.3 Projected Impacts
Water pollution and climate stressors have been considered major 
challenges to ecosystem sustainability, and now it has been shown 
that the combined effect these two stressors would be more damaging 
(Buchanan et  al., 2019). For seagrass beds the pollution stress was 
found to increase by 2.6% (from 39.7 to 42.3%) when climate factors 
were added. Assuming the pollution levels remain at the 2014 levels,

different scenarios including RCP2.6 and RCP8.5 were worked out for 
the Bohai Sea, and the results indicated amplification of the impacts 
on the ecosystem. Pollutants like petroleum hydrocarbons, dissolved 
inorganic nitrogen and soluble reactive phosphorus were the major 
pollution stressors (Lu et al., 2018). In the future, policies that focus 
strictly on pollution control should be changed and take into account 
the interactive effects of climate change for better forecast and 
management of potential ecological risks (Lu et al., 2018).
Projected changes in catch potential (in percent) by 2050 and 2100 
relative to 2000 under RCP2.6 and RCP8.5, based on outputs from the dynamic bioclimate envelop model and the dynamic size-based 
food-web models, indicate that the marine and coastal resources of 
most Asian countries will be impacted with varying intensity (FAO, 
2018b).
Better management of resources through projections of resource

distribution, abundance and catch is required; however, lack of data 
(e.g., oceanographic surveys) and scientific knowledge is a constraint 
to this aim (Maung Saw Htoo et al., 2017). Effective forecasts of areas 
of resource abundance based on habitat preference have to be worked 
out for Asian regions.
Modelling and assessment of the vulnerability and habitat suitability of 
the Persian Gulf for 55 species to climate change indicated that there 
is a high rate of risk of local extinction in the southwest part of the 
Persian Gulf, off the coast of Saudi Arabia, Qatar and the United Arab 
Emirates (UAE). Likelihood of reduced catch was observed, and Bahrain 
and Iran were found to be more vulnerable to climate change (Wabnitz 
et al., 2018). Projected changes in fish catches can impact the supply 
of fish available for local consumption (i.e., food security) and exports 
(i.e., income generation) (Wabnitz et  al., 2018). As per (UNESCAP ,

2018a), over 40% of coral reefs and 60% of coastal mangroves in the 
Asia-Pacific region have already been lost, and approximately 80% of 
the region’s coral reefs are currently at risk.
Regionally, the escalation in thermal stress estimated for the different 
global warming scenarios is greatest for Southeast Asia and least 
for the Pacific Ocean (Lough et al., 2018). For the 100 reef locations 
examined here and given current rates of warming, the 1.5°C global 
warming target represents twice the thermal stress they experienced 
in 2016 (Lough et al., 2018).
In the Southeast Asia region threats from both warming and acidification 
has indicated that by 2030, 99% of reefs will be affected, and by 
2050, 95% are expected to be in the highest levels of the ‘threatened’ 
category (Burke et al., 2011), similar to global corals (Frieler et al., 2013; 
Bruno and Valdivia, 2016). Modelling results indicate that even under

RCP scenarios, the functional traits of coral reefs can be affected (van 
der Zande et al., 2020) and coral communities will mainly consist of 
small numbers of temperature-tolerant and fast-growing species 
(Kubicek et al., 2019). Increases in temperature (+3°C) and p CO2 (+400 
matm) projected for this century can reduce the sperm availability for 
fertilisation, which along with adult population decline either due to 
climate change or anthropogenic impacts (Hughes et al., 2017) can affect 
coral reproductive success thereby reducing the recovery of populations 
and their adaptation potential (Albright and Mason, 2013; Hughes et al., 
2018; Jamodiong et al., 2018). In the southern Persian Gulf, increased 
disturbance frequency and severity has caused progressive reduction 
in coral size, cover and population fecundity (Riegl et al., 2018), and 
this can lead to functional extinction. Connectivity required to avoid 
extinctions has increased exponentially with disturbance frequency

and correlation of disturbances across the metapopulation. In the 
Philippines experiments have also proved that scleractinian corals, such 
as A. tenuis, A. millepora and F . colemani, which spawn their gametes 
directly into the water column, may experience limitations from sperm 
dilution and delays in initial sperm–egg encounters that can impact 
successful fertilisation (de la Cruz and Harrison, 2020).

10
1483Asia  Chapter 10
Apart from these threats, natural hazards have also been found to 
affect coral reefs of Asia. The extensive and diverse coral reefs of 
Muscat, Oman, in the northeast Arabian Peninsula were found to have 
long-term effects from Cyclone Gonu, which struck the Oman coast in 
June 2007, more than coastal development (Coles et al., 2015).
Sandy beaches are subject to highly dynamic hydrological and 
geomorphological processes, giving them more natural adaptive 
capacity to climate hazards (Bindoff et  al., 2019). Progress is being 
made towards models that can reliably project beach erosion under 
future scenarios despite the presence of multiple confounding drivers 
in the coastal zone (Chapter 3). Assuming minimal human intervention 
and projected impacts of SLR by 2100 under RCP8.5-like scenarios, 
57–72% of Thai beaches (Ritphring, 2018), at least 50% loss of area on 
around a third of Japanese beaches (Mori, 2018) will disappear.

Marine heatwaves (MHWs) in Asia have been making changes to the 
structure and functioning of coastal and marine ecosystems (Kim and 
Han, 2017; Oliver et al., 2017; Frölicher and Laufkötter, 2018; Oliver 
et  al., 2019; Smale et  al., 2019), affecting resources like copepods 
(Doan et al., 2019) and coral reefs (Zhang et al., 2017c). Coral reefs 
of the southeast Indian Ocean have been affected by MHWs (Zhang 
et al., 2017c).
Simulation of RCP scenarios have shown that continued warming can 
drive a poleward shift in distribution of the seaweed Ecklonia cava 
of Japan, and under the lowest-emissions scenario (RCP2.6) most 
populations may not be impacted, but under the highest-emissions 
scenario (RCP8.5) the existing habitat may become unsuitable and it 
can also increase predation by herbivorous fishes (Takao et al., 2015).
10.4.3.4 Adaptation Options
The UN (2019) has identified establishment of protected areas, restoring

ecosystems like mangroves and coral reefs, integrating coastal-zone 
management practices, sand banks and structural technologies, and 
implementing local monitoring networks for increasing adaptive 
capacity and protecting the biodiversity of the coastal ecosystem. 
In Asia, management of marine sites by earmarking protected areas 
(SDG 14) has been found to be low with only 27% of areas being 
protected. In India, detailed CCA guidelines for coastal protection and 
management has been prepared considering various environmental 
and social aspects (Black et  al., 2017). The Ocean Health index for clean waters was also low (54.6), and the threat to the ecosystem due 
to the combined effects of pollution and climate change was high. 
Table 10.2 shows the ocean and MPAs.
Conservation and restoration of mangroves were found to be effective 
tools for enhancing ecosystem carbon storage and an important part 
of Reducing Emissions from Deforestation and forest Degradation plus

(REDD+) schemes and climate-change mitigation (Ahmed and Glaser, 
2016). In East Asia, restoration success has been attributed to choosing 
the right geomorphological locations (Van Cuong et al., 2015; Balke 
and Friess, 2016) and co-management models (Johnson and Iizuka, 
2016; Veettil et al., 2019).
In South Asia, restoration programmes have been largely successful 
(Jayanthi et  al., 2018) but in some regions partly a failure due to 
inappropriate site selection, poor post-planting care and other issues 
(Kodikara et al., 2017). Using remote sensing it has been observed that 
there are high recovery rates of mangroves in a relatively short period 
(1.5 years) after a powerful typhoon, indicating that natural recovery 
and regeneration would be a more economically and ecologically 
viable strategy. Better mangrove management through mapping is 
suggested (Castillo et al., 2018; Gandhi and Jones, 2019). Statistical

tools developed for modelling biomass and timber volume (Phan et al., 
2019), and allometric models to estimate above-ground biomass and 
carbon stocks (Vinh et al., 2019), will be useful in estimating stocks in 
mangroves. Future mangrove loss may be offset by increasing national 
and international conservation initiatives that incorporate mangroves, 
such as the SDGs, Blue Carbon, and Payments for Ecosystem Services 
(Friess et al., 2019). Since seagrass meadows and marine macroalgae 
are important habitats capable of combating impacts of climate 
change, the need for a global networking system with participation of 
stakeholders has been suggested (Duffy et al., 2019).
10.4.4 Freshwater Resources
In Asia, freshwater resources, an important component of ecosystem 
services, are widely used for agriculture, domestic, irrigation, navigation, 
energy and industry. Freshwater availability is changing at the global 
scale because of unsustainable use of surface water and groundwater,

pollution and other environmental changes. These changes in space 
and time, directly or indirectly, affect water-use sectors and services 
(Wheater and Gober, 2015; Rodell et  al., 2018). About 82% of the Table 10.2 |  Status of ocean health and mean of marine protected areas (MPA)a
Ocean Health 
index: Clean 
waters (0–100)Fish stocks over -
exploited or 
collapsed (%)Ocean Health 
index: Fisheries 
(0–100)Fish caught by 
trawling (%)Ocean Health 
index: Biodiversity 
(0–100)Mean MPA (%)
Eastern Asia 54.0 29.1 49.5 39.8 89.6 32.5
Southeast Asia 54.1 28.5 54.9 34.7 84.6 25.0
Western Asia 54.3 28.3 46.2 20.4 89.4 18.3
Southern Asia 50.3 17.4 51.0 15.1 88.3 41.2
Northern Asia 91.6 55.4 57.6 60.0 93.4 30.0
Asia (whole) 54.6 26.9 50.3 27.3 87.9 27.0
(a) Data are from Sachs et al. (2018).

10
1484Chapter 10 Asia
global population served by freshwater provisions from upstream 
areas are exposed to high threat (Green et al., 2015). Given that some 
of the fastest-growing economies in the world are in Asia, and the 
geographies of development are highly uneven, both CIDs and non-
climate drivers, such as socioeconomic changes, have contributed to 
water stress conditions in both water supply and demand in diverse 
sub-regions of Asia. In the case of Asia, therefore, the entanglement 
between the non-climate drivers and CIDs makes it difficult to attribute 
environmental changes—both present and projected—neatly and 
exclusively to CIDs.
Immerzeel et  al. (2020) have ranked all mountain-dependent water 
towers according to their water-supplying role and the downstream 
dependence of ecosystems, societies and economies. The resulting 
Global Water Tower index indicates that the upper Indus basin is both 
the most important and the most vulnerable water tower unit (WTU)

in the world. A WTU is defined as ‘the intersection between major river 
basins and a topographic mountain classification based on elevation 
and surface roughness’. Whereas all important transboundary WTUs 
in Asia remain highly vulnerable, it is the Indus WTU (inhabited 
by approximately 235 million people in the basin in 2016 (which is 
projected to increase by 50% by the middle of 21st century) where the 
average annual temperature is projected to increase by 1.9°C between 
2000 and 2050, with wide-ranging consequences and trans-sectoral 
spillovers. The Indus WTU faces a deep risk produced by a combination 
of factors including water stress, ineffective governance, hydropolitical 
tensions, population growth and density, urbanisation and social 
transformations, with a significant bearing on SDG 6 on water, SDG 2 
on food and SDG 7 on energy.
10.4.4.1 Key Drivers
Across Asia and its various sub-regions, the key drivers behind an

increasingly inadequate supply of freshwater resources, affecting the 
livelihood security of millions, are varied, complex and intersect with 
multiple social, cultural, economic and environmental stressors (Luo 
et al., 2017; Tucker et al., 2015; Kongsager et al., 2016). Water stress 
has been defined as the situation ‘when the demand for water exceeds 
its supply, during a certain period of time, or when poor quality restricts 
its use’(Felberg et al., 1999; see also Figure 4.32 in Lee et al., 2021a).
Freshwater resources in Asia, which include both surface water and 
groundwater, are considerably strained, and changing climate is 
likely to act as a major stress multiplier (Dasgupta et al., 2015; Fant 
et al., 2016; Gao et al., 2018c; Mack, 2018). In Southern and Eastern 
Asia (SEA) nearly 200  million people are at risk of serious water-
stressed conditions. Effective mitigation might reduce the additional 
population under threat by 30% (60  million people), but still there

is a 50% chance that 100  million people across SEA might face a 
50% increase in water stress and a 10% chance that water stress will 
almost double in the absence of wide-ranging, multi-scalar adaptive 
measures (Gao et al., 2018c). With Millennium Development Goal 7c, 
which aimed to halve the population that had no sustainable access to 
water and basic sanitation before 2015, not having been fully realised, 
and Sustainable Development Goal 6 on water and sanitation not 
having been effectively operationalised, the water stress is likely to 
increase by the end of 2030 (Weststrate et al., 2019).In Asia and elsewhere the interplay between the challenge of 
sustainability and climate change poses major policy challenges (von 
Stechow et al., 2016). The pursuit of SDG 6—protection and restoration 
of water-related ecosystems, universal and equitable access to safe 
and affordable drinking water for all, improvement in water quality by

reducing pollution, elimination of dumping and significant reduction 
in release of hazardous chemicals and materials, and treatment of 
waste water through recycling and safe reuse globally—could be 
directly or indirectly challenged and undermined by climate change 
(Parkinson et al., 2019). Dissolved organic materials from sewage can 
enhance CO 2 emissions, especially in rapidly urbanising river systems 
which receive untreated waste water and/or sewage across developing 
countries (Kim et al., 2019b). Conversely, policy interventions aimed 
at significant augmentation in water-use efficiency across all sectors, 
ensuring sustainable withdrawals and supply of freshwater to address 
water scarcity and a significant reduction in the number of victims of 
water scarcity, especially the poor and marginalised, could mitigate 
vulnerabilities caused by climate change. More interdisciplinary 
research is needed on highly precarious future pathways and the

intersection between CIDs and non-climate drivers in order to 
anticipate and mitigate diverging and uncertain outcomes.
10.4.4.2 Sub-regional Diversity
According to a quantitative scenario assessment for future water 
supply and demand in Asia to 2050, based on global climate change 
and socioeconomic scenarios (Satoh et  al., 2017), water demand in 
sectors such as irrigation, industry and households will increase by 30–
40% around 2050 in comparison with 2010. Water stress is likely to be 
more pronounced in Pakistan, and northern parts of India and China. 
By mid-21st Century, the international transboundary river basins of 
Amu Darya, Indus, Ganges could face severe water scarcity challenges 
due to climatic variability and changes acting as stress multipliers 
(high confidence). Within a country as well, the water scarcity could 
be exacerbated, such as in India and China, due to various drivers like 
population increase and climate change. Research on the differentiated

impacts of climate change on freshwater sources across the Asian 
sub-regions remains inconclusive and requires assessment at the sub-
regional scale (IPCC, 2014b; Wester et al., 2019).
10.4.4.3 Observed Impact
The climate-change impact on different parts of freshwater ecosystems 
(Section 10.4.2) has affected water supply in various sub-regions of 
Asia. While headwater zones are susceptible to change in snow cover, 
permafrost and glaciers, the downstream plain areas of these river 
systems are vulnerable to the increasing high demand of freshwater 
which will affect water availability in space and time. The observed 
impact of climate change has also been seen in direct physical losses 
such as precipitation (Mekong Delta), floods (Vietnam) and saltwater 
intrusion leading to low agricultural productivity (Mora et al., 2018; 
Almaden et al., 2019a; Pervin et al., 2020).
The HKH region extends 3,500 km from Afghanistan in the west to

Myanmar in the east. It is a source of major river systems originating in 
Asia, supporting livelihoods, energy, agriculture and the ecosystem for 
240 million people in the mountains and hills and 1.65 billion people

10
1485Asia  Chapter 10
500 km
Kabul
NuDaduIndus
Shiqua
nChena
bHelmandZa HuangPeshawar
SrinagarXining
LhasaPopulation size
in main citiesWater Stress 
in the Hindu 
Kush-Himalaya 
region
Boundary of the
Hindu
province
Low
(<10%)Low to medium
(10–20%)Medium to high
(20–40%)High
(40–80%)Extremely high
(>80%)Arid and 
low water use
Baseline water stress< 50,000
1,000,000–5,000,00050,000–100,000
100,000–250,000
250,000–500,000
500,000–1,000,00 0
Boundary of
mayor river
basins
Figure 10.5 |  Water stress in the Hindu Kush Himalaya (HKH) region according to Wester et al. (2019) and Hu and Tan (2018).
in the plains (Sharma et al., 2019). The HKH region stores about half 
of the ice mass in HMA, provisioning freshwater to almost 869 million 
people in the Indus, Tarim, Ganges and Brahmaputra river basins. 
While the warming climate increases the melt-water runoff enhancing 
water supply, it is indeed at the cost of glacier-mass reduction that

would eventually reduce melt water and impact the people’s livelihood 
downstream in the future (Nie et al., 2021). The melt runoff from the 
region plays an important role in downstream agriculture such as in the 
case of Indus where two-thirds of total irrigation withdrawal is from 
melt runoff in the pre-monsoon season (Biemans et al., 2019). Changes 
in cryosphere and other environmental changes have already impacted 
people living in high-mountain areas and are likely to introduce new 
challenges for water, energy and food security in the future (Borodavko 
et al., 2018; Adler et al., 2019; Bolch, 2019; Hoelzle et al., 2019; Rasul 
and Molden, 2019; Shen et al., 2020).
With climate-change impacts resulting in the shrinking and melting 
of snow, ice, glacier and permafrost, and correspondingly causing 
an increase in melt water, the incidences of flash floods, debris flow, 
landslides, snow avalanches, livestock diseases and other disasters

in the HKH region have become more frequent and intense. Some of 
the key factors that get in the way of assigning confidence levels to 
climate-change impacts include lack of sufficient observed data on 
factors such as river discharges, precipitation and glacier melt (You 
et al., 2017). Climate-change impacts cryospheric water sources in the 
Hindu Kush, Karakoram and Himalayan ranges which, in turn, carry 
consequences for the Indus, Ganges and Brahmaputra basins.The combined impacts of climate change and non-climate drivers 
on hydrological processes and water resources in transboundary 
rivers in diverse regions of Asia were well noted in AR5. In Central 
Asia, withdrawal is approximately equal to water availability, with 
Turkmenistan and Uzbekistan as the most water-stressed countries 
in the region (Karthe et  al., 2017; Russell, 2018). A study on water 
availability in mainland South Asia has pointed in the direction of

decreasing precipitation trends in recent years, which have also 
contributed to the increasing incidence and severity of droughts (Liu 
et al., 2018b). There are reports of increase in occurrence and severity of 
different forms of droughts in the Koshi River basin (Central Himalaya) 
(Wu et al., 2019a; Hamal et al., 2020; Dahal et al., 2021; Nepal et al., 
2021). Figure 10.5 shows the water stress in the HKH region. The water 
stress is relatively higher in the western region compared with the 
central and eastern regions.
Climate change is also having an impact on stream flows. The changes 
in snowmelt water can explain 19% of the variations in rivers of arid 
regions like Xinjiang, China (Bai et  al., 2018) (medium confidence), 
and the 10.6% of the runoff of the upper Brahmaputra River was 
contributed by snow during 2003–2014 (Chen et al., 2017c) (medium 
confidence). A recent study (Chen et al., 2018 f) has shown that with

the average temperature after 1998 being 1.0°C higher than that 
during 1960–1998 in the Tienshan Mountains, the process of glacier 
shrinkage and decreases in snow cover are causing earlier peak 
runoff and aggravated extreme hydrological events, affecting regional 
water availability and adding to the future water crisis in Central 
Asia. The magnitude and frequency of flooding have increased across

10
1486Chapter 10 Asia
the Himalayan region, such as in the Tarim basin in China (Zhang 
et  al., 2016c) and the higher Indus, Ganges and Brahmaputra, in 
the past six decades (Elalem and Pal, 2015). The latter also reported 
the highest number of flood disasters and greater spatial coverage 
in recent decades as compared with previous decades. In the Middle 
Yellow River basin, which has become much warmer and drier, climate 
variability accounts for 75.8% of streamflow decrease during 1980–
2000, whereas during 2001–2016, change in land use and cover 
was the main factor in streamflow decrease, accounting for 75.5% 
of the decline (Bao et al., 2019). The changes in hydrological regime 
and extreme floods cause changes in river morphology and the river 
channel system which impact water availability.
In China, a quantitative assessment based on a multi-model dataset 
(six global hydrological models driven by three observation-based

global forcings) during 1971–2010 suggested that climate variability 
dominated the changes in streamflow in more the 80% of river 
segments, while direct human impact dominated changes mostly in 
northern China (Liu et al., 2019b). In the Lancang-Mekong River basin, 
climate variability would have contributed 45% more flood occurrences 
in the middle of the basin, while reservoir operation reduced it by 36% 
during 2008–2016 as compared with 1985–2007 (Yun et al., 2020).
In western China, the total annual snow mass declines at a rate of 
3.3 × 109 pg per decade (p < 0.05), which accounts for approximately 
0.46% of the mean of annual snow mass (7.2 × 1011 pg). The loss 
could be valued in terms of replacement cost at 0.1 billion CN¥ (at 
the present value) every year (1 USD = 7 CN¥) compounded over the 
past 40 years (Wu et al., 2021). In the Mekong River Delta in Vietnam, 
climate-change impacts include a 30% annual increase in rainfall,

shifting rainfall patterns, an average temperature increase of 0.5°C 
over the past 30 years and an average SLR of 3 mm yr–1 over the past 
three decades, resulting in a greater flooding threat (Wang et  al., 
2021a).
A recent study (Wang et al., 2021b) has shown that during 1936–2019, 
due largely to intensified precipitation induced by a warming climate, 
the streamflow of the Ob, Yenisei and Lena rivers has increased by 
∼7.7, 7.4 and 22.0%, respectively. While rising temperatures can 
reduce streamflow via evapotranspiration, it can enhance groundwater 
discharge to rivers due to permafrost thawing. In permafrost-
developed basins, the thawing permafrost will continue to result in 
increased streamflow. However, with further permafrost degradation 
in the future, the positive effect of permafrost thaw on streamflow 
would probably be offset by the negative effect of the increase in 
basin evapotranspiration. This could result in a situation where runoff

reaches threshold level and then declines. This is clearly marked in the 
Ob River basin, which is characterised by the highest precipitation, 
whereas in the case of the Yenisei and Lena rivers, further research is 
needed.
The HKH region is susceptible to floods and related hazards caused 
by a cloud burst and other landscape-based processes such as glacial 
lake outburst floods, which can seriously damage property, lives and 
infrastructure (Shrestha et  al., 2010). The likely increased frequency 
of hazards caused by abnormal glacier changes, such as the glacier 
collapses happened on two glaciers in western Tibetan Plateau in 2016 (Kääb et al., 2018), and also surges which were frequently found 
in this vast region (e.g. Bhambri et al., 2017; Mukherjee et al., 2017; 
Ding et  al., 2018), threatening the security of the local and down 
streaming societies (high confidence).The total amount and area of 
glacier lakes increased during last decade (Zhang et al., 2015; Chen

et al., 2017c) (high confidence). Himalayan rivers are frequently hit by 
catastrophic floods caused by the failure of glacial lakes (Cook et al., 
2018; Ahluwalia et al., 2016). In Kedarnath, India (western Himalaya), 
a flash flood was triggered by glacier lake outburst flood (GLOF) 
released from the Chorabari glacial lake in June 2013 which caused 
extensive flooding, erosion of riverbanks and damage to downstream 
villages and towns, as well as the loss of several thousand lives (Rafiq 
et al., 2019; Das et al., 2015). Nepal has experienced 24 GLOF events 
which have caused considerable loss of life and damage to property 
and infrastructure (Icimod, 2011). There is high confidence that current 
glacier shrinkages have caused more glacial lakes to form in most 
mountainous regions, including HMA, but there is limited evidence 
that the frequency of GLOF has changed (Hock et  al., 2019). Veh 
et al. (2018) reported no clear trend of increasing GLOF events in the

Himalayan region, although the southern Himalaya was identified as 
a hotspot region compared with the western Himalaya. Research has 
shown a decrease in glacier area of 24% in Nepal between 1980 and 
2010 (Bajracharya et al., 2014).
Climate-change impacts on both the quantity and quality of 
freshwater resources will hinder the attainment of SDG-6 (Water, 
2020). Contamination of drinking water is caused by wildfires and 
drought that contribute to elevated levels of nutrients (nitrogen, 
phosphorus and sulphates), heavy metals (lead, mercury, cadmium 
and chromium), salts (chloride and fluorides), hydrocarbons, pesticides 
and even pharmaceuticals. Heavy rains and flooding also increase 
nutrients, heavy metals and pesticides, as well as turbidity and faecal 
pathogens in water supplies–especially when sewage treatment plants 
are overwhelmed by runoff (Mora et al., 2018). Pharmaceuticals and 
personal-care products (from source to disposal) are contributing to

the vulnerability of urban waters. A study of vulnerability assessment 
of urban waters in highly populated cities in India and Sri Lanka, 
through analysing the concurrence of Pharmaceuticals and Personal 
Care Products (PPCPs), enteric viruses, antibiotic-resistant bacteria, 
metals, faecal contamination and antibiotic resistance genes (ARGs), 
also underlines the need for a resilience strategy and action plan (Rafiq 
Adequate water supply for various uses is crucial for millions of 
people living in the mountains of Asia. Particularly in the HKH region, 
mountain springs play an important role in generating stream flow for 
non-glaciated catchments and in maintaining dry-season flows across 
many watersheds (Scott et al., 2019; Stott and Huq, 2014). There is a 
good deal of evidence that the springs are drying up or yielding less 
discharge (Tambe et al., 2012; Tiwari and Joshi, 2014; Sharma et al.,

2016), threatening local communities who depend on spring water for 
their lives and livelihoods. Some of the main reasons for drying springs 
include anthropogenic impacts (deforestation, exploitative land use), 
infrastructure (road construction), socioeconomic changes (increasing 
demand and modernisation of facilities) and climatic changes (changes 
in rainfall regime and higher temperature) (Stott and Huq, 2014; Tiwari 
and Joshi, 2014; Sharma et al., 2016).

10
1487Asia  Chapter 10
The Ganges–Brahmaputra region also faces the threat increased 
frequency of flood events (Lutz et al., 2019). Floods and extreme events 
can impact river channel systems (Grainger and Conway, 2014). One of 
the challenges in South Asia is the shifting boundaries of river channels. 
For instance, the major floods on the Indus in July 2010 altered the 
river’s course in Pakistan, moving it closer to the Indian district of Kutch 
(Grainger and Conway, 2014). In the eastern tributary of the Ganges 
system, the alluvial fan of the Koshi River basin has shifted by more 
than 113 km to the west in the past two centuries (Chakraborty et al., 
2010), which may be due to heavy sediment load from the Himalayan 
rivers in which about 50 million tons of sediment is deposited annually 
in the alluvial plains (Sinha et al., 2019; Chakraborty et al., 2010).
Asia is no exception to the global trend of lake ecosystems, which

provide drinking water to millions of people, being degraded (Jenny 
et  al., 2020) and severely threatened at the same time by climate 
change (Mischke, 2020). Lake surface conditions, such as ice cover, 
surface temperature, evaporation and water level react dramatically 
to this threat, and there are negative implications for water quantity 
and quality, food provisioning, recreational opportunities and 
transportation (Woolway et  al., 2020). Due to substantial regional 
variability, the quantum of future changes in lake water storage 
remains uncertain. A recent study (Liu et al., 2019a) using Moderate 
Resolution Imaging Spectroradiometer 500-m spatial resolution global 
water product data, and applying the least squares method to analyse 
changes in the area of 14 lakes in Central Asia from 2001 to 2016, has 
shown that the area-shrinkage changes for all plains lakes in the study 
region could be attributed to climate change and human activities.
10.4.4.4 Projections

Asian and global water demands for irrigation, despite geographic 
variation in terms of water availability, are very likely to surpass 
supply by 2050 (Chartres, 2014). A regional quantitative assessment 
(Lutz et al., 2019) of the impacts of 1.5°C versus 2°C global warming 
for a major global climate-change hotspot–the Indus, Ganges and 
Brahmaputra river basins (IGB) in South Asia–shows adverse impacts 
of climate change on agricultural production, hydropower production 
and human health. A global temperature increase of 1.5°C with respect 
to pre-industrial levels would imply a ≈ 2.1°C temperature increase 
for IGB, whereas under a 2.0°C global temperature increase scenario, 
these river basins would warm by ≈ 2.7°C. Future warming is expected 
to further increase rain-on-snow events that can cause snowmelt flood 
during winter (Ohba and Kawase, 2020), affecting hydropower and 
resulting in river flooding, avalanches and landslides.

In the Mekong River Delta (in Vietnam), with an area of 40,500 km2 
and home to 17.8 million people in 2018, climate change is projected 
to increase the average temperature by 1.1–3.6°C, and the maximum 
and minimum monthly flow are projected to increase and decrease, 
respectively, and are likely to result in a high risk of food during the 
wet season and water shortages during the dry season (Wang et al., 
2021a).
Researchers have found that the southern Tibetan Plateau has been 
consistently melting from 1998–2007 and is projected to continue 
melting until 2050 (Lutz et  al., 2014b) (high confidence). In HMA, glacier ice is projected to decrease by 49 ± 7% and 64 ± 5% by the 
end of the century under RCP4.5 and RCP8.5 scenarios, respectively 
(Kraaijenbrink et  al., 2017). Local- and regional-scale projections 
suggest that peak water will generally be reached around the middle 
of the century, followed by steadily declining glacier runoff thereafter

(Hock et al., 2019). A global-scale projection suggests that a decline 
in glacier runoff by 2100 (RCP8.5) may reduce basin runoff by about 
10% for at least 1 month of the melt season (Huss and Hock, 2018). 
Significantly, research on climate change and its impact across Asia 
remains inconclusive and requires an assessment at the sub-regional 
scale (IPCC, 2014a; Wester et al., 2019).
There is a projection of an increase in runoff until the 2050s mainly 
due to an increase in precipitation in the upper Ganges, Brahmaputra, 
Salween and Mekong basins, where it could be due to accelerated 
melting in the upper Indus basin. The runoff could increase in the 
range of 3–27% (7–12% in Indus, 10–27% in Ganges and 3–8% in 
Brahmaputra) by mid-century compared with the reference period 
(1998–2008) for Himalayan river basins depending on the different 
RCP scenarios (Lutz et  al., 2014a). Likewise, Khanal et  al. (2021) 
suggest contrasting responses to climate change for HMA rivers in

which, on the seasonal scale, the earlier onset of melting causes a 
shift in magnitude and peak of water availability, whereas on the 
annual scale, total water availability increases for the headwaters. The 
future flow would increase in Nepal’s Central Himalaya region (Nepal, 
2016; Ragettli et al., 2016; Bajracharya et al., 2018). These changes in 
water availability in space and time will have serious consequences 
in downstream water availability for various sectoral uses and 
ecosystem functioning in Asia (Nepal et al., 2014; Green et al., 2015; 
Arfanuzzaman, 2018; Wijngaard et al., 2018; Rasul and Molden, 2019); 
however, future water availability is largely uncertain due to significant 
variation in climate-change projections among different global climate 
models (Nepal and Shrestha, 2015; Lutz et al., 2016; Li et al., 2019a).
A recent study (Didovets et al., 2021) covering eight river catchments 
having diverse natural conditions within Central Asia, where water

availability or scarcity is also a major developmental concern, and 
using the eco-hydrological model SWIM (including scenarios from five 
bias-corrected GCMs under RCP4.5 and RCP8.5) has show an increase 
in mean annual temperature in all catchments for both RCPs to the 
end of the 21st century. The projected changes in annual precipitation 
indicate a clear trend to increase in the Zhabay and decrease in the 
Murghab catchments, and for other catchments, they were smaller. 
Both the projected trends for river discharge and precipitation show an 
increase in the northern and decrease in the southern parts of the study 
region, whereas seasonal changes include a shift in the peak of river 
discharge up to one month, a shortening of the snow accumulation 
period and a reduction in discharge during the summer months.
The intensity and frequency of extreme discharges are very likely to 
increase towards the end of the century. The future of the upper Indus

basin water availability is highly uncertain in the long term due to 
uncertainty surrounding precipitation projections (Lutz et  al., 2016). 
The future hydrological extremes of the upper Indus, Ganges and 
Brahmaputra river basins suggest an increase in the magnitude of 
extremes towards the end of the 21st century by applying RCP4.5 and 
RCP8.5 scenarios, mainly due to an increase in precipitation extremes

10
1488Chapter 10 Asia
(Wijngaard et  al., 2017). In the Brahmaputra, Ganges and Meghna, 
including the downstream component, the runoff is projected to 
increase by 16, 33 and 40%, respectively, under the climate-change 
scenarios by the end of the century during which the changes in 
runoff are larger in the wet seasons than the dry seasons (Masood 
et  al., 2015). In the Mekong River basin also, extremely high-flow 
events are likely to increase in both magnitude and frequency, which 
can exacerbate flood risk in the basin (Hoang et al., 2016); however, 
uncertainty is high regarding future hydrological response due to large 
variation in precipitation projections, modelling approaches and bias-
correction methods (Nepal and Shrestha, 2015; Lutz et  al., 2016; Li 
Current research on the adverse relationship between climate change 
and river flows suggests that there is a high possibility that some of the

river basins affected by floods could be Brahmaputra, Congo, Ganges, 
Lena and Mekong, with a return period of 10 years (Best, 2018).
In most parts of the upper Ganges and Brahmaputra rivers, the 50-
year return level flood is likely to increase and to a lesser degree 
in Indus River. Similarly, the extreme precipitation events are also 
expected to increase to a higher degree in the Indus than the Ganges 
and Brahmaputra basins (Wijngaard et al., 2017). Increase in extreme 
precipitation events is likely to cause more flash-flood events in the 
future (medium confidence). In the case of the Indus, increasing 
temperature trend in the future may lead to accelerated snow and ice 
melting which may increase the frequency and intensity of floods in 
the downstream areas (Hayat et al., 2019). The Ganges–Brahmaputra 
region also faces the threat of increased frequency of flood events 
(Lutz et al., 2019). Additionally, the Ganges basin also shows a higher

sensitivity to changes in temperature and precipitation (Mishra and 
Lilhare, 2016).
Assessing the impact of climate change on water resources in nine 
alpine catchments in arid and semiarid Xinjiang of China (Li et  al., 
2019a), it has been noted that even though the total discharge revealed 
an overall increasing trend in the near future, the impact of climate 
change on different hydrological components indicated significant 
spatio-temporal heterogeneity in terms of the area, elevation and 
slope of catchments, which could be usefully factored into climate-
adaptation strategies.
It was noted early on (Singh et al., 2011) that the main drivers that 
influence the provisioning of ecosystem services and human well-
being in the HKH region are a mix of environmental change in general 
and climate change in particular, but much more data and knowledge 
on the HKH region are needed in order to develop either a regional or 
global understanding of climate-change processes.

Climate change impacts cryospheric water sources in the Hindu Kush, 
Karakoram and Himalayan ranges which in turn carry consequences 
for the Indus, Ganges and Brahmaputra basins. The impact of climate 
change on spring-fed rivers in the HKH is under-researched and 
therefore makes projections difficult. Further research is needed for 
understanding the impact of deforestation, urbanisation, development 
and introduction of water infrastructures, such as tube wells, in the 
hill region (Aayog, 2017). This in turn calls for greater investment in research and development for the HKH by both the national and 
regional organisations. There is high confidence that due to global 
warming, Asian countries could experience an increase in drought 
conditions (5–20%) by the end of this century (Prudhomme et  al., 
2014; Satoh et al., 2017).
Soil erosion in high-mountain areas is particularly sensitive to climate 
change. A recent study (Wang et al., 2020) that focused on the mid-

Yarlung Tsangpo River, located in the southern part of the Tibetan 
Plateau, has revealed dramatic land surface environment changes due 
to climate change during recent decades. It has further shown that 
increasing precipitation and temperature would lead to increasing soil-
erosion risk in ~2050 based on the Coupled Model Intercomparison 
Project (CMIP5) and RUSLE models.
High-resolution climate-change simulations suggest that due to deadly 
heatwaves projected in some of the densely populated agricultural 
regions of South Asia (i.e., the Ganges and Indus river basins), 
those regions are likely to exceed the critical threshold of wet-bulb 
temperature of 35°C under the business-as-usual scenario of future 
GHG emissions (Im et al., 2017).
10.4.4.5 Climate Vulnerability and Adaptation: Interfaces and 
Interventions
In Asia and its diverse sub-regions, the challenge of adaptation to 
climate change at diverse sectors, sites and scales of vulnerability in the

domain of freshwater resources is compounded by the nexus between 
long-standing non-climatic vulnerabilities and climatic impacts, both 
observed and projected. Water insecurities in Asia are increasing due 
to excessive freshwater withdrawals (Satoh et al., 2017), economic and 
population growth (Gleick and Iceland, 2018), urbanisation and peri-
urbanisation (Roth et al., 2019), food insecurity (Demin, 2014) and lack 
of access to clean and safe drinking water (Cullet, 2016), which mostly 
affects the health of the most vulnerable members of society.
Significantly, climate change will add to already existing vulnerabilities. 
In the case of the Yellow River basin in China, underlining the interface 
between future water scarcity and hydroclimatic and anthropogenic 
drivers, a recent study expects moderate-to-severe water scarcity 
over six Yellow River sub-catchments under the RCP4.5 scenario, and 
anticipates that human influences on water scarcity will be worse than

that of climate change, with water availability in the downstream being 
impacted by concurrent changes in land use and high temperature (Omer 
et  al., 2020). Nearly 8% of internationally shared or transboundary 
aquifers (TBAs), ensuring livelihood security for millions of people 
through sustaining drinking water supply and food production, are 
currently overstressed due to human overexploitation (Wada and 
Heinrich, 2013). The Asia Pacific region has the highest annual water 
withdrawal due to its geographic size, growing population and irrigation 
practices, and water for agriculture continues to consume 80% of the 
region’s resources (Taniguchi et al., 2017b; Visvanathan, 2018).
In South Asia, surface water and groundwater resources are already 
under stress (both in terms of quality and quantity) due to population 
growth, economic development, poor governance and management, 
and poor efficiency of use in economic production. In the past 40 years,

10
1489Asia  Chapter 10
there has been an increasing reliance on groundwater in South Asia 
for irrigation (Rodell et al., 2009; Tiwari et al., 2009; Surie and Prasai, 
2015; Bhanja et al., 2016; Shrestha et al., 2016; Mukherjee, 2018; Shah 
et  al., 2018). It is noteworthy that India, Bangladesh, Pakistan and 
China together account for more than 50% of the world’s groundwater 
withdrawals (Scott et al., 2019). A study conducted in the Shahpur and 
Maner districts of Bihar, India, in which drinking water sourced from 
the groundwater of 388 households was tested, showed that 70–90% 
of the sampled household’s drinking water contained either arsenic 
or iron, or both (Thakur and Gupta, 2019). Given the nexus between 
CIDs and non-climate drivers, an effective adaptation to the impacts 
of climate change would also demand sustainable development and 
management of shared aquifer resources, which in turn require reliable 
TBA inventories and improved knowledge production and knowledge

sharing on the shared groundwater systems (Lee et al., 2018a).
A study of peri-urban spaces involving four South Asian cities, Khulna 
(Bangladesh) (Pervin et al., 2020), Gurugram and Hyderabad (India), 
and Kathmandu (Nepal), has shown the nexus between intensifying use 
and deteriorating quality of water and the impact of climate change, 
resulting in peri-urban water insecurity and conflict (Roth et al., 2019). 
The challenge of ensuring access to water resources and their (re)
allocation and prioritisation for marginalised communities remains on 
the agenda of policy-oriented interdisciplinary research and demands 
effective implementation of its findings at the grassroots level by the 
administrative agencies. Taking water security as a key CCA goal at the 
urban-city scale of Bangkok, a study (Babel et al., 2020) has shown the 
usefulness of a generic framework with 5 dimensions, 12 indicators 
and a set of potential variables to support national-level initiatives and

plans in diverse climatic and socioeconomic conditions across various 
sub-regions of Asia.
In the Kathmandu valley in Nepal, where groundwater resources are 
under immense pressure from multiple stresses, including overextraction 
and climate change, mapping groundwater resilience to climate change 
has been demonstrated as a useful tool to understand the dynamics 
of groundwater systems, and thereby facilitate the development of strategies for sustainable groundwater management (Shrestha et al., 
2020).
In the Mekong Delta, the groundwater storage is projected to decline by 
more than 120 and 160 million m3 under RCP4.5 and RCP8.5 scenarios, 
respectively, by the end of the 21st century, in conjunction with land 
subsidence and SLR. This in turn calls for proactive planning and 
implementation of adaptation strategies that address multiple stresses 
in order to ensure sustainable utilisation of groundwater resources in

the Mekong Delta in the context of future climatic conditions and 
associated uncertainties (Wang et al., 2021a). Proposed CCA strategies 
for the Mekong River basin include a better understanding of the 
complex linkages between climate change, technological interventions, 
land-use change, water-use change and socioeconomic developments 
both in the upstream and downstream riparian countries (Evers and 
Pathirana, 2018).
While South Asian countries have done well in attaining Goal 6 of 
Sustaining Development Goals, access to safe and clean drinking 
water remains a challenge. Taking Indian rivers as an example, it is 
suggested that participatory river protection and rehabilitation, based 
on comprehensive knowledge of the river-system dynamics, and local 
awareness at the community level, may act as a multiplier for river 
conservation measures (Nandi et al., 2016).
Hydroclimatic extremes in the HKH region could adversely impact

the Ganga, Brahmaputra and Meghna basins (Wijngaard et al., 2017; 
Acharya and Prakash, 2019). Studies have recommended watershed 
or basin analysis to address the challenge of adaptation  in urban 
spaces (Lele et al., 2018). A study of northern Bangladesh that focused 
on encouraging traditional ways of cultivation suggests that rural 
women have Indigenous knowledge and their participation can play 
a useful role (Kanak Pervez et al., 2015). The knowledge pertains to 
agriculture, soil conservation, fish and animal production, irrigation 
and water conservation. There has also been a focus on gendered 
construction of local flood-forecasting knowledge in rural communities 
in India living in the Gandak River basin (Acharya and Prakash, 2019). Box 10.4 | Case Study on Climate Vulnerability and Cross-Boundary Adaptation in Central Asia
In Central Asia, water scarcity has been ranked in the top five global risks (Gleick, 1993; Zhupankhan et al., 2018). Cross-boundary

adaptation remains critically important in this region with abundant glaciers in the Pamir Plateau of Tajikistan (Hu et al., 2017) and areas 
with severe glacier retreat in the Tianshan Mountains (Liu and Liu, 2015). The spatial variations of glacier and other climate variables 
have added to uncertainty related to the dynamic of the water cycle. The headwater regions, such as Pamir area, would be significantly 
affected by the climate parameters, such as the stronger rainfall intensity, more frequent rainfall and higher temperature (Luo et al., 
2019). The water resources in the Pamir Plateau will range from −0.48 to 5.6% (Gulakhmadov et al., 2020), and the crop phenological 
period in Tajikistan and Kyrgyzstan will be about 1–2 weeks earlier. The threat of agricultural water stress is increasing as well. The oasis in 
downstream areas will face more complex water resource fluctuations, water crisis and desertification. In particular, rain-fed agriculture in

northern Kazakhstan, Uzbekistan and western Turkmenistan is particularly dependent on water resources. Under the RCP2.6 and RCP4.5 
scenarios, considering CO 2 fertilisation effects and land-use projections, the increase in CO 2 atmospheric concentration and accumulated 
temperature can contribute to a 23% increase in cotton yield in Central Asia (Tian and Zhang, 2019), but extreme climate, such as 
drought, heatwaves and rainstorms, will have a 10% negative impact on agricultural production and the ecological environment (Zhang 
and Ren, 2017). High-efficiency water-saving technology will help the upstream and downstream water resource management in Central 
Asian countries to adapt to the variation in water resources quantity, frequency and spatial pattern.

10
1490Chapter 10 Asia
While designing the adaptation options, understanding the water–
energy–food (WEF) nexus among different water-use sectors is crucial 
(Section 10.5.3). Understanding of the WEF nexus could be beneficial 
for achieving water security in developing countries in Asia (Nepal 
AR5 identified a number of adaptation challenges and options 
facing the stakeholders in the wake of climate-change-induced 
vulnerabilities, uncertainties and risks in the freshwater sector, and 
underlined the importance of an integrated management approach as 
well as acknowledging diverse socioeconomic contexts, differentiated 
capacities and the uneven pace of impacts. Further validated by recent 
research in terms of their usefulness, these adaptation options include 
building and improving capital-intensive physical water infrastructure 
such as irrigation channels, flood-control dams and water storage 
(Nüsser and Schmidt, 2017). Drawing upon customary institutions

and combining Indigenous knowledge systems with scientific 
knowledge, innovative structures, including artificial glaciers, ice 
stupas and snow barrier bands, have been built by local communities 
in Ladakh, Zanskar and Himachal Pradesh in India (Hock et al., 2019; 
Nüsser et al., 2019). Communities in Solukhumbu, Nepal, in response 
to depleting water flow in snow-fed rivers, have chosen adaptation 
through changing practices by collecting water from distant sources 
for domestic consumption (McDowell et al., 2013). Taking the IPCC 
concept of climate risk as a basis for adaptation planning, a pilot 
study of flood risk in Himachal Pradesh, India (Allen et  al., 2018), 
integrating assessment of hazard, vulnerability and exposure in the 
complementary domains of CCA and DRR, has identified stakeholder 
consultation, knowledge exchange and institutional capacity building 
as key steps in adaptation planning. Aquifer storage and recovery

has been proposed as an ‘alternative climate-proof freshwater 
source’ for deltaic regions in Asia, particularly those with a history of 
saline groundwater aquifers (Hoque et al., 2016). It is further argued 
(Hadwen et al., 2015) that water, sanitation and hygiene objectives 
would need to be addressed as a component of a wider integrated 
water resource management (IWRM) framework.
Ensuring sustainability of the rivers and ecosystems requires 
coordinated and collaborative action on the part of all countries, with 
the long-term goal of synergising political, social, cultural and ecological 
facets associated with the riverine system. Daunting as this challenge 
is, evidence suggests that a long-term view of transboundary basins 
is not very optimistic as big rivers of Asia contribute heavily towards 
urban and agricultural activities, and are experiencing challenges of 
increasing sedimentation, large-scale damming and pollution, among

others (Best, 2018). In the case of China, Sun et al. (2016) have shown 
that the localised vulnerabilities within the Yangtze River basin prompt 
an ‘integrated basin-wide approach’ that is able to account for the 
specific needs of each of its sub-basins.
In HMA, factors that undermine effective adaptation to climate change 
include both sudden-onset and slow-paced disasters along with the 
knowledge deficit regarding cryospheric change and its adverse 
impacts on water resources and also the agriculture and hydropower 
sectors. Other key barriers include a sectoral approach, overemphasis 
on structural approaches and the lack of context-sensitive, community-
centric understanding of how these changes influence perceptions, options and decisions about migration, relocation and resettlement 
(Rasul et al., 2020; Hock et al., 2019). More interdisciplinary research 
is needed on highly precarious future pathways and the intersection

between CIDs and non-climate drivers in order to anticipate and 
mitigate diverging and uncertain outcomes.
10.4.5 Agriculture and Food
Asia accounts for 67% of global agricultural production (Mendelsohn, 
2014) and employs a large portion of the population in many 
developing countries and regions (Briones and Felipe, 2013; ADB, 
2017b; ILO, 2017a). Since the release of IPCC AR5, more studies 
have reinforced the earlier findings on the spatio-temporal diversity 
of climate-change impacts on food production in Asia depending 
on the geographic location, agroecology and crops grown (Hoegh-
Guldberg et al., 2018; Ahmad et al., 2019), recognising that there are 
winners and losers associated with the changing climate across scales 
(Dasgupta et  al., 2013a; Yong-Jian et  al., 2013; Bobojonov and Aw-
Hassan, 2014; Hijioka et al., 2014; Li et al., 2014a; Prabnakorn et al., 
2018; Trisurat et al., 2018; Matsumoto, 2019). Despite the observed

increase in total food production in terms of crops and food yields 
from 1990 to 2014 in Asia (FAO, 2015), there is high confidence that 
overall, at the regional level, the projected total negative impacts will 
far outweigh the expected benefits, with India emerging as the most 
vulnerable nation in terms of crop production (Figure  10.6). Recent 
evidence also indicates that climate-related risks to agriculture and 
food security in Asia will progressively escalate as global warming 
reaches 1.5°C and higher above pre-industrial levels (IPCC, 2018b) 
with differentiated impacts across the Asian continent.
10.4.5.1 Observed Impacts
There remains a paucity of data for observed climate-change impacts 
on Asian agriculture and food systems since the release of IPCC AR5. 
Most of these impacts have been associated with drought, monsoon 
rain and oceanic oscillations, the frequency and severity of which 
have been linked with the changing climate (Heino et al., 2018; Heino

et al., 2020). In general, major impacts to agricultural production, such 
as those observed by the farmers in the Philippines and Indonesia, 
include among others delays in crop harvesting, declining crop yields 
and quality of produce, increasing incidence of pests and diseases, 
stunted growth, livestock mortality and low farm income (Stevenson 
et al., 2013). In South Asia, the series of monsoon floods from 2005 to 
2015 contributed to a high level of loss in agricultural production with 
peaks in 2008 and 2015 (FAO, 2018a). Similarly, in Pakistan, farmers 
are experiencing a decline in crop yields and increasing incidence 
of crop diseases as a result of climate extremes, particularly floods, 
droughts and heatwaves (Fahad and Wang, 2018; Ahmad et al., 2019).
Limited studies have quantified the actual impacts of climate change 
on agricultural productivity and the economy. In a study in the Mun 
River basin, northeast Thailand, yield losses of rice due to past climate

trends covering the period 1984–2013 was determined to be in the 
range of < 50 kg ha–1 per decade or 3% of actual average yields with 
a high possibility of more serious yield losses in the future (Prabnakorn 
et al., 2018). Likewise, in China, an economic loss of 595–858 million

10
1491Asia  Chapter 10
USD for the corn and soybean sectors was computed from 2000 to 
2009 (Chen et al., 2016b). On the other hand, the intensive wheat–
maize system in China seems to have benefited from climate change 
with the northward expansion of the northern limits of maize and 
multi-cropping systems brought about by the rising temperatures (Li 
and Li, 2014).
There is high agreement in more recent studies that linked the 
frequency and extent of the El Niño phenomenon with global warming 
(Thirumalai et  al., 2017; Wang et  al., 2017a; Hoegh-Guldberg et  al., 
2018) that can trigger substantial loss in crop and fishery production. 
The 2004 El Niño caused the Philippines an 18% production loss during 
the dry season and a 32% production loss during the wet season 
(Cruz et al., 2017). In the 2015 El Niño event, the Indian oil sardine 
fishery declined by more than 50% of previous years (Kripa et  al., 
2018) severely impacting coastal livelihoods and economies (Shyam

et  al., 2017). The 2015–2016 El Niño also inflicted adverse impacts 
on agricultural productivity and food security, especially affecting the 
rural poor in middle- and lower-income countries in Southeast and 
South Asia (UNDP ESCAP OCHA RIMES APCC, 2017).
10.4.5.2 Projected Impacts
10.4.5.2.1 Fisheries and aquaculture
The fisheries and aquaculture production from Asia in 2019 was 
estimated at 159.67 mmt contributing to 74.7% of the global 
production (FAO, 2020). This sector provides employment to an 
estimated 50.46  million people where fishing and aquaculture are 
important socioeconomic activities and fish products are a substantial 
source of animal protein (Bogard et al., 2015; Azad, 2017; FAO, 2018c). 
The economic contribution could be as high as 44% of the coastal 
communities’ GDP as in the case of Sri Lanka (Sarathchandra et al., 
2018). Five Asian countries (i.e., China, Indonesia, India, Vietnam 
and Japan) are in the top ten of global fish producers, representing a

cumulative share of 36% in 2018 (FAO, 2020). As a top producer with 
15% global share, China also remains a top exporter of fish and fish 
products with 14% global market share.
There is high agreement in the literature that Asian fisheries and 
aquaculture, including the local communities depending on them for 
livelihoods, are highly vulnerable to the impacts of climate change. 
Asia has been impacted by SLR (Panpeng and Ahmad, 2017), a 
decrease in precipitation in some parts (Salik et  al., 2015) and an 
increase in temperature (Vivekanandan et al., 2016), all of which have 
drastic effects on fisheries and aquaculture (FAO, 2018c). Its coastal 
fishing communities is exposed to disasters, which are predicted to 
increase (Esham et  al., 2018). Fisheries in most of South Asia and 
Southeast Asia involve small-scale fishers who are more vulnerable 
to climate-change impacts compared with commercial fishers (Sönke

Kreft et al., 2016; Blasiak et al., 2017), although there is a general 
decreasing trend in the number of small units (Fernandez-Llamazares 
et al., 2015; ILO, 2015). A regional study of South Asia forecast large 
decreases in potential catch of two key commercial fish species (hilsa 
shad and Bombay duck) in the Bay of Bengal (Fernandes et al., 2016), 
which forms a major fishery and food source for coastal communities. 
About 69% of the commercially important species of the Indian marine fisheries were found to be impacted by climate change and 
other anthropogenic factors (Dineshbabu et  al., 2020). Likewise, 
water salinisation brought about by SLR is expected to impact the 
availability of freshwater fish in southwest coastal Bangladesh with 
adverse implications to poor communities (Dasgupta et al., 2017a). 
Analysis of fishery has indicated that there will be a continued 
decrease in catch impacting the seafood sector in the Philippines,

Thailand, Malaysia and Indonesia (Nong, 2019). Climate change is 
predicted to decrease total productive fisheries potential in South and 
Southeast Asia, driven by a temperature increase of approximately 
2°C by 2050 (Barange et al., 2014).
Like fisheries, Asian aquaculture is highly vulnerable to climate 
change. Shrimp farmers and fry catchers of Bangladesh are 
frequently affected by extreme climatic disruptions like cyclones and 
storm surges that severely damage the entire coastal aquaculture 
(Islam et al., 2016a; Kais and Islam, 2018). The majority of shrimp 
farmers also observed that weather has changed abruptly during 
the past 5  years and that high temperature is most detrimental 
because it lowers growth rate, increases susceptibility to diseases, 
including deformation, and affects production (Islam et al., 2016a). 
Low production in shrimp farming is also attributed to variation and 
intensity of rainfall perceived by the majority of farmers as part of

climate-change impacts (Ahmed and Diana, 2015; Islam et al., 2016a; 
Henriksson et al., 2019). In Vietnam, small-scale shrimp farmers are 
likewise vulnerable to climate change, although those who practise 
an extensive type of farming with low inputs are more vulnerable 
compared with those who practise a more intensive type with more 
capital investment (Quach et al., 2015; Quach et al., 2017). Seaweed 
farming in Asia is very popular, and the significance of seaweed 
aquaculture beds in capturing carbon is recognised, but most of the 
farmed seaweeds are susceptible to climate change (Chung et  al., 
2017a; Duarte et al., 2017).
Marine heatwaves are a new threat to fisheries and aquaculture 
(Froehlich et al., 2018; Frölicher and Laufkötter, 2018) including disease 
spread (Oliver et al., 2017), live feed culture (copepods) (Doan et al., 
2018) and farming of finfishes like Cobia (Le et al., 2020). Predicting 
MHWs is considered a prerequisite for increasing the preparedness of

farmers (Frölicher and Laufkötter, 2018). In Southeast Asian countries 
more than 30% of aquaculture areas are predicted to become 
unsuitable for production by 2050–2070 and aquaculture production 
is predicted to decrease 10–20% by 2050–2070 due to climate change 
(Froehlich et al., 2018).
10.4.5.2.2 Crop production
Since IPCC AR5, more studies have been done on different scales from 
local to global that focus on the differentiated projected impacts of 
climate change on the production and economics of various crops 
with rice, maize and wheat among the major crops receiving more 
attention. New research findings affirm that climate-change impacts, 
and will continue to significantly affect, crop production in diverse 
ways in particular areas all over Asia (Figure  10.6). An increasing 
number of sub-regional and regional studies using various modelling 
tools provide significant evidence on the overall projected impacts of

climate change on crop production at the sub-regional and regional

10
1492Chapter 10 Asia
Projected impacts of climate change to agriculture and food systems in sub-regions of Asia IPCC-AR5 studies
South East Asia
Direction of change: Decrease Incresse+Prec.Projected
year
2080
2050 & 2100
2080
2080
2080–2099
2080 & 2100
2080Commodity
Rice
Rice
Rice
Crops
Livestoc kImpact on
yield
45%
4%
14%
10%
2.6%
22.7%5.5–8.5%Country/
Region
Cambodia
Philippines
SE Asia
(5 countries)NE ThailandNW Vietnam
Vietnam+1°C
+1°C
+1°CTemp.
+East Asia
Aquaculture
2030 2.3%
8.62%
10–30%
25–70%
5–17%India
South IndiaRajasthan
(India)South Asia
Rice/Wheat/
Pluses/
Coarse
cereals
Wheat/
Barley/
Maize
Rice
Maize
Rice
RiceHyderabao
(India)
North IndiaIndia
Bangladesh Rice
Wheat
Rice
Rice
Tea
Rice
Wheat
Wheat
FisheriesSri Lanka
South Asia
Bangladesh/
India/Pakistan
South India
(other than
India/Pakistan2050
2050
+1°C,
+2°C, +4°C
2030
12.4–61%
RiceBhutan
Med latitude
Bhutan
Med latitude12.1–17%
6.7%
12.6%2050
2050
3.6–19.8%
8–17% 2050–2070

2040 Up to 5%
2040 5 to 10%
2040 5–7%
Trishuli River,
NepalPrec.Projected
year
2100CommodityImpact on
yieldCountry/
Region
ChinaTemp.
Maize
Grain
Grain
MaizeGrain
Crops
GrainCorn
Soya
WheatGrain
NE China
South China
North China
SW China
MongoliaNorth China+100 mm
+100 mm
+30 mm
+100 mm
+100 mm
+30 mm+1.32°C
+1.32°C
+
Seaweed/
Fish/
Shrimp/
AbaloneRice
Maize
FisheriesRice
PotatoJapan
Rep. of Korea
(Yellow Sea)
(Strait)Fisheries
D.P.R. of
Korea
RCP8.5 RCP6.0 RCP4.5 No RCP 
analysis+1°C
+1°C3–12%
7–19%
17%
0.59–1.19%
3%
22%0.1–0.52%1.31%2100
2100
End of 
1.74%
3%
2030
20302030
>25%
10–20%
30%End of 
End of 
End of 
1.9%West
Kazakhstan
semi-arid & 
sub-humi d
regionWheat
Barley
Wheat
Crops
General
Potato
Cotton
Wheat4.8%
CropsTajikistan
KyrgyztanIncreaseNegative impact (80–157%)
Decrease
No change
Increase+1°C+1 mmIncrease export by 0.7%

Decrease import by 1.7%
Increase export by 21.9 %
Decrease import by 49.4 %
Figure 10.6 |  Projected impacts of climate change to agriculture and food systems in sub-regions of Asia based on post-IPCC-AR5 studies. The figure illustrates 
the spatio-temporal diversity of projected future impacts on food production highlighting that there are winners and losers associated with the changing climate at different scales. 
AGRI: agriculture; E: east; N: north; NRCP: no RCP analysis; Pre: precipitation; PY: production yield; RCP: representative concentration pathway; S: south; Temp: temperature; W: west. 
(Refer to Table SM10.2 for details and supporting references.)

10
1493Asia  Chapter 10
scales with clear indications of winners and losers among and within 
nations (see, for instance, Mendelsohn, 2014; Cai et al., 2016; Chen 
et al., 2016b; Schleussner et al., 2016).
Beyond the usual research interest in crop yields which has dominated 
the current literature, recent studies, such as those in Japan, focus on 
the impacts of climate change on the quality of crops (see, for instance, 
Sugiura et  al., 2013, for apple; as well as Morita et  al., 2016, and 
Masutomi et al., 2019, for rice). A large-scale evaluation by Ishigooka 
et al. (2017) shows that the increased risk in rice production brought 
about by temperature increase may be avoided by selecting an 
optimum transplanting date considering both yield and quality. More 
studies of this nature have to be conducted for other crops in different 
locations to better understand and adapt to the negative impacts of 
the changing climate on the quality of crops (Ahmed and Stepp, 2016).

New studies have projected the likely negative impact of pests in 
Asian agriculture. The golden apple snail (Pomacea canaliculate), 
which is among the world’s 100 most notorious invasive alien species, 
threatens the top Asian rice-producing countries, including China, India, 
Indonesia, Bangladesh, Vietnam, Thailand, Myanmar, the Philippines 
and Japan, with the predicted increase in climatically suitable habitats 
in 2080 (Lei et al., 2017). Similarly, a study by (Shabani et al., 2018) 
in Oman projected that the pest of date palm trees, Dubas bug 
(Ommatissus lybicus Bergevin), could reduce the crop yield by 50% 
under future climate scenarios.
While there is general agreement that CO 2 promotes growth and 
productivity of plants through enhanced photosynthesis, there 
remains uncertainty on the extent to which carbon fertilisation will 
influence agricultural production in Asia as it interacts with increasing

temperatures, changing water availability and the different adaptation 
measures employed (Ju et al., 2013; Jat et al., 2016; ADB, 2017b). As 
global warming compounds beyond 1.5°C, however, the likelihood of 
adverse impacts on agricultural and food security in many parts of 
developing Asia increases (Mendelsohn, 2014; IPCC, 2018b). There 
is a growing trend towards more integrated studies and modelling 
that combines biophysical and socioeconomic variables (including 
management practices) in the context of changing climate to reduce 
uncertainty associated with future impacts of climate change on the 
agriculture sector (see, for instance, Mason-D’Croz et al., 2016; Smeets 
Kristkova et al., 2016; Gaydon et al., 2017).
10.4.5.2.3 Livestock production
There is hardly any mention about the impacts of climate change 
on livestock production in the Asia chapter of AR5 due to limited 
studies on this area. This scarcity of information persists to the current

assessment with very scant information on the projected impacts and 
adaptation aspects of livestock production (Escarcha et  al., 2018a). 
The use of scenarios and models to determine alternative futures 
with participatory engagement processes has been recommended 
for informed policy and decision making with potential application 
in the livestock sector (Mason-D’Croz et  al., 2016). Of the limited 
assessment available, a study on the smallholders’ risk perceptions 
of climate change impacts on water-buffalo production systems in 
Nueva Ecija, the Philippines, identified feed availability and animal health as the production aspects most severely affected by multiple 
weather extremes (Escarcha et  al., 2018b). In the Mongolian Altai 
Mountains, early snowmelt and an extended growing season have 
resulted in reduced herder mobility and prolonged pasture use, which 
has in turn initiated grassland degradation (Lkhagvadorj et al., 2013a).

Furthermore, reduced herder mobility has increased the pressure on 
forests resulting in increased logging for fuel and construction wood 
and reduced regeneration due to browsing damage by increasing goat 
populations (Khishigjargal et al., 2013; Dulamsuren et al., 2014).
In terms of direct impacts, climate-change-induced heat stress and 
reduced water availability are likely to generally have negative effects 
on livestock (ADB, 2017b). In the HKH region, climate change has 
induced severe impacts on livestock through degradation of rangelands, 
pastures and forests (Hussain et  al., 2019). However, indirect effects 
may be positive such as in Uzbekistan and South Asia where alfalfa 
and grassland productivity is expected to improve under warming 
conditions, which have beneficial effects on livestock production 
(Sutton et al., 2013; Weindl et al., 2015).
At the global level, analysis involving 148  countries in terms of

the potential vulnerability of their livestock sector to climate and 
population change shows that some Asian nations, particularly 
Mongolia, are likely to be the most vulnerable while South Asia is the 
most vulnerable region (Godber and Wall, 2014).
10.4.5.2.4 Farming systems and crop areas
There is new evidence since AR5 that farming systems and crop areas 
will change in many parts of Asia in response to climate change. In 
South Asia, a study in Nepal showed that farmers are inclined to 
change practices in cropland use to reduce climate-change risk (Chalise 
and Naranpanawa, 2016). In India, climate change is also predicted to 
lead to boundary changes in areas suitable for growing certain crops 
(Srinivasa Rao et  al., 2016). A study in Bangladesh revealed a shift 
in crop choices among farmers, implying changes in the future rice-
cropping pattern. Specifically, a temperature increase will compel 
farmers to choose irrigation-based Boro, Aus and other crops in favour

of the rain-fed Aman rice crop (Moniruzzaman, 2015).
In the coastal area of Odisha in India, adverse impact on the agriculture 
sector is anticipated considering the increasing temperature trends 
over the past 30 years for all the seasons (Mishra and Sahu, 2014). In a 
national study that groups Bangladesh into 16 sub-regions with similar 
farming areas, simulations of a 62 cm rise in mean sea level project 
damages to production because of area loss in excess of 31% in sub-
region 15 and nearly 40% in sub-region 16 (Ruane et al., 2013). Also 
in Bangladesh, a study on predicting the design of water requirements 
for winter paddy rice under climate change conditions shows that 
agricultural water resource management will help minimise drought 
risk and implement future agricultural water resource policies (Islam 
et al., 2018) that may have important implications for crop areas and 
production.
In East Asia, the observed changes in agricultural flooding in different

parts of China could influence farming systems and crop areas (Zhang 
et al., 2016b) as extreme events intensify in the context of changing

10
1494Chapter 10 Asia
climate. Agricultural management practice in China may also change 
to optimise soil organic carbon sequestration (Zhang et al., 2016a). A 
study on projected irrigation requirements under climate change using 
a soil-moisture model for 29 upland crops in the Republic of Korea 
showed that water scarcity is a major limiting factor for sustainable 
agricultural production (Hong et  al., 2016). In terms of drought, 
despite increasing future precipitation in most scenarios, crop-specific 
agricultural drought is expected to be a significant risk due to rainfall 
variability (Lim et al., 2019a). On the other hand, a projected rise in 
water availability in the Korean Peninsula using multiple regional 
climate models and evapotranspiration methods indicates that it will 
likely increase agricultural productivity for both rice and corn, but 
would decrease significantly in rain-fed conditions (Lim et al., 2017b).

Thus, irrigation and soil-water management will be a major factor in 
determining future farming systems and crop areas in the country.
Global studies on climate-change-induced hotspots of heat stress on 
agricultural crops show that large suitable cropping areas in Central 
and Eastern Asia, and the northern part of the Indian subcontinent, 
are under heat stress risk under the A1B emissions scenario (Teixeira 
et al., 2013) and hence may reduce cropping areas in these regions. In 
Japan, the projected decline in rice yield in some areas suggests that 
the current rice-producing regions would be divided into suitable and 
unsuitable areas as temperatures increases (Ishigooka et al., 2017), with 
important implications regarding the possible shift in cropping area. 
Similarly, it has been shown that there will be change in the geographic 
distribution of the occurrence of poor skin colour of table-grape berries 
(Sugiura et al., 2019) and suitable areas for cultivation of subtropical

citrus (Sugiura et al., 2014) in Japan by the middle of the 21st century.
There is emerging evidence from modelling and field experimentation 
that designing future farming systems and crop areas that will promote 
sustainable development in Asia in the context of climate change would 
have to incorporate not only productivity and price considerations 
but also how to moderate temperature increase, enhance water 
conservation and optimise GHG mitigation potential (Sapkota et al., 
2015; Zhang et al., 2016a; Ko et al., 2017; Lim et al., 2017b). The effects 
of agricultural landscape change on ecosystem services also need to 
be understood and taken into account in designing farming systems 
and allocating farm areas (Lee et al., 2015b; Zanzanaini et al., 2017).
10.4.5.3 Food Security
FAO (2001) defines food security as ‘a situation that exists when all 
people, at all times, have physical, social and economic access to

sufficient, safe and nutritious food that meets their dietary needs and 
food preferences for an active and healthy life’. There is significant 
evidence that climate change significantly undermines both agricultural 
production and food security in Asia (ADB, 2017b). Increasing evidence 
from sub-regions and individual countries suggests that climate-related 
hazards, such as increasing temperature, changing rainfall, SLR, drought, 
flooding and the more frequent and intense occurrences of El Niño–
Southern Oscillation events, all impact agricultural production with 
significant effects on food security. All these hazards interact with non-
climatic factors, such as competing demand for scarce water resources, 
rural–urban migration, food prices and increasing food demand in the long term, and poor governance, among other things, that may worsen 
food insecurity in the region (Montesclaros and Teng, 2021).
In West Asia, particularly in Saudi Arabia and Yemen, increasing water

scarcity brought about by temperature rise is anticipated to have a 
severe impact on agriculture and food production that undermines 
food security (Al-Zahrani et al., 2019; Baig et al., 2019). Saudi Arabia, 
for instance, was forced to phase out its wheat production starting in 
2016 and fully rely on importation to conserve its drying fossil water 
resources (Al-Zahrani et al., 2019), a situation which is also linked to a 
water governance issue.
In Central Asia, a study using a bioeconomic farm model shows very 
large differences in climate change impacts across farming systems at 
the subnational level. Large-scale commercial farms in the northern 
regions of Kazakhstan will have positive income gains, while small-
scale farms in arid zones of Tajikistan will experience a negative impact 
with likely effects on farm income security (Bobojonov and Aw-Hassan, 
2014). Impacts on farmers’ income in western Uzbekistan will also

significantly vary and could fall by as much as 25% depending on the 
extent of temperature increase and water-use efficiency (Bobojonov 
In a regional study among South Asian countries using an integrated 
assessment modelling framework, changes in rice and wheat 
productions brought about by climate change are anticipated to 
engender wild price volatilities in the markets (Cai et al., 2016). Price 
spikes are projected for 2015–2040 in all South Asian regions with 
India, Pakistan and Sri Lanka predicted to experience increasingly 
much higher rice and wheat prices than under the baseline scenario, 
creating major concerns about food affordability and food security. 
This will likely severely affect the overall economic growth of these 
countries since they are mainly agriculture-driven economies.
A study on mapping global patterns of drought risk projected an 
increase in drought frequency and intensity in the populated areas of

South to Central Asia extensively used for crop and livestock production 
with serious repercussion to food security and potential civil conflict 
in the medium to long term (Carrão et al., 2016). In Southeast Asia, 
a Philippine study on the relationship between seasonal rainfall, 
agricultural production and civil conflict suggests that the projected 
change towards wetter rainy seasons and drier dry seasons in many 
parts of the country will lead to more civil conflict (Crost et al., 2018) 
with negative implications for food and human security. Similarly, 
floods and higher food prices are also associated with higher risks of 
social unrest in Asia that may undermine food security (Hendrix and 
Haggard, 2015; Ide et al., 2021).
Food insecurity will be localised across Asia where one part of the 
country or sub-region will be more food secured while the others, 
more insecure. This will require in-country or sub-regional trade and

development cooperation to minimise the adverse impacts of food 
insecurity associated with the changing climate (Li et al., 2014a; Abid 

10
1495Asia Chapter 10
10.4.5.4  K ey Drivers to Vulnerability
There is high confidence that agriculture will continue to be among

the most vulnerable sectors in Asia in light of the changing climate (Mendelsohn, 2014; ADB, 2017b). Among the more vulnerable areas include mountain agriculture where fluctuation in crop production (Poudel and Shaw, 2016; Hussain et al., 2019), and food insufficiency, is more widespread than in lowland areas (Poudel and Shaw, 2015; Kohler and Maselli, 2009). Also vulnerable are flood-prone areas like the Vietnam Mekong River Delta where 39% of the total rice area is exposed to sustained flood risks (Wassmann et al., 2019a). Increasing temperatures and changing precipitation levels will persist to be important vulnerability drivers that will shape agricultural productivity particularly in South Asia, Southeast Asia and Central Asia as well as in selected areas of the region. With the increasing likelihood of extreme weather events, such as strong typhoons in the Philippines, the agriculture sector in the typhoon-prone areas of Southeast and East Asia, as well as the Indus Delta, will

be more vulnerable to crop destruction (Mallari and Ezra, 2016). Projections on increasing SLR and flooding, such as those in Bangladesh and the Mekong Delta, will submerge and decrease crop production areas and severely affect agriculture and fishery sectors, but will also trigger outmigration from these areas (ADB, 2017b).

Vulnerability of aquaculture-related livelihoods to climate change was 
assessed at the global scale using the MAGICC/SCENGEN climate modelling tools, and Vietnam and Thailand were identified as most vulnerable in brackish-water aquaculture production (Handisyde et al., 2017). China, Vietnam and the Philippines are also ranked highly vulnerable in marine production. Moreover, a recent vulnerability assessment of Korean aquaculture based on predicted changes in seawater temperature and salinity according to RCP8.5 indicated that vulnerability was highest for seaweed, such as laver and sea mustard, while fish, shrimp and abalone are relatively less vulnerable as they are less sensitive to high water temperature and their farming environments are controllable to a large extent (Kim et  al., 2019a). In Indonesia, farming of whiteleg shrimp (Litopenaeus vannamei) has been found to be vulnerable to increased rainfall and temperature decrease (Puspa et al., 2018).

Climate-change-induced vulnerability, however, is complicated by 
non-climate drivers. In Thailand, for instance, a 38% reduction (from 21,486 to 13,328 million at the present value (1 USD = 33.54 THB) in the export values of rice and products in the last quarter of 2011 has been attributed not only to the impact of tropical cyclone Nock-Ten on Thai rice export but also the economic slowdown in Thailand during 2011–2012 (Nara et al., 2014).
Considering the high vulnerability of Asia to climate change as a whole,

there is a need to look at the drivers of vulnerability in an integrated and comprehensive manner. The increasing interest on nexus studies that links climate-change impacts on agriculture with the other sectors like water, energy, land-use change, urbanisation, poverty, economic liberalisation and others (see, for example, Takama et al., 2016; Aich et al., 2017; Eslamian et al., 2017; Duan et al., 2019b) could contribute to a systemwide vulnerability reduction and an important initial step towards a more climate-resilient future.10.4.5.5  Adaptation Options

Since AR5, there has been a surge in the volume of literature that documents and assesses the different adaptation practices already employed in Asian agriculture as well as those that provide future adaptation options. There is robust evidence that a variety of adaptation practices already employed in agriculture and fisheries are valuable in reducing the negative effects of current climate anomalies but may not be sufficient to fully offset the adverse impacts of future climate scenarios. Recent literature, therefore, focuses on how to build on current adaptation initiatives and processes to improve current and future outcomes (Iizumi, 2019).
Asian farmers and fishers already employ a variety of adaptation

practices to minimise the adverse impacts of climate change. In a recent systematic and comprehensive review of farmers’ adaptation practices in Asia, Shaffril et  al. (2018) categorised these practices into different forms such as crop management, irrigation and water management, farm management, financial management, physical infrastructure management and social activities. ‘Climate-smart agriculture’–an integrated approach for developing agricultural strategies that address the intertwined challenges of food security and climate change–is increasingly being promoted in many parts of the region, especially in Southeast and South Asia, with potentially promising outcomes (Chandra et al., 2017; Khatri-Chhetri et al., 2017; Shirsath et al., 2017; Westermann et al., 2018; Wassmann et al., 2019b). Site-specific adaptations, such as those in Pakistan, include farmers’ utilisation of several adaptation techniques which include changing crop type and variety, and improving seed quality;

fertiliser application and use of pesticides, and planting of shade trees; and water storage and farm diversification (Fahad and Wang, 2018), as well as the implementation of comprehensive climate information services for farming communities (World Meteorological Organization, 2017).

Adaptation measures are also beneficial to small-scale fishers and fish 
farmers (Miller et al., 2018), and through fisheries management plans (FMP) and Early warning systems, the Asian region is reducing climate impact (FAO, 2018c). The most common FMPs adopted in different Asian countries are limits to fishing gear, licensing schemes and seasonal closures (ILO, 2015), protection of nursery grounds, providing alternative livelihoods (Azad, 2017), limiting fish aggregating devices (FADs) and introduction of monitoring and control tools (Department of Fisheries (Thailand), 2015). Fishers’ strong sense of belonging to their place of residence and the sense of responsibility to protect the vulnerable fish stock has been advantageously used for developing cooperatives and starting community-based fisheries management (FAO, 2012; ILO, 2015; Shaffril et al., 2017), and these initiatives have yielded positive results.
In aquaculture, most households in shrimp communities rely on process-

oriented multiple coping mechanisms such as consumption smoothing, income smoothing and migration that enhance farmers’ resilience to climate anomalies (Kais and Islam, 2018). Diversification and integration of varied resources and interventions in feed and husbandry are seen to help the aqua farmers increase their profits and overcome the impacts of climate change (Henriksson et al., 2019). Strategies like polyculture, integrated multitrophic aquaculture (IMTA) and recirculating aquaculture

10
1496Chapter 10 Asia
systems (RAS) have been suggested to increase aquaculture productivity, 
environmental sustainability and climate change adaptability (Ahmed 
et  al., 2019c; Tran et  al., 2020). In Bangladesh, several adaptation 
measures, such as integrated community-based adaptation strategies 
(Akber et al., 2017) and integrated coastal zone management (Ahmed 
and Diana, 2015), have been recommended to increase climate resilience 
among shrimp farmers.
More recently, nature-based solutions (NbS) have gained attention 
globally to enhance climate adaptation. In the context of agriculture, 
NbS are seen as cost-effective interventions that can increase resilience 
in food production while advancing climate mitigation and improving 
the environment (Iseman and Miralles-Wilhelm, 2021). Experiences in implementing NbS in agricultural landscapes have been documented 
both in agriculture and fisheries sectors that promote production

while providing co-benefits such as environmental protection and 
sustainability (Miralles-Wilhelm, 2021).
Despite the numerous adaptation measures already employed, there 
is sufficient evidence that farmers’ current adaptation practices 
are inadequate to offset the worsening climate change impacts. A 
more comprehensive approach that integrates economic and social 
strategies with other measures is seen to reduce climate vulnerability. 
For instance, agriculture insurance is viewed as a promising 
adaptation approach to reduce risks and increase the financial 
resilience of farmers and herders in many Asian countries (Prabhakar ASIAN
AGRICULTURE1
65432Adaptation-related strategies in Asian agriculture to enhance current and future adaptations
CREATE ENABLING POLICIES
Create enabling policies (e.g. security of land tenure and agricultural insurance policy)
and enhance institutional capacity (e.g. ef fective extensions services and early warning

system) to support farmers’  adaptation
IMPROVE ADAPTATION PLANNING AND DECISION-MAKING
Improve adaptation planning and decision-making by: 
/g122 Conducting integrated and multi-sectoral adaptationassessment that explores synergies 
 and trade-of fs among energy , GHG emission, wate r, land use, urbanization, and food production
/g122 Recognizing and integrating indigenous and local knowledge and practices ; an d
/g122 Mainstreaming adaptation into ongoing development planning and decision-makin g
PROMOTE SCIENCEBASED ADAPTATION MEASURES
Promote science-based adaptation measures. (e.g. development of drought-tolerant and 
short-maturing varieties, promotion of conservation-agriculture practices and technologies,  
use of weather and climate information to enhance farmers’  adaptation
ADDRESS FARMERS’ ADAPTATION BARRIERS
Address farmers’  adaptation barriers (e.g. labor shortage, insecure land tenure,
limited market access, poor governmental and institutional support, povert y,

lack of access to assets and technology , limited water sources, absence of credit facility ;
inadequate access to near-term and medium-term climate knowledge, etc.) to advance 
transformational adaptionINVEST ON CRITICAL INFRASTRUCTURE
Invest on critical infrastructure such as irrigation and transportation systemsADOPT AN INTEGRATED APPROACH TO IMPROVE ADAPTATION
Adopt an integrated approach to improve adaptation interventions from field-level 
practices and technological interventions, to management strategies shifting crops and 
sowing dates, to enacting adaptation policies and reforming agricultural institutions    
Figure 10.7 |  Adaptation-related strategies in Asian agriculture to enhance current and future adaptations.

10
1497Asia  Chapter 10
et al., 2018; Matheswaran et al., 2019; Nguyen et al., 2019; Stringer 
et  al., 2020). Similarly, participation of multiple stakeholders from 
all relevant sectors at different levels in adaptation planning and 
decision making is seen as an important factor in improving outcomes 
(Arunrat et al., 2017; Hochman et al., 2017; Chandra and McNamara, 
2018). Moreover, while adaptation is local and context specific, the 
following general adaptation-related strategies are distilled from the 
current literature, based on the Asian experience, to enhance current 
and future adaptations (see Figure  10.7 for details or examples of 
each strategy):
• Create enabling policies (Chen et  al., 2018d) and enhance 
institutional capacity (Wang et  al., 2014; Hirota and Kobayashi, 
2019)
• Improve adaptation planning and decision making (Xu and 
Grumbine, 2014; Asmiwyati et al., 2015; Dissanayake et al., 2017; 
Hochman et al., 2017; Qiu et al., 2018; Shuaib et al., 2018; Aryal

et al., 2020b; Ruzol and Pulhin, 2021; Ruzol et al., 2021)
• Promote science-based adaptation measures (Alauddin and Sarker, 
2014; Sapkota et al., 2015; Lim et al., 2017b)
• Adopt an integrated approach to improve adaptation (Teixeira 
et al., 2013; Yamane, 2014; Abid et al., 2016; Sakamoto et al., 2017; 
Sawamura et al., 2017; Trinh et al., 2018)
• Invest in critical infrastructure (Cai et al., 2016; Rezaei and Lashkari, 
2018)
• Address farmers’ adaptation barriers (Alauddin and Sarker, 2014; 
Pulhin et  al., 2016; Fahad and Wang, 2018; Gunathilaka et  al., 
2018; Almaden et al., 2019b)
10.4.6 Cities, Settlements and Key Infrastructures
Cities across Asia have large populations exposed to climate risks but 
also present an opportunity for concerted climate action (Revi et al., 
2014; Chu et al., 2017; Revi, 2017; Khosla and Bhardwaj, 2019) and 
report numerous examples of adaptation actions at various stages 
of planning and implementation (Dulal, 2019; Singh et  al., 2021b).

However, challenges specific (though not exclusive) to Asian cities such 
as uneven economic development, rapid land-use changes, increasing 
inequality, growing exposure to extreme events and environmental 
change, such as land subsidence (with antecedent impacts on people 
and infrastructure), and large, socially differentiated vulnerable 
populations, remain key concerns as Asian cities simultaneously tackle 
challenges of sustainable development and equitable climate action.
10.4.6.1 Sub-regional Diversity
By 2050, urban areas are expected to add 2.5 billion people, 90% of 
whom will be in Asia and Africa (UNDESA, 2018). Critically, this urban 
population increase will be concentrated in India, China and Nigeria, 
with India and China adding 416  million and 255  million urban 
dwellers, respectively, between 2018 and 2050 (UNDESA, 2018).

4 Average annual loss is the average amount that a country could expect to lose each year over the long term due to hazard incidence. It corresponds to the expected average loss per year considering 
all the events that could occur over a long time frame, including very intensive events. It is a probabilistic indication of the direct economic losses expected due to total or partial damage of physical 
assets existing in the affected area (UNISDR, 2017).Asia is home to 54% of the world’s urban population, and by 2050, 64% 
of Asia’s 3.3 billion people will be living in cities. Asia is also home to the 
world’s largest urban agglomerations: Tokyo (37 million inhabitants), 
New Delhi (29 million) and Shanghai (26 million) are the top three with 
Cairo, Mumbai, Beijing and Dhaka home to nearly 20 million people 
each (UNDESA, 2018). By 2028, New Delhi is projected to become the 
most populous city in the world. In certain parts of Asia (e.g., some

cities in Japan and the Republic of Korea), a steep decline in urban 
population is projected, mainly due to declining birth rates (Hori et al., 
2020). Within Asia, rates of urban  isation differ sub-regionally. Eastern 
Asia has seen the most rapid urban growth with the percentage of 
urban population having more than tripled from 18 to 60% between 
1950 and 2015, while rates of urbanisation have decreased in West 
Asia and remained steady in Central Asia (UNDESA, 2018).
Asian cities are seeing growing income inequality, with rural poverty 
being replaced by urban poverty (ADB, 2013). Regional studies show 
high and growing inequality within Indian and Chinese urban areas 
and decreasing rural–urban income gaps in Thailand and Vietnam 
(Baker and Gadgil, 2017; Imai and Malaeb, 2018). Critically, East Asia 
and the Asia–Pacific in general continue to house the world’s largest 
population of slum dwellers at 250 million, with most of them in China,

Indonesia and the Philippines, and the highest rates of urban poverty 
in Papua New Guinea, Vanuatu, Indonesia and the Lao PDR (McIlreavy, 
2015; Baker and Gadgil, 2017). A lot of urbanisation, especially in 
South Asia, is also ‘hidden’ due to poor, competing definitions of what 
is ‘urban’ and limited data (Ellis and Roberts, 2016).
10.4.6.2 Key Drivers of Vulnerabilities
In Asian cities, climatic hazards such as changes in precipitation and, 
during the Asian monsoon, SLR, cyclones, flooding, dust storms, 
heatwaves and permafrost thawing (Byers et  al., 2018; Hoegh-
Guldberg et al., 2018; Rogelj et al., 2018; Shiklomanov, 2019), as well 
as non-climatic vulnerabilities such as non-climatic hazards (e.g., seismic 
hazards), inadequate infrastructure and services, unplanned urbanisation, 
socioeconomic inequalities and existing adaptation deficits (Johnson 
et  al., 2013; Araos et  al., 2016; de Leon and Pittock, 2017; Meerow,

2017; Dulal, 2019) interact to shape overall urban risk (Shaw et  al., 
2016a; Rumbach and Shirgaokar, 2017; Dodman et al., 2019). Caught 
at the intersection of high exposure, socioeconomic vulnerability and 
low adaptive capacities, informal settlements in urban and peri-urban 
areas are particularly at risk (robust evidence, high agreement) (Meerow, 
2017; Rumbach and Shirgaokar, 2017; Byers et al., 2018).
10.4.6.3 Observed and Projected Impacts
10.4.6.3.1 Multi-hazard risk
Of the multi-hazard global average annual loss (AAL)4 of 293  billion 
USD, 170 billion USD (58%) is in the Asia Pacific region (UNISDR, 2017). 
Of the top ten highest AALs associated with multi-hazards, six are in

10
1498Chapter 10 Asia
Asia (Japan, China, Republic of Korea, India, the Philippines and Taiwan, 
Province of China) (UNISDR, 2017). As per Gu et al. (2015), 56% of cities 
with populations greater than 300,000 in 2014 are exposed to at least 
one of the six physical hazards (cyclones, floods, droughts, earthquakes, 
landslides and volcanic eruptions). Cities in areas highly exposed and 
vulnerable to multiple hazards were also the ones that grew rapidly in 
population between 1950 and 2014, implying greater infrastructural 
investments in climate-sensitive areas. Among 27 cities highly exposed 
to multiple disasters, 13 cities had a population of 1 million or more in 
2014. Among them were three megacities, Tokyo (Japan), Osaka (Japan) 
and Manila (the Philippines), with more than 10  million inhabitants 
exposed to three or more hazards. Seven other cities with 1  million 
inhabitants or more in Asia were at high risk of three or more types of

disaster. Manila is highly vulnerable to economic losses and disaster-
related mortality from all six types of disasters. Moscow (Russia) is the 
only megacity not exposed to the risk of any of the six types of physical 
hazards analysed (cyclones, floods, droughts, earthquakes, landslides and 
volcano eruptions). Of the eight megacities most vulnerable to disaster-
related mortality, seven–Tokyo, Osaka, Karachi, Kolkata, Manila, Tianjin 
and Jakarta, totalling 143 million people–are in Asia (Gu et al., 2015).
10.4.6.3.2 Extreme temperatures and heatwaves
Urbanisation and climate change interact to drive an urban heat 
island (UHI) effect across Asian cities (Hauck et al., 2016; Chapman 
et al., 2017; also see Figure 6.4 in Chapter 6). Three regions which are 
expected to see higher maximum wet-bulb temperature than global 
averages are southwest Asia around the Persian Gulf and Red Sea, 
South Asia in the Indus and Ganges river valleys, and eastern China (Im

et al., 2017; Perkins-Kirkpatrick et al., 2020).
Impacts of heatwaves at 1.5°C and 2°C in cities are substantially larger 
than under the present climate (Hoegh-Guldberg et al., 2018). In South 
Asia particularly, more intense heatwaves of longer durations and 
occurring at a higher frequency are projected with medium confidence  
over India (Murari et al., 2015) and Pakistan (IPCC AR6, WGI, Table 11.5; 
Ali et al., 2018; Nasim et al., 2018; Ali et al., 2020; IPCC, 2021). At the city 
level, these projections could translate into significant impacts: at 1.5°C, 
on average, every year Kolkata will experience heat equivalent to the 
2015 record heatwaves; Karachi about once every 3.6 years; and under 
2°C warming, both regions could expect such heat annually (Matthews 
et al., 2017). In Pakistan, Hyderabad is likely to be the hottest city by 
2100 with the highest average temperature reaching 29.9°C (RCP4.5) to 
32°C (RCP8.5) followed by Jacobabad, Bahawalnagar and Bahawalpur

cities (Ali et  al., 2020). The frequency of heatwave days (HWF) will 
increase by 22.8, 22.3 and 26.5 d yr–1 in northern, eastern and western 
Japan, respectively, with megacities such as Tokyo, Osaka and Nagoya 
seeing large increases in HWF and related deaths (Nakano et al., 2013).
In China’s urban agglomerations, an increase in the global warming from 
1.5°C to 2°C is likely to exacerbate the intensity of extreme maximum 
temperature 4.1 times (Yu et al., 2018d). From 1995 to 2014 China’s urban 
agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, Middle 
Yangtze River, Chongqing–Chengdu and Pearl River Delta) experienced 
no more than three heat danger d yr–1, which is projected to increase to 
3–13 d by 2041–2060 and 8–67 d by 2081–2100 under high-emissions 
shared socioeconomic pathways SSP3-7.0 and SSP5-8.5, resulting in approximately 260 million people (19% of the total population of China) 
and 310  million people (39% of the total population), respectively,

facing more than three heat-danger days annually (Zhang et al., 2021). 
This projected risk exposure is reduced under low-emissions pathways 
(SSP1-2.6 and SSP2-4.5), where annual heat-danger days will remain 
similar to current levels or increase slightly (Zhang et al., 2021).
Critically, these projections of higher temperatures will have a significant 
impact on heat-related morbidity and mortality, labour productivity, 
mental health, and health and well-being outcomes across all sub-regions 
of Asia (medium evidence, high confidence) (Pal and Eltahir, 2016; Im 
et al., 2017; Arifwidodo et al., 2019; Arshad et al., 2020). In West Asia and 
the North China Plain especially, extreme wet-bulb temperatures are 
expected to approach, and possibly exceed, the physiological threshold 
for human adaptability (35°C) (Pal and Eltahir, 2016; Kang and Eltahir, 
2018). By the end of the century, under higher projections (RCP8.5),

the daily maximum wet-bulb temperature is expected to exceed the 
survivability threshold across most of South Asia (Im et al., 2017). City-
specific studies articulate what these regional projections will mean for 
urban populations. For example, at 1.5°C warming, without adaptation, 
annual heat-related mortality in 27 major cities across China is projected 
to increase from 32.1 per million inhabitants annually in 1986–2005 to 
48.8–67.1 per million. This number increases to 59.2–81.3 per million for 
2°C warming (Wang et al., 2019a). In the Republic of Korea, deaths from 
heat disorders are expected to increase approximately fivefold under the 
RCP4.5 and 7.2-fold under RCP8.5 by 2060 compared with the current 
baseline value of ~23 people per summer (Kim et al., 2016a). Importantly, 
heat exposure is differentiated within cities: it disproportionally affects 
the poorest populations (Lohrey et  al., 2021) and those with lesser

access to green spaces (Arifwidodo and Chandrasiri, 2020).
10.4.6.3.3 Precipitation extremes: excess rainfall, drought and water 
scarcity
Warming from 1.5°C to 2°C will increase extreme precipitation events 
across Asia especially over East and South Asia (medium evidence, 
high agreement) (Zhang et al. 2018; Supari et al., 2020; Zhang et al., 
2020b). In East and Central Asia, under 1.5°C warming, extreme 1- and 
5-d precipitation will increase by 28 and 15% relative to 1971–2000 
(Zhang et al., 2020b). In China’s urban agglomerations, an increase in 
global warming from 1.5°C to 2°C is likely to increase the intensity of 
total precipitation of very wet days 1.8 times and double maximum 
5-d precipitation (Yu et  al., 2018d). Extreme rainfall has direct and 
increasing consequences on urban flooding risk (Dasgupta et  al., 
2013b), which is further exacerbated by urbanisation trends that 
reduce permeability, divert water flow and disrupt watersheds (Chen

et al., 2015b; Duan et al., 2016).
Urban extent in drylands in expected to increase from 2000 to 2030 
with large expansions in West Asia, Central Asia, South Asia and China 
and antecedent impacts on exposure to drought and water scarcity 
(Güneralp et al., 2015). Urban dryland extent in West Asia will increase 
from 19,400 to 67,400 km2 (Güneralp et al., 2015). In the Haihe River 
basin in China, the proportion of people exposed to droughts at 1.5°C 
(without accounting for population growth) is projected to decrease 
by 30.4% but increase by 74.8% at 2°C relative to people exposed 
in 1986–2005 (339.65 million) (Sun et al., 2017). About 411 million

10
1499Asia  Chapter 10
people living in 330 cities above 300,000 population are exposed to 
drought risk, which include three Asian megacities Delhi (India), Karachi 
(Pakistan) and Kolkata (India). Drought-related economic losses are 
also high in Dhaka (Bangladesh) (Pervin et al., 2020), Istanbul (Turkey), 
Manila (the Philippines) and Shenzhen (China), and Manila is also 
highly vulnerable to drought-related mortality (Gu et al., 2015).
Increasing urban drought risk will also have cascading impacts on 
regions from where water is imported, exacerbating drought exposure 
beyond urban settlements and limiting water availability in certain 
regions (Chuah et al., 2018; Garrick et al., 2019; Zhang et al., 2020c; 
Zhao et al., 2020). There is medium evidence (high agreement) that 
urban water insecurity is experienced differentially based on income, 
risk exposure, and assets, and that urban drought and water scarcity

is causing material and non-material losses and damage (Singh et al., 
2021a). Importantly, in several Asian cities, flood and drought risk 
is expected to occur concurrently, especially in South Asia which is 
projected to see the largest increase in urban land exposed to both 
floods and droughts (25–32% increase in flood and drought risk 
between 2000 and 2030).
10.4.6.3.4 Sea level rise and coastal flooding
Global assessments identify Asia as the most exposed region to SLR 
(see Section CCP2.2.1) in terms of the number of people living in low-
elevation coastal zones and the number of people exposed to flooding 
from 1-in-100-years storm surge events (Neumann et al., 2015; Jevrejeva 
et al., 2016; Kulp and Strauss, 2019; Abadie et al., 2020; Haasnoot, 2021). 
Twelve of the top 20 countries exposed to SLR and associated flood events 
are in Asia, and of these, China, India, Bangladesh, Indonesia and Vietnam 
are estimated to have the highest total coastal population exposure

(Neumann et  al., 2015; Edmonds et  al., 2020). Critically, regardless of 
the emissions scenario, 70% of the global population exposed to SLR 
and land subsidence are in eight Asian countries: China, Bangladesh, 
India, Vietnam, Indonesia, Thailand, the Philippines and Japan (Kulp and 
Strauss, 2019). This is particularly worrisome since in highly populated 
low-lying coastal cities across Asia, it is estimated that land subsidence 
could be as influential as climate-induced SLR over the 21st century (Cao 
et  al., 2021; Nicholls et  al., 2021). In East Asia and the Asia–Pacific in 
general (expected to see 0.2–0.5 m SLR), without adaptation, 1 million 
people (range of 0.3–2.2  million) are projected to be affected by 
submergence under RCP8.5 by 2095. Limiting warming will reduce this 
risk, and under RCP4.5, these numbers of people at risk will be reached by 
2140. However, continuing on RCP8.5 increases risk exposure to 7 million

(estimated range of 2–24  million people) (Haasnoot, 2021). Notably, 
assuming present-day population and adaptation (in the form of existing 
protection standards), East and South Asia already have a large number 
of people at risk of a 1-in-100-years flooding event (63 million) because 
of relatively lower flood protection (except in China and Malaysia).
These global scenarios will have significant impacts on national and 
subnational populations. For example, in Bangladesh, under 0.44 and 
2 m mean SLR, direct inundation is estimated to drive migration of 
5 Guangzhou, Mumbai, Kolkata, Shenzhen, Tianjin, Ho Chi Minh, Jakarta, Chennai, Surat, Zhanjiang, Bangkok, Xiamen and Nagoya0.73–2.1 million people by 2100 (Davis et al., 2018). Such migration 
will have direct development implications: for example, destination 
locations could see additional demands on jobs (594,000), housing 
(197,000) and food (783 × 109 calories) by mid-century as a result of

those displaced by SLR (Davis et al., 2018).
Among the 20 largest coastal cities with the highest flood losses by 
2050, 13 are in Asia5, with a regional concentration in South, Southeast 
and East Asia (Hallegatte et al., 2013). Furthermore, 9 of these cities 
(Guangzhou, Kolkata, Tianjin, Ho Chi Minh City, Jakarta, Zhanjiang, 
Bangkok, Xiamen, Nagoya) also have an additional risk of subsidence 
due to SLR and flooding (Hallegatte et al., 2013). Guangzhou, China, 
is estimated to be the most economically vulnerable city in the world 
to SLR by 2050, with estimated losses of 254 million USD yr–1 under 
0.2 m SLR (Jevrejeva et al., 2016). With a 2°C warming, Guangzhou 
is expected to see SLR of 0.34 m; under 5°C warming, this number 
would rise to 1.93 m. A more recent estimate calculates expected 
damage in Guangzhou due to SLR under RCP8.5 to reach 331 billion 
USD by 2050 and 420 billion USD under the high-end scenario with

figures doubling by 2070. By 2100, expected damage could reach 1.4 
trillion USD under RCP8.5 and 1.8 trillion USD under the high-end 
scenario. Similarly, in Mumbai (India) SLR damages amount to US$ 
112–162 billion by 2050 and could increase by a factor of 2.8–2.9 by 
2070 (Abadie et al., 2020). In coastal cities such as Bangkok and Ho 
Chi Minh City, projected land subsidence rates, mainly due to excessive 
groundwater extraction, are comparable to, or exceed, expected rates 
of SLR, resulting in an additional 0.2 m SLR by 2025 (Jevrejeva et al., 
2016). In Shanghai, current annual damage by coastal inundation is 
estimated at 0.03% of local GDP; under RCP4.5, this increases to 0.8% 
by 2100 (uncertainty range of 0.4–1.4%) and is further exacerbated by 
land subsidence and socioeconomic development (Du et al., 2020). It 
is important to note that these projections assume (a) no adaptation 
and (b) that damage repairs are undertaken and completed annually.

Given these assumptions, while these estimates communicate the 
scale of projected impacts, they are indicators of possible damages in 
the absence of adaptation and not actual projections.
The SLR affects economic growth, its drivers and welfare outcomes 
(Hallegatte, 2012; Pycroft et al., 2016; Lee and Asuncion, 2020) through 
(a) permanent loss of land and natural capital, (b) loss of infrastructure 
and physical capital, (c) loss of social capital and migration, (d) 
temporary floods, food insecurity and loss of livelihoods and (e) added 
expenditure for coastal protection. Without adaptation, direct damage 
to the GDP by 2080 due to SLR would be highest in Asia (robust 
evidence, medium agreement), with China losing between 64.2 billion 
USD (under A1B of 2.4°C by the 2050s and 3.8°C by the 2090s at 
0.47 m), 95.8 billion USD (under the RAHM scenario of 1.4 m SLR by 
2100 at 1.12 m) and 118.4 billion USD (at a high SLR of 2 m by 2100 at

1.75 m) in direct damages, and an additional 5.7, 4.5 and 4.5 billion 
USD, respectively, due to migration (Pycroft et al., 2016). Closely after 
China will be India, the Republic of Korea, Japan, Indonesia and Russia. 
Overall, Asia can experience direct losses of about 167.6 billion USD 
(at 0.47 m), 272.3  billion USD (at 1.12 m) or 338.1  billion USD (at

10
1500Chapter 10 Asia
1.75 m), and an additional 8.5, 24 or 15 billion USD at the respective 
SLR projections, due to migration6.
10.4.6.3.5 Tropical cyclones
Globally, there is high confidence that the proportion of intense 
tropical cyclones is expected to increase despite the total number of 
tropical cyclones being expected to decrease or remain unchanged 
(Arias et  al., 2021), especially in Southeast and East Asia (Knutson 
et al., 2015; Yamamoto et al., 2021). Historical trends from South Asia 
indicate that more lives are lost due to storm surge levels than the 
intensity of the cyclone (Niggol and Bakkensen, 2017). The number of 
people exposed to 1-in-100-years storm surge events is highest in Asia. 
China, India, Bangladesh, Indonesia and Vietnam have the highest 
numbers of coastal populations exposed (Neumann et al., 2015) with 
Guangzhou, Mumbai, Shenzen, Tianjin, Ho Chi Minh City, Kolkata and 
Jakarta incurring losses of 1520 million USD due to coastal flooding in

2005 alone (Dulal, 2019), although Jakarta is exposed to monsoonal 
storm surge. It is projected that by 2050, without adaptation, the 
annual losses incurred in these cities will increase to approximately 
32 billion USD (Dulal, 2019).
Globally, six of the top ten countries/places with the highest AAL 
associated with tropical cyclones are in Asia (Japan, Republic of Korea, 
the Philippines, China, Taiwan, Province of China, and India) (Mori et al., 
2021a). The AAL associated with storm surge is primarily concentrated 
in Japan, China, Hong Kong SAR of China, and India. The AAL associated 
with wind and storm surge relative to the existing capital stock in 
the country is highest in New Caledonia, Tonga, Vanuatu, Palau, the 
Philippines, Fiji and the Solomon Islands, indicating less resilience. For 
example, in Ise Bay, Japan, the current storm surges are estimated to 
lead to property and business damage of approximately 100.04 billion

JPY with current adaptation (protective sea wall), but this can more 
than double to 236.49  billion JPY under climate-change-induced 
increases in storm surge intensity (Jiang et al., 2016).
10.4.6.3.6 Riverine floods
Over one-third of Asian cities and about 932 million urban dwellers live 
in areas with high risk of flooding (Gu et al., 2015). Of 437 cities at low 
risk of flood exposure but highly vulnerable to flood-related economic 
losses, approximately half are in Asia (Gu et al., 2015).
Globally, China and India have the highest AALs associated with 
riverine floods, with a magnitude of 13 and 6 billion USD, respectively. 
Other countries from Asia among the top ten of absolute AALs are 
Japan, Bangladesh and Thailand. There is an increased flood risk for 
habitations on the deltas influenced by both riverine and coastal 
drivers of flooding (Szabo et al., 2016a), globally exposing 9.3% more 
people annually to riverine flooding than otherwise estimated without

the compounded influence (Eilander et al., 2020). Simultaneously, SLR 
and subsidence are also expected to increase the risk due to frequent 
flood events for these delta regions than the longer-return periods 
otherwise associated with SLR (Yin et al., 2020).
6 Cumulative migration in high SLR scenarios is always higher, but since much of the migration has already occurred in earlier decades, the additional migration is lower in the high-SLR scenarios than 
the A1B scenario.10.4.6.3.7 Permafrost thawing and associated risks
In Northern Eurasia, observed and projected climate-change impacts 
are especially pronounced. On land, the presence of permafrost, 
which occupies substantial areas of eastern Russia, Mongolia and 
mountain regions of China, creates specific challenges for economic 
development and human activities. By 2050, it is likely that 69% of 
fundamental human infrastructure in the Pan Arctic will be at risk

(RCP 4.5 scenario)(medium confidence), including more than 1200 
settlements (Hjort et al., 2018). The majority of the population and 
the absolute majority (85%) of large settlements on permafrost are 
located in Russia, and 44% of those are expected to be profoundly 
affected by permafrost thaw by 2050 (Streletskiy et al., 2019; Ramage 
et al., 2021). Under RCP8.5, the climate-induced decrease of bearing 
capacity and, in regions with ice-rich permafrost, thaw subsidence, 
is projected to affect 54% of all residential buildings on permafrost 
with a combined worth of 20.7 billion USD; 20% of commercial and 
industrial structures and 19% in critical infrastructure with a total 
worth of 84.4 billion USD (Streletskiy, 2019). Transport infrastructure 
in Russia and China are impacted by thaw subsidence and, to a lesser 
degree, from frost heave, which add significant operational costs and 
limit accessibility to remote settlements (Porfiriev et  al., 2019; Ni 

Especially in Russia, significant populations and fixed infrastructure 
assets are located in urban centres on permafrost that is degrading 
significantly. Two major risks associated with permafrost degradation 
are loss of permafrost bearing capacity and ground subsidence 
(Streletskiy et al., 2015). The former determines the ability to support 
foundations of buildings and structures and is a vital characteristic of 
sustainability of the economic centres, while the latter impacts the ability 
of critical infrastructure (roads, railroads) to provide transportation and 
support accessibility of remote populations and economic centres on 
permafrost. The proximity of some settlements to the coasts or areas 
with uneven topography may further increase risks associated with 
permafrost degradation as ice-rich coasts characterised by high rates 
of coastal erosion, while settlements located on slopes may experience 
higher rates of mass wasting processes.

Changes in climate have resulted in permafrost warming and increased 
thaw depth in undisturbed locations (Biskaborn et al., 2019), but in 
built up areas these transformations have been exacerbated by human 
activities (Grebenets et  al., 2012). Norilsk, the largest city built on 
permafrost above the Arctic Circle (Shiklomanov et  al., 2017b), was 
found to have one of the highest trends of near-surface permafrost 
warming (Streletskiy et al., 2012). Anomalous high temperatures and 
earlier snowmelt in 2020 may have contributed to oil storage collapse 
and the resulting spill of 20,000 tons of diesel fuel in Norilsk area 
(Rajendan et al., 2021). The ability of foundations to support structures 
has decreased by 10–40% relative to the 1960s in the majority of 
settlements on permafrost in Russia (Streletskiy et  al., 2012) and is 
expected to further decrease by 20–33% by 2050–2059 relative to 
2006–2015 (Streletskiy et al., 2019).

10
1501Asia  Chapter 10
10.4.6.3.8 Risks and impacts on infrastructure
South Asia and Africa bear the highest losses from unreliable 
infrastructure, and climate change will increase these losses due to 
hazards and necessitate additional infrastructure investments to 
address new risks (Hallegatte et  al., 2019; Lu, 2019)7. Specifically, 
power generation and transport infrastructure incur losses of 30 billion 
USD a year on average from hazards (about 15 billion USD each), with 
low- and middle-income countries shouldering about 18 billion USD of 
the total amount (Koks et al., 2019; Nicholls et al., 2019).
Among the top 20  countries that are rapidly expanding their 
infrastructure stock while facing high disaster risk and low infrastructure 
quality, the Asian countries are Lao PDR, the Philippines, Bangladesh, 
Cambodia, Kyrgyzstan, Bhutan and Vietnam. (UNISDR, 2017; WEF , 
2018). The losses are due to direct damage to infrastructure, disruption

7 While Hallegatte et al. (2019) estimate that in low- and middle-income countries, the cost of infrastructure disruptions ranges from 391 to 647 billion USD, they emphasise that ‘while these estimates 
are incomplete, they highlight the substantial costs that unreliable infrastructure impose on people in low- and middle-income countries’.
8 Estimates are based on the DIVA model, which uses SSP2, 3 and 5, and RCP2.6, 4.5 and 8.5 in Nicholls et al. (2019), and investments from Ward et al. (2017). According to this study, uncertainty 
regarding socioeconomic changes and climate change is small compared with the uncertainty around construction costs and tolerance to risk.in services and affected supply chains (Hallegatte et al., 2019). East 
Asia and the Pacific and South Asia have the highest adaptation 
deficits in coastal protection with 75  billion USD in the former and 
49 billion USD in the latter (Nicholls et al., 2019). If overall damages

are minimised, low- and middle-income countries may need to invest 
0.1–0.5% of their GDP annually up to 2030 for protection against both 
coastal and river floods, varying based on level of acceptable risks, 
construction costs, urbanisation and climate uncertainties8.
• Power disruption: Contrasting with high-income, countries such 
as the USA, where hazards, particularly storms, are responsible 
for 50% of power outages, this share is much lower in countries 
like Bangladesh or India, because system failures due to unnatural 
causes are very frequent. However, outages caused by hazards 
tend to be longer and geographically more widespread than other 
outages (Rentschler et al., 2019). Climate-change-induced SLR is 
Key risks and adaptation options in select cities across Asia
Moderate Large Small = Insufficient literature/Central
Asia
TashkentEast
Asia
Guangzhou ShanghaiNorth
Asia
SalekhardWest
Asia
RiyadhSouth
Asia
Ahmedabad Dhaka Mumbai
Observed (2020)
Projected (2035)

Extreme rain
BehaviouralPopulation
(thousands
of people)
ProgressCyclones
Ecosystem-basedPermafrost thawHeat, urban
heat island
Drought,
water scarcity
InfrastructuralSea level riseFloodsSoutheast Asia
KualaLumpur JakartaHo Chi
Minh City
13,923 7,231 7,997 8,602
18,649 9,058 10,467 12,236916
1,38813,302 27,058
16,741 34,34155 8,059 20,411 21,006
11,295 27,343 31,234
/
/
/
//
/
///
/
/
/
//
//
/na na na na
na na na na na
na na na na na na na nana
na
Negligible//
/
/
= not applicablena Progress evidence Progress level
Medium High LowRisk levelKey risks
Risk evidence
No reported
adaptationHigh Medium Low
Figure 10.8 |  Risks and key adaptation options in select cities across Asia. Cities were chosen to ensure coverage of different sub-regions of Asia, represent different 
risk profiles, different city sizes (based on current population and projected growth) and reported progress on different adaptation strategies (infrastructural, institutional, ecosystem

based and behavioural). There is a full line of sight in SM10.4.

10
1502Chapter 10 Asia
expected to impact power infrastructure, even necessitating power 
plant relocation (Hallegatte et al., 2019). In Bangladesh, to avoid 
inundations caused by SLR (SSP2, RCP8.5), approximately one-third 
of power plants may need to be relocated by 2030. An additional 
30% of power plants are likely to be affected by increased salinity 
of cooling water and increased frequency of flooding, while power 
plants in the northern region will probably see a decrease in output 
because of droughts (Hallegatte et al., 2019). In 2013 in Chittagong 
(Pervin et al., 2020), users experienced about 16 power outages 
due to storms alone (Hallegatte et al., 2019). Furthermore, low-
carbon technology diffusion might make certain infrastructures 
redundant, leading to stranded assets.
Across Asia, infrastructure impacts are mixed: net importers, such as 
China and India, will see GDP gains, while extreme examples include

Russia, a net exporter, which could see steep declines in fossil fuel 
production (Mercure et al., 2018). In low- and middle-income countries 
globally, disruption in power supply can impact firms directly (up to 
120 billion USD yr–1), with coping costs (up to 65 billion USD yr–1) and 
other indirect impacts. Similarly, for households, the direct impact and 
cost of coping could be between 2.3 and 190 billion USD yr–1. Although 
all power outage is not due to natural hazards, there is a significant 
number that is attributed to disasters. Besides, outages caused by 
natural hazards tend to be longer and geographically larger than other 
causes (Hallegatte et al., 2019).
• Transportation disruption: Of the 20  countries in which the 
road and railway infrastructure is expected to be most affected 
in absolute terms due to multi-hazards, half are Asian (Koks et al., 
2019). In low- and middle-income countries globally, the direct

losses to firms on account of transportation disruption are about 
107  billion USD yr–1, excluding the costs due to sales losses or 
delayed supplies and deliveries alone (Hallegatte et al., 2019). In 
the transport sector, floods and other hazards disrupt traffic and 
cause congestion, taking a toll on people and firms in rich and 
poor countries alike.
• Water supply and disposal infrastructure disruption: In 
low- and middle-income countries, disruption of water supply 
could lead to direct losses of about 6  billion USD yr–1 for firms, 
and between 88 and 153 billion USD yr–1 for households (due to 
willingness to pay to avoid disruption). Additionally, there are 
second-order costs associated with finding alternate sources of 
water and also health issues (on the order of 6–9 billion USD yr–1 
accounting for medical bills and missed income) (Hallegatte et al., 
2019). In China, climate models project that an increasing number

of wastewater-treatment-plant assets face climate-induced flood 
hazards in both the near and distant future, potentially affecting as 
many as 208 million users by 2050 (Hu et al., 2019).
10.4.6.4 Adaptation in Cities Across Asia
A review of urban adaptation in South, East and Central Asia found 
examples of 180 adaptation activities across 74 cities (Dulal, 2019). 
Most adaptation actions in Asia are in the initial stages (Araos 
et  al., 2016) with 57% focused on preparatory actions, such as 
capacity building and vulnerability assessment, and 43% focused on implemented adaptation (see also SM10.4). Most adaptation actions 
were focused on disaster risk management (Dulal, 2019), although the 
proportion of climate finance spent on disaster preparedness is not 
very high (as Georgeson et al., 2016, show in the megacities of Beijing, 
Mumbai and Jakarta). Although key port cities across Asia are at high 
risk of climate impacts, it is estimated that adaptation interventions

constitute only a small proportion of cities’ climate efforts (Blok and 
Tschötschel, 2015). Figure 10.8 shows risks and key adaptation options 
in select cities across Asia.
Critically, most urban adaptation in South, East and Central Asia is 
reactive in nature (Dulal, 2019; Singh et al., 2021b), raising questions 
on preparedness, proactive building of adaptive capacities and whether 
present actions can lock certain cities or sectors into maladaptive 
pathways (Friend et al., 2014; Gajjar et al., 2018; Salim et al., 2019; Chi 
et al., 2020). China, India, Thailand and the Republic of Korea record 
the most number of urban adaptation initiatives, driven mainly by 
supportive government policies (Lee and Painter, 2015; Dulal, 2019). 
The number of actors working on urban adaptation is growing: in 
addition to national governments and local municipalities, civil society, 
private-sector actors (Shaw, 2019) and transnational municipal

networks (Fünfgeld, 2015) are emerging as important for knowledge 
brokering, capacity building and financing urban adaptation (Karanth 
and Archer, 2014; Chu et al., 2017; Bazaz et al., 2018).
Adaptation options include: (a) infrastructural measures such as 
building flood protection measures and sea walls, and climate-resilient 
highways and power infrastructure (Shaw et  al., 2016b; Ho et  al., 
2017); (b) sustainable land-use planning through zoning, developing 
building codes (Knowlton et  al., 2014; Nahiduzzaman et  al., 2015; 
Rahman et  al., 2016; Ahmed et  al., 2019b); (c) ecosystem-based 
adaptation measures such as protecting urban green spaces, improving 
permeability, mangrove restoration in coastal cities, etc. (Brink et al., 
2016; Fink, 2016; Yu et al., 2018d); (d) relocation and migration out 
of risk-prone areas (McLeman, 2019; Hauer et  al., 2020; Maharjan 
et al., 2020); and (e) disaster management and contingency planning

such as through Early warning systems (EWS), improved awareness 
and preparedness measures (Shaw et al., 2016a). Asian cities are also 
focusing on institutional adaptation measures which cut across the 
five categories mentioned above such as through building capacity and 
local networks (Anguelovski et al., 2014; Friend et al., 2014; Knowlton 
et al., 2014), improving awareness (Knowlton et al., 2014), and putting 
local research and monitoring mechanisms in place (Lee and Painter, 
2015) to enable adaptation. Figure  10.9 shows the effectiveness of 
select adaptation options in cities across Asia.
10.4.6.4.1 Infrastructural adaptation options
The challenge of adapting infrastructure to climate change across Asia 
is twofold: there are significant infrastructure deficiencies, especially 
in low-income countries, and key infrastructures are at high risk due 
to climate change (Hallegatte et al., 2019; Lu, 2019). Infrastructural

adaptation options in cities attempt to enable networked energy, 
water, waste and transportation systems to prepare for, and deal 
with, climate risks better (Meerow, 2017) through interventions such 
as improved highways and power plants, climate-resilient housing, 
improved water infrastructure and so forth (ADB, 2014).

10
1503Asia  Chapter 10
• Power infrastructure: Adaptations in electricity systems include 
climate-resilient power infrastructure, particularly essential for 
coastal megacities such as Manila, Mumbai, Bangkok and Ho 
Chi Minh City (Meerow, 2017; Duy et  al., 2019), which double 
as regional economic hubs and are home to tens of millions of 
people. In the Philippines, solar panels at water pumping stations 
are installed to operate and maintain a minimal capacity to pump 
water if the electricity grid were to break down (Stip et al., 2019).
• Water infrastructure: Sustainable water supply and resource 
management are key to urban adaptation through improved 
water service delivery, wastewater recycling and storm-water 
diversion (Deng and Zhao, 2015; Xie et  al., 2017; Yu et  al., 
2018d). Infrastructure-based adaptation options in urban water 
management include building water storage facilities, storm-water 
management and enhancing water quality improving permeability,

managing runoff and enabling groundwater recharge. One 
example is of Shanghai (China), where infrastructural and policy 
incentives come together to enable adaptation: the city has been 
divided into 14 water conservancy zones, including 348 polder 
areas with 2517 km of dykes, 1499 pump stations and 2203 sluices 
(Yu et al., 2018d). It also depends on a regional inundation control 
system, flood Early warning system and an emergency plan to deal 
with flood risk and mitigate waterlogging (Chen et al., 2018e; Yu 
et  al., 2018d). Another example is Ho Chi Minh City (Vietnam), 
where, given significant increases in area at risk of flooding under 
climate change, the city has invested in storm sewer upgradation, 
dike works, improving drainage and increasing the height of road 
embankments and minor bridges (Storch and Downes, 2011; ADB, 
2014; Ho et  al., 2017). These infrastructural interventions were 
complemented by designing an Early warning system to initiate

flood mitigation procedures, such as isolating critical electrical and 
mechanical operating systems from water.• Built infrastructure: Current built-infrastructure adaptation 
interventions are mostly reactive (e.g., strengthening housing 
units, using sandbags during flooding, storing of food, evacuation) 
rather than preventive (e.g., relocation, building multi-storey and 
stronger housing units), mainly due to limited resources within 
most vulnerable households for investing in proactive measures 
(Francisco and Zakaria, 2019). For cities in North Asia seeing per -
mafrost thawing, adequate land-use practices, permafrost mon-
itoring, maintenance of infrastructure and engineering solutions 
(e.g., using thermosiphons) may temporarily offset the negative 
effects of permafrost degradation in small, economically vital 
areas, but are unlikely to have an effect beyond the immediate 
areas (Shiklomanov et al., 2017b; Streletskiy, 2019). Importantly,

thawing permafrost and GHG emissions create feedbacks where 
emissions amplify warming and drive additional thaw. Reducing 
these impacts through mitigation will reduce the need for adapta-
tion significantly (Schaefer et al., 2014).
• Infrastructure and technology: Several infrastructural options 
employ technology, such as smart meters, to monitor water usage 
and service delivery, but these are differentially adopted across 
Asian sub-regions with higher adoption across East Asia. Examples 
include: the Yokohama smart city project in Japan, which has been 
smart eco-urbanism interventions since 2011 (e.g., energy saving 
and storage infrastructure, wastewater management, behavioural 
change towards renewable energy and low-carbon transportation) 
(IUC, 2019); the Tianjin Eco-city mega-project in China, which is 
testing a range of measures to meet urban sustainability goals in 
partnership with Singapore (ICLEI, 2014b; Blok and Tschötschel,

2015); development in New Songdo (Republic of Korea), which is 
experimenting with interventions, such as embedded smart waste 
management (Anthopoulos, 2017), and national policy initiatives 
such as the Smart Cities Mission covering 100 cities in India (e.g. 
technology-enabled water, energy and land management for urban 
/
a
b
c
dEffectiveness of select adaptation options in cities across Asia
Illustrative examples of adaptation plans and actions
Evidence EfectivenessThermosiphons
Salekhard
Legislation to
reduce season
heat exposure
Riyadh
Coastal Zone Regulation
MumbaiaHeat Action Plan
Ahmedabad
Bangladesh Delta Plan
Dhakab Sea walls
Shanghai
Sponge City
Guangzhou
c Greening KL
Kuala Lumpur
Great Garuda
Sea Dike
Jakartad
Medium High Low High Medium Low
Figure 10.9 |  Effectiveness of select adaptation options in cities across Asia. Effectiveness is assessed based on the option’s ability to reduce risk as reported in the 
literature.

10
1504Chapter 10 Asia
agriculture in Nashik city) (ICLEI, 2014a). However, the efficacy of 
such measures, especially for larger sustainability and climate-
change goals, remains to be seen (ICLEI, 2014a; ICLEI, 2014b; 
Caprotti et al., 2015; Anthopoulos, 2017).
Infrastructural measures alone are seldom effective in building urban 
resilience as seen in the examples of the 2011 floods in Bangkok and 
the 2005 typhoon in Manila (Duy et al., 2019), or projected estimates 
by Pervin et  al. (2020) who found that structural interventions in 
existing drainage systems reduce flooding risk by 7–19% in Sylhet 
(Bangladesh) and Bharatpur (Nepal); however, without proper 
solid waste management, areas under flood risk could increase to 
18.5% in Sylhet and 7.6% in Bharatpur in five  years, rendering the 
infrastructural interventions ineffective over time. While in some cities 
it is estimated that infrastructural adaptation through ‘hard’ flood

protection strategies (e.g., storm surge barriers and floodwalls) is more 
effective than institutional or ecosystem-based adaptation by 2100, for 
example, Shanghai. A hybrid approach where hard strategies protect 
from flood risk, and soft strategies reduce residual risk from hard 
strategies, is suggested (Du et al., 2020). In Japan, without adaptation, 
estimated damage costs of floods (caused by tropical cyclones and 
altered precipitation) by 2081–2100 under RCP2.6 will be 28% higher 
(compared with 1981–2000), rising to 57% higher under RCP8.5 
(Yamamoto et al., 2021). With a combination of adaptation measures 
(such as land-use control, piloti building and flood control measures), 
estimated damage costs can be reduced even below the 1981–2000 
levels, and with a combination of mitigation and these adaptation 
measures, an estimated 69% reduction in flood damage costs are 
expected–demonstrating the importance of concerted and immediate 
climate action in reducing damage.

Infrastructural interventions can sometimes be maladaptive when 
assessed over longer time periods: for example, the Mumbai Coastal 
Road (MCR) project aimed at reducing flood risk and protecting against 
SLR will potentially cause damages to intertidal fauna and flora and 
local fishing livelihoods (Senapati and Gupta, 2017); and Jakarta’s 
Great Garuda project aimed at reducing flood risk is expected to 
increase flood risk for the poorest urban dwellers (Salim et al., 2019).
10.4.6.4.2 Sustainable land-use planning and regulation
Land use in cities impacts resource use (e.g., water, energy), risk (a 
function of population density, service provision and hazard exposure) 
and adaptive capacity, all of which influence the efficacy of urban 
adaptation (de Coninck et al., 2018). Locally suited land-use planning 
and regulation (such as appropriate zoning or building codes and 
safeguarding land rights) can have adaptation co-benefits (Mitchell

et  al., 2015; Dhar and Khirfan, 2016): for example, strict building 
regulations can protect urban wetlands and associated ecosystem 
services (Jiang et  al., 2015); appropriate land zoning can safeguard 
green spaces, ensure improvements in permeability and obviate new 
development in risk-prone locations (Duy et al., 2019); and ensuring 
9 Ecosystem-based adaptation (EbA) is defined by IPBES as the conservation, sustainable management and restoration of natural ecosystems to help people adapt to climate change (Glossary, 2019). 
In urban areas, EbA includes improving ecological structures (e.g., maintaining watersheds, forests, green roofs), ecological functions and processes (e.g., wetland functioning for flood protection), 
valuation measures (including monetary or non-monetary values to ecosystem service benefits) and investing in ecosystem management practices (i.e., enabling adaption co-benefits through the

maintenance, preservation and restoration or creation of ecological structures) (Liu et al., 2014a; Brink et al., 2016). Thus, EbA adaptation actions include protecting urban green spaces, improving 
permeability, fostering urban agriculture, mangrove restoration in coastal cities, improved wetland management and so forth (Doswald et al., 2014; Brink et al., 2016; de Coninck et al., 2018).tenurial security or regularising informal settlements can incentivise 
improvements to housing quality, thereby alleviating vulnerability of 
the most marginal people (Mitchell et al., 2015).
Land tenure arrangements strongly shape urban dwellers’ vulnerability 
and their adaptive capacities (Roy et  al., 2013; Michael et  al., 2018). 
For example, in Khulna (Bangladesh), Roy et al. (2013) found significant 
differences between the adaptive strategies of homeowners and renters 
in low-income settlements, a finding echoed in Bangalore (India)

(Deshpande et al., 2018) and Phnom Penh (Cambodia) (Mitchell et al., 
2015). In Riyadh (Saudi Arabia), land-based adaptation strategies 
include land zoning to control population and building density, 
demarcating environmental protection zones, and sub-urbanisation 
(Nahiduzzaman et al., 2015; Rahman et al., 2016). In many Asian cities, 
land subsidence control can serve as an adaptation strategy since it is 
estimated to significantly reduce relative SLR (high confidence). This has 
an important implication in that subsidence control would be a good and 
complementary measure to climate mitigation and climate adaptation 
in many coastal urban settings in Asia (Cao et al., 2021; Nicholls et al., 
2021). Urban land-use planning, if used proactively, can incentivise 
adaptation–mitigation synergies and obviate unintended negative 
consequences of urbanisation as Xu et al. (2019) have shown in Xiamen.
10.4.6.4.3 Ecosystem-based adaptation

The literature on urban ecosystem-based adaptation (EbA)9, especially 
across Asia, has grown significantly since AR5 (Demuzere and al., 2014; 
Yao et al., 2015; Brink et al., 2016; Bazaz et al., 2018; de Coninck et al., 
2018; Ren, 2018). This growing literature reflects the wide recognition 
that infrastructural adaptation can often have ecological and social 
trade-offs (Palmer et  al., 2015) and need to be complemented by 
ecosystem-based actions to manage risk more effectively (Du et al., 
2020), build adaptive capacity, and in some cases, meet mitigation and 
SDGs (Huang et al., 2020).
Illustrative examples of EbA in Asian cities include sponge cities 
in China for sustainable water management, flood mitigation and 
minimising heatwave impact (Jiang et al., 2018; Yu et al., 2018d; Wang 
et  al., 2019a; Zhanqiang et  al., 2019), Singapore’s Active, Beautiful, 
Clean Waters (ABC Waters) Programme, which uses bio-engineering

approaches to protect river channels and prevent localised flooding, 
improve water quality and create community spaces, and Dhaka’s 
green roofs and urban agriculture (Zinia and McShane, 2018).
The EbA approaches to manage floods, capture and store rainwater, 
restore urban lakes and rivers, and reduce surface runoff often blend 
infrastructural and ecosystem-based approaches. For example, in 
Tokyo, stormwater management is done by sophisticated underground 
infrastructure and an artificial infiltration stormwater system (Saraswat, 
2016; Mishra et  al., 2019). China’s Sponge City Programme aims to 
reduce the impacts of flooding through low-impact development 
measures, urban greenery and drainage infrastructure, such that 80% of

10
1505Asia  Chapter 10
Evidence of effectiveness of urban ecosystem-based adaptation in Asia
using examples of four commonly used ecosystem-based adaptation options
Risk
reductionEcosystem
benefitsEconomic
benefits
Extreme heat,
urban heat islad
Mangrove
restorationEcologica l
stormwater
managementUrban parks,
green spacesEffectiveness
ConfidenceRiskEcosystem-
based optionHuman
wellbeing
Urban
agricultureSea level riseFlooding
Food insecurityInsufficien t
evidenc eMedium High Low
Figure 10.10 |  Evidence of the effectiveness of ecosystem-based adaptation (EbA) using examples of four commonly used EbA options10. Effectiveness is 
assessed qualitatively based on the evidence (for a full line of sight see SM10.5) and is examined through four framings: potential to reduce risk (e.g., reduced exposure to hazard

means to reduce risk); benefits to ecosystems (through improved ecosystem health and high biodiversity); economic benefits (e.g., improved incomes, fewer man-days lost, better 
livelihoods); and human well-being outcomes (e.g., health, quality of life, etc.). The darker shading signifies high effectiveness and the lightest shade signifies low effectiveness of 
an EbA option (i.e., the option scores low on the indicator).
10 Assessing the effectiveness of adaptation actions is challenging because of the lack of a clear goal that signifies effective adaptation, varied conceptual framings and metrics used to assess 
effectiveness, and low empirical evidence on the effectiveness of implemented adaptation actions (Owen, 2020; Singh et al., 2021a).urban areas reuse 70% of rainwater by 2020, which would help ensure 
the resilience of these cities to floods (Li et al., 2016b; Stip et al., 2019).
Case studies on urban EbA also raise equity concerns (medium

evidence, medium agreement) such as interventions biased 
towards suburban areas in Haizhu District, Guangzhou (China) (Zhu 
et  al., 2019); inadequate consideration of low-income, vulnerable 
populations (Blok and Tschötschel, 2015; Meerow, 2017; Mabon and 
Shih, 2021); and low familiarity with interventions such as artificial 
wetlands, water retention ponds as well as green façades and walls 
can restrict inclusiveness (Zinia and McShane, 2018). Furthermore, 
urban EbA is constrained by a range of factors such as inadequate 
institutional structures and processes for connecting different remits 
and knowledge systems along with trade-offs in land use for different 
purposes (Mabon and Shih, 2021; Singh et al., 2021b).
The EbA interventions are not uniform across Asian cities: in a global 
study on urban EbA, Brink et al. (2016) found that Eastern Asia, India 
and Israel report most EbA interventions and that there is variable and

limited evidence on effectiveness and scalability (SM10.5). Using a 
risk framing (i.e., the extent to which an option reduces risk), urban 
EbA options in Asian cities score as being ‘low to medium’ effective 
(see SM10.5); however, when the assessment is expanded to include 
the ecosystem benefits, economic impacts and human well-being co-
benefits of EbA, effectiveness increases. Figure 10.10 shows evidence 
of the effectiveness of EbA.
For example, urban agriculture is identified as offering multiple benefits 
such as mitigating emissions associated with food transportation 
from rural to urban areas, improving food and nutritional security, 
strengthening local livelihoods and economic development, improved 
microclimate, soil conservation, improved water and nutrient recycling, 
and efficient water management (Padgham and Dietrich, 2015; Patil et al., 2019). However, it can potentially undermine ecosystem services

through land-use changes, water overextraction or applying chemical 
fertilisers (Ackerman et al., 2014), exposure of smallholders to volatile 
markets and crops that are not consumed by farming households 
themselves (thus undermining food security) or increasing the work 
burdens on women, as well as health externalities (e.g. through use 
of untreated wastewater, or rearing poultry and livestock in unsanitary 
conditions). There remain gaps in understanding the differential impacts 
of urban agriculture at different scales as well as its effectiveness in 
improving adaptive capacity at scale.
10.4.6.4.4 Migration and planned relocation
There is medium evidence with high agreement that climatic risks 
are exacerbating internal and international migration across Asia 
(see Box 10.2; IDMC, 2019; Maharjan et al., 2020). In coastal cities, 
formal ‘retreat’ measures, such as forced displacement and planned 
relocation (Oppenheimer et al., 2019), are commonly considered ‘last

resort’ adaptation strategies once other infrastructural and ecosystem-
based protect-and-accommodate strategies are exhausted (CCP2.3) 
(Haasnoot et al., 2019). In contrast, migration (which can take various 
forms from seasonal, temporary mobility to circular or permanent 
movement) is a regular feature across Asian urban settlements 
(Box 10.2, CCB MIGRATE, Maharjan et al., 2020).
There is robust evidence (medium agreement) that across Asia, 
migration (and increasingly planned relocation) will continue to be a 
key risk management strategy, especially in low-lying flood-prone cities 
(e.g., in Southeast and South Asia) and across drylands (e.g., in South 
and Central Asia) (Davis et al., 2018; Ajibade, 2019; Lincke and Hinkel, 
2021). While there is insufficient evidence to project migration numbers 
under different warming levels, it is well established that migration as 
an adaptation strategy is not equally available to all (Ayeb-Karlsson,

10
1506Chapter 10 Asia
2020), and climatic risks might reduce vulnerable populations’ ability 
to move due to loss of assets, thus reinforcing existing inequalities and 
differential adaptive capacities (Blondin, 2019; Zickgraf, 2019; Singh 
and Basu, 2020; Cundill et al., 2021; Gavonel et al., 2021).
There is medium evidence (low agreement) about the effectiveness of 
migration and planned relocation in reducing risk exposure. Evidence 
on climate-driven internal migration shows that moving has mixed 
outcomes on risk reduction and adaptive capacity. On one hand, 
migration can improve adaptive capacity by increasing incomes and 
remittances as well as diversifying livelihoods (Maharjan et al., 2020); 
on the other, migration can expose migrants to new risks. For example, 
in Bangalore (India), migrants often face high exposure to localised 
flooding, insecure and unsafe livelihoods, and social exclusion, which 
collectively shape their vulnerability (Michael et al., 2018; Singh and

Basu, 2020). In greater Manila (the Philippines) and Chennai (India), 
planned relocations to reduce disaster risk have often exacerbated 
vulnerability, due to relocation sites being in environmentally sensitive 
areas, inadequate livelihood opportunities and exposure to new risks 
(Meerow, 2017; Ajibade, 2019; Jain et al., 2021).
10.4.6.4.5 Disaster management and contingency planning
There is rich case-based evidence across Asia on urban adaptation 
to extreme events with relatively more evidence on rapid-onset 
events such as cyclones and flooding than slow-onset disasters such 
as drought (see Box  10.6; Ray and Shaw, 2019; UNESCAP , 2019; 
Singh et al., 2021a). Overall, there has been a growing emphasis on 
‘build back better’ interventions (Mannakkara and Wilkinson, 2013; 
Hallegatte et al., 2018) that approach disaster management holistically 
through infrastructural solutions such as climate-resilient housing

or sea walls and soft approaches such as strengthening livelihoods, 
developing EWS11,increasing awareness about disaster risks and 
impacts, and building local capacities to deal with them (Bhowmik 
et al., 2021). Notably, urban disaster management is effective when 
land-use planning processes, including greenfield development, zoning 
and building codes, and urban redevelopment, are leveraged to reduce 
and/or obviate risk, thereby averting potential maladaptation (Kuhl 
There is relatively lower empirical evidence on how microenterprises 
and businesses are adapting to increased risk, but recent examples 
in Mumbai, India (Schaer and Pantakar, 2018), and Kratie, Cambodia 
(Ngin et al., 2020), suggest that businesses primarily adopt temporary 
and reactive responses rather than long-term, anticipatory adaptation 
measures.
A review of innovative DRR approaches notes the use of geographic 
information system (GIS) and drone-based technologies for mapping

risk exposure and impacts, mobile-based payments for post-disaster 
compensation, and transnational initiatives and learning networks to 
promote urban resilience (Izumi et al., 2019). Furthermore, technology-
based innovations, such as using big data (Yu et al., 2018b), improved 
11 The set of technical, financial and institutional capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organisations 
threatened by a hazard to prepare to act promptly and appropriately to reduce the possibility of harm or loss. Depending on context, EWS may draw upon scientific and/or Indigenous knowledge. 
These EWS are also considered for ecological applications (e.g., conservation, where the organisation itself is not threatened by hazard but the ecosystem under conservation is; an example is coral

bleaching alerts), in agriculture (e.g., warnings of ground frost and hailstorms) and in fisheries (e.g., storm and tsunami warnings) (IPCC, 2018a).warnings through mobile phones or mobilising relief through social 
media (Carley et al., 2016), are proving effective for disaster prepared-
ness, relief and recovery. Community-based DRR is consistently rank-
ed as most effective for its role in transforming DRR towards being 
more context relevant and inclusive. Ecosystem-based DRR (EbDRR) 
is also gaining prominence and includes strategies such as mangrove 
plantation and rejuvenation in vulnerable coastal areas. Nature-based 
solutions for flood protection and reducing drought incidence have 
emerged as an alternative to costlier ‘hard’ infrastructure (UN-Water, 
2018; Zevenbergen et al., 2018; Rozenberg and Fay, 2019). Some cities 
are also reporting adaptation to heat risk. For example, Ahmedabad 
(India) has pioneered preparedness for extreme temperatures and

heatwaves by developing annual Heat Action Plans, building regula-
tions to minimise trapping heat, advisories about managing heat stress 
and instituting a cool-roofs policy (Ahmedabad Municipal, 2018).
Financing, regulations and institutional processes play a significant 
role in incentivising DRR and resilience in large-scale, city-level built 
infrastructure by the private sector and other actors. Currently there 
are gaps in these mechanisms, leading to infrastructure development 
in disaster-prone areas, increasing exposure to people, property, 
economy and systems (Jain, 2013). Both firms and governments need 
to take disaster risks into consideration in supply-chain management 
to avoid disruptions and subsequent negative effects (Abe and Ye, 
2013). There are several institutional challenges faced during DRR 
and CCA implementation including overlapping efforts and inefficient 
use of scarce resources due to inappropriate funding mechanisms,

a lack of coordination and collaboration, a lack of implementation 
and mainstreaming, scale mismatches, poor governance, the social–
political–cultural structure, competing actors and institutions, and lack 
of information, communication, knowledge sharing, and community 
involvement, as well as policy gaps (Seidler et al., 2018; Islam et al., 
2020).
10.4.6.5 Enabling Urban Adaptation Across Asia
There is growing empirical evidence of conditions enabling and 
constraining urban adaptation (Table 10.3) with relatively more literature 
from South, Southeast and East Asia. Governance and capacity-related 
deficits are repeatedly identified as significant barriers to urban 
adaptation (robust evidence, high agreement) and interact with financial 
and informational constraints to mediate adaptation action.
10.4.7 Health and Well-Being
Climate change is increasing risks to human health in Asia by increasing 
exposure and vulnerability to extreme weather events such as

heatwaves, flooding and drought, and air pollutants, increasing vector- 
and water-borne diseases, undernutrition, mental disorders and allergic 
diseases (high confidence). Sub-regional diversity in socioeconomic and 
demographic contexts (e.g., ageing, urban compared with agrarian 
society, increasing population compared with reduced birth rate,

10
1507Asia  Chapter 10
high income compared with low to middle income), and geographic 
characteristics, largely define the differential vulnerabilities and impacts 
in Asia (high confidence).
10.4.7.1 Observed Impacts
High temperatures affect mortality and morbidity in Asia (high 
confidence). In addition to all-cause mortality (Dang et al., 2016; Chen 
et  al., 2018e), deaths related to circulatory, respiratory, diabetic (Li 
et al., 2014b) and infectious diseases (Ingole et al., 2015), as well as 
infant mortality (Son et al., 2017), are increased with high temperature 
(high confidence). Increased hospital admissions (Giang et al., 2014; 
Lin et  al., 2019) and ambulance transport (Onozuka and Hagihara, 
2015) coincide with increased ambient temperature (high confidence). 
Heatwaves are particularly detrimental to all-cause and cause-specific 
mortality (Chen et al., 2015a; Lee et al., 2016; Guo et al., 2017b; Yin et al.,

2018). Both rural and urban populations are vulnerable to heat-related 
mortality (Ma et  al., 2015; Chen et  al., 2016a; Wang et  al., 2018a). 
Individuals with lower degrees of education and socioeconomic status, 
older individuals and individuals living in communities with less green 
space are more susceptible to heat-related mortality (high confidence) 
(Yang et al., 2012a; Huang et al., 2015b; Seposo et al., 2015; Son et al., 
2016; Kim and Kim, 2017). These heat effects have been attenuating 
over recent decades in East Asian countries, although the driving force behind this remains unknown (high confidence) (Chung et al., 2017c; 
Chung et al., 2018).
Rising ambient temperature accelerates pollutant formation reactions 
and may modify air-pollution-related health effects (medium 
confidence). Higher temperatures are associated with the increased 
effects of ozone on mortality (Shi et al., 2020). Climate change causes

intensified droughts and greater wind erosion resulting in increased 
intensity and frequency of sand and dust storms (Akhtar et al., 2018). 
Mortality and hospital admissions for circulatory and respiratory 
diseases are increased after exposures to Asian dust events (high 
confidence) (Hashizume et al., 2020). El Niño has a major influence 
on weather patterns in various regions. For example, it causes dry 
conditions that sometimes result in forest fires and transboundary 
haze that increased all-cause mortality in children by 41% in Malaysia 
(Sahani et al., 2014).
Ambient temperature is associated with the risk of an outbreak of 
mosquito-borne disease in South and Southeast Asia (high confidence) 
(Servadio et al., 2018). Warmer climates are associated with a higher 
incidence of malaria (Xiang et  al., 2018). Moderate rainfall also 
promotes malaria infection, while excessive rainfall decreases the risk 
of malaria (Wu et al., 2017b). El Niño intensity is positively associated

with malaria incidence in a single year in India (Dhiman and Sarkar, Table 10.3 |  Barriers and enablers to climate adaptation across Asian cities
Indicator As an enabler As a barrier
Governance and 
planningNational policy directives to adapt: for example, strong national climate 
commitments in China, India and Thailand (Dulal, 2019); and dedicated public–
private councils on climate change in Seoul, Republic of Korea (Lee and Painter, 
2015)
Participatory planning, co-producing solutions and engaging multiple 
stakeholders: for example, Surat (Anguelovski et al., 2014; Karanth and Archer, 
2014; Chu et al., 2017) and Guwahati (Archer et al., 2014) in India; Bandar 
Lampung and Semarang in Indonesia (Archer et al., 2014); and Seoul, Republic of 
Korea (Lee and Painter, 2015)
Devolving decision making to city governments (ADB, 2013) and strong political 
leadership helps to institutionalise adaptation programmes (Anguelovski et al.,

2014; Friend et al., 2014; Lee and Painter, 2015): for example, in Moscow where 
the city mayor has spearheaded climate action (van der Heijden et al., 2019).
Mainstreaming climate adaptation in city plans (UN-HABITAT and UNESCAP , 
2018)Low accountability and transparency in planning processes with inadequate 
spaces for public dialogue (Friend et al., 2014) and limited accountability to the 
most economically and politically marginalised people within cities (Garschagen 
and Marks, 2019)
Of 180 urban adaptation interventions across Asia, 65% are reactive in nature 
(Dulal, 2019), thus missing opportunities for risk prevention and preparedness 
(Francisco and Zakaria, 2019).
Lack of forward-looking, learning-oriented processes constrain adaptation with 
short-term development priorities often overshadowing long-term climate-action 
needs (Friend et al., 2014; de Leon and Pittock, 2017; Gajjar et al., 2018; Khaling 
et al., 2018; Garschagen and Marks, 2019; Jain et al., 2021).

Fragmented governance, lack of mainstreaming between CCA and DRR (Fuhr 
et al., 2018; Khaling et al., 2018): for example, in Vietnam, Thailand, Indonesia 
(Friend et al., 2014) and greater Manila (Meerow, 2017)
InformationKnowledge sharing through transnational municipal networks such as C40, 
ACCRN and A-PLAT (Fünfgeld, 2015)
City-level knowledge creation and knowledge-transfer institutions (Lee and 
Painter, 2015)Data gaps on projected climate risks and impacts in certain sub-regions and 
small settlements (Revi et al., 2014)
Numerous tools for assessing vulnerability and adaptation planning (Nordgren 
Technology and 
infrastructureEarly warning systems, climate information and modelling studies inform 
adaptation decision making (Reed et al., 2015; Singh et al., 2018a)Inadequate regional downscaled data at the city scale (ADB, 2013; Khaling et al., 
2018);
inadequate cost–benefit analyses of different adaptation strategies (Khaling et al. 
2018)
Capacity and

awarenessA focus on learning, experimentation, awareness and capacity building leads to 
more sustained, legitimate and inclusive adaptation (ADB, 2013; Anguelovski 
et al., 2014; Reed et al. (2015).Limited access to, and capacity to use, risk assessment tools (ADB, 2013; Shaw 
et al., 2016b)
FinanceDedicated adaptation financing (e.g., in Beijing, adaptation spending is 0.33% of 
the city’s GDP) (Georgeson et al., 2016); steering international and local funding 
to leverage adaptation benefits in urban development programmes such as 
in Surat (India) (Cook and Chu, 2019); mainstreaming climate adaptation into 
development programming to leverage developmental finance for adaptation 
action (Cuevas et al., 2016; Narender and Sethi, 2018)Inadequate adaptation funding and lack of financial devolution to city 
governments (Fuhr et al., 2018; Garschagen and Marks, 2019)

10
1508Chapter 10 Asia
2017). The duration and survival rate of dengue mosquito development, 
mosquito density, mosquito biting activity, mosquito spatio-temporal 
range and distribution, and mosquito flying distance are all affected 
by temperature (high confidence) (Li et  al., 2018b). Temperature, 
precipitation, humidity and air pressure are major weather factors 
associated with dengue fever transmission (high confidence) (Sang 
et al., 2014; Choi et al., 2016; Xu et al., 2017).
Climate change alters the hydrological cycle by increasing the 
frequency of extreme weather events such as excess precipitation, 
storm surges, floods and droughts (high confidence). Water-borne 
diseases, such as diarrhoea, leptospirosis and typhoid fever, can 
increase in incidence following heavy rainfall, tropical cyclones and 
flooding events (high confidence) (Deng et al., 2015; Levy et al., 2016; 
Li et al., 2018b; Matsushita et al., 2018; Zhang et al., 2019b). Droughts

can cause increased concentrations of pathogens, which overwhelm 
water-treatment plants and contaminate surface water. A positive 
association between ambient temperature and bacterial diarrhoea 
has been reported, compared with a negative association with viral 
diarrhoea (Carlton et al., 2016; Wang et al., 2018c).
Asia has the highest prevalence of undernourishment in the world, 
which was 11.4% in 2017, representing more than 515 million people. 
Southeast Asia has been affected by adverse climate conditions such 
as floods and cyclones, with impacts on food availability and prices 
(FAO, 2018d). Crop destruction due to tropical cyclones can include salt 
damage from tides blowing inland (medium confidence) (Iizumi and 
Ramankutty, 2015). Sea level rises result in intrusion of saline water 
into the coastal area of Bangladesh and people living in this area face 
an increased risk of hypertension resulting from high salt consumption 
(Scheelbeek et al., 2016).

Weather conditions have been linked to mental health. High temperatures 
increase the risk of mental problems including mental disorders, 
depression, distress and anxiety in Vietnam (Trang et al., 2016), Hong 
Kong SAR of China (Chan et al., 2018) and the Republic of Korea (Lee 
et al., 2018d). In addition, high temperatures are reported to increase 
the risk of mortality from suicide in Japan, the Republic of Korea, Taiwan, 
Province of China (Kim et al., 2016c), India (Carleton, 2017) and China 
(Luan et  al., 2019). Extreme weather events, such as storms, floods, 
hurricanes and cyclones, increase injuries and mental disorders (post-
traumatic stress disorder and depressive disorders) (Rataj et al., 2016), 
thereby negatively affecting well-being (high confidence).
Higher temperatures and increased CO 2 elevate the level of allergens 
such as pollen, which can result in increased allergic diseases, such as 
asthma and allergic rhinosinusitis. The association between variations in

ambient temperature and the occurrence of asthma has been reported 
in several Asian countries/regions such as Japan (Yamazaki et al., 2015), 
the Republic of Korea (Kwon et al., 2016), China (Li et al., 2016a) and 
Hong Kong SAR of China (medium confidence) (Lam et al., 2016).
10.4.7.2 Projected Impacts
Climate change is associated with significantly increased mortality (high 
confidence). Figure 10.11 shows projected health impacts due to climate 
change in Asia. The global estimates of excess deaths due to malnutrition, malaria, diarrhoea and heat stress will be approximately 250,000 
deaths per year in 2030–2050 under the medium-to-high emissions 
scenario, assuming no adaptation (World Health Organization, 2014). 
The impacts are expected to be greatest in South, East and Southeast 
Asia. Another projection showed that the change in heat-related deaths 
is largest in Southeast Asia, which was a 12.7% increase at the end of

the century under a high-emissions scenario (Gasparrini et al., 2017). As 
the proportion of older individuals in the population rises, the number of 
years lost due to disability increases more steeply (Chung et al., 2017b). In 
the 2080s, the number of annual temperature-related deaths is estimated 
to reach twice that in the 1980s in China (Li et al., 2018c). Over a 20-year 
period in the mid-21st century (2041–2060), the incidence of excess heat-
related mortality in 51 cities in China is estimated to reach 37,800 (95% 
CI: 31,300–43,500) deaths per year under RCP8.5 (Bazaz et al., 2018).
Increased concentrations of fine particulate matter and ozone 
influenced by extreme events such as atmospheric stagnations and 
heatwaves are projected to result in an additional 12,100 and 8,900 
deaths per year due to fine particulate matter and ozone exposure, 
respectively, in China in the mid-century under RCP4.5 (Hong et al.,

2019a). Excess ozone-related future premature deaths is noticeable 
in 2030 in East Asia and India for RCP8.5 (over 95% of global excess 
mortality) (Silva et al., 2016).
The global estimates for increases in malaria and dengue deaths 
(annual estimates) are approximately 32,700 and 280 additional 
deaths, respectively, in 2050 under the medium-to-high emissions 
scenario (World Health Organization, 2014). Among these additional 
deaths, 9,300 and 200 deaths, respectively, are projected to occur in 
South Asia. The population at risk of malaria infection is estimated to 
increase by 134 million by 2030 in South Asia under the medium-to-
high emissions scenario, considering socioeconomic development. If 
no actions are taken, malaria incidence in northern China is projected 
to increase by 69–182% by 2050 (Song et al., 2016). Another study 
suggested a decrease in climate suitability for malaria in northern and 
eastern India, southern Myanmar, southern Thailand, the Malaysia

border region, Cambodia, eastern Borneo and Indonesia by 2050 
(Khormi and Kumar, 2016). By contrast, climate suitability for malaria is 
projected to increase in the southern and southeast mainland of China 
and Taiwan, Province of China (Khormi and Kumar, 2016).
Dengue incidence is projected to increase to 16,000  cases per year 
by 2100 in Dhaka, Bangladesh, if ambient temperatures increase by 
3.3°C without any adaptation measures or changes in socioeconomic 
conditions (Banu et  al., 2014). This would represent an increase in 
incidence of over fortyfold compared with 2010. Higher numbers of 
dengue fever cases are projected to occur under RCP8.5 than RCP2.6 
in China (Song et  al., 2017). Compared with the average numbers 
in 1997–2012, the annual number of days suitable for dengue fever 
transmission in the 2020s, 2050s and 2080s will increase by 15, 25 
and 40 d, respectively, in southern China under RCP8.5. In addition,

areas in which year-round dengue fever epidemics occur will likely 
increase by 4500, 8800 and 20,700 km2 in the 2020s, 2050s and 2080s, 
respectively, under RCP8.5 (Nahiduzzaman et al., 2015).
The global estimates for increases in deaths due to diarrhoeal 
disease (annual estimates) in children under 15  years in 2030 and

10
1509Asia  Chapter 10
2050 are approximately 48,000 and 33,000 additional deaths, 
respectively, under the medium-to-high emissions scenario (World 
Health Organization, 2014). Among these additional deaths, 14,900 
and 7,700 deaths, respectively, are projected to occur in South Asia. 
An updated projection with a pathogen-specific approach estimated 
25,000 additional annual diarrhoeal deaths in Asia in 2080–2095 
under the high-emissions scenario (Chua et al., 2021), while in some 
countries, such as Japan, net reductions in temperature-induced 
infectious diarrhoeal cases were estimated, because viral infections 
are dominant in these countries during the cold season (Onozuka 
South and Southeast Asia are projected to be among the highest-risk 
regions for reduced dietary iron intake among women of childbearing 
age and children under five years due to elevated CO 2 concentrations 
(medium confidence) (Smith and Myers, 2018). The estimated number

of additional deaths due to climate change in children aged under 
five years attributable to moderate and severe stunting in 2030 and 
2050 are approximately 20,700 and 16,500, respectively, in South 
Asia, under the medium-to-high emissions scenario (World Health 
Organization, 2014). In Bangladesh, due to climate change, river 
salinity is projected to be increased in coastal and freshwater fishery communities leading to significant shortages of drinking water in the 
coastal urban areas (Dasgupta et al., 2014c).
10.4.7.3 Adaptation Options/Co-benefits
The health co-benefits of GHG mitigation measures in energy 
generation have been reported to reduce disease burden. In China, 
the implementation of GHG policies would reduce the air-pollution-
associated disease burden by 44% in 2020 under the Integrate Carbon 
Reduction scenario compared with the business-as-usual scenario 
(Liu et al., 2017b). Transition to a half-decarbonised power supply for

the residential and transport sectors would prevent 55,000–69,000 
deaths in 2030 compared with the business-as-usual scenario (Peng 
et al., 2018). A shift in travel modes from private motor vehicles to the 
use of mass rapid-transit lines is estimated to reduce CO 2-equivalent 
emissions by 6% in greater Kuala Lumpur and bring important health 
co-benefits to the population (Kwan et  al., 2017). The 25 measures 
developed for reducing air pollution levels in Asia and the Asia–
Pacific in general would reduce CO 2 emissions in 2030 by almost 
20% relative to baseline projections and decrease warming by 0.3°C 
by 2050, which could eventually reduce heat-related excess deaths 
in the region (UNEP , 2019). The 25 measures include conventional Projected health impacts due to climate change in Asia
related mortality
0%10%
5%15%and Heat Cold
Excess mortality (%)
according to four
different climate
change scenarios
12.7% 
largest 
increase
in Asia

South and Southeast Asia: highest risk regions for reduced 
dietary iron intake among women of childbearing age and 
children under 5 years due to elevated CO2 concentrations37,800  deaths per year excess heat-related mortality in 51 chinese cities 
12,100   death per year atmospheric stagnations due to Increased concentrations of fine particulate matter 
8,900  deaths per year due to ozone influenced by extreme events such as heatwaves
Double  amount of temperature-related deaths since 1980s  
Malaria  and dengue  mortality
32,700  additional deaths globally
9,300   additional deaths  in South Asia 
134 million population to be infected in South Asia 
69%–182%  increase in malaria incidence in Northern China Northern and eastern India
Southern Myanmar
Southern Thailand
Malaysia border region
Cambodia
Eastern Borneo
IndonesiaSouthern and 
south-eastern ChinaClimate suitability for malaria and dengue by 2050
280  additional deaths globally
200  additional deaths in South Asia

16,000 cases per year
(40-fold increase compared to 2010)Dhaka
BangladeshIncrease in area per year of 
round dengue fever epidemics
4,500 km2
2020
8,800 km2
205020,700 km2
2080Diarrhoeal disease  mortality
48,000 approx. global increase in additional deaths by 2030 &
33,000  approx. global increase in additional deaths by 2050
in children aged under 15 years, globally
14,900 approx. global increase in additional deaths by 2030 &
7,700  approx. global increase in additional deaths by 2050
in children aged under 15 years, South Asia
Japan
Net reductions in temperature-induced
infectious diarrhoeal cases 
Reduce
dietary IronSouth Asia: additional deaths in children aged under 5 years 
attributable to moderate and severe stunting in 2030 and 
2050 are approximately 20,700 and 16,500, respectivelyOther deaths
due to climate changeSouth Korea Thailand Vietnam China East Asia Japan Southeast Asia Philippines

2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099 2010–2019 2050–2059 2090–2099
Figure 10.11 |  Projected health impacts due to climate change in Asia.

10
1510Chapter 10 Asia
emissions controls focusing on emissions that lead to the formation 
of fine particulate matter (PM2.5), next-stage air-quality measures for 
reducing emissions that lead to the formation of PM2.5 and are not 
yet major components of clean-air policies in many parts of the region, 
and measures contributing to development-priority goals with benefits 
for air quality. Health co-benefits outweigh mitigation costs in the 
Republic of Korea up to 2050 (Kim et al., 2020). Low-carbon pathways 
consistent with the 2°C and 1.5°C long-term climate targets defined in 
the Paris Agreement are associated with the largest health co-benefits 
when coordinated with stringent air pollution controls in Asia followed 
by Africa and Middle East (Rafaj et al., 2021).
Strategies to increase energy efficiency in urban environments by 
compact urban design and circular-economy policies can reduce GHG 
emissions and reap ancillary health benefits; for example, compared

with conventional single-sector strategies, national CO 2 emissions 
can be reduced by 15–36%, and the annual deaths from 25,500 to 
57,500 are avoidable from air pollution reduction in 637 Chinese cities 
(Ramaswami et al., 2017). In a city in China, the existing mitigation 
policies (e.g., promotion of tertiary and high-tech industry) and the 
one-adaptation policy (increasing resilience) increased the co-benefits 
for well-being (Liu et al., 2016a).
Changing dietary patterns, particularly reducing red meat consumption 
and increasing fruit and vegetable consumption, contributes to reduced 
GHG emissions as well as reduced premature deaths. The adoption of 
global dietary guidelines was estimated to prevent 5.1 million deaths 
per year relative to the reference scenario in which the largest number 
of avoidable deaths occurred in East Asia and South Asia, and GHG 
emissions would be reduced most in East Asia (Springmann et  al.,

2016). In China, dietary shifts to meet national dietary reference 
intakes reduced the daily carbon footprint by 5–28% depending on the 
scenario (Song et al., 2017). In India, the optimised healthy diets (e.g., 
lower amounts of wheat and increased amounts of legumes) could 
help reduce up to 30% water use per person for irrigation and reduce 
diet-related GHG emissions. This would result in 6800 life-years gained 
per 100,000 population in 2050 (Milner et al., 2017).
10.5 Adaptation Implementation
10.5.1 Governance
10.5.1.1 Points of Departure
Climate-change governance is characterised by a scalar/stakeholder 
turn which includes: (a) acknowledgement of the importance of both 
subnational and transnational–regional scales along with the global 
scale; (b) involvement of diverse stakeholders in decision-making 
systems; (c) reliance on bottom-up architectures of governance that 
are supported by the framework given by the SDGs; (d) emphasis

on developmental and environmental co-benefits; (e) recognition of 
diverse experiences of marginalisation and social stratification, and 
their impacts on participation in governance-related activities; and (f) 
greater decentralisation and strengthening of local institutions.10.5.1.2 Findings
In order to facilitate local adaptation, especially in a context characterised 
by regional diversity and spatio-temporal variation, climate-adaptive 
governance invites greater policy attention to institution building 
(formal and informal) at multiple scales and across sectors (Mubaya 
and Mafongoya, 2017). An incremental EbA approach underlines 
the advantage of drawing upon ecosystem services for reducing 
vulnerabilities, increasing resilience of communities to adapt to climate 
change, and minimising threats to social systems and human security, 
provided climate change remains below 2°C or, better yet, below the 
1.5°C of global warming (Barkdull and Harris, 2018).

Focus on multi-level governance, both below and beyond the state level, 
is steadily growing (Jogesh and Dubash, 2015; Jörgensen et al., 2015; 
Beermann et al., 2016). Discernible diversity across political systems and 
sectors in Asia notwithstanding, issues relevant to multi-level climate 
governance includes interplay between top-down national initiatives, 
which stem from supranational, regional and sub-regional levels. In the 
case of India, national climate governance has proliferated beyond the 
National Action Plan on Climate Change to include State Action Plans on 
Climate Change of over 28 states and union territories, demonstrating 
graphically the shared ‘co-benefit’ in terms of creating greater space for 
innovation and experimentation (Jörgensen et al., 2015).
In Japan’s Climate Change Adaptation Act, enacted by the Japanese 
Diet in June 2018, the national government shall formulate a national 
action plan to promote adaptation in all sectors. This Act recommends

that prefectures and municipalities designate a ‘local climate change 
adaptation centre’ as a local climate-change data collection and 
provision centre to provide more locally specific information and 
support for adaptation planning at the level of local municipalities. 
The Japanese government, in partnership with the private sector, has 
formulated a new comprehensive strategy, named Society 5.0, which 
aims at devising a number of technologically innovative solutions 
(Mavrodieva and Shaw, 2020).
Significantly, the co-benefit concept for international city partnerships 
along with comparative analysis of the challenges, capabilities and 
limitations of urban areas in Asia with regard to CCA governance 
remains under-researched (Beermann et al., 2016).
In the case of Vietnam, especially at district and community levels, 
where the policy capacities in hierarchical governance systems to 
deal with climate-change impacts are generally constrained, the value

of clear legal institutions, provision of financing for implementing 
policies and the training opportunities for governmental staff has been 
well demonstrated (Phuong et al., 2018b). A key finding is that any 
effort to support local actors (i.e., smallholder farmers) should ensure 
augmentation of policy capacity through necessary investments.
In the case of China, a combination of market-based policies, 
emissions trading systems, a growing number of environmental non-
governmental organisations (NGOs) and international networks appear 
to be serving as an important tool for climate governance (Ramaswami 
et al., 2017; Wang et al., 2017b). Public private partnership (PPP) too 
is receiving increasing focus, especially with regard to climate-related

10
1511Asia  Chapter 10
cost-effective and innovative infrastructure projects. In the absence 
of major investments in resilience, climate change may force up to 
77  million people into a poverty trap by 2030 (World Bank, 2016). 
As seen in the case of Japan, most of the countries in Asia face the 
challenge of contractual allocation of risks associated with natural 
hazards and climate change between the public and private sectors 
and its long-term management in the face of uncertainty. Risk sharing, 
therefore, could be addressed by clear definition and allocation (World 
Bank, 2017). Given that in Asia, especially Singapore, China, Japan 
and Republic of Korea, where the water sector is a target of industrial 
and technology policy, PPPs could prove to be mutually beneficial. As 
a middle ground, key findings of a study on Indonesia (Yoseph-Paulus 
and Hindmarsh, 2016) underline the importance of building, sustaining

and augmenting local capacity by addressing inadequacies with regard 
to resource endowment and capacity building, public awareness about 
climate change, government–community partnerships, vulnerability 
assessment and providing inclusive decision-making spaces to 
Indigenous knowledge systems and communities.
In the agriculture sector, farmers in Asia are adapting to climate 
change at the grassroots level (Tripathi and Mishra, 2017). A recent, 
comprehensive and systematic review (Shaffril et al., 2018) shows how 
farmers in diverse sub-regions of Asia have adopted diverse adaptation 
strategies through management of crops, irrigation and water, farms, 
finances, physical infrastructure and social activities. Much more 
qualitative research on farmers’ perceptions and decision-making 
processes about adaptation practices is needed in order to capture 
their location-specific priorities and get a diverse understanding of

the risks and threats. A study of Vietnamese smallholder farmers’ 
perceptions of their current and future capacity to adapt to climate 
change (Phuong et al., 2018a) found considerable differences between 
farmers in crop production and livestock production in terms of their 
motives behind adopting particular planned adaptation options.
A study on farmers’ awareness of, and adaptation to, climate change in 
the dry zones of Myanmar, critically dependent on agriculture, indicates 
how those at the front line of the adverse effects of climate change 
are steadily abandoning the common sesame/groundnut cropping 
pattern, and trying to adapt to risks and uncertainties with the aid 
of conventional agricultural practices such as rainwater collection, 
water-harvesting techniques and even traditional weather forecasting 
techniques for weather prediction. Similarly, a case study of the Gandak 
basin in Nepal showed that incorporation of local knowledge into

agricultural practices and weather warning systems works best when 
coupled with multiple sources of information based on a method of 
triangulation. This also intersects with gender outcomes, where women 
frequently receive information from the men of their households rather 
than directly from state institutional sources (Acharya and Prakash, 
2019). Climate-change adaptive governance is facilitated by improved 
cross-scalar and cross-sectoral cooperation, exchange of information 
and experiences, and best practices (Smith et al., 2014; Watts et al., 
2015; Gamble et al., 2016; Gilfillan et al., 2017).
An integrated approach informed by science, which examines 
multiple stressors along with Indigenous knowledge, appears to be 
of immense value (Elum et al., 2017). A study on Pakistan concluded 
that poor agricultural communities are among the worst victims of climate change (Ali and Erenstein, 2017) and that farmers who are

younger, better educated, belong to joint families and possess more 
landholdings are likely to adapt sooner and better. Correspondingly, 
this category achieved higher levels of income and food security. 
The climate-development nexus suggests that CCA practices at the 
farm level can have significant development outcomes, besides 
reducing risk posed by changing weather patterns. Central to the 
CCA process is the growing recognition of the role that institutions 
play in both the hierarchical setting and across different scales to 
influence implementation of CCA in diverse areas of governance 
across social and political domains. Cuevas (2018) highlights the 
usefulness of mainstreaming CCA into local land-use planning in 
Albay, the Philippines, by involving networks of interacting institutions 
and institutional arrangements for overcoming obstacles that are 
potentially counterproductive and conflictual.
As noted by AR5 (IPCC, 2014a), research on issues related to both

climate-change impacts on livestock production–demand for which is 
expected to double by 2050 in a world of 10 billion people–and policy 
choices with regard to adaptation, especially at the local scale, is still 
limited but progressing (Rojas-Downing et al., 2017). The promise of 
diversification of livestock animals (within species), crop diversification 
and transition to mixed crop–livestock systems needs to be further 
explored. A study of livestock farmers in Pakistan showed that risk-
coping mechanisms, such as purchasing livestock insurance and 
increasing land areas for fodder, are far more rewarding policy options 
in comparison with selling livestock and migrating to another place. 
Relatedly, the association of migration with adaptation measures is 
context specific and involves a number of factors pertaining to the 
socioeconomic circumstances of vulnerable agricultural groups in 
countries like India and Bangladesh (Ojha et al., 2014). In the 2010

United Nations Framework Convention on Climate Change’s Cancun 
Adaptation Framework, migration was recognised as a form of 
adaptation that should be included in a country’s long-term adaptation 
planning where appropriate (Paragraph 14 f).
Furthermore, agricultural climate-adaptation policy targeting livestock 
farmers in rural areas is very likely to benefit from better education and 
awareness as well as increased access to extension services among 
livestock farmers on climate risk-coping choices and strategies (Rahut 
and Ali, 2018). In Myanmar, the lack of adequate agricultural extension 
strategies has had a negative impact on adaptation outcomes in what 
is labelled the ‘central dry zone’. Farmers’ perceptions of climate change 
contribute to a comprehensive understanding of the context where 
they identify deforestation and related activities as the main culprits. 
Their adaptive methods include agricultural land preparation and crop

rotation practices in addition to rainwater-harvesting techniques (Swe 
et al., 2015). A study of vulnerable areas in Bangladesh (Alam et al., 2017) 
has shown that with policy support, livestock rearing can prove to be a 
viable substitute for crop production in areas prone to riverbank erosion. 
Carefully developed partnerships between government organisations 
and NGOs can come to the rescue of poor farmers and their precarious 
households by providing information about best practices for local 
adaptation strategies, including credit options with various institutions 
and creating an enabling environment for the promotion of agro-based 
industries. A study in community forestry in the Indian Himalayan region 
(Gupta and Koontz, 2019) has shown how the synergies and successful

10
1512Chapter 10 Asia
partnerships could evolve between government and NGOs in local forest 
governance, with the former providing technical and financial support, 
and the latter directing the communities to those resources, and in the 
making up for each other’s limitations thereby enabling and augmenting 
community efforts in forest governance.
A study of Pakistan (Ali and Erenstein, 2017) shows that factors such 
as enhanced awareness about various climate risk-coping strategies, 
better education and agricultural extension services, augmenting 
farm-household assets, lowering the cost of adaptation, improving 
access to services and alternative livelihoods, and providing support 
to poorer households appear to have paid rich dividends. Countries 
such as Bhutan and Sri Lanka have included provisions for ‘climate-
smart agriculture’ in their nationally determined contributions (NDCs) 
(Amjath-Babu et al., 2019).

In the domain of forest adaptive governance, ever since the introduction 
of Reducing Emissions from Deforestation and forest Degradation plus 
(REDD+) at COP 13 in 2007 in Bali, the Indonesian experience suggests 
that some of the major challenges include curbing emissions, changes 
in cross-sectoral land-use as well as practice within forestry and lack 
of effective, efficient and equitable implementation of diverse forest 
governance practices. The issue of how forest governance institutions 
are conceived and managed, both at national and subnational levels, 
involving state, private sector and civil society, also needs serious 
attention (Agung et al., 2014).
In an example from Nepal, Clement (2018) showed that deliberative 
governance mechanisms can create the space for alternative framings 
of climate change to take a hold in ways that are cognisant of both the 
local and global contexts; this moves beyond a dependence on techno-

managerialism in the construction of solutions, where local governance 
solutions can support institutional changes. The possibilities more 
incorporating deliberative methods into wider governance architecture 
are also expanded through an acknowledgment of the role of social 
learning; this is observable in the multi-stakeholder involvement that 
this approach fosters in regions of South Asia such as the Brahmaputra 
River basin (Varma and Hazarika, 2018). Additionally, recent studies 
have reconfirmed the importance of linking Indigenous knowledge 
with the scientific knowledge of climate change in diverse regions 
of the globe, including Asia and Africa (Hiwasaki et al., 2014; Etchart, 
2017; Taremwa et al., 2017; Vadigi, 2017; Apraku et al., 2018; Inaotombi 
and Mahanta, 2018; Makondo and Thomas, 2018) for building farmers’ 
resilience, enhancing CCA, ensuring cross-cultural communication, 
promoting local skills, drawing upon Indigenous Peoples’ intuitive

thinking processes and geographic knowledge of remote areas.
A study of the Sylhet Division in Bangladesh, deploying a knowledge 
quality assessment tool, found significant correlation between a narrow 
technocratic problem framing, divorced from traditional knowledge 
strongly rooted in local sociocultural histories and relatively low 
project success due to skewed risk-based calculations disconnected 
from the ground realities (Haque et  al., 2017; Wani and Ariana, 
2018). Highlighting the vulnerability of the Bajo tribal communities, 
who inhabit the coastal areas of Indonesia, to climate change, the 
study showed how they share several examples of their Indigenous 
knowledge and traditions of marine resource conservation, and how this wisdom, a valuable asset for climate adaptation governance, has 
been passed from generation to generation through oral tradition.
10.5.1.3 Knowledge Gaps and Future directions
One of the major knowledge gaps in the domain of climate adaptation

governance relates to implementation by various stakeholders at 
multiple scales, and sharing of information and experiences in this 
regard. There is a need to assuage the perceptions of distrust in global 
information, through governance methods that engage multiple 
stakeholders in open and lucid channels of communication (Stott 
and Huq, 2014). This is observable in the structure of the New Urban 
Agenda which formed part of the SDGs pertaining to cities and has 
been shaped by a bottom-up process marked by diverse participation 
including communities, experts and activists, rather than the top-
down variant that is observable in the Millenium Development Goals 
(Barnett and Parnell, 2016). This approach could also be evidenced 
in the Paris Agreement, which placed the onus of a successful global 
governance regime on the development of efficient systems of regional 
governance. However, these emerging systems of regional governance

could equally pose a challenge to the global governance in a way that 
can be witnessed through the development of financial groups such 
as the BRICS (associated economies of Brazil, Russia, India, China and 
South Africa) and Asian Infrastructure Investment Bank, which resulted 
from a perception of inadequate institutional transformation at the 
global level. From another perspective, a comprehensive approach 
would require simultaneous implementation of both bottom-up and 
top-down models of governance, retaining flexibility of scale.
Given the concerns surrounding food security, especially in light of the 
principles of common but differentiated responsibilities, under the NDCs 
submitted by South Asian nations under the Paris Agreement, emission 
reduction commitments are less likely to include the agriculture sector. 
Prospects for enhancing both adaptive capacity and food security 
could be improved by strengthening resilience and profitability

through the introduction of a basket of policy choices and actions 
including structural reforms, agriculture value-chain interventions 
and landscape-level efforts for climate resilience. Correspondingly, 
the substantial adaptation finance gap could be closed with the help 
of both private finance (autonomous adaptation) and international 
financial transfers (Amjath-Babu et al., 2019).
For nearly five decades, integrated coastal management (ICM), 
advocated by several international organisations (e.g., IMO, UNEP , 
WHO, FAO) and adopted by over 100 countries, has been acknowledged 
as a holistic coastal governance approach aimed at achieving coastal 
sustainability and reducing the vulnerability of coastal communities 
in the face of multiple environmental impacts (high confidence). In 
view of threats posed to coastal ecological integrity by climate-
change-induced tropical storm activity, accelerated SLR and littoral

erosion and social–ecological impacts on the livelihood security of 
vulnerable coastal communities, the pressing need for approaches that 
innovatively combine coastal zone management and CCA measures 
is widely acknowledged (Rosendo et al., 2018) yet under researched. 
A study focusing on the three coastal cities of Xiamen, Quanzhou and 
Dongying, in China, a country with nearly 12% of its national coastline 
already covered under the ICM governance framework, suggests that

10
1513Asia  Chapter 10
whereas the ICM approach has been found to be effective in promoting 
the overall sustainability of China’s coastal cities (Ye et  al., 2015) 
using accurate and reliable data, in addition, the developing unified 
standards could usefully reveal changing conditions and parameters 
related to ICM performance.
Steadily the regional scale of climate adaptive governance is acquiring 
salience in diverse sub-regions of Asia, and more policy-oriented 
empirical research is needed on how various regional forums, agencies 
and multilateral organisations could further contribute by way of in-
house expertise and other resources, including financial. A study of 
climate adaptation in the health sector in Southeast Asia (Gilfillan, 
2018) highlights the growing role of the Asian Development Bank 
(ADB) and the Asia-Pacific Regional Forum on Health and Environment, 
and shows that their mandates and goals could mutually benefit from

the institutionalisation of coordination mechanism. An example from 
the Maldives shows that the 2014 Tsunami, climate change and the 
risk of extreme weather events have led to the legitimisation of state-
led population resettlement programmes. In China, this has occurred 
through the renaming of previously existing resettlement initiatives as 
climate adaptation initiatives; however, the efficacy of resettlement as 
a CCA measure requires further scrutiny (Arnall, 2019). In India, the 
National Adaptation Fund on Climate Change has been instituted in 
order to enable states to implement adaptation programmes; however, 
this does not address the question of mainstreaming CCA into designs 
for development (Prasad and Sud, 2019). This is closely related to the 
development of National Adaptation Programmes of Action (NAPAs) 
where the mainstreaming of adaptation within countries has been

an important concern. Insights from developing countries indicate that there is still much ground to cover. The NAPA of the Maldives 
prioritises food security, coastal resources and public health, while 
Nepal has prioritised ecosystem management and public health, 
and food security, among other concerns (Saito, 2013). Importantly, 
Bangladesh’s NAPA has shown that there is potential for ‘reflexivity’ 
in the integration of adaptation objectives with sectoral objectives (Vij 
et  al., 2018). Conspicuous by their absence are the transboundary-
scale adaptation policies in South Asia (Vij et al., 2017).
A distinguishing feature of the case of Japanese apple growers is the co-
existence of both top-down and bottom-up adaptation practices. The 
former pertains to farmers who rely on the support of the cooperative 
for agricultural support and follow institutional mechanisms. The 
latter pertains to non-co-op farmers who have been responsible for

innovative practices of cultivation such as the shift to peaches and 
the sale in the market of apples without leaf-picking. Importantly, 
the non-co-op group also have access to sales channels that may 
not be accessible to the former owing to their direct interactions 
with customers, among other factors (Fujisawa and Kobayashi, 2011; 
Fujisawa et  al., 2015). The significance of this combination of top-
down and bottom-up approaches to agricultural adaptation practices 
may be further sharpened by formulating approaches for Asia and 
the Pacific region in ways that contribute to the fortification of food 
security objectives and the idea of co-benefits. This may be carried 
out by enhancing the ability of farmers to better manage cultivation 
practices in the context of climatic variability (FAO, 2018d).
There exist numerous barriers to the mainstreaming of CCA measures 
across Asia. The integration of CCA into the dissemination of localised Box 10.5 | Bangladesh Delta Plan 2100

The Bangladesh Delta Plan (BDP) 2100 is the plan moving Bangladesh forward for the next 100 years. We have formulated BDP 2100 in 
the way we want to build Bangladesh. (Commission, 2018).
The vision of BDP is revealed by the foregoing statement from Sheikh Hasina, the prime minister of Bangladesh. The government approved 
BDP 2100 in 2018. Achievement of a safe, climate-resilient and prosperous delta is the aspiration of the delta plan. Ensuring water and 
food security with economic growth, environmental sustainability, climate resilience, vulnerability reduction to natural hazards and 
minimising different challenges of the delta through robust, adaptive and integrated strategies, and equitable water governance, are the 
mission of this mega plan. Under this mission, three higher-level goals and six specific goals have been determined. Three higher-level

goals include elimination of extreme poverty by 2030, achievement of upper middle-income status by 2030 and becoming a prosperous 
country beyond 2041. Six specific goals of BDP 2100 are fully linked with SDG Goals 2, 6, 13 and 14 and partially linked with Goals 1, 
5, 8, 9, 11 and 15. These specific goals comprise a wide range of issues, including land and water resources, climate change, disaster, 
wetlands and ecosystems, river systems and estuaries. The vision, mission and goals of BDP 2100 reveal that this mega plan is a holistic 
and integrated approach considering diversified themes and sectors for the whole country. The implementation of the BDP 2100 requires 
total spending of an amount of about 2.5% of the GDP per annum. A series of strategies have been formulated for better implementation 
of the mega plan.
Water is the key and complicated resource of Bangladesh, and therefore BDP 2100 has kept water at the centre of the plan. It aims to

promote wise and integrated use of water and other resources through development of effective institutions and equitable governance 
for in-country and transboundary water resource management.
Along with water, for the first time in any development planning, BDP 2100 has taken the climate-change issue as an exogenous variable 
in developing the macroeconomic framework of the plan. In a brief, it is stated that the principle of BDP 2100 is ‘Living with Nature’.

10
1514Chapter 10 Asia
climatic information and its uptake and implementation through 
institutional policy arrangements remain areas of concern (Cuevas, 
2018). Institutional incentives to agricultural production, for instance, 
are frequently compounded by the negative impacts they have on 
existing bases of natural resources. The disconnected operations of local 
governmental agencies coupled with inadequacies of cross-sectoral 
coordination further highlights the prevalent food–water–energy nexus 
(Rasul, 2016). One possible way of addressing these intersecting sources’ 
complexity is by locating emerging CCA measures in educational 
development. The introduction of CCA thinking into land-use planning in 
the Philippines is an example of the successful role of enhancing public 
education and awareness through the dissemination of information 
by institutional channels. The linkages between the strength of local

leadership and the inclusion of CCA in localised planning activities are 
also well illustrated by the case study of Cuevas (2018).
As shown in the case of Pakistan, level of education shares a positive 
relationship with the implementation of adaptation measures (Ali and 
Erenstein, 2017). However, a closer examination of the educational 
imperatives that drive CCA in ways that improve the representational 
architecture of adaptation actions through a focus on gender is needed. 
Mainstreaming of gender into CCA would involve addressing a host 
of barriers to education and involvement that are often rooted in the 
differential structures of households, social norms and roles, and the 
domestic division of labour (Rao et al., 2019). A study from the Indian 
state of Bihar shows that gender plays a major role in determining 
intra-household decision making and also inhibits the ability of female-
headed households to establish access to agricultural extension

services (Mehar et al., 2016). Even within wider female farmer-operated 
federations, such as the Bangladesh Kishani Sabha (BKS), the barriers 
to participation stem from social factors that include the limitation of 
female mobility through the gendered division of labour and a lack of 
recognition of female agency (Routledge, 2015). Gendered inequalities 
in educational attainment and outcomes viewed through the lens of 
social vulnerability thus intersect with environmental vulnerabilities in 
ways that affect the ability of women to participate in CCA, owing 
also to a lack of access to health and sanitation facilities. These factors 
have a direct impact on the ability of adaptation to be effective in 
the global South, and are especially important in the context of the 
commitments of the UN Convention on the Elimination of All Forms 
of Discrimination Against Women countries to the objective of gender 
equality (Roy, 2018).
10.5.2 Technology and Innovation

10.5.2.1 Point of Departure
Much like any other field, CCA is greatly facilitated by science, 
technology and continuous innovation. These range from the 
application of existing science, to the development of new scientific 
tools and methods, to the utilisation of Indigenous knowledge and 
citizen science. Many of the pressing problems in Asia, including water 
scarcity, rapid urbanisation, loss of natural habitats, biodiversity, 
rising coastal and river basin hazards, and agricultural loss can be 
effectively minimised through the adoption of suitable scientific and 
technological methods. Despite the current challenges in the region, many significant advances in science and technology have been made, 
and the future prospects look bright. The following sections outline 
the present status and future prospects of science and technology in 
scaling up adaptation actions in four key sectors, namely (a) DRR, (b)

urbanisation, (c) water and agriculture, and (d) forests and biodiversity.
10.5.2.2 Findings
10.5.2.2.1 Disaster risk reduction
Technological advances have enhanced the capabilities of Asian 
countries to monitor and prepare for climate-related hazards. Remote 
sensing technologies and GIS are widely used for DRR (Kato et  al., 
2017), for example, to assess and mitigate risks of an area to potential 
climate-related disasters (Wu et al., 2018b). The potential impacts of 
different types of hazards can be visualised using interactive maps 
(Lee, 2017), which help local communities to understand risks and 
find appropriate evacuation areas (Cadiz, 2018). These maps provide a 
situational overview and instant risk assessment (Yang et al., 2012b). 
As an emerging technology, artificial intelligence (AI) can identify 
conditioning factors of a landslide disaster (Hong et al., 2019b). Mobile 
virtual reality is used for disaster mitigation training through a three-

dimensional visualisation of a past disaster (Ghosh et al., 2018).
A community-based DRR system provides risk investigation, training 
and information analysis (Liu et  al., 2016b). Sharing information 
enhances establishment of such a system and contributes to disaster 
prevention (Nakamura et  al., 2017). One example is an online 
mapping tool, which has been developed by volunteers (Sakurai and 
Thapa, 2017). Social media enables the population to access real-
time information on a disaster (Ghosh et al., 2018), raises situation 
awareness (Yin et  al., 2012) and empowers communities towards 
appropriate emergency actions (Leong et  al., 2015). Among the 
various forms of social media, Twitter is widely used as a social sensor 
to detect what is happening in a disaster event (Sakaki et al., 2013). 
Accuracy of information on Twitter has been proved in collecting local 
details about floods (Shi et al., 2019b); however, it has been noticed

that Twitter generates rumours as well (Ogasahara et  al., 2019). 
Artificial intelligence is expected to reduce human error when they 
operate a decision-making system (Lin et al., 2018). Since technologies 
supporting DRR completely depend on electricity, the loss of power 
supply and communication constrains the recovery work in disaster-
affected areas (Sakurai et al., 2014).
10.5.2.2.2 Urban sector
In the urban sector, a wide variety of sensor technologies are being 
used to monitor urban land-use and climate changes over time, and 
to better understand the potential impacts of future changes. These 
sensors range from large optical–thermal–radar satellite instruments 
with (near) global coverage–for example, Landsat (US Geological 
Service), Sentinel (European Space Agency), ALOS (Japan Aerospace 
Exploration Agency) and MethaneSAT–to portable sensors embedded 
in mobile phones (e.g., phone cameras or temperature sensors) whose

data are collected into centralised databases through crowdsourcing 
(Fenner et al., 2017; Meier et al., 2017). To combine and extract useful 
information from these heterogeneous sensor data–for example, for

10
1515Asia  Chapter 10
conducting climate risk assessments (Perera and Emmanuel, 2018; 
Bechtel et al., 2019) and/or simulations of future land-use or climate 
changes in urban areas (Bateman et al., 2016; Iizuka et al., 2017; Liu 
et al., 2017c)–AI technologies (e.g., machine-learning algorithms) are 
now being widely adopted (Johnson and Iizuka, 2016; Joshi et  al., 
2016; Mao et  al., 2017). Thanks to advances in cloud-computing 
technology, which allows for online processing of massive volumes 
of remote sensing data, high-resolution (~30 m) global urban-area 
maps from the late 1990s to 2018 are now available from several 
different sources (Gong et  al., 2020). Using these historical maps, 
researchers have been able to generate maps of future urban land-use 
changes at the global level to 2100 (Chen et al., 2020a), which can 
help to elucidate the potential impacts of this future urban expansion 
and identify adaptation needs. Technology also plays a major role in

urban planning and design in the context of adaptation. To mitigate 
rising urban temperatures and reduce the impacts of climate-related 
hazards, many new ‘grey’ infrastructure and ‘green’ infrastructure 
technologies are being adopted in urban areas in Asia, for example, 
cool (i.e., high solar reflectance) rooftops and pavements as well as 
green (i.e., vegetated) rooftops to mitigate high temperatures; and 
porous pavements to mitigate flooding (Akbari and Kolokotsa, 2016).
10.5.2.2.3 Water and agriculture
The majority of the Asian region is experiencing water stress in terms 
of both quantity and quality, due to poor management systems and 
governance. This has dire consequences for the national GDP as the 
majority of the population belongs to agrarian communities and 
their water-dependent agriculture system. Despite a substantial 
investment and progress in research and development, and capacity 
building in the recent past, the majority of developing countries in

Asia are struggling to manage both water resources and agriculture 
sectors heavily dependent on water resources, in response to rapid 
global changes. Considering the frequent extreme weather conditions, 
progress in management tasks is even more consequential; hence, 
critically important for these countries, to achieve better CCA, are 
advanced science and technology viz. smart agriculture, robust 
EWS using downscaled meteorological information, a participatory 
approach, IWRM and so forth.
Having scientific knowledge relevant at the local scale through 
placed knowledge is important to identify climate-change risk and 
vulnerability. Moreover, once integrated with socioeconomic attributes, 
it can be useful for natural resource management, agriculture and so 
forth (Leith and Vanclay, 2017). Role of big data and data mining is 
undeniably very huge to get reliable climatic information and hence 
for designing appropriate adaptation measures for natural resource

measurement. For example, use of big data in terms of EWS and real-
time observation data provides more accurate information on hydro-
meteorological extreme weather conditions or hazards like drought and 
flood, and will help farmers and local governments to improve their 
perception and hence preparedness for better adaptation (Hou et al., 
2017; Ong and G.L.B.L., 2017). Using big data, different adaptation 
measures, such as new cultivar breeding, cropping-region adjustment, 
irrigation-pattern change, crop rotation and cropping-practice 
optimisation, are being designed in the agriculture sector, and these 
practices have greatly increased crop yield, leading to higher resource-use efficiency as well as greatly increased soil organic carbon content 
with reduced GHG emissions. It results in a win–win situation in terms 
of enhancing food security and mitigating climate warming (Deng et al., 
2017). However, usability and application of this technology are still

not common, especially in data-scarce regions. Integrated numerical 
simulations are efficient tools for estimating the current status and 
predicting the risk and efficiency of the adaptive capabilities of different 
countermeasures for sustainable natural resource management 
such as with water (Kumar, 2019). Similarly, the agent-based model 
is commonly used to estimate risk of food-borne diseases due to 
climate change, using tunable parameters such as hygiene level, the 
microorganism’s growth rate and the number of consumers, and hence 
it has the potential to be a useful tool for optimising decision making 
and urban planning strategies related to health and climate change 
(Gay Garcia et al., 2017). The integrated assessment model under the 
shared-socioeconomic pathway (SSP) framework is effectively used to 
estimate future energy development and possible mitigation strategies 
to reduce GHG emissions related to the energy sector (Bauer et al., 2017).

Sound understanding of different drivers, pressures and stress factors, 
such as abnormal temperature, rainfall, insect pests or pathogens and 
their interaction pattern with the genetic makeup of crops, is the key 
to produce high-yielding varieties of wheat with better nutritional 
quality and resistance to major diseases (Goel et  al., 2017). Another 
critical point to address this water security is inclusive, polycentric and 
adaptive governance. Polycentric governance is a means by which 
water management plans and policies should be framed and agreed by 
all relevant stakeholders. For adaptive governance, more emphasis will 
be on finding the best pathways to make robust water management 
plans amid rapid global changes. The benefit of such plans should 
reach the end users in terms of providing clean water, protection from 
hydrological hazards and maintaining the health of the ecosystem. In 
addition, there is urgent need for co-management, which includes the

cycle of co-design, co-implementation and co-delivery throughout the 
whole water cycle. The best suitable example is using the circulating 
and ecological sphere (CES) approach. The CES is a concept that 
complements and supports regional resources by building broader 
networks, which are composed of natural connections (connections 
among forests; city and countryside; groundwater, rivers and the sea) 
and economic connections (human resources, funds and others), thus 
complementing each other and generating synergy (Mavrodieva and 
Shaw, 2020). Another suitable example for managing water resources is 
the participatory watershed land-use management (PWLM) approach. 
The PWLM is another very innovative and successful approach for more 
robust water resource management as explained by Kumar et al. (2020). 
It helps to make land-use and CCA policies more effective at the local 
scale. This is an integrative method using both participating tactics and

computer-simulation modelling for water resource management at the 
10.5.2.2.4 Forests and biodiversity
Technologies and their applications to identify habitat degradation, 
ecosystem functions and biodiversity conservation are increasing in 
Asia, with many countries looking to new and improved means for 
forest and biodiversity monitoring and conservation. In particular, there 
has been an impressive use of temporal satellite data, particularly from 
the Landsat and the Moderate Resolution Imaging Spectroradiometer

10
1516Chapter 10 Asia
(MODIS) series for widespread monitoring of forests and ecological

resources. These series have provided reliable information on forests and ecosystem services at the country level, in difficult terrains, such as the mountains, cross-boundaries and otherwise inaccessible areas. For instance, Yin et al. (2017) estimated cross-boundary forest resources in Central Asia, a region which traditionally suffered from lack of reliable forest data, using remote sensing techniques. In a separate study, Reddy et al. (2020) used long-term MODIS forest fire data from 2003 to 2017 to characterise fire frequency, density and hotspots in South Asia. Archives of scientific data, backed by state-of-the art modelling techniques, advanced-computing methods and innovations in big-data analysis, particularly helped the provisioning of scientific research. A number of studies simulated forest futures from the local to the continent scale under different socioeconomic and climate scenarios. For instance, at the local scale, Dasgupta et al. (2018) projected the future extent

of mangroves in the Sundarban Delta under four local scenarios, while Estoque et al. (2019) modelled and developed spatial maps of regional forest futures in Southeast Asia using the five SSP scenarios. Science and technology also helped the monitoring of species diversity and abundance, pivotal for sustaining an ecosystem and ecosystem-based adaptation. Digital camera traps and radio-collaring methods have largely replaced old film cameras and labour-intensive methods of photo screening to count target species (Pimm et al., 2015). This enhanced scientific capacities to monitor biodiversity and facilitate better conservation in difficult terrains, control poachers and maintain steady ecological balance. Umapathy et al. (2016), for example, used VHF radio collars and satellite-based tracking tools to monitor the movement of Bengal tigers over hostile island terrain. Photo recognition and other non-invasive techniques for individual identification have been rising in Asia. For example,

a study by Gray et al. (2014) used faecal-DNA samples to estimate the population density of the Asian elephant in Cambodia. Furthermore, the advancement of citizen science programmes has greatly facilitated better monitoring of forest resources, including invasive floral and faunal species (Chandler et al., 2017; Johnson et al., 2020). In Asia, citizen science has been used effectively in India (Chandler et al., 2017), and also in Malaysia for the monitoring urban bird abundance (Puan et al., 2019).

With rapid advances of technologies, the use of appropriate technologies generates some degree of management problems. To resolve such problems, the enhancement of information science is essential to understand design, implementation and adoption of digital tools under crisis (Xie et  al., 2020). For example, social media research reveals a way of controlling malicious information (Tanaka et al., 2014), and its characteristics under COVID-19–showing a plain text message–can be more powerful in the context of citizen engagement than media-rich communications (Chen et  al., 2020b). Information behaviour needs more investigation to understand how people survive and connect in the era of information overload (Pan et al., 2020). Moreover, a new set of data (e.g., travel history record, personal health data, etc.) becomes an important base of DRR (Xie et al., 2020). Analysis of these personal data requires careful consideration, as it generates ethical issues (Sakurai and Chughtai, 2020).

Indicators or measurements of technology-enabled crisis response needs to be developed for further risk reduction (Akbari and Kolokotsa, 2016; Wong et  al., 2020). On the other hand, adopting infrastructure technologies requires investment, and due to the inherent uncertainties of climate projections, the future payoffs of these investments are also somewhat uncertain (Ginbo et al., 2020). In the water and infrastructure sectors, for example, various options exist for conducting cost–benefit analysis considering future uncertainty (i.e., so-called robust approaches, which are able to identify adaptation projects and infrastructure that can achieve their intended purpose(s) across a wide range of climate scenarios) (Dittrich et al., 2016). Despite substantial investment and progress in research, development and capacity building in the recent past, the majority of the developing countries in Asia are struggling to manage both water resources and the agriculture sector. Considering the

frequent extreme weather conditions, progress in management tasks has become even more mammoth and, hence, we need a holistic solution, which is currently missing in field implementation. This solution should be based on advanced science and technology in association with other attributes like social, economic and political dynamics, which play a pivotal role in sustainable management of water resources and agriculture, as a way forward.

10.5.3  Lifestyle Changes and Behaviour al Factors
10.5.3.1
 P
oint of Departure

Understanding the motivations and processes underpinning decisions to adapt or not is key to enabling adaptation (see Section 17.2.2.1; Clayton et  al., 2015; de Coninck et  al., 2018; Taylor, 2019; van Valkengoed and Steg, 2019a; van Valkengoed and Steg, 2019b), because how and why certain people adapt is shaped by sociocultural factors, ways of making sense of risks and uncertainty, and personal motivations to undertake action (Nguyen et al., 2016; Mortreux and Barnett, 2017; Singh et al., 2018b; van Valkengoed and Steg, 2019b). The IPCC’s Assessment Report 5 was critiqued for silences on how perceptions shape climate action and the behavioural drivers of adaptation responses (Lorenzoni and Whitmarsh, 2014). Addressing this gap and assessing the growing literature from social sciences, notably psychology, behavioural economics and risk perception studies, the IPCC Special Report on 1.5°C (de Coninck et  al., 2018) comprehensively assessed behavioural dimensions of CCA for the first

time; however, compared with studies on mitigation behaviour, the literature on what motivates adaptation remains incomplete (Lorenzoni and Whitmarsh, 2014; Clayton et al., 2015).

10.5.3.2  F indings
There are three key aspects of adaptation to which psychology and behavioural science contribute: understanding perceptions of climate risk, identifying the behavioural drivers of adaptation actions and analysing the impacts of climate change on human well-being (Clayton et al., 2015). Overall, there is growing acknowledgement that individual adaptation is significantly shaped by perceptions of risk, perceived self-efficacy (i.e., beliefs about which options are effective and one’s ability to implement specific adaptation interventions), sociocultural norms and beliefs within which adaptation decisions are taken, past experiences of risk management and the nature of the intervention itself (Grothmann and Patt, 2005; Werg et al., 2013; Clayton et al., 2015; Truelove et al., 2015; Pyhälä et al., 2016; Deng

10
1517Asia  Chapter 10
Table 10.4 |  Sectors and sub-regions where behavioural aspects of adaptation have been assessed
Sub-
regionSector Adaptation interventions Behavioural aspects affecting adaptationSupporting 
West Asia AgricultureSoil and water conservation activities to mitigate drought 
impactsResponse efficacy and perceived severity shape water conservation.Iran (Keshavarz and Karami, 
2016)
Central 
AsiaNEaNEaNEaNEa
South AsiaAgricultureConservation agriculture, adjusting agricultural practicesRisk perceptions shape adoption of adaptation strategy (e.g., 
perceptions of decreasing rainfall motivate building water storage 
tanks).Nepal (Piya et al., 2013; 
Halbrendt et al., 2014)
Sustainable water management practices, adjusting 
agricultural practicesRisk perception is shaped by sociocultural context, memories, 
experiences and expectations (of future change).India (Singh et al., 2016)
Alternate wetting and drying irrigation, alternative crop

selection, using drought-resistant seedsA combination of attitudes, self-efficacy, outcome efficacy, and 
community efficacy predict intent to adapt strongly.Sri Lanka (Truelove et al., 
2015)
Adjustment in farm management including growing short 
duration or drought-tolerant varieties, pest-resistant 
varieties, changing planting distance, increasing weeding, 
soil conservation techniques, cultivation of direct-seeded 
rice, switching to non-rice cropsFarmers’ education, access to credit and extension services, experience 
with climate-change impacts such as drought and flood, information on 
climate-change issues, belief in climate change and the need to adapt 
all variously determine their decision making.Nepal (Khanal et al., 2018)
Disaster 
managementFlood and cyclone preparedness measures such as using 
durable building materials, raising plinth levels, storing 
food and waterDisaster management behaviour is intuitive: low evidence to suggest

outcome expectancy, self-efficacy, and preparedness intention follow 
linear patterns.India (Samaddar et al., 
2014); Bangladesh 
(Dasgupta et al., 2014b)
Use of emergency toolkits and evacuation plansRisk perception and knowledge of adaptation options shape uptake 
and perceived benefits.Pakistan, Bangladesh (Alvi 
and Khayyam, 2020)
Insurance to deal mitigate financial losses from floods and 
droughtsFrequency, the severity of previous extreme events, socioeconomic 
settings and ability to pay shape decisions to take crop insurance.
Acceptability of flood insurance depends on the perceived efficacy of 
the insurance (among other factors such as age of household head, 
land ownership and off-farm income sources).Pakistan (Arshad et al., 
2016; Abbas et al., 2015)
Embankments and dikes for flood risk mitigationWillingness to contribute manual labour to flood protection measures is 
positively influenced by the number of adult family members, livestock

damage, compensation received and expected effectiveness of the 
intervention, but is negatively influenced by age and education of the 
household head, farm income and the distance of the farm from the 
river.Pakistan (Abbas et al., 
2019)
Southeast 
AsiaAgriculture Changing agricultural practices, diversifying livelihoods Values along with personal and social beliefs of risk shape adaptation.Vietnam (Le Dang et al., 
2014; Cullen and Anderson, 
2016; Nguyen et al., 2016; 
Arunrat et al., 2017)
Disaster 
managementRaising floor height to avoid flooding, retrofitting housesPerceived probabilities and perceived consequences of flood shape 
preparedness.Vietnam (Reynaud et al., 
2013; Ling et al., 2015)
Flood insuranceLikelihood of purchasing flood insurance increased with higher physical 
exposure and subjective perceptions of vulnerability.Malaysia (Aliagha, 2013; 
Aliagha et al., 2014)
EvacuationIndividual risk perceptions lead to learning, but only where previous

disaster experiences are traumatic.Philippines, India (Walch, 
2018)
Disaster preparedness measures such as having kits, 
undertaking precautionary measuresPerceived self-efficacy was the most significant measure affecting 
reactive adaptation; education had the highest effect size on 
anticipatory adaptation.Cambodia (Ung et al., 
2015)
East AsiaAgricultureChanging agricultural practices, diversifying incomes, 
adopting water-saving technology, purchasing weather 
insurancePerceived self-efficacy strongly predicts adaptive intent.China (Jianjun et al., 
2015; Zhang et al., 2016a; 
Burnham and Ma, 2017; 
Feng et al., 2017)
Disaster 
managementGeneralHigher education and being in environments where climate is discussed 
leads to stronger risk perceptions.Taiwan, Province of China 
(Sun and Han, 2018)
Drought management through early warnings, prevention 
informationPolicies can positively shape adaptation decision making depending on

how information is given and what support is provided.China (Wang et al., 2015)
(a) No evidence

10
1518Chapter 10 Asia
et al., 2017; Sullivan-Wiley and Short Gianotti, 2017; Taylor, 2019; van 
Valkengoed and Steg, 2019a). This is in addition to more commonly 
understood factors shaping adaptation behaviour such as technical 
know-how and the cost and benefits associated with an option.
Across Asia, behavioural aspects of adaptation have been studied 
to a lesser extent: a global meta-analysis of 106 studies found that 
most research focused on North America and Europe with only 12% 
of papers from Asia (van Valkengoed and Steg, 2019a). Within Asia, 
behavioural drivers of adaptation decision making have been studied 
primarily in agriculture (in South, East and Southeast Asia) and disaster 
risk management (from Southeast and East Asia) (Table 10.4) and tend 
to focus on technical adaptation interventions rather than how and 
why people adapt (Sun and Han, 2018).
In agriculture, studies demonstrate how perceptions of risk (e.g., climate

variability) (Singh et  al., 2016; Zheng and Dallimer, 2016; Burnham 
and Ma, 2017; Feng et  al., 2017), sociocultural norms and personal 
experiences (Masud, 2016; Nguyen et al., 2016; Singh et al., 2016), and 
perceived efficacy of adaptation interventions in having a positive and 
desirable impact (Halbrendt et  al., 2014; Truelove et  al., 2015; Feng 
et al., 2017), affect adaptation decisions. Policies on providing early 
warnings of drought or information on prevention techniques shape 
farmer decisions to undertake adaptation interventions (Wang et al., 
2015).
In disaster risk management, risk appraisal (Samaddar et al., 2014; Rauf 
et al., 2017; Hung et al., 2018), previous experience and losses (Said 
et al., 2015; Hung et al., 2018; Walch, 2018)12,perceived probabilities 
and consequences (Reynaud et al., 2013), perceived self-efficacy (Ung 
et al., 2015; Hung et al., 2018) and awareness (Hung et al., 2018; Wu

et al., 2018a; Alvi and Khayyam, 2020) shape preparedness. Individual 
risk management is nested within public policies, such as those on 
flood management, which shape individual flood risk perception 
and protective behaviours (Reynaud et al., 2013) as well as personal 
factors such as religious beliefs (Alshehri et al., 2013). For example, 
communities often perceive disasters as ‘acts of God’ (Birkmann 
et  al., 2019) or punishment for wrongdoings (Alshehri et  al., 2013; 
Iqbal et al., 2018), which might constrain adaptive action. However, 
religious faith can also motivate people to prepare for extreme events, 
as Alshehri et al. (2013) showed in Saudi Arabia demonstrating how 
‘Islam urges that it is most important to prepare the people to escape 
from disaster’ (p.1825).
Trust in public action as a mediator of risk management has had 
conflicting evidence: some studies have discussed trust as being 
critical to effective preparedness (Kittipongvises and Mino, 2015;

Walch, 2018), while others have found that trust in public actions, 
such as structural interventions to mitigate flood impacts, can lower 
individual motivations to act since they feel protected (Hung et al., 
2018).
12 Two exceptions to this were found. One is a survey in Saudi Arabia which tested public perceptions of disaster risk and found that direct experience with such disasters does not directly influence risk 
perception (Alshehri et al., 2013). Second, a study on flood experience and ensuing adaptation behaviour in Pakistan found that those with prior flood experience do not make significantly different 
choices than those who have no experience of flooding. What is more significant is repeated exposure to flooding events (Said et al., 2015).
13 Some studies compare nationally representative surveys on climate perceptions and their impacts on climate action to demonstrate that higher risk perception leads to higher motivations to undertake

climate action (Corner et al., 2014; Smith and Mayer 2018). Others, however, highlight that higher risk perception can lead to a normalisation of risks, leading to lower climate action (Luís et al., 2018). 
In all of these papers, there is a recognition that the literature on perceptual drivers of climate action is USA-centric and is negligible in Asia (Capstick et al. 2015).Belief in climate variability and change significantly shapes adaptation 
decision making (Le Dang et al., 2014; Singh et al., 2016; Khanal et al., 
2018; Liu et al., 2018a) with those believing in climate change and 
associated impacts tending to engage in adaptation. Crucially, those 
who do not believe in climate change can be influenced by social norms 
(Arunrat et  al., 2017) thereby incentivising adaptation behaviour. 
While risk perception is a critical step in adaptation decision making, 
higher risk perception does not necessarily signal better capacity to

cope: in Taiwan, Province of China, Sun and Han (2018) highlight 
how perceptions of climate risk as a global problem tends reduce its 
urgency as an individual issue. Providing information on climate risks, 
impacts and possible adaptation options enables adaptation behaviour 
(Piya et al., 2013; Zheng and Dallimer, 2016; Rauf et al., 2017), but 
information alone is not sufficient to motivate adaptive behaviour. 
Specifically, awareness building on concrete measures and outcomes, 
such as the amount of water saved or number of deaths averted, rather 
than abstract notions of climate change, motivate adaptation (Deng 
et al., 2017; Rauf et al., 2017).
10.5.3.3 Lifestyle changes
Changes in current lifestyles and consumption patterns are 
acknowledged as critical to climate action (de Coninck et al., 2018; 
IGES, 2019). With rapidly changing diets and increasing purchasing 
power, lifestyle changes in countries across Asia, especially those

with large populations such as China and India, will be critical to 
contributing to global climate solutions (IGES, 2019). Lifestyle shifts 
that can contribute towards adaptation include:
• Engaging in urban agriculture through rooftop gardening, 
community gardens in urban and peri-urban areas and so forth 
(with implications for food-associated footprints but also nutritional, 
livelihood and well-being benefits) (Mohanty et al., 2012; Ackerman 
et al., 2014; Padgham and Dietrich, 2015)
• Shifts towards organic farming and creating demand for organically 
sourced food and other materials
• Shifts towards water-saving behaviour such as rainwater harvesting, 
water conservation, reducing water usage and so forth
10.5.3.4 Knowledge gaps
Overall, understanding behavioural factors shaping adaptation 
implementation and uptake is important (medium evidence, high 
agreement). While there is a growing literature on behavioural drivers

of adaptation at the individual and household levels, gaps remain 
in understanding how socio-cognitive factors affect adaptation 
behaviour at higher scales (e.g., at local or subnational government, 
in the private sector, etc.)13.More empirical evidence is needed in 
sectors beyond agriculture and disaster risk management (e.g., factors 
motivating urban adaptation) and better coverage across Asia’s sub-

10
1519Asia  Chapter 10
regions. Importantly, there are no studies on the behavioural aspects 
of adaptation from Central Asia.
10.5.4 Costs and Finance
10.5.4.1 Point of Departure
Estimates of adaptation costs and financial needs have evolved 
significantly since the previous IPCC assessments. These developments 
are based on improvements in the understanding of how the hazard 
interacts with the physical and socioeconomic elements, and how 
to capture these interactions in systematic modelling frameworks. 
The developments are also clearly reported especially in the area of 
addressing the underestimates in adaptation costs that the previous 
studies suffered from as the previous studies tended to rely on data 
from wealthy economies (Hochrainer-Stigler et  al., 2014; Carleton 
et  al., 2020). The adaptation cost estimates have also improved 
since the previous IPCC reports due to constant improvements in 
capturing the loss and damages of disaster events (Hochrainer-Stigler

et al., 2014). The reliance of earlier studies on correlations to derive 
adaptation costs was addressed to some extent by addressing the 
endogeneity of disaster measures (Kousky, 2014), especially by relying 
upon the physical measures of disasters such as wind speed, although 
more work is needed in this area.
10.5.4.2 Findings
Climate change can cause significant impacts and as a result can 
impose considerable adaptation costs on countries and people. Despite 
the importance, the research on adaptation costs is limited in Asia, 
especially on the economy-wide costs, while fragmented literature 
is available on sector-level adaptation costs. Most of the available 
literature on adaptation costs at the regional level originate from the 
work carried out by development finance institutions such as ADB.
Estimates suggest that climate-change impacts could result in a loss 
of 2% of the GDP of South Asian countries by 2050 and 9% by 2100

(Ahmed and Suphachalasai, 2014). These impacts will be felt in major 
vulnerable sectors, including agriculture, water, coastal, marine, health 
and energy, and will have significant impact on the economic growth 
and poverty reduction in the region. Countries could differ widely in 
terms of the economic costs they face. In South Asia, the economic 
costs were projected to be 12.6% of the GDP for the Maldives, which is 
the highest among the South Asian countries, and 6.6% for Sri Lanka, 
the least among the South Asian countries. The resultant adaptation 
costs for countries were projected to range from 0.36% (Copenhagen 
Cancun Scenario for 2050) to 1.32% (business-as-usual scenario) of 
the GDP in various scenarios during 2010–2050 (Medium agreement, 
limited evidence) (Ahmed and Suphachalasai, 2014).
Arto et al. (2019) have reported the adaptation costs of the Mahanadi 
Delta in India for agriculture, fisheries and infrastructure sectors (Arto

et  al., 2019). The cumulative adaptation costs for 2015–2016 were 
reported to be 276 million USD for agriculture and 0.163 million USD 
for fisheries. In comparison, the modelled cumulative agricultural 
GDP loss due to climate-change impacts was reported to be 5% up to 2050, and 8% for infrastructure. Adaptation interventions, such as 
embankments, were found to provide an avoided losses (adaptation 
benefits) to the tune of 2.2% of the delta’s GDP by 2050. Similarly, 
input subsidies in seeds, fertilisers and biofertilisers were found to 
buffer the shocks in agriculture by 10%, and buffer the GDP per capita 
by 3% (Arto et al., 2019).
Markandya and González-Eguino (2019) have estimated the 
adaptation costs and residual adaptation costs accrued due to 
insufficient adaptation using integrated assessment models. Using 
the residual damages as a measure of loss and damage, the authors 
have estimated adaptation costs and residual costs under scenarios

of high damages–low discount rate and low damages–high discount 
rate. The estimates suggested adaptation costs of 182 and 193 billion 
USD by 2050, and 737 and 783 billion USD by 2100 for South Asia 
and East Asia, respectively, under the scenario of high damages–low 
discount rate. The residual costs for the same scenario were 289 and 
76% for 2050 and 238 and 62% for 2100 for South Asia and East 
Asia, respectively. Estimates for low damages–high discount rate were 
significantly lower adaptation costs and residual costs for both of 
these sub-regions of Asia.
The CCA efforts can be characterised as fragmented, incoherent and 
lacking perspective (Ahmed et al., 2019a), and the picture on adaptation 
financing can be stated as similarly fragmented with very limited 
literature published in peer-reviewed journals. Adaptation financing is 
crucial for supporting vulnerable countries and enhances adaptation, as 
it is evident that the enhanced adaptation finance support has positively

affected the pace of adaptation in low-income countries (Ford et al., 
2015). At the organisational level, adaptation financing has provided 
multiple functions that include risk assessment functions, valuation 
functions and risk disclosure functions (Linnenluecke et al., 2016).
Of the total global public adaptation finance of 28 billion USD, East 
Asia and the Asia–Pacific attracted 46% of the total funding, while 
South Asian countries attracted only 9% of the total funding (UNEP , 
2016). These differences reflect the capacity of countries to attract 
adaptation finance. Some of the important adaptation-targeted 
climate funds are Pilot Programmes for Climate Resilience, Green 
Climate Fund, and Least-Developed Countries Fund, and South Asian 
countries have significantly benefited from these dedicated climate 
funds. Due to the disaster implications of climate change, there is a 
need to allocate adaptation finances for DRR. Estimates suggest that

East Asia and the Asia–Pacific in general allocated 27% of the total 
adaptation funds to DRR, while South Asia allocated 25% (Caravani, 
2016). Low-income economies tend to allocate more adaptation funds 
to DRR (46%), while lower-middle-income economies allocated 22%.
The least developed countries lack the capacity to adapt to climate 
change and the Least Developed Country Fund (LDCF) has made 
significant contributions to adaptation in these countries (High 
agreement, limited evidence). Based on the interview-based field 
research in four least developed countries, Sovacool et  al. (2017) 
opined that the LDCF projects are contributing to the adaptive capacity 
of these countries (Sovacool et al., 2017). They also found that these 
projects are taking a marginal approach, rather than a radical or 
transformational one, to adaptation.

10
1520Chapter 10 Asia
Box 10.6 | Loss and Damage Across Asia: Mapping the Evidence and Knowledge Gaps
Losses and damages are climate impacts after implementing adaptation and mitigation actions, signifying the presence of residual risks 
(Chapter 1; Kugler and Sariego, 2016; Mechler et al., 2019). These residual risks indicate that despite adaptation, there are soft and 
hard adaptation limits (Mechler et al., 2019). This box reviews the adaptation literature across 51 countries in Asia on loss and damage 
(L&D), and adaptation barriers and limits, and identifies knowledge and regional gaps. The key messages are that (a) climate-induced 
L&D is already occurring across Asia (medium evidence, high agreement), (b) these L&D are very likely to increase at higher warming 
levels (medium evidence, high agreement) and (c) measuring and attributing non-economic and intangible L&D remains a challenge (low 

Findings on losses and damages in Asia: Evidence on climate-related L&D highlights tangible or material losses and damages such 
as loss to life, property, infrastructure and livelihoods (medium evidence, high agreement); and intangible or non-material losses and 
damages such as increasing conflict and civil unrest, erosion of sociocultural practices and decreased well-being (low evidence, high 
agreement). The main constraint in assessing past and future L&D is that this terminology is not used prominently or consistently in the 
disaster management and climate risk literature in Asia, which potentially leads to under-reporting. In contrast, there is robust evidence 
(high agreement) on adaptation constraints, notably on governance, informational and physical constraints, to adapting, but regional 
evidence is very uneven with gaps in Central, North and West Asia. Table 10.5 presents a summary of L&D but draws on national and 
subnational studies.
The knowledge gaps are as follows:

• Attribution studies linking anthropogenic climate change and L&D remain focused on rapid-onset extreme events, and evidence on 
L&D from slow-onset events, such as drought and water scarcity, is low (Pereira et al., 2019; Singh et al., 2021a).
• Regional evidence gaps in Central, North and West Asia; and low evidence of national-level projected L&D (Uchiyama et al., 2020; 
Singh et al., 2021a).
• Disproportionate emphasis on economic L&D while intangible, non-economic L&D are relatively less measured and reported (Chiba 
et al., 2017; Bahinipati, 2020). Economic loss estimates are largely approximations and therefore suffer from various methodological, 
assumption and data-related uncertainties.
• Insufficient literature differentiating L&D under future adaptation scenarios, which makes assessment of residual damages and future 
L&D difficult. The L&D projections are constrained by limited understanding on how vulnerabilities will evolve with economic and

demographic changes. Most projected L&D are based on the population and GDP projections. More future projections are based on 
the RCP scenarios, and the least number of studies were conducted on the combination of RCP and SSPs.
• Mitigation will have L&D and adaptation co-benefits (Kugler and Sariego, 2016; Toussaint, 2020), especially at the lower temperature 
stabilisation 1.5°C (Nishiura et al., 2020), but the literature is currently insufficient to assess these L&D co-benefits of mitigation 
efforts.
• Negligible regional evidence on limits to adaptation.
Way forward: Developing robust metrics and institutions for measuring and reporting L&D at national and regional scales, especially 
non-economic damages and L&D due to slow-onset events, is critical. In addition to vulnerability assessments, assessing L&D and limits 
to adaptation can inform adaptation prioritisation and enhance adaptation effectiveness (e.g., Craft and Fisher, 2016; Leiter et al., 2019).

Lessons are available from biodiversity and ecosystem services monitoring frameworks that have well-developed metrics and processes 
(e.g., Díaz et al., 2020).
Kissinger et al. (2019) have estimated the climate financing needs in 
the land sector under the Paris Agreement. The estimates suggested 
adaptation needs of 2.5 billion USD for Bangladesh, 40.5 million USD 
for Lao PDR and 31  million USD for Mongolia, for the forest sector 
alone (Low agreement, limited evidence).
Financing green growth and low-carbon development can provide 
resilience benefits (high agreement, limited evidence). Kameyama 
et al. (2016) have estimated the cost of low-carbon investments that 
can provide resilience benefits in Asia and reported that such low-
carbon development will cost in the range of 125–149  billion USD annually. A combination of public, private, bilateral and multilateral 
funding sources, and carbon-market offsets, were suggested to

achieve this level of funding. In terms of the total resources available, 
a combination of public, private and bi- and multi-lateral funding could 
help the region to raise as much as 222.3–412.5 billion USD annually, 
with a possibility to reach higher amounts depending on the future 
economic growth of countries in the region. Soil carbon sequestration 
in agricultural soils was found to be a win–win solution for both 
mitigation and adaptation as it can help improve soils while increasing 
farm yields and incomes of smallholders (Aryal et al., 2020a).

10
1521Asia  Chapter 10
Table 10.5 |  Tangible and intangible losses and damages across Asiaa
Sub-
region 
(no. of 
papers)Key risks 
reported in 
L&D papersLosses and damagesAdaptation constraints (bold ticks denote strong 
barrier)Adaptation 
limits
Tangible Intan-
gibleE S H G F I P B Soft Hard
Past RCP2.5 RCP4.5 RCP8.5
East Asia 
(32)Coastal 
flooding, 
heatwaves, 
SLR*** * ** ** * ✓ ✓✓✓✓ NE NE
Southeast 
Asia (4)Coastal 
flooding, 
SLR* * ✓✓ ✓ NE NE
South 
Asia (18)Coastal 
flooding, 
drought, SLR, 
heatwaves*** * ** ** ** ✓ ✓✓✓ * **
Central 
Asia (3)Snowmelt, 
drought* * * ✓✓ NE NE
North 
Asia (2)Permafrost 
thaw* * * ✓ NE NE
West Asia 
(9)Heatwaves, 
drought** * * * ✓ * **
Magnitude of losses and 
damagesEvidence Adaptation constraints
High (>50% 
sector/
affected 
reported 
baseline)*** High (≥10 papers)E Economic
S Sociocultural
Medium 
(25–50% 
sector/
affected)** Medium (5–9 papers)H Human capacity
G Governance
Low (<25%

sector/
affected)* Low (≤4 papers)F Financial
I Informational/technological
Not assessed 
due to 
evidenceNE No evidenceP Physical
B Biological
Notes:
East Asia: Tezuka et al. (2014); Elliott et al. (2015); Lei et al. (2015); Li et al. (2015a); Li et al. (2015b); Kim et al. (2016a); Lee and Kim (2016); Yu (2016); Zhao et al. (2016b); 
Abadie et al. (2017); Chen et al. (2017a); Chen et al. (2017b); Chung et al. (2017b); Lee et al. (2017); Feng et al. (2018a); Lee et al. (2018b); Lee et al. (2018c); Udo and Takeda 
(2018); Yu et al. (2018a); Yu et al. (2018c); Lee et al. (2019); Liu et al. (2019c); Liu et al. (2019d); Wang et al. (2019b); Wu et al. (2019d); Kim and Lee (2020); Liu (2020); Liu and 
Chen (2020); Yu et al. (2020). Southeast Asia: Giuliani et al. (2016); Dau et al. (2017); Vu and Ranzi (2017); Mehvar et al. (2018). South Asia: Wijetunge (2014); Ahmed et al.

(2016b); Jevrejeva et al. (2016); Patankar and Patwardhan (2016); Abadie et al. (2017); Aslam et al. (2017); Chiba et al. (2017); Mishra et al. (2017); van der Geest (2017); Chhogyel 
and Kumar (2018); Jevrejeva et al. (2018); Leng and Hall (2019); Bahinipati (2020); Bahinipati and Patnaik (2020); Khan et al. (2020); Bhowmik et al. (2021). Central Asia: Groll 
et al. (2015); Babagaliyeva et al. (2017); Otto et al. (2017). North Asia: Gleick (2014); Hjort et al. (2018); Tschakert et al. (2019). West Asia: Mantyka-Pringle et al. (2015); Pal and 
Eltahir (2016); Ghomian and Yousefian (2017); Gohari et al. (2017); Ashrafzadeh et al. (2019b); Bierkens and Wada (2019); Houmsi et al. (2019); Mosavi et al. (2020).
(a) For definitions on losses and damages and limits, see Cross-Chapter Box LOSS in Chapter 1.

10
1522Chapter 10 Asia
New adaptation financing sources have been emerging which could 
provide country-specific adaptation financing suiting local-level 
adaptation needs in Asia. The newly established Asia Infrastructure 
Investment Bank (AIDB), and newly emerging developing-country 
finance institutions, are known to provide an additional adaptation 
finance (Neufeldt et  al., 2018); however, despite these emerging 
financial sources, the region will fall short of the adaptation target in 
the Paris Agreement (Neufeldt et al., 2018).
10.5.4.3 Knowledge Gaps
Adaptation cost estimates can vary between various studies due 
to the differences in methodologies they adopt. Some studies have 
conducted cost assessments using a combination of stakeholder 
consultations and quantitative modelling of climate-change impacts 
and adaptation (Ahmed and Suphachalasai, 2014), while others 
depended solely on the quantitative modelling. Studies also differ in

the coverage of sectors too: they either have focused on the multiple 
vulnerable sectors (Ahmed and Suphachalasai, 2014) or on a single 
sector (Hossain et al., 2019). Studies have differed in their estimates 
depending on their ability to take into consideration the transition 
costs of sudden adaptation (Hossain et  al., 2019), the nature of 
social cost and/or damage functions employed (Arto et al., 2019), the 
discount rates applied (Markandya and González-Eguino, 2019) and 
consideration for the effects of GHG mitigation on adaptation needs 
(Duan et al., 2019a). In addition, the assumptions made on the pace 
of adaptation in estimating adaptation costs can make a difference 
in adaptation cost estimates. Adaptation at a slow or normal pace 
could require more adaptation finance, as large amounts of damage 
are not eliminated, than when adaptation is implemented at a faster 
rate (Markandya and González-Eguino, 2019). Although there have

been improvements in adaptation cost estimates, there is a need to 
address the issue of endogeneity (Kousky, 2014; Samuel et al., 2019). 
The vast majority of studies that rely on databases, such as EM-DAT, 
tend to suffer from such endogeneity problems due to their inability 
to control the causality between GDP and damages (Kousky, 2014). 
Costs attributable to non-economic losses and damages are the least 
reported and least quantified in the adaptation costs literature due to 
lack of sufficient, robust and accessible methodologies (Chiba et al., 
2017; Chiba et al., 2019; Serdeczny, 2019). This is a major limitation 
in assessing adaptation costs and financial needs, and it can lead 
to gross underestimation of adaptation costs. A detailed description 
of issues related to non-economic losses and damages, and its 
importance in strengthening adaptation, is provided in Box 10.6 and 
Table 10.5.
10.5.5 Risk Insurance
10.5.5.1 Point of Departure

Risk insurance approaches and tools have significantly evolved during 
recent years. The emphasis has been mainly on mitigating the adverse 
selection and moral hazard that have been the limitations of traditional 
area-based crop insurance approaches (He et al., 2019). This has been 
achieved by shifting the indemnity calculations on to the specific 
weather parameters and developing a weather index (Greatrex et al., 2015; Fischer, 2019). Technological applications in the development 
of insurance products have seen significant progress, including that 
of the blockchain and smart contracts (Gatteschi et al., 2018). There 
are technological developments in loss estimation, which has been a 
major limitation in the traditional insurance approaches in the past 
that either delayed the indemnity payment or misjudged the losses. 
Application of multi-model and multi-stage decision support systems 
has begun to make crop loss assessments for insurance more efficient

(Aggarwal et al., 2020). Technological applications also include remote 
sensing (Di et al., 2017) and mobile phone app technologies (Meena 
et al., 2018) to provide accurate and quick damage assessments, and 
application of Internet-based indemnity approvals have enabled quick 
payment of indemnities (OECD, 2017b).
10.5.5.2 Findings
As against financing post-disaster relief and reconstruction, which 
has been the norm of disaster management for decades in Asia, the 
evolution of ex-ante risk financing in the form of risk insurance has 
seen a steady rise globally and in Asia. The rise in popularity for risk 
financing in general and insurance in specific stem from the observation 
that governments have recognised the burden of mainly financing the 
post-disaster relief and reconstruction only (Juswanto and Nugroho, 
2017; UNESCAP , 2018c; ADB, 2019), and from the realisation of cost 
savings and efficiency that risk financing for risk mitigation brings

overall risk reduction (high agreement, medium evidence). As a result, 
a gamut of risk-financing instruments have been introduced to finance 
DRR and CCA initiatives in Asia among which risk insurance has gained 
prominence for it provides a low-cost and easy option for individuals, 
provides an opportunity for the governments to effectively engage 
the private sector in implementation and has the ability to inculcate 
risk-aware decision making at various levels (high agreement, medium 
evidence) (Hazell and Hess, 2017; UNESCAP , 2018c).
Several Asian countries, including India, the Philippines and China, 
have a significant experience of offering agricultural insurance against 
typhoons, droughts and floods (Yang, 2018). For the most part, these 
insurance systems have followed a traditional indemnity-based 
insurance which faces several challenges in implementation including 
moral hazard and adverse selection, disagreements and delays in

crop-damage assessments that relied upon crop-cutting experiments, 
often leading to a delay in processing indemnity payments, costly 
insurance premiums and poor insurance expansion (high agreement, 
robust evidence) (Patnaik and Swain, 2017; Ghosh et al., 2019). Other 
factors contributing to poor penetration of insurance include limited 
awareness on the importance of insurance, and poor access.
To tackle the problem of costly insurance premiums, governments 
have subsidised the premiums (Ghosh et al., 2019). Premium subsidies 
have been reported to undermine the ability to convey the real cost of 
risks by the insured (price distortion), and have encouraged adverse 
selection and moral hazard (Nguyen and Jolly, 2019). On the contrary, 
subsidies have been suggested to address the issue of adverse selection 
associated with the insurance (Zhao et al., 2017c).
Despite the fact that the insurance programmes are able to obtain

high participation rates due to subsidised premiums, their impact

10
1523Asia  Chapter 10
on farmers’ income seems to be insignificant especially under the 
conditions of low indemnities, low guarantee and wide coverage 
(Zhao et  al., 2016a). The subsidy burden of insurance on national 
governments is found to be significant with an estimated equivalent of 
6 billion USD spent by China alone on insurance (Hazell et al., 2017). 
In addition, the insurance programmes in Asian countries are reporting 
higher producer claim ratios, and often governments have to spend 
more than the money being transferred to the insured through the 
insurance programmes (Hazell et al., 2017).
To address the issues associated with traditional indemnity insurance, 
efforts have been made to develop weather-index insurance in Asia 
that bases the payouts on the rainfall or temperature index, rather 
than on the direct damage measurements. The parametric insurance 
products help avoid the delays in insurance payouts as they are based

on modelled risks, rather than actual damage measurements, and 
control the adverse selection and moral hazard, although basis risks 
could be increased due to improper matching of payouts with the 
index (De Leeuw et al., 2014). Index insurance is known to promote 
public–private partnerships that in turn will enhance the efficiency of 
overall programme delivery (Hazell and Hess, 2017). Several countries, 
including India, Bangladesh, Thailand, Indonesia, Myanmar and the 
Philippines, either are currently piloting or expanding the weather-index 
insurance (Surminski and Oramas-Dorta, 2014; Tyagi and Joshi, 2019). 
Index insurance is constantly expanding with an estimated 194 million 
farmers already enrolled in China and India, which is much lower than 
the potential number of farmers it can reach (Hazell et al., 2017).
Few significant bottlenecks that are limiting the scaling up of weather-
index insurance include lack of reliable weather data, low density

of weather stations leading to high basis risk, and limited data on 
damage and hazard for parametric modelling of the insurance 
(Shirsath et al., 2019). Several innovations are being tried and tested 
to overcome the limitations associated with the index insurance 
which include developing multiscale index insurance, application of 
remote sensing, smartphone-based near-surface remote sensing and 
building insurance based on vegetation indices instead of relying on 
weather data alone (Hufkens et  al., 2019). Alternative indices, such 
as the NDVI, are being tested for their applications in designing 
index-based insurance in India (IFAD, 2017). Agro-meteorology-based 
statistical analysis and crop growth modelling have been suggested 
to calibrate and rectify faulty weather indices (Shirsath et al., 2019; 
Zhu et al., 2019). Establishing automatic weather stations can improve 
data accuracy while preventing the delay in acquiring the weather

data (Sinha and Tripathi, 2016). These technological applications have 
already started finding space within insurance programmes designed 
by national governments in Asia. For example, the government of India 
has released new operational guidelines for the application of new 
technologies such as drones, remote sensing and mobile phone apps 
in implementation of the national agricultural insurance, which is the 
third largest insurance in the world (Department of Agriculture, 2019).
10.5.5.3 Knowledge Gaps
Despite these developments, several issues still seem to hinder the 
penetration of insurance in Asia. Issues such as lack of sufficient 
choices, lack of clear model, lack of legal support, limited or absence of proper monitoring and evaluation, and limited data for underwriters 
to properly evaluate claims have been suggested (Nguyen and Jolly, 
2019). Low interest among the potential buyers due to unaffordable

insurance premiums, lack of provision for partial-loss claim settlement, 
big hassles in the claim settlement process and lack of timely 
settlement of claims are reported (Parappurathu et  al., 2017). In 
addition, insurance has been reported to have expanded the coverage 
of cash crops at the expense of drought-resistant subsistence crops 
with effects on natural capital and a potential increase in farmer’s 
vulnerability to market price fluctuations (Müller et al., 2017).
Regional catastrophic insurance pools have also received attention 
in Asia. With the formation of Southeast Asia Disaster Risk Insurance 
Facility (Haraguchi and Lall, 2019), the regional insurance pool has 
been introduced in Southeast Asia, initially being piloted in Lao PDR 
and Myanmar and to be expanded to the rest of the ASEAN region. 
Regional catastrophic insurance allows vulnerable countries to buffer 
climatic shocks by diversifying the risks beyond country boundaries.
10.5.6 Social Protection

10.5.6.1 Point of Departure
Social protection (SP) encompasses initiatives that involve transfer 
income or assets to the poor, protect the vulnerable against risks 
to their livelihood, and enhance the social status and rights of the 
marginalised (Béné et al., 2014; Kothari, 2014). Social protection offers 
a wide range of instruments (e.g., cash transfers, insurance products, 
pension schemes and employment guarantee schemes) that can be 
used to support households that are exposed to climate changes 
(Bank, 2015). It also presents an opportunity to develop inclusive 
comprehensive risk management strategies to address L&D from 
climate change as well as a means to CCA (Aleksandrova, 2019).
Social protection programmes assist individuals and families, especially 
the poor and vulnerable, cope with crises and shocks, finds jobs, improve 
productivity, invest in the health and education of their children, and 
protect the ageing population (Bank, 2018b). Social protection that

is well designed and implemented in a more long-term approach can 
enhance human capital and productivity, reduce inequalities, build 
resilience and empowerment, and end the inter-generational cycle 
of poverty (medium evidence, medium agreement) as indicated from 
various experiences in the region such as (a) cash transfer programmes 
in Indonesia (Kwon and Kim, 2015), (b) the Benazir Income Support 
Programme in Pakistan (Watson et al., 2017), (c) the Chars Livelihoods 
Programme in Bangladesh (Pritchard et  al., 2015) and (d) Minsei-in 
designated volunteer social workers in Japan (Boeckmann, 2016). A 
key consideration in strengthening resilience through SP programmes 
is to design with climate and disaster risk considerations in mind 
and implement in close synergy with existing programmes, such as 
on sustainable livelihoods, EWS and financial inclusion (Coirolo et al., 
2013; Bank, 2018a).

10
1524Chapter 10 Asia
10.5.6.2  F indings
Asia is already the most disaster-prone region in the world, with over 
200,000 lives lost and almost 1 billion people affected by storms and floods alone between 2005 and 2014, while a heatwave in North and Central Asia in 2010 killed 56,000 people (United Nations, 2015). Climate change is increasing the frequency and intensity of these sudden and slow-onset disasters, among them, hydrological changes in major river basins where 1.5  billion people live (such as the Indus, Ganges, Brahmaputra, Mekong, Yellow, Yangtze, Tarim, Amu and Syr Darya rivers) (Bank, 2017a). According to the latest estimates of the International Labour Organisation (ILO), 55% of the global population (around 4 billion people) remain without any SP benefits, and the SP coverage gap is highest in Africa (82.2%) and the Asia–Pacific (61%) (ILO, 2017b).
Risks are generally amplified for people without SP or essential

infrastructure and services, and for people with limited access to land and quality housing, especially those in exposed areas and informal settlements without secure tenure (ESCAP , 2017). Stateless people are disproportionately affected by climate change and disasters as they tend to reside in hazard-prone areas and their statues as non-citizens often limits access to assistance (Connell, 2015). The three main types of SP are: (a) social safety nets (also known as social assistance), which include conditional and unconditional cash transfers, public work programmes, subsidies and food stamps; (b) social insurance, which consists of contributory pensions and contributory health insurance; and (c) labour market measures, which include instruments such as unemployment compensation (Bank, 2018b). The potential for an integrated adaptive SP is not yet harnessed by policymakers in tackling the structural causes of vulnerability to climate change (Tenzing, 2019). Public works programmes

(i.e., India’s Mahatma Gandhi National Rural Employment Guarantee Act, MGNREGA) should take into account climate risk in planning and support development of community assets to increase collective resilience.

Aligning SP with climate-change interventions is an attempt to develop 
more durable pathways out of poverty and climate vulnerability; examples from MGNREGA depicting the attempt to align through a mainstreaming approach has helped women and their households (Adam, 2015; Steinbach et al., 2016). On another note, the Catastrophe Insurance Framework, the first model introduced in Shenzen, China, provides timely relief for citizens and operates as a safety net, particularly for the poorest residents who do not have disposable income to cover the costs associated with bodily injuries arising from disasters (Telesetsky and He, 2016). The Department of Labour and Employment’s Integrated Livelihood and Emergency Employment Programme in the Philippines is part of the recovery efforts after Typhoon Haiyan, providing short-term wage employment, and facilitates entrepreneurship for people affected by natural calamities and economic shocks (Bank, 2018b).

populations suffering from climate change or are adversely affected by structural, pro-climate economic reforms (Hallegatte et al., 2015). However, additional research is still needed and new tools need to be developed to inform policy design and support the implementation of ‘green’ SP , as well as to measure the net-welfare impacts of such policies (Canonge, 2016). In order to enhance SP programmes, one of the cross-cutting issues is to discuss the linkages between gender roles and responsibilities, food security, agricultural productivity and the mediating role that SP programmes can have (Jones et al., 2017). Social protection has a potentially important role to play in contributing to food security and agricultural productivity in a gender-responsive way (Holmes and Jones, 2013). As such, experience from Challenging the Frontiers of Poverty Reduction: Targeting the Ultra Poor programme in Bangladesh has promoted social innovation by creating social and economic values, fostering

microenterprises, increasing food security and fostering inclusive growth, all while empowering ultra-poor women (Emran et al., 2014; Mahmuda et al., 2014). Although there is increasing evidence that SP programmes are having a positive impact in terms of reducing vulnerability in women’s everyday lives (Jones et al., 2017), the transformative impact of these programmes is rare due to limitations in recognising women’s access to productive inputs and resources (Tanjeela and Rutherford, 2018; Cameron, 2019).

On the other hand, poor governance practices affect delivery of SP 
programmes and the ability of beneficiary households to reap the benefits from such support (Sijapati, 2017). In Nepal, a closer look at public expenditure shows that about 60% of the SP budget is used by social insurance programmes that predominantly consist of public-sector pensions (Babken Babajanian, 2014; Koehler, 2014). Towards this end, more effort is needed to improve its existing programmes so that there is an equality of opportunities, along with secured human rights. The example from Nepal’s Child Grant is an indicative of an incremental approach to social policy (Garde et al., 2017). Meanwhile, in the Philippines, despite the existence of flagship national interventions that cover a significant number of people in need and have clear and robust implementation rules, there are still many programmes with overlapping mandates and target population, and several gaps in their monitoring systems (Bank, 2018b).

Having an integrated SP information system would allow policymakers

to better monitor inputs, outputs and outcomes (e.g., who the beneficiaries are, what they are receiving, at what frequency, what the existing gaps are) (OECD, 2017a; Samad and Shahid, 2018). Based on evidence from the assessments of three countries (Mongolia, Nepal and Vietnam), the political and institutional arrangements (i.e., the software) is as important as the technical fixes (i.e., the hardware) in the success of using information and communication technology for delivering SP programmes (ADB, 2016). By 2050, climate-induced migration will likely be a major policy aspect of the rural–urban nexus as slow-onset impacts of climate change in sub-Saharan Africa, South Asia and Latin America will likely force over 143  million people to migrate within their national borders (Kumari Rigaud, 2018). This will have major implications for SP systems, and therefore national SP strategies should be designed to anticipate and address climate-induced internal mobility (Schwan and Yu, 2017).

For instance, it does not offer a solution for maintaining Indigenous cultures which are often strongly affected by, or even disrupted by, climate change (Olsson, 2014). Hence, an effective approach needs to combine different policy instruments to support protection, adaptation and migration (O’Brien et al., 2018).

Evidently, SP has been financed typically through the combination of 
government tax revenues and official development assistance, and

10
1525Asia  Chapter 10
the challenges of the increasing frequency and intensity of natural 
and economic crises are straining these traditional financial sources 
(Durán-Valverde, 2020). In this context, innovative financing schemes 
are seen as critical to achieve the sustainable financing of SP (Asher, 
2015; UNICEF , 2019) via social and solidarity economy, as seen in 
women’s autonomous adaptation measures in precautionary savings 
and flood preparedness in Nepal (Banerjee et al., 2019), and self-help 
groups as development intermediaries (Anderson, 2019). Still, there are 
constraints of SP to reach those who are most vulnerable to climate 
change and other hazards due to their legal status, such as the fact 
that attention to forcibly displaced populations within the SP field has 
been limited (Sabates-Wheeler, 2019).
10.5.6.3 Knowledge Gaps
Government SP can attenuate the negative impacts in facing disasters, 
depending on the differences in political systems and the focus put

on sociopolitical measures (medium evidence, medium agreement), 
not only in restoring livelihoods but also in easing mental burdens 
faced by rural households in developing countries (Dalton et al., 2016; 
Kosec and Mo, 2017; Liebenehm, 2018). However, limited government 
capacities and fiscal feasibility may impede the expansion and effective 
implementation of SP as developing countries need further support to 
design, adjust and implement SP schemes effectively (Klonner, 2014; 
Schwan and Yu, 2017). Most countries have comprehensive strategies 
for both SP and climate change, but few have attempted to align them, 
as in practice they remain in separate institutional homes, governed 
by their own intra-sector coordination groups and funding channels 
(Steinbach et  al., 2016; Bank, 2018b). Thus, significant knowledge 
gaps remain in terms of understanding the potential of SP to build 
long-term resilience to climate change (Ulrichs et  al., 2019). Future

efforts should be geared to develop climate-responsive SP policies that 
consider a broad range of issues including urbanisation and migration, 
the impact of green policies on the poor, access to essential health care 
and risks to socially marginalised groups (Aleksandrova, 2019). Along 
with strengthening links to climate information and EWS, finance for 
enabling SP systems to address climate-related shocks and stresses 
dynamically needs to be scaled up (Kuriakose et al., 2013; Ulrichs et al., 
2019).
10.5.7 Education and Capacity Development
10.5.7.1 Point of Departure
Asian areas with the least capacity to respond, such as the Himalayan 
region and densely populated deltas, are hit first and hardest by 
climate impacts (De Souza et al., 2015; Khan, 2017). Acknowledging 
the limitations in terms of capacity and coping mechanisms towards 
climate change, education, training and awareness building is 
central to sustaining long-term capacity building (Clemens et  al.,

2016). Education has a lot more to offer in terms of improvements in 
addressing climate change, particularly in the climate hotspots of Asia 
where mostly poor, disadvantaged communities vulnerable to climate 
change reside (Mani et  al., 2018). In particular, when disseminated, 
climate-change awareness and information need more explanation 
(Steg et  al., 2014; Wi and Chang, 2018; Cho, 2020). In addition, international and national support through institutions and financing 
is critical for successful capacity building (Hemachandra, 2019), which 
must be designed for the long term and be self-sustaining (Gustafson 
et al., 2018). National ownership by recipient countries and members 
of communities of capacity-building efforts is key to ensuring their 
success (Roberts and Pelling, 2016; Mikulewicz, 2017).
10.5.7.2 Findings
The need to develop tailored climate communication and education 
strategies for individual nations as public awareness and risk

perceptions towards climate change vary greatly (medium evidence, 
high agreement) (Lee et al., 2015a). Improving on, and investing in, 
basic education, climate literacy and location-based strategies of 
climate change are vital to enhance public engagement, societies’ 
adaptive capacity and support for climate action (Lutz et al., 2014c; 
Hu and Chen, 2016). As stated in the IPCC Special Report 1.5°C, 
sustainable development has the potential to significantly reduce 
systemic vulnerability, enhance adaptive capacity and promote 
livelihood security for poor and disadvantaged populations (Roy et al., 
2018). Hence, various concepts are introduced to foster awareness, 
understanding, knowledge, participation as well as commitment 
towards managing climate change in a sustainable manner. One such 
concept is education for sustainable development (ESD), which is aimed 
at integrating the principles and practices of sustainable development

in all aspects of education, and training individuals who will contribute 
to the realisation of a more sustainable society (Kitamura, 2017).
Climate-change education (CCE) is also now addressed in the 
context of ESD and allows for learners to understand the causes and 
consequences of climate change, and teaches them how to take action 
(Mochizuki and Bryan, 2015). Both ESD and CCE are gaining broader 
attention, for instance, in China (Han, 2015) and the Republic of Korea 
(Sung, 2015); however, development of policies and implementation 
of initiatives regarding ESD and CCE still face a handful of challenges 
which require a strong political will and consensus of key stakeholders 
(Læssøe and Mochizuki, 2015). Effective communication on CCE 
particularly for younger-generation engagement is also essential, as 
they are our future leaders as climate change is an inter-generational 
equity issue (Corner et al., 2015). Action for Climate Empowerment of

Article 6 of the UNFCCC target youth as a major group for effective 
engagement in the formulation and implementation of decisions on 
climate change (UNFCCC, 2015). Increasing attention from countries 
in Asia, such as Thailand and India, will encourage innovative ways 
to provide adequately in educating and engaging youth in climate-
change issues (Narksompong and Limjirakan, 2015; Dür and Keller, 
2018).
An integrated approach to knowledge about climate change embraces 
both the importance in bridging knowledge of climate science and 
respecting IKLK, and should be at the heart of any effort to educate 
citizens to have a deeper understanding of the causes and consequence 
of climate change in a holistic manner (Aswani et al., 2018). Indigenous 
Peoples, comprising about 6% of the global population, play a crucial 
role in the fight against climate change for two interlinked reasons. 
First, they have a particular physical and spiritual relationship with

10
1526Chapter 10 Asia
most vulnerable group to climate change (Magni, 2017). Second, they 
have a specialised ecological and traditional knowledge relevant to 
finding the best solutions to climate change (Rautela and Karki, 2015). 
Indigenous knowledge systems and resource management practices 
are important tools for both mitigating and adapting to climate 
change (Fernandez-Llamazares et al., 2015). Indigenous knowledge is 
increasingly recognised as a powerful tool for compiling evidence of 
climate change over time (Ahmed et al., 2016a). Knowledge of CCA 
and DRR provide a range of complementary approaches in building 
resilience and reducing the vulnerability of natural and human systems 
to the impacts of climate change and environmental hazards (Mall 
et al., 2019). The adaptation dimension involves developing knowledge 
and utilising existing IKLK, skills and dispositions to better cope with 
already evident and looming climate impacts (Aghaei et al., 2018). It

is also important to ensure inclusive efforts in DRR across different 
nations and communities as well as increasing skills and capacities of 
women towards DRR efforts (Alam and Rahman, 2014; Drolet et al., 
2015; Islam et  al., 2016b; Reyes and Lu, 2016; Hemachandra et  al., 
2018). More effective and efficient teaching and learning strategies, as 
well as collaborative networks, are needed to increase preparedness 
and DRR activities across various levels of community (Oktari et al., 
2015; Takahashi et  al., 2015; Tuladhar et  al., 2015b; Shiwaku et  al., 
2016; Gampell et al., 2017).
Table  10.6 shows education and capacity-building aspects affecting 
adaptation by sub-region examples.
10.5.7.3 Knowledge gaps
Capacity building at national and local levels still needs to address 
gaps in research and practice, such as impacts and results of different 
preparedness measures (Alcayna et  al., 2016). Ad-hoc and localised

documentations and monitoring of efforts to build adaptive capacities 
has rendered it difficult to assess success (Cinner et  al., 2018). 
Recommendations for strengthened capacity building are sometimes 
made or understood in isolation from the underlying structural issues 
shaping vulnerability, or without adequately recognising the political 
relationships that mediate the ways in which particular technical 
interventions result in differentiated outcomes for different groups 
(Archer and Dodman, 2015). Thus, design- and decision-based tools, 
such as rapid assessment for community resilience to climate change 
as well as rapid approach to monitor the effectiveness of aid projects, 
support the community-based adaptation to climate change to analyse 
using a multi-dimensional approach, procedural, distributional, rights 
and responsibilities (Nkoana et al., 2018; Jacobson et al., 2019).
As a model of communication and engagement, citizen science has

the potential to promote individual and collective climate-change 
action (Groulx et al., 2017). More than information provision is needed 
to mobilise public action on climate change (Kyburz-Graber, 2013). 
Citizen science links communication and engagement in a manner that 
holds important lessons on ways to promote collective responses to 
climate (Wals et al., 2014; Bonney et al., 2016). The power of science-
based citizen engagement lies in citizen group contribution in drawing 
upon their local knowledge to enrich the knowledge base required for 
management decisions (Sayer et al., 2015). Scientific evidence may be 
less attuned to the complexity of local realities in managing climate change; thus, citizen science has the potential in bridging this gap and 
has many advantages for climate mitigation and adaptation practice 
and policy (Ford et  al., 2016). While citizen science uses citizens as 
policy-passive objects for research in conducting measurements for

big datasets, citizen social science is gaining momentum where it 
repositions citizens as central co-learners who can widen the climate-
science evidence base to achieve a more holistic understanding for the 
benefit of all (Kythreotis et al., 2019).
10.6 Climate Resilient Development Pathways
10.6.1 Climate Resilient Development Pathways in Asia
Climate resilient development pathways (CRDPs) are ‘trajectories 
poverty and reduce inequalities while promoting fair and cross-scalar 
adaptation to and resilience in a changing climate’ (Roy et al., 2018). 
Moving beyond a business-as-usual scenario, CRDPs involve not only 
adaptation and mitigation choices but also sustainable development 
implications and societal transformation (Roy et  al., 2018). This 
basic understanding of CRDPs explicitly reflects that climate action 
(mitigation and adaptation) and sustainable development are

fundamentally integrated and interdependent.
There is high confidence that currently implemented climate action 
in Asia (such as climate-smart agriculture, ecosystem-based DRR and 
investing in urban blue–green infrastructure) can meet adaptation, 
mitigation and SDGs simultaneously, presenting opportunities for 
climate resilient development (CRD). However, there also exist 
technologies (in situ observation and remote sensing, a variety of new 
sensor technologies, citizen science, AI and machine learning tools); 
regional partnerships and learning; improved forecasting capabilities; 
and better risk awareness (high confidence).
Asian countries are repeatedly identified as the most vulnerable

to climatic risks with key sectors such as agriculture, cities and 
infrastructure, and terrestrial ecosystems expected to see high exposure 
to multiple hazards (Section 10.3). Owing to rapid development and 
large populations, Asian countries have large and growing GHG 
emissions: in 2018, five of the top ten emitters in the world were Asian–
China (1), India (3), Japan (5), the Republic of Korea (8) and Indonesia 
(10) (Friedlingstein et al., 2019)–although it is critical to note that per-
capita emissions and cumulative emissions are relatively lower than 
in developed economies (Raupach et al., 2014). However, in the 2020 
Sustainability Index and Dashboard, only two Asian countries made it 
into the top 30 countries in the world: Japan (17) and the Republic of 
Korea (20) (Sachs et  al., 2020). Finally, Asia has varied capacities to 
adapt with high heterogeneity in adaptation progress across the region.
Given this context of high risks, growing emissions and varied adaptive

capacities in Asia, CRDPs can enable (a) reducing existing vulnerability 
and inequality, (b) sustainable development and meeting the SDGs and

10
1527Asia  Chapter 10
Table 10.6 |  Education and capacity-building aspects affecting adaptation by sub-regions examples
Sub-region Sectors Adaptation interventionsEducation and capacity-building factors 
affecting adaptationSupporting references
North AsiaHuman 
well-beingPEEX (Pan Eurasian Experiment) originating 
from a bottom-up approach by the science 
communities aiming at resolving major 
uncertainties in Earth system science and global 
sustainability issues concerning the Arctic and 
boreal pan-Eurasian regions, as well as ChinaEducating the next generation of multidisciplinary 
experts and scientists capable of finding tools in solving 
future environmental, socioeconomic and demographic 
development problems of the Arctic and boreal regions, as 
well as ChinaPan Eurasian regions, as well as 
China (Kulmala et al., 2015)
West Asia Agriculture Smallholder farmers’ vulnerability assessmentHigh level of education, more human capacity and adaptive

capacity, less vulnerabilityIran (Jamshidi et al., 2019)
Central AsiaAgriculture, 
water 
resources 
and energyCarrying out the selection and cultivation of 
drought-tolerant, salt-tolerant crops, preservation 
of the upper watershed of the rivers, improving 
climate resilience of hydro-facilitiesPlacing the focal point for the preparation and 
implementation of programmes for climate change at 
the regional level, increasing capacity of professionals 
in targeted areas and networking between them, and 
strengthening institutional, technical and human resources 
to promote adaptation and research in fields of climate and 
hydrological investigations, geographic information systems, 
environmental impact assessment, and protection and 
re-cultivation of landsKazakhstan, Tajikistan and 
Kyrgyzstan mountain societies in 
Central Asia (Schmidt-Vogt et al., 
2016; Xenarios et al., 2019)
South Asia AgricultureProductivity, net crop income, improvement in

livelihoods and food securityFarmers’ education, easy access to farm advisory services, 
weather forecasting and marketing informationPakistan (Abid et al., 2016)
Passive adaptation in agricultural and farming 
practices implicitly to cope with climate changeIncreasing knowledge on climate change so that concrete 
steps can be taken in dealing with perceived climate 
changesIndia (Tripathi and Mishra, 2017)
Having farmers’ perceptions shape knowledge, 
and vice versa on climate changeAge, education, occupation, farming experience, knowledge 
about coping strategies (all significantly related to farmers’ 
perceptions about climate change)India (Aslam Ansari, 2018)
Disaster risk 
reductionLocal institutions’ preparedness and capacity for 
managing disaster at the local scaleCapacity building, technical support and financial capacity, 
as well as adopting a proactive approach, to achieve a 
higher level of disaster preparednessPakistan (Shah et al., 2019)
Southeast

AsiaAgricultureFarming cultural practices adopted to minimise 
production losses due to extreme weatherSmall-scale farmers’ attendance at climate-change training 
to enhance adaptive capacityVietnam (Trinh et al., 2018)
Removing farmers’ barriers to adopting 
adaptation measures, and provide funds and 
timely informationKnowledge of crop variety, increasing educational outreach 
and communicating climate-change-related information to 
increase the likelihood of employing adaptive strategiesChina (Zhai et al., 2018)
Making farmers in rural, under-resourced 
communities in the Lower Mekong basin aware of 
how climate change will affect themScientific findings which can be merged with local 
knowledge at a community level to help raise awareness, 
and knowledge gaps on both which can be filled for better 
understanding and adaptation planningCambodia, Lao PDR, Vietnam and 
Thailand (USAID, 2015; Gustafson 
Coastal areasReducing households’ vulnerability due to

variation in socioeconomic and livelihoods assetsIncreasing resilience by establishing effective an 
communication system, improving knowledge on climate 
changeVietnam and Indonesia (Nanlohy 
et al., 2015; Huynh and Stringer, 
2018)
reductionCapacity building through learning labs on 
disaster risk management for sustainable 
development (DRM-SD)Transfer of learning initiatives to provide approach 
guidelines and innovative mechanisms for DRM practitioners 
who will have the know-how and potential for leadership 
in DRM-SDFour ASEAN countries: Malaysia, 
Vietnam, Lao PDR and Cambodia 
(Ahmad Shabudin et al., 2017)
East AsiaDisaster risk 
reductionConducting community participation and disaster 
education so that people can take action in 
disaster managementEducational-resilience system tested and revised through 
experiences from past disasters; recognising and integrating 
gender perspectives into mainstream disaster management;

‘school-based recovery concept’ facilitating short-term 
recovery and the longer-term community building needs, 
which can also help communities in building new networks 
and solving chronic social problemsJapan (Matsuura and Shaw, 2014; 
Saito, 2014; Shiwaku et al., 2016)
Bridging 
knowledge 
and scientific 
knowledgeIn efforts to solve real-world problems, engaging 
first with those local communities that are most 
affected, beginning with the perspective of 
Indigenous knowledge and then seeking relevant 
scientific knowledgePaying attention to the Indigenous perception of a hazard 
and risk with the aim of increasing the effectiveness of 
projects implemented by practitioners who might need to 
communicate risks in the future; empowering the younger 
generation to ensure continuity of Indigenous cultures and 
their linked ecosystems(Mistry and Berardi, 2016; Roder 

10
1528Chapter 10 Asia
Table 10.7 |  Adaptation options that can have mitigation and SDG synergies and trade-offs, providing opportunities for the triple wins necessary for climate resilient development 
pathways
Adaptation optionMitigation impacts 
(H/M/L/NA)aImplications on SDGs
Positive Negative
Wetland protection, restorationMedium synergy
Carbon sequestration through mangrovesSDGs 8, 14, 15 Southeast Asia: Griscom et al. (2020)
Solar drip irrigationHigh synergyShift to cleaner energySDGs 2, 7, 12 SDG 10 South Asia: Alam et al. (2020)
Climate-smart agricultureHigh synergy bNo till practices and improved residue management can reduce soil carbon emissions.SDGs 2, 12South Asia: Aggarwal et al. (2019); Aryal et al. (2020b); Aryal et al. (2020a); Tankha et al. (2020)
Southeast Asia: Chandra and McNamara (2018)
Integrated smart water gridsHigh synergy
Reduced energy needs for supplying waterSDGs 6, 9, 11, 13 Asia: Kim (2017)

Disaster risk management (including early warning systems)
cNot applicable SDGs 9, 11 SDGs 5, 10South and Southeast Asia: Ajibade et al. (2019); Herbeck and Flitner (2019); Aryal et al. (2020b); Mishra (2020)
Asia: Iturrizaga (2019)Bhutan: Sovacool et al. (2012)The Philippines: Grefalda et al. (2020)
Carefully planned resettlement 
and migration (including decongestion of urban areas)Inadequate evidence to make an assessmentSDGs 8, 10, 11 SDGs 6, 10, 11Asia: Arnall (2019)
South Asia: Maharjan et al. (2020)The Philippines: Estoque et al. (2020)Nepal: Banerjee et al. (2019)
Aquifer storage and recovery Low synergy SDGs 6, 12Saudi Arabia: Lopez et al. (2014)South and Southeast Asia: Hoque et al. (2016)
Nature-based solutions in urban 
areas: green infrastructure (including urban green space, blue–green infrastructure)High synergyBlue–green infrastructure acts as a carbon sinkSDGs 3, 9, 11Japan: Mabon et al. (2019)

The Philippines: Estoque et al. (2020)China: Byrne et al. (2015); Zhang et al. (2020a)Taiwan, Province of China: Mabon and Shih (2018)Singapore: Liao (2019); Radhakrishnan et al. (2019)
Coastal green infrastructure High synergy SDGs 9, 11, 13, 14, 15Bangladesh: Sovacool et al. (2012); Chow (2018); Zinia and 
(McShane, 2018)
Southeast Asia: Koh and Teh (2019); Herbeck and Flitner 
(2019)
Asia: Giffin et al. (2020)
Notes:
(a) Expert judgment.(b) The CCA options in the agriculture sector include soil management, crop diversification, cropping system optimisation and management, water management, sustainable land

management, crop pest and disease management, and direct seeding of rice (Aryal et al., 2020b). Other specific agricultural practices that have adaptation and mitigation synergies include between-tillage and residue management, alternate wetting and drying, site-specific nutrient management, crop diversification for less-water-intensive crops, such as maize, and improved livestock management (Aryal et al., 2020a).
(c) Risk management strategies in agriculture include crop insurance, index insurance, social networking and community-based adaptation, collective international action and
integrated agro-meteorological advisory services (Aryal et al., 2020b).
(c) managing multiple and often concurrent risks, including climate-
change 
and disaster risks. Potentially, combinations of adaptation and

mitigation options will be required to lead to CRD (see Figure  18.1on CRDPs ), and some of them are shown in Table 10.7. For example,climate-smart agriculture strengthens food security (SDG 2, ZeroHunger) (Aggarwal et al., 2019); urban disaster management, such asthe Jakarta Disaster Risk Reduction Education Initiative, contributes toSDG 11 (Sustainable Cities) (Ajibade et al., 2019).The following subsections examine CRDPs through three approaches that are particularly important in Asia and have a large body of evidence to assess implications for adaptation, mitigation and sustainable development. The three illustrative approaches are: (a) disaster risk management and adaptation synergies, (b) the food–water–energy nexus and (c) poverty alleviation and meeting equity goals.

10
1529Asia  Chapter 10
10.6.2 Disaster Risk Reduction and Climate-Change 
Adaptation Linkages
10.6.2.1 Point of Departure
There is growing evidence on the interconnectedness of extreme 
weather, climate change and disaster impacts (Asia, 2017; Reyer, 
2017). In Asia, climate-related disasters have become more recurrent 
and destructive in terms of both economic and social impacts (Bhatt 
et al., 2015; Aich et al., 2017; Vij et al., 2017). Projections of increasing 
frequency, intensity and severity of climate-related disasters call for 
better integration of CCA and DRR (Sapountzaki, 2018) in policy 
development to address risks efficiently (Rahman et al., 2018) and to 
promote sustainable development pathways for reduced vulnerability 
and increased resilience (Seidler et  al., 2018). Connecting CCA and 
DRR efforts in both policy and practice continue to be a challenge, 
however, because the convergence of national policy and planning

processes on CCA and DRR within Asia is in its early stages (Cousins, 
2014) and structural barriers persist (Mall et  al., 2019). Both CCA 
and DRR have developed as separate policy domains because of the 
different temporal and spatial scales considered, the diversity of actors 
involved, the policies and institutional frameworks adopted and the 
differences in tools and methodological approaches used. This has 
resulted in the CCA and DRR communities, and the knowledge and 
research they produce to support planning and decision making, not 
always being well connected (Street et al., 2019).
10.6.2.2 Findings
Climate risk management in Asia is approached by focusing on hazards 
that are associated with extremes (i.e., extreme weather events with 
increased frequency and severity) as well as climate- and weather-
related events. For example, farming has been affected by climatic 
variability and change in a wide variety of ways that include an

increase in drought periods and intensity, a shortage of irrigation water 
availability, an increase in flooding and landslides, pest infestation of 
crops, a rising number of crop diseases, the introduction of invasive 
species and crop weeds, land degradation and an overall reduction 
in crop yields (Khanal et al., 2019). Estimation of the number of daily 
patients of heat-related illness based on the weather data and newly 
introduced metrics shows that the effects of age, successive days and 
heat adaptation are key variables (Kodera et al., 2019).
Because most developing countries in Asia are highly vulnerable to the 
impacts of climate change due to a number of factors, many studies 
have focused on understanding vulnerability, for instance, gendered 
vulnerability at the micro scale, which limits capacity to respond to 
both climatic and socioeconomic stressors (Ferdous and Mallick, 
2019); vulnerability of urban poor communities due to the interaction

of environmental and social factors (e.g., low incomes, gender, migrant 
status) and heightens the impacts of climate change on the poor (Porio, 
2014); social–ecological vulnerability where a degraded environment 
influences hazard patterns and vulnerability of people (Depietri, 
2020); and livelihood vulnerability due to perceived climate risks and 
adaptation constraints (Fahad and Wang, 2018; Hossain et al., 2020).Risk assessments have been undertaken for different hazards such as 
flood (Al Saud, 2015; Al-Amin et al., 2019; Jha and Gundimeda, 2019; 
Mahmood et al., 2019; Zhang et al., 2019e), drought (Guo et al., 2019; 
Mainali et al., 2019), rainfall-induced landslide (Li et al., 2019b), SLR 
(Imaduddina and Subagyo, 2014; Suroso and Firman, 2018) and heat 
stress (Onosuka et al., 2019), among others, as well as environmental 
assessment, for example, in coastal zones (Islam and Zhang, 2019). 
Different types of strategies for climate risk management have also

been studied including: (a) in situ adaptation through ecosystem- and 
community-based adaptation (Jamero et al., 2017); (b) managed retreat 
or relocation (Buchori et al., 2018; Doberstein et al., 2020); (c) planned 
sheltering in flood zones (Wu et al., 2019c); (d) sustainable livelihoods 
that consider long-term CCA measures of farmers and fishermen 
(Nizami et  al., 2019; Shaffril et  al., 2019); (e) coastal afforestation 
through mangrove plantation (Rahman et al., 2018); (f) management 
of ecosystem services to mitigate the effects of droughts (Tran and 
Brown, 2019); (g) pre-investments, including holistic assessment of the 
basin (Inaoka et al., 2019); (h) institutionalisation, where entry points 
are identified in efforts to build resilience (Lassa, 2019) and adaptive 
governance (Walch, 2019); and (i) linking science and local knowledge 
(Mehta et al., 2019; van Gevelt et al., 2019).
The sectors to which CCA and DRR have been linked are varied. For

example, Filho et al. (2019) assessed adaptive capacity and resilience to 
climate change based on urban poverty, infrastructure and community 
facilities; Mabon et  al. (2019) looked at adaptation via the built 
environment, green roofs, and citizen and private-sector involvement 
in smaller-scale greening actions; Lama and Becker (2019) focused on 
adaptation to reduce risk in conflicts; Banwell et al. (2018) studied the 
link between health, CCA and DRR; and Izumi et al. (2019) surveyed 
science, technology and innovation for DRR. Vulnerable groups have 
been given much attention, such as farmers (Afroz, 2017; Gupta 
et al., 2019; Jawid and Khadjavi, 2019; Khanal et al., 2019; Shi et al., 
2019a), women (Goodrich et al., 2019; Hossain et al., 2019; Udas et al., 
2019), and children, elderly and refugees (Asia, 2017). Finally, issues 
identified include water resource management (Bhatta et al., 2019; Sen 
et al., 2019; Zhang et al., 2019a); food security (Aleksandrova et al.,

2016; Le, 2016); disaster governance (Blanco, 2015); climate boundary 
shifting wherein impacts of climate change are significant for crop 
production, soil management and DRR (Talchabhadel and Karki, 2019); 
and institutional dimensions of CCA (Cuevas, 2018; Islam et al., 2020).
Case studies on climate risk management and integrated CCA and 
DRR actions highlight some key lessons including: an integrated 
and transformative approach to CCA, which focuses on long-term 
changes in addressing climate impacts (Filho et al., 2019); adoption 
of an adaptive flood risk management framework incorporating 
both risk observation and public perceptions (Al-Amin et  al., 2019); 
a holistic approach and non-structural and technological measures 
in flood control management (Chan, 2014); monitoring of changes in 
urban surface water in relation to changes in seasons, land covers, 
anthropogenic activities and topographic characteristics for managing

watersheds and urban planning (Faridatul et  al., 2019); removing 
‘gender blindness’ in agrobiodiversity conservation and adaptation 
policies (Ravera et al., 2019); understanding uncertainties in CCA and 
DRR at the local level (van der Keur et al., 2016; Djalante and Lassa, 
2019); promoting the use of IKLK alongside scientific knowledge

10
1530Chapter 10 Asia
(Hiwasaki et  al., 2014); and increasing information, education and 
communication activities, and capacity development on DRR at the local level (Tuladhar et al., 2015a).
Several studies also have identified enabling conditions to effectively

implement CCA and DRR actions. In the Arab region of Asia (ARA), the following are critical: capacity building to develop knowledge and awareness; mainstreaming CCA and DRR in the national strategies and policies (e.g., water and environmental strategies); empowering the role of CCA and DRR actors, notably women and rural societies; adopting lessons learned from regions with physical characteristics similar to those of ARA; establishing forecasting and prediction platforms that are supported by advanced monitoring technologies (e.g., remote sensing); and encouraging universities and research centres to develop studies on CCA and DRR. In Southeast Asia, laws and policies, institutional and financial arrangements, risk assessment, capacity building, and planning and implementation are entry points in integrating CCA and DRR (Lassa and Sembiring, 2017; Agency, 2018). According to Cutter et  al. (2015), holistic solutions and integrated approaches, rigorous risk research that shows

coherent science-based assessment and knowledge transfer from research to practice, and aligned targets on disaster risk management, climate change and sustainable development targets, are critical. Social capital and SP measures could promote pro-poor and gender-responsive adaptation as well as socially inclusive policies (Dilshad et al., 2019; Yari et al., 2019). Community-based approaches could allow local perceptions of climate change and experience of place to be included in planning (Dujardin et  al., 2018; Dwirahmadi et  al., 2019; Widiati and Irianto, 2019), and multi-stakeholder participation could engage various actors such as the private sector in CCA and DRR. Furthermore, multi-level climate governance could benefit from vertical and horizontal interactions at different levels and layers in the city (Zen et al., 2019). To mainstream and secure funding commitments, CCA and DRR could be integrated into national development plans and sectoral long-term plans (Ishiwatari and

Adaptation follows knowledge on risks, and literature exists that systematically identifies and characterises exposure and vulnerability, but gaps still exist. Decision making under uncertainty is challenged by the lack of data for adapting to current and uncertain future climate, the different perceptions of risk, and the potential solutions across different cultures and languages (van der Keur et  al., 2016). Lack of downscaled climatic data, diverse institutional structures, and missing links in policies, are among the challenges in South Asia (Mall et al., 2019). In agriculture, there are gaps in the use of advanced farming techniques such as drought-resistant crops, and information on climate change to support farming households in making adaptation decisions (Akhtar et  al., 2019; Khanal et  al., 2019; Ullah et  al., 2019). Better understanding of effective water management is crucial due to conflicts for shared water in ARA (Shaban and Hamze, 2017; UNDP , 2018). For delta

regions, gaps identified are methodologies and approaches appropriate for understanding social vulnerability at various scales, pathways required for adaptation policy and response in the deltas that transcend development, and the lessons from implemented policy and how practice can build on these lessons in the deltas, among others (Lwasa, 2015). Approaches in tackling the challenges of climate change and disasters in the cities of developing countries could be better understood, and shared between cities so they can learn from one another (Filho et al., 2019).

10.6.3  F ood–Water–Energy Nexus
10.6.3.1
 P
Food, energy, water and land are vital elements for sustainable

development as well as enhancing resilience to both climatic and non-climatic shocks. All these resources are highly vulnerable to climate change (Sections  10.3.1, 10.3.4). Poor people are most affected due to changes in resources availability and accessibility. Food, water and energy security are interconnected (Bizikova et al., 2013; Ringler et al., 2013; Rasul, 2014; Chang et al., 2016; Ringler et al., 2016). Although adapting to climate change is one of the core components of the global, regional, national and subnational agendas, the focus of adaptation action has remained sectoral. Undermining the interlinkages of food, energy and water security may increase trade-offs between sectors or places, which may lead to maladaptation (Barnett and O’Neill, 2010; Howells et  al., 2013; Lele et  al., 2013). Therefore, focusing on the nature of trade-offs and synergies across the food–water–energy nexus for integrated management of resources is a potential strategy for adaptation to both

climatic and non-climatic challenges (Bhaduri et al., 2015; Zaman et al., 2017). Due to its importance to the Paris Agreement and SDGs, the food–water–energy nexus approach has gotten increasing attention in terms of capturing synergies and minimising trade-offs in this interconnected system, which is also critical for enhancing adaptation together (Bazilian et  al., 2011; Lawford et  al., 2013; UNESCAP , 2013; FAO, 2014; Rasul and Sharma, 2014; Taniguchi et al., 2017a; Sukhwani et al., 2019; Sukhwani et al., 2020).

The food–water–energy nexus can be evaluated in the two-way interactions between water–food, water–energy and food–energy (Taniguchi et  al., 2017a). The water–energy nexus includes water for energy and energy for water (Rothausen and Conway, 2011; Hussey and Pittock, 2012; Byers et al., 2014), the water–food nexus includes water for food and the impact of food production on water (Hoekstra and Mekonnen, 2012) and the energy–food nexus includes energy consumption for food production and food crops for biofuel production (Tilman et al., 2009). The food–water–energy–land nexus has diverse implications at the sub-regional level in Asia. The increase in the water-supply gap raises questions about the sustainability of the main mode of electricity generation in South Asia. Thermal power generation and hydropower generation are both threatened by water shortages in South Asia (Luo, 2018b; Mitra et al., 2021). Furthermore, policy-mismatch-driven anthropogenic causes lead to unsustainable

water use for food production in India. For example, subsidised electricity supply for watering agriculture plays a key role in losing groundwater’s buffer capacity against the various changes including climate variabilities (Badiani et al., 2012; Mitra, 2017). In the Mekong River basin of Southeast Asia, massive and rapid export-oriented

10
1531Asia  Chapter 10
hydropower development will have direct implications on regional food 
security and livelihoods through a major negative effect on the aquatic 
ecosystem (Baran and Myschowoda, 2009; Dugan et al., 2010; Arias 
et al., 2014). Similarly, in Central Asia, the shifting of water storage for 
irrigation to power development has increased risks on reliable water 
supply and quality of water (Granit et al., 2012). Deforestation-driven 
agro-environmental changes have led to a decreased forest water 
supply, an increased irrigation water demand and a negative effect on 
cropland stability and productivity (Lim et al., 2017a; Lim et al., 2019b).
10.6.3.3 Knowledge gaps
Many challenges remain in both scientific research and policy 
actions at the global, regional, national and subnational levels. The 
scientific challenges include data, information and knowledge gaps 
in understanding the food, energy, water and land interlinkages,

and lack of systematic tools to address trade-offs (Liu et al., 2017a). 
Until very recently, implementation of the food–energy–water nexus 
focused primarily on technical solutions, whereas governance (i.e., the 
institutions and processes governing the food–energy–water nexus) 
has not received much consideration (Scheyvens and Shivakoti, 2019). 
At the policy end, the common challenges for implementation of the 
water–energy–food–land nexus are absence of sectoral coordination 
(Pahl-Wostl, 2019), the influence of political priorities on decisions and 
lack of processes for scientific knowledge to shape decisions, lack of 
capacity to understand interlinkages between sectors, lack of multi-
stakeholder engagement in planning and decision-making processes, 
and lack of incentive mechanisms and adequate finance to support the 
approach (Bao et al., 2018; Scheyvens and Shivakoti, 2019).
10.6.4 Social Justice and Equity

Social justice focuses on the justice-related implications of social and 
economic institutions, examined in different ways such as distributional 
justice (distribution of benefits and burdens across different societal 
groups), procedural justice (the design of just institutions and processes 
for decision making), inter-generational justice (duties of justice to 
future generations) and recognitional justice (recognition of historical 
inequality) (Thaler et  al., 2017). Climate change is affecting every 
aspect of our society and economy; thus, it is pertinent to understand 
the interactions between social justice and climate-change impacts 
(Tol, 2018), in particular, focusing on how vulnerability to various 
impacts is created, maintained and distributed across geographic, 
social, demographic and economic dimensions (Bulkeley et al., 2014; 
Schlosberg and Collins, 2014; Van de Vliert, 2014; Burke et al., 2016). 
For instance, environmental and health consequences of climate

change, which disproportionately affect low-income countries and 
poor people in high-income countries, profoundly affect human rights 
and social justice (Levy and Patz, 2015). Furthermore, great concern is 
expressed about the plight of the poor, disadvantaged and vulnerable 
populations when it comes to climate, but not in other policy domains 
(Winters, 2014).
Evidence is increasing on the importance of focusing on environmental 
sustainability, and relieving poverty and social injustice are not 
conflicting aims; in fact, there is a further need for mainstreaming such approaches in order to respond to the climate-change challenge 
in a socially just manner (Mayrhofer and Gupta, 2016). These non-
conflicting aims are described as co-benefits as reiterated in the 
IPCC reports as a central concept that refers to ‘the positive effects 
that a policy or measure aimed at one objective might have on other 
objectives, irrespective of the net effect on overall social welfare’

(IPCC, 2014b). Better understanding of how social justice affects, 
and is affected by, efforts to build adaptive capacity will be crucial 
to avoiding unintended, and even perverse, outcomes. For example, 
on the Andaman coast of Thailand, responses to climate-change 
trends and events tended to be reactive rather than proactive, making 
already vulnerable people even more vulnerable and undermining 
their capacity to adapt in the future (Bennett et al., 2014). Different 
forms of inequality, moreover, render some groups more vulnerable 
than others to damage from climate hazards. In Mumbai, India, for 
example, the houses of poorer families required repeated repairs to 
secure them against flood damage, and the cumulative cost of those 
repairs consumed a greater proportion of their income than for richer 
populations (UN, 2016). Building the resilience of vulnerable groups 
requires strong community and government institutions that can

support efforts to cope with devastating events, offering SP and social 
development initiatives to support at-risk or vulnerable groups (Drolet 
et al., 2015). In addition, agencies need to consider how they can best 
work in ways which potentially support longer-term positive change 
to gender roles and relations. For example, post-disaster activities 
must build and resource women’s resilience and adaptive capacity 
in practice and challenge the constraints that impinge on their lives 
(Sadia et al., 2016; Sohrabizadeh et al., 2016; Hadiyanto et al., 2018; 
Yumarni and Amaratunga, 2018; Alam and Rahman, 2019).
Insights from the environmental justice literature show that an 
overemphasis on emission reductions at national levels obscures 
the negative impacts on disadvantaged communities, including low-
income communities (Burch and Harris, 2014). The issue of social 
justice and adaptation is particularly relevant because of the politics

that drive how adaptation and recovery efforts, as well as investments, 
are targeted towards specific populations, places and capacities 
(Klinsky et  al., 2017). Hence, climate justice and equity need to be 
highlighted more explicitly in integrative approaches to mitigation and 
adaptation (medium evidence, high agreement) (Moellendorf, 2015; 
Henrique KP , 2020).
The term ‘climate justice’ is used to problematise global warming in 
ethical and political contexts. It does so by employing the concepts 
of environmental justice and social justice to examine inequalities 
and violation of human collective rights in relation to climate-change 
impacts (Ghimire, 2016).
At the heart of climate-justice concerns lies the asymmetry that those 
who have contributed least to the problem of climate change (i.e., GHG 
emissions) are the ones who will be affected by its adverse impacts 
the most. It is about sharing the burden and benefits equitably (a)

among developed and developing countries in the context of historical 
responsibility, and (b) within nations to uplift the marginalised and 
affected populations who have contributed the least to the problem 
in the contexts of per-capita equity and local vulnerability (Joshi, 2014; 
Chaudhuri, 2020; Shawoo, 2020).

10
1532Chapter 10 Asia
An ethical analysis of the climate regime reveals an abidingly strong 
interconnection between economic circumstances, geopolitical power 
and the justice claims that nations can assert in negotiations (Okereke, 
2016). Events within the climate regime highlight the importance of 
questioning the extent to which claims of justice can ever be truly 
realised in the context of international regimes of environmental 
governance, as well as how much concerns for justice are motivated 
by other concerns such as relative economic gains or geopolitical 
objectives (Sikor, 2014).
The global land rush and mainstream climate-change narratives 
have broadened the ranks of state and social actors concerned about 
land issues while strengthening those opposed to social-justice-
oriented land policies (Borras, 2018). The five deep social reforms (redistribution, recognition, restitution, regeneration and resistance) of

socially just land policy are necessarily intertwined. But the global land 
rush amid deepening climate change calls attention to the linkages, 
especially between the pursuit of agrarian justice, on the one hand, 
and climate justice, on the other. Here, the relationship is not without 
contradictions and warrants increased attention as both unit of 
analysis and object of political action. Understanding and deepening 
agrarian-justice imperatives in climate politics, and understanding and 
deepening climate-justice imperatives in agrarian politics, is needed 
more than ever in the ongoing pursuit of alternatives. For example, 
the intersection between land grabs and climate-change mitigation 
politics in Myanmar has created new political opportunities for scaling 
up, expanding and deepening the struggles towards ‘agrarian climate 
justice’(Sekine, 2021).

FAQ 10.1 | What are the current and projected key risks related to climate change in each sub-region of Asia?
Climate-change-related risks are projected to increase progressively at 1.5°C, 2 °C and 3°C of global warming in many parts of Asia. Heat stress 
and water deficit are affecting human health and food security. Risks due to extreme rainfall and sea level rise are exacerbated in vulnerable Asia.
Climatologically, the summer surface air temperature in South, Southeast and Southwest Asia is high, and its coastal 
area is very humid. In these regions, heat stress is already a medium risk for humans. Large cities are warmer by more 
than 2°C compared with the surroundings due to heat island effects, exacerbating heat stress conditions. Future 
warming will cause more frequent temperature extremes and heatwaves especially in densely populated South 
Asian cities, where working conditions will be exacerbated and daytime outdoor work will become dangerous. For

example, incidence of excess heat-related mortality in 51 cities in China is estimated to reach 37,800 deaths per year 
over a 20-year period in the mid-21st century (2041–2060) under the RCP8.5 scenario.
Asian glaciers are the water resources for local and adjacent regions. Glaciers are decreasing in Central, Southwest, 
Southeast and North Asia, but are stable or increased in some parts of the Hindu Kush Himalaya region. The glacier 
melt water in the southern Tibetan Plateau increased during 1998–2007, and the total amount and area of glacier 
lakes has increased during recent decades. In the future, maximum glacial runoff is projected in High Mountain 
Asia. Glacier collapses and surges, together with glacier lake outburst flood due to the expansion of glacier lakes, 
will threaten the securities of the local and down streaming societies.
With much of the Asian population living in drought-prone areas, water scarcity is a prevailing risk across Asia

through water and food shortage leading to malnutrition. Populations vulnerable to impacts related to water are 
going to increase progressively at 1.5°C, 2°C and 3°C of global warming. Aggravating drought condition is projected 
in Central Asia. Water quality degradation also has profound impact on human health.
Extreme rainfall causes floods in vulnerable rivers. Observed changes in extreme rainfall vary considerably by region 
in Asia. Extreme rainfall events (such as heavy rainfall >100 mm per day) have been increasing in South and East Asia. 
In the future, most of East and Southeast Asia are projected to experience more intense rainfall events as soon as by 
the middle of the 21st century. In those regions, the flood risk will become more frequent and severe. It is estimated 
that over one-third of Asian cities and about 932 million urban dwellers are living in areas with high risk of flooding.

Sea level rise is continuing. Higher than the global mean sea level rise is projected on Asian coasts. Storm surge and 
high wave by tropical cyclones of higher intensity are high risk for a large number of Asian megacities facing the 
ocean: China, India, Bangladesh, Indonesia and Vietnam have the highest numbers of coastal populations exposed 
and thus are most vulnerable to disaster-related mortality.
Changes in terrestrial biome have been observed that are consistent with warming, such as an upward move of 
treeline position in mountains. Climate change, human activity, lightning and quality of forest governance and 
management have increased wildfire severity and area burned in North Asia in recent decades. Changes in marine 
primary production also have been observed: a decrease up to 20% over the past six decades in the western Indian 
Ocean, due to ocean warming and stratification, has restricted nutrient mixing. The risk of irreversible loss of many

10
1533Asia  Chapter 10
The likelihood of adverse impacts to agricultural and food security in many parts of developing Asia will progressively 
escalate with the changing climate. The potential of total fisheries production in South and Southeast Asia is also 
projected to decrease.
Key risks related climate change in Asia
Wildfir ePermafrost thawing
Dust storms
Heat wave
Extreme rainfall
Sea level riseFloodsHeat wave
Heat islandBiodiversity
Figure FAQ10.1.1 |  Key risks related to climate change in Asia.FAQ 10.1 (continued)

10
1534Chapter 10 Asia
FAQ 10.2 | What are the current and emerging adaptation options across Asia?
Mirroring the heterogeneity across Asia, different countries and communities are undertaking a range of reactive and proactive strategies to 
manage risk in various sectors. Several of these adaptation actions show promise, reducing vulnerability and improving societal well-being. 
However, challenges remain around scaling up adaptation actions in a manner that is effective and inclusive while simultaneously meeting 
national development goals.
Asia exhibits tremendous variation in terms of ecosystems, economic development, cultures and climate risk 
exposure. Mirroring this variation, households, communities and governments have a wide range of coping and 
adaptation strategies to deal with changing climatic conditions, with co-benefits for various non-climatic issues such 
as poverty, conflict and livelihood dynamics.

Currently, Asian countries have rich evidence on managing risk, drawing on long histories of dealing with change. 
For example, to deal with erratic rainfall and shifting monsoons, farmers make incremental shifts such as changing 
what and when they grow or adjusting their irrigation practices. Communities living in coastal settlements are 
using Early warning systems to prepare for cyclones or raising the height of their houses to minimise flood impacts. 
These types of strategies, seen across all Asian sub-regions, based on local social and ecological contexts, are termed 
autonomous adaptations that occur incrementally and help people manage current impacts.
Currently and in the future, Asia is identified as one of regions most vulnerable to climate change, especially 
on extreme heat, flooding, sea level rise and erratic rainfall. All these climatic risks, when overlaid on existing

development deficits, show us that incremental adaptation will not be enough; transformational change is 
required. Recognising this, at subnational and national levels, government and non-governmental actors are also 
prioritising planned adaptation strategies which include interventions like ‘climate-smart agriculture’ as seen in South 
and Southeast Asian countries, or changing labour laws to reduce exposure to heat as seen in West Asia. These are 
often sectoral priorities governments lay out through national or subnational policies and projects, drawing on 
various sources of funding: domestic, bilateral and international. Apart from these planned adaptation strategies in 
social systems, Asian countries also report and invest in adaptation measures in natural systems such as expanding 
nature reserves to enable species conservation or setting up habitat corridors to facilitate landscape connectivity 
and species movements across climatic gradients.

Overall, the fundamental challenges that Asia will see exacerbated under climate change are around water and 
food insecurity, poverty and inequality, and increased frequency and severity of extreme events. In some places 
and for some people, climate change, even at 1.5°C and more so at 2°C, will significantly constrain the functioning 
and well-being of human and ecological systems. Asian cities, villages and countries are rising to this current and 
projected challenge, albeit somewhat unevenly.
Some examples of innovative adaptation actions are China’s ‘Sponge Cities’ which are trying to protect ecosystems 
while reducing risk for people, now and in the future. Another example is India’s Heat Action Plans that are using 
‘cool roofs’ technologies and awareness-building campaigns to reduce the impacts of extreme heat. Across South 
and Southeast Asia, climate-smart agriculture programmes are reducing GHG emissions associated with farming

while helping farmers adapt to changing risks. Each country is experimenting with infrastructural, nature-based, 
technological, institutional and behavioural strategies to adapt to current and future climate change with local 
contexts shaping both the possibility of undertaking such actions as well as the effectiveness of these actions to 
reduce risk. What works for ageing cities in Japan exposed to heatwaves and floods may not work for pastoral 
communities in the highlands of Central Asia, but there is progress on understanding what actions work and for 
whom. The challenge is to scale current adaptation action, especially in the most exposed areas and for the most 
vulnerable populations, as well as move beyond adapting to single risks alone (i.e., adapt to multiple coinciding 
risks such as flooding and water scarcity in coastal cities across South Asia or extreme heat and flash floods in

West Asia). In this context, funding and implementing adaptation is essential, and while Asian countries are 
experimenting with a range of autonomous and planned adaptation actions to deal with these multiple and often 
concurrent challenges, making current development pathways climate resilient is necessary and, some might argue, 
unavoidable.

10
1535Asia Chapter 10
Table FAQ10.2.1 |  System transitions, sectors and illustrative adaptation options
System transitions Sectors Illustrative adaptation options
Energy and industrial systems Energy and industriesDiversifying energy sources
Improving energy access, especially in rural areasImproving resilience of power infrastructureRehabilitation and upgrading of old buildings
Land and ecosystemsTerrestrial and freshwater ecosystemsExpanding nature reservesAssisted species migrationIntroducing species to new regions to protect them from climate-induced extinction riskSustainable forest management including afforestation, forest fuel management, fire management
Ocean and coastal ecosystemsMarine protected areasMangrove and coral reef restorationIntegrated coastal zone managementSand banks and structural technologies

FreshwaterIntegrated watershed managementTransboundary water managementChanging water access and use practices to reduce/manage water demandHigh-efficiency water-saving technologyAquifer storage and recovery
Agriculture, fisheries and foodChanging crop type and variety, improving seed qualityWater storage, irrigation and water managementClimate-smart agricultureEarly warning systems and use of climate information servicesFisheries management plans (e.g., seasonal closures, limited fishing licenses, livelihood diversification)
Urban systemsCities and settlementsFlood protection measures and sea wallssustainable land-use planning and regulationProtecting urban green spaces, improving permeability, mangrove restoration in coastal citiesPlanned relocation and migrationDisaster management and contingency planning
Key infrastructuresClimate-resilient highways and power infrastructureRelocating key infrastructure

Health systemsReducing air pollutionChanging dietary patternsFAQ 10.2 (continued)
FAQ 10.3 | How are Indigenous knowledge and local knowledge being incorporated in the design and 
implementation of adaptation pr
ojects and policies in Asia?
Indigenous People, comprising about 6% of the global population, play a crucial role in managing climate change for two important reasons. 
First, they have a physical and spiritual connection with land, water and associated ecosystems, thus making them most vulnerable to any environmental and climatic changes. Second, their ecological and local knowledge are relevant to finding solutions to climate change.
Indigenous knowledge and local knowledge (IKLK) play an important role in the formulation of adaptation

governance and related strategies (IPCC 2007), and best quality, locality-specific knowledge can help address the serious lack of education on climate change and uncertainties surrounding quality, salience, credibility and the legitimacy of the available knowledge base.
Key findings across Asia underline the importance of building, sustaining and augmenting local capacity through 
addressing inadequacies in terms of resource base, climate-change awareness, government–community partnerships and vulnerability assessment. Furthermore, inclusion of  as well as related practices will improve adaptation planning and decision-making processes concerning climate change.

10
1536Chapter 10 Asia
In climate-sensitive livelihoods, an integrated approach informed by science that examines multiple stressors, along 
with IKLK, appears to be of immense value. For instance, in building farmers’ resilience, enhancing CCA, ensuring cross-cultural communication and promoting local skills, Indigenous People’s intuitive thinking processes and geographic knowledge of remote areas are very important.
There is also a widespread recognition that IKLK are important in ensuring successful ecosystem-based adaptation

(EbA). However, this recognition requires more practical application and translation into IKLK-driven EbA projects. For instance, in the Coral Triangle region, creating historical timelines and mapping seasonal calendars can help to capture IKLK while also feeding this information into climate science and climate adaptation planning. Identifying indigenous crop species for agriculture by using IKLK is already identified as an important way to localise climate adaptation: an example is Bali’s vital contribution of moral economies to food systems which have long built resilience among groups of communities in terms of food security and sovereignty, even with the challenges faced due to modernising of local food systems.
Many of the pressing problems of Asia, including water scarcity, rapid urbanisation, deforestation, loss of species,

rising coastal hazards and agricultural loss can be effectively negated, or at least minimised, through proper adoption of suitable science and technological methods. Climate-change adaptation is greatly facilitated by science, technology and innovation. This ranges from application of existing science, new development on scientific tools and methods, application of IKLK and citizen sciences. Deploying Knowledge Quality Assessment Tool found significant co-relation between science-based and IKLK framing would help to address, acknowledge and utilise by an integrated approach the wisdom of IKLK, a valuable asset for climate adaptation governance. The IKLK-based environmental indicators need to be seen as part of a separate system of knowledge that coexists with, but is not submerged into, another conventional knowledge system.
In the context of education and capacity development of climate change, an integrated approach of embracing

both the importance of climate science and IKLK is acknowledged. The IKLK is increasingly recognised as a powerful tool for compiling evidence of climate change over time. Such as knowledge of CCA and DRR provide a range of complementary approaches in building resilience and reducing the vulnerability of natural and human systems. Developing knowledge and utilising existing IKLK, skills and dispositions to better cope with already evident and looming climate impacts. Engaging communities in the process of documenting and understanding long-term trends and practices will enable both IKLK as well as Western scientific assessments of climate change to contribute in designing appropriate climate adaptation measures.FAQ 10.3 (continued)
FAQ 10.4 | How can Asia meet multiple goals of climate-change adaptation and sustainable development within 
the coming decades?

Asian countries are testing ways to develop in a climate-resilient manner to meet the goals related to climate change and sustainable development simultaneously. Some promising examples exist, but the window of opportunity to put some of these plans in place is small and closing fast, highlighting the need for urgent action across and within countries.
In order to achieve the multiple goals of CCA, mitigation and sustainable development, critical are rapid, system

transitions across (a) energy systems, (b) land and ecosystems and (c) urban and infrastructural systems. This is especially important across Asia, which has the largest population exposed to current climate risks and high sub-regional diversity, and where risks are expected to rise significantly and unevenly under higher levels of global warming. However, such transformational change is deeply challenging because of variable national development imperatives; differing capacities and requirements of large, highly unequal and vulnerable populations; and socioeconomic and ecological diversity that requires very contextual solutions. Furthermore, issues such as growing transboundary risks, inadequate data for long-term adaptation planning, finance barriers, uneven institutional capacity and non-climatic issues, such as increasing conflict, political instability and polarization, constrain rapid, transformational action across systems.

10
1537Asia Chapter 10
Despite these challenges, there are increasing examples of actions across Asia that are meeting climate adaptation 
goals and SDGs simultaneously, such as through climate-smart agriculture, disaster risk management and NbS. To enable these system transitions, vertical and horizontal policy linkages, active communication and cooperation between multiple stakeholders, and attention to the root causes of vulnerability are essential. Furthermore, rapid systemic transformation can be enabled by policies and finances to incentivise capacity building, new technological innovation and diffusion. The effectiveness of such technology-centred approaches can be maximised by combining them with attention to behavioural shifts such as by improving education and awareness, building local capacities and institutions, and leveraging IKLK.
Obviously, time is of the essence. If system transitions are delayed, there is high confidence that climatic risks will

increase human and natural system vulnerability, as well as increase inequality and erode the achievements of multiple SDGs. Thus, urgent systemic change that is suited to national and subnational social–ecological contexts across Asia is imperative.
Adaptatation option Mitigation impactsImplications on SDGs
Wetland protection, 
restorationMedium synergy (carbon sequestration through mangroves)
Solar drip irrigitationHigh synergy (shift to cleaner energy)
Climate-smart agricultureHigh synergy (no till practices and improved residue management can reduce soil carbon emissions)
Integrated smart water gridsHigh synergy (reduced energy needs for supplying water)
Disaster risk management (including early warning systems)Not applicable
Aquifer storage and recoveryLow synergy
Nature-based solutions in urban areas: green infrstructureHigh synergy (blue-green infrastructure act as carbon sinks)
Coastal green infrastructureHigh synergy

Figure FAQ10.4.1 |  Adaptation options, mitigation impacts and implications on Sustainable Development Goals.FAQ 10.4 (continued)

11
1583Australasia Chapter 11
Observed changes and impacts
Ongoing climate trends have exacerbated many extreme

events (very high confidence). The Australian trends include further warming and sea level rise sea level rise (SLR), with more hot days and heatwaves, less snow, more rainfall in the north, less April–October rainfall in the southwest and southeast and more extreme fire weather days in the south and east. The New Zealand trends include further warming and sea level rise (SLR), more hot days and heatwaves, less snow, more rainfall in the south, less rainfall in the north and more extreme fire weather in the east. There have been fewer tropical cyclones and cold days in the region. Extreme events include Australia’s hottest and driest year in 2019 with a record-breaking number of days over 39°C, New Zealand’s hottest year in 2016, three widespread marine heatwaves during 2016–2020, Category 4 Cyclone Debbie in 2017, seven major hailstorms over eastern Australia and two over New Zealand from 2014–2020, three major floods in eastern Australia and three over New Zealand during 2019–2021

and major fires in southern and eastern Australia during 2019–2020. {11.2.1, Table 11.2, 11.3.8}

Climate trends and extreme events have combined with exposure 
and vulnerabilities to cause major impacts for many natural systems, with some experiencing or at risk of irreversible change in Australia (very high confidence)  and in New Zealand (high 
confidence) . For example, warmer conditions with more heatwaves,

droughts and catastrophic wildfires have negatively impacted terrestrial and freshwater ecosystems. The Bramble Cay melomys, an endemic mammal species, became extinct due to loss of habitat associated with sea level rise (SLR) and storm surges in the Torres Strait. Marine species abundance and distributions have shifted polewards, and extensive coral bleaching events and loss of temperate kelp forests have occurred due to ocean warming and marine heatwaves across the region. In New Zealand’s southern Alps, from 1978 to 2016, the area of 14 glaciers declined 21%, and extreme glacier mass loss was at least 6 times more likely in 2011 and 10 times more likely in 2018 due to climate change. The end-of-summer snowline elevation for 50 glaciers rose 300
 m from
 
1949 to 2019. {11.3.1.1, 11.3.2.1, Table 11.2b, Table 11.4, Table 11.6, Table 11.9}
Climate trends and extreme events have combined with

exposure and vulnerabilities to cause major impacts for some human systems (high confidence). Socioeconomic costs arising 
from climate variability and change have increased. Extreme heat has led to excess deaths and increased rates of many illnesses. Nuisance and extreme coastal flooding have increased due to sea level rise (SLR) superimposed upon high tides and storm surges in low-lying coastal and estuarine locations, including impacts on cultural sites, traditions and lifestyles of Aboriginal and Torres Strait Islander Peoples in Australia and Tangata Whenua Māori in New Zealand. Droughts have caused financial and emotional stress in farm households and rural communities. Tourism has been negatively affected by coral 

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.bleaching, fires, poor ski seasons and receding glaciers. Governments, 
business and communities have experienced major costs associated with extreme weather, droughts and sea level rise (SLR). {11.3, 11.4, 11.5.2, Table 11.2, Boxes 11.1–11.6}
Climate impacts are cascading and compounding across sectors

and socioeconomic and natural systems (high confidence). Complex connections are generating new types of risks, exacerbating existing stressors and constraining adaptation options. An example is the impacts that cascade between interdependent systems and infrastructure in cities and settlements. Another example is the 2019–2020 southeast Australia wildfires, which burned 5.8 to 8.1  million hectares, with 114 listed threatened species losing at least half of their habitat and 49 losing over 80%, over 3,000 houses destroyed, 33 people killed, a further 429 deaths and 3230 hospitalisations due to cardiovascular or respiratory conditions, AUD$1.95 billion in health costs, AUD$2.3 billion in insured losses and AUD$3.6 billion in losses for tourism, hospitality, agriculture and forestry. {11.5.1, Box 11.1}
Increasing climate risks are projected to exacerbate existing 
vulnerabilities and social inequalities and inequities (high

confidence). These include inequalities between Indigenous and non-Indigenous Peoples and between generations, rural and urban areas, incomes and health status, increasing the climate risks and adaptation challenges faced by some groups and places. Resultant climate change impacts include the displacement of some people and businesses and threaten social cohesion and community well-being. {11.3.5, 11.3.6, 11.3.10, 11.4}
Projected impacts and key risksFurther climate change is inevitable, with the rate and 
magnitude largely dependent on the emission pathway (very high confidence
1). Ongoing warming is projected, with more hot

days and fewer cold days (very high confidence). Further sea level rise (SLR), ocean warming and ocean acidification are projected (very high confidence). Less winter and spring rainfall is projected in southern Australia, with more winter rainfall in Tasmania, less autumn rainfall in southwestern Victoria and less summer rainfall in western Tasmania (medium confidence), with uncertain rainfall changes in northern

Australia. In New Zealand, more winter and spring rainfall is projected in the west and less in the east and north, with more summer rainfall in the east and less in the west and central North Island (medium confidence). In New Zealand, ongoing glacier retreat is projected (very high confidence). More extreme fire weather is projected in southern and eastern Australia (high confidence) and over northern and eastern New Zealand (medium confidence). Increased drought frequency is projected for southern and eastern Australia and northern New Zealand (medium confidence). Increased heavy rainfall intensity is projected, with fewer tropical cyclones and a greater proportion of severe cyclones (medium confidence). {11.2.2, Table 11.3, Box 11.6}

11
1584Chapter 11 Australasia
Climate risks are projected to increase for a wide range of 
systems, sectors and communities, which are exacerbated by 
underlying vulnerabilities and exposures (high confidence) 
{11.3; 11.4}. Nine key risks have been identified, based on magnitude, 
likelihood, timing and adaptive capacity {11.6, Table 11.14}:
Ecosystems at critical thresholds, where recent climate change has 
caused significant damage and further climate change may cause 
irreversible damage, with limited scope for adaptation
1) Loss and degradation of coral reefs and associated biodiversity 
and ecosystem service values in Australia due to ocean warming 
and marine heatwaves. For example three marine heatwaves on 
the Great Barrier Reef (GBR) during 2016–2020 caused significant 
bleaching and loss (very high confidence). {11.3.2.1, 11.3.2.2, 
Box 11.2}
2) Loss of alpine biodiversity in Australia due to less snow. For example

loss of alpine vegetation communities (snow patch Feldmark and 
short alpine herb-fields) and increased stress on snow-dependent 
plant and animal species (high confidence). {11.3.1.1, 11.3.1.2}
Key risks that have potential to be severe but can be reduced 
substantially by rapid, large-scale and effective mitigation and 
3) Transition or collapse of alpine ash, snowgum woodland, pencil 
pine and northern jarrah forests in southern Australia due to hotter 
and drier conditions with more fires. For example declining rainfall 
in southern Australia over the past 30 years, has led to drought-
induced canopy dieback across a range of forest and woodland 
types and death of fire-sensitive tree species due to unprecedented 
wildfires (high confidence). {11.3.1.1, 11.3.1.2}
4) Loss of kelp forests in southern Australia and southeast New 
Zealand due to ocean warming, marine heatwaves and overgrazing 
by climate-driven range extensions of herbivore fish and urchins. For

example less than 10% of giant kelp in Tasmania was remaining by 
2011 due to ocean warming (high confidence). {11.3.2.1, 11.3.2.2}
5) Loss of natural and human systems in low-lying coastal areas due 
to sea level rise (SLR). For example for 0.5 m sea level rise (SLR), 
the value of buildings in New Zealand exposed to 1-in-100-year 
coastal inundation could increase by NZ$12.75  billion and the 
current 1-in-100-year flood in Australia could occur several times a 
year (high confidence). {11.3.5; Box 11.6}
6) Disruption and decline in agricultural production and increased 
stress in rural communities in southwestern, southern and 
eastern mainland Australia due to hotter and drier conditions. For 
example by 2050, a decline in median wheat yields of up to 30% 
in southwestern Australia and up to 15% in South Australia and 
increased heat stress in livestock by 31–42  days per year (high 
confidence). {11.3.4; 11.3.5; Box 11.3}

7) Increase in heat-related mortality and morbidity for people and 
wildlife in Australia due to heatwaves. For example heat-related 
excess deaths in Melbourne, Sydney and Brisbane are projected 
to increase by about 300/year (low emission pathway) to 600/year 
(high emission pathway) during the 2031–2080 period relative 
to 142/year in the period 1971–2020 (high confidence). {11.3.1, 
11.3.5.1, 11.3.5.2, 11.3.6.1, 11.3.6.2}Key cross-sectoral and system-wide risk
8) Cascading, compounding and aggregate impacts on cities, 
settlements, infrastructure, supply chains and services due to 
wildfires, floods, droughts, heatwaves, storms and sea level rise 
(SLR). For example in New Zealand, extreme snow, heavy rainfall 
and wind events have combined to impact road networks, power 
and water supply, interdependent wastewater and stormwater 
services and business activities (high confidence) {11.3.3, 11.5.1, 
11.8.1}.
Key implementation risk

9) Inability of institutions and governance systems to manage climate 
risks. For example the scale and scope of projected climate impacts 
overwhelm the capacity of institutions, organisations and systems 
to provide necessary policies, services, resources and coordination 
to address socioeconomic impacts (high confidence) {11.5.1.2, 
11.5.1.3, 11.5.2.3, 11.7.1, 11.7.2, 11.7.3}.
There are important interactions between mitigation and 
adaptation policies and their implementation (high confidence). 
Integrated policies in interdependent systems across biodiversity, 
water quality, water availability, energy, transport, land use and 
forestry for mitigation can support synergies between adaptation 
and mitigation. These have co-benefits for the management of land 
use, water and associated conflicts and for the functioning of cities 
and settlements. For example, projected increases in fire, drought, 
pest incursions, storms and wind place forests at risk and affect their

ongoing role in meeting New Zealand’s emissions reduction goals. 
{11.3.4.3, 11.3.10.2, 11.3.5.3, Box 11.5}
Challenges and solutions
The ambition, scope and progress of the adaptation process have 
increased across governments, non-government organisations, 
businesses and communities (high confidence). This process 
includes vulnerability and risk assessments, identification of strategies 
and options, planning, implementation, monitoring, evaluation and 
review. Initiatives include legislated institutional frameworks for 
risk assessment and national adaptation planning and monitoring 
in New Zealand, a National Recovery and Resilience Agency and 
National Disaster Risk Reuction Framework in Australia, deployment 
of new national guidance, decision tools, collaborative governance 
approaches and the introduction of climate risk and disclosure regimes 
for the private sector. The focus, however, has been on adaptation

planning, rather than on implementation. {11.5.1, 11.7.1.1, Box 11.6, 
Table 11.15a, Table 11.15b, Table 11.17}
Adaptation progress is uneven, due to gaps, barriers and limits to 
adaptation and adaptive capacity deficits (very high confidence). 
Progress in adaptation planning, implementation, monitoring and 
evaluation is lagging. Barriers include lack of consistent policy 
direction, competing objectives, divergent risk perceptions and values, 
knowledge constraints, inconsistent information, fear of litigation, up-
front costs and lack of engagement, trust and resources. Adaptation 
limits are being approached for some species and ecosystems. Adaptive

11Australasia  Chapter 11
1585capacity to address the barriers and limits can be built through greater 
engagement with groups and communities to build trust and social 
legitimacy through the inclusion of diverse values, including those 
of Aboriginal and Torres Strait Islander Peoples and Tangata Whenua 
Māori. {11.4, 11.5, 11.6, 11.7, 11.8, Table 11.4, Table 11.5, Table 11.6, 
Table 11.16, Box 11.2}
A range of incremental and transformative adaptation options 
and pathways is available as long as enablers are in place to 
implement them (high confidence). Key enablers for effective 
adaptation include shifting from reactive to anticipatory planning, 
integration and coordination across levels of government and sectors, 
inclusive and collaborative institutional arrangements, government 
leadership, policy alignment, nationally consistent and accessible 
information and decision-support tools, along with adaptation funding

and finance, and robust, consistent and strategic policy commitment. 
Over 75% of people in Australia and New Zealand agree that climate 
change is occurring and over 60% believe climate change is caused by 
humans, giving climate adaptation and mitigation action further social 
legitimacy. {11.7.3, Table 11.17}
New knowledge on system complexity, managing uncertainty 
and how to shift from reactive to adaptive implementation is 
critical for accelerating adaptation (high confidence). Priorities 
include a greater understanding of impacts on natural system dynamics; 
the exposure and vulnerability of different groups within society, 
including Indigenous Peoples; the relationship between mitigation and 
adaptation; the effectiveness and feasibility of different adaptation 
options; the social transitions needed for transformative adaptation; 
and the enablers for new knowledge to better inform decision-making 
(e.g., monitoring data repositories, risk and vulnerability assessments,

robust planning approaches, sharing adaptation knowledge and 
practice). {11.7.3.3}
Aboriginal and Torres Strait Islander Peoples and Tangata 
Whenua Māori can enhance effective adaptation through the 
passing down of knowledge about climate change planning 
that promotes collective action and mutual support across the 
region (high confidence). Supporting Aboriginal and Torres Strait 
Islander Peoples and Tangata Whenua Māori institutions, knowledge 
and values enable self-determination and create opportunities to 
develop adaptation responses to climate change. Actively upholding 
the UN Declaration on the Rights of Indigenous Peoples and Māori 
interests under the Treaty of Waitangi at all levels of government 
enables intergenerational approaches for effective adaptation. {11.3, 
11.4, 11.6, 11.7.3; Cross-Chapter Box INDIG in Chapter 18}
A step change in adaptation is needed to match the rising 
risks and to support climate resilient development (very high

confidence). Current adaptation is largely incremental and reactive. 
A shift to transformative and proactive adaptation can contribute 
to climate resilient development. The scale and scope of cascading, 
compounding and aggregate impacts require new, larger-scale and 
timely adaptation. Monitoring and evaluation of the effectiveness of 
adaptation progress and continual adjustment is critical. The transition 
to climate resilient development pathways can generate major co-benefits, but complex interactions between objectives can create 
trade-offs. {11.7, 11.8.1, 11.8.2}
Delay in implementing adaptation and emission reductions 
will impede climate resilient development, resulting in more 
costly climate impacts and greater scale of adjustments (very 
high confidence). The region faces an extremely challenging future. 
Reducing the risks would require significant and rapid emission 
reductions to keep global warming to 1.5°C–2.0°C, as well as robust

and timely adaptation. The projected warming under current global 
emissions reduction policies would leave many of the region’s human 
and natural systems at very high risk and beyond adaptation limits. 
{11.8, Table 11.1, Table 11.14, Figure 11.6}

11
1586Chapter 11 Australasia
This chapter assesses the observed impacts, projected risks, vulnerability 
and adaptation, and the implications for climate resilient development 
for the Australasia region, based on the literature published up to 1 
September 2021. It should be read in conjunction with other Working 
Group (WG) II chapters, the climate science assessment in the WGI 
report and the greenhouse gas emissions and mitigation assessment 
in the WGIII report.
11.1.1 Context
The Australasia region is defined as the Exclusive Economic Zones 
(EEZs) and territories of Australia and New Zealand. In both countries, 
climate adaptation is largely implemented at a sub-national level 
through the devolution of functions constitutionally or by statute, 
alongside disaster risk reduction (COAG, 2011; Lawrence et al., 2015; 
Macintosh et al., 2015).
Australia’s economy is dominated by financial and insurance services,

education, mining, construction, tourism, health care and social 
assistance (ABS, 2018) with Australian exports accruing mostly from 
mining (ABS, 2018; ABS, 2019). In New Zealand, service industries, 
including tourism, collectively account for around two-thirds of 
GDP (NZ Treasury, 2016). The primary sector contributes 6% of New 
Zealand’s GDP and over half of the country’s export earnings (NZ 
Treasury, 2016).
Existing vulnerabilities expose and exacerbate inequalities between 
rural, regional and urban areas, Indigenous and non-Indigenous Peoples, 
those with health and disability needs, and between generations, 
incomes and health status, increasing the relative climate change risk 
faced by some groups and places (high confidence) (Jones et al., 2014; 
Bertram, 2015; Perry, 2017; Hazledine and Rashbrooke, 2018).
Previous IPCC reports (Table 11.1) have documented observed climate 
impacts, projected risks, adaptation challenges and opportunities.

This chapter presents more evidence of observed climate impacts 
and adaptation, better quantification of socioeconomic risks, new 
information about cascading and compounding risks, greater emphasis 
on adaptation enablers and barriers, and links to climate resilient 
11.1.2  Economic, Demographic and Social Trends
Economic, demographic and sociocultural trends influence the exposure, 
vulnerability and adaptive capacity of individuals and communities (high 
confidence) (Elrick-Barr et al., 2016; Smith et al., 2016; Hayward, 2017; 
B. Frame et al., 2018; Plummer et al., 2018; Smith et al., 2018; Gartin 
et  al., 2020). In the absence of proactive adaptation, climate change 
impacts are projected to worsen inequalities between Indigenous and 
non-Indigenous peoples and other vulnerable groups (Green et  al., 
2009; Manning et  al., 2014; Ambrey et  al., 2017) (high confidence). 
Socioeconomic inequality, low incomes and high levels of debt, poor

health and disabilities increase vulnerability and limit adaptation (Hayward, 2012) (11.7.2). A lack of services, such as schools and medical 
services, in poorer and rural areas and decision-making processes that 
privilege some voices over others exacerbate inequalities (Kearns et al., 
2009; Hinkson and Vincent, 2018).
Changes to the composition and location of different demographic 
groups in the region contribute to increased exposure or vulnerability 
to climate change (medium confidence). Australia’s population reached 
25 million in 2018 and is projected to grow to 37.4–49.2 million by 2066, 
with most growth in major cities (accounting for 81% of Australia’s 
population growth from 2016 to 2017) (ABS, 2018), although COVID-19 
is expected to slow the growth rate (CoA, 2020c). The highest growth 
rates outside of major cities occurred mostly in coastal regions (ABS, 
2017), which have built assets exposed to sea level rise (SLR). New

Zealand’s population was 5.1 million at the end of 2020 and is projected 
to increase to 6.0–6.5 million by 2068, assuming no marked changes 
in migration patterns (Stats NZ, 2016; Stats NZ, 2021). Although the 
population densities of both countries are much lower than other OECD 
countries, they are highly urbanised with over 86% living in urban 
areas in both countries (Productivity Commission, 2017; World Bank, 
2018). This proportion is projected to increase to over 90% by 2050 (UN 
DESA, 2019) mostly in coastal areas (Rouse et al., 2017). Consideration 
of climate change impacts when planning and managing such growth 
and associated infrastructure could help avoid new vulnerabilities being 
created, particularly from wildfires, sea level rise (SLR), heat stress and 
flooding.
The region has an increasingly diverse population through the arrival 
of migrants, including those from the Pacific, whose innovations,

skills and transnational networks enhance their and others’ adaptive 
capacity (De et al., 2016; Fatorić et al., 2017; Barnett and McMichael, 
2018), although language barriers and socioeconomic disadvantage 
can create vulnerabilities for some (11.7.2).
Climate change inaction exacerbates intergenerational inequity, 
including prospects for the current younger population (Hayward, 
2012). Increasing transient worker populations (ABS, 2018) may 
diminish social networks and adaptive capacity (Jiang et al., 2017). The 
region has an ageing population and increasing numbers of people 
living on their own who are highly vulnerable to extreme events, 
including heat stress and flooding (Zhang et al., 2013).
Socioeconomic trends are affected by global mega trends (KPMG, 
2021), which are expected to influence the region’s ability to implement 
climate change adaptation strategies (World Economic Forum, 2014). 
Digital technological advances have potential benefits for building

adaptive capacity (Deloitte, 2017a).
11.2 Observed and Projected Climate Change
11.2.1 Observed Climate Change
Regional climate change has continued since AR5 was released in 2014, 
with trends exacerbating many extreme events (very high confidence). 
The following changes are quantified with references in Tables 11.2a 
and 11.2b. The region has continued to warm (Figure 11.1), with more

1587Table 11.1 |  Summary of key conclusions from the IPCC 5th Assessment Report (AR5) Australasia chapter (Reisinger et al., 2014) and relevant conclusions from the IPCC Special 
Reports on Global Warming of 1.5°C (IPCC, 2018), Climate Change and Land (IPCC, 2019a) and Oceans and Cryosphere (IPCC, 2019b).
Conclusions Report
Our regional climate is changing (very high confidence) and warming will continue through the 21st century (virtually certain) with more hot days, fewer cold days, 
less snow, less rainfall in southern Australia and the northeast of both of New Zealand’s islands, more rainfall in western New Zealand, more extreme rainfall, SLR, 
increased fire weather in southern Australia and across New Zealand and fewer cyclones but a greater proportion of intense cyclones.(Reisinger et al., 
2014)

Key risks include changes in the structure and composition of Australian coral reefs, loss of montane ecosystems, increased flood damage, reduced water resources 
in southern Australia, more deaths and infrastructure damage during heatwaves, more fire-related impacts on ecosystems and settlements in southern Australia and 
across New Zealand, greater risk to coastal infrastructure and ecosystems and reduced water availability in the Murray-Darling Basin (MDB) and southern Australia 
(high confidence). Benefits are projected for some sectors and locations ( high confidence), including reduced winter mortality and energy demand for heating, 
increased forest growth and enhanced pasture productivity.
Adaptation is occurring and becoming mainstreamed in some planning processes ( high confidence). Adaptive capacity is considered generally high in many human

systems, but adaptation implementation faces major barriers, especially for transformational responses (high confidence). Some synergies and trade-offs exist between 
different adaptation responses and between mitigation and adaptation, with interactions occurring both within and outside the region (very high confidence).
Vulnerability remains uncertain due to incomplete consideration of socioeconomic dimensions ( very high confidence), including governance, institutions, patterns of 
wealth and ageing, access to technology and information, labour force participation and societal values.
Emissions reductions under Nationally Determined Contributions from signatories to the Paris Agreement are consistent with a global warming of 2.5°C–3.0°C above 
pre-industrial temperatures by 2100. Much deeper emission reductions are needed prior to 2030 to limit warming to 1.5°C. There are limits to adaptation and adaptive

capacity for some human and natural systems at global warming of 1.5°C, with associated losses.(IPCC, 2018)
Climate impacts will disproportionately affect the welfare of impoverished and vulnerable people because they lack adaptation resources. Strengthening 
the climate-action capacities of national and sub-national authorities, civil society, the private sector, Indigenous People and local communities can support 
implementation of actions.
Land-related responses that contribute to climate change adaptation and mitigation can also combat desertification and land degradation and enhance food security. (IPCC, 2019a)
Appropriate design of policies, institutions and governance systems at all scales can contribute to land-related adaptation and mitigation while facilitating the pursuit 
of climate-adaptive development pathways.

Mutually supportive climate and land policies have the potential to save resources, amplify social resilience, support ecological restoration and foster collaboration 
between stakeholders.
Near-term action to address climate change adaptation and mitigation, desertification, land degradation and food security can bring social, ecological, economic and 
development co-benefits. Delaying action (both mitigation and adaptation) will be more costly.
The rate of global mean SLR of 3.6 mm yr−1 for 2006–2015 is unprecedented over the last century. Extreme wave heights, coastal erosion and flooding have increased 
in the Southern Ocean by around 1.0 cm yr−1 over the period 1985–2018.(IPCC, 2019b)
Some species of plants and animals have increased in abundance, shifted their range and established in new areas as glaciers receded and the snow-free season

lengthened. Some cold-adapted or snow-dependent species have declined in abundance, increasing their risk of extinction, notably on mountain summits.
Many marine species have shifted their range and seasonal activities. Altered interactions between species have caused cascading impacts on ecosystem structure and 
functioning.
Mean SLR projections are higher by 0.1 m compared to AR5 under RCP8.5 in 2100. Extreme sea level events that are historically rare (once per century) are projected 
to occur frequently (at least once yr−1) at many locations by 2050.
Projected ecosystem responses include losses of species habitat and diversity and degradation of ecosystem functions. Warm water corals are at high risk already and 
are projected to transition to very high risk even if global warming is limited to 1.5°C.
Governance arrangements (e.g., marine protected areas, spatial plans and water management systems) are too fragmented across administrative boundaries and

sectors to provide integrated responses to the increasing and cascading risks. Financial, technological, institutional and other barriers exist for implementing responses.
Enabling climate resilience and sustainable development depends critically on urgent and ambitious emissions reductions coupled with coordinated, sustained and 
increasingly ambitious adaptation actions. This includes better cooperation and coordination among governing authorities, education and climate literacy, sharing of 
information and knowledge, finance, addressing social vulnerability and equity, and institutional support.
extremely high temperatures and fewer extremely low temperatures. 
Snow depths and glacier volumes have declined. Sea level rise and 
ocean acidification have continued. Northern Australia has become 
wetter, while April–October rainfall has decreased in south-western 
and south-eastern Australia. In New Zealand, most of the south has

become wetter, while most of the north has become drier (Figure 11.2). 
The frequency, severity and duration of extreme fire weather conditions 
have increased in southern and eastern Australia and eastern New 
Zealand. Changes in extreme rainfall are mixed. There has been a 
decline in tropical cyclone frequency near Australia.Reliable measurements are limited for some types of storms, particularly 
thunderstorms, lightning, tornadoes and hail (Walsh et al., 2016). Many 
high-impact events are a combination of interacting physical processes 
across multiple spatial and temporal scales (e.g., fires, heatwaves and 
droughts), and better understanding of these extreme and compound 
events is needed (Zscheischler et al., 2018).
Some of the observed trends and events can be partly attributed to 
anthropogenic climate change, as documented in Chapter 16. Examples 
include regional warming trends and sea level rise (SLR), terrestrial

and marine heatwaves, declining rainfall and increasing fire weather

11
Observed temperature changes in Australia and New Zealand
0.5 0.1 0.2 0.3 0 0.4Trend 1960–2019
(°C / decade)
New Zealand
 AustraliaTemperature change
(Baseline 1961–1990)Australia
1940 1980 2020 1900-1°C01°C2°C-1°C01°C2°C
Figure 11.1 |  Observed temperature changes in Australia and New Zealand. Annual temperature change time series are shown for 1910–2019. Mean annual temperature 
trend maps are shown for 1960–2019 using contours for Australia and individual sites for New Zealand. Data courtesy of BOM and NIWA.
Observed rainfall changes in Australia and New Zealand
0.5 0.1 0.2 0.3 0 0.4Trend 1960–2019 (% / decade)New Zealand
(December-February)
Australia (December-February)Rainfall change (Baseline 1961–1990)Northern Australia
100 mm200 mm300 mm
South-western Australia
(June-August)
1940 1980 2020 1900Southeast Australia
(June-August)0 mm
-100 mm
0 mm
-100 mm
0 mm
-100 mmAustralia (June-August)New Zealand

Regions used
for the time-seriesAreas of low rainfall
< 0.25 mm/day
Figure 11.2 |  Observed rainfall changes in Australia and New Zealand. Rainfall change time series for 1900–2019 are shown for Northern Australia (December–February: 
DJF), southwest Australia (June–August: JJA) and southeast Australia (JJA). Dashed lines on the maps for Australia show regions used for the time series. Rainfall trend maps are 
shown for 1960–2019 (DJF and JJA) using contours for Australia and individual sites for New Zealand. Areas of low Australian rainfall (less than 0.25 mm/day) are shaded white 
in JJA. Data courtesy of BOM and NIWA.

1589Table 11.2a |  Observed climate change for Australia.
Climate variable Observed change References
Air temperature over landIncreased by 1.4°C from 1910 to 2019, with 2019 being the warmest year; 9 of the 10 warmest on 
record have occurred since 2005; clear anthropogenic attribution.(BoM and CSIRO, 2020; Trewin et al., 2020; 
BoM, 2021a; Gutiérrez et al., 2021)
Sea surface temperatureIncreased by 1.0°C from 1900 to 2019 (0.09°C/decade), with an increase of 0.16°C–0.20°C/decade 
since 1950 in the southeast. Eight of the 10 warmest years on record have occurred since 2010.(BoM and CSIRO, 2020)
Air temperature extremes 
over landMore extremely hot days and fewer extremely cold days in most regions. Weaker warming trends 
in minimum temperatures in southeast Australia compared to elsewhere during 1960–2016. Frost 
frequency in southeast and southwest Australia has been relatively unchanged since the 1980s. Very

high monthly maximum or minimum temperatures that occurred around 2% of the time in the past 
(1960–1989) now occur 11–12% of the time (2005–2019). Multi-day heatwave events have increased 
in frequency and duration across many regions since 1950. In 2019, the national average maximum 
temperature exceeded the 99th percentile on 43 days (more than triple the number in any of the years 
prior to 2000) and exceeded 39°C on 33 days (more than the number observed from 1960 to 2018 
combined).(Perkins-Kirkpatrick et al., 2016; Alexander 
and Arblaster, 2017; Pepler et al., 2018; BoM 
and CSIRO, 2020; Perkins-Kirkpatrick and 
Lewis, 2020; Trancoso et al., 2020)
Sea temperature extremesIntense marine heatwave in 2011 near western Australia (peak intensity 4°C, duration 100 days). 
The likelihood of an event of this duration is estimated to be about five times higher than under 
pre-industrial conditions. Marine heatwave over northern Australia in 2016 (peak intensity 1.5°C,

duration 200 days). Marine heatwave in the Tasman Sea and around southeast mainland Australia and 
Tasmania from September 2015 to May 2016 (peak intensity 2.5°C, duration 250 days)—likelihood of 
an event of this intensity and duration has increased about 50-fold. Marine heatwave in the Tasman 
Sea from November 2017 to March 2018 (peak intensity 3°C, duration 100 days). Marine heatwave on 
the GBR in 2020 (peak intensity 1.2°C, duration 90 days)(BoM and CSIRO, 2018; BoM, 2020; Laufkötter 
et al., 2020; Oliver et al., 2021)
RainfallNorthern Australian rainfall has increased since the 1970s, with an attributable human influence. April 
to October rainfall has decreased 16% since the 1970s in southwestern Australia (partly due to human 
influence) and 12% from 2000–2019 in south-eastern Australia. The lowest recorded average rainfall in 
Australia occurred in 2019.(Delworth and Zeng, 2014; Knutson and Zeng, 
2018; Dey et al., 2019; BoM and CSIRO, 2020; 
BoM, 2021a)

Rainfall extremesHourly extreme rainfall intensities increased by 10–20% in many locations between 1966 to1989 and 
1990 to 2013. Daily rainfall associated with thunderstorms increased 13–24% from 1979 to 2016, 
particularly in northern Australia. Daily rainfall intensity increased in the northwest from 1950 to 2005 
and in the east from 1911 to 2014 and decreased in the southwest and Tasmania from 1911 to 2010.(Donat et al., 2016; Alexander and Arblaster, 
2017; Evans et al., 2017; Guerreiro et al., 
Bruyère et al., 2020; Dowdy, 2020; Dunn et al., 
2020; Gutiérrez et al., 2021)
DroughtMajor Australian droughts occurred in 1895–1902, 1914–1915, 1937–1945, 1965–1968, 1982–1983, 
1997–2009 and 2017–2019. Fewer droughts have occurred across most of northern and central 
Australia since the 1970s, and more droughts have occurred in the southwest since the 1970s; drought

trends in the southeast have been mixed since the late 1990s.(Gallant et al., 2013; Delworth and Zeng, 
2014; Alexander and Arblaster, 2017; Dai and 
Zhao, 2017; Knutson and Zeng, 2018; Dey 
et al., 2019; Spinoni et al., 2019; Dunn et al., 
2020; Rauniyar and Power, 2020; BoM, 2021b; 
Seneviratne et al., 2021)
Wind speedWind speed decreased 0.067 m/s/decade over land in the period 1941–2016, with a decrease of 
0.062 m/s/decade over land from 1979 to 2015, and a decrease of 0.05–0.10 m/s/decade over land 
from 1988 to 2019. Wind speed increased 0.02 m/s/year across the Southern Ocean during 1985–2018.(Troccoli et al., 2012; Young and Ribal, 2019; 
Blunden and Arndt, 2020; Azorin-Molina 
Sea level riseRelative SLR was 3.4 mm/year from 1993 to 2019, which includes the influence of internal variability 
(e.g., ENSO) and anthropogenic greenhouse gases.(Watson, 2020)
FireAn increase in the number of extreme fire weather days from July 1950 to June 1985 compared to

July 1985 to June 2020, especially in the south and east, partly attributed to climate change. More 
dangerous conditions for extreme pyro convection events since 1979, particularly in south-eastern 
Australia. Extreme fire weather in 2019–2020 was at least 30% more likely due to climate change.(Dowdy and Pepler, 2018; BoM and CSIRO, 
2020; van Oldenborgh et al., 2021)
Tropical cyclones and other 
stormsFewer tropical cyclones since 1982, with a 22% reduction in translation speed over Australian 
land areas in the period1949–2016. No significant trend in the number of East Coast Lows. From 
1979 to 2016, thunderstorms and dry lightning decreased in spring and summer in northern and 
central Australia, decreased in the north in autumn, and increased in the southeast in all seasons. 
Convective rainfall intensity per thunderstorm increased by about 20% in the north and 10% in the 
south. An increase in the frequency of large to giant hail events across southeastern Queensland and

northeastern and eastern New South Wales in the most recent decade. Seven major hail storms over 
eastern Australia from 2014 to 2020 and three major floods over eastern Australia from 2019 to 2021.(Pepler et al., 2015b; Ji et al., 2018; Kossin, 
2018; BoM and CSIRO, 2020; Dowdy, 2020; 
ICA, 2021; Bruyère et al., 2020)
SnowAt Spencers Creek (1830 m elevation) in NSW, annual maximum snow depth decreased 10% and 
length of snow season decreased 5% during 2000–2013 relative to 1954–1999. At Rocky Valley Dam 
(1650 m elevation) in Victoria, annual maximum snow depth decreased 5.7 cm/decade from 1954 to 
2011. At Mt Hotham, Mt Buller and Falls Creek (1638–1760 m elevation), annual maximum snow depth 
decreased 15%/decade from 1988 to 2013.(Bhend et al., 2012; Fiddes et al., 2015; Pepler 
et al., 2015a; BoM and CSIRO, 2020)
Ocean acidificationAverage pH of surface waters has decreased since the 1880s by about 0.1 (over 30% increase in 
acidity).(BoM and CSIRO, 2020)

11
1590Chapter 11 Australasia
in southern Australia and extreme rainfall and severe droughts in New 
Zealand.
11.2.2 Projected Climate Change
There are three main sources of uncertainty in climate projections: 
emission scenarios, regional climate responses and internal climate 
variability (CSIRO and BOM, 2015). Emission scenario uncertainty 
is captured in Representative Concentration Pathways (RCPs) for 
greenhouse gases and aerosols. RCP2.6 represents low emissions, 
RCP4.5 medium emissions and RCP8.5 high emissions. Regional climate 
response uncertainty and internal climate variability uncertainty are 
captured in climate model simulations driven by the RCPs.
Further climate change is inevitable, with the rate and magnitude 
largely dependent on the emission pathway (very high confidence) 
(IPCC, 2021). Preliminary projections based on Climate Model 
Intercomparison Project Phase 6 (CMIP6) models are described in

the IPCC Working Group I Atlas. For Australia, the CMIP6 projections broadly agree with CMIP5 projections except for a group of CMIP6 
models with greater warming and a narrower range of summer rainfall 
change in the north and winter rainfall change in the south (Grose 
et al., 2020). For New Zealand, the CMIP6 projections are similar to 
CMIP5, but the CMIP6 models indicate greater warming, a smaller 
increase in summer precipitation and a larger increase in winter 
precipitation (Gutiérrez et al., 2021).
Dynamical and statistical downscaling offer the prospect of improved 
representation of regional climate features and extreme weather events 
(IPCC 2021: Working Group I Chapter 10 (Doblas-Reyes et al., 2021)), 
but the added value of downscaling is complex to evaluate (Ekström 
et  al., 2015; Rummukainen, 2015; Virgilio et  al., 2021). Downscaled 
simulations are available for New Zealand (MfE, 2018) and various

Australian regions (Gutiérrez et al., 2021). Further downscaling was 
recommended by the Royal Commission into National Natural Disaster 
Arrangements (CoA, 2020e). Projections for rainfall, thunderstorms, 
hail, lightning and tornadoes have large uncertainties (Walsh et  al., 
2016; MfE, 2018).Table 11.2b |  Observed climate change for New Zealand.
Air temperatureIncreased by 1.1°C in the period 1909–2019. Warmest year on record was 2016, followed by 2018 and 1998, which tied for second 
warmest. The six years between 2013 and 2020 were among New Zealand’s warmest on record.(MfE, 2020a; NIWA, 2020)
temperatureIncreased by 0.2°C/decade from 1981 to 2018. (MfE, 2020a)
Air temperature 
extremesNumber of frost days (below 0°C) decreased at 12 of 30 sites, the number of warm days (over 25°C) increased at 19 of 30 sites,

and the number of heatwave days increased at 18 of 30 sites during 1972–2019. Increase in the frequency of hot February days 
exceeding the 90th percentile between 1980–1989 and 2010–2019, with some regions showing more than a five-fold increase.(Harrington, 2020; MfE, 
2020a)
Sea temperature 
extremesThe eastern Tasman Sea experienced a marine heatwave in 2017/2018 lasting 138 days with a maximum intensity of 4.1°C, and 
another marine heatwave in 2018/2019 lasting 137 days with a maximum intensity of 2.8°C.(NIWA, 2019; Salinger 
et al., 2019b; Salinger et al., 
2020; Oliver et al., 2021)
RainfallFrom 1960 to 2019, almost half of the 30 sites had an increase in annual rainfall (mostly in the south) and 10 sites (mostly in the north) 
had a decrease, but few of the trends are statistically significant. Rainfall increased by 2.8% per decade in Whanganui, 2.1% per decade

in Milford Sound and 1.3% per decade in Hokitika. Rainfall decreased by 4.3% per decade in Whangarei and 3.2% per decade in 
Tauranga.(MfE, 2020a)
Rainfall extremesThe number of days with extreme rainfall increased at 14 of 30 sites and decreased at 11 sites during 1960–2019. Most sites with 
increasing annual rainfall had more extreme rainfall, and most sites with decreasing annual rainfall had less extreme rainfall.(MfE, 2020a)
DroughtDrought frequency increased at 13 of 30 sites from 1972 to 2019 and decreased at 9 sites. Drought intensity increased at 14 sites, 11 
of which are in the north, and decreased at 9 sites, 7 of which are in the south.(MfE, 2020a)
Wind speedSince 1970, the wind belt has often shifted to the south of New Zealand, bringing an overall decrease in wind speed over the country. 
For 1980–2019, the annual maximum wind gust decreased at 11 of the 14 sites that had enough data to calculate a trend and 
increased at 2 of the 14 sites.(MfE, 2020a)

Sea level rise Increased 1.8 mm/year during 1900–2018 and 2.4 mm/year during 1961–2018, mostly due to climate change. (Bell and Hannah, 2019)
FireOf the 28 sites, 6 sites (Napier, Lake Tekapo, Queenstown, Gisborne, Masterton, and Gore) had an increase in days with very high 
or extreme fire danger during 1997–2019 and 6 sites (Blenheim, Christchurch, Nelson, Tara Hills, Timaru, and Wellington) had a 
decrease. An increase in fire impacts during 1988–2018 included homes lost, damaged, threatened and evacuated.(Pearce, 2018; MfE, 2020a)
Tropical cyclones 
and other stormsNo significant change in storminess. Three major floods and two major hail storms during 2019–2021. (MfE, 2020a; ICNZ, 2021)
Snow and iceFrom 1978 to 2019, the snowline rose 3.7 m/year. From 1977 to 2018, glacier ice volume decreased from 26.6 to 17.9 km3 (a loss of 
33%). From 1978 to 2016, the area of 14 glaciers in the southern Alps declined 21%. The end-of-summer snowline elevation for 50

glaciers rose 300 m from 1949 to 2019. In the southern Alps, extreme glacier mass loss was at least 6 times more likely in 2011 and 
10 times more likely in 2018 due to climate change.(Salinger et al., 2019a; 
Baumann et al., 2020; 
Chinn and Chinn, 2020; 
MfE, 2020a; Salinger et al., 
2021; Vargo et al., 2020)
Ocean acidification The Sub-Antarctic ocean off the Otago coast became 7% more acidic from 1998–2017. (MfE, 2020a)

1591Future changes in climate variability are affected by the El Niño 
Southern Oscillation (ENSO), Southern Annular Mode (SAM), Indian 
Ocean Dipole (IOD) and Interdecadal Pacific Oscillation (IPO). An 
increase in strong El Niño and La Niña events is projected (Cai, 2015), 
along with more extreme positive phases of the IOD (Cai et al., 2018) 
and a positive trend in SAM (Lim et al., 2016), but potential changes 
in the IPO are unknown (NESP ESCC, 2020). There is uncertainty about 
regional climate responses to projected changes in ENSO (King et al., 
2015; Perry et al., 2020; Virgilio et al., 2021).
Australian climate projections are quantified with references in 
Table 11.3a. Further warming is projected, with more hot days, fewer 
cold days, reduced snow cover, ongoing sea level rise (SLR) and ocean 
acidification (very high confidence). Winter and spring rainfall and soil 
moisture are projected to decrease, with higher evaporation rates,

decreased wind over southern mainland Australia, increased wind 
over Tasmania, and more extreme fire weather in southern and eastern 
Australia (high confidence). Heavy rainfall intensity is projected to 
increase, with more droughts over southern and eastern Australia 
(medium confidence). Increased winter rainfall is projected over 
Tasmania, with decreased rainfall in southwestern Victoria in autumn 
and in western Tasmania in summer, fewer tropical cyclones with a 
greater proportion of severe cyclones and decreased soil moisture in 
the north (medium confidence). Hailstorm frequency may increase (low 
New Zealand climate projections are quantified with references in 
Table 11.3b. Further warming is projected, with more hot days, fewer 
cold days, less snow and glacial ice, ongoing sea level rise (SLR) 
and ocean acidification (very high confidence). Increases in winter 
and spring rainfall are projected in the west of the North and South

Islands, with drier conditions in the east and north, caused by stronger 
westerly winds (medium confidence). In summer, wetter conditions are 
projected in the east of both islands, with drier conditions in the west 
and central North Island (medium confidence). Fire weather indices 
are projected to increase over northern and eastern New Zealand 
(medium confidence). Heavy rainfall intensity is projected to increase 
over most regions, with increased extreme wind speeds in eastern 
regions, especially in Marlborough and Canterbury, and reduced 
relative humidity almost everywhere, except for the west coast in 
winter (medium confidence). Drought frequency may increase in the 
north (medium confidence).
11.3 Observed Impacts, Projected Impacts and 
This section assesses observed impacts, projected risks and adaptation 
for 10 sectors and systems. Boxes provide more details on specific 
issues. Risk is considered in terms of vulnerability, hazards (impact

driver), exposure, reasons for concern and complex and cascading risks 
(Chapter 1; Figure 1.2).11.3.1 Terrestrial and Freshwater Ecosystems
11.3.1.1 Observed Impacts
Widespread and severe impacts on ecosystems and species are 
now evident across the region (very high confidence) (Table  11.4). 
Climate impacts reflect both ongoing change and discrete extreme 
weather events (Harris et  al., 2018), and the climatic change signal 
is emerging despite confounding influences (Hoffmann et al., 2019). 
Fundamental shifts are observed in the structure and composition 
of some ecosystems and associated services (Table  11.4). Impacts 
documented for species include global and local extinctions, severe 
regional population declines and phenotypic responses (Table 11.4). In 
terrestrial and freshwater ecosystems, land use impacts are interacting 
with climate, resulting in significant changes to ecosystem structure, 
composition and function (Bergstrom et  al., 2021), with some

landscapes experiencing catastrophic impacts (Table  11.4). Some of 
the observed changes may be irreversible where projected impacts 
on ecosystems and species persist (Table 11.5). Of note is the global 
extinction of an endemic mammal species, the Bramble Cay melomys 
(Melomys rubicola), from the loss of habitat attributable in part to sea 
level rise (SLR) and storm surges in the Torres Strait (Table 11.4).
Natural forest and woodland ecosystem processes are experiencing 
differing impacts and responses depending on the climate zone (high 
confidence). In Australia, an overall increase in the forest fire danger 
index, associated with warming and drying trends (Table  11.2a), 
has been observed particularly for southern and eastern Australia in 
recent decades (Box  11.1). The 2019–2020 mega wildfires of south 
eastern Australia burnt between 5.8 and 8.1 million hectares of mainly 
temperate broadleaf forest and woodland, but with substantial areas of

rainforest also impacted, and were unprecedented in their geographic 
location, spatial extent and forest types burnt (Boer et al., 2020; Nolan 
et  al., 2020; Abram et  al., 2021; Collins et  al., 2021; Godfree et  al., 
2021). The human influence on these events is evident (Abram et al., 
2021; van Oldenborgh et al., 2021) (Box 11.1). The fires had significant 
consequences for wildlife (Hyman et al., 2020; Nolan et al., 2020; Ward 
et al., 2020) (Box 11.1) and flow-on impacts for aquatic fauna (Silva 
et al., 2020). In southern Australia, deeply rooted native tree species can 
access soil and groundwater resources during drought, providing a level 
of natural resilience (Bell and Nikolaus Callow, 2020; Liu et al., 2020). 
However, the Northern Jarrah forests of south western Australia have 
experienced tree mortality and dieback from long-term precipitation 
decline and acute heatwave-compounded drought (Wardell-Johnson

et al., 2015; Matusick et al., 2018). While there is limited information 
on observed impacts for New Zealand, increased mast seeding events 
in beech forest ecosystems that stimulate invasive population irruptions 
have been recorded (Schauber et al., 2002; Tompkins et al., 2013).
11.3.1.2 Projected Impacts
In the near term (2030–2060), climate change is projected to become 
an increasingly dominant stress on the region’s biodiversity, with 
some ecosystems experiencing irreversible changes in composition 
and structure and some threatened species becoming extinct (high 
confidence). Climate change will interact with current ecological 
conditions, threats and pressures, with cascading ecological impacts,

11
1592Chapter 11 Australasia
Table 11.3a |  Projected climate change for Australia. Projections are given for different RCPs (RCP2.6 is low, RCP4.5 is medium, RCP8.5 is high) and years (e.g., 20-year period 
centred on 2090). Uncertainty ranges are generally 10th–90th percentile, and median projections are given in square brackets where possible. The four Australian regions are shown 
in Chapter 2 of (CSIRO and BOM, 2015). Preliminary projections based on CMIP6 models are included for some climate variables from the IPCC (2021) WGI report.
Climate variable Projected change (year, RCP) relative to 1986–2005 References
Air temperatureAnnual mean temperature
 –+0.5–1.5°C (2050, RCP2.6), +1.5–2.5°C (2050, RCP8.5), +0.5–1.5°C (2090, RCP2.6), +2.5–5.0°C (2090, RCP8.5)
 –Weaker increase in the south, stronger increase in the centre
 –Preliminary CMIP6 projections: +0.6°C–1.3°C (2050, SSP1-RCP2.6), +1.2°C–2.0°C (2050, SSP5-RCP8.5), +0.6°C–1.5°C (2090,

SSP1-RCP2.6), +2.8°C–4.9°C (2090, SSP5-RCP8.5) relative to 1995–2014(NESP ESCC, 2020; IPCC, 
2021)
temperature –+ 0.4–1.0°C (2030, RCP8.5)
 –+2–4°C (2090, RCP8.5)(CSIRO and BOM, 2015)
extremes –Annual frequency of days over 35°C may increase 20–70% by 2030 (RCP4.5) and 25–85% (RCP2.6) to 80–350% (RCP8.5) by 2090
 –Heatwave frequency may rise by 85% if global warming increases from 1.5°C to 2.0°C, and it may rise by four times for xxxx 3°C 
warming
 –Annual frequency of frost days may decrease by 10–40% (2030, RCP4.5), 10–40% (2090, RCP2.6) and 50–100% (2090, RCP8.5)(CSIRO and BOM, 2015; 
Trancoso et al., 2020)
RainfallAnnual mean rainfall
 –South: −15 to +2% (2050, RCP2.6), −14 to +3% (2050, RCP8.5), −15 to +3% (2090, RCP2.6), −26 to +4% (2090, RCP8.5)
 –East: −13 to +7% (2050, RCP2.6), −17 to +8% (2050, RCP8.5), −19 to +6% (2090, RCP2.6), −25 to +12% (2090, RCP8.5)

–North: −12 to +5% (2050, RCP2.6), −8 to +11% (2050, RCP8.5), −12 to +3% (2090, RCP2.6), −26 to +23% (2090, RCP8.5)
 –Rangelands: −18 to +3% (2050, RCP2.6), −15 to +8% (2050, RCP8.5), −21 to +3% (2090, RCP2.6), −32 to +18% (2090, RCP8.5)(Liu et al., 2018; NESP 
ESCC, 2020)
Rainfall extremesIntensity of daily total rain with 20-year recurrence interval
 –+4 to +10% (2050, RCP2.6)
 –+8 to +20% (2050, RCP8.5)
 –+4 to +10% (2090, RCP2.6)
 –+15 to +35% (2090, RCP8.5)(NESP ESCC, 2020)
DroughtTime in drought (Standardised Precipitation Index below −1)
 –Southern Australia: 32–46% [39%] (1995), 38–68% [54%] (2050, RCP8.5), 41–81% [60%] (2090, RCP8.5)
 –Eastern Australia: 25–46% [37%] (1995), 24–67% [47%] (2050, RCP8.5), 19–76% [56%] (2090, RCP8.5)
 –Northern Australia: 26–44% [34%] (1995), 18–54% [40%] (2050, RCP8.5), 9–81% [39%] (2090, RCP8.5)
 –Australian Rangelands: 29–43% [34%] (1995), 26–58% [42%] (2050, RCP8.5), 23–70% [46%] (2090, RCP8.5)(Kirono et al., 2020)

Wind speed 0–5% decrease over southern mainland Australia and 0–5% increase over Tasmania (2090, RCP8.5) (CSIRO and BOM, 2015)
Sea level rise –South (Port Adelaide): 13–29 cm [21 cm] (2050, RCP2.6), 16–33 cm [25 cm] (2050, RCP8.5), 23–55 cm [39 cm] (2090, RCP2.6), 
40–84 cm [61 cm] (2090, RCP8.5)
 –East (Newcastle): 14–30 cm [22 cm] (2050, RCP2.6), 19–36 cm [27 cm] (2050, RCP8.5), 22–54 cm [38 cm] (2090, RCP2.6), 
46–88 cm [66 cm] (2090, RCP8.5)
 –North (Darwin City Council, 2011): 13–28 cm [21 cm] (2050, RCP2.6), 17–33 cm [25 cm] (2050, RCP8.5), 22–55 cm [38 cm] (2090, 
RCP2.6), 41–85 cm [62 cm] (2090, RCP8.5)
 –West (Port Hedland): 13–28 cm [20 cm] (2050, RCP2.6), 16–33 cm [24 cm] (2050, RCP8.5), 22–55 cm [38 cm] (2090, RCP2.6), 
These projections have not been updated to include an Antarctic dynamic ice sheet factor which increased global sea level projections

for RCP8.5 by approx. 10 cm. Preliminary CMIP6 projections indicate +40–50 cm (2090, SSP1-RCP2.6) and +70–90 cm (2090, 
SSP5-RCP8.5)(McInnes et al., 2015; 
Zhang et al., 2017; IPCC, 
2019b)
(IPCC, 2021)
Sea level extremesIncrease in the allowance for a storm tide event with 1% annual exceedance probability (100-year return period)
 –South (Port Adelaide): 21 cm (2050, RCP2.6), 25 cm (2050, RCP8.5), 41 cm (2090, RCP2.6), 66 cm (2090, RCP8.5)
 –East (Newcastle): 24 cm (2050, RCP2.6), 30 cm (2050, RCP8.5), 49 cm (2090, RCP2.6), 86 cm (2090, RCP8.5)
 –North (Darwin): 21 cm (2050, RCP2.6), 26 cm (2050, RCP8.5), 43 cm (2090, RCP2.6), 71 cm (2090, RCP8.5)
 –West (Port Hedland): 21 cm (2050, RCP2.6), 26 cm (2050, RCP8.5), 43 cm (2090, RCP2.6), 70 cm (2090, RCP8.5)(McInnes et al., 2015)
Fire –East: annual number of severe fire weather days 0 to +30% (2050, RCP2.6), 0 to +60% (2050, RCP8.5), 0 to +30% (2090, RCP2.6), 
0 to +110% (2090, RCP8.5)

–Elsewhere: number of severe fire weather days +5 to +35% (2050, RCP2.6), +10 to +70% (2050, RCP8.5), +5 to +35% (2090, 
RCP2.6) +20 to +130% (2090, RCP8.5)(Clarke and Evans, 2019; 
Dowdy et al., 2019; Virgilio 
et al., 2019; Clarke et al., 
2020; NESP ESCC, 2020; 
Clark et al., 2021)
and other storms –Eastern region tropical cyclones: −8 to +1% (2050, RCP2.6), −15 to +2% (2050, RCP8.5), −8 to +1% (2090, RCP2.6), −25 to +5% 
(2090, RCP8.5)
 –Western region tropical cyclones: −10 to −2% (2050, RCP2.6), −20 to −4% (2050, RCP8.5), −10 to −2% (2090, RCP2.6), −30 to 
−10% (2090, RCP8.5)
 –East coast lows: −15 to −5% (2050, RCP2.6), −30 to −10% (2050, RCP8.5), −15 to −5% (2090, RCP2.6), −50 to −20% (2090, 
RCP8.5)
 –Hailstorm frequency may increase, but there are large uncertainties(NESP ESCC, 2020; 
Raupach et al., 2021)

1593Table 11.3b |  Projected climate change for New Zealand. Projections are given for different RCPs (RCP2.6 is low, RCP4.5 is medium, RCP8.5 is high) and years (e.g., 20-year period 
centred on 2090). Uncertainty ranges are 5th–95th percentiles, and median projections are given in square brackets where possible. Preliminary projections (10th–90th percentiles) 
based on CMIP6 models are included for some climate variables from the IPCC (2021) WGI report.
 –+0.2–1.3°C [0.7°C] (2040, RCP2.6), +0.5–1.7°C [1.0°C] (2040, RCP8.5), +0.1–1.4°C [0.7°C] (2090, RCP2.6), +2.0–4.6°C [3.0°C] 
 –More warming in summer and autumn, less in winter and spring
 –More warming in the north than the south
 –Preliminary CMIP6 projections: +0.4°C–1.1°C (2050, SSP1-RCP2.6), +0.9°C–1.7°C (2050, SSP5-RCP8.5), +0.5°C–1.5°C (2090,

SSP1-RCP2.6), +2.2°C–4.1°C (2090, SSP5-RCP8.5) relative to 1995–2014(MfE, 2018);
temperature –+1.0°C (2045, RCP8.5),
 –+2.5°C (2090, RCP8.5).(Law et al., 2018b)
extremes –Annual frequency of days over 25°C may increase 20–60% (2040, RCP2.6) to 50–100% (2040, RCP8.5), and 20–60% (2090, 
RCP2.6) to 130–350% (2090, RCP8.5)
 –Annual frost frequency may decrease 20–60% (2040, RCP2.6) to 30–70% (2040, RCP8.5), and 20–60% (2090, RCP2.6) to 70–95% 
(2090, RCP8.5).(MfE, 2018)
 –Waikato, Auckland and Northland: −7 to +7% (2040, RCP2.6), −8 to +5% (2040, RCP8.5), −5 to +11% [+2%] (2090, RCP2.6), −15 
to +12% [−2%] (2090, RCP8.5)
 –Hawke’s Bay and Gisborne: −8 to +8% [−1%] (2040, RCP2.6), −12 to +7% [−2%] (2040, RCP8.5), −9 to +4% [−2%] (2090, 
RCP2.6), −15 to +15% [−3%] (2090, RCP8.5)
 –Taranaki, Manawatū and Wellington: −4 to +9% [+1%] (2040, RCP2.6), −6 to +10% [+1%] (2040, RCP8.5), −6 to +15% [+3%]

(2090, RCP2.6), −14 to +14% [+2%] (2090, RCP8.5)
 –Tasman-Nelson and Marlborough: −3 to +5% [+1%] (2040, RCP2.6), −3 to +8% [+1%] (2040, RCP8.5), −4 to +8% [+2%] (2090, 
RCP2.6), −3 to +15% [+5%] (2090, RCP8.5)
 –West coast and Southland: −4 to +12% [+3%] (2040, RCP2.6), −4 to +12% [+4%] (2040, RCP8.5), −2 to +18% [+5%] (2090, 
RCP2.6), −8 to +23% (2090, RCP8.5)
 –Canterbury and Otago: −7 to +15% [+3%] (2040, RCP2.6), −7 to +19% [+3%] (2040, RCP8.5), −6 to +18% (2090, RCP2.6), −9 to 
+28% [+8%] (2090, RCP8.5)(Liu et al., 2018; MfE, 2018)
Rainfall extremesIntensity of daily rain with 20-year recurrence interval
 –+2.8 to 7.2% [5%] (2040, RCP2.6)
 –+4.2 to 10.4% [7%] (2040, RCP8.5)
 –+2.8 to 7.2% [5%] (2090, RCP2.6)
 –+12.6 to 31.5% [2%] (2090, RCP8.5)(MfE, 2018)
DroughtIncrease in potential evapotranspiration deficit
 –Northern and eastern North Island: 100–200 mm (2090, RCP8.5)
 –Western North Island: 50–100 mm (2090, RCP8.5)
 –Eastern South Island: 50–200 mm (2090, RCP8.5)

–Western South Island: 0–50 mm (2090, RCP8.5)(MfE, 2018)
Wind speed99th percentile of daily mean wind speed
 –Northern North Island: 0 to −5% (2090, RCP8.5)
 –Southern North Island: 0 to +5% (2090, RCP8.5)
 –South Island: 0 to +10% (2090, RCP8.5)(MfE, 2018)
Sea level rise –23 cm (2050, RCP2.6)
 –28 cm (2050, RCP8.5)
 –42 cm (2090 RCP2.6)
 –67 cm (2090 RCP8.5)
These projections have not been updated to include an Antarctic dynamic ice sheet factor which increased global sea level projections for 
RCP 8.5 by approx. 10 cm. Preliminary CMIP6 projections indicate 40–50 cm (2090, SSP1-RCP2.6) and 70–90 cm (2090, SSP5-RCP8.5).(MfE, 2017a; IPCC, 2019b)Climate variable Projected change (year, RCP) relative to 1986–2005 References
Snow and ice –Maximum snow depth at Falls Creek and Mt Hotham may decline 30–70% (2050, B1) and 45–90% (2050, A1FI) relative to 1990
 –Maximum snow depth at Mt Buller and Mt Buffalo may decline 40–80% (2050, B1) and 50–100% (2050, A1FI) relative to 1990

–Length of Victorian ski season may contract 65–90% and mean annual snowfall may decline 60–85% (2070–2099, RCP8.5) 
relative to 2000–2010.
 –The snowpack may decrease by about 15% (2030, A2) to 60% (2070, A2)(Bhend et al., 2012; Harris 
et al., 2016; Di Luca et al., 
2018)
Ocean acidification pH is projected to drop by about 0.1 (2090, RCP2.6) to 0.3 (2090, RCP8.5)(CSIRO and BOM, 2015; 
Hurd et al., 2018)

11
1594Chapter 11 Australasia
including population declines, heat-related mortalities, extinctions 
and disruptions for many species and ecosystems (high confidence) 
(Table  11.5). These include inadequate allocation of environmental 
flows for freshwater fish (Vertessy et al., 2019), native forest logging 
for old-growth-forest-dependent fauna (Lindenmayer et  al., 2015; 
Lindenmayer and Taylor, 2020a; Lindenmayer and Taylor, 2020b), and 
invasive species (Scott et al., 2018). Climate change has synergistic and 
compounding impacts, particularly in bioregions already experiencing 
ecosystem degradation, threatened endemics and collapse of keystone 
species, including those of value to Indigenous Peoples, and high 
extinction rates as a consequence of human activities (Table  11.4) 
(Gordon, 2009; Australia SoE, 2016; Weeks et al., 2016; Cresswell and 
Murphy, 2017; Hare et al., 2019; MfE, 2019; Lindenmayer and Taylor,

2020a; Lindenmayer and Taylor, 2020b; Bergstrom et al., 2021). Some 
native species are projected to have potentially greater geographic 
range if they can colonise new areas, while other species may be 
resilient to projected climate change impacts (Bulgarella et al., 2014; 
K.E. Lawrence et al., 2017; Conroy et al., 2019; Rizvanovic et al., 2019).
In southern Australia, some forest ecosystems (alpine ash, snow gum 
woodland, pencil pine, northern jarrah) are projected to transition to a 
new state or collapse due to hotter and drier conditions with more fires 
(high confidence) (Table  11.5). In Australia, most native eucalyptus 
forest plants have a range of traits that enable them to persist with 
recurrent fire through recovery buds (sprouters) or regenerate through 
seeding (Collins, 2020), affording them a high level of resilience. For 
high-end projected 2060–2080 fire weather conditions in southeast 
Australia (Clarke and Evans, 2019), stand-killing wildfires could occur

at a severity and frequency greater than the regenerative capacity 
of seeders (Enright et al., 2015; Clarke and Evans, 2019). Most New 
Zealand native plants are not fire resistant and are projected to be 
replaced by fire-resistant introduced species following climate-change-
related fires (Perry et al., 2014).A loss of alpine biodiversity in the southeast Australian Alps bioregion 
is projected in the near-term as a result of less snow on snow patch 
feldmark and short alpine herb fields as well as increased stress on snow-
dependent plant and animal species (high confidence) (Table  11.3, 
Table 11.5). In Australia, invasive plants’ and weeds’ response rates 
are expected to be faster than for native species, and climate change 
could foster the appearance of a new set of weed species, with many 
bioregions facing increased impacts from non-native plants (medium 
confidence) (Gallagher et  al., 2013; Scott et  al., 2014; March-Salas

and Pertierra, 2020) (Table 11.5), along with declines in some listed 
weeds (Duursma et al., 2013; Gallagher et al., 2013). In New Zealand, 
climate change is projected to enable invasive species to expand to 
higher elevations and southwards (medium confidence) (Table 11.5) 
(Giejsztowt et al., 2020; MfE, 2020a).
Projected responses of ecosystem processes are uncertain in part due 
to complex interactions of climate change with soil respiration, plant 
nutrient availability (Hasegawa et al., 2015; Orwin et al., 2015; Ochoa-
Hueso et  al., 2017) and changing fire regimes (Table  11.5) (Scheiter 
et al., 2015; Dowdy et al., 2019). For aquatic biota, responses will reflect 
seasonal differences in water temperature (Wallace et al., 2015) and 
changes in rainfall intensity, productivity and biodiversity (Jardine et al., 
2015). Extreme floods may have negative impacts on New Zealand 
river biota, by mobilising nutrients, sediments and toxic chemicals and

aiding the dispersal of invasive species. These effects are compounded 
by homogenisation of rivers through channelisation (Death et al., 2015).
Improved coastal modelling, experiments and in situ studies are 
reducing uncertainties at a local scale about the impact of future sea 
level rise (SLR) on coastal freshwater terrestrial wetlands (medium 
confidence) (Shoo et al., 2014; Bayliss et al., 2018; Grieger et al., 2019). 
Low-lying coastal wetlands are susceptible to saltwater intrusion from 
sea level rise (SLR) (Shoo et al., 2014; Kettles and Bell, 2015; Finlayson 
et al., 2017) with consequences for species dependent on freshwater 
habitats (Houston et  al., 2020). Saline habitat conditions will move Climate variable Projected change (year, RCP) relative to 1986–2005 References
Sea level extremesFor a rise in sea level of 30 cm, the 1-in-100-year high water levels may occur about
 –Every 4 years at the port of Auckland
 –Every 2 years at the port of Dunedin

–Once a year at the port of Wellington
 –Once a year at the port of Christchurch(PCE, 2015)
Fire –Seasonal Severity Rating (SSR) increases 50–100% in coastal Marlborough and Otago, 40–50% in Wellington and 30–40% in 
Taranaki and Whanganui, 0–30% elsewhere (2050, A1B).
 –Number of days with very high or extreme fire weather increase >100% in coastal Otago, Marlborough and the lower North Island, 
50–100% in Taupō and Rotorua, 20–50% in the rest of the North Island, and little change in the rest of the South Island (2050, 
A1B).(Pearce et al., 2011)
and other stormsPoleward shift of mid-latitude cyclones and potential for a small reduction in frequency (MfE, 2018)
Snow and ice –Maximum snow depth on 31 August may decline by 0–10% (2040, A1B) and 26–54% (2090, A1B).
 –Annual snow days may be reduced by 5–15 days (2040, RCP2.6), 10–25 days (2040, RCP8.5), 5–15 days (2090, RCP2.6) and 
15–45 days (2090 RCP8.5).

–Relative to 2015, New Zealand glaciers are projected to lose 36%, 53% and 77% of their mass by the end of the century under 
RCP2.6, RCP4.5 and RCP8.5, respectively.
 –Over the period 2006–2099, New Zealand glaciers are projected to lose 50 to 92% of their ice volume for RCP2.6 to RCP8.5.(Hendrikx et al., 2013; 
MfE, 2018; Marzeion et al., 
2020; Anderson et al., 
2021)
Ocean acidification pH is projected to drop by about 0.1 (2090, RCP2.6) to 0.3 (2090 RCP8.5).(CSIRO and BOM, 2015; 
Hurd et al., 2018; Law 
et al., 2018b)

1595Table 11.4 |  Observed impacts on terrestrial and freshwater ecosystems and species in the region where there is documented evidence that these are directly (e.g., a species 
thermal tolerances are exceeded) or indirectly (e.g., through changed fire regimes) the result of climate change pressures.
Ecosystem Climate-related pressure Impact Source
Australia
Forest and woodlands of southern and 
southwestern Australia30-year declining rainfallDrought-induced canopy dieback across a 
range of forest and woodland types (e.g., 
northern jarrah)(Matusick et al., 2018; Hoffmann et al., 
2019)
Multiple wildfires in short succession 
resulting from increased fire risk conditions, 
including declining winter rainfall and 
increasing hot daysLocal extirpations and replacement 
of dominant canopy tree species and 
replacement by woody shrubs due to 
seeders having insufficient time to reach 
reproductive age (alpine ash) or vegetative

regeneration capacity is exhausted (snow 
gum woodlands)(Slatyer, 2010; Bowman et al., 2014; 
Fairman et al., 2016; Harris et al., 2018; 
Zylstra, 2018)
Background warming and drying created 
soil and vegetation conditions that 
are conducive to fires being ignited by 
lightning storms in regions that have rarely 
experienced fire over the last few millenniaDeath of fire-sensitive trees species from 
unprecedented fire events (Palaeo-endemic 
pencil pine forest growing in sphagnum, 
Tasmania, killed by lightning-ignited fires 
in 2016)(Hoffmann et al., 2019)
Australian Alps Bioregion and Tasmanian 
alpine zonesSevere winter drought; warming and 
climate-induced biotic interactionsShifts in dominant vegetation with a decline 
in grasses and other graminoids and an 
increase in forb and shrub cover in Bogong 
High Plains, Victoria, Australia(Bhend et al., 2012; Hoffmann et al., 2019)
Snow loss, fire, drought and temperature 
changesChanging interactions within and among

three key alpine taxa related to food 
supply and vegetation habitat resources: 
The mountain pygmy-possum (Burramys 
parvus), the mountain plum pine 
(Podocarpus lawrencei) and the bogong 
moth (Agrostis infusia)(Hoffmann et al., 2019)
Retreat of snow line Increased species diversity in alpine zone (Slatyer, 2010)
Reduced snow coverLoss of snow-related habitat for alpine zone 
endemic and obligate species(ACE CRC, 2010; Pepler et al., 2015a; 
Thompson, 2016; Mitchell et al., 2019)
Wet Tropics World Heritage AreaWarming and increasing length of dry 
seasonSome vertebrate species have already 
declined in both distribution area and 
population size, both earlier and more 
severely than originally predicted(Moran et al., 2014; Hoffmann et al., 2019)
Sub-Antarctic Macquarie islandReduced summer water availability for 
17 consecutive summers, and increases 
in mean wind speed, sunshine hours and 
evapotranspiration over four decadesDieback in critically endangered

habitat-forming cushion plant Azorella 
macquariensis in the fellfield and herb field 
communities(Bergstrom et al., 2015; Hoffmann et al., 
2019)
Mass mortality of wildlife species (flying 
foxes, freshwater fish)Extreme heat events; rising water 
temperatures, temperature fluctuations, 
altered rainfall regimes including droughts 
and reduced in-flowsFlying foxes—thermal tolerances of 
species exceeded; fish—amplified extreme 
temperature fluctuations, increasing 
annual water basin temperatures, extreme 
droughts and reduced runoff after rainfall(AAS, 2019; Ratnayake et al., 2019; Vertessy 
et al., 2019)
Bramble Cay melomys (mammal)
Melomys rubicolaSLR and storm surges in Torres Strait Loss of habitat and global extinction(Lunney et al., 2014; Gynther et al., 2016; 
Waller et al., 2017; CSIRO, 2018)
Koala, Phascolarctos cinereusIncreasing drought and rising temperatures, 
compounding impacts of habitat loss, fire

and increasing human populationPopulation declines and enhanced risk of 
local extinctions(Lunney et al., 2014)
Tawny dragon lizard, Ctenophorus decresiiDesiccation stress driven by higher body 
temperatures and declining rainfallPopulation decline and potential local 
extinction in Flinders Ranges, south 
Australia(Walker et al., 2015)
BirdsChanging thermal regimes including 
increasing thermal stress and changes in 
plant productivity are identified as being 
causalChanges in body size, mass and condition 
and other traits linked to heat exchange(Gardner et al., 2014a; Gardner et al., 
2014b; Campbell-Tennant et al., 2015; 
Gardner et al., 2018; Hoffmann et al., 2019)

11
1596Chapter 11 Australasia
inland and new coastal ecosystem states may emerge, including the 
World Heritage listed Kakadu’s freshwater wetland (Bayliss et al., 2018) 
(Table 11.5). Increasingly, sea level rise (SLR) will shrink the intertidal 
zone, having implications for wading birds which use this zone (Tait and 
Pearce, 2019) (Box 11.6). The ecology of freshwater wetlands in New 
Zealand are projected to be impacted by the intersection of warming, 
drought and heavy rainfall (Pingram et al., 2021) (Table 11.5).
The impacts on species from projected global warming depend on their 
physiological and ecological responses for which knowledge is limited 
(Table 11.5) (Bulgarella et al., 2014; Carter et al., 2018; Green et al., 
2021). Knowledge of projected impacts is constrained by uncertainties 
about the influence of physiological limits, barriers to dispersal, 
competition, the availability of habitat resources (Worth et al., 2014)

and disruptions to ecological interactions (Lakeman-Fraser and Ewers, 
2013; Parida et  al., 2015; Porfirio et  al., 2016). Gaps in ecological 
modelling of future climate impacts include consideration of long-term 
rainfall and temperature changes (Grimm-Seyfarth et al., 2017; Grimm-
Seyfarth et al., 2018), species dispersal rates, evolutionary capacity and 
phenotypic plasticity and the thresholds at which they are considered 
adequate to counter the impacts of climate change (Ofori et al., 2017b), 
as well as indirect effects including sea level rise (SLR) and altered fire 
regimes (Shoo et al., 2014; Cadenhead et al., 2016; He et al., 2016).
11.3.1.3 Adaptation
Managing climate change risks to ecosystems is primarily based 
on reducing the impact of other anthropogenic pressures, including 
invasive species, and facilitating natural adaptation (high confidence). 
This approach is most feasible within protected areas on public, private

and Indigenous land and sea (Bellard et al., 2014; Liu et al., 2020) but 
is also applicable elsewhere (Barnes et al., 2015). Effective strategies 
promote ecosystem resilience by changing unsustainable land uses 
and management practices, increasing habitat connectivity, controlling 
introduced species, restoring habitats, implementing appropriate fire 
management, integrated risk assessment and adaptation planning (B. 
Frame et al., 2018; Lindenmayer et al., 2020; Macinnis-Ng et al., 2021). 
Complementary approaches include ex situ seed banks (Morrison and 
Pickering, 2013; Christie et al., 2020).
Best practice conservation adaptation planning is informed by data 
on key habitats, including refugia, and restoration that facilitates 
species movements and employs adaptive pathways (very high confidence) (Guerin and Lowe, 2013; Reside et al., 2014; Shoo et al., 
2014; Keppel et al., 2015; Andrew and Warrener, 2017; Baumgartner

et al., 2018; Harris et al., 2018; Jacobs et al., 2018a; Das et al., 2019; 
Walker et al., 2019; Molloy et al., 2020). Landscape planning (Bond 
et al., 2014; McCormack, 2018) helps reduce habitat loss, facilitates 
species dispersal and gene flow (McLean et al., 2014; Shoo et al., 2014; 
Lowe et al., 2015; Harris et al., 2018; McCormack, 2018) and allows for 
new ecological opportunities (Norman and Christidis, 2016). Coastal 
squeeze is a threat to freshwater wetlands and requires planning 
for the potential inland shift (Grieger et al., 2019). Adaptations that 
maintain critical volumes and periodicity of environmental flows 
will help protect freshwater biodiversity (Box 11.3) (Yen et al., 2013; 
Barnett et al., 2015; Wang et al., 2018b).
Adaptation planning for ecosystems and species requires monitoring 
and evaluation to identify trigger points and thresholds for new actions 
to be implemented (high confidence) (Tanner-McAllister et al., 2017;

Williams et al., 2020). Best planning practice includes keeping options 
open (Barnett et al., 2015; Dunlop et al., 2016; Finlayson et al., 2017) 
and updating management plans in light of new information. New 
insights are emerging into how species’ natural adaptive capacities 
can inform adaptation planning (Llewelyn et al., 2016; Steane et al., 
2017; Hoeppner and Hughes, 2019). Physiological limits to adaptation 
in some species are being identified (Barnett et  al., 2015; Sorensen 
et al., 2016), and where natural responses are not feasible, human-
assisted translocations may be warranted (Becker et  al., 2013; 
Chauvenet et  al., 2013; Innes et  al., 2019) for some species (Ofori 
et al., 2017a; Ofori et al., 2017b). Legal reform may be needed to better 
enable climate adaptation for biodiversity conservation that recognises 
species’ natural adjustments to their distributions and the difficulties 
encountered in predicting the consequences for ecological interactions

and ecosystem services (McCormack, 2018; McDonald et al., 2019).
Adaptation research priorities include understanding of the 
interactions and cumulative impacts of existing stressors and 
climate change and the implications for managing ecosystems and 
natural resources (Williams et  al., 2020). For Australia, research on 
implementation strategies for conservation and managing threats, 
stress and natural assets is a priority (Williams et  al., 2020). For 
New Zealand, understanding how terrestrial ecosystems and species 
respond to climate change is a priority, and where existing stressors 
are affecting freshwater quantity and quality, in situ monitoring 
to detect and evaluate projections of climate change impacts on Ecosystem Climate-related pressure Impact Source
Forest birds WarmingIncreasing invasive predation pressure on 
endemic forest birds surviving in cool forest 
refugia, particularly larger-bodied bird 
species that nest in tree cavities and are

poor dispersers(Walker et al., 2019)
Coastal ecosystems More severe storms and rising sea levelsErosion of coastal habitats, including dunes 
and cliffs, is reducing habitat(Rouse et al., 2017)
Beech forest ecosystemsIncreasing mean temperatures and 
indirectly through effects of events like 
ENSOIncreased beech mast seeding events that 
stimulate population irruptions for invasive 
rodents and mustelids, which then prey on 
native species(Schauber et al., 2002; Tompkins et al., 
2013)

1597Table 11.5 |  An indicative selection of projected climate-change impacts on terrestrial and freshwater ecosystems and species in Australia and New Zealand respectively.
Ecosystem, species Climate-related pressure Projected Impact Source
Australia
Floristic composition of vegetation 
communitiesIncreases in temperature and reductions in 
annual precipitation by 2070. Many plant 
species based on median projection from 
five global climate models (ACCESS1.0, 
CNRM-CM5, HADGEM2-CC, MIROC5, 
NorESM1-M) centred on the decade 2070 
under RCP8.547% of vegetation types have characteristic 
plant species at risk of their climatic 
tolerances being exceeded from increasing 
mean annual temperature by 2070 with 
only 2% at risk from reductions in annual 
precipitation by 2070(Gallagher et al., 2019)
Some south east Australian temperate 
forestsReduction in winter rainfall and rising 
spring temperatures resulting in an 
increase in the frequency of very high fire

weather conditions and increased risk of 
catastrophic wildfires; based on output from 
15 CMIP5 GCMs using RCP8.5 for years for 
2060–2079 as compared to 1990–2009Increase in fire frequency prevents 
recruitment of obligate seeder resulting 
in changing dominant species and 
vegetation structure including long lasting 
or irreversible shift in formation from tall 
wet temperate eucalypt forests dominated 
by obligate seeder trees (e.g., alpine ash) to 
open forest or in worst case to shrubland
Declining rainfall and regolith drying, more 
unplanned, intense fires and declining 
productivity place stress on tree growth 
and compromise biodiversity in northern 
jarrah forest(Doherty et al., 2017; Zylstra, 2018; 
Bowman et al., 2019; Dowdy et al., 2019; 
Naccarella et al., 2020)
(Wardell-Johnson et al., 2015)
Tree line stasis or regression (snow gum)(Doherty et al., 2017); (Bowman et al., 2019; 
Increase in lightning-ignited landscape fires

along with contracting palaeo-endemic 
refugia due to warmer and drier climatesPopulation collapse and severe range 
contraction of slow-growing, fire-sensitive 
palaeo-endemic temperate rainforest 
species (e.g., pencil pine)(Doherty et al., 2017); (Bowman et al., 
2019)
Rhizosphere responses or accelerated rates 
of soil organic matter decompositionPlant nutrient availability may be enhanced(Hasegawa et al., 2015; Ochoa-Hueso et al., 
2017)
Alpine ecosystemsIncreasing global warming and rising 
temperatures, ongoing reduction in snow 
cover and winter rain and increasing 
frequency and magnitude of wildfiresLoss of alpine vegetation communities 
(snow patch feldmark and short alpine 
herb fields) and increased stress on 
snow-dependent plant and animal species; 
changing suitability for invasive species(Slatyer, 2010; Morrison and Pickering, 
2013; Pepler et al., 2015a; Williams et al., 
2015; Harris et al., 2017)

Northern tropical savannahs Rainfall and CO 2 effectsPotentially resulting in an increase in 
ecosystem carbon storage(Scheiter et al., 2015)
Murray-Darling River Basin Drought Reduced river flow; mass fish kills (Grafton et al., 2014; AAS, 2019)
Unimpaired river basins Elevated CO 2 levelsIncrease plant water use reduces stream 
flow(Ukkola et al., 2016)
Bearded dragons (lizards), Pogona spp. Changes in precipitationP . henrylawsoni and P . microlepidota to gain 
suitable habitat, P . nullarbor and P . vitticeps 
showing the most potential loss(Wilson and Swan, 2017; Silva et al., 2018)
Xeric beesBroad temperate tolerances, arid climate 
adaptedClimate-resilient, only small response (Silva et al., 2018)
Great desert skink Liopholis kintoreiBuffering capacity of underground 
microclimates, for nocturnal and crepuscular 
ectothermsWarming impacts projected to be indirect (Moore et al., 2018)
22 narrow-range fish species in imminent

risk of extinctionProjected changes in rainfall, run-off, air 
temperatures and the frequency of extreme 
events (drought, fire, flood) compound risk 
from other key threats especially invasive 
speciesExtinction projected within next 20 years (Lintermans et al., 2020)
Freshwater taxa (freshwater fish, crayfish, 
turtles and frogs)Changed hydrological regimesSubstantial changes to the composition of 
faunal assemblages in Australian rivers well 
before the end of this century, with gains/
losses balanced for fish but suitable habitat 
area predicted to decrease for many crayfish 
and turtle species and nearly all frog species(James et al., 2017)

11
1598Chapter 11 Australasia
biodiversity and a national data repository are lacking (MfE, 2020a). 
The projected increase in invasive species indicates the importance 
of a step-up in pest management efforts to ensure native species 
persistence as invasive species spread from climate change (Firn 
et al., 2015). There remains a gap between the knowledge generated, 
potential adaptation strategies and their incorporation into conservation instruments (medium confidence) (Graham et al., 2019; 
Hoeppner and Hughes, 2019), though there is increasing recognition 
of the need to improve governance and management structures for 
their implementation (Christie et al., 2020).Ecosystem, species Climate-related pressure Projected Impact Source
Modified lowland wetlandsIntersection of warming, drought and heavy 
rainfall (ex-tropical cyclones)Prolonged anoxic conditions in waterways 
(blackwater events) leading to mortality of 
fish (e.g., shortfin eels) and invertebrates,

while botulism outbreaks can lead to 
impacts on waterfowl(Pingram et al., 2021)
Native forests and landsElevated CO 2 levels, warming, increased 
precipitation.Short-term beneficial effects on carbon 
storage; droughts in eastern areas would 
decrease productivity and rates of carbon 
storage in the medium term(Ausseil et al., 2019b)
Increased fire intensity and frequency in hot 
and dry parts of New ZealandMuch of the native vegetation has no fire 
adaptations, causing vulnerability to local 
extinction due to ‘interval squeeze’(Perry et al., 2014)
Freshwater rivers Rainfall variationCascading effects of warming, drought, 
floods and algal blooms compounded by 
water abstraction(Macinnis-Ng et al., 2021)
Three species of naturalised woody weeds Warming and increased CO 2 levels Increased geographic range (Sheppard and Stanley, 2014)
Kauri tree, Agathis australisLower than average rainfall stimulates 
a drought-deciduous response in this

evergreen speciesIncreased litter fall (Macinnis-Ng and Schwendenmann, 2015)
Windmill palm Warming Increased geographic range (Aguilar et al., 2017)
New Zealand tussock grasslands Warming Enhanced respiration (Graham et al., 2014)
Invasive species WarmingIncreased invasive species abundance and 
increased predation on native species(Tompkins et al., 2013; Macinnis-Ng et al., 
2021)
WarmingExpanded ranges of invasive species in 
higher/cooler areas(Sheppard and Stanley, 2014; Walker et al., 
2019)
WarmingChange in flowering phenology and 
pollination competition(Giejsztowt et al., 2020)
WarmingIncrease in invasive plants, insects and 
pathogens from sub-tropical/tropical 
climates(Macinnis-Ng et al., 2021)
Tuatara (reptile), Sphenodon punctatus WarmingTemperature-dependent sex determination 
with more male hatches threatening small, 
isolated populations(Grayson et al., 2014)
Warming Increased geographic range (Carter et al., 2018)

Cattle tick WarmingIncreased geographic range and risk of 
tick-spread anaemia in cattle(K.E. Lawrence et al., 2017)
Brown mudfish, Neochanna apoda DroughtReduced flow regimes associated with 
drought interact with reduced habitat due 
to land use change, leading to population 
declines and potential local extinction(White et al., 2016b; White et al., 2017)
Suter’s skink (lizard) Oligosoma suteri WarmingIncreased suitable range but unclear if 
dispersal is possible because habitats are 
isolated(Stenhouse et al., 2018)
Threatened endemic passerine bird, 
Notiomystis cinctaFluctuations in total precipitation, 
particularly increased and more variable 
rainfallHeavy rainfall can flood nests and kill 
fledglings while droughts can cause 
population-wide reproductive failure(Correia et al., 2015)
Feral cats Warming Increased geographic range (Aguilar et al., 2015b)

159911.3.2 Coastal and Ocean Ecosystems
Australia’s EEZ covers over 8.1 million km2 of marine territory, including 50,000 km of coastline (Dhanjal-Adams et  al., 2016), spanning sub-
Antarctic islands in the south to tropical waters in the north. New 
Zealand’s marine territory extends from the sub-tropics to sub-
Box 11.1 | Escalating Impacts and Risks of Wildfire
Fire activity depends on weather, ignition sources, land management practices and fuel flammability, availability and continuity (Bradstock 
et al., 2014). Increased fire activity in southeast Australia associated with climate change has been observed since 1950 (Abram et al., 
2021), though trends vary regionally (medium confidence) (Bradstock et al., 2014). In New Zealand, there has been an increased frequency 
of major wildfires in plantations (FENZ, 2018) and at the rural–urban interface (medium confidence) (Pearce, 2018). In northern Australia,

increased wet season rainfall (Gallego et al., 2017) has increased dry season fuel loads (Harris et al., 2008).
In Australia, the frequency and severity of dangerous fire weather conditions is increasing, with partial attribution to climate change 
(very high confidence) (Dowdy and Pepler, 2018; Abram et al., 2021) (11.2.1, Figure Box 11.1.1), especially in southern and eastern 
Australia during spring and summer (Harris and Lucas, 2019). Although Australia’s eucalyptus forests and woodlands are fire adapted 
(Collins, 2020), increasing intensity and frequency of fires may exceed their resilience because of the shorter intervals between high-
severity fires (Bowman et al., 2014; Etchells et al., 2020; Lindenmayer and Taylor, 2020a). Recent fires have severely impacted eastern 
rainforests, including significant Gondwana refugia (Abram et al., 2021). In New Zealand, the trends in very high and extreme fire weather

(1997–2019) have not yet been attributed to climate change (MfE, 2020a).
Fire weather is projected to increase in frequency, severity and duration for southern and eastern Australia (high confidence) and most of 
New Zealand (medium confidence) (11.2.2), with projected increases in pyro-convection risk for parts of southern Australia (Dowdy et al., 
2019) and increased dry-lightning and fire ignition for southeast Australia (Mariani et al., 2019; Dowdy, 2020). Increased fire risk in spring 
may reduce opportunities for prescribed fuel-reduction burning in some regions (Harris and Lucas, 2019; Di Virgilio et al., 2020). Fuel 
dryness is a key constraint on wildfire occurrence (Ruthrof et al., 2016). Vegetation change will affect fuel load and fire risk in different 
areas in complex ways (Watt et al., 2019; Alexandra and Max Finlayson, 2020; Clarke et al., 2020; Sanderson and Fisher, 2020).

Direct effects of wildfire include death and injury to people and animals and damage to ecosystems, property, agriculture, water supplies 
and other infrastructure (Brodison, 2013; Pearce, 2018; de Jesus et al., 2020; Johnston et al., 2020; Maybery et al., 2020). Indirect effects 
include electricity and communication blackouts leading to cascading impacts on services, infrastructure and communities (Bowman, 
2012; Schavemaker and van der Sluis, 2017).
For New Zealand, there has been recent increased frequency and magnitude of property losses due to wildfire (Pearce, 2018). The 
1660-hectare Port Hills fire in 2017 resulted in the greatest house losses (9) in almost 100 years (Langer et al., 2018), but the subsequent 
5540-hectare Lake Ohau fire destroyed 53 houses in 2020 (Waitaki District Council, 2020).
In Australia, between 1987 and 2016, there were 218 deaths, 1000 injuries, 2600 people left homeless and 69,000 people affected by

wildfire (Deloitte, 2017b). Wildfires cost about AUD$1.1 billion per year on average (11.5.2).
The Australian wildfires of 2019–2020 resulted in 33 deaths, over 3000 houses destroyed, AUD$2.3  billion in insured losses and 
AUD$3.6 billion in losses for tourism, hospitality, agriculture and forestry (CoA, 2020e; Filkov et al., 2020) (Figure Box 11.1.2). Smoke caused 
a further 429 deaths and 3230 hospitalisations as a result of respiratory distress and illness, with health costs totalling AUD$1.95 billion 
(Johnston et al., 2020). These fires burnt about 5.8 to 8.1 million hectares of forest in eastern Australia (Ward et al., 2020; Godfree et al., 
2021), resulting in the loss or displacement of nearly 3 billion vertebrate animals (CoA, 2020e; Wintle et al., 2020). Further, 114 listed 
threatened species lost at least 50% of their habitat, and 49 lost 80% (Wintle et al., 2020), among other severe ecological impacts

(Hyman et al., 2020). Smoke carried over 4000 km to New Zealand, where it increased snow/glacier melt through darkening surfaces 
and produced a detectable odour (Pu et al. 2021;(Filkov et al., 2020). The fire season of 2019–2020 was at least 30% more likely than 
a century ago due to the influence of climate change (van Oldenborgh et  al., 2021). Following the fires, a Royal Commission into 
National Natural Disaster Arrangements made 80 recommendations, most of which were accepted by government, including establishing 
a disaster advisory body and a resilience and recovery agency (11.5.2.3) (CoA, 2020e).
In the face of climate change and the increased cost of fire damage and suppression, there has been considerable investment in fire risk 
reduction (Table Box 11.1.1). Recent analysis of 8800 fires in Australia shows resource constraints in response capacity are a barrier to

effectively containing fires (Collins et al., 2018b), compounded by lengthened and more extreme fire seasons.

11
1600Chapter 11 Australasia
Change in number of dangerous  fire weather days
Change in the annual (July to June) number of days
that the Forest Fire Danger Index (FFDI) exceeds
its 90th percentile from July 1985 to June 2020
relative to July 1950 to June 1985
0
-20-15-10
-25-5101520
525
Figure Box 11.1.1 |  Change in the annual (July to June) number of days that the Forest Fire Danger Index (FFDI) exceeds its 90th percentile from July 
1985 to June 2020 relative to July 1950 to June 1985 (BoM and CSIRO, 2020; Abram et al., 2021).
Table Box 11.1.1 |  Examples of adaptation options and enablers to reduce wildfire risk (Hart and Langer, 2011; Mitchell, 2013; Price et al., 2015; Tolhurst 
and McCarthy, 2016; Deloitte, 2017b; Miller et al., 2017; Steffen et al., 2017; Kornakova and Glavovic, 2018; Newton et al., 2018; Pearce, 2018; CoA, 2020e; McKemey 
Land management Communications Infrastructure
Prescribed burning to reduce fuel load close to built

assets.Clearer communication of existing exposure and 
vulnerability to enable informed decisions about risk 
tolerance and management, including sites of key 
biodiversity that are sensitive or susceptible to fire.Enhanced training and support for firefighters and 
aerial firefighting assets, including sharing of resources 
nationally and internationally to address the increasing 
overlap of fire seasons, which are lengthening across 
the world.
Engagement with Australia’s Aboriginal and Torres 
Strait Islander Peoples to utilise and learn from their fire 
management knowledge and skills to assist in landscape 
management and greenhouse gas mitigation.Increased research to understand interactions between 
fire, fuel, weather, climate and human factors to enhance 
projections of fire occurrence and behaviour.Nationally consistent response to exceedance of air 
quality standards.
Locating power lines appropriately or underground and

decentralising power supply to reduce ignitions.Community education and engagement, encouraging 
house and property maintenance, improving 
early-warning systems, more targeted messaging and 
increased emergency evacuation planning and sheltering 
options.Improved governance arrangements to ensure 
greater accountability and coordination between 
agencies, sharing of data and resources for emergency 
planning and greater understanding of risks to critical 
infrastructure and supply chains.
Preventive, community-based interventions to reduce 
ignitions from arson and accidental fires.Development of new systems to augment capability of 
fire services and technological advances to detect and 
respond to fires.
Reduced exposure of new assets through statutory 
spatial planning and land use regulations, building codes 
and building design standards.Box 11.1 (continued)

1601
Fires in southern and eastern Australia from Sep 2019 to Feb 2020
Extreme air pollution
5.8 to 8.1 million hectares burned with net emissions of up to 830 million tonnes of CO2-eq
Respiratory illness and disruption of outdoor activities and transport
Massive fire-fighting ef fort, saving many lives and at least 16,000 buildings
Building and facility closures, sporting events cancelled, holidays cancelled, workplace closures
Degraded and destroyed: Wineries, fruit, livestock, dairy , plantations
Loss of or displacement of 3 billion animals, with possible extinctions
Change in framework vegetation species and depletion of vegetation habitat resources
Smoke and ash transported to New Zealand, af fecting air quality and glaciers
Destroyed and damaged utilities and infrastructure, e.g. roads closed for weeks, power and communication outages, fuel shortage s, back-up generators  
without diesel, phone batteries run flat

Emergency evacuations of thousands of people by road, sea and air involving State Emergency Services and National Defence Force
Contamination of rivers and water supply with ash and sediment
Economic impacts: Estimates of the national financial impacts are over $8 billion
Social impacts: 33 people killed by fires, 429 killed by smoke, 3,103 houses destroyed, social disruption, injuries, exhaustion  and mental health issues
Environmental impacts: Loss of ecosystem service benefitsA
B
C
D
E
F
G
H
I
J
K
N
OL
MKey
Figure Box 11.1.2 |  Cascading impacts on people, economic activity, built assets, ecosystems and species arising from the Black Summer fires of 
2019–2020 in eastern and southern Australia (Boer et al., 2020; CoA, 2020e; CoA, 2020b; CoA, 2020a; CSIRO, 2020; Filkov et al., 2020; Johnston et al., 2020; 
Ward et al., 2020; Wintle et al., 2020; Abram et al., 2021; Godfree et al., 2021).Box 11.1 (continued)

11
1602Chapter 11 Australasia
Antarctic waters, encompassing an EEZ of 4 million km2, 18,000 km 
of coastline and 700 smaller islands and islets, in addition to the two 
main islands (Costello et al., 2010a; MfE, 2016).
The marine environment is important to the culture, health and well-
being of the region’s diverse Indigenous Peoples, including those who 
had sovereign ownership, governance, resource rights, and stewardship 
over ‘Sea Country’ for many thousands of years before the current sea 
level stabilised approximately 6000 years ago and before current coastal 
ecosystems were established (Rist et al., 2019). Marine environments 
contribute AUD$69 billion per year to Australia’s economy (Eadie et al., 
2011), and NZD$4  billion per year to New Zealand’s economy (MfE, 
2016). They have a high proportion of rare and endemic species (Croxall 
et al., 2012) and provide ecosystem services including food production,

coastal protection, tourism and carbon sequestration (Croxall et  al., 
2012; Kelleway et al., 2017). Half of the species within New Zealand’s 
seas are endemic (Costello et al., 2010b).
11.3.2.1 Observed Impacts
Climate change is having major impacts on the region’s oceans (very 
high confidence) (Table  11.6) (Law et  al., 2016; Sutton and Bowen, 
2019). Rising sea surface temperatures (SSTs) have exacerbated marine 
heatwaves, notably near western Australia in 2011, the GBR in 2016, 
2017 and 2020 and the Tasman Sea in 2015/2016, 2017/2018 and 
2018/2019 (Table 11.2) (BoM and CSIRO, 2018; AMS, 2019; NIWA, 2019; 
Salinger et al., 2019b; Sutton and Bowen, 2019; BoM, 2020; Salinger 
et al., 2020; Oliver et al., 2021). Temperature anomalies ranged from 
1.2°C to 4.0°C and durations ranged from 90–250 days (Table 11.2).
Ocean carbon storage and acidification has led to decreased surface 
pH in the region (Table 11.2), including the sub-Antarctic waters off

the East Coast of New Zealand’s South Island (very high confidence) 
(Law et al., 2016). The depth of the Aragonite Saturation Horizon has 
shallowed by 50–100 m over much of New Zealand, which may limit 
and/or increase the energetic costs of growth of calcifying species 
(low confidence) (Anderson et al., 2015; Bostock et al., 2015; Mikaloff-
Fletcher et al., 2017).
In the estuaries of southwestern Australia, sustained warming and 
drying trends have caused dramatic declines in freshwater flows of 
up to 70% since the 1970s and increased frequency and severity of 
hypersaline conditions, enhanced water column stratification and 
hypoxia and reduced flushing and greater retention of nutrients 
(Hallett et al., 2017).
Extensive changes in the life history and distribution of species have 
been observed in Australia’s (very high confidence) (Gervais et  al., 
2021) and New Zealand’s marine systems (medium confidence) 
(Table 11.6) (Cross-Chapter box MOVING SPECIES in Chapter 5). New

occurrences or increased prevalence of disease, toxins and viruses are 
evident (de Kantzow et al., 2017; Condie et al., 2019), along with heat 
stress mortalities and changes in community composition (Wernberg 
2 In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is 
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.et al., 2016; Zarco-Perello et al., 2017; Thomsen et al., 2019). Extreme 
climatic events in Australia from 2011 to 2017 led to abrupt and 
extensive mortality of key habitat-forming organisms — corals, kelps,

seagrasses and mangroves — along over 45% of the continental 
coastline of Australia (high confidence) (Babcock et al., 2019).
In 2016 and 2017, the GBR experienced consecutive occurrences 
of the most severe coral bleaching in recorded history (very high 
confidence) (Box 11.2), with shallow-water reef in the top two-thirds 
of the GBR affected and the severity of bleaching on individual reefs 
tightly correlated with the level of local heat exposure (Hughes et al., 
2018b; Hughes et al., 2019c). Mass mortality of corals from these two 
unprecedented events resulted in larval recruitment in 2018 declining 
by 89% compared to historical levels (Hughes et al., 2019b). southern 
reefs were also affected by warming, although significantly less than 
in the north (Kennedy et  al., 2018). Coral reefs in Australia are at 
very high risk of continued negative effects on ecosystem structure 
and function (very high confidence) (Hughes et  al., 2019b), cultural

well-being (very high confidence) (Goldberg et al., 2016; Lyons et al., 
2019), food provision (medium confidence) (Hoegh-Guldberg et  al., 
2017), coastal protection (high confidence) (Ferrario et al., 2014) and 
tourism (high confidence) (Deloitte Access Economics, 2017; Prideaux 
and Pabel, 2018; GBRMPA, 2019). If bleaching persists, an estimated 
10,000 jobs and AUD$1 billion in revenue would be lost per year from 
declines in tourism alone (Swann and Campbell, 2016).
11.3.2.2 Projected Impacts
Future ocean warming, coupled with periodic extreme heat events, 
is projected to lead to the continued loss of ecosystem services and 
ecological functions (high confidence) (Smale et al., 2019) as species 
further shift their distributions and/or decline in abundance (Day et al., 
2018). Compounding climate-driven changes in the distribution of 
habitat-forming species, invasive macroalgae are predicted to exhibit 
higher growth under all higher pCO 2 and lower pH conditions (Roth-

Schulze et al., 2018). Corals and mangroves around northern Australia 
and kelp and seagrass around southern Australia are of critical 
importance for ecosystem structure and function, fishery productivity, 
coastal protection and carbon sequestration; these ecosystem services 
are therefore extremely likely2 to decline with continued warming. 
Equally, many species provide important ecosystem structure and 
function in New Zealand’s seas including in the deep sea (Tracey 
and Hjorvarsdottir, 2019). The future level of sustainable exploitation 
of fisheries is dependent on how climate change impacts these 
ecosystems. Native kelp is projected to further decline in southeastern 
New Zealand with warming seas (Table 11.6). Climate change could 
affect New Zealand fisheries’ productivity (Cummings et  al., 2021), 
and both ocean warming and acidification may directly affect shellfish 
culture (Cunningham et al., 2016; Cummings et al., 2019) and indirectly

through changes in phytoplankton production (Pinkerton, 2017).
Climate-change-related temperature and acidification may affect 
species sex ratios and, thus, population viability (medium confidence)

1603(Table 11.3) (Law et al., 2016; Tait et al., 2016; Mikaloff-Fletcher et al., 
2017). Acidification may alter sex determination (e.g., in the oyster 
Saccostrea glomerate), resulting in changes in sex ratios (Parker et al., 
2018), and may thus affect reproductive success (low confidence). 
Decreasing river flows (Chiew et  al., 2017) are projected to cause 
periodically open estuaries across southwest Australia to remain closed 
for longer periods, inhibiting the extent to which marine taxa can 
access these systems (Hallett et al., 2017) and with warming predicted 
to constrain activity in some large fish (Scott et  al., 2019b). Major 
knowledge gaps include environmental tolerances of key life stages, sources of recruitment, population linkages, critical ecological (e.g., 
predator–prey interactions) or phenological relationships and projected 
responses to lowered pH (Fleming et al., 2014; Fogarty et al., 2019).

Black-browed albatrosses breeding on Macquarie Island may be more 
vulnerable to future climate-driven changes to weather patterns in the 
Southern Ocean and potential latitudinal shifts in the sub-Antarctic 
Front (Cleeland et  al., 2019). New Zealand coastal ecosystems face 
risks from sea level rise (SLR) and extreme weather events (MfE, 
2020a).
Table 11.6 |  Observed climate-change-related changes in the marine ecosystems of Australia and New Zealand. Climate-related impacts have been documented at a range of 
scales from single-species or region-specific studies to multi-species or community-level changes.
Type of change Examples Climate-related Pressure Source
Australia
Reduced activity and increased 
energetic demandsCoral trout (Plectropomus leopardus), one of Australia’s most 
important commercial and recreational tropical finfish speciesIncreased temperature (experimental laboratory 
study) and ocean warming(Johansen et al., 2014; Scott 
Estuaries warming and

fresheningAustralian lagoons and rivers warming and decreasing pH at 
a faster rate than predicted by climate modelsWarming and reduction in rainfall (leading to 
reduced flows and therefore being less frequently 
open to the sea)(Scanes et al., 2020)
Changes in life-history traits, 
behaviour or recruitmentReduced size of Sydney rock oysters (for commercial sale)Limited capacity to bio mineralise under 
acidification conditions(Fitzer et al., 2018)
Reduced growth in tiger flathead fish in equatorward range Ocean warming(Morrongiello and Thresher, 
2015)
55% of 335 fish species became smaller and 45% became 
larger as seas warmed around AustraliaOcean warming (over three decades) (Audzijonyte et al., 2020)
Rock lobster display reduced avoidance of predators at 23°C 
compared to 20°CIncreased temperature (experimental laboratory 
study)(Briceño et al., 2020)
Analysis of stress rings in cores of corals from the GBR dating 
back to 1815 found that following bleaching events, the coral

was less affected by subsequent marine heatwavesHeat events (DeCarlo et al., 2019)
Mortality and reductions in spawning stocks of fishery 
important abalone, prawns, rock lobsters2011 marine heatwave (Caputi et al., 2019)
Recruitment of coral on GBR reduced to 11% of long-term 
averageWarming-driven back-to-back global bleaching 
events(Hughes et al., 2019b)
Green turtle hatchlings from southern GBR 65–69% female 
and hatchlings from northern GBR 100% female for last two 
decadesIncreased sand temperatures (Jensen et al., 2018)
New diseases, toxinsFirst occurrence of virulent virus causing Pacific Oyster 
Mortality Syndrome (POMS), up to 90% of all farmed oysters 
died in impacted areasDetected during heatwave (de Kantzow et al., 2017)
Mussels, scallops, oysters, clams, abalone and rock lobsters 
on east coast of Tasmania found to have high levels of 
Paralytic Shellfish toxins, originating from a bloom of harmful 
Alexandrium tamarenseWarming and extension of the East Australian

Current(Hallegraeff and Bolch, 2016)
Range expansion of phytoplankton Noctiluca, which can be 
toxicWarming and extension of the East Australian 
Current(Hallegraeff et al., 2020)
Mortality of fish following algal blooms in South Australia 2013 marine heatwave (Roberts et al., 2019)
Changes in species distributionsRange extensions at the poleward range limit have been 
detected in: fish, cephalopods, crustaceans, nudibranchs, 
urchins, coralsOcean warming(Baird et al., 2012; Robinson 
et al., 2015; Sunday et al., 2015; 
Ling et al., 2018; Nimbs and 
Smith, 2018; Ramos et al., 2018; 
Smith et al., 2019; Caswell et al., 
2020)
Contractions in range at the equatorward range edge have been 
detected in anemones, asteroids, gastropods, mussels, algaeOcean warming(Pitt et al., 2010; Poloczanska 
et al., 2011; Smale et al., 2019)

11
1604Chapter 11 Australasia
Australia’s most southern dominant reef building coral, 
Plesiastrea versipora, in eastern Bass Strait, increasing in 
abundance at the poleward edge of the species’ range and in 
western AustraliaOcean warming(Tuckett et al., 2017; Ling et al., 
2018)
Southwestern Australia fish assemblages—warm-water fish 
increasing in density at poleward edge of distributions and 
cool-water species decreasing in density at equatorward 
edge of distributions; increase in warm-water habitat 
forming species leading to reduced habitat for invertebrate 
assemblagesCombination of increased temperatures and 
changes in habitat-forming algal species(Shalders et al., 2018; Teagle 
Predicted reduction range of rare Wilsonia humilis herb in 
Tasmanian saltmarsh but no change in rest of communityWetter and drier climate (Prahalad and Kirkpatrick, 2019)

Changes in abundanceShift towards a zooplankton community dominated by 
warm-water small copepods in southeast AustraliaOcean warming (Kelly et al., 2016)
Diebacks of tidal wetland mangroves2015–2016 heatwaves combined with moisture 
stress(Duke et al., 2017)
Decline in giant kelp in Tasmania, Australia, less than 10% 
remaining; loss of kelp Australia-wide totalling at least 
140,187 hectaresOcean warming and change in East Australian 
Current (lower nutrients)(Wahl et al., 2015; Butler 
et al., 2020; Filbee-Dexter and 
Wernberg, 2020)
Regional loss of seagrass in Shark Bay World Heritage Area, 
western AustraliaHigh air and water temperatures during 2011 
heatwave(Strydom et al., 2020)
Increased annual dugong and inshore dolphin mortality 
across QueenslandSustained low air temperature and increased 
freshwater discharge during high Southern 
Oscillation Index (SOI) (ENSO) index(Meager and Limpus, 2014)
Predicted equatorward decline and poleward shift of sea

urchin in eastern AustraliaOcean warming (Castro et al., 2020)
Increasing mortality of Australian fur seal pups in low-lying 
coloniesStorm surges and high tides amplified by ongoing 
SLR(McLean et al., 2018) (Box 11.6)
Rapid shifts in community 
composition, structure and 
integrityCommunity-wide tropicalisation in Australian temperate 
reef communities; temperate species replaced by seaweeds, 
invertebrates, corals, and fishes characteristic of sub-tropical 
and tropical watersExtreme marine heatwaves led to 100-km range 
contraction of extensive kelp forests(Vergés et al., 2016; Wernberg 
Ongoing declines in habitat-forming seaweeds Climate-driven shift of tropical herbivores(Thomson et al., 2015; Nowicki 
et al., 2017; Zarco-Perello et al., 
2017; Wernberg et al., 2016)
Dieback of temperate seagrass in Shark Bay, Australia, 
subsequently replaced by tropical early successional seagrass 
with seagrass-associated megafauna (sea turtles) declining

in health status2011 marine heatwave (Strydom et al., 2020)
Increased herbivory by fish on tropicalised reefs of western 
AustraliaChange in species composition due to ocean 
warming(Zarco-Perello et al., 2019)
No recovery 2 years after coral bleaching and macroalgae 
mortality in western Australia2011 marine heatwave (Bridge et al., 2014)
Mass mortality of particular coral species on affected reefs 
during heatwaves on GBR (Eastern Australia) led to altered 
coral reef structure and species composition 8 months later.2016 marine heatwave (Hughes et al., 2018c)
Community-wide restructuring along GBR 1 year after the 
2016 mass bleaching event2016 marine heatwave (Stuart-Smith et al., 2018)
Changes in life-historyAlteration of shell of pāua (black footed abalone, Haliotis iris) 
under lowered pH (calcite layer thinner, greater etching of 
external shell surface)Lowered pH (experimental laboratory study) (Cummings et al., 2019)

Decline in maximum swimming performance of kingfish and 
snapperElevated CO 2 (experimental laboratory study)(Watson et al., 2018; McMahon 
Increased mortality and faster growth in juvenile kingfish Increased temperature (Watson et al., 2018)
Earlier spawning of snapper in South Island 2017–2018 heatwave (Salinger et al., 2019b)
Increase in mortalityHeat stress mortality in salmon farms off Marlborough, New 
Zealand, where 20% of salmon stocks died2017–2018 marine heatwave (Salinger et al., 2019b)

1605Type of change Examples Climate-related Pressure Source
Changes in species distributionsSpecies increasingly caught further south (e.g., snapper and 
kingfish)Ocean warming and 2017–2018 marine heatwave (Salinger et al., 2019b)
Non-breeding distribution of New Zealand nesting seabird 
(Antarctic prion) shifting south with long-term climate 
inferred from stable isotopesClimate warming (Grecian et al., 2016)
Less phytoplankton production in Tasman Sea but more on 
sub-tropical frontOcean warming (Chiswell and Sutton, 2020)
Loss of bull kelp (Durvillaea) populations in southern New 
Zealand subsequently replaced by introduced kelp Undaria2017–2018 heatwave when sea and air 
temperatures exceeded 23°C and 30°C respectively(Salinger et al., 2019b; Thomsen 
et al., 2019; Salinger et al., 2020)
Box 11.2 | The Great Barrier Reef in Crisis

The GBR is the world’s largest coral reef system, comprising 3863 reefs over an area of 348,700 km2, stretching for 2300 km. The GBR 
is a central cornerstone of the beliefs, knowledges, lores, languages and ways of living for over 70 geographically and culturally diverse 
Traditional Owner groups spanning the length of the GBR (Dale et al., 2018), and it contributes an estimated AUD$6.4 billion per year 
(pre-COVID) to the Australian economy, mainly via tourism. As the world’s most extensive coral reef ecosystem, the GBR is a globally 
outstanding and significant entity, with practically the entire ecosystem inscribed as a World Heritage Site in 1981 (UNESCO, 1981).
The GBR is already severely impacted by climate change, particularly ocean warming, through more frequent and severe coral bleaching 
(very high confidence) (Hughes et al., 2018b; Hughes et al., 2019c). The worst coral bleaching event on record affected over 90% of reefs

in 2016 (Hughes et al., 2018b). In the most northern 700-km-long section of the GBR in which the heat exposure was the most extreme, 
50% of the coral cover on reef crests was lost within 8 months (Hughes et al., 2018c). Throughout the entire GBR, including the southern 
third where heat exposure was minimal, the cover of corals declined by 30% between March and November 2016 (Hughes et al., 2018b). 
In 2017, the central third of the reef was the most severely affected and the back-to-back regional-scale bleaching events has led to an 
unprecedented shift in the composition of GBR coral assemblages, transforming the northern and middle sections of the reef system 
(Hughes et al., 2018c) to a highly degraded state (very high confidence). Coral recruitment to the GBR in 2018 was reduced to only 11% 
of the long-term average (Hughes et al., 2019b). A mass bleaching event also occurred in 2020, making it the third event in 5 years (BoM, 
2020) (Figure Boxes 11.2.1 and 11.2.2).

Increased heat exposure also affects the abundance and distribution of associated fish, invertebrates and algae (high confidence) (Stuart-
Smith et al., 2018). Thus, coral bleaching is an indicator of thermal effects on coral habitat, fauna and flora. Bleaching is expected to 
continue for the GBR and Australia’s other coral reef systems (virtually certain). Bleaching conditions are projected to occur twice each 
decade from 2035, annually after 2044 under RCP8.5 and annually after 2051 under RCP4.5 (Heron et al., 2017). Global warming of 3°C 
would result in over six times the 2016 level of thermal stress (Lough et al., 2018).
Increases in cyclone intensity projected for this century, and other extreme weather events, will greatly accelerate coral reef degradation 
(Osborne et al., 2017). Additionally, through interactions between elevated ocean temperature and coastal runoff (nutrient and sediment),

extreme weather events may contribute to an increased frequency and/or amplitude of crown-of-thorns starfish outbreaks (Uthicke et al., 
2015), further reducing the spatial distribution of coral.
Recovery of coral reefs following repeated disturbance events is slow (Hughes et al., 2019b; IPCC, 2019b), and it takes at least a decade 
after each bleaching event for the very fastest growing corals to recover (high confidence) (Gilmour et al., 2013; Osborne et al., 2017). 
Estimates of future levels of thermal stress, measured as degree heating months, which incorporates both the magnitude and duration 
of warm season SST anomalies, suggest that achieving the 1.5°C Paris Agreement target would be insufficient to prevent more frequent 
mass bleaching events (very high confidence) (Lough et al., 2018), although it may reduce their occurrence (Heron et al., 2017), and 
occurrences of warming events similar to 2016 bleaching could be reduced by 25% (King et al., 2017).

Tourist motivations for visiting the GBR are changing, with a recent survey finding that two-thirds of tourists were visiting ‘before it was 
gone’ and a similar number were reporting damage to the reef—an example of ‘last chance tourism’ (Piggott-McKellar and McNamara, 
2016). The Australian government is investing AUD$1.9 billion to support the GBR through science and practical environmental outcomes, 
including reducing other anthropogenic pressures, which can suppress natural adaptive capacity (CoA, 2019b; GBRMPA, 2019). However, 
adaptation efforts on the GBR aimed specifically at climate impacts, for example coral restoration following marine heatwave impacts 
(Boström-Einarsson et al., 2020), may slow the impacts of climate change in small discrete regions of the reef or reduce short-term 
socioeconomic ramifications, but they will not prevent widespread bleaching (Condie et al. 2021).

11
1606Chapter 11 Australasia
The Great Barrier Reef
(a) Spatial patterns in heat exposure
14 24 08Degree Heating Weeks (°C / week)
(c) Coral recruitment
(b) Geographic footprint of recurrent coral bleaching
<1% 1–10% 10–30% 30–60% >60%Footprint of bleaching (% bleached)
Recruits
per
panel
200
100
10
Spawners Brooders
500 km2016 2017 2016 2017
Long-term
average2018
Figure Box 11.2.1 |  Top panels: spatial patterns in heat exposure along the GBR in 2016 (left pair) and 2017 (right pair), measured from satellites 
as Degree Heating Weeks (DHW, °C-weeks). Middle panels: geographic footprint of recurrent coral bleaching in 2016 (left) and again in 2017 (right), measured by 
aerial assessments of individual reefs (adapted from (Hughes et al., 2019c)]). Bottom panels: density of coral recruits (mean per recruitment panel on each reef), measured

over three decades, from 1996 to 2016 (n = 47 reefs, 1784 panels) (left), compared to the density of coral recruits in 2018 after the mass mortality of corals in 2016 and 
2017 due to the back-to-back bleaching events (n = 17 reefs, 977 panels) (right). The area of each circle is scaled to the overall recruit density of spawners and brooders 
combined. Yellow and blue indicate the proportion of spawners and brooders respectively (from (Hughes et al., 2019b)]).Box 11.2 (continued)

1607Nutrient availability and productivity in the sub-tropical waters 
of New Zealand are projected to decline due to increased SST and 
strengthening of the thermocline, but they may increase in sub-
Antarctic waters, potentially bringing some benefit to fish and other 
species (low confidence) (Law et al., 2018b). For New Zealand waters 
as a whole, declines in net primary productivity of 1.2% and 4.5% are 
projected under RCP4.5 and RCP8.5 respectively by 2100, and declines 
in the primary production of surface waters by an average 6% from the 
present day under RCP8.5, with sub-tropical waters experiencing the 
largest decline (Tait et al., 2016).
The pH of surface waters around New Zealand is projected to decline 
by 0.33 under RCP 8.5 by 2090 (Tait et al., 2016), and the depth at 
which carbonate dissolves is projected to be significantly shallower 
(Mikaloff-Fletcher et  al., 2017), affecting the distribution of some

species of calcifying cold water corals (medium confidence) (Law 
et al., 2016). However, model projections suggest that the top of the 
Chatham Rise may provide temporary refugia for scleractinian stony 
corals from ocean acidification because the Chatham Rise sits above 
the aragonite saturation horizon (Anderson et al., 2015; Bostock et al., 
2015). For sub-tropical corals, skeletal formation will be vulnerable 
to the changes in ocean pH, with implications for their longer-term 
growth and resilience (Foster et al., 2015).11.3.2.3 Adaptation
Climate change adaptation opportunities and pathways have been 
identified across aquaculture, fisheries, conservation and tourism 
sectors in the region (MacDiarmid et  al., 2013; Fleming et  al., 
2014; MPI, 2015; Jennings et al., 2016; MfE, 2016; Royal Society Te 
Apārangi, 2017; Ling and Hobday, 2019), and some stakeholders 
are already autonomously adapting (Pecl et al., 2019). Some fishing

and aquaculture industries use seasonal forecasts of environmental 
conditions to improve decision-making, risk management and business 
planning (Hobday et  al., 2016), with the potential to use 5-yearly 
forecasts similarly (Champion et al., 2019). Shifts in the distribution 
and availability of target species (e.g., oceanic tuna) would impact 
the ability of domestic fishing vessels to continue current fishing 
practices, with potential social and economic adjustment costs (Dell 
et  al., 2015), including disruption to supply chains (Fleming et  al., 
2014; Plagányi et  al., 2014) (Cross-Chapter Box  MOVING SPECIES 
in Chapter 5). Species abundance data are insufficient to enable 
projections of climate impacts on fishery productivity. However, fishery 
and aquaculture industries are considering adaptation strategies, 
such as changing harvests and relocating farms (Pinkerton, 2017). 
Thus, while climate change is extremely likely to affect the abundance

and distribution of marine species around New Zealand, insufficient 
monitoring means there is limited evidence of ecosystem level change 
in biodiversity to date and no quantitative projections of which species 
may win and lose to climate change (Table 11.6) (Law et al., 2018a; 
Law et al., 2018b).Figure Box 11.2.2 |  Variation in the severity of mass-bleaching episodes 
recorded on Australia’s GBR over the last four decades (1980–2020). The 
overall number of reefs surveyed was substantially higher in 1998, 2002, 2016, 2017 
and 2020 when aerial surveys were undertaken, whereas the severity of other more 
localised bleaching episodes was documented with in-water surveys (adapted from 
(Pratchett et al., 2021). Extent of bleaching in 2020 was similar in severity to that of 
2016 but more geographically widespread and included southern reefs.Variation in the severity of mass-bleaching 
episodes recorded on Australia’s Great Barrier Reef over the 
last four decades (1980–2020)

1020%30%40%50%70%
60%
10%
10 100 1000
Number of reefs surveyed% reef with severe bleaching2016
2020
20172010 1980 1990 2000 2020
YearBox 11.2 (continued)

11
1608Chapter 11 Australasia
11.3.3 Freshwater Resources
Climate change impacts on freshwater resources cascade across 
people, agriculture, industries and ecosystems (Boxes 11.3 and 11.5). 
The challenge of satisfying multiple demands with a finite resource is 
exacerbated by high interannual and inter-decadal variability of river 
flows, particularly in Australia (Chiew and McMahon, 2002; Peel et al., 
2004; McKerchar et al., 2010).
11.3.3.1 Observed Impacts
Streamflow has generally increased in northern Australia and decreased 
in southern Australia since the mid-1970s (high confidence) (Zhang 
et al., 2016). Declining river flows since the mid-1970s in southwest 
Australia have led to changed water management (WA Government, 
2012; WA Government, 2016). The large decline in river flows during 
the so-called 1997–2009 Millennium drought in southeast Australia 
resulted in low irrigation water allocations, severe water restrictions

and major environmental impacts (Potter et  al., 2010; Chiew and 
Prosser, 2011; Leblanc et al., 2012; van Dijk et al., 2013). The drying in 
southern Australia highlighted the need for hydrological models that 
adequately account for climate change (Vaze et al., 2010; Chiew et al., 
2014; Saft et al., 2016; Fowler et al., 2018). The decline in streamflow 
was largely due to the decline in cool-season rainfall (which has been 
partly attributed to climate change) (Figure 11.2) (Timbal and Hendon, 
2011; Post et al., 2014; Hope et al., 2017; DELWP , 2020), when most of 
the runoff in southern Australia occurs.
In New Zealand, precipitation has generally decreased in the north 
and increased in the southwest (Figure 11.2) (Harrington et al., 2014), 
but it is difficult to ascertain trends in the relatively short streamflow 
records. Glaciers in New Zealand’s southern alps have lost one third of 
their mass since 1977 (Mackintosh et al., 2017; Salinger et al., 2019b),

and glacier mass loss in 2018 was at least 10 times more likely to occur 
with anthropogenic forcing than without (Vargo et al., 2020).
11.3.3.2 Projected Impacts
Projections indicate that future runoff in southeast and southwest 
Australia are likely to decline (median estimates of 20% and 50% 
respectively under 2.2°C global average warming) (Figure 11.3) (Chiew 
et al., 2017; Zheng et al., 2019). These projections are broadly similar 
to those reported previously and in AR5 (Teng et al., 2012; Reisinger 
et al., 2014). The range of estimates arises mainly from the uncertainty 
in projected future precipitation (Table 11.2a).
The runoff decline in southern Australia is projected to be further 
accentuated by higher temperature and potential evapotranspiration 
(Potter and Chiew, 2011; Chiew et al., 2014), transpiration from tree 
regrowth following more frequent and severe wildfires (Brookhouse 
et al., 2013) (Box 11.1), interceptions from farm dams (Fowler et al.,

2015) and reduced surface–groundwater connectivity (limiting 
groundwater discharge to rivers) in long dry spells (high confidence) 
(Petrone et al., 2010; Hughes et al., 2012; Chiew et al., 2014). In the 
longer term, runoff will also be affected by changes in vegetation 
and surface–atmosphere feedback in a warmer and higher CO 2 
environment, but the impact is uncertain because of the complex interactions, including changes in climate inputs, fire patterns 
(Box 11.1) and nutrient availability (Raupach et al., 2013; Ukkola et al., 
2016; Cheng et al., 2017).
Climate change is projected to affect groundwater recharge and the 
relationship between surface waters and aquifers and through rising 
sea levels where groundwater has a tidal signature (PCE, 2015; MfE, 
2017a). Groundwater recharge across southern Australia has decreased 
in recent decades (Fu et al., 2019), and this trend is expected to continue 
(high confidence) (Barron et  al., 2011; Crosbie et  al., 2013). Climate

change is also projected to impact water quality in rivers and water 
bodies, particularly through higher temperature and low flows (Jöhnk 
et  al., 2008) (Box  11.5) and increased sediment and nutrient load 
following wildfires (high confidence) (Biswas et al., 2021) (Box 11.1) 
and floods (Box 11.4).
The projected changes in river flows in New Zealand are consistent 
with the precipitation projections (Table 11.2), with increases in the 
west and south of the South Island and decreases in the east and 
north of the North Island (Figure 11.4). In the South Island, the runoff 
increase occurs mainly in winter due to increasing moisture-bearing 
westerly airflow, with more precipitation falling as rain and snow 
melting earlier. In the North Island, the runoff decrease occurs in spring 
and summer (Caruso et  al., 2017; Collins et  al., 2018a; Jobst et  al., 
2018; D. Collins, 2020).
11.3.3.3 Adaptation
In Australia, prolonged droughts and projections of a drier future

have accelerated policy and management change in urban and rural 
water systems. Adaptation initiatives and mechanisms, like significant 
government investment to enhance the Bureau of Meteorology online 
water information (Vertessy, 2013; BoM, 2016), funding to improve 
agricultural water use and irrigation efficiency (Koech and Langat, 
2018), enhanced supply through inter-basin transfers and upgrading 
water infrastructure and an active water trading market (Wheeler 
et al., 2013; Kirby et al., 2014; Grafton et al., 2016) are helping to buffer 
regional systems against droughts and facilitating some adaptation to 
climate change (medium confidence). However, these measures could 
also be maladaptive because they may perpetuate unsustainable water 
and land uses under ongoing climate change (Boxes 11.3 and 11.5).
The widespread 2017–2019 drought across eastern Australia (BoM, 
2021b) has led to the Australian government establishing a Future

Drought Fund (Australian Government, 2019) to enhance drought 
resilience and a National Water Grid Authority to develop regional 
water infrastructure to support agriculture. Nevertheless, the ability to 
adapt to climate change is compounded by uncertainties in future water 
projections, complex interactions between science, policy, community 
values and political voice, and competition between different sectors 
dependent on water (Boxes 11.3 and 11.5). The impact of declining 
water resources on agricultural, ecosystems and communities in 
southeastern Australia would escalate with ongoing climate change 
(medium confidence) (Hart, 2016; Moyle et  al., 2017), highlighting 
the importance of more ambitious, anticipatory, participatory and 
integrated adaptation responses (Bettini et al., 2015; Abel et al., 2016; 
Marshall and Lobry de Bruyn, 2021).

1609Altered water regimes resulting from the combined effects of climatic 
conditions and water policies carry uneven and far-reaching implications 
for communities (high confidence).  Acting on Indigenous Peoples’ claims 
to cultural flows (to maintain their connections with their country) is 
increasingly recognised as an important water management and social 
justice issue (Taylor et al., 2017; Hartwig et al., 2018; Jackson, 2018; 
Jackson and Moggridge, 2019; Moggridge et al., 2019). Compounding 
stressors, such as coal and coal seam gas developments, can also 
severely impact local communities, water catchments and water-
dependent ecosystems and assets, exacerbating their vulnerability to 
climate change (Navi et al., 2015; Tan et al., 2015; Chiew et al., 2018).
In Australian capital cities and regional centres, water planning

has focused on securing new supplies that are resilient to climate change. This includes increasing use of stormwater and sewage 
recycling and managed aquifer recharge (Bekele et  al., 2018; Page 
et al., 2018; Gonzalez et al., 2020). All major coastal Australian cities 
have desalination plants. Household scale adaptation, like rainwater 
harvesting, water-smart gardens, dual flush toilets, water-efficient 
showerheads and voluntary residential use targets, can help reduce 
water demand by up to 40% (Shearer, 2011; Rhodes et  al., 2012; 
Moglia et al., 2018). Water utilities across Australia have established 
climate change adaptation guidelines (WSAA, 2016). Coordinated 
efforts to reduce demand, design and retrofit infrastructure to reduce 
flood risk and harvest water and to practice water-sensitive urban 
design are evident (WSAA, 2016; Kunapo et al., 2018; Rogers et al., 
2020b). Transitioning centralised water systems to a more sustainable

basis represents adaptation progress but is complex and faces many Projected changes in mean annual runoff
2046–2075 relative to 1976–2005 for RCP8.5 from hydrological modelling with future climate projections informed by 42 CMIP5 GC Ms 
0 -30 -20 -10 -50 -5 10 20 30 55 0Percent change in mean annual  runoffMedian
estimate
Wet estimate
(90th percentile)Dry estimate
(10th percentile)
0 -30 -20 -10 -50 -5 10 20 30 55 0Absolute (mm) change in mean annual runoff
Figure 11.3 |  Projected changes in mean annual runoff for 2046–2075 relative to 1976–2005 for RCP8.5 from hydrological modelling with future climate 
projections informed by 42 CMIP5 GCMs. Projections for RCP4.5 are about three quarters of the aforementioned projections. Plots show median projection and the 10th and 
90th percentile range of estimates. The boundaries are based on hydroclimate regions and major drainage basins. Source: (Zheng et al., 2019).

11
1610Chapter 11 Australasia
barriers and limits (medium confidence) (Morgan et  al., 2020). 
Developing multiple redundant or decentralised systems can enhance 
community resilience and promote autonomous adaptations that may 
be more sustainable and cost effective in the longer term (Mankad and 
Tapsuwan, 2011; WSAA, 2016; Iwanaga et al., 2020).
In New Zealand, many water supplies are at risk from drought, extreme 
rainfall events and sea level rise (SLR), exacerbated by underinvestment 
in existing water infrastructure (in part due to funding constraints) 
and urban densification (high confidence) (CCATWG, 2017; MfE and 
Stats NZ, 2021). Lessons can be learned from global experience (e.g., 
Cape Town, South Africa; Section 4.3.4). Water quality has diminished, 
with hotter conditions and drought causing algal blooms, combined 
with intensification of agricultural land uses in some areas, and heavy 
rainfall and sea level rise (SLR) causing flooding and sedimentation

of water sources and health impacts (11.3.6; Box 11.5). Some towns are only partially metered or not metered at all, which exacerbates 
the adaptation challenge (Hendy et al., 2018; WaterNz, 2018; Paulik 
2019a). Unregulated or absent water supplies accentuate risks to 
vulnerable groups of people (MfE, 2020b). Māori view water as the 
essence of all life, which makes any impacts on water a governance 
and stewardship concern, and increasingly, the subject of legal claims 
(MfE, 2020a; MfE, 2020b; MfE, 2020c) (11.4.2). Māori understanding 
of time can also open up new spaces for rethinking freshwater 
management in a climate change context that does not reinforce or 
rearticulate multiple environmental injustices (Parsons et al., 2021).
Water resource adaptation in New Zealand is variable across local 
government and water authorities but they all actively monitor water 
availability, demand and quality, and most have drought management

plans. The 2019/2020 drought led to water shortages in the most 
populated areas of Waikato, Auckland and Northland, resulting in 
Projected percentage change in mean annual runoff
2086–2099 relative to 1986–2005 from hydrological modelling informed by six CMIP5 GCMs for four RCPs
RCP2.6 RCP4.5 RCP6.0 RCP8.5
20%
-50%-20%-10%050%Projected
change in
mean annual
runoff
Change is
not statistically
Figure 11.4 |  Projected percentage change in mean annual runoff for 2086–2099 relative to 1986–2005 from hydrological modelling informed by six CMIP5 
GCMs for four RCPs. Maps show median projection from the six modelling runs. White indicates that the change is not statistically significant. Source: (D. Collins, 2020).
Box 11.3 | Drought, Climate Change and Water Reform in the Murray-Darling Basin
The MDB is Australia’s largest, most economically important and politically complex river system (Figure Box 11.3.1). The MDB supports

agriculture worth AUD$24 billion/year, 2.6 million people in diverse rural communities and important environmental assets including 16 
Ramsar listed wetlands (DAWE, 2012). Climate change is projected to substantially reduce water resources in the MDB (high confidence), 
with the median projection indicating a 20% decline in average annual runoff under 2.2°C average global warming (Figure  11.3) 
(Whetton and Chiew, 2020). This reduction, plus increased demand for water in hot and dry conditions, would increase the already 
intense competition for water (high confidence) (CSIRO, 2008; Hart, 2016).

1611The economic, environmental and social impacts of the 1997–2009 Millennium Drought in the MDB (Chiew and Prosser, 2011; Leblanc 
et al., 2012; van Dijk et al., 2013) and projections of a drier future under climate change have accelerated significant water policy reforms 
costing more than AUD$12 billion (Bark et al., 2014; Docker and Robinson, 2014; Hart, 2016). These reforms included the development 
of a Basin Plan (MDBA, 2011; MDBA, 2012) requiring consistent regional water resource plans (MDBA, 2011; MDBA, 2012; MDBA, 
2013) and environmental watering strategies (MDBA, 2014) across the MDB. Despite contestation, the reforms have resulted in some 
substantive achievements, including returning an equivalent of about one-fifth of consumptive water to the environment through the 
purchase of irrigation water entitlements and infrastructure projects (medium confidence) (Hart, 2016; Gawne et al., 2020; MDBA, 2020).

However, the overall impacts of these water management initiatives are difficult to measure due to hydroclimatic variability, time lags 
and environmental, social and institutional complexity (Crase, 2011; Bark et al., 2014; Docker and Robinson, 2014; MDBA, 2020).
Reform initiatives such as water markets, improving agriculture water use efficiency (Koech and Langat, 2018), and increasing 
environmental water are helping buffer the system against droughts (medium confidence) (Moyle et al., 2017), but they can also be 
maladaptive by perpetuating unsustainable water and land use under ongoing climate change. While water markets can allow users 
to adapt and shift water to higher value uses, they can also have adverse impacts unless supported by wider policy goals and planning 
processes (Wheeler et al., 2013; Kirby et al., 2014; Grafton et al., 2016; Qureshi et al., 2018).

Adapting MDB management to climate risks is an escalating challenge, with the projected decline in runoff being potentially greater than 
the water recovered for the environment (Chiew et al., 2017). While the Basin Plan includes mechanisms for climate risk management 
(Neave et al., 2015), it does not require altering pre-existing rules that distribute the impacts of anticipated reductions in water resources 
between users (Hart, 2016; Capon and Capon, 2017; Alexandra, 2020). The intense drought conditions in 2017–2019 (BoM, 2021b), the 
South Australian Royal Commission investigation into the MDB reforms (SA Government, 2019b) and major fish kills in the lower Darling 
River in the summer of 2018/2019 (AAS, 2019; Vertessy et al., 2019) have increased concerns about the Basin Plan’s climate adaptation 
deficit (medium confidence). Consequently, the MDB Authority (MDBA) is undertaking an assessment of climate change risks and

developing adaptation mechanisms (MDBA, 2019) that can feed into the revisions to the Basin Plan scheduled for 2026. The MDB reforms 
to date illustrate the difficulties in integrating climate change science and projections into management (Alexandra, 2018; Alexandra, 
2020). Anticipatory and participatory governance and adaptive management approaches supported by structural and institutional 
reforms would support the effectiveness of the reforms (Abel et al., 2016; Alexandra, 2019; Hassenforder and Barone, 2019; Marshall and 
Lobry de Bruyn, 2021).
(a) The Murray-Darling Basin (b) Average annual river flows in the Basin
     under pre-development conditions 
assessment location
water availability
1234 Regional water
availability (Gl/y)
based on
locations
Figure Box 11.3.1 |  (A) The Murray-Darling Basin, and (B) average annual river flows in the basin under pre-development conditions (from (CSIRO,

2008) showing that most of the runoff comes from the southeastern highlands. The borders show key drainage basins.Box 11.3 (continued)

11
1612Chapter 11 Australasia
water reduction advisories and 5 to 8 weeks’ waiting time for water 
tank refills and water rationing. The Havelock North water supply 
contamination, which arose after an extreme rainfall event (DIA, 
2017a; DIA, 2017b), was exacerbated by fragmented governance and 
led to passage of the Taumata Arawai-Water Services Regulator Act 
of 2020 and the Water Services Bill of 2020 aimed at the protection 
of source water. The 2017 update to the National Policy Statement for 
Freshwater Management contains guidelines for implementation at the 
regional level (MfE, 2017b), including consideration of climate change, 
which creates opportunities for adaptation. However, there remain tensions between land, water and people which are exacerbated by 
climate changes and have yet to be addressed (Box  11.5). The first 
National Adaptation Plan and the Resource Management law reform 
have the potential to help resolve these tensions (11.7.1) (CCATWG, 
2017; MfE, 2020a).

Box 11.4 | Changing Flood Risk
Pluvial (flash flood from high intensity rainfall) and fluvial (river) flooding are the most costly natural disasters in Australia, averaging 
AUD$8.8 billion per year (Deloitte, 2017b). In New Zealand, insured damages for the 12 costliest flood events from 2007 to 2017 exceeded 
NZD$472 million, of which NZD$140 million has been attributed to anthropogenic climate change (Frame et al., 2020). Extreme rainfall 
intensity in northern Australia and New Zealand has been increasing, particularly for shorter (sub-daily) duration and more extreme high 
rainfall (high confidence) (Westra and Sisson, 2011; Griffiths, 2013; Laz et al., 2014; Rosier et al., 2015) (Table 11.2b). Changes are also 
occurring in spatial and temporal patterns and seasonality (Wasko and Sharma, 2015; Zheng et al., 2015; Wasko et al., 2016).
Extreme rainfall is projected to become more intense (high confidence), but the magnitude of change is uncertain (Evans and McCabe,

2013; Bao et al., 2017) (Table 11.3). The insured damage in New Zealand from more intense extreme rainfall under RCP8.5 is projected 
to increase 25% by 2080–2100 (Pastor-Paz et al., 2020). In urban areas, extreme rainfall intensity is projected to increase pluvial flood 
risk (high confidence). In New Zealand, 20,000 km2 of land, 675,000 people, and 411,000 buildings with a NZD$135 billion replacement 
value are exposed to flood risk (Paulik et al., 2019a).
In non-urban areas, where the flood response is also dependent on antecedent catchment conditions (Johnson et al., 2016; Sharma et al., 
2018), there is no evidence of increasing flood magnitudes in Australia (Ishak et al., 2013; Zhang et al., 2016; Bennett et al., 2018), except 
for the most extreme events (Sharma et al., 2018; Wasko and Nathan, 2019). Modelling studies project increases in flood magnitudes in

northern and eastern Australia and in western and northern New Zealand (high confidence) (Hirabayashi et al., 2013; Collins et al., 2018a; 
Do et al., 2020). The change in flood magnitude in southern Australia is uncertain because of the compensating effect of more intense 
extreme rainfall versus projected drier antecedent conditions (Johnson et al., 2016; Pedruco et al., 2018; Wasko and Nathan, 2019). Higher 
rainfall intensity and peak flows also increase erosion and sediment and nutrient loads in waterways (Lough et al., 2015) and exacerbate 
problems from ageing stormwater and wastewater infrastructure (Jollands et al., 2007; WSAA, 2016; Hughes et al., 2021).
There is some recognition of the need for flood management and planning to adapt to climate change (medium confidence) (COAG, 
2011; CCATWG, 2018; CoA, 2020d). Australian flood estimation guidelines recommend a 5% increase in design rainfall intensity per

degree global average warming (Bates et al., 2015). In New Zealand, the recommended increase ranges from 5% to more than 10% for 
shorter-duration and longer-return-period storms (MfE, 2010; Carey-Smith et al., 2018). Both guidelines also indicate the potential for 
higher increases in extreme rainfall intensity.
Adaptation to reduce flooding and its impacts have included improved flood forecasting (Vertessy, 2013; BoM, 2016) and risk management 
(AIDR, 2017), accommodating risk through raising floor levels and sealing external doors (Queensland Government, 2011; Wang et al., 
2015), deploying temporary levee structures and reducing risk through spatial planning and relocation. Adaptation options in urban 
areas include improved stormwater management (Hettiarachchi et al., 2019; Matteo et al., 2019), ecosystem-based approaches such as 
maintaining floodplains, restoring wetlands and retrofitting existing flood control systems to attenuate flows, and water-sensitive urban

design (WSAA, 2016; Radcliffe et al., 2017; Radhakrishnan et al., 2017; Rogers et al., 2020b).
Adaptation to changing flood risks is currently mostly reactive and incremental in response to flood and heavy rainfall events (high 
confidence). For example, the 2010–2011 flooding in eastern Australia resulted in changes to reservoir operations to mitigate floods 
(QFCI, 2012) and insurance practice to cover flood damages (Phelan, 2011; Phelan et al., 2011; QFCI, 2012; Schuster, 2013). Nevertheless, 
adaptation planning that is pre-emptive and incorporates uncertainties into flood projections is emerging (medium confidence) 
(Schumacher, 2020). Examples from New Zealand include the use of Dynamic Adaptive Pathways Planning (DAPP) (Lawrence and 
Haasnoot, 2017) with Real Options assessment (Infometrics and PSConsulting, 2015) and designing decision signals and triggers to

161311.3.4 Food, Fibre, Ecosystem Products
The food, fibre and ecosystem product sectors are economically 
important in the region. Agriculture contributes around 4% of New 
Zealand GDP and 2% of Australian GDP and over 50% of New 
Zealand’s and 11% of Australia’s exports (NZ Treasury, 2016; Jackson 
et al., 2020). Forestry contributes 1% of New Zealand GDP and 0.5% 
Australian GDP (NZ Treasury, 2016; Whittle, 2019). With processing 
and indirect effects, the primary sector of New Zealand contributes 
25% of GDP (Saunders et al., 2016). The region has the lowest level 
of agricultural subsidies across the OECD (OECD, 2017) and highly 
responsive producers to market drivers but limited strategic, longer-
term approaches to environmental challenges and adaptation 
(Wreford et  al., 2019). Both countries receive government financial 
drought assistance (Pomeroy, 2015; Downing et al., 2016).
Impacts resulting from climate change are observed across sectors and

the region (high confidence). While more intense changes are observed 
in Australia, New Zealand is also experiencing impacts, including the 
economic impacts of drought attributable to climate change (Frame 
et  al. 2020). Overall, modelling indicates that negative impacts will 
intensify with increased levels of warming in both countries, with 
declining crop yield and quality, and negative effects on livestock 
production and forestry. Although benefits are identified, particularly 
in the short term for New Zealand (MfE, 2020a), an absence of studies 
that consider the totality of climatic variables, including extremes, 
moderate the benefits identified from considering only selected 
variables and systems in isolation.
Incremental adaptation is occurring (Hochman et  al., 2017; Hughes 
and Lawson, 2017; Hughes and Gooday, 2021). In the longer term, 
transformative adaptation, including land use change, will be required

(Cradock-Henry et al., 2020a), both as a result of sectoral adaptations 
and mitigation (medium confidence) (Grundy et  al., 2016). Specific 
changes are context specific and challenging to project (Bryan 
et  al., 2016). Future adaptive capacity may be limited by declining 
institutional and community capacity resulting from high debt, 
unavailability of insurance, increasing regulatory requirements and 
funding mechanisms that lock in ongoing exposure to climate risk, 
creating mental health impacts (Rickards et al., 2014; Wiseman and 
Bardsley, 2016; McNamara and Buggy, 2017; McNamara et al., 2017; 
Moyle et al., 2017; Robinson et al., 2018; Ma et al., 2020; Yazd et al., 
2020).11.3.4.1 Field Crops and Horticulture
11.3.4.1.1 Observed impacts
Drought, heat and frost in recent decades have shown the vulnerability 
of Australian field crops and horticulture to climate change (Cai 
et  al., 2014; Howden et  al., 2014; CSIRO and BOM, 2015; Lobell

et  al., 2015; Hughes and Lawson, 2017; King et  al., 2017; Webb 
et al., 2017; Harris et al., 2020) as recognised by policymakers (CoA, 
2019a) (high confidence). Northern Australia’s agricultural output 
losses are on average 19% each year due to drought (Thi Tran et al., 
2016). In southern Australia, the frequency of frost has been relatively 
unchanged since the 1980s (Dittus et  al., 2014; Pepler et  al., 2018; 
BoM and CSIRO, 2020). Drier winters have increased the irrigation 
requirement for wine grapes (Bonada et al., 2020), while smoke from 
the 2019/20 fires, which occurred early in the season, caused significant 
taint damage (Jiang et al., 2021). In New Zealand, reduced winter chill 
has a compounded impact on the kiwifruit industry, resulting in early 
harvest and increased energy demand for refrigeration and port access 
problems (Cradock-Henry et al., 2019) (11.5).
Across all types of agriculture, drought and its physical flow-on effects

have caused financial and emotional disruption and stress in farm 
households and communities (Austin et al., 2018; Bryant and Garnham, 
2018; Yazd et  al., 2019) (11.3.6). Severe and uncertain climate 
conditions are statistically associated with increases in farmer suicide 
(Crnek-Georgeson et  al., 2017; Perceval et  al., 2019). Rural women 
often carry extra stress and responsibilities, including increased unpaid 
and paid work and emotional load (Whittenbury, 2013; Hanigan et al., 
2018; Rich et al., 2018).
11.3.4.1.2 Projected impacts
Australian crop yields are projected to decline due to hotter and drier 
conditions, including intense heat spikes (high confidence) (Anwar 
et  al., 2015; Lobell et  al., 2015; Prokopy et  al., 2015; Dreccer et  al., 
2018; Nuttall et al., 2018; Wang et al., 2018a). Interactions of heat and 
drought could lead to even greater losses than heat alone (Sadras and 
Dreccer, 2015; Hunt et al., 2018). Australian wheat yields are projected

to decline by 2050, with a median yield decline of up to 30% in 
southwest Australia and up to 15% in southern Australia, with possible 
increases and decreases in the east (Taylor et al., 2018; Wang et al., 
2018a). In temperate fruit, accumulated winter chill for horticulture 
is projected to further decline (Darbyshire et  al., 2016). Winegrape 
maturity is projected to occur earlier due to warmer temperatures 
(high confidence) (Webb et al., 2014; van Leeuwen and Darriet, 2016; monitor changes before physical and coping thresholds are reached (Stephens et al., 2018). Implementing adaptive flood risk management 
relies upon an understanding of how such risks change in uncertain and ambiguous ways necessitating adaptive and robust decision-
making processes. These can enable learning through participatory adaptive pathways approaches (Lawrence and Haasnoot, 2017;

Bosomworth and Gaillard, 2019) and through coordination across different levels of government and statutory mandates, adaptation 
funding and individual and community adaptations (Glavovic et al., 2010; Boston and Lawrence, 2018; McNicol, 2021).Box 11.4 (continued)

11
1614Chapter 11 Australasia
Jarvis et al., 2018; Ausseil et al., 2019b), leading to potential changes 
in wine style (Bonada et al., 2015). Rice is susceptible to heat stress, 
and average grain yield losses across rice varieties range from 83% 
to 53% in experimental trials when heat stress is applied during 
plant emergence and grain fill stages (Ali et al., 2019). In Tasmania, 
wheat yields are projected to increase, particularly at sites presently 
temperature-limited (Phelan et al., 2014).
New Zealand evidence on impacts across crops is very limited. 
Precipitation and temperature changes alone show minor effects on 
crop yield, and winter yields of some crops may increase (e.g., wheat, 
maize) (Ausseil et al., 2019b). For temperate fruit, loss of winter chill 
may reduce yields in some regions and trigger impacts across supply 
chains (Cradock-Henry et al., 2019) (11.5.1). Increased pathogens could 
damage the cut flower, guava and feijoa fruit growing and the honey

and related industries (Lawrence et al., 2016). The combined effects of 
changes in seasonality, temperature, precipitation, water availability 
and extremes, such as drought, have the potential to escalate impacts, 
but understanding of these effects is limited.
Other climate-change-related factors complicate crop climate 
responses. When CO 2 was elevated from present-day levels of 400 to 
550 ppm in trials, yields of rainfed wheat, field pea and lentil increased 
approximately 25% (0–70%). However, there was a 6% reduction in 
wheat protein that could not be offset by additional nitrogen fertilizer 
(O’Leary et al., 2015; Fitzgerald et al., 2016; Tausz et al., 2017). Elevated 
CO 2 will worsen some pest and disease pressures, for example, 
barley yellow dwarf virus impacts on wheat (Trębicki et  al., 2015). 
Warmer temperatures are also expanding the potential range of the 
Queensland fruit fly, including into New Zealand (Aguilar et al., 2015a),

threatening the horticulture industry (Sultana et  al., 2017; Sultana 
et al., 2020). Some crop pests (e.g., the oat aphid) are projected to be 
negatively affected by climate change (Macfadyen et al., 2018), but 
so too are beneficial insects. There is large uncertainty in rainfall and 
crop projections for northern Australia (Table 11.3). For sugarcane, an 
impact assessment for CO 2 at 734 ppm using the A2 emission scenario 
at Ayr in Queensland projected modest yield increases (Singels et al., 
2014). Climate change is projected to adversely impact tropical fruit 
crops such as mangoes through higher minimum and maximum 
temperatures, reducing the number of inductive days for flowering 
(Clonan et al., 2020).
Climate change is projected to shift agro-ecological zones (high 
confidence) (Lenoir and Svenning, 2015; Scheffers et al., 2016). This 
includes the climatically determined cropping strip bounded by the 
inner arid rangelands and the wetter coast or mountain ranges in

mainland Australia (Nidumolu et al., 2012; Eagles et al., 2014; Tozer 
et al., 2014). A narrowing of grain-growing regions is projected with 
a shift of the inner margin towards the coast under drier and warmer 
conditions (Nidumolu et al., 2012; Fletcher et al., 2020). The economic 
impact of the shift depends on adaptation (Sanderson et  al., 2015; 
Hunt et al., 2019) and how resources, support industries, infrastructure 
and settlements adapt. Shifts in agro-ecological zones present some 
opportunities, for example warming is projected to be beneficial for 
wine production in Tasmania (Harris et al., 2020).11.3.4.1.3 Adaptation
Some farmers are adapting to drier and warmer conditions through 
more effective capture of non-growing-season rainfall (e.g., stubble 
retention to store soil water), improved water use efficiency and 
matching sowing times and cultivars to the environment (high 
confidence) (Kirkegaard and Hunt, 2011; Fitzer et al., 2019; Haensch

et  al., 2021). Observed adaptations include new technologies that 
improve resource efficiencies, professional knowledge and skills 
development, new farmer and community networks and diversification 
of business and household income (Ghahramani et  al., 2015; De 
et al., 2016). For Australian wheat, earlier sowing and longer-season 
cultivars may increase yield by 2–4% by 2050, with a range of −7 to 
+2% by 2090 (Wang et al., 2018a). In the wheat industry, breeding 
for improved reproductive frost tolerance remains a priority (Lobell 
et al., 2015). Modelling suggests that, since 1990, farm management 
has held Australian wheat yields constant, but declining rainfall and 
increasing temperature may have contributed to a 27% decline in 
simulated potential Australian wheat yield (Hochman et al., 2017).
Other observed incremental adaptations include later pruning in the 
grape industry to spread harvest period and partially restore wine

balance, with neutral effects on yield and cost (Moran et  al., 2019; 
Ausseil et al., 2021). The cotton sector increasingly requires shifts in 
sowing dates to avoid financial impacts (Luo et al., 2017). During years 
of low water availability, rice growers have been trading water and/or 
shifting to dry land farming (Mushtaq, 2016).
Growers in New Zealand are changing the timing of their operations, 
growing crops within covered enclosures and purchasing insurance 
(Cradock-Henry and McKusker, 2015) Teixeira et al. 2018). Investment 
of capital in irrigation infrastructure has increased (Cradock-Henry 
et al., 2018a), although its effectiveness as an adaptation depends on 
water availability (Box 11.5). In industries based on long-lived plants, 
such as the kiwifruit and grape industries, many of the adaptations 
(e.g., breeding and growing heat-adapted and disease-resistant 
varieties) have long lead times and require greater investment than

in the cropping sector (Cradock-Henry et al., 2020a). While breeding 
programmes for traits with enhanced resilience to future climates 
are beginning, there is little evidence of  strategic industry planning 
(Cradock-Henry et al., 2018a).
For drought management, balancing near-term needs with long-term 
adaptation to increasing aridity is essential (Downing et  al., 2016). 
Insufficient and maladaptive decisions can have far-reaching effects, 
including changes to resources, infrastructure, services and supply 
chains to which others must adapt (Fleming et al., 2015; Graham et al., 
2018). While there is potential for a greater proportion of agriculture 
to be located to northern Australia, there are significant and complex 
agronomic, environmental, institutional, financial and social challenges 
for successful transformation, including the risk of disruption (medium 
confidence) (Jakku et al., 2016).

161511.3.4.2 Livestock
11.3.4.2.1 Observed impacts
Both the seasonality and annual production of pasture is changing (high 
confidence). In many regions, warming is increasing winter pasture 
growth (Lieffering, 2016); the effects on spring growth are more mixed, 
with some regions experiencing increased growth (Newton et al., 2014) 
and others experiencing reduced spring growth (Perera et al., 2020). 
Droughts are causing economic damage to livestock enterprises, with 
drought and market prices significantly affecting profit (Hughes et al., 
2019a), in addition to the impacts on animal health and the livelihoods 
of pastoralists, periods of drought contribute to land degradation, 
particularly in the cattle regions of northern Australia (Marshall, 2015). 
Heat load in cattle leads to reduced growth rates and reproduction, and 
extreme heat waves can lead to death (Lees et al., 2019; Harrington,

2020). Temperatures over 32°C reduce ewe and ram fertility along with 
the birth weight of lambs (van Wettere et al., 2021).
11.3.4.2.2 Projected impacts
Some areas may experience increased pasture growth, but others 
may experience a decrease that cannot be fully offset by adaptation 
(high confidence) (Moore and Ghahramani, 2013; Lieffering, 2016; 
Kalaugher et al., 2017). Climate change may modify the seasonality 
of pasture growth rates more than annual yields in New Zealand 
(Lieffering, 2016). In eastern parts of Queensland, climate change 
impacts on pasture growth are equivocal, with simple empirical 
models suggesting a decrease in net primary productivity (Liu et al., 
2017), while mechanistic models that include increases in length 
of the growing season and the beneficial effects of CO 2 fertilisation 
indicate increases in pasture growth (Cobon et al., 2020). In Tasmania, 
annual pasture production is projected to increase by 13–16%, even

with summer growth projected to decline with increased interannual 
variability, resulting in a projected increase in milk yields by 3–16% per 
annum (Phelan et al., 2015).
Extreme climatic events (droughts, floods and heatwaves) are 
projected to adversely impact productivity for livestock systems 
(medium confidence). This includes reduced pasture growth rates 
between 3–23% by 2070 from late spring to autumn and elevated 
growth in winter and early spring (Cullen et al., 2009; Hennessy et al., 
2016; Chang-Fung-Martel et al., 2017). Heavy rainfall and storms are 
projected to lead to increased erosion, particularly in extensively grazed 
systems on steeper land, reducing productivity for decades, reducing 
soil carbon (Orwin et al., 2015) and increasing sedimentation. Increased 
heat stress in livestock is projected to decrease milk production and 
livestock reproduction rates (high confidence) (Nidumolu et al., 2014;

Ausseil et al., 2019b; Lees et al., 2019). In Australia, the average number 
of moderate to severe heat stress days for livestock is projected to 
increase 12–15 d by 2025 and 31–42 d by 2050 compared to 1970–
2000 (Nidumolu et al., 2014). In New Zealand, an extra 5 (RCP2.6) to 
7 (RCP8.5) moderate heat stress days per year are projected for 2046–
2060 (high confidence) (Ausseil et al., 2019b), which would especially 
affect animals transported long distances (Zhang and Phillips, 2019) and 
strain the cold chains needed to deliver meat and dairy products safely. 
The distribution of existing and new pests and diseases are projected to increase, for example, new tick- and mosquito-borne diseases such as 
bovine ephemeral fever (Kean et al., 2015).
11.3.4.2.3 Adaptation
Adaptations in both grazing and confined beef cattle systems require 
enhanced decision-making skills capable of integrating biophysical, 
social and economic considerations (high confidence). Social learning

networks that support integration of lessons learned from early 
adopters and involvement with science-based organisations can help 
enhance decision-making and climate adaptation planning (Derner 
et al., 2018). Pasture management adaptations for livestock production 
include deeper rooted pasture species in higher rainfall regions 
(Cullen et  al., 2014) and drought-tolerant species (Mathew et  al., 
2018). Soil and land management practices are important in ensuring 
soils maintain their supporting and regulating services (Orwin et al., 
2015). Adaptations in the primary sector in New Zealand are now 
positioned within the requirements of the National Policy Statement 
on Freshwater (MfE, 2020b). Adaptations to manage heat stress in 
livestock include altering the breeding calendar, providing shade and 
sprinklers, altering nutrition and feeding times and more heat-tolerant 
animal breeds (Chang-Fung-Martel et al., 2017; Lees et al., 2019; van 
Wettere et al., 2021).

Beef rangeland systems in Queensland are projected to have benefits 
in the southeast through higher CO 2 and temperatures extending the 
growing season and reducing frost, but a warmer and drier climate in 
the southwest may reduce pasture and livestock production (Cobon 
et  al., 2020). Northern Queensland is most resilient to temperature 
and rainfall changes (production limited by soil fertility) while western/
central west Queensland is most sensitive to rainfall changes, that is, 
low rainfall is associated with lower productivity (Cobon et al., 2020). 
The social context of climate change impacts and the processes 
shaping vulnerability and adaptation, especially at the scale of the 
individual, are critical to successful adaptation efforts (Marshall and 
Stokes, 2014).
11.3.4.3 Forestry
11.3.4.3.1 Observed impacts
Climate change may have increased tree mortality in Australia’s 
commercial Eucalyptus globulus and Pinus radiata plantation forests

(Crous et al., 2013; Pinkard et al., 2014). Climate warming enhanced 
tree water use and vulnerability to heat (Crous et al., 2013). Increases 
in fire frequency and intensity in forests of southern Australia are 
leading to diminishing resources available for timber production 
(Pinkard et al., 2014) (Box 11.1).
11.3.4.3.2 Projected impacts
The projected declines in rainfall in far southwest and far southeast 
mainland Australia are projected to reduce plantation forest yields 
(high confidence). Warmer temperatures are projected to reduce forest 
growth in hotter regions (between 7 and 25%), especially where 
species are grown at the upper range of their temperature tolerances, 
and increase plantation forest growth (>15%) in cooler margins like

11
1616Chapter 11 Australasia
Tasmania and the Victorian highlands (2030, A2); emission scenario A2 
creates a warming trajectory slightly higher than the RCP6.0 warming 
scenario, but less than RCP8.5 (Rogelj et al., 2012; Battaglia and Bruce, 
2017). Elevated CO 2 is projected to increase forest growth if other 
biophysical factors are not limiting (medium confidence) (Quentin 
et al., 2015; Duan et al., 2018).
Forestry plantations are projected to be negatively impacted from 
increases in fire weather (Box 11.1), particularly in southern Australia 
(high confidence) (Pinkard et al., 2014).  Increased pest damage due 
to temperature increases may reduce eucalyptus and pine plantation 
growth by as much as 40% in some Australian environments by 2050 
(Pinkard et  al., 2014). Increased heat and water stress may enhance 
insect pest defoliation for P . radiata in Australia (e.g., Sirex noctilio,  Ips 
grandicollis and Essigella californica) (Mead, 2013; Pinkard et al., 2014).

Combined impacts from heavy rainfall, soil erosion, drought, fire and 
pest incursions are projected to increase risks to the permanence of 
carbon offset and removal strategies in New Zealand for meeting its 
climate change targets (PCE, 2019; Watt et al., 2019; Anderegg et al., 
2020; Schenuit et al., 2021). Effective management of the interactions 
between mitigation and adaptation policies can be achieved through 
governance and institutions, including Māori tribal organisations 
and sectoral adaptation, to ensure effective and continued carbon 
sequestration and storage as the climate changes (medium confidence) 
(Lawrence et al., 2020b) (11.4.2) (Box 11.5). The productivity of radiata 
pine (P . radiata D. Don) in New Zealand due to higher CO 2 is projected to 
increase by 19% by 2040 and 37% by 2090, but greater wind damage 
to trees is expected (Watt et  al., 2019). Changes in the distribution 
of existing weeds, pests and diseases with potential establishment

of new sub-tropical pests and seasonal invasions are projected (Kean 
et al., 2015; Watt et al., 2019; MfE, 2020a). Increased pathogens such 
as pitch canker, red needle cast and North American bark beetles could 
damage plantations (Hauraki Gulf Forum, 2017; Lantschner, 2017; 
Watt et al., 2019).
11.3.4.3.3 Adaptation
Adaptation options include increased investment in monitoring forest 
condition and functioning; early detection and management of insect 
pests, diseases and invasive species; improved selection of land with 
appropriate growing conditions for plantation timber production 
under current and future conditions; trialling new species and genetic 
varieties; changing the timing and frequency of planned fuel reduction 
fires; introducing more fire-tolerant tree species where appropriate; 
reducing ignition sources; and maintaining access and emergency 
response capacity (Boulter, 2012; Pinkard et al., 2014; Keenan, 2017).
11.3.4.4 Marine Food
11.3.4.4.1 Observed impacts

The ecological impacts of climate change on fisheries species have 
already emerged (high confidence) (Morrongiello and Thresher, 2015; 
Gervais et al., 2021). This includes loss of habitats for fisheries species 
(Vergés et  al., 2016; Babcock et  al., 2019) and poleward shifts in 
the distribution of barrens-forming urchins (Ling and Keane, 2018) impacting abalone and rock lobster fisheries. The percentage of reef 
as barrens across eastern Tasmania grew from 3.4% to 15.2% from 
2001/2002 to 2016/2017, an approx. 10.5% increase per annum 
over the 15-year period (Ling and Keane, 2018). Oysters farmed from 
wild spat (Sydney rock oysters Saccostrea glomerata) are most at risk 
from climate change, primarily due to observed increases in summer 
temperatures and heatwave-related mortalities (Doubleday et  al., 
2013). The exceptional 2017/2018 summer heatwave caused significant 
losses of farmed salmon in New Zealand, with farm owners seeking

consent to move operations to cooler water (Salinger et al., 2019b).
11.3.4.4.2 Projected impacts
Aquaculture is projected to be more easily adapted than wild fisheries 
to avoid excessive exposure to the physio-chemical stresses from 
acidification, warming and extreme events (Richards et al., 2015). In 
New Zealand, wild and cultured shellfish are identified as being most 
at risk from climate change (Capson and Guinotte, 2014). Changes 
in ocean temperature and acidification and the downstream impacts 
on species distribution, productivity and catch are projected concerns 
(medium confidence) (Law et al., 2016) that impact Māori harvesting 
of traditional seafood and the social, cultural and educational 
elements of food gathering (mahinga kai) (MfE, 2016). Warm 
temperate hatchery-based finfish species (yellowtail kingfish Seriola 
lalandi) are projected to be the least at risk, because of well-controlled 
environmental conditions in hatcheries and temperature increases,

which are expected to increase growth rates and productivity during 
the grow-out stage (Doubleday et al., 2013). For wild fisheries, multi-
model projections suggest temperate and demersal systems, especially 
invertebrate shallow-water species, would be more strongly affected by 
climate change than tropical and pelagic systems (medium confidence) 
(Pecl et al., 2014; Fulton et al., 2018; Pethybridge et al., 2020). In New 
Zealand waters, available habitat for both albacore tuna and oceanic 
tuna (Cummings et al., 2021) is expected to widen and shift.
11.3.4.4.3 Adaptation
Selective breeding in oysters is projected to be an important global 
adaptation strategy for sustainable shellfish aquaculture that can 
withstand future climate-driven change to habitat acidification (Fitzer 
et al., 2019). Less than a quarter of fisheries management plans for 
99 of Australia’s most important fisheries considered climate change,

and only to a limited degree (Fogarty et al., 2019; Fogarty et al., 2021). 
Implementation of management and policy responses to climate 
change have lagged in part because climate change has not been 
considered as the most pressing issue (Hobday and Cvitanovic, 2017; 
Fogarty et al., 2019; Fogarty et al., 2021) (Cross-Chapter Box MOVING 
SPECIES in Chapter 5).
11.3.5 Cities, Settlements and Infrastructure
Almost 90% of the population of Australia and New Zealand is urban 
(World Bank, 2019). Each country has vibrant and diverse urban, 
rural and remote settlements, with some highly disadvantaged areas 
isolated by distance and limited infrastructure and services (Argent 
et al., 2014; Charles-Edwards et al., 2018; Spector et al., 2019). Some

11Australasia Chapter 11
1617areas in northern Australia and New Zealand, especially those with 
higher proportions of Indigenous inhabitants, face severe housing, health, education, employment and services issues (Kotey, 2015), which increases their vulnerability to climate change.
Infrastructure within and between cities and settlements is critical for

activity across all sectors, with interdependencies increasing exposure to climate hazards (11.5.1). Previous planning horizons for existing infrastructure are compromised by now having to accommodate ongoing sea level rise (SLR), warming and increasing frequency of extreme rainfall and storm events (Climate Institute, 2012; MfE, 2017a). There is almost no information on the costs and benefits of adapting vulnerable and exposed infrastructure in Australia or New Zealand. Given the value of that infrastructure and the rising damage costs, this represents a large knowledge gap that has led to an adaptation investment deficit.11.3.5.1  Observed Impacts

Critical infrastructure, cities and settlements are being increasingly affected by chronic and acute climate hazards, including heat, drought, fire, pluvial and fluvial flooding and sea level rise (SLR), with consequent effects on many sectors (high confidence) (Instone et al., 2014; Loughnan et  al., 2015; Zografos et  al., 2016; Hughes et  al., 2021). Risks and impacts vary with physical characteristics, location, connectivity and socioeconomic status of settlements because of the ways these influence exposure and vulnerability (high confidence) (Loughnan et al., 2013; MfE, 2020a).
Weather-related disasters are causing significant disruption and 
damage (Paulik et  al., 2019a; CSIRO, 2020; Paulik et  al., 2020). In Australia, during 1987–2016, natural disasters caused an estimated 971 deaths and 4370 injuries, 24,120 people were made homeless Box 11.5 | New Zealand’s Land, Water and People Nexus under a changing climate

New Zealand’s economy, dominated by the primary sector and the tourist industry (pre-COVID), relies upon a ‘clean green’ image of water, natural ecosystems and pristine landscapes (Foote et al., 2015; Roche and Argent, 2015; Hayes and Lovelock, 2017). Water is highly valued by Māori for its mauri or life force and for its intrinsic values and multiple uses (Harmsworth et al., 2016). Increasingly, these diverse values are coming into conflict (Hopkins et al., 2015) due to increasing pressures from how land is used and managed and the effects on water availability and quality. Such tensions will be further challenged as temperatures rise and extreme events intensify beyond what has been experienced, thus stressing current adaptive capacities (high confidence) (Hughey and Becken, 2014; Cradock-Henry and McKusker, 2015; Hopkins et al., 2015; MfE and Stats NZ, 2021) (11.2.2; 11.3.4).

Irrigation has increasingly been used to enhance primary sector productivity and regional economic development (Srinivasan et  al., 
2017; Fielke and Srinivasan, 2018; MfE and Stats NZ, 2021) (Srinivasan et al., 2017; Fielke and Srinivasan, 2018; MfE and Stats NZ, 2021). Pressure for long-term access to groundwater or large-scale water storage is increasing to ensure the ongoing viability of the primary sector as the climate changes. While investment in irrigation infrastructure may reduce climate change impacts in the short term, maladaptive outcomes cannot be ruled out longer term, which means that focusing attention now on adaptive and transformational measures can help increase climate resilience in areas exposed to increasing drought and climate extremes that disrupt production (medium confidence) (Abel et al., 2016; Cradock-Henry et al., 2019) (Yletyinen et al., 2019).

Furthermore, overallocation raises further tensions from competing uses of water such as for horticulture and urban water supplies,

as well as for ecological requirements. The deterioration of water quality and loss of places of social, economic, cultural and spiritual significance creates increasing tension for Māori in particular (Harmsworth et al., 2016; Salmon, 2019; MfE and Stats NZ, 2021). Public concern has increased over the deterioration of New Zealand’s waterways and the profiting of some land uses at the expense of environmental quality and human health—tensions that make adaptation to climate change more challenging (Duncan, 2014; Foote et al., 2015; Scarsbrook and Melland, 2015; McDowell et al., 2016; McKergow et al., 2016; Greenhalgh and Samarasinghe, 2018). A lack of precautionary governance of water resources linked to unsustainable land use practices degrading water quality (Scarsbrook and Melland, 2015; Salmon, 2019) highlights the role that foresight could play in managing the nexus between land, water and people in a changing climate (11.3.3). Adaptive planning holds potential for navigating

these multi-dimensional challenges (Sharma-Wallace et al., 2018; Cradock-Henry and Fountain, 2019; Hurlbert et al., 2019) (11.7).

Furthermore, land and, in particular, plantation and native forests play a critical role in meeting New Zealand’s emissions reduction goals.

However, the persistence of land and forests as a carbon sink is uncertain, and the sequestered carbon is at risk from future loss resulting from climate change impacts, including from increased fire, drought and pest incursions, storms and wind (IPCC, 2019a; PCE, 2019; Watt et al., 2019; Anderegg et al., 2020) (11.3.4.3), underlining the importance of interactions between mitigation and adaptation policy and implementation. Integrated climate change policies across biodiversity, water quality, water availability, land use and forestry for mitigation can support the management of land use, water and people conflicts, but there is little evidence of such coordinated policies (Cradock-Henry et al., 2018b; Wreford et al., 2019). Implementation of the National Policy Statement for Freshwater Management 2020 (MfE, 2020b) and the National Adaptation Plan (due out in August 2022) present opportunities for such interconnections and diverse values to be addressed, as well as enabling sector

and community benefits to be realised across New Zealand (Awatere et al., 2018; Lawrence et al., 2020b).

11
1618Chapter 11 Australasia
and about 9  million people were affected (Deloitte, 2017a). More 
than 50% of these deaths and injuries came from heatwaves in 
cities and 22% from fires. During the 2007–2016 period, Australia 
natural disaster costs averaged AUD$18.2 billion yr−1, with the largest 
contributions from floods (AUD$8.8  billion), followed by cyclones 
(AUD$3.1 billion), hail (AUD$2.9 billion), storms (AUD$2.3 billion) and 
fires (AUD$1.1 billion) (Deloitte, 2017a). The Australian fires in 2019–
2020 cost over AUD$8 billion, with devastating impacts on settlements 
and infrastructure (Box 11.1)
Sea level rise affects many interdependent systems in cities and 
settlements, which increases the potential for compounding and 
cascading impacts (11.5.1). Seaports, airports, water treatment plants, 
desalination plants, roads and railways are increasingly exposed to 
sea level rise (SLR) (very high confidence), impacting their longevity

and levels of service and maintenance (high confidence) (McEvoy and 
Mullett, 2014; Woodroffe et al., 2014; PCE, 2015; Ranasinghe, 2016; 
Newton et  al., 2018; Paulik et  al., 2020) (Box  11.6). Compounding 
coastal hazards in New Zealand, such as elevated water tables 
associated with rising sea level and intense rainfall (Morgan and 
Werner, 2015; McBride et  al., 2016; White et  al., 2017; Hughes 
et  al., 2021), are exerting pressure on stormwater and wastewater 
infrastructure and drinking water supply and quality (MfE, 2020a).
Extreme heat events exacerbate problems for vulnerable people and 
infrastructure in urban Australia, where urban heat is superimposed 
upon regional warming, and there are adverse impacts for population 
and vegetation health, particularly for socioeconomically disadvantaged 
groups (Tapper et al., 2014; Heaviside et al., 2017; Filho et al., 2018; 
Gebert et  al., 2018; Rogers et  al., 2018; Longden, 2019; Marchionni

et al., 2019; Tapper, 2021) (11.3.6), energy demand, energy supply and 
infrastructure (very high confidence) (Newton et  al., 2018) (11.3.10). 
Extreme heat is increasingly threatening liveability in some rural areas 
in Australia (Turton, 2017), particularly given their reliance on outside 
physical work and older populations. Settlement design and the level of 
greening interact with climate change to influence local heating levels 
(Tapper et al., 2014; Wong et al., 2020; Tapper, 2021).
Floods cause major damage. The floods of early 2019 in North Queensland 
cost AUD$5.68  billion (Deloitte, 2019), while Cyclone Yasi and the 
Queensland floods of 2011 cost A$6.9 billion (Deloitte, 2016). Floodplains 
in New Zealand have considerably higher overall national exposure of 
buildings and population than coasts (Paulik et al., 2019a) (Box 11.4). The 
insured losses from the 12 costliest floods in New Zealand from 2007 to

2017 totalled NZD$471.56 million, of which NZD$140.48 million could 
be attributed to climate change (Frame et al., 2020).
Climatic extremes are exacerbating existing vulnerabilities (high 
confidence). Long supply chains, poorly maintained infrastructure, 
social disadvantage and poor health and lack of skilled workers 
(Eldridge and Beecham, 2018; Mathew et al., 2018; Rolfe et al., 2020) 
are contributing to serious stress and disruption (Smith and Lawrence, 
2014; Kiem et al., 2016). In many rural settlements, population ageing 
and reliance on an overstretched volunteer base for recovery from 
extreme events are increasing vulnerability to climate change (Astill 
and Miller, 2018; Davies et  al., 2018). Recovery from long, intense, 
more frequent and compounding climatic events in rural areas has been disrupted by the erosion of natural, financial, built, human and 
social capital (De et al., 2016; Sheng and Xu, 2019). Delayed recovery

from extreme climatic events has been compounded by long-term 
displacement, which in turn prolongs the impacts (Matthews et  al., 
2019). Severe droughts have contributed to poor health outcomes for 
rural communities, including extreme stress and suicide (Beautrais, 
2018; Perceval et  al., 2019). In Australia, competition among water 
users has left some rural communities experiencing extreme water 
shortage and insecurity with associated health impacts (Wheeler et al., 
2018; Judd, 2019) (Box 11.3).
11.3.5.2 Projected Impacts
Changes in heat waves, droughts, fire weather, heavy rainfall, storms 
and sea level rise (SLR) are projected to increase negative impacts for 
cities, settlements and infrastructure (high confidence) (Table  11.3a, 
Table 11.3b; Box 11.1, Box 11.3, Box 11.4).
Increased floods, coastal inundation (assuming a sea level rise (SLR) 
of 1.6 m by 2100), wildfires, windstorms and heatwaves may cause

property damage in Australia estimated at AUD$91 billion per year by 
2050 and AUD$117 billion per year by 2100 for RCP8.5, while damage-
related loss of property value is estimated at AUD$611 billion by 2050 
and AUD$770  billion by 2100 for RCP8.5 (Steffen et  al., 2019). For a 
1.0-m sea level rise (SLR), the value of exposed assets in New Zealand 
would be NZD$25.5 billion (Box 11.6). For a 1.1-m sea level rise (SLR), 
the value of exposed assets in Australia would be AUD$164–226 billion 
(Box  11.6). These exposure estimates exclude impacts on personal 
livelihood, well-being and lifestyle.
Extreme heat risks are projected to exacerbate existing heat-related 
impacts on human health, vegetation and infrastructure (Tapper et al., 
2014; Tapper, 2021) (11.3.6). In Australia, the annual frequency of days 
over 35°C is projected to increase 20–70% by 2030 (RCP4.5), and 
25–85% (RCP2.6) to 80–350% (RCP8.5) by 2090 (Table  11.3a). For

example, Perth may average 36 d over 35°C by 2030 (RCP4.5). In New 
Zealand, the annual frequency of days over 25°C may increase 20–
60% (RCP2.6) to 50–100% (RCP8.5) by 2040 and 20–60% (RCP2.6) to 
130–350% (RCP8.5) by 2090 (Table 11.3b). For example, Auckland may 
average 39 d over 25°C by 2040 (RCP8.5). Unprecedented extreme 
temperatures, as high as 50°C in Sydney or Melbourne, could occur 
with global warming of 2.0°C (Lewis et al., 2017). Heat-related costs 
for Melbourne during 2012–2051 are estimated at AUD$1.9  billion, 
of which AUD$1.6  billion is human health/mortality costs (AECOM, 
2012). Extreme heat is threatening liveability in some rural areas in 
Australia (Turton, 2017), particularly given their reliance on outside 
physical work and older populations.
Key infrastructure and services face major challenges. Structural metal 
corrosion rates are projected to increase significantly at coastal locations

but decrease inland (Trivedi et al., 2014). A drier climate may decrease 
the rate of deterioration of road pavements, but extreme rainfall events 
and heat pose a significant risk (Taylor and Philp, 2015), especially to 
unsealed roads in northern Australia (CoA, 2015). Critical infrastructure 
on coasts is at risk from sea level rise (SLR) and storm surges (Box 11.6). 
Facilities such as hospitals face weather-related hazards exacerbated 
by climate change and not originally anticipated in building and

1619infrastructure design (Loosemore et al., 2011; Loosemore et al., 2014). 
By 2050, increased risks are projected for the availability and quality of 
potable water supplies, delivery of wastewater and stormwater services 
to communities, transport systems, electricity infrastructure, operating 
municipal landfills and contaminated sites located near rivers and 
the coast (Gilpin et al., 2020; MfE, 2020a; Hughes et al., 2021). These 
then create risks to social cohesion and community well-being from 
displacement of individuals, families and communities, with inequitable 
outcomes for vulnerable groups (Boston and Lawrence, 2018).
11.3.5.3 Adaptation
In cities and settlements, climate adaptation is under way and is 
being led and facilitated by state and local government leadership 
and facilitation, particularly in Australia (high confidence) (Hintz 
et al., 2018; Newton et al., 2018) (Table 11.7, Supplementary Material 
Table SM11.1a).

Effective adaptations to urban heat include spatial planning, expanding 
tree canopy and greenery, shading, sprays and heat-resistant and 
energy-efficient building design, including cool materials and reflective 
or green roofs (very high confidence) (Broadbent et al., 2018; Jacobs 
et al., 2018b; Haddad et al., 2019; Haddad et al., 2020a; Yenneti et al., 
2020; Bartesaghi-Koc et al., 2021; Tapper, 2021). Reducing urban heat 
not only benefits human health but reduces the demand for, and cost 
of, air conditioning (Haddad et al., 2020b) and the risk of electricity 
blackouts (11.3.10).
Adaptation progress is being hampered by current urban 
redevelopment practice and statutory planning guidelines that are 
leading to the removal of critical urban green space (Newton and 
Rogers, 2020). Reform of approaches to urban redevelopment would 
facilitate adaptation (Newton and Rogers, 2020). Several cities in 
Australia and New Zealand are part of the 100 Resilient Cities global

network, which helped facilitate the metropolitan Melbourne Urban 
Forest Strategy across councils (Fastenrath et al., 2019; Coenen et al., 
2020), and in New Zealand, restoration of the urban forest in Hamilton 
is reducing heat stressors (Wallace and Clarkson, 2019). In peri-urban 
zones, adapting to fire risk is a contested issue, raising difficult trade-
offs between heat management, ecological values and fuel reduction 
in treed landscapes (Robinson et al., 2018).
The resilience of Australia’s major cities to flooding and drought 
has been advanced through a range of economic and physical 
interventions. Water-sensitive urban design irrigates vegetation with 
harvested storm water that improves water security, flood risk, carbon 
sequestration, biodiversity and air and water quality and delivers 
cooling that can save human lives in heatwaves (Wong et al., 2020). 
Stormwater harvesting is supported by some councils in New Zealand

and can deliver recycled water for households (Attwater and Derry, 
2017), improving climate resilience and reducing water demand 
(White et al., 2017). Addressing infrastructure vulnerability is essential 
given the long lifetime of assets, criticality of services and high costs of 
maintenance (Chester et al., 2020; Hughes et al., 2021).
Climate risk management is evolving, but adaptive capacity, implemen-
tation, monitoring and evaluation are uneven across all scales of cities, settlements and infrastructure (very high confidence) (Table  11.15a 
and Table  11.15b; Supplementary Material Tables SM11.1a and 
SM11.1b). There is increasing awareness of the need to move from 
incremental coping and defensive coastal strategies (Jongejan et al., 
2016) to transformational adaptation, for example managed retreat 
(Torabi et al., 2018; Hanna, 2019), and to consider the flow-on effects 
(e.g., for housing and employment) (Fatorić et al., 2017; Torabi et al.,

2018). Strategies limited to building household and community self-
reliance (Astill and Miller, 2018) are increasingly inadequate given sys-
temic and interconnected stressors and cascading impacts across in-
terdependent systems (Lawrence et al., 2020b). Integrated approaches 
to climate change adaptation and emissions reduction have potential 
for addressing interdependent systems (e.g., nature-based approaches, 
climate-sensitive urban design, energy and transport systems) (Nor -
man et al., 2021). Climate risk assessment and adaptation guidelines 
have been prepared for transport infrastructure authorities and organ-
isations (Finlayson et al., 2017; Byett et al., 2019; Yenneti et al., 2020).
Infrastructure service vulnerability in New Zealand is supported by 
new institutional adaptations including the Infrastructure Commission 
to develop a 30-year national infrastructure strategy. The Climate 
Change Commission (Climate Change Commission, 2020) has issued

six principles for climate-relevant infrastructure investments and is 
mandated to monitor the National Climate Change Adaptation Plan 
based on the first National Climate Change Risk Assessment (MfE, 
2020a). A National Disaster Resilience Strategy addresses integrated 
planning for risk reduction and awareness-raising in New Zealand 
(Department of the Prime Minister and Cabinet, 2019).
Successive inquiries and reviews highlight potential synergies between 
disaster risk management and climate resilience (11.5.1) (Smith 
and Lawrence, 2018; Ruane, 2020). In Australia, there is a National 
Disaster Risk Reduction Framework (CoA, 2018b) and a National 
Recovery and Resilience Agency (CoA, 2021) that help underpin 
the development of national support systems for rural and regional 
emergency management and associated volunteer sectors (McLennan 
et al., 2016) and wildfire smoke impacts (CoA, 2020e). The National 
Heatwave Framework Working Group uses a Heatwave Forecast

Service, and heatwave early-warning and adaptation systems that 
operate in Adelaide, Melbourne, Sydney and Brisbane have reduced 
potential death rates (Nitschke et al., 2016).
Infrastructure planning is lagging behind international standards 
for climate resilience evaluation and guidance for adaptation to 
climate risk (high confidence) (CSIRO, 2020; Kool et al., 2020; Hughes 
et  al., 2021). Some companies have examined their exposure to 
climate risk and developed strategies to minimise their vulnerability 
(Climate Institute, 2012) (11.3.8). Climate risk assessments have been 
conducted for the electricity sector in both Australia and New Zealand 
(11.3.10). Climate change is considered in Australian infrastructure 
plans for national and regional water supply security, water for 
irrigated agriculture, a coastal hazards adaptation strategy and the 
Tanami Road upgrade (Infrastructure Australia, 2016; Infrastructure 
Australia, 2019; Infrastructure Australia, 2021)

Industry associations are beginning to facilitate climate adaptation 
for infrastructure, including the Australian Green Infrastructure

11
1620Chapter 11 Australasia
Council (CoA, 2015), the Green Building Council of Australia Green 
Star Programme (GBCA, 2020), the Water Services Association of 
Australia, Climate Change Adaptation Guidelines (WSAA, 2016) and 
the Australian Sustainable Built Environment Council Built Environment 
Adaptation Framework (ASBEC, 2012). The Infrastructure Sustainability 
Rating Scheme measures the social, environmental, governance and 
cultural outcomes delivered by more than AUD$160  billion worth of 
infrastructure, and it is projected to deliver a cost-benefit ratio of 1:1.6 
to 1:2.4 during the period 2020–2040 (RPS, 2020). There is scope for 
engagement of industry in transitioning to a low-carbon green economy 
that is adapted to climate change, but less certainty on how to develop 
appropriate business cases (Newton and Newman, 2015).
There are tensions between settlement-scale adaptation options, such 
as managed retreat, that focus on the long term and people’s values,

place attachments, needs and capacities (Gorddard et al., 2016; Fatorić 
et  al., 2017; Graham et  al., 2018; O’Donnell, 2019; Norman et  al., 
2021). Tensions also exist between climate change adaptation and mitigation goals (e.g., current energy efficiency standards in Australian 
buildings can worsen their heat resistance and increase dependence 
on air-conditioning) (Hatvani-Kovacs et al., 2018). Where there is a lack 
of coordination between jurisdictions, there can be flow-on effects 
from failure to adapt, for example in coastal local government areas 
(Dedekorkut-Howes et al., 2020) (Box 11.6). There is limited information 
across the region on climate change impacts and adaptation options for 
telecommunications (NCCARF , 2013) (Table 11.7). There is an emerging 
recognition that implementing and evaluating the adaptation process 
(vulnerability and risk assessments, identification of options, planning, 
implementation, monitoring, evaluation and review) in local contexts

can advance more effective adaptation (Moloney and McClaren, 2018). 
For example, the Victorian state government has built monitoring, 
evaluation and adaptation components into its adaptation plan 
(Table 11.15a).Table 11.7 |  Cities, settlements and infrastructure: key risks and adaptation options.
Sector Key Risks Adaptation Options Inter-Sector Dependencies Sources
RoadHeat, SLR, coastal surges, floods 
and high-intensity rainfall impacts 
on road foundationsRe-routing, coastal protection, 
improved drainagePorts (fuel supply), rail (fuel 
supply), electricity(NCCARF , 2013; CoA, 2018a; MfE, 
2020a)
RailExtreme temperatures, flooding, 
SLR, high-intensity rainfall impacts 
on track foundationsDrainage and ventilation 
improvements, systematic risk 
assessments, overhead wire and 
rail/sleeper upgrades, re-routingElectricity, telecommunications, 
fuel supply (transport, ports)(CoA, 2018a; MfE, 2020a)
Urban and Rural Built 
Environment1Extreme temperatures, floods,

extreme weather events, wildfire 
(at urban–rural interface), SLRMultiple options from the 
building-to-city scale to reduce 
heat impacts and improve climate 
resilience, behavioural change, 
coastal defences and managed 
retreatRoad, rail, electricity, air and 
seaports, telecommunications, 
water and wastewater(CoA, 2018a; Newton et al., 2018; 
Haddad et al., 2019; MfE, 2020a; 
Paulik et al., 2020; Tapper, 2021)
(Box 11.1)
(Box 11.4)
ElectricityHigh-wind/ temperature events, 
wildfire, lightning, dust storms, 
drought (hydro)Demand management, 
re-engineering and new 
technology, network intelligence, 
smart metering, improved 
planning for outagesRoad, rail, water(CoA, 2017; MfE, 2020a)
(11.3.10.)
Ports: Air and SeaSLR, coastal surges, wind, heat, 
extreme weather eventsAir: improved coastal, pluvial 
and fluvial flood protection, 
on-site services; sea: widening 
operational limits, raising wharfs,

roads and breakwatersElectricity, road, rail, water(McEvoy and Mullett, 2014; MfE, 
2020a)
Telecommunications Floods, wildfires, extreme windProtect, place underground, 
wireless systemsElectricity, digital connectivity, 
all sectors serviced, rural 
communities(NCCARF , 2013)
Stormwater Wastewater and 
Water supplya.High-intensity rainfall, increased 
and extreme temperatures, 
flooding, drought, SLRLarge investments in upgrading 
centralised infrastructure and 
capacity, increasing investment 
in decentralised infrastructure 
and capacity (e.g., water-sensitive 
urban design), demand 
management, fewer options in 
smaller communities, governance 
at scaleElectricity, telecommunications, 
urban and rural built environment(White et al., 2017; CoA, 2018a; 
Gilpin et al., 2020; MfE, 2020a; 
Wong et al., 2020; Hughes et al., 
2021)
(Box 11.4)
Notes:

(a) Water supply safety and security and exposure of buildings have been identified as the most significant risks for New Zealand in terms of urgency and consequence (MfE, 2020a). 
No such ranking of risk has been done for Australia.

1621Box 11.6 | Rising to the Sea Level Challenge
Many of the region’s cities and settlements, cultural sites and place attachments are situated around harbours, estuaries and lowland rivers 
(Black, 2010; PCE, 2015; Australia SoE, 2016; Rouse et al., 2017; Hanslow et al., 2018; Birkett-Rees et al., 2020) exposed to ongoing relative sea 
level rise (RSLR). RSLR includes regional variability in oceanic conditions (Zhang et al., 2017) and vertical land movement along New Zealand’s 
tectonically dynamic coasts (Levy et al., 2020) and some Australian hotspots for subsidence (Denys et al., 2020; King et al., 2020; Watson, 2020).
Table Box 11.6.1 |  Observed and projected impacts from higher mean sea level
Impacts from increase in mean sea level References
Nuisance and extreme coastal flooding have increased from higher mean sea level in 
New Zealand. Projected SLR will cause more frequent flooding in Australia and New

Zealand before mid-century (very high confidence)(Hunter, 2012; McInnes et al., 2016; Stephens et al., 2017; Stephens et al., 2020); 
(Steffen et al., 2014; PCE, 2015; MfE, 2017a; Hague et al., 2019; Paulik et al., 2020)
Squeeze in intertidal habitats (high confidence)(Steffen et al., 2014; Peirson et al., 2015; Mills et al., 2016a; Mills et al., 2016b; Pettit 
et al., 2016; Rouse et al., 2017; Rayner et al., 2021)
Significant property and infrastructure exposure (high confidence)(Steffen et al., 2014; PCE, 2015; Harvey, 2019; LGNZ, 2019; Paulik et al., 2020) (Table 
Box 11.5.2 and Table Box 11.6.2)
Loss of significant cultural and archaeological sites and projected to compound with 
several hazards over this century (medium confidence)(Bickler et al., 2013; Birkett-Rees et al., 2020; NZ Archaeological Association, 2020)
Increasing flood risk and water insecurity with health and well-being impacts on Torres

Strait Islanders (high confidence)(Steffen et al., 2014; McInnes et al., 2016; McNamara et al., 2017)
Degradation and loss of freshwater wetlands (high confidence)(Pettit et al., 2016; Bayliss and Ligtermoet, 2018; Tait and Pearce, 2019; Grieger et al., 
2020; Swales et al., 2020)
Coastal shoreline position is driven by a complex combination of natural drivers, past and present human interventions, climate variability 
(Bryan et al., 2008; Helman and Tomlinson, 2018; Allis and Hicks, 2019) and variation in sediment flux (Blue and Kench, 2017; Ford and 
Dickson, 2018). RSLR, to date, is a secondary factor influencing shoreline stability (medium confidence), and in Australia no definitive SLR 
signature is yet observed in shoreline recession, nor is one documented in New Zealand, due to variability in shoreline position responding 
to storms and seasonal, annual and decadal climate drivers (Australian Government, 2009; McInnes et al., 2016; Sharples et al., 2020).

The primary impacts of rising mean sea level (Table Box 11.6.1) are being compounded by climate-related changes in waves, storm 
surge, rising water tables, river flows and alterations in sediment delivery to the coast (medium confidence). The net effect is projected 
to increase erosion on sedimentary coastlines and flooding in low-lying coastal areas (McInnes et al., 2016; MfE, 2017a; Hanslow et al., 
2018; Wu et al., 2018). Waves are projected to be higher in southern Australasia and lower elsewhere (Morim et al., 2019) and storm 
surge slightly higher in the south, slightly lower further north in New Zealand (Cagigal et al., 2019) and small robust declines in southern 
Australia, with potentially larger changes in the Gulf of Carpentaria (Colberg et al., 2019).
The cumulative direct and residual risk from RSLR and associated impacts are projected to continue for centuries, necessitating ongoing

adaptive decisions for exposed coastal communities and assets (high confidence) (MfE, 2017c; Oppenheimer et al., 2019; Tonmoy et al., 
2019).
Prevailing decision-making assumes shorelines can continue to be maintained and protected from extreme storms, flooding and erosion, 
even with RSLR (Lawrence et al., 2019a). Rapid coastal development has increased exposure of coastal communities and infrastructure 
(high confidence) (Helman and Tomlinson, 2018; Paulik et al., 2020), reinforcing perceptions of safety (Gibbs, 2015; Lawrence et al., 2015) 
and creating barriers to retreat and nature-based adaptations (very high confidence) (Schumacher, 2020). The efficacy and increasing costs 
of protection and accommodation risk reduction approaches and rebuilding after extreme events have been questioned and have limits 
(PCE, 2015; MfE, 2017a; Harvey, 2019; LGNZ, 2019; Paulik et al., 2020; Haasnoot et al., 2021). Future shoreline erosion is often signalled

using defined coastal setback lines(s) and using probabilistic approaches to signal uncertainty (Ramsay et al., 2012; Ranasinghe, 2016).

11
1622Chapter 11 Australasia
Table  Box  11.6.2 |   Observed relative SLR (variance-weighted average) with uncertainty range (standard deviation) and projected impacts on infrastructure and 
population of 1.1 m in Australia and 1 m in New Zealand. SLR projections for 2050 and 2090 are given in Table 11.3a and Table 11.3b.
CountryObserved relative sea 
level riseProjected impacts of SLR (1.1 m Australia, 1.0 m New Zealand)
Value of coastal urban 
infrastructureNumber of buildings 
exposedNumber of 
residents 
exposedPublic council assets 
exposed
Australia2.2±1.8 mm/year to 2018 
for four >75-year records (or 
an average of 0.17 m over 
75 years), 3.4 mm/year from 
1993–2019 (Watson, 2020)AUD$164 to >226 billion (DCCEE, 
2011; Steffen et al., 2019)
111% rise in inundation cost from 
2020 to 2100 (Mallon et al., 2019)187,000 to 274,000 residential 
buildings, 5800 to 8600 
commercial buildings, 3700 to 
6200 light industrial buildings 
(DCCEE, 2011)N/A27,000 to 35,000 km roads and

1200 to 1500 km rail lines and 
tramways (DCCEE, 2011)
New 
Zealand1.8 mm/year from 1900–2018, 
1.2 mm/year from 1900–1960 
and 2.4 mm/year from 
1961–2018 (Bell and Hannah, 
2019)NZD$25.5 billion (Paulik et al., 2020) 75,890 (Paulik et al., 2020)105,580 
(Paulik 
et al., 2020)4000 km pipelines, 1440 km 
roads, 101 km rail, 72 km 
electricity transmission lines 
(Paulik et al., 2020)
NZD$5 billion (2018) (reserves, 
buildings, utility networks, 
roads) (LGNZ, 2019)
Flooding from high spring (‘king’) tides or storm tides during extreme weather events are raising public awareness of SLR (Green Cross 
Australia, 2012), including through media coverage (Priestley et al., 2021). The use of adaptive decision tools (11.7.3.1; Table 11.17) is 
increasing the understanding of changing coastal risk (Bendall, 2018; Lawrence et al., 2019b; Palutikof et al., 2019b) and how dynamic

adaptive pathways and monitoring of them can aid implementation (Stephens et  al., 2018; Lawrence et  al., 2020b). Collaborative 
governance between local governments and their communities, including with Māori tribal organisations, is emerging in New Zealand 
(OECD, 2019b) assisted by national direction (DoC NZ, 2010) and guidance on adaptive planning (Table 11.15b). This shift from reactive 
to pre-emptive planning is better suited to ongoing RSLR (Lawrence et al., 2020b).
In Australia, adaptation to SLR remains uneven across jurisdictions in the absence of clear federal or state guidance, rendering Australia 
unprepared for flooding from SLR (Dedekorkut-Howes et al., 2020). Risk-averse coastal governance at the local level has led to shifts in 
liabilities to other actors and to future generations (Jozaei et al., 2020). Managed retreat has emerged as an adaptation option in New

Zealand (Rouse et al., 2017; Hanna, 2019; Kool et al., 2020; Lawrence et al., 2020c), where protective measures are transitional (DoC 
NZ, 2010) and where managed retreat has arisen from collaborative governance (Owen et al., 2018). Remaining adaptation barriers are 
social or cultural (the absence of licence and legitimacy) and institutional (the absence of regulations, policies and processes that support 
changes to existing property rights and the funding of retreat) (high confidence) (O’Donnell and Gates, 2013; Tombs et al., 2018; Grace 
et al., 2019; O’Donnell et al., 2019).
Legacy development, competing public and private interests, trade-offs among development and conservation objectives, policy 
inconsistencies, short- and long-term objectives and the timing and scale of impacts compound to create contestation over 
implementation of coastal adaptation (high confidence) (Mills et al., 2016b; McClure and Baker, 2018; Dedekorkut-Howes et al., 2020;

McDonald, 2020; Schneider et al., 2020). Legal barriers to coastal adaptation remain (Schumacher, 2020) with a risk that the courts will 
become decision makers (Iorns Magallanes et al., 2018) due to legislative fragmentation, status quo leadership, lack of coordination 
between governance levels and agreement about who pays for what adaptation (very high confidence) (Waters et al., 2014; Boston and 
Lawrence, 2018; Palutikof et al., 2019a; Noy, 2020). The nexus of climate, law, place and property rights continues to expose people and 
assets to ongoing SLR (Johnston and France-Hudson, 2019; O’Donnell, 2019), especially where the risks of SLR are not being reflected 
in property valuations (Cradduck et al., 2020). Risk signalling through land use planning, flooding events and changes in insurance 
availability and costs is projected to increase recognition of coastal risks (medium confidence) (Storey and Noy, 2017; CCATWG, 2018;

Lawrence et al., 2018a; Harvey and Clarke, 2019; Steffen et al., 2019; Cradduck et al., 2020; ICNZ, 2021). Proactive local-led engagement 
and strategy are key to effective adaptation and incentivising and supporting communities to act (Gibbs, 2020; Schneider et al., 2020). 
Adopting ‘fit for purpose’ decision tools that are flexible as sea levels rise (11.7.3) can build adaptive capacity in communities and 
institutions (high confidence) .Box 11.6 (continued)

162311.3.6 Health and Well-being
11.3.6.1 Observed Impacts
There is ample evidence of health loss due to extreme weather in 
Australia and New Zealand, and rising temperatures, changing 
rainfall patterns and increasing fire weather have been attributed to 
anthropogenic climate change (11.2.1). Extreme heat leads to excess 
deaths and increased rates of many illnesses (Hales et  al., 2000; 
Nitschke et al., 2011; Lu et al., 2020). Between 1991 and 2011 it is 
estimated that 35–36% of heat-related mortality in Brisbane, Sydney 
and Melbourne was attributable to climate change, amounting to 
about 106 deaths a year on average over the study period (Vicedo-
Cabrera et  al., 2021). Exposure to high temperatures at work is 
common in Australia, and the health consequences may include more 
accidents, acute heat stroke and chronic disease (Kjellstrom et  al., 
2016).  Long-term rise in temperatures is changing the balance of

summer and winter mortality in Australia (Hanigan et al., 2021). The 
Black Summer wildfires in Australia in 2019/2020 (Box 11.1) caused 
33 deaths directly (Davey and Sarre, 2020) and exposed millions of 
people to heavy particulate pollution (Vardoulakis et  al., 2020). In 
the Australian states most heavily affected by the fires, 417 deaths, 
3151 hospital admissions for cardiovascular or respiratory conditions 
and about 1300 emergency department presentations for asthma are 
attributed to wildfire smoke exposure (Borchers Arriagada et al., 2020). 
Immediate smoke-related health costs from the 2019–2020 fires are 
estimated at AUD$1.95 billion (Johnston et al., 2020).
Extreme heat is associated with decreased mental well-being, more 
marked in women than men (Ding et  al., 2016). Changing climatic 
patterns in western Australia have undermined farmers’ sense of 
identity and place, heightened anxiety and increased self-perceived

risks of depression and suicide (Ellis and Albrecht, 2017). Following the 
Black Saturday wildfires in Victoria in 2009, 10–15% of the population 
in the most severely affected areas reported persistent fire-related 
post-traumatic stress disorder, depression and psychological distress 
(Bryant et al., 2014). Repeated exposure to the threat of wildfires in 
Australia, either directly (Box 11.1) or through media coverage (Looi 
et al., 2020), may compound effects on mental health. In March 2017, 
31,000 people in New South Wales and Queensland were displaced 
by Tropical Cyclone Debbie. Six months post-cyclone, adverse mental 
health outcomes were more common among those whose access to 
health and social care was disrupted (King et al., 2020).
Dengue fever remains a threat in northern Australia and variations in 
rainfall and temperature are related to disease outbreaks and patterns 
of spread, although most outbreaks are sparked by travellers bringing

the virus into the country (Bannister-Tyrrell et al., 2013; Hall et al., 2021). 
Cases of dengue fever and other arboviral diseases have been increasing 
among recent arrivals to New Zealand from overseas, but to date there 
have been no reports of local transmission (Ammar et al., 2021).
In 2016 in New Zealand, it is estimated 6000 to 8000 people became 
ill due to contamination of the Havelock North water supply with the 
bacteria Campylobacter (Gilpin et al., 2020). The infection was traced 
to sheep faeces washed into the underground aquifer that feeds the 
town’s (untreated) water supply after an extraordinarily heavy rainfall event. This is not an isolated finding: increases in paediatric hospital 
admissions are seen across New Zealand two days after heavy rainfall 
events (Lai et al., 2020).
11.3.6.2 Projected impacts
Climate change is projected to have detrimental effects on human 
health due to heat stress, changing rainfall patterns including floods

and drought climate-sensitive air pollution (including that caused 
by wildfires) (high confidence) and vector-borne diseases (medium 
confidence). Vulnerability to detrimental effects of climate change will 
vary with socioeconomic conditions (high confidence).
The greatest number of people affected by compounding effects of 
heat, wildfires and poor air quality will be in urban and peri-urban 
areas of Australia. By 2100 the proportion of all deaths attributable 
to heat in Melbourne, Sydney and Brisbane may rise from about 0.5% 
to 0.8% (under RCP 2.6), or 3.2% (under RCP 8.5) (Gasparrini et al., 
2017). Heatwave related excess deaths in Melbourne, Sydney and 
Brisbane are projected to increase to 300/year (RCP2.6) or 600/year 
(RCP8.5) during 2031–2080 relative to 142/year during 1971–2020, 
assuming no adaptation and high population growth (Guo et  al., 
2018). High temperatures amplify the risks due to local air pollution:

without adaptation, ozone-related deaths in Sydney may increase by 
50–60/year by 2070 (Physick et al., 2014).
Unless there is more effective control of nutrient runoff, bacterial 
contamination of drinking water supplies is projected to increase due 
to more intense rainfall events, exacerbating risks to human health 
(Gilpin et  al., 2020; Lai et  al., 2020), and higher temperatures will 
increase freshwater toxic blooms (Hamilton et al., 2016).
In general, the area of Australia suitable for the transmission of dengue 
is projected to increase (Zhang and Beggs, 2018; Messina et al., 2019), 
but estimates of local disease risk vary considerably according to 
climate change scenario and socioeconomic pathways (Williams 
et  al., 2016). The spread of Wolbachia among Aedes mosquitoes in 
northern Australia has already reduced dengue transmission and may 
decrease the influence of climate in the future (Ryan et al., 2019). In

New Zealand, the risk of dengue remains low for the remainder of this 
century (Messina et al., 2019). Higher temperatures and more intense 
rainfall may also increase pollen production and the risk of allergic 
illness throughout the region (Haberle et al., 2014).
11.3.6.3 Adaptation
Strengthening basic public health services can rapidly reduce 
vulnerability to death and ill-health caused by climate change; however, 
this opportunity is often missed (very high confidence). The 2020 New 
Zealand Health and Disability System Review pointed to shortcomings 
in leadership and governance, structures that embed health inequity, 
lack of transparency in planning and reporting and underinvestment 
in public health personnel and systems (HDSR, 2020). An Australian 
study found that without deliberate planning the health system 
‘would only be able to deal with climate change in an expensive, ad 
hoc crisis management manner’ (Burton, 2014). In both Australia and

11
1624Chapter 11 Australasia
in information systems, primary care for marginalised groups and 
intersectoral planning (Salvador-Carulla et  al., 2020; Skegg and Hill, 
2021): all these deficiencies are relevant to climate adaptation.
Underlying health and economic trends affect the vulnerability of the 
population to extreme weather (high confidence). Poor housing quality 
is a risk factor for climate-related health threats (Alam et al., 2016). 
Homeless people lack access to temperature-controlled or structurally 
safe housing and often are excluded  from disaster preparation and 
responses (Every, 2016). These inequalities are reversible. For example, 
a government partnership with social housing providers in Australia 
improved the thermal performance of housing for low-income tenants 
(Barnett et al., 2013). A postcode-level analysis of the vulnerability of 
urban populations to extreme heat in Australian capital cities (Loughnan

et  al., 2013) led to the development of an interactive website for 
purposes of planning and emergency preparedness (Figure 11.5) as well as subsequent work on green urban design for cooler, more liveable 
cities (Tapper, 2021).
Heatwave responses, from public education to formal heat-warning 
systems, are the best-developed element of adaptation planning for 
health in Australia, but many metropolitan centres are still not covered 
(high confidence) (Nicholls et  al., 2016; Nitschke et  al., 2016). Air 
conditioning (AC) in Australian homes reduces mortality in heatwaves 
by up to 80% (Broome and Smith, 2012), but heavy reliance on AC 
carries risks. It is estimated that a power outage on the third day 
of extreme heatwaves would result in an additional 10–21 deaths 
in Adelaide, 24–47 in Melbourne and 7–13 in Brisbane (Nairn and 
Williams, 2019). Multiple interventions at the landscape, building and 
individual scale are available to reduce the negative health effects of

extreme heat (Jay et al., 2021).
Housing and socio-economic disadvantage is correlated with the use of emergency services on hot days
(a) A community vulnerability index (VI (PCA)) by deciles 
9VI (PCA)
8 7 6 5 4 3 2 11 09Ambulance callouts
8 7 6 5 4 3 21 0(b) Ambulance call-outs on days above daily mean of 34°C
30 km BrisbaneNorth North
Figure 11.5 |  Housing and socioeconomic disadvantage are correlated with the use of emergency services on hot days (rho = 0.55, p<0.01). The spatial 
distribution of (A) a community vulnerability index (VI) (PCA) by deciles and (B) ambulance call-outs on days above the daily mean of 34°C, in Brisbane, Australia.  Ambulance 
call-out data are expressed as deciles based on per-capita calls during 2003–2011 (Loughnan et al., 2013)

1625Heat extremes receive most policy attention, but the numbers of 
deaths are less than those resulting from more frequent exposures to 
moderately high temperatures (Longden, 2019). Melbourne, with its 
Urban Forest Strategy, provides a case study in long-term planning 
for cooler cities (Gulsrud et al., 2018). Australian workers’ perceptions 
of heat and responses to high temperatures show that heat policies 
on their own are insufficient for full protection; workers also require 
knowledge and agency to slow down or take breaks on their own 
initiative (Singh et al., 2015; Lao et al., 2016).
The first national climate change risk assessment in New Zealand (MfE, 
2020a) highlighted the risk to potable water supplies. An inquiry into the 
Havelock North outbreak recommended that all registered drinking water 
supplies (which supply about 80% of the national population) in New 
Zealand should be disinfected and have stronger oversight by a national

regulatory body (Government Inquiry into Havelock North Drinking 
Water, 2017). The use of local and Indigenous knowledge strengthens 
interventions to protect water supplies to remote settlements that may 
be affected by climatic changes (Henwood et al., 2019).
Adaptation requires better protection of health facilities and supply 
chains, but hospital managers seldom have capacity to invest in long-
term improvements in infrastructure (Loosemore et al., 2014). However, 
health services in the region are required to prepare disaster plans: these 
could be expanded to explicitly cover health adaptation and local threats 
from climate change, including flooding events (Rychetnik et al., 2019).
11.3.7 Tourism
11.3.7.1 Observed Impacts
Tourism is a major economic driver in the region, accounting for 3% 
(Australia) and 6% (New Zealand) of GDP pre-COVID-19 (WTTC, 2018). 
Climate change is having significant impacts on tourism due to the

heavy reliance of the sector on natural heritage and outdoor attractions 
(11.3.1; Box  11.2). Furthermore, because Australia and New Zealand 
are both long-haul destinations, a global increase in ‘flygskam’ (flight 
shame) will likely impact travel patterns (Becken et al., 2021).
Impacts of climate change are being observed across the tourism 
system (high confidence) (Scott et al., 2019a), most notably the GBR 
(Box  11.2) (Ma and Kirilenko, 2019). Australia’s ski industry is very 
sensitive to climatic change, due to reductions in snow depth and 
snow season length (Table  11.2) (Steiger et  al., 2019; Knowles and 
Scott, 2020). The 2019–2020 summer wildfires (Box  11.1) impacted 
tourism and travel infrastructure, affecting air quality, vineyards and 
wineries (CoA, 2020e; Filkov et al., 2020). Global media coverage of 
the wildfires, alongside Australia’s climate change policy response, 
profoundly and negatively, affected Australia’s destination image

(Schweinsberg et al., 2020; Wen et al., 2020). In New Zealand’s South 
Island, Fox and Franz Josef Glaciers have retreated approximately 
700 m since 2008, with ice melt and retreat resulting in increased 
rock fall risks and negatively affecting the tourist experience (Purdie, 
2013; Stewart et al., 2016; Wang and Zhou, 2019). The west coast of 
New Zealand is extremely prone to flooding events, impacting amenity 
values and access (Paulik et al., 2019a). Damage to tracks, huts and bridges have closed popular destinations, including the Hooker Glacier 
and the popular Routeburn and Heaphy Tracks during heavy rainfall 
events (Christie et al., 2020). Climate-driven damage is motivating ‘last 
chance’ tourism to see key natural heritage and outdoor attractions, 
for example, GBR (Piggott-McKellar and McNamara, 2016) and Franz 
Josef and Fox Glaciers (Stewart et al., 2016).
11.3.7.2 Projected Impacts
Widespread impacts from projected climate change are very likely

across the tourism sector. The World Heritage listed Kakadu National 
Park in Australia is projected to experience increasing severity of 
cyclones (Turton, 2014), and sea level rise (SLR) is projected to affect 
freshwater wetlands (11.3.1.2; Table 11.5) (McInnes et al., 2015) and 
Indigenous rock art (Higham et al., 2016; Hughes et al., 2018a). The 
projected increase in the number of hot days in northern and inland 
Australia may impact the attractiveness of the region for tourists 
(Amelung and Nicholls, 2014; Webb and Hennessy, 2015). Coastal 
erosion and flooding of Australasian beaches due to sea level rise (SLR) 
and intensifying storm activity are estimated to increase by 60% on 
the Sunshine Coast by 2030, causing significant damage to tourist-
related infrastructure (Hughes et al., 2018a). Urgent ‘hard’ and ‘soft’ 
adaptation strategies are projected to help reduce sea level rise (SLR) 
impacts (Becken and Wilson, 2016).

Glacier tourism, a multi-million-dollar industry in New Zealand, 
is potentially under threat because glacier volumes are projected 
to decrease (very high confidence) (Purdie, 2013). Glacier volume 
reductions of 50–92% by 2099 relative to the present reflect the large 
range of temperature projections between RCP2.6 and RCP8.5. Under 
RCP2.6 at 2099, the glaciers retain a similar configuration to present, 
although clean-ice glaciers will retreat significantly. For RCP4.5, 
RCP6.0 and RCP8.5, the clean-ice glaciers will retreat to become small 
remnants in the high mountains (Anderson et al. 2021).
Snow skiing faces significant challenges from climate change (high 
confidence). In Australia, the annual maximum snow depth is estimated 
to decrease from current levels by 15% (2030) and 60% by 2070 (SRES 
A2) (Di Luca et al., 2018). By 2070–2099, relative to 2000–2010, the 
length of the Victorian ski season is projected to contract by 65–90%

under RCP8.5 (Harris et al., 2016). The New Zealand tourism destination 
of Queenstown is expected to experience declining snowfall, increased 
wind and more severe weather events (Becken and Wilson, 2016). Ski 
tourism stakeholders have been responding to longer-term climate 
risks with an increase in snow-making machines in New Zealand since 
2013 (Hopkins, 2015) and in Australia (Harris et al., 2016).
11.3.7.3 Adaptation
Current snow-making technologies are expected to sustain the ski 
industry until mid-century. However, with warmer winter temperatures 
and declining water availability, snow-making is projected to decrease 
to half at most resorts by 2030 (Harris et al., 2016). New Zealand’s ski 
industry may benefit from Australian skiers visiting New Zealand due 
to lower relative vulnerability (Hopkins, 2015). However, tourists may 
substitute destinations or ski less in the absence of snow (medium

11
1626Chapter 11 Australasia
agreement, limited evidence) (Cocolas et  al., 2015; Walters and 
Ruhanen, 2015).
With the exception of the ski industry (Becken, 2013; Hopkins, 2015), 
tourism stakeholders generally focus on coping with short-term weather 
events, rather than longer-term climate risks, but they do exhibit high 
adaptive capacity by diversifying their activities (Stewart et al., 2016). 
Post-COVID-19 pandemic economics and recovery policies challenge 
this sector’s prospects, and the combination of COVID-19 and climate 
change (e.g., fires, floods) has also highlighted the need for the tourism 
sector to be able to respond to multiple, overlapping crises.
There is limited evidence that research into the impact of climate 
change on tourism in Australia and New Zealand is translating 
into policy or action (Moyle et al., 2017). New Zealand government 
tourism sector strategies acknowledge this and the need for greater

understanding of climate change for the sector (TIA, 2019) but do not 
offer solutions (MBIE, 2019b; MfE, 2020a). The COVID-19 pandemic 
and the global pause of international travel offer an opportunity to 
potentially ‘reset’ tourism to account for the impacts of climate change 
(Prideaux et al., 2020).
11.3.8 Finance
11.3.8.1 Observed Impacts
The finance sector has significant exposure to climate variability and 
extreme events (high confidence).
Aggregated insured losses from weather-related hazard events 
from 2013 to 2020 were almost AUD$15 billion for Australia (1.2% 
of GDP) and almost NZD$1  billion for New Zealand (0.4% of GDP) 
(NIWA, 2020; ICA, 2021) (ICA, 2020a; NIWA, 2020). However, there is 
no trend in normalised losses because the rising insurance costs are 
being driven by more people living in vulnerable locations with more 
to lose (McAneney et al., 2019). In New Zealand, two major hailstorms 
during 2014–2020 and three major floods during 2019–2021 caused

significant insurance losses (ICNZ, 2021). Insured losses exceeded 
NZD$472  million for the 12 costliest floods from 2007 to 2017, of 
which NZD$140 million could be attributed to anthropogenic climate 
change (Frame et al., 2020). In Australia, insured damage was almost 
AUD$1.0 billion for the Queensland hailstorm in 2020, AUD$1.7 billion 
for east coast flooding in 2020, AUD$2.3 billion for the 2019–2020 fires, 
AUD$2.3 billion for the Queensland hailstorm in 2019, AUD$1.2 billion 
for the North Queensland floods in 2019, AUD$1.4 billion for the NSW 
hailstorm in 2018, AUD$1.8  billion for Cyclone Debbie in 2017 and 
AUD$1.5 billion for the Brisbane hailstorm in 2014 (ICA, 2020b). The 
insured loss from the seven costliest hailstorms in Australia from 2014 
to 2021 totalled AUD$7.6 billion (ICA, 2021).
Some homes in the highest-risk areas tend to be in lower socioeconomic 
groups that may not buy insurance (Actuaries Institute, 2020). For

example, a quarter of residents that experienced loss or damage in 
the 2019 Townsville floods did not have insurance (ACCC, 2020). 
Underinsurance reduces people’s capacity to recover from adverse 
events, while over-reliance on private insurance undermines collective disaster recovery efforts (Lucas and Booth, 2020). In Australia, those 
in high-risk areas minimise house and contents insurance for financial 
reasons (Booth and Harwood, 2016; Osbaldison et al., 2019; Actuaries 
Institute, 2020). Insurance premiums in northern Australia are almost 
double those in the rest of Australia, and rising, mainly due to cyclone 
damage (ACCC, 2020).
11.3.8.2 Projected Impacts
Risks for the finance sector are projected to increase (medium 
confidence). The potential impact of increased coastal and inland 
flooding, soil desiccation and contraction, fire and wind could lead to 
higher insurance costs, reduced property values and difficulties for some

customers to service loans (CBA, 2018). Under a high-emissions scenario 
(RCP8.5), estimated annual losses to home-lending customers may 
increase 27% by 2060, and the proportion of properties with high credit 
risk may rise from 0.01% in 2020 to 1% in 2060, assuming no portfolio 
changes (CBA, 2018). In New Zealand, weather-related insurance claims 
between 2000 and 2017 totalled NZD$450 million, 40% of which was 
due to extreme rainfall. Using six climate model projections of extreme 
rainfall, the insured damage is projected to increase by 7% (RCP2.6) 
to 8% (RCP8.5) by 2020–2040 and 9% (RCP2.6) to 25% (RCP8.5) by 
2080–2100, relative to 2000–2017 (Pastor-Paz et al., 2020). By 2050–
2070, tropical cyclone risk for properties not in flood plains or storm 
surge zones in south-east Queensland may increase by 33% under a 
2°C scenario and by 317% under a 3°C scenario for properties in flood 
plains and storm surge zones (IAG, 2019).
11.3.8.3 Adaptation

Banks, insurers and investors increasingly recognise the risks posed 
by climate change to their businesses (high confidence) (Paddam 
and Wong, 2017). Collaborations between banks, insurers and 
superannuation funds in Australia and New Zealand are driving 
efforts aimed at achieving the Paris Agreement goals, including 
the New Zealand Centre for Sustainable Finance and Australian 
Sustainable Finance Initiative (AFSI, 2020; TAO, 2020; NZCFSF , 2021). 
Company directors, including superannuation fund directors, have 
legal obligations to disclose and appropriately manage material 
financial risks (Barker et al., 2016; Hutley and Davis, 2019). Financial 
regulators are aware of climate risks for financial stability and financial 
institutions (RBNZ, 2018; RBA, 2019) and are closely supervising 
climate risk disclosure practices (TCFD, 2017; RBNZ, 2018; APRA, 
2019; CMSI, 2020; IGCC, 2021b). In Australia, regulatory action (APRA,

2021) includes issuing prudential guidelines for financial institutions 
on managing climate risk, aligned with guidelines developed by the 
Climate Measurement Standards Initiative (NESP ESCC, 2020). In New 
Zealand, the financial sector (climate-related disclosure and other 
matters) amendment bill aims to ensure that the effects of climate 
change are routinely considered in business, investment, lending and 
insurance underwriting decisions (NZ Government, 2021).
Banks and insurers are beginning to undertake climate risk analyses 
(CRO Forum, 2019; Bruyère et  al., 2020) and disclose their risks 
(Paddam and Wong, 2017; ANZ, 2018; CBA, 2018). For example, the 
agricultural banking sector has analysed climate risk and embedded 
climate adaptation financing into its risk scoring and lending practices

1627(CBA, 2019). However, the overall number of disclosures continues 
to lag expectations, suggesting the need for mandatory climate risk 
disclosure in Australia (IGCC, 2021a).
Climate adaptation finance is not evident (medium confidence). There 
is an adaptation finance gap (Mortimer et al. 2020). Private sector 
initiatives are beginning to emerge through large scale projects or 
public–private partnerships, such as the Queensland Betterment 
Fund (Banhalmi-Zakar et al., 2016; Ware and Banhalmi-Zakar, 2020). 
Addressing investor pressure (IGCC, 2017) could increase investment in 
adaptation. However, ongoing policy uncertainty in Australia continues 
to be the key barrier to allocating additional capital to invest in climate 
solutions for 70% of investors (IGCC, 2021a).
Current and future insurance affordability pressures could be addressed 
by increased mitigation, revisions to building codes and standards

and better land use planning (ACCC, 2020; Actuaries Institute, 2020). 
In New Zealand, insurance signals are motivating the government 
to address adaptation funding mechanisms (Boston and Lawrence, 
2018; CCATWG, 2018). Some insurers offer premium discounts to 
customers with reduced risk (Drill et  al., 2016), with increasing 
premiums reflecting known risk and no cover for some hazards in risky 
locations (CCATWG, 2017). Special excess payments are available for 
flood hazard so customers take responsibility for part of the claim, 
with increasing premiums to reflect known and foreseeable risk and 
downgrading cover from replacement value to market value (Bruyère 
et  al., 2020). Retreat by private insurers from risky locations could 
increase the unfunded fiscal risk to the government (Storey and 
Noy, 2017), creating moral hazard (Boston and Lawrence, 2018). The 
litigation risk from failing to take adaptation action (Hodder, 2019)

could affect financial markets and government policy settings, creating 
cascading impacts across society (Lawrence et al., 2020b) CRO Forum, 
2019). For some climate risks, national governments act as ‘last resort’ 
insurers (CCATWG, 2017), but this could become unsustainable (CRO 
Forum, 2019).
11.3.9 Mining
Many mines are exposed and sensitive to climate extremes (high 
confidence), but there is little available research on climate change 
impacts on them (Odell et al., 2018). Most Australian mines face higher 
temperatures, cyclones, erosion and landslides and hazards such as sea 
level rise (SLR) and storms across their supply chains, including ports 
(Cahoon et al., 2016). Impacts include operational disruptions such as 
acute drainage problems (Loechel and Hodgkinson, 2014) and heat-
induced illness, irritation and absenteeism among workers (McTernan 
et al., 2016), lost revenue and increased costs (Pizarro et al., 2017).

Damage and disruption from climate impacts can cost operators 
billions of dollars (Cahoon et al., 2016). Climatic extremes increase the 
risk and impact of spillages along transportation routes (Grech et al., 
2016), exacerbate mining’s effects on hydrology, ecosystems and air 
quality (Phillips, 2016; Ali et al., 2018), increase contamination risks 
(Metcalfe and Bui, 2016) and disrupt and slow mine site rehabilitation 
(Wardell-Johnson et al., 2015; Hancock et al., 2017). Adaptations such 
as improved water management are emerging slowly (Gasbarro et al., 2016; Becker et al., 2018). Some companies are spatially diversifying 
and relocating (Hodgkinson et al., 2014). Others are replacing workers 
with automation and remote operations (Halteh et al., 2018; Keenan 
11.3.10 Energy
Australia’s energy generation is a mix of coal (56%), gas (23%) and 
renewables (21%) (DISER, 2020), with ageing coal-fired infrastructure

being replaced by a growing proportion of renewable and distributed 
energy resources (AEMO, 2018). In New Zealand, 60% of energy 
generation comes from hydro-electricity and 15% from geothermal 
(MBIE, 2021), with coal (2%) and gas (13%) generation capacity to 
be retired, and total renewable energy to increase from 82% in 2017 
to around 95% by 2050, mostly through wind generation (MBIE, 
2019a).
11.3.10.1 Observed Impacts
The energy sector is highly vulnerable to climate change (high 
confidence). Oil and gas systems are vulnerable to storms, fires, 
drought, floods, sea level rise (SLR), extreme heat and fires, which can 
damage infrastructure, slow production and add to operational costs 
(Smith, 2013). The electricity system is vulnerable to high temperatures 
reducing generator and network capacity and increasing failure 
rates and maintenance costs (AEMO, 2020a). Fires (including those 
sparked by electrical distribution lines) pose risks to assets. Smoke

can cause electricity transmission to trip, and high winds reduce wind-
energy capacity and threaten the integrity of transmission lines. Low 
rainfall reduces hydro-energy capacity and increases the demand 
for desalination energy. Higher sea level may affect some low-lying 
generation, distribution and transmission assets, and compound 
extreme weather events can cause outages (Vose and Applequist, 
2014; Lawrence et al., 2016; AEMO, 2020b; AEMO, 2020a; ESCI, 2021). 
For example, in September 2016, a major windstorm in South Australia 
damaged 23 transmission towers and cut power to over 900,000 
households. In February 2017, the South Australian energy system 
failed to cope with a heatwave-related jump in demand, causing power 
cuts to 40,000 homes (Steffen et  al., 2017). In April 2018, a storm 
over Auckland, New Zealand left 182,000 properties without power 
(Bell, 2018). The 2019/2020 Australian heatwaves and fires caused

widespread blackouts that disrupted communications, transport and 
emergency response capacity (Box 11.1).
11.3.10.2 Projected Impacts
Risks for the energy sector are projected to increase with climate change 
(medium confidence). Projected increases in the frequency and intensity 
of heatwaves, fires, droughts and wind-storms would increase risks for 
energy supply and demand (AEMO, 2020b; ESCI, 2021). Households 
are unevenly vulnerable to energy sector risks due to varying housing 
quality and health dependencies (11.3.6). In New Zealand, a warmer 
climate and increasing energy efficiency is projected to marginally 
reduce annual average peak electricity heating demand (Stroombergen 
et  al., 2006; MBIE, 2019a). Winter and spring inflows to main hydro 
lakes are projected to increase 5–10% and may reduce hydroelectric

11
1628Chapter 11 Australasia
energy vulnerability (McKerchar and Mullan, 2004; Poyck et al., 2011; 
Stevenson et al., 2018). However, major electricity supply disruptions 
are projected to increase as dependence on electricity grows from 25% 
of total energy in 2016 to 58% in 2050 (Transpower, 2020).
In Australia, the total heating and cooling energy demand of 5-star 
energy-rated houses is projected to change by 2100 (Wang et  al., 
2010). At 2°C global warming, the estimated change in demand is 
−27% in Hobart, −21% in Melbourne, +61% in Darwin, +67% in Alice 
Springs and +112% in Sydney. For a 4°C global warming, the changes 
are −48%, −14%, +135%, +213% and +350% respectively.11.3.10.3 Adaptation
Options to manage risks include adaptation of energy markets, 
integrated planning, improved asset design standards, smart-
grid technologies, energy generation diversification, distributed 
generation (e.g., roof-top solar, microgrids), energy efficiency, demand

management, pumped hydro storage, battery storage and improved 
capacity to respond to supply deficits and balance variable energy 
resources across the network (Table  11.8) (high confidence). With 
increasing electrification, diversification and resilience can contribute 
to security of supply as fossil fuels are retired from the energy mix 
(AEMO, 2020b). In Australia, the AEMO (2020) Integrated System Plan 
has evaluated various options, costs and benefits. Risks associated Table 11.8 |  Adaptation options for energy sector.
Adaptation options References
Diversification of electricity supplies geographically and technically, including distributed energy resources and variable renewable energy (AEMO, 2020b)
Integrated planning, improved asset design and management and disaster recovery to build resilience to more extreme weather (AEMO, 2020b; Transpower, 2020)

Augmentation of transmission grid to support change in generation mix using interconnectors and renewable energy zones, coupled with 
energy storage, adds capacity and helps balance variable resources across the network(Blakers et al., 2017; ICCC, 2019; AEMO, 
2020b)
Climate change risks included in the design, location and rating of future infrastructure and consideration of the implications for future 
transmission developments(Bridge et al., 2018; AEMO, 2020b)
Increased design and construction standards, flood defence measures, insurance, improved water efficiency, improved insulation of 
supercooled LNG processes, more efficient air conditioning and creating fire breaks for the oil and gas sector(Smith, 2013; Gasbarro et al., 2016)
Technological developments to strengthen existing resilience under climate change that reinforce the relative advantage of western Australia 
and Tasmania for new wind energy installations(Evans et al., 2018)

Energy generation diversity, demand management, pumped hydro storage and battery storage (Keck et al., 2019; Transpower, 2020)
Tools and strategies to manage winter energy deficits and dry years alongside renewable electricity generation deployment (Transpower, 2020)
Improved insulation and heating of buildings and flexible electricity consumption to reduce significance of winter electricity demand peak(Stroombergen et al., 2006; MBIE, 2019a; 
Transpower, 2020)
Table 11.9 |  Examples of observed impacts that can be partly attributed to climate change.
Impact Source
Mass bleaching of GBR in 2016/2017 due to a marine heatwave Box 11.2
In the New Zealand southern Alps, extreme glacier mass loss, which was at least 6 times more likely in 2011 and 10 times more likely in 2018, due to warming 11.2.1, 11.3.3
In the Australian Alps bioregion, loss of habitat for endemic and obligate species due to snow loss and increases in fire, drought and temperature Table 11.4

In the Australian wet tropics world heritage area, some vertebrate species have declined in distribution area and population size due to increasing temperatures and 
length of dry seasonTable 11.4
Extinction of Bramble Cay melomys due to loss of habitat caused by storm surges and SLR in Torres Strait Table 11.4
In New Zealand, increasing invasive predation pressure on endemic forest birds surviving in cool forest refugia due to anthropogenic warming Table 11.4
In New Zealand, erosion of coastal habitats due to more severe storms and SLR Table 11.4, Box 11.6
In Australia, estuaries warming and freshening with decreasing pH Table 11.6
Changes in life-history traits, behaviour or recruitment of fish and invertebrates due to ocean acidification or warming, severe decline in recruitment of coral on GBR 
due to ocean warming, aquaculture stock deaths due to heat stressTable 11.6
New diseases and toxins due to warming and extension of East Australian Current Table 11.6

Changes in almost 200 marine species’ distributions and abundance due to ocean warming Table 11.6
Temperate marine species replaced by seaweeds, invertebrates, corals and fishes characteristic of sub-tropical and tropical waters Table 11.6
River flow decline in southern Australia is largely due to the decline in cool-season rainfall partly attributed to anthropogenic climate change 11.3.3
In New Zealand, the 2007/2008 drought and 2012/2013 drought were 20% attributed to anthropogenic climate change 11.3.3
In New Zealand, about 30% of the insured damage for the 12 costliest flood events from 2007 to 2017 can be attributed to anthropogenic climate change 11.3.8
In Australia, 35–36% of heat-related excess mortality in Melbourne, Sydney and Brisbane from 1991–2018 can be attributed to anthropogenic climate change 11.3.6

1629with an increasing reliance on weather-dependent renewable energy 
(e.g., solar, wind, hydro) (ESCI, 2021) can be managed through strong 
long-distance interconnection via high-voltage powerlines and storage 
(Blakers et al., 2017; Blakers et al., 2021; Lu et al., 2021). However, 
implementation of adaptation options remains inadequate (Gasbarro 
11.3.11 Detection and Attribution of Observed Climate 
Impacts
Detection and attribution of observed climate trends and events is called 
‘climate attribution’. This has been assessed by IPCC WGI (Gutiérrez 
et  al., 2021; Ranasinghe et  al., 2021; Seneviratne et  al., 2021) and 
summarised in Chapter 16. Trends that have been formally attributed in 
part to anthropogenic climate change include regional warming trends 
and sea level rise (SLR), decreasing rainfall and increasing fire risk in 
southern Australia. Events include extreme rainfall in New Zealand

during 2007–2017, the 2007/2008 and 2012/2013 droughts in New 
Zealand, high temperatures in Australia during 2013–2020, the 2016 
northern Australian marine heatwave, the 2016/2017 and 2017/18 
Tasman Sea marine heatwaves and 2019/2020 fires in Australia.
Detection and attribution of climate impacts on natural and human 
systems is called ‘impact attribution’. This often involves a two-step 
approach (joint attribution) that links climate attribution to observed 
impacts. Impact attribution is complicated by confounding factors, for 
example, changes in exposure arising from population growth, urban 
development and underlying vulnerabilities.
Impact attribution is considered in Sections  11.3.1–11.3.10 and 
summarised in Table  11.9. More literature is available for natural 
systems than human systems, which represents a knowledge gap rather 
than an absence of impacts that are attributable to anthropogenic 
climate change. Fundamental shifts in the structure and composition of

some ecosystems are partly due to anthropogenic climate change (high 
confidence). In human systems, the costs of droughts and floods in New 
Zealand, and heat-related mortality and fire damage in Australia, are 
partly attributed to anthropogenic climate change (medium confidence).
11.4 Indigenous Peoples
Indigenous perspectives of well-being embrace physical, social, 
emotional and cultural domains, collectiveness and reciprocity and, 
more fundamentally, connections between all elements across past, 
present and future generations (Australia. NAHS Working Party, 1989; 
MfE, 2020a). Changing climate conditions are expected to exacerbate 
many of the social, economic and health inequalities faced by Aboriginal 
and Torres Strait Islander Peoples in Australia and Māori in New Zealand 
(high confidence) (Bennett et  al., 2014; Hopkins et  al., 2015; AIHW, 
2016; Lyons et al., 2019). As a consequence, effective policy responses

are those that take advantage of the interlinkages and dependencies 
between mitigation, adaptation and Indigenous Peoples’ well-being 
(Jones, 2019) and those that address the transformative change 
needed from colonial legacies (high confidence) (Hill et al., 2020). There 
is a central role for Indigenous Peoples in climate change decision-making that helps address the enduring legacy of colonisation through 
building opportunities based on Indigenous governance regimes, 
cultural practices to care for land and water and intergenerational 
perspectives (very high confidence) (Nursey-Bray et al., 2019; Petzold 
et al., 2020) (Cross-Chapter Box INDIG in Chapter 18).
11.4.1 Aboriginal and Torres Strait Islander Peoples of 
Australia
The highly diverse Aboriginal and Torres Strait Islander Peoples of 
Australia have survived and adapted to climate changes such as 
sea level rise (SLR) and extreme rainfall variability during the late

Pleistocene era, through intimate place-based Indigenous knowledge 
in practice and while losing traditional land and sea country 
ownership (Liedloff et al., 2013) (Cross-Chapter Box INDIG in Chapter 
18) including during the Late Pleistocene era (Golding and Campbell, 
2009; Nunn and Reid, 2016). They belong to the world’s oldest 
living cultures, continually resident in their own ancestral lands, or 
‘country’, for over 65,000 years (Kingsley et al., 2013; Marmion et al., 
2014; Nagle et al., 2017; Tobler et al., 2017; Nursey-Bray and Palmer, 
2018). The majority of the Australian Indigenous Peoples live in urban 
areas in southern and eastern Australia, but are the predominant 
population in remote areas.
Climate-related impacts on Aboriginal and Torres Strait Islander 
Peoples, countries (traditional estates) and cultures have been observed 
across Australia and are pervasive, complex and compounding (high 
confidence) (Green et  al., 2009) (11.5.1), for example, the loss of

biocultural diversity, nutritional changes through the availability of 
traditional foods and forced diet change, water security and loss of land 
and cultural resources through erosion and SLR (Table 11.10) (TSRA, 
2018). Moreover, these impacts are being experienced now particularly 
in low-lying geographical areas—especially in the Torres Strait Islands 
(Mosby, 2012; Kelly, 2014; Murphy, 2019; Hall et al., 2021). Estimates 
of the loss from fire impacts on ecosystem services that contribute to 
the well-being of remotely located Indigenous Australians were found 
to be higher than the financial impacts from the same fires on pastoral 
and conservation lands (Sangha et al., 2020) and could increase with 
both financial and non-financial impacts (Box 11.1).
Due to ongoing impacts of colonisation, Aboriginal and Torres Strait 
Islander Peoples have, on average, lower income, poorer nutrition, lower 
school outcomes and employment opportunities, higher incarceration

and higher levels of removal of children than non-Indigenous 
Australians, represented in high comorbidities of chronic diseases and 
mental health impacts (Marmot, 2011; Green and Minchin, 2014; AIHW, 
2015). This relative poverty can reduce climate-adaptive capacities 
while exacerbating climate change vulnerabilities (Nursey-Bray and 
Palmer, 2018). In remote country, this can combine with lack of security 
for food and water, non-resilient housing and extreme weather events, 
contributing to migration off traditional country and into towns and 
cities—with flow-on social impacts such as homelessness, dislocation 
from community and family and disconnection from country and 
spirituality (Mosby, 2012; Brand et al., 2016).

11
1630Chapter 11 Australasia
Recognition of the role of Aboriginal and Torres Strait Islander Peoples 
in identifying solutions to the impacts of climate change is slowly 
emerging (UN, 2018), having been largely excluded from meaningful 
representation from the conception of climate change dialogue through 
to debate and decision-making (Nursey-Bray et  al., 2019). Honouring 
the United Nations Declaration on the Rights of Indigenous Peoples and 
social justice values would support self-determination and the associated 
opportunity for Indigenous Australians to develop adaptation responses 
to climate change (Langton et al., 2012; Nursey-Bray and Palmer, 2018; 
Nursey-Bray et al., 2019), including the adaptive capacity opportunities 
available through Indigenous knowledge (Liedloff et al., 2013; Petheram 
et al., 2015; Stewart et al., 2019) (Cross-Chapter Box INDIG in Chapter 
18). The Uluru Statement from the Heart proposes a pathway and

roadmap forward for enhanced representation of Aboriginal and Torres 
Strait Islander Peoples in decision-making in Australia (Ululru Statement, 
2017). Table 11.11 provides examples of traditional Indigenous practices 
of adaptation to a changing climate. However, due to Indigenous 
methods of knowledge sharing and knowledge holding, such knowledge 
relies disproportionately on elders and seniors, who form a very small 
portion of the total Aboriginal and Torres Strait Islander Peoples of 
Australia, and is limited in the formal literature (ABS, 2016).11.4.2 Tangata Whenua—New Zealand Māori
Māori society faces diverse impacts, risks and opportunities from 
climate change (Table  11.12). Studies exploring climate change 
impacts, scenarios, policy implications, adaptation options and tools 
for Māori society have increased substantially (e.g., (King et al., 2012; 
Bargh et al., 2014; Jones et al., 2014; Bryant et al., 2017; Awatere et al.,

2018; Colliar and Blackett, 2018). Māori priorities surrounding climate 
change risks and natural resource management have been articulated 
in planning documents by many Māori kin groups (e.g., (Ngāti Tahu- 
Ngāti Whaoa Rūnanga Trust, 2013; Raukawa Settlement Trust, 2015; 
Ngai-Tahu, 2018; Te Urunga Kea - Te Arawa Climate Change Working 
Group, 2021), reflecting the importance of reducing vulnerability and 
enhancing resilience to climate impacts and risks through adaptation 
and mitigation.
Māori have long-term interests in land and water and are heavily 
invested in climate-sensitive sectors (agriculture, forestry, fishing, 
tourism and renewable energy) (King et  al., 2010). Large proportions 
of collectively owned land already suffer from high rates of erosion 
(Warmenhoven et al., 2014; Awatere et al., 2018), which are projected 
to be exacerbated by climate-change-induced extreme rainfalls (high

confidence) (RSNZ, 2016; Awatere et  al., 2018). Changing drought Table 11.10 |  Climate-related impacts on Aboriginal and Torres Strait Islander Peoples, country and cultures.
Impacts Implications
Loss of biocultural diversity 
(land, water and sky) (medium 
confidence)Healthy country is critical to Indigenous Australians’ livelihoods, caring for country responsibilities, health and well-being. Damage to land can 
magnify the loss of spiritual connection to land from dispossession from traditional country and leads to disruption of cultural structures. Climate 
change impacts can exacerbate and/or accelerate existing threats of habitat degradation and biodiversity loss and create challenges for traditional 
stewardship of landscapes (Mackey and Claudie, 2015)
Climate-driven loss of native 
title and other customary lands

(medium confidence)Traditional coastal lands lost through erosion and rising sea level, with associated mental health implications from loss of cultural and traditional 
artefacts and landscapes, including the destruction and exhumation of ancestral graves and burial grounds. This is also occurring and predicted 
to intensify in the low-lying islands of the Torres Strait (TSRA, 2018; Hall et al., 2021) and was also noted during the extreme bushfires in Eastern 
Australia in late 2019 and early 2020.
Changing availability of traditional 
foods and forced diet change 
(medium confidence)Human health impacts can be exacerbated by climate change through the changing availability of traditional foods and medicines, while outages and 
the high costs of electricity can limit the storage of fresh food and medication (Kingsley et al., 2013; Spurway and Soldatic, 2016; Hall and Crosby, 
2020)
Changing climatic conditions 
for subsistence food harvesting

(medium confidence)Climate-change-induced SLR and saltwater intrusion can limit the capacity for traditional Indigenous floodplain pastoralism and affect food security, 
access to and affordability of healthy, nutritional food (Ligtermoet, 2016; Spurway and Soldatic, 2016)
Extreme weather events triggering 
disasters (high confidence)Increasing frequency or intensity of extreme weather events (floods, droughts, cyclones, heatwaves) can cause disaster responses in remote 
communities, including infrastructure damage of essential water and energy systems and health facilities (TSRA, 2018; Hall and Crosby, 2020)
Heatwave impacts on human 
health (high confidence)Heatwaves can occur in many regions of Australia. Tropical regions can experience prolonged seasons of high temperatures and humidity levels, 
resulting in extreme heat stress risks. For example, the Torres Strait Islands are already categorised under the U.S. National Oceanic and Atmospheric

Administration (NOAA) Heat Index as a danger zone for extreme human health risk during summer (TSRA, 2018)
Health impacts from changing 
conditions for vector-borne 
diseases (high confidence)Climate change can alter exposure and increase risk for remote Indigenous Peoples to infection from waterborne and insect-borne diseases, especially 
if medical services are limited or damaged by extreme weather events. For example, in the Torres Strait Islands the changing climate is affecting the 
range and extension of the Aedes albopictus and Aedes aegypti mosquitoes that can carry and transmit dengue and other viruses (Horwood et al., 
2018; TSRA, 2018)
Unadaptable infrastructure for 
changing environmental conditions 
(high confidence)Poorly designed, inferior quality and unmaintained housing can create health challenges for tenants in extreme heat (Race et al., 2016). Essential

community-scale water and energy service infrastructure, unpaved roads, sea walls and stormwater drains can fail in extreme weather events 
(McNamara et al., 2017)
Drinking water security (medium 
confidence)Predicted continued increases in arid conditions in Australia are expected to reduce the recharge rate of finite groundwater supplies (Barron et al., 
2011). For remote communities reliant on groundwater for drinking supplies, this water insecurity creates vulnerabilities from over-extraction and 
lack of access (Jackson et al., 2019; Hall and Crosby, 2020). This groundwater can also have microbial contamination from sewage and chemicals 
supporting bacterial growth, such as high iron levels supporting the growth of Burkholderia pseudomallei that causes melioidosis in humans and 
animals (Kaestli et al., 2019). In the Torres Strait, increasing reliance on desalination for drinking water raises costs for fuel and its associated 
transport (Beal et al., 2018)

1631occurrence, particularly across eastern and northern New Zealand, 
is also projected to affect primary sector operations and production 
(medium confidence) (King et  al., 2010; Smith et  al., 2017; Awatere 
et al., 2018). Further, many Māori-owned lands and cultural assets, such 
as marae and urupa, are located on coastal lowlands vulnerable to sea 
level rise (SLR) impacts (high confidence) (Manning et al., 2014; Hardy 
et al., 2019). Māori tribal investment in fisheries and aquaculture faces 
substantial risks from changes in ocean temperature and acidification 
and the downstream impacts on species distribution, productivity and 
yields (medium confidence) (Law et al., 2016). A clearer understanding of 
climate change risks and the implications for sustainable outcomes can 
enable more informed decisions by tribal organisations and governance 
groups.
Changing climate conditions are projected to exacerbate health

inequities faced by Māori (medium confidence) (Bennett et al., 2014; 
Jones et  al., 2014; Hopkins, 2015). The production and ecology of 
some keystone cultural flora and fauna may be impacted by projected 
warming temperatures and reductions in rainfall (medium confidence) 
(RSNZ, 2016; Bond et al., 2019; Egan et al., 2020). Obstruction of access 
to keystone species is expected to adversely impact customary practice, cultural identity and well-being (medium confidence) (Jones et  al., 
2014; Bond et  al., 2019). Social-cultural networks and conventions 
that promote collective action and mutual support are central features 
of many Māori communities, and these practices are invaluable for 
initiating responses to, and facilitating recovery from, climate stresses 
and extreme events (King et  al., 2011; Hopkins et  al., 2015). Māori 
tribal organisations have a critical role in defining climate risks and 
policy responses (Bargh et al., 2014; Parsons et al., 2019), as well as

entering into strategic partnerships with business, science, research 
and government to address these risks (high confidence) (Manning 
et al., 2014; Beall and Brocklesby, 2017; CCATWG, 2017).
More integrated assessments of climate change impacts, adaptation 
and socioeconomic risk for different Māori groups and communities, 
in the context of multiple stresses, inequities and different ways of 
knowing and being (King et al., 2013; Schneider et al., 2017; Henwood 
et al., 2019), would assist those striving to evaluate impacts and risks 
and how to integrate these assessments into adaptation plans (high 
confidence). Better understanding of the social, cultural and fiscal 
implications of sea level rise (SLR) is urgent (PCE, 2015; Rouse et al., 
2017; Colliar and Blackett, 2018), including what duties local and Table 11.11 |  Examples of Aboriginal and Torres Strait Islander Peoples’ practices of adaptation to a changing climate

‘Caring for Country’: Traditional Practices for Holistic Land and Cultural Protection and Adaptation in a Changing Climate Source
Indigenous Protected Area (IPA) management plans enable culturally and ecologically compatible development that contribute to local Indigenous economies(Mackey and Claudie, 
2015).
IPAs can avoid the potential for ‘nature–culture dualism’ that locks out Indigenous access in some protected area legislation because they are based on 
relational values informed by local Indigenous knowledge(Lee, 2016)
Fire management using cultural practices can achieve greenhouse gas emission targets while maintaining Indigenous cultural heritage. (Robinson et al., 2016)
Indigenous Ranger programmes provide a means for Indigenous-guided land management, including for fire management and carbon abatement, fauna studies, 
medicinal plant products, weed management and recovery of threatened species(Mackey and Claudie, 
2015)

Faunal field surveys can engage local, bounded and fine-scale intuitive species location by Indigenous knowledge holders and their knowledge used for 
conservation planning(Wohling, 2009; 
Ziembicki et al., 2013)
Cultural flows in waterways are a demonstration of cultural knowledge, values and practice in action as they are informed by Indigenous knowledge, bound by 
water-dependent values, and define when and where water is to be delivered, particularly in a changing climate(Bark et al., 2015; Taylor 
Table 11.12 |  Climate-related impacts and risks for Tangata Whenua New Zealand Māori
Impact Risks
Changes in drought occurrence and 
extreme weather eventsRisks to Māori tribal investment in forestry, agriculture and horticulture sector operations and production, particularly across eastern and 
northern New Zealand (medium confidence) (King et al., 2010; Awatere et al., 2018; Hardy et al., 2019)
Changes in rainfall, temperature, drought,

extreme weather events and ongoing SLRRisks to potable water supplies (availability and quality) for remote Māori populations ( medium confidence) (RSNZ, 2016; Henwood et al., 
2019)
Changes in rainfall, temperature, drought, 
extreme weather events and ongoing SLRRisks of exacerbating existing inequities (e.g., health, economic, education and social services), social cohesion and well-being (medium 
confidence) (Bennett et al., 2014; Jones et al., 2014)
Changes in rainfall regimes and 
more intense drought combined with 
degradation of lands and waterRisks to the distribution and survival of cultural keystone flora and fauna, as well as cascading risks for Māori customary practice, cultural 
identity and well-being (high confidence) (King et al., 2010; RSNZ, 2016; Bond et al., 2019)
Changes in ocean temperature and 
acidificationRisks to nearshore and ocean species productivity and distribution, as well as cascading risks for Māori tribal investment in the fisheries and

aquaculture sectors (medium confidence) (King et al., 2010; Law et al., 2016)
Sea-level-rise-induced erosion, flooding 
and saltwater intrusionRisks to Māori-owned coastal lands and economic investment as well as risks to community well-being from displacement of individuals, 
families and communities (high confidence) (Manning et al., 2014; Smith et al., 2017; Hardy et al., 2019)
Sea-level-rise-induced erosion, inundation 
and saltwater intrusionRisks to Māori cultural heritage as well as cascading risks for tribal identity and spiritual well-being (medium confidence) (King et al., 2010; 
Manning et al., 2014; RSNZ, 2016)
Impacts of climate change, adaptation and 
mitigation actionsRisks that governments are unable to uphold Māori interests, values and practices under the Treaty of Waitangi, creating new, modern-day 
breaches of the Treaty of Waitangi (high confidence) (Iorns Magallanes, 2019; MfE, 2020a)

11
1632Chapter 11 Australasia
central government might have with respect to actively upholding 
Māori interests under the Treaty of Waitangi (high confidence) (Iorns 
Magallanes, 2019). Intergenerational approaches to climate change 
planning will become increasingly important, elevating political 
discussions about conceptions of rationality, diversity and the rights 
of non-human entities (high confidence) (Ritchie, 2013; Carter et al., 
2018; Ruru, 2018; Munshi et al., 2020).
11.5 Cross-Sectoral and Cross-Regional 
Implications
The impacts and adaptation processes described in Sections 11.3 and 
11.4 are focused on specific sectors, systems and Indigenous Peoples. 
Added complexity, risk and adaptation potential stem from cross-
sectoral and cross-regional interdependencies.
11.5.1 Cascading, Compounding and Aggregate Impacts
11.5.1.1 Observed Impacts
Climate impacts are cascading, compounding and aggregating across 
sectors and systems due to complex interactions (high confidence)

(Pescaroli and Alexander, 2016; Challinor et  al., 2018; Zscheischler 
et al., 2018; Steffen et al., 2019; AghaKouchak et al., 2020; CoA, 2020e; 
Lawrence et al., 2020b; Simpson et al., 2021) (Boxes 11.1, 11.3, 11.4, 
11.5 and 11.6). Cascading impacts propagate via interconnections 
and systemic factors, including supply chains, shared reliance on 
connected biophysical systems (e.g., water catchments and ecosystems), 
infrastructure, essential goods and services and the exercise of 
governance, leadership, regulation, resources and standard practices 
(e.g., in planning and building codes), including lock-in of past decisions 
and experience (CSIRO, 2018; Lawrence et al., 2020b). The capacity of 
critical systems such as information, communication and technology, 
water infrastructure, health care, electricity and transport networks, is 
being stretched, with impacts cascading to other systems and places, 
exacerbating existing hazards and generating new risks (Cradock-

Henry, 2017) (11.3.6; 11.3.10; Box  11.1). Temporal or spatial overlap 
of hazards (e.g., drought, extreme heat and fire; drought followed by 
extreme rainfall) are compounding impacts (Zscheischler et al., 2018) 
and affecting multiple sectors.
Extreme events such as heatwaves, droughts, floods, storms and 
fires have caused deaths and injuries (Deloitte, 2017a) (11.3.5.1), 
and affected many households, communities and businesses via 
impacts on ecosystems, critical infrastructure, essential services, food 
production, the national economy, valued places and employment. This 
has created long-lasting impacts (e.g., mental health, homelessness, 
health incidents and reduced health services) (Brown et  al., 2017; 
Brookfield and Fitzgerald, 2018; Rychetnik et al., 2019) and reduced 
adaptive capacity (Friel et al., 2014; O’Brien et al., 2014; Ding et al., 
2015; CoA, 2020e) (Box 11.1, Box 11.3, 11.3.1–11.3.10).
In New Zealand, extreme snow, rainfall and wind events have

combined to impact road networks, power and water supplies and have 
impeded interdependent wastewater and stormwater services and business activities (Deloitte, 2019; Lawrence et al., 2020b; MfE, 2020a) 
(Box  11.4). Community and infrastructure services are periodically 
disrupted during extreme weather events, triggering impacts from the 
interdependencies across enterprises and individuals (Glavovic, 2014; 
Paulik et al., 2021).
Slow-onset climate change impacts have also had cascading and 
compounding effects. For example, degradation of the GBR by ocean 
heating, acidification and non-climatic pressures (Marshall et al., 2019), 
repeated pluvial, fluvial and coastal flooding of some settlements (Paulik 
et al., 2019a; Paulik et al., 2020), long droughts and water insecurity 
in rural communities (Tschakert et al., 2017) and the gradual loss of 
species and ecological communities have caused substantial ecological,

social and economic losses. Indigenous Peoples have especially been 
impacted by multiple and complex losses (Johnson et al., 2021) (11.4).
11.5.1.2 Projected Impacts
Cascading, compounding and aggregate impacts are projected to grow 
due to a concurrent increase in heatwaves, droughts, fires, storms, 
floods and sea level (high confidence) (CSIRO, 2020; Lawrence et al., 
2020b). Urban wastewater, stormwater and water supply systems are 
particularly vulnerable in New Zealand (Paulik et al., 2019a; Hughes 
et al., 2021) to pluvial flooding (Box 11.4) and to sea level rise (SLR) 
(Box 11.6), with flow-on effects to settlements, insurance and finance 
sectors, and governments (Lawrence et  al., 2020b). Furthermore, 
consecutive heavy rainfall events in late summer and autumn, 
following drought conditions in low-lying modified wetland areas, 
have implications for the operation of flood control infrastructure as 
increased rainfall intensity, land subsidence and sea level rise (SLR)

compound and result in the retention of floodwaters (Pingram et al., 
2021).
In Australia, the aggregate loss of wealth due to climate-induced 
reductions in productivity across agriculture, manufacturing and service 
sectors is projected to exceed AUD$19 billion by 2030, AUD$211 billion 
by 2050 and AUD$4 trillion by 2100 for RCP8.5 (Steffen et  al., 
2019) (Table  11.13). Projected impacts also cascade across national 
boundaries via value chains, markets, movement of humans and other 
organisms and geopolitics (e.g., migration from near-neighbours as a 
pathway for adaptation, mobile climate-sensitive diseases and changes 
in production and trade patterns) (Lee et al., 2018; Nalau and Handmer, 
2018; Schwerdtle et al., 2018; Dellink et al., 2019). The scale of impacts 
is projected to challenge the adaptive capacity of sectors, governments 
and institutions (Steffen et al., 2019), including the insurability of assets 
and risks to lenders (Storey and Noy, 2017).

11.5.1.3  Adaptation
Coordinating adaptation strategies and addressing underlying 
exposure and vulnerability can increase resilience to cascading, 
compounding and aggregate impacts (high confidence) (Table 11.17; 
11.7.3). Systems understanding, network analysis, stress testing, spatial 
mapping, collaboration, information sharing and interoperability across 
states, sectors, agencies and value chains, as well as national-scale 
facilitation, can increase adaptive capacity (Espada et al., 2015; CoA, 
2020e; Cradock-Henry et al., 2020b; Jozaei et al., 2020). Greater system

1633diversity, modularity, redundancy, adaptability and decentralised control 
can reduce the risk of cascading failures and system breakdown (Sinclair et  al., 2017; Sellberg et  al., 2018). Addressing existing vulnerabilities in systems can reduce susceptibility and improve the resilience of interdependent systems (11.7.3). Multi-level leadership, including national and sub-national policies, laws and finance can reduce and manage aggregate risks supported by the enablers in Table 11.17.
Anticipatory governance and agile decision-making can build resilience

to cascading, compounding and aggregate impacts (high confidence) (Boston, 2016; Deloitte, 2016; Steffen et al., 2019; CoA, 2020e; CSIRO, 2020; Lawrence et  al., 2020b; MfE, 2020c). There is uncertainty about whether standard integrated assessment models can estimate cascading and compounding impacts across systems and sectors, but systems methodologies and social network analysis hold promise (Stoerk et  al., 2018; Cradock-Henry et  al., 2020b). Interventions at the landscape, building and individual scales can reduce the negative health effects of current and future extreme heat, if integrated in well-communicated heat action plans with robust surveillance and monitoring (Jay et al., 2021).
In Australia, the National Disaster Risk Reduction Framework (CoA,

2018b), National Recovery and Resilience Agency and Australian Climate Service (CoA, 2021) can provide some support for adaptation across multiple sectors. New Zealand has effective partnerships across critical infrastructure through lifelines groups, but organisational silos and lack of stress testing of plans hamper coordinated decision-making during crises and for adaptation (Brown et al., 2017; Lawrence et al., 2020b). The New Zealand national risk assessment, national adaptation plan, forthcoming Climate Change Adaptation Act and monitoring of adaptation progress by the Climate Change Commission provide a framework for anticipating climate change risks (MfE, 2020a).
11.5.2  Implications for National Economies

The implications of climate change for national economies are significant (high confidence). The costs associated with lost productivity, disaster relief expenditure and unfunded contingent liabilities represent a major risk to financial system stability (MfE, 2020a). Costs include significant and often long-term social impacts, temporary dislocation, business disruption and impacts on employment, education, community networks, health and well-being (Deloitte, 2017a). Climate change disrupts international patterns of agricultural production and trade in ways that may be negative but that also may lead to new opportunities for agriculture (Mosnier et al., 2014; Nelson et al., 2014; Lee et al., 2018). Net exports may increase following global climate shocks (Lee et al., 2018), but the longer-term effects on GDP are likely to be negative (Dellink et al., 2019).
11.5.2.1  Observed Impacts

In Australia, during 2007–2016, total economic costs from natural disasters averaged AUD$18.2  billion per year
 (Deloitte, 2017a). 
Individual weather-related disaster costs across multiple sectors have exceeded AUD$4 billion, such as the 2009 fires in Victoria (Parliament of Victoria, 2010), the 2010–2011 floods in south-east Queensland (Deloitte, 2017b), the 2019 floods in northern Queensland (Deloitte, 2019) and the 2019–2020 fires in southern and eastern Australia (Box 11.1).
In New Zealand, the annual cost of rural fire to the economy has been

estimated at NZD$67 million, with indirect ‘costs’ potentially two to three times the direct costs (Scion, 2018). Insured losses from weather-related disasters cost almost NZD$1 billion during 2015–2021 (ICNZ, 2021). Floods cost the New Zealand economy at least NZD$120 million for privately insured damages between 2007 and 2017 (D. Frame et al., 2018). The 2007/2008 drought cost NZD$3.2 billion and the 2012/13 drought cost NZD$1.6 billion, of which about 20% could be attributed to anthropogenic climate change (Frame et al., 2020) (11.3.11).
The intangible costs of climate impacts, including death and injury,

impacts on health and well-being, education and employment, community connectedness and the loss of ancestral lands, cultural sites and ecosystems (Barnett et al., 2016; Warner et al., 2019), affect multiple sectors and systems and exacerbate existing vulnerabilities. While often incommensurable, intangible costs may be far higher than the tangible costs. For example, following the Victorian fires in 2009, the tangible costs were AUD$3.1  billion while the intangible costs were AUD$3.4 billion; following the Queensland floods in 2010/2011, the tangible costs were AUD$6.7  billion while the intangible costs were AUD$7.4 billion (Deloitte, 2016).
11.5.2.2  Projected Impacts

The economic long-run impact increases with higher levels of warming (high confidence), but there is a wide range in projections. Conservative estimates for the long-run impacts of a 1°C, 2°C or 3°C global warming (relative to 1986–2005) on Australian GDP are −0.3, −0.6 and −1.1%/year, respectively, while for New Zealand the estimates are −0.1, −0.4 and −0.8%/year respectively (Kompas et al., 2018). Moredetailed modelling indicates a loss in Australia’s GDP of 6% by 2070 for 3°C global warming, while a 2.6% GDP rise by 2070 is possible for 1.5°C global warming (Deloitte, 2020). The potential for much more severe effects on GDP is shown in recent estimates, which attempt to account for the increased severity of uncertain effects (e.g., up to 18.5% reduction in Australia’s GDP by mid-century) (Swiss Re, 2021).
In Australia, the total annual cost of damage due to floods, coastal

inundation, forest fires, subsidence and wind (excluding cyclones) is estimated to increase 55% between 2020 and 2100 for RCP8.5 (Mallon et  al., 2019). National damage costs and impacts on asset values could be significant (Table 11.13). The macroeconomic shocks induced from climate change, including reduced agricultural yields, damage to property and infrastructure and commodity price increases, could lead to significant market corrections and potential financial instability (Steffen et al., 2019). Under a ‘slow decline’ scenario by 2060 where Australia fails to adequately address climate change and sustainability challenges, GDP is projected to grow at 0.7% less per year and real wages would be 50% lower than under an ‘outlook scenario’ where Australia meets climate change and sustainability challenges (CSIRO, 2019).
In New Zealand, the value of buildings exposed to coastal inundation 
could increase by NZD$2.55 billion for every 0.1-m increment in sea

11
1634Chapter 11 Australasia
level, that is, NZD$25.5 billion for a 1.0-m sea level rise (SLR) (Paulik 
et al., 2020). Greater understanding is required of the distributional 
impacts, the rate of change of costs over time and the economic 
implications of delayed action (Warner et al., 2020).
11.5.2.3 Adaptation
Investments in mitigation and adaptation can help reduce or prevent 
economic losses now and in the coming decades (IPCC, 2018; Steffen 
et  al., 2019); however, the costs and benefits of mitigation and 
adaptation are not well understood in the region (high confidence) 
(CSIRO, 2019; MfE, 2020a).
In New Zealand, the emphasis has been on rebuilding after climate 
disasters, rather than anticipatory adaptation (Boston and Lawrence, 
2018). Australia is similarly focused on disaster response and recovery, 
even though investment in disaster resilience can provide a cost:benefit 
ratio of 1:2 to 1:11 through reduced post-disaster recovery and

reconstruction (GCA, 2019). Recent Australian and state government 
spending on direct recovery from disasters was around AUD$2.75 billion 
per year, compared to funding for natural disaster resilience of 
approximately AUD$0.1 billion per year (Deloitte, 2017b). The Australian 
government is supporting most of the 80 recommendations from the 
Royal Commission into National Natural Disaster Arrangements, 
including establishing a disaster advisory body and a resilience and 
recovery agency (CoA, 2020e; CoA, 2020b). Australia and New Zealand 
provide humanitarian and disaster assistance across the Pacific, which 
is increasingly focused on climate adaptation and the SDGs (Brolan 
et  al., 2019) as cyclones and floods become amplified by climate 
change (Fletcher et al., 2013) (Table 11.3). Climate change may increase 
current migration flows to and impacts on diaspora in Australia and New Zealand from near-neighbour island nations as they become

increasingly stressed by rising seas, higher temperatures, more droughts 
and stronger storms (Nalau and Handmer, 2018).
Delaying adaptation to climate risks may result in higher overall costs 
in future when adaptation is more urgent and impacts more extreme 
(medium confidence) (Boston and Lawrence, 2018; IPCC, 2018). 
Estimates of the magnitude of adaptation costs and benefits in the 
region are localised and sectoral (e.g., (Thamo et al., 2017) or regionally 
aggregated (Joshi et  al., 2016). Adaptation costs are expected to 
increase markedly for higher RCPs, for example, a tripling of expected 
costs between RCP2.6 and RCP8.5 for sea level rise (SLR) protection 
in Australia (Ware et al., 2020). Existing governance arrangements for 
funding adaptation are inadequate for the scope and scale of climate 
change impacts anticipated; dedicated funding mechanisms that can 
be sustained over generations can enable more timely adaptation 
(Boston and Lawrence, 2018).

11.6 Key Risks and Benefits
Nine key risks have been identified (Table 11.14) based on four criteria: 
magnitude, likelihood, timing and adaptive capacity (Chapter 16). 
Most of the key risks are similar to those in the IPCC AR5 Australasia 
chapter (Reisinger et al., 2014), but the emphasis here is on specific 
systems affected by multiple hazards rather than specific hazards 
affecting multiple systems. The selection of key risks reflects what has 
been observed, projected and documented, noting that there are gaps 
in knowledge, and a lack of knowledge does not imply a lack of risk 
(11.7.3.3). Key risks are grouped into four categories:Table 11.13 |  Economy-wide projected costs (AUD$) of climate change in Australia. (Estimates are not comparable across studies because different methods have been used. 
Estimates for later in the century are speculative because both impacts and adaptation are uncertain.)
Impact 2030 2050 2090 Reference

Damage-related loss of property value in Australia $571 billion $611 billion $770 billion (Steffen et al., 2019)
Property damage in Australia $91 billion/year $117 billion/year (Steffen et al., 2019)
Loss of asset value of road infrastructure (including freeways, main roads 
and unsealed roads) in Australia at risk of a SLR of 1.1 m by 2100$46–60 billion (DCCEE, 2011)
Loss of asset value of rail and tramway infrastructure in Australia at risk 
of a SLR of 1.1 m by 2100$4.9–6.4 billion (DCCEE, 2011)
Loss of asset value of residential buildings in Australia at risk of a SLR of 
1.1 m by 2100 (2008 replacement value)$51–72 billion (DCCEE, 2011)
Loss of asset value of light industrial buildings (used for warehousing, 
manufacturing and assembly activities and services) in Australia at risk of 
a SLR of 1.1 m by 2100$4.2–6.7 billion (DCCEE, 2011)
Loss of asset value of commercial buildings (used for wholesale, retail,

office and transport activities) in Australia at risk of a SLR of 1.1 m by 
2100 (2008 replacement value)$58–81 billion (DCCEE, 2011)
Accumulated loss of wealth due to reduced agricultural productivity and 
labour productivity$19 billion $211 billion $4.2 trillion (Steffen et al., 2019)
Wind damage to dwellings in Cairns, Townsville, Rockhampton and 
south-east Queensland (assuming a 4% discount rate)$3.8 billion $9.7 billion $20 billion(Stewart and Wang, 
2011)
Damage to Australian coastal residential buildings due to SLR (A1B 
scenario, 3.5°C global warming)$8 billion (Wang et al., 2016)

1635Ecosystems at critical thresholds where recent climate change has 
1) Loss and degradation of coral reefs in Australia and associated 
biodiversity and ecosystem service values due to ocean warming 
and marine heatwaves (11.3.2.1, 11.3.2.2, Box 11.2).
2) Loss of alpine biodiversity in Australia due to less snow (11.3.1.1, 
11.3.1.2).
3) Transition or collapse of alpine ash, snow gum woodland, pencil 
and drier conditions with more fires (11.3.1.1, 11.3.1.2)
Zealand due to ocean warming, marine heatwaves and overgrazing

by climate-driven range extensions of herbivore fish and urchins 
(11.3.2.1, 11.3.2.2).
to sea level rise (SLR) (11.3.5, Box 11.6).
stress in rural communities in southwestern, southern and eastern 
mainland Australia due to hotter and drier conditions (11.3.4, 
11.3.5, Box 11.3).
wildlife in Australia due to heatwaves (11.3.5.1, 11.3.5.2, 11.3.6.1, 
11.3.6.2).
Key cross-sectoral and system-wide risk
(SLR) (11.5.1.1, 11.5.1.2, Box 11.1, Box 11.4, Box 11.6).
risks (11.5; 11.7.1, 11.7.2, 11.7.3).

At higher levels of global warming, adaptation costs increase, options 
become limited and risks grow. The ‘burning embers’ diagram in 
Figure 11.6 has four IPCC risk categories: undetectable, moderate, high 
and very high, with transition points defined by different global warming 
ranges. The embers are indicative, based on an assessment of available 
literature and expert judgement (Supplementary Material SM 11.2). 
Outcomes for low and moderate adaptation have been compared, with the 
latter including both incremental and transformative options. Illustrative 
examples of adaptation pathways are shown in Figure 11.7 for low-lying 
coastal areas and Figure  11.8 for heat-related mortality. These figures 
highlight thresholds at which adaptation options become ineffective and 
possible combinations of strategies and options implemented at different 
times to manage emerging risks and changing risk profiles.Caveats: (a) key risks are assessed at regional scales, so they do not

include other risks for finer scales or specific groups; (b) non-climatic 
vulnerabilities are held constant for simplicity; (c) the assessment of 
risk ratings at different levels of global warming is limited by available 
literature; (d) risks increase with global warming, despite the lack 
of an IPCC risk rating beyond very high; and (e) the feasibility and 
effectiveness of adaptations options were not assessed due to limited 
literature (11.7.3.3).
The New Zealand National Climate Change Risk Assessment (MfE, 
2020a) identified the priority risks from climate change for New 
Zealand based on a literature review and expert elicitation. The top 
two risks in each of five domains are as follows:
1. Natural environment
a) risks to coastal ecosystems due to ongoing sea level rise (SLR) and 
extreme weather events
b) risks to indigenous ecosystems and species from invasive species
2. Human environment
a) risks to social cohesion and community well-being from 
displacement of people

b) risks of exacerbating existing inequities and creating new and 
additional inequities from distribution impacts
3. Economy
a) risks to governments from economic costs associated with lost 
productivity, disaster relief expenditure and unfunded contingent 
liabilities
b) risks to the financial system from instability
4. Built environment
a) risk to potable water supplies due to changes in rainfall, 
temperature, drought, extreme weather events and ongoing sea 
level rise (SLR)
b) risks to buildings due to extreme weather events, drought, 
increased fire weather and ongoing sea level rise (SLR)
5. Governance
a) risk of maladaptation due to practices, processes and tools that do 
not account for uncertainty and change over long time frames
b) risk that climate change impacts across all domains will be 
exacerbated, because current institutional arrangements are not 
fit for adaptation
Not all of these risks feature as key risks for the wider Australasia

region; nonetheless, they are reflected across Chapter 11 and remain

11
1636Chapter 11 Australasia
Table 11.14 |  Key risks from climate change based on assessment of the literature and expert judgement (Supplementary Material SM 11.2). Assessment criteria are magnitude, 
timing, likelihood and adaptive capacity. Risk drivers are hazards, exposure and vulnerability. Adaptation options describe ways in which risks can be reduced. Confidence ratings 
are based on the amount of evidence and agreement between lines of evidence.
Key risk
(confidence rating) 
(Chapter reference)Consequences influenced by hazards, exposure, vulnerability and adaptation options
1. Loss and degradation 
of tropical shallow coral 
reefs and associated 
ecosystem service 
values in Australia due 
to ocean warming and 
marine heatwaves
(very high confidence)
(11.3.2, Box 11.2)Consequences:  Widespread destruction of coral reef ecosystems and dependent socio-ecological systems. Three mass bleaching events from 2016 to 2020 have

already caused significant loss of corals in shallow-water habitats across the GBR. Globally, bleaching is projected to occur twice each decade from 2035 and 
annually after 2044 under RCP 8.5 and annually after 2051 under RCP4.5. A 3°C global warming could cause over six times the 2016 level of thermal stress.
Hazards:  Increase in background warming and marine heatwave events degrade reef-building corals by triggering coral bleaching events at a frequency greater 
than the recovery time. Fish populations also decline during and following heatwave events.
Exposure:  Increasing geographic area affected by rate and severity of ocean warming
Vulnerability:  Vulnerability to increases in sea temperature is already very high because of other stressors on the ecosystem, including sediment, pollutants and 
overfishing.
Adaptation options:  Minimising other stressors. Efforts on the GBR may slow the impacts of climate change in small sections or reduce short-term socioeconomic

ramifications, but they will not prevent widespread bleaching.
2. Loss of alpine 
biodiversity in Australia 
due to less snow
(11.3.1, Table 11.2, 
Table 11.3, Table 11.4, 
Table 11.5)Consequences: Loss of endemic and obligate alpine wildlife species and plant communities (feldmark and short alpine herb fields) as well as increased stress on 
snow-dependent plant and animal species.
Hazards: Projected decline in annual maximum snow depth by 2050 is 30–70% (low emissions) and 45–90% (high emissions); projected increases in 
temperature and decreases in precipitation.
Exposure: Alpine species face elevation squeeze due to lack of nival zone, and alpine environments have restricted geographic extent.
Vulnerability: Narrow ecological niche of species including snow-related habitat requirements; encroachment from sub-Alpine woody shrubs; vulnerability 
generated by non-climatic stressors including weeds and feral animals, especially horses

Adaptation options: Reducing pressure on alpine biodiversity from land uses that degrade vegetation and ecological condition, along with weed and pest 
3. Transition or collapse 
of alpine ash, snow gum 
woodland, pencil pine 
and northern jarrah 
forests in southern 
Australia due to hotter 
and drier conditions 
with more fires
(11.2, 11.3.1, 11.3.2, 
Box 11.1)Consequences: If regenerative capacities of the dominant (framework) canopy tree species are exceeded, a long-lasting or irreversible transition to a new 
ecosystem state is projected with loss of characteristic and framework species, including loss of some narrow-range endemics.
Hazards: Hotter and drier conditions have increased extreme fire weather risk since 1950, especially in southern and eastern Australia. The number of severe fire 
weather days is projected to increase 5–35% (RCP2.6) and 10–70% (RCP8.5) by 2050

Exposure: Shift in landscape fire regimes to larger, more intense and frequent wildfires over extensive areas (~10 million hectares) of forests and woodlands 
from longer fire seasons and more hazardous fire conditions and increasing human-sourced ignitions from urbanisation and projected increase in frequency of 
lightning strikes
Vulnerability: The resilience and adaptive capacity of the forests is being reduced by ongoing land clearing and degrading land management practices
Adaptation options: Increased capacity to extinguish wildfires during extreme fire weather conditions; avoiding and reducing forest degradation from 
inappropriate forest management practices and land use.
4. Loss of kelp forests in 
southern Australia and 
southeast New Zealand 
due to ocean warming, 
marine heatwaves 
and overgrazing by 
climate-driven range 
extensions of herbivore 
fish and urchins

(11.3.2)Consequences: Observed decline in giant kelp in Tasmania since 1990, with less than 10% remaining by 2011 due to ocean warming. Extensive loss of kelp, 
−140,187 hectares across Australia, loss of bull kelp in southern New Zealand, replaced by the introduced kelp following the 2017/2018 marine heatwave. 
Further loss of native kelp is projected with warming oceans.
Hazards: Ocean warming and marine heatwave events
Exposure: Coastal waters around Australia and New Zealand
Vulnerability: Giant kelp are already federally listed in Australia as an endangered marine community type. In Australia, kelp forests are vulnerable to 
nutrient-poor East Australian Current waters pushing further south, warming waters and increased herbivory from range-extending species.
Adaptation options: Minimising other stressors, local restoration and transplantation of heat-tolerant phenotypes.
5. Loss of human and 
natural systems in 
low-lying coastal areas 
from ongoing SLR

(11.2, 11.3.2, 11.3.5, 
11.3.10, 11.4, 
Table 11.3; Box 11.6)Consequences: Nuisance and extreme coastal flooding are already occurring due to SLR. For 0.2- to 0.3-m SLR, coastal flooding is projected to become more 
frequent, for example, the current 1-in-100-year flood would occur every year in Wellington and Christchurch. For 0.5-m SLR, the value of buildings in New 
Zealand exposed to coastal inundation could increase by NZD$12.75 billion and the current 1-in-100-year flood in Australia could occur several times a year. 
For 1.0-m SLR, the value of exposed assets in New Zealand would be NZD$25.5 billion. For 1.1-m SLR, the value of exposed assets in Australia would be 
AUD$164–226 billion. This would be associated with the displacement of people, disruption and reduced social cohesion, degraded ecosystems, loss of cultural 
heritage and livelihoods and loss of traditional lands and sacred sites.

Hazards: Rising sea level (0.2–0.3 m by 2050, 0.4–0.7 m by 2090), storm surges, rising groundwater tables.
Exposure: Population growth, new and infill urbanisation, tourism developments in low-lying coastal areas. Buildings, roads, railways, electricity and water 
infrastructure. Torres Strait Island and remote Māori communities are particularly exposed and sensitive.
Vulnerability: Ineffective planning regulations, reduced availability and increased cost of insurance and costs to governments as insurers of last resort. 
Inadequate investment in avoidance and preparedness exacerbating underlying social vulnerabilities. Financial and physical capacities to cope and adapt are 
uneven across populations, creating equity issues.
Adaptation options: Risk reduction coordinated across all levels of government with communities. Statutory planning frameworks, decision tools and funding

mechanisms that can address the changing risk. Planning and land use decisions, including managed retreat where it is inevitable. Improved capacity of 
emergency services, early-warning systems, improved planning and regulatory practice and building and infrastructure design standards. Options that anticipate 
risk and adjust as conditions change.

1637Key risk
6. Disruption and 
decline in agricultural 
increased stress in rural 
south western, southern 
and eastern mainland 
and drier conditions.
(11.2, 11.3.4, 11.3.6.3, 
11.4.1, Table 11.11, 
Boxes 11.1, 11.3)Consequences: Projected decline in crop, horticulture and dairy production, for example, decline in median wheat yields by 2050 of up to 30% in southwest 
Australia and up to 15% in southern Australia. Increased heat stress in livestock by 31–42 days per year by 2050. Reduced winter chilling for horticulture. 
Increased smoke impacts for viticulture. Flow-on effects for agricultural supply chains, farming families and rural communities across southwestern, southern and 
southeastern Australia, including the MDB.

Hazards: Hotter and drier conditions with constraints on water resources and more frequent and severe droughts in southwestern, southern and eastern Australia.
Exposure: Across southwestern, southern and eastern Australia, many production regions are exposed, including the MDB, which supports agriculture worth 
AUD$24 billion/year, 2.6 million people in diverse rural communities and important environmental assets containing 16 Ramsar Convention-listed wetlands.
Vulnerability: Existing financial, social, health and environmental pressures on rural, regional and remote communities. Existing competition for water resources 
among communities, industries and environment and uncertainty about sharing of water under a drying climate.
Adaptation options: Improved governance and collaboration to build rural resilience, including regional and basin-scale initiatives. Improved water policies and

initiatives (e.g., MDB plan) and changes in management and technologies. Resilience-focused planning for rural settlements, land use, industry, infrastructure 
and value chains. Adoption of information, tools and methods to better manage uncertainty, variability and change. Incremental changes in farm management 
practices (e.g., stubble retention, weed control, water-use efficiency, sowing dates, cultivars). In some regions, major changes may be necessary, for example, 
diversification in agricultural enterprises, transition to different land uses (e.g., carbon sequestration, renewable energy production, biodiversity conservation) or 
migration to another area. Flows in waterways based on Indigenous knowledge to protect cultural assets.
7. Increase in 
heat-related mortality 
and morbidity for people 
and wildlife in Australia
(11.2, 11.3.1, 11.3.5,

11.3.6, 11.4)Consequences: During 1987–2016, natural disasters caused 971 deaths and 4370 injuries, with more than 50% due to heatwaves. Annual increases are 
projected for excess deaths, additional hospitalisations and ambulance callouts. Heatwave-related excess deaths in Melbourne, Sydney and Brisbane are 
projected to increase by about 300/year (RCP2.6) to 600/year (RCP8.5) during 2031–2080 relative to 142/year during 1971–2020, assuming no adaptation. 
Significant heat-related mortality of wildlife species (flying foxes, freshwater fish) has been observed and is projected to increase.
Hazards: Increased frequency, intensity and duration of extreme heat events
Exposure: Pervasive but differentially affecting some wildlife species depending on their thermal tolerances and occupational groups (e.g., outdoor workers) and 
those living in high exposure areas (e.g., urban heat islands). Health risks multiply with other harmful exposures, for example, to wildfire smoke.

Vulnerability: Lower adaptive capacity for young/old/sick people, those in low-quality housing and of lower socioeconomic status, and areas served by fragile 
utilities (power, water). Remote locations with extreme heat and inadequate cooling in housing infrastructure (such as remote indigenous communities). For 
wildlife, impacts of extreme heat events are being amplified by habitat loss and degradation.
Adaptation options: Urban cooling interventions including irrigated green infrastructure and increased albedo, education to reduce heat stress, heatwave/fire 
early-warning systems, battery/generator systems for energy system security, building standards that improve insulation/cooling, accessible / well-resourced 
primary health care. For wildlife, removing human stressors, reducing pressures from ferals and weeds, and ensuring suitable habitat.
8. Cascading, 
compounding and 
aggregate impacts 
on cities, settlements, 
infrastructure, supply 
chains and services due

to extreme events
(11.2, 11.3.4, 11.3.5, 
11.3.6, 11.3.7, 11.3.8, 
11.3.9, 11.3.10, 11.4, 
11.5.1, Boxes 11.1, 11.4, 
11.6)Consequences: Widespread and pervasive damage and disruption to human activities generated by the interdependencies and interconnectedness of 
physical, social and natural systems. Examples include failure of transport, energy and communication infrastructure and services, heat stress, injuries and 
deaths, air pollution, stress on hospital services, damage to agriculture and tourism, insurance loss from heatwaves and fires; failure of transport, stormwater 
and flood-control infrastructure and services from floods and storms; water restrictions, reduced agricultural production, stress for rural communities, 
mental health issues, lack of potable water from droughts; damage to buildings, roads, railways, electricity and water infrastructure, loss of assets and lives,

displacement of people, reduced social cohesion, degraded ecosystems from extreme SLR. Large aggregate costs due to lost productivity and major disaster 
relief expenditures, creating unfunded liabilities and supply chain disruption, e.g., 2019–2020 Australian fires cost AUD$8 billion. The long-run impact of a 
1°C, 2°C or 3°C global warming (relative to 1986–2005) on Australian GDP is estimated at −0.3%/year, −0.6%/year and −1.1%/year respectively, while for 
New Zealand estimates are −0.1%/year, −0.4%/year and −0.8%/year respectively. Impacts on Māori tribal investments in forestry, agriculture, horticulture, 
fisheries and aquaculture.
Hazards: Heatwaves, droughts, fires, floods, storms and SLR. This includes cascading and compound events such as heatwaves with fires, storms with floods or 
droughts followed by heavy rainfall and extreme sea levels.

Exposure: Highly populated areas, rural and remote settlements, traditional lands and sacred sites. Greater urban density and population growth increases 
exposure in high-risk areas. Different exposure for different hazards, for example, heatwaves: urban and peri-urban areas; fire: peri-urban areas and settlements 
near forests; floods: people, property and infrastructure from pluvial floods in cities and settlements and fluvial floods on floodplains; storms: buildings and 
infrastructure in cities and settlements.
Vulnerability: Existing social and economic challenges (e.g., those caused by COVID-19) and socioeconomic and cultural inequalities; competing resource and 
land use demands across sectors; inadequate planning, policy, governance, decision-making and disaster resilience capacity; and non-climatic stresses on 
ecosystems. Vulnerabilities generated by interdependencies and interconnectedness of physical, social and natural systems.

Adaptation options: Flexible and timely adaptation strategies that prepare socioeconomic and natural systems for surprises and unexpected threats. Multi-sector 
coordinated actions that address widespread impacts, redress existing vulnerabilities and building adaptive capacity and systemic resilience. Improved 
coordination between and within levels of governments, communities and private sector. Greater use of dynamic decision frameworks and suitable economic 
and social assessment tools. Improved emergency services and early-warning systems; use of climate-resilient standards for buildings and infrastructure. 
Transformational adaptations (e.g., managed retreat) that can be planned in stages.

11
1638Chapter 11 Australasia
01°C2°C3°C4°CLow
adaptationModerate
Loss of kelp forests in
southern Australia and
southeast New ZealandLow
Loss of alpine
biodiversity in
AustraliaLow
Forest ecosystem
collapse or transition in
southern AustraliaLow
Loss of natural and
human systems in low-lying
coastal areas
Disruption/decline in
agricultural production in
southern and eastern
heat-related
mortality in AustraliaLow
Cascading impacts on
cities and settlements in
Australia and New Zealand Low
Failure of institutions and
governance systems to
manage climate risksBurning embers diagram for each of the nine key risks for low and moderate adaptation
Confidence rating•
••
•••
••••

rangeGlobal warming relative to 1850–1900
Very high
High
Moderate
UndetectableRisk
categoriesLow
Loss and degradation
of coral reefs in
AustraliaGlobal warming relative to 1850–1900•••••••••
••••••••
••••••••
••••••
••••••••
••••••••••••••
••••••••
••••••••
••••••••
•••••••
•••••••••••••
•••••••
••••••••
••••••••••••••••
•••••••••
Figure 11.6 |  Burning embers diagram for each of the nine key risks for low and moderate adaptation. The risk categories are undetectable, moderate, high and 
very high. While there is no risk category beyond very high, risks obviously get worse with further global warming, and the risk for coral reefs is already very high. The assessment 
is based on available literature and expert judgement, summarised in Table 11.14 and described in Supplementary Material SM 11.2. The global warming range associated with

each risk transition has a confidence rating (**** very high, *** high, ** moderate, * low) based on the amount of evidence and level of agreement between lines of evidenceKey risk
9. Inability of 
institutions and 
governance systems to 
manage climate risks
(11.2, 11.3.5, 11.3.6, 
11.3.7, 11.3.8, 11.3.10, 
11.4, 11.5.1, 11.7.2, 
Boxes 11.1–11.6)Consequences: Climate hazards overwhelm the capacity of institutions, organisations, systems and leaders to provide necessary policies, services, resources, 
coordination and leadership. Failed adaptation at the institutional and governance levels has widespread, pervasive impacts on all areas of society. This 
includes a reliance on reactive, short-term decision-making that locks in existing exposures, leaves perverse incentives and interconnected and systemic impacts

unaddressed and generates high costs and fiscal impacts. This worsens vulnerability and leads to maladaptation, inequities and injustices within and across 
generations, as well as actions that do not uphold the rights, interests, values and practices of Indigenous Peoples. Resultant failure to take adaptation action 
generates litigation risk.
Hazards: Increasing frequency, duration, severity and complexity of extreme weather events, droughts and SLR
Exposure: All sectors, communities, organisations and governments
Vulnerability: Fragmented institutional and legal arrangements, under-resourcing of services, lack of dedicated adaptation funding instruments and 
resources to support communities and local government, uneven capability to manage uncertainty and conflicting values and competing policy and 
political interests.
Adaptation options: Pre-emptive options that avoid and reduce risks. Redesign of policy and statutory frameworks and funding instruments for addressing

changing risks and uncertainties that enable just and collaborative governance across scales and domains. Addressing existing vulnerabilities and capacity, 
capability and leadership deficits within and across all levels of government, all sectors, Indigenous Peoples and communities. Risk and vulnerability assessment 
methodologies and decision-making tools that build resilience and address changing risks and vulnerabilities. Co-designed adaptation approaches implemented 
with communities, including Māori tribal organisations and Australian Aboriginal and Torres Strait Island peoples.

16390.1 m 0.2 mIllustrative adaptation pathway for risk to natural and human systems
in low-lying coastal areas due to sea level rise
Managed retreat
Retreat
Accommodate
ProtectNo build zones
Clean-up responses
Elevated buildings
Insurance
premium incentives
Now
Engineering
options
Change stormwater
systems
Large-scale
engineeringi. Long lead time to align with social goals.= Decision trigger points
ii. Shifting from gravity to pumped stormwater 
and groundwater systems buys time 
before managed retreat becomes 
necessary.
iii. Elevated building costs can be reduced by 
insurance incentives.
iv. Ecosystem based adaptation is effective 
for initial sea level rise, but may need to 
combine with engineering options to be 
effective in the long term.
v. Large-scale engineering, e.g. tidal 
barrages, will be effective long term but 
only for large populations willing to pay 
the high cost.
0.5 m
r
i.
ii.
iii.
iv.
v.

Figure 11.7 |  Illustrative adaptation pathway for risk to natural and human systems in low-lying coastal areas due to sea level rise.
1°C 1.9°CIllustrative adaptation pathway for risk of heat-related mortality and morbidity
for people and wildlife in Australia due to heatwaves
2.6°CEducation and behaviour change
Air conditioning
Improvements in healthcare services
Heatwave early warning systems
Retreat from the heat
Now
Passive building design
Heat-reducing urban landscapes
Rooftop solari. Behaviour change is a reliable adaptation 
option and can work in tandem with other 
options.
ii. Air-conditioning will reduce heat stress to its 
thermal limits, reliance on heat-sensitive 
electricity generation, solar power can 
support its use.
iii. Heat warning systems in tandem with 
improved healthcare services.
iv. Incremental, temporary relocation of people, 
animals, or events to cooler locations.
v. Transformative retreat permanent relocation 
of people, animals, or events to cooler

locations.
vi. Can be done at low cost and incrementally, 
increased water demand needed for 
vegetation can reduce effectiveness as heat 
increases.
s
s
t
w
s
ri.
ii.
iii.
iv.
vi.v.
Global warming level= Decision trigger points
Figure 11.8 |  Illustrative adaptation pathway for risk of heat-related mortality and morbidity for people and wildlife in Australia due to heatwaves.

11
1640Chapter 11 Australasia
priorities for New Zealand to address through the National Adaptation 
Plan, its implementation and monitoring.
Short-term benefits from climate change may include reduced winter 
mortality, reduced energy demand for winter heating, increased 
agriculture productivity and forest growth in south and west New 
Zealand and increased forest and pasture growth in southern Australia, 
except where rainfall and soil nutrients are limiting (11.3.4, 11.3.6, 
11.3.10) (medium confidence).
11.7  Enabling Adaptation Decision-Making
11.7.1 Observed Adaptation Decision-Making
The ambition, scope and progress on adaptation by governments 
have risen but are uneven, with a focus on high-level strategies at 
the national level, adaptation planning at sub-national levels and 
new enabling legislation (very high confidence) (Table  11.15a, 
Table  11.15b) (Lawrence et  al., 2015; Macintosh et  al., 2015; MfE, 
2020a). The adaptation process comprises vulnerability and risk

assessments, identification of options, planning, implementation, 
monitoring, evaluation and review. Large gaps remain, especially in 
effective implementation, monitoring and evaluation (Supplementary 
Material SM 11.1) (CCATWG, 2017; Warnken and Mosadeghi, 2018), 
and current adaptation is largely incremental and reactive (very high 
confidence) (Box 11.4, Box 11.6, Table 11.14).
Australia has a National Climate Resilience and Adaptation Strategy, 
a National Recovery and Resilience Agency (11.5.2.3), and the First 
National Action Plan to implement the National Disaster Risk Reduction 
Framework which acknowledges climate change as a disaster risk 
driver (Home Affairs, 2020). States and territories have climate change 
adaptation strategies with plans to address them (Table 11.15a), with 
some adaptation implementation at the state level and, increasingly, at 
the local government level (Jacobs et al., 2016; Warnken and Mosadeghi,

2018) (Table 11.15a). In coastal zones, however, few local government 
planning instruments are being applied (Warnken and Mosadeghi, 
2018; Harvey, 2019; Robb et  al., 2019; Elrick-Barr and Smith, 2021). 
Some businesses and industry sectors are recognising climate-related 
risks and adaptation planning (11.3.4; 11.3.7; 11.3.10) (Harris et  al., 
2016; Hennessy et al., 2016; CBA, 2019). There is an opportunity for 
Australia to undertake a national risk assessment and to develop a 
national climate adaptation implementation plan that is aligned with 
Paris Agreement expectations of a national-level system for adaptation 
planning, monitoring and reporting (Morgan et al., 2019).
New Zealand’s Climate Change Response Act 2019 creates a legal 
mandate for national climate change risk assessments (first one 
completed) (MfE, 2020a) and national adaptation plans (first in 
preparation), as well as a Climate Change Commission to monitor and

report on adaptation implementation. Preparation of natural and built 
environment, strategic planning and climate change adaptation acts is 
under way, including provision for funding and managed retreat (MfE, 
2020c). National coastal guidance is available for adaptation planning 
to address changing climate risks (MfE, 2017a) (Table  11.15b). 
Meanwhile, several local authorities have developed integrated climate change strategies and plans and revised policies and rules to enable 
adaptation (Table 11.15b). Different adaptation approaches continue 
to create confusion and inertia while development pressures continue 
(Schneider et al., 2017). Opportunities for integrated adaptation and 
mitigation planning in regional policies and plans have arisen through 
the Resource Management Amendment Act 2020 (Dickie, 2020), the 
National Policy Statement on Freshwater Management (MfE, 2020b) 
and the revised national coastal guidance (MfE, 2017a), but rely on

funding instruments to be in place and statutes are aligned for their 
effectiveness (very high confidence) (Boston and Lawrence, 2018; 
CCATWG, 2018).
There is growing awareness of the need for more proactive adaptation 
planning at multiple scales and across sectors, and a better 
understanding of future risks and limits to adaptation is emerging 
(medium confidence) (Evans et al., 2014; Archie et al., 2018; Christie 
et al., 2020; MfE, 2020a). Disaster risk reduction is being positioned as 
part of climate change adaptation (Forino et al., 2017; CDEM, 2019; 
Forino et al., 2019; CoA, 2020e; CSIRO, 2020). Public and private climate 
adaptation services are informing climate risk assessments, but they 
are characterised by fragmentation, duplication, inconsistencies, poor 
governance and inadequate funding; addressing these gaps presents 
adaptation opportunities (CCATWG, 2018; Webb et  al., 2019; NESP 
ESCC, 2021) (Table 11.15a, Table 11.15b). Large infrastructure asset

planning is starting to factor in climate risks, but implementation is 
uneven (Gibbs, 2020). Local governments in Australia are increasingly 
implementing adaptation plans, but few monitor or evaluate actual 
outcomes or know how to (Scott and Moloney, 2021).
Observed and projected rates of sea level rise (SLR) (Box 11.6) and 
increased flood frequency (11.3.3) are challenging established uses of 
modelling, risk assessment and cost-benefit analysis, where climate 
change damage functions cannot be projected or are unknown (deep 
uncertainty) or impacts on communities are ambiguous (Infometrics 
and PSConsulting, 2015; Lawrence et  al., 2019a; MfE, 2020a). New 
tools are available in the region (Table 11.17), but uptake cannot be 
assumed (high confidence) (Lawrence and Haasnoot, 2017; Palutikof 
et al., 2019c).
Resilience and adaptation approaches are beginning to converge 
(White and O’Hare, 2014; Aldunce et  al., 2015) (Supplementary

Material SM 11.1) but widespread ‘bounce-back’ resilience-driven 
responses that lock in risk by discounting ongoing and changing 
climate risk (Leitch and Bohensky, 2014; O’Hare et al., 2016; Wenger, 
2017; Torabi et al., 2018) can create maladaptation and impede long-
term adaptation goals (high confidence) (Glavovic and Smith, 2014; 
Dudney et al., 2018).
Local government engagement with communities on adaptation is 
starting to motivate a change towards more collaborative engagement 
practices (Archie et  al., 2018; Bendall, 2018; MfE, 2019; Schneider 
et al., 2020). Nature-based adaptations (Colloff et al., 2016; Lavorel 
et al., 2019; Della Bosca and Gillespie, 2020) and ‘green infrastructure’ 
(medium confidence) (Lin et al., 2016; Alexandra and Norman, 2020) 
are increasingly being adopted (Rogers et al., 2020a).

1641Table 11.15a |  Examples of Australian adaptation strategies, plans and initiatives by government agencies at national, sub-national and regional or local levels. These examples 
have not been assessed for their effectiveness (see Supplementary Material Table SM11.1a).
Jurisdiction Strategies/Plans/Actions
National Level
AustraliaNational Climate Resilience and Adaptation Strategy 2015 (CoA, 2015)
National Disaster Risk Reduction Framework (2018) (CoA, 2018b)
National Recovery and Resilience Agency and Australian Climate Service (CoA, 2021)
Sub-national
Australian Capital Territory (ACT)ACT Climate Change Strategy 2019–2025 (ACT Government, 2019)
Canberra’s Living Infrastructure Plan: Cooling the City (ACT Government, 2020b); ACT Well-being Framework (ACT Government, 2020a)
New South Wales NSW Climate Change Policy Framework (NSW Government, 2016)
Coastal Management Framework (OEH, 2018b) including

Coastal Management Act 2016, State Environmental Planning Policy (Coastal Management) 2018, NSW Coastal Management Manual (OEH, 
2018c; OEH, 2018a)
Northern Territory Northern Territory Climate Change Response: Towards 2050 (DENR, 2020b) three-year action plan (DENR, 2020a)
Queensland Pathways to climate-resilient Queensland: Queensland Climate Adaptation Strategy 2017–2030 (DEHP , 2013)
Sector adaptation plans: https://www.qld.gov.au/environment/climate/climate-change/adapting/sectors-systems
State heatwave risk assessment, 2019 (QFES, 2019)
Planning Act 2016 (Queensland Government, 2020) and the Coastal Protection and Management Act 1995 (Queensland Government, 1995), plus 
supporting initiatives: Coastal Management Plan (DEHP , 2013) and Shoreline Erosion Management Plans (DES, 2018)
Queensland’s QCoast2100 program
South Australia Directions for a Climate Smart South Australia (SA Government, 2019a)

Tasmania Climate Action 21: Tasmania’s Climate Change Action Plan 2017–2021 (State of Tasmania, 2017a)
Tasmania’s 2016 State Natural Disaster Risk Assessment (White et al., 2016a)
Tasmanian Planning Scheme—State Planning Provisions 2017, Coastal Inundation Hazard Code and a Coastal Erosion Hazard Code (Government 
of Tasmania, 2017).
VictoriaIn accordance with the Climate Change Act 2017, Victoria has a Climate Change Adaptation Plan 2017–2020 (Victoria State Government DELWP , 
2016) including a Monitoring, Evaluation, Reporting and Improvement (MERI) framework for Climate Change Adaptation in Victoria (DELWP , 
2018), Victorian Climate Projections (2019) and multiple resources for regions and local government (Victoria DELWP 2020).
Heatwaves in Victoria. A 2018 vulnerability assessment of the state to heatwaves using a Damage and Loss Assessment methodology (Natural 
Capital Economics, 2018)
Western AustraliaWestern Australian Government Adapting to our changing climate

2012 (WA Government, 2016)
State Planning Policy 2.6 – Coastal Planning (SPP2.6)
Regional and local (examples only)
104 have declared climate emergencies to leverage climate action as of September 2021 covering 36.6% of the Australian population (Climate Emergency Declaration, 2022)
Tasmania2017: Tasmanian Planning Scheme – State Planning Provisions. State of Tasmania, 514.
(State of Tasmania, 2017a; State of Tasmania, 2017b)
South Australia Regional integrated vulnerability assessments (IVAs) and adaptation plans (SA Government, 2019a)
NSW Enabling Regional Adaptation (Jacobs et al., 2016)
VictoriaEvery region and catchment management authority in Victoria has an adaptation plan, as does virtually every local government. There are also 
three alliances of multiple local governments working on climate change and new initiatives such as the Climate Change Exchange: https://www.
parliament.vic.gov.au/967-epc-la/inquiry-into-tackling-climate-change-in-victorian-communities

NSW Coastal Zone Management Plan for Bilgola Beach (Bilgola) and Basin Beach (Mona Vale) (Haskoning Australia, 2016)
Queensland Torres Strait Climate Change Strategy (TSRA, 2014), Torres Strait Regional Adaptation and Resilience Plan 2016–2021 (TSRA, 2016)
Climate Risk Management Framework for Queensland Local Government (Erhart et al., 2020)
Douglas Shire Coast Strategic Plan 2019 (Douglas Shire Council, 2019)
Northern Territory Climate Change Action Plan (2011–2020) (Darwin City Council, 2011)

11
1642Chapter 11 Australasia
Some businesses have initiated active adaptation (Aldum et al., 2014; 
Linnenluecke et al., 2015; Bremer and Linnenluecke, 2017; CCATWG, 
2017; MfE, 2018), with most focused on identifying climate risks 
(Aldum et  al., 2014; Gasbarro et  al., 2016; Cradock-Henry, 2017). 
Businesses are more likely to engage in anticipatory adaptation when 
the frequency of climate events is known (McKnight and Linnenluecke, 
2019). Effective cooperation and a positive innovation culture can 
contribute to the collaborative development of climate change 
adaptation pathways (medium confidence) (Bardsley et al., 2018).
Some areas in northern Australia and New Zealand, especially those 
with higher proportions of Indigenous populations, face severe housing, 
health, education, employment and services deficits that exacerbate 
the impacts of climate change (Kotey, 2015) (11.3.5; 11.4; 11.6). 
Where adaptation relies upon an ageing population and an overstretched

volunteer base, vulnerability to climate change impacts is being 
exacerbated (Astill and Miller, 2018; Davies et al., 2018). Adaptation 
options that succeed within remote Indigenous communities are 
founded on connections to traditional lands, alignment with cultural 
values and contribute to social, cultural and economic goals (Nursey-Bray and Palmer, 2018). Knowledge co-production for Indigenous 
adaptation pathways can enable transformative change from colonial 
legacies (Hill et  al., 2020). Learning and experimentation across 
governance boundaries and between agencies and local communities 
enable adaptation to be better aligned with changing climate risks 
and community (high confidence) (Fünfgeld, 2015; Howes et al., 2015; 
Bardsley and Wiseman, 2016; Lawrence et al., 2019b).
There is increasing focus on improving adaptive capacity for transitional 
and transformational responses, but reactive responses dominate (very

high confidence) (Smith et  al., 2015; Schlosberg et  al., 2017; Boston 
and Lawrence, 2018). While extreme events can provide opportunities 
for positive transitions within communities (Cradock-Henry et  al., 
2018b) (e.g., Queensland Reconstruction Authority Building Back 
Better scheme), often rebuilding occurs in at-risk places to aid quick 
recovery (Lawrence and Saunders, 2017). Community-based adaptation 
innovations (Bendall, 2018; Kench et  al., 2018; Forino et  al., 2019) 
include relationship building; use of new decision tools, pathways 
planning with communities, visualisation and serious games (Lawrence 
and Haasnoot, 2017; Schlosberg et al., 2017; Flood et al., 2018; Reiter Table 11.15b |  Examples of New Zealand’s adaptation strategies, plans and initiatives by government agencies at national, sub-national and regional or local levels. NB: These 
examples have not been assessed for their effectiveness (see Supplementary Material Table SM11.1b)

New Zealand central GovernmentThe New Zealand Government’s adaptation policy framework is based on the following legislation: Resource Management Act 1991, Local 
Government Act 2002, National Disaster Resilience Strategy 2019 (CDEM, 2019) and the Climate Change Response (Zero Carbon Amendment) Act 
2002 (CCRA 2002)
Adaptation preparedness report 2020/2021 baseline is the reporting organisation responses from the first information request under the CCRA 
2002 (MfE, 2021) to assist the monitoring of progress and effectiveness of adaptation by the Climate Change Commission
The Department of Conservation’s Climate Change Adaptation Action plan sets out a long-term strategy for climate research, monitoring and 
action; DOC climate adaptation plan
Local GovernmentIn July 2017, a group of 66 local government mayors and council chairs (of 78 in total) endorsed a 2015 local government declaration calling for

urgent responsive leadership and a holistic approach on climate change, with the government needing to play a vital enabling leadership role 
(LGNZ, 2017; Schneider et al., 2017).
Seventeen councils have declared climate emergencies to leverage climate action plans as of September 2021, covering 75.3% of the New Zealand 
population.
The MfE adaptation preparedness report states that 18% of councils (11 of 61 surveyed in 2021) have some sort of plan or strategy to increase 
resilience to climate impacts (MfE, 2021). Out of New Zealand’s 15 regional and unitary councils, 2 have climate adaptation strategies in place. 
One council has conducted a climate risk assessment, and four have one in development. Five councils have climate action plans, and three are in 
Regional Councils (examples only)
Bay of Plenty Regional Council Climate Action Plan July 2019 (non-statutory) Climate Action Plan
Waikato Regional Council Long-Term Plan 2018–2028 (LTP)

Greater Wellington Regional CouncilGWRC’s Climate Change Strategy (October 2015) Climate change strategy implementation
Hutt River Flood Risk Management Plan
Unitary Authorities (examples only)
Auckland CouncilAuckland Unitary Plan
AUP RPS B10
Table B11.9 (bottom of doc)
E36. Natural hazards and flooding
Marlborough District Council Marlborough Environment Plan first to integrate DAPP into plan policies.
Gisborne District Council Tairāwhiti Resource Management Plan (District Plan) March 2020
District Council (example only)
Waimakariri District CouncilInfrastructure Strategy in the Long Term Plan 2017.
Long-Term-Plan-Further-Information-Document-WEB.pdf
Page 113/31

1643et al., 2018; Serrao-Neumann and Choy, 2018); communities of practice; 
and climate information sharing (Astill et al., 2019; Stone et al., 2019).
11.7.2 Barriers and Limits to Adaptation
Major gaps in the adaptation process remain across all sectors 
and at all levels of decision-making (very high confidence) (11.3; 
Table  11.115a, Table  15b). Efforts to build, resource and deploy 
adaptive capacity are slow compared to escalating impacts and risks 
(Stephenson et al., 2018; CoA, 2020e). Barriers to effective adaptation include governance inertia at all levels, hindering the development of 
careful and comprehensive adaptation plans and their implementation 
(Boston and Lawrence, 2018; MfE and Hawke’s Bay Regional Council, 
2020; White and Lawrence, 2020). Lack of clarity about mandate, roles 
and leadership and inadequate funding for adaptation by national and 
state governments and sectors, are slowing adaptation (Lukasiewicz

et al., 2017; Waters and Barnett, 2018; LGNZ, 2019; MfE, 2020c) (11.3; 
11.7.1). Established planning tools and measures were designed for 
static risk profiles, and practitioners are slow to take up tools better 
suited to changing climate risks (CoA, 2020e; Schneider et al., 2020) 
(11.5; Box  11.5). The communication of relevant climate change Table 11.16 |  Examples of barriers to adaptation action in the region
Barrier Source
Lack of consistent policy direction from higher levels and frequent policy reversals (Dedekorkut-Howes et al., 2020)
Conflicts between community-based initiatives, city councils and business interests (Forino et al., 2019)
Different framings of adaptation between local governments (risk) and community groups 
(vulnerability, transformation)(Smith et al., 2015; Schlosberg et al., 2017; McClure and Baker, 2018)
Competing planning objectives (McClure and Baker, 2018)

Divergent perceptions of risk concepts (Button and Harvey, 2015; Mills et al., 2016b; Tonmoy et al., 2018)
Focus on climate variability rather than climate change (Dedekorkut-Howes and Vickers, 2017)
Low prioritisation of climate change adaptation among competing institutional objectives (Glavovic and Smith, 2014; Lawrence et al., 2015; McClure and Baker, 2018)
Constraints in using new knowledge (Temby et al., 2016)
Lack of institutional and professional capabilities and capacity (e.g., to monitor and evaluate 
adaptation outcomes)(Lawrence et al., 2015; Scott and Moloney, 2021)
Lack of understanding of Indigenous knowledge and practices (Parsons et al., 2019)
Lack of authority and political legitimacy (Hayward, 2008; Boston and Lawrence, 2018; CCATWG, 2018; Parsons et al., 2019)
Fear of litigation (Tombs et al., 2018; Iorns Magallanes and Watts, 2019; O’Donnell et al., 2019)

Upfront costs of adaptation relative to competing demands on government expenditure (Gawith et al., 2020; Warren-Myers et al., 2020b)
Private sector
Governance and policy uncertainty, lack of cross-sector coordination, lack of capital 
investment in climate solutions(CCATWG, 2017; Forino et al., 2017; IGCC, 2021a)
Inconsistent hazard information and incomplete understanding of adaptation (CCATWG, 2017; Harvey, 2019)
Mismatch in duration of insurance cover (annual) lending (decades) and infrastructure and 
housing investment (50–100 years)(Storey and Noy, 2017; O’Donnell, 2020)
Perceived unaffordability of adaptation, lack of client demand and awareness of climate 
change risks and limited and inconsistent climate risk regulation in the construction industry(Hurlimann, 2008; Hurlimann et al., 2018)
Translating information into organisations to address disinterest among clients in the 
property industry(Warren-Myers et al., 2020b; Warren-Myers et al., 2020a)

Erosion of adaptive capacity and challenges of transformational adaptation in agriculture 
and rural communities(Jakku et al., 2016)
Communities
Nature of government engagement with communities (Public Participation, 2014; MfE, 2017a; Archie et al., 2018; OECD, 2019b)
Lack of clarity regarding roles and responsibilities (Gorddard et al., 2016; Elrick-Barr et al., 2017; Goode et al., 2017; Waters and Barnett, 2018)
Lack of resourcing of adaptation (Singh-Peterson et al., 2015; Lukasiewicz et al., 2017; Brookfield and Fitzgerald, 2018)
Lack of deep engagement with climate change (Kench et al., 2018; Pearce, 2018)
Diverging perceptions, values and goals within communities (Austin et al., 2018; Fitzgerald et al., 2019; Marshall et al., 2019)
Inequities within and between communities (Eriksen, 2014; Parkinson, 2019)
Lack of sustained engagement, learning and trust between community, scientists and policy 
makers(Serrao-Neumann et al., 2020)

11
1644Chapter 11 Australasia
information remains ad hoc (Stevens and O’Connor, 2015; CCATWG, 
2017; Palutikof et al., 2019c; Salmon, 2019). In Australia, the lack of 
national guidance or adaptation laws creates barriers to adaptation, 
reflected in uneven coastal adaptation based on a wait-and-see 
approach (Dedekorkut-Howes et al., 2020).
There are many barriers to starting adaptation pre-emptively (very 
high confidence) (CCATWG, 2018) (Table 11.16). Recent institutional 
changes in New Zealand indicate that this is changing (11.7.1; 
Table 15b). Many groups are yet to engage deeply with climate change 
adaptation (Kench et al., 2018), and some adaptation processes are 
being blocked (Pearce et al., 2018; Garmestani et al., 2019; Alexandra, 
2020) or exploited to deflect from mitigation responsibilities (Smith 
and Lawrence, 2018; Nyberg and Wright, 2020). Some actors 
are resistant to using climate change information (Tangney and

Howes, 2016; Alexandra, 2020). Fear of litigation and demands for 
compensation can contribute to this reluctance (Tombs et al., 2018; 
O’Donnell et al., 2019)and is increasingly inviting litigation and other 
costs (Hodder, 2019; Bell-James and Collins, 2020). Jurisprudence 
is evolving from cases on projects to cases about decision-making 
accountability in the public and private sectors (Bell-James and Collins, 
2020; Peel et  al., 2020) and rights-based cases (Peel and Osofsky, 
2018). National and sub-national governments may become exposed 
to unsustainable fiscal risk as insurers of last resort, which can lead 
to inequitable outcomes for vulnerable groups and future generations 
(11.3.8), path dependencies and negative effects on physical, social, 
economic and cultural systems (Hamin and Gurran, 2015; Boston and 
Lawrence, 2018). Cross-scale governance tensions can prevent local 
adaptation initiatives from performing as intended (Tschakert et al.,

2016; Piggott-McKellar et al., 2019). Adaptation that draws on Māori 
cultural understanding in partnership with local government in New 
Zealand can lead to more effective and equitable adaptation outcomes 
(MfE, 2020a).
Communities’ vulnerabilities are dynamic and uneven (high confidence). 
In Australia, 435,000 people in remote areas face particular challenges 
(CoA, 2020e). Some groups do not have the time, resources or oppor -
tunity to participate in formal adaptation planning as it is currently or -
ganised (Victorian Council of Social Service, 2016; Tschakert et al., 2017; 
Mathew et al., 2018). Linguistically diverse groups can be disadvantaged 
by social isolation, language barriers and others’ ignorance of the knowl-
edge and skills they can bring to adaptation (Shepherd and van Vuuren, 
2014; Dun et al., 2018) (11.1.2). Social, cultural and economic vulnerabil-
ities, biases and injustices, such as those faced by many women (Eriksen,

2014; Parkinson, 2019) and non-heterosexual groups and gender mi-
norities (Dominey-Howes et al., 2016; Gorman-Murray et al., 2017), can 
deepen impacts and impede adaptation; (Fitzgerald et al., 2019; Mar -
shall et al., 2019) (Cross-Chapter Box GENDER in Chapter 18).
Potential biophysical limits to adaptation for non-human species and 
ecosystems where impacts are projected to be irreversible, with limited 
scope for adaptation, are signalled in key risks 1–4 (11.6). In some 
human systems, fundamental limits to adaptation include thermal 
thresholds and safe freshwater (Alston et al., 2018) (Table 11.14) and 
the inability of some low-lying coastal communities to adapt in place 
(Box 11.6) (very high confidence). Some individuals and communities 
are already reaching their psycho-social adaptation limits (Evans et al., 2016). A lack of robust and timely adaptation means key risks will 
increasingly manifest as impacts, and numerous systems, communities

and institutions are projected to reach limits (Table 11.14, Figure 11.6), 
compounding current adaptation deficits and undermining society’s 
capacity to adapt to future impacts (very high confidence).
11.7.3 Adaptation Enablers
Adaptation enablers include understanding relevant knowledge, 
diverse values and governance, institutions and resources (very high 
confidence) (Gorddard et  al., 2016). Skills and learning, community 
networks, people–place connections, trust-building, community 
resources and support and engaged governance build social resilience 
that support adaptation (Maclean et al., 2014; Eriksen, 2019; Phelps 
and Kelly, 2019). A multi-faceted focus on the role societal inequalities 
and environmental degradation play in generating climate change 
vulnerability can enable fairer adaptation outcomes (McManus et al., 
2014; Ambrey et al., 2017; Schlosberg et al., 2017; Graham et al., 2018).
The feasibility and effectiveness of adaptation options will change

over time depending on place, values, cultural appropriateness, social 
acceptability, ongoing cost-effectiveness, leadership and the ability 
to implement them through the prevailing governance regime (Singh 
et al., 2020). The capacity and commitment of the political system can 
drive early action that can reduce risks (Boston, 2017).
Decision makers face the challenge of how to adapt when there are 
ongoing knowledge gaps and uncertainties about when some climate 
change impacts will occur and their scale, for example coastal flooding 
(Box  11.6) or extreme rainfall events and their cascading effects 
(Box  11.4) (very high confidence). No-regrets decisions are likely to 
be insufficient (Hallegatte et al., 2012). A perception exists in some 
sectors that all climate risks are manageable based on past experience 
(CCATWG, 2017). Projected impacts, however, are outside the range 
experienced, meaning that decisions must be made now for long-lived

assets, land uses and communities exposed to the key risks (Paulik 
et  al., 2019a; Paulik et  al., 2020) often under contested conditions 
where adaptation competes with other public expenditures (Kwakkel 
et al., 2016). New planning approaches being used across the region 
can enable more effective adaptation, for example continual iterative 
adaptation (Khan et  al., 2015), rapid deployment of decision tools 
appropriate for addressing uncertainties (Marchau et  al., 2019) 
and transformation of governance and institutional arrangements 
(Boston and Lawrence, 2018) (Table 11.17). Recognising co-benefits 
for mitigation and sustainable development can help incentivise 
adaptation (11.3.5.3, 11.8.2).
11.7.3.1 Planning and Tools
Adaptation decision support tools enable a shift from reactive to 
anticipatory planning for changing climate risks (high confidence). The 
available tools are diversifying with futures and systems methodologies

and dynamic adaptive policy pathways being increasingly used 
(Bosomworth et al., 2017; Prober et al., 2017; Lawrence et al., 2018a; 
CoA, 2020e; Rogers et al., 2020a; Schneider et al., 2020) (11.5; Box 11.6) 
to facilitate the shift from static to dynamic adaptation by highlighting

1645path dependencies and potential lock-in of decisions, system 
dependencies and the potential for cascading impacts (Table 11.17) 
(Wilson et al., 2013; Clarvis et al., 2015; Pearson et al., 2018; Cradock-
Henry et al., 2020b; Lawrence et al., 2020b). Modelling and tools to 
test the robustness and cost-effectiveness of options (Infometrics and 
PSConsulting, 2015; Qin and Stewart, 2020) can be used alongside 
adaptation strategies with decision-relevant and usable information 
(Smith et  al., 2016; Tangney, 2019; Serrao-Neumann et  al., 2020), 
particularly when supported by effective governance and national and 
sub-national guidance (Box 11.6).
More inclusive, collaborative and learning-oriented community 
engagement processes are fundamental to effective adaptation 
outcomes (11.7.3.2) (very high confidence) (Boston, 2016; Lawrence 
and Haasnoot, 2017; Sellberg et  al., 2018; Serrao-Neumann et  al.,

2019a; Simon et al., 2020). More participatory vulnerability and risk 
assessments can better reflect different knowledge systems, values, 
perspectives, trade-offs, dilemmas, synergies, costs and risks (Jacobs 
et  al., 2019; Ogier et  al., 2020; Tonmoy et  al., 2020). A shift from 
hierarchical to more cooperative governance modalities can assist 
effective adaptation (Vermeulen et al., 2018; Steffen et al., 2019; CoA, 
2020e; Lawrence et al., 2020b; MfE, 2020a; Hanna et al., 2021).
Regular monitoring, evaluation, communication and coordination of 
adaptation are essential for accelerating learning and adjusting to 
dynamic climate impacts and changes in socioeconomic and cultural conditions (high confidence) (Moloney and McClaren, 2018; Palutikof 
et al., 2019a; Cradock-Henry et al., 2020a). Training to improve decision 
makers’ ‘evaluative capacity’ can play a role (Scott and Moloney, 
2021). Climate action benchmarking, diagnostic tools and networking

can enhance the adaptation process across diverse decision settings 
(e.g., water, coasts, protected areas and Indigenous Peoples) (Ayre and 
Nettle, 2017; Davidson and Gleeson, 2018; Coenen et al., 2019; Gibbs, 
2020). Effective adaptation requires cross-jurisdictional and cross-
sectoral policy coherence and national coordination (Delany-Crowe 
et al., 2019; Rychetnik et al., 2019; MfE, 2020c).
11.7.3.2 Attitudes, Engagement and Accessible Information as 
Enablers
Concern for climate change has become widespread (Hopkins, 2015; 
Borchers Arriagada et  al., 2020), giving climate adaptation social 
legitimacy (high confidence). Over three quarters of Australians 
(77%) agree that climate change is occurring, and 61% believe 
climate change is caused by humans (Merzian et al., 2019). A growing 
proportion of Australians perceive links between climate change and 
high temperatures experienced during heatwaves and extremely hot

days (Summer 2018/2019) (48%), droughts and flooding (42%) and 
urban water shortages (30%) (Merzian et al., 2019). Rural populations 
in NSW perceive climate change impacts as stressing their well-being 
and mental health and requiring leadership and action (Austin et al., 
2020). In New Zealand, between 2009 and 2018, the proportion of New Table 11.17 |  Key enablers for adaptation
Enabler Example
frameworksClear climate change adaptation mandate
Measures that inform a shift from reactive to anticipatory decision-making (e.g., decision tools that have long time frames)
Institutional frameworks integrated across all levels of government for better coordination
Revised design standards for buildings, infrastructure, landscape such as common land use planning guidance and codes of practice that integrate consideration 
of climate risks to address existing and future exposures and vulnerability of people and physical and cultural assets

(11.3.1, 11.3.2, 11.3.3, 11.3.4.3, 11.3.5, 1.3.6, 11.4.1, 11.4.2, 11.5.1, 11.5.2, 11.6, 11.7.1, 11.7.2, 11.8.1, 11.8.2, Table 11.7, Table 11.14, Box 11.1, Box 11.3, 
Box 11.5, Box 11.6)
Building capacity for 
adaptationProvision of nationally consistent risk information through agreed methodologies for risk assessment that address non-stationarity
Targeted research including understanding the projected scope and scale of cascading and compounding risks
Education, training and professional development for adaptation under changing risk conditions
Accessible adaptation tools and information
(11.1.2, 11.3.4, 11.3.5, 11.4.1, 11.5.1, 11.6, 11.7.1, 11.7.2, Table 11.14, Table 11.16, Table 11.18, Box 11.6)
Community partnership 
and collaborative 
engagementCommunity engagement based on principles that consider social and cultural and Indigenous Peoples’ contexts and an understanding of what people value and

wish to protect (e.g., International Association of Public Participation) (Public Participation, 2014)
Use of collaborative and learning-oriented engagement approaches tailored for the social and informed by the cultural context
Community awareness and network building
Building on Indigenous Australian and Māori communities’ social-cultural networks and conventions that promote collective action and mutual support
(11.3.5, 11.4, 11.7.1, 11.7.3.2, Table Box 11.1.1, Table 11.14, Box 11.6)
Dynamic adaptive 
decision-makingIncreased understanding and use of decision-making tools to address uncertainties and changing risks, such as scenario planning and DAPP to enable effective 
adaptation as climate risk profiles worsen
(11.7.3.1, 11.7.3.2, Table 11.14, Table 15b, Table 11.18, Box 11.4, Box 11.6)
Funding mechanismsAdaptation funding framework to increase investment in adaptation actions
New private-sector financial instruments to support adaptation
(11.7.1, 11.7.2, Table 11.16)

Reducing systemic 
vulnerabilitiesEconomic and social policies that reduce income and wealth inequalities
Strengthening social capital and cohesion
Identifying and redressing rigid or fragmented administrative and service delivery systems
Reviewing land use and spatial planning to reduce exposure to climate risks
Restoring degraded ecosystems and avoiding further environmental degradation and loss.
(11.1.1, 11.1.2, 11.3.5, 11.3.11, 11.4.1, 11.5.1.3, 11.7.2, 11.8.1; Table 11.10, Table 11.13)

11
1646Chapter 11 Australasia
Zealanders who agreed or strongly agreed that climate change is real 
increased from 58% to 78% (a 34.5% increase), while those agreeing 
or strongly agreeing that it was caused by humans increased from 
41% to 64% (a 56.1% increase) (Milfont et al., 2021). Nevertheless, 
New Zealanders have a tendency to overestimate the amount of sea 
level rise (SLR), especially among those most concerned about climate 
change and incorrectly associate it with melting sea ice, which has 
implications for engagement and communication strategies (Priestley 
et al., 2021).The use of more systemic, collaborative and future-oriented engagement 
approaches is facilitating adaptation in local contexts (high confidence) 
(Rouse et al., 2013; MfE, 2017a; Leitch et al., 2019). Local ‘adaptation 
champions’ and experimental and tailored engagement processes 
can enhance learning (McFadgen and Huitema, 2017; Lindsay et  al.,

2019). Dynamic adaptive pathways planning (Lawrence et al., 2019a) 
and inclusive community governance (Schneider et al., 2020) can help 
progress difficult decisions such as the relocation of cultural assets 
and managed retreat, and contestation about which public goods Table 11.18 |  Examples of adaptation decision tools
Tools Application Source
Scenario analysis, modelling, futures 
narrativesFor futures planning in coastal, urban, 
agriculture and health sectors(Randall et al., 2012; Jones et al., 2013; CSIRO, 2014; Bosomworth et al., 2015; Infometrics and 
PSConsulting, 2015; Knight-Lenihan, 2016; Maier et al., 2016; Stephens et al., 2017; B. Frame et al., 
2018; Stephens et al., 2018; Ausseil et al., 2019a; Coulter et al., 2019; Serrao-Neumann et al., 2019b)
Dynamic Adaptive Pathways Planning 
(DAPP)For conditions of deep uncertainty for 
short-term and long-term options and

flexibility, and with communities(Cradock-Henry et al., 2018b; Cradock-Henry et al., 2020a) (agriculture); (Lawrence et al., 2019b) 
(flood risk management)
(Lawrence and Haasnoot, 2017; Colliar and Blackett, 2018) (coastal communities)
(Tasmanian Climate Change Office, 2012; Lin et al., 2017; Ramm et al., 2018) (capacity building)
(Moran et al., 2014; Colloff et al., 2016; Dunlop et al., 2016; Bosomworth et al., 2017) (natural 
resource, management)
(Hadwen et al., 2012; Barnett et al., 2014; Fazey et al., 2015; Lazarow, 2017; Ramm et al., 2018) 
(coastal)
(Siebentritt et al., 2014; Zografos et al., 2016) (regional development)
(Maru et al., 2014) (disadvantaged communities)
(Hertzler et al., 2013; Sanderson et al., 2015) (agriculture)
(Ren et al., 2011) (infrastructure and resilient cities)
(Cunningham et al., 2017) (social network analysis with communities)
Serious GamesTo catalyse learning, raise awareness

and explore attitudes and values(Lawrence and Haasnoot, 2017; Colliar and Blackett, 2018; Flood et al., 2018; Edwards et al., 2019)
Signals and Triggers for monitoring 
DAPPFor where there is near-term certainty 
and longer-term deep uncertainty 
(e.g., SLR)(Stephens et al., 2017; Stephens et al., 2018)
Shared Socioeconomic PathwaysFor where there is deep uncertainty 
and scenarios are used(B. Frame et al., 2018)
Hybrid Multi-criteria analysis and DAPP 
(deep uncertainty)For conditions of deep uncertainty for 
short-term and long-term options and 
flexibility desired(Lawrence et al., 2019a)
Real Options Analysis (ROA) For conditions of deep uncertainty (Infometrics and PSConsulting, 2015; Infometrics, 2017; Lawrence et al., 2019a; Wreford et al., 2020)
Scenario-based cost-benefit analysis For conditions of deep uncertainty (Guthrie, 2019)
Portfolio analysis For uncertainties in the land use sector (Monge et al., 2016; Awatere et al., 2018; West et al., 2021)

Cost Benefit Analysis Where decisions can be easily reversed (Hadwen et al., 2012; Little and Lin, 2015; Stewart, 2015; Luo et al., 2017; Thamo et al., 2017)
Vulnerability assessmentFor assessing and prioritising physical 
and social place-based risks, using 
indices, modelling and participatory 
approaches(Ramm et al., 2017; Moglia et al., 2018; Pearce et al., 2018; Tonmoy and El-Zein, 2018)
Statutory toolsFor planning direction
For planning and design of adaptation(DoC NZ, 2010; DoC NZ, 2017a; DoC NZ, 2017b; NSW Government, 2018)
(MfE, 2017a)
Standards For adaptation best practice (ISO, 2019)
JurisprudenceFor adaptation implementation and 
legal interpretation(O’Donnell and Gates, 2013; McAdam, 2015; Iorns Magallanes and Watts, 2019; Peel et al., 2020)
GuidanceFor adaptation and use of uncertainty 
tools(CSIRO and BOM, 2015; MfE, 2017a; Lawrence et al., 2018b; Palutikof et al., 2019b)
Information delivery and decision

support portalFor adaptation decision making https://coastadapt.com.au/
Monitoring, evaluation and reporting 
on adaptation progress (incl. adaptation 
indices and web-based tools)For local government, private sector 
and finance sector to benchmark, track 
progress(Goodhue et al., 2012; Little et al., 2015; IGCC, 2017; Lawrence et al., 2020a; LGAQ and DES, 2020; 
Rogers et al., 2020b; WAGA, 2020)
(Moloney and McClaren, 2018)

1647to prioritise and how adaptation should be implemented (Kwakkel 
et al., 2016) (Colliar and Blackett, 2018). Participatory climate change 
scenario planning can test assumptions about the present and the 
future (Mitchell et  al., 2017; Serrao-Neumann and Choy, 2018; 
Chambers et al., 2019; Serrao-Neumann et al., 2019c) and help envision 
people-centred, place-based adaptation (Barnett et al., 2014; Lindsay 
et  al., 2019). Social network analysis can inform engagement and 
communication of adaptation (Cunningham et al., 2017). Knowledge 
brokers, information portals and alliances can help communities, 
governments and sector groups to better access and use climate 
change information (Shaw et al., 2013; Fünfgeld, 2015; Lawrence and 
Haasnoot, 2017). Novel approaches to building climate change literacy 
and adaptation capability go hand in hand with dedicated expert 
organisational support (Stevens and O’Connor, 2015; CCATWG, 2018;

Palutikof et al., 2019c; Salmon, 2019). All of these approaches depend 
on adequate resourcing (very high confidence).
11.7.3.3 Knowledge Gaps and Implementation Enablers
There are two priority areas where new knowledge is critical for 
accelerating adaptation implementation.
1) System complexity and uncertainty in observed and projected 
impacts
• Regionally relevant projections of rainfall, runoff, compound and 
extreme weather (11.2.1, 11.3.3; Box 11.4)
• Inclusion of cascading and compounding impacts in integrated 
assessments (11.5.1), including for infrastructure (11.3.5), tourism 
(11.3.7) and health (11.3.6) and for different groups, including 
Aboriginal and Torres Strait Islander Peoples and Tangata Whenua 
Māori communities (11.4)
• Impacts on terrestrial and freshwater ecosystems, including in 
situ monitoring to detect ongoing changes especially in New 
Zealand (11.3.1), and marine biodiversity, including environmental 
tolerances of key life stages (11.3.2)

• Repository of indigenous species distribution data for monitoring 
responses to climate change and climate advisory services for New 
Zealand (11.3.1.3)
• National risk assessment for Australia (11.7.1)
• The interactions between adaptation and mitigation, particularly 
where land carbon mitigation is impacted by climate change 
(11.3.4.3; Box 11.5)
2) Supporting adaptation decision making
• Better understanding of who and what is exposed and where and 
their vulnerability to climate hazards (11.3, 11.4)
• National assessments of the costs and benefits of climate change, 
with and without different levels and timings of adaptation and 
mitigation (11.5.2.3) (11.7.1)
• Understanding available adaptation strategies and options, their 
feasibility and effectiveness as the climate changes, including their 
intended and unintended outcomes (11.7, 11.8)
• Understanding how to embed robust planning approaches into 
decision making that retain flexibility to change course in the

future (11.7.1).• Mechanisms for sharing knowledge and practice of adaptation 
(11.7).
• The role of development paradigms, values and political economy 
in adaptation framing and effective implementation (11.8).
• Understanding social transitions and social licence, for timely, 
robust and transformational adaptation (11.8.2).
11.8 Climate Resilient Development Pathways
Adaptation to climate risks and global mitigation of greenhouse 
emissions determine whether development pathways are climate-
resilient (Chapter 18). In the near term, progress towards climate resilient 
development can be monitored by progress on the SDGs. According to 
government reports (Figure 11.6) (OECD, 2019a), current and projected 
trajectories fall short of meeting all targets (Allen et  al., 2019). Key 
climate risks for the region (11.6, Table 11.14) affect all of the SDGs, 
and pre-existing societal inequalities exacerbate climate risks (11.3.5).

Projected climate risks combined with underlying SDG indicators will 
increasingly impede the region’s capacity to achieve and maintain a 
number of SDGs, including sustainable agriculture, affordable and clean 
energy, sustainable cities and communities, life below water and life on 
land (OECD, 2019a). Reducing these risks would require significant and 
rapid emission reductions to keep global warming to 1.5°C–2.0°C and 
robust and timely adaptation (IPCC, 2018).
11.8.1 System Adaptations and Transitions
A step change in adaptation action is needed to address climate 
risks and to be consistent with climate resilient development (very 
high confidence) . Current adaptation falls short on the assessment of 
complex risks, implementation, monitoring and evaluation. It is largely 
incremental and temporary given the scale of projected impacts; it has 
limits and is mainly reactive rather than anticipatory. Furthermore,

risks are projected to cascade and compound, with impacts and costs 
that challenge adaptive capacities (11.5) and call for transformational 
responses (11.6, Table  11.15a, Table  11.15b; Supplementary Tables 
SM11.1a and SM11.1b).
Current global emissions reduction policies are projected to lead to a 
global warming of 2.1°C–3.9°C by 2100 (Liu and Raftery, 2021), leaving 
many of the region’s human and natural systems at very high risk and 
beyond adaptation limits (high confidence). With higher levels of warming, 
adaptation costs increase, loss and damages grow, and governance and 
institutional responses reduce adaptive capacity. Underlying social and 
economic vulnerabilities and injustices further reduce adaptive capacity, 
exacerbating disadvantage in particular groups in society. Sustainable 
development across and beyond the region will help reduce shared 
adaptation challenges (11.5.1.2). Effective adaptation avoids lock-in and

path dependency, reduces vulnerabilities, increases flexibility to change, 
builds adaptive capacity and advances SDGs, thereby improving intra- 
and intergenerational justice (11.5, 11.6, 11.7). Reducing greenhouse 
gas emissions and structural inequalities is key to achieving the SDGs 
and contributing to climate resilient development.

11
1648Chapter 11 Australasia
Integrated and inclusive adaptation decision-making can contribute to 
climate resilient development by better mediating competing values, 
interests and priorities and helping to reconcile short- and long-
term objectives, as well as public and private costs and benefits, in 
the face of rapidly and continuously changing risk profiles (very high 
confidence) (Gorddard et al., 2016; MfE, 2017a; Schlosberg et al., 2017) 
(11.5.2). Use of new tools and approaches (Table 11.18) to address 
system interactions that match the scale and scope of the problem 
can result in more effective adaptation, including proactive and 
anticipatory governance and institutional enablers (11.7, Table 11.17) 
(Schlosberg et al., 2017; Boston and Lawrence, 2018). Building cities 
and settlements that are resilient to the impacts of climate change 
requires the simultaneous consideration of infrastructural, ecological,

social, economic, institutional and political dimensions of resilience, 
including political will, leadership, commitment, community support, 
multi-level governance and policy continuity (Torabi et al., 2021).11.8.2 Challenges for Climate Resilient Development 
Pathways
Implementing enablers can help drive adaptation ambition and action 
consistent with climate resilient development (very high confidence) 
(11.7.3, Table  11.17). However, the scale and scope of cascading, 
compounding and aggregate impacts (11.5.1) calls for new and timely 
adaptation, including more effective ongoing monitoring, evaluation, 
review and continual adjustment (11.7.3) towards the transformations 
that can break through the path dependencies that define the way things 
are done now (Cradock-Henry et al., 2018b; UN et al., 2018; Head, 2020). 
However, complex interactions between objectives can create social and 
economic trade-offs (Table 11.1, 11.3.5.3, 11.7.3.1, Box 11.6).

Delay in implementing climate change adaptation and emissions re-
ductions will impede climate resilient development, resulting in more 
costly climate impacts and greater scale of adjustments in the future 
FAQ 11.2 | What systems in Australia and New Zealand are most at risk from ongoing climate change?
The nine key risks to human systems and ecosystems in Australia and New Zealand from ongoing climate change are shown in Figure FAQ 
11.2.1. Some risks, especially on ecosystems, are now difficult to avoid. Other risks can be reduced by adaptation if global mitigation is effective.
Risk is the combination of hazard, exposure and vulnerability. For a given hazard (e.g., fire), the risk will be greater 
in areas with high exposure (e.g., many houses) and/or high vulnerability (e.g., remote communities with limited 
escape routes). The severity and type of climate risk varies geographically (Figure FAQ11.2.1). Everyone will be

affected by climate change, with disadvantaged and remote people and communities the most vulnerable.Frequently Asked Questions
FAQ 11.1 | How is climate change affecting Australia and New Zealand?
Climate change is affecting Australia and New Zealand in profound ways. Some natural systems of cultural, environmental, social and economic 
significance are at risk of irreversible change. The socioeconomic costs of climate change are substantial, with impacts that cascade and 
compound across sectors and regions, as demonstrated by heatwaves, wildfire, cyclone, drought and flood events.
Temperature has increased by 1.4°C in Australia and 1.1°C in New Zealand over the last 110  years, with more 
extreme hot days. The oceans in the region have warmed significantly, resulting in longer and more frequent 
marine heatwaves. Sea levels have risen and the oceans have become more acidic. Snow depths have declined

and glaciers have receded. Northwestern Australia and most of southern New Zealand have become wetter, while 
southern Australia and most of northern New Zealand have become drier. The frequency, severity and duration 
of extreme wildfire weather conditions have increased in southern and eastern Australia and northeastern New 
Zealand.
The impacts of climate change on marine, terrestrial and freshwater ecosystems and species are evident. The 
mass mortality of corals throughout the Great Barrier Reef during marine heatwaves in 2016–2020 is a striking 
example. Climate change has contributed to the unprecedented south-eastern Australia wildfires in the spring 
and summer of 2019–2020, loss of alpine habitats in Australia, extensive loss of kelp forests, shifts further south in 
the distribution of almost 200 marine species, decline and extinction in some vertebrate species in the Australian

wet tropics, expansion of invasive plants, animals and pathogens in New Zealand, erosion and flooding of coastal 
habitats in New Zealand, river flow decline in southern Australia, increased stress in rural communities, insurance 
losses for floods in New Zealand, increase in heatwave mortalities in Australian capital cities and fish deaths in the 
Murray-Darling River in the summer of 2018–2019.

1649The risks to natural and human systems are often compounded by impacts across multiple spatial and temporal 
scales. For example, fires damage property, farms, forests and nature with short- and long-term effects on 
biodiversity, natural resources, human health, communities and the economy. Major impacts across multiple sectors 
can disrupt supply chains to industries and communities and constrain delivery of health, energy, water and food 
services. These impacts create challenges for the adaptation and governance of climate risks. When combined, they 
have far-reaching socioeconomic and environmental impacts.
Loss of alpine biodiversit y
in AustraliaKey risks for Australasia
Loss of kelp forests in coastal  
waters in southern Australia and 
southeast New ZealandDisruption and decline in agricultural production
and increased stress in rural communities in south-
western, southern and mainland eastern AustraliaLoss and degradation of coral reefs

in tropical AustraliaCascading compounding and aggregate  
impacts on cities, settlements, infrastructure,  
supply-chains and services
Inability of institutions and governance  
systems to manage climate risks
Loss of natural and human systems in  
low-lying coastal areasRisks across Australia and New Zealand
Increase in heat-related mortality and  
morbidity across Australia
Transition or collapse of alpine ash,  
snowgum woodland, pencil pine and 
northern jarrah forest in southern Australia
Figure FAQ11.2.1 |  Key risks from climate changeBox FAQ 11.2 (continued)

11
1650Chapter 11 Australasia
FAQ 11.3 | How can Indigenous Peoples’ knowledge and practice help us understand contemporary climate impacts 
and inform adaptation in Australia and New Zealand?
In Australia and New Zealand, as with many places around the world, Indigenous Peoples with connections to their traditional country and 
extensive histories hold deep knowledge from observing and living in a changing climate. This provides insights that inform adaptation to 
Indigenous Australians—Aboriginal and Torres Strait Islanders—maintain knowledge regarding previous sea level 
rise, climate patterns and shifts in seasonal change associated with the flowering of trees and emergence of food 
sources, developed over thousands of generations of observation of their traditional country. Knowledge of localised 
contemporary adaptation is also held by many Indigenous Australians with connections to traditional lands. With

assured free and prior informed consent, this provides a means for Indigenous-guided land management, including 
for fire management and carbon abatement, fauna studies, medicinal plant products, threatened species recovery, 
water management and weed management.
Tangata Whenua Māori in New Zealand are grounded in Mātauranga Māori knowledge, which is based on human–
nature relationships and ecological integrity and incorporates practices used to detect and anticipate changes 
taking place in the environment. Social-cultural networks and conventions that promote collective action and 
mutual support are central features of many Māori communities and these customary approaches are critical to 
responding to, and recovering from, adverse environmental conditions. Intergenerational approaches to planning 
for the future are also intrinsic to Māori social-cultural organisation and are expected to become increasingly

important, elevating political discussions about conceptions of rationality, diversity and the rights of non-human 
entities in climate change policy and adaptation.
FAQ 11.4 | How can Australia and New Zealand adapt to climate change?
There is already work under way by governments, businesses, communities and Indigenous Peoples to help us adapt to climate change. 
However, much more adaptation is needed in light of the ongoing and intensifying climate risks. This includes coordinated laws, plans, 
guidance and funding that enable society to adapt and the information, education and training that can support it. Everyone has a part to 
play working together.
We currently mainly react to climate events such as wildfires, heatwaves, floods and droughts and generally rebuild 
in the same places. However, climate change is making these events more frequent and intense, and ongoing

sea level rise and changes in natural ecosystems are advancing. Better coordination and collaboration between 
government agencies, communities, Aboriginal and Torres Strait Islanders and Tangata Whenua Indigenous 
Peoples, not-for-profit organisations and businesses will help prepare for these climate impacts more proactively, in 
combination with future climate risks integrated into their decisions and planning. This will reduce the impacts we 
experience now and the risks that will affect future generations.
Some of the risks for natural systems are close to critical thresholds and adaptation may be unable to prevent 
ecosystem collapse. Other risks will be severe, but we can reduce their impact by acting now, for example coastal 
flooding from sea level rise, heat-related mortality and managing water stresses. Many of the risks have the 
potential to cascade across social and economic sectors with widespread societal impacts. In such cases, really

significant system-wide changes will be needed in the way we currently live and govern. To facilitate such changes, 
new governance frameworks, nationally consistent and accessible information, collaborative engagement and 
partnerships with all sectors, communities and Indigenous Peoples and the resources to address the risks are needed 
(Figure FAQ11.4.1).
However, our ability to adapt to climate change impacts also rests on every region in the world playing its part in 
reducing greenhouse gas emissions. If mitigation is ineffective, global warming will be rapid and adaptation costs 
will increase, with worsening losses and damages.

1651Action options:
More options to reach 
Paris Agreement
existed compared to now .Past T ime: AR5
Critical status, high urgency to 
adapt sucessfully .
Fewer options are left to reach the
SDG goals, Paris Agreement
and CBD goalToday: AR6
Equity ,
human-wellbeing &
planetary healthPossible future:Climate resilience
Urgencyactionoptions
Options will get less
if adaptation, mitigation &
development action fail.Possible future:No resilience
UrgencyactionoptionsAdaptation pathways for Australia and New Zealand 
Coral systems 
(marine
heat waves)Forest ecosystem
(fire)Natural
ecosystem s
and 
specie sHeat
mortalityLowlying 
coastal
Alpine ecosystem
(temperature rise)Water
and
system sEveryone adapts
Strong leadership, partnership,
engagement
Institutions and governanc e
facilitates adaptation
Funding/Resources availabl e
2022 2015 20502100
Paris
AgreementPast
IPCC
AR6Action options

in the pastPathwaysnottaken
unsuccessfuladaptation
Present
situation &
adaptation option sPathwaysnottaken
Sustainable Development Goals2030System tipping points
Irreversible damageUrgencyactionoptionsUrgencyactionoptions
Urgencyactionoptionspossible shock seffectiveadaptation
Increasing adaptation challenge We are now here:
Critical status, high urgency to adapt sucessfull y.
possible shocks
Kelp forests 
(marine
heat waves)Partnership, social transformatio n
Unsuccessful adaptation, 
growth of CO2 emissions
Pathway towards unsustainable spac ethresholds/
limits to adapationPast Ti me Today Possible future
Fewer adaptation options
Higher costs of adaptation
Figure FAQ11.4.1 |  Developing adaptation plans in the solutions space showing system tipping points, thresholds and limits to adaptation, 
unsustainable pathways, critical systems and enablers to climate resilient developmentBox FAQ 11.4 (continued)

11
1652Chapter 11 Australasia
(IPCC, 2018) (11.5.1, 11.5.2, Box 11.6) and legal risks for those with 
adaptation mandates and for financial institutions (11.5.1) (very high confidence). The scale and scope of societal change needed for the region to transition to more climate resilient development pathways requires close attention to governance, ethical questions, the role of civil society and the place of Aboriginal and Torres Strait Islander Peoples and Tangata Whenua Māori in the co-production of ongoing adaptation at multiple scales (Loorbach et  al., 2017; Koehler et  al., 2019; Hill et al., 2020).
The region faces an extremely challenging future that will be highly

disruptive for many human and natural systems (IPCC, 2018) (UNEP , 2020; AAS, 2021; IPCC, 2021) (11.5.1, 11.6, 11.7; Boxes 11.1–11.6; Table 11.14). The extent to which the limits to adaptation are reached depends on whether global warming peaks this century at 1.5°C, 2°C or 3+°C above pre-industrial levels. Whatever the outcome, adaptation and mitigation are essential and urgent (very high confidence).

12
1691Central and South America Chapter 12
Vulnerability and observed impacts:
Central and South America (CSA) are highly exposed, vulnerable 
and strongly impacted by climate change, a situation amplified by inequality, poverty, population growth and high population density, land use change particularly deforestation with the consequent biodiversity loss, soil degradation, and high dependence of national and local economies on natural resources for the production of commodities (high confidence
1). Profound 
economic, ethnic and social inequalities are exacerbated by climate change. High levels of widespread poverty, weak water governance, unequal access to safe water and sanitation services and lack of infrastructure and financing reduce adaptation capacity, increasing and creating new population vulnerabilities (high confidence). {12.1.1, 12.2, 12.3, 12.5.5, 12.5.7, Figure 12.2}
The Amazon forest, one of the world’s largest biodiversity

and carbon repositories, is highly vulnerable to drought (high confidence). The Amazon forest was highly impacted by the unprecedented droughts and higher temperatures observed in 1998, 2005, 2010 and 2015/2016, which are attributed partly to climate change. This resulted in high tree mortality rates and basin-wide reductions in forest productivity, momentarily turning pristine forest areas from a carbon sink into a net source of carbon to the atmosphere (high confidence). Other terrestrial ecosystems in CSA have been impacted by climate change, through persistent drought or extreme climatic events. The combined effect of anthropogenic land use change and climate change increases the vulnerabilities of terrestrial ecosystems to extreme climate events and fires (medium confidence). {12.3, 12.4, Figure 12.7, Figure 12.9, Figure 12.10}
The distribution of terrestrial species has changed in the Andes

due to increasing temperature (very high confidence). Species have shifted upslope, leading to range contractions for highland species and range contractions and expansions for lowland species, including crops and vectors of diseases (very high confidence). {12.3.2.4}
Ocean and coastal ecosystems in the region, such as coral reefs,

estuaries, salt marshes, mangroves and sandy beaches, are highly sensitive and negatively impacted by climate change and derived hazards (high confidence). Observed impacts include the reduction in coral abundance, density and cover in Central America (CA), northwestern South America (NWS) and northeastern South America (NES) and an increasing number of coral bleaching events in CA and NES; other observed impacts are changes in the plankton community and in ocean and coastal food web structures, loss of vegetated wetlands and changes in macrobenthic communities in CA, NWS, northern South America (NSA) and southeastern South America (SES). {12.3, 12.5.2, Figure 12.8, Figure 12.9, Table SM12.3}
1 In this report,  the following summary terms are used to describe the available evidence: limited, medium and robust; and for the degree of agreement: low, medium and high. A level of confidence

is expressed using five qualifiers: very low, low, medium, high and very high; they are typeset in italics, for example, medium confidence. Different confidence levels can be assigned to evidence and 
agreement statements, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.Global warming has caused glacier loss in the Andes from 30% 
to more than 50% of their area since the 1980s. Glacier retreat, temperature increase and precipitation variability, together with land use changes, have affected ecosystems, water resources and livelihoods through landslides and flood disasters (very high confidence). In several areas of the Andes, flood and landslide disasters have increased, and water availability and quality and soil erosion have been affected by both climatic and non-climatic factors (high confidence). {12.3.2, 12.3.7, Figure 12.9, Figure 12.13, Table SM12.6}
The scientific evidence since the IPCC’s Fifth Assessment Report

(AR5) increased the confidence in the synergy among fire, land use change, particularly deforestation, and climate change, directly impacting human health, ecosystem functioning, forest structure, food security and the livelihoods of resource-dependent communities (medium confidence). Regional increases 
in temperature, aridity and drought increased the frequency and intensity of fire. On average, people in the region were more exposed to high fire danger between 1 and 26 additional days depending on the sub-region for the years 2017–2020 compared to 2001–2004 (high confidence). {12.2, 12.3, Figure 12.9, Figure 12.10, Table 12.5}
Changes in the timing and magnitude of precipitation and

extreme temperatures are impacting agricultural production (high confidence). Since the mid-20th century, increasing mean precipitation has positively impacted agricultural production in SES, although extremely long dry spells have become more frequent, affecting the economies of large cities in southeastern Brazil. Conversely, reduced precipitation and altered rainfall at the start and end of the rainy season and during the mid-summer drought (MSD) is impacting rainfed subsistence farming, particularly in the Dry Corridor in CA and in the tropical Andes, compromising food security (high confidence). The crop growth duration for maize in those regions was reduced by at least 5% between 1981–2010 and 2015–2019. {12.3.1, 12.3.2, 12.3.6, Table 12.4}
Climate change affects the epidemiology of climate-sensitive 
infectious diseases in the region (high confidence). Examples

are the effects of warming temperatures on increasing the suitability of transmission of vector-borne diseases, including endemic and emerging arboviral diseases such as dengue fever, chikungunya and Zika (medium confidence). The reproduction potential for the transmission of dengue increased between 17% and 80% for the period 1950–1954 to 2016–2021, depending on the sub-region, as a result of changes in temperature and precipitation (high confidence). {12.3.1, 12.3.2, 12.3.3, 12.3.5, 12.3.6, Table 12.1}
The Andes, northeastern Brazil and the northern countries in 
CA are among the more sensitive regions to climatic-related migrations and displacements, a phenomenon that has increased since AR5 (high confidence). Climatic drivers interact with social, political, geopolitical and economic drivers; the most common climatic drivers for migration and displacements are droughts, tropical storms

12
1692Chapter 12 Central and South America
and hurricanes, heavy rains and floods (high confidence). {12.3.1.4, 
12.3.2.4, 12.3.3.4, 12.3.5.4, 12.5.8.4}
The impacts of climate change are not of equal scope for men 
and women (high confidence). Women, particularly the poorest, are 
more vulnerable and are impacted in greater proportion. Often they 
have less capacity to adapt, further widening structural gender gaps 
(high confidence). {12.3.7.3, 12.5.2.4, 12.5.2.5, 12.5.7.3, 12.5.8.1, 
12.5.8.3, 12.5.8.4}
Current adaptation responses:
Ecosystem-based adaptation is the most common adaptation 
strategy for terrestrial and freshwater ecosystems (high 
confidence). There is a focus on the protection of native terrestrial 
vegetation through the implementation of protected areas and payment 
for ecosystem services (PES), especially those related to water provision. 
The adaptation measures in place, however, are insufficient to safeguard

terrestrial and freshwater ecosystems in the CSA from the negative 
impacts of climate change (high confidence). {12.5.1, 12.5.3, 12.6}
Adaptation initiatives in ocean and coastal ecosystems mainly 
focus on conservation, protection and restoration) (high 
confidence). The main adaptation measures are ocean zoning, the 
prohibition of productive activities (e.g., fisheries, aquaculture, mining 
and tourism) on marine ecosystems, the improvement of research and 
education programmes and the creation of specific national policies 
(high confidence). {12.5.2}
Adaptive water management has mainly centred on enhancing 
the quantity and quality of water supply, including large 
infrastructure projects, which, however, are often contested 
and can exacerbate water-related conflicts (high confidence). 
Inclusive water regimes that overcome social inequalities and 
approaches including nature-based solutions, such as wetland 
restoration and water storage and infiltration infrastructure, with

synergies for ecosystem conservation and disaster risk reduction, have 
been found to be more successful for adaptation and sustainable 
development (high confidence). {12.5.3, 12.6.1, 12.6.3}
Adaptation strategies for agricultural production are increasing 
in the region as a response to current and projected changes in 
climate (high confidence). The main observed adaptation strategies 
in agriculture and forestry are soil and water management conservation, 
crop diversification, climate-smart agriculture, early-warning systems 
(EWSs), upward shifting for plantations to avoid warming habitat 
and pests and improved management of pastures and livestock. 
Adaptation requires governance improvements and new strategies to 
address the changing climate; nevertheless, barriers limiting adaptive 
capacity persist such as lack of educational programmes for farmers, 
adequate knowledge of site-specific adaptation and institutional and 
financial constraints (high confidence). {12.5.4}

Urban adaptation in the region includes solutions on regulation, 
planning, urban water management and housing (high 
confidence). Regulation, planning and control systems are central 
tools for reducing risk associated with the security of buildings and their location and the proper supply of basic urban services and 
transport (high confidence). The adoption of nature-based solutions 
(e.g., urban agriculture and river restoration) and hybrid (grey-green) 
infrastructure is still in the early stages, with weak connections to 
poverty and inequality reduction strategies (medium confidence). 
Focusing on risk reduction encompasses upgrading informal and 
precarious settlements, built environments and housing conditions, 
which offer an important but still limited contribution to urban 
adaptation (high confidence). {12.5.5, 12.5.7, 12.6.1}
Adaptation initiatives for the health sector are mainly focused on 
the development of climate services such as integrated climate-

health surveillance and observatories, forecasting climate-
related disasters and vulnerability maps (high confidence). 
Climate services for the health sector are largely focused on epidemic 
forecast tools and associated EWSs for vector-borne diseases and heat 
and cold waves. Political, institutional and financial barriers reduce 
the feasibility of implementing these tools (high confidence). {12.5.6, 
Table 12.9, Table 12.11}
Indigenous knowledge and local knowledge (IKLK) are crucial 
for the adaptation and resilience of social-ecological systems 
(high confidence). IKLK can contribute to reducing the vulnerability 
of local communities to climate change (medium confidence). {12.5.1, 
12.5.8, 12.6.2}
What are the projected impacts and key risks?
Climate change is projected to convert existing risks in the 
region into severe key risks (medium confidence). Key risks are 
assessed as follows: 1. Risk of food insecurity due to droughts; 2. Risk to

people and infrastructure due to floods and landslides; 3. Risk of water 
insecurity due to declining snow cover, shrinking glaciers and rainfall 
variability; 4. Risk of increasing epidemics, particularly of vector-borne 
diseases; 5. Cascading risks surpassing public service systems; 6. Risk 
of large-scale changes and biome shifts in the Amazon; 7. Risks to 
coral reef ecosystems; and 8. Risks to coastal socioecological systems 
due to sea level rise (SLR), storm surges and coastal erosion. {12.3, 
12.4, Figure 12.9, Figure 12.11, Table 12.6, Table SM12.5}
Impacts on rural livelihoods and food security, particularly for 
small and medium-sized farmers and Indigenous peoples in 
the mountains, are projected to worsen, including the overall 
reduction of agricultural production, suitable farming area and 
water availability (high confidence). Projected yield reductions 
by 2050 under the A2 scenario are as follows: beans 19%, maize

4–21%, rice 23% in CA with seasonal droughts projected to lengthen, 
intensify and increase in frequency. Small fisheries and farming of 
seafood will be negatively affected as El Niño Southern Oscillation 
(ENSO) events become more frequent and intense and ocean warming 
and acidification continues (medium confidence). {12.2, 12.3, 12.4, 
Figure 12.9, Figure 12.11, Table 12.4}
Extreme precipitation events, which result in floods, landslides 
and droughts, are projected to intensify in magnitude and 
frequency due to climate change (medium confidence). Floods and 
landslides pose a risk to life and infrastructure; a 1.5°C increase would

12
1693Central and South America  Chapter 12
result in an increase of 100–200% in the population affected by floods 
in Colombia, Brazil and Argentina, 300% in Ecuador and 400% in Peru 
(medium confidence). {12.3, Figure 12.7, Figure 12.9, Table SM12.5}
Increasing water scarcity and competition over water are 
projected (high confidence). Disruption in water flows will 
significantly degrade ecosystems such as high-elevation wetlands and 
affect farming communities, public health and energy production (high 
confidence). {12.3, Figure 12.3, Figure 12.9, Figure 12.11}
In coming decades, endemic and emerging climate-sensitive 
infectious diseases are projected to increase (medium 
confidence). This can happen through expanded distribution of 
vectors, especially viral infectious diseases of zoonotic origin in 
transition areas between urban and suburban, or rural settings, and 
upslope in the mountains (medium confidence). {12.3.2, 12.3.5, 12.3.7,

Figure 12.5, Figure 12.9, Figure 12.11, Table 12.6, Table SM12.5}
The positive feedback between climate change and land use 
change, particularly deforestation, is projected to increase the 
threat to the Amazon forest, resulting in the increase of fire 
occurrence, forest degradation (high confidence) and long-term 
loss of forest structure (medium confidence). The combined effect 
of both impacts will lead to a long-term decrease in carbon stocks 
in forest biomass, compromising Amazonia’s role as a carbon sink, 
largely conditioned on the forest’s responses to elevated atmospheric 
CO 2 (medium confidence). The southern portion of the Amazon has 
become a net carbon source to the atmosphere in the past decade 
(high confidence). {12.3.3, 12.3.4, Figure 12.9, Figure 12.11, Table 12.6, 
Table SM12.5}
Up to 85% of natural systems (plant and animal species, habitats 
and communities) evaluated in the literature for biodiversity

hotspots in the region are projected to be negatively impacted 
by climate change (medium confidence). Available studies focus 
mainly on vertebrates and plants of the Atlantic Forest and Cerrado in 
Brazil and in CA, with a large knowledge gap on freshwater ecosystems 
{12.3, 12.5.1, CCP1}
Ocean and coastal ecosystems in the region will continue to 
be highly impacted by climate change (high confidence). Coral 
reefs are projected to lose their habitat, change their distribution range 
and suffer more bleaching events driven by ocean warming. In the 
Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, 
by 2050, virtually every coral reef will experience at least one severe 
bleaching event per year (high confidence). Under all RCP scenarios of 
climate change, there will be changes in the geographical distribution of 
marine species and ocean and coastal ecosystems such as mangroves, 
estuaries and rocky shores, as well as those species held in fisheries

(medium confidence). {Figure 12.9, Table SM12.3, Table SM12.4}
Contribution of adaptation to solutions and barriers to adaptation
Policies and actions at multiple scales and the participation 
of actors from all social groups, including the most exposed 
and vulnerable populations, are critical elements for effective 
adaptation (high confidence). Engaging social movements and local actors in policymaking and planning for adaptation generate positive 
synergies and better results. Adaptation policies and programmes 
that consider age, socioeconomic status, race and ethnicity are more 
efficient because these factors determine vulnerability and potential 
benefits of adaptation. Socioeconomic and political factors that 
provide some level of safety and continuity of policies and actions 
are critical enablers of adaptation (high confidence). {12.5.1, 12.5.2, 
12.5.7, 12.5.8, 12.6.4}
The knowledge and awareness of climate change as a threat

have been increasing since AR5 due to the increasing frequency 
and magnitude of extreme weather events in the region, 
information available and climate justice activism (high 
confidence). Conflicts in which the direct biophysical impacts of 
climate change play a major role can unleash protests and strengthen 
social movements (medium confidence). {12.5.8, 12.6.4}
Research approaches that integrate IKLK systems with natural 
and social sciences have increased since AR5 (high confidence) 
and are helping to improve decision-making processes in the region, 
reduce maladaptation and foster transformational adaptation through 
the integration with ecosystem-based adaptation and community-
based adaptation (high confidence). {12.5.1, 12.5.8, 12.6.2}
The most widely reported obstacle to adaptation in terrestrial, 
freshwater, ocean and coastal ecosystems is financing (high 
confidence). There is also a significant gap in identifying limits to

adaptation and weak institutional capacity for implementation. This 
hinders the development of comprehensive adaptation programmes, 
even under adequate funding. {12.5.1, 12.5.2}
The use of climate-smart agriculture technologies strengthening 
synergies among productivity and mitigation is rising as an 
important adaptation strategy in the region (high confidence). 
Pertinent information for farmers provided by climate information 
services is facilitating the understanding of the role of climate 
compared with other drivers in perceived productivity changes. Index 
insurance builds resilience and contributes to adaptation by protecting 
farmers’ assets in the face of major climate shocks, by promoting 
access to credit and by the adoption of improved farm technologies 
and practices. {12.5.4}
Institutional instability, fragmented services and poor water 
management, inadequate governance structures, insufficient 
data and analysis of adaptation experience are barriers to

addressing the water challenges in the region (high confidence). 
{12.5.3}
Inequality, poverty and informality shaping cities in the region 
increase vulnerability to climate change, while policies, plans 
or interventions addressing these social challenges with 
inclusive approaches represent opportunities for adaptation 
(high confidence). Initiatives to improve informal and precarious 
settlement, guaranteeing access to land and decent housing, are 
aligned with comprehensive adaptation policies that include the 
development and reduction of poverty, inequality and disaster risk 
(medium confidence). {12.5.5, 12.5.7}

12
1694Chapter 12 Central and South America
Adaptation policies often address climate impact drivers, but seldom 
include the social and economic underpinnings of vulnerability. 
This narrow scope limits adaptation results and compromises their 
continuity in the region (high confidence). In a context of unaddressed 
underdevelopment, adaptation policies tackling poverty and inequality 
are marginal, underfunded and not clearly included at national, regional 
or urban levels. Dialogue and agreement that include multiple actors 
are mechanisms to acknowledge trade-offs and promote dynamic, 
site-specific adaptation options (medium confidence). {12.5.7}

12
1695Central and South America  Chapter 12
12.1 Introduction
12.1.1 Central and South America Region
Central and South America (CSA) is a highly diverse region, both 
culturally and biologically. It has one of the highest levels of biodiversity 
on the planet (Hoorn et al., 2010; Zador et al., 2015; IPBES, 2018a) 
(Cross-Chapter Paper [CCP] 1: Biodiversity Hotspots) and a wealth of 
cultural diversity resulting from more than 800 Indigenous Peoples 
who share the territory with European and African descendants and 
more recent Asian migrants (CEPAL, 2014). Moreover, it is one of the 
most urbanised regions in the world, with some of the most populated 
metropolitan areas (UNDESA, 2019). Several countries in the region 
have experienced sustained economic growth in recent decades, 
making important advances in reducing poverty in the area. Yet it is a 
region of substantial social inequality including the highest inequality

in land tenure, where a large percentage of the population remains 
below the poverty line, unequally distributed between rural and 
urban areas and along aspects like gender and race; these groups are 
highly vulnerable to climate change and natural extreme events that 
frequently affect the region (high confidence) (ECLAC, 2019b; Busso 
and Messina, 2020; Poveda et al., 2020).
Land use changes in the region, particularly deforestation, are significant, 
mostly due to agricultural production for export purposes, one of the main sources of income for the area (Salazar et al., 2016) (Figure 12.2c). 
Additional pressure on the land comes from illegal activities, pollution 
and induced fires. These changes exacerbate the impacts of climate 
change and make the region a key player in the future of the world 
economy and food production (IPBES, 2018a). The region boasts the 
largest tropical forest on the planet and other important biomes of high

biodiversity on mountains, lowlands and coastal areas. It can potentially 
continue its agricultural expansion and development at the expense of 
substantially reducing the areas of natural biomes. Indigenous Peoples 
and smallholder families are lacking adequate climate policies combined 
with institutions to protect their property rights; this could result in a 
more sustainable process of agricultural expansion, without substantially 
increasing greenhouse gas (GHG) emissions and the vulnerability of 
those populations (high confidence)  (Sá et al., 2017).
CSA is divided into eight climatic sub-regions by Working Group (WG) 
I (Figure 12.1). Though the southern part of Mexico is included in the 
climatic sub-region South Central America (SCA) for WGI, Mexico is 
assessed in Chapter 14 (North America). In this chapter, we refer to this 
sub-region as CA because it excludes southern Mexico. The climate-
change literature for the region occasionally includes Mexico, and in

those cases, our assessment makes reference to Latin America but 
when only southern Mexico is included, the term Mesoamerica is used. 
Figure  12.2 and Table  SM12.1 summarise relevant characteristics of 
the sub-regions included in this chapter.
Poligon delineations represent the boundaries used 
for the regional synthesis of historical trends and 
future climate change projections used in the 
Assessment Reports of the IPCC WGI.
(a) Central America (CA) *
(b) Northwestern South America (NWS)
(c) Northern South America (NSA) 
(d) South America Monsoon (SAM )
(e) Northeastern South America (NES)
(f) Southwestern South America (SWS)
(g) Southeastern South America (SES)
(h) Southern South America (SSA )
* Different from the WGI South Central America (SCA) 
which includes the southern part of Mexico .Geographical scope of 
(CA)
(NWS)(NSA )
(SAM)
(SES)(NES)
(SWS)
(SSA )
6,000 m
5,000 m
4,000 m
3,000 m
2,000 m
1,000 m
500 m
250 m
0 m

Figure 12.1 |  Sub-regions included in CSA region. Note that the WGI climatic sub-region SCA corresponds to CA in this chapter, as southern Mexico is included in Chapter 
14. Small islands in the region are covered in Chapter 15 in more detail.

12
1696Chapter 12 Central and South America
12.1.2 Approach and Storyline for the Chapter
The chapter is divided into two main sections. The first section follows 
an integrative approach in which hazards, exposure, vulnerability, 
impacts and risks are discussed following the eight climatically 
homogeneous sub-regions described in WGI AR6 (Figure  12.1). The 
second section assesses the implemented and proposed adaptation 
practices by sector; in doing so, it connects to the WGII AR6 cross-
chapter themes. The storyline is then a description of the hazards, 
exposure, vulnerability and impacts providing as much detail as is available in the literature at the sub-regional level, followed by the 
identification of risks as a result of the interaction of those aspects. 
This integrated sub-regional approach ensures a balance in the text, 
particularly for countries that are usually underrepresented in the 
literature but that show a high level of vulnerability and impacts, such

as those observed in CA. The sectoral assessment of adaptation that 
follows is useful for policymakers and implementers, usually focused 
and organised by sectors, government ministries or secretaries that can 
easily locate the relevant adaptation information for their particular 
sector. To ensure coherence in the chapter, a summary of the assessed 
(d) Human Development Index (2019)*
Very high human development
0.650.700.750.80>0.85(a) Population density (1995–2014)
People
2,50001,000,000
100,000
The Human Development Index (HDI) is a composite index measuring 
average achievement in the following three basic dimensions of human 
development, knowledge, a decent standard of living and a long and 
healthy life. * Data for French Guiana corresponds to 2018
Gross national income per capita Expected years of schooling Life expectancy at birth(b) Biodiversity
Mean years of schooling
0.60High human development
Medium human development
7,0009,00011,00013,000>15,000
5,0008101214>16

68101214>16
6
 USD
(2017 PPP)0.40.60.8
0.2Scaled
richness
01
YearsYearsSocioeconomic and biophysical characterization of the region
(c) Land cover (2019)
Sparse vegetation
urbanMosaicShrublandCroplandTree cover
Bare/snow/iceGrassland
7173757779–81
69Years
Figure 12.2 |  Characterisation of the region. Population data from ISIMIP (2021) after Klein Goldewijk et al. (2017). Biodiversity expressed as marine and terrestrial species 
richness adapted from Gagné et al. (2020). Land cover data from ESA (2018). Human Development Index (HDI) and its components from UNDP (2020). HDI and components for 
French Guiana from Global Data Lab (2020).

12
1697Central and South America  Chapter 12
adaptation options by key risks is presented, followed by a feasibility 
assessment for some relevant adaptation options. The chapter closes 
with case studies and a discussion of the knowledge gaps evidenced 
in the process of the assessment.
12.2 Summary of AR5 and Recent IPCC Special 
Reports
CSA shows increasing trends of climatic change and variability and 
extreme events severely impacting the region, exacerbating problems 
of rampant and persistent poverty, precarious health systems and 
water and sanitation services, malnutrition and pollution. Inadequate 
governance and lack of participation escalates the vulnerability and 
risk to climate variability and change in the region (high confidence) 
(WGII AR5 Chapter 27) (Magrin et al., 2014).
Increasing trends in precipitation had been observed in SES 
(Figure  12.1), in contrast to decreasing trends in CA and central-

southern Chile (high confidence) (WGII AR5 Chapter 27) (Magrin et al., 
2014). The frequency and intensity of droughts have increased in many 
parts of SA (IPCC, 2019c). Warming has been detected throughout CSA, 
except for a cooling trend reported for the ocean off the Chilean coast.
Climate projections indicate increases in temperature for the entire 
region by 2100 for RCP4.5 and RCP8.5, but rainfall changes will vary 
geographically, with a notable reduction of −22% in northeastern Brazil 
and an increase of +25% in SES. Significant dependency on rainfed 
agriculture (>30% in Guatemala, Honduras and Nicaragua) indicates 
high sensitivity to climatic variability and change and represents a 
challenge for food security (high confidence) (SRCCL Chapter 5, Mbow 
et al., 2019). Undernutrition has worsened since 2014 in CSA (SRCCL 
Chapter 5, Mbow et  al., 2019). Evidence of climate-change impacts 
on food security is emerging from IKLK studies in SA. Municipalities

in CA with a high proportion of subsistence crops tend to have fewer 
resources for adaptation and more vulnerable to climate change (SRCCL 
Chapter 5, Mbow et  al., 2019). Rising temperature and decreased 
rainfall could reduce agricultural productivity by 2030, threatening the 
food security of the poorest populations (WGII AR5 Chapter 27, Magrin 
et  al., 2014). Though reduced suitability and yield for beans, coffee, 
maize, plantain and rice are expected in CA (SRCCL Chapter 5, Mbow 
et al., 2019), limiting the warming to 1.5°C, compared with 2°C, are 
projected to result in smaller net reductions in yields of maize, rice, 
wheat and other cereal crops for CSA (high confidence) (SR15 Chapter 
3, Hoegh-Guldberg et al., 2018). The heat stress is expected to reduce 
the suitability of Arabica coffee in Mesoamerica, but it can improve 
in high-latitude areas in SA (SRCCL Chapter 4, Olsson et  al., 2019). 
There is limited evidence that these declines in crop yields may result in

significant population displacement from the tropics to the sub-tropics 
(SR15 Chapter 3, Hoegh-Guldberg et al., 2018).
There is a high confidence that heatwaves will increase in frequency, 
intensity and duration, becoming, under high emission scenarios, 
extremely long, over 60 d in duration in SA; the risk of wildfires will 
also increase significantly in SA (SRCCL Chapter 2, Jia et al., 2019). 
These processes are leading and will continue to lead to increased 
desertification that will cost between 8% and 14% of gross agricultural product in many CSA countries (SRCCL Chapter 3, Mirzabaev et al., 
2019). Distinguishing climate-induced changes from land use changes 
is challenging, but 5–6% of biomes in SA are expected to change by 
2100 due to climate change (SRCCL Chapter 4, Olsson et al., 2019).
Changes in weather and climatic patterns are negatively affecting 
human health in CSA, in part through the emergence of diseases in

previously non-endemic areas (WGII AR5 Chapter 27, Magrin et  al., 
2014). Projections of potential impacts of climate change on malaria 
confirm that weather and climate are among the drivers of geographic 
range, intensity of transmission, and seasonality; the changes of risk 
become more complex with additional warming (very high confidence) 
(SR15 Chapter 3, Hoegh-Guldberg et al., 2018). There is high confidence 
that constraining the warming to 1.5°C would reduce risks for unique 
and threatened ecosystems safeguarding the services they provide 
for livelihoods and sustainable development (food, water) in CA and 
Amazon (SR15 Chapter 5, Roy et al., 2018).
Observed changes in streamflow and water availability affect vulnerable 
regions (WGII AR5 Chapter 27, Magrin et  al., 2014). Glacier mass 
changes in the Andes in recent decades are among the most negative 
ones worldwide (SROCC Chapter 2, Hock et al., 2019). This reduction

has modified the frequency, magnitude and location of related natural 
hazards, while the exposure of people and infrastructure has increased 
because of growing population, tourism and economic development 
(high confidence) (SROCC Chapter 2, Hock et al., 2019).
The negative impacts of climate change in the region are exacerbated 
by deforestation and land degradation attributed mainly to expansion 
and intensification of agriculture and cattle ranching, usually under 
insecure-tenure land. This conversion of natural ecosystems is the main 
cause of biodiversity and ecosystem loss and is an important source 
of GHG emissions (high confidence) (WGII AR5 Chapter 27, Magrin 
et al., 2014).
The combination of continued anthropogenic disturbance, particularly 
deforestation, with global warming may result in dieback of forest in 
the region (medium confidence) (SR15 Chapter 3, Hoegh-Guldberg 
et al., 2018). Losses of biomass as high as 40% are projected in CA

with a warming of 3°C–4°C, and the Amazon may experience a 
significant dieback at similar warming levels (SR15 Chapter 3, Hoegh-
Guldberg et  al., 2018). Advances in second-generation bioethanol 
from sugarcane and other feedstock will be important for mitigation. 
However, agricultural expansion results in large conversions in tropical 
dry woodlands and savannahs in SA (Brazilian Cerrado, Caatinga and 
Chaco) (high confidence) (SRCCL Chapter 1, Arneth et al., 2019). The 
expansion of soybean plantations in the Amazonian state of Mato 
Grosso in Brazil reached 16.8% yr−1 from 2000 to 2005; and oil palm, a 
significant biofuel crop, is also linked to recent deforestation in tropical 
CA (Costa Rica and Honduras) and SA (Colombia and Ecuador), 
although lower in magnitude compared to deforestation from soybean 
and cattle ranching (WGII AR5 Chapter 27, Magrin et al., 2014).
Ocean and coastal ecosystems in the region already show important

changes due to climate change and global warming (SROCC Chapter 
5, Bindoff et al., 2019).

12
1698Chapter 12 Central and South America
Adaptation to future climate change starts by reducing the 
vulnerability to the present climate considering the deficient welfare 
of people in the region. Generalising to the region cases of synergies 
among development, adaptation and mitigation planning requires a 
governance model where development needs, vulnerability reduction 
and adaptation strategies are intertwined (WGII AR5 Chapter 27, 
Magrin et al., 2014).
12.3 Hazards, Exposure, Vulnerabilities and 
Impacts
12.3.1 Central America Sub-region
12.3.1.1 Hazards
Since the mid-20th century, extreme warm temperatures have increased 
and extreme cold temperatures have decreased in the region (medium 
confidence). The magnitude and frequency of extreme precipitation 
events have increased, but droughts have mixed signals (low confidence) 
(WGI AR6 Table 11.13, Table 11.14, Table 11.15, Seneviratne et al., 2021). 
Spatially variable trends have been detected for the MSD timing, the

amount of rainy-season precipitation, the number of consecutive and 
total dry days and extreme wet events at the local scale since the 
1980s. At the regional scale, a positive trend in the duration, but not the 
magnitude, of the MSD was found (Anderson et al., 2019).
Significant increases in tropical cyclone (TC) intensification rates in the 
Atlantic basin, highly unusual compared to model-based estimates of 
internal climate variations, have been observed (Bhatia et al., 2019). 
TCs contributed approximately 10% of the annual precipitation 
(Khouakhi et al., 2017). During the TC season more TC-driven events of 
extreme sea level exceed a 10-year return period (Muis et al., 2019).
Massive heatwave events and increase in the frequency of warm 
extremes are projected at the end of the 21st century (high confidence). 
When comparing 2.0°C with 1.5°C of warming, the longest annual 
warm wave is projected to increase more than 60 d (Taylor et al., 2018).

General decrease in the magnitude of heavy precipitation extremes 
(Chou et al., 2014; Giorgi et al., 2014) (in 1.5°C projection) but increase 
in the frequency of extreme precipitation (R50mm) (Imbach et al., 2018) 
are projected for both 2°C and 4°C global warming level (GWL). Strong 
declines in mean daily rainfall are projected for July in Belize (Stennett-
Brown et al., 2017; WGI AR6 Table 11.14, Seneviratne et al., 2021) and 
decreased rainfall through the year for all capital cities except Panama 
City (medium confidence: limited evidence, high agreement) (Pinzón 
The main climate impact drivers like extreme heat, drought, relative 
SLR, coastal flooding, erosion, marine heatwaves, ocean aridity (high 
confidence) and aridity, drought and wildfires will increase by mid-
century (medium confidence) (Figure  12.6, WGI AR6 Table  12.6, 
The rainy season in CA will likely experience more pronounced MSD by

the end of this century, with a signal for reduced minimum precipitation by mid-century for the June July August (JJA) and September October 
November (SON) quarters, and a broader second peak is projected, 
consistent with the future south displacement of the Intertropical 
Convergence Zone (ITCZ) (high confidence) (Fuentes-Franco et  al., 
2015; Hidalgo et al., 2017; Maurer et al., 2017; Imbach et al., 2018; 
Naumann et  al., 2018; Ribalaygua et  al., 2018; Corrales-Suastegui 
Climate projections indicate a decrease in frequency of TCs in CA 
accompanied by an increased frequency of intense cyclones (WGI AR6 
Section 12.4.4.3, Ranasinghe et al., 2021).
12.3.1.2 Exposure
Of the 47 million Central Americans in 2015, 40% lived in rural areas, 
with Belize being the least urbanised (54% rural) and Costa Rica 
the most (21% rural) (CELADE, 2019); 10.5  million lived in the Dry 
Corridor region, an area recently exposed to severe droughts that have

resulted in 3.5 million people in need of humanitarian assistance (FAO, 
2016a). Except in Belize and Panama, the majority of the countries’ 
populations—ranging from 56% in Honduras to 95% in El Salvador—
were exposed to two or more risks derived from natural extreme events, 
affecting between 57% and 96% of the GDP of the countries (UNISDR 
and CEPREDENAC, 2014). CA is one of the regions most exposed to 
climatic phenomena; with long coastlines and lowland areas, the 
region is repeatedly affected by drought, intense rains, cyclones and 
ENSO events (high confidence) (ECLAC et al., 2015).
Large urban centres are located on mountains or away from the 
shore, with the notable exceptions of Panama City, Belmopan and 
Managua, capital cities housing around 3  million people. Urban 
development in the capital cities and suburbs has almost tripled in 
the last 40  years, reaching population densities as high as 11,000 
inhabitants/km2 in Guatemala City and Tegucigalpa, with the spread

of poor neighbourhoods in steep ravines and other marginal high-risk 
areas (Programa Estado de la Nación – Estado de la Región, 2016).
12.3.1.3 Vulnerability
Climate change is exacerbating socioeconomic vulnerability in CA, a 
region with high levels of socioeconomic, ethnic and gender inequality, 
high rates of child and maternal mortality and morbidity, high levels 
of malnutrition and inadequate access to food and drinking water 
(ECLAC et al., 2015). Disasters from adverse natural events exacerbate 
CA’s economic vulnerability, accounting for substantial human and 
economic losses (UNISDR and CEPREDENAC, 2014). Vulnerability 
in most sectors is considered high or very high (high confidence) 
(Figure 12.7).
Approximately 40% of the CA population live in poverty. Guatemala 
(62%), Honduras (60%), Nicaragua (46%) and Belize (42%, 2009) 
had the highest poverty rates in CSA in 2018 (ECLAC, 2019b; BCIE, 
2020). Rural poverty rates are higher—82% in Honduras and 77%

in Guatemala in 2014—as is poverty among Indigenous Peoples, up 
to 79% in Guatemala. Rural poor are the most sensitive to climate 
extremes as their main economic activity is based on agriculture in 
vulnerable terrains (NU CEPAL, 2018). In 2014, all CA countries, except

12
1699Central and South America  Chapter 12
for El Salvador (excluding Belize), had higher GINI coefficients (more 
inequality) than the average for Latin America (0.473), which in itself 
is the most unequal region in the world (ECLAC, 2019b); in 2018 the 
situation remained similar, with El Salvador showing the lowest GINI 
coefficient (40) and the remaining countries showing values higher 
than the Latin American average (BCIE, 2020).
12.3.1.4 Impacts
The countries in the region are consistently ranked highest in the world 
by risk of being impacted by extreme events (high confidence). The 
economic costs of climate-change impacts in 2010 were estimated as 
being from 2.9% of GDP for Guatemala to 7.7% for Belize (ECLAC 
et  al., 2015). For the period 1992–2011, Honduras, Nicaragua and 
Guatemala were among the 10 most impacted countries in the world 
by extreme weather events (UNISDR and CEPREDENAC, 2014). The 
number of these events has increased 3% annually in the last 30 years

(Bárcena et al., 2020a).
Human and economic losses, changing water availability and 
increasing food insecurity are the most studied impacts of climate 
change in CA (Figure 12.9) (Harvey et al., 2018; Hoegh-Guldberg et al., 
2019). Hydro-meteorological events, such as storm surges and TCs, are 
the most frequent extreme events and have the highest impact (high 
confidence) (Reyer et al., 2017). From 2005 to 2014, the cumulative 
impacts were over 3410 people dead, hundreds of thousands 
displaced and damages estimated around USD 5.8  billion (Ishizawa 
and Miranda, 2016). One standard deviation in the intensity of a 
hurricane windstorm leads to a decrease in both the growth of total 
GDP per capita (0.9% to 1.6%) and total income and labour income by 
3%, whereas it increases moderate and extreme poverty by 1.5% in CA 
(Ishizawa and Miranda, 2016).
Food insecurity is a serious impact of climate change in a region where

10% of the GDP depends on agriculture, livestock and fisheries (very 
high confidence) (ECLAC et al., 2015; CEPAL et al., 2018; Harvey et al., 
2018; BCIE, 2020). Crop losses largely result from highly variable 
rainfall and seasonal droughts, which have increased significantly in 
recent decades (Table 12.3) (CEPAL and CAC-SICA, 2020), particularly 
the observed changes in the MSD that reduces rainfall at the onset 
of the rainy season (May–June) (Anderson et  al., 2019). Small and 
subsistence farmers experience the highest impact because they 
practice rainfed agriculture (Imbach et  al., 2017), along with poor 
neighbourhoods, which face socioeconomic and physical barriers for 
adapting to climate change (Kongsager, 2017). In 2015, precipitation 
diminished between 50% and 70% of its historic average, causing 
a loss of up to 80% of beans and 60% of maize, leaving 2.5 million 
people food insecure, 1.6 million of whom were in the Dry Corridor of

CA (ECLAC et al., 2015; FAO, 2016a). In 2019, the region entered its 
fifth consecutive drought year, with 1.4 million people in need of food 
aid. Seasonal-scale droughts are projected to lengthen by 12–30%, 
intensify by 17–42% and increase in frequency by 21–42% in RCP4.5 
and RCP8.5 scenarios by the end of the century (Depsky and Pons, 
2021).
Studies have shown that the incidence of some vector-borne and 
zoonotic diseases in CA is correlated to climatic variables, particularly temperature and rainfall (high confidence) (Figure 12.4; Table 12.1). 
In Honduras, rainfall and relative humidity were positively correlated 
with the occurrence of haemorrhagic dengue cases (Zambrano et al., 
2012). In Costa Rica, temperature and rainfall were correlated to 
cattle rabies outbreaks and mortality during 1985–2016 (Hutter et al., 
2018); incidence of leishmaniasis showed cycles of 3  years related 
to temperature changes (Chaves and Pascual, 2006); and snakebites

were more likely to occur at high temperatures and were significantly 
reduced after the rainy season for the period 2005–2013 (Chaves 
et  al., 2015). In Panama, rainfall was associated with an increased 
number of malaria cases among the Gunas, an Indigenous People 
with high vulnerability living in poverty conditions on small islands 
affected by SLR (Hurtado et al., 2018). These correlations point to a 
possible change in disease incidence with climate change; evidence of 
that change is yet to be reported in the literature because longitudinal 
studies are lacking in the region.
Heat stress is another health concern in this already warm and humid 
part of the world (high confidence) (Table  12.2); it is an increasing 
occupational health hazard with potential impacts on kidney disease 
(Sheffield et  al., 2013; Dally et  al., 2018; Johnson et  al., 2019). SLR 
exacerbating wave-driven flooding is expected to impact infrastructure

and freshwater availability in small islands and atolls off the coast 
of Belize (Storlazzi et  al., 2018). Observed and expected impacts in 
the coastal and ocean ecosystems of the sub-region are described in 
Figure 12.9.
Decreasing water availability is another impact of climate change 
(high confidence). Under a climate-change scenario of 3.5°C warming 
and a 30% reduction in rainfall, a reduction in the production and 
export of crops and livestock is projected, affecting the wages and 
decreasing the GDP of Guatemala by 1.2%, thereby increasing food 
insecurity (Vargas et al., 2018b). By 2100, water availability per capita 
is projected to decrease 82% and 90% on average for the region under 
B2 (low emissions) and A2 (high emissions) scenarios respectively 
(Figure 12.3) (CEPAL, 2010).
Impacts on rural livelihoods, particularly for small and medium-sized 
farmers and Indigenous Peoples in mountains, include an overall

reduction in production, yield (Table 12.4), suitable farming area and 
water availability (high confidence) (Walshe and Argumedo, 2016; 
Bouroncle et al., 2017; Hannah et al., 2017; Imbach et al., 2017; Harvey 
et al., 2018; Batzín, 2019; Donatti et al., 2019). Bean production in El 
Salvador, Nicaragua, Honduras and Guatemala, is projected to decrease, 
using the Decision Support for Agro-Technology Transfer (DSSAT) 
under the A2 scenario, by 19% for 2050, whereas maize production, 
depending on the water retention capacity of soils, will drop between 
4% and 21% by 2050 (CEPAL et al., 2018). In Guatemala, the yield of 
rainfed maize is expected to decrease by 16% by 2050 under RCP8.5 
using the Global Gridded Crop Model Intercomparison GGCMI; yields 
for rainfed sugarcane are expected to drop by 44% and irrigated 
sugarcane by 36% under the same modelling conditions (Castellanos 
et al., 2018). Rice production is expected to decrease by 23% under

scenario A2 by 2050 (CEPAL and CAC/SICA, 2013).
The extent and quality of suitable areas for basic grains are expected to 
contract (high confidence). The suitable area for maize will experience

12
1700Chapter 12 Central and South America
a 35% reduction of cultivated area expected by 2100 under the A2 
scenario. The area suitable for beans is expected to shrink by 2050. 
Projections show that suitable areas with excellent capacity under 
current conditions will decrease by 14%, mainly in Panama (41%) Costa 
Rica (21%) and El Salvador (20%). The Species Distribution Model, 
using the IPSL GCM, projects that the suitable zones for cacao and 
coffee will shrink between 25% and 75% under RCP6.0 (Fernandez-
Manjarrés, 2018; Fernández Kolb et al., 2019). Warmer and dryer lower 
areas will become unsuitable for coffee and will drive its production 
to higher land (Läderach et al., 2013; Bunn et al., 2015). Under the 
A2 climate-change scenario, areas with excellent capacity for Arabica 
coffee will decrease by 12% in CA; coffee yield will decrease in suitable 
zones whereby the extent of high yield (>0.8 T ha−1) zones is projected

to shrink from 34% to 12%, whereas low-yield (<0.3 T ha−1) zones will 
expand from 14% to 36% by 2100 under the A2 scenario (CEPAL and 
CAC/SICA, 2014).
Mesoamerica, a biodiversity hotspot spanning across CA and southern 
Mexico, is a global priority for terrestrial biodiversity conservation, and 
it is projected to be negatively impacted by climate change, especially 
through the contraction of distribution of native species as the area 
becomes increasingly dryer (high confidence) (Section CCP1.2.2) 
(Feeley et al., 2013; Manes et al., 2021). A significant reduction in net 
primary productivity in tropical forests is expected under both RCP4.5 
and RCP8.5 as a result of temperature increase, precipitation reduction 
and droughts (Lyra et al., 2017; Castro et al., 2018; Stan et al., 2020).

lies within the 17–83% probability range.Aridity index models show that the dry, sub-humid vegetation of the 
dry corridor will expand to neighbouring areas and replace the humid 
forests in the Pacific lowlands and the northern parts of Guatemala by 
2050 under RCP4.5 and RCP8.5 scenarios (Pons et al., 2018; CEPAL 
and CAC-SICA, 2020). A warming of 3°C would shrink the tropical

rainforest and replace it with savannah grassland. Wetlands are also 
expected to be highly affected by climate change in the region (Hoegh-
Guldberg et al., 2019).
12.3.2 Northwestern South America Sub-region
12.3.2.1 Hazards
Significant increases in the intensity and frequency of hot extremes and 
significant decreases in the intensity and frequency of cold extremes 
(Dereczynski et  al., 2020; Dunn et  al., 2020) were likely2 observed 
(Figure 12.6; WGI AR6 Table 11.13) (Seneviratne et al., 2021).
Insufficient data coverage and trends in available data are generally 
not significant for heavy precipitation (low confidence) (Dereczynski 
et al., 2020; Dunn et al., 2020; Sun et al., 2021) (Figure 12.6; WGI AR6 
Table 11.14) (Seneviratne et al., 2021).
ENSO is the dominant phenomenon affecting weather conditions in 
all of CSA and along the Pacific Coast of NWS, causing heavy rains, Belize
Panama
Nicaragua
Costa Rica
GuatemalaHonduras
El Salvador7,000
6,000
5,000
4,000
3,000

1,0002,000
037,558
20,064
16,772
13,389Projected: 2100
(Baseline scenario)Projected: 2100 
Climate change scenario B2)Projected: 2100
(Climate change scenario A2)
m3 / person / year
El SalvadorHonduras
GuatemalaNicaragua
Costa RicaPanamaBelize10,826
El SalvadorHondurasNicaragua
GuatemalaCosta RicaBelize
PanamaWater availability
reduction per capita
in Central America
Hydric stress
Chronic scarcity
Absolute stress
Figure  12.3 |   Reduction of water availability per capita projected to 2100 without climate change (baseline scenario) and with two climate-change 
scenarios (CEPAL, 2010).

12
1701Central and South America  Chapter 12
storms, floods, landslides, heat and cold waves and extreme SLR 
(Ashok et  al., 2007; Reguero et  al., 2015; Wang et  al., 2017b; Muis 
et al., 2018; Rodríguez-Morata et al., 2018; Rodríguez-Morata et al., 
2019; Cai et  al., 2020). There is medium confidence that extreme 
ENSO will increase long after 1.5°C warming stabilisation according 
to CMIP5 (Cai et al., 2015, 2018; Wang et al., 2017b). It is very likely 
that ENSO rainfall variability, used for defining extreme El Niño and 
La Niña, will increase significantly, regardless of amplitude changes in 
ENSO sea surface temperature (SST) variability, by the second half of 
the 21st century in scenarios SSP2-4.5, SSP3-7.0 and SSP5-8.5 (WGI 
AR6 Chapter 4) (Lee et al., 2021).
Warming and drier conditions are projected through the reduction 
of total annual precipitation, extreme precipitation and consecutive 
wet days and an increase in consecutive dry days (Chou et al., 2014).

Heatwaves will increase in frequency and severity in places close to 
the equator like Colombia (Guo et al., 2018; Feron et al., 2019), with 
a decrease but strong wetting in coastal areas, pluvial and river flood 
and mean wind increase (Mora et al., 2014). Models project a very likely 
2°C GWL increase in the intensity and frequency of hot extremes and 
decrease in the intensity and frequency of cold extremes. Nevertheless, 
models project inconsistent changes in the region for extreme 
precipitation (low confidence) (Figure  12.6; WGI AR6 Table  12.14) 
(Ranasinghe et  al., 2021). The main climate impact drivers in the 
region, like extreme heat, mean precipitation and coastal and oceanic 
drivers, will increase and snow, ice and permafrost will decrease with 
high confidence (WGI AR6 Table 12.6) (Ranasinghe et al., 2021).
12.3.2.2 Exposure
There is high confidence that coastal lowlands are exposed to SLR in 
the form of coastal flooding and erosion, subsidence and saltwater

intrusion (Hoyos et al., 2013). Those hazards can affect settlements, 
ports, industries and other infrastructures. Mangrove and aquaculture 
areas are among the most exposed systems (Gorman, 2018). The 
Eastern Tropical Pacific, particularly Sector Niño 3.4, will see the worst 
increase in SST, affecting industrial and small-scale fisheries (very 
high confidence) (Castrejón and Defeo, 2015; Reguero et  al., 2015; 
Eddy et al., 2019; Bertrand et al., 2020; Castrejón and Charles, 2020; 
Escobar-Camacho et al., 2021).
Settlements and agriculture on different scales and hydroelectric 
infrastructures, especially near big rivers or in plains, are exposed to 
floods. Exposure and vulnerabilities to precipitation, overflows and 
related landslides are increasing (Briones-Estébanez and Ebecken, 
2017).
The Andean piedmont (500–1200 metres above sea level [MASL]) 
ecosystems and crops and elevation ranges above the treeline are

more exposed to thermal anomalies (very high confidence) (Urrutia 
and Vuille, 2009; Vuille et al., 2015; Aguilar-Lome et al., 2019; Pabón-
Caicedo et  al., 2020). Temperature rise, combined with precipitation 
and floods, leaves people more exposed to epidemics (very high 
confidence) (Stewart-Ibarra and Lowe, 2013; Sippy et  al., 2019; 
Petrova et al., 2020). A more significant exposure is related to lower 
socioeconomic conditions, poor health and marginalisation (Oliver-
Smith, 2014).12.3.2.3 Vulnerability
Local economies reliant on limited and specialised resources, highly 
dependent on ecosystem services such as water and soil fertility, 
such as alpaca and llama herders or small-scale fishers, are among 
the more vulnerable (very high confidence) (Hollowed et  al., 2013; 
Postigo, 2013; Glynn et al., 2017; Duchicela et al., 2019), along with the 
agricultural sector in the face of extreme events (Coayla and Culqui,

2020). Their vulnerabilities increase as a result of unequal chains of 
value, incomplete transfers of technology and other socioeconomic 
and environmental drivers (high confidence) (Ariza-Montobbio and 
Cuvi, 2020; Gutierrez et al., 2020).
Informal housing and settlements, usually located in areas exposed 
to the highest level of risk, exacerbates vulnerability (very high 
confidence) (Miranda Sara and Baud, 2014; Cuvi, 2015; Miranda 
Sara et al., 2016). The absence of proper drainage systems in urban 
areas increases this vulnerability, especially to floods. Most cities and 
infrastructure are considered highly vulnerable to climate change (high 
confidence) (Figure 12.7).
Regions dependent on glacier runoff are particularly vulnerable 
(Jiménez Cisneros et al., 2014; Mark et al., 2017; Polk et al., 2017). 
Also biodiversity and water-dependent activities where seasonality 
and rainfall patterns are changing and where other non-climatic

sources of change, such as land use, affect the capacity of ecosystems 
to provide hydrological services (very high confidence) (Cerrón et al., 
2019; Molina et al., 2020). The countries in this sub-region (Colombia, 
Ecuador and Peru) are among the most vulnerable in terms of well-
being and health (Figure 12.7; Nagy et al., 2018).
12.3.2.4 Impacts
An increase in the frequency of climate-related disasters has been 
reported (high confidence) (Huggel et al., 2015a; Stäubli et al., 2018) 
(WGI AR6 Chapter 12) (Ranasinghe et al., 2021). Scale studies indicate 
an increase of flood risk during the 21st century, consistent with 
more frequent floods, with the risk being worse in higher emission 
scenarios (high confidence) (Arnell and Gosling, 2013; Hirabayashi 
et al., 2013; Alfieri et al., 2017; WGI AR6 Chapter 12, Ranasinghe et al., 
2021). Those living on riverbanks and in slums built on steep slopes 
are among the most affected by floods of all kinds (high confidence)

(Emmer et al., 2016; Emmer, 2017). There is still uncertainty in relation 
to future drought intensity and frequency (Pabón-Caicedo et al., 2020).
Increased SST, coupled with stronger ENSO events, will affect marine 
life and fisheries by loss of productive habitat, disruption of nutrient 
structure, productivity and alteration of species migration patterns, 
leading to changes in fishing rates, which will impact coastal 
livelihoods (high confidence) (Bayer et al., 2014; Cai et al., 2015; Ding 
et  al., 2017; Mariano Gutiérrez et  al., 2017; Bertrand et  al., 2020). 
Figure 12.8 shows other observed sensitivities in several ecosystems 
and in such places as the Galapagos and Malpelo islands and coastal 
economic exclusion zones (EEZs).
ENSO events coupled with climate change lead to warmer ocean 
temperatures, heavy rains, floods and heavy river discharges, which

12
1702Chapter 12 Central and South America
will continue to impact several activities, including small-scale fishery 
infrastructure (very high confidence). In Peru alone, wet extremes are 
estimated to be at least 1.5 times more likely to happen compared to 
pre-industrial times. The extremely wet ENSO event of 2017 resulted 
in 6–9  billion USD in monetary losses in that country, 1.7  million 
inhabitants affected and crops, roads, bridges, homes, schools and 
health service facilities damaged or destroyed. Distinct types of ENSO 
events can have differentiated impacts (French and Mechler, 2017; 
Christidis et al., 2019; Takahashi and Martínez, 2019; Bertrand et al., 
2020; Coayla and Culqui, 2020).
Irrigation, potable water, health and education infrastructures, as well 
as roads, bridges, cities and residential constructions, are frequently 
damaged or destroyed by extreme precipitation events, which also 
impact sediment transport, river erosion and annual discharge (very

high confidence) (Martínez et al., 2017; Morera et al., 2017; Isla, 2018; 
Rosales-Rueda, 2018; Salazar et  al., 2018; Puente-Sotomayor et  al., 
2021). The increasing variability of precipitation has compromised 
rainfed agriculture and power generation, particularly in the dry season 
(high confidence) (Bradley et al., 2006; Bury et al., 2013; Buytaert et al., 
2017; Carey et al., 2017; Vuille et al., 2018; Orlove et al., 2019). For the 
Amazon–Andes transition zone, the impacts of hydrological variability and transport of sediments have been noticed in riparian agriculture 
and biodiversity (high confidence) (Maeda et al., 2015; Espinoza et al., 
2016; Vauchel et  al., 2017; Ronchail et  al., 2018; Ayes Rivera et  al., 
2019; Armijos et al., 2020; Figueroa et al., 2020; Pabón-Caicedo et al., 
2020). Changes in seasonality and rain patterns are affecting coffee 
producers (Lambert and Eise, 2020).
Increases in vector-borne diseases can be related to increases in

rainfall and minimum temperatures during ENSO events (Stewart-
Ibarra and Lowe, 2013) and the expansion of the diseases’ altitudinal 
distribution (high confidence) (Lowe et  al., 2017; Lippi et  al., 2019; 
Portilla Cabrera and Selvaraj, 2020). ENSO events have been related 
to such diseases as dengue and leptospirosis (Quintero-Herrera et al., 
2015; Sánchez et  al., 2017; Arias-Monsalve and Builes-Jaramillo, 
2019); they can also lead to an increased incidence of chikungunya 
(Sections  7.2.2.1 and 7.3.1.3). Precipitation, relative humidity and 
temperature have influenced dengue incidence in recent years (Mattar 
et  al., 2013) (Table  12.1). Dengue cases are predicted to increase 
in the 1.5°C and the 3.7°C warming scenarios by 2050 and 2100, 
with increases ranging from 28,900 to 88,800 in Peru, 34,600 to 
110,000 in Ecuador, and 97,400 to 317,000 in Colombia, although 
these scenarios do not consider the potential effects of vaccines or

socioeconomic trajectories (Colón-González et al., 2018). Other studies 
found that Aedes aegypti (arbovirus vector) will shift into higher 
elevations, increasing the populations at risk (Figure 12.5) (Lippi et al., 
2019). Climate change will contribute to increased malaria vectorial 
capacity (high confidence) (Section  7.2.2.1) (Laporta et  al., 2015). 
Increases in minimum temperature were associated with historical 
malaria transmission when taking into consideration disease control 
interventions and climate factors (Fletcher et  al., 2020). Figure  12.4 
shows mixed changes in the number of months suitable for malaria 
transmission, with low-lying areas in coastal regions becoming 
more suitable. Zoonotic tick-borne diseases and the epidemiology 
of tuberculosis are also influenced (Garcia-Solorzano et  al., 2019; 
Rodriguez-Morales et al., 2019).
Accelerated warming is reducing tropical glaciers. Glacier volume loss

and permafrost thawing will continue in all scenarios (high confidence) 
(Ranasinghe et  al., 2021). On average, the tropical Andes have lost 
about 30% and more of their area since the 1980s (Basantes-Serrano 
et al., 2016; Mark et al., 2017; Thompson et al., 2017; Rabatel et al., 
2018; Vuille et al., 2018; Reinthaler et al., 2019a; Seehaus et al., 2019; 
Masiokas et al., 2020). In a low-emissions scenario, by the end of the 
21st century, Peru will lose about 50% of its present glacier surface, 
while in a high-emission scenario there will remain very small areas 
of only about 3–5% on the highest peaks (Schauwecker et al., 2017).
Changing glaciers, snow and permafrost (Figure 12.13), in synergy with 
land use change, have implications for the occurrence, frequency and 
magnitude of derived floods and landslides (high confidence) (Huggel 
et al., 2007; Iribarren Anacona et al., 2015; Emmer, 2017; Mark et al., 
2017), as well as for landscape transformation through lake formation

or drying and for alterations in hydrological dynamics, with impacts 
on water for human consumption, agriculture, industry, hydroelectric 
generation, carbon sequestration and biodiversity (high confidence) 
(Michelutti et al., 2015; Carrivick and Tweed, 2016; Kronenberg et al., 
2016; Emmer, 2017; Mark et al., 2017; Milner et al., 2017; Polk et al., Historical changes (1950–59 to 2010–2019)
in suitability for malaria transmission
CA
NWSNSA
SAM
SESNES
SWS
SSA
7 6 5 4 3 2 1 0.5 -6 -5 -4 -3 -2 -1-0.5 -7Central America (CA)
Northwestern South America (NWS )
Northern South America (NSA) 
South America Monsoon (SAM)
Northeastern South America (NES)
Southwestern South America (SWS)
Southeastern South America (SES)
Southern South America (SSA)
Change in number of suitable months for malaria transmission
Figure 12.4 |  Change in average number of months in a given year suitable 
for malaria transmission by Plasmodium falciparum, from 1950–1959 to

2010–2019. The threshold-based model used incorporates precipitation accumulation, 
average temperature and relative humidity (Grover-Kopec et al., 2006; Romanello et al., 
2021).

12
1703Central and South America  Chapter 12
2017; Reyer et al., 2017; Young et al., 2017; Vuille et al., 2018; Cuesta 
et  al., 2019; Drenkhan et  al., 2019; Hock et  al., 2019; Motschmann 
Water flow has decreased in several basins, such as the Shullcas River 
in the Cordillera Huaytapallana in Peru, and is expected to decrease 
in the near future in places such as the Cordillera Blanca in Peru (very 
high confidence) (Baraer et al., 2012; Vuille et al., 2018; Somers et al., 
2019; Molina et al., 2020). Disruptions in water flows will significantly 
degrade or eliminate high-elevation wetlands (high confidence) (Bury 
et al., 2013; Dangles et al., 2017; Mark et al., 2017; Polk et al., 2017; 
Cuesta et  al., 2019). Impacts on wetlands are affecting the wild 
vicuña and the domesticated alpaca (Duchicela et  al., 2019). New 
lakes represent a source of future hazards and water scarcity, as well 
as opportunities to serve as water reservoirs (Colonia et  al., 2017;

Drenkhan et al., 2019). The timing and extent of peak water due to 
glacier shrinkage is spatially highly variable and has passed for a large 
number of tropical Andes glaciers (Hock et al., 2019). Cities dependent 
on glacier melt have experienced high variability in domestic water 
supply (Chevallier et al., 2011; Soruco et al., 2015; Mark et al., 2017), as 
shown in Case Study 2.7.3, but an increase in demand may also have 
an effect (Buytaert and De Bièvre, 2012). Water provision is related to 
socioeconomic issues (Drenkhan et al., 2015). Glacier retreat impacts 
Andean pastoralists (high confidence), as shown in Case Study 2.6.5.4.
NWS houses several global priority areas for biodiversity conservation, 
including the Tropical Andes and Tumbes-Chocó-Magdalena terrestrial 
biodiversity hotspots (Section CCP1.2.2; Manes et  al., 2021). 
Biodiversity in the Tropical Andes and Tumbes-Chocó-Magdalena is 
projected to suffer negative impacts (medium confidence: medium

evidence, high agreement) (Figure  12.9). Invasive plant species 
might benefit from climate change in these hotspots (Wang et  al., 
2017a). Species distribution is changing upslope due to increasing 
air temperature, leading to range contraction and local extinctions 
of highland species, whereas lowland species are experiencing range 
contractions at the rear end and expansions in the front end, including 
vectors of diseases (high confidence) (Crespo-Pérez et al., 2015; Duque 
et al., 2015; Morueta-Holme et al., 2015; Moret et al., 2016; Aguirre 
et al., 2017; Cuesta et al., 2017a; Seimon et al., 2017; Fadrique et al., 
2018; Tito et al., 2018; Zimmer et al., 2018; Cauvy-Fraunié and Dangles, 
2019; Cuesta et  al., 2019; Moret et  al., 2020; Rosero et  al., 2021). 
Vegetation in summits of the northern Andes is particularly vulnerable 
because of a high abundance of endemic species with narrow thermal 
niches and lowland dispersal capacity in comparison to the central

Andes (Cuesta et al., 2020).
The upper limit of alpine vegetation (paramo) shifted upslope 500 m 
in the Chimborazo (Morueta-Holme et al., 2015), yet the upper forest 
limit (the ecotone between forest and alpine vegetation) is migrating 
at slower rates or not at all (Harsch et  al., 2009; Rehm and Feeley, 
2015b), so it is expected to be a major barrier to migration to several 
montane species, leading to population reductions and biodiversity 
losses (Lutz et  al., 2013; Rehm and Feeley, 2015a). Shifts in tree 
species distribution may result in decreased above-ground carbon 
stocks and productivity in tropical mountain forests (high confidence) 
(Feeley et al., 2011; Duque et al., 2015; Fadrique et al., 2018; Duque 
et al., 2021), a biomass loss that will only be partially offset through increased recruitment and growth of lowland species migrating 
upslope. Water scarcity can enhance tree mortality and decrease 
above-ground carbon stocks (Álvarez-Dávila et  al., 2017; McDowell

et  al., 2020). The agricultural frontier of crops, such as potatoes or 
maize, is moving upwards (high confidence), following the freezing 
level height upward displacement (Morueta-Holme et  al., 2015; 
Skarbø and VanderMolen, 2016; Schauwecker et al., 2017; Vuille et al., 
2018). Modelling exercises agree with the observed impacts in species, 
ecosystem processes, crop impacts and related pests and diseases 
(high confidence) (Cernusak et al., 2013; Tovar et al., 2013; Ramirez-
Villegas et al., 2014; Ovalle-Rivera et al., 2015; van der Sleen et al., 
2015; Lowe et al., 2017). Agricultural options are changing as a result 
of intra-seasonal temperature variation (Ponce, 2020). Changes in the 
timing and amount of precipitation are also impacting agriculture 
(Table 12.4) (Heikkinen, 2017; Altea, 2020).
Species distribution is changing in dry lowland forests, where 
deforestation is the more intense driver and climate change is intensely

acting (Aguirre et  al., 2017; Manchego et  al., 2017). Extinctions in 
amphibians have been related to temperature rises acting in synergy 
with diseases (Catenazzi et  al., 2014). The fungus Batrachochytrium 
dendrobatidis successfully accompanied and caused disease in high-
elevation Andean frogs as they expanded their ranges to 5200–
5400 m (Seimon et al., 2017). Several groups of freshwater species of 
the tropical Andes represent 35% of threatened freshwater species in 
the world (Gardner and Finlayson, 2018). Potential impacts of species 
turnover in key areas for biodiversity conservation have been identified 
(Cuesta et al., 2017b).
Climate-change-related hazards could foster rural poverty, and its 
impacts have led to the modification of agriculture calendars and 
irrigation adjustments (Postigo, 2014). Livestock populations are 
diminishing due to rising temperatures, changing water flows and 
shrinkage of pastures, particularly cattle and pig production (Bayer

et al., 2014; Tapasco et al., 2015; Bergmann et al., 2021). In some cases 
farmers respond to extreme temperatures by increasing use of land and 
crop intensity (Aragón et al., 2021). Climate change has prompted and 
will continue to prompt internal and international migrations (Løken, 
2019; Bergmann et al., 2021). A change in fire regimes and fire risk is 
expected in highland ecosystems, although it is difficult to determine 
the influence of human activities and climate change influence on fire 
patterns (Oliveras et al., 2014, 2018; Armenteras et al., 2020).
12.3.3 Northern South America Sub-region
12.3.3.1 Hazards
A significant increase in the intensity and frequency of warm extremes 
and length of heatwaves and a decrease in the frequency of cold 
extremes (Skansi et al., 2013) were likely observed (Figure 12.6) (WGI 
AR6 Table 11.13) (Donat et al., 2013; Almeida et al., 2017; Seneviratne 
et al., 2021). Precipitation showed increasing trends in annual and wet

season totals over the eastern part and decreasing trends in the dry 
season (Almeida et al., 2017). An increase in the frequency of anomalous 
severe floods (Gloor et al., 2015) was observed, but insufficient data 
coverage for extreme precipitation and trends in the available data

12
1704Chapter 12 Central and South America
result in low confidence (Avila-Diaz et  al., 2020; Dereczynski et  al., 
2020; Dunn et  al., 2020; Sun et  al., 2021) (WGI AR6 Table  11.14) 
(Seneviratne et al., 2021). Droughts presented mixed trends between 
sub-regions, but evidence indicates an increasing length of dry periods 
(low confidence) (WGI AR6 Tables 11.15 and 12.3) (Skansi et al., 2013; 
Marengo and Espinoza, 2016; Spinoni et al., 2019; Avila-Diaz et al., 
2020; Dereczynski et al., 2020; Dunn et al., 2020; Seneviratne et al., 
2021; Ranasinghe et al., 2021).
An overall increase in temperature by the end of the century is 
projected for all seasons, from 2°C to 6°C depending on the scenario 
(Chou et al., 2014). Projections also suggest increases in the intensity 
and frequency of hot extremes and decreases in the intensity and 
frequency of cold extremes (very likely for a 2°C GWL) (WGI AR6 
Table 11.13) (López-Franca et al., 2016; Seneviratne et al., 2021). In

the entire region, extreme maximum temperature estimates under 
the RCP4.5 scenario are projected to increase. Major tropical cities 
are expected to be strongly affected by heatwaves and daily record 
temperatures (Feron et al., 2019).
A decrease in precipitation over the tropical region but regional 
changes, such as increases in rainfall amounts in western NSA of up to 
40 mm, are expected by mid-century under RCP8.5 (Teichmann et al., 
2013; Sánchez et al., 2015). Changes in the dry season in the central 
part of South America (SA) due to the late onset and late retreat of 
monsoon, decreases in precipitation over the Amazon and central Brazil 
are expected (Coppola et al., 2014; Giorgi et al., 2014; Llopart et al., 
2014). Further, an increase in the frequency and geographic extent of 
meteorological drought in the eastern Amazon and the opposite in 
the west are expected with medium confidence (Duffy et al., 2015).

A decrease in the total annual precipitation but an increase in heavy 
precipitation (Seiler et al., 2013; Chou et al., 2014) are projected for a 
2°C GWL (Figure 12.6; WGI AR6 Table 11.15) (Seneviratne et al., 2021).
Mean precipitation will decrease, and heavy precipitation, aridity and 
drought are projected to increase with medium confidence, whereas 
mean temperature, extreme heat, fire weather and coastal and oceanic 
climate impact drivers will all increase with high confidence (WGI AR6 
Table 12.6 and Figure 12.8) (Sun et al., 2019; Ranasinghe et al., 2021).
12.3.3.2 Exposure
In NSA the percentage of the national population living in low elevation 
coastal zones (LECZs) and exposed to SLR is 68% for Suriname, 56% for 
Guyana and 6% for Venezuela (Nagy et al., 2019). In these countries, 
the exposure of populations, land areas and built capital to coastal 
floods is projected to continue and increase (Neumann et al., 2015; 
Reguero et al., 2015).

In the Amazon basin, approximately 80% of the population is 
concentrated in cities due to migrations in search of improvements 
in education, job opportunities, health and goods and services (Eloy 
et al., 2015; Pinho et al., 2015). These populations settle in areas prone 
to flooding combined with various levels of sanitation due to limited 
economic access to areas of lower risk (Pinho et  al., 2015; Mansur 
et al., 2016; Andrade and Szlafsztein, 2018; Parry et al., 2018). In these 
areas, poor urban planning and high population densities increase exposure levels (Mansur et al., 2016). In this context, 41% of the total 
population of urban centres of the Amazon delta and estuary (ADE) 
are exposed to flooding (Mansur et  al., 2016), while in Santarem, 
population and infrastructure are highly exposed to floods and flash 
floods (Andrade and Szlafsztein, 2018).
Exposure of the Brazilian Amazon to severe to extreme drought has

increased from 8% in 2004/2005 to 16% in 2009/2010 and 16% in 
2015/2016 (Anderson et  al., 2018b); a similar trend is reported in 
other regions (Table 12.3). During the extreme drought of 2015/2016 
in the Amazonian forests, 10% or more of the area showed negative 
anomalies of the minimum cumulative water deficit (Anderson et al., 
2018b). This extreme drought also caused an increase in the occurrence 
and spread of fires in the basin (medium confidence: medium evidence, 
high agreement) (Aragão et al., 2018; Lima et al., 2018; Silva Junior 
et  al., 2019; Bilbao et  al., 2020). Exposure to anomalous fires in 
ecosystems such as savannahs, which are more fire-prone, increases 
the exposure and vulnerability of adjacent forest ecosystems not 
adapted to fire, such as seasonally flooded forests (Bilbao et al., 2020; 
Flores and Holmgren, 2021).
12.3.3.3 Vulnerability
NSA is one of the most vulnerable sub-regions in the region, after CA,

as evidenced by its very high vulnerability in four of the six sectors 
assessed (Figure 12.7). The LECZ of Venezuela, Guyana and Suriname 
are highly vulnerable to climate change due to SLR (high confidence) 
(CAF , 2014; Mycoo, 2014; Reguero et al., 2015; Villamizar et al., 2017; 
Nagy et al., 2019). In Guyana, the combined effect of increased rainfall 
intensity and SLR has caused flooding over the past two decades, 
increasing the vulnerability of the agricultural sector (Tomby and 
Zhang, 2019).
The unprecedented extreme events of floods (2009, 2012 and 2014) 
and drought (2010) in the Amazon basin led to increased societal 
vulnerability (medium confidence: medium evidence, high agreement) 
(Mansur et  al., 2016; Debortoli et  al., 2017; Marengo et  al., 2018; 
Menezes et al., 2018). The disruption of the region’s natural hydrology 
dynamics as a consequence of extreme events increases the sensitivity 
of the food and transport systems of the Indigenous Peoples and rural

resource-dependent communities (Pinho et al., 2015).
Migration by Indigenous Peoples and rural resource-dependent 
communities to cities has increased due to urbanisation, development 
of extractive activities, agroindustry and infrastructure. Upon 
migrating, they are forced to abandon their livelihoods in order to 
acquire temporary jobs and to live in poverty and exclusion conditions 
on the periphery of the city (Cardoso et al., 2018). Between 60% and 
90% of the population in the urban centres of ADE live in conditions 
of moderate to high degree of vulnerability (Mansur et  al., 2016) 
(Figure  12.7). Amazon populations located in remote urban centres 
with limited or non-existent roads are more vulnerable to extreme 
events in relation to more connected urban centres (Parry et al., 2018). 
These highly risky circumstances reduce the adaptive capacity of these 
populations (Cardoso et al., 2018). Nevertheless, the dynamics of the

adaptive capacity of Indigenous Peoples and rural resource-dependent 
communities is a complex issue. There is a robust and growing body

12
1705Central and South America  Chapter 12
of literature showing that resource-dependent communities located in 
remote areas address climate anomalies by reducing the vulnerability 
of socioecological systems through IKLK (high confidence) (Mistry 
et al., 2016; Vogt et al., 2016; Bilbao et al., 2019, 2020; Camico et al., 
2021).
Amazonian forests constitute one of the major carbon (C) sinks on 
Earth (Pan et al., 2011), playing a pivotal role in the climate system and 
regional balance of C and water (Marengo et al., 2018; Molina et al., 
2019). Deforestation, temperature increase and any factor affecting 
forest ecosystem dynamics will have an impact on atmospheric CO 2 
concentrations and, hence, on the global climate (Ruiz-Vásquez et al., 
2020; Sullivan et al., 2020).There is robust scientific evidence of the 
high vulnerability of Amazon rainforests to increasing temperature 
and repeated extreme drought events (high confidence) (Figure 12.7)

(Brienen et al., 2015; Olivares et al., 2015; Feldpausch et al., 2016; Zhao 
et al., 2017; Anderson et al., 2018b; Anjos and De Toledo, 2018; Yang 
et al., 2018; Barkhordarian et al., 2019; Sampaio et al., 2019; Rammig, 
2020; Sullivan et al., 2020).
12.3.3.4 Impacts
Suriname has experienced coastal erosion and flooding, which 
causes damage to infrastructure, agriculture and ecosystems, while 
Georgetown has suffered a significant number of floods (CAF , 2014). 
In Guyana, coastal flooding has negatively impacted agricultural 
activity (Tomby and Zhang, 2018) (Figure 12.9). Sugarcane production 
has been one of the most impacted cash crops. The impact on sugar 
production has affected Guyana’s sugar industry (Tomby and Zhang, 
2019). Among the main impacts observed in the sugar industry are an 
increase in production costs, greater use of pesticides and fertilizers 
and a reduction in worker income (Tomby and Zhang, 2018).

Indigenous Peoples and resource-dependent rural communities in the 
Amazon have been impacted over the last decade by extreme drought 
and flood events in various dimensions of their livelihoods (Pinho 
et al., 2015). Food security has been strongly impacted since it is based 
on fishing and small-scale agriculture, two sectors highly vulnerable 
to climate change. During extreme events, fishing decreases due to 
limited access to fishing grounds (medium confidence: low evidence, 
high agreement) (Figure 12.9) (Pinho et al., 2015; Camacho Guerreiro et  al., 2016. Overfishing, deforestation and dam construction are a 
threat to fishing in the sub-region (Lopes et al., 2019) and therefore 
contribute to exacerbating the impacts of climate change. Small-scale 
agriculture practices (e.g., floodplain agriculture and slash and burn) 
are highly coupled with natural hydrological cycles and therefore 
severely affected by extreme events (Figure 12.9) (Cochran et  al.,

2016). Livelihoods are also impacted by disruptions in land and river 
transport, restrictions in drinking water access, increased incidence 
of forest fires and disease outbreaks (medium confidence: medium 
evidence, high agreement) (Figure 12.9) (Marengo et al., 2013; Pinho 
et al., 2015; Marengo and Espinoza, 2016; Marengo et al., 2018). In 
addition, flood events have caused losses of homes and disruption of 
public and commercial services (Figure 12.9) (Parry et al., 2018).
Several vector-driven diseases such as malaria and leishmaniasis 
are endemic in the Amazon region; however, socio-environmental 
changes are altering their natural dynamics (Confalonieri et  al., 
2014b). An important relationship between the outbreak of infectious 
diseases and changes in climatic events (e.g., droughts, floods, heat 
waves, ENSO) or environmental events (e.g., deforestation, dam 
construction and habitat fragmentation) has been found to exist for

the Brazilian Amazon (medium confidence: medium evidence, high 
agreement) (Pan et al., 2014; Filho et al., 2016; Nava et al., 2017; 
Ellwanger et  al., 2020). These impacts are more severe in poor 
populations with limited access to health services (Pan et al., 2014; 
WHO and UNFCCC, 2020). In the case of Venezuela, the impact of 
climate change on the epidemiology of malaria has been studied, 
showing significant influence on transmission in the Amazonia area 
of the country (Figure 12.4) (Laguna et al., 2017). Other studies from 
Venezuela have documented the role of ENSO in dengue outbreaks 
(Vincenti-Gonzalez et  al., 2018). Table  12.1 shows the changes 
observed in reproduction potential for dengue in the different sub-
regions due to changes in rainfall and temperature. Forest fires pose 
a major threat to public health in the region because they relate 
to an increase in hospital admissions due to respiratory problems,

mainly among children and the elderly (Figure  12.5). The amount 
of air pollutants detected is sometimes higher than that observed 
in large urban areas, especially during dry seasons when biomass 
burning increases (Aragão et al., 2016; de Oliveira Alves et al., 2017; 
Paralovo et al., 2019).
Table 12.1 |  Environmental suitability for transmission of dengue by Aedes aegypti as modelled by the influence of temperature and rainfall on vectorial capacity and vector 
abundance; this is overlaid on human population density data to estimate the reproduction potential for these diseases (R 0, expected number of secondary infections resulting from 
one infected person). The southwestern South America (SWS) and southern South America (SSA) sub-regions are not presented because the vector is not abundant in these areas 
and the estimated R 0 is lower than 0.01. Data were derived from Romanello et al. (2021).
Sub-region Average R 0 1950–1954 Average R 0 2016–2020Absolute change in R 0 from

1950–1954 to 2016–2020Percentage change in 
R0 from 1950–1954 to 
2016–2021
Central America (CA) 3.00 3.53 0.53 18%
Northwestern South America 
(NWS)1.85 2.40 0.55 30%
Northern South America (NSA) 1.31 2.05 0.74 56%
South America Monsoon (SAM) 0.93 1.67 0.74 80%
Northeastern South America (NES) 2.11 2.47 0.36 17%
Southeastern South America (SES) 0.64 0.81 0.17 26%

12
1706Chapter 12 Central and South America
Climate-change impacts have also been observed in the oceans, coastal 
ecosystems (coral reefs and mangroves), EEZs and saltmarshes in NSA; 
further impacts are expected in coral reefs, estuaries, mangroves and 
EEZs in the sub-region (Figure 12.9). Species in freshwater ecoregions 
(e.g., the Orinoco and Amazon Rivers and their flooded forests) are 
predicted to suffer a decrease in range and climatic suitability (medium 
confidence: low evidence, high agreement) (Section CCP1.2.3; Manes 
et al., 2021). A significant decrease in climate refugia (90%) for multiple 
vertebrate and plant species in the region has been projected for a 4°C 
scenario, with considerable benefits of mitigation and reducing risks to 
40% for a 2°C scenario (Warren et al., 2018).
Droughts in 2009/2010 and 2015/2016 increased tree mortality rate 
in Amazon forests (Doughty et  al., 2015; Feldpausch et  al., 2016;

Anderson et  al., 2018b), while productivity showed no consistent 
change; some authors reported a drop in productivity (Feldpausch 
et al., 2016), while others found no significant changes (Brienen et al., 
2015; Doughty et  al., 2015). Nevertheless, the combined effect of 
increasing tree mortality with variations in growth results in a long-
term decrease in C stocks in forest biomass, compromising the role 
of these forests as a C sink (high confidence) (Brienen et  al., 2015; 
Rammig, 2020; Sullivan et al., 2020) (Figure 12.9). Under the RCP8.5 
scenario for 2070, drought will increase the conversion of rainforest 
to savannah (medium confidence: medium evidence, high agreement) 
(Anadón et al., 2014; Olivares et al., 2015; Sampaio et al., 2019). The 
transformation of rainforest into savannah will bring forth biodiversity 
loss and alterations in ecosystem functions and services (medium 
confidence: medium evidence, high agreement) (Anadón et al., 2014;

Olivares et al., 2015; Sampaio et al., 2019). In the Amazon basin, the 
synergistic effects of deforestation, fire, expansion of the agricultural 
frontier, infrastructure development, extractive activities, climate 
change and extreme events may exacerbate the risk of savannisation 
(medium confidence: medium evidence, high agreement) (Nobre et al., 
2016b; Bebbington et al., 2019; Sampaio et al., 2019; Rammig, 2020).
12.3.4 South America Monsoon Sub-region
12.3.4.1 Hazards
Temperature extremes have likely increased in the intensity and 
frequency of hot extremes and decreased in the intensity and frequency 
of cold extremes (Donat et  al., 2013; Bitencourt et  al., 2016) (WGI 
AR6 Table  11.13, Seneviratne et  al., 2021). In a vast transition zone 
between the Amazon and the Cerrado Biomes within the region, 
analysis of seasonal precipitation trends suggested that almost 
90% of the observational sites showed a reduction in the length of

the rainy season in the region (Debortoli et  al., 2015), in the period 
1971–2014 (Marengo et al., 2018), confirming the growth in length of 
the dry season. Changes in the hydrological and precipitation regimes, 
characterised by a reduction in rainfall in southern Amazonia, in 
contrast to an increase in northwestern Amazonia, and overall increases 
in extreme precipitation and in the frequency of consecutive dry days 
have been reported by several authors (Fu et al., 2013; Almeida et al., 
2017; Marengo et al., 2018; Espinoza et al., 2019a) with low confidence  
(WGI AR6 Table 11.14) ( Seneviratne et al., 2021) due to insufficient 
data coverage and trends in available data generally not significant.The Amazon has been identified as one of the areas of persistent and 
emergent regional climate-change hotspots in response to various 
representative concentration pathways (Diffenbaugh and Giorgi, 
2012). In Bolivia, CMIP3/5 models projected an increase in temperature

(2.5°C–5.9°C), with seasonal and regional differences. In the lowlands, 
both ensembles agreed on less rainfall (–19%) during drier months 
(June–August and September–November), with significant changes in 
interannual rainfall variability, but disagreed on changes during wetter 
months (January–March) (Seiler et  al., 2013). As a consequence of 
higher temperatures and reduced rainfall, an increased water deficit 
would be expected in the Brazilian Pantanal (Marengo et  al., 2016; 
Bergier et  al., 2018; Llopart et  al., 2020) with high confidence. The 
largest increases in warmer days and nights, and aridity, drought and 
significant increases in fire occurrence are calculated over the Amazon 
area (Huang et  al., 2016). Over the entire region, by mid-century 
(RCP4.5) there is medium confidence of increases in river and pluvial 
floods, aridity and mean wind speed, and extreme heat, fire weather 
and drought are projected to increase with high confidence (WGI AR6

Table 12.6; Ranasinghe et al., 2021).
12.3.4.2 Exposure
A large expansion in cropland area (soybean, corn and sugarcane) was 
observed in the past two decades in SAM, in response to increased 
local and global demand for biofuels and agricultural commodities 
(high confidence) (Lapola et al., 2014; Cohn et al., 2016). Feedbacks 
to the climate system resulting from such land use changes are 
intricate. The clear-cutting of Amazon forest and Cerrado savannah in 
the region led to a local warming due to an increase in the energy 
balance and evapotranspiration (Malhado et  al., 2010); in contrast, 
the replacement of pasture by agriculture have led to a local cooling 
effect, due to changes in the surface albedo (medium confidence: 
medium evidence, medium agreement). Deforestation of the Amazon 
for pastures and soybean have decreased evapotranspiration during 
drought months and caused a localised lengthening of the dry season

in northwestern SAM by 6.5 (± 2.5) d since 1979 (medium confidence: 
medium evidence, medium agreement) (Fu et al., 2013).
It is not surprising, therefore, that while SAM is the region in CSA that 
experienced the highest temperature increase in the last century, it is 
where most of the fire spots in the sub-continent are located, owing 
also to the prevalent use of fires in pasturelands (medium confidence: 
medium evidence, high agreement) (Bowman et al., 2009). Recently, 
da Silva Junior et al. (2020) reported 6,708,350 and 6,188,606 fire foci 
in Cerrado and Amazonia, between 1999 and 2018, corresponding to 
80% of the total observed in Brazil. The occurrence of extreme droughts 
has affected the carbon and water cycles in large areas of the Amazon 
rainforest (high confidence) (Lapola et al., 2014; Agudelo et al., 2019), 
in particular in its southern and eastern portions, where deforestation 
rates are higher. The loss of carbon in the Amazon region considering

the combined effect of land use change in the southern portion of the 
region bordering Cerrado and Pantanal and global carbon emission 
scenarios can be as high as 38% at 4°C of warming, but limited to 8% 
if the Paris Agreement limit of 1.5°C is achieved (medium confidence, 
medium evidence, high agreement) (Burton et al., 2021), driving the 
region to be a net carbon source to the atmosphere (Gatti et al., 2021). 
A recent extreme drought was estimated to affect the photosynthetic

12
1707Central and South America  Chapter 12
capacity of 400,000 km2 of the forest (Anderson et  al., 2018b); 
nevertheless, there are considerable uncertainties regarding the 
effects of CO 2 fertilisation in tropical forests and ecosystems (medium 
confidence: medium evidence, high agreement) (Sampaio et al., 2021). 
Extreme drought events increase forest vulnerability to fire, directly 
affecting the biodiversity and the forest structure and its plant species 
distribution (high agreement) (Brando et al., 2014). Production sectors 
are also exposed. SAM is pointed out as a region where agricultural 
production will be especially impacted by climate change, affecting the 
production of annual crops, fruits and livestock (medium confidence: 
medium evidence, high agreement) (Lapola et  al., 2014; Zilli et  al., 
2020).
12.3.4.3 Vulnerability
The largest expanses of remaining vegetation in the Cerrado biome 
are located in SAM, but the region has a small number of protected

areas (only 7.5% of Cerrado vegetation occurs inside protected areas), 
which will leave fauna and flora with little room for moving across the 
landscape in the face of climate change. Protected areas—Indigenous 
lands included—have significantly reduced forest clear-cutting in 
the Amazon deforestation arc (most of which is inside SAM) (high 
confidence) (Nolte et al., 2013). However nearly 100 protected areas 
in the Amazon, Cerrado and Pantanal biomes inside SAM have been 
identified as highly or moderately vulnerable to future climate change 
and demand serious adaptation interventions (medium confidence: 
medium evidence, high agreement) (Feeley and Silman, 2016; Lapola 
et al., 2019b). Yet the maintenance of these protected areas or even the 
halting of deforestation may do little to impede a large-scale persistent 
ecosystem shift to an alternative state (crossing a tipping point) of 
the Amazon rainforest or even more subtle changes caused by climate

change in the region (medium confidence: medium evidence, high 
agreement) (Aguiar et al., 2016a; Boers et al., 2017; Lapola et al., 2018; 
Lovejoy and Nobre, 2018).
The agriculture in the region is highly dependent on the climate (high 
confidence), responsible for three-fourths of the variability in agricultural 
yields in the region (Table 12.4). Irrigation is an important strategy for 
agricultural production in part of the region, but it accounts for no 
more than 8% of the total agricultural area in SA and 7% in CA (OECD 
and FAO, 2019). This practice faces potential impacts from reductions 
in surface water availability in future climate scenarios (Ribeiro Neto 
et al., 2016; Zilli et al., 2020), enhanced by non-climate drivers such 
as land use changes (medium confidence: medium evidence, high 
agreement) (Spera et  al., 2020). The remaining fluctuation in yields 
relates to issues of infrastructure, market, economy, policy and social

aspects. Good infrastructure, transport logistics, quality of roads and 
storage strongly influence the vulnerability of the agricultural sector 
The combined effects of extreme climate events and ecosystem 
fragmentation, for example, by deforestation or fire, lead to changes 
in forest structure, with the death of taller trees and reduction in 
diversity of plant species, loss of productivity and carbon storage 
(high agreement) (Brando et al., 2014; Reis et al., 2018). The rise of 
a large-scale soybean agroindustry in the early 2000s led to a faster 
increase in human development indicators in some regions, tightly linked to the agricultural production chain (high confidence) (Richards 
et al., 2015). Such a development also came at considerable cost to 
the environment (e.g., Neill et al. 2013) and the regional climate, even 
though a moratorium implemented in 2006 on new soy plantations 
on deforested areas reduced deforestation by a factor of five (high

confidence) (Macedo et  al., 2012; Kastens et  al., 2017). The same 
sort of supply chain interventions along with incentive-based public 
policies applied to the beef supply chain could minimise the need for 
agricultural expansion in the SAM deforestation frontier (medium 
confidence: medium evidence, high agreement) (Nepstad et al., 2014; 
Pompeu et al., 2021).
SAM has a low population density, and the majority of the population 
is located in cities. The populations of some of these cities are 
reported as being highly vulnerable considering the enormous social 
inequalities embedded in these cities (high confidence) (Filho et  al., 
2016). Inequalities and uneven access to infrastructure, housing and 
healthcare increase populations’ vulnerability to atmospheric pollution 
and drier conditions (high confidence) (Rodrigues et al., 2019; IPAM, 
2020; Machado-Silva et al., 2020).
12.3.4.4 Impacts
The Amazon and the Cerrado are among the largest and unique

phytogeographical domains in SA. The Brazilian Cerrado is one of the 
most diverse savannah in the world, with more than 12,600 plant 
species, with 35% being endemic (high confidence) (Forzza et  al., 
2012). Historic land cover change and concurrent climate change in 
the region strongly impacted the biodiversity and led to the extinction 
of 657 plant species for the Cerrado, which is more than four times the 
global recorded plant extinctions (high confidence) (Strassburg et al., 
2017; Green et al., 2019). The effects of climate change, expressed by 
drought and heatwaves, lead to plant stress, compromising growth 
and increasing mortality (Yu et  al., 2019). The fauna dependent on 
dew water was strongly impacted by the 1.6°C temperature rise that 
occurred from 1961 to 2019 (medium confidence: medium evidence, 
medium agreement) (Hofmann et  al., 2021). Modelling outcomes 
project impacts in forest ecosystems in the region, with persistent

warming and significant moisture reduction (Anjos et al., 2021), leading 
to a potential change in the ecosystem structure and distribution in the 
region (medium confidence: medium evidence, medium agreement) 
(Government of Brazil, 2020).
The observed impact on plant species in SAM is projected to worsen 
in a warmer world (Warszawski et al., 2013). An increasing dominance 
of drought-affiliated genera of tree species has been reported in the 
southern part of the Amazon rainforest in the last 30 years (medium 
confidence: medium evidence, medium agreement) (Esquivel-Muelbert 
et al., 2019). Due to the tight relationship between drought and fire 
occurrence, an increase of 39% to 95% of burned area is modelled 
to impact the Cerrado region under RCP4.5 and RCP8.5, while under 
RCP2.6, a 22% overshoot in temperature is estimated to impact the 
area in 2050 decreasing to 11% overshoot by 2100 (Silva et al., 2019d),

leading to a high impact on agricultural production (high confidence).
SAM hosts the headwaters of important South American rivers, such as 
the Paraguay, Madeira, Tocantins-Araguaia and Xingu. The impact from

12
1708Chapter 12 Central and South America
climate change is expressed differently in several sub-regions. Extreme 
floods in the southern Amazon and Bolivian Amazon floodplains were 
described and related to the exceptionally warm sub-tropical South 
Atlantic ocean (high confidence) (Espinoza et  al., 2014), causing 
high economic impacts (losses in crop and livestock production and 
infrastructure) and number of fatalities (very high confidence) (Ovando 
et al., 2016). In contrast, declines in stream flow, particularly in the dry 
season, expressed by the ratio of runoff to rainfall, are observed for 
the southern part of the Amazon basin (high evidence) (Molina-Carpio 
et al., 2017; Espinoza et al., 2019b; Heerspink et al., 2020). Observed 
precipitation reduction in the Cerrado region impacted main water supply 
reservoirs for important cities in the Brazilian central region, leading to 
a water crisis in 2016/2017 (Government of Brazil, 2020) and affecting

hydropower energy generation (Ribeiro Neto et al., 2016). Modelling 
studies project decreases in the river discharge rate on the order of 27% 
for the Tapajós basin and 53% for the Tocantins-Araguaia basin for the 
end of the century, which may affect freshwater biodiversity, navigation 
and generation of hydroelectric power (medium confidence: medium 
evidence, high agreement) (Marcovitch et al., 2010; Mohor et al., 2015). 
This region also holds one of the largest floodplains in the world, the 
Pantanal. The climatic connection of Pantanal regions to the Amazon, 
and the influence of deforestation in local precipitation (Marengo et al., 
2018) has implications for conservation of ecosystem services and water 
security in Pantanal (high confidence) (Bergier et al., 2018). Impacts of 
extreme drought, with increasing numbers of dry days and the peak of fire foci, were recently reported (robust evidence) (Lázaro et al., 2020;

Garcia et al., 2021). The projected impacts of climate change will lead to 
profound changes in the annual flood dynamics for Pantanal wetlands, 
altering ecosystem functioning and severely affecting biodiversity (high 
confidence) (Thielen et al., 2020; Marengo et al., 2021).
Soybean and corn yields in the Cerrado region will suffer one of 
the strongest negative impacts under the estimates of the RCP4.5 
and RCP8.5 scenarios and will require high levels of investment in 
adaptation should they continue to be cultivated in the same areas 
as currently (high confidence) (Oliveira et  al., 2013; Camilo et  al., 
2018). Changes in precipitation patterns are related to reductions 
in agricultural productivity and revenues in the southern portion of 
the Amazon region (medium confidence: medium evidence, high 
agreement) (Costa et  al., 2019; Leite-Filho et  al., 2021). Thus, the 
future socioeconomic vigour of the region will be, to a large extent,

connected to an unlikely stability of the regional climate and eventual 
fluctuations of global markets potentially affecting the agricultural 
supply chain (high confidence) (Nepstad et al., 2014).
Observations from recent past droughts in SAM indicate how the 
incidence of respiratory diseases may worsen under a drier and warmer 
climate. Northwest SAM had an approximately 54% increase in the 
incidence of respiratory diseases associated with forest fires during 
the 2005 drought compared to a no-drought 10-year mean (high 
confidence) (Ignotti et al., 2010; Pereira et al., 2011; Smith et al., 2014). 
It is estimated that more than 10 million people are exposed to forest 
fires in the deforestation arc, a region comprising several Brazilian 
states in the southern and western parts of the Amazon rainforest, with 
several impacts on human health including potential exacerbation of the 
COVID-19 crisis in Amazonia (medium confidence: medium evidence,

high agreement) (de Oliveira et  al., 2020) (Table  12.5). Increases in 
hospital admissions, asthma, DNA damage and lung cell death due to 
the inhalation of fine particulate matter  represent an increase in public 
health system costs (high confidence) (Ignotti et al., 2010; Silva et al., 
2013; de Oliveira Alves et  al., 2017; Machin et  al., 2019). The patchy 
landscape created by forest clearing contributes to a rising risk of 
zoonotic disease emergence by increasing interactions between wildlife, 
livestock and humans (medium confidence: low evidence, medium 
agreement) (Dobson et al., 2020; Tollefson, 2020). Recent studies also 
suggest an influence of climate change in zoonotic diseases, such as 
Orthohantavirus and Chapare viral infections, rodent-borne diseases, 
in some areas of Bolivia (Escalera-Antezana et  al., 2020a; Escalera-
Antezana et  al., 2020b). Extreme fluctuations in Amazon River levels

were associated with a significant increase in the incidence of diarrhoea, 
leptospirosis and dermatitis (de Souza Hacon et al., 2019; Government 
of Brazil, 2020). According to a comprehensive characterisation of future 
heatwaves and alternative RCPs scenarios, Brazilian urban areas in 
the SAM region are projected to face increasing related mortality from 
400% to 500% in the period 2031–2080 compared to the period 1971–
2020, under the highest emission scenario and high-variant population 
scenario (medium confidence: low evidence, medium agreement) (Guo 
et  al., 2018). Table  12.2 shows the increase in days of exposure to 
heatwaves already observed in the region.
The high risk of floods (high-frequency and costly damage) is centred 
in the Brazilian states of Acre, Rondônia, Southern Amazonas and Pará Table 12.2 |  Average change in mean number of days exposed to heatwaves (defined 
as a period of at least 2 d where both the daily minimum and maximum temperatures are

above the 95th percentile for their respective climatologies) in the over-65 population in 
2016–2020 relative to 1986–2005. Temperature data taken from the European Centre 
for Medium-Range Weather Forecasts (ECMWF) ERA5 data set; calculations were 
derived from Romanello et al. (2021).
CountryNumber of additional days of heatwave exposure 
in 2016–2020 relative to 1986–2005
Argentina 4.9
Belize 8.8
Bolivia 2.2
Brazil 3.1
Chile 3.3
Colombia 9.3
Costa Rica 0.8
Ecuador 7.6
El Salvador 2.2
Guatemala 8.4
Guyana 8.2
Honduras 11.2
Nicaragua 2.2
Panama 2.6
Paraguay 2.6
Peru 3.6
Suriname 15.2
Uruguay 2.7
Venezuela 8.5

12
1709Central and South America  Chapter 12
(Andrade et  al., 2017). Global-scale studies indicate an increase of 
flood risk for the SAM region during the 21st century (consistent with 
floods that are more frequent) (high confidence) (Hirabayashi et al., 
2013; Arnell et al., 2016; Alfieri et al., 2017). Higher emission scenarios 
result in substantially higher flood risks than low emission scenarios 
(Alfieri et al., 2017).
12.3.5 Northeastern South America Sub-region
12.3.5.1 Hazards
The region has likely experienced an increase in temperature, with 
significant increases in the intensity and frequency of hot extremes and 
(Donat et al., 2013) (WGI AR6 Table 11.13, Seneviratne et al., 2021).
A decrease in the frequency and magnitude of extreme precipitation 
was observed, but with low confidence, due to insufficient data 
coverage and trends in available data being generally insignificant. An

increase in drought duration was observed with high confidence but 
medium confidence with respect to the increase of drought intensity 
(WGI AR6 Table 11.14, Seneviratne et al., 2021). Table 12.3 shows the 
estimates of changes in land area per sub-region affected by drought 
events; NES sub-region presented the highest changes in CSA.
The projected warming for the extreme annual maximum temperatures 
(TXx) over NES is +2°C for the 1.5°C scenario and about +2.5°C 
for the 2°C scenario (Hoegh-Guldberg et  al., 2018). An increased 
number of tropical nights with minimum temperatures exceeding 
the 20°C threshold is projected (Orlowsky and Seneviratne, 2012). 
In general, extreme heat will increase and cold spells decrease with 
high confidence. A decrease in total precipitation is projected with 
high confidence, with an increase in heavy precipitation events and an 
increase in dryness (medium confidence). Increases in drought severity

due to the combination of increased temperatures, less rainfall and 
lower atmospheric humidity (5 to 15% relative humidity reduction) 
create water deficits, which are projected for the entire region after 
2041 (3–4 mm d−1 reduction), particularly over western NES and over 
the semiarid region (Marengo and Bernasconi, 2015; Marengo et al., 
2017). Fire will significantly increase (high confidence) (Figure 12.6).12.3.5.2 Exposure
NES is home to about 60 million people (estimate for 2019 fromIBGE 
[2020]]), with >70% living in urban areas (data for 2010 from IBGE 
[2020]; Silva et  al. [2017]) and high poverty levels (>50%, data for 
2003 fromIBGE [2020]]). People are exposed to intense drought and 
famine (high confidence), and about 94% of the region has moderate 
to high susceptibility to desertification (Marengo and Bernasconi, 
2015; Spinoni et  al., 2015; Vieira et  al., 2015; Mariano et  al., 2018; 
Tomasella et  al., 2018; Marengo et  al., 2020c). The most severe dry

spell of 2012–2013 affected about 9 million people, who were exposed 
to water, food and energy scarcity (Marengo and Bernasconi, 2015).
People, infrastructure and economic activities are exposed to SLR in 
the 3800 km of coastline (medium confidence). The high concentration 
of cities on the coast is a concern (Martins et  al., 2017), with all 
state capital cities but one on the coast, totalling almost 12 million 
vulnerable people (estimate for 2019 fromIBGE [2020]]). The ports 
of São Luís, Recife and Salvador are important exporters of Brazilian 
commodities, and the beaches in the sub-region are an international 
touristic destination, generating considerable revenues (Pegas et al., 
2015; Ribeiro et al., 2017).
Natural systems in NES are also exposed to climate change. In 
terrestrial ecosystems, 913,000 km2 of NES’ dry forest Caatinga 
vegetation (Silva et  al., 2017) is exposed to predicted increases in 
dryness. Despite what has been previously suggested, the Caatinga has

high biodiversity and endemism (Silva et al., 2017), which vulnerable 
to habitat reduction due to climate change and agricultural expansion 
(Silva et  al., 2019b). Fifty-two percent of the freshwater fish (203 
species) are endemic (Lima et al., 2017) and are exposed to predicted 
reduction in river flow due to climate change (Marengo et al., 2017; 
de Jong et al., 2018). The coastal waters contain a separate marine 
ecoregion due to its uniqueness (Spalding et al., 2007). The region is 
responsible for 99% of Brazilian shrimp production, which is exposed 
to SLR and increases in ocean temperature and acidification (Gasalla 
et  al., 2017). Most coral reefs in the Southern Atlantic Ocean are 
along NES’s coast (Leão et al., 2016), increasing its conservation and 
touristic value. The 685 km2 of coral reefs along NES’s coast (likely 
an underestimate [Moura et al. 2013; UNEP-WCMC et al. 2018]) are 
exposed to increased sea temperatures.

Table 12.3 |  Change in percentage of land area affected by extreme drought in 2010–2019, in relation to 1950–1959   using Standardised Precipitation-Evapotranspiration Index 
(SPEI); extreme drought is defined as SPEI ≤ −1.6 (Federal Office of Meteorology and Climatology MeteoSwiss, 2021). Data were derived from Romanello et al. (2021).
Average change in percentage of land area in drought in 2010–2019 with respect to 1950–1959
Sub-region At least 1 month in drought At least 3 months in drought At least 6 months in drought
Central America (CA) 38.8% 17.6% 6.1%
Northwestern South America (NWS) 51.8% 25.3% 7.0%
Northern South America (NSA) 52.5% 18.3% 2.5%
South America Monsoon (SAM) 48.0% 34.4% 12.2%
Northeastern South America (NES) 64.5% 38.4% 12.0%
Southeastern SouthAmerica (SES) 16.4% 6.7% 0.4%
Southwestern South America (SWS) 20.5% 13.9% 7.5%
Southern South America (SSA) −23.5% −8.8% —

12
1710Chapter 12 Central and South America
12.3.5.3 Vulnerability
NES is the world’s most densely populated semiarid land and its 
population is highly vulnerable to droughts (high confidence), which have 
well-documented impacts on water and food security, human health and 
well-being in the region (e.g., Confalonieri et al. 2014a; Marengo et al. 
2017; Bedran-Martins et al. 2018) (Figure 12.7). The region’s relatively 
low economic development and poor social and health indicators 
increase vulnerability, especially that of poor farmers and traditional 
communities (Confalonieri et al., 2014a; Bech Gaivizzo et al., 2019). In 
state capital cities, about 45% of the population live in poverty (data 
for 2003 fromIBGE [2020]]), often in slums with already deficient water 
supply and sewage systems and poor access to health and education. 
Climate change will increase pressures on water availability, threatening 
water, energy and food security (Marengo et al., 2017).

Natural systems in NES are also vulnerable (Figure 12.7). The Caatinga 
vegetation is particularly sensitive to variations in water availability 
and climate change (Seddon et al., 2016; Rito et al., 2017; Dantas et al., 
2020). It has already lost about 50% of its original vegetation cover 
(Souza et al., 2020), with only about 2% of the remaining vegetation 
within fully protected areas (CNUC and MMA, 2020). Caatinga’s high 
vulnerability to climate change is further increased by the extensive 
conversion of native vegetation (high confidence) (Rito et al., 2017; 
Silva et al., 2019b, c).
Studies with terrestrial animals show that habitat loss increases 
the vulnerability of species to climate change (high confidence) (de 
Oliveira et al., 2012; Arnan et al., 2018; da Silva et al., 2018b). NES’s 
coral reefs have shown some resilience to bleaching, but vulnerability 
is intensified by the synergy between chronic heat stress caused by

increased SST (Teixeira et  al., 2019) and other well-documented 
stressors, such as coastal runoff, urban development, marine tourism, 
overexploitation of reef organisms and oil extraction (high confidence) 
(Figure 12.8) (Leão et al., 2016).
12.3.5.4 Impacts
The impacts of intense drought have been reported in NES since 1780, 
with severe losses in agricultural production, livestock death, increase 
in agricultural prices and human death (Figure 12.9) (Marengo et al., 
2017, 2020c; Martins et al., 2019; Government of Brazil, 2020; Silva 
et al., 2020). The rural population already suffers from natural water 
scarcity in the countryside. The drought in 2012 was responsible for 
reducing up to 99% of the corn production in Pernambuco state 
(Government of Brazil, 2020). A predicted increase in drought, coupled 
with inadequate soil management practices by small farmers and 
agribusiness, increases the region’s susceptibility to desertification

(Spinoni et al., 2015; Vieira et al., 2015; Mariano et al., 2018; Tomasella 
et al., 2018; Marengo et al., 2020c). In NES, 70,000 km2 have reached a 
point at which agriculture is no longer possible (Government of Brazil, 
2020). Intense droughts have triggered migration to urban centres 
within and outside NES (Confalonieri et  al., 2014a; Government of 
Brazil, 2020). More than 10 million people have been impacted by the 
drought of 2012/2014 in the region, which was responsible for water 
shortage and contamination, increasing death by diarrhoea (Marengo 
and Bernasconi, 2015; Government of Brazil, 2020).There is growing evidence of the impacts of climate change on human 
health in NES, mostly linked to food and water insecurity caused by 
recurrent long droughts (e.g., gastroenteritis and hepatitis) (high 
confidence) (Figure 12.9) (Sena et al., 2014; de Souza Hacon et al., 
2019; Marengo et  al., 2019; Government of Brazil, 2020; Salvador

et al., 2020). From 2071 to 2099, thermal conditions in NES might 
improve for vectors of dengue, chikungunya and Zika (de Souza 
Hacon et al., 2019). Additionally, a high risk of mortality associated 
with climatic stress in the period of 2071–2099 is expected in the 
São Francisco river basin (de Oliveira et al., 2019; de Souza Hacon 
Recent studies predict strong negative impacts of climate change on 
NES’s agriculture (high confidence) (Ferreira Filho and Moraes, 2015; 
Nabout et al., 2016; Gateau-Rey et al., 2018) (Figure 12.9; Table 12.4). 
NES concentrates the bulk of the predicted loss of regional gross 
domestic product (GDP) associated with agriculture in Brazil (Ferreira 
Filho and Moraes, 2015; Forcella et  al., 2015). Although agriculture 
makes a modest contribution to the region’s economy, its drop could 
have a severe impact on the poorest rural household by shrinking the 
agricultural labour market and increasing food prices (Ferreira Filho

and Moraes, 2015; Government of Brazil, 2020). The expected increase 
in dryness is also predicted to impact the region’s hydroelectric power 
generation (Marengo et al., 2017; de Jong et al., 2018). SLR has also 
been reported to impact coastal cities such as Salvador, destroying 
urban constructions (Government of Brazil, 2020). SLR, increased 
ocean temperature and acidification may also negatively impact NES’s 
shrimp aquaculture production (Figure  12.8) (Gasalla et  al., 2017). 
Along with climate change, overfishing has driven exploited marine 
fish species to collapse (Verba et al., 2020).
Biodiversity in NES is severely threatened by climate change in 
terrestrial (medium confidence: medium evidence, high agreement) 
and freshwater (low confidence: low evidence, high agreement) 
ecosystems (Figure 12.9). There are few studies projecting the likely 
impact of climate change on NES’s biodiversity, especially its endemic

freshwater fish. Recent studies have already reported the reduction in 
several endemic plant species affecting pollination and seed dispersal 
(Bech Gaivizzo et al., 2019; Cavalcante and Duarte, 2019; Silva et al., 
2019b). Studies with terrestrial animals predict that most groups will 
be negatively impacted by climate change (de Oliveira et  al., 2012; 
Arnan et al., 2018; da Silva et al., 2018b; Montero et al., 2018). Changes 
in the abundance of coral reef community and extreme reduction in 
coral cover have been observed in NES (de Moraes et al., 2019; Duarte 
et al., 2020). A number of observed coral bleaching events associated 
with an abnormal increase in sea temperatures have occurred in NES 
(Krug et al., 2013; Leão et al., 2016; de Oliveira Soares et al., 2019) 
(Figure  12.8), but thus far mortality has remained low and corals 
have been able return to normal values or remain stable following 
sea water temperature rise (medium confidence: medium evidence,

high agreement) (Leão et  al., 2016). Mangroves in the region have 
shown increased mortality, but they have also expanded their range 
inland (Figure 12.6) (Godoy and Lacerda, 2015; Cohen et al., 2018). 
Future projections include mangrove landward expansion and lower 
migration rates by 2100 (Cohen et al., 2018).

12
1711Central and South America Chapter 12
12.3.6  Southeastern South America Sub-region
12.3.6.1
 Hazards
An increase in the intensity and frequency of hot extremes and decrease 
in the intensity and frequency of cold extremes were observed with high confidence (Rusticucci et al., 2017; Wu and Polvani, 2017) (WGI AR6 Table 11.13) (Seneviratne et al., 2021). There is low confidence 
that the decrease in hot extremes over SES is related to an increase in extreme precipitation (Wu and Polvani, 2017).
Over SES most stations have registered an increase in annual rainfall, 
largely attributable to changes in the warm season; this is one of few sub-regions where a robust positive trend in precipitation and significant intensification of heavy precipitation have been detected since the early 20th century (high confidence) but with medium 
confidence in a reduction of hydrological droughts (Vera and Díaz,

2015; Saurral et al., 2017; Lovino et al., 2018; Avila-Diaz et al., 2020; Carvalho, 2020; Dereczynski et al., 2020; Dunn et al., 2020; Marengo et al., 2020a; Olmo et al., 2020) (WGI AR6 Table 11.14) (Seneviratne et al., 2021). A higher observed frequency of extratropical cyclones in the region has been detected (Parise et al., 2009; Reboita et al., 2018) with three cyclogenetic foci: south-southeastern Brazil, extreme south of Brazil and Uruguay, and southeastern Argentina.
In Montevideo, mean sea levels have increased over the past 20 years, 
reaching 11
 cm from 1902 to 2016,
 and a recent accelerating trend 
has been observed (Gutiérrez et al., 2016b). A value of water level rise and its acceleration for Buenos Aires was calculated from a record of annual mean water levels obtained from hourly levels (1905–2003). Annual mean water level showed a trend of +1.7 ± 0.05
 mm yr−1 and 
an acceleration of +0.019 ± 0.005
 
mm yr−2 (D’Onofrio et al., 2008).

Increasing trends in mean air temperature and extreme heat and decreasing cold spells are projected (high confidence) (WGI AR6 Table 12.6) (Ranasinghe et al., 2021). The increase in the frequency of warm nights is larger than that projected for warm days, consistent with observed past changes that have been related to changes in cloud cover that affect daytime temperatures differently than nighttime temperatures (López-Franca et al., 2016; Menéndez et al., 2016; Feron et al., 2019).
Increases in mean precipitation (high confidence), pluvial floods and 
river floods are projected (medium confidence) (Nunes et  al., 2018) (WGI AR6 Table 12.6) (Ranasinghe et al., 2021). Droughts in the River Plate basin will be more frequent in the medium term (2011–2040) and the distant future (2071–2100) (with respect to the 1979–2008 period), but also shorter and more severe, for the more extreme emission scenario (RCP8.5) (low confidence) (Carril et al., 2016).

Negative trends in the annual number of cyclone events in the long term 
of 3.6% to 6.5% (2070–2098) are projected and showed an increase of 3% to 11% (2080–2100 for the A1B scenario) (Grieger et al., 2014; Reboita et al., 2018). All coastal and oceanic climate impact drivers (relative sea level, coastal flood and erosion, marine heatwaves and ocean aridity) are expected to increase by mid-century in the RCP8.5 scenario (high confidence) (WGI AR6 Table 12.6) (Ranasinghe et al., 2021).12.3.6.2  Exposure

Higher temperatures and SLR, changes in rainfall patterns, and an increased frequency and intensity of extreme weather events could generate risks to the energy and infrastructure sectors and to the mining and metals industry. In the River Plate basin, urban floods have become more frequent, causing infrastructure damage and sometimes substantial mortality (high confidence) (Barros et  al., 2015; Zambrano et al., 2017; Nagy et al., 2019; Mettler-Grove, 2020; Morales-Yokobori, 2021; Oyedotun and Ally, 2021). A large increase in landslides and flash floods is also predicted for the Brazilian portion of SES, where they are responsible for the majority of deaths related to disasters in the country (high confidence) (Debortoli et al., 2017; Haque et al., 2019; Saito et al., 2019; Marengo et al., 2020d; da Fonseca Aguiar and Cataldi, 2021). Due to uncontrolled urban growth, 21.5  million people living in the large Brazilian cities of São Paulo, Rio de Janeiro and Belo Horizonte (estimate

for 2019 fromIBGE [2020]]) are expected to be exposed to water scarcity, despite widespread water availability in the region (medium evidence, medium agreement) (Marengo et al., 2017, Marengo et al., 2020b; Lima and Magaña Rueda, 2018).

The expected increase in temperature will also expose the populations

in large cities to extreme heat. Urban heat islands are already a reality in large cities in the region, such as Buenos Aires (high confidence) (Wong et al., 2013; Sarricolea and Meseguer-Ruiz, 2019; Wu et al., 2019; Mettler-Grove, 2020), Rio de Janeiro (high confidence) (Ceccherini et al., 2016; Neiva et al., 2017; Geirinhas et al., 2018; Peres et al., 2018; Sarricolea and Meseguer-Ruiz, 2019; Wu et al., 2019; de Farias et al., 2021) and São Paulo (high confidence) (Mishra et  al., 2015; Barros and Lombardo, 2016; Ceccherini et al., 2016; Vemado and Pereira Filho, 2016; de Azevedo et al., 2018; Lima and Magaña Rueda, 2018; Ferreira and Duarte, 2019; Lapola et al., 2019a; Sarricolea and Meseguer-Ruiz, 2019; Wu et al., 2019), with reported impact on human health in the latter (medium confidence: medium evidence, medium agreement) (e.g., Araujo et al. 2015; Son et al. 2016; Diniz et al. 2020). These cities alone represent 22 million people exposed to increased heat (estimate for 2019

The sub-region presents a high frequency of occurrence of intense 
severe convection events (Section 12.3.6.1). Because of this situation, strong winds from the south or southeast and high water levels affect the whole Argentine coast, as well as the River Plate shores, Uruguay and southern Brazil (Isla and Schnack, 2009). The coast of the River Plate is subject to flooding when there are strong winds from the southeast (sudestadas). As sea level rises as a result of global climate change, storm surge floods will become more frequent in this densely populated area, particularly in low-lying areas (high confidence) (Figure 12.8) (D’Onofrio et al., 2008; Nagy et al., 2014a; Santamaria-Aguilar et  al., 2017; Nagy et  al., 2019 impacts and adaptation in Central and South America coastal areas; Cerón et al., 2021).
The region’s natural systems are also exposed to climate change.

The SES region is home to two important biodiversity hotspots, with high levels of species endemism: the Cerrado and the Atlantic Forest, where about 72% of Brazil’s threatened species can be found (PBMC, 2014).

12
1712Chapter 12 Central and South America
12.3.6.3 Vulnerability
The River Plate basin and the city of Buenos Aires are highly vulnerable 
to recurring floods, and the increasing number of newcomers to the 
area reduce the collective cultural adaptation developed by older 
neighbours (high confidence) (Barros, 2006; Nagy et al., 2019; Mettler-
Grove, 2020; Morales-Yokobori, 2021; Oyedotun and Ally, 2021). 
Extreme events, including storm surges and coastal inundation/
flooding, cause injuries and economic/environmental losses on the 
urbanised coastline of Southern Brazil (States of São Paulo and Santa 
Catarina) (high confidence) (Muehe, 2010; Khalid et  al., 2020; Ohz 
et al., 2020; de Souza and Ramos da Silva, 2021; Quadrado et al., 2021; 
Silva de Souza et al., 2021).
Cities like Rio de Janeiro and São Paulo are overpopulated, where most 
people live in poor conditions of inadequate housing and sanitation, 
such as slums, with little and no trees and high temperatures. These

people have low access to sanitation, public health and residential 
cooling and are vulnerable to the effects of heat islands on human 
comfort and health (Figure  12.7). These include cardiopulmonary, 
vector-borne diseases and even death (medium confidence: medium 
evidence, medium agreement) (Araujo et  al., 2015; Mishra et  al., 
2015; Geirinhas et al., 2018; Peres et al., 2018). Heat stress is known 
to worsen cardiovascular, diabetic and respiratory conditions (Lapola 
et al., 2019a). In connection with the heat island effect, these people 
are also vulnerable to injuries and casualties due to increased 
thunderstorms, causing economic losses and other social problems 
(Vemado and Pereira Filho, 2016).
12.3.6.4 Impacts
Despite the observed increase in rainfall in the region, between 2014 
and 2016 Brazil endured a water crisis that affected the population and 
economy of major capital cities in the SES region (Blunden and Arndt,

2014; Nobre et al., 2016a). Extremely long dry spells have become more 
frequent in southeastern Brazil, affecting 40  million people and the 
economies in cities such as Rio de Janeiro, São Paulo and Belo Horizonte, 
which are the industrial centres of the country (medium confidence: 
medium evidence, medium agreement) (PBMC, 2014; Nobre et  al., 
2016a; Cunningham et al., 2017; Marengo et al., 2017, 2020b; Lima and 
Magaña Rueda, 2018). They have also impacted agriculture, affecting 
food supply and rural livelihoods, especially in Minas Gerais (Nehren 
et al., 2019). Agricultural prices increased by 30% in some cases, and 
harvest yields of sugar cane, coffee and fruits suffered a reduction of 15–
40% in the region. The number of fires increased by 150%, and energy 
prices increased by 20–25%, as most electricity comes from hydroelectric 
power (Nobre et  al., 2016a). In Argentina, projected changes in the 
hydrology of Andean rivers associated with glacier retreat are predicted

to have negative impacts on the region’s fruit production (low evidence, 
medium agreement) (Barros et al., 2015).
Heat islands affect ecosystems by increasing the energy consumption 
for cooling, the concentration of pollutants and the incidence of fires 
(high confidence) (Wong et  al., 2013; Akbari and Kolokotsa, 2016; 
Singh et al., 2020b; Ulpiani, 2021). It also affects human health, as well 
increasing the incidence of respiratory and cardiovascular diseases 
(medium confidence: medium evidence, medium agreement) (Araujo et  al., 2015; Barros and Lombardo, 2016; de Azevedo et  al., 2018; 
Geirinhas et al., 2018).
Warming temperatures have been implicated in the emergence of 
dengue in temperate latitudes, increasing populations of Aedes aegypti 
(high confidence) (Natiello et  al., 2008; Robert et  al., 2019, 2020; 
Estallo et al., 2020; López et al., 2021) (Table 12.1), and field studies 
have demonstrated the role of local climate in vector activity (Benitez

et al., 2021). Figure 12.5 shows the modelled transmission suitability 
for dengue for two climate-change scenarios. Future increases in the 
number of months suitable for transmission of dengue will be highest 
in SES (see SM12.8 for additional information). There is additional 
evidence of the spread of arbovirus into southern temperate latitudes 
(Basso et al., 2017); however, a longer historical time series is needed 
to understand climate–disease interactions, given the relatively recent 
emergence of arborvirus in this region.
SLR impacts the port complex in Santa Catarina, which during the last 
6 years has interrupted its activities 76 times due to strong winds or 
big waves, with estimated losses varying between USD 25,000 and 
50,000 for each 24 idle hours (Ohz et al., 2020). Historically, extratropical 
cyclones associated with frontal systems cause storm surges in the city 
of Santos. Although there are no fatality records, these events cause

several socioeconomic losses, especially in vulnerable regions, including 
the Port of Santos, the largest port in Latin America (São Paulo). In an 88-
year time span (1928–2016), the frequency of storm surge events was 
three times greater in the last 17 years (2000–2016) than in the previous 
period of 71 years (1928–1999) (Souza et al., 2019).
There are many projected impacts of climate change on natural 
systems. The impacts of SLR are habitat destruction and the invasion of 
exotic species, which affect biodiversity and the provision of ecosystem 
services (Figure 12.8) (Nagy et al., 2019).
SES is a global priority for terrestrial biodiversity conservation and 
is home to two important biodiversity hotspots—the Atlantic Forest 
and Cerrado—which are among the world’s most studied biodiversity 
hotspots in connection with climate-change impact on biodiversity, 
especially for terrestrial vertebrates (Section CCP1.2.2; Manes et al.,

2021). An increasing number of studies show that the Atlantic Forest 
and Cerrado are at risk of biodiversity loss, largely due to projected 
reductions of species’ geographic distributions in many different 
taxa (e.g., Loyola et al. 2012, 2014; Ferro et al. 2014; Hoffmann et al. 
2015; Martins et al. 2015; Aguiar et al. 2016b; Vale et al. 2018; Borges 
et al. 2019; Braz et al. 2019; Vale et al. 2021). Cerrado savannahs are 
projected to be the hotspot most negatively impacted by climate 
change within SA, mostly though range contraction of plant species 
(very high confidence), while the Atlantic Forest is projected to be 
highly impacted especially though the contraction of the distribution of 
endemic species (very likely) (Section CCP1.2.2; Figure 12.10) (Manes 
et al., 2021). Reductions in species’ distribution are also projected in 
the River Plate basin for sub-tropical amphibians (Schivo et al., 2019) 
and the river tiger (Salminus brasiliensis), a keystone fish of economic

value (Ruaro et al., 2019). Farming of mussels and oysters in the region 
is predicted to be negatively impacted by climate change, particularly 
SLR, and ocean warming and acidification (Gasalla et al., 2017). Some 
more localised habitats are also at risk of losing area due to climate

12
1713Central and South America  Chapter 12
change, such as the meadows of northwestern Patagonia (Crego et al., 
2014) and mangroves of southern Brazil (Godoy and Lacerda, 2015). 
Predicted changes in global climate along with agricultural expansion 
will strongly affect South American wetlands, which comprise around 
20% of the continent and bring many benefits, such as biodiversity 
conservation and water availability (Junk, 2013).
12.3.7 Southwestern South America Sub-region
12.3.7.1 Hazards
have likely been observed for the region (Skansi et al., 2013; Ceccherini 
et al., 2016; Meseguer-Ruiz et al., 2018; Vicente-Serrano et al., 2018; 
Dereczynski et al., 2020; Dunn et al., 2020; Olmo et al., 2020) (WGI 
AR6 Table 11.13) (Seneviratne et al., 2021). In particular, a significant

increment in the duration and frequency of heatwaves mainly in 
central Chile from 1961 to 2016 has been observed (Piticar, 2018).A robust drying trend for Chile (30°S–48°S) has been recorded (medium 
confidence) (Saurral et  al., 2017; Boisier et  al., 2018). However, 
inconsistent trends over the region in the magnitude of precipitation 
extremes with both decreases and increases (Chou et al., 2014; Giorgi 
et al., 2014; Heidinger et al., 2018; Meseguer-Ruiz et al., 2018) (WGI 
AR6 Table 11.14) (Seneviratne et al., 2021) have been observed (low 
confidence). The glacier equilibrium line altitude has presented an 
overall increase over central Chilean Andes (Barria et al., 2019).
For central Chile, a significant increase (5% to 20% in the last 60 years) 
in wave heights in the sea has been observed (Martínez et al., 2018). 
From 1982 to 2016, sea levels at central Chile have increased 5 mm 
yr−1, where El Niño events of 1982–1983 and 1997–1998 caused an

extreme increase of 15 to 20 cm in the mean sea level (Campos-Caba, 
2016; Martínez et al., 2018).
From 1946 to 2017, the number of fires and areas burned have 
increased significantly in Chile (high confidence) (González et  al., 
2011; Jolly et al., 2015; Úbeda and Sarricolea, 2016; de la Barrera et al., 
2018; Urrutia-Jalabert et al., 2018). Fires are attributed to changes in 
Predicted thermal suitability for transmission of 
dengue by Aedes aegypti mosquitoes
CA
NWSNSA
SAM
SESNES
SWS
SSA
1 2 3 4 5 6 7 8 12 11 10 9MonthsCurrent2030
RCP4.5
2050
RCP4.5
2080
RCP4.52030
RCP8.5
2050
RCP8.5
2080
RCP8.5
Central America (CA) • Northwestern South America  (NWS) • Northern South America 
(NSA) • South America Monsoon (SAM) • Northeastern South America (NES) • Southwestern 
South America (SWS) • Southeastern South America (SES) • Southern South America (SSA)

Figure 12.5 |  Predicted thermal suitability for transmission of dengue by Aedes aegypti mosquitoes, mapped as the number of months of the year suitable 
under baseline or current conditions (2015), and in 2030, 2050 and 2080 under RCP4.5 and RCP8.5. Adapted from Ryan et al. (2019). See SM12.8 for additional data 
on population at risk for dengue and Zika in the sub-regions and methodological details.

12
1714Chapter 12 Central and South America
temperature regimes (González et al., 2011; de la Barrera et al., 2018; 
Gómez-González et  al., 2018) and precipitation regimes (medium 
confidence) (Gómez-González et  al., 2018; Urrutia-Jalabert et  al., 
2018).
The glaciers of the southern Andes (including the SWS and SSA regions) 
show the highest glacier mass loss rates worldwide (high confidence) 
contributing to SLR (Jacob et al., 2012; Gardner et al., 2013; Dussaillant 
et  al., 2018; Braun et  al., 2019; Zemp et  al., 2019). Since 1985, the 
glacier area loss in the sub-region is in a range of 20 up to 60% (Braun 
et al., 2019; Reinthaler et al., 2019b).
Four sets of downscaling simulations based on the Eta Regional 
Climate Model forced by two global climate models (Chou et al., 2014) 
projected warmer conditions (more than 1°C) for the entire sub-region 
by 2050 under the RCP4.5 scenario (medium confidence). Extremely 
warm December–January–February days as well as the number of

heatwaves per season are expected to increase by 5–10  times in 
northern Chile (Feron et al., 2019), likely increasing in the intensity and 
frequency of hot extremes over the entire region (WGI AR6 Table 11.13) 
(Seneviratne et  al., 2021). Drier conditions (medium confidence), by 
means of a decrease in total annual and extreme precipitation, are 
expected to increase for southern Chile, but inconsistent changes are 
expected in the sub-region (low confidence) (Chou et al., 2014) (WGI 
AR6 Table 11.14) (Seneviratne et al., 2021) with high confidence upon 
an increase in fire weather and a decrease in permafrost and snow 
extent (WGI AR6 Table 12.6, Ranasinghe et al., 2021).
Regional sea-level change for the region predicted by 2100 shows that 
total mean SLR along the coast will lie between 34 and 52 cm for the 
RCP4.5 scenario and between 46 and 74 cm for the RCP8.5 scenario 
with high confidence (Albrecht and Shaffer, 2016; WGI AR6 Table 12.6, 

12.3.7.2 Exposure
There is high confidence that age and socioeconomic status are key 
factors determining health exposure and quality of life in SWS, where 
low-income areas show an insufficient number of public spaces to 
provide acceptable environmental quality in comparison with the high-
income areas (Romero-Lankao et al., 2013; Fernández and Wu, 2016; 
Paz et al., 2016; Hystad et al., 2019; Smith and Henríquez, 2019; Jaime 
et al., 2020; Pino-Cortés et al., 2020).
Profound social inequalities, urban expansion and inadequate city 
planning (e.g., drainage network) increase exposure to flooding 
events and landslides (high confidence) (Müller and Höfer, 2014; Rojas 
et al., 2017; Lara et al., 2018), heat hazards such as heatwaves (high 
confidence) (Welz et al., 2014; Qin et al., 2015; Inostroza et al., 2016; 
Welz and Krellenberg, 2016; Krellenberg and Welz, 2017) and the loss 
and fragmentation of green infrastructure (GI) (Hernández-Moreno and

Reyes-Paecke, 2018). SWS cities show the highest levels of air pollution 
of CSA (medium confidence: medium evidence, high agreement) 
(Pino et al., 2015; Huneeus et al., 2020; González-Rojas et al., 2021), 
where state air quality alerts have limited effect on protective health 
behaviours, since public perceptions about air pollution vary widely 
among the population (Boso et  al., 2019). In particular, human communities living in coastal cities show a negative safety perception 
about the performance of the infrastructure and coastal defences to 
flood events (low confidence) (González and Holtmann-Ahumada, 
2017; Igualt et al., 2019).
Although climate change is critically important for the current and 
future status of mining activity in SWS (Odell et al., 2018), and SWS 
areas subjected to mining activities are highly exposed to water risk 
(Northey et al., 2017), to date there is low evidence of climate change 
impacting mining activities (Corzo and Gamboa, 2018; Odell et  al.,

2018).
12.3.7.3 Vulnerability
Rapid changes in temperature and precipitation regimes make 
terrestrial ecosystems highly vulnerable to climate change (high 
confidence) (Salas et  al., 2016; Fuentes-Castillo et  al., 2020) 
(Figure 12.7). Terrestrial ecosystems dominated by exotic species (e.g., 
pine) with lower landscape heterogeneity and degraded soils and that 
are close to settlements and roads are highly vulnerable to wildfires 
in comparison to forests dominated by native trees (high confidence) 
(Altamirano et al., 2013; Castillo-Soto et al., 2013; Cóbar-Carranza et al., 
2014; Salas et al., 2016; Bañales-Seguel et al., 2018; Gómez-González 
et  al., 2018; Sarricolea et  al., 2020). Changes in land use, artificial 
forestation, deforestation, agricultural abandonment and urbanisation 
have provoked a permanent degradation of old-growth forests, 
putting at risk the biodiversity, recreation and ecotourism (medium

confidence: medium evidence, high agreement) (Rojas et  al., 2013; 
Nahuelhual et al., 2014). Marine coastal ecosystems such as dunes, 
sandy beaches and wetlands show high deterioration, decreasing their 
ability to mitigate extreme events (medium confidence: low evidence, 
high agreement) (González and Holtmann-Ahumada, 2017; Ministerio 
de Medio Ambiente de Chile, 2019).
The water sector shows a very high vulnerability (high confidence) 
(Figure 12.7) mainly due to weak water governance focused on market 
aspects (e.g., inter-sectoral water transactions, setting rates, granting 
concessions, waiving the water right) (high confidence) (Hurlbert and 
Diaz, 2013; Valdés-Pineda et al., 2014; Barría et al., 2019; Hurlbert and 
Gupta, 2019; Muñoz et al., 2020a; Urquiza and Billi, 2020b). Potable 
water and adequate sanitation are available in SWS; however, water 
availability in Chile is unevenly distributed in rural communities (high

confidence) (Valdés-Pineda et  al., 2014; Nelson-Nuñez et  al., 2019). 
Spatial differences in water availability are enhanced by strong 
population growth, economic development, mining activities and 
the high dependence of agriculture on irrigation (high confidence) 
(Stathatou et  al., 2016; Northey et  al., 2017; Fercovic et  al., 2019). 
Droughts in SWS are a major threat to water security (high confidence) 
(Aitken et  al., 2016; Núñez et  al., 2017) as river streamflows are 
highly dependent on the interannual to decadal climate conditions, 
snow melting processes and rainfall events (Boisier et al., 2016) and 
impacted by land uses and changes in irrigated agriculture (medium 
confidence: medium evidence, high agreement) (Vicuña et al., 2013; 
Fuentes et al., 2021).
Energy and water needs of large-scale mining activities make this 
socioeconomic sector particularly vulnerable to climate change;

12
1715Central and South America  Chapter 12
additionally, the relative lack of power of resource-poor communities 
living in areas where such mining makes claims on water and energy 
resources renders these communities even more vulnerable (Odell 
et al., 2018). Given new conditions generated by changes in a growing 
demand and climate change, mining companies will need to increase 
resilience to extreme events; additionally, the declining concentrations 
of minerals of interest in raw materials require greater energy input 
for extraction and processing and new methods to avoid associated 
emissions are required (Hodgkinson and Smith, 2018).
Urban and agriculture sectors are vulnerable to climate change 
(medium confidence: medium evidence, high agreement) (Figure 12.7), 
increasing problems and demand for water (high confidence) 
(Monsalves-Gavilán et al., 2013; Meza et al., 2014; Fercovic et al., 2019). 
Important health problems (e.g., pathogenic infections, changes in

vector-borne diseases, heat-related mortality, lower neurobehavioural 
performance) have been associated with agriculture, mining and 
thermal power production activities in SWS (high confidence) (Muñoz-
Zanzi et al., 2014; Valdés-Pineda et al., 2014; Pino et al., 2015; Cortés, 
2016; Berasaluce et al., 2019; Muñoz et al., 2019a; Ramírez-Santana 
Large-scale agricultural growth has increased vulnerability to climate 
change by disfavouring traditional agriculture, the homogenisation 
of the biophysical landscape and the replacement of traditional 
crops and native forests with exotic species like pines and eucalyptus 
(high confidence) (Torres et al., 2015), where farmers’ perceptions of 
climate change are highly dependent on educational level and access 
to meteorological information (low confidence) (Roco et  al., 2015). 
Agricultural systems owned by Indigenous Peoples (i.e., Mapuche, 
Quechua and Aymara farmers) seem to pose a lower level of vulnerability

to drought and higher response capacity than non-Indigenous farmers 
thanks to the use of the traditional knowledge of specific management 
techniques and the tendency to conserve species or varieties of crops 
tolerant to water scarcity (low confidence) (Montalba et  al., 2015; 
Saylor et al., 2017; Meldrum et al., 2018). Fishery- and aquaculture-
related livelihoods are vulnerable to climate and non-climate drivers 
(medium confidence: medium evidence, high agreement), such as 
sea surface warming and precipitation reduction (Handisyde et  al., 
2017; Soto et al., 2019; González et al., 2021), changes in upwelling 
intensity (low confidence) (Oyarzún and Brierley, 2019; Ramajo et al., 
2020), eutrophication and harmful algal bloom (HAB) events (Almanza 
et al., 2019), a lack of observational elements and data management 
(Garçon et al., 2019) and events such as earthquakes and tsunamis 
(Marín, 2019).
Chile has experienced accelerated economic growth, which has reduced

poverty; however, important geographical, economic and educational 
inequalities remain (Repetto, 2016). The Chilean healthcare system has 
become more equitable and responsive to the population’s needs (e.g., 
the Bono AUGE healthcare reform programme); however, the high 
relative inequalities in terms of income (OECD, 2018), education level 
and rural–urban factors are determinants of quality of care, health 
system barriers and differential access to healthcare (high confidence) 
(Frenz et al., 2014). Exposure and vulnerability to psychosocial risks 
in SWS show significant inequalities in times of disasters such as 
earthquakes according to socioeconomic, geographic and gender factors (high confidence) (Labra, 2002; Vitriol et  al., 2014; Quijada 
et  al., 2018), which are increased by the absence of local planning 
and drills and the lack of coordination (Vitriol et al., 2014). Indigenous 
Peoples have the highest levels of vulnerability in Chile in terms of

income, basic needs and access to services to climate change (low 
confidence) (Parraguez-Vergara et al., 2016).
12.3.7.4 Impacts
Increasing temperatures in SWS have impacted temperate forests 
(high confidence) (Peña et  al., 2014; Urrutia-Jalabert et  al., 2015; 
Camarero and Fajardo, 2017; Fontúrbel et al., 2018; Venegas-González 
et  al., 2018b; Peña-Guerrero et  al., 2020). Increasing temperatures 
and decreasing precipitation have increased the impacts of wildfires 
on terrestrial ecosystems (high confidence) (Boisier et al., 2016; Díaz-
Hormazábal and González, 2016; Martinez-Harms et al., 2017; de la 
Barrera et al., 2018; Gómez-González et al., 2018; Urrutia et al., 2018; 
Bowman et  al., 2019), creating conditions for future landslides and 
floods (de la Barrera et al., 2018).
Future projections show important changes in the productivity, 
structure and biogeochemical cycles of SWS temperate and rainforests 
(medium confidence: medium evidence, high agreement) (Gutiérrez

et  al., 2014; Correa-Araneda et  al., 2020) and their fauna (low 
confidence) (Glade et  al., 2016; Bourke et  al., 2018). The Chilean 
Winter Rainfall-Valdivian Forests are a biodiversity hotspot (Manes 
et  al., 2021) (Section CCP1.2.2) projected to suffer habitat change, 
with loss of vegetation cover in the future due to climate change 
(medium confidence: medium evidence, high agreement) (Jantz et al., 
2015; Mantyka-Pringle et  al., 2015). Species are projected to suffer 
changes in their distribution, including a decrease in climatic refugia 
for vertebrates (low confidence) (Cuyckens et al., 2015; Warren et al., 
2018).
Increasing temperatures have enlarged the number and areal extent 
of glacier lakes in the central Andes, northern Patagonia and southern 
Patagonia (high confidence) (Wilson et  al., 2018), while decreased 
rainfall and rapid glacier melting have provoked changes in the 
environmental, biogeochemical and biological properties of central-

southern and Andes Chilean lakes (low confidence) (Pizarro et  al., 
2016).
Increasing glacier lake outburst floods (GLOFs), ice and rock 
avalanches, debris flows and lahars from ice-capped volcanoes have 
been observed in SWS (Iribarren Anacona et al., 2015; Jacquet et al., 
2017; Reinthaler et al., 2019b). There is low evidence on the effects of 
warming and degrading permafrost on slope instability and landslides 
in these regions (Iribarren Anacona et al., 2015).
Increasing temperatures, decreasing precipitation regimes and an 
unprecedented long-term drought have decreased the annual average 
river streamflows that supply SWS megacities such as Santiago (high 
confidence) (Meza et al., 2014; Muñoz et al., 2020a), with important 
and negative effects on water quality (Bocchiola et al., 2018; Yevenes 
et  al., 2018), threatening irrigated agriculture activities (medium 
confidence: medium evidence, high agreement) (Yevenes et al., 2018;

Oertel et  al., 2020; Peña-Guerrero et  al., 2020). Large reductions in

12
1716Chapter 12 Central and South America
the availability of groundwater in the SWS region (Meza et al., 2014) 
and a sustained decrease in the mean annual flows (Ragettli et al., 
2016; Bocchiola et al., 2018), especially during the snowmelt season 
(Vargas et al., 2013), have been observed in SWS. Drought has affected 
wetlands (low confidence) (Zhao et  al., 2016; Domic et  al., 2018) 
and desert ecosystems (medium confidence: medium evidence, high 
agreement) (Acosta-Jamett et al., 2016; Neilson et al., 2017; Díaz et al., 
2019).
There is low evidence on shoreline retreat attributed to climate change 
(Martínez et al., 2018; Ministerio de Medio Ambiente de Chile, 2019), 
although increasing wind intensity along the central Chilean coast has 
caused serious damage in coastal infrastructure and buildings (Winckler 
et al., 2017) and changes in seawater properties and processes (low 
confidence) (Schneider et  al., 2017; Aguirre et  al., 2018). Ocean and

coastal ecosystems in SWS are sensitive to upwelling intensity, which 
affects the abundance, diversity, physiology and survivorship of coastal 
species (high confidence) (Anabalón et  al., 2016; Jacob et  al., 2018; 
Ramajo et al., 2020) (Figure 12.8). Increasing radiation and temperatures 
and reduced precipitation, in conjunction with increased nutrient load, 
have increased HAB events, producing massive fauna mortalities (high 
confidence) (León-Muñoz et al., 2018; IPCC, 2019b, SPM A8.2 and B8.3; 
Quiñones et al., 2019; Soto et al., 2019; Armijo et al., 2020). Multiple 
resources subjected to fisheries and aquaculture are highly vulnerable 
to storms, alluvial disasters, ocean warming, ocean acidification, 
increasing ENSO extreme events and lower oxygen availability (high 
confidence) (Figure 12.8; García-Reyes et al., 2015; Silva et al., 2015; 
Duarte et al., 2016, 2018; Lagos et al., 2016; Navarro et al., 2016; Lardies

et  al., 2017; IPCC, 2019b; Mellado et  al., 2019; Ramajo et  al., 2019; 
Silva et al., 2019a; Bertrand et al., 2020). Ocean and coastal ecosystems, 
especially EEZs, will be highly impacted by climate change in the near 
and long term (high confidence) (Figure 12.8; Table SM12.3; Silva et al., 
2015; Silva et al., 2019a).Changes in temperature and droughts have impacted crops 
significantly (medium confidence: medium evidence, high agreement) 
(Ray et al., 2015; Zambrano et al., 2016; Lesjak and Calderini, 2017; 
Ferrero et al., 2018; Piticar, 2018; Haddad et al., 2019; Zúñiga et al., 
2021). Table 12.4 shows the changes in crop growth duration, which 
affects yields. Higher negative numbers then indicate yield reduction 
for the crop. Increasing temperatures and decreasing precipitation are 
expected to impact the agriculture sector (i.e., fruits crops and forests) 
across the entire sub-region, with the largest impacts in the northern

and central zone (high confidence) (Mera et al., 2015; Zhang et al., 
2015; Silva et al., 2016; Lizana et al., 2017; Reyer et al., 2017; Toro-
Mujica et  al., 2017; Beyá-Marshall et  al., 2018; Lobos et  al., 2018; 
O’Leary et al., 2018; Aggarwal et al., 2019; Ávila-Valdés et al., 2020; 
Fernandez et al., 2020; Melo and Foster, 2021). Observed impacts and 
future projections warn that increasing temperatures and decreasing 
precipitation will largely impact water demand by agricultural sectors 
(high confidence) (Novoa et  al., 2019; Peña-Guerrero et  al., 2020; 
Webb et al., 2020). Extreme climate events have caused Indigenous 
Peoples (e.g., Mapuche, Uru and Aymara) to experience water 
scarcity, a reduction in agricultural production and a displacement of 
their traditional knowledge and practices (medium confidence: low 
evidence, high agreement) (Parraguez-Vergara et al., 2016; Meldrum 
et al., 2018; Perreault, 2020).

SWS cities have been largely impacted by wildfires, water scarcity and 
landslides affecting highways and local roads, as well as potable water 
supply (Sepúlveda et al., 2015; Araya-Muñoz et al., 2016). Increasing 
temperature and heat extreme events in cities have increased the 
demand for water, damage to urban infrastructure (Monsalves-
Gavilán et al., 2013) and accelerated ageing and death of trees (high 
confidence) (Moser-Reischl et al., 2019). Increasing temperature will 
modify energy demand in cities in northern and central Chile (Rouault 
et al., 2019).Table 12.4 |  Average percentage change in crop growth duration for the period 2015–2019. Crop growth duration refers to the time taken in a year for crops to accumulate the 
reference period (1981–2010) average growing season accumulated temperature total (ATT). As temperatures rise, the ATT is reached earlier (higher negative changes), the crop

matures too quickly, and thus yields are lower. “No data” means no data are available for the growth of that crop in the specified region. NP means that the crop is not present in 
significant areas in that region. Data were derived from Romanello et al. (2021).
Region Winter wheat Spring wheat Rice Maize Soybean
Central America (CA) −4.8% No data −1.9% −5.0% −4.7%
Northwestern South 
America (NWS)−3.8% −5.2% −5.2% −5.6% −3.1%
Northern South America 
(NSA)NP NP −0.7% −3.1% 0.0%
South America Monsoon 
(SAM)−5.3% −0.7% −1.4% −2.9% −1.5%
Northeastern South 
America (NES)−1.0% −1.3% −0.7% −3.5% −2.6%
Southeastern South 
America (SES)−2.3% −3.5% −2.3% −2.4% −2.7%
Southwestern South 
America (SWS)−2.3% −5.2% −10.0% −5.2% No data
Southern South America 
(SSA)−0.8% −6.5% No data −1.6% No data

12
1717Central and South America  Chapter 12
Increasing temperature, heat extreme events and air pollution in SWS 
have significantly impacted population health (cardiac complications, 
heat stroke and respiratory diseases) (high confidence) (Table  12.2; 
Leiva G et al., 2013; Monsalves-Gavilán et al., 2013; Pino et al., 2015; 
Herrera et al., 2016; Henríquez and Urrea, 2017; Ugarte-Avilés et al., 
2017; de la Barrera et al., 2018; Johns et al., 2018; Bowman et al., 2019; 
González et al., 2019; Matus C and Oyarzún G, 2019; Sánchez et al., 
2019; Terrazas et al., 2019; Cakmak et al., 2021; Zenteno et al., 2021). 
There is low confidence regarding areal changes in Chagas disease 
(Tapia-Garay et al., 2018; Garrido et al., 2019) and transmission rates 
in the future (Ayala et al., 2019).
12.3.8 Southern South America Sub-region
12.3.8.1 Hazards
There were inconsistent trends and insufficient data coverage on 
extreme temperatures and precipitation (low confidence), but an

increase in the frequency of meteorological droughts was observed 
with medium confidence (Dereczynski et al., 2020; Dunn et al., 2020; 
WGI AR6 Tables 11.13, 11.14, 11.15, Seneviratne et al., 2021; WGI AR6 
Table  12.3, Ranasinghe et  al., 2021). An increase in precipitation in 
Trelew, no change for Comodoro Rivadavia, both stations located in 
eastern Patagonia, and negative trends in austral summer rainfall in 
the southern Andes were observed (Vera and Díaz, 2015; Saurral et al., 
2017). Chile’s wildfires in Patagonia (fire frequency and intensity) have 
grown at an alarming rate (Úbeda and Sarricolea, 2016). Decreasing 
rainfall patterns in Punta Arenas is closely associated with variability 
at interannual to inter-decadal time scales of the main forcing system 
of climate in Patagonia. Snow cover extension (SCE) and snow cover 
duration decreased by an average of approximately 13 ± 2% and 
43 ± 20 d respectively from 2000 to 2016, due to warming rather

than drying (Rasmussen et al., 2007). In particular, analysis of spatial 
patterns of SCE indicates a slightly greater reduction on the eastern 
side (approximately 14 ± 2%) of the Andes Cordillera compared to 
the western side (approximately 12 ± 3%). According to the longest 
time series of glacier mass balance data in the Southern Hemisphere, 
the Echaurren Norte glacier lost 65% of its original area in the period 
1955–2015 and disaggregated into two ice bodies in the late 1990s 
(Malmros et al., 2018; Pérez et al., 2018).
Mean temperatures in the SSA sub-region are projected to continue to 
rise up to +2.5°C by 2080 with respect to the present climate (Kreps 
et al., 2012). A rise in temperature means that an isotherm of 0°C will 
move up mountains, leaving less surface for accumulation of snow 
(Barros et al., 2015).
An increase in the intensity and frequency of hot extremes and a 
decrease in the intensity and frequency of cold extremes are projected

to be likely (WGI AR6 Table 11.13, Seneviratne et al., 2021); CMIP6 
models project an increase in the intensity and frequency of heavy 
precipitation (medium confidence).
It is expected that an increase in the intensity of heavy precipitation, 
droughts and fire weather will intensify through the 21st century in 
SSA, but mean wind will decrease (medium confidence) (Kitoh et al., 2011; WGI AR6 Tables 11.14 and Table 11.15, Seneviratne et al., 2021). 
The probability of extended droughts, such as the recently experienced 
mega-drought (2010–2015), increases to up to 5 events/100 yr 
(Bozkurt et al., 2017). Snow, glaciers, permafrost and ice sheets will 
decrease with high confidence (WGI AR6 Table 12.6, Ranasinghe et al., 
2021). The observed area and the elevation changes indicate that the 
Echaurren Norte glacier may disappear in the coming years if negative 
mass balance rates prevail (medium confidence) (Farías-Barahona 
12.3.8.2 Exposure

Grasslands make a significant contribution to food security in 
Patagonia by providing part of the feed requirements of ruminants 
used for meat, wool and milk production. There is a lack of information 
regarding the combined effects of climate change and overgrazing and 
the consequences for pastoral livelihoods that depend on rangelands. 
Temperature and the amount and seasonal distribution of precipitation 
were important controls of vegetation structure in Patagonian 
rangelands (Gaitán et al., 2014). They found that over two-thirds of the 
total effect of precipitation on above-ground net primary production 
(ANPP) was direct, and the other third was indirect (via the effects 
of precipitation on vegetation structure). Thus, if evapotranspiration 
and drought stress increase as temperature increases and rainfall 
decreases in water-limited ecosystems, a greater exposure of ranchers 
to a reduction in stocking rate and, therefore, family income would be

expected (medium confidence). The number of farmers (mainly family 
enterprises) exposed to climatic hazards (drought) is approximately 
70,000–80,000, who have 14–15  million sheep in Argentina (Peri 
The main Argentinian Patagonia cities have developed as a result of 
oil and gas extraction, which requires massive quantities of water due 
to fracking and drilling techniques. Vaca Muerta is the major region 
in SA, where those techniques are used to extract oil and gas, and 
this will lead to an exacerbation of current water scarcity issues and 
to competition with irrigated agriculture (Rosa and D’Odorico, 2019), 
which in the context of drought may exacerbate socioenvironmental 
conflicts (medium confidence).
12.3.8.3 Vulnerability
There are reports related to a decrease in survival, growth and higher 
vulnerability to drought and fire severity for species of native forests 
due to climate change and wildfires (high confidence) (Mundo et al.,

2010; Landesmann et  al., 2015; Whitlock et  al., 2015; Jump et  al., 
2017; Camarero et  al., 2018; Venegas-González et  al., 2018a). A 
coincidence has been reported between major changes in regional 
decline in the growth of forests with severe droughts due to climatic 
variations over northern Patagonia (Rodríguez-Catón et  al., 2016). 
Once the forest decline begins, other contributing factors, such as 
insects (e.g., defoliator outbreaks), increase forest vulnerability or 
accelerate the loss of forest health of previously stressed trees (Piper 
et  al., 2015). This region hosts unique temperate rainforests, which 
are particularly rich in endemic and long-lived conifer species (e.g., 
Fitzroya cupressoides) and which may be vulnerable to declines in soil 
moisture availability (Camarero and Fajardo, 2017). Patagonia will

12
1718Chapter 12 Central and South America
probably be vulnerable to a decrease in precipitation regimes due to 
climate change, and consequently many species that rely on meadows 
in an arid environment will also be impacted (Crego et al., 2014). The 
floods triggered by strong ENSOs caused significant changes in crop 
production (Isla et al., 2018).
The development of various human activities and water infrastructure 
is depleting water sources, changing river basins from exoreic to 
endoreic and the disappearance of a lake in 2016 (Scordo et  al., 
2017). Numerous dams for irrigation, some of which are also used 
for hydropower, have been and are planned to be built despite wind 
power generation potential (Silva, 2016). Oil and gas have played an 
important role in the rise of Neuquén-Cipolletti as Patagonia’s most 
populous urban area and in the growth of Comodoro Rivadavia, Punta 
Arenas and Rio Grande as well.
12.3.8.4 Impacts

The potential impact of climate change is of special concern in arid 
and semiarid Patagonia, a >700,000 km2 region of steppe-like plains 
in Argentina. Thus, melting snow and ice in the glaciers of Patagonia 
and the Andes will alter surface runoff into interior wetlands. A SLR of 
20–60 cm will destroy coastal marshes, and an increase in extreme 
events, such as storms, floods and droughts, will affect biodiversity in 
wet grasslands (medium confidence: low evidence, high agreement) 
(after Junk et al. 2013; Joyce et al. 2016). Three species of lizard from 
Patagonia are at risk of extinction as a result of global warming 
(Kubisch et al., 2016).
Patagonian ice fields in SA are the largest bodies of ice outside of 
Antarctica in the Southern Hemisphere. They are losing volume due 
partly to rapid changes in their outlet glaciers, which end up in lakes 
or the ocean, becoming the largest contributors to eustatic SLR in

the world per unit area (Foresta et al., 2018; Moragues et al., 2019; 
Zemp et al., 2019). Most calving glaciers in the southern Patagonia 
ice field retreated during the last century (high confidence). Upsala 
glacier retreat generated slope instability, and a landslide movement 
destroyed the western edge in 2013. The Upsala Argentina Lake 
has become potentially unstable and may generate new landslides (Moragues et al., 2019). The climate effect on the summer stratification 
of piedmont lakes is another issue in connection with glacier dynamics 
(Isla et al., 2010).
Between 41° and 56° South latitude, the absolute glacier area loss 
was 5450 km2 (19%) in the last approximately 150 years, with an 
annual area reduction increase of 0.25% yr−1 for the period 2005–
2016 (Meier et al., 2018). The small glaciers in the northern part of the 
Northern Patagonian Ice Field had over all periods the highest rates 
of 0.92% a−1. In this sub-region, increased melting of ice is leading to

changes in the structure and functioning of river ecosystems and in 
freshwater inputs to coastal marine ecosystems (medium confidence: 
low evidence, high agreement) (Aguayo et  al., 2019). In addition, 
in the case of coastal areas, the importance of tides and rising sea 
levels in the behaviour of river floods has been demonstrated (Jalón-
Rojas et al., 2018).
Suitable areas for meadows (very productive areas for livestock 
production) will decrease by 7.85% by 2050 given predicted changes 
in climate (low confidence) (Crego et al., 2014).
A major drought from 1998 to 1999, coincident with a very hot summer, 
led to extensive dieback in a Nothofagus species (Suarez et al., 2004). 
In another dominant Nothofagus species, several periodic droughts 
have triggered forest decline since the 1940s (Rodríguez-Catón et al., 
2016).
Climate-change-impacted ocean ecosystems by reducing kelp 
coverage, increasing reproductive failure and chick mortality of

penguins and spurring the poleward expansion of saltmarshes in the 
Atlantic Patagonia. SSA houses the Patagonian Steppe Global-200 
terrestrial ecoregion, which is a conservation priority on a global scale, 
but with a clear lack of studies on likely future climate-change impacts 
(Section CCP1.2.2.2) (Manes et al., 2021). The Patagonian Steppe may 
suffer pronounced expansion in invasive species’ ranges under climate 
change (low confidence) (Wang et al., 2017a).
Fire has been found to promote or halt biological invasions (medium 
confidence: medium evidence, high agreement). For example, an 
Table 12.5 |  Change in population-weighted exposure to very high or extremely high wildfire risk. Data were derived from Fire Danger Indices (FDIs) produced by the Copernicus 
Emergency Management Service for the European Forest Fire Information System (EFFIS) (available at Copernicus Emergency Management Service [2021]). High and very high

wildfire danger are defined as FDI ≥ 5. Data were derived from Romanello et al. (2021).
Population-weighted mean days of exposure to extremely high and very high wildfire danger
Sub-region 2001–2004 2017–2020Change from 2001–2004 to 
2017–2020
Central America (CA) 30.4 26.9 −3.5
Northwestern South America (NWS) 4.2 4.6 0.5
Northern South America (NSA) 19.7 21.2 1.5
South America Monsoon (SAM) 16.0 27.8 11.8
Northeastern South America (NES) 47.9 53.3 5.4
Southeastern SouthAmerica (SES) 4.2 8.2 4.0
Southwestern SouthAmerica (SWS) 31.9 58.4 26.5
Southern South America (SSA) 88.7 104.9 16.2

12
1719Central and South America  Chapter 12
analysis of Pinus spread following wildfires in Patagonia revealed a high 
risk that pines will become invasive if ignition frequency increases as a 
result of climate change (Raffaele et al., 2016). According to Inostroza 
et al. (2016), the Magellan Region is one of the most fragile regions in 
Patagonia, and despite its low population densities, it is undergoing a 
silent process of anthropogenic alteration where between 53.1% and 
68.1% of the area needs to be considered to be influenced by humans 
who are occupying pristine ecosystems, even some with extensive 
conservation designations (Inostroza et al., 2016). Fire exposure can 
result in several health problems for human populations; Table 12.5 
shows that SSA is the region with the highest exposure to wildfire 
danger.
Observed and projected hazards in Central and South America
NSA
SAMNESNWS
SESSWS
SSACA
NSA
SAMNESNWS
SESSWS
SSACAProjected Observed
Direction of hazard

Increase IncreaseanddereaseDecreaseCentral America (CA) • Northwestern South 
America  (NWS) • Northern South America 
(NSA) • South America Monsoon (SAM) • 
Northeastern South America (NES) • 
Southwestern South America (SWS) • 
Southeastern South America (SES) • 
Southern South America (SSA)Confidence in attribution
Low Medium HighHazards
Flood and landslidesMean temperature
Extreme precipitationExtreme heat
Cold spell and frost Mean precipitation
Wildfire
Wind speed Sea levelDrought, dryness and aridity
Figure 12.6 |  Observed trends (WGI AR6 Tables 11.13, 11.14, 11.15) (Seneviratne et al., 2021) and summary of confidence in direction of projected change in climatic 
impact drivers, representing their aggregate characteristic changes for mid-century for RCP4.5, SSP3-44 4.5 and SRES A1B scenarios, or above within each AR6 region, approximately

corresponding (for CIDs that are independent of SLR) to global warming levels between 2°C and 2.4°C (WGI AR6 Table 12.6) (Ranasinghe et al., 2021).

12
1720Chapter 12 Central and South America
(b) References used  and vulnerability level attributed by subregion and sector
Sectoral distribution of vulnerability to climate change for Central and South America
Central America (CA) • Northwestern South 
Southern South America (SSA)Confidence scale
of vulnerability level
Low Medium HighSectors
(a) Vulnerability and confidence level by subregion and sector
Vulnerability level
High
Moderate
LowVery highFood systems
Cities and
poverty and
their livelihoodsWater
Health
Terrestrial and
Sectors Subregions
Cities and
their livelihoods
Water
HealthFood systems
ecosystemsSSA SES SAM NWS NSA NES CA SWS

Figure 12.7 |  Sectoral distribution of vulnerability levels to climate change for sub-regions. The vulnerability levels are based on studies that include: (a) databases with 
climate-change vulnerability indexes by country and sector, (b) studies that apply climate-change vulnerability indexes by sector at the local, national, regional or global scale, and (c) 
studies that define some vulnerability level based on the authors’ expert judgment.  
Panel (a) shows the vulnerability and confidence levels for each sub-region. 
Panel (b) indicates the references used and the level of vulnerability by sub-region. The numbers within the table indicate the reference used for the assessment in the following 
order: (1) Aitken et al. (2016); (2) Anderson et al. (2018b); (3) Bañales-Seguel et al. (2018); (4) Bouroncle et al. (2017); (5) CAF (2014); (6) Carrão et al. (2016); (7) Donatti et al. (2019);

(8) Eguiguren-Velepucha et al. (2016); (9) FAO (2020a); (10) FAO (2020b); (11) FAO (2021a); (12) FAO (2021b); (13) FAO (2021c); (14) FAO et al. (2021); (15) FAO and ECLAC (2020); 
(16) Ferreira Filho and Moraes (2015); (17) Filho et al. (2016); (18) Fuentes-Castillo et al. (2020); (19) FSIN and Global Network Against Food Crisis (2021); (20) Global Health Security 
Index (2019); (21) Godber and Wall (2014); (22) Handisyde et al. (2017); (23) Hannah et al. (2017); (24) Immerzeel et al. (2020); (25) Inform Risk Index (2021); (26) Koutroulis et al. 
(2019); (27) Krishnamurthy et al. (2014); (28) Lapola et al. (2019a); (29) Li et al. (2018); (30) Lin et al. (2020); (31) Mansur et al. (2016); (32) Martins et al. (2017); (33) Menezes et al. 
(2018); (34) Nagy et al. (2018);35) ND-Gain (2020); (36) Northey et al. (2017); (37) Olivares et al. (2015); (38) Pacifici et al. (2015); (39) Qin et al. (2020); (40) Romeo et al. (2020); (41)

Liu and Chen (2021); (42) Silva et al. (2019b); (43) Soto Winckler and Del Castillo Pantoja (2019); (44) Soto et al. (2019); (45) Tomby and Zhang (2019); (46) Venegas-González et al. 
(2018b); (47) Yeni and Alpas (2017); (48) Marengo et al. (2017); (49) Bedran-Martins et al. (2018); (50) Confalonieri et al. (2014a). Detailed methodology can be found in SM12.2.

12
1721Central and South America  Chapter 12
Less MoreRelative amount of times
each driver action was reported:Sensitivity of ocean, coastal ecosystems, and Exclusive Economic Zones (EEZs)
to climate and non-climate drivers in Central and South America 
Synthesis of field and laboratory experiments reporting drivers generating sensitivity on ocean, coastal ecosystems and EEZs
MangrovesOcean warming
Coral reefsOcean acidification
EstuariesSea level rise
ENSO (El-Niño)
KelpsChanges in salinity
Exclusive
economic
zones
SaltmarshesWind speed
Rocky shoresWave height
Hypoxia
Sandy beachesFlooding
Extreme events
DroughtReduced rainfallHeatwavesCold spellAtmospheric CO2Atmospheric circulation
Hydrological changes
Nutrient loss
Ozone
Southern Annular Mode
Upwelling intensity
Non-climateOcean and coastal
ecosystemsDrivers
Southeast
(SES)
Northeast 
(NES)
(CA)
Northern
(NSA)
Southwest
(SWS)

Southern
(SSA)
Northwest
(NWS)Central and
Sub-regions
Figure 12.8 |  Climate and non-climate sensitivity drivers of ocean, coastal ecosystems and EEZs of Central and South America.
12.4 Key Impacts and Risks
This section synthesises key risks across the CSA region. It follows 
the definition and concept of risk given in AR5, distinguishing the 
risk components, climatic hazards, exposure and vulnerabilities of 
people and assets (IPCC, 2014). This concept is further developed in 
AR6, defining key risks as potentially severe risks (Section 16.5). Key 
risks may refer to present or future conditions, with a focus on the 
21st century. Both mitigation and adaptation can moderate the extent 
or severity of risks. The identification and evaluation of risks imply 
sociocultural values, which may vary across individuals, communities 
or cultures.
In line with Chapter 16 of this report, this chapter uses a risk outcome

perspective, that is, the focus is on consequences related to risks, 
which could result from different combinations of hazards, exposure 
and vulnerabilities. Few studies in the literature focus on severe risks 
in the CSA region, and scant studies consider specifically and explicitly 
risk drivers such as level of warming, level of exposure, vulnerability 
Criteria for identifying key risks for this chapter include the magnitude 
of the consequences, in particular the number of people potentially 
affected; the severity of the negative effects of the risk (e.g., lives 
threatened, major negative effects on livelihoods, well-being or the economy); the importance of the affected system (e.g., for vital 
ecosystem services, for large population groups); the irreversibility of 
either the process leading to the risk or the consequences; and the 
potential to reduce the risk.
Several of the key risks identified for the CSA region align well with

the overarching key risks assessed in AR5 (Oppenheimer et al., 2014) 
and later in O’Neill et al. (2017), as well as with the representative 
key risks assessed in Section  16.5 of this report. The identified key 
risks include the following: KR1: risk of food insecurity due to frequent 
and/or extreme droughts; KR2: risk to life and infrastructure due to 
floods and landslides; KR3: risk of water insecurity; KR4: risk of severe 
health effects due to increasing epidemics (in particular vector-borne 
diseases); KR5: systemic risks of surpassing infrastructure and public 
service systems capacities; KR6: risk of large-scale changes and 
biome shifts in the Amazon; KR7: risk to coral reef ecosystems due 
to coral bleaching; KR8: risks to coastal socioecological systems due 
to SLR, storm surges and coastal erosion (Table  12.6; Figure  12.11; 
Table SM12.5).
Identification and assessment of key risks are informed by 
observed and projected impacts in the different sub-regions of CSA

(Section 12.3). Figure 12.10 shows a summary of different levels of 
observed and future impacts per sub-region for different sectors, 
based on a detailed assessment of climate-change impacts on various

12
1722Chapter 12 Central and South America
Table 12.6 |  Synthesis of key risks identified and assessed for Central and South America region
Consequence that would make the 
risk severeAssociated changes in hazards Associated changes in exposure Associated changes in vulnerability
1. Risk of food insecurity due to frequent/extreme droughts
Substantial decrease in yield for key crops, 
disruption of food provision chains, reduced 
capacity or production of goods, reduced 
food security and increased malnutrition.More frequent and/or longer drought 
periods; decrease in annual rainfall, severe 
decrease in rainfall at onset of rainy season; 
desertification of semiarid regions.More people exposed to food insecurity 
due to spatially more extensive drought, 
high population growth rate (including rural 
areas) and greater population dependence 
on agricultural goods.Reduced capacity of farmers (especially 
small-scale) to adapt to changing climatic

conditions; soil degradation, insufficient 
government support of adaptation 
measures, financial contributions, 
infrastructure, insurance, and research 
efforts; inefficient water management.
2. Risk to life and infrastructure due to floods and landslides
Death and severe health effects, disruption 
of critical infrastructure and service systems.More frequent and severe storms and 
heavy precipitation events; changing 
snow conditions and thawing permafrost; 
retreating glaciers, formation of glacier 
lakes, increased GLOF hazard.More people exposed to floods and 
landslides due to changing hazards, land 
use and increased population; occupation of 
more risk-prone areas such as flood plains 
and steep slopes.Low-income and marginal populations 
and low resilience of infrastructure and 
critical service systems; limited government 
support through insurance, monitoring, 
EWSs and recovery.
3. Risk of water insecurity
Seasonal water availability change and

decline due to glacier shrinkage, snow 
cover change, more pronounced dry periods 
and poor or failed water management and 
governance.Glacier shrinkage, snow cover change, more 
pronounced dry periods, precipitation and 
circulation changes.Increase in population dependent on 
contribution of glacier/snow melt, especially 
during drought conditions; increased 
demand from intensification of agriculture, 
mining, hydropower and urbanisation.Unequal water consumption systems, 
failed water management and government 
capacities, low water infrastructure 
efficiency, growing urban areas.
4. Risk of severe health effects due to increasing epidemics (in particular vector-borne diseases)
Increased rate of epidemics of vector-borne 
diseases (malaria, dengue, Zika, 
leishmaniasis) together with diarrheal 
diseases. Severe health effects and damage 
to health systems in countries with low 
adaptive capacity and where original

endemicity is high and control status poor.Higher temperatures increase the 
geographical range of vectors, leading to 
expansion of climate suitable areas.Increased population density and mobility 
through urbanisation results in high 
transmission rate. Increased population 
exposed to arboviruses due to expansion 
of vectors, including higher altitudes and 
latitudes.Poor sanitation conditions, particularly 
in low-income communities and for 
Indigenous Peoples. Insufficient coverage 
of appropriate water provision and sewage 
systems. Low structural or economic 
capacity to cope; underfunding of health 
systems. Increase in infections can increase 
incidence of more severe forms of dengue.
5. Systemic risks of surpassing infrastructure and public service systems capacities
Breakdown of public service systems, 
including infrastructure and health services 
due to cascading impacts of natural hazards 
and epidemics, affecting a large part of the

population.Higher frequency and magnitude of 
climate-related events (storms, floods, 
landslides), together with an increase 
in spatial and temporal distribution of 
pathogens/vectors for malaria, dengue, Zika 
and leishmaniasis.More people and infrastructure exposed 
to climate/weather events; increase in 
population exposed to arboviruses due to 
spatial expansion of vectors.Increasing vulnerability of public service 
and infrastructure systems; insufficient 
disaster management; little improvement, 
maintenance and expansion of public 
healthcare systems; low system resilience.

12
1723Central and South America  Chapter 12
systems and components for the corresponding sector (Figure 12.9). 
This assessment is consistent with and complementary to the 
assessment in Section 12.3. A synthesis of these impacts (Figure 12.10) 
indicates the following: Climate change has or will have a major 
impact on the observed and future decline of Andean glaciers and 
snow (high confidence) and lead to the degradation of permafrost 
and destabilisation of related landscapes (medium evidence, high 
agreement). Water quality is a major concern across the region, but 
there is limited evidence of impacts of climate change on water quality 
as well as on groundwater. Climate change has had a significant 
impact on terrestrial and freshwater ecosystems in the NWS, SES and 
SWS sub-regions and a medium impact in the other sub-regions, but 
the level of confidence varies across sub-regions. Projections indicate 
a strong impact of climate change on these ecosystems for the future

(medium confidence: medium evidence, high agreement). Many 
aspects and assets of ocean and coastal ecosystems (e.g., mangroves, 
coral reefs, saltmarshes) were identified as being strongly impacted by 
climate change, both for observed and future periods (high confidence) 
(Section 12.5.2; Figure 12.9).
In most sub-regions, crop, livestock, fisheries and food systems in 
general show medium to high impacts of climate change over the 
observed period, as does the remaineder of the 21st century (medium 
confidence: medium evidence, high agreement). For some sub-regions, 
the available literature does not allow for an assessment of impacts 
on various human systems, including cities and infrastructure, health, 
poverty, livelihoods, migration, conflicts and IKLK, especially for future 
time periods. This points to important knowledge gaps about climate-change impacts on human systems. The indication of high impacts on 
several human systems and sub-regions points to the need to close

these knowledge gaps.
Assessment of key observed and projected impacts and risks shows 
that in the CSA region several systems are already approaching critical 
thresholds under current warming levels, in particular glaciers in the 
Andes, coral reefs in CA (high confidence) and ocean and coastal 
ecosystems in virtually all sub-regions (medium confidence: medium 
evidence, high agreement). Some systems could cross these thresholds 
with different levels of reversibility depending on the degree of future 
warming, that is, glaciers in the Andes and coral reefs in CA, which will 
show partial but irreversible loss already under low levels of warming 
(RCP2.6) (high confidence). The risk of large-scale ecological changes 
and biome shifts of the Amazon rainforest, that is, a transition from 
tropical forest into other biomes such as seasonal forest or savannah, 
is now assessed with medium confidence, with the extent of the

changes depending on the level of future warming and non-climatic 
drivers (land use change, deforestation, forest fire practices).
Systemic risks where critical infrastructure and public service system 
capacities are surpassed due to storms, floods and epidemics, with 
cascading impacts through vulnerable systems and populations 
and economic sectors, have the potential to affect large parts of 
populations and are therefore of major concern (medium confidence: 
limited evidence, high agreement). The COVID-19 crisis has exposed 
the existing vulnerabilities in important systems, in particular health 
systems and public services (Phillips et  al., 2020). However, tipping Consequence that would make the 
6. Risk of large-scale changes and biome shifts in Amazon
Transition from tropical forest into 
other biomes such as seasonal forest or 
savannah through forest degradation and

deforestation; risk of shifting from carbon 
sink to source.More frequent, stronger and more 
persistent drought periods; temperature 
increase and reduction in annual rainfall.Reduced availability of natural sources 
for local people; land use and land cover 
change (mining, deforestation); loss of 
biodiversity and ecosystem services; 
health impacts from increased forest fires 
particularly for Indigenous Peoples.Strong dependence on non-climatic drivers, 
in particular land use change, deforestation, 
forest fire practices; low capacity to monitor 
and control deforestation.
7. Risk to coral reef ecosystems due to coral bleaching
Degradation and possible death of 
Mesoamerican coral reef, the second largest 
reef in the world; severe damage to habitat 
for marine species, degrading coastal 
protection and other ecosystem services, 
decreased food security from fisheries, lack 
of income from tourism.Ocean SST increase, lowered seawater 
pH and carbonate levels due to increased

atmospheric CO 2 levels, leading to ocean 
acidification and coral bleaching.Continued exposure to increased 
atmospheric CO 2 levels and SSTs together 
with destruction from coastal development, 
fishing practices and tourism.Ecosystem highly sensitive to water 
temperature and pH fluctuations; high 
levels of negative human interference with 
reefs including runoff and pollution.
8. Risks to coastal socioecological systems due to SLR, storm surges and coastal erosion
Coastal flooding and erosion causing 
severe damage to coastal population 
and infrastructure; loss of fisheries, 
reef degradation and decline in coastal 
protection due to increased storm surges 
and waves;saltwater intrusion and land 
subsidence.High continuing trajectories of SLR; More 
intense and persistent coastal flooding, 
saltwater intrusion, coastal erosion.Coastal population growth; increased 
number of people, infrastructure and 
services (coastal tourism) exposed; need for

relocation of millions of people.Poor planning in coastal development 
and infrastructure, disproportionate 
vulnerability and limited adaptation options 
for rural communities and Indigenous 
Peoples, increasing urbanisation in coastal 
cities; significant economic losses and 
unemployment from declining tourism.

12
1724Chapter 12 Central and South America
Impact levelObserved and projected impacts for the subregions of Central and South America
Obs. Proj. Obs. Proj. Obs. Proj. Obs. Proj. Obs. Proj. Obs. Proj. Obs. Proj. Obs. Proj.NES CA NWS NSA SAM SES SWS SSA
= not assessedna = not applicable/
Observed impactsConfidence level:
Projected impactsCentral America (CA) • Northwestern South America  
(NWS) • Northern South America (NSA) • South America 
Monsoon (SAM) • Northeastern South America (NES) • 
Southwestern South America (SWS) • Southeastern 
South America (SES) • Southern South America (SSA)Temperate forests
Tropical forests
Grassland and savanna
Deserts
Mountains
Estuaries
Mangroves
Kelps
Exclusive Economic Zones (EEZs)
Cryosphere reservoir
Stream flow
Annual crop systems
Livestock and pasture 
Permanent crops (fruit production)

Forestry and wood production 
Urban land and built environment
Land Use
Housing stock
Water supply, rainwater drainage and sewer infrastructure
Energy
Mobility and transport systems
Labor productivity
Morbidity
Mortality
Territory
Livestock mortality
Income
Migration and displacement
Conflicts
Indigenous knowledge and local knowledgeTerrestrial and
ecosystems and
their services
Ocean and
coastal
Water
Food, fibre
and other
ecosystem
products
Cities and
Health
Poverty, livelihoods
and sustainable
Human
dimension/
/
/
/
/
nananananananana/
/
/
nananana
na
na
na
na
na
nana
na
nanananana
na
na
nananananananana
/
//
/
//
/
/
/
/
//
/
nana
na
na
nana
na
nananana
nananana
na
nana/
/na
na
na
nananana
na
na
nanananana/
//
na/
//
na
na
na
nana
na
na
nananana
na
na
nana/
/
//
nana
na
na
nananana
na
nana/
/
/
/
/
nananananana
nana/
/
nanana
na
na
na
na
na
nanana
nanana
/
na
nananana
na
na

nanananananana
/
//
/
na
nanana
na
na//
/
/
/
/
//
nana
na
nana
na
na/
nana
na
na
nananana
na
nana/nana
nana/
/
na
na
na
na
nana/nana
nanana
nana
na
nana
/
na
na
nananana
na
nanana
nana
na
na
nanana
na
na
na/
//
na
nana
nana
na
nananana
na
nanana
na
nana
na
nana
na
nana
na/
/
//
nana
nananana
nanana
na
nana
nana
na
na
nanana
na
na
nana
nanana
na
Medium High Low
Figure 12.9 |  Observed and projected impacts for sub-regions of CSA. Impacts are distinguished for main sectors and for their corresponding systems (or components). 
Observed impacts relate to the last several decades. Projected impacts represent a synthesis across several emission and warming scenarios, indicative of a time period from the 
middle to end of the 21st century. For each system (e.g., coral reefs) climate-change impacts are identified as being low, medium or high. The references underlying this assessment 
can be found in SM12.4.1.

12
1725Central and South America Chapter 12
na naCA
NWSNSA
SAM
SESNES
SWS
SSASynthesis of observed and projected impacts to main sectors
in Central and South America 
Projections averaged across scenarios and 21st century
Terrestrial and freshwater
ecosystems and their services
Ocean and coastal ecosystems 
Water
Food, fibre and
other ecosystem products
Cities and infrastructure
Health
Populations in poverty 
and their livelihoods
Migration, conflict and indigenous
knowledge and local knowledgeObs. Proj. Obs. Proj. Obs. Proj. Obs. Proj.
//
//Obs. Proj.
//
/
///Obs. Proj.
/
/
///
/
////NES CA NWS NSA SAM SES SWS SSA
/
Impact level Medium High Low Confidence level: Observed impacts
Confidence level: Projected impacts
High Medium Low = not assessed/ = not applicablena• Central America (CA) 
• Northwestern South America (NWS)• Northern South America (NSA) • South America Monsoon (SAM)

• Northeastern South America (NES) • Southwestern South America (SWS)• Southeastern South America (SES)  
• Southern South America (SSA)
Figure 12.10 |  Synthesis of observed and projected impacts, distinguished for different sectors and each sub-region of CSA. Observed impacts relate to the last 
several decades. Projected impacts represent a synthesis across several emission and warming scenarios, indicative of a time period from the middle to end of the 21st century. For each sector (e.g., health) climate-change impacts are identified as being low, medium or high. The references underlying this assessment can be found in SM12.4.1 and the methodology to complete the synthesis is found in SM12.4.2.
points in social systems are poorly understood (Bentley et al., 2014; 
Milkoreit et  al., 2018), and there is limited evidence to inform understanding about which level of compound climatic, environmental and socioeconomic stressors social systems will withstand in CSA.

Overall, most key risks and their severity and extent are strongly 
driven and determined by system exposure, vulnerability and adaptive capacity. In particular, the high vulnerability of large populations, infrastructure and service systems, such as health, food and energy production and supply, are important factors, along with high levels of inequality and poor governance, for creating and increasing key risks (high confidence). Prevailing low levels of available information and understanding exacerbate the uncertainties surrounding key risks and so pose limitations to adaptation. An example is CA, with its high levels of vulnerability and exposure, but there is limited evidence and understanding on impacts and risks, making this region susceptible to inappropriate adaptation to expected future climate-change impacts.
12.5 Adaptation

Adaptation initiatives across the region have increased since AR5. National Communications (NCs), Nationally Determined Contributions (NDCs) and National Adaptation Plans (NAPs) (https://unfccc.int) recently published are providing guidance for adaptation in CSA. There is also a diversity of non-governmental adaptation initiatives, at both the national and sub-national levels. In this context, this section assesses, through a sectoral approach, the main challenges, opportunities, trends and initiatives to adapt to climate change in the region.
12.5.1  T errestrial and Freshwater Ecosystems and Their 
Services

CSA is one of the most biodiverse regions in the world, hosting unique socioecosystems that will be strongly impacted by climate change (high confidence) (Section 12.3; Cross-Chapter Paper 1; CAF , 2014; Camacho Guerreiro et al., 2016; IPBES, 2018a; Li et al., 2018; Retsa et  al., 2020). Warming has generated extreme heat events in many parts of CSA (IPCC, 2019a) that, together with droughts and floods, will seriously affect the integrity of terrestrial and freshwater ecosystems in the entire region (Section 12.3; CAF , 2014). A reduction in net primary productivity in tropical forests and glacier retreat in the Andes, for example, are expected to cause significant negative socioecological impacts (Feldpausch et  al., 2016; Lyra et  al., 2017; Cuesta et al., 2019) (Case Study, 12.7.1). Biodiversity hotspots in the region are well assessed in the literature compared to other regions of the world, especially the Atlantic Forest, Mesoamerica and Cerrado (Section CCP1.2.2; Manes et  al.,

2021). Up to 85% of evaluated natural systems (species, habitats and communities) in the literature for biodiversity hotspots since AR5 were projected to be negatively

12
1726Chapter 12 Central and South America
impacted by climate change (high confidence), with 26% of projections 
predicting species extinctions (Section CCP1.2.2; Manes et al., 2021). 
IKLK play an important role in adaptation and are vital components of 
many socioecological systems, while also being threatened by climate 
change (high confidence) (Box 7.1) (Valdivia et al., 2010; Tengö et al., 
2014; Mistry et al., 2016; Harvey et al., 2017; Diamond and Ansharyani, 
2018; Camico et al., 2021).
12.5.1.1 Challenges and Opportunities
The conversion of natural ecosystems to agriculture, pasture and other 
land uses in CSA has been identified as a major challenge to climate-
change adaptation in the region (high confidence) (Scarano et  al., 
2018; IPCC, 2019a). In the last three decades, SA has been a significant 
contributor to the growth of agricultural production worldwide (OECD/
Food and Agriculture Organization of the United Nations, 2015), driven

partly by increased international demand for commodities, especially 
soybeans and meat (IPCC, 2019a). Between 2001 and 2015 about 65% 
of all forest disturbance in the region was associated with commodity-
driven deforestation (Curtis et al., 2018). High rates of native vegetation 
conversion in Argentina, Bolivia, Brazil, Colombia, Ecuador, Paraguay 
and Peru threaten important ecosystems (Amazon, Cerrado, Chacos and 
Llanos savannahs, Atlantic rainforest, Caatinga and Yungas) (Graesser 
et al., 2015; FAO, 2016c). Almost two-thirds of soy consumed in EU+ 
comes from Brazil, Argentina and Paraguay (IDH, 2020), increasing 
conversion risk in the Amazon, Cerrado and Gran Chaco. Despite growing commodity production traceability, in 2018 only 19% of the 
soybean meal consumed in EU+ was certified deforestation-free and 
38% compliant with the FEFAC Soy Sourcing Guidelines (IDH, 2020), 
which poses a serious challenge at the international level (Negra et al.,

2014; Curtis et al., 2018; Lambin et al., 2018; IDH, 2020).
Investing in actions aimed at protection, restoration and the 
sustainable use of biodiversity and ecosystems represents a good 
approach to maintaining critical ecosystem services and constitutes 
part of a common strategy for adaptation, mitigation and disaster 
risk reduction in the region (high confidence) (Kabisch et  al., 2016; 
Scarano et al., 2018). These strategies also satisfy international forest 
and water conservation agendas in terms of optimising resources and 
solutions (Strassburg et al., 2019). Global conservation and sustainable 
development commitments, such as the Aichi Targets (Convention on 
Biological Diversity [CBD]), Sustainable Development Goals (UN), 
the NDCs under the Paris Agreement and the New York Declaration 
on Forests, strongly rely on nature-based solutions (NbS) to achieve 
their objectives (Brancalion et al., 2019) (Figure 12.12). The COVID-19

outbreak also brought attention to the need to preserve tropical 
forests as a means of preventing spillover of viruses from wildlife 
to humans, with concerns over that risk in the Amazon (Allen et al., 
2017b; Dobson et al., 2020; IPBES, 2020; Ferreira et al., 2021). These 
represent an important opportunity for ecosystem-based adaptation 
(EbA) to be at the core of NbS for climate change, access finance and 
promote climate resilient development pathways in CSA.key risks by subregion
in Central and South America
Central America (CA) • 
Northwestern South America  
(NWS) • Northern South 
America (NSA) • South America  
Monsoon (SAM) • Northeastern 
South America (NES) • 
Southwestern South America  
(SWS) • Southeastern South 
America (SES) • Southern 
South America (SSA)<0.9°C1.5°C2.0°C2.5°C>3.0°CMean temperature change
Model projection CMIP6, period 2081–2100,
scenario SSP2 4.5. Baseline 1986–2005
(1) Risk of food insecurity due to frequent/extreme droughts

• Central and South America ( Medium confidence )
(2) Risk to life and infrastructure due to floods and landslides
 • CA, NWS, NSA, SAM, SES, SWS ( Medium confidence )
(3) Risk of water insecurity
 • CA, NWS, SAM, NES, SES, SWS ( High confidence )
(4) Risk of severe health effects due to increasing epidemics (in particular vector-borne diseases)
 • CA, NWS, NSA, SAM, NES, SES, SWS ( High confidence )
(5) Systemic risks of surpassing infrastructure and public service systems
 • Central and South America ( Medium confidence )
(6) Risk of large-scale changes and  biome shifts in the Amazon
 • NSA, SAM, NES ( Medium confidence )
(7) Risk to coral reef ecosystems due to coral bleaching
 • CA, NSA, NES  (High confidence )
(8) Risk to coastal socio-ecological systems due to sea level rise, storm surges and coastal erosion
 • CA, NWS, NSA, NES, SES, SWS, SSA ( Medium confidence )
Key risksCA
NWSNSA
SAM
SESNES
SWS
SSA

Figure 12.11 |  Synthesis of key risks for the CSA region. The base map indicates the mean temperature change between the SSP2 4.5 scenario using CMIP6 model 
projections for 2081–2100 and a baseline period of 1986–2005 (WGI AR6 Atlas, Gutiérrez et al., 2021).

12
1727Central and South America  Chapter 12
The Declaration on Protected Areas and Climate Change, presented by 
18 CSA countries during the United Nations Framework Convention 
on Climate Change (UNFCCC) Conference of the Parties 21 (COP21), 
highlights the fundamental role of protected areas in providing the 
so-called GI needed to implement climate-change mitigation and 
adaptation and safeguard the provision of essential ecosystem services 
and the livelihoods of Indigenous Peoples and local communities 
(Gross et  al., 2016). Protected area in CSA are underfunded (very 
high confidence). Latin American (including Mexico) governments 
allocate just about 1% of their national environmental budgets on 
protected areas (about USD 1.18 ha−1 on average). This figure only 
covers 54% of their basic needs, resulting in insufficient management. 
The financing gap to achieve optimal needs for protected areas in 
CSA is approximately USD 700  million yr−1 (Bovarnick et  al., 2010).

This seriously compromises the management and delivery capacity 
of protected areas for climate-change adaptation and preparedness 
for ongoing ecological transformation (van Kerkhoff et  al., 2019). 
Furthermore, to become a relevant mechanism for resilience, 
protected areas need to be managed for this purpose (Mansourian 
et  al., 2009). About 40% of protected areas in Latin America and 
the Caribbean (including Mexico) have undertaken management 
effectiveness evaluations (UNEP-WCMC and IUCN, 2020a). This is 
hardly representative of Aichi’s Target 11, although far better than 
the 11% global average. Collaborations with Indigenous Peoples and 
local communities are also an important issue to consolidate protected 
areas (Gross et al., 2016). In addition to protected areas as solutions 
for climate-change adaptation and mitigation, there is also a need to 
protect or restore ecosystems outside the protected areas, as illustrated

by the Mesoamerican Biological Corridor (Imbach et al., 2013).
Despite some local and specific assessments (e.g., Warner (2016)), 
there is a significant gap when it comes to identifying barriers to 
adaptation or maladaptation in the region (Dow et  al., 2013). In 
their NCs, NDCs and/or NAPs, most countries identified inadequate 
financing and access to technology as barriers to adaptation relevant 
to terrestrial and freshwater socioecosystems (high confidence). 
Insufficient institutional coordination is also frequently mentioned 
(Rangecroft et al., 2013; Cameron et al., 2015). These limitations could 
be partially addressed through multi-lateral cooperation, incorporation 
of synergies from local to national scales, local empowerment and 
poverty alleviation (Rangecroft et al., 2013; Harvey et al., 2017; Murcia 
et al., 2017; Calispa, 2018; Chain-Guadarrama et al., 2018).
12.5.1.2 Governance and Financing
All CSA countries have formulated policies that include measures

relevant for socioecosystem adaptation in their NCs, NDCs and NAPs, 
with an emphasis on protecting and restoring water and forests (high 
confidence). Existing proposed measures, instruments and programmes, 
however, do not yet reflect the vision needed to integrate the ecosystem 
and human dimensions of vulnerability. Administration coordination and 
the progress in adaptive ecosystem management are still in their early 
stages, due in part to the lack of stable financial resources and scientific 
knowledge and IKLK about adapting ecosystems to climate change 
(Bustamante et al., 2020). Brazil was an exception, showing dramatic 
policy-driven reduction in deforestation in the Amazon between 
2004–2012, with a concomitant 70% increase in soy production, the most profitable Amazon crop (Hansen et  al., 2013; Nepstad et  al., 
2014). Policies included territorial planning (protected areas, Indigenous 
territories and land tenure), satellite monitoring and market and credit

restrictions on high-deforesting municipalities, plus some incentives to 
small farmers (Boucher et al., 2013; Hansen et al., 2013; Nepstad et al., 
2014; Castelo, 2015; Cunha et al., 2016a). It is important to highlight the 
important role of Indigenous territories, in addition to protected areas, in 
forest conservation in the Amazon (high evidence, medium agreement) 
(Schwartzman et  al., 2013; Barber et  al., 2014; Nepstad et  al., 2014; 
Walker et  al., 2014b). These policies were partially funded by results-
based compensation through the Amazon Fund. Since 2012, however, 
policies and institutions have weakened, and Amazon deforestation 
rates have started to rise (Carvalho et al., 2019), becoming more acute 
in recent years (Silva Junior et al., 2021). Conservation incentives, a new 
complementary and allegedly cost-effective approach, is increasingly 
being implemented in the region (Magrin et al., 2014). They include PES,

REDD+, environmental certification and conservation easements, but 
remain controversial, and more research is needed on their effectiveness, 
possible negative side effects, participatory management systems 
and collective decision-making processes (Larson and Petkova, 2011; 
Locatelli et al., 2011; Pinho et al., 2014; Strassburg et al., 2014; Mistry 
et al., 2016; Gebara and Agrawal, 2017; Scarano et al., 2018; Ruggiero 
et al., 2019; To and Dressler, 2019; Vallet et al., 2019).
12.5.1.3 Adaptation Options to Avert and Reduce Key Risks to 
Terrestrial and Freshwater Ecosystems
Research, monitoring systems and other initiatives for knowledge 
management are promoted in the region on terrestrial and freshwater 
socioecosystem adaptation (high confidence) (NCs, NDCs and NAPs, 
https://unfccc.int). In Chile, for example, the Eco-social Observatory 
of Climate Change Effects for High Altitude Wetlands of Tarapacá has

been collecting information on physical, biological and social variables 
since 2013 (Uribe Rivera et al., 2017). Other examples in the Andes 
are the GLORIA-Andes network (Cuesta et  al., 2017a), the Andean 
Forest Network (Malizia et al., 2020) and the Initiative of Hydrological 
Monitoring in the Andes (IMHEA), with measures to optimise 
watershed management and protection and reduce the risk of water 
insecurity (Correa et al., 2020).
Poverty is a driver of climate-change risk, while the sustainable use of 
ecosystems fosters adaptation (Kasecker et al., 2018) (high confidence). 
Most of the 398 ecosystem-based adaptation hotspots identified in 
Brazil on this premise are located in some of the ecosystems that are 
most vulnerable to climate change (Kasecker et al., 2018). Although 
conservation and restoration are reported as being effective at 
reducing risk (medium confidence: medium evidence, high agreement)

(Anderson et  al., 2010; Borsdorf et  al., 2013; Keenan, 2015; Pires 
et  al., 2017; Ramalho et  al., 2021), their effectiveness depends on 
the integration of conservation actions with enhancements of local 
socioeconomic conditions (medium confidence: medium evidence, high 
agreement) (Scarano and Ceotto, 2015; Pires et  al., 2017; Kasecker 
et al., 2018; de Siqueira et al., 2021; Vale et al., 2021).
Since AR5, there has been an increase in the number of adaptation 
measures through natural resource and ecosystem service management. 
The main approaches are EbA and community-based adaptation (CbA)

12
1728Chapter 12 Central and South America
(high confidence) (NCs, NDCs and NAPs, https://unfccc.int). IKLK can 
be very detailed and usually relates to people’s priorities as identified 
by collective decision-making (Box 7.1) (Hurlbert et al., 2019, SRCCL 
Section 7.6.4; SRCCL Cross-Chapter Box 13 ILK; de Coninck et al., 2018, 
SR1.5 Section 4.3.5.5). In Manaus, central Amazon, fishermen perceive 
reductions in fish size, diversity and capture levels caused by droughts, 
while recognising that floods hinder access to fishing grounds 
(Keenan, 2015; Camacho Guerreiro et  al., 2016). In the Amazon 
floodplains, small-scale fisher and farmer communities incorporate 
their knowledge on natural hydrologic and ecological processes into 
management systems that reduce climate-change risk and impacts 
(Oviedo et  al., 2016). Smallholder grain farmers in Guatemala and 
Honduras implement EbA practices based on local knowledge (e.g.,

live fences, home gardens, shade trees in coffee plantations, dispersed 
trees in corn fields and other food insecurity risk reduction practices) 
(Harvey et al., 2017; Chain-Guadarrama et al., 2018). There is, therefore, 
great potential for terrestrial and freshwater ecosystem adaptation to 
climate change in CSA, provided the right incentives and sociocultural 
protective measures are in place (high confidence) (Section 12.5.10.4; 
Table SM12.7).
Disarticulation between policy and implementation is a common 
problem. Ecuadorian climate public policy points towards a CbA 
approach, but it is often downsized when implemented (Calispa, 
2018). Important adaptation actions have been undertaken in 
Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, 
Paraguay, Peru and Uruguay, both in policymaking and institutional 
arrangements, but they tend to be poorly coordinated with policies on 
development, land planning and other sectoral policies (Ryan, 2012).

Some type of community participation mechanisms is present in most 
country strategies, but their levels of implementation vary considerably 
(medium confidence: medium evidence, high agreement) (Ryan, 2012; 
Pires et al., 2017; Calispa, 2018).
There is an ecosystem bias in adaptation priorities for research and 
implementation, hindering the development of comprehensive 
adaptation programmes. Most scientific research on adaptation in Peru 
focuses on the highlands and coastal regions, while mitigation research 
focuses on forests (Chazarin et al., 2014). Combined adaptation and 
mitigation strategies can produce positive results, but they are often 
disconnected (Locatelli et al., 2015). Most reviewed cases in agriculture 
and forestry in Latin America (84% of 274  cases) reported positive 
synergies between adaptation and mitigation. Nevertheless, research 
on Latin American forests tend to focus on mitigation, while studies on

agriculture are usually oriented towards adaptation (high confidence) 
(Locatelli et al., 2015, 2017).
Rural communities in the Cusco region, Peru, ground their ability to 
adapt to climate change on four cultural values, known in Quechua 
as ayni (reciprocity), ayllu (collectiveness), yanantin (equilibrium) 
and chanincha (solidarity), but policies oriented towards so-called 
modernisation undermine these traditional mechanisms. Adaptation 
strategies could benefit from integrating these and other insights 
from traditional cultures, fostering risk reduction and transformational 
adaptation towards intrinsically sustainable systems (medium 
confidence: medium evidence, high agreement) (Walshe and Argumedo, 
2016).Protected areas have become an important component as enablers 
of national climate-change adaptation strategies. They increase 
ecosystems’ adaptive potential, reducing climate risk and delivering 
numerous ecosystem services and sustainable development benefits

while playing an important role in climate-change mitigation (high 
confidence) (Mackey et  al., 2008; Dudley et  al., 2010; Gross et  al., 
2016; Bebber and Butt, 2017; Dinerstein et  al., 2019; IPCC, 2019a). 
CSA already has a greater percentage of land (24.1%) under protected 
status than the world average (14.7%) (UNEP-WCMC and IUCN, 
2020b). Some countries, including Belize, Bolivia, Brazil, Guatemala, 
Nicaragua and Venezuela, have already met or surpassed the 30% CDB 
and IUCN goal (Dinerstein et al., 2019), and others, like Costa Rica and 
Honduras, are very close to doing so. In some cases, the establishment 
of protected areas not accompanied by collective decision-making 
processes has displaced local people or denied them access to natural 
resources, increasing their vulnerability to climate change (Brockington 
and Wilkie, 2015).
In addition to better managing and expanding protected area 
networks, other effective area-based conservation measures (OECMs),

recently defined by the Parties to the Convention on Biological 
Diversity (Dudley et al., 2018), could also enhance ecosystem resilience 
(low confidence). Private protected areas in the mountain regions of 
the Americas (e.g., Andes) play an important role in closing the gaps 
in fragmented biomes and expanding protection in underrepresented 
areas (Hora et  al., 2018). In Brazil, there is also huge potential for 
conservation and sustainable management in private areas, as roughly 
53% of the country’s native vegetation is within private land (Lapola 
et al., 2014; Soares-Filho et al., 2014).
Large-scale restoration is also seen as pivotal to limiting both climate 
change (IPCC, 2019a) and species extinction (IPBES, 2018a) (very high 
confidence). A new multi-criteria approach to optimising multiple 
restoration outcomes (for biodiversity, climate-change mitigation 
and cost), for example, indicate that SA has the greatest extension of

converted lands, evenly distributed in the top 50% of global priorities 
(Strassburg et al., 2020).
12.5.2 Ocean and Coastal Ecosystems and Their Services
Ocean and coastal ecosystems provide suitable habitats for a high 
number of species that support important local fisheries, the tourism 
sector and the regional economy (high confidence) (Section  3.5; 
Table  3.9; González and Holtmann-Ahumada, 2017; Venerus and 
Cedrola, 2017; CEPAL, 2018; Carvache-Franco et  al., 2019; SROCC 
Section 5.4, Bindoff et al., 2019). There is high confidence that CSA ocean 
and coastal ecosystems are already being impacted by climate change 
(Figure 12.9, 12.10; Table SM12.3; Section 3.4; Section 5.4 in SROCC, 
Bindoff et al., 2019) and are highly sensitive to non-climatic stressors 
(Figure  12.8; Table  SM12.3; Section  3.4). Projections for CSA ocean 
and coastal ecosystems warn about significant and negative impacts 
(high confidence), which include major loss of ecosystem structure and

functionality, changes in the distributional range of several species 
and ecosystems, major mortality rates and increasing numbers of coral 
bleaching events (Figure 12.9; Figure 12.10; Table SM12.3; Section 3.4; 
SROCC Sections 5.3, 5.4, Bindoff et al., 2019).

12
1729Central and South America  Chapter 12
CSA sub-regions are highly dependent on ocean and coastal ecosystems 
and, thus, vulnerable to climate change (FAO, 2018). Fisheries and 
aquaculture contribute significantly to food security and livelihoods 
by creating employment (more than two million people), income 
and economic growth for the region (Section 3.5; FAO, 2018). More 
than 45% of the total fisheries in CSA are based on marine products 
(CEPALSTAT, 2019). Peru, Chile, Argentina and Ecuador are among the 
15  countries with the largest marine capture production worldwide 
(Gutiérrez et al., 2016a; FAO, 2018; Vannuccini et al., 2018), while more 
than 90% of the hydrological resources produced by aquaculture in 
CSA have a marine origin (CEPALSTAT, 2019). There is high confidence 
about important current and future impacts of climate-change 
hazards in marine resources used by fisheries; however, there is low

evidence regarding impacts on regional economies (Figure 12.9, 12.10; 
Table SM12.3).
12.5.2.1 Adaptation Measures and Strategies Applied to Oceans 
and Coasts of Central and South America
Similar to those strategies pointed out by WGII AR5 Chapter 27 
(Magrin et al., 2014) and Chapter 3 (Section 3.5; Section 3.6.2; Box SLR 
in Chapter 3), adaptation strategies in ocean and coastal ecosystems 
in CSA remain focused on ecosystem protection and restoration and 
the sustainable use of marine resources (high confidence). There is 
low evidence on how coastal urban areas and tourist settlements of 
CSA countries are adapting to SLR and extreme events (Calil et  al., 
2017; Villamizar et al., 2017). Some of these strategies include planned 
relocation (Dannenberg et al., 2019) and the use of grey infrastructures 
like seawalls and bulkheads (Silva et al., 2014; Isla et al., 2018).
There is medium confidence that EbA is the main strategy used in

CSA coral reef ecosystems. The set of strategies applied include the 
protection, restoration (e.g., coral gardening, larval propagation) and 
conservation of coral reef areas through the application of spatial 
ocean zoning schemes such as marine protected areas (MPAs), marine 
managed areas (MMAs), national parks, wildlife refuges, special zones 
of marine protection, special management zones, responsible fishing 
areas and the establishment of management plans with some level 
of participatory processes. These strategies are complemented by 
actions that promote the development of research and education 
programmes, recreational and cultural activities, the use of community-
based approaches and the creation of national specific laws (Graham, 
2017) and the adherence to international treaties (e.g., Convention 
on International Trade in Endangered Species of Wild Fauna and Flora 
[CITIES], AGENDA 21, United Nations Convention on the Law of the

Sea (UNCLOS), Ramsar Convention on Wetlands of International 
Importance Especially as Waterfowl Habitat) (Cruz-Garcia and Peters, 
2015; Gopal et al., 2015; Graham, 2017; Bayraktarov et al., 2020).
Adaptation measures in mangrove ecosystems are mainly focused on 
the application of EbA strategies (high confidence). These measures 
include the application of restoration programmes, the creation 
of management plans, which also have significant co-benefits 
with mitigation (Section  3.6.2.1), and the establishment of coastal 
protected areas, followed by the development of research activities 
and the creation of specific mangrove policies through new laws and 
resolutions (e.g., Colombia) (Cvitanovic et  al., 2014; Krause, 2014; Blanco-Libreros and Estrada-Urrea, 2015; Carter et al., 2015; Estrada 
et  al., 2015; Ferreira and Lacerda, 2016; Oliveira-Filho et  al., 2016; 
Rodríguez-Rodríguez et al., 2016; Alvarado et al., 2017; Álvarez-León

and Álvarez Puerto, 2017; Baptiste et  al., 2017; Borges et  al., 2017; 
Jaramillo et  al., 2018; Salazar et  al., 2018; Armenteras et  al., 2019; 
Blanco-Libreros and Álvarez-León, 2019; Maretti et al., 2019; Ellison 
The use of territorial planning tools, the promotion of sustainable 
resource exploitation, the adherence to certification schemes and the 
implementation of management instruments, such as ecosystem-based 
management (EbM), followed by the use of an integrated coastal zone 
management, coastal marine spatial planning and capacity building, 
ecological risk assessments, have been the main strategies used to 
ensure the sustainability of marine resources in fisheries across EEZs 
of CSA (high confidence) (Hellebrandt et al., 2014; Gelcich et al., 2015; 
Singh-Renton and McIvor, 2015; Gutiérrez et  al., 2016a; Karlsson 
and Bryceson, 2016; Oyanedel et al., 2016; Debels et al., 2017; Isaac 
and Ferrari, 2017; Mariano Gutiérrez et  al., 2017; Barragán and

Lazo, 2018; Bertrand et al., 2018; Lluch-Cota et al., 2018; Guerrero-
Gatica et al., 2020). Other strategies include the application of local 
regulations (e.g., closed seasons) (Fontoura et al., 2016) and the use of 
participative programmes (Hellebrandt et al., 2014; Arroyo Mina et al., 
2016; Matera, 2016).
12.5.2.2 Adaptation Success in Ocean and Coastal Ecosystems 
of Central and South America
There is low evidence about how the strategies and actions taken and 
implemented in ocean and coastal systems of CSA have contributed 
to advance in the protection and conservation of ocean and coastal 
ecosystems. However, some important advances are visible in 
Colombian Pacific areas with coral reefs (new conservation plans, 
research monitoring and conservation practices) (low confidence) 
(Cruz-Garcia and Peters, 2015; Alvarado et al., 2017; Bayraktarov et al., 
2020). In Panama, actions taken have allowed the protection of a high

number of marine areas with coral reefs, as well as the incorporation of 
management approaches that include several sectors such as fisheries, 
tourism, coral protection and coral conservation (low confidence) 
(Alvarado et al., 2017). In the case of Costa Rica, 80% of coral habitats 
are located inside of MPAs, multiple research coral-related activities 
have been performed, and several training activities have favoured the 
engagement of the local community in their protection against climate 
and non-climate hazards (low confidence) (Alvarado et al., 2017).
There is low evidence of how the incorporation of mangroves as 
Ramsar sites, the reforms of legislations (e.g., fines and stronger 
regulations), and the creation of reserves and private protection 
initiatives (e.g., Belize Association of Private Protected Areas BAPPA), 
and capacity-building projects or new educational programmes have 
promoted the protection of mangroves in CSA countries such as

Honduras, Guatemala and Belize (Cvitanovic et al., 2014; Carter et al., 
2015; Ellison et al., 2020). In Brazil, between 75–84% of mangroves 
are under some level of protection which has improved the forest 
structures, and multiple research programmes (e.g., Mangrove 
Dynamics and Management, MADAM, and ‘GEF-Mangle’) have 
been developed (medium confidence) (Krause, 2014; Medeiros et al.,

12
1730Chapter 12 Central and South America
2014; Estrada et al., 2015; Ferreira and Lacerda, 2016; Oliveira-Filho 
et al., 2016; Borges et al., 2017; Maretti et al., 2019; Strassburg et al., 
2019). In Colombia, research projects (e.g., Mangroves of Colombia 
Projects, MCP), the installation of a geographic information system 
for mangroves (e.g., SIGMA Sistema de Información para la Gestión 
de los Manglares en Colombia), surveillance monitoring plans (e.g., 
EGRETTA Herramientas para el Control y Vigilancia de los Manglares), 
and the establishment of protected areas have contributed to decrease 
loss of the mangrove forest (high confidence) (Blanco-Libreros and 
Estrada-Urrea, 2015; Rodríguez-Rodríguez et  al., 2016; Álvarez-León 
and Álvarez Puerto, 2017; Baptiste et al., 2017; Jaramillo et al., 2018; 
Salazar et  al., 2018; Armenteras et  al., 2019; Blanco-Libreros and 
Álvarez-León, 2019).
There is low evidence whether the establishment of MPAs and the

creation of legal instruments have allowed the development of new 
research activities have increased the environmental awareness, 
decreased the illegal extraction, and improved the local coordination 
which have promoted the sustainable use of marine resources, 
and improved the community-government cooperation in marine 
ecosystems (Alvarado et  al., 2017). The experience in countries 
like Chile demonstrates the importance of implementing robust 
management plans that guarantee the protection objectives and the 
sustainability through the implementation of EbA measures such as 
MPAs (Petit et al., 2018).
There is low confidence about how measures adopted are ensuring 
the sustainability of marine resources used by fisheries. In Peru, 
industrial fisheries follow an adaptive management approach (i.e., 
stock assessments, catch limits), while in Chile, small-scale fisheries 
of benthic-demersal resources is managed through the granting of

exclusive territorial use rights (called TURFS) with established quotas 
defined by the central authority (Bertrand et  al., 2018). In addition, 
MPAs in Chile play a key role in climate-change adaptation for fisheries 
(medium confidence) (Gelcich et al., 2015; Petit et al., 2018), and an 
increasing amount of funds have been invested in initiatives to reduce 
the vulnerability of fishery and aquaculture sectors to climate change 
(OECD, 2017). Since 2016, Argentina has been developing a strategy to 
implement EbM on fisheries with support from the Global Environment 
Facility (GEF) programme. Also, Argentina and Chile are promoting 
the local consumption of seafood and the certification of its fishery 
products (OECD, 2017), while Brazil and Chile have advanced in their 
response to climate change through the development of new research 
studies and methodologies incorporating research institutions (Nagy 
et  al., 2015). Uruguay is incorporating stakeholders in its climate-

change adaptation strategies (low confidence) (Nagy et  al., 2015), 
while Colombia is supporting the capacity building of fishers, promoting 
livelihood diversification to increase the resilience of the sector (medium 
confidence: medium evidence, high agreement) (Hellebrandt et al., 2014; 
Arroyo Mina et  al., 2016; Matera, 2016). Chile and Peru have made 
certain advances in the development of guidelines for the management 
of the coast line and the implementation of the EbM, which has 
favoured the collaboration of diverse and multiple stakeholders (fishers, 
academics, municipal institutions), the development of outreach and 
educational activities, the creation of networks and the interests of other 
fishery communities to implement EbM (medium confidence: medium 
evidence, high agreement) (Hellebrandt et al., 2014; Gelcich et al., 2015; Gutiérrez et al., 2016a; Oyanedel et al., 2016; Guerrero-Gatica et al.,

2020). In countries like Peru and Chile, there is an increasing presence of 
intergovernmental and international cooperation agencies, in addition 
to new funding (e.g., GEF) and projects (Inter-American Development, 
SPINCAM) related to change adaptation for the fishery sector (medium 
confidence: medium evidence, high agreement) (Galarza and Kámiche, 
2015; Barragán and Lazo, 2018).Table 12.7 |  National plans with adaptation goals for ocean and coasts in CSA.
CSA 
countryAdaptation initiative Year
ArgentinaPlan Nacional de Adaptación y Mitigación al Cambio 
Climático12019
BrazilNational Climate Change Adaptation Plan (Volume 1); 
General Strategies22016
National Climate Change Adaptation Plan (Volume 2); 
Sectoral and thematic strategies32016
Chile Plan Nacional de Adaptación al Cambio Climático42014
Plan Sectorial de Adaptación al Cambio Climático en 
Biodiversidad52014
Plan Sectorial de Adaptación al Cambio Climático en Pesca 
y Acuicultura62015

Plan de Adaptación y Mitigación de los Servicios de 
Infraestructura al Cambio Climático72017
Plan de Adaptación al Cambio Climático Sector Salud82017
Colombia Plan Nacional de Adaptación al Cambio Climático92016
Costa Rica Política Nacional de Adaptación al Cambio Climático102018
Ecuador Plan Nacional de Cambio Climático112015
El Salvador Plan Nacional de Cambio Climático122015
Guatemala Plan de Acción Nacional de Cambio Climático132018
Guyana Política de Adaptación y Plan de Implementación142001
Honduras Plan Nacional de Adaptación al Cambio152018
NicaraguaPlan de Adaptación a la Variabilidad y el Cambio Climático 
en el Sector Agropecuario, Forestal y Pesca162013
Peru Plan Nacional de Adaptación al Cambio Climático del Peru172021
Suriname Suriname National Adaptation Plan182019
Uruguay Plan Nacional de Respuesta al Cambio Climático192010
Belize Not Available 2019
Panamá Not Available
Venezuela Not Available

References: 1(Ministerio de Ambiente y Desarrollo Sostenible de la República de 
Argentina, 2019) 2(Ministry of Environment of Brazil, 2016a) 3(Ministry of Environment 
of Brazil, 2016b) 4(Ministerio de Medio Ambiente de Chile, 2014b) 5(Ministerio de Medio 
Ambiente de Chile, 2014a) 6(Ministerio de Economía Fomento y Turismo de Chile, 2015) 
7(Ministerio de Medio Ambiente de Chile, 2017) 8(Ministerio de Salud de Chile, 2017) 
9(Ministerio de Ambiente y Desarrollo Sostenible de Colombia, 2016) 10(Ministerio de 
Ambiente y Energía de la República de Costa Rica, 2018) 11(Gobierno Nacional de la 
República del Ecuador, 2015) 12(Ministerio de Medio Ambiente y Recursos Naturales 
de El Salvador, 2015) 13(Consejo Nacional de Cambio Climático y la Secretaría de 
Planificación y Programación de la Presidencia de Guatemala, 2018) 14(National Ozone 
Action Unit of Guyana, 2016) 15(Secretaría de Recursos Naturales y Ambiente del

Gobierno de la República de Honduras, 2018) 16(Ministerio Agropecuario y Forestal de 
Nicaragua, 2013) 17(Ministerio del Ambiente Gobierno del Perú, 2021) 18(Government of 
Suriname, 2019) 19(Ministerio de Vivienda Ordenamiento Territorial y Medio Ambiente 
de la República de Uruguay, 2010)

12
1731Central and South America  Chapter 12
Adaptation Goals identified for ocean and coastal systems
in National Adaptation Plans of Central and South American countries
1>30Risk management
Infrastructure and tech
Traditional practices
Water and residues management
Productive management
Capacity building and awareness Research and monitoring
Number of
adaptation goals
Total 4 6 13 16 17 34 35 84Legislation and policies
53Financial management
14112
1221
24141
442111
1115134
2214232
15427144
52278631
1354938810
33236424930 Chile
Brazil
Suriname
GuyanaGuatemala
Honduras
Argentina
Uruguay
Costa RicaPeru
El Salvado r1 1 1
61
11 Colombia
Nicaragua
EcuadorBiodiversity and conservation
51482332167
4
2
Figure 12.12 |  Type and amount of adaptation goals identified in NAPs for ocean and coastal systems of CSA countries.
12.5.2.3 National Climate Change Commitments for Ocean and 
Coasts
Beyond the protection, conservation and climate-change adaptation

strategies implemented on CSA ocean and coastal areas and their 
ecosystems, a high number of adaptation goals to address climate-
change impacts on ocean and coastal ecosystems and their services 
are incorporated into most of the national climate-change adaptation 
commitments of CSA countries (Table 12.7).
Current goals in national and sectoral adaptation plans attempt 
to promote research and monitoring (e.g., new research actions, 
modelling, knowledge management), the development of new 
legislative tools and policies (e.g., inter-institutional and territorial 
coordination, improvement of public policies), the conservation of 
ocean and coastal ecosystems and their biodiversity (e.g., creation 
of new MPAs, protection tools), the management of climate risks 
(e.g., warning systems), the management of productive activities 
(e.g., diversification of resources), the promotion of the construction 
of new infrastructure and technology (e.g., grey-green infrastructure

[GGI]), the creation of new financial tools (e.g., types of insurance), 
improved the capacity building (e.g., education, awareness), water 
and residue management (e.g., sewage and freshwater availability), 
social inclusion (e.g., strategies to support vulnerable sectors, gender 
inclusion) and the incorporation of traditional practices (e.g., restoring 
traditional practices including Indigenous knowledge [IK]). However, 
the amount and type of adaptation goals differ enormously from 
country to country (Figure 12.12).12.5.2.4 Limits and Barriers to Adaptation in Ocean and Coastal 
Although current NAPs and many other actions and strategies focus 
on improving the conservation and restoration of ocean and coastal 
ecosystems, as well as the suitability of marine resources throughout 
CSA, these measures are still not able to reduce the vulnerability 
and sensitivity of these ecosystems to climate-change hazards (high

confidence) (Figure 12.6; Table SM12.3; Leal Filho, 2018; Nagy et al., 
2019). There is high confidence that sandy beach ecosystems of CSA 
countries have suffered significant losses of dunes as a consequence 
of the construction of infrastructures that have caused interruptions 
in the natural dynamic of beaches, reducing protection against tides, 
waves, extreme events or tsunamis (high confidence) (Amaral et  al., 
2016; Bernardino et al., 2016; González and Holtmann-Ahumada, 2017; 
Obraczka et  al., 2017). Also, adaptation measures to cope with SLR 
and coastal extreme events sometimes fail because they exacerbate 
coastal erosion and damage (medium confidence: medium evidence, 
high agreement) (Spalding et  al., 2014; Lins-de-Barros and Parente-
Ribeiro, 2018). There is medium evidence but high agreement that the 
most serious barriers limiting the success of adaptation strategies in 
ocean and coastal systems in CSA stem from a lack of coordination

(e.g., absence of participatory processes, overlapping among fishing 
and protection activities), lack of knowledge (e.g., poor monitoring, 
poor control and surveillance, no long-term studies) and lack of 
adequate metrics for evaluating adaptation actions informing decision 
makers hinder the continuity and adjustment of measures and lead to 
weak governance (e.g., perverse incentives, resource overexploitation,

12
1732Chapter 12 Central and South America
conflicts), a lack of financial resources and long-term commitments 
(e.g., crisis, lack of budgets, market fluctuations), weak policies, cultural 
constraints, poverty, low flexibility, lack of awareness of climate risks 
and lack of engagement by stakeholders (Leal Filho, 2018; Nagy et al., 
2019; Moreno et al., 2020b; Aburto et al., 2021).
Some important limits and barriers have been detected for productive 
systems such as fisheries and tourism in CSA (medium confidence: 
medium evidence, high agreement). Major Brazilian fisheries do not 
follow an ecosystem approach to management, although some small-
scale fisheries apply a precautionary approach (Singh-Renton and 
McIvor, 2015). The management of Peruvian artisanal (medium and 
small-scale) fisheries is minimal and is governed by a lack of regulations, 
control and management actions (Bertrand et al., 2018). In Argentina,

recreational marine fisheries have been largely unregulated, and there 
exists a lack of monitoring programmes, which has contributed to the 
overexploitation of some key coastal stocks (Venerus and Cedrola, 
2017). Moreover, womenfisher in CSA are excluded of the decision-
making processes (FAO, 2016b; Bruguere and Williams, 2017). Due to 
the lack of monitoring programmes, it is unknown how this tourist 
industry will respond to long-term changes driven by climate change 
(Weatherdon et al., 2016).
12.5.2.5 Challenges and Opportunities
There is low evidence and high agreement that empowering local 
stakeholders (e.g., multi-lateral fisheries agreements) improves public 
awareness and simplifies regulations and increases the flexibility and 
sustainability of marine resources managed in fisheries under future 
scenarios (Weatherdon et al., 2016; Kalikoski et al., 2019). Ecosystem-
based fisheries management (EBFM) has emerged as a suitable tool to

minimise the risk to climate change, avoid ecosystem degradation and 
related services (Gullestad et al., 2017). Further, when EBFM includes 
climate complexity and the relationships among species within the 
ecological systems it contributes to maintain long-term socioeconomic 
benefits (Long et al., 2015). There is high confidence that EbA is more 
successful and feasible than hard coastal defences for the protection, 
management and restoration of ocean and coastal ecosystems and 
their resources (Spalding et  al., 2014; González and Holtmann-
Ahumada, 2017; Scarano, 2017).
There is high confidence that ecological and social resilience is 
improved by the presence of adequate metrics to evaluate adaptation 
measures to allow dynamic changes; and by increasing basic research 
and climate data (Moreno et al., 2020b). Resilience also increases with 
the existence of EWSs, improved local institutions, the construction of

adequate infrastructure, major funding for capacity building and the 
enhanced engagement and empowerment of women (FAO, 2016b; 
Harper et al., 2017; Frangoudes and Gerrard, 2018; Gallardo-Fernández 
and Saunders, 2018; Leal Filho, 2018).
12.5.3 Water
CSA is one of the regions most affected by current and future hydrological 
risks to water security with an increasing number of vulnerable people 
depending on water from mountains (high confidence) (Sections 4.3, 4.4, 4.5; Immerzeel et al., 2020; Viviroli et al., 2020; WWAP , 2020). Adaptation 
to changing water availability is therefore a priority, but most efforts are 
documented only in the grey literature (e.g., governmental documents, 
project reports) with highly variable standards of quality and evidence. 
Most of the documented adaptation initiatives are in an early planning 
or implementation stage and evidence on successful outcomes is quite 
limited (Berrang-Ford et  al., 2021). However, the growing number

of adaptation initiatives across the CSA region has contributed to 
improved understanding of complex interlinkages of climate change, 
human vulnerabilities, local policies and feasible adaptation approaches 
(McDowell et al., 2019).
12.5.3.1 Challenges and Opportunities
In several regions of CSA, water scarcity is a serious challenge to local 
livelihoods and economic activities. Regions that are (seasonally) dry, 
partly with large populations and increasing water demand, exhibit 
particularly significant water stress. These include the Dry Corridor in 
CA, coastal areas of Peru (SWS) and northern Chile (SWS), the Bolivian-
Peruvian Altiplano (NWS, SAM), the Dry Andes of Central Chile (SWS), 
Western Argentina and Chaco in northwestern Paraguay (SES) and Sertão 
in northeastern Brazil (NES) (high confidence) (Kummu et  al., 2016; 
Mekonnen and Hoekstra, 2016; Schoolmeester et al., 2018). In NWS and 
SWS, downstream areas are increasingly affected by decreasing and

unreliable river runoff due to rapid glacier shrinkage (high confidence) 
(Table SM12.6; Carey et al., 2014; Drenkhan et al., 2015; Buytaert et al., 
2017). Many regions in CSA rely heavily on hydroelectric energy, and 
as a result of rising energy demand, hydropower capacity is constantly 
being extended (Schoolmeester et al., 2018). Worldwide, SA features 
the second-fastest growth rate, with about 5.2 GW additional annual 
capacity installed in 2019 (IHA, 2020). This development requires 
additional water storage options, which entail the construction of large 
dams and reservoirs with important social-ecological implications. 
River fragmentation and corresponding loss of habitat connectivity 
due to dam constructions have been described for, for example, the 
NSA, SAM, NES and SES (high confidence) (Grill et al., 2015; Anderson 
et al., 2018a), with important implications for freshwater biota, such 
as fish migration (medium confidence) (Pelicice et al., 2015; Herrera-R

et al., 2020). Furthermore, examples in, for instance, NWS (Carey et al., 
2012; Duarte-Abadía et  al., 2015; Hommes and Boelens, 2018) and 
SWS (Muñoz et al., 2019b) showcase unresolved water-related conflicts 
between local villagers, peasant communities, hydropower operators 
and governmental institutions in a context of distrust and lack of water 
Increasing water scarcity is also shaped by poor water quality, 
which has barely been assessed in CSA. Declining water quality can 
be observed, for example, due to intense agricultural and industrial 
activities in SWS, SES and SSA (medium confidence) (Mekonnen 
et al., 2015; Gomez et al., 2021), mining in Andean headwaters (NWS, 
SWS and Western SAM) and tropical lowlands (eastern SAM and 
NSA) (medium confidence) (Bebbington et al., 2015 risk and climate 
resilience; Vuille et  al., 2018), urban domestic use (Desbureaux and 
Rodella, 2019), decreasing meltwater contribution (Milner et al., 2017)

and acid rock drainages from recently exposed glacial sediments 
(Santofimia et al., 2017; Vuille et al., 2018). The level of water pollution 
is often exacerbated by missing water treatment infrastructure and

12
1733Central and South America  Chapter 12
low governance levels (medium confidence) (Mekonnen et al., 2015), 
with considerable negative implications for human health (Lizarralde 
Oliver and Ribeiro, 2016).
Water scarcity risks are projected to affect a growing number of people 
in the near and mid-term future in view of growing water demand in 
most regions (medium confidence: medium evidence, high agreement) 
(Veldkamp et al., 2017; Schoolmeester et al., 2018; Viviroli et al., 2020), 
expected precipitation reductions in western and northern SAM and 
SWS (medium confidence: medium evidence, medium agreement) 
(Neukom et al., 2015; Schoolmeester et al., 2018), substantial vanishing 
of glacier extent in NWS, SAM and SWS (Table SM12.6; Rabatel et al., 
2018; Vuille et al., 2018; Cuesta et al., 2019; Drenkhan et al., 2019) 
and increasing evaporation rates in CA (medium confidence) (CEPAL, 
2017). Furthermore, flood risk is a serious concern (Arnell et al., 2016)

and expected to increase, especially in NWS, SAM, SES and SWS in the 
mid- and long-term future (high confidence) (Arnell and Gosling, 2016; 
Alfieri et al., 2017).
Risks of water scarcity and flood threaten people unevenly across 
the region. In CSA, about 26% (130 million people) of the population 
have no access to safe drinking water, and strong disparities prevail 
regarding its spatial distribution; for example, in Chile, 99% of the 
population have access, compared to 50% in Peru, 73% in Colombia, 
52% in Nicaragua or 56% in Guatemala (high confidence) (UNICEF and 
WHO, 2019). Inequalities can be further exacerbated by unregulated 
or privately owned water rights and allocation systems (e.g., in Chile) 
(Muñoz et  al., 2020a). The most vulnerable people belong to low-
income groups in rural areas and informal settlements of large urban 
areas (high confidence) (WWAP , 2020).
Considerable uncertainties remain concerning future hydrological risks

that strongly depend on the respective pathways of human intervention, 
management, adaptation and socioeconomic development. The 
combination of (seasonally) reduced water supply, growing water 
demand, declining water quality, ecosystem deterioration and habitat 
loss and low water governance could lead to increasing competition 
and conflict associated with high economic losses (high confidence) 
(Vergara et  al., 2007; Vuille et  al., 2018; Desbureaux and Rodella, 
2019). This situation threatens human water security in the long 
term and poses an increasing risk to adaptation success in CSA (high 
confidence) (Drenkhan et al., 2015; Huggel et al., 2015b; Urquiza and 
Billi, 2020a).
Important progress has been made on climate change and water 
management policies in combination with more inclusive stakeholder 
processes. For instance, the implementation of NDCs in most countries 
of the region provides an important baseline for improving water

efficiency, quality and governance at a multi-sectoral level and, thus, 
long-term adaptation planning (UNEP , 2015).
12.5.3.2 Main Concepts and Approaches
Adaptation in the water sector includes a broad set of responses to 
improve and transform, for example, water infrastructure, ecosystem 
functions, institutions, capacity building and knowledge production, 
habits and culture and local-national policies (Section 4.6).Most adaptive water management approaches in CSA centre around 
extending the water supply side, including large infrastructure projects. 
However, ‘hard path’ interventions are now strongly contested 
because negative effects exacerbate local water conflicts (Carey et al., 
2012; Boelens et al., 2019; Drenkhan et al., 2019), potentially leading 
to increasing water demand, vulnerabilities and water shortage risks 
(Di Baldassarre et al., 2018), thereby limiting adaptive capacity (high 
confidence) (Ochoa-Tocachi et al., 2019). More integrated approaches

focus on multiple uses of water storage with shared stakeholder vision, 
responsibilities, rights and costs, as well as risks and benefits, and often 
integrating water and risk management (Branche, 2017; Haeberli et al., 
2017; Drenkhan et al., 2019). In this chapter, a feasibility assessment 
was carried out for six major dimensions of multi-use water storage 
for the entire CSA (Table  12.11). While geophysical and economic 
aspects allow for the implementation of water storage projects under 
a multi-use approach, the institutional, social and environmental 
dimensions pose a major barrier (Section  12.5.3). Further demand-
oriented approaches focus on incentives for the reduction of water 
use through changes in people’s habits, efficiency increase and smart 
water management (Gleick, 2002). These are promoted in some 
regions, such as in CA and NWS (e.g., Colombia, Ecuador and Peru), 
to foster a sustainable water culture (Bremer et al., 2016; Paerregaard 

Major emphasis has been placed on NbS, that is, catchment 
interventions that are inspired and supported by nature and leverage 
natural processes and ecosystem services to contribute to the improved 
management of water. NbS potentially enhance water infiltration, 
groundwater recharge and surface storage, contribute to disaster risk 
reduction and can replace or complement grey (i.e., conventionally 
built) infrastructure that is often socioenvironmentally contested 
(WWAP , 2018). Some examples include the reactivation of ancestral 
infiltration enhancement systems in the Peruvian Andes (NWS) 
(Ochoa-Tocachi et al., 2019), the use of erosion control structures in 
the Bolivian Altiplano (SAM) (Hartman et al., 2016) and the potential 
improvement of drinking water quality and flood risk reduction in urban 
areas of CSA (Tellman et al., 2018) (Section 12.5.5.3.2). Additionally, 
NbS in combination with ecosystem and community-based adaptation

potentially generate important co-benefits, including increasing water 
security and the attenuation of social conflicts in Chile (SWS) (Reid 
et  al., 2018), water conservation in coastal Peru (NWS) and flood 
protection in Guyana (NSA) (medium confidence: medium evidence, 
medium agreement) (Spencer et al., 2017). However, the evaluation of 
implementation success of NbS is often hampered by limited evidence 
on actual benefits (WWAP , 2018).
In recent years, the inclusion of IKLK in current adaptation baselines 
has attracted increasing attention, particularly in regions with a high 
share of Indigenous Peoples (NWS, SAN, SWS, NSA) (high confidence) 
(Reyes-García et al., 2016; Schoolmeester et al., 2018; McDowell et al., 
2019). One example is the adapted use of agrobiodiversity when 
dealing with more frequent and intense tidal floods in the Amazon 
delta (NSA) (Vogt et  al., 2016). In another context, IKLK has been

considered for the evaluation of water scarcity and GLOF risks in Peru 
(NWS) (Motschmann et al., 2020b). Additionally, local citizen science-
based initiatives (Buytaert et al., 2014; Tellman et al., 2016; Njue et al., 
2019) can support the production of multiple forms of knowledge

12
1734Chapter 12 Central and South America
with flexible and extensive data collection. Important questions centre 
around how to integrate IKLK and other types of knowledge from the 
early planning stages on, to achieve enhanced or transformational 
adaptation building on co-produced knowledge (Kates et  al., 2012; Klenk et  al., 2017). NbS combined with community engagement 
and integration of diverse knowledge can foster transformational 
adaptation of social-ecological systems (Palomo et al., 2021).
NDC CCL GLL INV
2018
u2020i2018 - 2017
2019 -- 2017NDC CCL GLL INV
2017 i2016 - 2019
2018
u20202018 i2018 2016
2015
u2020i2018 i2005 2015
2016
20202019 2019 2017Inner-tropical
Andes
Outer-tropical
Andes
Southern
Andes
(Dry and Wet Andes)NDC CCL GLL INV
2016 i???? - 2010
6962
10004000metres
above
sea leve l30005000
02000CO
PE
CHECVE

ARBOOverview map of observed glacier changes, associated impacts, adaptation and policy efforts across the Ande s
(a) Impacts
(b) Adaptation
(c) Policies and inventory
-1-0.75-0.5-0.250
naPE VE CO EC BO CH ARGeodetic glacier mass balance 
(m w.e./year)(f) Glacier mass balance
error  ± m w.e./year0
0.25
0.50
0.75
1CH
-1.6-1.2-0.8-0.4nanaPE VE CO EC BO ARGlacier area change
(%/year)(e) Glacier area change
0Glacier lake development
EconomySociocultureRiver runoff
Ecosystem
Management and Planning
Monitoring system
Financing
Awareness and behaviourNature- based solutions
110100100010000100000Glacier area
(km2 [ln])(d) Glacier area
PE VE CO EC BO CH ARVenezuela (VE)
Colombia (CO)
Ecuador (EC)
Peru (PE)
Bolivia (BO)
Chile (CH)
Argentina (AR)
NDC  = Submission year(s) of Nationally Determined Contributions 
CCL = Climate change law
GLL = Glacier law
INV = Last national glacier inventory
Glaciers
Snow
Mountain ecosystemsu = update
i = initialized framework
el

mass balan ceGrey colour = not come into force
na = no data available
Figure 12.13 |  Overview map of observed glacier changes, associated impacts, adaptation and policy efforts across the Andes.
(a) Selected impacts from glacier shrinkage.
(b) Selected adaptation efforts (see upper-right map for the location of each adaptation measure).
(c) Policies and glacier inventory: NDC = submission year(s) of Nationally Determined Contributions (u = update), CCL = climate change law, GLL = glacier law (i = initialised 
framework), INV = last national glacier inventory. The explicit mention of glaciers, snow and mountain ecosystems within each law/inventory is highlighted with the corresponding 
symbols (grey = has not come into force).
(d) Glacier area (km²) according to last national inventory.
(e) Glacier area change (%/year) according to baseline of last national inventory.

(f) Geodetic glacier mass balance in metres water equivalent per year (m w.e./year) and error estimate (±m w.e./year) retrieved from Dussaillant et al. (2019). nd = no data 
available. Further details can be found in the appendix in Table SM12.6.

12
1735Central and South America  Chapter 12
12.5.3.3 Policies, Governance and Financing
National policies on climate change, water protection, regulation 
and management laws are important focal areas of adaptation 
in the water sector (Section  4.7). Notable in the jurisdiction field is 
the Glacier Protection Law in place in Argentina (2010–2019) and 
under construction in Chile (since 2005). This first glacier law in 
the world represents a milestone for high-mountain conservation 
but is also criticised for hindering effective disaster risk adaptation 
measures and excluding local socioeconomic needs (Anacona et al., 
2018). Furthermore, the first Framework Law on Climate Change was 
implemented in Peru (2018) and is under way in Colombia, Chile 
and Venezuela (Figure  12.13; Table  SM12.6). Overarching regional 
institutions (e.g., OAS [2016]) and most countries in CSA promote a 
move towards more integrative and sustainable management of water

resources through new legislation and financing mechanisms. For 
instance, new water laws that include principles of integrated water 
resource management (IWRM) have entered into force, for example, 
in Nicaragua (2007), Peru (2009), Ecuador (2014) and Costa Rica 
(2014), or are under way, such as in Colombia (since 2009). However, 
current realities in all regions show major challenges in implementing 
IWRM mechanisms and policies, related but not limited to political and 
institutional instabilities, governance structures, fragmented service 
provision, lack of economies of scale and scope, corruption and social 
conflicts (high confidence) (WWAP , 2020).
Many water-related conflicts in CSA are rooted in inequitable water 
governance that excludes water users from decisions on water 
allocation (high confidence) (Drenkhan et  al., 2015; Vuille et  al., 
2018). In turn, inclusive water regimes leverage long-term adaptation

planning. These have been addressed in some national strategies, such 
as in Brazil (Ministry of Environment of Brazil, 2016a). At the local 
level, a decentralised and participatory bottom-up water governance 
model was induced by civil society and research institutions to foster 
rainwater harvesting technologies reducing drought risk in semiarid 
Brazil (NES) (Lindoso et al., 2018).
Water fund programmes can generate important co-benefits for 
sustainable development, contributing to improved governance and 
conservation of watershed systems in CSA. Nevertheless, only a few 
experiences have been evaluated as successful due to insufficient 
implementation, low decision-making ability of some stakeholder groups 
and poor evidence-based approaches (medium confidence) (Bremer 
et al., 2016; Leisher et al., 2019). Furthermore, financing mechanisms 
that produce incentives for sustainable water management have been 
promoted, tested or implemented. PES for water provision represents

such an example and such mechanisms have been implemented across 
CSA since the 1990s (Grima et al., 2016).
Only about 50–70% of required financial resources are currently 
allocated per year to meet the national targets in the water, sanitation 
and hygiene (WASH) sector for the Sustainable Development Goal 
(SDG) 6 agenda in several regions of CSA. This share drops down 
to less than 50% in NSA (Venezuela) and SES (Argentina, Uruguay, 
Paraguay), except for Panama in CA, which allocates more than 75% 
of the required financial resources. For the implementation of NbS, 
evidence suggests that the overall expenditure remains well below 1% of total investment in water resource management infrastructure 
(WWAP , 2018). These funding deficits set important limitations on 
future water provision, adaptation to changing water resources and 
the achievement of the SDGs by 2030 (high confidence) (WHO, 2017).
12.5.3.4 Successful Adaptation and Limitations

Although a growing body of adaptation initiatives exists for CSA, 
evidence on their effectiveness is scarce. In many parts of CSA the level 
of success of adaptation measures depends largely on the governance 
of projects and stakeholder-based processes and is closely related to 
their effectiveness, efficiency, social equity and sociopolitical legitimacy 
(high confidence) (Adger et  al., 2005; Rasmussen, 2016b; Moulton 
et al., 2021). Several PES experiences across CSA have been described 
as successful measures for watershed conservation and adaptation 
(high confidence). An example of success is the Quito water fund in 
Ecuador, which aims to improve the city’s water quality by integrating 
public and private stakeholder interests with ecosystem conservation 
and local community development since the 2000s (Bremer et  al., 
2016; Grima et  al., 2016) (Case Study 12.6.1). At the same time, in 
Moyobamba in Peru, the development of a watershed protection

programme was leveraged by a multi-stakeholder platform process 
that enabled deep social learning (Lindsay, 2018). In turn, initiatives 
that do not consider the entire set of social-ecological dimensions and 
dynamics of adaptation or unintentionally increase vulnerabilities of 
human or natural systems are at risk of leading to reduced outcomes 
(McDowell et al., 2021) or maladaptation (Reid et al., 2018; McDowell 
et al., 2019; Eriksen et al., 2021). However, systematic assessments of 
maladaptation in the water sector have barely been provided for CSA.
In CSA, only limited information on the limits of adaptation in relation 
to water is available, for instance on the possible path dependency of 
institutions and associated resistance to change (Barnett et al., 2015). 
Examples of soft adaptation limits (i.e., options to avoid intolerable 
risks currently not available) include lack of trust and stakeholder

flexibility, associated with unequal power relations that lead to reduced 
social learning and poor outcomes for improved water management, 
as reported in, for example, NWS (Lindsay, 2018). An example of hard 
adaptation limits (i.e., intolerable risks cannot be avoided) in the 
region is the loss of livelihoods and cultural values associated with 
glacier shrinkage in NWS (Jurt et al., 2015).
Most barriers to advance adaptation in CSA correspond to soft limits 
associated with missing links of science–society–policy processes, 
institutional fragilities, pronounced hierarchies, unequal power 
relations and top-down water governance regimes (high confidence). 
One example is the abandonment of long-term hydrological monitoring 
sites within tropical Andean ecosystems (paramo) in Venezuela 
(Rodríguez-Morales et  al., 2019) due to the lack of governmental 
support during the political crisis. In that regard, the collection and

availability of consistent hydroclimatic and socioeconomic data at 
adequate scales represent an important challenge in CSA. Major 
adaptation barriers are furthermore reported from central Chile in 
the context of a mega-drought since 2010, related to socioeconomic 
factors and a deficient bottom-up approach to informing and 
developing public policy (Aldunce et al., 2017). These gaps could be 
bridged by strengthening transdisciplinary approaches at the science–

12
1736Chapter 12 Central and South America
policy interface (Lillo-Ortega et  al., 2019) with blended bottom-up 
and top-down adaptation to include scientific knowledge with impact 
and scenario assessments in local adaptation agendas (Huggel et al., 
2015b). For instance, a new allocation rule for the Laja reservoir in 
southern Chile (SWS), based on consistent water balance modelling 
results, could inform policy and water management and potentially 
improve local water management and reduce water conflicts over the 
long term (Muñoz et al., 2019b).
12.5.4 Food, Fibre and Other Ecosystem Products
The CSA region globally has the greatest agricultural land and water 
availability per capita. With 15% of the world’s land area, it receives 29% 
of global precipitation and has 33% of globally available renewable 
resources (Flachsbarth et al., 2015). Agricultural commodities (coffee, 
bananas, sugar, soybean, corn, sugarcane, beef livestock) are some of

the highest users of ecosystem resources such as land, water, nutrients 
and technology. These exports have gained importance in the past 
two decades as international trade and globalisation of markets have 
shaped the global agri-food system. However continuous overuse of 
the environment might account for resource depletion (deforestation, 
land degradation, nutrient depletion, pollution), affecting the natural 
capital base. The effects of climate change on humans, via ecological 
systems, exacerbate the impact related to the depletion of ecosystem 
services (Scholes, 2016; IPBES, 2018b; Castaneda Sanchez et al., 2019; 
Clerici et al., 2019; Tellman et al., 2020; Pacheco et al., 2021).
12.5.4.1 Challenges and Opportunities
Even though several regions have seen significant improvements in 
food availability, many countries are also experiencing a declining 
trend in food self-sufficiency (Porkka et al., 2013; Rolando et al., 2017).

Drought conditions in CA and the Caribbean increased in line with 
climate model predictions (Herrera et al., 2018a). The direct social and 
economic consequences for the sector are evident in CA’s so-called 
Dry Corridor, with a growing dependence on food imports (Porkka 
et al., 2013), and these degrees of dependency make the region more 
vulnerable to price variability, climatic conditions (Bren d’Amour et al., 
2016; ECLAC, 2018) and, therefore, to food insecurity in the absence 
of adaptation actions (high confidence) (Porkka et  al., 2013; Bren 
d’Amour et  al., 2016; López Feldman and Hernández Cortés, 2016; 
Eitzinger et al., 2017; Imbach et al., 2017; Lachaud et al., 2017; Harvey 
et al., 2018; Niles and Salerno, 2018; del Pozo et al., 2019; Alpízar et al., 
2020; Anaya et al., 2020).
Given these circumstances, some regions in CSA (Andes region and 
CA) will just meet, or fall below, the critical food supply/demand ratio

for their populations (Bacon et al., 2014; Barbier and Hochard, 2018b). 
Meanwhile, the more temperate part of SA in the south is projected to 
have agricultural production surpluses (low confidence) (Webb et al., 
2016; Prager et al., 2020). The challenge for this region will be to retain 
the ability to feed and adequately nourish its internal population as 
well as making food supplies available to the rest of the world.
The access of other markets to the region’s agricultural products might 
be conditioned on the adoption of low-carbon-agriculture measures. Achieving net-zero emissions while improving standards of living 
will be possible but will also require developing transition policy 
frameworks to reach the target (Frank et al., 2019; Mahlknecht et al., 
2020; Cárdenas et al., 2021).
12.5.4.2 Governance and Barriers for Adaptation
The governance of adaptation for CSA implies modifying agricultural, 
socioeconomic and institutional systems in response to and in

preparation for actual or expected impacts of climate variability and 
change, to reduce harmful effects and exploit beneficial opportunities 
(high confidence). CSA agriculture has a diversity of systems 
and segments of producers. While small-scale farmers contribute 
significantly to food production and food security, especially in 
developing economies, they face global policies oriented towards 
global commodity markets (Knapp, 2017; Fernández et  al., 2019). 
Climate action initiatives that consider CSA’s high levels of poverty 
and inequality to reduce these pervasive problems are central for 
adaptation in the region (Crumpler et al., 2020; Locatelli et al., 2020).
Since AR5, important advances at the institutional level have occurred 
based on the development and implementation of NAPs for the 
agriculture and forestry sector among countries. Adapting to climate 
change entails the interaction of decision makers, stakeholders and

institutions at different scales of government, from local to national. The 
Climate-Adapted Sustainable Agriculture Strategy for the region of the 
Central American Integration System (EASAC) of the Central American 
Agricultural Council of Ministers of Agriculture constitutes a valuable 
example of how to undertake climate action in the agricultural sector, 
as a block of countries and in an intersectoral manner, to enhance 
results and make better use of resources (IICA, 2019).
In Brazil, the Low-Carbon Agriculture (LCA) programme (Programa ABC) 
funds practices for reducing GHG emissions in the sector (Government 
of Brazil, 2012), accounting for about 15% of the total agriculture 
official finance portfolio, although it faces challenges to advance (Souza 
Piao et al., 2021). Costa Rica offers an example on how reforestation 
can help achieve Paris Agreement objectives. Reforestation through 
natural regeneration on abandoned pastures boosted forest cover from

48% in 2005 to 53.4% in 2010 (Reid et al., 2019; Cárdenas et al., 2021). 
Some key success factors included a strong institutional context, fiscal 
and financial incentives for reforestation, conservation measures such 
as payment for environmental services, cattle ranch subsidy reform and 
a historically strong enforcement and focus on land titles that favoured 
the restoration of lands. Uruguay offers another example, with the farm 
sector contribution of 32.8% of all exports and 73.8% of the country’s 
emissions, so decarbonisation is not just an environmental issue but 
an economic competitiveness one as well. In the Intended Nationally 
Determined Contributions (INDCs) submitted to the UNFCCC in 2015, 
Uruguay set a specific target for the agriculture sector to reduce enteric 
methane emissions intensity per kilogram of beef (live weight) by 
33% to 46% in 2030 by improving efficiency of beef production by

controlling the grazing intensity to increase animal intake, reproductive 
efficiency and daily weight gain (Picasso et al., 2014).
It is relevant to create conditions for the development of sustainable 
agricultural practices in a framework where factors associated with

12
1737Central and South America  Chapter 12
climate have become important for producers, given recent experiences 
of drought and lack of water (high confidence) (Clarvis and Allan, 2014; 
Roco et  al., 2016; Hurlbert and Gupta, 2017; Pérez-Escamilla et  al., 
2017; Cruz et al., 2018; Zúñiga et al., 2021). Solutions that consider 
relevant drivers that have demonstrated a positive effect in diffusion of 
adaptation strategies are more efficient (Table 12.8). Some conditions, 
such as the promotion of education programmes, participation in 
cooperatives, credit access and land tenure security, can help. In the 
same line, in CSA some elements, such as technology and information 
access and local knowledge, reinforce climate-change adaptation 
(Khatri-Chhetri et al., 2019; Piggott-McKellar et al., 2019). As stated 
in Table  12.8, barriers of various origins persist in connection with 
climate-change adaptation in the region increasing the vulnerability of

farming systems and rural livelihoods.
Limited information regarding cost-benefit analyses of adaptation 
is available in the region and regarding avoiding maladaptation 
effects and promoting site-specific and dynamic adaptation options 
considering available technologies (medium confidence) (Roco et al., 
2017; Zavaleta et al., 2018; Ponce, 2020; Shapiro-Garza et al., 2020).
Climate information services has an important role in climate-change 
adaptation and there is a recognised gap between climate science 
and farmers (high confidence) (Vaughan et  al., 2017; Loboguerrero 
et  al., 2018; Tall et  al., 2018; Thornton et  al., 2018; Ewbank et  al., 
2019). Such services should address the challenges of ensuring that 
climate information and advisory services are relevant to the decisions 
of smallholder and family farmers, providing timely climate service 
access to remote rural communities with marginal infrastructure and

ensuring that farmers own climate services and shape their design 
and delivery. An interesting case facing this gap is the implementation 
of local technical agro-climatic committees in Colombia, which make 
it possible to share and validate climatic and weather forecasts, as 
well as crop model results for seasonal drought events (Loboguerrero 
et al., 2018). Another example is the web service AdaptaBrasil-MCTI, 
which forecasts the risk of climate-change impacts on strategic sectors 
(e.g., food, energy, water) in Brazil (Government of Brazil and Ministry 
of Science Technology and Innovation Secretariat of Policies and 
Programs, 2021).
Barriers to financial access are present in the region, restricting effective 
adaptation to extreme weather events (high confidence) (Chen et al., 
2018; Fisher et  al., 2019; Piggott-McKellar et  al., 2019; Vidal Merino 
et al., 2019; de Souza Filho et al., 2021). In 2014, the penetration rate

of this type of insurance in the region averaged 0.03% of GDP , and a 
few countries dominate the market (Brazil, Argentina). Beyond these 
countries, some initiatives also exist in Uruguay, Paraguay, Chile and 
Ecuador. In most Latin American and Caribbean countries, the public 
sector plays an important role in providing insurance or reinsurance 
and coexists with private-sector companies (Cárdenas et  al., 2021). 
Insurance protections represent a strategy to transfer climate risk 
to protect the well-being of vulnerable small farmers and accelerate 
uptake (recovery) after a climate-related extreme weather event. 
Lack of finance and proper infrastructure is compounded by limited 
knowledge of sustainable farming practices and high rates of financial 
illiteracy (high confidence) (Hurlbert and Gupta, 2017; Piggott-McKellar 
et al., 2019).Insufficient access to digital services and technologies further widens 
the gap between the rural poor and more urban populations of Latin

America and the Caribbean (medium confidence: insufficient evidence, 
high agreement). In turn, these factors compromise productivity and 
competitiveness. Support for the rural poor can be focused on both 
economic competitiveness and social development. Finally, to align the 
identified adaptation options as a priority for achieving future food 
security in the NDCs of CSA countries to mitigation commitments, it 
will be essential to highlight synergies by generating evidence (national 
research) in relation to progress towards increasing productivity and 
resilience and reducing GHG, in addition to demonstrating its added 
value as a development initiative (Rudel et  al., 2015 sustainable; 
Loboguerrero et al., 2019).
12.5.4.3 Adaptation Options
To contextualise the adaptation options at the regional level, the 
majority of the NDCs of the CSA countries reported observed and/or 
projected climate-related hazards: occurrence of droughts and floods

(80% of countries each), followed by storms (45%) and landslides 
(30%), as well as extreme heat, wildfire and invasion by pests and 
non-native species in agriculture (25% each) (Crumpler et al., 2020).
The main adaptation options for climate change in the region include 
preventive measures against soil erosion; climate-smart agriculture, 
which provides a framework for synergies between adaptation, 
mitigation and improved food security; climate information systems; 
land use planning; shifting plantations at high altitudes to avoid 
temperature increases and plagues; and improved varieties of pastures 
and cattle (Lee et  al., 2014  ; Jat et  al., 2016; Crumpler et  al., 2020; 
Moreno et al., 2020a; Aragón et al., 2021). Agricultural technologies 
are not necessarily changing, but the economic activity is shifting to 
accommodate increasing climate variation and adapt to changes in 
water availability and ideal growing conditions (high confidence), as

is observed in Argentina, Colombia and Brazil (McMartin et al., 2018; 
Rolla et al., 2018; Sloat et al., 2020; Gori Maia et al., 2021). Coffee 
plantations are moving further up mountain regions, with the land at 
lower elevations converted for other uses. In Brazil, crop modelling 
suggests the need for the development of new cultivars, with a longer 
crop cycle and with higher tolerance to high temperatures, a necessary 
technological advance for maize, an essential staple crop, to be 
produced in the future. Additionally, irrigation becomes essential for 
sustaining productivity in adverse climate-change scenarios in several 
regions of CSA (McMartin et al., 2018; Lyons, 2019; Reay, 2019).
Livestock production is for small farmers one of the main sources of 
protein and contributes to food security (Rodríguez et al., 2016). The 
importance of this sub-sector in CSA will continue to increase as the 
demand for meat products does as well in the coming years, driven

by growing incomes in the region (OECD and FAO, 2019). However, 
the increase in animal production has been associated with land 
degradation, triggered by the conversion of native vegetation to 
pastureland and aggravated by overgrazing and abandoning of the 
degraded pastures (Baumann et  al., 2017; ECLAC, 2018; Müller-
Hansen et  al., 2019). Sá et  al. (2017) simulated the adoption of 
agricultural systems based on LCA strategies towards 2050. According 
to the simulation, the adoption of LCA strategies in the SA region can

12
1738Chapter 12 Central and South America
Table 12.8 |  Recent studies related to climate-change adaptation of agricultural systems and its determinants in Central and South America region.
Reference CountriesSample 
size (n)Study 
approachCrop systemsAdaptation 
strategiesMain drivers 
promoting 
climate-change 
adaptationMain barriers 
limiting climate- 
change adaptationMain barriers 
detected
de Souza Filho 
et al. (2021)Brazil 175 Quant. Cattle farmersIntegrated 
crop-livestock 
and 
livestock-forestry 
systemsCredit access, extension 
servicesLack of resourcesLack of agricultural 
market access 
strategies
Magalhães et al. 
(2021)Brazil 94 Qual. Several cropsFarm 
managementPrevious experience 
with risksInadequate 
infrastructure, low 
purchasing powerOpportunities 
limited by 
Carrer et al. 
(2020)Brazil 175 Quant. Several cropsAgricultural 
insuranceSchooling, technical 
assistanceHigher risk propensityLimited financial 
market access
Quiroga et al.

(2020)Nicaragua 212 Quant. CoffeeSeveral 
measuresFarm size, awareness 
of climate change, 
schoolingLimited access to rain 
waterAbsence of 
education
Bro et al. (2019) Nicaragua 236 Quant. CoffeeCrop,
soil and waterSchooling, participation 
in cooperatives, radioHousehold sizeInstitutional 
framework 
to promote 
cooperatives
Leroy (2019)Venezuela 
and 
Colombia73 Qual.Several crops at 
high altitudesIrrigation 
managementPerception of water 
scarcity, local 
knowledgeDegradation of fragile 
areasIneffectiveness of 
local institutions
Cherubin et al. 
(2019)Colombia 6 Quant.Several crops 
and pastureAgroforestry 
systemsImproving soil quality 
and biotaDegradation of 
conventional pastureLack of crop 
diversification
Harvey et al. 
(2018)Costa Rica, 
Honduras 
and 
Guatemala860 Quant.Coffee, beans 
and maizeSeveral 
practicesAwareness of climate 
changeAffordability of 
adaptation practicesLack of adaptation 
involving 
agroecological and

contexts
Chen et al. 
(2018)Costa 
Rica and 
Nicaragua559 Quant. Several cropsIntensification 
and 
diversificationAccess to weather 
information, 
participation in 
organisations, credit 
access, farming 
experienceLand rentingLack of crop 
and practices 
Vidal Merino 
et al. (2019)Peru 137 Quant. Several cropsWater 
managementFarm size, capital, 
irrigated proportionLimited access to 
off-farm activities, 
small cultivated areaLack of site-specific 
design of 
Meldrum et al. 
(2018)Bolivia 193 Quant.Potato, quinoa 
and othersDiversification of 
crop portfolioWeather informationLoss to traditional 
knowledgeLack of resilience 
and actions to 
expand and 
maintain variety 
portfolio
Lan et al. (2018) Nicaragua 180 Quant. CocoaCrop 
managementSchooling, household 
size, farm sizeLack of incomeIncome inequality, 
gaps of profitability 
of practices, 
benefits of 
practices depends 
on costs
Kongsager

(2017)Belize 125 Qual. Maize Alley cropping SchoolingLand tenure, market 
distance, degradation 
of fragile areasLack of land 
tenure, lack of 
market access, lack 
of trust
Schembergue 
et al. (2017)Brazil 5485aQuant. Several cropsAgroforestry 
systemsFinancing, presence 
of associations, credit 
accessHigh potential for 
agriculture, lack of 
climate informationAdaptation 
conditioned 
by agricultural, 
socioeconomic and 
climatic conditions

12
1739Central and South America  Chapter 12
alter the growing trend of land use and land use change emissions, 
and at the same time, it can increase meat production by 55 Mt for 
the entire period (2016–2050). The restoration of degraded pasture 
and livestock intensification account for 71.2% and integrated crop–
livestock–forestry system contributes 28.8% of total meat production 
for the entire period. These results indicate that combined actions 
in agricultural management systems in SA can result in synergistic 
responses that can be used to make agriculture and livestock 
production an important part of the solution of global climate 
change and advance food security (medium confidence: insufficient 
evidence and high agreement) (Zu Ermgassen et  al., 2018; Pompeu 
et al., 2021). Crop–livestock–forestry systems are also important for 
climate-change adaptation as they provide multiple benefits, including 
the coproduction of food, animal feed, organic fertilizers and soil

organic carbon sequestration (Sharma et al., 2016; Rodríguez et al., 
2021), achieving mitigation and adaptation goals (high confidence) 
(Picasso et al., 2014; Modernel et al., 2016, 2019; Rolla et al., 2019; 
Locatelli et  al., 2020). A recent analysis of agroforestry in Brazil 
showed positive and relevant impacts on the heads/pasture area rate 
in livestock production and that the system may have also stimulated 
a shift towards other production activities with higher gross added 
value (Gori Maia et al., 2021). Agroforestry has also proven to have 
protective benefits to obtain more stable, less fluctuating yields due 
to climate-related damage in coffee production (high confidence) 
(Bacon et al., 2017; Durand-Bessart et al., 2020; Ovalle-Rivera et al., 
2020). In the same way, the production of plant-based fibre can be 
less vulnerable to economic and climatic variability through farming 
system diversification. Textile fibre crops for the case of cotton include

crop rotation, agroecological intercropping and agroforestry (Oliveira 
Duarte et al., 2019).
Adaptation strategies also concern Indigenous agriculture, that is, the 
vast majority of the 44 million Amerindians (CEPAL, 2014). IKLK can 
play an important role in adaptation (Zavaleta et al., 2018). On one 
hand, they ensure the conservation of a very rich agrobiodiversity that 
is likely to meet the challenges of climate change (high confidence) 
(Carneiro da Cunha and Morim de Lima, 2017; Magni, 2017; Emperaire, 2018; Donatti et al., 2019), while on the other hand, the sustainability 
of large territories that assure their livelihood (Singh and Singh, 2017; 
Mustonen et al., 2021). In the Andes, ancient technologies increased 
the quantity of crops produced and made it possible to cope with 
climatic changes and water scarcity, while nutrition conditions were 
improved (high confidence) (López Feldman and Hernández Cortés,

2016; Parraguez-Vergara et  al., 2018; Carrasco-Torrontegui et  al., 
2020 food). Also, fire prevention management and protection against 
forest and biodiversity loss are recognised as important elements in IK 
(Mistry et al., 2016; Bowman et al., 2021).
12.5.5 Cities, Settlements and Infrastructure
CSA is the second most urbanised region of the world, with 5 
megacities and half of the urban population in 129 secondary cities 
(UNDESA, 2019), huge metropolitan areas concentrated on the coast 
and an increasing number of small cities by the sea (Barragán and 
de Andrés, 2016). Besides the many climatic events threatening urban 
areas in the region (extreme heat, droughts, heavy storms, floods, 
landslides), cities by the coast are also exposed to SLR (Section 12.3; 
Figure  12.6; Dawson et  al., 2018; Leal Filho et  al., 2018; Le, 2020). 
The main determinants of urban vulnerability assessed in the region 
are poor and unevenly distributed infrastructure, housing deficits and

informality, poverty and the occupation of risk areas, including LECZs 
(Section 12.3). Those features of urban systems increase the risks to 
health, ecosystems and its services, water, food and energy supplies 
(Section  12.4). Impacts of climate events on urban water supply, 
drainage and sewer infrastructures are the most frequently reported in 
the region (Section 12.3; Figure 12.9).
12.5.5.1 Challenges and Opportunities
The inequality, poverty and informality shaping cities in the region 
increase vulnerability to climate change (high confidence) (Romero-
Lankao et al., 2014; Rasch, 2017; Filho et al., 2019) and can hinder 
adaptation (Section  12.5.7.1), while interventions addressing these Reference CountriesSample 
promoting 
detected
(2017)Guatemala, 
Honduras 
and Costa 
Rica300 Quant.Coffee and

maizeEcosystem-based 
adaptationSchooling, age, farming 
experience, access to 
technological supportLack of land tenureLack of access 
to training and 
finance
Roco et al. 
(2016)Chile 665 Quant. Several cropsWater 
managementFarm size, access to 
weather informationLocations, ageLack of availability 
and access to 
Mussetta and 
Barrientos 
(2015)Argentina 41 Qual. Vine and othersCrop and water 
managementOrganisation of 
producers, labour 
availability, knowledge 
and information access, 
technology accessWater allocation 
systemLack of water 
and distribution 
Notes:
(a) municipalities; Quant.: mainly quantitative; Qual.: mainly qualitative.

12
1740Chapter 12 Central and South America
social challenges and the existing development deficits (e.g., build 
or improve infrastructure and housing applying climate-adapted 
patterns) can go hand in hand with adaptation and mitigation (medium 
confidence: high agreement, medium evidence) (Section  12.5.7.3; 
Creutzig et al., 2016; Le, 2020; Satterthwaite et al., 2020). Over 20% of 
the urban population in Latin America and the Caribbean live in slums 
and many other forms of precarious and segregated neighbourhoods, 
settled in risk areas and lacking infrastructure (Rasch, 2017; UN-
Habitat, 2018; Rojas, 2019). This vulnerable condition is boosted by 
unstable political and governmental institutions, which suffer from 
ongoing corruption, weak governance and reduced capacity to finance 
adaptation (Rasch, 2016). Facing governance challenges by including 
diverse stakeholders and encouraging and learning from community-

based experiences has also presented the opportunity to improve 
adaptation strategies (Archer et al., 2014). An example of this is the 
Regional Climate Change Adaptation Plan of Santiago (Krellenberg 
and Katrin, 2014).
12.5.5.2 Governance and Financing
The lack of a high multi-level and intersectoral governance capacity 
with strong multi-player horizontal and vertical coordination and 
long-term support limit adaptation in the region (high confidence) 
(Anguelovski et al., 2014; Bai et al., 2016; Chu et al., 2016; Schaller et al., 
2016; Miranda Sara et al., 2017). The ability to enrol stakeholders and 
include community-based initiatives can determine adaptation success, 
particularly considering their impact in the decision-making arena (high 
confidence) (Section  12.5.8.1; Section  6.4; Anguelovski et  al., 2014; 
Archer et al., 2014; Chu et al., 2017; Rosenzweig et al., 2018).
Lima’s Climate Action Strategy is an example (Metropolitan

Municipality of Lima, 2014). It was approved following a participatory 
and consultative process with the technical group on climate change 
from the Metropolitan Environmental Commission, focusing on the 
reduction of water vulnerabilities to drought and heavy rain, on the 
basis of which 10 (out of 51 with Callao) Lima district municipalities 
are developing and starting to implement adaptation measures (Foro 
Ciudades Para la Vida, 2021). In 2021 the municipality of Lima also 
approved its Local Climate Change Plan (Metropolitan Municipality of 
Lima, 2021) under a similar process. The engagement of local players 
was central to spreading and mobilising different types of knowledge 
and creating networks able to support adaptation (Section  12.6.3; 
Miranda Sara and Baud, 2014; Miranda Sara et al., 2017). The inclusive 
process is also a goal based on the example of Chile Municipalities 
Network Facing Climate Change (RedMuniCC) engaged in developing

participatory strategic plans for climate adaptation and mitigation 
(RedMuniCC, 2021).
New forms of financing and leadership focused on community-based 
approaches have been developed to overcome the funding challenge 
and enable adaptation in the region (medium confidence: medium 
evidence, medium agreement) (Castán Broto and Bulkeley, 2013; 
Archer et al., 2014; Paterson and Charles, 2019). Systems for measuring, 
reporting and verifying adaptation financing, as in Colombia (Guzmán 
et  al., 2018), or a national legislation geared towards adaptation, 
can also help access funds. The Peruvian Law on the Retribution 
Mechanism of Eco-Systemic Services and Code (Miranda Sara and Baud, 2014; MINAM Peru, 2016) in addition to the Ley Marco de la 
Gestión y Prestación de los Servicios de Saneamiento and Its Code 
(Ministerio de Vivienda, Construcción y Saneamiento de Perú, 2017), 
allowed potable water companies to add 1% to the bill to guarantee

ecosystem services, water treatment and reuse with GI. Another 4% 
of bill go to developing and implementing adaptation plans and 
measures (Government of Peru, 2016).
12.5.5.3 Adaptation Options in Urban Design and Planning
Both the shape and activities of a city have an impact on carbon 
emissions, adaptation and mitigation opportunities (high confidence) 
(Raven et  al., 2018; Satterthwaite et  al., 2018). Combining urgent 
measures, strategic action (Chu et  al., 2017) on long-term planning 
is central for transformative adaptation and avoiding maladaptation 
(Filho et  al., 2019). Urban planning, considering climate risk 
assessments, and regulation (e.g., land use and building codes), 
including climate-adapted parameters, are central to coordinating 
and fostering private and public investments in adaptation, reducing 
risks related to features of the built environment (infrastructure and 
buildings) and the occupation of risk areas (e.g., threatened by floods

and landslides) (Rosenzweig et al., 2018). A lack of information at the 
local scale, human resources and clear liability for climate-change 
response planning can limit adaptation (Aylett, 2015).
Strategic adaptation approaches have been adopted by many cities 
in dealing with the multi-level and intersectoral complexity of urban 
systems, with gains in fostering leadership and facing the predominant 
pattern of uneven urban development in the region (medium 
confidence: limited evidence, high agreement) (Chu et  al., 2017). 
Medellin’s metropolitan green belt, for example, focuses on problems 
such as irregular settlements, inequality and poor governance, 
formulating programmes and projects of the municipality of Medellin 
and the municipalities of the Vale do Aburra in a strategic long-term 
plan. Places with informal and precarious settlements were slated to 
be transformed with the belt’s integration areas: eco-parks and eco-

gardens (Alcaldía de Medellín, 2012; Chu et al., 2017).
12.5.5.3.1 Housing, Informality and Risk Areas
Informality and precariousness in housing is one of the most sensitive 
issues for adaptation in CSA cities (medium confidence: medium 
evidence, high agreement) (Satterthwaite et  al., 2018; UN-Habitat, 
2018). Housing deficit in 2009, as a regional baseline, estimated 
that 37% of households suffered from quantitative or qualitative 
deficiencies due to the high cost of housing and the incidence of 
poverty (Blanco Blanco et  al., 2014; McTarnaghan et  al., 2016; NU 
CEPAL et al., 2016; Vargas et al., 2018a; Rojas, 2019).
Policies and programmes have been implemented accumulating good 
practices and reducing the percentage of population in informal and 
precarious settlements (33.7% in 1990 to 21% in 2014) (NU CEPAL 
et al., 2016; Satterthwaite et al., 2018; Teferi and Newman, 2018; UN-
Habitat, 2018). Slum upgrading and built-environment interventions

(housing and infrastructure improvement and provision) in informal 
settlements can enhance adaptation (high confidence) (Teferi and 
Newman, 2018; Núñez Collado and Wang, 2020; Satterthwaite et al.,

12
1741Central and South America  Chapter 12
2020) while reducing floods, landslides and cascading impacts of 
storms, floods and epidemics, as observed with the ‘incremental 
housing approach’ in Quinta Monroy (Rojas, 2019) and the ‘social 
urbanism’ in Medellin (Garcia Ferrari et al., 2018).
The climate adaptation plans of several large CSA cities include 
efficient land use and occupation planning and urban control systems 
(comprising regulation, monitoring), fostering the articulation with 
housing and environmental policy (by means of intersectoral and multi-
level governance), inhibiting and reducing the occupation of risk areas 
(mainly flooding and landslides risks); increasing population density in 
areas already served by infrastructure; expanding slum urbanisation 
and technical assistance programmes to improve and expand social 
housing (high confidence) (Municipio del Distrito Metropolitano de 
Quito, 2020; Prefeitura Municipal do Salvador, 2020; Municipalidad de

Lima, 2021; Prefeitura da Cidade do Rio de Janeiro, 2021; Prefeitura do 
Município de São Paulo, 2021).
Housing programmes and initiatives that consider resilient construction 
and site selection strategies are still in their nascent stages (Martin 
et al., 2013). Initiatives in slum upgrading, social housing improvement 
and regularising land tenure, associated with infrastructure provision, 
do not usually focus on adaptation, although they often focus on 
risk reduction. Those initiatives, associated with a housing policy 
that guarantees access to land and decent housing, represent a 
comprehensive intervention in vulnerable neighbourhoods for their 
adaptation to climate change, and CbA (community-based adaptation) 
strategies, including housing self-management and the participation 
of cooperatives, demonstrate the need and opportunity to transition 
to a transformative urban agenda that encompasses sustainable 
development, poverty reduction, disaster-risk reduction, climate-

change adaptation and climate-change mitigation (high confidence) 
(Muntó, 2018; UN-Habitat, 2018; Valadares and Cunha, 2018; Bárcena 
et  al., 2020b; Núñez Collado and Wang, 2020; Satterthwaite et  al., 
2020).
Several large cities are implementing municipal risk management 
plans and management and restoration plans for hydrologically 
relevant areas, considering threats of drought and heat waves, 
integrated watershed management and flood control programmes 
(high confidence) (Municipio del Distrito Metropolitano de Quito, 2020; 
Prefeitura Municipal do Salvador, 2020; Municipalidad de Lima, 2021; 
Prefeitura da Cidade do Rio de Janeiro, 2021; Prefeitura do Município 
de São Paulo, 2021). Quito and Rio de Janeiro are two examples of 
comprehensive and effective city-level climate action that includes 
creating environmental protected areas, managing appropriate land 
use, household relocation and EWSs in areas vulnerable to high levels

of precipitation associated with EbA, such as reforestation projects, to 
address natural hazards (ELLA, 2013; Anguelovski et al., 2014; Calvello 
et al., 2015; Alcaldía de Quito, 2017; Sandholz et al., 2018; Prefeitura 
da Cidade do Rio de Janeiro, 2021) (Section 12.6.1). EWS and the use 
of mapping tools as undertaken in La Paz proved to be an effective 
adaptation measure in the face of increasing hydro-climatic extreme 
events (Aparicio-Effen et al., 2018).12.5.5.3.2 Green and Grey Infrastructure
Hybrid solutions, combining green and grey infrastructure (GGI), have 
been adopted for better efficiency in flood control (Ahmed et al., 2019; 
Drosou et  al., 2019; Romero-Duque et  al., 2020), sanitation, water 
scarcity, landslide prevention and coastal protection (high confidence) 
(Section 12.5.6.4; Mangone, 2016; Depietri and McPhearson, 2017; Leal 
Filho et al., 2018; McPhearson et al., 2018). The adoption of NbS, which

embraces well-known approaches such as GI and EbA (Pauleit et al., 
2017; Le, 2020), has increased (Box 1.3). The Fund for the Protection 
of Water (FONAG) and the Participative Urban Agriculture (AGRUPAR) 
are initiatives using NbS in Quito (Section 12.6.1). An example of GGI 
is a stormwater detention pond as a water storage solution to flood 
prevention, allowing multiple uses of an urban space, and adapting 
and revitalising a degraded area in Mesquita, Rio’s metropolitan 
region (Jacob et al., 2019). These systemic and holistic solutions still 
need to overcome governance and sectorial barriers to be more widely 
adopted (Herzog and Rozado, 2019; Wamsler et al., 2020; Valente de 
Macedo et al., 2021).
Managing water in cities in an adaptive way has been central to 
reducing impacts such as floods and contributes to water security (high 
confidence) (Van Leeuwen et al., 2016; Okumura et al., 2021). Many 
cities facing frequent heavy storms that impact mostly underprivileged

communities, slums and vulnerable areas could benefit from integrated 
NbS for disaster risk reduction and adaptation (high confidence) 
(Sandholz et al., 2018; Ronchi and Arcidiacono, 2019). A study covering 
70 Latin American cities estimated that 96  million people would 
benefit from improving main watersheds with GI (Tellman et al., 2018). 
In several municipal climate plans, NbSs were introduced mainly to 
enhance rainwater management, reduce energy consumption and 
urban heat areas, improve water quality, prevent landslides and set 
aside green areas (high confidence) (Gobierno de la Ciudad de Buenos 
Aires, 2015; Municipio del Distrito Metropolitano de Quito, 2020; 
Prefeitura Municipal de Curitiba, 2020; Alcaldía de Medellín, 2021; 
Municipalidad de Lima, 2021; Prefeitura da Cidade do Rio de Janeiro, 
2021; Prefeitura do Município de São Paulo, 2021). São Paulo’s project 
for Jaguaré River proposes a large-scale landscape transformation

applying innovative multi-functional NbSs instead of exclusively large, 
expensive and monofunctional hard-engineered solutions to manage 
stormwater (Marques et  al., 2018; Herzog and Rozado, 2019). In 
Bogota, the Humedales Foundation has restored wetlands to enhance 
areas near the Van Der Hammen reserve to improve water quality and 
quantity, restore habitat for biodiversity and provide flood protection 
(Portugal Del Pino et  al., 2020). In Petrópolis, a medium-sized city 
in the hills of Rio de Janeiro state, the water service company has 
implemented 10 NbS multi-functional micro wastewater treatment 
plants in low-income areas, helping to reduce cascading impacts of 
storms, floods and epidemics (Herzog and Rozado, 2019). In Costanera 
Sur, Buenos Aires, a public initiative to protect an auto-regenerated 
River Plate bank, which had received demolition material to create 
land, currently offers numerous ecosystem services for residents and

attract visitors, activating the tourist industry and helping reducing 
riverine floods (Bertonatti, 2021; OICS, 2021).
A hybrid solution to water management that merges traditional 
interventions in urban areas with sustainable urban drainage systems

12
1742Chapter 12 Central and South America
(SUDSs) (Davis and Naumann, 2017), considering small-scale low-
impact development (LID) measures scattered over the watershed 
instead of concentrate huge hydraulic grey structures, can help reduce 
the risk and damage of flooding (high confidence) (Miguez et al., 2014, 
2015a; Depietri and McPhearson, 2017; Da Silva et  al., 2018a; de 
Macedo et al., 2018). Quito’s climate plan explicitly cites the strategy 
for implementing blue and grey infrastructure to reduce risk due to 
extreme precipitation and its associated impacts such as flooding 
and landslides and the possible impact of water scarcity (Municipio 
del Distrito Metropolitano de Quito, 2020). The Integrated Iguaçu-
Sarapuí River Basin Flood Control Master Plan, in Rio’s metropolitan 
area, combines different solutions to flood protection, focusing on 
river restoration by retrofitting levee systems combined with adapting

land use to provide a multi-functional landscape as an alternative 
to bring together green and grey solutions, creating urban parks to 
prevent further paving and avoid irregular occupation of riverbanks 
and provide storage capacity for damping flood peaks (Miguez et al., 
2015b).
Many cities are implementing adaptation measures on integrated 
water and flood management systems (Sarkodie and Strezov, 2019), 
improving basic sanitation services (medium confidence: medium 
evidence, high agreement). The main strategies are established by 
NAPs periodically focusing on improving water distribution network 
and reservoir systems, as in Honduras (Government of Honduras, 
2018) and Ecuador (Mills-Novoa et al., 2020), sewage and effluent 
treatment, as in Guatemala, Brazil and Paraguay (Government 
of Brazil, 2007; Government of Guatemala, 2016; Government of 
Paraguay, 2017), facing water scarcity and environmental degradation.

Local authorities follow this guideline in an effort to maintain and 
upgrade existing drainage systems in Georgetown (Mycoo, 2014) 
or in Medellin, focusing on improving drainage systems to prevent 
landslides or flooding (Núñez Collado and Wang, 2020; Alcaldía de 
Medellín, 2021). Rio de Janeiro has constructed three large stormwater 
detention reservoirs to deal with frequent flood, (Prefeitura da Cidade 
do Rio de Janeiro, 2015), adopting a set of exclusively grey solutions, 
not combined into a NbS that could improve urban flood resilience 
(Rezende et  al., 2019). The main proposed actions still consider 
the traditional approach in improving the hydraulic capacity of 
urban drainage systems as an adaptive measure (high confidence) 
(Gobierno de la Ciudad de Buenos Aires, 2020; Prefeitura Municipal 
do Salvador, 2020; Municipalidad de Lima, 2021; Prefeitura da Cidade 
do Rio de Janeiro, 2021). In addition to this strategy, several local

plans propose actions for the retention and storage of rainwater, both 
in urban drainage networks on a smaller intervention scale (Prefeitura 
Municipal de Curitiba, 2020) and along rivers and canals with large-
scale works (medium confidence: medium evidence, high agreement) 
de Curitiba, 2020; Alcaldía de Medellín, 2021; Prefeitura da Cidade do 
Rio de Janeiro, 2021).
12.5.5.3.3 Mobility and Transport System
Mobility and transport systems play a key role in urban resilience 
(high confidence) (Walker et  al., 2014a; Caprì et  al., 2016; Espinet 
et  al., 2016; Lee and Lee, 2016; Ford et  al., 2018; Mehrotra et  al., 
2018; Quinn et al., 2018). Examples reported in the scientific literature assessed focus on mitigation strategies, even when they are labelled 
as adaptation measures (da Silva and Buendía, 2016; Di Giulio et al., 
2018; Valderrama et al., 2019; Goes et al., 2020).

The integration of transport and land use planning and the 
improvement of public transport, also as important mitigation 
actions, has emerged as a consensus in countries’ adaptation plans; 
nevertheless, emphasis on mobility and transport systems in the many 
published NAPs is low (medium confidence: medium evidence, high 
agreement). The NAPs of Honduras, Costa Rica and El Salvador do 
not approach adaptation or mitigation in the sector, while those of 
Peru, Ecuador, Guatemala and Paraguay focus on mitigation only. 
The NAPs of Chile, Colombia and Brazil focus on both mitigation and 
adaptation of mobility and transport systems. Chile’s and Colombia’s 
plans dedicate specific action lines to adapting mobility and transport 
systems to climate change, while Brazil published a complementary 
volume to accompany its NAP that is dedicated exclusively to sectoral 
strategies, although it presents only general guidelines (Government

of Peru, 2010; Government of Chile, 2014; Government of Ecuador, 
2015; Government of Brazil, 2016; Government of Colombia, 2016; 
Government of Guatemala, 2016; Government of Paraguay, 2017; 
Government of Costa Rica, 2018; Government of Honduras, 2018; 
Government of El Salvador, 2019).
On the municipal scale, among the biggest cities, São Paulo, Rio 
de Janeiro, Lima and Santiago stand out for including mobility and 
transport as a strategic axis of its climatic plans, though they prioritise 
mitigation, while Buenos Aires and Bogota do not delve into the issue 
in their plans (Gobierno de la Ciudad de Buenos Aires, 2015; Prefeitura 
da Cidade do Rio de Janeiro, 2016; Alcaldía Mayor de Bogotá D.C., 
2018; Municipalidad de Lima, 2021; Municipalidad de Santiago, 2021; 
Prefeitura do Município de São Paulo, 2021). Most of those same cities 
have sectoral mobility plans, which are key tools in urban resilience. 
Those plans, however, do not focus on adaptation actions, instead

emphasising mitigation (Government of Peru, 2005; Gobierno de la 
Ciudad de Buenos Aires, 2011; Prefeitura do Município de São Paulo, 
2015; Alcaldía Mayor de Bogotá D.C., 2017; Ilustre Municipalidad de 
Santiago, 2019; Município de Rio de Janeiro, 2019).
12.5.6 Health and Well-being
The most common adaptation strategies include the development of 
climate services such as epidemic forecast tools, integrated climate-
health surveillance and observatories and forecasting climate-related 
disasters (floods, heat waves). Geographic information system (GIS) 
technologies are being used to identify locations where vulnerable 
populations are exposed to climate hazards and associated health 
risks.
12.5.6.1 Climate Services for Health
The measures most directly linked to diminishing risk are those related 
to climate services for health (high confidence). Climate services 
provide tailored, sector-specific information from climate forecasts to

support decision-making (WHO and WMO, 2016); they allow decision 
makers and practitioners to plan interventions in anticipation of a

12
1743Central and South America  Chapter 12
weather/climate event (Mahon et  al., 2019). More recently, climate 
services, such as EWSs and forecast models, have been promoted for 
the health sector (WHO and WMO, 2012, 2016; WMO, 2014; Thomson 
and Mason, 2018) and are an important adaptation measure to 
reduce the impacts of climate on health (high confidence). To guide 
this process, the Global Framework for Climate Services (GFCS) issued 
a Health Exemplar (Lowe et  al., 2014; WMO, 2014), which aims to 
foster stakeholder engagement between health and climate actors at 
all levels to promote the effective use of climate information within 
health research, policy and practice.
There exist at least 24 EWS in SA to avoid deaths and injuries from 
floods in the countries such as Argentina, Colombia, Ecuador, Bolivia, 
Brazil, Peru, Uruguay and Venezuela (Bravo et  al., 2010; Bidegain, 
2014; Moreno et  al., 2014; Dávila, 2016; del Granado et  al., 2016;

López-García et  al., 2017; Carrizo Sineiro et  al., 2018). A total of 
149 emergency prevention and response systems are reported in CA 
(UNESCO, 2012). In addition, some countries implement programmes 
for the relocation of families who are in risk condition, like in Bogota 
and Medellin, Colombia (World Bank, 2014; Watanabe, 2015).
Epidemic forecast tools are an example of an adaptation measure 
being developed and/or implemented in this region (high confidence). 
Climate-driven forecast models have been developed for dengue in 
Ecuador, Puerto Rico, Peru, Brazil, Mexico, Dominican Republic, and 
Colombia (Lowe et al., 2013; Eastin et al., 2014; Johansson et al., 2016; 
Lowe et al., 2017; Johansson et al., 2019); for Zika virus infections across 
the Americas (Muñoz et al., 2017); for cutaneous leishmaniasis in Costa 
Rica and Brazil (Chaves and Pascual, 2006; Lewnard et al., 2014); for 
Aedes-borne diseases across the Americas (Muñoz et al., 2020b); and

a nowcast model for chikungunya virus infections across the Americas 
(Johansson et  al., 2014). In Ecuador, a prototype system utilised 
forecasts of seasonal climate and ENSO forecasts of to predict dengue 
transmission, providing the health sector with warnings of increased 
transmission several months ahead of time (Stewart-Ibarra and Lowe, 
2013; Lowe et  al., 2017). Despite these advances, few tools have 
become operational and mainstreamed in decision making processes. 
However, Brazil and Panama have been able to operationalise an EWS 
for the surveillance of dengue fever transmission (Codeço et al., 2016; 
McDonald et al., 2016).
One of the most promising climate services for the health sector are 
heat and cold early-warning and alert systems (medium confidence). 
These have been developed by the national meteorological institutes 
in Peru, Argentina, and Uruguay (Bidegain, 2014). A heat alert system 
was implemented in Argentina in 2017 and daily alerts are issued for

57 localities across the country. A stoplight colour scheme is used to 
issue alerts, identifying specific groups at risk and actions to be taken 
to reduce the risk (Herrera et al., 2018b).
The public dissemination of climate–health warnings via bulletins, 
websites and other outlets can be an adaptation measure to address 
climate change and weather variability to reduce health risks 
(high confidence). The information produced is systematised to be 
communicated to authorities and the general public. The Caribbean 
Health-Climatic Bulletin has been issued quarterly since 2018 to health 
ministries across the region, including CA and NSA. Regional climate and health authorities meet to review 3-month climate forecasts and 
issue statements about the probable impacts on health (Trotman et al., 
2018). In Panama, information on dengue is distributed in a monthly 
bulletin that is used by health authorities to inform vector control

activities (McDonald et  al., 2016). Another example is the climate-
driven forecast of dengue risk that was produced prior to Brazil’s 2014 
FIFA World Cup to inform disease prevention interventions (Lowe 
et al., 2014, 2016). In Colombia, the Intersectoral National Technical 
Commission for Environmental Health publishes a monthly bulletin with 
regional weather forecasts and potential effects on health (CONASA, 
2019). Paraguay improves epidemiological surveillance and trains 
first-level health staff via information campaigns on the prevention 
of climate-sensitive diseases and promotes health networks with the 
participation of civil society (Environmental Secretariat of Paraguay, 
2011).
12.5.6.2 Integrated Climate–Health Surveillance and 
Observatories
Integrated climate–health surveillance systems are another key 
adaptation strategy (medium confidence). This information can be 
used by the health sector to inform decision-making about when

and where to deploy a public health intervention. It can also feed 
into an EWS, particularly if the data are compatible in format and 
spatiotemporal scales. An integrated climate–health surveillance 
system for vector-borne disease control was developed in southern 
coastal Ecuador through a partnership among the climate and health 
sectors and academia (Borbor-Cordova et al., 2016; Lowe et al., 2017). 
Additionally, an interdisciplinary multi-national team working at 
the border of Ecuador and Peru created a cooperation network for 
climate-informed dengue surveillance (Quichi et al., 2016), and their 
successful binational collaboration resulted in the local elimination of 
malaria (Krisher et al., 2016). A similar tool is innovative community-
based data collection to understand and find solutions to rainfall-
related diarrheal diseases in Ecuador (Palacios et al., 2016).
Climate and health observatories represent a promising strategy that

is being developed at sub-national, national (e.g., Brazil, Argentina) 
and regional levels (high confidence) (Muñoz et al., 2016; Rusticucci 
et al., 2020). The Brazilian Observatory of Climate and Health brings 
together climate and health information for the Amazon region 
of Manaus (Barcellos et  al., 2016). At the national level, Brazil has 
created a climate and health observatory, where information and 
data visualisations are available for various climate-sensitive health 
indicators (Ministério da Saúde and FIOCRUZ, 2021).
12.5.6.3 Vulnerability and Risk Maps
Vulnerability and risk maps have been widely used as an adaptation 
strategy to understand the potential impacts of climate on health 
outcomes both directly (e.g., maps of disease risk) and indirectly (e.g., 
maps of populations vulnerable to climate disasters) (high confidence). 
There are many examples of where climate services have been used 
to construct vulnerability maps for health outcomes, including maps

in the aforementioned climate–health observatories. Dengue, malaria 
and Zika vulnerability maps using climate, social and environmental 
information have been developed in Brazil and Colombia (Cunha et al.,

12
1744Chapter 12 Central and South America
2016b; López-Álvarez, 2016; Pereda, 2016; IDEAM, 2017). Argentina 
focuses on improving its health system using a climate change risk 
map system as a tool that identifies the risks and allows assessing 
their management (OPS and WHO, 2018).
Vulnerability and risk maps for climate disasters have been developed 
at the city level, for example in Bogota, Cartagena de Indias and Mocoa 
in Colombia (Yamin et al., 2013; Guzman Torres and Barrera Arciniegas, 
2014; Tehelen and Pacha, 2017; Zamora, 2018), and for the metropolitan 
district of Quito in Ecuador (Tehelen and Pacha, 2017). In addition, 
vulnerability maps were created for the primary road network of 
Colombia (Tehelen and Pacha, 2017). At the regional level, vulnerability 
maps using climate-change probability, disaster risk and food insecurity 
variables have been produced for the Andean region (WFP , 2014). In

Brazil, vulnerability maps that consider exposure, sensitivity and adaptive 
capacity, coupled with climate scenarios, were designed to support the 
NAP on a municipal scale (Chang and Garcia, 2018; Duval et al., 2018; 
Marinho and Silva, 2018; Menezes, 2018; Santos and Marinho, 2018; 
Silva et  al., 2018). A Climate Change Vulnerability Index was used to 
generate vulnerability maps for countries of the Latin American and 
Caribbean region (Vörösmarty et al., 2013; CAF , 2014).12.5.6.4 Other Adaptation Actions
Diverse adaptation measures are being implemented through public 
policies, private household responses and communal management 
that directly or indirectly reduce the impacts of climate change on 
human health (high confidence) (Table 12.9). Private and communal 
management measures could be considered indirect measures because 
they might be adopted even in the absence of climate change.
Participatory management can be relevant in the case of mosquito-

borne disease prevention (e.g., dengue fever or malaria), where the 
reduction in mosquito habitat in one area or ‘hotspot’ can reduce 
the risk for all surrounding households. This approach is also relevant 
when considering new places where vector-borne diseases can emerge 
because of changes in climate (Andersson et al., 2015).
Adaptation strategies implemented by the public sector include a 
diverse suite of strategies ranging from the creation of green spaces 
in urban areas, relocation of families located in disaster-prone areas, 
ecosystem restoration and improved access to clean water, among 
many others (high confidence) (Table  12.9). Building GGI has been 
a popular public adaptation measure to reduce deaths and injuries 
because of floods (Section 12.5.5.3.2). Infrastructure has been improved 
at schools, public buildings and drainage systems in cities such as

Table 12.9 |  Hazards from climate change that impact human health and examples of adaptation strategies proposed or implemented in CSA. Based on McMichael et al. (2006), 
Miller et al. (2013a, b, c, d), Hardoy et al. (2014), IPCC (2014), Janches et al. (2014), Lee et al. (2014), Mejia (2014), Sosa-Rodriguez (2014), Vergara et al. (2014), Lemos et al. (2016), 
Villamizar et al. (2017), Magoni and Munoz (2018) and Zhao et al. (2019).
Hazard and
impacts on human healthExamples of adaptation strategies
Public Private Communal
Extreme heat and cold: deaths/illness by 
thermal stress –Creation of urban green spaces
 –Health promotion campaigns
 –Shelters during heatwaves
 –Technology transfer for home heating –Cooling by swamp coolers, air 
conditioning, open windows, wet floors, 
shade trees
 –Bioclimatic building design –Training of community health volunteers 
to recognise and treat heat strain
Extreme rainfall, wildfire, wind speed: 
injuries/deaths from floods, storms,

cyclones, bushfires and landslides (Key risk 
2, Table 12.6) –EWSs for extreme climate events
 –Safe housing programmes and relocation
 –GGI (e.g., channels, drainage systems) –GGI to prevent landslides
 –Insurance mechanisms and financing for 
long-term recovery –Communal efforts to clear debris from 
canals to reduce flood risk
 –Cooperative efforts to rebuild following 
flood events
Drought and dryness: poor nutrition due to 
reduced food yields and dehydration due 
to limited or inadequate management of 
freshwater (Key risk 1, Table 12.6) –Formalising land ownership for small 
farmers and Indigenous people
 –Address emerging water conflicts –Water infrastructure and irrigation
 –Soil moisture retention techniques
 –Insurance mechanisms
 –Selection of drought-resistant crops –Incorporation of local stakeholders in 
formulating adaptation responses
 –Recognition of Indigenous and local 
wisdom and knowledge
Changes in climate that promote microbial

proliferation: food poisoning and unsafe 
drinking water (Key risk 3, Table 12.6). –Restoration of watersheds
 –Integrated health-climate surveillance
 –Improve access to drinking water, 
drainage, sanitation and waste removal –Water disinfection: boiling, chlorination
 –Purchasing water or water filters –Participatory water management 
strategies, including protection of 
drinking water sources
Changes in climate that affect vector–
pathogen host relations and infectious 
disease geography/seasonality (Key risk 4, 
Table 12.6) –Vector control
 –EWS for epidemics
 –NbS (e.g., forest conservation) –Use of bed nets and screens
 –Use of repellent and insecticides
 –Elimination of standing water –Community volunteers to collect blood 
smears for malaria diagnosis
 –Community-led elimination of vector 
habitat
SLR and storm surges: impaired crop, 
livestock and fisheries yields; unsafe 
drinking water, leading to impaired nutrition

(Key risk 8, Table 12.6) –Improve governance of water utilities
 –Address emerging water conflicts
 –Protection, restoration and soil 
conservation to recharge aquifers –Improve water efficiency in agriculture –Incorporation of local stakeholders in 
Environmental degradation: loss of 
livelihoods and displacement leading to 
poverty and adverse health outcomes 
(related to Key risk 6, Table 12.6) –Long-term risk management planning 
for cities
 –Sustainable forestry programmes
 –Protection and restoration of lacustrine 
areas –Identification of alternative livelihoods –Community-led efforts to reforest and 
restore/protect watersheds

12
1745Central and South America  Chapter 12
Bogota, Colombia (World Bank, 2014) and La Paz, Bolivia (Fernández 
and Buss, 2016). In Brazil, channel works were implemented to reduce 
the flooding of the Tiete River, which crosses the metropolitan area 
of São Paulo; these projects were designed based on simulated flood 
scenarios (Hori et al., 2017).
Another example of a public adaptation measure is the protection and 
restoration of natural areas, which have the potential to decrease the 
transmission of water- and vector-borne infectious diseases (medium 
confidence: robust evidence, low agreement). Studies have shown that 
these measures can diminish the cases of malaria and diarrhoea in 
Brazil and cases of diarrhoea in children in Colombia (Bauch et  al., 
2015; Herrera et al., 2017; Chaves et al., 2018). However, deforestation 
and malaria have a complex relationship that relies on local context 
interactions, where land use and land cover changes play an important

role due to vector ecology alterations and social conditions of human 
settlements (Rubio-Palis et al., 2013). Forest conservation can improve 
hydrological cycle control and soil erosion that can help to improve 
water quality and reduce the burden of water-borne diseases. In 
addition, forest cover can help to diminish the habitat for larval 
mosquitoes that transmit malaria. These measures can help to design 
policies at sites where these problems do not currently exist but can 
emerge as a consequence of climate change and the increase in the 
frequency of weather extreme events.
12.5.6.5 Challenges and Opportunities
Despite the proliferation of disaster EWSs in the region, only 37 can be 
considered operational, because many of these systems do not operate 
or function properly or do not meet the requirements that would allow 
them to be considered EWSs (UNESCO, 2012). Sustainable financing 
and political support are needed to ensure the functioning of disaster

EWSs (high confidence) (Table  12.11). Several studies identified 
difficulties in implementing disaster EWSs due to a lack of community 
engagement and response to the alerts that are issued (del Granado 
et al., 2016; López-García et al., 2017). To address these challenges, the 
document ‘Developing Early Warning Systems: A Checklist’ provides 
guidance for the implementation of a people-centred approach to 
early warning systems, as proposed in the Hyogo Framework for Action 
2005–2015 (Wiltshire, 2006).
With respect to the development of climate-driven epidemic forecasts, 
efforts are needed to improve the utility of such forecasts for the 
health sector. Few such forecasts have been operationalised to inform 
health-sector decision-making. A review of 73  studies that predicted 
and forecasted Zika virus infections (42% from the Americas) identified 
a high degree of variation in access, reproducibility, timeliness and

incorporation of uncertainty (Kobres et al., 2019). A recent systematic 
review of epidemic forecasting and prediction studies found that no 
reporting guidelines exist; the development of guidance to improve the 
transparency, quality and implementation of forecast models in the public 
health sector was recommended (Pollett et al., 2020). An earlier review of 
dengue early-warning models found that few models incorporated both 
spatial and temporal aspects of disease risk (Racloz et al., 2012), limiting 
their potential application as an adaptation strategy by the health sector. 
Advances have been made in the last decade with respect to modelling 
and computing tools, increasing access to digital climate information and health records and the use of Earth observations to forecast climate-
sensitive diseases (Fletcher et al., 2021; Wimberly et al., 2021).
The growing field of implementation science—defined as ‘a discipline

focused on systematically examining the gap between knowledge and 
action’—represents another opportunity to address the challenges 
and barriers to using climate information for health-sector decision-
making (Boyer et  al., 2020). Implementation science in the health 
sector in CSA is nascent; research in this area could help to address 
barriers to mainstreaming climate information in the health sector as 
an adaptation strategy (Table 12.11; Table SM12.7).
12.5.6.6 Governance and Financing
A description of the governance and financing dimensions of the 
feasibility of implementing EWSs is presented in Table  12.11 and 
Table SM12.7.
12.5.6.6.1 National Health Plans
Some countries have developed national plans on health including the 
role of climate. Chile has a Climate Change Adaptation Plan of the 
Health Sector that proposes several actions to enhance monitoring, 
institutions and citizen information and education (Ministry of

Health of Chile and Ministry of Environment of Chile, 2016). Based 
on the identification of vulnerability to climate change, Colombia has 
developed 11 regional adaptation plans to strengthen institutional 
capacities, climate-change education for behavioural changes and cost 
estimation to promote health resilience (WHO and UNFCCC, 2015). 
In addition, El Salvador implemented actions to strengthen health 
infrastructure using high latrines for housing in flood communities, as 
well as other measures focused on water supply and quality based 
on an education and awareness programme (Ministry of Environment 
and Natural Resources of El Salvador, 2013). Only Brazil and Peru have 
implemented actions so far in the region derived from national health 
adaptation plans, and only Brazil completed a national assessment of 
impacts, vulnerability and adaptation for health (Watts et al., 2018). 
Some countries include health as a priority sector in their NAPs, as

in the case of Ecuador and Costa Rica, which has a national plan 
addressing the prevention and care of climate-sensitive diseases 
coupled with a National Health Plan (2016–2020) (Ministry of Health 
Costa Rica, 2016; Jiménez, n.  d.).
12.5.6.6.2 National Disaster Management Plans
National Risk Management Plans or National Disaster Response Plans 
are tools for adapting to climate change that can help to diminish death 
and injuries from disasters (high confidence). These plans are generally 
promoted by governments as national instruments that guide the 
processes of estimating, preventing and reducing disaster risk. Updated 
National Risk Management Plans have been found for Guatemala 
(CONRED, 2014), Honduras (COPECO, 2014), El Salvador (Ministry of 
Health of El Salvador, 2017), Costa Rica (CNE, 2016), Ecuador (SGR, 
2018), Peru (SGRD et  al., 2014), Argentina (Ministerio de Seguridad 
de Argentina, 2018), Bolivia (VIDECI, 2017), Chile (ONEMI, 2015) and

Colombia (UNGRD, 2015). It has been shown in Brazil that information

12
1746Chapter 12 Central and South America
on drought conditions can be used to reduced health impacts of drought 
using a national disaster risk reduction framework (Sena et al., 2016).
12.5.7 Poverty, Livelihood and Sustainable Development
Climate-change impacts are increasing and exacerbating poverty and 
social inequalities, affecting those already vulnerable and disfavoured, 
generating new and interlinked risk and challenging climate resilient 
development pathways (high confidence) (Section 8.2.1.4; Shi et al., 
2016; Otto et al., 2017; Johnson et al., 2021). Poverty, high levels of 
inequality and pre-existing vulnerabilities can also be worsened by 
climate-change policies (Antwi-Agyei et  al., 2018; IPCC, 2018; Roy 
et al., 2018; Eriksen et al., 2021). Those already suffering are losing their 
livelihoods and reducing their development options; poor populations 
and countries are more vulnerable and have lower adaptive capacity

to climate change compared to rich ones (very high confidence) 
(Section 8.5.2.1; Rao et al., 2017).
Inequality is growing, a CSA structural characteristic; the Gini index 
average for Latin American countries (including Mexico) decreased to 
0.466 in 2017, where 1% of the richest got 22  times more income 
than 10% of the poorest (ECLAC, 2019b; Busso and Messina, 2020), 
but in 2018, 29.6% of Latin American populations were poor (which 
increased to 182 million) and 10.2% were living in extreme poverty; 
in 2018 (increased to 63  million) (ECLAC, 2019b) and in 2020, due 
to the COVID crisis, the Gini coefficient projection of increases range 
from 1.1% to 7.8% (ECLAC and PAHO, 2020), with poverty increasing 
to 33.7% (209 millions) and extreme poverty to 12.5% (78 millions) 
(ECLAC and PAHO, 2020; ECLAC, 2021). Those poverty and extreme 
poverty rates are higher among children, young people, women, 
Indigenous Peoples (Reckien et al., 2017; Busso and Messina, 2020),

migrants (Dodman et al., 2019) and rural populations. Climate change 
has differential impacts, and even within a household there may be 
important differences in relation to age, gender, health and disability; 
these factors may intersect with one another (high confidence) 
(Reckien et al., 2017; Busso and Messina, 2020).
In IPCC’s Third Assessment Report (TAR), AR4 and AR5, WGII recognised 
higher risks associated with poor living conditions, substandard housing, 
inadequate services, location of hazardous sites stemming from a lack 
of alternatives and the need to work more seriously on strengthening 
governance structures involving residents and community organisations, 
among others (Wilbanks et al., 2007; Revi et al., 2014). The AR5 CSA 
chapter stated that poverty levels remained high (45% for CA and 30% 
for SA in 2010) despite years of sustained economic growth. Poor and 
vulnerable groups are disproportionately affected in negative ways by

climate change (Section 8.2.1.4; Section 8.2.2.3; SR15 Section 5.2 and 
Section 5.2.1, Roy et al., 2018) due to physical exposure derived from 
their place of residence or work, illiteracy, low income and skills, political 
and institutional marginalisation tied to a lack of recognition of informal 
settlements and employment, poor access to good-quality services and 
infrastructure, resources and information and other factors (very high 
confidence) (UN-Habitat, 2018; SR15 Sections 5.2.1, 5.6.2, 5.6.3, 5.6.4, 
Roy et al., 2018).International agreements aim for climate resilient development 
pathways where efforts to eradicate poverty, reduce inequality 
and promote fair and cross-scalar adaptation and mitigation are 
strengthened. The first and second objectives of the SDGs aim to 
reduce poverty, allowing no one to fall through the cracks (UN General 
Assembly, 2015). Researchers argue that poverty is mischaracterised 
and has multiple dimensions (Castán Broto and Bulkeley, 2013)

(Section 8.1.1), that biodiversity loss, climate change and pollution will 
undermine efforts on 80% of assessed SDG targets, that biodiversity 
and climate change must be tackled together (Pörtner et  al., 2021; 
United Nations Environment Programme, 2021) and due to the COVID 
crisis LAC countries have made uneven progress in terms of meeting 
SDGs (high confidence) (ECLAC, 2020).
12.5.7.1 Challenges and Opportunities
Climate change exacerbates pre-existing vulnerability conditions 
and can drive societies further away from achieving resilience, equity 
and sustainable development (Tanner et  al., 2015b; Bartlett and 
Satterthwaite, 2016; Kalikoski et  al., 2018; Bárcena et  al., 2020a). 
Existing inequalities in the provision and consumption of services are 
bound to be exacerbated by future risks and uncertainties associated 
with climate-change scenarios (Miranda Sara et  al., 2017). Climate 
change will be a major obstacle in reducing poverty (high confidence)

(Bartlett and Satterthwaite, 2016; Allen et al., 2017a; Hallegatte et al., 
2018; UN-Habitat, 2018; United Nations Environment Programme, 
2021), affecting even wealthier populations that become vulnerable 
facing climate-change scenarios (WGI AR6 Chapter 12, Ranasinghe 
et al., 2021), dragging them into poverty and erasing decades of work 
and asset accumulation.
CSA is highly urbanised, and the vast majority of the region’s poor live 
in urban areas (except in CA), while urban extreme poverty is becoming 
more pronounced (Rosenzweig et  al., 2018; Dodman et  al., 2019; 
Almansi et al., 2020; Sette Whitaker Ferreira et al., 2020), with those 
living in informal settlements and working within informal economy 
being critical factors in each city’s economy (Satterthwaite et  al., 
2018, 2020). Many households in the region’s cities live in precarious 
neighbourhoods with insufficient infrastructure and substandard 
housing (Adler et al., 2018; Rojas, 2019). On average, between 21%

and 25% of urban populations live in informal settlements (Jaitman, 
2015; UN-Habitat, 2015; Rojas, 2019; Sandoval and Sarmiento, 2019). 
This hides important disparities: Habitat III reports, by individual 
countries, the percentage of urban population living in informal 
settlements, which ranged from 5% to 60% and in absolute terms 
means 105 million people living in precarious conditions (106 million 
estimated in 1990) (Section 12.5.5; Sandoval and Sarmiento, 2019).
High levels of inequality and informality remain the biggest challenges 
in terms of adaptation measures being effective (Rosenzweig et al., 
2018; Dodman et al., 2019). The interaction of projected impacts with 
existing vulnerabilities in the region (such as hunger, malnutrition 
and health inequalities, arising from the region’s social, economic 
and demographic profile) affects CSA development and well-being in 
different ways (Reyer et al., 2017) increasing poverty and inequality

and threatening paths to sustainable development (Section  18.1.1; 
Reckien et al., 2017).

12
1747Central and South America  Chapter 12
The uneven enforcement of land use regulations, relocations and 
evictions in connection with environmental risk management and 
climate adaptation is a contested issue (Brockington and Wilkie, 
2015; Lavell, 2016; Quimbayo Ruiz and Vásquez Rodríguez, 2016a; 
Quimbayo Ruiz and Vásquez Rodríguez, 2016b; Anguelovski et  al., 
2018; Anguelovski et  al., 2019; Shokry et  al., 2020; Chávez Eslava, 
2021; Oliver-Smith, 2021). This suggests that caution in framing 
climate adaptation and resilience related interventions equally benefits 
everyone (high confidence) (Brown, 2014; Chu et al., 2016; Connolly, 
2019; Romero-Lankao and Gnatz, 2019; Johnson et al., 2021) and that 
equality and justice dimensions should be incorporated into decision-
making (very high confidence) (Section 18.1.2.2; Agyeman et al., 2016; 
Meerow and Newell, 2016; Romero-Lankao et  al., 2016; Shi et  al., 
2016; Reckien et al., 2017; Leal Filho et al., 2021).

Poor rural households in marginal territories that have a low productive 
potential and/or that are far from markets and infrastructure are 
highly vulnerable to climate-change impacts and could easily fall into 
poverty-environment traps (high confidence) (Barbier and Hochard, 
2019; Heikkinen, 2021). Climate change is one of the main threats to 
rural livelihoods in CA, since agriculture is a pillar of rural economies 
and food security, especially in the poorest sectors, which rely on 
subsistence crops in areas with low soil fertility and rainfall seasonality 
(Bouroncle et al., 2017).
Impacts are likely to occur simultaneously, exacerbating the challenges 
faced by the poorer segments of society, but also creating new groups 
at risk (Miranda Sara et al., 2016; Rosenzweig et al., 2018; Dodman 
et  al., 2019). The material basis for poor and vulnerable urban and 
rural populations’ adaptations is in a critical state across the CSA

region, magnifying extreme events’ impacts, making CSA less resilient. 
Consequences in terms of social vulnerability and livelihood will be 
widely felt, inasmuch as the security and protection of critical assets 
(housing, infrastructure and water, land and ecosystem services) 
continue to lag behind. Small businesses are usually located within 
homes, and if the home is affected, so is the business (Stein and Moser, 
2015), adding another layer of vulnerability for this population.
As productivity declines, outside sources of income are sought, and 
people rely on resource extraction for subsistence and for income, 
further increasing their vulnerability to climate change (Barbier and 
Hochard, 2018a). Cycles of declining productivity, environmental 
degradation, wildlife poaching and trafficking, the search for outside 
employment, reduced incomes, livelihood opportunities and poverty 
have been observed in rural El Salvador, Honduras, Amazonia (López-

Feldman, 2014; Graham, 2017; Barbier and Hochard, 2018a). The 
protection of communities that defend and are dependent on wildlife 
and natural environments requires immediate attention. Latin America 
is home to eight million forest-dependent people, which represents 
about 82% of the region’s rural extreme poor (FAO and UNEP , 2020).
Poverty and disaster risk reduction interlinked with climate-change 
adaptation share a focus on identifying and acting on local risks and 
their root causes, even though they view risk through different lenses 
(very high confidence) (IPCC, 2014; Allen et al., 2017a; Satterthwaite 
et  al., 2018, 2020; UN-Habitat, 2018). Construction of climate 
knowledge and risk perceptions affect decision-making to define implementation priorities, but the poor are less able to cope with and 
adapt so as to avoid so-called adaptation injustices (high confidence) 
(Mansur et al., 2016; Miranda Sara et al., 2017; Reckien et al., 2017; 
Hardoy et al., 2019).

Adaptation, social policies, poverty reduction and inequality are weakly 
articulated to daily or chronic risk reduction. Poor residents are often 
caught in ‘risk traps’, accumulated cycles of everyday risks and small-
scale disasters (medium confidence: medium evidence, high agreement) 
(Bartlett and Satterthwaite, 2016; Mansur et al., 2016; Allen et al., 2017a; 
Leal Filho et al., 2021), which are exacerbated by climate risks and COVID 
pandemic with the most vulnerable populations suffering. Chronic and 
everyday risks (poor access to infrastructure, services, incomes, housing, 
land tenure, education, security, location and poor-quality environment 
and networks and lack of a voice) are often exacerbated and 
generate new unknown risks by climate change (medium confidence: 
medium evidence, high agreement) (Bartlett and Satterthwaite, 2016; 
Mansur et al., 2016; Satterthwaite et al., 2018; Leal Filho et al., 2021),

extreme events and risks related to ENSO oscillation. All these risks 
need to be considered simultaneously (UN-Habitat, 2018). Risks are 
seldom distributed equally, highlighting socioeconomic inequalities 
and governance failures (high confidence) (IPCC, 2014; Bartlett and 
Satterthwaite, 2016; Rasch, 2016; Romero-Lankao et al., 2018).
Adaptation, disaster risk reduction and social and poverty reduction 
policies contribute to sustainable development (Hallegatte et al., 2018; 
Satterthwaite et al., 2020) and improve prospects for climate-resilient 
pathways (Section 18.1.1). Without pro-poor interventions, adaptation 
options could reinforce poverty cycles (Kalikoski et al., 2018). Secure 
locations, good-quality infrastructure, services and housing are critical 
to reducing risks from extreme climate events (Satterthwaite et  al., 
2018; Dodman et al., 2019).
12.5.7.2 Governance and Finance
Poor and most vulnerable groups have limited political influence,

fewer capacities and opportunities to participate in decision and 
policymaking and are less able to leverage government support to 
invest in adaptation measures linked with poverty, inequality and 
vulnerability reduction (very high confidence) (Chapter 8; Miranda 
Sara et al., 2017; Reyer et al., 2017; Kalikoski et al., 2018; Dodman 
Existing imbalances in power relations, corruption, historic structural 
problems and high levels of risk tolerance (Miranda Sara et al., 2016) 
constitute climate governance barriers to implementing more effective 
adaptation and preventive measures. Corruption, particularly in the 
construction and infrastructure sectors, has proven to be a barrier 
to CSA development, even reproducing and reconstructing the same 
risks (French and Mechler, 2017; Vergara, 2018; Durand, 2019). Critical 
infrastructure and valuable assets continue to be placed in vulnerable

areas (Calil et  al., 2017; Escalante Estrada and Miranda, 2020), 
demonstrating the persistence of maladaptation and adaptation 
deficit (Villamizar et al., 2017).
Social organisation, participation and governance reconfiguration are 
essential for building climate resilience (very high confidence) (Stein

12
1748Chapter 12 Central and South America
and Moser, 2015; Kalikoski et  al., 2018; Satterthwaite et  al., 2018, 
2020; Stein et  al., 2018; Hardoy et  al., 2019; Stein, 2019; Miranda 
Sara, 2021). Adaptation measures have trade-offs that need to be 
acknowledged and acted upon, most importantly by developing the 
capacity to convene discussions that draw in all key actors and commit 
them to do things differently (Almeida et al., 2018; Hardoy et al., 2019). 
Collaborative approaches integrate groups and organisations (e.g., 
saving, women’s groups, clubs, vendor associations, cooperatives) 
contributing to the exchange of information to visibilise people’s 
needs, generate safety networks and negotiate for improvements and 
enhance adaptive capacity.
12.5.7.3 Adaptation Options
Effective adaptation can be achieved by addressing pre-existing 
development deficits, particularly the needs and priorities of informal 
settlements and economies (Revi et  al., 2014; UN-Habitat, 2018).

There is urgency in making sure that social systems are better able to 
respond to climate-related risks and increase their adaptive capacity 
(Lemos et al., 2016), focusing on path dependency, lock-ins and poor 
specific needs (Leal Filho et al., 2021).
The linkages between climate adaptation and poverty are not clearly 
addressed at the national level (Kalikoski et al., 2018). A revision of 
some NDCs presented by CSA countries (https://unfccc.int) shows 
that NDCs are developed with almost no connection to poverty and 
livelihoods. Exceptions include Bolivia, whose NDC developed the 
‘good life’ concept as an alternative development pathway, supporting 
sustainable livelihoods as a means to eradicate poverty. Honduras 
asserts that climate action should improve living conditions. Peru 
defined a poverty and vulnerability reduction approach. Finally, El 
Salvador conditioned its NDCs to macroeconomic stability, economic

growth and poverty reduction. A sustainable development approach 
permeates the proposed actions for sectors such as energy, agriculture, 
transport, water and forestry.
Adaptive capacity is linked to addressing climate-related risks (specific 
capacity) and structural deficits (generic capacity) and synergies, and a 
strategic balance between both is necessary (Eakin et al., 2014; Lemos 
et al., 2016). Adaptation institutional context can undermine one form 
of capacity with repercussions for the other compromising overall 
adaptation and sustainable development (Eakin et al., 2014).
The literature assessing the effectiveness of pro-poor or community-
based adaptation practices and livelihood options continues to be 
weak, though such practices and options are being increasingly 
documented, as in AR5 (Magrin et al., 2014). A great variety of measures 
and financial instruments are being applied to strengthen and protect

livelihoods and assets: collective insurance schemes, micro-credit, 
financial instruments for transferring risks, agricultural insurance and 
PES (Dávila, 2016; Hardoy and Velásquez, 2016; Lemos et al., 2016; 
Porras et  al., 2016; Kalikoski et  al., 2018). Small-scale household 
businesses in poor neighbourhoods develop adaptation strategies 
to keep operations going, showing how household-level adaptation 
strategies are multi-purpose (Stein et al., 2018; Stein, 2019). There are 
emerging interinstitutional communities of practice whose aim is to 
share practices and lessons learned (ECLAC, 2013, 2015, 2019a).There is also increasing evidence of human mobility associated with 
climate change and disaster risk (IOM, 2021) and the adoption of 
sustainable tourism, diversification of livelihood strategies, climate 
forecasts, appropriate construction techniques, neighbourhood layout, 
integral urban upgrading initiatives, territorial and urban planning,

regulatory frameworks, water harvesting and NbS (Stein and Moser, 
2014; Hardoy and Mastrangelo, 2016; Almeida et  al., 2018; Barbier 
and Hochard, 2018a; Desmaison et al., 2018; Satterthwaite et al., 2018, 
2020; Villafuerte et al., 2018; Hidalgo, 2020). Mostly, socioeconomical 
and sociopolitical factors show that safety and continuity measures 
are critical enablers of adaptation.
At the municipal level, a study in CA highlighted that adaptive capacity 
in rural areas is associated with the satisfaction of basic needs (safe 
drinking water, school, quality dwelling, gender parity index), access to 
resources for innovation and action (road density, economically active 
population with non-agricultural employment and rural demographic 
dependency ratio) and access to credit and technical support 
CSA adaptation initiatives to reduce poverty, improve livelihoods and 
achieve sustainable development in scale and scope, from planned and

collective interventions to autonomous and individual actions. Many of 
them are bottom-up, community-led initiatives together with civil society 
organisations; others are government-led, including local governments, 
or a combination of them (McNamara and Buggy, 2017; Berrang-Ford 
et al., 2021). Vulnerable groups are a focus to achieve equity at planning 
and as a target including mainly rural low-income, Indigenous Peoples 
and women and migrants in most references. Responses detected were 
focused on behavioural and cultural followed by ecosystem-based 
responses, institutional, and technological/infrastructural responses. 
Out of 55  articles analysed from CSA (Berrang-Ford et  al., 2021) 
about poverty, equity and adaptation options, half covered adaptation 
planning and early implementation, but only 2% could show evidence 
of risk reduction associated with adaptation efforts.
Tensions and conflicts may result from differing perceptions and

knowledge of vulnerabilities and risk, which can hinder the acceptance 
of adaptation measures or the implementation of stronger adaptive 
or preventive actions (Miranda Sara et al., 2016). There is a need to 
better understand complex interactions and community responses to 
climate change in the Amazonian and Andean regions. Climate-change 
hotspot impacts have shown that poverty reduction measures alone 
were not enough to improve adaptive capacity because people will 
not necessarily invest in their enhancement (Pinho et al., 2014; Filho 
et al., 2016; Nelson et al., 2016; Lapola et al., 2018; Zavaleta et al., 
2018). Current adaptation strategies and methods may be neglecting 
cultural values, even eroding them, in the Peruvian Andes, indicating 
that success of adaptation practices is tied to deep cultural values 
(Walshe and Argumedo, 2016).
Limits to adaptation include access to land, territory and resources 
(Mesclier et  al., 2015), poor labour opportunities coupled with

knowledge gaps, weak multi-actor coordination, and lack of effective 
policies and supportive frameworks (Berrang-Ford et al., 2021).

12
1749Central and South America  Chapter 12
Low participation of women in income-earning opportunities contrasts 
with their role in unpaid activities (ECLAC, 2019b). Despite the progress 
that has been made, gender differences in labour markets remain an 
unjustifiable form of inequality (OIT, 2019), and women easily fall back 
on the informal labour market during crisis situations, such as those 
generated by climate events (Collodi et al., 2020).
Participatory processes are leveraging adaptation measures throughout 
CSA; they contribute to the prioritisation of specific adaptation 
measures as well as the strengthening of local capacities. Results of 
participatory processes show how climate adaptation needs to be part 
of larger transformation processes to that have vulnerable communities 
at the center and reduce vulnerability drivers (Stein and Moser, 2015; 
Stein et al., 2018; Stein, 2019). Stronger national policies interlinking

poverty and inequality reduction to adaptation considering the coupled 
human-environmental systems to comprehend poor and vulnerable 
groups’ capacity to adapt are urgently needed.
12.5.8 Cross-cutting Issues in the Human Dimension
12.5.8.1 Public Policies, Social Movements and Participation
Public policies related to adaptation must be seen in the wider 
context of environmental policies and governance, as they usually 
address climatic processes in synergy with other environmental 
and socioeconomic drivers (very high confidence) (Ding et al., 2017; 
Aldunce Ide et  al., 2020; Comisión Europea, 2020; Lampis et  al., 
2020; Scoville-Simonds et  al., 2020). However, some people point 
to education, sanitation or social assistance, among other sectors 
(Bonatti et  al., 2019). In Brazil, for example, it would be difficult to 
clearly separate climate-change adaptation and urban policies (high 
confidence) (PBMC, 2016; Barbi and da Costa Ferreira, 2017; Marques

Di Giulio et al., 2017; Empresa de Pesquisa Energética, 2018; Checco 
and Caldas, 2019; Canil et al., 2020).
Many public policies related to climate change have become symbolic, 
in conflict with prevailing economic policies and practices (medium 
confidence: low evidence, high agreement). Urban adaptation plans 
can be in conflict with other policies, and there may exist insufficient 
support in multiple areas such as social attitudes and behaviour, 
knowledge, education and human capital, finance, governance, 
institutions and policy (Villamizar et  al., 2017; Koch, 2018). Some 
policies around climate-related displacements and migrants have been 
considered in NDCs (Priotto and Salvador Aruj, 2017; Yamamoto et al., 
2018; de Salles Cavedon-Capdeville et al., 2020).
Because there are asymmetries among populations regarding the 
vulnerability and benefits of adaptation, along the lines of gender, 
age, socioeconomic conditions and ethnicity, it has been noticed that

adaptation policies and programmes must be adequate to diverse 
conditions and actors (very high confidence) (Kaijser and Kronsell, 
2014; Walshe and Argumedo, 2016; Baucom and Omelsky, 2017; 
Harvey et al., 2018).
Effective adaptation and mitigation depend on policies and measures 
at multiple scales, especially when it comes to the involvement of more exposed and vulnerable people. The participation of experts, 
communities and citizens has shown to be effective (FAO and Fundación 
Futuro Latinoamericano, 2019), particularly through partnerships 
between grassroots organisations and impoverished communities, 
providing valued expertise and capacities to support the implementation 
of government climate resilience strategies (World Bank Group, 2015). 
More inclusive planning processes correspond to higher climate equity 
and justice outcomes in the short term, but an emphasis on building 
dedicated multi-sector governance institutions may also enhance long-

term programmes’ stability while ensuring civil society has a voice in 
adaptation planning and implementation (Chu et al., 2016). Some local 
organisations and people have succeeded when they were in charge of 
their own resiliency efforts, where international projects and protocols 
proved less effective (Doughty, 2016). Some decentralised governmental 
programmes have tried to increase public responsiveness to the 
adaptation needs of the people, but such programmes have proven to 
be only mildly successful and provoke the mobilisation of communities 
against existing governance structures (Thompson, 2016).
IKLK participation is thought to be more considered in adaptation 
policies because it yields good results (high confidence) (Nagy et al., 
2014b; Jurt et  al., 2015; Arias et  al., 2016; Stensrud, 2016). IK has 
been adaptive for long periods in the Andes (Cuvi, 2018), but there 
might be limits to adaptation in the face of present climatic and other

environmental and socioeconomic drivers (Postigo, 2019). Approaches 
integrating IK with more formal sciences, to address research and 
policies, have improved adaptation processes, but they carry their own 
complications (high confidence) (Doswald et  al., 2014; Metternicht 
et  al., 2014; Tengö et  al., 2014; Drenkhan et  al., 2015; Keenan, 
2015; Lasage et  al., 2015; Camacho Guerreiro et  al., 2016; Hurlbert 
and Gupta, 2016; Roco et al., 2016; Santos et al., 2016; Walshe and 
Argumedo, 2016; Uribe Rivera et  al., 2017; Kasecker et  al., 2018; 
Cuesta et  al., 2019; Ulloa, 2019; Ariza-Montobbio and Cuvi, 2020). 
More interdisciplinary and transdisciplinary research will help to 
better understand and manage the relationships among governance, 
implementation, management priorities, wealth distribution and trade-
offs between adaptation, mitigation and the SDGs.
Representations of climate change can also emerge as critiques

and resistances that reveal that climate-change-labelled politics or 
interventions have posed even greater risks or do not address poverty 
issues (medium confidence: medium evidence, high agreement) 
(Lampis, 2013; Pokorny et  al., 2013; Ojeda, 2014). Indigenous and 
social movements have joined with climate justice activists, calling 
for action to address climate change (Hicks and Fabricant, 2016; 
Ruiz-Mallén et al., 2017; Charles, 2021). The Bolivian Platform against 
Climate Change, a coalition of civil society and social movement 
organisations working to address the effects of global warming in 
Bolivia and to influence the broader global community, reflects an 
innovative dimension that, though at times conflictual, has shown how 
increasing climate variability hinders the right of Indigenous Peoples 
to the conservation of their culture and practices and illustrates how 
grassroots movements are increasingly taking over climate-change

policy in the region (Hicks and Fabricant, 2016). Social movements 
have engaged with international networks, such as Blokadia, which 
surged after COP 23, whose claims try to go beyond the protection

12
1750Chapter 12 Central and South America
of the environment and delve into issues of democracy and resource 
control (Martínez-Alier et al., 2018).
Many social movements address adaptation to climate change. Some 
engage and participate in policy and planning, often producing good 
results at the local level. In contrast, top-down approaches without 
citizen or community participation have shown to be less effective 
(high confidence) (Krellenberg and Katrin, 2014; Nagy et al., 2014b; 
Stein and Moser, 2014; Ruiz-Mallén et  al., 2015; Sherman et  al., 
2015; Waylen et al., 2015; Bizikova et al., 2016; Chelleri et al., 2016; 
Merlinsky, 2016; Villamizar et al., 2017).
Some conflicts in which the direct biophysical impacts of climate 
change play a major role can unleash social protests and strengthen 
social movements (Section  12.6.4). In Cartagena, since 2010, the 
increase in precipitation has increasingly impacted the barrio

Policarpa, promoprting residents to call for solutions to the problems 
caused by the coupled effect of flooding and industrial pollution. Also, 
in El Cambray II, in Guatemala City, in 2015 a nearby hill collapsed, 
causing the deaths of 280 people, 70 missing and the destruction of 
hundreds of homes. The affected community entered into a conflict 
with the municipality demanding resettlement and a reform of land-
use planning (Stein Heinemann, 2018).
12.5.8.2 Perceptions
Perception and understanding of climate change can be seen as an 
adaptive feature. In CSA, the awareness of climate change as a threat 
is increasing, a situation related to growth in climate justice activism 
and to the occurrence of extreme weather events of all kinds (high 
confidence) (Forero et al., 2014; Magrin et al., 2014; Capstick et al., 2015). 
Perception of climate change is positively associated across countries 
with the HDI and ND-Gain Readiness Index and negatively associated

with the Vulnerability Index and, within countries, with education level, 
while perception is negatively associated with the degree of political 
affinity for the market economy (Azócar et al., 2021). However, some 
communities do not associate their problems with the scientific concept 
of climate change, so discussions on whether it is human induced and 
its causes or relationship with other problems can become irrelevant 
(Sapiains Arrué and Ugarte Caviedes, 2017). Even communities affected 
by the same changes do not necessarily perceive them in the same way 
(Bonatti et al., 2016). The interpretations of change, as well as its causes 
and effects, can vary widely (Paerregaard, 2018; Scoville-Simonds, 2018). 
Rather than adapting to climate change, some people adapt climate 
change to their social worlds (Rasmussen, 2016a).
Perceptions tend to be different in rural and urban areas (Sherman et al., 
2015). In rural areas, it largely relates to temperature rise and changes

in rainfall patterns, changes in agriculture (pests, calendars), biodiversity 
loss, solar radiation or changes in the oceans, and their impacts are 
sometimes related or even more attributed to socioeconomic and 
environmental drivers, as well as to negative financial outcomes (high 
confidence) (Infante and Infante, 2013; Postigo, 2014; Jacobi et al., 2015; 
Barrucand et al., 2017; Harvey et al., 2018; Martins and Gasalla, 2018; 
Meldrum et al., 2018; Córdoba Vargas et al., 2019; Leroy, 2019; Viguera 
et al., 2019; Gutierrez et al., 2020; Iniguez-Gallardo et al., 2020; Lambert 
and Eise, 2020). In such places as Amazonia, perception increases with age (Funatsu et al., 2019). In Mediterranean Chile, younger, more 
educated producers and those who own their land tend to have clearer 
perceptions than older, less educated or tenant farmers, but they do not 
have a clear perception or how it may affect their yields and farming

operation (Roco et  al., 2015). In some dry and humid Ecuadorian 
montane forests, peasantss perceptions are in line with the scientific 
data, but they have a lot of difficulties to predict the changes and believe 
that they may not be prepared and can only be reactive (Herrador-
Valencia and Paredes, 2016). In an Andean community, perceptions of 
climate change are homogeneous and do not vary according to gender, 
age or ethnicity (Cáceres-Arteaga et al., 2020). Among representatives 
of five municipalities of Lima, it was found that climate change is not 
well understood and residents have trouble distinguishing it from 
other environmental issues (Siña et al., 2016). In an Amazonian region, 
farmers provided a more accurate description than regional institutions 
of how it affects the local livelihood system (Altea, 2020). In Cuenca 
Auqui peasants attribute recently experienced challenges in agricultural 
production mainly to perceived changes in precipitation patterns, but

statistical analyses of daily precipitation records at nearby stations do 
not corroborate those perceived changes (Gurgiser et al., 2016).
12.5.8.3 Gender and Intersectionality
There is ample empirical evidence that the impacts of climate change 
are not of equal scope for men and women. Women, particularly 
the poorest, are more vulnerable and are impacted in greater 
proportion. Often, for several economic and social reasons, women 
(high confidence) (Box 7.4; Arana Zegarra, 2017; Casas Varez, 2017; 
Segnestam, 2017; Acosta et al., 2019; Aldunce Ide et al., 2020; Olivera 
et al., 2021; Silva Rodríguez de San Miguel et al., 2021). Gender equity 
is deemed to be central to discussions on climate-change adaptation 
policies. In issues such as drinking water, energy, disasters, impacts on 
health and agriculture and capacity to migrate, women (poor women

in particular) are affected in greater proportion, further widening 
structural gender gaps. In a rural community vulnerable to drought, 
short-term coping was more common among the women, especially 
among female heads of household, while adaptive actions were more 
common among the men; there are gendered inequalities in access 
to and control over different forms of capital that lead to a gender-
differentiated capacity to adapt, where men are better able to adapt 
and women experience a downward spiral in their capacity to adapt 
and increasing vulnerability to drought (Segnestam, 2017).
However, women are not always the more vulnerable group. While in a 
broad sense climate-change impacts women more severely, there are 
situations where they have reacted, adapted better to or been more 
resilient. Grassroots women self-help groups can be active agents 
of change for their communities, designing and delivering gender-

responsive adaptation solutions (Huairou Commission, 2019). Some 
studies suggest that women establish friendlier relationships with the 
environment and towards natural resources; studies on masculinity and 
environment confirm this tendency (Brough et al., 2016). In a multi-
country study, some female-headed households tend to be slightly 
less vulnerable and more resilient than male-headed households, 
though some exceptions were found among sub-groups (Andersen 
et al., 2017). In Chile, women are more likely to modernise irrigation

12
1751Central and South America  Chapter 12
and infrastructure, and gender appears to be an important element 
in drought adaptation (Roco et al., 2016). A change to agroecological 
practices has improved gender equality and adaptive capacity to 
climate change (Cáceres-Arteaga et al., 2020).
Recent studies emphasise that a gender approach to social inequalities 
ought to move beyond just looking at men and women as experiencing 
impacts in a differentiated manner; rather, an intersectional analysis 
illuminates how different individuals and groups relate differently to 
climate change due to their situatedness in power structures based on 
context-specific and dynamic social categorisations (high confidence) 
(Kaijser and Kronsell, 2014; Djoudi et al., 2016; Thompson-Hall et al., 
2016; Olivera et  al., 2021). Thus, the relationship between gender 
and adaptation demands an analytical framework that connects 
environmental problems with social inequalities in a complex way

(Godfrey, 2012). An intersectional approach helps to better capture 
the diversity of adaptive strategies that men and women adopt vis-à-
vis climate change. Particular constellations of race, gender, class, age 
or nationality reveal more complex realities (high confidence).
12.5.8.4 Migrations and Displacements
Migration and displacements are multi-causal phenomena, and climate 
may exacerbate political, social, economic or other environmental 
drivers (high confidence) (Kaenzig and Piguet, 2014; Brandt et  al., 
2016; Priotto and Salvador Aruj, 2017; Sudmeier-Rieux et  al., 2017; 
Radel et  al., 2018; Heslin et  al., 2019; Hoffmann et  al., 2020; Silva 
Rodríguez de San Miguel et al., 2021). Many case studies have been 
conducted on the region, but data to assess and monitor precisely 
the effects of climate- and weather-related disasters in migration and 
displacements from a broad perspective remain inaccurate (Priotto and

Salvador Aruj, 2017; Abeldaño Zuñiga and Fanta Garrido, 2020). The 
most common climatic drivers include tropical storms and hurricanes, 
heavy rains, floods and droughts (Kaenzig and Piguet, 2014). Positive 
climatic conditions also can facilitate migration (Gray and Bilsborrow, 
2013). Peru, Colombia and Guatemala are among the countries with 
the largest average displacements caused by hydro-meteorological 
causes; Brazil had 295,000 people displaced because of disasters in 
2019 (Global Internal Displacement Database, https://www.internal-
displacement.org/database/displacement-data).
These processes can be interpreted as impacts on vulnerable peoples, 
but also as adaptation strategies to manage risks and reduce exposure 
when people continue with their lives, temporarily or permanently, 
in a different but stable situationor when family members send 
remittances to those that remain in the affected areas (Section 7.4.3.2;

Cross-Chapter Box  MIGRATE in Chapter 7). The remittances create 
opportunities for adaptive capacity building because they reduce some 
vulnerabilities in the form of infrastructures, agricultural supplies, food, 
education or health, as in northern CA (NU CEPAL, 2018). Anyhow, 
migration as adaptation is not available to everyone (Kaenzig and 
Piguet, 2014), and the idea has also been contested because it may 
not help to overcome structural problems or point to in situ options 
(Radel et  al., 2018; Ruiz-de-Oña et  al., 2019). The causal processes 
are complex. Surveys of migrants usually find that the main reported 
reason for migration is to find a job or to increase household income 
(Wrathall and Suckall, 2016; OIM, 2017; Radel et al., 2018), but the underlying reason for the lack of a job or income is rarely examined 
and at times may be related to climatic hazards.
Migration most often originates in rural areas, with people moving to

other rural or urban areas within their home countries (Table Cross-
Chapter Box MIGRATE 1 in Chapter 7). In the Amazon, approximately 
80% of the population are concentrated in cities due to rural–urban 
migrations in search of better income, livelihoods and services, in cases 
associated with extreme floods and droughts (Pinho et  al., 2015). In 
Ecuador, environmental variables are most likely to enhance international 
than internal migration (Gray and Bilsborrow, 2013). Hurricanes have 
been seen as positive triggers for international migration in CA (Spencer 
and Urquhart, 2018). The highlands of Peru see different patterns, 
including daily circular migration to combine the scarce income from 
agricultural production with urban income, rather than abandoning farm 
land (Milan and Ho, 2014; Zimmerer, 2014; Bergmann et al., 2021).
Migration to cities can mean opportunities for migrants and for urban 
areas, but it can also worsen existing problems, as urban poor people

can become even more exposed and vulnerable, and the pressure on 
urban capacities may not be well absorbed (high confidence) (Chisari 
and Miller, 2016; Gemenne et al., 2020). Internal migration to cities is 
likely to exacerbate pre-existing vulnerabilities related to inequality, 
poverty, indigence and informal activities and housing (Warn and 
Adamo, 2014). Immigration can make cities/residents more vulnerable 
to climate-change risks (Sections 12.5.5 and 12.5.7). Groups such as 
children, Indigenous Peoples and the poor are usually among the most 
vulnerable in migrations and displacements, which poses challenges 
to national policies and international aid (Sedeh, 2014; Gamez, 2016; 
Ulla, 2016; Priotto and Salvador Aruj, 2017; Ramos and de Salles 
Cavedon-Capdeville, 2017; Amar-Amar et  al., 2019; Gemenne et  al., 
2020). In migration or displacement driven by climate effects, women 
are prone to lose their leadership, autonomy and voice, especially in

new organisational structures imposed by authorities. This is especially 
the case in temporary accommodation camps created after disasters, 
exacerbating existing differentiated vulnerabilities (Aldunce Ide et al., 
2020). International migration has become more dangerous and 
difficult as border controls have become stricter, but programmes 
such as one to help temporary agricultural workers from Guatemala 
to Canada have proven successful (Gabriel and Macdonald, 2018). At 
the same time, emigration may lead to the loss of IKLK for adaptation 
(Moreno et al., 2020b).
Some areas are more likely to generate climatic migration: the Andes, 
the dry areas of Amazonia, northern Brazil and northern countries in 
CA (high confidence). Northeastern Brazil will lose population that 
will move to the south, deepening existing inequalities (Oliveira and 
Pereda, 2020). In a study of eight countries around the world, including 
Guatemala and Peru, a link was found between rainfall variability

and food insecurity, which could lead to migration in areas of high 
prevalence of rainfed agriculture and low diversification (Warner and 
Afifi, 2014). In CA, younger individuals are more likely to migrate in 
response to hurricanes and especially to droughts (Baez et al., 2017).
The perception of gradual changes lowers the likelihood of internal 
migration, while sudden-onset events increase movement (Koubi et al., 
2016). On the other hand, it has been seen that extreme events like

12
1752Chapter 12 Central and South America
floods or droughts can hinder population mobility, immobilising them 
in their localities (Thiede et  al., 2016). These immobilised populations 
are supposed to face a double set of risks: they are unable to move 
away from environmental threats, and their lack of capital makes them 
especially vulnerable to environmental changes (Black et al., 2011). In 
CSA, migrating to the US is becoming dangerous and expensive because 
that country is restricting entry; these trends expose local populations to 
the risk of becoming immobile in the near future in a place where they 
are extremely vulnerable (Ruano and Milan, 2014; McLeman, 2019). A 
survey in Guatemala found no correlation between migration to the US 
and severe food insecurity in households, but the correlation became 
significant if the level of food insecurity was moderate, suggesting that 
families in extreme hardship did not have the resources to migrate

(Aguilar et  al., 2019). At the same time, some populations just have 
chosen not to move, as in Peru, where immobility among dissatisfied 
people is more likely to be caused by attachment to place than resource 
constraints (Adams, 2016; Correia and Ojima, 2017). Some populations 
have chosen to adapt relying on their IKLK (Boillat and Berkes, 2013).
Migration is often the last resort for rural communities facing water 
stress problems (Magrin et al., 2014; Ruano and Milan, 2014). In Bolivia, 
glacial retreat has not triggered new migration flows and had a limited 
impact on the existing migratory patterns (Kaenzig, 2015). In SA, 
climatic variability increases the likelihood of interprovince migration, 
rather than trapping populations. In a study of interprovincial migration 
motivated by temperature, an exception arose in Bolivia, and even if 
that could suggest an immobilised population (Thiede et al., 2016), it is

not clear whether they want to stay and adapt. In some cases, people 
want to move but wait for relocation until after the climate-related 
disasters have subsided (Priotto and Salvador Aruj, 2017).
12.5.8.5 Financing
Climate-change financing is unequally distributed among CSA 
countries (high confidence). Financing of climate-change adaptation 
remains very much delegated to multilateral and bilateral cooperation, 
and the governments in the region have heavily relied on it. Still, there 
are some concerns regarding justice in the distribution of these funds 
(Khan et al., 2020). The UNFCCC has created financing mechanisms 
throughout its functioning years, but a wide range of issues that 
present challenges for access by recipients (Hickmann et  al., 2019). 
These include a lack of technical capacity, difficulties in following 
the procedures established by the various financial entities and low 
levels of awareness about the need for action, as well as the different

sources of funds available. The fiscal policies of various countries 
have contributed to government financing in the fight against climate 
change (World Bank, 2021). Since the Paris Agreement, countries 
have pledged NDCs that introduce the need to design and implement 
carbon budgets with a corresponding consideration of the efficiency 
and costs and benefits involved in each mitigation or adaptation to 
climate-change projects (Fragkos, 2020).
According to UNFCCC, Latin America and the Caribbean, for 
the period 2015–2016, obtained 22% of climate financing from 
multilateral climate funds. In this section we use data from https://
climatefundsupdate.org/data-dashboard, and most of the reported 
information for Latin American and the Caribbean includes Mexico, since the scope of this chapter does not include Mexico, so we must 
rely on the raw data included in the data dashboard mentioned in 
the link (see also Guzmán et al. [2016]). According to the data, 76%

of climate-related financing went to mitigation projects, with the 
remaining 24% going to adaptation. Of the total financing provided 
by the multilateral climate funds to the region, 51% took the form of 
concessional loans, while 47% was provided as grants. For the region, 
approvals in the 2015–2016 period were concentrated in Argentina, 
Chile, Brazil and Colombia, where large-scale mitigation projects 
were launched supported by the Green Climate Fund (GCF) and the 
Clean Technology Fund (CTF). For the period 2003–2019, the total 
contribution of climate financing to SA and the Caribbean is about 
USD 3558 million. The largest contributors to climate financing in the 
region come from the GCF , which approved USD 824.2 million for 23 
projects. Brazil is the top recipient with USD 195 million, followed by 
Argentina with about USD 162  million. The second provider is the 
Amazon Fund with USD 717 million allocated to 102 projects in Brazil.

In 2018, the CTF became the third largest source of financing, with USD 
483 million dollars approved for 24 projects; the main recipient is Chile 
with USD 16,207 million, followed by Colombia with USD 170 million. 
The five largest projects approved in the region in 2018 were through 
the GCF . Brazil (USD 195 million) received support for reducing energy 
consumption across Brazilian cities, while Argentina (USD 103 million) 
received support to scale up investments by small and medium-sized 
enterprises (SMEs) in renewable energy and energy efficiency. In both 
cases, financing is predominantly provided as concessional loans.
Climate financing in CSA is mainly focused on mitigation actions (high 
confidence). In SA and the Caribbean, 73% (USD 2579  million) of 
funding to date has supported mitigation. Only 21% (USD 761 million) 
of the funding supports adaptation projects, and the remaining 
4% (USD 217  million) supports multi-focus projects. Of the 51 new

projects in SA and the Caribbean approved in 2018–2019, the GCF 
financed USD 508 million over ten projects. Amazon Fund was next 
with USD 81 million for 10 projects. While the GCF focuses on large 
and transformative projects and programmes, and in connection with 
broader reform of the policy framework in the region, the Amazon 
Fund targets smaller project interventions.
Climate financing in the region is concentrated in Brazil, which receives 
a third of the region’s funding, with 41 mitigation activities receiving 
more than 6 times that of adaptation from multilateral climate funds. By 
the size of its GDP , Brazil receives the largest amount of financing; this 
leaves the poorest countries with little or no financing and therefore 
reinforces a vicious circle of poverty and vulnerability. Whether this is 
due to Brazil’s being more successful at presenting eligible projects, a 
lack of commitment from other developing countries or some other

structural factors is an open question. In any case, compensation 
schemes for the most vulnerable countries appear as needed, given 
the differences in vulnerability to climate-related damage (Antimiani 
et  al., 2017). This is aggravated by the fact that fund management 
is in the hands of supranational entities while inequalities remain in 
regions within a country, particularly in highly centralised countries, as 
is the case for countries in the region.
COVID-19 recovery plans can have synergistic effects for climate-change 
adaptation (medium confidence: low evidence, high agreement). A

12
1753Central and South America  Chapter 12
key decision point for adaptation will be how the world responds to 
the pandemic. The global recovery can serve as a catalyst to increased 
and more equitable climate financing. Globally, recovery packages 
will likely have the power to change the global trajectory towards 
meeting the targets of the Paris Agreement and building a more just 
future (Forster et al., 2020). Several factors are relevant to the design 
of economic recovery packages: the long-run economic multiplier, 
contributions to the productive asset base and national wealth, speed 
of implementation, affordability, simplicity, impact on inequality and 
various political considerations (Hepburn et al., 2020). A key objective of 
any recovery package is to stabilise expectations, restore confidence and 
channel desired surplus savings into productive investment. However, 
‘business as usual’ implies temperature increases over 3°C, implying

great future uncertainty, instability and climate damage. An alternative 
way to restore confidence is to steer investment towards a productive 
and balanced portfolio of sustainable physical capital, human capital, social capital, intangible capital and natural capital assets (Zenghelis 
et  al., 2020), consistent with global goals on climate change. Finally, 
any recovery package, including climate-friendly recovery, is unlikely to 
be implemented unless it also addresses existing societal and political 
concerns—such as poverty alleviation, inequality and social inclusion—
which vary from country to country.
12.5.9 Adaptation Options to Address Key Risks in 
This section integrates, in Table 12.10 as follows, the sectoral assessment 
of adaptation options (Sections 12.5.1–12.5.8) with the eight key risks 
assessed in the region (Section 12.4). Table 12.10 presents a list of the 
summarised adaptation options, which are detailed in their adaptation

sections, from Section 12.5.1 to Section 12.5.8 in this chapter.
Table 12.10 |  Adaptation options addressing key risks organised by sector. See the note at the end for descriptions of the sector name abbreviations.
T&F ecosystems EbA: Agroecosystem resilience practices
O&C ecosystems Not assessed (NA)
Water Water infrastructure and irrigation; NbS and PES; participatory water management; multi-purpose water use
Food Climate information services; EWSs; insurance; land use planning; LCA strategies; agroforestry; IKLK
Cities NA
Health and well-being EWS, insurance; participatory water management; water infrastructure and irrigation
Poverty and SD CbA; government and institutional support
Human Dimension Participatory management; incorporation of IKLK in water and crop management; education and communication
T&F ecosystems NA
O&C ecosystems NA

Water NbS; land use regulation; EWSs; integrated risk management
Food NA
CitiesUrban planning; climate-adapted parameters in land use and building regulation; intersectoral and multi-level governance; slum upgrading; 
social housing improvement; urban control systems; CbA; risk management plans; integrated watershed management; flood control programmes; 
environment protected areas; household relocation; EWS; NbS; mapping tools; GGI; water storage solutions; wetland restoration; SUDSs; LID; river 
restoration; multi-functional landscapes; improving basic sanitation services
Health and well-being EWS; GGI; community-led and managed relocation; insurance
Poverty and SD Secure location; social housing policies; EWS
Human dimensions Education and communication
T&F ecosystems Monitoring systems; EbA; forest protection and restoration; watershed protection
O&C ecosystems CbA; land use and development regulation

Food Management and planning; NbS; soil and water conservation
CitiesIntersectoral and multi-level governance; CbA; risk management plans; integrated watershed management; environment protected areas; NbS; GGI; 
wetland restoration; improving basic sanitation services; reservoir system
Health and well-being Protection and restoration; NAPs; participatory water management
Poverty and SD NbS: water harvesting; equitable water distribution
Human dimensions Participatory management; incorporation of IKLK in water management; education and communication
 4. Risk of severe health effects due to increasing epidemics

12
1754Chapter 12 Central and South America
Water Water infrastructure; sanitation improvement
Food NA
Cities NA
Health and well-being EWS; health-climate surveillance systems; national plans on health; communal management; GGI; protection and restoration
Poverty and SD CbA; transparent democratic governance; equitable services; education
5. Systemic risks of surpassing infrastructure and public service systems
O&C ecosystems EWS; EbA; territorial planning; CbA; land use and development regulation; GGI
Water Water infrastructure; land use regulation; water retention capacity; EWS; capacity building
Food NA
CitiesUrban planning; climate-adapted parameters in land use and building regulation; intersectoral and multi-level governance; slum upgrading; social 
housing improvement; CbA; improving basic sanitation services; micro wastewater treatment plants
Health and well-being EWS; vulnerability and risk maps; NAPs; GGI

Poverty and SD Transparent, democratic governance
Human dimensions NA
T&F ecosystems Monitoring systems; EbA; protected areas; forest protection and restoration; watershed protection
Water IWRM
Food Territorial planning
Cities NA
Health and well-being Protection and restoration
Poverty and SD Insurance; micro-credits; PES; CbA
Human dimensions Participatory management; incorporation of IK and LK in forest management; education and communication
O&C ecosystems Zoning schemes; MPAs; EbA; CbA; adherence to international treaties
Water NA
Food NA
Cities NA
Poverty and SD NA
8. Risks to coastal socioecological systems due to sea level rise, storm surges and coastal erosion
O&C ecosystems EbA; planned relocation; GGI
Water NA
Food NA

CitiesUrban planning; climate-adapted patterns in land use and building regulation; intersectoral and multi-level governance; CbA; risk management plans; 
household relocation; NbS; GGI
Health and well-being GGI; communal management; protection and restoration
Poverty and SD Secure location; CbA relocation
Human dimensions Participatory management; education and communication
Notes:
Some sectors are represented by abbreviations: Terrestrial and freshwater ecosystems and their services (T&F ecosystems); ocean and coastal ecosystems and their services (O&C 
ecosystems); food, fibre and other ecosystem products (food); cities, settlements and key infrastructure (cities); poverty, livelihood and sustainable development (poverty and SD); 
cross-cutting issues in the human dimension (human dimensions).

12
1755Central and South America  Chapter 12
12.5.10 Feasibility Assessment of Adaptation Options
This section assesses the feasibility of selected adaptations options by 
sector, relevant for CSA, in five dimensions (economic, technological, 
institutional, social, environmental and geophysical), according to the 
methodology developed by Singh et al. (2020a). Table 12.11 shows the 
summary of results and Table SM12.7 the details of the assessment 
and the supporting literature.
12.5.10.1 Food, fibre and other ecosystem products: agroforestry
For agrifood systems, the adoption of agroforestry provides for 
more diverse and sustainable agricultural production, where farmers 
maintain or improve their current production by incorporating suitable 
trees that ameliorate climatic conditions. Thus, in the same unit of 
land, these systems incorporate exotic tree species or managed native 
forests into farming systems allowing for the simultaneous production

of trees, crops and livestock with different spatial arrangements 
or temporal sequences. On the other hand, it is recognised that the 
initial investment and time until trees start to produce may create 
an economic vulnerability. Therefore, there is a need to design 
adequate programmes and allocate resources for agroforestry system 
implementation and technical assistance and training (medium 
confidence). Also, some market schemes such as PES and certification 
can help to reduce this vulnerability.
12.5.10.2 Health and Well-being: Early-warning Systems
For the health sector, we assessed the barriers and facilitators for the 
implementation of climate-driven EWSs under natural extreme events 
and epidemic situations. We found institutional dimensions to be 
potential barriers. These included the legal and regulatory feasibility, 
institutional capacity and administrative feasibility, transparency and 
political acceptability (high confidence). The fewest barriers were

identified for the economic and environmental dimensions.
One of the main institutional challenges is the lack of policy with 
climate–health linkages. Opportunities include a national plan for the 
health sector to address the impacts of climate change by formalising 
collaborations via agreements memoranda of understanding (MOUs). Another key barrier is that relatively few institutions in the region 
have the human technical and administrative capacity to implement 
and operate an EWS. Regional platforms may provide a solution for 
technical assistance at national levels.
On the other hand, the economic dimensions faced relatively few 
barriers, although the initial costs of designing, implementing, 
equipping and maintaining the system are a potential barrier for health-
related sectors with reduced budgets. However, the health benefits and 
economic savings (due to averted epidemics or damage from disasters) 
may offset these costs. The resilience built into the health sector by

these systems may be applicable to other economic sectors that could 
benefit from the early warning of an immenent extreme event and 
associated health impacts.
12.5.10.3 Water—Multi-use Water Storage Approaches
For the water sector, geophysical and economic dimensions do not 
pose a major barrier thanks to the potential reduction of flood hazard 
exposure, physical-technical viability of project implementation, 
different suitable economic mechanisms for joint public-private 
financing and more efficient water use. However, limited institutional 
capacities and the social-environmental impacts of large water 
infrastructure (Section  12.5.3) reduce the institutional, social, 
environmental and, to some extent, technological feasibility. This may 
be a potential barrier to the adaptive approach of multi-use water 
storage (medium confidence).
12.5.10.4 Freshwater and Terrestrial Ecosystems—Ecosystem-
based Adaptation

In the terrestrial and freshwater ecosystem sector, we assessed the 
feasibility of implementing EbA options in the CSA region. Given that 
EbA encompasses a wide range of projects, techniques and political 
and socioeconomic arrangements, extreme care should be taken when 
applying these general findings to particular cases. EbA can enhance 
food sovereignty and carbon stocks and foster SDGs by protecting and 
restoring ecosystems’ health and productivity. EbA is a strategy that 
frequently involves bottom-up decision-making and local communities’ 
empowerment and usually contributes to inequality reduction. EbA Table 12.11 |  Feasibility assessment of selected adaptation options for CSA region.
SystemAdaptation 
optionEvidence AgreementDimension assessed
EconomicTechnolog-
icalInstitutional SocialEnviron-
mentalGeophysical
Food, fibre 
and other 
ecosystem 
productsAgroforestry Medium HighInsignificant 
barriersMixed effectSignificant 
barriersMixed effectInsignificant

barriersMixed effect
Health and 
well-beingEWSs Robust HighInsignificant 
barriersMixed effectInsignificant 
WaterMulti-use of 
water storage 
approachesRobust MediumInsignificant 
barriersMixed effect Mixed effect Mixed effect Mixed effectInsignificant 
barriers
and terrestrial 
ecosystemsEbA Medium HighInsignificant 
barriersMixed effect Mixed effectInsignificant 
barriersInsignificant 
barriers

12
1756Chapter 12 Central and South America
tends to benefit vulnerable groups, but aspects such as the impact on 
socioeconomic inequalities when implemented should be taken into 
account.
In general, EbA does not require advanced technologies for local 
communities. However, limitations in technical assistance and 
funding for specific key technologies and training may act as a barrier 
for EbA adoption (medium confidence). EbA practices can reduce 
risk in several ways by increasing awareness among communities 
and providing food diversity and production. EbA is recognised as a 
desirable policy for most stakeholders in CSA, particularly because 
as a strategy it incorporates environmental and social concerns. 
Nonetheless, it is important that all stakeholders agree on the 
goals and methods for EbA to be effective. A lack of institutional 
coordination, clear goals and strategies were identified as a potential 
barrier for EbA implementation. EbA is heavily based in local and IK,

as well as academic ecological knowledge.
For the adaptation options analysed, significant barriers and mixed 
effects were observed for the institutional dimension, which indicates 
the relevance of the design and implementation of public policies 
and institutional arrangements for effective adaptation in the 
region. Considering the results, there is a need to advance initiatives, 
programmes and projects that facilitate adaptation to climate 
change. In the same way, barriers were apparent in the technological 
dimension, which indicates the importance of increasing access and 
diffusion of appropriate techniques and technologies in order to face 
the challenges of climate change in the region.
12.6 Case Studies
12.6.1 Nature-based Solutions in Quito, Ecuador
NbS are related to the maintenance, enhancement and restoration of 
biodiversity and ecosystems as a means of addressing multiple concerns 
simultaneously (Kabisch et  al., 2016). NbS can trigger sustainability

transitions. For example, the conservation and restoration of natural 
ecosystems are prone to promote synergy between mitigation, 
adaptation and sustainable development. EbA can be seen as a type 
of NbS deployed in response to climate-change vulnerability and risk 
(Greenwalt et  al., 2018), combining the objectives of reducing the 
vulnerability of human systems and increasing the resilience of natural 
systems (IPCC, 2014).
The Municipal Quito District in Ecuador covers 4235 km2 of 
mountainous territory that ranges from 500 to 5000 MASL. That 
territory has followed a pattern of urbanisation common in Latin 
America: its population has increased from around 500,000 people 
in the 1970s to nearly 3 million inhabitants by 2020, of which 80% 
live in urban areas (Municipio del Distrito Metropolitano de Quito, 
2016). A massive inflow of people immigrated in the early 1970s 
due to various causes, including the search for the rents created as

a result of the oil boom in the Ecuadorian Amazon, better working 
conditions, health, education and cultural services, in comparison with 
the rural areas or in mid-sized cities. As a result, the city underwent 
exponential growth, claiming valuable agricultural and forestry areas, as well as natural ecosystems, in the peripheries. Many of the 
new neighbourhoods were established through land invasions or 
informal markets, in many cases over steep slopes, in water sources 
and agricultural or conservation areas (high confidence) (Cuvi, 2015; 
Gómez Salazar and Cuvi, 2016). That exponential population growth, 
coupled with urban sprawl, poses many challenges to the city, 
including those related to climate change.
Mean air temperature and annual rainfall (measured by instruments 
since 1891 and inferred through historical records of rogation 
ceremonies since 1600), are increasing, combined with an increase 
in seasonality (i.e., longer periods of drought) and extreme weather

events, particularly higher levels of precipitation (Serrano Vincenti 
et  al., 2017; Domínguez-Castro et  al., 2018). Two impacts related 
to warmer air conditions are the displacement of the freezing 
line currently placed at 5100 MASL (Basantes-Serrano et  al., 
2016), followed by glacier retreat and the upward displacement 
of mountainous ecosystems (very high confidence) (Vuille et  al., 
2018; Cuesta et al., 2019). The key ecosystem that regulates water 
provision for the city is the paramo, and only about 5% of this 
process is related to glaciers, so the combined effects of climate 
change on both systems, coupled with land use change and fires, can 
reduce the availability of water for agriculture, human consumption 
and hydropower. Other important climatic hazards and impacts 
are the increase of solar radiation, the heat island effect and fires 
(high confidence) (Anderson et  al., 2011; Armenteras et  al., 2020;

Ranasinghe et al., 2021). On almost half of the days of each year, 
Quito’s population is exposed to levels of UV radiation above 11 
according to the World Health Organization scale (Municipio del 
Distrito Metropolitano de Quito, 2016).
Various policies, programmes and projects have been created for 
the promotion of urban green spaces, protected areas, water source 
and watershed monitoring, conservation and ecosystem restoration, 
air pollution monitoring and control and urban agriculture. Among 
those actions, three recent ones are commonly highlighted. The first 
is the FONAG, established in 2000 with funds from national and 
international organisations to promote the protection of the water 
basins that supply most of the drinking water. It is a PES scheme 
enabled through a public–private escrow. The projects include 
conservation, ecological restoration and environmental education for 
a new culture of water, in a context opposed to the commodification

of natural resources (Kauffman, 2014; Bremer et al., 2016; Coronel T, 
2019). FONAG was innovative in the use of trust funds in a voluntary, 
decentralised mechanism and has inspired more than 21 other water 
funds in the region; nevertheless, its narrative of success has also 
been said to oversimplify and misrepresent some complex interactions 
between stakeholders as well as within communities and their land 
management practices (Joslin, 2019).
The second highlighted initiative is the AGRUPAR (Participatory 
Urban Agriculture) programme, launched as a public initiative in 
2002 initially with international cooperation funds. It was aimed at 
providing assistance to poorer urban and peri-urban populations, to 
initiate and manage orchards as well as domestic animals such as 
chickens and guinea pigs, with the goal of promoting self-sustenance 
and commerce. AGRUPAR provides and finances training, seeds and

12
1757Central and South America  Chapter 12
seedlings, greenhouses, certifications and marketing support and 
spaces where farmers can sell directly their products to consumers. In 
2016, AGRUPAR gave assistance to more than 4000 farmers managing 
orchards of various scales that combined produce, more than 500 
tonnes annually. The programme has direct impacts on nutrition, 
generation of work for women, production of healthy food, reduction 
of runoff, recycling of organic waste and social cohesion, among others 
(very high confidence) (Thomas, 2014; Cuvi, 2015; Rodríguez-Dueñas 
and Rivera, 2016; Clavijo Palacios and Cuvi, 2017).
A third initiative is the creation of a municipal system of protected 
areas, locally named Áreas de Conservación y Uso Sustentable (ACUS). 
This system covers an area of 1320 km², nearly a third of the Municipal 
Quito District. Half of this landscape (680 km2) is covered by montane 
forests and paramos (Torres and Peralvo, 2019). These forests provide

direct water, food and fibres for about 20,000 people and indirectly a 
rural landscape for a growing number of urban citizens and foreign 
tourists that practice ecotourism and look for fresh and healthy 
food. During the last three decades, this area has witnessed a high 
density of public and private conservation and restoration efforts that 
aim to regain ecological integrity and improve human well-being in 
deforested and degraded landscapes (Mansourian, 2017; Zalles, 2018; 
Wiegant et al., 2020). Quito’s system of protected areas constitutes a 
primary strategy for fostering links between urban and rural citizens 
as a means of understanding the ecological dependence of urban 
metropolises to their surrounding natural landscapes. Along the same 
lines, these areas constitute a key element to increase the adaptive 
capacity of rural livelihoods and contribute to mitigating climate 
change through landscape restoration, sustainable production and

forest conservation (high confidence).
Other NbS actions include the restoration of small basins, locally 
called quebradas, under different schemes of management and 
participation (medium evidence, very high agreement) (da Cruz e 
Sousa and Ríos-Touma, 2018) and the transformation since 2013 
of a large portion of the old Quito airport into an urban park. 
Nevertheless, Quito city continues to face challenges in the social, 
economic, infrastructural and environmental spheres. A major 
pending environmental issue is air pollution; a high level of pollutants 
affects the city in general and especially the most vulnerable groups 
(high confidence) (Zalakeviciute et al., 2018; Alvarez-Mendoza et al., 
2019; Estrella et al., 2019; Hernandez et al., 2019; Rodríguez-Guerra 
and Cuvi, 2019). Another major issue is the continuous sprawl of new 
neighbourhoods, mainly through informal processes, that diminish 
urban resilience because of the destruction of conservation and food

production areas, sources of water and the dispersion of settlements 
without primary services, among other consequences (Gómez Salazar 
and Cuvi, 2016).12.6.2 Anthropogenic Soils, an Option for Mitigation 
and Adaptation to Climate Change in Central and 
South America. Learning from the “Terras Pretas 
de Índio” in the Amazon
Amazonian dark earths (ADEs), also known as Terras Pretas de Índio, 
are anthropogenic soils derived from the activities associated with 
the settlements and agricultural practices of pre-Hispanic societies 
in the Amazon (Woods and McCann, 1999; Lehmann et  al., 2003; 
Sombroek et  al., 2003). Most of the ADEs identified so far are 500 
to 2500  years old (de Souza et  al., 2019). According to Maezumi 
et al. (2018a), polyculture agroforestry allowed for the development 
of complex societies in the eastern Amazon around 4500 years ago. 
Agroforestry was combined with the cultivation of multiple crops and

the active and progressive increase in the proportion of edible plant 
species in the forest, along with hunting and fishing. The formation 
of ADEs as a result of these activities served as the basis for a food 
production system that supported a growing human population in the 
area (Maezumi et al., 2018a).
ADEs are the result of the accumulation and incomplete combustion of 
waste materials such as ceramic artefacts and organic residues from 
harvesting, weeding, food processing (including cooking) and other 
activities (Lima et al., 2002; Hecht, 2003; Kämpf et al., 2003). ADEs are 
characterised by their increased fertility in relation to adjacent soils, 
with high contents of organic carbon (C) (mainly as charcoal) as well 
as inorganic nutrients, especially phosphorus (P) and calcium (Ca) and 
high carbon/nitrogen ratios (high confidence) (Moline and Coutinho, 
2015; Alho et al., 2019; Barbosa et al., 2020; Pandey et al., 2020; Soares

et al., 2021; Zhang et al., 2021). They also exhibit a high cation exchange 
capacity and moisture retention, among other properties (Hecht, 2003; 
Kämpf et  al., 2003; Falcão et  al., 2009). Charcoal content is a key 
indicator of pre-Hispanic fire activity and sedentary occupation, which 
is evidence of the anthropic origin of these soils (high confidence) 
(Hecht, 2017; Maezumi et al., 2018b; Alho et al., 2019; Barbosa et al., 
2020; Iriarte et al., 2020; Montoya et al., 2020; Shepard et al., 2020).
Accumulation of organic residues and low-intensity fire management 
are recognised key elements in ADE formation. ADEs originating 
around settlements show a relatively high density of ceramic artefacts 
and are called terras pretas. They present a higher content of calcium 
and phosphorus than those originating from agricultural activities, 
which are known as terras mulatas (Hecht, 2003).
There is a robust and growing body of research from various disciplines

that assigns a high relevance to ADEs in the region. It has been shown 
through archaeological and palaeoclimatic data that Amazonian 
societies that based their agricultural management on Terras Pretas 
de Índio were more resilient to the changing climate due to increased 
soil fertility and water retention capacity (de Souza et  al., 2019). 
Additionally, low organic carbon degradability over long time periods, 
associated with high contents of charcoal or pyrogenic carbon, makes 
these soils an important C sink (medium confidence: robust evidence, 
medium agreement) (Lehmann et al., 2003; Guo, 2016; Trujillo et al., 
2020), which is particularly relevant in an area like the Amazon, that 
could change from a net carbon sink to a net carbon source as a 
consequence of anthropogenic climate change (Maezumi et al., 2018b).

12
1758Chapter 12 Central and South America
The Indigenous agricultural practices that led to ADEs are thought 
to be associated with a more sedentary agricultural model than the 
current slash-and-burn and shifting cultivation practices. Although 
this is a controversial topic, as the precise definitions of slash and 
burn and shifting cultivation are presently under discussion (Hecht, 
2003), several present-day local and Indigenous agricultural practices, 
including in-field burning and nutrient additions from food processing 
and residue management, have been recognised as promoting high 
organic carbon and nutrient soil contents similar to those found in 
ADEs (Hecht, 2003; Winklerprins, 2009).
At present, ADEs are estimated to cover up to 3.2% of the Amazon 
basin and are highly valued for their persistent fertility, and they 
have become a key resource for sustainable agriculture for Amazon 
communities in a climate-change context (Altieri and Nicholls,

2013; Maezumi et  al., 2018a; de Souza et  al., 2019). Based on the 
lessons learned from the Terras Pretas de Índio, some researchers 
have proposed the development of technologies to promote a new 
generation of anthropogenic soils (e.g., Kern et  al. 2009; Lehmann 
2009; Schmidt et al. 2014; Bezerra et al. 2016; Kern et al. 2019). Among 
the technologies based on ADE findings, biochar, obtained by the slow 
pyrolysis of agricultural residues, is the most explored application found 
in the literature (Mohan et al., 2018; Matoso et al., 2019; Amoah-Antwi 
et al., 2020). The dual purpose of increased soil fertility and carbon 
sequestration is considered an important goal in connection with 
developing sustainable agriculture in a climate-change context (Kern 
Preservation of the practices and knowledge associated with these 
soils is vital for sustainable agriculture in a climate-change scenario in

the Amazon. It will greatly contribute to the preservation of valuable IK 
as well as the contribution to the development of new adaptation and 
mitigation technologies, among other unexplored solutions.
12.6.3 Towards a Metropolitan Water-related Climate 
Proof Governance (Re)configuration? The case of 
Lima, Peru
Lima-Callao Metropolitan Area, capital of Perú, is facing recurrent 
climate disasters providing lessons on water-related climate-proof 
governance reconfiguration. The first lesson is that when disasters 
affect poor and rich populations, dominant actors prioritise the integral 
city’s resilience and development and coordinate and collaborate 
within a concertation framework across institutional levels and 
geographical scales (Hommes and Boelens, 2017; Miranda Sara, 2021), 
even having different ideas, discourses, and power, recognising that no 
single actor has enough power. Second, water-related climate-change

scenarios require comprehensive, transverse, multi-sectoral, multi-
scalar, multiple types of actor knowledge (expert, tacit, codified and 
contextual embedded) (Pfeffer, 2018) and transparent information to 
manage the tensions and even conflicts when some knowledge is not 
shared or restricted, particularly when lower risk perception and higher 
risk tolerance are present. Finally, a concertative (processes involving 
a variety of actors, which has become mandatory in Peru) strategy to 
localise climate-changed-related action shows quicker, more effective 
and more transparent results (medium confidence, robust evidence, medium agreement) (Miranda Sara and Baud, 2014; Pepermans and 
Maeseele, 2016; Siña et al., 2016; Miranda Sara et al., 2017).
As the second driest city in the world, Lima is highly vulnerable to 
drought and heavy rainfall in the nearby Andean highlands (Schütze 
et al., 2019). Located on the Pacific coast with more than 10 million

inhabitants, it suffers from flooding, mudslide disasters and water 
stress, and is more frequently affected by heavy rain peak events 
(1970, 1987, 1998, 2012, 2014, 2015 and 2017) (very high confidence) 
(Mesclier et al., 2015; Miranda Sara et al., 2016; French and Mechler, 
2017; Vázquez-Rowe et  al., 2017; Escalante Estrada and Miranda, 
2020). In addition to unequal water distribution in quantity and 
pricing, one million inhabitants lack water connections (Ioris, 2016; 
Miranda Sara et al., 2017; Vázquez-Rowe et al., 2017) as a result of a 
lack of long-term city planning and lack of integration with water and 
risk management. Climate-change scenarios were ignored or denied, 
particularly when budget allocations for preventive actions were 
necessary (high confidence) (Miranda Sara et  al., 2016; Allen et  al., 
2017a).
In 2014, the water company (SEDAPAL), together with the Lima 
Metropolitan Municipality (LMM), National Water Authority (ANA) and

other organisations, agreed on a Lima Action Plan for Water (Schütze 
et  al., 2019). The same year, the LMM approved a climate change 
strategy defining adaptation and mitigation measures (Miranda Sara 
and Baud, 2014) based on technical and scientific action research 
within interactive and iterative concertation multi-actor processes.
However, in 2015, municipal elections shifted Lima’s and, later, 
Peru’s political power to parties associated with climate deniers at 
a high cost to the people, city infrastructure and housing. In early 
2017, buildings along rivers, ravines and slopes suffered from floods, 
huaycos (mudslides), and the whole city experienced potable water 
cuts (Vázquez-Rowe et al., 2017) and vector-borne diseases affecting 
especially poorer but also richer inhabitants.
A so-called coastal El Niño affected the whole country, and as a 
consequence, in 2018 the Peruvian government passed the Framework

Law for Climate Change, Law No. 30754, a unique political decision, to 
assure the integration of climate-change concerns in public policies and 
investment projects. The law defines local government mandates on 
local climate action plans. The 2019 municipal elections brought new 
local authorities to Lima, and by 2020, 19 district municipalities had 
developed adaptation measures, adopting the metropolitan climate 
change strategy with support from Cities for Life Foro and GIZ (Foro 
Ciudades Para la Vida, 2021). In 2021, LMM approved its local climate 
change plan (LCCP), and 10 (out of 51 with Callao) more municipalities 
finalised their LCCP with the support of the Global Covenant of Mayors 
for Climate & Energy and the European Union.
The institutionalised culture of participation in Peru did lead to a 
broader concept of concertation, wherein practices of collaborative 
planning were developed to allow actors to build up socially supported

agreements and decisions and take action without losing sight of their 
principles. These processes have been applied to reduce risks, to adapt 
and to anticipate uncertain and unknown futures; they also introduced 
climate-change concerns within a complex political and institutional

12
1759Central and South America  Chapter 12
environment surrounded by corruption scandals (Vergara, 2018; 
Durand, 2019) and growing political polarisation.
Several processes have been set in motion to engage citizen 
participation and promote climate action planning. First, the LMM with 
Climate Action Plan processes reopened the Climate Change Technical 
Group of the Municipal Environmental Commission, whose work 
ended in the approval of the Lima Local Action Plan of Climate Change 
(MML, 2021). Second, the River Basin Council is developing the River 
Basin Management Plan led by the National Water Authority.Finally, 
the Metropolitan Lima Urban Development Plan is finalising a citizen 
consultation, with the support of a high-level consultation group.
Such processes include heated discussions, conflicts and the recognition 
of other discourses and types of knowledge so as to build up scenarios 
that ‘visualise’ and anticipate what might happen. These processes

require democratic, transparent and decentralised institutions, 
providing clear mandates and strong political will to support them, so 
that the poor and vulnerable can make their views known and are able 
to make themselves heard, even if their power remains limited (Chu 
et  al., 2016). Opportunities for the reconfiguration of sociopolitical 
and technological water governance are emerging based on socially 
supported agreements (Miranda Sara and Baud, 2014; Miranda 
Sara, 2021). However, the water governance configuration faces the 
paradox that the current water demands of all users combined may no 
longer be feasible within ecological limits and future climate-change 
consequences (Miranda Sara et al., 2016; Schütze et al., 2019).
12.6.4 Strengthening Water Governance for Adaptation 
to Climate Change: Managing Scarcity and Excess 
of Water in the Pacific Coastal Area of Guatemala
Guatemala experiences high climate interannual variability, now

increased from the effect of climate change (INSIVUMEH, 2018; 
Bardales et al., 2019). Impacts on human settlements, agriculture and 
ecosystems result from both excess and reduced precipitation (high 
confidence) (Section  12.3.1.4). Guerra (2016) argues that deficient 
IWRM in the country is the main reason for those impacts. A case 
in point is that of the Madre Vieja and Achiguate rivers, where an 
intense El Niño event triggered dryer conditions and, in turn, a crisis 
and conflict that reached national proportions. Progress in local water 
governance helped to solve that crisis and helped tackle challenges 
posed by reduced precipitation and flood risk in southern Guatemala.
The ENSO event that started in November 2014 and ended in July 
2016 (CIIFEN, 2016) was the most intense since records commenced 
in 1950 (NOAA, 2019). Its effects were felt in different parts of the 
world, and Guatemala and the rest of CA experienced intense water

scarcity due to a significant reduction in rainfall (high confidence) 
(IICA, 2015; Scientific American, 2015). River flow in the dry months 
is related to precipitation levels in the previous rainy season, so ENSO 
has an effect on river flow rates. Two of the main rivers in the Pacific 
coast of Guatemala, Madre Vieja and Achiguate, dried out completely 
at the beginning of 2016, triggering a nearly violent local conflict that 
caught the attention of national leaders (Guerra, 2016; Gobernación 
de Escuintla et al., 2017). In addition to the severe drought, the rivers dried because of overextraction by multiple users (60 in the case 
of Madre Vieja). This had happened before to a lesser extent in the 
last 20 years during the critical months of the dry season. A lack of 
regulation, coordination mechanisms, information and other elements 
of water governance was the root cause of the problem, exacerbated 
by the drier conditions during the intense El Niño event, resulting in the

intensification of an existing conflict (high confidence) (Guerra, 2016).
Roundtables were set up to foster dialogue between numerous 
stakeholders, including communities, agri-export companies, 
governmental organisations and municipalities, all led by the local 
governor (Gobernación de Escuintla et  al., 2017). Agreements 
included keeping a minimum river flow all the way to the sea, setting 
up a monitoring and verification system for levels of river flow and 
restoring riparian forests. A system was set up to monitor river 
flow at different points along the rivers on a daily basis in the dry 
season using a simple WhatsApp-based system to communicate the 
warnings and monitor compliance. Four years on, the rivers had not 
dried out and conflict was kept to a minimum. Rural communities 
can use rivers for recreational purposes and for fishing all year round, 
while plantations (large and small) can use water for irrigation

(rationally) and keep producing. Similar schemes and interactions 
started happening in other rivers in the Pacific coast of Guatemala, 
with positive results, in particular, rivers kept flowing all through the 
dry season, as can be seen in the report of river flows for the years 
2017, 2018 and 2019 (ICC, 2019b).
A key actor in the improvement of water governance has been the 
private Institute for Climate Change Research (ICC). This is a unique 
initiative that was created in 2010 and is funded primarily by the 
private sector of Guatemala to help the country advance in climate-
change mitigation and adaptation (Guerra, 2014). The institute works 
alongside local governments, communities and private companies 
in several areas besides integrated water management. Its role is 
merely technical-scientific: it oversees the water monitoring system, 
generating data on weather and hydrology and providing support to 
other stakeholders.

Local governance was also essential for the implementation of flood 
risk management actions (high confidence). Guerra et  al. (2017) 
explained how impacts were significantly reduced in the Coyolate 
River watershed, as well as on the Pacific coast of Guatemala, 
thanks to flood protection that was designed and implemented in a 
technical and integrated manner. This was a result of the strong and 
active participation of local communities, companies and the local 
municipality, which demanded that the central government invest 
effectively. The stakeholders provided some resources (financial 
and in-kind) and inspected the works. Some flat areas of the lower 
Coyolate watershed used to flood annually, causing economic damage 
in communities. The areas covered by flood risk measures have not 
flooded and so have avoided losses and created conditions that 
attract investment and create jobs, improving living conditions for the

locals. Other processes of participation and interaction between the 
authorities, the private sector and communities have taken place in 
other watersheds for planning, action and investment in connection 
with flood risk management. The ICC has played a role by studying 
flood-prone areas, building capacities in communities, fostering

12
1760Chapter 12 Central and South America
public–private coordination mechanisms and providing much needed 
technical assistance to local governments (ICC, 2019a).
Although some may argue that water governance is in the realm of 
development, it has made contributions in reducing direct and indirect 
impacts of climate events and, therefore, can be seen as a key element 
for climate adaptation (high confidence).
12.7 Knowledge Gaps
Data deficiencies and heterogeneity in quantity, quality and 
geographical bias in knowledge limit people’s understanding of 
climate change, evaluation of its impacts and the implementation 
of adaptation and mitigation measures (Harvey et al., 2018) in CSA. 
The number of publications is not representative of the sensitivity to 
climate change and vulnerability contexts of different sub-regions 
and sectors. This lack of representation in the mainstream literature 
may lead to a bias and, therefore, an underestimation of the overall

climate-related impact for some CSA sub-regions (Sietsma et  al., 
2021). The reason for the relatively few quantitative studies might 
be the complexities of socio-demographic and economic factors and 
the lack of long-term and reliable data in these areas (Harvey et al., 
2018), along with other social, economic and technical constraints.
Most studies that assess vulnerability to climate change do not yet 
follow the concept adopted since AR5, which isolates exposure as an 
external variable (WGII AR5 Figure SPM 1) (IPCC, 2014), and many still 
use the A and B system of climate-change scenarios from AR4, because 
the adoption of the RCP models has been slow. There is still limited 
literature on severe risks and little specific and explicit consideration 
of risk drivers in the region. Moreover, limits to adaptation and 
the effectiveness of adaptation measures in CSA remain largely 
understudied.
Research on the interactions between climate change and socioeconomic

processes is not extensive (Barnes et al., 2013; Leichenko and O’Brien, 
2019; Thomas et al., 2019). There is limited understanding of the multi-
level synergistic effects of climate change and other drivers, including 
economic development from the household to the country level 
(Wilbanks and Kates, 2010; Leichenko and Silva, 2014; Tanner et  al., 
2015a; Carey et al., 2017). In the region, this deficit is greater for sectors 
other than agriculture, water and food.
12.7.1 Knowledge Gaps in the Sub-regions
The knowledge gaps in the eight sub-regions are quite heterogeneous. 
In CA, climate-change research is notably insufficient in all sectors 
included in this report, considering that climatic change, variability 
and extremes are impacting and will continue to severely impact this 
sub-region, and the vulnerability of the social and natural systems 
is high. Data deficiencies must be overcome as renewed research 
on climate change updates models, scenarios and projected impacts

across sectors and levels (i.e., household to country). In NWS, there 
is a lack of studies on the relationships with increased fire events, 
and the impacts on the infrastructure of all kinds, on certain lowland, marine and coastal ecosystems and on ecosystem functioning and the 
provision of environmental services. Experimental studies are rare and 
most necessary to identify critical ecological thresholds to support 
decision-making processes, linking glacier retreat to its consequences 
on biodiversity and ecosystems, combined with different land use 
trajectories. Complex interactions with processes such as peace 
agreements in Colombia are yet to be investigated (Salazar et  al., 
2018). In NSA, there remains a limited amount of peer-reviewed 
literature addressing the implications of climate change on Indigenous 
cultures and their livelihoods. In SAM, further data are needed on the 
vulnerability of traditional populations, impacts on water availability

and soil degradation, risks to biodiversity and resilience of ecosystems 
in connection with climate change.
There is a knowledge gap about the likely impact of climate change 
on NES biodiversity, soil degradation and best adaptation measures. 
SES is the most urbanised sub-region of CSA, but there are severe 
knowledge deficits related to the design, implementation and 
evaluation of adaptation policy plans with respect to climate change. 
Forecasts related to risk prevention require new studies that address 
down-scaled climate-change models with concrete solutions to 
increase cities’ resilience. In SWS, there is a lack of long-term studies 
addressing climate-change impacts on terrestrial, freshwater and 
marine ecosystems, which is mainly due to the lack of integrated 
observational systems. There is a lack of studies projecting future 
impacts of climate change on the cryosphere, water resources, hazards, 
risks and disasters on natural and human systems. This is mainly due

to the lack of systematic documentation, analysis and evaluation of 
adaptation strategies adopted, as well as their limitations and the 
lessons learned from maladaptation processes. There is scant evidence 
about transformational adaptation to climate change and system 
resilience. In SSA, there is a need for information related to the cities’ 
vulnerability to climate change and the impacts of the direct effects 
of future climate change on cities, energy infrastructure and health. 
Also, there is a knowledge gap about the financing of climate-change 
adaptation in SSA.
12.7.2 Knowledge Gaps by Sector
12.7.2.1  Terrestrial and Freshwater Ecosystems and their 
Services
Advances in scientific knowledge on the risks of climate-change 
impacts and the vulnerability and resilience of ecosystems to climate 
change are needed (Bustamante et  al., 2020). Persistent climate 
change in tropical rainforests requires deeper study and understanding,

overall in connection with the role of nutrients, deep-water availability 
and biodiversity. Further research is needed to understand feedback 
to climate systems of large-scale changes in the land surface in SA 
biomes. The region has important freshwater Global 200 Ecoregions, 
including the Orinoco River and Flooded Forests, Upper Amazon River 
and streams, and Amazon River and Flooded Forests, which represent 
a priority for freshwater biodiversity conservation at a global scale 
(Manes et  al., 2021) (Cross-Chapter Paper 1; Figure  12.8). There is, 
however, a clear knowledge gap on the impacts of climate change on 
freshwater biodiversity in the region (Section CCP1.2.3; Manes et al.,

12
1761Central and South America  Chapter 12
2021). Lastly, more interdisciplinary research is needed regarding 
conservation strategies and stable financial resources focusing on 
adaptation of ecosystems in the region (Mistry et al., 2016; Gebara 
and Agrawal, 2017; Ruggiero et al., 2019; To and Dressler, 2019).
12.7.2.2 Ocean and Coastal Ecosystems and Their Service
There is an important lack of knowledge about the state of health of the 
ocean and coastal ecosystems along CSA (i.e., social-ecological data 
integration, poor sampling efforts, lack of information about the value 
of ecosystem services, lack of information about ecosystems cover 
and distribution, lack of studies about climate-change perceptions 
and social concerns), including marine fisheries (i.e., landing statistics 
not available, lack of reliable information on the scope of resource 
extraction, among others). Poor or absent monitoring programmes

(physical, environmental and biological variables) that feed alert and 
surveillance systems are lacking in CSA. There is a general absence of a 
continuous line of scientific research or adequate baseline information 
about the impacts of climate change, as well as continuous monitoring 
of the adaptation plans adopted in ocean and coastal ecosystems 
that limit the formulation of adequate conservation and management 
programmes. When studies are performed, inadequate access to 
data limits the analyses of the existing information, making difficult 
to detect climate-change trends and impacts and develop effective 
12.7.2.3 Water
As in other sectors and environmental systems, for the water sector 
there are important limitations in terms of monitoring and data 
collection. High-quality, long-term hydrological data are unevenly 
available for different sub-regions and limit the possibility of obtaining

a deeper understanding of changes in river runoff and lake or 
groundwater changes. Groundwater data are particularly scarce. There 
are important gaps related to the projections of water resources for 
the future. Much of current knowledge on future changes in water 
resources and water scarcity and flood risks is based on information 
from global-scale studies because studies specific to this region are 
scarce. Several elements that are important for IWRM, such as water 
quality, water demand, privatisation and other economic dynamics, 
and nutrient, pollutant and sediment flux are poorly known currently 
due to missing data and insufficient efforts to monitor them.
12.7.2.4 Food, Fibre and Other Ecosystem Products
Integrative evaluation on impacts on food security, including agricultural 
production, distribution and access, leading to adaptation strategies is 
limited within the region. Limited information regarding cost-benefit

analyses of adaptation in the food production sector is available in 
the region. It is also important to obtain a better understanding of 
adaptation effects to avoid maladaptation and promote site-specific 
and dynamic adaptation options considering available technologies. 
Compiling and systematising existing scientific and local knowledge 
on the relationship between forests, land cover/use and hydrological 
services is a gap that must be filled from a broader perspective in the 
region, which could contribute to formulating recommendations and 
inform restoration practices and policies. The literature also highlights widespread gaps between farmers’ information needs and services 
that are routinely available. There is evidence that when climate 
information services are created with farmer input and targeted in 
a timely and inclusive manner, they are a positive determinant of 
adaptation through the adoption of more resilient farm-level practices.

However, current assessments of the economic impacts of climate 
information services are scarce; hence, more such studies are needed.
12.7.2.5 Cities, Settlements and Infrastructure
Despite the high level of urbanisation in the region, studies on urban 
adaptation initiatives are still underreported by municipalities, and 
several practical results have not yet been demonstrated (Araos et al., 
2016). This issue is particularly relevant to medium-sized cities because 
most of the literature and data available on adaptation refer to the 
major capital cities. The potential of applying new resilient parameters 
in building and land use regulations for adaptation is underreported. 
The same can be said about the impact of housing improvement and 
slum upgrading on climate resilience, even when initiatives are focused 
on reducing environmental and climate risk. Also relevant in the region 
is a gap in research about NbS applied to urban area adaptation, as in

the case of the urban forestry potential for adaptation (Barona et al., 
2020). Even though the importance of urban ecological infrastructure 
in providing ecosystem services, such as flood control, is reasonably 
well documented, its practical application in urban planning in CSA 
remains limited (Romero-Duque et al., 2020). Added to this is the lack of 
monitoring data on adaptation initiatives in general and, in particular, 
on adaptation initiatives in water systems that have already been 
implemented and their effects on risk reduction. A lack of monitoring 
data contributes to the lack of information about maladaptation in 
urban areas and its consequences. Mobility and transport system 
adaptation options remained virtually entirely unexplored, while 
mitigation options receive significant attention.
12.7.2.6 Health and Well-being
There is a growing body of evidence that climate variability and 
climate change (CVC) cause harm to human health in CSA. However,

there is a lack of information about the current and future projected 
impacts of CVC events on overall illness and death in this region. It is 
challenging to attribute specific health outcomes to CVC in models 
and field experiments due to multiple factors, including the following:
• lack of long-term, high-quality health surveillance data
• multiple interacting infectious disease and chronic health issues
• mismatch in the spatial and temporal scales of CVC and health 
• complex climate and human system dynamics, including non-
linear time lags
• limited longitudinal data on non-climate factors that influence 
health outcomes (e.g., public health interventions, migration of 
human populations, seasonal patterns in livelihoods).
The uncertainty inherent in predictive models also makes it challenging 
to expand current localised knowledge on the impacts of infectious 
diseases associated with CVC to other regions or future climate 
scenarios (UNEP , 2018).

12
1762Chapter 12 Central and South America
Improved risk assessments based on better models and empirical 
research are needed to bridge the knowledge gap and inform the 
design of adaptation strategies. A systematic multi-scalar analysis of 
the impact of CVC on human health is needed across distinct social-
ecological contexts. Data collection systems need to be strengthened 
to accurately estimate the burden of mortality and morbidity from 
heat and extreme events. The data deficit is a common problem in 
functioning civil registration and vital statistics systems, including lack 
of information on causes of death (UNEP , 2018). In addition, there is a 
lack of consensus on globally accepted and operational definitions for 
both climate-related extremes and exposures/outcomes.
For infectious disease (vector-borne and water-borne), the technology 
available to estimate current and future risk areas is often limited by

human or financial resource constraints in developing countries. There is 
a geographical mismatch between the areas producing the technology 
and knowledge (in the global north) and the areas most affected 
by CVC (in the global south). User-friendly tools that bring together 
climate and health information—without the need for modelling or GIS 
expertise—are needed for health sector decision makers.
There is a lack of studies that assess the feasibility of health adaptation 
measures (Section  12.5.10), thereby limiting the ability of decision 
makers to compare different health interventions and identify 
bottlenecks for implementation. The growing field of implementation 
science could help to address barriers to mainstreaming climate 
information in the health sector as an adaptation strategy.
Finally, there is an almost complete absence of studies that address 
relationships of climate change with well-being in CSA, broadly

understood as including emotions and moods, satisfaction with life, 
sense of meaning and positive functioning, including the capacity 
for unimpaired cognitive functioning and economic productivity 
(Section 7.1.4.1).
12.7.2.7 Poverty, Livelihood and Sustainable Development
Climate change is becoming a major obstacle to poverty reduction and 
overcoming poverty traps. There is a need to better understand how 
poor and vulnerable communities are affected and the more effective 
ways to prevent it. The large majority of the poor in the region are 
living in urban areas (UNDESA, 2019); extreme urban poverty is 
increasingly more relevant, including the needs and priorities of 
informal settlements and economies, but less studied within the 
interaction with climate change. There is little reporting of major 
adaptation options implemented by or for vulnerable and poor urban 
dwellers (Ryan and Bustos, 2019; Berrang-Ford et al., 2021).

Adaptation options are being increasingly documented for poverty-
related impacts, despite the fact that the uncertain context from 
climate impacts is not uniform across communities and the very local 
scale of the type of adaptation responses needed (Miranda Sara et al., 
2016; Rosenzweig et al., 2018; Dodman et al., 2019). There is a huge 
gap in understanding how the poor respond to climate change, what is 
needed to support them and the interconnections among development 
policies, poverty and risk reduction with climate-change actions (Ryan 
and Bustos, 2019; Satterthwaite et al., 2020).The literature to assess the effectiveness of pro-poor or low-income 
adaptation options continues to be weak; a very small proportion 
shows results associated with adaptation efforts (Magrin et al., 2014; 
Berrang-Ford et  al., 2021). Without this kind of approach and in-
depth understanding there is the risk that top-down climate-change

adaptation options could reinforce poverty cycles and neglect cultural 
values, even eroding them (Bartlett and Satterthwaite, 2016; Walshe 
and Argumedo, 2016; Allen et  al., 2017a; Hallegatte et  al., 2018; 
Kalikoski et al., 2018; UN-Habitat, 2018).
The impacts of climate change on vulnerable groups remain 
understudied. There are little or no climate data on the remote 
mountain regions of CSA as well as research measuring the 
vulnerability of smallholders living there, making it hard to assess the 
expected changes or the possible adaptation measures (Pons et  al., 
2016; Donatti et al., 2019).
12.7.2.8 Cross-Cutting Issues in the Human Dimension
A significant number of studies address the impacts of climate change 
on the Amazon rainforest (Brienen et al., 2015; Doughty et al., 2015; 
Feldpausch et al., 2016; Rammig, 2020; Sullivan et al., 2020); however, 
assessment of the tangible and intangible impacts of climate change

on Indigenous Peoples’ cultures and livelihoods in this forest need to be 
further advanced (Brondízio et al., 2016; Hoegh-Guldberg et al., 2018).
Studies on the perception of climate change in rural and urban 
populations throughout the region have increased, but there is a lack 
of more specific research on the perception of specific groups, such 
as economic or political actors, that influence public institutions and 
policies at the local, regional and national levels.
While studies on climate-change gender-differentiated impacts 
have grown over the past 10  years in CSA, studies on how gender 
intersects with other dimensions such as race, ethnicity, age or rural/
urban setting are still needed. This will help to further understand 
how gender inequalities are connected to broader power structures 
in society and, thus, to produce evidence on the importance of an 
intersectional approach to climate change.
Regarding the relation of social movements and climate-change

adaptation, institutions and politics, two major issues stand out: youth 
movements for climate change and the resistances, mainly urban, 
to climate-change adaptation policies. Little connection is found in 
research concentrating on resistance to climate-change adaptation 
policies and their interaction with the politics of place. Conflictivity 
related to climate change is another understudied issue.
Although there are several case studies on migrations and 
displacements caused by strong and immediate climatic threats, such 
as hurricanes or floods, and on slow-onset impacts, such as droughts 
or temperature increase, there are gaps in the attribution or relative 
weight of climate change in these processes.
Still important to note is that synergies between mitigation, adaptation, 
risk reduction and sustainable development have not been jointly 
explored, which would better facilitate adaptation policy approaches.

12
1763Central and South America  Chapter 12
There are critical knowledge gaps in the interlinkages between social 
and environmental dynamics that are important for climate-change 
adaptation, as in Andean forest landscapes. A salient knowledge 
gap in this thematic area is the need to characterise how multi-level 
and multi-actor governance systems can enable sustainable land 
management practices, including ecosystem restoration (Mathez-
Stiefel et  al., 2017). More capacities are needed to increase the 
generation of relevant knowledge. Even small grant programmes can 
sustain research projects that target the linkages between knowledge 
and decision-making at multiple scales (Báez et al., 2020).
12.8 Conclusion
CSA is a broadly heterogeneous region with respect to topography, 
ecosystems, urban and rural territories, demography, economy, 
cultures and climates. The region relies on a strong agrarian economy 
in which small producers and large industries participate, but also

large industrialised urban centres, oil production and mining. The 
region is one of the most urbanised areas of the world and home to 
many Indigenous Peoples, some still in isolation, and exhibits one 
of the highest rates of inequality, which is a structural and growing 
feature of CSA. Poverty and extreme poverty rates are higher among 
children, young people, women, Indigenous Peoples and migrant and 
rural populations, but urban extreme poverty is also growing (very high 
confidence). Socioeconomic challenges are being intensified by the 
COVID crisis. Most countries in CA are already ranked as the highest 
risk level worldwide due to the region’s high vulnerability to climate 
change and low adaptive capacity; the lack of climate data and proper 
downscaling are challenging the adaptation process (high confidence).
Many extreme events are already impacting the region and are 
projected to intensify; such events include warming temperatures

and dryness, SLR, coastal erosion and ocean and lake acidification, 
resulting in coral bleaching and an increasing frequency and severity 
of droughts in some regions, with a concomitant decrease in water 
supply, which impact agricultural production, traditional fishing, food 
security and human health (high confidence). In CA, 10.5  million 
people are living in the so-called Dry Corridor, a region with an 
extended dry season and, now, more erratic rainfall patterns. A water 
crisis in Brazil affected the major cities of the country between 2014 
and 2016, having become more frequent since then. Severe droughts 
have also been reported in Paraguay and Argentina. In contrast, the 
urbanised areas of NSA are highly exposed to extreme floods (41% of 
urban population in the Amazon Delta and Estuary). Urban areas in the 
region are vulnerable for many reasons, notably high rates of poverty 
and informality, poor and unevenly distributed infrastructure, housing

deficits and the recurrent occupation of risk areas (high confidence).
Socioecological systems in the region are highly vulnerable to climate 
change, which acts in synergy with other drivers such as land use 
change and deep socioeconomic inequalities. Most biodiversity 
hotspots in the region will be negatively impacted. The Cerrado 
and the Atlantic Forest (two important biodiversity hotspots where 
about 72% of Brazil’s threatened species can be found) are exposed 
to different hazards (extreme events, mean temperature increase) 
due to climate change. Many coastal areas and their concentrated urban populations and assets are exposed to SLR. Climate change is 
threatening several systems (glaciers in the Andes, coral reefs in CA, 
the Amazon rainforest) that are already approaching a critical state at 
risk of irreversible damage.
Extreme heat, droughts and floods will seriously affect CSA terrestrial 
and freshwater ecosystems. The high poverty level increases the

region’s vulnerability to droughts, both in cities and rural areas, where 
people already suffer from natural water scarcity (high confidence). 
The conversion of natural ecosystems to other land uses exacerbates 
the adaptation challenges. IKLK play an important role in adaptation 
but are also threatened by climate change (high confidence). EbA 
and CbA have increased since AR5, with an emphasis on freshwater 
ecosystems and forests, including protected areas. Inadequate access 
to finance and technology is widely identified as an adaptation barrier 
Climate change is expected to have many impacts on the economy. 
Subsistence farmers and the urban poor are expected to be the 
most impacted by droughts and variable rainfall in the region (high 
confidence). The increasing water scarcity is and will continue to impact 
food security, human health and well-being. The impacts of the many 
landslides and floods affect mainly the urban poor neighbourhoods

and are responsible for the majority of the deaths related to disasters. 
SLR and intense storm surges are expected to impact the tourism and 
hospitality industry in general. Internal and international migrations 
and displacements are expected to increase (high confidence). Climatic 
drivers, such as droughts, tropical storms and hurricanes, heavy rains 
and floods, interact with social, political, geopolitical and economic 
drivers (high confidence).
The common patterns and problems, however, also highlight the 
possibilities for collaboration and learning among the countries and 
institutions in the region in order to strengthen the interface between 
knowledge and policy in climate-change adaptation. All countries in 
the region have submitted their first and updated NDCs, and many 
have published their NAPs, establishing priorities and formulating their 
own policies to cope with climate change.
Various adaptation initiatives have been launched in various sectors

that focus on reducing poverty, improving livelihoods and achieving 
sustainable and resilient development. An increasing number of 
planned and autonomous initiatives has been seen, led by communities, 
governments or a combination of the two, involving engineering 
or NbS. Climate-smart agriculture is an effective option, in several 
conditions and regions, to mitigate the negative impacts of climate 
change. Disaster reduction solutions are increasingly being used, such 
as EWSs. Many and diverse initiatives are still poorly reported and 
evaluated in the scientific literature, leading to challenges in assessing 
and improving them, including consideration of tacit IKLK. The lack 
of climate data and proper downscaling, weak governance, obstacles 
to financing, and inequality constrain the adaptation process (high 
Adaptation measures have been increased and improved since AR5 
in ocean and coastal ecosystems. The majority of these measures are

focused on EbA application through the application of protection and

12
1764Chapter 12 Central and South America
recovery of already impacted ecosystems. Another battery of measures 
is focused on the management and sustainability of marine resources 
managed in fisheries; however, these measures do not assess current 
and future climate-change impacts but rather focus on decreasing the 
impact of other non-climate factors, such as overfishing or pollution. 
To date, throughout CSA there is an important lack of long-term 
research addressing ocean and coastal ecosystem health and their 
species through continuous monitoring, which is one of the main 
barriers to adaptation. The number and type of adaptation measures 
for ocean and coastal ecosystems and their contributions to humans 
are very different among CSA countries, which highlights the number 
of measures related to increasing scientific research and monitoring 
followed by the conservation of biodiversity and changes in legislation

(high confidence). On the other hand, those measures that include 
changes in financing (an important barrier) or the incorporation of 
traditional knowledge are not always considered in NAPs by CSA 
In the water sector, a lack of systematic analysis and evaluation of 
adaptation measures predominates, although important progress has 
been made since AR5 in terms of understanding the interlinkages 
among climate change, human vulnerabilities, governance, policies and 
adaptation success (high confidence). NbS, PES, IWRM and integration 
of IKLK hold great potential for success, in particular if adopting 
approaches with inclusive negotiation formats for water management 
with clear, just and transparent rights and responsibilities.
Climate change poses several challenges to the agri-food sector, 
impacting agricultural production and productivity and posing a 
risk to food security and the economy (high confidence). Adapting

agriculture while conserving the environment represents a challenge 
for sustainable and resilient food production (high confidence). 
Adaptation in the region presents persistent barriers and limitations 
(Table  12.8) associated with investments and knowledge gaps 
(medium confidence). Climate change urges advances in initiatives to 
improve education, technology and innovation in farming systems in 
the CSA region.
Urban adaptation is limited by financing constraints, weak 
intersectoral and multi-level governance and deficits in the housing 
and infrastructure sectors, the overcoming of which represents an 
opportunity for transformative adaptation (high confidence). Short-
term interventions are more common than long-term planning 
(high confidence). Adaptation has taken place throughout the 
region in planning, land use and building regulation, urban control 
systems and risk management. Initiatives in social housing focus on

reducing risk and overcoming urgent deficits but also adding to a 
transformative adaptation pathway (high confidence). Hybrid (green-
grey) infrastructure has been adopted for better efficiency in flood 
control, sanitation, water scarcity and landslide prevention and coastal 
protection (high confidence). NbS, including GI and EbA, are increasing 
in urban areas (high confidence), although isolated engineering 
solutions are still widely practiced. The integration of transport and 
land use plans and the improvement of public transport are key to 
urban adaptation; mitigation prevails over adaptation in the sector 
(high confidence).There is a growing body of evidence that climate variability and climate 
change are causing harm to human health in CSA—including the 
increasing transmission of vector-borne and zoonotic diseases, heat 
stress, respiratory illness associated with fires, food and water insecurity 
associated with drought, among others (medium confidence). In

response, countries in the region are developing innovative adaptation 
strategies to inform health decision-making such as integrated 
climate-health surveillance systems and observatories, forecasting 
of climate-related disasters and epidemic forecast tools. However, 
institutional barriers (limited resources, administrative feasibility and 
political mandates) need to be addressed to ensure the sustained 
implementation of adaptation strategies (high confidence).
Poor and vulnerable groups exert limited political influence; the fewer 
channels and opportunities that exist to participate in decision-making 
and policymaking make these groups less able to leverage government 
support to invest in adaptation measures (very high confidence). 
Participatory processes spur adaptation measures strengthening local 
capacities, though the literature assessing the success of such initiatives 
remains limited. Limits to adaptation include access to land, territory

and resources, labour and livelihood opportunities, knowledge gaps 
and poor multi-actor coordination. Social organisation, participation 
and governance reconfiguration are essential for building climate 
resilience (very high confidence).
Social organisation, participation, governance, education and 
communications to increase perception and knowledge are essential 
for building the resilience to adapt and overcome expected and 
unexpected climate impacts (very high confidence). The focus on 
inclusion and enrolling of the full range of actors in adaptation 
processes, including vulnerable populations, have yielded good 
results in the region (high confidence). However, existing poverty and 
inequality, imbalances in power relations, corruption, weak governance 
and institutions, structural problems and high levels of risk tolerance 
may reinforce poverty and inequality cycles (high confidence). In 
addition, the continued exposure of critical infrastructure and valuable

assets are signs of persisting maladaptation.
The development model prevalent in the region in recent decades 
has proven to be unsustainable, with the emphasis on financial 
sources based on natural resource depletion and extraction and 
the persistence and growing inequality. It is widely recognised that 
climate adaptation measures, if carefully selected considering coupled 
human-environment systems, will provide significant contributions to 
the sustainable development pathways of the region and to achieve 
the SDGs if implemented together with comprehensive strategies to 
reduce poverty, inequality and risks (high confidence). Adaptation and 
the construction of resilience offer not only an opportunity to reduce 
climate-change impacts but also an opportunity to reduce inequality 
and development gaps, to achieve dynamic economies and to regulate 
the sustainable use and transformation of the territory.

12
1765Central and South America  Chapter 12
FAQ 12.1 | How are inequality and poverty limiting options to adapt to climate change in Central and South 
America?
Poverty and inequality decrease human capacity to adapt to climate change. Limited access to resources may reduce the ability of individuals, 
households and societies to adapt to the impacts of climate change and variability because of the narrow response portfolio. Inequality limits 
responses available to vulnerable segments as most adaptation options are resource-dependent.
Though poverty in Central and South America has decreased over the last 12 years, inequality remains as a historic 
and structural characteristic of the region. In 2018, 29.5% of Latin America’s population (including Mexico) were 
poor (182 million) and 10.2% were extremely poor (63 million), more than half of them living in urban areas. In

2020, due to COVID crisis Gini coefficient projection of increases is ranging from 1.1% to 7.8%, poverty increased to 
33.7% (209 millions) and extreme poverty to 12.5% (78 millions).
Poor populations have little or no access to good-quality education, information, health systems and financial 
services. They have fewer chances to access resources, such as land and water, good-quality housing, risk-reducing 
infrastructure, and services, such as running water, sanitation and drainage. Their lack of political clout and 
endowments limits their access to assets for withstanding and recovering from shocks and stresses. Poverty, 
inequality and high vulnerability to the impacts of climate change are interrelated processes. Poor populations are 
highly vulnerable to the impacts of climate change and are usually located in areas of high exposure to extreme 
events. The constant loss of assets and livelihoods in both urban and rural areas drives communities into chronic

poverty traps, exacerbating local poverty cycles and creating new ones.
For instance, climate-related reduced yields in crops, fisheries and aquaculture have a substantial impact on 
the livelihoods and food security of families and affect their options for coping with and adapting to climate 
change and variability. The impact of climate change in agriculture for CSA depends on determinants such as 
the availability of natural resources, access to markets, diversity of inputs and production methods, quality and 
coverage of infrastructure and socioeconomic characteristics of the population. Impacts from climate change on 
small-scale farmers compromise the livelihoods and food security of rural areas and, consequently, the food supply 
for urban areas.
Governments in the region have implemented several poverty-reduction programmes. However, policies of income 
redistribution and poverty alleviation do not necessarily improve climate risk management, so complementary

policies integrating both social and material conditions are required. A study in northern Brazil showed that risk 
management strategies for droughts and food insecurity did not change poverty rates between 1997–1998 and 
2011–2012. Major shocks, such as climate and extreme weather events (e.g., floods, heavy rains, droughts, frost), 
reduce and destroy public and private property. For instance, the ENSO event of 2017 in Peru caused losses estimated 
between USD 6 and 9 billion, affected more than a million inhabitants and generated 370,000 new poor. In total, 
losses by unemployment, deaths, destruction and damage to infrastructure and houses were around 1.3% of the 
GDP of Peru.
Low government spending on social infrastructure (e.g., health, education), ethnic discrimination and social 
exclusion reduce healthcare access, leaving poor people in entire regions mostly undiagnosed or untreated. In

a context of privatisation policies of healthcare systems, research shows that marginal people lack identifying 
documents needed to access public services in Buenos Aires (Argentina), Mexico City (Mexico) and Santiago de Chile 
(Chile), some of the most developed cities in the region. The consequences of this situation are underreporting, 
low diagnosis and low treatment of diseases such as vector-borne diseases such as dengue and risk of diarrhoeal 
diseases originating from frequent flooding in Amazonian riverine communities. Bias in reporting on access to 
healthcare and the incidence of diseases in marginal populations is usually region-dependent. For example, in 
Brazil’s Amazonian north in 2018, there were 2.2 medical doctors per 1000 inhabitants, while 4.95 medical doctors 
per 1000 inhabitants and 9.52 doctors in São Paulo and Santa Catarina respectively. Another example is pregnant

women in remote Amazonian municipalities, who receive less prenatal care than women in urban areas. These social 
inequities underlie systemic biases in health data quality, hindering reliable estimation of disease burdens such as 
the distribution of disease or birth and death registrations. For example, in Guatemala, alternative Indigenous 
healthcare systems are responding to local needs in Mayan communities. However, this remains unrecognised. The 
existence of health institutions based on IK can reinforce the lack of universal coverage by central government 
healthcare, addressing the miscalculation of morbidity, mortality and cause of death among disadvantaged groups.

12
1766Chapter 12 Central and South America
Inequality, informality and precariousness are particularly relevant barriers to adaptation. A significant part of the 
construction sector in the region is informal and does not follow regulations for land use and construction safety 
codes, and there is a lack of public strategies for housing access. Adaptive construction is based on up-to-date 
regulation and codes, appropriate design and materials, and access to infrastructure and services. Decreasing 
inequality and eradicating poverty are crucial for achieving proper adaptation to climate change in the region. 
Some anti-poverty initiatives, such as savings groups, microfinance for improving housing or assets and community 
enterprises, may also support specific adaptive measures. These mechanisms should be widely accessible to poor 
groups and be complemented by comprehensive poverty alleviation programmes that include climate-change 
adaptation.FAQ 12.1 (continued)

FAQ 12.2 | How have urban areas in Central and South America adapted to climate change so far, which further 
actions should be considered within the next decades and what are the limits of adaptation and sustainability?
Cities are becoming focal points for climate-change impacts. Rapid urbanisation in CSA, together with accelerating demand for housing, 
resource supplies and social and health services, has put pressure on the already stretched physical and social infrastructure. In addition, 
migration is negatively affecting the opportunities of cities to adapt to climate change.
CSA is the second most urbanised region in the world after North America, with 81% of its population being 
urban. In addition, 129 secondary cities with 500,000 inhabitants are home to half of the region’s urban population 
(222 million). Another 65 million people live in megacities of over 10 million each. The population migrates among

cities, resulting in more secondary cities and creating mega regions and urban corridors.
Rapid growth in cities has increased the urban informal housing sector (e.g., slums, marginal human settlements 
and others), which increased from 6% to 26% of the total residences from 1990 to 2015. Coastal areas in CSA 
increasingly concentrate more urban centres. Researchers indicate that between 3 and 4 million inhabitants will 
experience coastal flooding and erosion from SLR in all emission scenarios by 2100 considering South America alone.
A study on cities with more than 100,000 inhabitants showed that the number of coastal cities significantly increased 
from 42 to 420 between 1945 and 2014; they are located close to fragile ecosystems such as bays, estuaries and 
mangrove forests, resulting in higher concentrations of population and economic activities. This process degraded

the ability of coastal ecosystems, such as mangroves, to reduce risks and provide essential ecosystem services, which 
help to prevent coastal erosion or maintain fish stocks. Moreover, it reduced ports and tourism, along with income 
opportunities.
Climate-change impacts on cities in CSA are strongly influenced by ENSO, which is associated with an increase in 
more-extreme rainfall events. Urban areas are increasingly dealing with floods, landslides, storms, TCs, water stress, 
fires, spread of vector-borne and infectious diseases, damaging infrastructure, economic activities, built and natural 
environments and the population’s overall well-being.
Glacier retreat in the mountains will affect water runoff and water provision to metropolitan areas such as Lima, La 
Paz, Quito and Santiago, which rely on rivers that originate in the high Andes. Lima, the second driest capital city

in the world, is vulnerable to drought and heavy rain peak events associated with climate change. In Bogota, lower 
precipitation levels and a tendency towards increasing extreme events are expected in the coming decades. Hence, 
the protection of fragile ecosystems such as paramo (fields at 3000 to 4000 MASL) will be crucial for supplying water 
to the city.
SLR impacts cities located in LECZs, not only because of direct coastal flooding, coastal erosion and subsidence, but 
also because it aggravates the impact of storm surges, heat wave energy and saltwater intrusion. In Suriname and 
Guyana 68% and 31% of the population respectively live below 5 MASL, while many sectors of Georgetown, the 
capital of Guyana, are below sea level. Floods with increased frequency and severity of storm surges will also impact 
the River Plate estuary and lower delta of the Parana River where metropolitan Buenos Aires is located.

12
1767Central and South America  Chapter 12
Over 80% of losses associated with climate-related risks are concentrated in urban areas, and between 40% and 
70% of losses occur in cities with less than 100,000 inhabitants, most likely as a result of limited capacities to 
manage disaster risks and low levels of investment.
Despite consistent political and economic barriers, many cities in the region have adopted sustainable local 
development agendas, which work to bring about balanced urban development. The shortcomings of poor 
development patterns remain prominently on display in cities and present important obstacles to adaptation 
investment, as public investment in basic needs (mainly housing and sanitation) must be prioritised.
Cities struggle to address the immediate needs of their population while addressing longer-term needs associated 
with climate adaptation, emissions reduction and sustainable development. Some cities are moving forward to

transformative adaptation, addressing drivers of vulnerability, building robust systems and anticipating impacts. 
Besides government-led adaptation planning and action, individuals, communities and enterprises have been 
incrementally adapting to climate change autonomously over time. Municipalities from Argentina, Peru, Chile, 
Equator, Brazil and Costa Rica are developing and implementing their Local Climate Action Plans, experimenting 
with and revealing best practices in adaptation. Both anticipatory adaptation measures—choosing safe locations, 
building structurally safe houses, choosing elevated places to store valuables, building on stilts—and reactive 
adaptation measures are used, the latter incorporating measures such as relocation, slope stabilisation, afforestation 
and greening of riverbanks. With variations, these cities have included mechanisms to work across sectors and

actors on the understanding that it is collective planning and actions that will ensure that long-term programmes 
continue independently of particular city administrations.
Cities are interconnected systems operating beyond administrative boundaries. Improved collaboration and 
coordination are needed for integrated responses. Aside from good planning, cities need access to external 
adaptation funds. Climate-change adaptation requires long-term funding and investments, which are beyond 
cyclical political considerations. It is crucial to rethink how to ensure that international adaptation funds will reach 
cities and innovate. For example, member cities of Global Covenant of Mayors for Climate & Energy in the region, 
together with Cities for Life Forum in Peru, the Red Argentina de Municipios por el Cambio Climático (RAMCC), the 
Capital Cities of the Americas facing Climate Change (CC35) and others, are pursuing this goal and applying directly

for international grants. New funding sources are required to help local governments and civil society. Cities and 
locally driven adaptation initiatives can be funded by national governments and international organisations.FAQ 12.2 (continued)
FAQ 12.3 | How do climatic events and conditions affect migration and displacement in Central and South America, 
will this change due to climate change, and how can communities adapt?
Migration and displacements associated with climatic hazards are becoming more frequent in CSA, and they are expected to continue to 
increase. These complex processes require comprehensive actions in their places of origin and reception, to improve both adaptation in more 
affected places and the conditions of mobilisation.
The migration, voluntary and involuntary, of individuals, families and groups is common in CSA. People migrate

nationally and internationally, temporarily or permanently, predominantly from rural areas—often immersed in 
poverty—to urban areas. Common social drivers of migration in the region are the economy, politics, land tenure 
and land management change, lack of access to markets, lack of infrastructure, and violence; environmental drivers 
include loss of water, crops and livestock, land degradation and sudden or gradual onset of climate hazards.
The increasing frequency and magnitude of droughts, tropical storms, hurricanes and heavy rains producing 
landslides and floods have amplified internal movements, overall rural to urban. For instance, rural-to-urban 
migration in northern Brazil and international migration from Guatemala, Honduras and El Salvador to North 
America are partly a consequence of prolonged droughts, which have increased the stress of food availability in

these highly impoverished regions. Diminished access to water is also a result of privatisation of that resource. 
In CA, the majority of migrants are young men, reducing the labour force in their places of origin. However, the

12
1768Chapter 12  Centr al and South America
FAQ 12.4 | How is climate change impacting and how is it expected to impact food production in Central and South 
America in the next 30 year
s, and what effective adaptation strategies are and can be adopted in the region?
Agriculture is a fundamental sector in the development of societies from economic and social perspectives, and so it is a major component of 
CSA countries’ adaptive strategies. Implementation of sustainable agriculture practices, such as improved management on native grasslands or agroforestry systems for crop and livestock production, can increase productivity while improving adaptability.
Over the last two decades, countries throughout CSA have been developing rapidly. The agricultural sector is 
fundamental to this development from economic and social perspectives. Some countries in the region are major global food exporters:
•

Corn: three of the top 10 exporters are Brazil, Argentina and Paraguay;
•Soybean exports: Brazil and Argentina are among the top 5 and Paraguay and Bolivia rank in the top 12;
•Cof
fee exports: 5 of the top 10 export countries are Brazil, Colombia, Honduras, Peru and Guatemala;
•Fruits: 2 of the top 10 fresh fruit exporting countries are Chile and Ecuador;
•Fishmeal exports globally are led by Peru, Chile and Ecuador;
•Beef: four of the top exporting countries are from this region: Brazil, Argentina, Uruguay and Paraguay.
CSA is among the regions with the highest potential to increase food supplies, particularly to more densely 
populated regions in Asia, the Middle East and Europe. A better understanding of the impact of the economy on the environment and the contribution of the environment to the economy is critical for identi
fying opportunities

for innovation and promoting activities that could lead to sustainable economic growth without depleting natural resources and increasing sensitivity to climate change and climate variability. The consideration of food as a commodity instead of a common resource leads to the accumulation of underpriced food resources at the expense migrants send back substantial amounts of money, which have become the main source of foreign exchange for their countries and the main source of income for their families.
Because poor people have fewer resources to adapt to changing conditions, they are usually the most impacted

by climate hazards since they are already struggling to survive under normal conditions. These populations are the most likely to migrate, chiefly because of the loss of their livelihoods, their precarious housing and settlements and the lack of money and international aid. Other important factors are the minimal governmental support and assistance through social safety nets and extension services, the scarcity and low quality of education and health services, their isolation and marginality and the insecurity of land rights. These same conditions, though, may hinder their mobility or even render them immobile. Nevertheless, in some cases, despite worsening conditions, people decide not to move.
The magnitude and frequency of droughts and hurricanes are projected to keep increasing by 2050, which may

force millions of people to leave their homes. Climate models show some dry regions becoming even dryer in the coming decades, increasing the stress on small farmers who rely on rainfall to water their fields. Glacier retreat and water scarcity are becoming strong drivers of migration in the Andes. SLR affects activities such as fishing and tourism, which will foster further migration. In Brazil, at least 0.9 million more people will migrate interregionally under future climate conditions.
Addressing migration and displacement requires diverse interventions: in dry regions it is recommended to improve

water management in the places of origin of migration, including storage, distribution and irrigation. Wet regions, lowlands and floodplains will benefit from preventing construction in areas prone to landslides and flooding. Government and international aid are also important for improving people’s options to adapt and enhance their resilience to climate impacts. In northern Brazil, for example, government financial support has significantly reduced drought-related migration. There exists between Guatemala and Canada a temporary migration programme to bring in migrant workers during the harvest season. The United States is also increasing these types of legal temporary migration.FAQ 12.3 (continued)

12
1769Central and South America  Chapter 12
of natural capital. Without serious emissions reduction measures, climate models project an average 1°C to 4°C 
increase in maximum temperatures and a 30% decrease in rainfall up to 2050, across CSA. Tropical SA is projected 
to warm at higher rates than the southern part of SA. Given these circumstances, some regions in CSA (Andes 
region and CA) will just meet or fall below the critical food supply/demand ratio for their population. Meanwhile, 
the temperate southern-most region of SA is projected to have agricultural production surplus. The challenge for 
this region will be to retain the ability to feed and adequately nourish its internal population as well as make an 
important contribution to food supplies available to the rest of the world.
The NDCs of most CSA countries expressly include agriculture as a major component of their adaptive strategy.

From the recommendations presented, five general adaptive themes, or imperatives, emerge: (a) inclusion 
of climate-change projections as a key element for ministries of agriculture and research institutes in their 
decision-making processes, (b) support of research on and adoption of drought- and heat-tolerant crop varieties, (c) 
promotion of sustainable irrigation as an effective adaptive strategy, (d) recovery of degraded lands and sustainable 
intensification of agriculture to prevent further deforestation, and (e) implementation of climate-smart practices 
and technologies to increase productivity while improving adaptability.
Climate-smart practices provide a framework to operationalise actions aimed at understanding synergies 
among productivity, adaptation and mitigation. A significant amount of evidence supports the potential for 
climate-smart-practice technologies to produce such triple wins as natural pastoral systems in the southern

region of SA. Such systems allow for the combination of food production and environmental sustainability. The 
production of meat based on native grasslands with grazing management that optimises forage allowance can 
achieve high production levels while providing multiple ecosystem benefits. Optimal forage allowance means 
offering animals enough forage in order to meet requirements while avoiding overgrazing. This management 
practice simultaneously increases productivity, reduces GHG emissions while improving soil carbon sequestration 
and minimises other environmental impacts such as excess of nutrients, fossil-based energy use and biodiversity loss. 
Pastoral farming systems that manage grazing and feeding efficiently are an example of the integration of food 
security, environmental conservation and nature-based adaptation to climate change.
Agroforestry systems are present in the tropical region of CSA. Trees are present in a large part of the agricultural

landscape of this region, either dispersed or in lines, supporting the production of coffee, cocoa, fruits, pastures and 
livestock in various agroforestry configurations. In CA, shade-grown coffee reduces weed control and improves the 
quality and taste of the product. Agroforestry uses nitrogen-fixing trees (Leguminosae), such as Leucaena in Colombia 
and Inga in Brazil, to restore soil nitrogen fertility. Tropical forest soils are generally nutrient-poor and unsuited to 
long-term agricultural use. Land converted to agriculture by cutting and burning natural vegetation tends to 
remain productive for only a few years. Agroforestry and so-called silvopastoral systems, which incorporate trees 
into crop and livestock systems, have been shown to have a dramatic impact on the maintenance and restoration 
of long-term productivity in agricultural landscapes, including degraded and abandoned land. Agroforestry systems

can provide major benefits through enhanced food security, stronger local economies and increased ecosystem 
services such as carbon storage, regulation of climate and water cycles, control of pests and diseases and maintenance 
of soil fertility. Because of these multiple goods and services, agroforestry practices are considered one of the key 
strategies for the development of climate-smart agriculture.FAQ 12.4 (continued)

12
1770Chapter 12 Central and South America
FAQ 12.5 | How can Indigenous knowledge and practices contribute to adaptation initiatives in Central and South 
America?
Indigenous Peoples have knowledge systems and practices that allow them to adapt to many climatic changes. Adaptation initiatives based on IK 
and practices are more sustainable and legitimate among local communities. It is important to build effective and respectful partnerships among 
Indigenous and non–Indigenous researchers to co-produce climate-relevant knowledge to enhance adaptation planning and action in the region.
There are 28 million Indigenous Peoples in CSA (around 6.6% of the total population of the region). They belong 
to more than 800  groups living in territories covering a wide range of ecosystems—from drylands to tropical 
rainforests to savannahs, coasts to mountains—and that share the land with many other cultural and ethnic groups.

In the region, Indigenous Peoples are often categorised as groups that are highly vulnerable to climate change 
because they are frequently affected by socioeconomic inequalities and the dominance of external powers. They 
often experience internal and external pressures on their communal lands in the forms of pollution, oil and mining, 
industrial agriculture and urbanisation. On the other hand, it is important to recognise that Indigenous Peoples 
have knowledge systems and practices that allow them to adapt to many climatic changes. Increasing scientific 
evidence shows that adaptation initiatives based on Indigenous knowledge and practices are more sustainable and 
legitimate among local communities.
The wide range of adaptation practices based on IK in the region include, among others, increasing species and 
genetic diversity in agricultural systems through community seed exchanges; promotion of highly diverse crop

systems; ancient systems to collect and conserve water; fire prevention strategies; observing and monitoring changes 
in communal ecological–agricultural calendar cycles; recognising changes in ecological indicators like migration 
patterns in birds, the behaviour of insects and other invertebrates and the phenology of fruit and flowering species; 
and systematisation and knowledge exchange among communities. These practices represent a valuable cultural 
and biological heritage.
The Kichwa in the Ecuadorian Amazon cultivate Chakras (plots) within the rainforest. These plots combine crops 
and medicinal herbs for both self-consumption and selling. Similar systems, like the Chakras in the high Andes, the 
Milpas in CA, and the Conucos in northern SA, have been resilient to social and environmental disturbances due 
to their outstanding agrobiodiversity (more than 40 species and varieties can be present in one plot), microhabitat

management and the associated knowledge and institutions.
Traditional fire management among Indigenous Peoples of Venezuela, Brazil and Guyana is another adaptation 
strategy based on a fine-tuned understanding of environmental indicators associated with their culture and 
worldviews. In these countries, Indigenous lands have the lowest incidence of wildfires, significantly contributing 
to maintaining and enhancing biodiversity. These traditional practices have helped to prevent large-scale and 
destructive wildfires, reducing the risks posed by rising temperature and dryness due to climate change.
The traditional agriculture of Mapuche Indigenous Peoples in Chile includes a series of practices that result in a 
system that is more resilient to climate and non-climate stressors. Practices include water management, native seed 
conservation and exchange with other producers (trafkintu), crop rotation, polyculture and tree–crop association.

Similar practices can be found in Mayan communities in Guatemala at the other end of the sub-continent.
Despite the increasing recognition and integration of IK in adaptation practices and policies in the region, 
important barriers for a more effective and transformative integration remain. Some of the most relevant barriers 
include limited participation of Indigenous Peoples and local communities in adaptation planning and the lack 
of sufficient consideration of non-climatic socioeconomic drivers of vulnerability such as poverty and inequality. 
Also, scientific knowledge is commonly prioritised over traditional IKLK. However, some transformative efforts are 
emerging. Bolivian Indigenous organisations represent a notable example by contesting normative conceptions of 
development as economic growth and replacing them with more comprehensive views like harmony with Mother 
Earth and ‘Sumak Kawsay’ or ‘Good Living’.

Several strategies have been proposed to overcome existing barriers, including building effective and respectful 
partnerships among Indigenous and non-Indigenous researchers, co-producing climate-change-relevant knowledge 
and recognising Indigenous Peoples as active participants in the continual development of autonomous strategies to 
preserve their practices, beliefs and knowledge. The implementation of these and other strategies can significantly 
enhance adaptation planning and action in the region.

13
1819Europe Chapter 13
Where Are We Now?
Our current 1.1°C warmer world is already affecting natural and 
human systems in Europe (very high confidence1). Since AR5, there 
has been a substantial increase in detected or attributed impacts of climate change in Europe, including extreme events (high confidence). Impacts of compound hazards of warming and precipitation have become more frequent (medium confidence). Climate change has resulted in losses of, and damages to, people, ecosystems, food systems, infrastructure, energy and water availability, public health and the economy (very high confidence) {13.1.4;13.2.1;13.3.1;13.4.1;  
13.5.1;13.6.1;13.7.1;13.8.1;13.10.1}.
As impacts vary both across and within European regions,

sectors, and societal groups (high confidence), inequalities have deepened (medium confidence). Southern regions tend to be more negatively affected, while some benefits have been observed, alongside negative impacts in northern and central regions. Traditional lifestyles, for example in the European Arctic, are threatened already (high confidence). Poor households have lower capacity to adapt to, and recover from, impacts (medium confidence) {13.5.1;13.6.1;13.7.1;13.8.1.;13.8.2;13.10.1;Box 13.2}.
The range of options available to deal with climate-change 
impacts has increased in most of Europe since AR5 (high confidence). Growing public perception and adaptation knowledge

in public and private sectors, the increasing number of policy and legal frameworks, and dedicated spending on adaptation are all clear indications that the availability of options has expanded (high confidence). Information provision, technical measures and government policies are the most common adaptation actions implemented. Nature-based Solutions (NbS) that restore or recreate ecosystems, build resilience and produce synergies with adaptation and mitigation are increasingly used. Many cities are taking adaptation action, but with large differences in level of ambition and implementation (high confidence) {13.2.2;13.3.2;13.4.2;13.5.2;13.6.2;13.7.2;13.8.2;13.10.2;13.11.1;13.11.2;13.11.3}.
Observed adaptation actions are largely incremental with only

a few examples of local transformative action; adaptation actions have demonstrated different degrees of effectiveness in reducing impacts and feasibility of implementation (high confidence). For example, adaptation actions such as flood defences 
and early warning systems have reduced flood damages and heat-related mortality in parts of Europe. Despite progress in adaptation, impacts are observed. Adaptation actions in the private sector are limited, with many businesses and regions remaining under-prepared. A gap remains between planning and implementation of adaptation action (high confidence) {13.2.2;13.5.2;13.6.2;13.7.2;13.11}.

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.What Are the Future Risks?
Warming in Europe will continue to rise faster than the global 
mean, widening risk disparities across Europe in the 21st century (high confidence). Largely negative impacts are projected for southern 
regions (e.g., increased cooling needs and water demand, losses in agricultural production and water scarcity) and some short-term benefits are anticipated in the north (e.g., increased crop yields and forest growth) {13.1.4;13.2.1;13.3.1;13.4.1;13.5.1;13.6;13.7.1;13.10.2}.
Four key risks (KR) have been identified for Europe, with most

becoming more severe at 2°C global warming levels (GWL) compared with 1.5°C GWL in scenarios with low to medium adaptation (high confidence). From 3°C GWL and even with high 
adaptation, severe risks remain for many sectors in Europe (high confidence). Key risks are: mortality and morbidity of people and ecosystems disruptions due to heat (KR1: heat); loss in agricultural production due to combined heat and droughts (KR2: agriculture); water scarcity across sectors (KR3: water scarcity); impacts of floods on people, economies and infrastructure (KR4: flooding) {13.10.2}.
KR1: The number of deaths and people at risk of heat stress

will increase two- to threefold at 3°C compared with 1.5°C GWL (high confidence). Risk consequences will become severe more rapidly in Southern and Western Central Europe and urban areas (high confidence). Thermal comfort hours during summer will decrease significantly (high confidence), by as much as 74% in Southern Europe at 3°C GWL. Above 3°C GWL, there are limits to the adaptation potential of people and existing health systems, particularly in Southern Europe, Eastern Europe and areas where health systems are under pressure (high confidence) {13.6.1;13.6.2;13.7.1;13.7.2;13.8.1;13.10.2.1}.
KR1: Warming will decrease suitable habitat space for current 
terrestrial and marine ecosystems and irreversibly change their composition, increasing in severity above 2°C GWL (very high

confidence). Fire-prone areas are projected to expand across Europe, threatening biodiversity and carbon sinks (medium confidence). Adaptation actions (e.g., habitat restoration and protection, fire and forest management, and agroecology) can increase the resilience of ecosystems and their services. Trade-offs between adaptation and mitigation options (e.g., coastal infrastructure and NbS) will result in risks for the integrity and function of ecosystems (medium confidence) {13.3.1;13.3.2;13.4.1;13.4.2;13.10.2.1; Cross-Chapter Box  SLR in Chapter 3; Cross-Chapter Box NATURAL in Chapter 2}.
KR2: Due to a combination of heat and drought, substantive 
agricultural production losses are projected for most European areas over the 21st century, which will not be offset by gains in Northern Europe (high confidence). Yield losses for maize

will reach 50% in response to 3°C GWL, especially in Southern Europe. Yields of some crops (e.g., wheat) may increase in Northern Europe if warming does not exceed 2°C (medium confidence).

13Chapter 13 Europe
1820While irrigation is an effective adaptation option for agriculture, the 
ability to adapt using irrigation will be increasingly limited by water 
availability, especially in response to GWL above 3°C (high confidence) 
{13.5.1;13.5.2;13.10.2.2}.
KR3: Risk of water scarcity will become high at 1.5°C and very 
high at 3°C GWL in Southern Europe (high confidence), and 
increase from moderate to high in Western Central Europe 
(medium confidence). In Southern Europe, more than a third of 
the population will be exposed to water scarcity at 2°C GWL; under 
3°C GWL, this risk will double, and significant economic losses in 
water- and energy-dependent sectors may arise (medium confidence). 
For Western Central and Southern Europe, and for many cities, the 
risk of water scarcity will be strongly increasing under 3°C GWL. 
Adaptation becomes increasingly difficult at 3°C GWL and above, 
due to geophysical and technological limits; hard limits are likely2 first

reached in parts of Southern Europe {13.2.1;13.2.2;13.6.1;13.10.2.3}.
KR4: Due to warming, changes in precipitation and sea level rise 
(SLR), risks to people and infrastructures from coastal, riverine and 
pluvial flooding will increase in Europe (high confidence). Risks of 
inundation and extreme flooding will increase with the accelerating pace 
of SLR along Europe’s coasts (high confidence). Above 3°C GWL, damage 
costs and people affected by precipitation and river flooding may double. 
Coastal flood damage is projected to increase at least tenfold by the end 
of the 21st century, and even more or earlier with current adaptation 
and mitigation (high confidence). Sea level rise represents an existential 
threat for coastal communities and their cultural heritage, particularly 
beyond 2100 {13.2.1;13.2.2;13.6.2;13.10.2.4;Box  13.1; Cross-Chapter 
European cities are hotspots for multiple risks of increasing

temperatures and extreme heat, floods and droughts (high 
confidence). Warming beyond 2°C GWL is projected to result 
in widespread impacts on infrastructure and businesses (high 
confidence). These impacts include increased risks for energy supply 
(high confidence) and transport infrastructure (medium confidence), 
increases in air conditioning needs (very high confidence) and high 
water demand (high confidence) {13.2.2;13.6.1;13.7.1;13.10.2}.
European regions are affected by multiple key risks, with 
more severe consequences in the south than in the north (high 
confidence). These risks may co-occur and amplify each other, but 
there is uncertainty about their interactions and their quantifications. 
There is high confidence that consequences for socioeconomic and 
natural systems will be substantial: the number of people exposed to 
KRs and economic losses are projected to at least double at 3°C GWL 
compared with 1.5°C GWL (medium confidence); and increased risks are

also projected for biodiversity and ecosystem services, such as carbon 
regulation. The risks resulting from changes in climatic and non-climatic 
drivers in many sectors is a key gap in knowledge (high confidence). This 
gap prevents the precise assessment of systemic risks, socio-ecological 
tipping points and limits to adaptation {13.10.2;13.10.3;13.10.4}.
as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely 95–100%, more likely than not >50–100% and extremely unlikely 0–5%) 
may also be used when appropriate. Assessed likelihood is typeset in italics (e.g., very likely).Climate risks from outside Europe are emerging due to a 
combination of the position of European countries in the global

supply chain and shared resources (high confidence). There 
is emerging evidence that climate risks in Europe may also impact 
financial markets, food production and marine resources beyond 
Europe. Exposure of European countries to inter-regional risks can be 
reduced by international governance and collaboration on adaptation 
in other regions (medium confidence) {13.5.2;13.9.1;13.9.2;13.11; 
Cross-Chapter Box INTEREG in Chapter16}.
What Are the Solutions, Limits and Opportunities of Adaptation?
There are a growing range of adaptation options available today 
to deal with future climate risks (high confidence). Examples of 
adaptation to the key risks include: behavioural change combined with 
building interventions, space cooling and urban planning to manage 
heat risks (KR1); restoration, expansion and connection of protected 
areas for ecosystems, while generating adaptation and mitigation 
benefits for people (KR1: heat); irrigation, vegetation cover, changes

in farming practices, crop and animal species, and shifting planting 
(KR2: agriculture); efficiency improvements, water storage, water reuse, 
early warning systems and land-use change (KR3: water scarcity); 
early warning systems, reserving space for water and ecosystem-
based adaptation, sediment or engineering-based options, land-use 
change and managed retreat (KR4: flooding). Nature-based Solutions 
for flood protection and heat alleviation are themselves under threat 
from warming, extreme heat, drought and SLR (high confidence) 
{13.2.2;13.3.2;13.4.2;13.5.2;13.6.2;13.7.2;13.8.2;13.9.4;13.10.2;13.11}.
In many parts of Europe, existing and planned adaptation measures 
are not sufficient to avoid the residual risk, especially beyond 1.5° C 
GWL (high confidence). Residual risk can result in losses of habitat and 
ecosystem services, heat related deaths (KR1), crop failures (KR2), water 
rationing during droughts in Southern Europe (KR3) and loss of land

(KR4) (medium confidence). At 3°C GWL and beyond, a combination 
of many, maybe even all, adaptation options are needed, including 
transformational changes, to reduce residual risk (medium confidence). 
Although adaptation is happening across Europe, it is not 
implemented at the scale, depth and speed needed to avoid 
the risks (high confidence). Many sectors and systems, such as 
flood risk management, critical infrastructure and reforestation, are 
on self-reinforcing development paths that can result in lock-ins and 
prevent changes needed to reduce risks in the long term and achieve 
adaptation targets. Forward-looking and adaptive planning can prevent 
path dependencies and maladaptation, and ensure timely action (high 
confidence). Monitoring climate change, socioeconomic developments 
and progress on implementation is critical in assessing if and when

further actions are needed, and evaluating whether adaptation is 
successful {13.2.2;13.10.2;13.11.1;13.11.2;13.11.3; Cross-Chapter 
Box DEEP in Chapter 17}.

13
1821Europe  Chapter 13
Systemic barriers constrain the implementation of adaptation 
options in vulnerable sectors, regions and societal groups (high 
confidence). Key barriers are limited resources, lack of private-sector 
and citizen engagement, insufficient mobilisation of finance, lack of 
political leadership and low sense of urgency. Most of the adaptation 
options to the key risks depend on limited water and land resources, 
creating competition and trade-offs, also with mitigation options 
and socioeconomic developments (high confidence). Europe will 
face difficult decisions balancing these trade-offs. Novel adaptation 
options are pilot tested across Europe, but upscaling remains 
challenging. Prioritisation of options and transitions from incremental 
to transformational adaptation are limited due to vested interests, 
economic lock-ins, institutional path dependencies and prevalent 
practices, cultures, norms and belief systems {13.11.1;13.11.2;13.11.3}.

Several windows of opportunity emerge to accelerate climate 
resilient development (CRD) (medium confidence). Such windows 
are either institutionalised (e.g., budget cycles, policy reforms and 
evaluations, infrastructure investment cycles) or open unexpectedly 
(e.g., extreme events, COVID-19 recovery programmes). These 
windows can be used to accelerate action through mainstreaming and 
transformational actions (medium confidence). This CRD is visible in 
European cities, particularly in green infrastructure, energy-efficient 
buildings and construction, and where co-benefits (e.g., to health, 
biodiversity) have been identified. Private-sector adaptation takes 
place mostly in response to extreme events or regulatory, shareholder 
or consumer pressures and incentives (medium confidence) {13.11.3; 
Box 13.3; Cross-Chapter Box COVID in Chapter 7}.
Closing the adaptation gap requires moving beyond short-term 
planning and ensuring timely and adequate implementation

(high confidence). Inclusive, equitable and just adaptation pathways 
are critical for CRD. Such pathways require consideration of SDGs, 
gender and Indigenous knowledge and local knowledge (IKLK) and 
practices. The success of adaptation will depend on our understanding 
of which adaptation options are feasible and effective in their local 
context (high confidence). Long lead times for nature-based and 
infrastructure solutions or planned relocation require implementation 
in the coming decade to reduce risks in time. To close the adaptation 
gap, political commitment, persistence and consistent action across 
scales of government, and upfront mobilisation of human and financial 
capital, is key (high confidence), even when the benefits are not 
immediately visible {13.2.2;13.8;13.11; Cross-Chapter Box GENDER in 
Chapter 18}.

182213.1 Point of Departure
13.1.1 Introduction and Geographical Scope
This regional chapter on climate-change impacts, vulnerabilities and 
adaptations in Europe examines the impacts on the sectors, regions and 
vulnerable populations of Europe, assesses the causes of vulnerability 
and analyses ways to adapt, thereby considering socioeconomic 
developments, land-use change and other non-climatic drivers. 
Compared with AR5 and in the context of the Paris Agreement (2015), we 
place emphasis on the planned and implemented solutions, assess their 
feasibility and effectiveness, and consider the Sustainable Development 
Goals (SDG) and shared socioeconomic pathways (SSPs). Global 
warming level (GWL) refers to global climate-change emissions relative 
to pre-industrial levels, expressed as global surface air temperature 
(Section 1.6.2; Chen et al., 2021).
The chapter generally follows the overall structure of AR6 WGII. We

first present our point of departure (the present section) followed by 
the key sectors, starting with water, as water is interconnected and of 
fundamental importance to subsequent sections (Sections 13.2–13.8). 
For each section, we assess the observed impacts and projected risks, 
solution space and adaptation options, and knowledge gaps. The solution space is defined as the space within which opportunities and 
constraints determine why, how, when and who adapts to climate risks 
(Haasnoot et al., 2020a). Section 13.9 discusses impacts and adaptation 
beyond Europe, followed by the key risks for Europe (Section 13.10). 
The chapter ends with an assessment of the adaptation solution space, 
CRD pathways and SDGs (13.11), although recognising that scientific 
literature on these aspects is only slowly beginning to emerge.
With the rapidly growing body of scientific literature since WGII 
AR5 (Callaghan et  al., 2020), our assessment prioritises systematic

reviews, meta-analyses, and synthesis papers and reports. Feasibility 
and effectiveness assessments use revised methods developed for the 
Special Report of Global warming of 1.5°C (de Coninck et al., 2018; 
Singh et al., 2020). Protocols, as well as supporting material for figures 
and tables, can be found in the Supplementary Material.
The geographical scope and subdivision of European land, coastal and 
ocean regions is largely the same as in WGII AR5 Chapter 23 (Kovats 
et al., 2014): Southern Europe (SEU), Western Central Europe (WCE), 
Eastern Europe (EEU) and Northern Europe (NEU). Note that WGI 
assesses a larger region for the Mediterranean (MED) which includes 
North Africa and the Middle East compared with the assessment 
in this chapter (SEU). The European part of the Arctic region is not 
Polygon delineations represent the 
boundaries used for the regional synthesis  
of historical trends and future climate 
change projections used in the Assessment

Reports of the IPCC WGI.
    (a) Northern Europe (NEU )
    (b) Eastern Europe (EEU )
    (c) Western and Central Europe (WCE )
    (d) Southern Europe (SEU ) *
European marine sub-regions
    (i) Northern European Seas (NEUS)
    (ii)Temperate European Seas (TEUS )
    (iii) Southern European Seas (SEUS)
* Different from the WGI Mediterranean (MED) 
which includes also the eastern and southern 
countries bordering the Mediterranean.Geographical subdivision of
land and ocean regions of Europe
(a)
(c)(b)
(d)       
       
       (i)
(ii)
(iii)       
       
Figure 13.1 |  Geographical subdivision of land (a,b,c,d) and ocean (i,ii,iii) regions of Europe. The overlay represents the WGI AR6 (IPCC, 2021) subdivisions for 
climate-change projections of land, while the colour coding indicates the European countries (or, in case of the Russian Federation, the European part of the country, EEU, used for

this chapter). Note that in the WGI AR6 report, MED includes both Southern Europe and Northern Africa, while this chapter includes only the northern (European) part of the MED 
region. To distinguish between the two the region is called SEU here.

13
1823Europe  Chapter 13
systematically assessed here, as it is extensively captured in Cross-
Chapter Paper 6. Information relevant to Europe is also synthesised 
in the CCPs (Cross-Chapter Papers), including European biodiversity 
hotspots (Cross-Chapter Paper 1), coastal cities and settlements 
(Cross-Chapter Paper 2), Mediterranean regions (Cross-Chapter Paper 
4) and mountains (Cross-Chapter Paper 5). European seas are broadly 
divided by latitude into (i) European Arctic waters (NEUS), (ii) European 
temperate seas (TEUS) and (iii) southern seas with the Mediterranean 
and the Black Sea (SEUS) (Figure 13.1).
13.1.2 Socioeconomic Boundary Conditions
The adaptive capacity, as measured by the GDP per capita, tends to be 
higher in northern and western parts of Europe (Figure 13.2a). In recent 
decades, climate change has led to substantial losses and damages to 
people and assets across Europe, mostly from riverine flooding, heatwaves

and storms (Figure 13.2b). Public concern about climate change, which 
is an indicator of the intention to mitigate and adapt, is particularly high 
in parts of SEU and WCE (Figure 13.2c). Current vulnerability to extreme weather and climatic events in European countries is low to moderate 
compared with the rest of the world (Figure 13.2d).
13.1.3 Impact Assessment of Climate Change Based on 
Previous Reports
The main findings of previous reports, particularly the WGII AR5 (Kovats 
et  al., 2014) and the IPCC Special Report on 1.5°C (Hoegh-Guldberg 
et al., 2018), highlighted the impacts of warming and rainfall variations 
and their extremes on Europe, particularly SEU and mountainous areas. 
At 2°C GWL, 9% of Europe’s population was projected to be exposed 
to aggravated water scarcity, and 8% of the territory of Europe were 
characterised to have a high or very high sensitivity to desertification 
(UNEP/UNECE, 2016). These impacts are driven by changes in

temperature, precipitation, irrigation developments, population growth, 
agricultural policies and markets (EEA, 2017a). Heat is a main hazard for 
high-latitude ecosystems (Kovats et al., 2014; Jacob et al., 2018; Hock 
et al., 2019). The majority of mountain glaciers lost mass during the past 
two decades, and permafrost in the European Alps and Scandinavia 
(a) Gross domestic 
product (GDP) per capita
Constant 
international 
dollars
(average 
2013–2018)
No data
(c) Population very or 
extremely worried about 
Percentage 
of 
respondents
No data1000020000400006000080000>100000
(d) Vulnerability of 
population to disasters 
and humanitarian crises
Vulnerability 
dimension of 
the 2021 
INFORM RISK 
index
No dataVery 
lowVery
high
15%20%30%40%>50%(b) Reported damages 
and fatalities from 
climate-related events 
(1999–2018) 
Global rank of 
the Climate 
Risk Index 
(period 
1999–2018)

No data1112151>100Damages to people and assets, vulnerability and adaptive capacity across Europe
Figure 13.2 |  Indicators of reported damages to people and assets, vulnerability and adaptive capacity across European countries:
(a) GDP per capita (average 2013–2018), in constant 2011 international dollars (World Bank, 2020); 
(b) exposure as measured by the global rank of the Climate Risk index, which is based on economic damages and fatalities due to climate-related extreme weather events between 
1999 and 2018 (Germanwatch, 2020); 
(c) level of climate-change concern among a representative weighted sample of residents 15 years and older in private households (European Social Survey, 2020); and 
(d) vulnerability to disasters and humanitarian crisis in 2021. The index is based on socioeconomic factors (development, inequality and aid dependency) and vulnerable groups 
(DRMKC, 2020).

1824is decreasing (Hock et  al., 2019). In Central Europe, Scandinavia and 
Caucasus, mountain glaciers were projected to lose 60–80% of their mass 
by the end of the 21st century (Hock et al., 2019). The combined impacts 
on tourism, agriculture, forestry, energy, health and infrastructure were 
suggested to make SEU highly vulnerable and increase the risks of failures 
and vulnerability for urban areas (Kovats et al., 2014). Previous reports 
stated that the adaptive capacity in Europe is high compared with other 
regions of the world, but that there are also limits to adaptation from 
physical, social, economic and technological factors. Evidence suggested 
that staying within 1.5°C GWL would strongly increase Europe’s ability 
to adapt to climate change (de Coninck et al., 2018).
13.1.4 European Climate: Main Conclusions of WGI AR6
Changes in several climatic-impact drivers have already emerged in all

regions of Europe: increases in mean temperature and extreme heat, 
and decreases in cold spells (Ranasinghe et  al., 2021; Seneviratne 
et al., 2021). Lake and river ice has decreased in NEU, WCE and MED, 
and sea ice in NEUS (Fox-Kemper et al., 2021; Ranasinghe et al., 2021). 
With increasing warming, confidence in projections is increasing for more drivers (Figure  13.3). Mean and maximum temperatures, 
frequencies of warm days and nights, and heatwaves have increased 
since 1950, while the corresponding cold indices have decreased 
(high confidence) (Ranasinghe et al., 2021; Seneviratne et al., 2021). 
Average warming will be larger than the global mean in all of Europe, 
with largest winter warming in NEU and EEU and largest summer 
warming in MED (high confidence) (Gutiérrez et al., 2021; Ranasinghe 
et  al., 2021). An increase in hot days and a decrease in cold days 
are very likely (Figure  13.4a,b). Projections suggest a substantial

reduction in European ice glacier volumes and in snow cover below 
elevations of 1500–2000 m, as well as further permafrost thawing 
and degradation, during the 21st century, even at a low GWL (high 
confidence) (Ranasinghe et al., 2021).
The assessment of climate change in WGI AR6 concludes that during 
recent decades mean precipitation has increased over NEU, WCE 
and EEU, while magnitude and sign of observed trends depend 
substantially on time period and study region in MED (medium 
confidence) (Douville et al., 2021; Gutiérrez et al., 2021; Ranasinghe 
et al., 2021). Precipitation extremes have increased in NEU and EEU 
(high confidence) (Seneviratne et  al., 2021), vary spatially in WCE 
Observed and projected climate impact drivers for Europe
Observations from 1970–2019, Projected changes based on warming levels
Northern
EuropeEastern
EuropeWestern
and Central
Europe MediterraneanClimate impact
drivers
Observed
Proj. 1.5°C
Proj. 4.0°C
Observed
Observed

Observed
Observed
Observed
Proj. 4.0°C(a) European land areas (b) European seas
Sea level
driven
coastal flooding
AcidificationObserved
Observed
Proj. 4.0°CClimate impact
drivers
Mean
warming
Heat
extremes
Cold
extremes
Mean
Heavy
Droughts
(for all types)
Severe
wind stormsObserved
Proj. 4.0°C1 = For December, January, February
2 = For June, July, August.
3 = There are subregional differences with decreases or no change for the 
southern part of Europe, such as the southern Mediterranean.
4 = There are differences among types, areas, seasons and metrics.
5 = Increased intensity is associated with decreased frequency.
6 = Future increase in Northern European Seas does not apply to the northern 
Baltic Sea.Northern
European
SeasTemperate
European
SeasSouthern
European
Seas
= not asssessedna Observations

High MediumIncreasing DecreasingIncreasing
and decreasingno change
Low= assessed, but 
   low confidence in 
   direction of change/Confidence assessment of change //
//na na na
6
6
3
3
44
4 4
44
5
52 1
1
Figure 13.3 |  Observed and projected direction of change in climate-impact drivers at 1.5°C and 4°C GWL for European sub-regions and European seas. 
(Assessment from Gutiérrez et al., 2021; Ranasinghe et al., 2021; Seneviratne et al., 2021).

13
1825Europe  Chapter 13
Climate impacts drivers and socio-ecological vulnerabilities
Change in 
Consecutive
Dry Days
(Days per year)
Annual harvested
rainfed area
Smaller areaLarger areaExtreme heat
Change in 
annual count
maximum
(Tx) ≥ 35°C
LowHigh1–1010–2020–3030–40> 40
Maximum
one day
(% change)
Built-up area≤ -10-10– -5-5– 55– 10>10
≤ 00– 1515– 30≤ 1
(a)
 (b)
(c)
 (d)
(e)
 (f)+ 1.5°C + 3.0°C

1826Marine
protected
areas
(Natura 2000)Sea surface
(Change
in SST °C)
≤ 00–11–1.51.5–2> 2
Coastal population
densityMean sea
level rise
(Change
in metres)
≤ 0.40.4–0.50.5–0.60.6–0.7> 0.7
(h)
(j)
(g)
(i)+ 1.5°C + 3.0°C
Figure 13.4 |  Changes in climate hazards for global warming levels of 1.5°C and 3°C based on the CMIP6 ensemble (Gutiérrez et al., 2021) with respect to 
the baseline period 1995–2014, combined with information on present exposure or vulnerability: 
(a,b) number of days with temperature maximum above 35°C (TX35) and population density (European Comission, 2019); 
(c,d) daily precipitation maximum (R × 1 d) and built-up area (JRCdatacatalogue, 2021); 
(e,f) consecutive dry days and annual harvested rain-fed area (Portmann et al., 2010); 
(g,h) sea surface temperature and marine protected areas (EEA, 2021b); and

(k,l) sea level rise (SLR) and coastal population (Merkens et al., 2016). The SLR data consider the long-term period (2081–2100) and SSP1–2.6 for (i) and SSP3–7.0 for (j).
(medium confidence) and have not changed in MED (low confidence). 
For >2°C GWL, of mean precipitation in NEU in winter is increasing and 
decreasing in MED in summer (high confidence). A widespread increase 
of precipitation extremes is projected for >2°C GWL for all sub-regions 
(high confidence), except for MED where no change or decrease 
is projected in some areas (Figure  13.4c,d; Gutiérrez et  al., 2021; 
Ranasinghe et al., 2021). WGI assessed projections for meteorological, 
agricultural/ecological and hydrological drought (Ranasinghe et  al., 2021) with low confidence in the direction of change in NEU, WCE and 
EEU at 1.5°C GWL. MED is projected to be most affected within Europe 
with all types of droughts increasing for 1.5°C (medium confidence)

and 4°C GWL (high confidence). At 4°C GWL, hydrological droughts 
in NEU, WCE and EEU will increase (medium confidence). Projections 
for the 21st century show increases in storms across all of Europe 
(medium confidence) for >2°C GWL with a decrease in their frequency 
in the MED (Ranasinghe et al., 2021).

13
1827Europe  Chapter 13
Sea surface warming between 0.25°C and 1°C has been observed in all 
regions over recent decades (high confidence) (Ranasinghe et al., 2021) 
and are projected to continue increasing (high confidence), particularly 
in the SEUS and at the NEUS (Figure 13.4g,h; Gutiérrez et al., 2021). 
Salinity has increased in the SEUS and decreased in NEUS and is 
projected to continue (medium confidence) (Fox-Kemper et al., 2021). 
European waters have been, and will continue, acidifying (virtually 
certain) (Eyring et  al., 2021; Szopa et  al., 2021), resulting in a mean 
decrease of surface pH of about 0.1 and 0.3 pH units at 1.5°C and 3°C 
GWL with the largest changes at high latitudes (Gutiérrez et al., 2021).
Relative sea level has risen along the European coastlines (Ranasinghe 
et al., 2021), regionally mitigated by post-glacial rise of land masses in 
Scandinavia (Fox-Kemper et al., 2021). This SLR will very likely continue

to increase during the 21st century (Figure 13.4k,l) (high confidence), 
with regional deviations from global mean SLR (low confidence). 
Extreme water levels, coastal floods and sandy coastline recession 
are projected to increase along many European coastlines (high 
13.2 Water
13.2.1 Observed Impacts and Projected Risks
13.2.1.1 Risk of Coastal Flooding and Erosion
Almost 50  million Europeans live within 10 m above mean sea 
level (Vousdoukas et  al., 2020; McEvoy et  al., 2021). Without further 
adaptation (Section 13.2.2), flood risks along Europe’s low-lying coasts 
and estuaries will increase due to SLR compounded by storm surges, 
rainfall and river runoff (high confidence) (Mokrech et al., 2015; Arns 
et al., 2017; Sayol and Marcos, 2018; Vousdoukas et al., 2018a; Bevacqua 
et al., 2019; Couasnon et al., 2020). The population at risk of a 100-year 
flood event starts to rapidly increase beyond 2040 (Vousdoukas et al.,

2018a) reaching 10 million people under RCP8.5 by 2100, but it stays just 
below 10 million people under RCP2.6 by 2150 (Figure 13.5; Haasnoot 
et al., 2021b) assuming present population and protection. The number 
of people at risk is projected to increase and risk to materialise earlier 
especially in response to increasing population under SSP5 (Vousdoukas 
et al., 2018a; Haasnoot et al., 2021b). Under high rates of SLR resulting 
from rapid ice sheet loss from Antarctica, risks may increase by a third by 
2150 (Haasnoot et al., 2021b). Expected annual (direct) damages due to 
coastal flooding are projected to rise from 1.3 billion EUR today to 13–
39 billion EUR by 2050 between 2°C and 2.5°C GWL and 93–960 billion 
EUR by 2100 between 2.5° and 4.4°C GWL, largely depending on 
socioeconomic developments (Cross-Chapter Box  SLR in Chapter 3; 
Vousdoukas et al., 2018a) (high confidence in the sign; low confidence  
in the numbers). UNESCO World Heritage sites in the coastal zone are

at risk due to SLR, coastal erosion and flooding (Section 13.8.1.3; Cross-
Chapter Paper 4; Marzeion and Levermann, 2014; Reimann et al., 2018b) 
as are coastal landfills and other key infrastructures in Europe (AR6/
SROCC; Brand et al., 2018; Beaven et al., 2020).
Observations indicate that soft cliffs and beaches are most affected by 
erosion in Europe with, for example, 27–40% of Europe’s sandy coast 
eroding today, without climate change being identified as the main driver so far (Pranzini et al., 2015; Luijendijk et al., 2018; Mentaschi 
et  al., 2018; Oppenheimer et  al., 2019). SLR will increase coastal 
erosion of sandy shorelines (high confidence) (Ranasinghe et  al., 
2021), but there is low confidence in quantitative values assessment 
of erosion rates and amounts (Athanasiou et al., 2019; Le Cozannet 
et al., 2019; Thieblemont et al., 2019). Without nourishment or other 
natural or artificial barriers to erosion, sandy shorelines could retreat

by about 100 m in Europe at 4°C GWL; limiting warming to 3°C GWL 
could reduce this value by one-third (Vousdoukas et al., 2020).
13.2.1.2 Risks Related to Inland Water
13.2.1.2.1 Riverine and pluvial flooding
Precipitation has raised river flood hazards in WCE and the UK by 11% 
per decade from 1960 to 2010 and decreased in EEU and SEU by 23% per 
decade (Douville et al., 2021; Ranasinghe et al., 2021). The most recent 
three decades had the highest number of floods in the past 500 years 
with increases in summer (Blöschl et al., 2020). Economic flood damages 
increased strongly, reflecting increasing exposure of people and assets 
(Visser et al., 2014; Hoegh-Guldberg et al., 2018; Merz et al., 2021).
Projections indicate a continuation of the observed trends of river 
flood hazards in WCE (high confidence) of 10% at 2°C GWL and 18% 
at 4.4°C GWL, and a decrease in NEU and SEU (medium confidence) 
with, respectively, 5 and 11% in NEU and SEU for a 100-year peak

flow, making Europe one of the regions with the largest projected 
increase in flood risk (Di Sante et al., 2021; Ranasinghe et al., 2021). 
While there is disagreement on the magnitude of economic losses 
and people affected, there is high agreement on direction of change, 
particularly in WCE (Alfieri et  al., 2018). New research increases 
confidence in AR5 statements that without adaptation measures, 
increases in extreme rainfall will substantially increase direct flood 
damages (e.g., Madsen et al., 2014; Alfieri et al., 2015a; Alfieri et al., 
2015b; Blöschl et al., 2017; Dottori et al., 2020; Mentaschi et al., 2020). 
With low adaptation, damages from river flooding are projected to be 
three times higher at 1.5°C GWL, four times at 2°C GWL and six times 
at 3°C GWL (Alfieri et al., 2018; Dottori et al., 2020). At 2°C GWL, the 
incidence of summer floods is expected to decrease across the whole 
alpine region, whereas winter and spring floods will increase due

to extreme precipitation (Gobiet et  al., 2014) and snowmelt-driven 
runoff (Coppola et al., 2018).
Pluvial flooding and flash floods due to intense rainfall constitute 
most flood events in SEU and a substantial risk in other European 
regions (Cross-Chapter Paper 4; Llasat et al., 2016; Rudd et al., 2020). 
The majority (56%) of flood events between 1860 and 2016 were 
flash floods (Paprotny et  al., 2018a). These floods had considerable 
impacts including danger to human lives, for example, causing total 
economic damage of 1 billion USD in Copenhagen (Denmark) in 2011 
(Wójcik et  al., 2013), damage to private households of more than 
70 million EUR in Münster (Germany) in 2014 (Spekkers et al., 2017) 
and during the 2021 floods in Belgium, Germany and the Netherlands 
over 200 deaths, damage to thousands of homes and disrupted 
water and electricity supply (Kreienkamp et  al., 2021). The intensity 
and frequency of heavy rainfall events is projected to increase (high

confidence) (Figure  13.3; Ranasinghe et  al., 2021). Combined with

1828increasing urbanisation, the risk of pluvial flooding is projected to 
increase (Westra et al., 2014; Rosenzweig et al., 2018; Papalexiou and 
Montanari, 2019). Small catchments, steep river channels and cities 
are particularly vulnerable due to large areas of impermeable surfaces 
where water cannot penetrate (Section 13.6).
13.2.1.2.2 Low Flows and Water Scarcity
The frequency and severity of low flows are projected to increase, making 
streamflow drought and water scarcity more severe and persistent in 
SEU and WCE (medium confidence) (Figure  13.3; Ranasinghe et  al., 
2021), but decreases are projected in most of NEU except the southern 
UK (Forzieri et al., 2014; Prudhomme et al., 2014; Schewe et al., 2014; 
Roudier et al., 2016; Ranasinghe et al., 2021). In EEU, uncertainty about 
changes in water scarcity pose distinct challenges for adaptation (Greve 
et  al., 2018). At 1.5°C GWL, the number of days with water scarcity

(water availability as opposed to water demand) and drought will 
increase slightly in SEU (Schleussner et al., 2016; Naumann et al., 2018), 
resulting in 18% of the population exposed to at least moderate water 
scarcity, increasing to 54% at 2°C GWL (Byers et al., 2018). Moderate 
water scarcity is emerging in some parts of WCE (Bisselink et al., 2018) 
increasing to16% of the population under 2°C GWL and SSP2 (Byers 
et al., 2018). Under 4°C GWL, areas in WCE experience water scarcity, 
especially in summer and autumn. Future intensive water use can 
aggravate the situation, in particular in SEU (Sections 13.5.1, 13.10.3).Groundwater abstraction rates reach up to 100 million m³ yr–1 across 
WCE and SEU, and exceed 100 million m³ yr–1 in parts of SEU (Wada, 
2016). Low recharge rates lead to a depletion of groundwater resources 
in parts of SEU and WCE (Doll et al., 2014; Wada, 2016; de Graaf et al., 
2017), increasing the impacts on water scarcity in SEU. Groundwater

pumping and declines in groundwater discharge already threaten 
environmental flow limits in many European catchments, especially in 
SEU, extending to almost all basins and sub-basins within the next 
30–50 years (de Graaf et al., 2019).
The combined effect of increasing water demand and successive 
dry climatic conditions further exacerbates groundwater depletion 
and lowers groundwater levels in SEU but also WCE (Goderniaux 
et al., 2015). Declines in groundwater recharge of up to 30% further 
increase groundwater depletion (Aeschbach-Hertig and Gleeson, 
2012) especially in SEU and semiarid to arid regions (Moutahir et al., 
2017). Even in WCE and NEU, projected increases in groundwater 
abstraction will impact groundwater discharge, threatening sustaining 
environmental flows under dry conditions (de Graaf et al., 2019).
The risks for soil moisture drought are projected to increase in WCE 
and SEU for all climate scenarios (Grillakis, 2019; Tramblay et al., 2020;

Ranasinghe et al., 2021). At 3°C GWL compared with 1.5°C GWL, the 
drought area will increase by 40% and the population under drought 
by up to 42%, especially affecting SEU, and to a lesser extent in WCE 
(Samaniego et al., 2018 ).
Box 13.1 | Venice and Its Lagoon
Venice and its lagoon are a UNESCO World Heritage Site. This socio-ecological system is the result of millennia of interactions between 
people and the natural environment. It is exposed to climatic and non-climatic hazards: more frequent floods, warming, pollution, 
invasive species, reduction of salt marshes, hydrodynamic and bathymetric changes, and waves generated by cruise ships and boat traffic.
The elevation of the average city pedestrian level and of its inner historic area are, respectively, 105 and 55 cm above the present relative 
mean sea level (RMSL). Consequently, even small surges and compound events cause floods when they coincide with high tide (Lionello

et al., 2021a). During the 20th century, RMSL rose at about 2.5 mm yr–1 due to SLR and land subsidence (Zanchettin et al., 2021). The 
frequency of floods affecting the city has increased from once per decade in the first half of the 20th century to 40 times per decade in 
the period 2010–2019 (Figure Box 13.1.1a).
In 1973, the Italian government established a legal framework for safeguarding Venice and its lagoon. Construction of the flood protection 
system started in 2003 and was used for the first time in October 2020 (Lionello et al., 2021b). This system of mobile barriers (MoSE) 
closes the lagoon inlets to avoid floods when needed, while under normal conditions they lay on the seabed, thus allowing ship traffic 
and the exchange between the lagoon and the sea (Molinaroli et al., 2019). To prevent flooding of the central monument area, additional

measures have been proposed including inlets, expansion of salt marshes and pumping seawater into deep brackish aquifers to raise the 
city’s level (Umgiesser, 1999; Umgiesser, 2004; Teatini et al., 2011).
Without adaptation, potential economic damages between 7 and 17 billion EUR have been estimated for the next 50 years (Caporin 
and Fontini, 2016). Additionally, the ecosystem is vulnerable to warming (Solidoro et al., 2010) and SLR (Day Jr et al., 1999; Marani et al., 
2007). The duration of the closure of the lagoon inlets is expected to increase from 2 to 3 weeks yr–1 for RMSL rises of 30 cm, to 2 months 
yr–1 for 50 cm and 6 months yr–1 for 75 cm (Figure Box 13.1.1b; Umgiesser, 2020; Lionello et al., 2021b), resulting in disconnection from 
the sea for most of the time for RMSL rise exceeding 75 cm. Frequent closures of the inlets would prevent ship traffic and in/outflow

of water. For Venice, adaptation pathways considering the full range of plausible RMSL (Figure Box 13.1.1c) levels are not available, 
indicating a long-term adaptation gap. As planning and implementation of adaptation of this extent can take several decades (Haasnoot 
et al., 2020b; Cross-Chapter Box SLR in Chapter 3), this increases the risk that the city will not be prepared in case of rapid SLR.

13
1829Europe  Chapter 13
Barrier islands
separating the lagoon
from the seainlets  connecting
the lagoon
to the sea
(closed by MoSE to avoid floods) Venice
city
center(a) Observed relative sea level rise and flood events in Venice
-10010203040
1900 1920 1940 1960 1980 2000 2020cm of 
relative
sea level
rise
Number of
per decadeRelative sea level in Venice
Mean global sea level
Floods per decadeProtecting Venice from sea level rise and coastal flooding
(b) Projected timing of sea level rise milestones
2000Expected annual duration of the closures of the lagoon inlets
if sea level thresholds are exceeded:
The lagoon is transformed in a coastal lake, only 
occasionally subjected to the tides 
The lagoon is disconnected from the sea for the larger 
part of the year
Severe limitation of ship traffic and lagoon water 
exchange1973 the Italian government approves the 
first special law: a legal framework for 
safeguarding Venice and its lagoon2003 MoSE
construction
begins

1952–1969 period of 
most intense artesian 
water withdrawal1966 highest 
ever-recorded
flood1994 MoSE
structure design 
approved
2020 Full MoSE system 
operated for the first time to 
prevent a predicted flood
Year by which
a rise of 120 cm
is expected
75 cm
50 cm
(c) Projected sea level rise in Venice
0100200300400500
cm of
relative
sea level
rise
2000 2025 2050 2075 2100 2125 215010 km(d) Venice and its lagoon
Medium
confidenice rangeSSP5-8.5
upper limit of the medium confidence range
if low confidence processes are include dSSP5−8.5SSP1−2.610.5 months
6 months
2 months
2100 2200 2300+ 2050 2150 2250likelyConfidence rangeMedian
projected
year
SSP5−8.5SSP1−2.6
Low-confidence
proceses includedvery likely
Figure Box 13.1.1 |  Venice sea level rise (SLR) and coastal flooding: (a) evolution of relative and mean sea level in Venice and decadal frequency of floods

above the safeguard level in the city centre (Frederikse et al., 2020; Lionello et al., 2021a; Lionello et al., 2021b; Zanchettin et al., 2021); (b) projected relative SLR at 
the Venetian coast (Fox-Kemper et al., 2021); “very likely” corresponds to 5–95th percentile range, “likely” to 17–83rd percentile range; (c) timing when critical relative 
sea level thresholds will be reached depending on scenarios and confidence level (Lionello, 2012; Umgiesser, 2020; Lionello et al., 2021a), the upper limit of the medium 
confidence range under SSP5–8.5 represents a low-likelihood, high-impact storyline, low confidence processes include ice sheet instability; (d) Landsat view of Venice and 
its lagoon with the three inlets connecting it to the Adriatic Sea.Box 13.1 (continued)

183013.2.1.2.3 Water Temperature and Quality
Water temperatures in rivers and lakes have increased over the past 
century by ~1–3°C in major European rivers (CBS, 2014; EEA, 2017a; 
Woolway et al., 2017). Warming is accelerating for all European river 
basins (Wanders et al., 2019) increasing by 0.8°C in response to 1.5°C 
GWL and 1.2°C for 3°C GWL relative to 1971–2000 (van Vliet et al., 
2016a) aggravated by declines in summer river flow.
(Ground)water extractions or drainage have caused saltwater 
intrusions (Rasmussen et  al., 2013; Ketabchi et  al., 2016). During 
summer, seawater will also penetrate estuaries further upstream in 
response to reduced river flow and SLR, resulting in more frequent 
closure of water inlets in the downstream part of the rivers in a period 
when water is most needed (high agreement, low evidence) (e.g., 
Haasnoot et al., 2020b).
13.2.2 Solution Space and Adaptation Options

In recent decades water management in Europe has increasingly 
shifted towards integrated and adaptive strategies, with the most 
noticeable shifts in WCE (high confidence) (e.g., Kreibich et al., 2015; 
Bubeck et  al., 2017). While adaptive strategies are increasingly 
considered as an approach to strengthen flexibility and implement 
climate-change adaptation actions, given deep uncertainty about the 
future (Ranger et  al., 2013; Klijn et  al., 2015; Bloemen et  al., 2019; 
Hall et al., 2019; Pot et al., 2019), more traditional water management 
approaches still dominate across Europe (OECD, 2013; OECD, 2015; 
Wiering et  al., 2017). Current measures focus on structural flood 
protection and water resources supply and play an important role to 
preserve present land use and development patterns. The long-term 
effectiveness of such measures is increasingly challenged by their 
reinforcing path dependency (e.g., flood defence and water supply

attract developments which require further protection and supply). 
This path dependency limits the solution space and may hamper 
implementation of transformative measures, such as land-use change, 
to accommodate the water system (medium confidence) (Cross-
Chapter Paper 2; Di Baldassarre et al., 2015; Kreibich et al., 2015; Alfieri 
et al., 2016; Gralepois et al., 2016; Welch et al., 2017; Di Baldassarre 
et al., 2018; Haer et al., 2020).
Water laws, policies and guidance documents increasingly mainstream 
climate impacts and adaptation options (Runhaar et al., 2018; Mehryar 
and Surminski, 2021), though not everywhere. Differences are apparent, 
for example, in coastal adaptation where most, but not all, countries 
are planning for SLR (Figure 13.5; McEvoy et al., 2021). Although the 
planning horizon of 2100 and 1-m SLR are most common (adjusted for 
local conditions), there are significant differences between countries

(e.g., the high-end SLR value in 2100 ranges from 0.3 to 3 m), which 
may lead to unequal impacts over time (McEvoy et al., 2021).
13.2.2.1 Flood Risk Management
Across Europe a range of measures have been implemented to address 
flood risk (Figure 13.6), with protection as the most used strategy (high 
confidence). Early warning and flood protection have been successful in reducing vulnerability to coastal and riverine flooding (Jongman et al., 
2015; Kreibich et al., 2015; Bouwer and Jonkman, 2018). Consequently, 
fatalities due to river flooding have decreased in Europe, despite similar 
numbers of people exposed (1990–2010 compared with 1980–1989) 
(Jongman et al., 2015; Paprotny et al., 2018a).
13.2.2.1.1 Coastal flood risk management
Further protection against coastal flooding is considered economically 
beneficial for densely populated areas (Lincke and Hinkel, 2018; 
Tiggeloven et al., 2020). At least 83% of flood damages due to coastal

flooding could be avoided by elevating dykes along ~23–32% of 
Europe’s coastline by 2100 (RCP4.5-SSP1, RCP8.5-SSP5) (Vousdoukas 
et al., 2020). Limitations of building flood defences include cost–benefit 
considerations in rural areas, available land and social acceptability in 
densely populated areas (Haasnoot et  al., 2018; Hinkel et  al., 2018; 
Meyerhoff et al., 2021).
Nature-based Solutions (NbS) (e.g., wetlands) and sediment-based 
solutions (e.g., sand nourishment) are increasingly considered for 
environmental, economic and/or societal reasons (Cross-Chapter 
Box NATURAL in Chapter 2; Stive et al., 2013; Pranzini et al., 2015; 
Pinto et al., 2020; de Schipper et al., 2021). Coastal wetlands can 
be effective to reduce wave height and form habitats, but their 
feasibility and effectiveness is limited for densely populated areas 
with competing land use, runoff of pollution, sediment-starved deltas 
like the Rhine Delta (Edmonds et al., 2020) and rapid SLR (Kirwan

et  al., 2016; Oppenheimer et  al., 2019; Haasnoot et  al., 2020b). 
While losses of wetlands could be minor if warming stays below 
1.7°C GWL, at high warming or SLR above 0.5 m large-scale losses 
of these habitats will impact their ecological importance, ecosystem 
function (Section  13.4; KR 1, Section  13.10.2) and their ability to 
protect coastlines (Roebeling et al., 2013; van der Spek, 2018; Wang 
et al., 2018; Xi et al., 2021). A combination with structural defences 
could reduce risk in urbanised coastal regions (high confidence). 
Accommodation through elevated or floating houses have been 
implemented and proposed locally within cities as part of a hybrid 
strategy together with protection and as a way of innovative urban 
development (Section  13.6.2; Cross-Chapter Paper 2; Penning-
Rowsell, 2020; Storbjörk and Hjerpe, 2021).
Avoidance through restricting new developments in flood prone 
areas is applied along the coast of WCE and SEU (Harman et  al.,

2015; Lincke et al., 2020) and is considered a low-cost alternative 
to coastal defence at lower SLR. In SEU, an integrated coastal zone 
management (ICZM) protocol has been developed which requires a 
setback zone of 100 m from the coast in unprotected areas. Setback 
zones are projected to reduce impacts considerably in urbanised 
regions (Lincke et  al., 2020). Planned relocation is increasingly 
considered as a realistic adaptation option in cases of extreme SLR 
(Haasnoot et al., 2021a; Lincke and Hinkel, 2021; Mach and Siders, 
2021), for example, UK Shoreline Management Plans (Nicholls et al., 
2013; Buser, 2020). Retreat is rarely applied in Europe (medium 
confidence), though it can have greater benefit-to-cost outcomes 
than protection, particularly in less populated parts of Europe (Lincke 
and Hinkel, 2021). Along parts of the coast in the UK (e.g., The 
Wash), Germany (e.g., Langeoog Island) and the Netherlands (e.g.,

13
1831Europe  Chapter 13
0510152030
25
25Risk and national adaptation planning to sea level rise in Europe
(a) Amount of sea level rise used in national level planning per country and population at risk by amount of sea level rise per  country
(b) Millions of people at risk of a 10-year flood eventNLD
BGRRUS
LVA
ALBBEL
BIHHRVESTFIN
FRA
MCOIRL
MNELTU
ROUUKR
SVNNOR
DEUPOLISL
TURESP ITA PRT
GRC CYPDNK
GBR*SWE*
* Typical values (large variation in values  
are used for GBR and SWE)
**SLR guidance is used differently 
depending on the country considred 
(e.g., enforcing avoidance in land use 
planning, engineering guidance, long 
term planning of adaptation)Amount of Sea Level Rise 
used per time horizonMetres of Sea Level Rise
(Height of the bar)
2 m
1 m
0 m4 m
3 mPreparation for sea level rise (SLR) is 
ongoing, but the amount of SLR 
considered and the amount of people at 
risk varies by country. **
Largest populations at risk for 2 m of SLR:
• NLD 9,928,043 people

• DEU 1,448,106 people
• GBR 1,557,030 peopleGrey = Coastal European Countries   Hashed = Non-coastal European countries
(c) Millions of people at risk of a 100-year flood event
2040 2060 2080 2100 2120 2140 2020 2040 2060 2080 2100 2120 2140 2020SSP1-2.6 population 2020
SSP1-2.6 population change SSP1
SSP1-2.6 constant population
beyond 2100
SSP5-8.5 population 2020
SSP5-8.5 population change SSP5
SSP5-8.5 constant population
21002150
21002150Mid-
CenturyEnd-
Century
(mean)End-
Century
(high-end)Long
term
2020 population at risk
of a 100-year coastal event
for amount of Sea Level Rise
>500,000 250,000 100,000 <100,000
Figure 13.5 |  Sea level rise (SLR) vulnerability and national planning in Europe: 
(a) map of countries in Europe summarising the amount of SLR each country is planning for, at different time horizons (blue bars), and the present population (2020) at risk of a

100-year coastal flood event (orange bars) (Haasnoot et al., 2021b). The amounts of SLR and time horizons reflect national guidance or planning (local or project-based levels may 
differ) (McEvoy et al., 2021); 
(b) projected population at risk to experience a 1-in-10-year coastal flood event under RCP2.6-SSP1 and RCP8.5-SSP5 assuming present protection and population levels, as well 
as population change according to, respectively, SSP1 and SSP5, based on Merkens (2016); 
(c) projected population at risk to experience a 1-in-100-year coastal flood event under RCP2.6-SSP1 and RCP8.5-SSP5, assuming the present protection and population levels, as 
well as population change according to, respectively, SSP1 and SSP5, based on Merkens (2016) (based on Haasnoot et al., 2021b).

1832Westerschelde) retreat has been applied to restore salt marshes and 
to aid coastal defence (Haasnoot et  al., 2019; Kiesel et  al., 2020; 
Lincke and Hinkel, 2021).
13.2.2.1.2 Riverine and pluvial flood risk management
Structural flood protection (e.g., levees) is considered economically 
beneficial in densely populated areas (Alfieri et al., 2016; Dottori et al., 
2020) and could reduce flood damage by ~45% as estimated under 
1.5°C GWL and ~70% under 3°C GWL (Dottori et al., 2020).
Providing more room for water through NbS is increasingly considered 
(Kreibich et al., 2015) as they can reduce risk effectively at lower costs, 
except in places with limited space or in areas with large protection. Such measures include (forest) restoration for upstream retention, 
restoration of river channels and widening riverbeds for natural flood 
retention (Kreibich et  al., 2015; Barth and Döll, 2016; Wyżga et  al.,

2018). Natural retention areas are estimated to be the most effective 
option to reduce riverine flood risk across Europe in the 21st century, 
followed by protection (low evidence) (Dottori et al., 2020).
Wet and dry proofing of buildings can be applied at household level. 
While measures taken at household level can reduce the risk of flooding, 
there is often insufficient investment (medium confidence) (Bamberg 
et  al., 2017; Aerts et  al., 2018). Reasons include low awareness or 
under-estimation of the risk (Kellens et al., 2013), low perceived efficacy 
of adaptation measures (van Valkengoed and Steg, 2019) and lack 
of financial support (Kreibich, 2011). In the long term, risk reduction Effectiveness and feasibility of adaptation options
for water-related climate impacts and risk in Europe
Flood defenses (Protect)
Flood preparedness and early warning plans (Protect/Accommodate)
Planned relocation (Retreat)
No-build zone, restrict new developments (Avoidance)

Flood insurance (Supporting)
Ecosystem based (e.g. wetlands, oyster reefs) (Protect)
Sediment based (e.g. nourishment) (Protect)
Wet and dry proofing (Accommodate)
Ecosystem based (e.g. floodplain restoration, widening riverbed) (Protect)
Retention and diversion (Accommodate)
Retention: green roofs (Accommodate)
Retention: parks (Accommodate)
Update drainage systems and pumps (Accommodate)
Supply: Storage (reservoirs)
Supply: Water diversion and transfer
Supply: Desalinization
Supply: Water reuse
Demand: Water saving and efficiency
Demand: Regulate distribution
Demand: Economic instruments
Demand: Land management and cover change
Monitoring and operational management, drought early warning systemsConfidence
EffectivenessFeasibilityEconomic
Institutiona lTechnological
Socio-cultural
Geophysica lEcological
Evidence
Agreement
Flooding - Coast/River
Flooding - Coast 
Flooding - River
Flooding - Pluvial
Water scarcityImpact type
Assessement score

= no/limited evidence/1 = Physically hampered in highly urbanized regions.
2 = Low on preventing damage, medium on preventing fatalities.
3 = Availability of sand can hamper feasibility in Southern Europe.
4 = In Southern Europe, no evidence for other parts of Europe.
5 = Medium in Southern Europe and high in Western and Central Europe/Northern Europe.// ////
/// ////
//
/// /
////
/
////// /
//
/
/
/
//
/
//52
41
1
3
//
Figure 13.6 |  Effectiveness and feasibility of water-related adaptation options to achieve objectives under increasing climate hazards (Section SM13.9; 
Table SM13.1)

13
1833Europe  Chapter 13
measures by governments are projected to outweigh floodproofing at 
household level, in particular in WCE, while for near-term household 
adaptation or regionally in SEU this could reduce risk more effectively 
(Haer et al., 2019). Relocation of households has occurred in response 
to river flood events (e.g., the 2013 flood events along the Danube River 
in Austria), with financial compensation playing a crucial role (Mayr 
et al., 2020; Thaler and Fuchs, 2020; Thaler, 2021).
Urban drainage infrastructure is designed based on historical rainfall 
intensities, and thus may not have sufficient capacity for increased 
future intensities (Dale et  al., 2018). Adaptation options to pluvial 
flooding include large retention ponds, local green spaces and green 
roofs within cities (Zölch et al., 2017; Maragno et al., 2018; Babovic 
and Mijic, 2019; Ribas et al., 2020).
Early warning systems, insurance and behaviour change can

complement protect and accommodate measures to limit residual risk 
(high confidence). Early warning systems have high monetary benefits 
(Pappenberger et al., 2015). Behavioural adaptation to flooding relies 
on recognition of the threat and capacity to respond, both of which are 
often lacking (Section 13.11.2.2; Bamberg et al., 2017; Haer et al., 2019). 
Flood risk insurance and compensation systems vary across European 
countries, ranging from post-disaster payments by governments and 
compulsory flood insurance, to public–private partnerships where the 
state acts as reinsurer (Keskitalo et al., 2014; Surminski et al., 2015; 
Hanger et al., 2018). Risk-based insurance premiums can induce risk-
averting behaviour but may become unaffordable to poor households 
and some households in high-risk zones (Hudson, 2018; Surminski, 2018). 
Increasing future flood risks due to both climatic and socioeconomic 
change could overburden government budgets (medium confidence)

(Section 13.11.2; Paudel et al., 2015; Mysiak and Perez-Blanco, 2016; 
Schinko et al., 2017; Mochizuki et al., 2018), resulting in unavailable or 
unaffordable insurance for private customers (Section 13.8.3; Hudson 
et  al., 2016; Surminski, 2018), and underfunding and insufficient 
solvency of insurance companies (Section  13.6.2.5; Lamond and 
Penning-Rowsell, 2014). Local knowledge about disastrous flood events 
in the past can be lost across generations, leading to (re)-settlement in 
flood-prone areas (Fanta et al., 2019).
Limits to adaptation to extremely high SLR scenarios have been 
identified for coastal defences, such as the Venice MoSE barrier (see 
Box  13.1), Thames Barrier in the UK (Ranger et  al., 2013) and the 
Maeslant Barrier in the Netherlands (Kwadijk et al., 2010; Haasnoot 
et al., 2020b). However, the scale and pace of adaptation required to 
face high-end SLR scenarios along all coasts of Europe has been poorly

studied. Given the lead and long lifetime of large critical infrastructures, 
there is a growing need to look beyond 2100 to support the design of 
new infrastructures (Cross-Chapter Box SLR in Chapter 3).
13.2.2.2 Water Resources Management
Planning adaptation to water scarcity has centred on increasing 
the availability and supply of freshwater through water storage, 
diversification of sources and water diversion and transfer (high 
confidence). Reservoirs are costly, have negative environmental 
impacts and will not be sufficient under higher warming levels in every 
place (Papadaskalopoulou et  al., 2015a; Di Baldassarre et  al., 2018; Garnier and Holman, 2019). Wastewater reuse is considered a low-cost 
and effective measure where wastewater is available (Lavrnic et  al., 
2017; De Roo et al., 2020), but public acceptance for domestic reuse is 
presently limited (high confidence) (Papadaskalopoulou et al., 2015b;

Morote et al., 2019). Increasing desalination capacity is used particularly 
in SEU but has high energy demands and produces brine waste (Garnier 
and Holman, 2019; Jones et al., 2019; Morote et al., 2019).
Adaptation measures on the demand side include monitoring (e.g., water 
meters, early warning systems of drought) and regulating demand, for 
example, water restrictions, water pricing, water saving and efficiency 
measures, and land management and cover change (Papadaskalopoulou 
et al., 2015b; Varela-Ortega et al., 2016; Manouseli et al., 2018; Garnier 
and Holman, 2019). Prolonged water restrictions and prioritising sectoral 
supply could result in economic losses (e.g., for irrigated agriculture) 
(Section 13.5.2; Wimmer et al., 2014; Salmoral et al., 2019). Economic 
instruments, such as water pricing, can be effective when combined with 
incentives for water saving and efficiency (Kayaga and Smout, 2014;

Esteve et  al., 2018; Crespo et  al., 2019). Water saving and efficiency 
measures, such as leakage repair, education and improved irrigation, 
could limit conflicts across sectors but necessitate technological 
advances and changes in practice together with a willingness to 
cooperate (Garnier and Holman, 2019; Papadimitriou et  al., 2019; 
Teotónio et al., 2020). Increased irrigation efficiency has reduced water 
scarcity, particularly in SEU (Section 13.5; De Roo et al., 2020), and occur 
at farm level in WCE and NEU (Papadaskalopoulou et al., 2015b; van 
Duinen et  al., 2015; Rey et  al., 2017) but come with increasing path 
dependency on supply and trade-offs which may not be sustainable in 
the long term (high confidence) (Di Baldassarre et al., 2018).
The assessment of the effectiveness and feasibility of adaptation 
options shows that a portfolio of supply-and-demand measures is 
needed to reduce water scarcity (Key Risk 3, Section 13.10.3), although

locally demand-side measures could be sufficient (Kingsborough et al., 
2016). Under high warming levels, adaptation to drought and low 
flows by water saving and efficiency measures may not be sufficient 
to counteract reduced availability (medium agreement, low evidence) 
(Collet et al., 2015; De Roo et al., 2020). Successful adaptation in the 
water sector depends on integrating water considerations into sectoral 
policies (Collet et al., 2015; Papadaskalopoulou et al., 2016). Inclusive 
and participatory approaches where (local) stakeholders are actively 
involved in the initiation and execution of water management can 
enhance problem ownership, the quality and democratic legitimacy 
of processes and decisions, enhance support and accelerate decisions 
(Edelenbos et al., 2017; Begg, 2018).
13.2.3 Knowledge Gaps
An assessment of the full solution space of adaptation options and 
pathways under low to high GWL, including the long term, is lacking.

A quantification of the effectiveness of measures in reducing risk is 
limited in the scientific literature. The available assessments consider 
adaptation by incremental measures. Transformative options, such as 
land-use changes, planned relocation from exposed areas or restricting 
future development, are rarely considered. While high-end scenarios 
describing low confidence processes and scenarios beyond 2100 are

1834considered to be useful for risk-averse decision making, in particular 
coastal adaptation (Hinkel et al., 2019; Haasnoot et al., 2020b), they 
are rarely considered in practice.
13.3 Terrestrial and Freshwater Ecosystems 
13.3.1 Observed Impacts and Projected Risks
13.3.1.1 Observed Impacts on Terrestrial and Freshwater 
European land and freshwater ecosystems (Figure  13.7) are already 
strongly impacted by a range of anthropogenic drivers (very high 
confidence), particularly habitats at the southern and northern margins, 
along the coasts, up mountains and in freshwater systems (Cross-Chapter 
Paper 1). Interacting with climate change are non-climatic hazards, such 
as habitat loss and fragmentation, overexploitation, water abstraction, nutrient enrichment and pollution, all of which reduce resilience of 
biotas and ecosystems (very high confidence). Peatlands in NEU and

EEU and other historically important cultural landscapes in Europe are 
overexploited for forestry, agriculture and peat mining (Page and Baird, 
2016; Tanneberger et al., 2017; Ojanen and Minkkinen, 2020). Inland 
wetland RAMSAR convention sites in Europe, which constitute 47% of 
the global sites have lost area in WCE and gained in SEU from 1980 to 
2014 (Xi et al., 2021). Forests in WCE were impacted by the extreme heat 
and drought event of 2018, with effects lasting into 2019 (Schuldt et al., 
2020) and losses in conifer timber sales in Europe (Hlásny et al., 2021).
Extirpation (e.g., local losses of species) have been observed in response 
to climate change in Europe (medium confidence) (Wiens, 2016; EEA, 
2017a; Soroye et al., 2020). Strong climate-induced declines have been 
detected in thermosensitive taxa (Hellmann et al., 2016), including many 
freshwater groups, insects (Habel et al., 2019; Harris et al., 2019; Seibold

et al., 2019; Soroye et al., 2020), amphibians, reptiles (Falaschi et al., 
2019), birds (Lehikoinen et al., 2019) and fishes (Myers et al., 2017a; 
Jarić et al., 2019). The loss of native species, especially specialised taxa, 
Fenno-Scandia Alpine Tundra and Taiga
Ural Mountains Taiga and Tundra
Köppen-Geiger climate classification and biodiversity hotspots in Europe
(a) 1995–2014 (b) 2076–2100
dry summer continentalsubartic 
continental subpolar oceanictundra
i. Northern Europe (NEU)
ii. Eastern Europe (EEU)
iii. Western and Central Europe (WCE)
iv. Southern Europe (SEU) Mediterraneani.
iii.ii.
iv.

Figure 13.7 |  Köppen-Geiger climate classification and biodiversity hotspots in Europe. Boundaries are of the
(a) Northern European (NEU),
(b) Western–Central European (WCE),
(c) Southern European (SEU) and
(d) Eastern European (EEU) regions for 1985–2014 (left) and 2076–2100 (right, A1FI scenario, ~4°C GWL), based on Rubel and Kottek (2010).

13
1835Europe  Chapter 13
is changing biodiversity; however, overall biodiversity could remain 
stable because losses may be offset by range shifts of native, and the 
establishment of non-native, species (Dornelas et al., 2014; McGill et al., 
2015; Hillebrand et al., 2018; Outhwaite et al., 2020).
Range shifts are leading to northward and upwards expansions of 
warm-adapted taxa (very high confidence) (Figure  13.8; Chapter 2). 
These shifts have altered species living in the boreal and alpine tundra 
(Elmhagen et al., 2015; Post et al., 2019; Mekonnen et al., 2021) and 
are greening the high Arctic tundra with shrubs and trees (Myers-
Smith et  al., 2020). Plants display more stable distributions at low 
than at higher mountain altitudes (Rumpf et al., 2018 ). Microclimatic 
variability in some locations can buffer warming impacts (medium 
confidence) (Suggitt et al., 2018; Zellweger et al., 2020; Carnicer et al.,

2021). Northward shifts of tree species distributions is documented in 
north-western Europe (Bryn and Potthoff, 2018; Mamet et al., 2019) but 
not consistently detected (Cudlín et al., 2017; Vilà-Cabrera et al., 2019).
The timing of many processes, including spring leaf unfolding, autumn 
senescence and flight rhythms, have changed in response to changes in 
seasonal temperatures, water and light availability (very high confidence) 
(Chapter 2; Szabó et al., 2016; Asse et al., 2018; Peaucelle et al., 2019; 
Menzel et  al., 2020; Rosbakh et  al., 2021), resulting, for example, in 
earlier arrival dates for many birds and butterflies (Karlsson, 2014; 
Bobretsov et al., 2019; Lehikoinen et al., 2019). The largest increase in 
length of growing season in plants has been detected in WCE, NEU and 
EEU, but shortening in parts of SEU driven by later senescence (Garonna 
et  al., 2014), increasing population growth for butterflies and moths

(Macgregor et al., 2019) and birds (Halupka and Halupka, 2017), and 
residence time for migrant birds (Newson et al., 2016).
13.3.1.2 Projected Risks for Terrestrial and Freshwater 
Risks for terrestrial ecosystems will increase with warming (very 
high confidence) with high impacts at >2.4°C GWL and very high 
impacts >3.5°C GWL (medium confidence) (Section 13.10.3.1). Land-
use changes will increase extirpation and extinction risk (very high 
confidence) (Vermaat et al., 2017). In NEU, biodiversity vulnerability is 
projected to be lower as new climate and habitat space is becoming 
available (Warren et al., 2018; Harrison et al., 2019). Warming <1.5°C 
GWL would limit risks to biodiversity, while 4°C GWL and intensive land 
use could lead to a loss of suitable climate and habitat space for most 
species (low confidence) (Warren et al., 2018; Harrison et al., 2019).
Disruption of habitat connectivity reduces resilience and is projected to

impact 30% of lake and river catchments in Europe by 2030, through 
drought and reduced river flows (medium evidence) (Markovic et al., 
2017). Average wetland area is not projected to change at 1.7°C GWL 
across Europe, while for >4°C GWL expanding sites in NEU are not 
sufficient to balance losses in SEU and WCE (high confidence) (Xi et al., 
2021). At 3°C GWL the alpine tundra habitat and its associated species 
are projected to be lost in the Pyrenees and shrink dramatically in NEU, 
WCE and EEU (Anisimov et al., 2017; Barredo et al., 2020).
Population range shifts (Figures 13.7, 13.10) are projected to continue 
(medium confidence at 1.5° GWL, high confidence at 3.0°C GWL) (Figure  13.8). The largest losses of suitable climatic conditions are 
projected for plants and insects, with different taxon-specific regions 
of highest risk, while proportions of species projected to lose suitable 
climates are lower for other groups (medium confidence) (Figure

Box 13.1.1; Table SM13.3; Warren et al., 2018). Temperatures >1.5°C 
GWL will lead to a progressive subtropicalisation in SEU, expanding 
into WCE at >3°C GWL, a northward shift in the temperate domain 
into NEU (medium confidence) (Feyen et al., 2020) and an expansion 
of desert biomes in EEU (Sergienko and Konstantinov, 2016). Changes 
in distribution are projected for major tree species in all European 
regions at 1.7°C GWL (Dyderski et  al., 2018; Leskinen et  al., 2020), 
with economic implications for managed forests (Section 13.5.1.4). The 
longer growth season in NEU and WCE will support the establishment 
of invasive species (Cross-Chapter Paper 1). Temperatures <1.5°C GWL 
would limit expansion and novel appearances of pests, while >3.4°C 
GWL would make large parts of SEU and WCE suitable for pests, for 
example, wood beetles (Urvois et  al., 2021), and increase economic 
losses due to lower harvest quality of timber (Toth et al., 2020).

Risks emerging from climate change for phenology are uncertain, given 
asynchrony between species, taxa and trophic responses (Thackeray 
et  al., 2016; Posledovich et  al., 2018; Keogan et  al., 2021) and the 
complexity of phenological events and their cues (medium confidence) 
(Delgado et al., 2020; Ettinger et al., 2020). Spring events may continue 
to occur earlier (Gaüzère et al., 2016), but reduced chilling may decrease 
this temporal shift (Wang et  al., 2020). Projections for autumn are 
mixed, with continuing delays (Prislan et al., 2019) or earlier onset of 
leaf senescence (Wu et al., 2018), but reduced chilling may also decrease 
these developments (Wang et al., 2020). Advancement, combined with 
longer autumn growth, may extend the growing season of trees by two 
days per decade in SEU (Prislan et al., 2019). Warming to >3°C GWL will 
impact forest planning in NEU (Caffarra et al., 2014).
13.3.1.3 Observed Impacts and Projected Risks of Wildfires

Fires affect over 400,000 ha every year in the EU (San-Miguel-Ayanz 
et al., 2019), with 85% of the area located in SEU (Khabarov et al., 
2016; de Rigo et al., 2017; Gomes Da Costa et al., 2020), where ‘fire 
weather’ conditions (determined by temperature, precipitation, wind 
speed and relative humidity) are most pronounced (Figure 13.10). Fire 
hazard conditions, including heatwaves (Boer et al., 2017), increased 
throughout Europe from 1980 to 2019 (Figure 13.10), with substantive 
increases in SEU and WCE (high confidence) (Urbieta et al., 2019; Di 
Giuseppe et  al., 2020; Fargeon et  al., 2020). Extreme wildfires have 
been observed in recent years, including 2017 in Portugal, 2018 in 
Sweden (Krikken et al., 2021) and 2021 in south-eastern Europe. In 
SEU, WCE and NEU human activities have caused more than 90–95% 
of the fires, while natural ignition accounts for a substantial portion of 
burned areas in EEU (Wu et al., 2015; Filipchuk et al., 2018).

Except for Portugal, burned area in SEU has shown a slightly 
decreasing trend since 1980, with high interannual variability (Cross-
Chapter Paper 4; Turco et al., 2016; de Rigo et al., 2017). In SEU, burned 
terrestrial biomass declined from 2003 to 2019 (Turco et  al., 2016), 
despite increasing fire risks. This trend is parallel to increasing fire 
management measures implemented (Fernandez-Anez et  al., 2021). 
The slight increase in burned biomass in WCE and NEU is associated

1836with more hazardous landscape configurations and warming in recent 
decades (Turco et al., 2016; Urbieta et al., 2019).
Projections of wildfire risks are uncertain due to multiple factors, 
including compound events, fire–vegetation interaction and social 
factors (Thompson and Calkin, 2011; San-Miguel-Ayanz et al., 2019). 
Wildfire risks could increase across all regions of Europe at 1.5°C and 
3°C GWL (medium to high confidence) (Figure 13.8). In SEU, the fre-
quency of heat-induced fire weather is projected to increase by 14% at 
2.5°C GWL and rise to 30% at 4.4°C GWL (Turco et al., 2018; Gomes 
Da Costa et al., 2020; Ruffault et al., 2020). In the European Arctic, 
the extent and duration of extreme fire seasons will increase because 
of increasing extreme fire weather, increased lightning activity, and drier vegetation and ground fuel conditions due to prolonged droughts 
(McCarty et al., 2021). Projections suggest that new fire-prone regions

in Europe could emerge, particularly in WCE and NEU where wildfires 
have been uncommon and fire management capacity is slowly increas-
ing (Wu et al., 2015; Forzieri et al., 2021).
13.3.1.4 Observed Impacts and Projected Risks on Ecosystem 
Functions and Regulating Services
European temperate and boreal forests, wetlands and peatlands 
hold important carbon stocks (Bukvareva and Zamolodchikov, 2016; 
Yousefpour et al., 2018). Effects of warming and increasing droughts 
on soil moisture, respiration and carbon sequestration have been 
Directionof changeImpacts and risks for terrestrial and freshwater ecosystems and their services
Observed and projected for two different warming levels: 1.5ºC and 3.0ºC
NEU Europe SEU WCE EEU
= no evidencena
Projections• Northern Europe (NEU)
• Eastern Europe (EEU)
• Western and Central Europe (WCE)
• Southern Europe (SEU)Reduction in
habitat availability
of cold-adapted
groups
Reduction in
biodiversity of

cold-adapted
groups
Changes
in phenology
Decrease in
ecosystem
Rising
incidence of fire
Reduced
pollination
services 
Increased
soil erosion  Warming,
heatwaves,
drought
Warming,
drought
Warming,
change in
Warming
Warming,
drought
Warming,
drought
Warming,
drought
Warming,
drought,
precipitationLand-use change,
habitat fragmentation
Land-use change,
management Rare, cold-adapted,
endemic species,
low dispersal
capacity groups
Rare, cold-adapted, thermosensitive
and drought-sensitive
species, endemic species,
low dispersal capacity groups
Northward shifts and
altitudinal movements of
species and populations. 
Species and populations
Ecosystem productivity,
and nutrient and
carbon cycling
Pollination and crop yields

Soil erosion Observed
Proj. +1.5°C
Proj. +3.0°C
Observed
Observed
Observed
Observed
Observed
Observed
Observed
Proj. +3.0°CDirection of change by regions
Increase Decrease Bothnana
High Medium Low High Medium Low High Medium Low High Medium LowImpact/Risk Climatic hazardsInteracting 
non-climatic hazardsHazards
Affected systems
and processeson / to
Figure 13.8 |  Summary of major impacts on, and risks for, terrestrial and freshwater ecosystems in Europe for 1.5°C and 3°C GWL (Table SM13.2)

13
1837Europe  Chapter 13
detected across European regions (high confidence) (Figure  13.8; 
Sanginés de Cárcer et al., 2018; Carnicer et al., 2019; Green et al., 2019; 
Schuldt et al., 2020). Forest expansion in boreal regions results in net 
warming (Bright et  al., 2017), possibly influencing cloud formation 
and rainfall patterns (medium confidence) (Teuling et al., 2017). These 
changes are affecting climate, pollination and soil protection services 
(Figure 13.8; Verhagen et al., 2018). If not managed through increased 
reforestation and/or revegetation or peatland restoration, future 
climate-change impacts will progressively limit the climate regulation 
capacity of European terrestrial ecosystems (medium confidence) 
(Figure 13.8), especially in SEU (Peñuelas et al., 2018; Xu et al., 2019). 
Predominantly positive CO 2 fertilisation effects at current warming will 
change into increasingly negative effects of warming and drought on

forests at higher temperatures (medium confidence) (Peñuelas et al., 
2017; Green et al., 2019; Ito et al., 2020; Wang 2020; Yu et al., 2021). 
In NEU and EEU, peatlands are projected to shrink with 1.7°C GWL, 
and become carbon sources at 3°C GWL (Qiu et al., 2020), peat bogs 
to lose 50% carbon at 2°C GWL, and blanket peatland to shrink or 
regionally disappear (Gallego-Sala et al., 2010; Ferretto et al., 2019).
Declines in pollinator ranges in response to climate change are 
occurring for many groups in Europe (high confidence) (Figure 
Box 13.1.1; Figure 13.8; Kerr et al., 2015; Soroye et al., 2020; Zattara 
and Aizen, 2020), with observed shifts to higher elevations in southern and lower elevation in northern species (Kerr et al., 2015) resulting in 
higher pollinator richness in NEU (Franzén and Öckinger, 2012). Lags in 
responses to climate change suggest that current impacts on pollination 
have not been fully realised (IPBES, 2018). Pollinators are also declining

due to lack of suitable habitat, pollution, pesticides, pathogens and 
competing invasive alien species (Settele et al., 2016; Steele et al., 2019).
Projected climate impacts on pollinators show mixed responses across 
Europe but are greater under 3°C GWL (medium confidence) (Rasmont 
et al., 2015). Increasing homogenisation of populations may increase 
vulnerability to extreme events (Vasiliev and Greenwood, 2021). 
Geographical changes to the climatic niche of pollinators are similar to 
those of insects, with mixed trends, depending on group and location 
(Figure 13.9; Kaloveloni et al., 2015; Rasmont et al., 2015; Radenković 
et al., 2017). In NEU, species richness may increase for some groups 
(Rasmont et al., 2015), with unclear trends for bumblebees (Fourcade 
et al., 2019; Soroye et al., 2020). Future land use will have important 
effects on pollinator distribution (Marshall, 2018) as habitat 
fragmentation in densely populated Europe decreases opportunities

for range shifts and microclimatic buffering (Vasiliev and Greenwood, 
2021).
Soil erosion varies across Europe, with higher rates in parts of SEU and 
WCE, but lower rates in NEU (high confidence) (Figure 13.8; Petz et al., Species projected to remain in suitable climate conditions in Europe
0–20%20–40%40–60%60–80%80–100%Fractional
climatic niche
remainingPlants
+ 1.5°C
i. Northern Europe (NEU) ii. Eastern Europe (EEU) iii. Western and Central Europe (WCE) iv. Southern Europe (SEU) Mammals
+ 1.5°C
Mammals
+ 3.2°C
Plants
+ 3.2°C
Insects
+ 1.5°C
Insects
+ 3.2°C
i.
iii.ii.
iv.
Figure 13.9 |  Species projected to remain within their suitable climate conditions at increasing levels of climate change. Colour shading represents the proportion 
of species projected to remain within their suitable climates averaged over 21 CMIP5 climate models (Warren et al., 2018). Areas shaded in green retain a large number of species

with suitable climate conditions, while those in purple represent areas where climates become unsuitable for more than 80% of species without dispersal (Table SM13.3).

18382016; Polce et al., 2016; Borrelli et al., 2020), related to vegetation type 
and amount of cover, slope and soil type (Panagos et al., 2015a). Short-
term land-use change and management may impact soil erosion more 
than climate (Verhagen et al., 2018). Where conservation agriculture is 
practised or vegetation cover increasing, erosion is slightly decreasing 
(Panagos et al., 2015b; Guerra et al., 2016). Reduced soil loss due to 
reduced spring snowmelt has been observed in EEU (Golosov et al., 
2018), while fire exacerbates soil loss especially in SEU (Borrelli et al., 
2016; Borrelli et al., 2017).
Projected increase in rainfall could increase soil erosion, while warming 
enhances vegetation cover, leading to overall mixed responses (medium 
confidence) (Berberoglu et al., 2020; Ciampalini et al., 2020). In Europe, 
rainfall erosion could increase by >81% (Panagos et al., 2017) at 2°C

GWL, especially in NEU (Borrelli et al., 2020) where risks can be limited 
by soil erosion control (Polce et al., 2016). Decreased rainfall projected 
for parts of SEU could reduce erosion, although increases in rainfall 
intensity could offset this (Serpa et al., 2015). Soil losses from fire will 
increase in SEU in response to 2°C GWL (Pastor et al., 2019), especially 
if combined with extreme rainfall (Morán-Ordóñez et  al., 2020). 
In northern regions, reduced soil losses are projected during spring 
snowmelt (Svetlitchnyi, 2020).
13.3.2 Solution Space and Adaptation Options
Autonomous species adaptation, via range shifts towards higher 
latitudes and altitudes and changes in phenology, but also extirpation, 
have been documented in all European regions (very high confidence) 
(Figure 13.8). Lowering vulnerability by reducing other anthropogenic 
impacts (Gillingham et  al., 2015), such as land-use change, habitat

fragmentation (Eigenbrod et al., 2015; Oliver et al., 2017; Wessely et al., 
2017), pollution and deforestation (Chapter 2), enhances adaptation 
capacity and biodiversity conservation (high confidence) (Ockendon 
et al., 2018). Protected areas, such as the EU Natura 2000 network, have 
contributed to biodiversity protection (medium confidence) (Gaüzère 
et al., 2016; Sanderson et al., 2016; Santini et al., 2016; Hermoso et al., 
2018), but 60% of terrestrial species at these sites could lose suitable 
climate niches at 4°C GWL (Figure Box 13.1.1; EEA, 2017a).
Most protected areas are static and thus do not take species migration 
into consideration (high confidence) (Gillingham et al., 2015; Heikkinen et al., 2020b). More dynamic areas of protection, such as networks of 
protected areas with corridors, buffer zones and zoning, can facilitate 
population shifts (Barredo et al., 2016; Nila et al., 2019; Crick et al., 
2020; Keeley et  al., 2021) and thereby reduce but not eliminate

vulnerability (Wessely et al., 2017; Pavón-Jordán et al., 2020).
Rehabilitation and restoration of land (Prober et al., 2019), particularly 
abandoned agricultural areas in SEU and NEU (Terres et  al., 2015), 
are long-term strategies to improve regulating services and enhance 
biodiversity conservation (Morecroft et al., 2019; Campos et al., 2021). 
Their success will depend on consideration of the future climate 
niche when restoring peatlands (Bellis et  al., 2021) or long-lived 
species with limited mobility (high confidence) (Hazarika et al., 2021). 
The combination of supporting the resilience of species, increasing 
functional diversity of habitats and assisting the migration of species 
at the limit of their adaptive capacity (Park and Talbot, 2018) is needed 
to protect and restore ecosystems (e.g., forests) (Boiffin et al., 2017; 
Messier et  al., 2019). Successful interventions consider habitat and 
the ecological and evolution interactions of species (Šeho et  al.,

2019; Diallo et  al., 2021) combined with monitoring to assess their 
effectiveness (Casazza et al., 2021).
Fire management plans and programmes are in place in most of SEU 
and increasingly developed in the parts of Europe where wildfires 
are less common (Fernandez-Anez et  al., 2021). The capacity to 
implement and maintain these options remains limited, however 
(medium confidence). The dominant fire management paradigm of 
fire suppression in some regions of SEU has been questioned, as it 
contributes to fuel accumulation. Approaches are advocated which 
combine fire-risk mitigation, prevention and preparation (Moreira 
et  al., 2020), recovery through post-fire management (Lucas-Borja 
et al., 2021) and diverse fuel treatment (Mirra et al., 2017), including 
prescribed burning (Fernandes et al., 2013).
Ecosystem-based adaptations (EbA) and NbS that restore or recreate 
ecosystems, build resilience and produce synergies with adaptation

and mitigation in other sectors are increasingly used in Europe (high 
confidence) (Cross-Chapter Box NATURAL in Chapter 2; Berry et al., 
2015; Chausson et  al., 2020). Planting trees or recreating wetlands 
can function as part of natural flood management (Dadson et  al., 
2017; Cooper et al., 2021), while urban green infrastructure can reduce 
flooding (Section 13.2.2) and heat stress as well as provide recreation 00.20.40.60.81.0
Fire
Weather
Index
(FWI)
1980 1990 2000 2010 2020Northern Europe (NEU )Western and
Central Europe (WCE )Southern Europe (SEU)Observed fire weather in European regions (1980–2019)
Figure  13.10 |   Geographical variability and dynamic changes in fire 
danger in Europe over recent decades. Significant increases in fire hazard 
at the multi-decadal scale and unprecedented years of elevated fire hazard have 
occurred over the past decade in Southern and Western Central Europe (SEU,

WCE). The environmental conditions required for fires to spread and intensify 
were evaluated using fire hazard estimates (Fire Weather index, FWI, based on 
meteorological variables such as temperature, precipitation, wind speed and 
relative humidity). The FWI trends were calculated with the ECMWF ERA-5 FWI 
reanalysis dataset (Copernicus, 2019; Copernicus, 2020a; Copernicus, 2020b).

13
1839Europe  Chapter 13
opportunities and health benefits (Section  13.6.2.3; see Box  13.3; 
Kabisch et al., 2016; Choi et al., 2021).
Appropriately implemented ecosystem-based mitigation, such as 
reforestation with climate-resilient native species (Section  13.3.1.4), 
peatland and wetland restoration, and agroecology (Section 13.5.2), 
can enhance carbon sequestration or storage (medium confidence) 
(Seddon et al., 2020). Salt marsh protection or recreation can increase 
carbon storage capacity, enhance coastal flood protection and provide 
cultural services (Beaumont et al., 2014; Bindoff et al., 2019). Trade-offs 
between ecosystem protection, their services and human adaptation 
and mitigation needs can generate challenges, such as loss of habitats, 
increased emissions from restored wetlands (Günther et  al., 2020) 
and conflicts between carbon capture services, and provisioning of 
bioenergy, food, timber and water (medium confidence) (Lee et  al.,

2019; Krause et al., 2020).
The solution space for responding to climate-change risks for terrestrial 
ecosystems has increased in parts of Europe (medium confidence). 
For example, EbA and NbS figure prominently in the EU Adaptation 
Strategy (2021a) and climate-change adaptation is mainstreamed in 
the EU Biodiversity Strategy for 2030 (European Comission, 2020), 
the EU Forest Strategy for 2030 (European Comission, 2021b), the EU 
Green Infrastructure Strategy (European Comission, 2013), as well as 
several national and regional policies. Yet, in the northern parts of EEU 
and NEU (e.g., Greenland, Iceland, northwest Russian Arctic), areas 
which are often sites of pronounced biodiversity shifts and changes, 
solutions are lacking or slow in emergence, due to remoteness, lack 
of resources and sparse populations (Canosa et al., 2020). In the EU, 
innovative financing schemes, such as the Natural Capital Financing 
Facility, are being explored by the European Investment Bank and

the European Commission which supports projects delivering on 
biodiversity and climate adaptation through tailored loans and 
investments. Multiple EU-level service platforms have been promoted 
to track climate-change impacts on land ecosystems and adaptation 
(e.g., Climate-Adapt, Copernicus Land and Fire Monitoring Service, 
Forest Information System of Europe) (Section 13.11.1).
Despite an expanding solution space, widespread implementation and 
monitoring of natural and planned adaptation across Europe is currently 
limited, due to high management costs, undervaluation of nature, and 
conservation laws and regulations that do not consider species shifts 
under future socioeconomic and climatic changes (high confidence) 
(Kabisch et al., 2016; Prober et al., 2019; Fernandez-Anez et al., 2021). 
Climate risks are not perceived as urgent due to a continuing perception 
of the high adaptive capacity of ecosystems (Uggla and Lidskog, 2016;

Esteve et al., 2018; Vulturius et al., 2018). Limited financial resources 
prevent widespread implementation of large-scale and connected 
conservation areas (high confidence) (Hermoso et al., 2017; Lee et al., 
2019; Krause et al., 2020). Particularly in WCE, competition for land use 
with other functions, including mitigation options, is a critical barrier 
to implementation of adaptation. Risks to terrestrial and freshwater 
ecosystems are rarely integrated into regional and local land-use 
planning, land development plans, and agro-system management 
(medium confidence) (Nila et al., 2019; Heikkinen et al., 2020a).13.3.3 Knowledge Gaps
Despite growing evidence of climate-change impacts and risks, including 
attributed changes to terrestrial ecosystems (Section  13.10.1), this 
information is geographically not equally distributed, leaving clear gaps 
for some processes or regions (high confidence). For processes such as

wildfire, the Fire Weather index (Section 13.3.1.3) suggests increasing risk 
of fires in Europe, but robust projections on incidents and magnitudes of 
wildfire and their impacts on ecosystems and other sectors is currently 
limited, particularly for NEU, EEU and WCE (high confidence).
Many studies consider only individual climate drivers, though new 
research shows strong interactions between hazards such as warming 
and drought (Section 13.3.1), as well as non-climatic drivers (Chapter 
2). This creates uncertainty about the emergence of extinctions and 
the magnitudes of impacts for European ecosystems and the services 
they provide (high confidence), such as pollination on food production. 
RCP-SSP combinations to assess risks are only just emerging (Harrison 
Assessments of the long-term effectiveness of adaptation actions 
are missing, due to the time lag in determining the effectiveness of 
an action and attributing risk reduction (Morecroft et al., 2019). For

example, many landscape restoration actions have been discussed, but 
it is unclear which would bring the greatest benefits and which species 
should be used for the restoration (Ockendon et al., 2018). Furthermore, 
adaptation actions will depend on local implementation and benefit 
from being assessed using cultural and Indigenous knowledge where 
applicable, but this is hardly studied (medium confidence).
13.4 Ocean and Coastal Ecosystems and Their 
Services
13.4.1 Observed Impacts and Projected Risks
13.4.1.1 Observed Impacts
Warming continues to be the key climate hazard for European seas 
(Figure 13.1). Interacting with other climatic and non-climatic drivers, 
it has detectable and attributable impacts at a wide range of biological 
and ecological organisational levels (Figure 13.11).
Particularly habitat loss in shallow coastal waters and at the coasts 
themselves, and northward distribution shifts of populations and

communities, are evident across all European marine sub-regions 
(high confidence) (Figure 13.11; Chapter 3). Marine heatwaves have 
had severe ecological impacts in SEUS (high confidence) (Cross-
Chapter Paper 4), threatening sessile benthic biotas and coastal 
habitats (Munari, 2011; Kersting et  al., 2013; Rivetti et  al., 2014; 
Garrabou et  al., 2019). Range contractions, extirpations (medium 
confidence) (Smale, 2020) and species redistributions have been 
observed (high confidence) in TEUS (Cottier-Cook et al., 2017) and 
SEUS (Castellanos-Galindo et al., 2020). Habitat losses, range shifts, 
species invasions and species thermal preferences have altered 
community compositions (Vasilakopoulos et  al., 2017), resulting in 
the ‘subtropicalisation’ of TEUS and ‘tropicalisation’ of SEUS (Chapter

18403; Cross-Chapter Paper 4) and temperature-dependent timing of 
abundance and reproduction cycles (Hjerne et al., 2019; Polte et al., 
2021; Uriarte et al., 2021).
Reductions in growth and reproductive success of calcifying species 
are not yet unambiguously detected and attributed in European 
seas (medium confidence) (Figure  13.11), as many show resilience 
(Kroeker et al., 2010; Wall et al., 2015). However, fish population sizes 
are shrinking (Queirós et al., 2018; Ikpewe et al., 2021), and growth, 
reproduction and recruitment are negatively impacted (Lindegren et al., 
2018; Goldberg et al., 2019; Hidalgo et al., 2019; Vieira et al., 2019; 
Denechaud et al., 2020; Maynou et al., 2020; Polte et al., 2021), though 
positive effects also occur (Sguotti et al., 2019; Tanner et al., 2019). 
Biodiversity changes depend on region, habitat and taxon (medium 
confidence) (Figure  13.11) overall resulting in the redistribution of

biodiversity in Europe (García Molinos et al., 2016), and biodiversity 
declines in some sub-regions (high confidence) (IPBES, 2018).Biological and ecological impacts have cascading effects for marine 
ecosystem functioning (Chivers et  al., 2017; Baird et  al., 2019) and 
biogeochemical cycling (Huete-Stauffer et  al., 2011; Munari, 2011; 
Kersting et al., 2013; Rivetti et al., 2014; Garrabou et al., 2019). In TEUS, 
increased water-column stratification (Section  13.1) and decreasing 
eutrophication, result in reduced primary production (high confidence) 
(Figure 13.11; Capuzzo et al., 2018) and productivity at higher trophic 
levels (high confidence) (Free et  al., 2019), while in NEUS sea ice 
decline has resulted in primary production increase by 40–60% (high 
confidence) (Figure  13.11; Arrigo and van Dijken, 2015; Borsheim, 
2017; Lewis et al., 2020). Climate-related deoxygenation impacts are 
small in most European waters (medium confidence) (Figure 13.11),

expect for semi-enclosed seas such as the Baltic and Black seas (Frolov 
et al., 2014; Jacob et al., 2014; Reusch et al., 2018). Here warming and 
eutrophication have altered ecosystem functioning (high confidence), 
reduced potential fish yield and increased harmful algal blooms 
(Alekseev et al., 2014; Carstensen et al., 2014; Berdalet et al., 2017; 
Directionof changeImpacts and risks for marine and coastal ecosystems and their services
Europe SEUS TEUS NEUS
= no evidence/• Southern European Seas (SEUS)
• Temperate European Seas (TEUS)
• Northern European Seas (NEUS)Observed
Observed
Observed
Observed
Observed
Observed
Observed
Increase Decrease BothImpact/Risk
(a) Loss of
habitat

(b) Shifts in ranges
(incl. invasions), compositions
(taxonomic, functional),
phenologies
(c) Reduction in
growth and
reproductive
success
(d) Loss in
(e) Decline in
(f) Emergence
of harmful algal
blooms and
pathogens
(g) Reduction
in ecosystem
servicesClimatic hazardsInteracting 
Warming,
Heatwave,
Sea-level rise,
Sea-ice decline
Warming,
Acidification
Warming,
Warming,
Heatwaves,
Warming
Warming,
Acidification,
Warming,
Deoxygenation,
Sea-level riseFishing,
Eutrophication,
Coastline modification,
Pollution
Shipping
Fishing,
Pollution
Fishing,
PollutionAffected systems
Populations,
Species,
Communities,
Biomes
Species
Species,
Ecosystems:
Species,
Regulating,

Provisioning,
Coastal protection//
/
//
/
/
ProjectionsHigh Medium Low High Medium Low High Medium Low High Medium Low*
Mainly in the semi-enclosed seas.**
*
Figure 13.11 |  Major impacts and risks for marine and coastal ecosystems in Europe for observed and projected 1.5°C and 3.0°C GWL (Table SM13.4)

13
1841Europe  Chapter 13
Daskalov et al., 2017; Riebesell et al., 2018; Stanev et al., 2018) along 
with the risks of Vibrio pathogens and vibriosis (Section 13.7.1; Baker-
Austin et al., 2017; Semenza et al., 2017). Across all European seas 
there is only low confidence of a consistent change in provisioning 
ecosystem services (e.g., fishing yields) (Section 13.5), because of inter-
regional variability, but high confidence in the decrease in regulating 
services and coastal protection because of the cascading effects of 
ecosystem impacts (Figure 13.11).
13.4.1.2 Projected Risks
Risks to marine and coastal European ecosystems are very likely to 
intensify (Figure  13.11) in response to projected further warming. 
Since the capacity of natural systems for autonomous adaptation is 
limited (medium confidence) (Thomsen et al., 2017; Miller et al., 2018; 
Bindoff et al., 2019), pronounced changes in community composition

and biodiversity patterns are projected by 2100 for TEUS and the 
eastern Mediterranean Sea (SEUS) for >3°C GWL (García  Molinos 
et  al., 2016), challenging conservation efforts (Corrales et  al., 2018; 
Cramer et al., 2018; Kim et al., 2019). At 1.5°C GWL, particularly in 
winter, Mediterranean coastal fish communities are projected to lose 
~10% of species, increasing to ~60% at 4°C GWL (Dahlke et  al., 
2020), exacerbating regime shifts linked to overexploitation (medium 
confidence) (Clark et  al., 2020). Warming at this level will threaten 
many species currently living in marine protected areas (MPAs) in 
TEUS and NEUS (Bruno et  al., 2018). Increasing marine heatwaves 
(MWHs), particularly in SEUS at 4°C GWL (Darmaraki et al., 2019a), 
elevate risks for species (Galli et al., 2017), coastal biodiversity, and 
ecosystem functions, goods and services (Smale et al., 2019); however, 
MWH-related risk levels differ among biotas (Pansch et al., 2018) and

across European seas (Smale et al., 2015).
Marine primary production is projected to further decrease by 2100 
in most European seas between 0.3% at 1.5°C GWL to 2.7% at 4°C 
GWL (high confidence) (Figure 13.11), mainly caused by stratification-
driven reductions in nutrient availability, impacting food webs (Doney 
et al., 2012; Laufkoetter et al., 2015; Wakelin et al., 2015; Salihoglu 
et al., 2017; Holt et al., 2018; Bryndum-Buchholz et al., 2019; Carozza 
et al., 2019; Kwiatkowski et al., 2019). In the Barents Sea, however, 
largely stable primary production is projected under all scenarios in 
response to sea ice decline (Slagstad et al., 2011) and in the eastern 
Mediterranean due to reduced stratification (Macias et  al., 2015; 
Moullec et al., 2019). These changes in productivity are projected to 
increase fish and macroinvertebrate biomass between 5 and 22% 
(Moullec et al., 2019). Decreasing net primary production will impact

higher trophic levels (Section 13.5.1), for example, in TEUS (Holt et al., 
2016; Holt et al., 2018). Marine animal biomass is projected to likely 
decline in most European waters, with decreases <10% under all 
scenarios until the 2030s but losses growing to 25% at 2°C GWL and 
50% at 4°C GWL in coastal waters of the northeast Atlantic (Lotze 
et al., 2019; Bryndum-Buchholz et al., 2020).
Ocean acidification and its biological and ecological risks are projected 
to rise in European waters by impeding growth and reproductive 
success of vulnerable calcifying organisms (medium confidence) 
(Figure  13.11). Coralline algae are projected to reduce skeletal 
performance at 3°C GWL, with negative consequences for habitat formation (medium confidence) (Ragazzola et  al., 2016). Regionally 
(Brodie et  al., 2014), differences in species-specific vulnerability 
will result in community shifts from calcifying macroalgae (medium

confidence) (Ragazzola et al., 2013) to non-calcifying macroalgae (high 
confidence) (Gordillo et al., 2016). Experimental studies demonstrated 
high resilience of some important habitat formers, such as the deep-
water coral Lophelia pertusa (Wall et al., 2015; Morato et al., 2020), 
and habitat engineers, such as Mediterranean limpets (Langer et al., 
2014), facilitated by energy reallocation. However, if not supported 
by sufficient food availability (Thomsen et  al., 2013; Clements and 
Darrow, 2018), such energy reallocation will negatively impact growth 
or reproduction (medium confidence) (Thomsen et al., 2013; Büscher 
et  al., 2017). This suggests that acidification risks will be amplified 
by increased stratification and reduced primary production (medium 
confidence). The emergence of harmful algal blooms and pathogens 
at higher GWLs is unclear across all European seas (low confidence) 
(Figure 13.11).
Risks to marine biotas and ecosystems in European seas are projected

to impact important ecosystem services (Figure 13.11). Elevated CO 2 
levels predicted at 4°C GWL will affect the C/N ratio of organic-matter 
export and, hence, the efficiency of the biological pump (low confidence), 
depending on the shifts in plankton composition and, hence, food-web 
structure (Taucher et al., 2020). Atlantic herring (Clupea harengus) will 
benefit with enhanced larval growth and survival from indirect food-
web effects (Sswat et al., 2018a), whereas Atlantic cod (Gadus morhua) 
will face overall negative impacts (medium confidence) (Section 13.5; 
Stiasny et al., 2018; Stiasny et al., 2019). Anoxic dead zones in the Black 
(Altieri and Gedan, 2015) and the Baltic (Jokinen et al., 2018; Reusch 
et al., 2018) seas are projected to increase, for example, by 5% in the 
Baltic Sea at 4°C GWL (Saraiva et al., 2019). Europe’s coastal vegetated 
‘blue carbon’ ecosystems (subtidal seagrass meadows and intertidal

salt marshes) are highly vulnerable (Spencer et  al., 2016; Schuerch 
et al., 2018; Spivak et al., 2019), particularly in microtidal areas such as 
the Baltic and Mediterranean coast. Losses are projected for Posidonia 
oceanica seagrass habitats in the Mediterranean by up to 75% at 
2.5°C GWL (low confidence) (Chapter 3). The Wadden Sea, the world’s 
largest system of intertidal flats, is projected to reduce in surface area 
and height, as the sediment transport capacity limits the possibility of 
growth with rapidly rising sea levels (Wang et al., 2018; Jiang et al., 
2020). For the Dutch Wadden Sea, the critical rate of 6–10 mm yr–1, at 
which intertidal flats will start to ‘drown’, will be reached by 2030 at 
1.5°C GWL (medium confidence), or even earlier through subsidence 
due to human activities (van der Spek, 2018). European coastal zones 
provided a total of 494 billion EUR of ecosystem services in 2018, and

4.2–5.1% of this value will be lost due to coastal erosion by 2100 at 
2.5°C and 4.6°C GWL, respectively (medium confidence) (Paprotny 
13.4.2 Solution Space and Adaptation Options
Human adaptation options for marine systems encompass socio-
institutional adaptation, technology and measures supporting 
autonomous adaptation (Chapter 3). Integrated coastal zone 
management (ICZM) and marine spatial planning (MSP) are 
frameworks for addressing climate-change adaptation needs as well

1842as operationalising and enforcing marine conservation; however, ICZM 
and MSP commonly do not explicitly take climate-change adaptation 
into consideration (Elliott et  al., 2015). Transboundary ICZM and/or 
MSP (Gormley et al., 2015) will become even more important with the 
projected acceleration of range extensions and ecological regime shifts 
due to climate change (IPCC, 2019).
Many climate-change adaptation governance and implementation 
measures are embedded in international strategies, such as HELCOM 
(Baltic Marine Environment Protection Commission) (Backer et  al., 
2010), OSPAR (Convention for the Protection of the Marine Environment 
of the North-East Atlantic) (OSPAR, 2009), and the Marine Strategy 
Framework Directive (MSFD) and European Water Framework Directive 
(EWFD) of the EU. In the Russian Arctic, mainly the Barents Sea, 
conservation priority areas (CPA) have been identified as Ecologically

and Biologically Significant Areas (EBSA) (Solovyev et  al., 2017); 
however, plans are generally at a relatively early stage (Miller et al., 
2018) and assessments of the effectiveness of these policy frameworks 
to accelerate climate-change adaptation are ongoing (Haasnoot et al., 
2020a).
‘Green’ adaptations, either EbA or NbS, are part of adaptive management 
strategies (European Comission, 2011) that facilitate coastal flood 
protection (Section 13.2.2; Chapter 3; CCC SLR) and generate benefits 
beyond habitat creation (medium confidence), for example, from 
avoided expenditures for flood defence infrastructure and avoided 
loss of the built assets (Gedan et al., 2010).MPAs have been identified 
as adaptation options for natural areas, including permitted and non-permitted uses (Chapter 3; Selig et  al., 2014; Hopkins et  al., 2016a; 
Roberts et al., 2017). The extent of MPAs has been increasing in Europe,

albeit with strong regional variations (Figure 13.12). These MPAs provide 
protection from local stressors, such as commercial exploitation, and 
enhance the resilience of marine and coastal ecosystems, thus lessening 
the impacts of climate change (medium confidence) (Narayan et  al., 
2016; Roberts et al., 2017); however, climate-change risk reduction is 
only a limited MPA objective (Hopkins et al., 2016b; Rilov et al., 2019). 
The implementation of the legal frameworks, such as the EC Habitats 
Directive and EC Birds Directive, allows for enabling adaptation 
(Verschuuren, 2015) as does the incorporation of climate considerations 
in management of Natura 2000 sites (European Comission, 2014). There 
is evidence that better international cooperation is required to increase 
the effectiveness of the MSFD (Cavallo et  al., 2019), and the Good 
Environmental Status is currently not effectively monitored (Machado 

The greatest benefits are obtained from large, long-established, no-
take MPAs (Edgar et al., 2014), yet most MPAs in Europe are partially 
protected or multi-use areas, and existing no-take areas tend to be 
very small (<50 km2). No-take areas account, in total, for less than 
0.4% of the area of European waters (Figure 13.12) and are often 
nested within multi-use MPAs. In some partially protected MPAs, 
local stressors, such as fishing, are higher than adjacent unprotected 
areas (medium confidence) (Zupan et  al., 2018a; Mazaris et  al., 
2019). Despite evidence for climate mitigation benefits of no-take 
zones (Roberts et  al., 2017), the efficacy of partially protected 
MPAs is debated and dependent on local management (Zupan 
Current protection status of Marine Protected Areas (MPA) across European seas
Together, the three marine sub-regions encompass an approximate total 11 million km2
Projected average increase of 
surface sea temperatures at a

4.0°C global warming level by 2100Northern
European Seas (NEUS)
Temperate
European Seas (TEUS)
Southern
European Seas (SEUS)Implemented and No-take MPA: 0.4%
Implemented and partially protected MPA: 0.4 %
Implemented MPAs but protection unknown: 7.0 %
Designated but not implemented MPAs: 0.7 %
Proposed MPAs: 0.3%
Remaining area (non-MPA): 91.2%
NEUS
+ 4.2°C
TEUS
+ 2.3°C
SEUS
+ 3.7°CCoverage of Marine Protected Area (MPA)
Temperate
European Seas (TEUS )
Northern
European Seas (NEU S)
Figure 13.12 |  Marine protected areas (MPAs) in European seas. Shown are proportions of designated and proposed MPAs in the total areas of northern (NEUS), temperate 
(TEUS) and southern (SEUS) European seas, as well as the shares of no-take, partial, unimplemented and unknown protection levels of designated MPAs (Marine Conservation 
Institute, 2021). Moreover, the average increase of surface sea temperatures at 4.0°C GWL by 2100 in NEUS, TEUS and SEUS is indicated.

13
1843Europe  Chapter 13
et al., 2018b). Marine protected areas of all types require effective 
management to contribute to mitigating climate-change impacts, 
including effective monitoring and enforcement (Watson et al., 2014), 
yet the management effectiveness of European MPAs has repeatedly 
been called into question (Batista and Cabral, 2016; Amengual and 
Alvarez-Berastegui, 2018; Fraschetti et al., 2018; Rilov et al., 2019). 
Many MPAs lack management plans, and insufficient resources are 
frequently an issue (Álvarez-Fernández et  al., 2017; Schéré et  al., 
2020). Thus, while substantial in potential, the current capacity of the 
European MPA network to reduce climate-change impacts is limited 
(Jones et al., 2016; Claudet et al., 2020).
Conservation approaches (e.g., MPAs, climate refugia), habitat 
restoration efforts (Bekkby et al., 2020) and further ecosystem-based 
management policies do support alleviation of, or adaptation to,

climate-change impacts (medium confidence) but are themselves 
impacted by climate change (Chapter 3). Moreover, the interaction of 
adaptation and mitigation measures poses risks to marine systems. 
Many coastal regions of the North Sea, especially in the south, are 
particularly susceptible to rising sea levels because of the strong 
tidal regime and the effects of storm surges (Figure  13.3). Hard 
measures to protect human infrastructure against SLR (Section 13.2) 
will lead to loss of coastal habitats, with negative impacts on marine 
biodiversity (Cross-Chapter Box SLR in Chapter 3; Airoldi and Beck, 
2007; Cooper et al., 2016). While rising sea levels will also directly 
threaten intertidal and beach ecosystems, coastal wetlands will benefit 
(medium confidence), in case lateral accommodation space and the 
opportunity for systems to migrate landward and upwards is provided, 
enhancing their ability to capture and store carbon (Lecocq et  al.,

2022; Rogers et al., 2019). In general, European coastal blue carbon 
ecosystems (e.g., seagrass meadows, kelp forests, tidal marshes) 
(Bekkby et  al., 2020) are potentially effective as carbon sinks in 
climate mitigation, akin to reforestation efforts on land (Section 13.3); 
however, their expansion has the potential to interfere with other 
ecosystem services (Cadier et al., 2020) and biodiversity conservation 
(Howard et  al., 2017; Chausson et  al., 2020). The ‘Blue Growth’ 
strategy of the European Commission with the aim to increase offshore 
activities (European Comission, 2012) will increase the pressures on 
the marine environments (medium confidence). Large-scale offshore 
wind-park infrastructure is currently developed in European seas, 
mostly in the North Sea (WindEuropeBusinessIntelligence, 2019), as 
a major component of climate-change mitigation efforts (Clarke et al., 
2022). The introduction of novel hard-substrate intertidal habitats

has, and will continue to have, profound ecological ramifications for 
marine systems, including hydrodynamic changes, stepping stones for 
non-native species, noise and vibration, and changes in the food web 
(high confidence) (Lindeboom et al., 2011; De Mesel et al., 2015; Gill 
et al., 2018; Dannheim et al., 2019).
13.4.3 Knowledge Gaps
Major knowledge gaps are uncertainties and shortcomings in our 
understanding of combined, cascading and interacting impacts of 
climatic and non-climatic pressures on European marine and coastal 
socio-ecological systems (Korpinen et al., 2021). Further observational, 
experimental and modelling work will enhance the insight into multiple drivers, processes and their interactions, strengthen the confidence of 
risk projections and provide a foundation for future adaptation actions.
There is limited knowledge about the connectivity among populations, 
species and ecosystems which would provide new recruits, enable

gene flow in MPA networks (Dubois et al., 2016; Sahyoun et al., 2016) 
and facilitate assisted migration. Such MPAs cover a wide range of 
protection status with limited evidence regarding which level of 
protection and connectivity is needed to achieve adaptations goals in 
response to future warming.
Although European seas and coasts are comparatively well studied 
on a global scale, the spatial and temporal resolution and coverage of 
open-access data is still limited in many regions, particularly in EEU. 
The detection and attribution of ongoing or emerging environmental 
and biological changes are therefore limited. Some efforts are in place, 
such as the six ‘Sea-basin Checkpoints’ (North Sea, Mediterranean Sea, 
Arctic, Atlantic, Baltic, Black Sea) that were established in 2013 under 
The European Marine Observation and Data Network, but high-quality 
observations of key ocean characteristics at the level of regional sea

basins are still too scarce to support decision making for marine 
adaptation (Míguez et al., 2019).
13.5 Food, Fibre and Other Ecosystem Products
13.5.1 Observed Impacts and Projected Risks
13.5.1.1 Crop Production
Agriculture is the primary user of land in Europe. In 2013, Europe 
provided 28% of cereals, 59% of sugar beet and 60% of wine 
produced globally, as well as being part of a globalised food system 
with a third of the commodities produced and consumed in Europe 
traded internationally (FAOSTAT, 2019).
Observed climate change has led to a northward movement of agro-
climatic zones in Europe and earlier onset of the growing season (high 
confidence) (Ceglar et al., 2019). Warming and precipitation changes 
since 1990 explain continent-wide reductions in yield of wheat and 
barley, as well as increases in maize and sugar beet (high confidence) 
(Fontana et al., 2015; Moore and Lobell, 2015; Ray et al., 2015; Ceglar

et al., 2017). Heat stress has increased in SEU in spring, in summer 
throughout Central and Southern Europe, and recently expanded into 
the southern boreal zone (Fontana et al., 2015; Ceglar et al., 2019). 
Drought, excessive rain and the compound hazards of drought and 
heat (Sections 13.2.1, 13.3.1, 13.10.2) have increased costs and cause 
economic losses in forest productivity (Schuldt et al., 2020), annual and 
permanent crops, and livestock farming (Stahl et al., 2016), including 
losses in wheat production in the EU (van der Velde et al., 2018) and 
EEU (high confidence) (Ivanov et al., 2016; Loboda et al., 2017), with 
the severity of impacts from extreme heat and drought tripling over 
the past 50  years (Brás et  al., 2021). Meteorological extremes due 
to compound effects of cold winters, excessive autumn and spring 
precipitation, and summer drought caused production losses (up to 
30% relative to trend expectations) in 2012, 2016 and 2018 (Ben-Ari

et al., 2018; van der Velde et al., 2018; Zscheischler et al., 2018; Toreti

1844et  al., 2019b) that were exceptional compared with recent decades 
(Webber et al., 2020). Regionally, warming caused increases in yields of 
field-grown fruiting vegetables, decreases in root vegetables, tomatoes 
and cucumbers (Potopová et al., 2017) and earlier flowering of olive 
trees (high confidence) (Garcia-Mozo et  al., 2015). Delayed harvest, 
due to both wet conditions and earlier harvests in Central Europe in 
response to warming, has impacted wine quality (Cook and Wolkovich, 
2016; van Leeuwen and Darriet, 2016; Di Lena et al., 2019).
Evidence for growing regional differences of projected climate risks is 
increasing since AR5 (high confidence). While there is high agreement 
of the direction of change, the absolute yield losses are uncertain due 
to differences in model parameterisation and whether adaptation 
options are represented (high confidence) (Donatelli et  al., 2015; 
Moore and Lobell, 2015; Knox et al., 2016; Webber et al., 2018). At

1.5°C GWL, compound events which led to recent large wheat losses 
are projected to become 12% more frequent (Ben-Ari et  al., 2018). 
Growing regions will shift northward or expand for melons (Bisbis 
et al., 2019), tomatoes and grapevines reaching NEU and EEU in 2050 
under 1.5°C GWL (high confidence) (Hannah et  al., 2013; Litskas 
et al., 2019), while warming would increase yields of onions, Chinese 
cabbage and French beans (Bisbis et al., 2019) (medium confidence). 
In response to 2°C GWL, agro-climatic zones in Europe are expected to 
move northward 25–135 km per decade, fastest in EEU (Ceglar et al., 
2019). Negative impacts of warming and drought are counterbalanced 
by CO 2 fertilisation for crops such as winter wheat (medium confidence, 
medium agreement), resulting in some regional yield increases with 
climate change (Zhao et al., 2017; Webber et al., 2018).
Reductions in agricultural yields will be higher in the south at 4°C

GWL, with lower losses or gains in the north (high confidence) 
(Figure 13.5; Trnka et al., 2014; Webber et al., 2016; Szewczyk et al., 
2018). The largest impacts of warming are projected for maize in SEU 
(high confidence) (Deryng et al., 2014; Knox et al., 2016) with yield 
losses across Europe of 10–25% at 1.5°C–2°C GWL and 50–100% at 
4°C GWL (Deryng et al., 2014; Webber et al., 2018; Feyen et al., 2020).
Use of longer-season varieties can compensate for heat stress on maize 
in WCE and lead to yield increases for NEU, but not SEU for 4°C GWL 
(medium confidence) (Siebert et al., 2017; Ceglar et al., 2019). Irrigation 
can reduces projected heat and drought stress, for example, for wheat 
and maize (Ruiz-Ramos et  al., 2018; Feyen et  al., 2020), but use is 
limited by water availability (KR3, Section  13.10.2). The advantages 
of a longer growing season in NEU and EEU are outbalanced by the 
increased risk of early spring and summer heatwaves (Ceglar et al., 
2019).

Warming causes range expansion and alters host pathogen association 
of pests, diseases and weeds affecting the health of European crops 
(high confidence) (Caffarra et al., 2012; Pushnya and Shirinyan, 2015; 
Latchininsky, 2017) with high risk for contamination of cereals (Moretti 
et al., 2019). Regionally predicted reduction in rainfall (Section 13.1) 
can lead to carryover of herbicides (Karkanis et al., 2018).
Net yield losses will reduce economic output from agriculture in the 
EU, reaching a reduction of 7% for the EU and the UK combined, and 
10% in SEU at 4°C GWL (Naumann et al., 2021). Farmland values are projected to decrease by 5–9% per degree of warming in SEU (Van 
Passel et al., 2017). Increased heat and drought stress, and reduced 
irrigation water availability, will decrease profitability and cause 
abandonment of farmland in SEU (limited evidence, low confidence) 
(Holman et al., 2017).
13.5.1.2 Livestock Production

Heat and humidity affect livestock, such as dairy cows and goats, 
directly exposed in open barns and outdoors (Gauly et  al., 2013; 
Bernabucci et  al., 2014; Silanikove and Koluman, 2015), and cold-
adapted husbandry (high confidence) (see Box 13.2; Section 13.8.3). 
Heat impacts animal health (Sanker et  al., 2013; Lambertz et  al., 
2014), nutrition, behaviour and welfare (Heinicke et  al., 2019), 
performance and product quality (Gauly and Ammer, 2020). Climate 
change also impacts grassland production, fodder composition and 
quality, particularly in SEU (Dumont et al., 2015) and EEU (Bezuglova 
et  al., 2020), as well as alters the prevalence, distribution and load 
of pathogens and their vectors (high confidence) (Section  2.4.2.7.3; 
Morgan et al., 2013; Charlier et al., 2016). Projected impacts on poultry 
and pigs are low due to temperature control in large parts of Europe, 
but are greater in SEU where open systems prevail (Chapter 5).

Warming increases the pasture growing season and farming period 
in NEU and at higher altitudes (Fuhrer et  al., 2014), while longer 
drought periods and thunderstorms can influence abandonment of 
remote Alpine pastures, reducing cultural and landscape ecosystem 
services and losing traditional farming practices (high confidence) 
(Section  13.8.3; Herzog and Seidl, 2018). At 2–4°C GWL grassland 
biomass production for forage-fed animals will increase in NEU and 
the northern Alps, while forage production will decrease in SEU and 
the southern Alps due to heat and water scarcity (Gauly et al., 2013; 
Jäger et al., 2020), causing regional reductions of cow milk production 
in WCE and SEU (high confidence) (Silanikove and Koluman, 2015).
13.5.1.3 Aquatic Food Production
Seafood production in Europe provides jobs for >250,000 people, 
predominantly in SEU (Carvalho et al., 2017). Marine fisheries contribute 
80% to European aquatic food production, while marine aquaculture

provides 18% and freshwater production 3% (Blanchet et al., 2019). 
The Russian Federation provides 25% of seafood production in Europe 
(FAOSTAT, 2019).
Climate change has impacted European marine food production 
(high confidence); however, extraction is still the major impact on 
commercially important fish stocks in Europe (Mullon et  al., 2016), 
with 69% of stocks overfished and 51% outside safe biological limits 
(Froese et al., 2018). The North Sea, the Iberian Coastal Sea and the 
Celtic Sea–Biscay Shelf are globally among the areas most negatively 
affected by warming with losses of 15–35% in maximum sustainable 
yields (MSY) during recent decades (Free et  al., 2019). Warming 
has caused ongoing northward movement and range expansion of 
Northeast Atlantic fish stocks (Section 13.4; Baudron et al., 2020). In 
the North Sea, cuttlefish (van der Kooij et al., 2016; Oesterwind et al., 
2020) and tuna (Bennema, 2018; Faillettaz et al., 2019) have become

new target species (medium confidence). In SEU, warm-water species

13
1845Europe  Chapter 13
increasingly dominate fisheries landings (Fortibuoni et  al., 2015; 
Teixeira et al., 2016; Vasilakopoulos et al., 2017).
European countries are assessed to be globally among the least 
vulnerable to the impacts of climate change on fisheries-related 
food security risks (high confidence) due to low levels of exposure to 
climate hazards, low dependency of economies on fisheries and a high adaptive capacity (Barange et al., 2014; Ding et al., 2017). European 
freshwater production is suggested to be less vulnerable than marine 
sectors and marine production vulnerability increases with latitude 
(Blanchet et  al., 2019). In the aquaculture sector, Norway is highly 
vulnerable due to the high sensitivity of salmon farming to warming 
and high per-capita production (Handisyde et al., 2017). In the fisheries 
sector, vulnerability for fishing communities is highest in SEU and the 
Future vulnerability and risks for aquatic food production

Blue MusselPacific Oyster
Atlantic SalmonGilt-head Bream
European SeabassRainbow Trout
European Carp
SW PortugalNetherlandsDenmark
N Ireland
Spain
TurkeyPoland
(a) Risk to fisheries in European coastal regions (b) Vulnerability of national European aquaculture sectors
(c) Difference (%) in projected population sizes of major
fisheries species between 1.5°C and 4°C global warming(d) Difference (%) in projected population sizes of major 
aquaculture species between 1.5°C and 4°C global warming
Sole
Sardine
Saithe
Red Mullet
plaice
Mackerel
Horse mackerel
Herring
Hake
Haddock
Dolphinefish
Cod
Bluefin tuna
Anchovey
Aegean Sea
Bay of Bisca y
NE Atlantic
North Sea
W MedLowestHighest
LowestHighest
60% 40% 20%0 -20% -40% -60% -80%
 0 -10 -20% -30%
Figure 13.13 |  Future vulnerability and risks for aquatic food production:

(a) vulnerability for fisheries in 105 coastal regions across 26 countries based on biological traits and physiological metrics of 556 resource populations (Payne et al., 2021); 
(b) vulnerability of major aquaculture species in European countries on physiological attributes, farming methods and economic output (Peck et al., 2020); 
(c,d) differences (%) between projected changes for 1.5°C and 4°C GWL (Peck et al., 2020), with (c) changes in abundance of major fish species by region, and (d) changes in 
productivity of major aquaculture species by country

1846Effectiveness and feasibility of adaptation options
for food system to climate impacts and risk in Europe
InstitutionalTechnological
Evidence
Agreement Impact type
= no/limited evidence/Adaptation option
Change of sowing/harvest date
Change of cultivars
Soil management
Plant and livestock breeding, including GMO
Mixed use - agroecology and agroforestry
Agricultural policy changes
Training and information
Crop selection changes
Land cover change, including agricultural land abandonment
Management, including high frequency rotations
International trade changes
Consumer shifts in consumptionHeat stress
Drought
Flooding
Compound and extreme weather
Disease pathogen and vectors
Combined impacts on productivity//
/// /// //

// //// /////////
Figure 13.14 |  Effectiveness and feasibility of the main adaptation options for food systems in Europe (Section SM13.9, Table SM13.5)UK (Figure  13.9A; Handisyde et  al., 2017; Payne et  al., 2021), while 
for aquaculture sectors, it is highest in SEU and some NEU and WCE 
countries (Figure 13.9B, 2020).
Future vulnerabilities, risks and opportunities are projected to strongly 
vary regionally and between major fisheries and aquaculture species 
(Figure  13.13 c,d; Peck et  al., 2020). Assuming MSY management, 
projections suggest reduced abundance of most commercial fish stocks 
in European waters of 35% (up to 90% for individual stocks) between 
1.5°C and 4.0°C GWL (medium confidence) (Figure 13.13; Peck et al., 
2020; Payne et al., 2021). In response to 4°C GWL, higher trophic-level 
biomass is projected to increase in the SEUS mainly due to increases 
in small pelagic and thermophilic, often exotic, species (Moullec et al., 
2019).

Ocean acidification (Section 13.4; Chapter 4) will develop into a major risk 
for marine food production in Europe under 4°C GWL (high confidence), 
affecting recruitment of important European fish stocks, such as those 
of cod in the Western Baltic and Barents Sea, by 8 and 24%, respectively 
(Swat et al., 2018b; Stiasny et al., 2018; Voss et al., 2019). Acidification 
is also projected to negatively affect marine shellfish production and 
aquaculture in Europe with 4°C GWL (medium confidence) (Fernandes 
et al., 2017; Narita and Rehdanz, 2017; Mangi et al., 2018).13.5.1.4 Forestry and Forest Products
Climate change is altering the structure and function of European 
forests via changes in temperature, precipitation and atmospheric CO 2, 
as well as through interaction with pests and fire (high confidence) 
(Section 13.3.1; Moreno et al., 2018; Morin et al., 2018; Senf et al., 2018; 
Orlova-Bienkowskaja et al., 2020). Species-specific responses of trees to

drier summers (Vitali et al., 2018) shape regional variability in European 
forest productivity in response to water and nutrient availability, 
heatwave and evaporative demand (Reyer et al., 2014; Kellomäki et al., 
2018). While warming and extended growing seasons have positive 
impacts on forest growth in cold areas in WCE and NEU (Pretzsch 
et al., 2014; Matskovsky et al., 2020), EEU (Tei et al., 2017) and higher 
altitude (Sedmáková et al., 2019), drought stress across Europe has been 
increasing (high confidence) (Primicia et al., 2015; Marqués et al., 2018; 
Ruiz-Pérez and Vico, 2020). Combined with land use, climate change has 
increased large-scale forest mortality since the 1980s (Senf et al., 2018). 
Extreme events, such as the 2018 drought in WCE, caused widespread 
leaf shedding and tree mortality (Buras et al., 2020) with carryovers into 
2019 (Schuldt et al., 2020), as well as bark beetle outbreaks (Netherer

et al., 2019) resulting in felling and cutting of more than 1 million ha of 
spruce forest and disrupting timber markets (Mauser, 2021).

13
1847Europe  Chapter 13
In response to 3°C GWL, forest productivity is projected to increase 
in NEU and altitudes, show mixed trends in WCE and decrease in 
SEU (medium confidence) (Reyer et  al., 2014). This trend is driven by 
increases in productivity of pine and spruce, and decreases of beech 
and oak, and excludes disturbances and management options (Reyer 
et al., 2014). Water stress exacerbates the incidence from and effects of 
fire and other natural disturbances (Section 13.3.1), resulting in forest 
productivity declines or cancelling out productivity gains from CO 2 (high 
confidence) (Seidl et al., 2014; Reyer et al., 2017). In response to 1.7°C 
GLW, managed forest and unmanaged woodland areas are projected 
to decrease only minimally, while at GWL >2.5°C losses are increasing 
for managed forest and unmanaged woodland (Harrison et al., 2019). 
Reducing warming from 4°C GLW to below 1.7°C GLW would reduce the

Europe-wide impacts on managed forest by 34% (Harrison et al., 2019).
13.5.2 Solution Space and Adaptation Options
The solution space for climate-change adaption for food and timber 
includes production-related options (Sections  13.5.2.1–13.5.2.3) 
and market-based changes to consumer demand and trade 
(Section 13.5.2.4). The assessment of effectiveness and feasibility of 
options in the food system is summarised in Figure 13.14.
13.5.2.1 Crops and Livestock
Farm management adaptation options to climate change include 
changing sowing and harvest dates, changes in cultivars and irrigation, 
and selecting alternative crops (Figures 13.14, 13.15; Donatelli et al., 
2015). Irrigation is effective at reducing yield loss from heat stress 
and drought, for example, for wheat and maize (Figures 13.14, 13.15), 
but it increases demand for water withdrawals (Siebert et al., 2017; 
Ruiz-Ramos et al., 2018; Feyen et al., 2020). Where sufficient water

and infrastructure is available, irrigation of wheat reverses yield 
losses across Europe at 2°C GWL to become gains, while yield losses 
in maize in SEU are reduced from as much as 80 to 11% (Feyen 
et al., 2020). Extensive droughts during the past two decades have 
caused many irrigated systems in SEU to cease production (Stahl 
et al., 2016) indicating limited adaptive capacity to heat and drought 
(medium confidence). Water management for food production on land 
is becoming increasingly complex due to the need to satisfy other 
social and environmental water demands (KR3, Section 13.10) and is 
limited by costs and institutional coordination (Iglesias and Garrote, 
2015). Agricultural water management adaptation practices include 
irrigation, reallocating water to other crops, improving use efficiency 
and soil water conservation practices (Iglesias and Garrote, 2015). In-
season forecasts of climate impacts on yield were successfully used

for European wheat during the 2018 drought (van der Velde et al., 
2018).
Changes to cultivars and sowing dates can reduce yield losses 
(Figure 13.15) but are insufficient to fully ameliorate losses projected 
>3°C GWL, with an increase of risk from north to south and for crops 
growing later in the season such as maize and wheat (high confidence) 
(Ruiz-Ramos et  al., 2018; Feyen et  al., 2020). Adaptations for early 
maturing reduce yield loss by moving the cycle towards a cooler part of 
year, and also constrains the increases in irrigation water demands, but reduce the period for photosynthesis and grain filling (high confidence) 
(Ruiz-Ramos et  al., 2018; Holzkämper, 2020). Crop breeding for 
drought and heat tolerance can improve sustainability of agricultural 
production under future climate (Costa et  al., 2019), particularly in 
SEU where drought-tolerant varieties provide 30% higher yields than 
drought-sensitive varieties at 3°C GWL (Senapati et  al., 2019). Soil

management practices, such as crop residue retention or improved 
crop rotations, generally undertaken as a mitigation option to increase 
soil carbon sequestration, are not commonly evaluated for adaptation 
in European agriculture (Hamidov et al., 2018).
Adaptation practices for livestock systems on European farms commonly 
focus on controlling cooling, shade provision and management of 
feeding times (Gauly et al., 2013). These options are used in indoors-
reared species (Gauly et al., 2013) but are limited in mountain pastures 
(high confidence) (Deléglise et al., 2019). Response options to insufficient 
amounts and quality of fodder include changing feeding strategies 
(Kaufman et al., 2017; Ammer et al., 2018), feed additives (Ghizzi et al., 
2018), relocating livestock linked to improved pasture management, 
organic farming (Rojas-Downing et  al., 2017; EEA, 2019c), importing 
fodder and reducing stock (Toreti et  al., 2019b). Dairy systems that

maximise the use of grazed pasture are considered more environmentally 
sustainable but are not fully supported by policy and markets (medium 
confidence) (Hennessy et al., 2020). Genetic adaptation of crops, pasture 
and animals could be a long-term adaptation strategy (Anzures-Olvera 
et al., 2019; Deléglise et al., 2019). Control strategies for pathogens and 
vectors include indoor or outdoor rearing and applying new diagnostic 
tools or drugs (Bett et al., 2017; Vercruysse et al., 2018), and regulations 
to ensure safe trade and reduce the risk of introducing or spreading pests 
(European Comission, 2016).
Agroecological systems provide adaptation options that rely on 
ecological process (e.g., soil organic matter recycling and functional 
diversification) to lower inputs without impacting productivity 
(Cross-Chapter Box  NATURAL in Chapter 2; Aguilera et  al., 2020). 
High-frequency rotational grazing and mixed livestock systems are

agroecological strategies to control pathogens (Aguilera et al., 2020). 
Agroforestry, integrating trees with crops (silvoarable), livestock 
(silvopasture), or both (agrosilvopasture), can enhance resilience 
to climate change (Chapter 5), but implementation in Europe needs 
improved training programmes and policy support (high confidence) 
Technological innovations, including ‘smart farming’ and knowledge 
training, can strengthen farmers’ responses to climate impacts (Delé-
glise et  al., 2019; Kernecker et  al., 2019), although strong belief in 
‘technosalvation’ by farmers (Ricart et al., 2019) can reduce the solu-
tion space and timing of adaptation options. Agricultural policy, market 
prices, new technology and socioeconomic factors play a more impor -
tant role in short-term farm-level investment decisions than climate-
change impacts (high confidence) (Juhola et al., 2016; Hamidov et al., 
2018).

Effective policy guidance is needed to increase the climate resilience 
of agriculture (Spinoni et  al., 2018; Toreti et  al., 2019b). Financial 
measures include simplifying procedures for obtaining subsidies, and 
insurance premiums and interest rates that incentivise adoption of

1848Projected yield changes with climate change, altered crop management and associated water demand
(d)
Water demand for 
300 400 500 600 700Irrigation water use (km3 yr-1)Relative yield change (%)Maize
Wheat
(winter )
0100200300
-100-50050100(a)
Yield changes for current 
production systems(b)
Yield increase with 
crop irrigation
RCP2.6 RCP4.5 RCP8. 5 RCP2.6 RCP4.5 RCP8. 5 Baseline RCP2.6 RCP4.5 RCP8. 5 RCP2.6 RCP4.5 RCP8. 5 Baseline-100 % -50 %0 50 % 100 %
Relative yield change (%) Irrigation water use  (mm yr-1)(c)
Irrigated yield increases 
from new varieties
GWL
1.5°CGWL
1.7°C GWL
2°CGWL
1.5°CGWL
1.7°C GWL
2°CGWL
1.5°CGWL
1.7°C GWL
2°CGWL
1.5°CGWL
1.7°C GWL
2°CMaize Wheat (winter)
Figure 13.15 |  Projected yield changes with climate change for 1.5°C (RCP2.6), 1.7°C (RCP4.5) and 2°C GWL (RCP8.5). Altered crop management and associated 
water demand shows:

(a) relative yield changes under climate change and elevated CO 2 for current production systems (i.e., rain-fed and irrigated simulations weighted by current the share of rain-fed 
and irrigated areas); 
(b) yield increase if current predominantly rain-fed areas are fully irrigated; 
(c) additional yield increases for irrigated production systems if new varieties are used to avoid losses associated with faster development and earlier maturity under climate 
change; and 
(d) water demand for irrigated systems with current varieties in currently rain-fed areas (Webber et al., 2018). Relative yield changes to a period centred on 2055 relative to a 
baseline period centred on 1995. Box plots are Europe’s aggregate results considering current production areas (a) or current rain-fed areas (b,c), showing uncertainty across crop 
models and general circulation models. The maps are for the crop model median for RCP4.5 (1.7°C GWL) with GFDL-CM3.

13
1849Europe  Chapter 13
climate-friendly agricultural methods (Garrote et al., 2015; Iglesias and 
Garrote, 2015; Zakharov and Sharipova, 2017; Hamidov et al., 2018; 
Wiréhn, 2018). The EU’s Common Agricultural Policy has increasingly 
focused on environmental outcomes (Alliance Environnement, 2018) 
but does not sufficiently provide for adaptation measures (Leventon 
et  al., 2017; Pe’er et  al., 2020). Limits to European farm-level 
adaptation include lack of resources for investment, political urgency 
to adapt, institutional capacity, access to adaptation knowledge and 
information from other countries (EEA, 2019c).
13.5.2.2 Aquatic Food
Climate-resilient fish production in Europe is the goal of the EU’s 
Common Fisheries Policy (CFP) rebuilding fish stocks to MSY levels, but 
success has been variable (Froese et al., 2018; Stecf, 2019). Adaptation 
is largely ignored in related EU policy frameworks such as the CFP , the

MSFD and the ‘Strategic guidelines for the sustainable development 
of EU aquaculture’. (Pham et al., 2021). A major governance challenge 
for adaptation will be the redistribution of the fixed allocation scheme 
for total allowable catches (Harte et al., 2019; Baudron et al., 2020). 
Inflexible and non-adaptive allocation schemes can result in conflicts 
among European countries (medium confidence), as demonstrated by 
the case of the Northeast Atlantic mackerel (Spijkers and Boonstra, 
2017).
The development of adaptation strategies for seafood production 
since the Paris Agreement is insufficient in Europe (high confidence) 
(Kalikoski et al., 2018; Pham et al., 2021). Concrete plans for adaptation 
planning towards climate-ready fisheries and aquaculture are lacking in 
all parts of Europe (European Comission, 2018), especially accounting 
for the expected reduced landings of traditional target species and in 
preparation for a new portfolio of resource species (Blanchet et  al.,

2019).
Recent scientific progress towards adaptation in European fisheries 
and aquaculture include conceptual guidance and demonstration 
cases on climate adaptation planning (Pham et al., 2021) and climate 
vulnerability assessments (Blanchet et al., 2019; Peck et al., 2020; Payne 
et al., 2021). Sociopolitical scenarios for European aquatic resources 
have been developed and have the potential to inform adaptation 
planning by European fisheries and aquaculture sectors (Kreiss et al., 
2020; Hamon et al., 2021; Pinnegar et al., 2021).
13.5.2.3 Forests
Forest management has been adopted as a frequent strategy to cope with 
drought, reduce fire risk, and maintain biodiverse landscapes and rural 
jobs (Hlásny et al., 2014; Fernández-Manjarrés et al., 2018). Successful 
adaptation strategies include altering the tree species composition to 
enhance the resilience of European forests (high confidence) (Schelhaas

et al., 2015; Zubizarreta-Gerendiain et al., 2017; Pukkala, 2018). Greater 
diversity of tree species reduces vulnerability to pests and pathogens 
(Felton et  al., 2016), and increases resistance to natural disturbances 
(high confidence) (Jactel et al., 2017; Pukkala, 2018; Pardos et al., 2021). 
Depending on forest successional history (Sheil and Bongers, 2020), tree 
composition change can increase carbon sequestration (high confidence) 
(Liang et al., 2016), biodiversity and water quality (Felton et al., 2016). Conservation areas can also help climate-change adaptation by keeping 
the forest cover intact, creating favourable microclimates and protecting 
biodiversity (low confidence) (Jantke et al., 2016).
Reforestation reduces warming rates (Zellweger et  al., 2020) and 
extremely warm days (Sonntag et  al., 2016) inside forests, reducing 
natural disturbances and fires (high confidence). Active management 
approaches can limit the impact of fires (Section  13.3.1) on forest

productivity, including fuel reduction management, prescribed 
burning, changing from conifers to deciduous, less flammable species, 
and recreating mixed forests (Feyen et  al., 2020) and agroforestry 
(Damianidis et al., 2020).
13.5.2.4 Demand and Trade
An increasing globalised food system makes European nations 
sensitive to supply chain disturbances in other parts of the world, but 
also provides capacity to adapt to production shifts within Europe 
through changes in international trade (Section  13.9.1) (Alexander 
et al., 2018; Challinor et al., 2018; Ercin et al., 2021). Consumer demand 
for food and timber products can adapt to productivity changes and be 
mediated by price (e.g., in response to production changes or policies 
on food-related taxation), reflect changes in preferences (e.g., towards 
plant-based foods motivated by environmental, ethical or health 
concerns) or reductions in food waste (high confidence) (Alexander

et  al., 2019; Willett et  al., 2019). Although mitigation potentials 
of dietary changes have received increasing attention, evidence is 
lacking on potential for adaptation through changes in European food 
consumption and trade, despite these socioeconomic factors being a 
strong driver for change (medium confidence) (Harrison et al., 2019; 
Kebede, 2021). Calls are increasing across Europe for sustainable and 
resilient agri-food systems acknowledging interdependencies between 
producers and consumers to deliver healthy, safe and nutritional foods 
and services (Section 13.7) (Venghaus and Hake, 2018).
13.5.3 Knowledge Gaps
Aggregated projections of impacts, especially of combined hazards, 
are still rare despite many physiological papers on species-specific 
responses to warming in all food sectors (high confidence). This 
is specifically true for scenarios that consider land-use change 
and population growth, although Agri SSPs are currently being

developed (Mitter et  al., 2019). Effectiveness of adaptation options 
is predominantly qualitatively mentioned but not assessed, and the 
effectiveness of combinations of measures is rarely assessed (high 
confidence) (Ewert et  al., 2015; Holman et  al., 2018; Müller et  al., 
2020). Effective adaptation planning would be supported by better 
modelling and scenario development including improved coupled 
nature–human interactions (e.g., with more realistic representation of 
behaviours beyond economic rationality and ‘bottom-up’ autonomous 
farmer adaptations) as well as greater stakeholder involvement.
Coverage of impacts and adaptation options in Europe are biased 
towards the EU-28 and have gaps within the eastern part of WCE 
and EEU, despite dramatic changes in land use over recent decades 
in Russia and Ukraine (high confidence) which have the potential to

1850increase production and export of agricultural products, especially 
wheat, meat and milk (Swinnen et al., 2017).
A bias towards modelling of cereals, specifically wheat and maize, 
results in gaps in knowledge for fruit and vegetables, especially for 
temperate regions in Europe (Bisbis et  al., 2019). The assessment 
of irrigation needs and the impact of CO 2 and O 3 tend to focus on 
individual species and processes hindering upscaling to multiple 
stressors and mixed production (high confidence) (Challinor et  al., 
2016; Webber et al., 2016).
There is a lack of actionable adaptation strategies for European 
fisheries and aquaculture. Knowledge gaps include adaptive capacities 
of local fishing communities to a new mix of target species and 
consumer acceptance of the product. Increased knowledge on the 
effects on freshwater fisheries and their resources is also needed.
13.6 Cities, Settlements and Key 

Urban areas in Europe house 547 million inhabitants, corresponding to 
74% of the total European population (UN/DESA, 2018). In the EU-28, 
39% of the total population lives in metropolitan regions (i.e., areas 
with at least 1  million inhabitants) where 47% of the total GDP is 
generated (Eurostat, 2016). Apart from urban settlements, this section 
also covers energy and transport systems, as well as tourism, industrial 
and business sectors which are key for livelihood, economic prosperity 
and the well-being of residents.
13.6.1 Observed Impacts and Projected Risks
13.6.1.1 Energy Systems
The energy sector in Europe already faces impacts from climate extremes 
(high confidence). Significant reductions and interruptions of power 
supply have been observed during exceptionally dry and/or hot years of 
the recent 20-year period, for example, in France, Germany, Switzerland 
and the UK during the extremely hot summer of 2018 which led to water-

cooling constraints on power plants (van Vliet et al., 2016b; Abi-Samra, 
2017; Vogel et al., 2019). Heating-degree days decreased and cooling-
degree days increased during 1951–2014, with clearer trends after 1980 
(De Rosa et al., 2015; Spinoni et al., 2015; EEA, 2017a). Projected climate 
risks for energy supply are summarised in Figure 13.16.
New studies reinforce the findings of AR5 on risks for thermoelectric 
power and regional differences between NEU and SEU regarding risks 
for hydropower (Figure 13.16). In NEU and EEU, extremely high water 
inflows to dams are projected to increase flooding risks for plant and 
nearby settlements (Chernet Haregewoin et al., 2014; Porfiriev et al., 
2017), while increasing temperatures could reduce the efficiency of 
steam and gas turbines (Porfiriev et  al., 2017; Cronin et  al., 2018; 
Klimenko et  al., 2018a). Water scarcity may limit onshore carbon 
capture and storage in some regions (Byers et al., 2016; Murrant et al.,

2017; EEA, 2019a).Reduced surface wind speeds during 1979–2016 (Frolov et al., 2014; 
Perevedentsev and Aukhadeev, 2014; Tian et  al., 2019) support 
projected trends in decreasing onshore wind energy potential. Seasonal 
changes may result in reductions in many areas in summer (by 8–30% 
in Southern Europe) and increases in most of NEU during winter. 
Increasing probabilities and persistence of high winds over the Aegean 
and Baltic seas (Weber et al., 2018a) could create new opportunities 
for offshore wind. The future configuration of the wind fleet will affect 
the spatial and temporal variability of wind power production (Tobin 
et al., 2016). Total backup energy needs in Europe could increase by 
4–7% by 2100 (Wohland et al., 2017) with potentially larger seasonal 
changes (Weber et al., 2018b).
There is low evidence and limited agreement on projections of solar 
power potential due to differences in the integration of aerosols and

the estimated cloud cover between climate models (Bartok et al., 2017; 
Boé et al., 2020; Gutiérrez et al., 2020). Studies on climate risks for 
bioenergy are also limited.
Energy demand is projected to display regional differences in response 
to warming beyond 2°C GWL, with a the significant southwest-to-
northeast decrease of heating-degree days by 2100 (particularly in 
northern Scandinavia and Russia), and a smaller north-to-south increase 
of cooling-degree days (Porfiriev et  al., 2017; Spinoni et  al., 2018; 
Coppola et  al., 2021). Under the present population numbers, total 
energy demand would decrease in almost all of Europe, whereas it could 
increase in some countries (e.g., UK, Spain, Norway) when considering 
Eurostat’s population projections (Klimenko et  al., 2018b; Spinoni 
et al., 2018). There is medium confidence that peak load will increase 
in SEU and decrease in NEU (Damm et  al., 2017; Wenz et  al., 2017;

Bird et al., 2019). Beyond 2°C GWL, a shift of peak load from winter to 
summer in many countries is possible (Wenz et al., 2017). Together with 
water-cooling constraints for thermal power, this change in load may 
challenge the stability of electricity networks during heatwaves (EEA, 
2019a). Technological factors, increased electricity use and adaptation 
influence significantly the temperature sensitivity of electricity demand 
and consequently risks (Damm et al., 2017; Wenz et al., 2017; Cassarino 
et  al., 2018; Figueiredo et  al., 2020). Potential power curtailments or 
outages during climatic extremes may increase electricity prices (Pechan 
and Eisenack, 2014; Steinhäuser and Eisenack, 2020).
13.6.1.2 Transport
Heatwaves in 2015 and 2018 in parts of WCE and NEU caused road 
melting, railway asset failures and speed restrictions to reduce the 
likelihood of track buckling (Ferranti et al., 2018; Vogel et al., 2019).

Recent studies on projected risks focus mainly on infrastructure and 
much less on transport flows and disruptions.
Sea level rise (Section  13.2) may disrupt port operations and 
surrounding areas, mainly in parts of NEU and WCE (Christodoulou 
et al., 2018), while changes of waves agitation could increase the 
non-operability hours of some Mediterranean ports beyond 2°C GWL 
(Sierra et al., 2016; Camus et al., 2019; Izaguirre et al., 2021). Low-
water-level days at some critical locations for inland navigation at 
the Rhine River are projected to increase beyond 2°C GWL, while

13
1851Europe  Chapter 13
decreases at the Danube River are possible (van Slobbe et al., 2016; 
Christodoulou et al., 2020).
Risks of rutting and blow-ups of roads (particularly in low altitudes) 
due to high summer temperatures are expected to increase in WCE 
and EEU at 3°C GWL (medium confidence) (Frolov et  al., 2014; 
Matulla et  al., 2018; Yakubovich and Yakubovich, 2018). In EEU 
and northern Scandinavia, the higher number of freezing–thawing 
cycles of construction materials will increase risks for roads (Frolov 
et al., 2014; Yakubovich and Yakubovich, 2018; Nilsen et al., 2021), 
while warming beyond 2°C GWL could significantly reduce road 
maintenance costs in NEU (Lorentzen, 2020), but limit off-road 
overland transport in northwest Russia (Gädeke et al., 2021). Beyond 
3°C GWL, more frequent hourly precipitation extremes are projected 
over WCE and NEU in summer (e.g., a twofold and tenfold increase, 
respectively, for events exceeding the present-day 99.99th percentile

in Germany and the UK) but more widely across Europe in autumn 
and winter (an increase higher than tenfold for 99.99th percentile 
events in SEU in autumn (Chan et  al., 2020), potentially severely 
damaging roads as happened in Mandra, Greece, in 2017 (Diakakis 
et al., 2020). Landslide risks in WCE and SEU could increase beyond 
a 2°C GWL, threatening road networks (Schlogl and Matulla, 2018; 
Rianna et al., 2020).
The current flood risk for railways could double or triple at 1.5–3°C 
GWL, particularly in WCE, increasing public expenditure for rail 
transport in Europe by 1.22 billion EUR annually under 3°C GWL and no adaptation (Bubeck et  al., 2019). Thermal discomfort in urban 
underground railways is expected to increase, even at a high level of 
carriage cooling (Jenkins et al., 2014a).
The number of airports vulnerable to inundation from SLR and storm 
surges may double between 2030 and 2080 without adaptation,

especially close to the North Sea and Mediterranean coasts (Christodoulou 
and Demirel, 2018). Rising temperatures reducing lift generation could 
impose weight restrictions for large aircraft at 2°C GWL and beyond in 
airports of France, the UK and Spain (Coffel et al., 2017). There is a lack of 
studies quantifying the effect of future extreme events on flight arrivals 
at, and departures from, European airports.
13.6.1.3 Business and Industry
European industrial and service sectors contribute 85% to gross value 
added in EU-28 (Eurostat, 2020); while their direct exposure and 
vulnerability is smaller compared with sectors directly reliant on weather, 
they are directly and indirectly affected by heat, flooding, water scarcity 
and drought (Weinhofer and Busch, 2013; Gasbarro and Pinkse, 2016; 
Meinel and Schule, 2018; Schiemann and Sakhel, 2018; TEG, 2019). 
Heat reduces the productivity of labour particularly in construction,

agriculture and manufacturing (Section 13.7.1; García-León et al., 2021; 
Schleypen et al., 2021). Direct losses from floods in Europe are highest 
for manufacturing, utilities and transportation; indirect losses arise, for 
example, for manufacturing, construction, and banking and insurance 
(Koks et  al., 2019a; Sieg et  al., 2019; Mendoza–Tinoco et  al., 2020). 
Projected climate change risks and opportunities for energy supply in Europe
Northern
EuropeSouthern
EuropeMajor sources
or energy supply
Wind power
potential
(onshore)
Wind power
potential
(off-shore)
Solar power
potential
potential
Bioenergy
potential
Thermoelectric
power capacity
(incl. nuclear)Proj. +1.5°C
Proj. +2.0°C
Proj. ≥3.0°C
Proj. ≥3.0°C*
High: change within <-20% and >+20%

Moderate: change within +5% and +20%, -20% and -5% 
Low: change within -5% and +5%Sign and magnitud of change *
= no evidence/
High Medium LowDecrease
High
Moderate
Low
/
/
//
/
// /
/Increase
The whole
Baltic Sea and 
North Sea are 
allocated to  
Northern EuropeThe whole
Black Sea is 
Southern 
Europe
Figure 13.16 |  Projected climate-change risks for energy supply in Europe for major sources and under 1.5°C, 2°C and >3°C GWL (Tables SM13.5–13.13)

1852Drought and water scarcity directly affect European industries in the 
sectors of pulp and paper, chemical and plastic manufacturing, and food 
and beverages (Gasbarro et al., 2019; Teotónio et al., 2020); additionally, 
drought may indirectly affect sectors relying on shipping, hydropower 
or public water supply (Naumann et al., 2021). The European financial 
and insurance sector is affected by climate-change impacts via their 
customers and financial markets (Bank of England, 2015; Georgopoulou 
et al., 2015; Battiston et al., 2017; TCFD, 2017; Bank of England, 2019; 
de Bruin et al., 2020; Monasterolo, 2020).
The vulnerability to climate hazards varies by European region, type of 
risk, sector and business characteristics (Gasbarro et al., 2016; Forzieri 
et al., 2018; ECB, 2021a; Kouloukoui et al., 2021). Current damages 
are mainly related to river floods and storms, but heat and drought 
will become major drivers in the future (medium confidence). Until

2050, the probability of default of firms located in particularly exposed 
locations may increase to up to four times that of an average firm in 
all sectors (ECB, 2021a).
Many European sectors are exposed to multiple and cross-cutting 
risks (Gasbarro et  al., 2019; Schleypen et  al., 2021). Indirect effects 
via supply chains, transport and electricity networks can be as high as, 
or substantially higher than, direct effects (medium confidence) (Koks 
et al., 2019a; Koks et al., 2019b; Knittel et al., 2020).
13.6.1.4  Tourism
Snow-cover duration and snow depth in the Alps has decreased since 
the 1960s (Klein et al., 2016; Schöner et al., 2019; Matiu et al., 2021). 
Despite snowmaking, the number of skiers to French resorts at low 
elevations during the extraordinary warm and dry winters of 2006–
2007 and 2010–2011 was 12–26% lower (Falk and Vanat, 2016).
Due to reduced snow availability and hotter summers, damages are

projected for the European tourism industry, with larger losses in 
SEU (high confidence) and some smaller gains in the rest of Europe 
(medium confidence) (Ciscar Martinez et al., 2014; Roson and Sartori, 
2016; Dellink et al., 2019).
At 2°C GWL, the operation of low-altitude resorts without snowmaking 
will likely be discontinued, while beyond 3°C GWL, snowmaking will be 
necessary, but not always sufficient, for most resorts in many European 
mountains and parts of NEU (Pons et al., 2015; Joly and Ungureanu, 
2018; Scott et al., 2019; Spandre et al., 2019). Expanding snowmaking 
is capital intensive and will strongly increase water and energy 
consumption, particularly at 3°C GWL and beyond (Spandre et  al., 
2019; Morin et al., 2021), adversely affecting the financial stability of 
small resorts (Pons et al., 2015; Falk and Vanat, 2016; Spandre et al., 
2016; Joly and Ungureanu, 2018; Moreno-Gené et al., 2018; Steiger

and Scott, 2020). Permafrost degradation due to rising temperatures is 
expected to create stability risks for ropeway transport infrastructure 
at high-altitude Alpine areas (Duvillard et al., 2019).
Climatic conditions from May to October at 1.5–2°C GWL are projected 
to become more favourable for summer tourism in NEU and parts of 
WCE and EEU, while there is medium confidence on opposite trends 
for SEU from June to August (Grillakis et al., 2016; Scott et al., 2016; Jacob et al., 2018; Koutroulis et al., 2018). The amenity of European 
beaches may decrease as a result of SLR amplifying coastal erosion 
and inundation risks, although less in NEU (Section 13.2; Ebert et al., 
2016; Toimil et al., 2018; Lopez-Doriga et al., 2019; Ranasinghe et al., 
2021).
13.6.1.5 Built Environment, Settlements and Communities
The expected shift of European residents to large cities and coastal 
areas will increase assets at risk (Section  13.2). The share of urban

population in Europe is projected to increase from 74% in 2015 to 
84% in 2050, corresponding to 77 million new urban residents (UN/
DESA, 2018), with most of this increase in SEU and WCE (particularly 
in Turkey and France). In the EU-28, urban residents in 2100 may 
increase by about 30 million under SSP1 and SSP5, and decrease by 
90–110 million under SSP3 and SSP4 (Terama et al., 2019).
About 32% of 571 European cities in the GISCO Urban Audit 2014 
dataset show a medium to high or relatively high vulnerability against 
heatwaves, droughts and floods (Tapia et  al., 2017). Under current 
vulnerabilities, future climate hazards will augment climate risks for 
several cities, particularly beyond 3°C GWL (Figure  13.17). In many 
NEU cities, a high increase in pluvial flooding risk by the end of the 
century is possible, while in WCE cities may face a high increase in 
pluvial flooding risks, moderate to very high increase in extreme heat

risk, and to some extent moderate to high increase in drought risk. 
Many SEU cities could face a high to very high increase in risks from 
extreme heat and meteorological drought.
13.6.1.5.1.  Risks from coastal, river and pluvial flooding
New studies increase confidence in AR5 statements that flood 
damages will increase in coastal areas due to SLR and changing social 
and economic conditions (Section 13.2.1.1). Except for areas affected 
by land uplift, it is projected that further adaptation will be required 
to maintain risks at the present level for most coastal cities and 
settlements (Haasnoot et al., 2013; Ranger et al., 2013; Malinin et al., 
2018; Hinkel et al., 2019; Umgiesser, 2020).
In many cities, the sewer system is older than 40  years, potentially 
reducing their capacity to deal with more intense pluvial flooding (EEA, 
2020b). Apart from climate change, urbanisation is an important driver 
for increases in flooding risks as it results in growth of impervious

surfaces. Flash floods are particularly challenging, causing the 
overburdening of drainage systems (Dale et al., 2018), urban transport 
disruptions, and health and pollution impacts due to untreated sewage 
discharges (Kourtis and Tsihrintzis, 2021).
More than 25% of the population in nearly 13% of EU cities live within 
potential river floodplains. In many of these places (e.g., 50% of UK 
cities), a significant increase in the 10-year high river flow is possible 
beyond 2°C GWL under a high-impact scenario (i.e., 90th percentile of 
projections) (Guerreiro et al., 2018; EEA, 2020b).

13
1853Europe  Chapter 13
010 30 40 50 60 70 80 90100 2060
50
40
3070
20
10
50
40
3070
20
10Extreme heat
In cities where heat is a risk already 
today, extreme heat risks increase 
from mid to end century, but not for 
Northern Europe and some cities in 
Western and Central Europe
Unit: Additional days per year when 
maximum temperature exceeds 35°C 
compared to the period 1995–2014010 30 40 50 60 70 80 90100 204.0
3.0
2.0
1.0
-1.0
-2.0010 30 40 50 60 70 80 90100 204.0
3.0
2.0
1.0
-1.0
-2.0Pluvial flooding
Increase in risk from pluvial flooding 
is expected to be high to very high in 
Western Central Europe
Unit:  Additional events of extreme 
precipitation (i.e. p>P99) per year  
compared to the period 1995–2014
010 30 40 50 60 70 80 90100 205.0
4.0
3.0
2.0
1.0
-1.0
-2.0
-3.0010 30 40 50 60 70 80 90100 205.0
4.0
3.0
2.0
1.0
-1.0
-2.0
-3.0Meteorological drought
Increase in risk from drought is

expected to be high in Southern 
Europe, being high by mid cetunry 
and very high by the end of the 
century
Unit: Additional drought events per  
decade compared to the period 
1995–2014Risks of pluvial flooding, extreme heat and meteorological droughts
Projected changes in risk levels across the 65 largest European cities
Far future (2081–2100) Mid future (2041–2060)Western Central Europe
Southern EuropeVery high risk
High risk
Moderate risk
Low risk
Vulnerabilit yH igh Low Vulnerabilit yH igh LowNorthern Europe
Medium population size
Low population sizeLarge population size
Figure 13.17 |  Projected changes in pluvial flooding, extreme heat and meteorological drought risks for the 65 largest cities in EU-28 plus Norway and 
Switzerland for 2.5°C and 4.4°C GWL compared with the baseline (1995–2014) (Tapia et al., 2017). Exposure is expressed in terms of current population. Values of 
climatic impact drivers are derived from the Euro-CORDEX regional climate model ensemble.

1854(a) Aggregation by  sectorOverall climate hazard risk to critical infrastructures in Europe
Energy
TransportSocial
010203040
Baseline 2020’s 2050’s 2080’sOverall climate risk  (billion EUR/year)(b) Aggregation by cllimate hazard
Heatwaves
Cold wavesDroughts
Wildfires
010203040
Baseline 2020’s 2050’s 2080’sOverall climate risk  (billion EUR/year)River floods
Coastal floodsWindstorms
Figure 13.18 |  Climate risks to critical infrastructures, aggregated at European (EU+) level under the SRES A1B scenario (Forzieri et al., 2018). Baseline: 
1981–2010; 2020s: 2011–2040; 2050s: 2041–2070; 2080s: 2071–2100
13.6.1.5.2 Risks from heatwaves, cold waves and drought
Heatwave days and number of long heatwaves increased in most 
capitals from 1998–2015 compared with 1980–1997 (Morabito et al., 
2017; Seneviratne et al., 2021). In the summer of 2018, many cities 
suffered from heatwaves attributed to climate change (Vogel et  al.,

2019; Undorf et al., 2020). As a result, indoor overheating and reduced 
outdoor thermal comfort, often coupled with urban heat island (UHI) 
effect, have already impacted European cities (see also Section 13.7.1; 
Di Napoli et al., 2018; EEA, 2020b).
Heatwaves are likely to become a major threat, not only for SEU but 
also for WCE and EEU cities (Russo et al., 2015; Guerreiro et al., 2018; 
Lorencova et al., 2018; Smid et al., 2019). At 2°C GWL and SSP3, half 
of the European population will be under very high risk of heat stress 
in summer (Rohat et  al., 2019). The UHI effect will further increase 
urban temperatures (Estrada et  al., 2017). In many cities, hospitals 
and social housing tend to be located within the intense UHI, thus 
increasing exposure to vulnerable groups (EEA, 2020b). There is 
high confidence that overheating during summer in buildings with 
insufficient ventilation and/or solar protection will increase strongly,

with thermal comfort hours potentially decreasing by 74% in SEU at 
3°C GWL (Jenkins et al., 2014a; Hamdy et al., 2017; Heracleous and 
Michael, 2018; Dino and Meral Akgül, 2019; Shen et al., 2020). Highly 
insulated buildings, following present building standards, will be 
vulnerable to overheating, particularly under high GWL levels, unless 
adequate adaptation measures are applied (Williams et al., 2013; Virk 
et al., 2014; Mulville and Stravoravdis, 2016; Fosas et al., 2018; Ibrahim 
and Pelsmakers, 2018; Salem et al., 2019; Tian et al., 2020). Cities in 
NEU and WCE are more vulnerable due to limited solar shading and fewer air conditioning installations (Ward et al., 2016; Thomson et al., 
2019). Cooling energy demand in SEU buildings has been projected 
to increase by 81–104% by 2035 and 91–244% after 2065 compared 
with 1961–1990 depending on GWL (Cellura et al., 2018). Increases of 
31–73% by 2050 and 165–323% by 2100 compared with 1996–2005

were estimated for buildings in NEU (Dodoo and Gustavsson, 2016) 
with risks modified by adaptation (Section 13.6.2; Viguié et al., 2020). 
Cold waves beyond 3°C GWL will not represent an effective threat for 
European cities at the end of the century, and only a marginal hazard 
under 2°C GWL (Smid et al., 2019).
At 2°C GWL and beyond, cities in SEU and large parts of WCE 
would exceed the historical maximum 12-month Drought Severity 
index of the past 50 years (see Section 13.2 on drought risks) and 
30% will have at least 30% probability of exceeding this maximum 
every month (Guerreiro et al., 2018). This could adversely affect the 
operation of municipal water services (Kingsborough et al., 2016). For 
example, under 2°C GWL, the reservoir storage volume is predicted 
to decrease for all of England and Wales catchments, resulting in a 
probability of years with water-use restrictions doubling by 2050 and 
quadrupling by 2100 compared with 1975–2004 (Dobson et al., 2020).

The combination of high temperatures, drought and extreme winds, 
potentially coupled with insufficient preparedness and adaptation, 
may amplify the damage of wildfires in peri-urban environments 
(Section 13.3.1.3). High fuel load combined with proximity of the built 
environment to wildland highly increases fire risks (EEA, 2020b).
Extreme heat and drought causes shrinking and swelling of clays, 
threatening the stability of small houses in peri-urban environments 
(Pritchard et al., 2015), with damage costs of 0.9–1 billion EUR during

13
1855Europe  Chapter 13
the 2003 heatwave (Corti et al., 2011). In WCE and SEU, mean annual 
damage costs could increase by 50% for 2°C GWL, and by a factor of 
2 for 3°C GWL (Naumann et al., 2021).
13.6.1.5.3 Risks from thaw of permafrost and mudflows
Increasing temperatures in NEU and the Alps has led to accelerated deg-
radation of permafrost, negatively affecting the stability of infrastruc-
tures (Stoffel et al., 2014; Beniston et al., 2018; Duvillard et al., 2019). 
In the Caucasus, glacial mudflows due to permafrost degradation and 
modern tectonic processes pose a significant danger to the infrastruc-
ture (Vaskov, 2016). In the past 30 years, the permafrost temperature in the European part of the Russian Arctic has increased by 0.5–2°C, result-
ing in damage to buildings, roads and pipelines, and to significant ex-
penditure for stabilising soils (Porfiriev et al., 2017; Konnova and Lvova, 
2019). Beyond 3°C GWL, the bearing capacity for infrastructure in the

permafrost region of the European Russia could decrease by 32–75% 
by mid-century and by 95% by 2100, potentially affecting settlements in 
northern EEU (Shiklomanov et al., 2017; Streletskiy et al., 2019). The in-
creasing number of cycles of freezing and thawing, observed in EEU, has 
led to accelerated ageing of building envelopes (Section 13.8.1.4; Frolov 
et al., 2014). Permafrost degradation due to higher temperatures could 
increase the potential of debris flow detachment in Alpine locations 
(Section 13.6.1.4; Damm and Felderer, 2013).Table 13.1 |  Present status of planned and implemented adaptation in European cities, energy sector, tourism sector, transport and industry (Table SM13.17)
General commitments / Adaptation Plans Implemented adaptation actionsCities –An increasing number of cities acknowledge the critical role of 
adaptation in building resilience to climate change.
 –Of 9609 European municipalities in the Covenant of Mayors for

Climate & Energy (CoM), 2221 reported on adaptation through 
the CoM platform; 429 provided some information on adaptation 
goals, risk and vulnerability assessments/action plans, and less 
than 300 reported adaptation goals and funds. Extreme heat, 
drought and forest fire were the most often reported hazards.
 –Most urban adaptation plans include ecosystem-based measures, 
but often with limited baseline information and convincing 
implementation actions.
 –Adaptation to risks from climate extremes (mostly flooding) is 
often addressed through municipal emergency plans. –Large cities (e.g., Helsinki, Copenhagen, Rotterdam, Barcelona, Madrid, London, Moscow) are in the 
process of implementing adaptation actions.
 –Current climate policies implemented at city-scale are primarily addressing mitigation and, to a lesser 
extent adaptation. Though many cities have implemented measures potentially supporting adaptation, 
they are not labelled as such.

–Nature-based Solutions and ecosystem-based adaptation are increasingly used to address urban heat 
and flooding risks that are enhanced by surface sealing and limited infiltration.
 –Strategic and emergency measures have been applied for drought management in some cities (e.g., 
London, Istanbul).Energy –In 2020, 29 countries had an adaptation plan for the energy 
sector. Some of them included specific adaptation actions (mostly 
preparatory) in their national or energy-specific risk assessments. –In 2020, 11 countries had implemented adaptation actions in the energy sector.
 –Measures undertaken by some distribution system operators and energy companies, focus on adaptation 
of transmission lines, water cooling, actions to avoid flooding (e.g. dams) and secure fuel supply.Tourism –Consideration of tourism in national adaptation strategies is 
limited, and national tourism strategies rarely mention adaptation.
 –In some countries there is legally binding consideration of climate

change when constructing new tourism units (e.g., the 2016 
French Mountain Act).
 –Many tourism operators focus on near-term coping strategies and 
do not consider longer term adaptation. –Snow making is widely applied in the Alps and Pyrenees ski resorts; e.g. from 18% of ski slopes 
in Germany to 67% in Austria. Some resorts already offer nocturnal skiing (e.g., Spain) and other 
snow-based activities.
 –There is already some transformation to year-round mountain resorts (e.g., in 70% of Spanish ski 
resorts).
 –Some diversification of tourism products is offered in Mediterranean coastal destinations.
 –Water saving measures, primarily for cost reduction, have been implemented, e.g. in hotels.Transport –At the national level, 10 countries have started coordination 
activities or identified adaptation measures. Some countries 
are mainstreaming adaptation within transport planning and 
decision-making (e.g., the ‘Low-water Rhine’ action plan, in 
Germany).

–Some action is undertaken in the public and private sector, e.g., 
revised manuals/guidelines/ protocols that consider climate change 
impacts and extreme events (e.g., Deutsche Bahn, Norwegian 
Public Roads Administration).
 –An integrated, transmodal approach to transport adaptation is 
lacking. –Most adaptation actions are preparatory; 5 countries have implemented specific actions. Planned and 
implemented actions mostly focus on infrastructure and much less on services, although the latter are 
increasing (e.g., operational forecasts for water levels in rivers).
 –Transport modes often compete for public funds and political priorities often influence adaptation for 
specific modes.
 –Some public and private actors are moving faster: new railway drainage standards (Network Rail/ UK), 
adverse weather event predictions (Spanish rail service operator), measures against coastal flooding

(Copenhagen Metro), measures for sea level rise (Rotterdam port, France).Industry and business –Some businesses are following recommendations of the High-Level 
Expert Group on Sustainable Finance, endorsed by the European 
Commission, and implementing the guidelines provided by the 
Task Force on Climate-Related Financial Disclosure in 2019. –Fifty large European publicly listed companies disclosed their climate risks in 2020, yet only a small 
percentage provided specifics on sectoral risks, as well as how risks differ over time and according to 
different climate scenarios.
 –Large national and multinational companies, and companies regulated by mitigation policy are the first 
movers in corporate adaptation, while small and medium-sized enterprises often lack the knowledge 
and resources to address risks and adaptation options.
 –Climate service providers, insurance companies and central banks have developed different tools for

climate risk assessment, such as, stress testing, scenario analysis, value at risk.
Well-established adaptation Advancing adaptation Low adaptation

1856Increased precipitation falling on local topography can increase land-
slide and mudflow risks, as seen in settlements at the Caucasus moun-
tainous region (Marchenko et al., 2017; Efremov and Shulyakov, 2018; 
Kerimov et al., 2020). At the Umbria region in Italy, landslide events 
could increase by 16–53% under 2°C GWL and by 24–107% beyond 
3°C GWL, mostly during winter (Ciabatta et al., 2016). Risks from shal-
low landslides are expected to increase in the Alps and Carpathians 
if no adequate risk mitigation measures are put in place (CCP5.3.2; 
Gariano and Guzzetti, 2016).
13.6.2 Solution Space and Adaptation Options
Monetary assessments of future damages from climate extremes on 
critical infrastructures show an escalating sevenfold increase by 2080s 
(Figure  13.18) compared with the baseline (Forzieri et  al., 2018), 
highlighting the need for adaptation.
13.6.2.1 Current Status of Adaptation

There is new evidence on increasing adaptation planning in cities, 
settlements and key infrastructures, but less on implemented adaptation 
(Table 13.1; see Box 13.3; Figure 13.36), adaptation by private actors 
and by cities against SLR (Chapter 16; Cross-Chapter Paper 2).
Although urban adaptation is underway, many small, economically weak 
(i.e., with low GDP per capita) or cities facing high climate-change risks 
lack adaptation planning (Reckien et al., 2015; EEA, 2016). While almost 
all large municipalities in NEU and WCE report implemented actions at 
least in one sector, this is not the case for 39% of municipalities in SEU 
(Aguiar et al., 2018). In the UK, the legal requirement to develop urban 
adaptation plans has been a significant driver for their widespread 
adoption (Reckien et al., 2015). The availability of, and access to, funding 
for adaptation is also crucial for plan development (Section 13.11.1).

Network membership (e.g., ICLEI, C40, Covenant of Mayors for Climate 
& Energy) is an important driver for city planning and transfer of best 
practices (Heikkinen et al., 2020a). Stakeholder engagement is key for 
successful adaptation (Chapter 17; Bertoldi et al., 2020).
Only 29% of local adaptation plans are mainstreamed in cities, 
which could reduce the effectiveness of implementing adaptation 
(Section 13.11.1.2; Reckien et al., 2019). Although large municipalities 
usually fund the implementation of their adaptation plans, smaller 
and less populated municipalities (particularly in SEU and EEU) often 
depend on intergovernmental, international and national funding.
13.6.2.2 Adaptation Options as a Function of Impacts
Examples of adaptation options in Europe are presented in Figure 13.19.
Both NbS and EbA, such as green spaces, ponds, wetlands and 
green roofs for urban stormwater management and vegetation for 
heat mitigation, represent an emerging adaptation option in cities.

Combined with traditional water infrastructure, they can contribute to 
managing urban flood events (Kourtis and Tsihrintzis, 2021), playing a 
role in mitigating flood peaks (Pour et al., 2020) and protecting critical 
urban infrastructure (Ossa-Moreno et al., 2017). For example, in the Augustenborg district of Malmö, Sweden, using nature to manage 
stormwater runoff has resulted in capturing an estimated 90% of 
runoff from impervious surfaces and reduced the total annual runoff 
volume from the district by about 20% compared with the conventional 
system (EEA, 2020b). Urban greening is associated with lower ambient 
air temperature and relatively higher thermal comfort during warm 
periods (Bowler et al., 2010; Oliveira et al., 2011; Cohen et al., 2012; 
Cameron et al., 2014). The scale and relative degree of management 
or integration of approaches drawing on nature with ‘engineered’ 
solutions affect their vulnerability to climate change. Small-scale

urban NbS are relatively less vulnerable due to increased capacity for 
intervention, while the relatively greater contact between stakeholders 
and urban NbS (compared with larger-scale, rural approaches) provides 
greater opportunity for human intervention to ensure the survival of 
urban vegetation during droughts or heatwaves.
When selecting and combining adaptation options, challenges remain 
on how to address the uncertainties of climate projections and climatic 
extremes (Fowler et  al., 2021) and to translate scientific input into 
practical guidance for adaptation (Section 13.11.1.3; Dale, 2021).
An assessment of the feasibility and effectiveness of the main 
adaptation options, based on the literature, is presented in Figure 13.20. 
(For adaptation to flood risk, see Figure 13.6.)
There are gaps in knowledge on the social, environmental and 
geophysical dimensions of feasibility for many options, and a holistic

assessment of different options is largely lacking. This latter issue could 
reveal unintended impacts from, and synergies or trade-offs between, 
options, as in water and wastewater services (Dobson and Mijic, 2020).
13.6.2.3 Adaptation Limits, Residual Risks, and Incremental and 
Transformative Adaptation
Adaptation in cities, settlements and key infrastructures in Europe faces 
technical, environmental, economic and social limits (Figure 13.21).
Adaptation options for many sectors will not be sufficient to remove 
residual risks, for example, regarding (a) overheating in buildings under 
high GWL (Tillson et al., 2013; Virk et al., 2014; Dodoo and Gustavsson, 
2016; Mulville and Stravoravdis, 2016; Hamdy et al., 2017; Heracleous 
and Michael, 2018; Dino and Meral Akgül, 2019); (b) snowmaking 
beyond 3°C GWL (Scott et al., 2019; Steiger and Scott, 2020; Steiger 
et  al., 2020); (c) hydropower (Gaudard et  al., 2013; Ranzani et  al.,

2018); (d) electricity transmission and demand (Bollinger and Dijkema, 
2016; EEA, 2019a; Palkowski et al., 2019); (e) urban subways (Jenkins 
et al., 2014a); and (f) flood mitigation in cities (Skougaard Kaspersen 
et  al., 2017; Umgiesser, 2020). Some adaptation actions in a sector 
may also have side effects on others, increasing their vulnerability 
(Sections 13.2.2, 13.2.3; Pranzini et al., 2015).
Examples of transformative adaptation in urban areas have been 
observed (e.g., the Benthemplein water square, the Floating Pavilion in 
Rotterdam and the Hafencity flood proofing in Hamburg), but they often 
remain policy experiments and prove challenging to upscale (Jacob, 
2015; Restemeyer et al., 2015; Restemeyer et al., 2018; Holscher et al., 
2019). Active involvement of local stakeholders, public administration

13
1857Europe Chapter 13
Water and
wastewater
Urban water
Expand drainage systemExpand water supplyUpdate flood defenses
Urban wastewater
Expand sewer systemTransport
CoolingImprove/renew existing
Climate servicesImprove maintenanceRevise guidelines &
standardsUrban areas
Buildings
Flood-proof buildingsUse resistant materialsInterventions at the 
buildings’ shell
Increase flood &
heat risk standards
Passive & active cooling
City/town
Climate servicesEmergency plans,
evacuation, early warning
Facilitate recoveryUpdate flood defensesGreen, blue and grey
NbS to manage water runoffWater roads and pavementsMove to nearby regions
during heatwaves
Planned relocationEconomic activities
and leisure
Winter Tourism
Protection & conservation
Management optionsAlternative/complementary
SnowmakingExpansionShift to other destinations
Summer, coastal &
other tourism
Hard defensesInland shift of activitiesExtend activitiesWater supply & demand
measures

‘Soft’ measures
Business-Finance-
Insurance
Individual & corporate
Extend financial regulations
for risk monitoring
Public reinsurance -
Extension of risk monitoringEnergyTransport
Economic activities
and leisureWater & wastewater
Urban areasDemand managementWater management
Move to nearby regions during heatwaves
Planned relocationAdaptation options for cities, settlements and key infrastructure
Potential synergies and trade-offs with mitigationEnergy
Thermoelectric power
Cooling towers/Dry cooling/
Seawater cooling
Replace by better plantsInstitutional measuresShift production to coastsUse ‘non-traditional’ waterInter-basin transfer
Warning systemsAdjust managementRecalibrate spillwaysMonitor/ forecast snowpack,
Increase plant capacity
Transmission-Distribution
Improve electricity
interconnections
Switch to RES with low
water use
Figure 13.19 |  Adaptation options in cities, settlements and key infrastructures in Europe (Table SM13.7)

1858Effectiveness and feasibility of main adaptation options to
climate impacts and risk for cities, settlements and key infrastructure
in Europe
Evidence
High Medium Low = no/limited evidence/Adaptation option
Interventions in the building shell
Ventilation (natural/mechanical, including night)
Shading
Green roofs, green walls
Urban green spaces
Use of 'cool' paints and coatings
Escape to nearby non-urban destinations
Improvements in cooling systems
Shifting production to less water-intensive plants
Regulatory measures
Management measures
Use of heat-resilient materials
Replace vulnerable infrastructure with resilient oneReduction of thermal comfort
due to increasing temperatures
and extreme heat
Loss of critical services
due to heatwaves
and drought
// ////// / /// ///
//// // / ///// ///
//

Figure  13.20 |   Effectiveness and feasibility of the main adaptation options for cities, settlements and key infrastructures in Europe (Section  SM13.9; 
Table SM13.8)
and political leaders are drivers for community transformation, whereas 
lack of local resources and/or capacities are frequently reported barriers 
to change (Fünfgeld et al., 2019; Thaler et al., 2019).
13.6.2.4 Governance and Insurance
Urban adaptation plans can enhance resilience, and their development 
is mandatory in the UK, France and Denmark (Reckien et al., 2019). 
There is medium confidence that the development of urban adaptation 
planning is much more influenced by a city’s population size, present 
adaptive capacity and GDP per capita than by anticipated climate risks 
(Reckien et al., 2018). A high organisational capacity in a municipality 
may not be a necessary condition for forward-looking investment 
decisions on urban water infrastructure, although enablers differ

for small versus medium-to-large municipalities (Pot et  al., 2019). 
There is large in-country variation in policy mixes utilised by local 
governments for supporting adaptation (Lesnikowski et  al., 2019). 
In early-adapter cities (e.g., Rotterdam), adaptation is institutionally 
embedded in climate, resilience and sustainability-related actions, as 
well as collaboration between city departments, government levels, 
businesses and other stakeholders (Holscher et al., 2019). In most other 
cities, however, adaptation planners rarely consider collaborations 
with citizens, and there are difficulties in departmental coordination 
and upscaling from pilot projects (Brink and Wamsler, 2018).
The level and type of collaboration between the public and private 
sectors in managing climate risks varies across Europe (Wiering et al., 
2017; Alkhani, 2020). For example, in flood management (Section 13.2), the private-sector involvement in Rotterdam is much more pronounced

and there are joint public–private responsibilities throughout most of 
the policy process due to the large share of private ownership of land 
and real estate (Mees et al., 2014).
In large infrastructure networks, the lack of a leading and powerful 
institutional body, with sufficient research resources targeted to 
climate-change risk assessment, may limit adaptive capacity, as for 
example in railways (Rotter et al., 2016).
The European insurance industry has developed tailored products 
for specific climate risks threatening cities, settlements and key 
infrastructures, such as risk-based flood insurance for homeowners 
and companies (Section  13.2.3). The European insurance industry 
is developing new services (such as risk analysis and catastrophe 
modelling embedding climate change, early warning and post-event 
recovery recommendations), and it has recently started to play a role 
as communicator of future risks and as institutional investor with the

aim of risk reduction (Jones and Phillips, 2016; Marchal et al., 2019).
13.6.2.5 Links Between Adaptation and Mitigation
Evidence from transport in Europe shows that adaptation actions do not 
consider enough long-term transition paths embedded in mitigation, 
while mitigation strategies are often not assessed under future 
climate scenarios (Aparicio, 2017). Without rapid decarbonisation of 
electricity supply, greenhouse gas emissions will increase due to the 
increased use of air conditioning installations in cities. This trade-off

13
1859Europe  Chapter 13
can be reduced to some extent through use of more efficient cooling 
technologies (IEA, 2018) and complementary adaptation measures 
such as large-scale urban greening, building policies and behavioural 
changes in air conditioning use (Viguié et al., 2020; Sharifi, 2021; Viguié 
et al., 2021). Greenhouse gas emissions from transport may increase 
due to the temporary relocation of city residents to cooler locations 
during heatwaves (Juschten et al., 2019), and from increased energy 
use for snowmaking in European ski resorts (Scott et al., 2019).
13.6.3 Knowledge Gaps
A key knowledge gap is the lack of a quantitative European-wide 
integrated assessment of future climate-change risks on water and 
energy, including different socioeconomic futures. Models capable 
of representing integrated policies for energy and water are lacking 
(Khan et  al., 2016) including quantitative modelling of impacts on

energy transmission and coastal energy infrastructure (Cronin et al., 
2018). These lacks are especially pertinent when combined with the 
small number of studies considering SSP population projections and 
adaptation tipping points. The limited social vulnerability assessments, 
mapping and validation (Rufat et al., 2019) contribute further to these 
knowledge gaps.While compound, concurrent and consecutive climate extremes 
become more frequent, there is limited knowledge on sectoral risks or 
on cascading risks for through transport, telecommunications, water, 
and banking and finance. While heat is well studied, studies on risks for 
cities and key infrastructures from hailstorms and lightning are missing.
Empirical data on the damage of transport infrastructure (e.g., railways) 
covering different European countries have not been systematically 
collected, and indirect economic effects of interruptions of transport

networks have not been well studied (Bubeck et al., 2019). These deficits 
result in uncertainties associated with impacts of climate change on 
transport flows and indirect impacts (e.g., delays, economic losses).
There is limited knowledge on interactions created by synchronous 
adaptation in ski tourism supply and demand, and models do not yet 
include individual snowmaking capacity and a higher time resolution 
(Steiger et  al., 2019). Furthermore, there is no European-wide 
assessment of coastal flooding risks on tourism.
Many studies lack consideration of market characteristics (e.g., competitors) 
in their risk assessment, which would be improved by location- and sector-
specific knowledge on climate risks for firm assets, operations, business, 
industry, finance and insurance needed to inform adaptation actions (de 
Bruin et al., 2020; Feridun and Güngör, 2020; Monasterolo, 2020).Supply of 
energy & water  
Technical limits
Technical/ management
measures not possible due to

plant characteristics
Socio-economic limits
High installation costs for 
large-scale adaptation
Too risky investments when 
in highly vulnerable locations
Environmental &
regulatory limits
Limited water resources
Competitive water usesCity / townHousehold / BuildingEconomic activities
Limited resources for
implementing adaptation
Technological limits
Shift to other locations is
prohibited
Limited areas for expansionSocio-economic limits
High investments needed
Small size of enterprisesIndicative adaptation limits in cities, settlements and key infrastructure in Europe
Limited efficacy of measures
under high/ rapidly changing
climate hazards
High investments to upgrade
municipal facilities
High installation cost for new
Space constraints for expanding
green infrastructureTechnical limits

Physical characteristics of
building stock
Low probability hazards prohibit
adaptation payoff
Poverty 
Comfort and safety
Legislation on buildings and
appliances
Figure 13.21 |  Indicative adaptation limits in cities, settlements and key infrastructures in Europe (Table SM13.16)

186013.7 Health, Well-Being and the Changing 
13.7.1 Observed Impacts and Projected Risks
13.7.1.1  Mortality Due to Heat and Other Extreme Events
Attribution studies show that human-induced climate change is 
increasing the frequency and intensity of heatwaves and has already 
impacted human health in Europe (Section  13.10.1; Vicedo-Cabrera 
et al., 2021); for example, the 2010 heatwave in EEU resulted in 55,000 
heat-related deaths (Barriopedro et al., 2011; Russo et al., 2015); also, 
the 2018 heatwave in NEU (Ebi et al., 2021) and the 2019 heatwave 
in WCE and NEU both had significant health impacts (Cross-Chapter 
Box DISASTER in Chapter 4; Vautard et al., 2020; Watts et al., 2021). 
Elderly, children, (pregnant) women, socially isolated people and those 
with low physical fitness are particularly exposed and vulnerable to 
heat-related risks, as are those people suffering from pre-existing

medical conditions, including cardiovascular disease, kidney disorders, 
diabetes and respiratory diseases (de’Donato et al., 2015; Sheridan and 
Allen, 2018; Szopa et  al., 2021). An ageing population in Europe is 
increasing the pool of vulnerable individuals, resulting in higher risk 
of heat-related mortality (Montero et al., 2012; Carmona et al., 2016b; 
WHO, 2018b; Watts et al., 2021).
A GWL of 1.5°C could result in 30,000 annual deaths due to extreme 
heat, with up to threefold the number under 3°C GWL (high confidence) 
(Roldán et al., 2015; Forzieri et al., 2017; Kendrovski et al., 2017; Naumann 
et al., 2020). The risk of heat stress, including mortality and discomfort, 
is dependent on socioeconomic development (Figure  13.22; Rohat 
et al., 2019; Ebi et al., 2021). Heat stress risks will be lower under SSP1 
than the SSP3 or SSP4 scenarios (high confidence) (Hunt et al., 2017; 
Rohat et al., 2019; Wang et al., 2020; Ebi et al., 2021). The incidence of

heat-related mortality and morbidity will be highest in SEU, where their 
magnitude is also expected to increase more rapidly (Forzieri et al., 2017; 
Gasparrini et al., 2017; Guo et al., 2018; Díaz et al., 2019; Vicedo-Cabrera 
et al., 2021). WCE, NEU and SEU will experience accelerating negative 
consequences beyond 1.5°C GWL, particularly under SSP3 and SSP4 
due to higher vulnerability compared with SSP1 (Figure  13.22; Rohat 
et al., 2019). The number of heat-related respiratory hospital admissions 
is projected to increase from 11,000 (1981–2010) to 26,000 annually 
(2021–2050), particularly in SEU mainly due to a relative increase in 
the number of extremely hot days (Åström et al., 2013). Cold spells are 
projected to decrease across Europe, particularly in Southern Europe, 
but do not compensate for the additional heat-related deaths projected 
(Lhotka and Kysely, 2015; Carmona et al., 2016a; Martinez et al., 2018).

Among Europeans, 74% live in urban areas (Section 13.6), where the 
effect of heatwaves on human health is exacerbated by microclimates 
due to buildings and infrastructure, UHI effects and air pollution (WHO, 
2018a; Smid et al., 2019). In large European cities, stabilising climate 
warming at 1.5°C GWL would decrease premature deaths by 15–22% 
in summer compared with stabilisation at 2°C GWL (high confidence) 
(Mitchell et al., 2018).
Although there is very high confidence that risk consequences will 
inevitably be more pervasive and widespread in a warmer Europe, evidence of higher heat tolerance is also emerging across most 
European regions (Todd and Valleron, 2015; Åström et al., 2016; Follos 
et  al., 2020). Future projections of mortality rates in Europe under 
the assumption of complete acclimatisation suggest constant or even 
decreasing rates of mortality in spite of global warming (Åström et al., 
2017; Guo et al., 2018; Díaz et al., 2019); however, there are large

uncertainties in the ability to adapt to future heat extremes which 
might fall outside of historical ranges (Vanos et al., 2020).
Other extreme events already result in major health risks across 
Europe. Between 2000 and 2014, for example, floods in Russia 
killed approximately 420 people, mainly older women (Belyakova 
et al., 2018). Fatalities associated with coastal and riverine flooding 
(Section 13.2.2), wildfires (Section 13.3.4) and windstorms could rise 
substantially by 2100 (Forzieri et al., 2017; Feyen et al., 2020). Lifetime 
exposure to extreme weather events for children born in 2020 will be 
about 50% greater at 3.5°C compared with 1.5°C GWL (Thiery et al., 
2021).
13.7.1.2 Air Quality
Air pollution is already one of the biggest public health concerns 
in Europe: in 2016, roughly 412,000 people died prematurely due 
to long-term exposure to ambient PM2.5, 71,000 due to NO 2 and 
more than 15,000 premature mortalities occurred due to near-

surface ozone (EEA, 2019b; Lelieveld et  al., 2019). The impacts 
of air pollution are determined by air-quality policies, changes 
to temperature, humidity and precipitation (Szopa et  al., 2021). 
Climate change could increase air pollution health effects, with the 
size of the effect differing across European regions and pollutants 
(medium confidence) (Jacob and Winner, 2009; Orru et  al., 2017; 
Tarin-Carrasco et al., 2021). Increases in temperature and changes 
in precipitation will impact future air quality due to increased risk 
of wildfires and related air pollution episodes. Data on the health 
impacts of wildfires in Europe is currently limited (Section 13.3.1.4), 
but examples, such as the 2017 fires, suggest that more than 100 
people died prematurely in Portugal alone as a result of poor air 
quality (Oliveira et al., 2020).
At 2.5°C GWL, mortalities due to exposure to PM2.5 are projected to 
increase by up to 73% in Europe (medium confidence) (Silva et al.,

2017; Lelieveld et al., 2019; Tarin-Carrasco et al., 2021). At 2°C GWL, 
annual premature mortalities due to exposure to near-surface ozone 
are projected to increase up to 11% in WCE and SEU and to decrease 
up to 9% in NEU (under RCP4.5) (medium confidence) (Orru et al., 
2019). A projected increase in wildfires and reduced air quality is 
expected to increase respiratory morbidity and mortality, especially 
in SEU (Slezakova et  al., 2013; de Rigo et  al., 2017). Constant or 
lower emissions, combined with stricter regulations and new policy 
initiatives, might improve air quality in the coming decades (medium 
agreement, low evidence). The ageing population in Europe will 
augment the air-quality mortality burden 3–13% by 2050 (Geels 
et al., 2015; Orru et al., 2019). Besides ambient air quality, projected 
increases in flood risk and heavy rainfall could decrease indoor air 
quality (Section 13.6.1.5.2) due to dampness and mould, leading to

increased negative health impacts, including allergies, asthma and 
rhinitis (EASAC, 2019; EEA, 2019b).

13
1861Europe  Chapter 13
Projected heat stress risks for people in Europe
(2040–2060)
SSP1-2.6
SSP3-8.5 SSP4-4.5Risk deciles
5th4th3rd2nd1st10th9th8th7th6th<1 inhabitant/km2
Baseline
Figure 13.22 |  Scenario matrix for multi-model median heat stress risks for the baseline 1986–2005, and different SSP–RCP combinations for the period 
2040–2060. The SSPs are extended for Europe (EU28+). Heat stress risk is calculated by geometrical aggregation of the hazard (heatwave days), population vulnerability and 
exposure. Risk values are normalised using a z-score rescaling with a factor-10 shift. Details of the methodology are provided by Rohat et al. (2019).

1862
Direction
of changeImpacts and risks of climate sensitive infectious diseases
= no evidence/• Northern Europe (NEU)
• Southern Europe (SEU)Climate sensitive
disease Hazard Vulnerability Exposure
Observed
Observed
Observed
Observed
Observed
Proj. +3.0°CDirection of change of
climatic suitability by
European regionsImpact/Risk
Increase Decrease BothOverall
risk for
European
society
Affected by:
ticks, hosts,
geography, habitat
encroachment, hunting
mosquitoes,
geography, land use,
standing water
mosquitoes, geography,
land use, urbanization,
land use, standing water
geography,
recreational water use,
sea food consumptionRecreation, low socio-economic status,

disadvantaged groups,
access to health care,
forest gatherers
Age, housing (A/C), poverty, lack of
window/door screens, access to
health care, uninformed, vector control,
lack of efficatious vaccine or medication 
bed nets and screens, access to health
care, uninformed, vector control, insecticide /
drug resistance, lack of efficatious vaccine
Age, access to care, pre-existing
medical conditions, open wounds,
immunocompromizedPathogens,
climatic suitability
Pathogen, vector,
bird hosts,
vectorial capacity,
precipitation,
population mobility
 Pathogen, vector,
Pathogen,
Sea Surface
Temperature, Sea
Surface Salinity Tick-borne diseases :
tick-borne,
encephalitis,
and borreliosis

West Nile
fever
Dengue,
Chikungua,
Zika
Malaria
VibrioHigh
High
Medium
Medium
Medium
Medium
Low
Medium
Medium
Low
Low
Low
Medium*
Medium*
Medium*
* only the case 
  for the Baltic region/
//
/
/
/
/
/
/
no observation* ***
**
***
Overall risk for European society:       Low = Low weighted risk for society         Medium = Medium weighted risk for society        High = High weigthed risk for soci etyConfidence level:
ProjectionsHigh Medium Low High Medium Low High Medium Low High Medium Low
Figure 13.23 |  Assessment of climate-sensitive infectious diseases. The assessment considers the main drivers of hazard (climate-impact drivers, pathogens and vectors), 
vulnerability (lack of safeguards and a predisposition to these hazards) and exposure (humans to be affected by these pathogens and vectors), the direction of change in climatic

suitability (i.e., temperature, precipitation, relative humidity, extreme weather events) of observed changes and at 1.5°C and 3°C GWL, and the overall infectious disease risks across 
Europe (Chapters 7.3, 7.4; Lindgren et al., 2012; Semenza and Paz, 2021). The assessment does not consider incidence of disease infections through autochthonous transmission 
(Table SM13.18).
13.7.1.3 Climate-Sensitive Infectious Diseases
Figure  13.23 summarises the observed and projected changes in 
climatic suitability and assesses the risk for selected climate-sensitive 
infectious diseases in Europe.
Among the tick-borne diseases, Lyme disease is the most prevalent 
disease in Europe. There has been a temperature-dependent range 
expansion of ticks that is projected to expand further north in Sweden, 
Norway and the Russian Arctic (Jaenson et al., 2012; Jore et al., 2014; 
Tokarevich et al., 2017; Waits et al., 2018), and to higher elevations

in Austria and the Czech Republic (medium confidence) (Daniel et al., 
2003; Heinz et al., 2015). A potential habitat expansion of these ticks 
of 3.8% across Europe, relative to 1990–2010, is projected for 2°C 
GWL (Porretta et al., 2013; Boeckmann and Joyner, 2014). In contrast, 
there are projected habitat contractions for these ticks in SEU due to 
unfavourable climatic conditions (Semenza and Suk, 2018).The Asian tiger mosquito (Aedes albopictus) is present in many 
European countries and can transmit dengue, chikungunya and zika 
(Liu-Helmersson et al., 2016; Tjaden et al., 2017; Messina et al., 2019). 
There is a moderate climatic suitability projected for chikungunya 
transmission, notably across France, Spain and Germany, but also 
contractions particularly in Italy. Europe experienced an exceptionally 
early and intense transmission season of the West Nile virus in 2018, 
with elevated spring temperature abnormalities (Haussig et al., 2018;

Marini et al., 2020). Projections for Europe show the West Nile virus 
risk to expand: by 2025, the risk is projected to increase in SEU and 
southern and eastern parts of WCE (medium confidence) (Semenza 
et  al., 2016). Although climatic suitability for malaria transmission 
in Europe is increasing and will lead to a northward spread of the 
occurrences of Anopheles vectors, the risk from malaria to human 
health in Europe remains low due to economic and social development 
as well as access to health care (medium confidence) (Sudre et al., 
2013; Hertig, 2019).

13
1863Europe  Chapter 13
Water-borne diseases are also associated with changes in climate such 
as heavy precipitation events (Semenza, 2020). Warming has been 
linked with elevated incidence of campylobacteriosis outbreaks in 
various European countries (Yun et al., 2016; Lake et al., 2019). Marine 
bacteria, such as Vibrio, thrive under elevated sea surface temperature 
and low salinity such as that of the Baltic Sea. Under further warming, 
the number of months with risk of Vibrio transmission increases and 
the seasonal transmission window expands, thereby increasing the risk 
to human health in the future (high confidence) (Baker-Austin et al., 
2017; Semenza et al., 2017).
13.7.1.4.   Allergies and Pollen
The main drivers of allergies are predominantly non-climatic (e.g., 
increased urbanisation, adoption of westernised lifestyles, social and 
genetic factors), but climate change strongly contributes to the spread

of some allergenic plants, thus exacerbating existing allergies and 
causing new ones in people across Europe (high confidence) (D’Amato 
et al., 2016; EASAC, 2019). The prevalence of hay fever (allergic rhinitis), 
for example, is between 4 and 30% among European adults (Pawankar 
et al., 2013). The invasive common ragweed (Ambrosia asteraceae) is 
a key species already causing major allergy in late summers (including 
hay fever and asthma), particularly in Hungary, Romania and parts of 
Russia (Ambelas Skjøth et al., 2019). Across Europe, sensitisation to 
ragweed is expected to increase from 33 million people in 1986–2005 
to 77 million people at 2°C GWL (Lake et al., 2017).
Warming will result in an earlier start of the pollen season and 
extending it, but this differs across regions, species, traits and flowering 
periods (Ziello et al., 2012; Bock et al., 2014; EASAC, 2019; Revich et al., 
2019). For instance, in different parts of WCE and NEU, the start of

birch-season flowering has been shifted and extended up to 2 weeks 
earlier during recent decades (Biedermann et al., 2019). Airborne pollen 
concentrations are projected to increase across Europe (Ziello et  al., 
2012). In south-eastern Europe, where pollen already has a substantive 
impact, the pollen count could increase more than 3 to 3.5 times at 
2.5°C GWL and can become a more widespread health problem 
across Europe, particularly where it is currently uncommon (medium 
agreement, low evidence) (Lake et al., 2017).
13.7.1.5.  Labour Productivity and Occupational Health
Extreme heat and cold waves have been linked to an increased risk of 
occupational injuries (Martinez-Solanas et al., 2018) and changes in 
labour productivity (Orlov et al., 2019; García-León et al., 2021), while 
evidence on the consequences of other extreme events is lacking. 
The sectors with a high percentage of high-intensity outdoor work in

Europe, mainly agriculture and construction, have the highest risk of 
increased injury and labour productivity losses, but also manufacturing 
and service sectors can be affected when air conditioning is not 
available (Section  13.6.1.3; Gosling et  al., 2018; Szewczyk et  al., 
2018; Dellink et al., 2019; Orlov et al., 2019). The heatwaves of August 
2003, July 2010 and July 2015 were concentrated in SEU and led to 
reductions in monthly worker productivity of on average 3–3.5% in 
SEU, ranging up to 8–9% in Cyprus (2003, 2010) and Italy (2015) 
(Orlov et al., 2019); in contrast, the heatwave of 2018 centred on NEU 
but also led to pronounced productivity reductions in WCE and SEU (García-León et al., 2021). Each of these major European heatwaves 
led to considerable economic losses in agriculture and construction 
(high confidence) and reduced GDP in Europe (except EEU) by 0.3–
0.5% (García-León et al., 2021). At 2.5°C GWL and beyond, GDP losses

are projected to increase fivefold compared with 1981–2010, ranging 
from 2–3.5% in SEU to 0.5–1.5% in WCE, and below 0.5% in NEU and 
EEU (Section 13.10.3; Roson and Sartori, 2016; Takakura et al., 2017; 
Szewczyk et al., 2018; Dellink et al., 2019; García-León et al., 2021).
13.7.1.6.  Food Quality and Nutrition
There is growing evidence that climate change will negatively affect 
food quality (diversity of food, nutrient density and food safety) and 
food access, although the risks for European citizens are significantly 
lower compared with other regions (Fanzo et al., 2018; IFPRI, 2018). 
Projected changes in crop and livestock production (Section 13.5.1), 
particularly reduced access to fruits and vegetables and foods with 
lower nutritional quality, will impact already vulnerable groups 
(Swinburn et al., 2019). The effects of climate change on food quality 
and access varies by income, livelihood and nutrient requirements,

with low-income and more vulnerable groups in Europe most affected 
(IFPRI, 2018). Spikes in food prices due to changing growing conditions 
in Europe (Section 13.5.1), increased competition for land (e.g., land-
based climate-change mitigation) and feedbacks from international 
markets are expected to decrease access to affordable and nutritious 
food (Section 13.9.1; EASAC, 2019; Loopstra, 2020). Reduced access to 
healthy and varied food could contribute to being overweight or obese, 
which is a growing health concern across Europe (Springmann et al., 
2016). Increased rates of obesity and diabetes further exacerbate risks 
from heat-related events (EASAC, 2019).
13.7.1.7.  Mental Health and Well-Being
Extreme weather events can trigger post-traumatic stress disorder 
(PTSD), anxiety and depression; this is well-documented for flooding 
in Europe (high confidence) but less for other extreme weather events. 
For example, in the UK, flooded residents suffered stress and identity

loss from the flood event itself, but also from subsequent disputes with 
insurance and construction companies (Carroll et  al., 2009; Greene 
et al., 2015). Residents displaced from their homes for at least 1 year 
due to 2013–2014 floods in England were significantly more likely to 
experience PTSD, depression and anxiety, with stronger effects in the 
absence of advance warning (Munro et al., 2017; Waite et al., 2017). 
There is emerging evidence across Europe that young people may 
be experiencing anxiety about climate change, although it is unclear 
how widespread or severe this is (Hickman, 2019). In northern Italy, 
the number of daily emergency psychiatric visits and mean daily air 
temperature has been linked (Cervellin et al., 2014).
13.7.2 Solution Space and Adaptation Options
Adaptation to health impacts has generally received less attention 
compared with other climate impacts across Europe (EASAC, 2019). 
Progress on health adaptation can be observed. Between 2012 and

2017, at least 20 European countries instituted new governance 
mechanisms, such as interdepartmental coordinating bodies for health

1864adaptation and adopted health adaptation plans (Kendrovski and 
Schmoll, 2019). Progress on city-level health adaptation is generally 
limited (Araos et al., 2015), with most activities occurring in SEU (high 
agreement, medium evidence) (Paz et al., 2016).
Figure 13.24 presents the assessment of the feasibility and effectiveness 
of key heat-related health adaptation actions. It shows that substantial 
social–cultural and institutional barriers complicate widespread 
implementation of measures; studies on the implementation of new 
blue–green spaces in existing urban structures in, for example, Sweden 
(Wihlborg et al., 2019), the UK (Carter et al., 2018) and the Netherlands 
(Aalbers et al., 2019), point to important feasibility challenges (e.g., access 
to financial resources, societal opposition, competition for space) (high 
confidence). Lower perception of health risks has been observed among

vulnerable groups which, in conjunction with perceived high costs of 
protective measures, act as barriers to implementing health adaptation 
plans (van Loenhout et al., 2016; Macintyre et al., 2018; Martinez et al., 
2019). Key barriers to mental health adaptation actions include lack 
of funding, coordination, monitoring and training (e.g., psychological 
first aid) (Hayes and Poland, 2018). Existing health measures, such as 
monitoring and early warning systems, play an important role in detecting 
and communicating emerging climate risks and weather extremes (high 
confidence) (Confalonieri et al., 2015; Casanueva et al., 2019; Linares 
et  al., 2020). Stricter enforcement of existing health regulations and 
policies can have a positive effect in reducing risks (Berry et al., 2018).
The effectiveness of most options in reducing climate-induced health 
risks is determined by many co-founding factors, including the extent

of the risk, existing sociopolitical structure and culture, and other 
adaptation options in place (high agreement, medium evidence). 
Successful examples include the implementation of heatwave plans 
(Schifano et al., 2012; van Loenhout and Guha-Sapir, 2016; de’Donato 
et  al., 2018), improvements in health services and infrastructure 
of homes (Section  13.10.2.1; Vandentorren et  al., 2006). A study of 
nine European cities, for example, showed lower numbers of heat-
related deaths in SEU and attributed this to the implementation of heat prevention plans, a greater level of individual and household 
adaptation, and growing awareness about exposure to heat (de’Donato 
et al., 2015). Long-term national prevention programmes in NEU have 
been shown to reduce temperature-related suicide (Helama et  al., 
2013). The physical fitness of individuals may increase resilience to 
extreme heat (Schuster et  al., 2017). Combining multiple types of

adaptation options into a consistent policy portfolio may have an 
amplifying effect in reducing risks, particularly at higher GWL (medium 
confidence) (Chapter 7; Lesnikowski et al., 2019).
Health adaptation actions have demonstrable synergies and trade-
offs (Cross-Chapter Box HEALTH in Chapter 7). For example, increasing 
green–blue spaces in Europe’s densely populated areas can be effective 
in improving microclimates, reducing the impact of heatwaves, improving 
air quality and improving mental health by increasing access to fresh air 
and green (restorative) environments (Gascon et al., 2015; Kondo et al., 
2018; Kumar et al., 2019). Health adaptations can also have negative 
trade-offs, be inconsistent with mitigation ambitions and could lead 
to maladaptation. Green–blue spaces, for example, may create new 
nesting grounds for carriers of vector-borne diseases, increase pollen 
and allergies (Kabisch et al., 2016), enlarge freshwater use for irrigation

(Reyes-Paecke et al., 2019) and could raise climate equity and justice 
issues such as green gentrification (Yazar et  al., 2019). Similarly, air 
conditioning and cooling devices are considered highly effective but 
have low economic and social feasibility as well as negative trade-
offs due to increasing energy consumption, raising energy costs which 
is particularly challenging for the poor (Section  13.8.1.1), enhancing 
the UHI effect and increasing noise pollution (Fernandez Milan and 
Creutzig, 2015; Hunt et al., 2017; Macintyre et al., 2018).
The solution space for implementing health adaptation options is slowly 
expanding in Europe. Health adaptation can build on, and integrate into, 
established health system infrastructures, but these differ significantly 
across Europe, as do existing capacities to deal with climate-related 
extreme events (Austin et al., 2016; Austin et al., 2018; Orru et al., 2018;

Watts et al., 2018; Austin et al., 2019; Martinez et al., 2019). Despite some 
Effectiveness and feasibility of main adaptation options
to reduce heat related impacts and risks to human health in Europe
Evidence
High Medium Low = no/limited evidence/Mortality, morbidity,
exposure, stress from heatAdaptation option
Behavior change measures
Natural cooling
Building interventions
Green infrastructures
Heat proof land management
Heat health action plans
Bundle of options //
///
/// ///
//
//
//
/
Figure 13.24 |  Effectiveness and feasibility of the main adaptation options to reduce heat-related impacts and health risks in Europe (Section SM13.9, Table 
SM 13.19)

13
1865Europe  Chapter 13
progress, limited mainstreaming of climate change has been observed, 
particularly due to low societal pressure to change, confidence in existing 
health systems and lack of awareness of links between human health 
and climate change (medium confidence) (Austin et  al., 2016; WHO, 
2018b; Watts et  al., 2021). Coordination of health adaptation actions 
across scales and between public sectors is needed to ensure timely and 
effective responses for a diversity of health impacts (high confidence) 
(Austin et al., 2018; Ebi et al., 2018). Key enabling conditions to extend 
the solution space include increasing the role for national and regional 
governments in facilitating knowledge sharing across scales, allocating 
dedicated financial resources, and creating dedicated knowledge and 
policy programmes on climate and health (Wolf et al., 2014; Akin et al., 
2015; Curtis et al., 2017). Investing in public healthcare systems more

broadly increases their capacity to respond to climate-related extreme 
events and will ensure wider societal benefits as the COVID-19 pandemic 
has demonstrated (Cross-Chapter Box COVID in Chapter 7).
Despite a range of options available, there are limits to how much 
adaptation can take place, and residual risks remain. These risks 
are predominantly discussed in the context of excess mortality and 
morbidity due to heat extremes (Hanna and Tait, 2015; Martinez et al., 
2019). Future heatwaves are expected to stretch existing adaptation 
interventions well beyond levels observed in response to the observed 
events of 2003 and 2010 (Section 13.10.2.1; Hanna and Tait, 2015).
13.7.3 Knowledge Gaps
Literature on the link between public health, climate impacts, vulnerability 
and adaptation is skewed across Europe, with most studies focusing 
on region-specific impacts (e.g., flood injuries in WCE, heatwaves in 
SEU). In general, attributing health impacts to climate change remains

challenging, particularly for mental health and well-being, (mal)nutrition 
and food quality and climate-sensitive infectious diseases, where other 
socioeconomic determinants play an important role. The connection 
between climate change and health risks under different socioeconomic 
development pathways is hardly studied comprehensively for Europe, 
with some exceptions for extreme events; however, these interactions 
seem to play an important role in better understanding projected risks 
and inform choices on adaptation planning.
Some climate-related health issues are emerging, but evidence is too 
limited for a robust assessment, for example, the links between climate 
change and violence in Europe (Fountoulakis et al., 2016; Mares and 
Moffett, 2016; Sanz-Barbero et al., 2018; Koubi, 2019).
The solution space for public health adaptation in Europe, and 
the effectiveness of levers for interventions, are hardly assessed.

Although health adaptations are documented, these are particularly 
around mortality and injuries due to extreme events, predominantly 
floods (Section  13.2.1) and heatwaves (Section  13.7.1.1). There 
are very few studies assessing the barriers and enablers of health 
adaptations, nor systematic assessment of the effectiveness of (the 
portfolio of) options. Limited insights into what works, and where, 
hamper upscaling these insights across Europe and constrains the 
ability to evaluate whether investments in health adaptation have 
actually reduced risks.13.8 Vulnerable Livelihoods and Social 
This section addresses the social consequences of climate change for 
Europe by looking into the consequences for poor households and 
minority groups, migration and displacement of people, livelihoods 
particularly vulnerable to climate change (indigenous and traditional 
communities) and cultural heritage.
13.8.1 Observed Impacts and Projected Risks

13.8.1.1 Poverty and Social Inequality
While climate change is not the main driver of social inequality in 
Europe, poor households and marginalised groups are affected more 
strongly by flooding, heat and drought, as well as health risks due to 
spreading diseases, than other social groups (medium confidence).
Urban poor and ethnic minorities often settle in more vulnerable 
settlement zones, and are therefore impacted more by flooding 
(medium confidence) (Medd et al., 2015; Župarić-Iljić, 2017; Efendić, 
2018; Fielding, 2018; Winsemius et  al., 2018; Puđak, 2019; Inuit 
Circumpolar Council, 2020). Yet, in some Western European residential 
waterside developments this pattern is reversed by flooding impacting 
high-income residents more strongly (Walker and Burningham, 2011).
The health of the poor is disproportionately affected, for example, during 
heatwaves in the Mediterranean (Jouzel and Michelot, 2016). Women,

those with disabilities and the elderly are disproportionately affected by 
heat (Section 13.7.1). Floods in the Western Balkans in 2014 resulted 
in heavy metal pollution of water and land threatening the health 
condition of the poorer rural population (Filijović and Đorđević, 2014). 
Access to water and sanitation is less available to poorer households 
and marginalised groups in Europe (Ezbakhe et al., 2019; Anthonj et al., 
2020); this effect could be intensified by increasing water scarcity in 
certain parts of Europe under future climate change (Section 13.10.3).
Food self-provisioning is a widespread practice in many parts of 
Europe (Aleynikov et al., 2014; Corcoran, 2014; Church et al., 2015; 
Mustonen and Huusari, 2020), reaching over half of German rural areas 
(Vávra et al., 2018). While it strengthens resilience for disadvantaged 
households (Church et al., 2015; Boost and Meier, 2017; Promberger, 
2017; Vávra et al., 2018; Ančić et al., 2019; Pungas, 2019) and renews

their local knowledge, it can become a risk in regions with projected 
crop yield reductions (high confidence) (Hallegatte et al., 2016; Quiroga 
and Suárez, 2016; Myers et  al., 2017b; Inuit Circumpolar Council, 
2020), and after extreme weather events (Filijović and Đorđević, 2014).
Energy-poor households often live in thermally inefficient homes and 
cannot afford air conditioning to adapt to overheating in summer 
(Sanchez-Guevara et  al., 2019; Thomson et  al., 2019). While energy 
poverty is much more prevalent in SEU and EEU (Bouzarovski and 
Petrova, 2015; Pye et al., 2015; Atsalis et al., 2016; Monge-Barrio and 
Sánchez-Ostiz Gutiérrez, 2018), climate change will also exacerbate 
energy poverty in European regions where heating thus far has been 
the major share of energy costs (medium confidence) (Sanchez-
Guevara et al., 2019; Randazzo et al., 2020).

1866Table 13.2 |  Examples of losses and damages to vulnerable livelihoods in Europe, differentiated by category according to non-economic loss and damage (Table SM13.20)
Human life Communal and production sites and intrinsic value
Sense of place Agency and identity
Cultural artefactsPsychological and emotional distressTeiltabellen 
zusammengezogen. Bitte prüfen.
Biodiversity and ecosystems
Climate hazard Change in exposure and vulnerability Observed impact and/or projected risk
Loss of livelihood, culture, health and well-being of the Sámi and the Nenets
Decrease and alterations in snow and ice sheet, unstable 
winter weather, especially in the form of rain-on-snow 
events; increased precipitation and thawing permafrost, in 
tundra; unstable loss/flux of marine ice coverLand-use change (e.g., expansion of renewable energy) resulting in 
pasture loss and disconnection of ecosystemsLoss of livelihood (e.g., reindeer herding), food

security (for cold-dependent species), culture, health 
(impact on safety; psychological impacts from stress 
to reindeer and indigenous way of life), and cultural 
and linguistic well-being; release of anthrax from 
permafrost soils in the Nenets area
Loss of key species in high-Arctic freshwater habitats, proliferation of introduced species and disruption of local food systems in Greenland, Finland, Sweden, northwest Russia and Scotland
Warmer water temperatures in high-Arctic freshwater 
habitats (Section 13.3.1) increase productivity in 
oligotrophic systems and eventually lead to loss of oxygen 
in water; warming temperatures and changes to ice cover 
and cryosphere lead to access issues to freshwater fisheries.Introduced Pacific pink salmon has expanded in range since the 
1970s, affecting endemic species through competition and reducing 
their abundance. Increased nutrient loading of rivers and rapid

expansion of algae increase the risks for cold-dependent fish.Shifts in freshwater aquatic habitats and loss of 
endemic cold-dependent fish, such as Arctic char 
and Arctic salmon, cause disruptions to local food 
supply, and local extinctions threaten livelihood 
safety and cultural well-being.
Warmer winters leading to loss of income from ice fishing and cultural heritage in Finland
The start of ice cover on lakes, e.g., Lake Puruvesi (Finland), 
has changed from November to February; ice breakup 
occurs much earlier in the year.The quality of the water in the lakes used for fishing depend on ice 
cover during most of the year, and the season of open water is now 
much longer, increasing nutrient flow and loss of water quality in 
these lakes.Lack of winter ice combined with delayed freeze-up 
and earlier ice breakup reduce fish harvest for 
important species by up to 50% and impacts local 
safety, ecosystems, oral-history maintenance and 
the local economy.

Changes to marine food web resulting in loss of Indigenous knowledge and food insecurity in Greenland
Warmer ocean waters are moving further north (so-called 
atlantification of Greenland waters); higher temperatures 
are melting sea ice.Traditional practices and knowledge based on sea ice uses and 
hunting are being lost; species are being replaced with southern 
fish.Loss of Indigenous knowledge of how to deal with 
and use sea ice regarding species and navigation is 
occurring, as is loss of access to seals and walruses, 
as well as food insecurity.
Reduced yields on managed alpine grasslands decreasing the self-sufficiency of pastoral livestock farming in the Austrian, French and Swiss Alps
Increase in heat, precipitation variability and agricultural 
as well as hydrological drought; less snow on the ground, 
increase in glacier melt, landslide susceptibility and erosionLand-use change resulting in natural reforestation of abandoned

pastoral land; shifts in alpine plant communities; more intensive 
cultivation of grasslands; change in agricultural markets and 
support policyAbandonment of summer pastures and farms, with 
negative consequences for farming income, tourism, 
and cultural and aesthetic values

13
1867Europe  Chapter 13
Reduced yields on semi-natural grasslands, compromising livestock feeding in winter and ultimately decreasing viability of pastoralism in the Spanish Pyrenees
Higher temperatures and more variable precipitation, less 
snow, change in seasonality and droughtDemographic change, change in policy and market conditions, 
simplification of pastoral practices and agroecosystems, land 
abandonment or afforestation of marginal pastoral lands 
and intensification of more favourable lands in the lowlands, 
troublesome coexistence with tourism and nature conservation 
initiativesDecreasing viability of pastoralism, concentration 
of pastoral production on most profitable locations 
for intensive rearing of livestock with abandonment 
of the rest of the land; pastoral land encroachment 
both by shrubs and other activities; grassland 
degradation; biodiversity loss

Retreating glaciers and changes in the landscape leading to loss of identity, culture and self-reliance in the Italian Alps (Alto Adige)
Glacier volume loss from increasing temperatures Vulnerability mainly driven by reliance on tourismLoss of sense of community through shared 
memories, and history; sadness caused by the loss 
of what feels like ‘home’; loss of well-being due to 
uncertainty and fear of the future
Drought resulting in a reduction of provisioning (water) and regulating services (protection against floods) in the Western and Eastern Alps, Iberian Mountains and Dinaric Mountains
Increase in drought, particularly under high-end GWLForest management strategies, including that of natural forests, 
which can enhance or reduce vulnerabilityCritical importance of alpine natural forests and 
meadows for regulating services; negative impacts 
of climate change found mainly at low elevations 
and for specific species (e.g., Norway spruce);

decrease in soil moisture due to abandonment of 
pastoralism resulting in reduced water provision for 
downstream water users
Increase in sea temperature leading to shifts in distribution of cold-water species, reducing productivity at lower latitudes; artisanal fisheries in southern European coastal areas 
(Mediterranean) that rely on local, nearshore stocks can have difficulties to adapt
Increase in sea temperatureSubstitution of artisanal fisheries by industrial fisheries; less support 
by governments; shift in employment (e.g., tourism) which does 
not match the skill sets, education or desires of small-scale fishers; 
national quota system leading to prices too high to buy or lease 
quotas and an immense amount of bureaucracy and regulationsDue to their low investment capacity and boat 
size, fishers are limited in their movement to 
other fishing places when local fish stocks decline. 
Increasing sea temperatures are increasing the

threat of invasive species in coastal ecosystems.
13.8.1.2 Migration and Displacement of People
Most migration and displacement due to climate change is taking place 
within national borders and single regions (Cross-Chapter Box MIGRATE 
in Chapter 7). There is low confidence in climate change contributing to 
migration from outside Europe into Europe (Gemenne, 2011; Topilin, 
2016; Gemenne and Blocher, 2017; Selby et al., 2017). Some economic 
models project that asylum applications to the EU might increase by 
a third at 2.5°C GWL and more than double beyond 4°C GWL by end 
of the century (Missirian and Schlenker, 2017), but empirical evidence 
shows that applications might decrease due to growing economic and 
legal barriers in the capacity of populations to emigrate from Africa or 
other regions (Kelley et al., 2015; Zickgraf, 2018; Borderon et al., 2019).
Migration of people within Europe is predominantly triggered 
by economic disparities among European countries (Fischer and

Pfaffermayr, 2018). There is limited evidence and low agreement for 
climate-driven impacts on these movements (Hoffmann et al., 2020). Small-scale climate-induced displacement within Europe occurs in the 
aftermath of flood and drought disasters and over short distances 
(Cattaneo et al., 2019). The unequal distribution of future climate risks 
(Section  13.1) and adaptive capacity across European regions may 
increase pressure for internal migration (Williges et al., 2017; Forzieri 
et al., 2018). For instance, projected SLR (Section 13.2.1; Cross-Chapter 
Box  SLR in Chapter 3) may result in planned relocation of coastal 
settlements and inland migration in the UK, the Netherlands and the 
northern Mediterranean (Mulligan et al., 2014; Antonioli et al., 2017). 
The number of people living in areas at risk in Europe is projected to 
increase with future SSPs increasing exposure (Merkens et al., 2016; 
Byers et al., 2018; Harrison et al., 2019).

13.8.1.3 Loss and Damage to Vulnerable Livelihoods in Europe
A number of livelihoods maintaining unique cultures in Europe are 
particularly vulnerable to climate change (Table  13.2): indigenous 
communities in the European polar region because of their dependence

1868Box 13.2 | Sámi Reindeer Herding in Sweden
Reindeer (Rangifer tarandus) are keystone species in northern landscapes (Vors and Boyce, 2009). Reindeer herding is a traditional, semi-
nomadic livelihood of the Sámi. Reindeer migrate between seasonal pastures that cover 55% of Sweden and are simultaneously used for 
multiple other purposes (Sandström et al., 2016). Reindeer herding is recognised as an indigenous right, protected by the UN Declaration 
on the Rights of Indigenous Peoples, several UN conventions and through Swedish national legislation.
Temperatures in Arctic and sub-Arctic regions have increased on average by 2°C over the past 30 years (very high confidence) (Ranasinghe 
et al., 2021). Future warming is expected to further increase winter precipitation (high confidence) (Ranasinghe et al., 2021) and rain-on-
snow events, creating a hard ice crust on the snow after refreezing (Bokhorst et al., 2016; Rasmus et al., 2018).

The documented and projected impacts on reindeer are complex and varied. Warming and CO 2 increase result in higher plant productivity 
(Section  13.3), changes in plant community composition and higher parasite harassment; unstable ice conditions affect migration; 
extreme weather conditions during critical winter months, more frequent forest fires and changes in plant community composition 
reduce pasture quality (medium confidence) (see Figure Box 13.2.1; Mallory and Boyce, 2018). High snow depth and rain-on-snow events 
impede reindeer access to ground lichen in winter and delay spring green-up during the critical calving period; both cause malnutrition 
and negative impacts on reindeer health, mortality and reproductive success (medium confidence) (Hansen et al., 2014; Forbes et al., 
2016; Mallory and Boyce, 2018). Lower slaughter weights and increased mortality reduce the income of herders (high confidence) (Tyler 
et al., 2007; Helle and Kojola, 2008).

Reindeer herders already autonomously adapt to changing conditions through flexible use of pastures and supplementary feeding (high 
confidence), reducing and thereby hiding some of the negative impacts of climate change (Uboni et al., 2016). However, adaptive herding 
practices have themselves added significant burden through increased workload, costs and stress (high confidence) (Furberg et al., 2011; 
Löf, 2013; Rosqvist et al., 2021). Supplementary feeding increases the risk of infectious diseases and implies culturally undesirable herding 
practices (low confidence) (Lawrence and Kløcker Larsen, 2019; Tryland et al., 2019).
Rapid land-use change reduces the ability to adapt (high confidence) (Tyler, 2010; Löf, 2013). National and EU policies expand land uses 
for mining, wind energy and bioeconomy in the area, causing loss, fragmentation and degradation of pastures, and increasing human

disturbance to animals (medium confidence) (Kivinen et al., 2012; Skarin and Åhman, 2014; Kivinen, 2015; Skarin et al., 2015; Sandström 
et al., 2016; Beland Lindahl et al., 2017; Österlin and Raitio, 2020). The cumulative impacts of these land uses on pastures are not 
adequately assessed or recognised in land-use planning (Kløcker Larsen et al., 2017; Kløcker Larsen et al., 2018). Herding communities 
face strong barriers to protecting their rights and halting further degradation of pastures (medium confidence) (Allard, 2018; Kløcker 
Larsen and Raitio, 2019; Raitio et al., 2020). Attempts by herding communities to stop mining projects have led to conflicts with other 
actors, including racist hate incidences (Persson et al., 2017; Beland Lindahl et al., 2018). Combined with land-use conflicts, climate 
impacts cause reduced psycho-social health and increase suicidal thoughts among herders (low confidence) (Kaiser et al., 2010; Furberg 

Reindeer herding is significantly affected by climate change directly and indirectly (Figure Box 13.2.1) (Pape and Löffler, 2012; Andersson 
et al., 2015). The cumulative effects of land-use and climate change have already increased vulnerability and reduced the adaptive 
capacity of reindeer herding to the extent that its long-term sustainability is threatened (medium confidence) (Löf, 2013; Horstkotte et al., 
2014; Kløcker Larsen et al., 2017).
Maintaining and improving the solution space to adapt reindeer herding is crucial for reducing existing impacts and projected risks 
of climate and land-use change (Andersson et al., 2015; Turunen et al., 2016; AMAP , 2017; Hausner et al., 2020). Lack of control over 
land use is the biggest and most urgent threat to the adaptive capacity of reindeer herding and the right of Sámi to their culture (high 
confidence) (Pape and Löffler, 2012; Andersson et al., 2015; Kløcker Larsen and Raitio, 2019).

13
1869Europe  Chapter 13
Climate change-related impacts affecting nomadic reindeer herding
This Indigenous way of  life is still in place in 
Northern Europe. It is dependent on :
• Access to pastures (lack of barriers )
• Quality of pastures (vegetation)
• Connectivity of pasture areas (lack of fragmentation)
• Grazing peace (lack of disturbance)
Political
boundaries
200 km(a)  Boundaries of the reindeer herding areas in Sweden
Obs.Proj.
Increased snow amounts
Frequent freeze-thaw cycles
Unstable ice condition s
Late snow melting during spring
Heat waves during summe ri.
ii.
iii.
iv.
v.Mining
Forestry
Wind power
Infrastructure developmen t
Tourismxii.
xiii.
xiv.
xv.
xvi.
xvii.Spread of new diseases
Psychological stres s
Work load & cost s
Conflicts
Self-determination & adaptive capacity
Insect harassmentvi.
vii.
viii.
ix.
x.
xi.Obs.Proj. Obs. Proj.(b) Changing weather condition s( d) Combined effects from land pressure s (c) Effects on people and animal s
na
na na

vi. vii. viii.
ix.
xii.
xiii.xiv.
xv.xvi.
xi.i. iv. v.
iii. ii.x.xvii.na
Type of impact:
Both increase and decrease
NegativeDecrease
= not assessedna
High MediumL ow High Medium LowConfidence level: Observed ImpactsBoth negative and positive PositiveIncrease (b)
(c), (d)
Figure  Box  13.2.1 |   Cumulative impacts of climate and land-use change on reindeer herding as a traditional, semi-nomadic Sámi livelihood 
(Table SM13.21)Box 13.2 (continued)

1870on cryosphere ecosystems (high confidence) (Cross-Chapter Paper 6; 
Hayashi, 2017; Huntington et  al., 2017; Hock et  al., 2019; Meredith 
et  al., 2019; Inuit Circumpolar Council, 2020; Douville et  al., 2021; 
Fox-Kemper et al., 2021) and communities dependent on small-scale 
fisheries, traditional farming and unique cultural landscapes (medium 
confidence) (Kovats et al., 2014; Ruiz-Díaz et al., 2020).
For Sámi reindeer, herding impacts cascade due to a lack of access to key 
ecosystems, lakes and rivers, thereby threatening traditional livelihoods, 
food security, cultural heritage (e.g., burial grounds, seasonal dwellings 
and routes), mental health (see Box 13.2; Figure 13.13; Feodoroff, 2021) 
and growing costs, for example, as a result of the need for artificial 
feeding of reindeer.
13.8.1.4 Cultural and Natural Heritage
Climate change poses a serious threat to the preservation of cultural

heritage in Europe, both tangible and intangible (high confidence) 
(Haugen and Mattsson, 2011; Daire et  al., 2012; Dupont and Van 
Eetvelde, 2013; Macalister, 2015; Phillips, 2015; Fatorić and Seekamp, 
2017; Graham et al., 2017; Carroll and Aarrevaara, 2018; Sesana et al., 
2018; Iosub et  al., 2019; Daly et  al., 2020). At higher GWL, building 
exteriors and valuable indoor collections become at risk (Leissner et al., 
2015). Coastal heritage, such as along the North Sea and Mediterranean, 
are under water-related threats (see Box 13.1; Cross-Chapter Paper 4; 
Reimann et al., 2018b; Walsh, 2018; Harkin et al., 2020).
Disappearing cultural heritage can reduce incomes due to loss of 
tourism (Hall et al., 2016), as exemplified by glacier retreat, for example, 
in the Swiss Alps and Greenland (CCP5.3.2.4; Bjorst and Ren, 2015; 
Bosson et al., 2019). Glacier retreat can create a sense of discomfort, 
loss of sense of place, displacement and anxiety in people (Section 13.7;

Albrecht et al., 2007; Brugger et al., 2013; Allison, 2015; Jurt et al., 2015). 
Intangible cultural heritage, such as place names, and lost traditional 
practices can also be affected (Mustonen, 2018; Dastgerdi et al., 2019).
13.8.2 Solution Space and Adaptation Options
As climate change is interacting with many other drivers of poverty, 
improving the social position of the currently poor may increase their 
climate resilience (low confidence) (Hallegatte and Rozenberg, 2017; 
Fronzek et al., 2019). Some adaptation actions have the potential to 
alleviate poverty (Section 13.11.3), but adaptation can also increase 
social inequalities, for instance, when practices of disaster recovery 
focus on high-visibility areas and not on low-income neighbourhoods 
or marginalised communities (D’Alisa and Kallis, 2016). Risk 
communication and management reliant on new information 
technologies can exclude the elderly and populations with lower 
educational attainment (Kešetović et al., 2017).

Unlike migration within the EU, migration from outside Europe to Europe 
is heavily constrained by restrictive migration and asylum policies 
(Fielding, 2011; Mulligan et al., 2014), eventually leaving people to stay in 
more exposed and risk-prone regions (Benveniste et al., 2020). To reduce 
vulnerability in these regions, Europe can contribute to adaptation and 
development in regions outside Europe (Section 13.9.4).IKLK, embedded, for example, in fishers, farmers and navigators, 
can be a vehicle for detecting, monitoring and observing impacts 
(Section  13.11.1.3; Arctic Council, 2013; Brattland and Mustonen, 
2018; Madine et al., 2018; Meredith et al., 2019). Regarding risks to 
northern traditional livelihoods and indigenous communities, small-
scale adaptation is taking place, for example, by ecological restoration 
of habitats (Section 13.3; Mustonen and Kontkanen, 2019); however, 
limited access to resources outside the jurisdictions of the communities

limits the scope of community-based adaptation (Arctic Council, 2013; 
Mustonen et al., 2018; Meredith et al., 2019).
European cultural heritage in general and world heritage sites specifically 
lack adaptation strategies to preserve key cultural assets (Haugen 
and Mattsson, 2011; Howard, 2013; Heathcote et  al., 2017; Reimann 
et al., 2018b; Harkin et al., 2020). Key reasons are the underdeveloped 
adaptation actions available, resources for implementing them and the 
absence of overarching policy guidance (Phillips, 2015; Fernandes et al., 
2017; Sesana et al., 2018; Daly et al., 2020; Fatorić and Biesbroek, 2020; 
Sesana et al., 2020).
13.8.3 Knowledge Gaps
There is limited understanding of how different social groups are 
affected by the four European key risks under future climate change 
(Section 13.11.2), and by adaptation to them. Similarly, the interaction 
of multiple risks across sectors and how this interaction results in

displacement, migration or immobility of people both within and 
from outside Europe is insufficiently understood. For indigenous 
and traditional livelihoods in Europe, the understanding of how 
risks will change at different warming levels is very limited, due to 
complex interactions with socioeconomic and political change. For 
European cultural heritage, there is also a lack of tailored knowledge 
and understanding of the impacts and how to translate them into 
adaptation measures.
13.9 Inter-regional Impacts, Risks 
and Adaptation
This section addresses inter-regional risks between Europe and other 
parts of the world. Global risk pathways affecting sectors and supply 
chains relevant for European economies and societies involve (a) 
ecosystems, (b) people (e.g., through migration), (c) financial flows 
and (d) trade; and these pathways ultimately impact security, health, 
well-being and food supply (Cross-Chapter Box INTEREG in Chapter 
16; Yokohata et al., 2019).

13.9.1 Consequences of Climate-Change-Driven Impacts, 
Risks and Adaptation Emerging in Other Parts of 
the World for Europe
Recent literature (Wenz and Levermann, 2016; Hedlund et al., 2018; 
Benzie et al., 2019) strengthens the confidence in the AR5 statement 
that ‘with increasing globalisation, the impacts of climate change 
outside the European region are likely to have implications for countries

13
1871Europe  Chapter 13
within the region’ (Kovats et  al., 2014). The exposure of European 
countries to trans-European climate impact and risk pathways varies 
depending on their territorial settings, national policies and position in 
the global supply chain (high confidence) (Berry et al., 2015; Hedlund 
et al., 2018; Benzie et al., 2019). There is limited evidence that Europe 
is more exposed to inter-regional risks than North America, and less 
than Africa and Asia (Hedlund et al., 2018). The social and governance 
context in Europe make the region less vulnerable to conflicts driven 
by climate change than other regions, at least up to 2°C GWL (Buhaug 
et al., 2014; Mach et al., 2019; Ide et al., 2020).
Climate risks in other parts of the world can be transmitted to 
European economies via trade networks (Figure  13.25). European 
agricultural imports exert a high water footprint in originating 
countries already today (Dolganova et al., 2019; Ercin et al., 2019),

and some crop imports, such as tropical fruits, are highly vulnerable to 
future climate change (Brás et al., 2019). Simultaneous breadbasket 
failures, and trade restrictions, increase risks to food supply (medium 
confidence) (Fellmann et al., 2014; d’Amour et al., 2016; Gaupp et al., 
2017; Gaupp et al., 2020). There is high confidence that the European 
economy could be negatively affected by supply chain disruptions 
due to flooding destroying facilities, heatwaves and malaria 
reducing productivity in labour-intensive industries and regions (Section 13.7.1), and SLR affecting ports and cities along coastlines 
(Section 13.6.1.2; Nicholls and Kebede, 2012; Challinor, 2016; Wenz 
and Levermann, 2016; Hedlund et al., 2018; Koks, 2018; Szewczyk 
et al., 2018; Willner et al., 2018; Knittel et al., 2020; Kulmer et al., 
2020; Carter et al., 2021).
13.9.2 Inter-regional Consequences of Climate Risks and 
Adaptation Emerging from Europe

New literature since AR5 suggests that climate risks in Europe can 
propagate worldwide in response to 3°C GWL (medium confidence). 
Key concerns include climate impacts on European agriculture 
threatening global food security (Section  13.5.1; Berry et  al., 2017; 
van der Velde et  al., 2018) and the European demand limiting the 
adaptation potential for ecosystems in South America, Africa and 
Asia (IPBES, 2018; Pendrill et al., 2019; Fuchs et al., 2020). Emerging 
literature suggests that coastal and riverine flood risks in Europe 
could be amplified through the global financial system and generate 
a systemic financial crisis (Figure 13.26; Mandel et al., 2021). For 3°C 
GWL and without adaptation, northern Atlantic flight routes and 
European ports are projected to be increasingly disrupted by changing

winds, waves and SLR (Section  13.6.1.2; Williams and Joshi, 2013; Virtual water flows (of blue and green water) embodied in imports of agricultural products to the European Union
Canada 4%
USA  8%
Colombia 2%
Brazil 20%
Paraguay 3%
Argentina 10%Nigeria ≤1%
Cameroon 1%Tunisia ≤ 1%
India 1%
Malaysia 3%Vietnam 1%
Indonesia 6%
Côte D’ivoire 1%Ghana ≤ 1%EU28Turkey 1% Ukraine 14%
Percent (%) of import share for main 
goods to Europe per source country
Rest of the world adds 25% Cocoa Coffee Maize Palm Oil Rice Soybean Sunflower Almonds Sugarcane Cotton Grapes Olives
Maximum risk level
to each goodNot
availableVery
highHigh Moderate Low <102.5Virtual Water Flow
(103 m3)
103.0105.0104.5104.0103.5
Figure 13.25 |  Trans-European climate risks in trade: virtual water flows embodied in agricultural imports to Europe in 2018 and the vulnerability to climate 
change of the most important crops in the originating countries (Dolganova et al., 2019; Ercin et al., 2019)

1872Irvine et al., 2016; Williams, 2016; Becker et al., 2018; Camus et al., 
2019; Verschuur et al., 2020).
13.9.3 European Territories Outside Europe
European territories outside Europe are critically exposed to climate 
risks such as increased forest fires (e.g., in Russian Siberia) (Chapter 
10; Sitnov et  al., 2017), climate-change-induced biodiversity losses 
and SLR (e.g., in British, Spanish, Portuguese, French and Dutch 
overseas regions and territories) (Chapters 12, 15; Ferdinand, 2018; 
Sieber et  al., 2018). Climate risks emerging from these territories 
include smoke and dust from Siberian forest fires (Sitnov et al., 2017) 
and, depending on European health-risk mitigation measures, dengue 
and other mosquito-transmitted diseases (Section 13.7; Schaffner and 
Mathis, 2014). Some MPAs (Section  13.4.3) in European overseas 
territories are increasingly affected by changes originating in far-field

upstream areas. These changes ultimately undermine their ability to 
curb biodiversity losses and provide ecosystem services (Schaffner 
and Mathis, 2014; Robinson et  al., 2017). Adaptation options and 
regulations developed within Europe apply in these territories, 
despite low confidence that they meet local and regional adaptation challenges and address the aspiration for social justice, promotion of 
local solutions and consideration of traditional knowledge (Ferdinand, 
2018; Terorotua et al., 2020).
13.9.4 Solution Space and Adaptation Options
European countries can address inter-regional risks at the place of origin 
or destination, for example, by (a) developing local adaptation capacity 
in trading-partner countries and in European territories outside Europe 
(Petit and Prudent, 2008; Benzie et al., 2019; Adams et al., 2020; Terorotua 
et al., 2020), (b) providing international adaptation finance (Dzebo and 
Stripple, 2015; BMUB, 2017), (c) developing insurance mechanisms

suitable for adaptation or (d) providing European climate services to 
support global adaptation (Cross-Chapter Box  INTEREG in Chapter 
16; Linnerooth-Bayer and Mechler, 2015; Brasseur and Gallardo, 2016; 
Street, 2016; Cavelier et al., 2017). Along the supply chain, risks can be 
reduced by trade diversification and alternative sourcing (Benzie and 
Persson, 2019; Adams et al., 2020). Within Europe, risks can be reduced by 
integrating inter-regional climate risks into national adaptation strategies 
and plans, and mainstreaming them into EU policies (e.g., Common 
Agricultural Policy, trade agreements) (Benzie and Persson, 2019; Benzie 
Central AsiaNorthern AfricaSub-Saharan AfricaSouthern AsiaAustralia and New ZealandSouth-Eastern AsiaLatin America and the CaribbeanWestern AsiaEastern AsiaNorthern AmericaTransmission of flood risks via finance 
flows from Europe to the rest of the world
Arcs shows how European regions are 
connected via the global financial system to

other regions of the world in 2019.
The circles below illustrate how 
these financial linkages distribute 
the regional damage costs of a 
20-year return period coastal or 
riverine flood event in 2080 
(RCP8.5-SSP5, with current 
adaptation) from Europe to 
the rest of the world. 
For Europe in total, global 
costs exceed regional 
costs by a factor of 2.5 
(with high adaptation) to 5 
(with current adaptation).
Eastern
EuropeWestern and
Central EuropeNorthern
Europe
Global
damage costsUnited
Kingdom Italy Portugal Germany France All Europe
Million USDAverage
amplification
ratio
110
5With
high
Regional
damage costsWith 
current
1
1,000
2,000
3,000
4,000
5,000
Figure 13.26 |  The transmission of coastal and riverine flood risks via finance flows from Europe to the rest of the world. (From Mandel et al., 2021).

13
1873Europe  Chapter 13
et al., 2019; Groundstroem and Juhola, 2019; Adams et al., 2020). There is 
high confidence that the exposure of European countries to inter-regional 
risks can be reduced by international governance (Cross-Chapter Paper 4; 
Dzebo and Stripple, 2015; Cramer et al., 2018; Persson and Dzebo, 2019), 
for example, fulfilling the targets of environmental agreements such as 
the Convention for Biological Diversity (IPBES, 2018). There is emerging 
evidence that supporting adaptation outside Europe may generate 
economic co-benefits for Europe (Román et al., 2018).13.10 Detection and Attribution, Key Risks and 
Adaptation Pathways
13.10.1 Detection and Attribution of Impacts
Since AR5, scientific documentation of observed changes attributed 
to global warming have proliferated (high confidence). These include 
ecosystem changes detected in previous assessments, such as earlier 
annual greening and onset of faunal reproduction processes, relocation

of species towards higher latitudes and altitudes (high confidence), and 
impacts of heat on human health and productivity (high confidence) 
(Figure  13.27; Table  SM13.22; Vicedo-Cabrera et  al., 2021). Formal 
attribution of impacts of compound events to anthropogenic climate 
change is just emerging, for example, in the recent crop failures due to 
heat and drought (Toreti et al., 2019a). Also, there is high agreement 
and medium evidence that particular events attributed to climate 
Direction
of change= no evidence/ Increase Decrease Mixed Confidence
levelHigh Medium Low High Medium Low• Southern Europe (SEU)
• Northern Europe (NEU)Impact category (number of papers)Detection and attribution of climate-related impacts in Europe 
during the period 1970–2020
Land
OceanSub-
tropicalTem-
perate ArcticMountain
regions NEU SEU WCE EEU
//
// / /
// //
//
/ / /
/ /
/ /
// ///
/
///

//Crops, change in yield due to temperature related regional changes (19)
Forest, change in net primary production and forest area expansion (22)
Tundra vegetation, change in growth and expansion of shrubs (3)
Terrestrial species, relocation towards higher latitude and altitude (10)
Aquatic species, relocation to higher latitude and tropicalization (8)
Vector borne diseases, northward expansion (6)
Winter tourism, change due to reduced snow cover and natural snow reliability (5)
Energy demand, change in cooling demand due to higher temperature (6)
Energy demand, change in heating demand due to higher temperature (3)
Infrastructure, change in damages from thaw of permafrost, rockslides, flood or heatwave related extreme temperatures (7)
Heatwaves, change in mortality from overheating (4)
Drought and water scarcity, change in damages (12)
Wildfire, change in burned area (10)
River floods, change in damages (8)
Coastal floods, change in damages (7)

Economic output, change in GDP due to temperature increase (3)
Heatwaves, change in frequency and duration (10)
Marine species, relocation towards higher latitudes (8)
Fisheries, change in frequency of small specimens in catch (6)
Figure 13.27 |  Detected changes and attribution (D&A) of climate-related impacts on land (top) and in the ocean (bottom) are shown. Assessment is based on 
peer-reviewed literature in this chapter that reported observed evidence with at least 90% significance (usually with 95% significance or more) (Table SM13.22).

1874
0234
1.5°C
1.09Key risks for Europe under low to medium adaptation 
relative to (1850–1900)
Human heat
stress, 
mortality
and morbidity Terrestrial
distruptionsMarine
distruptionsLosses
in crop
productionWater
scarcity in
Southern
Europe
(SEU)River and
pluvial
floodingCoastal
floodingWater
Western
Europe (WCE)The ember colour gradient indicates the 
level of additional risk to society and 
ecosystems as a function of global 
temperature change. Confidence is 
provided for the change of risk level at 
given temperature ranges .Very high
High
Moderate
UndetectableLevel of risk Confidence••• ••• ••
•• •• ••••• ••••
•• •• •
••• ••• ••
••• •• ••
•• •• •••• •• •
•• ••
Delayed risks
for cultural
heritage and 
long-living
infrastructureLow High•
•••••
Key Risk 2 Key Risk 1 Key Risk 3 Key Risk 4

Figure 13.28 |  Burning ember diagrams for low to medium adaptation. (More details on each burning ember are provided in Sections 13.10.2.1–13.10.2.4 and 
SM13.10. Some burning embers are shown again in Figures 13.29–13.34 alongside burning embers with high adaptation.)
Health
action plans
to enable
implementation
High Medium Low(b) Pathway to achieve high adaptation to heat stresss, mortality and morbidity in Northern Europe
i. Heat proof land development takes time to 
become effective (•••) and is bundled with 
other measures to achieve high adaptation 
as warming increases (•••).
ii. Air conditioning alone is not enough and is 
combined with behaviour changes and/or 
building interventions (••).
iii. For high warming heat proof land 
development might still be needed (••).
iv. Building interventions have low to medium 
effectiveness and need to be combined with  
other measures at higher warming (••).
i.  Measures are to be implemented and

combined earlier in Southern Europe due to  
higher risk (••).
ii. There is less that can be achieved with 
behaviour change because there is already  
extensive culture of heat in Southern Europe 
(••).
iii. Building interventions are crucial to be 
combined with other measures earlier since  
they have low to medium effectiveness (••).
iv. Heat proof land development is needed for 
high warming levels (•••).(c) Pathway to achieve high adaptation to heat stresss, mortality and morbidity in Southern EuropeHealth
to enable
implementationHeat proof
land
Air conditionin g
Building
Behaviour
change
High Medium Lowi. iii.
ii.
iv.
Heat proof
land
Building
Behaviour
changeiv.
ii.iii.i.
NowNow(a) Heat stresss, mortality 
and morbidityGlobal mean temperature change
Present or
medium
adaptationHigh
adaptation••• ••• ••
••• ••
The ember colour gradient 
indicates the level of additional

risk to society and ecosystems 
as a function of global 
temperature change. 
Confidence is provided for the 
change of risk level at given 
temperature ranges.Very high
High
Moderate
UndetectableLevel of risk ConfidenceBurning embers and illustrative adaptation pathways for risks to human health from heat, in Europe (Key Risk 1)
Global warming levelLead tim e
Low High•
•••••
Figure 13.29 |  Burning embers and illustrative adaptation pathways for risks to human health from heat (Key Risk 1)
(a) Burning ember diagrams for the risk to human health from heat are shown. The low to medium adaptation scenario corresponds to present, SSP2 and SSP4 socioeconomic 
conditions. The high adaptation includes SSP1 and adaptation needed to maintain current risk levels. 
(b,c) Illustrative adaptation pathways for NEU (top) and SEU (bottom), and key messages based on the feasibility and effectiveness assessment in Figures 13.20 and 13.24. Grey

shading means long lead time and dotted lines signal reduced effectiveness. The circles imply transfer to another measure and the bars imply that the measure has reached a tipping 
point (Tables SM13.24, SM13.25).

13
1875Europe  Chapter 13
change have induced cascading impacts and other impact interactions 
(Smale et al., 2019; Vogel et al., 2019). In recent decades (2000–2015), 
economic losses intensified in SEU (high confidence) and were detected 
for parts of WCE and NEU (medium confidence). (The methodology for 
detection and attribution is presented in Section 16.2.)
13.10.2 Key Risks Assessment for Europe
Key risks (KRs) are defined as a subset of climate risks that can 
potentially become, or are already, severe (Section  16.5). The 
selection process included a review of KRs already identified in 
AR5 Chapter 23 (Kovats et al., 2014) and a review of the large body 
of new evidence on projected risks presented in Sections  13.2–
13.9. Key risks are reinforced by evidence from the detection and 
attribution assessment (Section  13.10.1) and new evidence from 
WGI AR6 Chapters 11 and 12 on regional climatic impact drivers and

extremes (Ranasinghe et al., 2021; Seneviratne et al., 2021). Several 
expert opinion workshops of lead and contributing authors led to 
further refinements, adjustment and consensus building around the 
characteristics of KRs, which ultimately guided the construction of 
the burning embers (Figures 13.28–13.32; SM13.10). There is high 
confidence that under low or medium adaptation, high to very high 
risks are projected at 3°GWL (Figure  13.28; Sections  13.10.2.1–
13.10.2.4). Most risks are assessed as moderate up to 1.5°GWL 
(Figure 13.28).
This section also includes an assessment of the solution space using 
illustrative adaptation pathways which show alternative sequences of 
options to reduce risks as climate changes (SM13.10). Low-effectiveness 
measures are followed by measures of higher effectiveness, while 
accounting for path dependency of decisions (Toreti et  al., 2019b; 
Haasnoot et  al., 2020a). The process to derive the pathways draws

on evidence from the feasibility and effectiveness assessments 
(Sections 13.2, 13.5–13.7).
13.10.2.1 KR1: Risks of Human Mortality and Heat Stress, and 
of Ecosystem Disruptions Due to Heat Extremes and 
Increases in Average Temperatures
Key risk 1 has cut across humans and ecosystems, and severe 
consequences are mainly driven by an increasing frequency, intensity 
and duration of heat extremes and increasing average temperatures 
(high confidence) (Urban, 2015; Forzieri et al., 2017; Feyen et al., 2020; 
Naumann et al., 2020; Ranasinghe et al., 2021). The risk of human heat 
stress and mortality is largely influenced by underlying socioeconomic 
pathways, with consequences being more severe under SSP3, SSP4 
and SSP5 scenarios than SSP1 (very high confidence) (Figure  13.22; 
Sections  13.6.1.5.2, 13.7.1.1; Hunt et  al., 2017; Kendrovski et  al., 
2017; Rohat et  al., 2019; Casanueva et  al., 2020). The SSPs impact

natural systems as well but are not yet well studied. The impact of 
warming in marine systems are often synergistic with SLR in coastal 
systems and ocean acidification driven by the rise in CO 2, while habitat 
fragmentation and land use have important synergies in terrestrial 
systems (high confidence) (Sections 13.3.1.2, 13.4.1.2). More intense 
heatwaves on land and in the ocean, particularly in Mediterranean 
Europe (Section 13.4; Cross-Chapter Paper 4; Darmaraki et al., 2019b; Fox-Kemper et al., 2021), are expected to cause mass mortalities of 
vulnerable species, and species extinction, altering the provision of 
important ecosystem goods and services (Marbà and Duarte, 2010).
The burning embers on risks for humans (Figure 13.29a) differentiate 
between present and medium adaptation conditions, drawing on SSP2 
and SSP4 (and to a lesser extent SSP3), and high adaptation conditions, 
drawing on SSP1 and papers using various temperature adjustment

methods (Table  SM13.25). There is high confidence that the risk is 
already moderate now because it has been detected and attributed 
with high confidence (Section 13.10.1). The transition from moderate 
to high risk for human health is assessed to happen after 1.5°C GWL 
in a scenario with present to medium adaptation and implies a two- to 
threefold increase (compared with moderate risk levels) in magnitude 
of consequences such as mortality, morbidity, heat stress and thermal 
discomfort (Rohat et  al., 2019; Casanueva et  al., 2020; Naumann 
et al., 2020). At this level, the risk will also become more persistent 
across the continent due to increase in heat events exceeding critical 
thresholds for health (high confidence on the direction of change and 
temperature transition, but medium confidence on the magnitude) 
(Ranasinghe et al., 2021).
The burning embers on risk for terrestrial and marine ecosystems, 
and some of their services, are shown in Figure 13.28 (second and

third ember from the left) (Tables SM13.26, SM13.27). The transition 
to moderate risk is currently happening as warming already results in 
changes in timing of development, species migration northward and 
upwards, and desynchronisation of species interactions, especially 
at the range limits, with cascading and cumulative impacts through 
ecosystems and food webs (high confidence) (Sections  13.3, 13.4; 
Figures 13.8, 13.12). While some terrestrial ecosystems are already 
impacted today, such as Alpine, cryosphere and peatlands, the 
impacts are not widespread and severe yet across a wide range of 
terrestrial systems. Around 2°C GWL, losses accelerate in marine 
ecosystem and appear across systems, including habitat losses 
especially in coastal wetlands (Roebeling et al., 2013; Clark et al., 
2020), biodiversity and biomass losses (Bryndum-Buchholz et  al., 
2019; Lotze et al., 2019) and ecosystem services such as fishing (high

confidence on the direction of change, but medium confidence on the 
local and regional magnitude) (Raybaud et al., 2017). The transition 
is happening at slightly higher warming in terrestrial systems due 
to a higher number of thermal refugia in terrestrial systems causing 
relocation but not already severe impacts (medium confidence) 
(Chapter 2).
There is medium confidence that high adaptation or conditions posing 
low challenges for adaptation (e.g., SSP1) in the context of human 
health can delay the transition from moderate to high risk (Åström 
et al., 2017; Ebi et al., 2021). The illustrative adaptation pathways in 
Figure 13.29b,c show the sequencing of options to a high adaptation 
future for NEU and SEU. Whether or not adaptation measures are 
effective to reduce risk severity for people’s health depends on local 
context (high confidence) (Figure  13.29; Sections  13.6.2, 13.7.2). 
Some adaptation options are found to be highly effective across

Europe irrespective of warming levels, including air conditioning 
and urban planning (high confidence) (Sections  13.6.2, 13.7.2; 
Jenkins et  al., 2014b; Donner et  al., 2015; Dodoo and Gustavsson,

18762016; Åström et al., 2017; Dino and Meral Akgül, 2019; Venter et al., 
2020), although air conditioning increasingly faces some feasibility 
constraints (Figure  13.20). Building interventions alone have low to 
medium effectiveness independent of the region. Many behavioural 
changes, such as personal and home heat protection, have already 
been implemented in SEU (Section  13.7.2; Martinez et  al., 2019). 
To reach high adaptation, a combination of low, medium and high 
effectiveness measures in different sectors and sub-regions is needed, 
many of which entail systems’ transformations (e.g., heat-proof land 
management) (Chapter 16) and remain effective at higher warming 
levels (medium confidence) (Díaz et al., 2019). These transformations 
have long lead times, thereby requiring timely start of implementation 
including regions that are not yet experiencing high heat stress (e.g., 
NEU) (high agreement, medium evidence).

Autonomous adaptation of species via migration in response to climate 
change is well documented in contemporary, historical and geological 
records (Chapter 2; Cross-Chapter Box PALEO in Chapter 1); however, 
the projected rate of climate change can exceed migration potential, 
leading to evolutionary adaptation or increased extinction risk (Chapters 
2, 3; Sections 13.3, 13.4). A reduction of non-climatic stressors, such as 
nutrient loads, resource extraction, habitat fragmentation or pesticides 
on land, are considered important adaptation options to increase the 
resilience to climate-change impacts (high confidence) (Sections 13.3, 
13.4; Ramírez et al., 2018). A major governance tool to reduce climatic 
and non-climatic impacts is the establishment of networks of protected 
areas (Sections 13.3.2, 13.4.2) especially when aggregated, zoned or 
linked with corridors for migration (high confidence), as well as a cost-

effective adaptation strategy with multiple additional co-benefits (Berry 
et al., 2015; Roberts et al., 2017). Reforestation, rewilding and habitat 
restoration are long-term strategies for reducing risk for biodiversity 
loss supported by assisted migration and evolution (Section  13.3.2, 
13.4), though current laws and regulations do not include species 
migration (high confidence) (Prober et al., 2019; Fernandez-Anez et al., 
2021).
Very high risks are expected beyond 3°C GWL due to the magnitude 
and increased likelihood of serious consequences, as well as to the 
limited ability of humans and ecosystems to cope with these impacts. 
There is high confidence that even under high adaptation scenarios 
for human systems or autonomous adaptation of natural systems, the 
risk will still be high at 3°C GWL and beyond (Section 13.7.2; Hanna 
and Tait, 2015; Spencer et al., 2016) with medium confidence on the 
temperature range of the transition. Projected SLR will strongly impact

coastal ecosystems (high confidence), minimising their contribution to 
shoreline protection (Section 13.10.2.4).
13.10.2.2 KR2: Risk of Losses in Crop Production, Due to 
Compound Heat and Dry Conditions, and Extreme 
Weather
Key risk 2 encompasses agriculture productivity (Figure 13.30a). It is 
mainly driven by the increase in the likelihood of compound heat and 
dry conditions and extreme weather, and their impact on crops. There 
is high confidence that climate change will increase the likelihood 
of concurrent extremely dry (Table SM13.28) and hot warm seasons 
with higher risks for WCE, EEU (particularly northwest Russia) and SEU leading to enhanced risk of crop failure and decrease in 
pasture quality (Section 13.5.1; Zscheischler and Seneviratne, 2017; 
Sedlmeier et al., 2018; Seneviratne et al., 2021). The risk is already 
moderately severe due to multiple crop failures in the past decade 
in WCE and Russia (Section 13.5.1; Hao et al., 2018; Pfleiderer et al.,

2019; Vogel et al., 2019). Under high-end scenarios, heat and drought 
extremes are projected to become more frequent and widespread as 
early as mid-century (Toreti et al., 2019a). For present to moderate 
adaptation and at least up to 2.5°GWL, negative consequences are 
mostly in SEU (Bird et al., 2016; EEA, 2019c; Moretti et al., 2019; Feyen 
et al., 2020). The transition from moderate to high risk is projected 
to happen around 2.7°C GWL when hazards and risk will become 
more persistent and widespread in other regions (Section  13.1; 
Deryng et al., 2014; Donatelli et al., 2015; Webber et al., 2018; Ceglar 
et al., 2019; Ranasinghe et al., 2021; Seneviratne et al., 2021). This 
temperature increase will trigger shifts in agricultural zones, onset 
of early heat stress, losses in maize yield of up to 28% across EU-
28 and regional disparity in losses and gains in wheat, which are 
not able to offset losses across the continent (Deryng et al., 2014;

Szewczyk et al., 2018; Ceglar et al., 2019). There will be also broader 
adverse impacts such as reduction of grassland biomass production 
for fodder, increases in weeds and reduction in pollination (medium 
confidence) (Castellanos-Frias et al., 2016; Nielsen et al., 2017; Brás 
et al., 2019). Combined with socioeconomic development, increased 
heat and drought stress, and reduced irrigation water availability, in 
SEU are projected to lead to abandonment of farmland (Holman et 
al., 2017). Around 4°C GWL, the risk is very high due to persistent 
heat and dry conditions (Ben-Ari et al., 2018) and the emergence of 
losses also in NEU which would be much higher without the assumed 
CO 2 fertilisation (Deryng et al., 2014; Szewczyk et al., 2018; Harrison 
Farmers have historically adapted to environmental changes, and 
such autonomous adaptation will continue. Higher CO 2 levels have 
a fertilisation effect on plants that is considered to decrease crop

production risks (Deryng et al., 2014). Adaptation solutions to heat and 
drought risks include changes in sowing and harvest dates, increased 
irrigation, changes in crop varieties, the use of cover crops and mixed 
agricultural practices (Section  13.5.2; Figures  13.14, Figure  13.30b). 
Under high adaptation, the use of irrigation can substantially reduce 
risk by both reducing canopy temperature and drought impacts (high 
confidence) (Section 13.5.2; Webber et al., 2018). Some reductions of 
maize yields in SEU are still possible, but are balanced by gains in other 
crops and regions (Deryng et al., 2014; Donatelli et al., 2015; Webber 
et al., 2018; Feyen et al., 2020). At 3°C GWL and beyond, the adaptive 
capacity is reduced (Ruiz-Ramos et  al., 2018). Crop production is a 
major consumer of water in agriculture (Gerveni et al., 2020), yet a 
potentially scarcer supply of water in some regions must be distributed

across many needs (KR3, Section  13.10.2.3), limiting availability to 
agriculture which is currently the main user of water in many regions of 
Europe (high confidence) (Section 13.5.1). Where the ability to irrigate 
is limited by water availability, other adaptation options are insufficient 
to mitigate crop losses in some sub-regions, particularly at 3°C GWL 
and above, with an increase in risk from north to south and higher risk 
for late-season crops such as maize (high confidence). Under these 
conditions, land abandonment is projected (low confidence) (Holman 

13
1877Europe  Chapter 13
13.10.2.3 KR3: Risk of Water Scarcity to Multiple Interconnected 
Sectors
Risks related to water scarcity across multiple sectors can become 
severe in WCE and, to a much larger extent, in SEU based on projections 
of drought damage, population and sectors exposed, and they 
increase in water exploitation (Figure 13.31a; Table SM13.29). In EEU, 
uncertainty in hydrological drought projections and risk consequences 
is higher (Greve et al., 2018; Ranasinghe et al., 2021; Seneviratne et al., 
2021) and the available number of publications is lower, not allowing a 
conclusion on how risk levels change with GWL. Yet, there is emerging 
evidence that drought-related risks increase with warming beyond 
3°C GWL also in EEU (Seneviratne, 2021, for hydrological drought and 
4°C GWL; Kattsov and Porfiriev, 2020). Evidence from the detected 
changes and attribution assessment suggests that the risk is already

moderate in SEU (e.g., 48 million people exposed to moderate water 
scarcity between 1981 and 2010) (high confidence) (Section 13.10.1; 
Figure 13.31a).
Risk of water scarcity has a high potential to lead to cascading impacts 
well beyond the water sector. These materialize in a number of highly 
interconnected sectors from agriculture and livestock farming to 
energy (hydropower and cooling of thermal power plants) and industry (e.g., shipping) (Blauhut et  al., 2015; Stahl et  al., 2016; Bisselink 
et al., 2020; Cammalleri et al., 2020). Extensive water extraction will 
augment pressures on water reserves, impacting the ecological status 
of rivers and ecosystems dependent on them (Grizzetti et al., 2017). 
Socioeconomic conditions contributing to severe consequences are 
when more residents settle in drought-prone regions, or when the 
share of agriculture in GDP declines (high confidence). For Europe, risks 
of water scarcity will be higher under SSP5 and SSP3 than under SSP1

(medium confidence) (Byers et al., 2018; Arnell et al., 2019; Harrison 
et al., 2019). Transition to high risks is projected to occur below 
2°C GWL in SEU and be associated with more persistent droughts 
(Section  13.1.3), and at 2°C GWL to show a 54% increase of the 
population facing at least moderate levels of water shortage (Byers 
et  al., 2018). This transition will happen at higher warming in WCE 
since risks are projected to increase less rapidly (transition between 
2°C and 3°C GWL) (medium confidence) (Section 13.2.1.2; Byers et al., 
2018). At 3°C GWL and beyond, water scarcity will become much more 
widespread and severe in already water-scarce areas in SEU (high 
confidence) and will expand to currently non-water-scarce regions 
in WCE (medium confidence) (Section 13.2.1.2; Bisselink et al., 2018; 
Naumann et al., 2018; Harrison et al., 2019; Koutroulis et al., 2019; 
Cammalleri et al., 2020; Spinoni et al., 2020). Decrease in hydropower

potential in SEU and WCE are expected beyond 3°GWL (Figure 13.16).(b) Adaptation pathways agriculture risk
Mixed, diversifying
systems, agroecology
and forestry
Soil management,
change in crops, sowing
and harvest dates
High global warming Medium Lowv.
iii.ii.Now(a) Risks of losses in crop 
productionGlobal mean temperature change
Present/
medium
•••• •• •
i. Current management practices are insufficient to respond to extreme events (•••).
ii. Change in crops, sowing and harvest dates and soil management reduce the risk at low warming (•••).
iii. Irrigation is highly effective but it is limited by water availability (•••). Beyond medium warming, it needs to be combin ed with other 
measures to take into account water constraints (••).
iv. Breeding of cultivars for heat tolerance and drought resistance extends the effectiveness of management adaption options (• •).

v. Mixed, diversified systems, agro forestry and -ecology have long lead times due to farmer socio-economic and policy constrai nts 
(••).
vi. At high warming a mix of most measures will be needed (•••). Limitation of water availability and other adaptation options  would be 
insufficient to fully ameliorate losses in all regions (••).IrrigationTraining/Information
Breeding of cultivars
i.iv.vi.
ch
indicates the level of additional 
risk to society and ecosystems  
High
Moderate
UndetectableLevel of risk ConfidenceGlobal warming levelBurning embers and illustrative adaptation pathways for losses in crop production in Europe (Key Risk 2)
Lead time
Low High•
•••••
Figure 13.30 |  Burning embers and illustrative adaptation pathways for losses in crop production (Key Risk 2)

(a) Burning ember diagrams for losses in crop production with present or medium adaptation conditions, and with high adaptation, are shown.
(b) Illustrative adaptation pathways and key messages based on the feasibility and effectiveness assessment in Figure 13.14. Grey shading means long lead time and dotted lines 
signal reduced effectiveness. The circles imply transfer to another measure and the bars imply that the measure has reached a tipping point (Table SM13.28).

1878To reduce risk to water scarcity, adaptation measures, at both the 
supply and the demand side, have been suggested (Section  13.2.2; 
Figures  13.6, 13.31b; Garnier and Holman, 2019; Hagenlocher 
et  al., 2019). Several measures are already in place showing high 
technical and institutional feasibility (Sections  13.2.2.2, 13.5.2.1). 
The effectiveness of options varies regionally (in particular between 
northern and southern regions). For example, in SEU many water 
reservoirs are already in place. Irrigation is used to support agriculture 
where rain-fed supplies are not sufficient (Section 13.5.2). Their future 
extension depends on available precipitation. Also, wastewater reuse 
can only be effective if sufficient wastewater is available. Improvements 
in water efficiency and behavioural changes are very effective in 
SEU (>25% of damages avoided) (Section  13.2.2.2). Investments in 
large water infrastructures and advanced technologies (including

storage), water transfer, water recycling and reuse, and desalination 
will allow to buy time and therefore to cope with additional warming 
(Papadaskalopoulou et  al., 2016; Greve et  al., 2018). Beyond 2.5°C 
GWL, transformational adaptation is needed to lower risk levels, such 
as planned relocation of industry, abandonment of farmland or the 
development of alternative livelihoods (Holman et al., 2017). In WCE, 
the solution space to water scarcity is expanding with considerable 
potential for investments in large water infrastructure and advanced 
technologies (including storage), for reducing risks above 3°C GWL 
(Greve et  al., 2018). Under medium warming a larger portfolio of measures might be needed in SEU in particular, although it may not be 
able to completely avoid water shortages at high warming.
13.10.2.4 KR4: Risks to People, Economies and Infrastructures 
Due to Coastal and Inland Flooding
Damages and losses from coastal and river floods are projected to

increase substantially in Europe over the 21st century (high confidence) 
(Section 13.2.1; SM13.10). Coastal areas have already started to be 
affected by SLR (see Box 13.1; Section 13.10.1) and human exposure 
to coastal hazards is projected to increase in the next decades (high 
confidence), but less under SSP1 (20%) than SSP5 (50%) by the end of 
the century (medium confidence) (Merkens et al., 2016; Reimann et al., 
2018a). Under low adaptation (i.e., coastal defences are maintained 
but not further strengthened), severe consequences include an increase 
in expected annual damage by a factor of at least 20 for 1.5°C–2.1°C 
GWL (i.e., high risks) and by two to three orders of magnitude between 
2°C and 3°C GWL in EU-28 (i.e., very high risk) (medium confidence) 
(Figures  13.28, 13.34c; Section  13.2.1.1; Vousdoukas et  al., 2018b; 
Haasnoot et  al., 2021b). Under high adaptation (i.e., lowlands 
are protected where it is economically efficient), expected annual

damages still increase by a factor of 5 above 2°C GWL (Section 13.2; 
Vousdoukas et al., 2020). Sea levels are committed to rise for centuries 
(Fox-Kemper et al., 2021), submerging at least 10% of the territory in (b) Adaptation pathways water scarcity (a) People at risk of water scarcity
i. Presently there is already a gap between water demand and water availability in some parts of Europe (•••), 
which is increasing due to climate change and socio-economic developments (••).
ii. A portfolio of demand-side measures can reduce risk to medium global warming level (GWL) (•••).
iii. Water reservoirs and transfer can have distributional impacts and when used for irrigation they intensify 
dependency on water (•••).
iv. Desalination is effective and can be expanded, but has adverse effect on the environment and energy 
demand. Water re-use is effective, but depends on water availability, has a long lead time for infrastructure 
development and overcome hesitation for household use (•••).

v. Under medium GWL, the portfolio of demand side measures needs to be combined with transformative 
measures inc diversification of sources or land-use/cover changes (••).
vi. Under high global warming a large portfolio of measures is needed to reduce risk to water scarcity sufficiently, 
and this may not be possible to avoid water shortage (dashed lines) (••).High Medium Lowv.iii.
ii.Water reservoirs and storage
i.
vi.iv. Diversification of sources
(re-use, desalination)
Water diversion and transfer
Regulate distributio nPricing, taxation
land-use management,
land cover changeWater savings and efficienc yMonitoring an d
operational managemen tNowSupply DemandGlobal mean temperature change
••• ••• ••
•• •• •
•• •• •
•• ••
No/low
adaptationNo/low
adaptationWestern and
Central Europe Southern Europe
Lead time
The ember colour gradient indicates the level of 
additional risk to society and ecosystems as a

function of global temperature change. Confidence 
is provided for the change of risk level at given 
High
Moderate
Low High•
•••••Burning embers and illustrative adaptation pathways for risk of water scarcity to people in Europe (Key Risk 3)
Figure 13.31 |  Burning embers and illustrative adaptation pathways for risk of water scarcity to people (Key Risk 3)
(a) Burning ember diagrams for the risk of water scarcity with no or low adaptation, and with high adaptation for SEU and WCE, are shown.
(b) Illustrative adaptation pathways and key messages (see Figure 13.6). Grey shading means long lead time and dotted lines signal reduced effectiveness. The circles imply transfer 
to another measure and the bars imply that the measure has reached a tipping point (Table SM13.29).

13
1879Europe  Chapter 13
(b) Adaptation pathways riverine flood riskGlobal mean temperature change
i. Continuing a protect pathway by strengthening existing dyke systems is cost-effective, but with regional variation in benefi t 
cost ratio. This comes with increasing path-dependency and residual risk (•••).
ii. In cities where there is no place or no support to further heighten structure, upstream retention and movable barriers 
combined with an early warning system can be added (••).
iii. Natural retention and diversion of peak flows can reduce risk effectively and have co benefits for the environment and 
climate mitigation. A combination with flood defenses in highly urbanized regions can further reduce risk (•••).
iv. Insurance can limit consequences of residual risk for people (•••).
v. Wet and dry proofing can be taken at household level and can reduce residual risk as levees are raised (••).

vi. Planned relocation has been implemented locally to restore floodplain both pre and post-hoc events and can ultimately
  remove risk (•••) .
(d) Adaptation pathways coastal flood risk (c) Coastal flooding risks
Global mean temperature change
i. Continuing a protect pathway has a high benefit cost ratio in particular in urbanized coast, but comes with path-dependency 
and residual risk (•••).
ii. There is lack of evidence of long-term consequences and the need to switch to alternative measures under long-term and/or 
high global warming level (GWL) (•••).
iii. Ecosystem based solutions (e.g. wetlands) can reduce waves and provide co-benefits for the environment and climate 
mitigation. They can be effective to low to medium GWL. Beyond they can reduce costs for flood defences (•••).
iv. Wet and dry proofing measures are effective under low GWL. A combination with protection could extend the functional 
lifetime. Floating houses are in experiment stage (••).

v. No-build zones exist and can mitigate risk (•••). With higher GWL planned relocation is an option. Impacts can be delayed by  
wet and dry proofing of buildings (•).
vi. Planned relocation has been implemented locally for ecosystem restoration and in support of coastal defence, but is 
increasingly considered for less populated areas and ultimately removes risk (•••).
* Mostly flood defences and early warning.•••• ••
•• ••River and 
pluvial flooding 
risks with
low to medium
adaptationRiver and 
pluvial flooding
risks with
high adaptatio n
Delayed
risks fo r
cultural
heritage and
long-livin g
infrastructur eCoastal
floodin g
risks
with low to
medium
adaptationCoastal
floodin g
risks
with high
adaptation••• •• ••
•• ••
High Medium Lowv.iii.ii.
Retention and diversion
(including ecosystem based)i.
iv.
Wet and dry proofin gNow *Protect
Accomodate
Global warming levelRetreat/avoidNo-build zones
Planned relocatio nFlood defence
Flood insuranc e
vi.
High Medium Lowv.iii. ii.i.

iv. Wet and dry proofin gNow *Protect
vi.Ecosystem-base d
(e.g. wetlands)
Sediment-based
(e.g. nourishment)
AA
Very high
High
Moderate
Low High•
•••••
Very high
High
Moderate
UndetectableLevel of risk ConfidenceLow High•
•••••
Low High•
•••••(a) Inland flooding risksBurning embers and illustrative adaptation pathways for inland and coastal flooding in Europe (Key Risk 4)
Lead time
Lead time•• ••
Figure 13.32 |  Burning embers and illustrative adaptation pathways for inland and coastal flooding (Key Risk 4)
(a) Burning ember diagrams for the risks from riverine and pluvial flooding, with and without adaptation, are shown.
(b) Illustrative adaptation pathways to riverine flooding risks.
(c) Burning ember diagrams for the risks from coastal flooding, with and without adaptation, are shown.

(d) Illustrative adaptation pathways to coastal flooding risks. Grey shading means long lead time and dotted lines signal reduced effectiveness. The circles imply transfer to another 
measure and the bars imply that the measure has reached a tipping point (Tables SM13.30, SM13.31).

188012 countries in Europe if GWL exceed 1.5°C–2.5°C (Clark et al., 2016), 
and this represents a major threat for the European and Mediterranean 
cultural heritage (Figure 13.28; Cross-Chapter Box SLR in Chapter 3; 
Cross-Chapter Paper 4; Marzeion and Levermann, 2014; Reimann 
et al., 2018b).
Pluvial and riverine flood events in Europe have been attributed to 
climate change, but the associated damages and losses also depend 
on land-use planning and flood risk management practices (medium 
confidence) (Section  13.10.1; Ranasinghe et  al., 2021). Exposure to 
urban flooding will increase with urbanisation (Jongman et al., 2012; 
Jones and O’Neill, 2016; Dottori et al., 2018; Paprotny et al., 2018b). 
Flooding is projected to rise with temperature in Europe with, for 
example, a doubling of damage costs and people affected from river 
flood for low adaptation above 3°C GWL (Alfieri et al., 2018). Inland

flooding represents a KR for Europe due to the extent of settlements 
exposed, the frequency of the hazards, the risks to human lives 
associated with flash floods and the limited adaptation potential to 
pluvial flooding (e.g., difficulty to upgrade urban drainage systems) 
(Dale et al., 2018; Dale, 2021); hence, risks can become very high from 
3°C GWL (Figure 13.32a).
A range of adaptation options to coastal flooding exists, and 
adaptation is possible in many European regions if started on time 
(Section  13.2; Figure  13.32d). Continuing a protection pathway is 
cost-effective in urbanised regions for this century (Vousdoukas 
et al., 2020), but there is high agreement that it comes with residual 
risk if coastal defences fail during a storm. This residual risk can 
be reduced through early warning and evacuations, insurance and 
accommodate measures (Section  13.2.2). Soft limits to protection 
have been identified under high GWL, in particular due to the rate of

change and delayed impacts of long-term SLR (medium confidence) 
(Hinkel et  al., 2018; Haasnoot et  al., 2020a). Ecosystem-based 
solutions, such as wetlands, can reduce waves’ propagation, provide 
co-benefits for the environment and climate mitigation, and reduce 
costs for flood defences (medium confidence) (Section  13.2.2.1). 
At higher GWL, ecosystems are projected to experience reduced 
effectiveness due to temperature increases and an increased rate 
of SLR combined with a lack of sediment and human pressures 
(Cross-Chapter Box SLR in Chapter 3). Retention and diversion can 
be effective for compound flooding or for estuaries with a limited 
storm surge duration, but there is a lack of knowledge on their 
effectiveness (Sections 13.2.2).
In the case of river flooding, adaptation has the potential to contain 
damage and losses up to 3°C GWL (Figure 13.32b; Jongman et al., 2014; 
Alfieri et al., 2016), provided they are implemented on time and that the

technical, social and financial barriers are addressed (Sections 13.2.2, 
13.6.2). Residual risks can be reduced through early warning and 
evacuations, insurance and accommodate measures (Section  13.2.2; 
Kreibich et  al., 2015). Accommodation strategies, such as retention 
and ecosystem-based solutions, require space, which is not always 
available in cities. Both protection and flood retention are effective in 
reducing inland flooding risk across Europe, but with regional variation 
in the benefit-to-cost ratio (medium confidence) (Alfieri et al., 2016; 
Dottori et  al., 2020). Furthermore, upgrading drainage systems to accommodate increase in pluvial flooding is costly, technically complex 
and requires time (Dale et al., 2018; Dale, 2021).
Avoiding developments in risk-prone areas can reduce both coastal 
and inland flooding risks and can be followed by planned relocation, 
particularly in less populated areas. To align relocation with social

goals and achieve positive outcomes, long lead times are needed 
(Haasnoot et al., 2021a).
13.10.3 Consequences of Multiple Climate Risks for Europe
European regions are affected by multiple KRs simultaneously. While 
there is a wide range in quantifications, there is high agreement that 
the consequences for socioeconomic and natural systems can be 
substantial, with more severe consequences in the south than in the 
north (very high confidence); and there is some indication also for a 
west-to-east gradient, with higher uncertainty in eastern WCE and 
EEU, which makes adaptation more challenging (medium confidence). 
Furthermore, the food–water–energy–land nexus plays an important 
role in amplifying overall risk levels in Europe (medium confidence) 
(Forzieri et al., 2016; Harrison et al., 2016; Byers et al., 2018; Arnell 
et  al., 2019; Harrison et  al., 2019; Kebede et  al., 2021). Southern 
Europe, European cities and coastal areas are projected to become

hotspots of multiple risks (high confidence) (Cramer et  al., 2018; 
Forzieri et  al., 2018; Guerreiro et  al., 2018). The number of people 
exposed to multiple KRs in Europe are projected to at least double 
at 3°C GWL compared with 1.5°C GWL (Forzieri et al., 2017; Byers 
et al., 2018; Arnell et al., 2019), but risk levels are already higher at 
1.5°C GWL than today for a number of KRs (medium confidence) 
Economic losses and damages for European economies from multiple 
KRs are projected to increase (high confidence) (Figure  13.34; 
Szewczyk et al., 2018; Feyen et al., 2020; Kalkuhl and Wenz, 2020) 
and potentially quadruple at 3°C GWL compared with 1.5°C GWL 
(Feyen et al., 2020). Existing estimates of projected economic costs for 
Europe, based on integrated assessment or computable general equi-
librium models, are, however, likely to be underestimations of the true 
costs because of incomplete coverage of biophysical impacts, in par -

ticular low-probability high-impact events, and disruptive risk prop-
agation channels (Lamperti et al., 2018; Stoerk et al., 2018; Schewe 
et al., 2019; Piontek et al., 2021). The main driver for this increase in 
economic losses and damages is mortality due to heat stress (me-
dium confidence), followed by reduced labour productivity, coastal 
and inland flooding, water scarcity and drought (medium confidence) 
(Figure 13.33; Section 13.6.1.3). While losses are highest in SEU for 
both 1.5°C and 3°C GWL, and increase by a factor of more than 3 
between these GWLs, the projected economic damages and losses 
also increase significantly in WCE (by a factor of 4 from 1.5°C to 3°C 
GWL; 40% of total losses in EU-28 at 3°C GWL) and in NEU (almost 
10% of total losses at 3°C GWL) (Szewczyk et  al., 2018; Szewczyk 
et al., 2020). Adaptation is projected to reduce macroeconomic costs, 
but residual costs will remain particularly for warming above 3°C

GWL (medium confidence) (De Cian et al., 2016; Bosello et al., 2018; 
Parrado et al., 2020).

13
1881Europe  Chapter 13
13.10.4 Knowledge Gaps
Information on risk levels and development are available for 1.7°C, 
2.5°C and >4°C GWL, making the determination of transitions for 
the burning embers challenging and impairing a comprehensive 
assessment across KRs. Further efforts to extend the SSP narratives 
to Europe can contribute to a more disaggregated understanding of 
risk severity for different vulnerability and exposure conditions, but the 
evidence to date remains limited to few sectors (Cross-Chapter Paper 
4; Kok et al., 2019; Pedde et al., 2019; Rohat et al., 2019). There is only 
very limited evidence on the extent and timing of residual risks under 
different GWL, even with high adaptation.
There is medium confidence on the effectiveness of adaptation beyond 
3°C GWL particularly where risks are high to very high (Figures 13.28–
13.32). There is limited evidence on the effectiveness of specific 
adaptation options at different levels of warming that also include

consideration of lead and lifetimes. An integrated assessment, which 
projects the impacts on crop production by examining the potential 
availability of water for agricultural purposes together with other 
adaptation measures, is missing.
Transboundary risks, interactions between commodity and financial 
markets, market imperfections, non-linear socioeconomic responses 
and loss of ecosystem services may amplify losses for European 
economies. Available models may underestimate the full costs of 
climate change as they generally neglect systemic risks, tipping points, indirect and intangible losses, and limits to adaptation (Dafermos 
et al., 2018; Lamperti et al., 2018; van Ginkel et al., 2020; Dasgupta, 
2021; Ercin et al., 2021; Piontek et al., 2021). With increasing global 
warming, compound, low likelihood, or unprecedented extremes 
such as the European dry and hot summer of 2018 or the extreme 
rainfall following storm Desmond in the UK in 2015, become more

frequent (AR6 WGI Cross-Chapter Box 11.2). These events could have 
catastrophic consequences for Europe, but the extent of economic and 
non-economic damages and losses remain largely uncertain.
13.11 Societal Adaptation to Climate Change 
Across Regions, Sectors and Scales
Building on our sectoral analysis in previous sections, this section looks 
across European sectors, regions and vulnerable groups to assess how 
climate-change impacts are being responded to generally by state 
(Section  13.11.1) and non-state (Section  13.11.2) actors, and their 
synergies and dependencies. Section  13.11.3 assesses if and how 
system transformations have emerged and implications for the SDGs 
and climate resilient development pathways (CRDPs).
Economic damages and gains due to projected climate risks
for 1.5°C and 3°C Global Warming Levels (GWL) relative to no additional warming
Northern
Europe Economic risk
1.5°C
3.0°C
1.5°C
3.0°C
1.5°C

3.0°C
1.5°C
3.0°C
1.5°C
3.0°C
1.5°C
3.0°C
1.5°C
3.0°C
1.5°C
3.0°C= limited evidence/
ConfidenceChange in
agricultural yields
Change in
labour productivity
Change in
energy demand
Change in
mortality due to heat
Damage to economic sectors
from water scarcity and drought
Change in
energy supply
Damage to infrastructure
from coastal flooding
from inland floodingKey risk
KR1, KR2
KR1
KR1
KR1
KR3
KR3
KR4
KR4Number of
3
6
5
6
2
3
2
5
4
2
2
3
4
8
6
7GWL
Economic
damagesEconomic
gain
>1% (Very high)% of GDP or welfare
0.1–1% (High)
0.01–0.1% (Moderate)
<0.01% (No)
= both/
/
/
/
/
//
Figure  13.33 |   Economic damages and gains due to projected climate risks are shown for 1.5°C and 3°C GWL relative to no additional warming; 
macroeconomic effects are measured in GDP or welfare. Effects for EEU are reported for Russia as a whole country, deviating from the definition of EEU in this chapter.

Effects may deviate from sectoral assessments in Sections 13.2–13.7 due to different degrees of coverage of risk channels (Table SM13.23).

188213.11.1 Policy Responses, Options and Pathways
13.11.1.1 Progress on Adaptation Planning and ImplementationThe solution space for climate change adaptation has expanded across 
European regions since AR5 (high confidence). European countries 
are increasingly planning to adapt to observed impacts and projected 
Step A
Preparing the ground for adaptation
Step B
Assessing risks and vulnerabilities to climate change 
Step C
Identifying adaptation optionsStep D
Implementing adaptation actionStep E
Monitoring and evaluation of adaptation activities
Status of National Adaptation Strategies and Plans
Eastern EuropeSouthern Europe Western and Central EuropeHigher
progress
No data availableLower
progress
Finland
Ireland
United Kingdom
Denmark
Estonia
Lithuania
Latvia
Norway
Iceland
Austria
France
Germany
Luxembour g
Netherlands
Romania
Belgium
Czechia
Liechtenstein
Poland
Belarus
Moldov a
Monaco
Ukraine
Russian Federation
Portugal
Spain
Turkey
Montenegro

Greece
Italy
Kosovo
Malta
Slovenia
Albania
Andorra
Bosnia & Herzegovina
North Macedonia
San Marino
SerbiaUpdated Climate Adaptation Strategy or Plan
Climate Adaptation Plan and Strategy in place
Climate Adaptation Plan or Strategy in place
No National Adaptation Plan or Strategy adoptedProgress of National Adaptation in Europe
Sweden
Bulgaria
Slovakia
SwitzerlandHungary
Cyprus
CroatiaSelf-reported, 2018
Figure 13.34 |  Progress of national adaptation in Europe in 2018 and status of national adaptation plans and strategies in 2020. Data on the progress of 
national adaptation are from the self-reported status of EU member states, as documented in the Adaptation Scoreboard for Country fiches (SWD(2018)460). The status of national 
adaptation plans and strategies data are from EEA Report 6/2020 (EEA, 2020a), the ClimateADAPT portal (EEA, 2021a) and the Grantham Institute database ‘Climate Change Laws 
of the World’ (Grantham Research Institute, 2021).

13
1883Europe  Chapter 13
Box 13.3 | Climate Resilient Development Pathways in European Cities
Climate resilient development (CRD) in European cities offers synergies and co-benefits from integrating adaptation and mitigation with 
environmental, social and economic sustainability (Geneletti and Zardo, 2016; Grafakos et al., 2020). Climate networks (e.g., Covenant 
of Mayors), funding (e.g., Climate-KIC), research programmes (e.g., Horizon Europe), European and national legislation, international 
treaties and the identification of co-benefits contribute to the prioritisation of climate action in European cities (Heidrich et al., 2016; 
Reckien et al., 2018; CDP , 2020). Still, mitigation and adaptation remain largely siloed and sectoral (Heidrich et al., 2016; Reckien et al., 
2018; Grafakos et al., 2020). An assessment of the integration of mitigation and adaptation in urban climate-change action plans in

Europe found only 147 cases in a representative sample of 885 cities (Reckien et al., 2018).
In European cities, CRD is most evident in the areas of green infrastructure, energy-efficient buildings and construction, and active and 
low-carbon transport (Pasimeni et al., 2019; Grafakos et al., 2020). Nature-based Solutions, such as urban greening, often integrate 
adaptation and mitigation in sustainable urban developments and are associated with increasing natural and social capital in urban 
communities, improving health and well-being, and raising property prices (Geneletti and Zardo, 2016; Pasimeni et al., 2019; Grafakos 
et  al., 2020). Barriers to CRD in European cities include limitations in: funding, local capacity, guidance documents and quantified 
information on costs, co-benefits and trade-offs (Grafakos et al., 2020). Pilot projects are used to initiate CRD transitions (Nagorny-Koring

and Nochta, 2018). Malmö (Sweden) and Milan (Italy) are two examples to illustrate the strategies and challenges of two European cities 
attempting to implement CRDP .
Malmö (population 300,000): Since the 1990s, Malmö has been transitioning towards an environmentally, economically and socially 
sustainable city, investing in eco-districts (redeveloped areas that integrate and showcase the city’s sustainability strategies) and adopting 
ambitious adaptation and mitigation targets. The city has focused on energy-efficient buildings and construction, collective and low-carbon 
transportation, and green spaces and infrastructure (Anderson, 2014; Malmo Stad, 2018). Malmö has developed creative implementation 
mechanisms, including a ‘climate contract’ between the city, the energy distributor and the water and waste utility to co-develop the 
climate-smart district, Hyllie (Isaksson and Heikkinen, 2018; Kanters and Wall, 2018; Parks, 2019). Flagship eco-districts play a central role

in the city’s transition, in the wider adoption of CRD and in securing implementation partners (Isaksson and Heikkinen, 2018; Stripple and 
Bulkeley, 2019). The city has also leveraged its status as a CRD leader to attract investment. The private sector views CRD as profitable, due 
to the high demand and competitive value of these developments (Holgersen and Malm, 2015). Malmö adopted the SDGs as local goals 
and the city’s Comprehensive Plan is evaluated based on them, for example, considering gender in the use, access and safety of public 
spaces, and emphasising development that facilitates climate-resilient lifestyles (Malmo Stad, 2018). Malmö also engages stakeholders via 
dialogue with residents, collaboration with universities and partnerships with industry and service providers (Kanters and Wall, 2018; Parks, 
2019). Despite measurable and monitored targets, and supportive institutional arrangements, sustainability outcomes for the flagship

districts have been tempered by developers’ market-oriented demands (Holgersen and Malm, 2015; Isaksson and Heikkinen, 2018) and 
there is limited low-income housing in climate-resilient districts (Anderson, 2014; Holgersen and Malm, 2015).
Milan (population 1.4 million): Milan is taking a CRD approach to new developments (Comune di Milano, 2019). From 2020, new 
buildings must be carbon neutral and reconstructions must reduce the existing land footprint by at least 10%. The Climate and Air 
Plan (CAP) and the city’s Master Plan (Comune di Milano, 2019) focus on low-carbon, inclusive and equitable development. The CAP 
is directed at municipal and private assets, and individual- to city-scale actions. In 2020, Milan released a revised Adaptation Plan and 
the Open Streets Project to ensure synergies between the COVID-19 response and longer-term CRD. Examples include strengthening

neighbourhood-scale disaster response and reallocating street space for walking and cycling (Comune di Milano, 2020). Milan emphasises 
institutionalisation of CRD via a dedicated resilience department, and through active participation in climate networks and projects that 
support learning and exchange. Climate network commitments are cited in the city’s Master Plan and CAP guidelines as driving more 
ambitious deadlines and emissions targets (Comune di Milano, 2019). Implementation of Milan’s plans remains a challenge, despite 
dedicated resources and commitment.
climate risks across scales of government (high confidence) (Lesnikowski 
et al., 2016; Russel et al., 2020). Whereas in 2009, only nine EU countries 
had developed a National Adaptation Strategy (NAS) (Biesbroek et al., 
2010; EEA, 2014), by mid-2020 all EU member states and several 
other European countries had adopted at least a NAS and/or revised 
and updated prior strategies (Figure 13.34, bottom; Klostermann et al.,

2018; EEA, 2020a). Progress is also observed at the level of the EU with 
the adoption of the new EU strategy on adaptation to climate change in 2021 (European Comission, 2021a), and regionally, particularly 
in federalist and decentralised states (Steurer and Clar, 2018; EEA, 
2020b; Pietrapertosa et  al., 2021), and locally, with an increasing 
number of European cities planning for climate risks (high confidence) 
(Section 13.6.2.1; see Box 13.3; Chapter 6; Aguiar et al., 2018; Reckien 
et al., 2018; Grafakos et al., 2020). There is evidence of action across 
sectors and scales, even in European countries where national adaptation 
frameworks are absent (medium confidence) (Figure 13.34; De Gregorio

1884Hurtado et  al., 2015; Pietrapertosa et  al., 2018; Reckien et  al., 2018). 
However, the implementation gap identified in AR5 (Chambwera et al., 
2014), that is, the gap between defined goals and ambitions and actual 
implemented actions on the ground, persists in Europe (Aguiar et  al., 
2018; Russel et al., 2020; UNEP , 2021).
The drivers of adaptation progress in Europe differ across sectors and 
regions. Common drivers include: experienced climatic events, improved 
climatic information, societal pressures to act, projected economic and 
societal costs of climate change, participation in (city) networks, societal 
and political leadership, and changes in national and European policies 
and legislation (medium evidence, high agreement) (EEA, 2014; Massey 
et al., 2014; Reckien et al., 2018). The availability of knowledge, human 
and financial resources appears important for proactive adaptation

(Termeer et al., 2012; Sanderson et al., 2018), while adaptation is 
also strongly dependent on economic and social development (high 
confidence) (Sanderson et  al., 2018). How adaptation is governed 
differs substantially across Europe (Clar, 2019; Lesnikowski et al., 2021). 
Political commitment, persistence and consistent action across scales of 
government is critical to move beyond planning for adaptation (Steps 
A–C in Figure 13.34) and to ensure adequacy of implementation (Steps 
D and E in Figure 13.34) (Howlett and Kemmerling, 2017; Lesnikowski 
et al., 2021; Patterson, 2021).
The scope of climate risks included in European adaptation policies 
and plans (Step B in Figure 13.34) is generally broad (EEA, 2018a). 
Systemic and cascading risks (Section 13.10) are often recognised, but 
most conventional risk assessment methods that inform adaptation 
planning are ill-equipped to deal with these effects (Adger et  al.,

2018). For example, transboundary risks emerging in regions outside 
of Europe are considered only by a few countries such as the UK 
and Germany (Section 13.9.3). European climate change adaptation 
strategies and national policies are generally weak on gender, sexual 
orientation, as well as other social equality issues (Cross-Chapter 
Box GENDER in Chapter 18; Boeckmann and Zeeb, 2014; Allwood, 
2020).
Many near-term investment decisions have long-term consequences, 
and planning and implementation (Steps C and D in Figure 13.34) can 
take decades, particularly for critical infrastructure planning in Europe 
(Zandvoort et al., 2017; Pot et al., 2018). Consequently, there are calls 
to expand planning horizons, to consider long-term uncertainties to 
prevent lock-in decision dependencies, to seize opportunities and 
synergies from other investments (e.g., socioeconomic developments 
and systems transitions) and to broaden the range of considered possible

impacts (e.g., Frantzeskaki et al., 2019; Marchau, 2019; Oppenheimer 
et  al., 2019; Haasnoot et  al., 2020b ). Yet, high GWL scenarios 
beyond 2100 are often not considered in climate-change adaptation 
planning due to a lack of perceived usability, missing socioeconomic 
information, constraining institutional settings and conflicting 
decision-making timeframes (medium confidence) (Lourenco et  al., 
2019; Taylor et al., 2020). High GWL scenarios are often seen as having 
a low probability of occurrence, resulting in inaction or incremental 
rather than transformative adaptation responses to projected climate 
risks (Dunn et  al., 2017). Extending planning horizons to beyond 
2100 increases deep uncertainties for decision makers as a result of 
unclear future socioeconomic and climatic changes. For adaptation to SLR along Europe’s coast, for example, there are already considerable 
uncertainties during this century (Fox-Kemper et al., 2021).

Adaptive planning and decision making are still limited across Europe 
(high confidence). Prominent examples of adaptive plans include the 
flood defence systems for the City of London (Ranger et  al., 2013; 
Kingsborough et  al., 2016; Hall et  al., 2019) and the Netherlands 
(Van Alphen, 2016; Bloemen et al., 2019). Adaptation pathways also 
have been developed for planning urban water supply (Kingsborough 
et al., 2016; Erfani et al., 2018), urban drainage (Babovic and Mijic, 
2019) and wastewater systems (Cross-Chapter Box DEEP in Chapter 
17; Sadr et al., 2020). Flexible strategies are increasingly considered 
by European countries (e.g., Stive et al., 2013; Kreibich et al., 2015; 
Bubeck et  al., 2017; Haasnoot et  al., 2019) but require appropriate 
design to be effective (Metzger et al., 2021).
Monitoring and evaluation of adaptation action is done only in some 
European countries (Step E in Figure 13.34) but is important for adjusting

planning, if needed (Hermans et al., 2017; Haasnoot et al., 2018), and 
enhancing transparency and accountability of progress (Mees and 
Driessen, 2019). In the Netherlands, a comprehensive monitoring system 
has been put in place, including signals for adaptation that support 
decisions on when to implement adaptation options or to adjust plans 
(Hermans et al., 2017; Haasnoot et al., 2018; Bloemen et al., 2019).
13.11.1.2 Mainstreaming and Coordination
Coordinated responses are necessary to prevent inefficient and 
costly action (Biesbroek, 2021), balance under- and overreaction to 
climate risks (Peters et al., 2017; Biesbroek and Candel, 2019), prevent 
redistributing vulnerability and maladaptive actions (Atteridge and 
Remling, 2018; Albizua et al., 2019; Neset et al., 2019), and ensure 
timely implementation (high confidence) (Benson and Lorenzoni, 
2017). Since AR5, progress has been made to increase coordinated

adaptation actions, but so far this is limited to a few sectors (mostly 
water management and agriculture) and European countries 
and regions (mostly SEU, and WCE depending on impact) (high 
confidence) (Section 13.11.2; Lesnikowski et al., 2016; Biesbroek and 
Delaney, 2020; Booth et  al., 2020). Despite evidence of emerging 
bottom-up (e.g., citizens and business) and top-down initiatives (e.g., 
governmental plans and instruments to ensure action), there are 
considerable barriers to mainstreaming adaptation (high confidence) 
(Runhaar et al., 2018).
While mainstreaming of adaptation into other policy domains has 
been advocated as an enabler for adaptation, it may have resulted in 
incremental rather than transformational adaptation, and may not be 
sufficient to close the adaptation gap (Andersson and Keskitalo, 2018; 
Remling, 2018; Scoville-Simonds et al., 2020).
13.11.1.3 Climate Services and Local Knowledge

Climate services to support adaptation decision making of governments 
and businesses across Europe have rapidly increased since AR5, partly 
as a result of national and EU investments such as the Copernicus 
C3S service (high confidence) (Street, 2016; Soares and Buontempo, 
2019). These services are increasingly used in NEU, SEU and WCE, for

13
1885Europe  Chapter 13
example, in energy and risk prevention in coastal and riverine cities, 
stimulating regulations and bottom-up initiatives (Cavelier et al., 2017; 
Le Cozannet et al., 2017; Reckien et al., 2018; Howard et al., 2020). 
However, climate service efficacy is rarely systematically evaluated 
(Cortekar et  al., 2020). Barriers to use include: lack of perceived 
usefulness of climate information to organisations and expertise to 
use the information, outdated statistics, mismatch between needs and 
type of information made available, insufficient effective engagement 
between providers and recipients of climate information and lack of 
business models to sustain climate services over time (high evidence, 
medium agreement) (Cavelier et al., 2017; Räsänen et al., 2017; Bruno 
Soares et al., 2018; Christel et al., 2018; Oberlack and Eisenack, 2018; 
Hewitt et al., 2020). Adaptation-decision support platforms also face

challenges regarding updating, training and engagement with users 
(EEA, 2015; Palutikof et al., 2019).
In addition to scientific knowledge, traditional and local knowledge can 
enable adaptation action (Huntington et al., 2017) as is the case with 
indigenous-led ecosystem restoration in the European Arctic (Brattland 
and Mustonen, 2018). There is a need to draw on surviving Indigenous 
knowledge systems in Europe (Greenland, Nenets, Khanty, Sámi, Veps, 
Ingrian) as unique, endemic ways of knowing the world that can position 
present and historical change in context and offer unique reflections of 
change in the future (Ogar et al., 2020; Mustonen et al., 2021).
13.11.1.4 Financing Adaptation and Financial Stability
Dedicated financial resources for the implementation of NAS and 
plans are a key enabling factor for successful adaptation (high 
confidence) (Chapter 17; Russel et  al., 2020). Yet, only 14 EU 
countries have announced such budget allocations in their plans

and strategies; and even if budget numbers are available, they are 
difficult to compare (EEA, 2020a). Current adaptation spending 
varies greatly across and within European countries, partly reflecting 
(sub)national adaptation priorities or financing sources targeting 
investment projects (López-Dóriga et al., 2020; Russel et al., 2020) 
and competing statutory priorities (Porter et  al., 2015). European 
government budgets are also burdened by climate-change damages 
today, particularly after huge flooding events, and austerity following 
financial crises, limiting anticipatory action (Penning-Rowsell and 
Priest, 2015; Miskic et  al., 2017; Schinko et  al., 2017; Slavíková 
et  al., 2020). National adaptation funding in EU member states 
is complemented by EU funding (e.g., European Structural and 
Investment Funds, European Regional Development Funds, and LIFE 
program). While the EU spending target on climate action increased

from 20% in 2016–2020 to 25% in 2021–2026, most spending is 
going into mitigation, not adaptation (Berkhout et al., 2015; Hanger 
et al., 2015; EEA, 2020a).
With higher warming levels, financing needs are likely to increase 
(high confidence) (Mochizuki et al., 2018; Bachner et al., 2019; Parrado 
et al., 2020), and governments can address this higher need by cutting 
other expenditures, increasing taxes or by increasing the fiscal deficit 
(Miskic et al., 2017; Mochizuki et al., 2018; Bachner et al., 2019). Yet, 
the requirement for fiscal consolidation that will be needed after the 
COVID-19 pandemic (Cross-Chapter Box  COVID in Chapter 7) may 
also lead to a cessation of adaptation spending, as evidenced by the expenditure drop in coastal protection in Spain after the financial crisis 
in 2008 (López-Dóriga et al., 2020). Governments can shift the financial 
burden to beneficiaries of adaptation, as suggested, for example, for

coastal protection and riverine flooding (Jongman et al., 2014; Penning-
Rowsell and Priest, 2015; Bisaro and Hinkel, 2018). There is also an 
increase in financial mechanisms to accelerate private adaptation 
actions, including adaptation loans, subsidies, direct investments 
and novel public–private arrangements. For example, the European 
Investment Bank created a finance facility to support European regions 
through loans to implement adaptation projects (EEA, 2020a).
Since AR5, new evidence has emerged that climate change may 
deteriorate financial stability both at the global and European scales 
(Campiglio et al., 2018; Dafermos et al., 2018; Lamperti et al., 2019; 
ECB, 2021a). The European Central Bank, the European Systemic 
Risk Board, and several national central banks in NEU and WCE have 
started to systematically assess the consequences of climate risks for 
financial stability and plan to integrate climate stress testing into their

supervisory tools (Batten et al., 2016; ECB, 2021a; ECB, 2021b).
13.11.2 Societal Responses, Options and Pathways
13.11.2.1 Private Sector
Within the private sector, there tends to be a preference for ‘soft’ (e.g., 
knowledge generation) than ‘hard’ (e.g., infrastructure) adaptation 
measures (Goldstein et  al., 2019), in contrast to government-led 
responses typically favouring hard measures (Pranzini et  al., 2015). 
However, there also remains diversity across sectors and organisations in 
the degree and type of adaptation response (Trawöger, 2014; Dannevig 
and Hovelsrud, 2016; Ray et al., 2017; Ricart et al., 2019). Whereas some 
sectors, such as flood management, banking and insurance, and energy 
(Bank of England, 2015; Gasbarro and Pinkse, 2016; Bank of England, 
2019; Botzen et al., 2019), have generally made moderate progress on 
adaptation planning across Europe, there are key vulnerable economic 
sectors that are in earlier stages, including aviation (Burbidge, 2018),

ports and shipping (Becker et al., 2018; Ng et al., 2018), and ICT (high 
confidence) (EEA, 2018b). There is also some evidence of ‘short-sighted’ 
adaptation or maladaptation; for example, in winter tourism there is a 
preference for technical and reactive solutions (e.g., artificial snow) that 
will not be sufficient under high levels of warming (Section 13.6.1.4).
Where adaptation is considered by companies, it is typically triggered 
either by the experience of extreme weather events that led to business 
disruptions (McKnight and Linnenluecke, 2019) or is included into 
corporate risk management in response to regulatory, shareholder or 
customer pressure (Averchenkova et al., 2016; Gasbarro et al., 2017). 
For instance, following the implementation of the recommendations 
of the Task Force on Climate-Related Financial Disclosure by the 
European Commission in 2019, 50 publicly listed companies revealed 
their exposure to their physical climate risks in 2020 (CDSB, 2020). But

even if companies experience extreme weather events or stakeholder 
pressure, they may not adapt because they underestimate their 
vulnerability (Table  13.1; Pinkse and Gasbarro, 2019). For example, 
key barriers to adaptation among Greek firms include both external 
(e.g., lack of support and/or guidance) and internal factors (e.g.,

1886few resources, managerial perceptions) (Halkos et al., 2018). Lack of 
knowledge, feeling climate change is not a salient risk, and lack of 
social learning or collaboration appear to be key barriers to private-
sector adaptation (Section 13.16.2.2; Dinca et al., 2014; André et al., 
2017; Romagosa and Pons, 2017; Esteve et  al., 2018; Luís et  al., 
2018; Ng et al., 2018). There remains little research on private-sector 
awareness of, or responses to, cascading or compound risks associated 
with climate change (Miller and Pescaroli, 2018; Pescaroli, 2018).
13.11.2.2 Communities, Households and Citizens
Planned behavioural adaptation remains limited among European 
households (high confidence), with few examples that can be consid-
ered transformative (e.g., structural, long-term, collective) (medium 
confidence) (Wilson et al., 2020). One Swedish survey of householders 
at risk of extreme weather events (e.g., floods, storms) found evidence

of some organisational measures (e.g., bringing possessions inside 
prior to a storm, preparing for power cuts with candles, etc.), but very 
few households took any other (technical, social, nature-based, or eco-
nomic) measures (Brink and Wamsler, 2019). Similarly, few at risk of 
flooding are taking action (Sections  13.2.1, 13.6.1; Stojanov et  al., 
2015); for example, there is little public take-up of available municipal 
support for individual adaptation in Germany (Wamsler, 2016). Water 
efficiency measures in anticipation of, or response to, drought are also 
limited (Bryan et  al., 2019), although water reuse in Mediterranean 
and some other EU (e.g., the UK and the Netherlands) countries is 
increasing (Section  13.2; Aparicio, 2017). Among the adaptation re-
sponses recorded, few are perceived as opportunities (Taylor et  al., 2014; Simonet and Fatorić, 2016). There is currently little European 
research on public responses to risks other than flooding, heat stress

and drought, such as vector-borne disease, and to multiple and cas-
cading risks (Section 13.7; van Valkengoed and Steg, 2019).
Perceived personal responsibility for tackling climate change remains 
low across the EU (Figure 13.35) and partly explains why household 
adaptation remains limited (high confidence) (Taylor et  al., 2014; 
van Valkengoed and Steg, 2019), despite risk perception apparently 
growing (Figure Box 13.2.1; Capstick et al., 2015; Poppel et al., 2015; 
BEIS, 2019). Householders’ risk perception and concern about climate 
change fluctuates in response to media coverage and significant 
weather or sociopolitical events (high confidence) (Capstick et  al., 
2015). On average across Europe, and particularly in relation to gradual 
change, compared with experts, non-experts continue to underestimate 
climate-change risks (medium confidence) (Taylor et  al., 2014), have 
low awareness of adaptation options, and confuse adaptation and

mitigation (Harcourt, 2019), suggesting a need for improved climate 
literacy among the public. Indeed, fostering learning and coping 
capacity supports robust adaptation pathways (Jäger et al., 2015).
There is strong public support for adaptation policy (e.g., building flood 
defences), particularly within the UK, France, Norway and Germany 
(Doran et al., 2018). Although, in some cases such public adaptation can 
undermine motivation for householders to take adaptation measures 
(Section  13.2), public adaptation can also increase householder 
motivations, with perceived efficacy of action a strong predictor of 
adaptation (high confidence) (Moser, 2014; van Valkengoed and Steg, (a) Perceived seriousness of climate change
Percentage of respondents
10%20%30%40%50%60%70%
2011 2013 2015 2017A very serious proble m
A fairly serious proble m
Not a serious problem
Don’t knowAnd how serious a problem 
do you think climate change

is at this moment? (b) Perceived responsibility for tackling climate change
In your opinion, who within the European Union (EU) is responsible for 
tackling climate change?
National governments
The EU
Business & industry
Regional & local authorities
You personally
Enviromental
groupsOther
All of themNone
2011 2013 2015 2017Don’t knowProportional distribution of respondents per answerTrends in perceived climate change risks and responsibility for tackling climate change across Europe
Figure 13.35 |  Trends in perceived climate-change risks and responsibility for tackling climate change across EU-28; data collected from around 1000 
respondents per country for each year surveyed (European Comission, 2017)

13
1887Europe  Chapter 13
2019). However, there are also structural and economic barriers to 
household adaptation due to lack of policy incentives or regulations. 
For example, water-saving devices in homes could halve consumption, 
but lack of economic benefits to householders are barriers to adoption; 
and lack of standards as well as societal hesitation may explain low 
levels of water reuse in Europe (Section 13.2; EEA, 2017b). Conversely, 
water meters and higher tariffs have been found to reduce water 
consumption only in combination with other measures (EEA, 2017b; 
Bryan et al., 2019).
As well as temporal trends in climate-change risk perception, the lit-
erature since AR5 continues to show much heterogeneity (both within 
and between nations) among householders in respect of risk percep-
tion (high confidence). Higher climate-change risk perceptions have 
been observed in Spain, Portugal, Iceland and Germany (Figure 13.2);

at the individual level, women, younger age groups, more educated, 
left-leaning and those with more ‘self-transcendent’ values perceive 
more negative impacts from climate change, although the strength 
of these relationships varies across European nations (Clayton et al., 
2015; Doran et al., 2018; Poortinga et al., 2019; Duijndam and van 
Beukering, 2021). Stronger evidence exists since AR5 that experience 
of extreme weather events can shape climate-change risk perceptions, 
if these events are attributed to climate change or evoke negative 
emotions (high confidence) (Clayton et al., 2015; Demski et al., 2017; 
Ogunbode et al., 2019). Proximity to climate hazards does not pre-
dict adaptation responses in a straightforward way: in Portugal, those 
living by the coast were more likely to attribute local natural hazards 
to climate change and to take some adaptive measures (Luís et al., 
2017); while waterside residents in flood-prone regions of France and

Austria were more resistant to relocation, due to higher place attach-
ment (Adger et al., 2013; Rey-Valette et al., 2019; van Valkengoed and 
Steg, 2019; Seebauer and Winkler, 2020). Migration from threatened 
regions is discussed in Section 13.8.1.3.
13.11.3 Adaptation, Transformation and Sustainable 
Development Goals
The implementation of far-reaching and rapid systemic changes, 
including both adaptation and mitigation options (de Coninck et  al., 
2018), remains less researched in societal systems than natural ones 
(Salomaa, 2020) that enhance multi-level governance and institutional 
capabilities, and enables lifestyle and behavioural change as well as 
technology innovation. Adaptation responses across European regions 
and sectors are more often incremental than transformative (medium 
confidence), with possible exceptions including water-related examples 
in, for example, the Netherlands (Section 13.2.2) and some cities (see

Box  13.3). Transformative options may be better able to exploit new 
opportunities and co-benefits (see Box 13.3; Cross-Chapter Box HEALTH 
in Chapter 7; EEA, 2019a). Transitions towards more adaptive and climate-
resilient systems are often the result of responses to crises which create 
windows of opportunity for systemic changes (Chapter 18; Johannessen 
et al., 2019). This includes extreme weather events, financial crises, for 
example in Malmö (Anderson, 2014; Isaksson and Heikkinen, 2018), and 
the COVID-19 pandemic (e.g., Milan), all of which have disrupted the 
status quo and accelerated innovation and implementation (e.g., Milan; 
see Box 13.3; Cross-Chapter Box COVID in Chapter 7).Considerable barriers exist that prevent system transitions from taking 
place in Europe, including institutional and behavioural lock-ins such 
as administrative routines, certain types of legislation and dominant 
paradigms of problem solving (high confidence) (Johannessen et  al.,

2019; Roberts and Geels, 2019). For example, near-term and sectoral 
decision-making constrains transformative options for water-related risks 
(Section 13.2). Breaking through these lock-ins requires substantive (i.e., 
political) will, (un)learning of practices, resources, and evidence of what 
works. Trade-offs exist between the depth, scope and pace of change 
in transitioning from one system to another, suggesting that designing 
system transformations is a delicate balancing act (Termeer et al., 2017). 
Aspiring in-depth and comprehensive transformational changes might 
create a consensus framework to which to aspire, but it might not offer 
concrete perspectives to act on the ground. Taking small steps and quick 
wins offer an alternative pathway (Termeer and Dewulf, 2018).
Adaptation responses can also be understood in terms of their trade-
offs and synergies with SDGs (Papadimitriou et al., 2019; Bogdanovich

and Lipka, 2020). In terms of synergies, analysis of the Russian NAP 
found that successful completion of the NAP’s first phase could lead to 
significant progress towards 15 of the 17 goals (Bogdanovich and Lipka, 
2020). European water adaptation (e.g., flood protection) can similarly 
support freshwater provision; and water-secured environments support 
socioeconomic growth (Sadoff et  al., 2015) since people and assets 
tend to accumulate in areas protected from flooding and supplied with 
water, reducing the incentive for autonomous adaptation (de Moel et al., 
2011; Hartmann and Spit, 2016; Di Baldassarre et al., 2018). In health, 
behavioural measures to reduce mental health impacts (e.g., gardening, 
active travel) can have broader health benefits (SDG 3) as well as help 
reduce emissions (Section 13.7; SDGs 7 and 13). Conversely, growing 
use of air conditioning for humans and livestock represents a potential 
trade-off between adaptation and mitigation (Sections  13.5–13.7,

13.10). As noted in Section 13.8, addressing poverty (SDG 1)–including 
energy poverty (SDG 7) and hunger (SDG 2); and addressing inequalities 
(SDG 10), including gender inequality (SDG 5)–improves resilience to 
climate impacts for those groups that are disproportionately affected 
(women, low-income and marginalised groups). Also, more inclusive and 
fair decision making can enhance resilience (SDG 16; Section 13.4.4), 
although adaptation measures may also lead to resource conflicts (SDG 
16; Section  13.7). Climate adaptation, particularly NbS, also supports 
ecosystem health (SDGs 14 and 15) (Dzebo et al., 2019).
Economic trade-offs appear to be more common across adaptation 
strategies, for example, reduced employment arising from land-use-
change measures (Papadimitriou et  al., 2019). There are also trade-
offs between large-scale mitigation measures (e.g., wind farms) and 
adaptation options that rely on ecosystem services (e.g., water regulation)

(Sections  13.3–13.4); and conversely, some adaptation options (e.g., 
air conditioning) may negatively impact mitigation. Figure  13.36 
summarises the synergies between adaptation and SDGs as identified 
by 167 European cities in 2019; particularly prominent are reported 
biodiversity and health benefits most often arising from societal (e.g., 
informational) and structural (e.g., technological and/or engineering) 
measures. Beyond the urban context, biodiversity co-benefits from 
agroecology are also recognised (Section 13.5). Sustainable behaviour-
change measures have been found to be particularly likely to lead to 
synergies with SDGs (Papadimitriou et al., 2019).

1888Extreme heat
River/coastal
flood
Heavy
precipitationSDG 14: Life below water
SDG 15: Life on land
SDG 6: Clean water and sanitation
Hydrological
droughtSDG 12: Responsible consumption
               and production
SDG 2: Zero hunger
SDG 7: Affordable and clean energy
SDG 16: Peace and justice strong institutions
SDG 3: Good health and well-being
SDG 13: Climate action
SDG 1 1: Sustainable cities and communities
LandslideSDG 9: Industry , innovation and infrastructure
SDG 10: Reduced inequality
SDG 5: Gender equality
SDG 8: Decent work and economic growthCold spell
Wind storm
SDG 4: Quality educationWild fire
Other
SDG 1: No poverty
Less MoreRelative number of times
each adaptation action was reportedOverview of adaptation actions reported in European cities
and their co-benefits
2 Economic10 Laws and regulations76 Government policies and programs11 Services41 Technological52 Ecosystem-based75 Engineered and built environmen t52 Educational171 Informational

0 BehaviouralSocial
Structural
InstitutionalAdaptation actions Sub-categoryA mount of actions per sub-category
Other 52
Social Structural InstitutionalClimate impact drivers Adaptation
actionsCo-benefits of adaptationSustainable
Goals(a)
(b)
Amount of
adaptation actions
per city
1510152025(c)
Figure 13.36 |  Co-benefits for SDGs from adaptation actions. Shown is how European cities have assessed the sustainability co-benefits of taking adaptation actions. 
Data were extracted from the Carbon Disclosure Project (CDP) database using the 2019 dataset; of the 861 European cities submitting data, 167 provided data on their adaptation 
actions, and these data are shown here (CDP , 2019). The CDP categories of climate hazards were re-categorised into WGI Climate Impact Drivers (e.g., cold spell, heavy precipitation);

CDP adaptation actions were re-classified into AR5 adaptation options (‘social’, ‘structural’ and ‘institutional’; ‘other’ includes actions falling outside these AR5 categories); and CDP 
co-benefits were re-categorised as SDGs. The upper panel shows that all SDGs except one (SDG 17) were identified as a co-benefit of adaptation, although more environmental 
co-benefits were identified than social or economic ones. The lower left panel shows that societal actions were most common, followed by structural, then institutional. Informational 
measures were particularly common. The lower right panel shows how many actions were taken by different European cities.

13
1889Europe  Chapter 13
FAQ 13.1 | How can climate change affect social inequality in Europe?
The poor and those practising traditional livelihoods are particularly exposed and vulnerable to climate change. They rely more often on 
food self-provisioning and settle in flood-prone areas. They also often lack the financial resources or the rights to successfully adapt to 
climate-driven changes. Good practice examples demonstrate that adaptation can reduce inequalities.
Social inequalities in Europe arise from disparities in income, gender, ethnicity, age as well as other social 
categorisations. In the EU, about 20% of the population (109 million people) live under conditions of poverty or 
social exclusion. Moreover, poverty is unequally distributed across Europe, with higher poverty levels in EEU. The 
oldest and youngest in society are often most vulnerable.

The poor and those practising traditional livelihoods are particularly vulnerable and exposed to climate risks. Many 
depend on food self-provisioning from lakes, the sea and the land. With higher temperatures, the availability 
of these sources of food is likely to be reduced, particularly in SEU. Poorer households often settle in flood-prone 
areas and are therefore more exposed to flooding. Traditional pastoralist and fishing practices are also negatively 
affected by climate change across Europe. Semi-migratory reindeer herding, a way of life among Indigenous and 
traditional communities (i.e., Komi, Sámi, Nenets) in the European Arctic, is threatened by reduced ice and snow 
cover. Almost 15% of the EU population (in some countries more than 25%) already cannot meet their health care 
needs for financial reasons, while they are at risk of health impacts from warming.

In addition to being more exposed to climate risks, socially vulnerable groups are also less able to adapt to these 
risks, because of financial and institutional barriers. More than 20% of people in SEU and EEU live in dwellings 
that cannot be cooled to comfortable levels during summer. These people are particularly vulnerable to risks from 
increasing heatwave days in European cities (e.g., when they already face energy poverty). They may also lack 
the means to protect against flooding or heat (e.g., when they do not own the property). Risk-based insurance 
premiums, which are intended to help people reduce climate risks, are potentially unaffordable for poor households. 
The ability to adapt is also often limited for Indigenous people, as they often lack the rights and governance of 
resources, particularly when in competition with economic interests such as resource mining, oil and gas, forestry 
and expansion of bioenergy.

Adaptation actions by governments can both increase and decrease social inequality. The installation of new, or 
the restoration of existing, green spaces may increase land prices and rents due to a higher attractiveness of these 
areas, leading to potential displacement of population groups who cannot afford higher prices. On the other hand, 
rewilding and restoration of ecosystems can improve the access of less privileged people to ecosystem services and 
goods, such as the availability of freshwater. At city level, there are examples of good practice in CRD that consider 
social equity which integrate a gender-inclusive perspective in its sustainable urban planning, including designing 
public spaces and transit to ensure that women, persons with disabilities and other groups can access, and feel safe 
using, these public amenities.

1890Frequently Asked Questions
FAQ 13.2 | What are the limits of adaptation for ecosystems in Europe?
Land, freshwater and ocean organisms and ecosystems across Europe are facing increasing pressures from human activities. Climate change is 
rapidly becoming an additional and, in the future, a primary threat. Ongoing and projected future changes are too severe and happen too fast 
for many organisms and ecosystems to adapt. More expensive and better implemented environmental conservation and adaptation measures 
can slow down, halt, and potentially reverse biodiversity and ecosystem declines, but only at low or intermediate warming.
Ecosystem degradation and biodiversity loss have been evident across Europe since 1950, mainly due to land use and 
overfishing; however, climate change is becoming a key threat. The unprecedented pace of environmental change

has already surpassed the natural adaptive capability of many species, communities and ecosystems in Europe. For 
instance, the space available for some land ecosystems has shrunk, especially in Europe’s polar and mountain areas, 
due to warming and thawing of permafrost. Across Europe, heatwaves and droughts, and their impacts such as 
wildfires, add further acute pressures, as seen in the 2018 heatwave, which impacted forest ecosystems and their 
services. In the Mediterranean Sea, plants and animals cannot shift northward and are negatively affected by 
marine heatwaves. Food-web dynamics of European ecosystems are disrupted as climate change alters the timing 
of biological processes, such as spawning and migration of species, and ecosystem composition. Moreover, warming 
fosters the immigration of invasive species that compete with–and can even out-compete–the native flora and 
fauna.

In a future with further and even stronger warming, climate change and its many impacts will become increasingly 
more important threats. Several species and ecosystems are projected to be already at high risk at 2°C GWL, including 
fishes and lake and river ecosystems. At 3°C GWL, many European ecosystems, such as coastal wetlands, peatlands 
and forests, are projected to be at much higher risk of being severely disrupted than in a 2°C warmer world. For 
example, Mediterranean seagrass meadows will very likely become extinct due to more frequent, longer and more 
severe marine heatwaves by 2050. Several wetland and forest plants and animals will be at high risk to be replaced 
by invasive species that are better adapted to increasingly dry conditions, especially in boreal and Arctic ecosystems.
Current protection and adaptation measures, such as the Natura 2000 network of protected areas, have some

positive effects for European ecosystems; however, these policies are not sufficient to effectively curb overall 
ecosystem decline, especially for the projected higher risks above 2°C GWL. NbS, such as the restoration of wetlands, 
peatlands and forests, can serve both ecosystem protection and climate-change mitigation through strengthening 
carbon sequestration. Some climate-change mitigation measures, such as reforestation and restoration of coastal 
ecosystems, can strengthen conservation measures. These approaches are projected to reduce risks for European 
ecosystems and biodiversity, especially when internationally coordinated.
Not all climate-change adaptation options are beneficial to ecosystems. When planning and implementing 
adaptation options and NbS, trade-offs and unintended side effects should be considered. On one hand, engineering 
coastal protection measures (seawalls, breakwaters and similar infrastructure) in response to SLR reduce the space

available for coastal ecosystems. One the other hand, NbS can also have unintended side effects, such as increased 
methane release from larger wetland areas and large-scale tree planting changing the albedo of the surface.

13
1891Europe Chapter 13
FAQ 13.3 | How can people adapt at individual and community level to heatwaves in Europe?
Heatwaves will become more frequent, more intense and will last longer. A range of adaptation measures are available for communities 
and individuals before, during and after a heatwave strikes. Implementing adaptation measures are important to reduce the risks of future heatwaves.
Heatwaves affect people in different ways; risks are higher for the elderly, pregnant women, small children, people 
with pre-existing health conditions and low-income groups. By 2050, about half of the European population may be exposed to high or very high risk of heat stress during summer, particularly in SEU and increasingly in EEU and WCE. The severity of heat-related risks will be highest in large cities, due to the UHI effect.
In SEU, people are already aware of the risks of heat extremes. Consequently, governments and citizens have

implemented a range of adaptation responses to reduce the impacts of heatwaves; however, there are limits to how much adaptation can be implemented. At 3°C GWL, there will be substantial risks to human lives and productivity, which cannot be avoided. In the parts of Europe where heatwaves are a relatively new phenomenon, such as many parts of NEU and WCE, public awareness of heat extremes is increasing and institutional capacity to respond is growing.
Preparing for heatwaves is an important first step. Implementing and sustaining effective measures, such as national

or regional early warning and information systems, heatwave plans and guidelines, and raising public awareness through campaigns, are successful responses. Evidence suggests that such measures have contributed to reduced mortality rates in SEU and WCE. At city level, preparing for heatwaves can sometimes require urban re-design. For example, green–blue spaces, such as recreational parks and ponds in cities, have been shown to reduce the average temperature in cities dramatically and to provide co-benefits, such as improved air quality and recreational space. The use of cool materials in asphalt, increasing reflectivity, green roofs and building construction measures are being considered in urban planning for reducing heat risks. Citizens can prepare themselves by using natural ventilation, using approaches to stay cool in heatwaves, green roofs and green façades on their buildings.

During heatwaves, public information that is targeted at people and social care providers is critical, particularly for 
the most vulnerable citizens. Governments and NGOs play an important role in informing people about how to prepare and what to do to avoid health impacts and reduce mortality. Coordination between vital emergency and health services is critical. Individuals can take several actions to effectively protect themselves from heat including (a) decrease exposure to high temperatures (e.g., avoid outdoor during hottest times of the day, access cool areas,wear protective and appropriate clothing), (b) keep hydrated (e.g., drink enough proper fluids, avoid alcohol, etc.)and (c) be sensitive to the symptoms of heat illness (dizziness, heavy sweating, fatigue, cool and moist skin withgoosebumps when in heat, etc.).
Once the heatwave has ended, evaluation of what worked well and how improvements can be made is key

to prepare for the next heatwave. Governments can, for example, evaluate whether the early warning systems provided timely and useful information, whether coordination went smoothly and assess the estimated number of lives saved, to determine the effectiveness of the measures implemented. Sharing these lessons learned is critical to allow other cities and regions to plan for heat extremes. After the heatwave, citizens can reflect if their responses were sufficient, whether investments are needed to be better prepared and draw key lessons about what (not) to do when the next heatwave strikes.

13Chapter 13 Eur ope
1892Frequently Asked Questions
FAQ 13.4 | What opportunities does climate change generate for human and natural systems in Europe?
Not all climate-change impacts across Europe pose challenges and threats to natural communities and human society. In some regions, and for 
some sectors, opportunities will emerge. Although these opportunities do not outweigh the negative impacts of climate change, considering these in adaptation planning and implementation is important to benefit from them. Nevertheless, Europe will face difficult decisions balancing the trade-offs between the adaptation needs of different sectors, regions and adaptation and mitigation actions.
Opportunities of climate change can be (a) positive effects of warming for specific sectors and regions, such as

agriculture in NEU, and (b) co-benefits of transformation of cities or transport measures that reduce the speed and impact of climate change while improving air quality, mental health and well-being. Windows of action for transformation opportunities for large-scale transitions and transformation of our society may be accelerated through new policy initiatives in response to the COVID-19 crisis, such as the European New Green Deal and Building Back Better.
As warming and droughts impact SEU most strongly, direct opportunities from climate change are primarily in 
northern regions, thereby increasing existing inequalities across Europe. Across Europe, positive effects of climate change are fewer than negative impacts and are typically limited to some aspects of agriculture, forestry, tourism and energy sectors. In the food sector, opportunities emerge by the northward movement of food production zones, increases in plant growth due to CO

2 fertilisation and reduction of heating costs for livestock during cold 
winters. In the energy sector, positive effects include increased wind energy in the southwest Mediterranean and reduced energy demand for heating across Europe. While climatic conditions for tourist activities are projected to decrease for winter tourism (e.g., insufficient snow amount) and summer tourism in some parts of Europe (e.g., too much heat), conditions may improve during spring and autumn in many European locations. Fewer cold waves will reduce risks on transport infrastructure, such as cracking of road surface, in parts of NEU and EEU particularly by the end of the century.
Indirect opportunities emerge from the co-benefits of implementing adaptation actions. Some of these co-benefits

are widespread but need careful consideration in order to be utilised. For example, an NbS approach to adaptation can make cities and settlements more liveable, increase the resilience of agriculture and protect biodiversity. Ecosystem-based adaptation can attract tourists and create recreational space. There are opportunities to mainstream adaptation into other developments and transitions, including the energy or agricultural transitions as well as COVID-19 recovery plans. Transformative solutions to achieve sustainability may be accelerated through larger changes of, for example, behaviour, energy, food or transport, to better exploit new opportunities and co-benefits. Implementation of adaptation actions can also help to make progress towards achieving the SDGs.
Inclusive, equitable and just adaptation is critical for CRD considering SDGs, gender as well as IKLK and practices.

Implementation requires political commitment, persistence and consistent action across scales of government. Upfront mobilisation of political, human and financial capital in implementation of adaptation actions is key, even when the benefits are not immediately visible.

14
1931North America Chapter 14
Since AR5, climate-change impacts have become more frequent, 
intense and have affected many millions of people from every region and sector across North America (Canada, USA and Mexico). Accelerating climate-change hazards pose significant risks to the well-being of North American populations and the natural, managed and human systems on which they depend (high confidence
1). Addressing these risks has been made more 
urgent by delays due to misinformation about climate science that has sowed uncertainty and impeded recognition of risk (high confidence). {14.2, 14.3}
Without limiting warming to 1.5°C, key risks to North America

are expected to intensify rapidly by mid-century (high confidence). These risks will result in irreversible changes to ecosystems, mounting damages to infrastructure and housing, stress on economic sectors, disruption of livelihoods, and issues with mental and physical health, leisure and safety. Immediate, widespread and coordinated implementation of adaptation measures aimed at reducing risks and focused on equity have the greatest potential to maintain and improve the quality of life for North Americans, ensure sustainable livelihoods and protect the long-term biodiversity, and ecological and economic productivity, in North America (high confidence). Enhanced sharing of resources and tools for adaptation across economic, social, cultural and national entities enables more effective short- and long-term responses to climate change. {14.2, 14.4, 14.5, 14.6, 14.7}
Past and Current Impacts and AdaptationOver the past 20  years, climate-change impacts across North

America have become more frequent, intense and affect more of the population (high confidence). Despite scientific certainty 
of the anthropogenic influence on climate change, misinformation and politicisation of climate-change science has created polarisation in public and policy domains in North America, particularly in the USA, limiting climate action (high confidence). Vested interests have generated rhetoric and misinformation that undermines climate science and disregards risk and urgency (medium confidence). Resultant public misperception of climate risks and polarised public support for climate actions is delaying urgent adaptation planning and implementation (high confidence). Drawing upon Indigenous knowledge, enhancing communication and outreach and undertaking collaborations to co-create equitable solutions are critical for successful climate action. {Box 14.1, 14.3, 14.7}
Climate change has negatively impacted human health and well-

being in North America (very high confidence). High temperatures have increased mortality and morbidity (very high confidence), with impacts that vary by age, gender, location and socioeconomic conditions (very high confidence). Changes in temperature and precipitation have 
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.increased risk of vector-borne (very high confidence), water-borne

(high confidence) and food-borne diseases (very high confidence). Changes in climate and extreme events have been linked to wide-ranging negative mental health outcomes (high confidence). The loss of access to marine and terrestrial sources of protein has impacted the nutrition of subsistence-dependent communities across North America (high confidence). Climate change has increased the extent of warmer and drier conditions favourable for wildfires (medium confidence) that increase respiratory distress from smoke (very high confidence). {14.5.2, 14.5.6, Box 14.2}
North American food production is increasingly affected by

climate change (high confidence), with immediate impacts on the food and nutritional security of Indigenous Peoples. Climate change and extreme weather events have impacted North American agroecosystems (high confidence), with crop-specific effects that vary in direction and magnitude by event and location. Climate change has generally reduced agricultural productivity by 12.5% since 1961, with progressively greater losses moving south from Canada to Mexico and in drought-prone rain-fed systems (high confidence) while favourable conditions increased yields of maize, soybeans in regions like the USA Great Plains. Loss of availability and access to marine and terrestrial sources of protein has impaired food security and nutrition of subsistence-dependent Indigenous Peoples across North America (high confidence). Climate change has impacted aquaculture (high confidence) and induced rapid redistribution of species (very high confidence), and population declines of multiple key fisheries

Climate change has impaired North American freshwater re-
sources and reduced supply security (high confidence). Reduced snowpack and earlier runoff (high confidence) have adversely affected aquatic ecosystems and freshwater availability for human uses (medi-um confidence). Recent severe droughts, floods and harmful algal and pathogen events have caused harm to large populations and key eco-nomic sectors (high confidence). Heavy exploitation of limited water supplies, especially in the western USA and northern Mexico, and deteriorating freshwater management infrastructure, have heightened the risks (high confidence). Effective examples of freshwater resource adaptation planning are already underway, but coordinated adapta-tion implementation across multiple conflicting interests and users is complicated and time-consuming (high confidence). {14.5.1, 14.5.2, 14.5.3}
Extreme events and climate hazards are adversely affecting

economic activities across North America and have disrupted supply chain infrastructure and trade (high confidence) . Larger losses 
and adaptation costs are observed for sectors with high climate exposures, including tourism, fisheries, and agriculture (high confidence) and outdoor labour (medium confidence). Disaster planning and spending, insurance, markets, and individual and household-level adaptation have acted to moderate effects to date (medium confidence). Entrenched socioeconomic vulnerabilities have amplified climate impacts for marginalised groups,

14
1932Chapter 14 North America
including Indigenous Peoples, due to the impact of colonialism and 
discrimination (medium confidence). {14.5.4, 14.5.5, 14.5.6, 14.5.7, 14.5.9, 
Box 14.1, Box 14.5, Box 14.6}
North American cities and settlements have been affected 
by increasing severity and frequency of climate hazards and 
extreme events (high confidence), which has contributed to 
infrastructure damage, livelihood losses, damage to heritage 
resources and safety concerns. Impacts are particularly apparent for 
Indigenous Peoples for whom culture, identity, commerce, health and 
well-being are closely connected to a resilient environment (very high 
confidence). Higher temperatures have been associated with violent 
and property crime in the USA (medium confidence), yet the overall 
effects of climate change on crime and violence in North America are 
not well understood. {14.4, 14.5.5, 14.5.6, 14.5.8, 14.5.9, Box 14.1}
Terrestrial, marine and freshwater ecosystems are being

profoundly altered by climate change across North America (very 
high confidence). Rising air, water, ocean and ground temperatures 
have restructured ecosystems and contributed to the redistribution 
(very high confidence) and mortality (high confidence) of fish, bird 
and mammal species. Extreme heat and precipitation trends on land 
have increased vegetation stress and mortality, reduced soil quality 
and altered ecosystem processes including carbon and freshwater 
cycling (very high confidence). Warm and dry conditions associated 
with climate change have led to tree die-offs (high confidence) and 
increased prevalence of catastrophic wildfire (medium confidence) 
with an increase in the size of severely burned areas in western 
North America (medium confidence). Nature-based Solutions (NbS) 
and ecosystem-based management have been effective adaptation 
approaches in the past but are increasingly exceeded by climate 
extremes (medium confidence). {14.5.1–3, Box 14.7}

Climate-driven changes are particularly pronounced within 
Arctic ecosystems and are unprecedented based on observations 
from multiple knowledge systems (very high confidence).  
Climate change has contributed to cascading environmental and 
sociocultural impacts in the Arctic (high to very high confidence) that 
have adversely, and often irreversibly, altered Northern livelihoods, 
cultural activities, essential services, health, food and nutritional 
security, community connectivity and well-being (high confidence). 
{14.5.2, 14.5.4, 14.5.6, 14.5.7, 14.5.8, Box 14.6}
Future Risks and Adaptation
Climate hazards are projected to intensify further across North America 
(very high confidence). Heatwaves over land and in the ocean, as well as 
wildfire activity, will intensify; subarctic snowpack, glacial mass and sea ice 
will decline (virtually certain); and sea level rise will increase at geographically

differential rates (virtually certain). Humidity-enhanced heat stress, aridification 
and extreme precipitation events that lead to severe flooding, erosion, debris 
flows and ultimately loss of ecosystem function, life and property are projected 
to intensify (high confidence). {14.2}
Health risks are projected to increase this century under all future 
emissions scenarios (very high confidence), but the magnitude 
and severity of impacts depends on the implementation and effectiveness of adaptation strategies (very high confidence).  
Warming is projected to increase heat-related mortality (very high 
confidence) and morbidity (medium confidence). Vector-borne disease 
transmission, water-borne disease risks, food safety risks and mental 
health outcomes are projected to increase this century (high confidence). 
Available adaptation options will be less effective or unable to protect 
human health under high-emission scenarios (high confidence). {14.5.6, 
Box 14.2}

Climate-induced redistribution and declines in North American 
food production are a risk to future food and nutritional 
security (very high confidence). Climate change will continue 
to shift North American agricultural and fishery suitability ranges 
(high confidence) and intensify production losses of key crops (high 
confidence), livestock (medium confidence), fisheries (high confidence) 
and aquaculture products (medium confidence). In the absence of 
mitigation, incremental adaptation measures may not be sufficient to 
address rapidly changing conditions and extreme events, increasing the 
need for cross-sectoral coordination in implementation of mitigation 
and adaptation measures (high confidence). Combining sustainable 
intensification, approaches based on Indigenous knowledge and local 
knowledge, and ecosystem-based methods with inclusive and self-
determined decision making, will result in more equitable food and

nutritional security (high confidence). {14.5.1–4, 14.5.6, 14.7, Cross-
Chapter Box INDIG in Chapter 18, Cross-Chapter Box MOVING PLATE 
in Chapter 5}
Escalating climate-change impacts on marine, freshwater and 
terrestrial ecosystems (high confidence) will alter ecological 
processes (high confidence) and amplify other anthropogenic 
threats to protected and iconic species and habitats (high 
confidence). Hotter droughts and progressive loss of seasonal water 
storage in snow and ice will tend to reduce summer season stream 
flows in much of western North America, while population growth, 
extensive irrigated agriculture and the needs of threatened and 
endangered aquatic species will continue to place high demands on 
those flows (high confidence). {14.2.2, 14.5.1, 14.5.2, 14.5.3, 14.5.4, 
14.5.6, Box 14.7.1}
Market and non-market economic damages are projected 
to increase to the end of the century from climate impacts

(high confidence). Estimates for the costs of climate inaction are 
substantial across economic sectors, infrastructure, human health and 
disaster management. Hard limits to adaptation may be reached for 
outdoor labour (medium confidence) and nature-based winter tourism 
activities (very high confidence). At higher levels of warming, climate 
impacts may pose systemic risks to financial markets through impacts 
on transportation systems, supply chains and major infrastructure, as 
well as global-scale challenges to trade (medium confidence). {14.2.2, 
14.5.4, 14.5.8, 14.5.7, 14.5.9, 14.5.5, Box 14.5, Box 14.6}
Solution Space and Governance
Self-determination for Indigenous Peoples is critical for effective 
adaptation in Indigenous communities (very high confidence). 
Throughout North America, Indigenous Peoples are actively addressing 
the compound impacts of climate change, and historical and ongoing

14
1933North America  Chapter 14
forms of colonialism (very high confidence). Indigenous knowledge 
underpins successful understanding of, responses to, and governance of 
climate-change risks. Western scientific practices and technology may 
not be sufficient in addressing future natural resource management 
challenges. Supporting Indigenous self-determination, recognising 
Indigenous Peoples’ Rights, and supporting adaptation underpinned by 
Indigenous knowledge are critical to reducing climate-change risks to 
achieve adaptation success (very high confidence). {14.7.3, Box 14.1}
Equitable, inclusive and participatory approaches that integrate 
climate-impact projections into near- and long-term decision 
making reduce future risks (high confidence). Government and 
private investment are increasingly focusing on early warning and 
rapid response systems, climate and ecological forecasting tools, and 
integrated climate scenario planning methods. Widespread adoption

of these practices and tools for infrastructure planning, disaster risk 
reduction, ecosystem management, budgeting practices, insurance, 
and climate risk reporting supports planning for a future with more 
climate risks (high confidence). Increased capacity to support the 
equitable resolution of existing and emerging resource disputes 
(local to international) will reduce climate impacts on livelihoods and 
improve the effectiveness of resource management (high confidence). 
{14.5.5, 14.5.10, 14.7}
Near- and long-term adaptation planning, implementation and 
coordination across sectors and jurisdictions supports equitable 
and effective climate solutions (high confidence). Recognition of 
the need for adaptation across North America is increasing, but action 
has been mostly gradual, incremental and reactive (high confidence). 
Current practices will be increasingly insufficient without coordination 
and integration of efforts through equitable policy focused on

modifying land-use impacts, consumption patterns, economic activities 
and emphasising NbS (high confidence). Transformational, long-term 
adaptation action that reduces risk and increases resilience can address 
rapidly escalating impacts in the long-term, especially if coupled with 
moderate to high mitigation measures (high confidence). {14.7}

14
1934Chapter 14 North America
14.1 Introduction and Point of Departure
Earth’s climate is currently changing in significant ways as a result 
of human activities, and future projections indicate continued and 
possibly accelerating change without reductions in greenhouse 
gas (GHG) emissions (Gutiérrez et  al., 2021; IPCC, 2021). Climate 
change affects human and natural systems; this chapter provides an 
assessment of present and future climate-change impacts, risks and 
adaptation for North America, including Mexico, Canada, the USA and 
coastal waters within the 370-km exclusive economic zone. We do not 
consider Hawaii and other island territories of the USA in depth as 
they are assessed in Chapter 15. Chapter 14 assesses evidence from 
Arctic Canada and Alaska, which is synthesised in Cross-Chapter Paper 
6 Polar Regions (CCP6).
Evidence from Indigenous knowledge (IK) systems is included in this 
chapter to assess climate-change risks and solutions in North America

following the framing provided in Chapter 1 Special Report on the Ocean 
and Cryosphere in a Changing Climate (SROCC) (Abram et  al., 2019) 
and Special Report on Climate Change and Land (SRCCL) (IPCC, 2019a). 
Indigenous contributing authors provided this assessment, reflecting the 
importance of meaningfully including IK in assessment processes (Ford, 
2012; Ford et  al., 2016; Hill et  al., 2020). This addition represents an 
important advancement since AR5 (IPCC, 2013; IPCC, 2014).
Our main point of departure was the Fifth Assessment Report (AR5) for 
WGII (IPCC, 2014). Key findings drawn from the Executive Summary for 
the North America chapter are summarised in Table 14.1. Subsequent 
IPCC reports, such as Special Report on Global Warming of 1.5°C 
(SR1.5) (Hoegh-Guldberg et  al., 2018; IPCC, 2018), SROCC (IPCC, 
2019b) and SRCCL (IPCC, 2019a), also informed the assessment. We 
additionally incorporated recent national climate assessments of the

USA (USGCRP , 2018) and Canada (Bush and Lemmen, 2019; Warren 
and Lulham, 2021) as well as the Sixth National Communication of 
Mexico to the United Nations Framework Convention on Climate 
Change (SEMARNAT and INECC, 2018).Chapter 14 sections are organised to address themes and content as 
contained in the IPCC-approved outline for regions. Regional climate 
changes assessed within North America are keyed to Figure 14.1 using 
four-letter abbreviations (e.g., CA-ON, US-SE, MX-NW). The assessment 
addresses recent and future climate for North America, the impacts, risks 
and adaptations within sectors, key risksacross sectors (KR), the nature 
of adaptation and sustainable development pathways as well as two 
additional sections on Indigenous Peoples and perceptions of climate 
change. Seven boxes are used to highlight topics of interdisciplinary 
nature while four frequently asked questions (FAQ) were produced in 
plain language for communication to the public. The chapter utilises

the framework as well as designated terms in the standardised 
process for evaluating and characterising the degree of certainty in 
assessment findings developed through the expert judgement process 
(Section  1.3.4; Mach et  al., 2017). The Glossary [Annex II] provides 
definitions for terms and concepts used across the report.
14.1.1 Context
With a 2019 total population of over 494 million people (USA 329 million, 
Mexico 128 million and Canada 37 million), North America comprises 
6.4% of the global population. Relative to other countries, North 
American countries have low population densities per square kilometre 
(Mexico 64 people, USA 35 people and Canada 4 people) (United 
Nations, 2019). Population projections indicate a steady growth in the 
three countries, which will exert pressure on consumption and increase 
risks under climate change (United Nations, 2019). North America is 
also responsible for about a quarter of global greenhouse gas (GHG)

emissions. Since 1990, North American GHG emissions have increased 
by almost 18% (Ritchie and Roser, 2020), and in 2019 the region was 
responsible for 5.9 MtCO 2 emissions worldwide (Friedlingstein et  al., 
2020). In terms of annual CO 2 emissions per capita, in 2019 Canada 
had 15 metric tons of CO 2 per person (tCO 2 per person), the USA had 
16 tCO 2 per person and Mexico had 3.4 tCO 2 per person (Friedlingstein 
Table 14.1 |  Key findings from AR5 North America chapter (Romero-Lankao et al., 2014b)
General topic AR5 finding
Climate hazardsClimate has changed in North America, with some changes attributed to human activities.
Climate hazards, especially related to heatwaves, heavy precipitation and snowpack, are expected to change in ways that are adverse to natural and human 
systems.
Natural ecosystems Warming, increasing carbon dioxide (CO 2) concentrations, sea level rise (SLR) and climate extremes are stressing ecosystems.

Human systemsWater resources that are already stressed in many parts of North America are expected to become further stressed by climate change. Current adaptation options 
can address water supply deficits, but responses to flooding and water quality concerns are more limited.
Climate change has affected yields of major crops, and projections indicate continued declines, although with variability.
Extreme climate events have affected human health, although climate-change-related trends and attribution to climate change have not been confirmed.
Multiple aspects of climate change have affected livelihoods, economic activities, infrastructure and access to services.
Much infrastructure is vulnerable to extreme weather events, and unless adaptation investments are made, vulnerability to future climate change will persist and 
increase.

Most sectors of the North American economy have been affected by and have responded to extreme weather, including hurricanes, flooding and intense rainfall.
AdaptationTechnological innovation, institutional capacity-building, economic diversification and infrastructure design are adaptations for reducing current climate impacts as 
well as future risks due to a changing climate.
North American governments predominantly have undertaken incremental adaptation assessment and planning at the municipal level. Limited proactive, 
anticipatory adaptation is directed at long-term investment for energy and public infrastructure.

14
1935North America  Chapter 14
North American regions and subregions
US Midwest
US Northwest
US Northern Great Plains
US Northeast
US Southwest
US Southern Great Plains
US Arctic
US Southeast
US HawaiiUS-MW
US-NW
US-NP
US-NE
US-SW
US-SP
US-AK
US-SE
US-HICA-ON
CA-BC
CA-PR
CA-QC
CA-NW
CA-NE
CA-A TCanada Ontario 
Canada British Columbia
Canada Prairies
Canada Québec
Canada W estern Arctic
Canada Eastern Arctic
Canada AtlanticNorth America Sub-Regions
Canadian Arctic Archipelag o
Caribbean Sea
Gulf of Alaska
Gulf of California
Gulf of Main e
Gulf of Mexic o
Hudson Bay
Northern California Current
British Columbia coast
Southern California Curren tBS
CAA
CAS
CS
EBS
GOA
GOC
GOM
GOMX
HB
N-CC
P-BC
S-CCNorth America Marine Basins
Mexico Northwest
Mexico North
Mexico Northeast
Mexico Southeast
Mexico Southwest
Mexico V eracruz
Mexico CentreMX-NW
MX-N
MX-NE
MX-SE
MX-SW
MX-VC
MX-CE
300km
Canada
USAEEZ
Mexico

Figure 14.1 |  North American regions and sub-regions, adapted from national climate assessments, and city names, referred to in discussion of local and 
regional climate-change impacts and adaptation. White dashed line denotes the southern boundary across North America of the Arctic region defined in Cross-Chapter 
Paper 6.

14
1936Chapter 14 North America
14.2 Current and Future Climate in North 
America
Trends in observed and projected physical climate variables, and 
changes in extreme weather and climate events, are summarised in 
this section. Many of the assessments here are adapted from AR6 WGI 
(IPCC, 2021), especially Chapters 11 (Seneviratne et al., 2021) and 12 
(Ranasinghe et  al., 2021), and the Atlas (Gutiérrez et  al., 2021a,  b). 
Ranasinghe et  al., 2021, Section  12.4.6, assesses North American 
climatic impact drivers without assessing their impacts or associated 
risks. The WGI assessments are augmented in this section with 
regionally specific support from recent national climate assessments 
or original literature.
14.2.1 Observed Changes in North American Climate
Climate changes directly related to increasing mean and extreme 
temperature, including reduced snowpack, sea and lake ice and glacier 
extent, and marine heatwaves (MHWs), can be attributed to human

activity and are affecting most of North America (high confidence). 
Upward trends in annual mean temperature across North America 
since 1960 are widespread (Gutiérrez et al., 2021a) but non-uniform 
(Figure 14.2A). Pronounced polar amplification of warming is observed 
in high latitudes (Figure 14.2A), particularly in winter (Gutiérrez et al., 
2021a; Vose et al., 2017; Zhang et al., 2019a). As average temperature 
rises, extreme high temperature records across North America are being 
set more frequently than extreme cold records (Meehl et  al., 2016) 
and the probability of cold extreme events is reduced (WGI Chapter 
11 [Seneviratne et al., 2021]). Trends in daily maximum and minimum 
temperature are significant in high latitudes (US-AK, CA-NW, CA-NE). 
Summer daily maximum temperature is increasing in southwest desert 
regions (US-SW, MX-NW) (Martinez-Austria et  al., 2016; Martinez-
Austria and Bandala, 2017; Navarro-Estupinan et al., 2018).

Annual precipitation has increased in recent decades in northern and 
eastern areas (CA-PR, CA-QU, US-NP , US-SP , US-MW, US-NE, US-AK) 
(high confidence), and has decreased across the western part of the 
continent (CA-BC, US-SW, US-NW, MX-NW) (medium confidence), 
with considerable spatial variability within these regions (Zhang 
et  al., 2019a; Gutiérrez et  al., 2021a). Elsewhere across North 
America there is limited evidence and low agreement on detection 
of observed trends in total precipitation and river flood hazards. The 
intensity and frequency of 1-day heavy precipitation events have 
very likely2 increased since the mid-20th Century across most of 
the USA (US-NP , US-MW, US-NE, but not in US-SE) and in Mexico, 
but no detectable trend is reported in Canada (Seneviratne et  al., 
2021; Zhang et al., 2019a). Recent flooding events along the mid-
latitude Pacific Coast have been attributed to increasingly intense

atmospheric river events ( Douville et  al., 2021; Gershunov et  al., 
2019; Vano et  al., 2019), but there is low confidence in detecting 
trends in atmospheric river activity.
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about 
as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10% and exceptionally unlikely 0–1%. Additional terms (extremely likely: 95–100%, more likely than not >50–100% and extremely unlikely 
0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics (e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome 
lies within the 17–83% probability range.Snowpack and snow extent across much of Canada and the western 
USA have declined as temperatures have increased (very high

confidence) (Ranasinghe et al., 2021; Gutierrez et al., 2021a; Kunkel 
et al., 2016; Mote et al., 2018; Mudryk et al., 2018; Derksen et al., 2019). 
Warm ‘snow droughts’ describing a deficit of snowpack available for 
runoff, even in the absence of a winter precipitation deficit (Cooper 
et al., 2016; Harpold et al., 2017), have become more common in North 
American mountains (Sproles et al., 2016; Nicholls et al., 2018; Pershing 
et al., 2018). Glaciers have retreated over the past half-century at high 
elevation across North America (Frans et al., 2018; Zemp et al., 2019) 
and in the Arctic (Burgess, 2017; Box et al., 2019; Derksen et al., 2019). 
Lake ice in Canada, south of the Arctic region delineated in Figure 14.1, 
has declined (Alexeev et al., 2016; Derksen et al., 2019).
There is limited evidence of trends in meteorological or hydrological 
droughts over the historical record (see Douville et  al. (2021) and

Seneviratne et al. (2021) for multiple perspectives on drought; Wehner 
et al., 2017), but there is medium confidence in increasing atmospheric 
evaporative demand acting to intensify surface aridity during recent 
droughts (e.g., US-SW) (Seneviratne et al., 2021; Williams et al., 2020). 
The ongoing multi-decadal dry period in the Colorado River basin is as 
extreme as any drought in the past 1000 years (Murphy and Ellis, 2019; 
Williams et al., 2020).
The proportion of hurricanes in stronger categories has likely increased 
globally over the past 40  years, with medium confidence that the 
onshore propagation speed of hurricanes making landfall in the USA 
has slowed detectably since 1900 (Seneviratne et  al., 2021; Kossin, 
2018), contributing to detectable increases in local rainfall and coastal 
flooding associated with these storms. There is high confidence 
(Seneviratne et  al., 2021) that anthropogenic climate change has

contributed to extreme precipitation associated with recent intense 
hurricanes, such as Harvey in 2017.
North American sea ice extent and volume (thickness) have declined 
up to 10% per decade since 1981 (Fox-Kemper et  al., 2021; Ding 
et al., 2017; Mudryk et al., 2018; Derksen et al., 2019; IPCC, 2019b), 
with changes accelerating during this time (robust evidence, high 
agreement) (Schweiger et  al., 2019), resulting in longer and larger 
periods of open water (Wang et  al., 2018a). Recent (2018) sea ice 
extent in the Bering Sea was the lowest in a 5500-year record and 
appears to lag atmospheric CO 2 by about two decades (Jones et al. 
2021). High Arctic sea ice retreat since 1971 and increases in open-
water duration in the most recent decade are unprecedented (Box 
et al., 2019) and most pronounced in the Chukchi, Bering and Beaufort 
seas (US-AK, CA-NW) (high confidence) (Wang and Overland, 2015; 
Jones et al., 2020).

Warming of North American offshore waters is significant and 
attributable to human activities, particularly along the Atlantic coast, 
contributing to sea level rise (SLR) through thermal expansion (very 
high confidence) (Fox-Kemper et al., 2021; IPCC, 2019b). Rates of SLR 
have accelerated along most North American coasts during the past

14
1937North America  Chapter 14
Observed and projected climate changes across North America
8°CHistorical temperature trend+2°C
6°C
4°C
2°C
0°C
30%
20%
10%
0%
-30%-10%
-20%
45 d yr-1
35 d yr-1
25 d yr-1
15 d yr-1
5 d yr-1∆T °C
∆PR
Bias Adj.
∆TX40(a) (b) (c)
(d) (e)
(f) (g)Daymet 1980–2015 observations
mean temperature trend (°C decade-1)
-0.3 0.1 0.3 0.5 -0.5 -0.1
Figure 14.2 |  Observed and projected climate changes across North America. Black boundary lines delineate North American sub-regions (Figure 14.1). Data were 
extracted from Gutiérrezet al. 2021a via http://interactive-atlas.ipcc.ch/ (WGI Interactive Atlas) (Gutiérrez et al., 2021b), where dataset details can be found. (A) Recent observations; 
(B) to (G) are from an ensemble of CMIP6 projections. 
(A) Observed annual mean temperature trend over land for 1980–2015.

(B,C) Projected change in annual mean temperature over land relative to the 1986–2005 average, associated with 2°C or 4°C global warming. 
(D,E) Like (B,C) but for projected percentage change in annual precipitation.
(F,G) Like (B,C) but for projected change in number of days per year with maximum temperature >40°C (‘TX40’).
three decades, excepting coastlines in southern Alaska (US-AK) and 
northeastern Canada (CA-QC, CA-NE) where land is rising (Ranasinghe 
et  al., 2021; Greenan et  al., 2018). Tidal flooding frequency has 
increased in the North Pacific from once every 1–3  years to every 
6–12 months (Sweet et al., 2014).
Acidification of North American coastal waters has occurred in 
conjunction with increased atmospheric CO 2 concentration (Mathis 
et al., 2015; Jewett and Romanou, 2017; Claret et al., 2018) combined 
with other local acidifying inputs such as nitrogen and sulphur 
deposition (Doney et  al., 2007) and freshwater nutrient input (very

high confidence) (Strong et al., 2014; IPCC, 2019b). Oxygen minimum 
zones, particularly in the North Pacific south of US-AK, have expanded 
in volume and O 2 has declined since 1970 (IPCC, 2019b).
14.2.2 Projected Changes in North American Climate
Climate changes related to warming temperature, including more 
intense heatwaves over land and in the ocean, diminished snowpack, 
sea ice reduction and SLR, are projected with high confidence and are strongly sensitive to future GHG concentrations (Figure  14.2). 
Climatic hazards affected by hydrological change, including humidity-
inclusive heat stress, extreme precipitation and more intense storms, 
are projected to intensify.
Pronounced amplification of warming across the Arctic and continental 
intensification of warming (Figure  14.2B,C) is projected with high 
confidence (Doney et al., 2007; Vose et al., 2017). Extreme heatwaves 
are projected to intensify, particularly in MX-NW, MX-N, MX-NE, US-

SW, US-NP and US-SP (Figure  14.2F ,G) and become more frequent 
and longer in duration as average temperature rises across North 
America (Seneviratne et al., 2021). Extreme cold events are projected 
to decrease in severity (Ranasinghe et al., 2021; Wuebbles et al., 2014).
Total precipitation is projected to increase across the northern half 
of North America (very high confidence) and decrease in southwest 
North America (MX-SW, MX-NW, US-SW) (medium confidence) 
(Figure  14.2D,E; Gutiérrez et  al., 2021b). Further increases in the 
intensity of locally heavy precipitation are very likely across the 
continent, as a greater fraction of precipitation falls in intense events 
(Easterling et al., 2017; Prein et al., 2017a; Zhang et al., 2019a).

14
1938Chapter 14 North America
High-humidity hazards are projected to increase (medium confidence) 
in regions around the Gulf of Mexico and southeast North America 
(US-SE, US-SP , MX-NE, MX-SE) (Zhao et  al., 2015). In subtropical 
regions that are less influenced by moisture from the Gulf of Mexico 
(including US-SW, US-SP , MX-NW and MX-N), the combination of 
higher temperature and less total precipitation leads to projections of 
increased aridity: drier surface conditions, higher evaporative demand 
by plants and more intense droughts (Ranasinghe et al., 2021; Jones 
and Gutzler, 2016; Easterling et  al., 2017; Escalante-Sandoval and 
Nuñez-Garcia, 2017).
As temperatures rise, snow extent, duration of snow cover and 
accumulated snowpack are virtually certain to decline in subarctic 
regions of North America (Gutierrez et al., 2021a; McCrary and Mearns, 
2019; Mudryk et  al., 2021), with corresponding effects on snow-

related hydrological changes (high confidence). These changes include 
declines in snowmelt runoff (Li et  al., 2017), increased evaporative 
losses during snow ablation (Foster et  al., 2016; Milly and Dunne, 
2020), as well as increases in the frequency of rain-on-snow events 
(Jeong and Sushama, 2018a) and consecutive snow drought years in 
western North America (Marshall et al., 2019a).
Climate change is projected to magnify the impact of tropical cyclones 
in US-NE, MX-NE, US-SP , and US-SE by increasing rainfall (Patricola and 
Wehner, 2018) and extreme wind speed (high confidence) and slowing 
the speed of land-falling storms (limited evidence, low confidence) 
(Seneviratne et al., 2021; Kossin, 2018). The coastal region at severe 
risk from tropical storms is projected to expand northward within US-
NE (medium confidence) (Kossin et al., 2017).Additional reduction in polar sea ice is virtually certain (Ranasinghe et al.,

2021; Mudryk et  al., 2021), with the North American Arctic projected  
to be seasonally ice free at least once per decade under 2°C of global 
warming (high confidence) (IPCC, 2019b; Mioduszewski et  al., 2019; 
Mudryk et al., 2018). Duration of freshwater lake ice across the northern 
USA and southern Canada is projected to diminish (high confidence) 
(Ranasinghe et al., 2021; Dibike et al., 2012; Mudryk et al., 2018; Sharma 
Ocean surface temperature is very likely to increase in future decades 
in waters around North America (Jewett and Romanou, 2017; Greenan 
et  al., 2018), but at a slower rate than air temperature over the 
continent. Rates of change are projected to be relatively higher in 
northern latitudes, with most rapid warming in summer in the Arctic 
and Bering Sea (US-AK, CA-NW) (Wang and Overland, 2015; Wang 
et al., 2018a; Hermann et al., 2019).
Sea level rise is virtually certain to continue along North American

coastlines except for parts of US-AK and around Hudson Bay (HB) 
with geographically variable rates of rise (Fox-Kemper et  al., 2021; 
Ranasinghe et  al., 2021; see Box  14.4). Relatively greater SLR is 
projected along the US-SE and MX-SW coastlines and relatively less 
along CA-BC and US-NW (Fox-Kemper et al., 2021; Ranasinghe et al., 
2021; see Box 14.4) (Fasullo and Nerem, 2018; Greenan et al., 2018 
IPCC, 2019b).
Ocean acidification (OA) along North American coastlines is projected 
to increase (very high confidence) (Jewett and Romanou, 2017). The 
frequency and extent of oxygen minimum and hypoxic zones are 
FAQ 14.1 | How has climate change contributed to recent extreme events in North America and their impacts?
Multiple lines of evidence indicate that climate change is already contributing to more intense and more frequent extreme events across North

America. The impacts resulting from extreme events represent a huge challenge for adapting to future climate change.
Extreme events are a fundamental part of how we experience weather and climate. Exceptionally hot days, torrential 
rainfall and other extreme weather events have a direct impact on people, communities and ecosystems. Extreme 
weather can lead to other impactful events such as droughts, floods or wildfires. In a changing climate, people 
frequently ask whether extreme events are generally becoming more severe or more frequent, and whether an 
actual extreme event was caused by climate change.
Because really extreme events occur rarely (by definition), it can be very difficult to assess whether the overall severity 
or frequency of such events has been affected by changing climate. Nevertheless, careful statistical analysis shows that 
record-setting hot temperatures in North America are occurring more often than record-setting cold temperatures

as the overall climate has gotten warmer in recent decades. The area burned by large wildfires in the western USA 
has increased in recent decades. Observed trends in extreme precipitation events are more difficult to detect with 
confidence, because the natural variability of precipitation is so large and the observational database is limited.
Our understanding of how individual extreme weather events have been influenced by climate change has improved 
greatly in recent years. Climate scientists have developed a formal technique (‘event attribution’, described in WGI 
FAQ 11.3) for assessing how climate change affects the severity or frequency of a particular extreme event, such 
as a record-breaking rainfall event or a marine heatwave. This is a challenging task, because any particular event 
can be caused by a combination of natural variability and climate change. Event attribution is typically carried out

using models to compare the probability of a specific event occurring in today’s climatic environment relative to

14
1939North America  Chapter 14
the probability that the same event might have occurred in a modelled climate in which atmospheric GHGs have 
not risen due to human activities. Using this strategy, multiple studies have estimated that the historically extreme 
rainfall amount that fell across the Houston area from Hurricane Harvey (2017) was three to ten times more likely as 
the result of climate change.
The impacts from extreme events depend not just on physical climate system hazards (temperature, precipitation, 
wind, etc.), but also on the exposure and vulnerability of humans or ecosystems to these events. For example, 
damage from land-falling hurricanes along the coast of the Gulf of Mexico is expected to increase as very strong 
hurricanes become more frequent and intense due to climate change. But damage would also increase with 
additional construction along the shoreline, because coastal development increases exposure to hurricanes. And if

some structures are constructed to poor building standards, as was the case when hurricane Andrew made landfall 
in Florida in 1992, then vulnerability to hurricane-caused impacts is increased.
Climate change also contributes to impacts from extreme events by making some building codes and zoning restrictions 
inadequate or obsolete. Many North American communities limit development in areas known to be flood-prone, 
to minimise exposure to flooding. But as climate change expands the areas at risk of exposure to flooding beyond 
historical floodplains, the impacts of potential flooding are increased, as Hurricane Harvey demonstrated. Adapting to 
climate change may require retrofits for existing structures and revised zoning for new construction. Some structures 
and neighbourhoods may need to be abandoned altogether to accommodate expanded flooding risk.
Climate change can be an added stress that increases impacts from extreme events, combined with other non-climatic

stressors. For example, climate change in western North America has contributed to more extreme fire weather. 
The devastating impacts of recent wildfire outbreaks, such as occurred across western Canada in 2016 and 2017, the 
western United States in 2018 and 2020, and both countries in 2021, are to some extent associated with expanded 
development and forest management practices (such as policies to suppress low-intensity fires, allowing fuel to 
accumulate). The effects of development and forest management have dramatically increased the exposure and 
vulnerability of communities to intense wildfires. Climate change has added to these stressors: warming temperature 
leads to more extreme weather conditions that are conducive to increasingly severe wildfires.
Biodiversity is affected by climate change in this way too. For example, numerous bird populations across North 
America are estimated to have declined by up to 30% over the past half-century. Multiple human-related factors,

including habitat loss and agricultural intensification, contribute to these declines, with climate change as an added 
stressor. Increasingly extreme events, such as severe storms and wildfires, can decimate local populations of birds, 
adding to existing ecological threats.Box FAQ 14.1 (continued)
projected to increase, with less confidence, exacerbated by climate-
driven eutrophication and increasing stratification (Altieri and Gedan, 
2015; IPCC, 2019b).
14.3 Perception of Climate-Change Hazards, 
Risks and Adaptation in North America
14.3.1 Climate Change as a Salient Issue
The majority of the climate science community has reached consensus 
that mean global temperature has increased and human activity is a 
major cause (Oreskes, 2004; Anderegg et al., 2010; Cook et al., 2013; 
Cook et al., 2016; IPCC, 2021), setting the context for public policy 
action. Despite expert scientific consensus on anthropogenic climate

change, there is polarisation and an ongoing debate over the reality of 
anthropogenic climate change in the public and policy domains, with 
attendant risks to society (high confidence) (Doran and Zimmerman, 
2009; Ballew et al., 2019; Druckman and McGrath, 2019; Hornsey and Fielding, 2020; Wong-Parodi and Feygina, 2020). Public perception 
of consensus regarding anthropogenic climate change can be an 
important gateway belief, which establishes a crucial precondition 
for public policy action (van der Linden et al., 2015; van der Linden 
et  al., 2019) by influencing the assessment of climate-change risks 
and opportunities, and formulation of appropriate mitigation and 
adaptation responses (Ding et al., 2011; Bolsen et al., 2015; Drews 
and Van den Bergh, 2016; Doll et al., 2017; Mase et al., 2017; Morton 
et al., 2017). Trust in experts, institutions and environmental groups 
is also important (Cologna and Siegrist, 2020; Termini and Kalafatis, 
2021).

Rhetoric and misinformation on climate change and the deliberate 
undermining of science have contributed to misperceptions of the 
scientific consensus, uncertainty, disregarded risk and urgency, and 
dissent (high confidence) (Ding et  al., 2011; Oreskes and Conway, 
2011; Aklin and Urpelainen, 2014; Cook et al., 2017; van der Linden 
et al., 2017). Additionally, strong party affiliation and partisan opinion 
polarisation contribute to delayed mitigation and adaptation action,

14
1940Chapter 14 North America
most notably in the USA (high confidence) (van der Linden et  al., 
2015; Cook and Lewandowsky, 2016; Bolsen and Druckman, 2018; 
Chinn et  al., 2020) but with similar patterns in Canada (medium 
confidence) (Lachapelle et al., 2012; Kevins and Soroka, 2018). Vocal 
groups can affect public discourse and weaken public support for 
climate mitigation and adaptation policies (medium confidence) 
(Aklin and Urpelainen, 2014; Lewandowsky et  al., 2019). Vested 
economic and political interests have organised and financed 
misinformation and ‘contrarian’ climate-change communication 
(Brulle, 2014; Farrell, 2016a; Farrell, 2016b; Supran and Oreskes, 2017; 
Bolsen and Druckman, 2018; Brulle, 2018). Traditional media–print 
and broadcast–frame and transmit climate-change information and 
play a crucial role in shaping public perceptions, understanding and 
willingness to act (Happer and Philo, 2013; Schmidt et  al., 2013;

Hmielowski et al., 2014; Bolsen and Shapiro, 2018; King et al., 2019; 
Chinn et al., 2020). The journalistic norm of ‘balance’ (giving equal 
weight to climate scientists and contrarians in climate-change 
reporting) biases coverage by unevenly amplifying certain messages 
that are not supported by science, contributing to politicisation of 
science, spreading of misinformation and reducing public consensus 
on action (Boykoff and Boykoff, 2004; Boykoff and Boykoff, 2007; Cook 
et al., 2017). Much online social media discussion of climate change 
takes place in ‘echo chambers’–a social network among like-minded 
people in communities dominated by a single view that contributes to 
polarisation (Williams et al., 2015; Pearce et al., 2019) and the spread 
of misinformation (Treen et al., 2020).
14.3.2 Public Perceptions, Opinions and Understanding of 
In a 2018 survey across 26 nations, people in Canada and Mexico

ranked climate change as the top global threat, whereas in the USA 
climate change ranked third (Poushter and Huang, 2019). The public’s 
responses to the causes of climate change and risk perceptions in 
Canada (Mildenberger et al., 2016) and the USA (Howe et al., 2015) 
have revealed variations among regions (Figure  14.3) and less 
acceptance of climate change in rural regions than in urban areas. 
Canadian regions have higher acceptance of climate change (e.g., 
recognise it is happening and attributable to human activity) than the 
most liberal areas in the USA (Lachapelle et al., 2012; Mildenberger 
et  al., 2016). Western Canadian regions with high carbon intensity 
economies had lower acceptance of climate change than the rest of 
Canada, whereas in the USA perceptions were more stable across 
regions (Lachapelle et al., 2012). A recent survey in Mexico found that 
for 73% of respondents climate change represents a major economic,

environmental and social threat, and in the most vulnerable states 
(MX-SE), the perception is that climate-change impacts and 
extreme events have considerable implications for the way of life in 
communities (Zamora Saenz, 2018). In a 2017 survey, Azócar et al. 
(2021) found that 85% of respondents from Mexico acknowledged 
anthropogenic climate change. Peoples’ experience with extreme 
events (e.g., hurricanes, high temperatures), socio-demographic 
characteristics, level of marginalisation and economic and social 
exclusion, as well as education levels, were important factors 
influencing perception of climate change in Mexico (Corona-Jimenez, 
2018; Alfie and Cruz-Bello, 2021; Azócar et al., 2021). Drawing upon Indigenous knowledge (see Box 14.1) as well as lived experience of 
recent changes in ice, weather patterns, and species’ phenology and 
distribution, Indigenous Peoples recognise that change is occurring in

their communities and have effective solutions that are grounded in 
Indigenous world views (Harrington, 2006; Turner and Clifton, 2009; 
Norton-Smith et al., 2016a; Savo et al., 2016; Maldonado et al., 2017; 
Chisholm Hatfield et al., 2018).
14.3.3 Building Consensus on Climate Change
Building consensus for action on climate change is influenced by 
individual factors (e.g., ideology, world view, trust, partisan identity, 
religion, education, age) and the broader societal context (e.g., 
culture, media coverage and content, political climate, economic 
conditions) (high confidence) (McCright and Dunlap, 2011; Brulle 
et  al., 2012; Hornsey et  al., 2016; Arbuckle, 2017; Pearson et  al., 
2017; Bolsen and Shapiro, 2018; Ballew et al., 2020; Cologna and 
Siegrist, 2020; Goldberg et al., 2020).  In a multi-country assessment 
of acceptance of global warming influenced by ideology (e.g., 
conspiratorial ideation, individualism, hierarchy, and left–right and

liberal–conservative political orientation), the USA uniquely had 
the strongest link to doubt out of 25 countries for all factors, while 
Canada’s dominant influence on non-acceptance was conservative 
political ideology, and for Mexico, there were no ideological effects 
(Hornsey et al., 2018).
Political affiliation and partisan group identity contribute to polarisation 
on the causes and state of climate change, most notably in the USA 
(medium confidence). Fewer US republicans hold the belief that human 
activity causes climate change than democrats (Bolsen and Druckman, 
2018; Druckman and McGrath, 2019). Partisanship in the USA with 
respect to climate change has evolved over the period 1997–2016; 
initially, it was limited, but since 2008, there has been a widening, 
more entrenched partisan ‘divide’ (Dunlap et al., 2016). The millennial 
generation (born in the 1980s and 1990s), emerging as the largest 
US population cohort, has a potentially important political influence–

reduction in polarisation–as they show relatively higher levels of concern 
and acceptance of climate-change science than older age groups. 
Political affiliation does not have as strong an effect on their climate 
change beliefs (Corner et al., 2015; Ross et al., 2019).
Communicating to educate or enhance knowledge on climate-change 
science or consensus can, but does not necessarily lead individuals 
to revise their beliefs (medium confidence) (Bolsen et  al., 2015; 
Druckman and McGrath, 2019). People may reject new information 
that conflicts with their beliefs or not consider it credible, as political 
ideology and partisan affiliation are strong influences (Arbuckle, 
2017). The climate-change issue may create resistance from 
individuals with conservative political ideologies and hierarchical, 
individualistic world views because it ascribes responsibility to 
developed, industrialised countries for emissions and brings about

more environmental regulation (Stevenson et al., 2015). Lack of trust 
in scientific consensus on climate change may actually originate 
from opposition by US conservatives to the perceived advocacy for 
different climate-change policy approaches that challenge their world 
views (Bolsen and Druckman, 2018).

14
1941North America  Chapter 14
Estimated percent (%) of adults who think earth is getting warmer
60%70%80%90%
= not available
Figure 14.3 |  Regional distribution of public perception that ‘the Earth is getting warmer’ as a surrogate for public acceptance that climate change is 
happening (percent of population). Scale is the Canadian federal electoral district or riding level and US congressional district. The three northern territories and Labrador, in 
Canada, did not meet population thresholds for modelling. The figure updates Mildenberger et al. (2016) and is based on equivalent public surveys in both countries: Canadian 
‘Earth is getting warmer’ and US ‘global warming is happening’ undertaken in 2019. Equivalent surveys and modelling for Mexico are not available at the time of writing.
14.3.4 Factors Influencing Perceptions of Climate-Change 
Risks and Adaptation Action
Projected climate-change risk, urgency and necessary adaptations

are perceived and understood differently by the public, communities, 
professional groups, climate scientists and public policy makers (high 
confidence) (Bolsen et  al., 2015; Drews and Van den Bergh, 2016; 
Morton et  al., 2017; Treuer et  al., 2018). People can engage with 
climate change across three dimensions: cognitive (knowledge), 
affective (feelings) and behavioural (responses and actions) (Galway, 
2019; Brosch, 2021). Risk assessment can be influenced by values 
regarding the subject under evaluation (Allison and Bassett, 2015; 
Stevenson et al., 2015) and can interact with other risks and change 
over time (Mach et  al., 2016). Communities and practitioners (e.g., 
farmers, foresters, water managers) are influenced in their willingness 
to modify current practices and adopt new measures based on how 
they perceive, understand and experience climate-change uncertainty,

risk and urgency as well as political and social norms (van Putten et al., 2015; Doll et al., 2017; Mase et al., 2017; Morton et al., 2017; 
Zanocco et  al., 2018). Place-based and local-focused assessments 
allow individuals to more readily assess and adapt to risks as well as 
identify roles and responsibilities in the face of multiple, interacting 
and often unequally distributed climate-change impacts (Khan et al., 
2018; Galway, 2019). Interest in preserving local archaeological 
sites threatened by SLR initiated collaboration and co-production of 
knowledge among disparate US communities: citizens, archaeologists, 
preservationists, planners, land managers and Indigenous Peoples 
(Fatorić and Seekamp, 2019; Dawson et al., 2020).
Psychological distancing–the perception that the greatest impacts 
occur sometime in the distant future and to people and places far 
away–can lead to discounting of risk and the need for adaptation

(medium confidence) (Leviston et al., 2014; Mildenberger et al., 2019). 
Communication directed at local and personal framing of climate-
change impact and risk information is one option for addressing low 
salience (Bolsen et al., 2019) particularly related to established risks

14
1942Chapter 14 North America
such as SLR, flooding and wildfires in North America (Mildenberger 
et  al., 2019). ‘Personalised’ risk communications have had mixed 
results creating behavioural change and policy support, and even 
caused resistance (Schoenefeld and McCauley, 2016). Communication 
focused extensively on risks and dangers of climate change can 
produce fear or dread, lessen agency and create fatalism that hinders 
action (Giddens, 2015; Mayer and Smith, 2019); it also can be labelled 
alarmist (Leiserowitz, 2005). Detailed SLR flooding maps for the 
San Francisco Bay area did not increase climate risk assessment but 
lessened personal risk perception of those with a strong belief in 
climate change, although policy preferences and support for adaptation 
did not change (Mildenberger et al., 2019). Defining coherent groups 
based on variations in beliefs, risk perceptions and policy preferences 
offers opportunities for effectively engaging with segments of the

population instead of using the same approach for everyone (low 
confidence) (Maibach et al., 2011; Chryst et al., 2018). As an example, 
the US population was segmented into a continuum ranging from 
the ‘Alarmed’, the dominant group who were ‘Concerned’, then the 
Cautious, Disengaged, Doubtful, and least prevalent, the Dismissive 
(Chryst et al., 2018).
14.4 Indigenous Peoples and Climate Change
Indigenous knowledge and science are resources for understanding 
climate-change impacts and adaptive strategies (very high confidence) 
(SM14.1; Table SM14.1). The Indigenous Peoples of North America have 
contributed substantially to, and continue to contribute to, the growing 
literature, scholarship and research on climate change (Barreiro, 1999; 
Houser et al., 2001; Mustonen, 2005; Bennett et al., 2014; Maynard, 
2014; Merculieff et al., 2017; FAQI, 2019; Ijaz, 2019; BIA, 2021). For 
thousands of years, Indigenous Peoples have developed and relied

on their own knowledge systems for sustaining their health, cultures 
and arts, livelihoods and political security (Battiste and Henderson, 
2000; Colombi, 2012; Nelson and Shilling, 2018). Diverse IK systems in 
North America consider weather and climate as major dimensions of 
understanding the relationship between society and the environment. 
Indigenous Peoples have distinct knowledge of climate change, over 
extensive temporal measures (Trosper, 2002; Barrera-Bassols and 
Toledo, 2005; Gearheard et  al., 2013). The basis of this knowledge 
is often Indigenous Peoples’ long and profound relationships with 
the environment, that is, to the ecosystems, waters, ice, lands, 
territories and resources in their homelands. The relationships have 
been forged by adaptation to a particular environment and involve 
systematic activities. Indigenous harvesters, including hunters, 
fishers, agriculturalists and plant gatherers, observe and monitor

environmental change, and engage in systematic reflection with one 
another about trends over short- and long-term periods (Sakakibara, 
2010; Sánchez-Cortés and Chavero, 2011; Kermoal and Altamirano-
Jiménez, 2016; Metcalfe et al., 2020b). The holistic perspective of the 
interrelated and interdependent nature of ecosystems is a distinct 
characteristic of IK and often contrasts with findings and results of 
science alone. Indigenous harvesters, agriculturalists, leaders, culture 
bearers, educators and government employees develop theoretical 
and practical knowledge of seasonal and climate change that seeks 
to furnish the best available knowledge and information to inform 
climate-change policy and decisions (Barrera-Bassols and Toledo, 2005; McNeeley and Shulski, 2011). Examples of theoretical knowledge 
systems include Indigenous calendars of seasonal change and systems 
of laws and protocols for environmental stewardship (see Box 14.1)

(Kootenai Culture Committee, 2015; Donatuto et al., 2020).
The practice and use of IK systems is recognised and affirmed by 
the United Nations Declaration on the Rights of Indigenous Peoples 
(UNDRIP) (UNGA, 2007), and consistent with reports and guidance 
from UN bodies including the High Commissioner for Human Rights 
(Bachelet, 2019), Expert Mechanism on the Rights of Indigenous Peoples 
(UNGA, 2015; UNGA, 2018), the Permanent Forum of Indigenous Issues 
(Dodson, 2007; Cunningham Kain et al., 2013; Sena and UNPFII, 2013; 
Sena, 2014; Quispe and UNPFII, 2015) and the Special Rapporteur on 
the Rights of Indigenous Peoples (Cross-Chapter Box INDIG in Chapter 
18; Toledo, 2013; UNGA, 2017). The right to self-determination, control 
over territorial development and cultural integrity make it important 
that climate scientists practise equitable engagement of IK and IK 
holders. There is a growing literature of success and lessons learned

from co-production of knowledge between IK systems and diverse 
scientific traditions relating to climate change (Behe et  al., 2018; 
Latulippe and Klenk, 2020; Camacho-Villa et al., 2021).
Current and projected climate-change impacts disproportionately harm 
Indigenous Peoples’ livelihoods and economies (very high confidence). 
Indigenous Peoples’ livelihoods in North America include a range of 
activities closely tied to traditional lands, waters and territories. These 
activities support a core economic base and an array of sustenance, 
including financial stability, food security, health and nutrition, safety, 
and adequate provisions and reserves of important supplies and 
resources, as well as the passing down of traditional knowledge. 
Indigenous lives and livelihoods are at risk in the following ways: 
Indigenous persons are more at risk of losing their lives due to factors 
that are exacerbated by climate-change impacts (Ford et  al., 2006;

Barbaras, 2014; Khalafzai et al., 2019). Indigenous Peoples’ livelihood 
practices are being distressed, interrupted and, in some cases, made 
entirely inaccessible. Livelihood activities known and anticipated to be 
impacted by climate change are food security (Meakin and Kurtvits, 
2009; Wesche and Chan, 2010; Nyland et al., 2017), harvesting of fish, 
plants and wildlife (Dittmer, 2013; Parlee et  al., 2014; Jantarasami 
et al., 2018b; ICC Alaska, 2020), agriculture (St Regis Mohawk Tribe, 
2013; Shinbrot et al., 2019; Settee, 2020), transportation (Swinomish 
Indian Tribe Community, 2010; Hori et al., 2018a; Hori et al., 2018b), 
and tourism and recreation (ICC Canada, 2008). Indigenous Peoples 
have been active in gathering to assess the impacts of climate 
change on their livelihoods, one example being the Bering Sea Elders 
Advisory Group (Bering Sea Elders Advisory Group and Alaska Marine 
Conservation Council, 2011; Bering Sea Elders Group, 2016).

Climate-change impacts have harmful effects on Indigenous Peoples’ 
public health, physical health and mental health, including harmful 
effects connected to the cultural and community foundations of 
health (very high confidence). Health and climate change is a major 
issue for Indigenous Peoples (Section 14.5.6; Ford, 2012; Ford et al., 
2014; Gamble et al., 2016; Jantarasami et al., 2018b; Middleton et al., 
2020a; Donatuto et al., 2021). Climate-change impacts and risks affect 
Indigenous Peoples’ health negatively in different ways. Indigenous 
health, as tied to nutrition and exercise, is threatened when local foods

14
1943North America  Chapter 14
Box 14.1 | Integrating Indigenous ‘Responsibility-Based Thinking’ into Climate-Change 
Adaptation and Mitigation Strategies
Indigenous Peoples throughout North America have experienced five centuries of territorial expropriation, loss of access to natural 
resources and, in many cases, barriers to the use of their sacred sites (Gabbert, 2004; Louis, 2007). The history of Indigenous struggles to 
preserve distinct cultural knowledge and assert autonomy in the face of colonialism has shaped land-use patterns and relationships with 
traditional territories (Cross-Chapter Box INDIG in Chapter 18; Alfred and Corntassel, 2005; Tuhiwai Smith, 2021). Climate change is now 
creating additional challenges for Indigenous Peoples. For example, increased water scarcity due to higher temperatures and diminished 
precipitation have led to reduced crop yields for Maya farmers in Yucatan (Sioui, 2019). Thawing permafrost in subarctic Canada (Quinton

et al., 2019) has interfered with the land-based livelihoods of the Indigenous Dene Peoples (CCP6).
Recent climate-related changes represent cultural threats similar to the ones that occurred when European settlement began in the 
Americas over 500 years ago (Whyte, 2016; Whyte, 2017). Thus, for Indigenous Peoples, who often disproportionately bear the impacts 
of climate change, such changes are not novel, but seen as déjà vu (Whyte, 2016). Since livelihoods and subsistence are often directly 
dependent on the land and water, Indigenous Peoples have direct insights into the localised impacts of global environmental change. 
Indeed, Indigenous Peoples consider themselves stewards of the land (and water), and have a spiritual duty to care for the land and its 
flora, fauna and aquatic community, or ‘Circle’ of beings. Indigenous knowledge (IK) has gained recognition for its potential to bolster

Western scientific research about climate change. Many recent examples demonstrate the scientific value of IK for resource management 
in climate-change adaptation and mitigation (e.g., Kronik and Verner, 2010; Maldonado et al., 2013; Wildcat, 2013; Etchart, 2017; Nursey-
Bray et al., 2019). For example, Indigenous practices have not only contributed to the present understanding of North American forest 
fires, but also that the practice of frequent small-scale anthropogenic fires, also called cultural burns, is a key method to prevent large-
scale destructive fires (Section 14.7.1). The growing interest and recognised value in these practices, particularly in California, has led to 
formal agreements with state and federal agencies (Long et al., 2020a; Lake, 2021).
Indigenous relationships with the land are commonly informed and guided by a cultural ethic of ‘responsibility-based thinking’ (Sioui

and McLeman, 2014). The Indigenous cultural ethic informs and mediates personal and collective conduct with a sense of duty or 
responsibility towards human and other-than-human relations (see Sioui, 2020). The Indigenous responsibility-based outlook stems from 
a cultural paradigm that understands that it is human beings who must learn to live with the land (Cajete, 1999; Pierotti and Wildcat, 
2000; McGregor et al., 2010a; McGregor, 2014). This way of thinking instils in its adherents an inherent awareness that the other-than-
human realm is capable of existing and thriving without humans. Thus, it is for our own sake (as humans) that we learn to live according 
to certain ever-shifting parameters, requiring us to remain acutely attuned to our physical surroundings. This Indigenous cultural precept 
is perhaps among the most significant contributions of Indigenous Peoples to the rest of humanity in the face of climate change.

Indigenous relationships with natural systems continue to be mediated by cultural orders of governance and legal systems that pre-
date, by several millennia, European traditions in North America. Napolean (2012) describes Indigenous legal orders as dynamic and 
encompassing knowledge that is simultaneously legal, religious, philosophical, social and scientific. Customary Indigenous legal orders 
(e.g., Borrows, 2002; Napolean, 2012) stand in contrast to Eurocentric understandings of law, which are closely related to, and founded 
on, the Western principles of rights. Indigenous legal orders are based on duties, obligations and responsibilities to the land and all beings, 
including humans, animals, plants, future generations and the departed/ancestors (Borrows, 2002; Borrows, 2010a; Borrows, 2010b; 
Borrows, 2016). Indigenous spiritual laws are centred on the values of responsibility and accountability to the land, and how these differ,

in theory and in practice, from Western law, which is based on ‘universal’ principles, with little consideration for the local environmental 
context (Craft, 2014). Research has elucidated these Indigenous understandings about how their land-based responsibilities act as the 
foundation of how humans must operate according to the land on which they live and depend.
With increasing climate-change threats to land-based subsistence and cultural practices, Indigenous Peoples are increasingly taking their 
rightful leadership roles in resource co-management arrangements and other stewardship activities (Section 14.5.2.2). Indeed, Indigenous 
Peoples are increasingly assuming leadership positions with regard to land governance and climate-change action, as the stewards of 
their traditional territories since time immemorial. Therefore, it is imperative for Indigenous scholars, Elders and knowledge holders to

occupy leadership roles in climate-change adaptation and mitigation, especially when their territories are concerned (Section 14.7; CCP6). 
For instance, Indigenous ‘resurgence’ paradigms draw on the strengths of traditional land-based culture and knowledge with regard to 
Indigenous leadership in land governance and stewardship (Alfred and Corntassel, 2005; Alfred, 2009; Simpson, 2011; Corntassel and 
Bryce, 2012; Coulthard, 2014; Alfred, 2015). Indigenous leadership in climate-change policy, therefore, can ensure that Indigenous right 
to self-determination is respected and upheld to allow Indigenous Peoples to continue to carry out their cultural responsibilities to the 
land, for the benefit of all North Americans (Powless, 2012; Etchart, 2017).

14
1944Chapter 14 North America
In northern Canada, a fusion of leading-edge Western science and IK on permafrost informed the co-development of predictive decision-
support tools and risk management strategies to inventory and manage permafrost and adapt to permafrost thaw (CCP6). Permafrost 
thaw in the Dehcho region of Canada is widespread and occurring at unprecedented rates (WGI). The Dehcho Collaborative on Permafrost 
(DCoP) aims to improve the understanding of and ability to predict and adapt to permafrost thaw3.DCoP’s collaborative approach, 
which places Indigenous Peoples in leadership positions, generates the new knowledge, predictive capacity and decision-support tools to 
manage natural resources that support Indigenous Dene Peoples’ ways of life. Indigenous–academic partnerships can enhance climate-
change adaptation and mitigation capacity, and provide openings for more holistic co-management approaches that recognise and

affirm the central role of Indigenous Peoples as stewards of their ancestral territories, especially as they face accelerating climate-change 
impacts. Academic researchers and their Indigenous partners can support climate-change resilience via mobilising IK in stewardship 
and adaptation; researching governance arrangements, economic relationships and other factors that hinder Indigenous efforts in these 
areas; proposing evidence-based policy solutions at international and national scales; and outlining culturally relevant tools for assessing 
vulnerability and building capacity will also support climate-change resilience. Such IK underpins successful climate-change adaptation 
and mitigation (very high confidence) (see Green and Raygorodetsky, 2010; Kronik and Verner, 2010; Alexander et al., 2011; Powless, 
2012; Ford et al., 2016; Nakashima et al., 2018). The inclusion of IK in adaptation and mitigation not only supports Indigenous cultural

survival but also enables governments to recognise the territorial sovereignty of Indigenous Peoples.
Responsibility-based philosophies of Indigenous Peoples from across the continent support the development of climate-change adaptation 
and mitigation strategies that promote responsible and respectful relationships with the environment over the long term. Adapting to 
change, in all its forms, has since time immemorial been one of the defining characteristics of Indigenous cultures on Turtle Island (the 
American continent). In Yucatan, one Elder explained that with regards to climate-change impacts in the region, the Maya have always 
dealt with k’ech, or change, and that accepting and responding to change is part of the Maya identity and responsibility (Sioui, 2020). 
Given successive failures in adequately and effectively responding to climate change, it has become urgent for the rest of the human

collective to (re)learn from Indigenous cultures to (re)consider our responsibility/ies to the land—the world over—and to reorient our 
societal imperatives to better respond and react to change. Such a process of learning from IK could foster the development of climate-
change policies that promote responsible and respectful relationships with the environment over the long term, and prove to be more 
effective and holistic. Although most inhabitants of North America are non-Indigenous, it is possible and beneficial for our societies to 
learn to think and act in a more responsibility-based way about our relations to the land, and, by extension, about climate-change policy. 
A collective commitment to protecting and advancing Indigenous territorial rights, so Indigenous Peoples can continue to reassert their 
spiritual duty and role as stewards of their traditional territories, benefits all human and other-than-human ‘Peoples’.
3 See http://scottycreek.com/DCoPBox 14.1 (continued)

are less available and harvesting activities are less possible to practise 
(Norton-Smith et al., 2016b; Rosol et al., 2016; Gonzalez et al., 2018). 
Indigenous Peoples experience widespread public health concerns from 
severe droughts (Stewart et al., 2020; Schlinger et al., 2021; Wiecks 
et  al., 2021), extreme heat (Doyle et  al., 2013; Campo Caap, 2018; 
Kloesel et al., 2018a; Meadow et al., 2018; ITK, 2019; Ute Mountain 
Ute Tribe and Wood Environment Infrastructure Solutions Inc, 2019; 
Whyte et  al., 2021), unpredictable precipitation patterns (Chavarria 
and Gutzler, 2018; Tom et al., 2018; Tlingit and Haida, 2019; Schlinger 
et al., 2021), flooding and coastal erosion (Jamestown S’klallam Tribe, 
2016; Norton-Smith et  al., 2016b; Puyallup Tribe of Indians, 2016; 
Marks-Marino, 2019; Ristroph, 2019; Marks-Marino, 2020b; Schlinger 
et al., 2021), wildfires and wildfire smoke (Edwin and Mölders, 2018; 
USEPA, 2018; Christianson et  al., 2019a; ITK, 2019; Marks-Marino,

2020a; Mottershead et al., 2020; Woo et al., 2020; Wiecks et al., 2021), 
algal blooms (Peacock et al., 2018; Gobler, 2020; Donatuto et al., 2021; 
Preece et al., 2021; Schlinger et al., 2021), storms and hurricanes (Rioja-
Rodríguez et al., 2018), influxes of invasive species (Pfeiffer and Huerta 
Ortiz, 2007; Pfeiffer and Voeks, 2008; Voggesser et al., 2013; Bad River 
Band of Lake Superior Tribe of Chippewa Indians and Abt Associates Inc., 2016; Scott et al., 2017; Reo and Ogden, 2018; Middleton et al., 
2020a) and changing production systems (Rioja-Rodríguez et  al., 
2018). Indigenous Peoples’ mental health is at risk and has already 
been affected negatively by climate change (Donatuto et  al., 2021). 
Water security is one of the most serious concerns to Indigenous 
Peoples’ health and well-being (Vanderslice, 2011; Cozzetto et  al., 
2013a; Redsteer et al., 2013; Hanrahan et al., 2014; Chief et al., 2016; 
Gamble et al., 2016; Jantarasami et al., 2018b; Kloesel et al., 2018a;

Tom et al., 2018; Martin et al., 2020a; Arsenault, 2021). When some 
people are less able to practise traditional, cultural, social and family 
activities, they can become alienated, compounding the negative 
effects of traumas Indigenous persons already experience. Traumas 
include historic and continuing land dispossession, assimilation, social 
marginalisation and discrimination, and food and financial insecurities. 
The practise of cultural traditions are associated with education, 
harvesting and agriculture, exercise, positive social relationships 
and family life, which play foundational roles in the achievement of 
physical, public and mental health (Bell et  al., 2010; Cunsolo Willox 
et al., 2015; Jantarasami et al., 2018b; Norgaard and Tripp, 2019; Billiot 
et al., 2020b; Adams et al., 2021; Donatuto et al., 2021).

14
1945North America  Chapter 14
Indigenous Peoples are affected dramatically by climate-related 
disasters and other climate-related extreme environmental events (very 
high confidence). Indigenous Peoples face numerous threats and have 
already been harmed by, and are planning for, extreme weather events 
with associations to climate change, including hurricanes and tornadoes 
(Oneida Nation Pre-Disaster Mitigation Plan Steering Committee 
and Bay-Lake Regional Planning Commission, 2016; Emanuel, 2019; 
Cooley, 2021; Marks-Marino, 2021; Zambrano et al., 2021), heatwaves 
(Confederated Tribes of the Umatilla Indian Reservation, 2016; Wall, 
2017; La Jolla Band of Luiseno Indians, 2019; Mashpee Wampanoag, 
2019; Wiecks et  al., 2021), ocean warming and MHWs (Hoh Indian 
Tribe, 2016; Port Gamble S’klallam Tribe, 2016; Port Gamble S’klallam 
Tribe, 2020; State of Alaska, 2020; Muckleshoot Tribal Council, 2021; 
Port Gamble S’klallam Tribe, 2021), wildfires (Voggesser et  al., 2013;

Billiot et  al., 2020a; Cozzetto et  al., 2021b; Gaughen et  al., 2021; 
Morales et  al., 2021; National Tribal Air Association, 2021; Zambrano 
et al., 2021), permafrost thaw (Haynes et al., 2018; Low, 2020), flooding 
(Riley et al., 2011; Ballard and Thompson, 2013; Brubaker et al., 2014; 
Thompson et  al., 2014; Burkett et  al., 2017; Quinault Indian Nation, 
2017; Ristroph, 2019; Sharp, 2019; Thistlethwaite et  al., 2020) and 
drought (Knutson et al., 2007; Chief et al., 2016; Redsteer et al., 2018; 
Sioui, 2019; Bamford et al., 2020; Sauchyn et al., 2020). Some Indigenous 
Peoples are facing climate-change impacts that generate community-
led permanent relocation and resettlement as an adaptation option 
(Maldonado et al., 2021). Coastal erosion is one climate-change issue 
that is often connected to Indigenous Peoples planning to resettle, 
including vulnerability connected to higher sea levels and storm surges

(Quinault Indian Nation, 2017; Bronen et al., 2018; Affiliated Tribes of 
Northwest Indians, 2020). Adapting to new settlement areas threatens 
the continuity of communities. In a number of cases, Indigenous Peoples’ 
having less access to adequate infrastructure is a driver of vulnerability 
to climate-related disasters and extreme weather events (Doyle et al., 
2018; Patrick, 2018; Cozzetto et al., 2021a; Indigenous Climate Action 
et al., 2021). Disasters and extreme events are particularly severe when 
their impacts are compounded by inadequate infrastructure. Lack of 
flood protection infrastructure on Indigenous reserve communities leads 
to displacement, loss of homes and perpetuates disproportionate levels 
of risk to extreme weather events (Cunsolo et al., 2020; Fayazi et al., 
2020; Yellow Old Woman-Munro et al., 2021).
Indigenous self-determination and self-governance are the foundations 
of adaptive strategies that improve understanding and research on

climate change, develop actionable community plans and policies 
on climate change, and have demonstrable influence in improving 
the design and allocation of national, regional and international 
programmes relating to climate change (very high confidence). Historical 
and contemporary developments have crystallised international norms 
recognising the distinct status, role and rights of Indigenous Peoples in 
the form of significant international human rights instruments. Premier 
among them is the UNDRIP (UNGA A/RES/61/295), which has received 
universal consensus since its adoption by the UN General Assembly. 
The UN member States have affirmed the right of self-determination 
(Article 3, UNDRIP) regarded as the prerequisite to the exercise and 
enjoyment of all other human rights.
The integrity of the environment is impacting all of humanity, including

Indigenous Peoples, their lands, territories, resources and their communities. Through self-determination, durable, sustainable and 
robust contributions from those with close, symbiotic relationships with 
the environment can be revealed in favour of all humanity. Indigenous 
Peoples of North America have been engaged in wide-ranging activities 
to address climate change (Doolittle, 2010; Parker and Grossman, 
2012; Abate and Kronk, 2013; STACCWG, 2021). They include actions 
in the spheres of education (Donatuto et  al., 2020; McClain, 2021; 
Morales et al., 2021), development of IK and science (Maldonado et al., 
2016; AFN, 2020; Ferguson and Weaselboy, 2020; Huntington et  al., 
2021a; Jones et al., 2021; Sawatzky et al., 2021), adaptation planning 
and implementation (Angel et  al., 2018a; Tribal Climate Adaptation 
Guidebook Writing Team et al., 2018; Hepler and Kronk Warner, 2019; 
Tribal Adaptation Menu Team, 2019; Metcalfe et al., 2020b), and political

action and diplomacy (including treaty-based diplomacy) (Grossman, 
2008; Kronk Warner and Abate, 2013; Callison, 2015).
14.5 Observed Impacts, Projected Risks and 
Adaptation by Sector
14.5.1 Terrestrial and Freshwater Ecosystems and  
14.5.1.1 Terrestrial Ecosystems: Observed Impacts  
 and Projected Risks
Evidence continues to mount about the impacts of recent climate change 
on species and ecosystems (very high confidence) (Table 14.2; Weiskopf 
et  al., 2020). Ranges and abundances of species continue to shift in 
response to warming throughout North America (very high confidence) 
(Cross-Chapter Box MOVING PLATE in Chapter 5; Cavanaugh et al., 2014; 
Molina-Martínez et al., 2016; Tape et al., 2016; Miller et al., 2017; Pecl 
et al., 2017; Zhang et al., 2018a). Future climate change will continue 
to affect species and ecosystems (high confidence) (IPBES, 2018), with 
differential responses related to species characteristics and ecology

(D’Orangeville et  al., 2016; Weiskopf et  al., 2019). Climate change is 
projected to adversely affect the range, migration and habitat of caribou, 
an important food and cultural resource in the Arctic (CCP6; Leblond 
et al., 2016; Masood et al., 2017; Barber et al., 2018b; Borish, 2022).
Climate-induced shifts in the timing of biological events (phenology) 
continue to be a well-documented ecological response (very high 
confidence) (Table 14.2; Vose et al., 2017; Lipton et al., 2018; Vose et al., 
2018; Molnar et al., 2021). Reduced snow season length may potentially 
lead to adverse camouflage effects on animals that change coat colour 
(Mills et al., 2013; Mills et al., 2018). Human conflicts with bears are 
expected to increase in response to shifts in hibernation patterns 
(Johnson et al., 2018) and food resources (Wilder et al., 2017; Wilson 
Severe ecosystem consequences of warming and drying are well

documented (very high confidence) (Table 14.2). Significant ecosystem 
changes are expected from projected climate change (high confidence), 
such as in Mexican cloud forests (Helmer et al., 2019), North American 
rangelands (Polley et al., 2013; Reeves et al., 2014) and montane forests 
(Stewart et al., 2021; Wright et al., 2021). Permafrost thaw is projected to 
increase in Alaska and Canada (DeBeer et al., 2016; see also Ranasinghe

14
1946Chapter 14 North America
et al., 2021), accelerating carbon release (CCP6, see also Canadell et al., 
2021) and affecting hydrology. Predicting which species or ecosystems 
are vulnerable is challenging (Stephenson et al., 2019), although palaeo-
ecological data (e.g., pollen, tree rings) provide context from past events 
to better understand current and future transformations (Nolan et al., 
2018).
Climate-change impacts on natural disturbances have affected 
ecosystems (very high confidence) (Table  14.2; see Box  14.2), and 
these impacts will increase with future climate change (medium 
confidence). Facilitated by warm, dry conditions, ‘mega-disturbances’ 
and synergies between disturbances that include wildfires, insect 
and disease outbreaks, and drought-induced tree mortality continue 
to affect large areas of North America (Cohen et  al., 2016; Young 
et al., 2017a; Hicke et al., 2020), overwhelming adaptive capacities

of species and degrading ecosystem services (Millar and Stephenson, 
2015; Stewart et al., 2021). This era of mega-disturbances is expected 
to become more widespread and severe in coming decades (Cook 
et al., 2015; Seidl et al., 2017; Buotte et al., 2019), with potentially 
significant impacts on ecosystems (Allen et al., 2015; Crausbay et al., 
2017; Schwalm et  al., 2017; Coop et  al., 2020; Dove et  al., 2020 
Thompson et al. 2020, Stewart et al. 2021). Effects include widespread 
tree mortality (Allen et  al., 2015; Kane et  al., 2017; van Mantgem 
et  al., 2018) and accelerated ecosystem transformation (medium 
confidence) (Guiterman et al., 2018; Crausbay et al., 2020; Munson 
14.5.1.2 Freshwater Ecosystems: Observed Impacts and  
Climate change, either directly (warming water) or indirectly (glacier 
and snow inputs), has affected biogeochemical cycling and species 
composition in North American aquatic ecosystems (very high

confidence) (Table 14.2; Moser et al., 2005; Saros et al., 2010; Preston 
et al., 2016), possibly amplifying other human-caused stresses on these 
systems (Richter et  al., 2016). Excess nutrients associated with high 
farm animal density can be transported during intense rainfall events 
(expected to increase with climate change) causing algal blooms, fish 
kills and other detrimental ecological effects (Huisman et  al., 2017; 
Coffey et al., 2019).
Projected climate change will cause habitat loss, alter physical and bio-
logical processes, and decrease water quality in freshwater ecosystems 
(high confidence) (Poesch et  al., 2016; Crozier et  al., 2019). Projected 
river warming of 1°C–3°C is expected to reduce thermal habitat for 
important salmon and trout species in the northwest USA by 5–31% 
(Isaak et  al., 2018) and in Mexico (Meza-Matty et  al., 2021), and for 
multiple fish species in Canada (Poesch et al., 2016). Cold-water streams

at higher elevations will warm less and therefore may become climate 
refugia (Isaak et  al., 2016). Projected warming of mountain lake eco-
systems (Roberts et al., 2017b; Redmond, 2018) will affect ecosystem 
processes (Preston et al., 2016; Redmond, 2018; Moser et al., 2019). Loss 
of cold-water inputs from retreating glaciers are expected to adversely 
affect alpine stream ecosystems (Fell et al., 2017; Giersch et al., 2017). 
For anadromous fish species (e.g., Chinook salmon), future warming 
will reduce habitat suitability from river headwaters to oceans (Crozier 
et al., 2021).Freshwater ecosystems across North America are increasingly at risk 
from extreme drought, compounded by human demands for water 
(Section  14.5.3; Kovach et  al., 2019). Implications for aquatic and 
riparian species can vary, but it is widely agreed that these systems 
are highly sensitive to fluctuations in the hydrological cycle, which can

increase competition by invasive species and compromise connectivity 
between potential cold-water refugia (Melis et al., 2016; Poff, 2019).
14.5.1.3 Adaptation in Terrestrial and Freshwater Ecosystems
Adaptation efforts to assess vulnerability of species and ecosystems, 
predict adaptive capacity and identify conservation-oriented options 
have increased markedly across North America (e.g., Hagerman and 
Pelai, 2018; Keeley et  al., 2018; Thurman et  al., 2020; Peterson St-
Laurent et al., 2021; Thompson et al., 2021). Scenario-based planning, 
an approach for addressing uncertainty, continues to gain traction and 
is regularly applied by the US National Park Service (Star et al., 2016). 
Nonetheless, barriers to implementation of specific actions often exist 
(e.g., inflexible policies, lack of resources and stakeholder buy-in, 
political will), hampering progress (Stein et al., 2013; Shi and Moser, 
2021). Efforts to evaluate the efficacy of implemented adaptation

actions are also lacking (Prober et  al., 2019), but some cases show 
progress. For example, ongoing efforts are quantifying how variable 
water releases from the Colorado River’s Glen Canyon Dam affect 
endangered fish species (Melis et al., 2016). Nature-based Solutions 
(NbS) for adaptation (see Box 14.7) are increasingly being evaluated, 
especially at larger scales.
Effective climate-informed ecosystem management requires a well-
coordinated suite of adaptation efforts (e.g., assessment, planning, 
funding, implementation and evaluation) that is co-produced among 
stakeholders, Indigenous Peoples and across sectors (high confidence) 
(Millar and Stephenson, 2015; Dilling et al., 2019). New applications 
of conventional strategies can be modified to achieve conservation 
goals under climate change (USGCRP , 2019). For example, mechanical 
thinning and prescribed burning (to reduce fuel loads and benefit 
ecosystems) could be used in combination with planting species better

suited to new conditions to build resilience in western US forests to 
longer and hotter drought conditions (Bradford and Bell, 2017; Vernon 
et al., 2018). Protection of buffer areas, such as riparian strips in arid 
regions and boreal ecosystems, reduces water temperature, builds 
resistance to invasive species, increases suitable habitat (Johnson and 
Almlof, 2016) and facilitates protection of freshwater systems from 
runoff during and after intense rain events (National Research Council, 
2002).
Innovative approaches may facilitate species’ responses to climate 
change, particularly when vulnerability is exacerbated by habitat loss 
and fragmentation. Strategies include improved landscape connectivity 
for species dispersal (Carroll et al., 2018; Littlefield et al., 2019; Lawler 
et al., 2020; Thomas, 2020) or assisted migration (also called managed 
relocation) to climatically suitable locations (Schwartz et  al., 2012;

Dobrowski et al., 2015). Examples include translocation of salmon in 
the Columbia River (Holsman et al., 2012), genetic rescue (i.e., assisted 
gene flow increases genetic diversity to address local maladaptation) 
(Aitken and Whitlock, 2013) and locating and conserving climate 
refugia, such as in alpine meadows of the Sierra Nevada (Javeline et al.,

14
1947North America  Chapter 14
Table 14.2 |  Examples of observed climate-change impacts on terrestrial and freshwater ecosystems
Impact References
Local extinctions Pomara et al. (2014); Wiens (2016)
Greening and increased productivity of North American vegetation from CO 2 fertilisation Smith et al. (2016b); Zhu et al. (2016); Huang et al. (2018)
Changes in phenology, including migration as well as mismatches between species and with 
human visitationMayor et al. (2017); Zaifman et al. (2017); Breckheimer et al. (2020)
Vegetation conversions, including
 –shifts to denser forests with smaller trees
 –trees to savannas and grasslands
 –woody plant encroachment into grasslands
 –changes in tundra plant phenology and abundance
 –expansion of boreal and subalpine forests into tundra, meadows
 –reduced or lack of recovery following severe fireMcIntyre et al. (2015)
Bendixsen et al. (2015)
Archer et al. (2017)
Myers-Smith et al. (2019)
Juday et al. (2015); Lubetkin et al. (2017)

Coop et al. (2020); O’Connor et al. (2020); see Box 14.2
Warmer droughts reducing plant productivity and carbon sequestration Mekonnen et al. (2017); Gampe et al. (2021)
Slowing ecosystem function recovery of vegetation to pre-disturbance conditions following 
droughtsSchwalm et al. (2017); Crausbay et al. (2020)
Warming streams and lakes, and changes in seasonal flows that have affected freshwater 
fish distributions and populationsO’Reilly et al. (2015); Lynch et al. (2016); Poesch et al. (2016); Roberts et al. (2017b); Isaak 
et al. (2018); Christianson et al. (2019b); Zhong et al. (2019)
Upstream expansion of human-mediated invasive hybridisation and enhanced risk of 
extinction of native salmonid speciesMuhlfeld et al. (2014)
Declining wetlands in western North America important for bird migrations Donnelly et al. (2020)
Increases in harmful freshwater algal blooms See Section 14.5.3
2015; Morelli et al., 2016). Maintaining diverse spawning habitats and

salmon runs can increase resilience of salmonid populations to climate 
change (Schoen et  al., 2017; Crozier et  al., 2021). Newer modelling 
approaches can facilitate the visualisation of future management 
scenarios, per a recent study of fires in the southwest USA (Loehman 
et al., 2018), in addition to technologies in genomics for monitoring 
species and modifying adaptive traits (Phelps, 2019).
Adaptation actions have important limitations (Dow et  al., 2013), 
particularly in the context of biodiversity conservation goals. ‘Hard’ limits 
include species extinctions and vegetation mortality events, despite 
conservation action (i.e., besides significant emissions reductions to 
mitigate warming, few if any interventions could have prevented these 
losses). In contrast, ‘soft’ adaptation limits exist primarily as a function 
of the social–ecological value systems of local communities and 
government entities that are reflected as goals and objectives in their

management plans for ecosystems and species across North America. 
Soft limits are often mutable or can be removed altogether (Dow et al., 
2013). In contrast, human modifications of landscapes that change or 
irreparably damage can limit adaptation by reducing connectivity and 
therefore range shifts (Parks and Abatzoglou, 2020).
14.5.2 Ocean and Coastal Social–Ecological Systems
14.5.2.1 Observed Impacts and Projected Risks of Climate 
Change
Warming of surface and subsurface ocean waters has been broadly 
observed across all North American marine ecosystems from the polar 
Arctic to the subtropics of Mexico (virtually certain) (Hobday et  al., 
2016; Jewett and Romanou, 2017; Pershing et al., 2018; Smale et al., 2019). Higher ocean temperatures have directly affected food-web 
structure (Gibert, 2019) and altered physiological rates, distribution, 
phenology and behaviour of marine species with cascading effects 
on food-web dynamics (very high confidence) (Gattuso et  al., 2015;

Pinsky and Byler, 2015; Sydeman et al., 2015; Poloczanska et al., 2016; 
Frölicher et al., 2018; Le Bris et al., 2018; Free et al., 2019; Stevenson 
and Lauth, 2019; Barbeaux et al., 2020; Dahlke et al., 2020). Pacific 
coastal waters from Mexico to Canada and US mid-Atlantic coastal 
waters have a high proportion of species (>5% of all marine species) 
near their upper thermal limit, representing hotspots of risk from MHWs 
(medium confidence) (Smale et  al., 2019; Dahlke et  al., 2020). Kelp, 
a macroalgae, forms important habitat for other marine species, and 
its biomass has decreased 85–99% in the past 40–60 years off Nova 
Scotia, Canada, replaced by invasive and turf algae; this is associated 
directly with warming waters (Filbee-Dexter et al., 2016).
Climate change has induced phenological and spatial shifts in primary 
productivity with cascading impacts on food webs (high confidence) 
(Siddon et al., 2013; Stortini et al., 2015; Sydeman et al., 2015; Stanley

et al., 2018). This includes widespread starvation events of fish, birds 
(e.g., tufted puffins in Bering Sea in 2016–2017 and Cassin’s Auklets 
in British Columbia in 2014–2015) and marine mammals (grey whales 
along both coasts of North America) (Sydeman et  al., 2015; Duffy-
Anderson et al., 2019; Jones et al., 2019b; Cheung and Frölicher, 2020; 
Piatt et  al., 2020), which challenge protected species and fisheries 
management (Section 14.5.4; Chasco et al., 2017; Wilson et al., 2018; 
Barbeaux et al., 2020; Free et al., 2020; Fisher et al., 2021; Cheung and 
Frölicher, 2020). Climate change has altered foraging behaviour and 
distribution of North Atlantic right whales and their target copepod 
prey (Record et  al., 2019) increasing entanglement rates in lobster 
and snow crab fishing gear on the east coast of the USA and Canada 
as lobster and crab distributions also shift due to changing water

14
1948Chapter 14 North America
Box 14.2 | Wildfire in North America
Recent Observations, Attribution to Climate Change and Projections
Anthropogenic climate change has led to warmer and drier conditions (i.e., fire weather) that favour wildland fires in North America 
(high confidence) (see AR6, WGI, Chapter 12, Ranasinghe et al., 2021). In response, increased burned area in recent decades in western 
North America has been facilitated by anthropogenic climate change (medium confidence). Annual numbers of large wildland fires and 
area burned have risen in the past several decades in the western USA (USGCRP , 2017; USGCRP , 2018), and area burned has increased 
in Canada (although the number of large fires has declined slightly recently) (Gauthier et al., 2014; Natural Resources Canada, 2018; 
Hanes et al., 2019). Attribution studies have reported that climate change increased burned area in Canada (1959–1999) (Gillett et al.,

2004) as well as the western USA (1984–2015) (Abatzoglou and Williams, 2016) and California (1972–2018) (Williams et al., 2019a). 
Decreased precipitation was the primary climate-change cause of increased burned area in the western USA, with warming a secondary 
influence (Holden et al. 2018), whereas warming (through aridity) was most important in a California study (Williams et al., 2019a). A 
drier atmosphere (including reduced precipitation) has been linked to climate change through altered large-scale atmospheric circulation, 
which then facilitated greater burned area in the western USA (Zhang et al., 2019c). Through anomalous warm and dry conditions, 
anthropogenic climate change contributed to the extreme fires of 2016 (Kirchmeier-Young et al., 2019; Tan et al., 2019) in western 
Canada and the extreme fire season in 2015 in Alaska (Partain et al., 2017). These studies did not include human activities that influence 
fire–climate relationships (Syphard et al., 2017).

Warming has led to longer fire seasons (Westerling, 2016) and drier fuels (Williams et al., 2019a). Warmer and drier fire seasons in the 
western USA during 1985–2017 have contributed to greater burned area of severe fires (Parks and Abatzoglou, 2020). Simultaneity in 
fires increased during 1984–2015 (Podschwit and Cullen, 2020), challenging firefighting effectiveness and resource sharing. In Mexico, 
fires have been correlated with dry conditions (Kent et al., 2017; Marin et al., 2018; Zuniga-Vasquez et al., 2019). Wildland fire activity 
in the grasslands of the US Great Plains has increased during the past several decades (Donovan et al., 2017) related to antecedent 
precipitation or aridity that affected fuel quantity (Littell et al., 2009).
Climate change is projected to increase fire activity in many places in North America during the coming decades (see also AR6, WGI,

Chapter 12, Ranasinghe et al., 2021) (Boulanger et al., 2014; Williams et al., 2016; Halofsky et al., 2020), via longer fire seasons (Wotton 
and Flannigan, 1993; USGCRP , 2017), long-term warming (Villarreal et al., 2019; Wahl et al., 2019) and increased lightning frequency in 
some areas of the USA and Canada (medium confidence) (Romps et al., 2014; Finney et al., 2018; Chen et al., 2021). Unusually extensive 
and severe fires have occurred in the Arctic tundra during recent extremely warm and dry years, suggesting that continued warming may 
increase the probability of such fires in the future (Hu et al., 2015). In drier non-forest ecosystems in the western USA, fires are limited 
by fuel availability and vegetation productivity; warming will decrease productivity, leading to lower burned area (Littell et al., 2018).
Impacts on Natural Systems
Although fire is a natural process in many North American ecosystems, increases in burned area and severity of wildland fires have had

significant impacts on natural ecosystems (medium confidence). The length of streams and rivers impacted by fire has increased in the 
USA along with burned area (Ball et al. 2021). Mega-fires can cause major changes in the structure and composition of ecosystems, 
particularly where human alterations are significant (Stephens et al., 2014; Loehman et al., 2020). Unusually severe fires may have led to 
the conversion of forest to grassland in the southwest USA (Haffey et al., 2018). Recent warming and drying have limited post-fire tree 
seedling and shrub establishment, limiting ecosystem recovery (Davis et al., 2019; O’Connor et al., 2020; Rodman et al., 2020). In boreal 
forests, soil carbon is being lost through increasingly severe or frequent fires (Walker et al., 2019).
Projected future fire activity will continue to affect ecosystems and alter their structure and function (medium confidence) (Coop et al.,

2020; Loehman et al., 2020). Increased fire activity (Stevens-Rumann et al., 2018; Stevens-Rumann and Morgan, 2019; Turner et al., 
2019a; Cadieux et al., 2020), further warming and drying that stresses tree seedlings, and model projections of stand-replacing fires 
at the forest–non-forest boundary in the western USA (Parks et al., 2019) have raised the possibility of shifts in species composition 
or vegetation type (Halofsky et al., 2020). These projections suggest high variability in ecosystem responses depending on interactions 
between vegetation type, moisture stress, disturbances regimes and human alterations (Hurteau et al., 2008; Kitzberger et al., 2017; Littell 
et al., 2018; Hurteau et al., 2019; Loehman et al., 2020; O’Connor et al., 2020).
Impacts on Human Systems
Increased fire activity, partly attributable to anthropogenic climate change, has had direct and indirect effects on mortality and morbidity,

economic losses and costs, key infrastructure, cultural resources and water resources (medium confidence), although other factors, such 
as increasing populations in the wildland–urban interface, have also contributed. During 2000–2018, significant fire events claimed 
315 lives in the USA (NOAA, 2019); the economic impacts (e.g., capital, health, indirect losses from economic disruption) from the 2018

14
1949North America  Chapter 14
California fires were 149 billion USD (Wang et al., 2021). Poor air quality from fires caused increased respiratory distress (very high 
confidence); exposure extends long distances from the fire source (Section 14.5.6.3). In addition to public and private property damage 
and loss, fires have caused irretrievable losses from archaeological and historical sites (Ryan et al., 2012). Post-fire conditions have 
created unanticipated challenges for communities’ water supply operations (Bladon et al., 2014; Návar, 2015; Martin, 2016) by altering 
water quality and availability (Smith et al., 2011; Bladon et al., 2014; Robinne et al., 2020) or public safety by increasing exposure to mass 
wasting events after extreme rainfall events (Cui et al., 2019; Kean et al., 2019). California utilities have proactively shut down parts of

their electricity grid to reduce risk of fire during extreme weather, and substantial numbers of people will be increasingly vulnerable to 
this action in the coming decades (Abatzoglou et al., 2020).
In the USA, annual costs of federal wildland fire suppression have increased by a factor of 4 since 1985 (USGCRP , 2018) and were 
1.5–3 billion USD during 2016–2020 (NIFC, 2021). Annual costs of fire protection in Canada have risen two- to threefold from 1970 
to 2017, to $1.0–1.4 billion CAD during 2015–2017 (considering the 2017 CAD value) (Natural Resources Canada, 2021). In one of its 
worst fire seasons, British Columbia expended over 500 million CAD in 2017 for fire suppression (Natural Resources Canada, 2018). The 
number of days of synchronous fire danger is expected to double in the western USA by 2051–2080, thereby increasing demands on fire 
suppression resources (Abatzoglou et al., 2021).

The 2016 Fort McMurray fire ranks as the costliest natural disaster in Canada to date (3 billion CAD in insured damages) (Mamuji and 
Rozdilsky, 2018; IBC, 2020). More than 88,000 people were evacuated; many were not aware of the high pre-existing fire risk and had 
limited warning to prepare and leave (McGee, 2019). The community subsequently required extensive social support and experienced 
mental health challenges (Government of Alberta, 2016; Cherry and Haynes, 2017; Mamuji and Rozdilsky, 2018; Brown et al., 2019a; 
McGee, 2019). Although a broad recovery plan was developed (Regional Municipality of Wood Buffalo, 2016), reconstruction and 
economic recovery has been slow (Mamuji and Rozdilsky, 2018).
Wildland fire was identified as a top climate-change risk facing Canada (Council of Canadian Academies, 2019) and poses a challenge to 
communities and fire management (Coogan et al., 2019). Projected area burned in Canada using RCP2.6 will increase annual fire suppression

costs to 1 billion CAD the by end of century (60% increase relative to 1980–2009) and to 1.4 billion CAD using RCP8.5 (119% increase) (Hope 
et al., 2016). In the USA, cumulative costs of fire response through 2100 are projected to be 23 billion USD (considering the 2015 USD value) 
yr−1 under RCP8.5 (EPA, 2017). Lower-emissions scenarios reduce these future cumulative costs by 55 million USD (EPA, 2017) to 7–9 billion 
USD (considering the 2005 USD value) (Mills et al., 2015a). Fire increases from future warming will reduce timber supply in eastern Canada 
(Gauthier et al., 2015; Chaste et al., 2019) and increase post-fire sedimentation in watersheds of the western USA (Sankey et al., 2017).
Wildland fire risks are not equitably distributed as they intersect with exposure and socioeconomic attributes (e.g., age, income, ethnicity) 
to influence vulnerability and adaptive capacity (medium confidence) (Wigtil et al., 2016; Davies et al., 2018; Palaiologou et al., 2019).

Individuals in rural areas, low-income neighbourhoods and immigrant communities, as well as renters in California, had less capacity to 
prepare for and recover from fire (Davies et al., 2018). In the USA, 29 million people live in areas with significant potential for wildfires 
and 12 million are socially vulnerable (Davies et al., 2018). In Canada, there are 117 million ha (14% of total land area) of wildland–
human interface, and 96% of populated places have some wildland–urban interface within 5 km (Johnston and Flannigan, 2018).
There is growing recognition of the need to shift fire management and suppression activities to co-exist with more fire on the landscape. 
This includes widespread use of prescribed fire across landscapes to increase ecological and community-based resilience (high agreement, 
medium evidence) (Schoennagel et al., 2017; McWethy et al., 2019; Tymstra et al., 2020). Otherwise, the unprecedented combination of

increased human exposure and size of recent mega-fires creates community risks that may exceed conventional operational and forest 
management response capacity and budgets (Podur and Wotton, 2010; Wotton et al., 2017; Loehman et al., 2020; Moreira et al., 2020; 
Parisien et al., 2020) particularly with ongoing population and infrastructure expansion into the wildland–urban interface (Canadian 
Council of Forest Ministers, 2016; Coogan et al., 2019).
Climate-informed post-fire ecosystem recovery measures (e.g., strategic seeding, planting, natural regeneration), restoration of habitat 
connectivity and managing for carbon sequestration (e.g., soil conservation through erosion control, preservation of old growth forests, 
sustainable agroforestry) are critical to maximise long-term adaptation potential and reduces future risk through co-benefits with carbon

mitigation (Davis et al., 2019; Hurteau et al., 2019; Coop et al., 2020; Stewart et al., 2021). Innovation in and scaling up the use of 
prescribed fire and thinning approaches are contributing to pre- and post-fire resilience goals, including use of Indigenous Peoples 
burning practices that are receiving a new level of awareness (see Box 14.1; Kolden, 2019; Marks-Block et al., 2019; Long et al., 2020b).Box 14.2 (continued)

14
1950Chapter 14 North America
The tools FireSmart Canada1, Firewise USA2 and Think-Hazard Mexico3 were devised to reduce fire risks and create fire-resilient communities. 
They provide design guidance at building, lot, subdivision and community scales, and instruct citizens on creating defensible space 
(National Fire Protection Association, 2013; Firesmart Canada, 2018). Implementation has been fragmented and variable as it depends 
on voluntary uptake by individuals, businesses and communities across a range of adaptive capacities and fire-exposed landscapes (Smith 
et al., 2016a). Many vulnerable groups do not have access to financial or physical resources to reduce fire risk (Collins and Bolin, 2009; 
Palaiologou et al., 2019).
Although innovative, holistic approaches to wildland fire management are becoming more common across North America, broader

application is necessary to address the growing risks (medium confidence). A social–ecological perspective blends ecosystem complexity, 
scale and processes into land-use planning along with community values, perception and capacities as well as institutional arrangements 
(Smith et al., 2016a; Spies et al., 2018). A risk assessment perspective expands from short-term, reactive fire response to landscape-scale, 
long-term prevention, mitigation, and preparedness with community and practitioner engagement (Coogan et al., 2019; Sherry et al., 
2019; Johnston et al., 2020; Tymstra et al., 2020).
4 See www.firesmartcanada.ca
5 See www.nfpa.org
6 See https://thinkhazard.orgBox 14.2 (continued)
temperatures (Meyer-Gutbrod et al., 2018; Davies and Brillant, 2019). 
Similarly, whale entanglements in fishing gear along the Pacific coast 
has increased twentyfold (Hazen et al., 2018). Projected shifts in the 
North Pacific Transition Zone by up to 1000 km northward (by the

end of the century under RCP8.5) combined with changes in coastal 
upwelling (Polovina et al., 2011; Hazen et al., 2013; Rykaczewski et al., 
2015) could alter up to 35% of elephant seal and bluefin tuna foraging 
habitat (Robinson et al., 2009; Kappes et al., 2010).
In North American Arctic marine systems, rapid warming is significant, 
with cascading impacts beyond polar regions (CCP6), and presents 
limited opportunities (tourism, shipping, extractive) but high risks 
(shipping, fishing industries, Indigenous subsistence and cultural 
activities) (high confidence) (Sections  14.5.4, 14.5.9, 14.5.11; CCP6 
Gaines et  al., 2018; IPCC, 2019b; Samhouri et  al., 2019; Free et  al., 
2020; Holsman et al., 2020). Both direct hazards and indirect food-web 
alterations from sea ice loss have imperilled seabirds, marine mammals, 
small-boat operators, subsistence hunters and coastal communities 
(CCP6; Sigler et al., 2014; Allison and Bassett, 2015; Huntington et al.,

2015; Hauser et  al., 2018; Raymond-Yakoubian and Daniel, 2018; 
Dezutter et al., 2019). Increasingly favourable environmental conditions 
due to warming combined with shipping and other activities has raised 
the rate of invasive species movement into the Arctic (Mueter et al., 
2011). Sea ice loss due to climate change is expected to accelerate over 
the next century (Section 14.2, Fox-Kemper et al., 2021).
Coral reefs in the Gulf of Mexico and along the coasts of Florida and the 
Yucatan Peninsula are facing increasing risk of bleaching and mortality 
from warming ocean waters interacting with non-climate stressors 
(very high confidence)  (Cinner et al., 2016; Hughes et al., 2018; Sully 
et  al., 2019; Williams et  al., 2019b). Coral reefs are contracting in 
equatorial regions and expanding poleward (Lluch-Cota et  al., 2010; 
Jones et al., 2019a). Loss of coral habitat leads to loss of ecosystem

structure, fish habitat, food for coastal communities and impacts tourism opportunities (Section 14.5.7; Weijerman et al., 2015a; Weijerman et al., 
2015b). Without mitigation to keep surface temperatures below a 2°C 
increase by the end of the century, up to 99% of coral reefs will be lost; 
however, 95% of reefs will still be lost even if warming is kept below 
1.5°C (high confidence) (Hoegh-Guldberg et al., 2018; Hoegh-Guldberg 
et al., 2018). In Florida, by 2100, an estimated 24–55 billion USD may 
be lost in recreational use and value derived by people knowing the reef 
exists and is healthy (Lane et al., 2013; Hoegh-Guldberg et al., 2019b) as 
coral reefs decline (Section 14.5.9).
Sea level rise has led to flooding, erosion and damage to infrastructure 
along the western Gulf of Mexico, the southeast US coasts and the 
southern coast of the Gulf of St Lawrence (very high confidence) 
(Section  14.2; Daigle, 2006; Lemmen et  al., 2016; Frederikse et  al.,

2020). Mangroves, important nurseries for fish and climate refugia for 
corals (Yates et al., 2014), are under threat from climate change along 
the east coast of Mexico (Pedrozo Acuña, 2012). This SLR, storm surge 
and attendant erosion of coastlines and barrier habitats are projected 
to have large impacts on coastal ecosystems, maritime industries 
(Section 14.5.9), urban centres and cities (Section 14.5.5) along the 
Gulf of Mexico, Caribbean Sea, southeast USA, southern Gulf of St 
Lawrence and Pacific Coast of Mexico (see Box 14.4; Semarnat, 2014; 
Sweet et al., 2017; Vousdoukas et al., 2020). Coastal archaeological 
and historical sites are especially vulnerable to SLR (Anderson et al., 
2017; Hestetune et al., 2018; Hollesen et al., 2018).
Future seawater CO 2 levels have been shown in laboratory studies 
to negatively impact Pacific and Atlantic squid, bivalve, crab and fish 
species (Pacific cod), and indirectly alter food-web dynamics (high

confidence) (Kaplan et al., 2013; Long et al., 2013b; Gledhill et al., 2015; 
Seung et al., 2015; Punt et al., 2016; Swiney et al., 2017; Hurst et al., 
2019; Wilson et  al., 2020). Long-term exposure to CO 2 has reduced 
growth of Atlantic halibut (Gräns et al., 2014), whereas some cultured 
oysters (Fitzer et al., 2019) and key Alaskan commercial fish species

14
1951North America  Chapter 14
show tolerance for high CO 2 waters (i.e., juvenile walleye pollock) 
(Hurst et al., 2012). Ocean acidification has already caused shellfish 
growers in the USA and Canada to modify hatchery procedures and 
farming locations to protect the most vulnerable life stages (Cross 
et al., 2016) and is projected to increasingly impact shellfish resources 
in the central and northeast Pacific and Atlantic coasts (Section 14.5.4; 
Seung et al., 2015; Punt et al., 2016).
Open ocean oxygen minimum zones (OMZ) are expanding in the North 
Atlantic, the North Pacific California Current and tropical oceans due to 
warming waters, stratification and changes in precipitation (medium 
confidence) (WGI Section  3.6.2; Deutsch et  al., 2015b; Breitburg 
et al., 2018; Claret et al., 2018; Ito et al., 2019). Hypoxic events along 
coasts, which are partially influenced by climate change, have been 
documented for all three countries, with events more prevalent

on the east coast and around the Gulf of Mexico due to a regional 
oceanography dominated by rivers and estuaries carrying land-based 
nutrients (Breitburg et  al., 2018). Hypoxia has directly caused large 
mortality events for fish and crabs in US estuaries in the Northwest 
Atlantic (Chesapeake Bay), Northeast Pacific (Puget Sound) and the 
Gulf of Mexico (Froehlich et al., 2015; Rakocinski and Menke, 2016; 
Sato et al., 2016; Kolesar et al., 2017). The OMZs and hypoxic events 
are projected to increase over the next century and may limit where 
fish can move (medium confidence) (Deutsch et  al., 2015b; Stortini 
et al., 2015; Bianucci et al., 2016; Li et al., 2016).
Favourable conditions for harmful algal blooms (HABs) have expanded 
due to warming, more frequent extreme weather events (Gobler 
et al., 2017; Pershing et al., 2018; Trainer et al., 2019) and increased stratification, CO 2 concentration and nutrient inputs (high confidence)

(Wells et  al., 2015; Gobler et  al., 2017; Griffith and Gobler, 2019). 
Increased occurrence of HABs (McCabe et al., 2016; Yang et al., 2016; 
Gobler et  al., 2017; USGCRP , 2018) has induced ecological impacts 
and societal costs (see Section 14.5.4 for fishery closures). During the 
2013–2016 Pacific MHW (see Box  14.3), a Pseudo-nitzschia diatom 
bloom off the west coast of the USA caused extensive closures of 
crab and razor clam fisheries (Fisher et al. 2021), with economic and 
sociocultural impacts beyond those in the fisheries sector (Ritzman 
Beaching of massive Sargassum seaweed mats (Sargassum natans 
and S. fluitans) have been reported across the Caribbean and Gulf of 
Mexico from 2011 to the present, affecting US and Mexico nearshore 
ecosystems, human health and the tourism industry (Franks et  al., 
2016; Resiere et al., 2018; Wang et al., 2019). Costs of beach clean-up 
is high, with Texas spending over 2.9 million USD annually (Webster

and Linton, 2013). Attribution of Sargassum blooms to climate 
change is still tenuous and complicated by multiple drivers and few 
observational data sources (low confidence) (Wang et al., 2019).
14.5.2.2 Adaptation: Current State, Barriers and Opportunities
Emerging technologies and cooperative marine management are 
approaches to facilitate adaptation but require coordination and 
investment for implementation (high confidence) (Gattuso et  al., 
2018; Miller et  al., 2018; Holsman et  al., 2019; Karp et  al., 2019). 
Advancements in oceanographic and ecological nowcasting and 
forecasting tools (i.e., O 2, pH, temperature, aragonite saturation state, 
Box 14.3 | Marine Heatwaves
Marine heatwaves are periods of discrete anomalously high (compared with a 30-year history) sea surface temperatures that persist for 
a minimum 5 d but up to several months (Hobday et al., 2016; Frölicher et al., 2018; Holbrook et al., 2019; Laufkötter et al., 2020). There

have been MHWs attributed to climate change in every marine system of North America including large areas of the Northwest Atlantic 
(2012), Caribbean Sea (2015), Bering Sea (2016–2018) and central through Northeast Pacific (2013–2016) (NOAA, 2018; Holbrook et al., 
2019; Smale et al., 2019). Such MHW events have affected kelp forests (Arafeh-Dalmau et al., 2019), corals (Eakin et al., 2018), seagrasses, 
bottom-dwelling organisms, marine birds (Loredo et al., 2019; Smale et al., 2019), mammals (Suryan et al., 2021), fish and shellfish, and 
marine-dependent human communities (Huntington et  al., 2020; Fisher et  al., 2021; Suryan et  al., 2021). Increased sea temperatures 
directly increase metabolic demand and change productivity and behaviour of fish species (Stock et al., 2017; Free et al., 2019) as well as 
induce rapid redistribution of species poleward and to deeper, colder waters (Pecl et al., 2017; Rheuban et al., 2017; Crozier et al., 2019;

Stevenson and Lauth, 2019; Yang et al., 2019; Barbeaux et al., 2020; Cheung and Frölicher, 2020). In the Pacific, from the Baja Peninsula 
to the Bering Sea, there is evidence of widespread shifts in coastal biota and multi-trophic-level starvation of seabirds and whales from 
combined metabolic demand and reduced prey quality associated with protracted MHWs across multiple regions ((CCP6); Sydeman et al., 
2015; Duffy-Anderson et  al., 2019; Sanford et  al., 2019; Smale et  al., 2019; Suryan et  al. 2021). The distribution of two economically 
important North American species, Bering Sea Pacific cod (Pinsky et al., 2013b; Stevenson and Lauth, 2019; Barbeaux et al., 2020; Spies 
et al., 2020) and American lobster (Rheuban et al., 2017), have shifted north. The MHW-induced loss of coral reefs across tropical North 
American waters has varied in severity regionally. For instance, in 2015 and 2016, extensive, severe bleaching affected more than 30%

of corals off the southeast USA and a large proportion of US Hawaiian Islands, but had moderate to no impact off the Mexican Yucatan 
Peninsula (Frieler et al., 2013; Weijerman et al., 2015a; Weijerman et al., 2015b; Cinner et al., 2016; van Hooidonk et al., 2016; Hughes 
et al., 2018; Sully et al., 2019; Williams et al., 2019b). Some reefs are exhibiting recovery following efforts focused at reducing non-climate 
stressors (e.g., overfishing, nutrient pollution and tourism use). Such MHWs are increasing in intensity and frequency (Hobday et al., 2016; 
Smale et al., 2019) with the largest increases in frequency and spatial coverage projected for the Gulf of Mexico, US southern east coast 
and US Pacific Northwest (Ranasinghe et al., 2021) and pose a key risk to marine systems in North America (Section 14.5.2; Chapters 3, 16).

14
1952Chapter 14 North America
sea ice conditions) can reduce climate impacts by supporting fisheries 
and aquaculture adaptation along US coasts (Section 14.5.4; Cooley 
et  al., 2015; Irby et  al., 2015; Siedlecki et  al., 2015; Siedlecki et  al., 
2016; Siddon and Zador, 2017). Forecasts and warnings reduce human 
exposure to HAB toxins in the Great Lakes, the west coast of Florida, 
east coast of Texas and the Gulf of Maine (Anderson et al., 2019).
Ocean management that utilises a portfolio of nested, multi-scale, 
climate-informed and ecosystem-based management approaches 
in North American waters can increase the resilience of marine 
ecosystems by addressing multiple stressors simultaneously (high 
confidence) (Marshall et al., 2018; Holsman et al., 2019; Smale et al., 
2019; Holsman et al., 2020). Integrated ecosystem assessments (Foley 
et al., 2013; Levin et al., 2014) are increasingly used to provide strategic

advice and context for harvest allocations and bycatch avoidance 
(Zador et al., 2017) as well as early warnings of ecosystem-wide change 
(e.g., sentinel species, ecological indicators) (Cavole et al., 2016; Hazen 
et al., 2019; Moore and Kuletz, 2019). Dynamic ocean management 
policies may improve resilience of marine species and ecosystems 
to climate (medium confidence) (Hyrenbach et  al., 2000; Maxwell 
et al., 2015; Dunn et al., 2016; Tommasi et al., 2017a; Tommasi et al., 
2017b; Hazen et al., 2018; Wilson et al., 2018; Holsman et al., 2019; 
Karp et al., 2019). New proactive and rapid management approaches 
have been developed to minimise impacts of increasingly frequent 
entanglements of protected species, caused by climate-driven changes 
in prey and fishery activities (Corkeron et  al., 2018; Meyer-Gutbrod 
et al., 2018). Dynamic closure areas are being used to address these 
issues and reduce loggerhead turtle bycatch in Hawaiian shallow-set

longline fisheries (Howell et al., 2015; Lewison et al., 2015), blue whale 
ship-strike risk in near-real time (Hazen et al., 2017; Abrahms et al., 
2019b) and bycatch of multiple top predator species in a west coast 
drift gillnet fishery (Hazen et al., 2018).
Improved coordination and planning at multiple scales will be important 
for marine species conservation and recovery as species redistribute 
across fishery areas, marine protected zones, and international and 
jurisdictional boundaries (Section 14.5.4; Cross-Chapter Box MOVING 
PLATE in Chapter 5; Pinsky et al., 2018; Karp et al., 2019). Indigenous 
Peoples’ co-management with federal and state partners of 
marine resources and protected species is an important approach 
(Section 14.5.4; Chapters 5 and 6; CCP6; Galappaththi et al., 2019).
Securing broodstocks for rebuilding and supplementation can 
be challenging for marine populations already in decline (e.g.,

blue king crab in Alaska, steelhead salmon in Puget Sound, white 
abalone in California, groundfish in the northeast USA and Canada) 
(Section 14.5.4; Table SM14.8). Marine protected areas can attenuate 
climate impacts through trophic redundancy, preserving ecological 
processes, biodiversity and climate refugia (Roberts et  al., 2017a; 
Schoen et  al., 2017), although benefits decrease after mid-century 
(or sooner for high-latitude marine protected areas) as species reach 
their thermal limit, unless coupled with GHG mitigation (Bruno et al., 
2018). Transport, relocation and cultivation of resistant breeds of 
salmon, oysters, corals, marine mammals and other keystone species, 
as well as hatchery supplementation of impaired populations of fish 
and shellfish, are species conservation and recovery methods that will 
be in greater demand under climate change, although unintended environmental impacts must be considered. Options for protecting

and restoring coral reefs to prevent loss of ecosystem function are 
under development with Florida reef species (Gattuso et al., 2018; 
National Academies of Sciences, 2019). An emerging approach for 
financing the protection of reefs involves re-categorising reefs as 
‘natural infrastructure’ which has allowed for use of insurance to 
rebuild lost reefs (Storlazzi et al., 2019).
14.5.3 Water Resources
Climate change poses increasing threats to North American aquatic 
ecology, water quality, water availability for human uses, and flood 
exposure, through reductions in snow and ice, increases in extreme 
precipitation and hotter droughts. Adaptation will be impeded in cases 
where there are conflicts over competing interests or unintended 
consequences of uncoordinated efforts, heightening the importance of 
cooperative, scenario-based water resource planning and governance 
14.5.3.1 Observed Impacts
North American water resources continue to be affected by ongoing

warming, with impacts driven by reductions in snow and ice, increases 
in extreme precipitation and hotter droughts (high confidence) 
(Section 14.2; Fleming and Dahlke, 2014; Mortsch et al., 2015; Dudley 
et  al., 2017; Fyfe et  al., 2017; McCabe et  al., 2017; Chavarria and 
Gutzler, 2018; Lall et al., 2018; Bonsal et al., 2019; USGCRP , 2019). The 
cascading effects of severe droughts, floods, sediment mobilisation, 
HABs and pathogen contamination episodes have revealed the 
vulnerability and exposure of large numbers of people and economic 
activities to those hazards.
North America’s dams, levees, wastewater-management and water 
conveyance facilities have improved water supply safety and have 
reduced flood and drought risks, but a substantial portion of that 
infrastructure is ageing and inadequate for modern conditions (Ho 
et  al., 2017; Tellman et  al., 2018; Carlisle et  al., 2019; FEMA, 2019; 
ASCE, 2021). Increasingly heavy precipitation from a variety of

storm types has affected parts of North America (Feng et  al., 2016; 
Prein et al., 2017a; Kunkel and Champion, 2019; Kunkel et al., 2020), 
contributing to contamination from combined sewer overflows (Olds 
et al., 2018) and increased flood damages that are partially attributed 
to anthropogenic climate change (van der Wiel et al., 2017; Davenport, 
2021). Extreme precipitation events have overwhelmed water control 
infrastructure, imperilling public safety and contributing to extensive 
damages in parts of North America (Kytomaa et al., 2019; Vano et al., 
2019; White et al., 2019). Damages stem from extremity of the event 
and prior land-use and infrastructure decisions (high confidence).
In South Carolina, 5 days of heavy rainfall in October 2015 caused the 
failure of more than 50 dams and some levees, significantly magnifying 
destruction from the floodwaters (FEMA, 2016). Slow-moving, 
destructive storms like hurricanes Harvey (2017) and Florence (2018)

have caused significant flooding (van Oldenborgh et al., 2017; Paul et al., 
2019b). In those cases, urban sprawl may have altered storm dynamics 
(Zhang et al., 2018b), while increased asset exposure to the flood hazard

14
1953North America  Chapter 14
amplified the multi-billion-dollar losses (Klotzbach et al., 2018; Trenberth 
et  al., 2018). A substantial fraction of the damage from hurricane 
Harvey’s extreme rainfall has been attributed to anthropogenic climate 
change (see Box 14.5; Emanuel, 2017; Risser and Wehner, 2017). A near 
disaster at California’s Oroville dam in 2017 was caused by inadequate 
infrastructure design and maintenance together with an unusually large 
number of atmospheric river (AR) storms. The event required emergency 
reservoir spills while the state was beginning recovery from the extreme 
2012–2016 drought (Vano et al., 2019; White et al., 2019).
In Mexico, some poor neighbourhoods and informal settlements are 
located in areas exposed to recurrent flooding. Residents often lack 
access to public services and technical resources for risk reduction, 
which heightens their vulnerability (Castro and De Robles, 2019).

Population growth and urban development have increased the 
exposure and vulnerability of Canadian communities to flood damages, 
with cumulative damages (including uninsured losses) exceeding 
10  billion USD in the past decade (The Geneva Association et  al., 
2020). Recurring floods are particularly costly (e.g., New Brunswick) 
(Beltaos and Burrell, 2015; Kovachis et al., 2017). Floods in High River, 
AB (2013) and Gatineau, QC (2017, 2019) initiated considerations of 
building flood resilience including planned retreat (Saunders-Hastings 
Extended and severe droughts in the western USA, northern Mexico 
and Canadian Prairies, exacerbated by higher temperatures, have 
caused economic and environmental damage (Williams et al., 2013; 
Agha Kouchak et  al., 2015; Diaz et  al., 2016; Bain and Acker, 2018; 
Lopez-Perez et  al., 2018; Ortega-Gaucin et  al., 2018; Xiao et  al., 
2018; Martinez-Austria et al., 2019; Bonsal et al., 2020; Martin et al.,

2020b; Milly and Dunne, 2020; Overpeck and Udall, 2020). Droughts 
have intensified tensions among competing water-use interests and 
accelerated depletion of groundwater resources (high confidence) 
(Section 14.5.4; Pauloo et al., 2020).
Climate trends are affecting riverine, lake and reservoir water quality 
(medium confidence). Droughts and increased evapotranspiration have 
impaired water quality by concentrating pollutants in diminished water 
volumes (Paul et  al., 2019a). Cyanobacterial blooms and pathogen 
exposure events are increasing in frequency, intensity and duration in 
North America (Taranu et al., 2015). They are closely associated with 
observed changes in precipitation intensity and associated nutrient 
loading (e.g., agricultural runoff, sanitary sewer overflows), elevated 
water temperatures and eutrophication (Michalak et  al., 2013; 
Michalak, 2016; Trtanj et al., 2016; Chapra et al., 2017; IBWC, 2017;

Williamson et al., 2017; Olds et al., 2018; Coffey et al., 2019). These 
events endanger human and animal health, recreational and drinking 
water uses and aquatic ecosystem functioning, and cause economic 
losses (Michalak et  al., 2013; Bullerjahn et  al., 2016; Chapra et  al., 
2017; Huisman et al., 2018). Households and communities dependent 
on substandard wells, unimproved water sources or deficient water 
provision systems are more exposed than others to experience climate-
related impairment of drinking water quality (Section 14.5.6.5; Allaire 
et  al., 2018; Baeza et  al., 2018; California State Water Resources 
Control Board, 2021; Navarro-Espinoza et al., 2021; Water and Tribes 
Initiative, 2021).14.5.3.2 Projected Impacts and Risks
Climate change is projected to amplify current trends in water resource 
impacts, potentially reducing water supply security, impairing water 
quality and increasing flood hazards to varying degrees across North

America (high confidence). Examples are presented in Table 14.3.
Projected long-term reduction in water availability in the southwest 
US and northern Mexico (e.g., from the Colorado and Rio Grande 
rivers) will have substantial ecological and economic impacts given 
the region’s heavy water demands (high confidence) (Lall et al., 2018; 
Paredes-Tavares et al., 2018; Martinez-Austria et al., 2019; Milly and 
Dunne, 2020; Williams et  al., 2020). Increased water scarcity will 
intensify the need to address competing interests across state and 
national boundaries, including honouring commitments to Indigenous 
Peoples who have long struggled with inadequate access to their water 
entitlements and marginalisation in water resource planning (Mumme, 
1999; Cozzetto et al., 2013b; Mumme, 2016; McNeeley, 2017; Radonic, 
2017; Robison et al., 2018; Curley, 2019; Water and Tribes Initiative, 
2020; Wilder et al., 2020).
Increased scarcity of renewable water relative to legally allocated

or desired uses may develop in many parts of North America. A 
detailed analysis of projected water demands (consumptive uses) and 
availability found increasingly frequent shortages in several watersheds 
across the USA (Brown et al., 2019b). This might lead to maladaptive 
increased groundwater mining, or alternatively to policies promoting 
sustainable balancing of water consumption with renewable supplies, 
for example, by facilitating voluntary water transfers or improving 
enforcement of groundwater rights (Colorado River Basin Stakeholders, 
2015; California Natural Resources Agency et al., 2020; Colorado Water 
Conservation Board, 2020; Pauloo et al., 2020).
Climate change is projected to reduce groundwater recharge in 
major southwest US aquifers (e.g., Southern High Plains, San Pedro 
and Wasatch Front), exacerbating their ongoing depletion due to 
unsustainable pumping. Other aquifers, especially those farther north,

face uncertain or possibly increasing recharge (medium confidence) 
(Meixner et al., 2016).
Projected changes in temperature and precipitation present direct risks 
to North American water quality, varying with regional and watershed 
contexts (Chapra et al., 2017; Coffey et al., 2019; Paul et al., 2019a), 
and related to streamflow, population growth (Duran-Encalada et al., 
2017) and land-use practices (medium confidence) (Mehdi et al., 2015). 
Harmful algal blooms increase in frequency across the USA (Wells 
et al., 2015) with the highest risk projected for the Great Plains and 
Northeast USA, and greatest economic impacts from lost recreation 
value in the southeast USA (Chapra et al., 2017).
The diversity of climate regimes across North America results in regional 
differences in water-related climate-change risks (Figure 14.4).
14.5.3.3 Adaptation
North American water planners and policy makers have abandoned

stationarity assumptions (Milly et al., 2015) to address climate change.

14
1954Chapter 14 North America
Table 14.3 |  Selected projected water resource impacts in North America
Climate drivers and 
processesExamples of future risks and impactsLocation  
(see Figure 14.1)References
Warming-induced reductions 
in mountain snow and 
glacial massProjected decreases in annual and late-summer streamflow 
from high-elevation reaches of snow-fed rivers, affecting 
stream ecology and water supplies (high confidence)US-NW, US-SW, CA-BC, 
CA-PRJost et al. (2012); Solander et al. (2018); Bonsal et al. (2019); 
Milly and Dunne (2020)
Earlier seasonal snowmelt 
runoffGreater winter/early spring flooding risks and reduced 
summer surface water availability, intensifying seasonal 
mismatch with water demands (high confidence);
increased challenges for balancing multi-purpose reservoir 
objectives (e.g., flood management, water supply, ecological 
protection and hydropower) (high confidence)US-NW, US-SW, CA-BC,

CA-PRCohen et al. (2015); Dettinger et al. (2015); Bonsal et al. 
(2019); Bonsal et al. (2020); RMJOC (2020); Bureau of 
Reclamation (2021d)
runoffPossible reductions in water supply security (medium 
confidence); reduced viability of some small-scale irrigation 
systems (medium confidence)US-SWMedellin-Azuara et al. (2015); Ullrich et al. (2018); Bai et al. 
(2019); Milly and Dunne (2020); Ray et al. (2020); Bureau 
of Reclamation (2021b); Bureau of Reclamation (2021a); 
Bureau of Reclamation (2021c)
Changes in seasonal timing 
and/or total annual runoffImpacts on electric power generation (medium confidence) 
varying by location and type of generationUS-SW, US-NW, CA-QCHaguma et al. (2014); Bartos and Chester (2015); Guay 
et al. (2015); Turner et al. (2019b); RMJOC (2020); Bureau of 
and/or total annual runoffImpacts on urban water supplies CA-QC Foulon and Rousseau (2019)
Warming-related increased

imbalance between 
renewable surface water 
supplies and consumptive 
water demandsGreater pressures on groundwater resources, possible 
increased aquifer depletion, reduced baseflow into surface 
streams and reduced long-term water supply sustainability 
(medium confidence)US-SW, US-SP , US-SE, 
MX-N, MX-NWBauer et al. (2015); Molina-Navarro et al. (2016); Russo and 
Lall (2017); Brown et al. (2019b); Nielsen-Gammon et al. 
(2020); Bureau of Reclamation (2021b)
Warming-related drought 
amplificationReduced water availability for human uses and ecological 
functioning (medium to high confidence) varying by location; 
increased evaporative losses from reservoirsWidespread especially:
US-SW, US-NP , US-SP , 
CA-PR, MX-NW, MX-NPrein et al. (2016); Dibike et al. (2017); Lall et al. (2018); 
Paredes-Tavares et al. (2018); Martinez-Austria et al. (2019); 
Tam et al. (2019); Martin et al. (2020b); Milly and Dunne 
(2020); Overpeck and Udall (2020); Williams et al. (2020);

Bureau of Reclamation (2021b)
Heavier and/or prolonged 
rainfall eventsFlooding, infrastructure and property damage (medium 
to high confidence) varying by location; increased erosion 
and debris flows with impacts on public safety, reservoir 
sedimentation and stream ecology (hazards amplified in 
watersheds affected by wildfires)Widespread especially:
US-SE, US-NE, US-NP , 
US-SP , US-SW, CA-BC, 
MX-CE, MX-NE, MX-SEFeng et al. (2016); Emanuel (2017); Prein et al. (2017a); Prein 
et al. (2017b); Haer et al. (2018); Kossin (2018); Mahoney 
et al. (2018); Thistlethwaite et al. (2018); Curry et al. (2019); 
Larrauri and Lall (2019); Wobus et al. (2019); Ball et al. (2021)
rainfall eventsWater quality impairment, increasing HAB events due to 
increased sediment and nutrient loading together with 
warming; greatest impacts in humid areas with extensive 
agriculture (medium to high confidence) varying by locationUS-MW, US-NE, US-SE, 
US-NP , US-SP , CA-ON,

CA-AT, MX-NE, MX-NWAlam et al. (2017); Chapra et al. (2017); Sinha et al. (2017); 
Ballard et al. (2019)
Increasingly variable 
precipitationHighly variable precipitation poses challenges for water 
management, worsening water supply and flooding 
risks; atmospheric river events are projected to increase 
variability by dominating future North American west coast 
precipitation (medium confidence)US-SW, US-NW, CA-BC Gershunov et al. (2019); Huang et al. (2020)
Hotter summer seasonEvaporative losses from reservoirs are projected to increase 
significantly (very high confidence)US-SW, US-NW, US-NP Bureau of Reclamation (2021b)
Transboundary institutions, government agencies and professional or -
ganisations are taking the lead on adaptation planning and implemen-
tation (ASCE, 2018b; Clamen and Macfarlane, 2018; International Joint 
Commission, 2018). Major water agencies are using climate scenarios 
to identify vulnerabilities and evaluate adaptation options (Yates et al.,

2015; Vogel et  al., 2016; California Department of Water Resources, 
2019; Ray et al., 2020; Bureau of Reclamation, 2021d).
The Water Utility Climate Alliance advises municipal water providers 
to address uncertainty by considering a wide range of plausible 
future climate conditions (WUCA, 2010). In some areas, the impacts of wildfires on water supply resiliency are being considered (Martin, 
2016). Many North American Indigenous Peoples are engaged in 
climate-change adaptation planning, although these efforts may be 
hampered by the complicated legal and administrative setting in which 
they must operate (Norton-Smith et al., 2016a; McNeeley, 2017).
Recent climate extremes have heightened governmental attention 
to climate-change impacts (e.g., California Natural Resources 
Agency et al., 2020). Droughts have exposed shortcomings in water 
management and governance (Gray et al., 2015; Xiao et al., 2017b; 
Lopez-Perez et  al., 2018) spurring legislation and administrative

14
1955North America  Chapter 14
changes to improve groundwater regulation and documentation of 
water rights (California Department of Food and Agriculture, 2017; 
Miller, 2017; Lund et al., 2018; Hanak et al., 2019). Water allocation 
policies are being reassessed to enhance equity, sustainability and 
flexibility through shortage sharing agreements, improved groundwater 
regulation and voluntary water transfers. Developments include an 
interstate drought management agreement for the Colorado River (US 
Law, 2019), and agreements between the USA and Mexico to provide 
pulse flows to benefit the ecology of the Colorado River Delta (Pitt and 
Kendy, 2017). Statewide water planning in Colorado has emphasised 
building drought resilience (e.g., by facilitating temporary water 
transfers) (Colorado State Government, 2015; Yates et  al., 2015). At 
local scales, there have been innovations in cooperative watershed 
protection and water resource planning (Cantú, 2016). Indigenous

Peoples are playing an increasing role in identifying equitable and 
resilient options for adaptation by contributing their knowledge and 
voicing their perspectives on the importance of healthy water bodies 
for human and environmental well-being (Norton-Smith et al., 2016a; 
Water and Tribes Initiative, 2020). Collaboration between stakeholders, 
policymakers and scientists is increasingly common in water resources 
adaptation planning and assessment.
Examples of adaptation include increasing adoption of water-saving 
irrigation methods in California (Cooley, 2016), experimentation with 
using flood waters to enhance groundwater recharge (Kocis and Dahlke, 
2017; California Department of Water Resources, 2018) and agricultural 
land management programmes, including developing riparian buffers to 
protect water quality (Section 14.5.4; Mehdi et al., 2015; Schoeneberger 
et al., 2017). Indigenous Peoples are building upon traditional practices

to adapt to the effects of climate change, for example, by working 
jointly to recharge local aquifers (Basel et al., 2020).
Water-right laws, interstate compacts and international treaties 
regarding transboundary water shape the context for climate-change 
adaptation, but the possibility of long-term climate change typically 
was not contemplated at their inception. Gaps in coverage and vaguely defined terms can lead to tensions and disputes, especially in areas 
facing increased aridity, creating difficulties for adaptation. For example, 
unregulated pumping of groundwater for irrigation during short-term 
droughts can serve as an adaptation to acute conditions (Section 14.5.4), 
but if persisting in the long term, it can deplete finite groundwater 
resources and de-water hydrologically connected rivers. Such outcomes 
have engendered bitter and costly interstate conflicts in the USA, some 
even reaching the US Supreme Court including Texas v. New Mexico (Rio

Grande) and Florida v. Georgia (Apalachicola-Chattahoochee-Flint).
Transboundary rivers that exemplify the need to address climate impacts 
include the Colorado (Gerlak et al., 2013), Columbia (Cosens et al., 2016) 
and Rio Grande/Rio Bravo (Mumme, 1999; Mumme, 2016; Garrick et al., 
2018; Payne, 2020). Drought emergencies can open opportunities for 
progress on collaborative adaptive governance, but such windows may 
quickly close when wetter conditions return (Sullivan, (2019).
Water serves a wide variety of environmental functions and human 
uses as it moves through North America’s river basins, so the impacts 
of climate change are expected to be widespread and multifaceted. 
This increases the importance of collaborative adaptation efforts 
that are equitable, transparent and give voice to differing values, 
perspectives and entitlements across a broad socioeconomic spectrum 
of urban and rural, Indigenous and non-Indigenous participants

(Miller et  al., 2016; Cosens et  al., 2018). Adaptation planning may 
be hampered by conflicting interests, jurisdictional boundaries and 
inherent interconnections between actions and impacts at different 
points throughout a watershed or river basin. Differential power 
relationships, decision-making authority and access to information 
also can interfere with effective adaptive governance, while equitable 
processes for decision making bolstered by reliable shared information 
can help to overcome those impediments (Cosens et al., 2016; Arnold 
et al., 2017; Cosens et al., 2018; Porter and Birdi, 2018).
Across North America, there are growing signs of progress towards 
adaptive water governance and implementation of climate-resilient, North American freshwater risks
Global mean temperatur e
additional risk to society as a function of global

temperature change. Confidence is provided for the 
change of risk level at given temperature ranges .Very high
High
Moderate
Low High•
•••••5°C
Snow and
ice decline
-seasonal
flows
•• •• ••
Heavy
flooding
•• •• ••
Water
quality
impacts
•• •• ••
scarcity
-droughts;
aridification
•• •• •• •• •• ••
Figure 14.4 |  Freshwater resource risks as a function of global mean surface temperature increase relative to pre-industrial (1850–1900) levels. Estimated 
sensitivities are based on references cited in Table 14.3 (SM14.4).

14
1956Chapter 14 North America
and ecosystem-based, water management solutions (Colorado River 
Basin Stakeholders, 2015). California’s approach to groundwater 
sustainability regulation intends to foster such collaborative problem-
solving by giving local Groundwater Sustainability Agencies the 
authority to design locally appropriate plans to meet state-defined 
sustainability goals (State of California, 2014; Miller, 2017). As 
evidenced by the US interstate disputes, the greatest difficulties arise 
in cases where stark upstream–downstream differences in interests 
leave little room for mutual benefit. Severe aridification may test the 
limits of adaptive capacity.
Research on water diplomacy recommends broadening negotiations 
beyond a narrow focus on zero-sum issues, like rigid water allocations, 
to embrace a more diverse set of shared interests including the need 
for flexibility to respond to changing conditions. A process for ongoing

inclusive engagement of a watershed’s stakeholders in mutual social, 
policy and science learning is important. Such mutual learning can build 
trust and establish a common platform of credible information for co-
creation of adaptation solutions. In addition, better understanding of 
the policy positions and constraints of others can help stakeholders to 
identify workable solutions to contentious water management issues 
(Payne, 2020; Wilder et al., 2020). Cooperation between Mexico and 
the USA on mapping and assessment of transboundary aquifers is a 
product of such ongoing engagement (Callegary et al., 2018; Sanchez 
et al., 2018). Other examples of the benefits of sustained engagement 
are provided by a set of co-management arrangements between 
state, federal and Indigenous authorities on water management for 
fishery restoration in the US Pacific Northwest (Tsatsaros et al., 2018) 
and Indigenous involvement in multi-level co-management of water

resources in Canada’s Northwest Territories (Latta, 2018).
14.5.4 Food, Fibre and Other Ecosystem Products
14.5.4.1 Observed Impacts and Projected Risks: Agriculture, 
livestock and forestry
Climate change has affected crops across North America through 
changes in growing seasons and regions, extreme heat, precipitation, 
water stress and soil quality (Table 14.1; Figure 14.5; Section 5.4.1; 
Figure 5.3) (Mann and Gleick, 2015; Galloza et al., 2017; Otkin et al., 
2018). These changes directly influence crop productivity, quality 
and market price (high confidence) (Kistner et al., 2018; Reyes and 
Elias, 2019). Effects of historical climate change on maize, soybean, 
barley and wheat crop yields vary from strong increases to strong 
decreases (e.g., > −0.5 to > +0.5 t ha−1 yr−1 for maize) within North 
America’s agroecological regions, even for the same crop (Ray et al., 
2019). Across North America, climate change has generally reduced

agricultural productivity by 12.5% since 1961, with progressively 
greater losses moving south from Canada to Mexico (Ortiz-Bobea 
et  al., 2021), yet responses are highly differential across regions 
and crops. Some crop loss events are partially attributed to climate 
change (high confidence) such as the 2012 Midwest and Great Plains 
drought, which cost agriculture 30 billion USD (Smith and Matthews, 
2015; Rupp et al., 2017). Aridity is extending northward, altering crop 
suitability ranges (Figure 14.4); up to 50% of distributional shifts in 
growing regions for US crops between 1970 and 2010 may be related to climate change (Lant et al., 2016; Cho and McCarl, 2017). Irrigation 
is expanding to areas formerly largely dependent on rainfall (Wang 
Without adaptation, climate change is projected to reduce overall 
yields of important North American crops (e.g., wheat, maize, 
soybeans) (high confidence) (Tables SM14.3, SM14.4; Chen et  al.,

2017; Levis et al., 2018). For example, projected heat stress (RCP8.5) 
reduced mid-century (2040–2069) maize and cotton yields by 12–
15% of historical yields (1950–2005), with the US-SW suffering the 
largest impacts (Table SM14.5; Elias et al., 2018). Warming and heat 
extremes will delay or prevent chill accumulation, affecting perennial 
crop development (e.g., fruit set failure), yield (e.g., walnuts, pistachios, 
stone fruit) and quality (e.g., grapes) (medium confidence) (Parker 
et al., 2020). Warming will alter the length of growing seasons of cold-
season crops (e.g., broccoli, lettuce) and will shift suitability ranges 
of warm-season California crops (e.g., tomatoes) (medium confidence) 
(Marklein et al., 2020). Increasing atmospheric CO 2 will enhance yields 
yet reduce nutrient content of many crops (high confidence); a CO 2 
concentration of 541 ppm (seen by 2050 in RCP8.5) would reduce 
per-capita nutrient availability in North American diets by 2.5–4.0%

(Beach et al., 2019). Crop pest and pathogen outbreaks are expected to 
worsen under climate change (high confidence) (Deutsch et al., 2018; 
Wolfe et al., 2018; Zhang et al., 2019a).
Climate change is anticipated to cause declines in livestock production 
across North America (high confidence) (Table 14.4; SM14.6; Havstad 
et  al., 2018; Murray-Tortarolo et  al., 2018). Increases in extreme 
temperature raise the risk of livestock heat stress, disease and pest 
impacts (Rojas-Downing et al., 2017). Projected aridification reduces 
forage production in the southwest USA and northern Mexico (high 
confidence) (Polley et  al., 2013; Reeves et  al., 2014; Cooley, 2016; 
Bradford et al., 2020) and transforms grasslands into woody shrublands 
(Briske et al., 2015; Murray-Tortarolo et al., 2018), while warmer and 
wetter conditions in the northern regions (CA-PR, US-NW, US-NP) may 
enhance rangeland production by extending growing seasons (high

confidence) (Hufkens et  al., 2016; Derner et  al., 2018; Zhang et  al., 
2019a). Increased CO 2 will enhance production (medium confidence) 
but reduce forage quality (high confidence) in US-NP and US-NW 
(Table SM14.6; Derner et al., 2018).
Climate-change impacts on forests (Section  14.5.1; see Box  14.2) 
may affect timber production by altering tree species distributions, 
productivity, and wildfire and insect disturbances (medium confidence). 
Southern or drier locations may shift from forests to other vegetation 
types, whereas higher-latitude areas may experience forest expansion 
(Brecka et al., 2018). Tree species composition is projected to change 
with climate change (Wang et al., 2015; Bose et al., 2017). Tree growth 
may increase or decrease from changes in temperature or moisture 
depending on location, with lower growth expected from warming 
in water-limited areas (Littell et  al., 2010). Increased productivity

associated with more favourable climate conditions is projected for 
boreal forests (Brecka et al., 2018), although in some regions, growth 
will reverse and decline with additional warming (D’Orangeville et al., 
2018; Chaste et al., 2019). As a result of these changes, timber yields 
in North America either may increase in the future (Beach et al., 2015; 
EPA, 2015a) or decrease (Boulanger et al., 2014; McKenney et al., 2016;

14
1957North America Chapter 14
Crop A:  yield & more variable
Crop B:  yield & more variable
Crop C:  climate now unsuitable
Crop D:  climate now suitableGradual climate change
long-term warming
longer growing seasonIncreased water demandCO2 fertilizatio n
Indirect effects of climate change
weeds, pests, pathogens, pollinator s
infrastructure (e.g., planting, harvesting, processing )
ozone exposureCrop type
water, energy requirement s
seasonal timing of cropwinter chill unit sFactors affecting crop yields
Future plant hardiness zonesMean climate
temperature, precipitationlatitude, elevation
flooding, extreme storms, drought, heat wave sClimate change
C
B
A
DB
A
Historical plant hardiness zonesCrop responses to climate change will depend on existing mean climate, the type of climate change,and characteristics of crop types
Adaptation actions (examples)
new varieties, different crop types, soil and water management

Figure 14.5 |  Crop responses to climate change will depend on existing mean climate, type of climate change and characteristics of crop types. Hypothesised 
responses for Crop Types A, B, C and D include changing crop yields or changing crop area. Adaptation actions may alter hypothesised responses. (Maps from Matthews et al., 2019.)D’Orangeville et  al., 2018; Thorne et  al., 2018; Chaste et  al., 2019) 
depending on location and the mechanisms included. Wildfires and insect outbreaks are projected to increase with future climate change, thereby limiting biomass (Gauthier et  al., 2015; Bentz et  al., 2019; Chaste et al., 2019).
14.5.4.2  Observed Impacts and Projected Risks:  Fisheries and 
Climate impacts outlined in Section 14.5.2 have induced yield losses for multiple subsistence, recreational and commercial fisheries (very high confidence), and contributed to commercial fishery

closures across North America (Sections 14.5.1, 14.5.3; Figure 14.6; Table SM14.7; Lynn et al., 2014; Barbeaux et al., 2020; Fisher et al., 2021). Climate-driven declines in productivity are widespread (high confidence) (Figure 14.6), although a few increases are observed in northern regions (medium confidence) (Cunningham et  al., 2018; Crozier et al., 2019; Zhang et al., 2019b). Redistribution of species has increased travel distance to fishing grounds, shifted stocks across regulatory and international boundaries, and increased interactions with protected species (very high confidence) (Figure  14.6; Table  SM14.7; Cross-Chapter Box  MOVING PLATE in Chapter 5; Morley et  al., 2018; Free et  al., 2019; IPCC, 2019b; Rogers et  al., 2019; Stevenson and Lauth, 2019; Young et al., 2019). Climate shocks have reduced yield and increased instability in fishery revenue (high confidence) (Fisher et al., 2021).
Declines in yield and poleward stock redistributions (an average 
of ~20.6
 km

change and increase in magnitude with atmospheric carbon (high confidence) (Table 14.4; Hare et al., 2016; Pecl et al., 2017; Rheuban et al., 2017; Morley et al., 2018; Smale et al., 2019; Szuwalski et al., 2021). For example, without adaptation, end-of-century losses of Bering Sea pollock yield (relative to persistence scenarios) is likely to reach 50% under moderate (RCP4.5) and 80% under low (RCP8.5) mitigation scenarios, respectively (Holsman et  al., 2020). Expanding HABs, pathogens and altered ocean chemistry (OA and dissolved oxygen) will reduce yields and increase closures of fisheries along all North American coasts (medium confidence) (Section 14.5.2; Deutsch et al., 2015a; Ekstrom et al., 2015; Seung et al., 2015; Punt et al., 2016; Howard et al., 2020). For fisheries that represent 56% of current US fishing revenue, projected annual net losses under high-emission scenarios (RCP8.5, 2021–2100) may reach double that of low-emission scenarios (RCP2.6) (Moore et al., 2021).

Warming waters and OA have impacted aquaculture production in North 
America (high confidence) (Figure  14.6; Clements and Chopin, 2017; Reid et  al., 2019; Stewart-Sinclair et  al., 2020). Under climate change

14
1958Chapter 14 North America
(RCP8.5), declines in marine finfish and bivalve aquaculture become 
likely by mid-century (Froehlich et al., 2018; Stewart-Sinclair et al., 2020). 
Adaptation is possible but uncertain (Bitter et al., 2019; Fitzer et al., 2019; 
Reid et  al., 2019), especially with increasing extreme events. Nature-
based aquaculture solutions (e.g., conservation aquaculture, restorative 
aquaculture) could aid carbon mitigation and local-level adaptation, 
especially for seaweed and bivalve culture (see Box 14.7; Froehlich et al., 
2017; Froehlich et al., 2019; Reid et al., 2019; Theuerkauf et al., 2019).
14.5.4.3  Food and Fibre Adaptation: Cross-Cutting Themes
Across food and fibre systems, climate resilience is enhanced through 
diversifying income and harvest portfolios as well as increasing local 
biodiversity and functional redundancy (high confidence) (Messier 
et  al., 2019; Rogers et  al., 2019; Young et  al., 2019; Aquilué et  al.,

2020; Fisher et al., 2021). Ecosystem-based practices and sustainable 
intensification (increasing yields while minimising resource demand 
and ecosystem impacts) (Cassman and Grassini, 2020; Rockström 
et al., 2021) will help the sector meet food production demands under 
climate change (medium confidence), but effectiveness generally 
declines and is less certain after 2050 in scenarios without carbon 
mitigation (high confidence) (Bermeo et al., 2014; Gaines et al., 2018; 
Costello et al., 2020; Free et al., 2020; Holsman et al., 2020). Across the sector, successful adaptation is underpinned by approaches that 
meaningfully consider the coupled social–ecological networks around 
food and fibre production and value IK (very high confidence) (see 
Box 14.1; FAO, 2018; Steele et al., 2018; Calliari et al., 2019). Integrated 
modelling, participatory planning and inclusive decision making 
promote effective and equitable adaptation responses (very high

confidence) (Figure 14.7, Section 14.7) Toledo-Hernández et al., 2017; 
Eakin et al., 2018; Monterroso and Conde, 2018; Alexander et al., 2019; 
Hodgson and Halpern, 2019; Holsman et  al., 2019; Samhouri et  al., 
2019; Barbeaux et al., 2020; Hollowed et al., 2020), while a paucity 
of high-resolution and locally tailored climate change information 
remains a barrier to adaptation (Ekstrom et al., 2015; Donatti et al., 
2017; Young et al., 2019).
14.5.4.4  Food and Fibre Adaptation: Agriculture, Livestock and 
Forestry
Land management and horticulture approaches that preserve and 
improve soil structure and organic matter can reduce erosion (high 
confidence) (Sections  14.5.1, 14.5.3; Lal et  al., 2011; Bisbis et  al., 
2018). Preserving biodiversity and water, changing planting dates and 
double cropping are also effective climate adaptation strategies (Bisbis 
et  al., 2018; Hernandez-Ochoa et  al., 2018; Monterroso-Rivas et  al., 
Froehlich et al 2018 (RCP  8.5 2100

ensemble model )negative
Bivalves high probability of declines  
due to changes in temp. CHLa, & OA .
Clements & Chopin et al 2018, Reid 2019,  
Stewart/uni2010Sinclair et al (2020)  negative Bivalves  
impacted  by OA  & temp. across Pacific coast.Filgueira 2013 (field/BACI) positive 
Storm increased turnover (thus food) for 
mussels in an inlet. “Dramatic increases  
in mussel production occurred in the year  
following the opening of the new inlet.”
Arnold et al 2012 (experiments)  negative Region shown 
where most catfish farming is located. Found “high 
temperatures decrease growth in Channel Catfish,  
largely due to reduced food consumption & feed 
conversion & increased levels of activity .”Gibble et al 2016 
(field/experiment) negative 
Drought then rain events 
following periods of higher HAB 
activity may increase dispersal of  
toxins to oysters/mussels .
Linan-Cabello et al 2012 (social 
case-study) likely negative
Tilapia production likely vulnerable,

but more research is needed.  Barbeaux et al 2020, Cheung & 
Frölicher 2020 negative Climate 
change attributed marine heatwave driven 
declines in Pacific cod fisher y. Declines in  
AK fish biomass & shifts in distribution were  
4 times higher during MHWs .
Jardine et al 2020; Fisher et al  
2021;Cheung & Frölicher 
2020 negative Fishery 
closures during the 2013-2016 
MHW & HAB event, closed 
multiple crab fisheries along 
the west coast (US-NW , 
US-SW), diff erentially  
impacted small & large 
vessels; pelagic fish had large 
declines in biomass, as did 
sockeye salmon & California  
anchovy.Poloczanska et al 2016; Miller et al 2018, Morley et al 2018 
positive/negative Species distributions have shifted poleward & 
phenology has shifted earlier with strongest ef fects on bony fish.Spies et al 2020; Stevenson et al 2019  negative 
Northward shifts > 1000 km observed for groundfish 
(e.g.,Pacific cod) associated with record high bottom

temperatures & record low sea ice in the EBS.
(N Am. wide)(N Am. wide)Weatherdon et al. 2016 negative (projected) CA-BC, 
projected declines in abundance of key Indigenous  
subsistence resources (e.g., salmon, halibut, herring,  
rockfish & shellfish) are greater for RCP8.5 than RCP  2.6.Le Bris et al 2018 (modeling/observations) 
positive/negative  American Lobster abundances declined 
(78%) in South New England and have increased (515%) in  
the Gulf of Maine due to water temperature changes and 
differing conservation measures (1985-2014 for GOM and 
1997-2014 for Southern New England). Climate change impacts on North American fisheries and aquaculture
Free et al 2019 (positive/negative) Changes in mean MS Y of fisheries in multiple regions are 
associated with warming temperatures over the last century (2001-2010) -(1930-1939) including 
declines along the entire west coast of North America that range from -14% in the EBS to -29% in

the S-CC. Along the east coast, declines of -3% to -9% were observed in the GOMX & US-SE,  
while increases of 8-15% were observed in the US-NE & CA-QC .Guyondet et al 2015 (model 2050)  positive/negative 
Possible 30% increase in blue mussel production 
largely due to greater spring bloom (CHLa), but  
temperature could negatively impact growth .
Figure 14.6 |  Case studies of climate-change impacts on North American fisheries (blue text) and aquaculture (gray text).

14
1959North America  Chapter 14
UN Sustainable Development Goals
Fisherie s
Aquaculture    Marin e
ecosystemsClimate change
LivestockFood & forage
crops Terrestrial & 
freshwater ecosystem s
Fertilizers & feed
              Food &  livelihood security            Integrated Food System            Equitable Nature Based Solutions    |   Disaster Risk Reduction                                                                       |   Local & IK-Based Approaches   |  Community-Based Approaches    |                      Adaptation in North American food sectors
Figure 14.7 |  Adaptation in North American food sectors is shown, modified from Cottrell et al. (2019).
2018; Wolfe et  al., 2018). Traditional agriculture inherently includes 
climate adaptive practices that enhance biodiversity, soil quality and 
agricultural production (e.g., multiple cultivars, heat-tolerant heritage 
cattle breeds) (Bermeo et  al., 2014; Gomez-Aiza et  al., 2017; Ortiz-

Colón et  al., 2018). Agroecology and agroforestry (see Box  14.7) in 
North America has expanded from (but not replaced) traditional and 
rural practices in Mexico (Metcalfe et al., 2020a) as a sustainable and 
climate-resilient alternative to industrial agriculture (Schoeneberger 
et  al., 2017) that increases productivity (by 6–65% depending on 
the crop), enhances microclimates and provides co-benefits for GHG 
mitigation (Abbas et al., 2017; Cardinael et al., 2017; Schoeneberger 
et al., 2017; Snapp et al., 2021). Irrigation is an effective adaptation 
strategy in key agricultural areas (Miller, 2017; Lund et al., 2018) and 
could stabilise food security in rain-fed regions (e.g., southeast Mexico) 
(Spring, 2014); water allocation must balance multiple needs and rights 
(medium confidence) (Section 14.5.3; Brown et al., 2015b; Levis et al., 
2018; Gomez Diaz et al., 2019). Heritage livestock breeds, changing

species and precision-ranching technology may promote ranch and rangeland resilience (Zhao et al., 2013). In loblolly pine plantations in 
the southern USA, effective adaptation includes reducing tree density 
and, less effectively, shifting to slash pine (Susaeta et  al., 2014). 
Salvage logging following forest disturbances (e.g., insect outbreaks) 
can increase timber harvest (Bogdanski et al., 2011; USDA Forst Service, 
2011; Han et al., 2018; Morris et al., 2018a).
14.5.4.5  Food and Fibre Adaptation: Fisheries and Aquaculture
Proactive and ecosystem-based management increases climate 
resilience in fisheries (high confidence), but effectiveness after 2050 
may be limited without global carbon mitigation (medium confidence) 
(Gaichas et al., 2017; Gaines et al., 2018; Kritzer et al., 2019; Barbeaux 
et al., 2020; Free et al., 2020; Holsman et al., 2020). Flexibility (e.g., 
mobility, diverse incomes or harvest portfolios) underpins climate

resilience across regions, management policies and fisheries, although 
small-scale fisheries have less scope for adaptation (Aguilera et  al., 
2015; Young et al., 2019). Climate-informed and dynamic management

14
1960Chapter 14 North America
Table 14.4 |  Observed and projected impacts to food and fibre resources
Climate driver Observed changeaReferences Projected change References
Agriculture and livestock (Tables SM14.2–SM14.5)
Extreme eventsEstimates of yield reduction from heat 
stress for both maize and cotton indicate 
that historically, US-SW heat stress reduced 
cotton yield by 26% and maize yield by 18% 
compared with potential yield. Extreme heat 
was associated with increased crop failure in 
MX-CE, US-SW. Hailstorm increased frequency 
observed in MX coinciding with the most 
vulnerable stage or flowering period of 
maize. Extreme precipitation damages to soil, 
increased erosion, and reduced crop yields 
observed in Mexico and US-MW.Altieri and 
Nicholls (2009); 
Mastachi-Loza et al. 
(2016); Elias et al. 
(2018); Kistner et al. 
(2018); Reyes and 
Elias (2019)Heat stress (RCP8.5) reduces mid-century (2040–2069) 
maize and cotton yields by 12–15% of historical yields

(1950–2005) with largest impacts in US-SW, and additional 
drought-related stress in US-MW could reduce maize and 
soybean yields by ~5 and ~10%, respectively, by late 
century under RCP4.5. Warming and extreme heat (>35%) 
will delay (or prevent) chill accumulation, impacting 
perennial crop development, yields and quality (US-SW). 
Increases in extreme temperature raise the risk of livestock 
heat stress, disease and pest impacts.Jin et al. (2017); 
Rojas-Downing et al. (2017); 
Elias et al. (2018); Parker 
Mean growing 
season 
decline, mean 
increase, droughtAcross the US Great Plains (US-SP , US-NP) 
between 1968 and 2013 climate change 
induced 3.55, −0.55 and 0.94% change 
in yield for (irrigated and non-irrigated) 
maize, sorghum and soybeans, respectively. 
Droughts and increasing temperatures 
reduced soil fertility in Mexico and 
contributed to soil erosion and degradation, 
and suitability loss of 18–22%. Experimental

and simulated reductions in water supply of 
25–50% result in similar-magnitude declines 
in yield for multiple food and forage crops 
(e.g., wheat, maize).Frisvold and Konyar 
(2012); Leskovar 
et al. (2012); 
Aladenola and 
Madramootoo 
(2014); Galloza et al. 
(2017); Havstad et al. 
(2018); Kukal and 
Irmak (2018)Warming alters the length of growing seasons of 
cold-season crops and shifts suitability ranges of 
warm-season California crops. Aridification reduces forage 
production in US-SW and MX-N. Warming is associated 
with reduced livestock growth and fertility, increased 
pathogens in US-SE, US-SP , US-MW and US-NE, and 
reduced milk production in US-MW.St-Pierre et al. (2003); 
Polley et al. (2013); Key and 
Sneeringer (2014); Reeves 
et al. (2014); Cooley (2016); 
Hufkens et al. (2016); Derner 
et al. (2018); Hristov et al. 
(2018); Ortiz-Colón et al. 
(2018); Zhang et al. (2019b); 
Bowling et al. (2020); 
Bradford et al. (2020); 
Marklein et al. (2020)

Multiple driversClimate change reduced total factor 
productivity of agriculture and livestock 
in North America by 12.5% (ranging from 
approximately −35 to 8%) between 2016 
and 2015. Losses have been greatest in 
Mexico (−30 to −25%) (Figure 14.5), and 
lowest in Canada (>0%). Reduced yield in 
Mexico and the USA; increased weed and 
pest pressure in US-NE, US-MW, US-NP and 
US-NW.Garruña-Hernández 
et al. (2012); Loreto 
et al. (2017); Wolfe 
et al. (2018); Torres 
Castillo et al. (2020; 
Ortiz-Bobea et al. 
(2021)Declines in yield and changes in suitability ranges for maize 
(−18 to 5%), sorghum (−16 to 12%) and wheat (−38 to 
−15%) in Mexico (RCP4.5, 8.5; 2040–2099); northward 
shifts in the suitable area for six crops from the central 
USA (2100). Warming accompanied by increased CO 2 
may benefit crop production of small grains in southern 
Canada up to 3°C global warming level (GWL), although 
benefits decline after 2.5°C GWL. Increased CO 2 enhances

production but reduces forage quality in US-NP and US-NW. 
Without adaptation, 2°C GWL increases insect-caused 
production losses ~36 and ~44% for maize and wheat, 
respectively.Calderón-García et al. 
(2015); Herrera-Pantoja 
and Hiscock (2015); Lant 
et al. (2016); Chen et al. 
(2017); Montiel-González 
et al. (2017); Reyer et al. 
(2017); Derner et al. 
(2018); Deutsch et al. 
(2018); Levis et al. (2018); 
López-Blanco et al. (2018); 
Murray-Tortarolo et al. 
(2018); Wolfe et al. (2018); 
Gomez Diaz et al. (2019); 
Qian et al. (2019); Zhang 
et al. (2019b); Arce Romero 
Aquaculture and fisheries (Tables SM14.6, SM14.8)
Extreme eventsMHW and HAB events of 2014–2016 
resulted in multiple fishery closures along 
the west coast (US-NW, US-SW); disparate 
impacts observed between small and large 
vessels with greatest impacts on small vessel 
revenue and fishery participation; impacts 
highest for ports in the N-CC and least for

fishing communities with diverse livelihoods 
and harvest portfolios. In the EBS, GOA and 
N-CC, declines in fish biomass and shifts 
in distribution were four times higher and 
greater during MHWs than that of general 
warming over the same period. Pelagic fish 
showed largest decrease in biomass (7%), as 
did Sockeye salmon and California anchovy; 
increased risk to hatcheries and low-lying 
pond systems from severe storms. Extreme 
heat is associated with reduced productivity 
of aquaculture species.Handisyde et al. 
(2017); Food 
Agriculture 
Organization of 
the United Nations 
(2019); Froehlich 
et al. (2019); Reid 
Bertrand et al. 
(2020); Cheung and 
Frölicher (2020); 
Jardine et al. (2020); 
Sippel et al. (2020); 
Fisher et al. (2021)Doubling of MHW impact levels by 2050 among the most 
important fisheries species (over previous assessments that 
focus only on long-term climate change).Cheung and Frölicher (2020)

14
1961North America  Chapter 14
Multiple driversClimate shocks reduce catch, revenue 
and county-level wages and employment 
among commercial harvesters in US-NE. 
Climate variability during 1996–2017 is 
responsible for a 16% (95% CI: 10–22%) 
decline in county-level fishing employment 
in New England; impacts mediated by local 
biology and institutions. Seafood is an 
important source of nutrients and protein 
for Indigenous Peoples in CA-BC. Polices 
that incorporate nutrition in fisheries 
management are limited in North America.Marushka et al. 
(2019); Oremus 
(2019); also see 
Section 14.5.6Declines in North American catch potential of flatfish 
under RCP8.5 for the EBS, GOA, GOMX, US-SE and US-NE; 
declines in productivity for multiple species in Mexico, with 
the largest declines in productivity (>35%) for abalone and 
Pacific sardine. Impacts are greatest for artisanal species;

declines in fish community biomass for all North American 
coasts except US-SW and the Canadian Arctic; declines are 
greater under RCP8.5 than RCP2.6. Modest increases (up 
to 10%) in landings of CA-QC and CA-AT surf clams and 
shrimp are projected under RCP2.6 by 2100 and declines in 
snow crab up to 16% are expected (RCP2.6, 8.5). Mussel 
landings increase 21%, while declines in shellfish and lobster 
landings (2090) are twice as high under RCP8.5 (42–54%) as 
RCP2.6. Shellfish and snow crab landings decline in CA-QC 
and CA-QT; declines under RCP8.5 are double those of 
RCP2.6. Climate change reduces EBS blue king crab recovery 
in simulations. Relative to the USA and Canada, Mexico has 
the strongest benefits in net catch under RCP2.6 relative 
to RCP8.5 ( >30% increase in catch); increases of 70% in 
catch potential projected for the Canadian Arctic (CA-NE, 
CA-NW) under RCP8.5 (versus minimal changes under 
RCP2.6). High-resolution and size-spectrum models project

declines in groundfish catch and biomass in S-EBS. Shifting 
transboundary stocks may increase challenges.Weatherdon et al. (2016); 
Cheung (2018); Carozza 
et al. (2019); Cisneros-Mata 
et al. (2019); Reum et al. 
(2019); Tai et al. (2019); 
Mendenhall et al. (2020); 
Wilson et al. (2020)
Ocean and lake 
acidificationOcean acidification (OA) reduced maximum 
sustainable yield, catch and profits of EBS 
Tanner crab in simulations. Survival of 
larval and juvenile red king crab in the 
lab decreased 97–100% with decreasing 
pH; no appreciable effects of pH on larval 
growth of walleye pollock in the lab (Hurst, 
2013); mixed evidence of impacts of 
changes in pH on freshwater or saltwater 
finfish aquaculture; OA reduced growth, 
calcification, attachment and increased 
mortality in calcifying molluscs and 
seaweeds in the USA and Canada; OA may 
benefit non-calcifying seaweeds.Long et al. (2013a); 
Seung et al. (2015); 
Punt et al. (2016); 
Clements and Chopin 
(2017); Handisyde

et al. (2017); Swiney 
Food Agriculture 
Stewart-Sinclair et al. 
(2020)Declines for some shellfisheries and flatfish due to OA and 
temperature. OA conditions under RCP8.5 reach critical 
risk thresholds for mollusc harvests earlier in northern 
regions than southern areas. OA risk to shellfisheries is 
highest in N-CC. OA causes 1% additional decline in Arctic 
cod populations by 2100 under RCP8.5. OA influences 
management reference points of Northern Rock sole. 
OA and temperature reduce probability of recovery in 
simulations of EBS blue king crab.Ekstrom et al. (2015); Reum 
et al. (2019); Steiner et al. 
(2019); Wilson et al. (2020); 
Punt et al. (2021)
Mean 
increaseSpecies distributions have shifted poleward 
and phenology has shifted earlier with the 
strongest effects on bony fish. Warming over 
the past century (2001–2010 to 1930–1939)

is associated with declines in maximum 
sustainable yield along the entire west coast 
of North America that range from −14% in 
the EBS to −29% in the CC-S. Along the east 
coast, declines of −3 to −9% were observed 
in the GOMX and US-SE, while increases 
of 8–15% were observed in the US-NE and 
CA-CQ; mixed positive and negative growth 
and mortality responses for aquaculture 
species in North America. Juvenile red king 
crab survival decreases as temperatures 
increase in lab experiments. American 
Lobster abundances declined (78%) in South 
New England and have increased (515%) in 
the Gulf of Maine due to water temperature 
changes and differing conservation measures 
(between 1985 and 2014 for the GOM, 
and 1997 and 2014 for southern New 
England).Poloczanska et al. 
(2016); McCoy et al. 
(2017); Swiney 
et al. (2017); Le 
Bris et al. (2018); 
Miller et al. (2018); 
(2019); Free et al. 
(2019); Reid et al.

(2019); Weiskerger 
(2020); Le et al. 
(2020)By end of century, North American fish biomass, catch 
potential and revenue are ~9% higher under RCP2.6 than 
RCP8.5 and differences are greatest for US fisheries (relative 
to Canada and Mexico; poleward redistributions (reported 
ranges of 10.3–39.1 km per decade) and to depth decrease 
access to shellfisheries in CA-QC and subsistence species in 
CA-BC (−28% by 2100), with impacts increasing north to 
south and under RCP8.5 as compared with RCP2.6. Climate 
change (RCP8.5) shifts the relative percentage of catch and 
profits for the USA–Canada transboundary stocks under 
RCP8.5 (but not RCP2.6); decreases in biomass of historically 
large fisheries in US-NA and CA-QC, and US-AK and 
important subsistence species in CA-WA and CA-BC, while 
some increases in the North Atlantic. Declines are greater 
under RCP8.5 relative to RCP2.6. In EBS (US-AK), community

biomass, catches and mean body size decreases by 36, 61 
and 38%, respectively, under RCP8.5 (2100). Climate change 
causes declines in global marine aquaculture production 
under RCP8.5 with impacts greater for bivalve than finfish 
and with significant disparities among regions in direction 
and magnitude of changes; greatest declines for finfish 
aquaculture expected in northern regions (GOA, CA-BC, 
CA-CQ), and large declines for bivalve production (declines 
of 20–100%) for Canada. Declines become more probable 
by 2050–2070.Weatherdon et al. (2016); 
Cheung (2018); Froehlich 
et al. (2018); Morley et al. 
(2018); Greenan et al. 
(2018); Steiner et al. (2019); 
Sumaila et al. (2019); 
Bryndum-Buchholz et al. 
(2020); Holsman et al. 
(2020); Palacios-Abrantes 
et al. (2020); Reum et al. 
(2020); Sumaila and Zwaag 
(2020); Whitehouse and 
Aydin (2020); Wilson et al. 
(2020)
Notes: See Figure 14.1 for region acronym definitions. (a)

14
1962Chapter 14 North America
(Hazen et al., 2018) improves modelled fishery performance (medium 
confidence) (Section  14.5.2; Froehlich et  al., 2017; Tommasi et  al., 
2017a; Tommasi et al., 2017b; Karp et al., 2019; Barbeaux et al., 2020), 
yet planning and policies that directly incorporate climate-change 
information remain limited (Skern-Mauritzen et  al., 2015; Marshall 
et al., 2019b). Expanding aquaculture across North America will likely 
address deficits in nutritional and protein yields (Gentry et al., 2019; 
Costello et al., 2020), yet aquaculture initiatives have largely progressed 
without explicitly considering climate impacts (FAO, 2018; Froehlich 
et al., 2019), and critical elements for climate adaptation (e.g., climate-
informed zoning, monitoring, insurance) are not widely implemented 
(Liñan-Cabello et al., 2016; FAO, 2018; Stewart-Sinclair et al., 2020). 
Climate-informed and standardised aquaculture governance, and

increased coordination with fishery and coastal management, is 
needed for climate resilience (high confidence) (Brugère et al., 2019; 
Froehlich et al., 2019; Free et al., 2020; Galparsoro et al., 2020).
14.5.5 Cities, Settlements and Infrastructure
Cities are complex social–ecological systems with large populations, 
concentrated wealth, ageing infrastructure, reliance on extrinsic and 
increasingly stressed natural systems, social inequality, differential 
institutional capacities and impervious, heat-retaining surfaces 
(Maxwell et  al., 2018a; Schell et  al., 2020). These factors interact 
with location (e.g., proximity to coast, in a floodplain) to create 
city-specific vulnerabilities to climate change and requirements for 
resilience initiatives (Mercer Clarke et al., 2016). Cities are home to 
diverse cultural and social communities, including large Indigenous 
populations who can be uniquely affected by climate change yet who

bring valuable IK and leadership to urban adaptation efforts (Statistics 
Canada, 2020; Brown et al., 2021). The rural and remote settlements of 
North America also experience similar hazards and risks; however, such 
challenges are due to different factors such as geographic isolation, 
dependence on local food resources and socioeconomic conditions 
(Kearney and Bell, 2019; Vodden and Cunsolo, 2021).
14.5.5.1 Observed Impacts
14.5.5.1.1 Rising temperatures and extreme heat
Extreme heat events are affecting natural assets and built infrastructure 
as well as individuals in cities and rural settlements across North 
America (high confidence) (Maria Raquel et  al., 2016; Amec Foster 
Wheeler and Credit Valley Conservation, 2017; Howell and Brady, 
2019; Martinich and Crimmins, 2019). Key urban infrastructure systems 
(e.g., services in buildings, energy distribution) are interdependent and 
susceptible to cascading impacts (e.g., electricity supply disruption

during a heatwave compromising another system like water delivery, 
high-rise cooling) (Brown et  al., 2021). Urban social inequality and 
systemic racism has led to disproportionately higher exposure to urban 
heat island effects in low-income and minority neighbourhoods in US 
cities, due in part, to less green space and tree cover to offset heat 
retained in the built environment (Hoffman et al., 2020; Schell et al., 
2020; Hsu et al., 2021). In the rural context, extreme heat contributes 
to migration out of small communities; for example, see cases reported 
in Mexico (Nawrotzki et  al., 2015a). Extreme heat events pose a significant risk to residents of small towns across North America due 
to limited resources to address heat impacts and attendant increased 
morbidity and mortality (Section 14.5.6.1; McDonald et al., 2016; Guo 
et al., 2018; D’ulisse, 2019).
Hot and dry conditions increase risk of wildfires close to human

settlements through collateral impacts on properties, economic 
activity and human health (see Box  14.2; Section  14.5.6.3). These 
environmental conditions also stress natural assets (e.g., urban forests, 
wetlands, household gardens, green walls) and performance of green 
infrastructure leading to higher operation and maintenance costs (high 
confidence) (Kabisch et al., 2017; Terton, 2017).
14.5.5.1.2 Storms and flooding
Short-duration, high-intensity rainfall and other extreme events (e.g., 
hurricanes, atmospheric river events) create significant flooding risks 
and impacts for cities in North America and negatively affect the 
lives, livelihoods, economic activities, infrastructure and access to 
services (high confidence) (Amec Foster Wheeler and Credit Valley 
Conservation, 2017; Curry et al., 2019). In 2016, US flooding events 
caused 126 fatalities and 11 billion USD (considering the 2016 USD 
value) in damages (NOAA, 2019). In Canada, flooding accounts for

40% of the costs associated with weather-related disasters recorded 
since 1970 (Canadian Institute for Climate Choices, 2020); the most 
costly event was the 2013 Calgary flood (CA-PR) (1.8 billion CAD in 
catastrophic insurance losses and 6 billion CAD in direct costs such 
as uninsured losses) (Office of the Auditor General of Canada, 2016). 
Mexico City is seasonally impacted by high-intensity rainfall events 
that generate local flooding (de Alba and Castillo, 2014). Rural and 
remote settlements are also threatened by floods; Indigenous lands in 
Canada are disproportionately exposed to flooding, with almost 22% 
of residential properties at risk of a 1-in-100-year flood (Thistlethwaite 
et al., 2020; Yumagulova, 2020).
Wind storms and hurricanes are significant climate hazards for North 
American cities and settlements, affecting urban forests, electricity 
distribution and service delivery, and damaging buildings and trans-

portation infrastructure (Amec Foster Wheeler Environment and Infra-
structure, 2017; British Columbia Hydro, 2019; Smith, 2020), with 
enduring impacts on small villages due to lost livelihoods and limited 
recovery capacity (e.g., Rio Lagartos and Las Coloradas in MX-SE after 
Hurricane Isidore) (Audefroy and Cabrera Sánchez, 2017). The Pacific 
coast of Mexico is also experiencing hurricanes such as Patricia (cate-
gory IV) in 2015 and Newton (category I) in 2016 (CONAGUA, 2015; 
CONAGUA, 2016); hurricane Patricia affected 56 municipalities in the 
states of Colima, Nayarit and Jalisco (MX-CE, MX-NW) (Calleja-Reina, 
2016).
14.5.5.1.3 Sea level rise
Sea level rise interacts with shoreline erosion, storm surge and 
wave action, saline intrusion and coastal flooding to directly 
threaten coastal cities and small communities in North America with 
impacts to public and private buildings and infrastructure, port and 
transportation facilities, water resources (high confidence) (NOAA

National Weather Service, 2017; Boretti, 2019) and cultural heritage

14
1963North America  Chapter 14
sites (see Box 14.4; Dawson et al., 2020). Sea level rise is creating 
conditions where considerable financial investments are needed 
and, in many cases, are being raised to address adaptation needs 
(see Box 14.4; CCP6, Fatorić and Seekamp, 2017; Hinkel et al., 2018; 
Greenan et al., 2018). Across North America, high population density 
and concentrated development along the coast generates exposure 
to SLR impacts.
14.5.5.2 Projected Impacts and Risks
Evidence since the AR5 highlights increased risk to quality of life in 
cities and rural communities as a result of exposure to intensifying 
climate-change hazards, and the compounding and interacting effects 
of climate and non-climate factors (medium confidence).
14.5.5.2.1 Rising temperatures and extreme heat
Extreme heat events are projected to increase in frequency and 
intensity across North America in the coming decades (Section 14.2.2;

Figure 14.2F ,G). Inland urban areas in the southern and eastern USA 
are susceptible to urban heat island effects, particularly the Midwest/
Great Lakes regions (Krayenhoff et  al., 2018) and also Mexico City 
and many other cities in Mexico (Vargas and Magaña, 2020). Climate 
change (RCP8.5) interacting with urban form, development and 
systemic racism (Schell et  al., 2020; Hsu et  al., 2021) could worsen 
risks from extreme heat in North American cities, especially where 
there is limited adaptation (high confidence) (Krayenhoff et al., 2018). 
Impacts from extreme heat will be exacerbated when multiple hazards 
occur simultaneously (e.g., heatwaves concurrent with droughts) 
(Mora et  al., 2018; Zscheischler et  al., 2018). Extreme heat events 
increase energy demand for space cooling in buildings, especially 
during peak demand periods and heatwaves (IEA, 2018a). This can 
decrease cooling efficiency, increase emissions of GHG from electricity

generation, increase refrigerant loads and associated emissions, and 
negatively affect air quality (IEA, 2018a). Major electrical grid failure 
(i.e., blackouts) have increased across the USA and will continue to 
be particularly dangerous for human health when they coincide with 
extreme heat events (Stone et al., 2021). Efforts to increase resilience 
of the infrastructure that cities rely on are increasing (Climate-Safe 
Infrastructure Working Group, 2018).
Warmer and/or drier conditions may reduce water supply reliability 
for cities and small communities that rely on surface water sources 
fed by rain or snowmelt runoff, for example, Victoria and Vancouver, 
Canada (CA-BC) (Metro Vancouver, 2016; Vadeboncoeur, 2016; Islam 
et  al., 2017); San Pedro, Hermosillo and Los Pargas, Aguascalientes, 
México (MX-NW, MX-CE) (Vadeboncoeur, 2016; Soto-Montes-de-Oca 
and Alfie-Cohen, 2019); New York City (US-NE) (NYC Department of

Environmental Protection, 2014); and Washington State (US-NW) 
(Section 14.5.3.2; Fosu et al., 2017).
14.5.5.2.2 Storms and flooding
Annual and winter precipitation is expected to increase for most of 
Canada (Section  14.2; Figure  14.2D,E) and will increase flooding in 
cities and settlements (high confidence) (Bonsal et al., 2019). Although 
there is more geographic variation across the continental USA (e.g., between high-latitude and subtropical zones), extreme precipitation 
events are projected to increase in frequency and intensity with 
impacts on flood hazards (Section  14.5.3.2; Easterling et  al., 2017). 
Winter (snow and ice) storms are expected to increase in northern 
North America and decrease in southern North America under RCP8.5 
(Jeong and Sushama, 2018b). Projected increases in wind-driven rain 
exposure is an emerging consideration for moisture-resilient design 
and management of buildings, especially in western and northern 
Canada (Jeong and Cannon, 2020).

14.5.5.2.3 Sea level rise
In the USA, many people are projected to be at risk of flooding 
from SLR (high confidence) (see Box 14.4). A projected SLR of 0.9 m 
by 2100 could place 4.2  million people at risk of inundation in US 
coastal counties, whereas a 1.8-m SLR exposes 13.1  million people 
(Hauer et al., 2016). In California, under an extreme 2-m SLR by 2100, 
150  billion USD (2010) of property or more than 6% of the state’s 
GDP and 600,000 people could be affected by flooding (Barnard 
et  al., 2019). A 1-m SLR would inundate 42% of the Albemarle-
Pamlico Peninsula in North Carolina and incur property losses of up 
to 14 billion USD (considering the 2016 USD value) (Bhattachan et al., 
2018). In nine southeast US states, a 1-m SLR would result in the loss 
of more than13,000 recorded historical and archaeological sites with 
over 1000 eligible for inclusion in the National Register for Historic 
Places (Anderson et al., 2017). This SLR raises groundwater levels by

impeding drainage and enhancing runoff during rain events (Hoover 
et al., 2017); coastal flooding enhances saltwater intrusion affecting 
drinking water supply in settlements (e.g., coast of Texas) (Anderson 
and Al-Thani, 2016).
In Canada, SLR is expected to increase the frequency and magnitude 
of extreme high-water-level events (Greenan et al., 2018) and to create 
widespread impacts on natural and human systems (high confidence) 
(see Box  14.4; Lemmen et  al., 2016). Although coastal sensitivity is 
high in the Arctic, Canada’s more populated regions are also sensitive 
to the impacts of SLR (Manson et al., 2019). The Mi’kmaq community 
of Lennox Island First Nation is exploring relocation options because of 
erosion from SLR (Savard et al., 2016).
In Mexico, crucial coastal tourism cities, such as Cancun, Isla Mujeres, 
Playa del Carmen, Puerto Morelos and Cozumel (MX-SE), are at risk 
of SLR with an estimated economic impact of 1.4–2.3  billion USD

(Section  14.5.7; Ruiz-Ramírez et  al., 2019). Negative effects of the 
‘coastal squeeze’ phenomena (generated by SLR, land subsidence, 
sediment deficit and current urbanisation processes) have been 
documented on tourist destinations along the coasts of the Mexican 
Gulf of Mexico and Mexican Caribbean. Zoning, limiting urbanisation 
along the coastline and using NbS (see Box  14.7) are alternatives 
that could be applied to improve the adaptation of these destinations 
(Martínez et  al., 2014; Salgado and Luisa Martinez, 2017; Lithgow 
Rural low-lying coastal areas are at risk from SLR where natural 
barriers or shoreline infrastructure are deteriorating and this interacts 
with remoteness, resource-dependent economies and socioeconomic 
challenges to adaptive capacity (Bhattachan et  al., 2018; Manson

14
1964Chapter 14 North America
et al., 2019). The Northeast Atlantic region of North America (CA-AT, 
US-NE) is exposed to high risk by combined effects of land subsidence 
and climate-driven SLR (see Box  14.4; Lemmen et  al., 2016; Sweet 
et al., 2017; Fleming et al., 2018; Greenan et al., 2018).
14.5.5.3 Adaptation
In North American cities, present-day adaptation responses extend 
beyond the traditional focus on infrastructure to include measures 
aimed to protect people, property and ecosystems (medium confidence). 
Barriers to adaptation include challenges related to the local physical 
and environmental setting, effects of colonialism and racism, 
socioeconomic attributes of the population, institutional frameworks 
and competing interests of city stakeholders (medium confidence). 
The current scale of adaptation is generally not commensurate with 
reducing risks from projected climatic hazards, although resources exist

that provide guidance and examples of effective adaptation (medium 
confidence). Some remote Canadian communities have demonstrated 
strengths (e.g., strong social networks) that support resilience to 
climate change (Kipp et  al., 2020; Vodden and Cunsolo, 2021). In 
some US cities with political resistance to action on climate change, 
adaptation measures focused on addressing extreme events (rather 
than climate-change impacts) have been able to make progress (Hamin 
et  al., 2014). Enhanced public awareness of the risks from extreme 
events associated with climate change is important for motivating 
adaptation (Section 14.3; Howe et al., 2019) and developing a climate-
change agenda (Aragón-Durand, 2020).
Community-level planning tailors adaptation responses and disaster 
preparedness to the local context but misalignment of policies within 
and between levels of government can prevent implementation 
(Oulahen et al., 2018). Coordination, planning and national support

are needed to provide sufficient financial resources to implement 
climate-resilient policies and infrastructure (Section 14.7.3; USGCRP , 
2018).
Public health measures to address extreme heat events are more 
common across North America, with a focus on vulnerable populations 
(e.g., City of Toronto, 2019) and innovative approaches for reaching 
at-risk populations with an overarching intent of prevention (medium 
confidence) (Section  14.4.6.1; Guilbault et  al., 2016). The heatwave plan for Montreal includes visits to vulnerable populations, cooling 
shelters, monitoring of heat-related illness and extended hours for 
public pools (Lesnikowski et al., 2017); efforts have reduced heatwave-
related mortalities (Benmarhnia et al., 2016).
Other adaptation responses to reduce temperature effects include 
modifying structures (roofs, engineered materials) and the urban 
landscape through green infrastructure (e.g., urban trees, wetlands,

green roofs), which increases climate resilience and quality of life 
by reducing urban heat island effects, while additionally improving 
air quality, capturing stormwater and delivering other co-benefits 
to the community (e.g., access to food, connection to nature, social 
connectivity) (high confidence) (see Box 14.7; Ballinas and Barradas, 
2016; Emilsson and Sang, 2017; Kabisch et al., 2017; Krayenhoff et al., 
2018; Petrovic et al., 2019; Schell et al., 2020). Green infrastructure can 
be flexible and cost-effective (Ballinas and Barradas, 2016; Emilsson 
and Sang, 2017; Kabisch et al., 2017). Initiatives can be ‘bottom-up’ 
community-led adaptation with support from municipal governments 
(e.g., East Harlem in New York City) (Petrovic et  al., 2019). Valuing 
municipal natural assets (e.g., assigning economic value to cooling 
from urban forests or stormwater retention by urban wetlands) is 
becoming increasingly common in Canada and the USA (Wamsler,

2015; Roberts et  al., 2017a; Municipal Natural Assets Initiative, 
2018). Guidance assists municipalities to identify, value and account 
for natural assets in their financial planning and asset management 
programmes (O’Neil and Cairns, 2017) and consider future climate 
(Municipal Natural Assets Initiative, 2018).
Meeting increasing demand for indoor space cooling with equitable 
access requires new approaches to providing cooling (e.g., equipment 
efficiencies, refrigerants with lower global warming potential) and 
electricity production and transmission innovation (Shah et al., 2015; 
IEA, 2018a). While energy efficiency and building code standards are 
not directly established by local governments, they can encourage 
behaviour change via incentives (e.g., rebates on efficient equipment) 
or disincentives (e.g., more onerous permit approvals).
Experience with droughts, heatwaves and other weather extremes 
has led many municipal water managers to accept the importance

of building resilience to the risks of future water shortages and costs 
posed by climate change (Metro Vancouver, 2016; Misra et al., 2021; 
Box 14.4 | Sea Level Rise Risks and Adaptation Responses for Selected North American Cities 
and Settlements
Approximately 95 million Americans lived in coastal communities in 2017 (US Census Bureau, 2019) and in 2013, Canada had roughly 
6.5 million coastal residents (Lemmen et al., 2016), while Mexico had 19 million people living in coastal municipalities in 2015 (Azuz-
Adeath et al., 2018). Sea level rise around North American coastlines (Figure Box 14.4.1) is projected to be greatest along the coasts of 
Atlantic Canada, northern Gulf of Mexico for the USA and the Pacific coast of Mexico (IPCC, 2021). Sections 14.5.2.1, 14.5.5.1.3 and 
14.5.5.2.3 describe SLR impacts. The status of adaptation to SLR by local governments is variable (see Table Box 14.4.1, where progress

is indicated by colour coding) and ranges from financed implementation to preliminary, preparatory or scoping studies and workshops. 
Adaptation planning and implementation to address SLR and coastal flooding have been initiated across many cities and settlements in 
North America, but preparedness varies (high confidence).

14
1965North America  Chapter 14
Sea Level Rise projections for selected North American cities
Projection changes relative to 2005
01m2m3m01m2m3m01m2m3m
Tuktoyuktu k
Prince Edward
Island
Truro
Halifax
New York
Norfolk
Miami
Cancun
New Orleans
Ciudad
del Carmen
VeracruzUnalaska
Surrey
Seattle
Quinault I. R.
San Francisco
Los Angeles
Acapulco
Sea Level Rise
in 2050
in 2100
in 2150SSP5-8.5 SSP1-2.61 Tuktoyuktu k
2 Prince Edward Island
3 Truro4 Halifax
5 New York
6 Norfolk7 Miami
8 Cancun
9 New Orleans10 Ciudad del Carmen
11 Veracruz
12 Unalaska13 Surrey
14 Seattle
15 Quinault I. R.16 San Francisco
17 Los Angeles
18 Acapulco
Pacific Atlantic Gulf of Mexico Arctic
Readiness
Levels
Specific plan, progress on actions
Specific plan, no evidence of actions takenSpecific plan, no actions specified
General Climate Change plan, mentions sea level riseNo Climate Change plan, but processes underway1
2
3
4
5
89
101113
14
15
16
17
186
712

Figure Box 14.4.1 |  Sea level rise projections for 2050, 2100 and 2150 for selected North American cities. Projections changes are relative to 2005, which 
is the central year for the 1994–2014 reference period. Horizontal lines in the boxes represent the median projection, boxes represent 25th to 75th percentile and whiskers 
the 10th to 90th percentile of SLR projections from all CMIP6 models as well as other lines of evidence (see Fox-Kemper et al., 2021 Table 9.7 for more details). Two SLR 
scenarios are provided for lower (SSP126) and higher emissions (SSP585), and are consistent with the WGI AR6 Interactive Atlas (Gutiérrez et al., 2021b. Numbers and 
colours (see Table Box 14.4.1 for detailed readiness definitions) on the map and in the projections represent the sites and status of SLR adaptation progress. Information 
supporting SLR adaptation status is summarised in Table Box 14.4.1.Box 14.4 (continued)

14
1966Chapter 14 North America
Table Box 14.4.1 |  Status of adaptation actions for locations on the SLR map above according to level of SLR preparedness through adaptation (as discoverable on 
government websites)
Ocean 
basinSite 
no.Area/city Exposure (not exhaustive)Adaptation 
readiness 1Does the area/city have an adaptation plan for SLR? If so, are 
they taking actions to implement it? (Status)
Arctic 1Tuktoyuktuk, 
CAInfrastructure, municipal services, 
transportation, homes, 900 peopleTuktoyaktuk Coastal Erosion Study completed March 2019. Additional 
investments in both planning and actual adaptation measures have occurred. 
Limited financial resources remain a barrier (Government of Canada, 2020).
Atlantic 2Prince 
Edward 
Island
with Lennox 
Island, CAPEI: residential, industrial and 
commercial infrastructure;
Lennox Island: 10 of 79 homes, 
causeway to the island, sacred

grounds, sewage treatment systemsPrince Edward Island government released a 5-year climate change action plan 
in 2018 which includes both adaptation and mitigation (Prince Edward Island 
Government, 2018). Biennial progress reports were issued (Prince Edward Island 
Government, 2019). The Mi’kmaq community of Lennox Island First Nation has 
explored relocation options (Daigle et al., 2015).
3 Truro, CAA regional centre of 12,000 residents, 
which has been vulnerable to 
repeated floods for decadesTown of Truro, County of Colchester and Millbrook First Nations, commissioned 
a flood risk study 2014–2017 (CBCL, 2017; Sherren et al., 2019) triggered by the 
2012 flooding. The outcome was Truro-Onslow dyke project–a voluntary retreat 
with realignment of dyke infrastructure and habitat restoration by conversion of 
agricultural land into salt marsh habitat (Saunders-Hastings et al., 2020).
4 Halifax, CATransportation causeways and 
bridges, marine facilities, municipal

infrastructureHalifACT 2050 is a comprehensive plan adopted as of 2020 by the Halifax 
regional council which includes reducing GHGs and adapting to climate change 
including a section on coastal preparedness (Halifax Regional Council, 2020).
5New York, 
USA20 million people at risk by 2050; 
40% of water treatment plans will 
be compromised by flooding, 60% 
of power plants will need to be 
relocated, transportation systems will 
need to be upgraded to avoid floodingNew York City has developed many adaptation plans for sustaining NYC in light 
of SLR and other climate hazards and impacts, especially since Hurricane Sandy 
affected the city in 2012. It is unclear how much of the planning has moved 
forward into implementation (NYC, 2013; New York City, 2015; NYC Mayor’s 
Office of Resiliency, 2020).
6 Norfolk, USAHomes, massive US naval base, 
shipyards, active waterfront and 
deep-water portsCity of Norfolk published a very specific Coastal Resilience Strategy in 2014.

Capital improvement projects highlighted in this strategy have been funded 
(City of Norfolk Virginia, 2014). A plan for protecting Naval base and shipyard 
is not evident.
7 Miami, USAHomes, port, transportation 
infrastructure, tourism (hotels, 
restaurants, beaches)Miami Dade County released a specific SLR Strategy in 2021. Actions in the 
plan include elevating roads and other infrastructure, designing ways to 
accommodate more water in and around buildings, building on higher ground 
and expanding waterfront parks and canals. The plan includes a map with 
current and planned adaptation projects in the county (Miami-Dade County, 
2021).
8 Cancun, MXTourism infrastructure (hotels, 
restaurants, beaches), homes, markets, 
service industry, transportationThe 2013 Climate Change Plan assigns adaptation in general to different 
government levels. There is no evidence of specific adaptation plan for SLR 
(Government of Quintana Roo, 2013).
Gulf of 
Mexico9New 
Orleans,

USAEntire city, especially low-lying, 
low-income areas, vulnerable as 
evidenced by Hurricane Katrina in 
2005City of New Orleans adaptation is incorporated in the broader Louisiana coastal 
climate-change adaptation plan (CPRA, 2023). The process includes very specific 
projects with updates on risk-based implementation.
10Ciudad del 
Carmen, MXFreshwater access, 11,000 homes, 
aquacultureThe Campeche State Climate Change Plan was released in 2013 (Government of 
Campeche, 2013). The plan does not include any specific recommended actions 
to adapt to SLR in Cuidad del Carmen. Flood-risk maps for Ciudad del Carmen 
were created in 2011 (Audefroy, 2019).
11Veracruz, 
MXFreshwater access, sewage treatment 
systems, electrical and petrochemical 
industriesState of Veracruz published a climate-change plan in 2008 (Government 
of Veracruz, 2008). The plan includes specific tables of actions needed to 
monitor and adapt to SLR. The World Bank funded coastal adaptation in

Veracruz focused on mangroves to dissipate storm surge but no investments in 
infrastructure to mitigate SLR.
Pacific 12Unalaska, 
USALoss of cultural resources, salinisation 
of rivers and lakesClimate Change Adaptation and Vulnerability Assessment workshops have been 
held with discussion of coastal erosion. SLR is not viewed to be as important as 
impacts from sea ice and permafrost loss (Poe et al., 2016).
13Surrey 
(Greater 
Vancouver 
Area), CADisruption in flow of goods in 
and out of Port of Vancouver, 
communication facilities, road, rail 
and air transportation infrastructure, 
businesses and agricultureSurrey has a Coastal Flood Adaptation Strategy (CFAS) approved by the council 
(City of Surrey, 2019) with 46 actions (policy and programme, local area 
infrastructure). Some local area infrastructure improvements have received 
capital funding.Box 14.4 (continued)

14
1967North America  Chapter 14
Ocean 
basinSite 
14 Seattle, USALow-lying areas, near-shore habitats, 
stormwater drains, roads, homes, 
businesses, socially vulnerable 
communitiesSeattle released a Climate Change Response Plan in 2017 which includes 
general approaches including development of risk maps for SLR which are also 
available online (City of Seattle, 2017).
15Quinault 
Indian 
Reservation 
(Tahola), 
USA650 residents and buildingsQuinault Indian Reservation has a plan to move Tahola to higher ground, 0.5 
miles from the existing village (EPA, 2021).
16San 
Francisco, 
USA37,200 residents, 17,200 businesses 
and 167,300 jobs vulnerable to 
inundation by 2100 at upper bounds 
of SLR, mostly along the bay side of 
the citySF has an active, SLR planning process as well as an iterative Sea Level

Rise Action Plan (City of San Francisco, 2016), planning tools and iterative 
assessment (City and County of San Francisco, 2020).The process specifically 
addresses wastewater, water, transportation, power, public safety, open space, 
port, neighbourhoods and changing shoreline.
17Los Angeles, 
USAPower plants, wastewater treatment 
plants, Port of Los Angeles, beaches, 
tourismLos Angeles has commissioned a projected SLR impact report but not an action 
plan. The Port of Los Angeles is particularly vulnerable and, as of 2019, has an 
SLR Adaptation Plan (Newbold et al., 2019).
18Acapulco, 
MXTourism infrastructure (hotels, 
restaurants beaches), homes, markets, 
service industry, transportationNo climate-change plan exists, although the Mexican Tourism Sector conducted 
a climate-change vulnerability assessment covering Acapulco (Guerrero, 2017).
Sea Level Rise Adaptation Readiness Levels

Specific plan, progress on actions - specific plan for SLR with evidence of progress on taking actions including allocating funding for projects
 Specific plan, no evidence of actions taken - specific plan for SLR with concrete actions identified but no evidence of actions taken to date
 Specific plan, no actions specified - specific plan for SLR but does not include specific actions
 General Climate Change plan, mentions sea level rise - general climate-change adaptation action plan, which mentions SLR as a risk, issue or impact but no concrete actions, developed
 No Climate Change plan, but processes underway - No climate-change adaptation action plan but processes underway such as workshops, studies and vulnerability assessmentsBox 14.2 (continued)
WUCA, 2021). In the southwest USA, water utilities have introduced 
demand-management programmes to encourage water conservation 
(e.g., tiered pricing, incentives for water-efficient appliances and

fixtures, and rewards for replacing water-guzzling lawns with water-
thrifty native vegetation) (Section 14.5.3.3; Luthy et al., 2020; Baker, 
2021). Water providers also have increased their adaptive capacity by 
diversifying water sources (Hanak et al., 2015).
Adaptation to the risks of wildland–urban interface fire is underway 
(see Box 14.2; Kovacs et al., 2020), but the scope of adaptation required 
to sufficiently minimise wildfire risks for cities and settlements across 
North America has not been assessed (medium confidence). Leadership 
at the local level is increasingly supported by federal resources that 
provide guidance on hazard and exposure assessment, property 
protection, community resilience and emergency planning (National 
Research Council of Canada, 2021).
Cities and settlements in North America can be susceptible to multiple 
flooding hazards (i.e., coastal SLR, pluvial or fluvial flooding); each

presents unique adaptation challenges that can be addressed through 
structural (e.g., armouring coastlines, reservoirs, levees, floodgates; New 
York City commuter tunnels) and non-structural approaches (e.g., land-
use planning and zoning, expanding green infrastructure; Chetumal, 
Mexico) (high confidence) (Hardoy et al., 2014). Green infrastructure 
practices (e.g., open-space preservation, floodplain restoration, urban 
forestry, de-channelisation of streams) (see Box  14.7) can reduce urban flooding, erosion and harmful runoff (Kovacs et al., 2014; Angel 
et al., 2018b; Government of Canada, 2021c). Structural approaches 
have limitations and require trade-offs that could be addressed with 
a focus on social–ecological solutions and stronger institutional 
coordination (e.g., flood risk management in Mexico City) (Aragón-
Durand, 2020). In response to high-intensity rainfall events, Mexico 
City invested in stormwater infrastructure, although additional benefits

could have been realised if water supply needs had been incorporated 
(de Alba and Castillo, 2014). Some programmes exist to facilitate 
stormwater and wastewater infrastructure updating to accommodate 
increased precipitation across North America. The US federal Clean 
Water State Revolving Fund provides low-interest loans for states 
to upgrade infrastructure for climate change, with 42  billion USD 
provided since 1987 (ASCE, 2019). In Canada, local governments are 
important leaders in managing engineered and green infrastructure 
decisions, incentivising property-level flood protection and ensuring 
service delivery (Government of Canada, 2021c). The civil engineering 
profession is playing an active role in facilitating an understanding 
of risks and prioritisation of adaptation investments in communities 
(Tye and Giovannettone, 2021).The high concentration of valuable 
assets in cities requires mechanisms to facilitate replacement of

assets including use of existing and proposed insurance mechanisms 
(medium confidence) (Section 14.7).
Adaptation planning and implementation to address SLR and coastal 
flooding has been initiated across cities and settlements in North America

14
1968Chapter 14 North America
but varies in preparedness (high confidence) (see Box 14.4). Efforts are 
supported by SLR design guidelines. In Canada, the Government of 
British Columbia provided SLR projections for 2050 (i.e., +0.5 m) and 
2100 (i.e., +1 m) in order to initiate community vulnerability and risk 
assessment, and adaptation planning (The Arlington Group Planning 
+ Architecture Inc et al., 2013). Based on recent hurricane impacts in 
Yucatan, Mexico, recommendations to enhance the rules governing the 
Mexican Recovery Program included incorporating local knowledge and 
IK when rebuilding houses and other structures on coasts (Audefroy 
and Cabrera Sánchez, 2017). Where in-place adaptation is insufficient, 
planned retreat is being considered as a sustainable option for reducing 
future risks (Saunders-Hastings et al., 2020).
14.5.6 Health and Well-being
Research examining climate-change impacts on human health in North

America has increased substantially since AR5 (Harper et al., 2021a). 
Using a systematic approach (Harper et  al., 2021b), the assessment 
focused on advancements since AR5.
14.5.6.1 Heat-Related Mortality and Morbidity
High temperatures currently increase mortality and morbidity in North 
America (very high confidence), with impacts that vary by age, gender, 
location and socioeconomic factors (very high confidence). Observed 
increases in heat-related mortality have been attributed to climate 
change in North America (Vicedo-Cabrera et al., 2021). Temperature 
effects on health vary based on how unusual the temperature is for that 
time and location (medium evidence, high agreement), highlighting 
the important role that temperature extremes and variability play in mortality and morbidity (Li et al., 2013; Lee et al., 2014; Barreca et al., 
2016; Allen and Sheridan, 2018). Adaptation has played an important 
role in reducing observed heat-related deaths (Vicedo-Cabrera et al.,

2018b).
Rising temperatures are projected to increase heat-related mortality 
across emission scenarios this century in North America (very high 
confidence), although the magnitude of increase varies geographically 
(Isaksen et al., 2014; Petkova et al., 2014; Wu et al., 2014; Weinberger 
et al., 2017; Anderson et al., 2018a; Limaye et al., 2018; Marsha et al., 2018; 
Morefield et al., 2018). Elderly people (Isaksen et al., 2014; Limaye et al., 
2018) and urban areas (Limaye et al., 2018) are projected to experience 
the greatest increase in heat-related mortality this century. Warming 
temperatures are also projected to increase heat-related morbidity 
(medium confidence). For instance, the incidence and treatment costs of 
asthma attributed to warmer temperatures are projected to increase in 
Texas by 2040–2050 (A1B) (McDonald et al., 2015).
While heat-related mortality is projected to increase across emissions 
scenarios and shared socioeconomic pathways, fewer deaths are

projected under both lower-emissions scenarios and higher-adaptation 
scenarios in North America (very high confidence). Heat-related 
mortality was projected to be 50% less under RCP4.5 compared with 
RCP8.5 in the USA for SSP3 and SSP5 (Table  14.5; Wu et  al., 2014; 
Marsha et al., 2018).
14.5.6.2 Cold-Related Mortality
Winter season mortality rates are generally high in high-income regions 
such as North America, with most of that mortality due to cardiovascular 
diseases (Ebi and Mills, 2013). It is important to differentiate between 
Table 14.5 |  A summary of adaptation options for different health outcomes in North America
Health outcome Adaptation options
Heat-related mortality and 
morbidityFuture temperature-related health impacts can be reduced by adaptation measures (Petkova et al., 2014; Wu et al., 2014; Mills et al., 2015b; Kingsley et al.,

2016; Anderson et al., 2018b; Marsha et al., 2018; Morefield et al., 2018), including more effective warning and response systems and building designs, 
enhanced pollution controls, urban planning strategies and resilient health infrastructure ( very high confidence) (Figure Box 14.7.1).
Wildfire-related mortalityAir quality indices are correlated with many respiratory conditions (Yao et al., 2013; Hutchinson et al., 2018), suggesting that providing air quality information 
to the public could reduce smoke-related health impacts (Yao et al., 2013; Rappold et al., 2017). Enhanced coordination between the health sector and fire 
suppression agencies can also reduce the health impacts of wildfire smoke via improving communication, weather forecasting, mapping, fire shelters and 
coordinated decision making (Withen, 2015), including transnational and cross-jurisdictional actions.

Vector-borne diseasePrevention of vector-borne disease currently involves surveillance, reducing environmental risks and promoting individual behaviours to reduce human–vector 
contact. Top-ranked Canadian West Nile interventions include individual protection (i.e., window screens, wearing lightly coloured clothing), and regional 
management and mosquito-targeting interventions (i.e., larvicides, vaccination of animal reservoirs, modification of human-made larval sites) (Hongoh et al., 
2016).
Water-borne diseaseClimate change is projected to increase water-borne disease risks (medium confidence), particularly in areas with ageing water and wastewater infrastructure 
in North America (high confidence). In Wisconsin, USA, precipitation changes are projected to increase gastrointestinal illness in children this century (A1B, A2,

B1) (Uejio et al., 2017). Slight reductions in precipitation-associated gastrointestinal illness is projected if water treatment infrastructure is upgraded slowly 
over time; however, if water treatment infrastructure is installed more rapidly, large decreases in precipitation-associated gastrointestinal illness incidence are 
projected (Uejio et al., 2017), highlighting the benefits of rapidly implementing adaptation actions.
Food-borne diseaseFood safety programmes play important roles in reducing the risk of climate-related food-borne disease (high confidence). Integrated health surveillance, more 
stringent refrigeration temperature controls to limit pathogen growth, targeted communication to the public and food sector, and enhanced coordination 
between the health and food sectors can reduce risk (Hueffer et al., 2013; Jones et al., 2013; Fillion et al., 2014; Doyle et al., 2015). In Mexico, the projected

risk of Vibrio parahaemolyticus in oysters was 11 times higher in a high-emissions scenario compared with a low-emissions scenario by the end of the century; 
however, this risk could be substantially lowered with adaptation measures, including improving temperature control (Ortiz-Jiménez, 2018).
Mental healthEffectiveness of individual and/or group therapy, and place-specific mental health infrastructure, to treat mental health challenges is well proven; yet, there is 
limited evidence evaluating these interventions within the context of climate change (e.g., Tschakert et al., 2017; Young et al., 2017b; Cunsolo and Ellis, 2018).

14
1969North America  Chapter 14
mortality related to cold temperatures and mortality due to other 
factors that vary with season (Ebi and Mills, 2013; Ebi, 2015). Warmer 
temperatures do not always equate to lower winter mortality: many 
cold-related deaths do not occur during the coldest times of year or in 
the coldest places (high confidence) but occur during the beginning or 
end of the winter season (Barnett et al., 2012; Lee et al., 2014; Schwartz 
et al., 2015; Sarofim et al., 2016b; Smith and Sheridan, 2019). Warmer 
US cities generally experience more mortality from extreme cold events 
and cold temperatures than colder cities in the USA and Canada (Lee 
et al., 2014; Gasparrini et al., 2015; Schwartz et al., 2015; Wang et al., 
2016; Smith and Sheridan, 2019). While mortality rates linked to direct 
cold exposure (e.g., hypothermia, falls and fractures) is generally low, 
the relatively higher mortality during milder temperatures is thought to

be largely due to respiratory infections and cardiovascular impacts (Lee 
et al., 2014; Gasparrini et al., 2015), which, although correlated with 
temperature, may not be caused by cold temperatures (Ebi and Mills, 
2013; Ebi, 2015; Sarofim et al., 2016a). When separating the effects 
of cold temperatures from the effects of the winter season, one study 
found that cold temperature did not drive mortality and suggested that 
winter season excess mortality was due to seasonal factors other than 
temperature (e.g., influenza, seasonal gatherings) (Kinney et al., 2015).
Mortality attributed to cold temperatures has increased in the USA 
and remained stable in Canada from 1985 to 2012 despite increasing 
winter temperatures (Vicedo-Cabrera et al., 2018b). Some attenuation 
in cold-related mortality in Mexico and warmer US states is projected 
under climate change, but less so in colder climates in northeast USA 
and Canada, with statistically insignificant trends in some regions

and increasing cold-related mortality in other regions (Li et al., 2013; 
Mills et al., 2015b; Schwartz et al., 2015; Sarofim et al., 2016a; Wang 
et al., 2016; Gasparrini et al., 2017; Vicedo-Cabrera et al., 2018a; Lee 
et al., 2019). These reductions in cold-related mortality are generally 
considered relatively small.
Observed and projected trends in winter mortality highlight that non-
climate factors may have a greater role in driving winter mortality than 
cold temperature, and that these deaths are expected to occur with or 
without climate change (Ebi and Mills, 2013; Ebi, 2015; Sarofim et al., 
2016a). This challenges the assumption that warmer winters due to 
climate change would dramatically lower winter season mortality 
14.5.6.3 Wildfire-Related Morbidity
Smoke from intensified wildfire activity in North America is associated 
with respiratory distress (very high confidence), and persists long

distances from the wildfire and beyond the initial high-exposure time 
(see Box  14.2; Hutchinson et  al., 2018). Exposure to wildfire smoke 
increases hospital admissions (McLean et  al., 2015; Alman et  al., 
2016; Reid et al., 2016; Yao et al., 2016; Rojas-Downing et al., 2017). 
Increased wildfire smoke from climate change is projected to result 
in more respiratory hospital admissions in the western USA by 2046–
2051 (A1B) (Liu et al., 2016; Rojas-Downing et al., 2017).
The magnitude of health risks varies by age (Le et al., 2014; Reid et al., 
2016; Liu et al., 2017a; Liu et al., 2017b), gender (Delfino et al., 2009; 
Rojas-Downing et  al., 2017), socioeconomic conditions (Henderson et  al., 2011; Rappold et  al., 2012; Reid et  al., 2016) and underlying 
medical conditions (Liu et  al., 2015). The intersectionality of these 
subgroups plays an important role in health-related vulnerability 
to wildfire smoke. Among the elderly in the western USA, risks of

respiratory admissions from wildfire smoke was significantly higher for 
African American women in lower-education counties (Liu et al., 2017b). 
For Indigenous Peoples, medical visits for respiratory distress, heart 
disease and headaches increased during a wildfire in California (Lee 
et al., 2009). In northern Canada, Indigenous livelihoods were disrupted 
during a wildfire, which negatively impacted mental, emotional and 
physical health (Dodd et al., 2018a; Howard et al., 2021).
14.5.6.4  Vector-Borne Disease
Climate change creates conditions that enable earlier seasonal 
activity and general northern expansion of ticks (Ogden et al., 2014), 
increasing human exposure to tick-borne diseases in North America 
(very high confidence). Lyme disease incidence and geographic extent 
has already increased in Canada and the USA (Eisen et  al., 2016), 
which has been associated with climate change (Ogden et al., 2014),

including warmer temperatures (Cheng et al., 2017; Lin et al., 2019). 
Climate change is projected to increase disease spread into new 
geographic regions, lengthen the season of disease transmission and 
increase tick-borne disease risk in North America across emissions 
scenarios throughout this century (very high confidence), with regional 
variability (Roy-Dufresne et  al., 2013; Feria-Arroyo et  al., 2014; 
Monaghan et al., 2015; Robinson et al., 2015; McPherson et al., 2017). 
Chagas disease is transmitted by triatomines, and most of the Mexican 
population (88.9%) already reside in areas with at least one infected 
vector species in both rural and urban populations (Carmona-Castro 
et al., 2018). Chagas has already extended its range into the southern 
USA, and the triatomines’ niche is projected to expand northward this 
century (Garza et al., 2014; Carmona-Castro et al., 2018) in both rural 
and urban areas (Carmona-Castro et al., 2018).

Climate change is projected to impact the distribution, abundance 
and infection rates of mosquitoes in North America (high confidence), 
which will increase risk of mosquito-borne diseases including West Nile 
virus, chikungunya and dengue (medium confidence). The geographic 
distribution of West Nile virus is projected to expand in North America 
this century (A1B) (Harrigan et  al., 2014). In the USA and Canada, 
mosquitoes are projected to emerge earlier in the year and remain 
active longer into the fall; however, mosquito population dynamics 
vary by location with northern locations projected to have an increased 
vector abundance, and currently hot areas may become too hot, thus 
negatively affecting mosquito survival (A2, A1B, B1) (Chen et al., 2013; 
Morin and Comrie, 2013; Brown et al., 2015a).
Local transmission of chikungunya virus has emerged in Mexico and 
the USA since AR5, and areas suitable for transmission are projected

to expand (RCP4.5 and RCP8.5) (Tjaden et  al., 2017). Although 
chikungunya virus is not currently in Canada, climate change is 
projected to make southern British Columbia suitable for virus 
transmission this century, particularly under RCP8.5 (Ng et al., 2017).
The dengue mosquito vector is well established in Mexico and the 
southeast USA. In northwest Mexico, incidence of dengue cases is

14
1970Chapter 14 North America
associated with minimum monthly temperature (Diaz-Castro et  al., 2017), 
and the geographic range of the vector in the USA is restricted, in part, 
by low temperatures. Thus, a northward range expansion is projected; 
however, future dengue risk also depends on built environments and 
competition with other mosquito species (Colón-González et al., 2013a; 
Eisen and Moore, 2013). Climate change is projected to increase the 
geographic range and extend the seasonal activity of the dengue vector 
in the southern USA by 2045–2065 (A1B); however, transmission is 
projected to be limited by low winter temperatures in the mainland 
USA, potentially preventing its permanent establishment (Butterworth 
et  al., 2017). In Mexico, increased dengue cases are projected this 
century (A1B, A2, B1) (Colón-González et al., 2013b).
14.5.6.5 Water-Borne Disease
Heavy precipitation events are associated with contaminated

drinking water and water-borne disease in North America (high 
confidence). Acute gastrointestinal illnesses increase with many 
hydro-climatological variables, including precipitation, streamflow 
and snowmelt (Harper et al., 2011; Wade et al., 2014; Galway et al., 
2015). Extreme precipitation is associated with Campylobacter and 
Salmonella infections in the USA, particularly in counties characterised 
by farms and private well water (Soneja et  al., 2016). In Canada, 
human Giardia infections are associated with increased temperature, 
precipitation, pathogen presence in livestock manure, and river water 
level and flow (Brunn et al., 2019). Land-use patterns and aquifer-types 
are associated with water-borne disease, and ecological zones with 
higher water-borne rates are projected to expand in range in Canada 
by 2080 (Brubacher et al., 2020).
In North America, stormwater and water treatment infrastructure 
play important roles in reducing water-borne disease risk during

precipitation events (high confidence). In the USA, heavy precipitation 
events are associated with higher rates of childhood gastrointestinal 
illness in municipalities with untreated drinking water, but not in 
municipalities with treated drinking water (Uejio et  al., 2014). In 
Mexico, disparities in access to treated water are a key determinant of 
morbidity in children under age 5 years (Jiménez-Moleón and Gómez-
Albores, 2011; Romero-Lankao et al., 2014a). In remote communities 
in Alaska and Northern Canada, challenges in water service provision 
and maintenance can increase risk of water-borne disease during high-
impact weather events (Harper et al., 2011; Bressler and Hennessy, 2018; 
Harper et al., 2020). In older sections of many North American cities, 
sewage treatment plant capacity is exceeded by overflow of combined 
sanitary and storm sewer systems during heavy precipitation events, 
resulting in bypass of untreated and microbiologically contaminated

wastewater discharge into drinking water sources (Jagai et al., 2017; 
Olds et al., 2018; Staley et al., 2018). These sewer overflow events are 
associated with increased gastrointestinal illness across age groups 
(Jagai et al., 2017).
14.5.6.6  Food-Borne Disease
Warmer air temperature, changes in precipitation, extreme weather 
events and ocean warming can increase microbial pathogen loads 
in food (very high confidence). Indeed, temperature and extreme 
weather are top factors influencing food safety in Canada (Charlebois and Summan, 2015). Outbreaks of Vibrio parahaemolyticus have been 
associated with the consumption of raw oysters harvested from higher-
than-usual ocean temperatures in Canada and Alaska (McLaughlin 
et  al., 2005; Taylor et  al., 2018). Warmer air temperature increases 
Campylobacter, Salmonella and E. coli prevalence in Canadian meat 
products (Smith et al., 2019), higher microbial load in American produce

(Ward et al., 2015) and increased Campylobacter spp., pathogenic E. 
coli and Salmonella spp. infections in humans (Akil et al., 2014; Valcour 
et al., 2016; Uejio, 2017).
Climate change is projected to increase food safety risks (medium 
confidence); however, the actual burden of food-borne disease will 
depend on the efficacy of public health interventions (high confidence). 
Increased ciguatera fish poisoning is associated with increased 
sea surface temperatures (SSTs) and tropical storm frequency, and 
this risk is projected to increase this century (Gingold et  al., 2014). 
Campylobacter infection in humans due to food contamination from 
flies is projected to increase this century in Canada (Cousins et  al., 
2019), and increased housefly populations are projected this century 
in Mexico (Meraz Jimenez et al., 2019). Climate change may also lead 
to new emerging food-borne disease risks. For instance, V. cholerae is

a pathogen previously restricted to tropical regions; however, due to 
warming ocean temperatures, its detection has significantly increased 
along Canadian coasts (Banerjee et al., 2018).
Climate change is projected to increase human food-borne exposure 
to chemical contaminants (medium confidence). Increases in SST have 
been associated with greater accumulation of mercury in seafood, 
marine mammals and fish (Ziska et al., 2016). This particularly increases 
food safety risks in the Arctic, with methylmercury and polychlorinated 
biphenyl concentrations in high trophic animals projected to increase 
under high-emission scenarios by 2100 (Alava et al., 2017; Alava et al., 
2018).
Climate-related food-borne disease risks vary temporally, and are 
influenced, in part, by food availability, accessibility, preparation and 
preferences (medium confidence). For example, seafood risks are 
more pronounced in coastal regions due to high seafood consumption

(Radke et  al., 2015). In Alaska and northern Canada, where locally 
harvested foods are critical to diet, climate change may introduce 
new pathogens to local food sources through wildlife range changes, 
warming temperatures affecting safe fermentation and drying 
preparation methods, and food temperature control in below-ground 
cold storage in or near permafrost (King and Furgal, 2014; Harper 
et al., 2015; Rapinski et al., 2018).
14.5.6.7 Nutrition
Agricultural productivity declines due to climate change (Section 14.5.4) 
are projected to lower caloric availability and increase the prevalence 
of underweight people and climate-related deaths in North America by 
2050 (IMPAACT) (Springmann et al., 2016a; Springmann et al., 2016b; 
Springmann et al., 2018); however, this lower caloric availability could 
also reduce obesity, which could result in deaths avoided (Springmann 
et al., 2016a; Springmann et al., 2016b). The climate-related deaths per

capita due to reduced fruit and vegetable consumption is projected to 
exceed the mortality due to reduced caloric intake in North America

14
1971North America  Chapter 14
by 2050, particularly in Canada and the USA (Springmann et  al., 
2016a; Springmann et  al., 2016b). These climate-change projections 
underscore the importance of focusing on nutritional security in North 
America, instead of only considering caloric intake.
Shifting to a more sustainable diet can have adaptation and mitigation 
co-benefits while simultaneously improving health outcomes for 
North Americans. Transitioning to more plant-based diets is projected 
to reduce climate-related deaths in Canada, the USA and Mexico by 
2050 (Springmann et  al., 2016a; Springmann et  al., 2016b), while 
simultaneously reducing food-related GHG emissions per capita in 
North America by 2050 (Springmann et al., 2018).
Nutrition impacts will not be experienced uniformly within countries 
(Shannon et  al., 2015; Zeuli et  al., 2018). In Alaska and Canada, IK 
has documented how climate change has already impacted locally

harvested foods and challenged nutrition security (CCP6; Lynn et  al., 
2013; Petrasek MacDonald et al., 2013; Harper et al., 2015; Hupp et al., 
2015; Bunce et  al., 2016). For First Nations coastal communities in 
western Canada, decreased access to traditionally harvested seafood 
is projected to reduce nutritional status by 2050 (RCP2.5, RCP8.5), with 
higher nutritional impacts for men and older adults (Marushka et al., 
2019). Substitution of seafood with non-traditional foods (e.g., chicken, 
canned tuna) would not replace the projected nutrients lost (Marushka 
et al., 2019), challenging assumptions that market food substitutions 
could be effective adaptation strategies for Indigenous Peoples
14.5.6.8 Mental Health and Wellness
Climate change has had, and will continue to have, negative impacts 
on mental health in North America (high confidence) (Figure  14.8). 
Climate change impacts mental health through multiple direct and

indirect pathways stemming from extreme weather events, slower, 
cumulative events, and vicarious or anticipatory events (Cunsolo Willox 
et al., 2013; Cunsolo Willox et al., 2014; Durkalec et al., 2015; Yusa 
et al., 2015; Schwartz et al., 2017; Trombley et al., 2017; Burke et al., 
2018b; Cunsolo and Ellis, 2018; Dodd et al., 2018b; Hayes et al., 2018; 
Middleton et al., 2020b). Climate-change disruptions to infrastructure, 
underlying determinants of health and changing-place attachment are 
also stressors on mental health (Vida et al., 2012; Cunsolo Willox et al., 
2013; Burke et al., 2018b; Obradovich et al., 2018).
In North America, climate change has been linked to strong emotional 
reactions; depression and generalised anxiety; ecological grief and loss; 
increased drug and alcohol usage, family stress and domestic violence; 
increased suicide and suicide ideation; and loss of cultural knowledge 
and place-based identities and connections (Cunsolo Willox et  al.,

2013; Durkalec et  al., 2015; Harper et  al., 2015; Fernández-Arteaga 
et al., 2016; Schwartz et al., 2017; Trombley et al., 2017; Burke et al., 
2018b; Cunsolo and Ellis, 2018; Clayton, 2020; Dumont et al., 2020).
Suicide is projected to increase in Mexico and the USA by 2050 due to 
rising temperatures (RCP8.5) (limited evidence) (Burke et al., 2018b). 
Literature on climate change and mental health in North America is 
increasing; however, few population-level quantitative studies exist, 
although they are increasing (e.g., Burke et al., 2018b; Kim et al., 2019; 
Dumont et al., 2020; Middleton et al., 2021).14.5.7 Tourism and Recreation
Tourism is one of the largest and fastest-growing industries in North 
America, contributing 2.5 trillion USD to North America’s GDP in 2019 
(WTTC, 2018; Duro and Turrión-Prats, 2019). The USA is the world’s 
largest tourism economy (with a 1.839 trillion USD contribution to the 
global GDP in 2019), Mexico is ranked ninth (196  billion USD) and

Canada thirteenth (108 billion USD) (WTTC, 2018). The tourism industry 
is both impacted by climate change and significantly contributes to it 
through the emission of GHGs from travel and activities (Becken and 
Hay, 2007). By 2060, under RCP8.5, Canada and the USA are projected 
to benefit from climate-induced changes in tourism expenditures of up 
to 92 and 21%, respectively, whereas Mexico could experience a 25% 
decrease (OECD, 2015; Scott et al., 2019a).
14.5.7.1 Observed Impacts and Projected Risks of Climate 
Change
14.5.7.1.1 Alpine and Nordic skiing, snowmobiling and other winter 
sports
Winter tourism activities with hard limits to adaptation, particularly 
those that occur at sea level where less precipitation is expected to 
fall as snow (i.e., Nordic skiing, snowmobiling, snowshoeing), are at 
the highest risk from climate change and may experience irreversible 
impacts well before 2°C of warming above pre-industrial levels (high

confidence) (Figure  14.9). During record warm winters, alpine ski 
resorts in eastern Canada experienced reductions in ski season lengths 
of between 11 and 17 d (Rutty et al., 2017) and resorts in the northeast 
USA (US-NE) experienced decreased skier visits by 11.6% and 
reductions in operational profits of 33% amounting to 40–52 million 
USD (Dawson et  al., 2009). Even with advanced snowmaking as an 
adaptation to warmer temperatures, average ski season lengths are 
projected to decrease 8% (RCP2.6, 2050s) to 73% (RCP8.5, 2080s) in 
Ontario, Canada (CA-ON) (Scott et al., 2019b), 12% (RCP4.5, 2050s) to 
22% (RCP8.5, 2080s) in Quebec, Canada (CA-QC), and 13% (RCP4.5, 
2050s) to 45% (RCP8.5, 2080s) in the northeast USA (US-NE) (Wobus 
et al., 2017; Scott et al., 2020). Season length for snowmobiling and 
cross-country skiing is projected to decrease more dramatically (high 
confidence), that is, by 80% (RCP4.5) to 100% (RCP8.5) by mid-century

(CCP5; Wobus et al., 2017). The number of outdoor skating days may 
decrease by 34% in Toronto and Montreal, and 19% in Calgary, by 
2090 under RCP8.5 (Robertson et al., 2015). The skating season length 
for the Rideau Canal in Ottawa, Canada, a UNESCO World Heritage Site 
attracting 1.3 million visitors annually, may decrease by 3.8±2.0 d per 
decade with later opening dates of 2.6±1.5 d per decade (Jahanandish 
and Alireza, 2019).
14.5.7.1.2 Beach, coral reef and protected areas tourism
Sea level rise, increased storm surge, wave action, algae blooms, 
extreme air temperatures, and changes in wind and precipitation 
patterns threaten coastal tourism infrastructure, submerge beaches, 
erode walking paths on coasts, and impact destination attractiveness, 
tourism demand and recreation economies (very high confidence). Warm 
weather tourism activities, including beach tourism, snorkelling and 
national park visitation, will have more time to implement adaptation

14
1972Chapter 14 North America
and adaptation responses in North America
2 1
(e.g., chronic physical and mental  
health conditions )
Occupation (e.g., farmers,  
Indigenous livelihoods, fishers)
Gender (genders differentially  
affected)
Age (e.g., youth and seniors 
(e.g., displacement, relocation, 
disruptions to food systems, cultural  
activities, place-based knowledge 
sharing and livelihoods )
change, anticipating future changes )Key adaptation responses
Enhanced locally-based and 
culturally-relevant mental health services, 
informed policies, early interventions

Community
climate-informed wellness programming, 
climate-focused training for health provider s
health supports, land-based programming and 
nature therapy3 4
Diminished social relations  
(e.g., domestic violence, loss of culture,  
reduced social capital )52. Vulnerability
Physiological factor s
Social factor s
Indirect exposure(s)  
Community
Individual s1. Hazard
Acute events  
(e.g., PTSD, depression, suicide)Diminished wellbein g
(e.g., stress, sadness, climate-related grief
and anxiety, impaired mental functioning)Hazard
(e.g., storms, floods, heat events,
wildfires)
Chronic changes

(e.g., drought, changing seasonal
and environmental norms)
Figure 14.8 |  Pathways through which climate change impacts mental health risk in North America

14
1973North America  Chapter 14
strategies to reduce climate risks as significant and widespread 
impacts are not expected until 3°C–4°C of warming (Figure  14.9; 
Rutty and Scott, 2015; Atzori et al., 2018; Santos-Lacueva et al., 2018; 
Duro and Turrión-Prats, 2019). Thirty percent of hotels along the Gulf 
of Mexico and Caribbean Sea are exposed to flooding and 66% are 
located on eroding beaches (Lithgow et al., 2019). Coral reef cover in 
Akumal Bay, Mexico, decreased by 79% between 2011 and 2014 (Gil 
et al., 2015; Manuel-Navarrete and Pelling, 2015). The recreation value 
of coral reef tourism in Florida, Puerto Rico, and Hawaii is expected to 
decrease by 90% by mid-century under RCP8.5 (Section 14.4.2; EPA, 
2017). Wildfires and insect outbreaks have contributed to reduced 
desirability for tourism across forest and mountain regions (Bawa, 
2017; Hestetune et  al., 2018; White et  al., 2020). Visitors to Utah’s 
National Parks declined 0.5–1.5% during wildfire years between

1993 and 2015, resulting in 2.7–4.5 million USD in lost revenue (see 
Box 14.2; Kim and Jakus, 2019). Trees damaged by insects have caused 
campground and hiking trail closures in the western USA and Alaska 
(Arnberger et  al., 2018). Seal level rise, flooding, coastal erosion, 
changing air and sea temperatures, changing humidity and extreme 
weather events are putting cultural heritage sites at risk (Fatorić and 
Seekamp, 2017; Hollesen et al., 2018; Tetu et al., 2019).
14.5.7.1.3 Arctic tourism
Cruise and yacht tourism in the North American Arctic have increased 
rapidly over the past decade as changes in sea ice has expanded open-
water areas and season length (Johnston et al., 2016; Pizzolato et al., 
2016; Dawson et al., 2018). The risk of a major accident or incident 
among Arctic-going yachts and some expedition passenger vessels is 
very high relative to other ships (high confidence) due to the combined 
increases in mobile ice, especially along the Northwest Passage (Barber

et al., 2018a; Howell and Brady, 2019; Copland et al., 2021; Lemmen 
et al., 2021), limited regulation for private yachts (Dawson et al., 2014; 
Dawson et al., 2017), the propensity for cruise ships to travel into newly 
ice-free and poorly charted areas, and the increasing number of non-
ice-strengthened vessels operating in the region (Dawson et al., 2018; 
Copland et al., 2019; Copland et al., 2021). Compounding risks include 
a lack of hydrographic charting and the lack of emergency response 
infrastructure (e.g., spill response, search and rescue, salvage) (Amap, 
2017). Tourism demand for polar bear viewing in Churchill, Manitoba, 
Canada, may change due to climate-related declines in polar bear 
health (Gil et  al., 2015; Manuel-Navarrete and Pelling, 2015), but 
may be offset by ‘Last Chance Tourism’ (LCT), a niche tourism market 
of individuals who explicitly seek to visit vanishing landscapes and/
or disappearing flora and fauna (Lemelin et al., 2010). The ethics of

promoting LCT has been questioned considering that more visitation 
to sensitive sites increases local impacts as well as travel-related 
emissions (Groulx et al., 2016; Groulx et al., 2019).
14.5.7.2 Emerging Responses and Adaptation
Compared with other economic sectors (Section 14.5.8), the tourism 
industry has high adaptive capacity (high confidence) (Figure  14.9). 
Investments in climate-resilient infrastructure within Canadian 
National Parks have increased visitation rates during the shoulder 
seasons (Fisichelli et  al., 2015; Lemieux et  al., 2017; Wilkins et  al., 
2018), regional collaboration among US and Canadian park agencies has enhanced adaptive capacity through integrated planning and 
management (Lemieux et al., 2015), and technological advancements 
have reduced the vulnerability of alpine winter sports from warming 
temperatures (e.g., snowmaking, refrigerated surfaces, chemical 
additives) (Rutty and Scott, 2015; Scott et al., 2019b; Scott et al., 2020).

Snowmaking as an adaptation strategy affects mitigation efforts by 
increasing the need for energy and fuel (Scott et al., 2019b).
Tourists are also highly adaptable and, depending on their levels of 
place attachment, location loyalty and socio-demographics, are very 
likely to substitute the timing or location of their travel activity based 
on climate and climatic-driven environmental changes (Rutty and Scott, 
2015; Atzori et al., 2018). Lemieux (2017) found that if the state of the 
Athabasca Glacier (CA-PR) (Figure 14.1) were to change negatively as 
a result of climate change, 83% would travel elsewhere, and if large 
infrastructure were built as an adaptive measure for viewing receding 
glaciers at Jasper National Park, 40% of tourists would no longer visit.
Hard and soft limits to adaptation exist in the tourism sector (Manuel-
Navarrete and Pelling, 2015). For example, machine-made snow, without 
the use of environmentally harmful chemical additives that are banned

in most jurisdictions, can only be made efficiently in temperatures 
below −2°C, but projections indicate warming temperatures above this 
threshold (Wobus et al., 2017; Scott et al., 2019a). Multi-jurisdictional 
adaptation planning for parks and protected areas in the USA has 
been hindered by a lack of funding and communication, and funding 
trade-offs that could be remedied through coordination (Lemieux 
et al., 2015). Social inequalities generated by the tourism development 
process must also be considered by climate-related interventions to 
prevent the perpetuation of inequalities that may exist, particularly in 
less developed regions and rapidly developing regions. For example, 
new developments in Hawaii, Florida, Quebec and popular resort areas 
in Mexico have led to social inequalities through increased property 
taxes leading to the marginalisation of local residents away from these 
areas in favour of wealthy tourists (Section 14.5.9; Manuel-Navarrete

and Pelling, 2015).
14.5.8 Economic Activities and Sectors in North America
Economic sectors highly reliant on climate, such as agriculture, tourism, 
fisheries and forestry, have higher levels of exposure and sensitivity 
(high confidence) and greater overall risk to climate change compared 
with other economic sectors such as mining, construction and 
manufacturing (medium confidence). However, the cascading nature 
of climate impacts related to trade (see Box 14.5), labour productivity 
(Section 14.5.8.1.5) and infrastructure (Section 14.5.8.1.2) means that 
there is no economic sector in North America that will be unaffected by 
climate change (very high confidence) (Figure 14.10). For Canada, this 
assessment is further supported by the Canadian Climate Assessment 
(Lemmen et al., 2021). The combined economies of Canada, Mexico 
and the USA represented ~28% of the global GDP in 2019, with the 
USA accounting for almost 90% of the total activity for North America

(World Bank, 2020a). The risks posed at different global warming 
levels (GWLs) for any given economic activity or sector are presented 
in Figure  14.10. By combining expert judgement with a systematic 
review of the literature for each sector, the information in Figure 14.10

14
1974Chapter 14 North America
represents a broader synthesis, especially for sectors with a smaller 
literature base and at higher GWLs. The assessment of the risks of 
climate change on tourism (Section  14.5.7) and the interactions 
between sectors through trade (see Box 14.5) are discussed separately.
14.5.8.1 Observed Impacts and Projected Risks of Climate 
Change
14.5.8.1.1 Agriculture, fisheries and forestry
The wide range of observed and projected impacts of climate hazards 
on food and fibre in North America are documented in Section 14.5.4 
(also see Chapter 5). Agriculture (US-NW: corn and soybeans), fisheries 
(cod and pollock) and forestry (Boreal Forest timber yield) are expected 
to experience substantial and widespread risks by 2°C of global 
warming above pre-industrial levels (medium to high confidence) 
(Figure 14.10). Economic models generally show economic losses in 
the agricultural sector across North America, especially at higher GWL

(Section  14.5.4; EPA, 2017; Boyd and Markandya, 2021), although 
the effects in local economies, especially rural areas of the USA that 
are highly dependent on agriculture, will be substantial even at lower 
GWLs (Gowda et al., 2018). Full evaluations of climate risks for forestry 
and fisheries are presented in Sections  14.5.1 and 14.5.4 (also see 
Section 14.6), respectively.
14.5.8.1.2 Transportation
Transportation infrastructure, including roads, bridges, rail, air, sea and 
pipelines, are highly vulnerable to rising temperatures, SLR, weather 
extremes, changing ice conditions, permafrost degradation and flooding 
(high confidence), resulting in damage, disruption to operations, unsafe 
conditions and supply chain impacts (see Box 14.5; Board and Council, 
2008; Natural Resources Conservation Service; Andrey and Palko, 
2017; Jacobs et al., 2018; Lemmen et al., 2021). In the Mexican states 
of Veracruz, Tabasco, San Luis Potosí, Chiapas and Oaxaca, 105,000

infrastructure sites, mostly major connecting roads, were found to be at risk of flooding from tropical storms (De la Peña et al. 2018). Low 
water levels in the Great Lakes has severely impacted US grain transport 
(Attavanich et al., 2013). High-intensity rain events destroyed 1000 km 
of roads and washed out hundreds of bridges and culverts in 2013 
resulting in an estimated 6 billion CAD (considering the 2013 CAD value) 
in damages and recovery costs in Alberta, Canada (Palko and Lemmen, 
2017). In 2019, the rail line from Winnipeg to Churchill Manitoba, which 
is the only ground transportation to the community and to Canada’s only 
deep-water Arctic port, was reopened after being closed for over 2 years 
due to the cumulative effects of flooding, permafrost degradation and 
political challenges (Lin et al., 2020). In the USA, the number of heat-
related train delays has increased (Bruzek et al., 2013; Chinowsky et al.,

2019) and, by the end of the century, may cause economic losses of 25–
45 billion USD (RCP4.5) or 35–60 billion USD (RCP8.5) (Chinowsky et al., 
2019). Sea ice reduction in the North American Arctic has led to a rapid 
increase in ship traffic (Huntington et al., 2015; Phillips, 2016; Pizzolato 
et  al., 2016; Huntington et  al., 2021b; Li et  al., 2021) with cascading 
risks related to invasive species introduction, accident rates, black carbon 
emissions, underwater noise pollution for marine mammals and risks to 
subsistence harvesting activities in Indigenous communities (Ware et al., 
2014; Council of Canadian Academies, 2016; Huntington, 2021; Verna 
et al., 2016; Chan et al., 2019).
14.5.8.1.3 Energy, oil and gas, and mining
Climate change is increasing the demand for electric power for cooling 
and threatens existing power supply (high confidence) (Section 14.5.5). 
Increased energy demand often occurs during peak energy usage and

especially during heatwaves (Cruz and Krausmann, 2013; Leong and 
Donner, 2015). Cooling represented 74% of peak electricity demand in 
Philadelphia on a particularly hot day in July 2011 (Waite et al., 2017; IEA, 
2018b). In Canada, warming temperatures are expected to reduce demand 
for heating by 18–33% and increase demand for cooling by 14–126% 
by 2070 compared with 1959–1989 and 1998–2014 baseline periods, 
respectively (Berardi and Jafarpur, 2020). The effects on hydropower are 
uneven across the region with the potential for increases in capacity in Relative risks to select tourism activities in North America
High
Moderate
Low High•
•••••
Nordic skiing and

snowmobiling
••• ••• •
••• ••• •
Alpine skiing
•••• •• •
••• •••
Beach tourism and
coral reef snorkeling
••• • •
•••• • •
Parks and protected
areas visitation
•• ••
•• ••••Without
adaptatio nWith
adaptatio nWithout
Figure 14.9 |  Burning ember of the relative risks to select tourism activities in North America with and without adaptation as a function of global mean 
surface temperature increase since pre-industrial times. Risks to tourism activities include: (a) season length reductions from warming temperatures for Nordic skiing and 
snowmobiling, (b) season length reductions from warming temperatures and precipitation changes for alpine skiing, (c) visitor-experience changes as a result of warming surface 
and ocean temperatures for beach tourism and degrading coral reef systems for snorkelling and (d) visitor-experience changes related to warming temperatures and changing

landscape aesthetic for Parks and Protected Areas. Risks assessed cover all of North America (c,d), or are specific to certain regions (a,b) . The supporting literature and methods 
are provided in Supplementary Material (SM14.4).

14
1975North America  Chapter 14
Canada but declines of over 20% in Mexico (RCP4.5 and RCP8.5) (Turner 
et  al., 2017). Electricity demand in the USA is projected to increase by 
5.3% per degree Celsius rise in temperature (Hsiang et al., 2017). Energy 
infrastructure, such as drilling platforms, refineries and pipelines, and 
evacuation routes, are also increasingly vulnerable to higher sea levels, 
hurricanes, storm surges, mobile multi-year sea ice, erosion, inland flooding, 
wildfires and other climate-related changes (Zamuda et al., 2018).
Operational efficiency and human safety at mining and energy production 
sites is expected to be adversely affected by increases in extreme 
events (Section  14.2), including storms, heavy rains, riverine flooding 
and wildfires (high confidence). General remoteness of many mining 
sites (especially in the North American Arctic) exacerbates risks related 
to emergency responses to extreme events such as wildfire (medium

confidence). The 2016 Fort McMurray wildfire in Alberta, Canada, forced 
the evacuation of 88,000 people and the shutdown of mine operations. 
Damages were minimal because companies had undertaken proactive 
FireSmart interventions specifically developed for the industry (see 
Box  14.1; Council of Canadian Academies, 2019). Onshore oil field 
production in Tabasco, Mexico, which accounts for 16% of the country’s 
daily output, was interrupted by extensive flooding (Cruz and Krausmann, 
2013). Two-thirds of mine operators globally, including major operators 
in North America, have experienced production challenges related to 
water shortages and flooding (Carbon Disclosure Project, 2013). Water 
availability stress due to climate change is lower in Canada than in the 
USA and Mexico, and mines in Canada may be less exposed to this risk 
(World Resourcs Institute, 2012) with some exceptions, that is, water-
intensive oil sands mining in the Athabasca River basin in Canada

(Section 14.5.3; Leong and Donner, 2016).Warming temperatures also 
have the potential to alter the nature, characteristics and quality of 
mineral resources such as kaolin or limestone (Phillips, 2016).
14.5.8.1.4 Construction
In the USA, construction workers comprise 6% of the total workforce but 
accounted for 36% of all occupational heat-related deaths from 1992 to 
2016 (Dong et al., 2019). It is expected that total labour hours among 
outdoor construction workers will decrease by 0.53% (±0.01%) per 
degree Celsius based on existing warming trends (Hsiang et al., 2017; also 
see EPA, 2017). Risks are expected to be exacerbated as SLR and storm 
surge expands the risk zone for coastal flooding exposing more property 
to inundation and enhancing construction demand (see Box  14.4; 
Section 14.5.5.1.3; EPA, 2017). Meeting existing and projected demand 
for water in affected regions could also require building new desalination

plants. For example, Texas has constructed over 44 desalination plants 
across the state because of a lack of freshwater to meet potable water 
demand and due to climate-driven droughts (Kloesel et al., 2018b). Other 
infrastructure damaged by floods and SLR will need to be reassessed and 
perhaps relocated away from the coast. Relocation requires availability of 
land that frequently does not exist within urban areas (Lithogow, 2019). 
Some US tribes and Indigenous groups in Canada lack the financial 
resources to build climate-resilient infrastructure, such as housing and 
sewage treatment facilities, to assure clean drinking water (Martínez 
et al., 2014; Salgado and Luisa Martinez, 2017; Lithgow et al., 2019).
Permafrost thaw in northern North America will result in increased 
construction and reconstruction needs (medium confidence) related to direct damage to buildings, roads, airport runways and other critical

infrastructure including decreased bearing capacities of building and 
pipeline foundations, damage to road surfaces, and deterioration 
of reservoirs and impoundments used for wastewater and mine 
tailings containment (Pendakur, 2017; Meredith et  al., 2019). Ice 
roads have become less safe due to warming, pavement damage has 
increased related to seasonal thaw–freeze cycles and there have been 
interruptions in airport operations, water and sewage service, and 
school operations in the Canadian territories of Yukon and Nunavut 
(Canadian Western and Eastern Arctic, i.e., CA-WA and CA-EA in 
Figure 14.1) (Council of Canadian Academies, 2019). By the end of the 
century, the economic impact of projected reconstruction of Alaska’s 
public infrastructure due to climate change (mainly from permafrost 
thaw) is estimated to range from 4.2 billion USD (RCP4.5) to 5.5 billion 
USD (RCP8.5) (Melvin et al., 2017; Markon et al., 2018).
14.5.8.1.5 Manufacturing

Twelve million Americans (Bureau of Labor Statistics, 2015), 1.5 million 
Canadians (Statistics Canada, 2020) and 9  million Mexicans (Statistics 
Mexico, 2021) are employed in manufacturing. The southeast USA and 
Texas have the highest manufacturing output, with 34% of total US output 
(700 billion USD yr–1). The impact of climate change on manufacturing 
varies greatly by region. Vulnerability of the sector to climate change 
stems from exposure of workers to increasing temperatures and humidity, 
exposure of facilities to SLR and flooding, and changes in water supply 
and quality required in many manufacturing processes (Lall et al., 2018).
14.5.8.1.6 Labour Productivity
Climate change is negatively affecting working conditions and labour 
productivity in North America (medium confidence) (Section 14.5.6.1; 
see Box  14.5). Working conditions in temperatures above a heat 
index of 85°F (29.4°C) are correlated with potentially hazardous

health conditions (Tustin et  al., 2018), and for every degree Celsius 
increase in temperature, labour productivity is estimated to be reduced 
by 0.11% for low-risk workers and 0.53% for high-risk workers (i.e., 
construction, mining, agriculture and manufacturing) (Hsiang et  al., 
2017). By mid-century (RCP8.5), temperature increase, changing water 
availability and SLR are projected to result in a 0.6% drop in labour 
productivity in auto, timber, textile and chemical manufacturing in the 
southeast and Texas regions (Kinniburgh et  al., 2015; Hsiang et  al., 
2017). Labour productivity in the US automobile industry decreases by 
8% for every six or more days of consecutive unusually hot weather 
(above 90°F/32.2°C) (Cachon et al., 2012). Thirty percent of California 
workers are employed in high-risk industries, such as agriculture, with 
exposure to high temperature leading to loss in productivity (Rogers 
et al., 2015). Under RCP8.5 increases in extreme temperatures, labour

productivity in the USA is projected to decrease, costing 190 billion 
USD in lost wages by 2090 (EPA, 2017; Kjellstrom et al., 2019; also see 
Gubernot et al., 2014; Kiefer et al., 2016; Carter et al., 2018).
14.5.8.2 Current and Potential Adaptation
Adaptation options are highly diverse and sector specific (EPA, 2017). 
Regardless of economic sector, companies that implement effective 
and rapid response options that address climate change stressors

14
1976Chapter 14 North America
will have a competitive advantage (Gasbarro et  al., 2016, Lemmen, 
2021). Most companies focus on short-term risk management and, 
consequently, short-term adaptation is often favoured over long-term 
approaches particularly in the private sector, which will be ineffective 
for climate-change risk reduction over the long term (Gasbarro et al., 
2016).
Investment and coordination of climate services (forecasting) can 
support many economic sectors across North America. In 2017, 15% 
of Standard and Poor’s (S&P , US industry credit rating agency) 500 
companies publicly disclosed an effect on earnings from weather 
events, reflecting a growing trend (Williams et  al., 2018). Existing US 
federal-sponsored planning tools provide guidance to states and to 
plan for SLR and flooding with large threats to commercial sectors (US 
Department of Transportation, 2015). The NOAA Coastal Services Center

SLR and coastal inundation viewer7,the Army Corps of Engineers Sea 
Level Change Curve simulator, and Climate Central’s interactive portal 
(Ocean at the Door) all provide access to visualisations of future SLR 
that are available to US coastal cities and towns for commercial planning 
purposes. Similar resources are being developed and are available for 
Canada including Canada’s Climate Atlas8.
Adaptation options for transportation and related infrastructure in-
clude engineering and technological solutions, as well as innovative 
policy, planning, management and maintenance approaches (Natural 
Resources Conservation Service, 2008; Jacobs et al., 2018). For north-
ern transportation, new technologies and infrastructure adaptations 
can be employed to facilitate heat extraction (e.g., air convection em-
bankments, heat drains, thermosyphons, high albedo surfacing, gentle 
embankment slopes) (McGregor et al., 2010b; United Nations, 2020)

Adaptation options for roads include changing pavement mixes to be 
more tolerant to heat or frost heaving, expanding drainage capacity, 
7 See https://coast.noaa.gov/digitalcoast/tools/slr.html
8 See https://climateatlas.ca
9 See https://living-future.org/basicsreducing flood risks, enhancing travel advisories and alerts, elevating 
or relocating new infrastructure where feasible and changing infra-
structure design requirements to include climate-change considera-
tions or to introduce new flood event thresholds (Natural Resources 
Conservation Service, 2008; EPA, 2017; Pendakur, 2017). Railroads are 
testing temperature sensors on rail tracks to provide early warning of 
buckling. Sensors that signal when tracks are approaching dangerous 
temperatures may help to avoid accidents (Hodge et al., 2014; Chi-
nowsky et al., 2019).
Adapting building codes more uniformly to changing climate conditions, 
such as SLR, storms, winds and wildfires, reduces risk (Olsen, 2015;

Maxwell et al., 2018b). North America has not, on the whole, adapted 
its building code regulations to consider the dynamic challenges 
of climate change, although some specific efforts have been made, 
including the addition of requirements for wildfire within California’s 
building codes and Canada’s climate-resilient building and core public 
infrastructure initiative, which involves updating building codes and 
standards to improve climate resiliency (see Box 14.4; Lacasse et al., 
2020). To enhance safety, some outdoor workers have been fitted with 
heat sensors to analyse or assess how warming may affect productivity 
and well-being (Runkle et al., 2019). Other options include raising public 
roads and seawalls, initiating buy-outs of property owners in flood risk 
areas and improving storm water drainage. Adopting approaches like 
the International Future Living Institute’s Living Building Challenge 
(LBC) may inform future regulatory processes (Eisenberg, 2016). The

LBC9has seven thematic areas that inform building design, although 
only a subset of those are relevant for climate change including water, 
energy and materials considerations.Relative risks to economic sectors in North America
High
Moderate
Low High•
•••••
• ••
Forestry
•••• ••
Tourism
••• ••••
Transportation
•••• ••
Fisheries
••• •• ••
Energy and
mining
•• •
Construction
••
Figure 14.10 |  Burning ember of the relative risks to economic sectors in North America as a function of projected global mean surface temperature increase

since pre-industrial times. Impacts on economic sectors include: (a) changing crop yield leading to economic loss for agriculture, (b) changes in the quality and quantity of timber 
yields, (c) reductions in season length and economic viability for tourism activities, (d) increased maintenance and reconstruction costs to transportation infrastructure, (e) changes 
in fisheries catch, (f) reduced productivity in mining and energy operations, (g) reduced labour productivity in outdoor construction and (h) increased maintenance and reconstruction 
costs to transportation systems. Risks to economic sectors and activities were sometimes assessed across all of North America (c, d), within specific regions (a, b) and for specific crops 
or species (a: corn and soybean, e: cod and pollock). The supporting literature and methods are provided in Supplementary Material (SM14.4).

14
1977North America  Chapter 14
Box 14.5 | Climate-Change Impacts on Trade Affecting North America
Trade, defined as the sum of exports and imports, accounts for 30% of North American GDP . Trade flows within North America are valued 
at $1.3 trillion USD annually (2019 dollars). Variations within the region are notable: Mexico relies on trade for 80% of its GDP and 
Canada for 66% (World Bank, 2020a). Canada and the USA traded over 55.2 billion USD worth of products related to the agriculture 
industry between 2015 and 2018 (Government of Canada, 2019). Canada, the USA and Mexico have the longest-running trade pacts 
globally and these agreements have played a major role in supporting economic and social development in the region (see (Frankel and 
Rose, 2005; Eaton et al., 2016; World Bank, 2020b); however, recent changes to the North American Free Trade agreement do not clearly 
address climate change (Lucatello, 2019).

Climate risks may create shocks to the trade system by damaging infrastructure and disrupting supply chains in North 
America (medium confidence). Sea level rise, flooding, permafrost thaw, landslides and increased frequency and magnitude of extreme 
weather events are projected to impact transportation infrastructure which will pose challenges to the movement of goods, especially in 
coastal areas (Lantuit et al., 2012; Doré et al., 2016; Hjort et al., 2018; Koks et al., 2019; Lemmen et al., 2021). Maritime ports are at the 
greatest risk from climate hazards (Messner et al., 2013; Slack and Comtois, 2016), followed by roads, rail and airports (Anarde et al., 
2017). Due to the transnational nature of trade, extreme weather disruptions in one region are likely to lead to cascading effects in other 
regions (high confidence) (Lemmen et al., 2021). For example, climate change will have negative impacts for global food and energy

trade where reductions in crop production and fish stocks in some regions could cause food and fish price spikes elsewhere (Figure 14.10; 
Sections 14.5.4 and 5.11.8; Beaugrand et al., 2015; Lam et al., 2016; IPCC, 2019a).
Climate-change impacts may alter current trade practices and patterns with implications for regional economic development 
in North America, especially in the Arctic (medium confidence). Climate change is causing modal shifts in cargo shipping. For 
example, lower water levels in lakes and rivers (e.g., Mackenzie River, Mississippi River) impact freight transport and may cause a shift 
from marine transport to more GHG-intensive rail, road or air transport (Koetse and Rietveld, 2009; Du et al., 2017; Pendakur, 2017). 
Sea ice change is creating new Arctic marine trade corridors (Melia et al., 2016; Pizzolato et al., 2016; Ng et al., 2018; Bennett et al.,

2020; Mudryk et al., 2021), including shorter and potentially more economical routes such as the Northwest Passages (see Box CCP6.1). 
Warming temperatures have also reduced the season length for ice roads, which are heavily relied upon to service remote communities 
and remote industries including forestry and mining (Section 14.5.8.1.2; Pendakur, 2017).
Effective and equitable trade policies can act as important adaptation strategies (medium confidence). Higher temperatures 
have had no direct effect on developed countries’ exports, but have significantly reduced growth in exports among developing countries, 
which in turn can increase the price of goods that developed countries then import (Costinot et al., 2016; Constant and Davin, 2019). 
Schenker (2013) estimated that the climate impacts on trade from developing to developed countries could be responsible for 16.4%

of the total expected cost of climate change in the USA in 2100 and, thus, North America would benefit from increased investment in 
effective and equitable trade policies and adaptation in developing regions. Under an RCP8.5 scenario (~2.6–4.8°C warming) and within 
current trade integration, climate change could lead to up to 55 million undernourished people by 2050. These projections decrease 
by 64% (20 million people) with the introduction of reduced trade tariffs and the lessening of institutional and infrastructure barriers 
(Janssens et al., 2020). Although most studies focus on global food security (i.e., agriculture), it is likely that the same challenges exist for 
other commodities and manufactured goods.
14.5.9 Livelihoods
Exposure and vulnerability to climate hazards have varied across North 
America by region and population (high confidence). These differences 
have been often underpinned by social and economic inequalities and

have been observed between households, social groups, rural and 
urban communities, and Indigenous Peoples (high confidence). These 
vulnerabilities have also been observed to contribute to maladaptation 
(medium confidence) (Section 14.5.9.1). Social and economic trends 
and development will determine near-term impacts on livelihoods from 
projected climate hazards; livelihoods will also adapt to the risks and 
opportunities (high confidence) (Section 14.5.9.2). Actions to enhance 
the livelihoods of the most vulnerable social groups in North America 
will lessen the impacts of climate hazards on them (high confidence) 
(Section 14.5.9.3).14.5.9.1 Observed Impacts
Livelihoods are ‘the resources used and the activities undertaken in 
order to live. Livelihoods are usually determined by the entitlements and 
assets to which people have access’ (Section 8.1.1; IPCC, 2018). While 
often understood as subsistence or traditional ways of life (Oswal, 1991),

livelihoods are often conceptualised more broadly as encompassing 
the economic, cultural, and social capitals or assets, capabilities, and 
activities that individuals, households and social groups use as the 
means to make a living (DFID, 1999; Obrist et al., 2010).
Past and current patterns of development in North America have 
propagated and perpetuated vulnerabilities that have created 
differential impacts on livelihoods from climate hazards (high 
confidence). Predatory and extractive economies have underpinned

14
1978Chapter 14 North America
Box 14.6 | The Costs and Economic Consequences of Climate Change in North America
Extreme weather events, including hurricanes, droughts and flooding, and wildfires, have been partly attributed to anthropogenic climate 
change (high confidence) (Table SM 16.21; e.g., Rupp et al., 2015; Emanuel, 2017). Direct, indirect and non-market economic damages 
from extreme events have increased in some parts of North America (high confidence). The number of extreme events with inflation-
adjusted damages totalling more than 1 billion USD has risen in the USA over the past decades (NOAA, 2020; Smith, 2020), and similar 
increases have been observed in Canada (Boyd and Markandya, 2021). Factors other than climate change, including increases in exposure 
and the value of the assets at risk, also explain increasing damage amounts (Freeman and Ashley, 2017; Vano et al., 2018). Climate

change explains a portion of long-term increases in economic damages of hurricanes (limited evidence, low agreement). Studies of US 
hurricanes since 1900 have found increasing economic losses that are consistent with an influence from climate change (Estrada et al., 
2015; Grinsted et al., 2019), although another study found no increase (Weinkle et al., 2018).
Formal attribution of economic damages from individual extreme events to anthropogenic climate change has been limited, but climate 
change could account for a substantial fraction of the damages (limited evidence, medium agreement). Two recent studies have shown 
approaches for how damages may be attributed for individual events in the USA. Assuming a direct proportionality between attributable 
risk of the event to the attributable economic damages, one study suggested that 30–75% of the direct damages from Hurricane Harvey

was caused by climate change, with a best estimate of 67 billion USD out of an estimated 90 billion USD total of attributable damages 
(Frame et al., 2020). Another study modelled the component of the flooding from Hurricane Sandy due to rising SLR and mapped that to 
coastal damages. That study estimated that 8.1 billion USD (13% of the total) was attributable to the climate influence on SLR (Strauss 
The effect of climate change has been identified in aggregate measures of economic performance, such as GDP , in North America and 
globally (medium confidence), although the magnitude of these changes is difficult to constrain (medium confidence). Climate change 
has been observed to affect national GDP level and economic growth (low confidence). The extent to which climate has affected GDP 
may be challenging to identify statistically (Cross-Working Group Box ECONOMIC in Chapter 16). Observed GDP effects are generally

slightly negative in the USA, higher and negative for Mexico, and the directionality of the effects in Canada varies by study and modelling 
approach (Burke et al., 2015; Colacito et al., 2018; Kahn et al., 2019).
Projections of market and non-market economic damages demonstrate the substantial economic risks of climate impacts associated 
with high-temperature pathways (RCP8.5) (high confidence). Since AR5, a wide range of estimates of the costs of climate change have 
been developed for the USA (EPA, 2015a; Houser et  al., 2015; EPA, 2017; Hsiang et  al., 2017; Martinich and Crimmins, 2019), with 
ongoing processes to update national estimates for Canada and Mexico (Semarnat, 2009; NRTEE, 2011; Estrada et al., 2013; Sawyer 
et al., 2020). While the magnitudes of the estimates depend on approach and assumptions in the methods and expectations of future

socioeconomic conditions, these studies show substantial projected economic damages across North America by the end of the century, 
especially for warming greater than 4°C (high evidence, high agreement). Whether these damages translate into GDP effects is not clear for 
Canada. Some modelling approaches show modest GDP increases in 2050 and 2100, while others suggest modest decreases although it 
is anticipated that the economic effects for Canada will be large and negative (Boyd and Markandya, 2021). Large costs and risks, such as 
those associated with extreme events such as wildfires (Hope et al., 2016) and the increased need for infrastructure replacement (Neumann 
et al., 2015; Maxwell et al., 2018a), will have compounding effects in the markets by disrupting economic activities (see Box 14.5).
Market and non-market risks and costs will not be experienced equally across countries, sectors and regions in North America (high

confidence). For the USA, energy expenditures and improvements in agricultural yields are projected to result in net gains in the north 
and Pacific Northwest whereas in the south, higher heat-related mortality, increases in energy expenditures, SLR and storm surge are 
projected to result in economic losses by the end of century (Hsiang et al., 2017). No region in the USA is expected to avoid some level of 
adverse effects (medium evidence, high agreement) (EPA, 2017; Martinich and Crimmins, 2019). Economic models generally show losses 
in the agricultural sector across North America, especially at higher GWL (Boyd and Markandya, 2021; EPA 2017). Some models show 
large gains in parts of Canada, although these models do not capture the full range of climate hazards including change in precipitation 
or extreme events (Boyd and Markandya, 2021).
Economics of Adaptation Opportunities

Economic analysis can help reveal where the avoided economic damages are greater than the costs of adaptation, improving decision 
making for adaptation planning and efforts in North America (high confidence). Detailed assessment of total needs and costs of climate 
adaptation are limited (Sussman et al., 2014), but estimates suggest that the costs are large (low evidence, high agreement). Cost–benefit

14
1979North America  Chapter 14
economic activity in North America historically and currently. While 
generating substantial wealth, these patterns have also driven social 
and economic inequality (medium evidence, high agreement) (Jasanof, 
2010; Shove, 2010; Klinsky et  al., 2016; Robinson and Shine, 2018). 
Patterns of development that reinforce these structures remain a large 
contributor to current social–environmental risks and have affected all 
kinds of contemporary livelihoods (Chapter 18; Cannon and Müller-
Mahn, 2010; Koch et al., 2019).
Climate impacts have damaged livelihoods across North America, 
especially those of marginalised people (high confidence) and 
deepened inequalities for these groups (medium confidence). Across 
North America, climate change has affected livelihoods with larger 
effects on individuals, households and communities that are already 
more vulnerable due to a range of pre-existing social and environmental

stressors (Olsson et al., 2014; Hickel, 2017; Koch et al., 2019) such as 
Indigenous Peoples, urban ethnic minorities and immigrants (Guyot 
et al., 2006; Gronlund, 2014; Klinenberg, 2015). These impacts have 
also contributed to a deepening of inequalities for marginalised groups 
(medium evidence, high agreement) (Audefroy and Cabrera Sánchez, 
2017; García et  al., 2018). As climate hazards further degrade their 
livelihoods, these groups have faced additional challenges to avoiding 
or escaping poverty (Ruiz Meza, 2014). Furthermore, these groups 
have needed to use their more limited resources to manage present 
challenges, restricting their future capacities to adapt (Tolentino-
Arévalo et al., 2019). Climate impacts have also affected the livelihoods 
of the middle classes (Domínguez et al., 2020) who have become more 
vulnerable due to changes in their social and economic security (Garza-
Lopez et al., 2018). Gender has also been recognised as a determinant

of differential vulnerability with implications for women’s livelihoods 
(Cross-Chapter Box GENDER in Chapter 18).
Migration and mobility have been an important part of livelihoods in 
North America (high confidence). Movement across North America has 
been reinforced by social, cultural and economic ties (see Box 14.5). 
For example, middle class retirees from Canada and the USA engage from temporary, seasonal to permanent migration to the warmer 
climates of the southern USA and Mexico, often benefiting from the 
lower cost of living (Domínguez et  al., 2018). Temporary or semi-
permanent labour migration, generally followed by remittances, has 
been an important part of livelihoods for rural areas in Mexico (high 
confidence) and has been employed as a response to climate hazards 
(low evidence). Drought in rural areas which are highly dependent on 
subsistence agriculture have observed migration to urban areas in

Mexico (Nawrotzki et al., 2017). Evidence of international migration 
in response to climate hazards is sparse with difficulties in identifying 
a climate signal due to the multi-causal nature of migration decision 
making (Cross-Chapter Box MIGRATE in Chapter 7). There is limited 
evidence of extreme weather events or climate hazards on migration 
from Mexico to the USA (Nawrotzki et  al., 2015b; Nawrotzki et  al., 
2015c; Nawrotzki et al., 2016; Murray-Tortarolo and Salgado, 2021).
Pre-existing social vulnerabilities have also led to forced displacement 
from extreme weather events (low confidence). In the USA, 
compounding effects of SLR and storm surge interacted with pre-
existing social vulnerabilities of local communities to generate large-
scale displacement after the effects of Hurricane Katrina on New Orleans 
in 2005 (Jessoe et al., 2018). The processes of relocation and recovery in 
New Orleans was further shaped by vulnerability where out-migration

was more likely to be minorities and economically disadvantaged, 
while the recovery was predominantly in neighbourhoods that were 
wealthier prior to the disaster (Fussell et al., 2014; Fussell, 2015). Newer 
evidence from Hurricane Maria in Puerto Rico in 2017 has shown an 
initial spike in displacement with slower recovery with more vulnerable 
communities returning at higher rates (DeWaard et al., 2020); however, 
overall out-migration trends have been consistent with long-term 
economic migration (Santos-Lozada et  al., 2020). Interactions of 
slower onset climate hazards with displacement, such as observed 
in Shishmaref, Alaska, have revealed the challenges in attribution of 
migration to climate as it intersects with socioeconomic conditions and 
lived experiences (Marino and Lazrus, 2015).and other economic analyses that incorporate damage estimates are expanding for adaptation decision making (Li et al., 2014), especially

for technical options in areas with high exposure such as coastal areas in Mexico (Haer et al., 2018) and Alaskan infrastructure (Melvin 
et al., 2017). Cost–benefit analysis has also been applied to coordinating planning across jurisdictions in North America for SLR and flood 
control (Adeel et al., 2020). Adaptation costs in the USA are lower on RCP4.5 compared with RCP8.5 emission pathways (Martinich and 
Crimmins, 2019). Adaptation, however, cannot be based solely on the cost–benefit analysis due to the high level of uncertainty related to 
climate risks (Cross-Chapter Box DEEP in Chapter 17).
Improving projections of future economic risk and damages facilitates the development of tools that can be used for economic analysis of 
climate policies (high confidence). Monetised estimates of the damages from climate change have been developed and refined since AR5,

motivated in part by efforts to estimate the Social Cost of Carbon (SCC) (National Academies of Sciences, 2017). Support for these efforts 
and the use of SCC in regulatory analysis of mitigation and adaptation efforts have been pledged across the national and subnational 
governments of Canada, the USA and Mexico. Harmonising SCC and consistent use can further enhance coordination of mitigation and 
adaptation decision making (Auffhammer, 2018; Aldy et al., 2021). Using these damages estimates can also inform other policy and 
tools that improve the consideration of climate impacts in markets and decision making (Report of the Climate-Related Market Risk 
Subcommittee, 2020).Box 14.6 (continued)

14
1980Chapter 14 North America
Maladaptation has also been occurring in livelihoods, especially as it 
relates to agricultural practices that are less resilient to climate hazards and 
competition for land use (limited evidence, high agreement). Focusing on 
examples in Mexico (see Section 14.5.4.3 for US and Canada examples), 
for some Mexican Indigenous Peoples, the replacement of ancestral 
farming practices with technological adaptations like transgenic crops 
has reduced their resilience by making them more dependent on external 
inputs and more expensive supplies while increasing putting their health 
at risk with herbicide and insecticide use (Mercer et al., 2012). Existing 
power structures have also interacted with climate hazards to generate 
maladaptive outcomes (Quintana, 2013). Mennonite communities in 
the northern state of Chihuahua, Mexico, have pursued commercial 
agricultural markets that lead them to shift to transgenic crops and to

overexploit local groundwater resources in a region experiencing multi-
year droughts. These actions have led to conflict with other local farming 
groups with less economic capital to access groundwater (Quintana, 
2013). Climate mitigation measures may also have adverse effects on 
local livelihoods with implications for adaptive capacity. The Reducing 
Emissions from Deforestation and Forest Degradation in Developing 
Countries (REDD+) mitigation programme has been highlighted as a 
trade-off between an international/national carbon mitigation strategy 
and the ability of some Mexican rural communities to improve their food 
security (Section 5.6.3.3; Barbier, 2014).
14.5.9.2 Projected Risks
Livelihoods will evolve as a result of both challenges presented directly 
or indirectly from climate impacts as well as socioeconomic changes 
and technological developments (high confidence). Livelihoods, 
however, can be undermined by many of the projected climate risks

with the impacts depending on adaptive capacity and adaptation 
limits (high confidence) (Section  8.4.5.1). Real areas in Mexico and 
the southern USA with agriculture-based livelihoods and projected 
reduction in precipitation will be adversely affected (Section 14.5.4; 
Esperon-Rodriguez et al., 2016). Outdoor workers in rural and urban 
areas will be exposed to higher health risks from higher temperatures 
and heatwaves (Section  14.5.8). Reduced livelihoods will also be 
associated with adverse mental health effects (Section 14.5.6.8).
Future climate hazards will deepen patterns of social inequality as 
vulnerable groups may also experience intersecting impacts that 
adversely affect their livelihoods (medium confidence). Health, in 
particular, will be a key intersection as marginalised and disadvantaged 
groups often have poorer health status and hold occupations that 
may involve higher exposure to climate hazards. African Americans

are expected to experience the largest impacts on their health status 
due to differential exposure and vulnerability to climate hazards 
(Section 14.5.6; Marsha et al., 2016).
Displacement, migration and resettlement will increase along higher-
emission pathways (medium confidence). Combining projections of 
SLR and population scenarios for the USA, Haer et al. (2013) and Hauer 
et al. (2016) have estimated the magnitude of the population at risk 
in coastal communities, numbering in the millions. In the near term, 
where climate hazards influence out-migration, it will mostly augment 
existing patterns as migration is strongly influenced by existing social 
networks (Section  7.3.2). Planned relocation and resettlements will reduce the exposure to climate hazards for the involved populations 
but could adversely affect their livelihoods in the absence of supportive 
programmes (Section 7.3.2; Jantarasami et al., 2018a), since livelihood

outcomes strongly depend on socioeconomic conditions.
14.5.9.3 Adaptation
Climate hazards undermine adaptation by damaging livelihoods 
(high confidence). Many actions that enhance and promote resilient 
livelihoods can have substantial benefit for adaptation to climate 
hazards (medium confidence). Livelihoods in the context of climate 
change are characterised by adjustments that then feed back into 
the assets that comprise a livelihood. Social capital–in the form of 
household and community cohesion–facilitates the development of 
adaptation strategies to the impacts of climate change in rural and 
urban communities at the household level and for small groups (Barbier, 
2014; Nawrotzki et al., 2015b; Nawrotzki et al., 2015c). Cultural capital, 
especially in the form of Indigenous knowledge and local knowledge, 
can guide adaptation practices in North America (Akpinar Ferrand and 
Cecunjanin, 2014), preserving Indigenous cultures and enhancing future

adaptation and resilience (see Box 14.1; Pearce et al., 2012; Audefroy 
and Cabrera Sánchez, 2017). In Mexico, rainwater harvesting (practised 
by some Mayan communities) and the use of local–traditional varieties 
of maize have assisted in the adaptation to climate impacts and 
promoted food security (Akpinar Ferrand and Cecunjanin, 2014; Hellin 
et al., 2014). Funding and support for these social adaptation strategies 
have been uneven (Barbier, 2014; Romeo-Lankao et al., 2014). The legacy 
of colonialism and historical patterns of development will continue to 
shape the adaptation responses and resiliency of Indigenous Peoples 
(Todd, 2015; Davis and Todd, 2017; Whyte, 2017; Cameron et al., 2019).
Migration is a common adaptation strategy to maintain and diversify 
people’s livelihoods and will continue to play an important role 
when households manage climate and social risks (high confidence) 
(Section 7.4.3). In the near term, actions that enhance in situ adaptive

capacities as well as foster safe and orderly migration can result in 
synergies for both adaptation and development (Cross-Chapter 
Box MIGRATE in Chapter 7). Populations that experience less mobility 
or cannot engage in voluntary migration as an adaptation may need 
additional support to adapt to climate hazards, for example, northern 
communities that are at risk of climatic events (Hamilton et al., 2016). 
Policies associated with the transition from high-GHG intensive 
extractive industries, sometimes referred to as ‘just transitions’, may 
also support in situ livelihoods if they also aim to address and redress 
existing inequalities to reduce vulnerabilities (McCauley, 2018); 
however, these policies could result in maladaptation if they create 
new inequalities or generate other environmental damages.
14.5.10 Violence, Crime and Security
Elevated rates of various types of crime have been associated with 
higher temperatures in the USA and Mexico (medium confidence

based on limited evidence and high agreement) (Section 14.5.10.1). 
If social relationships prevailing now and in the recent past continue, 
projections show future crime rates in the USA and Mexico increasing 
with increasing temperatures (low confidence) (Section  14.5.10.2).

14
1981North America  Chapter 14
Degradation of human security and conflicts exacerbated by climate 
change–even outside of North America–will increase the demand 
for humanitarian assistance, foreign aid and resettlement (medium 
confidence) (Section 14.5.10.2).
14.5.10.1 Observed Impacts
14.5.10.1.1 Violence and crime in the past and present
Crime, including violent crime, has been associated with higher 
temperatures in the USA (medium confidence). Studies of crime 
statistics in the USA have revealed a relationship between temperature 
and a range of violent crimes including aggravated assaults, rapes and 
homicides; effects for property crimes are weaker (limited evidence, 
medium agreement) (Ranson, 2014; Houser et  al., 2015; Heilmann 
and Kahn, 2019; Mares and Moffett, 2019). These effects have been 
observed in US urban centres (Hsiang et al., 2013; Mares, 2013; Ranson, 
2014; Schinasi and Hamra, 2017; Heilmann and Kahn, 2019) and more

generally across the USA (Mares and Moffett, 2019). Differential effects 
have also been observed within urban areas. Observed higher rates 
of domestic and intimate partner violence during periods of high heat 
in less affluent neighbours in Los Angeles have been associated with 
disparities in access to air conditioning and greenery (Heilmann et al., 
2021). By contrast, Lynch et al. (2020a) found no significant correlation 
between annual homicide rate and annual temperature for New York City 
(Lynch et al., 2020b). For Mexico, Burke et al. (2018a) found temperature 
linkages with intergroup killings by drug-trafficking organisations, 
homicides and suicides. No linkages between temperature and crime 
have been reported for Canada. Differences in spatial and temporal 
aggregation of the crime statistics as well as in the measure of climate 
change or variability explain some of the differences between studies. 
Several causal pathways can explain these relationships (Miles-Novelo

and Anderson, 2019; Lynch et al., 2020b). The dominant theory is that 
weather changes result in changes in behavioural patterns that lead to 
more opportunities for crimes. For example, studies that disaggregate 
by month often report significant positive associations between 
temperature anomalies and violent crime (especially aggravated 
assaults, rapes and homicides), particularly in the cold season (Harp 
and Karnauskas, 2018; Mares and Moffett, 2019). Smaller increases in 
crime during positive warm-season temperature anomalies may be due 
to people seeking shelter in cooler indoor spaces, decreasing crimes of 
opportunity (Section 7.2.7; Gamble and Hess, 2012).
The archaeological record has been used to infer linkages be-
tween climatic variability and social process, including violence 
(inferred with medium confidence). Past North American societies 
have been exposed to greater climatic variability than is documented

in the instrumental record. Because future climatic conditions are likely 
to exceed those known for the recent past (Cross-Chapter Box PALEO 
in Chapter 1), the North American archaeological record can illuminate 
possible relationships between climate variability and violence that 
cannot be observed in the present record. In the upland southwest 
US between 600 and 1280 CE, one study found that violence signifi-
cantly increased as climatically controlled maize production decreased 
and interannual variability increased (low evidence, high agreement) 
(Kohler et al., 2014); massive emigration from the northern Southwest 
in the last half of the 1200s CE is connected with, though not com-pletely explained by, climatic variability (Scheffer et al., 2021). In the 
central and southern Maya lowlands, following centuries of increasing 
populations and attempts to produce more maize (Roman et al., 2018), 
episodes of drought and/or increased summer temperatures in the 9th

and 10th centuries (Dunning et al., 2012; Kennett et al., 2012) accom-
panied increased conflicts and social disintegration including collapse 
of long-lived dynasties, cessation of monumental inscriptions (Carleton 
et al., 2017) and emigration (medium evidence, medium agreement). 
Such findings reinforce research on contemporary societies that cli-
mate-induced farming shortfalls in regions dependent on agriculture 
may induce or exacerbate conflict, especially in interaction with un-
favourable demographic, political and socioeconomic factors (medium 
evidence, medium agreement) (Section 7.2.7; e.g., Koubi, 2019).
14.5.10.1.2 Security
Climate change poses risks to peace (Section 16.5.2.3.8) that 
could affect North America (medium confidence). Military and 
security communities are adapting their planning, operations and 
infrastructure to current impacts of climate change in North America 
and globally (medium agreement, medium evidence). Arctic nations

are renewing their military capacity and expanding their constabulary 
presence around their existing boundaries (Choi, 2020). There is 
increasing awareness that climate change causes weather patterns 
and extreme events that directly harm military installations and 
readiness through infrastructure damage, loss of utilities, and loss of 
operational capability (Duffy-Anderson et  al., 2019). Transboundary 
disputes and competition over resources, such as fish (Østhagen, 
2020), are a concern in the changing Arctic and increases in military 
and constabulary operations are being observed (Jönsson et al., 2012; 
Smith et al., 2018; Eyzaguirre et al., 2021).
14.5.10.2 Projected Risks
14.5.10.2.1 Violence and crime
Projections of future crime derived from the empirical relation-
ships between temperature and crime in the USA show the 
potential for increased criminality under RCP8.5 compared with 
RCP4.5 (low confidence). For RCP8.5, holding all socioeconomic

conditions at 2015 levels, violent crime could increase 0.6–2.1% by 
mid-century and 1.9–4.5% by late century (Houser et al., 2015). The 
rise in property crime is projected to be smaller as property crime 
flattens at higher temperatures (Hsiang et al., 2013). Using relation-
ships between crime and monthly temperatures established for five 
US regions by Harp and Karnauskas (2018), Harp and Karnauskas 
(2020) project 18,800 additional violent crimes annually beyond 2014 
levels by the end of the 21st century under 1.5°C warming, rising to 
48,200 under 4°C warming. Aggregating data by states weighted by 
population density, Mares and Moffett (2019) project an average an-
nual increase of 0.94% across seven categories of violent and prop-
erty crime for each anomalous degree Celsius of warming (an average 
annual increase of about 100,000 crimes). Changing socioeconomic 
conditions in the future may either reduce or exacerbate the projected

contemporaneous relationship between temperature anomalies and 
crime (Agnew, 2011; Lynch et al., 2020b), whereas adaptation could 
weaken these relationships.

14
1982Chapter 14 North America
14.5.10.2.2 Defence and security
Climate change will affect ecosystems (Section 16.5.2.3), living 
standards (Section  16.5.2.3.4), health (Section  16.5.2.3.5) and 
food security (Section  16.5.2.3.6) globally, and these changes 
may exacerbate violence and political instability (medium 
confidence)  with implications for national security in North 
America (medium confidence) . Climate variability, hazards and trends, 
to date, have played a role in exacerbating conflict, but the influence of 
climate appears to be minor and more uncertain than the roles of low 
socioeconomic development, low state capability and high intergroup 
inequality (Mach et  al., 2019). More profound impacts from climate 
change on weather and seasons, as well as changing socioeconomic 
conditions, could lead to patterns of violence that cannot be predicted 
by projecting relationships between current climate and violence into

the future (Section 14.6.3; Mach et al., 2019). If global levels of violence 
increase, there will be increased demand for international efforts, 
including disaster aid and humanitarian efforts (Eyzaguirre et al., 2021). 
Climate change and geopolitical goals interact in the Arctic (Smith et al., 
2018). New transportation corridors and the potential access to natural 
resources could lead to competition for access to and control over the 
region (Section CCP6.2.6; see Box CCP6.1; FAQ CCP6.2; Estrada, 2021). 
Governance structures exist to manage geopolitical manoeuvring and 
to protect the human security of Arctic populations (Sections 14.5.10.3, 
7.2.7.1).
14.5.10.3 Adaptation Options
14.5.10.3.1 Violence and crime
Co-benefits from adaptation options include improving the live-
ability of, and quality of life in, cities, reducing socioeconomic 
vulnerability and exposure to locally higher temperatures (me-
dium confidence). Urban settings in the USA have disproportionately

higher exposure to urban heat island effects in low-income and mi-
nority neighbourhoods in US cities (Section 14.5.5.1). Co-benefits from 
adaptation responses in the urban landscape can reduce socioeconomic 
vulnerabilities and exposure to higher temperatures (Section 14.5.5.3). 
Evaluation of adaptation efforts to reduce crime rates that have been 
associated with temperature are limited. In Los Angeles, a link has been 
inferred between violence and older buildings that may lack air con-
ditioning (Heilmann et al., 2021). By contrast, access to air conditioning 
did not appear to lessen crime rates in Mexico (Baysan et al., 2019).
14.5.10.3.2 Defence and security
Existing environmental and international agreements that 
consider climate risks can contribute to cooperation (medium 
confidence). Strengthening and empowering existing environmental 
and diplomatic avenues (e.g., the Arctic Council and international 
agreements such as the United Nations Convention on the Law of the

Sea, and various subnational actors and agreements) (Section CCP6.3.2) 
to incorporate risks from climate impacts could enhance cooperative 
avenues for defusing conflict (Huebert et  al., 2012). Improving the 
consideration of climate risks in efforts to expand economies and trade 
(see Box  14.5), and improvements in peacekeeping (Section  7.4.4; 
Barnett, 2018) could also reduce future conflict risks.14.6 Key Risks
Ten key risks from climate change were identified for North American 
based on definitions and assessment approaches outlined in Chapter 
16, which were extended to include the development of a risk database 
and analysis that included expert evaluation of interactions between 
climate hazards and sectors (Figure 14.11; SM14.3).
14.6.1 Key Risks of Climate Change for North America
In North America, divergent perceptions regarding the attribution 
and implications of climate change pose a key risk to adaptation

mainstreaming (KR1). This lack of adequate adaptation in turn amplifies 
threats to human life and safety from intensifying extreme events, fires 
and storms (KR2). Climate change hazards pose risks to economic and 
social well-being (KR3), marine social–ecological systems (KR4), unique 
terrestrial ecosystems and their services (KR5), freshwater services 
(KR6), physical and mental health (KR7), food and nutritional security 
(KR8), and commerce and trade (KR9). Cumulatively, these risks interact 
to imperil the quality of life for North American communities, cities and 
towns (KR10).
14.6.2 Key Risks Across Sectors in North America
KR1: In the public and policy domains, divergent perceptions of 
anthropogenic climate change which pose a risk of inaction on 
adaptation efforts to reduce exposure and socioeconomic vulnerability
Complex factors, including individual beliefs, ideology, world view, par -
tisan identity as well as societal context, influence how the public, as

well as professional groups, communities and policymakers, perceive 
and understand climate change (high confidence) (Sections  14.3.3, 
14.3.4). While there is expert scientific consensus on anthropogenic 
climate change, rhetoric, misinformation and politicisation of science 
have contributed to misperceptions (high confidence), polarisation on 
the severity of impacts and risks to society, indecision and delayed ac-
tion (high confidence) (Section 14.3.1). In North America, this impedes 
adaptation efforts (Section 14.3.4) and inflates climate risks (high con-
fidence).
KR2: Risk to life, safety and property from intensifying extreme events
Human life and safety across North America, and especially along the 
coasts of Mexico, the Hawaiian Islands, Gulf of Mexico, Atlantic Canada 
and southeast USA, will be placed at risk from SLR and severe storms and 
hurricanes, even at 1.5°C GWL (very high confidence) (Sections 14.5.2,

14.5.5; see Box  14.4). Warming, heatwaves and increases in wildfire 
activity in many regions of North America pose risks to air quality, 
health, lives and property (see Box 14.2). More extreme precipitation and 
flooding pose a risk to human morbidity, mortality and safety in fluvial 
flood zones and areas downstream of levees, dams and flood culverts. 
The increasing intensity of storm events poses a risk of landslides, erosion 
and flooding in shoreline and urban communities, especially high-bank 
areas along exposed coasts, in Arctic and temperate areas where winter 
sea ice has diminished and in low-lying coastal areas where SLR and

14
1983North America  Chapter 14
Rapid assessment of relative risk by sector and climate hazard for North America
Mortality
Cold spell
Frost
Landslide
Aridity
Drought

Hail
Radiation at surfaceAtmospheric CO2 Permafrost
High
Moderate
Figure 14.11 |  Rapid assessment of relative risk by sector (y-axis) and climate hazard (x-axis) for North America based on an assessment of asset-specific 
vulnerability and exposure across climate hazards (see SM14.3 for methodological details). For each unique combination, the hazard-by-sector risk was ranked as

medium confidence), low or not detected (risk is low or not detectable). Blank cells are those where the assessment was not applicable or not conducted. Risks identified through 
the rapid assessment were further evaluated in the chapter assessments (see corresponding sector text for full assessment of risk and impacts).

14
1984Chapter 14 North America
storm surge often overwhelm existing natural coastal features and 
engineered structures (Section 14.5.5; see Box 14.4).
KR3: Cumulative damages from climate hazards which pose a 
substantial risk to economic well-being and shared prosperity
Climate-change impacts are projected to cause large market and non-
market damages (high confidence). By end of century under higher GWL 
scenarios (>4°C), these damages are expected to reach several tens of 
billions of USD annually in Canada and hundreds of billions annually 
in the USA. Losses in labour productivity and wages, and damages 
to coastal properties, will be especially large; however, all sectors in 
the USA and most sectors in Canada are projected to see substantial 
relative damages on high-emission pathways by mid- to end of century 
compared with lower-emission pathways. Economic sectors with hard 
limits to adaptation (i.e., winter tourism) or that are highly affected by

climate variability (i.e., agriculture and fisheries) will be at more risk 
at lower temperatures than other economic sectors (Sections 14.5.7, 
14.5.8). Strategic implementation of adaptation strategies coupled 
with lower-emissions scenarios result in multi-billion-dollar reductions 
in economic damages (Section 14.5.8; see Box 14.6).
KR4: Risk of degradation of marine and coastal ecosystems, including 
loss of biodiversity, function and related services with cascading 
effects for communities and livelihoods
Ocean warming will increase the frequency and intensity of MHWs 
(see Box  14.3), accelerate unprecedented rates of sea ice loss, and 
alter ocean circulation, chemistry and nutrient cycling in ways that 
profoundly impact marine productivity, biodiversity and food webs 
(very high confidence) (Section  14.5.2). Collectively these impacts 
pose a risk to nearshore ecological and human systems (high 
confidence), increasing the probability of phenological mismatches,

large-scale redistribution of species, and species population declines 
(Section  14.5.4) with cascading impacts that strain cultural and 
economic systems reliant on marine productivity across North America 
(high confidence). Nearshore areas of Chesapeake Bay (USA) and 
Akimiski Island, mid-western James Bay and the coasts in the Pacific 
ranging from the Gulf of Alaska through Baja Peninsula, have a high 
proportion of species near their upper thermal limit, and are areas that 
are particularly susceptible to climate-change risk.
KR5: Risk to major terrestrial ecosystems leading to disruptions of 
species, ecosystems and their services
Major risks to terrestrial ecosystems across North America, such as 
semiarid landscapes, rangelands, boreal and temperate forests, and 
Arctic tundra, include significant ecosystem transformations and shifts 
in species abundances and ranges, and major vegetation types (e.g., 
transitions from forests to grasslands), with cascading implications

for regional biodiversity (very high confidence). Warming increases 
the risk of permafrost thaw with propagating impacts on species 
and communities in the Canadian and US Arctic (high confidence) 
(CCP6). 6Forest disturbances, including wildfire, drought, insects 
and pathogens, are expected to increase with warming, acting 
synergistically to raise the prevalence of tree mortality and ecosystem 
transformation (medium confidence) (Section 14.5.1). These changes will reduce services provided by terrestrial ecosystems, including 
timber yields and carbon sequestration (medium confidence).
KR6: Risk to freshwater resources with consequences for ecosystems, 
reduced surface water availability for irrigated agriculture and other 
human uses
Droughts and earlier snowmelt runoff will increase water scarcity 
during the summer peak water demand period especially in regions 
with extensive irrigated agriculture, leading to economic losses and

increased pressures on groundwater as a substitute for diminished 
surface water supplies (medium to high confidence) (Section 14.5.3). 
Streams in North America are expected to continue to warm, with 
important ramifications for aquatic ecosystems (high confidence), 
reducing habitat for salmon and trout species that are economically 
and culturally important (Section 14.5.1). Warming and drying coupled 
with other stressors (e.g., pollutants, nutrients and invasive species) 
pose a risk to ecosystem structure and function in lakes, streams 
and reservoirs across many parts of North America (high confidence) 
(Sections  14.5.1, 14.5.3). Warming increases in heavy rainfall and 
nutrient loading pose risks for water quality and HABs (medium to 
high confidence) (Section 14.5.3).
KR7: Risk to human health and well-being, including mental health
Heat-related human mortality is projected to increase in North America 
as a result of climate change and ageing populations, poverty, chronic

diseases and inadequate public health systems (very high confidence) 
(Section 14.5.6.1). Gradual changes to temperature and precipitation 
are impacting urban ecosystems and creating ecosystem regime 
changes resulting in the poleward expansion among insects that 
bring risks related to vector-borne diseases such as West Nile virus 
and Lyme disease (high confidence) (Section 14.5.6). Climate change 
is expected to lead to wide-ranging mental health challenges related 
to an increase in the psychological burdens of climate change (high 
confidence), particularly for individuals with existing mental health 
conditions, who live in severely impacted areas or who are reliant 
on climate for livelihoods and cultural well-being (e.g., Indigenous 
Peoples and farmers) (Section 14.5.6.8).
KR8: Risk to food and nutritional security through changes in 
agriculture, livestock, hunting, fisheries and aquaculture productivity 
and access

Cascading and interacting impacts of climate change threatens food 
systems as well as food and nutritional security for many North 
Americans, especially those already experiencing food and nutritional 
scarcity, women and children with high nutritional needs and 
Indigenous Peoples reliant on subsistence resources (high confidence) 
(Section 14.5.6). In agricultural regions experiencing aridification and 
where water scarcity precludes substantial expansion of irrigation, 
warming and extreme heat pose a risk to food and forage crop 
and livestock production (high confidence) (Section  14.5.4). Ocean 
warming and MHWs will continue to disrupt commercial capture 
fisheries through species redistribution and changes to yield (high 
confidence), and warming waters and OA will increasingly impact 
aquaculture production (high confidence) (Section 14.5.4). Interactions

14
1985North America  Chapter 14
between competing aspects of human security (e.g., food, energy 
and water) will be exacerbated by climate change (high confidence) 
(Sections 14.5.3, 14.5.4, 14.5.8).
KR9: Risks to major infrastructure supporting commerce and trade 
with implications for sustainable economic development, regional 
connections and livelihoods
Climate change and extreme events are expected to increase risks 
to the North American economy via infrastructure damage and 
deterioration (high confidence), disruption to operations, unsafe 
conditions for workers (medium confidence) and interruptions to 
international and inter-regional supply chains (medium confidence) 
(Section  14.5.8; see Box  14.5). These climatic impacts will have 
cascading implications for local livelihoods, sustainable economic 
development pathways and regional connectivity, and will reinforce 
pre-existing social inequities (medium confidence). Infrastructure

damage will also disrupt economic activities, including manufacturing, 
tourism, fisheries, natural resource extraction and energy production 
(high confidence) (Section 14.5.8).
KR10: Risk to the quality of life in North American communities, cities 
and towns
In major North American cities and settlements, vulnerability to climate 
change has increased and is projected to continue to rise (medium 
confidence) (Section 14.5.5). Concentrated populations with unequal 
adaptive capacities, exposure of valuable assets, ageing infrastructure, 
and differing degrees of institutional capacity and effectiveness 
will underpin climate hazards (Section  14.5.5). Coastal, riverine and 
urban flooding displacing communities and coastal ecosystems 
(Section 14.5.5.2) will become a dominant risk to urban centres (high 
confidence) and will cause disruptions to transportation and trade 
infrastructure (Section  14.5.8). Large wildfires endangering lives,

livelihoods, property and key infrastructure, and economic activities 
will contribute to compromised air quality and municipal water 
contamination (Section 14.5.6; see Box 14.2).
14.6.3 Cumulative Risk, Tipping Points, Thresholds and 
Limits
Across North America, climate change poses a risk to social–ecological 
systems increasingly destabilised by compounding climate impacts and 
non-climate pressures (high confidence) (Sections 14.5.1–14.5.3) that 
erode the connectivity and redundancy underpinning system resilience 
(Sections 14.5.1–14.5.5; Xiao et al., 2017a; Koven et al., 2020; Malhi 
et  al., 2020; Turner et  al., 2020). Accelerating climate change and 
increasingly severe hazards and shocks may induce abrupt changes 
or push systems, people and species to critical points–tipping points–
where a small additional change causes a disproportionately large 
response, triggering feedbacks that lock systems into novel regimes

(Scheffer et al., 2001; Scheffer, 2010; Anderies et al., 2013; Lenton, 2013; 
Iglesias and Whitlock, 2020; Lenton, 2020a). Climate-change tipping 
points can compound and amplify climate impacts and risk, induce 
disparate climate burdens and benefits across human and ecological 
systems, and irreversibly restructure ecosystems and livelihoods (e.g., species extinctions, fisheries collapse, community-managed relocation) 
(Lynham et al., 2017). Examples of systems with potential tipping points 
in North America include (a) permafrost and sea ice loss triggering 
transformation of ecological and human systems (including substantial 
shipping opportunities) in the Arctic that are permanent and irreversible 
except on geological timescales, and which are potentially underway 
(high agreement, low evidence) (Section 14.6.2; see Box 14.3, CCP6), 
(b) mid-latitude forest ecosystems at low to middle elevations in 
western North America where wildfire and cumulative climate and

non-climate pressures may restructure forests and succession in ways 
that promote transition to new vegetation types (Section 14.5.1) and 
(c) agricultural communities in northern Mexico and the southwest 
USA where aridification and drought may interact with water resource 
policies, economic opportunities and pressures, and farm practices 
to induce either adaptation (via changes in irrigation practices) or 
farm abandonment, land-use transformation and livelihood changes 
(due to heat stress, soil deterioration or reduced economic viability) 
(Sections 14.5.3, 14.5.4, CCP6, Yumashev et al., 2019; Turner et al., 
2020; Heinze et al., 2021).
Identification of critical thresholds, elements and connections within 
a system may also help identify potential positive tipping points, that 
is, focal components or processes in a system where a relatively small 
investment or intervention can induce a large benefit and enable

self-reinforcing transformative adaptation (Section 14.7; Chapter 17; 
Tàbara et  al., 2018; Lenton, 2020b; Otto et  al., 2020). Under low-
mitigation scenarios, compounding risks and higher-carbon-emission 
scenarios increase the potential that amplifying feedback loops and 
fatal synergies across sectors could lead to existential threats to the 
social–ecological systems of North America (medium confidence). 
Societal collapse has been linked to shifts in climate regimes, especially 
when societies have lost resilience due to slowly mounting social–
ecological challenges, while other studies reveal that social continuity 
and flexibility enable historical climate resilience and prosperity under 
changing environments (FAQ 14.2; Lenton et  al., 2019; Otto et  al., 
2020; Degroot et al., 2021; Richards et al., 2021).
Accounting for tipping points, interactions and reinforcing dynamics 
among ecological, social and climate processes is necessary for

comprehensive analyses of climate-change risk, cost and urgency, 
as well as effective adaptation design and implementation 
(Section  14.7; Cai et  al., 2015; Steffen and et  al., 2018; Lenton 
et al., 2019; Narita et al., 2020; Dietz et al., 2021). Multiple lines of 
evidence across sectors assessed in this chapter suggest that after 
mid-century and without carbon mitigation, climate-driven changes 
to ecological and social boundary conditions may rapidly push many 
systems into disequilibrium (medium confidence), emphasising the 
importance of prioritising adaptation actions with co-benefits for 
mitigation (Section 14.5.4; see Box 14.3). Reducing climate hazards 
through mitigation and removing catalysts of system instability 
through adaptation measures that increase system resilience (e.g., 
ecosystem restoration) will help reduce the risk that systems move 
across a tipping point from a desirable to an alternate or undesirable

state (Sections 14.5.4, 14.7; see Box 14.3; Narita et al., 2020; Turner 
et al., 2020; Heinze et al., 2021).

14
1986Chapter 14 North America
FAQ 14.2 | What can we learn from the North American past about adapting to climate change?
The archaeology and history of Indigenous Peoples and Euroamerican farmers show that climate variability can have severe impacts on 
livelihoods, food security and personal safety. Traditional societies developed numerous methods to cope with variability but have always 
expanded to the limits of what those adaptations permit. Current knowledge and technology can buffer societies from many negative effects 
of climate change already experienced but will be severely challenged by the novel conditions we are now creating.
People came into North America more than 15,000  years ago and have experienced both massive and minor 
shifts in climate ever since. At the end of the last very cold phase of the most recent Ice Age, about 11,500 years

ago, temperatures rose extremely rapidly—as much as 10°C (18°F) in a decade in some regions. This undoubtedly 
contributed to the extinction of large mammals like mammoths and mastodons that people hunted alongside 
many other resources (see Cross-Chapter Box PALEO in Chapter 1). There were so few people on the land, though, 
and other resources were so abundant, that the long-standing human means of coping with climate variability—
switching foods and moving on—were sufficient.
Following the end of the Ice Age, populations across North America grew for the next few thousand years, at a 
rate that increased once people began to domesticate corn (maize), beans and squash (the ‘three sisters’) as well 
as other crops. However, more people meant less mobility, and farmers traditionally are also more invested in their 
fields and remaining in place than foragers are to hunting grounds. Other means of coping with vulnerability to

food shortage caused by climate variability included some continued hunting and gathering of wild resources, 
planting fields in multiple locations and with different crops, storage in good years, and exchange with neighbours 
and neighbouring groups.
According to archaeological evidence, however, these adaptation strategies were not always sufficient during times 
of climate-induced stress. Human remains showing the effects of malnutrition are fairly common, and conflict caused 
in part by climate-induced shortfalls in farming has left traces that include fortified sites, sites placed in defensible 
locations and trauma to human bone. Larger and more hierarchical groups emerged, first in Mesoamerica and then 
in the southwest and southeast USA as well as the Midwest USA. These groups offered the possibility of buffering 
poor production in one area with surplus from another, but they also tended to increase inequality within their

borders and often attempted to expand at the expense of their neighbours, introducing new sources of potential 
conflict. Dense hierarchical societies also arose in other areas such as the northwest coast where agriculture was not 
practised but resources, such as salmon and roots, were abundant and either relatively constant or storable.
These societies were not immune to climate hazards despite their greater population and more formal organisation. 
Archaeological evidence strongly suggests that drought, or growing conditions that were too hot or cold, 
contributed to the decline of groups ranging from Classic-period Maya states in Mesoamerica, to the somewhat less 
hierarchical societies of Chaco in the southwest USA and Cahokia in the Midwest USA (Figure FAQ14.2.1). The usual 
pattern seems to be that climatic variability compounded social and environmental problems that were already 
challenging these societies.

If societies in North America prior to the Euroamerican colonisation were vulnerable to climate variability, surely 
were not the more recent and technologically advanced societies of North America at lower risk? The 20th century 
Dust Bowl created in the US and Canadian prairies suggests otherwise. Severe drought conditions throughout 
the 1930s—which, to make matters worse, peaked during the Great Depression—did not cause either the USA 
or Canada to collapse. But both countries suffered massive economic losses, regional loss of topsoil and regional 
human strife (including loss of crops, income and farms) leading to migration. Yet anthropogenic global climate 
change was of little or no consequence in the 1930s. While farming practices made climate stress worse, the climate 
variability itself was either completely, or mostly, within the envelope of historical climate variability that earlier 
human societies had experienced.

Indigenous Peoples and Euroamerican farmers and ranchers have a long history of mostly successful adaptation 
to changing weather patterns. The wisdom held by Indigenous Peoples deep knowledge of how plants, animals 
and atmospheric conditions provide early warning signals of approaching weather shifts, and stories about how 
past communities have tried to cope with climate-related resource shortfalls. Long-standing community-level 
management of resources also helps prevent shortfalls, and institutions such as kin groups, church groups, clubs and 
local governments (which exist in communities of both Euroamericans and Indigenous Peoples, in different forms) 
can be powerful aids in ameliorating shortfalls and resolving conflict.

14
1987North America  Chapter 14
Examples of areas where past climate variability has contributed to crises
Like the N. Pueblo area, the mound complex of  
Cahokia at the center of this zone was af fected 
by droughts in the 12th and 13th centuries CE,  
and possibly by flooding .
 
Image credit: Ira Block/National Geographic CreativeDust-bowl conditions caused by drought and land 
management were especially severe in this area. 
 Photo credit Arthur Rothstein.
Many cities in the Central Maya Lowlands declined 
or disappeared in the 9th and 10th centuries CE  
under pressure from drought, increased summer  
heat, deforestation, and warfare.  
Image credit: Image credit: iStock/id 543832440 Large scale droughts in the 12th and 13th 
centuries CE, and cooling temperatures in the 
13th centur y, contributed to farmers leaving the  
northern Pueblo area in the 13th centur y. Image credit Nate Crabtree

Figure FAQ14.2.1 |  Examples of areas where past climate variability has contributed to crises.  Climatic variability is most likely to lead to crisis when 
it is accompanied by social, demographic and political conditions or environmental mismanagement that compound climatic impacts on societies.Box FAQ 14.2 (continued)

14
1988Chapter 14 North America
FAQ 14.3 | What impacts do changes in the North American Arctic have within and outside the region?
The North American Arctic is warming at nearly three times the global average, creating a cascading web of local, regional and global impacts 
within and beyond polar regions. Changes in the Arctic not only effect global ocean circulation and climate regulation, but also facilitate new 
Arctic transportation routes and support transboundary resources with geopolitical, environmental and cultural implications as conditions 
change.
Rapid warming and extreme temperatures in the Arctic is leading to unprecedented seasonal sea ice loss, permafrost 
thaw and increasing ocean temperatures. Cascading from these biophysical changes are cultural, socioeconomic 
and political consequences that are widespread and largely unprecedented in human history. Changes in sea ice

create safety hazards for Indigenous Peoples and northerners who rely on frozen seas and rivers for transportation 
between remote communities and to subsistence hunting areas. Thawing permafrost, especially that of ice-rich 
permafrost, creates challenges and costs for a region with low population density and a small tax base to support 
major infrastructure investments. Warmer ocean temperatures induce large-scale distributional shifts and reduced 
productivity and access to the largest North American fisheries. Ice-associated marine mammals, such as polar bears, 
seals and walruses, have declined precipitously with decreasing sea ice in the Bering Sea, and widespread ecosystem 
changes from fish through birds and marine mammal species have altered the system with uncertain outcomes for 
these productive ice-driven ecosystems. Newly ice-free shipping routes are increasing regional and geopolitical

tensions and may facilitate novel threats like the spread of invasive species and safety hazards to local hunters and 
fishers. The local and regional impacts of climate change in the North American Arctic are profound and span social, 
cultural, health, economic and political imperatives.
Although the region is remote, changes in the Arctic impact the rest of the world. The Arctic serves as a regulator 
of global climate and other ecological processes through large-scale patterns related to air and ocean circulation. 
These vitally important processes are nearing points beyond which rapid and irreversible (on the scale of multiple 
human generations) changes are possible. The magnitude of cascading changes over the next two centuries includes 
regional warming and temperature extremes, permafrost declines and sea ice loss beyond that experienced in 
human existence. This includes macro-scale risks related to SLR from the melting of glaciers and thermal expansion

of oceans. Changes in the Arctic are more pronounced than elsewhere and portend climate-change impacts in other 
areas of the globe.
Adaptation in the Arctic is underway and lessons learned on what works and what is effective and feasible to 
implement can provide global insights. Successful adaptation in the North American Arctic region has been 
attributed, in part, to the explicit and meaningful inclusion of IK and Indigenous self-determination, and diverse 
perspectives in decision-making processes, strong local leadership, co-management approaches, technological 
investment in integrated climate modelling and projections, and multilateral cooperation.Still, Indigenous knowledge and traditional knowledge among Euroamerican farming communities provide 
guidelines for how to cope with traditional problems. Contemporary governmental restrictions (such as legal 
water-rights allocations, international borders and tribal-lands boundaries) have limited the adaptive capacity that

Indigenous societies have developed over the centuries. Now human-caused climate forcing, if not mitigated by 
reducing heat-trapping GHGs, is expected to produce climates in North America that have no local analogues in 
human history even as it destroys heritage sites that are sources of knowledge about palaeoclimates and the diverse 
ways of coping with them that past peoples have discovered. Just as past peoples often avoided local climate change 
by moving on, in a world where mobility options are severely limited, a lesson from archaeology and history is that 
we should use our hard-won knowledge of the causes of climate change to avoid creating futures with no past 
analogues to provide useful guidance.Box FAQ 14.2 (continued)

14
1989North America  Chapter 14
14.7 Adaptation in North America
14.7.1 Overview of Observed Adaptation  
in North America
Climate adaptation efforts have increased across all North American 
regions and sectors (high confidence). Support for, and implementation 
of, adaptation policies, plans and measures have not been equal across 
the public and private sectors, regions or varying levels of governance 
(high confidence) (Table  14.7). To date, reactive (coping-based) and 
incremental adaptations have helped North Americans avoid greater 
damages from observed climate impacts (medium confidence). There 
is increasing agreement that worsening impacts and expanding risk 
conditions may exceed current adaptation capacities by mid-century 
under high-emissions scenarios (RCP8.5) (medium confidence).
14.7.1.1 Individuals and Households
Across North America, individuals and households have taken action to 
reduce climate-influenced risks (high confidence). These autonomous

adaptations comprise the majority of the observed responses in the 
peer-reviewed literature (Berrang-Ford and et al., 2021). The increased 
use of cooling systems (which could be maladaptive unless there 
are innovations) (Section  14.5.5.3; Barreca et  al., 2016), creating 
defensible space around homes in wildfire-prone areas (see Box 14.2), 
and the modification or redesign of housing structures along coasts 
(Koerth et al., 2017), are important household responses to existing 
risks. Although these actions have played a role in reducing risks, the 
capacity to undertake such actions is not uniform across individuals 
in North America and has exacerbated existing social inequities, 
especially in coastal areas (Keenan et al., 2018; de Koning and Filatova, 
2020). Additionally, these adaptation activities often are taken without 
consideration of the impact on mitigation efforts (Kates et al., 2012; 
Fedele et al., 2019; Shi and Moser, 2021).

14.7.1.2 Local and Subnational Governments
The majority of local jurisdictions in North America have undertaken 
some level of adaptation. These efforts largely have focused on planning 
and less on implementation (high confidence). Some subnational 
governments, namely states and provinces, have engaged in advanced 
adaptation planning efforts (high confidence). Indigenous Peoples in 
North America have undertaken substantial activities (Section  14.4; 
see Box 14.1).
Many cities across North America have undertaken adaptation 
planning (Section  14.5; Hughes, 2015; Reich et  al., 2016; Moser 
et  al., 2017; Auditors General, 2018; McMillan et  al., 2019) with 
some financing adaptation implementation, for example, in the case 
of SLR (see Box 14.4). Adaptation actions commonly implemented 
in cities include climate-informed building codes, enacting energy 
conservation measures, modifying zoning and increasing green

infrastructure (Section  14.5.5.3; see Box  14.7; Binder et  al., 2015; 
Maxwell et al., 2018a; Moss et al., 2019; Brown et al., 2021). The 
majority of cities have formed practitioner networks to share 
information (ICLEI Canada, 2016; Vogel et  al., 2016; C40 Cities, 
2018) and supporting learning and collaboration through regional collaborations that include utility managers and the private sector 
(Fünfgeld, 2015; Moser et al., 2017).
In Canada, the Map of Adaptation Actions10presents over 200 
adaptation case studies addressing a variety of climate-related impacts 
(Warren and Lulham, 2021). The City of Saskatoon, in developing its 
Climate Action Plan (which includes a Corporate Climate Adaptation 
Strategy), engaged with local businesses, non-governmental 
organisations (NGOs), residents and experts to identify potential risks 
(and benefits) requiring action (City of Saskatoon, 2019). Similarly, 
the City of Surrey specifically used community outreach programmes

to develop its Coastal Flood Adaptation Strategy (CFAS) through a 
value-based planning approach (City of Surrey, 2019). Municipal 
asset management, local services and community well-being were 
key considerations for the City of Selkirk, Manitoba, when developing 
an adaptation strategy as well as ensuring a budgeting process that 
supports implementation (City of Selkirk, 2019). As of 2019, 8 of 13 
Canadian provinces and territories have high-level climate adaptation 
strategies. The scope of these efforts vary by jurisdiction as a review 
conducted by federal and provincial auditors in Canada identified 
several deficiencies related to a lack of detailed implementation plans, 
obligated funding and specific timelines (Auditors General, 2018).
Progress in Mexico on adaptation implementation at the local level 
has been extensive (INECC and Semarnat, 2018). Activities include 
executing programmes for relocating infrastructure in high-risk zones

in priority tourist sites, incorporating adaptation criteria in public 
investment projects that involve construction and infrastructure 
management, water management, application of climate adaptation 
norms for the construction of tourist buildings in coastal zones, 
and improving the security of key water, communication and 
transportation infrastructure (Sections  14.5.5, 14.5.7, 14.5.8). 
Additionally, local capacity and protocol to respond to extreme 
weather events as a function of climate change have been integrated 
more regularly into community-based hazard mitigation plans. States 
and municipalities in Mexico must have climate policies that are 
consistent with the guidelines of national strategies (Section 14.7.1.5) 
and state-level programmes on climate change, in addition to other 
state and municipal laws. As a result, these entities have developed 
and implemented early warning systems designed to protect the

population from climate-related risks, such as strong storms and 
hurricanes (INECC and Semarnat, 2018).
Implementation of adaptation initiatives and specific actions in US 
cities has increased in the approximately 5 years between the 3rd US 
National Climate Assessment (NCA3) (Melillo et al., 2014) and the 4th 
Assessment (NCA4), and adaptation responses have been observed 
widely (Lempert et al., 2018). ICLEI-USA provides numerous resources 
for adaptation planning and implementation for cities, Indigenous 
Peoples and Regional Governments11.The Georgetown Center for 
Climate maintains a comprehensive resource for tracking adaptation 
progress for States12.As of 2021, 18 US states have completed 
climate adaptation plans, and six states have plans underway as of 
10 See https://changingclimate.ca/case-studies
11 See https://icleiusa.org
12 See www.georgetownclimate.org/adaptation/plans.html

14
1990Chapter 14 North America
the time of this report (Georgetown Climate Center, 2021). California, 
in particular, has adopted sustained climate assessment to allow for 
more rapid iterations on adaptation planning (Bedsworth et al., 2018; 
Miao, 2019). Across all US states, however, adaptation activities do not 
have readily accessible budgets, such that levels of funding cannot be 
assessed directly (Gilmore and St. Clair, 2018).
14.7.1.3  National and Multi-National Governance
The federal government of each North American country has developed 
policies and actions that promote climate adaptation (Figure 14.12). 
Recognising the cultural, economic and social networks that span 
North America, the federal governments have also committed to 
engagement on adaptation and resilience across borders and through 
cooperation on domestic adaptation efforts (The White House, 2016). 
Each country also outlines their respective adaptation efforts through

submissions under the UN Framework Convention on Climate Change, 
including their nationally determined contributions (NDCs) under the 
Paris Agreement. The federal governments also support adaptation 
efforts in other countries through international climate negotiations 
as well as related agreements, such as the Sendai Framework for 
Disaster Risk Reduction and efforts to support the achievement of the 
Sustainable Development Goals (SDGs).
Mexico’s 2020 update to its first NDC communicated extensive 
adaptation efforts (Government of Mexico, 2020). The measures 
outlined in this document highlight the importance of co-benefits for 
adaptation efforts as they relate to the SDGs and to support mitigation 
commitments. Ecosystem-based solutions and NbS (see Box 14.7) are 
the basis for much of the synergies between adaptation and mitigation 
efforts. These plans are supported by domestic legislation through the 
General Law on Climate Change, which includes the Climate Change

Adaptation Process (CCAP). The CCAP provides a holistic systems 
approach for identifying instruments and institutional arrangements 
for adaptation implementation (Semarnat and INECC, 2015; INECC and 
Semarnat, 2018). This approach includes guidance for planning (e.g., 
the Climate Change Mid-Century Strategy, the Special Climate Change 
Program 2014–2018) and formalises its adaptation commitments to 
the Paris Agreement.
In Canada, the Federal Adaptation Policy Framework (Government 
of Canada, 2011) guides domestic action to develop adaptation 
knowledge, build adaptive capacity, and mainstream adaptation 
into federal policy, in support of the Pan-Canadian Framework on 
Clean Growth and Climate Change (Government of Canada, 2016), 
which included specific adaptation measures and investments to 
build resilience. In August 2021, the government initiated a National 
Adaptation Strategy with development anticipated through 2022.

Additionally, the government facilitates efforts and funds research, 
capacity building and information sharing across sectors and among 
government departments (Government of Canada, 2021a). The 
Canadian Centre for Climate Services provides access to climate 
data, tools and information13.In Canada’s revised NDC, near-term 
commitments to protecting land and oceans, and efforts related to 
sustainable and resilient energy systems, are highlighted as examples 
13 See www.canada.ca/en/environment-climate-change/services/climate-change/canadian-centre-climate-services.htmlof co-benefits between climate-change adaptation and mitigation 
(Government of Canada, 2021b).
The USA has experienced substantial revisions to its climate policy and 
its international engagement since AR5 with implications still unclear 
(Bomberg, 2021). Since AR5 and until early 2020, many congressionally 
mandated federal efforts (Beavers et  al., 2016; Parris et  al., 2016;

Rockman et al., 2016; Caffrey and Hoffman, 2018) faced programmatic 
challenges, but most continued to provide research and capacity 
development to support adaptation implementation across the USA. 
Importantly, the US government sustained the national climate 
assessments (Lempert et al., 2018). Recently, the administration has 
re-engaged with the Paris Agreement and the USA has submitted an 
NDC (Government of the United States of America, 2021); however, 
adaptation was not directly addressed. Subsequent Executive orders 
mandate adaptation planning at the federal level (e.g., USEO 13754; 
USEO 14008). As of the time of this report, the US climate policy 
landscape is rapidly evolving, including major legislative initiatives 
(e.g., Green New Deal) (Boyle et al., 2021).
14.7.1.4  Private Sector, Including Companies, NGOs, 
Professional Organisations, Academic Institutions and 
Communities of Practice
The private sector comprises a diverse set of actors who influence,

interact with and support adaptation efforts, generally through shared 
governance with the public sector. The weight of evidence points to the 
benefits of these collaborations and the importance of voluntary code-
making and self-regulation (Section  17.4.2.1.6). In North America, 
NGOs and professional organisations have been important agents of 
change in the adaptation field (Bennett and Grannis, 2017; Stults and 
Meerow, 2017). Efforts have included supporting community-based 
resilience, network building, Internet-based guidance and resources, 
case studies, workshops and other services to support adaptation 
action (e.g., vulnerability assessments, scenario-based planning).
Market and financial mechanisms have provided important buffering 
capacity against climate shocks in North America. Insurance products 
are being developed to meet emerging climate risks, especially related 
to availability and pricing of flood insurance in Canada (Thistlethwaite,

2017; Davies, 2020) and the USA (Kousky et al., 2021). Some existing 
US flood insurance products provided through joint public and private 
arrangements has led to rebuilding in flood-prone locations (Zellmer 
and Klein, 2016). The price of these products may limit their uptake in 
low-income neighbourhoods (Cannon et al., 2020).
Professional organisations have participated in the development and 
adoption of measures to integrate climate resilience into the built 
environment. This includes new designs, guidelines, codes, standards 
and specifications, in addition to infrastructure inventories that 
incorporate evaluation of vulnerabilities and identification of priority 
at-risk areas (Amec Foster Wheeler and Credit Valley Conservation, 
2017; ASCE, 2018a). These efforts are supported by provincial/state and 
federal initiatives (e.g., Canada’s Climate Lens (Infrastructure Canada, 
2018), and California’s Climate-Safe Infrastructure Working Group

14
1991North America  Chapter 14
(Climate-Safe Infrastructure Working Group, 2018)). Infrastructure 
Canada has undertaken Canada-wide initiatives to improve 
infrastructure resilience to climate change14.The Standards Council of 
Canada (SCC) established the Northern Infrastructure Standardization 
Initiative15engaging stakeholders, including Indigenous Peoples, to 
develop standards specific for addressing climate-change impacts 
on northern infrastructure design, planning and management, and 
community development (Standards Council of Canada, 2020).
Professional organisations in the USA (e.g., National Medical Association, 
American Institute of Architects, Association of Metropolitan Water 
Agencies, Water Utility Climate Alliance, American Society of Adaptation 
Professionals) have engaged with their members particularly through 
training about urban adaptation (Stults and Meerow, 2017). The 
private sector and citizens (Klein et al., 2018) have been involved in the

management of increasing flood risk, such as the adoption of property-
level flood protection (Thistlethwaite and Henstra, 2018; Valois et al., 
2019), implementing FireSmart Canada and Firewise USA guidance (see 
Box 14.2). In Canada, Engineers Canada developed the PIEVC Protocol 
to provide guidance for professionals in engineering and geoscience16.
Research-based institutions have accelerated the development of 
Internet-based tools for visualising and exploring climate information, 
in addition to furthering the scholarship on adaptation. In the USA, joint 
university, foundation and government programmes have contributed 
to advancing the field with products such as oceanographic and fishery 
climate forecasting tools (Section 14.5.2), in addition to methods for 
evaluating water resource plans under uncertainty about future mean 
and extreme conditions (ASCE, 2018a; Ray et al., 2020). Some regional 
research centres focus on stakeholder engagement in addition to

research; these include the National and Regional Climate Adaptation 
Science Center Network of the US Geological Survey17,the US 
Department of Agriculture’s Climate Hub Network18and the Climate 
Program Office of NOAA19which includes the Regional Integrated 
Science Assessment Network20to support delivery of climate services. 
So-called networks of networks, consisting of NGOs as well as state 
and city government programmes, have provided an alternative to 
federal support. For example, the Science for Adaptation Network was 
formed subsequent to dismantling the federal advisory group to the US 
National Climate Assessment (Moss et al., 2019).
14.7.2 The Solution Space
14.7.2.1 Incremental Adaptation, Barriers and Limits
Adaptation actions to moderate the effects of climate impacts are well 
documented in North America and have buffered much of the past and 
currently observed climate impacts (e.g., Lempert et al., 2018; Lemmen

14 See www.infrastructure.gc.ca/plan/crbcpi-irccipb-eng.html
15 See www.scc.ca/en/nisi
16 See www.pievc.ca
17 See www.usgs.gov/ecosystems/climate-adaptation-science-centers
18 See www.climatehubs.usda.gov
19 See https://cpo.noaa.gov
20 See https://cpo.noaa.gov/Meet-the-Divisions/Climate-and-Societal-Interactions/RISA/About-RISAet al., 2021). While it is challenging to catalogue adaptation activities, 
as many are not published or are not necessarily undertaken with 
climate adaptation as the primary rationale (Section 1.3.2.2), most of 
the activities identified by sector in this chapter have been primarily 
incremental adaptation measures (medium evidence, high agreement). 
Many actions are extensions of existing practices for managing 
climate variability and there is broad agreement that worsening future 
conditions will exceed the capacity of many of these efforts (Kates 
et al., 2012; Termeer et al., 2017; Fazey et al., 2018; Fedele et al., 2019; 
Shi and Moser, 2021).

Progress in adaptation planning and implementation between regions 
in North America is uneven (Table 14.6; see Box 14.7; Bierbaum et al., 
2013; Moser et al., 2017; Auditors General, 2018; INECC and Semarnat, 
2018; Shi and Moser, 2021). At the local level (cities) in the USA, 
commitment of elected officials, financial resources and awareness 
of climate-change hazards and risks have been identified as driving 
the variation in climate adaptation (Shi et  al., 2015). Adaptation 
programmes have come under budgetary and political pressures 
that limit continuity of efforts (Moss et al., 2019). Implementation of 
adaptation has also faced challenges due to institutional arrangements, 
constraints and gaps that prevent different levels of government, 
social organisations and academia to act in an integrated and timely 
way to consider biodiversity, agriculture and water systems (e.g., see 
Box 14.7; Bourne et al., 2016; Nalau et al., 2018)

Adaptive capacity in the face of climate risks and impacts has not 
been equal across North American communities (Sarkodie and 
Strezov, 2019). Lack of representation, health inequities and economic 
constraints adversely affect the capacity to respond to change and 
further exacerbate marginalisation. For example, within many water 
basins in Canada and the USA, planning processes are often hampered 
by conflicting interests, asymmetrical information and differential 
power (ICLEI Canada, 2016; Nordgren et al., 2016; Woodruff and Stults, 
2016).
The absence of evidence about the current effectiveness of proposed 
adaptation actions to guide future actions and investments presents 
a serious risk to North America, especially at higher GWLs (medium 
confidence). Evaluating the limits to adaptation and the effectiveness 
of adaptation actions is hindered by a lack of monitoring and evaluation 
(Auditors General, 2018; Dilling et al., 2019; Berrang-Ford et al., 2021).

Incremental, passive adaptations are often characterised by soft limits 
due to differing access to resources and by perceptions and tolerance 
of risk (Moser, 2010; Dow et  al., 2013). At current warming levels, 
social–ecological systems have been reaching limits to adaptation in 
regions with high exposure and high sensitivity (medium confidence). 
However, the implications for adaptation are unclear as soft 
adaptation limits are mutable and change with evolving knowledge,

14
1992Chapter 14 North America
Table 14.6 |  Adaptation trends and progress across sectors. Adaptation progress consists of assessment (A), planning (P), implementation of strategies (I) and evaluation of 
efficacy (E).
Adaptation progress Limits
Sector Strategies Cases A P I E Soft Hard
(Section 14.5.1.1)Broad use of tools such 
as scenario planning, 
structured decision making 
and adaptation planning 
frameworksPlanning for climate refugia in the Sierra 
Nevada of California, USA (Morelli et al., 
2016)H HL 
to 
MLManagement agency internal 
policies which may prevent 
the flexibility required for 
strategiesSome species may 
face local extirpation 
or even extinction if 
adaptive capacity is 
overwhelmed
Oceans 
(Section 14.5.2)Proactive and rapid 
management approaches 
to minimise impacts of 
increasingly frequent 
entanglements of protected 
species, caused by 
climate-driven changes in

prey and fishery activitiesDynamic closure areas to reduce 
loggerhead turtle bycatch in Hawaiian 
shallow-set longline fisheries (Howell 
et al., 2015; Lewison et al., 2015), blue 
whale ship-strike risk in near-real time 
(Hazen et al., 2017; Abrahms et al., 2019a) 
and bycatch of multiple top predator 
species in a West Coast drift gillnet fishery 
(Hazen et al., 2018)H H M MLack of coordination and 
planning at multiple scales 
as species redistribute across 
fishery areas, marine protected 
zones and international and 
jurisdictional boundariesMarine species 
mortality events
resources 
(Section 14.5.3)Forecasting and warning of 
harmful algal blooms (HABs) 
that affect water qualityReduced human exposure to the increased 
risk of toxins from HABs in the Great 
LakesML 
to 
ML 
to 
ML 
to 
MFinancial resources required 
to enhance water treatment 
facilities to deal with HABs, 
technological innovation to 
improve treatment and removal

of HABs, closure of recreational 
water useSevere human health 
effects, mortality of 
aquatic species
Water availability 
(Section 14.5.3)Water allocation policies 
reassessed to enhance 
equity, sustainability 
and flexibility in times of 
shortage through sharing 
agreements, improved 
groundwater regulation and 
voluntary water transfersUS Colorado River interstate shortage 
sharing agreementH H ML 
to 
MComplex legal and 
administrative challenges, 
heightening lengthy disputes 
and costly interstate legal 
battlesDepletion of finite 
resources and 
reduced flow in 
hydrologically 
connected rivers
Food and fibre 
(Section 14.5.4)Improved climate resilience 
through increasing income 
and harvest/crop portfolio 
diversificationFishing communities in the US-SW and 
US-NE through nature-based aquaculture 
solutions (Messier et al., 2019; Rogers 
et al., 2019; Young et al., 2019; Fisher 
et al., 2021)H HM 
to 
HMLack of high-resolution and

locally tailored climate-change 
informationCollapse of fisheries 
and loss of crops 
due to excessive 
Cities and 
(Section 14.5.5)Consideration of the value 
of green infrastructure and 
natural assets to meet a 
range of adaptation needs 
related to flooding, extreme 
urban heat, SLR and droughtMunicipal Natural Assets Initiative to 
assist Canadian municipalities to integrate 
natural assets in financial planning and 
asset management programmes and 
consider projected climate changes 
(Municipal Natural Assets Initiative, 2018)H H ML 
to 
MOrganisations’ willingness 
to take on solutions that are 
emergent and less tested;
capacity for municipalities to 
undertake the development 
and assessment of this new 
infrastructureRate and magnitude 
of climate changes 
exceeding capacity 
of natural/green 
infrastructure to 
cope
Health and 
communities 
(Sections 14.5.5, 
14.5.6)Access to green spaces, 
cooler infrastructure and

cooling stationsThe heatwave plan for Montreal which 
includes visits to vulnerable populations, 
cooling shelters, monitoring of 
heat-related illness and extended hours 
for public pools (Lesnikowski et al., 2017)H HL 
to 
ML 
to 
MLack of effective warning 
and response systems, ability 
to reach at-risk populations, 
building designs, enhanced 
pollution controls, urban 
planning strategies, and 
affordable, resilient health 
infrastructureExtreme increase 
in heat-related 
morbidity
Tourism and 
recreation 
(Section 14.5.7)Diversification of 
winter-focused recreation 
and tourism opportunitiesInvestments in climate-resilient 
infrastructure within Canadian National 
Parks which have increased visitation 
rates during the shoulder seasons 
(Fisichelli et al., 2015; Lemieux et al., 2017; 
Wilkins et al., 2018)H H M LSocial inequalities generated 
by the tourism development 
process not considered, such 
as increased property taxes 
leading to the marginalisation

of local residents in favour of 
wealthy touristsLack of precipitation 
that falls as snow 
lower-elevation 
areas

14
1993North America  Chapter 14
values, interests and perspectives involved in decision making (Adger 
et al., 2009; Moser et al., 2017). Hard limits have been identified for 
some natural systems, such as species extinctions (Sections 14.5.2.1, 
14.5.1.3; Table 14.2).
Adaptation actions in one place or sector can have adverse side 
effects elsewhere (medium confidence). For example, increased use 
of groundwater for irrigation in response to aridification can reduce 
baseflows into rivers with adverse impacts on stream ecology and 
water availability for communities far downstream (Section  14.5.3). 
Additionally, across multiple sectors in North America, adaptation 
actions have tended to be sector specific rather than integrating 
across systems (Gao and Bryan, 2017; Fulton et al., 2019), despite the 
increasing awareness of cascading impacts and interdependencies 
(Zimmerman and Faris, 2010; C40 Cities and AECOM, 2017) and

risks from possible ecological and social thresholds that have been 
identified under higher GWL (Section 14.6.3). For example, the water, 
energy and food nexus in North America has highlighted that food, 
water and energy security depend on transportation infrastructure 
(Section 14.5.8.1.2; Romero-Lankao et al., 2018).
14.7.2.2  Adaptation Through Participatory and Robust Decision 
Making, Indicators and Sustained Assessments
In response to some of the challenges presented in Section 14.7.2.1, 
substantial progress has been made in the North American context on 
the development of climate services, indicators, sustained assessments, 
and participatory and stakeholder-driven robust decision making 
(medium confidence) (Fazey et  al., 2018; Fedele et  al., 2019; Moss 
et al., 2019; Boon et al., 2021; Werners et al., 2021).
Decision making related to adaptation policies, plans and projects has 
become more formalised, emphasising participatory governance and

co-production of knowledge. Canada has improved capacity with its 
Canadian Expert Panel on Climate Change Adaptation and Resilience 
Results and the recent National Adaptation Plan (Section  14.7.1.5), 
with the development of a series of indicators to measure progress 
on adaptation (EPCCAR, 2018; Government of Canada, 2021a). In the 
USA, indicators have been developed to communicate climate risks 
and guide adaptation efforts from federal (Kenney et al., 2020) to more 
regional initiatives (Kenney and Gerst, 2021). These climate indicators 
have been used to support user-driven assessments and to articulate adaptation goals (Moss et  al., 2019; Kenney et  al., 2020); however, 
these frameworks have not sufficiently incorporated monitoring and 
evaluation into adaptation plans (Lempert et al., 2018; Kenney et al., 
2020). Tools and services to facilitate risk assessment and action 
planning have been made available through federal government

climate service efforts, and guidance for their use has been developed 
(Vano et al., 2018); however, these products have been characterised 
as insufficiently developed to allow all adaptation practitioners to use 
these services (Meerow and Mitchell, 2017).
Throughout North America, co-development (or co-production) of adap-
tation efforts among stakeholders who share common climate vulner -
abilities or risk levels (e.g., individuals, groups, communities, businesses 
or institutions) has been a core attribute of adaptation planning (Mees 
et al., 2016) and ranges across many sectors (e.g., Sections 14.5.2.2, 
14.5.3.3, 14.5.4.3). Participatory efforts and robust decision making 
have also been observed; some integrated watershed planning proc-
esses have high degrees of sustained stakeholder involvement (Sec-
tion 14.5.3.3; FAQ 14.4; Harris-Lovett et al., 2015; Cantú, 2016).
14.7.2.3 Transformational Adaptation and Climate Resilience

Climate change and its projected impacts pose a substantial risk to North 
America as a region as well as to sectors, communities and individuals 
(Section 14.6.2). Incorporating different values and knowledge systems, 
consideration of equity and justice as core objectives and addressing 
underlying vulnerabilities are principles that can guide transformational 
adaptation and resilience (medium confidence).
Approaches that advance adaptation within the existing contexts 
(finances, institutions and processes) have been increasingly promoted 
by governments to mainstream climate risk into all considerations 
(Rosenzweig and Solecki, 2014; Van der Brugge and Roosjen, 2015; 
Boon et  al., 2021; Shi and Moser, 2021). Policies and programmes 
that build upon existing approaches that have inherent climate 
resilience including Indigenous knowledge-based land and resource 
management (Section  14.5.4), co-management of agriculture and

freshwater resources (Section  14.5.3), NbS (see Box  14.7), links 
between health and equity, and ecosystem-based management 
(Sections  14.5.2–14.5.4) have advanced sustainable and equitable 
climate resilience. Implementing the recommendations in the ASCE 
committee’s report on adaptation to a changing climate (2018a) Adaptation progress Limits
Commerce and 
transportation 
(Section 14.5.8)Improved engineering 
and technological 
solutions, in addition 
to innovative policy, 
planning, management and 
maintenance approaches, to 
enhance climate resilience 
for transportation and 
related commerceFor roads, changing pavement mixes 
to be more tolerant to heat or frost 
heaving, expanding drainage capacity, 
reducing flood risks, enhancing travel 
advisories and alerts, elevating or 
relocating new infrastructure where 
feasible and changing infrastructure 
design requirements (Natural Resources 
Conservation Service, 2008; EPA, 2017;

Pendakur, 2017)H H M LLack of financial resources 
to build climate-resilient 
infrastructure, particularly in 
marginalised communitiesExtreme events 
which may cause 
significant and 
irreversible impacts 
on the transportation 
sector with major 
implications for 
supply chains and 
global trade
Note:
L: low, M: moderate, H: high

14
1994Chapter 14 North America
and Canada’s Infrastructure and Buildings Working Group report has 
been identified as an opportunity to improve social equity by ensuring 
the resilience of infrastructure and the services it provides, through 
adoption of standards and good asset management practices (Amec 
Foster Wheeler Environment and Infrastructure, 2017; ASCE, 2018a).
Long-term policy signals to incentivise ongoing, scalable adaptation 
action that is coordinated with mitigation efforts will increase actions 
and prevent potential maladaptive investment (Moser, 2018; Shi 
and Moser 2021). Using SDG goals and the NDCs as a framework 
for inclusive and coordinated partnership and vertical integration 
across subnational, national and regional planning can promote 
climate resilient development (CRD) (Section 18.1.3). Coordination of 
policies and responses have been identified as supporting longer-term, 
transformational adaptation and minimising risk (Termeer et al., 2017;

Fazey et  al., 2018). New approaches for enabling and incentivising 
transformative adaptation in North America are rapidly emerging 
(Colloff et al. 2017; Fedel et al. 2019; Werners et al. 2021). Evaluation 
of the feasibility of evolving adaptation strategies is only in the early 
stages, but recent work has provided the foundation for assessing 
these considerations (Table 14.7; Chapter 16).
Differing values, perspectives, interests and needs of relevant actors 
(Dittrich et  al., 2016) through participatory processes, such as co-
production of knowledge (Meadow et  al., 2015; Wall et  al., 2017), 
have been incorporated through the Resilience Dialogues21and the 
development of guidance on climate scenarios (Chaumont, 2014). 
Framing of adaptation goals strongly determines beneficiaries of 
resultant policies and underscores the importance of a plurality of 
21 See www.resiliencedialogues.orgperspectives in adaptation governance (Cochran et al., 2013; Plummer,

2013; Allison and Bassett, 2015; Raymond-Yakoubian and Daniel, 2018). 
Sustained engagement through iterative knowledge development, 
learning and negotiation has been identified as core for addressing 
climate risks (Kates et al., 2012; Seijger et al., 2014). Interdisciplinary 
and inclusive adaptation programmes that embrace and plan for conflict 
and resolution, and address inequalities, have been part of broadening 
the opportunities for engagement (Cantú, 2016; Termeer et al., 2017; 
Parlee and Wiber, 2018; Sterner et al., 2019; Haasnoot et al., 2020).
Equity and justice in climate adaptation have been identified as providing 
a foundation for resilience in natural, social and built systems (Cochran 
et  al., 2013; Reckien et  al., 2017; Schell et  al., 2020). This approach 
recognises that social vulnerability undermines efforts to increase adaptive 
capacity and that adaptation may also entrench existing social inequities,

such as marginalisation of communities of colour, gender discrimination, 
legacy effects of colonisation and gentrification of coastal communities 
(Schell et al., 2020; Thomas, 2020). Thus, identifying systemic racism and 
the effects of colonialism within and across institutions has also been 
identified as part of achieving more just and equitable adaptation (Shi and 
Moser 2021). Acknowledgement and incorporation of IK in adaptation 
planning and implementation also recognises Indigenous sovereignty 
issues and the importance of the equitable role of Indigenous self-
determination in governance and planning (see Box 14.1; Section 14.4; 
Raymond-Yakoubian and Daniel, 2018).
Strategies have been emerging to facilitate progress by including specific 
guidance on tools for financing and funding climate-change adaptation

infrastructure (Berry and Danielson, 2015; Chen et  al., 2016; Zerbe, Table 14.7 |  Simplified example for transitioning from incremental to transformative adaptation approaches to support future climate-resilient sustainable development
Adaptation approaches Mitigation Feasibility dimensions
Hazard Response Incremental TransformationalEvidence/
agreementCo-benefits Barriers Enablers
Extreme 
storms causing 
severe flooding 
and erosionIntegrated 
ecosystem and 
watershed 
managementRestoration of stream 
corridors to incorporate 
environmental flows; 
continuing to build 
hardened surfaces and 
stream diversions in urban 
areas to accommodate 
infrequent, yet extreme, 
storm eventsRestoration of streambanks 
and beds to stabilise 
and slow flows; use of 
drought-tolerant plantings 
and shade trees to 
reduce evaporation rates; 
incorporation of pervious 
surfaces in urban settings 
in combination with 
designating wide buffer 
area within floodplains to 
accommodate increased

frequency of extreme 
events; integration of equity 
and justice considerationsMediumConservation 
of soil and 
increased 
opportunity 
for carbon 
sequestrationSectors working in 
silos, inadequate 
financing, inability to 
identify shared goals 
(EC, INST, SOC, GEO)Development of a 
coordinated suite of 
adaptation efforts, 
co-produced among 
stakeholders and 
across sectors (INST, 
SOC, ENV, TEC)
Notes:
This table is modified from the IPCC SR1.5 adaptation feasibility assessment for Land and Ecosystem Transitions (IPCC, 2018). Feasibility dimensions (can be barriers and/or 
enablers) are as follows: Economic (EC), Technological (TEC), Institutional (INST), Sociocultural (SOC), Environmental/Ecological (ENV) or Geophysical (GEO) (Chapter 16).

14
1995North America  Chapter 14
2019). This includes facilitating transitions between incremental and 
transformational efforts to facilitate CRD (Figure 14.12; Chapter 18).
The extent to which resilient infrastructure contributes to social 
justice and equity has also been taken into consideration (Climate-
Safe Infrastructure Working Group, 2018; Doorn, 2019). Proactive 
actions focused on small towns and rural areas—including the interdependencies between cities and surrounding areas—increases 
the potential that small and medium cities can build adaptive capacity 
at a pace that is commensurate with present and future risks (Moss 
et al., 2019; Vodden and Cunsolo, 2021). This coordination also creates 
greater opportunity for translation of knowledge into practice and 
assessing knowledge in the context that it is to be applied to improve 
decision making across scales (Enquist et al., 2017; Moss et al., 2019).

Conceptual diagram of the key elements for expanding the adaptation solution space
and implementing climate-resilient development
Figure 14.12 |  Conceptual diagram of the key elements for expanding the adaptation solution space and implementing climate resilient development 
(Chapter 18). Adapted from Shi and Moser (2021).

14
1996Chapter 14 North America
Box 14.7 | Nature-based Solutions to Support Adaptation to Climate Change
Nature-based Solutions (NbS) are ‘actions to protect, sustainably manage, and restore natural or modified ecosystems, that address 
societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits’ (IUCN, 2016). Such 
NbS in the context of climate change, or nature-based adaptation see (Box 1.3), can jointly address multiple social–ecological issues 
related to climate-change hazards, impacts, adaptation and mitigation (Figure Box 14.7.1; Cross-Chapter Box NATURAL in Chapter 2). 
Successful nature-based adaptation draws from existing adaptation approaches (Borsje et al., 2011; Temmerman et al., 2013; Law et al., 
2018; Reguero et al., 2018; Buotte et al., 2019) and is applied across ecological and human systems (high confidence) (Table Box 14.7.1; 
Figure Box 14.7.1).
Adaptations: Forest thinning; prescribed

burning; cultural burning
Benefits: Increase carbon storage; protect 
biodiversity; increase resilience to fire and 
drought
Caution: potential for failed regeneration
Adaptation: Green cities and urban 
spaces; green infrastructure; habitat  
Benefits : Protection from flooding; 
reduce heat-island ef fects and 
related human health 
risks; maintain and 
enhance carbon 
storage and 
Adaptation:  Protect and restore barrier habitats; combined 
natural and built infrastructur e
Benefits : Wave attenuation, erosion and flood reduction 
from storm events exacerbated by SL RAdaptation: Forest preservation and 
Benefits: Enhance carbon storage;  
protect biodiversity; reduce soil erosio nAdaptation: Integrated watershed 
biodiversity; regulate seasonal  
streamflows; reduce water treatment costs;  
improve water quality and quantity; reduce 
soil erosio n

Adaptation: Agroforestry; winter cover crops; 
revegetate stream buf fers; wetland protectio n
Benefits: Maintain crop yields; reduce soil erosion; 
reduce crop heat stress; enhance carbon 
storage; enhance biodiversity
Adaptation: Protect critical habitats, kelp forests and coral reefs; 
ecosystem-based management
Benefits: Support fish and shellfish resources; promote ecosystem  
resilience; maintain and enhance carbon storage and biodiversityClimate hazards protection services provided by nature-based solutions
Figure Box 14.7.1 |  Climate hazard protection services provided by Nature-based Solutions
Through a capacity to evolve to keep pace with climate change, these approaches can impart self-sustaining and cost-efficient long-term 
protection in addition to serving as biodiverse, carbon sinks (Scyphers et al., 2011; Cheong et al., 2013; Temmerman et al., 2013; Rodriguez

et al., 2014; Herr and Landis, 2016; Sasmito et al., 2016; Reguero et al., 2018). Nature-based adaptation is generally less expensive and 
strengthens over time, as compared with built infrastructure which erodes with time (medium confidence) (Narayan et al., 2016; Smith 
et al., 2017; Sutton-Grier et al., 2018). Analysis of the impacts of Hurricane Sandy determined that communities located behind wetlands 
experienced 20% less damage (Narayan et al., 2016). Coral reefs are providing 544 million USD yr−1 (Beck et al., 2018a) and mangroves 
22 billion USD yr−1 in property protection for coastal communities in the USA and Mexico (Beck et al., 2018b). By 2030, flooding from 
changes in storms, SLR (based on RCP8.5) and increases in built infrastructure in the US Gulf Coast may result in net economic losses of

14
1997North America Chapter 14
up to 176 billion USD, of which 50 billion USD could be avoided through implementation of nature-based measures including wetland 
and oyster reef restoration and other green infrastructure (see Box 14.4; Section 14.5.2; EPA, 2015b; Reguero et al., 2018).
Innovative approaches in Canada (Borsje et al., 2011; Spalding et al., 2014; Soto-Navarro et al., 2020) and the USA (Law et al., 2018;

Buotte et al., 2019; Soto-Navarro et al., 2020) have led to social and environmental co-benefits and could address both future climate risk and long-standing social injustices (Hobbie and Grimm, 2020; Schell et al., 2020; Cousins, 2021). Effective nature-based adaptation requires a well-coordinated suite of adaptation efforts (e.g., assessment, planning, funding, implementation and evaluation) that is co-produced among stakeholders and across sectors (high confidence) (Millar and Stephenson, 2015; Kabisch et al., 2016; Dilling et al., 2019; Morecroft et al., 2019; Lavorel et al., 2020). Evaluating the efficacy of nature-based adaptation may become more tractable with more uniform guidelines for implementation (Scarano, 2017; Malhi et al., 2020; Seddon et al., 2020), and coordination in scaling-up local-level nature-based adaptation measures is likely to facilitate long-term success (Gao and Bryan, 2017).
Table Box 14.7.1 |  Nature-based adaptation in North America

Sector NbS actions Benefits References
CoastsConservation and restoration of barrier 
habitats, salt marshes, mangroves, coral and oyster reefs, sand dunes and river deltas; combined natural and built infrastructure (e.g., oyster reef in front of breakwall)Wave attenuation; erosion and flood reduction from storm events exacerbated by SLR; novel, created habitats, connectivity; recreation, quality of lifeBorsje et al. (2011); Scyphers et al. (2011); Cheong et al. (2013); Pinsky et al. (2013a); Temmerman et al. (2013); Ferrario et al. (2014); Möller et al. (2014); Rodriguez et al. (2014); Spalding et al. (2014); Yates et al. (2014); EPA (2015b); Grenier et al. (2015); Brandon et al. (2016); Herr and Landis (2016); Narayan et al. (2016); Sasmito et al. (2016); Ward et al. (2016); Aerts et al. (2018); Beck et al. (2018a); Morris et al. (2018b); Moudrak et al. (2018); Reguero et al. (2018); Sutton-Grier et al. (2018)

Watershed approaches such as protecting and restoring forests and wetlands in coastal watersheds, adopting stream buffers in agricultural areas (see agriculture below)Creation of a less flashy/variable hydrology; reduction in sediment, nutrient, hazardous chemical input to coastal waters and reduction in eutrophication and other water quality impairments, notably in deep waters where fish seek refuge from rising sea surface temperaturesDeutsch et al. (2015b); Boesch (2019); CENR (2010)
AquacultureControlled culture of fish, bivalves, corals and other marine speciesEnhancement and restoration of, and reduction in pressure on, wild species and ecosystems; restoration of threatened species such as coral reef species; storage of carbonFroehlich et al. (2017); Reid et al. (2019); Theuerkauf et al. (2019)

AgricultureRe-vegetation of stream buffer zones; planting of winter cover crops; wetland protection and restoration; agroforestrySelf-sustaining and cost-efficient long-term protection from soil erosion; maintenance and enhancement of crop yields; enhancement of carbon sinks; enhancement of biodiversity; reduction in nutrient input to coastsCENR (2010); Boesch (2019); Seddon et al. (2020)
Urban areasReplacement of impervious surfaces with permeable pavement, green space, parks, wetlands and green infrastructure (e.g., stormwater ponds, bioswales, rain gardens, green roofs); community gardens and urban forests; restoration of natural habitatsReduction in urban heat island effects and air pollution; self-sustaining and cost-efficient long-term protection from flooding, erosion and SLR; enhancement of carbon sequestration biodiversity, habitat and connectivity; improvement in quality of life and human health benefitsHobbie and Grimm (2020); Brown et al. (2021)Box 14.7 (continued)

14
1998Chapter 14 North America
TerrestrialForest conservation based on productivity 
and vulnerability to drought and fire; 
longer harvest rotationsIncrease in carbon storage and biodiversityLaw et al. (2018); Buotte et al. (2020); Soto-Navarro et al. (2020); 
Mori et al. (2021)
Forest thinning; prescribed burning; 
cultural burningReduction in wildfire risk and severity; 
increase in forest resilience to fire; 
reduction in forest drought stress; increase 
in carbon storageSee Box 14.2 and citations therein.
Protection and restoration of natural 
forestsRegulation of stream flow; reduction in soil 
erosion; protection and enhancement of 
biodiversityLawler et al. (2020); Seddon et al. (2020)
Beaver (Castor canadensis) reintroduction Regulation of seasonal stream flow McKelvey and Buotte (2018); Vose et al. (2018)
FreshwaterForests to Faucets and other watershed 
restoration projects for stream and

drinking water protectionImprovement in water quality; reduction in 
drinking water treatment costs; increase in, 
and regulation of, streamflowGartner et al. (2017); Claggett and Morgan (2018); Price and 
Heberling (2018)Box 14.7 (continued)
FAQ 14.4 | What are some effective strategies for adapting to climate change that have been implemented across 
North America, and are there limits to our ability to adapt successfully to future change?
Climate adaptation is happening across North America. These efforts are differential across sectors, scale and scope. Without more integrative 
and equitable approaches across broad scales, known as transformational adaptation, the continent may face limits to the future effectiveness 
of adaptation actions.
Across North America, progress in introducing climate adaptation is steady, but incremental. Adaptation is typically

limited to planning, while implementation is often hindered by ‘soft’ limits, such as access to financial resources, 
disparate access to information and decision-making tools, the existence of antiquated policies and management 
frameworks, lack of incentives and highly variable political perceptions of the urgency of climate change.
Cities and other state and local entities are taking the lead in adaptation efforts, particularly in terms of mainstreaming 
the use of many approaches to adaptation. These approaches include a suite of efforts ranging from assessment of 
impacts and vulnerability (relative to individuals, communities, jurisdictions, economic sectors, natural resources, 
etc.), planning processes, implementation of identified strategies and evaluation of the effectiveness of these 
strategies. Other institutions (e.g., NGOs, professional societies, private engineering and architecture businesses)

also are making significant progress in the adaptation arena, particularly at local to regional levels.
The water management and utilities sectors have made significant progress towards implementation of adaptation 
strategies using broad-based participatory planning approaches. Consideration of climate change is now folded 
into some ongoing watershed-wide planning efforts. An example is provided by the One-Water-One-Watershed 
(OWOW) approach followed by the Santa Ana Watershed Project Authority (SAWPA) in southern California. SAWPA 
is a joint powers authority comprising five regional water districts that provide drinking water to more than 6 million 
people as well as industrial and irrigation water across the 2400-square-mile watershed. The OWOW perspective 
focuses on integrated planning for multi-benefit projects and explicit consideration of the impacts of any planning

option across the entire watershed. Planning is supported by stakeholder-driven advisory bodies organised along 
themes that consider a full suite of technical, political, environmental and social considerations. SAWPA provides 
member agencies with decision-support tools and assistance to implement water conservation policies and pricing 
regimes, and one member agency is an industry leader on potable water recycling.
The marine and coastal fisheries sector also has shown considerable progress in climate adaptation planning, 
particularly in terms of assessing impacts and vulnerability of fisheries. Along the Pacific Northwest coast of the 
USA and Alaska, seasonal and sub-seasonal forecasts of ocean conditions exacerbated by warming (e.g., O 2, pH,

14
1999North America Chapter 14
temperature, sea ice extent) already have informed fisheries and aquaculture management. Similarly, forecasts and 
warnings have reduced human exposure to the increased risk of toxins from HABs in the Gulf of Mexico, the Great Lakes, California, Florida, Texas and the Gulf of Maine.
Professional organisations and insurance play an important part in mainstreaming climate adaptation. Government 
and private-sector initiatives can help address adaptation efforts through building-design guidelines and engineering standards, as well as insurance tools that reflect the damages from climate impacts. Through the identification of climate risks and proactive adaptation planning, the private sector can contribute to reducing risks throughout North America by securing operations, supply chains and markets.
Indigenous Peoples and rural community efforts across the continent show great potential for enhancing and

accelerating adaptation efforts particularly when integrated with Western-based natural resource management approaches, such as cultural burning and other traditional practices that reduce the buildup of fuels, in addition to prescribed fire and mechanical thinning. In the agricultural sector, examples include planting and cultivation of culturally significant plants, as a traditional practice of soil conservation, in addition to food crops or in lieu of synthetic or mechanical soil treatments.
Future changes in climate (e.g., more intense heatwaves, catastrophic wildfire and post-fire erosion, SLR and forced

relocations) could exceed the current capacity of human and natural systems to successfully adapt (or ‘hard limits’). The inclusion and equitable contribution of Indigenous Peoples and rural communities in decision-making and governance processes—including recognition of the interdependencies between cities and surrounding areas—increases the likelihood of building adaptive capacity at a pace that is commensurate with present and future climate-change risks.
Large-scale, equitable transformational adaptation likely will be required to respond to the growing rate and

magnitude of changes before crossing tipping points where hard limits exist, beyond which adaptation may no longer be possible. Increasingly, there are calls for accelerating and scaling up adaptation efforts, in addition to aligning policies and regulatory legislation at multiple levels of government. Improved processes for adaptation decision making, governance and coordination, across sectors and jurisdictions, could enhance North America’s capacity to adapt to rapid climatic change. These actions include a focused societal shift, across governments, institutions and transnational boundaries, from primarily technological approaches to NbS that help foster changes in perception of risk and, ultimately, human behaviour.Box FAQ 14.4 (continued)

15
2045Small Islands Chapter 15
A sense of urgency is prevalent among small islands in the 
combating of climate change and in adherence to the Paris Agreement to limit global warming to 1.5°C above pre-industrial levels. Small islands are increasingly affected by increases in temperature, 
the growing impacts of tropical cyclones (TCs), storm surges, droughts, changing precipitation patterns, sea level rise (SLR), coral bleaching and invasive species, all of which are already detectable across both natural 
and human systems (very high confidence
1) {15.3.3.1, 15.3.3.2, 15.3.3.3, 
15.3.4.1, 15.3.4.2, 15.3.4.3, 15.3.4.4, 15.3.4.5, 15.3.4.7}.
The observed impacts of climate change differ between urban 
and rural contexts, island types and tropical and non-tropical islands (high confidence). Coastal cities and rural communities on

small islands have been already impacted by SLR, heavy precipitation events, tropical cyclones and storm surges. Climate change is also affecting settlements and infrastructure, health and well-being, water and food security, and economies and culture, especially through compound events (high confidence). As of 2017, an estimated 22 million people in the Caribbean live below 6-m elevation and 50% of the Pacific’s population lives within 10
 km of the coast along with 
≥50% of their infrastructure concentrated within 500
 m of the coast 
{15.3.4.1,
 15.3.4.2, 15.3.4.3, 15.3.4.4, 15.3.4.5, 15.3.4.7}.

TCs are severely impacting small islands (high confidence). The TC intensity and intensification rates at a global scale have increased in the past 40 years with intensity trends generally remaining positive. Intense TCs including Categories 4 and 5 TCs have threatened human life and destroyed buildings and infrastructural assets in small islands in the Caribbean and the Pacific. Among 29 Caribbean islands, 22 were affected by at least one Category 4 or 5 TC in 2017. TC Maria in 2017 destroyed nearly all of Dominica’s infrastructure and losses amounted to over 225% of the annual GDP . Destruction from TC Winston in 2016 exceeded 20% of Fiji’s current GDP . TC Pam devastated Vanuatu in 2015 and caused losses and damages to the agricultural sector valued at USD 56.5 million (64.1% of GDP). Coast-focused tourism is already extremely impacted by more intense TCs. {15.2.1, 15.3.3.1, 15.3.3.3, 15.3.4.1, 15.3.4.2, 15,3.4.4, 15.3.4.5}.

Scientific evidence has confirmed that globally and in small 
islands tropical corals are presently at high risk (high confidence). Severe coral bleaching, together with declines in coral abundance, has been observed in many small islands, especially those in the Pacific and Indian oceans (high confidence). In the Pacific, median return time between two severe bleaching events has diminished steadily since 1980. The return time is now 6 years and often associated with the warm phase of El Niño–Southern Oscillation (ENSO) events (high 

can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.confidence). In mid-2016, a new ENSO event occurred which reduced 
living coral cover by 75% in the Maldives {15.3.3.1.3, 15.3.4.8}.
Freshwater systems on small islands are exposed to dynamic 
climate impacts and are among the most threatened on the planet. An 11–36% reduction is estimated in the volume of fresh groundwater lens of the small atoll islands (area < 0.6
 km²) of the

Maldives due to SLR. The El Niño-related 2015–2016 drought in Vanuatu led to reliance on small amounts of contaminated water left at the bottom of household tanks. A Caribbean high-resolution drought atlas spanning 1950–2016 indicates that the region-wide 2013–2016 drought was the most severe event during the multi-decadal period. In Puerto Rico, the island experienced 80 consecutive weeks of moderate drought, 48  weeks of severe drought and 33 weeks of extreme drought conditions between 2014 and 2016. Increasing trends in drought are apparent in the Caribbean although trends in the western Pacific are not statistically significant {15.3.3.2, 15.3.4.3}.
Small islands host significant levels of global terrestrial species 
diversity and endemism. Due to the large range of insular-related vulnerabilities, almost 50% of terrestrial species presently considered at risk of global extinction also occur on islands (high confidence). Despite encompassing approximately 2%

of the Earth’s terrestrial surface, oceanic and other high-endemicity islands are estimated to harbour substantial proportions of existing species including ~ 25% extant global flora, ~ 12% birds and ~10% mammals {15.3.3.3}.
Projected ImpactsProjected climate and ocean-related changes will significantly 
affect marine and terrestrial ecosystems and ecosystem services, which will in turn have cascading impacts across both natural and human systems (high confidence). Changes in 
wave climate superimposed on SLR will significantly increase coastal flooding (high confidence) and low-coastal and reef island erosion (limited evidence, medium agreement). The frequency, extent, duration

and consequences of coastal flooding will significantly increase from 2050 (high confidence), unless coastal and marine ecosystems are able to naturally adapt to SLR through vertical growth (low confidence). These changes are a major concern for small islands given that a high percentage of their population, infrastructure and economic assets are located in the low-elevation coastal zone of below 10-m elevation {15.3.3.1.1, 15.3.3.1.2, 15.3.3.1.3, 15.3.3.1.4}.
Projected changes in the wave climate superimposed on SLR 
will rapidly increase flooding in small islands, despite highly contrasting exposure profiles between ocean sub-regions (high confidence). A 5–10-cm additional SLR (expected for ~2030–2050) will double flooding frequency in much of the Indian Ocean and

15
2046Chapter 15 Small Islands
Tropical Pacific, while TCs will remain the main driver of (rarer) flooding 
in the Caribbean Sea and Southern Tropical Pacific. Some Pacific atoll 
islands will likely2 undergo annual wave-driven flooding over their 
entire surface from the 2060s–2070s to 2090s under RCP8.5, although 
future reef growth may delay the onset of flooding (limited evidence, 
low agreement) {15.3.3.1.1}.
Modelling of both temperature and ocean acidification effects 
under future climate scenarios (RCP4.5 and RCP8.5) suggest that 
some small islands will experience severe coral bleaching on 
an annual basis before 2040 (medium confidence). Above 1.5°C, 
globally inclusive of small islands, it is projected there will be further 
loss of 70–90% of reef-building corals, with 99% of corals being lost 
under warming of 2°C or more above the pre-industrial period. Intact 
coral reefs, seagrass meadows and mangroves provide a variety of

ecosystem services that are important to island communities (high 
confidence). These include provisioning services, regulating services, 
cultural services and those that support community resilience (high 
confidence). If coastal ecosystems are degraded and lost, then the 
benefits they provide cannot be easily replaced (medium confidence) 
{15.3.3.1.3, 15.3.3.1.4}.
Projected changes in aridity are expected to impose freshwater 
stress on many small islands, especially SIDS (high confidence). 
It is estimated that with a warming of 1.5°C or less, freshwater stress 
on small islands would be 25% less as compared to 2.0°C. While some 
island regions are projected to experience substantial freshwater 
decline, an opposite trend is observed for some western Pacific and 
northern Indian Ocean islands. Drought risk projections for Caribbean 
SIDS aligned with observations from the Shared Socioeconomic 
Pathway (SSP) 2 scenario indicate that a 1°C increase in temperature

(from 1.7°C to 2.7°C) could result in a 60% increase in the number 
of people projected to experience severe water resources stress from 
2043 to 2071. In some Pacific atolls, freshwater resources could be 
significantly affected by a 0.40-m SLR. Similar impacts are anticipated 
for some Caribbean countries with the worst-case scenario (RCP8.5) 
indicating a 0.5-m SLR by the mid-century (2046–2065) and a 1-m 
SLR by the end-of-century (2081–2100). SIDS with high projected 
population growth rates are expected to experience the most severe 
freshwater stress by 2030 under a 2°C warming threshold scenario 
{15.3.3.2}
The continued degradation and transformation of terrestrial 
and marine ecosystems of small islands due to human-
dominated will amplify the vulnerability of island peoples to 
the impacts of climate change (high confidence). New studies 
highlight large population reductions with an extinction risk of 100%

for endemic species within insular biodiversity hotspots including 
within the Caribbean, Pacific and Sundaland regions by 2100 for > 
3°C warming {15.3.3.3}. This is likely to decrease the provision of 
resources (e.g., potable water) to the millions of people living on small 
islands, resulting in impacts upon settlements and infrastructure, food 
and water security, health, economies, culture and migration (high 
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as 

lies within the 17–83% probability range.confidence) {15.3.3.2, 15.3.3.3, 15.3.4.1, 15.3.4.2, 15.4.3, 15.3.4.4, 
15.3.4.5, 15.3.4.6, 15.3.4.7}.
Reef island and coastal area habitability in small islands is 
expected to decrease because of increased temperature, extreme 
sea levels and degradation of buffering ecosystems, which 
will increase human exposure to sea-related hazards (high 
confidence) . Climate and non-climate drivers of reduced habitability 
are context specific. On small islands, coastal land loss attributable to 
higher sea level, increased extreme precipitation and wave impacts 
and increased aridity have contributed to food and water insecurities 
that are likely to become more acute in many places (high confidence).

In the Caribbean, additional warming by 0.2°–1.0°C, could lead to a 
predominantly drier region (5–15% less rain than present day), a greater 
occurrence of droughts along with associated impacts on agricultural 
production and yield in the region. Crop suitability modelling on several 
commercially important crops grown in Jamaica found that even an 
increase of less than + 1.5°C could result in a reduction in the range 
of crops that farmers may grow. Most Pacific Island Countries could 
experience ≥ 50% declines in maximum fish catch potential by 2100 
relative to 1980–2000 under both an RCP2.6 and RCP8.5 scenario 
{15.3.4.3, 15.3.4.4}.
Future Risks
The reduced habitability of small islands is an overarching 
significant risk caused by a combination of several key risks 
facing most small islands even under a global temperature 
scenario of 1.5°C (high confidence). These are loss of marine and 
coastal biodiversity and ecosystem services; submergence of reef

islands; loss of terrestrial biodiversity and ecosystem services; water 
insecurity; destruction of settlements and infrastructure; degradation 
of health and well-being; economic decline and livelihood failure); and 
loss of cultural resources and heritage. Climate-related ocean changes, 
including those for slow-onset events, and changes in extreme events 
are projected to cause and/or amplify Keys Risks in most small islands. 
Identification of key risks facilitates the selection of optimal context-
specific adaptation options. Moreover, it can distil the benefits and/or 
disadvantages and long-term implications of choosing such options 
(high confidence) {15.3.4.9}.
The vulnerability of communities in small islands, especially 
those relying on coral reef systems for livelihoods, may exceed 
adaptation limits well before 2100 even for a low greenhouse 
gas emission pathway (high confidence). The impacts of climate 
change on vulnerable low-lying and coastal areas present serious

threats to the ability of land to support human life and livelihood 
(high confidence). Climate-related migration is expected to increase, 
although the drivers and outcomes are highly context-specific and 
insufficient evidence exists to estimate numbers of climate-related 
migrants now and in the future (medium evidence, high agreement) 
{15.3.4.1, 15.3.4.6, CCB7-1}.

15
2047Small Islands  Chapter 15
Small islands are already reporting losses and damages 
particularly from tropical cyclones and increases in SLR (high 
confidence). Despite the loss of human life and economic damage, 
the methods and mechanisms to assess climate-induced loss and 
damage remain largely undeveloped for small islands. Further, there 
are no robust methodologies to infer attribution and such assessments 
are limited. A research gap on losses and damages includes how to 
assess the economic costs of losses and damages. Specific data on 
experienced losses and damages across socioeconomic groups and 
demographics are needed. Monitoring and tracking slow-onset events 
are equally important and require robust data {15.7, 15.8}.
Options, Limits and Opportunities of Adaptation
Some island communities are resilient with strong social 
safety nets and social capital that support responses and 
actions already occurring, but there is limited information on

the effectiveness of the adaptation practices and the scale 
of action needed ( high confidence). This is in part due to a need 
for a better understanding of the limits to adaptation and of what 
constitutes current resilience and/or successful adaptation in small 
island contexts. Greater insights into which drivers weaken local and 
indigenous resilience, together with recognition of the sociopolitical 
contexts within which communities operate, and the processes by 
which decisions are made, can assist in identifying opportunities at 
all scales to enhance climate adaptation and enable action towards 
climate resilient development pathways (medium evidence, high 
agreement) {15.6.1, 15.6.5, 15.7}.
In small islands, despite the existence of adaptation barriers 
several enablers can be used to improve adaptation outcomes 
and to build resilience (high confidence). These enablers include 
better governance and legal reforms; improving justice, equity and

gender considerations; building human resource capacity; increased 
finance and risk transfer mechanisms; education and awareness 
programmes; increased access to climate information; adequately 
downscaled climate data and embedding Indigenous knowledge and 
local knowledge (IKLK) as well as integrating cultural resources into 
decision-making (high confidence) {15.6.1 15.6.3, 15.6.4, 15.6.5}.
Small islands present the most urgent need for investment in 
capacity building and adaptation strategies (high confidence) but 
face barriers and constraints which hinder the implementation 
of adaptation responses. Barriers and constraints arise from 
governance arrangements, financial resources and human resource 
capacity. Additionally, institutional and legal systems are often 
inadequately prepared for managing adaptation strategies such 
as large-scale settlement relocation and other planned and/or 
autonomous responses to climate risks (high confidence). Adaptation

strategies are already being implemented on some small islands 
although barriers are encountered including inadequate up-to-date 
and locally relevant information, limited availability of finance and 
technology, lack of integration of IKLK in adaptation strategies, and 
institutional constraints (high confidence) {15.5.3, 15.5.4, 15.6.3, 
15.6.4, 15.6.5}.For many small islands, adaptation actions are often incremental 
and do not match the scale of extreme or compounding events 
(high confidence). Much of the currently implemented adaptation 
measures remain small in scale (e.g., community-based adaptation 
projects), sectoral in focus and do not address the needed structural 
and system-level adaptations to combat climate impacts and achieve 
long-term sustainability of adaptation interventions. To address these 
shortcomings, enablers are being integrated into National Adaptation 
Plans and Disaster Risk Reduction Plans (high confidence) {15.6.3}.

Although international climate finance has increased in magnitude, 
small islands face challenges in accessing adaptation finance to 
cope with slow- and rapid-onset events (high confidence). In the 
Caribbean, 38% of flows were concessional loans and 62% were grants, 
whereas in the Atlantic and Indian oceans nearly 75% of the flows were 
in the form of concessional loans and 25% were grants. Solutions to 
these barriers are being explored and some small islands have started 
adopting enablers such as insurance and microfinance at both the 
national and local levels in responding to adaptation needs and to 
facilitate resiliency building. COVID-19 has caused, however, economic 
shock in many small islands, which will limit adaptation, undermine the 
attainment of Sustainable Development Goals and slow down climate 
resilient development transitions {15.8.3}.
The unavailability of up-to-date baseline data and contrasting 
scenarios/temperature levels continue to impair the generation

of local-to-regional observed and projected impacts for small 
islands, especially those that are developing nations (high 
agreement). Climate model data based on the most recent suite 
of scenarios (RCPs and especially SSPs) are still not widely available 
to primary modelling communities in most small island developing 
nations (high agreement). Coastal sites of small islands are not well 
represented in global gridded population and elevation data sets, 
thereby making estimation of population exposure to SLR difficult. The 
lack of data continues to impede the development of robust impacts-
based modelling output (e.g., for terrestrial biodiversity). Downscaling 
is pivotal for small islands due to their high diversity, which makes 
generalisation invalid.

15
2048Chapter 15 Small Islands
15.1 Introduction
This chapter examines the climate change impacts and projected 
risks faced by small islands, including the detection and attribution 
of observed impacts, the losses and damages they experience, and 
the enablers, limits and barriers to the implementation of adaptation 
options applicable to them. The implications of climate change impacts 
on the attainment of the Sustainable Development Goals (SDGs), the 
need for more climate resilient development pathways based on a 
systems transitions approach, and how both of these intersect with 
future potential responses are assessed within the context of small 
island states.
The small islands covered in this chapter are located within the 
tropics of the southern, northern, and western Pacific Ocean, the 
central, eastern and western Indian Ocean, the Caribbean Sea, the 
eastern Atlantic off the coast of West Africa, and in the temperate

Mediterranean Sea. In contrast to the Intergovernmental Panel on 
Climate Change (IPCC) Fifth Assessment Report (AR5), non-sovereign 
island states and territories dependent on continental states and 
islands of semi-autonomous, sub-national island jurisdictions are 
included in this chapter. Further, Small Island Developing States 
(SIDS) consisting of 39 small island and low-lying coastal developing 
states which belong to the Alliance of Small Island States (AOSIS) are 
covered in this assessment. Islands in the polar and sub-polar regions, 
North Atlantic Ocean, the Baltic Sea, the North Sea, the Black Sea and 
the Arctic Ocean are not included.
Small islands share similarities such as geographical remoteness, 
isolation, narrow resource bases, heavy dependency on external trade, 
vulnerability to exogenous economic shocks, economic volatility, and 
limited access to development finance. Many are biodiversity hotspots

and experience a disproportionate impact of natural hazards associated 
with climate change. They are also diverse in physical and biophysical 
characteristics, economic systems, political/governance systems, and 
exhibit social and cultural differences. Adaptation responses vary 
among small islands because such diversity requires place-specific and 
culturally specific adaptation responses.
The chapter is structured in accordance with the overall format of 
the AR6 Working Group II report (Figure 15.1). This section presents 
points of departure from AR5. As shown in Figure 15.1, this is followed 
by an assessment of current and future risks that are expected to be 
experienced by small islands (Sections 15.3 and 15.4), what measures 
have been implemented (Section 15.5) and enablers, limits and barriers 
that are being encountered (Section 15.6). Section 15.7 deals with the 
SDGs, climate resilient development pathways and potential future

responses. The chapter ends with an identification of research gaps 
(Section 15.8).
15.2 Points of Departure from AR5
Points of departure from AR5 are highlighted in this section in relation 
to exposure, vulnerability, impacts and risks (Section  15.2.1), and 
adaptation options (Section 15.2.2).15.2.1 Points of Departure on Exposure, Vulnerability, 
Scientific studies since AR5 confirm that global temperature will 
continue to increase even if greenhouse gas emissions are drastically 
reduced and will escalate the vulnerability, impacts and multiple 
interrelated risks experienced by small islands (high confidence) (IPCC, 
2018). A greater sense of urgency in lowering global greenhouse gas 
emissions and a call for action now is resonating among small island 
states.
Post-AR5 new studies confirm observed impacts on the natural and 
human systems and indicate projected risks in both these systems over

time. Over the past four decades, there was a significant increase in 
the probability of the global exceedances of tropical cyclones (TCs) 
of major intensity (Kossin et  al., 2020), a trend confirmed by the 
occurrence of a growing number of intense TCs affecting the Atlantic 
and Pacific regions since AR5 (Magee et al., 2016; Bhatia et al., 2019; 
Knutson et al., 2019). Also, scientific evidence since AR5 has confirmed 
that tropical corals are presently at high risk (very high confidence) and 
if global warming exceeds 1.5°C, known coral reef restoration options 
may be ineffective (IPCC, 2018). Even achieving emission reduction 
targets consistent with the ambitious goal of 1.5°C of global warming 
under the Paris Agreement will result in the further loss of 70–90% of 
reef-building corals compared to today, with 99% of corals being lost 
under warming of 2°C or more above the pre-industrial period (high 
confidence) (Hoegh-Guldberg et al., 2018).

Additionally, since the last assessment, more robust scientific evidence 
exists on the impacts of sea level rise (SLR) and extreme sea level (ESL) 
events on small islands. Under Representative Concentration Pathways 
emission scenarios, RCP2.6, RCP4.5 and RCP8.5, many low-lying 
coastal areas at all latitudes, including small islands, will experience 
SLR and ESL events such as coastal storm surges and coastal flooding 
more frequently in the coming decades (Section 4.2.3.4.1; IPCC, 2019). 
SLR and ESL events will affect atoll islands and islands with higher 
elevations differently. New studies forecast that small islands are likely 
to experience some of the largest increases in endemic extinctions 
and may substantially contribute to future global biodiversity loss 
as well as to impaired ecosystem functioning (Fortini et  al., 2015; 
Vogiatzakis et al., 2016; Cramer et al., 2018). Scientific evidence points 
to large population reductions with an extinction risk of 100% for

endemic species within insular biodiversity hotspots by 2100 (IPBES, 
2018; Manes et al., 2021). An overarching concern since AR5 is the 
reduced habitability of small islands. Eight key risks (KRs) affecting 
the habitability of small islands are identified in this assessment and 
these are covered in the pertinent sections of this chapter which assess 
adaptation responses.
15.2.2 Points of Departure on Adaptation
New knowledge of adaptation responses used in small islands has 
grown significantly since AR5. Strategies include hard protection, land 
reclamation and permanent relocation, with improved appreciation for 
when each strategy is relevant (IPCC, 2019). Evidence of migration as 
an adaptation response to climate change remains limited (Roland and

15
2049Small Islands Chapter 15
15.3 OBSERVED IMPACTS AND PROJECTED RISKS 
15.3.3: Observed impacts &
projected risks on natural systems
/g122/g3Marine & coastal systems
/g122Freshwater systems
/g122Terrestrial biodiversity systems15.3.1: Physical basis
/g122Warming (ocean & land)
/g122Rainfall
/g122Drought
/g122Extreme events
/g122Relative Sea-Level Rise
15.3.4: Observed impacts & projected risks 
on human systems
/g122Settlements & infrastructure
/g122Human health & well-being
/g122Water security
/g122Fisheries & Agriculture
/g122Economie s
/g122Migratio n
/g122Culture
/g122Transboundary risks
15.5. ADAPTATION OPTIONS & THEIR IMPLEMEN TATION
/g122Hard protection
/g122Accommodation & Advance*
/g122Migration
/g122Ecosystem-based measures
/g122Community-based adaptation
/g122Livelihood responses
/g122Disaster Risk Management,
Early 
Warning Systems & Climate Services 15.6. ENABLERS, LIMITS & BARRIERS
15.7. CLIMAT E-RESILIENT DEVELOPMENT PATHWA YS & FUTURE SOLUTIONS
15.8. RESEARCH GAPS

/g122Island-scale data availability
/g122Vulnerability and resilience
/g122Adaptation15.3.2: Exposure and vulnerability
/g122Ecosystem dimensions
/g122Human dimensions
Key examples of compound events: Box 15.1
What measures have been implemented? What are the enablers, limits & barriers encountered?
What are priority needs to reduce uncertainty?/g122Governance
/g122Health-related strategies
/g122Finance & Risk Transfer Mechanisms
/g122Education & awareness-raising
/g122Unavailability of adequately downscaled
c
limate data
*Advance: advancing the shoreline through the creation of new elevated landLoss and damage & Small Islands: Box 15.2
Key Risks in Small Islands
15.4. DETECTION & ATTRIBUTION OF OBSERVED IMPACTSSchematic illustration of the interconnections of Chapter 15 themes
What risks exist and are likely to be  increasingly experienced by small islands?

Figure 15.1 |  Schematic illustration of the interconnections of Chapter 15 themes, including on observed impacts and projected risks (Section 15.3) and on 
adaptation options and their implementation (Sections 15.5 and 15.6).

15
2050Chapter 15 Small Islands
Curtis, 2020). Understanding of ecosystem-based adaptation (EbA) 
has improved considerably but there is medium agreement regarding 
its benefits (Doswald et  al., 2014; Nalau et  al., 2018a) and limited 
evidence and low agreement on its economic efficiency and long-term 
effectiveness (Renaud et al., 2016; Oppenheimer et al., 2019).
Since the previous assessment, integration of IKLK into adaptation is 
recognised as a major benefit in preparing and recovering from TCs 
and EbA (Narayan et  al., 2020). The roles of social capital, health-
related adaptation strategies and livelihood responses are more fully 
understood (Nalau et al., 2018b; Nunn and Kumar, 2018; Abram et al., 
2019; IPCC, 2019). Gender equity, climate justice, climate services, early 
warning systems and disaster risk reduction (DRR) (Vaughan and Dessai, 
2014; Newth and Gunasekera, 2018), which were data gaps in AR5,

have received more treatment, especially in the context of small islands. 
Stronger evidence confirms that education and awareness-raising 
enhance household and community adaptation (high confidence).
Knowledge has improved on limits to adaptation, including 
projected timeframes of limits for hard protection (high confidence) 
and EbA (medium confidence) (IPCC, 2019). There is also a better 
understanding that barriers and governance challenges vary by island 
and island groups (high confidence) and result in them having different 
adaptive capacities (IPCC, 2019). A major barrier to adaptation is 
limited information on the feasibility, outcomes and sustainability of 
adaptation responses in small islands. Moreover, limited time series 
data on monitoring and evaluation make evaluating the feasibility of 
adaptation responses difficult.
Adaptation financing for small islands has increased since AR5 
although leveraging finance is a constraint and remains complex

(Robinson and Dornan, 2017). Informal microfinancing has grown and 
risk transfer mechanisms are being explored although funding and 
access to insurance schemes are limited (Handmer and Nalau, 2019; 
Nunn and Kumar, 2019a; Petzold and Magnan, 2019). In small islands 
the methods and mechanisms to assess climate-induced losses and 
damages remain undeveloped (medium confidence) (Thomas and 
Benjamin, 2017; Handmer and Nalau, 2019).
Many small islands have experienced economic shock arising from 
COVID-19 and have had to re-direct investment previously targeting 
sustainable development (Sheller, 2020). Adaptation will be affected 
by economic contraction and indebtedness. Framing adaptation within 
climate resilient development pathways (CRDPs) that emphasize 
systems transition and are implemented at scale may bolster small 
islands’ resilience to multiple shocks such as COVID-19.
15.3 Observed Impacts and Projected Risks of 

Compared to larger landmasses, many climate change-driven impacts 
and risks are amplified for small islands. This is due largely to their 
boundedness (surrounded by ocean), their comparatively small land 
areas, and often their remoteness from more populated parts of the 
world, which restricts the global connectivity of islands. This is true on 
all types of islands (Figure 15.2).15.3.1 Synthesis of Observed and Projected Changes in 
the Physical Basis
There is increased evidence of warming in the small islands, particularly 
in the latter half of the 20th century (high confidence). The diversity 
of metrics and timescales used across studies makes it impossible to 
provide explicit comparisons; however, Table 15.1 provides a summary 
of observed changes.
Some phenomena have no demonstrable trends in a region because of 
limited observed data, these include TC frequency in the northeastern 
Pacific and Indian oceans (Walsh et  al., 2016); other phenomena

are too variable to detect an overarching trend, including rainfall in 
regions where inter-annual and decadal variabilities such as the El 
Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Decadal 
Variability, Atlantic Multidecadal Variability are dominant (Jones et al., 
2015; McGree et al., 2019).
There are also marked regional variations in the rates of SLR (Merrifield 
and Maltrud, 2011; Palanisamy et al., 2012; Esteban et al., 2019) and 
relative SLR (RSLR; that is, incorporating land movement). Various 
factors, including interannual and decadal sea level variations 
associated with low-frequency modulation of ENSO and the Pacific 
Decadal Oscillation (PDO) and vertical land motion contribute to both 
relative sea level variations and related uncertainties. Increased distant-
source swell height from extra-tropical cyclones (ETCs) also contributes 
to ESLs (Mentaschi et al., 2017; Vitousek et al., 2017). Together, these

stressors increase ESLs and their impacts, including coastal erosion and 
marine flooding and their impacts on both ecosystems and ecosystem 
services and human activities (Section 15.3.3.1 and Table 15.3).
Like observed impacts, projected impacts include some high confidence 
assessments, which are distributed across a diversity of models, 
timescales and metrics. Generalised trends, and specific projections 
when available, are provided in Table  15.2. However, actual values 
and spatial distribution of precipitation changes remain uncertain as 
they are strongly model dependent (Paeth et al., 2017). Furthermore, 
the current capabilities of climate models, to adequately represent 
variability in climate drivers including ENSO, and the topography 
of small islands limit confidence in these future changes (Cai et al., 
2015a; Harter et al., 2015; Guilyardi et al., 2016).
15.3.2 Trends in Exposure and Vulnerability
Most of the research that has been conducted on exposure and

vulnerability from climate change demonstrates that factors including 
those that are geopolitical and political, environmental, socioeconomic 
and cultural together conspire to increase exposure and vulnerability 
of small islands (Box 15.1; Betzold, 2015; McCubbin et al., 2015; Duvat 
et al., 2017b; Otto et al., 2017; Weir et al., 2017; Taupo et al., 2018; 
Barclay et al., 2019; Hay et al., 2019a; Ratter et al., 2019; Salmon et al., 
2019; Bordner et al., 2020; Douglass and Cooper, 2020; Duvat et al., 
2020a). Additional pressures on coastal and marine environments, 
including overexploitation of natural resources, may further exacerbate 
possible impacts in the future (Bell et al., 2013; Pinnegar et al., 2019; 
Siegel et al., 2019).

15
2051Small Islands  Chapter 15
Classification of small island types
Physical:
/g122>80% continental rock
/g122Usually (but not always)  
 adjacent to a large landmas s
/g122Typically larger
/g122Diverse topography (can be  
 high or low)
/g122Subject to higher storm surge  
 because of slope bathymetry
Human Exposure:
/g122Rich diversity of soils and  
 vegetation
/g122Often more resources (as  
 larger and near mainland)
/g122Inhabitants can sometimes  
 migrate to the mainlandPhysical:
/g122/g3>80% igneous rock
/g122 Sometimes volcanically active   
 or recently extinc t
/g122/g3Worldwide distribution
/g122/g3Typically high and distant   
 from larger landmasse s
/g122/g3Fertile soils, often high rainfall
/g122/g3Limited fringing coral reefs
/g122/g3Prone to landslides and flash   
 flooding 
/g122 Settlements around the coast   
 (coastal squeeze)
/g122 Inhabitants can sometimes   
 move up-slopePhysical:
/g122/g3>80% calcareous rock, reef

and unconsolidated sediments
/g122/g3Typically low and flat
/g122/g3Often with well developed coral  
 reefs (atolls )
/g122/g3Poorer soil s
/g122/g3Settlements subject to coastal  
 inundation by ‘king tides ’
/g122/g3Surface water limited
/g122/g3Often hard sea defences are  
 necessary (expensive)
/g122/g3Land reclamation to accommo  
 date growing populationsPhysical:
/g122/g3>80% calcareous rock, often  
 reef-associated
/g122/g3Commonest in those places  
 where (tectonic) uplift has been  
 occurring
/g122/g3Can be high or low
/g122/g3Often steep sided but flat topped
/g122/g3Lack of surface wate r
/g122/g3Need for collection and storage  
 of water
/g122/g3Poor soils (food insecurity )
/g122/g3Inhabitants can sometimes  
move up-slope or inlandPhysical:
/g122/g3<80 % volcanic and <80 %  
 limestone rock
/g122/g3Typically formed by alternating  
 volcanism and uplift 
/g122/g3Diverse topography
 Can be high or low
 Lack of surface wate r

/g122/g3Rich diversity of soils and  
 move up-slope or inlandContinental islands Reef islands/atolls Raised limestone islands Composite islands Volcanic islands
Figure 15.2 |  Classification of small island types showing island characteristics and elements of human exposure (based on Nunn et al., 2016; Kumar et al., 2018).

15
2052Chapter 15 Small Islands
Table 15.1 |  Observed changes in basic climate metrics.
Phenomenon Location Basic trends Specific metric Time period Reference literature
Air temp West Pacific WarmerIncrease in daily mean minimum temp by 0.14°C 
per decade1951–2015 McGree et al. (2019)
Air temp Caribbean WarmerIncrease in daily minimum temp by 0.28°C per 
decade1961–2010 Stephenson et al. (2014)
Air temp Mediterranean WarmerIncrease in annual mean surface temp 
0.19–0.25°C per decade1960–2005 Mariotti et al. (2015)
Land and sea 
tempMediterranean WarmerAnnual mean temperatures are now 1.54°C 
above the 1860–1890 level for land and sea(MedECC, 2020)
Rainfall Mediterranean DrierDecrease in annual mean precipitation by 
−0.6 mm d–1 and decade1960–2005Mariotti et al. (2015); Ducrocq 
Rainfall Pacific Ocean No clear pattern No significant long-term trends in rainfall 1951–2015 McGree et al. (2019)
Rainfall Indian Ocean No clear pattern 1983–2015 Nguyen et al. (2018)

Rainfall Caribbean No clear patternNo significant long-term trends in rainfall in the 
Caribbean over the 20th century1901–2012 Jones et al. (2015)
Drought CaribbeanLow confidence in the 
direction of changeInconsistent between sub-regions and not 
statistically significant1950–2016 Herrera and Ault (2017)
Drought Pacific OceanLow confidence in the 
statistically significant in the tropical Pacific. 
Significant decrease in Hawaii and sub-tropical 
South Pacific1951–2015McGree et al. (2016); McGree 
Tropical Cyclones North AtlanticIncrease in intensity and 
decrease in frequency1975–2009 Walsh et al. (2016)
Tropical Cyclones Western North Pacific Decreasing frequencyDecrease in frequency except over central North 
Pacific1977–2010 Walsh et al. (2016)
Tropical Cyclones South PacificIncrease in intensity and 
decrease in frequency1989–2009Walsh et al. (2016)
Kuleshov et al. (2020)

Tropical Cyclones Indian Ocean No clear pattern Poor data coverage 1961–2008Tauvale and Tsuboki (2019); 
RSLR East Caribbean Greater than average 3–5 mm yr–11993–2014 Becker et al. (2019)
RSLR West/North Caribbean Greater than average 2.5–3 mm yr–11993–2014 Becker et al. (2019)
RSLR Western Tropical Pacific Greater than average 5–11 mm yr–11993–2014 Becker et al. (2019)
RSLR Mauritius/Indian Ocean Greater than average 4 mm yr–11993–2014 Becker et al. (2019)
RSLR Rodrigues/Indian Ocean Greater than average 6 mm yr–11993–2014 Becker et al. (2019)
Notes:
RSLR: relative sea-level rise
Furthermore, these factors exacerbate climate change-induced 
problems such as coastal flooding and erosion faced by small islands. 
These impacts continue to worsen, putting small islands at increasingly 
higher risk to the impacts of climate change (Box  15.1). There are 
multiple stressors that affect the vulnerability of small islands to 
climate change (McNamara et al., 2019).

The problems of increasing exposure and vulnerability are most clearly 
seen in atoll islands. For example, in the capital of Tuvalu, economic 
stressors, food-related stressors and overcrowding make the islands 
much more vulnerable to climate impacts including changing 
precipitation patterns, ESLs, intense strong winds, warming sea 
surface temperature (SST) and ocean acidification (McCubbin et al., 
2015). Small islands, in trying to address the problem of limited land 
availability, put in place practices that lead to increasing exposure 
for island people. In Majuro, Marshall Islands (Ford, 2012), Tarawa, Kiribati (Biribo and Woodroffe, 2013; Duvat, 2013), and the Maldives 
Islands (Kench, 2012; Naylor, 2015; Duvat and Magnan, 2019b), 
population growth has led to land reclamation and the building of 
coastal protection structures, such as seawalls. Land reclamation and 
coastal protection structures negatively impact coastal and marine

ecosystems, including reefs and mangroves, which compromise the 
protection services that they deliver to island communities through 
wave energy attenuation and sediment supply (Gracia et al., 2018; 
Curnick et  al., 2019; Duvat and Magnan, 2019a) and may impact 
the long-term sustainable adaptive planning of islands (Giardino 
et al., 2018). In addition, these construction activities disrupt natural 
coastal processes, thereby causing coastal erosion, which in turn 
increases the risk of flooding (Yamano et  al., 2007; Duvat et  al., 
2017b) (Figure 15.3). This becomes a vicious cycle, with more land 
reclamation necessary to accommodate growing populations. Land 
reclamation requires stabilisation by protection structures, which

15
2053Small Islands  Chapter 15
then contributes to environmental degradation that increases the 
exposure and vulnerability of the communities living in these atolls 
(Duvat et al., 2017b).
15.3.3 Observed Impacts and Projected Risks on Natural 
Systems
15.3.3.1 Impacts on Marine and Coastal Systems
15.3.3.1.1 Submergence and flooding of islands and coastal areas
Recent studies confirmed that observed ESL events causing extensive 
flooding generally resulted from compound effects, including the 
combination of SLR (Section  3.2.2.2 and Cross-Chapter Box  SLR 
in Chapter 3) with ETCs, TCs and tropical depressions (WGI AR6 
Sections  11.7.1 and 11.7.2, Seneviratne, 2021), ENSO-related high-
water levels associated with high or spring tide and/or local human 
disturbances amplifying impacts (high confidence). For example, the 
major floods that occurred in 1987 and 2007 in the Maldives involved 
the combination of distant-source swells and high spring tides and the

settlement of reclaimed low-lying areas (Box 15.1; Wadey et al., 2017). 
In the Tuamotu atolls, French Polynesia, the 1996 and 2011 floods were 
due to the combination of distant-source swells causing lagoon filling 
and the obstruction of inter-islet channels by human-built structures 
(Canavesio, 2019). In 2011, the flooding of the lagoon-facing coast of 
Majuro Atoll, Marshall Islands, resulted from the combination of high 
sea levels occurring during La Niña conditions and seasonally high 
tides (Ford et al., 2018). Another example is the widespread flooding 
caused by distant TC Pam (2015) in Kiribati and Tuvalu, which was 
attributed to the strong swell generated, the long duration of the 
event and exceptionally high regional sea levels (Hoeke et al., 2021). 
On high tropical islands, major floods often occurred during TC events, 
due to the cumulative effects of storm surge and river flooding, the impacts of which were exacerbated by human-induced changes to

natural processes in urban areas. This, for example, occurred in 2014 
(TC Bejisa) in Reunion Island, France, in a harbour area favourable 
to water accumulation (Duvat et al., 2016); in 2015 (TC Pam) in Port 
Vila, Vanuatu, where urbanisation and human-induced changes to the 
river exacerbated flooding (Rey et al., 2017); and in 2017 (TC Irma) in 
Saint-Martin, Caribbean, where urbanisation had the same effect (Rey 
et al., 2019). Successive tropical depressions generating heavy rains 
were also involved in extensive flooding, for example, in 2012 in Fiji 
(Kuleshov et al., 2014) and in 2014 in the Solomon Islands (Ha’apio 
Reconstructions of past storm surges and modelling studies 
assessing storm surge risk similarly highlighted high variations of 
risk along island coasts, due to variations in exposure, topography 
and bathymetry (high confidence). For example, the storm surge 
caused by TC Oli (2010) on the high volcanic island of Tubuai, French

Polynesia, ranged from a few centimetres to 2.5 m, depending on coast 
exposure (Barriot et al., 2016). Investigating the contribution of reef 
characteristics to variations in wave-driven flooding on Roi-Namur 
Island, Kwajalein Atoll, Marshall Islands, Quataert et al. (2015) found 
that the coasts fronted by narrow reefs with steep fore reef slopes 
and smoother reef flats are the most flood-prone. Modelling studies 
assessing storm surge risk in Fiji (McInnes et  al., 2014) and Samoa 
(McInnes et al., 2016) confirmed the influence of coast exposure and 
water depth on risk distribution. In Apia, Samoa, Hoeke et al. (2015, p. 
1117) found ‘differences in extreme sea levels in the order of 1 m at 
spatial scales of less than 1 km’ and estimated (p. 1131) that a ‘1 m 
SLR relative to constant topography increases wave energy reaching 
the shore by up to 200% during storm surges.’ These studies reaffirmed 
the main control exerted by SLR on ESL events and associated storm

surges compared to ENSO (high confidence). In Hawaii and the 
Caribbean, SLR is projected to exponentially increase flooding, with 
nearly every centimetre of SLR causing a doubling of the probability of Population living in small islands
that may be exposed to 
coastal inundation
by 2100 under RCP4.5
For selected islands, each dot 
represents the corresponding 
percentage of the population 
occupying vulnerable land, 
coastal inundation either by 
permanently falling below 
mean higher high water 
(MHHW), or temporarily falling 
below the local annual flood 
height.
<10%10–30%31–50%>50%Percentage of island’s
population exposed to
Figure 15.3 |  Percentage of current population in selected small islands occupying vulnerable land (the number of people on land that may be exposed 
to coastal inundation—either by permanently falling below MHHW, or temporarily falling below the local annual flood height) in 2100 under an RCP4.5

scenario (adapted from Kulp and Strauss, 2019, using the CoastalDEM_Perm_p50 model).  Positions on the map are based on the capital city or largest town.

15
2054Chapter 15 Small Islands
Table 15.2 |  A small subset of projected changes in basic climate metric. Med: Mediterranean; n.c.: no change.
Phenome-
nonLocationGeneral 
trendMetricSpecific projections 
2040–2060Specific projections 
2080–2100 Comments Reference
RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5
Air 
temperatureCaribbeanHotter, 
the EastMonthly mean 
temperature compared 
to 1971–2000n.a. 1.2°C rise 1.6C rise 3.0°C riseSpecific to Lesser 
AntillesBowden et al. 
(2020); Cantet 
et al. (2014)
East 
AtlanticHotterAverage annual 
to 1971–20001.5–2°C 
rise2.5°C rise n.a. n.a.Low-confidence, 
specific to Sao Tome 
and PrincipeChou et al. (2020)
MedHotter, 
summerAverage maximum daily 
temperature during 
summer compared to 
1970–20001.6–1.9°C 
rise2–2.5°C 
risen.a. n.a.Specific to Sicily, Crete 
and CyprusVarotsos et al. 
(2021)
Pacific HotterAverage temperature 
1986–20050.5–1.5°C 
rise1.0–
2.0°C 
rise1.0–
2.0°C

rise2.0–4.0°C 
riseConsistent in tropical 
latitudesLough et al. 
(2016)
Global 
small 
islandsHotterHeat index compared to 
1986–20051°C rise 1.5°C rise1.3°C 
rise2.8°C riseEquatorial, coastal and 
continental islands 
hotter than oceanicHarter et al. 
(2015)
ENSO PacificMore frequent 
extreme 
eventsFrequency compared to 
~1900–1999n.a. n.a. n.a.100% more 
El Niños, 
73–100% 
more La 
NiñasHigh natural variability 
limits statistical 
significance in related 
patternsCai et al., (2014); 
Cai et al. (2015b)
Inconclusive 
change in 
variabilityAmplitude change 
1979–20050.02°C 
drop0.01°C 
rise0.04°C 
drop0.04°C riseSpecific projections 
are not statistically 
significantCai et al., (2018); 
Beobide-Arsuaga 
PrecipitationEast 
CaribbeanSlightly wetter, 
more extreme 
seasonalityTotal rainfall 
compared to wet/dry 
season compared to 
1971–2001n.a. n.a.5% 
rise/10% 
drop8% 
rise/15% 
dropSignificant local 
variabilityCantet et al. 
(2014)
West/North

CaribbeanDrierAnnual rainfall 
compared to 1986–
2005; consecutive 
dry days compared to 
1961–1990n.a.9% less 
rainn.a.Up to 327% 
more dry 
daysSpecific to Puerto Rico 
and US Virgin IslandsStennett-Brown 
Bowden et al. 
(2020)
East 
AtlanticInconclusive 
changeMonthly rainfall 
1971–200010–25-
mm rise10–25-
mm dropn.a. n.a.Low-confidence, 
West PacificWetter, 
especially 
after 
mid-centuryAnnual average rainfall 
compared to 1971-
–20052% rise 6% rise 3% rise 8% riseLow-confidence, 
specific to BorneoSa’adi et al. 
(2017)
Central 
PacificDrier, more 
extreme 
seasonalityTotal rainfall compared 
to 1975–200515% drop20% 
drop17% 
drop30% dropLow-confidence, 
specific to HawaiiTimm et al. (2015)
Southwest 
Indian 
OceanDrier during 
the wet 
season, 
south of 10SAverage change in daily 
rainfall compared to 
1971–2000n.a. n.a. n.a.0.2 mm d–1 
dropLow confidenceLazenby et al. 
(2018)

MedDrier, but 
highly variedAnnual mean 
precipitation compared 
to 1960–199070–100-
mm drop60–150- 
mm dropn.a. n.a.Specific to Malta; no 
significant change 
in Sicily, Crete and 
CyprusVarotsos et al. 
(2021)
Global 
small 
islandsSlightly 
wetter, highly 
variableMean annual 
to 1986–2005<1% rise <1% rise 1.8% rise 3.2% riseConfidence limited 
by high standard 
deviationHarter et al. 
(2015)

15
2055Small Islands  Chapter 15
Phenome-
Tropical 
CyclonesNorth 
Indian 
OceanMore storms 
in the west, 
fewer in the 
eastFrequency compared to 
1990–2013n.a. n.a. n.a.30–60% 
rise/20–40% 
dropSpecific to Arabian 
sea/Bay of BengalBell et al. (2020)
South 
Indian 
OceanFewer storms, 
fewer strong 
storms in eastStorm/Category 4–5 
frequency compared to 
1979–2010n.a. n.a. n.a.20–40% 
drop/0–20% 
dropBell et al. (2019a)
Northwest 
PacificSlightly more 
and stronger 
storms at 
increasingly 
high latitudesStorm density compared 
to 1970–2000; 
poleward shift in 
annual mean of 
location of maximum 
intensity compared to 
1980–2005n.a. n.a. n.a.15–40% 
rise; 0.2°10–40N, 140–170EKossin et al. 
(2016); Chand 
Southwest 
and 
low-latitude 
PacificLess frequent 
stormsStorm density compared 
to 1970–2000n.a. n.a. n.a.0–20% 
drop/20–

30% dropSouth/North Pacific up 
to 20N, 100–140EBell et al. (2019a) 
Chand et al. 
(2019)
Northeast 
stormsStorm frequency 
1970–2016n.a. n.a. n.a. 2–13% dropNo data for Southern 
HemisphereBell et al. (2019b)
Central 
North 
PacificMore and 
stronger 
stormsMean annual TC/
Category 4–5 
composition compared 
to 1979–2010n.a. n.a. n.a. 31–88% rise Specific to HawaiiYoshida et al. 
(2017)
CaribbeanSlightly fewer 
stormsMinor/major cyclones 
1984–2013n.a.12% 
drop/n.c.n.a. n.a.Specific to lesser 
AntillesCantet et al. 
(2021)
East 
AtlanticMore storms 
and slightly 
more frequent 
intense stormsStorms per decade 
1979–2010n.a. n.a. n.a. 0–3 riseSpecific to latitude 
>15NYoshida et al. 
(2017)
Extratropical 
cycloneMedDecreased 
frequency 
but increased 
intensityFrequency of 
storms compared to 
1986–2005n.c. n.a.12% 
dropn.a.González-Alemán 

flooding (Taherkhani et al., 2020). Simulations of SLR-induced flooding 
resulting from the combination of (a) direct marine flooding, (b) flow 
reversal in drainage networks caused by extreme tide levels and (c) the 
elevation of groundwater levels, at Honolulu, Hawaii, highlighted the 
major influence of this latter component (which is the most difficult to 
manage), as well as the increase of the proportion of triple-mechanism 
flooding as sea level rises (Habel et al., 2020). Where coral reefs buffer 
flooding through wave attenuation, flooding will be further aggravated 
by reef decline over time (Section 15.3.3.1.3).
Larger-scale studies confirmed that projected changes in the wave 
climate superimposed on SLR will rapidly increase flooding in small 
islands, despite highly contrasting exposure profiles between ocean 
sub-regions (high confidence) (Shope et  al., 2016; Mentaschi et  al., 
2017; Shope et al., 2017; Vitousek et al., 2017; Morim et al., 2019).

In particular, Vitousek et  al. (2017) showed that even a 5–10-cm 
additional SLR (expected for ~2030–2050) will double flooding 
frequency in much of the Indian Ocean and Tropical Pacific, while TCs 
will remain the main driver of (rarer) flooding in the Caribbean Sea and Southern Tropical Pacific (Figure 15.3). Some Pacific atoll islands, 
which already experience major floods, will likely undergo annual 
wave-driven flooding over their entire surface from the 2060s–2070s 
(Storlazzi et al., 2018) to 2090s (Beetham et al., 2017) under RCP8.5, 
although future reef growth may delay the onset of flooding (limited 
evidence, low agreement) (key risk KR2 in Figure 15.5).
15.3.3.1.2 Reef island destabilisation and coastal erosion
Over the past three to five decades, shoreline changes were dominated 
by stability on reef islands and erosion on high islands; attribution of 
observed erosion to SLR and other climate change-related drivers is

challenged by the complex interplay of multiple climatic, ecological 
and human drivers (high confidence). Since the 1950s–1970s, and 
even in regions exhibiting higher than global-averaged SLR rates, 
atoll islands maintained their land area (high confidence). A literature 
review including 709 Indian Ocean and Pacific Ocean atoll islands 
showed that 73.1% of these islands were stable in area, while, 
respectively, 15.5% and 11.4% increased and decreased in area

15
2056Chapter 15 Small Islands
(Duvat, 2018). The rates of change did not correlate with SLR rates, 
suggesting that the impact of SLR on island land area was obscured 
by other climate drivers and human disturbances on some islands 
(high confidence) (Kench et al., 2015; McLean and Kench, 2015; Duvat, 
2018). However, reef island disappearance and reduction in land area 
was clearly observed in New Caledonia and the Solomon Islands, and 
was attributed to the synergistic interactions of gradual SLR with 
stronger trade winds causing higher sea levels and local tectonics in 
the Solomon Islands (Albert et al., 2016; Garcin et al., 2016). Despite 
important knowledge gaps on coastal erosion in high tropical islands, 
recent studies confirmed increasing shoreline retreat and beach loss 
over the past decades, mainly due to TC and ETC waves and human 
disturbances (high confidence) (e.g., in the Caribbean region: Anguilla,

Saint-Kitts, Nevis, Montserrat, Dominica and Grenada (Cambers, 2009; 
Reguero et al., 2018)), and Pacific (Hawaii (Romine and Fletcher, 2013); 
Tubuai, French Polynesia (Salmon et  al., 2019)) and Indian Oceans 
(Anjouan, Comoros (Ratter et al., 2016).
Despite storm-induced erosion prevailing along some shoreline 
sections, recent studies reaffirmed the contribution of TC and ETC 
waves to coastal and reef island vertical building through massive reef-
to-island sediment transfer (high confidence). For example, TC Ophelia 
(1958) and Category 5 TC Fantala (2016), which eroded the islands 
of Jaluit Atoll, Marshall Islands (Ford and Kench, 2016), and Farquhar 
Atoll, Seychelles (Duvat et  al., 2017c), respectively, also contributed 
to island and beach expansion. Likewise, tropical depressions can 
have constructional effects, as reported on Fakarava Atoll, French 
Polynesia (Duvat et  al., 2020b). On Saint-Martin/Sint Maarten and

Saint-Barthélemy, the 2017 hurricanes, which caused marked shoreline 
retreat at most beach sites, also enabled beach formation and beach 
ridge development along some natural coasts (Duvat et  al., 2019a; 
Pillet et al., 2019). Similarly, El Niño and La Niña were involved in rapid 
and highly contrasting shoreline changes (high confidence), including 
reef island accretion in the Ryukyu Islands, Japan (Kayanne et  al., 
2016), beach shifts on Maiana and Aranuka atolls, Kiribati (Rankey, 
2011), and beach erosion on Hawaii, USA (Barnard et  al., 2015). 
These contrasting shoreline responses were, respectively, due to coral 
reef degradation from past bleaching events providing material to 
islands, wave directional shifts, and increased wave energy. The role 
of bleaching events in increasing short-term sediment generation in 
atoll contexts was confirmed by a study conducted on Gaafu Dhaalu 
Atoll, Maldives, which reported an increase of sediment production

from ~0.5 kg CaCO 3 m–2 yr-1 to ~3.7 kg CaCO 3 m–2 yr-1 between 2016 
(pre-bleaching) and 2019 (bleaching + 3 years) (Perry et al., 2020).
There is high confidence that accelerating SLR and increased wave height 
will affect the geomorphology of reef islands (Baldock et al., 2015; Costa 
et al., 2019; Tuck et al., 2019) and coastal systems on high islands (Grady 
et  al., 2013; Barnard et  al., 2015; Bindoff et  al., 2019), and that the 
responses of these systems will highly depend on changes in boundary 
conditions (wave regime and direction, exposure to extreme events, 
impacts of ocean warming and acidification on supporting ecosystems, 
bathymetry and reef flat roughness) and the degree of disturbance of 
their natural dynamics by human activities (Smithers and Hoeke, 2014; 
McLean and Kench, 2015; Bheeroo et al., 2016; Ratter et al., 2016; Shope 
et al., 2016; Duvat et al., 2017a; Kench and Mann, 2017; Kench et al.,

2018; Duvat et  al., 2019a). Reef islands and beach and beach-dune systems that are not disturbed by human activities are, respectively, 
expected to migrate lagoonward (Webb and Kench, 2010; Albert et al., 
2016; Beetham et al., 2017; Costa et al., 2019; Tuck et al., 2019) and 
landward (Bindoff et al., 2019), and to also experience increased erosion 
as well as changes in configuration, volume and elevation (Kench and 
Mann, 2017; Tuck et al., 2019) (Bramante et al., 2020; Kane and Fletcher, 
2020). Small reef islands and narrow coastal systems affected by human 
disturbances will increasingly be at risk of disappearance due to SLR 
(KR2 in Figure 15.5), enhanced sediment loss caused by extreme events 
(Duvat et  al., 2019a) and/or human activities (high confidence), as 
reported in Hawaii (Romine and Fletcher, 2013), Puerto Rico (Jackson 
et al., 2012), Sicily (Anfuso et al., 2012), and Takuu, Papua New Guinea

(Mann and Westphal, 2014). SLR will also increase coastal erosion in the 
Mediterranean Sea, (e.g., in the Aegean Archipelago, Greece (Monioudi 
et al., 2017), and Mallorca, Spain (Enríquez et al., 2017).
15.3.3.1.3 Impacts on marine and coastal ecosystems
Loss of marine and coastal biodiversity and ecosystem services is a key 
risk in small islands (see KR1 in Figure  15.5). Coral bleaching caused 
by elevated water temperatures is the most visible and widespread 
manifestation of a climate change impact on coastal ecosystems in 
most small islands but is far from being the only one (Sections 3.4.2.1 
and Section  5.3.4; Spalding and Brown, 2015; Hoegh-Guldberg et  al., 
2017; IPCC, 2018; Bindoff et al., 2019; Sully et al., 2019). Severe coral 
bleaching, together with declines in coral abundance have been 
documented in many small islands, especially those in the Pacific Ocean 
and Indian Ocean (e.g., Guam, Fiji, Palau, Vanuatu, Chagos, Comoros,

Mauritius, Seychelles, and the Maldives (high confidence) (Box  15.1; 
Golbuu et  al., 2007; Woesik et  al., 2012; Perry and Morgan, 2017; 
Hughes et al., 2018). During severe bleaching events, not only do reefs 
lose a significant amount of live coral cover, but they also experience 
a decrease in growth potential, and thus reef erosion surpasses reef 
accretion (Perry and Morgan, 2017). Median return time between two 
severe bleaching events has diminished steadily since 1980 and is now 
only 6 years (e.g., Hughes et al., 2017b; Hughes et al., 2018) and is often 
associated with warm phase of ENSO events (high confidence) (Lix et al., 
2016). Modelling of both bleaching and ocean acidification effects under 
future climate scenarios suggested that some Pacific small islands (e.g., 
Nauru, Guam, Northern Marianas Islands) will experience conditions that 
cause severe bleaching on an annual basis before 2040 and that 90%

of the world reefs are projected to experience conditions that result in 
severe bleaching annually by 2055 (medium confidence) (van Hooidonk 
et al., 2016). Models are currently predicting the large-scale loss of coral 
reefs by mid-century under even low-emission scenarios. Even achieving 
emission reduction targets consistent with the ambitious goal of 1.5°C 
of global warming under the Paris Agreement will result in the further 
loss of 70–90% of reef-building corals compared to today, with 99% of 
corals being lost under warming of 2°C or more above the pre-industrial 
period (high confidence) (Hoegh-Guldberg et al., 2018).
Satellite data and local field studies at 3351 sites in 81  countries 
including small islands show that not all coral reefs are equally 
exposed to severe temperature stress events, and even similar coral 
reefs exposed to similar conditions show local and regional variation 
and species-specific responses (Sully et  al., 2019). There is great

variability in terms of sensitivity of corals to climate change, as also

15
2057Small Islands  Chapter 15
demonstrated in the Comoros Archipelago (Cowburn et al., 2018), in 
the Pacific (Fox et al., 2019; Mollica et al., 2019; Romero-Torres et al., 
2020) and globally (Sully et al., 2019; McClanahan et al., 2020). It has 
been hypothesised that low-latitude tropical reefs bleached less than 
those in higher latitudes because: (a) of the geographical differences 
in species composition, (b) of the higher genotypic diversity at low 
latitudes, and (c) some corals were pre-adapted to thermal stress 
because of consistently warmer temperatures at low latitude prior to 
thermal stress events (Sully et al., 2019). However, latitudinal variation 
was not reported in other global surveys of coral bleaching occurrence 
(Donner et  al., 2017; Hughes et  al., 2017a; Hughes et  al., 2017b; 
McClanahan et al., 2019). Ainsworth et al. (2016) and Ateweberhan 
et  al. (2013) showed that coral bleaching can be mitigated by pre-

exposure to elevated temperatures. Regionally, recovery is also highly 
variable. While some reefs in the Seychelles and Maldives were shown 
to recover to pre-disturbance levels of coral cover after previous 
bleaching events (Box 15.1; Pisapia et al., 2016; Koester et al., 2020), 
other reefs underwent seemingly permanent regime shifts toward 
domination by fleshy macro algae (Graham et  al., 2015), or major 
declines in carbonate budgets, and thus the capacity of reefs to sustain 
vertical growth under rising sea levels (Perry and Morgan, 2017).
Despite their vital social and ecological value, substantial declines in 
seagrass communities have been documented in many small islands 
(Section 3.4.2.5; Arias-Ortiz et al., 2018; Kendrick et al., 2019; Brodie 
et al., 2020), including Fiji (Joseph et al., 2019), Reunion Island (Cuvillier 
et al., 2017), Bermuda, Cayman Islands, US Virgin Islands (Waycott et al., 
2009), Kiribati (Brodie et  al., 2020), Federated States of Micronesia,

and Palau (Short et  al., 2016), but attribution of such declines to 
climatic influences remains weak (low confidence). The impact of 
climate change on seagrasses goes beyond the loss of seagrass but 
includes acceleration of seagrass decomposition (Kelaher et al., 2018), 
palatability (Jimenez-Ramos et al., 2017) and the cumulative effect of 
warming and eutrophication (Ontoria et al., 2019). Seagrasses face a 
multitude of threats including physical disturbance and direct damage 
caused by rapidly growing human populations, declines in water 
quality, and coastal erosion (Short et al., 2016). Experimental studies 
have shown increased mortality, leaf necrosis, and respiration when 
seagrasses are exposed to higher-than-normal temperatures (Hernan 
et al., 2017). As such, seagrass meadows growing near the edge of their 
thermal tolerance are at risk from rising temperatures (Pedersen et al., 
2016). In the Mediterranean, seagrass meadows are already showing

signs of regression, which may have been aggravated by climate change 
(high confidence). Some studies suggest seagrasses have potential for 
acclimation and adaptation (Duarte et al., 2018; Ruiz et al., 2018; Beca-
Carretero et al., 2020). Chefaoui et al. (2018) attempted to forecast the 
distribution of two seagrasses in the future, including around the islands 
of Cyprus, Malta, Sicily and the Balearic Islands. Under the worst-case 
scenario, Posidonia oceanica was projected to lose 75% of suitable 
habitat by 2050. Conversely, it has been suggested that seagrasses 
could actually benefit from an increase in anthropogenic CO 2because 
of increased growth and photosynthesis (Hopley et al., 2007; Waycott 
et al., 2011; Sunday et al., 2016; Repolho et al., 2017). However, Collier 
et al. (2017) argued that when faced with increased heat waves, thermal 
stress will rarely be offset by the benefit of elevated CO 2 and therefore

that the widespread belief that seagrasses will be a ‘winner’ under 
future climate change conditions seems unlikely (low confidence).Since 2011, the Caribbean region has been experiencing unprecedented 
influxes of the pelagic seaweed Sargassum. These extraordinary 
sargassum ‘blooms’ have resulted in mass strandings of sargassum 
throughout the Lesser Antilles, with significant damage to coastal 
habitats, mortality of seagrass beds and associated corals (van 
Tussenbroek et al., 2017), as well as consequences for fisheries and 
tourism. Whether or not such events are related to long-term climate 
change remains unclear; however, it has been suggested that the influx 
may be related to strong Amazon discharge, enhanced West African 
upwelling, together with rising seawater temperatures in the Atlantic 
(low confidence) (Oviatt et al., 2019; Wang et al., 2019). Since 2011, the 
Pacific atoll nation of Tuvalu has also been affected by algal blooms,

the most recent being a large growth of Sargassum on the main atoll 
of Funafuti, and this phenomenon has been related to anthropogenic 
eutrophication and high seawater temperatures (De Ramon N’Yeurt 
and Iese, 2014).
Mangroves face serious risks from deforestation and unsustainable 
coastal development (Section  3.4.2.5; Gattuso et  al., 2015). Large-
scale die-offs around many small islands suggest that mangroves face 
increased risks from climate change (Sippo et  al., 2018). Mangrove 
seaward edge retreat has been demonstrated in American Samoa and at 
Tikina Wai in Fiji, in Bermuda, West Papua, Grand Cayman and attributed 
to long-term SLR or tectonic subsidence (Ellison, 1993; Ellison, 2005; 
Gilman et al., 2007; Ellison and Strickland, 2015). Inundation-related 
mortality of mangroves could, in theory, be mitigated if mangrove 
substrates can ‘keep up’ with rising sea level by accretion. Pacific 
Island studies using radionuclides (e.g., 210Pb, 137Cs) have suggested

that most mangroves are keeping up with current rates of SLR (Alongi, 
2008; MacKenzie et  al., 2016), while surface elevation tables (SETs) 
suggest otherwise. Lovelock et al. (2015) reported that nearly 70% of 
the mangroves monitored with SETs are not keeping up with current 
SLR rates. If SLR exceeds 6 mm yr–1, mangroves may be unable to 
maintain their elevation relative to sea level, a threshold likely to be 
surpassed in the next 30 years under high emission scenarios (Ellison, 
1993; Saintilan et al., 2020). In these worst-case scenarios, flooding 
would result in tree, root and rhizome death and an abrupt change 
in elevation through peat collapse (Krauss et al., 2010; Lang’at et al., 
2014), creating a positive feedback loop between SLR and elevation 
loss. Geomorphology, hydrology, tidal range and suspended sediments 
are important factors that will determine if mangroves will survive 
increased rates of SLR (Lovelock et  al., 2015; Sasmito et  al., 2015;

Rogers et al., 2019). TCs can cause extensive damage to mangroves 
(Short et  al., 2016). While immediate physical damage is often 
considerable, trees can sometimes recover by re-foliating, re-sprouting 
or regenerating (Kauffman and Cole, 2010). Examples of substantive 
mangrove recovery include the regrowth of trees in the Bay Islands 
of Honduras following Hurricane Mitch (October 1998) (Fickert, 2018) 
and in the Nicobar Islands, India, following the December 2004 Indian 
Ocean Tsunami (Nehru and Balasubramanian, 2018).
Sandy beaches are an important ecosystem in small islands, with high 
socioeconomic as well as ecosystem services value (Ellison, 2018). 
Turtles and many seabirds nest just above the high-water mark on 
sandy beaches or among sand dunes, but TCs, rising seas, storm surges 
and heavy rainfall as well as inappropriate coastal development can 
erode beaches (Section 15.3.1.2) resulting in damage to nests and eggs

15
2058Chapter 15 Small Islands
(Fuentes et al., 2011). Beach-nesting turtle populations are projected 
to become threatened around many small islands as a result of future 
climate change (e.g., Bonaire – Netherlands Antilles (Fish et al., 2005), 
Bioko Island – Equatorial Guinea (Veelenturf et al., 2020), Cyprus (Varela 
et al., 2019), Raine Island – Australia (Pike et al., 2015)), although other 
populations such as those around the Cape Verde Islands are projected 
to remain relatively robust (Abella Perez et al., 2016). Turtles are also 
threatened by temperature rise around some small islands as warmer 
temperatures on nesting beaches can lead to an unbalanced sex ratio in 
the population (e.g., St. Eustatius island, (Laloë et al., 2016)).
15.3.3.1.4 Marine and coastal ecosystem services
Intact coral reefs (Woodhead et al., 2019), seagrass meadows (Hejnowicz 
et al., 2015) and mangroves (UNEP , 2014b) (Friess, 2016) provide a variety

of ecosystem services that are key to island communities, including 
provisioning services (e.g., timber, fisheries, aquaculture), regulating 
services (e.g., coastal protection, carbon storage, filtering of pollutants), 
cultural services (Pascua et al., 2017) as well as supporting community 
resilience (Förster et al., 2019). If coastal ecosystems are degraded and 
lost, then the benefits they provide are also lost (Oleson et al., 2018; 
Förster et  al., 2019; Brodie et  al., 2020). In small islands where the 
risk of loss to ecosystem services is high (Cross-Chapter Box DEEP in 
Chapter 17), many of these ecosystem services cannot be easily replaced 
(medium confidence). The beneficial role that coral reefs play in coastal 
protection through wave attenuation, and therefore enhancing climate 
resilience in small islands, has been extensively studied (e.g., Elliff and 
Silva, 2017; Harris et al., 2018; Reguero et al., 2018). Indeed, it has been

demonstrated that in small islands (such as the Cayman Islands, Grenada, 
Bahamas) averted damages as a result of protecting intact coral reefs 
can be considerable when expressed as a percentage of GDP (Beck et al., 
2018). Ferrario et al. (2014) conducted a global meta-analysis including 
many small islands across the Atlantic, Pacific and Indian oceans and 
found that coral reefs reduce wave height by an average of 84% (and 
wave energy by 97%) and that reef crests alone dissipate most of this 
energy. Based on another meta-analysis of 69 case studies worldwide 
(wave heights measured before and after the habitat), Narayan et al. 
(2016) observed that coral reefs, mangroves and seagrass reduced 
wave height by 70%, 31% and 36%, respectively (Figure 15.4) and thus 
perform an essential role in protecting human lives and livelihoods (high 
confidence). Post-TC studies have provided additional evidence for the 
protection services offered by coastal ecosystems. On some Caribbean

islands (e.g., Saint-Martin/Sint Maarten) where the dense indigenous 
vegetation belt was preserved, the vegetative structure buffered the 
waves of TCs Irma and José (2017), reducing the extent of marine 
inundation and shoreline retreat to a 30-m-wide coastal strip against 
values >160 m in deforested areas (Duvat et  al., 2019a; Pillet et  al., 
2019). By contrast, the destruction of mangrove ecosystems, even a few 
trees around the fringes, can accelerate coastal erosion, as exemplified 
by observations in Micronesia (Krauss et al., 2010; Nunn et al., 2017a).
As corals, mangroves and seagrasses disappear, so do fish and other 
dependent organisms that directly benefit industries such as ecotourism 
and fisheries (high confidence) (Graham et al., 2015; Cinner et al., 2016). 
These impacts are sometimes exacerbated by catastrophic events such 
as tropical storms and marine heatwaves that destroy habitats and

hence the resources upon which coastal fisheries depend (Sainsbury et  al., 2018). There is high confidence that climate change impacts, 
together with local human disturbances, will continue to denude coastal 
and marine ecosystem services in many small islands with serious 
consequences for vulnerable communities (Elliff and Silva, 2017; Bindoff 
15.3.3.2 Impacts on Freshwater Systems
Freshwater systems on small islands are exposed to dynamic climate 
impacts and are considered to be among the most threatened on the 
planet (key risk 3 in Box 15.1; Settele et al., 2014; IPCC, 2018; Butchart 
et al., 2019). Hoegh-Guldberg et al. (2019) estimated that freshwater 
stress on small islands would be 25% less with a warming of 1.5°C or 
less as compared to 2.0°C. While some island regions are projected 
to experience substantial freshwater decline, an opposite trend is 
observed for some western Pacific and northern Indian Ocean islands

(Holding et al., 2016; Karnauskas et al., 2016). Island topography and 
ecophysiology influence water storage capacity and rainfall response 
potential (Dunn et  al., 2018). On high volcanic and granitic islands, 
freshwater ecosystems are often closely connected with coastal 
spaces, and changes in freshwater supply from river systems have 
direct implications for salinity and sediment loads (high confidence) 
(Yang et al., 2015; Zahid et al., 2018). Climate impacts on streamflow 
patterns in tropical islands also create shifts in water supply for 
downstream users and habitat conditions for organisms supporting 
a wide range of ecosystem services (high confidence) (Strauch et al., 
2015; Frazier and Brewington, 2019; Frauendorf et al., 2020).
Projected changes in aridity are expected to impose freshwater stress 
on many small islands, especially SIDS (high confidence).These changes 
are congruent with drought risk projections for Caribbean SIDS (Lehner

et al., 2017; Taylor et al., 2018) and aligned with observations from the 
Shared Socioeconomic Pathway (SSP) 2 scenario, where a 1°C increase 
in temperature (from 1.7°C to 2.7°C) could result in a 60% increase in 
the number of people projected to experience severe water resources 
stress from 2043 to 2071 (Schewe et  al., 2014; Karnauskas et  al., 
2018). In the Mediterranean region, freshwater resources will decline 
by 10–30% (medium confidence) (Koutroulis et al., 2016; Kumar et al., 
2020). For example, analysis of annual and seasonal streamflow data 
on the island of Mallorca shows a decreasing trend during spring and 
summer, with a reduction of up to 17% in some basins (Garcia, 2017).
The influence of climate change spans several variables for atoll 
islands with multiple, interacting forces that exacerbate impacts on 
freshwater ecosystems (Connell, 2016), including groundwater and 
freshwater resources (Warix et  al., 2017). Analysis of groundwater

resources on Roi-Namur, in the Marshall Islands, reveals that the 
extent of salinisation of fresh groundwater lenses varies with the scale 
of the overwash (Gingerich et al., 2017). Alsumaiei and Bailey (2018) 
estimated an 11–36% reduction in the fresh groundwater lens volume 
of the small atoll islands (area < 0.6 km²) of the Maldives due to SLR. 
Small overwash events lead to saline conditions that last for up to 
3 months (Oberle et al., 2017).
SLR undermines the long-term persistence of freshwater-dependent 
ecosystems on islands (Goodman et al., 2012) and is one of the greatest 
threats to the goods and services these environments provide (Box 16.1;

15
2059Small Islands  Chapter 15
Ridge-to-reef interrelated protection services delivered by ecosystems on small islands
Mangrove Beach
Mangrove Beach SeagrassOcea n
ecosystemsCoastal
ecosystem sTerrestrial
HEAL THY CONTINUUM OF ECOSYSTEM S
DEGRADE DCONTINUUM OF ECOSYSTEM SContinuum of ecosystems...................... from ridge ..... ......................... .................. to reef
Upward migration of high altitude grasslands
Upward migration of lower altitude speciesAgroforestry & other ecologically base d Agroforestry & other ecologically base d
adaptation/conservation measures  adaptation/conservation measures 
Soil healthSoil health
FloodingThe seagras s
traps sand
Trees in grasslan d
LandslidesCoral reef
Coral reefHealthy grasslan d
limited
sediment production 
Healthygroundwater
Percolation of
artificial fertilizersSustainable agricultureSustainable agricultur e
Unhealthy
water qualityaffects agricultur
e
Mudflows
Intensive
mono agricultur e

& settlements
overland flow of fertilizers into river'Water Quality
chemicallycontaminatedgroundwater+saltwaterintrusionReforestationReforestation
Reef 
ecosystem
increased
wave energy
Figure 15.4 |  Ridge-to-reef interrelated protection services delivered by ecosystems on small islands. On small islands, terrestrial, coastal and marine ecosystems are 
interconnected and interdependent, with each ecosystem contributing towards maintaining the health of the others. Together, these ecosystems provide protection services against 
natural hazards (including flooding, erosion, landslides, mudflows, glacial melting and sedimentation) to human populations living on islands. As a consequence, the degradation of 
one or more of these ecosystems significantly reduces the protection services provided by this continuum of ecosystems. Conversely, the protection or restoration of one or more of

these ecosystems also provides benefits to the other ecosystems and enhances the protection services provided to island inhabitants . See Box CCP1.1 for more details.

15
2060Chapter 15 Small Islands
Mitsch and Hernandez, 2013). Hoegh-Guldberg et al. (2019) posit that as 
sea level rises, managing the risk of salinisation of freshwater resources 
will become increasingly important. On Roi-Namur, Marshall Islands, 
Storlazzi et al. (2018) found that the availability of freshwater is impacted 
by the compounding effect of SLR and coastal flooding. In other Pacific 
atolls, Terry and Chui (2012) showed that freshwater resources could be 
significantly affected by a 0.40-m SLR. Similar impacts are anticipated for 
some Caribbean countries (Stennett-Brown et al., 2017). Such changes 
in SLR could increase salinity in estuarine and aquifer water, affecting 
ground and surface water resources for drinking and irrigation water 
(Mycoo, 2018a) across the region (high confidence). SLR also affects 
groundwater quality (Bailey et al., 2016), salinity (Gingerich et al., 2017) 
and water-table height (Masterson et al., 2014).

15.3.3.3 Impacts on Terrestrial Biodiversity Systems
Despite encompassing approximately 2% of the Earth’s terrestrial 
surface, oceanic and other high-endemicity islands are estimated to 
harbour substantial proportions of existing species including ~25% 
extant global flora, ~12% birds and ~10% mammals (Alcover et al., 
1998; Wetzel et  al., 2013; Kumar and Tehrany, 2017). Islands also 
have higher densities of critically endangered species, hosting just 
under half of all species currently considered to be at risk of extinction 
(Spatz et  al., 2017a; 2017b), hence making the loss of terrestrial 
biodiversity and related ecosystem services a KR (KR3) for small islands 
(Figure  15.5). Impacts from developing synergies between changing 
climate, natural and anthropogenic stressors on islands (Cross-Chapter 
Box DEEP in Chapter 17) could lead to disproportionate changes in 
global biodiversity. The most prominent drivers include: SLR, increasing

intensities of extreme events (human activities—especially continuing/
accelerating habitat destruction/degradation) and the introduction of 
invasive alien species (IAS) (Tershy et al., 2015). When coupled with 
characteristic small island traits such as spatial and other resource 
limitations, these synergies play a critical role towards increasing the 
vulnerability of these insular ecosystems (Box  CCP1.1). This is likely 
to hinder the adaptation response of terrestrial biota–increasing the 
risk of biodiversity loss and, in turn, impairing the resilience capacity 
of ecosystem functioning and services (high confidence) (Heller and 
Zavaleta, 2009; Ferreira et al., 2016; Vogiatzakis et al., 2016).
Current observations of insular species response to climate change 
generally report geographic range shifts/reductions for species and 
vegetation associations in addition to resulting impacts on local 
ecology (Virah-Sawmy et al., 2016; Koide et al., 2017; Maharaj et al.,

2019). These include changes in plant/animal phenology and resulting 
community alterations such as for the common Mediterranean island 
species Quercus ilex (holly oak) and Ficus carica (common fig). Species 
have been shifting greater distances to access not only suitable climate 
conditions but also, by association, suitable breeding conditions and 
seasonal food. Examples include: migratory birds such as Coturnix 
coturnix now having earlier spring arrival dates in the Mediterranean 
compared to six decades ago and the increased mortality of the iconic 
Argyroxiphium sandwicense (Hinahina) as result of warmer drier 
trends at Hawaiian high altitudes (Krushelnycky et al., 2012; Taylor and 
Kumar, 2016a; Vogiatzakis et al., 2016). There have also been die-offs 
of some species from temperature extremes (e.g., flying fox species: 
Pteropus species) within the Pacific islands (Taylor and Kumar, 2016a).Recorded alterations of ecological interactions include increased

competition, changes to migratory routes (Harter et  al., 2015) and 
mismatches between species, such as increased pathogen attacks on 
Mediterranean forest species (Vogiatzakis et al., 2016). Also, in some 
areas of Madagascar there has been increased vulnerability to fire, due 
to the replacement of succulents by less fire-resilient species (Virah-
Sawmy et al., 2016). Further, the low functional redundancy of island 
ecosystems implies a comparatively higher proportion of keystone 
species than continents, many of them being endemic (Harter et al., 
2015), with potentially unpredictable system consequences due to 
climate-induced ecological changes. For example, Caribbean land crabs 
have been observed to alter their food intake as a response to drying 
conditions (McGaw et  al., 2019) and Aldabra giant land tortoises 
have reduced their activity in response to increasing temperature and 
decreasing precipitation (Falcon and Hansen, 2018); such changes in

both these ecosystem engineers are of potential consequence for seed 
dispersal, among other ecological functions.
The majority of studies modelling geographical range changes of small 
island species, to even the most optimistic 21st century climate change 
scenarios, imply a reduction in climate refugia (Table 15.3, Box CCP1.1). 
This is due to projected strong shifts, reductions or even complete losses 
of climatic niches resulting from inadequate geographic space for species 
to track suitable climate envelopes (high confidence) (e.g., Maharaj and 
New, 2013; Fortini et al., 2015; Struebig et al., 2015b). Because of the 
high proportion of global endemics hosted within small and especially 
isolated islands, the resulting increased extinction risk of such species 
(up to 100%) could lead to disproportionate losses in global biodiversity 
(medium to high confidence) (Harter et al., 2015; Manes et al., 2021).
SLR has been projected to impact the terrestrial biodiversity of low-

lying islands and coastal regions via large habitat losses both directly 
(e.g., submergence) and indirectly (e.g., salinity intrusion, salinisation 
of coastal wetlands and soil erosion) at even the 1-m scenario (medium 
to high confidence). However, these impacts vary depending on the 
islands’ topographical differences. In a study of SLR impacts on insular 
biodiversity hotspots, Bellard et al. (2013a) reported that the Caribbean 
islands, Sundaland and the Philippines were projected to suffer the 
most habitat loss while the East Melanesian islands were projected 
to be less (but not minimally) affected. The most threatened of these, 
the Caribbean, was projected to have between 8.7% and 49.2% of its 
islands entirely submerged, respectively, from 1-m to 6-m SLR (Bellard 
et  al., 2013a). However, many current projection studies consider 
marine flooding directly and seldom incorporate other indirect impacts

such as increased habitat losses from horizontal erosion loss, increased 
salinity levels, tidal ranges and extreme events. These projections are 
considered to be conservative, underestimating the extent of habitat 
loss to terrestrial biodiversity (Bellard et al., 2013b).
Marine flooding is expected to destroy habitats of coastal species, 
particularly range-restricted coastal and/or single-island endemics 
(many already listed as at least ‘threatened’ by the International Union 
for Conservation of Nature) within the limited terrain on atoll islands. 
These species have limited opportunities to accommodate such direct 
impacts of climate change apart from shifting further inland or to other 
neighbouring atolls which might have favourable habitat. However, 
fragmentation of habitat due to anthropogenic activity may hinder

15
2061Small Islands  Chapter 15
migration further inland, while shifting to neighbouring islands is 
not viable due to the water barrier between islands (high confidence) 
(Bellard et al., 2013b; Wetzel et al., 2013; Kumar and Tehrany, 2017). 
Additionally, migratory birds, which use small islands (e.g., atolls) for 
stopovers or breeding/nesting sites, are projected to become impacted. 
Within the Mediterranean and Caribbean, significant losses to coastal 
wetlands—critical habitat for migratory birds—has already been 
observed, with further significant habitat losses, redistribution and 
changes in quality being projected across island systems such as the 
Bahamas (Caribbean) and Sardinia (Mediterranean) (Vogiatzakis et al., 
2016; Wolcott et al., 2018).
Indirect impacts of SLR may potentially result in equal or more 
biodiversity loss than direct impacts (medium confidence). Relocation 
of displaced coastal human populations and associated intensive

agriculture and urban areas inland to natural habitat may result in 
greater biodiversity loss than direct impacts—especially on islands 
with large coastal populations and urban centres (Wetzel et al., 2012; 
Bellard et al., 2013b). Given the dense population of insular hotspots 
(~31.8% of existing humans within ~15.9% of inhabited global land 
area) and the fact that on many islands, large proportions of human 
populations live within coastal regions, it has been suggested that 
immense impacts from such relocations should be factored into 
projection and adaptation studies (Wetzel et al., 2012).
Tropical island natural habitats/systems are highly vulnerable 
to extreme weather events such as TCs, due to their small size, 
unique ecological systems and often low socioeconomic capacity 
(high confidence) (Box  15.2; Goulding et  al., 2016; Schütte et  al., 
2018). Growing evidence suggests high resilience of forest habitats

(Keppel et al., 2014; Luke et al., 2017), especially within intact forest 
ecosystems to hurricanes and cyclones (Goulding et al., 2016). While 
initial damage can be high, relatively fast recovery rates have been 
reported for both floral and faunal components of these ecosystems 
(Cantrell et al., 2014; Shiels et al., 2014; Monoy et al., 2016; Richardson 
et al., 2018). Within the Caribbean in particular, high resilience of forest 
types has been associated with the current intensity and return rate of 
hurricanes over the past 150 years.
It should, however, be underscored that these relatively fast recovery 
rates are associated with the present intensity and return rate of TCs. 
They do not reflect the impacts of increasingly intense events such as 
Hurricane Dorian (2019), which resulted in almost complete inundation 
of several low-lying islands of the Bahamas from storm surges. Severe 
weather events also have indirect effects on the biodiversity of islands—

interacting synergistically with other stressors, such as increased invasion 
by non-native species and land use change. For example, TCs within 
Papua New Guinea resulted in the destruction of subsistence gardens, 
which led inhabitants to clear forest areas for new farming areas and for 
harvesting of timber resources to rebuild (Goulding et al., 2016).
The most recent projections suggest that TC intensity is predicted to 
increase as climate continues to change (Walsh et  al., 2016; Kossin 
et al., 2017). There are too few studies available to suggest potential 
future response trends of these ecosystems to this increased intensity; 
however, it seems plausible that present resilience capacities may be 
adversely impacted (medium confidence) (Marler, 2014). Further, the potential for stressors such as forest fragmentation/degradation or IAS 
combining with these increasingly intense events to cause precipitating 
ecosystem cascades is a real concern (Goulding et al., 2016).

Continued high rates of habitat loss and degradation have been 
reported for many small islands as natural habitats continue to be 
cleared to meet increasing demands upon natural resources from rising 
human populations, agriculture, urbanisation, unsustainable tourism, 
overgrazing and fires. This increases the vulnerability of ecosystems 
within especially oceanic islands—where isolation has given rise to high 
levels of endemism but simple biotic communities, with low functional 
redundancy (Box CCP1.1). There is high confidence that climate change 
may exacerbate the effects of this habitat loss upon the biodiversity 
of these islands as the climate refugia (Table  15.3) and the upslope 
shifts of range-restricted, dispersal-limited and poorly competitive 
species, confined within narrow latitudinal (and decreasing altitudinal) 
gradients, are increasingly challenged by fragmented and degraded 
landscapes (e.g., Struebig et al., 2015a; IPBES, 2019). Additionally, high-

altitude ecosystems such as cloud forests which harbour high levels 
of endemism are projected to shrink due to increasing atmospheric 
temperature and competition from upward-shifting lowland species 
(Taylor and Kumar, 2016a). These may ultimately increase the risk of 
multiple extinctions, negatively impacting upon global biodiversity 
levels (high confidence) (Taylor and Kumar, 2016a; Portner et al., 2021).
Analyses of historical and current threats indicate that IAS and 
disease have been the primary drivers of insular extinctions in modern 
history (Bellard et al., 2016). Impacts of IAS on islands are projected 
to increase with time due to synergies between climate change and 
other traditional drivers such as increasing global trade, tourism, 
agricultural intensification, overexploitation and urbanisation (Bellard 
et al., 2014; Russell et al., 2017). Changing climate conditions may not 
necessarily increase the rate of IAS introductions but is expected to

improve chances of IAS establishment via (a) altering IAS transport and 
introduction mechanisms, (b) increasing the impacts and distributions 
of existing IAS and (ci) altering the effectiveness of existing control 
strategies (Hellmann et al., 2008; Russell et al., 2017). These are likely 
to enhance IAS impacts on islands including: restructuring of ecological 
communities leading to declines and extinctions/extirpations in flora 
and fauna, habitat degradation, declining ecosystem functioning, 
services and resilience and, in extreme cases, potential community 
homogenisation (high confidence) (Russell and Blackburn, 2017; IPBES, 
2019). Given the high degree of endemicity within oceanic islands and 
their associated vulnerabilities, such exacerbation by changing climate 
poses a serious threat to decreasing global biodiversity (medium to 
high confidence) (van Kleunen et al., 2015).
Compared to continents, terrestrial IAS are disproportionately prevalent

on islands (almost three quarters of global species currently threatened 
by IAS and disease are found on islands) and also generate stronger 
impacts (e.g., within alpine ecosystems of high islands) than on 
continents (high confidence)(Bellard et al., 2014; Bellard et al., 2016; 
Frazier and Brewington, 2019). Russell and Blackburn (2017) suggested 
a correlation between small island size and increased numbers of IAS. 
SIDS within the Indian Ocean and in particular the Pacific SIDS region 
were reported to have significantly more IAS (medium confidence), 
while the Caribbean and Atlantic SIDS have fewer numbers but faster

15
2062Chapter 15 Small Islands
Table 15.3 |  Percentage of selected islands classified as refugia for biodiversity at increasing levels of warming. While protected land is still ‘protected’ this table demonstrates the 
difficulty of protecting lands which might be ‘more resilient’ to climate change under increasing levels of warming and current land use practices. Derived from current and future 
projected distributions of ~130,000 terrestrial fungi, plants, invertebrates and vertebrates (Warren et al., 2018a). Refugia=areas remaining climatically suitable for >75% of the 
species modelled (Warren et al., 2018b). Projections: based on mean impacts from 21 CMIP5 climate model patterns (no dispersal) and elevationally downscaled to 1 km under 
interpolated warming levels derived from RCP2.6, 4.5, 6.0 and 8.0 (Warren et al., 2018a). First column-set: % island/island chain classified as a refugia based on climate alone;

second column-set: % natural land projected to be climate refugia—illustrating potential refugia ‘space’ already lost to habitat conversion. Colour key: white: > 50%; yellow: 
30–50%; red: 17–30%; dark red: <17% of land classified as refugia.
Island(s) Climate °C Climate + land use °C
0.5 1 1.5 2 2.5 3 3.5 4 0.5 1 1.5 2 2.5 3 3.5 4
Aegean 
Islands98 89 85 68 39 19 12 6 66 62 60 50 32 16 11 6
American 
Samoa100 100 100 100 83 52 39 25 39 39 39 39 34 24 18 11
Andaman 
Nicobar100 95 90 46 7 2 1 0 92 88 84 45 7 2 1 0
Balearic 
Islands99 97 95 82 26 6 4 2 29 28 28 25 13 6 3 2
Bangka 100 100 97 3 1 0 0 0 20 20 19 1 0 0 0 0
Barbados 94 67 53 25 5 0 0 0 10 7 6 3 1 0 0 0
Borneo 98 92 89 60 25 14 10 6 67 62 60 43 24 13 10 6
Bougainville 92 81 77 62 39 28 24 19 87 77 74 58 37 27 23 18
British 
Indian Ocean 
Territory100 100 94 0 0 0 0 0 47 47 47 0 0 0 0 0
Corsica 72 61 57 43 29 18 15 10 64 53 50 38 26 16 13 8
Crete 91 83 80 68 52 35 27 20 51 47 46 42 35 26 22 17

Cuba 97 94 92 69 14 4 3 1 48 46 45 36 10 4 3 1
Cyprus 53 51 49 44 32 20 14 8 48 46 44 37 24 14 9 6
Dominica 79 66 63 51 41 28 20 14 79 66 63 51 41 28 20 14
French 
Polynesia100 100 100 100 100 81 68 54 38 38 38 38 38 32 28 23
Galapagos 91 82 79 67 50 27 18 13 93 88 86 74 54 33 21 14
Grenada 73 49 43 29 18 10 6 3 71 48 43 29 18 10 6 3
Guadeloupe 91 71 64 27 19 13 9 6 57 46 42 26 19 13 9 6
Guernsey 100 52 41 0 0 0 0 0 13 7 5 0 0 0 0 0
Hispaniola 77 60 54 35 22 15 12 9 55 43 40 28 19 13 11 8
Indonesia 95 87 81 54 28 17 14 11 60 55 51 36 23 15 12 10
Jamaica 77 65 61 47 31 17 10 5 64 54 51 40 27 15 9 4
Java 91 74 65 37 24 17 13 10 27 24 22 18 14 11 9 7
Kiribati 100 55 38 14 0 0 0 0 15 12 12 5 0 0 0 0
Madagascar 98 90 87 70 47 28 22 13 84 77 73 58 37 21 16 10
Maldives 100 38 1 0 0 0 0 0 16 0 0 0 0 0 0 0
Marajo 100 58 33 0 0 0 0 0 91 55 33 0 0 0 0 0
Marshall 
Islands100 99 99 55 22 0 0 0 46 46 46 15 10 0 0 0
Mauritius 100 100 100 100 100 100 92 74 27 27 27 27 27 27 25 23

Micronesia 100 100 100 78 59 31 16 6 86 86 86 72 56 29 15 6
Montserrat 61 43 39 27 20 9 9 4 56 38 35 23 17 9 7 4
Nauru 100 100 97 0 0 0 0 0 11 11 11 0 0 0 0 0
New 
Caledonia100 100 99 97 89 62 45 31 76 75 75 74 69 53 41 28
New Guinea 95 84 73 47 32 25 22 19 86 76 67 43 30 23 21 18

15
2063Small Islands Chapter 15
Northern 
Mariana Islands100 100 99 95 58 29 19 11 49 49 49 46 35 22 16 9
Orinoco Delta100 31 9 0 0 0 0 0 93 29 9 0 0 0 0 0
Palau 100 79 73 21 0 0 0 0 74 59 55 17 0 0 0 0
Palawan 86 70 64 36 21 12 9 6 55 47 44 31 20 12 9 6
Philippines 90 74 66 41 27 16 12 8 34 30 28 21 15 10 8 6
Prince Edward100 100 100 100 100 97 9 0 35 35 35 35 35 33 2 0
Puerto Rico 84 66 59 41 25 15 11 7 63 52 49 36 24 14 11 7
Saint Lucia 77 50 45 29 14 6 3 1 72 50 45 29 14 6 3 1
Saint Vincent and the Grenadines73 57 50 37 27 18 13 8 63 50 44 34 23 15 10 5
Samoa 100 100 100 99 89 67 56 46 34 34 34 34 31 24 22 20
Sardinia 95 87 83 65 34 16 10 5 41 38 37 31 22 12 8 4
Seychelles 100 100 98 83 57 25 16 9 25 25 25 22 18 8 6 5
Sicily 93 84 80 60 35 18 11 7 16 15 15 13 10 7 6 4
Singapore 100 100 100 98 9 0 0 0 14 14 14 13 3 0 0 0
Solomon Islands93 79 74 48 28 15 10 6 92 78 73 48 28 15 10 6
Sri Lanka 98 94 89 64 23 11 7 5 47 46 44 36 16 7 5 4

Sulawesi 86 75 71 58 44 33 28 23 60 54 52 46 38 30 26 21
Sumatra 96 90 87 65 24 16 13 11 40 37 36 30 18 13 11 9
Sumba 98 90 86 70 49 23 11 4 36 33 31 26 18 9 4 2
Timor 92 84 80 66 48 30 22 15 11 10 9 8 7 5 4 3
Trinidad and Tobago88 24 16 6 3 1 0 0 64 20 14 6 3 1 0 0
Tuvalu 100 100 100 34 0 0 0 0 3 3 3 0 0 0 0 0
Wallis and Futuna100 100 100 65 32 11 3 0 35 35 35 33 21 7 1 0
accumulation of IAS. Finally, while there have been developments in 
the eradication of IAS on islands (Jones et al., 2016), there is sparse evidence and hence assessment of the degree to which measures designed to prevent introduction and to manage invasion pathways and establishment have been successful.
15.3.4  Observed Impacts and Pr ojected Risks on Human 
Systems
15.3.4.1
 Island Settlements and Infrastructure

As a result of slow-onset ocean and climate changes and changes in extreme events, settlements and infrastructure of small islands are at growing risk due to climate change in the absence of adaptation measures (high confidence). Ocean acidification and deoxygenation, increased ocean temperatures and relative SLR are impacting marine, coastal and terrestrial biodiversity and ecosystem services, making settlements more exposed and vulnerable to climate-related hazards. Changes in rainfall patterns such as heavy precipitation result in annual flood events that damage major assets and result in a loss of human life. Examples of settlements where this has occurred are Port of Spain (Mycoo, 2014b; 2018a), Haiti (Weissenberger, 2018), Viti Levu (Brown et al., 2017; Singh-Peterson and Iranacolaivalu, 2018), urban areas of Fiji and Kiribati (McAneney et al., 2017; Cauchi et al., 2021), Male’, Maldives (Wadey et al., 2017), and Mahé, in the Seychelles (Etongo, 2019).

The main settlements of small islands are located along the coast 
and with decades of high-density coastal urban development, their population, buildings and infrastructure are currently exposed to multiple climate change-related hazards (Kumar and Taylor, 2015; Mycoo, 2017) and face key risks (high confidence) (KR5 in Figure 15.5). In many small islands, population is concentrated in the low-elevation coastal zone (LECZ), which is defined as coastal areas below 10-m elevation. Approximately 22 million in the Caribbean live below 6-m elevation (Cashman and Nagdee, 2017) and an estimated 90% of Pacific Islanders live within 5
 km of the coast,
 if Papua New 
Guinea is excluded (Andrew et  al., 2019). In the Solomon Islands

15
2064Chapter 15 Small Islands
and Vanuatu, over 60% of the population lives within 1 km of the 
coast (Andrew et al., 2019). Most Pacific islands have ≥50% of their 
infrastructure within 500 m of the coast (Kumar and Taylor, 2015), and 
in Kiribati, Marshall Islands and Tuvalu, >95% of the infrastructure is 
located in the LECZ (Andrew et al., 2019) (Figure 15.3). Sustainable 
development challenges including insufficient land use planning and 
land use competition contribute to increased vulnerability of human 
settlements to climate change in small islands (Kelman, 2014; Mycoo, 
2021).
Categories 4 and 5 TCs are severely impacting settlements and 
infrastructure in small islands. TC Maria in 2017 destroyed nearly all of 
Dominica’s infrastructure and losses per unit of GDP amounted to more 
than 225% of the annual GDP (Eckstein et al., 2018). Destruction from 
TC Winston in 2016 amounted to more than 20% of Fiji’s current GDP

(Cox et al., 2018). Additionally, living conditions in human settlements 
are changing due to storm surge which is already penetrating further 
inland compared with a few decades ago (IPCC, 2018, Section 3.4.4.3; 
Brown et al., 2018).
A growing percentage of the population in small islands lives in informal 
settlements which occupy marginal lands leading to increased population 
exposure and vulnerability to climate-related hazards (Mycoo and 
Donovan, 2017). Unplanned settlements have compounded flooding 
brought on by slow-onset hazards such as coastal and riverine flooding 
and fast-onset events such as TCs and storm surges (Butcher-Gollach, 
2015; Chandra and Gaganis, 2016; Mycoo, 2017). Unsustainable land 
use practices and difficulties in enforcing land use zoning and building 
guidelines in informal settlements make them highly vulnerable to such 
events (Butcher-Gollach, 2015; Mecartney and Connell, 2017; Mycoo, 
2017; 2018b; 2021; Trundle et al., 2018).

TC intensification in the future is likely to cause severe damage to 
human settlements and infrastructure in small islands. Additionally, SLR 
is expected to cause significant losses and damages (Martyr-Koller et al., 
2021). Based on SLR projections, almost all port and harbour facilities in 
the Caribbean will suffer inundation in the future (Cashman and Nagdee, 
2017). In Jamaica and St. Lucia, SLR and ESLs are projected to be key 
risks to transport infrastructure at 1.5°C unless further adaptation is 
undertaken (Monioudi et al., 2018). Similar findings were reported for 
Samoa (Fakhruddin et al., 2015). Even islands of higher elevation are 
expected to be threatened, given the high amount of infrastructure 
located near the coast, for example, Fiji (Kumar and Taylor, 2015).
15.3.4.2 Human Health and Well-Being
Small islands face disproportionate health risks associated with 
changes in temperature and precipitation, climate variability, and

extremes (Cross-Chapter Box  INTERREG in Chapter 16; KR4 in 
Section 15.3.9, Figure 15.5). Climate change is projected to increase 
the current burden of climate-related health risks (Weatherdon et al., 
2016; Ebi et  al., 2018; Schnitter et  al., 2019). Health risks can arise 
from exposures to extreme weather and climate events, including 
heatwaves; changes in ecological systems associated with changing 
weather patterns that can result, for example, in more disease 
vectors, or in compromised safety and security of water and food; and 
exposures related to disruption of health systems, migration, and other factors (see Cross-Chapter Box  ILLNESS in Chapter 2; McIver et  al., 
2016; Mycoo, 2018a; WHO, 2018).
Extreme weather and climate events, particularly TCs, floods, drought, 
and heatwaves can cause injuries, infectious diseases, and deaths 
(Box 15.1; Schütte et al., 2018). For example, Category 5 TC Winston 
hit Fiji on 20 February 2016. During the national state of emergency

(7 March and 29 May 2016), the World Health Organization portable 
toolkit for an early warning alert and response system (EWARS in a 
Box) was deployed within 24 h; it recorded 34,113 cases of the nine 
syndromes among 326,861 consultations in a population of about 
900,000; 48% of cases were influenza-like illnesses, 30% were acute 
watery diarrhoea, and 13% were suspected cases of dengue. There also 
were 583 cases of Zika-like illness (1.7% of all cases) and two large 
outbreaks of viral conjunctivitis (total of 880 cases). During TC Maria 
in Puerto Rico, there were more deaths per 100,000 among individuals 
living in municipalities with the lowest socioeconomic development 
and for men 65  years of age or older (Santos-Burgoa et  al., 2018); 
this excess risk persisted for at least 1 year after the event. The first 
human cases of leptospirosis in the U.S. Virgin Islands occurred in 2017 
after TC Irma and Maria. TCs also can affect treatment and care for

people with non-communicable diseases, including exacerbation or 
complications of illness and premature death (Ryan et al., 2015).
Heat-related mortality and risks of occupational heat stress in small 
island states are projected to increase with higher temperatures (Hoegh-
Guldberg et al., 2018; Mendez-Lazaro et al., 2018). Higher temperatures 
can also affect the productivity of outdoor workers (Taylor et al., 2021). 
Climate change, urbanisation, and air pollution are risk factors for the 
rise of allergic diseases in Asia Pacific (Pawankar et al., 2020).
Tropical and subtropical islands face risks from vector-borne diseases, 
such as malaria, dengue fever, and the Zika virus. El Niño events can 
increase the risk of diseases such as Zika virus by increasing biting rates, 
decreasing mosquito mortality rates and shortening the time required 
for the virus to replicate within the mosquito (Caminade et al., 2017).

By combining disease prediction models with climate indicators that are 
routinely monitored, alongside evaluation tools, it is possible to generate 
probabilistic dengue outlooks in the Caribbean and early warning 
systems (Oritz et al., 2015; Lowe et al., 2018). Projections suggest that 
more individuals will become at risk of dengue fever by the 2030s 
and beyond because of an increasing abundance of mosquitos and 
larger geographic range (Ebi et al., 2018). Projected increases in mean 
temperature could double the dengue burden in New Caledonia by 2100 
(Teurlai et al., 2015). In the Caribbean, Saharan dust transported across 
the Atlantic can interact with Caribbean seasonal climatic conditions 
to become respirable and contribute to asthma presentations at the 
emergency department (See Table 15.5; Akpinar-Elci et al., 2015).
Ciguatera fish poisoning (CFP) is a foodborne illness caused by toxic 
dinoflagellate algae that proliferate on degraded coral reefs and that can

contaminate reef fish; symptoms can remain for a few weeks to months. 
CFP occurs in tropical and subtropical regions, primarily in the South 
Pacific and Caribbean, but wherever reef fish are consumed (Traylor and 
Singhal, 2020). In the Caribbean Sea, increasing ocean temperatures 
are expected to stabilise or slightly decrease the incidence of CFP 
because of shifts in species distribution of dinoflagellates associated

15
2065Small Islands  Chapter 15
with CFP (Kibler et al., 2015). CFP is endemic in the Cook Islands and 
French Polynesia, where incidence is associated with SST anomalies 
(Zheng et al., 2020). In the Canary Islands, tropicalisation trends due 
to climate change are expected to increase CFP occurrence in the 
future (Rodriguez et al., 2017). In addition, in the Caribbean, increased 
density of Sargassum algae, possibly due to ocean temperature impacts 
on ocean currents compounded by agricultural pollution, may lead to 
increased respiratory illnesses (Resiere et al., 2018; 2019; 2020).
Climate-driven changes in the ability to access locally grown or harvested 
food, either through environmental degradation or changes in extreme 
event magnitude and/or frequency, can increase dependence on imported 
food and increase rates of malnutrition and non-communicable diseases 
(Springmann et al., 2016; WHO, 2018; Savage et al., 2019; Lieber et al.,

2020). Projections suggest that local food accessibility could be reduced 
by 3.2% in the low- and middle-income countries of the Western Pacific 
(including the Philippines, Fiji, Papua New Guinea, Solomon Islands, and 
other Pacific islands) by 2050, with approximately 300,000 associated 
deaths possible (Springmann et al., 2016). A climate change-related 20% 
decline in coral reef fish production in some Pacific Island countries by 
2050 could exacerbate the population growth-driven gap between volume 
of fish needed for nutritional security and fish available through sustained 
harvest (Bell et al., 2013; Cauchi et al., 2019; Savage et al., 2019)).
Heavy reliance on aquifers and rainwater harvesting in small islands, 
particularly atolls, coupled with overcrowding, population growth 
and contamination increase the risk of waterborne disease (McIver 
et al., 2014; 2016; Strauch et al., 2014). For example, seasonal rainfall

in Kiribati is associated with waterborne disease (such as diarrhoea, 
cholera, and typhoid fever). Future projections indicate increases in the 
number of days of heavy rainfall by 2050, suggesting future increases 
in risk in heavily populated areas (McIver et  al., 2014). Damage to 
water and sanitation services can cause infectious disease outbreaks, 
such as the cholera outbreak that occurred in Haiti following TC 
Matthew (Raila and Anderson, 2017; Hulland et al., 2019).
Evidence is emerging of the mental health impacts of climate change 
(limited evidence). Tuvaluans are experiencing distress because of the 
local environmental impacts caused or exacerbated by climate change, 
and by hearing about the potential future consequences of climate 
change (Gibson et al., 2020).
15.3.4.3 Water Security
Climate change impacts on freshwater systems frequently exacerbate 
existing pressure, especially in locations already experiencing water

scarcity (Section  15.3.3.2 and Cross-Chapter Box  INTERREG in 
Chapter 16; Schewe et  al., 2014; Holding et  al., 2016; Karnauskas 
et al., 2016), making water security a key risk (KR4 in Figure 15.5) in 
small islands. Small islands are usually environments where demand 
for resources related to socioeconomic factors such as population 
growth, urbanisation and tourism already place increasing pressure 
on limited freshwater resources. In many small islands, water demand 
already exceeds supply. For example, in the Caribbean, Barbados is 
utilising close to 100% of its available water resources and St. Lucia 
has a water supply deficit of approximately 35% (Cashman, 2014). On 
many Mediterranean islands, water demand regularly outstrips supply as a result of low average precipitation coupled with increasing water 
demand from economic activities such as irrigated agriculture and 
tourism (Hof et al., 2014; Papadimitriou et al., 2019).

Population growth plays a major role in projected future water stress 
(Schewe et al., 2014). Combining projected aridity change (fractional 
change compared to historical climatology) with population projections 
derived from SSP2 shows that the SIDS with high projected population 
growth rates are expected to experience the most severe freshwater 
stress by 2030 under a 2°C warming threshold scenario (Karnauskas 
et  al., 2018). For several SIDS (e.g., Belize and Jamaica), increasing 
aridity change is a prominent exacerbating factor, but for others (e.g., 
the Solomon Islands and Comoros) population growth is the main 
factor. An increase in temperature of 1°C (from 1.7°C to 2.7°C) could 
result in a 60% increase in the number of people projected to experience 
severe water resources stress in the period 2043–2071 (Schewe et al., 
2014; Karnauskas et al., 2018). Research on Jamaica concluded that the 
ability of rainwater harvesting to meet potable water needs between

the 2030s and 2050s will be reduced based on predicted shorter intense 
showers and frequent dry spells (Aladenola et al., 2016).
The Caribbean and Pacific regions have historically been affected by 
severe droughts (Peters, 2015; FAO, 2016; Barkey and Bailey, 2017; 
Paeniu et al., 2017; Trotman et al., 2017; Anshuka et al., 2018) with 
significant physical impacts and negative socioeconomic outcomes. 
Water quality is affected by drought as well as water availability. The 
El Niño related 2015–1016 drought in Vanuatu led to reliance on small 
amounts of contaminated water left at the bottom of household tanks 
(Iese et  al., 2021a). The highest land disturbance percentages have 
coincided with major droughts in Cuba (de Beurs et al., 2019). Drought 
has been shown to have an impact on rainwater harvesting in the 
Pacific (Quigley et al., 2016) and Caribbean (Aladenola et al., 2016), 
especially in rural areas where connections to centralised public water

supply have been difficult. Increasing trends in drought are apparent in 
the Caribbean (Herrera and Ault, 2017) although trends in the western 
Pacific are not statistically significant (McGree et al., 2016).
Areas where a freshwater lens is thinner are most likely to be impacted 
by multiple climate stressors, and these areas tend to be in coastal 
zones where populations are likely to be most concentrated (Holding 
et al., 2016). In Barbados, where groundwater is relied upon for food 
production, urban use and environmental needs, higher food prices 
are expected in the future if informed land use management and 
integrated water resources policies are not implemented to manage 
groundwater in the short term, even with modest climate change 
threats (Gohar et al., 2019).
15.3.4.4 Fisheries and Agriculture
Fisheries provide small islands with opportunities for economic 
development, revenues, food security and livelihoods (Bell et al., 2018).

Ten Pacific Island countries and territories derive between 5% and >90% 
of all government revenue (except grants) from access fees paid by 
industrial tuna-fishing fleets, mainly from distant-water fishing nations 
(Bell et al., 2018; SPC, 2019). Under a high greenhouse gas emissions 
scenario (RCP8.5), the total biomass of three tuna species in the waters 
of 10 Pacific SIDS could decline by an average of 13% (range = −5–

15
2066Chapter 15 Small Islands
−20%) due to a greater proportion of fish occurring in the high seas 
(Bell et al., 2021), while projected increases have been anticipated for 
Ascension Island and Saint Helena in the South Atlantic (Townhill et al., 
2021). Additionally, seafood plays an important role in achieving food 
security in many islands. In the Pacific, fish protein is estimated to make 
up 50–90% of animal protein consumption in rural areas and 40–80% 
in urban areas (Bell et al., 2009; Hanich et al., 2018) with similar values 
reported for some Indian Ocean and Caribbean islands (e.g., Maldives, 
Antigua and Barbuda). It has been suggested that island nations may 
need to retain more of their tuna catch rather than to rely solely on 
coastal fisheries to achieve food security in the future (Cross-Chapter 
Box MOVING PLATE in Chapter 5; Bell et al., 2015; Bell et al., 2018). 
Furthermore, small island fisheries can be severely impacted by extreme

events such as TCs, yet rapidly recovering pelagic fisheries can help to 
alleviate immediate food insecurity pressures in some circumstances, 
helping to build resilience (Pinnegar et al., 2019).
Observed impacts of climate change on fish and fisheries in small islands 
include declines in reef-associated species due to coral bleaching or 
cyclone damage (Robinson et al., 2019; Magel et al., 2020), oceanic-scale 
shifts in the distribution of large pelagic fish and hence their fisheries 
(Erauskin-Extramiana et  al., 2019), changes to the size structure or 
breeding behaviour of species (e.g., (Asch et al., 2018) (Sections 3.3.3.2 
and 3.4.3.1)). Many studies of future fishery productivity in a changing 
climate suggest that yields will fall as a result of ocean productivity 
reductions, local species extinction and/or migration (Nurse, 2011; Asch 
et al., 2018; Robinson et al., 2019). Asch et al. (2018) provided future

projections for biodiversity and the maximum catch potential of fisheries 
in Pacific Island countries and territories. These authors concluded that 
nine of 17 Pacific Island entities (Cook Islands, Federated States of 
Micronesia, Guam, Kiribati, Marshall Islands, Niue, Papua New Guinea, 
Solomon Islands, and Tuvalu) could experience ≥50% declines in 
maximum catch potential by 2100 relative to 1980–2000 under both an 
RCP2.6 and RCP8.5 scenario (medium confidence). In Wallis and Futuna, 
maximum catch potential was projected to increase slightly (around 
10%) by 2050, later declining by the year 2100. Similar projections 
have now been provided for all countries worldwide, including Pacific, 
Caribbean, Atlantic, Mediterranean and Indian Ocean small islands 
(Cheung et al., 2018). The small islands that show the largest anticipated 
decrease in the maximum catch potential of fisheries by the end of 
the century (according to an RCP4.5 and RCP8.5 scenario) include the

Federated States of Micronesia, Kiribati, Nauru, Palau, Tokelau, Tuvalu, 
São Tomé and Príncipe, whereas some other small islands such as 
Bermuda, Easter Island (Chile), and Pitcairn Islands (UK), might actually 
witness increases in fish catch potential (medium confidence) (Cheung 
et al., 2018). Monnereau et al. (2017)showed that for the fisheries sector, 
small island states are generally more vulnerable to climate change 
impacts compared to continental least-developed countries or coastal 
states because of their increased reliance on fisheries, the exposure 
of coastal communities to potential climatic threats and their limited 
adaptive capacity.
Projected impacts of climate change on agriculture and fisheries pose 
serious threats to dependent human populations (Ren et  al., 2018; 
Hoegh-Guldberg et al., 2019), making the risk caused to livelihoods a 
key risk in small islands (KR7 in Figure 15.5). On small islands, despite

biophysical commonalities (e.g., size and isolation), differences in economic status and level of dependence on agriculture and fisheries 
produce dynamic climate impacts (Balzan et al., 2018). Climate change 
is impacting agricultural production in small islands through slow-onset 
stressors such as rising average temperatures, shifting rainfall patterns, 
SLR and extreme events like TCs. For example, TC Pam, a Category 5 
cyclone, devastated Vanuatu in 2015 and caused losses and damages to 
the agriculture sector valued at USD 56.5 million (64.1% of GDP) (Nalau 
et al., 2017), and TC Winston in 2016 resulted in losses and damages 
in the agriculture sector in Fiji valued at USD 254.7 million (Iese et al., 
2020). In 2017, total losses and damages associated with hurricane 
Maria (Category 5) amounted to 224% of Dominica’s 2016 GDP (Barclay 
et  al., 2019). Losses and damages in agriculture often led to people 
eating imported processed foods affecting their diet and nutrition

(Haynes et  al., 2020). Small islands communities are also witnessing 
the indirect effects of the COVID-19 pandemic on agricultural systems 
(Hickey and Unwin, 2020). However, the limited diversity of agriculture 
production and reduced household incomes are contributing to low 
diet diversity (Iese et al., 2021b). Bell and Taylor (2015) assessed the 
effects of climate change on specific sectors of agriculture in the Pacific 
islands region and found that, by 2090, staple food crops of taro, sweet 
potato and rice are expected to suffer from moderate to high impact. 
Among export crops, coffee is expected to sustain the most significant 
impact due largely to increased temperatures in the highland areas 
of Papua New Guinea—a high production area (Bell et  al., 2016). 
Livestock is an important protein source in some small islands and is 
particularly vulnerable to changes in temperature through heat stress 
(Bell and Taylor, 2015; Lallo et  al., 2018). With the concentration of

island people along (often reef-fringed) coasts, there is a comparatively 
large dependence on nearshore marine foods and coastal agricultural 
systems (Ticktin et al., 2018).
In the Caribbean, additional warming by 0.2°–1.0°C could lead to a 
predominantly drier region (5–15% less rain than present-day), a greater 
occurrence of droughts (Taylor et  al., 2018) along with associated 
impacts on agricultural production and yield in the region (Gamble et al., 
2017; Hoegh-Guldberg et al., 2019; Nicolas et al., 2020). Crop suitability 
modelling on several commercially important crops grown in Jamaica 
found that even an increase of less than  1.5°C could result in a reduction 
in the range of crops that farmers may grow (Rhiney et al., 2018).
Sugar yield in Fiji could decline by 2–14% under projected scenarios 
(McGree et  al., 2020). Farmers in some small islands have utilised 
Indigenous knowledge systems built on local ontology to sharpen their

sensitivity to environmental conditions (Shah et al., 2018). However, 
projected climate change across the Pacific could undermine climate-
sensitive agricultural livelihoods and exacerbate food insecurity 
challenges (McCubbin et al., 2017; Campbell et al., 2021).
Projected climate impacts on island agroecosystem services could 
accentuate a myriad of social and ecological risks (Campbell, 2021). 
Without proactive farm management practices, the projected impacts of 
climate change on drought patterns is a major threat to cocoa pollination 
services (Arnold et al., 2018). Many tropical island agroforestry crops are 
completely dependent on insect pollination and it is therefore important to 
understand the climatic drivers of changing conditions related to pollinator 
abundance. Coastal agroforestry systems in small Pacific islands are vital 
to national food security but native biodiversity is rapidly declining (Ticktin

15
2067Small Islands  Chapter 15
et al., 2018). Biodiversity loss from traditional agroecosystems is a major 
threat to food and livelihoods security in SIDS (UNEP , 2014a). Additionally, 
while coastal-lowland salinisation and more frequent flooding attributable 
to SLR have impacted coastal agriculture on some islands (Cruz and 
Andrade, 2017; Wairiu, 2017), stronger TCs can sometimes shock island 
terrestrial food production warranting reconfiguration (Mertz et al., 2010; 
Duvat et al., 2016; Chakrabarti et al., 2017). Calls to conserve associated 
environments and to make terrestrial food production on islands more 
resilient to climate-driven shocks underscore concern about future food 
security (Connell, 2013; de Scally, 2014). Implicit in the latter is reversing 
the decades-long loss of Indigenous knowledge about food production in 
many island societies and incorporating it into future strategies (Mercer 
et al., 2014b; Janif et al., 2016).
15.3.4.5 Economies

Small island economies vary greatly in their nature, history/trends 
and viability under a changed climate. As elsewhere, few small island 
economies are overseen by governments that are adequately prepared 
for the economic impacts of climate change over the next few decades 
(Connell, 2013; Hay, 2013). In particular, the lack of diversity that 
characterises most small island economies means they are especially 
vulnerable to global (climate-driven) shocks (Cross-Chapter Box DEEP 
in Chapter 17), be these the impacts of extreme events or more gradual 
longer-term change, which makes the maintenance of traditional 
mechanisms for coping with such shocks in many island societies all 
the more important (Granderson, 2017; Wilson and Forsyth, 2018; 
Nunn and Kumar, 2019b). As a result, the risk from climate change to 
economies constitutes a key risk (KR7 in Figure 15.5) in small islands.
Many island environments have been commercially exploited by

external interests for much of their recent history. This is especially 
common for timber, the wholesale removal of forests, especially on 
tropical islands, exposing land to heavy rain that leads to denudation 
and increases lowland sedimentation (Wairiu, 2017; Eppinga and 
Pucko, 2018). Negative aspects of both processes will be exacerbated 
by climate change, demonstrating the practical need for reforestation 
in many island contexts (Thomson et  al., 2016). Some small island 
economies are sustained by extractive industries such as mining, 
creating dependencies that lead to their environmental impacts being 
downplayed (Tserkezis and Tsakanikas, 2016; Shepherd et al., 2018). 
It is important to address these impacts as they will add to negative 
impacts of climate change (Clifford et al., 2019).
Many small island economies are sustained by tourism and have 
invested heavily in associated infrastructure and capacity building

(Cannonier and Burke, 2018). Some rural island communities have 
become dependent on tourism to the point that it would be difficult 
to revert to subsistence living (Lasso and Dahles, 2018). Coast-focused 
(beach-sea) tourism in island contexts is already being impacted 
by beach erosion, elevated high SST causing coral bleaching, and 
associated marine-biodiversity loss, as well as more intense TCs 
(Tapsuwan and Rongrongmuang, 2015; Parsons et al., 2018; Wabnitz 
et  al., 2018). The COVID-19 pandemic travel disruption significantly 
affected the tourism sector of Caribbean islands by reducing incomes 
that would have been used to enhance climate resilience (Sheller, 
2020). Many tourism interests downplay the impacts and future risks from climate change (Shakeela and Becken, 2015), a position that may 
be borne out by sustained/rising demand for small island vacationing 
in some locales (Katircioglu et al., 2019). A way forward is for island

tourism to emphasize its low-carbon and sustainable attributes, and to 
encourage smaller-scale eco-friendly holiday opportunities (Lee et al., 
2018), in other words for island nations to embrace a ‘blue economy’ 
in line with SDG14 to conserve and utilise their oceans for sustainable 
futures (Hampton and Jeyacheya, 2020; Hassanali, 2020).
Given the high cost of imported goods, especially foodstuffs, larger 
island jurisdictions are striving to transform their economies to favour 
locally produced or locally constituted materials that employ local 
people and reduce their cost of living. The exposure of this component of 
island economies varies, yet manufacturing/commercial operations are 
usually found in the lowest-lying areas, often on reclaimed lands. This 
makes them especially vulnerable to rising sea level, part of a larger issue 
around the disproportionate exposure of infrastructure on small islands 
to climate change (Fakhruddin et al., 2015; Kumar and Taylor, 2015).

It is challenging to disentangle the role of climate change from 
that of globalisation and development in recent changes to human 
livelihoods on small islands, given that the latter have characterised 
many—especially SIDS—within the last few decades. However, recent 
climate change is clearly implicated in livelihood deterioration in many 
island contexts (Hernandez-Delgado, 2015; Nunn and Kumar, 2018). 
For example, livelihood impacts of climate-driven stressors (including 
shoreline/riverbank erosion, flooding and erratic rainfall) in three 
Mahishkhocha island chars (river-mouth sand islands of Bangladesh) 
have been amplified by inadequate/misguided policy (Saha, 2017).The 
subordination of IKLK in favour of external adaptation strategies has 
accelerated livelihood decline in many island contexts (Wilson and 
Forsyth, 2018). Although economic and financial development has the 
potential to reduce environmental (and livelihood) degradation in SIDS

(Seetanah et al., 2019), it is also clear that uneven development can 
steepen core–periphery disparities, especially in archipelagic contexts, 
resulting in deteriorating rural/peripheral livelihoods at the expense of 
improving urban ones (Wilson, 2013; Sofer, 2015) and increased rural–
urban migration (Birk and Rasmussen, 2014; Connell, 2015).
15.3.4.6 Migration
Climate-related migration is considered to be a particular issue for small 
islands because changes in extreme events and slow-onset changes 
affect increasingly highly exposed and vulnerable low-lying coastal 
populations, therefore causing a threat to small island habitability (KR9 
in Figure 15.5) (Storey and Hunter, 2010; Kumar and Taylor, 2015; Duvat 
et  al., 2017b; Weir and Pittock, 2017; Hoegh-Guldberg et  al., 2018; 
Mycoo, 2018a; Rasmussen et al., 2018). A typology of climate-related 
migration is provided in Cross-Chapter Box MIGRATE in Chapter 7. It is

assumed that climate-related migration will increase in small islands; 
however, as is the case globally, the causes, form and outcomes are 
highly context specific. Types of climate-related migration occur 
across a continuum of agency from involuntary displacement at one 
end to voluntary movement to strategically reduce risks and planned 
resettlement at the other end (Section 15.5.1, also see Chapter 7; Birk 
and Rasmussen, 2014; Betzold, 2015; McNamara and Des Combes, 
2015; Gharbaoui and Blocher, 2016; Stojanov et al., 2017; Weir, 2020).

15
2068Chapter 15 Small Islands
Table 15.4 |  Global mean sea level rise (SLR) at 2100 projections and associated population of SIDS exposed to permanent inundation for global mean surface temperature 
stabilisation targets of 1.5°C, 2.0°C and 2.5°C. Rasmussen et al. (2018).
Stabilised Warming at 2100a1.5°C 2.0°C 2.5°C
Percentile 50th 5th–95th 50th 5th–95th 50th 5th–95th
Global mean SLR (cm) by percentileb48 28–82 56 28–96 58 37–93
SIDS population exposure (thousands) by percentilec400 300–560 420 300–640 430 320–630
Notes:
(a) Above pre-industrial level.
(b) Values are centimetres above 2000 current-era baseline.
(c) Potentially affected population due to local mean SLR. Local mean SLR projections used for individual SIDS take account of variations from the global mean due to factors such 
as glacial isostatic adjustment, gravitational changes from ice melting, deltaic subsidence and tectonic movements.
Studies do not provide sufficiently robust evidence to attribute the

various forms of migration to anthropogenic climate change directly on 
small islands or to accurately estimate the current number of climate-
related migrants (see Chapter 7). Climate events and conditions 
strongly interact with other environmental stressors and economic, 
social, political and cultural reasons for migrating (robust evidence, 
high agreement) (Birk and Rasmussen, 2014; Campbell and Warrick, 
2014; Laczko and Piguet, 2014; Marino and Lazrus, 2015; Connell, 2016; 
Weber, 2016b; Stojanov et al., 2017; Cashman and Yawson, 2019).
Despite difficulties with attribution, the literature establishes that 
climate variability and extreme events and broad environmental 
pressures have contributed to some degree to human mobility on 
small islands over time (medium evidence, high agreement) (Birk 
and Rasmussen, 2014; Campbell, 2014a; Campbell and Warrick, 
2014; Donner, 2015; Kelman, 2015a; Connell, 2016; Stojanov et  al.,

2017; Barnett and McMichael, 2018; Martin et  al., 2018) and these 
studies can provide analogues from which to inform climate-migration 
responses (Birk and Rasmussen, 2014; Kelman, 2015a; Connell, 2016).
Similarly, studies do not provide robust evidence to project how the full 
range of climate drivers may influence migration patterns on small islands 
into the future, although studies are emerging that estimate populations 
affected as a consequence of projected SLR. Rasmussen et  al. (2018) 
estimated current populations of the world that are potentially subject 
to permanent inundation from projected local mean SLR associated with 
global mean surface temperature stabilisation targets of 1.5°C, 2.0°C 
and 2.5°C occurring in 2100. For the affected land area and population, 
this analysis included a subset of 58 SIDS, as defined by the United 
Nations, for which the results are shown in Table 15.4.
The aggregate figures of population that could potentially be affected

by permanent inundation shown in Table 15.4 and Figure 15.3 mask 
important differences in relative exposure between individual SIDS. 
Further, population affected by permanent inundation does not take 
into account the change in the frequency of ESL events and associated 
water-level attenuation (as per Vafeidis et  al., 2019), nor does it 
account for adaptation measures that may alleviate impacts, future 
population growth or the extent to which populations could adaptively 
migrate (Section  15.5.3). However, the analysis by Rasmussen et  al. 
(2018) shows that comparatively small changes in mean sea level can 
result in large increases in the frequencies of ESL events and, hence, 
the risk of coastal flooding of inhabited land, suggesting many areas of SIDS may become uninhabitable well before the time of permanent 
inundation (see also studies referenced in Section 15.3.3.1.1). A similar 
conclusion is drawn by Kulp and Strauss (2019), who show that land

area home to 10% or more of the population of many SIDS is at risk of 
chronic coastal flooding or permanent inundation by 2100.
Duvat et  al. (2021a) employed an integrated systems approach to 
analyse future risk to habitability in atoll islands, taking into account 
changes in various ocean and atmospheric climate drivers and a 
moderate adaptation scenario (i.e., adaptation responses that remain 
similar in nature and magnitude to currently observed responses). They 
found that, compared to present-day risk, additional risk to habitability 
in Male’, Maldives, and Fongafale, Tuvalu, is minimal under a low 
emissions scenario (RCP2.6) at 2050, although it may become moderate 
for Male and high for Fongafale by 2090. Under a worse-case emissions 
scenario (RCP8.5), future risk to habitability in these two urban islands 
may increase slightly in 2050, but may increase to moderate-to-high 
(for Male’) and high-to-very high (for Fongafale) by 2090.

Even where settlement locations and livelihoods remain secure, an 
increase in health diseases, decrease in the availability of potable water 
and increasing exposure to extreme events may reduce habitability 
(Section 15.3.4.9.2; Campbell and Warrick, 2014; Storlazzi et al., 2018). 
For example, the Fijian coastal community of Vunidogoloa made the 
decision to relocate in response to regular inundation during high tides. 
Raising houses on stilts and constructing a seawall failed to prevent 
regular flood damage to buildings and the entire community eventually 
relocated as a ‘last resort’ adaptation measure to a site within customary 
land. The availability of customary land for the new site was a key factor 
of success in this relocation example although this will not guarantee 
success in every case as relocation may expose communities to new risks 
(McNamara and Des Combes, 2015; Piggott-McKellar et al., 2019a).
15.3.4.7 Culture

Small island societies have developed IKLK-based responses to 
living in dynamic environments susceptible to climate variability and 
extremes, which are based in broader systems of culture and heritage 
(high confidence) (Barnett and Campbell, 2010; Lazrus, 2015; Nunn 
et al., 2017b; Bryant-Tokalau, 2018b; Nalau et al., 2018b; Perkins and 
Krause, 2018). As expanded upon in Section 15.6.5, cultural resources 
are thought to play an important role in climate change adaptation on 
small islands through contributing to adaptive capacity and resilience

15
2069Small Islands  Chapter 15
(McMillen et al., 2014; Petzold and Ratter, 2015; Nunn et al., 2017b; 
Warrick et al., 2017; Falanruw, 2018; Mondragón, 2018; Neef et al., 
2018; Parsons et al., 2018; Perkins and Krause, 2018; Hagedoorn et al., 
2019; 2020a) (robust evidence, medium agreement). Thus, loss of 
culture (KR8 in Figure 15.5) threatens adaptive capacity.
Some studies from the Pacific suggest that climate-migration linked 
to reduced habitability (Section 15.3.4.6) can have particularly severe 
cultural implications in a small island context where community solidarity 
and cohesion linked to place-based identity are important aspects of 
adaptive capacity (Hofmann, 2014; Lazrus, 2015; Warrick et al., 2017). 
In the Federated States of Micronesia, land is owned through the 
matrilineal system and hence puts women at the centre of decision-
making. The deterioration and loss of land (through saltwater intrusion,

flooding, drought, erosion) not only can lead to economic deprivation 
but it also compromises cultural identities: ‘Where land signifies political, 
social, and economic well-being, becoming bereft of land cuts off an 
important thread of people’s sense of belonging’ (Hofmann, 2017, p. 82) 
particularly for Chuuk women. Land degradation and loss involves the 
‘interruption to the matrilineal transmission of land’ (Hofmann, 2017; p. 
82), the loss of identities, relationships and their customary authority.
The unquantifiable and highly localised cultural losses resulting from 
climate drivers are less researched and less acknowledged in policy than 
physical and economic losses (Karlsson and Hovelsrud, 2015; Thomas 
and Benjamin, 2018a). In the Bahamas, prolonged displacement 
of the entire population of Ragged Island following Hurricane Irma 
(2017) highlighted the cultural losses that can result from climate-
induced displacement from ancestral homelands. Threats to identity,

sense of place and community cohesion resulted from displacement, 
although all were important foundational features of the Islanders’ 
self-initiated rehabilitation efforts and eventual return. Nonetheless, 
non-economic losses were not accounted for by policy addressing 
displacement (Thomas and Benjamin, 2018a). In the case of Monkey 
River Village in Belize, coastal erosion is threatening the community’s 
cemetery. Residents place significant spiritual and emotional value on 
the cemetery, which serves important community functions, and, thus, 
threats to it are perceived to be serious and necessary to be taken into 
account in any planned response (Karlsson and Hovelsrud, 2015). A 
similar situation exists on Carriacou in the West Indies where culturally 
and historically significant archaeological sites are being lost due to 
coastal erosion caused by a combination of sand mining and extreme 
climate-ocean events exacerbated by SLR (Fitzpatrick et al., 2006).

Population and settlement concentration in coastal areas and high 
exposure to climate-driven coastal hazards on small islands mean that 
threats to tangible cultural heritage (archaeological sites, buildings, 
historic sites, UNESCO World Heritage Sites etc.) are high (Marzeion 
and Levermann, 2014; Reimann et  al., 2018), although few studies 
examine this issue specifically in a small island context. On the island 
of Barbuda, archaeological sites containing important information on 
historical ecology and climatic shifts are at risk from coastal erosion and 
hurricanes. This loss of heritage represents identity loss, as “learning 
about the past is a crucial exploration of self that grounds and connects 
people to places” (Perdikaris et al., 2017; p. 145). Losses and damages 
to heritage sites may also impact tourism and thus have significant 
economic impacts for narrow small island economies (Section 15.3.4.5).15.3.4.8 Transboundary Risks/Issues

Inter-regional transboundary impacts are those generated by processes 
originating in another region or continent well beyond the borders 
of an individual archipelagic nation or small island. Intra-regional 
transboundary impacts originate from a within-region source (e.g., 
the Caribbean). Some transboundary processes may have positive 
effects on the receiving small island or nation, although most that are 
reported have negative impacts (Table 15.5).
15.3.4.9 Key Risks in Small Islands
15.3.4.9.1 Key risk approach
This section builds on cross-chapter work led by Chapter 16 aimed at 
identifying and assessing KRs across sectors and regions (Section 16.5 
and SM16). KRs are the risks of most pressing concern that are caused 
or exacerbated by climate change in a given region. A KR is defined as a 
‘potentially’ severe risk, which can either be already severe or projected 
to become severe in the future, as a result of (a) changes in associated

climate-related hazards and/or the exposure and/or vulnerability of 
natural and human systems to these hazards, and/or of (b) the adverse 
consequences of adaptation or mitigation responses to the risk. In line 
with the guidelines used in the WGII AR6, the identification of KRs in 
small islands is based on the chapter authors’ expert judgement, using 
scientific literature and five types of criteria: (1) importance of the 
affected system or dimension of the system, which is a value judgement 
left to readers to make; (2) magnitude of adverse consequences, based 
on their pervasiveness, degree and irreversibility, and on the potential for 
impact thresholds and cascading effects across the system; (3) likelihood 
of adverse consequences, although this probability is rarely quantifiable 
for small islands due to limited downscaled data at a small island level; 
(4) temporal characteristics of the risk, including its period of emergence,

persistence over time and trend; and (5) ability to respond to the risk, 
with the severity of the risk being inversely proportional to this ability.
15.3.4.9.2 Key risks in small islands
Slow-onset climate and ocean changes, and changes in extreme 
events, are expected to cause and/or to amplify nine KRs in small 
islands, through both direct (e.g., decrease in rainfall will increase 
water insecurity) and indirect, that is, cascading effects: For example, 
loss of terrestrial biodiversity and ecosystem services will increase 
water insecurity, which will in turn cause the degradation of human 
health and well-being (Figure 15.5, Table 15.6 and SM16).
These KRs include loss of marine and coastal biodiversity and ecosystem 
services (high confidence) (KR1; for details on KR coverage, see 
Section 15.3.3.1); submergence of reef islands (low confidence) (KR2; 
Section 15.3.3.1.1); loss of terrestrial biodiversity and ecosystem services

(high confidence) (KR3; Section  15.3.3.3); water insecurity (medium-
high confidence)  (KR4; Section 15.3.4.3); destruction of settlements and 
infrastructure (high confidence) (KR5; Section 15.3.4.1); degradation of 
human health and well-being (low confidence) (KR6; Section 15.3.4.2); 
economic decline and livelihood failure (high confidence) (KR7; 
Sections  15.3.4.4 and 15.3.4.5); and loss of cultural resources and 
heritage (low confidence) (KR8; Section 15.3.4.7).

15
2070Chapter 15 Small Islands
Table 15.5 |  Summary of inter- and intra-regional transboundary risks and impacts on small islands.
Transboundary risks/
issuesSmall island examples Reference
Large ocean waves from 
distant sourcesUnusually large deep ocean swells generated from sources in the mid- and high latitudes by extratropical cyclones (ETCs) cause 
considerable damage on the coasts of small islands thousands of kilometres away in the tropics. Impacts include inundation of 
settlements, infrastructure, and tourism facilities as well as coastal erosion. These waves can propagate to and influence reef islands 
in equatorial areas not usually exposed to high-energy waves.
Examples of extratropical swell waves causing flooding and inundation have been reported throughout the Pacific (French Polynesia, 
Fiji, Micronesia, the Marshall Islands, Kiribati, Papua New Guinea and the Solomon Islands). Modelling of future wave climates has

been carried out for 25 tropical Pacific islands, and results suggests that December–February extreme wave heights will decrease for 
most islands by 2100 under both an RCP4.5 and RCP8.5 scenario, although the frequency of the large winter wave events may increase 
around the Hawaiian Islands. In the Caribbean, northerly swells affecting the islands have been recognised as a significant coastal 
hazard. They cause considerable seasonal damage to beaches, marine ecosystems and coastal infrastructure throughout the region.Hoeke et al. (2013); 
Smithers and Hoeke 
(2014); Shope et al. 
(2016); Canavesio 
(2019); Wandres et al. 
(2020)
Jury (2018)
Transcontinental dust 
clouds and their impactsThe transport of airborne Saharan dust across the Atlantic into the Caribbean has been intensively studied. In the West African 
Sahel, where drought has been persistent since the mid-1960s, analysis has shown that there have been remarkable changes in dust

emissions since the late 1940s. Variability in Sahel dust emissions may be related not only to droughts, but also to changes in the 
North Atlantic Oscillation (NAO), North Atlantic SST and the Atlantic Multidecadal Oscillation (AMO). The frequency of dust storms 
has been on the rise during the last decade. Forecasts suggest that their incidence will increase further. Transboundary movement 
of Saharan dust into the island regions of the Caribbean and the Mediterranean has been associated with human health problems 
including asthma cases in the Caribbean, cardiovascular morbidity in Cyprus and pulmonary disease in the Cape Verde islands.Prospero and Lamb 
(2003); Goudie (2014); 
Schweitzer et al. 
(2018); Goudie (2020); 
Middleton et al. (2008); 
Martins et al. (2009); 
Akpinar-Elci et al. 
(2015); Sakhamuri and 
Cummings (2019)
Influx of Sargassum

from distant sourcesSince 2011, the Caribbean region has witnessed unprecedented influxes of the pelagic seaweed Sargassum. These extraordinary 
sargassum ‘blooms’ have resulted in mass deposition of seaweed on beaches throughout the Lesser Antilles, with damage to coastal 
habitats, mortality of seagrass beds and associated corals, as well as consequences for fisheries and tourism. This recent phenomenon 
has been linked to climate change as well as the possible influence of nutrients from Amazon River floods and/or Sahara dust.van Tussenbroek et al. 
(2017); Oviatt et al. 
(2019)
Franks et al. (2016); 
Putman et al. (2018)
Large-scale changes 
in the distribution of 
fisheries resourcesOcean warming and other climatic phenomena (e.g., ENSO events and Indian Ocean Dipole) have been linked to observed oceanic 
shifts in tuna distribution with significant impacts on revenue for vulnerable small island states that depend on fisheries licences

(e.g., 98% of national income in Tokelau, 66% of national income in Kiribati). The projected eastward redistribution of skipjack and 
yellowfin tuna due to climate change is expected to reduce the total tuna catch within the combined Exclusive Economic Zones of the 
10 Pacific Island Countries and territories (PICTs) where most purse-seine activity occurs by approximately 10% by 2050. Projected 
increases in tuna biomass have been anticipated for Ascension Island and Saint Helena in the South Atlantic.Bell et al. (2018); SPC 
(2019); Oremus et al. 
(2020); Bell et al. 
(2021); Townhill et al. 
(2021)
Movement and impact 
of introduced and 
invasive species across 
boundariesThe spread of IAS is regarded as a significant transboundary threat to the health of biodiversity and ecosystems worldwide.
The extent to which IAS (both animals and plants) successfully establish themselves at new locations in a changing climate will be

dependent on many variables, but non-climate factors such as transmission pathways, suitability of the destination, ability to compete 
and adapt to new environments, and susceptibility to invasion of host ecosystems are deemed to be critical. Modelling studies have 
been used to project the future ‘invisibility’ of small island ecosystems subject to climate change and therefore to anticipate marine 
and terrestrial habitat degradation in the future.
Evidence suggests that hurricanes may have hastened the spread of highly invasive Indo-Pacific lionfish (Pterois volitans) throughout 
the Caribbean in recent years. Two IAS, the Common Green Iguana (Iguana iguana) and Cuban Treefrog (Osteopilus septentrionalis) 
were reported in the Caribbean island of Dominica, following the passage of TC Maria in 2017. Observations 7 months after the 
hurricane, within close proximity to ports, suggest that these animals were stowaways on ships or within relief containers.Russell et al. (2017)

Vorsino et al. (2014); 
Taylor and Kumar 
(2016b)
Johnston and Purkis 
(2015); van den Burg 
Spread of pests and 
pathogens within and 
between island regionsIncreased climate instability has contributed to the emergence and spread of serious diseases carried by mosquitoes such as dengue, 
chikungunya and Zika. The incidence and severity of mosquito-borne diseases have increased significantly in Pacific, Indian Ocean 
and Caribbean islands during the past 10 years, which calls for a better understanding of how climate change is shaping disease 
prevalence and transmission.
Rising sea temperatures are thought to increase the frequency of disease outbreaks affecting reef buildings. Of the range of bacterial, 
fungal and protozoan diseases known to affect stony corals, many have explicit links to temperature. Global projections suggest that

disease is as likely to cause coral mortality as bleaching in the coming decades at many localities, with effects occurring earlier at 
sites in the Caribbean compared to the Pacific and Indian oceans. Model hindcasts suggest that climate-driven changes in SST as well 
as extreme heatwave events have all played a significant role in the spread of white-band disease throughout the Caribbean.
Global food security is threatened by climate-related increases in crop pests and diseases. Black Sigatoka disease of bananas has recently 
completed its invasion of Latin American and Caribbean banana-growing areas. Infection risk has increased by a median of 44.2% across 
the Caribbean since the 1960s, due to increasing canopy wetness and improving temperature conditions for the pathogen.Cao-Lormeau and 
Musso (2014); 
Caminade et al. 
(2017); Pecl et al. 
(2017); Filho et al. 
(2019)
Maynard et al. (2015); 
Randall and van 
Woesik (2015)
Bebber (2019)
Human migration and

displacementCurrently there is limited empirical evidence that long-term climate change is driving transboundary human migration from islands; 
however, following Hurricane Maria, Puerto Rico witnessed ‘depopulation’ of 14% in only 2 years as a result of emigration to the US 
mainland.Campbell (2014a); 
Melendez and 
Hinojosa (2017)
Transboundary risks to 
island food security. 
COVID-19 caused 
disruptions to food 
supply and disaster risk 
management operationsWhile SIDS are a diverse group of nations, most share such characteristics as limited land availability, insularity and susceptibility to 
natural hazards that make them particularly vulnerable to global environmental and economic change processes leading to regional 
food insecurity. The Pacific Islands Forum Secretariat (PIFS) has established a transboundary Framework for Action on Food Security, 
that promotes cooperation, investments, research and development, capacity-building, and adaptation to mitigate climate change

threats.Connell (2013);  
Islam and Kieu (2020); 
Sheller (2020)

15
2071Small Islands  Chapter 15
Box 15.1 | Key Examples of Cumulative Impacts from Compound Events: Maldives Islands and 
Caribbean Region
Cumulative Impacts of the Compound Events of the 1998–2016 Period in the Maldives Islands
Between 1998 and 2016, the Maldives Islands were affected by three major climate events, including the 1997–1998 ENSO event, the 
2007 flood event and the 2016 ENSO event, and by one tectonic event, the 2004 Indian Ocean tsunami (Morri et al., 2015). These events 
illustrate the cumulative and cascading risks that a series of events may cause in reef-dependent atoll contexts (Figure Box 15.1).
The 1997–1998 ENSO event was severe in the Maldives and as a result the living coral cover dropped to <10% (Bianchi et al., 2003). 
Recovery was still in progress in 2004 when the tsunami caused further (although not quantitatively assessed (Gischler and Kikinger,

2006)) damage to the reef ecosystem. Post-1998 recovery ultimately took 15 years, (i.e., longer than following the 1987 ENSO event, 
after which recovery had only taken a few years) and also longer than in the neighbouring undisturbed Chagos atolls, thereby suggesting 
the alteration of the recovery capacity of the reef ecosystem by human-induced reef degradation and climate change (Morri et al., 2015; 
Pisapia et al., 2017). Mid-2016, a new ENSO event occurred, which reduced living coral cover by 75% (Perry and Morgan, 2017). Future 
recovery of the reef ecosystem, which is critical to both current livelihoods and economic activities (especially diving-oriented tourism and 
fishing) and to long-term island persistence, will mainly depend first on the frequency and magnitude of future bleaching events, which 
are expected to increase due to ocean warming, and second on the highly variable effects of anthropogenic disturbances locally (Perry

and Morgan, 2017; Pisapia et al., 2017; Duvat and Magnan, 2019b).
Additionally, the 2004 Indian Ocean tsunami (Magnan, 2006) and the 2007 flood (Wadey et al., 2017) caused damage totalling 62% 
of the country’s GDP (Luetz, 2017). The tsunami also downgraded the Maldives (now a middle-income country) to the Least Developed 
Countries category and caused within-country migration, with 30,000 people (9.6% of the country’s population) displaced (Republic 
of Maldives, 2009). These successive events, which had cumulative devastating effects on the reef ecosystem and cascading effects on 
health and well-being, livelihoods and the economy, highlighted the risk posed by limited recovery time to the whole social–ecological 
system as well as the detrimental effect of local human disturbances on reef recovery.
Cumulative Impacts of the 2017 Hurricanes in the Caribbean Region

Among the 29 Caribbean SIDS, 22 were affected by at least one Category 4 or 5 TC in 2017. These events highlighted how the pre-cyclone 
high exposure and vulnerability of these islands and their populations has caused a ‘cumulative community vulnerability’ (Lichtveld, 2018, 
p. 28) that has amplified the impacts of these TCs, which will in turn increase the long-term vulnerability of affected islands. The exposure 
of these islands over their entire surface, combined with the concentration of people, infrastructure, utilities and public services in flood-
prone coastal areas, inadequate housing, limited access to healthy food and transportation, and unpreparedness explains widespread-to-
total devastation (Shultz et al., 2018; Briones et al., 2019). The destruction of transport systems (Lopez-Candales et al., 2018) and island 
supply chains (Kim and Bui, 2019), which heavily depend on ports, roads, power and communications, made rescue logistically complex,

explaining the lack of freshwater, food supplies, medications and fuel on some islands for several weeks after the event. This cumulative 
vulnerability caused ‘cascading public health consequences’ (Shultz et al., 2018, p. 9), including delayed (i.e., over the next year) mortality, 
physical injury during the clean-up and recovery phase and increased risk of chronic, vector-borne, contaminated water-related diseases 
as well as of mental sequelae (Kishore et al., 2018; Ferre et al., 2019).
The loss of mangroves (Branoff, 2018; Walcker et al., 2019; Taillie et al., 2020) and terrestrial forests (Eppinga and Pucko, 2018; Feng et al., 
2018; Hu and Smith, 2018; Van Beusekom et al., 2018) exacerbated the cyclone-induced economic crisis. In the most affected islands, the 
destruction of buildings and outmigration generated a significant loss of tangible (e.g., museums) and intangible (e.g., traditional artistry)

cultural heritage (Boger et al., 2019). Prolonged displacement of entire island populations (e.g., Ragged Island, the Bahamas, Barbuda) 
caused ‘non-economic loss and damage’, including threats to health and well-being, and loss of culture, sense of place and agency 
(Thomas and Benjamin, 2019), which may further exacerbate the long-term vulnerability of concerned communities.
In early 2020, while island communities were still recovering from the 2017 hurricanes, the COVID-19 pandemic caused the closure of 
global transportation, with devastating socioeconomic impacts on tourism-dependent Caribbean economies (Sheller, 2020), illustrating 
how compounding crises increase island vulnerability to both climate- and non-climate-related events.

15
2072Chapter 15 Small Islands
Cumulative and cascading impacts of recent compound events in the Maldives Island s
El Nino
Southern
Oscillation
(ENSO)
1998, 2016Sea surface
temperatures
(SST)
Coral
bleachingCoral
recoveryIndian Ocean
Tsunami
2004
Stressors Physical changesCumulative impacts
on human system sInfluences Ecological changesLivelihood
& economy
Diving-oriented tourism Fishing Land reclamation Aggregate extraction from reef fla tC oastal waters pollutio n
Local human
disturbances2007
flood event
Figure Box 15.1.1 |  Cascading and cumulative impacts of the compound events of the 1998–2016 period in the Maldives Islands.Cross-Working Group Box (continued)

15
2073Small Islands  Chapter 15
Box 15.2 | Loss and Damage and Small Islands
Loss and damage has a range of conceptualisations (Section 1.4.4.2; Cross-Chapter Box LOSS in Chapter 17) and is a critical issue for 
many small islands, closely related to issues of climate justice (Section 15.7). Small islands are already experiencing an array of negative 
climate change impacts while climate risks are projected to increase as global average temperatures rise (Sections 15.3, 16.2; Cross-
Chapter Paper 2). Barriers and limits to adaptation also contribute to greater levels of both economic and non-economic loss and damage 
for small islands (Sections 15.6, 16.4).
For SIDS in particular, loss and damage has negative implications for sustainable development (Benjamin et al., 2018). The costs of loss 
and damage, particularly from extreme events, can deplete national capital reserves (Noy and Edmonds, 2019). Thomas and Benjamin

(2017) show how loss and damage can lead to an ‘unvirtuous cycle of climate-induced erosion of development and resilience’. In this 
cycle, addressing loss and damage strains limited national resources, diverting public funding and other resources to address negative 
climate impacts. This in turn reduces resources and capacities which could be allocated to adaptation, building resilience and sustainable 
development, thereby increasing vulnerability to climate change and leading to further loss and damage where the cycle begins again. 
The cascading and cumulative impacts of extreme events experienced in Pacific and Caribbean SIDS exemplify that this cycle may already 
be in effect.
In addition to the strain on national resources that loss and damage currently presents, credit ratings of SIDS have recently begun to 
include vulnerability to climate change, which may have negative impacts on their abilities to borrow external funds, attract foreign

investment or access concessional financing (Buhr et al., 2018; Volz et al., 2020). Costs of addressing loss and damage may also affect the 
ability of SIDS to repay external debt, thus endangering eligibility for future access to funding (Baarsch and Kelman, 2016; Klomp, 2017; 
Shutter, 2020). These factors may place SIDS in situations where they face mounting costs of climate change with eroding capacities and 
resources to address loss and damage.
In the international policy arena, small islands—as part of the AOSIS—have been strong advocates for including loss and damage in 
the United Nations Framework Convention on Climate Change (UNFCCC); highlighting the increasing and irreversible risks that climate 
change poses for islands in particular (Roberts and Huq, 2015; Adelman, 2016; Mace and Verheyen, 2016). AOSIS, along with other 
developing countries and groups, have advocated that there is a pressing need for finance and resources to address loss and damage as

well as greater integration of loss and damage in the UNFCCC and the Paris Agreement, including in capacity building, technology and 
the global stocktake (Benjamin et al., 2018; Nand and Bardsley, 2020).
Risk accumulation and amplification through cascading effects from 
ecosystems and ecosystem services to human systems will likely 
cause reduced habitability of some small islands (high confidence) 
identified as the overarching KR (KR9). Habitability is understood 
as the ability of these islands to support human life by providing 
protection from hazards which challenge human survival; by 
assuring adequate space, food and freshwater; and by providing 
economic opportunities, which contribute to health and well-being—
recognising that both supportive ecosystems and sociocultural 
conditions (i.e., beliefs and values, institutions and governance 
arrangements, sense of community and attachment to place) play 
a critical role in habitability (Duvat et al., 2021a). The reduction of

island habitability is expected to cause increased migration, along the 
afore-mentioned involuntary displacement to planned resettlement 
spectrum (Section 15.3.4.6), which may eventually lead to population 
movements from exposed areas and depopulation of some islands. 
This risk is the highest for atoll nations, where some islands might 
become uninhabitable over this century (Section 15.3.4.6; Storlazzi 
et al., 2018; Duvat et al., 2021a). Despite a lack of literature assessing 
the risk of reduced habitability in non-atoll islands, the latter are 
also expected to experience decreased habitability, especially in their 
coastal areas.15.4 Detection and Attribution of Observed 
Impacts of Climate Change on Small 
Islands
As highlighted in AR5, detection of climate change impacts on the 
fragile environments of small islands is challenging because of 
other non-climate drivers that affect small islands. Determination of

attribution to incremental change of climate drivers is also challenging 
because of the natural climate variability. Therefore, there is limited 
scientific literature on observed impacts and attribution. A synthesis of 
findings on the impacts of climate change (Sections 15.3.3 and 15.3.4) 
shows that there is more information on impacts on ecosystems 
compared to human systems. There is high confidence in attribution 
to climate change of impacts on the coastal and marine as well as 
terrestrial ecosystems (Hansen and Cramer, 2015; Shope et al., 2016; 
van Hooidonk et al., 2016; Hoegh-Guldberg et al., 2017; Hughes et al., 
2017b; Mentaschi et al., 2017; Shope et al., 2017; Vitousek et al., 2017; 
Wadey et al., 2017; Ford et al., 2018; Hughes et al., 2018; IPCC, 2018; 
Storlazzi et al., 2018; Bindoff et al., 2019) and medium confidence in 
attribution to climate change of impacts on livelihoods, economics and 
health (Figure 15.6; McIver et al., 2016; Eckstein et al., 2018; Santos-

15
2074Chapter 15 Small IslandsIncreased frequency
of strong tropical 
cyclonesIncreased storm 
surgesIncreased frequency 
of extreme El Nino/La
 Nina eventsCHANGES IN EXTREME EVENTSSLOW ONSET  OCEAN CHANGES
Increase in ocean
 temperaturesOcean acidificationRelative
sea-level riseIncreased frequency of 
marine heat waves
See text sections for detailed description of  KR coverage.Increase in 
atmospheric temperaturesChange in rainfall patternsSLOW ONSET  CLIMA TE CHANGES
Key Risk 5 (KR5)
Destruction of settlements
and infrastructure
Key Risk 6 (KR6)
Degradation of human health 
and well-being
Key Risk 7 (KR7)
Economic decline and
livelihood failure
Key Risk 8 (KR8)
Loss of cultural
resources & heritageKey Risk 4 (KR4)
Water
insecurity
Key Risk 1 (KR1)
Loss of marine and
coastal biodiversity and
ecosystem services
Key Risk 2 (KR2)
of reef islands
Key Risk 3 (KR3)
Loss of
terrestrial biodiversity
and ecosystem services
Small island system Key Risksinfluences

Climate change-related influential factors ABKey Risk 9 (KR9)
Reduced habitability
of islandsKey Risks in small islands
Figure 15.5 |  Key risks in small islands.  KR1 to KR8 are interconnected as shown by arrows, which causes risk accumulation leading to reduced island habitability. The main 
interconnections are shown in this figure: for example, loss of marine and coastal and terrestrial biodiversity and ecosystem services (KR1 and KR3, respectively) are projected 
to cause the submergence of reef islands (KR2), water insecurity (KR4), destruction of settlements and infrastructure (KR5), degradation of human health and well-being (KR6), 
economic decline and livelihood failure (KR7), and loss of cultural resources and heritage (KR8). Importantly, KRs result from both direct effects (e.g., decrease in rainfall will increase

water insecurity) and indirect effects (e.g., loss of terrestrial biodiversity and ecosystem services will increase water insecurity, which will in turn cause the degradation of human 
health and well-being).

15
2075Small Islands  Chapter 15
As Figure  15.6 shows, there is high confidence that climate change 
causes changes in terrestrial ecosystems as well as coral reef 
bleaching through increases in SST and submergence and flooding of 
coastal areas through SLR and increased wave height. With respect to 
casualties, settlements and infrastructure loss and damage, as well as 
economic and livelihood loss, although confidence in detection is high, 
there is at present medium confidence in the attribution to climate 
change. Medium confidence in attribution frequently arises owing to 
the limited research available on small island environments.
15.5 Assessment of Adaptation Options and 
Their Implementation
Since AR5, small islands have experimented with new adaptation 
options, which has increased the lessons learnt from on-the-ground 
practices in these settings. Figure 15.7 shows some of the adaptation 
options that are being experimented with in small islands. This section

covers most common adaptation actions and approaches across 
small islands and assesses the many constraints, enablers and limits 
to adaptation. Adaptation also plays a key role in climate resilient 
development and the insights emerging from small islands on this 
topic are discussed after the adaptation section.
15.5.1 Hard Protection
Seawalls have been a popular coastal protection measure on islands 
(Figure 15.7). An analysis of National Communications shows that 28% 
of coastal protection actions are seawalls, followed by breakwater 
structures and coastal protection units (Robinson, 2017a). Coastal protection infrastructure has been heavily invested, for example, in 
the Caribbean region (Mycoo, 2014b) and Cuba (Mycoo, 2014a). A 
similar situation applies in many Indian Ocean islands, where coastal 
protection strategies are manifested by hard shoreline structures, many 
of which are proving challenging to maintain (Naylor, 2015; Betzold

and Mohamed, 2017; Magnan and Duvat, 2018). In the Pacific the 
situation is different given that many islands have been occupied for 
millennia by indigenous communities with extant knowledge for coping 
with adversity (Granderson, 2017). The latter generally favours ‘soft’ 
shoreline structures for coastal protection although the building of 
seawalls has been rapid, especially in urban islands (Umeyama, 2012; 
Duvat, 2013; Magnan et al., 2018; Morris et al., 2018), and also in some 
rural islands (e.g., Tubuai, French Polynesia (Salmon et al., 2019)).
Many rural communities have uncritically emulated structures in urban 
contexts built and maintained with external finances. As a result, in 
many Pacific SIDS, seawalls have collapsed without additional funding 
available for repairs (Nunn and Kumar, 2018; Piggott-McKellar et al., 
2020; Nunn et al., 2021). Similar cases have been recorded along the 
coast of Puerto Rico (Jackson et al., 2012) while on Indian Ocean islands

(e.g., Seychelles), the shorelines are littered with broken seawalls and 
groynes (Duvat, 2009). In Samoa, seawalls close to Apia need constant 
investments to remain viable.
On small islands, another widespread issue with seawalls and other 
hard shoreline structures is that they invariably shift problems of 
shoreline erosion and lowland inundation elsewhere (Donner and 
Webber, 2014). Even surrounding entire islands with such structures, 
as has happened on Male’ (Maldives), is not a long-term solution 
because of incidences of localised seawall collapse that can spread 
quickly if not addressed immediately (Naylor, 2015). Hard structures 
Confidence in the attrributionConfidence in the detection and
in the attribution of observed impacts of climate change
on tropical small islands High
Medium
Low
Confidence in the detectionHigh Medium Low• Casualties, settlements and infrastructure 
loss and damage• Economic and Livelihood loss• Relocation and migration

• Water stress from drought• Coral reef bleaching
• Submergence and flooding of marine and 
• Species geographic range shifts
• Phenological changes
• Fresh water stress• Degradation of mangroves
   • Changes in ecosystem services 
   • Changes in ecosystem structure
• Degradation of seagrass• Settlements and infrastructure damage and 
loss from erosion and sea level rise• Natural systems
• Human systems
• Wealth and wellbeing
• Agriculture production decline• Fisheries decline
Figure 15.6 |  A comparison of the degree of confidence in the detection of observed impacts of climate change on tropical small islands with the degree 
of confidence in attribution to climate change drivers.

15
2076Chapter 15 Small Islands
for coastal protection will become increasingly ineffective in the future, 
demonstrating the need for adaptation along most island coasts to 
become more transformative than has been the case over the past few 
decades. In the Bahamas, it has been suggested that coastal protection 
structures and strategies are implemented through ‘a rather piecemeal 
approach of single projects and small patches, partially resulting in 
maladaptation by further increasing processes of erosion’ (Petzold 
et  al., 2018, p. 95). In the village of Lalomalava, Samoa, national 
adaptation funding was spent on erecting a seawall to protect the 
village, but the wall was not long enough to protect the whole village, 
leading some families and properties to face increasing impacts from 
large waves (Crichton and Esteban, 2018).
15.5.2 Accommodation and Advance as Strategies
In most small island contexts, the costs of adaptation through

accommodation are prohibitive so that it has in most cases not been 
contemplated as a widespread option. However, accommodation 
measures such as the raising of dwellings and key infrastructure like 
coastal roads above ground level have been implemented to reduce the 
impacts of flooding in some islands (Figure 15.7). In the most populous 
islands of the Tuamotu atolls, French Polynesia, where between 48% 
and 98% of dwellings have already experienced flooding since the 
1980s, elevated houses with floors built 1.5 m above ground level 
are subsidised by the government as part of risk prevention plans 
(Magnan et  al., 2018). Despite this incentive, the opposition of the 
local authorities and population to these plans (which also include 
constraining setback guidelines) considerably limited implementation, 
hence elevated houses only represent 7% of the total housing stock. 
In the Philippines (Tubigon) and Indonesia (Jakarta area), residents

have elevated their houses by building stilted houses or raising the 
floor using coral stones to face increased flooding (Jamero et al., 2017; 
Esteban et al., 2020). Also, in Puerto Rico houses have been raised to 
address flooding (Lopez-Marrero, 2010).
In some small island settings, land reclamation (i.e., land gain through 
infilling) has been implemented for decades to allow for infrastructure 
construction and to address land shortages arising from high 
population growth. For example, land reclamation in Port of Spain, the 
capital city of Trinidad, has long been used as a solution space to meet 
land for housing, industrial development and infrastructure provision 
(Mycoo, 2018b). Likewise, one third of the land area of Male’, the 
capital island of the Maldives, results from land reclamation (Naylor, 
2015). Land reclamation is also common in Pacific atoll countries and 
territories, where it occurs both in urban islands facing high population

pressure, such as South Tarawa, Kiribati (Biribo and Woodroffe, 2013), 
Funafuti Atoll, Tuvalu (Onaka et al., 2017), and Rangiroa Atoll, French 
Polynesia (Duvat et  al., 2019b), and in rural islands, for example, 
Takapoto and Mataiva atolls, French Polynesia (Duvat et al., 2017b). 
In some cases, land reclamation has paved the way for land raising, 
which is increasingly considered to adapt to SLR in small island 
contexts (Figure 15.7). For example, since the 1990s, the capital area of 
the Maldives has been expanded through the construction of a large 
new island, Hulhumalé, which is still under construction and is built 
60 cm higher than Male’ to take into account SLR (Hinkel et al., 2018; 
Brown et al., 2020). More generally, in the Maldives, the 2004 Indian Ocean tsunami has boosted island raising as part of the ‘safe island 
development programme’ (Shaig, 2008). Recent studies suggest that 
land and island raising have some potential in small islands, especially

in urban high-value areas where this can generate substantial revenues 
through the sale or lease of new land, and therefore leverage public 
adaptation finance (Bisaro et al., 2019).
15.5.3 Migration
Migration, including planned resettlement, is increasingly occurring in 
small islands to intentionally respond to or prepare for climate change 
impacts (Figure 15.7; Magnan et al., 2019). There is currently limited 
evidence and low agreement in the literature as to whether migration 
of various types is an effective strategy to adapt to localised impacts 
of climate change, as outcomes are highly context specific (Donner, 
2015; McNamara et al., 2016; Hermann and Kempf, 2017; McMichael 
et al., 2019; Piggott-McKellar et al., 2019a; Tabe, 2019; Bertana, 2020; 
Weir, 2020).
In situ adaptation options are frequently the preference of communities 
over resettlement (Jamero et al., 2017) and in many documented cases, 
relocation—both planned and autonomous—is an adaptation option

of last resort due to high economic and sociocultural cost (McNamara 
and Des Combes, 2015; Jamero et al., 2017; Crichton et al., 2020). In 
small islands, there is medium evidence and high agreement that the 
degree of migrant agency and choice in decisions about whether to 
move, where, when and how is an important determinant of success and 
therefore ‘adaptiveness’ (see Cross-Chapter Box MIGRATE in Chapter 
7; McNamara and Des Combes, 2015; Hino et  al., 2017; McMichael 
et al., 2019; Piggott-McKellar et al., 2019a; Bertana, 2020). Two case 
studies of community relocation in Fiji (Denimanu and Vunidogoloa 
villages) recommend that participatory inclusion of all social groups 
in the relocation planning process, including in planning for livelihood 
sustainability in new locations, should be ensured in future planned 
community relocation to foster positive adaptive outcomes (Piggott-
McKellar et al., 2019a).
There are few examples of highly ‘successful’ and therefore adaptive

international resettlement or relocation in response to environmental 
pressures in history. For example, the experiences of Gilbertese 
resettled in the Solomon Islands highlight that tensions with host 
communities over land and resource rights and limited knowledge of 
new environments (such as when communities previously reliant on 
marine resources are resettled in high island locations) can create new 
vulnerabilities (Donner, 2015; Weber, 2016a; Tabe, 2019). Even where 
gradual international relocation is supported and planned through 
policy as in the case of Kiribati’s ‘migration with dignity’ strategy, strong 
cultural connection to land and uncertainty about life in receiving 
communities in Australia and New Zealand means that many remain 
opposed to indefinite or permanent migration (Allgood and McNamara, 
2017; Hermann and Kempf, 2017). The same challenges could apply 
where domestic migration occurs between significantly different

cultural, social and physical environments. However, planned migration 
for employment or education can reduce exposure in sending locations 
and spread risk through expanding economic opportunities and 
providing remittances, thus having inadvertent adaptation outcomes

15
2077Small Islands  Chapter 15
(Campbell, 2014a). Policies which support migration for employment 
by the most vulnerable—those that may wish to migrate but lack the 
resources to do so—may offer an adaptive strategy to environmental 
pressure, particularly where these incorporate adequate preparedness 
for life in host communities (Luetz, 2017; Curtain and Dornan, 2019; 
Drinkall et al., 2019). Research from the Maldives suggests that women 
and men do not possess equal capacities to use mobility as a strategy 
to adapt to climate change, with women less able to employ migration 
as an adaptation strategy due to gender roles, social expectations, 
economic structures, political laws and religious doctrines, and gender 
norms and cultural practices (Lama, 2018).
Forced relocation, involuntary displacement and low-agency migration 
(e.g., due to low migrant financial resources, or limited participation 
in migration planning) are commonly associated with unsuccessful

outcomes and can therefore be considered an impact of climate change 
rather than an adaptation strategy (Weber, 2016a; Thomas and Benjamin, 
2017; Tabe, 2019). Resettlement of households, communities and larger 
island populations is increasingly discussed in the context of losses and 
damages when in situ adaptation limits are thought to be reached. 
Limited data and research relating to adaptation limits, transformational 
adaptation, tolerable and intolerable risk levels in small islands, and 
limited ability to directly attribute climate change to migration decisions 
(in the context of both slow-onset changes and extreme events) mean 
that policy applications are currently limited (Thomas and Benjamin, 
2018b; Handmer and Nalau, 2019; Nand and Bardsley, 2020).
15.5.4 Ecosystem-Based Measures
Small islands have focused increasingly on EbA approaches and other 
nature-based solutions that bring benefits both for the ecosystems

and communities (Figure  15.7; Giffin et  al., 2020). There is robust 
evidence on implementation of EbA approaches across small islands, 
yet medium agreement on the exact benefits of these activities 
(Mercer et al., 2012; Doswald et al., 2014; Nalau et al., 2018a) given 
the difficulties in quantifying benefits and the absence of monitoring 
and evaluation frameworks (Doswald et al., 2014). Traditionally, EbA 
activities, especially at national and regional scales, have predominantly 
focused on restoring or conserving coastal and marine ecosystems 
(e.g., coral reefs, mangrove forests and seagrass meadows), with less 
emphasis on the services provided by natural inland forests (Mercer 
et al., 2012). Incorporation of forests is, however, increasing, in most 
cases as components of ridge to reef (Figure 15.4) (or Disaster Risk 
Reduction) projects (limited to medium evidence), and is geared 
towards integrated watershed management to establish downstream

water security, erosion control and ultimately to protect the health of 
coral reef ecosystems (Förster et al., 2019).
Additionally, some islands are constructing climate-smart development 
plans such as improved management of existing and newly established 
protected areas, restoration of riparian zones, urban forests/trees, 
sub-urban and peri-urban home gardens, and improved agroforestry 
practices towards increasing resilience to changing climate conditions, 
wildfires as well as decreasing food insecurity (e.g., Pedersen et al., 
2016; McLeod et  al., 2019). Paired terrestrial and marine protected 
areas (MPAs) have shown that forest conservation and rehabilitation yield better outcomes for coral health as forests stabilise soils and 
prevent erosion and sequester groundwater pollutants (limited to 
medium evidence, high agreement) (Carlson et al., 2019). The success 
of protected areas is, however, undermined by weak governance due

in part to limited financial resources which undermine management 
and the enforcement of regulations governing activity within them 
(Schleicher et al., 2019).
Since the 1990s, artificial reefs have been increasingly used in 
small islands to support reef restoration and reduce beach erosion, 
especially in the Caribbean region (e.g., Dominican Republic, Antigua, 
Grand Cayman, Grenada) and Indian Ocean (Maldives, Mauritius) 
(Fabian et  al., 2013; Reguero et  al., 2018). They have been more or 
less successful in reducing the destructive impacts of extreme events, 
depending on their technical characteristics and the local context. 
For example, while it resisted the waves generated by hurricanes 
Georges and Mitch in 1998, the artificial reef (Reef Ball breakwater 
type) implemented at Gran Dominicus Resort, Dominican Republic, 
did not prevent significant beach erosion. By contrast, the coral reef 
restoration project implemented to ‘build a beach’ on the resort island

of Ihuru, North Malé Atoll, Maldives, was successful as it enabled 
beach expansion and prevented the erosive impacts of the 2004 Indian 
Ocean tsunami on the beach (Fabian et al., 2013).
Over the past decades, beach nourishment has been implemented in 
small islands either to reduce beach erosion (e.g., in tourist areas) or 
to protect critical human assets (e.g., roads) that are highly exposed to 
storm waves. It has been increasingly used to maintain beaches in the 
islands of the Maldives (Shaig, 2011) and in Barbados (Mycoo, 2014b). 
However, islands have limited sand stocks, and sediment extraction 
can aggravate risks and/or accelerate ecosystem degradation if 
implemented without the necessary precautions.
In designing and implementing EbA, IKLK have high relevance 
especially amongst Pacific small islands as many communities are 
remote and still rely on ecosystems for their livelihoods (Nalau et al., 
2018b; Narayan et al., 2020). In Fiji, IKLK have informed EbA projects

by identifying native species suitable for strengthening the coastal 
environment to reduce coastal erosion and flooding in the villages 
(Nalau et al., 2018b). Whole-of-island approaches, like Lomani Gau in 
the Gau Island in Fiji, try to foster integrated management practices in 
small islands that are based on shared governance of resources and 
on understanding the interlinkages between sectors and ecosystems 
(Remling and Veitayaki, 2016). In the Caribbean, EbA approaches 
are somewhat absent in national and regional programmes and 
plans, yet at the local scale EbA strategies are used increasingly with 
implementation mostly led by NGOs (Mercer et al., 2012).
EbA approaches have many benefits but also face several challenges 
and limits. Biophysical limits can make some EbA and nature-based 
solutions ineffective: coral reefs are unlikely to withstand increased 
temperatures, reducing the effectiveness of coral reef-based EbA

options under higher temperature scenarios (Barkdull and Harris, 
2018; Cornwall et al., 2021). Likewise, many other coastal and marine 
ecosystems, such as mangroves, face severe limitations with increasing 
sea levels and other climate impacts (Morris et al., 2018; Thomas et al., 
2021).

15
2078Chapter 15 Small Islands
No respons e
Hard protection
Planned retreat
Ecosystem-base d
measures
2030 2020 2010 1970 2000 1990 1980 1960 2050MC StF
Risk of flooding/erosion and date at which it was recognised severe by stakeholders Tipping point
New action take n
Future trend expecte dMC: Mon Choisy public beac h1 2
2
1. Decreased public beach value
2. Nature of tipping point :
Increased coastal road disruptio n1
StF: Saint-Félix public beac hSLR:  sea-level rise IWE: increased wave energyNo response
SLRIWE
IWEHard Protection
SLR
Accommodation (e.g. raising of dwellings and infrastructure)
IWE
SLR
IWEAdvance with land or island raising
SLRPlanned retreat
IWE
SLR
Ecosystem-based measures
IWE
SLR- Indian Ocean: common
- Pacific Ocean: common
- Caribbean: common
- Indian Ocean : Male’, Maldives; Comoro s
- Pacific Ocean: South Tarawa, Kiribati; Majuro, Marshall Islands; Fiji;
 T ubuai, French Polynesia; Samo a
- Caribbean: Barbados; Cuba; Puerto Rico
- Indian Ocean:  /

- Pacific Ocean: T uamotu atolls, French Polynesia; Tubigon, Philippines ;
 Jakarta area, Indonesia
- Caribbean: P uerto Rico; Jamaica
- Indian Ocean : Mauritius
- Pacific Ocean: Carteret Islands, Papua New Guinea; Nuatambu Island & Taro, Solomon Island s
- Caribbean: /
- Indian Ocean: Hulhumale’  and other inhabited islands, Maldives ;
 Eden and Perseverance Islands, Seychelles
- Pacific Ocean: /
- Indian Ocean : Mauritius (mangrove planting, beach restoration); Ihuru resort ,
 Maldives (artificial reef)
- Pacific Ocean: Tuvalu (beach restoration); Samoa & Vanuatu (restoration of beach vegetation)
- Caribbean: D ominican Republic, Grenada (artificial reefs); Barbados (beach nourishment) ;
 Haiti, Bahamas (mangrovereplanting )
+ beach restoration
a
b
c d
a
b
c
dEmbankment and groynes
Beach nourishmen t
Artificial reef + seagrass restoration + beach restoration
Beach restoratio ne
20m-landward move of a 200m-long roa d

eTime before action is taken after a tipping point is reached21
Adaptation measures implemented to reduce coastal risks in small islands

15
2079Small Islands Chapter 15
Figure 15.7 |  Adaptation measures implemented to reduce coastal risks in small islands. Panel 1 provides examples of implementation of different types of measures 
aimed at reducing coastal erosion and flooding. The measures include no response (no intervention, widespread in small islands), hard protection through the construction of

engineering-based structures, accommodation through dwelling and infrastructure raising, planned retreat, advance (i.e., especially island raising) and ecosystem-based measures, in three small island regions, the Indian and Pacific oceans and Caribbean. It highlights the prevalence of no response, hard protection and the increasing use of ecosystem-based measures. Based on the example of two beach sites in Mauritius (Mon Choisy in the north and Saint-Félix in the south), panel 2 shows that the measures used at a given coastal site evolve over time (e.g., from no response to hard protection, and then planned retreat and ecosystem-based measures) and that recent DRR (Saint-Félix) and adaptation (Mon Choisy) projects often combine several types of measures, including retreat and ecosystem-based measures (Duvat et al., 2020a). Together, panels 1 and 2 emphasise the diversity and increasing complexity of the measures implemented in small islands.
15.5.5  Community-Based Adaptation

‘community-led process based on meaningful engagement and proactive involvement of local individuals and organisations’ (Remling and Veitayaki, 2016; p. 380). Enabling CBA projects to succeed relies on gaining a good understanding of the sociopolitical context within which the communities operate, including such key issues as land tenure arrangements and ownerships, gender and decision-making processes that operate on the ground (Nunn, 2013; Buggy and McNamara, 2016; Crichton and Esteban, 2018; Delevaux et al., 2018; Nalau et al., 2018b; Parsons et al., 2018; McNamara et al., 2020; Piggott-McKellar et al., 2020).This also includes the broader and often more urgent development issues that impact on communities’ well-being (Piggott-McKellar et  al., 2020). Community-based projects demonstrate in the Pacific that communities’ vulnerabilities, priorities and needs might be a better and more effective entry point for climate adaptation than framing projects solely around climate change

(Remling and Veitayaki, 2016; Weir, 2020).This is supported by a recent review of 32 CBA initiatives in the Pacific where initiatives that were locally funded and implemented were more successful than those with external international funding (McNamara et  al., 2020). Initiatives that integrated EbA and climate awareness raising also performed better (McNamara et al., 2020).

While CBA approaches to adaptation projects can increase community 
ownership and commitment to project implementation, these can also face challenges. In Pele Island, Vanuatu, implementation of CBA projects has experienced significant failures due to elite capture of project management, internal power dynamics within communities, and different priorities of communities living across the island that were supposed to be all responsible for implementing whole-of-island projects (Buggy and McNamara, 2016). Similarly, in Samoa, consultations with community leaders led to the misplacement of a revetment wall that increased flooding in the area against engineering advice (McGinn, 2020). Also, community scale might not always be the best fit if the best scale to leverage adaptation is across catchment or whole-of-island scale (Buggy and McNamara, 2016; Remling and Veitayaki, 2016).
15.5.6  Livelihood Responses

Communities across small islands are adapting to the impacts of climate change across a range of livelihood activities. Coastal fishers have adapted by employing several activities ranging from diversification of livelihoods to changing fishing grounds and considering weather insurance (Blair and Momtaz, 2018; Lemahieu et  al., 2018; Karlsson and McLean, 2020; Turner et  al., 2020). In Antigua and Vanuatu, fishers have undertaken adaptation in response to increases in air and ocean temperature, increases in wind and changes in rainfall. In Antigua, adaptation strategies amongst coastal fishers have included investments in improved technologies and equipment, changing fishing grounds, and seeking better training and education (Blair and Momtaz, 2018). In Efate (Vanuatu) the majority (87%) of the fishermen used livelihood diversification as an adaptation strategy whereas 53% also searched for new fishing areas as a result of the changing conditions (Blair and Momtaz, 2018). In southwest

Madagascar, due to deteriorated reef conditions, coastal fishermen now go further offshore to catch fish or have adapted their fishing techniques, while others closer to the tourism markets have opted for livelihood diversification (Lemahieu et al., 2018). Coastal fishers in the Dominican Republic have also diversified their livelihoods and use local knowledge in changing fishing practices and locations depending on environmental conditions (Karlsson and McLean, 2020). In the future, increased inland rainfall could, for example, provide new areas for inland aquaculture in the Solomon Islands as an adaptation strategy and also reduce pressure from coastal fishing (Dey et al., 2016).

In the agricultural sector in Jamaica, adaptation strategies include

varying expenditure on inputs (e.g., fertilisers, chemicals, labour), diversifying cropping patterns, expanding or prioritising other cash crops (e.g., fruits and vegetables), engaging in small-scale livestock husbandry (Guido et al., 2018), and investing in irrigation technologies due to increased drought and infrequent rainfall (Popke et  al., 2016). In many higher-elevation islands within the Pacific, including Vanuatu and Fiji, communities continue to use to varying degrees traditional adaptive strategies designed to reduce their vulnerability to tropical cyclones. These include planting a diversity of different crops within household and communal gardens, locating gardens in different areas within their customary lands to ensure that not all crops are destroyed by an extreme event, and the storage and preservation of certain foodstuffs (so-called famine foods) (Campbell, 2014b; McMillen et al., 2014; Le Dé et al., 2018; Moncada and Bambrick, 2019).

Given changes in climatic conditions, in Puerto Rico women in the coffee 
industry are now forming their own ‘micro-clusters’ of complementary activities, such as rebuilding of public spaces, running environmental education programmes for children, and opening new commercial enterprises (e.g., coffee shops, and food products) that do not rely on traditional coffee supply chains or government assistance (Borges-Méndez and Caron, 2019). Such alternative livelihood strategies parallel those undertaken by Pacific women working on various local-level climate change adaptation and environmental projects throughout small island nations of the Pacific. Women report testing and using adaptive strategies informed by IKLK, but which are being modified to suit the changing environmental conditions they are encountering and

15
2080Chapter 15 Small Islands
those projected in the future. This includes harvesting rainwater during 
droughts, planting native plants along coastlines to prevent erosion 
and flooding, developing plant nurseries, experimenting with growing 
salt-tolerant (taro) crops, and relocating crop cultivation inland (McLeod 
The tourism sector is increasingly a major source of cash-based 
livelihoods across small islands. Despite the high vulnerability and 
sensitivity of island tourism to climate change at a national scale (Scott 
et al., 2019), there is evidence from the South Pacific that local tourism 
operators’ adaptive capacity is high due to sociocultural factors. In 
Samoa, adaptive capacity consists of accommodation providers’ 
social networks, resources, past experiences and understanding of 
environmental conditions, and remittances as a form of informal 
insurance (Parsons et al., 2017). The adaptive capacity of Tongan tour

operators is strengthened by high awareness of climate change, strong 
social networks and remittances as well as perceived high resilience 
against climate change (van der Veeken et al., 2016).
Evidence from Vanuatu shows that climate risk to tourism destinations 
is influenced by multiple, interconnected economic, sociocultural, 
political and environmental factors suggesting that holistic approaches 
are needed to reduce risk and avoid negative knock-on effects (Loehr, 
2019). Tourism can strengthen mechanisms that reduce vulnerability 
and increase adaptive capacity of the wider destination, such as 
providing adaptation finance, investing in education and capacity 
building, and working with nature (Loehr, 2019). Examples include 
numerous EBA initiatives in the Caribbean including marine-protected 
areas in St. Lucia and Jamaica (Mycoo, 2018a). In Vanuatu, tourism 
businesses are engaged in establishing marine-protected areas to

address multiple risks from climate change, population growth and 
development (Loehr et al., 2020). In the Seychelles, coral restoration 
programmes and mangrove reforestation are promoted through 
public–private partnerships, generating new opportunities for wetland-
tourism livelihoods (Khan and Amelie, 2015).
The willingness of tourism businesses to finance adaptation measures 
varies. Islands have developed building codes which consider impacts 
from SLR but these are often not enforced (Hess and Kelman, 2017). 
In cases where tourist resorts have been part of climate adaptation 
projects, such as funding for hard coastal protection infrastructure, 
the resort owners find that these diminish the aesthetics of the beach 
destination (Crichton and Esteban, 2018). Adaptation taxes and levies 
imposed on tourism can provide funding (Mycoo, 2018a) as The 
Environmental Protection and Tourism Improvement Fund Act, 2017 of

British Virgin Islands shows (Smith, 2017). A lack of interaction between 
tourism and climate change decision makers is a commonly identified 
issue (Becken, 2019; Mahadew and Appadoo, 2019; Scott et al., 2019). 
A number of adaptation measures are recommended in the literature 
such as increasing climate change research, education and institutional 
capacities; product and market diversification away from coastal 
tourism to include terrestrial-based experiences and heritage tourism; 
and mainstreaming adaptation in tourism policies and vice versa (e.g., 
to include appropriate planning guidelines for tourism development, 
coastal setbacks and environmental impact assessments (Mycoo, 
2018a; Becken et al., 2020) Thomas et al., 2020; van der Veeken et al., 
2016).15.5.7 Disaster Risk Management, Early Warning Systems 
and Climate Services
Disaster risk management investments in small islands are commonly 
framed as reducing climate change-driven risk and contributing

to sustainable development (Johnston, 2014; Mercer et  al., 2014a; 
Kuruppu and Willie, 2015). Examples include strengthening the capacity 
of National Meteorological and Hydrological Services (NMHS ) to deliver 
effective (WMO et al., 2018); nurturing community-based disaster risk 
management to build social capital (Blackburn, 2014; McNaught et al., 
2014; Gero et al., 2015; Handmer and Iveson, 2017; Chacowry et al., 
2018; De Souza and Clarke, 2018; Currenti et  al., 2019; Cvitanovic 
et al., 2019; Hagedoorn et al., 2019), as well as processes that integrate 
IKLK with science (Hiwasaki et al., 2014; Carby, 2015; Bryant-Tokalau, 
2018a; CANARI, 2019).
Many small islands, especially those with the highest risks and the 
least resources, remain highly challenged in building and sustaining 
integrated, people-centred, end-to-end early warning systems (EWS) 
that are fully functional across the four interrelated components 
of EWS. Warning dissemination and communication, and disaster

preparedness and response capacities are particular components 
of EWS requiring strengthening in SIDS (WMO, 2020). More recent 
assessments of early warning capabilities in the Caribbean highlight 
improvements in EWS for weather, water and climate over time (WMO 
et al., 2018; Mahon et al., 2019). However, progress has been uneven 
across hazards, governance levels and spatial and temporal scales, with 
more advanced development of some sub-systems and EWS pillars than 
others. Significant progress has been made in the area of detection, 
monitoring, analysis and forecasting of severe weather systems but 
there is a need to strengthen this area for other climate-related hazards 
such as wildfires, localised intense rainfall, floods, as well as heatwaves 
and droughts, which become more important in a changing climate. 
Assessments also point to specific deficiencies including significant gaps 
in the area of disaster risk knowledge—particularly the development

of risk assessments, the variable capacity for interpreting scientific 
warning products across states, as well as effective communication of 
warning messages to populations at risk (Lumbroso et al., 2016; WMO 
There is increasing recognition and commitment at global 
(Section  3.6.3.2.4; WMO, 2014; UN, 2015c; UN, 2015b; UN, 2015a), 
regional (CCCCC, 2012; CDEMA, 2014; SPC, 2016; SPREP , 2017; CIMH 
et al., 2019) and national levels (SPREP , 2016a; WMO, 2016a) of the 
importance of climate services in supporting adaptation decision-
making in small islands (medium evidence, high agreement). A number 
of SIDS-focused climate service programmes have emerged, especially 
in the Caribbean and Pacific (Group, 2015; Martin et al., 2015; SPREP , 
2016b; WMO, 2016b; WMO, 2018a; WMO, 2018b) and at least one 
SIDS—Dominica—has been prioritised as a pilot implementation 
country under the Global Framework for Climate Services (WMO,

2016a). As is the case globally, climate services focused on decision-
making at seasonal (3–6  month) timescales has thus far been the 
focus of investment in small islands. Less attention has been given 
to investments in and assessments of climate services for decision-
making at longer timescales (Vaughan et al., 2018).

15
2081Small Islands  Chapter 15
Table 15.6 |  Adaptation options per key risk in small islands. This table summarises risk-oriented adaptation options, their level of implementation, enablers and effectiveness in 
reducing exposure and vulnerability, co-benefits and disbenefits in small islands. For KR2 (submergence of reef islands), not included, adaptation options are the same as for KR5.
Key risksRisk-oriented adaptation 
optionsEvidence 
and agree-
mentImplementa-
tionKey enablersReduction of 
vulnerabilityCo-benefits Disbenefits
KR1. Loss 
of marine 
biodiversity 
and 
ecosystem 
servicesEbA measures 
(15.4.4)MPAs; paired 
terrestrial and 
MPAsMedium 
evidence, low 
agreement 
with regard 
to climate 
change 
and benefitsWidespread 
across small 
islands, with 
climate resilience 
being a target of 
some MPAsStrong governance 
and sufficient 
financial resourcesReduces the 
ecosystem 
exposure to human 
disturbances, 
increasing their

resistance and 
resilience to climate 
eventsFor biodiversity, 
food supply, 
economics, 
human health 
Active 
restoration 
of coastal 
and marine 
ecosystemsLimited 
agreement 
to long-term 
successMostly 
small-scale: 
replanting of 
mangroves, 
seagrasses 
and beach 
vegetation; 
transplantation 
of corals; beach 
nourishmentFunding: adaptation 
taxes and levies 
imposed on tourism; 
blue bonds; public–
private partnershipsReduces the 
vulnerability of 
natural ecosystems 
by increasing their 
resilienceImproved 
water quality; 
reduction in 
coastal erosion 
and flood risks; 
economic 
benefits
Hard protection 
(15.5.1)Hard structures 
designed to 
enhance marine 
biodiversityMedium 
evidence, 
medium 
agreementArtificial reefsFunding: adaptation 
taxes and levies, with 
limited evidence of 
direct reinvestment 
in conservation and 
managementUncertainty on 
reduction of 
vulnerability of

marine ecosystems; 
reduces the 
exposure of 
population and 
coastal risksFor food supply, 
economies 
(tourism), human 
health and 
well-being
Diversifying 
livelihoods 
(15.5.6)Diversifying 
fisheries 
livelihoods (e.g., 
to aquaculture 
and tourism), 
changing fishing 
grounds and/or 
target speciesLimited to 
medium 
evidence, 
medium 
agreementExamples in the 
Caribbean region 
and in the Pacific 
and Indian 
OceansImproved governance 
and cooperation (e.g., 
through regional 
strategies); weather 
insurance to enhance 
resilienceReduces exposure 
and vulnerability of 
livelihoods through 
the diversification 
of income and 
spreading of risks; 
targeting less 
offshore pelagic 
species reduces 
coastal habitats to 
overfishingSustainably 
managed 
fisheries, 
improved food 
and income 
security, greater 
economic and 
social resilience
Reef-to-ridge 
ecosystem 
(Figure 15.4)Improved land 
use as a driver 
of marine 
ecosystem

health, 
including better 
management of 
forests, nutrients 
and wastewater 
upland 
catchmentsLimited 
evidence, 
medium 
agreementMostly in the 
and PacificImproved governanceReduces the 
exposure of coral 
reefs to human 
degradation, 
increasing their 
ecosystem 
protection 
services (e.g., 
against flooding, 
landslides and 
mudflows), 
biodiversity, 
and livelihoods

15
2082Chapter 15 Small Islands
and agree-
KR3. Loss of 
terrestrial 
and 
ecosystem 
servicesDecreased 
deforestation 
(15.5.4)Limited to 
medium 
evidence, 
high 
and PacificNational determined 
contributions 
(NDCs), external and 
long-term funding, 
engagement of local 
landowners and 
resolution of land 
ownership issues, 
gender-sensitive 
participationFor example, 
increase in forest 
extent, reduction 
in human exposure 
to natural disasters 
(hurricanes, 
landslides), 
improvement 
in vulnerability 
assessment scoresIncreased 
between forest 
fragments, 
reduced erosion, 
improved water 
supply and 
quality, improved 
and sanitation, 
improved 
livelihoods and 
soil health; 
decreased 
poverty; supports 
global mitigation
Increased 
reforestation

(native species) 
(15.5.4)Towards habitat 
connectivity, 
heterogeneity 
and diversityMedium 
evidence, 
high 
agreementRelatively 
widespread, with 
examples in the 
and PacificNDC, funding, 
technical assistance, 
supply materials, 
provision of land, 
awareness raising, 
enforcement of 
policies, sense of 
shared responsibility, 
inclusion of IKLK, 
social capitalGenerally limited 
evidence, lack 
of long-term 
monitoringIncreased DRR; 
fewer floods 
and landslides; 
reduced erosion; 
increased 
and well-being; 
increased quality 
of ecosystem 
services; 
increased 
adaptive 
capacity; 
supports global 
mitigation
EbA (15.5.4)Agroforestry 
and other 
silvicultural/
agroecological 
practices (e.g., 
climate-smart 
agriculture)Medium 
evidence, 
high 
agreementWidespread in 
the Caribbean 
region and 
Pacific OceanNDC, shared access 
and benefit, local 
knowledge and 
training, farmers, 
private sector 
for developing 
technology, financing,

data availability; 
political, institutional 
and socioeconomic 
conditionsLimited examples, 
some increases in 
adaptive capacityImproved 
awareness, 
increased 
well-being, 
improved gender 
equity, improved 
productivity and 
Watershed 
management/
(15.5.4)Reforestation, 
slope 
revegetationMedium 
evidence, 
high 
agreementWidespread (e.g., 
in the Caribbean 
region and 
Pacific Ocean)Less socially 
and politically 
acceptable than 
engineering solutions; 
communication 
and trust between 
stakeholders; 
sustainable financing 
mechanisms; 
island remoteness 
barrier to logistical 
implementationYes, through 
security, reduced 
costs, reduced 
vulnerability to 
droughtDRD, improved 
increased water 
security and 
quality, reduced 
run-off and 
sedimentation, 
increased 
well-being and 
financial stability
Ridge-to-reef 
ecosystem 
(Figure 15.4)Improved land

of terrestrial 
ecosystem 
healthMedium 
evidence, 
high 
agreementSee above See aboveLimited but slowly 
increasing evidence 
to date

15
2083Small Islands  Chapter 15
and agree-
and 
ecosystem 
servicesIncreasing the 
connectivity of 
protected areas 
(PAs) across 
elevation/climatic 
gradients 
to facilitate 
climate-driven 
redistribution 
of species 
(Figure 15.4)Establishment 
of new PAs, 
forested 
migration 
corridors across 
elevation/
climatic 
gradients, 
improving 
landscape 
by permanent 
protection of 
stepping stonesVery limited 
evidence, 
high 
agreementLow degree 
of new 
implementations 
due to terrain 
limitations 
combined with 
competition from 
human land use 
needs; large 
variation in PA 
coverage among 
islandsConservation of 
larger areas of forest 
habitat surrounding 
PAs, reforestation 
of degraded areas, 
increasing and 
forest cover within 
PAs, policies towards

the coordination 
of conservation 
actions/partnerships, 
incorporation of 
‘Other Effective 
area-based 
Conservation 
Measures’ (OECMs)Yes, especially 
if landscape 
connectivity is 
improved (migration 
corridors)Improved 
water security, 
improved coastal 
ecosystem 
health, greater 
resiliency 
and recovery 
from wildfires, 
reduced 
pollution, DRRMay facilitate 
movement of IAS
Eradication of 
IAS (15.3.3.3)Robust 
evidence, 
high 
agreementWidespread 
(>700 islands)Integration of 
changing climate 
conditions within 
ongoing prevention, 
control and 
eradication strategies, 
prevention via 
ongoing vigilance 
and biosecurity via 
quarantine, control 
incoming cargo and 
goods into islandsYes, positive 
demographic and 
distributional 
responses of native 
species following 
eradication of IASFood security, 
ecosystem health 
and services, 
increased 
livelihood 
securityA few native 
species harmed 
by eradication 
process
KR4. Water

insecurityRainwater 
harvesting 
(15.3.4.3)Robust 
evidence, 
high 
islands (e.g., 
Jamaica, 
Barbuda, 
Solomon Islands)Sociocultural and 
financialYesBiodiversity 
(watershed 
protection); 
health; economic 
(reduced 
public supply); 
food securityDependent 
on mode of 
implementation. 
Nothing 
mentioned in the 
chapter.
Desalination 
(15.6.1)Limited 
evidence, 
high 
agreementRelatively limited 
(e.g., Maldives)Financial YesHealth; economic 
(reduced 
public supply)Energy intensive 
(carbon 
footprint)
Reforestation 
(15.5.4)Medium 
evidence, 
high 
agreementExamples 
reported in the 
Pacific (e.g., 
Fiji, Papua New 
Guinea)Governance–
whole-of-island 
approaches 
foster integrated 
practices in small 
islandsYes, through 
supporting 
wetland-oriented 
tourismEconomic 
(agroforestry); 
restoration); food 
security; DRRDependent 
on mode of 
Nothing

chapter.PA management 
(terrestrial) 
evidence, 
high 
Samoa, Jamaica, 
Haiti, Grenada)Financial/governanceYes, through soil 
stabilisation and 
sequestration of 
pollutantsBiodiversity 
(forest 
conservation); 
DRR

15
2084Chapter 15 Small Islands
and agree-
KR5. 
Destruction of 
and 
infrastructureHard protection 
(15.5.1)Medium 
agreement, 
limited 
evidence 
to climate 
change 
and successWidespread in 
both urban and 
rural areas of 
the Caribbean, 
Pacific and 
Indian OceansExternal funding; 
sociocultural (meets 
the preference of 
the population); 
political–institutional 
(e.g., supported by 
business-as-usual 
approach of coastal 
risks); technical 
(requires materials 
and skills)Reduces exposure 
in some places 
but not in 
others; increases 
vulnerabilityLimited evidence 
of co-benefitsBeach loss; 
erosion 
acceleration; 
ecosystem 
through material 
extraction; 
increased SLR 
impacts
Accommodation 
(15.5.2)Limited 
evidence 
to climate 
change 

and successRelatively 
limitedTechnological, 
financial, institutional, 
socioculturalLimited evidence 
to dateMaintains the 
functionalities of 
coastal systems 
and allows their 
maintenance 
through 
landward 
migration, under 
SLR
Advance with 
land raising 
and/or through 
the creation of 
artificial islands 
evidence 
to climate 
change 
(driven by 
growth in the 
Maldives)Limited (e.g., 
Hulhumalé, 
Maldives)Technological, 
sociocultural, high 
potential in urban 
(compared to rural) 
areasReduces population 
exposure where 
high standard 
as in Hulhumalé, 
MaldivesOffers new land 
for economic 
development, 
generates 
revenues 
through sale or 
lease of land in 
urban areasWidespread 
ecosystem 
destruction, 
increased 
negative impacts 
of SLR
Migration 
including 
planned 
resettlement 
(15.5.3)Limited 
agreement 
to climate 
change 
adaptationVillage-scale 
planned

supported by 
government 
policy/legislation 
in the PacificParticipatory 
inclusion of all social 
groups; financial (for 
small and remote 
communities); social–
cultural connections; 
strong governance 
frameworks; 
enabling legislation; 
land availability or 
ownership; conditions 
in receiving locations; 
technical supportReduced exposure 
locally; has 
created new 
vulnerabilities at 
some locations by 
bearing significant 
economic cost, 
impacting social 
capital and 
reducing access to 
servicesNew livelihood 
opportunitiesLoss of cultural 
heritage, 
impacts on 
receiving 
EbA measures 
(15.4.4)Medium 
medium 
evidenceIncreasingly 
experienced; 
includes artificial 
reefs, beach 
nourishment 
and vegetation 
mangrove) 
restorationEnvironmental/
physical conditions; 
social acceptability; 
technical capacities 
(enhanced by external 
support); funding; 
inclusion in national 
adaptation policiesLimited evidence

to dateBiodiversity 
strengthening; 
increased 
food supply; 
increased 

15
2085Small Islands  Chapter 15
and agree-
KR6. Health 
degradationIncreasing public 
awareness of 
health risks 
associated with 
climate change; 
providing 
training to 
health sector 
staff; improving 
reliability and 
safety of water 
storage practices 
(15.6.2)Limited 
evidenceFew examplesFinancial and 
human resources to 
implement options; 
early warning and 
response systems; 
integrating climate 
services into health 
decision-making 
systems; public 
uptake and buy in; 
improving health data 
collection systemsPrimarily reduces 
vulnerabilityIncreased water 
security
KR7. 
Economic 
decline and 
failureCircular 
migration 
evidence 
to climate 
change 
(mostly 
driven by 
economic or 
social factors)Examples in 
Tuvalu from 
outer to 
capital atoll 
and locations

overseasLabour and education 
opportunities in 
Funafuti, Tuvalu, and 
overseasYes, on Nanumea 
Atoll, TuvaluJob and 
education for 
migrants
(15.5.6)Limited to 
medium 
agreementObserved in 
region and 
PacificUse of IKLK and 
areas; investment 
in technology and 
educationYes, in documented 
places (e.g., 
Antigua, Vanuatu, 
Madagascar, 
Dominican 
Republic)Reduction of 
pressure on 
previous fishing 
areasGreater 
catch putting 
pressure on fish 
stock
Improved 
technology and 
equipment/
training (15.5.6)Limited 
evidence, 
medium 
agreementExamples in 
region and 
PacificInvestments in 
technologies 
and education 
(e.g., irrigation 
technologies, growing 
salt-tolerant crops 
and relocating 
crop cultivation in 
Jamaica)Yes, in documented 
placesNew 
strengthening
Livestock 
husbandry 
(15.5.6)Limited 
evidenceLimited (e.g., 
small-scale 
livestock

husbandry in 
Jamaica)Farm inputs and 
investments in 
educationNo evidence to 
date. Limited 
examples of 
successful livestock 
husbandry only in 
JamaicaInvestments in 
farm inputs
Adaptive 
finance/
education 
evidence, 
medium 
agreementLimited (e.g., 
in Puerto 
Rico, women 
engage in new 
commercial 
enterprises 
that do not rely 
on traditional 
coffee supply 
chains or 
assistance)Tourism income; 
investment in 
education and 
capacity building; 
working with nature 
and EbAYes, reduces risk 
and avoids negative 
knock-on effectsGenerates 
opportunities 
(e.g., for wetland 
tourism)

15
2086Chapter 15 Small Islands
and agree-
KR7. 
Economic 
failureProduct/market 
diversification 
(15.5.6)Diversity 
of crops, 
gardening in 
different areas, 
preservation 
of foodstuffs, 
engagement of 
women in new 
enterprisesMedium 
evidence, 
high 
region and 
PacificAvailability of crops 
and land, new 
marketsReduces 
tropical cyclones in 
Fiji and Vanuatu; 
new markets in 
Puerto RicoIncreases 
food security 
and improves 
nutrition; 
increases income 
security
Adaptation in 
tourism policies 
evidence, 
high 
agreementLimited (e.g., in 
the British Virgin 
Islands, policies 
like adaptation 
imposed on 
tourism can 
provide funding 
for adaptation 
measures)Tourism regulations

and policies that 
mainstream climate 
change adaptations; 
imposed on tourismLimited evidence 
in reducing 
vulnerability
KR8. Loss 
of cultural 
heritageIntegrating 
IKLK with 
Western science 
to provide 
approaches to 
(15.6.5)Medium 
evidence, 
high 
agreementReported in 
the Pacific and 
CaribbeanUse of IKLK for 
preparing for 
disasters and 
change; social 
networks in sharing 
information and 
helping others; 
eco-theology 
increasing people’s 
awareness of the 
environmentYes, can reduce 
vulnerability when 
IKLK supports 
robust adaptation; 
No, can increase 
vulnerability if 
IKLK no longer 
provides accurate 
informationCan increase 
and its 
in communities, 
and increase 
culturally 
appropriate 
climate 
adaptationReports from 
Vanuatu indicate 
that IKLK are at 
times inaccurate 
(e.g., seasonal 
calendars, 
biophysical 
weather

indicators) 
due to climate 
change
(15.5.5.1)Medium 
limited 
evidence 
to climate 
change 
and successWidespread 
in protecting 
cultural sites 
and villages in 
(generally meets 
political-institutional 
but not in 
erosion 
ecosystem 
impacts
Studies from the Caribbean (Dookie et al., 2019; Mahon et al., 2019) 
and Indian Ocean (Hermes et  al., 2019), have found that NMHSs 
and regional intergovernmental bodies face capacity challenges in

translation, transfer and facilitation of the use of climate information 
to various end user groups. In many small island contexts a gap 
remains between investments in data quality and information services 
and uptake and use in risk reduction by policy and decision makers 
(Dookie et  al., 2019). Bringing policy makers and users together to 
guide investments in climate information services is recommended, 
as is provision of dedicated resources to develop applicable tools and 
products that turn data and information services into risk reduction 
measures (Dookie et al., 2019; Haines, 2019).Many of the outlined KRs (Section 15.3.4.9) can be addressed through 
the variety of adaptation options outlined in the previous sections 
in the context of small islands (Table  15.6, Supplementary Material 
15.1). Whereas some of these adaptation options are widespread (e.g., 
hard protection, reforestation or the creation of MPAs), others (e.g.,

accommodation, health awareness raising and training) have been 
little experimented with to date in small island contexts. Although most 
of these adaptation options provide diversified co-benefits to small 
island communities, there is still limited evidence with regard to their 
effectiveness in reducing climate change impacts. While some of them 
respond directly to a KR or a number of KRs (Table 15.6), others can be 
understood as overarching options that, for example, build adaptive 
capacity of communities and organisations and enable these actors to 
respond to a variety of KRs in an effective manner (see SM15.1).

15
2087Small Islands  Chapter 15
15.6 Enablers, Limits and Barriers to 
Since AR5, more literature has emerged on barriers, limits and enablers 
to climate change adaptation across small islands. Here, we cover 
barriers, limits and enablers as they relate to key themes across small 
islands and adaptation.
15.6.1 Governance
Specific governance-related barriers for effective adaptation include: 
lack of coordination between government departments and sectors 
and limited policy integration (Scobie, 2016; Robinson, 2018b), lack of 
ownership of adaptation implementation in cases where communities 
or national governments have not been part of the adaptation decision 
process (Conway and Mustelin, 2014; Kuruppu and Willie, 2015; 
Prance, 2015; Nunn and Kumar, 2018; Parsons and Nalau, 2019), and 
difficulties in integrating IKLK in adaptation initiatives. Specific barriers 
to effective sustained adaptation in the Pacific include variable climate

change awareness among decision makers, and the preference for 
short-term responses rather than longer-term transformative ones 
(Nunn et al., 2014). These barriers also stem from donors’ preferencing 
their own priorities that do not necessarily fit the country priorities or 
context (Conway and Mustelin, 2014; Kuruppu and Willie, 2015; Prance, 
2015), which has led to increasing calls for effective community/cultural 
engagement in adaptation, especially through CBA and EbA (Nalau 
et al., 2018b). In cases where recovery efforts are framed as purely a 
matter of infrastructure other important aspects, such as livelihoods 
and gender, are more easily overlooked in adaptation (Turner et  al., 
2020).
In the Caribbean small islands such as Jamaica and St. Lucia, and also 
in the Pacific, barriers to mainstreaming adaptation include competing 
development priorities, the absence of planning frameworks or 
‘undetected’ overlaps in existing frameworks, serious governance flaws

linked to the prevalence of corruption and corrupt people in political 
and public life, and insufficient manpower and human resources, linked 
to countries’ financial capacity (Robinson, 2018b). In addition, the lack 
of strong governance mechanisms for urban planning has contributed 
to urban sprawl and expansion that has increased the number of 
informal settlements, which together with population growth are 
driving Caribbean small islands to their limits (Enríquez-de-Salamanca, 
2018; Mycoo, 2018a; 2018b). In the Pacific, only a few countries have 
embedded climate change adaptation in existing legislation despite 
the overall regional agreement to A New Song for Coastal Fisheries—
Pathways to Change: The Noumea Strategy to improve coastal fisheries 
management in a changing climate (Gourlie et al., 2018). Many climate 
change-specific initiatives across small islands have a unidirectional 
focus on climate risks and shift limited resources away from other

important development objectives (Baldacchino, 2018). Local-level 
plans are often overlooked: For example, in Mauritius, local-level 
climate adaptation plans are currently nearly non-existent while district 
councils have rarely been successful in even accessing international 
adaptation finance (Williams et al., 2020). In Samoa, several national-
level programs on adaptation have had difficulties in engaging with 
the local level even if the decision-making powers on actual land 
management sit within the communities (McGinn and Solofa, 2020).Adaptation governance is also complicated further by the multitude 
of stakeholders involved, with differing agendas and priorities. In 
the Bahamas, private properties have significant say in how and 
what adaptation measures they decide to pursue and are not well 
regulated, with the tourism sector in particular dominated mainly by 
external investors (Petzold et  al., 2018). Social organisations, such

as the churches, which have significant influence in many Oceanic 
countries, are engaging in climate change discussions and governance. 
Many churches report, however, being constrained to act on climate 
adaptation due to lack of financial resources, low levels of professional 
knowledge on adaptation, and their members not perceiving climate 
change as an urgent risk (Rubow and Bird, 2016). Actors such as 
military services in the Indian and Pacific oceans also control a high 
number of assets in vulnerable locations and will need to integrate 
climate information into adaptive planning in the future (Finucane and 
Keener, 2015).
Low technical capacity and poor data availability and quality are 
reported as limiting adaptation in Caribbean small islands such as 
Dominica, St. Vincent and the Grenadines (Smith and Rhiney, 2016; 
Robinson, 2018a) and Trinidad and Tobago (Mycoo, 2020). These 
factors are, however, secondary to the lack of finances, which is seen

as a fundamental limit (Charan et al., 2017; Robinson, 2018a; Williams 
et  al., 2020). This was also reported in the Seychelles, despite its 
success with innovative financing streams and being a leader in the 
Indian Ocean in this regard (Robinson, 2018a).
Limited regional cooperation across sub-national island jurisdictions 
(jurisdictions with semi-autonomous status) along with limited 
regional-scale climate information are also stymying action 
(Petzold and Magnan, 2019). This is a concern given the need 
for pooled governance in response to capacity constraints across 
small jurisdictions (Dornan, 2014; Kelman, 2018). There is also an 
insufficient understanding of the role of regional and international 
actors such as the Caribbean Community Climate Change Centre and 
the Global Environment Facility, respectively (Middelbeek et al., 2014). 
Sometimes external pressure and, for example, transregional trade 
agreements are ‘useful for reducing unsustainable local sociopolitical

arrangements’ as seen in the Solomon Islands regarding fisheries 
management within the concept of blue economy (Keen et al., 2018, p. 
338). Similarly, in Samoa, the World Bank’s Pilot Program for Climate 
Resilience (PPCR) and the Adaptation Fund’s Enhancing Resilience of 
Samoa’s Coastal Communities to Climate Change illustrate successful 
examples of multilevel governance due to their programmatic and 
pragmatic approaches versus project-based approaches (McGinn 
and Solofa, 2020). Enabling factors in these programmes relate to 
strategic placements of funds and responsibilities in the relevant 
ministries, alignment with national priorities and pre-existing 
plans, pooling funding to fill existing finance gaps, and increased 
awareness across scales and departments of synergies and gaps 
between different initiatives (McGinn and Solofa, 2020). Initiatives 
such as the Pacific Adaptive Capacity Framework (Warrick et  al.,

2017) and regional strategies such as the Framework for the Disaster 
and Climate Resilient Development in the Pacific (FRDP) enable the 
localising of climate adaptation into cultural contexts in an integrated 
manner (SPC, 2016).

15
2088Chapter 15 Small Islands
Countries including the Seychelles and Maldives have developed 
national climate change plans that recognise linkages to food 
security, health and DRR, although these face significant resourcing 
issues when it comes to implementation (Techera, 2018). National-
level plans, such as National Adaptation Plans of Action (NAPAs), 
increasingly could include local government engagement and have 
a stronger focus on urban centres and adaptation (Mycoo, 2018a). 
Building codes act as supportive enablers for adaptation governance: 
Requiring more hurricane-resistant housing in the Caribbean, including 
incentives for informal settlements to build in a more resilient 
manner, can achieve multiple development and adaptation outcomes 
(Mycoo, 2018a). In Dominica, a Climate Resilience Executing Agency 
of Dominica (CREAD) established in 2019, aims to enable stronger 
climate resilience by bringing all sectors and services together for more

effective coordination (Turner et  al., 2020). Improvements in cross-
sectoral and cross-agency coordination are creating opportunities for 
improved disaster preparedness and resilience measures in Vanuatu 
(Webb et al., 2015). A range of mechanisms also exist in the tourism 
industry: Adaptation taxes and improved building regulations could 
reduce risk drastically, for example, in the Caribbean region (Mycoo, 
2018a).
15.6.2 Health-Related Adaptation Strategies
The term ‘health systems’ refers to the organisation of people, 
institutions and resources that work to protect and promote 
population health. The two components of health systems are 
public health and healthcare; adaptation is needed in both to 
develop climate-resilient health systems (WHO, 2015). Adaptation 
measures focus on each of the building blocks of health systems, 
including leadership and governance; a knowledgeable health 
workforce; health information systems; essential medical products

and technologies; health service delivery; and financing. Many small 
island states have policies to manage climate-sensitive health risks, 
although ministries of health are largely unprepared to adapt to a 
changing climate because few programmes take climate change 
into account (McIver et  al., 2016). Particularly vulnerable groups, 
such as Indigenous Peoples, are often inadequately represented in 
adaptation planning processes and implementation, resulting in less 
effective interventions (Jones, 2019).
A range of climate-sensitive diseases pose threats to island 
communities. A vulnerability and adaptation assessment conducted 
in Dominica identified vector-, water- and food-borne diseases and 
food security as priority threats from climate change (Schnitter et al., 
2019). Short-term adaptation options include strengthening solid 
waste management and enforcing current legislation; increasing public 
awareness; training health sector staff; improving the reliability and

safety of water-storage practices; improving climate change and health 
data collection methods and enhancing environmental monitoring; 
enhancing the integration of climate services into health decision-
making; strengthening the organisational structure of emergency 
response; and ensuring sufficient resources and surge capacity. 
Longer-term adaptation options include developing early warning and 
response systems for climate-sensitive health risks; enhancing data 
collection and information flow; increasing the capacity of laboratory facilities; and developing emergency plans. For example, rainfall is 
the best environmental predictor of malaria in North Guadalcanal, 
Solomon Islands, leading to the development of an early warning tool 
that could increase resilience to climate change (Smith et  al., 2017; 
Jeanne et al., 2018).
In small island states, water, sanitation and hygiene infrastructure are 
particularly vulnerable to climate change, with impacts on the burden of

diarrhoeal diseases. The resilience of types of sanitation infrastructure 
in urban and rural households in the Solomon Islands differs under 
scenarios of increased rainfall and flooding versus decreased rainfall 
and drought, reinforcing the centrality of taking the local context into 
account during adaptation decision-making (Fleming et  al., 2019). 
Healthcare facilities, including hospitals, clinics and community care 
centres, are vulnerable to extreme weather and climate events, such 
as flooding and TCs, and to climate-related outbreaks of infectious 
diseases that overwhelm their capacity to provide critical services 
(WHO, 2020). These facilities may lack functioning infrastructure 
and trained health workforce, and be predisposed to inadequate 
energy supplies and water, sanitation and waste management 
services. Adaptation is needed to build resilience and contribute to 
environmental sustainability.
Many major healthcare facilities in small island states are in exposed

coastal areas and have limited ability to provide health services 
during disasters when services are most needed (WHO, 2018). For 
example, in Vanuatu, TC Pam in 2015 severely damaged two hospitals, 
19 healthcare centres, and 50 healthcare dispensaries in 22 affected 
islands (Kim et al., 2015). A Smart Hospital Initiative in the Caribbean 
focuses on improving hospital resilience, strengthening structures 
and operations, and installing green technologies to reduce energy 
consumption and provide energy autonomy during extreme events 
and disasters (https://www.paho.org/en/health-emergencies/smart-
hospitals).
15.6.3 Adaptation Finance and Risk Transfer Mechanisms
In the majority of SIDS there is a high dependence on international 
financing to support adaptation to slow- and rapid-onset events 
(Robinson and Dornan, 2017; Petzold and Magnan, 2019). However, 
funds tend to be geared towards supporting sectoral-level adaptation

initiatives for vulnerable natural resource sectors such as water, 
biodiversity and coastal zones (Kuruppu and Willie, 2015). Considering 
low-income small islands such as Comoros, Haiti, and São Tomé and 
Príncipe, international modalities do little to address the root causes 
of vulnerability or to support system-wide transformations (Kuruppu 
and Willie, 2015). Although countries like Trinidad and Tobago have 
amassed oil wealth, the profits are not invested in a way that benefits 
environmental goals (Middelbeek et al., 2014). In Mauritius, a lack of 
financial resources for climate change adaptation has been recognised 
as a specific impediment at the district council level (Williams et al., 
2020).
Although small island jurisdictions have seen increased flows of 
adaptation finance through mostly top-town arrangements, they face 
large implementation difficulties (medium evidence, high agreement)

15
2089Small Islands  Chapter 15
(Weir and Pittock, 2017; Magnan and Duvat, 2018). There are growing 
concerns among policy- and decision-makers in small islands about the 
current levels and forms of adaptation finance, and about countries’ 
experience with accessing it (Robinson and Dornan, 2017). In the 
Caribbean, 38% of flows were concessional loans and 62% were 
grants (Atteridge et al., 2017); the situation in the Atlantic and Indian 
oceans is starkly different—nearly 75% of the flows were in the form 
of concessional loans and grants accounted for the remaining 25% 
(Canales et al., 2017). This raises questions about fairness and justice 
for small islands having to finance adaptation to climate impacts 
to which they have made a negligible contribution. In the Pacific, 
86% of aid was delivered as project-based support (Atteridge and 
Canales, 2017), which can undermine the long-term sustainability of 
adaptation interventions (Conway and Mustelin, 2014; Remling and

Veitayaki, 2016; Atteridge and Canales, 2017). Direct budget support 
was rare (Atteridge and Canales, 2017), signalling the importance of 
works such as Rambarran (2018) that support cross-regional lesson-
learning by, for example, showcasing the experience of Seychelles 
with successfully devising innovative financing mechanisms for 
supporting adaptation and conservation goals, and reducing its public 
debt. Regional catastrophe risk insurance schemes, however, such as 
Pacific Catastrophe Risk Insurance Company under the World Bank’s 
Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) 
Program are trying to enable a regional effort in increasing accessibility 
to insurance (PCRAFI, 2017) as does the Caribbean Catastrophe Risk 
Insurance Facility, although these funds are still rather small compared 
to the needs across the countries (Handmer and Nalau, 2019).
Microfinance is increasingly viewed as a positive mechanism to improve

access to climate adaptation funding (Di Falco and Sharma, 2018). In 
the Caribbean, a significant barrier in accessing climate finance relates 
to bureaucratic structures, which means that money intended for 
communities does not reach them (Mycoo, 2018a). Many adaptation 
projects even at the community level have upfront costs that need 
to be supported, especially in communities where there is little hard 
cash in use (Remling and Veitayaki, 2016). Despite such challenges, 
communities in the Pacific region have used ‘cashless adaptation’ for 
a long time, which involves trading of services and items as a form of 
indigenous microfinance (Nunn and Kumar, 2019b). Social networks 
also function as a source of informal microfinance where extended 
family members send back remittances from overseas to their families 
and communities especially after disasters. In Samoa, indigenous 
tourism operators receive remittances from overseas family members

(Crichton and Esteban, 2018; Parsons et  al., 2018), with similar 
processes observed among atoll communities in the Solomon Islands 
(Birk and Rasmussen, 2014), Vanuatu (Handmer and Nalau, 2019) and 
Jamaica (Carby, 2017). However, the role of migration and remittances 
is still poorly understood; it is difficult to quantify the informal flows 
and understand the extent to which they support effective adaptation 
(limited evidence, high agreement) (Campbell, 2014a; Parsons et al., 
2018; Handmer and Nalau, 2019).
In Old Harbour Bay, Jamaica’s largest fishing village, a high number 
of community members engaged in the fishing industry, particularly 
vendors and scalers, do not own the material assets needed to fully 
benefit from these livelihood activities (Baptiste and Kinlocke, 2016). 
Developing a broader asset portfolio by increasing access to such assets via adaptation finance investments could reduce vulnerability

across the community. This could function as an effective livelihood-
based adaptation strategy for the most vulnerable such as women, 
who are part-time employed and in peripheral roles in the fishing 
industry (Baptiste and Kinlocke, 2016). In Belize and the Dominican 
Republic, many coastal fishers, for example, use informal credit from 
food stores or captains to enable them to withstand financial losses 
that are often incurred during bad weather and extreme events 
(Karlsson and McLean, 2020).
In Vanuatu, discussions are ongoing on increasing insurance availability 
for TCs and droughts, but standardisation of housing designs to get 
insurance can become difficult where the costs make it prohibitive and 
run counter to traditional building designs and materials (Baarsch and 
Kelman, 2016). Empirical evidence from Belize, Grenada, Jamaica and 
St. Lucia indicates that there are also other factors why people do not

take insurance, including ‘the cost of premiums (44%), lack of trust 
in insurance companies (27%), having never considered insurance 
(26%), a lack of need for insurance (25%) and a lack of knowledge 
of insurance (22%)’ (Lashley and Warner, 2013, p. 108). Increasing 
trust could be addressed by seeking out domestic banks or credit 
unions with whom people are already engaging with, while also using 
social marketing campaigns to raise awareness of weather-related 
insurance to address knowledge gaps and lack of awareness of these 
tools (Lashley and Warner, 2013). In Dominica, many coastal fishers 
are suspicious of insurance schemes given past experiences of not 
being paid out on time or having to disclose catch data (Turner et al., 
2020). Yet, insurance is not capable of addressing all kinds of losses 
and damages accruing from climate impacts and should be used as an 
adaptation strategy in combination with other strategies (Lashley and 
Warner, 2013).

Insurance cover is a critical question in small islands. For example, in 
Vanuatu, some companies do not ‘cover storm damage from the sea or 
high tides…which is not helpful for properties damaged by a tropical 
cyclone’s storm surge’ (Baarsch and Kelman, 2016, p. 6). There is also 
limited access to insurance schemes due to lower demand in small 
markets (Petzold and Magnan, 2019) especially when many people 
do not have high cash-based incomes and likely cannot pay insurance 
premiums (Baarsch and Kelman, 2016). In St. Lucia and Grenada 
(via the Caribbean Oceans and Aquaculture Sustainability Facility), 
discussions are ongoing with regard to national-level parametric 
insurance, underpinned by financing from the US State Department, 
to help fishing communities recover more quickly following the 
passage of TCs in the future (Sainsbury et al., 2019; Turner et al., 2020). 
Likewise, Reguero et al. (2020) have suggested a resilience insurance

mechanism that could in theory reduce climate-related losses and 
damages through investments in nature-based adaptation projects 
(e.g., coral reef restoration and potentially mangrove restoration).
15.6.4 Education and Awareness-Raising
A significant barrier to effective climate adaptation is the lack of 
education and awareness around climate change both among the 
general public, for example, in the Bahamas (Petzold et al., 2018) and 
among decision makers in the more remote rural communities (Nunn,

15
2090Chapter 15 Small Islands
2013; Mycoo, 2015). Increasing knowledge on adaptation options 
and needs can increase adaptive capacity that is underpinned by ‘the 
ability of individuals to access, understand and apply the knowledge 
needed to inform their decision-making processes’ (Cvitanovic et al., 
2016 p. 54). This should, however, also be seen as a collective effort 
(Hayward et al., 2019).
Workshops and training are seen as crucial at the local scale to build 
communities’ capacity to take action and to integrate climate change 
considerations to the broader development processes (Remling and 
Veitayaki, 2016), although purely workshop-based short-term capacity 
building in adaptation has been questioned (Conway and Mustelin, 
2014; Lubell and Niles, 2019). More interactive community engagement 
strategies could include ‘participatory three-dimensional modelling 
(P3DM), participatory video, development of photo journals, and civil

society plans’ (Beckford, 2018, p. 46) that enables broader engagement. 
In Fiji, Laje Rotuma youth ecocamps have been used to engage younger 
Fijians to understand adaptation and increasing environmental 
stewardship with good outcomes (McNaught et  al., 2014). In Palau, 
Camp Ebiil provides a culturally based platform for younger generations 
to learn about nature and culture in an interactive camp (Singeo, 2011). 
Vanuatu’s Volunteer Rainfall Observer Network, in turn, engages 
volunteers to record their rainfall observations, demonstrating the use 
of IKLK that can be integrated with contemporary weather forecasting 
(Chand et al., 2014). Likewise, initiatives such as ePOP Petites Ondes 
Participatives aim to develop a citizen network to share environmental 
information (e.g., via mini-videos on smartphones). Across the Pacific, 
projects such as the European Union Pacific Technical Vocational 
Education and Training on Sustainable Energy and Climate Change

Adaptation Project (EU PacTVET) have sought to increase the capacity 
of Pacific islanders in disaster risk management and climate adaptation 
(Hemstock et al., 2018).
In Fiji, a study on adaptive behaviour and intention to invest in more 
adaptive portfolios found that the intent for adaptive behaviour 
increased with the supply of climate information (Di Falco and Sharma, 
2018). In the Pacific, high-performing CBA initiatives included climate 
awareness raising that equipped people with knowledge to understand 
occurring environmental changes and what to do (McNamara et al., 
2020). Lack of information can increase community vulnerability. 
Remote indigenous farming communities in St. Vincent, in the 
Caribbean, for example, have already observed decreased rainfall 
and increases in temperatures, but they have been largely excluded 
from agricultural training that includes information in how to improve 
agricultural strategies in times of climatic shocks and how to prepare

for changing climatic conditions (Smith and Rhiney, 2016). In the 
Bahamas, cultural background, income and education levels impact 
the extent to which people are aware of climate risks (Petzold et al., 
2018). In Dominica, access to information critical to fisheries is noted 
as a significant challenge, including data collection, its management 
and human resources in building capacity to process and use this 
information for evidence-based decision-making (Turner et al., 2020).
The Caribbean Climate Online Risk and Adaptation tool has been 
developed to assist the tourism industry in producing ‘climate-
sensitive developments’ (Mackay and Spencer, 2017, p. 55). Although 
some authors conclude on the low climate awareness/understanding among small islanders (Middelbeek et al., 2014; Betzold, 2015; Petzold 
et al., 2018), others indicate that many Caribbean islanders are acutely 
aware of past storm events (i.e., social memory) and have a certain

degree of self-reliance, which creates the capability to multitask and 
cope with limited resources (Petzold and Magnan, 2019). There is, 
however, a disconnect between knowledge, attitudes and practices—
knowledge sharing and learning need to be improved along with the 
take-up of an evidence-based decision-making approach (Lashley and 
Warner, 2013; Petzold et al., 2018; Saxena et al., 2018).
15.6.5 Culture
Culture can be defined as ‘material and non-material symbols that 
express collective meaning’ (Adger et al., 2014, p. 762) and includes 
worldviews and values, how individuals and communities relate to 
their environment, and what they perceive to be at risk and in need 
of adaptation (McNaught et al., 2014; Nunn et al., 2014; Remling and 
Veitayaki, 2016; Nunn et  al., 2017b; Granderson, 2017; Neef et  al., 
2018; Oakes, 2019). In small islands, culture plays an important role 
in individual and community decision-making on adaptation both as

an enabling factor and as a barrier (robust evidence, high agreement) 
(Nunn et al., 2017b; Parsons et al., 2017; Neef et al., 2018; Piggott-
McKellar et al., 2020). The concept of Vai Nui as the interconnectedness 
of Pacific Islanders continues to support the collective agency to 
plan and undertake adaptation efforts in the region (Hayward et al., 
2019). In Samoa, the principles of Fa‘asamoa (the Samoan way of life) 
impacts on how decisions are made, including the role of the aiga 
(extended family) that is a web of local, national and transnational 
kinship networks (Parsons et  al., 2018). Traditional village council 
structures and land stewardship enable an expanded range of coastal 
adaptation options in Samoa, including potential relocation, but at the 
same time may limit participation of all social groups in adaptation 
decision-making (Crichton et al., 2020). In Dominica, in the aftermath 
of Hurricane Maria (2017), social capital in the form of transboundary

nearby island networks enabled some communities to recover faster 
from the disaster including access to more livelihood opportunities and 
assets (Turner et al., 2020).
Yet, culture is often overlooked in adaptation policies and plans. For 
example, in the National Communications of 16 SIDS, only one country 
(Cook Islands) reported adaptation actions that addressed social issues, 
culture and heritage (Robinson, 2018b). Externally driven adaptation 
efforts in rural small island communities that exclude community 
priorities, ignore or undervalue IKLK, and are based on secular 
Western/global worldviews (Donner and Webber, 2014; Prance, 2015; 
McNamara et al., 2016; Nunn et al., 2017b; Schwebel, 2017; Mallin, 
2018; Nunn and McNamara, 2019; Piggott-McKellar et al., 2019b) are 
often less successful (high agreement, medium evidence). The World 
Bank Kiribati Adaptation Program (KAP), for example, builds mainly 
on Western knowledge and science despite consultations with the

Kiribati communities (Prance, 2015). Yet, in many contexts, most land 
and knowledge are embedded in traditional governance and culture 
while adaptation plans and decisions are made elsewhere on how that 
land should be used and what knowledge is used (high agreement) 
(Nunn, 2013; Prance, 2015; Charan et al., 2017; Nalau et al., 2018a; 
Parsons et al., 2018; McGinn and Solofa, 2020).

15
2091Small Islands  Chapter 15
In Kiribati, communities often use different timescales to evaluate the 
need for adaptation. I-Kiribati culture’s core concept of time is short 
term and medium term (Prance, 2015), which should be considered in 
adaptation policy and planning processes especially at the household 
and community level (Donner and Webber, 2014). Key stakeholders, 
especially community leaders, should be included and empowered to 
help design and sustain adaptation (Baldacchino, 2018; Weiler et al., 
2018). Focusing on values-as-relations (e.g., island communities’ 
relationship with the environment and each other) could diversify the 
values considered in adaptation decision-making processes (Parsons 
and Nalau, 2019). Indeed, those Pacific islands with a more island-
centric approach to climate adaptation tend to have overall more 
successful adaptation policies in place (Schwebel, 2017).
The cultural context and sources of knowledge are myriad and diverse

in small islands. Community members often use both IKLK as well as 
Western scientific-based weather forecasts to take actions to prepare 
for extreme weather events (Chand et al., 2014; Johnston, 2015; Janif 
et  al., 2016; Granderson, 2017; Kelman et  al., 2017), with specific 
examples from Niue, Tonga, Vanuatu and the Solomon Islands (high 
agreement, high evidence) (Chand et al., 2014; Chambers et al., 2017; 
Chambers et al., 2019). In Samoa, people keep particular areas reserved 
for disaster times such as TC seasons (Kuruppu and Willie, 2015), while 
in Vanuatu, IKLK indicators for TCs include mango trees flowering early 
and turtles going further inland to lay their eggs (Chand et al., 2014). 
IKLK are, however, not evenly distributed within communities due to 
IKLK being traditional intellectual property of particular roles in the 
villages (e.g., weathermen in Vanuatu), and not available to other 
community members or external actors directly (Chand et al., 2014;

Prance, 2015). In Tongoa Island, Vanuatu, communities are finding, 
however, that their IKLK-based seasonal calendars are out of sync 
given the changes in climatic conditions (Granderson, 2017), while 
erosion of IKLK remains a concern across most small island nations 
(Kuruppu and Willie, 2015; Granderson, 2017; Beckford, 2018).
Not all IKLK and other knowledge are necessarily helpful and IKLK can 
lead to maladaptation (Mercer et  al., 2012; Beckford, 2018). Elders 
from the Chuuk State (Federated States of Micronesia; Elders from 
Atafu Atoll, 2012), for instance, assign blame for changeable weather 
patterns, destructive typhoons and loss of biodiversity to people’s failure 
to maintain and employ their IKLK. Fatalism (belief that disasters are 
God’s will) is still reported as a major cultural barrier to adaptation. In 
Maldives, fatalism decreases direct adaptation action and influences 
perceptions of climate risks (Shakeela and Becken, 2015) while

indigenous communities in St. Vincent do not prepare for hurricanes or 
climatic shocks for the same reason (Smith and Rhiney, 2016). In Oceania, 
Christianity and the church play an important role in how issues, such as 
climate change, are communicated and thought about (Rubow and Bird, 
2016; Nunn et al., 2017b), including the Noah and flood story used as a 
justification that there is no need to worry about SLR (Rubow and Bird, 
2016). New emerging forms of eco-theology (theology that connects 
humans with land, sea and sky), however, situate climate change as part 
of environmental stewardship (Rubow and Bird, 2016) making churches 
active partners in caring for the environment.
Many studies also now demonstrate the value in considering multiple 
systems of knowledge through collaborative and co-production projects and strategies, which allow for culturally situated knowledge, 
values and practices to be positioned at the heart of sustainable climate

change adaptation (high agreement) (Chambers et al., 2017; Plotz et al., 
2017; Beckford, 2018; Malsale et al., 2018; Parsons et al., 2018; Suliman 
et al., 2019).  In the Caribbean context, Beckford (2018) suggests the 
establishment of the Caribbean Local and Traditional Knowledge 
Network, a shared regional platform, makes IKLK more available 
for climate adaptation and community resilience projects where 
appropriate. Likewise, indigenous research methodologies are emerging 
that introduce more culturally grounded concepts and methods into 
how research is conducted and that decolonise mainstream research in 
the Pacific Islands (Suaalii-Sauni and Fulu-Aiolupotea, 2014).
Despite widespread international evidence that the impacts of 
climate change and disaster events often negatively affect women 
(and gender minorities) more than men (McSherry et al., 2014; Aipira 
et al., 2017; Gaillard et al., 2017), attention to gender equality as a

concept is still only ‘embryonic in climate change adaptation in the 
Pacific’, and although recognised in some policies and project designs, 
it is not well supported by on-the-ground actions or well monitored 
(Aipira et al., 2017, p. 237). Many Pacific small island climate change 
adaptation policies do not mainstream gender across the activities 
(Aipira et  al., 2017), with women’s groups being excluded from 
climate grants due to patriarchal formal and informal governance 
structures, lack of resources, less access to educational and training 
schemes and no track record (or receiving grants or meeting grant 
milestones) (McLeod et  al., 2018). However, Pacific women identify 
several strategies that enable them to adapt to climate change more 
effectively. These include the recognition and support of women’s IKLK 
by governments, researchers and NGOs; increasing women’s access 
to climate change funding and support from organisations to allow

them to meet the requirements of international climate change grants; 
and specific education and training to women’s groups to allow them 
to develop strategic action plans, mission statements, learn financial 
reporting requirements as well as general leadership and institutional 
training (McLeod et al., 2018). These and other measures could enable 
a broader representation and participation in adaptation processes 
despite cultural constraints (Table 15.7 on Enabling Conditions).
15.7 Climate Resilient Development Pathways 
and Future Solutions in Small Islands
Synergies exist between climate resilient development pathways 
and implementation of SDGs in small islands because development 
decisions and outcomes are strengthened by consideration of climate 
and disaster risk (Robinson, 2017b; Hay et  al., 2019a). However, 
monitoring progress of SDGs is challenging for small islands, in part 
due to large numbers of indicators and inadequate data. Literature

on SDG implementation is generally lacking for small islands as is the 
integration of climate risk into infrastructure decisions.
Decisions that are optimal for adaptation may not be acceptable in 
the wider development context within which they operate. In the 
Pacific region, where 67% of infrastructure is located within 500 m 
of coastline, commercial, public and industrial infrastructures are 
particularly vulnerable due to the location of urban centres (Kumar

15
2092Chapter 15 Small Islands
Table 15.7 |  Enabling conditions and factors for adaptation in small islands.
Enabler Example Reference
Knowledge (indigenous, local, external)
IKLK in developing adaptation strategies 
(soft protective structures; disaster 
preparedness)Using IKLK in identifying Indigenous vegetation (e.g., ecosystem-based 
adaptation) to reduce erosion (Samoa, Vanuatu)Crichton and Esteban (2018); Nalau et al. (2018b)
Pacific storm prediction, disaster preparednessChand et al. (2014); Kuruppu and Willie (2015); Granderson 
(2017)
Shared resource governance and understanding of linkages between sectors 
and ecosystems based on IKLK (e.g., Lomani Gau village initiative (Fiji)Remling and Veitayaki (2016)
Increased access to climate informationIncreased access to climate information increasing individuals will and 
capacity to support/take adaptive actions (Fiji)Di Falco and Sharma-Khushal (2019)
Dissemination of adaptation skills and significance to youth (e.g., ecocamps

in Fiji)McNaught et al. (2014)
Increased access to climate information 
(continued)Pacific women’s improved participation in adaptation processes via training, 
access to information and decision-making
Improved climate data quality, management and associated observation, 
modelling and information services
Caribbean: improved climate data quality, management and associated 
observation, modelling and information services
Provision of user-tailored products and services through knowledge 
co-production processesMcLeod et al. (2018)
Martin et al. (2015); Hermes et al. (2019)
Trotman et al. (2018)
SPREP (2016a)
Economy and finance
Economic diversification and shifting to 
CRDPsTourism system transitions/cooperation from tourism sectorLoehr (2019); Mahadew and Appadoo (2019); Loehr et al. 
(2020); Sheller (2020)
Finance models for adaptationInnovative financing models that enable adaptation (e.g., Seychelles)
Parametric fisheries insurance products to increase fishery resilience funded

by Caribbean Catastrophe Risk Insurance Facility (Grenada and Saint Lucia)Rambarran (2018)
CCRIF (2019)
Transregional trade agreements/associated 
pressureRevised sociopolitical arrangements for better fisheries management 
(Solomon Islands)Keen et al. (2018)
Economic viability via revenue from sale 
of new landMaldives land raising on Hulhumalé
‘Safe island development programme’ after 2004 Indian Ocean Tsunami in 
the MaldivesBisaro et al. (2019)
Shaig (2008)
Government subsidies Tuamotu’s government subsidy of raised houses Magnan et al. (2018)
Co-investments and cooperation between 
agencies (donors, governments)Tuvalu use of beach nourishment in collaboration with JICA Onaka et al. (2017)
Diversification of livelihoods as basis for 
economic activityCoastal fishers’ diversification of livelihoods into the tourism sector (Vanuatu 
and Madagascar)
Fishermen varying fishing practices and locations depending on

environmental conditions (e.g., Dominican Republic)Blair and Momtaz (2018)
Karlsson and McLean (2020)
Changed governance arrangements 
resulting in improved coordinationImproved governance arrangements: cross-sectoral and cross-agency 
coordination (e.g., Vanuatu)Webb et al. (2015); Nalau et al. (2016)
resulting in improved coordination 
(continued)Agency explicitly tasked with coordinating sectors and services for climate 
resilience across government (Dominica)Turner et al. (2020)
Efficient and coordinated distribution of climate adaptation support across 
national projects and departments (e.g., Samoa)McGinn and Solofa (2020)
New strict/explicit building codesCaribbean infrastructure (esp. housing and hotels) now must be built to 
withstand strong hurricanesMycoo (2018a)
Localising climate adaptation plans, 
frameworks and policiesPacific Adaptive Capacity Framework

Framework for the Disaster and Climate Resilient Development in the Pacific 
(FRDP)
Island-centric adaptation policy and planningWarrick et al. (2017)
SPC (2016)
Schwebel (2017)

15
2093Small Islands  Chapter 15
Social and cultural
Social networks and capacity in disaster 
recoverySupport of social networks in hurricane recovery, access to livelihood 
opportunities (e.g., Dominica)Turner et al. (2020)
Increased indigenous resilience and adaptive capacity via social networks 
and capital (e.g., Samoa)Petzold and Ratter (2015); Parsons et al. (2018)
Informal credit for fishermen at food stores during and after disasters (e.g., 
Belize and Dominican Republic)Karlsson and McLean (2020)
Social networks and traditional familiarity 
with barter/microfinanceCommunity-level fundraising (e.g., Samoa, Solomons, Jamaica)Birk and Rasmussen (2014); Carby (2017); Crichton and 
Esteban (2018); Parsons et al. (2018); Nunn and Kumar 
(2019a)
Maintenance of home community Circular migration between Tuvalu and overseas Marino and Lazrus (2015)
Empowerment of the migrating individuals Relocations of villages (Fiji) Marino and Lazrus 2015)

and Taylor, 2015). Yet the Parliamentary Complex in Samoa was 
redeveloped at the original site owing to cultural and historical factors 
despite strong evidence of the need to relocate (Hay et al., 2019b).
Energy transitions in the Pacific islands demonstrate development 
synergies such as reduced dependency on volatile fossil fuel markets, 
increased resilience to weather-related disasters and less need for 
investment in large-scale centralised energy systems (Dornan, 2014; 
Cole and Banks, 2017; Weir, 2018; Weir and Kumar, 2020). However, 
high and rapid energy transition ambitions can lead to trade-offs for 
rural electrification (Box 18.4; Dornan, 2014; Cole and Banks, 2017; 
Hills et al., 2018).
Tourism system transitions can enable the sector to contribute to 
climate resilient development pathways through managing climate 
risks and improving ecological, economic and social outcomes for small 
islands (medium evidence, high agreement) (Loehr, 2019; Mahadew

and Appadoo, 2019; Loehr et al., 2020; Sheller, 2020).
There is a clear role for local governments to work closely with the 
informal private sector to achieve a ‘trifecta’ of climate change 
adaptation, economic development and DRR, especially for women 
(McNamara et al., 2020). Yet, many cities and local governments in the 
Pacific region are severely resource constrained (Kelman, 2014; Kiddle 
et al., 2017; Keen and Connell, 2019; Nunn and McNamara, 2019).
Broader innovation in climate resilient development policy-making has 
taken place in the Pacific (Hay et al., 2019a) and Caribbean (Mycoo, 
2018a). The Pacific region is bringing together DRM, low carbon 
growth and climate change adaptation with broader development 
efforts for the first time (SPC, 2016). Improvements in cross-sectoral 
and cross-agency coordination are creating opportunities for improved 
disaster preparedness and resilience measures in small islands

(Webb et al., 2015; Nalau et al., 2016). Further integration between 
development priorities and risk management in national budgetary 
and development processes is necessary, as is continued investment in 
coordination mechanisms (Hay et al., 2019a).
Early research on the response to COVID-19 indicates that existing 
disaster response mechanisms in the Caribbean islands have assisted in rapid responses to COVID-19 (Hambleton et al., 2020). Many small 
islands are highly dependent on tourism for their economies and are 
facing worsening crises associated with climate-related disasters 
and more recently COVID-19 disruptions of travel (Sheller, 2020). 
The adaptive capacity and innovations demonstrated by SIDS during 
COVID-19, moving beyond dependence on ‘extractive’ international 
tourism, demonstrate the potential benefits of diversified and 
sustainable economies (and ecologies) for the enhanced resilience of 
both human and ecological communities (Sheller, 2020).

In the context of small islands, climate justice research is expanding 
beyond initial debates about nation-states responsibilities for the 
causes and responses to climate change, to demonstrate complex and 
dynamic intergenerational and multiscalar dilemmas of climate justice 
(Ferdinand, 2018; Sheller, 2018; Baptiste and Devonish, 2019; Look 
et al., 2019; Douglass and Cooper, 2020; Kotsinas, 2020; Sheller, 2020). 
In Caribbean SIDS, research highlights how intersecting external and 
internal socioeconomic and political processes are leading marginalised 
populations to become increasingly socially and economically 
disadvantaged and politically marginalised, which in turn heightens 
climate vulnerability and impedes sustainable development efforts 
(Baptiste and Devonish, 2019) (Moulton and Machado, 2019; Gahman 
and Thongs, 2020; Rhiney, 2020; Duvat et al., 2021b). Inequity extends 
to how development and disaster aid was coordinated and distributed

within various nations after hurricanes Irma, Maria and Harvey in 2017.
15.8 Research Gaps
Despite intensive study, many knowledge gaps remain due to the 
complexity of biophysical and social interactions as well as the local 
and regional diversity of small islands. Research and data gaps exist 
in four areas: island-scale data availability; ecosystem services data; 
vulnerability and resilience, and adaptation (Table 15.8).

15
2094Chapter 15 Small Islands
Table 15.8 |  Research gaps in small islands.
Research gap Elaboration
Unavailability of 
adequately downscaled 
climate dataThere is a lack of oceanographic (e.g., tidal), meteorological, high-resolution topographic and bathymetric data, as well as future sea level and wave climate 
projections for most islands, which severely constrain modelling studies and therefore improved understanding of future coastal flooding, erosion, and rates of 
saline intrusion into aquifers (Giardino et al., 2018; Lal and Datta, 2019)
There is a need for further developing context-specific numerical models, especially through the inclusion of sediment transport, production and delivery (Shope 
and Storlazzi, 2019), coastal and marine ecosystems’ responses (Beetham et al., 2017), and various societal responses (e.g., engineering and ecosystem-based 
solutions (Giardino et al., 2018)) under different climate change and SLR scenarios

The complexity and specificities of small island environments and unavailability of robust baseline data considerably challenge modelling studies in small islands 
contexts, as reflected by the serious limitations of global modelling impact studies for these (Mentaschi et al., 2018; Vousdoukas et al., 2020)
Data and model developments are therefore urgently needed to assess the future habitability or exploitability of the islands that are the most critical to small 
island countries and territories, and to help identify and promote appropriate (especially in technical terms) solutions
Adequately downscaled Regional Climate Model (RCM) data (sub-5 km2) is also required to conduct modelling assessments for small island terrestrial 
ecosystems. This is particularly needed for islands with complex topography which could be important in providing much-needed climate refugia for the survival

of narrow range species such as endemics (Balzan et al., 2018). Such spatial data could be used to maximise the potential of islands to deliver critical ecosystem 
services (Katovai et al., 2015; Balzan et al., 2018)
Widely used WorldClim data may not be suitable when applied to the small island context (Box CCP1.1). Without such data, robust ecosystem-based adaptation 
strategies such as climate-smart PA planning and management under changing climate conditions cannot be developed
Thomas and Benjamin (2017) highlighted the lack of data as an area of concern related to assessing loss and damage at 1.5°C. Understanding losses 
and damages also requires more detail on island-specific losses and damages accruing from anthropogenic climate change impacts. At the moment, such 
assessments are limited, and most of the small islands have not yet documented these factors in their national adaptation plans or policies (Handmer and

Nalau, 2019). There is a need for specific studies also on biophysical variables and species (e.g., impact of temperature rise on mangroves); long-term impacts 
of ocean acidification on species, including relationship to disease outbreaks, and changing breeding grounds of marine species and impacts on fisheries and 
marine-based livelihoods; incorporating biophysical feedback and interconnectivity of environments into models; and more detailed datasets (e.g., bathymetry, 
coastal assets) (World Bank, 2016; McField, 2017; Wilson, 2017)
Vulnerability and 
resilienceThere is need for new research that investigates the variability of vulnerability within and between islands and states, typologies of best practice (Oculi and 
Stephenson, 2018), frequency of knowledge sharing among islands and regions (Foley, 2018), identification of regional framework mechanisms, and mapping

the complex impact and hazard interactions at a regional scale (Duvat et al., 2017b; Neef et al., 2018; Scandurra et al., 2018; Thiault et al., 2018). Research 
needs to also examine resilience-building efforts within the four domains of islandness (boundedness, smallness, isolation, and littorality) to effectively capture 
subjective nuances associated with climate development efforts on islands (Kelman, 2018)
Research gaps in place-based assessments of social service bundles coupled with policy actions (Balzan et al., 2018) highlight the need for new knowledge to 
strengthen communication, collaboration and networks between academia, donors, the private sector, community and government (Allahar and Brathwaite, 
2016; Schipper et al., 2016) so as to improve understanding of vulnerability and resilience in small islands
A paucity of research exists currently on the vulnerability of island ecosystem services to climate change (Balzan et al., 2018). While there is rich scientific

evidence on the pressures of habitat loss and degradation, impacts of natural hazards and invasive species, far less is known about the interactions of these 
factors with adaptive capacity and livelihood conditions on islands. In small island contexts, there is a specific need for assessing the effectiveness and cost of 
ecosystem- and community-based solutions where the latter have been implemented (Filho et al., 2020). The design of generic assessment methods and tools 
is required to allow for comparative analyses that will, in turn, provide useful guidance for the promotion of context-specific adaptation strategies (Blair and 
Momtaz, 2018). For many of the small islands, especially SIDS, the economic valuation of marine and coastal ecosystem services—coastal protection, fisheries, 
tourism—is of great importance, as well as the subsequent losses in these sectors and related livelihoods due to climate change impacts (Waite et al., 2014;

Schuhmann and Mahon, 2015; World Bank, 2016; Layne, 2017; Duijndam et al., 2020). There are few integrated modelling studies to inform future habitability of 
differentiated small island types and how these models can inform decision support processes for ridge to reef stewardship (Povak et al., 2020). Existing studies 
(Rasmussen et al., 2018) have progressed knowledge since AR5, but island-specific analyses are required to robustly estimate the future ability of land to support 
life and livelihoods, taking into account multiple climate-drivers, future population exposure, and adaptation responses
More research is also needed in understanding how ecosystem benefits are modified under changing climate conditions and how these benefits can be 
quantified (Doswald et al., 2014). For example, many small islands lack comprehensive (and disaggregated) data related to food security, which makes it

challenging to attribute climate impacts on local food systems (Taylor et al., 2019). Balzan et al. (2018) highlight the importance of quantifying the role of 
biodiversity in delivering key ecosystem services and demonstrate how such data could provide insights into the interrelatedness of island ecosystems and 
transboundary service benefits

15
2095Small Islands  Chapter 15
AdaptationIn the last decade or so, there has been a significant increase in climate-related financing for small island states. However, monitoring and tracking of funding 
and metrics to evaluate overall impact are lacking (Boyd et al., 2017; Mallin, 2018). Research into adaptation costs could benefit from the inclusion of indirect 
effects of climate change such as psychological costs (Vincent and Cull, 2014; Gibson et al., 2019) but to date this research is missing. Greater effort could also 
be placed on quantifying the relationship between adaptation costs and adverse events (Adelman, 2016). There is also a need for overall land use planning 
guidelines in small coastal communities, including small islands (Major and Juhola, 2016). The usefulness and utility of insurance mechanisms for building

resilience to climate hazards require up-to-date information on assets at risk (Tietze and van Anrooy, 2018) and further exploration of adaptation measures 
in small island contexts (Baarsch and Kelman, 2016). Additionally, the differences between theoretical adaptation practices and observed results from actual 
implementation, along with the integration of IKLK and external knowledge, are currently not well understood (Mercer et al., 2014b; Kelman, 2015b; Saint Ville 
et al., 2015; Robinson and Gilfillan, 2016; Robinson, 2017b). Documenting experience-based knowledge of adaptation projects and programme implementation 
could fill important data gaps. At the project design stage, the paucity of climate finance data is a barrier to accessing climate finance (Bhandary et al., 2021)
Although studies examining the association between climate and weather extremes, events and conditions and mobility in small islands have increased

since AR5 (Birk and Rasmussen, 2014; Kelman, 2015a; Connell, 2016; Stojanov et al., 2017; Barnett and McMichael, 2018), few studies robustly examine the 
attribution of migration of small island populations, communities and individuals to anthropogenic climate change and other non-climate migration drivers. 
Biophysical, socioeconomic and in situ adaptation thresholds that force small island populations to migrate remain under-explored (Barnett, 2017; Handmer 
and Nalau, 2019). The implications of forced and voluntary immobility (Allgood and McNamara, 2017; Farbotko, 2018; Suliman et al., 2019), the socioeconomic, 
health, psychological and cultural outcomes of climate migrants, and gender dimensions of climate migration all remain under-researched
Limits to adaptation is still a largely under-researched topic globally (Nalau and Filho, 2018) and specifically in small island contexts, as are the linkages

between adaptation limits, loss and damage and transformative adaptation (Thomas et al., 2020). In terms of projected risks and adaptation responses, further 
work is needed to improve knowledge of commonalities, differences, successes, and failures of natural and human adaptation responses (Kuruppu and Willie, 
2015). One of the failings of the current literature on limits to adaptation revolves largely around the use of barriers for sector-specific or small-scale scenarios, 
which provides an understanding only for that particular scenario and does not identify common constraints (Kuruppu and Willie, 2015). Research gaps on loss 
and damage include: how to assess the economic costs of loss and damage; mechanisms to develop robust policies in small island contexts; specific data on 
experienced loss and damage across socioeconomic groups and demographics; monitoring and tracking of slow-onset events (Thomas and Benjamin, 2017;

Thomas et al., 2020) and the non-economic aspects including sense of place, health and community cohesion (Thomas and Benjamin, 2019)
More studies are needed on the role that organisations (international, national and regional) play in adaptation efforts—their effectiveness at achieving desired 
outcomes, roles and accountability (Robinson and Gilfillan, 2016; Scobie, 2016; Mallin, 2018). It is also important that the impacts of sociopolitical relations 
inter-state are researched (Belmar et al., 2015) and more focus on climate justice (Baptiste and Devonish, 2019; Moulton and Machado, 2019; Gahman and 
Thongs, 2020) and gender is similarly needed (McLeod et al., 2018). Given the high number of place-specific case studies in the adaptation literature, more 
reviews are needed that synthesise key lessons and principles of adaptations in small island contexts from this knowledge. Further research is also needed to

capture the lessons from COVID-19 response in small islands and how these could enable more robust adaptation and climate resilient development transitions 
as has been suggested at a broader scale by Schipper et al. (2020). There is also little to no information on impacts upon terrestrial and freshwater biodiversity 
from the relocation of coastal human populations inland due to SLR
FAQ 15.1 | How is climate change affecting nature and human life on small islands, and will further climate change 
result in some small islands becoming uninhabitable for humans in the near future?
Climate change has already affected and will increasingly affect biodiversity, nature’s benefits for people, settlements, infrastructure, livelihoods 
and economies on small islands. In the absence of ambitious human intervention to reduce emissions, climate change impacts are likely to make

some small islands uninhabitable in the second part of the 21st century. By protecting and restoring nature in and around small islands as well 
as implementing anticipatory adaptation responses, humans can help reduce future risks to ecosystems and human lives on most small islands.
Observed changes—including increases in air and ocean temperatures, increases in storm surges, heavy rainfall 
events, and possibly more intense tropical cyclones—are already reducing the number and quality of ecosystem 
services, thereby causing the disruption of human livelihoods, damage to buildings and infrastructure, and loss 
of economic activities and cultural heritage on small islands. Widespread observed impacts include severe coral 
reef bleaching events, such as that associated with the 2015–2016 El Niño season, the most damaging on record 
worldwide. Additionally, the 2017 Atlantic hurricane season was unusually characterised by sequential severe TCs

that resulted in widespread cyclone-induced damage to ecosystems from the very interior of small islands to those 
of the ocean waters that surround them as well as damage to human settlements and economic activities within 
the whole Caribbean region. Although knowledge is limited regarding long-term increases in TC intensity, studies 
have shown that heavy rainfall and intense wind speed of individual TCs were increased by climate change. The 
combination of various climate events, such as TCs, extreme ocean waves, and El Niño or La Niña phases, with SLR 
causes increased coastal flooding, especially on low-lying atoll islands of the Indian and Pacific oceans.
The expected increased risk of such impacts under further climate change is significant. For example, some low-lying 
islands and areas may be extensively flooded at every high tide or during storms. As a result, their freshwater supplies

and soils would be repeatedly contaminated by saltwater, with adverse cascading consequences for freshwater and

15
2096Chapter 15 Small Islands
terrestrial food supplies, biodiversity and ecosystems, and economic activities. It is unlikely that these locations 
would remain habitable unless such impacts are mitigated through reduction of heat-trapping greenhouse gas 
emissions or adaptation solutions that are acceptable for the populations of these islands. Acceptable adaptation 
options may be limited in these locations. Additionally, drought intensity may challenge freshwater security in some 
regions such as the Caribbean. Likewise, remote atoll islands where inhabitants rely on reef-derived food and other 
resources and that are at high risk of widespread coral reef degradation may become uninhabitable. Strategies to 
reduce risk may include substituting the consumption of vulnerable inshore reef resources by developing onshore 
aquaculture (fish farming), or promoting access to tuna and other pelagic fish, and/or importing food to meet

nutritional needs. However, adoption of these strategies will depend on the acceptance of their local populations.
The intensity and timing of such impacts will be more severe under high warming futures compared to low warming 
futures accompanied by ambitious adaptation. Tailored, desirable and locally owned adaptation responses that 
incorporate both short- and long-term time horizons would certainly help to reduce future risks to nature and 
human life in small islands. Among the short-term measures frequently employed to address SLR and flooding are 
seawalls. Long-term measures include ecosystem-based adaptation such as mangrove replanting, relocation of 
coastal villages to upland sites, creation of elevated land through reclamation, revised building codes as part of a 
broader DRR strategy, shifting to alternative livelihoods and changes in farming and fishing practices.FAQ 15.1 (continued)

FAQ 15.2 | How have some small island communities already adapted to climate change?
Faced with rising sea levels and storm surges along their coastal areas which have significantly threatened people’s safety, buildings, 
infrastructure and livelihoods, small island communities have already embarked on the use of different adaptation strategies. These include 
reactive adaptation, which deals with short-term measures, and anticipatory adaptation, which takes action in advance to lessen climate 
change impacts in the long run. Reactive measures have not always proven to be effective. By contrast, anticipatory measures hold much 
promise for future adaptation.
The majority of people living on small islands occupy coasts, and thus the most widespread threats to people’s 
livelihoods are those from SLR, shoreline erosion, increased lowland flooding, and salinisation of groundwater and

soil. Humans can either adapt reactively or anticipate coming changes and prepare for them. Given the diversity of 
small islands across the world, and their capacities to adapt, there is no single solution that fits all contexts.
Coastal livelihoods in particular are already affected by climate impacts. Coastal fishers have adapted to these 
changes in environmental conditions by diversifying livelihoods, expanding aquaculture production, considering 
weather insurance, building social networks to cope with reduced catches and availability during extreme storms, 
switching fishing grounds, and changing target species. Similarly, farmers have diversified livelihoods to more cash- 
and service-based activities such as tourism, changed plant species that thrive better in altered conditions, and 
shifted planting seasons according to changes in climate.
A typical reactive adaptation along small island coasts involves the construction of hard impermeable structures

such as seawalls to stop the encroachment of the sea. Yet such structures, especially along rural island coasts, 
often fail to prevent flooding during extreme sea levels or extreme-wave impacts, and can inadvertently damage 
nearshore ecosystems such as mangroves and beaches. In the Caribbean, Indian Ocean islands and some Pacific 
islands, there are numerous examples of coastal engineering structures that have been destroyed already or are in 
grave danger from the encroaching sea. In many instances, citizens and governments are unable to access external 
advice or funding, communities have built such structures without assistance or knowledge of expected future SLR.
By contrast, anticipatory adaptation, which anticipates expected future impacts and acts in advance, requires a 
longer-term view as well as some understanding of future climate-change impacts in particular contexts. Along

small island coasts, anticipatory adaptation typically involves recognising that sea level will continue rising and 
that problems currently experienced will be amplified in the future. One strategy for anticipatory adaptation in 
response to SLR and flooding is relocation, which is the movement of coastal communities away from vulnerable

15
2097Small Islands  Chapter 15
(coastal-fringe) locations to sites that are further inland. Coastal setback policies have been applied to hotels in 
some islands such as Barbados. In coastal locations where the risks of rising sea level, flooding and erosion are very 
high and cannot effectively be reduced, ‘retreat’ from the shoreline is the only way to eliminate or reduce such risks.
Where relocation is successful, it is most commonly driven and funded by governments and non-government 
organisations, often within a specially designed policy framework. The Government of Fiji, for example, has 
introduced a relocation framework that specifically develops guidance on relocation processes, with several villages 
already having relocated. Evaluations to date recommend thorough cost–-benefit analyses of relocation be 
undertaken before this strategy is pursued. Relocation is often viewed as a ‘last resort’ adaptation option because

of high cost and because some sociocultural aspects of life cannot be maintained in locations separated from 
customary land. The Bahamas relocated a community on Family Island from the shoreline to an inland location and 
the community of Boca de Cachón in the Dominican Republic was relocated to higher ground. The Navunievu 
community (Bua, Fiji) has mandated that every young adult building their family home in the village should do so 
upslope rather than on the regularly flooded coastal flat where the existing village is located. Over the next few 
decades, this will result in the gradual upslope migration of the community, an example of autonomous adaptation. 
Such creative community-grounded solutions hold great promise for future adaptation on small islands, where they 
are undertaken inclusively.
Anticipatory adaptation has been aligned with DRR in some small islands. For example, Jamaica adopted such an

approach in relocating three communities. Recognising that a proactive approach is needed, Jamaica developed a 
Resettlement Policy Framework aligned with the National Development Plan and based on vulnerability assessments 
of communities at risk of climate change and disaster risk. A resettlement action plan was developed for the 
Harbour Heights community using community engagement to design successful planned relocation. In some islands 
revised building codes are implemented as an anticipatory adaptation measure. As part of the build-back-better 
strategy hurricane resistant roofs are being built to cope with strong winds associated with tropical cyclones.
Ecosystem-based adaptation can be a low-cost anticipatory adaptation measure that is often used in small islands. 
It is referred to as a ‘no-regret’ or ‘low-regret’ strategy because it is low-costing, brings co-benefits and requires less

maintenance in contrast to hard engineering structures. Ecosystem-based adaptation is used at different scales and 
in different sectors such as to protect fisheries, farming and tourism assets, and integrates various stakeholders from 
national to local governments and non-governmental agencies. Many islands have implemented ecosystem-based 
adaptation such as watershed management, mangrove replanting and other nature-based solutions to strengthen 
coastal foreshore areas that are subjected to coastal erosion and flooding caused by SLR and changing rainfall 
patterns. For example, mangroves have been planted on several cays in Belize and pandanus trees have been planted 
near the coastlines of the Marshall Islands. Agroforestry is another example of ecosystem-based adaptation. Planting 
trees and shrubs in combination with crops has been used to increase resilience of crops to droughts or excessive

rainfall run-off. Case studies show that people living on islands benefit even further from using ecosystem-based 
adaptation. Their health improves as well as their food and water supply, while risks of disasters caused by extreme 
events are reduced.FAQ 15.2 (continued)

15
2098Chapter 15 Small Islands
(e) Wholesale externally-sponsored relocation(d) Incremental autonomous relocation (c) In-situ adaptation 
mangrove plantingraised dwelling seawallcoastal settlement
coastal settlement(a) Contemporary situation without adaptation
mean high-tide level 2020
(b) The future challeng e
mean high-tide level 2100
()Adaptation options for rural coastal communities in small islands
Figure FAQ15.2.1 |  Adaptation options for rural coastal communities in small islands.FAQ 15.2 (continued)

15
2099Small Islands  Chapter 15
a: In many places today, coastal communities which have been established for hundreds of years are being more regularly inundated than ever before as a result 
of rising sea level. 
b: By the end of this century, sea level in such places may have risen 1 m or more, making many such settlements (largely) uninhabitable, underscoring the need 
for effective (anticipatory) adaptation. 
c: One option is in situ adaptation, popular because it is cheaper and less disruptive than other options; it is typically characterised by mangrove replanting, 
seawall construction and raising of dwellings. 
d: A second option is for communities to incrementally relocate upslope by building all new houses further inland.  
e: A third option is complete relocation of a vulnerable coastal community with external support upslope and inland.FAQ 15.2 (continued)

FAQ 15.3 | How will climate-related changes affect the contributions of agriculture and fisheries to food security in 
small islands?
Agriculture and fisheries are heavily influenced by climate, which means a change in occurrence of TCs, air temperature, ocean temperature 
and/or rainfall can have considerable impacts on the production and availability of crops and seafood and therefore the health and welfare of 
island inhabitants. Projected impacts of climate change on agriculture and fisheries in some cases will enhance productivity, but in many cases 
could undermine food production, greatly exacerbating food insecurity challenges for human populations in small islands.
Small islands mostly depend on rain-fed agriculture, which is likely to be affected in various ways by climate change, 
including loss of agricultural land through floods and droughts, and contamination of freshwater and soil through

salt-water intrusion, warming temperatures leading to stresses of crops, and extreme events such as cyclones. In 
some islands, crops that have been traditionally part of people’s diet can no longer be cultivated due to such 
changes. For example, severe rainfall during planting seasons can damage seedlings, reduce growth and provide 
conditions that promote plant pests and diseases.
Changes in the frequency and severity of TCs or droughts will pose challenges for many islands. For example, 
more pronounced dry seasons, warmer temperatures and greater evaporation could cause plant stress reducing 
productivity and harvests. The impacts of drought may hinder insects and animals from pollinating crops, trees and 
other vegetative food sources on tropical islands. For instance, many agroforestry crops are completely dependent 
on insect pollination, and it is, therefore, important to monitor and recognise how climate change is affecting the

number and productivity of these insects. Coastal agroforest systems in small islands are important to national 
food security but rely on biodiversity (e.g., insects for pollination services). Biodiversity loss from traditional 
agroecosystems has been identified as one of the most serious threats to food and livelihood security in islands. 
Ecosystem-based adaptation practices and diversification of crop varieties are possible solutions.
The continuous reduction of soil fertility as well as increasing incidences of pests, diseases and invasive species 
contribute to the growing vulnerability of the agricultural systems on small islands. Higher temperatures could 
increase the presence of food- or water-borne diseases and the challenge of managing food safety. Changes 
in weather patterns can also disrupt food transportation and distribution systems on islands where indigenous 
communities are often located in remote areas.

Impacts of climate change on fisheries in small islands result from ocean temperature change, SLR, extreme weather 
patterns such as cyclones, reducing ocean oxygen concentrations and ocean acidification. These combined pressures 
are leading to the widespread loss or damage to marine habitats such as coral reefs but also mangroves and seagrass 
beds and consequently of important fish species that depend on these habitats and are crucial both to the food 
security (a high proportion of dietary protein is derived from seafood) and incomes of island communities. Shifting 
ocean currents and warming waters are also changing the distribution of pelagic fish stocks, especially of open-water 
tuna, with further consequences for both local food security and national economies, where they are often highly 
dependent on income from fishing licenses (e.g., 98% of GDP in Tokelau, 66% of national income in Kiribati).

Climate change is projected to have profound effects on the future status and distribution of coastal and oceanic 
habitats, and consequently of the fish and invertebrates they support. High water temperature causes changes in 
the growth rate of fish species as well as the timing of spawning and migration patterns, with consequences for

15
2100Chapter 15 Small Islands
fisheries catch potential. Some small island countries and territories are projected to experience more than 50% 
declines in fishery catches by 2100. Other small islands such as Easter Island (Chile), Pitcairn Islands (UK), Bermuda, 
and Cabo Verde may actually witness increases in catch potential under certain climate scenarios. Food shortages 
are often apparent in small islands, following the passage of catastrophic TCs. Access to pelagic fisheries can help 
to alleviate immediate food insecurity pressures in some circumstances, whereas aquaculture (fish farming) is being 
viewed as a longer-term means of diversifying incomes and enhancing resilience in many Caribbean and Pacific 
islands.FAQ 15.3 (continued)

16
2414Chapter 16 Key Risks across Sectors and Regions
Introduction and framing
This chapter synthesises observed climate change impacts 
(Section 16.2), adaptation-related responses (Section 16.3), limits to 
adaptation (Section 16.4), and the key risks identified across sectors 
and regions (Section 16.5). We consider how these risks accrue with 
increasing global average temperature, how they depend on future 
development and adaptation efforts and what this implies for the 
Sustainable Development Goals and the five main Reasons for Concern 
about climate change (Section 16.6).
The impacts of changes in climate-related systems have been 
identified in a wide range of natural, human and managed 
systems (very high confidence1). Compared with the last 
Report (AR5), there is more evidence for impacts of long-term changes 
in climate-related systems (including the atmosphere, ocean and

cryosphere) on socioeconomic indicators and high confidence in the 
sensitivity of societies to weather conditions. There is also stronger 
evidence for impacts of long-term climate change on ecosystems, 
including the observed widespread mortality of warm water corals, far 
reaching shifts in phenology in marine and terrestrial ecosystems and 
the expansion of tropical species into the ranges of temperate species, 
and boreal species moving into Arctic regions (high confidence). 
{16.2.3, 16.2.3.1}
Increased rainfall intensity associated with tropical cyclones 
and rising sea levels have contributed to observed damages in 
local coastal systems (medium confidence). However, while the 
impact is expected to be widespread, formal attribution of damages 
to long-term changes in the climate-related systems is still limited by 
restricted knowledge about changes in exposure and vulnerability and 
the missing quantification of the contribution of sea level rise to the

extent of flooded areas. {16.2.3.3}
Due to complex interactions with socioeconomic conditions, 
evidence on the impact of long-term climate change on crop 
prices and malnutrition is largely lacking, while the sensitivity of 
malnutrition to weather conditions has become more evident in some 
regions, particularly Africa (medium to high confidence). A negative 
impact of long-term climate change on crop yields has been identified 
in some regions (e.g., wheat yields in Europe) (medium confidence), 
while studies are still inconsistent in other regions. {16.2.3.4}
Climate change has increased observed heat-related mortality 
(medium confidence) and contributed to the observed latitudinal 
or altitudinal range expansion of vector-borne diseases into 
previously colder areas (medium to high confidence), while 
evidence on the impact of long-term climate change on water-

expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, for example medium confidence. For a given evidence and agreement statement, different confidence 
levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.borne diseases is largely lacking. Overall, there is extensive 
observational evidence that extreme ambient temperatures increase 
human mortality (high confidence) and that the occurrence of water- 
and vector-borne diseases is sensitive to weather conditions (high 
confidence). {16.2.3.5, 16.2.3.6, 16.2.3.7}
Extreme weather events not only cause substantial direct 
economic damage (high confidence), but also reduce

economic growth in the short term (year of, and year 
after event) (high confidence) as well as in the long term 
(up to 15 years after the event) (medium confidence), with 
more severe impacts in developing than in industrialised 
economies (high confidence). Evidence has increased for all 
of these conclusions; however, evidence for impacts of long-term 
climate change is still limited. {16.2.3.7}
Climate variability and extremes are associated with increased 
prevalence of conflict, with more consistent evidence for low-
intensity organised violence than for major armed conflict 
(medium confidence). Compared with other socioeconomic drivers, 
the link is relatively weak (medium confidence) and conditional 
on high population size, low socioeconomic development, high 
political marginalisation and high agricultural dependence (medium 
confidence). Literature also suggests a larger climate-related influence 
on the dynamics of conflict than on the likelihood of initial conflict

outbreak (low confidence). There is insufficient evidence at present to 
attribute armed conflict to climate change. {16.2.3.8}
There is high confidence that anthropogenic climate forcing has 
had an impact on internal displacement, given the observed 
impact of anthropogenic climate forcing on the occurrence 
of weather extremes (high confidence, Table  SM16.21) and 
the strong contribution of weather extremes to observed 
displacement (high confidence). However, the link between long-
term changes in the climate-related systems has not been demonstrated 
systematically, and so far there is no attribution of observed trends in 
displacement to long-term changes in the climate-related systems. Links 
between weather fluctuations (including extreme events) and human 
mobility are complex and conditional on socioeconomic situations; for 
example, poor populations may more often be involuntarily displaced 
or ‘trapped’ and not be able to migrate. {16.2.3.9}

Observed adaptation in ecosystems
While species are increasingly responding to climate change, 
these responses may not be adaptive or sufficient to cope 
with the rate of climate change (high confidence). Responses 
have been documented in a range of species, including, for 
example, changes in the timing of breeding and migration. It is 
unclear whether these responses reflect long-term evolutionary 
adaptation or short-term coping mechanisms. Existing assessments 
indicate that some species’ responses will be insufficient to avert 
extinction. {16.3.1}

16
2415Key Risks across Sectors and Regions  Chapter 16
Observed adaptation-related responses in human systems
Responses across all sectors and regions reported in the 
scientific literature are dominated by minor modifications to 
usual practices or measures for dealing with extreme weather 
events, while evidence of transformative adaptation in human 
systems is low (high confidence). Responses have accelerated in 
both developed and developing regions since AR5, with some examples 
of regression. Despite this, there is negligible evidence in the scientific 
literature documenting responses that are simultaneously widespread 
and rapid, and that challenge norms and adaptation limits. {16.3.2.3}
There is negligible evidence that existing responses are 
adequate to reduce climate risk (high confidence). There is some 
evidence of global vulnerability reduction, particularly for mortality and 
economic losses due to flood risk and extreme heat (Section 16.3.2.4).

Evidence on the effectiveness of specific adaptations remains limited. 
There is negligible robust evidence to assess the overall adequacy of 
the global adaptation response to address the scale of climate risk. No 
studies have systematically assessed the adequacy and effectiveness of 
adaptation at a global scale, across nations or sectors, or for different 
levels of warming. {16.3.2.3}
Adaptation responses are showing co-benefits, for mitigation 
and other societal goals (high confidence). There is increasing 
evidence of co-benefits of adaptation responses. Co-benefits are 
most frequently linked to changes in agricultural practices (e.g., 
conservation agriculture), land use management (e.g., agroforestry), 
building technologies (e.g., building efficiency standards) and urban 
design (e.g., walkable neighbourhoods). {16.3.2.3}
Evidence of maladaptation is increasing (high confidence), that 
is, adaptation that increases climate risk or creates new risks in other

systems or for other actors. Globally, maladaptation has been reported 
most frequently in the context of agriculture and migration in the 
Global South. {16.3.2.6}
Limits to adaptation across natural and human systems
There is increasing evidence on limits to adaptation which 
result from the interaction of adaptation constraints and can be 
differentiated into soft and hard limits (high confidence). Soft 
limits may change over time as additional adaptation options become 
available. Hard limits will not change over time as no additional 
adaptive actions are possible. Evidence focuses on constraints that 
may lead to limits at some point of the adaptation process, with 
less information on how limits may be related to different levels of 
socioeconomic or climatic change (high confidence). {16.4.1, 16.4.2, 
16.4.3}
Limits to adaptation have been identified for terrestrial and 
aquatic species and ecosystems, coastal communities, water

security, agricultural production, and human health and heat 
(high confidence). Beginning at 1.5°C, autonomous and evolutionary 
adaptation responses by terrestrial and aquatic species and ecosystems 
face hard limits, resulting in biodiversity decline, species extinction 
and loss of related livelihoods (high confidence). Beginning at 3°C, hard limits are projected for water management measures, leading to 
decreased water quality and availability, negative impacts on health 
and well-being, economic losses in water and energy-dependent 
sectors and potential migration of communities (medium confidence). 
Adaptation to address risks of heat stress, heat mortality and reduced 
capacities for outdoor work for humans face soft and hard limits across 
regions beginning at 1.5°C, and are particularly relevant for regions 
with warm climates (high confidence). {16.4.2, 16.4.3}
Soft limits are currently being experienced by individuals and

households along the coast and by small-scale farmers (medium 
confidence). As sea levels rise and extreme events intensify, coastal 
communities face soft limits due to financial, institutional and 
socioeconomic constraints reducing the efficacy of coastal protection 
and accommodation approaches and resulting in loss of life and 
economic damages (medium confidence). {16.4.2, 16.4.3}
Hard limits for coastal communities reliant on nature-based 
coastal protection will be experienced beginning at 1.5°C 
(medium confidence). Soft and hard limits for agricultural production 
are related to water availability and the uptake and effectiveness of 
climate-resilient crops which are constrained by socioeconomic and 
political challenges (medium confidence). {16.4.2, 16.4.3}
Across regions and sectors, the most significant determinants 
of soft limits are financial, governance, institutional and policy 
constraints (high confidence). The ability of actors to overcome

these socioeconomic constraints largely influence whether additional 
adaptation is able to be implemented and prevent soft limits from 
becoming hard. While the rate, extent and timing of climate hazards 
largely determine hard limits of biophysical systems, these factors 
appear to be less influential in determining soft limits for human 
systems (medium confidence). {16.4.2, 16.4.3}
Financial constraints are important determinants of limits to 
adaptation, particularly in low-to-middle-income countries 
(high confidence). Impacts of climate change may increase financial 
constraints (high confidence) and contribute to soft limits to adaptation 
being reached (medium confidence). Global and regional evidence 
shows that climate impacts may limit the availability of financial 
resources, stunt national economic growth, result in higher levels 
of losses and damages and thereby increase financial constraints. 
{16.4.3.2, 16.4.3.3}
Key risks across climate and development pathways

Regional and sectoral chapters of this report identified over 
120 key risks (KRs) that could become severe under particular 
conditions of climate hazards, exposure, and vulnerability. These 
key risk are represented in eight so-called Representative Key 
Risks (RKRs) clusters of key risks relating to low-lying coastal 
systems; terrestrial and ocean ecosystems; critical physical 
infrastructure, networks and services; living standards; human 
health; food security; water security; and peace and human 
mobility (high confidence). A key risk is defined as a potentially 
‘severe’ risk, that is, one that is relevant to the interpretation of 
dangerous anthropogenic interference (DAI) with the climate system.

16
2416Chapter 16 Key Risks across Sectors and Regions
Key risks cover scales from the local to the global, are especially 
prominent in particular regions or systems, and are particularly large 
for vulnerable subgroups, especially low-income populations, and 
already at-risk ecosystems (high confidence). The conditions under 
which RKRs would become severe have been assessed along levels 
for warming, exposure/vulnerability, and adaptation: for warming, 
high refers to climate outcomes consistent with RCP8.5 or higher, 
low refers to climate outcomes consistent with RCP2.6 or lower, 
and medium refers to outcomes for scenarios between RCPs 2.6 and 
8.5; exposure/vulnerability levels are relative to the range of future 
conditions considered in the literature; for adaptation, high refers to 
near maximum potential and low refers to the continuation of today’s 
trends (and Sections 6.5.2.1, 16.5.2.2, SM16.7.4).
For most Representative Key Risks (RKRs), potentially global

and systemically pervasive risks become severe in the case of 
high warming, combined with high exposure/vulnerability, low 
adaptation, or both (high confidence). Under these conditions, 
there would be severe and pervasive risks to critical infrastructure 
(high confidence) and to human health from heat-related mortality, to 
low-lying coastal areas, aggregate economic output, and livelihoods 
(all medium confidence), of armed conflict (low confidence), and to 
various aspects of food security (with different levels of confidence). 
Severe risks interact through cascading effects, potentially causing 
amplification of RKRs over the course of this century (limited evidence, 
high agreement). {16.5.2.3, 16.5.2.4, 16.5.4, Figure 16.10}
For some RKRs, potentially global and systemically pervasive 
risks would become severe even with medium to low warming 
(i.e., 1.5–2°C) if exposure/vulnerability is high and/or adaptation

is low (medium to high confidence). Under these conditions, there 
would be severe and pervasive risks associated with water scarcity 
and water-related disasters (high confidence), poverty, involuntary 
mobility, and insular ecosystems and biodiversity hotspots (all medium 
confidence). {16.5.2.3, 16.5.2.4}
All potentially severe risks that apply to particular sectors or 
groups of people at more specific regional and local levels 
require high exposure/vulnerability or low adaptation (or both), 
but do not necessarily require high warming (high confidence). 
Under these conditions, there would be severe, specific risks to 
low-lying coastal systems, to people and economies from critical 
infrastructure disruption, economic output in developing countries, 
livelihoods in climate-sensitive sectors, waterborne diseases especially 
in children in low- and middle-income countries, water-related impacts 
on traditional ways of life, and involuntary mobility for example in

small islands and low-lying coastal areas (medium to high confidence). 
{16.5.2.3, 16.5.2.4}
Some severe impacts are already occurring (high confidence) 
and will occur in many more systems before mid-century 
(medium confidence). Tropical and polar low-lying coastal human 
communities are experiencing severe impacts today (high confidence), 
and abrupt ecological changes resulting from mass population-level 
mortality are already observed following climate extreme events. Some 
systems will experience severe risks before the end of the century 
(medium confidence), for example critical infrastructure affected by extreme events (medium confidence). Food security for millions of 
people, particularly low-income populations, also faces significant risks 
with moderate to high warming or high vulnerability, with a growing 
challenge by 2050 in terms of providing nutritious and affordable diets 
(high confidence). {16.5.2.3, 16.5.3}
In specific systems already marked by high exposure and

vulnerability, high adaptation efforts will not be sufficient 
to prevent severe risks from occurring under high warming 
(limited evidence, medium agreement). This is particularly the 
case for some ecosystems and water-related risks (from water scarcity 
and to indigenous and traditional cultures and ways of life). {16.5.2.3, 
16.5.2.4, 16.5.3}
Interconnectedness and globalisation establish pathways for the 
transmission of climate-related risks across sectors and borders, 
for instance through trade, finance, food and ecosystems (high 
confidence). Examples include semiconductors, global investments, 
major food crops like wheat, maize and soybean, and transboundary 
fish stocks. There are knowledge gaps on the need for, effectiveness 
of, and limits to adaptation to such inter-regional risks. {Cross-Chapter 
Box INTERREG in this Chapter}
Key risks increase the challenges in achieving global 
sustainability goals (high confidence). The greatest challenges will

be from risks to water (RKR-G), living standards (RKR-D), coastal socio-
ecological systems (RKR-A) and peace and human mobility (RKR-H). 
The most relevant goals are zero hunger (Sustainable Development 
Goal [SDG] 2), sustainable cities and communities (SDG11), life 
below water (SDG14), decent work and economic growth (SDG8), 
and no poverty (SDG1). Priority areas for regions are indicated by the 
intersection of hazards, risks and challenges, where, in the near term, 
challenges to SDGs indicate probable systemic vulnerabilities and 
issues in responding to climatic hazards (high confidence). {16.6.1}
The scale and nature of climate risks is partly determined by the 
responses to climate change, not only in how they reduce risk, but 
also how they may create other risks (sometimes inadvertently, and 
sometimes to others than those who implement the response, in other 
places, or later in time).
Solar radiation modification (SRM) approaches have potential

to offset warming and ameliorate other climate hazards, but 
their potential to reduce risk or introduce novel risks to people 
and ecosystems is not well understood (high confidence). SRM 
effects on climate hazards are highly dependent on deployment 
scenarios, and substantial residual climate change or overcompensating 
change would occur at regional scales and seasonal time scales (high 
confidence). Due in part to limited research, there is low confidence in 
projected benefits or risks to crop yields, economies, human health or 
ecosystems. Large negative impacts are projected from rapid warming 
for a sudden and sustained termination of SRM in a high-CO 2 scenario 
(high confidence). SRM would not stop CO 2 from increasing in the 
atmosphere or reduce resulting ocean acidification under continued 
anthropogenic emissions (high confidence). There is high agreement 
in the literature that for addressing climate change risks SRM is, at

best, a supplement to achieving sustained net zero or net negative CO 2

16
2417Key Risks across Sectors and Regions Chapter 16
emission levels globally. Co-evolution of SRM governance and research 
provides a chance for responsibly developing SRM technologies with broader public participation and political legitimacy, guarding against potential risks and harms relevant across a full range of scenarios. {Cross-Working Group Box SRM}
Recent global estimates of the economic cost of climate impacts

exhibit significant spread and generally increase with global average temperature, as well as vary by other drivers, such as income, population and composition of the economy (high confidence). The wide variation across disparate methodologies does not allow a robust range of damage estimates to be identified with confidence, though the spread of estimates increases with warming in all methodologies, indicating higher risk (in terms of economic costs) at higher temperatures (high confidence). Reconciling methodological variance is a priority for facilitating use of different lines of evidence; however, that some new estimates are higher than the AR5 range indicates that global aggregate economic impacts could be higher than previously assessed (low confidence due to the lack of robustness and comparability across methodologies). {Cross-Working Group Box ECONOMIC in Chapter 16}
Reasons for Concern across scalesThe five major Reasons for Concern (RFCs), describing risks associated

with (1) unique and threatened systems, (2) extreme weather events, (3) distribution of impacts, (4) global aggregate impacts, and (5)large-scale singular events, were updated using expert elicitation.RFC risk levels were assessed with no or low adaptation, but limitsto adaptation are a factor in the identification of very high risk levels.
Compared with AR5 and SR15, risks increase to high and very 
high levels at lower global warming levels for all five RFCs (high confidence), and transition ranges are assigned with greater confidence. Transitions from high to very high risk emerge in all five RFCs, compared with just two RFCs in AR5 (high confidence). {16.6.3, Figure 16.15}
•
F
or unique and threatened systems (RFC1), as before, levels of risk
at a given level of warming are higher than for the other RFCs.

Risks are already (at current warming of 1.1°C) in the transitionfrom moderate to high (very high confidence), compared withmoderate in AR5 and SR15, based on observed and modelledimpacts. The transition to very high risk occurs between 1.2°C and2.0°C warming (high confidence). {16.6.3.1}
•
F
or risks from extremes (RFC2), the transition to high risk is
between 1.0°C and 1.5°C (high confidence) and to very high risk(new in AR6) between 1.8°C and 2.5°C (medium confidence). {16.6.3.2}
•
F
or risks disproportionately affecting particularly vulnerable
societies and socio-ecological systems, including disadvantagedpeople and communities in countries at all levels of development(RFC3), current risk is moderate (high confidence) and the transition to high risk is between 1.5°C and 2.0°C warming (mediumconfidence). The transition to very high risk occurs at between2.0°C and 3.5°C warming (medium confidence). {16.6.3.3}
•
T

he risk of global aggregate impacts, including monetary damages, lives affected, species lost or ecosystem degradation at a global scale (RFC4), has begun to transition to moderate risk (medium confidence), with a transition to high risk between 1.5°C and 2.5°C (medium confidence) and to very high risk (new in AR6) at between 2.5°C and 4.5°C (medium confidence). {16.6.3.4}
•
Present-day risks associated with large-scale singular events(sometimes called tipping points or critical thresholds) (RFC5)are already moderate (
high confidence), with a transition to high
risk between 1.5°C and 2.5°C (medium confidence) and to veryhigh risk (new in AR6) between 2.5°C and 4°C (low confidence). {16.6.3.5}

Limiting global warming to 1.5°C would ensure risk levels remain moderate for RFC3, RFC4 and RFC5 (medium confidence), but risk for RFC2 would have transitioned to a high risk at 1.5°C and RFC1 would be well into the transition to very high risk (high confidence). Remaining below 2°C warming (but above 1.5°C) would imply that risk for RFC3 through 5 would be transitioning to high, and risk for RFC1 and RFC2 would be transitioning to very high (high confidence). By 2.5°C warming, RFC1 will be in very high risk (high confidence) and all other RFCs will have begun their transitions to very high risk (medium confidence for RFC2, RFC3 and RFC4, low confidence for RFC5).
RFC1, RFC2 and RFC5 include risks that are irreversible, such as species

extinction, coral reef degradation, loss of cultural heritage, or loss of a small island due to sea level rise. Once such risks materialise, as is expected at very high risk levels, the impacts would persist even if global temperatures would subsequently decline to levels associated with lower levels of risk in an ‘overshooting’ scenario (high confidence). {16.6.3}

16
2418Chapter 16 Key Risks across Sectors and Regions
16.1 Introduction and Framing
16.1.1 Objective of the Chapter
Anthropogenic climate change poses risks to many human and 
ecological systems. These risks are increasingly visible in our day-to-
day lives, including a growing number of disasters that already bear a 
fingerprint of climate change. There is increasing concern about how 
these risks will shape the future of our planet—our ecosystems, our 
well-being and development opportunities. Policymakers are asking 
what is known about the risks, and what can be done about them. 
Many people, especially youth, around the world are calling for urgency, 
ambition and action. Companies are wondering how to manage new 
threats to their bottom line, or how to grasp new opportunities. On top 
of this growing concern about climate change, the coronavirus disease 
2019 (COVID-19) pandemic has exposed vulnerabilities to shocks,

significantly aggravated climate-related risks, and posed new questions 
about how to achieve a green, resilient and inclusive recovery (see 
Cross-Chapter Box COVID in Chapter 7).
The three synthesis chapters of this report (Chapters 16, 17 and 18) aim 
to address these concerns. They synthesise information from across all 
thematic and regional chapters of the Working Group II (WGII) IPCC 
Sixth Assessment Report (AR6) and the recent IPCC Special Reports 
on Global Warming of 1.5°C, on Climate Change and Land, and on 
Ocean and Cryosphere in a Changing Climate (SR15 (IPCC, 2018a), 
SRCCL (IPCC, 2019a) and SROCC (IPCC, 2019b)), but also include an 
independent assessment of the literature, especially literature that cuts 
across sectors and regions.
Chapter 16 lays the groundwork by synthesising the state of knowledge 
on the observed impacts of climate change (Section  16.2) and 
ongoing adaptation responses (Section 16.3), the limits to adaptation

(Section 16.4), and the key risks we should be concerned about, how 
these risks evolve with global temperature change, and also how they 
depend on future development and adaptation efforts (Sections 16.5, 
16.6). It thus brings together elements that were assessed in different 
chapters in previous assessments, especially the Third, Fourth and Fifth 
Assessment Reports (TAR, AR4 and AR5, respectively). Background 
on specific methodological aspects of this chapter is provided in 
Supplementary Material (SM).
The strong link between risks, adaptation and development connects 
this chapter closely to Chapters 17 and 18. Chapter 17 assesses 
decision -making: what do we know about the ways to manage risks 
in a warming climate (including in the context of the key risks and 
limits to adaptation identified in this chapter)? Chapter 18 puts all of 
this information into the perspective of climate resilient development 
pathways: how can we achieve sustainable development given the

additional challenges posed by climate change?
16.1.2 Risk Framing
In the IPCC AR6, ‘risk’ is defined as the potential for adverse conse-
quences for human or ecological systems, recognising the diversity of 
values and objectives associated with such systems. Relevant adverse consequences include those on lives, livelihoods, health and well-being, 
economic, social and cultural assets and investments, infrastructure, ser -
vices (including ecosystem services), ecosystems and species (Chapter 
1 this volume, SR15 (IPCC, 2018a)). The AR6 definition explicitly notes 
that ‘risks can arise from potential impacts of climate change as well as 
human responses to climate change.’
The main risks assessed here relate to the potential impacts of 
climate change. In recent years, the growing visibility of current 
climate impacts has resulted in a stronger focus on understanding 
and managing such risk across time scales, rather than just for the

longer-term future. Examples include the rapid growth in attribution 
of specific extreme weather events, the use of scientific evidence of 
climate change impacts in legal cases, and the context of the Paris 
Agreement’s Article 8 on ‘averting, minimising and addressing loss 
and damage’ associated with climate change, but also the stronger 
links between adaptation and disaster risk reduction, including early-
warning systems, wider discussions on how to build resilience in the 
face of a more volatile climate, and attention for limits to adaptation 
that are already being reached.
Of course, the scale of these risks is also determined by the responses 
to climate change, mainly in how they reduce risk, but also how they 
may create risks (sometimes inadvertently, and sometimes to others 
than those who implement the response, in other places, or later in 
time). Our focus is on adaptation responses, given that mitigation is

covered in Working Group III (WGIII) AR6, but we acknowledge certain 
important interactions, such as biomass production as an alternative to 
fossil fuels which can compete with food production and thus aggravate 
adaptation challenges. Given that SRM could also be considered a 
response with significant implications for climate risks across scales, 
this chapter also includes Cross-Working Group Box SRM.
This assessment focuses primarily on adverse consequences of climate 
change. However, climate change also has positive implications 
(benefits and opportunities) for certain people and systems, although 
there are gaps in the literature on these positive effects. Some risks 
assessed in this chapter are actually about a balance between positive 
and negative effects of climate change (and of response options, 
especially adaptation). In those contexts, we assess the combined effect 
of both, aiming to identify not only the aggregate impacts (the balance

between positive and negative effects) but also the distributional 
aspects (winners and losers). A more comprehensive discussion of the 
decision-making related to such trade-offs in relation to adaptation is 
provided in Chapter 17.
This chapter’s assessment takes a global perspective, although many 
risks and responses materialise at the local or national scale. We use 
case studies to illustrate the ways these risks aggregate across scales, 
again with particular concern for distributional aspects.
16.1.3 Storyline of the Chapter, and What’s New 
Compared with Previous Assessments
Figure 16.1 illustrates the elements covered by the chapter, which can 
be summarised as four key questions.

16
2419Key Risks across Sectors and Regions Chapter 16
16.1.3.1  What Impacts Are Being Experienced?
This assessment of climate-related impacts that are already taking 
place is covered in Section 16.2, which aims to differentiate between observed changes in climate hazards (also called ‘climate impact drivers’ in IPCC Working Group I) and the exposure and vulnerability of human and ecological systems.
Observed impacts of climate change were synthesised in the TAR, 
AR4 and AR5. The TAR found that recent regional climate changes 
2 In this Report,  the following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about as 

0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, e.g., very likely). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies within the 17–83% probability range.had already affected many physical and biological systems, with 
preliminary indications that some human systems had been affected, primarily through floods and droughts. AR4 found likely
2 discernible 
impacts on many physical and biological systems, and more limited evidence for impacts on human environments. AR5 devoted a separate chapter to observed impacts, which found growing evidence of impacts on human and ecological systems on all continents and across oceans (Cramer et al., 2014).Vulnerability
Hazard
ExposureHazard
Exposure
Solution space for  Climate
Resilient development Residualr isks (i.e. aftera daptation )Actions  to reduce hazards, exposure
and vulnerability
NowI nthe futur eHard limits to adapt

Section in Chapte r16Adaptation-related
responses
Limits to adaptation& Residual risk s1. What impacts are being experienced? 3. What future risks are of greatest concern? 2. What responses are
being undertaken?
4. What are the limits
to adaptation?Vulnerability
Realised risks
(Observed impacts)Keyr isks
Reasons
forconcernIN THE FUTURE TODAY
Impacts  (observed)  andr isks (projected) Chapter structureFigure 16.1 illustrates the elements covered by the chapter, which can be summarised as four key questions
16.3
16.5
16.6
16.416.2
16.X
Figure 16.1 |  Illustrative storyline of the chapter highlighting the central questions addressed in the various sections, from realised risks (observed impacts)

to future risks (key risks and reasons for concern), informed by adaptation-related responses and the limits to adaptation. The arrows illustrate actions to reduce hazard, exposure and vulnerability, which shape risks over time. Accordingly, the green areas at the centre of the propeller diagrams indicate the ability for such solutions to reduce risk, up to certain adaptation limits, leaving the white residual risk (or observed impacts) in the centre. The shading of the right-hand-side propeller diagram compared with the non-shaded one on the left reflects some degree of uncertainty about future risks. The figure builds on the conceptual framework of risk–adaptation relationships used in SROCC (Garschagen et al., 2019).

16
2420Chapter 16 Key Risks across Sectors and Regions
Section 16.2 reports on the expanded literature since then, generally 
reflecting a growing and more certain impact of climate change on 
humans and ecological systems.
16.1.3.2 What Responses Are Being Undertaken?
Section 16.3 provides, for the first time, a comprehensive synthesis of 
observed adaptation-related responses to the rising risks.
Such adaptation responses were first covered in the TAR, and further 
developed in the AR4 and AR5. For instance, AR5 Chapter 15 notes 
that adaptation to climate change was transitioning from a phase of 
awareness to the construction of actual strategies and plans in societies 
(Mimura et al., 2014) but did not include a comprehensive mapping of 
Based on such a comprehensive mapping, Section 16.3 finds growing 
evidence of adaptation-related responses, although these are dominated 
by minor modifications to usual practices or measures for dealing with

extreme weather events, and there is limited evidence for the extent to 
which they reduce climate risk.
16.1.3.3 What are the limits to adaptation?
The literature on limits to adaptation, which is covered in Section 16.4, 
has strongly evolved since AR5, including links to discussions on loss 
and damage in the United Nations Framework Convention on Climate 
Change (UNFCCC). While the Summary for Policymakers (SPM) of 
AR4 noted that there was no clear picture of the limits to adaptation, 
or the cost, AR5 Chapter 16 (Klein et  al., 2014) reported increasing 
insights emerging from the interactions between climate change and 
biophysical and socioeconomic constraints, and highlighted the fact 
that limits could be both hard and soft. It also noted that residual 
losses and damages will occur from climate change despite adaptation 
and mitigation action. However, AR5 Chapter 16 still found that the 
empirical evidence needed to identify limits to adaptation of specific

sectors, regions, ecosystems or species that can be avoided with 
different greenhouse gas (GHG) mitigation pathways was lacking.
Section 16.4 provides a more comprehensive assessment of limits to 
adaptation, highlighting again that limits to adaptation are not fixed, 
but are properties of dynamic socio-ecological systems. They are shaped 
not only by the magnitude of the climate hazards (e.g., the amount of 
sea level rise in low-lying coasts and islands) and the exposure and 
vulnerability to those hazards (e.g., people and assets in those areas), 
but also by physical, infrastructural and social tolerance thresholds and 
adaptation choices of actors in societies (e.g., the decision to migrate 
from locations strongly impacted by climate change). The evolution of 
such socioeconomic systems over time, including their interaction with 
the changing physical climate, determines the evolution of limits to 
16.1.3.4 What Future Risks Are of Greatest Concern?

The fourth and final element of the chapter is the question about the 
risks we face, and which ones we should be most concerned about. 
This is addressed in Sections 16.5 and 16.6.Section 16.5.1 presents a full discussion of ‘key risks’, synthesised from 
across all chapters, defined as those risks that are potentially severe 
and therefore especially relevant to the interpretation of ‘dangerous 
anthropogenic interference with the climate system’ in the terminology 
of UNFCCC Article 2.
In 2015, the Paris Agreement established the goal of ‘holding the 
increase in the global average temperature to well below 2°C above 
pre-industrial levels and pursuing efforts to limit the temperature 
increase to 1.5°C above pre-industrial levels’. However, assessment 
of key risks across a range of future warming levels remains a high 
priority for several reasons: (1) understanding risks at higher levels of 
warming can help prepare for them, should efforts to limit warming

be unsuccessful (UNEP , 2017); (2) understanding risks at higher levels 
can inform the benefits of limiting warming to lower levels; (3) in 
addition, there is continued debate about whether warming limits 
should be at or rather somewhere below 2°C (in particular at 1.5°C); 
and (4) there is a more explicit recognition that key risks can result not 
only from increased warming, but also from changes in the exposure 
and vulnerability of society, and from a lack of ambitious adaptation 
efforts. Thus, relatively limited warming does not automatically imply 
that key risks will not occur. In assessing key risks, we have applied 
four criteria: magnitude of adverse consequences, likelihood of 
adverse consequences, temporal characteristics of the risk, and ability 
to respond. Of course, this is an aggregated approach to what is 
dangerous; it should be noted that in practice, ‘dangerous’ will occur 
at a myriad of temperature levels depending on who or what is at risk

(and their circumstances), geographic scale and time scale.
A new element is that we particularly look at a set of eight 
‘representative key risks’ that exemplify the underlying set of key 
risks identified in the earlier chapters: risk to the integrity of low-
lying coastal socio-ecological systems, risk to terrestrial and ocean 
ecosystems, risk to critical physical infrastructure and networks, risk to 
living standards (including economic impacts, poverty and inequality), 
risk to human health, risk to food security, risk to water security, and 
risk to peace and human mobility (Section 16.5.2.3).
Another increased focus relates to the issue of compound risks. This 
includes risks associated with compound hazards (Working Group I 
AR6 Chapter 11, Seneviratne et  al., 2021), but also implications for 
future risk when repeated impacts erode vulnerability, as well as 
through transboundary effects (including effects both from one system

to a neighbouring one, as well as from one system to a distant one), 
also discussed in the cross-chapter box on inter-regional risks and 
adaptation (Cross-Chapter Box INTEREG in this Chapter).
Section 16.6 maps the representative key risks in Section 16.5 to the 
SDGs, noting both direct and indirect implications for climate resilient 
development as assessed in Chapter 18.
Finally, Section 16.6 presents an updated assessment of the so-called 
Reasons for Concern (RFC): risks related to unique and threatened 
systems, extreme events, distribution of impacts, aggregate impacts 
(including the cross-chapter box on the global economic impacts of 
climate change and the social cost of carbon, Cross-Working Group 
Box ECONOMIC) and the risk of irreversible and abrupt transitions.

16
2421Key Risks across Sectors and Regions  Chapter 16
The AR4 and AR5 each also evaluated the most important climate risks, 
framed firstly in terms of the state of knowledge relevant to Article 2 
of the UNFCCC. The TAR first synthesised this knowledge in five RFCs. 
AR4 identified a set of ‘key vulnerabilities’ and provided an update of 
the RFCs. AR5 further refined a new risk framework developed in the 
IPCC Special Report on Managing the Risks of Extreme Events and 
Disasters to Advance Climate Change Adaptation (SREX), and used it 
to assess ‘key risks’ and provide another update of the overarching 
RFCs, drawing as well on Cramer et al.’s (2014) assessment of observed 
changes.
Our risk assessment also further builds on risk assessments from 
the Special Reports that are part of the AR6 cycle, that is, SR15, 
SRCCL and SROCC. While since AR4 the RFC assessment framework 
has remained largely consistent, refinements in methodology have

included the consideration of different risks, the role of adaptation, use 
of confidence statements, more formalised protocols and standardised 
metrics (Zommers et al., 2020). In subsequent assessment cycles, the 
risk level at a given temperature has generally increased, reflecting 
accumulating scientific evidence (Zommers et al., 2020).
16.1.4 Drivers of Exposure and Vulnerability
Climate-related impacts, risks and responses all take place against 
a backdrop of trends in exposure and vulnerability driven by 
demographics, socioeconomic development (including inequalities) and 
ecosystem degradation. Other global trends that are shaping climate 
risks include technological innovation, shifts in global power relations, 
and resource scarcity (Retief et  al., 2016). Note that these global 
trends may increase but also reduce exposure and/or vulnerability, for 
instance when growing incomes, savings and social protection systems

increase resilience in the face of shocks and stresses. Drivers and future 
trends in vulnerability and exposure—next to climate-induced changes 
in natural hazards—therefore need to be considered in comprehensive 
risk assessments and eventually adaptation solutions, but empirical 
research suggests that they remain to be underemphasised in current 
national adaptation planning (Garschagen et al., 2021a).
While these risk drivers are often listed separately, they are often closely 
interconnected, including between human and ecological systems, and 
increasingly also through climate risks and responses (e.g., Simpson 
et  al., 2021). Climate impacts increasingly affect these drivers, and 
may compete with financial resources that could otherwise be applied 
for development, mitigation, adaptation and resilience building, also 
affecting inequalities (e.g., Taconet et al., 2020).
16.1.4.1 Demographics
Population growth (or decline) can result in increasing (or decreasing)

pressure on natural resources (e.g., soils, water and fish stocks) (IPBES, 
2019), and can result in the expansion of densely populated areas 
(Cardona et al., 2012; Day et al., 2016). The majority of the population 
in the coming decades will be in urban areas. While urbanisation can 
have many benefits that reduce vulnerability, such as employment 
opportunities and increased income, better access to healthcare and 
education, and improved infrastructure, unsustainable urbanisation patterns can create challenges for resource availability, exacerbate 
pollution levels (Rode et  al., 2015) and increase exposure to some 
risks. For example, ~10% of the global population live in low-elevation 
coastal zones (in 2000; areas <10 m of elevation) (McGranahan et al., 
2007; Neumann et  al., 2015), which is expected to increase by 5% 
to 13.6% by 2100 depending on the population scenario (Neumann 
et al., 2015; Jones and O’Neill, 2016). Building assets and infrastructure

in naturally risk-prone areas are also projected to increase (Magnan 
et al., 2019), which may also lead to environmental degradation that 
can further aggravate risk, such as destruction of wetlands that buffer 
against floods (Schuerch et  al., 2018; Oppenheimer et  al., 2019). 
Demographic trends, coupled with changes in income, can also result 
in increasing demands for land, food, water and energy, and therefore 
in major changes in land use and cover change (Arneth, 2019). The 
observed and projected population decline in some rural areas also has 
implications for vulnerability and exposure. In addition, demographic 
changes such as ageing may increase vulnerability to some climate 
hazards, including heat stress (Byers et al., 2018; Rohat et al., 2019a; 
Rohat et al., 2019b).
16.1.4.2 Biodiversity and Ecosystems
Rapidly accelerating trends in human impacts on global ecosystems 
and biodiversity, especially in the past five decades, have resulted

in precipitous declines in the numbers of many wild species on land 
and in the ocean, transformation of the terrestrial land surface for 
agricultural production, and the pervasive spread of alien and invasive 
species (IPBES, 2019). As a result, the capacity of ecosystems to support 
human society is thought to be coming under threat. For instance, the 
fraction of all primary production being appropriated for human use 
has doubled over the course of the 20th century (to about 25% in 
2005), although it has grown at a slower rate than human population 
(Krausmann et  al., 2013). Future projections significantly depend 
on bioenergy production, signalling one of the feedbacks between 
responses to climate change and climate risks.
16.1.4.3 Poverty Trends and Socioeconomic Inequalities within 
and across Societies
Poverty contributes to exposure and vulnerability by limiting access 
of individuals, households and communities to economic resources

and restraining adaptive capacities (e.g., for food and energy supply, 
or for financing adaptation responses) (Hallegatte and Rozenberg, 
2017). Over the past decades, until the COVID-19 pandemic, global 
poverty rates have declined rapidly. Between 1981 and 2015, the 
share of global population living in extreme poverty (under the 
international poverty line of USD 1.90 d−1) declined from 42% to 
10%, leaving 736 million people in extreme poverty, concentrated in 
South Asia and Sub-Saharan Africa (World Bank, 2018). This general 
reduction in poverty across the world is accompanied by a decrease 
in vulnerability to many types of climate change impacts (medium 
confidence). However, the COVID-19 pandemic has significantly 
increased extreme poverty by about 100 million people in 2020, with 
disproportionate economic impacts on the poorest, most fragile and 
smaller countries (World Bank, 2021) and significant implications for

vulnerability to climate change (see also Cross-Chapter Box COVID 
in Chapter 7).

16
2422Chapter 16 Key Risks across Sectors and Regions
The majority of the population in poverty are smallholder farmers and 
pastoralists, whose livelihoods critically depend on climate-sensitive 
natural ecosystems, such as through semi-subsistence agriculture where 
food consumption is primarily dependent on households’ own food 
production (Mbow et al., 2019). A significant share of this population 
is affected by armed conflict, which deters economic development and 
growth and increases local dependence on subsistence agriculture 
(Serneels and Verpoorten, 2015; Braithwaite et  al., 2016; Tollefsen, 
2017), and aggravating humanitarian challenges (e.g., ICRC, 2020). 
Extreme weather events, particularly droughts, can result in poverty 
traps keeping people poor or making them poorer, resulting in widening 
inequalities within and across countries.
Climate risks are also strongly related to other inequalities, often

but not always intersecting with poverty. AR5 found with very high 
confidence that differences in vulnerability and exposure arise from 
multi-dimensional inequalities, often produced by uneven development 
processes. These inequalities relate to geographic location, as well 
as economic, political and socio-cultural aspects, such as wealth, 
education, race/ethnicity, religion, gender, age, class/caste, disability 
and health status (Oppenheimer et al., 2014). Since AR5, a number of 
studies have confirmed and refined this assessment, especially also 
regarding socioeconomic inequality and poverty (Hallegatte et  al., 
2016; Hallegatte and Rozenberg, 2017; Pelling and Garschagen, 2019; 
Hallegatte et al., 2020). Poor people more often live in exposed areas 
such as wastelands or riverbanks (Garschagen and Romero-Lankao, 
2015; Winsemius et al., 2018). Also, poor people lose more of their total 
wealth to climatic hazards, receive less post-shock support from their

often-times equally poor social networks, and are often not covered by 
social protection schemes (Leichenko and Silva, 2014; Hallegatte et al., 
2016). Countries with high inequality tend to have above-average 
levels of exposure and vulnerability to climate hazards (BEH UNU-EHS, 
2016). Many socioeconomic models used in climate research have 
been found to have a limited ability to capture and represent the poor 
at a larger scale (Rao et  al., 2019; Rufat et  al., 2019). However, an 
analysis of 92 countries found that relative income losses and other 
climate change impacts were disproportionately high among the 
poorest (Hallegatte and Rozenberg, 2017, see Section 16.2.6). There 
have also been advances in detecting and attributing the impacts of 
climate change and vulnerability at household scale and specifically 
on women’s agency and adaptive capacity (Rao et  al., 2019). The 
distribution of impacts and responses (adaptation and mitigation)

affects inequality, not just between countries but also within countries 
(e.g., Tol, 2020) and between different people within societies. 
Distribution has so far largely been thought of in a geographical sense, 
but identifying those most at risk requires an additional focus on the 
social distribution of impacts, responses, and resilience, as influenced 
for instance by differential social protection coverage (Tenzing, 2020).
Many climate responses interact with all of these global risk drivers. 
Some raise additional equity concerns about marginalising those most 
vulnerable and exacerbating social conflicts (Oppenheimer et  al., 
2019), leading to wider questions about the governance of climate 
risks (and impacts) across scales. Hence, our assessment of impacts, 
responses and risks is complemented by the assessment of governance 
and the enabling environment for risk management in Chapter 17, and

of climate resilient development in Chapter 18.16.2 Synthesis of observed impacts of changes 
in climate related systems
This section synthesises the observed impacts of changes in climate-
related systems (Section  16.2.1) on different natural, human and 
managed systems (outlined in Chapters 2–8) and regions (outlined 
in Chapters 9–15). To stay as specific as possible given the required 
level of aggregation, we decided in favour of a summary along 
specific prominent indicators such as ‘crop yields’ or ‘areas burned 
by wildfires’ instead of an assessment across broad categories 
such as ‘food production’ which could include a broad range of 
measures ranging from climate-induced changes in growing seasons 
to impacts on livestock and fisheries, etc., or ‘wildfires’ which could 
also cover impacts on the frequency, intensity, timing, or emissions 
and health impacts of wildfires. This decision for specificity certainly

implies a decision against comprehensiveness. In addition, the level 
of specificity has to be adjusted given the literature basis which is 
quite broad regarding crop yields but still limited and less harmonised 
regarding indicators when it comes to, for example, conflicts. A 
broader discussion can be found in the sectoral or regional chapters 
that all cover ‘observed impacts’ individually. Section 16.2.1 provides 
key definitions, followed by recent advances in available methods 
and data for climate impact attribution (Section  16.2.2), and the 
assessment of observed impacts of changes in climate related 
systems (Section 16.2.3). It is important to note that the assessment 
is primarily based on peer-reviewed literature, that is, it is limited to 
the regions and phenomena for which such studies are available. So 
‘no assessment’ in a certain region does not imply that the considered 
type of impact did not occur in this region.
16.2.1 Definitions

The section adopts the general definition of detection as ‘demonstra-
tion that a considered system has changed without providing reasons 
for the change’ and attribution as ‘identifying the causes of the ob-
served long-term change in an impact indicator or of the change in 
the temporal or spatial extent, the intensity or frequency of a specific 
event’ (see Glossary (Annex II)).
Based on these general definitions and following the approach 
applied in WGII AR5 Chapter 18 (Cramer et al., 2014), we define an 
observed impact as the difference between the observed state of a 
natural, human or managed system and a counterfactual baseline 
that characterises the system’s state in the absence of changes in the 
climate-related systems, defined here as climate system including 
the ocean and the cryosphere as physical or chemical systems.
The difference between the observed and the counterfactual baseline 
state is considered the change in the natural, human or managed

system that is attributed to the changes in the climate-related 
systems (impact attribution). The counterfactual baseline may be 
stationary or may change over time, for example due to direct human 
influences such as changes in land use patterns and agricultural or 
water management affecting exposure and vulnerability to climate-
related hazards (see Section 16.2.3 for methods on how to construct 
the counterfactual baseline).

16
2423Key Risks across Sectors and Regions  Chapter 16
In line with the AR5 definition, ‘changes in climate-related systems’ 
here refer to any long-term trend, irrespective of the underlying 
causes; thus, an observed impact is not necessarily an observed 
impact of anthropogenic climate forcing. For example, in this section, 
sea level rise is defined as relative sea level rise measured against 
a land-based reference frame (tide gauge measurements), meaning 
that it is driven not only by thermal expansion and loss of land ice 
influenced by anthropogenic climate forcing, but also by vertical land 
movements. As attribution of coastal damages to sea level rise does not 
distinguish between these components, it does not imply attribution 
to anthropogenic forcing. Where the literature does allow attribution 
of changes in natural, human or managed systems to anthropogenic 
climate forcing (‘joint attribution’, Rosenzweig et  al., 2007), this is

highlighted in the assessment. Often the attribution of changes in 
the natural, human or managed systems to anthropogenic forcing can 
be done in a two-step approach where (i) an observed change in a 
climate-related system is attributed to anthropogenic climate forcing 
(‘climate attribution’) and (ii) changes in natural, human or managed 
systems are attributed to this change in the climate-related system 
(‘impact attribution’).
For climate attribution, the main challenge is the separation of externally 
human forced changes in the climate-related systems from their internal 
variability, while for impact attribution it often is the separation of 
the effects of other external forcings (i.e., direct human influences or 
natural disturbances) from the impacts of the changes in the climate-
related systems. Direct influences not related to changes in the climate-
related systems could, for example, be pollution and land use changes

amplifying biodiversity losses, intensification of fishing reducing fish 
stocks, and increasing protection reducing losses due to river floods. 
The direct human or natural influences may counter the impacts of 
climate change (e.g., climate change may have reduced flood hazards, 
but exposure may have increased as people have moved to flood-prone 
areas, resulting in no change in observed damages). Given the definition 
of impact attribution, this means that there may be an observed impact 
of climate change without the detection of a change in the natural, 
human or managed system. This is different from ‘climate attribution’, 
where detection and attribution usually are consecutive steps.
Changes in climate-related systems can certainly also affect natural, 
human and managed systems through indirect effects on land use, 
pollution or exposure. However, these indirect effects are barely 
addressed in existing studies.

In addition to impact attribution, there is research on the identification 
of natural, human or managed systems’ response to short-term 
(typically daily, monthly or annual) weather fluctuations or individual 
extreme weather events. As different from impact attribution, we 
separately define:
‘Identification of weather sensitivity’ refers to the attribution of 
the response of a system to fluctuations in weather and short-term 
changes in the climate-related systems including individual extreme 
weather events (e.g., a heatwave or storm surge).
Typical questions addressed include: ‘How much of the observed 
variability of crop yields is due to variations in weather conditions compared to contributions from management changes?’ (e.g., Ray et al., 
2015; Müller et al., 2017) and ‘Can weather fluctuations explain part 
of the observed variability in annual national economic growth rates?’ 
(e.g., Burke et al., 2015). Identification of weather sensitivity may also

address the effects of individual climate extremes, for example asking, 
‘Was the observed outbreak of cholera triggered by an associated flood 
event?’ (e.g., Rinaldo et al., 2012; Moore et al., 2017b). It is important 
to note that sensitivity could be described in diverse ways and that, for 
example, the fraction of the observed variability in a system explained by 
weather variability differs from the strength of the systems’ response to 
a specific change in a weather variable. Nevertheless, all these different 
measures are integrated in the ‘identification of weather sensitivity’ 
assessment, where ‘sensitivity’ should not be considered a quantitative 
one-dimensional mathematical measure.
In this chapter, we explicitly distinguish between assessment statements 
related to ‘climate attribution’ (listed in Table  SM16.21), ‘impact 
attribution’ (listed in Table  SM16.22) and ‘identification of weather 
sensitivity’ (listed in Table  SM16.23). The identification of ‘weather

sensitivity’ does not necessarily imply that there also is an impact of 
long-term changes in the climate-related systems on the considered 
system. However, if the probability or intensity of an extreme weather 
event has increased due to anthropogenic forcing (‘climate attribution’) 
(NASEM, 2016; WGI AR6 Chapter 11 Seneviratne et al., 2021) and the 
event is also identified as an important driver of an observed fluctuation 
in a natural, human or managed system (‘identification of weather 
sensitivity’), then the observed fluctuation is considered (partly) 
attributed to long-term climate change (‘impact attribution’) and even 
to anthropogenic forcing.
16.2.2 Methods and Data for Impact Attribution 
Including Recent Advances
By definition, the counterfactual baseline required for impact 
attribution cannot be observed. However, it may be approximated by 
impact model simulations forced by a stationary climate, for example

derived by de-trending the observed climate (Diffenbaugh et al., 2017; 
Mengel et al., 2021), while other relevant drivers (e.g., land use changes 
or application of pesticides) of changes in the system of interest (e.g., a 
bird population) evolve according to historical conditions. To attribute 
to anthropogenic climate forcing, the anthropogenic trends in climate 
are estimated from a range of different climate models and subtracted 
from the observed climate (e.g., Abatzoglou and Williams, 2016, for 
changes in the extent of forest fires or Diffenbaugh and Burke, 2019, 
for effects on economic inequality) or the ‘no anthropogenic climate 
forcing’ baseline is directly derived from a large ensemble of climate 
model simulations not accounting for anthropogenic forcings (e.g., 
Kirchmeier-Young et al.., 2019b, for the extent of forest fires). In any 
case, it has to be demonstrated that the applied impact models are 
able to explain the observed changes in natural, human or managed

systems by, for example, reproducing the observations when forced 
by observed changes in climate-related systems and other relevant 
drivers.
In a situation where an influence of other direct human drivers can 
be excluded (e.g., by restriction to remote areas not affected by

16
2424Chapter 16 Key Risks across Sectors and Regions
direct human interventions), the ‘no climate-change’ baseline can 
also be approximated by data from early observational periods with 
no or minor levels of climate change. In particular, the contribution 
of climate change to the observed changes in ecosystems is often 
also determined by a ‘multiple lines of evidence’ approach where 
the baseline is not formally quantified but the observed changes are 
identified as a signal of climate change compared with a no-climate-
change situation based on process understanding from, for example, 
palaeo data and laboratory or field experiments in combination with 
individual long-term observational records and the large-scale spatial 
or temporal pattern of observed changes that can hardly be explained 
by alternative drivers (Parmesan et al., 2013).
To date, explicit accounting for direct human or natural influences 
is often hampered by an incomplete understanding of the processes

and limited observational data. There are, however, first studies 
demonstrating the potential of detailed process-based or empirical 
modelling that explicitly accounts for known variations in direct human 
or natural drivers and separate their effects from the ones induced 
by changes in the climate-related systems. Examples are Butler et al. 
(2018) for the separation of growing season adjustments from within 
growing season climate effects on US crop yields; Wang and Hijmans 
(2019) separating effects of shifts in land use from climate effects; 
Jongman et al. (2015); Formetta and Feyen (2019) and Tanoue et al. 
(2016) for the separation of changes in exposure and vulnerability 
from climate effects on river floods; Kirchmeier-Young et al. (2019b) 
for wildfire attribution; and Venter et  al. (2018) for the attribution 
of ecosystem structural changes to climate change versus other 
disturbances.
There also has been significant progress in the compilation of

fragmented and distributed observational data (e.g., Cohen et al., 
2018, for phenological ecosystem changes; Poloczanska et al., 2013, for 
distributional shifts in marine ecosystems; Andela et al., 2019, with the 
new global fire atlas including information about individual fire size, 
duration, speed and direction) as well as in the regional disaggregation 
(e.g., Ray et al., 2015, for crop yields ) allowing for the identification of 
an overall picture of the impacts of progressing climate change. Given 
the ever-increasing body of literature on observed changes in natural, 
human and managed systems, there also is a first machine learning 
approach for an automated identification of relevant literature that 
could complement or support expert assessments as the one provided 
here (Callaghan et al., 2021).
16.2.3 Observed Impacts of changes in climate-related 
systems
In this section, we synthesise observed impacts of changes in climate-

related systems across a range of ecosystems, sectors and regions. 
Figure  16.2 summarises the attribution of observed (regional) 
changes in natural, human or managed systems (orange symbols and 
confidence ratings), the quantification of weather sensitivity of those 
systems (blue symbols and confidence ratings) and the attribution of 
underlying changes in the climate-related systems to anthropogenic 
forcing (grey symbols and confidence ratings). The figure can be 
read as a summary and table of content for the underlying Tables SM16.21 on climate attribution, SM16.22 on impact attribution and 
SM16.23 on identification of weather sensitivity that provide the more 
detailed explanations behind each regional or global assessment, 
including all references. The synthesis was generated in collaboration 
with ‘detection and attribution contact persons’ from the individual 
chapters that each includes its own assessment of observed impacts,

and contributing authors on individual topics. The synthesis of ‘climate 
attribution’ studies in Table SM16.21 was particularly informed by the 
WGI assessment.
If Figure 16.2 only provides an assessment of attributed impacts on 
a given system (e.g., phenology shifts in terrestrial ecosystems) but 
does not include an associated ‘identification of weather sensitivity’ 
that does not mean that the system is not sensitive to weather 
fluctuations. The focus of our assessment was on ‘impact attribution’, 
and we only provide an assessment of ‘weather sensitivities’ if the 
literature has turned out to provide only limited evidence on impacts 
of long-term changes in climate-related systems but rather addressed 
the responses of natural, human or managed systems to short-term 
weather fluctuations in the climate-related ones.
16.2.3.1 Ecosystems
The collapse or transformation of ecosystems is one of the most 
abrupt potential tipping points associated with climate change.

Climate change has started to induce such tipping points, with the 
first examples including mass mortality in coral reef ecosystems (e.g., 
Donner et al., 2017; Hughes et al., 2018; Hughes et al., 2019) (high 
confidence), and changes in vegetation cover triggered by wildfires 
with climate change suppressing the recovery of the former cover 
(Tepley et al., 2017; Davis et al., 2019) (low confidence because of the 
still limited number of studies). Another example of an abrupt change 
in an ecosystem triggered by a climate extreme is the shift from 
kelp- to urchin-dominated communities along parts of the Western 
North America coast (Rogers-Bennett and Catton, 2019; McPherson 
et  al., 2021, see ‘Marine ecosystems—Kelp forest’, Table  SM16.22). 
The loss of kelp forests was induced by a marine heatwave where 
anthropogenic climate forcing has been shown to have increased the 
probability for an event of that duration by at least a factor of 33

(Laufkötter et al., 2020). Many terrestrial ecosystems on all continents 
show evidence of significant structural transformation, including 
woody thickening and ‘greening’ in more water-limited ecosystems, 
with a significant role played by rising atmospheric CO 2 fertilisation in 
these trends (high confidence) (Fang et al., 2017; Stevens et al., 2017; 
Burrell et al., 2020). Climate change is identified as a major driver of 
increases in burned areas in the Western USA (high confidence, see 
‘Terrestrial ecosystems—Burned areas’, Table SM16.22).
There is also a clear footprint of climate change on species distribution, 
with appreciable proportions of tropical species expanding into the 
ranges of temperate species, and boreal species moving into Arctic 
regions (high confidence, see ‘Marine ecosystems—Range reduction 
and shift’ and ‘Terrestrial ecosystems—Range reduction and shift’, 
Table  SM16.22). Climate change has also shifted the phenology of

animals and plants on land and in the ocean (high confidence, see 
‘Marine ecosystems—Phenology shift’ and ‘Terrestrial ecosystems—
Phenology shifts’, Table SM16.22). Both processes have led to emerging

16
2425Key Risks across Sectors and Regions  Chapter 16
hybridisation, competition, temporal or spatial mismatches in predator–
prey, guest–host relationships, and the invasion of alien plant pests 
or pathogens (Edwards and Richardson, 2004; Bebber et  al., 2013; 
Parmesan et al., 2013; Millon et al., 2014; Thackeray et al., 2016).
16.2.3.2 Water Distribution—River Flooding and Reduction in 
Water Availability
Observed trends in high river flows strongly vary across regions but 
also with the considered time period (Gudmundsson et  al., 2019; 
Gudmundsson et  al., 2021) as influenced by climate oscillations such 
as the El Niño-Southern Oscillation (Ward et al., 2014). On the global 
scale, the spatial pattern of observed trends is largely explained by 
observed changes in climate conditions as demonstrated by multi-
model hydrological simulations forced by observed weather, while the 
considered direct human influences play only a minor role on global

scale (Gudmundsson et  al., 2021, see ‘Water distribution—Flood 
induced economic damages’, Table SM16.22). The annual total number 
of reported fatalities from flooding shows a positive trend (1.5% yr−1 
from 1960 to 2013, Tanoue et al., 2016) which appears to be primarily 
driven by changes in exposure dampened by a reduction in vulnerability, 
while climate-induced increases in affected areas show only a weak 
positive trend on the global scale. However, the signal of climate change 
in flood-induced fatalities may be lost in the regional aggregation, 
where effects of increasing and decreasing hazards may cancel out. Thus, 
a climate-driven increase in flood-induced damages becomes detectable 
in continental subregions with increasing discharge, while the signal 
of climate change may not be detectable without disaggregation 
(Sauer et  al., 2021, see ‘Water distribution—Flood induced economic 
damages’, Table  SM16.22). Compared with river floods, the analysis

of impacts of long-term changes in the climate-related systems on the 
reduction in water availability is much more fragmented and reduced to 
individual case studies regarding associated societal impacts (see ‘Water 
distribution—Reductions in water availability + induced damages and 
fatalities’, Table SM16.22). At the same time, weather fluctuations have 
led to reductions in water availability with severe societal consequences 
and high numbers of drought-induced fatalities and damages in 
particular in Africa and Asia (see ‘Water distribution—Reductions in 
water availability + induced damages and fatalities’, Table  SM16.23) 
and impacts on malnutrition (see ‘Food system—Malnutrition’, 
Table SM16.23). Although anthropogenic climate forcing has increased 
droughts’ intensity or probability in many regions of the world (medium 
confidence), (see ‘Atmosphere—Droughts’, Table SM16.21) the existing 
knowledge has not yet been systematically linked to attribute long-

term trends in malnutrition, fatalities and damages induced by reduced 
water availability to anthropogenic climate forcing or long-term climate 
change. For impacts of individual attributable drought events, see 
Table  4.5 and ‘Water distribution—Reductions in water availability + 
induced damages and fatalities’, Table SM16.23.
16.2.3.3 Coastal systems
With their enormous destructive power, tropical cyclones represent 
a major risk for coastal systems (see ‘Coastal systems—Damages’, 
Table  SM16.23). Despite its relevance, confidence in the influence of 
anthropogenic climate forcing on the strength and occurrence probability 
of tropical storms themselves is still low (see ‘Coastal systems—Tropical cyclone activity’, Table  SM16.21). However, anthropogenic 
climate forcing has become the dominant driver of sea level rise (high 
confidence) (see ‘Coastal systems—Mean and extreme sea levels’, 
Table SM16.21) and has increased the risk of coastal flooding, including

inundation induced by tropical cyclones. In addition, anthropogenic 
climate forcing has increased the amount of rainfall associated with 
tropical cyclones (high confidence) (Risser and Wehner, 2017; Van 
Oldenborgh et  al., 2017; Wang et  al., 2018, for Hurricane Harvey in 
2017; Patricola and Wehner, 2018, for hurricane Katrina in 2005, Irma 
in 2017 and Maria in 2017, see ‘Atmosphere—Heavy precipitation’, 
Table SM16.21). Assuming that the extreme rainfall is a major driver 
of the total damages induced by the tropical cyclone, the contribution 
of anthropogenic climate forcing to the occurrence probability of the 
observed rainfall (fraction of attributable risk) can also be considered 
the fraction of attributable risk of the hurricane-induced damages or 
fatalities (Frame et al., 2020; Clarke et al., 2021, see ‘Coastal systems—
Damages’, Table SM16.22). However, first studies do not only quantify 
the change in occurrence probabilities but translate the actual change

in climate-related systems into the additional area affected by flooding 
in a process-based way (Strauss et  al., 2021 for the contribution of 
anthropogenic sea level rise (SLR) to damages induced by Hurricane 
Sandy; Wehner and Sampson, 2021 for the contribution increased 
precipitation to damages induced by Hurricane Harvey) and attribute 
a considerable part of the observed damage to anthropogenic climate 
forcing. In addition, disruption of local economic activity in Annapolis, 
Maryland and loss of areas and settlements in Micronesia and Solomon 
Islands have been attributed to relative SLR (Nunn et al., 2017; Albert 
et al., 2018; Hino et al., 2019), while permafrost thawing and sea ice 
retreat are additional drivers of observed coastal damages in Alaska 
(Albert et al., 2016; Smith and Sattineni, 2016; Fang et al., 2017).
16.2.3.4 Food system
Crop yields respond to weather variations but also to increasing

atmospheric CO 2, changes in management (e.g., fertilizer input, changes 
in varieties), diseases and pests. However, the weather signal is clearly 
detectable in national and subnational annual yield statistics in main 
production regions (see ‘Food system—Crop yields’, Table  SM16.23). 
Over the last decades, crop yields have increased nearly everywhere 
mainly due to technological progress (e.g., Lobell and Field, 2007 
[global]; Butler et al., 2018 [USA]; Hoffman et al., 2018 [Sub-Saharan 
Africa]; Agnolucci and De Lipsis, 2019 [Europe]), with only minor areas 
not experiencing improvements in maize, wheat, rice and soy yields. 
However, meanwhile, stagnation or decline in yields is also observed in 
parts of the harvested areas (high confidence) (~20–40% of harvested 
areas of maize, wheat, rice and soy with wheat being most affected) (Ray 
et al., 2012; Iizumi et al., 2018). Evidence on the contribution of climate

change to recent trends is still limited (see ‘Food system—Crop yields’, 
Table SM16.22). Current global-scale process-based simulations forced 
by simulated historical and pre-industrial climate lack an evaluation 
to what degree simulations reproduce observed yields (Iizumi et  al., 
2018). Global-scale empirical approaches do not explicitly account for 
extreme weather events but growing season average temperatures 
and precipitation (e.g., Lobell et al., 2011; Ray et al., 2019). In addition, 
studies are constrained by only fragmented information about changes 
in agricultural management such as growing season adjustments. Some 
of these limitations have been overcome in regional studies indicating a

16
2426Chapter 16 Key Risks across Sectors and Regions
climate-induced increase (28% of observed trend since 1981) in maize 
yields in the USA (Butler et al., 2018, based on a detailed accounting 
of impacts of extreme temperatures and growing season adjustments) 
and a climate-induced decrease in millet and sorghum yields (10–20% 
for millet and 5–15% for sorghum in 2000–2009 compared with 
pre-industrial conditions) in Africa and a negative effect of historical 
climate change on potential wheat yields (27% reduction from 1990 
to 2015) in Australia (Hochman et al., 2017; Sultan et al., 2019 based 
on detailed process-based modelling including a dedicated evaluation 
against observed yield fluctuations). These findings need additional 
support by independent studies. Results are relatively convergent that 
climate change has been an important driver of the recent declines in 
wheat yields in Europe (medium confidence) (Moore and Lobell, 2015;

Agnolucci and De Lipsis, 2019; Ray et al., 2019).
Due to complex interactions with socioeconomic conditions, climate-
induced trends in crop yields and production do not directly transmit 
to crop prices, availability of food, or nutrition status. This complexity, 
in addition to the limited availability of long-term data, has so far 
impeded the detection and attribution of a long-term impact of climate 
change on associated food security indicators. However, in a few cases, 
observed crop prices (e.g., domestic grain price in Russia and Africa, 
Götz et  al., 2016; Mawejje, 2016; Baffes et  al., 2019) are shown to 
be sensitive to fluctuations in local weather through its impact on 
production (see ‘Food system—Food prices’, Table  SM16.23). In 
addition, there is growing evidence that climate extremes (in particular, 
droughts) have led to malnutrition (in particular, stunting of children) 
in the historical period (medium confidence, see ‘Food system—

Malnutrition’, Table SM16.23) but without an attribution of changes to 
long-term climate change.
16.2.3.5 Temperature-Related Mortality
There is nearly universal evidence that non-optimal ambient temperatures 
increase mortality (high confidence), with notable heterogeneity only in 
the shape of the temperature–mortality relationship across geographical 
regions but often sharply growing relative risks at the outer 5% of the 
local historical temperature distributions (Gasparrini et  al., 2015; Guo 
et al., 2018; Carleton et al., 2020; Zhao et al., 2021; see ‘Other societal 
impacts—Heat-related mortality’, Table SM16.23). Significant advances 
have been made since AR5 regarding the analysis of temperature-
related excess mortality in previously under-researched regions, such 
as developing countries and (sub)tropical climates (e.g South-East Asia: 
Dang et al., 2016; Ingole et al., 2017; Mazdiyasni et al., 2017; South Africa:

Wichmann, 2017, Scovronick et  al., 2018; the Middle East: Alahmad 
et al., 2019, Gholampour et al., 2019; and Latin America: Péres et al., 
2020). Progress has also been made with regard to temporal changes 
in temperature-related excess mortality and underlying population 
vulnerability over time. Heat-attributable mortality fractions have 
declined over time in most countries owing to general improvements 
in health care systems, increasing prevalence of residential air 
conditioning, and behavioural changes. These factors, which determine 
the susceptibility of the population to heat, have predominated over the 
influence of temperature change (see ‘Other societal impacts—Heat-
related mortality’, Table  SM16.22, De’Donato et  al., 2015; Arbuthnott 
et al., 2016; Vicedo-Cabrera et al., 2018a). Important exceptions exist, 
for example, where unprecedented heatwaves have occurred recently. No conclusive evidence emerges regarding recent temporal trends in

excess mortality attributable to cold exposure (Vicedo-Cabrera et  al., 
2018b). Quantitative detection and attribution studies of temperature-
related mortality are still rare. One study (Vicedo-Cabrera et al. 2021), 
using data from 43 countries, found that 37% (range 20.5–76.3%) of 
average warm-season heat-related mortality during recent decades can 
be attributed to anthropogenic climate change (medium confidence, 
see ‘Other societal impacts—Heat-related mortality’, Table  SM16.22). 
Studying excess mortality associated with past heatwaves, such as 
the 2003 or 2018 events in Europe, even higher proportions of deaths 
attributable to anthropogenic climate change have been reported for 
France and the UK (Mitchell et  al., 2016; Clarke et  al., 2021). Formal 
attribution studies encompassing cold-related mortality are quasi non-
existent. The very few studies from Europe and Australia (Christidis et al., 
2010; Åström et al., 2013; Bennett et al., 2014) find weak impacts of

climate change on cold-associated excess mortality, with contradictory 
outcomes both towards higher and lower risks (low confidence, see 
‘Other societal impacts—Heat-related mortality’, Table SM16.22).
16.2.3.6 Waterborne Diseases
Infectious diseases with water-associated transmission pathways 
constitute a large burden of disease globally. Since the AR5, the 
evidence has strengthened that waterborne diseases, and especially 
gastrointestinal infections, are highly to moderately sensitive to weather 
variability (medium confidence, see ‘Water distribution—Waterborne 
diseases’, Table SM16.23). Increased temperature and high precipitation, 
with associated flooding events, have been shown to generally increase 
the risk of diarrhoeal diseases. There are, however, a number of studies 
that describe important exceptions and modifications to this general 
observation. While high temperatures favour bacterial diarrhoeal 
diseases, virally transmitted diarrhoea is on the contrary mostly

associated with low temperatures (Carlton et  al., 2016; Chua et  al., 
2021). Socioeconomic determinants, such as the existence of single-
household water supplies (Herrador et  al., 2015) or combined sewer 
overflows (Jagai et  al., 2017), have been shown to critically increase 
the risk of gastrointestinal infections linked to heavy rainfall in high-
income countries. Also, for both low- and high-income countries it has 
been found that gastrointestinal diseases increase following a heavy 
rainfall event only if preceded by a dry period (Carlton et al., 2014; Setty 
et al., 2018). Yet, so far there is no consistent evidence on the role of 
droughts in favouring waterborne disease transmission (Levy et  al., 
2016). As exemplified by the large cholera outbreak following the 2010 
earthquake in Haiti, the existence of functioning sanitation systems is 
critical for preventing waterborne disease outbreaks, while climatic

factors (especially rainfall) are important in driving the transmission 
dynamics once the outbreak has started (Rinaldo et  al., 2012). Other 
socioeconomic factors, such as human mobility and water management 
projects (e.g., dam constructions), also modify the strength of the 
association between climatic factors and waterborne diseases, as shown 
by recent studies in Africa (Perez-Saez et al., 2015; Finger et al., 2016).
Whereas the weather sensitivity of waterborne diseases is well 
established for all world regions (see ‘Water distribution—Water-
borne diseases’, Table SM16.23), studies attempting to attribute recent 
trends in waterborne disease to climate change are non-existent, 
except for investigations on the distribution of marine Vibrio bacteria

16
2427Key Risks across Sectors and Regions  Chapter 16
and associated disease outbreaks in the coastal North Atlantic and the 
Baltic Sea regions (Baker-Austin et al., 2013; Baker-Austin et al., 2016; 
Vezzulli et  al., 2016; Ebi et  al., 2017). These investigations provide 
evidence that increases in sea surface temperatures over recent decades 
as well as during recent summer heatwaves are linked to increased 
concentrations of Vibrio bacteria in coastal waters and an associated 
rise in environmentally acquired Vibrio infections in humans.
16.2.3.7 Vector-Borne Diseases
Vector-borne diseases constitute a large burden of infectious diseases 
worldwide and are highly sensitive to fluctuations of weather conditions 
including extreme events. Thus, both extreme rainfall and droughts have 
increased infections (high confidence,  see ‘Other societal impacts—
Vector-borne diseases’, Table  SM16.23). For example, in Sudan,

anomalous high rainfall increased Anopheles mosquito breeding sites, 
leading to malaria outbreaks (Elsanousi et al., 2018), while in Barbados 
and Brazil, drought conditions in urban areas have enhanced dengue 
incidence due to changes in water storage behaviour creating breeding 
sites for Aedes mosquitoes around human dwellings (Lowe et al., 2018; 
Lowe et al., 2021) . In the Caribbean and Pacific Island nations, weather 
extremes, such as storms and flooding, have led to outbreaks of dengue 
due to disruption to water and sanitation services, leading to increased 
exposure to Aedes mosquito breeding sites (Descloux et al., 2012; Sharp 
et al., 2014; Uwishema et al., 2021). In South and Central America, and 
Asia, dengue incidence has been shown to be sensitive to variations in 
temperature and the monsoon season in addition to variations induced 
by urbanisation and population mobility (high confidence [South 
and Central America]; medium confidence [Asia]; see ‘Other societal

impacts—Vector-borne diseases’, Table SM16.23).
The attribution of changes in disease incidence to long-term climate 
change is often limited by relatively short reporting periods often 
only covering 10–15 years. Most studies then attribute trends in the 
occurrence of vector-borne diseases to the trends in climate across 
the same observational period and do not refer to an early ‘no climate 
change’ baseline climate. This means that they also capture trends 
induced by longerterm climate oscillations. Nevertheless, we list them 
in Table  SM16.22 on ‘impact attribution’ to clearly distinguish them 
from the analysis of interannual fluctuations. The overall consistency of 
their findings across regions and time windows indicates that climate 
change is an important driver of the observed latitudinal or altitudinal 
range expansions of vector-borne diseases into previously colder areas 
(medium to high confidence, see ‘Other societal impacts—Vector-

borne diseases’, Table SM16.22). In highland areas of Africa and South 
America, epidemic outbreaks of malaria have become more frequent 
due to warming trends that allow Anopheles mosquitoes to persist at 
higher elevations (Pascual et al., 2006; Siraj et al., 2014). In the USA, 
ticks that transmit Lyme disease have expanded their range northwards 
because of warmer temperatures (high confidence; Kugeler et al., 2015; 
McPherson et al., 2017; Lin et al., 2019; Couper et al., 2020; see ‘Other 
societal impacts—Vector-borne diseases’, Table SM16.22). In Southern 
Europe, climate suitability for Aedes mosquitoes, which transmit dengue 
and chikungunya, and Culex mosquitoes, which transmit West Nile 
virus, has also increased and contributed to unprecedented outbreaks 
including the 2018 West Nile fever outbreak (medium confidence, Medlock et al., 2013; Paz et al., 2013; Roiz et al., 2015; ECDC, 2018, see 
‘Other societal impacts—Vector-borne diseases’, Table SM16.22).

16.2.3.8 Economic Impacts
Since the AR5, there has been significant progress regarding the 
identification of economic responses to weather fluctuations: 
evidence has increased that extreme weather events such as tropical 
cyclones, droughts, and severe fluvial floods have not only caused 
substantial immediate direct economic damage (high confidence, see 
‘Coastal Systems—Damages, Table  SM16.23, ‘Water distribution—
Reductions in water availability + induced damages and fatalities’, 
Table  SM16.23, and ‘Water distribution—Flood-induced economic 
damages’, Table SM16.22) but have also reduced economic growth 
in the short term (year of, and year after event) (Strobl, 2011; Strobl, 
2012; Fomby et al., 2013; Felbermayr and Gröschl, 2014, Loyaza et al. 
2012) (high confidence) as well as in the long term (up to 10–15 years 
after event) (medium confidence) (Hsiang and Jina, 2014; Berlemann 
and Wenzel, 2016; Berlemann and Wenzel, 2018; Krichene et al., 2020;

Tanoue et  al., 2020, see ‘Other societal impacts—Macroeconomic 
output’, Table SM16.23). Short- and long-term reductions of economic 
growth by extreme weather events affect both developing and 
industrialised countries, but have been shown to be more severe 
in developing than in industrialised economies, thereby increasing 
inequality between countries (high confidence, see ‘Other societal 
impacts—Between-country inequality’, Table  SM16.23). Further, 
extreme weather events have increased within-country inequality 
since poorer people are more exposed and suffer relatively higher 
well-being losses than richer parts of the population (medium 
confidence, see ‘Other societal impacts—Within-country inequality’, 
Table  SM16.23). Going beyond extreme weather events, economic 
production depends nonlinearly on temperature fluctuations: below 
a certain threshold temperature, economic production increases 
with temperature, whereas it decreases above a certain threshold

temperature (high confidence) (Burke et al., 2015; Pretis et al., 2018; 
Kalkuhl and Wenz, 2020; Kotz et al., 2021).
So far, there are few individual studies attributing observed economic 
damages to long-term climate change except for damages induced 
by river flooding, droughts and tropical cyclones (see ‘Coastal 
systems—Damages’, ‘Water distribution—Flood-induced damages’, 
and ‘Water distribution—Reduction in water availability + induced 
damages and fatalities’, Table  SM16.22). In addition, the empirical 
findings on the sensitivity of macroeconomic development to weather 
fluctuations and extreme weather events have been used to estimate 
the cumulative effect of historical warming on long-term economic 
development (see ‘Other societal impacts—Macroeconomic output’, 
Table SM16.22): anthropogenic climate change is estimated to have 
reduced gross domestic product (GDP) growth over the last 50 years, 
with substantially larger negative effects on developing countries

and in some cases positive effects on colder industrialised countries 
(low confidence) (Diffenbaugh and Burke, 2019). Globally, between-
country inequality has decreased over the last 50  years. Climate 
change is estimated to have substantially slowed down this trend, that 
is, increased inequality compared with a counterfactual no-climate-
change baseline (low confidence) (Diffenbaugh and Burke, 2019). On 
a regional level, decreasing rainfall trends in Sub-Saharan Africa may

16
2428Chapter 16 Key Risks across Sectors and Regions
have increased the GDP per capita gap between Sub-Saharan Africa 
and other developing countries (low confidence) (Barrios et al., 2010). 
Overall, more research is needed on the impact channels through which 
extreme weather events and weather variability can hinder economic 
development, especially in the long term.
16.2.3.9 Social Conflict
There are few studies directly attributing changes in conflict risk to 
climate change in the modern era (van Weezel, 2020), preventing a 
confident assessment of the effect of long-term changes in the climate-
related systems on armed conflict (see ‘Other societal impacts—Social 
conflict’, Table  SM16.22). However, a sizeable literature links the 
prevalence of armed conflict within countries to within- and between-
year variations in rainfall, temperature or drought exposure, often via 
reduced-form econometric analysis or statistical models that control

for important non-climatic factors, such as agricultural dependence, 
level of economic development, state capacity and ethnopolitical 
marginalisation (see ‘Other societal impacts—Social conflict’, 
Table SM16.23). Overall, there is more consistent evidence that climate 
variability has influenced low-intensity organised violence than major 
civil wars (Detges, 2017; Nordkvelle et al., 2017; Linke et al., 2018). 
Likewise, there is more consistent evidence that climate variability 
has affected dynamics of conflict, such as continuation, severity and 
frequency of violent conflict events, than the likelihood of initial 
conflict outbreak (Yeeles, 2015; Eastin, 2016; Von Uexkull et al., 2016, 
Section 7.2.7). Moreover, research suggests with medium confidence 
(medium evidence, medium agreement) that weather effects on 
armed conflict have been most prominent in contexts marked by a 
large population, low socioeconomic development, high political

marginalisation and high agricultural dependence (Theisen, 2017; 
Koubi, 2019; Buhaug et al., 2020; Ide et al., 2020).
Some studies also seek to evaluate potential indirect links between 
climate and weather anomalies and prevalence of armed conflict via 
food price shocks or forced migration. While there is robust evidence 
that the likelihood of social unrest in the developing world generally 
increases in response to rapid growth in food prices (Bellemare, 2015; 
Rudolfsen, 2018), the magnitude of the climate effect on unrest via 
food prices is less well established (Martin-Shields and Stojetz, 2019). 
Similarly, research shows with high confidence that climate variability 
and extremes have affected human mobility (see ‘Other societal 
impacts—Displacement and migration’, Table SM16.23), but there is 
low agreement and limited evidence that weather-induced migration 
has increased the likelihood of armed conflict (Section 7.2.7, Brzoska

and Fröhlich, 2016; Kelley et al., 2017; Selby et al., 2017; Abel, 2019). 
Research on weather-related effects on interstate security generally 
concludes that periods of transboundary water scarcity are more 
likely to facilitate increased international cooperation than conflict 
(Bernauer and Böhmelt, 2020).
In general, the historical influence of climate on conflict is judged to 
be small when compared with dominant conflict drivers (Mach et al., 
2019). Much of this research is limited to (parts of) Sub-Saharan Africa, 
which raises some concerns about selection bias and generalisability 
of results (Adams et al., 2018).16.2.3.10 Displacement and Migration
Given the complexity of human migration processes and decisions 
(e.g., Boas et  al., 2019, Cattaneo et  al., 2019) and the paucity of 
long-term, reliable and internally consistent observational data on 
displacement (IDMC, 2019; IDMC, 2020) and migration (Laczko, 2016),

the contribution of long-term changes in climate-related systems to 
observed human displacement or migration patterns has not been 
quantified so far, except for individual examples of displacement induced 
by inland flooding where the heavy precipitation has been attributed to 
anthropogenic climate forcing and coastal flooding (see ‘Other societal 
impacts—Displacement and migration’, Table SM16.22; Section CCP2).
However, new evidence has emerged since the AR5 that further 
documents widespread effects of weather fluctuations and extreme 
events on migration (see ‘Other societal impacts—Displacement 
and migration’, Table  SM16.23). Numerous studies find significant 
links between temperature or precipitation anomalies, or extreme 
weather events such as storms or floods, and internal as well as 
international migration (Coniglio and Pesce, 2015; Cattaneo and Peri, 
2016; Nawrotzki and DeWaard, 2016; Beine and Parsons, 2017, for

international migration; and IDMC, 2019, for internal displacement). 
Internal displacement of millions of people every year is triggered by 
natural hazards, mainly floods and storms (IDMC, 2019). The effects of 
weather fluctuations and extremes on migration are considered more 
important for temporary mobility and displacement than permanent 
migration, and more influential on short-distance movement, including 
urbanisation, than international migration (McLeman, 2014; Hauer 
et al., 2020; Hoffmann et al., 2020, Section 7.2.6). Importantly, these 
links are conditional on the socioeconomic situation in the origin; for 
example, poor populations may be ‘trapped’ and not able to migrate in 
the face of adverse climate or weather conditions (Black et al., 2013; 
Adams, 2016). Many studies have also explored the channels through 
which climate or weather influence migration, and have identified 
incomes in the agricultural sector as one of the main channels

(Nawrotzki et al., 2015; Viswanathan and Kavi Kumar, 2015; Cai et al., 
2016a). In particular, declines in agricultural incomes and employment 
due to changed weather variability may foster increased rural–urban 
movement, and the resulting pressures on urban wages in turn foster 
international migration (Marchiori et  al., 2012; Maurel and Tuccio, 
2016). Another possible but controversial channel is violent conflict, 
which may be fostered (though not exclusively caused) by adverse 
climate conditions such as drought, and in turn lead to people seeking 
refugee status, although evidence of such an indirect effect is weak 
(Brzoska and Fröhlich, 2016; Abel et al., 2019; Schutte et al., 2021).
16.3 Synthesis of Observed Adaptation-
Related Responses
A new development since AR5 is that there is now growing ev-
idence assessing progress on adaptation across sectors, geographies 
and spatial scales. Uncertainty persists around what defines adaptation

and how to measure it (Cross-Chapter Box FEASIB in Chapter 18, UNEP , 
2021). As a result, most literature synthesising responses is based on 
documented or reported adaptations only, and is thus subject to sub-
stantial reporting bias.

16
2429Key Risks across Sectors and Regions  Chapter 16
Box 16.1: Case Study on Climate Change and the Outbreak of the Syrian Civil War
Separating between climatic and non-climatic factors in impact attribution is often challenging, as highlighted by the debate surrounding 
the causes of the Syrian civil war. During the years 2006–2010, the Fertile Crescent region in Eastern Mediterranean and Western Asia 
was hit by the worst drought on meteorological record, compounding a consistent drying of the region over the past half century (Trigo 
et al., 2010; Hoerling et al., 2012; Mathbout et al., 2018, SR15 BOX 3.2 (Hoegh-Guldberg et al., 2018a)). The magnitude of the multi-year 
drought is estimated to have become two to three times more likely as a result of increased CO 2 forcing (Kelley et al., 2015). The drought 
had a devastating impact on agricultural production in the northeast of Syria. In 2007–2008 alone, average crop yields dropped by 32%

in irrigated areas and as much as 79% in rain-fed areas (De Châtel, 2014), and herders in the northeast lost around 85% of their livestock 
(Werrell et al., 2015). Successive years with little or no income eventually forced people to leave their farms in great numbers and seek 
employment in less affected parts of the country, adding to existing pressures on housing, labour market and public goods provision 
(Gleick, 2014; Kelley et al., 2015). In March 2011, by which time the ‘Arab Spring’ uprisings had gained momentum and spread across much 
of the region, anti-regime protests broke out in Syria, first in the southern city of Dara’a and then in Damascus and throughout the country.
Yet, the attribution of the Syrian civil war to climate change has triggered a heated debate. A number of studies argue that the principal 
drivers of the drought-induced economic collapse were political rather than environmental in nature, shaped by adverse economic

reforms and unsustainable agricultural policies, promoting water-intensive irrigation schemes for cotton cultivation and implementing 
abrupt subsidy cuts at the peak of the drought, implying that many poor farmers no longer could afford fertilis ers or fuel to power 
irrigation pumps (Barnes, 2009; De Châtel, 2014; Eklund and Thompson, 2017; Selby et al., 2017). Thus, the 2006–2010 drought did not 
precipitate similar devastating socioeconomic impacts on agrarian communities across the borders in Turkey, Iraq or Jordan, although 
environmental conditions were comparable (Trigo et al., 2010; Eklund and Thompson, 2017; Feitelson and Tubi, 2017).
However, the relevant attribution question is not whether the same drought would produce the same consequences under different 
political and socioeconomic conditions, but rather, given the same political and socioeconomic context, how would the outcomes have

differed in the absence of climate change? Research still provides very limited insights into whether and how the escalation process 
would have evolved differently in a counterfactual no-climate-change world.
Thus, the role of the drought in augmenting pre-existing internal migration, and the role of the distress migration in accentuating 
demographic, economic and social pressures in receiving areas, remain contested. Estimates of the number of people who abandoned 
their farms in response to the drought range from less than 40,000–60,000 families (Selby et al., 2017) to more than 1.5 million displaced 
(Gleick, 2014). However, the numbers have to be seen in the context of prevailing population growth, significant rural–urban migration, 
and the preceding inflow of around 1.5 million refugees from neighbouring Iraq (De Châtel, 2014; Hoffmann, 2016). In addition, research

suggests that the migrants played a peripheral role in the initial social mobilisation in March 2011 (Fröhlich, 2016).
While it is undisputed that the drought caused direct economic losses, its overall additional impact on the Syrian economy, relative to 
other prevalent drivers of economic misery, including rampant unemployment, increasing inequalities, declining rural productivity, and 
loss of oil revenues (Aïta, 2009; Landis, 2012; De Châtel, 2014; Selby, 2019), has not been quantified.
In addition, the protesters’ demands centred around contentious political rather than economic issues, including release of political 
prisoners, ending of torture and indiscriminate violence by security forces, and abolishment of the near 50-year-old state of emergency 
(Selby et al., 2017; Ash and Obradovich, 2020). The mobilisation in Syria in the spring of 2011 also made explicit references to events

across the Middle East and North African region. Analyses of regional and social media and networks show a high level of interaction 
across the Arab world, and the initial Syrian uprising adopted a mobilisation model and rhetorical frames similar to those developed in 
Tunisia and Egypt (Leenders, 2013; 2014). However, the Syrian uprising stands out in how it was met with overwhelming violent force by 
the police and security forces, which changed the character of the resistance and opened up for militarisation of non-state actors that 
further escalated the conflict (Heydemann, 2013; Leenders, 2013; Bramsen, 2020).
In summary, the drought itself is shown to be attributable to GHG emissions. The agricultural losses and internal migration from rural to 
urban areas can be directly linked to the drought and in this way are partly attributable to GHG emissions, although there are no studies

comparing the observed losses and number of people displaced with a counterfactual situation of a weaker drought in a ‘no climate 
change’ situation. Current research does not provide enough evidence to attribute the civil war to climate change. In contrast, it is likely 
that social uprisings would have occurred even without the drought.

16
2430Chapter 16 Key Risks across Sectors and Regions
Macroeconomic output (a)&
between country inequalit y(b)bab a b S31a S30
Within country
inequality
Europe
Asia
SmallI slandsCentral &SouthAmerica
GlobalAustralasia
NorthAmericaI.
II.
VII.V.III.
VI.IV.AfricaPolar
Temperate
GlobalTropicalOceanS01–S 31 link to
TablesSM16.21 (grey)
SM16.22 (orange )&
SM16.23 (blue)Symbolc olorr eferstocategory :
Climat eattribution:
Observed impact sof
anthropogenic climate
forcin gonclimate-related
systems . (SM16.21)Arrows  show
inﬂuence s
&LandR egionImpact attribution :Observed im-
pacts oflong-ter mchanges inthe
climate-related systems onnatural,
human, ormanaged systems.
(Table SM16.22)
High
Medium
Noassessment
MixedLowConﬁdence Level
High
Medium
MixedLowConﬁdence LevelIdentiﬁcation ofweather sensitivity:
Observed inﬂuenc eofﬂuctuation s
inthec limate-related system son
natural, humano rmanaged systems.
(Table SM16.23)Displacement &
migrationS29Heat-relatedmortalit yS28

Vector-borne diseasesS27
Socialc onﬂictS26Other societal impacts
Damages tocoastal
systemsS14Tropical cyclon e
activityS07Coastal systems
S25
Burned areasPhenology shiftsS24S23
Structural change
Sea levels: mean (a)&
extrem e(b)a bb S08aTerrestrial
MalnutritionS20Food pricesS19Crop yieldsS18Food system
Waterborne diseasesS17Reduction sinwatera vailability
&induced damages and fatalitiesS16Flooding: hazard s(a),fa-
talities (b)&damages( c)ba
cc b S15aWaterdistribution
Netprimary
productionS22Ranger eduction
orshiftS21
Kelp forest distributionS12Coral bleachin g&
associate deffectsS11
SeagrassS13Range reduction
orshiftS10S09
Phenology shift Marine heatwavesS06Marine ecosystemsHeatwavesS04
Mean precipitation (a),heavy
precipitation (b)&d rought( c)cbac b S05a
Permafros tS03
Glacier massS02
Seai ceextentS01 Cryosphere Atmosphere
High
Medium
Increase
Minor/No impact
Mixed
Inconclusiv estudiesDecreaseDirection ofinducedc hange

Strong
Moderate
Mixed
Inconclusiv
studie sMinor/NoStrengt hofsensitivityI. VI.
VII.II.
III.IV.V.Impacts of climate change or weather fluctuations
Figure 16.2 |  Impact of climate change or weather fluctuations.

16
2431Key Risks across Sectors and Regions  Chapter 16
We document implemented adaptation-related responses that 
could directly reduce risk. Adaptation as a process is more broadly 
covered in Chapter 17 (Section  17.4.2), including risk management, 
decision making, planning, feasibility (see Cross-Chapter Box FEASIB 
in Chapter 18), legislation and learning. Here, we focus on a subset 
of adaptation activities: adaptation-related responses of species, 
ecosystems, and human societies that have been implemented, 
observed, and could directly reduce risk. We consider all adaptation-
related responses to assumed, perceived or expected climate risk, 
regardless of whether or not impacts or risks have been formally 
attributed to climate change.
We use the term ‘adaptation-related responses’, recognising 
that not all responses reduce risk. While ‘adaptation’ implies risk 
reduction, we use the broader term ‘responses’ to reflect that responses

may decrease risk, but in some cases may increase risk.
It is not currently possible to conduct a comprehensive global assessment 
of effectiveness, adequacy or the contribution of adaptation-related 
responses to changing risk owing to an absence of robust empirical 
literature. This constrains assessment of adaptation progress and gaps 
in the context of over-shoot scenarios. Given limited evidence to inform 
comprehensive global assessment of effectiveness and adequacy, we 
assess evidence that adaptation responses in human systems indicate 
transformational change. Chapter 17 considers adaptation planning 
and governance, including adaptation solutions, success, and feasibility 
assessment (Cross-Chapter Box FEASIB in Chapter 18), discussed further 
in Box 16.2 (also see Cross-Chapter Box PROGRESS in Chapter 17).
In natural ecosystems or species, detectable changes can be 
considered as ‘impact’ or ‘response’. The distinction between

‘observed impacts’ (Section  16.2) and ‘observed responses’ (Section  16.3) 
is not always clear. For example, autonomous distributional shifts in 
wild species induced by increasing temperatures (an observed impact) 
may reduce risk to the species (an autonomous adaptation response), 
but this process can be enhanced or supported by human intervention 
such as intentional changes in land use. Observed autonomous changes 
in natural ecosystems or species unsupported by human intervention 
are treated as impacts (see Section 16.2).
Adaptation-related responses are frequently motivated by a 
combination of climatic and non-climatic drivers, and interact with 
other transitions to affect risk. For societal responses, it is difficult to 
say whether they are triggered by observed or anticipated changes in 
climate, by non-climatic drivers, or by a combination of all three. In the 
case of observed impacts, assessment typically focuses on detection

and attribution vis à vis a counterfactual of no climate change. 
While there has been some effort to attribute reduced climate risk to 
adaptation-related responses (Toloo et al., 2013a; Toloo et al., 2013b; 
Hess et al., 2018; Weinberger et al., 2018), in many cases this has not 
been feasible given difficulties in defining adaptation and empirically 
disentangling the contribution of intersecting social transitions and 
changing risks. Literature on adaptation-related response frequently 
draws on theories of change to assess the likely contribution of 
adaptations to changes in risk, including maladaptation and co-
benefits.16.3.1 Adaptation-Related Responses by Natural Systems
There is growing evidence of shifts in species distributions and 
ecosystem structure and functioning in response to climate change 
(Chapter 2). While many species are increasingly responding to climate 
change, there is limited evidence that these responses will be fully

adaptive, and for many species the rate of response appears insufficient 
to keep pace with the rate of climate change under mid- and high-range 
emissions scenarios (medium confidence). There is relatively limited, 
but growing, empirical data to document adaptation of natural systems 
in the absence of human interventions. For example, Scheffers et al. 
(2016) reviewed climate responses across diverse species, reporting 
widespread and extensive observed changes in organisms (genetics, 
physiology, morphology), populations (phenology, abundance and 
dynamics), species (distributions) and ecosystems. A systematic review 
by Franks et al. (2014) synthesised evidence from 38 empirical studies 
of changes in terrestrial plant populations, finding evidence to support 
a mix of plastic and evolutionary responses. Boutin and Lane (2014) 
similarly reviewed adaptive responses in mammals, finding most 
species’ responses to be due to phenotypic plasticity. Charmantier

and Gienapp (2014) reviewed responses to climate change among 
birds, finding emerging evidence that birds from a range of taxa show 
advancement in their timing of migration and breeding in response 
to warming. Aragão et  al. (2018) reviewed adaptation responses in 
marine systems, including 12  studies of live marine mammals. They 
observed widespread evidence of shifting distributions and timing of 
biological events (Chapter 2, Chapter 3, and Cross-Chapter Paper 1).
Some ecosystems and species’ responses may be insufficient to 
keep pace with rates of climate change. It is difficult to distinguish 
whether adaptations are due to genotypic change or to phenotypic 
plasticity. Long-term natural adaptations will require the former, but 
the latter may provide short-term coping mechanisms to ‘buy time’ 
to respond to climate changes or lay foundations for evolutionary 
adaptation. There is mixed evidence regarding evolutionary versus

plastic responses, with relatively limited evidence of longer-term 
evolutionary responses of species that can be associated with climate 
change. Similarly, it is difficult to assess whether responses are indeed 
potentially adaptive (e.g., coping, shifting, migrating) or simply 
reflective of impacts (e.g., stress, damage). Among mammal responses 
reviewed by Boutin and Lane (2014), for example, only 4 of 12 studies 
found some evidence that responses were adaptive. Even where 
adaptive responses are occurring, they may not be sufficient to keep 
pace with the rate of climate change. found, for example, that, among 
the 12 studies in their review that directly assessed the sufficiency of 
responses to keep pace with the rate of climate change, 8 concluded 
that responses would be insufficient to avert extinction.
16.3.2 Adaptation-Related Responses by Human Systems
The literature that seeks to assess adaptation progress is growing

at the global (Berrang-Ford et al., 2021a), regional (Bowen and Ebi, 
2015; England et al., 2018; Robinson, 2018a; Wirehn, 2018; Olazabal 
et al., 2019; Thomas et al., 2019a; Biesbroek et al., 2020; Canosa et al., 
2020; Robinson, 2020b), national (Hegger et al., 2017; Lesnikowski 
et al., 2019a; Lesnikowski et al., 2019b) and municipal (Araos et al.,

16
2432Chapter 16 Key Risks across Sectors and Regions
2016; Reckien et al., 2018; Reckien et al., 2019; Lesnikowski et al., 
2020; Singh et  al., 2021) levels, using National Communications 
(Gagnon-Lebrun and Agrawala, 2007; Lesnikowski et  al., 2015; 
Muchuru and Nhamo, 2017), local climate change action plans 
(Regmi et al., 2016b; Regmi et al., 2016a; Reckien et al., 2018; Reckien 
et al., 2019), adaptation project proposals, and reported adaptations 
in the peer-reviewed literature. There remains persistent publication 
bias in the evidence base on adaptation given the difficulty of 
integrating diverse knowledge sources (see Section 16.3.3). To better 
assess how adaptation is occurring in human systems, we draw on 
this literature base and characterise evidence of adaptation across regions and sectors in terms of five key questions (Table 16.4, Ford 
et  al., 2013; Biagini et  al., 2014; Ford et  al., 2015a; Bednar and

Henstra, 2018; Reckien et  al., 2018; Tompkins et  al., 2018): What 
types of hazards are motivating adaptation-related responses? Who 
is responding? What types of responses are being documented? What 
evidence is available on adaptation effectiveness, adequacy and 
risk reduction? To characterise evidence that adaptation responses 
indicate transformation, we use a typology based on four dimensions 
of climate adaptation: scope, depth, speed, and consideration of limits 
to adaptation (Section 16.4, Termeer et al., 2017; Berrang-Ford et al., 
2021a).
Salience of different types of hazards in the scientific literature on adaptation-related responses 
Food
Oceans/CoastalHealthCities
Poverty
Terrestrial/Freshwater
Water and Sanitation
Sea level riseHeat
Arctic sea ic e
OceansFloodPrecipitationDroughtEurope
Food
Poverty
OceansFloodPrecipitationDroughtSmall Island States
Food

Poverty
OceansFloodPrecipitationDroughtCenral and South
America
Food
Poverty
OceansFloodPrecipitationDroughtAfricaFood
Poverty
OceansFloodPrecipitationDroughtNorth America
Food
Poverty
OceansFloodPrecipitationDroughtAustralasiaFood
Poverty
OceansFloodPrecipitationDroughtAsia
0 100 300Number of publications
200 50 20

Figure 16.3 |  Salience of different types of hazards in the scientific literature on adaptation-related responses (i.e., responses that people undertake to 
reduce risk from climate change and associated hazards). Updated from a systematic review of 1682 scientific publications (2013–2019) reporting on adaptation-related 
responses in human systems (Berrang-Ford et al., 2021a). Numbers in table reflect the number of publications reporting. Darker colours denote more extensive reporting on a hazard 
as a motivating factor for the response. Publications are counted in all relevant regions or sectors.

16
2433Key Risks across Sectors and Regions  Chapter 16
16.3.2.1 What Hazards Are Motivating Adaptation-Related 
Responses?
Drought and precipitation variability are the most prevalent hazards 
in the adaptation literature, particularly in the context of food and 
livelihood security. Adaptation frequently occurs in response to specific 
rapid or slow-onset physical events that can have adverse impacts on 
people. In some cases, people adapt in anticipation of climate change in 
general or to take advantage of new opportunities created by hazards 
(e.g., increased navigability due to melting sea ice). There is evidence 
that prior experience with hazards increases adaptation response 
(Barreca et al., 2015). Following drought and precipitation variability, 
the next specific hazards that are most frequently documented in the 
global adaptation literature are heat and flooding. Heat, while less 
salient, appears to be a driver of adaptation across all regions and

sectors (Stone Jr et al., 2014; Hintz et al., 2018; Nunfam et al., 2018). 
Drought, extreme precipitation, and inland flooding are commonly 
reported in the context of water and sanitation (Bauer and Steurer, 
2015; Lindsay, 2018; Kirchhoff and Watson, 2019; Hunter et al., 2020; 
Simpson et al., 2020). Flooding is frequently reported as a key hazard 
for adaptation in cities, followed by drought, precipitation variability, 
heat, and SLR (Broto and Bulkeley, 2013; Araos et al., 2016; Georgeson 
et al., 2016; Mees, 2017; Reckien et al., 2018; Hunter et al., 2020).
16.3.2.2 Who Is Responding?
Individuals and households play a central role in adaptation 
globally. The most frequently reported actors engaged in adaptation-
related responses in the scientific literature are individuals and 
households, particularly in the Global South (Figure 16.4). Regionally, 
household- and individual-level adaptation is documented most

extensively in Africa and Asia, and to a lesser but still substantial 
extent in North America (Figure 16.4).National and local governments are also frequently engaged 
in reported adaptation across most regions. In Africa and Asia, 
reported adaptations have been primarily associated with individuals, 
households, national governments, non-governmental organisations 
(NGOs), and international institutions, with more limited reporting of 
involvement from sub-national governments or the private sector (Ford 
et al., 2015a; Ford and King, 2015; Hunter et al., 2020). Engagement by 
sub-national governments in adaptation is more frequently documented 
in Europe and North America (Craft and Howlett, 2013; Craft et al., 2013; 
Bauer and Steurer, 2014; Lesnikowski et al., 2015; Shi et al., 2015; Austin 
et al., 2016). Reporting of private sector engagement is generally low. 
Civil society participation in adaptations is reported across all regions.

Consistent with this, local governments are also widely reported 
in documented adaptation responses, particularly where municipal 
jurisdiction is high, including cities, infrastructure, water and sanitation.
16.3.2.3 What Types of Responses Are Documented?
Behavioural change is the most common form of adaptation. 
The scientific literature presents extensive evidence of behavioural 
adaptation—change in the strategies, practices and actions that 
people, particularly individuals and households, undertake to reduce 
risk (Figure  16.5). This includes, for example, household measures to 
protect homes from flooding, protect crops from drought, relocation 
out of hazard zones, and shifting livelihood strategies (Porter et  al., 
2014). This is followed by adaptation via technological innovation and 
infrastructural development, nature-based adaptation (enhancing, 
protecting or promoting ecosystem services) and institutional adaptation

(enhancing multi-level governance or institutional capabilities). 
Behavioural adaptation is most frequently documented in Asia, Africa 
and Small Island States, and in the agriculture, health and development 
sectors. In the agricultural sector, households are adopting or changing 
to crops and livestock that are more adapted to drought, heat, moisture, 
Who is responding, by geographic region and sector?
Food Oceans Health Cities Poverty Terrestrial Water Africa AsiaAustra-
lasiaCentral
and South
America EuropeSmall
Island
StatesNorth
America
Households or individuals
Civil society
Local government
National or
sub-national government
International/Multi-national
governance institutions
Number of publications
0 100 300
 200 50 20
 1,000
Figure 16.4 |  Who is responding, by geographic region and sector? Cell contents indicate the number of publications reporting engagement of each actor

in adaptation-related responses. Darker colours denote a high number of publications. Based on a systematic review of 1682 scientific publications (2013–2019) reporting on 
adaptation-related responses in human systems (Berrang-Ford et al., 2021a). SIS, Small Island States; Terr, terrestrial and freshwater ecosystems.

16
2434Chapter 16 Key Risks across Sectors and Regions
pests and salinity (Arku, 2013; Kattumuri et  al., 2017; Wheeler and 
Marning, 2019). Studies in Africa and Asia have documented shifts in 
farming and animal husbandry practice (Arku, 2013; Garcia de Jalon 
et al., 2016; Gautier et al., 2016; Chengappa et al., 2017; Epule et al., 
2017; Kattumuri et al., 2017; Abu and Reed, 2018; Asadu et al., 2018; 
Haeffner et al., 2018; Shaffril et al., 2018; Wiederkehr et al., 2018; Zinia 
and McShane, 2018; Currenti et al., 2019; Fischer, 2019a; Fischer, 2019b; 
Schofield and Gubbels, 2019; Sereenonchai and Arunrat, 2019; Wheeler 
and Marning, 2019; Mayanja et  al., 2020). In Small Island Nations, 
studies have documented household flood protections measures 
such as raising elevation of homes and yards, creating flood barriers, 
improving drainage, moving belongings and, in some cases, relocating 
(Middelbeek et al., 2014; Currenti et al., 2019; Klock and Nunn, 2019).

The mix of adaptation response types differs across regions 
and sectors. Technological and infrastructural responses are widely 
reported in Europe, and globally in the context of cities and water 
and sanitation (Mees, 2017; Hintz et  al., 2018). Responses to flood 
risk in Europe include the use of flood- and climate-resistant building 
materials, large-scale flood management, and water storage and 
irrigation systems (van Hooff et al., 2015; Mees, 2017). Technological 
and infrastructural responses are also documented to some extent in 
agriculture, including, for example, breeding more climate-resilient 
crops, precision farming and other high-tech solutions such as genetic 
modification (Makhado et  al., 2014; Fisher et  al., 2015; Costantini 
et al., 2020; Fraga et al., 2021; Grusson et al., 2021; Naulleau et al., 
2021). While less common, institutional responses are more prominent 
in North America and Australasia as compared with other regions,

and include zoning regulations, new building codes, new insurance 
schemes, and coordination mechanisms (Craft and Howlett, 2013; 
Craft et al., 2013; Parry, 2014; Ford et al., 2015b; Beiler et al., 2016; 
Lesnikowski et  al., 2016; Labbe et  al., 2017; Sterle and Singletary, 
2017; Hu et al., 2018; Conevska et al., 2019). Institutional adaptations are more frequently reported in cites than other sectors. Institutional 
adaptation may be particularly subject to reporting bias, however, with 
many institutional responses likely to be reported in the grey literature 
(see Chapter 17). Nature-based solutions are less frequently reported, 
except in Africa, where they are relatively well documented, and in 
the content of terrestrial systems where reports included species 
regeneration projects, wind breaks, erosion control, reforestation and 
riparian zone management (Munji et  al., 2014; Partey et  al., 2017; 
Muthee et al., 2018).

Some but not all adaptation-related responses are engaging 
vulnerable populations in planning or implementation (high 
confidence) (Araos et  al., 2021). Consideration of vulnerable 
populations is most frequently focused on low-income populations 
and women through the inclusion of informal or formal institutions or 
representatives in adaptation planning, or through targeted adaptations 
to reduce risk in these populations (high confidence). Consideration of 
vulnerable groups in adaptation responses is more frequently reported 
in the Global South (medium confidence). Engagement in adaptation 
planning of vulnerable elderly, migrants, and ethnic minorities remains 
low across all global regions (medium confidence). There is negligible 
literature on consideration of disabled peoples in planning and 
implementation of adaptation-related responses (medium confidence).
16.3.2.4 Adaptation Effectiveness, Adequacy and Risk Reduction

Despite a lack of systematic methods for assessing general adaptation 
effectiveness, there is some evidence of risk reduction for particular 
places and hazards, especially flood and heat vulnerability. There is some 
evidence of a reduction in global vulnerability, particularly for flood risk 
(Jongman et al., 2015; Tanoue et al., 2016; Miao, 2019) and extreme 
heat (Bobb et al., 2014; Boeckmann and Rohn, 2014; Gasparrini et al., 
2015; Arbuthnott et al., 2016; Chung et al., 2017; Sheridan and Allen, 
Type of adaptation responses by global region
Technological/Infrastructural
Behavioural/Cultural
Africa
Asia
Central
and South
AmericaEuropeSmall
Island
States
North
America100
Percentages reflect the number of articles mentioning each
type of adaptation over the total number of articles for that region

Figure 16.5 |  Type of adaptation responses by global region. Percentages reflect the number of articles mentioning each type of adaptation over the total number of 
articles for that region. Radar values do not total 100% per region since publications frequently report multiple types of adaptation; for example, construction of drainage systems 
(infrastructural), changing food storage practices by households (behavioural), and planting of tree cover in flood-prone areas (nature-based) in response to flood risk to agricultural 
crops. Data updated and adapted from Berrang-Ford et al. (2021a), based on 1682 scientific publications reporting on adaptation-related responses in human systems.

16
2435Key Risks across Sectors and Regions  Chapter 16
2018; Folkerts et al., 2020). Investment in flood protection, including 
building design and monitoring and forecasting, have reduced flood-
related mortality over time and are cost-effective (Bouwer and Jonkman 
2018; Ward et al. 2017). Declining heat sensitivity, primarily reported 
in developed nations, has also been observed, and has been linked to 
air conditioning, reduced social vulnerability and improved population 
health (Boeckmann and Rohn, 2014; Chung et al., 2017; Kinney, 2018; 
Sheridan and Allen, 2018). Formetta and Feyen (2019) demonstrate 
declining global all-cause mortality and economic loss due to extreme 
weather events over the past four decades, with the greatest reductions 
in low-income countries, and with reductions correlated with wealth. 
Studies that correlate changes in mortality or economic losses with 
wealth indicators, to infer changes in vulnerability or exposure, lack

direct empirical measures of vulnerability or exposure and are limited 
in their ability to assess how indirect effects of extreme events (e.g., 
morbidity, relocation, social disruption) may have changed or how 
changes may redistribute risk across populations.
There remain persistent difficulties in defining and measuring 
adaptation effectiveness and adequacy for many climate risks. No 
studies have systematically assessed the adequacy and effectiveness 
of adaptation at a global scale, across nations or sectors, or for 
different levels of warming. There has, however, been progress in 
operationalising assessment of adaptation feasibility (Cross-Chapter 
Box  FEASIB in Chapter 18). Effectiveness of adaptation-related 
responses reflects whether a particular response actually reduces 
climate risk, typically through reductions in vulnerability and exposure 
(Figure  1.7 in Section  1.4). Some adaptation-related responses may

increase risk or create new risks (maladaptation) or have no or 
negligible impact on risk. Adequacy of adaptation-related responses 
refers to the extent to which responses are collectively sufficient 
to reduce the risks or impacts of climate change (Figure  1.7 in 
Section 1.4). A set of adaptation-related responses may, for example, result in reduced climate risk (effectiveness), but these reductions may 
be insufficient to offset the level of risk and avoid loss and damages. 
Feasibility reflects the degree to which climate responses are possible 
or desirable, and integrates consideration of potential effectiveness. A 
feasibility assessment drawing on these methods is presented in Cross-
Chapter Box FEASIB in Chapter 18.
Global adaptation is predominantly slow, siloed and incremental with 
little evidence of transformative adaptation (high confidence). In the 
absence of a general method to assess the adequacy of adaptation

actions, we assessed evidence for transformational adaptation 
documented in peer-reviewed publications identified by a global 
stock-taking initiative (Berrang-Ford et al., 2021b) and in other AR6 
chapters (2–15) (see Supplemental Material, SM16.1 for details). 
‘Transformational adaptation’ refers to the degree to which adaptations 
have been implemented widely (scope), reflect major shifts (depth), 
occur rapidly (speed) and challenge limits to adaptation (limits, Pelling 
et al., 2015; Few et al., 2017; Termeer et al., 2017, Table 16.1).
Based on the literature, the overall transformative nature of adaptation 
across most global regions and sectors is low (high confidence)  
(Figure  16.6). Documented adaptations tend to involve minor 
modifications to usual practices taken to address extreme weather 
conditions (high confidence). For example, changing crop variety or timing 
of crop planting to address floods or droughts, new types of irrigation,

pursuing supplementary livelihoods, and home elevations are widely 
reported but typically do not reflect radical or novel shifts in practice or 
values and are therefore considered low depth (high confidence) (see 
SM16.1 for more examples). Adaptations documented in the literature 
are also frequently focused on a single sector or small geographic 
area (high confidence). Actions taken by individuals or households are 
generally small in scope (Hintz et al., 2018; Hlahla and Hill, 2018) unless 
they are widely adopted (e.g., by farmers across a region) or address 
Table 16.1 |  Evidence of transformational adaptation assessed across four components (depth, scope, speed and limits). Transformational adaptation does not imply adequacy 
or effectiveness of adaptation (low transformation may be sufficient for some climate risks, and high transformation may be insufficient to offset others). Nevertheless, these

components provide a systematic framework for tracking adaptation progress and assessing the state of adaptation-related responses. The ‘high’ categories across each component 
reflect more transformative scenarios. Methods are described in SM16.1.
Transformative potential of adaptation
Dimensions Low Medium High
OverallAdaptation is largely sporadic and consists 
of small adjustments to Business-As-Usual. 
Coordination and mainstreaming are 
limited and fragmented.Adaptation is expanding and 
increasingly coordinated, including 
wider implementation and multi-level 
coordination.Adaptation is widespread and implemented 
at or very near its full potential across 
multiple dimensions.
DepthAdaptations are largely expansions of 
existing practices, with minimal change in 
underlying values, assumptions or norms.Adaptations reflect a shift away from 
existing practices, norms or structures to 
some extent.Adaptations reflect entirely new practices 
involving deep structural reform,

complete change in mindset, major shifts 
in perceptions or values, and changing 
institutional or behavioural norms.
ScopeAdaptations are largely localised and 
fragmented, with limited evidence of 
coordination or mainstreaming across 
sectors, jurisdictions or levels of governance.Adaptations affect wider geographic 
areas, multiple areas and sectors, or are 
mainstreamed and coordinated across 
multiple dimensions.Adaptations are widespread and 
substantial, including most possible sectors, 
levels of governance, and actors.
Speed Adaptations are implemented slowly.Adaptations are implemented moderately 
quickly.Change is considered rapid for a given 
context.
LimitsAdaptations may approach but do not 
exceed or substantively challenge soft 
limits.Adaptations may overcome some soft 
limits but do not challenge or approach 
hard limits.Adaptations exceed many soft limits and 
approach or challenge hard limits.

16
2436Chapter 16 Key Risks across Sectors and Regions
numerous aspects of life. National policies are more likely to be broad 
in scope (Puthucherril et al., 2014), although they frequently focus on 
a single sector and are therefore still limited. The speed of adaptation 
is rarely noted explicitly, but the average speed documented in the 
literature is slow (medium confidence) (Cross-Chapter Box  FEASIB in 
Chapter 18). Adaptation efforts frequently encounter either soft or hard 
limits (see Section 16.4), but there is limited evidence to suggest these 
limits are being challenged or overcome (medium confidence).
Few documented responses are simultaneously widespread, rapid 
and novel (high confidence). Some examples exist, such as village 
relocations or creation of new multi-stakeholder resource governance 
systems (Schwan and Yu, 2018; McMichael and Katonivualiku, 2020), but these are rare. In general, adaptations that are broad in scope

tend to be slow (medium confidence), suggesting that achieving high 
transformation in all four categories (depth, scope, speed and limits) 
may be particularly challenging or even involve trade-offs.
16.3.2.5 Observed Maladaptation and Co-benefits
There is increasing reporting of maladaptation globally 
(Table  16.2, Section  17.5.1) (high confidence). Maladaptation 
has been particularly reported in the context of agricultural, forestry 
and fisheries practices, migration in the Global South, and some 
infrastructure-based interventions. Urban heat adaptations have 
been linked to maladaptation that increase health risks and/or energy 
Food
Poverty
Water and SanitationNorth America
Limits
OverallSpeedScopeDepth
Food
Poverty
Limits
Food
Poverty
Limits

OverallSpeedScopeDepthEvidence of transformative adaptation by sector and region
Food
Poverty
Limits
Food
Poverty
Limits
Food
Poverty
Limits
Food
Poverty
Limits
OverallSpeedScopeDepthEurope
Small Island States
Cenral and South
AmericaAfricaAustralasiaAsia
Evidence of
transformational
Medium
LowHigh
Insuficient information
or very low confidenceConfidence
Medium
LowHigh
**
****
Figure 16.6 |  Evidence of transformative adaptation by sector and region. Evidence of transformational adaptation does not imply effectiveness, equity or adequacy.

Evidence of transformative adaptation is assessed based on the scope, speed, depth and ability to challenge limits of responses reported in the scientific literature (see Supplementary 
Material for methods). Studies relevant to multiple regions or sectors are included in assessment for each relevant sector/region.

16
2437Key Risks across Sectors and Regions  Chapter 16
Table 16.2 |  Observed examples of maladaptation and co-benefits from adaptation-related responses in human systems.
Implemented adaptations Observed maladaptation References
Agricultural and forestry practices
Intensified cultivation of marginal lands: clearing of 
virgin forests for farmland; frequent weeding; poorly 
managed irrigation schemes; dependence on rainfed 
agricultureIncreased competition for resources such as water and nutrients; 
reduced soil fertility; invasive species; degraded environment; 
increased greenhouse gas emissions; reduced crops diversity and 
reduced harvest, thus increasing food insecurity in rural areas; 
accelerated illegal logging practices; increased vulnerability of 
herders, translated into poor health and working conditions 
(Mongolia)Bele et al. (2014); D’haen et al. (2014); Chapman et al. 
(2016); Ifeanyi-obi et al. (2017); Suvdantsetseg et al.

(2017); Villamayor-Tomas and Garcia-Lopez (2017); 
Afriyie et al. (2018); Ticehurst and Curtis (2018); Tran 
et al. (2018); Neset et al. (2019); Work et al. (2019); 
Yamba et al. (2019); Singh and Basu (2020)
Agroforestry systemsHigher water demand where trees were combined with crops and 
livestock; native trees replaced with non-indigenous trees; reduced 
resilience of certain plants (e.g., cocoa); degraded soil and water 
quality and accelerated environmental degradation in Africa and 
Asia (Pakistan, Nepal, India, China, Philippines)Nordhagen and Pascual (2013); D’haen et al. (2014); 
Hoang et al. (2014); Ruiz-Mallen et al. (2015); Kibet 
et al. (2016); Chengappa et al. (2017); Haji and Legesse 
(2017); Abdulai et al. (2018); Antwi-Agyei et al. (2018); 
Mersha and van Laerhoven (2018); Ullah et al. (2018); 
Krishnamurthy et al. (2019)
Agricultural transitions: commercialisation of common 
property; market integration and sedentarisation of

pastoralists; adoption and expansion of commercial 
cropsSoil degradation and high dependency on external inputs in South 
and Central America (El Salvador, Guatemala, Honduras, Nicaragua 
and Peru); dependency on foreign corporation seed systems; land 
enclosures; adaptation that forced local farmers in Costa Rica to 
switch crops to commercially viable products (e.g., from rice to 
sugar cane) impoverished the land by removing nutrients and 
affecting food security for smallholder farmersNordhagen and Pascual (2013); D’haen et al. (2014); 
Warner et al. (2015); Kibet et al. (2016); (Warner and 
Kuzdas, 2016); Haji and Legesse (2017); Antwi-Agyei 
et al. (2018); Mersha and van Laerhoven (2018); 
Krishnamurthy et al. (2019); Neset et al. (2019)
Proper, improper and increased use of agrochemicals, 
pesticides and fertilizersFertilizer and agrochemicals negatively affected soil quality 
and accelerated environmental degradation in several parts of

Africa (Ghana, Nigeria) and Asia (Pakistan, Nepal, India, China, 
Philippines). In Europe (Sweden and Finland), there are concerns 
about the risk of pests and weeds developing immunity to 
pesticides, and drainage systems and rain transferred chemicals 
to other fields, thereby affecting arable land. In South and Central 
America (El Salvador, Guatemala, Honduras, Nicaragua and Peru), 
agrochemicals led to soil degradation, and high dependency 
on external input was reported. Loss of soil nutrients, increased 
GHG emissions (Sweden, Finland); high nitrate and phosphate 
concentration (Great Britain)Postigo (2014); Rodriguez-Solorzano (2014); Fezzi et al. 
(2015); Sujakhu et al. (2016); Begum and Mahanta 
(2017); de Sousa et al. (2018); Tang et al. (2018); Yamba 
et al. (2019)consumption. Heat poses significant risks to the evolutionary tolerance 
levels of humans, animals and crops (Asseng et  al., 2021), and 
current adaptation interventions for reducing urban heat like cool or

evaporation roofs and street trees may be insufficient to reduce heat-
related vulnerabilities in some urban areas at higher levels of warming 
(Krayenhoff et al., 2018) (see also Section 16.4 on adaptation limits). 
There is evidence that autonomous adaptation by individuals and 
households can shift risk to others, with net increases in vulnerability. 
Intensification of pasture use as a coping response to climate-induced 
drought has been observed to increase risks to livestock reproduction 
and human life expectancy due to overgrazing, suggesting responses 
to pastoral vulnerability can cross tolerance limits for animals, humans 
and food available for foraging (Suvdantsetseg et al., 2017).
Evidence on realised co-benefits of implemented adaptation responses 
with other priorities in the SDGs is emerging among the areas of 
poverty reduction, food security, health and well-being, terrestrial and 
freshwater ecosystem services, sustainable cities and communities,

energy security, work and economic growth, and mitigation (Table 16.2) 
(high confidence). Evidence on co-benefits of adaptation for mitigation 
is particularly strong, and is observed in various agricultural, forestry 
and land use management practices like agroforestry, climate-smart 
agriculture and afforestation (Kremen and Miles, 2012; Christen and 
Dalgaard, 2013; Mbow et  al., 2014; Locatelli et  al., 2015; Suckall et  al., 2015; Wichelns, 2016; Kongsager, 2018; Debray et  al., 2019; 
Loboguerrero et al., 2019; Morecroft et al., 2019; Chausson et al., 2020) 
as well as in the urban built environment (Perrotti and Stremke, 2020; 
Sharifi, 2020). Evidence on co-benefits of implemented responses for 
other SDG priority areas is less developed, however, in the areas of 
education, gender inequality and reduced inequalities, clean water 
and sanitation, industry, innovation and infrastructure, consumption 
and production, marine and coastal ecosystem protection, and peace,

justice, and strong institutions. This indicates a gap between some 
assumed likely co-benefits of adaptation and empirical evidence on 
the realisation of these co-benefits within the context of implemented 
adaptation responses (Berga, 2016; Froehlich et  al., 2018; Gattuso 
et al., 2018; Morris et al., 2018; Chausson et al., 2020; Karlsson et al., 
2020; Krauss and Osland, 2020).
16.3.3 Knowledge Gaps in Observed Responses
Many adaptation responses are not documented, and reporting 
bias is a key challenge for assessment of observed responses. Evidence 
of absence (i.e., where no adaptations are occurring) is different from 
absence of evidence (where responses are occurring but are not 
documented), with implications for understanding trends in global 

16
2438Chapter 16 Key Risks across Sectors and Regions
Tree plantingThe lack of shaded trees increased vulnerability to landslides in 
areas where Robusta coffee was grown (Mexico); new tree species 
to cope with climate change increased sensitivity and displaced 
non-indigenous trees (India; Tanzania and Kenya); cocoa planted 
under shade trees had higher mortality rate and more stress 
(Ghana); eucalyptus trees planted to reduce soil erosion had high 
water demand (Pakistan); in certain urban areas, trees planted to 
provide shade damaged buildings during heavy stormsBenito-Garzon et al. (2013); Hoang et al. (2014); Ruiz 
Meza (2015); Chengappa et al. (2017); Abdulai et al. 
(2018); Ullah et al. (2018)
Fisheries and water management
Increased fishing activityFishery depletion and exacerbated negative trends in the ecosystem 
that threatened fishermen’s subsistenceGoulden et al. (2013); Mazur et al. (2013);

Rodriguez-Solorzano (2014); Pershing et al. (2016); 
Kanda et al. (2017); Kihila (2018); Pinsky et al. (2018)
Shrimp farmingA driver of deforestation of mangroves in Bangladesh; imposes 
external cost on paddy farmers; salinity levels are relatively higher 
in paddy plots closer to shrimp ponds; coral mining increased 
vulnerability to flooding (in small islands in the Philippines)Johnson et al. (2016); Jamero et al. (2017); Paprocki and 
Huq (2018); Sovacool (2018); Morshed et al. (2020)
Water irrigation infrastructure for agriculture; water 
desalination in response to water shortagesIncreased land loss; redistributed risk among agrarian stakeholders; 
affected the rural poor (Cambodia; Costa Rica); uneven distribution 
of cost and benefits (USA–Mexico border); desalination plants to led 
disproportionately high cost for low-income water usersBarnett and O’Neill (2013); Olmstead (2014); Warner 
and Kuzdas (2016); Work et al. (2019)

Storage of large quantities of water in the homeWater rendered unsafe for drinking due contamination by faecal 
coliforms in Zimbabwe; drought-induced changes in water 
harvesting and storage increased breeding sites for mosquitoes 
(Australia); water storage facilities and tanks provided ideal 
breeding conditions for mosquitoes and flies, bringing both vectors 
and diseases closer to people (Ethiopia)Boelee et al. (2013); Trewin et al. (2013); Kanda et al. 
(2017)
Increased number of farm dams for water storage; 
groundwater extraction and interbasin water transfersReduced river and ground water flow downstream; water grabs 
from shared surface or groundwater resources with poorly defined 
property rights shifted vulnerability to other groups and ecosystems 
(Cambodia; California): water extractions increased risks for the 
environment and food security, while transfers reduced hydropower 
generation and resulted in higher costs paid by electricity consumers

and health impacts from air pollution caused by more electricity 
generation from natural gas (California); increase the concentration 
in the hands of the more powerful large farmers (Argentina)Mazur et al. (2013); Christian-Smith et al. (2015); 
(Hurlbert and Mussetta, 2016); Work et al.)
Seawalls and infrastructural development along 
coastlinesCoastal erosion, beach losses, changes in water current, and 
destruction of natural ecosystems in Asia, Australasia, Europe 
and North America; increased or shifted erosion from protected 
to unprotected areas in Fiji, Marshall Islands, Nuie, Kiribati and 
Norway; failed or sped up flood waters and worsened conditions 
for riparian habitat and downstream residents; harmed nearby 
reefs and impeded autonomous adaptation practise that could be 
effective (Bangladesh)Macintosh (2013); Maldonado et al. (2014); Porio 
(2014); Betzold (2015); Renaud et al. (2015); Gundersen

et al. (2016); Sayers et al. (2018); Craig (2019); Javeline 
and Kijewski-Correa (2019); Loughran and Elliott (2019); 
Rahman and Hickey (2019); Piggott-McKellar et al. 
(2020); Simon et al. (2020) Dahl et al. (2017)
Smart or green luxury real estate development designed 
to reduce impacts from storm surges and erosion along 
coastal area; artificial islandsRedistributed risk and vulnerability; displaced and diminished 
adaptive capacity of vulnerable groups, created new population of 
landless peasants; negatively affected neighbouring coastal areas 
and local ecology (Lagos, Miami, Hanoi, Jakarta, Manila; Maldives)Caprotti et al. (2015); Magnan et al. (2016); Atteridge 
and Remling (2018); Ajibade (2019); Salim et al. (2019); 
Thomas and Warner (2019)
Subsidised insurance premiums for properties located in 
flood-prone areas, levees, dykesRebuilding in risky areasShearer et al. (2014); O’Hare et al. (2016); Craig (2019); 
Loughran and Elliott (2019)

Autonomous flood strategies such as sandbags, digging 
channels and sand walls around homesSandbags used to reduce coastal erosion released plastics into the 
sea and led to loss of recreational value of beaches; sand walls 
shifted the flood impacts across space and time and were more 
detrimental to poor informal urban settlers (Dakar); caused erosion 
and degraded coastal lands (South Africa)Schaer (2015); Wamsler and Brink (2015); (Chapman 
et al., 2016); Magnan et al. (2016); Mycoo (2018); 
Top-down technocratic adaptation with no 
consideration for ecosystem biodiversity, local adaptive 
capacity and gender issuesIgnored the complexities of the landscapes and socio-ecological 
systems; constrained autonomous adaptation due to time and 
labour demands of public work; increased gender vulnerability; 
hamper women’s water rights (South Africa); altered local gender 
norms (Ethiopia); led to a mismatch that undermine local-level

processes that are vital to local adaptive capacity (Rwanda)Cartwright et al. (2013); Goulden et al. (2013); 
Nordhagen and Pascual (2013); Carr and Thompson 
(2014); Nyamadzawo et al. (2015); Ruiz-Mallen et al. 
(2015); Djoudi et al. (2016); Gautier et al. (2016); 
Gundersen et al. (2016); Barnett and McMichael (2018); 
Kihila (2018); Mersha and van Laerhoven (2018); Clay 
and King (2019); Currenti et al. (2019); Yang et al. (2019)

16
2439Key Risks across Sectors and Regions  Chapter 16
Migration and relocation
Out-migration or rural-to-urban migration in response to 
food insecurity and agricultural livelihood depreciationMigration mostly undertaken by poorer households weakened 
local subsistence production capacity; disrupted family structures; 
reduced labour available for agricultural work; increased burden 
of responsibilities on women; fostered loss of solidarity within 
communities; increased divorce rates; exacerbated conflicts among 
different groups; increased pressure on urban housing and social 
services; expanded slum settlements around riparian and coastal 
areas including flood plains and swamplands (Ethiopia, Namibia, 
Benin, Botswana, Nigeria, Ghana, Kenya, Niger, Mail, Tanzania, 
Zimbabwe, South Africa, Morocco, Nepal, Pakistan, Bangladesh 
China, India, Australia, Nicaragua); out-migration from small

communities had devastating consequences on their fragile 
economies, thereby reducing community resilience in the long term 
(Australia)Su et al. (2017);Aziz and Sadok (2015);Bhatta and 
Aggarwal (2016);Clay and King (2019); Elagib 
et al. (2017);Gao and Mills (2018); Kattumuri et al. 
(2017); Magnan et al. (2016); Ofoegbu et al. (2016); 
Rademacher-Schulz et al. (2014);Rademacher-Schulz 
et al. (2014);Wiederkehr et al. (2018); Yegbemey et al. 
(2017); Yila and Resurreccion (2013); Nizami et al. 
(2019); Mersha and Van Laerhoven (2016); Ojha et al. 
(2014); Radel et al. (2018); Gioli et al. (2014); Hooli 
(2016); Koubi et al. (2016)
Certain autonomous, forced and planned relocation
Temporary resettlement (India)Expansion of informal settlements in cities (Solomon Islands); 
relocation to areas prone to landslide and soil erosion or insufficient 
housing (Fiji); disproportionate burden on vulnerable communities 
(China); temporary relocation created gender inequality

associated with minimal privacy; poor access to private toilets; 
sexual harassment; reduced sleep; insufficient or food rationing; 
exploitation and abuse of children (India); inadequate funding and 
governance mechanism for community-based relocation caused 
loss of culture, economic decline and health concerns (Alaska); 
relocation of supply chain to reduce exposure to climate change 
resulted in adverse outcomes for communities along the supply 
chainMonnereau and Abraham (2013); Maldonado et al. 
(2014); Pritchard and Thielemans (2014); Averchenkova 
et al. (2016); Lei et al. (2017); Barnett and McMichael 
(2018); Currenti et al. (2019)
Agricultural practices
Integrated agricultural practices (e.g., climate-smart 
agriculture, urban and peri-urban agriculture and 
forestry; agro-ecology; silvopasture; soil desalinisation; 
drainage improvement; integrated soil–crop system 
management; no tillage farming; rainwater harvesting;

check dams)Mitigation, especially carbon sequestration (but see Sommer et al., 
2018); improved household equity regarding farming decisions, 
particularly inclusion of women; food securityFurman et al. (2014); Lwasa et al. (2014); Kibue et al. 
(2015); Nyasimi et al. (2017); Aryal et al. (2018); Han 
et al. (2018); Kakumanu et al. (2018); Sikka et al. (2018); 
Debray et al. (2019); Kerr et al. (2019); (Teklewold et al., 
2019a); Teklewold et al. (2019b); Wang et al. (2020) 
Sommer et al. (2018)
Improved irrigation systems Mitigation, especially avoided emissions; improved crop yields Islam et al. (2020)
Conservation agriculture (e.g., crop diversification; soil 
conservation; cover cropping)Mitigation, especially carbon sequestration; increased crop yields; 
food security; reduced heat and water stress; increased food securityHelling et al. (2015); Sapkota et al. (2015); Kimaro 
et al. (2016); Mainardi (2018); Asmare et al. (2019); 
Gonzalez-Sanchez et al. (2019)

Return to traditional farming practices Mitigation, especially carbon sequestration Pienkowski and Zbaraszewski (2019)
Place-specific practices and innovations: animal 
cross-breeding; direct crop seeding; site-specific nutrient 
management; irrigation innovations; use of riparian 
buffer strips; use of green winter land; rice–rice systemMitigation, especially carbon sequestration; improved crop yields; 
food securitySushant (2013); Balaji et al. (2015); Helling et al. (2015); 
Jorgensen and Termansen (2016); Sen and Bond (2017); 
Wilkes et al. (2017); Kakumanu et al. (2018); Mainardi 
(2018); Sikka et al. (2018) Yadav et al. (2020)
Land and water management
AgroforestryMitigation, especially carbon sequestration; biodiversity and 
ecosystem conservation; improved food security; plant species 
diversification; diversification of household livelihoods; improved 
household incomes; improved access to forage material; energy 
access and reduced fuel wood gathering time and distance for

women; soil and water conservation; aesthetic improvements in 
landscapesHoller (2014); Suckall et al. (2015); Sharma et al. (2016); 
Nyasimi et al. (2017); Pandey et al. (2017); Schembergue 
et al. (2017); Ticktin et al. (2018); Debray et al. (2019); 
Jezeer et al. (2019); Krishnamurthy et al. (2019); 
Nyantakyi-Frimpong et al. (2019); Tschora and Cherubini 
(2020)
Afforestation and reforestation programs;
forest management practices (e.g., tree thinning)Mitigation, especially carbon sequestration; biodiversity and 
ecosystem conservation; new employment opportunities; 
diversification of household livelihoods; increased household 
incomes; improved access to fuel wood; harvesting opportunities 
from enclosuresHoller (2014); Etongo et al. (2015); Diederichs and 
Roberts (2016); Acevedo-Osorio et al. (2017); Nyasimi 
et al. (2017); Krishnamurthy et al. (2019); Rahman et al. 
(2019) Wolde et al. (2016)
Ecosystem-based adaptations such as mangrove

restoration and natural coastal defencesMitigation, especially carbon sequestration; habitat enhancement 
and protection for marine species; prevention of floor-related 
deaths, injuries and damage; improved nutrition and income 
generation for local communities, improved water qualityFedele et al. (2018)
Roberts et al. (2012); Morris et al. (2019); (Jones et al., 
2020)

16
2440Chapter 16 Key Risks across Sectors and Regions
Adaptation is being reported differently across different sources 
of knowledge. The peer-reviewed literature, for example, has been 
primarily reporting reactive adaptation at the individual, household 
and community levels, while the grey literature has been more mixed, 
reporting adaptation across governmental levels and civil society, with 
less focus on individuals and households (Ford et al., 2015a; Ford and 
King, 2015). Synthesis of impacts and responses within the private 
sector is particularly limited (Averchenkova et al., 2016; Minx et al., 
2017), further suggesting that knowledge accumulation on climate 
responses has been particularly slow, and that more robust evidence 
synthesis is required to fill key knowledge gaps.
The potential for under-reporting is most acute in the context 
of minorities and remote and marginalised groups, who are often 
also the most affected by the impacts of climate change and least able

to respond to, or benefit from, the responses to climate change (Araos 
et al., 2021). Deficits in reporting on impacts and responses are well 
recognised in the Global South, among vulnerable populations (e.g., 
women, socioeconomically disadvantaged, Indigenous, people living 
with disabilities) and within civil society (ibid.).
There is growing support for more comprehensive and systematic 
approaches to assess adaptation progress (Berrang-Ford et al., 2015; 
Ford et  al., 2015a; Ford and King, 2015; Ford and Berrang-Ford, 2016; 
Biesbroek et al., 2018). Since the AR5, there is increased recognition of 
the value of integrating diverse knowledge sources to fill knowledge 
gaps in observation of impacts and responses (Chapter 17; Cross-Chapter Box PROGRESS in Chapter 17). Van Bavel, for example, found that the 
involvement of local and diverse knowledge can improve the detection 
(medium confidence) and attribution (medium confidence) of health

impacts, and improve the action (high confidence) (Van Bavel et al., 2020).
A new development since AR5, there is now growing evidence 
assessing progress on adaptation across sectors, geographies and 
spatial scales. Uncertainty persists around what defines adaptation 
and how to measure it (Cross-Chapter Box  FEASIB in Chapter 18, 
UNEP , 2021). As a result, most literature synthesising responses is 
based on documented or reported adaptations only, and is thus subject 
to substantial reporting bias.
ecosystems, and human societies that have been implemented and

that not all responses reduce risk. While ‘adaptation’ implies risk Implemented adaptations Observed maladaptation References
Sustainable water managementMitigation, especially avoided emissions; reduced water demand; 
increased awareness about impacts of water consumption; 
decreased incidence of faecal–oral disease transmission; decreased 
use of drinking water for irrigation; reduced soil loss; increased 
groundwater retention; increased vegetation cover; increased food 
security and health and well-being; increased forage for livestock 
and amount of cultivated area; enhanced recreational areasSpencer et al. (2017); Siraw et al. (2018); 
Stanczuk-Galwiaczek et al. (2018)

Return to traditional land management practices (e.g., 
the Ngitili system)Mitigation, especially carbon sequestration; increased water 
availability for household and livestock use; increase in presence 
of edible and medicinal plants; regional economic growth; reduced 
land management conflicts; increased household income and 
access to education for children; improved access to wood fuel and 
reduced collection time for women; improved wildlife habitatDuguma et al. (2014)
REDD+ participation to maintain intact forest 
ecosystemsMitigation, especially carbon sequestration; improved air quality; 
water and soil conservation; slowed rate of vector-borne disease; 
improved mental well-being associated with cultural continuity; 
clean water; nutritional and spiritual value of forest-derived foods; 
protection from violence related to natural resource extractionMcElwee et al. (2017); Spencer et al. (2017)
Urban planning and design
Spatial planning—walkable neighbourhood design;

strategic densificationMitigation, particularly avoided emissions; public health—increases 
in physical activity, reductions in air pollution and urban heat island 
effectBeiler et al. (2016); Belanger et al. (2016)
Urban greening (e.g., tree planting; construction of 
stormwater retention areas; construction of green roofs 
and cool roofs; provision of rainwater barrels; pervious 
pavement materials)Mitigation, particularly avoided emissions; public health 
improvements—increases in physical activity, reductions in air and 
noise pollution, reduced urban heat island effect, improved mental 
health; urban flood risk management; water savings; energy savingsSamora-Arvela et al. (2017); Vahmani and Jones (2017); 
Newell et al. (2018); Alves et al. (2019); De la Sota et al. 
(2019)
Improved building efficiency standardsMitigation, particularly avoided emissions; improved air quality;

reduced urban heat island; improved natural indoor lightingBarbosa et al. (2015); Koski and Siulagi (2016); Balaban 
and Puppim de Oliveira (2017); Landauer et al. (2019)
Use of local building materials Mitigation, particularly avoided emissions Lundgren-Kownacki et al. (2018)

16
2441Key Risks across Sectors and Regions  Chapter 16
reduction, we use the broader term ‘responses’ to reflect that responses 
Given limited evidence to inform comprehensive global assessment 
of effectiveness and adequacy, we assess evidence that adaptation 
responses in human systems indicate transformational change. 
Chapter 17 considers adaptation planning and governance, including 
adaptation solutions, success, and feasibility assessment (Cross-
Chapter Box  FEASIB in Chapter 18). It is not currently possible to 
conduct a comprehensive global assessment of effectiveness, adequacy 
or the contribution of adaptation-related responses to changing risk 
due to an absence of robust empirical literature (discussed further in 
Cross-Chapter Box PROGRESS in Chapter 17).

‘observed impacts’ (Section 16.2) and ‘observed responses’ (Section 16.3) 
may reduce risk to the species (an autonomous adaptation response), but this process can be enhanced or supported by human intervention 
Adaptation-related responses are frequently motivated by a combination 
of climatic and non-climatic drivers, and interact with other transitions 
to affect risk. For societal responses, it is difficult to say whether they are 
triggered by observed or anticipated changes in climate, by non-climatic 
drivers or, as is the case in many societal responses, by a combination 
of all three. In the case of impacts, assessment typically focuses on

detection and attribution vis à vis a counterfactual of no climate change. 
adaptation-related responses (Toloo et  al., 2013a; Toloo et  al., 2013b; 
adaptations to changes in risk, including maladaptation and co-benefits.
Cross-Chapter Box INTEREG | Inter-regional Flows of Risks and Responses to Risk
Authors: Birgit Bednar-Friedl (Austria, Chapter 13), Christopher Trisos (South Africa, Chapter 9), Laura Astigarraga (Uruguay, Chapter 12), 
Magnus Benzie (Sweden/UK), Aditi Mukherji (India, Chapter 4), Maarten Van Aalst (the Netherlands, Chapter 16)

Our world today is characterised by a high degree of interconnectedness and globalisation which establish pathways for the transmission 
of climate-related risks across sectors and borders (high confidence) (Challinor et al., 2018; Hedlund et al., 2018). While the IPCC 5th 
Assessment Report (AR5) has pointed to this connection of risks across regions as ‘cross-regional phenomena’ (Hewitson et al., 2014), 
only a few countries so far have integrated inter-regional aspects into their climate change risks assessments (Liverman, 2016; Surminski 
et al., 2016; Adams et al., 2020), and adaptation is still framed as a predominantly national or local issue (Dzebo and Stripple, 2015; 
Benzie and Persson, 2019).
Inter-regional risks from climate change—also called cross-border, transboundary, transnational or indirect risks—are risks that are 
transmitted across borders (e.g., transboundary water use) and/or via teleconnections (e.g., supply chains, global food markets) (Moser

and Hart, 2015). The risks can result from impacts, including compound or concurrent impacts, that cascade across several tiers, in ways 
that either diminish or escalate risk within international systems (Carter et al., 2021). Risk transmission may occur through trade and 
finance networks, flows of people (Cross-Chapter Box MIGRATE in Chapter 7), biophysical flows (natural resources such as water) and 
ecosystem connections. However, not only risks are transmitted across borders and systems; the adaptation response may also reduce 
risks at the origin of the risk, along the transmission channel or at the recipient of the risk (Carter et al., 2021). This cross-chapter box 
discusses four inter-regional risk channels (trade, finance, food and ecosystems) and how adaptation can govern these risks.
Trade
Most commodities are traded on global markets, and supply chains have become increasingly globalised. For instance, specialised industrial

commodities such as semiconductors are geographically concentrated in a few countries (Challinor et al., 2017; Liverman, 2016). When 
climatic events like flooding or heat affect the location of these extraction and production activities, economies are not only disrupted 
locally but also across borders and in distant countries (high confidence), as exemplified by the Thailand flood 2011 that led to a shortage 
of key inputs to the automotive and electronics industry not only in Thailand but also in Japan, Europe and the USA (Figure Cross-Chapter 
Box INTEREG.1). For many industrialised countries like the UK, Japan, the USA and the European Union, there is increasing evidence that 
the trade impacts of climate change are significant and can have substantial domestic impacts (medium confidence) (Nakano, 2017; Willner 
et al., 2018, Section 13.9.1; Benzie and Persson, 2019; Knittel et al., 2020). Enhanced trade can transmit risks across borders and thereby

amplify damages (Wenz and Levermann, 2016), but it can also increase resilience (Lim-Camacho et al., 2017; Willner et al., 2018).

16
2442Chapter 16 Key Risks across Sectors and Regions
Source of risk
Major sources of flood risk in trade 
networks (e.g. Thailand flood 2011)Investments in flood resilience “at source” will 
reduce risk exposure of trading partners in 
other regions; increased stockpiling, 
diversifying suppliers (e.g. across multiple 
countries), supply chain insurance, etc. will 
reduce risks in importing countries. Adaptation solutions Highest  risk exposure
Highest risk exposure – countries that 
depend on delivery of raw materials and 
goods for their production and consumption$$Trade1
Finance2
Food3
Ecosystems4Interregional Risk Channels:
Asia North AmericaDirect effect for Japan:
Exports from Thailand to Japan: -14%
Indirect effects for Japan:
up to 174 days of production disruption for Japanese 
car manufactures; net profits reduced up to 50 %
$
$
Global financial networksTrade
Direct effect for the USA:
Exports from Thailand to USA: -21.4%
Indirect effect for the USA:

on car manufacturers  
(delays, supplier substitution), on electronic industry 
(higher prices of hard disk drives, delays,…)Europ e
$
TradeDirect effect for the European Union:
Exports from Thailand to EU: -35%
Indirect effects for the European Union:
on car manufacturers (delays, supplier substitution ,
also shortage of batteries), on electronic industry (higher prices 
of hard disk drives, delays,… )
Total global effect:
Price of hard disk drives increased by 80-190% ,
did not return to earlier price after 6 monthsDirect and indirects effects for Thailand:
USD 40 billion of economic damage, 813 fatalities, 
local insurance sector under stress;
affected products and sectors: cars, hard disk drives,  
airconditioners, refrigeratorsJapan
ThailandInterregional climate risks
Figure Cross-Chapter Box INTEREG.1 |  Inter-regional climate risks: the example of the trade transmission channel, illustrated for the Thailand flood

2011 (Abe and Ye, 2013; Haraguchi and Lall, 2015; Carter et al., 2021).
Finance
Climate risks can also spread through global financial markets (Mandel et al., 2021). For the case of coastal and riverine flooding with low 
adaptation 2080 (RCP 8.5-SSP5), the financial system is projected to amplify direct losses by a factor of 2 (global average), but reach up 
to a factor of 10 for countries that are central financial hubs (Mandel et al., 2021, Figure 13.28). Indirect impacts may also arise through 
indirect effects on foreign direct investment, remittance flows and official development assistance (Hedlund et al., 2018).Cross-Chapter Box INTEREG (continued)

16
2443Key Risks across Sectors and Regions  Chapter 16
Food
The global supply of agricultural products is concentrated to a few main breadbaskets (Bren d’Amour et al., 2016; Gaupp et al., 2020, 
Chapter 5). For instance, Central and South America is one of the regions with the highest potential to increase food supplies to more 
densely populated regions in Asia, the Middle East and Europe (Chapter 12). The exports of agricultural commodities (coffee, bananas, 
sugar, soybean, corn, sugarcane, beef livestock) have gained importance in the past two decades as international trade and globalisation 
of markets have shaped the global agri-food system (Chapter 5).
The export of major food crops like wheat, maize and soybeans from many of the world’s water-scarce area—the Middle East, North Africa, 
parts of South Asia, North China Plains, southwest USA, Australia—to relatively water-abundant parts of the world carries a high virtual

water content (the net volume of water embedded in trade) (high confidence) (Hoekstra and Mekonnen, 2012; Dalin et al., 2017; Zhao 
et al., 2019, Chapter 4). Both importing and exporting countries are exposed to transboundary risk transmission through climate change 
impacts on distant water resources (Sartori et al., 2017; Zhao et al., 2019; Ercin et al., 2021). Climate change is projected to exacerbate 
risk and add new vulnerabilities for risk transmission (medium confidence). Rising atmospheric CO 2 concentration is projected to decrease 
water efficiency of growing maize and temperate cereal crops in parts of the USA, East and Mediterranean Europe, South Africa, Argentina, 
Australia and Southeast Asia, with important implications for future trade in food grains (Fader et al., 2010). By 2050 (SRES B2 scenario), 
virtual water importing countries in Africa and the Middle East may be exposed to imported water stress as they rely on imports of food

grains from countries which have unsustainable water use (Sartori et al., 2017). Until 2100, virtual trade in irrigation water is projected 
to almost triple (for SSP2-RCP6.5 scenarios) and the direction of virtual water flows is projected to reverse, with the currently exporting 
regions like South Asia becoming importers of virtual water (Graham et al., 2020). An additional 10–120% trade flow from water-abundant 
regions to water-scarce regions will be needed to sustain environmental flow requirements on a global scale by the end of the century 
(Pastor et al., 2019). Exports of agricultural commodities contribute to deforestation, over-exploitation of natural resources and pollution, 
affecting the natural capital base and ecosystem services (Agarwala and Coyle, 2020; Rabin et al., 2020, Section 12.5.4).
Species and ecosystems
The spatial distributions of species on land and in the oceans are shifting due to climate change, with these changes projected to

accelerate at higher levels of global warming (Pecl et al., 2017). These ‘species on the move’ have large effects on ecosystems and human 
well-being, and present challenges for governance (Pecl et al., 2017). For example, the number of transboundary fish stocks is projected to 
increase as key fisheries species are displaced by ocean warming (Pinsky et al., 2018). Conflict over shifting mackerel fisheries has already 
occurred between European countries (Spijkers and Boonstra, 2017), because few regulatory bodies have clear policies on shifting stocks; 
this leaves species open to unsustainable exploitation in new waters in the absence of regularly updated catch allocations to reflect 
changing stock distributions (Caddell, 2018).
Human health will also be affected as vector-borne diseases such as malaria and dengue shift geographic distributions (Caminade et al.,

2014). There is also evidence that many warm-adapted invasive species, such as invasive freshwater cyanobacterium, have spread to 
higher latitudes because of climate change (Chapter 2).
Adaptation to inter-regional climate risks
Adaptation responses to reduce inter-regional risks can be implemented at a range of scales: at the point of the initial climate change 
impact (e.g., assistance for recovery after an extreme event, development of resilient infrastructure, climate-smart technologies for 
agriculture); at or along the pathway via which impacts are transmitted to the eventual recipient (e.g., trade diversification, re-routing 
of transport); in the recipient country (e.g., increasing storage to buffer supply disruptions), or by third parties (e.g., adaptation finance, 
technology transfer) (Bren d’Amour et  al., 2016; Carter et  al., 2021; Talebian et  al., 2021). A knowledge gap exits on the need for,

effectiveness of, and limits to adaptation under different socioeconomic and land use futures.
Due to regional and global interdependencies, climate resilience has a global, multi-level public good character (Banda, 2018). The 
benefits of adaptation are therefore shared beyond the places where adaptation is initially implemented. Conversely, adaptation may 
be successful at a local level while redistributing vulnerability elsewhere or even driving or exacerbating risks in other places (Atteridge 
and Remling, 2018). International cooperation is therefore needed to ensure that inter-regional effects are considered in adaptation and 
that adaptation efforts are coordinated to avoid maladaptation. However, regional- and global-scale governance of adaptation is only 
just beginning to emerge (Persson, 2019).
The United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement frames adaptation as a ‘global challenge’

(Article 7.2) and establishes the global goal on adaptation (Article 7.1), which provides space for dialogue between parties on the Cross-Chapter Box INTEREG (continued)

16
2444Chapter 16 Key Risks across Sectors and Regions
16.4 Synthesis of Limits to Adaptation across 
Natural and Human Systems
This section builds on previous IPCC Reports (i.e., AR5, SR15, SROCC, 
SRCCL) to advance concepts and emphasise remaining gaps in 
understanding about limits to adaptation. We provide case studies to 
illustrate these concepts and synthesise regional and sectoral limits 
to adaptation across natural and human systems that informs key 
risks (Section 16.5) and RFCs (Section 16.6). We also identify residual 
risks—risks that remain after efforts to reduce hazards, vulnerability 
and/or exposure—associated with limits to adaptation.
16.4.1 Definitions and Conceptual Advances since AR5
16.4.1.1 Limits to Adaptation since AR5
AR5 introduced the concept of limits to adaptation and provided 
a functional definition that has been used in subsequent Special 
Reports (SR15, SROCC, SRCCL) and is also used for AR6 (see also 
Chapter 1).

A limit is defined as the point at which an actor’s objectives or system’s 
needs cannot be secured from intolerable risks through adaptive actions 
(Klein et al., 2014). Tolerable risks are those where adaptation needed 
to keep risk within reasonable levels is possible, while intolerable 
risks are those where practicable or affordable adaptation options to 
avoid unreasonable risks are unavailable. This highlights that limits to 
adaptation are socially constructed and based on values that determine 
levels of reasonable or unreasonable risk as well as on available 
adaptation options, which vary greatly across and within societies.
Limits are categorised as being either ‘soft’ or ‘hard’. Soft limits may 
change over time as additional adaptation options that are practicable 
or affordable become available. Hard limits will not change over time as 
no additional adaptive actions are possible. When a limit is exceeded,

then intolerable risk may materialise and the actor’s objectives or 
system’s needs may be either abandoned or transformed (Box 16.2).
For human systems, soft and hard limits are largely distinguished by 
whether or not constraints to adaptation are able to be overcome. Constraints to adaptation (also called barriers) are factors that make 
it harder to plan and implement adaptation actions, such as limited 
financial resources, ineffective institutional arrangements or insufficient 
human capacity. Soft limits are mostly associated with human systems, 
due in part to the role of human agency in addressing constraints. 
For natural systems, the magnitude and rate of climate change and 
capacity of adaptation to such change largely determine the type of 
limit. Hard limits are largely associated with natural systems and are 
mostly due to inability to adapt to biophysical changes.
Using this understanding of limits, subsequent Special Reports have

assessed relevant literature (Mechler et  al., 2020). SR15 identifies 
several regions, sectors and ecosystems—including coral reefs, 
biodiversity, human health, coastal livelihoods, Small Island Developing 
States, and the Arctic—that are projected to experience limits at either 
1.5°C or 2°C. SRCCL states that land degradation due to climate 
change may result in limits to adaptation being reached in coastal 
regions and areas affected by thawing permafrost. SROCC details 
that risks of climate-related changes in the ocean and cryosphere may 
result in limits for ecosystems and vulnerable communities in coral reef 
environments, urban atoll islands and low-lying Arctic locations before 
the end of this century in case of high-emissions scenarios.
A key area of advancement since AR5 is how incremental adaptation 
and transformational adaptation relate to limits to adaptation. 
Incremental adaptation maintains ‘the essence and integrity of a

system or process at a given scale’, while transformational adaptation 
‘changes the fundamental attributes of a social-ecological system’ 
(IPCC, 2018b). Both incremental and transformational adaptation may 
expand the adaptive possibilities for a system, providing additional 
adaptation options after a system reaches a soft limit (Felgenhauer, 
2015; Pelling et  al., 2015; Termeer et  al., 2017, see also Chapters 1 
and 17; Alston et al., 2018; Panda, 2018; Mechler and Deubelli, 2021). 
However, it is critical to note that adaptation, whether incremental 
or transformational, must support securing an actor’s objectives or 
system’s needs from intolerable risks. Once objectives or needs have 
been abandoned or transformed, a limit to adaptation has occurred. 
However, objectives or needs may change over time as values of a 
society change (Taebi et al., 2020), thus adding further complexity to

assessing limits to adaptation.global-scale challenge of adaptation and the need for renewed political and financial investment in adaptation, including to address 
inter-regional effects (Benzie et al., 2018).
National Adaptation Plans (NAPs) can evolve to consider inter-regional effects as well as domestic ones (Liverman, 2016; Surminski et al., 
2016; European Environment, 2020). Regional and international coordination of NAPs, coupled with building capacities and addressing 
existing knowledge gaps at the country level, can help to ensure that resources are oriented towards reducing inter-regional risks and 
building systemic resilience to climate change globally (Booth et al., 2020; Wijenayake et al., 2020).
Given the important role of private actors in managing inter-regional climate risks (Goldstein et al., 2019; Tenggren et al., 2019), efforts

will be needed to align public and private strategies for managing inter-regional climate risks to avoid maladaptation and ensure just and 
equitable adaptation at different scales (Talebian et al., 2021).Cross-Chapter Box INTEREG (continued)

16
2445Key Risks across Sectors and Regions  Chapter 16
16.4.1.2 Residual Risk since AR5
The term ‘residual risk’ was not assessed in detail in AR5 and was 
used interchangeably with other terms, including ‘residual impacts’, 
‘residual loss and damage’ and ‘residual damage’. SR15 includes 
discussion of residual risks without an explicit definition and relates 
these to L oss and D amage and limits to adaptation, concluding 
that residual risks rise as global temperatures increase from 1.5°C to 
2°C. SRCCL refers to residual risks arising from limits to adaptation 
related to land management. Such residual risk can emerge from 
irreversible forms of land degradation, such as coastal erosion when 
land completely disappears, collapse of infrastructure due to thawing 
of permafrost, and extreme forms of soil erosion. SROCC advanced 
the conceptualisation of residual risk and integrated it within the risk 
framework, defining residual risk as the risk that remains after actions

have been taken to reduce hazards, exposure and/or vulnerability. 
Residual risk is therefore generally higher where adaptation failure, 
insufficient adaptation or limits to adaptation occur. We use the SROCC 
definition of residual risk for our assessment in the following sections 
and identify residual risks that are associated with limits to adaptation.
16.4.2 Insights from Regions and Sectors about Limits to 
Here we provide example case studies to highlight constraints that 
may lead to soft limits, potential incremental and transformational 
adaptation options that may overcome soft limits, evidence of hard 
limits, and residual risks.16.4.2.1 Small Island Developing States
An expanding volume of empirical research highlights existing 
adaptation constraints that may lead to soft limits in Small Island 
Developing States (SIDS). Investigation of national communications 
among 19 SIDS found that financial constraints, institutional

challenges and poor resource endowments were the most frequently 
reported as inhibiting adaptation for a range of climate impacts 
(Robinson, 2018b). Governance, financial and information constraints 
such as unclear property rights and lack of donor flexibility have led 
to hasty implementation of adaptation projects in Kiribati, whereas 
in Vanuatu and the Solomon Islands, limited awareness of rural 
adaptation needs and weak linkages between central governance 
and local communities have resulted in an urban bias in resource 
allocation (Kuruppu and Willie, 2015). Limited availability and use of 
information and technology also present constraints to adaptation; 
many SIDS suffer from lack of data and established routines to identify 
losses and damages, and the combination of poor monitoring of slow-
onset changes and influence of non-climatic determinants of observed 
impacts challenges attribution (Thomas and Benjamin, 2018). The fact

that climate information is often available only in the English language 
represents another common constraint for island communities (Betzold, 
2015). Although Indigenous and local knowledge systems can provide 
important experience-based input to adaptation policies (Miyan et al., 
2017), socio-cultural values and traditions such as attachment to 
place, religious beliefs and traditions can also constrain adaptation 
in island communities, particularly for more transformational forms of 
adaptation (Ha’apio et al., 2018; Oakes, 2019).
Box 16.2 | Linking Adaptation Constraints, Soft and Hard Limits
McNamara et al. (2017) provides an example of community-scaled adaptation that highlights how constraints affect limits, the relationship 
between soft and hard limits, and the potential need to abandon or transform objectives. Community members of Boigu Island, Australia,

are already adapting to perceived climate change hazards—including sea level rise and coastal erosion—to secure their objective of 
sustaining livelihoods and way of life in their current location. Existing seawall and drainage systems provide inadequate protection 
from flooding during high tides, leading residents to elevate their houses to prevent damages. However, these adaptation measures have 
proved to be insufficient. Standing saltwater for extended periods of time after floods has resulted in losses and damages, including 
erosion of infrastructure, increased soil salinity, and heightened public health concerns. Additional adaptation efforts are constrained by 
scarcity of elevated land, which inhibits movement of infrastructure within the community, and lack of financial, technical and human 
assets to improve coastal protection measures.
These constraints are leading to a soft limit to adaptation, where risks would become unreasonable as sea levels continue to rise and

practicable and affordable adaptation options are limited to currently available approaches. This soft limit could be overcome through 
addressing constraints and allowing further adaptation to take place, such as providing financial, technical and human resources for more 
effective coastal protection and drainage systems that would reduce flooding. However, if the effectiveness of these new adaptation 
measures decreases as sea levels rise further and if constraints are not able to be overcome, another soft limit may be reached. Eventually, 
if constraints are not addressed, no further adaptation measures are implemented, and climate hazards intensify, the area could become 
uninhabitable. This would then be a hard limit for adaptation; there would be no adaptation options available that would allow the 
community to sustain livelihoods and way of life in its present location. This hard limit to adaptation may necessitate abandoning the

objective of remaining in the community. The objective of the community may then transform to sustaining their livelihoods in a less 
vulnerable location, which would necessitate relocation. However, such transformation of the community’s objectives may be hindered 
by the expressed resistance of residents to migrate, due to their strong sense of place.

16
2446Chapter 16 Key Risks across Sectors and Regions
Soft limits to adaptation for coastal flooding and erosion are already 
being experienced in Samoa owing largely to financial, physical and 
technological constraints (Crichton and Esteban, 2018). While sea walls 
have been erected to minimise coastal erosion, these defences need 
regular upgrading and replacement as high swells, tropical cyclones 
and constant wave action erode their effectiveness. The high costs of 
installing, upgrading and enlarging such infrastructure has led to sea 
walls only being used in specific locations, leaving communities that 
are beyond the extent of these measures exposed to inundation and 
erosion. Native tree replanting has also been implemented, but coastal 
flooding and erosion persist as large swells lead to high failure rates 
of replanting efforts. Across SIDS, adaptation to coastal flooding and 
erosion in particular is increasingly facing soft limits due to high costs,

unavailability of technological options and limited physical space or 
environmental suitability for hard engineering or ecosystem-based 
approaches (Mackey and Ware, 2018; Nalau et al., 2018).
Retreat and relocation constitute transformative adaptation options, 
although evidence of permanent community-scale relocation in 
response to climate change remains limited at present (Kelman, 2015; 
McNamara and Des Combes, 2015). Material and emotional cost of 
emigration as well as loss of homeland, nationhood, and other intangible 
assets and values imply that relocation is generally considered a last 
resort (Jamero et al., 2017) and may mean abandoning objectives of 
remaining in existing locations, hence exceeding adaptation limits.
Hard limits in SIDS are mostly due to adaptation being unable to 
prevent intolerable risks from escalating climate hazards such as 
SLR and related risks of flooding and surges, severe tropical cyclones,

and contamination of groundwater. Emerging evidence suggests that 
shortage of water and land degradation have already contributed to 
migration of multiple island communities in the Pacific (Handmer and 
Nalau, 2019).
Residual risks for SIDS include loss of marine and terrestrial biodiversity 
and ecosystem services, increased food and water insecurity, 
destruction of settlements and infrastructure, loss of cultural resources 
and heritage, collapse of economies and livelihoods, and reduced 
habitability of islands (Section 3.5.1, Section 15.3).
16.4.2.2 Agriculture in Asia
Lack of financial resources is found to be a significant constraint that 
contributes to soft limits to adaptation in agriculture across Asia. Although 
smallholder farmers are currently adapting to climate impacts, lack of 
finance and access to credit prevents upscaling of adaptive responses 
and has led to losses (Bauer, 2013; Patnaik and Narayanan, 2015; Bhatta

and Aggarwal, 2016; Loria, 2016). Other constraints further contribute 
to soft limits, including governance and associated institutional factors 
such as ineffective agricultural policies and organisational capacities 
(Tun Oo et al., 2017), information and technology challenges such as 
limited availability and access to technologies on the ground (Singh 
et al., 2018), socio-cultural factors such as the social acceptability of 
adaptation measures that are affected by gender (Huyer, 2016; Ravera 
et al., 2016), and limited human capacity (Masud et al., 2017). A wide 
range of pests and pathogens are predicted to become problematic 
to regional food crop production as average global temperatures rise (Deutsch et al., 2018), increasing crop loss across Asia for which 
farmers are already experiencing a variety of adaptation constraints, 
including financial, economic and technological challenges (Sada et al., 
2014; Tun Oo et al., 2017; Fahad and Wang, 2018). Extreme heatwaves

are projected in the densely populated agricultural regions of South 
Asia, leading to increased risk of heat stress for farmers and resultant 
constraints on their ability to implement adaptive actions (Im et  al., 
2017). However, socioeconomic constraints appear to have a higher 
influence on soft limits to adaptation in agriculture than biophysical 
constraints (Thomas et  al., 2021). For example, an examination of 
farmers’ adaptation to climate change in Turkey found that constraints 
related to access to climate information and access to credit will 
likely limit the yield benefits of incremental adaptation (Karapinar 
and Özertan, 2020). In Nepal, conservation policies restrict traditional 
grazing inside national parks, which promotes intensive agriculture and 
limits other cropping systems that have been implemented as climate 
change adaptation (Aryal et al., 2014).
In Bangladesh, small and landless farm households are already

approaching soft limits in adapting to riverbank erosion (Alam et al., 
2018). While wealthier farming households can implement a range 
of adaptation responses, including changing planting times and 
cultivating different crops, poorer households have limited access 
to financial institutions and credit to implement such measures. 
Their adaptation responses of shifting to homestead gardening and 
animal rearing are insufficient to maintain their livelihoods, and these 
households are more likely to engage in off-farm work or migrate.
Palao et  al.. (2019) identify the possible need for transformational 
adaptation in Asian-Pacific agricultural practices due to changes in 
biophysical parameters as global average temperatures rise. In this 
context, transformational adaptation would consist of changing 
farming locations to different provinces or different elevations for 
the production of specific crops or introducing new farming systems.

Nearly 50% of maize in the region along with 18% of potato and 8% 
of rice crops would need to either be shifted in location or use new 
cropping systems, with the most significant transformation being 
needed in China, India, Myanmar and the Philippines. For maize 
suitability by 2030, seven provinces in the east and northeast of China 
are projected to experience over 50% reduction in suitability, and two 
northern states in India may experience 70% reduction in suitability. 
Cassava and sweet potato may play a critical role in food resilience in 
these areas, as these crops are more resilient to climate change (Prain 
and Naziri, 2019).
In terms of hard limits, the rate and extent of climate change is critical 
as agriculture is climate-dependent and sensitive to changes in climate 
parameters. Poudel and Duex (2017) document that over 70% of the 
springs used as water sources in Nepalese mountain agricultural 
communities had a decreased flow, and approximately 12% had dried

up over the past decade. While there are some adaptation measures to 
address reduced water availability—such as the introduction of water-
saving irrigation technology among Beijing farmers to alleviate water 
scarcity in metropolitan suburbs (Zhang et al., 2019)—these actions 
still depend on some level of water availability. If climate hazards 
intensify to the point where water supply cannot meet agricultural 
demands, hard limits to adaptation will occur.

16
2447Key Risks across Sectors and Regions  Chapter 16
Residual risks associated with agriculture in Asia include declines 
in fisheries, aquaculture and crop production, particularly in South 
and Southeast Asia (Section  10.3.5), increased food insecurity 
(Section  10.4.5), reductions of farmers’ incomes by up to 25% 
(Section 10.4.5), loss of production areas (Section 10.4.5) and reduced 
physical work capacity for farmers—between 5% and 15% decline in 
south-southwest Asia and China under RCP8.5 (Section 5.12.4).
16.4.2.3  Livelihoods in Africa
For livelihoods dependent on small-scale rain-fed agriculture in Africa, 
climate hazards include floods and droughts. However, governance, 
financial and information/awareness/technology challenges are 
identified as the most significant constraints leading to soft limits, 
followed by social and human capacity constraints (Thomas et  al., 
2021). Finance and land tenure constraints restrict Ghanaian farmers

when considering adaptation responses due to climate variability 
(Guodaar et  al., 2017). Similarly, in East Africa, farmers with small 
pieces of land have limited economic profitability, making it difficult to 
invest in drought and/or flood management measures (Gbegbelegbe 
Increasing droughts and floods require costlier adaptation responses 
to reduce risks, such as using drought-tolerant species (Berhanu and 
Beyene, 2015) and coping strategies for flood-prone households 
(Schaer, 2015; Musyoki et al., 2016), resulting in soft limits for poorer 
households who cannot afford these responses. In Namibia, weak 
governance and poor integration of information, such as disregarding 
knowledge of urban and rural residents in flood management 
strategies, has resulted in soft limits to adaptation, leading to 
temporary or permanent relocation of communities (Hooli, 2016). 
Shortage of land—namely high population pressure and small per

capita land holding—leads to continuous cultivation and results in 
poor soil fertility. This low productivity is further aggravated by erratic 
rainfall causing soft limits as farmers cannot produce enough and must 
depend on food aid (Asfaw et al., 2019).
Relocation due to flooding is discussed as a transformation 
adaptation action taken in Botswana where the government decided 
to permanently relocate hundreds of residents to a nearby dryland 
area (Shinn et  al., 2014). Some residents permanently relocated, 
whereas others only temporarily relocated against the government’s 
instructions. Such relocation processes must attend to micro-politics 
and risks of existing systemic issues of inequality and vulnerability.
In terms of hard limits, land scarcity poses a hard limit when 
implementing organic cotton production, an adaptation response 
supporting sustainable livelihoods (Kloos and Renaud, 2014).
Residual risks associated with livelihoods in Africa include poorer

households becoming trapped in cycles of poverty (Section  9.9.3), 
increased rates of rural–urban migration (Section  9.8.4), decline of 
traditional livelihoods such as in agriculture (Sections 9.9.3, 9.11.3.1) 
and fisheries (Section 9.11.1.2), and loss of traditional practices and 
cultural heritage (Section 9.9.2).16.4.3 Regional and Sectoral Synthesis of Limits to 
16.4.3.1 Evidence on Limits to Adaptation
There is high agreement and medium evidence that there are limits to 
adaptation across regions and sectors. However, much of the available 
evidence focuses on constraints that may lead to limits at some 
point with little detailed information on how limits may be related 
to different levels of socioeconomic or environmental change (high 
confidence). Figure 16.7 assesses evidence on constraints and limits 
for broad categories of region and sector. Small islands and Central 
and South America show most evidence of constraints being linked to

adaptation limits across sectors, while ocean and coastal ecosystems 
and health, well-being and communities show most evidence of 
constraints being linked to limits across regions (medium confidence).
There are clusters of evidence with additional details on limits to 
adaptation, as detailed in Table 16.3. Evidence on limits to adaptation 
is largely focused on terrestrial and aquatic species and ecosystems, 
coastal communities, water security, agricultural production, and 
human health and heat (high confidence).
Beginning at 1.5°C, autonomous and evolutionary adaptation responses 
by terrestrial and aquatic species and ecosystems face hard limits, 
resulting in biodiversity decline, species extinction and loss of related 
livelihoods (high confidence). Interventionist adaptation strategies 
to reduce risks for species and ecosystems face soft limits due to 
governance, financial and knowledge constraints (medium confidence) .

As sea levels rise and extreme events intensify, coastal communities 
face soft limits due to financial, institutional and socioeconomic 
constraints reducing the efficacy of coastal protection and 
accommodation approaches and resulting in loss of life and economic 
damages (medium confidence). Hard limits for coastal communities 
reliant on nature-based coastal protection will be experienced 
beginning at 1.5°C (medium confidence).
Beginning at 3°C, hard limits are projected for water management 
measures, leading to decreased water quality and availability, negative 
impacts on health and well-being, economic losses in water and energy 
dependent sectors and potential migration of communities (medium 
Soft and hard limits for agricultural production are related to water 
availability and the uptake and effectiveness of climate-resilient 
crops, which is constrained by socioeconomic and political challenges 

Adaptation measures to address risks of heat stress, heat mortality and 
reduced capacities for outdoor work for humans face soft and hard 
limits across regions beginning at 1.5°C and are particularly relevant 
for regions with warm climates (high confidence).

16
2448Chapter 16 Key Risks across Sectors and Regions
16.4.3.2 Constraints Leading to Limits to Adaptation
Across regions and sectors, a range of constraints (Figure  16.8) are 
identified as leading to limits to adaptation, particularly financial 
constraints and constraints related to governance, institutions and 
policy (high confidence). While individual constraints may appear 
straightforward to address, the combination of constraints interacting 
with each other leads to soft limits that are difficult to overcome (high 
confidence). The interplay of many different constraints that lead to 
limits makes it difficult to categorise limits beyond being either soft 
or hard.
16.4.3.3 Climate Change Impacts, Financial Constraints and 
Limits to Adaptation
Across regions and sectors, financial constraints are identified as 
significant and contributing to limits to adaptation, particularly in low-

to-middle-income countries (high confidence) (Sections  3.6.3, 4.7.2, 5.14.3, 6.4.5, 7.4.2, 8.4.5, 12.5.1, 12.5.2, 15.6.1, 15.6.3, Figure 16.8, 
Table 16.4, Section CCP2.4.2). Impacts of climate change may increase 
financial constraints (high confidence) and contribute to soft limits 
to adaptation being reached (medium confidence). Table 16.5 details 
climate impact observations that point to potentially substantial 
negative impacts on the availability of financial resources for different 
regions.
At the national level, negative macroeconomic responses to climate 
change may limit the availability of financial resources, impede access 
to financial markets and stunt economic growth (high confidence). 
Economic growth has been shown to decline under higher temperatures 
(Burke et al., 2015; Kahn et al., 2019, Section 16.5.2.3.4) and following 
extreme events (Hsiang and Jina, 2014; IMF , 2017), particularly for 
medium- and low-income developing countries (Section  18.1). The

most severe impacts of climate-related disasters on economic growth 
per capita have been observed in developing countries, although 
authors note a publication bias in the reporting of negative effects Evidence on constraints and limits to adaptation by region and sector
Africa Asia AustralasiaCentral
and South
America EuropeNorth
Water
Ocean and
coastal ecosystems
Wellbeing and
Cities, settlement and
key infrastructureEvidence
High
Food, fibre and other
ecosystem products
Medium
Low
Figure 16.7 |  Evidence on constraints and limits to adaptation by region and sector. Data from Thomas et al. (2021), based on 1682 scientific publications reporting 
on adaptation-related responses in human systems. See SM16.1 for methods. Low evidence: <20% of assessed literature has information on limits; literature mostly focuses on

constraints to adaptation. Medium evidence: between 20% and 40% of assessed literature has information on limits; literature provides some evidence of constraints being 
linked to limits. High evidence: >40% of assessed literature has information on limits; literature provides broad evidence of constraints being linked to limits.
Table 16.3 |  Adaptation limits and residual risks for select actors and systems. Asterisks indicate confidence level
Actor/system at risk Adaptation limits Residual risks
Terrestrial species in islands at risk to loss 
of habitatHard: autonomous adaptation unable to overcome loss of habitat 
and lack of physical space (c) (Box CCP1.1)Biodiversity decline, local extinctions, half of all species currently 
considered to be at risk of extinction occur on islands (Box CCP 1.1)
Terrestrial species across Africa at risk to 
habitat changesHard: beyond 2°C, many species will lack suitable climate conditions

by 2100 despite migration and dispersal (c) (Section 9.6.4.1)9% of species face complete range loss (a), mountaintop endemics 
and species at poleward boundaries of African continent at risk of 
range loss due to disappearing cold climates (c) (Section 9.6.4.1)
African aquatic organisms at risk to habitat 
changesHard: thermal changes above optimal physiological limits will reduce 
available habitats (Section 9.6.2.4)Greater risks of loss of endemic fish species than generalist fish 
species (Section 9.6.2.4)
African coastal and marine ecosystems at 
risk to habitat changesHard: at 2°C, bleaching of east African coral reefs (c) 
(Section 9.6.2.3)Over 90% of east African coral reefs destroyed at 2°C (c) 
(Section 9.6.2.3)
Coral reefs at risk to oceanic changesHard: coral restoration and management no longer effective after 
2°C (c), enhanced coal and reef shading no longer effective after 3°C 
(b) (Figure 3.23)Loss of more than 80% of healthy coral cover, loss of livelihoods

dependent on coral reefs (c) (Figure 3.23, Table 8.7)
Cold-adapted species whose habitats are 
restricted to polar and high mountaintop 
areas at risk to loss of climate spaceHard: evolutionary responses unable to keep pace with the rate of 
climate change and degraded state of ecosystems (Sections 2.6.1, 
CCP1.2.4.2)Species extinctions in the case of species losing their climate space 
entirely on a regional or global scale (Sections 2.6.1, CCP1.2.4.2)

16
2449Key Risks across Sectors and Regions  Chapter 16
Ecosystems in North America at risk to 
multiple climate hazardsSoft: governance constraints hinder implementation of adaptation 
Hard: some species unable to adapt (Table 14.8)
Ecosystems and species at risk to multiple 
climate hazardsSoft: financial and knowledge constraints lead to limits for 
interventionist approaches such as translocation of species or 
ecosystem restoration Hard: some habitats unable to be effectively 
restored (Section 2.6.6)Species extinctions and changes, irreversible major biome shifts 
(Section 2.6.6)
Coastal settlements in Australia and New 
Zealand at risk to sea level riseSoft and hard: limits in the efficacy of coastal protection and 
accommodation approaches as sea levels rise and extreme events 
intensify (Box 11.5)With 1–1.1 m of sea level rise, value of coastal urban infrastructure

at risk in Australia is AUD 164 to >226 billion, while in NZ it is NZD 
43 billion. Sea level rise will also result in significant cultural and 
archaeological sites disturbed and increasing flood risk and water 
insecurity with health and well-being impacts on Australia’s small 
northern islands (Box 11.5)
Human settlements in
coastal areas in the 1-in-100-year 
floodplain at risk to coastal floodingSoft: socioeconomic, institutional and financial constraints may lead 
to soft limits well in advance of technical limits of hard engineering 
measures (Sections CCP 2.3.2, CCP2.3.4) Hard: Nature-based 
measures (e.g., restoration of coral reefs, mangroves, marshes) reach 
hard limits beginning at 1.5°C of global warming. Retreat strategies 
reach hard limits as availability and affordability of land decreases 
(CCPs 2.3.2.3, 2.3.5)At 3°C, globally up to 510 million people and up to USD 
12,739 billion in assets at risk by 2100 (Section CCP 2.2.1)
Communities in small islands at risk to

freshwater shortagesHard: domestic freshwater resources unable to recover from 
increased drought, sea level rise and decreased precipitation by 
2030 (RCP8.5+ ice sheet collapse), 2040 (RCP8.5) or 2060 (RCP4.5) 
(Box 4.2, Section 4.7.2)Migration of communities due to water shortages with impacts 
on well-being, community cohesion, livelihoods and people–land 
relationships (Box 4.2)
Communities in North America at risk to 
poor water qualitySoft: financial and technological constraints lead to limits in ability 
to treat water for harmful algal blooms (Table 14.8)
Communities in Western and Central 
Europe at risk to water shortagesHard: at 3°C, geophysical and technological limits reached in 
Southern Europe (Section 13.10.3.3)At 3°C, two-thirds of the population of Southern Europe at risk 
to water security with significant economic losses in water- and 
energy-dependent sectors (b) (Sections 13.2.2, 13.6, 13.10.2.3)
Communities in Central and South America

at risk to water shortagesSoft: improved water management as an adaptation strategy unable 
to overcome lack of trust and stakeholder flexibility, unequal power 
relations and reduced social learning (Section 12.5.3.4)Increasing competition and
conflict associated with high economic losses (b); glacier 
shrinkage leading to loss of related livelihoods and cultural values 
(Section 12.5.3.1, Table 8.7)
Agricultural production in Europe at risk to 
heat and droughtSoft: above 3°C, unavailability of water will limit irrigation as an 
adaptation response (c) (Sections 13.5.1, 13.10.2.2)At 3–4°C, yield losses for maize may reach up to 50% (b) 
(Sections 13.5.1, 13.10.2.2)
Crops at risk to temperature increaseSoft: socioeconomic and political constraints limit uptake of 
climate-resilient crops (Section 5.4.4.3) Hard: after 2°C, cultivar 
changes unable to offset global production losses (Section 5.4.4.1)Costs of adaptation and residual damages are USD 63 billion

at 1.5°C. USD 80 billion at 2°C and USD 128 billion at 3°C, 
with greater risks and damages in tropical and arid regions 
(Section 5.4.4.1)
Human health in Europe at risk to heatSoft: many adaptation measures will not be able to fully mitigate 
overheating in buildings with high levels of global warming (c) 
(Section 13.6.2.3) Hard: above 3°C, people and health systems 
unable to adapt (c) (Sections 13.6.2.3, 13.7.2, 13.7.4, 13.10.2.1, 13.8)At 1.5°C, 30,000 annual deaths due to extreme heat with up to 
90,000 annual deaths at 3°C in 2100 (c) (Section 13.7.1); at 3°C, 
thermal comfort hours during summer will decrease by as much as 
74% in locations in southern Europe (c) (Section 13.6.1.5)
Human health at risk to heatSoft: socioeconomic constraints limit adaptation responses to 
extreme heat (Section 7.4.2.6, Table 8.7)Globally, the impact of projected climate change on 
temperature-related mortality is expected to be a net increase under

RCP4.5 to RCP8.5, even with adaptation, particularly for regions 
with warm climates (d) (Section 7.3.1, Table 8.7)
South Asian settlements at risk to coastal 
flooding, drought, sea level rise and 
heatwavesSoft and hard: at 4.5°C, maximum temperature is expected 
to exceed survivability threshGold across most of South Asia, 
particularly relevant for outdoor work (a) (Table 10.6)At RCP4.5, 25–50% of population affected; at RCP8.5, more than 
50% of population affected; at 4.5°C of warming, increase in 
heat-related deaths of 12.7% in South Asia (a) (Table 10.6)
Tourism in Europe reliant on snow at risk to 
higher levels of warmingSoft: at 3°C, snowmaking as an adaptation measure limited by 
biophysical and financial constraints (c) (Sections 13.6.1.4, 13.6.2.3)Damages in European tourism with larger losses in Southern Europe 
(c) (Section 13.6.1.4)
Rapidly growing towns/cities and smaller

cities at risk to range of climate hazardsSoft: governance and financial constraints lead to limits in ability to 
adapt (Sections 6.3, 6.4)
Notes:
(a) low confidence
(b) medium confidence
(c) high confidence
(d) very high confidence.

16
2450Chapter 16 Key Risks across Sectors and Regions
(Klomp and Valckx, 2014). Substantial immediate output losses and 
reduced economic growth due to extreme events have been observed 
in both the short and long term (Section  16.2.3). Estimates of the 
duration of negative effects of climate-related disasters differ, with 
some analyses suggesting that, on average, economies recover after 
2 years (Klomp, 2016) and others finding negative effects of cyclones 
to persist 15–20 years following an event (Hsiang and Jina, 2014; IMF , 
2017). Rising climate vulnerability has also been shown to increase 
the cost of debt (Kling et  al., 2018). Rising climatic risks negatively 
affect developing countries’ ability to access financial markets (Cevik 
and Jalles, 2020), and their disclosure may result in capital flight 
(Cross-Chapter Box FINANCE in Chapter 17). Overall, the direct and 
indirect economic effects of climate change represent a major risk to

financial system stability (Section 11.5.2). These risks and effects may 
further limit the availability of financial resources needed to overcome 
constraints, in particular for developing countries.
Sectoral studies indicate that climate impacts will result in higher 
levels of losses and damages and decreases in income, thereby 
increasing financial constraints (medium confidence). Yield losses 
for major agricultural crops are expected in nearly all world regions 
(Figure 5.7). Decreases in estimated marine fish catch potential and 
large economic impacts from ocean acidification are expected globally, 
leading to the risk of revenue loss (Section 5.8.3). Losses of primary 
productivity and farmed species of shellfish are expected in tropical 
and subtropical regions (Section 5.9.3.2.2). Economic losses have been 
observed in the power generation sector and transport infrastructure 
(Section 10.4.6.3.8), including economic losses from floods in urban

areas (Section  4.2.4.5). However, some positive sectoral climate 
change impacts have been identified for the timber and forestry sector 
(Section 5.6.2), for primary productivity and farmed species of shellfish 
in high-latitude regions (Section 5.9.3.2.2) and for agriculture in high-
latitude regions (Section 5.4.1.1).
At the household or community level, climate impacts may increase 
financial constraints (high confidence). Impacts on agriculture 
and food prices could force between 3 and 16  million people into 
extreme poverty (Hallegatte and Rozenberg, 2017). Within-country 
inequality is expected to increase following extreme weather events 
(Section  16.2.3.6 and Chapter 8). Households affected by climate-
related extreme events may be faced with continuous reconstruction 
efforts following extreme events (Adelekan and Fregene, 2015) or 
declines in critical livelihood resources in the agriculture, fisheries and

tourism sectors (Forster et  al., 2014, Section  3.5.1). Further erosion 
of livelihood security of vulnerable households creates the risk of 
poverty traps, particularly for rural and urban landless (Sections 8.2.1, 
8.3.3.1), for example in Malawi and Ethiopia (Section 9.9.3). Levels of 
labour productivity and economic outputs are projected to decrease as 
temperatures rise particularly in urban areas (Section 6.2.3.1). At the 
same time, higher utilities demand under higher urban temperatures 
exerts additional economic stresses on urban residents and households. 
Substantial, negative impacts on the livelihoods of over 180 million 
people are expected from changes to African grassland productivity 
(Section 5.5.3.1). In Western Uzbekistan, farmers’ incomes are at risk 
of declining (Section 10.4.5.3). For SIDS, loss of livelihoods is expected 
due to negative climatic impacts on coastal environments and

resources (Section 3.5.1). Negative effects on households from extreme events can also persist in the long term and in multiple dimensions. 
Exposure to disasters during the first year of life significantly reduces 
the number of years of schooling and increases the chances of being 
unemployed as an adult and living in a multidimensionally poor 
household (González et al., 2021). 
16.5 Key Risks across Sectors and Regions
This section builds on the analogous chapter in AR5 (Oppenheimer 
et al., 2014) to refine the definition of climate-related key risks (KRs) 
and criteria for identifying them (Section 16.5.1), and describe a broad 
range of key risks by sector and region as identified by the authors of 
WGII AR6 (Section 16.5.2, SM16.4). Based on this, eight clusters of key 
risks (i.e., Representative Key Risks, RKRs) are identified and assessed 
in terms of the conditions under which they would become severe. In

addition, the section assesses variation in KRs and RKRs by the level of 
global average warming, socioeconomic development pathways, and 
levels of adaptation, and illustrates the implications from resulting 
dynamics in all risk dimensions (hazard, exposure, vulnerability) along 
a case study of densely populated river deltas (Section 16.5.3). Last, 
interactions among RKRs are discussed (Section 16.5.4).
16.5.1 Defining Key Risks
A key risk is defined as a potentially severe risk and therefore especially 
relevant to the interpretation of dangerous anthropogenic interference 
(DAI) with the climate system, the prevention of which is the ultimate 
objective of the UNFCCC as stated in its Article 2 (Oppenheimer et al., 
2014). Key risks are therefore a relevant lens for the interpretation of this 
policy framing. The severity of a risk is a context-specific judgement based 
on a number of criteria discussed below. KRs are ‘potentially’ severe

because, while some could already reflect dangerous interference now, 
more typically they may become severe over time due to changes in the 
nature of hazards (or, more broadly, climatic impact drivers; IPCC, 2021) 
and/or of the exposure/vulnerability of societies or ecosystems to those 
hazards. They also may become severe due to the adverse consequences 
of adaptation or mitigation responses to the risk (on the former, see 
Section 17.5.1; the latter is not assessed separately here, except as it 
contributes to risks from climate hazards). Dangerous interferences in 
this chapter are considered over the course of the 21st century.
KRs may be defined for a wide variety of systems at a range of scales. 
The broadest definition is for the global human system or planetary 
ecological system, but KRs may also apply to regions, specific sectors 
or communities, or to parts of a system rather than to the system as 
a whole. For example, the population at the lower end of the wealth

distribution is often impacted by climate change much more severely 
than the rest of the population (Leichenko and Silva, 2014; Hallegatte 
and Rozenberg, 2017; Hallegatte et al., 2017; Pelling and Garschagen, 
2019).
KRs are determined not just by the nature of hazards, exposure, 
vulnerability and response options, but also by values, which determine 
the importance of a risk. Importance is understood here as the degree of 
relevance to interpreting DAI at a given system’s level or scale, and was

16
2451Key Risks across Sectors and Regions  Chapter 16
Constraints associated with limits by region and sector
Water
Ocean and
key infrastructureFood, fibre and other
ecosystem productsEconomicSocial/
CulturalHuman
and
policy FinancialInformation,
awareness
and
technology Physical Biological EconomicSocial/
and
awareness
and
technology Physical Biological
Africa Asia
Australasia Central and South America
Europe North America
Constraint
Secondary
MinorPrimary
Insuficient dataWater
Ocean and
ecosystem productsWater

Ocean and
Ocean and
Figure 16.8 |  Constraints associated with limits by region and sector. Data from Thomas et al. (2021), based on 1682 scientific publications reporting on adaptation-related 
responses in human systems. See SM16.1 for methods. Constraints are categorised as: (1) economic: existing livelihoods, economic structures, and economic mobility; (2) social/
cultural: social norms, identity, place attachment, beliefs, worldviews, values, awareness, education, social justice, and social support; (3) human capacity: individual, organisational,

and societal capabilities to set and achieve adaptation objectives over time including training, education, and skill development; (4) governance, institutions and policy: existing 
laws, regulations, procedural requirements, governance scope, effectiveness, institutional arrangements, adaptive capacity, and absorption capacity; (5) financial: lack of financial 
resources; (6) information/awareness/technology: lack of awareness or access to information or technology; (7) physical: presence of physical barriers; and (8) biologic/climatic: 
temperature, precipitation, salinity, acidity, and intensity and frequency of extreme events including storms, drought, and wind. Insufficient data: there is not enough literature to 
support an assessment (fewer than five studies available); Minor constraint: <20% of assessed literature identifies this constraint; Secondary constraint: 20–50% of assessed

literature identifies this constraint; Primary constraint: >50% of assessed literature identifies this constraint.

16
2452Chapter 16 Key Risks across Sectors and Regions
Table 16.4 |  Key constraints associated with limits to adaptation for regions
Region Key constraints associated with limits to adaptation
AfricaFinancial constraints inhibit implementation of a variety of adaptation strategies including ecosystem-based adaptation (Section 9.11.4.2) and adoption of 
drought-tolerant crops by farmers (Section 9.12.3).
Information constraints (including limited climate science information), governance constraints (such as communication disconnects between national, district and 
community levels) and human capacity constraints (limited capacities to analyse threats and impacts) are identified as negatively affecting the implementation of 
adaptation policies (Section 9.13.1).
Social/cultural constraints (social status, caste and gender) also affect adaptation in contexts with deep-rooted traditions (Section 9.12.4).

AsiaGovernance, human capacity, financial and informational constraints commonly present barriers to urban adaptation (Section 10.4.6.5).
Economic, governance, financial and informational constraints are related to both soft and hard limits to adaptation against a range of hazards in South Asia (Box 10.7), 
while in West Asia, physical constraints to heatwaves and drought have been associated with limits to adaptation (Box 10.7).
AustralasiaA range of constraints, including governance, information and awareness, social/cultural, human capacity and financial, have been identified as impeding adaptation 
action in the region (Section 11.7.2, Box 11.1).
Evidence of limits to adaptation are primarily for ecosystems (Sections 11.7.2, 11.6), although individuals and communities are also approaching soft limits owing to social 
constraints (Section 11.7.2).

South AmericaFinancial, governance, knowledge, biophysical and social/cultural constraints identified as most significant for adaptation (Section 12.5, Table 12.3).
Soft limits are largely related to governance constraints, while evidence of hard limits is related to biophysical constraints, such as glacier shrinking leading to loss of 
livelihoods and cultural values (Section 12.5.3.4).
EuropeKey constraints are identified as technical, biophysical, economic and social (Section 13.6.2.4).
For cities, settlements and key infrastructure, technical socioeconomic and environmental and regulatory constraints may lead to limits at a range of spatial scales 
(Figure 13.12).
Biophysical constraints may lead to limits to the ability of water saving and water efficiency measures to prevent water insecurity under high warming scenarios 
(Section 13.2.2.2).

North AmericaSocial/cultural, governance, financial, knowledge and biophysical constraints are identified as most significant for adaptation and leading to both soft and hard limits 
(Sections 14.5.2.1, 14.6, 14.6.2.1, Table 14.8).
Small islandsFinancial, governance, information/awareness, technological, cultural and human capacity constraints are identified as affecting adaptation and leading to soft limits 
(Sections 15.5.3, 15.5.4, 15.6.1, 15.6.3, 15.6.4).
Differences between constraints and soft limits in the small island context is marginal, with policymakers in the Caribbean and Indian Oceans seeing these as synonymous 
(Section 15.6.1).
Table 16.5 |  Evidence of climate change impacts affecting availability of financial resources.
Region Evidence of climate change impacts affecting availability of financial resources
AfricaNegative consequences for economic growth and GDP growth rate from higher average temperatures and lower rainfall (c) (Sections 9.9.1.1, 9.9.2, 9.9.3)

Economic losses from damage to infrastructure in the energy, transport, water supply, communication services, housing, health and education sectors (observed) 
(Sections 9.7.2.2, 9.8.2)
AsiaHigh coastal damages due to sea level rise (China, India, Korea, Japan, Russia) (c) (Section 10.4.6.3.4)
Decline in aquaculture production (Section 10.4.5.2.1)
Loss of coastal ecosystem services (Bangladesh) (Section 5.9.3.2.4)
AustralasiaLoss of wealth and negative impacts on GDP (Sections 11.5.1.2, 11.5.2.2)
High disaster costs (observed in Australia, New Zealand) (Section 11.5.2.1)
South AmericaHigh costs of extreme events relative to GDP (observed in Guatemala, Belize) (Section 12.3.1.4)
Decrease in growth of total GDP per capita and total income and labour income from one standard deviation in the intensity of a hurricane windstorm (Section 12.3.1.4)
EuropeNegative combined effect of multiple risks on economy for Europe in total (b) (Sections 13.9.1, 13.10.2)

Negative combined effect of multiple risks on economy for Southern Europe (c) (Sections 13.9.1, 13.10.2)
High economic costs in agriculture and construction following heatwaves and flooding (Sections 6.2.3.2, 7.4.2.2.1)
North AmericaSmall but persistent negative economy wide effect on GDP (observed in the USA and Mexico) (b) (Box 14.5)
Economic risks associated with high-temperature scenarios (c) (Box 14.5)
Small but persistent positive economy wide effect on GDP (observed in Canada) (b) (Box 14.5)
Significant economic costs for urban, natural and ecosystem infrastructure (USA) (Section 6.2.5.9)
High economic damages for a subset of sectors from high warming (southern and southeastern USA) (Box 14.5)
Adverse effects on municipal budgets due to costly liabilities, and disruption of financial markets (Box 14.5)
Small islandsHigh economic costs relative to GDP from extreme events, particularly tropical cyclones (observed) (Section 15.3.4.1)

Negative long-term implications of extreme events for state budgets (Section 8.2.1.4)
Inundation of almost all port and harbour facilities (Caribbean) (Section 15.3.4.1)
Notes:

16
2453Key Risks across Sectors and Regions  Chapter 16
an explicit criterion for identifying key vulnerabilities and risks in AR5 
(Oppenheimer et al., 2014). Because values can vary across individuals, 
communities or cultures, as well as over time, what constitutes a KR 
can vary widely from the perspective of each of these groups, or across 
individuals. For example, ecosystems providing indirect services and 
cultural assets such as historic buildings and archaeological sites may 
be considered very important to preserve by some people but not by 
others; and some types of infrastructure, such as a commuter rail, may 
be important to the well-being of some households but less so to others. 
Therefore, Chapter 16 authors do not make their own judgements about 
the importance of particular risks. Instead, we highlight importance as 
an overarching factor but identify and evaluate KRs based on four other 
criteria for what may be considered potentially severe.

Magnitude of adverse consequences. Magnitude measures the 
degree to which particular dimensions of a system are affected, 
should the risk materialise. Magnitude can include the size or extent 
of the system, the pervasiveness of the consequences across the 
system (geographically or in terms of affected population), as well 
as the degree of consequences. Consequences can be measured by 
a wide range of characteristics. For example, risks to food security 
can be measured as uncertain consequences for food consumption, 
access or prices. The magnitude of these consequences would be the 
degree of change in these measures induced by climate change and 
accounting for the interaction with exposure and vulnerability. In 
addition to pervasiveness and degree of change, several other aspects 
can contribute to a judgement of magnitude, although they refer to 
concepts that are difficult to capture and highly context-specific:

Irreversibility of consequences. Consequences that are irreversible, at 
least over long time scales, would be considered a higher risk than those 
that are temporary. For example, changes to the prevailing ecosystem in 
a given location may not be reversible on the decade to century scale.
Potential for impact thresholds or tipping points. Higher risks are 
posed by the potential for exceeding a threshold beyond which the 
magnitude or rate of an impact substantially increases.
Potential for cascading effects beyond system boundaries. Higher 
risks are posed by those with the potential to generate downstream 
cascading effects to other ecosystems, sectors or population groups 
within the affected system and/or to another system, whether 
neighbouring or distant (Cross-Chapter Box INTEREG in this Chapter).
Likelihood of adverse consequences. A higher probability of 
high-magnitude consequences poses a larger risk a priori, whatever

the scale considered. This probability may not be quantifiable, and it 
may be conditional on assumptions about the hazard, exposure or 
vulnerability associated with the risk.
Temporal characteristics of the risk. Risks that occur sooner, or that 
increase more rapidly over time, present greater challenges to natural 
and societal adaptation. A persistent risk (due to the persistence of 
the hazard, exposure and vulnerability) may also pose a higher threat 
than a temporary risk due, for example, to a short-term increase in 
the vulnerability of a population (e.g., due to conflict or an economic 
downturn).Ability to respond to the risk. Risks are more severe if the affected 
ecosystems or societies have limited ability to reduce hazards (e.g., 
for human systems, through mitigation, ecosystem management 
and possibly solar radiation management); to reduce exposure or 
vulnerability through various human or ecological adaptation options;

or to cope with or respond to the consequences, should they occur.
The relative influence of these different criteria is case-specific and left 
to author judgement in the identification of KRs (groups of authors in 
regional and sectoral chapters) and the assessment of representative 
key risks (author teams, see SM16.4). But in general, the more criteria 
are met, the higher is the risk.
16.5.2 Identification and Assessment of Key Risks and 
Representative Key Risks
16.5.2.1 Identification of Key Risks
The authors of the sectoral and regional chapters and cross chapter 
papers of the WGII AR6 Report identified more than 120 key risks 
(SM16.7.4). Authors were asked to rely on the above definition and 
criteria to identify risks that could potentially become severe according 
to changes in the associated hazards, the study systems’ exposure 
and/or vulnerability, and important adaptation strategies that could 
reduce these risks (see SM16.3 for methodology). Wherever possible,

identification is based on literature that includes projected future 
conditions for all three components of risk and adaptation. Where 
literature was insufficient, potential severity is based on current 
vulnerability and exposure to climate hazards and the expectation that 
hazards will increase in frequency and/or intensity in the future. This 
approach is more limited in that it does not consider future changes 
in exposure and vulnerability nor in adaptation, but has the benefit of 
being grounded in observed experience.
Table SM16.24 indicates that climate change presents a wide range of 
risks across scales, sectors and regions that could become severe under 
particular conditions of hazards, exposure and vulnerability, which 
may or may not occur. Some illustrations of the extent and diversity of 
KRs are provided here, and more detailed assessment can be found in 
the Chapters referenced in the table.
Global-scale KRs include threats to biodiversity in oceans, coastal

regions and on land, particularly in biodiversity hotspots, as well as 
other ecological risks such as geographic shifts in vegetation, tree 
mortality, reduction in populations and reduction in growth (such 
as for shellfish). These ecological risks include cascading impacts on 
livelihoods and food security. Global-scale risks also include risks to 
people, property and infrastructure from river flooding and extreme 
heat (particularly in urban areas), risks to fisheries (with implications 
for living standards and food security) and some health risks from 
food-borne diseases as well as psychopathologies.
Many KRs are especially prominent in particular regions or systems, 
or for particular subgroups of the population. For example, coastal 
systems and small islands are a nexus of many KRs, including those 
to ecosystems and their services, especially coral reefs; people (health,

16
2454Chapter 16 Key Risks across Sectors and Regions
livelihoods); and assets, including infrastructure. Risks to socio-
ecological systems in polar regions are also identified as KRs, as are 
ecological risks to the Amazon Forest in South America and savannahs 
in Africa. For some regions, risks from wildfire are of particular concern, 
including in Australasia and North America. Vector-borne diseases are 
a particular concern in Africa and Asia. Loss of cultural heritage is 
identified as a KR in small islands, mountain regions, Africa, Australasia 
and North America.
For many risks, low-income populations are particularly vulnerable to 
KRs. Climate-related impacts on malnutrition and other forms of food 
insecurity will be larger for this group, along with small-holder farming 
households and Indigenous communities reliant on agriculture, and for 
women, children, the elderly and the socially isolated (Section 5.12). 
KRs in coastal communities are expected to affect low-income

populations more strongly, including through risks to livelihoods of 
those reliant on coastal fisheries. KRs related to health are generally 
higher for low-income populations less likely to have adequate housing 
or access to infrastructure.
16.5.2.2 Identification of Representative Key Risks
As in AR5 Oppenheimer et al. (2014), major clusters of KRs are further 
analysed, and here referred to as ‘representative key risks’ (RKRs). 
RKRs were defined in a three-step process (SM16.3.1). First, half of 
Chapter 16 authors independently mapped the KRs (SM16.7.4) to a set 
of candidate RKRs. Second, all Chapter 16 authors discussed the set of 
independent results and proposed a list of RKRs, considering scope and 
overlap. Third, this proposal was discussed with a consultative group of 
about 20 WGII AR6 authors from other chapters closely involved in the 
KR identification process, and a final list of eight RKRs was identified 
(Table 16.6).

The RKRs are intended to capture the widest variety of KRs to 
human or ecological systems with a small number of categories that 
are easier to communicate and provide a manageable structure for 
further assessment. They expand the scope of some AR5 KR clusters 
(e.g., on coasts, health, food and water) and add new ones (e.g., 
on peace and human mobility). The RKRs encompass a diversity of 
types of systems, including an example of a geographically defined 
system (RKR-A on coastal regions), ecosystem well-being and integrity 
(RKR-B), a cross-cutting issue relevant to several outcomes of concern 
(RKR-C on critical infrastructure) and several topics focused directly on 
aspects of human well-being and security (RKR-D to RKR-H). This set 
of RKRs manages but does not eliminate overlap, instead providing 
alternative perspectives on underlying key risks that sometimes 
include complementary views on common risks. For example, the

water security RKR highlights the many key risks mediated by water 
quantity or quality, which are sometimes manifested as risk to food 
security (RKR-F) or health (RKR-E).
16.5.2.3 Assessment of Representative Key Risks
Each RKR was assessed by a team of four to nine members drawn 
from Chapter 16, other WGII AR6 chapters, and external contributing 
authors (SM16.4). The following subsections describe the scope of 
the category of risk (underlying KR considered) and the approach to defining ‘severe’ risks for each particular RKR. They also assess the 
conditions in terms of warming (more broadly, climatic impact drivers; 
(Ranasinghe et al., 2021), exposure/vulnerability and adaptation under 
which the RKR would become severe. For each of these dimensions, 
RKR teams considered generic levels ranging from High to Medium 
and Low. For warming levels, in line with WGI framing, High refers 
to climate outcomes consistent with RCP8.5 or higher, Low refers to

climate outcomes consistent with RCP2.6 or lower, and Medium refers 
to outcomes for scenarios between RCPs 2.6 and 8.5. For reference, 
the full range of warming levels (across all climate models) associated 
with RCP8.5 for the 2081–2100 period is 3.0–6.2°C; for RCP2.6 it is 
0.9–2.3°C; and for intermediate RCPs it is 1.8–3.6°C (Cross-Chapter 
Box  CLIMATE in Chapter 1). For Exposure-Vulnerability, levels are 
determined by the RKR teams relative to the range of future conditions 
considered in the literature, for example based on the Shared 
Socioeconomic Pathways (SSPs) in which future conditions based on 
SSPs 1 or 5 represent Low exposure or vulnerability and those based 
on SSPs 3 or 4 represent High exposure or vulnerability (O’Neill et al., 
2014; van Vuuren and Carter, 2014). For Adaptation, two main levels 
have been considered: High refers to near maximum potential, and Low 
refers to the continuation of today’s trends. Despite being intertwined

in reality, Exposure-Vulnerability and Adaptation conditions are 
distinguished to help understand their respective contributions to 
risk severity. Importantly, this assessment does not consider all risks, 
but only those that can be considered severe given the definition and 
criteria presented in Section 16.5.1. The assessment does not exclude 
the possibility that severe risks are already observed in some contexts, 
and considers projected risks through the end of this century.
Each RKR assessment followed a common set of guidelines (SM16.3) 
that included broad criteria for defining severity (Section  16.5.1), 
consideration of complex risks and interactions within and across 
RKRs, and consideration of risks across a range of scales, regions, and 
ecological and human development contexts. The specific definition of 
severity within each RKR was determined by the author teams of that 
assessment, applying different combinations of key risk criteria and

metrics as judged appropriate in each case. Definitions are transparent 
and use common criteria, but are nonetheless based on the respective 
author team’s judgement. Conclusions about severity and associated 
confidence statements are therefore conditional on those definitions.
Assessments are based on different types of evidence depending on 
the nature of the literature. In some cases, quantitative projections of 
potential impacts are available. In others and as for KR identification, 
the potential for severe risk is inferred from high levels of current 
vulnerability and the expectation that the relevant climate hazards 
(climatic impact drivers, CIDs) will increase in frequency or intensity 
in the future.
16.5.2.3.1 Risk to the integrity of low-lying coastal socio-
ecological systems (RKR-A)
RKR-A considers climate-change-related risks to low-lying coasts 
including their physical, ecological and human components. Low-

lying systems are those occupying land below 10 m of elevation that 
is contiguous and hydrologically connected to the sea (McGranahan 
et al., 2007). The assessment builds on Key Risks identified in Chapters

16
2455Key Risks across Sectors and Regions  Chapter 16
3 and 15, Cross Chapter Paper 2 as well as in the SROCC (Magnan 
et al., 2019; Oppenheimer et al., 2019). It highlights risks to (i) natural 
coastal protection and habitats; (ii) lives, livelihoods, culture and well-
being; and (iii) critical physical infrastructure; it therefore overlaps with 
several other RKRs (Figures 16.10, 16.11) but within a coastal focus. 
It encompasses all latitudes and considers multiple sources of climate 
hazards, including SLR, ocean warming and acidification, permafrost 
thaw, and sea ice loss and changes in weather extremes.
Severe risks to low-lying coasts involve irreversible long-term loss of 
land, critical ecosystem services, livelihoods, well-being or culture in 
relation to increasing combined drivers, including climate hazards and 
exposure and vulnerability conditions. The definition depends on the 
local context because of variation in the perception of tolerable risks

and the limits to adaptation (Handmer and Nalau, 2019). Accordingly, 
a qualitative range of consequences is presented here, in place of a 
quantitative global severe risk threshold.
The literature suggests that severe risks generally occur at the nexus 
of high levels and rates of anthropogenic-driven change in climate 
hazards (Section  16.2.3.2), concentrations of people and tangible 
and intangible assets, non-climate hazards such as sediment mining 
and ecosystem degradation (Section  3.4.2.1), and the reaching of 
adaptation limits (Section 16.4) (medium evidence, high agreement). 
In some Arctic communities and in communities reliant on warm-
water coral reefs, even 1.5–2°C warming will lead to severe risks from 
loss of ecosystem services (Section  3.4.2.2; Cross-Chapter Paper 6) 
(high confidence). Loss of land is already underway globally due to

accelerating coastal erosion and will be amplified by increased sea level extremes and permanent flooding (high confidence; Oppenheimer 
et al. 2019, Ranasinghe et al. 2021). Observed impacts of and projected 
increases in high-intensity extreme events (Ranasinghe et al. 2021) also 
provide evidence for severe risk to occur on livelihoods, infrastructure 
and well-being (Section 16.5.2.3.3) by mid-century (high confidence). 
Consequently, the combination of high warming, continued coastal 
development and low adaptation levels will challenge the habitability 
of many low-lying coastal communities in both developing and 
developed countries over the course of this century (limited evidence, 
high agreement) (Duvat et  al., 2021; Horton et  al., 2021). In some 
contexts, climate risks are already considered severe (medium 
evidence, medium agreement), and in others, even lower warming will 
induce severe risks to habitability, which will not necessarily be offset

by ambitious adaptation (limited evidence, medium agreement).
i) Natural coastal protection and habitats—severe risks from the 
loss of shoreline protection from reductions in wave attenuation 
(Beck et al., 2018, Sections 3.5.5.1, 3.5.4.5) and sediment delivery 
(Sections  3.4.2.5, 15.3.3) are already observed in some coastal 
systems (Section 16.2.3.1) and occur broadly even with 1.5°C of 
global warming (Hoegh-Guldberg et al., 2018a; Bindoff et al., 2019, 
Section 3.4.2). These impacts are the consequence of warming and 
SLR on coastal ecosystems.
Warm-water coral reefs are at risk of widespread loss of structural 
complexity and reef accretion by 2050 under 1.5°C global warming 
(Section 3.4.2.1) (high confidence). Kelp forests may experience shifts 
in community structure (Arafeh-Dalmau et al., 2019; Rogers-Bennett

and Catton, 2019; Smale, 2020; Smith et  al., 2021) with >2°C of Table 16.6 |  Climate-related representative key risks (RKRs). The scope of each RKR is further described in the assessments in Section 16.5.2.3. Relation to categories of 
overarching key risks identified in AR5 is provided for continuity.
CodeRepresentative key 
riskScopeRelation to AR5 overarching 
key risks;
for definitions, refer to Oppen-
heimer et al.. (2014)Subsection 
assessment
RKR-ARisk to low-lying coastal 
socio-ecological systemsRisks to ecosystem services, people, livelihoods and key infrastructure in low-lying 
coastal areas, and associated with a wide range of hazards, including sea level changes, 
ocean warming and acidification, weather extremes (storms, cyclones), sea ice loss, etc.Contains key risk (i), overlaps with key 
risks (iii) and (vii)16.5.2.3.1
RKR-BRisk to terrestrial and 
ocean ecosystemsTransformation of terrestrial and ocean/coastal ecosystems, including change in structure

and/or functioning, and/or loss of biodiversity.Contained in key risks (vii) and (viii) 16.5.2.3.2
RKR-CRisks associated 
with critical physical 
infrastructure, networks 
and servicesSystemic risks due to extreme events leading to the breakdown of physical infrastructure 
and networks providing critical goods and services.Overlaps with key risk (iii) 16.5.2.3.3
RKR-D Risk to living standardsEconomic impacts across scales, including impacts on gross domestic product (GDP), 
poverty and livelihoods, as well as the exacerbating effects of impacts on socioeconomic 
inequality between and within countries.Broader version of key risk (ii) 16.5.2.3.4
RKR-E Risk to human healthHuman mortality and morbidity, including heat-related impacts and vector-borne and 
waterborne diseases.Broader version of key risk (iv) 16.5.2.3.5
RKR-F Risk to food securityFood insecurity and the breakdown of food systems due to climate change effects on 
land or ocean resources.Overlaps with key risk (v) 16.5.2.3.6

RKR-G Risk to water securityRisk from water-related hazards (floods and droughts) and water quality deterioration. 
Focus on water scarcity, water-related disasters and risk to indigenous and traditional 
cultures and ways of life.Overlaps with key risk (iv) 16.5.2.3.7
RKR-HRisks to peace and to 
human mobilityRisks to peace within and among societies from armed conflict as well as risks to 
low-agency human mobility within and across state borders, including the potential for 
involuntarily immobile populations.New 16.5.2.3.8

16
2456Chapter 16 Key Risks across Sectors and Regions
global warming especially at lower latitudes (Section  3.4.2.2) (high 
confidence). In addition, depending on the local tide and sediment 
conditions, SLR associated with >1.5°C of global warming (SSP1–
2.6; 3.4.2.5) is sufficient to initiate shifts to alternate states in some 
seagrass and coastal wetland systems (van Belzen et  al., 2017; El-
Hacen et al., 2018, Section 3.4.2.5, Cross-Chapter Box SLR in Chapter 
3), and submergence of some mangrove forests (Section  3.4.2.5). A 
striking example of risks becoming severe at higher levels of warming 
is the one of coral islands with low elevation (Section 15.3.4, Box 15.1): 
the risk of loss of habitability transitions from Moderate-to-High under 
RCP2.6 for most island types (urban and rural) to High-to-Very High 
under RCP8.5 (Duvat et  al., 2021), even under a high adaptation 
scenario (Oppenheimer et al., 2019), partly due to declining sediment

supply (Perry et  al., 2018) and increased annual flooding (Giardino 
et al., 2018; Storlazzi et al., 2018).
More broadly, about 28,000 km2 of land have been lost globally 
since the 1980s due to anthropogenic factors (e.g., coastal structures, 
disruption of sediment fluxes) and coastal hazards (Mentaschi et al., 
2018), and an additional loss of 6000–17,000 km2 is estimated by the 
end of the century due to coastal erosion alone associated with SLR in 
combination with other drivers (Hinkel et al., 2013).
ii) Impacts to lives, livelihoods, culture and well-being—in the 
absence of effective adaptation, changing extreme and slow-onset 
hazards combined with anthropogenic drivers (e.g., increased 
population pressure at the coast between +5% and +13.6% by 
2100 compared with today, Jones and O’Neill, 2016) will lead to loss 
of lives, livelihoods, health, well-being and/or culture (McGregor 
et al., 2016; Pinnegar et al., 2019; Pugatch, 2019; Schneider and

Asch, 2020; Thomas and Benjamin, 2020; McNamara et al., 2021) 
(high confidence). Catastrophic examples that may foreshadow 
the future include Hurricane Sandy in 2012 (Strauss et al., 2021) 
and Super Typhoon Haiyan in 2013 (>6,000 deaths and inequities 
in access to safe housing; Trenberth et al. 2015) (Sections 6.2.2, 
6.3.5.1). Although there is no unique definition of ‘intolerable’ loss, 
risks are generally expected to become severe over this century 
(Tschakert et al., 2017; Dannenberg et al., 2019; Tschakert et al., 
2019). Globally, with High warming, 90–380 million more people 
will be exposed to annual flood levels by the mid- and end-century, 
respectively, compared with 250 million people today (Kulp and 
Strauss, 2019; Kirezci et  al., 2020), with potential implications 
on forced displacement or migration (Oppenheimer et al., 2019; 
Wrathall et al., 2019; Hauer et al., 2020; Lincke and Hinkel, 2021, 
Section 16.5.2.3.8). Some of the largest fish-producing and fish-

dependent ecoregions have already experienced losses of up to 
35% in marine fisheries productivity due to warming (Free et al., 
2019), and about 11% of the global population will face increasing 
nutritional risks if current trajectories continue (Golden et  al., 
2016). While difficult to measure, current climate-driven losses to 
(Indigenous) knowledge, traditions (Tschakert et al., 2019; Pearson 
et  al., 2021) and well-being (Ebi et  al., 2017; Cunsolo and Ellis, 
2018; Jaakkola et al., 2018) indicate such risk as already severe in 
some regions (limited evidence, medium agreement), jeopardising 
communities’ realisation of their rights to food, health and culture. 
In the Arctic, climate-driven changes to ice and weather regimes 
have substantially affected traditional coastal-based hunting and fishing activities (Fawcett et al., 2018; Galappaththi et al., 2019; 
Huntington et al., 2020; Nuttall, 2020, Cross-Chapter Paper 6), and

where permafrost thaw, SLR and coastal erosion are contributing 
to threatening cultural sites (Hollesen et al., 2018; Fenger-Nielsen 
iii) Critical physical infrastructure—severe risks are also illustrated 
through damages that lead to possibly long-lasting disruption 
of key services like transportation as well as energy generation 
and distribution in coastal areas (Section  16.5.2.3.3) under all 
RCPs (Section CCP2.2.3) and if no additional adaptation (medium 
confidence). Critical transport infrastructure is already suffering 
from structural failures in polar regions, for instance, due to 
permafrost thaw and increased erosion associated with ocean 
warming, storm surge flooding and loss of sea ice (Melvin et al., 
2017; Fang et al., 2018, Sections 14.5.2.8, 16.2.3.2, Cross-Chapter 
Paper 6). One hundred airports are projected to be below mean 
sea level in 2100 with 2°C of warming (i.e., 0.62 m SLR, Yesudian

and Dawson, 2021), including in small islands (Monioudi et  al., 
2018; Storlazzi et  al., 2018) and megacities. Projections show 
San Francisco International Airport, for instance, to be inundated 
by 2100 under the upper likely range of SLR in RCP8.5 (also 
considering subsidence trends, Shirzaei and Bürgmann, 2018). On 
the energy side, it is estimated that with 1.8 m SLR, for example, 
4 out of 13 US nuclear power plant facilities will become exposed 
to storm surges and 3 others will be surrounded or submerged by 
seawater (Jordaan et al., 2019; Jenkins et al., 2020).
16.5.2.3.2 Risk to terrestrial and ocean ecosystems (RKR-B)
This risk refers to transformations of terrestrial and ocean/coastal 
ecosystems that would include significant changes in structure and/
or functioning, and/or loss of a substantial fraction of species richness 
(commonly used to indicate loss of biodiversity). These are sourced 
mainly from Chapters 2 and 3, Cross-Chapter Paper 1, and reference

the 1.5C report, Chapter 4 from WGII AR5, and Chapter 4 from WGII 
AR4 Reports.
Severe adverse impacts on biodiversity include significant risk of 
species extinction (e.g., loss of a substantial fraction (one-tenth or 
more) of species from a local to global scale), mass population mortality 
(>50% of individuals or colonies killed), ecological disruption (order-
of-magnitude increases or abrupt reductions of population numbers or 
biomass), shifts in ecosystem structure and function (order-of magnitude 
increases or abrupt decreases in cover and/or biomass of novel growth 
forms or functional types) and/or a socioeconomically material increase 
in environmental risk (e.g., destruction by wildfire) or socioeconomically 
material decline in goods and services (e.g., carbon stock losses, loss of 
grazing, loss of pollination). Metrics relevant to SDGs are also germane.
A substantial proportion of biodiversity is at risk of being lost

below 2°C of global warming (Chapter 2), due to range reductions 
and loss globally, with this risk amplified roughly three times in 
insular ecosystems and biodiversity hotspots, due to the increased 
vulnerability of endemic species (Manes et  al., 2021). High-latitude, 
high-altitude, insular, freshwater, and coral reef ecosystems and 
biodiversity hotspots (Chapter 2, Cross-Chapter Paper 1) are at 
appreciable risk of substantial biodiversity loss due to climate change

16
2457Key Risks across Sectors and Regions  Chapter 16
even under Low warming (high confidence). These systems comprise a 
large fraction of unique and endemic biodiversity, with species impacts 
often exacerbated by multiple drivers of global change (Chapter 2, 
Chapter 3). Roughly one-third of all known plant species are extremely 
rare, vulnerable to climate impacts, and clustered in areas of higher 
projected rates of anthropogenic climate change (Enquist et al., 2019). 
Much evidence shows increased risk of the loss of 10% or more of 
terrestrial biodiversity with increasing anthropogenic climate change 
(Urban, 2015; Smith et  al., 2018) (medium confidence), likely with 
2°C warming above pre-industrial level (Chapter 2), with consequent 
degradation of terrestrial, freshwater and ocean ecosystems (Oliver 
et al., 2015) and adverse impacts on ecosystem services (Pecl et al., 
2017) and dependent human livelihoods (Dube et al., 2016). Adverse

impacts on biodiversity may show lagged responses (Essl et al., 2015), 
and loss of a substantial fraction of species could occur abruptly, 
simultaneously across multiple taxa, below 4°C of global warming 
(Trisos et al., 2020).
Mass population-level mortality (>50% of individuals or colonies 
killed) and resulting abrupt ecological changes can be caused by 
simple or compound climate extreme events, such as exceedance 
of upper thermal limits by vulnerable terrestrial species (Fey et  al., 
2015), who also note reduced mass mortality trends due to extreme 
low thermal events; marine heatwaves that can cause mortality, 
enhance invasive alien species establishment, and damage coastal 
ecological communities and small-scale fisheries (high confidence) 
(Section 3.4.2.7); and increased frequency and extent of wildfires that 
threaten populations dependent on habitat availability (like Koala 
Bears, Lam et al., 2020). Abrupt ecological changes are widespread and

increasing in frequency (Turner et al., 2020), and include tree mortality 
due to insect infestation exacerbated by drought, and ecosystem 
transformation due to wildfire (Vogt et al., 2020). Freshwater ecosystems 
and their biodiversity are at high risk of biodiversity loss and turnover 
due to climate change (precipitation change and warming, including 
warming of water bodies), due to high sensitivity of processes and 
life histories to thermal conditions and water quality (Chapter 2) (high 
confidence). In marine systems, heatwaves cause damages in coastal 
systems, including extensive coral bleaching and mortality (very high 
confidence) (Section  3.4.2.1), mass mortality of invertebrate species 
(low to high confidence, depending on system) (Sections  3.4.2.2, 
Section 3.4.2.5, Section 3.4.4.1), and abrupt mortality of kelp-forest 
(high confidence) (Section 3.4.2.3) and seagrass-meadow habitat (high 
confidence) (Section  3.4.4.2). The biodiversity of polar seas shows

strong impacts of climate change on phenological timing of plankton 
activity, Arctic fish species range contractions and species community 
change (Table  SM16.22) (high confidence). Extreme weather events 
and storm surges exacerbated by climate change have severe and 
sudden adverse impacts on coastal systems, including loss of seagrass 
meadows and mangrove forests (high confidence) (see Section 3.4.2.7, 
Section 3.4.2.8, Cross-Chapter Box EXTREMES in Chapter 2).
Ecological disruption (order-of-magnitude increases or abrupt 
reductions of population numbers or biomass) can occur due to 
unprecedented inter-species interactions with unpredictable outcomes 
in ‘novel ecosystems’ (Chapter 2) as species shift geographic ranges 
idiosyncratically in response to climatic drivers (Table  SM16.22). 
Idiosyncratic geographic shifts are now observed in an appreciable fraction of species studied (Chapter 2, Table 16.2). Commensal or

parasitic diseases may infect immunologically naive hosts (e.g., chytrid 
fungus in amphibians). Atypical disturbance regimes may be enhanced, 
for example, with the spread of flammable plant species (e.g., du Toit 
et  al., 2015), exacerbated by introduced species (e.g., Martin et  al., 
2015), thus significantly increasing risk of losses and damages to 
infrastructure and livelihoods, as well as ecological degradation, and 
challenging existing management approaches.
Landscape- and larger-scale shifts in ecosystem structure and function 
(order-of-magnitude increases or abrupt decreases in cover and/or 
biomass of novel growth forms or functional types) are occurring in non-
equilibrium ecosystems (systems which exist in multiple states, often 
disturbance-controlled) in response to changing disturbance regime, 
climate and rising CO 2 (high confidence) Woody plant encroachment 
has been occurring in multiple ecosystems, including subtropical and

tropical fire driven grassland and savanna systems, upland grassland 
systems, arid grasslands and shrublands (high confidence), leading 
to large-scale biodiversity changes, albedo changes, and impacts 
on water delivery, grazing services and human livelihoods (medium 
confidence). Expansion of grasses (alien and native) into xeric 
shrublands is occurring, causing increasing fire prevalence in previous 
fire-free vegetation (Cross-Chapter Paper 3). In tropical forests, 
repeated droughts and recurrence of large-scale anthropogenic 
fires increase forest degradation, loss of biodiversity and ecosystem 
functioning (high confidence) (Anderson et  al., 2018b; Longo et  al., 
2020). Accelerated growth rates and mortality of tropical trees is also 
adversely affecting tropical ecosystem functioning (McDowell et  al., 
2018; Aleixo et al., 2019). Projected changes in ecosystem functioning, 
such as via wildfire (Section 2.5.5.2), tree mortality (Section 2.5.5.3)

and woody encroachment under climate change (Chapter 2) would 
alter hydrological processes, with adverse implications for water yields 
and water supplies (Sankey et al., 2017; Robinne et al., 2018; Rodrigues 
et al., 2019; Uzun et al., 2020).
The loss of a substantial fraction of biodiversity globally, abrupt impacts 
such as significant local biodiversity loss and mass population mortality 
events, and ecological disruption due to novel species interactions 
have been observed or are projected at global warming levels 
below 2°C (Chapter 2 Table SM2.5, Cross Chapter Box: EXTREMES in 
Chapter 2, Section 2.4.4.3.1, Section 2.4.2.3.3) (medium confidence). 
Simple and compound impacts of extreme climate events are already 
causing significant losses and damages in vulnerable ecosystems, 
including through the facilitation of important global change drivers of 
ecological disruption and homogenisation like invasive species (high

confidence). Severe impacts on human livelihoods and infrastructure, 
and valuable ecosystem services, are all projected to accompany these 
changes. Adaptation potential for many of these risks is low due to 
the projected rate and magnitude of change, and to the requirement 
of significant amounts of land for terrestrial ecosystems (Hannah 
et al., 2020). Biodiversity conservation efforts may be hampered due 
to climate change impacts on the effectiveness of protected areas, 
with high sensitivity of effectiveness to forcing scenario (medium 
confidence). In addition, climate-related risks to ecosystems pose 
challenges to ecosystem-based adaptation responses (‘nature-based 
solutions’) (Section 2.1.3) (medium confidence).

16
2458Chapter 16 Key Risks across Sectors and Regions
16.5.2.3.3 Risk to critical physical infrastructure and networks (RKR-C)
RKR-C includes risks associated with the breakdown of physical 
infrastructure and networks which provide goods and services considered 
critical to the functioning of societies. It encompasses infrastructure 
systems for energy, water, transportation, telecommunications, health 
care and emergency response, as well as compound, cascading and cross-
boundary risks resulting from infrastructure interdependencies (Birkmann 
et  al., 2016; Fekete, 2019). Critical infrastructures such as transport 
or energy supply also play a central role in coping with climate risks, 
especially in acute disaster situations in which the services of transport 
infrastructure, communication technologies or electricity are particularly 
needed, despite the fact that these very systems are themselves exposed 
to disaster impacts (Garschagen et  al., 2016; Pescaroli et  al., 2018).

The major hazards driving such risks are acute extreme events such as 
cyclones, floods, droughts or fires (high confidence), but cumulative and 
chronic hazards such as SLR are also considered.
RKR-C is considered severe when the functioning of critical infrastructure 
cannot be secured and maintained against climate change impacts, 
resulting in the frequent and widespread breakdown of service delivery 
and eventually a significant rise of detrimental impacts on people (lives, 
livelihoods and well-being), the economy (including averted growth) or 
the environment (disruption and loss of ecosystems) above historically 
observed levels. Severity in this RKR is assessed on two levels for (i) 
direct impacts of climate change on infrastructure assets and networks 
(e.g., amount of port infrastructure damaged or destroyed by SLR, 
flooding and storms) on which most of the literature focuses, as well as 
(ii) indirect and cascading downstream impacts to people, economy and

environment (Markolf et al., 2019; Pyatkova et al., 2019; Chester et al., 
2020), for which attribution is more difficult and uncertainties tend to 
be much higher. Overall, the literature with quantified assessments of 
climate change infrastructure risks remains to be less extensive than 
for many other risks, particularly with regard to assessments focusing 
on the Global South. While climate-related changes in hazards are 
widely considered in the literature, changes in future exposure and 
vulnerability conditions are often not treated explicitly. In addition, 
the severity of infrastructure risks also depends on future trends in the 
capacity to maintain, repair and rebuild infrastructure and adapt it to 
new hazard intensities (medium evidence,  high agreement). These are 
mostly not quantified in a forward-looking manner in the literature; 
however, damage projections (see below) indicate a rapidly rising 
demand for investment, straining the financial capacity of countries

(medium evidence,  high agreement).
i) Risks related to direct impacts on critical infrastructure would 
become severe with high warming, current infrastructure 
development regimes and minimal adaptation (high confidence), 
and in some contexts even with low warming, current vulnerability 
and no additional adaptation (medium confidence), with severity 
defined as infrastructure damage and required maintenance 
costs exceeding multiple times the current levels. Transport and 
energy infrastructure in coasts and polar systems and along rivers 
are projected to face a particularly steep rise in risk, resulting in 
severe risk even under medium warming (high confidence). Risk in 
relation to the increasing intensity and frequency of extreme events 
might become severe before the middle of the century (medium confidence). Damages from multiple climate hazards to transport, 
energy, industry and social infrastructure in Europe could increase

10-fold by the 2080s, from 3.4 € billion annually to date, and 15-
fold for transport infrastructure, under Medium warming (A1B, 
~3°C by 2100) and with current adaptation levels, even if no further 
extension of the infrastructure in exposed areas is considered 
(Forzieri et al., 2018). Under High warming (RCP8.5) in 2100, the 
percent of roads in the USA that require rehabilitation due to high 
temperatures and precipitation is expected to increase to 23–33%, 
relative to 14% in 2100 when no climate change is considered 
(Mallick et  al., 2018). Projections of climate-induced changes in 
exposure are an incomplete measure of risk but in the absence 
of other metrics can serve as a proxy for the potential for severe 
impacts. In the circumpolar Arctic, 14.8% of critical infrastructure 
assets would be affected by climate change under RCP8.5 by 2050, 
with lifecycle replacement costs projected to increase by 27.7% if

infrastructure is to be preserved at current adaptation levels (Suter 
et al., 2019). Under RCP8.5, the number of ports under high risk 
will increase from 3.8% in the present day to 14.4% by 2100, as 
a result of increased coastal flooding and overtopping due to SLR, 
as well as the heat stress impacts of higher temperatures (Izaguirre 
et  al., 2021). In the UK under High warming (4°C), the number 
of clean and wastewater treatment sites located in the 1-in-75-
year floodplain will increase by a third relative to today by the 
2080s under current vulnerability and adaptation levels (Dawson 
et al., 2018). A global assessment of changing climate and water 
resources for electricity generation finds considerable reductions in 
usable hydropower and thermoelectric capacity by 2050 for a range 
of warming scenarios from Low to High, with absolute declines on 
average for most (61–74%) of the world’s hydropower resources 
and monthly maximum reductions above 30% of usable capacity

for over two-thirds of 1427 thermoelectric power plants worldwide 
(Van Vliet et al., 2016). Many studies find large technical potential 
for coordinated adaptation–mitigation policies in the electricity 
sector to avoid a significant portion of projected climate change 
impacts (e.g., a two-thirds reduction, and in some cases fully offset) 
(Ciscar and Dowling, 2014; Van Vliet et al., 2016; Gerlak et al., 2018; 
Allen-Dumas et al., 2019).
ii) Studies quantifying the indirect impacts of infrastructure failure 
on lives, livelihoods and economies are still rare but emerging, 
suggesting that risks would become severe in many contexts 
globally with high warming, current vulnerability and no additional 
adaptation (medium confidence). Severity in this context is defined 
as the potential to disrupt the lives, livelihoods and well-being 
of a significantly increased proportion of the population and to 
significantly forestall economic growth and development potential.

Global risks to air travel from SLR, expressed in terms of expected 
annual route disruptions, could increase by a factor of between 
17 and 69 by 2100 under the 1.5°C and the 95th percentile value 
of the RCP8.5 SLR scenario, respectively (Yesudian and Dawson, 
2021). By 2050, up to 185,000 airline passengers per year may be 
grounded due to extreme heat (48°C) if no additional adaptation 
is taken, roughly 23  times more than today (McKinsey Global 
Institute, 2020). In Africa, under RCP8.5 and without additional 
adaptation, a 250% increase in disruption time of the transport 
network is expected by 2050 due to extreme temperatures, a 76% 
increase due to precipitation, and 1400% increase due to flooding

16
2459Key Risks across Sectors and Regions  Chapter 16
(Cervigni et al., 2015). On the Dawlish railway section (UK), the 
number of days with line restrictions is set to increase by up to 
1170%, to as high as 84–120 yr–1 by 2100 due to 0.8 m SLR with 
High warming (Dawson et al., 2016). Next to the limited number of 
projections or scenarios of indirect impacts, additional inferences 
from studies focusing on past and current impacts can be drawn. 
Already today, climate-related impacts on transport and energy 
infrastructure reach far beyond the direct impacts on physical 
infrastructure, triggering indirect impacts on, for example, health 
and income (medium confidence). A case study of future flood 
hazard in Europe found that the indirect impact of a power outage 
on the local economy is six to eight times greater than the direct 
flood damage and asset repair costs, due to the interruption of daily 
economic activity (Karagiannis et al., 2019). In low- and middle-

income countries, the annual costs from infrastructure disruptions 
reach up to 300 billion USD for firms and 90 billion USD for private 
households, with natural hazards such as floods being responsible 
for 10–70% of these disruptions, depending on the sectors and 
regions (Hallegatte et al., 2019). Power outages triggered by floods 
or droughts have also been found to have substantial health 
implications, particularly among low-income populations (Klinger 
et al., 2014), and shown to impede disaster recovery efforts and 
severely disrupt local economies (Karagiannis et al., 2019; Nicolas 
et al., 2019). In addition, risks associated with infrastructure have 
the potential to become particularly severe when hazard-driven 
infrastructure disruptions undermine the capacity of emergency 
response in disaster situations (limited evidence, high agreement). 
A study on the UK shows, for example, that even a small increase in

minor road flooding leads to a disproportionately high disruption 
of the efficacy of emergency services (Yu et  al., 2020). Similar 
risks have been found for rural areas, particularly in developing 
countries (Alegre et al., 2020).
16.5.2.3.4 Risk to living standards (RKR-D)
This RKR includes risks to (i) aggregate economic output at the global 
and national levels, (ii) poverty and (iii) livelihoods, and their implications 
for economic inequality. It is informed by key risks identified by regional 
and sectoral chapters. Risks are potentially severe as measured by 
the magnitude of impacts in comparison with historical events or as 
inferred from the number of people currently vulnerable.
i) Risks to aggregate economic output would become severe at the 
global scale with high warming and minimal adaptation (medium 
confidence), with severity defined as the potential for persistent 
annual economic losses due to climate change to match or exceed

losses during the world’s worst historical economic recessions. With 
historically observed levels of adaptation, warming of ~4°C may 
cause a 10–23% decline in annual global GDP by 2100 relative to 
global GDP without warming, due to temperature impacts alone 
(Burke et  al., 2015; Kahn et  al., 2019; Kalkuhl and Wenz, 2020). 
These magnitudes exceed economic losses during the Great 
Recession (2008–2009, ~5% decline in global GDP , up to 15–18% 
in some countries) and the COVID-19 pandemic (2020, ~3% decline 
globally, up to 10% in some countries) (IMF , 2020; IMF , 2021). Unlike 
past recessions, climate change impacts would occur continuously 
every year. However, smaller effects (1–8%) are found when using alternative methodologies (Diaz and Moore, 2017; Nordhaus and 
Moffat, 2017; Kompas et  al., 2018; Kalkuhl and Wenz, 2020), 
assuming less warming (Kahn et al., 2019; Takakura et al., 2019), 
and assuming lower vulnerability and/or more adaptation (Diaz

and Moore, 2017); this literature is comprehensively summarised 
in Cross-Working Group Chapter Box ECONOMIC. Impacts at high 
levels of warming are particularly uncertain, as all methodologies 
require extrapolation and insufficiently incorporate possible tipping 
elements in the climate system (Kopp et al., 2016).
Annual economic output losses in developing countries could exceed 
the worst country-level losses during historical economic recessions 
(medium confidence). Assuming global warming of ~4°C by 2100, 
historical adaptation levels and high vulnerability, losses across Sub-
Saharan Africa may reach 12% of GDP by 2050 (Baarsch et al., 2020) 
and 80% by 2100 (Burke et  al., 2015), and ~9% on average across 
developing countries by 2100 (Acevedo et  al., 2017). The largest 
estimates are debated and depend on assumptions about development 
trends, adaptive capacity, and whether temperature impacts the level 
or growth rate of economic activity (Kalkuhl and Wenz, 2020). Severe

risks are more likely in (typically hotter) developing countries because 
of nonlinearities in the relationship between economic damages and 
temperature (Burke et al., 2015; Acevedo et al., 2017). These risks are 
highest in scenarios and countries with: a large portion of the workforce 
employed in highly exposed industries (Acevedo et al., 2017); a high 
concentration of population and economic activity on coastlines (Hsiang 
and Jina, 2014; Acevedo et al., 2017); and an increase in the frequency 
or intensity of disasters triggered by natural hazards (Berlemann and 
Wenzel, 2018; Botzen et al., 2019). Whether baseline economic growth 
may help avoid severe future risks is highly uncertain (Dell et al., 2012; 
Burke et al., 2015; Acevedo et al., 2017; Deryugina and Hsiang, 2017).
ii) Under medium warming pathways, climate change risks to poverty 
would become severe if vulnerability is high and adaptation 
is low (limited evidence, high agreement). We define poverty in

terms of absolute consumption levels and define severity as tens 
to hundreds of millions of additional people in poverty relative 
to the number without climate change (globally) or an absolute 
increase in the number of people living in poverty compared with 
today (nationally or locally). This global impact is comparable to 
the effect of the 2007 food price shock (De Hoyos and Medvedev, 
2009) and the 2020 COVID-19 pandemic (World Bank, 2020) and 
can be compared to about 700 million in poverty in 2017, down 
from 1.9 billion in 1990 (World Bank, 2020).
In a high-vulnerability development pathway, climate change in 2030 
could push 35–132 million people into extreme poverty, in addition to the 
people already in poverty assuming climate is unchanged (disregarding 
impacts from natural variability; Hallegatte and Rozenberg, 2017; Jafino 
et  al., 2020). In a low-warming pathway, risks from mitigation costs 
could also be severe if no progressive redistribution from carbon pricing

revenues is applied (Soergel et al., 2021). At the national level, there 
is limited evidence of climate change causing an absolute increase in 
poverty (e.g., absolute increase of ~1–2% yr−1 through 2040, Montaud 
et al., 2017). Potentially severe risks to poverty are also supported by 
(1) the observed impacts of past disasters (Winsemius et  al., 2018; 
Hallegatte et al., 2020; Rentschler and Melda, 2020) and previous crises

16
2460Chapter 16 Key Risks across Sectors and Regions
such as food price shocks (Ivanic and Martin, 2008) or current diseases 
(WHO, 2018) on poor people and on poverty; (2) the expectation that 
these events will become more intense or frequent in some regions 
(WGI Chapter 12, Ranasinghe et al., 2021); and (3) population growth 
and the low adaptive and coping capacities of the poor (Leichenko 
and Silva, 2014; Huynh and Stringer, 2018; Thomas et al., 2020). This 
literature provides indirect evidence that climate change will keep 
many people poor and may cause more than tens of millions to fall into 
poverty (limited evidence,  high agreement).
iii) Climate change poses severe risks to livelihoods at low levels 
of warming, high exposure/vulnerability and low adaptation in 
climate-sensitive regions, ecosystems and economic sectors (high confidence), where severity refers to the disruption of livelihoods 
for tens to hundreds of millions of additional people (Arnell and

Lloyd-Hughes, 2014; Liu et al., 2018). More widespread severe 
risks would occur at high levels of warming (with high exposure/
vulnerability and low adaptation) where there is additional 
potential for one or more social or ecological tipping points to 
be triggered (Cai et al., 2015; Cai et al., 2016b; Kopp et al., 2016; 
Steffen et al., 2018; Lenton et al., 2019), and for severe impacts 
on livelihoods to cascade from relatively more climate-sensitive 
to relatively less climate-sensitive sectors and regions (medium 
confidence) (Lawrence et al., 2020). Severity assessment is based 
on the current magnitude of exposure and vulnerability across 
multiple social and ecological systems, projected future exposure 
Illustrative examples from 
individual studies of risks to 
living standards and the 
conditions under which they 
could become severe
Type and 
level of
risk severity
conditionsHuma n
vulnerabilit yVery high
LowPopulation

densityCEV ALatin American and the Caribbean
• Poverty
5.8 million people pushed to 
extreme poverty by 2030 ( 7; 11 )
CEV ADeveloping Countries
• Aggregate GDP
9% average loss in GDP  by 2100 (1)
62% of the population are currently 
employed in climate-sensitive 
agricultural sector ( 16)
CCCC
EVEVEVEV
AAAA
GDP  losses of 80% by 2100 (3)
• Poverty
39.7 million people pushed to 
extreme poverty by 2030 ( 7; 11 )• Livelihoods• Aggregate GDP
GDP  losses of 10–15% by 2050 ( 2)Sub-Saharan AfricaCEV AAfrica
Projected convergence in 
country-level incomes by 2050 is 
delayed by 10 years ( 2)• Inequality
CEV ATropics and Coastal Regions
• Livelihoods
Climate-sensitive livelihoods, such
as agriculture and fisheries, would be 
severely impacted ( 8; 15)35.7 million people pushed
to extreme poverty by 2030
(7; 11 )C
CEV
EVA
ASouth Asia
• Poverty
40% of the population are
currently  employed in climate-
sensitive agricultural sector ( 16)CEV AEast Asia and Pacific
• Poverty

7.5 million people pushed to 
Exposure and
VulnerabilityClimate
(warming)
High
Medium
Low Not fully assessedC
C
CEV
EV
EVA
A
AWorld
Global GDP  losses of 10–23% by 
2100 due to temperature impacts 
alone (3; 12; 13)
• Poverty
35–132 million people pushed to 
extreme poverty by 2030 ( 6; 10)
330–396 million people could be 
exposed to lower agricultural yields 
and associated livelihood impacts ( 4)
CEV AUnited States of America
• Inequality
Economic damages as share of 
income in 2100 are 9 times larger in 
the poorest 5% of counties than in 
the richest 5% ( 5; 9)CEV AArctic Regions
Populations dependent on hunting 
and fishing face severe livelihood, 
cultural, and economic risks ( 14)Livelihoods
References:
1. Acevedo (2017); 2. Baarsch et al. (2020); 3. Burke et al. (2015); 4. Byers et al. (2018); 5. Carleton and Greenstone (2021) ; 6. Hallegatte (2017); 7. Hallegatte and Rozenberg (2017) ;

8. Hoegh-Guldberg (2018); 9. Hsiang et al. (2017); 10. Jafino (2020); 1 1. Jafino et al. (2020); 12. Kahn (2019); 13. Kalkuhl (2020); 14. Norden (2014); 15. Roy (2018); 16. World Bank (2020)
High
Medium
LowAdaptation
Low
Medium
High
Figure 16.9 |  Illustrative examples from individual studies of risks to living standards and the conditions under which they could become severe. Selected 
studies are not representative of the literature, but provide examples of potentially severe risks to aggregate economic output, poverty and livelihoods. High, medium and low levels 
of warming, exposure/vulnerability and adaptation are defined as in Figure 16.10.

16
2461Key Risks across Sectors and Regions  Chapter 16
and vulnerability, and the rate at which hazard frequency or 
intensity is expected to increase (Otto et  al., 2017; Roy et  al., 
2018; Li et al., 2019, Section 8.5). Without effective adaptation 
measures, regions with high dependence on climate-sensitive 
livelihoods—particularly agriculture and fisheries in the tropics 
and coastal regions—would be severely impacted even at low 
levels of warming (high confidence) (Hoegh-Guldberg et  al., 
2018b; Roy et al., 2018). For example, it is estimated that 330–
396 million people could be exposed to lower agricultural yields 
and associated livelihood impacts at warming between 1.5°C 
and 2°C (Byers et  al., 2018). Risks to the 200  million people 
with livelihoods derived from small-scale fisheries would also be 
severe, given sensitivity to ocean warming, acidification and coral 
reef loss occurring beyond 1.5°C (Cheung et al., 2018b; Froehlich

et al., 2018; Free et al., 2019; Barnard et al., 2021). Livelihoods in 
highly exposed locations, such as Small Island Developing States, 
low-lying coastal areas, arid or semiarid regions, the Arctic, and 
urban informal settlements or slums, are particularly vulnerable 
(Ford et al., 2015c; Hagenlocher et al., 2018; Ahmadalipour et al., 
2019; Tamura et al., 2019). Within populations, the poor, women, 
children, the elderly and Indigenous populations are especially 
vulnerable due to a combination of factors, including gendered 
divisions of paid and/or unpaid labour, as well as barriers in access 
to information, skills, services or resources (Bose, 2017; Thomas 
et al., 2019b; Anderson and Singh, 2020; Adzawla and Baumüller, 
2021) (high confidence). Future structural transformation 
could moderate risk severity by improving adaptive capacity, 
creating livelihoods in less climate-sensitive sectors, or by 
enabling sustainable migration to less climate-sensitive locations

(Henderson et al., 2017; Roy et al., 2018). However, successful 
risk moderation would depend upon simultaneous avoidance of 
both climate-change-related and mitigation-related (Doelman 
et  al., 2019; Fujimori et  al., 2019; Doelman et  al., 2020) or 
maladaptation-related risks (Magnan et  al., 2016; Benveniste 
et al., 2020; Schipper, 2020).
Climate change also could increase income inequality between 
countries (high confidence) as well as within them (medium evidence, 
high agreement) resulting from and exacerbating impacts on aggregate 
economic activity, poverty and livelihoods. Increasing inequality 
implies larger impacts on the least well-off, threatens their ability to 
respond to climate hazards, compromises basic principles of fairness 
and established global development goals, and potentially threatens 
the functioning of society and long-term progress (Roe and Siegel, 
2011; Cingano, 2014; van der Weide and Milanovic, 2018). There is

evidence that warming has slowed down the convergence in between-
country income in recent decades (Diffenbaugh and Burke, 2019). 
Future impacts may halt or even reverse this trend during this century 
owing to high sensitivity of developing economies (Burke et al., 2015; 
Pretis et al., 2018; Baarsch et al., 2020), although projections depend 
as much or more on future socioeconomic development pathways 
and mitigation policies as on warming levels (Takakura et al., 2019; 
Harding et  al., 2020; Taconet et  al., 2020). Within countries, studies 
that find adverse impacts on low-income groups imply an increase 
in inequality (Hallegatte and Rozenberg, 2017; Hsiang et  al., 2017), 
although evidence for long-term climate impacts on within-country 
inequality at global scale remains limited.16.5.2.3.5 Risk to human health (RKR-E)
This RKR includes (i) mortality from heat, and morbidity and mortality 
from (ii) vector-borne diseases and (iii) waterborne diseases. It builds

on KRs identified primarily in Chapter 7 and health risks in regional 
chapters.
A severe risk to health is the potential for a widespread, substantial 
worsening of health conditions due to climate change. We measure 
severity in terms of the magnitude of mortality and morbidity. We 
consider a severe mortality impact to be a sustained increase in the 
crude mortality rate (CMR) of more than about 2–4 deaths per 10,000 
people yr–1. This range of increase is consistent with current mortality 
impacts with substantial global effects, including traffic fatalities (CMR 
of 1.6/10,000 yr−1; IHME, 2019) and the COVID-19 pandemic (CMR 
of 4/10,000 yr−1, as of April 2021, expressed as an annualised rate; 
Ritchie et  al., 2021). We use these global rates as thresholds in all 
cases, recognising that they reflect substantial variation across regions 
and sub-populations (for other points of comparison, see IHME, 2019). 
Morbidity impacts are measured in numbers of disease cases or hospital

admissions. We find that severe health impacts are projected to occur for 
particular sub-populations and regions where vulnerability is currently 
high and is assumed to persist into the future; we focus our assessment 
on these cases. In other cases, literature is either inadequate or does not 
support severe outcomes.
i) Risks of heat-related mortality would become severe at global 
and regional scales with high levels of warming and vulnerability 
(medium confidence). Under these conditions (SSP3–8.5), 
accounting for adaptation, heat mortality would increase the 
global CMR by up to 7/10,000 yr−1 by 2100 (Carleton et  al., 
2020). For example, the USA would experience a CMR increase of 
2–4/10,000 yr−1 by the end of the century (medium vulnerability 
without adaptation, and recent vulnerability with adaptation, 
respectively) (Weinberger et al., 2017; Shindell et al., 2020). Also 
assuming no adaptation and recent vulnerability, most populations

of the world would experience an increase of 2–10  percentage 
points in the percentage of deaths attributable to heat by the end 
of the century (RCP8.5) (Vicedo-Cabrera, 2018a; Gasparrini, 2017). 
Harmful conditions for health are expected to increase in frequency 
and intensity over all land areas along with the rising temperatures 
in the coming decades (Pal and Eltahir, 2016; Russo et al., 2017; 
Ranasinghe et al., 2021; Saeed et al., 2021; Schwingshackl et al., 
2021). Projections of exposure are an incomplete measure of 
risk but suggest the potential for severe impacts. For example, 
the percent of global population exposed to deadly heat stress 
would increase from today’s 30% to 48–74% by the end of the 
century depending on level of warming and population distribution 
(Mora et al., 2017). Projected impacts are larger if exposure and/or 
vulnerability increases due to ageing of the population or increased

inequality (Weinberger et al., 2017; Chen et al., 2020a; IPCC, 2021) 
and with limited adaptation capacity (e.g., poor infrastructure, 
limited air conditioning, few medical and public health resources) 
(SM16.7.4) (Carleton et al., 2020). Higher risks are also expected in 
urban areas owing to hazard amplification (i.e., urban heat island 
effect) and in highly dense settlements with other environmental 
hazards such as air pollution (Zhao et al., 2018; Sera et al., 2019).

16
2462Chapter 16 Key Risks across Sectors and Regions
ii) Risks of vector-borne disease would become severe with high 
warming and current vulnerability, concentrated in children and 
in sensitive regions (medium confidence). Severity is defined by 
regionally substantial numbers of additional malaria deaths, 
disease cases and episodic hospitalisation demands (for dengue).
With high warming, the CMR for malaria among children under the 
age of 1 year could increase by 5.2–10.1/10,000 yr−1 in Africa under 
current vulnerability levels. This estimate assumes a net increase of 
70–130 million more people exposed to potential disease transmission 
due to climate change in a high-warming scenario (RCP8.5, end of 
century) (Caminade et  al., 2014; Colón-González et  al., 2021; Ryan 
et al., 2020), representing a 14–27% increase in the current population 
at risk (Ryan et al., 2020), and assumes children under 1 year of age

are facing the same crude mortality in the future as for the African 
region today (IHME, 2019). The largest increase is observed in Eastern 
Africa, where the population exposed could nearly double by 2080 
(Ryan et al., 2020) without accounting for population growth, driven 
mainly by changes among previously unexposed populations at higher 
altitude areas (Colón-González et  al., 2021). Actual future disease 
burden of malaria will be highly sensitive to regional socioeconomic 
development and the effectiveness of malaria intervention programs.
For dengue, with high warming and current levels of vulnerability there 
could be as much as a doubling of cases and hospital admissions per 
year globally, relative to today, driven by both warming and population 
growth. These estimates are derived by assuming similar relative 
incidence rates as today (Shepard et al., 2016) combined with projections 
of a more than doubling of the population exposed to potential disease

transmission by the end of the century in a high-warming scenario 
(RCP8.5), although much of this increase is driven by population growth 
(Colón-González et  al., 2018; Monaghan et  al., 2018; Messina et  al., 
2019). There are around 3 billion people exposed to dengue today.
iii) Climate change would lead to severe risks of morbidity and 
mortality caused by waterborne diseases, particularly for diarrhoea 
in children in many lower- and middle-income countries (LMICs) 
and where vulnerability remains high (medium confidence). The 
global CMR for diarrhoea is 1.98 for all ages, but varies by region 
and age group, reaching as high as 53 for <1-year-olds in Africa 
(IHME, 2019). In these vulnerable populations, even a small 
percentage increase can lead to substantial additional morbidity 
and mortality. For example, assuming no change in vulnerability 
or population, an increase in diarrhoea mortality of only 5% over 
2019 baseline rates would create a severe risk (CMR of 2.0) for

children under the age of 1 in the World Health Organization 
(WHO) Africa (AFRO) region. This percent increase due to climate 
change is plausible since diarrhoea incidence increases of 7% 
(95% confidence interval 3–10%) are associated with a 1°C 
increase in ambient temperature (WHO, 2014; Carlton et al., 2016), 
and diarrhoea is positively associated with heavy rainfall and 
flooding events (Levy et al., 2016), expected in some regions (WGI). 
Assuming vulnerability remains the same as today, mortality and 
morbidity rates would increase equivalently.
However, risks will be highly dependent on development trajectories, 
given that waterborne disease transmission is exacerbated by lack of clean drinking water and sanitation systems, inadequate food safety 
and hygiene conditions, lack of flood and drought protections, and 
interactions with other risks such as cholera outbreaks, food insecurity 
and infrastructure damage. Climate change threatens the progress that

has been made towards reducing the burden of diarrhoea. For example, 
in Sub-Saharan Africa, while overall diarrhoea rates are expected 
to continue to decline (GBD 2016 Diarrhoeal Disease Collaborators, 
2018), warming in 2030 (relative to the late 20th century) is projected 
to lead to diarrhoeal deaths in children under 15 equivalent to a CMR 
increase of 0.56/10,000 yr−1 (based on population projections for 
the region and age group; UN, 2020; WHO, 2014). In China, by 2030, 
climate change could delay progress towards reducing waterborne 
disease burden by 8–85 months (Hodges et al., 2014).
16.5.2.3.6 Risk to food security (RKR-F)
Climate change affects food security primarily through impacts on 
food production, including crops, livestock and fisheries, as well as 
disruptions in food supply chains, linked to global warming, drought, 
flooding, precipitation variability and weather extremes (Myers et al., 
2017; FAO et al., 2018; Mbow et al., 2019). This RKR builds on Key Risks

identified primarily in the Food, Fibre and Other Ecosystem Products 
Chapter, some sectoral (Health), and regional (Africa, Australasia, 
Central and South America, North America) chapters, as well as SR15, 
SRCCL and SROCC.
The severity of the risk to food security is defined here using a 
combination of criteria including the magnitude and likelihood of 
adverse consequences, affecting tens to hundreds of millions of people, 
timing of the risk and ability to respond to the risk. In this assessment, 
we use the number of undernourished people as a proxy outcome of 
these dimensions and their multiple interactions.
Climate change will pose severe risks in terms of increasing the number 
of undernourished people, affecting tens to hundreds of million people 
under High vulnerability and High warming, particularly among low-
income populations in developing countries (high confidence). Extreme 
weather events will increase risks of undernutrition even on a regional

scale, via spikes in food price and reduced income (high confidence) 
(FAO et al., 2018, Hickey and Unwin, 2020; Mbow et al., 2019). The 
timing of these impacts and our ability to respond to them vary based 
on the level of GHG emissions and Shared Socioeconomic Pathways 
(SSP).. Under a low vulnerability development pathway (SSP1), climate 
change starts posing a moderate risk to food security above 1°C of 
global warming (i.e., impacts become detectable and attributable to 
climate-related factors), while beyond 2.5°C the risk becomes high 
(widespread impacts on larger numbers or proportion of population 
or area, but with the potential to adapt or recover) (Hurlbert et al., 
2019). Under high vulnerability–high warming scenario (i.e., SSP3-
RCP6.0), up to 183 million additional people are projected to become 
undernourished in low-income countries owing to climate change by 
2050 (Mbow et al., 2019). Climate-related changes in food availability

and diet quality are estimated to result in a crude mortality rate of 
about 54 deaths per million people with about 2°C warming by 2050 
(SSP2, RCP8.5), most of them projected to occur in South and East Asia 
(67–231 deaths per million depending on the country) (Springmann 
et  al., 2016). In a medium vulnerability–high warming scenario

16
2463Key Risks across Sectors and Regions  Chapter 16
(SSP2, RCP6.0), Hasegawa et  al. (2018) project that the number of 
undernourished people increases by 24  million in 2050, compared 
with outcomes without climate change and accounting for the CO 2 
fertilisation effect. This number increases by around 78  million in a 
low-warming scenario (RCP2.6) accounting for the impacts of both 
climate change and mitigation policies. Caveats to these modelling 
studies are that most models (crop models in particular) are designed 
for long-term change in climate but not suited to project the impacts of 
short-term extreme events. The inclusion of adaptation measures into 
modelling estimates remains selective and partial.
Climate change risks of micronutrient deficiency will become severe 
in high-vulnerability development pathways and in the absence of 
societal adaptation, leading to hundreds of millions of additional 
people lacking key nutrients for atmospheric CO 2 levels above

500 ppm (high confidence) (Myers et  al., 2017; Nelson et  al., 2018; 
Mbow et al., 2019). For example, concentration of many micronutrients 
(e.g., phosphorus, potassium, calcium, sulphur, magnesium, iron, zinc, 
copper and manganese) can decrease by 5–10% under atmospheric 
CO 2 concentrations of 690 ppm (3.5°C warming). The decline in zinc 
content is projected to lead to an additional 150–220 million people 
affected by zinc deficiency with increases in existing deficiencies in 
more than 1  billion people (Myers et  al., 2017). Similarly, decrease 
in protein and micronutrient content in rice due to a higher CO 2 
concentration (568–590 ppm) can lead to 600  million people with 
rice as a staple at risk of micronutrient deficiency by 2050 (Zhu et al., 
2018). Additionally, the impact on protein content of increased CO 2 
concentration (>500 ppm) can lead an additional 150 million people 
with protein deficiency by 2050 (within the total of 1.4 billion people

with protein deficiency) in comparison with the scenario without 
increased CO 2 concentration (Medek et al., 2017).
16.5.2.3.7 Risk to water security (RKR-G)
Water security encompasses multiple dimensions: water for sanitation 
and hygiene, food production, economic activities, ecosystems, water-
induced disasters, and use of water for cultural purposes (Chapter 
4; Box 4.1; Section 4.6.1). Water security risks are a combination of 
water-related hazards such as floods, droughts and water quality 
deterioration, and exposure of vulnerable groups exposed to too little, 
too much or contaminated water. Reasons for these can include both 
environmental conditions and issues of safety and access influenced 
by effectiveness of water governance (Sadoff et al., 2020). These are 
manifest through loss of lives, property, livelihoods and culture, and 
impacts on human health and nutrition, ecosystems and water-related 
conflicts which in turn can drive forced human displacement.

This RKR focuses on three types of risks with the potential to become 
severe: those associated with water scarcity, those driven by water-
related disasters, and those impacting indigenous and traditional 
cultures and ways of life. Risk to water security constitutes a 
potentially severe risk because climate change could impact the 
hydrologic cycle in ways that would lead to substantial consequences 
for the health, livelihoods, property and cultures of large numbers 
of people. For those associated with water scarcity, ‘severe’ refers 
to magnitude (number of people in areas where water scarcity falls 
below recognised thresholds for adequate water supply per capita), along with the likelihood of unforeseen increases in water scarcity that 
outpace the ability to prepare for the increased risk by putting in place 
new large-scale infrastructure within the required time scale. For those 
associated with extreme events, ‘severe’ refers to magnitude (numbers

of people affected, including deaths, physical health impacts including 
disease, mental health impacts, loss of livelihoods, loss of or damage to 
property) and timing (e.g., events coinciding with other stresses, e.g., a 
pandemic occurring at a time when local infrastructures are weakened 
by an extreme weather event). Important water-related extreme 
events include river flooding caused by heavy and/or prolonged 
rainfall, glacial lake outburst floods, and droughts. For those impacting 
cultures, ‘severe’ refers to the loss of key aspects of traditional ways of 
life. This includes consequences of the above two KRs.
Risks associated with water scarcity have the potential to become 
severe based on projections of large numbers of people becoming 
exposed to low levels of water availability per person, where ‘water 
availability’ includes fresh water in the landscape, including soil 
moisture and streamflows, available for all uses including agriculture

as a dominant sector. Approximately 1.6  billion people currently 
experience ‘chronic’ water scarcity, defined as the availability of less 
than 1000 m3 of renewable sources of fresh water per person per 
year (Gosling and Arnell, 2016). In this context, we define a severe 
outcome as an additional 1  billion people experiencing ‘chronic’ 
water scarcity, relating to all uses of water, representing an increase 
of a magnitude comparable to current levels. The global number of 
people experiencing chronic water scarcity is projected to increase by 
approximately 800  million to 3  billion for 2°C global warming, and 
up to approximately 4 billion for 4°C global warming, considering the 
effects of climate change alone, with a 9 billion population (Gosling 
and Arnell, 2016). Severe outcomes are projected to occur even 
with no changes in exposure: present-day exposure is defined here 
as ‘medium’ since either an increase or decrease in exposure could

be possible. Vulnerability is not quantified in the literature assessed 
here, so in this assessment it is considered that severe outcomes 
could occur with present-day levels of vulnerability, again defined 
here as ‘medium’. Particularly severe outcomes (i.e., the high end 
of these ranges) are driven by regional patterns of climate change 
bringing severe reductions in precipitation and/or high levels of 
evapotranspiration in the most highly populated regions, leading 
to very substantial reductions in water availability compared with 
demand. There is strong consensus across models that water scarcity is 
projected to increase across substantial parts of the world even though 
projections disagree on which specific areas would see this impact. 
Moreover, a projected decrease in water scarcity in some regions does 
not prevent the increase in water scarcity in other regions becoming 
severe. Hence there is high confidence that risks to water scarcity have

the potential to become severe due to climate change. Consequences 
of water scarcity include potential competition and conflicts between 
water users (Vanham et  al., 2018), damaging livelihoods, hindering 
socioeconomic development and reducing human well-being, for 
example through malnutrition resulting from inadequate water 
supplies leading to long-term health impacts such as child stunting 
(Cooper et  al., 2019). The avoidance of these consequences at high 
levels of water scarcity would require transformational adaptations 
including large-scale interventions such as dams and water transfer 
infrastructure (Greve et al., 2018). Since these require many years or

16
2464Chapter 16 Key Risks across Sectors and Regions
even decades for planning and construction, and are also costly and 
irreversible and can potentially lead to lock-in and maladaptation, the 
potential for inadequate policy decisions made in the context of high 
uncertainties in regional climate changes brings the risk of a shortfall 
in adaptation. Around 2050, at approximately 2°C global warming, the 
risk of a substantial adaptation shortfall and hence severe outcomes 
for water scarcity have a relatively high likelihood across large parts 
of the southern USA and Mexico, northern Africa, parts of the Middle-
East, northern China, and southern Australia, as well as many parts of 
Northwest India and Pakistan (Greve et al., 2018).
Risks associated with water-related extreme events and disasters 
have the potential to become severe based on projections of large 
numbers of people or high values of assets being affected. The risks

to people from disasters can often only be quantified in terms of the 
hazard and exposure (the number of people affected), rather than the 
full consequences such as number of deaths, injuries or other health 
outcomes, as these often depend on complex or unpredictable factors 
such as the effectiveness of emergency and humanitarian responses or 
the access to healthcare. With approximately 50 million people per year 
currently affected by flooding (Alfieri et  al., 2017), we define severe 
outcomes as more than 100 million people affected by flooding. At 2°C 
global warming, between approximately 50  million and 150  million 
people are projected to be affected by flooding, with figures rising to 
110  million to 330  million at 4°C global warming. These projections 
assume present-day population and no additional adaptation, so no 
changes in exposure. Increased flood risk is projected by the WHO to 
lead to an additional 48,000 deaths of children under 15 years due to

diarrhoea by 2030, with Sub-Saharan Africa impacted the most (WHO, 
2014). Other consequences of floods that already occur include deaths 
by drowning, loss of access to fresh water, vector-borne diseases, 
mental health impacts, loss of livelihoods and loss of or damage to 
property. Many of these consequences depend on the vulnerability of 
individuals, households or communities to flooding impacts, for example 
through the presence or absence of measures to safeguard health 
and livelihoods, such as through infrastructure services, insurance or 
community support. The risks associated with these consequences 
could increase if there were no local adaptations to counter the effect 
of increased levels of hazard by reducing exposure and/or vulnerability. 
Climate-related changes to extreme events that would lead to these 
severe outcomes include increased frequency and/or magnitude of river 
floods of flash floods due to heavy or long-lasting precipitation, rapid

snowmelt, or catastrophic failure of glacial lake moraine dams. These 
climate conditions are projected to increase with global warming.
Risks to cultural uses of water can become severe if there is permanent 
loss of aspects of communities’ cultures due to changes in water, 
including loss of areas of ice or snow with spiritual meanings, loss 
of culturally important places of access to such places, and loss of 
culturally important subsistence practices including by Indigenous 
People (Chapter 4). This includes mountain regions where changes 
in the cryosphere are having profound impacts (Cross-Chapter Paper 
5). In these cases, severe outcomes would be defined locally rather 
than globally. Communities that lost a dominant environmental 
characteristic deeply associated with its cultural identity would be 
considered to be severely impacted. For example, due to the central

role that travel on sea ice plays in the life of Inuit communities, providing freedom and mental well-being, loss of sea ice can be 
argued to represent environmental dispossession of these communities 
(Durkalec et al., 2015). Traditional ways of life are therefore threatened, 
and resulting changes would be transformative rather than adaptive. 
Similarly, changes in streamflow affecting the availability of species for 
traditional hunting can also negatively impact Indigenous communities 
(Norton-Smith et al.). Such changes are already being seen at current 
levels of warming, but studies remain somewhat limited in number, so 
this assessment is assigned medium confidence because of medium 
evidence and medium agreement. WGI conclude that it is virtually 
certain that further warming will lead to further reductions in Northern 
Hemisphere snow cover, and mass loss in individual glacier regions 
is projected to be between approximately 30% and 100% by 2100

under high-warming scenarios (Chapter 4). Streamflows are projected 
to change in most major river basins worldwide by several tens of 
percent at 4°C global warming (Chapter 4).
There is strong potential for increases in water scarcity, flooding, loss of 
snow and ice and changes in water bodies to lead to severe outcomes 
such as deaths from water-related diseases, drowning and starvation, 
long-term health impacts arising from malnutrition and diseases, 
loss of property, loss of existence or access to places of cultural 
significance, loss of livelihoods and loss of aspects of culture especially 
for Indigenous People with traditional lifestyles. The numbers of people 
affected are projected to range from hundreds of millions to several 
billion, depending on the level of global warming and socioeconomic 
futures. A key aspect of the risk is the high uncertainty in future regional 
precipitation changes in many regions of high vulnerability, including

the potential for large and highly impactful changes, for which it may 
not be possible to provide adaptation measures before they become 
needed, leading to a high likelihood of adaptation deficits.
16.5.2.3.8 Risks to peace and to human mobility (RKR-H)
This RKR includes risks to peace within and among societies from armed 
conflict as well as risks to human mobility, epitomised by involuntary 
migration and displacement within and across state borders and 
involuntary immobility. Breakdown of peace and the inability of people 
to choose to move or stay challenge core elements of human security 
(Adger et al., 2014). Risks to peace also inform the agency and viability 
of mobility decisions. However, evidence does not indicate that human 
mobility constitutes a general risk to peace.
Breakdown of peace, materialised as overt or covert violence across 
social and spatial scales, constitutes a key risk because of its potential

to cause widespread loss of life, livelihood and well-being. Such 
impacts are considered severe if they result in at least 1000 excess 
battle-related deaths in a country in a year. This threshold is consistent 
with the conventional definition of war (Pettersson and Öberg, 2020). 
However, because armed conflict routinely causes significant material 
destruction, triggers mass displacement, threatens health and food 
security, and undermines economic activity and living standards 
(Baumann and Kuemmerle, 2016; FAO et al., 2017; de Waal, 2018), risks 
to peace can be considered severe also when conflict has cascading 
effects on other aspects of well-being and amplifies vulnerability 
to other RKRs. Beyond the magnitude of such impacts, the rapidity 
with which armed conflict can escalate and the challenges of ending

16
2465Key Risks across Sectors and Regions  Chapter 16
violence once it has broken out imply potentially very limited time and 
ability to respond for populations at risk.
Mobility is a universal strategy for pursuing well-being and managing 
household risks (Section 7.2.6; Cross-Chapter Box MIGRATE in Chapter 
7, UN, 2018) and, where it occurs in a safe and orderly fashion, can 
reduce social inequality and facilitate sustainable development 
(Franco Gavonel et al., 2021). Involuntary mobility constitutes a key 
risk because it implies reduced human agency with high potential for 
significant economic losses and non-material costs, an unequal gender 
burden, and amplified vulnerability to other RKRs (Schwerdtle et al., 
2018; Adger et al., 2020; Maharjan et al., 2020; Piggott-McKellar et al., 
2020). Climate change also may erode or overwhelm human capacity 
to use mobility as a coping strategy, producing involuntarily immobile

populations (Adams, 2016). A severe impact is when a large share of 
an affected population is forcibly displaced or prevented from moving, 
relative to normal mobility patterns, at local to global scale. However, 
because mobility may be a favourable mechanism for reducing risk or 
an adverse outcome of risk, depending on the circumstances under 
which it occurs, it is not possible to specify a simple quantitative 
threshold for when impacts become severe.
Complex causal pathways and lack of long-term projection studies 
presently prevent making confident quantitative judgements about how 
risks to peace and human mobility will materialise in response to specific 
warming levels, development pathways and adaptation scenarios. 
Literature concludes with medium confidence that risks to peace will 
increase with warming, with the largest impacts expected in weather-
sensitive communities with low resilience to climate extremes and high

prevalence of underlying risk factors (Theisen, 2017; Busby, 2018; Koubi, 
2019; von Uexkull and Buhaug, 2021). However, climate-driven impacts 
on societies will depend critically on future political and socioeconomic 
development trajectories (limited evidence, high agreement), suggesting 
that risks due to climate change are relevant primarily for highly 
vulnerable populations and for pessimistic development scenarios. 
Overall risks to peace may decline despite warming if non-climatic 
determinants are reduced sufficiently in the future.
Regular human mobility will continue regardless of climate change, 
but mobility-related risks will increase with warming, notably in 
densely populated hazard-prone regions, in small islands and low-lying 
coastal zones, and among populations with limited coping capacity 
(RKR-A; Section CCP2.2.2; Chapter 7) (high confidence). Such risks can 
become severe even with limited levels of warming for populations

with low adaptive capacity and whose settlements and livelihoods are 
critically sensitive to environmental conditions (medium evidence, high 
agreement). Likewise, risk of involuntary immobility could become 
severe for highly vulnerable populations with limited resources, even 
with moderate levels of warming (limited evidence, high agreement). 
Critically, population growth and shifting exposure will interact with 
warming to shape these risks (Davis et al., 2018; Hauer et al., 2020; 
Robinson, 2020a). Although climate-driven human mobility generally 
does not increase risks to peace (medium confidence), armed conflict 
is a major driver of forced displacement (high confidence).
Expert elicitation estimates that 4°C warming above pre-industrial 
levels will have severe and widespread effects on armed conflict with 26% probability, assuming no change from present levels in non-
climatic drivers (Mach et al., 2019). That judgement refers to impacts

that exceed the threshold for severity considered here, suggesting that 
global warming of 4°C would produce severe risks to peace under 
present societal conditions (low confidence). Future risks to peace will 
remain strongly influenced by socioeconomic development (Hegre et al., 
2016). A study of Sub-Saharan Africa that accounts for both temperature 
and socioeconomic changes, 2015–2065, concludes that determinants 
other than rising temperatures, notably quality of governance, will 
remain most influential in shaping overall levels of violence even in the 
high-warming RCP8.5 scenario (Witmer et al., 2017).
A larger empirical literature offers indirect evidence that climate change 
may produce severe risks to peace within this century by demonstrating 
how climate variability and extremes affect contemporary conflict 
dynamics, especially in contexts marked by low economic development, 
high economic dependence on climate-sensitive activities, high or

increasing social marginalisation, and fragile governance (medium 
confidence) (Sections  7.2.7, 16.2, Schleussner et  al., 2016a; Von 
Uexkull et al., 2016; Busby, 2018; Harari and Ferrara, 2018; Ide et al., 
2020; Scartozzi, 2020).
Climatic risks interact with economic, political and social drivers to 
create risks to human mobility both directly (through the threat of 
physical harm and destruction of property and infrastructure) and 
indirectly (via adverse impacts on livelihood and well-being). Extreme 
weather events are leading causes of forced displacement (Cross-
Chapter Box MIGRATE in Chapter 7, IDMC, 2020). Projected increases in 
the frequency and severity of extreme events ( Ranasinghe et al., 2021) 
in combination with future population growth in hazard-prone regions 
(e.g., Merkens et al., 2016) suggest that risks to mobility will increase 
in response to future global warming (Robalino et  al., 2015; Davis

et al., 2018; Rigaud et al., 2018). For example, moving from RCP2.6 
to RCP8.5 (entailing ~0.5°C additional global warming by 2050) is 
projected to increase internal migration by 2050 from 51 [31–72] 
million to 118 [92–143] million people across South Asia, Latin America 
and Africa (Rigaud et al., 2018), although those estimates principally 
comprise migrants, whose decisions are also informed by non-climatic 
drivers, rather than involuntarily displaced people. Global levels of 
flood displacement are estimated to increase by 50% with each 1°C 
warming (Kam et al., 2021). Should future warming reduce adaptation 
options for vulnerable populations ( Section 16.4), a consequence may 
be higher levels of involuntary migration and immobility (Grecequet 
et  al., 2017; Otto et  al., 2017). There is little evidence that climate-
driven mobility negatively affects peace (Brzoska and Fröhlich, 2016; 
Burrows and Kinney, 2016; Freeman, 2017; Petrova, 2021).

There is high agreement that even moderate levels of future SLR will 
severely amplify involuntary migration and displacement in small 
islands and densely populated low-lying coastal areas in the absence 
of appropriate adaptive responses (high confidence) (Hauer, 2017; IPCC, 
2019b; Hauer et al., 2020; McMichael et al., 2020, Sections 15.3.4, 16.4). 
In some contexts, climate change also may accelerate migration towards 
high-exposure coastal areas (Bell et al., 2021). Under a high-emissions 
RCP8.5 scenario (global median 0.7 m SLR by 2100), the number of 
people exposed to annual coastal flooding may more than double by 
2100 compared with present numbers (Kulp and Strauss, 2019). In the

16
2466Chapter 16 Key Risks across Sectors and Regions
USA alone, SLR of 0.9 m could potentially put 4.2 million people at risk 
of inundation by the end of this century (Hauer, 2017). However, number 
of people exposed to SLR does not evenly translate to forcibly displaced 
populations (Hauer et  al., 2020). Ascertaining how many people will 
move forcibly or as an adaptive response to SLR is inherently challenging 
because of the complex and highly individual nature of migration 
decisions (Black et al., 2013; Boas et al., 2019; Piguet, 2019; Bell et al., 
2021). Implications of climate change for risks to human mobility 
across borders are even harder to quantify and highly uncertain, due 
to unknown developments in legal and political conditions that govern 
international migration (McLeman, 2019; Wrathall et al., 2019).
16.5.2.4 Synthesis of the Assessment of Representative Key Risks
Figure 16.10 provides a synthesis of the RKRs and the conditions that

lead to severe risks over the course of the 21st century, as assessed 
in Sections  16.5.2.3.1–16.5.2.3.8 (see Table  SM16.14 for further 
description). It identifies sets of conditions—defined by levels of 
warming, exposure/vulnerability and adaptation—that would produce 
severe risk with a particular level of confidence. The risks are of two 
scopes: broadly applicable, meaning that the risks described by a 
particular KR or RKR would be severe pervasively and even globally; 
and specific, meaning that these risks would apply to particular areas, 
sectors or groups of people.
Five main messages arise from this synthesis:
Severe risk is rarely driven by a single determinant (warming, 
exposure/vulnerability, adaptation), but rather by a combination 
of conditions that jointly produce the level of pervasiveness of 
consequences, irreversibility, thresholds, cascading effects, likelihood 
of consequences, temporal characteristics of risk and the systems’

ability to respond (medium to high confidence). In other words, climate 
risk is not a matter of changing CIDs only, but of the confrontation 
between changing CIDs and changing socio-ecological conditions.
In most of the RKRs, severe risk for broadly applicable situations requires 
high levels of warming or exposure/vulnerability, or low adaptation. In 
many cases, it is associated with several of these conditions occurring 
simultaneously (e.g., high warming and high vulnerability). Examples 
include low-lying coastal areas (RKR-A; medium confidence), loss of 
livelihoods (RKR-D; medium confidence) or armed conflicts (RKR-H; 
low confidence).
High warming and exposure/vulnerability combined with low adaptation 
is, however, not necessarily required to lead to severe risk, and various 
other sets of conditions can lead to such an outcome. For example:
Without high levels of warming. T his is especially the case for terrestrial

and marine ecosystems (RKR-B) and water security (RKR-G) for which 
even medium to low levels of warming will generate severe risk, 
depending on the processes considered (e.g., mass population-level 
mortality and ecological disruption for ecosystems). This is also the 
case when more specific situations are considered, for example in the 
case of (in)voluntary mobility of vulnerable populations with limited 
resources (RKR-H), and for some critical infrastructure in already highly 
exposed and vulnerable contexts (RKR-C).With high levels of adaptation. H igh levels of adaptation will not 
necessarily avoid severe risk, as is illustrated by the cases of coral-
dependent and arctic coastal communities (RKR-A), some terrestrial 
and marine ecosystems (RKR-B), and water scarcity and the cultural 
uses of water (RKR-G).
All RKR assessments indicate that risks are higher in high-vulnerability 
development pathways, and in some cases high vulnerability can occur

in high-income societies. Examples include the possibility of increasing 
coastal settlement and the location of critical infrastructure in highly 
exposed locations (RKR-A, RKR-C), including to floods (RKR-G) and risks 
to terrestrial and marine ecosystems (RKR-B). The assessment therefore 
shows that, depending on socioeconomic trends especially in terms 
of equity, social justice and income sustainability, as well as on the 
ability to shift towards more climate-resilient economic and settlement 
systems (e.g., at the coast), higher-income societies also are at serious 
risk of being substantially affected in the decades to century to come.
In terms of the time frames, most of the RKRs conclude that severe 
risks to many dimensions (ecosystems, health, etc.) are expected to 
occur by the end of the 21st century and across the globe. Some RKRs, 
however, highlight that severe risk could occur far earlier, for example

as soon as a warming level of 1.5°C or 2°C is reached, which means 
potentially well before mid-century (IPCC, 2021). In some cases, risks 
are already considered severe, for example after major climatic events 
such as tropical storms (RKR-A).
16.5.3 Variation of Key Risks across Levels of Global 
Warming, Exposure and Vulnerability, and 
This section builds on Sections  16.5.1 and 16.5.2 as well as on 
additional literature to illustrate how consequences associated with 
KRs and RKRs are projected to vary with three types of determinants: 
global average warming level, as a proxy for associated changes 
in climate hazards (CIDs, Ranasinghe et  al., 2021); socioeconomic 
development pathway, as a means of capturing alternative future 
exposure and vulnerability conditions; and level of adaptation to 
reflect the extent to which successful adaptation is implemented. While 
these three dimensions are partly intertwined—for example, warming

and adaptation scenarios are constrained by development pathways 
(Chapter 18)—this section assesses the influence of each dimension 
separately (Sections  16.5.3.2–16.5.3.4) to highlight how sensitivity 
varies across these dimensions for different KRs and RKRs. We then 
bring the dimensions together in an illustrative example (large deltas; 
Section 16.5.3.5).
16.5.3.1 Warming Level, Including Risks Avoided by Mitigation
Studies illustrating sensitivity to warming level typically do so by 
contrasting projected impacts for the same socioeconomic conditions 
but different climate pathways or temperature levels, often based on 
Representative Concentration Pathways (RCPs) (van Vuuren and Carter, 
2014). We refer to future climate conditions either based on their 
global average warming level or as a ‘high warming’ scenario (based 
on RCP8.5), medium warming (RCP4.5 or RCP6.0) or low warming

16
2467Key Risks across Sectors and Regions  Chapter 16
Risk severity condition sbytheendo fthiscentury
N.B.: only setso fconditions assesse dinthechaptera rereported
Type andlevel Scope Conﬁdence levels
Climate (warming)C
EV Exposure and V ulnerabilityLowMediumHigh
Low
Notfully assessedHigh
A Adaptation*
**Broadly applicable
(risks are severe pervasivelya ndeven globally)
Speciﬁc
(risks aret oparticular areas
sectors or groups of people)
N.B.:f ordetails andexamples, see Table SM16.24 in the
supplementary information associated with the chapter.Loss oflives,
livelihoods&
well-being(a) Low-lying coastal systems
(e) Human health
 (g) Water security
Water
scarcityWaterborne
diseases
A*EVCVector-borne
diseasesHeat-related
mortality
disastersIndig. &trad.
cultures&
wayso flifeNat. coastal
protection&
habitatsDisruptiono f
transport
systems
**
A** * *
(c) Critical infrastructure, networks
 and services
A EVC
A EVC**
Impactso f
failureo nlives,
livelihoods, eco-

nomiesDamage&
disruption*A EVC
A EVC*
**
A EVC**
(d) Living standards
Loss of
livelihoodsIncreased
poverty Aggregate
economic
impacts EV
AA C
EVC*
**
A EVC
A EVC*
**A EVC*(b) Terrestrial and marine ecosystems
Losso f
biodiversityNat. coastal
habitatsLoss
ecosystem
goods/servicesChange struc-
ture/functioning*A EVC
/ *A EVC
C*A EV
A EVCA**EVC A EVC
A EVC EVCInvoluntary
(im)mobilityArmed
conﬂicts
A EVC
A EVC****/
Increased
hungerDecline provis.
ecosystem
servicesA EVC
A EVC
A EVC*
*
*(f) Food security
A EVC(h) Peace & human mobilitySynthesis of the severity conditions for Representative Key Risks by the end of this century
Figure 16.10 |  Synthesis of the severity conditions for Representative Key Risks by the end of this century. The figure does not aim to describe severity conditions 
exhaustively for each RKR, but rather to illustrate the risks highlighted in this report (Sections 16.5.2.3.1 to 16.5.2.3.8). Coloured circles represent the levels of warming (climate),

exposure/vulnerability, and adaptation that would lead to severe risks for particular key risks and RKRs. Each set of three circles represents a combination of conditions that would 
lead to severe risk with a particular level of confidence, indicated by the number of black dots to the right of the set, and for a particular scope, indicated by the number of stars to

16
2468Chapter 16 Key Risks across Sectors and Regions
(RCP2.6 or 1.5°C scenarios). Because some of these scenarios assume 
no or minimal mitigation (RCP8.5, RCP6.0) while others do (RCP4.5, 
RCP2.6), differences in outcomes between them reflect risks avoided 
by mitigation (assuming consistent socioeconomic assumptions).
Some ecological risks (Chapter 2) are particularly sensitive to warming. 
For example, warm-water coral reefs are already experiencing High risk 
levels and are expected to face Very High risks under 1.5°C of glob-
al warming (Hoegh-Guldberg et al., 2018a; Bindoff et al., 2019). Some 
societal risks, such as human mortality due to extreme heat, also are 
sensitive to warming. A medium-warming scenario (relative to high 
warming) reduces projected global average mortality due to heat 
from seven deaths per 10,000 people yr–1 (7/10,000 yr−1) by 2100 to 
~1/10,000 yr−1, assuming high-vulnerability societal conditions (Carle-

ton et al., 2020). At the national level, without considering adaptation, 
reductions in a broader measure of mortality are projected across a 
range of countries including Colombia, the Philippines, and several in 
Europe (Guo et al., 2018), and exposure of the US population to high-
mortality heatwaves is reduced by nearly half (Anderson et al., 2018a). 
Without considering changes in exposure or vulnerability, warming of 
1.5–2°C (compared with 4–5°C) reduces global mortality impacts from 
an increase of 2.1–13.0% to 0.1–2.2% (Gasparrini et al., 2017; Vicedo-
Cabrera et al., 2018a) and impacts in China from up to 4/10,000 yr−1 
(Weinberger et al., 2017) to 0.3–0.5/10,000 yr−1 (Wang and Hijmans, 
2019).
A low-warming scenario (relative to high warming) reduces 
aggregate economic impacts from around 7% of global GDP to less 
than 1% (Takakura et al., 2019), and changes impacts on the number 
of people suffering from hunger from an increase (by 7–55 million)

to a decrease (by up to 6 million) (Janssens et al., 2020). Low versus 
high warming also reduces the coastal population at risk of flooding 
due to SLR from tripling by 2100 (relative to today) to doubling 
(Kulp and Strauss, 2019, Section 16.5.2.3.2). The SROCC estimates 
that SLR risks are reduced from Moderate-to-High to Moderate for 
large tropical agricultural deltas and resource-rich megacities, and 
from High and Very High to Moderate-to-High for Arctic human 
communities and urban atoll islands, respectively (Oppenheimer 
Higher levels of warming are projected to also generate higher income 
inequality between countries (e.g., Pretis et al., 2018; Takakura et al., 
2019) as well as within them (Hallegatte et  al., 2016) even though 
other drivers will be more important (Section  16.5.2.3.5). Similarly, 
climate and weather events are expected to play an increasing role 
in shaping risks to peace (limited evidence, medium agreement)

and migration (medium evidence, high agreement) in the future, but 
uncertainty is high due to complex causal pathways and non-climate 
factors likely dominate outcomes (Section  16.5.2.3.8). There is high 
agreement that future SLR will amplify levels of forced migration from small islands and low-lying coastal areas in the absence of appropriate 
adaptive responses (Oppenheimer et al., 2019).
A synthesis of risk assessments in the recent IPCC Special Reports 
(Magnan et al., 2021) concludes that an integrated measure of today’s 
global climate risk level will increase by the end of this century by 
two- to four-fold under low and high warming, respectively (based on 
aggregated scores developed in the study). An additional comparison 
of risk levels under +1.5°C and +2°C suggests that every additional 
0.5°C of global warming will increase the risk level by about a third.
16.5.3.2 Exposure and Vulnerability Trends
Development pathways describe plausible alternative futures of

societal change and are critical to future risks because they affect 
outcomes of concern both through non-climate and climate-related 
channels (very high confidence).
Studies illustrating sensitivity to development pathways typically do 
so by contrasting projected impacts for the same climate pathway or 
temperature level but different levels of socioeconomic exposure and 
vulnerability, for example based on SSPs (O’Neill et al., 2014; Van Vuuren 
et al., 2014). Or, they infer sensitivity to future development pathways 
based on differences in impacts across current populations with different 
levels of exposure or vulnerability. We refer to future conditions based 
on SSPs 1 or 5 as ‘low exposure’ or ‘low vulnerability’ conditions, and 
those based on SSPs 3 or 4 as ‘high exposure’ or ‘high vulnerability’ 
conditions (O’Neill et al., 2014; van Vuuren and Carter, 2014).
A wide range of climate change impacts depend strongly on

development pathway (high confidence). A low (relative to high) 
exposure future, determined by limited population growth and 
urbanisation, results in about 30% fewer people exposed to extreme 
heat globally (Jones et  al., 2018b) and about 50% fewer in Africa 
(Rohat et al., 2019a), similar to the effect of a medium versus high 
level of global warming. Low-exposure conditions also reduce the 
fraction of the population in Europe at very high risk of heat stress 
from 39% to 11% (Rohat et al., 2019b). Demographic differences lead 
to a reduction in the global population exposed to mosquitos acting 
as viral disease vectors by more than half (Monaghan et al., 2018) and 
exposure to wildfire risk by nearly half (Knorr et al., 2016).
Studies are increasingly going beyond exposure to incorporate future 
vulnerability, finding that it is often the dominant determinant of risk 
(high confidence). A low (relative to high) vulnerability future reduces

the risk to global poverty by an order of magnitude, robustly across 
approaches that account for macroeconomic growth, structural change 
in the economy, inequality, and access to infrastructure services 
(Hallegatte and Rozenberg, 2017), or for the exposure of vulnerable 
populations to multi-sector climate-related risks (Byers et al., 2018). A the left of the set. The two scopes are ‘broadly applicable’, meaning applicable pervasively and even globally, and ‘specific’, meaning applicable to particular areas, sectors or groups 
of people. Details of confidence levels and scopes can be found in Section 16.5.2.3. In terms of severity condition levels (Section 16.5.2.3), for warming levels (coloured circles 
labelled ‘C’ in the figure), High refers to climate outcomes consistent with RCP8.5 or higher, Low refers to climate outcomes consistent with RCP2.6 or lower, and Medium refers to

intermediary climate scenarios. Exposure-Vulnerability levels are determined by the RKR teams relative to the range of future conditions considered in the literature. For Adaptation, 
High refers to near maximum potential and Low refers to the continuation of today’s trends. Despite being intertwined in reality, Exposure-Vulnerability and Adaptation conditions 
are distinguished to help understand their respective contributions to risk severity.

16
2469Key Risks across Sectors and Regions  Chapter 16
low (relative to high) vulnerability future also reduces the global mean 
number of temperature-attributable deaths in 2080–2095 due to 
enteric infections by an order of magnitude (from >80,000 to <7000; 
(Chua et al., 2021)). Low future socioeconomic vulnerability to flooding 
reduces global fatalities and economic losses by 69–96% (Jongman 
et  al., 2015). Low vulnerability as measured by indicators including 
per capita GDP , education, governance, water demand and storage 
potential reduces water insecurity by a factor of three (Koutroulis 
et  al., 2019). A scenario with reduced barriers to trade reduces the 
number of people at risk of hunger due to climate change by 64% 
(Janssens et al., 2020). Structural transformation of the economy (shift 
of the workforce from highly exposed sectors such as agriculture and 
fishing to less exposed sectors such as services) lowers GDP impact

projections by 25–30% in today’s developing countries by 2100 
(Acevedo et al., 2017).
The IPCC SRCCL supports the importance of societal conditions to 
climate-related risk (Hurlbert et  al., 2019), concluding that risks of 
water scarcity in drylands (i.e., desertification), land degradation and 
food insecurity are close to High3 beginning at 1.5°C under high-
vulnerability conditions (SSP3), but remain close to Moderate up to 
slightly above 2°C for low-vulnerability conditions (SSP1). Specifically, 
risk of water scarcity in drylands (i.e., desertification) at 1.5°C warming 
is reduced in low vulnerability (relative to high vulnerability) conditions 
from High to Medium. Similarly, under a 2°C warming, risk is reduced 
from High to Moderate for food security and High to Moderate-to-High 
for land degradation.
While climate change will increase risk to society and ecosystems, 
future exposure and vulnerability conditions will also greatly impact

outcomes of concern directly. Global economic damages to coastal 
assets from tropical cyclones are projected to increase by more than 
300% due to coastal development alone, a much larger effect than 
projected climate change impacts through 2100 even in RCP8.5 
(Gettelman et al., 2018). Similarly, global crop prices are more than 
three times more sensitive to alternative assumptions about changes 
in production technologies and demand than to alternative climate 
outcomes (Ren et al., 2016). Future water scarcity is driven mainly by 
both demographic change and socioeconomic changes affecting water 
demand and management. A measure of between-country inequality 
(Gini coefficient) would decline by more than 50% this century in 
low-vulnerability conditions, but would double in a high-vulnerability 
future (Crespo Cuaresma, 2017), outweighing the effect of climate 
(Taconet et al., 2020). Similarly, the global prevalence of armed conflict

will roughly double this century in a high-vulnerability future, whereas 
it will drop by half in a low-vulnerability future (Hegre et al., 2016). In 
Sub-Saharan Africa, assumptions about governance and political rights 
are estimated to be far more important to the future risk of violent 
conflict than climate change (Witmer et al., 2017).
3 The IPCC distinguishes between four qualitative risk levels, from Undetectable (risks that are undetected), to Moderate (detectable with at least medium confidence), High (significant and widespread) 
and Very high (very high probability of severe risks and significant irreversibility or persistence of impacts).16.5.3.3 Climate Adaptation Scenarios
One approach to understand adaptation benefits for risk reduction is 
to contrast projected impacts for the same climate and development 
conditions but different levels of adaptation. For example, global-scale 
coastal protection studies considering both RCPs and SSPs suggest

that, under a given RCP , the total flooded area may be reduced by 40% 
by using 1-m height dykes, compared with a no-adaptation baseline 
(Tamura et al., 2019). The global cost of SLR over the 21st century can be 
lowered by factor of two to four if local cost–benefit decisions consider 
migration an adaptation option, in addition to hard protection (Lincke 
and Hinkel, 2021). Under a low-warming scenario, it is estimated that 
adaptation (i.e., changes in crop variety and planting dates) could 
reduce the total number of people at risk of hunger globally by about 
4%, and by about 10% in a high-warming scenario (Hasegawa et al., 
2014). Impacts on heat-related mortality would be cut from 10 to 7 
deaths per 10,000 people yr–1 in 2100 by adaptation actions beyond 
those assumed to be driven by income growth (Carleton et al., 2020). 
In a regional example, proactive adaptation efforts on infrastructure 
(especially roads, runways, buildings and airports) in Alaska, USA,

could reduce damage-related expenditure by 45% under medium or 
high warming (Melvin et al., 2017).
Another approach infers the potential future effectiveness of 
adaptation based on current sensitivity of impacts to interventions. 
For example, the future disease burden of malaria is likely to be 
highly dependent on the future development of health services, 
deployment of malaria programs and adaptation. Investments in 
water and sanitation infrastructure are also recognised to have the 
potential to reduce severe risks of waterborne disease, although 
these improvements likely need to provide transformative change 
(Cumming et al., 2019). The potential for severe risks may also be 
substantially reduced through the development of vaccines for 
specific enteric diseases (Riddle et al., 2018), although most current 
vaccines target viral pathogens, incidence for which tends to be 
inversely correlated with ambient temperature (Carlton et al., 2016).

In addition, international migration as well as forced movement 
of people across borders will be influenced by developments in 
legal and political conditions (McLeman, 2019; Wrathall et  al., 
2019), but the fact that these developments are unknown strongly 
limits any forecasts on the magnitude of adaptation benefits 
(Section 16.5.2.3.8).
Last, there is growing concern that even ambitious adaptation efforts 
will not eliminate residual risks from climate change (Section 16.4.2). 
(Magnan et  al., 2021) concludes that high societal adaptation is 
expected to reduce the aggregated score—the proxy used in the 
study—of global risk from anthropogenic climate change by about 
40% under all RCPs by the end of the century, compared with risk 
levels projected without adaptation. It, however, also shows that, 
even for the lowest warming scenario, a residual risk one-third greater

than today’s risk level would still remain (with a doubling of today’s 
aggregated score under the high-emissions scenario).

16
2470Chapter 16 Key Risks across Sectors and Regions
16.5.3.4 Illustration: Risk and Adaptation Pathways in Densely 
Populated and Agricultural Deltas
Large deltas, which are very dynamic risk hotspots of global importance 
and interest (Wigginton, 2015; Hill et al., 2020; Nicholls et al., 2020), 
serve well to illustrate how risk pathways develop over time, determined 
by climatic as well as non-climatic risk drivers and by adaptation. Deltas 
occupy less than 0.5% of the global land area but host over 5% of the 
global population (Dunn et al., 2019) and contribute major fractions of 
food production in many world regions (Kuenzer et al., 2020). Future 
risk in these areas is heavily driven by climate change but also greatly 
depends on past, current and future socioeconomic changes which 
influence future trends in exposure, vulnerability and adaptive capacity 
of natural and human systems (high confidence) (Oppenheimer et al.,

2019). From a risk perspective, trends over the past decades have been 
unfavourable for many deltas, as most of them have experienced a 
simultaneous intensification of hazards, rise in exposure and stagnation 
or only limited reduction in vulnerability, particularly in low-income 
countries (high confidence) (Day et al., 2016; Tessler et al., 2016; Loucks, 
2019; Oppenheimer et al., 2019; Hill et al., 2020).
16.5.3.4.1 Hazard trends in deltas
Deltas face multiple interacting hazards, many of which over the past 
decades have been intensified by local and regional anthropogenic 
developments (e.g., the construction of dams, groundwater extraction, 
or agricultural irrigation practices) and most of which are expected 
to be exacerbated by climate change (high confidence) (Giosan 
et al., 2014; Tessler et al., 2015; Tessler et al., 2016; Arto et al., 2019; 
Oppenheimer et al., 2019). The most important hazards include SLR,

inundation, salinity intrusion, cyclones, storms and erosion, many of 
which occur in combination. The potential for flooding and inundation 
depends on the relative sea level rise (RSLR) which results from 
global and regional SLR as well as local subsidence within the deltas. 
Subsidence caused by natural and human drivers (mainly compaction 
and groundwater extraction) is currently the most important cause for 
RSLR in many deltas and can exceed the rate of climate-induced SLR 
by an order of magnitude (Oppenheimer et al., 2019). But in higher 
warming scenarios the relative importance of climate-driven SLR is 
expected to increase over time (Oppenheimer et al., 2019). In a global 
study covering 47 major deltas and assessing future trends of sediment 
delivery across four RCPs, three SSPs (1,2,3) and a projection of future 
dam construction, Dunn et al. (2019) find most deltas (33 out of the 
47) will experience a mean decline of 38% in sediment flux by the end

of the century when considering the average of the scenarios. Nienhuis 
et al. (2020) find in a global assessment that some deltas have gained 
land through increased sediment load (e.g., through deforestation), 
but recent land gains are unlikely to be sustained if SLR continues to 
accelerate. According to the latest assessments, it is virtually certain 
that global mean sea level will continue to rise over the 21st century, 
with SLR by 2100 likely to reach 0.28–0.55 m in a an SSP1–1.9 and 
0.63–1.01 m in an SSP5–8.5 scenario relative to 1995–2014 (IPCC, 
2021). The combined effects of local subsidence and GMSL rise result 
in a significant increase in the potential for inundation of low-lying 
deltas across all RCPs, with some variation according to regional sea 
level change rates, without significant further adaptation measures 
(very high confidence).In terms of salt-water intrusion and salinisation, global comparative

studies are still lacking but the general processes are well understood 
(e.g., White and Kaplan, 2017), and research on individual deltas is on 
the rise. In the Mekong Delta of Vietnam, one of the main rice-producing 
deltas globally, salinity intrusion has been observed to extend around 
15 km inland during the rainy season and around 50 km during the dry 
season (Gugliotta et al., 2017), resulting in rice yield losses of up to 4 
t ha−1 yr−1 (Khat et al., 2018). SLR, along with the expansion of dams 
and dry season irrigation upstream, is expected to further increase the 
salinity intrusion into the delta. This creates additional risk for food 
production as rice and other crops might be pushed beyond their 
adaptation limits in terms of salt tolerance, potentially affecting many 
of the 282,000 agriculture-based livelihoods in the Mekong Delta 
and increasing the pressure for cost-intensive adaptation (Smajgl 
et  al., 2015). Genua-Olmedo et  al. (2016) find for the Ebro that in

high scenario (RCP8.5, and SLR of almost 1 m by 2100), SLR-induced 
salinity intrusion will lead to almost a doubling of salinity levels and a 
decrease of mean rice productivity by over 20% in a high-SLR scenario 
with almost 1 m of SLR by the end of the century.
16.5.3.4.2 Exposure trends in deltas
Next to the trends in hazards, future exposure of and in deltas is shaped 
particularly by the increase of population and infrastructure and the 
intensification of land use. Over the recent years, the population has been 
rising in major deltas, roughly along with overall national population 
trends (Szabo et al., 2016). In 2017, 339 million people lived in deltas 
with a high exposure to flooding, cyclones and other coastal hazards 
(Edmonds et  al., 2020). Over 40% of the global population exposed 
to flooding from tropical cyclones lived in deltas, more than 90% of 
which in developing countries and emerging economies (ibid.). Looking

into the future, population in low-elevation coastal zones is expected 
to increase by 2050 across all SSPs with diverging developments in the 
second half of the century, and at the end of the century will reach well 
over 1 billion people in SSP3 (Jones and O’Neill, 2016; Merkens et al., 
2016). A major part of this population is expected to reside in deltas 
with large cities or mega-urban agglomerations such as the Pearl River 
Delta, China. One of the first studies using the SSP-RCP framework on 
the delta scale suggests a strong increase in intensive agricultural land 
by the middle of the century in three SSPs (2, 3, 5) in the Volta Delta, 
Ghana, while the Mahanadi, India, and the Ganges–Brahmaputra–
Meghna do not show a significant further increase (Kebede et  al., 
2018). Hence, the amount of population and infrastructure as well as 
agricultural land is expected to rise further under certain SSPs, further 
increasing the exposure to future climate hazards.

16.5.3.4.3 Vulnerability trends in deltas
Deltas are characterised by multi-faceted vulnerabilities of their 
environment and human populations. Over 200 indicators are being 
used in the literature to characterise and analyse vulnerability in 
deltas, spanning social, ecological and economic aspects (Sebesvari 
et al., 2016). However, only a few studies model or dynamically assess 
trends in vulnerability, particularly for the future, at global scale, or 
take a comparative approach. But overall, a global trend assessment 
suggests that social vulnerability to climate hazards has been 
improving over the past years in all world regions hosting major deltas

16
2471Key Risks across Sectors and Regions  Chapter 16
apart from Oceania, yet with emerging economies and developing 
countries in Africa showing less improvement than the Americas, Asia 
and Europe (Feldmeyer et al., 2017). An analysis of 48 major deltas 
finds that vulnerability therefore is a less dominant source of future 
increase in risk than exposure (Haasnoot et al., 2012). However, case 
study research from individual deltas suggests that delta populations, 
particularly those with agriculture-based livelihoods, have seen 
more limited vulnerability reduction due in particular to the impacts 
of environmental hazards, stress and disasters (high confidence). In 
the Mekong Delta, for instance, the strong economic growth since 
the beginning of Vietnam’s reform process has not led to a reduction 
of vulnerability across the board for all socioeconomic groups 
(Garschagen, 2015). Rather, issues such as widespread landlessness

or continued poverty have maintained and, in some respect, increased 
social vulnerability.
16.5.4 RKR Interactions
Multiple feedbacks between individual risks exist that have the 
potential to create cascades (WEF , 2018; IPCC, 2019c p. 680; Simpson 
et  al., 2021) and then to amplify systemic risks and impacts far 
beyond the level of individual RKRs (medium confidence). Scientific 
research, however, remains limited on whether such interactions 
would result in increasing or decreasing the initial impact(s), and 
hence risk severity across systems. Given the scope of this chapter on 
increasing risk severity, here we focus on assessing RKR interactions 
that lead to increasing risk. Drawing directly on RKR assessments 
(Sections 16.5.2.3.2–16.5.2.3.8), this section cites those assessments 
rather than primary literature. The arrows in Figure 16.11 are derived 
from a qualitative analysis by three authors of Chapter 16 of the material

provided by chapters on KRs and RKR assessments (Section 16.5.2.3), 
and do not result from any systematic and quantitative approach as 
done in some recent studies (e.g., WEF , 2018; Yokohata et al., 2019).
Interactions at the RKR level (Figure 16.11, panel A)—climate change 
will combine with pre-existing socioeconomic and ecological conditions 
(grey blocks on the left-hand-side of panel A in Figure  16.10) to 
generate direct and second-order effects (black plain arrows) both on 
the structure and/or functioning of ecosystems (RKR-B) and on some 
natural processes such as the hydrologic cycle (RKR-G), for example. This 
then translates into implications not only for biodiversity but also for 
natural resources that support livelihoods, which will in turn affect food 
security (especially food availability; RKR-F), water security (especially 
access to adequate quantities of acceptable quality water; RKR-G) 
and the living standards of already vulnerable groups and aggregate

economic outputs at the global level (RKR-D). CIDs (IPCC, 2021) will 
also directly affect infrastructure that are critical to ensure some basic 
conditions for economies to function (RKR-C), for example through 
transportation within and outside the country, energy production 
and international trade. Such disturbances to socioecological systems 
and economies pose climate-related risks to human health (RKR-E) as 
well as to peace and human mobility (RKR-H). Indeed, while health 
is concerned with direct influence of climate change, for example 
through hotter air temperatures impacting morbidity and mortality or 
the spatial distribution of disease vectors such as mosquitos, it is also 
at risk of being stressed by direct and secondary climate impacts on living standards, food security and water security (RKR-D, RKR-F , RKR-G, 
respectively). Increased poverty, increased hunger and limited access to 
drinkable water are well-known drivers of poor health conditions. The

role of impact cascades is even more prominent in the case of peace 
and human mobility (RKR-H), even though the scientific literature 
does not conclude on any clear and direct climate influence on armed 
conflict and human migration. Rather, climate-induced degradation of 
natural resources that are vital for subsistence agriculture and fisheries, 
transformational and long-term consequences on livelihoods (e.g., 
new risks, increasing precarious living conditions, gendered inequity, 
etc.), and erosion of social capital due to exacerbated tension within 
and between communities are considered among the main drivers of 
armed conflicts and forced displacement, therefore highlighting links 
with water security (RKR-G) and living standards (RKR-D), for example.
RKR assessments also suggest that some feedback effects are at work 
(arrows moving from the right to the left in panel A) that contribute to 
the potentially long-lasting effects of climate risks. RKR-H assessment,

for example, states that there is robust evidence that major armed 
conflicts routinely trigger mass displacement, threaten health and food 
security, and undermine economic activity and livelihoods, often with 
lasting negative consequences for living standards and socioeconomic 
development, therefore linking back to risks to living standards 
(RKR-D), human health (RKR-E) and food security (RKR-F).
Interactions at the KR level (Figure 16.11, panel B)—panel B illustrates 
risk connections at the Key Risk level (Section  16.5.2.1) and as de-
scribed in RKR assessments (Section 16.5.2.3). To only take one exam-
ple here, risk to livelihoods and economies is influenced by the loss of 
ecosystem services (RKR-B) and the loss or breakdown of critical infra-
structures (RKR-C), and it influences risks to human lives and health 
(RKR-E), food and water security (RKR-F , RKR-G), poverty (RKR-D) and 
peace and human mobility (RKR-H). As a third-order sequence, RKR

assessments show that increased risk to peace and human mobility 
affects lives and health as well as food security, which in turn threaten 
livelihoods and economies.
The above suggests that some vicious cycle effects play a central role 
in explaining impact processes. Cascading effects can indeed lead to 
cumulative risks that partly feed various drivers of the emergence of 
severe risks (Section  16.5.1), such as the acceleration of ecosystem 
degradation, or the reaching of thresholds and irreversible states 
in human systems at a decade-to-century time horizon (e.g., when 
permanent inundation questions the habitability of some low-lying 
coasts; RKR-A). The extent and duration of risk cascades are, however, 
expected to substantially vary depending on warming levels and 
development pathways, both separately (Section  16.5.3) and when 
combined (Sections 16.6.1, 16.6.2) (Figure 16.10).
In addition, RKR assessments converge to suggest that regions that

are already experiencing climate change impacts will experience 
severe impact cascades first (e.g., RKR-F), because they are in areas 
(i) that face development constraints and associated challenges such 
as poverty, inequity and social discrimination for example, and (ii) 
where climate change projections are the most intense for the next 
decades. That is especially a concern for Africa (RKR-F , RKR-G), Asia 
and Latin America (Chapters 9, 10, 12). RKR-E, for example, concludes

16
2472Chapter 16 Key Risks across Sectors and Regions
* CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. Indicated changes are system-dependent 
and can be detrimenlal, beneficial, neutral, or a mixture of each (see IPCC WG1 contribution to AR6, Summary for Policy Makers).(b) Illustration of interactions at the Key Risk level (e.g. from ecological risk to key dimensions for human societies)
Well-beingWater
Food Income
inequality 
Lives and 
healthN.B.: Anthropogenic drivers are 
not represented as such in 
panel B, but contribute to all 
risks considered in this figure
Loss/breakdown of 
infrastructure-based 
service delivery 
Livelihoods and 
economies 
(supply chains, aggregate 
economic outputs, etc.)
Migration and forced 
displacements 
(within/across state borders)PovertyPeace from 
armed 
conflictsSpecies extinction
& ecological
disruption
Changes in habitats 
and biodiversity
(all latitudes, land and

ocean)
Climatic 
impact-drivers
(CID) *
Other things that
societies value
(intangible assests, landscape, 
places, identity , etc.)Loss of 
life-supporting 
ecosystem
Climatic 
(CID) *
Living 
StandardsWater 
securityFood
security
InfrastructureEcosystem
(& ecosystem 
services)
Health
Peace and human
mobilityRecent development trends and 
ecological conditions
(exposure and vulnerability conditions)
Population growth
Settlement trends
Socioecon. inequalities
Resources use
Indigen./local knowledge
etc.
** Illustrative rather than comprehensive, and qualitative rather than quantitative, as suggested across the RKR assessments in  this report.Risk cascades ** Representative Key Risks
Across key risks
Climate-drivenIllustration of some connections across key risks
(a) lnteractions across the eight Representative Key Risk level
A (Low-lying coasts )B  (Ecosystems) C (Infrastructure) D (Living standards)

E (Human health) F (Food security) G (W ater security) H (Peace and human mobility)
Figure 16.11 |  Illustration of some connections across key risks. Panel A describes all the cross-RKR risk cascades that are described in RKR assessments (Sections 16.5.2.3.2–
16.5.2.3.8). Panel B builds on Section 16.5.2 and Table SM16.24 to provide an illustration of such interactions at the key risk level, for example from ecological risk to key dimensions 
for human societies. The arrows are representative of interactions as qualitatively identified in this chapter; they do not result from any quantitative modelling exercise.

16
2473Key Risks across Sectors and Regions  Chapter 16
that the likelihood of severe risks to human health is especially high 
for highly susceptible populations, particularly the poor and otherwise 
marginalised. RKR assessments, however, emphasise that middle- and 
high-income regions are also to be considered at serious risk because 
climate change is accelerating at the global level (IPCC, 2021), and 
because critical dimensions are exposed to severe risks such as major 
transportation (e.g., international airports) and energy (e.g., nuclear 
power plants) infrastructure for instance (RKR-C), and because of the 
interconnectedness of economies.
Finally, all RKR assessments suggest that enhanced adaptation has the 
potential to contain such feedback effects and cascading processes 
more broadly, and reduce the duration of the impacts on the system as a 
whole. There are, however, knowledge gaps on such a potential, as well

as on the nature of impact cascades (positive, negative, neutral, mixed).
16.6 Reasons for Concern Across Scales
This section builds on Section 16.5 which identifies and assesses key 
risks (KRs) and representative key risks (RKRs), including conditions 
contributing to their severity (i.e., Figure 16.10), in two ways. First, we 
consider those risks in the context of the global goal for sustainable 
development which can be impacted, as expressed in the United 
Nations 2030 Agenda for Sustainable Development and the SDGs. This 
discussion supports further assessment in Chapter 18 on sustainable 
system transitions and climate resilient development pathways. 
Second, the potential global consequences are then elaborated in an 
updated assessment of five globally aggregated categories of risk, 
designated as Reasons for Concern (RFCs), that evaluates risk accrual 
by global warming level.
16.6.1 Key Risks and Sustainable Development

The United Nations 2030 Agenda for Sustainable Development, and 
the SDGs (UN, 2015), since 2015, have become an important vision for the United Nations member countries (Chimhowu, 2019) as well 
as for corporations to contribute towards sustainable growth (UNDP 
et al., 2016; Ike et al., 2019; van der Waal and Thijssens, 2020). Climate 
change risks, as embodied in the RKR and RFCs, can affect attainment 
of the SDGs and have consequences for lives and livelihoods (related 
to SDGs 1, 4, 8 and 9), health and well-being (related to SDGs 2, 
3 and 6), ecosystems and species (related to SDGs 6, 14 and 15), 
economic (related to SDGs 1, 8 and 12), social and cultural assets 
(related to SDGs 5, 10, 11, 16 and 17), services including ecosystem 
services (related to SDGs 6, 7, 11, 12, 14 and 15), and infrastructure 
(related to SDGs 6, 7, 9, 11 and 12). This section assesses the level of 
linkages between key risks with sustainable development, in terms

of the SDG targets and indicators. This informs on the key risks which 
are most relevant to consider with respect to the attainment of the 
SDGs.
16.6.1.1 Links between Key Risks and Sustainable Development 
Goals
Within the AR6 cycle, the three IPCC Special Reports have all 
considered the relationships between climate change impacts and 
actions and the SDGs. SR15 discussed priorities for sustainable 
development in relation to climate adaptation efforts (Section 5.3.1, 
SR15); synergies and trade-offs of climate adaptation measures 
(Section 5.3.2, SR15); and the effect of adaptation pathways towards 
a 1.5°C warmer world (Section 5.3.3 SR15). The SRCCL considered 
impacts of desertification on SDGs 1 (no poverty), 2 (zero hunger), 13 
(climate), 15 (life on land) and 5 (gender) (IPCC, 2019a, Figure 3.9). 
Trade-offs and synergies between SDGs 2 (zero hunger) and 13 
(climate action) at the global level were recognised (IPCC, 2019a,

Section  5.6.6, Figure  5.16). Various integrated response options, 
interventions and investments were also evaluated within the SDG 
framework (IPCC, 2019a, Section  6.4.3). The SROCC (Chapter 5) 
concluded that climate change impacts on the ocean, overall, will 
negatively affect achieving the SDGs, with 14 (life below water) 
being most relevant (Singh et al., 2019).
Cross-Working Group Box SRM | Solar Radiation Modification
Authors: Christopher H. Trisos (South Africa), Oliver Geden (Germany), Sonia I. Seneviratne (Switzerland), Masahiro Sugiyama (Japan), 
Maarten van Aalst (the Netherlands), Govindasamy Bala (India), Katharine J. Mach (USA), Veronika Ginzburg (Russia), Heleen de Coninck 
(the Netherlands), Anthony Patt (Switzerland)
Proposed solar radiation modification schemes
This cross-working group box assesses solar radiation modification (SRM) proposals, their potential contribution to reducing or increasing

climate risk, as well as other risks they may pose (categorised as risks from responses to climate change in the IPCC AR6 risk definition 
in 1.2.1.1), and related perception, ethics and governance questions.
SRM refers to proposals to increase the reflection of shortwave radiation (sunlight) back to space to counteract anthropogenic warming 
and some of its harmful impacts (de Coninck et al., 2018) (Cross-Chapter Box 10; WGI Chapters 4, 5). A number of SRM options have 
been proposed, including: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ground-based albedo modifications 
(GBAM) and ocean albedo change (OAC). Although not strictly a form of SRM, cirrus cloud thinning (CCT) has been proposed to cool the 
planet by increasing the escape of longwave thermal radiation to space and is included here for consistency with previous assessments

16
2474Chapter 16 Key Risks across Sectors and Regions
(de Coninck et al., 2018). SAI is the most-researched proposal. Modelling studies show SRM could reduce surface temperatures and 
potentially ameliorate some climate change risks (with more confidence for SAI than other options), but SRM could also introduce a 
range of new risks.
There is high agreement in the literature that for addressing climate change risks SRM cannot be the main policy response to climate 
change and is, at best, a supplement to achieving sustained net zero or net negative CO 2 emission levels globally (de Coninck et al., 2018; 
MacMartin et al., 2018; Buck et al., 2020; National Academies of Sciences and Medicine, 2021b). SRM contrasts with climate change 
mitigation activities, such as emission reductions and carbon dioxide removal (CDR), as it introduces a ‘mask’ to the climate change

problem by altering the Earth’s radiation budget, rather than attempting to address the root cause of the problem, which is the increase 
in greenhouse gases (GHGs) in the atmosphere. In addition, the effects of proposed SRM options would only last as long as a deployment 
is maintained—for example, requiring ca. yearly injection of aerosols in the case of SAI as the lifetime of aerosols in the stratosphere 
is 1–3 years (Niemeier et al., 2011) or continuous spraying of sea salt in the case of MCB as the lifetime of sea salt aerosols in the 
atmosphere is only about 10 d—which contrasts with the long lifetime of CO 2 and its climate effects, with global warming resulting from 
CO 2 emissions likely remaining at a similar level for a hundred years or more (MacDougall et al., 2020) and long-term climate effects of 
emitted CO 2 remaining for several hundreds to thousands of years (Solomon et al., 2009).
Which scenarios?

The choice of SRM deployment scenarios and reference scenarios is crucial in assessment of SRM risks and its effectiveness in attenuating 
climate change risks (Keith and MacMartin, 2015; Honegger et al., 2021). Most climate model simulations have used scenarios with 
highly stylised large SRM forcing to fully counteract large amounts of warming in order to enhance the signal-to-noise ratio of climate 
responses to SRM (Kravitz et al., 2015; Sugiyama et al., 2018a; Tilmes et al., 2018; Krishna-Pillai et al., 2019).
The effects of SRM fundamentally depend on a variety of choices about deployment (Sugiyama et al., 2018b), including: its position 
in the portfolio of human responses to climate change (e.g., the magnitude of SRM used against the background radiative forcing), 
governance of research and potential deployment strategies, and technical details (latitude, materials, and season, among others, see

WGI Section 4.6.3.3). The plausibility of many SRM scenarios is highly contested, and not all scenarios are equally plausible because 
of socio-political considerations (Talberg et al., 2018b), as with, for example, CDR (Fuss et al., 2014; Fuss et al., 2018). Development of 
scenarios and their selection in assessments should reflect a diverse set of societal values with public and stakeholder inputs (Sugiyama 
et al., 2018a; Low and Honegger, 2020), as depending on the focus of a limited climate model simulation, SRM could look grossly risky 
or highly beneficial (Pereira and al., 2021).
In the context of reaching the long-term global temperature goal of the Paris Agreement, there are different hypothetical scenarios 
of SRM deployment: early, substantial mitigation with no SRM, more limited or delayed mitigation with moderate SRM, unchecked 
emissions with total reliance on SRM, and regionally heterogeneous SRM. Each scenario presents different levels and distributions of

SRM benefits, side effects and risks. The more intense the SRM deployment, the larger is the likelihood for the risks of side effects and 
environmental risks (e.g., Heutel et al., 2018). Regional disparities in climate hazards may result from both regionally deployed SRM 
options such as GBAM, and more globally uniform SRM such as SAI (Jones et al., 2018a; Seneviratne et al., 2018b). There is an emerging 
literature on smaller forcings of SAI to reduce global average warming, for instance, to hold global warming to 1.5°C or 2°C alongside 
ambitious conventional mitigation (Jones et al., 2018a; MacMartin et al., 2018), or bring down temperature after an overshoot (Tilmes 
et al., 2020). If emissions reductions and CDR are deemed insufficient, SRM may be seen by some as the only option left to ensure the 
achievement of the Paris Agreement’s temperature goal by 2100.Cross-Working Group Box SRM (continued)

16
2475Key Risks across Sectors and Regions  Chapter 16
Table Cross-Working Group Box SRM.1 | SRM options and their potential climate and non-climate impacts. Description, potential climate impacts, potential impacts 
on human and natural systems, and termination effects of a number of SRM options: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ocean 
albedo change (OAC), ground-based albedo modifications (GBAM) and cirrus cloud thinning (CCT).
SRM option SAI MCB OAC GBAM CCT
DescriptionInjection of reflective 
aerosol particles directly 
into the stratosphere or a 
gas which then
converts to aerosols that 
reflect sunlightSpraying sea salt or 
other particles in marine 
clouds, making them more 
reflectiveIncrease surface albedo 
of the ocean (e.g., by 
creating microbubbles 
or placing reflective 
foam on the surface)Whitening roofs, changes 
in land use management 
(e.g., no-till farming, 
bioengineering to 
make crop leaves more

reflective), desert albedo 
enhancement, covering 
glaciers with reflective 
sheetingSeeding to promote 
nucleation of cirrus clouds, 
reducing optical thickness 
and cloud lifetime to allow 
more outgoing longwave 
radiation to escape to 
space
Potential climate 
impacts other than 
reduced warmingChange precipitation and 
runoff
pattern; reduced 
precipitation extremes; 
precipitation reduction in 
some monsoon regions; 
decrease in direct and 
increase in diffuse sunlight 
at surface; changes to 
stratospheric dynamics and 
chemistry; potential
delay in ozone hole 
recovery; changes in surface 
ozone and UV radiationChange in land-sea 
contrast in temperature 
and precipitation, regional 
precipitation and runoff 
changesChange in land–sea 
and precipitation, 
regional, precipitation 
and runoff changesChanges in regional 
precipitation pattern, 
regional extremes and 
regional circulationChanges in temperature 
and precipitation pattern,

altered regional water 
cycle, increase in sunlight 
reaching the surface
Potential impacts on 
human and natural 
systemsChanges in crop yields, 
changes in land and ocean 
ecosystem productivity, 
acid rain (if using sulphate), 
reduced risk of heat stress 
to coralsChanges in regional ocean 
productivity, changes in 
crop yields, reduced heat 
stress for corals, changes 
in ecosystem productivity 
on land, sea salt deposition 
over landUnresearchedAltered photosynthesis, 
carbon uptake and side 
effects on biodiversityAltered photosynthesis and 
carbon uptake
Termination effectsSudden and sustained 
termination would result in 
rapid warming, and abrupt 
changes to water cycle. 
Magnitude of termination 
depends on the degree of 
warming offsetSudden and sustained
termination would 
result in rapid

warming. Magnitude of 
termination depends on 
the degree of warming 
offsetGBAM can be maintained 
over several years without 
major termination effects 
because of its regional 
scale of application.
rapid warming. Magnitude 
of termination depends 
on the degree of warming 
offset
References (also 
see main text of 
this box)Tilmes et al. (2018); 
Simpson et al. (2019); 
Visioni et al. (2017)Latham et al. (2012); Ahlm 
et al. (2017); Stjern et al. 
(2018)Evans et al. (2010); 
Crook et al. (2015a)Zhang et al. (2016); Field 
et al. (2018); Seneviratne 
et al. (2018a); Davin et al. 
(2014); Crook et al. (2015a)Storelvmo and Herger 
(2014); Crook et al. 
(2015a); Jackson et al. 
(2016); Gasparini et al. 
(2020); Duan et al. (2020)
SRM risks to human and natural systems and potential for risk reduction

Since AR5, hundreds of climate modelling studies have simulated effects of SRM on climate hazards (Kravitz et al., 2015; Tilmes et al., 
2018). Modelling studies have shown SRM has the potential to offset some effects of increasing GHGs on global and regional climate, 
including the increase in frequency and intensity of extremes of temperature and precipitation, melting of Arctic sea ice and mountain 
glaciers, weakening of Atlantic meridional overturning circulation, changes in frequency and intensity of tropical cyclones, and decrease 
in soil moisture (WGI, Chapter 4). However, while SRM may be effective in alleviating anthropogenic climate warming either locally or 
globally, it would neither maintain the climate in its present-day state nor return the climate to a pre-industrial state (climate averaged 
over 1850–1900, see WGI Chapter 1, Box 1.2) in all regions and in all seasons even when used to fully offset the global mean warming Cross-Working Group Box SRM (continued)

16
2476Chapter 16 Key Risks across Sectors and Regions
(high confidence) (WGI Chapter 4). This is because the climate forcing and response to SRM options are different from the forcing and 
response to GHG increase. Because of these differences in climate forcing and response patterns, the regional and seasonal climates of a 
world with a global mean warming of 1.5°C or 2°C achieved via SRM would be different from a world with similar global mean warming 
but achieved through mitigation (MacMartin et al.., 2019). At the regional scale and seasonal time scale, there could be considerable 
residual climate change and/or overcompensating change (e.g., more cooling, wetting or drying than just what is needed to offset 
warming, drying or wetting due to anthropogenic greenhouse gas emissions), and there is low confidence in understanding of the climate 
response to SRM at the regional scale (WGI, Chapter 4).

SAI implemented to partially offset warming (e.g., offsetting half of global warming) may have potential to ameliorate hazards in multiple 
regions and reduce negative residual change, such as drying compared with present-day climate, that is associated with fully offsetting 
global mean warming (Irvine and Keith, 2020), but may also increase flood and drought risk in Europe compared with unmitigated 
warming (Jones et al., 2021). Recent modelling studies suggest it is conceptually possible to meet multiple climate objectives through 
optimally designed SRM strategies (WGI, Chapter 4). Nevertheless, large uncertainties still exist for climate processes associated with 
SRM options (e.g., aerosol–cloud–radiation interaction) (WGI, Chapter 4) (Kravitz and MacMartin, 2020).
Compared with climate hazards, many fewer studies have examined SRM risks—the potential adverse consequences to people and

ecosystems from the combination of climate hazards, exposure and vulnerability—or the potential for SRM to reduce risk (Curry et al., 
2014; Irvine et al., 2017). Risk analyses have often used inputs from climate models forced with stylised representations of SRM, such 
as dimming the sun. Fewer have used inputs from climate models that explicitly simulated injection of gases or aerosols into the 
atmosphere, which include more complex cloud radiative feedbacks. Most studies have used scenarios where SAI is deployed to hold 
average global temperature constant despite high emissions.
There is low confidence and large uncertainty in projected impacts of SRM on crop yields due in part to a limited number of studies. 
Because SRM would result in only a slight reduction in CO 2 concentrations relative to the emission scenario without SRM (Chapter 5, 
WGI), the CO 2 fertilisation effect on plant productivity is nearly the same in emissions scenarios with and without SRM. Nevertheless,

changes in climate due to SRM are likely to have some impacts on crop yields. A single study indicates MCB may reduce crop failure rates 
compared with climate change from a doubling of CO 2 pre-industrial concentrations (Parkes et al., 2015). Models suggest SAI cooling 
would reduce crop productivity at higher latitudes compared with a scenario without SRM by reducing the growing season length, 
but benefit crop productivity in lower latitudes by reducing heat stress (Pongratz et al., 2012; Xia et al., 2014; Zhan et al., 2019). Crop 
productivity is also projected to be reduced where SAI reduces rainfall relative to the scenario without SRM, including a case where 
reduced Asian summer monsoon rainfall causes a reduction in groundnut yields (Xia et al., 2014; Yang et al., 2016). SAI will increase the 
fraction of diffuse sunlight, which is projected to increase photosynthesis in forested canopy, but will reduce the direct and total available

sunlight, which tends to reduce photosynthesis. As total sunlight is reduced, there is a net reduction in crop photosynthesis with the result 
that any benefits to crops from avoided heat stress may be offset by reduced photosynthesis, as indicated by a single statistical modelling 
study (Proctor et al., 2018). SAI would reduce average surface ozone concentration (Xia et al., 2017) mainly as a result of aerosol-induced 
reduction in stratospheric ozone in polar regions, resulting in reduced downward transport of ozone to the troposphere (Pitari et al., 2014; 
Tilmes et al., 2018). The reduction in stratospheric ozone also allows more UV radiation to reach the surface. The reduction in surface 
ozone, together with an increase in surface UV radiation, would have important implications for crop yields but there is low confidence 
in our understanding of the net impact.

Few studies have assessed potential SRM impacts on human health and well-being. SAI using sulphate aerosols is projected to deplete 
the ozone layer, increasing mortality from skin cancer, and SAI could increase particulate matter due to offsetting warming, reduced 
precipitation and deposition of SAI aerosols, which would increase mortality, but SAI also reduces surface-level ozone exposure, which 
would reduce mortality from air pollution, with net changes in mortality uncertain and depending on aerosol type and deployment 
scenario (Effiong and Neitzel, 2016; Eastham et al., 2018; Dai et al., 2020). However, these effects may be small compared with changes 
in risk from infectious disease (e.g., mosquito-borne illnesses) or food security due to SRM influences on climate (Carlson et al., 2020). 
Using volcanic eruptions as a natural analogue, a sudden implementation of SAI that forced the El Niño-Southern Oscillation (ENSO)

system may increase risk of severe cholera outbreaks in Bengal (Trisos et al., 2018; Pinke et al., 2019). Considering only mean annual 
temperature and precipitation, SAI that stabilises global temperature at its present-day level is projected to reduce income inequality 
between countries compared with the highest warming pathway (RCP8.5) (Harding et al., 2020). Some integrated assessment model 
scenarios have included SAI (Arino et al., 2016; Emmerling and Tavoni, 2018; Heutel et al., 2018; Helwegen et al., 2019; Rickels et al., 
2020) showing the indirect costs and benefits to welfare dominate, since the direct economic cost of SAI itself is expected to be relatively 
low (Moriyama et al., 2017; Smith and Wagner, 2018). There is a general lack of research on the wide scope of potential risk or risk Cross-Working Group Box SRM (continued)

16
2477Key Risks across Sectors and Regions  Chapter 16
reduction to human health, well-being and sustainable development from SRM and on their distribution across countries and vulnerable 
groups (Carlson et al., 2020; Honegger et al., 2021).
SRM may also introduce novel risks for international collaboration and peace. Conflicting temperature preferences between countries 
may lead to counter-geoengineering measures such as deliberate release of warming agents or destruction of deployment equipment 
(Parker et al., 2018). Game-theoretic models and laboratory experiments indicate that a powerful actor or group with a higher preference 
for SRM may use SAI to cool the planet beyond what is socially optimal, imposing welfare losses on others, although this cooling does not 
necessarily imply that excluded countries would be worse off relative to a world of unmitigated warming (Ricke et al., 2013; Weitzman,

2015; Abatayo et al., 2020). In this context, counter-geoengineering may promote international cooperation or lead to large welfare 
losses (Heyen et al., 2019; Abatayo et al., 2020).
Cooling caused by SRM would increase the global land and ocean CO 2 sinks (medium confidence), but this would not stop CO 2 from 
increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions (high confidence) (WGI 
Chapter 5).
Few studies have assessed potential SRM impacts on ecosystems. SAI and MCB may reduce risk of coral reef bleaching compared with 
global warming with no SAI (Latham et al., 2013; Kwiatkowski et al., 2015), but risks to marine life from ocean acidification would remain, 
because SRM proposals do not reduce elevated levels of anthropogenic atmospheric CO 2 concentrations. MCB could cause changes in

marine net primary productivity by reducing light availability in deployment regions, with important fishing regions off the west coast of 
South America showing both large increases and decreases in productivity (Partanen et al., 2016; Keller, 2018).
There is large uncertainty in terrestrial ecosystem responses to SRM. By decoupling increases in atmospheric greenhouse gas concentrations 
and temperature, SAI could generate substantial impacts on large-scale biogeochemical cycles, with feedbacks to regional and global 
climate variability and change (Zarnetske et al., 2021). Compared with a high-CO 2 world without SRM, global-scale SRM simulations 
indicate reducing heat stress in low latitudes would increase plant productivity, but cooling would also slow down the process of nitrogen 
mineralisation, which could decrease plant productivity (Glienke et al., 2015; Duan et al., 2020). In high-latitude and polar regions, SRM

may limit vegetation growth compared with a high-CO 2 world without SRM, but net primary productivity may still be higher than pre-
industrial climate (Glienke et al., 2015). Tropical forests cycle more carbon and water than other terrestrial biomes, but large areas of 
the tropics may tip between savanna and tropical forest depending on rainfall and fire (Beer et al., 2010; Staver et al., 2011). Thus, SAI-
induced reductions in precipitation in Amazonia and central Africa are expected to change the biogeography of tropical ecosystems in 
ways different from both present-day climate and global warming without SAI (Simpson et al., 2019; Zarnetske et al., 2021). This would 
have potentially large consequences for ecosystem services (Chapter 2 and Chapter 9). When designing and evaluating SAI scenarios, 
biome-specific responses need to be considered if SAI approaches are to benefit rather than harm ecosystems. Regional precipitation

change and sea salt deposition over land from MCB may increase or decrease primary productivity in tropical rainforests (Muri et al., 
2015). SRM that fully offsets warming could reduce the dispersal velocity required for species to track shifting temperature niches, 
whereas partially offsetting warming with SAI would not reduce this risk unless rates of warming were also reduced (Trisos et al., 2018; 
Dagon and Schrag, 2019). SAI may reduce high-fire-risk weather in Australia, Europe and parts of the Americas, compared with global 
warming without SAI (Burton et al., 2018). Yet SAI using sulphur injection could shift the spatial distribution of acid-induced aluminium 
soil toxicity into relatively undisturbed ecosystems in Europe and North America (Visioni et al., 2020). For the same amount of global 
mean cooling, SAI, MCB and CCT would have different effects on gross and net primary productivity because of different spatial patterns

of temperature, available sunlight, and hydrological cycle changes (Duan et al., 2020). Large-scale modification of land surfaces for GBAM 
may have strong trade-offs with biodiversity and other ecosystem services, including food security (Seneviratne et al., 2018a). Although 
existing studies indicate SRM will have widespread impacts on ecosystems, risks and potential for risk reduction for marine and terrestrial 
ecosystems and biodiversity remain largely unknown.
A sudden and sustained termination of SRM in a high CO 2 emissions scenario would cause rapid climate change (high confidence; WGI 
Chapter 4). More scenario analysis is needed on the potential likelihood of sudden termination (Kosugi, 2013; Irvine and Keith, 2020). 
A gradual phase-out of SRM combined with emission reduction and CDR could avoid these termination effects (medium confidence)

(MacMartin et al., 2014; Keith and MacMartin, 2015; Tilmes et al., 2016). Several studies find that large and extremely rapid warming 
and abrupt changes to the water cycle would occur within a decade if a sudden termination of SAI occurred (McCusker et al., 2014; 
Crook et al., 2015b). The size of this ‘termination shock’ is proportional to the amount of radiative forcing being masked by SAI. A sudden 
termination of SAI could place many thousands of species at risk of extinction, because the resulting rapid warming would be too fast for 
species to track the changing climate (Trisos et al., 2018).Cross-Working Group Box SRM (continued)

16
2478Chapter 16 Key Risks across Sectors and Regions
Public perceptions of SRM
Studies on the public perception of SRM have used multiple methods: questionnaire surveys, workshops, and focus group interviews 
(Burns et al., 2016; Cummings et al., 2017). Most studies have been limited to Western societies, with some exceptions. Studies have 
repeatedly found that respondents are largely unaware of SRM (Merk et al., 2015). In the context of this general lack of familiarity, the 
public prefers CDR to SRM (Pidgeon et al., 2012), is very cautious about SRM deployment because of potential environmental side effects 
and governance concerns, and mostly rejects deployment for the foreseeable future. Studies also suggest conditional and reluctant 
support for research, including proposed field experiments, with conditions of proper governance (Sugiyama et al., 2020). Recent studies

show that the perception varies with the intensity of deliberation (Merk et al., 2019), and that the public distinguishes different funding 
sources (Nelson et  al., 2021). Limited studies for developing countries show a tendency for respondents to be more open to SRM 
(Visschers et al., 2017; Sugiyama et al., 2020), perhaps because they experience climate change more directly (Carr and Yung, 2018). 
In some Anglophone countries, a small portion of the public believes in chemtrail conspiracy theories, which are easily found in social 
media (Tingley and Wagner, 2017; Allgaier, 2019). Since researchers rarely distinguish different SRM options in engagement studies, there 
remains uncertainty in public perception.
Ethics
There is broad literature on ethical considerations around SRM, mainly stemming from philosophy or political theory, and mainly focused

on SAI (Flegal et al., 2019). There is concern that publicly debating, researching and potentially deploying SAI could involve a ‘moral 
hazard’, with potential to obstruct ongoing and future mitigation efforts (Morrow, 2014; Baatz, 2016; McLaren, 2016), while empirical 
evidence is limited and mostly at the individual, not societal, level (Burns et al., 2016; Merk et al., 2016; Merk et al., 2019). There is 
low agreement whether research and outdoors experimentation will create a ‘slippery slope’ towards eventual deployment, leading 
to a lock-in to long-term SRM, or can be effectively regulated at a later stage to avoid undesirable outcomes (Hulme, 2014; Parker, 
2014; Callies, 2019; McKinnon, 2019). Regarding potential deployment of SRM, procedural, distributive and recognitional conceptions 
of justice are being explored (Svoboda and Irvine, 2014; Svoboda, 2017; Preston and Carr, 2018; Hourdequin, 2019). With the SRM

research community’s increasing focus on distributional impacts of SAI, researchers have started more explicitly considering inequality 
in participation and inclusion of vulnerable countries and marginalised social groups (Flegal and Gupta, 2018; Whyte, 2018; Táíwò and 
Talati, 2021), including considering stopping research (Stephens and Surprise, 2020; National Academies of Sciences and Medicine, 
2021a). There is recognition that SRM research has been conducted predominantly by a relatively small number of experts in the Global 
North, and that more can be done to enable participation from diverse peoples and geographies in setting research agendas and research 
governance priorities, and undertaking research, with initial efforts to this effect (e.g., Rahman et al., 2018), noting unequal power 
relations in participation could influence SRM research governance and potential implications for policy (Whyte, 2018; Táíwò and Talati,

2021; Winickoff et al., 2015; Frumhoff and Stephens, 2018; Biermann and Möller, 2019; McLaren and Corry, 2021; National Academies of 
Sciences and Medicine, 2021b)
Governance of research and of deployment
Currently, there is no dedicated, formal international SRM governance for research, development, demonstration or deployment (see 
WGIII Chapter 14). Some multilateral agreements—such as the UN Convention on Biological Diversity or the Vienna Convention on 
the Protection of the Ozone Layer—indirectly and partially cover SRM, but none is comprehensive, and the lack of robust and formal 
SRM governance poses risks (Ricke et al., 2013; Talberg et al., 2018a; Reynolds, 2019a). While governance objectives range broadly, 
from prohibition to enabling research and potentially deployment (Sugiyama et al., 2018b; Gupta et al., 2020), there is agreement that 
SRM governance should cover all interacting stages of research through to any potential, eventual deployment with rules, institutions

and norms (Reynolds, 2019b). Accordingly, governance arrangements are co-evolving with respective SRM technologies across the 
interacting stages of research, development, demonstration and—potentially—deployment (Rayner et al., 2013; Parker, 2014; Parson, 
2014). Stakeholders are developing governance already in outdoors research, for example for MCB and OAC experiments on the Great 
Barrier Reef (McDonald et al., 2019). Co-evolution of governance and SRM research provides a chance for responsibly developing SRM 
technologies with broader public participation and political legitimacy, guarding against potential risks and harms relevant across a full 
range of scenarios, and ensuring that SRM is considered only as a part of a broader portfolio of responses to climate change (Stilgoe, 
2015; Nicholson et al., 2018). For SAI, large-scale outdoor experiments even with low radiative forcing could be transboundary, and

those with deployment-scale radiative forcing may not be distinguished from deployment, such that MacMartin and Kravitz (2019) 
argue for continued reliance on modelling until a decision on whether and how to deploy is made, with modelling helping governance 
development. For further discussion of SRM governance, see Chapter 14, WGIII.Cross-Working Group Box SRM (continued)

16
2479Key Risks across Sectors and Regions Chapter 16
Many linkages between SDG 13 (climate action) and other SDGs have

been identified (very high confidence) (Blanc, 2015; Kelman, 2015; Northrop et  al., 2016; Hammill and Price-Kelly, 2017; ICSU, 2017; Mugambiwa and Tirivangasi, 2017; Dzebo et  al., 2018; Major et  al., 2018; Nilsson et al., 2018; Sanchez Rodriguez et al., 2018). In addition, interactions between different climate change actions and SDGs, and interactions among SDGs themselves, have also been assessed (Nilsson et al., 2016; IPCC, 2018a; McCollum et al., 2018; Fuso-Nerini et al., 2019; IPCC, 2019b; Cernev and Fenner, 2020). The Cross-Chapter Box GENDER in Chapter 18 assessment indicates the importance of gender considerations in achieving success and benefits in adaptation efforts. Aligning climate change adaptation to the SDGs could bring potential co-benefits and increased efficiency in funding, and reduce the gap between adaptation planning and implementation (very high confidence) (IPCC, 2018a; Sanchez Rodriguez et al., 2018; IPCC, 2019b; IPCC, 2019a).

able to reduce global disparities and support more climate resilient development pathways (IPCC WGII AR5, Chapter 13, p. 818; discussed further in Chapter 18). Nevertheless, we are still lagging in achieving the 2030 Goals (OECD, 2019; Sachs et al., 2021), and this affects societal vulnerability, readiness and risk response capacities (IPCC, 2019a, Chapters 6, 7, Chapters 6 and 8, this report). We assess the risk literature for linkages between key risks (grouped by RKRs) and the indicators of the SDGs (UN, 2015) using text analysis (details in SM16.5) to identify the potential level of effect of different risks on the SDGs. Some 940 documents were analysed. The SDG status is associated with projected climate hazards, also called climatic impact drivers (CIDs) (Ranasinghe et  al., 2021) (panel a), and RKRs (panel c), summarising hazard and exposure with vulnerability aspects, as expressed by challenges in achieving the SDGs (panel d), on a regional level (Figure 16.12).

Linkages between the 17 SDGs and the eight RKRs (Figure  16.12 bottom left panel) are mapped to the regional SDG status (Figure 16.12 bottom right panel) and related to the CIDs (Figure  16.12 top left panel). Interconnections between CIDs and RKRs are complicated by the possibility of concurrent weather events, extremes and longer-term trends. Risks are compounded by existing vulnerabilities (Iwama et al., 2016; Thomas et al., 2019b; Birkmann et al., 2021) and cascading consequences (Pescaroli and Alexander, 2015; Pescaroli and Alexander, 2018; Yokohata et al., 2019) (see, for example, Sections 3.4.3.5, 5.12, 6.2.6, 7.2.2.2) as well as interactions. The level of challenges faced in attaining the SDGs is one metric for assessing vulnerability and lack of capacity to manage risks (Cernev and Fenner, 2020). Other metrics are also available (Parker et al., 2019; Garschagen et al., 2021b; Birkmann et al., 2022). From Figure 16.12, aside from SDG13 (climate action), the strongest

connections and risk challenges are with zero hunger (SDG2), sustainable cities and communities (SDG11), life below water (SDG14), decent work and economic growth (SDG8), no poverty (SDG1), clean water and sanitation (SDG6) and good health and well-being (SDG3) (high confidence). Other SDGs have strong linkages with specific RKRs,

for example, terrestrial and marine ecosystems with life on land (SDG15); infrastructure (RKR-C) with industry, innovation and infrastructure (SDG9) and affordable and clean energy (SDG7); living standards (RKR-D) with gender equality (SDG5); and peace and human mobility (RKR-H) with peace, justice and strong institutions (SDG 16) (high confidence).
On a global scale, priority areas for regions can be evaluated from the 
intersection of climate hazards, risks and the level of challenges in SDG attainment (Moyer and Hedden, 2020; Sachs et al., 2021). The greatest linkages and effects on the SDGs will be due to risks to water (RKR-G), living standards (RKR-D), coastal socio-ecological systems (RKR-A) and peace and human mobility (RKR-H) (high confidence) (details in SM16.5).
In particular, coastal socio-ecological systems (RKR-A), living standards

(RKR-D), food security (RKR-F), water security (RKR-G) and peace and human mobility (RKR-H), have strong linkages with SDG 2 (zero hunger), for which there are significant to major challenges for all regions (high confidence). Almost all the RKRs are strongly linked to SDGs 8 (decent work and economic growth) and 11 (sustainable cities and communities) (high confidence), where regions such as Africa, Asia, and Central and South America face significant to major challenges in attaining targets. All regions also face major to significant challenges affecting SDGs 14 (life below water) and 15 (life on land), which relate to terrestrial and ocean ecosystems (RKR-B) (high confidence).
The analysis of RKR linkages to SDGs is also useful in identifying gaps

and susceptibilities, especially for developing future climate resilient development targets. This aspect is discussed further in Chapter 18. Gaps may arise as SDG targets and indicators are not specifically focused on systems affected by climate change risks or impacts. For example, in the SRCCL Section 7.1.2, Hurlbert et al. (2019) noted the absence of an explicit goal for conserving freshwater ecosystems and ecosystem services in the SDGs. Such gaps (Tasaki and Kameyama, 2015; Guppy et al., 2019) are inevitable as the current SDG targets and indicators focus on overall sustainable development. As another example, projected increases in frequency and intensity of hot temperature extremes are likely to result in increased heat-related illness and mortality, yet heat extremes are not called out as an SDG indicator under SDGs 3 (good health and well-being) or 13 (climate action). The gaps on climate-related metrics for impacts on health are just beginning to be evaluated (Lloyd and

Hales, 2019, see also Section  7.1.6). The current SDG 13 (climate action) targets also do not specifically track the possibility of differential impacts on society from disasters and extreme weather events (RFC2). For example, the first indicator (Section 13.1.1.1), ‘Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population’, does not include any requirement for disaggregated data, unlike several other socioeconomic and population SDG indicators, making it difficult to track the different effects that climate-related disasters are expected to have on men, women and children across different segments of society, relevant for distributional impacts (RFC3) (see also Section 8.3, Cross-Chapter Box GENDER in Chapter 18). The risk consequences identified and discussed in each RKR (Section 16.5.2) provide useful entry points for identifying indicators and metrics for monitoring and evaluating specific impacts of key climate change risks.

In addition, the sector and region chapters have considered various adaptation responses relevant to the SDGs (see, for example, Sections 3.6, 4.7.5, 5.13.3, 8.2.1.6, 10.6.1, 13.11.4, 14.6.3) with relevant metrics for evaluation.

16
2480Chapter 16 Key Risks across Sectors and Regions
Linkages between the projected climatic impact-drivers (CIDs) by region, Sustainable Development Goals (SDGs)
by region, and the Representative Key Risks (RKRs)
(a) Projected change in CIDs (b) CIDs relevance to RKRs
(c) SDG status (d) RKRs linkages with SDGsSDG17 Partnerships for the Goals  SDG16 Peace, Justice and 
Strong Institutions  SDG15 Life on Land SDG14 Life below Water SDG13 Climate Action SDG12 Responsible Consumption 
and ProductionSDG11 Sustainable Cities 
and Communities  SDG10 Reduced InequalitiesSDG9 Industry, Innovation and
InfrastructureSDG8 Decent Work and

Economic GrowthSDG7 Affordable and Clean EnergySDG6 Clean Water and SanitationSDG5 Gender EqualitySDG4 Quality EducationSDG3 Good Health and Well-beingSDG2 Zero HungerSDG1 No PovertySDGRKR Region   CIDMean air temperatur eExtreme heatCold spellFrostMean precipitatio nRiver floodHeavy precipitation and pluvial floodLandslideAridityHydrological droughtAgricultural and ecological droughtFire weatherMean wind speedSevere wind stormTropical cycloneSand and dust stor mSnow, glacier and ice sheetPermafrostLake, river and sea ic eHeavy snowfall and ice stor mHailSnow avalancheRelative sea levelCoastal floodCoastal erosio nMarine heatwaveOcean Acidit yAir pollution weatherAtmospheric CO 2at surfac eRadiation at surfac eSmall IslandsNorth AmericaEuropeCentral and
South AmericaAustralasiaAsiaAfrica
H:Peace and 
human mobilityG: Water securityF:Food securityE: Human healthD:Living standards 
and equityC: Critical physical 
infrastructure, 
networks & services  B: Terrestrial and

ocean ecosystemsT T TEC EC EC EC EC EC ECEC EC EC EC EC EC ECEC EC EC EC EC EC ECT T TEC EC EC EC EC EC ECEC EC EC EC EC EC ECTe Te Te TeEC EC EC EC EC EC ECEC EC EC EC EC EC ECT TEC EC EC EC EC EC ECEC EC EC EC EC EC ECTsg TgTTai Ta Ta TiEC EC EC EC EC EC ECT T T T T TE E E E E E EEC ECTT
T
EC
E
EC
Tai
T
EC
EC
EC
EC
EC
EC
EC
EC
ECTEl Nino –Southern Oscillatio n
Sea Surface Temperature T T TT T T T
(d) SDG status 2021 
(Sachs et al., 2021)
Not availabl e
Major challenges 
Significant challenges
Challenges remai n
Goal achievement(c) RKR linkages with SDGs 
Strong  (>0.4 )
Moderate  (0.2 to 0.4)
Some (0  to <0.2 )(b) CIDs relevance to RKRs 
(WGI AR6, 2021, Cross 
Chapter Box 12.1 Table 1)
Increasing relevance 
Extremes
Concurrent extremes
Trends
Arctic warmin g
SPEI global
Sea ice loss
N. hemisphere spring snow
Mass loss of glacier sE
aC
T
ie
s
g(a) Summary of confidence in  
direction of projected change  
in climatic impact -driver (CID) 
(WGI AR6, 2021, Tables 12.3 
to 12.9)

High confidence of 
decrease
of decrease
Low confidence in  
of increase
High confidence 
Not broadly relevant
A:Coastal socio-
ecological system s
Figure  16.12 |   Linkages between the projected climatic impact drivers (CIDs) by region, Sustainable Development Goals (SDGs) by region, and the 
representative key risks (RKRs).

16
2481Key Risks across Sectors and Regions  Chapter 16
In summary, key risks, and the consequences arising from them, 
are directly linked to and will affect specific indicators of the SDGs 
(high confidence). They also will be indirectly linked to, and thus 
affect, the SDGs overall, due to the interactions between the key 
risks (Section  16.5) and between the SDGs themselves (very high 
confidence). These results support previous findings that climate 
change impacts pose a risk to achieving sustainability (Ansuategi 
et al., 2015; Chirambo, 2016; ICSU, 2017; Pradhan et al., 2017; Gomez-
Echeverri, 2018; IPCC, 2018a; IPCC, 2019b; IPCC, 2019a; Cernev and 
Fenner, 2020). Not all observed or expected consequences arising from 
the key risks are fully captured by the SDG indicators, nor were they 
designed to be. Therefore, for monitoring and assessing the climate 
risk impacts, it is useful to consider specific climate change impact

indicators and metrics (Enenkel et al., 2020) to capture any realised 
impacts.
In the near term, the strength of connection between the RKRs and 
the SDGs, with respect to existing SDG challenges, indicate probable 
systemic vulnerabilities and issues in responding to climatic hazards 
(UN-IATFFD, 2019; Leal Filho et al., 2020; Weaver et al., 2020; Tiedemann 
et al., 2021) (high confidence). In the medium to long term (associated 
with global warming levels of between 2°C and 2.7°C under SSP2–4.5 
scenario), if such vulnerabilities and challenges cannot be substantially 
reduced, the hazards and risks resulting from the projected CIDs 
(Figure 16.12b, c) will further stress systems relevant for sustainable 
development, based on current experience of the COVID-19 pandemic 
(UN-IATFFD, 2021, see also Cross-Chapter Box  COVID in Chapter 7; 
Sections  8.2, 8.3) (medium confidence, based on medium evidence, 

The potential impacts of the various climate hazards, the occurrence 
of extreme events, and the projected trends of climate hazards 
give rise to complex risks for ecological and human systems, which 
are compounded by the exposure, vulnerability and sustainability 
challenges faced in different regions of the world. The potential global 
consequences are elaborated in the next section, which describes the 
framework and approach for the assessment of the five RFCs.
16.6.2 Framework and Approach for Assessment of RFCs 
and Relation to RKRs
The RFC framework communicates scientific understanding about 
accrual of risk in relation to varying levels of warming for five broad 
categories: risk associated with (1) unique and threatened systems, 
(2) extreme weather events, (3) distribution of impacts, (4) global 
aggregate impacts and (5) large-scale singular events (Smith et al., 2001; 
Mastrandrea and Schneider, 2004; Schneider and Mastrandrea, 2005).

The RFC framework was first developed during the Third Assessment 
Report (Smith et al., 2001) along with a visual representation of these 
risks as ‘burning embers’ figures, and this assessment framework 
has been further developed and updated in subsequent IPCC reports 
including AR5 (IPCC, 2014; Oppenheimer et al., 2014) and the recent 
IPCC Special Reports (SR15 (IPCC, 2018a); SRCCL (IPCC, 2019; SROCC 
(IPCC, 2019)).Relationship between RKRs and RFCs
RFCs reflect risks aggregated globally that together inform the 
interpretation of DAI with the climate system. The five RFC categories 
are maintained as previously defined for consistency with earlier 
assessments. Compared with the synthesis of risk across RKRs in 
Section  16.5, we note that the RKRs and RFCs are complementary 
methods that aggregate individual risks into different but 
interconnected categories (Figure 16.13).
We draw important distinctions between RFC and RKR. First, RFCs

assess risks that might be of global concern, while RKRs also include 
risks that may be of concern only locally or for specific population 
groups (Figure 16.13). RFCs focus on the full range of increasing risk, 
and locate transitions between four categories of risk: undetectable, 
moderate, high, and very high. RKRs focus on severe risks, and attempt 
to elaborate when/where severe impacts may occur. RKR assessments 
focus on the conditions under which some risks would become severe 
over the course of this century, while RFCs evaluate changes in risk 
levels against gradual increase in temperature levels. The RKR analysis 
used specific definitions of severity including quantified thresholds 
where possible, and this is distinct from the approach based on the 
combined elements of risk used in the RFC expert elicitation process. 
Severity as defined in the RKRs is associated with high or very high 
risk levels but does not align precisely with either of those categories,

and a further difference arises from a more explicit emphasis on 
irreversibility and adaptation limits in the very high risk category in the 
RFCs. Thus, RKR and RFC neither map directly to one another in terms 
of content, nor in terms of the response metric.
The treatment of vulnerability and adaptation is different in the RKR 
and RFC assessments. The RKR assessment considered specifically three 
alternative levels of vulnerability, whereas the RFC process did not 
explicitly differentiate risk by level of vulnerability. Therefore, the global 
warming levels at which the various RKR assessments identify risk of 
severe impacts are not directly comparable to risk transitions identified 
in the RFC assessments. In addition, RKRs consider implications of 
low versus high adaptation in order to illustrate the potential role of 
ambitious adaptation efforts to limit risk severity; RFCs consider risks in 
a no/low adaptation scenario only, although there is some discussion

of the potential role of adaptation in assessing the transition to very 
high risk. Last, both RKRs and RFCs focus on the 21st century scale, 
though recognising risk will continue to increase after 2100, but treat 
this timing issue differently: RKRs assess severe risks over the course 
of this century and distinguish risks that are already severe, that will 
become severe by the mid-century, or that will become severe by the 
end of the century; while RFCs assess risk level irrespective of their 
timing, but according to different temperature levels.
Many of the elements of risk which contribute to RKRs also contribute 
to risk within one or more RFCs. In turn, elements of risk within 
some RFCs, such as extreme weather and changes in the Earth 
system contribute to risk within one or more RKR. Hence, RFCs may 
incorporate elements of many different RKRs, and vice versa. There 
are therefore common elements between some particular RKRs and

RFCs: for example, risks to terrestrial and ocean ecosystems (RKR-B) 
contribute strongly to RFC1 (Unique and Threatened Systems) and RFC4

16
2482Chapter 16 Key Risks across Sectors and Regions
(Global Aggregate Impacts), while RFC2 (extreme weather events) has 
implications for all RKRs, including direct linkages with critical physical 
infrastructure, networks and services (RKR-C). Furthermore, risks 
emerging from the interaction of RKRs also contribute to the RFCs, 
but are only qualitatively described in Section  16.5.4. For example, 
the effects of risks to terrestrial and ocean ecosystems (RKR-A) affect 
living standards and equity (RKR-C), as does the associated decline in 
ecosystem services which then impacts livelihoods (RKR-D).
Elicitation Methodology
The method used to develop judgements on levels of risk builds 
on the approach described in WGII AR5 Chapter 19 (Oppenheimer 
et al., 2014) and outlined in more detail in the work of O’Neill et al. 
(2017), while integrating advances in the AR6 SRs including expert 
judgement (SRCCL, Zommers et al., 2020). We provide further details

on the underlying judgements of risk level compared with previous 
assessments by indicating key risk criteria associated with each 
judgement: magnitude of adverse consequences, likelihood of adverse 
consequences, temporal profile of the risk, and ability to respond to 
the risk (Section 16.5.1). The definitions of risk levels used to make the 
expert judgements are presented in Table 16.7 (Section 16.5.1).
A brief summary of the framework that was used to carry out the 
risk assessment, synthesis and expert elicitation is presented here, 
and details are provided in SM16.6. Expert judgements about the 
qualitatively defined levels of risk (i.e., undetectable, moderate, high, and very high) reached at various levels of global average 
warming are informed by evidence of observed impacts illustrated 
in Section 16.2 and variations in individual key risks under different 
scenarios of climate change, socioeconomics and adaptation effort

in Section  16.5. We follow the methodological advances from 
SRCCL Chapter 7 (Hurlbert et  al., 2019), which used an expert 
elicitation protocol for developing the burning embers (Zommers 
et  al., 2020). Specifically, we used expert participants from within 
the AR6 author team and a protocol based on the modified Delphi 
technique (Mukherjee et  al., 2015) and the Sheffield Elicitation 
Framework (Oakley and O’Hagan, 2010; Gosling, 2018). This 
approach (Figure  16.14) includes a two-round elicitation process 
with a first round of independent anonymous judgements about 
the global warming level at which risk levels transition from one 
to the next, and a final round of group discussion and deliberation 
to develop consensus. The results are then reported, and additional 
references are made to findings from other relevant chapters in this 
report. Then, authors who had not participated in the elicitation as 
part of independent appraisal review the results.

The resulting risk transition or ‘ember’ diagram illustrates the 
progression of socio-ecological risk from climate change as a function 
of global temperature change, taking into account the exposure and 
vulnerability of people and ecosystems, as assessed by literature-
based expert judgement. Section 16.6.3 presents these diagrams for 
each RFC, providing information about the most important literature-
based evidence that experts used to make their judgements. Similar 
Interconnections between the Key Risks, Representative Key Risks and the Reasons for Concern
Key Risks 
highlighted by
sectoral and regional chapters
Examples
in AR6Representative Key Risks (RKR)
Systems
- Risk to low-lying coasts
- Risk to terrestrial and ocean ecosystems
Sectors
- Risk to critical infrastructure
- Risk to living standards
- Risk to human health
- Risk to food security
- Risk to water security
Topics
- Risk to peace and human mobility
Reasons for Concern (RFC)

Aggregated; Cross-systems/Sectors/T opics; Global
- Risks to unique and threatened systems
- Risks associated with extreme weather events
- Risks associated with the distribution of impacts
- Risks associated with global aggregate impacts
- Risks associated with large-scale singular eventsCombination of
Key Risks 
including
interactions
Figure 16.13 |  Interconnections between the key risks, representative key risks and Reasons for Concern.

16
2483Key Risks across Sectors and Regions  Chapter 16
assessments for selected individual KRs are discussed in Chapters 2, 
7, 9, 12, 13 and 14.
Representation of warming levels
The RFC assessment reflects the latest understanding of warming 
reported in WGI AR6. Global surface temperature was 1.09 [0.95 to 
1.20]°C higher in 2011–2020 than 1850–1900, with stronger warming 
over land (1.59 [1.34 to 1.83]°C) than over the ocean (0.88 [0.68 to 
1.01]°C) (WGI AR6 Cross Chapter Box 2.3 Table 1, Eyring et al. in Gulev 
et al., 2021). Warming levels are commonly reported and studied in the 
impacts literature using two scales of spatially averaged temperature rise, 
global surface air temperature (GSAT), commonly produced by General 
Circulation Models (GCMs) when projecting climate changes, and global 
mean surface temperature (GMST), commonly used in empirical studies. 
Both have the same reference point of pre-industrial of 1850–1900.

The ember diagrams presented here use GSAT, which is consistent with 
most literature of projected risk (largely based on the output of climate 
models). To the extent that the embers also draw on the observed 
impacts literature using GMST, this potential variation is minimal as the 
average levels of GSAT and GMST have been shown to match closely (for further discussion on this, see Cross-Chapter Box CLIMATE in Chapter 
1). Hence, the diagrams are presented with a single y -axis representing 
global temperature change, generally referring to global temperature 
rise irrespective of when it occurs; however, the majority of the literature 
assessed considers alternative levels of warming during the 21st century. 
For example, a warming level of 2°C might occur in the 2050s, in the 
2080s or in 2100 (see next section).
Furthermore, climate-related hazards associated with each of the RFCs 
are assessed in WGI AR6 Cross-Chapter Box 12.1 Table 1 (Tebaldi et al.,

2021), which synthesises information from various chapters of WGI on 
35 such hazards according to global warming levels (GWLs) to inform 
understanding of their potential changes and associated risks with 
temperature levels in general.
Temporal dimension
When are the risks shown in the embers projected to occur? The issues 
associated with assessing transient risks are discussed in Chapter 3, 
SR15 (IPCC, 2018a). Some of the literature, however, does explore the 
dynamics within human and natural systems (i.e., the way in which Table 16.7 |  Definition of risk levels for Reasons for Concern.
Level Definition
Undetectable (white) No associated impacts are detectable and attributable to climate change.
Moderate (yellow)Associated impacts are both detectable and attributable to climate change with at least medium confidence, also accounting for the other specific criteria 
for key risks.

High (red) Severe and widespread impacts that are judged to be high on one or more criteria for assessing key risks.
Very high (purple)Very high risk of severe impacts and the presence of significant irreversibility or the persistence of climate-related hazards, combined with limited ability to 
adapt due to the nature of the hazard or impacts/risks.
Expert elicitation approach for assessment of RFC risk level transitions
Experts review the risk literature (of fline)
Assess the risk level transition locations independently (no discussion between experts )
Submit results to Facilitator
Facilitator summarises results, which are anonymise d
Group results disseminated to experts
Review and reflect on group results and the rationale given by others
Experts revise own assessment, if needed, in preparation for the group discussio n
Discussion conducted by Facilitator (online)
Experts present rationale for transitions
Build consensus for final risk transition levelsIndependent 
Individual

Round One
Group
Discussion & 
Consensus
Round Tw o
Figure 16.14 |  Expert elicitation approach for assessment of RFC risk level transitions. A more detailed description of the methodology used in this elicitation is 
provided in SM16.6.

16
2484Chapter 16 Key Risks across Sectors and Regions
systems respond when a transient level of warming is first reached, 
and then further, how they continue to develop if that transient level of 
warming is then maintained indefinitely). We note that this important 
factor is captured in the RFC assessment (and ember diagrams), since the 
timing of risk accrual is one of the criteria for the assessment of the level 
of risk (Section 16.5.1). Risks that are known to evolve only over very 
long-time scales contribute less to the level of risk than those which are 
known to occur rapidly. This is because SLR also depends on the dynamics 
of global warming, including the rate of change of radiative forcing, and 
time lags of several decades, including between atmospheric and ocean 
warming, and in reaching equilibrium sea level state (Oppenheimer 
et al., 2019; Fox-Kemper et al., 2021). However, longer-term risks that

would arise if those transient temperatures were maintained are also 
included, and this is particularly important in RFC5 (large-scale singular 
events). Note that risks that take place over a very long time scale are 
considered to be of lower concern than more imminent risks. However, 
changes of very large magnitude can still be very important even if far 
away in time, especially if these changes are irreversible (or reversible 
only on extremely long time scales) (see Section 16.5.1).
Although the embers do not indicate the decade in which certain 
risks are projected to occur, clearly this depends strongly on the level 
of mitigation action as well as the degree of adaptation. Hence, the 
ember diagram (Figure 16. 15 ) is shown alongside a graphic illustrating 
possible global temperature time series emerging from alternative future 
scenarios assessed by WGI AR6 which imply different levels of mitigation

effort. For example, in a scenario with a high level of mitigation effort 
(SSP1–1.9) reaching net zero emissions in the 2050s, it is extremely likely  
that global warming remains below 2°C and more than 50% likely that it 
will remain below 1.6°C (AR6 WGI 4.3.1.1, Meinshausen et al., 2020). On 
the other hand, a level of 2°C warming is extremely likely to be exceeded 
during the 21st century under the three scenarios assessed by WGI AR6 
in which GHG emissions do not fall below current levels before mid-
century (i.e., SSP2–4.5, SSP3–7.0, SSP-8.5) (WGI AR6 4.3.1.1, Lee et al., 
2021). WGI AR6 has assessed that ‘global surface temperature averaged 
over 2081–2100 is very likely to be higher by 1.0°C–1.8°C under the 
lowest CO 2 emission scenario considered in this report (SSP1–1.9) and 
by 3.3°C–5.7°C under the highest CO 2 emission scenario (SSP5–8.5)’. 
However, almost all scenarios assessed by IPCC AR6 WGI reach 1.5°C

global warming level in the early 2030s (WGI AR6 SPM, IPCC, 2021).
The concept of temperature overshoot, defined as ‘exceedance of a 
specified global warming level followed by a decline to or below that 
level during a specified period of time’ is a relevant consideration for 
this RFC risk assessment; however, the effect of overshoot has not 
explicitly been considered in the burning ember assessment because 
of the limited literature basis. However, despite the lack of directly 
assessed overshoot scenarios, the current literature provides several 
salient examples of irreversible changes that are projected to occur 
once global temperatures reach a particular level. For example, coral 
reefs are unable to survive repeated bleaching events that are too close 
together, leading to irreversible loss of the reefs even if bleaching were 
to cease (see Section 16.6.3.1 RFC1). Species extinction is irreversible,

and Chapter 2 assesses that, at ~1.6°C, >10% of species are projected 
to become endangered as compared with >20% at ~2.1°C (median), representing high and very high biodiversity risk, respectively (medium 
confidence) (Section 2.5.4). Similarly, WGI AR6 finds that ‘Over the 21st 
century and beyond, abrupt and irreversible regional changes in the 
water cycle, including changes in seasonal precipitation, streamflow 
and aridity, cannot be excluded’. Thus, information about irreversibility 
provides information about the potential outcome of temperature 
overshoot scenarios. Other types of losses, such as loss of human or 
species life, are irreversible even if the loss process ceases in the future. 
The less resilient a system is, the more likely it is to suffer irreversible 
damage during a temperature overshoot; the more resilient it is, the 
more likely it is to be able to withstand the overshoot or recover 
afterwards. Very high levels of risk, as assessed here in the RFC, are

associated with a wide range of criteria for risk assessment including 
irreversibility. While not all very high risks are irreversible, in general, 
risks reaching a very high level include a component of irreversible 
risks that would persist during and after an overshooting of a given 
temperature level.
Risks associated with socioeconomic development, mitigation and 
maladaptation
The ember diagrams in Figure 16. 15 capture only the risks arising from 
exposure of vulnerable socio-ecological systems to climatic hazards 
across a range of socioeconomic futures. They do not capture any 
risk component arising solely from changes in population or level of 
development. Importantly, they also do not capture additional risks 
that may arise from the human response to climate change, including 
climate change mitigation or unintended negative consequences of 
adaptation-related responses (i.e., maladaptation) (Section  17.5.1).

Such risks are discussed in SRCCL Chapter 7, for example, adverse 
effects of the very large-scale use of land and water for primary 
bioenergy production on food production and biodiversity (Hurlbert 
et al., 2019). Contributions of mitigation or maladaptation to risk can 
be important, however, and are discussed further in the context of 
specific RFCs in Section 16.6.3. In general, such components of risk are 
difficult to quantify, and can be minimised by good design of climate 
change mitigation and adaptation. Thus, the effect is excluded from 
the ember diagrams to allow a clearer representation of the accrual of 
climate change risk with global warming.
Emergent risk
AR5 Oppenheimer et al. (2014) defined ‘emergent risk’ as a risk that 
arises from the interaction of phenomena in a complex system. While 
emergent risk is a relevant consideration for this RFC risk assessment, 
this type of risk has not been explicitly accounted for in the burning

ember assessment because of the limited literature basis. Unlike known 
or identified risks, emergent risks are characterised by the uncertainty 
of consequences and/or probabilities of occurrence. The International 
Risk Governance Council (IRGC) suggests three categories of emergent 
risks: (1) high uncertainty and a lack of knowledge about potential 
impacts and interactions with risk-absorbing systems; (2) increasing 
complexity, emergent interactions and systemic dependencies that 
can lead to nonlinear impacts and surprises; and (3) changes in 
context (for example, social and behavioural trends, organisational 
settings, regulations, natural environments) that may alter the nature, 
probability and magnitude of expected impacts. Feedback processes

16
2485Key Risks across Sectors and Regions  Chapter 16
between climatic change, human interventions involving mitigation 
and adaptation actions, and processes in natural systems can be 
classified as emergent risks if they pose a threat to human security.
16.6.3 Global Reasons for Concern
In this section, we present the results of the expert elicitation in the 
form of the burning embers diagram, alongside a description of the 
recent literature and scientific evidence for each of the RFCs in turn. The 
consensus transition values are illustrated in Figure 16. 15 , an updated 
version of the burning embers diagram that describes the additional 
risk due to climate change for each RFC when a temperature level 
is reached and then sustained or exceeded (Table SM16.20 presents 
the consensus values of the transition range and median estimate in 
terms of global warming level by risk level for each of the five RFC 
embers). The shading of each ember provides a qualitative indication

of the increase in risk with temperature, and we retain the colour 
scheme employed in the most recent versions of this figure, where 
white, yellow, red and purple indicate undetectable, moderate, high 
and very high additional risk, respectively. These transitions were 
assessed under conditions of low to no adaptation compared with 
today, in accordance with definitions provided in 16.3 (i.e., adaptation 
consists of fragmented, localised, incremental adjustments to existing 
practices), though the effect of adaptation on risk for individual RFCs 
and related literature is discussed further below.
The following subsections present the expert assessment and 
judgements made during the elicitation process to identify consensus 
transition values for each RFC. The description of these transitions is 
further extended with additional references to findings from underlying 
chapters in this report, and reviewed by Chapter 16 authors as part of

independent appraisal. No changes were made to the transition values 
assessed through the expert elicitation.
16.6.3.1 Unique and Threatened Systems (RFC1)
This RFC addresses the potential for increased damage to or irreversible 
loss of a wide range of physical, biological and human systems that 
are unique (i.e., restricted to relatively narrow geographical ranges and 
have high endemism or other distinctive properties) and are threatened 
by future changes in climate (Smith et  al., 2001; Smith et  al., 2009; 
Oppenheimer et al., 2014). The specific examples of such systems given in 
previous IPCC assessment reports has remained broadly consistent, with 
AR4 including ‘coral reefs, tropical glaciers, endangered species, unique 
ecosystems, biodiversity hotspots, small island states, and indigenous 
communities’ (Smith 2009), AR5 including ‘a wide range of physical, 
biological, and human systems that are restricted to relatively narrow

geographical ranges’ and ‘are threatened by future changes in climate’ 
(Smith et  al., 2001), and SR15 Chapter 3 including ‘ecological and 
human systems that have restricted geographic ranges constrained by 
climate related conditions and have high endemism or other distinctive 
properties. Examples include coral reefs, the Arctic and its Indigenous 
People, mountain glaciers and biodiversity hotspots’. In this cycle, we 
retain the definition used in SR15 as most explicit and inclusive of the 
previous definitions.AR5 (Oppenheimer et  al., 2014) assessed the transition from 
undetectable to moderate risk for RFC1 to lie below recent global 
temperatures (1986–2005, which at the time was considered to 
correspond to a global warming level of 0.6°C above pre-industrial 
levels; AR6 WGI now considers this time period of 1986–2005 to 
correspond to a global warming or approximately 0.7°C). At that time, 
there was at least medium confidence in attribution of a major role for

climate change for impacts on at least one each of ecosystems, physical 
systems and human systems within this RFC. SR15 Section  3.5.2.1 
(Hoegh-Guldberg et al., 2018b), concurred with high confidence that 
the transition to moderate risk had already occurred before the time 
of writing.
The transitions here are informed by these assessments, along with 
the assessment in Chapter 2 on species high extinction risk and on 
ecosystem transitions. It also draws substantially from information 
in Cross-Chapter Paper 1 and Table  SM16.22 on risks to unique 
and threatened biological systems. Some unique and threatened 
systems, such as coral reefs and sea-ice-dependent ecosystems, were 
already showing attributable impacts with high confidence (see 
Table SM16.22 , Cross-Chapter Paper 1 and Chapter 2) based on data 
collected in the mid to latter 20th century, when global warming of 
0.5°C above pre-industrial levels had taken place, as noted already in

AR3. In this AR6 assessment, the temperature range for the transition 
from undetectable to moderate risk is still located at a median value 
of 0.5°C above pre-industrial levels, with very high confidence. Since 
impacts were first detected in coral reef systems in the 1980s when 
warming of ~0.4°C of global warming had occurred (SR15 Chapter 
3), this provides the temperature at which the transition begins. The 
September Arctic sea ice volume has declined by 55–65% between 
1979 and 2010 (AR6 WGI, Schweiger et al., 2019) as global warming 
increased from around 0.36°C in 1979 to around 0.9°C in 2010. These 
provide evidence of a start to the transition from undetectable to 
moderate risk at 0.4°C above pre-industrial levels. Recent evidence of 
observed impacts on mountaintop ecosystems and sea-ice-dependent 
species, and of range shifts in multiple ecosystems during 1990–2000, 
which AR6 WGI now assesses as corresponding to a global warming

of 0.69°C (see WGI AR6 Cross-Chapter Box 2.3, Figure 1, Gulev et al., 
2021), provides evidence for an upper limit to this transition of 0.7°C 
with very high confidence. Overall, the transition is located at a median 
of 0.5°C with lower and upper limits of 0.4°C and 0.7°C, respectively, 
with very high confidence.
AR5 assessed the transition from moderate to high risk to lie around 
1°C above 1986–2005 levels (which corresponded at that time to 
1.6°C above pre-industrial levels but has been reassessed by AR6 WGI 
to correspond to 1.7°C) to reflect projected ‘increasing risk to unique 
and threatened systems, including Arctic sea ice and coral reefs, as well 
as threatened species as temperature increases over this range.’ SR15 
relocated the transition slightly from 1.6°C to 1.5°C, owing to increased 
literature projecting the effects of climate change upon Arctic sea ice 
and new literature assessing projected impacts of climate change on 
biodiversity at 1.5°C warming.

In this AR6 assessment, the transition from moderate to high is based 
on the high level of observed impacts, and the areas projected to begin 
undergoing major transformations by 1.5°C (see Cross-Chapter Paper

16
2486Chapter 16 Key Risks across Sectors and Regions
1, Chapter 2 and SR15 (IPCC, 2018a)). A substantial number of unique 
and threatened systems are assessed to be in a high risk state owing 
to the influence of anthropogenic climate change by the 2000–2010 
period, when global warming had reached approximately 0.85°C 
(range 0.7–1°C) (see WGI AR6 Cross-Chapter Box  2.3, Gulev et  al., 
2021) using the 1995–2014 figure as a proxy for 2000–2010).
The most prominent example of a system assessed to be already in 
a high risk state is that of coral reefs, which are already degrading 
rapidly. Observed impacts on coral reefs increased significantly during 
2014–2017 (Table SM16.22 , corresponding to a global warming of 
about 0.9°C). This includes mass bleaching in the Indian Ocean in 
1998, 2010, 2015 and 2016 when bleaching intensity exceeded 20% 
in surveyed locations in the western Indian Ocean, eastern Indian 
Ocean and western Indonesia. In the tropical Pacific Ocean, climate-

driven mass bleaching was reported in all countries in the region, 
with most bleaching reports coinciding with 2014–2017 marine 
heatwaves. Fifty percent of coral within shallow-water reefs of the northern and central two-thirds of the Great Barrier Reef were killed 
in 2015/2016. Subsequent coral recruitment in 2018 was reduced to 
only 11% of the long-term average, representing an unprecedented 
shift in the ecology of the northern and middle sections of the reef 
system to a highly degraded state. A second key example are sea-
ice-dependent systems in the Arctic. During August to October of 
2010–2019, corresponding to a global warming of about 0.9°C, 
average Arctic sea ice area has declined in area by 25% relative to 
1979–1988 (high confidence, AR6 WGI, Figure 9.13). September Arctic 
sea ice volume has declined by about 72% between 1979 and 2016, 
with the latter deemed a conservative estimate (AR6 WGI, Schweiger 

Other important examples of observed impacts on unique ecosystems 
that indicate that risks are already at a high level (Table  SM16.22) 
include mass tree mortalities, now well recorded in multiple unique 
forest and woodland ecosystems around the world. Sections  2.4.3.3 
and 2.4.5 report that, between 1945 and 2007, drought-induced tree The dependence of risk associated with the Reasons for Concern on the level of climate change
Updated by expert elicitation and reflecting new literature and scientific evidence since AR5 and SR15
(a) Global surface temperature change
     Increase relative to the period 1850–1900(b) Reasons for Concern (RFC)
     Impact and risk assessments assuming low to no adaptation
234
1.5
1
0
2100 2050 2000 1950Projections for different scenarios°C
SSP1-1.9
SSP1-2.6 (shade representin g very likely range)
SSP2-4.5
SSP3-7.0 (shade representing very likely range)
SSP5-8.5
RFC4
Global
aggregate
impactsRFC1
Unique and
threatened
systemsRFC2
Extreme
weather

eventsRFC3
Distribution
of impactsRFC5
Large scale
singular
events5
High
Moderate
•••••
••••
range 0.95–1.20°CTransition range
•••• •••• ••••••• ••• ••••• •• ••••• •• ••• •• ••
Figure 16.15 |  The dependence of risk associated with the Reasons for Concern (RFCs) on the level of climate change, updated by expert elicitation and 
reflecting new literature and scientific evidence since AR5 and SR15.
(a) Global surface temperature (GST), relative to pre-industrial, 1850–1900 (WGI AR6 Figure SPM.8d). (IPCC, 2021a).
(b) Embers are shown for each RFC, assuming low to no adaptation (i.e., adaptation is fragmented, localised, incremental adjustments to existing practices). The dashed horizontal

line denotes the present global warming of 1.09°C (IPCC WGI Figure SPM.8a ) which is used to separate the observed, past impacts below the line from the future projected risks 
above it. RFC1 Unique and threatened systems: ecological and human systems that have restricted geographic ranges constrained by climate-related conditions and have high 
endemism or other distinctive properties. Examples include coral reefs, the Arctic and its Indigenous People, mountain glaciers and biodiversity hotspots. RFC2 Extreme weather 
events: risks/impacts to human health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, and 
coastal flooding. RFC3 Distribution of impacts: risks/impacts that disproportionately affect particular groups owing to uneven distribution of physical climate change hazards,

exposure or vulnerability. RFC4 Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, 
lives affected, species lost or ecosystem degradation at a global scale. RFC5 Large-scale singular events: relatively large, abrupt and sometimes irreversible changes in systems 
caused by global warming, such as ice sheet disintegration or thermohaline circulation slowing. Comparison of the increase of risk across RFCs indicates the relative sensitivity of 
RFCs to increases in GSAT. The levels of risk illustrated reflect the judgements of IPCC author experts from WGI and WGII.

16
2487Key Risks across Sectors and Regions Chapter 16
mortality (sometimes associated with insect damage and wildfire) has

caused the mortality of up to 20% of trees in western North America, the African Sahel, and North Africa, linked to a warming of 0.3–0.9°C above pre-industrial levels, and is implicated in more than 100 other cases of drought-induced tree mortality in Africa, Asia, Australia, Europe, and North and South America (high confidence). Species in biodiversity hotspots already show changes in response to climate change (CCP1, high confidence). Román-Palacios and Wiens (2020) attribute local extinctions of several taxonomic groups between the latter 20th century and 2003–2012, (corresponding to warming of less than 0.85°C) to climate-change-related temperature extremes for up to 44% (0–75%) of species. Widespread declines of up to 35% in the species richness of the unique pollinator group, bumble bees, between 1901–1974 and 2000–2014 are also attributed to climate change, via increasing exceedance of their thermal tolerance limits across Europe and North America (Soroye et al., 2020). The

first extinctions attributed to climate change have been now detected with the present 1.2°C warming, including that of the Bramble Cay melomys (Melomys rubicola), a sub-species of the lemuroid ringtail possum (Hemibelideus lemuroides), and golden toad (Incilius periglenes) (Chapter 2). An increasing frequency or unprecedented occurrence of mass animal mortality due to climate-change-enhanced heatwaves has also been observed in recent years on more than one continent, including temperature-vulnerable terrestrial birds and mammals in South Africa and Australia (Ratnayake et  al., 2019; McKechnie et al., 2021). There have also been 90% declines in sea-ice-dependent species such as sea lions and penguins in the Antarctic (Table SM16.22 ). A strong effect of climate change on the observed contraction of ranges of polar fish species and strong expansion of ranges of arcto-boreal or boreal fish was observed between 2004 and 2012 (Frainer et al.., 2017). Even if current human-driven habitat

loss is excluded, many hotspots are projected to cease to be refugia (i.e., to remain climatically suitable for >75% of the species they contain which have been modelled), at 1.0–1.5°C (Cross-Chapter Paper 1).

Based on observed and modelled impacts to unique and threatened 
systems, including in particular coral reefs, sea-ice-dependent systems and biodiversity hotspots, AR6 assesses that the transition to high risks for RFC1 have already occurred at a median level of 0.9°C, with a lower bound at 0.7°C and an upper bound at the present-day level of global warming of 1.2°C (WMO, 2020) (very high confidence).
Identification of the transition to very high risk is associated by 
definition with the reaching of limits to natural and/or societal adaptation. Adaptation which occurs naturally is already included in the risk assessment, but experts also discussed the effect of additional human-planned adaptation in reducing risk levels in RFC1. This additional adaptation could help species to survive in situ despite a

changing climate (for example, by reducing current anthropogenic stresses such as over-harvesting), or facilitate the ability of species to shift geographic range in response to changes in climate, and the potential benefits of nature-based solutions and restoration (see Cross-Chapter Box NATURAL, Section 2.6.5.1).
When considering planned adaptation, the main option often

considered in terrestrial ecosystems is the expansion of the protected area network, which is broadly beneficial in increasing the resilience of ecosystems to climate change (e.g., Hannah et al., 2020). However, this action is not effective if the unique and threatened systems in question reach a hard limit to adaptation (as in the case of the loss of Arctic summer sea ice, the submergence of a small island, the contraction and elimination of a species’ climatic niche from a mountaintop, or the degradation of a coral reef) (Section  16.4). Furthermore, adaptation benefits deriving from restoration rapidly diminish with increasing temperature (Cross-Chapter Paper 1). One study quantifies how land management (in terms of protecting existing ecosystems or restoring lost ones) might reduce extinctions in biodiversity hotspots or globally significant terrestrial biodiversity areas more generally (Warren et al., 2018b). While the latter suggests that substantial benefits can result globally

in terrestrial systems, allowing less unique systems to persist at higher levels of warming but only under a high adaptation scenario in which globally applied terrestrial ecosystem restoration and protected area expansion takes place, this is less likely for many of the unique and threatened terrestrial systems which are more vulnerable than the globally significant biodiversity areas treated in that study (which excludes coral reefs and Arctic sea-ice-dependent systems). Such high levels of adaptation globally are likely infeasible owing to competition for land use with food production (Pörtner et al., 2021). Novel targeted adaptation interventions for coral reefs such as artificial upwelling and local radiation management show some promise for reducing the adverse effects of thermal stress and resulting coral bleaching (Condie et al., 2021), but are far from implementation (Sawall et  al., 2020; Kleypas et  al., 2021). Larger benefits in this RFC could theoretically accrue only if

adaptation action became ubiquitous and extensive, which experts considered infeasible at the scales required. Small island communities are confronted by socio-ecological limits to adaptation well before 2100, especially those reliant on coral reef systems for their livelihoods, even for a low-emissions pathway (Chapter 3) (high confidence). At warming levels beyond 1.5°C, the potential to reach biophysical limits to adaptation due to limited water resources are reported for small islands (medium confidence) and unique systems dependent on glaciers and snowmelt (Chapter 4) (medium confidence).

AR5 assessed with high confidence that the transition from high to 
very high risks for RFC1 lies around 2°C above 1986–2005 levels (then considered to correspond to 2.6°C above pre-industrial levels) to reflect the very high risk to species and ecosystems projected to occur beyond that level as well as limited ability to adapt to impacts on coral reef systems and in Arctic sea-ice-dependent systems. Using the additional literature which became available on projected risks to Arctic sea ice, biodiversity and ecosystems at 1.5°C versus 2°C warming above pre-industrial levels, SR15 assessed that the transition from high to very high risks in RFC1 lay between 1.5°C and 2°C above pre-industrial levels.
In AR6, risks are considered to start to transition from high to very

high risks above 1.2°C warming (present day, WMO, 2020), with a median value of 1.5°C, owing in particular to the observation of a present-day onset of ecosystem degradation in coral reefs, which are projected in the SR15 report ‘to decline by a further 70–90% at 1.5°C (very high confidence)’ . The literature for projected increases in risk to other unique and threatened systems and their limited ability to adapt above 2°C warming is substantial and robust, and the confidence level in very high risk remains high. At 2°C, 18% of 34,000 insects are projected to lose >50% climatically determined geographic range, as compared with 6% at 1.5°C (Warren et al., 2018a). The risk of species

16
2488Chapter 16 Key Risks across Sectors and Regions
extinction increases with warming in all climate change projections, 
for all native species studied in biodiversity hotspots (Cross-Chapter 
Paper 1, high confidence), being roughly threefold greater for endemic 
than more widespread species for global warming of 3°C above pre-
industrial levels than 1.5°C) (Manes et al., 2021, Cross-Chapter Paper 
1) (medium confidence). The Arctic is projected to be practically ice free 
in September in some years for global warming of between 1.5°C and 
2°C (WGI AR6 Section 9.3.1.1, Fox-Kemper et al., 2021), undermining 
the persistence of ice-dependent species such as polar bears, ringed 
seals and walrus (Meredith et  al., 2019), and adversely affecting 
Indigenous communities. Warming of 1.5°C is also assessed (Chapter 
3) to reduce the habitability of small islands, due to the combined 
impacts of several key risks (high confidence). Hence, the transition

from high to very high risk in these systems is assessed to occur with 
high confidence beginning at 1.2°C, passing through a median value of 
1.5°C, and completing (i.e., reaching its upper bound) at 2°C warming.
16.6.3.2 Extreme Weather Events (RFC2)
This RFC addresses the risks to human health, livelihoods, assets and 
ecosystems from extreme weather events such as heatwaves, heavy 
rain, drought and associated wildfires, and coastal flooding (Hoegh-
Guldberg et al., 2018b). Previous assessments of this RFC have focused 
mainly on changes to the hazard component of the risk, using the 
projected increase in hazard as an indicator of higher risk. However, in 
AR6 an expanding (although still smaller) body of evidence now allows 
also incorporation of the exposure and/or vulnerability components of 
risk and, to a limited extent, their trends.
AR5 identified a transition from undetectable to moderate risk below 
‘recent’ temperatures (i.e., during 1986–2005, which then corresponded

to a global warming of 0.6°C above pre-industrial levels). SR15 
Section 3.5.2.2 (Hoegh-Guldberg et al., 2018b) concluded that differences 
of 0.5°C in global warming led to detectable changes in extreme weather 
and climate events on the global scale and for large regions. IPCC WGI 
AR6 Chapter 11 confirms this assessment and concludes that ‘new 
evidence strengthens the conclusion from SR15 that even relatively small 
incremental increases in global warming (+0.5°C) cause statistically 
significant changes in extremes on the global scale and for large regions’.  
Substantial literature is available for comparisons at +1.5°C versus +2°C 
of global warming, but the conclusions are assessed to also apply at 
lower global warming levels and smaller increments of global warming 
given the identified linearity of regional responses of several extremes in 
relation to global warming (Seneviratne et al., 2016; Wartenburger et al.,

2017; Tebaldi and Knutti, 2018) and the identification of emergence of 
global signals in climate extremes for global warming levels as small as 
0.1°C (Seneviratne and Hauser, 2020, WGI AR6, Chapter 11, Figure 11.8; 
WGI Cross-Chapter Box 12.1). Further analyses are consistent with this 
assessment, based on model simulations (Fischer and Knutti, 2015; 
Schleussner et  al., 2017; Kirchmeier-Young et  al., 2019a; Seneviratne 
and Hauser, 2020) and observational evidence (Zwiers et al., 2011; Dunn 
et al., 2020). A global warming of +0.5°C above pre-industrial conditions 
corresponds approximately to climate conditions in the 1980s (Chapter 2, 
Figure 2.11), a time frame at which detectable changes in some extremes 
were established at the global scale based on observations (Dunn et al., 
2020). Heat-related mortality has also been assessed to have increased

considerably because of climate change (Ebi et al., 2021; Vicedo-Cabrera et al., 2021). The onset, and also median location of the transitions of risk 
(Figure 16.15) from undetectable to moderate, is therefore considered to 
be 0.5°C. Further strong new evidence shows that changes in extremes 
emerged during the 1990s and 2000s (Dunn et al., 2020) by which time 
+0.7°C of global warming had taken place (IPCC SR15, Chapter 1; WGI 
AR6, Chapter 2). In AR5 Section  19.6.3.3 (Oppenheimer et  al., 2014), 
a transition to moderate risk was assessed to have taken place at the 
then ‘recent’ global warming level of 0.6°C, with high confidence. Owing 
to the increase in evidence, there is now very high confidence that the 
median value of the transition from undetectable to moderate risk is at 
0.5°C and led by heat extremes, with the lower estimate set at 0.5°C as 
well, and upper estimate at 0.7°C.
Further evidence of more recent observed changes in extreme

weather and climate events, and their potential for associated adverse 
consequences across many aspects of society and ecosystems, has 
continued to accrue (WGI AR6 Chapter 11; WGI AR6 Chapter 12). Since 
a necessary condition for ‘moderate’ levels of risk is the detection 
and attribution of observed impacts, the following text provides an 
overview of some salient examples of this evidence. In particular, WGI 
AR6 Chapter 11 (Seneviratne et al., 2021) concludes that some recent 
hot extreme events that happened in the past decade (2010s) would 
have been extremely unlikely to occur without human influence on the 
climate system. Global warming in that decade reached approximately 
1.09°C on average (IPCC WGI AR6 Chapter 2).
Assessment of a high level of risk requires a higher level of magnitude, 
severity and spatial extent of the risks. Events prior to that already 
had substantial impacts, such as the 2003 European heatwave (IPCC

SREX Chapter 9). Examples of impactful events in the early 2010s (at 
ca. 0.95°C of global warming; WGI AR6 Chapter 2, Gulev et al., 2021) 
include the 2010 Russian heatwave (Barriopedro et al., 2011) and the 
2010 Amazon drought (Lewis et al., 2011). Later impactful events include, 
among others, the 2013 heatwave in eastern China (Sun et al., 2014), the 
2017 tropical cyclone Harvey (Risser and Wehner, 2017; Van Oldenborgh 
et al., 2017) and the 2018 concurrent North Hemisphere heatwaves in 
Europe, North America and Asia (Vogel et al., 2019). Very recent events 
with severe and unprecedented impacts attributed to anthropogenic 
climate change indicate that thresholds to high risks may already 
have been crossed at recent levels of global warming (ca. 1.1–1.2°C), 
including the Siberian fires and the 2019 Australian bushfires that were 
linked to extreme heat and drought conditions (Van Oldenborgh et al., 
2017) and extreme precipitation linked to increased storm activity in the

USA (Van Oldenborgh et al., 2017). Severe and unprecedented impacts 
occurred with current low levels of adaptation (Section 16.2.3.4). The 
global-scale risk of wildfire considerably degrading ecosystems and 
increasing illnesses and death of people has been assessed to transition 
from undetectable to moderate over the range 0.6–0.9°C with high 
confidence (Chapter 2, Table SM2.5, Figure 2.11).
In addition, long-term trends in various types of extremes are now 
detectable (WGI AR6 Chapter 11, Seneviratne et al., 2021). This includes 
increases in hot extremes over most land regions (virtually certain), 
increases in heavy precipitation at the global scale and over most 
regions with sufficient observations (high confidence), and increases 
in agricultural and ecological droughts in some regions (medium 
confidence) (WGI AR6 Chapter 11). There has also been overall a likely

16
2489Key Risks across Sectors and Regions  Chapter 16
increase in the probability of compound events, such as an increase in 
concurrent heatwaves and droughts (high confidence) (WGI AR6 Chapter 
11). There is medium confidence that weather conditions that promote 
wildfires (fire weather) have become more probable in southern Europe, 
northern Eurasia, the USA and Australia over the last century (WGI AR6 
Chapter 11; SRCCL Chapter 2, Jolly et al., 2015; Abatzoglou and Williams, 
2016). Furthermore, food security and livelihoods are being affected by 
short-term food shortages caused by climate extremes (Section 5.12.1; 
Chapter 16, Food Security RKR) which have affected the productivity of 
all agricultural and fishery sectors (high confidence). The frequency of 
sudden food production losses has increased since at least mid-20th 
century on land and sea (medium evidence, high agreement). Droughts, 
floods and marine heatwaves contribute to reduced food availability and

increased food prices, threatening food security, nutrition and livelihoods 
of millions (high confidence). Changes in sea surface temperatures drive 
simultaneous variation in climate extremes, increasing the risk of multi-
breadbasket failures (Cai et al., 2014; Perry et al., 2017). Droughts induced 
by the 2015–2016 El Niño, partially attributable to human influences 
(medium confidence), caused acute food insecurity in various regions, 
including eastern and southern Africa and the dry corridor of Central 
America (high confidence). Human-induced climate change warming 
also worsened the 2007 drought in southern Africa, causing food 
shortages, price spikes and acute food insecurity in Lesotho (Verschuur 
et al., 2021). In the fisheries and aquaculture sector, marine heatwaves 
are estimated to have doubled in frequency between 1982 and 2016, 
as well as increasing in intensity and length, with consequences for fish

mortality (Chapter 5; Smale et al., 2019; Laufkötter et al., 2020). In the 
northeast Pacific, a recent 5-year warm period impacted the migration, 
distribution and abundance of key fish resources (high confidence). 
At 1°C warming, the number of people affected by six categories of 
extreme events was found to have already increased by a factor of 2.3 
relative to pre-industrial (Lange et al., 2020).
The general picture is one of annual or more frequent occurrences 
of severe extremes with widespread impacts (as also reflected in 
Section  16.2), and of multiple extremes, meeting the criteria for 
the ‘severe and widespread’ nature of risks that is required for 
classification at a ‘high’ level of risk. This is consistent with AR5 
Chapter 19 (Oppenheimer et al., 2014), and gives high confidence that 
the lower threshold for entering high risks associated with extreme 
weather events is +1°C, and that the best estimate is that this

transition already occurred now that global warming has reached its 
present-day level of ca. 1.2°C (WMO, 2020), slightly above the 1.09°C 
average conditions in the 2010s, that is, 2011–2020 (IPCC WGI AR6 
Chapter 2, Gulev et al., 2021).
A range of literature projects further substantial increases in several 
extreme event types with a global warming of +1.5°C, notably hot 
extremes in most regions, heavy precipitation in several regions,  and 
drought in some regions (IPCC SR15; WGI AR6 , Chapter 11). In particular, 
heavy precipitation and associated flooding are projected to intensify and 
be more frequent in most regions in Africa and Asia (high confidence), 
North America (medium to high confidence depending on the region) 
and Europe (medium confidence). Also, more frequent and/or severe 
agricultural and ecological droughts are projected in a few regions in all 
continents except Asia, compared with 1850–1900 (medium confidence);

increases in meteorological droughts are also projected in a few regions (medium confidence). Increases at 1.5°C of global warming are projected 
in marine heatwaves (Laufkötter et al., 2020) and the occurrence of fire 
weather (IPCC, 2019a).  Heat-related mortality is assessed to increase 
from moderate to high levels of risk under about 1.5°C warming under 
SSP3, a socioeconomic scenario with large challenges to adaptation 
(Ebi et al., 2021) especially in urban centres (Chapter 6). An additional 
350  million people living in urban areas are estimated to be exposed 
to water scarcity from severe droughts at 1.5°C warming (Sections 6.1, 
6.2.2; CCP2 Coastal Cities). In summary, there is high confidence that the 
best estimate for the transition from moderate to high risk is 1.2°C of 
global warming, with 1°C as lower estimate and 1.5°C as upper estimate. 
The latter would be set to 1.3°C for an assessment at medium confidence.

As in RFC1, one of the criteria for identification of very high risks is 
limits to adaptation. Though the literature explicitly considering societal 
adaptation to extreme weather events is limited, there is evidence 
that investments in hydro-meteorological information, early-warning 
systems and anticipatory forecast-based finance are a cost-effective 
way to prevent some of the most adverse effects of extreme events 
(Coughlan de Perez et  al., 2016; Fakhruddin and Schick, 2019; Merz 
et al., 2020). Despite a lack of systematic methods for assessing general 
adaptation effectiveness, there is some evidence of risk reduction for 
particular places and hazards, especially flood and heat vulnerability 
(Section  16.3.2.4), including investment in flood protection, building 
design and monitoring and forecasting, air conditioning, reduced social 
vulnerability, and improved population health. One study finds declining 
global mortality and economic loss due to extreme weather events

over the past four decades (Formetta and Feyen, 2019) especially in 
low-income countries. Using SSP2 as a proxy for expanded adaptation, 
Ebi et al. (2021) assess that the transition to high risk for heat-related 
mortality increases to 1.8°C (compared with 1.5°C with less adaptation 
under SSP3). There is evidence of adaptation avoiding heat-related 
mortality at low levels of global warming, using early-warning and 
response systems and sustainable alterations of the thermal environment 
at the individual, building, urban and landscape levels (Jay et al., 2021). 
Despite the evidence that adaptation can reduce risks of heat stress, the 
impact of projected climate change on temperature-related mortality 
is expected to be a net increase under a wide range of climate change 
scenarios, even with adaptation (Chapter 7, high confidence) . Much 
of the adaptation literature focuses on coping with long-term gradual 
climate change and largely does not take into account the increased

difficulty of adapting to climate extremes and general higher variability 
in climate that is projected to occur in the future. However, expanding 
and more coordinated adaptation, including wider implementation and 
multi-level coordination, has the potential to reduce the risks to crops 
from heatwaves at intermediate (but not high) levels of warming.(IPCC 
AR5 Ch7, Ahmed et al., 2018; Ahmed et al., 2019, Section 16.3.2.2; EEA, 
2019; Raza et al., 2019; Tripathi and Sindhi, 2020).
The transition from high to very high risk for the RFC2 was not assessed 
in the AR5 or in SR15. Some new evidence suggests, however, that 
very high risks associated with weather and climate extremes would  
be reached at higher levels of global warming. In particular, changes 
in several hazards would be more widespread and pronounced at 2°C 
compared with 1.5°C global warming, including increases in multiple 
and concurrent extremes (IPCC WGI AR6 SPM; IPCC WGI AR6 Chapter

11, IPCC WGI AR6 Chapter 12). On average over land, high temperature

16
2490Chapter 16 Key Risks across Sectors and Regions
events that would have occurred once in 50 years in the absence of 
anthropogenic climate change are projected to become 13.9  times 
more likely with 2°C warming, and 39.2  times more likely with 4°C 
warming (IPCC AR6 WGI SPM Figure  SPM.6, IPCC, 2021), indicating 
a nonlinear increase with warming. Chapter 2 assessed that risk of 
wildfire transitions from moderate to high over the range 1.5°C to 2.5°C 
warming (medium confidence, Table SM2.5 , Figure 2.11). The intensity 
of heavy precipitation events increases overall by about 7% for each 
additional degree of global warming (IPCC AR6 WGI SPM), while their 
frequency increases nonlinearly. Events that would have occurred once 
every 10 years in a climate without human influence are projected to 
become 1.7 times more likely with 2°C warming, and 2.7 times more 
likely with 4°C warming (IPCC AR6 WGI SPM Figure SPM.6). Several

AR6 regions are projected to be affected by increases in agricultural and 
ecological droughts at 2°C of global warming, including western North 
America, central North America, northern Central America, southern 
Central America, the Caribbean, northern South America, northeastern 
South America, South American Monsoon, southwestern South America, 
southern South America, West and Central Europe, the Mediterranean, 
western Southern Africa, eastern Southern Africa, Madagascar, 
eastern Australia and southern Australia (IPCC WGI AR6, Chapter 11, 
Seneviratne et al., 2021). This is a substantially larger number compared 
with projections at 1.5°C (IPCC WGI AR6, Chapter 11, Seneviratne et al., 
2021). In these drying regions, events that would have occurred once 
happen 2.4 times more frequently at 2°C of global warming (IPCC WGI 
AR6 SPM Figure SPM.6). Urban land exposed to floods and droughts

is very likely to have more than doubled between 2000 and 2030, 
and the risk of flooding accelerates after 2050 (Chapter 4). At 2°C of 
global warming, there are also significant projected increases in fluvial 
flood frequency and resultant risks associated with higher populations 
exposed to these flood risks (Alfieri et al., 2017; Dottori et al., 2018).
Heat-related mortality is assessed to increase from high to very high 
by 3°C under SSP3, a socioeconomic scenario with large challenges to 
adaptation (Ebi et al., 2021). SRCCL assessed that very high risks would be 
reached in association with wildfire above 3°C of global warming (IPCC, 
2019a). Chapter 2 has assessed that risk of fire weather itself transitions 
from high to very high over the range 3°C to 4.5°C warming (medium 
confidence,  Table  SM2.5, Figure  2.11). Matthews et  al. (2017) show 
that, at 1.5°C of global warming, about 40% of all megacities would be

affected at least 1 d yr−1 with a heat index above 40.6°C (i.e., with 40.6°C 
‘feels-like’ temperatures, accounting for moisture effects). This number 
would reach about 65% of megacities at 2.7°C and close to 80% at 4°C. 
In addition, there is evidence for a higher risk of concurrent heat extremes 
at different locations with increasing global warming (Vogel et al., 2019), 
meaning that several cities could be affected by deadly heatwaves 
simultaneously. Laufkötter et  al. (2020) found that marine heatwave 
events would become annual to decadal events under 3°C of global 
warming, with consequences for aquaculture (Chapter 5). Gaupp et al. 
(2019) conclude that risks of simultaneous crop failure across worldwide 
breadbasket regions, due to changes in maximum temperatures in the 
crop-growth-relevant season or cumulative precipitation in relevant time 
frames, increase disproportionately between 1.5°C and 2°C of global

warming. Populations exposed to extreme weather and climate events 
may consume inadequate or insufficient food, leading to malnutrition 
and increasing the risk of disease (Chapter 5, high confidence). Hence, there is the potential for very high risks associated with changes in 
climate extremes for food security in the low adaptation case, already 
above 2°C of global warming. Finally, studies suggest that regional 
thresholds for climate extremes could be reached at 2°C of global 
warming, for instance in the Mediterranean (Guiot and Cramer, 2016). 
Samaniego et  al. (2018) conclude that soil moisture droughts in that 
region would become two to three times longer than at the end of the 
20th century at 2°C, and three to four times longer (125 d long yr–1) 
at 3°C of global warming. There is clear evidence of very high risk at 
3°C global warming for wildfires, marine heatwaves and heatwaves in 
megacities (the latter being set at 2.7°C).

Based on the available evidence, we assess that there is medium 
confidence that the transition to very high risk would happen at a 
median value 2°C of global warming, considering the increased risk for 
breadbasket failure and irreversible impacts associated with changes 
in extremes at this warming level (e.g., damages to ecosystems, health 
impacts, severe coastal storms), but that due to the disproportionate 
increases in risk between 1.5°C and 2°C this transition begins already 
at 1.8°C. The higher range for this transition is set with medium 
confidence at 2.5°C in this low/no adaptation scenario, owing to the 
further projected nonlinear increases in risks associated with high 
temperature events above 2°C (WGI AR6 Figure SPM.6, IPCC, 2021; 
Cross-Chapter Box 12.1, Ranasinghe et al., 2021), and also the limits to 
adaptation associated with dealing with a rapid escalation of extreme 
weather events globally during this century; extreme events are

particularly difficult to adapt to and thus more often exceed hard limits 
to adaptation, particularly in natural ecosystem settings (Section 16.4).
16.6.3.3 Distribution of Impacts (RFC3)
RFC3 reflects how key risks are distributed unevenly across regions 
and different population groups, due to the non-uniform spatial 
distributions of physical climate change hazards, exposure and 
vulnerability across regions. It addresses how risks disproportionately 
affect particularly vulnerable societies and socio-ecological systems, 
including disadvantaged people and communities in countries at 
all levels of development. AR5 concluded that low-latitude and less 
developed areas generally face greater risk than higher-latitude and 
more developed countries, including for food- and health-related 
risks. This conclusion remains valid and is now supported by greater 
evidence across a range of sectors and geographic regions.
Note that the assessment here is largely based on the national and

regional distribution of impacts, rather than sub-national distribution or 
explicit consideration of vulnerable elements of society. Climate risks are 
also strongly related to inequalities, often but not always intersecting 
with poverty (Section  16.1), geographic location, and political and 
socio-cultural aspects. Thus, countries with high inequality tend to be 
more vulnerable, and more exposed, to climate hazards (Section 16.1). 
While the literature assessed here tends to be insufficiently granular 
to resolve local inequalities, it does confirm the AR5 finding that low-
latitude and less developed areas generally face greater risk.
AR6 continues to highlight the uneven regional distribution of 
projected climate change risks. Biodiversity loss is projected to affect 
a greater number of regions with increasing warming, and to be

16
2491Key Risks across Sectors and Regions  Chapter 16
highest in northern South America, southern Africa, most of Australia, 
and northern high latitudes (Section  2.5.1.3, medium confidence). 
Climate change is projected to increase the number of people at risk 
of hunger in mid-century, concentrated in Sub-Saharan Africa, South 
Asia and Central America (Chapter 5, high confidence), increasing 
undernutrition, stunting and related childhood mortality particularly 
in Africa and Asia and disproportionately affecting children and 
pregnant women (Chapter 7, high confidence), strongly mediated by 
socioeconomic factors (Sections 7.2.4.4, 7.3.1, very high confidence). 
Strong geographical differences in heat-related mortality are projected 
to emerge later this century, mainly driven by growth in regions with 
tropical and subtropical climates (Section 7.3.1, very high confidence).
In AR5 and SR15, the transition from undetectable to moderate risk

was located below what were at the time ‘recent’ temperatures of 
between 0.5°C and 0.8°C above pre-industrial levels, with medium to 
high confidence, based on evidence of distributional impacts on crop 
production and water resources. New literature has continued to confirm 
this transition has already taken place, including more recent observed 
impacts for regions and groups within the food and water sectors, 
strongly linked to Representative Key Risks for health, water and food 
security (Sections 16.2, 16.5, 5.4.1, 5.5.1, 5.8.1, 5.12; Chapter 7).
In AR6, moderate risks have already been assessed to have occurred 
in Africa for economic growth and reduced inequality, biodiversity 
and ecosystems, mortality and morbidity due to heat extremes and 
infectious disease, and food production in fisheries and crop production 
(Figure  9.6). In Europe, moderate risks to heat stress, mortality and 
morbidity have already been reached, as well as for water scarcity in

some regions (Figure 13.30, Figure 13.3 1). In Australasia, moderate 
risks are assessed as present already for heat-related mortality risk as 
well as cascading effects on cities and settlements, and also very high 
risks already present in coral reef systems, and high risks to kelp forests 
and alpine biodiversity (Figure 17.6). In North America, moderate risks 
have already been reached for freshwater scarcity, water quality 
(Figure  14.4), agriculture, forestry, tourism, transport, energy and 
mining, and construction (Figure 14.10).
For this assessment, the transition to moderate risk was assessed 
to have occurred between 0.7°C and 1.0°C of warming with high 
confidence, demonstrating that a moderate level of risk exists at 
present. The 0.2°C increase in this temperature range as compared 
with AR5 reflects the fact that AR6 WGI has assessed that the level 
of global warming reached by 1986–2005 was 0.52–0.82°C (as

opposed to 0.55–0.67°C in previous assessments), and also reflects 
the opportunity for observations to be have made of the observed 
consequences of the additional rise in temperature that has taken place 
since the literature underpinning the AR5 assessment was published.
In AR5, the transition from moderate to high risk was assessed to occur 
between 1.6°C and 2.6°C above the pre-industrial levels with medium 
confidence. In SR15, new literature on projected risks allowed this range 
to be narrowed to 1.5–2°C. There is now substantial literature providing 
robust evidence of larger regional risks at 2°C warming than 1.5°C and in 
a range of systems, including crop production (with risks of simultaneous 
crop failure) (Thiault et al.; Gaupp et al., 2019), aquaculture and fisheries 
(Cheung et al., 2018b; Froehlich et al., 2018; Stewart-Sinclair et al., 2020), nutrition-related health (Springmann et  al., 2016; Lloyd et  al., 2018;

Sulser et al., 2021) and exposure to stressors such as drought, floods 
(Alfieri et al., 2017; Hirabayashi et al., 2021) and extreme heat (Dosio 
et al., 2018; Harrington et al., 2018; Sun et al., 2019). One study (Gaupp 
et al., 2019) found that the risk of simultaneous crop failure in maize is 
estimated to increase from 6% to 40% at 1.5°C relative to the historical 
baseline climate. In particular, further research on projected regional 
yield declines of wheat and maize between 1.5°C and 2°C, especially 
in Africa, has accrued Asseng et al. (2015), including in Ethiopia (Abera 
et  al., 2018) with associated economic effects (Wang et  al., 2019). 
Optimum maize production areas in East Asia are projected to reduce in 
area by 38% for global warming of 1.5–2.0°C (He et al., 2019). A study 
of Jamaica also estimated that warming of less than 1.5°C will have an 
overall negative impact on crop suitability and a general reduction in

the range of crops, but above 1.5°C, irreversible changes to Jamaica’s 
agriculture sector were projected (Rhiney et al., 2018).
Projections of increasing flood risk associated with global warming of 
1.5°C and 2°C continue to highlight regional disparities, with larger-
than-average increases projected in Asia and Africa (Hirabayashi et al., 
2021), including in China, India and Bangladesh (Alfieri et al., 2017). 
Similarly, nearly 80% of the 8–80 million additional people projected 
to be at risk of hunger owing to climate change are located in Africa 
and Asia (Springmann et al., 2016; Lloyd and Oreskes, 2018; Nelson 
et al., 2018). Schleussner et al. (2016b) analysed hotspots of multi-
sectoral risks with 1.5°C and especially 2°C warming, and highlighted 
projected crop yield reductions in West Africa, Southeast Asia, and 
Central and northern South America; a reduction in water availability 
in the Mediterranean; and widespread bleaching of tropical coral 
reefs.

High risks to crop production are assessed to occur in Africa with ~1.5–
2°C warming (Figure 9.6), to agriculture in North America with ~1.5°C 
warming (Figure  14.10), and with ~2.8°C in Europe (Figure  13.30). 
High risks of mortality and morbidity due to heat extremes and 
infectious disease are assessed to be reached in Africa with ~1.5°C 
warming (Figure 9.6); heat stress, mortality and morbidity in Europe 
are assessed to reach a high level of risk at ~2°C (Figure 13.30). Heat-
related mortality risk transitions to a high level by ~1.5–2°C warming 
in Australasia, while cascading effects on cities reach high risk with 
~1.2°C warming (Figure 17.6). Risks to water scarcity, forestry, tourism 
and transportation in North America are projected to reach high levels 
with ~2°C warming (Figure 14.4, Figure 14.10).
Two complementary multi-sectoral analyses indicate that South Asia 
and Africa become hotspots of multi-sectoral climate change risk,

largely due to changes in water-related indicators which also affect 
crop production (Arnell et al., 2018; Byers et al., 2018). For instance, 
Byers et al. (2018) found that the doubling in global exposure to multi-
sector risks that accrues as warming increases from 1.5°C to 2°C is 
concentrated in Asian and African regions (especially East Africa), 
which together account for 85–95% of the global exposure.
Considering this evidence, for this assessment, the temperature range 
for the transition from moderate to high risk is located between 1.5°C 
and 2°C above pre-industrial levels, with high confidence in the lower 
bound of 1.5°C, but medium confidence in the upper bound of 2°C,

16
2492Chapter 16 Key Risks across Sectors and Regions
because simulation studies do not account for climate variability and 
therefore risks could be higher.
Very high risk implies limited ability to adapt. Adaptation potential not 
only differs across sectors and regions, but also occurs on different 
time scales depending on the nature and implementation level of the 
adaptation option under consideration and the system in which it is 
to be deployed. The costs of adaptation actions that would be needed 
to offset projected climate change impacts for major crop production 
are projected to rise once global warming reaches 1.5°C (Iizumi et al., 
2020). It has been estimated that the number of additional people 
at risk of hunger with 2.0°C global warming could be reduced from 
40 million to 30 million by raising the level of adaptation action (Baldos 
and Hertel, 2014), but beyond this level of warming residual impacts

are projected to escalate (Iizumi et al., 2020). Chapter 5 assessed the 
potential of existing farm management practices to reduce yield losses, 
finding an average 8% loss reduction in mid-century and 11% by end-
century (Section 5.4.4.1), which is insufficient to offset the negative 
impacts from climate change, particularly in currently warmer regions 
(Section 5.4.3.2). The literature indicates that, globally, crop production 
may be sustained below 2.0°C warming with adaptation, but negative 
impacts will prevail at 2.0°C warming and above in currently warm 
regions (Section  5.4.4.1). Importantly, residual damage (that which 
cannot be avoided despite adaptation) is projected to rise around 
2.0°C global warming (Iizumi et al., 2020). Evidence of constraints and 
limits for food, fibre and other ecosystem products for the different 
regions is evident for the various regions (Section 16.4.3.1) indicating 
limited ability to adapt. Adaptation costs are also higher relative to

GDP in low-income countries, for example for the building of sea dikes 
(Brown et al., 2021).
In previous reports, the transition from high to very high risk for the 
distribution of impacts was not assessed due to limited available 
literature, but there is now sufficient evidence to do so. A range of 
literature quantifies the increasing regional probability of drought as 
compared with the present day, with projected increases in the area 
exposed to drought (Carrão et al., 2018; Pokhrel et al., 2021), as well as 
in the duration (Naumann et al., 2018) and frequency of droughts, with 
higher warming levels. Naumann et al. (2018) showed that, for drying 
areas, drought durations are projected to rise from 2 months per °C 
below 1.5°C to 4.2 months per °C near 3°C warming. Most of Africa, 
Australia, southern Europe, southern and central USA, Central America, 
the Caribbean, northwest China, and parts of Southern America are

projected to experience more frequent droughts. Adverse effects of 
climate change on food production are projected to become much 
more severe (Section  5.4.3.2) when global temperatures rise more 
than 2°C globally, but there are predicted to be much more negative 
impacts experienced sooner on food security in low to mid-latitudes 
(Richardson et al., 2018a) (Section 5.4.1). For instance, climate change 
by 2050 is projected to increase the number of people at risk of hunger 
by between 8 and 80 million with 2–3°C warming compared with no-
climate-change conditions (Baldos and Hertel, 2014; Hasegawa et al., 
2018; Nelson et al., 2018; Janssens et al., 2020). In addition to effects 
upon crop yield, agricultural labour productivity, food access and 
food-related health are projected to be negatively impacted by 2–3°C 
warming (Springmann et al., 2016; de Lima et al., 2021). Regionally,

substantial regional disparity in risks to food production is projected to persist at these higher levels of warming. Risks for heat-related 
morbidity and mortality, ozone-related mortality, malaria, dengue, 
Lyme disease and West Nile fever are projected to increase regionally 
and globally (Chapter 7) with potential infestation areas for disease-
carrying vectors in multiple geographic regions that could be five times 
higher at 4°C than at 2°C (Liu-Helmersson et al., 2019).
Very high risks to crop production are assessed to occur in Africa above 
~2.5°C warming (Figure 9.6) and below 4°C in Europe (Figure 13.29 
). Very high risks of mortality and morbidity due to heat extremes and 
infectious disease are assessed to occur in Africa with 2.5°C warming 
(Figure 9.6); heat stress, mortality and morbidity in Europe are assessed 
to reach a very high level of risk at ~3.2°C (Figure 13.30). Heat-related 
mortality risk and cascading effects on cities both transition to a very

high level by ~2.5°C warming in Australasia (Figure  11.7). Risks to 
water scarcity in North America are projected to reach very high levels 
with 3.5°C warming (Figure 14.4). Hence, this assessment concludes 
with medium confidence that a transition from high to very high risks, 
in terms of distribution of impacts, begins at 2°C global warming, 
with a full transition to very high risks completed by 3.5°C. However, 
it should be noted that many studies upon which this assessment 
has been based have not taken into account the impacts of extreme 
weather events and oscillations in sea surface temperatures; hence, 
risks at a given level of global warming might be underestimated in 
the literature.
16.6.3.4 Global Aggregate Impacts (RFC4)
This RFC considers impacts to socio-ecological systems that can be 
aggregated globally into a single metric, such as monetary damages, 
lives affected, species lost or ecosystem degradation at a global

scale (Oppenheimer et  al., 2014; O’Neill et  al., 2017). RFC4 shares 
underlying key risk components with other RFCs (e.g., RFC1 and RFC2, 
see O’Neill et al., 2017) and thus draws on a similar literature as those 
assessments; however, this RFC focuses on impacts that reach levels of 
concern at the global level and also weighs the composite effect of risk 
elements ranging from economic to biodiversity.
In AR5 Section 19.6.3.5 (Oppenheimer et al., 2014), the transition from 
undetectable to moderate risk was assessed between 1.6°C and 2.6°C 
above pre-industrial levels (i.e., 1°C and 2°C above the 1986–2005 
level) based on impacts to both Earth’s biodiversity and the overall 
global economy with medium confidence. The risk transition between 
moderate and high risk was set around 3.6°C above pre-industrial 
levels (i.e., 3°C above the 1986–2005 level), based on literature 
finding extensive species vulnerability and biodiversity damage with

associated loss of ecosystem goods and services at 3.5°C (Foden et al., 
2013; Warren et al., 2013). In SR15 Section 3.5.2.4 (Hoegh-Guldberg 
et al., 2018b), economic literature on potential socioeconomic threshold 
events and empirical studies of global economic damages, combined 
with new evidence on biome shifts, extinction risk, species range loss 
(especially noting the integral role of insects in ecosystem function) 
and ecosystem degradation, were assessed, and the upper bound of 
the transition to moderate risk was lowered to 1.5°C warming above 
pre-industrial levels, and the transition from moderate and high risk 
was lowered to between 1.5°C and 2.5°C (medium confidence). The 
boundary between high risk and very high risk was not assessed in

16
2493Key Risks across Sectors and Regions  Chapter 16
either of these reports because the temperature threshold was beyond 
the scope of the assessment in the case of SR15 and literature available 
for this highest transition in AR5 was limited.
Since AR5, many new global estimates of the aggregate, economy-wide 
risks of climate change have been produced, though, as was the case 
in AR5, these continue to exhibit a low level of agreement, including 
for today’s level of global warming, due primarily to differences in 
methods. Cross-Working Group Box ECONOMIC in this chapter includes 
a more thorough discussion of advancements and limitations of global 
economic impact estimates and methodologies, finding significant 
variation in estimates that increases with warming, indicating higher risk 
in terms of economic costs at higher temperatures (high confidence). 
Climate change has been found to exacerbate poverty through declines

in agricultural productivity, changes in agricultural prices and extreme 
weather events (Hertel and Lobell, 2014; Hallegatte and Rozenberg, 
2017). In terms of biodiversity risks, the literature indicates that losses 
in terrestrial and marine ecosystems increase substantially between 
1.5°C and 2°C of warming (Hoegh-Guldberg et al., 2018b). Since SR15, 
further evidence of degradation of biodiversity and ecosystem services 
and ocean acidification at the global aggregate level has continued to 
accrue due to climate change (see Chapter 2).
For this RFC, the transition from undetectable to moderate risk to 
global aggregate impacts is assessed with medium confidence to occur 
between 1.0°C (start of transition) and 1.5°C (completion of transition) 
with a median judgement of transition at 1.3°C, based on evidence of a 
combination of economic consequences, widespread impacts to climate-
sensitive livelihoods, changes in biomes, and loss of terrestrial and marine

biodiversity. The start of the transition from undetectable to moderate 
risk is located at recent temperatures based on observed impacts 
to biodiversity (Section  16.2.3.1). Experts noted aggregate impacts 
on biodiversity are detectable, with damages that have had global 
significance (e.g., drought, pine bark beetles, coral reef ecosystems). 
Consistent with the start of this transition at 1°C, a similar elicitation 
conducted in Chapter 2 assessed that risks to biodiversity globally have 
already transitioned to a moderate level with 1°C warming, while risks 
of widespread tree mortality are already moderate with 0.9°C warming 
and moderate risks of ecosystem structure change began with warming 
of 0.5°C (Table  SM2.5, Figure  2.11). Human-induced warming has 
slowed growth of agricultural productivity over the past 50 years in mid- 
and low latitudes (Chapter 5; Hurlbert et al., 2019). Although there is

not yet strong evidence of attributable loss of life and livelihoods at the 
global level (Sections 16.5.2.3.4, 16.5.2.3.5), experts found that regional 
evidence of such observed impacts was still relevant to defining the 
beginning of the transition (e.g., Table SM16.22, Chapter 9). Informing 
the median value and upper bound of the transition to moderate risk, 
empirical studies and scenario analyses have found that regions with 
high dependence on climate-sensitive livelihoods like agriculture, 
fisheries and forestry would be severely impacted even at low levels of 
warming under conditions of low adaptation (RKR-D, Lobell et al., 2011; 
Hoegh-Guldberg et al., 2018b).
The transition to high risk is assessed with medium confidence to occur 
between 1.5°C (start of transition) and 2.5°C (completion of transition) 
with a median judgement of transition at 2.0°C. Though economic

estimates exhibit wide variation and low agreement at warming levels above 1.5°C, many estimates are nonlinear, with marginal economic 
impacts increasing with temperature (see Cross-Working Group 
Box ECONOMIC in this Chapter). At 1.5°C warming, most aggregate 
global impacts to GDP are negative across different estimation 
methods, including bottom-up estimation (e.g., Takakura et  al., 
2019), meta-analysis (e.g., Howard and Sterner, 2017) and empirical 
estimations (e.g., Pretis et al., 2018; Kalkuhl and Wenz, 2020). At 2°C 
Watts et al. (2021) estimate a relative decrease in effective labour by 
10%, which would have profound economic consequences. Byers et al. 
(2018) found that global exposure to multi-sector risks approximately 
doubles between 1.5°C and 2°C, while the percentage of the global 
population exposed to flooding is projected to rise by 24% with 1.5°C 
warming and by 30% with 2.0°C warning (Hirabayashi et al., 2021).

Section 16.5.2.3.4 (RKR-D, underlying key risk on poverty) reports that, 
under medium warming pathways, climate change risks to poverty 
would become severe if vulnerability is high and adaptation is low 
(limited evidence, high agreement). At and beyond 1.5°C, approximately 
200  million people with livelihoods derived from small-scale fisheries 
would face severe risk, given sensitivity to ocean warming, acidification 
and coral reef loss (Cheung et  al., 2018a; Froehlich et  al., 2018; Free 
et  al., 2019). Warming between 1.5°C and 2°C could expose 330–
396 million people to lower agricultural yields and associated livelihood 
impacts (Byers et al., 2018; Hoegh-Guldberg et al., 2018a), due to a high 
dependency of climate-sensitive livelihoods to agriculture globally (World 
Bank, 2020). Models project that climate change will increase the number 
of people at risk of hunger in 2050 by 8–80 million people globally, with

the range depending on the level of warming (1.5–2.9°C) and SSPs 
(Nelson et al., 2018; Mbow et al., 2019; Janssens et al., 2020). Higher 
atmospheric concentrations of carbon dioxide reduce the nutritional 
quality of wheat, rice and other major crops, potentially affecting millions 
of people at a doubling of carbon dioxide relative to pre-industrial 
(very high confidence) (Section  7.3.1). Global ocean animal biomass 
is projected to decrease on average by 5% per 1°C increase; hence, a 
2.5°C level of warming is associated with ~13% decline in ocean animal 
biomass, which would considerably reduce marine food provisioning, 
fisheries distribution and revenue value, with further consequences for 
ecosystem functioning (Chapter 5, medium confidence).
Losses in terrestrial and marine biodiversity increase substantially beyond 
1.5°C of warming (Hoegh-Guldberg et al., 2018b). Section 16.5.2.3.2 
(RKR-B, risks to terrestrial and marine ecosystems) finds that substantial

biodiversity loss globally, abrupt local ecosystem mortality impacts, and 
ecological species disruption are all projected at global warming levels 
below 3°C, with insular systems and biodiversity hotspots at risk below 
2°C (medium confidence). Insects play a critical role in providing vital 
ecosystem services that underpin human systems, with major losses 
of their climatically determined geographic range at 2°C warming 
implying adverse effects on ecosystem functioning. Consistent with 
the transitions presented here, a similar burning ember developed in 
Chapter 2 assessed a transition from moderate to high risks globally 
for marine and terrestrial biodiversity (e.g., widespread death of trees, 
damages to ecosystems, and reduced provision of ecosystem services, 
and structural change, including biome shifts) beginning between 1.0°C 
and 2.0°C warming ( Table SM2.5, Figure 2.11).

16
2494Chapter 16 Key Risks across Sectors and Regions
Though explicit treatment of adaptation is limited in the RFC4 impacts 
literature (i.e., studies that compare risks for specific adaptation scenarios 
in terms of globally aggregated impacts with quantified findings), 
there is evidence of the potential for investments in improved hydro-
meteorological information and early-warning systems to avoid some 
of the most adverse social and economic impacts from extreme weather 
events in both developed and developing countries, with benefits at a 
globally significant level (Hallegatte, 2012). Studies of adaptation in the 
agriculture sector (e.g., changing crop variety, timing of crop planting, 
new types of irrigation, etc.) and infrastructure (e.g., coastal protection, 
hardening of critical infrastructure, flood and climate-resistant building 
materials and water storage) show large potential benefits in terms of

reduced impacts to lives and livelihoods (van Hooff et al., 2015; Mees, 
2017). At higher warming levels, however, potential adaptations to 
address biodiversity loss are expected to be limited due to the projected 
rate and magnitude of change as well as the resources required (Hannah 
The transition to very high risks is assessed to occur within a range of 
2.5–4.5°C with medium confidence over the range, and low confidence  
assessed over a narrowed ‘best estimate’ range of 2.7–3.7°C. The 
lower end of the range reflects the loss of an increasingly large 
fraction of biodiversity globally. Chapter 2 has assessed a transition 
from high to very high risks globally for biodiversity (marine and 
terrestrial) completing at ~2.5°C warming, noting widespread death 
of trees, damages to ecosystems, and reduced provision of ecosystem 
services over the temperature range 2.5–4.5°C ( Table  SM2.5, 
Figure 2.11) and, similarly, a transition from high to very high risks of

ecosystem structure change (including biome shifts) between 3°C and 
5°C warming ( Table SM2.5, Figure 2.11). A global study of 115,000 
common species projects climatically determined geographic range 
losses of over 50% in 49% of insects, 44% of plants and 26% of 
vertebrates with global warming of 3.2°C, implying an associated 
effect on provisional and regulating ecosystem services that support 
human well-being, including pollination and detritivory (Warren 
et al., 2018a). The risk of abrupt impacts on ecosystems as multiple 
species approach tolerance limits simultaneously is projected to 
threaten up to 15% of ecological communities with 4°C of warming 
(Trisos et al., 2020). Under a 4°C warming scenario, models project 
global annual damages associated with SLR of $31,000 billion yr–1 in 
2100 (Brown et al., 2021)
In terms of global economic impact, while an emerging economic 
literature is addressing many gaps and critiques of previous damage

estimates for high warming (e.g., Jensen and Traeger, 2014; Burke et al., 
2015; Lontzek et al., 2015; Moore and Diaz, 2015; Lemoine and Traeger, 
2016; Moore et  al., 2017a; Cai and Lontzek; Takakura et  al., 2019, 
discussed further in Cross-Working Group Box ECONOMIC; Carleton 
et  al., 2020; Méjean et  al., 2020; Rode et  al., 2021), there remains 
wide variation across disparate methodologies, though the spread 
of estimates increases with warming in all methodologies, indicating 
higher risk in terms of economic costs at higher temperatures (high 
confidence). Section 16.5.2.3.4 (RKR-D) finds that risks to aggregate 
economic output would become severe at the global scale at high 
warming (~4.4°C) and minimal adaptation (medium confidence), 
defining severity as ‘the potential for persistent annual economic 
losses due to climate change to match or exceed losses during the world’s worst historical economic recessions’. Furthermore, climate

change impacts on income inequality could compound risks to living 
standards (high confidence, 16.5.2.3.4). Chapter 4 finds that, at 4°C, 
4 billion people are projected to be exposed to physical water scarcity 
16.6.3.5 Large-scale Singular Events (RFC5)
This RFC, large-scale singular events (sometimes called tipping points or 
critical thresholds), considers abrupt, drastic and sometimes irreversible 
changes in physical, ecological or social systems in response to smooth 
variations in driving forces (accompanied by natural variability) 
(Oppenheimer et al., 2014; O’Neill et al., 2017). SR15 Section 3.5.2.5 
presented four examples, including the cryosphere (West Antarctic ice 
sheet, Greenland ice sheet), thermohaline circulation (slowdown of 
the Atlantic Meridional Overturning Circulation), the El Niño-Southern 
Oscillation (ENSO) as a global mode of climate variability, and the role 
of the Southern Ocean in the global carbon cycle (Hoegh-Guldberg

et al., 2018b). While most of the literature assessed here focuses on 
the resultant changes to climate-related hazards such as sea level rise, 
in this assessment, evidence about the implications of accelerated 
sea level rise for human and natural systems is also considered. If sea 
level rise is accelerated by ice sheet melt, the associated impacts are 
projected to occur decades earlier than otherwise, directly affecting 
coastal systems including cities and settlements by the sea (CCP2) and 
wetlands (Chapter 2). The associated disruption to ports is projected 
to severely compromise global supply chains and maritime trade with 
local–global geo-political and economic consequences. To compensate 
for this acceleration, adaptation would need to occur much faster and 
at a much greater scale than otherwise, or indeed than has previously 
been observed (CCP2). The costs of accommodating port growth and 
adapting to sea level rise amount to USD 22–768 billion before 2050

globally (medium evidence, high agreement) (see Sections  2.1, 2.2; 
In AR5 Section  19.6.3.6 (Oppenheimer et  al., 2014), the boundary 
between undetectable and moderate risk is set at levels between 
0.6°C and 1.6°C above pre-industrial levels (i.e., 0°C and 1°C above 
the 1986–2005 level) with high confidence, based on emerging early-
warning signals of regime shifts in Arctic and warm water coral reef 
systems. The risk transition boundary between moderate and high 
risk was set between 1.6°C and 3.6°C above pre-industrial levels (i.e., 
1°C and 3°C above the 1986–2005 level), with medium confidence 
based on projections of ice sheet loss, with faster increase between 
1°C and 2°C than between 2°C and 3°C. The literature available at 
the time did not allow AR5 to assess the boundary between high and 
very high risk.
In SR15 Section 3.5.2.5 (Hoegh-Guldberg et al., 2018b), new assessments

of the potential collapse of the West Antarctic ice sheet (WAIS) initiated 
by marine ice sheet instability (MISI) resulted in lowering the upper end 
of the transition from undetectable and moderate risk from 1.6°C to 
1°C warming above pre-industrial levels, and lowering the upper end of 
the transition from moderate to high risk to 2.5°C. Although SR15 did 
not produce embers beyond 2.5°C, authors reported that the transition 
to very high risk was assessed as lying above 5°C in light of growing 
literature on ice sheet contributions to SLR.

16
2495Key Risks across Sectors and Regions  Chapter 16
Cross-Working Group Box ECONOMIC | Estimating Global Economic Impacts from Climate Change
Authors: Steven Rose (USA), Delavane Diaz (USA), Tamma Carleton (USA), Laurent Drouet (Italy), Celine Guivarch (France), Aurélie Méjean 
(France), Franziska Piontek (Germany)
This Cross-Working Group Box assesses literature estimating the potential global aggregate economic costs of climate change and the 
social cost of carbon (SCC), where the former are sometimes referred to as estimates of global ‘climate damages’ and the latter are 
estimates of the potential monetised impacts to society of an additional metric ton of carbon dioxide emitted to the atmosphere. These 
measures include the economic costs of climate change that could be felt in market sectors such as agriculture, energy services, labour 
productivity and coastal resources, as well as non-market impacts such as other types of human health risks (including mortality effects)

and ecosystems. Global economic impacts estimates can inform decisions about global climate management strategy, while SCC estimates 
can inform globally incremental emissions decisions. In practice, economic damage estimates have been used to explore economically 
efficient (‘economically optimal’) global emissions pathways (e.g., Nordhaus and Moffat, 2017), while SCCs have been used to inform 
federal and state-level policy assessment in some countries (Greenstone et al., 2013; Rose and Bistline, 2016), but the type of SCC and 
application matters (Rose, 2017). This literature has been assessed in previous WGII reports (e.g., Arent et al., 2014), and this box serves 
this need for this report. The assessment in this box was performed jointly across WGII and WGIII, building on the foundation of WGII AR6 
Chapter 16’s ‘Risk to living standards’ assessment (Section 16.5.2.3.4), which includes consideration of severe risks to global aggregate

economic output, and WGIII AR6 Chapter 3’s assessment of the benefits of mitigation. It also informs Chapter 16’s global aggregate 
impacts Reasons for Concern and supports Chapter 18’s assessment of global emissions transitions, risk management and climate resilient 
development. In keeping with the broad risk framing presented in Chapter 1 of this report, other lines of evidence regarding climate risks, 
beyond monetary estimates, should be considered in decision making, including key risks and Reasons for Concern.
Methods for estimating global economic costs of climate impacts
There are several broad approaches to estimating climate damages, including biophysical process models, structural economic models, 
statistical methods (also called empirical or econometric) and hybrid approaches, with each methodology having strengths and 
weaknesses. Process models simulate physical, natural science and/or engineering processes and their response to climate variables,

which are then monetised (e.g., Anthoff and Tol, 2014; Sieg et al., 2019; Narita et al., 2020). Process approaches have the advantage 
of being explicit and interpretable, though they can be computationally intensive; may omit relevant impact channels, interactions and 
market dynamics affecting valuation; and often lack a rigorous empirical basis for calibration (Fisher-Vanden et al.). Structural economic 
modelling represents climate impacts on inputs, production, household consumption, aggregate investment, and markets for economic 
sectors and regional economies (e.g., Reilly et al., 2007; Roson and Van der Mensbrugghe, 2012; Anthoff and Tol, 2014; Dellink et al., 2019; 
Takakura et al., 2019), often using computable general equilibrium (CGE) frameworks. Structural models can evaluate how market and 
non-market impacts might enter and transmit through economies, and adaptation responses within input and output markets, consumer

and investment choices, and inter-regional trade (e.g., Darwin and Tol, 2001; Dellink et  al., 2019; Takakura et  al., 2019). Statistical 
methods estimate economic impacts in a given sector (e.g., Auffhammer, 2018) or in aggregate (e.g., Dell et al., 2014; Burke et al., 2015; 
Hsiang et al., 2017; Pretis et al., 2018; Kahn et al., 2019), inferred from observed changes in economic factors, weather and climate, with 
responses and net results constrained by available data. Since AR5, hybrid approaches have taken different forms to integrate process, 
statistical and/or structural methods, and represent a potentially promising means of leveraging the strengths of different approaches 
(e.g., Moore and Diaz, 2015; and Hsiang et al., 2017; Moore et al., 2017a; Ricke et al., 2018; Yumashev et al., 2019; Chen et al., 2020b). 
There is also a small literature that uses expert elicitation to gather subjective assessments of climate risks and potential economic

impacts (Nordhaus, 1994; IPCC, 2019a; Pindyck, 2019).
In addition to differences in methods, there are also differences in scope—geographic, sectoral and temporal. Global estimates are 
frequently based on an aggregation of independent sector and/or regional modelling and estimates; however, there are examples of 
estimates from global modelling that simulate multiple types of climate impacts and their potential interactions within a single, coherent 
framework (e.g., Roson and Van der Mensbrugghe, 2012; Dellink et al., 2019; Takakura et al., 2019). Differences in scope also represent 
strengths and weaknesses between the methodologies, with narrower scope allowing for more detailed assessment, but missing potential 
interactions with the scope not covered (e.g., other geographic areas, sectors, markets or periods of time).
Comprehensive economic estimates are challenging to produce for many reasons, including complex interactions among physical, natural

and social systems; pervasive climate, socioeconomic and system response uncertainties; and the heterogeneous nature of climate 
impacts that vary across space and time. Critiques and commentaries of global estimation methods (Pindyck, 2013; Stern, 2013; van 
den Bergh and Botzen, 2015; Cropper et al., 2017; Diaz and Moore, 2017; Pindyck, 2017; Rose et al., 2017; Stoerk et al., 2018; DeFries 
et al., 2019; Pezzey, 2019; Calel et al., 2020; Warner et al., 2020; EPRI, 2021; Grubb et al., 2021; Newell et al., 2021) include, among 
other things, concerns about statistical methods estimating weather but not climate relationships, making out-of-sample extrapolations,

16
2496Chapter 16 Key Risks across Sectors and Regions
and model specification uncertainty, concerns about the observational grounding of structural modelling, and overall concerns about 
the lack of adaptation consideration, as well as representation and evaluation of potential large-scale singular events such as ice 
sheet destabilisation or biodiversity destruction, some questioning the ability to generate robust estimates (i.e., estimates insensitive 
to reasonable alternative inputs and specifications), and general concerns about methodological details, transparency and justification.
Additional methodological challenges to address (see, for instance, EPRI, 2021; Piontek et al., 2021) include how to capture and represent 
uncertainty and variability in potential damage responses for a given climate and societal condition, combine estimates from different methods

and sources (including aggregating independent sectoral and regional results), assess sensitivity and evaluate robustness of estimates 
(including sensitivity to model specification), capture interactions and spillovers between regions and sectors, estimate societal welfare 
implications (versus gross domestic product [GDP] changes) of market and non-market impacts, consider distributional effects, represent 
micro- and macro-adaptation processes (and adaptation costs), specify non-gradual damages and nonlinearities, and improve understanding 
of potential long-run economic growth effects. Note that the treatment of time preference, risk aversion and equity considerations have 
important welfare implications for the aggregation of both potential economic impacts and climate change mitigation costs.
In addition to updated and new methods and estimates, newer literature has explored non-gradual damages, such as climatic and

socioeconomic tipping points (Lontzek et al., 2015; Méjean et al., 2020), potential damage to economic growth (e.g., Burke et al., 2015; 
Moore and Diaz, 2015), valuing uncertainty in potential damages (Jensen and Traeger, 2014; Lemoine and Traeger, 2016; Cai and Lontzek) 
and representing adaptation (Takakura et al., 2019; Carleton et al., 2020; Rode et al., 2021). Going forward, to help advance science 
and decisions, a key research priority is to understand and evaluate methodological strengths and weaknesses in damage estimation, 
and reconcile the differences affecting comparability in such a way that it informs use of the different lines of evidence. This will require 
greater transparency and assessment of details and assumptions in individual methods, communication and evaluation of alternatives 
for specifying or calibrating climate damage functional representations with respect to climate and non-climate drivers and potential

nonlinearities, including evaluating data sufficiency for levels within and beyond observations and for characterising physical system 
dynamics, and evaluating the sensitivity of results to model specification and input parameter choices (Cropper et al., 2017). Improving 
the robustness of economic impact estimates is an active area of research. Below we describe the latest estimates.
Global estimates of the economic costs of climate impacts
Since AR5, many new estimates of the global economic costs of climate change have been produced. Figure Cross-Working Group 
Box  ECONOMIC.1 shows a wide spread of estimates, with growing variance at higher levels of warming, both within and across 
methodology types (i.e., statistical, structural or meta-analysis). Meta-analysis is used here to refer to studies that treat other studies’ 
estimates as data points in an attempt to derive a synthesised functional form.

Global aggregate economic impact estimates (Figure Cross-Working Group Box  ECONOMIC.1) are generally found to increase with 
global average temperature change, as well as vary by other drivers, such as income and population and the composition of the economy. 
Most estimates are nonlinear with higher marginal economic impacts at higher temperature, although some recover declining marginal 
economic impacts, and functional forms cannot be determined for all studies. The drivers of nonlinearity found in economic impact 
estimates, and the differences in nonlinearity across estimates (e.g., convex versus concave, degree of curvature), are not well understood, 
with methodology construction, assumptions and data all being potential factors. Relative to AR5, there have been more estimates and 
greater variation in estimates, including some recent estimates significantly higher than the range reported in AR5. For most of the

studies shown in Figure Cross-Working Group Box ECONOMIC.1, the visible variation within a study represents alternative socioeconomic 
projections and climate modelling, not economic impacts response uncertainty for a given socioeconomic and climate condition. Response 
uncertainty could be significant, as indicated by some of the results shown in the figure (e.g., Burke et al., 2015; Rose et al., 2017), but 
methodological differences in how uncertainty is characterised (model specification, errors and confidence intervals versus distributions 
of results) limit comparability and assessment. Note that modelling factors between global temperature change and the economic impact 
calculation, such as regional temperature pattern assumptions or assumed SLR dynamics, can also impact calculated estimates (e.g., 
Warren et al.., 2021 PAGE09 estimates versus those in Rose et al.., 2017, Chen et al.., 2020 PAGE-ICE estimates versus Burke et al.., 2015).

From Figure Cross-Working Group Box ECONOMIC.1, we find a large span of damage estimates, even without considering uncertainty/
confidence in damage responses, including for today’s level of warming (about 1°C). There is also evidence that some regions benefit 
from low levels of warming, leading to net benefits globally at these temperatures. The size of the span of estimates grows with global 
warming level, with variation across statistical estimates larger than variation in structural estimates. The structural and meta-analyses 
estimates appear to be in closer agreement, but that outcome is contingent on the meta-analyses’ data considerations and approach. 
Meta analyses to date have not assessed the alternative methods and dealt with the lack of comparability between methods.Cross-Working Group Box ECONOMIC (continued)

16
2497Key Risks across Sectors and Regions  Chapter 16
Global aggregate economic impact estimates by global warming level
(b) Structural modelin g
(d) AR5 various methods
1°C2 °C 3°C4 °C 5°C6 °C 7°C7 °CPercentage loss inglobal GDP(a) Statistical modeling
-10%0%10%20%30%40%50%60%70%80%
Global temperature change above pre-industrial1°C2 °C 3°C4 °C 5°C6 °C 7°C-10%0%10%20%30%40%50%60%70%80%(e) All estimates
(f) Global average temperature change(c) Meta analyses
1°C2 °C 3°C4 °C 5°C6 °C-10%0%10%20%30%40%50%60%70%80%Percentage loss inglobal GDP
Global temperature change above pre-industrial
Kahn et al. (2019)
Burke et al. (2018) - SR
Pretis et al. (2018)
Maddison & Rehdanz (2011)
Burke et al. (2015)Kalkuhl & Wenz (2020)(a) Statistical modeling
Takakura et al. (2019)
Dellink, Lanzi & Chateau (2019)
Kompas et al (2018)
Roson & van der Mensbrugghe (2012)
Bosello et al. (2012)
Rose et al. (2017)
Rose et al. (2017) - FUND 5th & 95t h
Rose et al. (2017) - PAGE 5th & 95t h(b) Structural modeling

AR5(d) AR5 various methods1°C2 °C 3°C4 °C 5°C6 °C 7°C
Global temperature change above pre-industrialNear 2000s
Mid 2050s
Long 2090sSSP1-1.9
SSP1-2.6
SSP2-4.5
SSP3-7.0
SSP5-8.5
Nordhaus & Moffat (2017)/Nordhaus (2016)
Tol (2018)
Howard & Sterner (2017)(c) Meta analyses
Figure Cross-Working Group Box ECONOMIC.1 |  Global aggregate economic impact estimates by global warming level (annual % global GDP loss 
relative to GDP without additional climate change). Top row panels present estimates by methodology type: (a) statistical modelling, (b) structural modelling and 
(c) meta-analyses, with all estimates from a paper in the same colour and estimates from methodologies other than that highlighted by the panel in grey for reference. 
Second row left panel (d) presents AR5 estimates. Second row right panel (e) presents all estimates in one figure, with the same colours as panels (a–d) using outlined

dots for the statistical modelling estimates, solid dots for structural modelling estimates, and triangles for meta-analysis estimates. In all panels, lines represent functions, 
with dashed and dotted lines 5th and 95th percentile functions from structural modelling. To avoid duplication, estimates from papers using the economic impacts 
estimates or model formulations already represented in the figure are not included (e.g., Diaz and Moore, 2017; Chen et al., 2020b; Glanemann et al., 2020; Warren et al., 
2021). The exception is Burke et al. (2018), with the different estimates shown representing variation across climate scenarios for a given aggregate economic impacts 
specification from Burke et al. (2015)—the ‘pooled, short run’ statistical specification. Results shown for the latter are estimates with the author’s different statistical

model specifications (and a fixed climate scenario, SSP5). From top to bottom, the Burke et al. (2015) estimates are for the ‘pooled, long run’, ‘differentiated, long run’, 
‘pooled, short run’ (authors’ base case) and ‘differentiated, short run’ statistical specifications. For Howard and Sterner (2017), the authors’ preferred function is shown. 
Overall, estimates shown in the figure can correspond to different future years, reflecting different socioeconomic conditions and climate pathways to a global warming 
level. Global average temperature change bars relative to the period 1850–1900 are shown below the economic cost estimates to provide context to potential future 
warming. Shown are the WGI AR6 assessed best estimates and 90% intervals for the illustrative emissions scenarios considered for the near term 2021–2040, mid-term 
2041–2060 and long term 2081–2100.Cross-Working Group Box ECONOMIC (continued)

16
2498Chapter 16 Key Risks across Sectors and Regions
Differences in methodology type and scope complicate comparison, assessment and synthesis (Cropper et al., 2017; Diaz and Moore, 
2017; EPRI, 2021; Piontek et  al., 2021). In particular, structural economic modelling and empirical aggregate output modelling are 
fundamentally different, which has been identified as an issue affecting the comparability of results (Cropper et al., 2017). The different 
methodologies affect outcomes, with global aggregate estimates based on statistical methodologies typically higher than those from 
structural modelling (Figure Cross-Working Group Box ECONOMIC.1). This is, in part, due to the relationships in observational data 
captured by statistical modelling, assumed persistence of impacts in statistical modelling, broader adaptation responses in structural 
modelling, and differences in the representation of future societies and how they might evolve, respond and interact. Within statistical

modelling, results are also found to be very sensitive to the statistical model specification (e.g., Burke et al., 2015; Newell et al., 2021). 
Within structural modelling, differences in representations of biophysical changes and economic structural dynamics contribute to 
differences across structural estimates (e.g., Rose et al., 2017).
The wide range of estimates, and the lack of comparability between methodologies, does not allow for identification of a robust range 
of estimates with confidence (high confidence). Evaluating and reconciling differences in methodologies is a research priority for 
facilitating use of the different lines of evidence (high confidence). However, the existence of higher estimates than AR5 indicate that 
global aggregate economic impacts could be higher than previously estimated (low confidence due to the lack of comparability across 
methodologies and robustness of estimates).

While Figure Cross-Working Group Box  ECONOMIC.1 summarises global aggregate estimates, the literature exhibits significant 
heterogeneity in regional economic impacts that are also sensitive to methodology, model specification and societal assumptions (with, for 
instance, larger estimates due to the assumed size of society, but offsetting adaptive capacity improvements and adaptation responses). 
Regional results illustrate the potential for overall net benefits in more temperate regions at lower levels of warming with potential lower 
energy demand and comparative advantages in agricultural markets; however, at higher levels of warming, net losses are estimated. In 
addition, economic impacts for poorer households and poorer countries represent a smaller share in aggregate quantifications expressed 
in GDP terms than their influence on well-being or welfare (Byers et al., 2018; Hallegatte et al., 2020).
Social cost of carbon methods and estimates

The global economic impact estimates discussed in the previous section serve as a key input into the calculation of the value of potential 
net damages caused by a marginal ton of carbon dioxide emissions, or the SCC. To compute an SCC, damage estimates are commonly 
combined in a multi-century modelling framework with socioeconomic and emissions projections, a physical model of the climate, 
including a SLR component, and assumptions about the discount rate, with current frameworks having highly stylised representations of 
these components. Though we do not present quantitative estimates here, due to the challenge of comparability, for economic impacts 
methodologies (as discussed above) as well as other SCC estimation elements, large variations in SCC estimates are found in the 
literature assessed due to, among other things, differences in modelling component representations, input and parameter assumptions,

considerations of uncertainty, and discounting, inflation, and emissions year (e.g., Tol, 2009; Tol, 2018; Pezzey, 2019; Iese et al., 2021). 
There are also different ‘variants’ of SCC estimates that differ conceptually, and in magnitude, depending on the reference condition for 
evaluating the impact of a marginal metric ton—is it being evaluated relative to a no-climate-policy baseline, an economically efficient 
pathway that weighs the benefits and costs of emissions mitigation, or a pathway based on a particular climate policy or goal such as 
2°C or a concentration target (Rose et al., 2017)? The variant of SCC has implications for its applicability to different policy contexts (Rose 
and Bistline, 2016).
In addition to the economic impacts methodological challenges discussed above with respect to aggregate economic impact estimates, 
the additional components needed for SCC calculations give rise to a new set of technical issues and critiques, including incorporation

of uncertainties in the components beyond climate damages, links between components, and discounting (van den Bergh and Botzen, 
2015; Cropper et al., 2017; Diaz and Moore, 2017; Pindyck, 2017; Rose et al., 2017; EPRI, 2021). For component-specific discussions and 
assessment, see Cropper et al. (2017), Rose et al. (2017) and EPRI (2021).
Substantial progress has been made in recent years to better reflect complexities in the global economy, the climate system, and their 
interaction. For example, recent studies have explored damages to natural capital (Bastien-Olvera and Moore, 2021), the influence of 
imperfect substitutability between environmental services and market goods (Sterner and Persson, 2008; Weitzman, 2012; Drupp and 
Hänsel, 2021), the implications of heterogeneous climate change impacts across income groups (Dennig et al., 2015; EPRI, 2021; Errickson

et al., 2021), the potential for persistent climate impacts to economic growth instead of effects on levels of economic output (Dietz and 
Stern, 2015; Moore and Diaz, 2015; Ricke et al., 2018; Kikstra et al., 2021; Newell et al., 2021), valuing the risks of climate tipping points 
(Cai and Lontzek, 2019; Rising et al., 2020), valuing uncertainty under risk aversion (Jensen and Traeger, 2014; Lemoine and Traeger, Cross-Working Group Box ECONOMIC (continued)

16
2499Key Risks across Sectors and Regions  Chapter 16
AR6 provides new evidence that relates to the location of the 
transition from undetectable to moderate risk. At the time of SR15, 
observations were suggesting that MISI might already be taking place 
in some parts of the WAIS, while AR5 supported assessment of an 
additional MISI contribution to SLR of several additional tenths of a 
metre over the next two centuries. Since SR15, new observations (WGI 
AR6 Section 9.4.2.1, Fox-Kemper et al., 2021) support the assessment 
of enhanced grounding line retreat and subsequent mass loss through 
basal melt in various parts of Antarctica, and year 2100 sea level 
projections for the RCP8.5 scenario have increased by 10–12 cm 
owing to ice dynamics. However, the onset of MISI is driven by ocean 
warming in specific locations (ice cavities beneath floating ice shelves), 
and the relation between these ocean temperatures and global mean

temperature is indirect and ambiguous. In addition, MISI implies a self-
sustaining instability in the absence of further forcing. Because forcing 
is still increasing, it cannot be unambiguously assessed whether MISI 
is driving the observed retreat of grounding lines in the WAIS, or 
whether this retreat is a purely forced response (and would stop if 
the warming stops) or is just a manifestation of natural variability in 
upwelling of warmer waters on the Antarctic continental shelves and, 
as a result, is just a temporary effect. Consistent with SROCC, AR6 
states with medium confidence that sustained mass losses of several 
major glaciers in the Amundsen Sea Embayment (ASE) are compatible 
with the onset of MISI, but that whether unstable WAIS retreat has 
already begun or is imminent remains a critical uncertainty.
Whether associated with MISI or not, WGI AR6 (Fox-Kemper et  al.,

2021) now assesses with very high confidence that mass loss from both the Antarctic (whether associated with MISI or not) and Greenland 
Ice Sheets, is more than seven times higher over the period 2010–
2016 than over the period 1992–1999 for Greenland and four times 
higher for the same time intervals for Antarctica. Given their multi-
century commitments to global SLR, this reinforces the assessment of 
estimating the boundary between undetectable and moderate risks for 
ice sheets to lie between 0.7°C (the level of global warming in the 
1990s when melting began to accelerate) and 1°C (as in SR15), with 
a median of 0.9°C.
In the Amazon Forest, increases in tree mortality and a decline in the 
carbon sink are already reported (Brienen et al., 2015; Hubau et al., 
2020), and old-growth Amazon Rainforest may have become a net 
carbon source for the period 2010–2019 (Qin et al., 2021). Estimates 
which include land use emissions indicate the region may have become

a net carbon source (Gatti et al., 2021). Fire activity is an important 
driver, and both bigger fires (Lizundia-Loiola et al., 2020) and longer 
fire season (Jolly et al., 2015) have been reported in South America, 
although this is strongly linked to land use and land use change as 
well as climate (Kelley et al., 2021), and indeed land use change may 
be a stronger driver of potential loss of the Amazon Forest than climate 
change. The risk of climate-change-related loss of the Amazon Forest is 
assessed already above ‘undetectable’, but has only emerged over the 
last few years, when global warming had reached 1°C, and is linked 
to land use as well as GSAT levels. Chapter 2 has assessed ecosystem 
carbon loss from tipping points in tropical forest and loss of Arctic 
permafrost, and finds a transition to moderate risk over the range

0.6–0.9°C (medium confidence). Specifically, WGII AR6 Table  SM2.5 2016), and modelling a distinction between intertemporal inequality aversion and risk aversion in the social welfare utility function (Crost 
and Traeger, 2013; Jensen and Traeger, 2014; Daniel et al., 2015). These new studies have, in general, raised estimates of the SCC (Crost 
and Traeger, 2013; Jensen and Traeger, 2014; Gerlagh and Michielsen, 2015; Moore and Diaz, 2015; Faulwasser et al., 2018; Guivarch and 
Pottier, 2018; Budolfson et al., 2019; Cai and Lontzek, 2019; Dietz and Venmans, 2019; Kalkuhl and Wenz, 2020), in some cases by an 
order of magnitude (Ricke et al., 2018). However, challenges persist in terms of moving from conceptual to practical application, such as 
pinning down parameter specifications, modelling specific mechanisms for impacts, and more fully representing adaptation.

Despite these scientific advances, SCC estimates vary widely in the literature. Technical issues with past and current modelling (e.g., 
Pezzey, 2019; Pindyck, 2019; EPRI, 2021) and the challenge of comparability across methodologies imply that many estimates are not 
robust (high confidence). Also, as a result, the issue of directional bias of past estimates remains unsettled. Better representation of 
uncertainty in methods can improve robustness, while detailed methodology assessment and comparison will help define the relative 
biases of methods (high confidence).
Application to decision making
The literature has also assessed the application of aggregate economic impact cost and SCC estimates (Rose and Bistline, 2016; Rose 
et al., 2017; Kaufman et al., 2020) and identified conceptual and technical issues that need to be considered when using results to inform

policy decisions. These issues include: accounting for endogenous marginal benefits and socioeconomic conditions in evaluating policies 
with non-incremental global emissions implications; consistency in assumptions and treatment of uncertainty across benefit and cost 
calculations; fully accounting for the streams of both mitigation costs and benefits over time; avoiding inefficiently valuing or pricing 
emissions more than once across policies and jurisdictions; and accounting for emissions leakage to capture net climate implications. 
Furthermore, concerns about the robustness of estimates have led some to recommend considering alternatives, such as using marginal 
mitigation cost estimates based on modelling of policy goals instead of the SCC (e.g., Rose, 2012; Pezzey, 2019; Kaufman et al., 2020), 
although this comes with its own set of assumptions and technical challenges.Cross-Working Group Box ECONOMIC (continued)

16
2500Chapter 16 Key Risks across Sectors and Regions
finds that ‘Primary tropical forest comprised a net source of carbon 
to the atmosphere, 2001–2019 (emissions 0.6 Gt y−1, net 0.1 Gt y−1) 
(Harris et al., 2021). Anthropogenic climate change has thawed Arctic 
permafrost (Guo et al., 2020), carbon emissions 1.7 ± 0.8 Gt y−1, 2003–
2017 (Natali et al., 2019)’. This also supports the upper limit for this 
transition lying at 1°C.
The potential global loss of an entire ecosystem type, coral reefs, is 
also considered a large-scale singular event. In the 1990s when 
global warming was around 0.7°C large-scale coral reef bleaching 
also became apparent (Section  16.2.3.1), also supporting the lower 
boundary for this transition in respect of coral reefs.
Overall, given the above evidence on ice sheets, Amazon Forest and 
coral reefs, the transition from undetectable to moderate risk is 
assessed to occur between 0.7°C and 1°C warming with a median of 
0.9°C with high confidence.

The transition from moderate to high risk is informed by an assessment 
of risks at higher levels of warming than present. Nearly all climate 
models show warmer temperatures around Antarctica in conjunction 
with rising global mean temperature, and all ice sheet models show 
sustained mass loss from the WAIS after temperature increase halts 
(thus implying MISI takes place) at various levels between 1.5°C and 
5°C. An increasing fraction of ice sheet models shows additional 
sustained mass loss from the East Antarctic Ice Sheet (EAIS) for peak 
warming between 2°C and 4°C, and all ice sheet models show mass 
loss for peak warming higher than 4°C. Therefore, we assess an 
increasing link between MISI, WAIS collapse and Antarctic mass loss, 
for increasing temperature levels (high confidence).
There is high confidence in the existence of threshold behaviour of 
the Greenland Ice Sheet in a warmer climate (WGI AR6 Ch 9, Fox-

Kemper et  al., 2021); however, there is low agreement on the nature 
of the thresholds and the associated tipping points. Similarly, the 
likelihood for accelerated and irreversible mass loss from Antarctica 
increases with increasing temperatures, but thresholds cannot yet be 
unambiguously identified. By the year 2100, sea level projections (AR6 
WGI Figure SPM.8 (IPCC, 2021)) now range from 0.57 m (0.37–0.85 m) 
for the SSP1–1.9 scenario to 1.35 m (1.02–1.89 m) for the SS5–8.5 
scenario and become 1.99 m for the latter scenario (1.02–4.83 m) in the 
case of low-likelihood, high-impact outcomes resulting from ice sheet 
instability, for which there is limited evidence. It should be noted that 
inclusion of such low-likelihood, high-impact outcomes dominated by 
not-well-understood processes affecting ice dynamics on the large ice 
caps of Greenland, and in particular Antarctica, would also enhance 
the sea level projections for other scenarios, but to a lesser extent for

increasingly weaker forcing. No quantitative assessment of their effect 
in other scenarios than SSP5–8.5 yet exists as such simulations with ice 
sheet models have not been carried out, or only in a very limited amount.
It should be noted that ice sheets may take many centuries to respond, 
implying that risk levels increase over time for the same warming level. 
Therefore, we base judgements about risk transitions related to ice 
sheets primarily on their implications for 2000-year commitments to 
SLR from sustained mass loss from both ice sheets as projected by 
various ice sheet models, reaching 2.3–3.1 m at 1.5°C peak warming and 2–6 m at 2.0°C peak warming (WGI AR6 TS, Box TS.4 Figure 1; 
Arias et  al., 2021). This is an important feature of the approach to 
this RFC (i.e., it is not primarily focused on implications for the next 
100–200  years). In addition, since the AR5, there is new evidence 
about the Last Interglacial (LIG), when global mean temperature was

about 0.5–1.5°C above the pre-industrial era. AR6 assesses that it is 
virtually certain that sea level was higher than today at that time, likely 
by 5–10 m (medium confidence) (B.5.4 WGI AR6 SPM,(IPCC, 2021)). 
Mid-Pliocene temperatures of 2.5°C (about 3 million years ago when 
global temperatures were 2.5–4°C higher) also provide evidence as 
an upper limit for the transition to high risk associated with long-
term equilibrium SLR of 5–25 m (WGI AR6 SPM B.5.4). Projected SLR 
for 2300 in an RCP8.5 or SSP5–8.5 scenario (consistent with a peak 
warming range of 4–6°C, varies between 1.7–6.8 m and 2.2–5.9 m, 
respectively (WGI AR6 TS Box  TS.4, Arias et  al., 2021), and when 
accounting for marine ice cliff instability taking place on Antarctica, 
these numbers may increase to a range of 9.5–16.2 m (WGI AR6 TS 
Box TS.4, Arias et al., 2021).
CMIP6 climate models project drying in the Amazon—especially in 
June–July–August, irrespective of future forcing scenario, but which

increases with GSAT/higher scenarios (Lee et  al., 2021). For higher 
GSAT levels, Burton et  al. (2021) explore different forcing scenarios 
and found, regardless of scenario, burned area increases markedly 
with GSAT. New understanding of the role of vegetation stomata will 
act to exacerbate this drying (Richardson et al., 2018b). A transition 
to high risk of savannisation for the Amazon alone was assessed to 
lie between 1.5°C and 3°C with a median value of 2.0°C. A mean 
temperature increase of 2°C could reduce Arctic permafrost area ~15% 
by 2100 (Comyn-Platt et al., 2018). Chapter 2 has assessed ecosystem 
permafrost, and finds a transition from moderate to high risk over 
the range 1.5°C to 3°C with a median of 2°C (medium confidence, 
Table SM2.5, Figure 2.11). Its assessment of the transition from high 
to very high risk is located over the range 3–5°C (low confidence,

Table SM2.5, Figure 2.11) based on the potential for Amazon Forest 
dieback between 4°C and 5°C temperature increase above the pre-
industrial period (Salazar and Nobre, 2010).
One of the criteria for locating a transition to very high risk is a limited 
ability to adapt. In natural systems, limiting warming to 1.5°C rather 
than 2°C would enhance the ability of coastal wetlands to adapt 
naturally to SLR, since natural sedimentation rates more likely keep 
up with SLR (SR15, Hoegh-Guldberg 2018). In human systems, there 
is medium confidence that technical limits will be reached for hard 
protection to SLR beyond 2100 under high-emissions scenarios, with 
limits associated with socioeconomic and governance issues reached 
before 2100 (CCP2).
We therefore estimate the boundary between moderate and high risk 
to lie between 1.5°C and 2.5°C, with a median at 2.0°C, with medium 
confidence based on projections for melting ice sheets and drying in

the Amazon. We also estimate the boundary between high and very 
high risk to lie between 2.5°C and 4°C, but with low confidence due to 
uncertainties in the projections of SLR at higher levels of warming and 
differences between levels of warming at which very high risks were 
assessed in different systems.

16
2501Key Risks across Sectors and Regions  Chapter 16
16.6.4 Summary
The updated RFCs show that transitions between levels of risk are now 
assessed to occur at lower levels of global warming than in previous 
assessments (high confidence), levels of confidence in assigning 
transitions have generally increased, evidence on the potential for 
adaptation to adequately address risks at different warming levels 
remains limited, and transitions from high to very high levels of risk 
have been assessed for all five RFCs, compared with just two RFCs in 
AR5, together showing how literature published since AR5 is informing 
us on our future climate risks.
• In particular, risks to unique and threatened systems (RFC1) are 
now assessed to be already at a high level today, as compared 
with a moderate level in previous assessments, and transition to 
a very high level is assessed to occur beginning at 1.2°C, passing 
through a median value of 1.5°C, and completing the transition at

2.0°C warming (high confidence).
• Risks associated with extreme weather events (RFC2) are assessed 
to have begun to transition to a high level already when global 
warming reached 1°C, with that transition projected to complete 
for a warming of 1.5°C (high confidence). Newly in AR6, a 
transition between high and very high levels of risk was assessed 
to lie at 2.0°C warming for RFC2 (range 1.8–2.5°C).
• For risks associated with the distribution of impacts (RFC3), there 
is now high confidence that a transition to moderate risk has 
already occurred, and the transition to high risk is now projected to 
occur between 1.5°C and 2.0°C warming with medium confidence. 
Furthermore, a transition from high to very high risk is provided for 
the first time in this AR6 assessment, between 2.0°C and 3.5°C 
• Global aggregate impacts (RFC4) are assessed to have begun 
to transition to a moderate level already when global warming

reached 1°C, and are projected to transition to a high level with 
warming of 1.5–2.5°C (median 2°C) with medium confidence. An 
assessment of a transition to very high risk is provided for the first 
time in AR6, over the range 2.5–4.5°C with low confidence.
• Risks associated with large-scale singular events are assessed 
to have already completed transitioning to moderate with 
1°C warming (high confidence), with a transition to high risk 
between 1.5°C and 2.5°C (median 2°C) (medium confidence). An 
In summary, risks to unique and threatened systems (RFC1) are higher 
at recent and projected levels of warming than assessed previously (very 
high confidence); risks associated with extreme weather events (RFC2) 
are assessed comparably to AR5 and SR15 at recent and low levels of 
warming, but notably much higher at projected warming above 1.8°C

(medium confidence); risks associated with distribution of impacts 
(RFC3) and global aggregate impacts (RFC4) are similar to SR15 and 
higher than AR5 above 2°C (medium confidence); and those associated 
with large-scale singular events (RFC5) are similar to SR15 and higher 
at both recent and projected warming than AR5 (medium confidence).
Limiting global warming to 1.5°C would ensure risk levels remain 
moderate for RFC3, RFC4 and RFC5 (medium confidence), but risk for RFC2 would have transitioned to a high risk at 1.5°C and RFC1 
would be well into the transition to very high risk (high confidence). 
Remaining below 2°C warming (but above 1.5°C) would imply that 
risk for RFC3 through 5 would be transitioning to high, and risk for 
RFC1 and RFC2 would be transitioning to very high (high confidence). 
By 2.5°C warming, RFC1 will be in very high risk (high confidence) 
and all other RFCs will have begun their transitions to very high risk

(medium confidence for RFC2, RFC3 and RFC4, low confidence for 
RFC5). These highest levels of risk are associated with an irreversible 
component, such that some impacts would persist even were global 
temperatures to subsequently decline in an ‘overshooting’ scenario.
Lack of evidence on the potential for adaptation to adequately reduce 
risk is a critical gap in our ability to assess global risk transitions at 
the RFC level, but not the only gap. In some cases, such as RFC1, 
the widespread nature and rapid speed of the escalating risks, in 
combination with limited ability to adapt, means that transitions 
to high risk may occur despite medium or even high levels of 
adaptation. Risks that are largely natural and not widely mediated 
by human vulnerability are thus less likely to have risk transitions 
that shift under higher societal adaptation. Risk transitions that 
are mediated through human systems, such as distribution impacts,

for example, are more likely to shift in response to adaptation as 
impacts are strongly mediated through vulnerability within human 
systems, but such a shift is difficult to quantify given knowledge 
gaps in the literature (Section 16.3). However, in some circumstances, 
expanded global adaptation could slow some of these transitions 
(low confidence); in the case of RFC2, RFC3 and RFC4, the literature 
suggests that coordinated global adaptation could increase the 
global temperature at which risks transition from moderate to high, 
for example the prevention of mortality associated with heat stress 
within RFC2.
A higher level of adaptation, applied globally and effectively, could 
have larger benefits for several RFC, either postponing the onset of 
a high level of risk until a higher level of warming is reached (and 
allowing time for mitigation efforts) or allowing a system to survive 
a temporary overshoot of a lower temperature threshold. Adaptations

are likely to have significant potential to reduce risks (Magnan et al., 
2021), in particular for risks mediated through human systems. 
However, there is limited evidence available to assess the extent to 
which current or potential adaptations are or would be adequate in 
reducing climate risks at different levels of warming, and adaptation 
implications for risk transitions will be highly localised. Pathways 
and opportunities for risk management and adaptation actions with 
transformational potential are discussed in Chapter 17, together with 
enabling factors, governance frameworks, financing, success factors, 
and monitoring and evaluation discussed in Chapter 18, supporting 
sustainable system transitions and leading to options for climate 
resilient development pathways.

16
2502Chapter 16 Key Risks across Sectors and Regions
FAQ 16.1 | What are key risks in relation to climate change?
A few clusters of key risks can be identified which have the potential to become particularly severe and pose significant challenges for 
adaptation worldwide. These risks, therefore, deserve special attention. They include risks to important resources such as food and water, risks 
to critical infrastructures, economies, health and peace, as well as risks to threatened ecosystems and coastal areas.
The IPCC defines key risks related to climate change as potentially severe risks that are relevant to the primary goal 
of the United Nations Framework Convention on Climate Change treaty to avoid ‘dangerous human interference 
with the climate system’, and whatever the scale considered (global to local). What constitutes ‘dangerous’ or 
‘severe’ risks is partly a value judgement and can therefore vary widely across people, communities or countries.

However, the severity of risks also depends on criteria like the magnitude, irreversibility, timing, likelihood of the 
impacts they describe, and the adaptive capacity of the affected systems (species or societies). The Working Group 
II authors use these criteria in various ways to identify those risks that could become especially large in the future 
owing to the interaction of physical changes to the climate system with vulnerable populations and ecosystems 
exposed to them. For example, some natural systems may be at risk of collapsing, as is the case for warm-water 
coral reefs by mid-century, even if global warming is limited to +1.5°C. For human systems, severe risks can include 
increasing restriction of water resources that are already being observed; mortality or economic damages that are 
large compared with historical crises; or impacts on coastal systems from SLR and storms that could make some 
locations uninhabitable.

More than 120 key risks across sectors and regions have been identified by the chapters of this report, which have 
then been clustered into a set of 8 overarching risks, called representative key risks, which can occur from global 
to local scales but are of potential significance for a wide diversity of regions and systems globally. As shown 
in Figure  FAQ16.1.1, the representative key risks include risks to (a) low-lying coastal areas, (b) terrestrial and 
marine ecosystems, (c) critical infrastructures and networks, (d) living standards, (e) human health, (f) food security, 
(g) water security and (h) peace and human mobility.
These representative key risks are expected to increase in the coming decades and will depend strongly not only 
on how much climate change occurs, but also on how the exposure and vulnerability of society changes, as well 
as on the extent to which adaptation efforts will be effective enough to substantially reduce the magnitude of

severe risks. The report finds that risks are highest when high warming combines with development pathways with 
continued high levels of poverty and inequality, poor health systems, lack of capacity to invest in infrastructure, 
and other characteristics making societies highly vulnerable. Some regions already have high levels of exposure and 
vulnerability, such as in many developing countries as well as communities in small islands, Arctic areas and high 
mountains; in these regions, even low levels of warming will contribute to severe risks in the coming decades. Some 
risks in industrialised countries could also become severe over the course of this century, for example if climate 
change affects critical infrastructure such as transport hubs, power plants or financial centres. In some cases, such 
as coral reef environments and areas already severely affected by intense extreme events (e.g., recent typhoons or 
wildfires), climate risks are already considered severe.

16
2503Key Risks across Sectors and Regions  Chapter 16
Loss oflives,
Disruptiono f
transport
systems
(g) Water security
Water
scarcityWater-related
cultures&
wayso flifeInvoluntary
conﬂicts(h) Peace & human mobilityPresentation of the 8 representative key risks assessed in this report
(and their underlying main key risks)
Change struc-
ture/functioningLoss
ecosystem
goods/servicesNat. coastal
habitatsNat. coastal
habitatsLoss of
biodiversity(b) Terrestrial and marine ecosystems
(c) Critical infrastructure, networks and services
Damage&
disruptionImpactso f
nomiesLoss of
povertyAggregate
economic
impacts(d) Living standards
Waterborne
diseasesVector-borne
mortality(f) Food security
Increased
ecosystem
services

Figure FAQ16.1.1 |  Presentation of the eight representative key risks assessed in this report (and their underlying main key risks).Box FAQ 16.1 (continued)

16
2504Chapter 16 Key Risks across Sectors and Regions
FAQ 16.2 | How does adaptation help to manage key risks and what are its limits?
Adaptation helps to manage key risks by reducing vulnerability or exposure to climate hazards. However, constraining factors make it harder to 
plan or implement adaptation and result in adaptation limits beyond which risks cannot be prevented. Limits to adaptation are already being 
experienced, for instance by coastal communities, small-scale farmers and some natural systems.
Adaptation-related responses are actions that are taken with the intention of managing risks by reducing 
vulnerability or exposure to climate hazards. While mitigation responses aim to reduce greenhouse gas emissions 
and slow warming, adaptations respond to the impacts and risks that are unavoidable, either due to past emissions 
or failure to reduce emissions. However, while these responses intend to reduce risks, it is difficult to determine

precise levels of risk reduction that can be attributed to adaptation. Changing levels of risk as well as other actions—
such as economic development—make it challenging to definitively connect specific levels of risk reduction with 
adaptation. Although it is not feasible to assess the adequacy of adaptation for risk reduction at global or regional 
levels, evidence from specific localised adaptation projects do show that adaptation-related responses reduce 
risk. Moreover, many adaptation measures offer near-term co-benefits related to mitigation and to sustainable 
development, including enhancing food security and reducing poverty.
Adaptation responses can occur in natural systems without the intervention of humans, such as species shifting their 
range, time of breeding, or migration behaviour. Humans can also assist adaptation in natural systems through, 
for example, conservation activities such as species regeneration projects or protecting ecosystem services. Other

adaptation-related responses by humans aim to reduce risk by decreasing vulnerability and/or exposure of people 
to climate hazards. This includes infrastructural projects (e.g., upgrading water systems to improve flood control), 
technological innovation (e.g., early-warning systems for extreme events), behavioural change (e.g., shift to new 
crop types or livelihood strategies), cultural shifts (e.g., changing perspectives on urban greenspace, or increased 
recognition of Indigenous knowledge and local knowledge) and institutional governance (e.g., adaptation 
planning, funding and legislation).
While adaptation is important to reduce risk, adaptation cannot prevent all climate impacts from occurring. 
Adaptation has soft and hard limits, points at which adaptive actions are unable to prevent risks. Soft limits can 
change over time as additional adaptation options become available, while hard limits will not change as there are

no additional adaptive actions that are possible. Soft limits occur largely due to constraints—factors that make it 
harder to plan and implement adaptation, such as lack of financial resources or insufficient human capacity. Across 
regions and sectors, the most challenging constraints to adaptation are financial and those related to governance, 
institutions and policy measures. Limited funding and ineffective governance structures make it difficult to plan 
and implement adaptation-related responses which can lead to insufficient adaptation to prevent risks. Small-scale 
farmers and coastal communities are already facing soft limits to adaptation as measures that they have put in 
place are not enough to prevent loss. If constraints that are limiting adaptation are addressed, then additional 
adaptation can take place and these soft limits can be overcome. Evidence on limits to adaptation is largely focused

on terrestrial and aquatic species and ecosystems, coastal communities, water security, agricultural production, and 
human health and heat.
Adaptation is critical for responding to unavoidable climate risks. Greater warming will mean more and more 
severe impacts requiring a high level of adaptation which may face greater constraints and reach soft and hard 
limits. At high levels of warming, it may not be possible to adapt to some severe impacts.

16
2505Key Risks across Sectors and Regions  Chapter 16
FAQ 16.3 | How do climate scientists differentiate between impacts of climate change and changes in natural or 
human systems that occur for other reasons?
We can already observe many impacts of climate change today. The large body of climatic impact data and research confirms this. To decide 
whether an observed change in a natural or human system is at least partly an impact of climate change, we systematically compare the 
observed situation with a theoretical situation without observed levels of climate change. This is detection and attribution research.
Global mean temperature has already risen by more than 1°C, and that also means that the impacts of climate change 
become more visible. Many natural and human systems are sensitive to weather conditions. Crop yields, river floods 
and associated damages, ecosystems such as coral reefs, or the extent of wildfires are affected by temperatures

and precipitation changes. Other factors also come into play. So, for example, crop yields around the world have 
increased over the last decades because of increasing fertilizer input, improved management and varieties. How do 
we detect the effect of climate change itself on these systems, when the other factors are excluded? This question 
is central for impact attribution. ‘Impact of climate change’ is defined as the difference between the observed 
state of the system (e.g., level of crop yields, damage induced by a river flood, coral bleaching) and the state of the 
system assuming the same observed levels of non-climate-related drivers (e.g., fertilizer input, land use patterns or 
settlement structures) but no climate change.
So:
‘Impact of climate change’ is defined as the difference between the observed state of the system and the state of 
the system assuming the same observed levels of non-climate-related drivers but no climate change. For example,

we can compare the level of crop yields, damage induced by a river flood, and coral bleaching with differences 
in fertilizer input, land use patterns or settlement structures, without climate change and with climate change 
occurring.
While this definition is quite clear, there certainly is the problem that, in real life, we do not have a ‘no climate 
change world’ to compare with. We use model simulations where the influence of climate change can be eliminated 
to estimate what might have happened without climate change. In a situation where the influence of other 
non-climate-related drivers is known to be minor (e.g., in very remote locations), the non-climate-change situation 
can also be approximated by observation from an early period where climate change was still minor. Often, a 
combination of different approaches increases our confidence in the quantification of the impact of climate change.

Impacts of climate change have been identified in a wide range of natural, human and managed systems. For 
example, climate change is the major driver of observed widespread shifts in the timing of events in the annual 
cycle of marine and terrestrial species, and climate change has increased the extent of areas burned by wildfires 
in certain regions, increased heat-related mortality, and had an impact on the expansion of vector-borne diseases.
In some other cases, research has made considerable progress in identifying the sensitivity of certain processes 
to weather conditions without yet attributing observed changes to long-term climate change. Two examples of 
weather sensitivity without attribution are observed crop price fluctuations and waterborne diseases.
Finally, it is important to note that ‘attribution to climate change’ does not necessarily mean ‘attribution to 
anthropogenic climate change’. Instead, according to the IPCC definition, climate change means any long-term

16
2506Chapter 16 Key Risks across Sectors and Regions
FAQ 16.4 | What adaptation-related responses to climate change have already been observed, and do they help 
reduce climate risk?
Adaptation-related responses are the actions taken with the intention of managing risks by reducing vulnerability or exposure to climate 
hazards. Responses are increasing and expanding across global regions and sectors, although there is still a lot of opportunity for improvement. 
Examining the adequacy and effectiveness of the responses is important to guide planning, implementation and expansion.
The most frequently reported adaptation-related responses are behavioural changes made by individuals 
and households in response to drought, flooding and rainfall variability in Africa and Asia. Governments are 
increasingly undertaking planning, and implementing policy and legislation, including, for example, new zoning

regulations and building codes, coordination mechanisms, disaster and emergency planning, or extension services 
to support farmer uptake of drought tolerant crops. Local governments are particularly active in adaptation-related 
responses, particularly in protecting infrastructure and services, such as water and sanitation. Across all regions, 
adaptation-related responses are strongly linked to food security, with poverty alleviation a key strategy in the 
Global South.
Overall, however, the extent of adaptation-related responses globally is low. On average, responses tend to be 
local, incremental, fragmented, and consistent with Business-As-Usual practices. There are no global regions or 
sectors where the overall adaptation-related response has been rapid, widespread, substantial and has overcome or 
challenged key barriers. The extent of adaptation thus remains low globally, with significant potential for increased

scope, depth, speed and the challenging of adaptation limits. Examples of low-extent adaptations include shifts by 
subsistence farmers in crop variety or timing, household flood barriers to protect houses and gardens, and harvesting 
of water for home and farm use. In contrast, high-extent adaptation means that responses are widespread and 
coordinated, involve major shifts from normal practices, are rapid, and challenge existing constraints to adaptation. 
Examples of high-extent adaptations include planned relocation of populations away from increasingly flood-prone 
areas, and widely implemented social support to communities to prevent migration or displacement due to climate 
hazards.
Increasing the extent of adaptation-related responses will require more widespread implementation and 
coordination, more novel and radical shifts from Business-As-Usual practices, more rapid transitions, and challenging

or surmounting limits—key barriers—to adaptation. This might include, for example, best-practice programmes 
implemented in a few communities being expanded to a larger region or country, accelerated implementation of 
behaviours or regulatory frameworks, coordination mechanisms to support deep structural reform within and across 
governments, and strategic planning that challenges fundamental norms and underlying constraints to change.
We have very little information on whether existing adaptation-related responses that have already been 
implemented are reducing climate risks. There is evidence that risks due to extreme heat and flooding have 
declined, though it is not clear if these are due to specific adaptation-related responses or general and incremental 
socioeconomic development. It is difficult to assess the effectiveness of adaptation-related responses, and even 
more difficult to know whether responses are adequate to adapt to rising climate risk. These remain unknown but

important questions in guiding implementation and expansion of adaptation-related responses.

16
2507Key Risks across Sectors and Regions  Chapter 16
FAQ 16.5 | How does climate risk vary with temperature?
Climate risk is a complex issue, and communicating it is fraught with difficulties. Risk generally increases with global warming, though it depends 
on a combination of many factors such as exposure, vulnerability and response. To present scientific findings succinctly, a risk variation diagram 
can help visualise the relationship between warming level and risk. The diagram can be useful in communicating the change in risk with warming 
for different types of risk across sectors and regions, as well as for five categories of global aggregate risk called ‘Reasons for Concern’.
A picture speaks a thousand words. The use of images to share ideas and information to convey scientific 
understanding is an inclusive approach for communicating complex ideas. A risk variation diagram is a simple way

to present the risk levels that have been evaluated for any particular system. These diagrams take the form of bar 
charts where each bar represents a different category of risk. The traffic light colour system is used as a basis for 
doing the risks, making it universally understandable. These diagrams are known colloquially as ‘burning ember’ 
diagrams, and have been a cornerstone of IPCC assessments since the Third Assessment Report, and further developed 
and updated in subsequent reports. The fact that the diagrams are designed to be simple, intuitive and easily 
understood with the caption alone has contributed to their longstanding effectiveness. Here, in Figure FAQ16.5.1 
below, we provide a simplified figure of this chapter’s burning embers for five categories of global aggregate risk, 
called Reasons for Concern (RFCs), which collectively synthesise how global risk changes with temperature. The

diagram shows the levels of concern that scientists have about the consequences of climate change (for a specified 
risk category and scope), and how this relates to the level of temperature rise.
The dependence of risk associated with the Reasons for Concern (RFC) on the level of climate change
234
1.5
1
0°C
5 Risk/impact
High
Moderate
•••••
••••
Historical average
RFC1
Unique and
systems•••• •••• •••
RFC2
Extreme
weather
events•••• ••• ••
RFC3
of impacts••• •• ••
RFC5
singular
events••• •• •
RFC4
Global
aggregate
impacts
•• •• ••Purple indicates very high risks of severe 
impacts and the presence of signiﬁcant 
irreversibility or the persistence of 
climate-related hazards, combined with

limited ability to adapt due to the nature of 
the hazard or impacts/risks.
Red indicates severe and widespread 
impacts/risks.
Yellow indcate that impacts/risks are 
detectable and attributable to climate change 
with at least medium conﬁdence, also 
accounting for specific criteria for key risks.
White indicates that no impacts are 
undetectable and attributable to climate 
change. Confidence level
assigned to transition
range
Figure FAQ16.5.1 |  Simplified presentation of the five Reasons for Concern burning ember diagrams as assessed in this report (adapted from 
Figure 16.15). The colours indicate the level of risk accrual with global warming for a low-adaptation scenario. RFC1 Unique and threatened systems: ecological 
and human systems that have restricted geographic ranges constrained by climate-related conditions and have high endemism or other distinctive properties.

Examples include coral reefs, the Arctic and its Indigenous People, mountain glaciers and biodiversity hotspots. RFC2 Extreme weather events: risks/impacts to 
human health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, and coastal 
flooding. RFC3 Distribution of impacts: risks/impacts that disproportionately affect particular groups owing to uneven distribution of physical climate change 
hazards, exposure or vulnerability. RFC4 Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, 
such as monetary damages, lives affected, species lost or ecosystem degradation at a global scale. RFC5 Large-scale singular events: relatively large, abrupt and 
sometimes irreversible changes in systems caused by global warming, such as ice sheet disintegration or thermohaline circulation slowing.

In this diagram, the risk variation bars or embers are shown with temperature on the y-axis, and the base of the 
ember corresponds to a baseline temperature. Typically, this baseline temperature is that before global warming

16
2508Chapter 16 Key Risks across Sectors and Regions
started (i.e., average temperatures for the pre-industrial period of 1850–1900). This area of the ember appears 
white, which indicates no to negligible impacts due to climate change. Moving up the ember bar, changing colours 
show the increase in risk as the Earth warms globally in terms of degrees Celsius—yellow for moderate risk, red 
for high risk, and purple for very high risk. Definitions of the risk levels are presented in Figure FAQ16.5.1 The risk 
transitions are informed by the latest literature and scientific evidence, and developed through consultation and 
development of consensus among experts. The bars depict an averaged assessment across the world, which has the 
disadvantage of hiding regional variation. For example, some locations or regions could face high risk even when 
the global risk level is moderate.

When the embers for different risk categories are placed next to each other, it is possible to compare risk levels at 
different levels of global warming. For example, at 1°C warming all embers appear yellow or white, so it is possible 
to say that keeping global warming below that particular temperature would help ensure risks remain moderate 
for all five categories of concern assessed. In contrast, at 2°C warming, risk levels have transitioned to high for all 
categories assessed, and even reach a very high level of risk in the case of unique and threatened systems.Box FAQ 16.5 (continued)
FAQ 16.6 | What is the role of extreme weather events in the risks we face from climate change?
Climate change has often been perceived as a slow and gradual process, but by now it is abundantly clear that many of its impacts arise through

shocks, such as extreme weather events. Many places are facing more frequent and intense extremes, and also more surprises. The impact of 
such shocks is shaped by exposure and vulnerability, where we live, and how we are prepared for and able to cope with shocks and surprises.
The rising risk of extreme events is one of the major RFCs about climate change. It is clear that this risk has already 
increased today. Many recent disasters already have a fingerprint of climate change.
There are large differences in such risks from country to country, place to place, and person to person. This is of 
course partly due to differences in hazards such as heatwaves, floods, droughts, storms, storm surges, etc., and the 
way those hazards are influenced by climate change. However, an even more important aspect is people’s exposure 
and vulnerability: do these hazards occur in places where people live and work, and how badly do they affect

people’s lives and livelihoods? Some groups are especially vulnerable, for instance elderly in the case of heatwaves, 
or people with disabilities in the case of floods. In general, poor and marginalised people tend to be much more 
affected than rich people, partly because they have fewer reserves and support systems that help them to prepare 
for, cope with and recover from a shock. On the other hand, absolute economic losses are generally higher in richer 
places, simply because more assets are at risk there.
Many problems caused by extreme weather do not just appear because of one weather extreme, but due to a 
combination of several events. For instance, dryness may increase the risk of a subsequent heatwave. But the 
increased risk may also cascade through human systems, for instance when several consecutive disasters erode 
people’s savings, or when a heatwave reduces the ability of power plants to produce electricity, which subsequently

affects availability of electricity to turn on air conditioning to cope with the heat. Many shocks also have impacts 
beyond the place where they occur, for instance when a failed harvest affects food prices elsewhere. Climate risks 
can also be aggravated by other shocks, such as in the case of coronavirus disease 2019 (COVID-19), which not 
only had a direct health impact, but also affected livelihoods around the world and left many people much more 
vulnerable to weather extremes.
Understanding the risks we face can help in planning for the future. This may be a combination of short-term 
preparation, such as early-warning systems, and longer-term strategies to reduce vulnerability, for instance through 
urban planning, as well as reducing greenhouse gases to avoid longer-term increases in risk. Many interventions to 
increase people’s resilience are effective in the face of a range of shocks. For instance, social safety nets can help

mitigate the impact of a drought on farmers’ livelihoods, but also of the economic impacts of COVID-19.
Climate-related shocks are threats to society, but they can also offer opportunities for learning and change. Recent 
disasters can motivate action during a short window of opportunity when awareness of the risks is higher and policy 
attention is focused on solutions to adapt and reduce risk. However, those windows tend to be short, and attention is 
often directed at the event that was recently experienced, rather than resilience in the face of a wider range of risks.

17
2542Chapter 17 Decision-Making Options for Managing Risk
Introduction and Framing
Chapter 17 assesses the options, processes and enabling conditions 
for climate risk management, a key component of climate resilient 
development. While Chapter 16 assesses the risks that society and 
ecosystems face, and residual risks after adaptation, this chapter focuses 
on the ‘how’ of climate risk management and adaptation. It covers: 
the adaptation and risk management options that are available; the 
governance and applicability of options in different contexts; residual 
risk and Loss and Damage; the methods and tools that can be drawn 
on to support climate risk management planning and implementation; 
enabling conditions and drivers for adaptation; the role of monitoring 
and evaluation for integrated risk management and tracking progress, 
success and the risk of maladaptation; and finally, integration of risk

management across sectors, jurisdictions and time horizons, under 
dynamic conditions of environmental and societal change.
Adaptation options for managing a wide range of climate risks 
have been proposed, planned or implemented across all sectors 
and regions, with prospects for wide-ranging benefits to nearly 
all people and ecosystems (high confidence1). Many options 
are widely applicable and could be scaled up to reduce vulnerability 
or exposure for the majority of the world’s population and the 
ecosystems they depend on (high confidence). These include nature 
restoration (high confidence), changing diets and reducing food waste 
(high confidence), infrastructure retrofitting (high confidence), building 
codes (medium confidence), disaster early warning (high confidence) 
and cooperative governance (medium confidence). The portfolio of 
adaptation options that could be successfully implemented varies 
across locations, with resource-limited and conflict-affected contexts

bearing large amounts of residual risk (high confidence) {17.2, 17.2.1, 
17.5.1}.
The majority of climate risk management and adaptation currently 
being planned and implemented is incremental (high confidence). 
Transformational adaptation will become increasingly necessary 
at higher global warming levels (medium confidence) but can be 
associated with significant and inequitable trade-offs (medium 
confidence). Adaptations with some of the highest transformative 
potential include migration (high confidence), spatial planning (medium 
confidence), governance cooperation (medium confidence), universal 
access to health care (medium confidence) and changing food systems 
(medium confidence). Options that tend to modify existing systems 
incrementally include early-warning systems (high confidence), insurance 
(medium confidence) and improved water use efficiency (high confidence) 
{17.2, 17.5.1}.
Governance, especially when inclusive and context sensitive, is

an important enabling condition for climate risk management 
and adaptation (very high confidence). The use of formal 
and informal governance approaches, often in polycentric 
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.arrangements of public, private and community actors, is being 
increasingly recognised as important across many decision-
making settings (high confidence). Public governance leadership has 
the largest role for social safety nets, spatial planning and building codes

(high confidence). Private sector governance is important for insurance 
and for minimising the stressors that can negatively impact ecosystems 
and their functions, especially in the absence of public regulations or 
enforcement (medium confidence). Communities and individuals play 
the largest role in governance of adaptations to farming and fishery 
practices and ecosystem-based adaptations (medium confidence). 
Informal or individual-led decision-making is more common in food 
security and livelihood-related adaptations, such as changes to diets, 
livelihood diversification and seasonal migration (high confidence). 
People who have experienced climate shocks are more likely to take 
on informal adaptation measures, and in places where people are more 
exposed to extreme events, autonomous adaptation is more common 
(high confidence) {17.2.1, 17.3.2, 17.4.2}.
National and international legal and policy frameworks 
and instruments support the planning and implementation

of adaptation and climate risk management across scales, 
especially when combined with guidelines for action (medium 
confidence). Nationally Determined Contributions (NDCs) have been 
drivers of national adaptation planning, with cascading effects on 
sectors and sub-national action, especially in developing countries 
(high confidence). Nearly all developing countries (particularly 
Small Island Developing States [SIDS]) that included an adaptation 
component in their NDCs consider adaptation the most urgent aspect 
of their national climate change response (high confidence). A steady 
increase in national and sub-national laws, policies and regulations 
that mandate reporting and risk disclosure has promoted adaptation 
response across public agencies, private firms and community 
organisations (high confidence). Greater adaptation is present where 
national climate laws and policies require adaptation action from 
lower levels of government and include guidelines on how to do so

(medium confidence) {17.4.2}.
Recognition of the critical role of financing for adaptation 
and resilience as an important enabler for climate risk 
management has strengthened (high confidence). Yet, since 
the Intergovernmental Panel on Climate Change (IPCC) 5th 
Assessment Report (AR5), the gap between the estimated costs 
of adaptation and the documented (tracked) finance allocated 
to adaptation has widened (high confidence). Estimated global 
and regional costs of adaptation vary widely due to differences in 
assumptions, methods and data; the majority of more recent estimates 
are higher than the figures presented in AR5 (high confidence). A 
high proportion of developing country NDC adaptation contributions 
are conditional on external financial support, emphasising the 
crucial role of international finance to achieving adaptation efforts 
commensurate with climate risks (high confidence). Developed country

climate finance leveraged for developing countries for mitigation and 
adaptation has fallen short of the 100 USD billion yr−1 Copenhagen

17
2543Decision-Making Options for Managing Risk  Chapter 17
commitment for 2020 (very high confidence). Substantial opportunities 
exist for improving access to climate finance, as well as its impact and 
effectiveness {17.4.2; Cross-Chapter Box FINANCE in this Chapter}.
Private sector financing for adaptation has been increasingly 
promoted as a response to realised adaptation finance needs 
(high confidence). However, private sector financing of 
adaptation has been limited, especially in developing countries 
(high confidence). Tracked private sector finance for climate change 
action has grown substantially since 2015, but the proportion 
directed towards adaptation has remained small (high confidence); 
in 2018, these contributions were 0.05% of total climate finance and 
1% of adaptation finance. A key challenge for private sector financing 
of adaptation is demonstrating financial return on investment, as 
many benefits of adaptation arise as avoided damages or public

goods, rather than direct revenue streams (medium confidence). 
Leveraging private finance in developing countries is often more 
difficult because of risk (perceived and real) to investors, reducing 
the pool of potential investors and/or raising the cost (interest) of 
investment (medium confidence) {17.4.3.; Cross-Chapter Box Finance 
in this Chapter}.
Information and knowledge on climate risk and adaptation 
options, derived from different knowledge systems, can support 
risk management and adaptation decisions (high confidence). 
Processes, such as co-production, that link scientific, Indigenous, local, 
practitioner and other forms of knowledge can make climate risk 
management processes and outcomes more effective and sustainable 
(high confidence) {17.3.2; 17.4.4}.
Climate services that provide reliable, relevant and usable 
climate information for the short or long term are increasingly 
being produced and used in climate risk management (high

confidence). In many regions and sectors, the utility of climate services 
is strengthened by sustained engagement between stakeholders and 
experts and by co-production (medium confidence). Significant gaps 
remain in the evaluation of climate services, and some studies indicate 
that climate services often do not reach the most vulnerable and more 
isolated people, maintaining or exacerbating inequality{17.4.4; Cross-
Chapter Box Climate Services WGI Chapter 12}.
Catalysing conditions and windows of opportunity can drive 
shifts in motivation and adaptation effort, stimulating more 
rapid uptake of existing and new adaptation options (medium 
confidence). Decision makers can take advantage of windows of 
opportunity to promote rapid and effective responses in reactive 
and proactive cases. Disaster events or shocks such as wildfires, 
tropical cyclones, heatwaves or coral bleaching have catalysing 
characteristics (high confidence). Additional types of catalysing

conditions include climate litigation and the presence of individuals 
and organisations that act as policy and decision innovators, including 
government and business innovators in cities (medium confidence), 
stimulating action within and beyond their immediate contexts 
(medium confidence). Litigation on failure of government and business 
to adapt is becoming more frequent and is expected to increase as 
climate impact attribution science matures further (high confidence) 
{Cross-Chapter Box LOSS in this Chapter; 17.4.5.2, 17.4.5.3}.Urgency can stimulate prompt climate risk management (high 
confidence). A moderate level of urgency contributes to enhanced 
climate action, while both high and low levels of urgency can impede 
response (high confidence). Well-designed communication strategies 
can move decision makers from low to moderate levels of urgency, 
stimulating action. As conditions approach a crisis state, however,

urgency can weaken decision-making rather than support it (medium 
confidence) {17.4.5.1}.
Decision support tools and decision-analytic methods are 
available and are being applied for managing climate risks in 
varied contexts, including where deep uncertainty is present 
(high confidence). These tools and methods have been shown 
to support deliberative processes where stakeholders jointly 
consider factors such as the rate and magnitude of change 
and their uncertainties, associated impacts and timescales of 
adaptation needed along multiple pathways and scenarios of 
future risks (high confidence). However, comparative evidence 
on the relative utility of different analytical methods in their use 
by decision makers for managing climate risks is an important gap 
(medium confidence). Nevertheless, robust decision-making, using 
pathway analyses to determine ‘no regrets’ options among trade-offs, 
has been shown to be a useful starting point under deep uncertainty

(medium confidence). Methods for analysing options differ across geo-
political scales, with modelling studies being a particularly prominent 
method across scales from community and urban to regional and 
national (high confidence) {17.3.1, 17.3.2, 17.6, Cross-Chapter 
Box DEEP in this Chapter}.
Successful adaptation and maladaptation form the opposite 
poles of a continuum (medium confidence). The evaluation of an 
adaptation option and its location on this continuum are context 
specific and vary across time, place and evaluation perspectives 
(high confidence). Despite knowledge gaps, adaptation options can 
be assessed according to several criteria, such as benefits to humans, 
benefits to ecosystem services, benefits to equity (marginalised ethnic 
groups, gender, low-income populations), transformational potential 
and contribution to greenhouse gas emission reduction (medium 
confidence). These factors can aid evaluation of co-benefits and trade-

offs within and between adaptation responses (high confidence) 
facilitating successful adaptation and reducing the likelihood of 
maladaptation (medium confidence) {17.5.1, 17.5.2}.
Adaptation options across a range of climate risk settings 
(Representative Key Risks) have potential for some degree of 
maladaptation alongside varied potential for success (very 
high confidence). Maladaptation can result from unaccounted trade-
offs with low-income groups and the transformational potential of 
adaptation (medium confidence). Success is greatest when adaptation 
enhances gender equity (medium confidence) and supports ecosystem 
function and services (medium confidence). Among adaptation options, 
coastal infrastructure is an example that has particularly high risk for 
maladaptation through trade-offs for natural system functioning and 
human vulnerability over time. Examples of options with high potential 
for successful adaptation are nature restoration (medium confidence),

social safety nets (medium confidence) and adaptations relating to 
changes of diets and reducing food waste (medium confidence) {17.5.2}.

17
2544Chapter 17 Decision-Making Options for Managing Risk
Monitoring and evaluation (M&E) are essential for tracking 
adaptation progress and learning about adaptation success 
and maladaptation (high confidence). M&E application has 
increased since AR5 at the local, project and national level, but 
is still at an early stage in most countries (high confidence) 
and underutilised as a way to assess adaptation outcomes 
at longer time frames (high confidence). About one-third of 
countries have undertaken steps to develop national adaptation M&E 
systems, but fewer than half of these are reporting on implementation 
(medium confidence). M&E, as well as tracking global progress 
on adaptation, are confronted with a number of challenges (high 
confidence), such as a comparability in what counts as adaptation 
and limited availability of data across scales. The relative strength 
and weaknesses of different approaches and their applicability have

not been systematically assessed, but the diversity of approaches 
being used could provide a more comprehensive assessment of 
global adaptation progress {17.5.2, Cross-Chapter Box PROGRESS in 
this Chapter}.
Understanding of residual impacts and risks in vulnerable 
regions and implications for Loss and Damage (L&D) has become 
increasingly relevant as the limits to adaptation are projected 
to be reached in natural and human systems (high confidence). 
The international L&D policy debate has seen heightened attention, 
with some coalescence around key issues, including risk management, 
limits to adaptation, existential risk, finance and support, including 
liability, compensation and litigation. Advisory groups have been set 
up with participation of policy and experts from research, civil society 
and practice to inform debate. Yet, the policy space and concrete remit 
for L&D has remained vague, which renders policy formulation complex

(high confidence) {17.2.2.5; Cross-Chapter Box LOSS in this Chapter }.
Effective management of climate risks is dependent on 
systematically integrating adaptations across interacting 
climate risks, ensuring that measures of success include factors 
important to climate resilient development, and accounting 
for the dynamic nature of climate risks over time (very high 
confidence). Across the Working Group II report are examples of 
how managing adaptations to reduce climate risks can negatively 
or positively affect sustainable development, thereby impacting the 
potential for climate resilient development. Climate risks can emerge 
at different rates and time horizons, and the interactions between 
risks vary from region to region (very high confidence). The need to 
manage these risks in an integrated manner is demonstrated by the 
diverse and interacting impacts of climate risks on ecosystems, cities, 
health, and poverty and livelihoods, such as in the water–energy–

food nexus (high confidence). Expertise and resources for integrated 
risk management vary between the developed and developing 
countries (high confidence). Integrated pathways for managing 
climate risks will be most suitable when ‘low regrets’ anticipatory 
options are established jointly across sectors in a timely manner, 
path dependencies are avoided in order to not limit future options for 
climate resilient development, and maladaptations across sectors are 
avoided (high confidence). National Adaptation Plans have potential 
to integrate participatory, iterative processes to monitor, review and 
update adaptations as knowledge, experience and resources become 
available {17.6; Cross-Chapter Box DEEP in this Chapter}.

17
2545Decision-Making Options for Managing Risk  Chapter 17
17.1 Objectives and Framing of the Chapter
17.1.1 Introduction
Addressing the impacts and risks associated with observed and 
projected climate change (Chapter 16) is fundamentally and 
intricately tied to the decision-making options available to manage 
those risks. Climate risk decision-making focuses on the processes 
needed to identify and characterise those risks as well as generate 
plans and policies to reduce the likelihood and/or magnitude of 
adverse potential consequences, based on assessed or perceived risks 
(derived from the definition of risk and risk management in Chapter 
1). This chapter presents an assessment of the evidence on climate 
risk decision-making as a set of processes that involve a range of 
actors in different contexts resulting in diverse outcomes. The climate 
risk decision makers and their actions are the central focus of the 
assessment. The chapter is an assessment of the evidence of the

decision-making options that are available in practice, and functions 
as a central pivot point between the identification of key climate 
risks (Chapter 16) and the means to integrate and leverage action on 
climate risk decision-making into the broader requirements of climate 
resilient development pathways (Chapter 18). This section introduces 
the main entry points on decision-making that have framed this 
assessment (Sections  17.1.1.1–17.1.1.5), as well as the key terms 
used to frame this assessment and its organisation in this chapter 
(Section 17.1.2).
A central framing point is the connection between climate risk 
decision-making and adaptation. Adaptation for human systems in 
this report is introduced in Chapter 1 and defined in the Glossary 
as ‘the process of adjustment to actual or expected climate and its 
effects, in order to moderate harm or exploit beneficial opportunities’. 
In natural systems, adaptation is the process of adjustment to actual

climate and its effects; human intervention may facilitate adjustment 
to expected climate and its effects (see IPCC 6th Assessment Report 
[AR6] Glossary [Annex II]). In this chapter, we consider adaptations 
that may be implemented by people, whether they be to support 
human, managed, or natural systems, and the processes and factors 
that underpin adaptation in these diverse settings. Different types of 
adaptation have been distinguished in Chapter 1, including anticipatory 
versus reactive, autonomous versus planned, and incremental versus 
transformational (IPCC WGII glossaries; Chapters 16–18). These 
dichotomies and interactions are assessed here. Implementation 
of adaptation through iterative risk management decision-making 
emphasises that anticipating and responding to climate change does 
not consist of a single set of judgements at a single point in time, but 
rather an ongoing cycle of assessment, action, reassessment, learn and 
response (Chapter 1).

17.1.1.1 Decision-Making for Managing Climate Risks in AR6
The UN 2030 Agenda for Sustainable Development and its 17 
Sustainable Development Goals (SDGs), as well the United Nations 
Framework Convention on Climate Change (UNFCCC) Paris Climate 
Agreement, the UN Sendai Framework Disaster Risk Reduction and the 
UN Habitat New Urban Agenda, helped push climate risk management 
and adaptation forward from the global to the national level, from the planning stage into implementation, and provide benchmarks for 
adaptation progress. To assess adaptation progress (Section 17.5), the 
interplay between top-down (institutional) and bottom-up (individual/
social/community) processes, multi-scale interaction (local, regional, 
national and international), iterative risk management, differing 
forms of knowledge, and equity are especially crucial (particularly 
Sections 17.2, 17.4). Parallel to these advances is an understanding

and assessment of appropriate decision support tools, methods and 
evaluation metrics (Section 17.3).
Since AR5, significant advances have been made in regard to the 
understanding of the drivers of decision-making and contexts in which 
climate risk decision-making takes place. Climate risk decision-making 
generally, and adaptation specifically, has been a focus within the IPCC 
special reports in the sixth assessment cycle. An overall goal of climate 
risk management is to eliminate or reduce the risk to levels that are 
socio-politically and economically acceptable. Risk management to an 
acceptable level may not be feasible because of limits or barriers to 
adaptation. Future potential risks are a more complex matter given 
the need to define time scales and spatial extent, and uncertainties. 
In the IPCC Special Report on Global Warming of 1.5°C (SR1.5) (IPCC, 
2018a), the risks associated with climate-related impacts were found

to be higher under emission scenarios above 1.5°C, raising awareness 
for the need to limit the impacts of warming through the acceleration 
of climate mitigation and both incremental and transformational 
adaptation (IPCC, 2018a).
The AR6 IPCC Special Report on Climate Change and Land (SRCCL) 
(IPCC, 2019b) added the dimensions of pace, intensity and scale of 
climate impacts and adaptation or mitigation responses and adverse 
consequences. Relevant land-based adverse consequences include 
those on lives, livelihoods, health and well-being, economic, social 
and cultural assets and investments, infrastructure, services (including 
ecosystem services), ecosystems and species.
While a generic understanding of the decision-making process has 
emerged from the literature, the chapter assesses how these components 
and their dimensions interact across a range of temporal (short, long term 
as defined in the IPCC Special Report on the Ocean and Cryosphere in a

Changing Climate [SROCC]), scalar (household to global), institutional/
governance (formal, informal, bottom-up, top-down) and magnitude (micro 
adaptation, small scale; macro adaptation, large scale) (Section 17.2). The 
IPCC SRCCL placed emphasis on acknowledging co-benefits and trade-
offs to avoid barriers to implementation, with particular attention to land 
use decisions. It states that this coordination can be supported by building 
networks of decision makers across scales and sectors, including local 
stakeholders from vulnerable groups, and by adopting and implementing 
policies in a flexible and iterative manner (IPCC, 2019b).
17.1.1.2 Approaches to Assess and Synthesise Options for 
Managing Risk
This chapter utilises several points of departure to assess climate risk 
management that emerge from AR5 and AR6, specifically. SR Climate 
Change and Land, especially Chapter 7 (IPCC, 2019b; Hurlbert et al., 
2019) and throughout SROCC (). These works provide foundational

17
2546Chapter 17 Decision-Making Options for Managing Risk
different spatial and temporal scales and diverse cultural contexts in 
which climate risk management takes place, the varying interactions 
of decision makers and their stakeholder groups, and the barriers 
and enablers to decision-making, including governance, finance and 
knowledge (Section 17.4).
Another significant advance is that, instead of cataloguing decision-
making strategies, the literature has now evolved to the point 
where adaptation progress, effectiveness and efficiency can be more 
meaningfully assessed through increased monitoring and evaluation 
capacity, although the ability to measure success and effectiveness 
is not fully developed and is hampered by lack of data, agreed 
methods and terms, and time to fully evaluate adaptation actions 
(see Sections 17.3.3 and 17.5, Cross-Chapter Box PROGRESS in this 
Chapter). The ambition to describe effectiveness and success illustrates

further maturation of the literature on climate risk decision-making 
as a system process. Overall, the process of climate risk decision-
making remains dynamic, and the chapter attempts to assess a variety 
of proactive management approaches being developed and tested 
to address adverse, diverse and complex risks in a wide range of 
developing and developed country contexts (Figure 17.1). The chapter 
provides a synthesis of how these new approaches are reflected in the 
sectoral and regional chapters and cross-chapter papers of this report 
(Chapters 2–15; Cross-Chapter Papers 1–7). Specifically, the goal is to 
provide a line of sight between the sectoral and regional chapters and 
cross-chapter papers’ decision-making assessment to sections in this 
chapter. This synthesis also helps to present the varying and context-
driven character of adaptation strategies now in practice and being 
considered.
17.1.1.3 Key Risks Considered in the Assessment of Climate Risk 
Decision-Making

In AR6 (Chapter 16 and cross-chapter papers), over 100 key risks 
have been identified across regions and sectors, which have the 
potential to manifest into severe impacts that are relevant to the 
interpretation of UNFCCC Article 2, specifically on the objective 
to avoid dangerous anthropogenic interference with the climate 
system. These risks are likely2 to become more severe under higher 
warming scenarios and social-ecological conditions that yield high 
exposure and vulnerability to the associated climate-related hazards. 
In this report, these key risks have been grouped into categories 
represented by eight overarching risks (called Representative Key 
Risks, RKRs) relating to: (1) coastal socio-ecological systems; (2) 
terrestrial and ocean ecosystems; (3) critical physical infrastructure, 
networks and services; (4) living standards; (5) human health; (6) 
food security; (7) water security; and (8) peace and human mobility

(Chapter 16). Decision-making options for managing these risks, such 
as selecting the relevant adaptation options to implement, require an 
assessment of the local context in which these impacts are likely to 
be experienced, as well as the local to global collective implications 
of those actions (Sections 17.2 and 17.5).
lies within the 17–83% probability range.17.1.2 Objectives and Key Terms

17.1.2.1 Drivers
AR5 provides a broad overview of drivers as the determinants of 
climate decision-making by individuals and organisations, including 
social, institutional and regulatory contexts, cultural values and norms, 
economic resources and constraints, and the availability of information 
and of tools to process it. This chapter expands the discussion of the 
contexts for decision-making in a number of ways (Section  17.4), 
including an examination of informal as well as formal decisions, 
an attention to emerging actors, particularly social movements, and 
consideration of several dimensions of governance. It expands the 
treatment of decision processes, with particular attention to framing 
and to the integration of multiple time frames (Sections 17.3 and 17.6).
Since AR5, there has been an increasing ambition for adaptation, 
signalled by growing attention to the adaptation gaps and deficits, 
which call for extensive and intensive levels of action (Chen et al., 2016;

UNEP , 2017; Tompkins et  al., 2018; Valente and Veloso-Gomes, 2020; 
UNEP , 2021a), as well as increased attention to co-benefits between 
climate risk reduction and other benefits, such as equity and biodiversity 
conservation (Colloff et  al., 2017, Section  17.5.1; Smith et  al., 2020). 
Climate risk decision-making as an object of study has emerged in a more 
central location within the literature as adaptation moves from planning 
into the realm of practice. The broad sense of urgency (summarised in 
Wilson and Orlove, 2019; Wilson and Orlove, 2021) shows growth of the 
term ‘urgency’ in both scholarly publications and the popular press since 
2014, building on earlier increases starting around 2005, and a dramatic 
spike of the terms ‘climate crisis’ and ‘climate emergency’. Paralleling 
this call for more extensive and rapid action is the emergence of the term 
‘transformational’ adaptation and decision-making. Transformational

adaptation (defined and deeply examined in Chapters 1 and 16 and 
Section 17.2) highlights efforts that involve large-scale, systemic change 
(Wilson et al., 2020) and involves ‘adapting to climate change resulting 
in significant changes in structure or function that go beyond adjusting 
existing practices including approaches that enable new ways of 
decision-making on adaptation’ (IPCC, 2018a). The complex relationship 
between incremental adaptation and transformational adaptation is 
presented and reviewed in Section 17.2. Furthermore, the literature since 
the AR5 report has moved beyond the question of limits and barriers 
to adaptation as relevant aspects for decision-making to additionally 
assessing drivers of change, with increasing focus devoted to more 
nuanced and differentiated contexts for action.
17.1.2.2 Enabling Conditions
AR5 extensively assessed the conditions of adaptation with a focus on 
the role of governance, finance, knowledge and capacity. AR6 extends

this examination of adaptation and the decision-making process 
around it by focusing on enablers. Adaptation enablers are defined 
as those conditions or properties that specifically promote or advance 
the adaptation process (Chapter 1). Enablers are positively associated

17
2547Decision-Making Options for Managing Risk  Chapter 17
with likelihood that adaptation planning occurs, and strategies will be 
put into practice. Three broad enabling conditions are presented in the 
chapter (Section 17.4): governance (legislation, regulation, institutions, 
litigation), finance (needs, sources, intermediaries, instruments 
flows, equity) and knowledge (capacities, climate services, big data, 
Indigenous/local knowledge, co-production, boundary organisations). 
As an extension of enabling conditions, the chapter also examines 
catalysing conditions for adaptation (Section  17.4.5). Catalysing 
conditions motivate and accelerate the process of decision-making, 
leading to more frequent and potentially substantial adaptations. The 
chapter recognises that the relative influence of enabling conditions 
and catalysing conditions is set within the human dimensions of 
climate change including vulnerability, inequality, poverty and the

achievement/non-achievement of SDGs (Figure 8.1).
17.1.2.3 Mechanisms for Decision-Making
The mechanisms and conditions for decision-making provide the 
basis for the chapter. AR5 provided a detailed chapter on the support 
of climate decision-making. Chapter 2 of AR5 (Jones et  al., 2014) 
concluded, with high confidence, that risk management provides a 
useful framework for most climate change decision-making, and that 
iterative risk management is most suitable in situations characterised 
by large uncertainties, long time frames, the potential for learning over 
time, and the influence of both climate as well as other socioeconomic 
and biophysical changes. Furthermore, decision support is situated 
at the intersection of data provision, expert knowledge and human 
decision-making at a range of scales from the individual to the 
organisation and institution.
The climate risk management decision-making process follows a set

of general considerations. The detail of each decision is often highly 
context specific. Climate risk decision-making is bound to the question 
of how and under what circumstance it is appropriate to alter, reduce or 
transfer and retain risk. Different types of risk (e.g., gradual compared 
with catastrophic) and conditions of risk (e.g., known versus uncertain) 
are associated with different types of responses (e.g., incremental versus 
transformational). As the risk decision process proceeds, individuals 
and organisations will formally or informally utilise any number of 
mechanisms to guide, aid or facilitate the decision-making process. 
Decision-making can then take place in a linear set of steps or through 
a complex iterative process involving reflexive and recursive steps.
17.1.2.4 Costs and Non-monetised Loss, Benefits, Synergies and 
Trade-Off
AR5 provided an extensive discussion of the costs to human and natural

systems associated with climate risks. It recognised the challenges 
which long time frames, uncertainty and the differing values held by 
stakeholders create for the monetisation of losses. The AR6 SROCC 
built on the discussion of cultural values—typically also difficult to 
monetise—through a consideration of cultural ecosystem services and 
cultural forms of valuation, with cases from high mountain areas and 
polar regions (Hock et al., 2019; Meredith et al., 2019; IPCC, 2019c). AR6 
expands this discussion of multiple forms of valuation in several ways. 
It considers regulation and litigation as mechanisms for promoting 
the consideration of both monetisable and non-monetisable losses in decision-making (Cross-Chapter Box LOSS in this Chapter). AR5 treated 
the issues of equity and justice primarily with regard to mitigation, 
especially in WGIII AR5 Chapter 3 (Kolstad et al., 2014); these issues in 
the adaptation sphere are considered extensively in this chapter in areas

such as finance, governance, success of adaptation, maladaptation, 
and monitoring and evaluation. The discussions of maladaptation and 
success of adaptation (Section  17.5) consider questions of synergies 
and trade-offs across values and goals, while the consideration of 
decision processes and tools shows opportunities to use co-benefits to 
promote effective decision-making, including approaches to decision-
making under conditions of deep uncertainty (Section  17.3; Cross-
Chapter Box  DEEP in this Chapter). Successful adaptation across the 
report (as specified in Chapter 1) is associated with conditions when 
co-benefits are high and (negative) trade-offs are low.
17.1.2.5 Monitoring and Evaluation
This chapter assesses the evidence of monitoring and evaluation 
(M&E) (see AR6 Glossary, Annex II) and their approaches as part of the 
adaptation process at the national, local and project level as well as 
in global assessments (Section 17.5.2; Cross-Chapter Box PROGRESS

in this Chapter). M&E can serve multiple functions, for example, to: 
(1) facilitate an understanding on whether and how interventions 
work in achieving intended objectives; (2) inform ongoing and future 
implementation; and (3) provide information that helps to substantiate 
upward and downward accountability (Preston et al., 2009; UNFCCC, 
2010b; Pringle, 2011; Spearman and McGray, 2011) (see BOX 17.1 for 
more discussion). This chapter also addresses the relevance of iterative 
learning as part of the design of M&E processes, as a means by which 
actors and institutions engaged in M&E acquire new insights on how 
these processes work (or not) to achieve set objectives.
17.1.3 Outline of the Chapter
The chapter is organised around the broad narrative of climate risk 
decision-making and management (Figure  17.1), building from the 
assessment of risks within RKRs (Chapter 16) and options available 
to address these risks and within a broader context of climate resilient

development pathways (Chapter 18). Decision-making is considered 
to be a reflexive and recursive process where different evidentiary 
threads and information inputs become relevant to the understanding 
and assessment of factors underlying specific decisions. Additionally, 
this is also a discursive process, whereby actors and institutions’ 
interpretations of climate risks are also key to these deliberations.
Decision-making processes of risk management and adaptation are 
varied and numerous. Section 17.2 assesses the risk management and 
adaptation options already in practice. Section 17.3 assesses decision-
support methods and tools available for application and the effectiveness 
of these in supporting climate decision-making across degrees of 
uncertainties and levels of governance and expected reach (scale) across 
populations from households to international cooperation. Closely 
interlinked across the decision-making process are the enabling and

catalysing conditions for decisions on adaptation and risk management 
(Section 17.4). Section 17.5 synthesises evidence on maladaptation and 
adaptation successes, and assesses the current knowledge on M&E

17
2548Chapter 17 Decision-Making Options for Managing Risk
Box 17.1 | How Is Success in Adaptation Characterised in Chapter 17?
Whether an adaptation is considered successful is context specific. It depends on who evaluates adaptation and at what time as well 
as on the ability to compare the outcome of adaptation with a hypothetical situation without adaptation and without other parallel 
changes, such as development interventions (Singh et al., 2021; Dilling et al., 2019). The ability to compare the risk situation post and 
prior adaptation is complicated through the long time horizons at which adaptation outcomes often become apparent (Cross-Chapter 
Box ADAPT in Chapter 1; Section 17.5.1; Dilling et al., 2019).
However, a wealth of information has recently become available on how success and effectiveness of adaptation could be assessed, 
defined or investigated in certain settings (Patt and Schröter, 2008; Morecroft Michael et al., 2019; Tubi and Williams, 2021) or across

a larger set of adaptations (Hegger et al., 2012; Eriksen et al., 2015; Gajjar et al., 2019a; Owen, 2020; Singh et al., 2021). Accordingly, 
successful adaptation is understood as effective adaptation, in that it reduces climate impacts, vulnerabilities and risk, and additionally 
balances synergies and trade-offs across diverse objectives, perspectives, expectations and values (Eriksen et al., 2015; Juhola et al., 2016; 
Gajjar et al., 2019a; Owen, 2020; Singh et al., 2021). Across this report, four factors are identified as enabling conditions of successful 
adaptation, which include a focus on recognitional, procedural and distributional justice as well as flexible and strong institutions that 
seek policy integration and account for long-term goals.
To operationalize ‘success’ in this chapter, it is characterised by the degree to which an adaptation response benefits (1) human systems

(number of people), (2) ecosystems or ecosystem services, (3) marginalised ethnic groups, (4) women and girls, (5) and low-income 
populations, and can be characterised as (6) transformational adaptation, and (7) contributing to greenhouse gas emission reductions 
(Section 17.5.1). Overarching to these factors are uncertainty and potential path dependency of decisions that may result in lock-in and 
maladaptation in the long term, and recognition that what is successful in the near term is not necessarily successful in the long term.
Success in adaptation is antithetical to maladaptation. Maladaptation refers to current or potential future negative consequences, 
including failed or partially successful adaptation (or risk reduction) but also trade-offs or side effects of adaptation (see Glossary, Annex 
II). Thus, success of adaptation and maladaptation form the ends of a continuum that represents the balancing of synergies and trade-offs

across regions, populations or sectors (Singh et al., 2016; Magnan et al., 2020; Schipper, 2020). Every adaptation action may be placed 
along such a continuum reflecting the empirical evidence of adaptation practices and their assessment (Section 17.5).
of adaptation, including financial accounting, to support learning on 
those, respectively. Here, M&E is considered distinct from the tracking 
of financial flows related to adaptation, given that financial accounting 
does not necessarily provide information on the implementation of 
adaptation measures and their results (see also Section  17.2.1.2). 
Finally, in Section  17.6, decision-making, climate risk responses and 
their relevance for climate resilient development are presented, where 
evidence on their respective contributions to facilitate actions in the 
adaptation solution space within a broader context for development is 
shown (Chapter 18). Throughout the decision-making process, crucial

feedback loops are present that define the results of specific actions and 
recursive nature of climate risk management and adaptation.
17.2 Risk Management and Adaptation Options
There has been substantial progress in risk management and adaptation 
responses around the world, as demonstrated in the sectoral and 
regional chapters of this report and illustrated in Chapter 16. This 
section presents an overview of different options available to manage 
risk, explaining how they are currently governed and the extent to 
which they can be applied around the world. The section contains an 
assessment of the ways in which different options are being combined 
to create adaptation portfolios, and describes how incremental and 
transformational change is starting to be considered. Based on the human dimension of climate change, as described in Chapter 8, vulnerability, 
inequality and poverty influence these portfolios of adaptation and

transformational change. Particularly for change where residual risks 
remain that may lead to exceeding the limits of adaptation, increasingly 
transformational adaptation and policy innovation will be important. 
Section  17.2.1 assesses options for climate risk management from 
around the world that reduce, manage or retain climate-related risks 
and assesses their contribution to reducing vulnerability and exposure, 
how they are governed, and the benefits to humans and ecosystems. 
Section  17.2.2 presents portfolios of risk management, including the 
design principles and observed variations across the globe, before it 
discusses the need and potential for transformational adaptation to 
complement incremental adaptation, for which we present evidence 
across the report for selected adaptation options and some key risks. 
The Cross-Chapter Box LOSS in this Chapter synthesises recent literature 
and assesses key strands of the international policy dialogue on Loss

and Damage, which discusses options that help to deal with impacts 
and residual risks in vulnerable countries.
17.2.1 Adaptation Options for Climate Risk Management
This section assesses options for climate risk management (CRM) across 
common risk settings that have been grouped into Representative 
Key Risks (RKRs; see Section 16.5.2.2). These risk management and

17
2549Decision-Making Options for Managing Risk  Chapter 17
(CH 16)Decision maker
Iterative Climate Risk Management (Ch. 17)
Monitor Progress
and Outcomes, 
Evaluate and
Learn
Implement 
and Risk
Management
Choose and 
Plan Content-
Specific
Responses
| Individual | Civil society |
Private sector | Institutions |
Intergovernmental organisations
NGOs and CBOs | Country |
Region | Municipality |
| Indigenous Peoples |
Assess Climate
Risk and Contextual
Factors
Risk is Reduced
or
Unchanged
or
 Increased
or
 TransferredRespond 
to
Risks Climate Resilient 
Pathways
(CH 18)Residual Risk
17.4
17.317.217.3
17.4
17.217.6
17.6
17.517.2
17.4
17.6
Adaptation Options & Risk 
Management: Adaptation 
Options, Benefits, Costs, 
Transformation, Context, Risk 
Layering, Loss & Damage17.2
Decision-Making Processes & 
Tools: T ools, Approaches, 
Scenarios, Models, Trade-of fs, 
Management, (Deep) 
Uncertainty , Participation, 
Feedbacks17.3
Enabling & Catalysing

Conditions: Governance, 
Finance, Knowledge, Capacity , 
Motivation, Champions, 
Litigation, Urgency , Windows of 
Opportunity17.4
Monitoring, Progress & 
Success: Success, 
Maladaptation, Trade-of fs, Equity 
and Justice, Monitoring, 
Evaluation, Learning17.5
Risk Management for Climate 
Resilient Development: 
Integrated Risk Management, 
Adaptation Pathways, Portfolios,
Cross-sector and Cross-scale 
Interactions17.6CCB 
PROGCCB 
LOSSSchematic representation of the climate risk management decision-making process
CCB
DEEP
CCB
FINANCE
Figure 17.1 |  Schematic representation of the climate risk management decision-making process as introduced in Chapter 1 (Figure 1.6) and the key 
elements of this chapter that address additional aspects of this process. In Chapter 17, climate risk management (middle box) is framed as the iterative response (i.e.,

what society could do and how it could be done) to the climate risks described in Chapter 16, with outcomes (ideally reduced risk) that can support (or perhaps hinder) climate 
resilient development, as assessed in Chapter 18. Decision makers from diverse contexts sit at the centre of the climate risk decision-making process and interact with and drive 
these processes as they play out. The main sections of Chapter 17 (bottom panel of boxes) address a wide range of issues (keywords in bottom panel) that manifest at one or more 
stages of climate risk management processes, illustrated by icons for section numbers and Cross-Chapter Boxes in the interactive risk management process.
adaptation actions target the components of risk: hazards, vulnerabilities 
and exposure associated with sudden or slow-onset events (see Chapter 
1 for more details on the definition of risk).
For each of the RKRs, three commonly discussed adaptation options

are identified across the regional, sectoral and cross-chapter papers 
of this report. These 24 options have been selected to cover a 
representative variety of strategies to adapt to climate change, while 
a particular adaptation option can be relevant to many of the RKRs. For example, the adaptations listed under the RKR of ‘Food security’ 
are also related to the RKR on ‘Human health’ (Ebi and Prats, 2015). 
See SM17.1 for more detail. The list is not comprehensive of all 
possible adaptations listed in the regional and sectoral chapters. 
For example, this does not include adaptations by institutions who 
might become unable to cope with increasing pace and magnitude of 
extreme events (Chapter 11).

17
2550Chapter 17 Decision-Making Options for Managing Risk
17.2.1.1 Adaptation Options and Their Contribution to Reduce 
Vulnerability and Exposure
Table 17.1 provides examples of each of these 24 adaptation options 
from across AR6 WGII. Detailed information about sectors and regions 
where these adaptations are being discussed can be found in the 
indicated chapters. Note that this list is curated to ensure a diversity of 
options; therefore, most of the options will apply to more than one RKR.
Of this list of adaptation options, many focus on reducing vulnerability 
to climate change (high confidence), as vulnerability is one of the 
components of risk (see Chapter 1 and Chapter 8). Vulnerability is 
the propensity or predisposition to be adversely affected, including 
sensitivity or susceptibility to harm and lack of capacity to cope and 
adapt (see Chapter 1 for more details). In the world’s threatened 
ecosystems, reducing vulnerability often means reducing other non-

climate negative pressures on ecosystems, such as pesticide use or 
fishery overexploitation (Section 3.3).
Vulnerability reduction is also a major focus in human systems, and 
this includes development of investments that help people adapt 
to climate change. Examples include irrigation or diversifying crops. 
Building infrastructure resilient to climate-related risks is another 
example; many of the structural and physical adaptation options can 
reduce sensitivity to disasters, such as elevating houses or doing beach 
nourishment in coastal areas (Section  15.5). Extreme events often 
catalyse investment in adaptation to reduce vulnerability for the future 
(Kreibich et al., 2017; Slavíková et al., 2021).
Next to vulnerability reduction, a large number of adaptation options 
focus on reducing exposure to climate change (high confidence). 
Selecting low-risk locations is the most basic example of reducing 
exposure; for example, private companies are relocating factories to

reduce flood-related disruptions to their supply chain (Neise and Revilla 
Diez, 2019), and species are autonomously adjusting their ranges 
to a changing climate (Section  2.4). Land use planning or investing 
in resilient infrastructure can avoid exposure in rapidly urbanising 
areas; however, the design and enforcement of these regulations can 
negatively impact marginalised people (Anguelovski et al., 2016).
Managed retreat is an example of exposure reduction that, while often 
controversial, is increasingly being considered and implemented (CCP 
2.2.2, Section  15.3.4; Cross-Chapter Box  LOSS in this Chapter; Siders 
et al., 2019). Examples include the US Hazard Mitigation Grant Program, 
which, among other activities, has helped people resettle outside of flood 
zones, and a ‘no-build zone’ established in the Philippines after Typhoon 
Haiyan (Hino et al., 2017). However, relocation is not always an option; 
immobility is sometimes involuntary, such as in the case of ‘trapped’

populations in Zambia (Nawrotzki and DeWaard, 2018; Section 8.2.1.3).
Adaptation efforts can have negative impacts on ecosystems and 
vulnerable groups (high confidence); see Figure 17.3 and Section 17.5 
for further information on maladaptation. While ‘hard’ structural 
investments have been popular to reduce exposure to climate 
extremes, barrier-type measures provide protection only up to a certain 
limit, and are designed to fail in more extreme events. Given the risk 
of catastrophe from a climate extreme overcoming a physical barrier, policy advancements in recent years encourage any investment in 
structural measures to be complemented by ‘softer’ vulnerability 
reduction measures, such as accommodating building construction 
(Wesselink, 2016).
When it comes to ‘softer’ vulnerability reduction initiatives, these were 
traditionally seen as ‘no regrets’ options for adaptation. However, 
subsequent studies have cautioned that notion as vulnerability is

a dynamic quality, and can be co-created while development or 
adaptation efforts are being implemented (Schipper and Pelling, 2006; 
Tempels and Hartmann, 2014; Dilling et al., 2015). Some scholars have 
suggested the application of a ‘do no harm’ principle to climate change 
adaptation efforts (Mayer, 2016).
17.2.1.2 Governance of Adaptation Options
For each adaptation option identified for the RKRs (Table 17.1), this 
section presents an assessment of how decisions are made and how 
the adaptations are being governed. The following section then covers 
benefits to humans and ecosystems, and potential for maladaptation 
is covered in Section 17.5. See SM17.1 for more information on the 
assessment methods and underlying citations.
The following analysis of adaptation options provides a synthesised 
overview of adaptation globally, but does not prescribe how important 
each adaptation should be in specific locations. Chapter 16 finds that

the ‘scope’ and ‘speed’ of adaptation is limited in many areas.
When it comes to decision-making, most of these 24 adaptations 
rely strongly on formal decision-making (high confidence), which 
follows the procedures of a group of people rather than ad hoc 
individual action. Formal decisions play a particularly strong role in the 
adaptations identified for infrastructure, early-warning systems and 
water systems (Kolen and Helsloot, 2014; Calvello et al., 2015; Zhao 
et al., 2017; Belčáková et al., 2019; Teo et al., 2019).
In contrast, informal or individual-led decision-making is more 
common in several food security-related and livelihood-related 
adaptations, such as changes to diets, livelihood diversification and 
seasonal migration (high confidence) (Li et al., 2017; Radel et al., 2018; 
Robinson et al., 2020). People who have experienced climate shocks 
are more likely to take individual decisions to implement adaptation

measures, and in countries where people are more exposed to extreme 
events, autonomous adaptation is more common (Koerth et al., 2017; 
Aerts et al., 2018b; van Valkengoed and Steg, 2019).
All adaptation options can occur under a range of governance 
arrangements (high confidence), with cases of either private, public or 
community governance typically playing the dominant role, as depicted 
in Figure  17.2. Public governance is the most frequent governance 
type for most adaptations considered. This is particularly true for social 
safety nets and spatial planning, where governments are often required 
to lead adaptation efforts (high confidence) (Mesquita and Bursztyn, 
2016; Hssaisoune et al., 2020; Wang et al., 2021). While government 
actors do the day-to-day management of these systems, civil society 
and international organisations also play a role in shaping agendas 
and priorities of government actors (Nagle Alverio et al., 2021).

17
2551Decision-Making Options for Managing Risk  Chapter 17
Table 17.1 |  Selected adaptation options per Representative Key Risk (RKR; see Section 16.5.2.2), with examples of each option from across the report. Many of the adaptation 
options are relevant to multiple RKRs, and have been selected to be representative of the wide variety of adaptation options implemented or suggested around the world.
RKR Adaptation option Examples from regional and sectoral chapters and cross-chapter papers
Risk to coastal 
socio-ecological 
systemsCoastal accommodationRaising of dwellings, raising of coastal roads (Section 15.5.2), amphibious building designs (CCP2), improved drainage 
(Section 11.3.5.3)
Coastal infrastructureSeawalls, beach and shore nourishment (Sections 3.6, 15.5.1), breakwater structures (Section 15.5.1), dykes, revetments, groynes or 
tidal barriers. (Section 6.3.4.8), land reclamation (Section 15.5.2)

Strategic coastal retreatRetreating from coastal areas (Section 3.6, Cross-Chapter Box SLR in Chapter 3, Section 6.3.5.1, CCP2), relocation/resettlement 
(CCP2)
Risk to terrestrial 
and ocean 
ecosystemsRestore/create natural areasMarine protected areas (FAQ 3.5), active restoration of coral reefs (Section 3.6.2.3.2), ridge-to-reef management (CCP1), restoring 
dunes (CCP4), planting salinity-tolerant trees (Section 4.5.2.1)
Increasing forest cover (CCP7), detect and manage forest pests (Section 11.3.4.3)
Reduce ecosystem stressReduce pollution and eutrophication (Section 3.3.3), reduce anthropogenic pressures on the Great Barrier Reef (Box 11.2), 
sustainable fisheries harvest (Section 3.6.2), increasing connectivity between natural areas (Section 2.6.2)
Ecosystem-based adaptationMarine habitats to protect against storm surge (Section 3.6), agroecology (Section 5.14.1.1), coastal and marine vegetation and

reefs (Section 6.3.3.4), vegetation corridors, greenspace, wetlands (FAQ 6.3), mangrove habitat restoration (Sections 8.5.2.2, 9.8.5.1), 
restoring coasts, rivers, wetlands to reduce flood risk (Section 2.6.3, CCP1), urban green space to reduce temperatures (Section 2.6.3)
Risks associated 
with critical 
physical 
networks and 
servicesInfrastructure retrofittingAir conditioning (Section 6.3.4), using thermosiphons for permafrost degradation (Section 10.4.6.4.1), increasing rooftop albedo (for 
reflectivity) (Section 11.3.5.3), shading (Section 13.A.4)
Building codesDrainage systems (Section 4.5.2.1), architectural and urban design regulations (Section 6.3.4.2), infrastructure standards initiatives 
(CCP6), Chile’s Sustainable Housing Construction Code (Section 12.5.5.3)
Spatially redirect 
developmentZoning/land use planning (Section 6.3.2.1), spatial development planning to regulate coastal development (CCP2)
Risk to living 
standards and

equityInsuranceAgricultural insurance and micro-credit (Sections 4.5.2.1, 10.4.5.5), index-based insurance, market and price insurance 
(Section 5.14.1.3), flood insurance (Section 10.5.3.2), collective insurance schemes (Section 12.5.7.5)
Diversification of livelihoodsCombining income-generating activities within fisheries sector (Section 3.6.2.2)
Community level adaptation by Pangnirtung Inuit through diversification to stabilise income and food resources (CCP6)
Social safety netsFood for work programmes (Section 4.5.2.1), school feeding programmes (Section 7.4.2.1.3), social protection programmes, such as 
unemployment compensation (Section 10.5.6)
Risk to human 
healthAvailability of health 
infrastructureSafe drinking water infrastructure (Section 4.5.2.1), temperature-controlled low-income housing (Section 11.3.6.3), health care clinics 
(Section 6.4 case study), place-specific mental health infrastructure and ‘nature therapy’ (Section 14.4.6.8)

Access to health careAccess to health care services (Section 11.3.6.3), access to health, nutrition services and healthy environments (water and sanitation) 
(Section 7.6), enhanced access to culturally appropriate mental health resources; ‘Telemedicine’ (information technologies and 
telecommunications for health and public health service delivery) (Section 12.6.1.5)
Disaster early warningEarly warning of marine heatwaves (Section 3.6.2.3.3) early warning for pests (Section 5.12.5), Heat Action Plans (HAP) 
(Section 7.4.2.1.2), raising public awareness through campaigns (FAQ13.3)
Risk to food 
securityFarm/fishery improvementsChanging fishing gear or vessel power (Section 3.6.2.2.3), change crop variety or timing (Section 4.5.2.1, CCP5, Section 8.5), close 
productivity gaps (Section 5.12.5), biotechnology (Section 5.12.5), irrigation schemes (Section 9.12.5.3), integrated crop/livestock

systems (Section 5.10.1), relocating livestock linked to improved pasture management (Section 13.5.2)
Food storage/distribution 
improvementsImprove transportation infrastructure and trade networks, shortened supply chains (Sections 5.12.5, 9.12.5.3), improved food storage 
(Sections 5.12.5, 7.4.2), local food production/chains (Cross-Chapter Box COVID in Chapter 7)
Behaviour change in diets 
and food wasteReduce food loss and waste (Section 5.12.5), shifts to more plant-based diets (Section 7.4.5.2), creating demand for organically 
sourced food (Section 10.5.3.2)
Risk to water 
securityWater capture/storageFarm ponds and revival of water bodies (Section 4.5.2.1), rain gardens, bioswales or retention ponds (Section 6.3.3.6), water storage 
tanks (Section 10.5.3.2), multi-purpose water reservoirs and dams (CCP5)
Efficient water use/demandPrecision/drip irrigation (Section 4.5.2.1), Managed Aquifer Recharge (MAR) (Section 9.4), cooperative policies across multiple sectors

(CCP4), changing water consumption patterns (CCP4)
Efficient water supply/
distributionConstructing irrigation infrastructure (Section 4.5.2.1), inter-basin transfers (Section 6.3.3.6), water reuse (Section 13.A.3), slum/
water upgrading (Section 6.4.3)
Risk to peace and 
migrationSeasonal/temporary mobilityFishing fleet mobility to follow species distribution (Section 3.6.2.2.2), mobility for seasonal employment and remittances 
(Section 4.5.2.1, Cross-Chapter Box MIGRATE in Chapter 7), legal/illegal labour migration (CCP3), pastoralist seasonal migrations 
(Cross-Chapter Box MIGRATE in Chapter 7)
Cooperative governanceTransboundary fishing agreements (Section 3.6.4.1), ocean governance (Section 3.6.2.2), collective water management 
(Section 4.5.2.1), indigenous water-sharing systems (Section 4.5.2.1), enforcing the land rights of indigenous populations (CCP7),

adaptive co-management in Arctic fisheries (CCP6), international compact on migration (Cross-Chapter Box MIGRATE in Chapter 7), 
policies for adaptive governance (Section 8.5)
Permanent migrationResettlement of flood-prone communities (Section 4.5.2.1), rural–urban migration (Section 6.1 case study), internal migration 
(Box 10.2), international migration and remittances (Sections 8.6.3, 14.4.7.3)

17
2552Chapter 17 Decision-Making Options for Managing Risk
The private sector plays a large role in governance of insurance, 
minimising ecosystem stressors, and livelihood diversification (medium 
confidence) (Allen et al., 2018; Mimet et al., 2020; Alam et al., 2020a). 
While having a key role in shaping and implementing many other 
adaptations, the private sector is not often the governing entity.
There are a number of adaptation options that tend to be governed 
by communities and individuals, including adaptations to farming and 
fishery practices and ecosystem-based adaptations (high confidence) 
(Reid, 2016; Basupi et al., 2019; Giffin et al., 2020; Karlsson and Mclean, 
2020). In rapidly urbanising areas of Asia and Africa, individual- or 
community-led adaptation is the norm in informal settlements that 
have poor governance structures. Residents of Mathare slum in Nairobi 
have established methods to pool risks, such as pooling labour to police

looting during flood events and developing community health centres 
in churches (Thorn et al., 2015). This is in addition to risk reduction 
measures such as building structures to withstand rising water levels 
(Thorn et  al., 2015). Residents in Bangkok have built walls around settlements, dug informal drainage channels to vacant lots, and filled 
areas of land (Limthongsakul et al., 2017). In these cases, individual-
led adaptation can have negative side effects, such as the building of 
flood defences in affluent communities increasing the flood impacts in 
less affluent regions of a city (Limthongsakul et al., 2017).
17.2.1.3 Benefit to Humans and Ecosystems
While some of the 24 adaptation options are specific to certain risk 
contexts (e.g., coastal areas, agricultural production), others are more 
widely applicable (e.g., early-warning systems, health care systems, 
creation/restoration of natural areas). Figure 17.3 depicts which of these

are most context specific, for example benefitting less than 1  billion 
people. This is contrasted with the extent to which each adaptation 
option is beneficial to ecosystem services. Many of the more generalisable 
adaptations have also been shown to have benefits to ecosystem services, 
such as nature restoration and changes to diets/food waste (medium 
confidence). While health care systems and the establishment of health-Efficient water use Water capture / storage Water supply / distributionPublic Private Community
(h) Risk to peace and migration (g) Risk to water security(c) Risks associated with critical physical infrastructure, 
networks and services(a) Risk to coastal socio-ecological systems
Coastal accomodation Strategic coastal retreat Coastal infrastructure(b) Risk to terrestrial and ocean ecosystems
Nature restoration Ecosystem-based
adaptationMinimizing ecosystem 
stressors

Infrastructure retrofitting Spatial planning Building codes(d) Risk to living standards and  equity
Insurance Social safety nets Diversification of livelihoodsWho is doing the governing?
(e) Risk to human health
Availability of health 
infrastructureDisaster early warning Access to health care(f) Risk to food security
Farm / fishery practice Diets / food waste Food storage / distribution
Seasonal / temporary 
mobilityPermanent migration Governance cooperationHeight is the relative amount
of adaptations managed
per governance sectorConfidence
levelHow are risk management options 
being run in society?
Figure 17.2 |  Governance of 24 major risk management options, grouped by relevance to the Representative Key Risks. Each option depicts the relative 
governance roles, between communities/individuals, private sector and public sector. The intensity of the colour refers to the level of confidence in the assessment.

17
2553Decision-Making Options for Managing Risk  Chapter 17
Benefit to humans and ecosystems
from representative adaptation options
Can reduce
the exposure or vulnerability
of specific groups of people
i.e. <1 billion peopleCan reduc e
of many people
i.e. between 1–5 billion peopleCan reduc e
for most people in the worl d
i.e. >5 billion peopleBreadth of applicability of each adaptation option benefiting humans
Benefits of each
adaptation option
for ecosystems and
ecosystem servicesHighly
beneficial
Moderately
No clea r
and differen t
benefits / harm sLow
- Ecosystem-based adaptation ( •••)
- Strategic coastal retreat (•••)
- Efficient water use/demand (••)
- Seasonal/temporary mobility (••)
- Permanent migration (••)
- Coastal accommodation (••)
- Food storage/distribution (••)
- Water supply/distribution (•)
- Insurance (••)- Minimizing ecosystem stressors (••)
- Diversification of livelihoods (••)

- Farm/fishery practice (•••)
- Social safety nets (•)
- Water capture/storage (•)- Spatial planning ( •)
- Availability of health infrastructure  ( ••)
- Access to health care ( ••)Confidence level
MediumH igh
Worsens
the situation •• • •••
- Coastal infrastructure (•••)- Nature restoration (•••)
- Diets/food waste ( •••)
- Infrastructure retrofitting ( •••)
- Building codes ( ••)
- Disaster early warning ( •••)
- Governance cooperation ( ••)
Figure 17.3 |  Benefit of representative adaptation options to humans and ecosystems. The breadth of applicability of each adaptation option benefiting humans 
is estimated by the degree to which each adaptation can be applied across multiple contexts, depicted on the x axis. The benefit of each adaptation option for ecosystems and 
ecosystem services is depicted on the y axis. See Annex A for literature underpinning each assessment. This figure uses the 24 representative adaptation options from Table 17.1

and Figure 17.2. Confidence levels are represented by dots.
related infrastructure can be widely used as adaptation options, their 
design and application to date have not generally benefitted ecosystems 
or ecosystem services (medium evidence,  low agreement).
As a general method related to adaptive management, ‘early 
warnings’ are the most frequently discussed adaptation option to 
deal with a changing climate across all key risks, sectors and regions. 
Early-warning systems are an adaptation that can benefit more than 
5 billion people (high confidence). Examples range from short-term 
disaster early-warning systems to revision of sea level rise plans 
based on monitoring. For example, the humanitarian community 
is investing in forecast-based financing systems to prepare for 
extreme events (Coughlan de Perez et  al., 2015; MacLeod et  al., 
2021). Forecasts are also used to manage hydropower dams (Ahmad 
and Hossain, 2020), to trigger interventions before public health

emergencies (Section 7.4.2) and to alert fishermen of algal blooms in 
the world’s oceans (Section 3.6.2.3.3). Table 17.2 provides examples 
of adaptations using early-warning systems that have been used to 
address each of the key risks.
In addition to immediate investments that reduce vulnerability and 
exposure, monitoring and early-warning systems allow people to take 
additional actions when there is an imminent event on the horizon (e.g., 
temporary evacuation during extreme events rather than permanent 
migration). This allows for ongoing adaptive decision-making (Alessa 
et  al., 2016; Ebi et  al., 2016; Barnard et  al., 2017; Haasnoot et  al., 
2018). However, these systems are only cost-effective for forecastable and actionable hazards, and require effective institutional governance 
(Wilkinson et al., 2018; IPCC, 2019c).
17.2.2 Combining Adaptation Options: Portfolios of Risk 
Management and Risk Governance

While the above assessments underlying Figures 17.2 and 17.3 isolate 
specific risk management options for specific risks, several adaptation 
measures are present in any given location, affecting the overall 
risk of a particular place. Policymakers are charged to evaluate risk 
comprehensively, deciding on a variety of measures that are effective, 
feasible and aligned with other policy goals for a specific place, or 
implementing a new activity because of how it complements the existing 
package of risk management activities (Girard et al., 2015).
17.2.2.1 From Risk Prevention to Risk Financing and Risk 
Retention
Portfolios of adaptation options generally include actions to reduce 
vulnerability and exposure, complemented by risk financing mechanisms 
that help people avoid the impacts of loss events, particularly very rare 
ones. There is also explicit or implicit risk retention, where further risk 
management is not desirable, cost-effective or feasible (Mechler and

Deubelli, 2021). Risk financing can include a variety of instruments, with 
insurance as the most widely known. Formal insurance uptake is lower 
in developing and emerging economies than in wealthier countries (Ali

17
2554Chapter 17 Decision-Making Options for Managing Risk
Table 17.2 |  Examples of adaptation investments and early-warning system options for adaptive management for each of the key risks in Chapter 16.
Key risk Adaptive early-warning systems-based measures
Risk to coastal socio-ecological systemsStorm surge early warnings (Section 15.5.7)
Early warnings of water-borne disease (Section 3.6.2.3.3)
Risk to terrestrial and ocean ecosystemsFishery marine heatwave warnings and mobile fishing equipment (Section 3.6.2.3, Chapter 13)
Forecast of shifts and regime changes in ecosystems (Pace et al., 2015; Bauch et al., 2016; Burthe et al., 2016).
Risks associated with critical physical infrastructure, networks and services Early warning for infrastructure and services (Sections 13.2.2.1, 10.4.6.4.1)
Risk to living standards and equity Adaptive social protection systems (Schwan and Yu, 2018; Ulrichs et al., 2019; Daron et al., 2021).

Risk to human healthHeat health early-warning systems (Section 7.4.2.1.2)
Health and disease monitoring and outbreak prediction (Sections 7.4.2.1.1, 12.5.6)
Risk to food securityForecasting rainfall and droughts for seed selection (Section 10.5.2.2.3)
Food price early warnings (Section 7.4.2.1.3)
Risk to water security Early warnings for flood and drought (Sections 4.4.1, 10.5.2.2.3, 15.5.7)
Risk to peace and migration Transboundary flood early warnings (Tuncok, 2015).
et al., 2020). To overcome some of the barriers to insurance uptake, 
index-based insurance has been offered for agriculture and livestock in 
many developing economies, with varying levels of success (Chantarat 
et al., 2013; Isakson, 2015; Dewi et al., 2018). In recent years, regional 
disaster insurance pools for sovereign states have been established, 
such as the Caribbean Catastrophe Risk Insurance Facility (CCRIF) 
(Iyahen and Syroka, 2018). Insurance can encourage the quantitative

evaluation of climate-related risks and adaptation limits, and it can 
incentivise risk reduction by charging lower premiums for less risky 
situations (Schäfer et al., 2019).
While insurance is increasingly accepted as an adaptation option 
(Linnerooth-Bayer and Hochrainer-Stigler, 2015), positive outcomes 
are not guaranteed (high confidence). First, there are concerns as to 
whether this will shift responsibility to the most vulnerable people 
to pay premiums (Surminski et al., 2016). There is also high risk for 
insurance to cause maladaptation (Müller et al., 2017); for example, 
Annan and Schlenker (2015) showed that insured crops were less well 
adapted to heat stress. To avoid this, people simultaneously invest in 
insurance and adaptations that reduce vulnerability/exposure (medium 
confidence) (Surminski et al., 2016; Highfield and Brody, 2017; Schäfer 
et al., 2019; Reguero et al., 2020).
The combination of interventions that reduce risk and risk financing for

residual risk (often through insurance for sudden-onset events, or social 
protection for risks including those linked to slow-onset processes) will 
reduce collective risk to a certain level. For very extreme and potentially 
catastrophic events, it is often impossible (or financially infeasible) to 
fully reduce vulnerability and exposure, and people, communities and 
countries therefore retain risk requiring the ex post management of 
unavoided and unavoidable residual impacts in case of events.
Ex-post risk management relies on national assistance, social safety 
nets (Section 7.4.2.1.3; Béné et al., 2012; Elmi and Minja, 2019) and 
support from social networks as well as lending from international 
institutions (high confidence) (Hochrainer-Stigler et al., 2014). Even in 
places where normalised losses have stabilised in recent years with 
investments in adaptation, effective planning to manage losses remains 
necessary (Jongman, 2018). Resilient recovery can support adaptation

goals in periods of  losses and damages (Slavíková et al., 2021).To coordinate between a suite of applicable risk management 
interventions, the concept of risk layering has been discussed and used in 
(financial) risk governance of disaster risk management (Mechler et al., 
2006; Cummins and Mahul, 2009; Clarke and Mahul, 2011) and climate 
risk management (Lal et al., 2012; Mechler et al., 2014; Herron et al., 
2015; Schäfer et al., 2016; Mechler and Deubelli, 2021). Incremental 
risk prevention and preparedness as well as risk financing occurs 
within national systems. Over the years, regional cooperation, such as 
through the regional sovereign insurance pools in the Caribbean, the 
Pacific and Africa, but also transboundary risk management elsewhere 
have become more important (medium confidence) (see Martinez-Diaz 
et al., 2019). Also, with risks increasingly experienced as severe and 
existential (Boyd et al., 2017), global governance and solidarity have

been invoked (see Linnerooth-Bayer et al., 2019; Pill, 2021), largely as 
part of the policy discourse on Loss and Damage (Mechler et al., 2019) 
with further momentum provided by discussions on the global goal of 
adaptation and recognition of climate risk as transboundary (Benzie 
and Persson, 2019; Cross-Chapter Box  INTERREG in Chapter 16). 
Transformational risk management has emerged where incremental 
and in situ adaptation is not effective in managing risks, such as for 
managed or strategic retreat for communities facing severe coastal 
and riverine flooding (Siders et al., 2019). Transformation has not been 
well documented, including as to its governance (Section 17.2.2.5).
17.2.2.2 Global Variation in Portfolios of Risk Management
While many studies assess adaptation trends by geographical region or 
by sector, the amount of residual risk varies across countries with different 
income and governance structures. Vulnerability, poverty and inequality,

which constitute the human dimensions of climate change, affect how 
these portfolios of adaptation options are structured around the world 
(Chapter 8). Figure 17.5 depicts several illustrative ‘typologies’ of how 
risk is addressed. While no country or location fits any one typology, this 
illustrates a range of risk portfolios found in different contexts.
Extensive protection category
The first category in this typology, that of ‘extensive protection’, 
requires substantial financial investment (Figure  17.5). In higher-
income contexts, this is often more feasible than in contexts with

17
2555Decision-Making Options for Managing Risk  Chapter 17
RiskRisk
governance  Risk managementA graphical representation of layered risk management
Risk retention
(Residual risk retained)
Risk finance
Insurance and social protection
(Residual risk financed)
Risk reduction
Risk prevention
and preparedness
(Risk avoided or reduced)Risk characteristicsUnavoidedUnavoidable
Avoided Risk governance and policy 
Regional 
collaborationInternational 
assistance
National  
Systems  Residual
risk Transformational
risk management
Managed retreat,
transformation
DRR
and
CCALoss
and
Damage
Figure 17.4 |  A graphical representation of layered risk management. Risks can be reduced or managed by risk finance (insurance and other means), but some residual 
risk remains, particularly for high-impact unavoided and unavoidable risk, which is retained implicitly or explicitly. Where incremental and in situ adaptation is not effective in

managing risks, transformational adaptation supports systemic change. Risk management occurs in national systems, and regional insurance systems have stimulated regional 
collaboration. Particularly for high impact risks and impacts in specific events, international assistance is required. Policy domains on disaster risk reduction (DRR) and climate 
change adaptation (CCA) as well as Loss and Damage overlap in their governance of risk management. Figure building on Mechler et al. (2014); Cummins and Mahul (2009); Lal 
et al. (2012); Mechler and Deubelli (2021).
limited resources, and adaptation investments are more likely to 
include structural measures to reduce exposure, complemented by 
vulnerability-reducing measures and insurance protection (medium 
confidence). While this typology is not universally representative of 
high-income areas (within or between countries), expensive exposure-
reduction measures tend to be easier to implement in high-income

countries. For example, flood protection is largest in countries with 
larger amounts of public spending and least amounts of corruption 
(Scussolini et al., 2016). It is seen as more economically efficient to 
invest in expensive protection measures in wealthy regions, under 
different scenarios of sea level rise and river flooding, although these 
calculations have equity and justice implications (Peduzzi, 2017; Lincke 
and Hinkel, 2018). After flood events happen in regions with high 
levels of protection, damages are comparatively limited, and people 
tend to continue living in close proximity to the protected river (Mard 
et al., 2018). In contrast, flood displacement is higher in low-income 
countries (Kakinuma et al., 2020).
Risk financing, especially insurance, is also common in higher-income 
countries with well-developed insurance markets and higher levels 
of insurance penetration than in lower-income countries, illustrated

by the purple bar in Figure 17.5 (high confidence) (Linnerooth-Bayer 
et  al., 2019). Of climate-related disasters, floods and storms cause the largest amount of reported economic losses; however, at least 
40% of these losses are uninsured, even in the regions with high 
insurance penetration (Baur et  al., 2018). Government involvement 
in insurance schemes is associated with higher penetration rates of 
the general population (Paleari, 2019). While some, predominantly 
high-income countries can make use of disaster contingency funds or 
dedicated budget items, these do not exist or are not well endowed 
to adequately support relief, recovery and reconstruction (Linnerooth-
Bayer and Hochrainer-Stigler, 2015). To help stabilise public finance 
in regions with little market-based insurance coverage and fiscal 
response mechanisms, regional public insurance pools have been set 
up with donor assistance, such as in the Caribbean, Africa and the

Pacific for flood and droughts (Schäfer et al., 2016; Surminski et al., 
2016; Linnerooth-Bayer et al., 2019).
Moderate investment focused on adaptive capacity
In contrast to the ‘extensive protection’ scenario, many regions 
of the world bear greater resemblance to the second typology in 
Figure  17.5 ‘moderate investment focused on adaptive capacity’ 
(medium confidence). These contexts see greater adaptation funding 
invested in capacity building activities to reduce vulnerability, rather 
than structural or ecosystem-based protection measures to reduce

17
2556Chapter 17 Decision-Making Options for Managing Risk
Several illustrative typologies for how risk has been manage d
Adaptation typologies
Extensive
protectionModerate investment
 focused on
adaptive capacityLittle adaptation
investmentTypes of 
investment
Reduce exposureReduce vulnerabilityRisk financeResidual risk retained
Figure 17.5 |  Several illustrative typologies for how risk has been managed. 
The first is ‘extensive protection’, in which the bulk of investments is made in reducing 
exposure, through protection up to limits (e.g., flood levees) and including retreat. 
The second category is ‘moderate investment focused on adaptive capacity’, in which 
the bulk of investment is made in reducing vulnerability (e.g., improved housing). The 
third category is ‘little adaptation investment’, in which there is little investment in 
either reducing vulnerability or exposure, and the bulk of risk is residual, borne by the 

exposure (Biagini et al., 2014). Because of limited international and 
domestic finance for large structural investments to reduce exposure, 
the most prevalent adaptation choices in low-income contexts are 
household-level vulnerability-reducing measures (Koerth et  al., 
2017).
Lack of access to finance can be one of the reasons countries engage 
more readily in adaptive capacity-building activities. Countries that 
rank highly on the Corruption Perceptions Index engage less in 
technological solutions for risk management (Berrang-Ford et  al., 
2014). In addition, countries with higher levels of corruption receive 
less adaptation aid (Betzold and Mohamed, 2017; Weiler et  al., 
2018). Countries are more likely to receive adaptation aid if they 
import goods from a donor country, or are a former colony of that 
donor (Betzold and Mohamed, 2017; Weiler et al., 2018). In countries 
with poor governance and limited aid flows, remittances make up a

substantial portion of finance available to the local population for risk 
management (Samuwai and Hills, 2018).
Risk financing does play a large role in the ‘moderate investment’ 
category; there are a variety of instruments in use globally. Many 
countries in the Global South have created national policies and 
a number of regional catastrophe risk insurance pools, subsidised 
by international assistance, which make pay-outs to the national 
government of affected nations when an extreme event happens and 
have helped to build risk awareness (Clarke et al., 2015; Thirawat et al., 
2017). Beyond this, residual risk is often borne directly by affected 
people (Andrianarimanana, 2015).Little adaptation investment typology
In the third typology, there are limited resources for adaptation, and 
populations bear large amounts of residual risk (depicted by the purple 
bar in the third typology in Figure 17.5, ‘little adaptation investment’).

SIDS can often find themselves in this situation, because small 
populations, small economies, lack of economies of scale, subsistence 
livelihoods and other challenges mean risk reduction and risk financing 
are both costly (Chapter 15).
Another example of this third typology are people living in conflict-
affected areas. These populations are highly vulnerable to the impacts 
of climate change (Basher, 2006; OCHA, 2011; IPCC, 2012; Zommers 
and Singh, 2014; Marktanner et  al., 2015; Walch, 2018; Eckstein 
et al., 2019; Peters et al., 2019). In conflict-affected areas similar to 
the third category of ‘little adaptation investment’, a combination of 
high vulnerability and relatively less support for adaptation means 
that there is a large amount of ‘residual risk’, in which residents 
cope with the impacts of extreme events on a regular basis (high 
confidence). For example, deaths from ‘natural’ disasters are 40% 
higher in areas that are undergoing armed conflict (Marktanner

et al., 2015) (Box 17.2).
17.2.2.3 Adaptation beyond Risk: Exploiting Opportunities
Several studies and many government planning documents reference 
how people can benefit from a changed climate, beyond reducing 
risks. For example, several regions are expecting an increase in 
visitors to eco-tourism sites or national parks with a changing climate 
(Fisichelli, 2015; Lwasa, 2015). In Europe, several national adaptation 
plans include planning for potential benefits of a changing climate, 
including reduced winter mortality and improved conditions for 
hydropower (Biesbroek et  al., 2010). Recognising the need for 
economic diversification, people working in certain industries, such as 
coastal management, perceive climate change as a factor increasing 
the need for their services (Fatorić et al., 2017). Northern countries 
are taking advantage of ice-free waters for shipping routes in the 
Arctic (Eguiluz et al., 2016; Melia et al., 2016; IPCC, 2019c). In Africa,

opportunistic adaptation has been observed by smallholder farmers, 
to plant crops that are better suited for a changing climate (Lalou 
et  al., 2019). Similar agricultural adaptation in Pakistan has been 
associated with improved food security and reduced poverty (Ali 
and Erenstein, 2017; Rahman et  al., 2020). In each of these cases 
documenting benefits, there are also potential negative impacts on 
other populations or ecosystems, such as ecosystem impacts from 
increased Arctic shipping (Ng et al., 2018).
While adaptation is rarely focused on taking advantage of opportunities 
presented by a changed climate, there are numerous co-benefits 
of adaptation opportunities, from health to reduced emissions to 
ecosystem services (high confidence) (Watts et al., 2015; Geneletti and 
Zardo, 2016; Spencer et al., 2016). There is also literature proposing 
that the actual process of adaptation planning can enable people to

take advantage of opportunities, including, for example, opportunities 
for larger policy and governance reform (Coleman and Sandhu, 1965; 
Ernst and Preston, 2017; Brown et al., 2017a).

17
2557Decision-Making Options for Managing Risk  Chapter 17
Box 17.2 | Climate Risk Management in Conflict-Affected Areas
Consequences of conflict that exacerbate vulnerability to climate change include: displacement, loss of access to employment leading to 
illegal livelihoods, gender-based violence, lack of land tenure, low literacy, poor access to social and health services, destruction, looting 
and theft of key assets, such as houses, food stocks and livestock, among others (Jaspars and Maxwell, 2009; Chandra et al., 2017; 
Anguita Olmedo and González Gómez del Miño, 2019). Such impacts perpetuate cycles of poverty (World Bank, 2013), making conflict-
affected populations more susceptible to suffer from climate-related events (Basher, 2006; Coughlan de Perez et al., 2019). For example, 
in Mindanao, Philippines, poverty is closely linked to long-standing armed conflicts; both climate change and conflict have significantly

increased smallholder vulnerability, resulting in loss of livelihoods, financial assets, agricultural yield and the worsening of debt problems 
(Chandra et al., 2017). In Colombia, displacement induced by conflict has pushed the population to live in high-risk areas such as steep 
slopes susceptible to landslides and river banks exposed to flooding (Albuja and Adarve, 2011). This conflict-induced vulnerability, with 
little adaptation activity, has in turn resulted in climate-related disasters (Kuipers, 2019; Siddiqi et al., 2019).
Conflict can also limit the effectiveness of adaptation measures that do exist; a study across Africa, the Caribbean and Asia concluded 
that poor governance can limit the effectiveness of early-warning systems in these regions (Lumbroso et al., 2016). Poor state services 
have health consequences and can limit social support networks (Peters, 2018). States are unable (even if they are willing) to assist or

protect citizens in disasters. Non-governmental stakeholders play a large role in these contexts, but questions of long-term implications 
and accountability remain unaddressed (Peters, 2018).
Climate risk management and adaptation in conflict-affected contexts is challenging, first, given the complex and dynamic nature of 
vulnerability (Hilhorst, 2003; Frerks et  al., 2004) and, second, given factors such as weak or non-existent disaster risk governance, 
restricted access, human rights violations, power dynamics between parties in conflict, and environmental degradation, among others 
(Kloos et al., 2013; Marktanner et al., 2015; ICRC, 2016; Quinn et al., 2017; Field and Kelman, 2018; Siddiqi, 2018). Climate can also 
be a contributing factor to conflict (Mach et al., 2019). There is little peer-reviewed documentation available on adaptation in climate-

affected contexts, and what exists is narrowly focused on agriculture at the expense of other sectors, such as cities, infrastructure and 
humanitarian operations (Sitati et al., accepted).
To address risks to livelihoods, conflict-sensitive livelihood programming has used vouchers to meet immediate needs, legal support 
to resolve land disputes, and disaster preparedness planning to identify safe places for displacement (Jaspars and Maxwell, 2009). 
For example, cooperation in the Philippines between Moro Islamic Liberation Front and United Nations agencies included training of 
farmers in disaster risk reduction, drought management and production of improved crop varieties to support a transition away from 
subsistence farming (Walch, 2018). In Mali, negotiations on fertilizer access and safe transport to agricultural lands were brokered by the 
International Committee of the Red Cross, and in Afghanistan, conflict-sensitive approaches have promoted ecosystem-based adaptation

to support reforestation (Walch, 2018; Mena and Hilhorst, 2020). Despite several examples of conflict-sensitive adaptation practices, 
little is known about the effectiveness of such efforts in reducing climate risks in these complex contexts (see Section 17.5 for further 
discussion of ‘effectiveness’).
17.2.2.4 The Spectrum from Incremental to Transformational 
Adaptation in Risk Management Portfolios
Section  1.4.5 noted that transformational adaptation is increasingly 
being considered necessary to allow a system to extend beyond its 
(soft) limits as incremental adaptation cannot guarantee to avoid 
intolerable risks. Section  16.4 presents evidence on RKRs where a 
need for transformational adaptation and climate risk management 
has been identified in order to further reduce climate risks and avoid 
breaching adaptation limits. The following section identifies how 
the 24 adaptation options representative of the RKRs may support

incremental and transformational risk management/adaptation that 
can lead to small, medium and large systemic change, often as part 
of portfolios of options. This subsection further discusses the role 
of transformational adaptation vis á vis incremental adaptation by 
reviewing evidence across chapters (see also Box  17.3). The Cross 
Chapter Box on Loss and Damage further expands on the international debate regarding the role of decision-making on incremental and 
transformational adaptation for dealing with residual risks to address 
soft as well as hard adaptation limits (see Cross-Chapter Box LOSS in 
this Chapter).
As the literature distinguishes active transformation to shape 
future risks from passive and unintended transformation (Lonsdale 
et al., 2015; Chapter 1), the section queries how to inspire actors to 
consider how to develop or implement transformational adaptation 
to complement incremental adaptation/risk management when and 
where appropriate.

In contrast to a broadening literature on conceptualisation and policy 
proposal, there has been little evidence reported in the literature 
on transformational adaptation and risk management at scale 
of implementation (high confidence) (Klein et  al., 2017; Ajibade 
and Egge, 2019; Tàbara et  al., 2019; Mechler and Deubelli, 2021).

17
2558Chapter 17 Decision-Making Options for Managing Risk
Deubelli and Venkateswaran (2021) review evidence on largely non-
governmental organisation (NGO)-implemented community-level 
adaptation for floods, heat and drought across the globe. They suggest 
that transformational adaptation success, while multi-facetted and 
challenging, depends on the availability of appropriate enabling 
environments including experiential and niche learning, alignment 
of transformational change objectives with strategic (government or 
other actor’s) priorities, strong bottom-up governance grounded in 
local contexts, phased long-term program support and appropriate 
financing.
To distinguish incremental from transformational adaptation, Lonsdale 
et  al. (2015), building on Mustelin and Handmer (2013), identify 
criteria related to framing, learning and decision-making, space and 
time, power, and type of change management. Tàbara et al. (2019),

additionally discuss transformation in light of informing climate 
pathways, strategies and solutions. Broadly considering these criteria, 
they identify 12 dimensions with additional discussion of change 
with regard to systems and dynamics, options and solutions, agency, 
and the consideration of equity (see also Chapters 1, 6, 18 for more 
discussion). In particular, the following key aspects for understanding 
the spectrum from incremental to transformational adaptation are 
of relevance: change, within or across the system; agency, single or 
heterogenous; a role for visioning and normative futures; the type of 
learning required (from first order, business-as-usual, to second order); 
and how equity and distributional issues are explicit.
Applying these key aspects to the list of 24 adaptation options from 
Table 17.1, certain options are assessed to be more transformational, 
often requiring large system changes that go beyond addressing

individual risks. Adaptations that are more transformational offer 
potential to lead to systemic change. Less transformational adaptations 
allow people to address specific climate-related risks while maintaining 
existing systems (see SM17.1 for more details; see also Box 17.3).
For example, several adaptations related to the RKR on risks to 
peace and migration, namely permanent migration, and cooperative 
governance, require moderate to high levels of transformation (high 
confidence). Some behavioural adaptations, such as changing diets 
and reducing food waste, can also require large transformations in 
land use and food culture (medium confidence). Spatial planning, 
including urban zoning, also tends to be more transformational 
On the other end of the spectrum, disaster early-warning systems 
tend to be incremental rather than transformational (high confidence), 
because they enable people to maintain/protect existing systems.

Several other adaptations allow people to maintain livelihoods and 
systems in the face of changing risks. For example, improvements 
in agricultural and fishing practices can be done with moderate 
transformation to systems (medium confidence). Similarly, insurance 
tends to require less transformation, as it can allow people to maintain 
existing systems while being more resilient to climate-related shocks 
None of the 24 adaptation options are consistently beneficial for 
vulnerable and marginalised groups (high confidence). For each adaptation, there are examples of how it has been implemented in 
a way that benefits poor, low-income, ethnic groups and/or females, 
and other examples of implementation in different contexts that have 
worsened the risks for those groups specifically. For example, while the 
goal of cooperative governance can be to support the marginalised, 
these same marginalised groups are usually excluded from

participating in the design of the solutions, and many articles criticise 
governance results as protecting only the interests of the wealthier and 
more powerful parties in the negotiations, especially in governance of 
migration (Groutsis et al., 2015; Pijnenburg et al., 2018). This reinforces 
the need for context-specific planning to ensure marginalised groups 
will benefit from an adaptation plan. See Table 17.3 for examples of 
how each adaptation option can have or not have equity benefits.
17.2.2.5 Incremental and Transformational Adaptation for 
Managing Risk in the Context of Adaptation Limits
With evidence on soft and hard limits being experienced in natural and 
human systems, including in terrestrial, aquatic and marine ecosystems, 
coastal and island systems, agriculture, health systems, urban spaces 
and tourism (Table 16.5, 16.4.2, medium confidence), transformation 
is also being considered to expand the adaptation space beyond

soft limits and before hard limits are being reached. As a key area 
of advancement since AR5, this  section assesses the relationship 
of residual risks, limits and incremental as well as transformational 
adaptation integrating the assessment of limits in Section 16.4 with 
Chapter 17 adaptation and risk management assessment along a 
spectrum of adaptation change. Section  17.2.2.5 thus contributes 
to understanding in which systems and regions transformational 
adaptation is increasingly required and considered once incremental 
adjustments are exhausted in the context of soft and hard limits.
Assessing risk and limits requires in-depth analysis of the adaptability 
of human and natural systems under different warming and risk levels, 
also considering socioeconomic exposure and vulnerability drivers, 
informed by perspectives on what breaching limits means, especially 
if significant change and losses and damages occur (Sections  16.4,

8.4). Assessments differ between natural systems (where adaptation 
potential is often very limited; Klein et al., 2014) and human systems 
where incremental and transformational adaptation can help to 
extend soft limits so that hard limits are not met or to buy time until 
hard limits are reached with higher levels of warming.
The assessment synthesises global and regional evidence across 
regional and thematic report chapters along a continuum from 
observed to projected impacts and risks, the spectrum of incremental 
and transformational adaptation, and finally any evidence on soft 
and hard limits. We present regional evidence for two types of salient 
natural and human systems and RKRs: RKR-B (risk to terrestrial and 
ocean ecosystems), where we assess risks from marine heatwaves to 
coral reefs; and RKR- E (risk of heat on human health as a human 
system). Both RKRs and systems are facing substantial (residual) risk,

characterised by adaptation limits and sharing heatwaves as the 
hazard, for which climate change has been considered the major driver 
of increasing intensity and frequency (high confidence) (IPCC, 2021). 
The assessment synthesises evidence on transformation as reported 
in the chapters as well as categorises identified adaptation options

17
2559Decision-Making Options for Managing Risk  Chapter 17
Table 17.3 |  The 24 adaptation options from Table 17.1 grouped and coloured by their potential for transformation. (See Appendix A for assessment methodology.) Adaptations 
in red tend to require small amounts of transformation, adaptations in orange tend to require middling levels of transformation, and adaptations in yellow tend to require large 
levels of transformation, or systemic change. Each option is paired with examples of how that adaptation can be done in a way that does not benefit or worsens, the situation 
for marginalised groups, as well as an example in which that adaptation has benefitted those groups. Examples of equity focus on benefits to poor, low-income, ethnic groups, or 
females.
AdaptationExample of the adaptation excluding or worsening the 
situation for marginalised groupsExample of the adaptation benefitting marginalised groups
Less transformation (small systemic change)

InsurancebIndex-based insurance policies in Mongolia were accessible primarily to 
wealthy herders (Taylor, 2016b).The availability of capital after disaster events can avoid a poverty trap from 
disasters (Alam et al., 2020a).
Coastal accommodationcAccommodation strategies in Jakarta have led to a false sense of security 
in an impoverished and vulnerable neighbourhood (Esteban et al., 2017).The mosaic restoration project provided training for women to support local 
accommodation of climate changes on Yap (Krishnapillai, 2018).
Early-warning systemscPeople of higher socioeconomic status tend to receive warnings, while 
marginalised groups can be left out (Baudoin et al., 2016).Famine and drought early-warning systems have helped avoid starvation 
among the world’s most vulnerable people (Funk et al., 2019).
Water use/demandcSmall farmers were unable to access supports to implement drip 
irrigation in Morocco, and uptake was greater among wealthy farmers

(Jobbins et al., 2015).Retrofits for water use efficiency were made available free of charge to 
low-income communities in the USA (Lee and Tansel, 2013).
Coastal hard protectionbConstruction of hard barriers increased flood risk for several low-income 
communities in Bangladesh (Adnan et al., 2020).Successful coastal embankments can help people avoid poverty traps in 
Bangladesh by reducing exposure to flood events (Borgomeo et al., 2017).
Moderate transformation (medium systemic change)
Infrastructure retrofittingbLow-income people often do not own their homes, and there are few 
incentives for landlords to upgrade (Tardy and Lee, 2019).Energy policy could promote solar infrastructure in Nigeria, which can offer 
electrification in underserved regions (Ohunakin et al., 2014).
Building codescBuilding codes in Nepal and Bangladesh often fail to increase resilience

because many buildings are built informally (Ahmed et al., 2019).Slum upgrading projects in Latin America reduced the vulnerability of informal 
settlements by improving built infrastructure (Núñez Collado and Wang, 2020).
Farm/fishery practicebMany agriculture improvement strategies create higher workloads for 
women and do not directly enfranchise them, as seen in Uganda, Ghana 
and Bangladesh (Jost et al., 2015).Improved crop varieties have supported the income of low-income farmers in 
Zambia (Khonje et al., 2015).
Diversification of livelihoodsaDiversifying livelihoods can increase women’s workloads, in a review of 
semiarid regions across Africa and Asia (Rao et al., 2020).A study on diversity of income sources in Ghana indicated that diversification 
can make people less vulnerable to extreme events (Baffoe and Matsuda, 
2017).
Social safety netsbSocial protection systems in Bangladesh focus on specific groups in

rural areas, and they often fail to reach urban poor and other very 
disadvantaged people (Coirolo et al., 2013).Adaptive social protection can help poor people avoid the impact of extreme 
events by scaling up support at critical moments (Bowen et al., 2020).
Infrastructure for healthcThe development of sanitary water infrastructure in Germany had less 
benefit in areas with higher income inequality (Gallardo-Albarrán, 2020).Improvements to water and sanitation infrastructure that avoid people 
fetching water are associated with improvements to women’s health (Geere 
and Hunter, 2020).
Food storage/distributionbIncreasing/improving livestock markets can favour high-income livestock 
producers (Gautier et al., 2016).Investments in large produce storage houses has supported indigenous 
livelihoods in the face of climate change (Mugambiwa, 2018).
Restoration/creation of 
natural areasbUrban greening programmes in the USA avoided minority

neighbourhoods or caused displacement of people of colour 
(Anguelovski et al., 2016; Watkins et al., 2016).Afforestation reduced landslide risk for informal settlements in Brazil 
(Sandholz et al., 2018).
Minimising ecosystem 
stressorsaFish quota reduction had negative economic impacts when done quickly 
(Barbeaux et al., 2020).South Africa’s Working for Water programme employed poor people to control 
invasive species (van Wilgen and Wannenburgh, 2016).
Ecosystem-based 
adaptationbPayments to Indigenous groups in return for protecting conservation land 
can be less than their original livelihoods and disadvantage those not 
receiving the payments, such as women (Bedelian and Ogutu, 2017).Integrated water resource management is proposed in the Caribbean as a way 
to maintain ecosystem services while improving economic welfare (Mycoo, 
2017).
Water supply/distributionbWater tariffs during the Cape Town drought negatively impacted poor

households (Millington and Scheba, 2021).City Water Forums in Nepal have focused on equitable water allocation as an 
adaptation (Pandey and Bajracharya, 2017).
Seasonal/temporary 
mobilitybWomen tend to have greater restrictions on mobility than men (Lama, 
2018).Indigenous communities in Guatemala use temporary migration to manage 
rainfall variability (Ruano and Milan, 2014).
Most transformation (largest systemic changes needed)
Spatial planningbSpatial planning in American cities has often resulted in less green space 
in ethnic minority neighbourhoods (Connolly and Anguelovski, 2021)While difficult, strategic approaches to urban planning can promote inclusive 
development (Chu et al., 2017).
Diets/food wasteaLow-income groups have less opportunity to diversify diets if certain 
foods become more expensive or difficult to obtain (Reynolds et al., 
2019).Changing dietary intake during heatwaves (e.g., eating cooler foods) is seen

as a low-cost adaptation accessible to low-income people in the UK (Porter 

17
2560Chapter 17 Decision-Making Options for Managing Risk
Health care systemsbFacilities in poor communities are often poorly sited and can lack 
capacity to support people during climate-related extreme events 
(Codjoe et al., 2020).Universal health coverage can be highly beneficial to poor people (Atun et al., 
2015), when needed for climate-related health outcomes.
Water capture/storagebMany Indigenous populations have been negatively affected by loss of 
their land when displaced for dam construction (Siciliano and Urban, 
2017).Improving water harvesting supports marginalised populations in dryland 
areas (Bobadoye et al., 2016).
Cooperative governancebInternational cooperation among national governments regarding 
migration can encourage human rights abuses and increase migration

(Crawley and Skleparis, 2018).International cooperation has the potential to remove barriers to adaptation 
in informal settlements in developing countries by sharing knowledge and 
expectations (Oberlack and Eisenack, 2014).
Permanent migrationcPermanent migration from small island nations can entail a loss of 
identity for Indigenous groups (Bordner et al., 2020).Migration supported by social protection systems can be sustainable for poor 
populations (Schwan and Yu, 2018).
Strategic coastal retreatcMinority groups faced tensions with host communities when relocated 
in India, and faced difficulties in terms of fishing access and land size 
(Mortreux et al., 2018).In several cases of post-disaster relocation, community members initiated the 
retreat and there were broader benefits to society (Hino et al., 2017).
Notes:
along an adaptation spectrum according to the criteria discussed in

Section  17.2.2.4, specifically whether adaptation leads to systems’ 
change or only change within a system is driven by multi-scale agency 
and considers equity impacts specifically.
Figure 17.6 organises global and regional findings for observed and 
projected health risks from heat (RKR-E) from chapters across the 
report and organises options according to findings on the potential for 
transformational change as presented in Section 17.2 and Table 17.3. 
The discussion shows that heat has become a significant health risk 
globally, incurring severe mortality and morbidity in all world regions 
with annual heat-related deaths estimated around 300,000 with 
millions affected (high confidence) (Section 9.3.1). Evidence shows that 
adaptation and risk management can be effective in reducing (relative) 
risks in developed countries, with inconclusive evidence in low-middle-
income states (Sections 9.2.4.1, 13.7.3, 13.6). In absolute terms, risk in

terms of heat-related mortality and morbidity is projected to increase 
under medium and high heating scenarios in many regions, even with 
implemented adaptation. By 2050 (compared with 1961–1991 and for 
a mid-range emissions scenario), an excess of 94,000 deaths yr−1 is 
projected globally as attributable to climate change (Section 9.3.1).
Planned and implemented adaptation interventions in all regions have 
remained largely incremental, while uptake is being intensified in some 
regions; options have included air conditioning (as autonomously 
deployed), public cooling spaces, heat action plans that incorporate 
early warning and response and heat-adapted building design 
(Sections 9.9.5, 11.3.6, 12.5.6.1.1,13.11.3, 13.11.3, 15.6.2).
Given increasing risks projected and soft and hard limits already 
reported, transformation is being considered as a complement potentially 
leading to systemic and transformational change. Adaptation, if upgraded

to also consider transformational interventions, will thus help to reduce 
heat risks (medium to high confidence, limited evidence), albeit with 
reduced effectiveness at higher levels of warming, particularly in regions (Africa, Asia) where lethal heatwaves are projected to occur almost 
annually towards later in the 21st (medium confidence) (Sections  9.1, 
10.4.7).
This may involve urban redesign using nature-based solutions (such 
as green roofs and infrastructure) as well as rescheduling of outdoor 
labour or cross-sectorial coordination. Integrated approaches across 
interdependent systems (e.g., ecosystem-based approaches and 
climate-sensitive urban design) are being proposed. Also, it may 
mean bolstering social safety nets and health systems that better 
attend to heat impacts by providing universal coverage. Societal 
and political transformations to reduce climate change risks for 
vulnerable groups are considered particularly relevant in some

regions (Sections  9.4.2.1.2, 9.9.5, 10.4.6.4.3, 12.5.3.2, 13.6.2.1, 
14.6). Yet, across all regions there is limited evidence on proposed 
transformational adaptation and very little evidence regarding 
implementation (high confidence).
As a consequence, studies project soft limits to be further reached as 
increased mortality and morbidity will add stress to health systems, 
and labour productivity will be severely hampered, impacting economic 
systems (medium to high confidence) at medium to higher levels of 
global warming (Sections  7.2.4.1, 9.10, 10.4.4.4, 11.9.1, 13.6.2.3, 
13.7.2, 13.7.4, 13.10.2.1, 13.8, 15.3.4.9).
Hard limits may be breached in some regions where critical heat 
tolerance thresholds are projected to be surpassed at medium to higher 
levels of global warming, such as physiological survivability thresholds, 
which, for example, may render urban outdoor labour in Asia, Africa 
and North America infeasible (Sections 10.4.6.3.2, 14.8, Box 9.1).

Marine heatwaves have affected tropical coral reefs, which are 
analysed as part of RKR-B (Table  SM17.20). Coral reefs across the 
tropics have recently seen massive bleaching events (such as for the 
Great  Barrier  Reefs) (very high confidence). Risks are projected to be

17
2561Decision-Making Options for Managing Risk  Chapter 17
Understanding the spectrum of incremental to transformational planned adaptation
for managing climate related heat risk to health including associated soft and hard adaptation limits
Change in days with TX above 35ºC
SSP2-4.5 (2041–2060 relative to 1850–1900)
10 20 >40 0
 30Risk to human health from heat (RKR-E)
Incremental adaptation complemented by transformational adaptation
Soft limit (to incremental adaptation) 
Hard limit
WG I Detection and attribution statement
Global
Heat is a significant health risk due to widespread urbanization, demographic changes and increase inhot weather (•••) 323,000 es timated heat-related deaths and 13 million heat-related DALYs in 2019 .

Temperature-related mortality expected to increase under medium and high heating scenarios even with adaptation. By 2050 (comp ared to 1961–1991) an excess of 94,000 deaths per year  
attributable to climate change projected due to heat for medium warming.
Implementation of heat warning systems has reduced relative mortality risk in developed countries (•••), unclear trends in low- middle income countries. Multi-sectoral integrated approach 
beneficial including heat early warning and response systems targeting vulnerable groups (•••) .
Longer term urban planning and design, including Nature based solutions (NBS) to reduce urban heat island effects. Improved ba sic protection for outdoor work including work reschedulin g
to cooler times of the day (•••) .
Some regions with heat stress conditions approaching upper limits of labour productivity (•••) .
Thresholds of survivability approached (•••) .

Hot extremes (including heatwaves) have become more frequent and more intense across most land regions since the 1950s (•••••) .
Human-induced climate change is the main driver of these changes (•••) .
Every additional 0.5°C of global warming causes clearly discernible increases in the intensity and frequency of heatwaves (•••) .
Africa  
Climate variability impacting the health of tens of millions of Africans through exposure to extreme heat. Heat extremes (hot days and hot nights) increased in frequency since 1980 (•••) .
Increasing temperatures will cause tens of thousands of additional deaths under moderate and high global warming scenarios, par ticularly in North, West and Central Africa (•••) .
Cooling stations, limited evidence of pro-active climate change adaptation in African cities (•••) .
Urgent need for improved societal and political transformations to reduce climate change risks for vulnerable groups (••). Depl oyment considered necessary of NBS with demonstrated

health, ecological, economic and social co-benefits. 
Morbidity and mortality will escalate with further global warming, placing additional strain on health and economic systems (• ••).
Under high warming scenarios annual exceedance of deadly heat thresholds in North, West and Central Africa (•••). 
Asia
Short-term effects of high temperatures on daily mortality and morbidity reported in several cities throughout Asia. 
More frequent hot days and intense heat-waves will increase heat-related risks and deaths in Asia (••) .
Urban technological solutions (e.g. smart cities, early warning systems);and behavioural adaptation growing from initial stages  but unevenly distributed across large and small cities (••) .
Transformational adaptation largely lacking, some incipient in larger cities, including NBS.
Heat stress likely to approach critical health thresholds in West and South Asia under medium warming scenario, and in some ot her regions such as East Asia under high warming (•••).

Australasi a
Heat-related deaths have increased with a third attributable to climate change in Australia (•••) .
Increase in heat-related mortality and morbidity for people and wildlife in Australia (•••) .
Urban cooling, education to reduce heat stress, heatwave early-warning systems, building standards that improve insulation/cool ing. Current levels of adaptation largely incremental and 
reactive inconsistent with rising risks (•••) .
NBS and well-resourced primary health care. 
Fundamental limits include thermal threshold, some individuals and communities are already reaching their psycho-social adaptat ion limits (•••) .
Heat stress a health concern (•••).
Significant increases in intensity, frequency and duration of heatwaves (•••), strong increases in heat-related mortality in ur ban areas.
Focus on early warning and surveillance systems for heat waves; political, institutional, and financial barriers limit feasibil ity to date (•••) .

NBS proposed to be combined with community engagement and integration of diverse knowledge to foster transformational adaptati on.
No limits for health risk discussed.
Europe  
70,000 and 54,000 deaths during 2003 and 2010 heatwaves, adaptation actions have reduced heat-related mortality in parts of So uthern Europe (•••).
Risk of heat mortality and morbidity to more than triple at 3°C compared to 1.5°C with projected 90,000 deaths in 2100 (•••) .
Air cooling, heat warning and response systems, building interventions, but largely incremental adaptation (•••). 
Increasing use and plans for NBS in urban spaces; large scale system transformations needed due to adaptation limits in Souther n Europe (••) involving strong behavioural change  
combined with large portfolios of preventive and planning options.
Above 3°C limits to the adaptation potential of people and existing health systems, particularly in Southern and Eastern Europe  and with health systems under pressure (•••).

North Americ a
High temperatures have increased mortality and morbidity (•••• ) with impacts varying by age, gender, location, and socioeconom ic conditions (•••• ).
Warming projected to increase heat-related mortality (•••• ) and morbidity (••) .
Air conditioning and cooling stations .
Transformational, long-term adaptation action to increase resilience such as through redesign of urban space (•••).
Available (incremental) adaptation options unable to protect human health under high-emission scenarios (•••). 
Hard limits to adaptation may be reached for rural and urban outdoor labor towards end of century (••) .
Disproportionate health risks associated with changes in temperature. Heatwaves cause injuries and deaths .
Heat-related mortality and risks of occupational heat stress in small island states projected to increase with higher temperat ures. Higher temperatures also can affect productivity of outdoor workers.

Limited evidence reported. Early warning and response systems; integrating climate services into health decision-making system s; public uptake and buy in; improving health data collection systems .
No evidence of transformational adaptation. 
Reduced habitability of small islands through a compounding of key risks including from heat-related health stress for warming of 1.5°C (•••).
(•••••) (••••) (•••) (••) (•)
Medium High Low Very
highVirtually
certain
Figure 17.6 |  Understanding the spectrum of incremental to transformational planned adaptation for managing climate related heat risk to health including 
associated soft and hard adaptation limits (Representative Key Risk-E (RKR-E)). Evidence from regional and thematic chapters. The figure from the WGI Atlas shows the 
change in extreme hot days (above 35°C) across regions for a medium-term scenario and medium global warming relative to 1850–1900. See Table SM17.19.

17
2562Chapter 17 Decision-Making Options for Managing Risk
further exacerbated by increases in intensity, frequency and duration 
of marine heatwaves (high confidence) as well as impacts from 
extreme events such as tropical cyclones (low to medium confidence) 
(Section 3.4.2).
Although there is some evidence of autonomous natural thermal 
adaptation, as indicated by the presence of stress-tolerant symbionts 
adapted to higher thermal thresholds observed in the Persian Gulf, there 
is low confidence (limited evidence, low agreement) that enhanced 
thermal tolerance can be maintained over time (Chapter 3 Box 5) as 
the adaptability in natural system is considered very limited and risks 
are driven by water temperature. Evidence suggests that already at 
further warming of 1.5°C coral reefs are put at high risk (very high 
confidence) (Section 3.4.2.1).
Planned adaptation can help to buy some limited time, including 
through recovery and restoration efforts that target resistant coral

populations and interventions to culture heat-tolerant algal symbionts 
as well as by setting up marine protected areas. Under higher warming 
levels, transformation has been proposed as possibly complementing 
available management approaches with high-risk interventions, 
including enhanced corals and reef shading, which may help to 
sustain some coral reef systems beyond 1.5°C of global warming. 
Modelling has shown, however, that the effectiveness of such high-
risk interventions declines beyond 2°C of global warming (Figure 3.23, 
Section 3.4.2.1) (medium confidence).
Already for limited warming beyond 1.5°C for mid-century with 
increasing intensity and frequency of marine heatwaves, hard limits are 
projected to become manifest in terms of widespread decline and loss 
of structural integrity (very high confidence) (Section 3.4.2.1), including 
for the two largest such systems, the Great Barrier Reef and the 
Mesoamerican coral reef (Section 11.3.2, Box 11.2, Tables 11.14, 12.4).

In terms of planned adaptation options that would provide benefits to 
populations, evidence suggests these are very limited and uncertain 
and bring along substantial risks to people, culture and ecosystems 
(Section  3.5.2, Cross-Chapter Box  SLR). Concurrent with the loss of 
coral reefs, important ecosystem services, including to fishery, tourism 
and coastal protection, would be lost. Transformational adaptation, 
while requiring difficult choices to be made, is being discussed to help 
overcome soft limits through livelihood diversification for alternative 
income sources, assisted migration and planned relocation of 
communities dependent on the services provided by the reef ecosystem 
(medium confidence) (Section 3.5.2).
17.3 Decision-Making Processes of Risk 
Management and Adaptation
AR5 (Chambwera et al., 2014; Jones et al., 2014; Klein et al., 2014; 
Kunreuther et al., 2014; Mimura et al., 2014) represented a significant

step forward in focusing attention on how decision-making may 
facilitate effective and robust responses to climate risks remaining 
after mitigation measures have been taken, following recognition of 
these needs in the IPCC 4th Assessment Report (AR4), including the 
diverse contexts that face decision makers (Klein et al., 2007).AR5 (Jones et al., 2014; Kunreuther et al., 2014) recognised that the 
decision-making procedures are as important to consider in managing 
risks as are the options for responding to climate change, mostly 
because the procedures can themselves constrain the choices of 
actions, which could, in turn, lead to constrained pathways which are 
undesirable. The importance of iterative risk management is emphasised 
because risk and adaptation are dynamic. It also identified that (i) risk 
assessments, decision-support tools, early-warning systems, accounting 
for uncertainty and delivering no-regret options by examining trade-offs

are important, (ii) integration across different governance portfolios is 
needed due to potential conflict of different actions between portfolios, 
and (iii) planning, implementation and decision-making, including the 
use of methods, are dependent on local context.
Since AR5, the IPCC special reports have provided the value of integrated 
assessment processes for assessing trade-offs and synergies (IPCC, 
2018a), adaptive management and governance, the roles of formal and 
informal decision-making (IPCC, 2019b) and the importance of developing 
policy and governance options for risk management, including managing 
disasters, enhancing resilience, addressing decision-relevant uncertainties 
and being prepared for abrupt change and extreme events (IPCC, 2019c)
Chapter 16 has shown that climate risks vary greatly from small to large, 
local to regional, uncertain to deeply uncertain. The plethora of risks 
means there are many types of decisions, and many forms of analyses

and processes that may be drawn on. Decisions can differ according 
to whether they are strategic, tactical or operational; whether there 
are one or many decision makers, from a domestic setting to national 
governments; the level of uncertainty present; the time available to 
take the decision; and many more factors (Chapter 1; Section 17.1).
The pathway to a decision may not be linear, depending on when and in 
what detail the decision-making or consultative group may need to be 
understanding the climate risk and its real-world context (sense-making, 
modelling), has sufficient background to analyse and explore options for 
ameliorating the risk (analysis, exploration), or is ready for interpreting 
the analyses and deciding on the requirements and strategies for 
implementing a chosen strategy (interpretation–implementation) 
(high confidence) (Figure  17.7; French et  al., 2020). The development 
of decision-support tools for climate risk management (Palutikof et al.,

2019a; Palutikof et al., 2019b) and more generally (Papathanasiou et al., 
2016), along with archives of experiences from practitioners (Watkiss 
and Hunt, 2013; Section 17.5; Bowyer et al., 2014; French, 2020), means 
that some aspects of the decision-making process can be circumvented 
or at least streamlined as that experience is re-used (high confidence).
No single approach to decision-making best suits an individual climate 
risk across any adaptation context (Richards et  al., 2013), although 
there is now a greater awareness of the methods and approaches that 
are available and their requirements for best practice (Hurlbert et al., 
2019) (high confidence). This section aims, firstly, to assess the factors 
that people responsible for organising and facilitating decision-making 
may wish to consider in choosing the methods and approach for them 
to make decisions in their context. It also assesses existing experience

in analysing the utility of methods for climate risk decision-making. 
The second part then assesses progress in integrating decision-making 
across a portfolio of risks.

17
2563Decision-Making Options for Managing Risk  Chapter 17
Cross-Chapter Box LOSS | Loss and Damage
Authors: Reinhard Mechler (Austria/Germany), Adelle Thomas (Bahamas), Christian Huggel (Switzerland), Emily Boyd (Sweden), Veruska 
Muccione (Italy), Ivo Wallimann-Helmer (Switzerland), Laurens Bouwer (the Netherlands), Sirkku Juhola (Finland), Chandni Singh (India), 
Carolina Adler (Switzerland/Chile/Australia), Kris Ebi (USA), Patricia Pinho (Brazil), Rawshan Ara Begum (Malaysia/Australia/Bangladesh), 
Adugna Gemeda (Ethiopia), Johanna Nalau (Australia/Finland), Katja Frieler (Germany), Richard Jones (UK), Riyanti Djalante (Japan), Rosa 
Perez (Philippines), Tabea Lissner (Germany), Anita Wreford (New Zealand), Mark Pelling (UK), Francois Gemenne (Belgium), Nick Simpson 
(Zimbabwe/South Africa), Doreen Stabinsky (USA)
An intensifying dialogue
This Cross-Chapter Box offers an assessment of the growing literature on Loss and Damage. Capitalised letter ‘Loss and Damage’ (L&D)

has been used to refer to negotiations under the UNFCCC. Research has used lowercase ‘losses and damages’ for residual effects from 
(observed) impacts and (projected) risks (see Glossary, Annex II).
Dialogue around L&D issues started with a proposal for insurance and compensation by the Alliance of Small Island States (AOSIS) (INC, 
1991) and has intensified over recent years with suggestions made to consider complements to adaptation in order to manage residual 
impacts and risks ‘beyond adaptation’ in vulnerable developing countries (Section 1.4.5). L&D was formally recognised in 2013 at the 
19th meeting of the Conference of the Parties (COP19) through the Warsaw International Mechanism on Loss and Damage (UNFCCC, 
2013), governed by an Executive Committee (ExCom), to advance knowledge, foster dialogue and enhance action and support. Article 8 
of the Paris Agreement provided a permanent legal basis for the Warsaw International Mechanism (WIM) (UN, 2015).

IPCC’s first assessment of L&D in 2018 found residual risks to rise with further global warming leading to soft and hard adaptation 
limits in some natural and human systems (e.g., coral reefs, human health, coastal livelihoods) (Roy et al., 2018). Sections 8.4.5.6, 16.4 
and 17.2 corroborate these findings concluding that, depending on mitigation and adaptation pathways, residual risks in key systems 
in many regions will create potential for negative impacts beyond adaptation limits (medium confidence). The assessment in 2018 also 
noted that there is ‘not one definition of L&D’. This ambiguity has persisted, and a policy space for L&D has not clearly been delimited 
(high confidence). There is, however, coalescence in dialogue among academia, civil society and policy around a distinct set of themes 
as identified by stakeholder surveys as well as literature, methods and evidence reviews (Vanhala and Hestbaek, 2016; Boyd et al.,

2017; Mechler et al., 2018; Calliari, 2019; McNamara and Jackson, 2019): risk management, limits to adaptation, existential risk, finance 
and support, including liability, compensation and litigation (Sections 8.3, 16.4; medium confidence; Figure Cross-Chapter Box LOSS.1). 
Various advisory groups have been set up with participation of policy and experts from research, civil society and practice to help inform 
the implementation of WIM workplans (UN, 2015; UN, 2019).
Risk management
An increasing body of research has focused on the role of climate risk management (Sections 8.3, 16.4 and 17.2; high confidence) 
(Birkmann and Welle, 2015; Gall, 2015; van der Geest and Warner, 2015; Mechler and Schinko, 2016; Boyd et al., 2017; IPCC, 2018b; IPCC, 
2019b; Boda et al., 2020; Broberg and Romera, 2020). A technical expert group on comprehensive risk management (TEG CRM) advises 
the WIM ExCom, while other expert groups focus on slow-onset events and non-economic L&D (UNFCCC, 2019a).

There is evidence that, without strong risk management and adaptation, losses and damages will continue to affect the poorest vulnerable 
populations, potentially creating poverty traps (high confidence) (Sections 8.3, 8.4.5.6 and Tables 8.7, 17.2; Serdeczny, 2019; Tschakert 
et al., 2019; Thomas et al., 2020). Research has started to develop global inventories on losses and damages, including on intangible 
effects (Tschakert et al., 2019; Otto et al., 2020), and engaged with the practice community for data collection. Practice has provided 
guidance to report on losses and damages in countries (I)NDCs (WWF & Practical Action, 2020). Yet, systematic risk assessments of 
climate-related losses and damages including adaptation limits (see, e.g., Leal Filho and Nalau, 2018; Robinson, 2018) have remained 
scarce (Section 16.4; high confidence). Thus, many vulnerable countries lack comprehensive data at scale of risk management including

on economic (e.g., loss of livelihood assets and infrastructure) and non-economic losses and damages (e.g., culture, health, biodiversity), 
thus hampering effective risk management (Thomas and Benjamin, 2018; Martyr-Koller et al., 2021; Singh et al. 2021). Van den Homberg 
and McQuistan (2019) propose a losses and damages inventory also to be used to monitor how technologies may shape risks as well as 
adaptation limits. While early warning and other risk reduction options as well as risk retention considerations are being discussed, L&D 
dialogue has strongly focused on risk finance for residual risks, particularly through the donor-supported provision of public insurance 
systems (Linnerooth-Bayer et al., 2019; Schäfer et al., 2019; Broberg and Romera, 2020; Nordlander et al., 2020).

17
2564Chapter 17 Decision-Making Options for Managing Risk
Charting out the Loss and Damage (L&D) discursive and policy space
Limits to 
Adapt ation
EG 
SOE
EG 
NELDTEC 
CRMTF D
Risk ManagementExistential
Adapt ation and 
MitigationRisk retentio n
Risk reductionMigration and 
retreat
transformatio n
Incremental Transformationa lEG 
A&S
Finance and suppor tWIM expert group s
EG SOE: Expert group on Slow Onset Events
ED NELD: Expert group on Non-Economic Losse s
TEG CRM: Technical Expert Group on Comprehensive Risk  
TF D: Task Force on Displacement
EG A&S: Expert group on Action and Suppor t
Distance from adaptatio n
Global      National      Loca l
Discussion of justic e
Focus on  
ex anteEx ante an d
ex postFocus on
ex post
Focus on  
climate changeAll clim ate-related 
stressors
Figure Box Cross-Chapter Box LOSS.1 |  Charting out the L&D discursive and policy space. The figure shows key discursive strands relevant for L&D, including

their inter-relationships with and distinction from adaptation. The figure also identifies expert groups set up under the WIM and showcases the scale of responses 
discussed, a focus on ex ante risk management and ex post attention to losses and damages as well as contributions by climate change and other stresses for the themes. 
Adapted from Boyd et al. (2017) and building on Vanhala and Hestbaek (2016), Mechler et al. (2018), McNamara and Jackson (2019) and Calliari (2019).
The role of transformation in risk management for overcoming any soft limits to adaptation is seeing emerging attention (medium 
confidence, limited evidence), and the TEG CRM has also been tasked to consider transformation. Relocation and retreat of assets 
and communities, where in situ adaptation is considered impossible, is increasingly being debated in research and practice, including

in terms of finance and L&D implications (Section 8.4.4; Boston et al., 2021; Desai et al., 2021; Mach and Siders, 2021; van der Geest 
and van den Berg, 2021; Zickgraf, 2021). Livelihood transformation occurs where current livelihoods become unfeasible in the face 
of multiple climatic and non-climatic stressors (Section 8.3.4.1) requiring change within sectors (such as switching from cropping to 
livestock rearing (Escarcha et al., 2020) or across sectors, when farming households relocate to offer labour elsewhere (Section 9.1; Rasel 
et al., 2013). Biermann and Boas (2017) suggest revamping global governance systems to effectively address the protection and voluntary 
resettlement of those displaced by climate variability and change. A WIM taskforce on displacement is tasked to further advise on human 
mobility, including migration, displacement and planned relocation (UNFCCC, 2019a).
The existential dimension

There has been less and often implicit discussion on the existential dimension of climate-related risk as pertaining to L&D (medium confidence).
McNamara and Jackson (2019) infer an existential dimension from notions of inevitability and irreversibility associated with migration and 
relocation of communities (Eckersley, 2015; Mayer, 2017; McNamara et al., 2018), socio-cultural impacts linked to glacial retreat (Jurt et al., 
2015) and adverse psychological and inter-subjective effects (Herington, 2017; Adams et al., 2021). Many SIDS in their NDCs refer to sea level 
rise in particular posing existential threats, and call for enhanced international support for L&D (Thomas and Benjamin, 2017).Cross-Chapter Box LOSS (continued)

17
2565Decision-Making Options for Managing Risk  Chapter 17
Finance and support
International support and finance, including compensation for losses and damages, have been in the spotlight from the beginning of the 
dialogue (high confidence), starting with AOSIS’ proposal (INC, 1991). Recent work has focused on finance sources, such as solidarity-
based donor and other support for experienced losses and damages and climate-induced displacement as well as questions of 
compensation and litigation (Roberts et al., 2017; Gewirtzman et al., 2018; Mechler and Deubelli, 2021; Robinson et al., 2021). A selection 
of finance options has also been explored such as donor-supported insurance systems with built-in risk reduction provisions (Gewirtzman 
et al., 2018) as well as roles for social protection (Aleksandrova and Costella, 2021). International policy and donors have provided 
technical assistance for insurance-related options (Insuresilience Global Partnership, 2018).

As national and donor-related funding for impacts and risk management remains limited (Schäfer and Künzel, 2019; 17.2; Serdeczny, 
2019) even at current global warming, many highly exposed developing countries remain financially constrained in their capacity to 
attend to residual impacts and risk management needs (Linnerooth-Bayer and Hochrainer-Stigler, 2015; Roberts et  al., 2017; UNEP , 
2021a) (high confidence). Discussion on options for the risk retention layer ‘beyond adaptation’ are likely to see further attention as the 
dialogue proceeds.
Although there is no explicit mandate regarding L&D, about a quarter of the Green Climate Fund’s approved projects explicitly refer to 
L&D, while 16% of projects have thematic links to L&D across their main project activities (Kempa et al., 2021). Any estimate of L&D 
finance needs and spending, however, remains highly speculative, as long as its exact remit including in relation to adaptation has not

been clarified politically (medium evidence, high agreement) (Markandya and González-Eguino, 2019).
Liability and compensation, implying legally defined reimbursement of losses and damages attributable to climate change, remain 
contentious in L&D dialogue (high confidence). Yet, in half of the academic and grey literature surveyed by McNamara and Jackson 
(2019), compensation is mentioned. Studies have laid out responsibility principles, such as historical responsibility based on the polluter 
pays principle, beneficiary pays and ability to pay. Discussions on compensation are closely linked to justice and equity scholarship which 
has studied compensatory, distributive and procedural equity considerations for burden sharing (Roser et al., 2015; Wallimann-Helmer, 
2015; Huggel et al., 2016; Boran, 2017; Page and Heyward, 2017; Roberts et al., 2017; Shockley and Hourdequin, 2017; Wallimann-Helmer 
et al., 2019; Garcia-Portela, 2020).

Litigation and liability are linked, and a growing research body has examined the role of litigation and international law for the L&D 
context finding that litigation risks for governments and business may increase as the science, particularly on attribution, matures further 
(Mayer, 2016; Banda and Fulton, 2017; WGI CWGB Attribution, 8.2.1.2); Marjanac and Patton, 2018; James et al., 2019; Simlinger and 
Mayer, 2019; Wewerinke-Singh and Salili, 2019; Toussaint and Martinez Blanco, 2020) (high agreement, medium evidence).
Outlook
The WIM has been reviewed twice as to its delivery on its key functions. As an outcome of the second review in 2019, an expert group 
on Action and Support has been set up to further discuss issues pertaining to finance, technology and capacity building and a Santiago 
Network for Technical Assistance will be established to consider providing technical support directly to developing countries (UNFCCC,

2019b). Overall, the L&D dialogue under the WIM supported by an increasing body of research has made important advances with regard 
to the two functions of knowledge generation and coordination, yet less so on action and support (medium confidence) (Calliari et al., 
2020). Resolution on the last item will need additional attention as, despite the coalescence of themes, the L&D dialogue continues to 
proceed across interlinked yet contested discussion strands.Cross-Chapter Box LOSS (continued)
Processes and methods to facilitate decision-making, from problem 
recognition to implementing a solution, have evolved in many 
contexts, disciplines and applications over the last century (high 
confidence). As a result, decision-making terminology has a vast 
number of synonyms that are not compiled here. For clarity, the term 
‘decision-analytic methods’ refers to procedures or tools that may 
be used by decision makers to help develop, analyse and contrast

alternative actions/adaptations; ‘approaches’ refers to processes that 
may be undertaken by decision makers to facilitate the development 
of proposed actions/adaptations; ‘decision-support tools’ refers to software or procedures that facilitate the use of knowledge and data 
(Papathanasiou et al., 2016).
17.3.1 Decision-Analytic Methods and Approaches
Different classes of decision-analytic methods have been variously 
presented in IPCC reports since AR4 but without a summary 
assessment of their capacity to deal with different contexts of the 
decision maker. ‘Communities-of-practice’ are developing tool boxes

17
2566Chapter 17 Decision-Making Options for Managing Risk
to support analysing and making of decisions generally (French, 
2020). These communities of decision analysts can act like broad-
based statisticians to advise on matching methods to the climate risk 
and its context, before individual decision specialists are consulted. 
Some scientific literature is presenting guides for choosing different 
methods, tools and approaches (Shi et al., 2019). This sub-subsection 
provides a summary guide for policy analysts and decision makers 
to help identify the classes of decision-analytic methods that may 
be suitable for their context for managing climate risks. It focuses 
on decision-analytic methods, noting that decision-support tools will 
underpin many of these methods by organising information (Bourne 
et  al., 2016; Papathanasiou et  al., 2016; Ceccato et  al., 2018; Haße 
and Kind, 2018) or support modelling (Papathanasiou et  al., 2016;

Kwakkel, 2017; Gardiner et  al., 2018), sometimes with a particular 
decision-analytic process in mind (Hadka et al., 2015; Torresan et al., 
2016; Tonmoy et al., 2018).17.3.1.1 Factors to Consider in Selecting Methods to Facilitate 
The choice of methods and approaches to decision-making for climate 
risks (next section) will depend on (i) the cognitive needs of the 
deliberations, otherwise considered to be the phase in developing a 
decision, (ii) the types of models and modelling available to facilitate 
the deliberations, (iii) the degree of uncertainty surrounding the 
choices and (iv) the context of a choice (high confidence) (Richards 
et al., 2013; Jones et al., 2014; Shi et al., 2019; French, 2021).
17.3.1.1.1 Cognitive phases of decision-making
The decision process often involves overlapping and iterative 
development of the components leading towards a decision, resulting in 
the blurring of stages but involving different phases of cognitive activity

(Figure 17.7; Holtzman, 1989; French, 2015; French, 2020). Framing the Relationships between different processes of decision-making to manage climate-related risks in the real worl d
noting that, when appropriate, some aspects may only require experience to be re-used
Real Worl d
Interacting system s:
Society, Industry,  
Economy, Environment, 
Earth SystemAssessed 
World
Sense -makin g
Modellin gScience, Data
Knowledge
People, Interests, Policy
Regulations, Politic s
Local
Sectors, Econom y
National, Multinationa l
UN (SDGs,  UNFCCC )Objectives Option sC osts Benefit sAlternative sets of 
actions to alter the 
Real World
Preparation, 
Implementation, 
Residual Risk,
Crossover impacts
Opportunities & 
Achievements as  
measures of  
successAnalysing
Explorin g
Decision -Make rInterpreting
Chosen actionsImplementin gPast impacts
Current impact s
Future risks2
3
1
4Review  
Adaptation  
Effectivenes sMulti- sectoral  
objectives & 
potential for  
trade -offsFeedback

ManagementMitigation, Adapta tion
Plans , Responses /RecoveryData
Assess ment
Adjustment
5
Figure 17.7 |  Relationships between different processes of decision-making to manage climate-related risks in the real world, noting that, when appropriate, 
some aspects may only require experience to be re-used. (1) Formulation of risks of concern and accompanying policies and objectives for managing those risks, forming 
prescriptive models for the decision maker. (2) Knowledge, understanding and observations of the real world are used to assess past and current impacts and future risks using 
descriptive models, based on the perspectives and prescriptive models arising from (1). If not well formulated from other experience, processes in (1) and (2) interact to make sense 
of the world and what needs to be done. In iterative management, (1) and (2) also form the basis for monitoring, reviewing and evaluating effectiveness of adaptations. (3) Use

of decision-support and decision-analytic tools to appraise costs and benefits of different options for ameliorating future risks. The double-headed arrow indicates where two-way 
interactions occur between different activities (likely to be iterative, feedback and nonlinear processes); modelling and assessments are repeated and revised in tandem with the 
planning and evaluation of options, based on interactions with the policymakers and stakeholders. (4) The decision maker, which may be a group of people, interacts with the 
evaluation of options (two-way interaction) and interprets the efficacy of the options and the implications for the real world, ultimately choosing one or more actions to satisfy the 
policy objectives to manage the risks. (5) Implementation of the actions in the real world, which may be once-only actions or instigation of a feedback management system that 
enables ongoing adjustments to meet objectives.

17
2567Decision-Making Options for Managing Risk  Chapter 17
problem (Orlove et al., 2020), by modelling its relationships with the 
human and natural systems and eliciting objectives, values and scope of 
the problem from stakeholders, is a precursor to analyses of options but 
may be returned to whenever a phase of ‘sense-making and modelling’ 
is required (high confidence) (Ackermann, 2012; Keeney, 2012; Slotte 
and Hämäläinen, 2014; Abbas and Howard, 2015; Marttunen et  al., 
2017; Korhonen and Wallenius, 2020; French, 2021).
The cognitive phase of ‘analysing and exploring’ uses models and 
existing data and/or knowledge services as available to explore the 
relevance/efficacy of adaptations to ameliorate risk or to meet other 
adaptation objectives, as well as possible flow-on effects of those 
actions (Section  17.3.1.4). Sensitivity and robustness analyses can 
be useful if conditions are favourable to supplement the decision

analysis, setting bounds on some of the residual uncertainty (high 
confidence) (Borgonovo and Plischke, 2016; Ferretti et  al., 2016). 
Validation of models and verification of data (Tittensor et al., 2018) are 
becoming highlighted as important steps in this phase or in the sense-
making phase, particularly in their capacity to understand and test 
decision makers and stakeholders’ perceptions (medium confidence). 
Randomisation methods, Bayesian methods, interval methods, multi-
criteria decision analysis (MCDA), decision-making under deep 
uncertainty (DMDU) and economic and financial approaches (e.g., Real 
Options Analysis) are tools of choice in this phase (high confidence) 
(Table  17.4) (Abbas and Howard, 2015; Bendoly and Clark, 2016; 
Borgonovo and Plischke, 2016; Iooss and Saltelli, 2017; Korhonen and 
Wallenius, 2020; Saltelli et  al., 2020). Decision-support tools in the 
provision of data and/or modelling methods are regularly used in this

and the sense-making phase (high confidence) (Section 17.3.1.2).
The phase of interpreting the analyses to make decisions on climate 
adaptation followed by implementation is the least described in the 
literature (Figure  17.8). Decision process management tools and 
methods for communicating choices, outcomes and implementation 
are expected to be used to provide support in this phase, particularly 
for understanding whether the advice is fit for purpose, and the efficacy 
of choices are clear (low confidence) (Spetzler et al., 2016).
17.3.1.1.2 Types and capacity of models to support decision-making
‘Descriptive models’ of socio-biophysical systems and their responses 
to different drivers (Argyris and French, 2017; French and Argyris, 
2018; Saltelli et  al., 2020) and ‘prescriptive models’, which capture 
the beliefs, values and objectives of decision makers and stakeholders 
(Parnell et  al., 2013; Keisler et  al., 2014; French and Argyris, 2018),

provide the foundations of sense making (high confidence) and thereby 
influencing the options and choices available in the phase of analysis 
and exploration (medium confidence) (Gorddard et al., 2016).
Socio-biophysical models may be qualitative network models, statistical 
models or dynamic mathematical models (Melbourne-Thomas et  al., 
2017). Qualitative network modelling can help assess the nature and 
consequences of the interactions, as well as facilitate understanding 
of possible structures to be used in dynamic models for assessing long-
term adaptation options (Reckien et al., 2013; Reckien, 2014; Reckien 
and Luedeke, 2014; Symstad et  al., 2017). These approaches help 
articulate the direct and indirect effects of fixed, long-term engineering or structural adaptations. Dynamic stochastic modelling (Fulton and 
Link, 2014; Ianelli et al., 2016) has been used to assess short- to medium-
term interactions of more dynamic and variable sectors, such as those

with annual adjustments and management of water, agriculture, land 
and marine uses (Holsman et  al., 2019; Hollowed et  al., 2020; Bahri 
et al., 2021). On a longer time frame, scenarios are used to test long-
term interactions but often with less variability and chance (Giupponi 
et al., 2013; Adam et al., 2014; Rosenzweig et al., 2017).
Many sensitivity analyses based on scenarios, including procedures 
to randomise across model uncertainty, relate to descriptive dynamic 
mathematical models with the user of the models characterised as an 
objective observer (Borgonovo and Plischke, 2016; Ferretti et al., 2016; 
Symstad et al., 2017; French, 2020). Bayesian approaches enable these 
descriptive analyses to take account of the subjective choices in model 
construction and implementation (Abbas and Howard, 2015; Sperotto 
et  al., 2017; Jäger et  al., 2018; Sperotto et  al., 2019; French, 2020). 
Organising descriptive analyses and deciding on a suitable option

across a diversity of opinions among stakeholders use prescriptive 
processes, which can be supported with prescriptive modelling tools 
(Williamson and Goldstein, 2012; Gelman et  al., 2013; Abbas and 
Howard, 2015; Dias et al., 2018; Phan et al., 2019; Hanea et al., 2021). 
These approaches are subjective, in that they are constrained or 
directed by the particular views and emphases of the decision-making 
group (Gorddard et al., 2016). Not all tools are appropriate for all these 
Decision makers will be better able to choose decision-analytic methods 
when they have an understanding of the types, scale and breadth of 
uncertainties around the climate risk (high confidence) (Symstad et al., 
2017). The Cynefin framework (Snowden, 2002; French, 2013) is a 
policy-driven framework that broadly categorises the decision context 
of uncertainty within which decision makers and policy analysts may 
find themselves (medium confidence) (Hurlbert et al., 2019; Helmrich

and Chester, 2020). As Cynefin has helped frame previous IPCC 
presentations on contexts of uncertainty (Hurlbert et al., 2019) and has 
a community of practice to consult on its use (French, 2020), it is used 
here, also because it considers the uncertainty in knowledge around 
cause and effect in general terms, rather than specifically focusing on 
uncertainty in formal models. Helmrich and Chester (2020) show how 
Cynefin can be used to frame climate adaptation decision-making in 
the infrastructure sector.
The Cynefin contexts relate to how well the system is understood for 
knowing precisely the outcomes of actions that may be taken, ranging 
from known, knowable and complex to chaotic. If a context is known 
or knowable, then it will be possible to build sophisticated models 
and make sound predictions. If the context is complex and chaotic the 
outcomes of actions will be less predictable, no matter how complex 
the models may be, although more complex dynamic models may be

useful to test ‘what if’ scenarios in these cases (Marchau et al., 2019). 
Under complex and chaotic circumstances an ensemble of models and 
approaches may be needed to help categorise a satisfactory ‘solution 
space’ across the broad knowledge of relationships and dependencies, 
but will need to have iterative processes to update and refine 
adaptations as knowledge improves (Marchau et al., 2019).

17
2568Chapter 17 Decision-Making Options for Managing Risk
17.3.1.1.3 Uncertainty and attitudes to risk
Uncertainty does not just relate to what might happen given climate 
drivers or adaptations, but also to how much one values potential 
consequences (Butler et al., 2016; Beven et al., 2018a; Cross-Chapter 
Boc DEEP; Beven et  al., 2018b; French, 2020) (high confidence); 
the balance between how particular decision analyses address 
uncertainties relating to the external world (descriptive models) and 
those relating to the values driving the decision-making (prescriptive 
models) is important (Butler et  al., 2016). Some analyses partially 
ignore uncertainties relating to the former in order to focus on conflicts 
in the values held by different stakeholders and help structure debate 
(Korhonen and Wallenius, 2020; French, 2020), while others build 
very sophisticated models of the external world to predict potential

consequences, but in doing so lose transparency and risk becoming 
untrustworthy black boxes to many stakeholders (low confidence) 
(Peterson and Thompson, 2020).
Much of the readily available literature on how uncertainties affect 
decision-making relates to the uncertainty in the biophysical models, 
with a recognition that the choice of tools will be influenced by 
the types of uncertainty to be addressed (Le Cozannet et  al., 2017; 
Symstad et al., 2017; Beven et al., 2018a; Beven et al., 2018b; Durbach 
and Stewart, 2020b; French, 2020). While terminology varies among 
disciplines, three types of uncertainty are important in understanding 
assessments of the future from descriptive models: epistemic 
(uncertainty in model construction relating to the lack of knowledge 
about the system being represented), analytic (the degree to which a 
model fits observations, and its accuracy) and stochastic (the natural 
variability or randomness in the system). The probability of an event

arising in the future is determined from all three uncertainties, noting 
that stochastic uncertainty is a property of the system rather than a 
limitation of research (Le Cozannet et al., 2017; Beven et al., 2018a; 
Beven et al., 2018b).
Uncertainty in what constitutes a risk of concern is increasingly identified 
as important to consider when managing risk (Chapter 16; Butler et al., 
2016; Prober et al., 2017; French et al., 2020; Reis and Shortridge, 2020). 
The uncertainty here arises from what is an acceptable risk. Acceptability 
relates to the value or importance of the consequence, which may 
include moral and ethical uncertainties (Prober et  al., 2017), as well 
as how ambiguous the understanding of the consequence may be 
between different groups (Beven et al., 2018a; Beven et al., 2018b). The 
development of strategies to ameliorate risk will benefit from considering 
these two uncertainties in specifying the risk to be managed (Prober

et al., 2017; French et al., 2020) because they can help set boundaries on a 
required likelihood of success, rather than simply casting stakeholders or 
decision makers as risk averse or risk tolerant, and can help identify and 
accept pathways of success (Gregory et al., 2012). This can be important 
when decisions need to be made well in advance of the actions needing 
to take effect, such as for many climate risks (Chapter 1; Chapter 16; 
Section 17.2.3; Cross-Chapter Box DEEP in this Chapter).
Elicitation methods help reduce these uncertainties (high confidence) 
(Butler et  al., 2016; Prober et  al., 2017; Symstad et  al., 2017; Beven 
et  al., 2018b). In addition, informal decision processes can assist in 
developing consensus in approaches and outcomes (Orlove et al., 2020).17.3.1.2 Decision-Analytic Methods Used in Decision-Making 
and Climate Risk Management
Entities making decisions (countries, regions, organisations and

individuals) select methods that best suit them in their context 
(Fünfgeld et al., 2018; Shi et al., 2019; French, 2020) (high confidence).
Classes of tools (Watkiss and Hunt, 2013; French, 2020) include 
Bayesian methods, interval methods, decision-making under deep 
uncertainty (DMDU; see Cross-Chapter Box  DEEP in this Chapter), 
cost–benefit analyses, multi-criteria decision analysis, elicitation and 
general decision support tools (Table  17.4). A summary guide for 
policy analysts and decision makers is presented in Table 17.4 to help 
identify the classes of decision-analytic methods that may be suitable 
for their context for managing climate risks. The table summarises how 
well the methods address the Cynefin context, the phase of decision-
making, the types of uncertainties that exist through the decision-
making process and the resources required. As terminology may vary 
between disciplines and research groups, suitable references to better

explain the methods within the class are provided. Also, there may be 
overlap between the classes as individual methods are often paired 
with other methods to address specific requirements and approaches 
(Buurman and Babovic, 2016; Haasnoot et al., 2019). In that respect, 
these methods are referred to in the next section discussing advances 
in the different approaches to managing climate risks.
Case studies in Table 17.4 describe the utility of classes of decision-
analytic tools to facilitate decisions about climate adaptations (SM 
17.2). These case studies are presented in Figure  17.8 according to 
the type of decision-making body and mapped according to their 
contribution to a decision outcome relative to the geopolitical scale 
of the actions being assessed. The effectiveness of these methods 
and tools in Table 17.4 in the context of climate change adaptation 
(Box 17.1) has yet to be evaluated.
Many published studies on the utility of decision-analytic methods in

managing climate risks are theoretical, and therefore it is difficult to 
find studies on the value of analytic methods for underpinning final 
decisions on climate risk adaptation. Bayesian, Deep Uncertainty and 
elicitation methods and tools to support decision-making were the 
most easily located classes of methods to be used in different contexts 
(Figure  17.7), while the other classes were more oriented towards 
government processes. This result highlights a key gap at present in the 
need to have real-world experiences published and mapped for their 
utility for different tasks, thereby creating a resource for policymakers 
to identify suitable tools, such as in emerging communities-of-practice 
of decision practitioners (Watkiss and Hunt, 2013; Street et al., 2019; 
French, 2020).
17.3.1.3 Approaches to Support Decision-Making
The common approaches presented here are not undertaken in 
isolation and are often combined throughout, or applied at different

17
2569Decision-Making Options for Managing Risk  Chapter 17
Table 17.4 |  Characteristics of the main approaches to decision analysis with respect to their Cynefin context, the manner in which they can be used to address different uncertainties, where they may be used in different cognitive phases of 
the decision-making process, the resources required and some case studies for further exploring how they might be used. Numbers in square brackets after references in case studies refer to the references plotted in Figure 17.8.
A: Bayesian methods (Keeney and Raiffa, 1993; Smith, 2010; Gelman et al., 2013; Reilly and Clemen, 2013; Abbas and Howard, 2015; Sperotto et al., 2017; Marchau et al., 2019)
A structured approach to assembling information around the consequences of choices, either by modelling, by analysis of multiple scenarios or by structuring deliberation; underpinned by a theoretical base, coherent assumptions and powerful computational

methods; can use both observational data and expert knowledge, weighting them appropriately; same approaches as in artificial intelligence algorithms. Biases (information, stakeholders, decision makers) can be made explicit. Traditionally, Bayesian methods 
computationally identify an ‘optimal’ decision, based on maximising the expected utility across a number of specified requirements, represented as functions.
Examples include the general application of decision network models (Richards et al., 2013; Sperotto et al., 2017); the use of decision network analyses based on elicitation to choose adaptations to coastal management in a lagoonal area in Italy (Catenacci 
and Giupponi, 2013) and coastal community in UK (Jäger et al., 2018); combination of economic models and decision models to assess research and development priorities (Baker and Solak, 2011); combining outputs from models, observations and opinions

in a decision framework for assessing climate impacts on water nutrient loads in Italy (Sperotto et al., 2019) and a general review for water resource management (Phan et al., 2019); combining results from different dynamic models to assess human 
mortality from ozone in the USA (Alexeeff et al., 2016); assessing adaptive capacity of surf lifesaving in Australia (Richards et al., 2016); and assessing urban flood risks in Denmark (Åström et al., 2014).
Cognitive phase Resources required Case studies
Sense-making and modelling Analysing and exploring Interpreting and implementing
Construction of hierarchical models, belief nets (Sperotto et al., 
2017; Phan et al., 2019), decision trees (Keeney and Raiffa, 
1993) and influence diagrams (Keeney and Raiffa, 1993; Reilly 
and Clemen, 2013) supplemented by many soft elicitation 
techniques helps build models for quantitative analysis 
(Gelman, 2003; Bendoly and Clark, 2016).Bayesian updating and expected 
utility analysis supplemented by

robustness and sensitivity analyses 
(Rios Insua, 1999; Rios Insua and 
Ruggeri, 2000; French et al., 2009; 
Smith, 2010; Reilly and Clemen, 
2013; Abbas and Howard, 2015).Use of graphical models (decision trees, 
belief nets and influence diagrams) 
and sensitivity plots can help make 
transparent and explain reasoning 
for strategy to stakeholders and 
implementers (Bendoly and Clark, 2016) 
and provide for auditable building of 
consensus.Bayesian decision-analytic models 
can be applied with increasing 
complexity and sophistication to any 
given problem. Coherence between 
different levels of sophistication can 
be maintained. Thus, the resources 
can be tailored to the time and 
support available for the analysis. 
The most sophisticated analyses are 
computationally demanding.Alexeeff et al. 2016) [1], Åström et al. (2014) [2], Baker 
and Solak (2011) [3], Catenacci and Giupponi (2013) [4], 
Jäger et al. (2018) [5], Phan et al. (2019) [6], Richards

et al. (2013) [7], Richards et al. (2016) [8], Sperotto et al. 
(2017) [9], Sperotto et al. (2019) [10]
Uncertainties Cynefin context
Stochastic, epistemic, analytical
(descriptive modelling)Ambiguity
value
(prescriptive modelling)Known Knowable Complex Chaotic
All can be modelled probabilistically, 
perhaps supplemented by sensitivity 
analysis (Rios Insua, 1999; Rios Insua 
and Ruggeri, 2000; Iooss and Saltelli, 
2017). Deep uncertainties can be 
investigated via scenarios (French, 
2020).Uncertainties resolved or reduced by 
discussion, then values modelled by 
multi-attribute values and utilities 
(Keeney, 1992; Keeney and Raiffa, 
1993; Gregory et al., 2012). Residual 
uncertainties explored via sensitivity 
analysis.Any stochastic uncertainties 
modelled probabilistically; otherwise, 
deterministic modelling with sensitivity 
analysis. Value functions tend to 
be used more than utility functions 
(Keeney and Raiffa, 1993; Goodwin

and Wright, 2014).Epistemic uncertainties updated 
via Bayesian statistics/machine 
learning, then remaining 
stochastic uncertainties modelled 
probabilistically. Full Bayesian decision 
modelling possible (French et al., 2009; 
Smith, 2010; Abbas and Howard, 
2015).More exploratory analysis (Gelman, 
2003) to understand behaviours with 
less complex Bayesian modelling 
support by sensitivity and robustness 
studies (Rios Insua, 1999; French, 2003). 
Scenario-focused decision analysis to 
cope with deep uncertainties (French, 
2020). Careful deliberations to construct 
values and utilities. (Keeney and Raiffa, 
1993; Gregory et al., 2012).Formal modelling impossible. Much 
exploratory work to identify potential 
causes and effects. Little if any 
complex analysis.
B: Decision-making under deep uncertainty (DMDU) (Hallegatte et al., 2012; Weaver et al., 2013; Marchau et al., 2019; Workman et al., 2021)

Deep uncertainty relates to circumstances in which data are too sparse, experts are in too much disagreement or time is too short to model the uncertainty. As such, DMDU methods are focused on working in the Cynefin Complex Space context. Approaches 
emphasise robustness (‘no regrets’ options) and the use of scenarios, and often link well with scenario-focused robust Bayesian studies (Cross-Chapter Box DEEP in this Chapter). DMDU studies draw in many other approaches to decision analysis, using them 
to identify robust rather than optimal strategies, as in robust decision-making (RDM). DMDU analyses can help decision makers to think contingently and build a more wide-ranging recognition of the risks. They often integrate with other classes of tools.

Examples include RDM for hydro-power design using down-scaled climate data in Sub-Saharan Africa (Taner et al., 2017), RDM for water management in California, USA (Lempert and Groves, 2010), the Colorado River, USA, and for international climate 
investment strategies (Groves et al., 2019), use of decision scaling (Brown et al., 2019), comparison of RDM and Info-gap methods (Hall et al., 2012) and review of using climate modelling in RDM (Weaver et al., 2013).

17
2570Chapter 17 Decision-Making Options for Managing RiskCognitive phase Resources required Case studies
Some of the simpler DMDU tools complement soft elicitation 
tools and can help to identify relevant scenarios and help 
formulate problems.Many Bayesian or MCDA tools can 
be used here but with DMDU’s 
additional emphasis on robustness 
and the exploration of several/many 
scenarios.DMDU with its emphasis on robustness 
encourages contingency planning in 
implementation with careful monitoring 
to identify emerging risks.Some of the simpler models do not 
require substantial resources, but the 
application of parallel sophisticated 
analyses in several scenarios can 
be computationally demanding. 
Also, the emphasis on discussion of 
robustness can be demanding on the 
time of problem-owners, experts and 
stakeholders.Brown et al. (2019) [11], Groves et al. (2019) [12], Hall

et al. (2012) [13], Lempert and Groves (2010), [14], Taner 
et al. (2017) [15], Weaver et al. (2013) [16]
value
Methods are designed for deep 
epistemic uncertainties. Some can 
deal with stochastic uncertainties. 
Analytical uncertainties seldom 
accounted for.Some DMDU methods draw on MCDA 
methods and thus consider ambiguity 
and value uncertainties. In any 
case, DMDU methods support wide 
deliberation with stakeholders.Deep uncertainty is absent, but 
the principles and processes of 
decision-making may be used.Deep uncertainty is absent, but the 
principles of decision-making may 
be used.The complex and chaotic spaces are 
home to deep uncertainties. DMDU 
tools and more particularly processes 
are relevant here. The emphasis 
on robustness is very relevant. 
The tools themselves are relatively

simply structured but are effective at 
stimulating discussion.Deep uncertainties are rife in the 
chaotic contexts. DMDU emphases 
on robustness and possible scenarios 
can stimulate creative discussions of 
ill-understood issues.
C: Decision process management (Raz and Micheal, 2001; Dalkir, 2005; Burstein and W. Holsapple, 2008; Jashapara, 2011; Bonczek et al., 2014; Sauter, 2014; Holsapple et al., 2019)
A range of tools and techniques to help manage the decision-making process and support risk management and the implementation of the chosen strategy. Some tools organise data and analyses, often being built on a geographic information system, known 
as decision support tools. Others manage processes, organising workflows. Some have inevitably expanded in function to support decision-making itself, even though their primary focus might be on, say, implementation and monitoring risks. Such tools are

closely related to knowledge management systems; knowledge management processes and decision process management differ more in terminology than in substance.
Examples include tools for agriculture (Biehl et al., 2017), evaluating and comparing CMIP climate models (Parding et al., 2020), development of action cycles (Park et al., 2012) and decision support systems across a range of sectors and decision-group 
applications (Papathanasiou et al., 2016).
Process, project, knowledge elicitation and risk management 
tools help identify how to structure decision-making processes. 
Decision process tools can capture details for implementation 
and document process for audit trail.Tools help structure decision-making 
processes and ensure timely 
involvement of problem owners, 
stakeholders, and experts. 
Knowledge management tools can

capture details for implementation 
and document process for audit trail.Project management tools plan 
implementation and risk management 
tools identify what to monitor 
during implementation. Knowledge 
management tools maintain audit trail 
and track reasoning for choices made 
during implementation.Decision process management tools 
can reduce resources needed in the 
decision-making process. However, 
this assumes that the tools are 
already installed on local information 
systems and that the analysis team is 
experienced in using them. Otherwise, 
resource is needed to understand and 
train in the use of the tools.Biehl et al. (2017) [17], Papathanasiou et al. (2016) [18], 
Parding et al. (2020) [19], Park et al. (2012) [20]

17
2571Decision-Making Options for Managing Risk  Chapter 17
value
Not designed to address uncertainties 
involved in the decision itself, but may 
handle project risks in the decision 
process, especially implementation.Not usually addressed, since 
ambiguities and value uncertainties 
will be addressed in the 
decision-making itself, but may use 
those values in risk management of 
implementation.Simple project management tools may 
be sufficient here.Project management and risk 
management tools apply easily here.Project management and risk 
management tools may be used, but 
attention needs to be paid to risks 
that are complex in nature with little 
knowledge of precise relationships 
between cause and effects.Project management and risk 
attention needs to be paid to risks

between cause and effects.
D: Economic and financial methods (Howell et al., 2001; Pearce et al., 2006; Boardman et al., 2017; Atkinson et al., 2018a; Hurlbert et al., 2019)
Stem from economic theory and accounting practices: for example, cost–benefit analysis, which seeks to price out all aspects of the consequence of a strategy, portfolio analysis, or real options theory, which seeks to value financial investments allowing for 
their risks and the contingent buying and selling. Such methods are perceived as objective when dealing with tangibles, but are more controversial in their valuing of intangibles. Since these methods model uncertainties with probabilities and then work with 
expectations, they share much in common with Bayesian methods. However, many applications of cost–benefit analysis omit any detailed treatment of uncertainty.

Examples examine the economic costs and benefits of adaptation pathways for storm water infrastructure in Singapore (Manocha and Babovic, 2017), and a coastal mega city, Los Angeles in the USA (de Ruig et al., 2019)
In themselves, these methods do not support sense-making and 
modelling, though discussions of how to value impacts, both 
tangible and intangible can be catalytic in understanding the 
issues.These tools focus mainly on analysis 
and evaluating the costs and 
benefits of various options. They are 
not designed to be used interactively 
so are more often deployed and 
communicated via reports than 
interactive workshops.Since community-based adaptation 
(CBA) methods do not emphasise the 
analysis of uncertainties and risks, they 
are less suited for use in developing 
and communicating an implementation 
plan. Real options with their emphasis

on contingency are much more suited 
(Fischhoff, 2015).Cost–benefit analysis for complex 
projects is a major undertaking, with 
much data collection needed to value 
outcomes. Real options also require 
data on risks and uncertainties. Both 
may have high computational needs.de Ruig et al. (2019) [21], Manocha and Babovic (2017) 
[22]
value
Cost–benefit methods usually deal 
with uncertainty via expectations 
with little attention to probability 
distributions; real options methods 
tend to treat uncertainty in much more 
sophisticated ways. Both methods, 
when applied fully have many points 
of contact with Bayesian methods 
(Neely and de Neufville, 2001; Bedford 
et al., 2005)These methods reduce all value and 
preference information to financial 
equivalents. The key issue is to find 
a market in which all outcomes may

be valued financially. Modern CBA 
methods use much more subtle 
techniques for this than those applied 
in the last century (Bedford et al., 
2005; Saarikoski et al., 2016).Although CBA and many financial 
methods work in theory, the 
complexity makes them seldom worth 
the effort.The methods may be applied to 
evaluate complex projects, but CBA 
tends to ‘average out’ rather than 
analyse uncertainty.The recognition of the need to treat 
deep uncertainties using real options 
has been investigated (Hallegatte 
et al., 2012; Buurman and Babovic, 
2016).Formal modelling impossible. Much 

17
2572Chapter 17 Decision-Making Options for Managing RiskE: Interval methods (Shafer, 1976; Pedrycz et al., 2011)
Because of concerns that the statistical accuracy of some data is unknown, and that decision makers and experts cannot make numerical judgements accurately, analyses have been suggested which work with ranges of values in categories (intervals) as their 
inputs. While avoiding accuracy issues, weakening the arithmetic may result in other foundational assumptions not being met, including some basic principles of rationality. Different types of uncertainty can often be confused, and the analyses can contradict 
basic probability theory. Interval models of semantics and imprecision can be useful in exploring ambiguity and value uncertainty, though modelling rather than resolving such uncertainties does not necessary help in decision-making. Some interval methods

can be thought of more as sensitivity techniques applied to other decision-analytic approaches. Typical approaches here relate to the fuzzy or possibility theory, and evidential reasoning.
Examples include using fuzzy methods to assess climate adaptations in ports in China (Yang et al., 2018), water supply vulnerability in South Korea (Kim and Chung, 2013) and resilience of the Nile River Delta (Batisha, 2015); and evidential reasoning in an 
environmental impact assessment for flood mitigation in Manila Philippines (Gilbuena et al., 2013).
The emphasis on modelling ambiguity may help structure a 
model initially, but the lack of structures to model and explore 
complex interdependencies may inhibit the ability to build a 
valid representation of the issues.If there are substantial data 
available, then even the simplest of 
these methods can produce useful

results. But with small quantities 
of data, their data analysis may be 
too inefficient. Evidential reasoning 
MCDA can be insightful on the 
preference side.The emphasis on linguistic uncertainty 
may in some cases mask some of the 
issues (French, 1995).Many methods are rather simple in 
application and require only moderate 
resources, but they may face issues in 
scaling up to major complex problems.Batisha (2015) [23], Gilbuena et al. (2013) [24], Kim and 
Chung (2013) [25], Yang et al. (2018) [26]
(descriptive modelling)Ambiguity 
value
There are issues of operational 
definition of quantities in some 
methodologies. Some simpler 
interval methods have no concept 
of conditionality so cannot model 
learning effectively, but there are some 
very sophisticated theories of evidence 
that can. Interval methods can also 
provide sensitivity analyses for

Bayesian and MCDA methods (Shafer, 
1976; Rios Insua, 1990).Some methods can be simplistic, with 
quantities not being operationally 
defined. The evidential reasoning 
approach to MCDA allows exploration 
of the relative weights on different 
criteria or between levels in criteria 
(Xu, 2012; Zhang et al., 2017).Methods can be applied here without 
major issue, possibly because the 
simple, repetitive nature of the 
problem allows access to much data 
and the possibility of tuning the 
methods to the application.Since the methods often capture rather 
than explore and resolve ambiguity 
and value uncertainties, they can hide 
issues. Also, the lack, in some cases, 
of operational definitions may mean 
that some quantification is dubious. 
Evidential reasoning methods can help 
analyse conflicting objectives (French, 
1995; Xu, 2012).The recognition of the need to treat 
et al., 2012; Buurman and Babovic,

2016).The ability to deal with ambiguity 
may be helpful in poorly understood 
situations, but the emphasis on 
capturing ambiguity may ultimately 
slow the building of understanding.
F: Multi-criteria decision analysis (MCDA): Full ranking and optimal seeking (Bell et al., 2001; Belton and Stewart, 2002; Bouyssou et al., 2006; Zopounidis and Pardalos, 2010; Tzeng and Huang, 2011; Velasquez and Hester, 2013; Kumar et al., 2017)
Covers many approaches: indeed, Bayesian, DMDU and interval methods are sometimes considered MCDA. Some MCDAs seek an optimal or best strategy; others form partial rankings, eliminating weak strategies but not discriminating fully between the 
better ones. Many MCDA methods eschew dealing with uncertainties and focus on modelling and exploring conflicting objectives and balancing these. MCDA techniques are especially useful in working with senior decision makers in setting policy and broad 
objectives, and in processes of stakeholder engagement.

Examples include ranking adaptation and mitigation priorities at a national level in the Netherlands (de Bruin et al., 2009), Lithuania (Streimikiene and Balezentis, 2013) and Bangladesh (Haque, 2016), in the forestry sector in Nicaragua (Guillén Bolaños 
et al., 2018) and in emissions trading in the European Union (Konidari and Mavrakis, 2007).

17
2573Decision-Making Options for Managing Risk  Chapter 17
There is growing experience in combining soft elicitation with 
tools to formulate problems (Marttunen et al., 2017). Many 
MCDA tools naturally encourage discussion and deliberation on 
developing appropriate value structures. However, exploration 
and formulation of stochastic and epistemological uncertainties 
is less developed (Durbach and Stewart, 2020a).Emphasis is usually on analysing 
and exploring, resolving conflicting 
objectives. MCDA methods come 
into their own at this stage of the 
process. Sensitivity tools and intuitive 
graphical displays exist for many of 
the methods (Gunawan and Azarm, 
2005; Boardman et al., 2017).Use of graphical models and sensitivity 
plots can help explain reasoning 
for strategy to stakeholders and

implementers (Bendoly and Clark, 2016).The more exploratory methods can be 
quite light in terms of computational 
resource, but require interactions with 
decision makers and stakeholders in 
workshops. Methods with use complex 
stochastic mathematical programming 
can be computationally demanding and 
require substantial data.(de Bruin et al., 2009) [27], (Guillén Bolaños et al., 2018) 
[28], (Haque, 2016) [29], (Konidari and Mavrakis, 2007) 
[30], (Streimikiene and Balezentis, 2013) [31] 
value
These methods tend to focus on 
balancing and resolving conflicting 
objectives and include little or no 
analysis of stochastic and epistemic 
uncertainties. Interactive methods that 
use complex objective functions do 
need to consider convergence criteria 
for analytic uncertainties.Many methods here use multi-attribute

value functions and focus on using 
weights to explore different emphases 
on conflicting objectives. One very 
popular method is analytic hierarchy 
processing (AHP) (Saaty, 1980) 
though this has issues in scaling up 
to evaluate more than a handful of 
policies.Usually in the known context, the 
objective function is well understood; 
but in cases where it is not, interactive 
multi-objective programming can offer 
a way forward (Klamroth et al., 2018).If the objective function is not well 
understood, then these methods can 
be useful and can be extended to 
stochastic programming, but epistemic 
uncertainties are not really addressed 
(Gutjahr and Pichler, 2016).Methods can explore conflicting 
objectives, but seldom are able to 
address deep epistemic uncertainties, 
unless combined with scenarios 
(Stewart et al., 2013; Marchau et al., 
2019; Durbach and Stewart, 2020a).Formal modelling impossible. Much 
causes and effects. Little if any

G: Multi-criteria decision analysis (MCDA): Partial ranking (Roy, 1996; Bell et al., 2001; Belton and Stewart, 2002; Bouyssou et al., 2006; Behzadian et al., 2010; Zopounidis and Pardalos, 2010; Tzeng and Huang, 2011; Bouyssou and others, 2012; De Smet 
and Lidouh, 2012; Velasquez and Hester, 2013; Figueira et al., 2016; Govindan and Jepsen, 2016)
Examples include developing criteria for assessing climate protection strategies and applying these to retrofitting a school to manage climate risks in Germany (Markl-Hummel and Geldermann, 2014); evaluating outranking approaches for managing heat 
stress in a large city in Australia (El-Zein and Tonmoy, 2015); using MCDA to manage the interactions of climate change with tourism in Greece (Michailidou et al., 2016); and identifying priorities to manage droughts and floods in agriculture in Bangladesh 
(Xenarios and Polatidis, 2015).
Cognitive phase Resources Required Case Studies

Graphical representations of partial orders are useful in model 
formulation, and the emphasis on exploring what can be said 
objectively about dominance relations can build a kernel of 
consensus between decision makers and stakeholders.ELECTRE and PROMETHEE 
implementations of outranking 
approaches have many tools for 
exploring partial relations and 
analysing agreements and the 
reasoning behind these.The analysis of dominance can provide a 
sound footing for building risk registers 
to aid implementation. Understanding 
the kernel of consensus can also aid 
communication.If an outranking algorithm is essentially 
combinatorial in its approach, then 
for complex problems there may be 
computational problems. Some of the 
methods may require less interaction 
with decision-makers and stakeholders 
if they can deduce many partial relations

from objective data.(El-Zein and Tonmoy, 2015) (Markl- [32], Hummel and 
Geldermann, 2014) [33], (Michailidou et al., 2016) [34], 
(Xenarios and Polatidis, 2015) [35]
value

17
2574Chapter 17 Decision-Making Options for Managing RiskModelling of all forms of uncertainty including epistemic 
uncertainty is not the primary objective of these methods. 
Stochastic uncertainty may be included as probability 
distributions, but there is no formalism for learning to address 
epistemic uncertainties (Hyde et al., 2003; Behzadian et al., 
2010; Gervásio and Simões da Silva, 2012).Partial ranking or outranking 
methods seek, first of all, to identify 
dominance between options and 
preference relations that can be 
agreed somewhat objectively. Thus, 
first they eliminate suboptimal 
alternatives before seeking a fuller 
ranking. Ambiguity and value 
uncertainty may also be quantified 
(Behzadian et al., 2010; Figueira 
et al., 2016; Govindan and Jepsen, 
2016).Usually in the 
known context, 
the objective 
function is well 
understood; 
but when it is 
not, outranking 
methods can 
identify a 
partial ranking 
without needing 
too many 
interactions 
with problem-

owners.Since epistemic uncertainties are not 
fully addressed, these methods can 
only help in relation to conflicting 
objectives, but robustness to 
uncertainties will need addressing 
(Hyde et al., 2003).Outranking methods may be combined 
with scenarios to explore and analyse 
decisions under deep uncertainty (Hyde 
et al., 2003; Durbach, 2014).Formal modelling impossible. Much 
H: Soft elicitation (Rosenhead and Mingers, 2001; Shaw et al., 2006; Shaw et al., 2007; Ackermann, 2012; Bendoly and Clark, 2016)
Also known as problem structuring, it is the process of asking problem owners, experts and stakeholders for the knowledge, perceptions, beliefs, uncertainties and values that a model needs to embody before being populated with numbers. Methods here

help in problem formulation, structuring understanding: for example, cognitive maps, soft operational research diagrams, soft systems, prompts such as PESTLE and other qualitative tools (Prober et al., 2017; Symstad et al., 2017). The output of soft elicitation 
can lead to the building of sophisticated quantitative models (Symstad et al., 2017) and can also structure communications and deliberations with stakeholders. Exploratory data analysis and visual analytics are also relevant. Soft elicitation has enormous 
advantages in setting the frame for communication between all parties (Prober et al., 2017); there are many cases in which the clarity brought by framing the issues well has obviated the need for formal quantitative analysis.
Examples include Adaptation Pathway planning and elicitation on managing a national park in the USA (Symstad et al., 2017), poverty alleviation in a province in Indonesia (Butler et al., 2016), woodland landscapes in Australia (Prober et al., 2017) and

general considerations for contested adaptations (Bosomworth et al., 2017).
Cognitive phase Resources required Case Studies
Soft elicitation tools provide much support to sense-making, 
formulating problems and identifying relevant issues to be 
addressed (Shaw et al., 2006; Shaw et al., 2007; Ackermann, 
2012).Soft elicitation is not relevant 
to quantitative analysis and 
evaluation per se, but can support 
the exploration of residuals to 
understand the quality of the models 
and detect further factors to be 
addressed.The results of soft elicitation provide 
the dimensions for communication 
by identifying the issues that are 
important to stakeholders and building 
understanding in those implementing 
the policies.Physical resources requirements are 
relatively slight: sometimes post-its 
and a white board can be sufficient, 
though modern visual analytics can

require substantial computing resource. 
However, the demands on the time 
of problem owners, stakeholders and 
experts can be significant.(Bosomworth et al., 2017) [36], (Butler et al., 2016) [37], 
(Prober et al., 2017) [38], (Symstad et al., 2017) [39]
value
Soft elicitation tools are available to elicit problem-owners’ 
and experts’ perceptions of these uncertainties and, more 
particularly, dependences and independences between them. 
Exploratory data analysis is also relevant (Steed et al., 2013; 
Bendoly and Clark, 2016).There are tools to catalyse 
deliberations and help 
problem-owners and stakeholders 
clarify their meanings and 
contextualise their values to the 
specific issues being considered 
(Keeney, 1992).Usually, 
problems falling 
into known 
contexts are 
well understood 
and there is 
little need 
to elicit or

structure models 
to perform 
analyses.Problems falling into knowable 
space are usually well structured and 
problem owners’ values are also well 
understood. However, there may be 
a need to explore error structures in 
preparation to estimate parameters in 
the models (Gelman, 2003; Steed et 
al., 2013; Fekete and Primet, 2016).Many soft elicitation tools were 
developed for complex contexts: 
'wicked' problems with deep 
uncertainties: e.g., soft systems, 
cognitive maps and similar tools to 
elicit perceptions of relationships 
between entities and problem owners' 
and stakeholder's values (Keeney, 
1992; Rosenhead and Mingers, 2001).Soft elicitation tools and processes can 
be used to catalyse creative thinking 
about poorly understood contexts.

17
2575Decision-Making Options for Managing Risk  Chapter 17
17.3.1.3.1 Role of informal processes
Informal decision-making pervades decision-making in all contexts 
(high confidence) (Orlove et  al., 2020); decisions relating to climate 
change are affected not only by rational processes but also by many 
informal, often behavioural responses to the situation, some of which 
may not require formal processes. Informal processes were officially 
studied in only a few of the publications contributing to Figure 17.8, but 
all of the studies have hints to informal decision-making that pervades 
all levels of governance. Although there are not many concrete 
studies, citing roles of study participants can lead to a perception of 
a disconnect between the process and the outcome that resulted (see 
Section 17.5.1 for enablers of success).
Generally, while governance requirements may define the processes

of formal deliberations and decision-making, informal deliberations will carry on in parallel, supported by social media, and these 
informal deliberations may be used to affect the outcome of the 
formal processes. Stakeholders may feel excluded from the formal 
deliberations either by governance structures or because they do not 
agree with their representatives. Conflicting value systems may cause 
some stakeholders to feel side-lined, particularly if some of the key 
decision makers are perceived holding different personal views and 
interests or to have engaged in political horse-trading, which connect 
independent decisions. There may be emotional responses, driven 
by poor comprehension of risk and probabilistic information, and 
potential for group biases or insularity of participants (Engler et al., 
2019). Well-designed decision processes recognise the informal and 
seek to gain information from it without introducing bias (medium 
confidence) (French and Argyris, 2018).

Decision-analytic tools used across different geo-political scales 
and how they contributed to decision outcomes
Decision to act
Pathway to
decision
established
Stakeholder
consultations
Recommendation
to decision-makers
Theoretical study
with realistic data
Review of
circumstance
Geo-political scaleHousehold
(individual)Community
(village/neighbourhood)City
(greater city area)Sub-national region
(state/province)Nation Trans-national
regions
(continents)International
(global agreements
and organizations)Contribution to decision
[  ]= Numbered references
         in the case studies of Table 17.5 Class of decision-analytic tool
• Bayesian
• Decision-Making under Deep Uncertainty (DMDU )
• Decision process management
• Economic and financial methods
• Interval methods
• MCDA full ranking
• MCDA partial ranking
• Soft elicitation
Type of decision-making body
C = Community
G = Government
B = Business/Industry
F = Finance
N = NGO
A = All categories

Figure 17.8 |  Decision-analytic tools used across different geo-political scales and how they contributed to decision outcomes. Points comprise the type 
of decision-making body (C = Community; G = Government; B = Business/Industry; F = Finance; N = NGO; A = All categories) coupled with the reference number in square 
brackets, which correspond to numbered references in the case studies of Table 17.4. Colours of the points correspond to the class of decision-analytic tool: Bayesian (red), DMDU 
(decision-making under deep uncertainty) (brown), decision process management (dark blue), economic and financial methods (purple), interval methods (light blue), MCDA—full 
ranking (light green) or partial ranking (dark green), soft elicitation (Black).

17
2576Chapter 17 Decision-Making Options for Managing Risk
17.3.1.3.2 Stakeholder engagement
Stakeholder engagement has become increasingly part of climate-
relevant decision processes (Orlove et  al., 2020). The degree of 
stakeholder engagement ranges from instructive and consultative to 
cooperative, which are equivalent to information exchange, influence 
and partners in decision-making (Sen, 2000; Cattino and Reckien, 
in press). Since the AR5, climate change adaptation and resilience 
literature has seen an increase in participatory approaches that deepen 
engagement and overcome challenges, as well as making some 
assessments of their effectiveness (Newton Mann et al., 2017; Wamsler, 
2017; Esteve et  al., 2018), including structured interactions among 
different types of stakeholders and the use of place-based boundary 
organisations to strengthen the interactions and heighten the awareness 
of the institutional context. A higher degree of public participation can

lead to more transformational adaptation as well as to higher ambition 
for local mitigation (medium confidence) (Section  17.4.4.2; Cattino 
and Reckien, in press). Challenges to stakeholder participation are 
access to state-of-the-art science, capacity to recognise and respond 
to non-reliable or false climate science information, and the removal 
of cognitive and other biases (high confidence) (Gorddard et al., 2016; 
Engler et al., 2019; Fulton, 2021).
Participatory and elicitation approaches, where the concerns and 
involvement of a broader range of interest groups and stakeholders are 
taken into account, can improve the effectiveness of decision-making 
(medium confidence) (Gregory et  al., 2012; Cvitanovic et  al., 2019). 
Participatory planning includes a variety of co-generative strategies 
and approaches (e.g., qualitative scenario or adaptation pathway 
development) through which goals and objectives, knowledge and

strategy implementation and evaluation can be decided collaboratively 
between practitioners, policymaking, local interests and groups, 
and scientists (Butler et al., 2016; Prober et al., 2017; Symstad et al., 
2017). Specifically, for climate change adaptation, these decision-
making strategies can incorporate expert, Indigenous and local 
knowledge (high confidence) (Cross-Chapter Box  INDIG; Gustafson 
et  al., 2016). The challenge will be to bring together these different 
actors, as stakeholders tend to act within rather than among systems 
and procedures, and it is important that platforms are developed to 
integrate data effectively (Rizzo et  al., 2020). Furthermore, reflexive 
and iterative risk management may further ensure acceptance by 
participating groups.
Bayesian methods are increasingly used in advancing approaches 
for decision-making and support in climate adaptation (Sperotto 
et al., 2017), by being able to include stakeholder and decision-maker

perceptions and biases (Dias et  al., 2018; Engler et  al., 2019; Phan 
et  al., 2019; Fulton, 2021) in a transparent modelling environment, 
thereby facilitating consensus and impartiality (medium confidence) 
(Catenacci and Giupponi, 2013; Gelman and Hennig, 2017). Increasing 
computational efficiency means that these methods can enable 
different approaches to be addressed and different descriptive 
and prescriptive models to be included within a single probabilistic 
environment, which also can be updated in iterative processes (high 
confidence) (Table 17.4; Sperotto et al., 2017; Phan et al., 2019).17.3.1.3.3 Scenario analyses
Scenarios are described in SR1.5 (IPCC, 2018a) and SRCCL (IPCC, 2019b) 
as a description of how the future may develop based on a coherent 
and internally consistent set of assumptions about key driving forces 
(e.g., rate of technological change, prices) and relationships. Scenarios

are neither predictions nor forecasts but are used to provide narratives 
and trajectories equipped with alternate outcomes. SR1.5 and the 
SRCCL describe a range of scenarios methods and how scenarios are 
used to guide risk management decision-making. Scenario analysis 
includes a range of potential future conditions from low-end and mid-
range to high-end projections. Scenarios can also include a temporal 
component, that is, short term, medium term and long term, as defined 
in the SROCC (IPCC, 2019c).
Scenarios and pathways, combined with elicitation methods, are 
becoming widely used to assess adaptation and resilience strategies (high 
confidence) (Butler et al., 2016; Prober et al., 2017; Symstad et al., 2017; 
Lawrence et al., 2019; Phan et al., 2019; Sperotto et al., 2019; Haasnoot 
et al., 2020a). They can support the consideration of a wide range of 
alternative possible futures (Catenacci and Giupponi, 2013; Jäger et al.,

2018), enabling identification of potential path dependencies caused 
by adaptation options (high confidence) (Pretorius, 2017; Haasnoot 
et  al., 2020a). They can also increase the willingness of stakeholders 
to consider costly actions, by placing them within broader sequences 
of action (limited evidence) (Barnett et  al., 2014). The development, 
consideration and understanding of scenarios can be enhanced by using 
visualisation tools to better display storylines, enabling the discussion of 
alternative futures by participants in decision-making processes (limited 
evidence) (Winters et al., 2016).
17.3.1.3.4 Evaluating trade-offs, robust decision-making and deep 
Trade-offs are pervasive in decision-making for climate change adap-
tation, including between adaptation and mitigation, economic/social 
and environmental cost including distributional/equity considerations, 
affordability and risk reduction, short- and long-term consequences,

and spatial variations (Borgomeo et al., 2016; Hudson et al., 2016; Gil 
et al., 2018; Landauer et al., 2019).
Trade-offs are often directly compared in cost–benefit analyses which 
require rigorous estimation of the monetised costs and benefits, 
where monetisation is feasible and values uncontested (such as for 
infrastructure) (high confidence) (de Ruig et  al., 2019; Table  17.4). 
Other tools can be employed, such as cost-effectiveness analysis 
and multi-criteria analysis in order to draw stakeholders into the 
process (Posner, 2004; Matheny, 2007; Mechler and Schinko, 2016). 
Stakeholder participation in measuring costs and benefits and in the 
modelling can aid the process (Doukas and Nikas, 2020).
Logic trees include a range of decision protocols and multi-criteria 
rules, either based on quantitative or qualitative categories (Roncoli 
et al., 2016), often termed multi-criteria analyses. The concept of the

logic tree has been increasingly applied in climate risk decision-making 
contexts (Nikas et al., 2018).

17
2577Decision-Making Options for Managing Risk  Chapter 17
Since the AR5, robust decision-making methods are increasingly used 
to account for deep uncertainty in many climate-related risks (high 
confidence) (Marchau et al., 2019; Table 17.4), particularly when decisions 
need to be made well in advance of when the adaptations need to be 
implemented (Cross-Chapter Box.5 in SROCC Chapter 1; Cross-Chapter 
Box DEEP in this Chapter). Reducing risk and building resilience under 
the context of these types of wicked problems require asking ‘what if’ 
questions about the future, remaining flexible in the face of uncertainty 
and seeking out policies that provide good outcomes no matter what the 
future climate might bring (high confidence) (Section 17.6; e.g., Larson 
et al., 2015; Bhave et al., 2016; Bhave et al., 2018). In these cases, trade-
offs can be assessed and options can be prioritised through iterative 
decision-making processes, such as multi-criteria decision-making,

robust decision-making and dynamic adaptation pathway planning 
(high confidence) (Table 17.4; Kwakkel et al., 2014; Kwakkel et al., 2016; 
Shortridge et al., 2016; Lawrence and Haasnoot, 2017; Haasnoot et al., 
2019; Lempert, 2019; Roelich and Giesekam, 2019; Haasnoot et  al., 
2020a). They can address limitations of data-intensive robust decision-
making in developing countries (Daron, 2015), use proxy data to enable 
the use of robust decisions in data-scarce contexts (Shortridge and 
Guikema, 2016; Ahmad et al., 2019), incorporate multiple-objectives into 
robust decision-making (Singh et al., 2015), and supplement pathway 
development with real options analysis (Buurman and Babovic, 2016; 
Smet, 2017; Haasnoot et al., 2019; Lawrence et al., 2019). Often, there 
are close synergies between the application of these methods and using 
scenario analyses (Workman et al., 2021).
17.3.1.3.5 Adaptive feedback management
Iterative decision-making requires that the implementation of

adaptations be reviewed to determine whether the adaptation 
effectively achieved the objectives, and whether adjustments or 
additional actions were required (Section  17.5). Adaptive feedback 
management is an approach to managing dynamic climate risks 
by designing a field monitoring programme to provide data to an 
assessment procedure which in turn advises on what adjustments need 
to be made to a ‘control action’, all of which are part of the adaptation to 
be implemented (Hurlbert et al., 2019; Figure 17.7). Adaptive feedback 
management is more able to account for the dynamic nature of risk and 
the future emergence of unforeseen risks because of the active design 
of how to adjust the management approach (Dickey-Collas, 2014).
Adaptive feedback management is important for managing climate risks 
that fall within the Cynefin context of chaos, relying on observations 
and indicators to learn about the system and to trigger actions (medium

confidence) (Helmrich and Chester, 2020). It has been a valued approach 
for managing wildfish fisheries in many oceans (high confidence) (Fulton 
et al., 2019; Hollowed et al., 2020; Bahri et al., 2021) and is important 
for responding to the challenges of climate change (high confidence) 
(Holsman et al., 2019; Hollowed et al., 2020; Bahri et al., 2021).
While the benefits of investment in data and assessments can 
outweigh the costs of implementation (low confidence) (Fulton et al., 
2019), the implementation may take time when resources are limited, 
particularly in developing nations, where low-cost approaches will be 
needed for deciding on pathways for adaptation (Bhave et al., 2016; 
Shortridge et al., 2016).Iterative decision-making and adaptive feedback management meet 
when the feedback management procedure is reviewed in total for 
its effectiveness in one of the review and adjustment iterations. 
At present, a common approach for assessing different adaptation

options and their interaction is using, for example, scenarios in 
dynamic models (Adam et al., 2014; Girard et al., 2015). An emerging 
field in adapting fisheries to climate change is to embed the decision-
making system in the scenario models in order to assess the capability 
of feedback management (decision-making, monitoring and capacity 
for adjustment of the options over time) to achieve satisfactory trade-
offs among the objectives of the different stakeholders (medium 
confidence) (Melbourne-Thomas et  al., 2017; Holsman et  al., 2019; 
Hollowed et al., 2020). This method can enable prospective evaluation 
of future whole-of-management scenarios described in this chapter.
17.3.2 Integration across Portfolios of Adaptation 
Responses
In recent years, methods for simultaneously considering multiple 
societal and sectoral objectives, climate risks and adaptation options 
have been emerging, often termed ‘integrated’ approaches (Hadka

et al., 2015; Garner et al., 2016; Rosenzweig et al., 2017; Giupponi and 
Gain, 2017a; Stelzenmuller et al., 2018; Marchau et al., 2019). Different 
decision-making approaches can be complementary (Kwakkel et  al., 
2016), and multiple approaches will be needed to manage risks across 
sectors, in space and over short to long time scales (Section 17.6).
Higher-level integration was first presented in the IPCC Special Report 
Climate Change Adaptation (SREX) (Burton et al., 2012; Lal et al., 2012; 
O’Brien et al., 2012) and includes concepts of planning, coordination 
and mainstreaming (Lal et  al., 2012), consideration of cross-scale 
dynamics and nested vulnerabilities (Klein et al., 2014), and decision-
making across governments and sectors (Denton et al., 2014; Mimura 
Since AR5, recognition of the importance of using integrated adaptation

to improve climate risk management across the nexus between many 
sectors and across regions has increased (high confidence) (Harrison 
et al., 2016; Challinor et al., 2018). This was highlighted in the Special 
Report on Climate Change and Land (Hurlbert et al., 2019); advanced 
planning and integration of adaptation responses are needed over 
many levels (medium confidence) (Göpfert et al., 2019; Section 17.6; 
Woodruff and Regan, 2019). The complexity of managing this nexus 
may be compounded by the potential for antagonistic or synergistic 
effects among and between climate impacts, and changes arising from 
local sectoral activities and independent adaptation responses to those 
risks (high confidence) (Crain et al., 2008; Piggott et al., 2015; Adger 
et al., 2018; Brown et al., 2018; Stelzenmuller et al., 2018; Simpson 
et al., 2021), such as the cross-sectoral demands for freshwater (Xue 
et al., 2015; Azhoni et al., 2018). Integrated adaptation will also help

facilitate management of new and emerging risks, help identify when 
response plans may need to be changed in light of the dynamics of risk 
over time, and help identify solutions that are less likely to constrain 
future options for adapting to future needs (Wise et al., 2016).

17
2578Chapter 17 Decision-Making Options for Managing Risk
Cross Chapter Box DEEP | Effective adaptation and decision-making under deep uncertainties
Authors: Carolina Adler (Switzerland/Chile/Australia), Robert Lempert (USA), Andrew Constable (Australia), Marjolijn Haasnoot (the 
Netherlands), Judy Lawrence (New Zealand), Katharine J. Mach (USA), Simon French (UK), Robert Kopp (USA), Camille Parmesan (USA), 
Mauricio Dominguez Aguilar (Mexico), Elisabeth A. Gilmore (USA), Rachel Bezner Kerr (Canada), Adugna Gemeda (Ethiopia), Cristina 
Tirado-von der Pahlen (USA/Spain), Debora Ley (Mexico), Rupa Mukerji (India).
Decision-relevant uncertainties for managing climate risk
Adaptation decision-making can benefit from assessments that support planning for both ‘what is most likely’ as well as for stress-testing 
adaptation options over a range of scenarios (Sections 11.7 and 17.3; Cross-Chapter Box.5 in SROCC Chapter 1). This Cross-Chapter Box

summarises how deep uncertainties (Section 1.2; IPCC, 2019a) can be assessed in decision-making and addressed practically for adaptation.
The concept of deep uncertainty has evolved in IPCC assessments, expanding beyond a focus on reducing uncertainty, to also considering 
a range of tools and approaches that guide robust and timely decisions to address climate risks. Deep uncertainty is defined as 
circumstances where experts or stakeholders do not know or cannot agree on one or more of the following: (1) appropriate conceptual 
models that describe relationships among drivers in a system; (2) the probability distributions used to represent uncertainty about 
variables and parameters; and/or (3) how to weigh and value desirable alternative outcomes (Cross-Chapter Box 5 in Chapter 1; Lempert 
et al., 2003; IPCC, 2019a; IPCC, 2019c).
Decisions by individuals, households, the private sector, governments and public–private partnerships are generally made with partial or

uncertain information. This is also the case for adaptation and development decisions where there is often deep uncertainty about the 
impacts and the societal conditions, preferences and priorities, and responses over time. Under such conditions, decision makers employ 
decision processes and scientific information differently from situations where most decision-relevant information is available, uncontested 
and confidently characterised with single joint probability distribution. Assuming scientific information is certain, when it is not, is a barrier to 
effective communication of risks and to successful decisions under uncertainty, increasing the potential for failure and regret of investments, 
lost opportunities and transfers of costs to future generations (Sarewitz and Byerly, 2000; Marchau et al., 2019; Sections 11.7 and 17.6).
Addressing deep uncertainty is contextual as it depends on the decision options available, outcomes at stake and the available scientific

information (Box 1.1. in Marchau et al., 2019). The IPCC uncertainty guidance note (Mastrandrea et al., 2010) addresses only the latter 
(see also Mastrandrea and Mach, 2011; Section 1.3.4). Deep uncertainty is generally more salient when policy-relevant statements have 
low confidence or lack relevant data or information, or in cases where significant uncertainty contributes to disagreements and disputes 
(Sriver et al., 2018). Recent work has also included moral uncertainty (MacAskill et al., 2020) by evaluating the outcomes of alternative 
strategies with analyses organised around different perspectives on the appropriate principles of justice (Ciullo et al., 2020; Section 17.3; 
Jafino et al., 2021; Lempert and Turner, 2021).
To better communicate deep uncertainty, WGI AR6 complements projections of likely global mean sea level change, driven by processes

in which there is at least medium confidence, with projections that incorporate ice-sheet processes in which there is low confidence 
(Section  9.6.3 in Fox-Kemper et  al., 2021). The latter are accompanied by storylines to highlight the physical processes that would 
generate extreme outcomes (Box 9.4 in Fox-Kemper et al., 2021). These low confidence projections and storylines are useful because the 
likelihood of high-end (>1.5 m) global mean sea level (GMSL) rise in the 21st century is difficult to determine but important to consider 
in coastal settings (e.g., Cross-Chapter Paper 2; Cross-Chapter Box SLR in Chapter 3). High-end GMSL rise by 2100 could be caused by 
earlier-than-projected disintegration of marine ice shelves, the abrupt, widespread onset of marine ice sheet instability and marine ice 
cliff instability around Antarctica, or faster-than-projected changes in the surface mass balance and dynamical ice loss from Greenland

(Box TS.4 in Arias et al., 2021; Box 9.4 in Fox-Kemper et al., 2021). In a low-likelihood, high-impact storyline and a high CO 2 emissions 
scenario, such processes could in combination contribute more than one additional metre of sea level rise by 2100 (Box TS.4 in Arias et al., 
2021; Section 9.6.3 and Box 9.4 in Fox-Kemper et al., 2021). Other hazards assessed in WGI AR6 that address similar aspects relevant 
for decision-making under deep uncertainty include drought (Section 8.4.1.6 in Douville et al., 2021; Section 11.6.5 in Seneviratne et al., 
2021), flood (Section 8.4.1.5 in Douville et al., 2021; Section 11.5.5 in Seneviratne et al., 2021) and wildfire weather (days) (Section 11.8.3 
and Box 11.2 in Seneviratne et al., 2021), among others.
Approaches and information requirements for managing deep uncertainty
Many approaches are available for evaluating robust decisions under conditions of deep uncertainty (Sections 17.3 and 11.7; Box 11.5

in Chapter 11). The majority use multiple scenarios to stress-test adaptation options and explore how alternative adaptation pathways 
might evolve under a range of different conditions (Swanson and Bhadwal, 2009). Approaches differ in terms of their focus, types of 
strategies best addressed, and data and other resources required (Marchau et al., 2019).

17
2579Decision-Making Options for Managing Risk  Chapter 17
‘Low regret’ options are one relatively simple and common approach to deep uncertainty (Sections 17.3 and 17.6) expected to perform 
well over a wide range of scenarios and represent one example of robust strategies. However, such options will generally be insufficient 
for adaptive responses to adapt over long time frames and to avoid lock-in of investments (Section 11.7; Box 11.5 in Chapter 11).
‘Adaptation pathways’ provide another approach for addressing deep uncertainty and staging decisions over time (Haasnoot et al., 2013), 
by linking the choice of near-term adaptation actions with pre-determined future thresholds. Observation of such thresholds trigger 
subsequent actions in the planning or implementation stages of adaptation strategies. Adaptation pathways can begin with low-regret,

near-term actions that aim to create and preserve future options to adjust if and when necessary. Alternative pathways can be explored 
and evaluated to design an adaptive plan with short-term actions and long-term options.
Climate resilient development (CRD), and the pathways (CRDPs) to it, can also involve decision-making under deep uncertainty. Literature 
assessed in sectoral and regional chapters of this report present several examples of potential risks to achieving development goals under 
climate change, at global as well as national and local levels (high confidence) (Chapter 18). Achieving CRD depends on negotiation, 
contestation and reconciliation of trade-offs among diverse actors, who in turn value preferred outcomes differently with respect to 
associated climate risks and uncertainties, hence the prospect for deep uncertainty to manifest (Section 18.5). Deep uncertainty also

characterises the development process itself, given that fundamental changes and disruptions are part of the transformational changes 
required to shift towards CRDPs.
The ‘keeping options open’ approach, i.e. plans that use a series of sequential decisions and actions in the near term to avoid closing off 
potentially promising future options (Rosenhead, 2001; Section 2.6) or, by using real options, takes near-term actions that create currently 
unavailable options in the future (Kwakkel, 2020). Deep uncertainty approaches use a wide range of storylines as scenarios to test low-
regret options and to provide information relevant for potential thresholds for use in adaptation pathways (Haasnoot et al., 2013; Boxes 
11.4, 11.6; Sections 11.7, 17.3).
Deep uncertainty approaches enhance the value of monitoring to detect signals of change in a timely manner (medium confidence).

Actionable warning can come from climate signals, and socioeconomic indicators/signposts, including drivers of change, vulnerability 
and impacts, best suited for timely, reliable and convincing signals for decision-making that anticipate future changes and the need for 
adaptation or the potential to seize opportunities (Hermans et al., 2017; Haasnoot et al., 2018; Stephens et al., 2018; Oppenheimer et al., 
2019). For early warning signals to be decision relevant, they need to have institutional connectivity to enable action (Haasnoot et al., 
2018; Sections 1.4, 11.4, 11.7; Table 11.18) (medium confidence).
Examples and case studies from across the WGII report
There are diverse examples of the practical application of deep uncertainty methods across different climate change hazards in many 
regions of the world. For instance, low-regret options have been used to address the impacts and risks of landslides and debris flows

in mountains (Section CCP5.2.6). Their frequency and magnitude are already widely experienced (Section CCP5.2.6) and projected to 
increase (Section CCP5.3.2.1). However, managing these associated risks also requires joint consideration of projected vulnerabilities 
and exposure of people and infrastructure, including the multiple and dynamic non-climate-related factors that are relevant for how the 
impacts manifest in context, such as population growth and land use planning (CCP5.2.6). Here, context-specific deliberative processes 
are used that include scenarios to guide and specify preventive measures with higher effectiveness than protective (infrastructure) 
measures could achieve alone. Low-regret adaptation involves raising awareness and accounting for long planning horizons to address 
the uncertainties associated with such risks, for instance in mountain regions, including education (Sections CCP5.4.1; CCP5.2.6), with

co-benefits such as addressing changes in water availability for supply and demand (CCP5.4.1).
Adaptation pathways have been used to address SLR and changes in extreme rainfall through flood risk and management (Cross-Chapter 
Box SLR in Chapter 3; CCP2; Sections 13.2, 11.3 and 11.7): for example, adaptive plans in the Netherlands (Van Alphen, 2016; Bloemen 
et al., 2019), climate resilient development in Bangladesh (Hossain et al., 2018; Zevenbergen et al., 2018), adaptive spatial pathways for 
infrastructure retreat and for flood risk management in New Zealand (Lawrence et al., 2019a; Kool et al., 2020) and adaptive strategies such 
as in the cities of London (Ranger et al., 2013; Hall et al., 2019), New York (Rosenzweig and Solecki, 2014) and Los Angeles (Aerts et al., 
2018a). This approach is mainstreamed into guidance documents such as the Climate Risk Informed Decision Analysis (CRIDA) (Mendoza

et al., 2018), national guidance and policy briefs to address coastal hazards and sea level rise planning in New Zealand (Lawrence et al., 
2018; Lawrence et al., 2019b), planning for sea level rise in California (OCP , 2018) and synthesis documents by the government of Canada 
on marine coasts (Lemmen et al., 2016). Furthermore, examples from the UK, New Zealand and the Netherlands point to the development 
of monitoring plans to detect signals for climate adaptation (Stephens et al., 2017; Haasnoot et al., 2018; Bloemen et al., 2019).Cross Chapter Box DEEP (continued)

17
2580Chapter 17 Decision-Making Options for Managing Risk
Climate-smart planning, with a focus on keeping options open, can play a role in reducing species extinction rates (Sections 2.5, 2.6). 
When and where and for whom particular irreversible impacts will occur is deeply uncertain, for example the extinction of a species. Even 
at the lowest emissions scenarios, some local species will become extinct, but estimates of extinction risk are highly uncertain, typically 
varying by factors of two to three even for one species (Section 2.5) (medium confidence). Risks of species’ extinctions are lowered by 
reducing emissions, but keeping options open for as long as possible and avoiding irreversible actions are key to developing a climate-
resilient adaptive pathway so that real-time climate-driven changes can inform actions. Nature-based solutions (NBS) are emerging as

key players for mitigation. With smart planning, NBS offer approaches that not only provide substantial mitigation, but also considerable 
adaptation benefit to biodiversity, and human health and well-being. Done poorly, such projects can result in large negative impacts on 
humans and nature. An NBS climate-sensitive decision framework leading to ‘win-win’ solutions for mitigation and adaptation is shown 
in Figure 1 Cross-Chapter Box NATURAL in Chapter 2 (see also Sections 2.4.2.5, 2.5, 2.6, 5.4.4.4 and 5.14.1; Cross-Chapter Box ILLNES in 
Chapter 2; Cross-Chapter Box COVID in Chapter 7).
In view of these multiple and diverse examples, it is evident that the application of deep uncertainty methods is enabling decisions to 
be made in a timely manner that avoid foreseeable and undesirable outcomes and take opportunities as they arise (high confidence).
Prospects for adaptation decision-making

Deep uncertainty is increasingly salient for decision-making as recognition of climate-related risks and related uncertainties has increased 
(high confidence). These risks can compound and cascade to become new risks, increasing the breadth, frequency and severity of climate 
change impacts and the consequently increasing scale and scope of adaptation (high confidence) (Cross-Chapter Box Extremes in Chapter 
2; Sections 1.3.1.2, 2.3, 2.5, 2.6, 11.5, 11.7, and CCP5.3.1). Waiting until uncertainties are resolved (if they ever can) may leave little or 
no time to adapt. The lead time for planning and implementation of adaptation can take decades (Haasnoot et al., 2020b; Cross-Chapter 
Box SLR in Chapter 3), and socioeconomic developments can lock in undesirable pathways where underlying vulnerabilities and exposure, 
such as poverty, conflict and their associated displacement of people, remain unaddressed (Sections 5.13.4, 16.5.2.3.8; Cross-Chapter 
Box Migrate in Chapter 7).

Overall, there is growing evidence that effective implementation of strategies developed for deeply uncertain problems require adequate 
mandates and funding frameworks, preparedness and disaster response plans, and monitoring and evaluation of the strategy outcomes, 
against how the future unfolds (medium confidence). Collaborative and adaptive governance arrangements, and education and 
awareness raising, promote learning environments for community engagement, and are essential for the effective implementation of 
robust adaptation plans (medium confidence) (Sections 5.14.1, 17.3 and 11.7).Cross Chapter Box DEEP (continued)
Implicit to managing cross-sectoral interactions, including the nexus 
concept, is that the interlinkages between multiple sectors are systemic, 
and therefore solutions to challenges arising from any one sector can 
only be satisfactorily addressed by considering the connections to other 
sectors at the same time (Wichelns, 2017). Challenges for integrated

adaptation include: (1) to sufficiently capture the complexities between 
the nexus dimensions (Weitz et al., 2017); (2) to adequately consider the 
time, costs and challenges of coordination and cooperation (Wichelns, 
2017); (3) to consider the political economy in which progress towards 
more integrated solutions could take place, not only accounting for 
technological requirements (Leck and Roberts, 2015); (4) to obtain 
sufficient temporal or spatial data to capture the interactions between 
natural and social processes (Shannak et  al., 2018); (5) to connect 
these considerations to decision-making and policy processes in order 
to gain insights into the conditions for collaboration and coordination 
across sectors, including external dynamics and political and cognitive 
factors determining change (Weitz et al., 2017); and (6) to develop a 
coherent framework against which to assess results and observations

(Crain et al., 2008; Wichelns, 2017).17.4 Enabling and Catalysing Conditions for 
Adaptation and Risk Management
17.4.1 Introduction
The WGII AR5 identified—with high confidence—a range of factors 
that could enable or limit planning and implementation of adaptation 
options and potentially their effectiveness (Klein et al., 2014; Mimura 
et  al., 2014; Noble et  al., 2014). These included governance, finance, 
knowledge and capacity as enabling factors, as well as cultural, social, 
political and economic differences that influence individual and 
collective willingness and capability to act. The AR6 Special Reports 
(specifically, de Coninck et al., 2018; Roy et al., 2018; Collins et al., 2019; 
Hurlbert et al., 2019) reinforced the AR5 findings, further noting that 
the transitions needed for climate resilient development would need to 
be supported by radical shifts in governance, knowledge development, 
technology application, finance and economics, and social norms.

This section builds on the AR5 and AR6 Special Reports by reviewing 
new evidence on three key enablers identified in the AR5: governance, 
finance and knowledge. The focus is on assessing new evidence on (i)

17
2581Decision-Making Options for Managing Risk  Chapter 17
understanding of these enabling conditions, (ii) how they have changed 
on the ground and (iii) whether these conditions have enabled progress 
on adaptation and risk management. The section also addresses an 
emerging related topic: the role of catalysing conditions and actors in 
accelerating action on climate change adaptation, such as litigation 
on failure to adapt, understandings of urgency, and the aftermath of 
extreme weather events. While enabling conditions are necessary for 
action, they are not by their presence enough; catalysing conditions 
emerge when game-changing circumstances become present, such as 
when a high-profile extreme weather event occurs or when a champion 
drives change in an organisation.
17.4.2 Enabling Condition 1: Governance
Governance is an inclusive concept of the range of means for deciding, 
managing, implementing and monitoring climate change responses.

It can involve contributions of various levels of government (global, 
international, regional, sub-national and local) along with those from 
the private sector, of non-governmental organisations and of civil 
society. The importance of supportive governance arrangements is re-
iterated widely across regional and sectoral chapters in this report, in 
multiple different contexts (very high confidence).
17.4.2.1  Legal, Policy and Regulatory Instruments
17.4.2.1.1 Climate legislation
Legal systems play an important governance role in facilitating 
responses to climate change across all levels of society (high 
confidence) (Ruhl, 2010; McDonald and Styles, 2014; Mehling, 2015). 
Laws can facilitate climate action in multiple ways, including through: 
(i) mandating and guiding the behaviour of governance structures 
and actors, (ii) fostering coordination between different levels of 
government, (iii) enforcing climate responses, (iv) its symbolic value

and (iv) aligning scientific evidence and societal norms (Mehling, 2015; 
Scotford et al., 2017). Laws also can embed climate change planning 
within the administrative structure of a state, rendering policy less 
vulnerable to revocation (Scotford et  al., 2017). Extensive revision 
to laws has occurred in the last decade: a survey of 164  countries 
showed that over 1200 climate-related national laws and policies have 
been published, with approximately 44% being acts of parliament 
(Nachmany et al., 2017).
National climate change laws are important for transposing ratified 
international commitments into domestic regimes, such as the Paris 
Agreement and the Convention on Biodiversity, as well as voluntary 
agreements such as the Sendai Framework for Disaster Risk Reduction. 
In turn, the enactment of domestic laws can yield useful experiences 
and foster engagements that positively influence and support 
the development of international commitments (Townshend and

Matthews, 2013; Mehling, 2015). Strong and consistent regulatory 
frameworks also support the flow of climate finance to developing 
countries that have such frameworks (Nachmany et  al., 2017). The 
successful implementation of national and sub-national climate 
change and related policies and strategies are often contingent upon 
the underlying legislative framework empowering, mandating or guiding their review, implementation and enforcement (Averchenkova 
and Matikainen, 2017; Scotford et al., 2017) (medium confidence).
Existing legal systems also pose potential barriers to adaptation, as 
described in Chapter 9 (Africa) and Chapter 8 (Poverty, Livelihoods 
and Sustainable Development). Laws may reinforce governance 
arrangements and regulations that do not support responses to climate 
change, and exacerbate existing vulnerabilities and inequalities (Craig, 
2010; Arnold and Gunderson, 2013; Wenta et al., 2019). In such cases,

laws may require review and revision or replacement, and at the same 
be written in ways that foster adaptive management (Craig, 2010; 
Ruhl, 2010; Cosens et al., 2017).
Even though there is no agreed definition of or typology for climate 
change laws (Mehling, 2015), studies have tended to classify climate 
change laws as being ‘framework’ or ‘sectoral’ (see Table  17.5 for 
examples). Framework laws offer a comprehensive, unifying basis 
for climate change policy, addressing multiple aspects or areas of 
climate change mitigation or adaptation (or both) in a holistic and 
overarching manner (Townshend et al., 2011; Fankhauser et al., 2014; 
Nachmany et al., 2015; Clare et al., 2017b); they are powerful levers 
for setting national and sub-national agendas, creating climate change 
institutional structures, enabling policy implementation and driving 
the passage of additional sectoral legislation and regulations (Clare

et al., 2017b). Prior to 2010, national framework laws tended to have a 
mitigation focus, while more recent laws or amendments thereto have 
an increased adaptation focus (Rumble, 2019b). No evidence indicates 
whether general or specific framework laws yield better outcomes; 
however, reviews of more recent examples of framework laws in Africa 
suggest a trend towards more specificity in the required content of 
adaptation strategies and duties (Rumble, 2019b).
A sectoral approach to climate change legislation grafts climate-related 
provisions into existing laws, such as environmental impact assessment, 
flood insurance and infrastructure planning, collectively creating an 
aggregated legal landscape (Townshend et  al., 2011; Gerrard and 
Fischer, 2012; Nachmany et  al., 2015; Scotford et  al., 2017; Rumble, 
2019a). This approach is particularly relevant to adaptation challenges 
which intersect with numerous bodies of law that are dedicated to other

societal concerns (Gerrard and Fischer, 2012). However, integrating such 
considerations can be challenging in certain areas of law, particularly 
those relating to property rights, water rights and endangered species 
protection (Gerrard and Fischer, 2012). The incorporation of adaptive 
management principles (including monitoring, periodic evaluation, and 
response modification) within existing laws can enhance their enabling 
role and foster greater resilience (Godden, 2012; Arnold and Gunderson, 
2013; McDonald and Styles, 2014).
The legal regime for adaptation is too embryonic for assessment of 
good practice design and content, although similarities can be seen 
in the framework laws and draft bills across several countries. Some 
studies highlight the importance of domestic ‘whole of legal system’ 
analysis prior to development of modifying law. This can identify the 
range of existing legislative instruments that can directly intersect

with climate change, along with related contextual factors such as 
national circumstances, governance frameworks, and political and 
economic realities as well as national administrative culture (Scotford

17
2582Chapter 17 Decision-Making Options for Managing Risk
et al., 2017). This helps any new climate change laws to be absorbed 
into, and harmonise with, the established legal system of each country 
(Scotford et al., 2017). Efforts are underway to assist countries in such 
assessments and the identification of areas for legislative reform, for 
example through the Commonwealth and UN Environment’s Law and 
Climate Change Toolkit.  Similarly, databases such as the Grantham 
Research Institute on Climate Change and the Environment and the 
Sabin Center on Climate Change Law are expanding the knowledge 
base of national climate legislation developments.
17.4.2.1.2 Climate change policies, strategies and plans
Climate change policies and plans are important in the translation 
of national commitments and legal requirements into specific on the 
ground strategies and guidelines, which enable actions across multiple 
spheres and scales of government and non-government institutions

and actors.
Substantial developments in adaptation policy have occurred since 
AR5 (high confidence). Perhaps the most significant is the NDCs 
required under the Paris Agreement, where 184 out of 197 parties 
to the UNFCCC have already submitted their first plans (UNDP and 
UNFCCC, 2019). The NDCs have allowed countries to articulate their 
priorities and ambition with respect to climate action, and it has 
been suggested that these can in turn lead to cascading policies (and 
laws) that drive and enable adaptation and climate risk management. 
Analysis of the first NDCs submitted in the lead-up to and after the 
Paris Agreement showed that adaptation priorities were more often 
articulated by developing countries and least developed countries, 
while developed countries and emerging economies focused mostly on 
mitigation (Pauw et al., 2019). As of 2019, over 90 developing nations 
are at various stages of preparing National Adaptation Plans and 112

nations have indicated their intention to revise their NDCs for the 2020 
update (UNDP and UNFCCC, 2019).
Several other international agreements, including the Sendai Framework 
for Disaster Risk Reduction and the UN Agenda 2030 Sustainable 
Development Goals, have had significant impacts on the adaptation 
and risk-management decision-making processes. For example, the 
Sendai Framework articulates the need for improved understanding 
of disaster risk in all its dimensions of exposure, vulnerability and 
hazard characteristics; accountability for disaster risk management; 
preparedness to ‘Build Back Better’; recognition of stakeholders and 
their roles; mobilisation of risk-sensitive investment to avoid the creation 
of new risk resilience of health infrastructure, cultural heritage and 
workplaces; strengthening of international cooperation and partnership; 
and risk-informed donor policies and programmes, including financial

support and loans from international financial institutions.
Specific adaptation policies have been formulated at national, 
regional/state and local levels across 68  countries and 136 coastal 
cities (Olazabal et al., 2019a). At the national level, the quantity and 
complexity of adaptation policies have increased since AR5, with most 
policies coming into force since 2009 (Nachmany and Setzer, 2018). 
Adaptation is addressed in the executive climate policies of at least 
170  countries (Nachmany et  al., 2019a). Documented sub-national 
adaptation policies are more prevalent in developed countries and emerging economies, as compared with low- and middle-income 
ones (Olazabal et  al., 2019b). For example, by 2017, 26% of large 
and medium-sized European cities had an adaptation plan or a joint 
adaptation–mitigation plan in place (Reckien et al., 2018a).
Adaptation policies often comprise multiple goals and instruments,

which develop over time, especially where jurisdiction over policy issues 
is shared among agencies or levels of government (Río and Howlett, 
2013). The increase in the number and complexity of policy instruments 
across geared towards adaptation raises questions of coherence and 
alignment between the selected policy mixes and their effectiveness 
(England et al., 2018; Ranabhat et al., 2018; Lesnikowski et al., 2019).
Evaluation of national adaptation plans (NAPs) has only recently 
been undertaken. Woodruff and Regan (2019) compared national 
adaptation plans from 38  countries and concluded that most were 
strong in identifying vulnerabilities and identifying potential adaptation 
options but were weaker in articulating implementation pathways and 
monitoring of progress; plans written by multi-agency teams were 
nearly always of higher quality. Garschagen et al. (2021) showed that, 
while most NAPs consider future changes in climate hazard, many do

not consider how vulnerability and exposure might change, concluding 
that this limits the potential effectiveness of the plans. Morgan et al. 
(2019) showed that NAPs that are consistent with the Paris Agreement 
can enable development pathways that promote synergies between 
environmental, social and economic goals.
17.4.2.1.3 Impact of legal and policy instruments
Commitment to act, and guidance on how to do so, from international 
and national governance levels can drive national and sub-national 
adaptation (Reckien et al., 2013; Heidrich et al., 2016; Reckien et al., 
2018a). For example, more local plans have been developed in 
European countries where it is obligatory for local municipalities to 
develop climate change plans (Reckien et al., 2018a). Local government 
have also drawn on non-binding national climate frameworks, as well 
as international frameworks (such as European law) or international 
networks (such as Global Covenant of Mayors for Climate and Energy)

to guide their actions (Reckien et al., 2013; De Gregorio Hurtado et al., 
2015; Reckien et al., 2015; Heidrich et al., 2016; Reckien et al., 2018a).
However, a national framework is not always sufficient to trigger 
climate change action on the lower level, in particular when the 
national guiding document fails to clearly formulate how it should be 
used and ‘translated down’ to lower governance levels (De Gregorio 
Hurtado et al., 2015). Guidance on how to apply a national framework 
at lower governance levels can assist in their uptake.
In the case of climate change legislation, research on the impact of 
adaptation laws is limited, save for a few studies (Averchenkova and 
Matikainen, 2017), because many framework laws, particularly those 
with more of an adaptation focus, have only been published recently 
(Rumble, 2019b). Reviews of the implementation of the risk assessment 
and adaptation components of the UK’s Climate Change Act 2008 sug-

gest that they had a weaker implementation record compared with mit-
igation provisions (Fankhauser et al., 2018), potentially because imple-
mentation of adaptation is more complex as compared with mitigation

17
2583Decision-Making Options for Managing Risk  Chapter 17
Table 17.5 |  Selected examples of framework and sectoral law approaches adopted by different nations that represent a variety of regional contexts.
Example Legal approach Description References
United Kingdom Climate 
Change Act 2008FrameworkProvides for development of climate change impact reports and programmes for adaptation. 
Dedicated institutional structure with advisory body, adaptation planning provision, reporting/
information obligations, climate change mainstreaming, climate change trusts or financial 
arrangements.Averchenkova et al. (2021)
Kenya Climate Change Act 
2016FrameworkModelled on the United Kingdom Climate Change Act. Provides for development of climate 
change impact reports and programmes for adaptation. Dedicated institutional structure with 
advisory body, adaptation planning provision, reporting/information obligations, climate change

mainstreaming, climate change trusts or financial arrangements.Rumble (2019b)
Mexican General Law on 
Climate Change 2012FrameworkImposes positive duties upon government to implement ‘adaptation actions’—conservation, 
sustainable use and rehabilitation of beaches and coasts; water programmes for watersheds; the 
establishment of protected areas and biological corridors; the development of risk atlases; human 
settlement and urban development programmes; and prevention programmes targeting diseases 
exacerbated by climate change. Includes development of economic instruments, including fiscal 
incentives, credits, bonds, civil liability insurance and market-based instruments.Averchenkova and Guzman 
Luna (2018)
New Zealand Exclusive 
Economic Zone and 
Continental Shelf 
(Environmental Effects) Act 
2012SectoralIncorporates adaptive management principles by regulating the issuance of marine consents with

conditions allowing change based on ecological change and indicators.Godden (2012)
Seychelles Conservation and 
Climate Adaptation Trust of 
Seychelles Act 18 of 2015SectoralProvides for the establishment of a dedicated trust fund for conservation measures and climate 
change adaptation measures.Etongo et al. (2021)
Commonwealth of Dominica 
Climate Resilience Act 16 
of 2018SectoralPromotes disaster recovery and resilience building. Establishes the Dominica Climate Resilience 
Policy Board and sets out its functions and duties. Requires the development of a Climate 
Resilience and Recovery Plan.Government of the 
(2018)
Swedish National Strategy 
for Climate Change 
Adaptation (Government 
Proposition 2017/18:163)SectoralAmends Sweden’s Planning and Building Act (2010: 900) by requiring municipalities to assess the 
risk of damage to the built environment from climate risks as well as how such risks may change

in the future; requires detailed plans for measures to address land permeability when issuing a 
land permit; adopts the Swedish National Climate Strategy into law.Government of Sweden (2017)
Argentinian Glaciers 
Preservation Law N 32.016 
(2010)SectoralProvides for minimum budgets to protect the national glacial water sources that supply the 
Mendoza oasis. Establishes that all of Argentina’s glaciers and its periglacial environment are to 
be protected, irrespective of size.Warner et al. (2019)
Netherlands Delta Act on 
Water Safety and Fresh 
Water SupplySectoralProtects the Netherlands from risks such as sea level rise and extreme rainfall. Establishes a Delta 
Programme to secure fresh water supply and address climate risks/sea level rise; a Delta Fund to 
operate the programme and a commissioner.Van Alphen (2016)
as shown for the local level (Reckien et al., 2019). However, the UK Act 
is considered to have made action on climate change more predictable,

more structured and more evidence based (Averchenkova et al., 2021).
There are numerous examples of regulatory and project-based 
innovations by local governments. Their impact, however, is uneven, with 
much depending on the implementation capacity of local governments 
and other socio-institutional barriers, including those relating to mandate 
and joint project implementation, cross-departmental working, planning 
cycles, concerns relating to legal liability and compensation, political 
appetite and cost (Godden, 2012; Taylor, 2016a). Notwithstanding 
implementation challenges, evidence is emerging that overarching 
framework laws play a foundational and distinctive role in supporting 
effective climate governance, including adaptation governance 
(Fankhauser et  al., 2018), and are drivers of subsequent activity 
(Townshend et al., 2011; Fankhauser et al., 2014; Clare et al., 2017b), 
especially when formulated with clear guidance for all related actors,

including lower level of governance (De Gregorio Hurtado et al., 2015). 
This may explain the rapid increase in both local and national climate 
change laws, now with an increased emphasis on regulatory provisions 
to increase resilience and reduce vulnerability.17.4.2.1.4 Regulations and standards
The presence and articulation of regulations and standards that 
address climate risk, such as building codes and land use zoning 
are key enabling factors for effective decision-making (Kim et  al., 
2020). Regulations and standards provide a framework for common 
understanding of when and under what conditions action should be 
taken specifically in relation to the construction and maintenance of 
the built environment, infrastructure and environmental and social 
practice (Grynning et al., 2020). Regulations and standards for climate 
action emerge primarily from two settings: first, as an addition or 
augmentation to existing regulations and standards that emerged

initially to address existing potential climate extremes and stresses 
(e.g., size of culverts in response to maximum rainfall and runoff 
conditions); and second, new regulations and standards that were 
developed in direct response to new or emergent climate risks (e.g., 
regulations in response to new presence of mean monthly high tide 
flooding) (Qiao et al., 2018). Commonly agreed upon social norms and 
conventions also can be described as regulatory and providing a set 
of standards.

17
2584Chapter 17 Decision-Making Options for Managing Risk
The regional and sectoral chapters of this report provide significant 
evidence of how regulations and standards enhance or hinder 
opportunities for climate risk management and adaptation. Relevant 
regulations and standards are especially evident in the oceans and 
coastal domains (Chapter 3 and CCP2, in cities and infrastructure 
(Chapter 6), and the water (Chapter 4) and food sectors (Chapter 5). 
Europe and North and South America (Chapters 12, 13 and 14) have 
the most frequent documented occurrences of examples of regulations 
and standards. Regulations and standards focused on building codes 
to protect against extreme event and loss, water regulations and 
agreements to protect water supply and lessen drought impacts, and 
health codes to limit heat exposure are the most frequent examples 
of such practices. Deficiencies of regulations and standards have been

noted with respect to their capacity to manage species migrating 
from climate change, and to provide opportunities for transformative 
adaptation. The evidence from the sectors and chapters illustrate 
that more comprehensive regulations and standards lead to positive 
adaptation outcomes.
17.4.2.1.5 Environmental and social governance
Environmental and social governance refers to voluntary or non-legally 
required actions taken by participating parties to achieve a commonly 
defined goal (Bodin, 2017; DeCaro et al., 2017; Partzsch, 2020). While 
not explicitly described in the sectoral and regional chapters of this 
report, the maintenance and exercise of environmental and social 
governance decision-making strategies do enable adaptation practice 
and have become especially important when formal legal and policy 
regimes are not yet present. As formal regulation promotes clear and 
common understanding of climate risks and mechanisms to develop

context specific appropriate solutions, voluntary code-making and self-
regulation can forestall the need for legal action or can function as 
precursors to the formulation and implementation of legislation, laws 
and regulations.
Social and environmental governance has long been presented within 
climate risk decision-making, although more typically in the domain 
of climate mitigation (Wright and Nyberg, 2016; Vandenbergh and 
Gilligan, 2017). Corporate climate decision-making emphasises 
the importance of profit motives in shaping decisions; however, 
reputational factors as appropriate environmental stewards can also 
be important when linked to sensitivity of other stakeholders such 
as investors, lenders, customers and employees (Vandenbergh and 
Gilligan, 2017). Pulver (2011) notes that climate issues influence 
corporate decision-making more strongly in organisations that are 
networked with other organisations that also consider these issues and

through direct experience with climate-related events and associated 
organisational learning.
Since AR5, more case studies of social and environmental govern-
ance within the domain of climate adaptation have become evident, 
especially within the context of adaptive management experimentation 
(Vella et al., 2016; Beunen and Patterson, 2019; Blühdorn and Deflo-
rian, 2019). Environmental and social governance strategies for climate 
adaptation are diverse and reflect context-specific conditions of the 
decision-making process, including the role of the state, the individual 
and private interests, formality/informality, social responsibility, sources of financing, and transparency. Environmental and social governance 
enables the testing and definition of implementation solutions, en-
hancing the opportunities for defining successful adaptation (Surmin-
ski, 2013). Several models and approaches to adaptive governance to

promote adaptation and resilience in response to extreme weather 
events have been observed. These include polycentric and multilayered 
institutions, participation and collaboration, self-organisation and net-
works, and learning and innovation (Djalante et al., 2011).
The effectiveness of social and environmental governance varies 
by sector. For example, in the private business sector, Aragòn-Correa 
et al. (2019) assess the effects of mandatory and voluntary regulatory 
pressure on firms’ environmental strategies.  In summary, they find 
that analyses of the effects of voluntary pressure demonstrate that by 
themselves they are unlikely to bring about significant improvement 
in environmental outcomes. Professional organisations, however, 
have made progress in addressing sectoral standards relative to the 
adaptation process. This includes the development of new industry 
guidelines, codes, standards and specifications, in addition to the

implementation of infrastructure inventories that incorporate evaluation 
of vulnerabilities and identification of priority at-risk areas (Chapter 14). 
Voluntary pressures by themselves are not likely to result in positive 
outcomes and instead should be coupled with mandatory regulatory 
pressure to achieve the environmental response desired (Bianco, 2020).
Since AR5, another key development in environmental and social 
governance has been the establishment of the Task Force on Climate-
related Financial Disclosures (TCFD), which aimed to develop guidelines 
for companies to voluntarily report the financial implications of two 
broad categories of climate risk: the transition risks of shifting to a 
lower-carbon economy and the physical risks of climate change 
itself (TCFD, 2017). As of 2019,  ~1340 companies with a market 
capitalisation of USD 12.6 trillion and financial institutions responsible 
for assets of USD 150 trillion have expressed support for the TCFD

(TCFD, 2020). An analysis of reports to the TCFD in 2016 showed that 
83% of companies report on physical risks of climate change, and of 
these, 82% reported on strategies to adapt to some of the identified 
risks (Goldstein et al., 2019). The same analysis also noted that: (i) the 
total of estimates of assets at risk were two orders of magnitude lower 
than generally accepted estimates of total financial risk; (ii) a minority 
of companies consider risks outside of their own operations or in their 
value chains; (iii) most underestimate or do not estimate the costs 
of adaptation; and (iv) many assume linear impacts and responses, 
neglecting the potential for tipping points or acceleration in risk and 
potentially transformative adaptation requirements.  At this stage, 
TCFD has influenced many companies’ thinking and comprehension 
of physical climate risk, but it appears too early to assess whether this 
has driven substantive responses to manage these risks.

17.4.3 Enabling Condition 2: Finance
Finance has long been recognised as an important enabling and 
catalysing factor for adaptation, climate resilient development and 
climate risk management. In Chapter 17, financing for adaptation and 
climate risk management is covered in the extended Cross-Chapter 
Box, Financing for Adaptation and Resilience (FAR), below. The Cross-

17
2585Decision-Making Options for Managing Risk  Chapter 17
Chapter Box aims to highlight key emerging evidence on financing of 
adaptation, covering both public and private sources and instruments. 
Climate finance is also covered in a dedicated chapter in the WGIII 
Report (WGIII AR6 Chapter 15 (Kreibiel et  al., 2022)), and readers 
should refer to this chapter for a more comprehensive assessment of 
this subject from both a mitigation and adaptation perspective.
17.4.4 Enabling Condition 3: Knowledge and Capacity
17.4.4.1 Overview of Knowledge Systems
AR5 emphasised the importance of knowledge systems as an enabling 
condition for decision-making, as did earlier ARs, all of which include 
a focus on the policy relevance of knowledge (Section  1.1.4). First 
introduced in IPCC reports in AR4, the term ‘knowledge system’ is used 
extensively in AR5 and the SRs. The discussion below follows a widely 
cited definition of knowledge systems as sets of interacting ‘agents,

practices and institutions that organize the production, transfer and 
use of knowledge’ (Cornell et al., 2013: 61). This definition emphasises 
the social nature of knowledge and the importance of the link between 
knowledge and action, rather than presenting knowledge simply as 
information about past, present and future states of the world which 
can be of use to decision makers.
This definition of knowledge systems indicates the importance of 
capacity—the ability and the motivation to use knowledge for 
action—since capacity is an important feature which allows knowledge 
systems to function. Capacity is a necessary enabling condition for 
knowledge to be put to use in adaptation activities (high confidence), 
as shown across sectors such as water (Section 4.5.2), food security 
(Sections 5.12.3, 5.14.3), cities and settlements (Sections 6.4.2, 6.4.4) 
and health and well-being (Sections 7.1.3, 7.2.6), and across regions,

including Africa (Sections 9.13.1, 9.14.5), Asia (Sections 10.3.6, 10.4.4) 
and North America (Section 14.4.5).
Some research on knowledge systems retains the earlier attention to 
information as a resource for decision makers. A major focus, discussed 
elsewhere in this chapter, has been increasing the precision about the 
certainty, likelihood and confidence with which certain statements are 
made in relation to underlying evidence (see Cross-Chapter Box DEEP 
in this Chapter). This topic, which was first introduced in AR4, advanced 
significantly in AR5 (Mach et al., 2017).
In addition to these characteristics of information, the social and 
organisational aspects of knowledge systems have also been the 
subject of recent research. One strand of this discussion emphasises the 
distinctiveness of different knowledge systems, often focusing on three 
types of knowledge: scientific, Indigenous and local, and the latter are

two sometimes grouped as ‘traditional’ knowledge (see Cross-Chapter 
Box INDIG in Chapter 18). This strand emphasises the specific forms of 
knowledge production and circulation in each type. Another strand of 
discussion emphasises the networks of interactions between different 
groups. This strand follows the influential ‘Knowledge systems for 
sustainable development’ (Cash et  al., 2003), which was cited in 
Chapters 2, 7 and 8 in WGII AR5; Cash et al. (2003) emphasise the 
usability and acceptability of scientific knowledge, and highlight the relations between knowledge producers and users. The discussion in 
Section 17.4.4 on knowledge as an enabling factor integrates these 
two strands of discussion of knowledge systems.
It was well established in AR5 and SRs that a component of knowledge 
systems for good climate decision-making is the production of 
‘information on climate, its impacts, potential risks, and vulnerability’

which can ‘be integrated into an existing or proposed decision-making 
context’ (Jones et  al., 2014: 200). Also important are two other 
components of knowledge: of response options and knowledge of 
other enabling conditions, particularly governance and finance, which 
were mentioned less frequently and more indirectly in AR5 and SR1.5, 
SROCC and SRCCL. Decision makers assess the feasibility of different 
alternatives (see Cross-Chapter Box  FEASIB) and develop strategies 
for the implementation and modification of the alternative, requiring 
a level of knowledge of the governance, policy and finance landscapes 
at national (Tanner et al., 2019; Lopes et al., 2020; Roberts et al., 2020) 
and international scales (Woodruff, 2018).
Examples of the importance of these other two components—
knowledge of response options and knowledge of enabling 
conditions—are provided by  networks of cities, including internal 
institutional networks (Aylett, 2015), intermunicipal networks (e.g.,

those supported by Local Governments for Sustainability [ICLEI] 
and the international United Cities and Local Governments [UCLG] 
network), transnational municipal networks (e.g., 100 Resilient Cities, 
Asian Cities Climate Change Resilience Network [ACCCRN]) and city-
to-city regional transdisciplinary learning networks (Ndebele-Murisa 
et al., 2020). These networks generate and exchange knowledge which 
can be critical to decision makers for understanding and evaluating the 
feasibility of different response options, identifying synergies across 
sectors and mainstreaming adaptation to climate change (Haupt et al., 
2020). However, the question of how to finance such network activities 
remains under-studied (Bracking, 2021; See Box 17.3).
In addition to these general considerations of knowledge systems, 
research since AR5 has contributed to the understanding of specific types 
of knowledge. Scientific knowledge is thoroughly discussed in Chapter

1, especially in Section 1.3 ‘Understanding and Evaluating Climate Risk’, 
which shows recent advances in the well-established IPCC categories 
of observation of past conditions and model-based projections of 
future conditions. We add here a consideration of a new area within 
scientific knowledge, artificial intelligence, which offers new methods for 
producing information that can be incorporated into knowledge systems.
Applying artificial intelligence (AI) to climate change is predominantly 
in the area of climate modelling and forecasting, inclusive of weather 
extremes (Monteleoni et  al., 2013; Jones, 2017; Huntingford et  al., 
2019). Recent efforts conceptualise the potential uses of AI for 
mitigation and adaptation (Rolnick et al., 2019; Cheong et al., 2020b) 
in addition to forecasting (Rolnick et al., 2019; Chattopadhyay et al., 
2020; Cheong et al., 2020b; Prabhat et al., 2021). There are very few 
cases to assess AI applications in these domains given that AI is

a new field for climate change impact and adaptation. To this date, 
sectoral applications of AI relevant to climate change adaptation and 
risk reduction mainly have advanced in the areas of crop yields, early-
warning systems and water management.

17
2586Chapter 17 Decision-Making Options for Managing Risk
Cross-Chapter Box FINANCE: Finance for Adaptation and Resilience
Authors: Mark New (South Africa), Madeleine Rawlins (UK), David Viner (UK), Charlene Watson (UK), Lily Burge (UK), Lionel Mok (Canada), 
Lauren Arendse (South Africa), Vita Karoblyte (UK), Liane Schalatek (USA), Neha Rai (UK), Baysa Naran (Mongolia), So-Min Cheong 
(Republic of Korea), Nicoletta Giulivi (Italy/Guatemala).
This Cross-Chapter Box reports on: (i) new evidence on the finance needed for adaptation and resilience, and uncertainties in these 
estimates; (ii) the emerging public and private climate finance architecture; (iii) the status of financing for AR, including sources, total flows, 
regional and sectoral distributions; (iv) equity considerations; (iv) opportunities and challenges for financing adaptation and resilience

during and after the coronavirus disease 2019 (COVID-19) pandemic. This Cross-Chapter Box does not focus on finance for mitigation, 
which is covered in WGIII Chapter 15 (Kreibiel et al., 20122), nor the economic damages of climate change or financial aspects of Loss and 
Damage, which are covered in Cross-Working Group Box ECONOMIC (Chapter 16) and Cross-Chapter Box LOSS (this chapter), respectively.
Successive reports of the IPCC (Vellinga et al., 2001; Mimura et al., 2008; Yohe et al., 2008; Klein et al., 2014) and the AR6 Special Reports 
have noted the importance of finance as an enabler for adaptation, across both developed and developing nations. While the UNFCCC 
and the UNFCCC has yet to arrive at a formally agreed definition of climate finance, numerous overlapping have been suggested and 
reported (e.g., Falconer and Stadelmann, 2014; UNFCCC, 2014; Roberts and Weikmans, 2017; Munira et al., 2021). However, there is

wide agreement across these definitions that climate finance refers to financial resources devoted to addressing climate change, both 
mitigation and adaptation to current and projected climate change, and that these resources can come from both public and private 
sources (high confidence). Climate finance includes, but in most definitions is not restricted to, international financial flows to developing 
countries. Finance can be delivered through a range of instruments including grants, concessional and non-concessional debt, and 
internal budget reallocations (high confidence) (Watson and Schalatek, 2019). Adaptation and resilience are often used interchangeably 
in climate finance discussions, although adaptation is a process, while resilience (to climate risk) is the ability to progress towards desired 
outcomes in the face of impacts from a changing climate (Section 1.2.1).
Box Cross-Chapter Box FINANCE.1 | The 100 Billion Climate Finance Commitment to 
Developing Countries

At the 16th session of the Conference of the Parties (COP16) in Copenhagen in 2009, developed country parties to the UNFCCC 
committed to a goal of jointly mobilising USD 100 billion yr−1 by 2020 to address the climate change needs of developing countries 
(UNFCCC, 2009). This was in response to a threat by developing countries to walk out of the negotiations, as they perceived 
developed country support to be lagging and lacking in ambition (Roberts et al., 2021). The commitment was formalised in the 
Cancun Agreements (Decision 1/CP .16) in 2010 and was re-affirmed as a key element of the Paris Agreement in 2015 (Article 
9, paragraph 4). At the 26th session of the Conference of the Parties (COP26) in 2021, formal deliberations will begin on a new 
climate finance goal to be adopted in 2025; the current USD 100 billion target will serve as the annual minimum until 2025 
(Chhetri et al., 2020).

The ‘100 Billion’ does not represent the total need to respond to climate change in developing countries, nor the global cost 
across all countries, as is sometimes interpreted in the literature and media. As shown below in this Cross-Chapter Box, the 
estimated cost of adaptation for developing countries ranges from 15 to 411 billion USD yr−1 for climate change impacts out to 
2030, with the majority of estimates being well above 100 billion.
Proposed sources for the developed country commitment included ‘a wide variety of sources, public and private, bilateral and 
multilateral, including alternative sources of finance’ and several instruments including grants and loans. Nonetheless, there 
remain differences of opinion on the types of finance that should count towards this goal, with several issues identified (high 
confidence) (Bodnar et al., 2015; Bhattacharya et al., 2020; Roberts et al., 2021), including: (i) counting non-grant finance, such

as market and concessional loans (public and private), where developing countries ultimately have to repay the investment; (ii) 
what is counted as ‘climate’ by different funders, especially when climate is not the prime objective; (iii) the extent to which some 
funds are ‘new and additional’ rather than a repurposing of development finance.
Progress towards the 100 Billion target has shown an upward trend over the last several years (high confidence), but will fall 
short in 2020, even when the most generous criteria are included (high confidence). In 2017/2018, the most recent year for 
which data have been comprehensively analysed, estimates using different (but overlapping) data sources and methods were in

17
2587Decision-Making Options for Managing Risk  Chapter 17
the range 48–75 billion USD yr−1, compared with 45–75 in 2015/2016 and 41–52 in 2013/2014 (Carty et al., 2020; SM17.3; CPI, 
2020; OECD, 2020; UNFCCC, 2020). The distribution between adaptation and mitigation has remained strongly weighted towards 
mitigation, although the proportion allocated to adaptation has increased from 17–25% in 2013/2014 to 19–30% in 2017/2018 
(high confidence). One analysis that excludes debt repayments indicates that the debt-adjusted flows are about half the total 
flows reported above, of which circa 31–33% was for adaptation between 2015/2016 and 2017/2018 (Carty et al., 2020).
Adaptation finance needs
Estimates of global, regional or national finance needs for adaptation and resilience vary depending on both analysis approach, the level 
of climate change, and the geographic and sectoral scope of analysis (high confidence) (UNEP , 2016; Chapagain et al., 2020; UNEP , 2020).

Recent estimates have adopted one of main approaches: (i) aggregation of individual case studies, along with scaling to generate global 
or regional costs; (ii) analysis of NDC adaptation cost estimates (Weischer et al., 2016; Hallegatte et al., 2018); (iii) integrated assessment 
model simulation of impacts and adaptation costs (Markandya and González-Eguino, 2019; Chapagain et al., 2020).
All approaches suffer from limitations that can cause both over- and underestimates, including incomplete coverage of sectors and 
risks; inability to account for autonomous/unreported adaptation; incorrect cost estimations; soft and hard limits to adaptation; balance 
between adaptation, mitigation and residual cost; benefits and co-benefits on cost; and learning and innovation as climate change 
progresses (UNEP , 2020). Global or developing region estimates based on scaling NDC data is particularly uncertain, as most NDCs did

not specify how the costs were calculated. Also, scaling from a relatively small set of NDCs with costs to the global scale is not particularly 
robust, indicating a need for more transparency and better guidance for calculating adaptation costs (Watkiss et al., 2015; Zhang and Pan, 
2016; Hallegatte et al., 2018; African Development Bank, 2019).
Most estimates of adaptation cost in the literature are for developing countries. Chapagain et al. (2020) assessed various estimates of 
adaptation for developing countries, under different emissions scenarios for 2030 and 2050. The median estimates (and range) from these 
studies are 127 (15–411) and 295 (47–1088) billion USD yr−1 for climate change impacts out to 2030 and 2050, respectively (see SM17.3). 
All but one study report adaptation costs higher than the 70–100 billion estimated in 2010 by the World Bank (World Bank, 2010).
1,100
Cost in
billion USD
in 2005
prices
01,000
900
800
700
600
500
400
300
200

100Comparison of recent studies that estimated developing country adaptation cost s
in billion USD (in 2005 prices) per year for 2030 and 2050
(h) Markandya et al. (2019 )(g) Chapagain et al. (2020): Bottom-up(f) Markandya et al. (2019)(e) Baarsch et al. (2015)(d) UNEP (2016)(c) Chapagain et al. (2020): National plan based(b) Chapagain et al. (2020): Bottom-up(a) World Bank (2010)Based on RCP2.6 
Based on RCP 8.5
(a)(b)(c)(d)(e)(f)(g)(h) (a)(b) (d)(e)(f)(g)(h)
year 2030year 2030 year 2050
Figure Cross-Chapter Box FINANCE.1 |  Comparison of recent studies that estimated developing country adaptation costs in billion USD (in 2005 
prices) yr−1, for 2030 and 2050. Figure based on Chapagain et al. (2020). Major studies are World Bank (2010), Chapagain et al. (2020), UNEP (2016), Baarsch et al. 
(2015) and Markandya and González-Eguino (2019). The solid-coloured bars are based on RCP2.6, and patterned bars are based on RCP 8.5; the width of the bars

indicates the range of estimates (maximum and minimum) produced in each study.Cross-Chapter Box FINANCE (continued)

17
2588Chapter 17 Decision-Making Options for Managing Risk
The cost of adaptation for developed countries is rarely reported; most literature either reports a global cost or developing country costs, 
or costs for a specific country or sector. Baarsch et al. (2015), using an Integrated Assessment Model (IAM), report adaptation annual 
costs (2012 prices) in 2030 (and 2050) as 272 (660) billion globally and 205 (521) in developing countries only under the RCP2.6 scenario, 
indicating that developed country costs are around 25% (21%) of total cost.
In addition to global estimated adaptation costs, there are many studies that have focused on specific regions, countries or sectors, such 
as estimated adaptation cost for coastal environments, water-related infrastructure, urban infrastructure, agriculture and energy (UNEP , 
2014; Watkiss et al., 2015; UNEP , 2016). Examples of such estimates are reported in various chapters in this report and summarised in 
SM17.3.

Estimating the benefit of adaptation, in terms of damage avoided, remains challenging. For example, Ricke et al. (2018) show that the 
social cost of carbon (monetary damage per tCO 2 emitted) varies by up to two orders of magnitude depending on country, socioeconomic 
scenario, damage function, total greenhouse gases (GHG) forcing, and local climate change. In addition, non-monetary benefits such as 
cultural identity, sacred places, human health and lives are often ignored (Tschakert et al., 2017; Serdeczny, 2019; see also Cross-Working 
Group Box ECONOMIC in Chapter 16; Cross-Chapter Box LOSS, this Chapter). Recent case studies and global level analyses continue 
to support the conclusion in IPCC AR5 WGII Chapter 17 (Chambwera et al., 2014) that the benefits of adaptation generally remain 
larger than the costs (medium confidence), but the cost–benefit ratio varies widely by context and assumptions (OECD, 2015; Global 
Commission on Adaptation, 2019; WRI, 2019)

The climate finance landscape
The adaptation and resilience finance landscape spans multiple sources, intermediaries, instruments and recipients, operating across 
global to sub-national scales (Buchner et al., 2019; Carter, 2020; Watson and Schalatek, 2021). Public finance is provided by national 
and sub-national governments and distributed directly by government or intermediaries such as development finance institutions 
and climate funds, either nationally or internationally. Private finance comes from five main sources: commercial financial institutions 
(banks), institutional investors (including asset managers, insurance companies and pension funds), other private equity (venture capital 
and infrastructure funds), non-financial corporations such as renewable energy or water companies, and individual households and 
communities. Across these different sources, the main instruments used are grants, concessional debt, market debt, internal budget

allocation, insurance, as well as personal savings in households (high confidence). Public and private sources of funding can be blended 
into a single instrument, for example for insurance where public funds provide capital for both sovereign catastrophe instruments and 
micro-insurance (Jarzabkowski et al., 2019) or for concessional loans. Similarly, public finance is often ultimately derived from commercial 
debt instruments such as bonds.
International public climate finance
International public climate finance flows are realised through bilateral and multi-lateral channels (Watson and Schalatek, 2021) where 
contributions to these channels are received from Annex II and non-Annex I countries (UNFCCC SCF , 2018; Buchner et al., 2019). Annex II 
countries contribute as part of their commitments in the Paris Agreement, while non-Annex I countries commit climate finance through

these channels on a voluntary basis (Pickering et al., 2015; Roberts and Weikmans, 2017; Egli and Stünzi, 2019). Bilateral intermediaries 
include development cooperation agencies and national development banks. These institutions often have long-standing development-
cooperation experience, and offer climate change projects, facilities and financial instruments based on their differing mandates, structures 
and priorities (Atteridge et al., 2009; Buchner et al., 2019).
Multi-lateral channels include the UNFCCC financial mechanisms, such as the Green Climate Fund, and the multi-lateral development 
banks (MDBs), such as the World Bank. Both pool contributor resources before committing such resources for climate change projects 
and programmes. Funding through multi-lateral channels promotes recipient country engagement in the governance and prioritisation of

funding decisions, with concurrent processes in the multi-laterals often existing to support country ownership of funded climate action 
(Ciplet et al., 2013; Ha et al., 2016).
There are five multi-lateral climate change funds of the UNFCCC and Paris Agreement financial mechanisms. There are further multi-lateral 
climate change funds that are not governed by the UNFCCC or Paris Agreement, the largest of which is the World Bank governed Climate 
Investment Funds (Watson and Schalatek, 2021). Some of the major multi-lateral climate change funds have been established with a 
specific focus on adaptation, while some bilateral donors have thematic or sectoral priorities. Multi-lateral climate change funds operate 
through accredited implementing entities. These have historically been multi-lateral in nature, such as the development banks, but recent

years have seen a rise in the accreditation of national and regional institutions (UNFCCC SCF , 2018). In addition to programming funds Cross-Chapter Box FINANCE (continued)

17
2589Decision-Making Options for Managing Risk Chapter 17
from external sources, such as through the multi-lateral climate change funds, the MDBs also raise and programme their own climate 
finance (UNFCCC SCF , 2018; MDBs, 2019).
Several major multi-lateral climate change funds work through grant-only programmes, whereas others include concessional loan, equity 
and guarantee instruments. The broader suite of instruments used by the MDBs includes grant, investment loan, equity, guarantee, line of credit, policy-based financing and results-based financing (MDBs, 2019).
Public funding of a concessional nature that flows from Annex II to non-Annex I countries supports research and capacity building and

can also facilitate private finance flows into climate action, with the intention to avoid creating a high debt burden in developing countries, in response to climate impacts for which they have little historic responsibility (Watson, 2016; Carter, 2020; Schalatek, 2020). Less concessional public finance flows include other official flows that are not developmental in nature and can be trade related, including, for example, export credits.
Critiques of the public climate finance architecture are aimed at the overlapping mandates of the institutions programming climate

finance, particularly the multi-lateral climate funds, and the challenges in accessing funding (Nakhooda et al., 2014; Amerasinghe et al., 2017; Pickering et al., 2017). However, Pickering et al. (2017) further note that institutional fragmentation of climate finance could result in more flexibility, resilience and innovation. There have also been important governance changes leveraged by some of these funds and instruments, such as integration of gender considerations into projects (Schalatek, 2020).
Private financing of adaptation and resilience

There is an increasing focus on the role of the private sector to support large-scale financing of adaptation and resilience (UNEP , 2016; UNEP , 2018). To date, it has been difficult to track adaptation and resilience finance within the private sector (UNEP , 2016) as it is either not disclosed or not easily identifiable, since it is often built into capital and operating expenditure and is not a standalone investment. Several private mechanisms are emerging as important sources of climate finance (Gupta et al., 2014; Eccles and Krzus, 2018; Miller et al., 2019).
Green, social impact and resilience bonds are similar to traditional bonds—fixed-income financial instruments raised on commercial

markets by companies, governments or financial institutions—but the proceeds are used to fund activities that have positive environmental, social or climate benefit (Tuhkanen, 2020). Green bonds align to voluntary principles, such as the Green Bond Principles set out by the International Capital Market Association, the Climate Bonds Initiative’s Climate Resilience Principles (Sartzetakis, 2020). Given the voluntary nature and lack of standardisation of green bond principles, there are concerns around their additionality, and there is also a lack of data on how green bonds contribute to a scaling up of green projects (Dupre et al., 2018).
Green bond annual issuance reached 260 billion in 2019 (CBI, 2020), but as of 2018, only 3–5% (USD 12 billion) of green bond total

proceeds can be explicitly traced to climate-resilience-related efforts (CBI, 2019). Examples of AR focused bonds include those issued by Fiji in 2017, dedicating 91% of spending to adaptation and resilience (Shukla and Peyraud, 2017; Ministry of Economy, 2019), and by the European Bank for Reconstruction and Development’s 2019 Climate Resilience Bond for USD 700 million to finance climate-resilient infrastructure, commercial operations, agriculture or ecological systems (EBRD, 2019).
Dedicated investment vehicles are equity funds that are created to invest in products and services that enhance resilience and reduce 
risks. An example is the Climate Resilience and Adaptation Finance and Technology Transfer Facility that is proposed as a USD 500 million 
private equity fund to invest in companies providing climate resilience solutions for developing countries. Initial funding has been provided by donors (Miller et al., 2019).

Balance sheet finance occurs when an entity directly invests in resilience and adaptation rather than as a separate project. This source 
of funding may be from exiting reserves, re-allocation from other budget lines, or via external commercial finance, but the investment is financed by the firm rather than as a separate project (Gupta et al., 2014; Buchner et al., 2019).
Insurance can play an important role in managing residual climate risks at any given level of adaptation, but insurers can also be 
important r risk assessment and risk reduction as part of any insurance package (Jarzabkowski et al., 2019; Section 11.3.8.3). While

traditional indemnity insurance is important for repair and rebuilding of damaged property and infrastructure, parametric insurance has become increasingly popular for supporting rapid post-disaster responses such as drought, hurricane damage and flooding. Examples include sovereign insurance facilities such as African Risk Capacity and the Caribbean Catastrophe Risk Insurance Facility (Broberg, 2019) Cross-Chapter Box FINANCE (continued)

17
2590Chapter 17 Decision-Making Options for Managing Risk
as well as weather-index insurance targeted at individuals, especially in agriculture (Greatrex et al., 2015; Isakson, 2015; Surminski et al., 
2016; Jensen and Barrett, 2017; Fischer, 2019). The role of insurance as a climate risk management option, as well as limitations, is 
covered in more depth in Section 17.2 and Cross-Chapter Box LOSS (this chapter).
Mainstreaming physical climate risks and resilience in the private sector
The data on tracked climate finance and green bond issuance for adaptation and resilience both show a substantial gap between the 
adaptation needs and the finance deployed. Scaling up these instruments is unlikely to close this gap given the challenges with financing 
adaptation projects, particularly from the private sector. There is therefore a need for more systematic action to manage climate risks and 
mainstream climate change considerations (Miller et al., 2019).

The financial case for mitigation investment can often be demonstrated through revenues from, for example, the sale of renewable 
electricity. On contrast, the benefits from investment in adaptation and resilience are typically considered in terms of avoided losses and 
cost benefit ratios. For example, the Global Commission on Adaptation (2019) estimates that the overall rate of return on investments in 
improved resilience is very high, with benefit–cost ratios ranging from 2:1 to 10:1, and in some cases even higher.
The private sector is becoming increasingly aware of the need to assess physical climate risks to avoid the long-term risks to assets and 
enhance climate resilience. The task force on climate-related financial disclosures (TCFD) is likely to create additional pressure from investors 
for companies to identify, manage and reduce risks from climate change (Eccles and Krzus, 2018; ERM and CBEY , 2018; Tuhkanen, 2020).

A key factor for the impact of the TCFD on mainstreaming of physical climate risks and demonstrating the case for investment in adaptation 
and resilience will be how investors systematically incorporate physical climate risks, adaptation and resilience into their investment 
decisions. The Coalition for Climate Resilient Investment (DFID et al., 2019) was established to look at this from the private sector viewpoint 
and is working to systematically incorporate resilience into cash flow modelling and asset valuation practices, so that investors may 
quantify the investment in resilience for an asset and the benefits associated with reduced costs and more reliable revenue streams.
Recent trends in climate finance flows
Considerable progress has been made in tracking climate finance since AR5, but substantial gaps remain, especially regarding domestic

public finance and private sector balance sheet investment in adaptation (Section 17.5.1.5; CPI, 2020; Richmond et al., 2020). The best 
documented information comes from international climate funds, which provide detail at the project level. Most bilateral and multi-lateral 
investment institutions report on whether debt, grants and other instruments are for climate projects, but with less detail. Private finance 
is harder to track, as reporting is voluntary; even for green bonds, where certification identifies the range of sectors a bond aims to cover, 
reporting of how the bond is spent is infrequent.
The Climate Policy Initiative (CPI) has been tracking climate finance since 2009, allowing for trends to be assessed; however, trends 
reported are a function of both real changes in finance and changes in methods and information sources (Richmond et al., 2020). Total

climate finance tracked by CPI has increased from USD 364 billion yr−1 in 2010/2011 to 579 billion in 2017/2018 (SM17.3). Tracked finance 
remained relatively constant from 2010/2011 to 2013/2014 but has increased steeply in more recent years. The proportion of finance 
allocated to adaptation has remained small throughout, between 4% and 8% (high confidence); a further 1–2% of global finance has 
been classified as ‘multiple-objectives’. The large majority of tracked adaptation finance is from public sources (high confidence), with 
only 2% coming from private sources in 2017/2018 (CPI, 2020). This is at least partly because of the difficulty in demonstrating financial 
(as opposed to public good and avoided damages) return on investment for adaptation.
The majority of the most recently (2017/18) tracked adaptation and multiple-objective finance was supplied through public donors,

largely through grants, concessional and non-concessional instruments (Figure FAR.1). Most finance (44.1%) was spent transregionally 
(allocated in specific projects to recipients in more than a single region). For regionally specific funding, Sub-Saharan Africa and South 
Asia, along with the Latin America and Caribbean region, received the largest gross amounts, although Oceania has received the greatest 
per-capita funding. The largest proportion of AR funding has been allocated to increasing the resilience of infrastructure, energy and the 
built environment, followed by agriculture, forestry and natural management, and then water and wastewater.
Across financial instruments, Sub-Saharan Africa received the highest relative proportion through grants (38%), followed by the Latin 
America and Caribbean region (23%), with other non-Organisation for Economic Co-operation and Development (OECD) regions receiving

between 16% and 10% (SM17.3). Concessional debt as a proportion of the regional total varies from 84% in South Asia to as low as 29% 
in Latin America and Caribbean, which has the highest proportion of non-concessional debt (48%).Cross-Chapter Box FINANCE (continued)

17
2591Decision-Making Options for Managing Risk  Chapter 17
Flow and distribution of globally tracked adaptation and resilience finance in 2018 from different sources, 
through different instruments into different sectors and regions
Regionper
capita
Sub-Saharan Africa (17%)
East Asia and Pacific (8.1%)
Latin America & Caribbean (7.4%)
South Asia (10.5%)
Transregional (44.1%)
US and Canada (2.9%)
Central Asia and Eastern Europe (4.0%)
Middle East and North Africa (3.6%)
Other Oceania (0.01%)
Western Europe (2.5%)Users Private/Public Instrument Sector
(75.3%)
Multiple
objective s
(24.7%)Public (98.3%)Private (1.7%)
Non-specifiedGrant
Concessional
Non-concessiona l
Equity
Non-specifiedDisaster risk managementPolicy and capacity buildingWater, wastewater and
waste managementCross-sectoralAgriculture, forestry and
natural resource managementInfrastructure, energy and
built environmentBalance shee tUSD 1.71
USD 0.80
USD 2.86
USD 0.76
USD 5.13
USD 3.88
USD 4.14
USD 61.86
USD 4.30

Figure Cross-Chapter Box FINANCE.2 |  The flow and distribution of globally tracked adaptation and resilience finance in 2018 from different 
sources, through different instruments into different sectors and regions. Each strand shows the relative proportion of finance flowing from one category 
to another (for example, from private or public sources to different instruments). Categories from left to right are: (a) whether the finance is solely for adaptation or for 
adaptation and other objectives, including mitigation (multiple objectives); (b) whether the finance comes from public or private sources; (c) the financing instrument; 
(d) the broad sectoral allocation; (e) the geographical distribution of funding (proportion of total in % and per-capita allocation). Based on data collated by CPI (2020).
The importance of public and private finance for adaptation and resilience

Adaptation finance provided by international public mechanisms remains the core source of tracked flows in support of adaptation and 
resilience to developing countries (Micale et al., 2018; UNEP , 2018), although these public funds alone are insufficient to meet rapidly 
growing needs and constitute only a minority share of all public climate finance flows (UNEP , 2016; Global Commission on Adaptation, 2019).
Public mechanisms can play a role in leveraging private sector finance for adaptation by addressing real and perceived regulatory, cost 
and market barriers through blended finance approaches, public–private partnerships or innovative financial instruments and structuring 
in support of private sector requirements for risk management and guaranteed investment returns (Pillay et al., 2017; Miller et al., 2019).
There is growing agreement on the sectors (such as infrastructure, agriculture or water management) and approaches (contingency

finance or insurance) where private sector adaptation investments alone, or leveraged by public mechanisms, might be best targeted, 
such as by reducing the risk of providing financial services for adaptation investments to domestic micro-, small and medium enterprises 
or agricultural smallholders, many of them women (Biagini and Miller, 2013; Chambwera et  al., 2014; Pauw et  al., 2016; Global 
Commission on Adaptation, 2019; Miller et al., 2019; Resurrección et al., 2019; Richmond et al., 2020). A remaining open question is how 
to allocate limited public adaptation funds in a way that is equitable, effective and efficient between mobilising private investments and 
safeguarding adequate financial support for necessary adaptation efforts, such as the provision of public goods, which the private sector 
will not invest in (Fankhauser and Burton, 2011; Abadie et al., 2013; Baatz, 2018; Omari-Motsumi et al., 2019).Cross-Chapter Box FINANCE (continued)

17
2592Chapter 17 Decision-Making Options for Managing Risk
Many adaptation interventions in the most vulnerable countries, communities and people provide no adequate financial return on 
investments and can therefore can only be funded with highly concessional public finance. Grant support is most appropriate for 
measures such as capacity building, planning, public policy and regulatory reforms, disaster risk management and response, community 
engagement or support for social safety nets, and for addressing social vulnerabilities, including poverty or gender inequality, which 
constrain adaptation (Grasso, 2010a; Pillay et al., 2017; Agrawal et al., 2019; Buchner et al., 2019).
Access to adequate adaptation grant finance is further constrained because several public mechanisms provide grants only for the 
additional costs of adaptation measures compared with a development baseline in the absence of climate impacts. Calculating the

incremental costs of adaptation measures imposes additional time and resource burden on the most vulnerable recipients, who are often 
faced with data gaps or technical capacity constraints (Chambwera et al., 2014; GCF , 2018; UNEP , 2018; Omari-Motsumi et al., 2019).
An exact delineation of respective costs for adaptation and development components is difficult and might be unsuitable as many 
adaptation measures are intrinsically linked to development. It may also prevent realising necessary synergies between both components 
(McGray et al., 2007; Smith et al., 2011; Denton et al., 2014; Resch et al., 2017; Micale et al., 2018).
Equality and fairness in climate finance
Climate finance literature recognises that poor and least developed households, communities and countries are most affected and 
marginalised by climate change, and least responsible for its causes, but receive relatively little financial support for adaptation (Chapters

15, 8; Olsson et al., 2014; Rozenberg and Hallegatte, 2015; Hallegatte et al., 2016; Rai and Fisher, 2017; Shakya and Byrnes, 2017).
While the gap between current financial flows to developing countries and their adaptation needs (see Box Cross-Chapter Box FINANCE.1) 
is a major factor undermining equity and fairness in financing, several other factors that can also affect fair and just financing in 
developing countries have been identified in recent literature (Klein et al., 2014; Colenbrander et al., 2018; Mfitumukiza et al., 2019; Khan 
et al., 2019a; Doshi and Garschagen, 2020). First, financing is skewed in favour of mitigation, and therefore towards fast-growing upper- 
and middle-income countries offering the biggest gains in emission reductions, especially in Southeast Asia, but also in Sub-Saharan 
Africa (Rai et al., 2016). Further, as much of current finance uses debt-based instruments, mitigation projects are further preferred as

returns are more assured (Lee and Hong, 2018; Carty et al., 2020).
Second, the requirement of many funders for readiness and fiduciary capacity means that least developed countries (LDCs) have been 
less able to access finance, despite many support mechanisms being offered. Additionally, geopolitical preferences of some countries 
mean that some developing countries are preferred to others for bilateral funding (Doshi and Garschagen, 2020). This is exacerbated for 
private sector investment, where lower credit ratings make finance more expensive, and increasing understanding of exposure to physical 
climate risks could lead to ‘capital flight’ from most vulnerable countries (Global Commission on Adaptation, 2019; Miller et al., 2019; 
Cooper, 2020).
Third, within climate-vulnerable countries, very little is channelled to local communities who need it most; the few analyses available

suggest that less than 10% of total climate finance supports decentralised actions (Rai et al., 2016; Soanes et al., 2017). Reasons include: 
(i) lack of consideration of procedural equity in programme design (Grasso, 2010b; Wang and Gao, 2018; Venn, 2019; Khan et al., 2019a); 
(ii) finance being managed by multi-lateral implementers, rather than agencies that are closer to local communities; (iii) the higher 
transaction costs of decentralised projects in low-income communities reduce their attractiveness to funders as well as the ability of local 
organisations to meet the fiduciary standards (Fonta et al., 2018; Omari-Motsumi et al., 2019).
It has been proposed that, as middle-income countries can leverage mitigation finance from the private sector, targeting scarce public 
finance towards LDCs and SIDS may be necessary to ensure sufficient funds reach these countries (Steele, 2015). Matching domestic

climate spending with international support is one way to ensure LDCs get the funds they need (Grasso, 2010b; Bird, 2014). Targeting 
specific marginalised communities and women within countries can also help make climate finance more effective and fairer, such as the 
Asian Development Bank’s efforts to make lending portfolios more inclusive and pro-poor (ADB, 2018).
Post-COVID recovery packages, debt relief and finance for adaptation and resilience
Recent literature has highlighted the opportunity that COVID recovery packages offer for environmentally sustainable, low-carbon and 
climate-resilient economic growth (Forster et al., 2020; Hepburn et al., 2020; Hanna et al., 2021). Assessment of whether this is indeed 
happening is limited, although the few available studies suggest that that this opportunity is not being realised in many nations

(O’Callaghan and Murdock, 2021; VIVID Economics, 2021). One study of the Group of Twenty (G20) and 10 other nations suggested that Cross-Chapter Box FINANCE (continued)

17
2593Decision-Making Options for Managing Risk  Chapter 17
stimulus packages would have net negative environmental impact in two-thirds of these countries (VIVID Economics, 2021), while 
another showed that around half of G20 recovery investment targeted at energy has had gone towards fossil fuels, rather than to cleaner 
energy sources (Dibley et al., 2021).
Concerns have also been raised about the interactions between debt service, COVID economic recession and post-COVID recovery in 
developing countries (Simmons et al., 2021; Volz et al., 2021). Debt service grows as a proportion of national budget during recession, 
reducing scope for investment in recovery, is a self-reinforcing cycle. It has been suggested that linking debt relief to Paris-aligned 
objectives can act as an additional source of climate finance (Fenton et al., 2014). The G20 has begun addressing this debt crisis through

its Debt Service Suspension Initiative and the Common Framework for Debt Treatments (IMF , 2020). It has been suggested that these 
initiatives could be expanded to prioritise climate-focused debt-relief instruments and to include more countries (Steele and Patel, 2020; 
Volz et al., 2021). If debt relief is used to invest in national instrument for green and inclusive recovery, national ownership of the use of 
the finance can occur, avoiding some of the negative connotations of historical debt restructuring (Volz et al., 2021).Cross-Chapter Box FINANCE (continued)
These sectoral advances using AI employ various learning techniques 
inclusive of supervised and unsupervised learning, multi-modal learning 
and transfer learning techniques to generate more accurate predictions 
than afforded by traditional climate projection methods (Cheong et al., 
2020b; Camps-Valls et  al., 2021). AI applications use finer-resolution 
data such as sub-daily weather-related data, remote and wearable

sensor data, text data and real-time survey data. They are fed into 
neural networks and semi-/unsupervised learning to configure detailed 
and more precise predictions of climate change impact on crop yields 
(Crane-Droesch, 2018), early warning (Moon et  al., 2019), impact of 
extreme heat on older adults (Cheong et al., 2020a), poverty in Africa 
(Oshri et  al., 2018) and multi-scale water management combining 
blockchain technology with remote water sensors (Lin et al., 2018).
Indigenous knowledge and local knowledge are thoroughly covered 
in SROCC (Abram et  al., 2019; IPCC, 2019c; IPCC, 2019d) and in 
Section 1.3.3. We here add relevant points to decision-making, and an 
additional form of knowledge, practitioner knowledge.
Indigenous knowledge and local knowledge are gaining recognition 
at multiple scales (Kleiche-Dray and Waast, 2016; David-Chavez and 
Gavin, 2018; Nakashima et  al., 2018). Of note is their association

with ecosystem-based adaptations, showcasing the long-term 
place-based knowledge of Indigenous Peoples (Johnson et al., 2015; 
Walshe and Argumedo, 2016; Carter, 2019; Mazzocchi, 2020). These 
knowledges and practices can be an important enabling condition in 
decision-making processes, complementing scientific information  by 
identifying impacts (Fernández-Llamazares et  al., 2017; Katz et  al., 
2020), emphasising values to consider (Huambachano, 2018), offering 
solutions (Chanza and de Wit, 2016; Cuaton and Su, 2020; Orlove 
et al., 2020), guiding land use and resource management (Brondízio 
et al., 2021) and filling gaps in scientific knowledge (Hiwasaki et al., 
2014; Audefroy and Sánchez, 2017; Makondo and Thomas, 2018; Son 
et al., 2019; Latulippe and Klenk, 2020; Wheeler et al., 2020).
Practitioner knowledge—the pragmatic, practice-based knowledge 
that comes from the regular exercise of craft or professional work—

was also acknowledged briefly in AR5 (Jones et al., 2014) and treated 
significantly in SROCC (Abram et  al., 2019). Practitioner knowledge resembles local knowledge in that it is acquired through participation 
in activities, and yet it differs from local knowledge, which is often 
place-based and tied directly to specific landscapes and communities. 
Local knowledge typically covers a variety of environmental domains. 
Practitioner knowledge may be shared with people in different locations 
and is often more focused on a narrower set of work activities. Recent 
calls have recommended bringing practitioners more fully into the 
IPCC assessment process, to promote more effective decision-making 
(Howarth et al., 2018).
Practitioner knowledge makes significant contributions to decision-
making by broadening the range of alternatives which are considered 
and by bringing in understandings of systems to the selection and

implementation of alternatives. Such knowledge is applicable to a large 
number of domains, including biodiversity management (Tengö et  al., 
2014; Rathwell et  al., 2015), and natural hazard risk management in 
urban settings, as reported in Denmark (Madsen et al., 2019), the USA 
(Matsler, 2019), Canada (Yumagulova and Vertinsky, 2019), Mexico 
(Aguilar-Barajas et al., 2019) and the Caribbean (Ramsey et al., 2019). 
Other contexts, all at regional scales, include watershed management 
in Peru (Ostovar, 2019), livestock management in Finland (Rasmus et al., 
2020), agricultural adaptation in a context of water scarcity in Iran (Zarei 
et al., 2020) and the water–energy nexus in the USA (Gim et al., 2019).
Literature indicates the importance of effective governance for 
promoting integration of local and practitioner knowledge with 
scientific knowledge (high confidence). This integration is most 
extensive and promotes a wider consideration of alternatives, where

governance arrangements promote ongoing exchanges of information 
and discussion of solutions, whether through formal mechanisms 
such as regional committees (Gim et al., 2019; Ostovar, 2019; Rasmus 
et  al., 2020; Zarei et  al., 2020) or informal mechanisms such as 
personal networks and local discussion groups (Madsen et al., 2019; 
Yumagulova and Vertinsky, 2019). Where such arrangements are 
absent, practitioner knowledge is side-lined from the formulation and 
implementation of decisions (Aguilar-Barajas et  al., 2019; Matsler, 
2019; Ramsey et al., 2019).

17
2594Chapter 17 Decision-Making Options for Managing Risk
17.4.4.2 Co-production and Other Composite Knowledge Systems
There is strong evidence that composite knowledge systems—
characterised by interactions between the producers and potential 
users of climate change information—can help facilitate climate-
related decision-making (Prokopy and Power, 2015; Richards, 2018; 
Ramsey et al., 2019). Several institutional forms and structures have 
been created to link scientific knowledge, Indigenous knowledge, and 
local and practitioner knowledge to climate change decision-making.
17.4.4.2.1 Co-production
The co-production of knowledge by different actors provides important 
avenues for exchanging and integrating climate-related knowledge 
in decisions made across society (high confidence). Though many 
definitions of co-production have been offered in recent years (Bremer 
and Meisch, 2017; Vincent et  al., 2018; Bremer et  al., 2019; Harvey

et  al., 2019a), most describe a set of individuals or organisations 
who work together to generate a set of products that entail new 
knowledge products and that guide action (Miller and Wyborn, 2020). 
Some major forms of co-production include action research (Baztan 
et  al., 2017; Laursen et  al., 2018; Zanocco et  al., 2018a), trans-
disciplinarity (Howarth and Monasterolo, 2016; Wamsler, 2017; Lanier 
et al., 2018; Scott et al., 2018; Knapp et al., 2019; Young et al., 2019), 
rapid assessment processes (Atkinson et al., 2018b) and participatory 
integrated assessments (Howarth et  al., 2018; Krkoška Lorencová 
et  al., 2018; Bitsura-Meszaros et  al., 2019; Carter et  al., 2019a; 
Cremades et  al., 2019; Leitch et  al., 2019; Martínez-Tagüeña et  al., 
2020; Section 17.3.1.3.1).
Co-production promotes iterative dialogue, experimentation, 
the tailoring of knowledge to context, needs and priorities, and 
learning, often promoting integration of Indigenous knowledge, local

knowledge and practitioner knowledge with scientific knowledge 
(high confidence). It generally entails long-lasting ties and fully 
inclusive partnerships between different parties (Kench et al., 2018). 
Governance measures and adequate financing can act as enablers of 
such co-production. This integration is most extensive, and promotes 
a wider consideration of alternatives where governance arrangements 
promote ongoing exchanges of information and discussion of solutions, 
whether through formal mechanisms such as regional committees 
(Gim et  al., 2019; Ostovar, 2019; Rasmus et  al., 2020; Zarei et  al., 
2020) or informal mechanisms such as personal networks and local 
discussion groups (Madsen et  al., 2019; Yumagulova and Vertinsky, 
2019). Where such arrangements are absent, practitioner knowledge 
is side-lined from the formulation and implementation of decisions 
(Orleans Reed et al., 2013; Aguilar-Barajas et al., 2019; Matsler, 2019; 
Ramsey et al., 2019).

An important mechanism of co-production is the boundary 
organisation, a knowledge-producing organisation composed of 
individuals who reflect different disciplines or knowledge systems 
and who represent different activities, sectors or forms of governance 
(Blades et al., 2016; Graham and Mitchell, 2016; Guido et al., 2016; 
Jeuring et al., 2019; Serrao-Neumann et al., 2020; Zarei et al., 2020). 
Boundary organisations themselves can be linked into boundary chains 
(Lemos et al., 2014; Meyer et al., 2015; Kirchhoff et al., 2015a; Pretorius et al., 2019; Daniels et al., 2020). When individuals and organisations 
from different disciplinary backgrounds and missions coordinate their 
activities informally, the resulting ties have been termed ‘knowledge 
networks’ (Ziaja and Fullerton, 2015; Brugger et al., 2016; Guido et al., 
2016; Davies et al., 2018; Klenk, 2018; Muccione et al., 2019; Ziaja, 
2019). When such networks interact with each other, the resulting

associations have been called ‘communities of practice’, which 
can work to collectively shape information to shared contextual 
circumstances (Orsato et al., 2018; Wang et al., 2019b).
There is extensive evidence that co-production can generate useful 
climate knowledge (Djenontin and Meadow, 2018; Bisbal, 2019; Ryan 
and Bustos, 2019; Hewitt et al., 2020; Jack et al., 2020; Lavorel et al., 
2020; Ruiz-Mallén, 2020) and that it can increase the likelihood that 
knowledge will be used in decision-making (Vogel et al., 2016; Prokopy 
et  al., 2017; Skelton et  al., 2017; Sylvester and Brooks, 2020). Co-
production is not without its costs, since it requires more time, money, 
facilitation expertise and personal commitment from participants 
than more conventional modes of knowledge production (Lemos 
et al., 2018; Sletto et al., 2019; Wamsler et al., 2019; Blair et al., 2020). 
Some research has shown ways to decrease the costs of co-production

for participants, such as funding and time to enable and sustain 
interactions and to build trust and legitimacy, or to create boundary 
organisations (Young et al., 2016; Klenk et al., 2017).
Co-production is supported by project cycles that provide for the 
involvement of stakeholders from the outset (Daly and Dilling, 2019; 
Brady and Leichenko, 2020); flexible research agendas that do not 
assume a climate related question (Daniels et al., 2020); support for 
interactivity and reflexivity (Araujo et al., 2020); and institutionalising 
incentives which address the different values, norms, perceptions 
and work patterns of scientists, policymakers and civil society 
representatives (Cvitanovic et  al., 2015; Vincent et  al., 2015; Bruno 
Soares and Dessai, 2016; Singh et al., 2017; Djenontin and Meadow, 
2018; Norström et al., 2020; Turnhout et al., 2020). Certain roles, such 
as policy entrepreneurs (Tanner et al., 2019), embedded researchers

(Pretorius et al., 2019) and knowledge brokers (Cvitanovic et al., 2015), 
can facilitate co-production.
17.4.4.2.2 Climate services
Climate services (refer to CWG Box on Climate Services) can be 
important enablers of climate risk management, provided they are 
credible, relevant and usable (high confidence), and will become 
increasingly important as human influence on weather and climate 
extremes grows across all regions (Chapter 11; Fischer et  al., 2021; 
IPCC, 2021). Climate services are more effective and more widely 
used when they are tailored to specific decisions and decision makers 
(high confidence). Sustained iterative engagement between climate 
information users, producers and translators can improve the quality 
of the information and the decision-making and avoid maladaptation 
Historically, climate services have been organised by climate information 
providers, based in meteorological, hydrological and agricultural

faculties and services, serving to improve through climate risk 
management, including the use of historical information, monitoring,

17
2595Decision-Making Options for Managing Risk  Chapter 17
seasonal forecasts and long-term climate projections (Hewitt et  al., 
2012; Blome, 2017; Bessembinder et al., 2019; Vaughan et al., 2019b).
Recent research on climate services shows that transdisciplinary 
knowledge co-production is a key enabler, starting to shift emphasis 
from the creation of climate services products to climate services 
processes (Vincent et  al., 2018; Carter et  al., 2019b; Daniels et  al., 
2020), potentially increasing uptake and sustainability (Norström 
et al., 2020). This shift is a result of the recognition of benefits which 
a co-production approach can offer, in addition to the provision of 
information; these additional benefits include building confidence, 
capacities, learning, knowledge, social capital, institutional capacity, 
stakeholder relationships, social networks, beneficial management 
practices and strengthened institutions (Bruno Soares and Dessai,

2016; Djenontin and Meadow, 2018; Bremer et al., 2019).
Cross-Chapter Box 12.2 in WGI AR6, ‘Climate information for climate 
services’, shows that users are widely distributed across civil society. 
Relevant users of climate services include humanitarian organisations 
(Coughlan de Perez and Mason, 2014; Harvey et  al., 2019b), 
government offices (Mahon et  al., 2019), international agencies 
(Perkins and Nachmany, 2019) and the private sector (Beckett, 2016; 
Hudson et al., 2019). Climate services currently exist at local, national, 
regional and international scales, at time scales which range from sub-
seasonal to decadal and longer (White et al., 2017; Hewitt et al., 2020) 
and in a range of different sectors (Bruno Soares and Buontempo, 
2019). Agriculture is the sector with the largest number of examples 
(Zebiak et  al., 2015; Burke and Emerick, 2016; Cliffe et  al., 2016; 
Haigh et  al., 2018; Buontempo et  al., 2020); others include  health

(Ghebreyesus et al., 2010; Ballester et al., 2016), forestry (Caurla and 
Lobianco, 2020), fisheries (Busch et al., 2016), disaster risk reduction 
(Street et al., 2019) and water resources management (van Vliet et al., 
2015; Golding et al., 2019). Evaluations of the extent to which climate 
services are accessed, used and deliver benefits to decision makers 
remain in an initial stage (Perrels, 2020), though studies suggest 
that these contributions vary widely depending on context. A review 
of evaluation of weather and climate agricultural services in Africa, 
for instance, found that most farmers use climate services when they 
are available , but that on-farm outcomes varied, with some farmers 
experiencing yield losses and others gains upward of 60% (Vaughan 
et al., 2019a). Other studies express concern that large climate service 
projects have run for decades at significant expense, without adequate 
evaluation (Gerlak et al., 2020).

Recent reviews (Carr and Onzere, 2018; Hewitt et al., 2020) provide 
evidence that the use of climate services is affected by (a) the quality, 
reliability and skill of the climate information (Zebiak, 2019); (b) the 
fit, tailoring and contextualisation of that information with respect 
to the specific decision-making needs of particular users (Clarkson 
et  al., 2019); (c) the mode and method by which the service is 
communicated (Golding et al., 2017); and (d) the characteristics of the 
users themselves, including the users’ access to resources that would 
allow them to alter their decisions based on the information provided 
(Clarkson et al., 2019).
A related literature characterises the extent to which the development, 
reach and effectiveness of climate services is affected by factors that can be termed ‘climate service governance’ (Stegmaier et al., 2020). 
Elements of this governance include the arrangements by which those

parties engage with each other (Vaughan et al., 2016; Daniels et al., 
2020) and the financial arrangements, and associated responsibilities, 
which support the service (Lourenço et  al., 2015; Bruno Soares and 
Buontempo, 2019). Though governance varies by context, evidence 
suggests that engaging a range of experts and potential users in the 
co-design and co-production of climate services increases the use and 
utility of services (Lemos et al., 2014; Pope et al., 2017; Masuda et al., 
2018; Harvey et al., 2019b). However, some studies warn that, even 
with broad and inclusive participation, power differentials can create 
barriers to co-production, reducing the usefulness of information 
products (Alexander et al., 2020) and the neglect of non-meteorological 
sources of information which may also possess useful predictive power 
(Coughlan de Perez et al., 2019).
A small but growing number of papers consider the business models

that support climate services, including, for instance, the role of 
open data (Iturbide et al., 2019; Chimani et al., 2020), the standards 
or institutional mandates by which users come to understand the 
credibility and legitimacy of certain services (Bruno Soares and 
Buontempo, 2019), and the role of public–private partnerships 
(Cortekar et al., 2020). While the commercialisation of climate services 
holds significant promise that more and more specifically targeted 
services will be provided, there is not yet agreement on which business 
models best support this in different contexts. There is also concern 
that commercialisation of climate services may disadvantage under-
resourced actors at the expense of wealthier or more powerful ones 
(Webber, 2017; Webber and Donner, 2017; Cortekar et  al., 2020). It 
has been noted that some climate services, such as weather forecasts 
and early warnings, are an example of a public good, best provided by

public agencies (high confidence) (Sutter, 2013; Kitchell, 2016; Hansen 
17.4.4.2.3 Capacity and motivation within knowledge systems
Knowledge of climate change influences decision-making not only by 
providing information but also by increasing the motivation to act and 
by promoting behaviour change. Evidence from many sectors (including 
water (Section 4.5.2), ocean and coastal ecosystems (Section 3.6.2), and 
agriculture (Section 5.4.2) and regions (including Africa [Section 9.8.4], 
Asia [Section 10.4.6] and North America [Section 10.4.5] shows that 
building capacity (e.g., adaptive capacity, institutional capacity, 
education/training in human capacity) can support adaptation and 
limited governance capacity can constrain it (high confidence). An 
emerging area of research examines the contribution of building 
capacity within public and technical organisations and agencies to draw 
on Indigenous knowledge and local knowledge (Adger et  al., 2017;

Hochman et al., 2017; Bacud, 2018). A number of factors influence the 
effect of knowledge on motivation and behaviour change, including 
values and education.
Decision makers who shape options for managing climate risk can 
evaluate stakeholders’ capacities and motivations to participate in the 
implementation process of these options. Stakeholder engagement in 
climate change risk management supports successful adaptation (Gray 
et al., 2014; Elsawah et al., 2015; Siders, 2017; Giordano et al., 2020).

17
2596Chapter 17 Decision-Making Options for Managing Risk
Research in psychology and related fields shows that the cognitive 
mechanisms by which individuals and organisations process climate 
information influence this capacity, motivation and engagement 
(Grothmann and Patt, 2005; Grothmann et  al., 2013; Masud et  al., 
2016; Nelson et al., 2016; Takahashi et al., 2016; Hügel and Davies, 
2020; Grothmann and Michel, 2021).
The perception of climate change as a major threat that requires action 
has increased since AR5, reflecting both the growth of information 
about climate change and the processing of that information (Lee 
et al., 2015; Fagan and Huang, 2019). Global social movements play 
an important role in raising public awareness of climate urgency 
(Thackeray et al., 2020). Climate change concern plays an important 
role in decision-making outcomes which entail public participation 
(Lammel, 2015; Chiang, 2018; van Valkengoed and Steg, 2019; Arıkan

and Günay, 2020). Nonetheless, public risk perception varies sharply 
on spatial and temporal scales, reflecting environmental changes, 
social influences (Kousser and Tranter, 2018; Rousseau and Deschacht, 
2020), economic capacities (Arıkan and Günay, 2020) and culture (Noll 
et al., 2020), as well as individual characteristics (van Valkengoed and 
Steg, 2019). The importance of values and norms is demonstrated by 
recent research which highlights how intrinsic motivation (altruistic, 
self-transcendental and eco-centric values) (Corner et al., 2014; Braito 
et  al., 2017; Xiang et  al., 2019; Bouman et  al., 2020) and extrinsic 
social motivation (e.g., economic gains and social desirability) (van 
Valkengoed and Steg, 2019) can drive action.
Recent research shows the importance of education as a predictor of 
risk perception, motivation and action. Education level is the strongest 
predictor of public awareness of climate change risk in a study across

119 countries of public awareness of climate change risk (Lee, 2015), 
though this relationship varies in different nations, and is influenced 
by mediating variables (Muttarak and Chankrajang, 2015; Blennow 
et al., 2016) (Ballew et al., 2020). Knowledge and awareness of climate 
change are correlated with the motivation to undertake action on 
climate change (Hornsey and Fielding, 2017). The integration of climate 
science in educational curricula has been shown to be effective (Hess 
and Maki, 2019; Molthan-Hill et al., 2019), including approaches such 
as integration of the complex system approach (Jacobson et al., 2017), 
experiential climate change education (Siegner, 2018), including climate 
games (O’Garra et al., 2021; Pfirman et al., 2021), massive open online 
courses and informal science learning centres (Geiger et al., 2017).
Attention to behavioural change of individuals has grown since AR5, 
including cases which address both adaptation and mitigation (e.g.,

dietary changes, modification of buildings, transport alternatives) 
(Azadi et  al., 2019; Fischer, 2019; Willett et  al., 2019; Sharifi, 2020; 
Sharifi, 2021). The interventions to promote behavioural change 
can be bottom-up, initiated by individuals, communities, non-
governmental organisations or the private sector, or top-down, coming 
from governments at various levels (Robertson and Barling, 2015; 
Stern et al., 2016). They are supported by a number of mechanisms, 
including education, information strategies, and campaigns, financial 
incentives, regulatory processes and legislation (Rosenow et al., 2017; 
Creutzig et al., 2018; Carlsson et al., 2019). These behavioural changes 
contribute significantly to effective risk management.17.4.5 Enabling Condition 4: Catalysing Conditions
A clear difference between enabling conditions and catalysing 
conditions is emerging in the climate mitigation literature (Hermwille

et  al., 2019; Michaelowa et  al., 2021), with some examples in the 
adaptation literature as well (Madsen et  al., 2019; Booysen et  al., 
2019a; Bolorinos et  al., 2020). Though enabling conditions are 
necessary pre-conditions that allow response options to be formulated 
and implemented, their presence alone does not guarantee that 
these response options will occur in a timely fashion or at a scale 
commensurate with the risk, or even that they will occur at all. 
Catalysing conditions address this deficit in advancing action. They 
serve to overcome the inertia that often operates as a barrier to action 
and motivate individuals and organisations to initiate or accelerate 
action. Different forms of catalysing conditions, described below, lead 
individuals and organisations to weigh more seriously the costs of 
delaying action or keeping action at low levels. Catalysing conditions 
focus the attention of individuals and organisations on particular risks,

leading actors to augment their decision-making processes and to 
allocate financial and social resources to respond to those risks. This 
attention and deliberation can lead to more frequent and potentially 
substantial adaptations, whether through more extensive action on 
existing forms of adaptation or through the adoption of entirely new 
adaptations (Bolorinos et al., 2020).
The first two catalysing conditions described below address the costs 
of delaying action. Urgency increases the awareness of individuals and 
organisations of such costs, while windows of opportunity, including 
extreme events, are time-bound periods during which certain actions 
are possible, but after which they are more difficult or impossible. 
The other two conditions stimulate new forms or levels of action by 
promoting or directing step changes from one policy or management 
regime to another (Solecki et  al., 2017). Litigation over adaptation

issues, for example, can open new lines of action or close off old ones, 
while catalysing agents advance action through a variety of means 
(e.g., communicating the urgency of climate action, revising agendas 
for action, expanding coalitions which undertake action). As detailed 
below, these four catalysing conditions can operate together as well as 
separately to promote more prompt and extensive adaptations.
17.4.5.1 Urgency
Urgency can catalyse action for individuals and organisations. A 
moderate level of urgency serves as an important driver of climate 
action, but both high and low levels of urgency impede response 
(high confidence). Wilson and Orlove (2021) review 5 experimental 
and 20 observational papers that examine the relationship between 
urgency and levels of response in climate decision-making, across a 
range of settings: from individuals and households to communities, 
managed ecosystems, sub-national regions and international river

basin. Urgency in the papers is defined primarily through objective 
and subjective time pressure, including the recognition of the costs of 
delaying action and the importance of using windows of opportunity 
during which new forms and higher levels of response are possible. 
All the experimental papers and all but three of the observational 
papers provide support for an inverted U-shaped relationship between 
urgency and response intensity (including motivation and action), with

17
2597Decision-Making Options for Managing Risk  Chapter 17
higher levels of response at intermediate levels of urgency and lower 
levels of response at low or high levels of urgency (Figure 17.9). The 
general shape of this relationship also is supported for other decision 
domains by a well-established line of research within psychology 
(Heitz, 2014; Zakay, 2014; Prem et al., 2017).
The synthesis of the studies on urgency offers two central lessons for 
policymakers, community groups and others involved in addressing 
climate change. First, greater levels of response to climate change-
induced challenges can be motivated by communication strategies 
that move decision makers from low to moderate levels of urgency 
(high confidence). In the case of drought, a number of studies show 
that urgent messages promote water conservation, especially when 
these messages are repeated, perceived as trustworthy and linked 
to concrete suggestions for action (Gonzales and Ajami, 2017;

Joubert and Ziervogel, 2019; Kam et al., 2019; Booysen et al., 2019a; 
Booysen et al., 2019b; Bolorinos et al., 2020). These effects are also 
demonstrated in experimental studies of adaptation planning in 
contexts including European flood preparations (Madsen et al., 2019; 
Pot et  al., 2019) and Pacific Island coastal planning (Donner and 
Webber, 2014).
Second, very high levels of urgency are a barrier to effective action 
(medium confidence) because last-minute actions to reduce risk 
during crises can create haste and panic, often leading to insufficient 
deliberation. In these cases, decision makers fail to consider a full 
range of alternative actions, make rash choices and poorly mobilise available resources (Asfaw et  al., 2019; Robins, 2019; Gee, 2020). 
Given that climate decision makers in many regions and sectors are 
experiencing greater pressure to act, this finding suggests the existence 
of windows for planning and action during which climate risks have

led to moderate levels of urgency, but before these risks have resulted 
in urgency exceeding some upper threshold (Section 17.4.5.2).
In addition, these studies point to potential weaknesses as well as 
strengths in strategic communication to modulate urgency. Such 
messages may instead lead to lower levels of response if they induce 
very high levels of urgency (Asfaw et al., 2019), though this effect may 
be somewhat mitigated by messages that simultaneously increase 
recipients’ sense of self-efficacy or they are experienced in the specific 
risk domain discussed in the messages (Bodin et  al., 2019). Future 
research on the relationships between urgency and effective risk 
management could help refine the measurement of urgency, how the 
relationship varies in different contexts, the role of different forms of 
messaging about urgency and action (Fesenfeld and Rinscheid, 2021), 
and the effects of urgency on decision-making by high-level decision

makers within polities and by climate social movements.
17.4.5.2 Windows of Opportunity
Windows of opportunity are time-bounded periods during which 
conditions are present for advancing and often accelerating climate 
adaptation strategies. They can act as significant catalysing conditions for 
climate action and are connected to a range of possible outcomes from A moderate level of urgency serves as an important driver of climate action, 
but both excessive high and low levels of urgency impede effective action responses
[4]–[17]
[22]
[3][1]–[2][23]
UrgencyResponse
attention,
motivation,
action
[21][18]–[20] 1.van Slobbe et al. (2016)
2.Gabillet et al. (2020)
3.Fesenfeld & Rinscheid (2021)
4.Rinscheid et al. (2020)
5.Rinscheid & Wüstenhagen (2019)
6.Rooney-Varga et al. (2018)
7.McMichael et al. (2019)
8.Gonzales & Ajami (2017)
9.Booysen et al. (2019)
10.Joubert & Ziervogel (2019)
11.Stavenhagen et al. (2018)
12.Archie et al. (2018)
13.Pot et al. (2019)
14.Donner & Webber (2014)

15.Kam et al. (2019)
16.Bolorinos et al. (2020)
17.Madsen et al. (2019)
18.Amelung & Funke (2015)
19.Robins (2019)
20.Asfaw et al. (2019)
21.Gee & Anguiano (2020)
22.Lohse et al. (2017)
23.Bodin et al. (2019)
Figure 17.9 |  A moderate level of urgency serves as an important driver of climate action, but both high and low levels of urgency impede response (derived 
from Wilson and Orlove, 2021).

17
2598Chapter 17 Decision-Making Options for Managing Risk
small incremental shifts to larger-scale more profound transformation 
adaptations (Novalia and Malekpour, 2020).
Windows can open because of extreme weather events (Birkmann and 
Fernando, 2008), political shifts, such as new institutions, new laws 
and regulations, and presence of a new policy entrepreneur or new 
policies (Haasnoot et al., 2013; Bell and Morrison, 2015), relevant and 
achievable policy goals, and emergence of new knowledge (Abunnasr 
et al., 2013), and close after the initial causes recede and become less 
efficacious. They also serve as focusing events whereby a coalition of 
groups address specific policy questions or response options (Rudel, 
2019). Recognising that windows of opportunity often catalyse action 
does not mean that action outside such windows is insignificant or 
impossible.
Extreme events such as disasters often act as proximate drivers of

windows of opportunity (Birkmann and Fernando, 2008; McSweeney 
and Coomes, 2011). Climate disasters in a specific location become 
significant windows for new debate, policymaking and financing 
(McSweeney and Coomes, 2011). Extreme events also can facilitate 
change at locations distant from the most impacted site when remote 
actors gain perspective on their own risks (Friedman et  al., 2019; 
Solecki et  al., 2019). Factors that facilitate extreme events driving 
proactive as opposed to reactive responses include access to relevant 
risk and vulnerability data, pre-existing experience with similar events, 
and appropriate governance (Brown et al., 2017a). Page and Dilling 
(2020) find that worldview or ideology plays a central role in sense-
making and in shaping what organisational decision makers ‘see’ in 
terms of acceptable actions in response to an extreme event.
Significant variation is present across the mix and intensity of conditions

that promote action through a window of opportunity. Capacity to 
respond to is a function of the presence of enabling conditions as well 
as tools and methods to aid decision-making (Shi et al., 2015). Political 
activism provides windows of opportunity for climate adaptation 
(Lauer and Eguavoen, 2016; see also Section 17.4.5.3.1).
Sudden shifts in institutions and legal framework can also catalyse 
climate action. For example, the year 2015 included a series of 
international frameworks such as the Sendai Framework for Disaster 
Risk Reduction 2015–2030 (van Niekerk et al., 2020; Hofmann, 2021), 
the 2030 Agenda for Sustainable Development, which established 
the Sustainable Development Goals (Sanchez Rodriguez et al., 2018), 
and the Paris Climate Agreement, which dramatically enhanced the 
promotion and implementation of altered the conditions under which 
climate adaptation occurred.
17.4.5.3 Climate Litigation on Adaptation

Litigation for Loss and Damage from climate change was first noted 
as a potential motivator for emissions reduction in AR4, and AR5 
noted that litigation was pending but not tested and that, while legal 
systems were beginning to define the boundaries of responsibility for 
climate change, it was ‘unclear liability exists’. The SR1.5 (IPCC, 2018a) 
reported, with high confidence, that litigation risks of government and 
business had increased, and the SRCCL (IPCC, 2019b) noted that recent developments in climate attribution improve the ability to detect 
human influence on climate and broaden liability.
Since AR5 there has been growing recognition of the potential of 
litigation for failure to take measures to adapt to climate change to 
drive climate risk management (Banda and Fulton, 2017; Peel et  al., 
2017; Bouwer, 2018). Litigation cases on adaptation and loss and 
damage account for about one-third of those covered in the literature

(Setzer and Vanhala, 2019). Reasons for this growth are: (i) the 
growing gap between projected climate change impacts and current 
adaptation efforts (Stezer and Byrnes, 2019) and (ii) expanded legal 
duty of government, business and others to manage foreseeable harms 
(Marjanac and Patton, 2018). Climate change litigation is expanding 
geographically into the Americas, Asia (and the Pacific region) and 
Europe, with several cases brought in low- and middle-income countries 
(Stezer and Byrnes, 2019) (Table 17.6).
Lawsuits against private entities contribute to articulating climate 
change as a legal and financial risk (medium confidence) (Peel and 
Osofsky, 2015; Ganguly et al., 2018; McCormick et al., 2018; Peel and 
Osofsky, 2018). Even if unsuccessful, Estrin (2016) concludes they are 
important in underlining the high level of public concern.
Climate-related, legal, financial disclosure requirements are improving

investment decision-making of corporations as well as augmenting ex 
post liability for failure to consider climate change risk in decision-
making. Organisations are required to disclose governance around 
climate-related risks (impact of climate change on businesses, 
products, services, supply or value chain, adaptation and mitigation 
activities, investment in research and development and operations). 
This functions as a vehicle for identifying climate-related risk and the 
organisation’s resilience strategy taking into consideration different 
climate- related scenarios including a 2°C or lower scenario (Sarra, 
2018). Institutions such as the G20 (Carney, 2019), the American 
Bar Association (Brammer and Chakrabarti, 2019) and the European 
Commission (Zadek, 2018) have adopted or endorsed these standards.
17.4.5.4 Catalysing Agents
Individuals and organisations often serve as catalysing agents of 
climate risk decision-making. They promote greater levels of new forms

of climate action by communicating the urgency of climate action 
and by developing coalitions which undertake action. Agents include 
individuals, organisations or collectives, or multiple organisations 
linked together.
17.4.5.4.1 Social movements and other mobilisations
Recent studies of climate-related social movements show that 
they can act as catalysing agents which promote action to manage 
climate-related risks (medium confidence). However, these studies use 
varying definitions of climate movements within the broader context 
of environmental movements. A prominent topic of research is the 
rapidity and the large scale of the proliferation of these movements 
around the world, primarily in urban settings but also in rural and 
Indigenous contexts (Claeys and Delgado Pugley, 2017).

17
2599Decision-Making Options for Managing Risk  Chapter 17
Table 17.6 |  Examples of types of climate-related litigation.
Litigation type Detail and examples Supporting literature
Challenge government 
decisions for not 
considering climate 
change risksChallenging government or administrative planning decisions for failure to consider, or adequately address, climate 
change in relation to developing and protecting coastal zones, water-stressed regions, flood-prone areas or decisions 
affecting endangered species whose habitat is at risk. For example, the Victorian Civil and Administrative Tribunal 
in Australia rejected a planned housing project in a coastal area, citing the risks from climate change (Gippsland 
Coastal Bd. v. South Gippsland Sc & Ors (No2), 2008).Banda and Fulton (2017); Peel et al. 
(2017); Bouwer (2018); Clarke and 
Hussain (2018)
Petitions to actConstitutional petitions to force governments to take adaptation measures. As an example, in Leghari v. Pakistan a

farmer initiated public interest litigation against federal and provincial governments for failure to develop climate 
change resilience through adaptation to floods, droughts and other impacts because it violated his rights to life and 
dignity. The High Court of Lahore found for Mr. Leghari and created a commission to develop and implement a wide 
range of adaptation actions.Banda and Fulton (2017); Ashgar 
Leghari v. Federation of Pakistan 
(April 2015); Ashgar Leghari v. 
Federation of Pakistan (September 
2015)
Regulatory proceedingsEnvironmental groups and city and state officials intervened in the application of the electric utility serving New 
York City, Consolidated Edison Company, to the New York State Public Service Commission for a rate increase. 
The intervenors argued that the company was not adequately preparing for flooding, heatwaves and other 
climate-related impacts. As a result, the Commission directed the company to undertake a study of its vulnerability

to climate change, and write and implement a plan to address these risks.Consolidated Edison Co. (2019)
Failure to act by public 
authoritiesLiability of public authorities for failure to undertake necessary adaptation actions to avoid damage to life or 
property, especially where statutory framework is proven ineffective or out of step with international commitments; 
in some areas these are class action suits. An example is private lawsuits for failure of a built environment to 
consider adaptation needs in a built environment (energy efficiency works, overheating because of increased 
temperatures).Banda and Fulton (2017); Peel et al. 
(2017); Bouwer (2018)
Failure by private sector 
to consider climate 
change adaptation in their 
business practiceExamples include: (i) a citizen suit against ExxonMobil for failure to adapt Everett Terminal to the impacts of climate 
change including increased precipitation, sea level rise and storm surges occurring with increasing frequency; (ii)

a citizen suit against Shell Oil Products US alleging Shell failed to incorporate climate risks in its investment in a 
bulk storage and fuel terminal in Rhode Island, USA; (iii) shareholder action against ExxonMobil for failure to report 
climate risks or complying with recommendations to do so and for issuing misleading corporate disclosure relied 
on by investors; (iv) a suit brought an NGO, the Conservation Law Foundation, against Exxon Mobil alleging that 
the company had taken insufficient precautions to protect a major oil tank farm near Boston, USA, from coastal 
storms that are worsened by climate change, creating a danger of an oil spill into Boston Harbour. The U.S. Court of 
Appeals for the First Circuit ruled in 2021 that the lawsuit could proceed, and that the NGO could attempt to make 
out its case that Exxon Mobil should take greater precautions.; (v) government and citizen claims for public nuisance

against fossil fuel companies for the costs of adaptation such as infrastructure to protect against sea level rise.Benjamin (2017); Stezer and Byrnes 
(2019); Street and Jude (2019); 
Wasim (2019); Conservation 
Law Foundation v. Exxon Mobil 
Corporation (2021)
Youth public trust claimsGovernment inter-generational liability for inadequate climate change mitigation and adaptation efforts. Our 
Children’s Trust (a non-profit organisation) and others brought an action against the USA and several executive 
branch individuals in 2015 claiming damages for their loss of the environment and the defendant’s failure to preserve 
a habitable climate system by the governments’ affirmative actions that actively cause and worsen the climate crisis. 
Similarly, a public trust claim could be brought in a coastal town for failure to adapt to climate change.Schneider et al. (2017); Bouwer 
(2018)

Human rights claimsHuman rights may be a powerful tool for organising and unifying adaptation decision-making, especially for the 
most vulnerable, through enforcement mechanisms of progressive realisation as well as ex post liability (Chapter 8). 
For example, a persons’ right to food implores state parties to take necessary actions to alleviate hunger caused by 
climate change; during natural and other disasters, rights to water and life are impacted; sea level rise and storm 
surges impact many coastal settlements and the right to adequate housing and an adequate standard of living. 
This is in part due to increasing acceptance of the impact of climate change on health, livelihoods, shelter and 
fundamental rights.Hall and Weiss (2012); Peel and 
Osofsky (2018); Setzer and Vanhala 
(2019); Stezer and Byrnes (2019)
These movements usually focus on climate mitigation but sometimes 
include adaptation. Their social bases include groups which had not

previously been active in climate politics, notably children and youth, 
as well as sectors with long traditions of environmental activism, such 
as women and Indigenous Peoples (see Cross-Chapter Boxes GENDER 
and INDIG in Chapter 18). Much of the literature on youth movements 
traces the emergence of the movements themselves (Sanson et al., 2019; 
Treichel, 2020), their framings of climate change as a social justice issue 
(Holmberg and Alvinius, 2019) and their presence in demonstrations and 
on social media (Boulianne et  al., 2020). Climate action catalysed by 
youth and other climate movements include visible international events 
such as the signing of Declaration on Children, Youth, and Climate Action 
at COP25 in Madrid 2019 (Han and Ahn, 2020), as well as national efforts, including lawsuits, and local events such as in tree-planting and 
waste reduction initiatives (Bandura and Cherry, 2019).
A recent review examines 2743 cases around the world of mobilisations

for environmental justice causes (Scheidel et al., 2020); roughly half the 
cases occurred between 1970 and 2007, and half between 2008 and 
2019. Of these environmental mobilisations, 17% are directly related 
to climate and energy, and others are related to climate-sensitive issues 
(15% for biomass and land use, 14% for water management). This study 
reports the proportion of positive outcomes for different strategies, 
defined as meeting the goals of the movements, which generally align 
with climate adaptation and sustainable resource management. These 
rates vary from 10% for negotiated solutions to 34% for court decisions.

17
2600Chapter 17 Decision-Making Options for Managing Risk
It notes the corresponding higher rates of failure, as well as the costs 
borne by the movements, which include criminalisation (20% of cases), 
violence (18%) and assassination (13%). These costs are significantly 
higher for Indigenous communities that engage in these mobilisations.
At a global scale, climate movements succeeded in pressing for the 
greater recognition of the importance of Indigenous knowledge 
within international agreements (Tormos-Aponte and García-López, 
2018) but did not achieve the major reforms of climate finance which 
they sought (Khan et al., 2019a); these differing outcomes reflect the 
sensitivity of the issues and the formation of coalitions which supported 
or opposed the movements. At national and local scales, one review of 
US cases reports limited effectiveness of climate movements because 
of the ability of governmental agencies to co-opt them (Pulido et al.,

2016), while another review in Pakistan shows a number of successes, 
because the movements were able to build alliances with other public 
sector and community groups (Shawoo and McDermott, 2020).
17.4.5.4.2 Policy leaders and entrepreneurs
Policy leaders, often described as policy entrepreneurs within the 
scholarly literature, are individuals in positions of leadership who set 
agendas and build coalitions to drive decision-making processes, and 
hence can function as catalysers of climate adaptation (Petridou and 
Mintrom, 2020). Political leaders who have taken on climate change 
as a key policy issue function as policy entrepreneurs at international, 
national and sub-national levels. City officials, including mayors and 
other executives, often play the role of climate policy entrepreneurs, 
while the absence of effective leadership negatively affects adaptation 
success (Becker and Kretsch, 2019). Such entrepreneurs can be

important forces for change in both reactive contexts following an 
extreme or focusing event and in proactive context. They can be effective 
especially in contexts where they navigate and link together formal and 
informal networks of complex climate governance systems (Tanner et al., 
2019). Their capacity to act has been increased when they and their 
institutions are embedded within partnership networks (Bellinson and 
Chu, 2019). It is in these contexts that the leadership and position of a 
policy entrepreneur becomes even more catalytic when operating at the 
interface of formal and informal networks (Mintrom, 2019; Stone, 2019).
Sub-national actors and city officials including mayors and other 
executives are among the individuals most often described and assessed 
as climate policy entrepreneurs (Kalafatis and Lemos, 2017). City-level 
climate policy entrepreneurs often operate using their own experience, 
connections and persistence to address issues of importance to their

constituency. Climate risk concerns are often inherently local, and in 
turn local decision makers perceive it as being appropriate to engage. 
Conversely, the absence of effective leadership negatively affects 
adaptation success (Kalafatis and Lemos, 2017; Becker and Kretsch, 
2019). Urban climate policy entrepreneurs operate in four key spheres 
of policy development and implementation: attention and support 
seeking strategies; linking strategies (e.g., coalition building); relational 
management strategies (e.g., networking and trusting building); and 
arena strategies including timing (Brouwer and Huitema, 2018). The 
presence and operation of urban climate policy entrepreneurs is positively 
associated in settings with multiple jurisdictions and across differing 
spatial scales (Kalafatis and Lemos, 2017; Renner and Meijerink, 2018). It is in these contexts that their capacity to operate simultaneously at

the interface of multiple networks is particularly valuable for promoting 
climate action. Urban climate policy entrepreneurs can directly engage 
with a range of constituent groups and offer and promote climate 
adaptation strategies that can have direct impact on the daily lives of 
these residents and their interests.
17.5 Adaptation Success and Maladaptation, 
Monitoring, Evaluation and Learning
17.5.1 Adaptation Success and Maladaptation
17.5.1.1 The Adaptation–Maladaptation Continuum
As evidence on adaptation implementation grows (Berrang-Ford et al., 
2021; Eriksen et al., 2021), there is a need to examine the outcomes of 
adaptation (Ford et al., 2011) for effectiveness, adequacy and justice/
equity in both outcomes and process, as well as synergies and trade-offs 
with mitigation, ecosystem functioning and other societal goals. There 
is also a growing recognition of the observed and potential negative 
consequences of some adaptation interventions, often referred to as

maladaptation (Juhola et  al., 2016; Magnan et  al., 2016; Schipper, 
2020; Eriksen et  al., 2021). This section advances a new framing to 
allow for an improved assessment of the potential positive or negative 
outcomes of adaptation options, therefore allowing navigation of the 
adaptation–maladaptation continuum.
17.5.1.1.1 Defining and assessing success in adaptation vis a vis 
The highly contextual nature of adaptation, a multitude of applied 
definitions of adaptation (e.g., cost effectiveness versus outcomes), 
its overlaps with development interventions, and the long time 
horizons over which outcomes accrue, deter a universal definition 
of adaptation success (Dilling et al., 2019; Section 17.5.1.2; Owen, 
2020; Singh et al., 2021). Moser and Boykoff (2013), Olazabal et al. 
(2019b) and Sherman and Ford (2013) suggest criteria against which 
successful adaptation could potentially be tracked. The literature

is converging to suggest that successful adaptation broadly refers 
to actions and policies that effectively and substantially reduce 
climate vulnerability, and exposure to and/or impacts of climate risk 
(Noble et  al., 2014; Juhola et  al., 2016), while creating synergies 
to other climate-related goals, increasing benefits to non-climate-
related goals (such as current and future economic, societal and 
other environmental goals) and minimise trade-offs (Grafakos et al., 
2019) across diverse objectives, perspectives, expectations and 
values (Eriksen et al., 2015; Gajjar et al., 2019a; Owen, 2020) (high 
Maladaptation refers to current or potential negative consequences 
of adaptation-related responses that lead to an increase in the climate 
vulnerability of a system, sector or group (Barnett and O’Neill, 2010) 
by exacerbating or shifting vulnerability or exposure now or in the 
future (Antwi-Agyei et al., 2014; Noble et al., 2014; Juhola et al., 2016;

Magnan et al., 2020) and eroding sustainable development (Juhola 
et  al., 2016). Conceptually, maladaptation differs from ‘failed’ or

17
2601Decision-Making Options for Managing Risk  Chapter 17
Box 17.3 | Climate Risk Decision-Making in Settlements: From Incrementalism to 
Transformational Adaptation
Cities are important sites of experimentation where the integration and management of adaptation decision-making complexity often 
takes place. These actions provide early evidence of what aspects of complex climate risk management decision-making functions well, 
but also what does not work (Revi et al., 2020). Cities are seen as locales where case examples of transformative adaptation can be 
examined (Rosenzweig and Solecki, 2018; Vermeulen et al., 2018). Cities act as testbeds of how to integrate climate response into issues 
of equity, health, resource allocation and sustainability in ways that utilise innovative use of new and emerging decision-support tools, 
methods and protocols.
Risk management has been an integral part of the community development and settlement building process. Three key sets of drivers

influence risk management decision-making in cities (Solecki et al., 2017). These include: (1) root, that is, cultural norms and social 
traditions; (2) context, that is, policy and governance conditions; and (3) proximate, that is, extreme events. Settlements have developed 
informal and formal strategies, including climate protection levels, to respond to local conditions of climate risk and hazards. In formal 
contexts, these strategies are contextualised in local climate change action plans (Araos et al., 2016a; Stults and Woodruff, 2017; Reckien 
et al., 2018a; Singh et al., 2021) and defined around a set of evaluation tools and methods and building codes, standards and regulations 
(see discussion in Section 17.4.4).
Climate change has begun to alter the environmental baseline of cities, changing their risk and hazard profiles. In recent years, national

and local risk management can benefit from assessments of current decision-making strategies and from evaluations of opportunities 
for change in risk management policy. These changes can be adjustments of existing policies or transitions to a new policy for current 
(i.e., conditions already experienced by getting worse) or emerging risks (i.e., conditions not previously or widely experienced but now 
increasingly present).
With increasing impacts of climate change, settlements of all sizes are considering how to make their communities more resilient 
to climate risk (see Cross-Working Group Box  URBAN in Chapter 6; Araos et  al., 2016a; Araos et  al., 2017; Reckien et  al., 2018a). 
In many settlements, demands for heightened resiliency are being coupled with opportunities to enhance the social and economic 
equity and quality of life of residents. Transformational adaptation (transformational, as being outcome-oriented; Vermeulen et al., 2018)

and associated adjustments to the urban risk management decision-making require an integration of climate resiliency pathways and 
conditions of sustainable development (Mendizabal et al., 2018). At the same time, growing conflict is present between requirements 
for greater resiliency and continued economic development, in particular in low-income environments (Ahenkan et al., 2020). Cities and 
their residents have the capacity to transform their own governance and decision-making systems (Birkmann et al., 2014; Chu, 2018; 
Romero-Lankao et al., 2018). Furthermore, cities have recognised the opportunity and demand to transform in order to be more ambitious 
(Mendizabal et al., 2018) and more successful, more equitable (Reckien et al., 2018b) and better able to connect the climate action to the 
sustainable development process (Singh et al., 2021).
In some cases, transformational adaptation is associated with large-scale, top-down, formal decision processes leading to significant

policy shifts. For coastal cities, this might include actions to build massive flood protection systems (as opposed to simple increase of 
existing structures) (Albers et al., 2015; Hinkel et al., 2018; Ajibade, 2019; see also Section 2.3.5, Cross-Chapter Paper 2) or policies 
to encourage managed retreat from increasing at risk locations (Hino et al., 2017; Rulleau and Rey-Valette, 2017). In more extreme 
instances, the relocation of cities is presented as a possibility, such as planned for the city of Jakarta (Garschagen et al., 2018b). However, 
acceptability of top-down approaches to relocation are usually low, and bottom-up drivers of relocation are important, especially to avoid 
inequitable outcomes (Mach and Siders, 2021). Intensity of extreme events and changing risk perceptions and expectations of property 
prices have been identified as important behavioural drivers of voluntary relocation (de Koning et al., 2019; de Koning and Filatova, 2020).

Yet, when not supported by equitable public adaptation policies, the transformational adaptation left to the influence of autonomous 
adaptation and market institutions alone leads to climate gentrification low-income households are priced out from the hazard-free 
zones (de Koning and Filatova, 2020).
These circumstances also have revealed potential advances in decision-making by encouraging greater participation, more effective 
generation and use of information and data, and more prominent inclusion of questions of social and economic equity (Ziervogel et al., 
2017; Reckien et al., 2018b; Solecki et al., In Press). Adaptation planning and decision-making, in general, within cities has increasingly 
focused on actively engaging residents in participatory and neighbourhood scale co-production processes (Broto et al., 2015; Sarzynski, 
2015; Wamsler, 2017; Foster et  al., 2019). However, engaging residents in risk management and adaptation has not always led to

transformative decision-making and resiliency, but can at times also reinforce existing maladaptive systems (D’Alisa and Kallis, 2016).

17
2602Chapter 17 Decision-Making Options for Managing Risk
Now increasing amounts of data are being collected via surveys or in participatory settings next to advanced methods, such as using 
citizen science, big data and AI, to integrate these social dimensions of climate adaptation decisions in cities in formal models (Abebe 
et al., 2019; Taberna et al., 2020). Linking to social data on individual decisions, risk perceptions, social norms and governmental policy, 
advanced social models trace and quantify how adaptation in cities evolve and would cumulatively induce transformational change. 
Although wider application of these models is outstanding, there is opportunity to simulate and learn from the integration of social and 
behavioural data with political and cultural norms (de Koning and Filatova, 2020).
Although non-urban areas could in many instances act in the same way as urban areas, the density of people, assets, infrastructure

and economical values drive cities to act as testbeds, implement adaptation and strive for resiliency. Cities are showcases for the larger 
environmental systems of governments that also support mitigation ambition of national actors and are therefore demanding to be 
recognised as valuable actors in the international negotiations, highlighting their contribution in emissions reductions (Chan et al., 2015; 
Hale, 2016), such as in the preparation for the first Global Stocktake of the Paris Agreement in 2023 (see Cross-Chapter Box PROGRESS 
in this Chapter).Box 17.3 (continued)
‘unsuccessful’ adaptation (Schipper, 2020), which ‘describes a failed 
adaptation initiative not producing any significant detrimental effect’ 
(Magnan et al., 2016: 648). Several frameworks have been proposed 
to explain and better assess maladaptation (Hallegatte, 2009; Barnett 
and O’Neill, 2010; Magnan, 2014; Magnan et al., 2016; Gajjar et al.,

2019b). To limit the risk of maladaptation, a common focus of these 
frameworks is on intentionally avoiding negative consequences of 
adaptation interventions, anticipating detrimental lock-ins and path 
dependence, and minimising spatio-temporal trade-offs/ dis-benefits.
The adaptation literature challenges the simplistic dichotomy of 
interventions being either successful or maladaptive (e.g., Moser and 
Boykoff, 2013; Singh et al., 2016; Magnan et al., 2020; Schipper, 2020). 
There is no clear-cut boundary between these two categories; rather, 
successful adaptation and maladaptation need to be considered as the 
two ends of a continuum of risk management strategies (Figure 17.10), 
emphasising that:
• no options are ‘bad’ or ‘good’ a priori with respect to reducing 
climate risk/vulnerability.
• positive and negative outcomes of adaptation depend on local 
context specificities (including the presence/absence of enabling

conditions [1]), how adaptation is planned and implemented, who 
is judging the outcomes (i.e., adaptation decision maker, planner, 
implementer or recipient) and when adaptation outcomes are 
assessed.
• ex ante assessment of where options fall on the continuum can 
help anticipate maladaptive outcomes.
Along the adaptation–maladaptation continuum, adaptation options 
can score high or low on different outcome criteria identified in this 
section such as: benefits to the number of people, benefits to ecosystem 
services, equity outcomes (for marginalised ethnic groups, gender, low-
income populations), transformational potential and contribution to 
GHG emission reduction (see SM 17.1 for full descriptions). Importantly, 
the outcome of the assessment, and consequently location of a given 
adaptation option along this continuum, is dynamic, depending on

multiple components, including changes in the characteristics of climate hazards and the effects of iterative risk management. Unfortunately, 
this temporal dimension is understudied in the literature (including 
studying thresholds or speed), preventing advances on this specific 
point.
17.5.1.1.2 Empirical evidence on success of adaptation vis a vis 
Although the empirical evidence on current and potential successful 
adaptation and maladaptation remains small and fragmented 
(Magnan et al., 2020; Berrang-Ford et al., 2021; see Section 17.3.2 in 
this Chapter), the above framing allows for moving a step further in 
assessing the potential contribution of a wide range of adaptation-
related options to success or maladaptation.
According to an assessment (Figure  17.11; see SM 17.1 for full 
descriptions) of maladaptation-relevant outcome dimensions, here 
called criteria, that is, benefits to people, benefits to ecosystem services,

benefits to equity (marginalised ethnic groups, gender, low-income 
populations), transformational potential and contribution to GHG 
emission reduction, no option is located at one or the other end of 
the adaptation-maladaptation continuum (Figure  17.11, right panel), 
showing that all options have some maladaptation potential, that 
is, trade-offs (very high confidence). This is also shown by the wide 
confidence ranges of most options (right panel) signifying that most 
adaptation can be done in a way that involves a higher or a lower risk of 
maladaptation (medium confidence; see also Figure 17.3). The option of 
‘coastal infrastructure’ signifies the highest risk for maladaptation. While 
it can be an efficient adaptation option in highly densely populated areas 
(Oppenheimer et al., 2019; CCP2.3), it has potential trade-offs for natural 
system functioning and human vulnerability over time. The options most

widely associated with successful adaptation are ‘nature restoration’, 
‘social safety nets’, ‘change of farm/fishery practice’ and ‘change of 
diets/reducing food waste’ (high confidence).
Some options show the dominant influence of certain criteria 
(Figure 17.11, central panel rows). For example, ‘availability of health 
infrastructure’ and ‘access to health care’ are dominated by the criterion

17
2603Decision-Making Options for Managing Risk  Chapter 17
Increases social vulnerability and/or 
causes unintended harm to humansDecreases social vulnerability; build adaptive 
capacity to new disturbancesBenefits to humans
Increases climate-related impacts on 
ecosystems and ecosystem servicesReduces climate-related impacts on 
ecosystems and ecosystem servicesBenefits to ecosystem services
Worsens present and/or future 
condition of the poor, low-income, 
ethnic groups and/or females Highly beneficial to the poor, low-income, 
marginalized ethnic groups and/or females Equity outcomes
Does not facilitate or unintendedly 
inhibits deep, systemic changeContributes to deep, systemic change of 
norms, practices, behaviors Transformation potential
Causes additional GHG emissions Does not increase GHG emissions OR has 
mitigation co-benefits (e.g. sequesters CO2)Reduced GHG emissionsTowards more vulnerable, inequitable 
adaptation that increases risk for humans

and ecosystems, has mitigation trade-offsTowards equitable and effective 
adaptation with human, ecosystem 
and mitigation co-benefitsMaladaptation Successful adaptationAdaptation-maladaptation continuum
Figure 17.10 |  Successful adaptation and maladaptation are conceptualised as the two end points of a continuum, with adaptation options being located 
along the continuum based on outcome criteria (how they benefit humans and ecosystems; how they contribute to or hinder equity goals; whether they 
enable transformative change to climatic risks; and synergies and trade-offs with climate mitigation). As indicated in SM 17.1 and Figure 17.10, adaptation options 
might rate largely positive and slightly negative across outcome criteria (tending towards successful adaptation), while other adaptation options might have small positive aspects

and larger negative ones across different outcome criteria (tending towards maladaptation). The figure draws on Singh et al. (2016), Magnan et al. (2020) and Schipper (2020).
‘greenhouse gas emissions’. Similarly, ‘spatial planning’ carries a high 
risk of disadvantages to marginalised ethnic and low-income groups. 
This means that these adaptations could be transformed into successful 
adaptations more easily than others, if attention is paid to the dominant 
criterion. For example, if health care could be provided with low GHG 
emissions, it would move closer towards successful adaptation (high 
confidence). For other options, the criteria’s influence is more evenly 
distributed, as illustrated for the ‘diversification of livelihoods’ and the 
three options to address climate risks to peace and mobility, denoting 
multiple entry points to reduce the risk of maladaptive outcomes for 
these options.
Some criteria score highly across a number of options (Figure 17.11,

central panel columns), showing that many adaptations do not pay 
attention to different trade-offs. For example, particular attention should 
be paid to prioritising benefits to low-income groups and leveraging the 
transformational potential of adaptation (having the largest number of 
large circles), that is, many evaluated options become maladaptive by 
exacerbating the vulnerability of low-income groups and by fortifying 
the status quo (medium confidence). On the contrary, most evaluated 
adaptation options are widely applicable across populations (benefits 
to humans) and deliver ecosystem services, while some also respect 
gender equity (largest number of small bubbles across options). Through 
these criteria, a number of adaptation options contribute to a higher 
potential for successful adaptation (high confidence).
The results displayed in Figure 17.11 are not rigorous predictions but 
illustrate the maladaptive potential of options based on a synthesis of

literature from underlying WGII chapters and cross-chapter papers. This leads to findings for general situations, potentially obscuring critical 
contextual specificities which can mediate successful adaptation or 
maladaptation outcomes. In a certain context, Figure 17.11 will appear 
different. Moreover, the analysis is based on a static interpretation of 
adaptation outcomes, while risk and risk reduction are dynamic. The 
current, underlying literature does not help understanding the temporal 
dimension of the options, their flexibility or risk of lock-in, and related 
potential contribution to long-term maladaptation or successful 
adaptation. The added value of the analysis lies in the approach to 
assess the potential contribution to maladaptation or successful 
adaptation (via the seven criteria at the top of the figure), rather 
than in the final results themselves. This overview illustrates how, in 
a particular context and for particular groups of people, adaptation

options and their location on the adaptation–maladaptation continuum 
can be assessed for a set of outcome dimensions, focusing on assessing 
potential contributions per and across criteria as well as per and across 
options (critical information to support the identification of adaptation 
pathways; Cross-Chapter Box DEEP in this Chapter).
17.5.1.1.3 Enabling successful adaptation and pre-empting 
Considering evidence on enabling successful adaptation in the 
sectoral (Chapters 2–8) and regional chapters (Chapters 9–15), 
four conditions stand out as particularly key to enabling adaptation 
success: recognitional equity and justice, including the integration of 
Indigenous and local communities and knowledge; procedural equity 
and justice; distributive equity and justice; and flexible and strong 
institutions that seek integration of climate risk management with

17
2604Chapter 17 Decision-Making Options for Managing Risk
Potential contribution of 24 adaptation-related options
to maladaptation and successful adaptation
na = Information not available in the literature considered (i.e. essentially from AR6 WGII underlying chapters)(a) Low-lying
(h) Peace and mobility(d) Living standards
(e) Human health(c) Critical infrastructure,
network and services
(f) Food security
(g) Water securityRepresentative Key Risks Adaptation optionsEquity
Seasonal/temporary mobilityWater use/demand
Water supply/distributionFarm/ﬁshery practice
Food storage/distribution
Diets/food waste
Water capture/storageInfrastructure retroﬁtting
Building codes
Social safety netsInsuranceSpatial planning
Diversiﬁcation of livelihoodsNature restoration
Ecosystem-based adaptationMinimizing ecosystem stressorsStrategic coastal retreatCoastal accomodation
Coastal infrastructure
Governance cooperation
Permanent migrationAvailability of health infrastructure

Access to health care
Disaster early warning(b) Terrestrial and
marine ecosystemsEthnic
groupsGreenhouse
gas
emissionsNumber of
beneﬁciaries GenderLow-
income
populationsEcosystems/
ecosystem
servicesTrans-
formational
potential
na
na
na
na
na
na
na
nananana
na
nana
na
na
nanato successful adaptation
Moderate / MediumMedium / ModerateHigh / Negligibl e
Negligible /HighPotential contribution
Range of scores
across criteriaAverage score per adaptation option
LowMediumHighConﬁdence levels
Average per
adaptation option 
and across criteriato risk of maladaptation
Potential contribution to the risk of maladaptation to climate change
High e.g. through dis-benefits that worsen the situation for the group/sector
Moderate e.g. through mixed or no clear benefits/dis-benefits
Small e.g. through moderate benefits for the group/sector
Negligible e.g. through large benefits for the group/sector

Figure 17.11 |  The potential contribution of 24 adaptation-related options to maladaptation and successful adaptation.  The figure builds on evidence provided 
in the underlying sectoral and regional chapters and the Cross-Chapter Papers (SM17.1) to map 24 adaptation options identified as relevant to the eight Representative Key Risks 
(see Section 16.5) onto the adaptation–maladaptation continuum. It assesses the potential contribution of each of these adaptation options to successful adaptation and the risk of 
maladaptation. The figure permits a review of options in multiple ways: (a) looking at adaptation options (first column), one can see which adaptation options score highest across 
the criteria (the central rows). Results by options show which ones carry the highest risk of maladaptation (largest circles per row); (b): looking at criteria (top centre), one can see

which criteria seem to be most influential to contribute to maladaptation outcomes (largest circles per central column); (c) panel on the right: merging the scores of each adaptation 
option across criteria helps highlight whether the options are likely to end up as successful adaptation or maladaptation.
other policies and address long-term risk reduction goals (Table 17.7). 
For a wider discussion of enablers for adaptation and climate risk 
management, see Section 17.4.
Recognitional equity and justice: Recognitional justice focuses on 
inclusion and agency, that is, examining who is recognised as a legitimate actor and how their rights, needs and interests are acknowledged and 
incorporated into action (Singh et al., 2021).
A global assessment of 1682 papers on adaptation responses yields 
that low-income groups (high agreement, 37% of 1682  articles), 
women (medium agreement, 20% articles), Indigenous peoples

17
2605Decision-Making Options for Managing Risk  Chapter 17
(10%), the elderly (8%), youths (5%), racial and ethnic minorities 
(4%), and migrants (4%) were the most frequently considered groups 
in adaptation responses. Individuals with disabilities are the least 
considered, with only 1% of articles including this group. There is a 
category of ‘other’ capturing characteristics of social disadvantage 
that are distinct from the categories above. This includes, for example, 
spatially marginalised populations (e.g., groups relegated to flood-
prone or cyclone-prone areas) and groups marginalised due to marital 
status or assets (education, farm size and land tenure) (Araos et al., 
2021).
Procedural equity and justice: Participation is employed to enable 
procedures that aim to redress power imbalances, which are assumed 
to be the root causes of vulnerability (i.e., the reasons that lead certain 
people and places to be differentially vulnerable to climate risks)

(Tschakert and Machado, 2012; Shackleton et al., 2015; Schlosberg et al., 
2017; Ziervogel et al., 2017). However, participation is often constrained 
by gender (Cross-Chapter Box GENDER in Chapter 18), social status, 
unequal citizenship (as concerns education, access to information, 
finance and media) (Wallimann-Helmer et  al., 2019), entrenched 
political interests (Shackleton et  al., 2015; Chu et  al., 2017), power 
dynamics (Rusca et  al., 2015; Taylor and Bhasme, 2018; Kita, 2019; 
Omukuti, 2020; Taylor and Bhasme, 2020) or institutional shortcomings 
(Nightingale, 2017, in Nepal), which allow the most powerful access 
to funding and reinforce marginalisation of the powerless (Schipper 
et al., 2014; Khatri, 2018; McNamara et al., 2020). Vulnerability is also 
sometimes used as a pretext to exclude groups from participation, 
often because vulnerable groups do not own land and lack legal status, 
time or the ability to commit labour or material inputs for adaptation,

all drivers of vulnerability in the first place (Nyantakyi-Frimpong and 
Bezner Kerr, 2015; Camargo and Ojeda, 2017; Nagoda and Nightingale, 
2017; Nightingale, 2017; Thomas and Warner, 2019; Mikulewicz, 2020).
Reporting from the global assessment of equity considerations in 
adaptation, procedural equity and justice was slightly more often 
mentioned (~52%) than not (~48%) (medium agreement). However, the 
robustness of the evidence on inclusion of vulnerable and marginalised 
groups in the planning of adaptation responses is low (63%) (high 
agreement). Only for ~6% of the articles that provide evidence for 
inclusion of vulnerable groups was the robustness of evidence high (low 
agreement). Globally, the categories of low income (~25%) and women 
(~13%) are most often included, although the robustness remains 
low. Most of the robust evidence comes from Africa and Asia, where 
adaptation responses mostly focus on low-income and women groups

in the food (28%) and poverty (32%) sectors (medium agreement). With 
regard to other vulnerability categories, such as disabled populations, 
almost negligible evidence was found for the inclusion of this group, 
globally. There is also little reporting of procedural equity in community-
based or ecosystem-based responses (Araos et al., 2021).
Distributive equity and justice: Attention to distributional equity 
and justice aims to ensure that adaptation interventions do not 
exacerbate inequities (Atteridge and Remling, 2018) and that the 
benefits and burdens of interventions are distributed fairly (Tschakert 
et  al., 2013; Reckien et  al., 2017; Reckien et  al., 2018b; Pelling and 
Garschagen, 2019).A global assessment of 1682 papers on adaptation (Araos et al., 2021) 
finds that about 60% of articles mentioned at least one vulnerable 
group being involved in the implementation of adaptation or targeted 
by it (medium confidence). Low-income groups (high agreement, 37%

of 1682 articles) and women (medium agreement, 20% articles) are 
the most frequently mentioned. Particularly in sectors and regions that 
incorporated coping measures in their adaptation response (poverty, 
food, Africa, Asia, Central and South America), these groups are 
prevalent. In sectors where responses were more strategic or planned, 
such as in cities, terrestrial and water, in a larger proportion of articles 
(51%, 47% and 47% of articles, respectively) vulnerable groups were 
not frequently included in the response (medium agreement). There 
was also a stark difference in inclusion of marginalised and vulnerable 
groups between high-income and low-income countries or regions, 
with the majority of the responses from Australia, Europe and North 
America, not including marginalised groups (high agreement with 
70%, 69% and 55% of articles, respectively), showing the need for 
increasing attention in particular on a cross-sectoral and cross-regional

relation (Araos et al., 2021).
Flexible and strong institutions: There is medium confidence that 
flexible institutions can enable adoption of new adaptation measures 
or course-correct established ones based on ongoing monitoring 
and evaluation, which is key to avoiding potential maladaptation 
(e.g., Granberg and Glover, 2014, in Australia; Magnan et al., 2016; 
Torabi et  al., 2018; Gajjar et  al., 2019a, in India). Cross-sectoral, 
cross-jurisdictional and cross-spatial institutional frameworks enable 
successful adaptation by improving the ability of societies to respond 
to changes in their environment in a timely manner. The latter points 
to the vital role of monitoring and evaluation, as the tool to detect 
change in risk and vulnerability, together with environmental or 
societal conditions determining risk and the effectiveness, efficiency, 
adequacy or success of adaptation responses.
17.5.2 Adaptation Monitoring, Evaluation & Learning

17.5.2.1 Purpose of Monitoring and Evaluation
Adaptation responses have been observed in every region and across 
a wide variety of sectors (Section 16.3), but little evidence exists of 
their outcomes in terms of climate risk reduction (high confidence) 
(Section  1.4.3; Ford and Berrang-Ford, 2016; Tompkins et  al., 2018; 
Berrang-Ford et al., 2021; Eriksen et al., 2021; UNEP , 2021a). To advance 
on that, the Paris Agreement is encouraging countries to engage in 
‘Monitoring and evaluating and learning from adaptation plans, 
policies, programmes and actions’ (UN, 2015, Article 7.9d). Monitoring 
and evaluation (M&E) is the systematic process of collecting, analysing 
and using information to assess the progress of adaptation and evaluate 
its effects—for example, risk reduction outcomes, co-benefits and 
trade-offs—mostly during and after implementation (AR6 Glossary, 
Annex II). Distinctions between monitoring and evaluation typically

view monitoring as a continuous process of tracking implementation 
and informing management to allow for corrective action including 
in situations of deep uncertainty (see Cross-Chapter Box DEEP in this 
Chapter), while evaluation is described as a more comprehensive 
assessment of achievements, unintended effects and lessons learned

17
2606Chapter 17 Decision-Making Options for Managing Risk
Table 17.7 |  Key factors that enable successful adaptation. The evidence and examples draw on the underlying sectoral and regional chapters as well as a synthesis of adaptation 
EnablersWhat this 
enablesKey characteristics Examples and traceability
Recognitional 
justicePluralising the 
ambit of who 
is ‘counted’ as 
vulnerable, drawing 
on multiple 
knowledge systems –Focuses on inclusion and agency, i.e., who is recognised as a 
legitimate actor and how their rights, needs and interests are 
acknowledged and incorporated into adaptation (Chu and Michael, 
2018; Singh et al., 2021).
 –Acknowledges how differential vulnerability to climate change 
stems from historical and structural inequalities, which can unevenly 
distribute adaptation benefits, especially for the poorest and the most 
marginalised (Tschakert and Machado, 2012; Shackleton et al., 2015;

Schlosberg et al., 2017; Ziervogel et al., 2017; Eriksen et al., 2021).
 –Informs more equitable adaptation priorities (Ziervogel et al., 2017), 
legitimises adaptation actions (Myers et al., 2018; Ellis and Tschakert, 
2019), supports inclusion of marginalised groups (Chu and Michael, 
2018) (medium confidence). –Co-production of knowledge and inclusion of Indigenous and local 
knowledge (Loboguerrero et al., 2018; Dannenberg et al., 2019, 
Cross-Chapter Box ILK; Ziervogel et al., 2019).
 –Co-production of knowledge and inclusion of marginalised groups 
across sectors, see, e.g., in the health sector (Chapter 7), food 
systems (Chapter 5) and fire management (Chapter 12).
Procedural 
justiceDifferential 
participation and 
power for more 
inclusive adaptation 
planning and 
implementation –Ensures that processes of representation and participation in 
adaptation planning, prioritisation and implementation are inclusive

(Holland, 2017; Reckien et al., 2017; Reckien et al., 2018b) (medium 
 –Enables adaptations to advance more quickly and generate higher 
levels of well-being (e.g., Dannenberg et al., 2019 comparing cases of 
strategic retreat), while also benefitting poorer households (Chu and 
Michael, 2018).
 –Higher participation can enable more legitimate outcomes, greater 
awareness about societal problems addressed, larger willingness for 
community cooperation, and increased individual behavioural change 
(Burton and Mustelin, 2013).
 –Participation in design and implementation of adaptation projects can 
be a critical element for avoiding maladaptive outcomes (Taylor, 2015; 
Nightingale, 2017; Forsyth, 2018; Mikulewicz, 2019). –Participation of multiple stakeholders enables co-production of 
adaptation strategies and devolution of decision-making (Ziervogel, 
2019) and often, if not always (D’Alisa and Kallis, 2016), a higher

level of transformational adaptation (and more ambitious local 
mitigation goals) (Cattino and Reckien, in press).
 –Participatory processes can have more equitable outcomes as 
evidenced in informal settlements (Ziervogel, 2019, South Africa), 
small farmers (Loboguerrero et al., 2018, Colombia), migrants 
(Gajjar et al., 2019b, India) and deliberative dialogues (Ojha and 
 –But participation does not always address unequal power relations 
(e.g., Buggy and McNamara, 2016; Karlsson et al., 2017).
Distributive 
justiceDelivering 
adaptation for 
vulnerable groups 
and correcting 
structural 
vulnerabilities –Ensures that adaptation interventions do not exacerbate inequities 
(Atteridge and Remling, 2018) and that the benefits and burdens of 
interventions are distributed fairly (Tschakert et al., 2013; Reckien 
et al., 2017; Reckien et al., 2018b; Pelling and Garschagen, 2019).
 –However, low levels of commitment to distributive justice, e.g., when

justice is one of many goals of adaptation instead of the prime one, 
are insufficient to promote equitable distribution of benefits and 
harms (medium evidence, high agreement) (Anguelovski et al., 2016; 
Pulido et al., 2016; Weinstein et al., 2019; Shawoo and McDermott, 
2020). –Women and men have very different access to mobile phones, 
entailing lower responsiveness with climate services among women 
(Partey et al., 2020, across Africa).
 –Slow progress on prioritising distributional and procedural justice 
limits the expansion of adaptation funding to poorest and most 
vulnerable social groups and nations (Khan et al., 2019a).
 –Focusing only on distributive justice alone is less effective than a 
holistic integration of recognitional and procedural justice ( limited 
evidence, medium agreement); e.g., only including poor households 
as recipients provides benefits to wealthier households, in sectors 
such as insurance for herders in Mongolia (Taylor, 2016b), urban

water supply in Malawi (Rusca et al., 2017), informal urban 
settlements in Kenya (Pelling and Garschagen, 2019) and forest 
management in Cambodia (Work et al., 2019).
Flexible 
and strong 
institutionsSeeks policy 
integration and 
dynamic risk 
and accounts for 
long-term goals –Institutional flexibility allows a society to respond quickly to the 
demands of a changing environment by developing new institutions 
or adjusting existing ones quickly (Davis, 2010); possibly avoiding 
lock-ins and addressing future climate risks (very robust evidence, 
high agreement) (Levi-Faur, 2012; Sherman and Ford, 2013; Boyd and 
Juhola, 2015; Magnan et al., 2016).
 –Stability (and familiarity) is often desired in governance 
arrangements, and balancing the need for stability with goals of 
flexibility without causing rigidity is key (Craig et al., 2017, in USA; 
Chapter 11). This is possible through deliberate, consultative changes

that build awareness, develop shared norms, rules and goals, and 
develop inclusive decision-making processes (Chapter 3). –Capacity building of adaptation funders, planners and 
implementers and re-orienting existing institutions to make 
decisions under uncertainty, institute long-term climate risk 
management that goes beyond typical political/planning cycles, and 
develop learning mechanisms between sectors, actors and projects 
needed (Moser and Boykoff, 2013; Granberg and Glover, 2014 in 
Australia; Boyd and Juhola, 2015 in cities; Ziervogel, 2019 in Africa 
and; Olazabal et al., 2019b in India; Chapter 3 Oceans; Chapter 10; 
Chapter 11; Chapter 12).
 –Flexible institutions enable adoption of new adaptation measures 
or course-correct based on ongoing M&E (e.g., Granberg and 
Glover, 2014 in Australia; Magnan et al., 2016; Torabi et al., 2018; 
Gajjar et al., 2019a in India) (medium evidence, high agreement).

–Sectoral or spatial policy integration (Chu et al., 2017; Section 17.6; 
Hino et al., 2017; Robinson and Wren, 2020); integration of 
jurisdictional frameworks of different agencies (Poesch et al., 
2016; Chapter 5; Chapter 9); and adaptive and flexible legal 
systems which disaggregate socio-ecological systems into smaller 
components (Arnold and Gunderson, 2013; Wenta et al., 2019) are 
key enablers.

17
2607Decision-Making Options for Managing Risk  Chapter 17
carried out at certain point in time (OECD, 2002). M&E is an important 
part of the adaptation process (Figure  1.9). It can help to generate 
information on adaptation success or maladaptive outcomes.
M&E of adaptation is undertaken for different purposes, including: (1) 
understanding whether responses have achieved their intended objectives 
and contributed to a reduction in climate risks and vulnerability or to 
an increase of adaptive capacity and resilience, (2) informing ongoing 
implementation and future responses, and (3) providing upward and 
downward accountability (Preston et al., 2009; UNFCCC, 2010a; Pringle, 
2011; Spearman and McGray, 2011). M&E is also commonly linked to 
learning (Section 17.5.2.7). By continuously monitoring implementation, 
for example, to assess whether adaptation is on track or needs to be 
accelerated, M&E can aid decision-making under uncertainty. Adaptation

M&E is distinct from tracking financial flows related to adaptation since 
financial accounting does not provide information on implementation 
and outcomes (Section  17.5.2.5; Adaptation Partnership, 2012; World 
Bank Independent Evaluation Group, 2012).
17.5.2.2 Adaptation M&E Approaches
Adaptation M&E can be conducted for various purposes and in a wide 
variety of different contexts ranging from the local to the global level 
(McKenzie Hedger et al., 2008; UNFCCC, 2010a; Spearman and McGray, 
2011). The context and specific purpose of M&E determine what 
information needs to be generated, and together with the available 
resources also determine the suitability of particular approaches and 
methods (Leiter, 2016; Leiter, 2017). Several frameworks and approaches 
have been proposed for M&E of adaptation and climate resilience (Bours 
et  al., 2014d; Schipper and Langston, 2015; Adaptation Committee, 
2016; ODI, 2016; Cai et al., 2018; Gregorowski et al., 2018), including

sector-specific ones for agriculture (FAO, 2017; FAO, 2019a; FAO, 2019b), 
health (Ebi et  al., 2018), ecosystem-based adaptation (Donatti et  al., 
2018; Donatti et al., 2020; GIZ, 2020) and cities (Section 6.4.6).
Adaptation M&E generally seeks to answer whether implementation 
is taking place and what effects it has (Figure  17.12). Accordingly, 
M&E can focus on the processes, activities and outputs or on their 
outcomes and ultimate impacts (Harley et al., 2008; Pringle, 2011; Ford 
et al., 2013). Most of the available guidance for the development of 
adaptation M&E systems is aimed at the household, local or project 
level (Pringle, 2011; Villanueva, 2012; Olivier et al., 2013; CARE, 2014; 
BRACED, 2015; Leiter, 2016; Jones, 2019b) with only limited guidance 
for national or cross-sectoral M&E systems (Price-Kelly et al., 2015) or 
frameworks that are applicable at different scales (Brooks et al., 2014). 
The available guidebooks take users through a series of steps which

are synthesised in Figure 17.12.
The majority of adaptation M&E efforts have so far focused on 
processes and outputs rather than on achieved outcomes such as 
climate risks, vulnerability, well-being or development (Droesch 
et al., 2008; GIZ and Adelphi, 2017; UNDP Cambodia, 2014; Fawcett 
et al., 2017) (high confidence) or use a combination thereof (Brooks 
et al., 2011; Brooks et al., 2014). Newly emerging approaches include 
perception-based measurements and the use of data collected via 
mobile phones (Jones et al., 2018; Jones, 2019a), which can be collected 
frequently (Clare et  al., 2017a; Knippenberg et  al., 2019; Jones and Ballon, 2020). Such advances call into question the common reliance 
on ‘objective’ indicators defined from an external perspective. Instead, 
they suggest that multiple complementary approaches combined with 
higher-frequency data collection produce a more elaborate picture of 
the effects of adaptation and resilience responses (Jones and d’Errico,

2019; Knippenberg et al., 2019; Singh et al., 2019; Jones, 2019a; see 
Cross-Chapter Box PROGRESS in this Chapter) (medium confidence).
Central to designing, monitoring and evaluating adaptation responses 
is outlining how activities are expected to lead to intended objectives, 
for example, via a theory of change (Bours et al., 2014c; Oberlack and 
al., 2019). Theories of change or similar change models provide a basis 
to decide what to measure, but more attention needs to be paid to 
how theories of change are constructed and who is involved (Mason 
and Barnes, 2007; Forsyth, 2018). Participatory approaches can support 
understanding how climate risks affect the respective population, 
how these risks interact with social and cultural processes, and how 
responses could most effectively address climate risks (Conway et al., 
2019). Inclusive M&E systems can facilitate ownership and enhance 
the meaningfulness and usability of the generated information

(CARE, 2014; Faulkner et al., 2015). Meaningfulness is not associated 
with a particular approach or method but depends on whether the 
chosen M&E design fits the M&E purpose and the information needs 
of the intended audience (Fisher et al., 2015; Leiter, 2017). Effective 
communication of M&E findings and feedback into decision-making 
processes is essential to achieve the respective M&E purpose and 
facilitate learning (Section 17.5.2.7).
17.5.2.3 Adaptation Indicators and Indices
A set of all-purpose and globally applicable standard indicators that 
could comprehensively measure adaptation does not exist (high 
confidence) (IPCC, 2014; Leiter and Pringle, 2018). A wide variety of 
indicators have been used to assess adaptation and its results (CARE, 
2010; Harvey et al., 2011; Lamhauge et al., 2013; Brooks et al., 2014; 
Hammill et al., 2014b; Mäkinen et al., 2018; HM Government, 2019). 
Literature has also noted unrealistic expectations of what indicators

can accomplish. For instance, decisions involving competing political 
interests would not be adequately informed through simple indicators; 
and learning requires knowledge of how and why change has 
happened, something that indicators often do not capture (Hinkel, 
2011; Bours et  al., 2014b). Indicators can also become misguided 
incentives and might steer attention away from what matters (Leiter 
and Pringle, 2018; Hallegatte and Engle, 2019; Klonschinski, 2021). 
Surveys, scorecards, interviews and focus groups are alternative 
methods of gaining insights on adaptation progress (Brooks et  al., 
2014; Porter et al., 2015; Das, 2019; McNamara et al., 2020).
The difficulties of assessing adaptation and an emphasis on short-term 
results have contributed to the common practice of relying on easily 
quantifiable indicators rather than assessing actual changes, that is, 
outcomes and impacts (World Bank Independent Evaluation Group,

2012; Fisher et  al., 2015). In fact, indicators used by international 
climate funds largely measure outputs which provide little evidence 
of the actual effectiveness of adaptation, that is, its outcomes and 
impacts (GCF Independent Evaluation Unit, 2018; Leiter et al., 2019; 
Pauw et al., 2020).

17
2608Chapter 17 Decision-Making Options for Managing Risk
Indices, the combination of multiple indicators into a single score, are 
common products of risk and vulnerability assessments to compare 
countries or other entities, often in the form of rankings or maps 
(Preston et al., 2011; Reckien, 2018; de Sherbinin and et al., 2019). They 
can indicate changes in vulnerability over time within their respective 
conceptualisation of vulnerability or risk. The construction of indices, 
including indicator selection, their weighting, normalisation and 
data sources, has a profound impact on their scores (Reckien, 2018). 
Research has consistently found large discrepancies between country 
vulnerability rankings (Brooks et  al., 2005; Eriksen and Kelly, 2007; 
Leiter et al., 2017b; Visser et al., 2020). Reviews of vulnerability and 
resilience indices identified ‘substantial conceptual, methodological 
and empirical weaknesses’ (Füssel, 2010: 8) and a widespread lack

of validation (Cai et al., 2018). Using countries as a unit of analysis 
also masks significant sub-national variation (Otto et  al., 2015; 
Mohammadpour et  al., 2019). Individual indices therefore ‘fail to 
convene a robust guidance for policy makers’ (Muccione et al., 2017: 
4) and should not present the sole basis for policy decisions (Brooks 
et al., 2005; Leiter and Pringle, 2018). Due to their limitations (Singh 
et  al., 2017), the OECD suggests that indices are primarily used for 
‘initiating discussion and stimulating public interest’ (OECD, 2008: 13).
17.5.2.4 Empirical Evidence of National Adaptation M&E Systems
Tracking the implementation of national adaptation plans is essential for 
understanding their effectiveness, that is, the progress made in addressing 
climate risks, and can support assessing the success of adaptation and 
the risk of maladaptation. Over 60 countries have developed or started developing national adaptation M&E systems, although less than half

are yet reporting on implementation (Leiter, 2021b; Table 17.8). Country-
specific adaptation M&E systems vary considerably regarding their 
legal mandate, purpose, content, involved actors and types of reporting 
(Hammill et al., 2014a; EEA, 2015; Leiter, 2015; Leiter et al., 2017a; EEA, 
2020). In most cases, they focus primarily on monitoring implementation 
rather than assessing outcomes, although some are linked to national 
climate risk or vulnerability assessments (e.g., in Germany and the UK) 
(EEA, 2018). At least 15 countries have published evaluations of national 
adaptation plans which help inform the development of successive 
adaptation plans or strategies (Table 17.8). Nevertheless, there is only 
limited empirical evidence of the ability of M&E systems to facilitate 
action or increase the level of ambition of revised policies. More research 
is needed to determine the quality of national adaptation M&E systems 
and how well they support the policy cycle.

Under the Paris Agreement, countries are encouraged to provide 
information on adaptation, including its adequacy and effectiveness 
(Möhner et al., 2017; Adaptation Committee, 2021). National adaptation 
M&E systems can inform both national as well as international 
reporting and contribute to the Global Stocktake (see Cross-Chapter 
Box  PROGRESS in this Chapter; Craft and Fisher, 2015; Leiter et  al., 
2017a). Guidance for and examples of national adaptation progress 
assessments are provided by Price-Kelly et  al. (2015), Brooks et  al. 
(2014), Brooks et al. (2019), EEA (2015), GIZ (2017), Karani (2018) and 
van Rüth and Schönthaler (2018). Global assessments of adaptation 
progress have so far often focused on adaptation planning and, to a 
lesser extent, implementation, while evidence of the collective effect Adaptation process (Figure 1.6)Adaptation monitoring, evaluation (M&E) and learning as part of the adaptation process
ContextPurpose of
evaluation
(M&E)

Information need s
M&E approach
Data sourcesOperationalisationCommunicatio n
M&E
Figure 17.12 |  Adaptation M&E and learning as part of the adaptation process (based on Hammill et al., 2014a; Price-Kelly et al., 2015; Leiter, 2016). This 
figure shows the main steps involved in developing an adaptation M&E system where the context informs the purpose of M&E, which in turn determines the information needs. 
To achieve the M&E purposes, the chosen approach and data sources need to be able to generate the needed information, which needs to be communicated in a suitable way to 
the target audiences.

17
2609Decision-Making Options for Managing Risk  Chapter 17
of adaptation globally remains limited (high confidence) (UNEP , 2021a; 
Cross-Chapter Box PROGRESS in this Chapter).
17.5.2.5 Challenges of Assessing Adaptation
To date, literature has largely focused on aspects prior to implementation 
such as assessments of climate vulnerability and risks or appraisals of 
adaptation options (Sietsma et al., 2021; Cross-Chapter Box Adaptation). 
To understand adaptation progress, the assessment of implemented 
adaptation actions and their outcomes requires more attention (very 
high confidence) (Cross-Chapter Box PROGRESS in this Chapter).
Outcomes on risk reduction are typically expressed in ways that are 
specific to the respective sector or context (e.g., as agricultural yields, 
health benefits or reduced water stress) highlighting that ‘adaptation 
has no common reference metrics in the same way that tonnes of 
GHGs or radiative forcing values are for mitigation’ (IPCC, 2014:

856). Assessments of adaptation progress therefore need to specify 
what they are measuring and how they are measuring it. The way 
adaptation is conceptualised, for example as a continuum between 
successful adaptation and maladaptation (Section  17.1.1), and the 
way adaptation is framed, for example as a technical challenge or 
a political process (Juhola et al., 2011; Bassett and Fogelman, 2013; 
Eriksen et  al., 2015), shape the understanding of progress and its 
subsequent measurement (Singh et al., 2021).
Furthermore, people can be differently affected even in the same 
location owing to, among others, differential vulnerability among 
the population (Reckien and Petkova, 2019; Thomas et  al., 2019). 
Different views and values can also affect what it means to adapt 
(Few et al., 2021). Assessments of adaptation progress therefore need 
to be transparent and reflective about how they define and measure 
adaptation and account for culturally and geographic contingent

concepts of what it means to adapt in light of the global diversity of 
livelihoods and concepts.
The lack of knowledge on adaptation progress is associated with further 
measurement challenges, including that avoided impacts are difficult 
to measure and that risk levels change over time, meaning what is 
effective today may not be effective in the future (Brooks et al., 2011; 
Pringle, 2011; Spearman and McGray, 2011; Villanueva, 2012; Bours 
et al., 2014a). Moreover, adaptation is embedded in complex political 
and social realities where power and politics shape outcomes and 
where simplistic views of how adaptation would take place may be ill-
conceived (Nightingale, 2017; Mikulewicz, 2018; Mikulewicz, 2020). In 
practice, this means that theories of change of adaptation projects may 
miss important causes of risks and could subsequently lead to inaccurate 
assessments (Forsyth, 2018). Measuring adaptation is therefore a matter

of understanding drivers of vulnerability and risk and of designing 
responses and M&E systems accordingly (UNFCCC, 2019a, section V).
The importance of context and the dependence on viewpoints 
make comparative assessments of adaptation across nations, 
regions or responses challenging. Comparison requires a consistent 
conceptualisation of adaptation, comparable units of analysis and 
access to relevant data sets (Ford et al., 2015; Ford and Berrang-Ford, 
2016). Comparative adaptation policy assessments to date often lack clarity in concepts and explanatory variables (Dupuis and Biesbroek, 
2013; Biesbroek R, 2018a). The trade-off between standardisation and 
context specificity also complicates attempts to aggregate adaptation 
progress across scales to the national or global level (Leiter and Pringle, 
2018; Cross-Chapter Box PROGRESS in this Chapter).
17.5.2.6 Tracking Adaptation Finance
Adaptation finance tracking is capturing the financial flows associated

with adaptation. It can indicate how much is being spent on 
adaptation, where funds are going to and whether spending matches 
allocated budgets. Thus, adaptation finance tracking can provide useful 
information for decision-making, but it does not provide information 
on the achievements resulting from the invested funds. Accordingly, 
it can complement, but not substitute, M&E of actions and outcomes. 
Adaptation finance tracking can be applied domestically (Guzmán 
et  al., 2017; Guzmán et  al., 2018) as well as internationally, for 
instance by developed countries to report on the goal to mobilise USD 
100 billion yr−1 by 2020 in climate finance (UNFCCC SCF , 2018). Data 
on adaptation finance can be used alongside information on planning 
and implementation to assess adaptation progress (UNEP , 2021a).
Tracking adaptation finance requires defining what counts as 
adaptation. Different definitions can lead to large variations in the

estimated amount of adaptation finance (Donner et  al., 2016; Hall, 
2017). A further challenge is how to account for adaptation that is 
mainstreamed, that is, where adaptation-specific investments form 
only part of a larger programme or budget line, or where actions 
contribute to adaptation without being labelled as adaptation. These 
challenges limit the direct comparability between adaptation and 
mitigation finance (UNFCCC, 2019a). In fact, tracking adaptation 
finance differs from tracking mitigation finance since activities cannot 
be a priori assumed to constitute adaptation but instead have to be 
assessed for their linkage to climate risks in a particular context (MDBs 
& IDFC, 2018). Methods for adaptation finance tracking continue to be 
further developed aiming at better comparability and completeness 
(Richmond and Hallmeyer, 2019; Richmond et al., 2021).
Various methods are used to track adaptation finance, which makes

comparisons between adaptation finance figures challenging (UNFCCC 
SCF , 2018; Weikmans and Roberts, 2019). For example, multi-lateral 
development banks use a different methodology than countries do 
under the OECD Development Assistance Committee (DAC) (Box 17.4; 
MDBs, 2019). One of the differences concerns the treatment of partially 
adaptation-relevant projects, namely whether only parts or the full 
amount of a given project volume are counted as adaptation finance (see, 
e.g., MDBs, 2019). Under the OECD DAC methodology, countries often 
use a fixed percentage (e.g., 50% of the total project value), whereas 
the MDB methodology attempts for a project-specific estimation of the 
adaptation-relevant proportion (MDBs & IDFC, 2018). Another aspect is 
whether tracking distinguishes between financial instruments, such as 
grants or loans. Different accounting rules can lead to large differences 
in reported amounts of adaptation finance and to a lack of comparability

between providers (Weikmans and Roberts, 2019). Studies identified an 
over-reporting (i.e., counting non-adaptation-related finance) by a factor 
of two to three, which suggests the need for a more consistent and 
transparent accounting system (Weikmans et al., 2017; CARE, 2021).

17
2610Chapter 17 Decision-Making Options for Managing Risk
Table 17.8 |  Countries in different stages of developing or operating a national adaptation M&E system as of 1 August 2021 (Source: Leiter, 2021b). Countries can appear twice 
if they have published both a progress report and an evaluation.
National adaptation M&E system
Stage Definition Country
Under developmentEarly stageTangible steps have been undertaken to develop a 
national adaptation M&E system, for example a stocktake 
of relevant existing data sources and engagement with 
stakeholders on the objectives of the M&E systemBenin, Cook Islands, Jordan, Paraguay, Sri Lanka, Uganda
Advanced stageDetails of the adaptation M&E system have been 
developed, including, for instance, institutional 
arrangements, indicators and data sources, but it has not 
yet been appliedAlbania, Bulgaria, Cameroon, Canada, Colombia, Ethiopia, Fiji, 
Grenada, Indonesia, Moldova, Morocco, Mozambique, Nauru, Peru,

Rwanda, Senegal, St. Lucia, St. Vincent and the Grenadines, Suriname, 
Thailand, Togo, Tonga, Turkey, Vietnam
In operationAdaptation progress 
report publishedA progress report on the implementation of the national 
adaptation plan or strategy has been publishedAustria, Belgium (Flanders), Brazil, Burkina Faso, Cambodia, Chile, 
Cyprus, France, Germany, Japan, Kenya, Kiribati, Lithuania, Mexico, the 
Netherlands (Delta Programme), Norway, Portugal, Slovakia, Spain, 
South Africa, South Korea, Switzerland, UK
Evaluation publishedAn evaluation of the implementation of the national 
adaptation plan or strategy has been undertaken and 
publishedBelgium, Cambodia, Chile, Czech Republic, Finland, France, Germany, 
Ireland, Mexico, the Netherlands, Philippines, South Korea, Spain, 
Switzerland, UK
Cross-Chapter Box PROGRESS | Approaches and Challenges to Assess Adaptation Progress at the 
Global Level

Authors: Matthias Garschagen (Germany), Timo Leiter (Germany/UK), Robbert Biesbroek (the Netherlands), Alexandre K. Magnan (France), 
Diana Reckien (the Netherlands/Germany), Mark New (South Africa), Lea Berrang-Ford (UK/Canada), So Min Cheong (Republic of Korea), 
Lisa Schipper (Sweden/USA), Robert Lempert (USA).
This Cross-Chapter Box responds to a growing demand for assessing global climate change adaptation progress, which currently faces 
the challenge of lacking consensus on how adaptation progress at this level can be tracked (high confidence). The box therefore assesses 
the rationale and methodological approaches for understanding adaptation progress globally across sectors and regions. It discusses 
strengths and weaknesses of existing approaches and sources of information, with a view towards informing the first Global Stocktake 
of the Paris Agreement in 2023.
Rationale for assessing adaptation progress at the global level

Global assessments of adaptation are expected to help answer key questions of climate policy (Ford et al., 2015; UNEP , 2017; Adaptation 
Committee, 2021) (limited evidence, high agreement), including: Do the observed, collective investments in adaptation lead humanity 
to being better able to avoid or reduce the negative consequences from climate change? Where is progress being made, and what gaps 
remain in the global adaptation response to climate risks?
While more than 170 countries have policies that address adaptation (Nachmany et al., 2019b; Section 17.4.2), very few have operational 
frameworks to track and evaluate implementation and results (Leiter, 2021a; Section 17.5.2.4). In Europe, for example, most countries have 
adopted a national adaptation plan or strategy, but only few are tracking whether ambitions are realised (EEA, 2020; Section 13.11.2).

Moreover, climate risks are interconnected across scales, regions and sectors (Eakin et al., 2009; Challinor et al., 2017; Cross-Chapter 
Box INTERREG in Chapter 16; Hedlund et al., 2018) (high confidence), complicating causal attribution. National assessments of progress 
usually do not assess private sector and non-governmental adaptation and barely account for climate risks that transcend across borders, 
for example through supply chains or shared ecosystems (EEA, 2018; Benzie and Persson, 2019). In addition, adaptation action in one 
place or time can potentially lead to negative effects elsewhere (externalities) (Magnan and Ribera, 2016; Atteridge and Remling, 2018; 
17.5.1). Hence, determining the collective adequacy and effectiveness (see Figure 1.7 in Chapter 1) of adaptation responses is different 
from simple aggregates of national and sub-national information (UNEP , 2017).

Assessing global progress on adaptation is therefore of high relevance to the scientific community, policymakers and other actors. Global 
assessments serve different information needs than local assessments, and their meaningfulness depends on the chosen approaches and 
their limitations. Aggregated global assessments of adaptation progress are therefore not meant to substitute place-specific ones but to 
complement them to enhance the knowledge base on adaptation beyond actions by or within individual countries. The Paris Agreement 
stipulates a Global Stocktake to be undertaken every 5 years to assess the collective progress towards its long-term goals, including

17
2611Decision-Making Options for Managing Risk  Chapter 17
on adaptation (UNFCCC, 2015, Article 14). Yet very few scientific studies have addressed the adaptation-specific aspects of the Global 
Stocktake (Craft and Fisher, 2018; Tompkins et al., 2018), and there are different views and options on how assessing global progress 
could take place (high confidence).
Considerations in designing global adaptation assessments
A number of key considerations for the design of global adaptation assessment approaches are discussed in the literature (Ford and 
Berrang-Ford, 2016; Berrang-Ford et al., 2017). Some of these involve trade-offs, such as global applicability versus context specificity, for 
which there is no simple solution. Design considerations directly depend on the objectives of global adaptation assessments, which can 
differ between actors and can include, for example, providing transparency, enabling accountability, understanding effectiveness or guiding

policy development (Section 17.5.2.1). The underlying objectives determine the suitability of approaches and the data requirements.
Comparability
Global assessments may have the objective to compare adaptation over time and across sectors and regions (Ford et al., 2015). Such 
comparison requires a consistent definition of concepts (Hall, 2017; Berrang-Ford et al., 2019) and the identification of variables that 
are both generic enough to be applicable from one context to another and specific enough to illustrate national circumstances. To date, 
finding such balance has proven to be challenging (Dupuis and Biesbroek, 2013). The context dependence of adaptation outcomes poses 
limits for meaningful comparisons. Even people exposed to the same climate hazard may be differentially affected due to varying levels 
of vulnerability and resilience (Jones et al., 2018; Thomas et al., 2019), meaning that perceptions on adaptation outcomes can also differ 
(Jones and d’Errico, 2019).

Aggregation
The aggregation of data from local or regional to global scales can take different forms ranging from qualitative synthesis to quantitative 
aggregation, which may involve condensing a diverse set of variables into a single score (Leiter, 2015; Section 17.5.2.3). In contrast to 
climate change mitigation, adaptation does not have a global reference metric against which adaptation levels could be assessed to 
identify progress or gaps. Experience from the Global Environment Facility, for example, has shown that mechanical aggregation based 
on standardised indicators fails to capture what makes the greatest difference on the ground (Chen and Uitto, 2014).
Results: Input, process, output or outcome
Adaptation progress at any spatial scale can in principle be assessed in terms of input (e.g., resources spent), process (i.e., the way 
adaptation is organised), output (i.e., adaptation capacities and actions) and outcomes (i.e., actual changes induced) (Section 17.5.2.2).

Due to the challenges inherent in measuring adaptation outcomes (Sections 16.3, 17.5.1 and 17.5.2.5), most global assessments to 
date have focused on outputs, such as whether countries have adopted adaptation plans (Berrang-Ford et al., 2021; UNEP , 2021a) (high 
confidence). Understanding the effectiveness of adaptation responses globally requires a way to conceptualise and capture outcomes, 
for example in terms of effective climate risk reduction, while avoiding simplifications that mask maladaptation at the global level, 
such as where climate risks are shifted to other countries, sectors or population groups (Cross-Chapter Box INTERREG in Chapter 16, 
Section 17.5.1).
Data
Global assessments typically require global availability of consistent data, be they quantitative or qualitative, which has proven to be a 
constraining factor for attempts to assess global adaptation (high confidence). For example, many countries face difficulties in reporting

adequately on progress in implementing the Sendai Framework and risk-related SDGs (UNDRR, 2019: vi). The availability of data also 
influences which variables can be eventually selected in an assessment. This limitation can affect the ability to meet the initial objectives 
and lead to biases in the framing and interpretation of assessment outcomes. For some variables, an alternative to relying on nationally 
provided data can be to develop new global data sets (Magnan and Chalastani, 2019) or utilise data from Earth Observation (Andries 
et al., 2018). Adaptation is hence faced with a dilemma between globally available yet generic data and regionally or locally more 
detailed yet patchy data (high confidence).
Assessment of existing approaches to assess adaptation progress at the global level
Only few global assessments of adaptation progress across sectors have been undertaken to date (high confidence). They focus, for

example, on whether countries have progressed their adaptation policies and actions over time (Lesnikowski et al., 2015; Nachmany 
et al., 2019b), the extent of implemented adaptation globally (Leiter, 2021a; Leiter, 2021b), and the type and actors of responses (Berrang-
Ford et al., 2021), evidence for reduced vulnerability to climate-related hazards (Formetta and Feyen, 2019; UNDRR, 2019) or adaptation 
planning in cities across the globe (Araos et al., 2016a; Reckien et al., 2018a; Olazabal et al., 2019a). Each of these assessments draws Cross-Chapter Box PROGRESS (continued)

17
2612Chapter 17 Decision-Making Options for Managing Risk
on different approaches and data, and all have particular potential but also limitations (Table Cross-Chapter Box PROGRESS.1) (high 
confidence). The application of differing approaches shows that there is no single ‘best’ approach or data source to assess global progress 
on adaptation (high confidence). Existing global assessments have provided valuable insights into the extent and types of responses and 
their level of planning and implementation (Section 16.3.2.4). However, they do not provide comprehensive and robust answers so far on 
whether climate risk and vulnerability have been reduced (Berrang-Ford et al., 2021) (high confidence). As a result, combining different 
approaches and integrating data on climate risk levels, policy measures, implemented actions and their effects on climate risk reduction 
is currently regarded as the most robust approach (Berrang-Ford et al., 2019) (medium evidence, high agreement).

Table Cross-Chapter Box PROGRESS.1 |  Key approaches and data sources used for global adaptation assessments.
Approach/data source Potential added value Limitations
Systematic assessment of adaptation responses reported 
in academic literature (e.g., systematic reviews, evidence 
synthesis, meta-analysis, large-n comparative studies)
Examples:
Berrang-Ford, 2011, Global Adaptation Mapping Initiative, 
Berrang-Ford et al. (2021)Provides an indication of the status, trends 
and gaps in adaptation responsesNot a representative sample; biased towards responses 
published in scientific literature; excludes grey literature; some 
topics and regions not well covered; challenges in terms of 
comparability and aggregation; inconsistency in definitions and 
use of concepts; English language bias
Self-reported progress documents by countries (e.g., 
National Communications, Biennial Transparency Reports or 
domestic progress and evaluation)
Examples:

Gagnon-Lebrun and Agrawala (2007); Lesnikowski et al. 
(2015); Lesnikowski et al. (2016); Leiter (2021a)Context-specific information; official 
government documents enable assessments of 
national progressMay only be available every few years; content is sensitive 
to political and policy changes; possible bias towards 
positive examples; challenges in terms of comparability and 
aggregation; inconsistency in definitions and use of concepts
Self-reported information from the private sector (e.g., 
information on actions taken in response to climate risks 
within the context of climate-related financial disclosure or 
in company reports).
Examples:
Committee on Climate Change (2017); Street and Jude 
(2019); UNFCCC (2021), responses reported under 
Climate-related Financial DisclosureProvides an indication of the status, trends 
and gaps in adaptation responses by the 
private sector; complements information 
published in the scientific literature; could

enable better understanding of supply chain 
risksSample biased towards larger companies; challenges in terms 
of comparability and aggregation; potential inconsistencies in 
definitions and use of concepts
Project documents and evaluations (e.g., from climate funds 
or implementing organisations)
Examples:
Leiter (2021b); Eriksen et al. (2021)Detailed information on context, intended or 
achieved results and activitiesActual implementation can differ from what was proposed; 
fragmented picture of local/regional actions; results may be 
challenging to aggregate; challenges in terms of comparability 
and aggregation; inconsistency in definitions and use of 
concepts
Existing global data sets of mostly quantitative indicators
Examples:
United Nations (UN, 2016a; UN, 2016b; UN, 2019; UNDRR, 
2019)Comparable information based on globally 
defined indicatorsGlobal data availability constrains indicator choice; reporting 
burden for new indicators; trade-off between global

applicability and national circumstances; usefulness and 
meaningfulness of global indicators is contested (Leiter and 
Pringle, 2018; Lyytimäki et al., 2020; Pauw et al., 2020).
Tracking financial flows
Examples:
CPI (2019), OECD (2018a), MDBs (2019)Comparable data on financial flows directed 
at adaptation; standardised methodologies 
(e.g., OECD RIO markers; climate finance 
tracking method of multi-lateral development 
banks; Section 17.5.2.6; Cross-Chapter 
Box FINANCE in this Chapter)No information about implementation of measures and 
their adaptation effect (Eriksen et al, 2021), i.e., it tracks 
inputs, not outputs or outcomes; inconsistency in what gets 
counted as adaptation finance (Donner et al., 2016; Doshi and 
Garschagen, 2020); evidence of over-reporting (Michaelowa 
and Michaelowa, 2011; Weikmans et al., 2017)
Conclusion—Combining approaches for assessing adaptation progress at the global level

Understanding to what extent the world is on track to adapt to climate change impacts and risks globally is a pressing question in scientific 
and policy communities, especially in light of the Global Stocktake under the Paris Agreement. Important considerations for a robust 
assessment framework (e.g., consistency), as well as the associated scientific challenges (e.g., aggregation, externalities, breadth versus 
depth of data) and the role of underlying objectives (e.g., on the contested issue of comparability) are increasingly understood (high 
confidence). There is also a growing and diverse body of information on adaptation progress, although most assessments of global progress 
undertaken to date focus on processes and outputs (e.g., policies and plans) rather than outcomes (i.e., risk reduction). A variety of approaches

and data sources are employed, such as systematic reviews of observed adaptation, formal communications by Parties to the UNFCCC, and Cross-Chapter Box PROGRESS (continued)

17
2613Decision-Making Options for Managing Risk  Chapter 17
project documents to international funding agencies. Novel approaches, including big data tools (Ford et al., 2016; Biesbroek et al., 2020), 
are also being explored but still have to prove their practical value. Each approach and source of information can contribute additional 
knowledge, but also demonstrates limitations, so that there is no single ‘best’ approach (high confidence). Yet, to date, the international 
community has not sufficiently explored the relative strengths and weaknesses of different approaches and their applicability and, therefore, 
their potential synergies in complementing each other. Triangulated assessments have only rarely been applied (high confidence) due to 
multiple conceptual and methodological challenges, despite their potential for increasing the robustness of knowledge. One overarching

conclusion of this Cross-Chapter Box therefore is that the combination of different approaches will provide a more comprehensive picture of 
global adaptation progress than is currently available from individual approaches (limited evidence,  high agreement).Cross-Chapter Box PROGRESS (continued)
Good coverage of adaptation finance data exists around international 
public finance flows, predominantly official development assistance 
flows from OECD DAC members and from multi-lateral development 
banks. Less data exist around domestic public finance and private 
finance flows to adaptation activities, but data sources continue to 
be further expanded, for example through climate change expenditure 
tagging and city-level data (Weikmans et al., 2017; UNFCCC SCF , 2018; 
Richmond et  al., 2021). Recent estimates of adaptation finance are 
provided in UNFCCC SCF (2018), Macquarie et al. (2020) and Cross-
Chapter Box FAR in this Chapter.
17.5.2.7 Evaluation and Learning

Most adaptation M&E frameworks and tools proposed to date refer 
to monitoring rather than evaluation (high confidence) (Adaptation 
Committee, 2016). Evaluations are envisioned to go beyond monitoring 
by examining how and why results have been achieved and what could 
be improved (Brousselle and Buregeya, 2018; Vähämäki and Verger, 
2019). Evaluations of adaptation outcomes are still rare, particularly 
quantitative impact evaluations (Weldegebriel and Prowse, 2013; Das, 
2019; Béné et  al., 2020). Impact evaluations of adaptation need to 
address several methodological as well as practical challenges (Dinshaw 
et  al., 2014; Fisher et  al., 2015; Béné et  al., 2017; Puri et  al., 2020). Different types of evaluations are appropriate for different evaluation 
questions (Silvestrini et al., 2015). Evaluations of the available evidence 
of effective adaptation, in particular topics or sectors, have emerged 
more recently, for instance on mainstreaming (Runhaar et al., 2018) and

agricultural climate services (Vaughan et al., 2019a). Impact evaluations 
of capacity building measures are important because capacity building 
is assumed to lead to adaptation, but its actual effects are seldom 
examined (Mortreux and Barnett, 2017; Alpizar F and Meiselman, 
2019). If well designed and utilised for learning, evaluations can play 
an important role in improving adaptation responses (Hildén, 2011).
Learning requires information about how and why change occurred and 
what experiences have been made (Feinstein, 2012). M&E is frequently 
associated with learning, but it is rarely made explicit how learning 
is supposed to take place (Armitage et al., 2008; Baird et al., 2015; 
Borras and Hølund, 2015). The design of adaptation M&E systems can 
support learning by gathering relevant information and disseminating 
it in a way that is accessible and effectively linked to decision-making 
processes (Spearman and McGray, 2011; Villanueva, 2012; Fisher

et al., 2015). Options include institutionalised feedback mechanisms, 
peer learning and knowledge sharing events, a learning culture and 
ways to gather in-depth insights beyond indicators (ibid; Oswald and 
Taylor, 2010). Since AR5, adaptation programmes and funds such as Box 17.4 | The Rio Markers Methodology to Track Climate Finance
The OECD Development Assistance Committee (DAC) introduced a methodology to track the amount of bilateral official development 
assistance (ODA) that is targeting climate change mitigation and/or adaptation. It distinguishes whether activities have adaptation as a 
‘principal’ objective (score ‘2’), as a ‘significant’ objective (score ‘1’) or as not targeting it (score ‘0’) (OECD, 2016). The associated project 
value is counted in full, in part, or not counted as adaptation finance, respectively. Countries count the volume of partial adaptation

projects (score ‘1’) to a different extent, which limits comparability and can lead to over-reporting (OECD, 2019). The first data on this 
‘adaptation marker’ became available in 2012 for the financial flows of 2010. It forms the basis for developed countries’ reporting to the 
UNFCCC Secretariat on their financial commitments towards developing countries (Weikmans and Roberts, 2019).
While a guidebook with requirements for adaptation as a principle or significant objective has been developed (OECD, 2016), several 
studies have shown that OECD DAC donors tend to overestimate the number of activities in their portfolio that genuinely have adaptation 
objectives (Michaelowa and Michaelowa, 2011; Weikmans et al., 2017; CARE, 2021). Hence, the amount of adaptation finance from 
public sources may be lower than reported. The use of just three categories leads to a broad range of the extent of adaptation being

concentrated in the middle category (‘significant objective’). Accordingly, the category ‘principle objective adaptation’ provides a more 
robust predictor of the relevance of an activity to adaptation (Donner et al., 2016).

17
2614Chapter 17 Decision-Making Options for Managing Risk
the BRACED programme, the Adaptation Fund, the Climate Investment 
Funds and the Green Climate Fund have created knowledge-sharing 
units and provide resources to support learning activities (BRACED, 
2015; Roehrer and Kouadio, 2015; Adaptation Fund, 2016; Leavy et al., 
2018; CIF , 2020; Puri et al., 2020), but there is little information about 
their longer-term effectiveness.
17.6 Managing and Adapting to Climate Risks 
for Climate Resilient Development
Actions to ameliorate a climate risk have consequences beyond the 
immediate effects on exposure or vulnerability to a hazard. They may 
aim to combat many risks, could adversely interact with other risks and 
actions, or may be nested within a suite of actions across many risks. 
Some actions may have negative consequences for climate resilient 
development. In this broader context, the effectiveness of adaptations

for supporting climate resilient development is now better articulated 
(Box 17.1). Importantly, adaptations need to be designed to not only 
combat current and future climate risks but also ensure that they 
do not lock in undesirable pathways in the future as risks develop 
and change (very high confidence) (Sections  17.2, 17.3.1, 17.5). 
Effective management of climate risks will therefore be dependent 
on satisfactorily managing current climate risks (Boxes 17.1, 17.2, 
17.5), coupled with assessing prognoses for future climate risks, and 
developing responses in advance for reducing those risks to tolerable 
residual levels (very high confidence) (Sections  1.4, 1.6, 16.6, 17.2, 
Box 16.1; e.g., water risks, Section 4.7.1). The dynamic nature of risk 
(Viner et al., 2019; Simpson et al., 2021; Sections 16.3, 16.6) also means 
that the contribution of current adaptations to ameliorating future 
risks needs to be regularly reviewed (high confidence) (Section 17.5.2).

Across the Working Group II report are examples of how managing 
adaptations to ameliorate climate risks can negatively or positively 
affect sustainable development, thereby impacting the potential for 
climate resilient development discussed in Chapter 18. Drawing on the 
assessment of sectoral and regional chapters in this report, this section 
examines three broad components for orienting decision-making for 
climate adaptation towards climate resilient development.
17.6.1 Need for Integrated Risk Management
The complex, interacting and compounding nature of climate risks 
means that single risks cannot be managed in isolation (very high 
confidence) (Section 16.5, Figure 16.11; Section 17.3.2; Nhamo et al., 
2018), including accounting for potential risks arising from adaptations 
(Simpson et al., 2021). Regional examples of needs for cross-sectoral 
integrated management include the water–energy–food nexus in Africa

(Section 10.5.1), Asia (Section 10.6.3), Australasia (Section 11.6), Europe 
(Section 13.2.2) and North America (Table 14.8), and ecosystem-oriented 
adaptations and/or nature-based solutions, in Africa (Section  9.6.5), 
Asia (Section 10.4.2), Australasia (Box 11.4, Section 11.3.5), Central and 
South America (Section 12.5.1), Europe (Section 13.3.2), North America 
(Section 14.6.1, Box 14.3) and Small Islands (Section 15.5.4). The cross-
sectoral interactions within humans systems, including impacts on cities, 
settlements and infrastructure, are reflected in those subjects as well as 
for health in Africa (Section 9.10.2), Asia (Section 10.4.5), Australasia (Section  11.3.6), Central and South America (Section  12.5.6), Europe 
(Section  13.7.2), North America (Section  14.6.1) and Small Islands 
(Section 15.6.2), and poverty and livelihoods in Africa (Section 9.11.3), 
Asia (Sections  10.4.5, 10.5), Australasia (Section  11.4), Central and

South America (Section 12.5.7), Europe (Section 13.8.2), North America 
(Section 14.6.1) and Small Islands (Section 15.3.4).
These examples demonstrate that the emergence of climate risks can 
be at different rates and different time horizons, and the interactions 
between risks vary from region to region (very high confidence). 
The need to manage these risks in an integrated manner is readily 
identified in the water–energy–food nexus (Box  9.5). However, in 
terms of climate resilient development, the need for integration is 
demonstrated by the diverse and interacting impacts of climate risks 
on ecosystems (Sections 2.7, 3.6), cities (Sections 6.2.3, 6.2.4, Boxes 
6.2, 6.3), health (Section 7.4), and poverty and livelihoods (Section 8.6).
17.6.2 Strategies for Managing a Portfolio of Climate 
Risks
Since WGII AR5, new methods for simultaneously considering multiple 
societal and sectoral objectives, climate risks and adaptation options

have emerged (Section 17.3.2; Adam et al., 2014; Hadka et al., 2015; 
Garner et al., 2016; Rosenzweig et al., 2017; Giupponi and Gain, 2017a; 
Stelzenmuller et al., 2018; Marchau et al., 2019), including methods 
for accounting for different sources of uncertainty and types of risk 
(Section 17.3.1; Giupponi and Gain, 2017a). Different decision-making 
approaches can be complementary (high confidence) (Section 17.3.1; 
Kwakkel et al., 2016), and multiple approaches will likely be necessary 
in managing the risks across sectors, over different spatial scales, and 
over short to long time scales (medium confidence) (Cross-Chapter 
Box PROGRESS in this Chapter; Girard et al., 2015; Rouillard and Spray, 
2016).
Deciding on which adaptations to adopt when managing climate 
risks inevitably needs examination of trade-offs in outcomes (very 
high confidence) (Sections 17.3.1, 17.5.1; Cross-Chapter Box FEASIB 
in Chapter 18). A current difficulty with integrated assessments is to

develop a set of metrics that are appropriately scaled for the different 
sectors or outcomes to be compared (e.g., Sections 12.5.2.6, 17.3.1, 
17.5.2; Cross-Chapter Box  PROGRESS in this Chapter). For climate 
resilient development, dimensions of poverty, equity, justice and health 
need to be factored into analyses (Boxes 17.1, 17.5), many of which 
are difficult to quantify (high confidence) (Section 18.2.4). Moreover, 
uncertainties on the interactions within and between sectors can 
make trade-off analyses uneven in their precision across sectors and 
uncertain as to the outcome of an implemented adaptation (medium 
confidence) (Sections 4.7.2, 17.4, 17.5).
Expertise and resources for using tools and approaches for integrated 
countries (high confidence) (e.g., Section  4.7.2). Exploration of 
adaptation scenarios can be derived from Earth System Models (high

confidence) (e.g., Sections 4.7.1.2, 11.7.3.1). However, the feasibility 
of possible adaptations and the degree to which they are likely to be 
effective (Box  17.1) will require further exploration as success will

17
2615Decision-Making Options for Managing Risk  Chapter 17
depend on appropriate enabling conditions, including institutional 
support and capacity, available financial resources and knowledge, 
and suitable conditions for stakeholder participation (high confidence) 
(Section  17.4). The current levels of uncertainty surrounding the 
effectiveness of many adaptation options (Section  17.5.2; Cross-
Chapter Box  PROGRESS in this Chapter) means that decision-
making approaches applicable to deep uncertainty (Cross-Chapter 
Box  DEEP in this Chapter; Section  17.3.1) will apply in many if not 
most cases (medium confidence). An early step in identifying suitable 
integrated pathways for managing climate risks, establishing ‘no 
regrets’ anticipatory options in a timely manner, and avoiding path 
dependencies is to jointly map the steps for adapting to sectoral 
risks and determine suitable ways to avoid maladaptations arising

(high confidence) (Section 17.3.1, Cross-Working Group Box URBAN 
in Chapter 6 and Cross-Chapter Boxes DEEP in this Chapter). 
The application of Dynamic Adaptive Pathway planning has been 
successfully used in this way in Australasia (Section 11.7.3) and Europe 
(Sections 13.6.2.2, 13.10.2) (Lawrence et al., 2019a; Haasnoot et al., 
2020a). Current experience suggests that synergies between sectors 
can save resources and effort (limited evidence) (Section  13.11.2). 
Iterative processes can then enhance adaptation programmes by 
including more detailed modelling, and updated knowledge as the 
experience is acquired (Section 17.3.1).
17.6.3 Mainstreaming Climate Risk Management in 
Support of Climate Resilient Development
This chapter has assessed and detailed a number of decision-making 
tools (Section 17.3) and enabling mechanisms and catalysing conditions 
(Section 17.4) that could be used in mainstreaming the management

of climate risk and adaptation in the sustainable development of 
communities, different sectors and nations. Since AR5, the challenges 
facing the management of climate risks have been articulated (Adger 
et  al., 2018; Balasubramanian, 2018), and greater clarity on the 
steps that could be taken to better mainstream adaptation has been 
developed (high confidence) (Cuevas, 2016; Giupponi and Gain, 2017a; 
Gomez-Echeverri, 2018; Sanchez Rodriguez et al., 2018). Nevertheless, 
the choice of decision processes is recognised as being dependent on 
a variety of local factors influencing development (Ayers et al., 2014; 
Szabo et al., 2016).
Adaptation strategies or plans, some of which incorporate elements 
of climate resilient development, have been developed in many 
jurisdictions from local (Cuevas, 2016; Araos et  al., 2016a; Reckien 
et al., 2018a; Göpfert et al., 2019) to provincial/state (Warnken and 
Mosadeghi, 2018) to national governments (Markolf et  al., 2015;

CSIRO, 2018; Warnken and Mosadeghi, 2018; Brown et  al., 2018a; 
Table 17.8). National Adaptation Plans have been a requirement under 
the UNFCCC and establish the general approach taken by nations for 
adapting to climate change (Woodruff and Regan, 2019). Integrated 
risk assessments and adaptation processes are being developed but 
with much less experience evident in their implementation (high 
confidence) (Wise et al., 2014; Woodruff and Stults, 2016; Brown et al., 
2018a).National Adaptation Plans (NAPs) submitted to the UNFCCC have 
been reviewed for quality by Woodruff and Regan (2019). In their 
review, Woodruff and Regan used a number of indicators grouped 
within established ‘quality principles’. They found that the plans 
were more oriented at the strategic level or at the level of specific 
projects rather than identifying methods for resolving cross-sectoral 
or cross-jurisdictional interactions or issues (medium confidence).

A key recommendation from their review and supported by other 
studies (e.g., Abutaleb et al., 2018) is that plans would be improved 
greatly by having inputs from multiple government agencies and 
multiple sectors (medium confidence), which could provide the basis 
for planning and review of integrated adaptation. Also, the plans need 
greater attention to implementation (Sections 9.4.1, 11.8, 13.11.2), 
and the identification of metrics by which success (Section  17.5.1) 
and performance can be measured (Cross-Chapter Box PROGRESS in 
this Chapter), a common issue for adaptation planning generally (e.g., 
Sections 12.5.2.6, 17.5).
Hence, satisfactorily managing intersecting climate risks in different 
settings, of which RKRs provide examples, is central to achieving 
sustainable development (high confidence) (Section 16.6.4), requiring 
integrated risk management within and across regions, jurisdictions, 
sectors and ecosystems (high confidence) (see CCP5.4.2, CCP5.4.3).

Iterative processes will enable measuring progress and updating 
adaptation at a satisfactory rate, to account for the different needs 
within regions and across sectors at different times (high confidence). 
The degree to which equity and justice will be achieved will be 
determined by the participatory processes in deciding on suitable 
adaptation options, the investment in the adaptation processes and 
the coordination and collaboration built among institutions and people 
across regions (high confidence).

17
2616Chapter 17 Decision-Making Options for Managing Risk
FAQ 17.1 | Which guidelines, instruments and resources are available for decision makers to recognise climate risks 
and decide on the best course of action?
Guidelines, instruments and resources to identify options for managing risks, and support decisions on the most 
suitable course of actions to take, can be collectively referred to as decision-support frameworks. These can include 
data services, decision-support tools, processes for making decisions and methods for monitoring and evaluating 
progress and success.  Data services enable the identification, location and timing of risks that could manifest 
with negative impacts, as well as potential opportunities.  Often, these are termed ‘climate services’ and assist 
with mapping hazards and how they are changing. Decision-support tools range from qualitative approaches to

determining overlap of areas of concern with those hazards in the future, to more quantitative and dynamic 
simulation approaches that enable dynamic stress-testing of adaptation options and strategies to determine if 
proposed plans for adapting to the future could be successful. An important consideration is whether options for 
risk management or capitalisation on opportunities will limit options and flexibility for responding to unforeseen 
events in the future. If these options have a negative effect on other areas of concern, then they could be identified 
in these planning scenarios as maladaptations, and therefore avoided.
A great challenge for decision makers is how to choose effective options when the future is uncertain. Uncertainty 
can arise not just in the statistical error of the magnitude of risk but also in the nature and consequence of risk from 
uncertainty about mechanisms that link areas of concern to hazards, uncertainty in the decision processes themselves

and so on. Methods are available to help develop no-regret options, commonly referred to as decision-making 
under conditions of deep uncertainty’.
Decision-support frameworks are most successful when they are iterative, integrative and consultative. Rather 
than a single decision be made, and an action taken, there are processes for making the best decision possible, 
then monitoring progress towards delivering a successful outcome. Given a set of suitable indicators with regular 
monitoring, decisions can be revised, updated or changed as the future unfolds and foundations for the original 
decision tested. This is important because climate responses need to be initiated well in advance of them being 
needed due to the time required to implement suitable responses. These forward-looking approaches allow errors 
to occur and corrections made before problems arise. They also enable action to be taken without having to wait

for the circumstances to arise, which if this were to occur could result in only limited reactions being available 
and the outcomes then dependent upon recovery from events rather than proactive planning and avoidance of 
events. Integrated approaches to risk management are available to help manage portfolios of interacting risks, 
including the potential for compounding and cascading risks when climate-related events arise.
Managing uncertainty with forward-looking processes needs to be more deliberative and oriented towards building 
trust in a collaborative process. Building relationships through informal, bottom-up processes enables this to occur. 
Top-down planning processes are important for ensuring that the management of risks and opportunities do not 
end up with maladaptations and that the approaches are equitable and proportional to that which is needed to 
manage the risks.

17
2617Decision-Making Options for Managing Risk  Chapter 17
FAQ 17.2 | What financing options are available to support adaptation and climate resilience?
What do we mean by ‘climate finance’?
The UNFCCC has no formally agreed upon definition of climate finance. The current IPCC definition is: ‘the financial 
resources devoted to addressing climate change by all public and private actors from global to local scales, including international financial 
flows to developing countries to assist them in addressing climate change’ (see Annex II: Glossary).
What needs to be financed?
Financial resources might be needed for a range of adaptation and resilience building activities. These include 
research, education and capacity building; development of laws, regulations and standards; provision of climate 
services and other information; reducing the vulnerability of existing assets, activities and services; and ensuring

future development—such as new infrastructure, settlements, health services and business activities—is climate 
resilient. Finance is also needed to recover and rebuild from the damage of climate hazards that cannot be 
completely avoided through adaptation. Adaptation actions can be undertaken by many different actors, alone or 
in partnership, including national and sub-national governments, public and private utilities, businesses of varying 
size, communities, households and individuals.
Table FAQ17.2.1 |  Examples of adaptation and resilience activities that might need to be financed
Training of agricultural extension officers so that their advice to small-holder farmers 
can support implementation of climate adapted agriculture. Additional financial 
support is needed for the costs of farmers transitioning to climate-resilient agricultural 
practices.A new urban development requires higher standards (and up-front costs) for buildings,

roads, stormwater systems and water re-use and to be resilient to expected changes in 
heavy rainfall, runoff, temperature and water supply reliability.
A water utility requires capital expenditure to increase supply through a desalination 
plant and to reduce leakage from its reticulation system in response to a scenario of 
reduced surface water availability and an increase in customers.A catastrophe risk insurance facility is established to provide post-disaster (drought, 
hurricane, flooding, pest outbreaks) recovery finance to national governments. The 
facility requires capital to be able to underwrite the insurance products it offers.
How much finance is needed?
The amount of adaptation finance depends on global, regional and local factors, including: the amount and timing 
of global warming, and how this translates into impacts and adaptation needs across the world; the levels of

adaptation already in place; the type of risk being adapted to; and the adaptation options being chosen, including 
whether the adaptation required is incremental or transformational.
The most-mentioned figure for finance need is the developed countries’ commitment to provide USD 100 billion per 
year by 2020 to support developing countries’ efforts in mitigation and adaptation. Negotiations will start in 2021 
on updating this amount for 2025. While sometimes thought to represent the actual cost of responding to climate 
change in developing countries, this is not the case. More recent estimates of the global cost of adaptation by 2030 
across developed and developing countries range between about USD 80 and 300 billion per year.
What types of finance are available?
Four main types (or instruments) of finance are currently being used to support adaptation. These different types 
are not mutually exclusive; grants can be combined with loans to provide blended finance.

17
2618Chapter 17 Decision-Making Options for Managing Risk
Table FAQ17.2.2 |  The main instruments through which adaptation is being financed.
Grants provide finance without any repayment requirements. Most grants for 
adaptation have been provided by multi-lateral funds such as the Green Climate 
Fund or a fund managed by a single OECD country such as Germany’s International 
Climate Initiative. Some countries have national climate or environment funds that 
provide grants for their own climate adaptation actions. Grants are also provided by 
philanthropic foundations and sometimes by companies as part of their environmental 
and social responsiveness mandate.Concessional loans require partial repayment of the finance provided. These involve 
either capital repayment coupled to below-market interest rates or capital repayment 
only. Concessional finance is almost entirely provided through multi-lateral

development banks such as the World Bank. This finance is particularly important for 
developing countries where market interests are high due to poor credit ratings or 
other risk factors, or where the return on investment is too low make a commercial 
loan viable.
Non-concessional loans (or debts) are commercial instruments, where capital 
repayment and market interest rates apply. These may be provided through 
development banks or private banks. Green bonds are a relatively new form of market 
loan, designed to meet climate and other environmental sustainability criteria in terms 
of how the proceeds are used. In recent years, green bonds have offered better interest 
than ordinary bonds owing to oversubscription by investors who are looking to move 
towards environmentally sustainable investment portfolios.Budget re-allocation does not require raising of new finance; rather, it involves 
moving funds already secured away from other purposes towards adaptation. In

government, this might involve re-allocation towards flood defence. In the private 
sector, a company might move budget from marketing, research and development, 
or perhaps dividends, towards increasing the climate resilience of operation, 
infrastructure or their value chain.
Where are different types of finance most useful?
Grants are useful for a range of adaptation actions where it is hard to generate a financial return. These include 
capacity-building activities, piloting new adaptation innovations, high-risk investment settings or projects where 
there are considerable non-financial benefits. In contrast, loans and other debt instruments can often support 
larger investments, for example for scaling out of successful pilot projects or for building adaptation and resilience 
into general development investment. To date, a large proportion of international climate finance for adaptation

in developing countries, especially in Sub-Saharan Africa and Oceania, has been grant led, sourced from OECD 
public funds, indicating that in many instances financing via loans is either considered too risky by the commercial 
investment sector or it has been hard to demonstrate sufficient return on investment.
Distribution of adaptation finance across different regions and different types of finance in 2015–2016
Grants
Project-level
market rate debt and equity
UnknownFinance type
Size relative to amount 
of finance (billions of USD)Americas
Latin America
and CaribbeanTrans-regionalWestern 
Europe
Sub-Saharan
Africa
Unknown Other
oceaniaEast Asia
and PacificCentral Asia and
South AsiaMidle East and
Figure FAQ17.2.1. |  The distribution of adaptation finance across different regions and different types of finance in 2015–2016, as tracked

by the Climate Policy Initiative. The size of each circle represents the amount of finance, with amount in billions USD superimposed.Box FAQ 17.2 (continued)

17
2619Decision-Making Options for Managing Risk  Chapter 17
FAQ 17.3 | Why is adaptation planning along a spectrum from incremental to transformational adaptation 
important in a warming world?
In a warming world, incremental adaptation, that is, proven standard measures of adaptation, will not always suffice 
to adjust to the negative impacts from climate change leading to substantial residual risks and, in some cases, the 
breaching of adaptation limits; transformational adaptation, involving larger system-wide change (as compared 
with in-system change), will increasingly be necessary as a complement for helping individuals and communities to 
cope with climate change. As an example of incremental adaptation, a farmer may decide to use drought-tolerant 
crops to deal with increasing occurrences of heatwaves. With further warming and increases in heatwaves and

drought, however, the impacts of climate change may necessitate the consideration of system-wide change, such 
as moving to an entirely new agricultural system in areas where the climate is no longer suitable for current 
practices, or switching to livestock rearing. Where on-site adaptation becomes infeasible and pull factors exist, the 
farming households may decide to seek employment in other sectors, which may also lead to migration for work. 
As another example, physical protection through sea walls to stop coastal flooding is a proven adaptation measure. 
With further projected flooding due to increasing sea level rise attributable to climate change transformational 
city planning, that would systemically change how flood water is managed throughout the whole city, potentially 
requiring deeper institutional, structural and financial support. Also, the deliberate relocation of settlements

(managed retreat) is seeing attention in the face of increasingly severe coastal or riverine flooding in some regions. 
While transformational adaptation is increasingly being considered in theory and planning, implementation is only 
beginning to see attention.
FAQ 17.4 | Given the existing state of adaptation, and the remaining risks that are not being managed, who bears 
the burden of these residual risks around the world?
A warming climate brings along increasing risks, part of which can be reduced or insured. What remains is called 
residual risks and needs to be retained by households, the private and public sectors. People living in conflict-affected 
areas benefit only marginally from adaptation investments by governments, private sector or other institutions. 
These people bear most of the changing climate risks themselves. Higher-income countries generally have invested

heavily in structural adaptation to make sure people are not exposed to extreme events (e.g., dykes) and have 
developed a variety of private or public insurance systems to finance the risk of the most rare or extreme events. In 
other, middle- or lower-income countries, these very extreme events are less likely to be insured, and the impacts 
are borne by the most vulnerable people. Absent risk reduction or insurance, coping with residual risks generally 
means reducing consumption (e.g., food) or drawing down assets (selling machinery, houses, etc.), which all can 
bring along longer-term adverse developmental implications. Adaptation investments in low-income countries tend 
to focus more heavily on increasing capacity and reducing vulnerability; people remain exposed to the changing 
climate risks and bear the burden of reacting and responding.

17
2620Chapter 17 Decision-Making Options for Managing Risk
FAQ 17.5 | How do we know whether adaptation is successful?
Adaptation aims to reduce exposure and vulnerability to climate change by responding to dynamic and multi-scalar 
combinations of climatic risks. What might be seen as successful at one scale or at one point in time might not be 
at another, particularly if climate risks continue to rise. Moreover, the benefits of adaptation interventions may not 
reach all intended beneficiaries or everyone affected by climate impact and risk, causing different people to have 
different views on how successful adaptation has been.
There is, therefore, no universal way to measure adaptation success, but there is high agreement that success is 
associated with a reduction of climate risks and vulnerabilities (for humans and ecosystems) and an equitable

balancing of synergies and trade-offs across diverse objectives, perspectives, expectations and values. Adaptation 
that is successful is also commonly expected to be inclusive of different socioeconomic groups, especially the most 
vulnerable, and to be based on flexible and integrative planning processes that take into account different climate 
scenarios.
Conceptually, the opposite of successful adaptation is maladaptation, that is, when adaptation responses produce 
unintended negative side effects such as exacerbating or shifting vulnerability, increasing risk for certain people 
or ecosystems, or increasing greenhouse gas emissions. Among the adaptation options assessed in this report 
(Figure  FAQ17.5.1), physical infrastructure along coasts (e.g., sea walls) has the highest risk for maladaptation 
over time through negative side effects on ecosystem functioning and coastal livelihood opportunities. However,

such adaptations may appear valuable in the short and even longer term for already densely populated urban 
coasts, demonstrating that an adaptation can be differently judged based on the context it is implemented in 
(Figure FAQ17.5.1). Many other adaptation options have a larger potential to contribute to successful adaptation 
(Figure FAQ17.5.1), such as nature restoration, providing social safety nets and changing diets/minimising food 
waste.
Assessments of adaptation need to be transparent about how they are measuring success. Monitoring and 
Evaluation (M&E) can be used to track progress and evaluate success and to identify if course corrections during 
adaptation implementation are needed to achieve the envisaged objectives. Given the diversity of adaptation 
actions and contexts, no one-size-fits-all approach to M&E and no common reference metrics for adaptation exist.

To date, assessments of progress of adaptation have often focused on processes and outputs (i.e., actions taken, 
such as adaptation plans adopted) that are easier to measure than the effects of these actions in terms of long-term 
reduction of risks and vulnerabilities. However, knowledge about the outcomes in terms of reducing climate risk, 
impact and vulnerability is critically required to know if adaptation has been successful.
Tracking progress, in particular outcomes and impacts of adaptation, involves a number of challenges. First, to 
determine progress over time, risk and vulnerability assessments need to be repeated at least once after starting an 
adaptation process. This is rarely done, as it demands resources that are usually not factored into the adaptation 
response. Second, attributing changes in climate risks and vulnerabilities to the adaptation response is often difficult

due to other influencing factors, such as socioeconomic development over time. Expected causal relationships 
between responses and their outcomes should already be outlined during the adaptation planning phase, for 
example by mapping the way from activities to outcomes, and they should be monitored during implementation. 
Third, as adaptation can occur in multiple forms and target multiple temporal and spatial scales, the engagement of 
a diversity of stakeholders is vital to understanding how responses enable adaptation and adaptation success across 
vulnerable groups. Although stakeholder engagement can be time intensive and costly, in particular when reaching 
out to populations that are usually not part of policy and planning processes, it can support evaluating co-benefits 
and trade-offs of adaptation responses. Consideration and analysis of co-benefits and trade-offs along with a focus

on short, medium and long time horizons of adaptation goals, which is usually possible through flexible and strong 
institutions, facilitate successful adaptation and reduce the likelihood of maladaptation.

17
2621Decision-Making Options for Managing Risk Chapter 17
Coastal accomodation
Strategic coastal retreat
Nature restoration
Minimizing ecosystem stressors
Ecosystem-based adaptation
Infrastructure retroﬁtting
Spatial planning
Insurance
Diversiﬁcationo f livelihoods
Social safety nets
Seasonal/temporary mobility
Permanent migrationFarm/ﬁshery practice
Water supply/distributionWater capture/storage
Water use/demandCoastal infrastructure
Availabilityo f health infrastructure
Accesst o health care
Disaster early warningSuccessul
adaptationRisko f
maladaptationContributiont o
Terrestrial &
marine ecosystems
(B)Low-lying coastal
systems (A)
Peace and mobility (H)Living standards (D)
Human health (E)Critical infrastructure,
networks and services
(C)
Food security (F)
Water security (G)Representative Key Risks Adaptation optionsContribution of adaptation options to potentially successful adaptation

and to the risk of maladaptation
Figure FAQ17.5.1 |  Contribution of adaptation options to potentially successful adaptation and to the risk of maladaptation. Note: A similar 
figure is part of Section 17.5.1.Box FAQ 17.5 (continued)

18
2657Climate Resilient Development Pathways Chapter 18
Climate resilient development (CRD) is a process of implementing

greenhouse gas mitigation and adaptation options to support sustainable development for all {Section  18.1}. Climate action and sustainable development are interdependent processes and climate resilient development is possible when this interdependence is leveraged. Pursuing these goals in an integrated manner increases their effectiveness in enhancing human and ecological well-being. Climate resilient development can help build capacity for climate action, including contributing to reductions in greenhouse gas emissions, while enabling the implementation of adaptation options that enhance social, economic and ecological resilience to climate change as the prospect of crossing the 1.5°C global warming level in the early 2030s approaches (WGI Table SPM1). For example, incorporating clean energy generation, healthy diets from sustainable food systems, appropriate urban planning and transport, universal health coverage and social protection, can generate substantial health and well-being

1) {Section 7.4.4, Cross-Chapter Box HEALTH in 
Chapter 7}. Similarly, universal water and energy access can help to reduce poverty and improve well-being while making populations less vulnerable and more resilient to adverse climate impacts (very high confidence) {Section 18.1, Box 4.7}.
Current development pathways, combined with the observed 
impacts of climate change, are leading away from, rather than towards, sustainable development, as reported in recent literature (moderate agreement, robust evidence). While

demonstrable progress has been made on some of the Sustainable Development Goals (SDGs), significant gains across a range of targets are still necessary, as is enhancing synergies and balancing and managing trade-offs. Severe risks to natural and human systems are already observed in some places (high confidence) and could occur in many more systems worldwide before mid-century (medium confidence) and by the end of the century at all scales, from the local to the global, and at all latitudes and altitudes (high confidence). The coronavirus disease 2019 (COVID-19) pandemic revealed the vulnerability of development progress to shocks and stresses, potentially delaying the implementation of the 2030 Agenda for all {Section 8.1, Cross-Chapter Box COVID in Chapter 7}. Various global trends, including rising income inequality, continued growth in greenhouse gas emissions, land use change, food and water insecurity, human displacement and reversals of long-term increasing life expectancy

trends in some nations, run counter to the SDGs (very high confidence), as well as efforts to mitigate greenhouse gas emissions and adapt to a changing climate {Section  18.2}. These development trends contribute to worsening poverty, injustice and inequity, and environmental degradation. Climate change can exacerbate these conditions by undermining human and ecological well-being {Section 18.2}.

Social and economic inequities linked to gender, poverty, race/
ethnicity, religion, age or geographic location compound vulnerability to climate change and have created and could further 
can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence.exacerbate injustices, as well as constrain the implementation of

CRD for all (very high confidence). Climate change intensifies existing vulnerability and inequality, with adverse impacts of climate change on the most vulnerable groups, including women and children in low-income households, Indigenous or other minority groups, small-scale producers and fishing communities, and low-income countries (high confidence). Most vulnerable regions and population groups, such as in East, Central and West Africa, South Asia, Micronesia and Melanesia, and Central America, present the most urgent need for adaptation (high confidence) {Chapters 10, 12, 15}. Climate justice initiatives explicitly address these multi-dimensional distributional issues as part of climate change adaptation. However, adaptation strategies can worsen social inequities, including gender, unless explicit efforts are made to change those unequal power dynamics, including spaces to foster inclusive decision making. Drawing upon Indigenous knowledge and local knowledge can contribute to

overcoming the combined challenges of climate change, food security, biodiversity conservation, and combating desertification and land degradation. {Section 18.2; Cross-Chapter Box GENDER; Cross-Chapter Box INDIG}

Opportunities for climate resilient development vary by location 
(very high confidence). Over 3.3 billion people live in regions that are

very high and highly vulnerable to climate change, while 2 billion people live in regions with low and very low vulnerability. Response to global greenhouse gas emissions trajectories, regional and local development pathways, climate risk exposure, socioeconomic and ecological vulnerability, and the local capacity to implement effective adaptation and greenhouse gas mitigation options, differ depending on local contexts and conditions {Table 18.3}. As an example, underlying social and economic vulnerabilities in Australasia exacerbate disadvantage among particular social groups and there is deep under-investment in adaptation, given current and projected risks {Chapter 11}. There is also significant regional heterogeneity in climate change, exposure and vulnerability, indicating different starting points for CRD, as well as mitigation, adaptation and sustainable development opportunities, synergies and trade-offs {Section 18.5}.

There are multiple possible pathways by which communities, 
nations and the world can pursue CRD. Moving towards different pathways involves confronting complex synergies and trade-offs between development pathways, and the options, contested values and interests that underpin climate mitigation and adaptation choices (very high confidence). Climate resilient

development pathways (CRDPs) are trajectories for the pursuit of CRD and navigating its complexities. Different actors, the private sector and civil society, influenced by science, local and Indigenous knowledges, and the media are both active and passive in designing and navigating CRDPs {Sections 18.1, 18.4}. Increasing levels of warming may narrow the options and choices available for local survival and sustainable development for human societies and ecosystems. Limiting warming to Paris Agreement goals will reduce the magnitude of climate risks to which people, places the economy and ecosystems will have to adapt. Reconciling the costs, benefits and trade-offs associated with

18
2658Chapter 18 Climate Resilient Development Pathways
adaptation, mitigation and sustainable development interventions and 
how they are distributed among different populations and geographies 
is essential and challenging, but also creates the potential to pursue 
synergies that benefit human and ecological well-being. For example, in 
parts of Asia, sustainable development pathways that connect climate 
change adaptation and disaster risk reduction can reduce climate 
vulnerability and increase resilience {Table  18.3, Section  10.6.2}. 
Different actors and stakeholders have different priorities regarding 
these opportunities, which can exacerbate or diminish existing social, 
economic and ecological vulnerabilities and inequities. For example, 
in parts of Africa, intensive irrigation contributes to the development 
of agriculture but has come at a cost to ecosystem integrity and 
human well-being {Table  18.3, Section  9.15.2}. Careful and explicit

consideration for the ethical and equity dimensions of policies and 
practices associated with a climate resilient development pathway can 
help limit these negative externalities.
Prevailing development pathways are not advancing CRD 
(very high confidence). Societal choices in the near term will 
determine future pathways. Some low-emissions pathways and 
climate outcomes are unlikely2 to be realised (very high confidence). 
Rapid climate change is affecting every region across the globe and 
affecting natural and human systems relevant to the pursuit of the 
SDGs  {Sections  18.1, 18.2, Figure  18.1}.  Even the most ambitious 
greenhouse gas mitigation scenarios indicate climate change will 
continue for decades to centuries  {WGI, Section  18.2}. Increasing 
mitigation effort across multiple sectors exhibits opportunities 
for synergies with sustainable development, but also trade-offs 
that increase with mitigation efforts, that need to be balanced and

managed (high confidence). The uncertainty associated with achieving 
specific pathways and climate outcomes is a risk factor to consider 
in planning, with plausibility and transformational challenges, as well 
as trade-offs and synergies, affected by technology, policy design and 
societal choices {Section 18.2}. For instance, restrictions on utilisation 
of individual mitigation options to manage trade-offs (e.g., bioenergy 
with carbon capture and storage [CCS], afforestation, nuclear power) 
can also affect the mitigation cost to households (e.g., energy security, 
commodity prices) and the likelihood of a desired climate outcome 
being realised. Developing and transitional economies are estimated 
as low-cost mitigation opportunities but are often at high risk from 
climate change due to their regional and development context (high 
confidence) {Sections  18.2, 18.5}. For example, in Africa, competing 
uses for water such as hydropower generation, irrigation and ecosystem

requirements can create trade-offs among different management and 
development objectives {Section 9.7.3}. In Asia, intensive irrigation and 
other forms of water consumption can have a negative effect on water 
quality and aquatic ecosystems {Section 10.6.3}. Developed countries 
also face trade-offs, including in Australasia where adapting to fire risk 
in peri-urban zones introduces potential trade-offs among ecological 
values and fuel reduction in treed landscapes {Section  11.3.5}, and 
in North America where new coastal and alpine developments 
generate economic activity but enhance local social inequalities 
{Section 15.4.10}.
2 In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, about

lies within the 17–83% probability range.Systems transitions can enable CRD when accompanied by 
appropriate enabling conditions and inclusive arenas of 
engagement (very high confidence). Five systems transitions 
are considered: energy, industry, urban and infrastructure, land 
and ecosystems, and societal. Advancing CRD in specific contexts 
may necessitate simultaneous progress on all five transitions. 
Collectively, these system transitions can widen the solution space, 
and accelerate and deepen the implementation of sustainable

development, adaptation, and mitigation actions by equipping actors 
and decision makers with more effective options. For example, urban 
ecological infrastructure linked to an appropriate land use mix, street 
connectivity, open and green spaces, and job-housing proximity 
provides adaptation and mitigation benefits that can aid urban 
transformation. {Table  18.4, Cross-Working Group Box  URBAN in 
Chapter 6} These system transitions are necessary precursors for more 
fundamental climate and sustainable-development transformations; 
but can simultaneously be outcomes of transformative actions. 
However, the way they are pursued may not necessarily be perceived 
as ethical or desirable to all actors. Hence, enhancing equity and 
agency are cross-cutting considerations for all five transitions. Such 
transitions can generate benefits across different sectors and regions, 
provided they are facilitated by appropriate enabling conditions,

including effective governance, policy implementation, innovation, 
and climate and development finance, which are currently insufficient 
{Sections 18.3, 18.4}.
There is a rapidly narrowing window of opportunity to implement 
system transitions needed to enable CRD. Past choices have 
already eliminated some development pathways, but other 
pathways for CRD remain (very high confidence). In spite of a 
growth in national net-zero commitments, the current prospects of 
surpassing 1.5°C global mean temperatures by the 2030s are high 
{WGI Table SPM1}. There is strong evidence of the worsening of multiple 
climate impact drivers in all regions, that will place additional pressures 
on ecosystem services that support food and water systems, increasing 
the risks of malnutrition, ill-health and poverty in many regions {WGI 
Fig SPM9, Table 18.4}. This implies that significant additional adaptation 
will be needed. Over the near-term, implementing such transformational

change could be disruptive to various economic and social systems. 
Over the long-term, however, they could generate benefits to human 
well-being and planetary health. Strengthening coordinated adaptation 
and mitigation actions can enhance the potential of local and regional 
development pathways to support CRD. Planning for CRD can support 
both adaptation and decarbonisation via effective land use, promoting 
resilient and low-carbon infrastructure; protecting biodiversity and 
integrating ecosystem services {Table 18.4}, assuming advancing just 
and equitable development processes.
Prospects for transformation towards CRD increase when key 
governance actors work together in inclusive and constructive 
ways to create a set of appropriate enabling conditions 
{Section  18.4.2} (high confidence). These enabling conditions 
include effective governance and information flow, policy frameworks

18
2659Climate Resilient Development Pathways  Chapter 18
that incentivise sustainability solutions; adequate financing for 
adaptation, mitigation and sustainable development; institutional 
capacity; science, technology and innovation; monitoring and 
evaluation of climate resilient development policies, programmes 
and practices; and international cooperation. Investment in social 
and technological innovation could generate the knowledge and 
entrepreneurship needed to catalyse system transitions and their 
transfer. The implementation of policies that incentivise the deployment 
of low-carbon technologies and practices within specific sectors such 
as energy, buildings and agriculture could accelerate greenhouse gas 
mitigation and deployment of climate-resilient infrastructure in urban 
and rural areas. Civic engagement is an important element of building 
societal consensus and reducing barriers to action on adaptation, 
mitigation and sustainable development {Section 18.4}.

CRDPs are determined through engagement in different arenas, 
the degree to which the emergent pathways foster just and 
CRD depends on how contending societal interests, values and 
worldviews are reconciled through inclusive and participatory 
interactions between governance actors in these arenas of 
engagement {Section 18.4.3} (high confidence). These interactions 
occur in many different arenas (e.g., governmental, economic and 
financial, political, knowledge, science and technology, and community) 
that represent the settings, places and spaces in which societal actors 
interact to influence the nature and course of development. For 
instance, the Agenda 2030 highlights the importance of multi-level 
adaptation governance, including non-state actors from civil society 
and the private sector. This implies the need for wider arenas and 
modes of engagement around adaptation that facilitate coordination, 
convergence and productive contestation among these diverse actors

to collectively solve problems and to unlock the synergies between 
adaptation and mitigation and sustainable development.
Regional and national differences mean different capacities 
for pursuing CRDPs. Economic sectors and global regions are 
exposed to different opportunities and challenges in facilitating 
CRD, suggesting adaptation and mitigation options should 
be aligned to local and regional context and development 
pathways (very high confidence). Given their current state of 
development, some regions may prioritise poverty and inequality 
reduction, and economic development over the near-term as a means 
of building capacity for climate action and low-carbon development 
over the long-term. For example, Africa, South Asia, and Central and 
South America are highly exposed, vulnerable and impacted by climate 
change, which is amplified by poverty, population growth, land use 
change and high dependence on natural resources for commodity

production. In contrast, developed economies with mature economies 
and high levels of resilience may prioritise climate action to transition 
their energy systems and reduce greenhouse gas emissions. Some 
interventions may be robust in that they are relevant to a broad range of 
potential development trajectories and could be deployed in a flexible 
manner. For example, conservation of land and water could be achieved 
through a variety of means and offer benefits to populations in the 
Global North and South alike. However, other types of interventions, 
such as those that are dependent upon emerging technologies, may 
require a specific set of enhanced enabling conditions or factors 
including infrastructure, supply chains, international cooperation, and education and training that currently limit their implementation 
to certain settings {Section  18.5}. Notwithstanding national and 
regional differences, development practices that are aligned to people,

prosperity, partnerships, peace and the planet, as defined in Agenda 
2030, could enable more CRD {Figure 18.1}.
People, acting through enabling social, economic and political 
institutions are the agents of system and societal transformations 
that facilitate CRD founded on the principles of inclusion, equity, 
climate justice, ecosystem health and human well-being (very  
high confidence). While much literature on climate action has focused 
on the role of technology and policy as the factors that drive change, 
recent literature has focused on the role of specific actors; citizens, 
civil society, knowledge institutions (including local and Indigenous 
Peoples and science), governments, investors and businesses. Greater 
attention to, and transparency of, which actors’ benefit, fail to benefit 
or are impacted by mitigation and adaptation choices actions could 
better support climate-resilient and sustainable development. For

example, grounding adaptation actions in local realities could help to 
ensure that adaptive actions do not worsen existing gender and other 
inequities within society (e.g., leading to maladaptation practices) (high 
confidence) . Differences in the ability of different actors to effect change 
ultimately influence which interventions for sustainable development 
or climate action are implemented and thus what development 
outcomes are achieved. Recent literature has focused on the social, 
political and economic arenas of engagement in which these different 
actors interact. More focused attention on these arenas of engagement 
could prove beneficial to reconciling divergent views on climate action, 
integrating Indigenous knowledge and local knowledges, and elevating 
diverse voices that have historically been marginalised from the policy 
discourse, thereby reducing vulnerability and deepening adaptive 
capacity and the ability to implement CRD {Section 18.4; Cross-Chapter

Box GENDER; Cross-Chapter Box INDIG}.
Pursuing CRD involves considering a broader range of sustainable 
development priorities, policies and practices, as well as enabling 
societal choices to accelerate and deepen their implementation 
(very high confidence). Scientific assessments of climate change 
have traditionally framed solutions around the implementation of 
specific adaptation and mitigation options as mechanisms for reducing 
climate-related risks. They have given less attention to a fuller set of 
societal priorities and the role of non-climate policies, social norms, 
lifestyles, power relationships and worldviews in enabling climate 
action and sustainable development. Because CRD  involves different 
actors pursuing plural development trajectories in diverse contexts, the 
pursuit of solutions that are equitable for all requires opening the space 
for engagement and action to a diversity of people, institutions, forms

of knowledge and worldviews. Through inclusive modes of engagement 
that enhance knowledge sharing and realise the productive potential of 
diverse perspectives and worldviews, societies could alter institutional 
structures and arrangements, development processes, choices and 
actions that have precipitated dangerous climate change, constrained 
the achievement of SDGs and, thus, limited pathways to achieving CRD 
{Box 18.1, Section 18.4}. Action over the next decade will be critical for 
charting CRD pathways that catalyse the transformation of prevailing 
development practices and offer the greatest promise and potential for 
human well-being and planetary health.

18
2660Chapter 18 Climate Resilient Development Pathways
18.1 Ways Forward for Climate Resilient 
The links between climate change and development have been long 
recognized by various research communities (Nagoda, 2015; Winkler 
et  al., 2015; Webber, 2016; Carr, 2019) and have been assessed by 
Working Group II in every IPCC Assessment Report since AR3 (Smit 
et  al., 2001; Yohe et  al., 2007; Denton et  al., 2014). For the AR 1-3 
reports, these links were largely framed in the context of sustainable 
development, a concept that has been well described in the literature 
for decades (Brundtland, 1987). The AR5 introduced the framing of 
climate-resilient pathways, which narrowed the discussion around 
sustainable development to specifically address the contributions 
of mitigation and adaptation actions to the reduction of risk to 
development and the various institutions, strategies and choices 
involved in risk management (Denton et al., 2014). That assessment

concluded that identifying and implementing appropriate technical 
and governance options for mitigation and adaptation as well as 
development strategies and choices that contribute to climate resilience 
are central to the successful implementation of such strategies. The 
AR5 also recognised that transformation of current development 
pathways in terms of wider political, economic and social systems may 
be necessary (Denton et al., 2014).
The literature presenting research findings on climate resilient 
development (CRD) and pathways and processes for successfully 
achieving CRD has expanded significantly in the several years 
since the AR5 (very high confidence). This includes both qualitative 
studies of development as well as illustrative, quantitative analyses 
of development trajectories linked to specific scenarios, such as the 
Shared Socioeconomic Pathways (SSPs) (Section 18.2.2). Furthermore, 
the literature describing the role of system transitions and societal

transformation in enabling climate action (Box  18.1, Section  18.3), 
compliance with the Paris Agreement (Sections  18.1.3, 18.2.1) and 
achievement of the SDGs (Section  18.1.3; Box  18.4) has expanded 
significantly (very high confidence). This expansion is comprised of 
studies spanning a broad range of disciplinary perspectives, some of 
which have been underrepresented in prior IPCC assessments (high 
agreement, limited evidence) (Minx et al., 2017; Pearce et al., 2018b).
This chapter therefore focuses on assessing this more recent literature 
and the diverse scientific understandings of CRD and the pathways 
for pursuing it. Notably, this chapter takes off where Chapters 16 
and 17 end: recognising the decision-making context to address the 
representative key risks and their intersections with development, 
among others. This chapter therefore highlights not only how climate 
risk undermines CRD, but also how current patterns of development

contribute to climate risk, both generally and in different sectoral and 
regional contexts. In particular, this chapter focuses on achieving CRD 
through systems transitions, discussing these in relation to societal 
transformation, and how different actors engage one another in order 
to pursue policy and practice consistent with CRD.18.1.1 Understanding Climate Resilient Development
Past IPCC Assessment Reports have consistently examined extensive 
literature on the links between climate change, adaptation and 
sustainable development (Smit et  al., 2001; Klein et  al., 2007; Yohe 
et al., 2007). However, studies that explicitly refer to CRD as a concept 
or a guide for policy and practice remain modest (very high confidence). 
The concept of CRD appeared in scholarly literature and development 
program documents over a decade ago (Kamal Uddin et al., 2006; Garg 
and Halsnæs, 2007) and has been used in more recent IPCC assessment

reports and special reports (e.g., Denton et al., 2014; Roy et al., 2018). 
Similarly, the use of the term climate resilient development pathways 
(CRDPs) dates to 2009 (Ayers and Huq, 2009), but its use accelerated 
after appearing in United Nations Framework Convention on Climate 
Change (UNFCCC) publications around the launch of the Green Climate 
Fund (UNFCCC, 2011). While this chapter prioritises the CRD literature, 
it also recognises that a broad range of literature, disciplinary expertise 
and development practice is relevant to the concept of CRD.
Much of this literature is assessed in recent IPCC Special Reports 
(Rogelj et al., 2018; Roy et al., 2018; Bindoff et al., 2019; Hurlbert et al., 
2019; Oppenheimer et al., 2019), but new studies have continued to 
emerge. More specific uses of CRD found in the literature describe 
development that seeks to achieve poverty reduction and adaptation 
to climate change simultaneously without explicit mention of

mitigation (USAID, 2014), as well as mitigation and poverty reduction, 
described as ‘low-carbon development’, without explicit mention of 
adaptation (Alam et  al., 2011; Fankhauser and McDermott, 2016). 
Other similar terms include ‘climate safe’, ‘climate compatible’ and 
‘climate smart’ development (Huxham et al., 2015; Kim et al., 2017b; 
Ficklin et al., 2018; Mcleod et al., 2018), each with varying nuances. 
Climate compatible development, coined by Mitchell and Maxwell 
(2010), specifically describes a ‘triple win’ of adaptation, mitigation 
and development (Antwi-Agyei et al., 2017; Favretto et al., 2018) (see 
also Section  8.6). In this spirit, AR5 specifically referred to CRD as 
‘development trajectories that combine adaptation and mitigation to 
realize the goal of sustainable development’ (Denton et al., 2014). This 
chapter builds on the AR5 and, for the purposes of assessment, formally 
defines CRD as a process of implementing greenhouse gas mitigation

and adaptation measures to support sustainable development for all. 
This extension of the earlier definition reflects the emphasis in recent 
literature on equity as a core element of sustainable development as 
well as the objective of the SDGs to ‘create conditions for sustainable, 
inclusive and sustained economic growth, shared prosperity and 
decent work for all, taking into account different levels of national 
development and capacities’ (United Nations, 2015: 3/35).
Past, present and future concentrations of greenhouse gases in the 
atmosphere are the direct result of both natural and anthropogenic 
greenhouse gas emissions which are, in turn, a function of past and 
current patterns of human and economic development (very high 
confidence, WGI SPM [IPCC, 2021b]). This includes development 
processes that drive land use change, extractive industries, 
manufacturing and trade, energy production, food production,

infrastructure development and transportation. These patterns of 
development are therefore drivers of current and future climate risk 
to specific sectors, regions and populations (Byers et  al., 2018), as

18
2661Climate Resilient Development Pathways  Chapter 18
well as the demand for both mitigation and adaptation as a means of 
preventing climate change from undermining development goals. The 
SDGs represent targets for supporting human and ecological well-being 
in a sustainable manner. Yet, while progress is being made towards a 
number of the SDGs, success in achieving all of the SDGs by 2030 
across all global regions remains uncertain (high agreement, medium 
evidence) (United Nations, 2021). Moreover, current commitments to 
reduce greenhouse gas emissions are not yet consistent with limiting 
changes in global mean temperature elevation to well-below 2°C or 
1.5°C (very high confidence) (IPCC, 2018a) (see also Section 18.2).
Atmospheric concentrations of greenhouse gases are just one of a 
number of planetary boundaries which define safe operating spaces for 
humanity and therefore opportunities for achieving sustainable and CRD.

Exceeding these boundaries poses increased risk of large-scale abrupt 
or irreversible environmental changes that would threaten human and 
ecological well-being (very high confidence) (Rockström et al., 2009a; 
Rockström et al., 2009b; Butler, 2017; Schleussner et al., 2021). Other 
planetary boundaries reported in the literature such as biodiversity loss, 
changes in land systems and freshwater use are also directly influenced 
by patterns of development as well as climate change (Sections 18.2, 
18.5). Current rates of species extinction, conversion of land for crop 
production and exploitation of water resources exceed planetary 
boundaries, thereby undermining CRD. Moreover, studies indicate that 
achievement of the SDGs, while consistent with maintaining some 
planetary boundaries, could undermine others (O’Neill et  al., 2018; 
Hickel, 2019; Randers et al., 2019) (Section 18.2), suggesting significant 
shifts in current patterns of development are necessary to maintain

development within planetary boundaries.
Exceedance of planetary boundaries contributes to human and ecological 
vulnerability to climate change and other shocks and stressors. People and 
regions that already face high rates of natural resource use, ecosystem 
degradation and poverty are more vulnerable to climate change impacts, 
compounding existing development challenges in regions that are 
already strained (IPCC, 2014a; Hallegatte et al., 2019). The International 
Monetary Fund, for example, found that for a medium- and low-income 
developing country with an annual average temperature of 25°C, the 
effect of a 1°C increase in temperature is a reduction in economic 
growth by 1.2% (Acevedo et  al., 2018). Countries whose economies 
are projected to be hard hit by an increase in temperature account for 
only about 20% of global gross domestic product (GDP) in 2016, but are 
home to nearly 60% of the global population. This is expected to rise

to more than 75% by the end of the century. These economic impacts 
are a function of the underlying vulnerability of low- and middle-income 
developing economies to the impacts of climate change (Section 18.5). 
Such vulnerability was also evidenced and enhanced by the COVID-19 
pandemic which slowed progress on the SDGs in multiple nations 
(Naidoo and Fisher, 2020; Srivastava et al., 2020; Bherwani et al., 2021).
18.1.2 Pathways for Climate Resilient Development
One approach for operationalising the concept of CRD in a decision 
making context is to link the concept of CRD to that of pathways 
(Figure  18.1). A pathway can be defined as ‘a trajectory in time, 
reflecting a particular sequence of actions and consequences against a background of autonomous developments, leading to a specific 
future situation’ (Haasnoot et  al., 2013; Bourgeois, 2015). As such, 
a pathway represents changes over time in response to policies and

practices, as well spontaneous and exogenous events. For example, 
the SR1.5 report suggested that CRD pathways are ‘a conceptual and 
aspirational idea for steering societies towards low-carbon, prosperous 
and ecologically safe futures’ (Roy et  al., 2018: 468), and a way to 
highlight the complexity of decision making processes at different 
levels. Here, consistent with the aforementioned definition of CRD, we 
define CRD pathways as development trajectories that successfully 
integrate mitigation, adaptation and sustainable development.
As illustrated in Figure 18.1, the ultimate aim of CRDPs is to support 
sustainable development for ensuring planetary health and human well-
being. CRD is both an outcome at a point in space and time, as observed 
through SDG achievement indicators, but also a process consisting of 
actions and social choices made by multiple actors—government, 
industry, media, civil society, and science (Section 18.4). These actions

and social choices are performed within different dimensions of 
governance—politics, institutions (norms, rules), and practice, and 
bounded by ethics, values and worldviews. The development outcomes 
and processes pertain to political, economic, ecological, socio-cultural, 
knowledge-technology and community arenas (Figure 18.2). A CRDP 
will, for example, aspire to achieve ecological outcomes in terms of 
planetary health and achievement of Paris Agreement goals as well as 
human well-being, solidarity and social justice, in addition to political, 
economic and science–technology outcomes. These outcomes are 
enabled by achieving progress in core system transitions that catalyse 
broader societal transformations (Figure 18.3).
While there are many possible successful pathways to future 
development in the context of climate change, history has shown that 
pathways that are positive for the vast majority often induce notable

impacts and costs, especially on marginal and vulnerable people 
(Hickel, 2017; Ramalho, 2019), placing them in direct contradiction 
with the commitment to ‘leave no one behind’ (United Nations, 
2015). Similarly, contemporary scenario analyses find that there are 
plausible development trajectories that lead towards sustainability 
(Figure  18.1, Section  18.2.2). Yet, a number of plausible trajectories 
that perpetuate or exacerbate unstainable forms of development also 
appear in the literature (Figure  18.1, Section  18.2.2). A significant 
challenge lies in identifying pathways that address current climate 
variability and change, while allowing for improvements in human 
well-being. Furthermore, while a given pathway might lead to a set of 
desired outcomes for one region or set of actors, the process of getting 
there may come at high environmental, socio- and economic cost to 
others (very high confidence) (Raworth, 2017; Faist, 2018). Frequently,

considerations of social difference and equity are not prioritised in the 
evaluation of different development choices. The assumption that a 
growing economy lifts opportunity for all could, for example, further 
marginalise those who are the most vulnerable to climate change 
(Matin et al., 2018; Diffenbaugh and Burke, 2019; Hickel et al., 2021).
Placing pathways and climate actions within development processes 
implies a broadening of enablers to include the ethical–political 
quality of socio-environmental processes that are required to shift 
such processes in directions that support CRD and the pursuit of

18
2662Chapter 18 Climate Resilient Development Pathways
There is a rapidly narrowing window of opportunity to enable climate resilient development
(a) Societal choices about adaptation,  
mitigation and sustainable development 
made in arenas of engagement
20222100 &
beyond
IPCC
AR6Present
situation
Development Goals2030(b) Illustrative development pathw ays
Illustrative climatic or non-climatic shock,  e.g. COVID-19,  drought or ﬂoods, that disrupts the development pathwa y
Past conditions
(emissions ,
climate change ,
development)
Warming limited to below 1.5°C;
adaptation enablessustainable development
CLIMA TE RESILIENT DEVELOPMENT          HIGHER LOWER(c) Actions and outcomes
characterizing development pathw aysOpportunitiesmissedforhigherclimateresilientdevelopmentWell-being
Low poverty
Equity and justice
Low global
warming levels 
Low risk                   
Vulnerabilit y
High poverty
Ecosystem degradation
Inequity and injustice
High global

warming levels
High risk                 SustainabledevelopmentactionSystemtransitionsTransformation
MitigationUnsustainabledevelopmentactionEntrenchedsystems
Risingemissions
Narrowing window of
opportunity for higher CRDDimensions that enable actions to wards 
higher climate resilient development
Dimensions that result in actions to wards 
lower climate resilient development                       Arenas of engagement:
Community
Socio-cultural   
Political
Ecological
Knowledge + technology
Economic + ﬁnancial 
Equityandjustice
InclusionArenasofengagementArenasofengagementEcosystemstewardshipKnowledgediversity
ExclusionInequityandinjusticeEcosystemdegradationSingularknowledgeIncreasing warming; path dependence and adaptation limits undermine sustainable development
Figure 18.1 |  Climate Resilient Development Pathways are development trajectories that successfully integrate GHG mitigation and adaptation efforts to support sustainable development for all.

18
2663Climate Resilient Development Pathways  Chapter 18
(a) Climate resilient development is a process that takes place through continuous societal choices towards higher CRD (illustrative green pathways) or lower CRD (illustrative red pathways).
(b) CRD is described by five development dimensions – people, prosperity, partnership, peace, planet – on which the SDGs build (18.2). 
Some societal choices have mixed outcomes for CRD (illustrative orange pathways). This figure builds on figure SPM.9 in AR5 WGII depicting climate resilient pathways by describing how CRDPs emerge from societal choices about adaptation, 
mitigation and sustainable development within multiple arenas – rather than solely from discrete decision points (18.4). Dimensions of CRD characterize both development outcomes as well as the interactions and societal choices that make

up the development process. Societal choices, often contested, are made in arenas of engagement through interactions between key actors in civil society, the private sector and government (see Figure 18.2). The quality of interactions, such 
as degree of inclusion and empowerment of diverse voices, determine whether societal choices and associated actions shift development towards or away from CRD. The five CRD dimensions underline the close interconnectedness between 
the biosphere and humans, the two necessarily intertwined in interactions, actions, transitions, and futures (see Figure 18.3). There is a narrow and closing window of opportunity to make transformational changes to move towards and not 
away from development futures that are more climate-resilient and sustainable (Box 18.1). Pathways not taken (dotted line) illustrate that opportunities have been missed for higher CRD pathways due to past societal choices and increasing

temperatures. Present societal choices determine whether we shift towards higher CRD in future or whether pathways will be limited to lower CRD.

18
2664Chapter 18 Climate Resilient Development PathwaysSocietal choices made in arenas of engagement shape actions and systems
(a) Societal choices away from climate resilient development (b) Societal choices towards climate resilient development (c) Interactions between arenas of 
engagement across scalesKNOWLEDGETECHNOLOGYARENASSOCIOCULTURALARENAS
POLITICALARENASECOLOGICALARENAS
ECONOMICARENAS
BUISNESSASUSUALAWAYFROMCRD
EcologicaldegradationSingular knowledge
SocialinjusticeInequity
Vulnerabi
lity
Growth-baseddevelopmentExploitationandindividualism
KNOWLEDGETECHNOLOGYARENASSOCIOCULTURALARENAS
Nature-societyconnect
ednessPluralknowledgesSocialjustice
EquityInclusion
Well-beingbaseddevelopment
Empowerme
nt
Planetary healthSolidarity
CLIMATEACTIONSCivil societ y
Private sectorGovernment LOCALGOVERNANCECOUNTRY-LEVELGOVERNANCEREGIONALGOVERNANCEGLOBALGOVERNANCEARENAS Nature - societ y
  disconnec t

Consumption growthDisempowerment and exclusion
Figure 18.2 |  Societal choices made in arenas of engagement shape actions and systems.  The settings, places and spaces in which key actors from government, civil society and the private sector interact to influence the nature 
and course of development can be called arenas of engagement, including political, economic, socio-cultural, ecological, knowledge-technology and community arenas (18.4) For instance, political arenas include formal political settings such 
as voting procedures to elect local representatives as well as less formal and transparent political arenas. Streets, town squares and post-disaster landscapes can become sites of interaction and political struggle as citizens strive to have their

voices heard. Arenas of engagement can take the form of “struggle arenas” – in which power and influence are used to include/exclude, set agendas, and make and implement decisions – with inevitable winners and losers. The quality of 
interactions in these arenas leads to development outcomes that can be characterized as CRD dimensions that underpin the SDGs – people, prosperity, partnership, peace, planet (see Figure 18.1).
(a) Interactions characterized by inequitable relations and domination of some actors over others may lead to societal choices away from CRD, including mitigation and adaptation actions that exacerbate vulnerability among marginalized groups.
(b) Prospects for moving towards CRD increase when governance actors work together constructively in these different arenas. Interactions and actions that are inclusive and synchronous, as opposed to fragmented or contradictory, enable

system transitions and transformational change towards CRD (see Figure 18.3). Most societal choices and associated decisions are characterized by a mix of the dimensions shown in (a) and (b), with mixed outcomes for CRD.
(c) Arenas exist across scales from the local to national level, and beyond. Community arenas of engagement constitute the many interactions between governance actors and the political, economic, socio-cultural, ecological, knowledge-technology 
arenas, reflecting emergent societal choices across scales. Together, the decisions made by multiple actors within and across these arenas of engagement form societal choices. Unlocking the potential of these societal choices and associated 
mitigation, adaptation and sustainable development actions is central to advancing human well-being and planetary health.

18
2665Climate Resilient Development Pathways  Chapter 18
Land, water & 
ecosystem
Industrial system transitionUrban & 
infrastructural 
systemBUISNESSASUSUALUNSUSTAINABLEACTIONSFRAGMENTEDCLIMATEACTIONSINACTIONTransformative actions and system transitions
(a) Societal choices that generate fragmented climate action or inaction and
 unsustainable development perpetuate business as usual development(b) Societal choices that support CRD involve transformative actions
 that drive five systems transitionszeroemissionEnergy system 
Industrial system 
transitionUrban & infrastructural 
system transitionSOCIETALTRANSITIONSUSTAINABLECLIMATEACTIONSTRANSFORMATIVECLIMATEACTIONSTRANSFORMATIVESOCIETALACTIONS
ecosystem 
Figure 18.3 |  Transformative actions and system transitions characterize Climate Resilient Development Pathways

(a) Societal choices that generate fragmented climate action or inaction and unsustainable development perpetuate business as usual and entrenched systems.
(b) Societal choices that support CRD involve transformative adaptation, mitigation and sustainable development actions that drive five systems transitions (energy, land and other ecosystems, urban and infrastructure, industrial and societal). 
There is close interdependence between these systems. The system transition framework allows for a comprehensive assessment of the synergies and trade-offs between mitigation, adaptation and sustainable development. For example, land 
and water use in one system impacts the other systems and their surrounding ecosystems, thus reflecting how agricultural practices can have an impact on energy usage in urban centers. Finally, societal system transitions within each of the 
other systems enable the transitions to occur (18.3, Box 18.1).

18
2666Chapter 18 Climate Resilient Development Pathways
sustainability outcomes. This chapter therefore departs from the AR5s 
alignment of CRD with adaptation pathways and the emphasis on 
decision points that enable one to manage (or fail to manage) climate 
risk, towards a framing that integrates a range of possible futures each 
offering different opportunities, risks and trade-offs to different actors 
and stakeholders (see WGII AR5, IPCC, 2014b, Figure SPM.9). Instead, 
CRD emerges from everyday formal and informal decisions, actions, 
and adaptation or mitigation policy interventions. This is inclusive of 
system transitions, increased resilience, environmental integrity, social 
justice, equity, and reduced poverty and vulnerability, all facets of 
human well-being and planetary health. Rather than encompassing a 
formula or blueprint for particular actions, sustainable development is 
a process that provides a compass for the direction that these multiple

actions should take (Anders, 2016). This creates opportunities for actors 
to apply a diverse toolkit of adaptation, mitigation and sustainable 
development interventions, thereby opening up the solution space.
This understanding of CRD implies that different actors—governments, 
businesses and civic organisations—will have to design and navigate 
their own CRD pathways towards climate-resilient and sustainable 
development. This includes determining the appropriate balance of 
adaptation, mitigation and sustainable development actions and 
investments that are consistent with individual actors’ development 
circumstances and goals, while also ensuring that the collective actions 
remain consistent with global agreements and goals (such as the SDGs, 
Sendai Framework and the Paris Agreement; Section 18.1.3), planetary 
boundaries and other principles of CRD including social justice and 
equity (Roy et al., 2018). Empowering individual actors to pursue CRD

in a context-specific manner while coordinating action among actors 
and a diversity of scales, local to global, is a key challenge associated 
with achieving CRD (high agreement, limited evidence).
18.1.3 Policy Context for Climate Resilient Development
As reflected in Chapter 1 of the AR6 WGII report, CRD is emerging as one 
of the guiding principles for climate policy, both at the international level 
(Denton et al., 2014; Segger, 2016), as reflected in the Paris Agreement 
(Article 2, UNFCCC, 2015), and within specific countries (Simonet and 
Jobbins, 2016; Kim et  al., 2017b; Vincent and Colenbrander, 2018; 
Yalew, 2020). This framing of development recognises the risks posed 
by climate change to development objectives (Section 18.2; see also 
Chapter 16); the opportunities, constraints and limits associated with 
reducing risk through adaptation; synergies and trade-offs between 
mitigation, adaptation and sustainable development (Sections 18.2.5,

18.5, Box 18.4); and the role of system transitions in enabling large-
scale transformations that limit future global warming to less than 
1.5°C, while boosting resilience (IPCC, 2018a) (Section 18.3, Box 18.1).
Since the AR5, the volume of research at the nexus of climate action and 
sustainable development has changed markedly (very high confidence). 
A rapidly growing, multi-disciplinary literature has emerged on CRD 
(Mitchell et al., 2015; Clapp and Sillmann, 2019; Hardoy et al., 2019; 
Yalew, 2020) and associated pathways (Naess et  al., 2015; Winkler 
and Dubash, 2016; Brechin and Espinoza, 2017; Solecki et al., 2017; 
Ellis and Tschakert, 2019) (Section 18.2.2). Nevertheless, the concept 
of resilience generally, and CRD specifically, has come under increasing criticism in recent years (very high confidence) (Joakim et al., 2015; 
Schlosberg et al., 2017; Mikulewicz, 2018; Mikulewicz, 2019; Moser 
et al., 2019), suggesting the need to enhance understanding of how

resilience is being operationalised at the programme and project level 
and the net implications for human and ecological well-being.
This expansion of research has been accompanied by a shift in the 
policy context for climate action including an increasingly strong link 
between climate actions and sustainable development. In particular, 
the SDGs represent a near-term framework linking sustainability 
and human development in a manner that not only addresses 
planetary health and human well-being, but also help better plan 
and implement mitigation and adaptation actions to achieve these 
linked goals (Conway et al., 2015; Griscom et al., 2017; Allen et al., 
2018b; Roy et al., 2018; P .R. Shukla E. Calvo Buendia, 2019). The SDGs 
explicitly identify climate action (SDG 13) among the goals needed 
to achieve sustainable development. Meanwhile, the text of the Paris 
Agreement makes explicit mention of the importance of considering

climate ‘in the context of sustainable development’ (Articles 2, 4, 6) 
or as ‘contributing to sustainable development’ (Article 7) (Article 
7, UNFCCC, 2015). Similarly, sustainable development appears 
prominently within the text of the Sendai Framework for Disaster 
Risk Reduction (UNDRR, 2015) and the Global Assessment Reports on 
Disaster Risk Reduction (UNDRR, 2019). At the local- or household-
level, a growing literature recognises that climate impacts tend to 
exacerbate existing inequalities within societies, even at the level of 
gender inequalities within households (Sultana, 2010; Arora-Jonsson, 
2011; Carr, 2013). Thus, climate change impacts threaten even short-
term gains in sustainable development (18.2, Box 18.4), which could 
be rolled back over longer adaptation and mitigation horizons. For 
example, the COVID-19 pandemic is estimated to have reversed gains 
over the past several years in terms of global poverty reduction (very

high confidence) (Phillips et al., 2020; Sultana, 2021; Wilhelmi et al., 
2021) (Cross-Chapter Box  COVID in Chapter 7), reflecting the risks 
posed by global, systemic threats to development.
The WGII AR5 Report noted that adapting to the risks associated with 
climate change becomes more challenging at higher levels of global 
warming (IPCC, 2014a). This was evidenced by contrasting impacts 
and adaptive capacity for 2°C and 4°C of warming. This relationship 
between levels of warming, climate risk and reasons for concern (see 
Chapter 16) is also relevant to the concept of CRD. For example, recent 
literature on CRD emphasises the urgency of climate action that achieve 
significant reduction in greenhouse gas emissions, as well as the 
implementation of adaptation options that result in significant gains 
in human and natural system resilience (very high confidence) (Haines 
et al., 2017; Shindell et al., 2017; Xu and Ramanathan, 2017; Fuso Nerini

et al., 2018). This was explored extensively in the IPCC’s SR1.5 report 
in its comparison of impacts associated with 1.5°C versus 2°C climate 
objectives and synergies and trade-offs with the SDGs (IPCC, 2018a). 
However, the SR1.5 report and other literature also identified potential 
trade-offs between aggressive mitigation and the SDGs (see also Frank 
et al., 2017; Hasegawa et al., 2018). This indicates that while future 
magnitudes of warming are a fundamental consideration in CRD, such 
development involves more than just achieving temperature targets. 
Rather, CRD considers the possible transitions that enable those targets 
to be achieved, including the evaluation of different adaptation and

18
2667Climate Resilient Development Pathways  Chapter 18
mitigation options and how the implementation of these strategies 
interacts with broader sustainable development efforts and goals. This 
interdependence between patterns of development, climate risk and 
the demand for mitigation and adaptation action is fundamental to the 
concept of CRD (Fankhauser and McDermott, 2016). Therefore, climate 
change and sustainable development cannot be assessed or planned 
in isolation of one another.
18.1.4 Assessing Climate Resilient Development
In operationalising the aforementioned definitions of CRD and 
CRDP , this chapter builds its assessment around five core elements 
that provide insights relevant to policymakers actively pursuing the 
integration of climate resilience into development. First, as noted 
above, climate change poses a potential risk to the achievement of 
development goals, including global goals such as the SDGs, as well as

nationally or locally specific goals. Accordingly, Chapter 16’s discussion 
of key risks, their implications for the SDGs and the options for risk 
management are fundamental to the pursuit of CRD. This includes 
the opportunities for implementing adaptation, mitigation or other 
risk management options. Yet the management of climate risk must 
be accompanied by interventions that address social and ecological 
vulnerabilities that enhance climate risk.
Second, CRD is dependent on achieving transitions in key systems 
including energy, land and ecosystem, urban and infrastructure, and 
industrial systems (very high confidence) (Box  18.1, Figure  18.3). In 
this context, CRD links to the discussion of system transitions in the 
SR1.5 report (IPCC, 2018b; IPCC, 2018a). However, in building on the 
SR1.5, here the assessment of CRD also recognises the importance of 
transitions in societal systems that drive innovation, preferences for

relationships among different actors that engage in CRD. In particular, 
the rate at which actors can achieve system transitions has important 
implications for the pursuit of CRD. Transitions that are slow to evolve 
or that are more incremental in nature may not be sufficient to enable 
CRD in comparison with faster transitions that contribute to more 
fundamental system transformations.
Third, equity and social justice are consistently identified in the literature 
as being central to CRD (very high confidence; Sections 18.1.1, 18.3.1.5, 
18.4, 18.5). This includes designing and implementing adaptation, 
resilience and climate risk management options in a manner that 
promotes equity in the allocation of the costs and benefits of those 
options. Similarly, the literature on CRD emphasises equity should 
be pursued in the implementation of options for greenhouse gas

mitigation, transitions in energy systems and low-carbon development. 
This emphasis on equity is consistent with the SDGs which place 
an emphasis on reducing inequality and achieving sustainable 
development for all.
Fourth, success in CRD and alignment of development interventions to 
CRDPs is contingent on the presence of multiple enabling conditions 
(very high confidence, Section 18.4.2), that operate at different scales 
ranging from those that provide capacity to implement specific 
adaptation options to those that enable large-scale transformational change (Box 18.1). The qualities that describe sustainable development 
processes (e.g., social justice, alternative development models, equity 
and solidarity, as described above and in Figure 18.1) lead to short-
term outcomes and conditions, such as those represented by SDGs, 
that in an iterative fashion enable or constraint subsequent efforts 
towards CRD. For example, success or failure in achieving the SDGs or

the Paris Agreement would shape future efforts in pursuit of CRD and 
the options available to different actors.
Fifth, CRD involves processes involving diverse actors, at different scales 
operating within an environmental, developmental, socioeconomic, 
cultural and political context, as typified in the SDG and the Paris 
Agreement negotiations (very high confidence) (Kamau et al., 2018) 
(Section  18.4). The dependence of CRD on processes of negotiation 
and reconciliation among diverse actors and interests leads to the 
dismissal of the notion that there is a single, optimal pathway that 
captures the objectives, values and development contexts of all actors, 
even for a particular sector, country or region. Rather, preferences for 
different pathways and specific actions in pursuit of those pathways 
will be subjected to intense scrutiny and debate among diverse actors 
within various arenas of engagement (Section  18.4), meaning the

settings, places and spaces in which key actors from government, civil 
society and the private sector interact to influence the nature and 
course of development.
18.1.5 Chapter Roadmap
This chapter engages with understanding CRD and the pathways to 
achieving it by building on the concepts introduced in Chapter 1 of this 
Working Group II report, as well as the regional and sectoral context 
presented in other chapters (Section 18.5). Notably, this chapter takes 
off where Chapters 16 and 17 end: recognising the significance of 
the representative key risks for CRD and the decision making context 
of different actors who are implementing policies and practices to 
pursue different CRD pathways and manage climate risk. Therefore, 
this chapter assesses options for pursuing CRD and the broader system 
transitions and enabling conditions in support of CRD.
This chapter hosts three Cross-Chapter Boxes, which have their natural

home here. The Cross-Chapter Box on Gender, Justice and Transformative 
Pathways (Cross-Chapter Box GENDER) assesses literature specifically 
on gender and climate change to uncover the importance of a justice 
focus to facilitate transformative pathways, both towards CRD, as well 
as a means to achieving gender equity and social justice. The Cross-
Chapter Box on The Role of Indigenous Knowledge in Understanding 
and Adapting to Climate Change (Cross-Chapter Box INDIG) highlights 
that achieving CRD requires confronting the uncertainty of a climate 
change future. There are many perspectives about what future is 
desired and how to reach it. Integrating multiple forms of knowledge 
is a strategy to build resilience and develop institutional arrangements 
that provide temporary solutions able to satisfy competing interests 
(Grove, 2018). Indigenous knowledge is proven to enhance resilience in 
multiple contexts (e.g., Chowdhooree, 2019; Inaotombi and Mahanta,

2019). Meanwhile, Cross-Chapter Box FEASIB acts as an appendix to 
the WGII report, synthesising information on the feasibility associated 
with different adaptation options for reducing risk.

18
2668Chapter 18 Climate Resilient Development Pathways
In assessing the opportunities and constraints associated with 
the pursuit of sustainable development, this chapter proceeds in 
Section  18.2 to assess the links between sustainable development 
and climate action, including examination of current patterns of 
development and consideration for synergies and trade-offs among 
different strategies and options. Then, in Section  18.3, the chapter 
assesses five systems transitions to identify the shifts in development that would enable CRD. Section  18.4 assesses the role of different 
actors in the pursuit of CRD as well as the public and private arenas 
in which they engage. Section 18.5 synthesises CRD assessments from 
different WGII sectoral and regional chapters to identify commonalities 
and differences. The chapter concludes in Section 18.6 with a summary 
of key opportunities for enhancing the knowledge needed to enable 
different actors to pursue CRD.

Box 18.1 | Transformations in Support of Climate Resilient Development Pathways
Transformational changes in the pursuit of climate resilient development pathways (CRDPs) involve interactions between individual, 
collective and systems change (Figures 18.1–18.3). There are complex interconnections between transformation and transition (Feola, 
2015; Hölscher et al., 2018), and they are sometimes used as synonyms in the literature (Hölscher et al., 2018). Much of the transitions 
literature focuses on how societal change occurs within existing political and economic systems. Transformations are often considered to 
involve deeper and more fundamental changes than transitions, including changes to underlying values, worldviews, ideologies, structures 
and power relationships (Göpel, 2016; O’Brien, 2016; Kuenkel, 2019; Waddock, 2019). Systems transitions alone are insufficient to achieve

the rapid, fundamental and comprehensive changes required for humanity and planetary health in the face of climate change (high 
confidence). Transformative action is increasingly urgent across all sectors, systems and scales to avert dangerous climate change and 
meet the SDGs (Pelling et al., 2015; IPCC, 2018a; IPCC, 2021b; Shi and Moser, 2021; Vogel and O’Brien, 2021) (high confidence). The SR1.5 
identified transformative change as necessary to achieve transitions within land, water and ecosystems systems; urban and infrastructural 
systems; energy systems; and industrial systems. This box summarises key points in the transformations literature relevant to CRD.
Transformative actions aimed at ‘deliberately and fundamentally changing systems to achieve more just and equitable outcomes’, (Shi and 
Moser, 2021: 2) shift pathways towards climate resilient development (CRD) (high confidence). Transformative action in the context of CRD

specifically concerns leveraging change in the five dimensions of development (people, prosperity, partnership, peace, planet) that drive 
societal choices and climate actions towards sustainability (Section 18.2.2; Figure 18.1). Climate actions that support CRD are embedded in 
these dimensions of development; for example, social cohesion and equity, individual and collective agency, and democratising knowledge 
processes have been identified as steps to transform practices and governance systems for increased resilience (Ziervogel et al., 2016b; 
Nightingale et al., 2020; Colloff et al., 2021; Vogel and O’Brien, 2021) (high confidence). Transformative actions towards sustainability and 
increased well-being, which are dominant components of CRD, include those that explicitly redress social drivers of vulnerability, shift 
dominant worldviews, decolonialise knowledge systems, activate human agency, contest political arrangements, and insert a plurality of

knowledges and ways of knowing (Görg et al., 2017; Fazey et al., 2018a; Brand et al., 2020; Gram-Hanssen et al., 2021; Shi and Moser, 
2021). They alter the governance and political economic arrangements through which unsustainable and unjust development logics and 
knowledges are implemented (Patterson et al., 2017; Shi and Moser, 2021) by shifting the goals of a system or altering the mindset or 
paradigm from which a system arises, for example, from individualism and nature-society disconnect to solidarity and nature-society 
connectedness along the CRD dimensions in Figure 18.1, and connecting inner and external dimensions of sustainability (Göpel, 2016; 
Abson et al., 2017; Wamsler and Brink, 2018; Fischer and Riechers, 2019; Horcea-Milcu et al., 2019; Wamsler, 2019).
There is no blueprint for how transformation is generated. An expanding literature suggests that transformation takes place through

diverse modalities and context-dependent actions (O’Brien, 2021). Transformation may require actions that disrupt moral or social 
boundaries and structures that are perpetuating unsustainable systems and pathways (Vogel and O’Brien, 2021) (high confidence). 
Extreme events and long-term climatic changes can trigger a realigning of practices, politics and knowledge (Carr, 2019; Schipper et al., 
2020b) (high confidence). While some see opportunities for generating social and political conditions needed for CRD in such actions and 
events (Beck, 2015; Han, 2015; Shim, 2015; Mythen and Walklate, 2016; Domingo, 2018), this is not guaranteed. Climate shocks, when 
managed within socio-political systems in ways that safeguard rather than alter practices and structures, can also reinforce rather than 
shift the status quo (Mosberg et al., 2017; Carr, 2019; Marmot and Allen, 2020; Arifeen and Nyborg, 2021) (high confidence). Further, in

the absence of equitable and inclusive decision making and planning, realignments resulting from disruptive actions and events can limit 
inclusiveness and lead to poor or coercive decision-making processes that undermine the equity and justice foundations of sustainable 
development (Orlove et al., 2020; Shi and Moser, 2021) and lead to adverse socio-environmental outcomes that generate transformations 
away from CRD (Vogel and O’Brien, 2021) (high confidence, see also CROSS-CHAPTER BOX 2).
Evidence for transformative actions largely exists at the community or city level. While identifying how to rapidly and equitably generate 
transformations at a global scale has remained elusive, there is high agreement but limited evidence from studies of ecosystem services 
that suggest facilitating a wide range of locally appropriate management decisions and actions can bring about positive global-scale

outcomes (Millennium Ecosystem Assessment, 2005). Diverse local efforts to transform towards sustainability in the face of climate

18
2669Climate Resilient Development Pathways  Chapter 18
change have been observed, such as community mobilisation for equitable and just adaptation actions and alternative visions of societal 
well-being (Shi, 2020b) and farmer-led shifts in agricultural production systems (Rosenberg, 2021). There has been an increase in 
transformative actions taking place through city-level resilience building aimed at shifting inequitable relations and opening up space for 
a plurality of actors (Rosenzweig and Solecki, 2018; Ziervogel et al., 2021) (high confidence).
Prospects for transformation towards CRD increase when key governance actors work together in inclusive and constructive ways through 
engagement in political, knowledge-technology, ecological, economic and socio-cultural arenas (high confidence, Section 18.4.3). Yet the 
interactions between key governance actors involve struggles and negotiations in addition to collaborations (Kakenmaster, 2019; Muok

et al., 2021). Transformative actions meet resistance by precisely the political, social, knowledge and technical systems and structures 
they are attempting to transform (Blythe et al., 2018; Shi and Moser, 2021) (high confidence). There is expanding evidence that many 
adaptation efforts have failed to be transformative, but instead entrenched inequities, exacerbated power imbalances and reinforced 
vulnerability among marginalised groups and that, instead, marginalised groups and future trends in vulnerability need to be placed 
at the centre of adaptation planning (Atteridge and Remling, 2018; Mikulewicz, 2019; Owen, 2020; Eriksen et al., 2021a; Eriksen et al., 
2021b; Garschagen et al., 2021) (high confidence). Beyond the enablers, drivers or modalities, another question tackled in the literature 
is how to evaluate transformation by establishing criteria for transformation assessments (Ofir, 2021; Patton, 2021; Williams et al., 2021),

experience-based lessons on managing transformative adaptation processes (Vermeulen et al., 2018), climate policy integration (Plank 
et al., 2021), investment criteria (Kasdan et al., 2021) and political economy analysis frameworks for climate governance (Price, 2021).Box 18.1 (continued)
Box 18.2 | Visions of Climate Resilient Development in Kenya
The government of Kenya’s (GoK) ambition through Vision 2030 is to create a globally competitive and prosperous country with a high 
quality of life by 2030. It aims to transform Kenya into a newly-industrialising, middle-income country providing a high quality of life to 
all its citizens in a clean and secure environment.
(Government of Kenya, 2008). Dryland regions in Kenya occupy 80–90% of the land mass, are home to 36% of the population (Government 
of Kenya, 2012) and contribute about 10% of Kenya’s gross domestic product (GDP) (Government of Kenya, 2012), which includes half

of its agricultural GDP (Kabubo-Mariara, 2009). In dryland regions, pastoralism has long been the predominant form of livelihood and 
subsistence (Catley et al., 2013; Nyariki and Amwata, 2019). The GoK seeks to improve connectivity and communication infrastructure 
within the drylands to better exploit and develop livestock, agriculture, tourism, energy and extractive sectors (Government of Kenya, 
2018). It argues that the transformation of dryland regions is crucial to enhance the development outcomes for the more than 15 million 
people who inhabit these areas (Government of Kenya, 2016: 17) and to help the country to realise its wider national ambitions including 
a 10% year on year growth in GDP (Government of Kenya, 2012). A key element within this vision is the promotion and implementation of 
the Lamu Port South Sudan Ethiopia (LAPSSET) project. The LAPSSET Corridor consists of two elements: the 500 meter wide Infrastructure

Corridor where the road, railway, pipelines, power transmission and other projects will be located and the Economic Corridor of 50 km 
on either sides of the infrastructure corridor which will be contain other industrial investments (Enns, 2018). Supporters of the LAPSSET 
project argue that it will help achieve priorities laid out in the Vision 2030 by opening up poorly connected regions, enabling the 
development of pertinent economic sectors such as agriculture, livestock and energy, and supporting the attainment of a range of social 
goals made possible as the economy grows (Stein and Kalina, 2019).
However, the development narrative surrounding LAPSSET remains controversial in its assumptions, not least because it is being promoted 
in the context of a highly complex and dynamic social, economic and biophysical setting (Cervigni and Morris, 2016; Atsiaya et al., 2019;

Chome, 2020; Lesutis, 2020). Some of the key trends driving contemporary and likely future change in dryland regions are changing 
household organisation, evolving customary rules and institutions at local and community levels, and shifting cultures and aspirations 
(Catley et al., 2013; Washington-Ottombre and Pijanowski, 2013; Tari and Pattison, 2014; Cormack, 2016; Rao, 2019). Dryland regions are 
also witnessing demographic growth and change in land use patterns linked to shifts in the composition of livestock (for example from 
grazers to browsers), a decrease in nomadic and increase in semi-nomadic pastoralism, and transition to more urban and sedentary 
livelihoods (Mganga et al., 2015; Cervigni et al., 2016; Greiner, 2016; Watson et al., 2016). At a landscape level, land is becoming more 
fragmented and enclosed, often associated with increases in subsistence and commercial agriculture and the establishment of

conservancies and other group or private land holdings (Reid et al., 2014; Carabine et al., 2015; Nyberg et al., 2015; Greiner, 2016; Mosley 
and Watson, 2016). In addition, there are political dynamics associated with Kenya Vision 2030 and decentralisation, the influence of

18
2670Chapter 18 Climate Resilient Development Pathways
international capital, foreign investors and incorporation into global markets (Cormack, 2016; Kochore, 2016; Mosley and Watson, 2016; 
Enns and Bersaglio, 2020), as well as increasing militarisation and conflict in the drylands (Lind, 2018). Allied to these social and political 
dynamics are ongoing processes of habitat modification and degradation and biophysical changes linked in part to climate variability 
(Galvin, 2009; Mganga et al., 2015). The interconnected nature of these drivers will intersect with LAPSSET in myriad ways. For example, 
the implementation of LAPSSET may accentuate some trends, such as increases in land enclosure and a shift towards more urban and 
sedentary livelihoods (Lesutis, 2020). Conversely, the perceived threat LAPSSET could pose to pastoral lifestyles may lead to greater 
visibility, solidarity and strength of pastoralist institutions (Cormack, 2016).

There is a recognised need to adapt and chose development pathways that are resilient to climate change while addressing key 
developmental challenges within dryland regions, notably, poverty, water and food insecurity, and a highly dispersed population with 
poor access to services (Government of Kenya, 2012; Bizikova et al., 2015; Herrero et al., 2016). The current vision for development of 
dryland regions comes with both opportunities and threats to achieve a more climate-resilient future. For example, the growth in and 
exploitation of renewable energy resources, made possible through increased connectivity, brings climate mitigation gains but also risks. 
These risks include the uneven distribution of costs in terms of where the industry is sited compared with where benefits primarily accrue, 
and may exacerbate issues around water and food insecurity as strategic areas of land become harder to access (Opiyo et al., 2016;

Cormack and Kurewa, 2018; Enns, 2018; Lind, 2018). While LAPSSET will bring greater freedom of movement for commodities, benefitting 
investors, improving access to markets and urban centres, supporting trade or ease of movement for tourists supporting economic 
goals, it can also result in the relocation of people and impede access to certain locations for the resident populations. Mobility is a key 
adaptation behaviour employed in the short and long term to address issues linked with climatic variability (Opiyo et al., 2014; Muricho 
et al., 2019). With modelled changes in the climate suggesting decreases in income associated with agricultural staples and livestock-
dependent livelihoods, development that constrains mobility of local populations could retard resilience gains (Ochieng et al., 2017; 
ASSAR, 2018; Enns, 2018; Nkemelang et al., 2018). The likely increase in urban populations and the growth in tourism and agriculture

may lead to increases in water demand at a time when water availability could become more constrained owing to the reliance on 
surface water sources and the modelled increases in evapotranspiration due to rising mean temperature, more heatwave days and 
greater percentage of precipitation falling as storms (ASSAR, 2018; Nkemelang et al., 2018; USAID, 2018). These pressures could make it 
harder to meet basic health and sanitation goals for rural and poorer urban populations, issues compounded further by likely increases 
in child malnutrition and diarrheal deaths linked to climate change (WHO, 2016; ASSAR, 2018; Hirpa et al., 2018; Nkemelang et al., 2018; 
Lesutis, 2020). Development must pay adequate attention to these interconnections to ensure that costs and benefits of achieving climate 
mitigation and adaptation goals are distributed fairly within a population.Box 18.2 (continued)
18.2 Linking Development and Climate Action

The AR5 examined the relationship between climate and sustainable 
development in Chapter 13 (Olsson et  al., 2014) and Chapter 20 
(Denton et al., 2014) in Working Group II and Chapter 4 (Fleurbaey 
et al., 2014) in Working Group III. It concluded that dangerous levels 
of climate change would limit efforts to reduce poverty (Denton 
et  al., 2014; Fleurbaey et  al., 2014). Since the AR5, the adoption of 
the Paris Agreement and Agenda 2030 have demonstrated increased 
international consensus regarding the need to pursue climate change 
as a component of sustainable development. For example, climate 
change impacts ‘undermine the ability of all countries to achieve 
sustainable development’ (United Nations, 2015) and can reverse or 
erase improvements in living conditions and decades of development 
(Hallegatte and Rozenberg, 2017). However, recent analysis shows that 
actions to meet the goals of the Paris Agreement can undermine progress

towards some SDGs (high agreement, medium evidence) (Pearce 
et al., 2018b; Liu et al., 2019; Hegre et al., 2020) (Section 18.2.5.3). 
Meanwhile efforts to achieve the SDGs can contribute to worsening 
climate change (high agreement, medium evidence) (Fuso Nerini 
et al., 2018). These findings in the literature highlight the importance 
of identifying clear goals and priorities for both climate action and sustainable development as well as mechanisms for capitalising on 
potential synergies between them and for managing trade-offs. In 
assessing literature relevant to the intersection between climate 
action and development, we first explore the implications of different 
patterns of development and development trajectories followed by 
more focused assessment of the links between development and 
climate risk.
18.2.1 Implications of Current Development Trends
Understanding the interactions between climate change, climate

action and sustainable development necessitates consideration for the 
current development context in which different communities, nations 
and regions find themselves. For example, wealthy economies of the 
Global North will encounter different opportunities and challenges vis-
à-vis climate change and sustainable development than developing 
economies of the Global South. Moreover, all economies are already 
following an existing development trajectory that has implications 
for the type and scale of interventions associated with pursuing CRD 
and managing climate risk. Some nations may experience particular 
challenges with reducing greenhouse gas emissions owing to the

18
2671Climate Resilient Development Pathways  Chapter 18
carbon-intensive nature of their energy systems (very high confidence) 
(Section  18.3.1.1). Others may experience acute challenges with 
adaptation due to existing vulnerability associated with poverty and 
social inequality (very high confidence) (Section 18.2.5.1). Overcoming 
such challenges is fundamental to the pursuit of CRD.
While demonstrable progress has been made towards the SDGs and 
improving human well-being, globally and in specific nations, some 
observed patterns of development are inconsistent with sustainable 
development and the principles of CRD (very high confidence) (van 
Dooren et al., 2018; Eisenmenger et al., 2020; Leal Filho et al., 2020). 
A significant literature, for example, links development to the loss of 
biodiversity and the extinction crisis (Ceballos et al., 2017; Gonçalves-
Souza et al., 2020; Oke et al., 2021). Meanwhile, in human systems,

indicators such as the limited convergence in income, life expectancy 
and other measures of well-being between poor and wealthy countries 
(with notable outliers such as China) (Bangura, 2019), and the increase 
in income inequality and the decline in life expectancy and well-
being in rich countries (Rougoor and van Marrewijk, 2015; Alvaredo 
et  al., 2017; Goda et  al., 2017; Harper et  al., 2017; Goldman et  al., 
2018), suggest limitations of the current development paradigm to 
successfully deliver universal human and ecological well-being by the 
2030s or even mid-century (TWI, 2019).
18.2.2 Understanding Development in CRD
Development in this report is defined as efforts, both formal and 
informal, to improve standards of human well-being, particularly in 
places historically disadvantaged by colonialism and other features 
of early global integration. Development is not limited to the SDGs, 
however these represent an internationally agreed sub-set of goals.

Prior IPCC reports employed development as a typological framing 
of the current state of a given country or population (IPCC, 2014a) 
(Section  1.1.4). Such framings frequently rest upon measures of 
economic activity, using them as proxies for the wider well-being of 
the population whose activity is measured. For example, the level of 
GDP is often equated with levels of social welfare, even though as a 
measure of market output, it can be an inadequate metric for gauging 
well-being over time, particularly in its environmental and social 
dimensions (Van den Bergh, 2007; Stiglitz et al., 2009).
The result of this broad framing linking economic growth to human well-
being has been decades of policies, programmes and projects aimed at 
growing economies at scales from the household to regional and global. 
However, linking development to past and current modes of economic 
growth creates significant challenges for CRD, as it implies that the very

processes that have contributed to current climate challenges, including 
economic growth and the resource use and energy regimes it relies 
upon, are also the pathways to improvements in human well-being. This 
places climate resilience and development in opposition to one another.
While there are many possible successful pathways to future development 
in the context of climate change, history shows that pathways positive 
for the vast majority of people typically induce significant impacts and 
costs, especially on marginal and vulnerable people (high confidence) 
(Hickel, 2017). Frequently, considerations for social difference and equity are side-lined in these processes, for example through the assumption 
that a growing economy lifts opportunity for all, further marginalising 
those who are the most vulnerable to climate change (Matin et  al., 
2018; Diffenbaugh and Burke, 2019).
The Agenda 2030 and its 17 SDGs and 169 targets seeks to ‘leave no

one behind’ through five pillars (5Ps): People, Planet, Prosperity, Peace 
and Partnership (United Nations, 2015). The five pillars align with the 
dimensions of development that influence motion towards or away from 
CRD. The focus on people refers to inclusion rather than exclusion, and 
the extent to which people are empowered or disempowered to make 
decisions about their well-being, determine their futures and be in a 
position to assert their rights. This means being able to make decisions 
that determine whether people are on a pathway towards or away 
from CRD (Figure 18.1–18.3). The focus on planet refers to protecting 
the planet, ensuring a balance of ecosystems, biodiversity and human 
activities, and giving equal space and respect for its integrity. The focus 
on prosperity refers to equity in well-being grounded in unanimity over 
shared goals and resources, rather than individualism, and economic, 
social and technological progress grounded in stewardship and care,

rather than exploitation. The focus on partnership refers to mutual 
respect embedded in solidarity that recognises multiple worldviews 
and their respective knowledges, rather than singular or hierarchy of 
knowledge, and acknowledges inherent nature-society connections, 
rather than posing nature as opposites or competitors. The focus on 
peace emphasises the need for just and equitable societies. These 
five pillars are inter-related but local and national contexts situate 
current status differently around the world. Successful achievement of 
Agenda 2030 is aligned with a safe climate with adequate mitigation 
and adaptation, and effective and inclusive systems transitions. With 
these conditions, a high CRD world can be attained, noting that when 
approached individually, the transformative potential of the SDGs is 
limited (Veland et al., 2021).
The need for transformational changes across sectors and scales to

address the urgency and scope of action needed to enable a climate-
resilient future in which goals such as the SDGs might be realised 
requires attention to the specific ways in which development action is 
defined and enacted (Box 18.1).
18.2.2.1 Development Perspectives
Development is about ‘improvement’. However there have been 
different and often conflicting viewpoints on the improvement of 
‘what’ and ‘how’ to improve. The diversity of positions has resulted 
in a multitude of metrics to track development, some more influential 
than others on policy. Alternative measures of development, while 
numerous, generally seek to nuance the connection between economic 
growth and human well-being. Because they maintain core notions of 
progress and, in some cases, economic growth seen in more mainstream 
models of development, they are less vehicles for transformation than 
continuations of thinking and action fundamentally at odds with

the needs of CRD. These include the Measure of Economic Welfare 
(Nordhaus and Tobin, 1973), the Index of Sustainable Economic Welfare 
(Cobb and Daly, 1989), the Genuine Progress Indicator (Escobar, 1995), 
the Adjusted Net Saving Index or the Genuine Savings Index (GSI), The 
Human Development Index (HDI), the Inequality-Adjusted Human

18
2672Chapter 18 Climate Resilient Development Pathways
Development Index (UNDP , 2016a), the Gender Development Index, 
the Gender Inequality Index, the Multidimensional Poverty Index, 
the Index of Sustainable Economic Welfare (ISEW) (Daly and Cobb, 
1989), the Genuine Progress Indicator (GPI) (Kubiszewski et al., 2013), 
Gross National Happiness (GNH) (Ura and Galay, 2004), Measures of 
Australia’s Progress (MAP) (Trewin and Hall, 2004), the OECD Better 
Life Index (OECD, 2019a) and the Happy Planet Index (NEF , 2016).
In terms of their historical trajectory, different perspectives on 
development can be broadly divided into five categories.
i) Development as economic growth (1950s onwards): Equating 
development with economic growth was a natural outcome of the 
dominance of economics as the major discipline to study problems 
of newly independent countries in the 1950s (Escobar, 1995), 
measured through GDP . Environment was not a policy concern in

the immediate period after decolonisation. The GDP measure has 
withstood the test of time, in spite of being an inexact measure of 
human well-being, and is the widely used metric globally to track 
development. Recent improvements to GDP have tried to account 
for environmental factors (Gundimeda et al., 2007; United Nations, 
2021).
ii) Development as distributional improvements (1970s onwards): 
That economic growth does not automatically result in decline in 
poverty and improved distribution of income became apparent in 
the 1970s. Welfare measures were thus promoted that involved 
‘redistribution with growth’ (Chenery, 1974). These distributional 
concerns have re-emerged in the last two decades with the 
widening gap between the richer and poorer groups of the 
population (Chancel and Piketty, 2019) and also the increased 
attention to ‘ecological distribution conflicts’ (Martinez-Alier, 
2021). The political economy perspective, highlighting continued

dependencies of countries in the Global South on the Global North, 
now evolved into political ecology highlighting environmental 
concerns between and within countries. Environment was not 
yet a policy priority, despite the links between development and 
environment becoming clearer.
iii) Development as participation (1980s onwards): Bottom-up 
responses emphasising sustainable livelihoods and local-level 
development emerged in the 1980s. The movement, which 
involved independent and uncoordinated efforts by grassroots 
activists, social movements and non-governmental organisations 
(NGOs), became ‘mainstreamed’ into development in the 1990s 
(Chambers, 2012). The multi-dimensional nature of poverty was 
acknowledged at the global policy level (World Bank, 2000) and 
there was wider acceptance of the role of non-economics social 
sciences as well as critical approaches in research on development 
and poverty (Thomas, 2008). Participatory development involved

decentralisation and local planning, emphasising protection of 
local natural resources in addition to improving living standards.
iv) Development as expansion of human capabilities (1980s onwards): 
The human development and capabilities approach was the first 
formidable response to the GDP-centric view of development 
(Sen, 2000; Deneulin and Shahani, 2009). Studies showed that 
improvements in income did not necessarily improve human 
well-being in other dimensions such as health and education, or 
more broadly put, ‘freedoms’ (Ruggeri Laderchi et al., 2003). The capabilities idea was influential in global policy making through 
Human Development Reports and metrics such as Human 
Development Index (HDI) and Multidimensional Poverty Index (MPI). 
However, environmental sustainability was not a major component 
in this approach until much later (Alkire and Jahan, 2018). Recent 
improvements to HDI such as the planetary pressures-adjusted HDI

(United Nations, 2020) is a step in this direction.
v) Development as post-growth (2010 onwards): The late 1980s saw a 
big push towards taking the environment to the centre of the global 
policy agenda (World Commission on Environment and Development, 
1987). However, progress in addressing environmental questions 
has been slow. As compared with Millennium Development Goals 
(MDGs), SDGs aim to tackle environmental concerns by explicitly 
tracking progress on multiple indicators. Nevertheless, the approach 
in these policy propositions sits largely within the economic growth 
framework itself. The climate change challenge and the financial crisis 
of 2008 led many scholars, ecological economists and environmental 
social scientists in particular, to argue for a post-growth world. 
Post-growth (Jackson, 2021), degrowth (Kallis, 2018; Hickel et  al., 
2021) and other environmentalist scholarship takes inspiration from

critiques of development such as post-development (Escobar, 1995). 
The argument here is not for better metrics but for imagining and 
working towards systemic change in the wake of the climate crisis. 
The challenge however is how to account for historical differences 
in economic growth and living standards between the Global North 
and the Global South and to protect the interests of Global South in 
the spirit of ‘common but differentiated responsibilities’ to climate 
change adaptation and mitigation. As empirical studies in the Global 
South have demonstrated (Lele et al., 2018), developing countries 
face multiple stressors, climate change being just one among them, 
and there are multiple normative concerns in developing country 
contexts, such as equity and justice, and not merely resilience (very 
high confidence).
Achieving CRD requires framings of development that move away 
from linear paradigms of development as material progress by

focusing on diversity and heterogeneity, well-being and equality, not 
only in contemporary practices, but also pathways of change over 
time (Gibson-Graham, 2005; Gibson-Graham, 2006). Such approaches, 
which are fundamentally aligned with ecological and ecosystem-
based environmental assessments that identified heterogeneity of 
approaches and actions as the most effective path to a sustainable 
world (Millennium Ecosystem Assessment, 2005), emphasise the 
importance of cultural, linguistic and religious diversity, not merely as 
alternative sources of information about the world, but as different 
paradigms of well-being (Kallis, 2018). These include Indigenous and 
local knowledge that provide alternatives to these framings of the 
world (Cross-Chapter Box INDIG). This broad reframing of development 
includes a focus on visions such as ‘buen vivir’ (Cubillo-Guevara et al., 
2014; Walsh, 2018; Acosta et  al., 2019), ecological Swaraj (Kothari

et  al., 2014; Demaria and Kothari, 2017; Shiva, 2017) and Ubuntu 
(Dreyer, 2015; Ewuoso and Hall, 2019), among others. All are linked 
by relationships with nature radically different from the Western 
mechanistic vision, presenting not only framings of development 
and the environment that yield locally appropriate CRDPs, but serve 
as examples of alternative ways of living in balance with nature that 
might inform similar thinking in other places.

18
2673Climate Resilient Development Pathways  Chapter 18
18.2.2.2.  Complexity of Development and Climate Action
Differing perspectives on development are in part determined by the 
multiple diverse priorities held by different actors and nations. Another 
reason is that development is not a linear process with a single goal, 
and active development planning requires simultaneously taking 
multiple processes and factors into account. This is well illustrated by 
growing attention to climate security. The AR5 delivered conflicting 
messages regarding climate change and security (Gleditsch and 
Nordås, 2014), yet the understanding of climate-related security 
risks has made substantial progress in recent years (von Uexkull and 
Buhaug, 2021). Although there remains considerable research gaps in 
certain regions (Adams et al., 2018), a large body of qualitative and 
quantitative studies from different disciplines provides new insight

into the relationship of climate change and security (Buhaug, 2015; 
De Juan, 2015; Brzoska and Fröhlich, 2016; Abrahams and Carr, 
2017; Sakaguchi et  al., 2017; Moran et al, 2018; Scheffran, 2020). 
Though not the only cause (Sakaguchi et al., 2017; Mach et al., 2019), 
climate change undermines human livelihoods and security, because 
it increases the populations vulnerabilities, grievances and political 
tensions through an array of indirect—at times nonlinear—pathways, 
thereby increasing human insecurity and the risk of violent conflict 
(van Baalen and Mobjörk, 2018; Koubi, 2019; von Uexkull and Buhaug, 
2021). Indeed, context, as well as timing and spatial distribution, 
matter and need to be accounted for (Abrahams, 2020).
In line with this better understanding, climate change and security 
have been reframed in the political space, to focus more on human 
security. The solutions to climate-related security risks cannot be

military, but are linked to development and people’s vulnerabilities 
in complex social and politically fragile settings (Abrahams, 2020). 
This has resulted in integration of climate-related security risk into 
institutional and national frameworks (Dellmuth et  al., 2018; Scott 
and Ku, 2018; Aminga and Krampe, 2020), including several Nationally 
Determined Contributions (NDCs) (Jernnäs and Linnér, 2019; Remling, 
2021). One example is the UN Climate Security Mechanism—set up 
in 2018 between UNDP , UNEP and UN DPPA to help the UN more 
systematically address climate-related security risks and devise 
prevention and management strategies. Yet work remains in bridging 
these concerns with practical responses on the ground (Busby, 2021). 
Especially since emerging research building on the maladaptation 
literature shows that this practice cannot just mean adding adaptation 
and mitigation to the mix of development strategies in a given

location, as this may have unintended and unanticipated effects and 
might even backfire completely (Dabelko et al., 2013; Magnan et al., 
2020; Mirumachi et al., 2020; Schipper, 2020; Swatuk et al., 2021). In 
extremely underdeveloped, fragile contexts such as Afghanistan, the 
local-level side effects of climate adaptation and mitigation projects 
might result in different development outcomes and question the 
potential for sustainable peace (Krampe et  al., 2021). Given the 
clearer understanding of the intertwined nature of climate change, 
security and development—especially in fragile and conflict affected 
regions—a rethinking of how to transfer this knowledge into policy 
solutions is necessary for the formulation of CRD.18.2.3 Scenarios as a Method for Representing Future 
Development Trajectories
Sustainable development represents specific development processes 
and priorities that can affect climate risk. As a result, sustainable

development both shapes the context in which different actors 
experience climate change and represents a potential opportunity, 
particularly by reducing climate risk by addressing vulnerability, 
inequity and shifting development towards more sustainable 
trajectories (IPCC, 2012; Denton et al., 2014; IPCC, 2014b; IPCC, 2014a; 
IPCC, 2018a; IPCC, 2019b). As assessed in past IPCC special reports 
and assessment reports, this same literature has also illustrated how 
different socioeconomic conditions affect mitigation options and costs. 
For example, variations in future economic growth, population size 
and composition, technology availability and cost, energy efficiency, 
resource availability, demand for goods and services, and non-climate-
related policies (e.g., air quality, trade), individually and collectively 
have all been shown to result in different climates and contexts for 
mitigation and adaptation.
One common approach for exploring the implications of different

development trajectories is the use of scenarios of future socioeconomic 
conditions, such as the SSPs (O’Neill et al., 2017). The SSPs represent 
sets of future global societal assumptions based on different societal, 
technological and economic assumptions that result in different 
development trajectories. Such scenarios often correspond to a small 
set of scenario archetypes (Harrison et  al., 2019; Sitas et  al., 2019; 
Fergnani and Song, 2020) in that they reflect core themes regarding 
the future of development such as sustainability versus rapid growth. 
Scenarios with assumptions more closely aligned with sustainability 
agendas (e.g., SSP1-Sustainability) commonly imply lower greenhouse 
gas emissions and projected climate change (Riahi et al., 2022), lower 
mitigation costs for ambitious climate goals (Riahi et al., 2022), lower 
climate exposure due in large part to the size of society (see Chapter 
16) and greater adaptive capacity (Roy et al., 2018) (see also Chapter

16). In contrast, scenarios with rapid global economic and fossil energy 
growth (e.g., SSP5 Fossil-Fueled Development) imply higher emissions 
and project climate change and higher mitigation costs, as well as 
greater social and economic capacity to adapt to climate change 
impacts (Hunt et al., 2012) (Table 18.1).
The SSPs incorporate various assumptions regarding population, 
GDP and greenhouse gas emissions, for example, that are relevant to 
development and climate resilience. In addition, the SSPs have been 
used to explore a broad range of development outcomes for human and 
ecological systems (Table  18.1), including multiple studies exploring 
futures for food systems, water resources, human health and income 
inequality. Limited, top-down modelling studies have used the SSPs 
to explore issues such as societal resilience (Schleussner et al., 2021) 
or gender equity (Andrijevic et al., 2020a). Such studies indicate that

different development trajectories have different implications for future 
development outcomes, but results vary significantly among different 
climate (e.g., representative concentration pathways [RCPs]) and 
development contexts, resulting in limited agreement among different 
SSPs (Table 18.1). Nevertheless, for some outcomes, SSPs are associated 
with generally similar outcomes. Over the near-term (e.g., 2030), those 
outcomes are strongly influenced by development inertia and path

18
2674Chapter 18 Climate Resilient Development Pathways
Table 18.1 |  Implications of different socioeconomic development pathways for CRD indicators. Studies presented in the above table include qualitative storylines and quantitative 
scenarios for two or more SSPs. Arrows and colour coding reflect the positive or negative impacts on sustainability based on aggregation of results for the 2030–2050 time horizon 
across the identified studies. Confidence language reflects the number of studies upon which results are based (evidence) and the agreement among studies regarding the direction 
of change (agreement).
Development indicatorRelevant 
SDGShared Socioeconomic Pathway
Evidence/
AgreementReferencesSustaina-
bility
(SSP1)Middle of 
the road
(SSP2)Regional 
rivalry
(SSP3)Inequality
(SSP4)Fossil-fuelled 
(SSP5)
Agriculture, food and 
forestry
 –Agriculture production
 –Forestry production
 –Food security
 –HungerSDG 2 ↗ ↔ ↘ ↘ ↘Low agreement/

robust evidence(Hasegawa et al., 2015; 
Palazzo et al., 2017; Riahi 
et al., 2017; Duku et al., 2018; 
Chen et al., 2019; Daigneault 
et al., 2019; Mitter et al., 2020; 
Mora et al., 2020)
 –Excess mortality
 –Air quality
 –Vector-borne disease
 –Life SatisfactionSDG 3 ↔ ↔ ↔ ↘ ↘Medium 
agreement/
robust evidence(Chen et al., 2017; Mora et al., 
2017; Aleluia Reis et al., 2018; 
Asefi-Najafabady et al., 2018; 
Chen et al., 2018; Harrington 
and Otto, 2018; Marsha et al., 
2018; Sellers and Ebi, 2018; 
Ikeda and Managi, 2019; 
Rohat et al., 2019; Wang et al., 
2019; Chae et al., 2020)
 –Water use
 –Sanitation access
 –Sewage dischargeSDG 6 ↗ ↘ ↘ ↔ ↔High agreement/
medium 
evidence(Wada et al., 2016); (van 
Puijenbroek et al., 2014; Yao 
et al., 2017); (Mouratiadou 
et al., 2016; Graham et al., 
2018)
 –Gini coefficientSDG 10 ↗ ↗ ↗ ↔ ↗Medium 
limited evidence(Rao et al., 2019b; Emmerling 
and Tavoni, 2021; Gazzotti

Ecosystems and ecosystem 
services
 –Aquatic resources
 –Urban expansion
 –Habitat provision
 –Carbon sequestration
 –BiodiversitySDG 14 
SDG 15↘ ↘ ↘ ↘ ↘High agreement/
medium 
evidence(Li et al., 2017; Chen et al., 
2019; Li et al., 2019b; Chen 
et al., 2020b; Song et al., 
2020b; McManamay et al., 
2021; Pinnegar et al., 2021)
Legend
↑ Balance of studies suggest large increasing threat to sustainable development
↗ Balance of studies suggest moderate increasing threat to sustainable development
↔ Studies suggest both threats and benefits to sustainable development
↘ Balance of studies suggest moderate increasing benefit to sustainable development
↓ Balance of studies suggest large increasing benefit to sustainable development
Studies presented in the above table include qualitative storylines and quantitative scenarios for two or more SSPs. Arrows and colour coding reflect the positive or negative impacts

on sustainability based on aggregation of results for the 2030–2050 time horizon across the identified studies. Confidence language reflects the number of studies upon which 
results are based (evidence) and the agreement among studies regarding the direction of change (agreement).
dependence, reducing differences among SSPs. Outcomes diverge later in 
the century, but fewer studies explore futures beyond 2050. Collectively, 
the scenarios reflect trade-offs associated with different development 
trajectories (Roy et al., 2018), with some SSPs foreshadowing outcomes 
that are positive in some contexts, but negative in others (Table 18.1). 
For example, pathways that lead to poverty reduction can have synergies 
with food security, water, gender, terrestrial and ocean ecosystems that 
support climate risk management, but also poverty alleviation projects 
with unintended negative consequences that increase vulnerability

(e.g., Ley, 2017; Ley et al., 2020).While the scenarios literature is useful for characterising the potential 
climate risk implications of different global societal futures, important 
limitations impact their use in climate risk management planning (very 
high confidence). The first is the often highly geographically 
aggregated nature of the SSPs and other scenarios, which, in the 
absence of application of nesting or downscaling methods, often 
lack regional, national, or sub-national context, particularly regarding 
social and cultural determinants of vulnerability (van Ruijven et  al., 
2014). Furthermore, there is limited understanding of the cost 
and process associated with transforming from today into each

18
2675Climate Resilient Development Pathways  Chapter 18
assumed socioeconomic future, or the opportunity to shift from one 
pathway to another (Section  18.3). Furthermore, the characteristics 
of the pathways suggest that they are not equally likely, there are 
relationships implied in assumptions that are uncertainties to consider 
(e.g., land productivity improvements are land saving), it is difficult 
to identify the role of different development characteristics, and 
policy implementation is stylised. In general, global assessments are 
not designed to inform local planning, given that there are many 
local circumstances consistent with a global future and unique local 
development context and uncertainties to manage—demographic, 
economic, technological, cultural and policy.
Overall, pursuing sustainable development in the future is shown to 
have synergies and trade-offs in its relationships with every element

of climate risk: the emissions and mitigation determining hazard; the 
size, location and composition of development determining exposure; 
and the adaptive capacity determining vulnerability. Importantly, the 
scenarios literature overall has found trade-offs such that none of the 
global societal projections achieve all the SDGs (very high confidence) 
(Roy et  al., 2018) (Section  18.2.5.3). Historical evidence supports 
this as well, for example, finding low-cost energy and food access 
is historically associated with higher emissions but greater adaptive 
capacity, and energy efficiency innovation contributing to lower 
emissions and greater adaptive capacity (e.g., Blanford et  al., 2012; 
Blanco et al., 2014; Mbow et al., 2019; USEPA, 2019). The literature 
suggests that trade-offs in the pursuit of sustainable development 
are inevitable. Managing those trade-offs, as well as capitalising on 
the synergies, will be important for CRD, particularly given trade-offs

have distributional implications that could contribute to inequities 
(Section 18.2.5.3).
18.2.4 Climate Change Risks to Development
In the near-term, additional climate change is expected regardless of 
the scale of greenhouse gas mitigation efforts (IPCC, 2021a). Across 
the global scenarios analysed in the AR6, global average temperature 
changes relative to the reference period 1850–1900 range from 1.2°C 
to 1.9°C for the period 2021–2040 and 1.2°C to 3.0°C for the period 
2041–2060 (WGI AR6 SPM [IPCC, 2021b], very likely range). However, 
the feasibility of emissions pathways (particularly RCP8.5) affect the 
plausibility of the associated climate projections, potentially lowering 
the upper end of these ranges because the likelihood of the higher 
warming levels is a function of the likelihood of the higher emissions 
scenarios (Riahi et  al., 2022) . There is significant overlap between 
climate scenario ensemble ranges from different emissions scenarios

through 2050, more so than through 2100 (Lee et al., 2021). There is 
also overlap between emissions scenario ensembles consistent with 
different temperature outcomes (Riahi et al., 2022) . Emissions pathway 
ranges represent uncertainties for policymakers and organisations to 
consider and manage (Rose and Scott, 2018, 2020) regarding, among 
other things, economic growth and structure, available technologies, 
markets, behavioural dynamics, policies and non-CO 2 climate forcings 
(Riahi et al., 2022), while climate pathway ranges represent bio-physical 
climate systems and carbon cycle uncertainties (Lee et al., 2021). For 
all climate projections and variables, there is significant regional 
heterogeneity and uncertainty in projected climate change (very high confidence) (IPCC, 2021a). Figure 18.4 apresents examples for average 
and extreme temperature precipitation change (see also Section 18.5 
and Tables 18.4–18.5 for more regional detail and ranges of climate

outcomes). Higher global warming levels also can affect geographic 
patterns of change and probability distributions of regional climate 
outcomes (Ahmad, 2019). Similarly, for all emissions projections, there 
is significant regional, sectoral and local heterogeneity and uncertainty 
regarding potential pathways for climate action (Lecocq et al., 2022; 
Riahi et al., 2022). Not all uncertainties are represented in projected 
emissions pathway ensembles, such as policy timing and design (e.g., 
Rose and Scott, 2018) or climate projection ensembles.
The projected ranges for near- and mid-term global average warming 
levels are estimated to result in increasing key risks and reasons for 
concern (Chapter 16). Chapter 16 developed aggregate ‘Representative 
Key Risks’ (RKRs) as indicators for subsets of approximately 100 
sectoral and regional key risks indicators. The RKRs include risks to 
coastal socio-ecological systems, terrestrial and ocean ecosystems,

critical physical infrastructure, networks and services, living standards 
and equity, human health, food security, water security, and peace and 
migration. The majority of these risks are directly linked to sustainable 
development priorities and the SDGs (Chapters 2 to 16; (Roy et  al., 
2018; IPCC, 2019d; IPCC, 2019b). Therefore, climate risks represent a 
potential additional challenge to pursuing sustainable development 
priorities, but also potential opportunities due to geographic variation 
in climate impacts. In addition, positive synergies have been found 
between sustainable development and adaptation, but trade-offs are 
also possible (e.g., Roy et al., 2018).
For all RKRs, additional global average warming is expected to increase 
risk. However, the increases vary significantly by RKR, and across the 
underlying key risks represented within each RKR. Geographic variation 
in key risk implications is only partially assessed in Chapter 16, but

evidence can be drawn from the WGII individual regional chapters. 
Regionally, key risks are found to be potentially greatest in developing 
and transition economies (Chapter 16 and sectoral chapters), which is 
also where the least-cost emissions reductions globally are projected 
to be (Riahi et al., 2022).See Figure 18.4 for an example of key risk 
geographic heterogeneity (see also Section 18.5 for regional detail). 
Chapter 16 also maps the RKRs to an updated aggregate ‘Reasons 
for Concern’ (RFC) framing. Thus, increasing RKR implies increasing 
RFC associated with unique and threatened systems, extreme weather 
events, distribution of impacts, global aggregate impacts and large-
scale singular events.
Climate risks are found to vary with future warming levels, the 
development context and trajectory, as well as by the level of investment 
in adaptation. Together, these three dimensions define risk—with 
projected climate changes defining the hazard, development defining

the exposure, and development and adaptation defining vulnerability. 
However, how these different dimensions interact and the level of 
scientific understanding vary significantly among different types of 
risk. For human systems, in general, the poor and marginalised are 
found to have greater vulnerability for a given hazard and exposure 
level. With some level of global average warming expected regardless 
of mitigation efforts, human and natural systems will be exposed to 
new conditions, but some level of adaptation should also be expected.

18
2676Chapter 18 Climate Resilient Development Pathways
18.2.5 Options for Managing Future Climate Risks to 
The pursuit of CRD requires not only the implementation of individual 
adaptation, mitigation and sustainable development initiatives, but 
also their careful coordination and integration. This section assesses the 
literature on CRD in the context of key climate change risks (Chapter 
16); gaps in adaptation that contribute to risk; potential synergies and 
trade-offs among mitigation, adaptation and sustainable development; 
and the mechanisms for managing those trade-offs.
18.2.5.1 Adaptation
18.2.5.1.1 Adaptation and Climate Resilient Development
Given that adaptation is recognised as a key element of addressing 
climate risk and CRD, the capacity for adaptation implementation is an 
important consideration for CRD. The AR5 noted a significant overlap 
between indicators of sustainable development and the determinants

of adaptive capacity, and suggested that adaptation presents an 
opportunity to reduce stresses on development processes and the socio-
ecological foundations upon which they depend (Denton et al., 2014). 
At the same time, it also noted that building adaptive capacity for 
sustainable development might require transformational changes that 
shift impacted systems to new patterns, dynamics or places (Denton 
et al., 2014). Thus, adaptation interventions and pathways can further 
the achievement of development goals such as food security (Campbell 
et al., 2016; Douxchamps et al., 2016; Richardson et al., 2018; Bezner 
Kerr et al., 2019) and improvements in human health (Watts et al., 2019) 
including in systems where animals and humans live in close proximity 
(very high confidence) (Zinsstag et al., 2018). However, to do so requires 
not only the avoidance of incremental adaptation actions that extend 
current unsustainable practices, but also the ability to manage and

overcome the barriers which arise when the limits of incremental 
adaptation are reached (high agreement,  medium evidence) (Few et al., 
2017; Vermeulen et al., 2018; Fedele et al., 2019).
Since AR5, the scientific community has deepened its understanding 
of the relationship between adaptation and sustainable development 
(very high confidence), particularly with regard to the place of 
resilience at the intersection of these two arenas. The literature has 
moved forward in its identification of specific overlaps in sustainable 
development indicators and determinants of adaptive capacity, 
how adaptation might reduce stress on development processes 
and their socio-ecological foundation, and how building adaptive 
capacity might facilitate needed transformative changes. Broadly 
speaking, work on these topics comes from one of two perspectives. 
One perspective speaks to adaptation practices that might further 
sustainable development outcomes, while another perspective draws

on deeper understandings of the socio-ecological dynamics of the 
systems in which we live, and which we may have to transform in the 
face of climate change impacts. These two literatures are not yet well 
integrated, leaving gaps in our knowledge of how best to implement 
adaptation in a manner that achieves sustainable development.
The literature considering adaptation and development in practice 
since AR5 suggests that efforts to connect adaptation to sustainable development should address proximate and systemic drivers of 
vulnerability (Wise et al., 2016), while remaining flexible and reversable 
to avoid the lock-in of undesirable or maladaptive trajectories (Cannon 
and Müller-Mahn, 2010; Wise et al., 2016). Such goals require critical 
reflection on processes for decision making and learning. In the AR5, 
more inclusive, participatory adaptation processes were presumed to 
benefit development planning by including a wider set of actors in

discussions of future goals (Denton et al., 2014). The post-AR5 literature 
expands on these critical perspectives to provide context regarding 
when participation is most effective. For example, (Eriksen et al., 2015) 
emphasise the need to build participatory adaptation processes to 
avoid subsuming adaptation goals to development-as-usual, while 
(Kim et al., 2017b) argues that this practice is most effective when it is 
focused on development efforts and considers how climate change will 
challenge the goals of those efforts. Adaptation, while presenting an 
opportunity to foster transformations needed to address the impacts of 
climate change on human well-being, is also a contested process that 
is inherently political (medium agreement, medium evidence) (Eriksen 
et al., 2015; Mikulewicz, 2019; Nightingale Böhler, 2019; Eriksen et al., 
2021b). How adaptation can challenge development and create a 
situation where CRD effectively becomes transformative adaptation,

adaptation that generates transformation of broader aspects of 
development, remains unclear (medium agreement, limited evidence) 
(Few et al., 2017; Schipper et al., 2020c).
The critical literature on socio-ecological resilience, which has grown 
substantially since the last AR (very high confidence), speaks to some 
of these questions. Since AR5, the IPCC and the wider literature on 
socio-ecological resilience have shifted their use of the term to reflect 
not only the capacity to cope with a hazardous event or trend or 
disturbance, but also the ability to adapt, learn and transform in ways 
that maintains socio-ecology’s essential function, identity and structure 
(Chapter 1; Glossary, Annex II). This change in usage is significant in 
that it shifts resilience from an emergent property of complex socio-
ecological systems to a deeply human product of efforts to manage 
ecology, economy and society to specific ends. This definition of

resilience recognises the need to define what is an essential identity, 
function and structure for a given system, questions rooted not in 
ecological dynamics, but in politics, agency, difference and power that 
emerge around the management of ecological dynamics (Cote and 
Nightingale, 2011; Brown, 2013; Cretney, 2014; Forsyth, 2018; Matin 
et al., 2018; Carr, 2019).
By connecting this framing of socio-ecological dynamics to the literature 
on the principles for adaptation efforts that meet development goals, 
new work has begun to identify 1) how adaptation can reduce stress 
on development processes, 2) how it might facilitate transformative 
change and 3) where adaptation interventions might either drive 
system rigidity and precarity, or otherwise challenge development goals 
(Castells-Quintana et al., 2018; Carr, 2020). For example, Jordan (2019) 
draws upon these contemporary framings of resilience to highlight the

ways in which coping strategies perpetuate the gendered norms and 
practices at the heart of women’s vulnerability in Bangladesh. Forsyth 
(2018) draws upon this work to highlight the ways in which the theory 
of change processes used by development organisations tend to 
exclude local experiences and sources of risk, and thus foreclose the 
need for transformative pathways to achieve development goals. Carr

18
2677Climate Resilient Development Pathways  Chapter 18
(a) Physical climate change indicatorsRegional projected select climate change and sustainable development-related climate impact indicators
by global warming level
Global Warming Level 1.5°C Global Warming Level 2.0°C Global Warming Level 3.0°C
0 2°C 3°C 4°C 6°C 1°C 5°C -2°C -3°C -4°C -6°C -1°C -5°CChange in
annual mean
from
1850–1900
(°C)
0 20 60 100Number of
days with
maximum
above 35°C
(days)
Change in
annual
maximum
1-day
amount 
from 1850–1900
(%)
-40 10 20 30 40Change in
consecutive
dry days
from
1850–1900
(days)
30 40 50 70 80 90 10
-55% -40% 20% 55% -10% 0 10% 30% 40% 50% -50% -20% -30%
0 -10 -20 -30

18
2678Chapter 18 Climate Resilient Development Pathways
(b) Sustainable development related climate impacts indicators
Global Warming Level ≈ 1.7°C Global Warming Level ≈ 2.5°C Global Warming Level ≈ 4.4°C
Days per year
population at 
risk of death
from extreme
heat
(Chapter 6)
with physical
work capacity
less than 40%
SSP8.5 2081–2100
(Chapter 5)
No days
1 day 365 daysLow (0 days) High (365 days)
Global Warming Level ≈ 5.0°C
40% 30% -30% -10% 0 10% -40% -20% 20%Global Warming Level ≈ 2.0°C Global Warming Level ≈ 5.3°C
Change in total
animal fish biomass
from 1990–1999
(Chapter 3)
for 100-year
river flood
(years)
1970–2000
Global Warming Level ≈ 2.1°C Global Warming Level ≈ 2.9°C Global Warming Level ≈ 4.7°C
1,000 500 50 95 105 125 25 75 250 5 2
Flood
frequency Decrease IncreaseHigh model
agreement

Figure 18.4 |  Regional projected select climate change and sustainable-development-related climate impact indicators by global warming level. Sources: WGI 
AR6 Interactive Atlas (https://interactive-atlas.ipcc.ch/) and WGII Figures 3.21, 4.17, 5.19, and 6.3. The GWLs shown are multi-model means derived from Hauser et al. (2019) for 
the respective RCP and SSP and time periods associated with each figure.

18
2679Climate Resilient Development Pathways  Chapter 18
(Carr, 2019; 2020) draws upon evidence from sub-Saharan Africa to 
develop more nuanced understandings of the ways in which different 
stressors and interventions either facilitate or foreclose transformative 
pathways, while pointing to the existence of yet poorly understood 
thresholds for transformation in systems that can be identified and 
targeted by interventions.
18.2.5.1.2 Adaptation gaps
Adaptation gaps are defined as ‘the difference between actually 
implemented adaptation and a societally set goal, determined 
largely by preferences related to tolerated climate change impacts 
and reflecting resource limitations and competing priorities’ (UNEP , 
2014; UNEP , 2018a). Adaptation deficit is a similar concept, described 
as an inadequate or insufficient adaptation to current conditions 
(Chapter 1). Adaptation gaps or deficits arise from a lack of adequate

technological, financial, social, and institutional capacities to adapt 
effectively to climate change and extreme weather events, which are 
in turn linked to development (very high confidence) (Fankhauser and 
McDermott, 2014; Milman and Arsano, 2014; Chen et al., 2016; Asfaw 
et al., 2018) (Section 18.2.2).
Currently, there is no consensus around approaches to assess the 
effectiveness of adaptation actions across contexts and therefore 
measure adaptation gaps at a global scale (Singh et al., 2021a). UNEP 
(2021) suggests that comprehensiveness, inclusiveness, implementability, 
integration and monitoring, and evaluation can be used to assess them 
(see also Cross-Chapter Box  FEASIB). However, limited information 
is available about future trends in national-level adaptation and the 
development of monitoring and evaluation mechanisms. Despite the 
challenges of measurement associated with adaptation gaps, available

evidence from smaller scales across several regions, communities and 
businesses suggest that significant adaptation gaps have existed in 
historical contexts of climate change, while expectations of extreme heat, 
increasing storm intensity and rising sea levels will create the context 
for the emergence of new gaps (very high confidence) (Hallegatte et al., 
2018; UNEP , 2018a; Dellink et al., 2019; UNEP , 2021). These adaptation 
gaps create risks to well-being, economic growth, equity, the health of 
natural systems and other societal goals. The negative impacts of these 
gaps can be compounded by adaptation efforts that are considered 
maladaptive or by development actions that are labelled as adaptation 
(see Chapter 16).
A higher level of adaptation finance is critical to enhance adaptation 
planning and implementation and reduce adaptation gaps, particularly 
in developing countries (very high confidence) (UNEP , 2021) (Cross-

Chapter Box  FINANCE in Chapter 17, Section  18.4.2.2). However, 
adaptation finance is not keeping pace with the rising adaptation 
costs in the context of increasing and accelerating climate change, as 
‘annual adaptation costs in developing countries alone are currently 
estimated to be in the range of US$70 billion, with the expectation of 
reaching US$140–300 billion in 2030 and US$280–500 billion in 2050’ 
(UNEP , 2021). Investment in attaining SDGs helps bridge adaptation 
gaps (Birkmann et  al., 2021), but care needs to be taken to avoid 
maladaptation through mislabelling. Integration of the Indigenous and 
local knowledge systems is anticipated to reduce existing adaptation 
gaps and secure livelihood transitions.Analysis of investments by four major climate and development 
funds (the Global Environment Facility, the Green Climate Fund, 
the Adaptation Fund and the International Climate Initiative) by 
UNEP (2021) suggests that support for green and hybrid adaptation

solutions has been increasing over the past two decades. These could 
be effective at reducing climate risks and bridging adaptation gaps 
while simultaneously bringing important additional benefits for the 
economy, environment and livelihoods (UNEP , 2021) (see also Cross-
Lately, the evidence of adaptation activity in the health sector has 
been increasing (Watts et al., 2019), yet substantial adaptation gaps 
persist (UNEP , 2018a; UNEP , 2021), including gaps in humanitarian 
response to climate-related disasters (Watts et  al., 2019). It is the 
under-investment in climate and health research in general and health 
adaptation in particular that has led to adaptation gaps in the health 
sector (Ebi et al., 2017).
Costs of implementing efficient adaptation measures and water-
related infrastructure in water-deficient regions have received 
attention at the global and regional level to bridge the ‘adaptation

gap’ (Hallegatte et al., 2018; UNEP , 2018a; Dellink et al., 2019; UNEP , 
2021). Livelihood sustainability in the drylands, which cover more 
than 40% of the land surface area, are home to roughly 2.5 billion 
people, and support approximately 50% of the livestock and 45% 
of the food production, is threatened by a complex and inter-related 
range of social, economic and environmental changes that present 
significant challenges to rural communities, especially women (Abu-
Rabia-Queder and Morris, 2018; Gaur and Squires, 2018). Adaptation 
deficits in arid and semi-arid regions are of high order (see CROSS-
CHAPTER BOX 3). To reduce adaptation deficit in arid and semi-
arid regions, comprehensive and efficient adaptation interventions 
integrating better water management, use of non-traditional water 
sources, changes in reservoir operations, soil ecosystem rejuvenation 
and enhanced institutional effectiveness are needed (Section  18.5)

(Makuvaro et al., 2017; Mohammed and Scholz, 2017; Morote et al., 
2019). Communities facing the lack of adequate technological, 
financial, human and institutional capacities to adapt effectively to 
current and future climate change often encounter adaptation deficits. 
To address current adaptation barriers and adaptation deficits, there 
is a need to promote efficient adaptation measures, coupled with 
inclusive and adaptive governance involving marginalised groups such 
as Indigenous communities and women.
Although unevenly distributed urban adaptation gaps exist in all world 
regions (see Chapter 6). Such gaps are higher in the urban centres 
of the poorer nations. Chapter 6 identified that the critical capacity 
gaps at city and community levels responsible for adaptation gaps are 
the ‘ability to identify social vulnerability and community strengths, 
and to plan in integrated ways to protect communities, alongside the

ability to access innovative funding arrangements and manage finance 
and commercial insurance; and locally accountable decision making 
support the application of adaptation solutions at scale’.
Insufficient financial resources are the main reasons for the coastal 
adaptation gap, particularly in the Global South (see CROSS-CHAPTER

18
2680Chapter 18 Climate Resilient Development Pathways
BOX 2). Engaging the private sector with a range of financial tools is 
crucial to address such gaps (see CROSS-CHAPTER BOX 2). An urgent 
and transformative action to institutionalise locally relevant integrative 
adaptation pathways is crucial for closing coastal adaptation gaps. 
Additional efforts are in place for assessing global adaptation progress 
(see Cross-Chapter Box PROGRESS in Chapter 17).
18.2.5.1.3 Adaptation implementation
As discussed in Chapter 16, adaptation is a key mechanism for 
managing climate risks, and therefore for pursuing CRD. The lower 
estimates in Table 18.2 are associated with higher levels of adaptation 
and more conducive development conditions. Furthermore, additional 
adaptation demand is associated with greater levels of climate change. 
Adaptation is a broad term referring to many different levels of response 
and options for natural and human systems, from individuals, specific

locations and specific technologies, to nations, markets, global dynamics 
and strategies at the system level. Adaptation also includes endogenous 
reflexive and exogenous policy responses. Perspectives on limits to 
adaptation, synergies, trade-offs and feasibility therefore depend on 
where the boundaries are drawn and the objective. Overall, there are 
a broad range of adaptation options relevant to reducing risks posed 
by climate change to development. However, current understanding 
of how such options are implemented in practice, their effectiveness 
across a range of possible climate futures and their potential limits, is 
modest.
The IPCC’s SR1.5 report evaluated individual adaptation options in terms 
of economic, technological, institutional, socio-cultural, environmental/
ecological and geophysical feasibility (de Coninck et  al., 2018). This 
analysis has been updated for AR6 (Cross-Chapter Box FEASIB). These

assessments identify types of barriers that could affect an option’s 
feasibility. Among other things, this work finds that every adaptation 
option evaluated had at least one feasibility dimension that represented a barrier or obstacle. The barriers also imply that there are trade-offs 
in these feasibility dimensions to consider. Overall, insights from this 
work are high-level and difficult to apply to a specific adaptation 
context. The feasibility and ranking of adaptation opportunities, as 
well as the list of opportunities themselves, for a given location will 
vary from location to location, with different criteria and weighting 
of criteria that reflect the priorities of society and decision-makers 
as well as differences in markets, technology options and policies for 
managing risks and trade-offs. Integrated evaluation of criteria and 
options is needed, that accounts for the relevant geographic context 
and interactions between options and systems (Section 18.5).

Sustainable development is regarded as generally consistent with 
climate change adaptation, helping build adaptive capacity by 
addressing poverty and inequalities and improving inclusion and 
institutions (Roy et  al., 2018). Some sustainable development 
strategies could facilitate adaptation effectiveness by addressing 
wider socioeconomic barriers, addressing social inequalities and 
promoting livelihood security (Roy et al., 2018). With a common goal of 
reducing risks, sustainable development and adaptation are relatively 
synergistic. For example, “low-regrets” adaptation strategies have 
been identified, such as improvements in health systems that reduce 
climate health impacts in cities (Barata, 2018). However, trade-offs 
also have been found and are important to consider and potentially 
address. Synergies have been found between adaptation and poverty 
reduction, hunger reduction, clean water access and health; while,

trade-offs have also been found, particularly when adaptation 
strategies prioritise one development objective (e.g., food security or 
heat-stress risk reduction) or promote high-cost solutions with budget 
allocation and equity implications (Roy et al., 2018) (Sections 18.2.5.3, 
18.5, Box 18.4). There are also opportunities for addressing the trade-
offs, in particular distributional effects—by recognising that there are 
trade-offs and considering alternatives and complementary strategies 
to address those trade-offs (Section 18.2.5.3).
Box 18.3 | Climate Resilient Development in Small Islands
Small islands are particularly vulnerable to climate change and many are already pursuing climate resilient development pathways 
that enable integrated responses (Allen et al., 2018a; Mycoo, 2018; Hay et al., 2019; Robinson et al., 2021). Countries such as Belize

have opted for a systems approach and are working across the sustainable development goals (SDGs) to increase integration (Allen 
et al., 2018a). This includes rethinking disaster reconstruction mechanisms in the Caribbean and introducing more diversified and 
sustainable tourism economies that can better withstand external shocks such as disruptions and loss of markets from COVID-19 
(Sheller, 2021). In the Seychelles, various government and tourism industry initiatives are focused on the promotion of sustainable 
tourism ventures that lower emissions, protect and promote biodiversity conservation (e.g., new marine protected areas with 
mitigation and adaptation benefits), and are climate resilient (Robinson et al., 2021). In 2016, the Seychelles signed the world’s first 
nature-for-debt swap, wherein a non-governmental organisation (NGO; The Nature Conservancy) agreed to pay off Seychelles’ public

debt to the Paris Club (foreign creditors) in return for the Seychelles government establishing marine conservation areas (Silver and 
Campbell, 2018).
One key area where enhanced climate risk integration is critical is infrastructure-related decisions, especially on coastal areas (World 
Bank, 2017). However, despite increasing awareness of climate risks and experienced impacts, decisions on, for example, infrastructure 
locations still reflect cultural preferences. For example, Hay et al. (2019) report that, despite recommendations to relocate the redevelopment 
site of the Parliamentary Complex in Samoa away from the coast, multiple cultural and historical factors influenced the decisions to 
redevelop at the original site. In the Solomon Islands, however, emerging evidence suggests that adaptation efforts to enhance the

18
2681Climate Resilient Development Pathways  Chapter 18
Box 18.4 | Adaptation and the Sustainable Development Goals
The achievement of the Sustainable Development Goals (SDGs) represents near-term positive sustainability as well as indicating the 
quality of development processes and actions (inclusion and social justice, alternative development models, planetary health, well-
being, equity, solidary, different forms of knowledge and human–nature connectivity) that enable climate resilient development (CRD) 
in the long term (Sections 18.2.2.2, 18.2.5.3). A key question is the extent to which adaptation actions (or non-action) may contribute 
to (or undermine) SDG achievement and, in particular, shift the quality of development processes and engagement within the political, 
economic, ecological, socio-ethical and knowledge-technology arenas, and hence contribute to climate resilient development pathways 
(CRDPs).

Table Box 18.4.1 (below) provides a set of examples of how adaptation actions can either contribute to or undermine SDG achievement 
for SDGs 2, 3, 6, 11 and 16. In general, formal adaptation policies as well as household and community-based adaptation strategies can 
generate positive outcomes, particularly if they are responsive to the local context and needs, with real participation and leadership by 
target populations (Remling and Veitayaki, 2016; Buckwell et al., 2020; McNamara et al., 2020; Owen, 2020). For example, integrated 
adaptation approaches to the water–energy–food (WEF) nexus aiming to build resilience in those sectors can lead to increased 
resource use efficiency and coherent strategies for managing the complex interactions and trade-offs among the water, energy 
and food SDGs (Mpandeli et al., 2018; Nhamo et al., 2020). One such approach could involve cultivating indigenous crops suited to

harsh growing conditions, which would allow for agricultural expansion for food and energy without increased water withdrawals 
(Mpandeli et al., 2018). Overall, adaptation commitments aiming to build resilience of vulnerable populations have typically shown to 
contribute to SDGs focused on ending extreme poverty (SDG 1), improving food security (SDG 2), improving access to water (SDG 6), 
ensuring clean energy (SDG 7), tackling climate change (SDG 13) and halting land degradation and deforestation (SDG 15) (Antwi-
Agyei et al., 2018).resilience of infrastructure are also serving to help urban areas address problems associated with rapid urbanisation and provide new 
opportunities for sustainable development (Robinson et al., 2021).
Energy system transitions in small islands can produce synergies with SDG implementation and can lead to transformational outcomes.

The Pacific Island territory of Tokelau has demonstrated a nationwide energy transition, sourcing 100% of their energy needs from solar 
power (Michalena and Hills, 2018), and many other countries such as Fiji, Niue, Tuvalu, Vanuatu, Solomon Islands and Cook Islands also 
have 100% renewable energy targets. Benefits of small island distributed energy systems (such as solar photovoltaic [PV] systems) 
include less need for large, centralised infrastructure; reduced reliance on volatile fossil fuel markets; enhanced international climate 
negotiations power; and enhanced local job markets/skills (Dornan, 2015; Cole and Banks, 2017; Weir, 2018). Additionally, renewable 
systems can enhance resilience to hydro-meteorological disasters (Weir and Kumar, 2020). For example, well-secured ground-based PV 
systems withstood cyclones in the Pacific Island of Tonga during cyclone Gita and across the Caribbean during Hurricane Maria, with

power restored in days rather than weeks associated with more centralised systems (Weir and Kumar, 2020). Yet a multitude of challenges 
remain. In the Pacific islands region, these include: the high up front capital investment of renewables; lack of private sector investment; 
limited renewable energy data for policymaking; land tenure/rent costs; ongoing infrastructure maintenance skills and requirements; 
political turnover; failed experimentation; difficulty in obtaining and transporting replacement parts; and a highly corrosive environment 
for equipment (Dornan, 2015; Cole and Banks, 2017; Lucas et al., 2017; Weir, 2018; Weir and Kumar, 2020). The example of Pacific energy 
transitions demonstrates that a nuanced and context specific analysis of synergies and trade-offs for energy transitions is required to 
lessen the impact on fragile economies and maximise benefits for remote populations.

Labour migration is increasingly recognised as a significant factor that can contribute to climate resilient development pathways for 
small islands. In the Pacific islands region, labour mobility schemes are already allowing for climate change adaptation and economic 
development to occur in labour migrants’ countries of origin (Smith and McNamara, 2015; Klepp and Herbeck, 2016; Dun et al., 2020). 
Dun et al. (2020) demonstrates that temporary or circular migrants from the Solomon Islands, working in Australia under its Seasonal 
Worker Programme (similar programmes operate in other developed countries), are using the money they earn to invest in adaptation 
and development activities back home. Similarly, labour migrants from Vanuatu, Kiribati and Samoa contribute to development and in 
situ climate change adaptation (at a household, village and regional level) that enable discussions about more resilient futures for their

countries (Barnett and McMichael, 2018; Parsons et al., 2018).Box 18.3 (continued)

18
2682Chapter 18 Climate Resilient Development Pathways
However, evidence also suggests limitations of adaptation actions, with the objectives and actions often being too narrow to address 
social justice and enable CRD. As such, adaptation actions can sometimes undermine SDG achievement through exacerbating social 
vulnerability, inequity and uneven power relations (Antwi-Agyei et al., 2018; Atteridge and Remling, 2018; Paprocki, 2018; Mikulewicz, 
2019; Satyal et al., 2020; Scoville-Simonds et al., 2020). This is due to adaptation practices often not accounting for the differentiated 
ways in which minority groups are especially vulnerable. For example, designs of emergency shelters should consider the fear of social 
stigma or abuse faced by women and girls (Pelling and Garschagen, 2019).
Such maladaptive adaptation practices can undermine SDG achievement through increasing vulnerability of marginalised groups by

failing to address the underlying root causes of vulnerability and poverty that are related to political economy, power dynamics and 
vested interests more broadly, instead treating the symptoms as the cause (Magnan et al., 2016; Ajibade and Egge, 2019; Schipper, 
2020). For example, evidence exists of flood defence measures through large-scale infrastructure development leading to the violent 
displacement of poor communities, forcibly resettling people in areas far from their employment or pushing up land and housing costs 
without providing compensation (Fuso Nerini et al., 2018; Reckien et al., 2018). Moreover, sectoral approaches to adaptation that fail 
to acknowledge the linkages between SDGs can counter development efforts and generate further trade-offs (Terry, 2009; Rasul and 
Sharma, 2016; von Stechow et al., 2016; Klinsky et al., 2017; Hallegatte et al., 2019).

The literature recommends a set of strategies for ensuring that adaptation actions are aligned with SDG achievement and do not further 
perpetuate poverty and inequality. These include ensuring that marginalised voices are central to adaptation decision making, with 
participatory approaches that empower and compensate affected communities (Moser and Ekstrom, 2011; Broto et al., 2015; Pelling 
and Garschagen, 2019; Palermo and Hernandez, 2020). Gender mainstreaming and gender transformative approaches within climate 
policies can also help ensure gender-sensitive design of adaptation projects, with appropriate equity analyses of policy (Klinsky et al., 
2017) decisions to identify the actual implications of trade-offs for vulnerable groups (Beuchelt and Badstue, 2013; Alston, 2014; Bowen 
et al., 2017; Fuso Nerini et al., 2018).
In addition, a substantial literature also argues for policy coherence measures that adopt whole-of-government approaches and

mainstream and nationalise SDG targets within national climate policies (Nilsson et al., 2012; Le Blanc, 2015; Ari, 2017; Collste et al., 
2017; Dzebo et al., 2017; Nilsson and Weitz, 2019). Institutional coordination mechanisms that aim to break down silos between different 
agencies and actors at the national level are suggested as beneficial for avoiding trade-offs between adaptation actions and SDGs 
(Mirzabaev et al., 2015; Howlett and Saguin, 2018; Scherer et al., 2018). However, these need to be paired with an investigation of the 
deep-seated ideologies and vested interests that are creating goal conflicts and negatively impacting marginalised groups to begin with 
(Purdon, 2014; Bocquillon, 2018). Ultimately, adaptation measures need to acknowledge and address the underlying drivers that make 
certain groups particularly vulnerable, such as social disenfranchisement, unequal power dynamics and historical legacies of colonialism

and exploitation (Magnan et al., 2016; Schipper, 2020)
Table Box 18.4.1 |  Examples of linkages between adaptation and the SDGs. For several key SDGs aligned with the concept of CRD, the table below identifies evidence 
from the literature where adaptation policies and practices contribute to achievement of the SDG, as well as where they undermine achievement of the SDG.
SDG Evidence of adaptation contributing to SDG Evidence of adaptation undermining SDG
SDG 2: Zero 
HungerAdaptation measures implemented by smallholder farmers (e.g., adjustments 
in farm operations timing, on-farm diversification, soil–water management) 
exhibit higher levels of productivity and technical efficiency in food 
production (Bai et al., 2019; Sloat et al., 2020; Khanal et al., 2021)
Some climate smart agriculture measures (e.g., intercropping) can 
significantly increase yields and contribute to zero hunger (Lipper et al., 2014;

Arslan et al., 2015; Saj et al., 2017)Some adaptation policies can increase land and food prices, negatively 
impacting smallholder farmers (Fuso Nerini et al., 2018; Zavaleta et al., 2018; 
Albizua et al., 2019)
Potential trade-offs for food production through adaptation actions within 
the water or energy sector, if integrated approaches not taken (Howells et al., 
2013; FAO, 2014; Biswas and Tortajada, 2016)
SDG 3: Good 
Health and 
WellbeingIncreased resilience of societies and reduced vulnerability through 
investments in public health care and access (Marmot, 2020; Mullins and 
White, 2020)
Adaptation measures that leverage solidarity, equity and nature 
connectedness contribute to physical and psychological health and 
well-being (Gambrel and Cafaro, 2009; Capaldi et al., 2015; Soga and 
Gaston, 2016; Woiwode, 2020)Societal measures beyond adaptation required to address underlying causes 
of inequities that drive poor health and well-being, including cuts in public

spending and neoliberalisation and commodification of healthcare (Hall, 
2020; Walsh and Dillard-Wright, 2020)Box 18.4 (continued)

18
2683Climate Resilient Development Pathways  Chapter 18
SDG 6: Clean 
Water and 
SanitationIntegrated water resources management as an adaptation strategy (Tan and 
Foo, 2018; Sadoff et al., 2020)Potential trade-offs for water security through adaptation actions within 
the food or energy sector, if integrated approaches not taken (Howells et al., 
2013; Rasul and Sharma, 2016; Mpandeli et al., 2018)
Local, regional or national ‘grabs’ for water from shared resources with 
poorly defined property rights (Olmstead, 2014)
SDG 11: 
Sustainable 
Cities and 
CommunitiesVulnerability reducing adaptation measures that aim to upgrade informal 
settlements, create affordable housing and protect populations living in 
disaster prone areas (Major et al., 2018; Sanchez Rodriguez et al., 2018; 
Ajibade and Egge, 2019)Need to ensure that adaptation measures understand how power dynamics

and cultural norms shape urban form and communities’ vulnerability and 
adaptive capacity (Sanchez Rodriguez et al., 2018)
Risk of built infrastructure aiming to increase resilience ignoring local 
population needs and creating low-skilled jobs that concentrate land, capital 
and resources in the hands of the elite (Ajibade and Egge, 2019)
SDG 16: 
Peace, Justice 
and Strong 
InstitutionsPotential for adaptation projects to support livelihood incomes and 
resource management, and thereby reduce tensions and the risk of conflicts 
(Matthew, 2014; Dresse et al., 2018; Barnett, 2019)Studies from Bangladesh, Cambodia and Nepal found that climate change 
adaptation-related policies and projects were an underlying cause of natural 
resource-based conflicts, as well as land dispossession and exclusion, 
entrenchment of dependency relations, elite capture and inequity (Sovacool, 
2018; Sultana et al., 2019)
Adaptation projects can reinforce top-down knowledge and decision-making

processes, asymmetric power relations and elite capture of adaptation 
resources (Nightingale, 2017; Eriksen et al., 2021b)
Need for conflict-sensitive adaptation approaches that aim to ‘do no harm’ 
(Babcicky, 2013; Ide, 2020)
18.2.5.2 Mitigation
Mitigation, including greenhouse gas emissions reductions, avoidance, 
and removal and sequestration, as well as management of other 
climate forcing factors (WGIII AR6), is a key element of addressing 
climate risk and pursuing CRD. There are numerous individual and 
system mitigation options throughout the economy and within human 
and natural systems (very high confidence) (Chapter 16; Section 18.5). 
Limiting global average warming has been found to reduce climate 
risks (IPCC, 2018a; IPCC, 2019b), and limiting global average warming 
to any temperature level has also been found to be associated with 
broad ranges of potential global emissions pathways that represent 
future uncertainty in the evolution of socioeconomic, technological,

market and physical systems (very high confidence) (Rose and Scott, 
2018; Rose and Scott, 2020). Pathways consistent with limiting 
warming to 2°C and below have been found to require significant 
deployment of mitigation options spanning energy, land use and 
societal transformation ((Lecocq et  al., 2022; Riahi et  al., 2022); 
Section 18.3). and substantial economic, energy, land use, policy and 
societal transformation (Lecocq et al., 2022; Riahi et al., 2022). Such 
emissions pathways would represent deviations from current trends 
that raise issues about their feasibility and therefore plausibility (Rose 
and Scott, 2018; Rose and Scott, 2020).
The technical and economic challenge of limiting warming has been 
found to increase nonlinearly with greater ambition, fewer mitigation 
options, less than global cooperative policy designs and delayed 
mitigation action ((Riahi et al., 2022); Table 18.2). Table 18.2 provides

a high-level summary of pathway characteristic ranges based on the 
WGIII AR6 assessment. Global pathways find large regional differences 
in mitigation potential, as well as the degree of regional nonlinearity with greater mitigation ambition. These represent opportunities for 
mitigation, but how this effort and cost would be facilitated and 
distributed respectively is a policy question.
Table  18.2 illustrates that greater climate ambition implies more 
aggressive emissions reductions in each region, and earlier regional 
peaking of emissions (if they have not peaked to date). Near-term 
regional emissions increases are possible, even for 1.5°C compatible 
pathways, but significantly lower emissions than today are shown in 
all regions by 2050. Increases in total regional energy consumption 
and fossil energy are observed for many pathways, even in the most 
ambitious where energy consumption growth is potentially slower

compared with less ambitious pathways. By 2050, regional fossil 
energy declines, but is not eliminated in any region. Regional growth in 
electricity use is substantial in all pathways, even the most ambitious, 
with the growth continuing and accelerating with time and regional 
dependence on electricity (share of total energy consumption) also 
growing significantly. The broad ranges are an indication of uncertainty 
and risk for regional transitions, noting that full uncertainty is likely 
broader than what is captured by emissions scenario databases 
(Rose and Scott, 2018; Rose and Scott, 2020). Among other things, 
pathways commonly assume idealised climate policies with immediate 
implementation, and model infeasibilities (i.e., models unable to 
solve) increase with climate ambition and pessimism about mitigation 
technologies (e.g., Clarke et al., 2014; Bauer et al., 2018; Rogelj et al., 
2018; Muratori et  al., 2020), highlighting the increasing challenge

and potential for actual infeasibility with lower global warming 
targets. Together, Table  18.2 provides insights into the increasingly 
demanding system and development transitions associated with lower 
global warming levels, as well as some of the low-carbon transition 
uncertainties and risks (see also Figure 18.5).Box 18.4 (continued)

18
2684Chapter 18 Climate Resilient Development Pathways
Past assessment has evaluated representative mitigation strategies 
in terms of economic, technological, institutional, socio-cultural, 
environmental/ecological and geophysical viability, as well as 
relationships to SDGs (de Coninck et  al., 2018). The strategies 
assessment analysis has been updated for AR6 (Cross-Chapter 
Box FEASIB). These assessments identify types of barriers that could 
affect an option’s feasibility. Among other things, this work finds that, 
other than public transport and non-motorised transport, every other 
mitigation option evaluated had at least one feasibility dimension that 
represented a barrier or obstacle. The barriers also imply that there are 
trade-offs in these feasibility dimensions to consider. The assessment 
of mitigation option-sustainable development relationships identifies 
related literature and derives aggregate characterisations. Concerns

about the potential sustainable development implications of some 
mitigation technologies may be motivation for precluding the use 
of some mitigation options. For instance, the potential food security 
and environmental quality implications of bioenergy have received 
significant attention in the literature (e.g., Smith et al., 2013). However, 
constraining or precluding the use of bioenergy without or with CCS 
could have significant implications for the cost of pursuing ambitious 
climate goals, and potentially the attainability of those goals (e.g., 
Clarke et al., 2014; Bauer et al., 2018; Rogelj et al., 2018; Muratori et al., 
2020). Bioenergy is not unique in this regard. Social, environmental, 
and sustainability concerns have also been raised about the large-
scale deployment of many low-carbon technologies, for example, 
REDD+, wind, solar, nuclear, fossil with CCS and batteries. See WGIII 
Chapter 3 (Riahi et al., 2022) for examples of the potential implications

of limiting or precluding different low-carbon technologies.
Overall, as with adaptation options, insights from this aggregate 
feasibility and sustainable development mapping work are high level 
and difficult to apply to a specific mitigation context. The feasibility, 
ranking and sustainable development implications of mitigation 
options, as well as the list of options themselves, for a given location 
will vary from location to location, with different criteria and weighting 
of criteria that reflect the relevant social priorities and differences 
in markets, technology options and policies for managing risks and 
trade-offs. Integrated evaluation of criteria and options is needed 
here as well, that accounts for the relevant geographic context and 
interactions between options, systems and implications.
Analyses of the potential implications of mitigation on sustainable 
development has various strands of literature—studies exploring

general greenhouse gas mitigation feedbacks to society, assessments 
of mitigation implications on specific societal objectives other than 
climate and literature evaluating mitigation implications specifically 
for sustainable development objectives (Denton et  al., 2022; Lecocq 
et al., 2022; Riahi et al., 2022). In general, mitigation alters development 
opportunities by constraining the emissions future society can produce, 
which affects markets, resource allocation, economic structure, income 
distribution, consumers and the environment (besides climate) (very 
high confidence). Examples of general development feedbacks from 
mitigation include estimated price changes, macroeconomic costs, 
and low carbon energy and land system transformations (Fisher et al., 
2007; Clarke et al., 2014; Popp et al., 2014; Rose et al., 2014; Weyant 
and Kriegler, 2014; Bauer et al., 2018; Rogelj et al., 2018). Examples of

mitigation implications for other specific variables of societal interest include evaluating potential effects on air pollutant emissions, crop 
prices, water and land use change (e.g., McCollum et  al., 2018b; Roy 
et  al., 2018), while the literature evaluating mitigation implications 
specifically for sustainable development objectives includes evaluations 
on energy access, food security and income equality (e.g., Roy et al., 2018; 
Arneth et al., 2019; Mbow et al., 2019). Proxy indicators are frequently 
used to represent whether there might be implications for a sustainable 
development objective. For example, changes in energy prices are used 
as a proxy for effects on energy security (e.g., Roy et al., 2018). This is 
common with aggregate modelling studies, such as those associated 
with global or regional emissions scenarios and energy systems.
Figure  18.5, derived from WGIII scenarios data, illustrates estimated

relationships between mitigation and various sustainable development 
proxy variables  for different global regions. Figure  18.5 illustrates 
synergies and trade-offs with mitigation, as well as regional 
heterogeneity, that can intensify with the level of climate ambition—
synergies in air pollutants, such as black carbon, NOx and SO 2; and 
trade-offs in overall economic development, household consumption, 
food crop prices and energy prices for electricity and natural gas. For 
comparison, recent IPCC assessments also observed similar synergies 
and trade-offs but did not directly make comparisons regarding overall 
development nor evaluate potential climates above 2°C (Rogelj et al., 
2018; Roy et al., 2018; Mbow et al., 2019). Regional nonlinearity in the 
economic costs of mitigation with greater climate ambition (i.e., costs 
rising at an increasing rate with lower warming goals) can be significant 
within individual models (Rose and Scott, 2018; Rose and Scott, 2020).

Figure 18.5 also illustrates transition risks in the potential for significant 
synergistic and trade-off implications with, for instance, potentially large 
regional commodity price implications and household consumption 
losses, as well as more significant air pollution benefits. Note that 
the 1.5°C results in Figure 18.5 (and Table 18.2) are biased by model 
infeasibilities. Many models are unable to solve, especially with less 
optimistic assumptions, resulting in small sample sizes and a different 
representation of models compared to the 2°C and higher results.
Results such as those in Figure  18.5 illustrate that mitigation–
development trade-offs are inevitable and need to be considered 
and addressed. For instance, Roy (2018) found that although limiting 
warming to 1.5°C would make it markedly easier to achieve most of 
the UN’s SDGs, none of the 1.5°C pathways assessed achieved all of 
the SDGs. A similar conclusion follows from the results in Figure 18.5

based on WGIII AR6 scenarios.. A newer literature is developing, 
evaluating the potential for managing SDG trade-offs. Results like 
those in Figure 18.5 provide insights regarding some of the types of 
strategy sets to consider. Roy et al. (2018) discuss the potential for 
policies that address distributional implications, such as payments, 
food support and revenue recycling, as well as education, retraining 
and technology outreach, subsidies or prioritisation. Recent studies 
have begun to estimate potential payments to offset trade-offs, such 
as related to food, water and energy access (e.g., McCollum et al., 
2018a). These analyses estimate investments to address specific 
trade-offs; however, with mitigation redirecting resources away 
from other productive activities, there is a need to also evaluate the 
aggregate economy-wide, distributional and welfare effects, including 
the redistribution effects of managing sustainable development 
trade-offs.

18
2685Climate Resilient Development Pathways  Chapter 18
Table 18.2 |  Emissions pathway regional characteristics from WGIII scenarios database for pathways associated with different global warming levels (1.5°C, 2°C, 3°C and 4°C). 
Sample sizes: n = 2, 120–126, 56, and 26 emissions pathways for 1.5°C, 2°C, 3°C and 4°C global warming levels, respectively. Sample size ranges indicate that the sample size 
varies by variable due to differences in model reporting. Sample size varies by warming level due to model infeasibilities and differences in model reporting.
VariablePeak 
global 
warm-
ing to 
2100Asia Latin America Middle East/Africa OECD Reforming economies n
Peak CO 2 
emissions 
year1.5°C 2020 2010 2020 2010 2015 2
2°C 2015 to 2030 2010 to 2035 2010 to 2030 2010 to 2020 2015 to 2030 126
3°C 2020 to 2080 2010 to 2100 2030 to 2100 2010 to 2020 2015 to 2100 56
4°C 2025 to 2100 2010 to 2100 2070 to 2100 2010 to 2100 2040 to 2100 26
global 
warm-
ing to

2100Asia Latin America Middle East/Africa OECD Reforming economies
2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 n
Net CO 2 
emissions 
(% change 
from 
2010)1.5°C −18 to −24% −73 to −69% −61 to −57% −94 to −92% −26 to −1% −65 to −50% −50 to −46% −91 to −90% −42 to −41% −92 to −91% 2
2°C −31 to 38% −89 to −33% −62 to 31% −98 to −3% −30 to 67% −73 to −1% −51 to −13% −97 to −59% −52 to 32%−105 to 
−30%126
3°C 10 to 50% −5 to 49% −58 to 16% −132 to 50% 7 to 84% 33 to 101% −44 to 2% −67 to −12% −18 to 33% −37 to 41% 56
4°C 26 to 76% 37 to 103% −49 to 5% −41 to 22% 19 to 121% 78 to 225% −34 to −8% −53 to −7% −13 to 38% 0 to 53% 26
Energy 
con-
sumption 
growth 
(% change 
from 
2010)1.5°C 48 to 48% 49 to 62% 23 to 27% 26 to 39% 40 to 46% 55 to 62% −15 to −12% −43 to −28 −21 to −15% −41 to −34% 2
2°C 17 to 90% 16 to 130% 3 to 72% 12 to 160% 18 to 82% 43 to 145% −16 to 10% −35 to 11% −15 to 37% −33 to 29% 125

3°C 43 to 80% 70 to 129% −9 to 74% 17 to 170% 21 to 82% 79 to 174% −16 to 13% −29 to 21% −3 to 37% −15 to 86% 56
4°C 47 to 91% 73 to 175% 19 to 65% 34 to 137% 46 to 95% 91 to 197% −9 to 3% −21 to 18% −8 to 18% −4 to 27% 26
Fossil 
energy use 
growth (% 
change 
from 20101.5°C 7 to 8% −34 to 34% −9 to −6% −53 to −46% 15 to 25% −23 to −20% −42 to −38% −81 to −76% −38 to −34% −81 to −80% 2
2°C −33 to 64% −73 to 14% −20 to 65% −78 to 61% −6 to 71% −78 to 61% −47 to −8% −81 to −32% −51 to 31% −85 to −5% 121
3°C 15 to 70% 29 to 89% −20 to 65% 3 to 124% 7 to 79% 31 to 158% −37 to 3% −57 to 3% −24 to 32% −30 to 43% 56
4°C 38 to 88% 59 to 149% 10 to 63% 21 to 149% 41 to 115% 103 to 247% −26 to −5% −45 to −1% 14 to 18% −5 to 32% 26
Elec-
tricity con-
sumption 
growth 
(% change 
from 
2010)1.5°C 159 to 165% 330 to 417% 91 to 93% 275 to 338% 119 to 132% 500 to 588% 3 to 12% 32 to 86% 28 to 30% 67 to 116% 2

2°C 41 to 231% 120 to 580% 34 to 127% 140 to 489% 64 to 172% 177 to 801% −2 to 33% 18 to 143% −1 to 112% 36 to 187% 120
3°C 57 to 198% 126 to 472% 34 to 129% 140 to 348% 75 to 175% 260 to 600% −3 to 39% 10 to 128% 3 to 112% 38 to 221% 56
4°C 107 to 208% 203 to 478% 47 to 123% 156 to 320% 84 to 200% 332 to 586% 1 to 33% 20 to 88% 36 to 83% 78 to 143% 26
Growth in 
electricity 
share of 
energy 
consump-
tion (% 
change 
from 
2010)1.5°C 76 to ‘79% 188 to 219% 53 to 56% 198 to 215% 56 to 60% 288 to 324% 22 to 27% 132 to 160% 54 to 61% 182 to228% 2
2°C −6 to 79% 13 to 240% 9 to 85% 43 to 238% 13 to 94% 77 to 386% −7 to 42% 22 to 182% −8 to 75% 7 to 262% 120
3°C −2 to 76% 6 to 158% 7 to 85% 37 to 180% 13 to 94% 70 to 204% 14 to 39% 8 to 112% −4 to 57% 7 to 127% 56
4°C 29 to 72% 41 to 150% 20 to 46% 37 to 103% 26 to 57% 70 to 149% 9 to 33% 22 to 79% 26 to 58% 43 to 102% 26

18
2686Chapter 18 Climate Resilient Development Pathways
Regional implications of climate mitigation pathways in 2050 
for various development and sustainable development proxy variables for different global mean peak temperature outcomes (during  the century )
Asia Latin AmericaMiddle East/
Africa OECDReforming
Economies
1.5 23 4
Price: 
Natural gas
% changePrice: 
Electricity
Food/feed 
crops
% changeSO2Fraction 
of referenceNOxFraction 
of referenceBlack carbon
Fraction 
of referenceConsumption
% changeGDP
% change
0%200%400%600%800%-70%-10%50%110%170%230%0%10%20%30%40%50%60%0.20.40.60.81.01.20.20.40.60.81.01.20.40.60.81.01.21.4-40%-30%-20%-10%0%10%20%-40%-30%-20%-10%0%°C 1.5 234 1.5 234 1.5 234 1.5 23 4
Goals
Figure 18.5 |  Regional implications of climate mitigation pathways in 2050 for different global mean peak temperature outcomes (during the century)

for various development and sustainable development proxy variables. Each row reports results for a different variable for each of the five global regions (columns) 
used by WGIII, and SDG associated with each variable is noted. Blue dots represent individual emissions scenario results from each of the respective WGIII climate outcome 
scenario categories, with red bars the median results. All results are changes (percentage or fraction) relative to each WGIII scenario’s reference scenario. In some circumstances 
the reference case emissions are below those from the scenario consistent with a global warming level, which can produce results that appear counter-intuitive (e.g., increases in 
GDP or consumption). Data sample sizes vary substantially across temperature levels for a given variable and across variables due to model infeasibilities and model differences in

reporting. Model infeasibilities, in particular, result in significantly fewer data points for 1.5°C compatible emissions pathways compared to 2°C pathways (i.e., models are more 
often unable to solve for a 1.5°C consistent pathway, than a 2°C pathway, with a given set of assumptions). Food/feed crop price results were not available for 1.5°C and 4°C 
warming levels. Sample sizes for each variable and warming level respectively—1.5°C, 2°C, 3°C, and 4°C—are as follows (and apply to all regions): GDP (n = 2, 93, 29, 12); 
Consumption (2, 93, 30, 13); Black Carbon (2, 100, 39, 16), NOx (2, 100, 39, 15), SO2 (2, 100, 39, 16), price food/feed crops (0, 44, 23, 0); price electricity (2, 94, 38, 15); price

18
2687Climate Resilient Development Pathways  Chapter 18
natural gas (10, 86, 44, 10). The sample sizes are very small for the 1.5°C and 4°C results; therefore, the medians for these warming levels are statistically unreliable, which should 
be considered in comparing across warming levels. Individual values in the samples exceed y-axis’ ranges in a few cases: black carbon 2°C Latin America minimum equals 0.08, 
food/feed price change 3°C minimums in Asia, Latin America, Middle East/Africa, OECD, and Reforming Economies equal respectively -33%, -28%, -28%, -29%, and -29%, natural 
gas price change 2°C maximums in Asia, Latin America, Middle East/Africa, OECD, and Reforming Economies equal respectively 962%, 1240%, 2768%, 917%, and 3588%. Figure 
developed from the WGIII AR6 scenarios database, with scenarios filtered according to WGIII exclusions and regional vetting.
There are a wide range of mitigation options and systems to consider,

with assessment suggesting that a diverse portfolio is practical for 
pursing climate policy ambitions. However, local context will impact 
mitigation choices, with unique sustainable development priorities, 
available mitigation options, sustainable development synergies and 
trade-offs, and policy design and implementation possibilities.
18.2.5.3 Combining Adaptation, Mitigation and Sustainable 
Development Options
In practice, adaptation, mitigation and sustainable development 
interventions are likely to be implemented in portfolio packages rather 
than as individual discrete options in isolation (high agreement,  limited 
evidence). However, there is a dearth of literature estimating optimal 
portfolios of global adaptation and mitigation strategies. This is not 
surprising given the geographic-specific nature of climate impacts 
and adaptation and the information and computational complexity of 
representing that detail, as well as mitigation options and interactions.

There are, however, different literatures relevant to considering potential 
combinations of adaptation, mitigation and sustainable development.
At the most aggregate level, there is a long-standing literature 
exploring economically optimal global trade-offs between climate 
risks and mitigation (e.g., Manne and Richels, 1992; Nordhaus, 2017; 
Rose, 2017), as well as global stochastic analysis exploring global 
risk hedging for a small number of uncertainties (e.g., (Lemoine and 
Traeger, 2014). Recent work has found optimal global emissions and 
climate pathways to be highly sensitive to uncertainties and plausible 
alternative assumptions, with uncertainties throughout the causal chain 
from society to emissions to climate to climate damages shown to imply 
a wide range of different possible economically optimal pathways (Rose, 
2017). Among other things, this work identifies assumptions consistent 
with limiting warming to different temperature levels. For example, the

combination of potential annual climate damages of 15% of global GDP 
at 4°C of warming and a less sensitive climate system were consistent 
with an economically efficient global pathway limiting warming to 
2°C. In addition, this work highlights the importance of characterising 
and managing uncertainties. These types of global aggregate analyses 
inform discussions regarding long-run global pathways and goals but 
are not designed to inform local planning.
As discussed in Section 18.2.5.3.1, there are synergies and trade-offs 
in mitigation, adaptation and sustainable development. For instance, 
the literature on the global cost-effectiveness of mitigation pathways 
provides insights regarding aggregate synergies and trade-offs 
between mitigation and sustainable development (e.g., Figure 18.5). 
Furthermore, linkages between mitigation and adaptation options 
have been shown, such as expected changes in energy demand due

to climate change interacting with energy system development and 
mitigation options, changes in future agricultural production practices 
to manage the risks of potential changes in weather patterns affecting land-based emissions and mitigation strategies, or mitigation strategies 
placing additional demands on resources and markets. This increases 
pressure on and costs for adaptation, or ecosystem restoration that 
provides carbon sequestration and natural and managed ecosystem 
resiliency benefits, but also could constrain mitigation and impact 
consumer welfare (WGIII AR6).
Nonlinearities are an important consideration in evaluating risk 
management combinations. Nonlinearities have been estimated in 
global and regional mitigation costs and potential economic damages 
from climate change (very high confidence) ((Riahi et al., 2022); (Clarke 
et al., 2014; Burke et al., 2015; Rose, 2017). Nonlinear mitigation costs

mean increasingly higher costs for each additional incremental reduction 
in emissions (or incremental reduction in global average temperature). 
Nonlinear increases in estimated economic climate damage means 
increasingly higher damages for each additional incremental increase 
in climate change (e.g., global average temperature). However, the 
evidence on whether damages increase at an increasing or decreasing 
rate is mixed (Chapter 16 CWGB: ECONOMIC). Nonlinearities are also 
suggested in estimated changes in key risks and adaptation costs 
(Chapters 2 to 16). However, to date, they have not been as explicitly 
characterised. These nonlinearities imply nonlinearities in climate risk 
management synergies and trade-offs with sustainable development. 
Not only do trade-offs vary by climate level, as do synergies, but they 
increase at an increasing rate and their relative importance can shift 
across climate levels (very high confidence). Some of this is evident in

results such as those shown in Figure 18.5 for mitigation (keeping in 
mind differences in sample sizes across temperature levels). Uncertainty 
about the degree of nonlinearity in mitigation, climate damages, key 
risks and adaptation costs creates uncertainties in the strength of 
the trade-offs and synergies, but also represents opportunities. For 
instance, additional mitigation options and more economically efficient 
policy designs have been shown to reduce mitigation costs and the 
nonlinearities in mitigation costs (very high confidence) (Riahi et al., 
2022). The same is true for adaptation options and adaptation costs.
Infeasibilities of mitigation and adaptation options (Sections 18.4.2.2.1, 
18.4.2.2.2), as well as global pathways (Riahi et al., 2022) , are also 
relevant to consideration of combinations of risk management options. 
Infeasibility of options implies higher costs and greater cost nonlinearity

due to fewer and/or more expensive options, while infeasibility of 
pathways bounds some of the uncertainty about the pathways relevant 
to decision making and planning.
18.2.5.3.1 Trade-offs and synergies in adaptation, mitigation and 
climate resilient development
Since AR5, a growing body of literature has emerged that frames 
adaptation processes as endogenous socioeconomic dynamics, 
exogenous driving forces and explicit decisions (Barnett et al., 2014; 
Maru et  al., 2014; Butler et  al., 2016; Kingsborough et  al., 2016;

18
2688Chapter 18 Climate Resilient Development Pathways
Werners et  al., 2018). Central to this framing is a shift away from 
viewing adaptation as discrete sets of options that are selected and 
implemented to manage risk, to thinking about adaptation as a social 
process that evolves over time, includes multiple decision points, and 
requires dynamic adjustments in response to new information about 
climate risk, socioeconomic conditions and the value of potential 
adaptation responses (very high confidence) (Haasnoot et al., 2013; 
Wise et al., 2016). This aligns adaptation with aspects of development 
thinking, including questions around the capacity and agency of 
different actors to effect change, the governance of adaptation, and 
the contingent nature of adaptation needs and effectiveness on the 
future evolution of society and climate change risk.
While ensuring development and adaptation produce synergies 
that allow for the achievement of sustainable development is

challenging, modelling exercises suggest that there are pathways 
where synergies among the SDGs are realised (very high confidence) 
(Roy et al., 2018; Van Vuuren et al., 2019) (Section 18.5), particularly 
if longer time horizons are used. These pathways require progress on 
multiple social, economic, technological, institutional and governance 
aspects of development, including building human capacity, 
managing consumption behaviour, decarbonisation of the global 
economy, improving food and water security, modernising cities and 
infrastructure, and innovations in science and technology (Van Vuuren 
et  al., 2019) (Section  18.3). In addition, Olsson et al, (Olsson et  al., 
2014) and Roy et al. (2018) emphasise the importance of integrating 
considerations for social justice and equity in the pursuit of sustainable 
development (Gupta and Pouw, 2017).
The significant overlaps and linkages between development and

adaptation practice and a lack of conceptual clarity about adaptation 
pose a conundrum for scholars (e.g., Bassett and Fogelman, 2013; 
Webber, 2016), who raise concerns that this potentially leads to trade-
offs or mislabelling (Few et  al., 2017). This framing of adaptation 
and development can result in competition between attainment 
of sustainable development and policies to reduce the impacts of 
climate change (Ribot, 2011). Such trade-offs are illustrated by (Moyer 
and Bohl, 2019) who use a baseline development trajectory based 
on current trends to project progress on SDGs by 2030. This work 
concluded that only marginal gains are likely to be achieved under 
that pathway over the next decade (Barnes et al., 2019).
Emerging evidence also suggests that many adaptation-labelled strategies 
may exacerbate existing poverty and vulnerability or introduce new 
inequalities, for example by affecting certain disadvantaged groups more

than others, even to the point of protecting the wealthy elite at the expense 
of the most vulnerable (Eriksen et al., 2019). Pelling et al. (2016) find that 
adaptation has been conceived and implemented in such a manner that 
most projects preserve rather than challenge the status quo. Specifically, 
the potential for knowledge and the goals of adaptation to be contested 
by different actors and stakeholders and the need to sustain progress over 
extended periods of time can constrain the ability to effectively implement 
actions that lead to sustainable development outcomes that are protected 
from the impacts of climate change while also delivering climate mitigation 
outcomes, that is, for CRD (Bosomworth et  al., 2017; Bloemen et  al., 
2019). This creates the possibility for specific adaptation actions to result 
in outcomes that undermine greenhouse gas mitigation and/or broader development goals (Fazey et al., 2016; Wise et al., 2016; Magnan et al.,

2020). For example, a study in Bangladesh revealed how local elites and 
donors used adaptation projects as a lever to push vulnerable populations 
away from their agrarian livelihoods and into uncertain urban wage labour 
(Paprocki, 2018). These types of outcomes are categorised as maladaptation, 
interventions that increase rather than decrease vulnerability, and/
or undermine or eradicate future opportunities for adaptation and 
development (Barnett and O’Neill, 2010; Juhola et al., 2015; Magnan et al., 
2016; Antwi-Agyei et al., 2017; Schipper, 2020). This inadvertent impact on 
equity appears to fundamentally contradict a benevolent understanding 
of transformative adaptation that also champions social justice (Patterson 
et  al., 2018), thus posing long-term maladaptation in opposition to 
transformative adaptation (Magnan et al., 2020).
Similarly, mitigation efforts, while reducing emissions, can also increase 
climate impacts vulnerability and undermine adaptation efforts.

The same can be said for some poverty alleviation and sustainable 
development efforts that increase vulnerability for specific segments of 
the population. For example, in Central America, an evaluation of 12 rural 
renewable energy projects (either forthe clean development mechanism, 
early warning systems or rural electrification goals) found that some 
mitigation and poverty alleviation projects increased vulnerability to 
families—by excluding them, not adhering to local safety and quality 
codes and standards, or significantly altering community power dynamics 
and contributing to conflict (Ley, 2017; Ley et al., 2020).
Synergies between adaptation, mitigation and sustainable development 
might be promoted by prioritising those CRD strategies most likely to 
generate synergies (very high confidence) (Roy et al., 2018; Karlsson 
et al., 2020). This could include focusing on poverty alleviation that 
improves adaptive capacity (e.g., Kaya and Chinsamy, 2016; Kuper

et al., 2017; Ley, 2017; Sánchez and Izzo, 2017; Stańczuk-Gałwiaczek 
et al., 2018; Ley et al., 2020); renewable energy systems that improve 
water management and preservation of river ecological integrity 
(e.g., Berga, 2016; Rasul and Sharma, 2016); or internalising positive 
externalities, such as subsidies for mitigation options thought to also 
improve water use efficiency (e.g., Roy et al., 2018). Similarly, trade-
offs might be managed by prioritising strategies such as disqualifying 
mitigation options thought to have negative social implications 
(Section 18.2.5.3.1), internalising externalities, such as placing a fee or 
constraint on a negative externality or related activity (Dubash et al., 
2022) (Bistline and Rose, 2018), or using complementary policies, 
such as transfer payments to offset negative mitigation, adaptation or 
sustainable development strategy implications (very high confidence) 
(e.g., McCollum et al., 2018b). Roy et al. (2018) discusses the latter,

noting, for instance, the possibility of complementary sustainable 
development payments to avoid global energy access, food security 
and clean water trade-offs (Box 4.7).
SR1.5 and AR6 assessments of system transitions also find opportunities 
for synergies and managing trade-offs (Section  18.3; Cross-Chapter 
Box FEASIB). Within each system, mitigation and adaptation options 
are assessed for their specific benefits and the impacts they can have 
on one another, as well as with sustainable development. For example, 
within energy system transitions, the three adaptation options (power 
infrastructure resilience, reliability of power systems, efficient water 
use management) have strong synergies with mitigation. While not

18
2689Climate Resilient Development Pathways  Chapter 18
all mitigation options have strong synergies, the trade-offs can be 
managed when adaptation and SDGs are also considered. Under 
land and other ecosystems system transitions, the main trade-off is 
the competition for land use between potential alternative uses, for 
example, sustainable agriculture, afforestation/reforestation, purpose-
grown biomass for energy. On the other hand, assessment of urban 
and infrastructure system transitions finds mainly synergies between 
mitigation and adaptation options with trade-offs that are considered 
manageable, and there is growing evidence of rural landscape 
infrastructure benefits to adaptation.
Overall, this literature is relatively new and still developing. It highlights 
the importance of societal priorities and policy design for realizing 
synergies. However, the literature is not well developed in terms of 
how to optimize mitigation, adaptation and sustainable development

interventions to achieve multiple priorities.
18.2.5.3.2 Risk management combinations with lower to higher 
Given the global climate system is committed to additional future 
warming, different portfolios of adaptation, mitigation, and sustainable 
development interventions are relevant for climate risk management. 
The different strands of literature discussed above can be integrated to 
help inform thinking about combinations of approaches to climate risk 
management. Globally, low climate change projections, versus higher 
climate change projections, imply greater mitigation, lower climate 
risks and less adaptation. This implies greater mitigation trade-offs 
in terms of overall economic development, food crop prices, energy 
prices and overall household consumption, but lower climate risk, with 
sustainable development synergies such as human health and lower 
adaptation trade-offs, and an uneven distribution of effects (very high 
confidence) (Roy et al., 2018).

Sustainable development considerations could be used to prioritise 
mitigation options, but as noted earlier, there are trade-offs, with a 
potentially significant impact on the economic cost of mitigation, as 
well as a potential trade-off in terms of the climate outcomes that are 
still viable (Riahi et al., 2022). For instance, all of the 1.5°C scenarios 
used in IPCC (2018a) deploy carbon dioxide removal technologies 
(Rogelj et al., 2018). Without these technologies, most models cannot 
generate pathways that limit warming to 1.5°C, and those that are 
able to adopt strong assumptions about global policy development and 
socioeconomic changes. Sustainable development might also affect 
the design of policies by prioritising specific sustainable development 
objectives. However, there are trade-offs here as well, with costs and 
the distribution of costs varying with alternative policy designs. For 
instance, prioritising air quality has climate co-benefits but does not

ensure the lowest cost climate strategy (Arneth et al., 2009; Kandlikar 
et al., 2009). Similarly, prioritising land protection has a variety of co-
benefits but could increase food prices significantly, as well as the 
overall cost of climate mitigation (IPCC, 2019b). In this context, with 
lower climate risk and adaptation levels and larger mitigation effort, 
managing mitigation trade-offs could be a sustainable development 
priority. Furthermore, sustainable development could also be tailored 
to facilitate adaptation and manage mitigation costs.Globally, high climate change projections imply lower mitigation 
effort, higher climate risks and greater adaptation. This implies lower 
mitigation trade-offs, but greater climate risk with greater demand of 
adaptation and potential for trade-offs in terms of competing sustainable 
development priorities. Sustainable development considerations could 
affect adaptation options. For instance, constraining options such as

relocation or facilitating adaptation capacity and community resilience. 
Sustainable development might also be tailored to affect the climate 
outcome by shaping the development of emissions. In this context, 
with greater climate risk and adaptation levels and less mitigation 
effort, facilitating adaptation addressing adaptation costs and trade-
offs could be a sustainable development priority.
Locally, there are many qualitative similarities to the global perspective 
in thinking about risk management combinations across lower versus 
higher levels of warming. However, there is one very important difference. 
Local decision makers are confronted with uncertainty about what others 
will do beyond their local jurisdiction. With future climate a function of 
the sum of global decisions, sustainable development planning needs 
to consider the possibility of more and less emissions reduction action 
globally and the potential associated climates. This implies the need for

sustainable development to manage for the possibility of higher levels 
of warming by further facilitating adaptation and managing adaptation 
trade-offs. Prioritising sustainable development locally is also supported 
by the insight that the impacts on poverty depend at least as much or 
more on development than on the level of climate change (very high 
confidence) (Wiebe et al., 2015; Hallegatte and Rozenberg, 2017).
With surpassing 1.5°C a distinct possibility, considering higher levels 
of warming is a necessity. CRD could be pursued with additional 
adaptation, recognizing increasing challenges for adaptation and 
sustainable development with higher warming, just as there are 
increasing challenges for mitigation and sustainable development with 
limiting warming to lower levels. There are many possible pathways for 
pursuing climate resilient development, though our understanding of 
the possibilities with different levels of warming is currently limited

(e.g., David Tàbara et al., 2018; O’Brien, 2018). The current literature 
suggests that different mixes of adaptation and mitigation strategies, 
and sustainable development and trade-off management priorities, 
measures and reallocations (Section  18.5.3.1), will be appropriate 
for different expected climates and locations (Section 18.1.2); while 
trade-offs between climates will be dictated by relative nonlinearities, 
feasibilities, shifts in priorities, and trade-off and reallocation options 
across future climates.
Finally, it is important to note that there is currently limited information 
available regarding the following: (1) local implications of 1.5°C versus 
warmer futures with respect to local climate outcomes, avoided impacts 
and sustainable development implications and interactions, given that 
applying global conclusions to local, national and regional settings 
can be misleading; (2) local context-specific synergies and trade-offs

with respect to adaptation, mitigation and sustainable development 
for 1.5°C futures; and (3) standard indicators for monitoring factors 
related to CRD (Roy et al., 2018).

18
2690Chapter 18 Climate Resilient Development Pathways
18.3 Transitions to Climate Resilient 
A key finding emerging from the IPCC SR1.5 is the critical role that 
system transitions play in enabling mitigation pathways consistent 
with a 1.5°C or less world (IPCC, 2018a; IPCC, 2019b).Such transitions 
are similarly critical for the broader pursuit of CRD, and the various AR6 
special reports as well as subsequent literature provide new evidence 
of why such transitions are needed for CRD, as well as both the 
opportunities for accelerating system transitions and their limitations 
for delivering on the goals of CRD.
18.3.1 System Transitions as a Foundation for Climate 
Resilient Development
In the AR6, system transitions are defined as ‘the process of changing 
(the system in focus) from one state or condition to another in a 
given period of time’ (IPCC, 2018a; IPCC, 2019b). In the climate 
change solution space, system transitions represent an important

mechanism for linking and enabling mitigation, adaptation and 
sustainable development options and actions (very high confidence). 
SR1.5C identified the need for rapid and far-reaching transitions in 
four systems—energy, land and terrestrial ecosystems, urban and 
infrastructure, and industrial systems (IPCC, 2018b; IPCC, 2018a) 
(Sections 1.5.1 and 18.1). The SRCCL expanded on this with a focus 
on terrestrial systems, while SROCC added additional evidence from 
ocean and cryosphere systems. This section assesses the four system 
transitions discussed in the SR1.5C assessment in the context of CRD, 
while also extending the assessment to consider societal transitions as 
a cross-cutting, fifth transition important for CRD. Literature to support 
this assessment is also drawn from AR6 regional and sectoral chapters, 
which is synthesised later in this chapter (Section 18.5).
As discussed in Box 18.3 (Hölscher et al., 2018), system transitions are

linked to system transformation, which is defined as ‘a change in the 
fundamental attributes of a system including altered goals or values’ 
(Figure 18.1) (IPCC, 2018a). In a systems context, transitions focus on 
‘complex adaptive systems; social, institutional and technological change 
in societal sub-systems’, while transformations are ‘large scale societal 
change processes … involving social-ecological interactions’  (IPCC, 
2018a) (Box 18.1). Although system transitions are often identified in the 
literature as being necessary processes for large-scale transformations 
(Roggema et al., 2012; Hölscher et al., 2018), thereby making them a core 
enabler of CRD, they are not necessarily transformative in themselves.
18.3.1.1 Energy Systems
Recent observed changes in global energy systems include continued 
growth in energy demand, led by increased demand for electricity 
by industry and buildings (very high confidence)(Dhakal et  al.,

2022) . Growth in energy demand has also been driven by increased 
demand for industrial products, materials, building energy services, 
floor space and all modes of transportation. This growth in demand, 
however, has been moderated by improvements in energy efficiency 
in industry, buildings and transportation sectors (very high confidence) 
(Dhakal et al., 2022). There is also a trend of moving away from coal towards cleaner fuels, owing to lower natural gas prices and lower 
cost renewable technologies, and structural changes away from more 
energy-intensive industry.
Features of sustainable development, such as enhanced energy access, 
energy security, reductions in air pollution and economic growth, 
continue to be the dominant influence on the evolution of energy systems 
and decision making regarding energy investments and portfolios 
(very high confidence) (Clarke et  al., 2022) . To date, climate policy 
has been comparatively less influential in driving energy transitions

globally. Yet there are examples at the local, regional and national 
level of policy incentivising rapid changes in energy systems (very 
high confidence) (Clarke et al., 2022) . Many sustainable development 
priorities have co-benefits in terms of climate mitigation, such as air 
pollution and conservation policies reducing short-lived climate forcers 
and sequestering carbon respectively, as well adaptation benefits, 
such as improved energy access and environmental quality enhancing 
adaptive capacity (very high confidence) (Clarke et  al., 2022) (de 
Coninck et al., 2018). Alternatively, sustainable development projects 
can have negative climate implications with, for instance, hydroelectric 
projects shut down by droughts or floods resulting in greater use of 
bunker and fuel oil, as well as natural gas.
In addition to sustainable development priorities driving change in 
energy systems, observed energy system trends have implications for

sustainable development (e.g., IEA et al., 2019). Observed changes in 
energy system size, rate of growth, composition and operations impact 
energy access, equity, environmental quality and well-being, with both 
synergies and trade-offs, including recent improvements in global 
access to affordable, reliable and modern energy services. For instance, 
in some countries, such as the USA, there has been a significant shift 
away from coal as a fuel source for electricity generation in favour 
of natural gas. More recently, however, renewables have emerged 
as the dominant form of new electricity generation (Gielen et  al., 
2019). Similarly, for energy access in developing countries, renewable 
energy or hybrid distributed generation systems are increasingly 
being prioritised because of challenges associated with access, costs 
and environmental impacts from traditional fossil fuel-based energy 
technologies (Mulugetta et al., 2019).

Energy systems have been a historical driver of climate change, but 
are also adversely affected by climate change impacts, including short-
term shocks and stressors from extreme weather as well as long-term 
shifts in climatic conditions (very high confidence). The potential for 
such factors is often incorporated into local system designs, operations 
and response strategies. There have been changes in observed weather 
and extreme event hazards for the energy system, but to date, many 
are not attributable solely to anthropogenic climate change (USGCRP , 
2017; IPCC, 2021a). Nevertheless, with observed extremes shifting 
outside of what has been observed historically, existing design criteria 
and operations may not be optimal for future climate conditions and 
contingencies (Chapters 2 to 16). Overall, there is limited historical 
evidence on the efficacy of adaptation responses in reducing 
vulnerability of energy systems (high agreement, limited evidence).

However, sustainable development trends, such as improving incomes, 
reducing poverty, and improving health and education have reduced 
vulnerability (Chapter 16), and improvements in system resiliency

18
2691Climate Resilient Development Pathways  Chapter 18
to extreme weather events and more efficient water management 
have occurred that have synergies with adaptation and sustainable 
development in general.
Available literature indicates that greenhouse gas emissions reductions 
have been achieved in response to climate actions including financial 
incentives to promote renewable energy, carbon taxes and emissions 
trading, removal of fossil fuel subsidies, and promotion of energy 
efficiency standards (very high confidence) (Clarke et  al., 2022). 
Such policies tend to lead to a lower carbon intensity of GDP , due 
to structural changes in the use of energy and the adoption of new 
energy technologies. However, other drivers of change are also present 
and thus ongoing energy transitions and their future evolution are 
a response to both climatic and non-climatic considerations, with 
broader sustainable development priorities being a significant driver

of change {Clarke, 2022 #4316.
18.3.1.2 Urban and Infrastructure Systems
Urban areas and their associated infrastructure are critical targets for 
CRD processes. This is a function of urban areas being the dominant 
settlement pattern, with over 55% of the global population living in 
cities (World Bank, 2021). As a consequence, urban areas are also the 
focal point for energy use, land use change and consumption of natural 
resources, thereby making them responsible for an estimated 70% of 
global CO 2 emissions (Johansson et al., 2012; Ribeiro et al., 2019). The 
trend towards increasing urbanisation is anticipated to create both 
challenges and opportunities for sustainable development, as well as 
climate action (Güneralp et al., 2017; Li et al., 2019a).
The built environment is increasingly exposed to climate stresses and 
more frequent co-occurrences of climate shocks than in the past. 
This has the potential to increase rates of building and infrastructure

degradation and increase damage from extreme weather events. 
The existing adaptation gaps and everyday risks within many cities, 
particularly those of the Global South, combined with escalating 
risk from climate change, makes rapid progress in enhancing urban 
resilience a high priority for CRD (Pelling et al., 2018; Davidson et al., 
2019; Lenzholzer et al., 2020). Strategic investments in disaster risk 
reduction, including climate-resilient green infrastructure, updated 
building codes and land use planning can provide significant long-
term cost savings and social benefits. Moreover, evaluating the relative 
merits of ‘fail safe’ versus ‘safe to fail’ approaches to infrastructure 
planning can help to identify more design principles that are more 
robust to the uncertainties of climate change and urbanisation (Kim 
et al., 2017a; Kim et al., 2019).
Much of the literature on urban resilience and sustainability focuses

on addressing discrete challenges for urban infrastructure subsystems. 
Climate change has the potential to enhance stress on lifeline 
infrastructure services such as the provision of electricity, water and 
wastewater, communications and transportation—subsystems which 
are often underdeveloped in many regions of the world (Arku and 
Marais, 2021; Sitas et al., 2021). For example, a warming and more 
variable climate can increase stress on electricity grids by reducing 
transmission efficiency, increasing cooling demand requirements, and 
by increasing exposure to climate shocks such as heatwaves, floods and storms (Bartos and Chester, 2015; Auffhammer et al., 2017; Perera 
et al., 2020). Accordingly, a significant focus on the energy transition 
is on achieving the dual goals of reducing the carbon footprint of 
energy while also increasing resilience of energy supply to current and 
future threats. For example, renewable energy generation and storage

technologies that are modular and distributed and provide enhanced 
resilience to shocks and stresses from climate change (Venema and 
Temmer, 2017a).
Similarly, building and maintaining urban water systems that are 
resilient to climate shocks requires significant changes in water demand, 
infrastructure and management. Enhancing redundancy in water 
supply and the flexibility to shift between surface and groundwater 
options aids adaptation. Decentralised water supply and sanitation 
options are now feasible and can provide greater resilience than 
most centralised systems (Parry, 2017), provided they have adequate 
supply (Leigh and Lee, 2019; Rabaey et al., 2020). Water conservation 
and green infrastructure options for stormwater management are 
proven approaches for reducing climate risks (Venema and Temmer, 
2017b), with adaptation and mitigation co-benefits. Water demand 
management and rainwater harvesting contribute to climate change

mitigation and increase adaptive capacity by increasing resilience 
to climate change impacts such as drought and flooding (Paton 
et  al., 2014; Berry et  al., 2015). In addition, they can contribute to 
restoring urban ecosystems that offer multiple ecosystem services 
to citizens (Berry et  al., 2015) {Lwasa, 2022 #4317}. The context-
appropriate development of green spaces, protecting ecosystem 
services and developing nature-based solutions, can increase the set 
of available urban adaptation options (IPCC, 2018b), while creating 
opportunities for more complex and dynamic approaches to urban 
water management (Franco-Torres et  al., 2020). For example, the 
Netherlands’ ‘Room for the River’ policy focuses on not only achieving 
higher flood resilience, but also improving the quality of riverine areas 
for human and ecological well-being (Busscher et al., 2019).
An overarching focus of urban sustainability is the reversal of long-

standing trends of ecosystem fragmentation and degradation that 
have resulted in growing separation between human and natural 
systems within urban environments (IPBES, 2019) (Lwasa et al., 2022). 
Urban ecosystems and the integration of nature-based solutions and 
green infrastructure into urban areas can yield benefits that facilitate 
achievement of the SDGs. There has been growing recognition of urban 
ecosystems as social, cultural and economic assets that can support 
economic development while also enhancing resilience to extreme 
weather events and improving air and water quality (Shaneyfelt 
et al., 2017; Matos et al., 2019). Investing in urban ecosystems and 
green infrastructure can provide lower-cost solutions to multiple 
urban development challenges when compared with traditional 
infrastructure systems (Terton, 2017). Relatedly, agriculture, while 
largely a rural system, is increasingly expanding within urban areas.

Urban agriculture enables citizens to fulfil some of their food needs, 
improving urban resilience to food shortages, enhancing biodiversity 
and increasing coping capacity during disasters (Demuzere et  al., 
2014; Clucas et al., 2018) (Lwasa et al., 2022). Strengthening urban 
agroecosystems therefore increases resilience to supply shocks from 
climate change impacts and can contribute to community cohesion 
(Temmer, 2017a).

18
2692Chapter 18 Climate Resilient Development Pathways
Overall, the discourse in the literature regarding the future of cities 
emphasises the importance of viewing cities as more than just their 
physical infrastructure that can be made more resilient through 
engineering solutions (Davidson et  al., 2019). Rather, urban areas 
are increasingly conceptualised as complex socio-ecological or socio-
technical systems (very high confidence) (Patorniti et al., 2017; Patorniti 
et al., 2018; Visvizi et al., 2018; Savaget et al., 2019). Such frameworks 
integrate physical, cyber, social and ecological elements of cities in 
pursuit of resilience and sustainability transitions, and they recognise 
the role of governance and engagement processes as being central 
to system change (Temmer, 2017b). Nevertheless, some authors have 
cautioned that urban transitions will be associated with synergies as 
well as trade-offs with respect to sustainable development (very high

confidence) (Maes et al., 2019; Sharifi, 2020).
18.3.1.3 Land, Oceans and Ecosystems
Land, oceans and terrestrial ecosystems are in transition globally, with 
anthropogenic factors including climate change being a major driving 
force (very high confidence) (IPBES, 2019) (Box 6). Seventy-five percent 
of the land surface has been significantly altered, 66% of the ocean 
area is experiencing increasing cumulative impacts and over 85% of 
wetland areas have been lost (IPBES, 2019). Since 1970, only four out 
of eighteen recognised ecosystem services assessed have improved 
in their functioning: agricultural production, fish harvest, bioenergy 
production and material harvests. The other 14 ecosystem services 
have declined (IPBES, 2019), raising concerns about the capacity of 
ecosystems and their services to support sustainable and CRD.
Given the pressures on land, oceans and ecosystems, enhancing 
resilience to climate change and other pressures of human development

is a core priority of transition in these systems. Yet there are a few 
recorded initiatives that provide evidence of successful improvement 
in ecosystem resilience (high agreement, limited evidence). Similarly, 
although there is significant evidence that a broad range of adaptation 
initiatives have been pursued across global regions and sectors, 
including a rapid expansion of nature- or ecosystem-based solutions 
(Mainali et al., 2020), there is limited evidence of how these planned 
climate adaptation efforts have contributed to enhanced ecosystem 
resilience. Additional research is necessary to evaluate these efforts in 
terms of their performance and also to identify mechanisms for scaling 
them up in different contexts. As an example, Paik et al. (Paik et al., 
2020) record the increased diffusion of salt tolerant rice varieties in the 
Mekong River Delta, which is at risk of sea level rise and an associated

saline intrusion. This is a low-cost adaption to saline ingress, that 
increases food productivity and reduces the risk of outmigration for 
this vulnerable agricultural region.
Evidence of the interactions between ecosystems and resilience come 
from a range of sources including both regional and sectoral examples 
(Box 18.2; Tables 18.7–18.8. For example, regional examples suggest 
that the use of land to produce biofuels could increase the resilience 
of production systems and address mitigation needs (Box  2.2). 
Nevertheless, the potential of bioenergy with carbon capture and 
storage (BECCS) to induce maladaptation needs deeper analysis 
(Hoegh-Guldberg et al., 2019). Climate Smart Forestry (CSF) in Europe 
provides an example of the use of sustainable forest management to unlock the EU’s forest sector potential (Nabuurs et al., 2017). This 
is in response to diverse climate impacts ranging from pressure on 
spruce stocks in Norway and the Baltics, on regional biodiversity in

the Mediterranean region, and the opportunity to use afforestation 
and reforestation to store carbon in forests (Nabuurs et al., 2019). CSF 
considers the full value chain from forest to wood products and energy 
and uses a wide range of measures to provide positive incentives 
to firmly integrate climate objectives into the forestry sector. CSF 
has three main objectives; (i) reducing and/or removing greenhouse 
gas emissions; (ii) adapting and building forest resilience to climate 
change; and (iii) sustainably increasing forest productivity and incomes 
(Verkerk et al., 2020).
Other solutions focus on specific subsectors. Mutually supportive 
climate and land policies have the potential to save resources, amplify 
social resilience, support ecological restoration, and foster engagement 
and collaboration between multiple stakeholders (IPCC, 2019 f, C.1). 
Land-based solutions can combat desertification in specific contexts:

water harvesting and micro-irrigation, restoring degraded lands using 
drought-resilient ecologically appropriate plants, agroforestry and 
other agroecological and ecosystem-based adaptation practices (IPCC, 
2019 f, B.4.1). Reducing dust, sandstorms and sand dune movement 
can lessen the negative effects of wind erosion and improve air quality 
and health. Depending on water availability and soil conditions, 
afforestation, tree planting and ecosystem restoration programmes 
using native and other climate-resilient tree species with low water 
needs, can reduce sand storms, avert wind erosion and contribute to 
carbon sinks, while improving micro-climates, soil nutrients and water 
retention (IPCC, 2019 f, B.4.2).
Coastal blue carbon ecosystems, such as mangroves, salt marshes 
and seagrasses, can help reduce the risks and impacts of climate 
change, with multiple co-benefits. Over 150 countries contain at least 
one of these coastal blue carbon ecosystems and over 70 contain

all three. Successful implementation of measures of carbon storage 
in coastal ecosystems could assist several countries in achieving a 
balance between emissions and removal of greenhouse gases. Carbon 
storage in marine habitats can be up to 1,000 tC ha–1, higher than 
most terrestrial ecosystems. Conservation of these habitats would 
also sustain a wide range of ecosystem services, assist with climate 
adaptation by improving critical habitats for biodiversity, enhance local 
fishery production and protect coastal communities from sea level rise 
(SLR) and storm events (IPCC, 2019b). Ecosystem-based adaptation is 
a cost-effective coastal protection tool that can have many co-benefits, 
including supporting livelihoods, contributing to carbon sequestration 
and the provision of a range of other valuable ecosystem services 
(IPCC, 2019b).
Diversification of food systems is another component of land, ocean 
and ecosystem transitions that are consistent with CRD. Balanced

diets, featuring plant-based foods such as those based on coarse 
grains, legumes, fruits and vegetables, nuts and seeds, and animal-
sourced food produced in a resilient, sustainable and low-greenhouse 
gas (GHG) emission manner, are major opportunities for adaptation 
and mitigation and improving human health. By 2050, dietary changes 
could free several million km2 of land and provide a mitigation potential 
of 0.7–8.0 Gt CO 2-eq yr-1, relative to Business-As-Usual projections.

18
2693Climate Resilient Development Pathways  Chapter 18
For coastal systems, many frameworks for climate resilience and 
adaptation have been developed since the AR5 (Hoegh-Guldberg 
et al., 2014; Settele et al., 2014) with substantial variations in approach 
between and within countries and across development status. Few 
studies have assessed the success of implementing these frameworks 
owing to the time-lag between implementation, monitoring, evaluation 
and reporting (IPCC, 2019g). As an example, the Nature-Based Climate 
Solutions for Oceans initiative has the potential to restore, protect 
and manage coastal and marine ecosystems, adapt to climate change, 
improve coastal resilience, and enhance their ability to sequester and 
store carbon (Hoegh-Guldberg et al., 2019).
Polar regions will be profoundly different in the future. The degree 
and nature of that difference will depend strongly on the rate and 
magnitude of global climate change, which will influence adaptation

responses regionally and worldwide. Future climate-induced changes 
in the polar oceans, sea ice, snow and permafrost will drive habitat and 
biome shifts, with associated changes in the ranges and abundance 
of ecologically important species (IPCC, 2019g). Innovative tools and 
practices in polar resource management and planning show strong 
potential in improving society’s capacity to respond to climate change. 
Networks of protected areas, participatory scenario analysis, decision 
support systems and community-based ecological monitoring that 
draws on local and Indigenous knowledge and self-assessments of 
community resilience contribute to strategic plans for sustaining 
biodiversity and limit risk to human livelihoods and well-being. 
Experimenting, assessing and continually refining practices while 
strengthening links with decision making has the potential to ready 
society for the expected and unexpected impacts of climate change 
(IPCC, 2019g).
18.3.1.4 Industrial Systems

Industrial emissions have been growing faster since 2000 compared 
with emissions in any other sector, driven by increased extraction and 
production of basic materials (Crippa et al., 2019; IEA, 2019) (very high 
confidence). About one-third of the total emissions are contributed 
by the industry sector, if indirect emissions from energy use are 
considered (Crippa et al., 2019). The COVID-19 pandemic has caused 
a significant decrease in demand for fuels, oil, coal, gas and nuclear 
energy (IEA, 2020). However, there is concern that the rebound in the 
crisis will reverse this trend (IEA, 2020). Accordingly, the literature 
suggests a combined set of measures is beneficial for facilitation a 
transition of industrial systems in support of CRD. This includes (i) 
dematerialisation and decarbonisation of industrial systems, (ii) 
establishment of supportive governance, policies and regulations, and 
(iii) implementation of enabling corporate strategies.

Decarbonisation and dematerialisation strategies have been proposed 
as key drivers for the transition of industrial systems (Fischedick 
et  al., 2014; Worrell et  al., 2016). The former involves limiting 
carbon emissions from industrial processes (IEA, 2017; Hildingsson 
et al., 2019), while the latter involves improving material efficiency, 
developing circular economies, raw material demand management, 
environmentally friendly product and process innovations, and 
environmentally friendly supply chain management (Worrell et  al., 
2016; Petrides et al., 2018).Recent modelling suggests that stocks of manufactured capital, including 
buildings, infrastructure, machinery and equipment, stabilise as countries 
develop and decouple from GDP (high agreement, medium evidence). 
For instance, Bleischwitz et  al. (2018) confirmed the occurrence of a 
saturation effect for materials in four energy-intensive sectors (steel,

cement, aluminum and copper) in five industrialised countries (Germany, 
Japan, the UK, the USA and China). High growth in the supply of materials 
may still drive global demand for new products in the coming years for 
developing countries that are still far from saturation levels. Therefore, 
accelerating industrial transitions to drive the decoupling of industrial 
emissions from economic growth and facilitate broader transformation 
in industrial systems can be one component of CRD.
Continued transitions in the industrial sector will be contingent 
on technological innovation. Although technologies exist to drive 
emissions in industrial sectors to very low or zero emissions, they 
require 5–15  years of innovation, commercialisation and intensive 
policies to ensure uptake (Åhman et  al., 2017) (high agreement, 
medium evidence). For instance, several options exist to reduce GHG 
emissions related to steel production process including increasing the

share of the secondary route (Pauliuk et  al., 2013), hydrogen-based 
direct reduced iron (Vogl et  al., 2018), aqueous electrolysis rout 
(Cavaliere, 2019) and plasma process (Quader et al., 2016).
Industrial transitions are also contingent upon consumer behaviour 
in terms of preferences for, and rates of, consumption of industrial 
products. Sustainable consumption can play an important role in 
sustainable production (Allwood et al., 2013; Allwood et al., 2019). This 
suggests feedbacks between industrial production and consumption 
in driving industrial transitions. For example, sustainable consumption 
could be triggered and/or enabled through sustainable production 
processes that provide more sustainable options to consumers as 
well as public or private promotional campaigns that promote those 
options. Meanwhile, demand from consumers for more sustainable 
options could help to drive the expansion of markets and innovation

among industrial producers to meet that demand. However, some 
have argued that such promotional campaigns that target consumers 
do little to incentivize sustainable development and climate action 
(Farrell, 2015; Grydehøj and Kelman, 2017).
18.3.1.5 Societal Systems
This chapter contributes a fifth system transition in addition to the 
four which have already been introduced by SR1.5: the societal 
systems transition. While society and people also feature in the 
other systems transitions, the purpose of defining a fifth transition 
is to explicitly highlight the challenges associated with changes in 
behaviour, attitudes, values and consciousness required to achieve 
CRD. One caveat of considering transitions in societal systems is the 
limit to which the nature of change is known: transitions accomplish 
reconfigurations towards a relatively known destination. Historical and 
current differences between and within nations translate to a multitude

of equally valid but diverse priorities for development, for example the 
understanding of development towards progress as linear has been 
challenged as being a Western concept by scholars of colonialisation 
(Sultana et al., 2019). Thus, societal transitions are understood as being 
intrinsically diverse for the purpose of achieving CRD.

18
2694Chapter 18 Climate Resilient Development Pathways
Box 18.5 | The Role of Ecosystems in Climate Resilient Development
Ecosystems and their services closely relate to climate resilient development (CRD). Climate change has impacted ecosystems across a 
range of scales, and those impacts have been exacerbated by other ecological impacts associated with human activities. Ecosystem-based 
adaptation strategies have been developed and are crucial to CRD. However, knowledge and evidence still missing, and cultural services—
in contrast to provision and regulation services as main benefits and supporting services as co-benefits—are less well addressed in the 
Ecosystems Play a Key Role in CRD
A key element of CRD is ensuring that actions taken to mitigate climate change do not compromise adaptation, biodiversity and 
human needs. Maintaining ecosystem health, linked to planetary health, is an integral part of the goals of CRD. The 2005 Millennium

Ecosystem Assessment (MEA) defined ecosystem services as ‘the benefits people obtain from ecosystems’, and categorised the services 
in to provisioning, regulating, supporting and cultural services (Millennium Ecosystem Assessment, 2005; IPBES, 2019). The 2019 
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) broadened the definition to ‘the contributions, 
both positive and negative, of living nature to the quality of life for people’, and developed a classification of 18 categories (IPBES, 2019).
Table Box 18.5.1 demonstrates how ecosystem services connect to sustainable development goals (SDGs) and CRD. MEA’s provisioning 
service generally connects to the IPBES’ material services, mostly contributing to the SDG cluster associated with nature’s contribution 
to people (NCP) (Millennium Ecosystem Assessment, 2005; IPBES, 2019) and to ‘Development’ in CRD. MEA’s regulating and supporting

services connect to IPBES’ non-material services, contributing to SDG clusters of Nature and Driver of change in nature and NCP and to 
‘Resilience’ in CRD. MEA’s cultural services connect to IPBES’ non-material services, contributing to SDG clusters of good quality of lift 
(GQL) and to Enabling conditions for CRD.
Table Box 18.5.1 |  Ecosystem services (based on the Millennium Ecosystem Assessment [MEA] and the Intergovernmental Science-Policy Platform on Biodiversity and 
Ecosystem Services [IPBES] classifications) and their connections to sustainable development goals (SDGs) and climate resilient development (CRD) (Millennium Ecosystem 
Assessment, 2005; IPBES, 2019).
SDGs CRD
MEA IPBES
Provisioning services11 Energy
12 Food and feed
13 Materials and assistance
14 Medicinal, biochemical and genetic resources1 No poverty
2 Zero hunger
3 Good health and well-being
11 Sustainable cities communities
7 Affordable clean energy
8 Decent work and economic growth

9 Industry, innovation and infrastructure
12 Responsible consumption and productionDevelopment
Regulating services3 Regulation of air quality
4 Regulation of climate
5 Regulation of ocean acidification
6 Regulation of freshwater quantity, location and timing
7 Regulation of freshwater and coastal water quality
9 Regulation of hazards and extreme events
10 Regulation of organisms detrimental to humans6 Clean water and sanitation
13 Climate action
Climate adaptation 
Supporting services1 Habitat creation and maintenance
2 Pollination and dispersal of seeds
8 Formation, protection and decontamination of soils and sediments
18 Maintenance of options14 Life below water
15 Life on land
Cultural services15 Learning and inspiration
16 Physical and psychological experiences
17 Supporting identities4 Quality education
5 Gender equality
10 Reduce inequality
16 Peace, justice and strong institutions
17 Partnerships for the goalsEnabling conditions

18
2695Climate Resilient Development Pathways  Chapter 18
Climate Change Impacts on Ecosystems and their Services
Climate change connects to ecosystem services through two links: climate change and its influence on ecosystems as well as its influence 
on services (Section 2.2). The key climatic drivers are changes in temperature, precipitation and extreme events, which are unprecedented 
over millennia and highly variable by regions (Sections 2.3, 3.2; Cross-Chapter Box EXTREMES in Chapter 2). These climatic drivers 
influence physical and chemical conditions of the environment and worsen the impacts of non-climate anthropogenic drivers including 
eutrophication, hypoxia and sedimentation (Section 3.4). Such changes have led to changes in terrestrial, freshwater, oceanic and coastal 
ecosystems at all different levels, from species shifts and extinctions, to biome migration, and to ecosystem structure and processes

changes (Sections 2.4, 2.5, 3.4, Cross-Chapter Box MOVING PLATE in Chapter 5). Changes in ecosystems leads to changes in ecosystem 
services including food and limber prevision, air and water quality regulation, biodiversity and habitat conservation, and cultural and 
mental support (Sections 2.4, 3.5). Table Box 18.5.2 presents examples of climate change’s impact on ecosystems and their services from 
other chapters in the WGII report. The degradation of ecosystem services is felt disproportionately by people who are already vulnerable 
because of historical and systemic injustices, including women and children in low-income households, Indigenous or other minority 
groups, small-scale producers and fishing communities, and low-income countries (Sections 3.5, 4.3, 5.13).
Table Box 18.5.2 |  Examples of key risks to ecosystems from climate change and their connections to ecosystem services (ES) in the WGII report and cross-chapter

papers (CCPs). (See Table 1 for the description of the categories of ES)
Climate factors Key riskES
P R S C
Terrestrial and freshwater ecosystems (Chapters 2, 4, 5; CCP 1; CCP 7; CCP 3; CCP 5)
 –Increase in average and extreme temperatures
 –Changes in precipitation amount and timing
 –Increase in aridity
 –Increase in frequency and severity of drought
 –Increased atmospheric CO 2Species extinction and range shifts X X X
Ecosystem structure and process change X X
Ecosystem carbon loss X X
Wildfire X X
Water cycle and scarcity X X
Ocean and coastal (Chapter 3; CCP 1; CCP 6)
 –Ocean warming
 –Marine heatwaves
 –Ocean acidification
 –Loss of oxygen
 –Sea level rise
 –Increased atmospheric CO 2
 –Extreme eventsSpecies extinction and range shifts X X X
Habitat loss X X
Ocean carbon sink less effective X
Erosion and land loss X X
Food, fibre and other ecosystem products (Chapter 5)
 –Global warming
 –Water stress
 –Extreme events

–Ocean acidification
 –Salt intrusionSpecies distribution X
Timing of key biological events change X
Corp productivity and quality decrease X
Diseases and insect X
Adaptation Practices and Enabling Conditions for CRD
Ecosystem protection and restoration, ecosystem-based adaptation (EBA), and nature-based solutions (NBS) can lower climate risk 
to people and achieve multiple benefits including food and material provision, climate mitigation and social benefits (Sections 2.6, 
3.6, 4.6, 5.13, 6.3, 8.6). Table Box 18.5.3 presents some examples of ecosystem adaptation practices reported in WGII sectoral and 
regional chapters and CCPs, as well as their co-benefits, potential for maladaptation and enabling conditions. Many of the strategies 
focus on integrated systems (managing for multiple objectives and trade-offs) as well as the fair use of resources. However, there is

limited evidence of the extent to which adaptation is taking place and virtually no evaluation of the effectiveness of adaptation in the 
scientific literature (Sections 2.6, 3.5). Enabling conditions for the successful implementation ecosystem-based practice include regional 
and community-based based approaches, multi-stakeholder and multi-level governance approaches, Integration of local knowledge and 
Indigenous knowledge, finance and social equity (Sections 2.6, 3.6).Box 18.5 (continued)

18
2696Chapter 18 Climate Resilient Development Pathways
Table Box 18.5.3 |  Examples of adaptation practices and their connections to ecosystem services (ES) and climate resilient development pathways (CRDP) in the WGII 
sectoral and regional chapters and cross-chapter papers (CCPs). (See Table 1 for the description of the categories of ES and CRDP)
Adaptation practices (and – examples)Main benefit (and & co-benefit; – trade off; + enabling 
conditions; X barrier and potential maladaptation)ES
P R S C
Agroforestry (Table 2.7; Table 5 ES; Section 5.10.4; 
Section 5.12.5.2; Box 5.10; Table 16.2)
 –Climate Adaptation and Maladaptation in Cocoa 
and Coffee Production (Box 5.7)Food provision
& Fuel (wood) provision, carbon sequestration, biodiversity and ecosystem 
conservation, diversification and improved economic incomes, water and 
soil conservation, and aesthetics
+ Secure tenure arrangements, supporting Indigenous knowledge,

inclusive networks and socio-cultural values, access to information and 
management skill
X Higher water demand; disruption of hydrology; loss of native biodiversity; 
reduced resilience of certain plants; degraded soil and water quality; 
improper and increased use of agrochemicals, pesticides and fertilizers*** ** **
Forest maintenance and restoration (Box 2.2; 
Table 16.2; Table Cross-Chapter Box NATURAL.1 in 
Chapter 2)
 –Protected Area Planning in Thailand 
(Section 2.6.5.3)
 –Conserving Joshua trees in the Joshua National 
Park (Section 2.6.5.6)
 –Addressing Vulnerability of Peat Swamp Forests in 
Southeast Asia (Section 2.6.5.10)
 –Reduce Emissions from Deforestation and Forest 
Degradation (REDD+) (Section 5.6.3.3; Table 16.2)Ecosystem conservation
& Food provision, fuel provision, job creation, carbon sequestration, 
biodiversity conservation, air quality regulation, water and soil 
conservation, vector-borne disease control, improved mental health,

cultural benefits, natural resources relative conflict prevention
+ Cooperation of Indigenous peoples and other local communities
X Planting large-scale non-native monocultures leads to loss of biodiversity 
and poor climate change resilience, increased vulnerability to landslide, 
increased sensitivity of new tree species, reduced resilience of certain 
plants, high water demand, trees planted damaged buildings during 
heavy storms, lack of carbon rights in national legislations** ** *** **
Traditional practices/Indigenous knowledge and 
local knowledge (IKLK) (Table 2.7; Section 5.6.3; 
Section 5.14.2.2; Table 16.2)
 –Crop and Livestock Farmers on Observed Changes 
in Climate in the Sahel (Box 5.6)
 –Karuk Tribe in Northern California (Section 5.6.3.2)Food and material provision
& Carbon sequestration
+ Partnerships between key stakeholders such as researchers, forest 
managers and local actors, Indigenous and local knowledge*** **
Restoring natural fire regimes (Table 2.7)

–Protecting Gondwanan wildfire refugia in 
Tasmania, Australia (Section 2.6.5.8)Fire regulation
& Biodiversity conservation***
Natural flood risk management (Table 2.7)
 –Natural Flood Management (NFM) in England, UK 
(Section 2.6.5.2)Water security, flood regulation, sediment retention
& Biodiversity and ecosystem conservation*** **
Coastal ecosystem conservation (Table 
Cross-Chapter Box NATURAL.1 in Chapter 2) 
(Tables 16.2, 2.7)
 –African Penguin On-Site Adaptation 
(Section 2.6.5.5)Coastal protection against sea level rise and storm surges
& Fisheries, carbon sequestration, biodiversity and ecosystem conservation, 
flood regulation, water purification, recreation and cultural benefits
X NH 4 emissions, digging channels and sand walls around homes, loss of 
recreational value of beaches, shifted the flood impacts to poor informal 
urban settlers, erosion and degraded coastal lands** *** **
Eco-tourism within protected areas (Table 2.7)Tourism
& Habitat protection*** **

Aquaculture (Section 5.9.4; Table 16.2; Table 
Cross-Chapter Box NATURAL.1 in Chapter 2)Food provision
& Biodiversity conservation
+ Farmer incentives, participatory adaptation to context
X Lack of financial, technical or institutional capacity; short value chains; 
productivity varies by system; over-fertilising; deforestation of mangroves; 
salt intrusion; increased flood vulnerability*** *
Water–energy–food (WEF) nexus (Box 4.7)
 –Food Water Energy Nexus in Asia (Section 10.6.3)
 –New Zealand’s Land, Water and People Nexus 
under a Changing Climate (Box 11.7)Water, energy and food provision
X Insufficient data, information, and knowledge in understanding the WEF 
inter-linkages; lack of systematic tools to address trade-offs involved in 
the nexus***Box 18.5 (continued)

18
2697Climate Resilient Development Pathways  Chapter 18
P R S C
Urban greening (Tables 2.7, 16.2; Table 
Cross-Chapter Box NATURAL.1 in Chapter 2)
 –Ecosystem-Based Adaptation in Durban, South 
Africa (Section 2.6.5.7)Urban flood management, water savings, urban heat island mitigation
& Reduced carbon emissions, air and noise regulation, improved mental 
health, energy savings, recreation and aesthetics
+ Meaningful partnerships, long-term financial commitments and 
significant political and administrative
X Storage of large quantities of water in the home; water contamination; 
increased breeding sites for mosquitoes and flies; vectors and diseases; 
intensified cultivation of marginal lands; clearing of virgin forests for 
farmland; frequent weeding; increased competition for water and

nutrients; reduced soil fertility, invasive species*** **
The four systems transitions identified in SR1.5 already include a 
component of societal change—for example, attitude change is 
part of public acceptance that facilitates shifts in energy including 
changing electricity to renewables (Chapter 4 SR1.5, Section 4.3.1.1) 
and developing nuclear power (Section  4.3.1.3), and behavioural 
change is a part of shifting irrigation practices to drive required 
land and ecosystems transitions (Section 4.3.2.1). Extracting societal 
transitions also allows for a detailed examination of other societal 
dimensions that facilitate systems transitions, for example justice 
issues relating to water and energy access and distribution, and land 
use. Societal transition, sometimes known as ‘societal transformation’, 
is an established concept in different literatures, as described below. 
Transformation and transition are terms often used as synonyms

(Hölscher et  al., 2018), although different schools of thought 
understand them as sub-components of each other, for example 
transition driving transformation, or transformation driving transition. 
For a more detailed discussion on the differences between transition 
and transformation represented in the literature, see Box 18.1.
Societal transitions for the purpose of this report are understood as the 
collection of shifts in attitudes, values, consciousness and behaviour 
required to move towards CRD. This builds on the SR1.5 (IPCC, 2018a: 
599) definition of societal (social) transformation: ‘A profound and 
often deliberate shift initiated by communities towards sustainability, 
facilitated by changes in individual and collective values and 
behaviours, and a fairer balance of political, cultural, and institutional 
power in society’. This includes accepting Indigenous knowledge 
and local knowledge (IKLK) as an equally valid form of knowledge

as compared with Western, scientific knowledge (see Cross-Chapter 
Box  INDIG) and recognition of the role of shifting gender norms to 
achieve climate resilience (see Cross-Chapter Box GENDER). Changes 
associated with societal transitions are not specific to defined systems 
(e.g., energy, industry, land/ecosystems or urban/infrastructure). Rather, 
these sectoral systems are embedded within broader societal systems, 
including for example political systems, economic systems, knowledge 
systems and cultural systems (Davelaar, 2021; Turnhout et  al., 2021; 
Visseren-Hamakers et al., 2021). Changes that happen in these broader 
social systems can therefore prompt changes in all systems embedded 
within them, meaning that societal transition is key to transforming 
across a range of sectors and topics (Leventon et al., 2021). Furthermore, societal transition requires changes in individual behaviours, but also 
in the broader conditions that shape these behaviours. These broader

conditions are largely related to questions of power, in enforcing 
dominant political economies and social-technological mindsets 
(Stoddard et al., 2021). This section also briefly describes the various 
trains of research on societal transitions and transformation.
Because of the multiple sectors, interests and scales that are involved 
in societal transitions, understanding and creating evidence on 
transitions requires shifting across system boundaries and finding ways 
to transcend disciplinary silos. Relevant research includes work within 
the topic of transformation and transitions (Hölscher et  al., 2018). 
Transformations literature can be split into multiple sub-concepts and 
requires engagement with multiple schools of thought (Feola, 2015; 
Feola et  al., 2021). Much focus within transformations research is 
currently related to biodiversity conservation (Massarella et al., 2021), 
and transitions work tends towards a focus in urban areas (Loorbach

et al., 2017). Though there is also work in both that is more broadly 
labelled as sustainability transformations or transitions (Luederitz et al., 
2017). Furthermore, there is likely to be much relevant literature that 
does not explicitly label itself as transformations or transitions (Feola 
et al., 2021). For example, we could look to political science theories 
on policy change (Leventon et al., 2021) and historical perspectives on 
social change. Bridging these divides will require a deeper rethinking 
in the research community to undo power structures that marginalise 
diverse knowledge (Caniglia et al., 2021; Lahsen and Turnhout, 2021).
There are a number of concepts proposed as pathways to creating societal 
transitions; usually centred around the idea of working with individuals 
and communities to change their mindsets as a way to change the 
way they manage their local environments or behave. Transformations

work explores how values are pathways towards sustainability, for 
example by changing values, through making values explicit, through 
negotiation and by eliciting values (Horcea-Milcu et al., 2019). Human 
nature connections is a further concept that is identified as a way to 
shift values and behaviours across a range of disciplines (Ives et  al., 
2017). The role of learning and Indigenous knowledge is also explored 
(Lam et al., 2020). These three concepts have had particular salience in 
discussions around transformations for biodiversity conservation and 
restoration, related to the IPBES assessment on Values (Pascual et al., Box 18.5 (continued)

18
2698Chapter 18 Climate Resilient Development Pathways
2017; Peterson et al., 2018). They largely focus on the need to engage 
with people’s values, connections and knowledge to better manage the 
social–ecological system they are in.
Focusing on bottom-up and community-led transformations, there is 
emphasis on the role of grassroots organisations in transformations. 
Community actions around specific locations or topics have parallels to 
the idea of transformative spaces. They are sites of innovative activity 
(Seyfang and Smith, 2007). Grassroots organisations can bridge the 
local and the political scales by politicising actors and creating new 
interactions between individuals and political processes (Novák, 
2021). They are a collective approach to pushing for both individual 
and societal change (Sage et al., 2021).
Despite a current lack of empirical evidence, there are numerous 
frameworks emerging for exploring societal transitions across levels.

There is focus on pathways for sustainability transitions, which tends 
to look at projected, normative scenarios for the future, and explore 
or back-cast the institutional and societal changes that are required 
to get there (Westley et al., 2011; Sharpe et al., 2016). There is also 
work that looks at scaling up of smaller sustainability initiatives, 
through processes of scaling up, scaling out and scaling deep (Moore 
et al., 2015; Lam et al., 2020). In particular, systems thinking provides 
an organising framework for bringing together multiple disciplines 
and perspectives, to understand problem framings, and normative 
and design aspects of social systems and behaviours (Foster-
Fishman et  al., 2007). Within this, Meadows (1999) framework of 
leverage points for systems transformation has been operationalised 
within the sustainability transformations debate (Abson et  al., 
2017). Here, system properties relating to system paradigms and

design are leverage points where interventions can create greatest 
system change; shallower leverage points relate to materials and 
processes. This framework is increasingly being used across a range 
of sustainability problems as boundary objects for cross-disciplinary, 
critical research (Fischer and Riechers, 2019; Leventon et  al., 2021; 
Riechers et al., 2021).
Analyses of societal transitions have had limited engagement with 
adaptation questions. The focus of the sub-field of sustainability 
transitions on a few industrialised nations, mostly in North America 
and Europe, limited the field’s development to assumptions born from 
the experiences in those areas. More recent studies have sought to 
understand sustainability transitions in other countries, especially 
emerging economies (Wieczorek, 2018; Köhler et  al., 2019). In 
particular, China has received attention from scholars on sustainability 
transitions (Huang et al., 2018; Lo and Castán Broto, 2019; Castán

Broto et al., 2020; Huang and Sun, 2020). As a result, some pressing 
issues related to societal transitions for adaptation have received 
limited attention compared with that paid to other system transitions. 
However, more recently, scholarship has begun examining transitions 
that have turned to nature and nature-based solutions. Adaptive 
transitions are an intermediary step towards sustainability transitions, 
whereby multiple actions at material and institutional levels are 
combined towards improving adaptation outcomes (Pant et al., 2015; 
Scarano, 2017).18.3.2 Accelerating Transitions
Successfully implementing climate actions and managing trade-
offs between mitigation, adaptation and sustainable development 
(Section  18.2.4) has important time considerations that imply 
significant urgency, making substantive progress in system transitions 
critical for CRD. Both the SDGs and the Sendai Framework, for 
example, have target dates of 2030. Meanwhile, the Paris Agreement

sets specific time horizons for NDCs and the SR1.5 indicated that 
limiting warming to 1.5°C would similarly require substantial climate 
action by 2030 (IPCC, 2018a). While the literature is unambiguous 
regarding the need for significant system transitions to achieve CRD 
(Section  18.1.3), the current pace of global emissions reductions, 
poverty alleviation and development of equitable systems of 
governance is incommensurate with these policy time tables (Rogelj 
et  al., 2010; Burke et  al., 2016; Oleribe and Taylor-Robinson, 2016; 
Kriegler et al., 2018; Frank et al., 2019; Sadoff et al., 2020). As noted 
previously in the AR5, ‘delaying action in the present may reduce 
options for climate-resilient pathways in the future’ (Denton et  al., 
2014: 1123). Accordingly, significant acceleration in the pace of system 
transitions is necessary to enable the implementation of mitigation, 
adaptation and sustainable development initiatives consistent with 
CRD (very high confidence).

Studies since the AR5 directly address the issue of how to accelerate 
transitions within the broader system transitions, sustainability 
transitions and socio-technical transitions literature (Frantzeskaki 
et al., 2017; Gliedt et al., 2018; Gorissen et al., 2018; Johnstone and 
Newell, 2018; Kuokkanen et  al., 2019; Markard et  al., 2020). Such 
literature explores several core themes to facilitate acceleration, 
which are aligned with the discussion later in this chapter on arenas 
of engagement for CRD (Section  18.4.3). One dominant theme is 
accelerating the implementation of sustainability or low-carbon policies 
that target specific sectors or industries (Bhamidipati et al., 2019). For 
example, Altenburg and Rodrik (Altenburg and Rodrik, 2017) discuss 
green industrial polices including taxes, mandated technology phase 
outs and the removal of subsidies as means of constraining polluting 
industries. Kivimaa et  al. (Kivimaa and Martiskainen, 2018; Kivimaa

et al., 2019a; Kivimaa et al., 2019b; Kivimaa et al., 2020) and Vihemäki 
et  al. (2020) discuss low-carbon transitions in buildings, noting the 
important role that intermediaries play in facilitating policy reform. 
Nikulina et al. (2019) identify mechanisms for facilitating policy change 
in personal mobility including political leadership, combining carrots 
and sticks to incentivise behavioural change and challenging current 
policy frameworks. These various examples reflect a fragmented 
approach to system transitions, suggesting a large portfolio of such 
transition initiatives would be required to accelerate change or 
more fundamental and cross-cutting policy drivers are needed (high 
agreement, limited evidence). Policies that seek to promote social 
justice and equity, for example, could ultimately catalyse a broader 
range of sustainability and climate actions than policies designed to 
address a specific sector or class of technology (Delina and Sovacool,

2018; White, 2020).
In contrast with formal government policies, a second theme 
in accelerating transitions is that of civic engagement (see also 
Section 18.4.3), which is reported to be an important opportunity for

18
2699Climate Resilient Development Pathways  Chapter 18
Table 18.3 |  Specific options for facilitating the five system transitions that can support CRD
Transition Examples Reference
Energy systemsFuel switching from coal to natural gas
Expansion of renewable energy technologies
Financial incentives to promote renewable energy
Reduced energy intensity of industry
Improvements in power system resilience and reliability
Increased water use efficiency in electricity generation
Energy demand management strategies(Gielen et al., 2019); (Mulugetta et al., 2019); (IEA et al., 2019); 
AR6 WGIII Chapter 2
Urban and 
infrastructure systemsIncreased investment in physical and social infrastructure
Enhance urban and regional planning
Enhanced governance and institutional capacity supports post-disaster recovery and 
reconstruction (Kull, 2016)(IPCC, 2018b): D3.1)
Land, oceans and 
ecosystemsExpanding access to agricultural and climate services

Strengthening land tenure security and access to land
Empowering women farmers
Improved access to markets
Facilitating payments for ecosystem services
Promotion of healthy and sustainable diets
Enhancing multi-level governance by supporting local management of natural resources
Strengthening cooperation between institutions and actors
Building on local, indigenous and scientific knowledge funding, and institutional support
Monitoring and forecasting
Education and climate literacy and social learning and participation(IPCC, 2019 f): C2.1; (IPCC, 2019 f): C4.5; (IPCC, 2019 f): C4
Industrial systemsPromote material efficiency and high-quality circularity
Materials demand management (IEA 2019, 2020)
Application of new processes and technologies for GHG emission reduction
Carbon pricing or regulations with provisions on competitiveness to drive innovation and 
systemic carbon efficiency
Low-cost, long-term financing mechanisms to enable investment and reduce risk

Better planning of transport infrastructure
Labour market training and transition support
Electricity market reform
Regulations—standards and labelling, material efficiency
Mandating technologies and targets
Green taxes and carbon pricing, preferential loans and subsidies
Voluntary action agreements, expanded producer responsibilities
Information programmes: monitoring, evaluation, partnerships, and research and 
Government provisioning of services—government procurements, technology push and 
market-pull(Åhman et al., 2017; Bataille et al., 2018; Material, 2019); (Tanaka, 
2011; Schwarz et al., 2020); (Ciwmb, 2003); (Romero Mosquera, 
2019); (Tanaka, 2011); (Ryan et al., 2011; Boyce, 2018); (Taylor, 
2008); (UNEP , 2018b); (Kaza et al., 2018); (Söderholm and Tilton, 
2012); (Bataille et al., 2018); (Ghisetti et al., 2017); (Taylor, 2008; 
Fischedick et al., 2014; Hansen and Lema, 2019); (Crippa et al., 
2019; IEA, 2019); (Cavaliere, 2019; IEA, 2020); Vogl et al. (2018);

(Pauliuk et al., 2013; Quader et al., 2016)
Societal systemsInclusive governance
Empowerment of excluded stakeholders, especially women and youth
Transforming economies
Finance and technology aligned with local needs
Overcoming uneven consumption and production patterns
Allowing people to live a life in dignity and enhancing their capabilities
Involving local governments, enterprises and civil society organisations across different 
scales
Reconceptualising development around well-being rather than economic growth (Gupta 
and Pouw, 2017),
Rethinking, prevailing values, ethics and behaviour
Improving decision making processes that incorporate diverse values and world views
Creating space for negotiating diverse interests and preferences(Fazey et al., 2018b; O’Brien, 2018; Patterson et al., 2018); (MRFCJ, 
2015; Dumont et al., 2019); (Popescu et al., 2017; David Tàbara 
et al., 2018); (de Coninck and Sagar, 2015; IEA, 2015; Parikh et al.,

2018); (Dearing et al., 2014; Häyhä et al., 2016; Raworth, 2017); 
(Klinsky and Winkler, 2018); (Hajer et al., 2015; Labriet et al., 
2015; Hale, 2016; Pelling et al., 2016; Kalafatis, 2017; Lyon, 2018); 
(Holden et al., 2017); (Cundill et al., 2014; Butler et al., 2016; 
Ensor, 2016; Fazey et al., 2016; Gorddard et al., 2016; Aipira et al., 
2017; Chung Tiam Fook, 2017; Maor et al., 2017); (O’Brien and 
Selboe, 2015; Gillard et al., 2016; DeCaro et al., 2017; Harris et al., 
2018; Lahn, 2018; Roy et al., 2018); Sections 5.6.1 and 5.5.3.1
driving transitions forward (high agreement, medium evidence). Ehnert 
et  al. (2018) describe local organisations and civic engagement in 
policy processes as an important engine for sustainability activities in 
European states. Similarly, Ruggiero et al. (2021) note the potential to 
use civic organisations to appeal to local identities in order to mobilise 
citizens to pursue energy transition initiatives among communities in

the Baltic Sea region. Gernert et  al. (2018) attribute such influence 
to the ability of grassroots movements to bypass traditional social and political norms and thereby experiment with new behaviours 
and processes. Moreover, civic engagement is also the foundation for 
collective action including protest and civil disobedience (Welch and 
Yates, 2018, Section  18.5.3.7). However, Haukkala (2018) observes 
that while green-transition coalitions in Finland could be an agent of 
change driving energy transitions, the diversity of views among the 
various grassroots actors could make consensus building difficult, 
thereby slowing transition initiatives.

18
2700Chapter 18 Climate Resilient Development Pathways
Cross-Chapter Box GENDER | Gender, Climate Justice and Transformative Pathways
Authors: Anjal Prakash (India), Cecilia Conde (Mexico), Ayansina Ayanlade (Nigeria), Rachel Bezner Kerr (Canada/USA), Emily Boyd 
(Sweden), Martina A Caretta (Sweden), Susan Clayton (USA), Marta G. Rivera Ferre (Spain), Laura Ramajo Gallardo (Chile), Sharina Abdul 
Halim (Malaysia), Nina Lansbury (Australia), Oksana Lipka (Russia), Ruth Morgan (Australia), Joyashree Roy (India), Diana Reckien (the 
Netherlands/Germany), E. Lisa F . Schipper (Sweden/UK), Chandni Singh (India), Maria Cristina Tirado von der Pahlen (Spain/USA), Edmond 
Totin (Benin), Kripa Vasant (India), Morgan Wairiu (Solomon Islands), Zelina Zaiton Ibrahim (Malaysia).
Contributing Authors: Seema Arora-Jonsson (Sweden/India), Emily Baker (USA), Graeme Dean (Ireland), Emily Hillenbrand (USA), Alison

Irvine (Canada), Farjana Islam (Bangladesh/ UK), Katriona McGlade (UK/Germany), Hanson Nyantakyi-Frimpong (Ghana), Nitya Rao 
(UK/India), Federica Ravera (Italy), Emilia Reyes (Mexico), Diana Hinge Salili (Fiji), Corinne Schuster-Wallace (Canada), Alcade C. Segnon 
(Benin), Divya Solomon (India), Shreya Some (India), Indrakshi Tandon (India), Sumit Vij (India), Katharine Vincent (UK/South Africa), 
Margreet Zwarteveen (the Netherlands)
Key Messages
• Gender and other social inequities (e.g., racial, ethnic, age, income, geographic location) compound vulnerability to climate change 
impacts (high confidence). Climate justice initiatives explicitly address these multi-dimensional inequalities as part of a climate 
change adaptation strategy (Box 9.2: Vulnerability Synthesis: Differential Vulnerability by Gender and Age in Chapter 9).
• Addressing inequities in access to resources, assets and services, as well as participation in decision making and leadership is

essential to achieving gender and climate justice (high confidence).
• Intentional long-term policy and programme measures and investments to support shifts in social rules, norms and behaviours are 
essential to address structural inequalities and support an enabling environment for marginalised groups to effectively adapt to 
climate change (very high confidence) (Equity and Justice box in Chapter 17).
• Climate adaptation actions are grounded in local realities so understanding links with Sustainable Development Goal (SDG) 5 
is important to ensure that adaptive actions do not worsen existing gender and other inequities within society (e.g., leading to 
maladaptation practices) (high confidence). [Section 17.5.1]
• Adaptation actions do not automatically have positive outcomes for gender equality. Understanding the positive and negative links 
of adaptation actions with gender equality goals, (i.e., SDG 5), is important to ensure that adaptive actions do not exacerbate existing

gender-based and other social inequalities [Section 16.1.4.4]. Efforts are needed to change unequal power dynamics and to foster 
inclusive decision making for climate adaptation to have a positive impact for gender equality (high confidence).
• There are very few examples of successful integration of gender and other social inequities in climate policies to address climate 
change vulnerabilities and questions of social justice (very high confidence).
Gender, Climate Justice and Climate Change
This Cross-Chapter Box highlights the intersecting issues of gender, climate change adaptation, climate justice and transformative 
pathways. A gender perspective does not centre only on women or men but examines structures, processes and relationships of power 
between and among groups of men and women and how gender, particularly in its non-binary form, intersects with other social

categories such as race, class, socioeconomic status, nationality or education to create multi-dimensional inequalities (Hopkins, 2019). A 
gender transformative approach aims to change structural inequalities. Attention to gender in climate change adaptation is thus central 
to questions of climate justice that aim for a radically different future (Bhavnani et al., 2019). As a normative concept highlighting the 
unequal distribution of climate change impacts and opportunities for adaptation and mitigation, climate justice (Wood, 2017; Jafry et al., 
2018; Chu and Michael, 2019; Shi, 2020a) calls for transformative pathways for human and ecological well-being. These address the 
concentration of wealth, unsustainable extraction and distribution of resources (Schipper et al., 2020a; Vander Stichele, 2020) as well as 
the importance of equitable participation in environmental decision making for climate justice (Arora-Jonsson, 2019).

Research on gender and climate change demonstrates that an understanding of gendered relations is central to addressing the issue of 
climate change. This is because gender relations mediate experiences with climate change, whether in relation to water (Köhler et al., 
2019) (see also Sections 4.7, 4.3.3, 4.6.4, 5.3), forests (Arora-Jonsson, 2019), agriculture (Carr and Thompson, 2014; Balehey et al., 2018; 
Garcia et al., 2020) (see also Chapter 4, Section 5.4), marine systems (Mcleod et al., 2018; Garcia et al., 2020) (see also Section 5.9) or 
urban environments (Reckien et al., 2018; Susan Solomon et al., 2021) (see also Chapter 6). Climate change has direct negative impacts 
on women’s livelihoods due to their unequal control over and access to resources (e.g., land, credit) and because they are often the ones 
with the least formal protection (Eastin, 2018) (see also Box 9.2 in Chapter 9). Women represent 43% of the agricultural labour force

globally, but only 15% of agricultural landholders (OECD, 2019b). Gendered and other social inequities also exist with non-land assets 
and financial services (OECD, 2019b) often due to social norms, local institutions and inadequate social protection (Collins et al., 2019b). 
Men may experience different adverse impacts due to gender roles and expectations (Bryant and Garnham, 2015; Gonda, 2017). These 
impacts can lead to irreversible losses and damages from climate change across vulnerability hotspots (Section 8.3).

18
2701Climate Resilient Development Pathways  Chapter 18
Participation in environmental decision making tends to favour certain social groups of men, whether in local environmental committees, 
international climate negotiations (Gay-Antaki and Liverman, 2018) or the IPCC (Nhamo and Nhamo, 2018). Addressing climate justice 
reinforces the importance of considering the legacy of colonialism on developing regional and local adaptation strategies. Scholars have 
criticised climate programmes for setting aside forestland that poor people rely on and appropriating the labour of women in the Global 
South without compensatory social policy or rights; where women are expected to work with non-timber forest products to compensate 
for the lack of logging and for global climate goals, but where their work of social reproduction and care is paid little attention (Westholm

and Arora-Jonsson, 2015; Arora-Jonsson et al., 2016). A global ecologically unequal exchange, biopiracy, damage from toxic exports or 
the disproportionate use of carbon sinks and reservoirs by high-income countries enhance the negative impacts of climate change. 
Women in Least Developed Countries (LDCs) and Small Island Developing States (SIDS) also endure the harshest impacts of the debt crisis 
due to imposed debt measures in their countries (Appiah and Gbeddy, 2018; Fresnillo Sallan, 2020). The austerity measures derived as 
conditionalities for fiscal consolidation in public services increases gender-based violence (Castañeda Carney et al., 2020) and brings 
additional burdens for women in the form of increasing unpaid care and domestic work (Bohoslavsky, 2019).
Gendered Vulnerability
Land, ecosystem and urban transitions to climate resilient development need to address gender and other social inequities to meet

sustainability and equity goals, otherwise, marginalised groups may continue to be excluded from climate change adaptation. In the 
water sector, increasing floods and droughts and diminishing groundwater and runoff have gendered effects on both production systems 
and domestic use (Sections 4.3.1, 4.3.3, 4.5.3). Climate change is reducing the quantity and quality of safe water available in many 
regions of the world and increasing domestic water management responsibilities (high confidence). In regions with poor drinking water 
infrastructure, it is forcing, primarily women and girls, to walk long distances to access water, and limiting time available for other 
activities, including education and income generation (Eakin et al., 2014; Kookana et al., 2016; Yadav and Lal, 2018). Water insecurity 
and the lack of water, sanitation and hygiene (WASH) infrastructure have resulted in psychosocial distress and gender-based violence,

as well as poor maternal and child health and nutrition (Collins et al., 2019a; Wilson et al., 2019; Geere and Hunter, 2020; Islam et al., 
2020; Mainali et al., 2020) (Sections 4.3.3 and 4.6.4.4) (high confidence). Climate-related extreme events also affect women’s health—by 
increasing the risk of maternal and infant mortality, disrupting access to family planning and prevention of mother to child transmission 
regimens for human immunodeficiency virus (HIV) positive pregnant women (UNDRR, 2019) (see also Section 7.2). Women and the 
elderly are also disproportionately affected by heat events (Sections 7.1.7.2.1, 7.1.7.2.3, 13.7.1).
Extreme events impact food prices and reduce food availability and quality, especially affecting vulnerable groups, including low-income 
urban consumers, wage labourers and low-income rural households who are net food buyers (Green et al., 2013; Fao, 2016) (Section 5.12).

Low-income women, ethnic minorities and Indigenous communities are often more vulnerable to food insecurity and malnutrition from 
climate change impacts, as poverty, discrimination and marginalisation intersect in their cases (Vinyeta et al., 2016; Clay et al., 2018) 
(Section 5.12). Increased domestic responsibilities of women and youth, due to migration of men, can increase their vulnerability due to 
their reduced capacity for investment in off-farm activities and reduced access to information (Sugden et al., 2014; O’Neil et al., 2017) 
(Sections 4.3, 4.6) (high confidence).
In the forest sector, the increased frequency and severity of drought, fires, pests and diseases, and changes to growing seasons, has 
led to reduced harvest revenues, fluctuations in timber supply and availability of wood (Lamsal et  al., 2017; Fadrique et  al., 2018; 
Esquivel-Muelbert et al., 2019). Climate programmes in the Global South such as REDD+ have led to greater social insecurity and the

conservation of the forests have led to more pressure on women to contribute to household incomes, but without enough supporting 
market access mechanisms or social policy (Westholm and Arora-Jonsson, 2015; Arora-Jonsson et al., 2016). In countries in the Global 
North, reduced harvestable wood and revenues have led to employment restructuring that has important gendered effects and negatively 
affects community transition opportunities (Reed et al., 2014).
Integrating Gender in Climate Policy and Practice
Climate change policies and programmes across regions reveal wide variation in the degree and approach to addressing gender inequities 
(see Table SMCCB GENDER.2). In most regions where there are climate change policies that consider gender, they inadequately address 
structural inequalities resulting from climate change impacts, or how gender and other social inequalities can compound risk (high confidence).

Experiences show that it is more frequent to address specific gender inequality gaps in access to resources. Regionally, Central and South 
American countries (Section  12.5.8) have a range of gender-sensitive or gender-specific policies such as the intersectoral coordination 
initiative Gender and Climate Change Action Plans (PAGcc), adopted in Perú, Cuba, Costa Rica and Panamá (Casas Varez, 2017), or the 
Gender Environmental policy in Guatemala that has a focus on climate change (Bárcena-Martín et al., 2021). However, countries often have 
limited commitment and capacity to evaluate the impact of such policies (Tramutola, 2019). In North and South America, policies have failed 
to address how climate change vulnerability is compounded by the intersection of race, ethnicity and gender (Radcliffe, 2014; Vinyeta et al., Cross-Chapter Box GENDER (continued)

18
2702Chapter 18 Climate Resilient Development Pathways
2016) (see also Section 14.6.3). Gender is rarely discussed in African national policies or programmes beyond the initial consultation stage 
(Holvoet and Inberg, 2014; Mersha and van Laerhoven, 2019), although there are gender and climate change action strategies in countries 
such as Liberia, Mozambique, Tanzania and Zambia (Mozambique and IUCN, 2014; Zambia and IUCN, 2017). European climate change 
adaptation strategies and policies are weak on gender and other social equity issues (Allwood, 2014; Boeckmann and Zeeb, 2014; Allwood, 
2020), while in Australasia, there is a lack of gender-responsive climate change policies. In Asia, there are several countries that recognise 
gendered vulnerability to climate change (Jafry, 2016; Singh et al., 2021b), but policies tend to be gender-specific, with a focus on targeting

women, for example in the national action plan on climate change as in India (Roy et al., 2018) or in national climate change plan as in 
Malaysia (Susskind et al., 2020).
Potential for Change and Solutions
The sexual division of labour, systemic racism and other social structural inequities lead to increased vulnerabilities and climate change impacts 
for social groups such as women, youth, Indigenous peoples and ethnic minorities. Their marginal positions not only affect their lives negatively 
but their work in maintaining healthy environments is ignored and invisible in policy affecting their ability to work towards sustainable 
adaptation and aspirations in the SDGs (Arora-Jonsson, 2019). However, attention to the following has the potential to bring about change:
Creation of new, deliberative policymaking spaces that support inclusive decision making processes and opportunities to (re)negotiate

pervasive gender and other social inequalities in the context of climate change for transformation (Tschakert et al., 2016; Harris et al., 
2018; Ziervogel, 2019; Garcia et al., 2020) (high confidence).
Increased access to reproductive health and family planning services, which contributes to climate change resilience and socioeconomic 
development through improved health and well-being of women and their children, including increased access to education, gender equity 
and economic status (Onarheim et al., 2016; Starbird et al., 2016; Lopez-Carr, 2017; Hardee et al., 2018) (Section 7.4) (high confidence).
Engagement with women’s collectives is important for sustainable environments and better climate decision making whether at the 
global, national or local levels (Westholm and Arora-Jonsson, 2018; Agarwal, 2020). The work of such collectives in maintaining their

societies and environments and in resisting gendered and community violence is unacknowledged (Jenkins, 2017; Arora-Jonsson, 2019) 
but is indispensable especially when combined with good leadership, community acceptance and long-term economic sustainability 
(Chu, 2018; Singh, 2019) (Section 4.6.4). Networking by gender experts in environmental organisations and bureaucracies has also been 
important for ensuring questions of social justice (Arora-Jonsson and Sijapati, 2018).
Investment in appropriate reliable water supplies, storage techniques and climate-proofed WASH infrastructure as key adaptation 
strategies that reduce both burdens and impacts on women and girls (Alam et al., 2011; Woroniecki, 2019) (Sections 4.3.3, 4.6.44).
Improved gender-sensitive early warning system design and vulnerability assessments to reduce vulnerabilities, prioritising effective 
adaptation pathways to women and marginalised groups (Mustafa et al., 2019; Tanner et al., 2019; Werners et al., 2021).

Established effective social protection, including both cash and food transfers, such as the universal public distribution system (PDS) 
for cereals in India, or pensions and social grants in Namibia, that have been demonstrated to contribute towards relieving immediate 
pressures on survival and support processes at the community level, including climate effects (Kattumuri et al., 2017; Lindoso et al., 2018; 
Rao et al., 2019a; Carr, 2020).
Strengthened adaptive capacity and resilience through integrated approaches to adaptation that include social protection measures, 
disaster risk management and ecosystem-based climate change adaptation (high confidence), particularly when undertaken within a 
gender-transformative framework (Gumucio et al., 2018; Bezner Kerr et al., 2019; Deaconu et al., 2019) (Cross-Chapter Box NATURAL in 
Chapter 2, Sections 5.12, 5.14).

For example, gender-transformative and nutrition-sensitive agroecological approaches strengthen adaptive capacities and enable more 
resilient food systems by increasing leadership for women and their participation in decision making and a gender-equitable domestic 
work (high confidence) (Gumucio et al., 2018; Bezner Kerr et al., 2019; Deaconu et al., 2019) (Cross-Chapter Box NATURAL in Chapter 2, 
Sections 5.12, 5.14)
New initiatives, such as the Sahel Adaptive Social Protection Program, represent an integrated approach to resilience that promotes 
coordination among social protection, disaster risk management and climate change adaptation. Accompanying measures include health, 
education, nutrition and family planning, among others (Daron et al., 2021).Cross-Chapter Box GENDER (continued)

18
2703Climate Resilient Development Pathways  Chapter 18
Climate Change Adaptation and SDG 5
Adaptation actions may reinforce social inequities, including gender, unless explicit efforts are made to change (Nagoda and Nightingale, 
2017; Garcia et al., 2020) (robust evidence, high agreement). Participation in climate action increases if it is inclusive and fair (Huntjens 
and Zhang, 2016). Roy et al. (2018) assessed links among various SDGs and mitigation options. Adaptation actions are grounded in local 
realities, especially in terms of their impacts, so understanding links with the goals of SDG 5 becomes more important to make sure that 
adaptive actions do not worsen prevalent gender and other social inequities within society (robust evidence, high agreement). In the 
IPCC 1.5°C Special Report, Roy et al. (2018) assessed links between various SDGs and mitigation options, adaptation options were not

considered. The current SDG 13 climate action targets do not specifically mention gender as a component for action, which makes it 
even more imperative to link SDG 5 targets and other gender-related targets to adaptive actions under SDG 13 to ensure that adaptation 
projects are synergistic rather than maladaptive (Section 16.3.2.6, Table 16.6) (Susan Solomon et al., 2021; Roy et al., Submitted).
This assessment is based on a systematic rapid review of scientific publications (McCartney et al., 2017; Liem et al., 2020) published on 
adaptation actions in nine sectors from 2014 to 2020 (see Table SMCCB GENDER.1) (Roy et al., Submitted)(Roy et al., Submitted)(Roy 
et al., Submitted)and how they integrated gender perspectives impacting gender equity. The assessment is based on over 17,000 titles 
and abstracts that were initially found through keyword search and were reviewed. Finally, 319 relevant papers on case studies, regional

assessments and meta-reviews were assessed. Gender impact was classified by various targets under SDG 5. Following the approach 
taken in Roy et al. (2018) and (Hoegh-Guldberg et al., 2019), the linkages were classified into synergies (positive impacts or co-benefits) 
and trade-offs (negative impacts) based on the evidence obtained from the literature review which is finally used to develop net impact 
(positive or negative) scores (see Table Cross-Chapter Box GENDER.1 and Supplementary Material).
Table Cross-Chapter Box GENDER.1 |  Inter-relations between SDG5 (gender equality) and adaptation initiatives in nine major sectors
Terrestrial and freshwater ecosystem
Ocean and coastal ecosyste m
Mountain ecosyste m
Food, fibre and others
Urban water and sanitation
Poverty, livelihood and sustainable developmen t
Cities, settlements and key infrastructure
Health, well-being, and changing communities’ structur eSector
Industrial system transitio nAdaptation categories
Ecosystem -

basedI nstitutionalTechnological /
infrastructure /
informationBehavioural /
cultural
All net positive link s
no literature/options/All net negative link sNet positive links > net negative links
Net negative links > net positive links
Very lowLowHigh
Medium/
/ ///
/
//
/Links with Sustainable
Development Goal 5:
Gender Equality
Potential net synergies and trade-offs between a sectoral portfolio of adaptation actions and SDG 5 are shown. Colour codes showing the 
relative strength of net positive and net negative impacts and confidence levels. The strength of net positive and net negative connections 
across all adaptation actions within a sector are aggregated to show sector-specific links. The links are only one-sided on how adaptation 
action is linked to gender equality (SDG 5) targets and not vice versa. 22 adaptation options were assessed in ecosystem-based actions,

10 options in technological/infrastructure/information, 17 in institutional and 13 in behavioural/cultural. The assessment presented here is 
based on literature presenting impacts on gender equality and equity of various adaptation actions implemented in various local contexts 
and in regional climate change policies (Table SMCCB GENDER.2).
Adaptation actions being implemented in each sector in different local contexts can have positive (synergies) or negative (trade-offs) 
effects with SDG 5. This can potentially lead to net positive or net negative connections at an aggregate level. How they are finally realised 
depends on how they are implemented, managed and combined with various other interventions, in particular, place-based circumstances. 
Ecosystem-based adaptation actions and terrestrial and freshwater ecosystems have higher potential for net positive connections (Roy

et al., 2018) (Table Cross-Chapter Box GENDER.1 and Supplementary Material). Adaptation in terrestrial and freshwater ecosystems has 
the strongest net positive links with all SDG 5 targets (medium evidence, low agreement). For example, community-based natural 
resource management increases the participation of women, especially when they are organised into women’s groups (Pineda-López Cross-Chapter Box GENDER (continued)

18
2704Chapter 18 Climate Resilient Development Pathways
et  al., 2015; de la Torre-Castro et  al., 2017) (Supplementary Material). For poverty, livelihood and sustainable development sectors, 
adaptation actions have generated more net negative scores (limited evidence, low agreement) (Table Cross-Chapter Box GENDER.1). For 
example, patriarchal institutions and structural discriminations curtail access to services or economic resources as compared with men, 
including less control over income, fewer productive assets and lack of property rights, as well as less access to credit, irrigation, climate 
information and seeds which devaluate women’s farm-related adaptation options (Adzawla et al., 2019; Friedman et al., 2019; Ullah 
et al., 2019) (Supplementary Material).
Among the adaptation actions, ecosystem-based actions have the strongest net positive links with SDG 5 targets (Table Cross-Chapter

Box GENDER.1, Table SMCCB GENDER.1). In the health, well-being and changing communities’ sector, this is with robust evidence and 
medium agreement, while in all other sectors there is medium evidence and low agreement. Net negative links are most prominent in 
institutional adaptation actions (Table Cross-Chapter Box GENDER.1). For example, in mountain ecosystems, changes in gender roles 
in response to climatic and socioeconomic stressors is not supported by institutional practices, mechanisms and policies that remain 
patriarchal (Goodrich et al., 2019). Additionally, women often have less access to credit for climate change adaptation practices, including 
post-disaster relief, for example, to deal with salinisation of water or flooding impacts (Hossain and Zaman 2018). Lack of coordination 
among different city authorities can also limit women’s contribution in informal settlements towards adaptation. Women are typically

under-represented in decision making on home construction and planning and home-design decisions in informal settlements, but 
examples from Bangladesh show they play a significant role in adopting climate-resilient measures (e.g., the use of corrugated metal 
roofs and partitions which is important in protection from heat) (Jabeen, 2014; Jabeen and Guy, 2015; Araos et al., 2017; Susan Solomon 
Towards Climate-Resilient, Gender-Responsive Transformative Pathways
The climate change adaptation and gender literature call for research and adaptation interventions that are ‘gender-sensitive’ (Jost et al., 
2016; Thompson-Hall et al., 2016; Kristjanson et al., 2017; Pearce et al., 2018a) and ‘gender-responsive’, as established in Article 7 of the 
Paris Agreement (UNFCCC, 2015). In addition, attention is drawn to the importance of ‘mainstreaming’ gender in climate/development

policy (Alston, 2014; Rochette, 2016; Mcleod et al., 2018; Westholm and Arora-Jonsson, 2018). Many calls have been made to consider 
gender in policy and practice (Ford et al., 2015; Jost et al., 2016; Rochette, 2016; Thompson-Hall et al., 2016; Kristjanson et al., 2017; 
Mcleod et al., 2018; Lau et al., 2021; Singh et al., 2021b). Rather than merely emphasising the inclusion of women in patriarchal systems, 
transforming systems that perpetuate inequality can help to address broader structural inequalities not only in relation to gender, but 
also other dimensions such as race and ethnicity (Djoudi et  al., 2016; Pearse, 2017; Gay-Antaki, 2020). Adaptation researchers and 
practitioners play a critical role here and can enable gender-transformative processes by creating new, deliberative spaces that foster 
inclusive decision making and opportunities for renegotiating inequitable power relations (Tschakert et al., 2016; Ziervogel, 2019; Garcia 

To date, empirical evidence on such transformational change is sparse, although there is some evidence of incremental change (e.g., 
increasing women’s participation in specific adaptation projects, mainstreaming gender in national climate policies). Even when national 
policies attempt to be more gendered, there is criticism that they use gender-neutral language or include gender analysis without 
proposing how to alter differential vulnerability (Mersha and van Laerhoven, 2019; Singh et al., 2021b). More importantly, the mere 
inclusion of women and men in planning does not necessarily translate to substantial gender-transformative action, for example in 
National Adaptation Programmes of Action across sub-Saharan Africa (Holvoet and Inberg, 2014; Nyasimi et al., 2018) and national and 
sub-national climate action plans in India (Singh et al., 2021b). Importantly, there is often an overemphasis on the gender binary (and

household headship as an entry point), which masks complex ways in which marginalisation and oppression can be augmented due to 
the interaction of gender with other social factors and intra-household dynamics (Djoudi et al., 2016; Thompson-Hall et al., 2016; Rao 
et al., 2019a; Lau et al., 2021; Singh et al., 2021b).
Climate justice and gender transformative adaptation can provide multiple beneficial impacts that align with sustainable development. 
Addressing poverty (SDG 1), energy poverty (SDG 7), WaSH (SDG 6), health (SDG 3), education (SDG 4) and hunger (SDG 2)––along with 
inequalities (SDG 5 and SDG 10)—improves resilience to climate impacts for those groups that are disproportionately affected (women, 
low-income and marginalised groups). Inclusive and fair decision making can enhance resilience (SDG 16; Section 13.4.4), although 
adaptation measures may also lead to resource conflicts (SDG 16; Section 13.7). Nature-based solutions attentive to gender equity also

support ecosystem health (SDGs 14 and 15) (Dzebo et al., 2019). Gender and climate justice will be achieved when the root causes of 
global and structural issues are addressed, challenging unethical and unacceptable use of power for the benefit of the powerful and 
elites (MacGregor, 2014; Wijsman and Feagan, 2019; Vander Stichele, 2020). Justice and equality need to be at the centre of climate 
adaptation decision-making processes. A transformative pathway needs to include the voice of the disenfranchised (MacGregor, 2020; 
Schipper et al., 2020a).Cross-Chapter Box GENDER (continued)

18
2705Climate Resilient Development Pathways  Chapter 18
A third theme is that of innovation, generally, and sustainability-oriented 
innovation, specifically (de Vries et al., 2016; Geradts and Bocken, 2019; 
Loorbach et  al., 2020), which creates opportunities for overcoming 
existing transition barriers (very high confidence). For example, Valta 
(2020) describes the role of innovation ecosystems—partnerships 
among companies, investors, governments and academics—in 
accelerating innovation (see also World Economic Forum, 2019). Burch 
et al. (Burch et al., 2016) describe the role of small- and medium-sized 
business entrepreneurship in promoting rapid innovation. Innovation 
extends beyond pure technology considerations to consider innovation 
in practices and social organisation (Li et  al., 2018; Psaltoglou and 
Calle, 2018; Repo and Matschoss, 2020). Zivkovic (2018), for example, 
discusses ‘innovation labs’ as accelerators for addressing so-called

wicked problems such as climate change through multi-stakeholder 
groups. Meanwhile, Chaminade and Randelli (2020) describe a case 
study where structural preconditions and place-based agency were 
important drivers of transitions to organic viticulture in Tuscany, Italy.
The fourth theme is that of transition management (Goddard and 
Farrelly, 2018), particularly vis-à-vis, disruptive technologies (Iñigo and 
Albareda, 2016; Kuokkanen et al., 2019) or broader societal disruptions 
(Brundiers, 2020; Davidsson, 2020; Hepburn et  al., 2020; Schipper 
et al., 2020b). Recent literature has given attention to how actors can 
use disruptive events, such as disasters, as a window of opportunity 
for accelerating changes in policies, practices and behaviours (high 
agreement, medium evidence) (Brundiers, 2018; Brundiers and Eakin, 
2018). This is consistent with concepts in resilience thinking around 
‘building back better’ after disasters (Fernandez and Ahmed, 2019).

For example, Hepburn et  al. discuss fiscal recovery packages for 
COVID-19 as a means of accelerating climate action, with a particular 
influence on clean physical infrastructure, building efficiency retrofits, 
investment in education and training, natural capital investment, and 
clean research and development (Andrijevic et al., 2020b).
18.4 Agency and Empowerment for Climate 
As reflected in the discussion of societal transitions (Section  18.3), 
people and their values and choices play an instrumental role in CRD. 
The agency of people to act on CRD is grounded in their worldviews, 
beliefs, values and consciousness (Woiwode, 2020), and is shaped 
through social and political processes including how policies and 
decision making recognise the voices, knowledges and rights of 
particular actors over others (very high confidence) (Harris and 
Clarke, 2017; Nightingale, 2017; Bond and Barth, 2020; Muok et al.,

2021). Since the AR5, evidence on diverse forms of engagement by 
and among social, political and economic actors to support CRD and 
sustainability outcomes, has increased. New forms of decision making 
and engagement are emerging within the formal policymaking 
and planning sphere, including co-production of knowledge, 
interventions grounded in the arts and humanities, civil participation 
and partnerships with business (Ziervogel et  al., 2016a; Roberts 
et  al., 2020). In addition, the set of actors that drive climate and 
development actions are recognised to extend beyond government 
and formal policy actors to include civil society, education, industry, 
media, science and art (Ojwang et  al., 2017; Solecki et  al., 2018; Heinrichs, 2020; Omukuti, 2020). This makes the power dynamics 
among actors and institutions critical for understanding the role of 
actors in CRD (Buggy and McNamara, 2016; Camargo and Ojeda, 
2017; Silva Rodríguez de San Miguel, 2018).

The formal space for national, sub-national and international 
adaptation governance emerged at COP 16 (UNFCCC, 2010) when 
adaptation was recognised as a similar level of priority as GHG 
mitigation. The Paris Agreement (UNFCCC, 2015) built on this and the 
2030 Sustainable Development Agenda (United Nations, 2015) to link 
adaptation to development and climate justice. It also highlighted the 
importance of multi-level adaptation governance, including new non-
state voices and climate actors that widen the scope of adaptation 
governance beyond formal government institutions. For example, 
individuals can act as agents of changes in their own behaviour, such 
as via change in their consumption patterns, but also generate change 
within organisations, fields of practice and the political landscape 
of governance. Accordingly, these interactions among actors across 
different scales implies the need for wider modes of, and arena for,

engagement around adaptation to accommodate a diversity of 
perspectives (high agreement, medium evidence) (Chung Tiam Fook, 
2017; Lesnikowski et al., 2017; IPCC, 2018a).
In most regions, such new institutional and informal arrangements are 
at an early stage of development (high agreement, limited evidence). 
Further clarification and strengthening are needed to enable the fair 
sharing of resources, responsibilities and authorities to enable climate 
action to enable CRD (Wood et al., 2017; IPCC, 2018a; Reckien et al., 
2018). These are strongly linked to contested and complementary 
worldviews of climate change and the actors that use these worldviews 
to justify, direct, accelerate and deepen transformational adaptation 
and climate action.
18.4.1 Political Economy of Climate Resilient 
Political economy studies (i.e., the origins, nature and distribution of 
wealth, and the ideologies, interests and institutions that shape it)

explicitly addressing CRD are quite limited. Yet there is an extensive 
post-AR5 literature on political economy associated with various 
elements relevant to CRD including climate change and development 
(Naess et  al., 2015); vulnerability, adaptation, and climate risk 
(Sovacool et al., 2015; Sovacool et al., 2017; Barnett, 2020); energy, 
decarbonisation and negative emissions technologies (Kuzemko et al., 
2019; Newell, 2019); degrowth and low-carbon economies (Perkins, 
2019; Newell and Lane, 2020); solar radiation management (Ott, 2018); 
planetary health and sustainability transitions and transformation 
(Kohler et al., 2019) (Gill and Benatar, 2020). Review and assessment of 
this literature reveals our key insights about the relationship between 
the political economy and CRD.
First, the political economy drives coupled development–climate 
change trajectories and determines vulnerability, thereby potentially

subjecting those least responsible for climate change to the greatest 
risk (Sovacool et  al., 2015; Barnett, 2020). The legitimacy, viability 
and sustainability of the prevailing political economy is being called

18
2706Chapter 18 Climate Resilient Development Pathways
into question because of its role in driving vulnerability in a changing 
climate (Barnett, 2020), thus undermining the prospects for CRD.As 
underpinning political economy ideologies, interests and institutions 
change, the cause of the vulnerable is being appropriated, the drivers of 
vulnerability and the adaptation agenda are depoliticised, and market-
based solutions advocated in ways that sustain the prevailing political 
economy at the expense of those most at risk. Political economy 
interests and institutions that drive vulnerability are thus themselves 
at risk because worsening climate change raises questions about their 
legitimacy and political and economic viability (Barnett, 2020).
Second, assessment of this literature suggests four attributes of the 
political economy of adaptation influence development trajectories 
in diverse settings, from Australia to Honduras and the Maldives

(Sovacool et al., 2015), as delivered through the Global Environment 
Facility’s Least Developed Countries Fund (Sovacool et al., 2017). These 
include enclosure (public resources or authority captured by private 
interests); exclusion (stakeholders are marginalised from decision 
making); encroachment (natural systems and ecosystem services 
compromised); and entrenchment (inequality exacerbated). These 
attributes hamper adaptation efforts, and reveal the political nature 
of adaptation (Dolšak and Prakash, 2018) and, by extension, CRD. 
Paradoxically, development initiatives labelled as ‘risk’ reduction or 
resilience building or ‘equitable and environmentally sustainable’, 
such as coastal restoration efforts in Louisiana, USA, can compound 
inequity and climate risk, and perpetuate unsustainable development 
(Gotham, 2016; Eriksen et al., 2021b).
Third, a long-held view is that the effects of mitigation are global,

while those of adaptation are local. A political economy perspective, 
however, underscores cross-scale linkages, and shows that local 
adaptation efforts, vulnerability and climate resilience are manifest in 
development trajectories that are shaped by both local and trans-local 
drivers, and defined by unequal power relations that cross scales and 
levels (Sovacool et al., 2015; Barnett, 2020; Newell, 2020), including in 
key sectors such as energy (Baker et al., 2014) and agriculture (Houser 
et al., 2019), as well as emergent blocs such as Brazil, Russia, India, 
China and South Africa (BRICS) (Power et al., 2016; Schmitz, 2017); and 
sub-national constellations such as cities (Fragkias and Boone, 2016; 
Béné et al., 2018).
Fourth, transitions towards CRD may be technically and economically 
feasible but are ‘saturated’ with power and politics (Tanner and 
Allouche, 2011) (Section  18.3), necessitating focused attention to

political barriers and enablers of CRD (Newell, 2019). With a narrow 
window of time to contain dangerous levels of global warming, 
political economy research calls for CRD trajectories that counter the 
tendency of the prevailing political economy to compound climate 
change impacts and risk (Newell and Lane, 2020), especially given the 
opportunity to realise co-benefits through pandemic recovery efforts 
that take into account vulnerability and the intersection of economic 
power and public health, environmental quality, climate change, and 
human and indigenous rights (Bernauer and Slowey, 2020; Schipper 
Given these insights, CRD can be understood as the sum of complex 
multi-dimensional processes consisting of large numbers of actions and societal choices made by multiple actors from government, the 
private sector and civil society, with important influences by science 
and the media (very high confidence). These actions and social choices

are determined by the available solution space and options, along 
with a range of enabling conditions (Section 18.4.2) that are largely 
bounded by individual and collective worldviews, and related ethics 
and values. This view is consistent with sustainable development being 
a process constituted by multiple inter-related societal choices and 
actions that are often contested as the needs and interests of current 
and future generations are addressed. Development choices have 
path dependencies and context-sensitive synergies and trade-offs 
with natural and embedded human systems, and they are bounded by 
multiple and contested knowledges and worldviews (Goldman et al., 
2018; Heinrichs, 2020; Nightingale et al., 2020; Schipper et al., 2020b). 
Consequently, societal choices about the political economy underpin 
prospects for moving towards or away from CRD.
18.4.2 Enabling Conditions for Near-Term System 
Transitions

Given actors, institutions and their engagement is fundamental to 
supporting system transitions needed for CRD (Section  18.3), this 
section assesses recent literature with respect to how the values, 
choices and behaviours of those actors enable or constrain specific 
enabling conditions. Such enabling conditions represent opportunities 
for policymakers to pursue actions that contribute to CRD beyond 
direct risk management options such as climate adaptation and GHG 
mitigation (Sections 18.2.5.1, 18.2.5.2).
18.4.2.1 Governance and Policy
An overarching enabling condition for achieving system transitions and 
transformations is the presence of enabling governance systems (very 
high confidence) . Recent literature on the translation of governance 
into system transitions in practice suggests four key actions are 
important. The first is the critical reflection on so-called ‘development 
solutions’, alternatively framed by some as ‘empty promises’,

that worsen climate risk, inequity, injustice and ultimately lead to 
unsustainable development (Mikulewicz, 2018; Mikulewicz and Taylor, 
2020). Examples include development aid (Scoville-Simonds et  al., 
2020), large-scale development projects such as biofuel production in 
Ethiopia (Tufa et al., 2018) and urban growth management in Vietnam 
(DiGregorio, 2015). The second is the recognition that while the power of 
different actors and institutions is often tied to access to resources and 
the ability to constrain the actions of others, other dimensions of power 
such as its ability to produce knowledge as well as its contingency on 
circumstances and relationships are also important in enabling energy 
transitions (Avelino et al., 2016; Avelino and Wittmayer, 2016; Lockwood 
et  al., 2016; Ahlborg, 2017; Avelino and Grin, 2017; Partzsch, 2017; 
Smith and Stirling, 2018). Third, governance systems can help to develop

productive interactions between formal government institutions, the 
private sector and civil society including the provision ‘safe arenas’ for 
social actors to deliberate and pursue transitional and transformational 
change (Haukkala, 2018; Törnberg, 2018; Strazds; Ferragina et al., 2020; 
Koch, 2020) (Section 18.3.1, Box 18.1). Fourth, governance can address 
challenges such as climate change from a systems perspective and

18
2707Climate Resilient Development Pathways  Chapter 18
pursue interventions that address the interactions among development, 
climate change, equity and justice, and planetary health (Harvey et al., 
2019; Hölscher et  al., 2019). This is evidenced by recent experience 
with the COVID-19 pandemic response as well as ongoing escalation 
of disaster risk associated with extreme weather events (Walch, 2019; 
Cohen, 2020; Schipper et al., 2020b; Wells et al., 2020).
One output from systems of governance is formal policy frameworks and 
policies that influence processes and outcomes of system transitions 
that support CRD (Section 18.1.3). The Paris Agreement, for example, 
provides a framework for CRD by defining a mitigation-centric goal 
of ‘limiting warming to well below 2°C and enabling a transition to 
1.5°C’ (UNFCCC, 2015). It also provides for a broadly defined global 
adaptation goal (UNFCCC, 2015: Art. 7.1). The NDCs are the core

mechanism for achieving and enhancing climate ambitions under the 
Paris Agreement. However, the pursuit of a given NDC within a specific 
country will likely necessitate a range of other policy interventions 
that have more immediate impact on technologies and behaviour, 
implicating transitions in energy, industry, land and infrastructure 
(very high confidence) (Section  18.3.1). SDG-relevant activities are 
increasingly incorporated into climate commitments in the NDCs (at 
last count 94 NDCs also addressed SDGs), contributing to several (154 
out of the 169) SDG targets (Brandi and Dzebo; Pauw et al., 2018). 
This reflects the potential of the NDCs as near-term policy instruments 
and signposts for progress towards CRD (medium agreement, limited 
evidence) (McCollum et al., 2018b).
As reflected by the SDGs (and SDG 13 specifically), the mainstreaming 
of climate change concerns into development policies is one mechanism

for pursuing sustainable development and CRD (very high confidence). 
However, such mainstreaming has also been critiqued for perpetuating 
‘development as usual’, reinforcing established development logics, 
structures and worldviews that are themselves contributing to climate 
change and vulnerability (O’Brien et al., 2015) and for obscuring and 
depoliticising adaptation choices into technocratic choices (Murtinho, 
2016; Webber and Donner, 2017; Benjaminsen and Kaarhus, 2018; Khatri, 
2018; Scoville-Simonds et al., 2020). The coordinated implementation 
of sustainable development policy and climate action is nonetheless 
crucial for ensuring that the attainment of one does not come at the 
expense of others (Stafford-Smith et al., 2017). For example, aggressive 
pursuit of climate policies that facilitate transitions in energy systems 
can undermine efforts to secure sustainability transitions in other 
systems (Sections 18.3.1.1, 18.2.5.3, Table 18.7).

Several non-climate international policy agreements provide context 
for CRD such as the 1948 UN Universal Declaration of Human Rights, 
the UN Declaration on the Rights of Indigenous Peoples (Hjerpe et al., 
2015) and the Convention on Biological Diversity (CBD; UNFCCC, 
1992), the UN Convention to Combat Desertification (UN, 1994), as 
well as the more recent Sendai Framework for Disaster Risk Reduction 
(UNDRR, 2015) and the ‘new humanitarianisms’ which seeks to reduce 
the gap between emergency assistance and longer term development 
(Marin and Naess, 2017). Collectively they provide a global policy 
framework that protects people’s rights that are potentially threatened 
by climate change (Olsson et al., 2014). These policies are relevant to 
transitions across multiple systems, particularly in societal systems 
towards more equitable and just development.18.4.2.2 Economics and Sustainable Finance
18.4.2.2.1 Economics

System transitions towards CRD is contingent on reducing the costs 
of current climate variability on society while making investments that 
prepare for the future effects of climate change. Climate change and 
responses to climate change will affect many different economic sectors 
both directly and indirectly (Stern, 2007; IPCC, 2014a; Hilmi et al., 2017). 
As a consequence, the characteristics of economic systems will play an 
important role in determining their resilience (very high confidence). 
These effects will occur within the context of other developments, 
such as a growing world population, which increases environmental 
pressures and pollution. This impact is higher for developing countries 
than for high-income countries (Liobikienė and Butkus, 2018). While 
looking for sustainable climate-resilient policies, many complex and 
interconnected systems, including economic development, must be 
considered in the face of global-scale changes (Hilmi and Safa, 2010).

Miller (2017) discusses some of the planning for, and application 
of, adaptation measures that improve sustainability, noting the 
importance of considering a range of factors including complexities 
of interconnected systems, the inherent uncertainties associated with 
projections of climate change impacts and the effects of global-scale 
changes such as technological and economic development for decision 
makers. For example, addressing climate impacts in isolation is unlikely 
to achieve equitable, efficient or effective adaptation outcomes (very 
high confidence). Instead, integrating climate resilience into growth 
and development planning allows decision makers to identify what 
sustainable development policies can support climate-resilient growth 
and poverty reduction and understand better how patterns and trends 
of economic development affect vulnerability and exposure to climate 
impacts across sectors and populations, including distributional

effects (Doczi, 2015). Markkanen and Anger-Kraavi (2019) highlighted 
that climate change mitigation policy can influence inequality both 
positively and negatively. Although higher levels of poverty, corruption, 
and economic and social inequalities can increase the risk of negative 
outcomes, these potential negative effects would be mitigated if 
inequality impacts were taken into consideration in all stages of policy 
making (very high confidence).
The primary objective of economic and financial incentives around 
carbon emissions is to redirect investment from high to low carbon 
technologies (Komendantova et  al., 2016). Recent years have seen 
policy interventions to incentivise transitions in energy, land and 
industrial systems to address climate change and sustainability 
focus on price-based, as opposed to quantity based, interventions. 
Price-based interventions aim at leveraging market mechanisms to 
achieve greater efficiency in the allocation of resources and costs

of mitigating climate change. For example, carbon pricing initiatives 
around the world today cover approximately 8 gigatons of carbon 
dioxide emissions, equivalent to about 20% of global fossil energy 
fuel emissions and 15% of total carbon dioxide GHG emissions (Boyce, 
2018). Meanwhile, environmental taxes and green public procurement 
push producers to eliminate the negative environmental effects 
of production (Danilina and Trionfetti, 2019). There are several 
advantages for environmental taxation including environmental

18
2708Chapter 18 Climate Resilient Development Pathways
effectiveness, economic efficiency, the ability to raise public revenue, 
and transparency (very high confidence). These gains can provide 
more resource-efficient production technologies and positively affect 
economic competitiveness (Costantini et al., 2018).
Policies encouraging eco-innovation, defined as ‘new ideas, behaviour, 
products, and processes that contribute to a decreased environmental 
burden’ (Yurdakul and Kazan, 2020), can positively affect economic 
competitiveness. By implementing policies to encourage eco-
innovation, countries enhance their energy efficiency. These gains can 
provide more resource-efficient production technologies and positively 
affect economic competitiveness (very high confidence) (Costantini 
et al., 2018; Liobikienė and Butkus, 2018). Other than eco-innovation, 
it is important to also consider exnovation, meaning the phasing out

of old technologies, as otherwise the expansion of supply could lead 
to a rebound owing to cheaper prices for carbon-based products (Arne 
Heyen et  al., 2017; David, 2017). Hence, decarbonisation strategies 
that set limits to carbon-based trajectories can be beneficial. Quantity-
based interventions—or so-called ‘command-and-control’ policies—
involve constraints on the quantity of energy consumption or GHG 
emissions through laws, regulations, standards and enforcement, with 
a focus on effectiveness rather than efficiency.
For a transition from dirty (more advanced) technologies to clean (less 
advanced) ones, market-based instruments such as carbon taxes should 
be considered alongside subsidies and other incentives that stimulate 
innovation (Acemoglu et al., 2016). Research and development in energy 
technologies, for example, can help reduce costs of deployment and 
therefore the costs of operating in a carbon-constrained world. Hémous

(2016) indicates that a unilateral environmental policy which includes 
both clean research subsidies and trade tax can ensure sustainable 
growth, but unilateral carbon taxes alone might increase innovation in 
polluting sectors and would not generally lead to sustainable growth.
18.4.2.2.2 Climate Finance
Achieving progress on system transitions will be contingent on the 
ability of actors and institutions to access the financing they need to 
invest in innovation, adaptation and mitigation, and broader system 
change (very high confidence). By greening their investment portfolios, 
investors can support reduction in vulnerability to the consequences 
of climate change and the reduction of GHG emissions. Finance can 
contribute to the reduction of GHG emissions, for example, by efficiently 
pricing the social cost of carbon, by reflecting the transition risks in 
the valuation of financial assets, and by channelling investments in

low-carbon technologies (OECD, 2017). At the same time, there is a 
growing need to spur greater public and private capital into climate 
adaptation and resilience including climate-resilient infrastructure 
and nature-based solutions to climate change. For instance, the Green 
Climate Fund, established within the framework of the UNFCCC, is 
assisting developing countries in adaptation and mitigation initiatives 
to counter climate change.
Recent evidence sheds light on the magnitude and pervasiveness 
of climate risk exposure for global banks and financial institutions. 
According to Dietz et al. (2016), up to about 17% of global financial 
assets are directly exposed to climate risks, particularly the impacts of extreme weather events on assets and their outputs. However, when 
indirect exposures via financial counterparts are considered, the share 
of assets subject to climate risks is much larger (40–54%) (Battiston

et al., 2017). Hence, the magnitude of climate change-related risks is 
substantial, and similar to those that started the 2008 financial crisis 
(high agreement, limited evidence).
Financial actors increasingly recognise that the generation of long-term, 
sustainable financial returns is dependent on stable, well-functioning 
and well-governed social, environmental and economic systems (very 
high confidence) (Shiller, 2012; Schoenmaker and Schramade, 2020). 
Institutional approaches to a variety of environmental domains (Krueger 
et  al., 2019) which seek to integrate the pursuit of green strategies 
with financial returns include targeted investments in green assets (e.g., 
green bonds, clean energy public equity) and specialised funds/vehicles 
for renewable energy infrastructure (Tolliver et al., 2019; Gibon et al., 
2020); cleantech venture capital and alternative finance (Gianfrate and 
Peri, 2019); investment screening to steer capital to green industries

(Nielsen and Skov, 2019; Ambrosio et al., 2020); and active ownership 
to influence organisational behaviour (Silvola and Landau, 2021).
Despite the expansion of green mandates across the investment chain, 
definitions of some of the asset classes associated with green investing 
are ambiguous and poorly defined. The EU taxonomy for sustainable 
activities is a promising step in the right direction. For example, a 
‘green’ label for bonds is often stretched to encompass financing 
facilities of issuers that misrepresent the actual environmental 
footprint of their operations (the so-called risk of ‘greenwashing’). 
Even in cases where the bonds’ proceeds are actually used to finance 
green projects, investors often remain exposed to both the green and 
‘brown’ assets of the issuers (Gianfrate and Peri, 2019; Flammer, 2020). 
The heterogeneity of metrics and rating methodologies (along with 
inherent conflict of interests between issuers, investors and score/

rating providers) results in inconsistent and unreliable quantification 
of the actual environmental footprint of corporate and sovereign 
issuers (Battiston et al., 2017; Busch et al.).
In order to promote financial climate-related disclosures for companies 
and financial intermediaries, the financial system could play a key 
role in pricing carbon and in allocating capital towards low-carbon 
emission companies (Aldy and Gianfrate, 2019; Bento and Gianfrate, 
2020; Aldy et al., 2021). Stable and predictable carbon-pricing regimes 
would significantly contribute to fostering financial innovation 
that can help further accelerate the decarbonisation of the global 
economy, even in jurisdictions which are more lenient in implementing 
climate mitigation actions (very high confidence) (Baranzini et  al., 
2017). A growing number of financial regulators are intensifying 
efforts to enhance climate-related disclosure of financial actors.

In particular, the Financial Stability Board created the Task Force on 
Climate-related Financial Disclosures (TCFD) to improve and increase 
reporting of climate-related financial information. Several countries 
are considering implementing mandatory climate risk disclosure in 
line with TCFD’s recommendations. Central Banks are also considering 
mandatory disclosure and climate stress testing for banks. For instance, 
in November 2020 the European Central Bank (ECB) published a guide 
on climate-related and environmental risks explaining how the ECB 
expects banks to prudently manage and transparently disclose such

18
2709Climate Resilient Development Pathways  Chapter 18
risks under current prudential rules. The ECB also announced that 
banks in the Euro-zone will be stress tested on their ability to withstand 
climate change-related risks. In addition to disclosure requirements 
and stress testing, some Central Banks are considering the possibility 
of steering or tilting the allocation of their assets to favour the less 
polluting issuers (Schoenmaker, 2019). This, in turn, would translate 
into lower cost of capital for cleaner sectors, significantly accelerating 
the greening of the real economy.
18.4.2.3 Institutional Capacity
Institutional capacity for system transitions refers to the capacity 
of structures and processes, rules, norms and cultures to shape 
development expectations and actions aimed at durable improvements 
in human well-being. The AR5 highlighted the need for strong 
institutions to create enabling environments for adaptation and GHG

mitigation action (Denton et al., 2014). Institutions stand within the 
social and political practices and broader systems of governance 
that ultimately drive adaptation and development processes and 
outcomes. They are thus produced by them and can become tools by 
which some actors constrain the actions of others (Gebreyes, 2018). As 
a consequence, they and can become a significant barrier to change, 
whether incremental or more transformational (very high confidence). 
The post-AR5 focus on transformational adaptation and resilience 
present in the literature suggests that institutions that enable system 
transitions towards CRD are secure enough to facilitate a wide range 
of voices, and legitimate enough to change goals or processes over 
time, without reducing confidence in their efficacy.
The limited literature on institutions and pathways relevant to system 
transitions and CRD suggests that institutions are most effective when

taking a development-first approach to adaptation. This is consistent 
with the principles of CRD which emphasise not simply reducing 
climate risk, but rather making development processes resilient to 
the changing climate. There is agreement in this literature that such 
an approach allows for the effective integration of climate challenges 
into existing policy and planning processes (very high confidence) 
(Pervin et  al., 2013; Kim et  al., 2017b; Mogelgaard et  al., 2018). 
However, this approach generally rests on an incremental framing of 
institutional change (Mahoney and Thelen, 2009) based on two critical 
assumptions. The first is that existing processes and institutions are 
capable of bringing about system transitions that generate desired 
development outcomes and thus can be considered appropriate 
vehicles for the achievement of CRD. A large critical literature questions 
the efficacy of formal state and multilateral institutions. The evidence

for the ability of local, informal institutions to achieve development 
goals remains uneven, with robust evidence of positive impacts on 
public service delivery, but more ambiguous evidence on behaviour 
changes associated with strengthened institutions (Berkhout et  al., 
2018). The second is that the mainstreaming of adaptation will bring 
about changes to currently unsustainable development practices and 
pathways, instead of merely strengthening development as usual by 
subsuming adaptation to existing development pathways and allowing 
them to endure in the face of growing stresses (Eriksen et al., 2015; 
Godfrey-Wood and Otto Naess, 2016; Scoville-Simonds et al., 2020). 
There is evidence that countries with poor governance have limited 
adaptation planning or action at the national level, even when other determinants of adaptive capacity are present (Berrang-Ford et  al., 
2014). This suggests that, in these contexts, adaptation efforts are

likely to be subsumed to existing government goals and actions, rather 
than having transformational impact.
18.4.2.4 Science, Technology and Innovation
Ongoing innovations in technology, finance and policy have enabled 
more ambitious climate action over the past decade, including 
significant growth in renewable energy, electrical vehicles and energy 
efficiency. However, access to, and the benefits of, that innovation have 
not been evenly distributed among global regions and communities, 
and continued innovation is needed to facilitate climate action and 
sustainable development (very high confidence). Policymakers need 
useful science and information (Cornell et al., 2013; Kirchhoff et al., 
2013; Calkins, 2015; IPCC, 2019 f; Guido et al., 2020) to make informed 
decisions about possible risks, and the benefits, costs and trade-offs 
of available adaptation, mitigation and sustainable development 
solutions (i.e., Article 4.1 of the Paris Agreement; UNFCCC, 2015).

Moreover, recent literature has emphasised the need for deep 
technological, as well social, changes to avert the risks of conventional 
development trajectories (Gerst et al., 2013; IPCC, 2014a).
An effective and innovative technological regime is one that is 
integrated with local social entities across different modes of life, local 
governance processes (Pereira, 2018; Nightingale et  al., 2020) and 
local knowledge(s), which increasingly support adaptation to socio-
environmental drivers of vulnerability (Schipper et al., 2014; Nalau et al., 
2018; IPCC, 2019 f). These actors and their knowledge are often ignored 
in favour of knowledge held by experts and policymakers, exacerbating 
uneven power relations (Naess, 2013; Nightingale et  al., 2020). For 
example, achieving sustainability and shifting towards a low carbon 
energy system (e.g., hydropower dams, wind farms) remains a contested 
space with divergent interests, values and future prospects (Bradley and

Hedrén, 2014; Avila, 2018; Mikulewicz, 2019), and potential impacts 
on human rights as embodied by the Paris Agreement (UNFCCC, 
2015). A number of studies have emphasised the limits of relying upon 
technology innovation and deployment (e.g., expansion of renewable 
energy systems and/or carbon capture) as a solution to challenges of 
climate change and sustainable development (Section 18.3.1.2). This is 
because such solutions may fail to consider the local historical contexts 
and barriers to participation of vulnerable communities, restricting their 
access to land, food, energy and resources for their livelihoods.
18.4.2.5 Monitoring and Evaluation Frameworks
Enabling system transitions towards CRD is dependent in part on 
the ability to monitor and evaluate system transitions and broader 
development pathways to identify effective interventions and 
barriers to their implementation (very high confidence). However, the

monitoring and evaluation of individual system transitions, much less 
CRD, remains highly challenging for multiple reasons (Persson, 2019). 
The highly contextual nature of resilience, adaptation and sustainable 
development means that, unlike climate mitigation, it is difficult to 
define universal metrics or targets for adaptation and resilience 
(Pringle and Leiter, 2018); (Brooks et al., 2014). This is demonstrated 
by the Paris Agreement’s global goal for adaptation, The mismatch

18
2710Chapter 18 Climate Resilient Development Pathways
Box 18.6 | ‘Green’ Strategies of Institutional Investors
Negative and Positive Screening. Investors assess the carbon footprint of issuers and identify the best and worst performers (Boermans 
and Galema, 2019). The issuers with excessive carbon footprint are divested and fall into the ‘exclusion lists’ (negative screening). 
Alternatively, the investors commit to pick only the best in class (positive screening). As a bare minimum, screening approaches force 
more transparent environmental reporting from issuers. In the most optimistic scenario, to avoid exclusion lists issuers may progressively 
divest their non-green operations. In the long term, the combination of positive and negative screening will reward sustainable issuers 
relative to non-green sectors, thus reducing the cost of capital for less polluting entities.

Active Ownership. Equity investors can exercise the voting rights at shareholders’ meetings in relation to governance and business 
strategy, including the environmental performance. In addition, institutional investors engage with the management and the boards 
of directors of investee companies. Active ownership is therefore defined as the full exercise of the rights that accrue to the ‘owners’ 
of the securities issued by companies (Dimson et al., 2015; Dimson et al., 2020). Active owners are entitled to question and challenge 
the robustness of financial analyses and the risk assessment behind strategic decisions including the environmental footprint ones. For 
instance, since fossil fuel businesses face the prospect of dramatic business decline (Ansar et al., 2013) and must revisit their business 
model to survive, active ownership by institutional investors may foster the transition to cleaner production and supply chain. Companies

more exposed to carbon risks particularly need the active support of long-term shareholders. In turn, investors adopting an active 
ownership approach can manage their holdings’ exposure to climate change risks, thus protecting the value of their investments on a 
long-term horizon (Krueger et al., 2019).
Specialized Financial Instruments and Investors. New asset classes have been created to address the climate change challenge. 
Also, specialised investment funds and vehicles came to life with the primary objective of addressing climate issues. While these financial 
instruments and funds prioritise the achievement of climate objectives, they do not sacrifice financial returns and are able to attract 
private capital. To mention a few examples:
• Green bonds are typically issued by companies, banks, municipalities and governments with the commitment to use the proceeds

exclusively to finance or refinance green projects, assets or business activities. These bonds are equivalent to any other bond issued 
by the same entity except for the label of ‘greenness’ that ideally is verified ex ante at the launch and ex post when the proceeds 
are actually used by the issuer. Early evidence show that green bonds do not penalise financially issuers (Gianfrate and Peri, 2019; 
Flammer, 2020).
• Carbon funds  are designed to help countries achieve long-term sustainability typically financing forest conservation. They are intended 
to reduce climate change impacts from forest loss and degradation.
• Project finance. New renewable energy initiatives are likely to recur more and more to project finance. Project finance relies on the 
creation of a special purpose vehicle (SPV), which is legally and commercially self-contained and serves only to run the renewable

energy project. The SPV is financed without (or very limited) guarantees from the sponsors (typically energy companies: investors are 
therefore paid back on the basis only of SPV’s future cash flows only and cannot recourse on the sponsors’ assets) (Steffen, 2018).
• Cleantech venture capital. These funds invest exclusively in early-stage companies working on innovative, but not yet fully tested, 
clean technologies. The risk profile of such investments is usually very high. The extent to which this segment of the financial 
industry can successfully support ‘deep’ energy innovations is still debated (Gaddy et al., 2017). When cleantech start-ups develop 
hardware requiring a high upfront investment, support from the public sector seems necessary to attract further investments from 
large corporations and patient institutional investors.
• Crowdfunding and alternative finance are emerging as a channel to both finance small-scale clean energy projects as well as fund

early-stage innovative clean technologies (Cumming et al., 2017; Bento et al., 2019).
between timescales associated with resilience and adaptation 
interventions and those over which the results of such interventions 
are expected to become apparent tends to result in a focus on the 
measurement of spending, outputs and short-term outcomes, rather 
than longer-term impacts (Brooks et al., 2014; Pringle and Leiter, 2018). 
The need to assess resilience and adaptation against a background of 
evolving climate hazards, and to link resilience and adaptation with 
development outcomes, present further methodological challenges 
(very high confidence) (Brooks et al., 2014).Currently, the ability to monitor different components of CRD are in 
various stages of maturity (very high confidence). Monitoring of the 
SDGs, for example, is a routine established practice at global and 
regional levels, and UNDP publishes annual updates on progress

towards the SDGs (United Nations, 2021). For resilience, Brooks et al. 
(2014) identify three broad approaches to its measurement, each of 
which could offer potential mechanisms for monitoring progress 
towards CRD. One is a ‘hazards’ approach, in which resilience is 
described in terms of the magnitude of a particular hazard that can 
be accommodated by a system, useful in contexts where thresholds

18
2711Climate Resilient Development Pathways  Chapter 18
in climate and related parameters can be identified and linked with 
adverse impacts on human populations, infrastructure and other 
systems (Naylor et al., 2020). An ‘impacts’ approach is one in which 
resilience is measured in terms of actual or avoided impacts and is 
suited for tracking adaptation success in delivering CRD over longer 
timescales, for example at the national level (Brooks et  al., 2014). 
Finally, a ‘systems’ approach is one where resilience is described in 
terms of the characteristics of a system using quantitative or qualitative 
indicators which are often associated with different ‘dimensions’ of 
resilience (Serfilippi and Ramnath, 2018; Saja et al., 2019). This allows 
measurement of key indicators that are proxies for resilience at regular 
intervals, even in the absence of significant climate hazards and 
associated disruptions (very high confidence) (Brooks et al., 2014) (see

also Cross-Chapter Box ADAPT in Chapter 1). Similar criteria could be 
applied to evaluating adaptation options and their implementation as 
well as various interventions in pursuit of SDGs.
18.4.3 Arenas of Engagement
Much of the enabling conditions for system transitions discussed 
in Section 18.4.2 are inherently linked to actors and their agency in 
pursuing system change. Yet a significant literature has developed 
since the AR5, exploring not only the role of different actors in pursuing 
adaptation, mitigation and sustainable development options, but also 
how those actors interact with one another to drive outcomes. CRDPs are 
determined by the interactions between societal actors and networks, 
including government, civil society and the private sector, as well as 
science and the media. The resultant social choices and cumulative 
private and public actions (and inactions) are institutionalised through 
both formal and informal institutions that evolve over time and seek to

provide societal stability in the face of change. The degree to which the 
emergent pathways foster just and CRD depends on how contending 
societal interests, values and worldviews are reconciled through these 
interactions. These interactions occur in many different arenas of 
engagement, that is, the settings, places and spaces in which societal 
actors interact to influence the nature and course of development, 
including political, economic, socio-cultural, ecological, knowledge–
technology and community arenas (Figures 18.1, 18.2).
For example, political arenas range from formalised election and voting 
procedures to more informal and less transparent practices, such as 
special interest lobbying. Town squares and streets can become sites 
of political struggle and dissent, including protests against climate 
inaction. As a more specific case in point, the formal space for national, 
sub-national and international adaptation governance emerged at

COP 16 (UNFCCC, 2010) when adaptation was recognised as having 
a similar level of priority as mitigation. The Paris Agreement (UNFCCC, 
2015) built on this and the 2030 Sustainable Development Agenda 
(United Nations, 2015) to link adaptation to development and climate 
justice, widening the scope of adaptation governance beyond formal 
government institutions. It also highlighted the importance of multi-
level adaptation governance, including non-state voices from civil 
society and the private sector. This implied the need for wider arenas 
and modes of engagement around adaptation (Chung Tiam Fook, 2017; 
Lesnikowski et al., 2017; IPCC, 2018a) that facilitate coordination and 
convergence among these diverse actors including individual citizens to collectively solve problems and unlock the synergies between 
adaptation and mitigation and sustainable development (IPCC, 2018a; 
Romero-Lankao et al., 2018).
There are many other visible and less visible arenas of engagement

in the other interconnected spheres of societal interaction spanning 
scales from the local to international level. The metaphor of arenas 
derives from diverse social and political theory, with applications 
in studies of, among other things, governance transformation and 
transitions (Healey, 2006; Jørgensen, 2012; Jørgensen et  al., 2017). 
It underscores that these arenas can be enduring or temporary in 
nature, are historically situated and often spatially bounded, and 
signifies the many different mechanisms by which societal actors 
interact in dynamic and emergent ways. Power and politics impact 
access and influence in these arenas of engagement—with varying 
levels of inclusion and exclusion shaping the nature and trajectory of 
development. In practice, some arenas of engagement are ‘struggle 
arenas’ as different societal actors strive to influence the trajectory of 
development, with inevitable winners and losers.

Institutional arrangements to foster CRD are at an early stage of 
development in most regions (medium agreement, limited evidence). 
They need to be further clarified and strengthened to enable a sharing of 
resources and responsibilities that facilitate climate actions embracing 
climate resilience, equity, justice, poverty alleviation and sustainable 
development (Wood et al., 2017; IPCC, 2018a; Reckien et al., 2018). 
These endeavours are strongly influenced by how contested and 
complementary worldviews about climate change and development 
are mobilised by societal actors to justify, direct, accelerate and deepen 
transformational climate action or entrench maladaptive business as 
usual practices (Section 18.4.3.1).
18.4.3.1 Worldviews
Worldviews are overarching systems of meaning and meaning-making 
that inform how people interpret, enact and co-create reality (De Witt 
et  al., 2016). Worldviews shape the vision, beliefs, attitudes, values,

emotions, actions and even political and institutional arrangements. 
As such, they can promote holistic, egalitarian approaches to enable, 
accelerate and deepen climate action and environmental care 
(Ramkissoon and Smith, 2014; De Witt et al., 2016; Lacroix and Gifford, 
2017; Sanganyado et al., 2018; Brink and Wamsler, 2019). Alternatively, 
they can also serve as significant barriers to system transitions 
and transformation, based on anthropocentric, mechanistic and 
materialistic worldviews and the utilitarian, individualist or skeptical 
values and attitudes they often promote (very high confidence) 
(Beddoe et al., 2009; van Egmond and de Vries, 2011; Stevenson et al., 
2014; Zummo et al., 2020).
Traditional, modern and post-modern worldviews have different, and in 
many ways, complementary potentials for enabling diverse approaches 
to climate action and sustainable development. They can also shift 
societal values and societal concern for climate change (Shi et  al.,

2015), resulting in changes in behaviour and acceptance of climate 
change policies (van Egmond and de Vries, 2011; Van Opstal and Hugé, 
2013; De Witt et al., 2016; Shaw, 2016) which are predictors of concern. 
Among the challenges of strongly different climate-related worldviews,

18
2712Chapter 18 Climate Resilient Development Pathways
is that they rarely co-exist. Some worldviews become incompatible or 
hostile to other worldviews, openly seeking to dominate, eliminate 
or segregate competing perspectives (medium agreement, medium 
evidence) (de Witt, 2015; Jackson, 2016; Nightingale, 2016; Xue et al., 
2016; Goldman et al., 2018).
To address these difficult contests, worldviews regarding climate and 
global environmental change are often expressed in scientific language 
and themes (Parsons et al., 2016; Goldman et al., 2018). This can exclude 
other worldviews grounded in other forms of knowledge or ways of 
knowing which ultimately narrows understanding of climate change 
and the solution space. Hence, the post-AR5 literature on worldviews 
focuses on the numerous meanings, associations, narratives and 
frames of climate change and how these shape perceptions, attitudes 
and values (Morton, 2013; Boulton, 2016; Hulme, 2018; Nightingale

Böhler, 2019). The recognition of the diversity of interpretations and 
meanings has led to multidisciplinary and transdisciplinary research 
that incorporates the humanities and the arts (Murphy, 2011; Elliott 
and Cullis, 2017; Steelman et al., 2019; Tauginienė et al., 2020), feminist 
studies (MacGregor, 2003; Demeritt et al., 2011; Bell, 2013; Brink and 
Wamsler, 2019; Plesa, 2019) and religious studies (Sachdeva, 2016; 
McPhetres and Zuckerman, 2018) to examine diverse understandings 
of reality and knowledge possibilities around climate change. In 
addition, literature on cultural cognition, epistemological plurality and 
relational ontologies draws on non-Western worldviews and forms of 
knowledge (Goldman et al., 2018)); .
On the other hand, the tendency for certain worldviews to dominate 
the policy discourse has the potential to exacerbate social, economic 
and political inequities as well as ontological, epistemic and procedural

injustices (very high confidence). Research aimed at exploring the 
existing political ontology and knowledge politics of exclusion that 
marginalise certain communities and actors originated in academic or 
scientific perspectives. This includes institutions such as the IPCC and is 
subsequently replicated in social representations, including the media, 
public policy and the development agenda, narrowing possibilities 
for social transformation (Jackson, 2014; Luton, 2015; Escobar, 2016; 
Burman, 2017; Newman et al., 2018; Sanganyado et al., 2018; Wilson 
and Inkster, 2018).
18.4.3.2 Political and Government Arenas
CRD is embedded in social systems, in the political economy and its 
underlying ideologies, interests and institutions (Section  18.4.1). 
The pursuit of CRD, and shifting development pathways away from 
prevailing trends, unfolds in an array of political arenas, from the 
offices of bureaucrats to parliament buildings, sidewalks and streets,

to discursive arenas in which governance actors interact—from the 
village level to global forums (Jørgensen et al., 2017; Montoute et al., 
2019; Sørensen and Torfing, 2019; Pasquini, 2020). Paradoxically, the 
post-AR5 literature suggests that political arenas are often used to 
shut down efforts to explore the solution space for climate change 
and sustainable development (medium agreement, robust evidence) 
(e.g., Kenis and Mathijs, 2012; Kenis and Mathijs, 2014; Beveridge and 
Koch, 2016; Kenis and Lievens, 2016; Driver et  al., 2018; Meriluoto, 
2018; Swyngedouw, 2018; Mocca and Osborne, 2019). Power 
relationships among different actors create opportunities for people to be included or excluded in collective action (Siméant-Germanos, 2019) 
(Sections 18.3.1.6, 18.4.3.5). Therefore, as evidenced by examples from 
the UK (MacGregor, 2019) and China (Huang and Sun, 2020), small-
scale collective environmental action has transformative potential

in part owing to its ability to increase levels of cooperation among 
different actors (medium agreement, limited evidence) (Green et al., 
2020; Blühdorn and Deflorian, 2021).
In addition to the ‘arm’s length’ acts of voting, social mobilisation, 
protest and dissent can be critical catalysts for transformative change 
(Porta, 2020). These are competitions for recognition, power and 
authority (Nightingale, 2017) that take place in settings. This is evidenced 
by experiences from the energy sector in Bangladesh which became 
a contested national policy domain and where social movements 
eventually transformed the nation’s energy politics (Faruque, 2017). 
Similarly, in Germany, the nation’s energy transition led to marked 
changes in agency and legal frameworks, and energy markets drove 
the proliferation of so-called municipalisations of energy systems—a 
reversal of years of system privatisation (Becker et  al., 2016).

Meanwhile, experience in Bolivia demonstrate that the transformative 
potential of political conflict depends on transcending narrow issues 
to form broad coalitions with a collective identity that challenge 
prevailing development objectives and trajectories (Andreucci, 2019). 
Such examples illustrate the power of the communities as a vanguard 
against environmentally destructive practices (Villamayor-Tomas 
and García-López, 2018). Social movements have been successful at 
countering fossil fuel extraction (Piggot, 2018) and open up political 
opportunities in the face of increasing efforts to capture natural 
resources (Tramel, 2018) and are bolstered by resistance from within 
some corporations and/or their shareholders (Fougère and Bond, 2016; 
Swaffield, 2017; Walton, 2018a; Walton, 2018b).
Coincident with these social movements targeting climate change and 
sustainability has been a rise of political conservatism and populism as

well as growth in misinformation (high agreement, medium evidence) 
(Mahony and Hulme, 2016; Swyngedouw, 2019). This reflects efforts 
to maintain the status quo by actors in positions of power in the face 
of rising social inertia for climate action (Brulle and Norgaard, 2019). 
Political arenas of the future could include a new body politic that 
integrates non-humans and a new geo-spatial politics (Latour et al., 
2018).
As introduced in the discussion of governance as an enabling condition 
(Section 18.4.2.1), a wide range of actors are involved in successful 
adaptation, mitigation, and sustainability policy and practice 
including national, regional and local governments, communities and 
international agencies (Lwasa, 2015). As of 2018, 197 countries had 
between them over 1500 laws and policies addressing climate change 
as compared with 60 countries with such legislation in 1997 when the 
Kyoto Protocol was agreed upon (Nachmany et al., 2017; Nachmany

and Setzer, 2018). In judicial branches, climate change litigation is 
increasingly becoming an important influence on policy and corporate 
behaviour among investors, activists and local and state governments 
(Setzer and Byrnes, 2019). There is enhanced action on climate change 
at both national and sub-national levels, even in cases where national 
policies are inimical, as in the USA (Carmin et al., 2012; Hansen et al., 
2013).

18
2713Climate Resilient Development Pathways  Chapter 18
Cross-Chapter Box INDIG | The Role of Indigenous Knowledge and Local Knowledge in 
Understanding and Adapting to Climate Change
Authors: Tero Mustonen (Finland), Sherilee Harper (Canada), Gretta Pecl (Australia), Vanesa Castán Broto (Spain), Nina Lansbury 
(Australia), Andrew Okem (Nigeria/South Africa), Ayansina Ayanlade (Nigeria), Jackie Dawson (Canada), Pauline Harris (Aotearoa-New 
Zealand), Pauliina Feodoroff (Finland), Deborah McGregor (Canada)
Indigenous knowledge refers to the understandings, skills and philosophies developed by societies with long histories of interaction 
with their natural surroundings (UNESCO, 2018; IPCC, 2019a). Local knowledge refers to the understandings and skills developed by 
individuals and populations, specific to the places where they live (UNESCO, 2018; IPCC, 2019a). Indigenous knowledge and local

knowledge are inherently valuable but have only recently begun to be appreciated and in western scientific assessment processes in their 
own right (Ford et al., 2016). In the past these often endangered ways of knowing have been suppressed or attacked (Mustonen, 2014). 
Yet these knowledge systems represent a range of cultural practices, wisdom, traditions and ways of knowing the world that provide 
accurate and useful climate change information, observations and solutions (very high confidence) (Table Cross-Chapter Box INDIG.1). 
Rooted in their own contextual and relative embedded locations, some of these knowledges represent unbroken engagement with the 
earth, nature and weather for many tens of thousands of years, with an understanding of the ecosystem and climatic changes over 
longer-term timescales that is held both as knowledge by Indigenous Peoples and local peoples, as well as in the archaeological record 
(Barnhardt and Angayuqaq, 2005; UNESCO, 2018).

Indigenous Peoples around the world often hold unique worldviews that link today’s generations with past generations. In particular, 
many Indigenous Peoples consider concepts of responsibility through intergenerational equity, thereby honouring both past and future 
generations (Matsui, 2015; McGregor et al., 2020). This can often be in sharp contrast to environmental valuing and decision making that 
occurs in Western societies (Barnhardt and Angayuqaq, 2005). Therefore, consideration of Indigenous knowledge and local knowledge 
needs to be a priority in the assessment of adaptation futures (Nakashima et al., 2012); Ford et al., 2016) (Chapter 1), although adequate 
indigenous cultural and intellectual property rights require legal and non-legal measures for recognition and protection (Janke, 2018).
Indigenous knowledge and local knowledge are crucial to address environmental impacts, such as climate change, where the uncertainty

of outcome is high and a range of responses are required (Mackey and Claudie, 2015). However, working with this knowledge in an 
appropriate and ethically acceptable way can be challenging. For instance, questions of data ‘validity’ and the requirement to communicate 
such knowledge in the dominant language can lead to inaccurate portrayals of Indigenous knowledge as inferior to science. This may 
overlook the uniqueness of Indigenous knowledge and then lead to the overall devaluation of indigenous political economies, cultural 
ecologies, languages, educational systems and spiritual practices (Smith, 2013; Sillitoe, 2016; Naude, 2019; Barker and Pickerill, 2020). 
Furthermore, Indigenous knowledge is too often only sought superficially—focusing only on the ‘what’, rather than the ‘how’ of climate 
change adaptation and/or seen through the lenses of ‘romantic glorification’ leaving little room for the knowledge to be expressed as

authored by the communities and knowledge holders themselves (Yunkaporta, 2019).
Multiple knowledge systems and frameworks
Indigenous knowledge systems include not only the specific narratives and practices to make sense of the world, but also profound 
sources of ethics and wisdom. They are networks of actors and institutions that organise the production, transfer and use of knowledge 
(Löfmarck and Lidskog, 2017). There is a pluralism of forms of knowledge that emerge from oral traditions, local engagement with 
multiple spaces, and Indigenous cultures (Peterson et al., 2018). Recognising such multiplicity of forms of knowledge has long been 
an important concern within sustainability science (Folke et  al., 2016). Less dominant forms of knowledge should not be put aside 
because they are not comparable or complementary with scientific knowledge (Brattland and Mustonen, 2018; Mustonen, 2018; Ford

et al., 2020; Ogar et al., 2020). Instead, Indigenous knowledge and local knowledge can shape how climate change risk is understood 
and experienced, the possibility of developing climate change solutions grounded in place-based experiences, and the development of 
governance systems that match the expectations of different Indigenous knowledge and local knowledge holders (very high confidence).
Different frameworks that enable the inclusion of Indigenous knowledge have emerged from efforts to utilise more than one knowledge 
system (robust evidence, high agreement). For example, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem 
Services (IPBES) has developed a ‘nature’s contribution to peoples’ framework that provides a common conceptual vocabulary and 
structural analysis (Díaz et al., 2015; Tengö et al., 2017; Díaz et al., 2018; Peterson et al., 2018). The IPBES approach complements other

efforts to study areas of intersection between scientific and indigenous worldviews (Barnhardt and Angayuqaq, 2005; Huaman and 
Sriraman, 2015) or ‘boundaries’ that illustrate ‘blind spots’ in scientific knowledge (Cash et al., 2003; Clark et al., 2016; Brattland and 
Mustonen, 2018). These frameworks highlight areas of collaboration but provide less guidance in areas where sources of evidence 
conflict across different knowledge systems (Löfmarck and Lidskog, 2017). These experiences suggest that the inclusion of Indigenous 
knowledge and local knowledge in international assessments may transform the process of assessment of scientific, technical and

18
2714Chapter 18 Climate Resilient Development Pathways
socioeconomic evidence (medium evidence, high agreement). These knowledge systems also point to novel discoveries that may be still 
unknown to the scientific world but have been known by communities for millennia (Mustonen and Feodoroff, 2020).
The importance of free and prior-informed consent
Obtaining free and prior-informed consent is a necessary but not sufficient condition to engage in knowledge production with Indigenous 
Peoples (Sillitoe, 2016). Self-determination in climate change assessment, response and governance is critical (Chakraborty and Sherpa, 
2021), and Indigenous Peoples are actively contributing to respond to climate change (Etchart, 2017). Climate change assessment and 
adaptation should be self-determined and led by Indigenous Peoples, acknowledge the importance of developing genuine partnerships,

respect Indigenous knowledge and ways of knowing, and acknowledge Indigenous Peoples as stewards of their environment (Country 
et al., 2016; Country et al., 2018; ITK, 2019; Barker and Pickerill, 2020; Chakraborty and Sherpa, 2021). Supporting Indigenous Peoples’ 
leadership and rights in climate adaptation options at the local, regional, national and international levels is an effective way to ensure that 
such options are adapted to their living conditions and do not pose additional detrimental impacts to their lives (very high confidence). 
Chapter 18 shows that the transformations required to deliver climate-resilient futures will create societal disruptions, with impacts that 
are most often unevenly experienced by groups with high exposure and sensitivity to climate change, including Indigenous Peoples and 
local communities (Schipper et al., 2020a). Climate-resilient futures depend on finding strategies to address the causes and drivers of

deep inequities (Chapter 18). For example, climate-resilient futures will depend on recognising the socioeconomic, political and health 
inequities that often affect Indigenous Peoples (Mapfumo et al., 2016; Ludwig and Poliseli, 2018) (very high confidence).
International conventions to support and utilise Indigenous knowledge and local knowledge
Several tools within international conventions may support instruments to develop equitable processes that facilitate the inclusion of 
Indigenous knowledge and leadership in climate change adaptation initiatives. The International Labour Convention 69 recognised 
Indigenous People’s right to self-determination in 1989 (ILO, 1989). The United Nations’ Declaration on the Rights of Indigenous Peoples 
(United Nations, 2007) includes articles on the right to development (Article 23), the right to maintain and strengthen their distinctive

spiritual relationship and to uphold responsibilities to future generations (Article 25), and the right to the conservation and protection 
of the environment and the productive capacity of their territories (Article 29). Article 26 upholds the right to the lands, territories and 
resources, the right to own, use, develop and control the lands, and legal recognition and protection of these lands, territories and 
resources. Indigenous Peoples are also recognised within the Sustainable Development Goals as a priority group (Carino and Tamayo, 
2019). International events such as the ‘Resilience in a time of uncertainty: Indigenous Peoples and Climate Change’ conference brought 
together Indigenous Peoples’ representatives and government leaders from around the world to discuss the role of Indigenous Peoples 
in climate adaptation (UNESCO, 2015).
The value of Indigenous knowledge and local knowledge in climate adaptation planning

There have been increasing efforts to enable Indigenous knowledge holders to participate directly in IPCC assessment reports (Ford 
et al., 2012; Nakashima et al., 2012; Ford et al., 2016). Adaptation efforts have benefited from the inclusion of Indigenous knowledge 
and local knowledge (IPCC, 2019e) (very high confidence). Moreover, it has been recognised that including Indigenous knowledge and 
local knowledge in IPCC reports can contribute to overcoming the combined challenges of climate change, food security, biodiversity 
conservation, and combating desertification and land degradation (IPCC, 2019c) (high confidence). Limiting warming to 1.5°C necessitates 
building the capability of formal assessment processes to respect, include and utilise Indigenous knowledge and local knowledge (IPCC, 
2018a) (medium evidence, high agreement).
However, these efforts have been accompanied by a recognition that ‘integration’ of Indigenous knowledge and local knowledge cannot

mean that those knowledge systems are subsumed or required to be validated through typical scientific means (Gratani et al., 2011; 
Matsui, 2015). Such a critique of ‘validity’ can be inappropriate, unnecessary, can disrespect Indigenous Peoples’ own identities and 
histories, limits the advancement and sharing of these perspectives in the formal literature, and overlooks the structural drivers of 
oppression and endangerment that are associated with Western civilisation (Ford et al., 2016). Moreover, by underutilising Indigenous 
knowledge and local knowledge systems, opportunities that could otherwise facilitate effective and feasible adaptation action can be 
overlooked. We should also reserve space for the understanding that each cultural knowledge system, building on linguistic-cultural 
endemicity, is unique and inherently valuable.
Indigenous Peoples have often constructed their ways of knowing using oral histories as one of the vehicles of mind and memory,

observance, governance and maintenance of customary law (Table Cross-Chapter Box  INDIG.2). These ways of knowing can also 
incorporate the relationships between multiple factors simultaneously which adds particular value towards understanding complex 
systems that is in contrast to the dominant reductionist, Western approach, noting that non-reductionist approaches also exist (Ludwig 
et al., 2014; Hoagland, 2017).Cross-Chapter Box INDIG (continued)Cross-Chapter Box INDIG (continued)

18
2715Climate Resilient Development Pathways Chapter 18
For climate research, the role of oral histories as a part of Indigenous knowledge and local knowledge is extremely relevant. For example,

ocean adaptation initiatives can be guided by oral historians and keepers of knowledge who can convey new knowledge and baselines of ecosystem change over long-time frames (Nunn and Reid, 2016). Oral histories can also convey cultural indicators and linguistic devices of species identification as a part of a local dialect matrix, and changes in ecosystems and species using interlinkages not available to science (Mustonen, 2013; Frainer et al., 2020). Oral histories attached to maritime place names, especially underwater areas (Brattland and Nilsen, 2011), can position observations relevant for understanding climate change over long ecological timeframes (Nunn and Reid, 2016). Species abundances, well-being and locations are some of the examples present in the ever-evolving oral histories as living ways of knowing. Indigenous knowledge and oral histories may also have the potential to convey governance, moral and ethical frameworks of sustainable livelihoods and cultures (Mustonen and

Shadrin, 2020) rooted in the particular Indigenous or local contexts that are not otherwise available in written or published forms.

Climate change research involving Indigenous Peoples and local communities has shown that the generation, innovation, transmission

and preservation of Indigenous knowledge is threatened by climate change (Kermoal and Altamirano-Jiménez, 2016; Simonee et al., 2021). This is because Indigenous knowledge is taught, local knowledge is gained through experience, and relationships with the land are sustained through social engagement within and among families, communities and other societies (Tobias J.K, 2014; Kermoal and Altamirano-Jiménez, 2016). The knowledge that has traditionally been passed on in support of identity, language and purpose has been disrupted at an intergenerational level (Lemke and Delormier, 2017). Many of these dynamics have affected local knowledge transfers equally (Mustonen, 2013). This scenario represents a tension for Indigenous Peoples, where Indigenous knowledge in the form of land-based life ways, languages, food security, intergenerational transmission and application are threatened by climate change, yet in parallel, these same practices can enable adaptation and resilience (McGregor

Table Cross-Chapter Box INDIG.1 |  Examples of Indigenous knowledge and local knowledge about climate change used in this Assessment Report
Issue Examples of Indigenous Peoples’ and local communities’ action Context, peoples and location Source
Climate 
forecasting/early warningPhenological cues to forecast and respond to climate change Smallholder farmers, Delta State, Nigeria
Chapter 9Forecasting of weather and climate variation through observation of the natural environment (e.g., changes in insects and wildlife).Afar pastoralists, north-eastern Ethiopia
Observation of wind patterns to plan response to coastal erosion/flooding Inupiat, Alaska, USA Chapter 14
Sky and moon observation to determine the onset of rainy season Maya, Guatemala Chapter 12
Fire hazards Prescribed burningIndigenous nations in Venezuela, Brazil, Guyana, Canada and USAChapter 12Chapter 14

Crop yield/food securityWater management, native seeds conservation and exchange, crop rotation, polyculture and agroforestryMapuche, Chile Chapter 12
Crop association (milpa) agroforestry, land preparation and tillage practices, native seed selection and exchange, adjusting planting calendarsMaya, Guatemala Chapter 12
Harvesting rainwater and the use of maize landraces by Indigenous farmers to adapt to climate impacts and promote food security in MexicoYucatán Peninsula, Mexico Chapter 14
Livelihood and well-beingCultural values ingrained in knowledge system: reciprocity, collectiveness, equilibrium and solidarityQuechua, Cusco, Peru Chapter 12
Ecosystem degradationEcosystem restoration including rewildingSámi, Nenets, and Komi, Scandinavia and SiberiaChapter 13
Collaboration with researchers, foresters and landowners to manage native black ash deciduous trees against emerald ash borerIndigenous Nations in Canada and USA Chapter 14

Selection and planting of native plants that reduce erosionWhole-of-island approaches that embed Indigenous knowledge and local knowledge in environmental governanceSmall Islands States (as defined by Chapter 15)Chapter 15
Fisheries Traditional climate-resilient fishing approachesIndigenous nations across North America and the ArcticChapter 14CCP6
Management of urban resourcesRestoration of traditional network of water tanksTraditional communities and activists in South Indian cities such as BengaluruChapter 6Cross-Chapter Box INDIG (continued)

18
2716Chapter 18 Climate Resilient Development Pathways
Table Cross-Chapter Box INDIG.2 |  Case study summary
Region Summary
AfricaMany rural smallholder farmers in Africa use their ingrained Indigenous knowledge systems to navigate climatic changes as many do not have access to 
Western systems of weather forecasting. Instead, these farmers have been reported to use observations of clouds and thunderstorms, and migration of 
local birds to determine the start of the wet season, as well as create temporary walls by rivers to store water during droughts. Indigenous knowledge 
systems should be incorporated into strategic plans for climate change adaptation policies to help smallholder farmers cope with climate change 
(Mapfumo et al., 2016).
ArcticFor local Inuit hunters and others who travel across Arctic land, ice and sea, there is evidence that the most accurate approach to reduce risk and enable

informed decision making for safe travel is to combine Indigenous knowledge and local observations of weather with official online weather and marine 
services information that is available nationally (Simonee et al., 2021). Combining Inuit and local knowledge of weather, water, ice and climate information 
with official forecasts has provided local hunters with more accurate, locally relevant information, and has on several occasions helped to avoid major 
weather-related accidents.
Latin AmericaIn Venezuela, Brazil and Guyana, Indigenous knowledge systems have led to a lower incidence of wildfires, reducing the risk of rising temperatures and 
droughts (Mistry et al., 2016). The Mapuche Indigenous Peoples in Chile use various traditional and sustainable agricultural practices, including native 
seed conservation and exchange (trafkintu), crop rotation, polyculture and tree-crop association. They also give thanks to Mother Earth through rituals

to nurture socio-ecological sustainability (Parraguez-Vergara et al., 2018). In the rural Cusco Region of Peru, ‘cultures values known in Quechua as ayni 
(reciprocity), ayllu (collectiveness), yanantin (equilibrium) and chanincha (solidarity)’ have led to successful adaptation to climate change (Walshe and 
Argumedo, 2016).
Māori
(Aotearoa New 
Zealand)The traditional calendar system (maramataka) used by the Māori in Aotearoa, New Zealand, incorporates ecological, environmental and celestial 
Indigenous knowledge. Māori practitioners are collaborating with scientists through the Effect of Climate Change on Traditional Māori Calendars project 
(Harris et al., 2017) to examine if climatic changes are impacting the use of the maramataka, which can be used as a framework to identify and explain 
environmental changes. Observations are being documented across Aotearoa, New Zealand to improve understandings of environmental changes and

explore the use of Indigenous Māori knowledge in climate change assessment and adaptation.
Skolt Sámi 
(Finland)In 2011, the Skolt Sámi in Finland began the first co-governance initiative where collaborative management and Indigenous knowledge were utilised to 
effectively manage a river and Atlantic Salmon (Salmo salar). This species is culturally and spiritually significant to the Skolt Sámi and has been adversely 
impacted by rising water temperatures and habitat loss (Brattland and Mustonen, 2018; Feodoroff, 2020; Ogar et al., 2020) (see also CCP Polar). Using 
Indigenous knowledge, they mapped changes in catchment areas and used cultural indicators to determine the severity of changes. Through collaborative 
management efforts that utilised both Indigenous knowledge and science, spawning and juvenile habitat areas for trout and grayling were restored,

demonstrating the autonomous community capacity (Huntington et al., 2017) of the Indigenous Skolt Sámi and the capacity of Indigenous knowledge to 
address climate change impacts and detection of very first microplastics pollution together with science (Pecl et al., 2017; Brattland and Mustonen, 2018; 
Mustonen and Feodoroff, 2020).
The strong role of governments in climate action has implications for 
the nature of democracy, the relationship between the local and the 
national state, and between citizens and the state (Dodman and Mitlin, 
2015). More integration of government policy and interventions across 
scales, accompanied by capacity building to accelerate adaptation is 
needed (very high confidence). Key needs include enhanced funding, 
clear roles and responsibilities, increased institutional capability, 
strategic approaches, community engagement and judicial integrity 
(Lawrence et al., 2015). More resources, and more active involvement

of the private sector and civil society can help maintain adaptation on 
the policy agenda. Multi-level adaptation approaches are also relevant 
in low-income countries where local governments have limited financial 
resources and human capabilities, often leading to dependency on 
national governments and donor organisations (Donner et  al., 2016; 
Adenle et al., 2017).
Unlike mitigation, adaptation has traditionally been viewed as a local 
process, involving local authorities, communities and stakeholders 
(Preston et al., 2015). The literature on the governance of adaptation 
continues to emphasise that local governments have demonstrated 
leadership in implementation by collaborating with the private sector 
and academia. Local governments can also play a key role (Melica 
et  al., 2018; Romero-Lankao et  al., 2018) in converging mitigation and adaptation strategies, coordinating and developing effective 
local responses, enabling community engagement and more effective

policies around exposure and vulnerability reduction (Fudge et  al., 
2016). Local authorities are well-positioned to involve the wider 
community in designing and implementing climate policies and 
adaptation implementation (Slee, 2015; Fudge et  al., 2016). Local 
governments also help deliver basic services and protect their integrity 
from climate impacts (Austin et al., 2015; Cloutier et al., 2015; Nalau 
et  al., 2015; Araos et  al., 2017). However, the resource limitations 
of local governments as well as their small geographic sphere of 
influence suggests the need for more funding for this from higher 
levels of government, particularly national governments, to address 
adaptation gaps (very high confidence) (Dekker, 2020). Local adaptation 
implementation gaps can be linked to limited political commitment 
at higher levels of government and weak cooperation between key 
stakeholders (Runhaar, 2018). Incongruities and conflicts can exist

between adaptation agendas pursued by national governments and 
the spontaneous adaptation practices of communities. There may be 
grounds for re-evaluating current consultative processes integral to 
policy development, if narrow technical approaches emerge as the 
norm for adaptation (Smucker et al., 2015).Cross-Chapter Box INDIG (continued)

18
2717Climate Resilient Development Pathways  Chapter 18
Therefore, the traditional view of adaptation as a local process 
has now widened to recognise it as a multi-actor process that 
transcends scales from the local and sub-national to national and 
even international (very high confidence) (Mimura et al., 2014). Many 
of the impacts of climate change are both local and transboundary, 
so that local, bilateral and multilateral cooperation is needed 
(Nalau et al., 2015; Donner et al., 2016; Magnan and Ribera, 2016; 
Tilleard and Ford, 2016; Lesnikowski et al., 2017). National policies 
and transnational governance should be seen as complementary, 
especially where they favour transnational engagement with sub- 
and non-state actors (Andonova et al., 2017). National governments 
typically act as a pivot for adaptation coordination, planning, 
determining policy priorities, and distributing financial, institutional 
and sometimes knowledge resources. National governments are also

accountable to the international community through international 
agreements. National governments have helped enhance adaptive 
capacity through building awareness of climate impacts, encouraging 
economic growth, providing incentives, establishing legislative 
frameworks conducive to adaptation and communicating climate 
change information (Berrang-Ford et al., 2014; Massey et al., 2014; 
Austin et al., 2015; Huitema et al., 2016).
18.4.3.3 Economic and Financial Arenas
The performance of local, national and global economies is a priority 
consideration shaping perceptions of climate risk and the costs and 
benefits of different policy responses to climate change. The most 
commonly used indicator of performance is GDP (Hoekstra et al., 2017). 
Traditionally, national development efforts have sought to maximise 
the growth of GDP under the assumption that GDP growth equates 
not only to economic prosperity (including poverty reduction) but

also to increased efficiency and reduced environmental externalities 
(Ota, 2017). Such assumptions often employ models such as the 
environmental Kuznets curve (EKC) that postulates that economic 
development initially increases environmental impacts, but these 
trends eventually reverse with continued economic growth. Wealthy 
nations of the Global North, including for example the USA, Great 
Britain, Iceland and Japan, have had success over the past decade in 
reducing their GHG emissions while growing their economies (very 
high confidence). However, attempts to empirically test EKC in different 
national contexts has yielded mixed results. Case studies in Myanmar, 
China and Singapore, for example, suggest that the impacts of GDP 
on environmental quality are contingent on the development context 
and the environmental impact under consideration (Aung et al., 2017; 
Lee and Thiel, 2017; Xu, 2018; Chen and Taylor, 2020). In addition, an

extensive literature now argues that current patterns of development, 
and the economic systems underpinning that development, are 
unsustainable (Washington and Twomey, 2016), and thus economic 
growth may not necessarily continue indefinitely in the absence of 
more concerted effort to pursue sustainable development, including 
reducing the impacts of climate change.
Given such criticisms of the link between development and economic 
growth, a growing number of researchers argue for the need for 
alternatives to GDP to guide development and evaluate the costs 
and benefits of different policy interventions (Hilmi et al., 2015). For 
example, while GDP growth can drive growth in income, it can also drive growth in inequality which can undermine poverty reduction 
efforts (very high confidence) (Fosu, 2017). Hence, recent years have 
seen significant interest in the concept of well-being as a more 
robust measure for linking policy and the economy with sustainable

development for a healthy Anthropocene era (Fioramonti et al., 2019).
Another mechanism for evaluating environmental performance is 
to include environmental data in the System of National Accounts 
(SNA) through the System of Environmental-Economic Accounting 
(SEEA) introduced by the UN. As the international statistical standard 
for environmental–economic accounting (Pirmana et  al., 2019), SEEA 
includes natural capital resources in national accounting. A number 
of recent studies conclude that failure to account for natural capital 
in macroeconomic impact assessments results in overly optimistic 
outcomes (Pirmana et  al., 2019; Jendrzejewski, 2020; Naspolini et  al., 
2020); (Banerjee et al., 2019; Kabir and Salim, 2019; Keith et al., 2019). For 
example, Jendrzejewski (2020) inserted natural capital into a computable 
general equilibrium model of the 2017 European windstorm on state-
owned forests in Poland. This resulted in more negative assessment of

impacts, suggesting excluding natural capital could lead to erroneous 
investments, strategies or policies. Similarly, other studies rely on Quality 
of life (QOL) measurements as alternatives for GDP . Estoque et al. (2018) 
suggested a ‘QOL-Climate’ assessment framework, designed to capture 
the social-ecological impacts of climate change and variability.
Another alternative to GDP is Green GDP which seeks to incorporate 
the environmental consequences of economic growth (Boyd, 2007; 
Stjepanović et  al., 2017; Stjepanović et  al., 2019). Green GDP is difficult to 
measure, because it is difficult to evaluate the environmental depletion 
and ecological damages of growth (Stjepanović et al., 2019). Although 
there is no consensus in measuring Green GDP , attempts have been 
made for select countries including the USA (Garcia and You, 2017), 
Europe (Stjepanović et al., 2019), China (Chi and Rauch, 2010; Yu et al., 
2019; Wang et al., 2020), Ukraine and Thailand (Harnphatananusorn

et al., 2019), and Malaysia (Vaghefi et al., 2015). Le (2016) illustrated 
the potential negative impacts of climate change vulnerability on green 
growth. Some studies have suggested that focusing on green growth 
as the only strategy to address climate change would be risky. Hickel 
and Kallis (2020) argue that green growth is likely to be a misguided 
goal due to the difficulties of separating economic growth from 
resource use and, therefore, carbon emissions (see also (Antal and van 
den Bergh, 2014). Therefore, alternative strategies are required (Hickel 
and Kallis, 2020). In addition, green growth should also be able to 
justly respond to social movements involving contestation, internal 
debates and tensions (Mathai et al., 2018).
The emphasis on Green GDP is mirrored by another concept, Blue 
Growth, that focuses on pursuing sustainable development through 
the ecosystem services derived from ocean conservation (Mustafa

et al., 2019). Synthesis studies suggest that more intensive use of ocean 
resources, such as scaling up seaweed aquaculture, can be used to 
enhance CO 2-eq sequestration, thereby contributing to GHG mitigation, 
while also achieving other economic goals (Lillebø et al., 2017; Froehlich 
et  al., 2019). Similarly, Sarker et  al. (2018) present a framework for 
linking Blue Growth and CRD in Bangladesh, with Blue Growth 
representing an opportunity for adapting to climate change. Bethel et al. 
(2021) also links Blue Growth to resilience, noting that a Blue economy

18
2718Chapter 18 Climate Resilient Development Pathways
can help facilitate recovery from the COVID-19 pandemic. Nevertheless, 
consistent with earlier assessment of enabling conditions for system 
transitions (Section 18.4.2.1), implementation of Blue Growth initiatives 
is contingent upon the successful achievement of social innovation as 
well as creating an inclusive and cooperative governance structure (very 
high confidence) (Larik et al., 2017; Soma et al., 2018).
A potential critique of the various alternative metrics and models for 
economic development is that they are all framed in the context of 
growth. Over the past decade, ecological economists and political 
scientists have proposed degrowth (e.g., Kallis, 2011; Demaria et al., 
2013) and managing without growth (e.g., Jackson, 2009) as a 
solution for achieving environmental sustainability and socioeconomic 
progress. Such concepts are a deliberate response to concerns

about ecological limits to growth and the compatibility between 
growth-oriented development and sustainability (Kallis et al., 2009). 
Sustainable degrowth is not the same as negative GDP growth, which 
is typically referred to as a recession (Kallis, 2011). Degrowth goes 
beyond criticising economic growth; it explores the intersection among 
environmental sustainability, social justice and well-being (Demaria 
et al., 2013). Under current economic and fiscal policies (see Box 18.7), 
degrowth has been argued as an unstable development paradigm 
because declining consumer demand leads to rising unemployment, 
declining competitiveness and a spiral of recession (Jackson, 2009: 
46). More comprehensive modelling of socioeconomic performance 
understands the segments of sufficient social transformation to 
guarantee maintenance and rises in well-being coupled with reduced 
‘footprints’ (Raworth, 2017; Hickel, 2019; D’Alessandro et al., 2020).

18.4.3.4 Knowledge–Technology and Ecological Arenas
Knowledge–technology arenas comprise the interaction in knowledge 
spaces connected to technology transitions. The institutional and 
political architecture through which knowledge and technology interact is described in sustainability transitions literature (Fazey et al., 
2018b; Sengers et al., 2019l Kanger, 2020 #3709). A common theme 
explored in that literature is the ability of actors to access and apply 
various forms of knowledge as a means of effecting change. Different 
forms of innovation are recognised as a core enabling condition for 
achieving system transitions for CRD (Section  18.3.3; Cross-Chapter 
Box  INDIG). However, while scientific and technology knowledge 
may be useful, in some cases, they remain subordinate to political 
agendas, or are controlled by actors in positions of power and thus 
not equitably distributed (very high confidence) (Mormina, 2019).

Participatory decision making, for example, assumes that multiple 
actors, with differing motivations, agency and influence, engage with 
climate decision making and co-produce actions. Yet some actors may 
not participate in the process if the proposed actions do not align with 
their motivations or if they do not have adequate agency (Roelich and 
Giesekam, 2019). Hence, effectively using knowledge to inform policy 
is challenging for both scientists, policymakers and civil society alike.
Science, technology and innovation (STI) policies are expected 
to shape expectations of the potential for a better world based on 
access to information, clean technologies, higher labour productivity, 
economic growth and a healthier environment (Brasseur and Gallardo, 
2016; Schot and Steinmueller, 2018; Singh et  al., 2018; Mormina, 
2019; Bamzai-Dodson et  al., 2021). STI policies are considered as 
‘social goods for development’. Hence, STI policies are often proposed

or implemented as means of addressing environmental challenges 
such as climate change along with SDGs such as the reduction of 
inequality, poverty and environmental pollution (Mormina, 2019). 
Realising the benefits of STI, however, may be contingent on building 
broader STI capacity and bolstering nations’ systems of innovation 
(very high confidence) (Mormina, 2019). This could include building 
global research partnerships to address priority STI needs as well 
as long-standing gaps between the Global North and South. Such 
an approach shifts the framing of STI as one focused on individual 
Box 18.7 | Macroeconomic Policies in Support of Climate Resilient Development
Climate change risk may differ from other economic and financial risks in a number of ways: climate change is global; it involves long-
term impacts and a great deal of uncertainty; and it has the possibility of irreversible change (Hansen, 2021). The macroeconomic

implications will differ across countries, with less developed countries likely to suffer more relative to more advanced ones (Batten, 
2018). Hence, policymakers need to understand the impact of climate change on macroeconomic issues such as potential output growth, 
capital formation, productivity and long-run levels of interest rates, in order to better design policy interventions, be it monetary or fiscal 
(Economides and Xepapadeas, 2018; Bank of England, 2019; Rudebusch, 2019). As discussed, below are a range of fiscal tools that can 
be leveraged to mitigate the effects of climate change (Krogstrup and Oman, 2019).
Monetary Policy
Changes in climate and subsequent policy responses could increase volatility of food and energy prices, resulting in higher headline 
inflation rates. Thus, Central Banks (CBs) have to pay careful attention to underlying inflationary factors to maintain their inflationary

targets. In response, CBs can take a number of actions. For example, they could require that collateral comprises assets that support 
the move to low-carbon economy, or their refinancing operations and crisis facilities could incentivise borrowers’ move to low-carbon 
activities, particularly in countries where CBs’ mandate has been expanded to account for climate impact (Papoutsi et al., 2021). Other 
actions that CBs could take include adoption of sustainable and responsible investment principles (Rudebusch, 2019) and requiring 
financial firms to disclose their climate-related risks (ECB, 2020; Lee, 2020). Despite these opportunities, there is ongoing debate regarding 
whether CBs should actively use monetary policy to address climate change and its risks (Honohan, 2019).

18
2719Climate Resilient Development Pathways  Chapter 18
Fiscal Policy
The application of green fiscal policies to address climate change could lead to environmental benefits, including environmental revenues 
that may be used for broader fiscal reforms (OECD, 2021). As the USA aims at becoming carbon neutral by 2050, fiscal policies at the 
national, sectoral and international level can help to achieve this goal, along with investment, regulatory and technology policies (Parry, 
2021). The effectiveness of green fiscal policies are through their fiscal potential, opportunities for efficiency gains, distributional and 
macroeconomic impacts, and their political economy implications (Metcalf, 2016). The International Monetary Fund argues public support 
for green policies may rise in response to the COVID-19 crisis (IMF , 2017). For example, Leibenluft (2020) argues that investments to

combat climate change should be an important component of the efforts to rebuild the economy in the wake of COVID-19. Such action is 
justified not only on ecological and social welfare grounds, but from a long-term fiscal perspective. For example, climate change impacts 
and/or efforts to adapt to those impacts drive increased spending in areas such as public health and disaster mitigation or response. 
Preventive and corrective actions would strengthen resilience to shocks and alleviate the financial constraints they create, particularly 
for small countries (Catalano et al., 2020). For example, Mallucci (2020) found that natural disasters exacerbate fiscal vulnerabilities and 
trigger sovereign defaults in seven Caribbean countries. Ryota (2019) illustrates how to include natural disaster and climate change in a 
fiscal policy framework to developing countries.
Carbon Pricing

Pricing of GHGs, including carbon, is a crucial tool in any cost-effective climate change mitigation strategy, as it provides a mechanism for 
linking climate action to economic development (IMF/OECD, 2021). By 2019, 57 nations around the world had implemented or scheduled 
implementation of carbon pricing. These initiatives cover 11 gigatons of carbon dioxide or about 20% of GHG emissions. Carbon prices in 
existing initiatives range between USD 1 and USD 127 per ton of carbon dioxide, while 51% of the emissions that are covered are priced 
more than USD 10 per ton of carbon dioxide. Moreover, in 2018, Governments raised about USD 44 billion in carbon pricing revenues 
(World Bank, 2019). However, the carbon prices are lower than the levels required for attaining the ambitious goal of climate change 
mitigation, and therefore, prices would need to increase if pricing alone is going to be used to drive compliance with the Paris Agreement.

Higher carbon prices would also be warranted if prices are based on the social cost of carbon, which represents the present value of the 
marginal damage to economic output caused by carbon emissions (Cai and Lontzek, 2018). This cost needs to be considered with the 
social benefits of reducing carbon emissions through cost-benefit analyses to make the intended regulation acceptable.
Taxes
Carbon taxes represent another financial mechanism for addressing climate change (Metcalf, 2019), 2019b). For example, the 
implementation of a carbon tax and a value-added tax on transport fuel in Sweden resulted in a reduction of CO 2 emissions from 
transport of about 11%, of which the carbon tax had the largest share (Andersson, 2019). In the USA, for example, a carbon tax could 
increase fiscal flexibility by collecting new revenues that can be redeployed to finance reforms and help stimulate economic growth.

However, US tax-inclusive energy prices would have to be 273% higher than laissez-faire levels in 2055 in order to meet international 
agreements (Casey, 2019). Similarly, limiting global warming to 2°C or less would likely require a carbon tax rate in the Asia/Pacific region 
to be significantly higher than USD 25 per ton (IMF , 2021). Therefore, using tax revenues to issue payments back to taxpayers that are 
disproportionately impacted, or to redistribute capital among regions, may be one of the most important features of carbon tax policies. 
Although the average effect of carbon tax on welfare would be positive, some regions (56%) will gain and some regions (44%) lose 
(Scobie, 2013). Therefore, large transfer payments are needed to compensate those losing from carbon tax (Krusell and Smith, 2018).
The International Monetary Fund (IMF (2019) argues that, of the various mitigation strategies to reduce fossil fuel CO 2 emissions, carbon

taxes are the most powerful and efficient, because they allow firms and households to find the lowest-cost ways of reducing energy use 
and shifting towards cleaner alternatives.
Subsidies
The World Bank has been encouraging both developed and developing states, especially those with petroleum reserves, to use the 
removal of subsidies as a mechanism for promoting energy transitions away from fossil fuels. The transition has led to social unrest in 
some cases, especially where there is a culture of entitlement to low-cost energy because it is an indigenous resource. Such reforms have 
been more effective when governments have been able to clearly show how savings are applied to social and health programs that 
benefit human well-being. Nevertheless, policymakers should not underestimate the complexity of issues involved in the removal of 
subsidies that will increase the cost of carbon and hasten the transition to cleaner fuels (Scobie, 2017; Scobie et al., 2018; Chen et al.,

2020a). A crucial issue to take into account is the harmful effects some subsidies have on biodiversity. Although governments agreed in 
2010 to make progress on reducing subsidies in 2010, by 2020 few governments had identified specific incentives to remove or taken 
action towards their removal. Further investigation of the positive and negative effects of subsidy redirection or elimination on people 
and the environment (Dempsey et al., 2020).Box 18.7 (continued)

18
2720Chapter 18 Climate Resilient Development Pathways
investigators to one comprised of building knowledge networks. It also 
creates opportunities for integration of disparate forms of knowledge 
and innovation, including local and Indigenous knowledge, into global 
knowledge systems (Cross-Chapter Box INDIG).
Furthermore, an extensive literature increasingly incorporates natural 
and ecological systems as knowledge domains relevant to understanding 
opportunities for sustainability and CRD. For example, the literature on 
socio-ecological systems (SES) (Sterk et al., 2017; Holzer et al., 2018; 
Avriel-Avni and Dick, 2019; Martínez-Fernández et al., 2021) as well 
as social, ecological and technological systems (SETS) (McPhearson 
and Wijsman, 2017; Webb et al., 2018; Ahlborg et al., 2019), explicitly 
integrate ecological knowledge into sustainability, including concepts 
such as planetary boundaries (Section  18.1.1), adaptation and

nature-based solutions, natural resources management, rights and 
access to nature, and understanding of how humans govern society–
nature interactions in the face of climate change (Benjaminsen and 
Kaarhus, 2018; Mikulewicz, 2019; Nightingale et  al., 2020). Some 
of these interactions are explained in Cross-Chapter Box  INDIG, 
including conflict over which knowledges are recognised as valuable 
in understanding and responding to climate change and therefore 
shape the nature of climate actions. Actor engagement in stewardship, 
solidarity and inclusion of multiple knowledges and nature–society 
connectedness can highlight the intertwined nature of ecological 
change and knowledge relations, thereby supporting shifts to 
sustainability (Pelling, 2010; Hulme, 2018; Ives et al., 2019; Nightingale 
et al., 2020) (see also Box 18.6).
The expanding definition of what constitutes credible, relevant and 
legitimate knowledge is leading to the democratisation of knowledge

and efforts to address historical inequities in access to knowledge (Ott 
and Kiteme, 2016; Rowell and Feldman, 2019). This is reflected in the 
communication of science, which is increasingly focused on reducing 
the distance between internal scientific and public communication 
and more engagement in public science governance and knowledge 
production (Waldherr, 2012; Peters, 2013). One innovative approach in 
co-production of knowledge is mobilising communities through citizen 
science (Heigl et al., 2019). This also presents additional opportunities 
to incorporate local knowledge with scientific research, and better 
match scientific capability to societal needs.
18.4.3.5 Community Arenas
Societal choices and development trajectories emerge from decisions 
made in different arenas which intersect and interact across levels 
and scales, in diverse institutional settings—some formal with their 
associated instruments and interventions, while others are informal.

Since AR5, both formal and informal setting are increasingly arenas 
of debate and contestation regarding development choices and 
pathways (very high confidence) (Section 18.4.4, Chapters 1, 6, 8, 10 
and 17). Community arenas exist from the local to the global scale 
and constitute the many interactions between governance actors, 
often transcending any one scale to reflect the emergent outcomes 
of interactions in political, economic, socio-cultural, knowledge-
technology and ecological arenas of engagement. Actions within and 
between these five arenas hence come together in the community 
arena of engagement. While community engagement is often described at the level of villages and cities (Ziervogel et al., 2021) (Chapter 8), 
communities in terms of people interacting with each other sharing 
worldviews, values and behaviours, also exist at the regional and 
global levels. For example, civil society engagement in climate action

reached a peak in 2019, notably through the global youth movement 
which led to large global mobilisation and street demonstrations on 
all continents and in many large cities (Bandura and Cherry, 2020; Han 
and Ahn, 2020; Martiskainen et al., 2020). Calling for enhanced climate 
action by governments and other societal actors, the youth movement 
was supported by many other societal groups and networks, including 
arenas of community interaction.
While the SR1.5 (de Coninck et al., 2018) for the first time comprehensively 
assessed behavioural dimensions of climate change adaptation, most 
literature still has a greater focus on what triggers mitigation behaviour 
(Lorenzoni and Whitmarsh, 2014; Clayton et al., 2015). Meanwhile, with 
CRD still a relatively young concept, there is little literature focused on 
what motivates action in pursuit of CRD rather than its sub-components 
of climate action and sustainable development. Nevertheless, a common

motivation that is emerging in the literature is clinically significant 
levels of climate distress among individuals (Bodnar, 2008), which is 
experienced as a continuing distress over a changed landscape which no 
longer offers solace, also known as solastalgia (high agreement, medium 
evidence) (Albrecht et  al., 2007). This is accompanied by a shift from 
blaming natural forces for disasters to attributing it to human negligence, 
which is known to lead to more acute perceptions of risk as well as more 
prolonged post-traumatic stress disorder (PTSD) than trauma arising 
from non-human causes. Improving social connections, acknowledging 
anxiety, reconnecting to nature and finding creative ways to re-engage 
are identified as ways of managing this growing anxiety (Lertzman, 
2010; Clayton et  al., 2017). Climate action in communities at various 
scales could fulfil many of these needs.
18.4.4 Frontiers of Climate Action

After decades of limited government action and social inertia to 
reduce the risk of climate change, there is also increasing social dissent 
towards the current political, economic and environmental policies to 
address climate (Brulle and Norgaard, 2019; Carpenter et al., 2019). 
Social movements are demanding radical action as the only option to 
achieve the mobilisation necessary for deep societal transformation 
(very high confidence) (Hallam, 2019; Berglund and Schmidt, 2020).
Prompted by SR1.5, new youth movements seek to use science-based 
policy to break with incremental reforms and demand radical climate 
action beyond emissions reductions (Hallam, 2019; Klein, 2020; 
Thackeray et al., 2020; Thew et al., 2020). Recent social movements 
and climate protests embrace new modalities of action related to 
political responsibility for climate injustice through disruptive collective 
political action (Young, 2003; Langlois, 2014). This is complemented

by a regenerative culture and ethics of care (Westwell and Bunting, 
2020). These new social movements are based on non-violent methods 
of resistance, including actions classified as dutiful, disruptive and 
dangerous dissent (O’Brien, 2018).

18
2721Climate Resilient Development Pathways  Chapter 18
The new climate movement mixes messages of fear and hope to propel 
urgency and the need to respond to a climate emergency (Gills and 
Morgan, 2020). While some consider the mix between fear and hope 
as beneficial to success, depending on psychological factors (Salamon, 
2019) or political geography (Kleres and Wettergren, 2017), others 
warn of the risks of a rhetoric of emergency and its political outcomes 
(Hulme and Apollo-University Of Cambridge Repository, 2019; Slaven 
and Heydon, 2020).
Research shows that new climate movements have increased public 
awareness, and also stimulated unprecedented public engagement 
with climate change (very high confidence) (Lee et al., 2020; Thackeray 
et al., 2020) and has helped rethink the role of science with society 
(Isgren et al., 2019). Such movements may represent new approaches 
to accelerate social transformation and have resulted in notable

political successes, such as declarations of climate emergency at the 
national and local level, as well as in universities. Their methods have 
also proven effective to end fossil fuel sponsorship (Piggot, 2018). 
Social demands for radical action are likely to continue to grow, as 
there is growing discontent with political inertia and a rejection of 
reformist positions (Stuart et al., 2020).
Box 18.8: The Role of the Private Sector in 
Climate Resilient Development via Climate 
Finance, Investments and Innovation
Climate finance broadly refers to resources that catalyse low-
carbon and climate resilient development. It covers the costs 
and risks of climate action, supports an enabling environment 
and capacity for adaptation and mitigation, and encourages 
research and development (R&D) and deployment of new 
technologies. Climate finance can be mobilised through a 
range of instruments from a variety of sources, international 
and domestic, public and private (Section 18.4.2.2).

The private sector has particular competencies which can make 
significant contributions to adaptation, through innovative 
technology, design of resilient infrastructure, development 
and implementation of improved information systems, and the 
management of major projects. The private sector can be seen 
as a ‘supplier of innovative goods and services’ to meet the 
adaptation priorities of developing countries with expertise in 
technology and service delivery (Biagini and Miller, 2013).
Future investment opportunities in climate resilient development 
(CRD) are in water resources, agriculture and environmental 
services. Provision of clean water is another opportunity, 
requiring investment in water purification and treatment 
technologies such as desalination and wastewater treatment. 
Weather and climate services are a possible area for private 
investment. (Hov et al., 2017; Hewitt et al., 2020).18.5 Sectoral and Regional Synthesis of 

Prior sections of this chapter assessed the literature relevant to 
CRD inclusive of climate risk management, systems transitions and 
transformation, and actors and the arenas in which they engage one 
another to enable or constrain CRD. Here, this knowledge is explored in 
different climatological and development contexts through a synthesis 
of CRD-relevant assessments within the WGII sectoral and regional 
chapters.
18.5.1 Regional Synthesis of Climate Resilient 
In synthesising regional knowledge relevant to the pursuit of CRD, 
this section first considers geographic heterogeneity in regional 
responses of common climate variables to increases in globally 
averaged temperatures. Such heterogeneity is a key driver of climate 
risk in different global regions, as well as human and natural systems 
within those regions. This is followed by synthesis of various national 
development indicators, aggregated to the regional level, as well as

various challenges, opportunities and options supporting CRD reported 
within WGII regional chapters.
18.5.1.1 Climate Change Risk for Different Global Regions
Two important elements of understanding the opportunities and 
challenges associated with the pursuit of CRD in different regional 
contexts are a) the geographic variability in climate conditions that 
shape livelihoods, behaviours and responses of human and natural 
systems; and b) how those conditions could shift in the future in 
response to climate change, which determines the additional burden 
that climate change could create for adaptation and sustainable 
The climate analyses of WGI provide information on regional differences 
in temperature, rainfall and sea surface temperatures for different global 
regions and how they are projected to change in response to different 
levels of aggregate global warming (Table  18.4). Such data reveal 
that even when aggregated to broad geographic regions, significant

variations exist for all of these parameters, which is a function of the 
baseline climatology of each region. For example, temperatures in 
Africa and Australia are, on average, warmer than in Europe or North 
America. Significant variations are also observed for rainfall variables. 
Such regional variation in climate conditions is part of the regional 
context that shapes current patterns of development of the past present 
and future. They influence biodiversity and natural resource availability 
as well as exposure to climatic extremes (tropical storms, heatwaves 
and drought) that contribute to disasters.
The WGI data also indicate that increases in globally averaged 
temperatures will have different consequences for regional climate 
change (Table  18.4), including variation in the magnitude and, for 
precipitation, even the direction of change (very high confidence). For 
example, although average temperatures, daily minimum temperature

and the number of days over a given threshold are projected to increase

18
2722Chapter 18 Climate Resilient Development Pathways
in all regions except Antarctica, the magnitude of the change varies. 
Moreover, little change is projected for daily maximum temperatures 
across different regions. Nevertheless, the number of days over different 
temperature thresholds such as 35°C increases markedly in most 
regions, reflecting the disproportionate impact that global warming 
has on the tails of temperature distributions. Given outcomes in many 
systems including public health, agriculture, ecosystems and biodiversity, 
and infrastructure are often associated with biophysical thresholds (e.g., 
physiological or design thresholds), those regions where such thresholds 
are increasingly exceeded due to climate change may experience 
disproportionately higher impacts (very high confidence). Given such 
temperatures occur more frequently in regions such as Africa and Central 
and South America, this disproportionate exposure is exacerbated by

disproportionate vulnerability, adaptation gaps and development needs 
(very high confidence; Section 18.2.4; Table 18.4).
The regional response of precipitation to globally averaged temperature 
increases is less clear than temperature, in part due to high intra-
region variability. Average daily precipitation remains fairly stable in 
all global regions in response to higher magnitudes of global warming 
(Table  18.4). However, 5-day precipitation totals provide a clearer 
signal of increasing hydrologic activity in response to higher globally 
averaged temperatures (Table 18.4). Such data does not necessarily 
reflect changes in rainfall extremes that could occur with downstream 
consequences for hazards such as drought or flooding. Similarly, while 
sea surface temperatures (SSTs) are more uniform across global ocean 
basins, all basins are anticipated to warm in response to higher globally 
averaged temperatures (Table 18.5). Unlike temperature, however, SST

increases are anticipated to be only a fraction of the globally averaged 
increase in temperature, due in large part to the heat capacity of the 
oceans. Nevertheless, such higher SSTs have implications not only for 
ocean ecosystems and the distribution of marine species, but also for 
weather patterns, such as formation and intensity of tropical cyclones 
The other aspect of the regional climate responses to global temperature 
increases that is important for CRD is the marked differences observed 
between changes in response to 1.5°C versus 4°C of warming. Higher 
levels of global warming are associated with higher regional changes, 
including changes in extremes of temperature. This in turn increases 
climate risk to exposed and vulnerable human and natural systems, 
thereby increasing demand for adaptation. If that demand is not met, 
then the adaptation gap will be larger, with greater risk of loss and

damage (very high confidence) (Schaeffer et  al., 2015; Chen et  al., 
2016; United Nations Environment Programme, 2021). This is true 
not only for regions, but also at the sectoral level (Section  18.5.2). 
Therefore, CRD pathways must balance the demands for emissions 
reductions to reduce exposure, adaptation to manage residual climate 
change risks, and sustainable development to address vulnerability 
and enhance capacity for sustainable development.
18.5.1.2 Regional Perspectives on Climate Resilient 
The various regional chapters within the AR6 WGII report each provide 
insights into progress towards CRD as well as the opportunities and 
challenges associated with future pursuit of different CRD pathways. Common indicators of development reflect the significant diversity 
that exists across different global regions with respect to their 
development context (very high confidence). For example, the Human

Development Index, recently adjusted to reflect the effect of planetary 
pressures (PPAHDI), illustrates the overall higher levels of development 
of North America and European countries of the Global North as well 
as Australasia compared with Asia, Africa, Central and South America 
and small islands of the Global South. Generally, this reflects the 
higher levels of vulnerability and greater need for both sustainable 
developments to reduce poverty and support sustainable economies 
as well as climate action to address climate risk (Table 18.6).
However, even within a given region, there is significant variation in 
PPAHDI among nations. Such differences reflect fundamental differences 
in historical patterns of development, as well as current development 
needs and challenges, and they imply differences in what future 
development pathways would be consistent with CRD. In addition, 
nations and regions with lower PPAHDI values suggests greater capacity

challenges for both GHG mitigation and climate adaptation. However, 
nations and regions with high PPAHDI values also tend to have higher 
per capita CO 2-e emissions production, indicating that economic 
development based on fossil fuel use undermines both efforts on 
climate action as well as the SDGs (very high confidence) (Figure 18.6). 
Such challenges are also reflected by differential Gini coefficients and 
metrics of state fragility among regions, which reflect inequities in 
income distribution and broader vulnerability of nations and regions 
to shocks and stressors (Figure  18.6). In addition, high variation is 
observed in CO 2 emissions production, even among comparatively 
wealthy nations, suggesting CO 2-e emissions of some nations are tightly 
coupled to development, while others have pursued more carbon neutral 
development trajectories. Even within regions such as Africa, Asia, 
Central and South America, and Europe, large within-region variations

are observed in inequality and state fragility, suggesting high variability 
among nations. Given the emphasis in the sustainable development and 
CRD literature on equity and vulnerability, addressing such determinants 
of vulnerability is a core design principle for CRDPs.
In addition to development indicators, the literature assessed in the 
WGII regional chapters indicates that different regions experience 
a range of development challenges and opportunities that affect the 
pursuit of CRD (very high confidence). These represent dimensions of 
governance, institutions, economic development, capacity, and social 
and cultural factors that shape decision making, investment and 
development trajectories. For example, significant challenges exist 
within regions with respect to managing debt and the ability to fund or 
finance climate action and sustainable development interventions (very 
high confidence). On the other hand, a broad range of opportunities

exist to pursue CRD including challenges with debt and financing of 
adaptation competing policy objectives, social protection programmes, 
economic diversification, investing in education and human capital 
development, and expanding disaster risk reduction efforts (very high 
There are a wide variety of more focused options for climate action 
and sustainable development (very high confidence). Such options 
have potential for synergies and trade-offs including implications for 
GHG mitigation, land use change and conservation, food and water,

18
2723Climate Resilient Development Pathways  Chapter 18
Table 18.4 |  Projected continental level result ranges for select temperature and precipitation climate change variables by global warming level. Ranges are 5th and 95th percentiles from SSP5-8.5 WGI CMIP6 ensemble results. There is little 
variation in the 5th and 95th percentile values by GWL across the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 projections. Source: WGI AR6 Interactive Atlas (Gutiérrez et  al., 2021). 
Climate variableGlobal warm-
ing levelAll Regions North America Europe AsiaCentral–South 
Mean temperature 
(°C)4°C 12 to 15 8 to 11 5 to 9 12 to 14 24 to 27 26 to 29 24 to 27 −33 to −27
Minimum of 
daily minimum

temperatures (°C)4°C −12 to −5 −25 to −15 −22 to −14 −18 to −9 11 to 15 10 to 14 5 to 10 −64 to −48
Maximum of 
temperatures (°C)4°C 32 to 37 32 to 38 28 to 33 35 to 40 36 to 43 40 to 47 41 to 49 −12 to −5
above 35°C—bias 
adjusted4°C 81 to 106 36 to 50 11 to 22 57 to 77 138 to 194 153 to 210 140 to 168 0 to 0

Near-surface total 
(mm/d)4°C 2 to 3 2 to 3 2 to 2 2 to 3 4 to 5 2 to 3 1 to 2 1 to 1
Maximum 5-day 

Table 18.5 |  Projected sea surface temperature change ranges by global warming level and ocean biome (°C). Ranges are 5th and 95th percentiles from SSP5-8.5 WGI CMIP6 ensemble results. There is little variation in the 5th and 95th 
percentile values by GWL across the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 projections. Source: WGI AR6 Interactive Atlas (Gutiérrez et al., 2021).
Global 
warming 
biomesNorthern Hem-
isphere; high 
Hemisphere; 
SubtropicsEquatorialSouthern 
SubtropicsSouthern Hem-
latitudesGulf of MexicoEastern 
BoundariesAmazon River Arabian SeaIndonesian 
flowthrough

1.5°C 0.2 to 0.7 0.1 to 0.9 0.2 to 1.0 0.2 to 0.8 0.2 to 0.6 0.1 to 0.5 0.2 to 1.0 0.2 to 0.9 0.2 to 0.9 0.2 to 0.9 0.1 to 0.8

18
2724Chapter 18 Climate Resilient Development Pathways
or social equity. Despite variation in development context, regional 
assessments suggest CRD efforts will be associated with some 
common features. For example, in all regions, existing vulnerability 
and inequality exacerbate climate risk and therefore pose challenges 
to CRD (very high confidence). Furthermore, low prioritisation of 
sustainability and climate action in government decision making, 
low perceptions of climate risk, and path dependence in governance 
systems and decision-making processes all pose barriers to system 
transitions, transformation and CRD (very high confidence).
18.5.2 Sectoral Synthesis of Climate Resilient 
The sectoral chapters of the WGII report provide insights regarding how 
development processes interact with sectors to shape the potential for 
CRD. Similar to global regions, each sector is associated with various

challenges, opportunities and options that enable or constrain CRD 
(Table 18.7). A number of challenges are common across sectors and 
mirror those associated with different regions. For example, issues 
associated with natural resource dependency, access to information 
for decision making, access to human and financial capital, and path 
dependence of institutions represent barriers that must be overcome 
if sectors are to support transitions that enable CRD. These challenges 
are more acute within vulnerable communities or nations where 
capacity to innovate and invest are constrained and social inequities 
reinforce the status quo (very high confidence). At the same time, a 
number of sector-specific opportunities for mitigation, adaptation and 
sustainable development can be used to integrate sectors into CRDPs. 
This could include policies and planning initiatives to enhance sector 
sustainability and resilience, as well as capacity building and greater

inclusion of different actors and groups in decision making including 
capitalising on local and Indigenous knowledge as a mechanism for 
more representative and equitable action.In addition, the sectoral assessments identify a broad range of specific 
adaptation, mitigation and sustainable development options that could 
play a role in facilitating CRD. Many of these options appear initially 
to be specific to a given sector. For example, options for the water 
sector (Chapter 4) are assessed independently from those for health 
and well-being (Chapter 7). In practice, however, evidence suggests 
the importance of thinking about sectoral options as cross-cutting, 
mutually supportive and synergistic packages rather than singular 
options. First, each of the sectoral chapters has links to multiple SDGs 
(Table 18.7), implying each sector is important for achieving a range of 
sustainability goals that extend beyond sectoral boundaries. Moreover,

progress across multiple sectors simultaneously creates opportunities 
for synergies for achieving the SDGs, but also enhances the risk of 
potential trade-offs (very high confidence). Second, a number of 
options are common to multiple sectors. For example, options 
associated with ecosystem-based adaptation and nature-based 
approaches to environmental management appear in multiple sectors 
(Table  18.7). Similarly, climate-smart agriculture and agroecological 
approaches to food systems create opportunities for food security, but 
those same options also benefit land-based ecosystems, water, poverty 
and livelihoods, and human well-being.
18.5.3 Feasibility and Efficacy of Options for Climate 
While both the sectoral and regional assessments indicate a rich toolkit 
of management options is available to decision makers to facilitate 
CRD, two key uncertainties undermine efforts to implement those

options. The first is the feasibility of implementation. Options that 
seem promising could nevertheless encounter implementation barriers 
due to cost, absence of necessary capacity, lack of public acceptance 
or competition with alternative options. Progress in the literature 
since the AR5 and SR1.5 reports enables improved consideration 
Relationship among development indicators relevant to climate-resilient development
120Gini Coefficient/Fragility States Index
0
Planetary Pressures-Adjusted Human Development Index0.1 0100
80
60
40
2040
Per Capita CO2 Emissions Production (tonnes)
030
20
10
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0Fragile State Index
Gini Coefficient
Per Capita CO2 emmissions Production
Figure 18.6 |  This figure presents National Gini coefficients (most recent year available; n = 141 (World Bank, 2021), the Fragile States Index (2021; n = 163; (Fund for

Peace, 2021), and per capita CO2 emissions (2018; n = 169), Human Development Report Office, 2020) plotted against the Planetary Pressures-Adjusted Human Development 
Index (2020, n = 163 (Human Development Report Office, 2020)

18
2725Climate Resilient Development Pathways  Chapter 18
Table 18.6 |  Regional synthesis of dimensions of climate resilient development. For each region, quantitative information is provided on common development indicators including the planetary pressures-adjusted human development 
index (PPHDI, 2020, n = 169 (Human Development Report Office, 2020), Gini coefficients (GINI, most recent year available; n = 156 (World Bank, 2021), Fragile States Index (FRAGILITY; 2021; n = 173 (Fund for Peace, 2021), and per capita 
CO 2 emissions production (CO 2/PC, 2018; n = 169 (Human Development Report Office, 2020). Each indicator is associated with a mean value among nations within a specific region as well as the range (minimum to maximum) value. In 
addition, the table contains evidence of sustainable development challenges and opportunities as well as adaptation/sustainable development options and potential synergies and trade-offs associated with their implementation. Synergies

and trade-offs are categorised as follows: (T) Trade-off among policies and practices; (S+) Synergy among policies and practices that enhances sustainability; (S-) Synergy among policies and practices that undermines sustainability.
RegionDevelopment indicators
mean (range)Challenges Opportunities Options Synergies and trade-offs
AfricaPPAHDI0.53
(0.39–0.72)
 –Institutional and financial challenges in 
programming and implementing activities to support 
concrete adaptation measures (Section 9.14.5)
 –High debt levels exacerbate fiscal challenges and 
undermine economic resilience (Section 9.14)
 –Insufficient development and adaptation finance 
and accessibility of finance (Section 9.14.5)
 –Complexity of estimating the costs and benefits 
for adaptation measures in specific contexts 
(Section 9.14.2)
 –Exclusions of migrants and other vulnerable 
populations from social programmes (Section 9.9.4)
 –Mismatch between the supply of, and demand for,

climate services (Section 9.5) –Climate change literacy can 
enable the mainstreaming of 
climate change into national 
and sub-national developmental 
agendas (Section 9.4.2)
 –Adaptive responses can be 
used as an opportunity for 
comprehensive, transformative 
change (Section 9.6.2)
 –Investments in human capital 
can facilitate socioeconomic 
development and poverty 
reduction (Section 9.9.1)
 –Strengthening the participation 
of women in decision making 
as well as advancing traditional 
and local knowledge can support 
climate action and sustainable 
livelihoods (Section 9.9.3) –Strengthening climate services (Section 9.4.2)
 –Ecosystem-based adaptation (Section 9.11.4.2)
 –Economic diversification (Section 9.12.3)
 –Intensive irrigation (Section 9.15.2)
 –Agricultural and livelihood diversification 
(Section 9.12.3)
 –Drought-resistant crop varieties (Section 9.15.2)
 –Soil and water conservation (Section 9.15.2) –(T) competing uses for water 
such as hydropower generation,

irrigation and ecosystem 
requirements create trade-offs 
among different management 
objectives (Section 9.7.3)
 –(T) migration in response to 
unfavourable environmental 
conditions provides opportunities 
for farmers but puts pressure 
on the provision of social 
services and reduces farm labour 
(Section 9.15.2)
 –(T) intensive irrigation contributes 
to the development of agriculture 
but has come at a cost to 
ecosystem integrity and human 
well-being (Section 9.15.2)GINI42.8
(27.6–63.4)
FRAGILITY87.3
(57.0–110.9)
CO2/PC1.1
(0.0–8.1)
AsiaHPAHDI0.65
(0.47–0.78)
 –Migration and displacement (Box 10.6)
 –Uneven economic development (Section 10.4.6)
 –Rapid land use change (Section 10.4.6)
 –Increasing inequality (Section 10.4.6)
 –Large, socially differentiated vulnerable populations 
(Section 10.4.6) –Investing in climate-resilient and 
sustainable infrastructure can be 
a source of green jobs as well 
as a means of reducing climate 
vulnerability (Section 10.6.2)

–Sustainable development 
pathways that connect climate 
change adaptation and disaster 
risk reduction efforts can reduce 
climate vulnerability and increase 
resilience (Section 10.6.2)
 –Social protection programmes 
can develop risk management 
strategies to address loss and 
damage from climate change 
(Section 10.5.6) –Risk insurance (Section 10.5.5)
 –Climate-smart agriculture (10.4.5.5, Table 10.6)
 –Wetland protection and restoration (Table 10.6)
 –Aquifer storage and recovery (Table 10.6)
 –Integrated smart water grids (Table 10.6)
 –Disaster risk management (Table 10.6)
 –Early warning systems (Table 10.6)
 –Resettlement and migration (Table 10.6)
 –Nature-based solutions in urban areas
 –Coastal green infrastructure (Table 10.6) –(S+) nature-based adaptation 
solutions, wetland protection, and 
climate-smart agriculture enhance 
carbon sequestration (Table 10.6)
 –(S+) disaster risk reduction 
and capacity building have 
synergistic interactions with

climate adaptation when the 
two are effectively integrated 
(Section 10.6.2)
 –(S+) environmental sustainability 
has benefits for relieving poverty 
and promoting social equity 
(Section 10.6.4)
 –(T) intensive irrigation and other 
forms of water consumption can 
have a negative effect on water 
quality and aquatic ecosystems 
(Section 10.6.3)GINI34.9
(26.6–43.9)
FRAGILITY73.6
(32.3–111.7)
CO 2/PC6.3
(0.3–38.0)

18
2726Chapter 18 Climate Resilient Development PathwaysRegionDevelopment indicators
AustralasiaPPAHDI0.75
(0.70–0.81) –Underinvestment in adaptation, particularly in public 
health systems, given current and projected risks 
(Section 11.3.6.3)
 –Underlying social and economic vulnerabilities 
exacerbate disadvantage among particular social 
groups (Section 11.8.2)
 –Competing policy and planning objectives within 
governments (Section 11.7.2)
 –Limits to adaptation across the region and among 
neighbours (Section 11.7.2)
 –Fear of litigation and demands for compensation 
create disincentives for climate adaptation 
(Section 11.7.2)
 –Different climate change risk perceptions among 
different groups (Section 11.7.2) –Implementation of national 
policies and guidance on climate 
adaptation and resilience 
(Box 11.5)
 –Cooperation among individual 
farmers for adaptation 
and regional innovation 
(Section 11.7.1)

–Enhancing understanding of 
Indigenous knowledge and 
practices (Table 11.11) –Climate adaptation services, planning and tools 
from government and private sector providers 
 –Enhancing governance frameworks (Table 11.17)
 –Building capacity for adaptation (Table 11.17)
 –Community partnership and collaborative 
engagement (Table 11.17)
 –Flexible decision making (Table 11.17)
 –Reducing systemic vulnerabilities (Table 11.17)
 –Providing adaptation funding and compensation 
mechanisms (Table 11.17)
 –Addressing social attitudes and engagement in 
adaptation and climate action (Table 11.17) –(T) adapting to fire risk in 
peri-urban zones introduces 
potential trade-offs among 
ecological values and fuel 
reduction in treed landscapes 
(Section 11.3.5)GINI34.4
(34.4–34.4)
FRAGILITY20.1
(18.4–21.8)
CO 2/PC12.1
(7.3–16.9)
Central 
and South 
AmericaPPAHDI0.71
(0.62–0.78) –Vulnerability of informal settlements with chronic 
exposure to everyday, non-climate risks

–Limited political influence of poor and most 
vulnerable groups
 –Poor market access of rural households
 –Little consideration of the implications of NDCs for 
poverty and livelihoods
 –Corruption, particularly in the construction and 
infrastructure sector
 –Gender inequities in labour markets
 –Limits to adaptation –Address existing development 
deficits, particularly the needs 
of informal settlements and 
economies
 –Adopt collaborative approaches 
to decision making that integrate 
civic groups and communities as 
well as the private sector
 –Enhance adoption of sustainable 
tourism and livelihood 
diversification –Upgrading of informal and vulnerable settlements
 –Capacity building in national and city level 
government institutions
 –Enhancing social protection programmes
 –Integrated land use planning and risk-sensitive 
zoning
 –Infrastructure greening
 –disaster risk mitigation and management
 –Emergency medical and public health preparedness

–Improving insurance mechanisms and climate 
financing
 –Ecosystem conservation, protection and restoration
 –Appropriate use of climate information and 
development of climate services –(S+) conservation and restoration 
of natural ecosystems have 
synergies with mitigation, 
adaptation and sustainable 
development (Section 12.7.1)GINI47.2
(38.6–57.9)
FRAGILITY 65.9 (35.9–92.6)
CO 2/PC2.2
(0.9–4.8)
EuropePPAHDI0.76
(0.52–0.83) –Mitigation and adaptation remain siloed around 
sectoral approaches (Box 13.3)
 –Institutional, policy and behavioural lock-ins 
constrain the rate of system transitions 
(Section 13.11.4)
 –Legislative and decision making process constraints 
on climate action (Section 13.11.4)
 –High adaptation costs and concerns about 
effectiveness and feasibility (Section 13.3.2, 
Table 13.A.5)
 –Competition for land use among adaptation and 
other uses (Section 13.3.2)
 –Perceptions of climate change as irrelevant or not 
urgent (Section 13.3.2)

–Public budget and human capital limitations 
(Section 13.3.2) –Engagement in climate change 
knowledge, policy and practice 
networks (Box 13.3)
 –National policies can lead to 
more ambitious and integrated 
climate planning and action with 
associated co-benefits (Box 13.3)
 –System transformations 
towards more adaptive and 
climate-resilient systems 
(Section 13.11.4, Box 13.3) –Ecological restoration of habitats agroforestry and 
reforestation (Section 13.8.2)
 –‘Smart farming’ and knowledge training 
(Section 13.5.2.1)
 –Soil management practices (Section 13.5.2.1)
 –Changing sowing dates and changes in cultivars 
 –Stricter enforcement of existing health regulations 
(Section 13.7.2)
 –Integrated coastal zone management and marine 
spatial planning (Section 13.4.2)
 –Nature-based solutions (Section 13.4.2)
 –Climate services (Section 13.6.2.3)
 –Tailored insurance products for specific physical 
climate risks (Section 13.6.2.5)

–Protection of world heritage sites (Section 13.8.2) –(T) wind farms support 
greenhouse gas mitigation but 
have ecosystem implications and 
impacts (Section 13.4.2)
 –(T) adapting and mitigating 
climate change through 
afforestation and forest 
management may be hampered 
by biophysical and land use 
trade-offs (Section 13.3.2)GINI31.9
(24.6–41.3)
FRAGILITY 41.1 (16.2–72.9)
CO 2/PC6.8
(1.3–21.3)

18
2727Climate Resilient Development Pathways  Chapter 18
North 
AmericaPPAHDI0.72
(0.72–0.73)
 –Lack of representation of all groups and 
communities in politics and decision making 
(Section 14.6.3)
 –Economic and financial constraints on adaptation 
within communities (Section 14.6.2)
 –Persistent social vulnerability and inequities 
(Sections 14.6.3, 14.4.7.3)
 –Adaptation actions that are maladaptive and 
exacerbate existing inequities (Section 14.6.2.1)
 –Constraints on capacity for data collection 
(Table 14.8)
 –Limited organisational willingness to implement 
new and untested solutions (Table 14.8) –Increased focus on building 
adaptive capacity in small towns 
and rural areas (Section 14.6.3)
 –Greater use of SDGs as a 
framework for equitable 
adaptation measures 
 –Broader and deeper recognition of 
the role of Indigenous knowledge

and local knowledge systems in 
adaptation (Section 14.6.3)
 –Greater emphasis on participatory 
governance and co-production of 
knowledge in adaptation decision 
making (Section 14.6.2.2)
 –Enhanced use of risk-based 
decision analysis frameworks 
and flexible adaptation pathways 
(Section 14.6.2.2)
 –Coordination of policies to 
support transformational 
adaptation (Section 14.6.2.2) –Indigenous knowledge-based land and resource 
management (Section 14.4.4)
 –Adaptive co-management of agriculture and 
freshwater resources (Section 14.4.3)
 –Ecosystem-based management and nature-based 
solutions (Box 14.3, Sections 14.4.2, 14.4.3, 14.4.4, 
Table 14.9)
 –Increased efficiency and equity of water 
management and allocation (Section 14.4.3.3)
 –Energy conservation measures (Section 14.6.1.3)
 –Guidelines, codes, standards and specifications for 
infrastructure (Section 14.6.1.6)
 –Modifying zoning and buying properties in 
floodplains (Section 14.6.1.3)

–Web-based tools for visualising and exploring 
climate information scenario planning and risk 
analyses (Section 14.6.1.6) –(S+) post-fire ecosystem recovery 
measures, restoration of habitat 
connectivity, and managing 
for carbon storage enhance 
adaptation potential and 
offers co-benefits with carbon 
mitigation (Box 14.1)
 –(T) REDD+ represents a trade-off 
between carbon mitigation 
and the ability of communities 
to improve their food security 
(Section 14.4.7)
 –(T) new coastal and alpine 
developments generate economic 
activity but enhance local social 
inequalities (Section 15.4.10)GINI40.0
(33.3–45.4)
FRAGILITY45.4
(21.7–69.9)
CO 2/PC11.9
(3.8–16.6)
Small 
IslandsPPAHDI0.68
(0.51–0.76)
 –High dependence of economic activity on tourism 
(Section 15.3.4.5)
 –Lack of coordination among government 
departments (Section 15.6.1)
 –Limited regional cooperation (Section 15.6.1)
 –Absence of planning frameworks (Section 15.6.1)

–Corruption and corrupt people in political and public 
life (Section 15.6.1)
 –Insufficient human capital (Section 15.6.1)
 –Competing development priorities (Section 15.5.5)
 –Lack of education and awareness around climate 
change (Section 15.6.4)
 –Failure of externally driven adaptation 
(Section 15.6.5)
 –Constraints on economic, legislative and technical 
capacity of local governments (Section 15.7) –Increasing women’s access to 
climate change funding and 
support from organisations 
(Section 15.6.5) promoting 
agroecology, food sovereignty 
and regenerative economies 
(Section 15.7)
 –Expanding sustainable tourism 
economies (Section 15.7)
 –Integrating climate change 
and disaster management 
with broader development 
planning and implementation 
 –Using climate risk insurance as 
a way to support development 
and adaptation processes 
 –Improving cross sectoral and 
cross agency coordination 
 –Enhanced integration between

development assistance, public 
financial management, and 
climate finance (Section 15.5.7) –Raising dwellings and other infrastructure 
(Section 15.5.2)
 –Land reclamation (Section 15.5.2)
 –Migration and planned resettlement 
 –Ecosystem-based adaptation including Indigenous 
and local knowledge (Section 15.5.2)
 –protected areas (Section 15.5.2)
 –Ecosystem restoration and improved agroforestry 
practices (Sections 15.5.2, 15.5.4)
 –Community-based adaptation (Section 15.5.5)
 –Livelihood diversification and use of improved 
technologies and equipment (Section 15.5.6)
 –Diversifying cropping patterns, expanding or 
prioritising other cash crops (Section 15.5.6)
 –Small-scale livestock husbandry (15.5.6)
 –Irrigation technologies (Section 15.5.6)
 –Diversification away from coastal tourism
 –Disaster risk management (DRM) (Section 15.5.7)
 –Early warning systems and climate services 
(Section 15.5.7) –(S+) development decisions and 
outcomes are strengthened by

consideration of climate and 
disaster risk (Section 15.7)
 –(S-) impacts of invasive alien 
species on islands are projected 
to increase with time due to 
synergies between climate change 
and other drivers (Section 15.3.3)
 –(S-) synergies between changing 
climate and other natural and 
anthropogenic stressors could 
lead to disproportionate impacts 
on biodiversity (Section 15.3.3)GINI40.2
(28.7–56.3)
FRAGILITY64.6
(38.1–97.5)
CO 2/PC3.7
(0.3–31.3)

18
2728Chapter 18 Climate Resilient Development PathwaysTable 18.7 |  Sectoral synthesis of dimensions of climate resilient development. For each sectoral chapter of the WGII report, this table identifies those SDGs that are discussed in the relevant chapter as being particularly relevant to the 
sector. In addition, the table contains evidence of sustainable development challenges and opportunities as well as adaptation/sustainable development options and potential synergies and trade-offs associated with their implementation. 
Synergies and trade-offs are categorised as follows: (T) Trade-off among policies and practices; (S+) Synergy among policies and practices that enhances sustainability; (S-) Synergy among policies and practices that undermines sustainability.
SectorRelevant 
SDGsChallenges Opportunities Options Trade-offs
freshwater 
and their 
servicesSDG 1, 
SDG 2, 
SDG 3, 
SDG 6, 
SDG 7, 
SDG 9, 
SDG 10, 
SDG 11, 
SDG 12, 
SDG 13, 
SDG 15,

SDG 17 –Low capacity for dispersal limits range shifts 
to match climate (Section 2.6.1)
 –Constraints on the evolution of greater stress 
tolerance among species (Sections 2.4.2, 
2.6.1)
 –Altered peatland drainage and repeated 
disturbances pose barriers to restoration of 
tropical peatlands (Section 2.4.3)
 –Demonstrating the efficacy of natural flood 
management efforts poses challenges to its 
deployment (Section 2.6.5)
 –Uncertainties in climate and socioeconomic 
projections constrain adaptation planning 
and implementation (Section 2.7) –Nature-based solutions offer the opportunity 
to address climate change and biodiversity 
problems in an integrated way (Section 2.6)
 –Adaptation can be integrated with the 
protection of biodiversity and land-based climate 
change mitigation initiatives (Section 2.6.2) –Habitat restoration, connectivity and creation of 
protected areas (Table 2.5)
 –Integrated landscape management (Table 

–Community-based natural resource management 
(Section 2.6.5.7)
 –Maintain or restore natural species and structural 
diversity (Table Cross-Chapter Box NATURAL.1 in 
Chapter 2)
 –Restoration of hydrological flows and catchment 
vegetation (Table Cross-Chapter Box NATURAL.1 in 
Chapter 2)
 –Control of feral herbivores Table Cross-Chapter 
Box NATURAL.1 in Chapter 2)
 –Reduce non-climatic stressors to land-based 
ecosystems (Table 2.6) –(S+) ecosystem-based adaptation measures, such 
as restoration of forests and wetlands for flood 
and erosion control help maintain freshwater 
supply and quality (Section 2.2.2)
 –(S-) over grazing/stocking of pastures and 
grasslands can result in soil erosion and the loss of 
biodiversity (Table Cross-Chapter BoxNATURAL1 
in Chapter 2)
 –(T) planting non-native monocultures for 
mitigation can reduce biodiversity and resilience
 –(T) inappropriate hydrological restoration can 
result in increased methane emissions (Table

Cross-Chapter Box NATURAL1 in Chapter 2)
 –(T) afforestation/reforestation and bioenergy 
initiatives can conflict with other land uses such as 
food and timber production (Table Cross-Chapter 
Box BECCS, Section 2.2.2, Box 2.2)
Ocean and 
coastal 
and their 
SDG 2, 
SDG 3, 
SDG 5, 
SDG 7, 
SDG 8, 
SDG 9, 
SDG 10, 
SDG 11, 
SDG 12, 
SDG 13, 
SDG 14 –Shifts in the distribution of fish species 
across exclusive economic zones present 
governance, ecological and conservation 
challenges (Section 3.4.3)
 –Resource constraints impede the 
implementation of ecosystem-based and 
community-based adaptation for low- to 
middle-income nations (Section 3.6.2)
 –Governance in marine social-ecological 
systems is highly complex with poorly defined 
legal frameworks (Section 3.6.2)
 –‘Coastal squeeze’ challenges adaptation, 
creating tensions between coastal 
development and coastal habitat 
management (Section 3.6.3) –Development assistance can help address

resource constraints associated with marine 
ecosystem management (Section 3.6.3)
 –Improving coordination among actors and 
projects will contribute to achieving SDGs 
(Section 3.6.3)
 –Private finance can support restoration of blue 
carbon systems (Section 3.6.3)
 –Joint implementation of coastal and marine 
management initiatives can address governance 
challenges across scales and sectors 
 –Ocean-based renewable energy options can 
reduce reliance on imported fuel (Section 3.6.3) –Maritime spatial planning and integrated coastal 
management (Section 3.6.2; Figure 3.2.6)
 –Adaptive and sustainable fisheries management 
(Section 3.6.2)
 –Habitat restoration (Section 3.6.2)
 –fishery mobility (Figure 3.6.2)
 –Assisted evolution (Figure 3.2.6)
 –Increase participation in management and 
governance (Figure 3.2.6)
 –Nature-based solutions (Section 3.6.2)
 –Hard and soft infrastructure (Figure 3.2.6)
 –Livelihood diversification (Figure 3.6.2)

–Disaster mitigation and response (Figure 3.2.6)
 –Finance and market mechanisms (Figure 3.2.6) –(S+) adaptation in ocean and coastal systems can 
be designed in ways that substantially contribute 
to the SDGs and not only support but allow the 
attainment of social, environmental and economic 
targets (Section 3.6.4)
 –(S+) blue/green economies can reduce emissions 
and finance adaptation pathways (Section 3.6.3)
 –(T) built infrastructure conflicts with mitigation 
goals and can create potential ecological, social 
and cultural impacts that undermine ecosystem 
health (Section 3.6.2)

18
2729Climate Resilient Development Pathways  Chapter 18
WaterSDG 1, 
SDG 2, 
SDG 3, 
SDG 6, 
SDG 7, 
SDG 10, 
SDG 11, 
SDG 13 –Uncertainty in future water availability 
(Box 4.1, Box 4.4)
 –Lack of sufficient data, information and 
knowledge in understanding the water–
energy–food nexus (Box 4.6)
 –Increasing urbanisation is creating new 
and difficult demands for urban water 
management. (Section 4.3.4)
 –Barriers to adapting water-dependent 
livelihoods in rural communities 
(Section 4.3.1)
 –Mainstreaming water management across 
sectors and enhancing finance for adaptation 
(Section 4.3.5)
 –Path-dependency of institutions, (and 
contingencies on decision-making processes 
(Section 4.5.3) –A resilient circular economy delivers access to 
water, sanitation, wastewater and ecological 
flows (Box 4.7)
 –Adaptive sanitation systems and sustainable 
urban drainage contribute to a ‘one health

approach’ which can prevent water and 
sanitation contamination risks during floods and 
droughts. (Box 4.7)
 –Climate-proof infrastructure would reduce 
infection risks in flood-prone areas (Box 4.7)
 –Governance can derive legitimacy from inclusion 
of multiple stakeholders, including women, 
Indigenous communities and young people 
(Section 4.6.6)
 –Indigenous and local knowledge can help ensure 
solutions align with the interests of communities 
(FAQ 4.5) –Changes in crop cultivars and agronomic practices 
(Section 4.5)
 –Changes in irrigation and water management 
practices (Section 4.5)
 –Water and soil conservation (Section 4.5)
 –Migration and off-farm livelihood diversification 
 –Collective action, policies and institutions 
 –Economic and financial incentives (Section 4.5)
 –Training and capacity building (Section 4.5)
 –Flood risk reduction measures (Section 4.5)
 –Urban water management (Section 4.5)
 –Water, sanitation and hygiene adaptations

 –Agro-forestry and forestry responses (Section 4.5)
 –Livestock and fishery responses (Section 4.5)
 –Indigenous and local knowledge (Section 4.5)
 –Energy-related adaptations (Section 4.5) –(S+) increasing the proportion of sewerage, 
treated wastewater, recycling and safe reuse 
would help reach climate and water targets 
(Box 4.7)
 –(S+) solar irrigation pumps provide for income 
diversification for small and marginal farmers 
while also generating renewable energy (Box 4.7)
 –(T) desalination of seawater or brackish inland 
water is energy intensive, with high salinity brine 
and other contaminants (Section 4.5.5)
 –(T) negative-emission technologies, such as direct 
air capture can result in a net increase in water 
consumption (Section 4.5.5)
Food, fibre, 
and other 
ecosystem 
productsSDG 1, 
SDG 2, 
SDG 3, 
SDG 4, 
SDG 5, 
SDG 6, 
SDG 7, 
SDG 9, 
SDG 9, 
SDG 10, 
SDG 11, 
SDG 12, 
SDG 13, 
SDG 14, 
SDG 15, 
SDG 16 –Increased cost and management challenges

of providing safe food (Section 5.2.2)
 –Warming-induced shifts of species create 
resource allocation challenges among 
different fishing fleets (Section 5.2.1)
 –Challenges related to REDD+ implementation 
and forest use (Section 5.6.3)
 –Differences in perceptions about the 
validity of different forms of knowledge 
(Section 5.8.4)
 –Inequality in access to climate services 
(Section 5.14.1)
 –Lack of support, policies and incentives for 
the adoption of agro-ecological approaches 
(BIOECO.1)
 –Financial barriers limit implementation of 
adaptation options in agriculture, fisheries, 
aquaculture and forestry (Section 5.14.3) –Integrated approaches to food, water, health, 
biodiversity and energy that involve vulnerable 
groups can help to address current and future 
food security challenges, reduce vulnerability of 
Indigenous People, small-scale landholders and 
pastoralists, and promote resilient ecosystems. 
(Sections 5.12.3, 5.13.2; 5.14)

–Agro-forestry delivers benefits for climate 
change mitigation, adaptation, desertification, 
land degradation and food security, and is 
considered to have broad adaptation and 
moderate mitigation potential (Section 5.10.4)
 –Partnerships between key stakeholders such 
as researchers, forest managers, and local 
actors can lead to a shared understanding of 
climate-related challenges and more effective 
decisions. (Section 5.6.3) –Livelihood diversification (Section 5.4.4)
 –Social protection policies and programmes 
(Section 5.4.4)
 –Changes in crop management including irrigation, 
fertilizers, planting schedules and crop varieties 
 –Adjusting water management for forage production 
(Section 5.5.4)
 –Rotational grazing of livestock (Section 5.5.4)
 –Fire management to control woody thickening of 
grass (Section 5.5.4)
 –Using more suitable livestock breeds or species 
 –Migratory pastoralist activities (Section 5.5.4)

–Monitor and manage the spread of pests, weeds, and 
diseases (Section 5.5.4)
 –Nature- or ecosystem-based strategies 
(Section 5.12.5.2) –(S+) agricultural production systems that integrate 
crops, livestock, forestry, fisheries and aquaculture 
can increase food production per unit of land, 
reduce climatic risk and reduce emissions (Chapter 
5 Executive Summary)
 –(S+) integrated approaches to food, water, health, 
biodiversity and energy can help address current 
and future food security challenges, reduce 
vulnerability of Indigenous People, small-scale 
landholders and pastoralists, and promote resilient 
ecosystems (Sections 5.12.3, 5.13.2, 5.14)
 –(T) growing biomass demand for producing 
sustainable bio products competes with food 
production, with potential effects on food prices 
and knock-on effects related to civil unrest 

18
2730Chapter 18 Climate Resilient Development PathwaysSectorRelevant 
Cities, 
and key 
infrastructureSDG 11, 
SDG 13, 
SDG 17 –Poor municipal funding, data collection 
and collaboration hinders sustainable 
development initiatives, capacity building and 
climate action (Sections 6.1.5, 6.4.5, 6.4.9)
 –High urbanisation rates pose challenges to 
areas that already have high levels of poverty, 
unemployment, informality and housing and 
service backlogs (Section 6.2.1)
 –Limited capacity for early warning systems in 
low-income countries (Section 6.3.2)
 –Lack of administrative capacities, 
coordination across sectors and efforts, 
transparency and accountability slows 
sustainability transitions and disaster risk 
reduction (Case Study 6.4) –Urban ecological infrastructure including green, 
blue, turquoise and others can be a source 
of nature-based solutions that can improve 
both adaptation and mitigation in urban areas

(Section 6.1.2)
 –Transition architecture movements can drive 
urban adaptation (Section 6.4.1)
 –Transformative capacities support adaptation 
efforts and systemic change processes 
(Section 6.4.4)
 –Incorporating Indigenous and local knowledge 
help generate more people-oriented and 
place-specific adaptation policies (Section 6.4.7)
 –Climate finance offers the opportunity to 
overcome structural impediments to climate 
action (Box 6.5)
 –Urban ecological infrastructure can be a source 
both adaptation and mitigation in urban areas 
(Cross-Chapter Box URBAN in Chapter 6)
 –High-density environments coupled with other 
design measures can provide mitigation and 
adaptation benefits (Cross-Chapter Box URBAN 
in Chapter 6) –Green infrastructure, sustainable land use and 
planning, and sustainable water management 
 –Nature-based solutions (Section 6.3.3)
 –Insurance (Section 6.3.2)

–switching to air cooling for thermal power plants 
(Section 6.3.4)
 –Increasing the efficiency of hydro- and thermoelectric 
power plants (Section 6.3.4)
 –Changing reservoir operation rules (Section 6.3.4)
 –Upgrading infrastructure and strengthening or 
relocating (critical) assets (Section 6.3.4)
 –Including green, blue, turquoise and nature-based 
solutions (Cross-Chapter Box URBAN in Chapter 6)
 –Cooling networks (Cross-Chapter Box URBAN in 
Chapter 6)
 –Early warning systems (Table 6.4)
 –Resource demand and supply side management 
strategies (Table 6.4)
 –Enhanced monitoring of air quality in rapidly 
developing cities (Table 6.4)
 –Investment in air pollution controls (Table 6.4)
 –Core and shell preservation, elevation and relocation 
for heritage buildings (Section 6.3.2) –(S+) sustainable urban energy planning that 
includes opportunities to avoid and reduce 
the UHI effect can provide synergies for both 
climate mitigation and adaptation in urban areas

 –(S+) natural ventilation and passive energy 
strategies can capture synergies between climate 
mitigation and adaptation (Cross-Chapter 
Box URBAN in Chapter 6)
 –(S+) community-based adaptation has potential 
to be better integrated to enhance well-being 
and create synergies with the Sustainable 
 –(T) urban mitigation efforts can create trade-offs 
with adaptation such as intensifying the 
urban heat island (UHI) effect (Cross-Chapter 
 –(T) efforts aimed at increasing adaptation may 
undermine mitigation objectives by increasing 
investment in hard infrastructure that increases 
emissions (Cross-Chapter Box URBAN in Chapter 6)
 –(T) lack of open and green spaces may induce 
long-distance leisure trips thereby increasing 
Health, well-
being and 
the changing 
structure of 
communitiesSDG 3, 
SDG 5, 
SDG 8, 
SDG 10,

SDG 13 –A lack of capacity for adaptation has resulted 
in only moderate or low levels of adaptation 
implementation across different countries 
(Section 7.4.2)
 –Transitioning to renewable energy sources 
presents opportunities for realising health 
co-benefits (Section 7.4.4)
 –Shifting to healthier plant-rich diets can 
reduce GHG emissions and reduce land use 
(Cross-Chapter Box HEALTH in Chapter 7)
 –Future flows of migration within and between 
countries are likely to respond strongly to 
particular combinations of climatic hazards 
and may present challenges for future 
adaptation policies and programmes
 –Climate change disruptions to natural 
environments can be expected to disrupt 
livelihood practices, stimulate higher rates 
of outmigration to urban centres, and in 
some instances necessitate planned or 
organised relocations of exposed settlements 
(Cross-Chapter Box MIGRATE in Chapter 7) –COVID-19 recovery investments offer an

opportunity to contribute to climate resilient 
development through a green, resilient, 
healthy and inclusive recovery (Cross-Chapter 
Box COVID in Chapter 7)
 –investing in basic infrastructure for all can 
transform development opportunities, increase 
adaptive capacity and reduce climate risk 
 –Integrated agroecological systems offer 
opportunities to increase dietary diversity while 
building local resilience to climate-related food 
insecurity (Section 7.4.2)
 –Incorporating climate change and health 
considerations into disaster reduction and 
management strategies could potentially 
improve funding opportunities (Section 7.4.2)
 –Adaptive urban design that provides access 
to healthy natural spaces can promote social 
cohesion and mitigate mental health challenges 
(Section 7.4.2) –Improved building and urban design including use of 
passive cooling systems (Table 7.2)
 –Better access to public health systems for the most

vulnerable (Table 7.2)
 –Deployment of renewable energy sources (Table 7.2)
 –Improved water, sanitation and hygiene conditions 
(Table 7.2)
Early warning system of vector-borne diseases, 
insecticide treated bed nets and indoor spraying of 
insecticide (Table 7.2)
 –Targeted efforts to develop vaccines for infectious 
diseases exacerbated by climate change (Table 7.2)
 –Improved personal drinking and eating habits 
 –Improved food storage, food processing and food 
preservation (Table 7.2)
 –Emergency shelters for people to escape heat 
 –Improved funding and access to mental health care 
 –Improved education for girls and women (Table 7.2)
 –Improved maternal and child health services 
(Table 7.2) –(T) energy strategies for energy efficiency and 
GHG emissions reductions can generate health 
co-benefits through improved air quality but may 
slow poverty reduction efforts (Sections 7.4.2, 
7.4.5)
 –(S+) investing in adaptation for health and

community well-being has the potential to 
generate considerable co-benefits in terms of 
reducing impacts of non-climate health challenges
 –(S+) investments in mitigating greenhouse gas 
emissions will not only reduce risks associated 
with dangerous climate change but will increase 
population health and well-being through a 
number of pathways. (Section 7.4)

18
2731Climate Resilient Development Pathways  Chapter 18
Poverty, 
developmentSDG 1, 
SDG 2, 
SDG 3, 
SDG 5, 
SDG 10, 
SDG 14 –Use of political frameworks for decision 
making that are unfavourable towards 
adaptation and system transitions (Table 8.4)
 –Attitudes towards risk and other cultural 
values limit responses (Table 8.4)
 –Psychological distress causes insecurity 
and behaviours that increase vulnerability 
(Table 8.4)
 –Limited financial resources to support 
adaptation projects (Section 8.2.2, Table 8.4)
 –Small-holder farmers have poor access to 
markets and land tenure (Section 8.6.1)
 –Unsuitable infrastructure may increase 
exposure (Table 8.4)
 –Lack of access to technologies that can 
support adaptation (Table 8.4)
 –Gender-based inequalities constrain women’s 
access to resources for adaptation (Table 8.7)
 –Poverty constrains livelihood diversification,

resilience or adaptive capacity (Table 8.7)
 –Indigenous Peoples and other populations 
with strong attachments to place face 
barriers to adaptation (Table 8.7)
 –Local institutions face ongoing challenges 
in gaining support from higher governance 
levels, particularly in developing countries. 
(Section 8.5.2) –Polycentric governance, adaptive governance, 
multi-level governance, collaborative governance 
or network governance are increasingly 
used to understand transitions towards 
climate-compatible development (Section 8.6.2)
 –Well coordinated and integrated nexus 
approaches to adaptation offer opportunities 
to build resilient systems while harmonising 
interventions, mitigating trade-offs and 
improving sustainability (Section 8.6.2)
 –Income from new livelihood activities can 
support recovery following disasters linked to 
climate variability and change (Section 8.4.5)
 –Improving industrial processes can contribute 
to the optimised use of energy, reuse of waste,

reducing GHG emissions, use of biomass and 
more efficient equipment (Table 8.3)
 –Industrialisation and technological innovation in 
rural areas may assist vulnerable communities 
through provision of resources, enhanced 
forecast information or reuse of biowaste 
(Table 8.3)
 –Responses to climate change can create 
significant development opportunities including 
job creation and livelihood diversification 
(Section 8.4.3) –Expanded private sector activity and public–private 
partnerships (Section 8.6.1)
 –Credit and insurance (Section 8.6.1)
 –Use of climate-smart agricultural practices and 
technologies (Section 8.6.1)
 –Crop insurance (Section 8.6.1)
 –Conservation agriculture (Section 8.6.1)
 –Changing farmers’ perception and enhancing 
farmers’ adaptive capacity (Section 8.6.1)
 –REDD+ (Section 8.6.1)
 –improving industrial processes (Table 8.3)
 –Renewable energy and energy efficiency (Table 8.3)
 –Smart electricity grids (Section 8.6.1)
 –Green buildings (Section 8.6.1)

–Efficient fuels (Section 8.6.1)
 –Pollution control investments (Section 8.6.1)
 –Public transit and non-motorised transport with 
increased use of biofuels (Section 8.6.1)
 –Integrated natural resource management (Table 8.2)
 –Disaster risk management (Table 8.2)
 –Relocation of vulnerable communities (Table 8.2)
 –Education and communication (Table 8.2)
 –Land use planning (Table 8.3) –(S+) agriculture technologies facilitate mitigation 
to climate change and adaptation such as saving 
water while maintaining grain yield (Section 8.6.1)
 –(S+) sustainable pastoralism increases carbon 
sequestration but can also contribute to 
adaptation by changing grazing management, 
livestock breeds, pest management and production 
structures (Section 8.6.1)
 –(S+) REDD+ may provide adaptation benefits 
by enhancing households’ economic resilience 
through positive livelihood impacts (Section 8.6.1)
 –(S+) solar energy contributes to reducing GHG

emissions and improving air quality (Section 8.6.1)
 –(S+) hydropower contributes to mitigation and 
adaptation through water resource availability for 
irrigation and drinking water (Section 8.6.1)
 –(S+) green roofed buildings contribute to cooler 
temperatures, thereby reducing energy use for 
air-conditioning (Section 8.6.1)
 –(T) mitigation measures such as bioenergy 
may result in trade-offs with efforts to achieve 
sustainable development, eradicate poverty and 
reduce inequalities (Section 8.6.1)
 –(T) migration to urban centres can be a form of 
adaptation, but can increase the vulnerability 
of communities of origin or at destinations 
(Section 8.2.2)

18
2732Chapter 18 Climate Resilient Development Pathways
for options feasibility for both mitigation (SR1.5 ref) and adaptation 
(Cross-Chapter Box  FEASIB). This assessment allows the range of 
available options to be considered in a more critical light, particularly 
when considering opportunities for implementation over the near 
term. Meanwhile, the other challenge is that of option efficacy. 
Significant uncertainties remain regarding how well a given option will 
perform in a specific context and whether it is capable of adequately 
addressing risk (Section 18.6.1). Such uncertainties can undermine the 
pursuit of CRD or at least efforts to accelerate system transitions that 
support CRD (medium evidence, medium agreement) (Section 18.3). 
Accordingly, closer examination of option implementation in the real 
world, including within different sectoral and regional contexts, would 
enhance the knowledge available to decision makers regarding which

options will best fit the needs of a given CRD pathway.
18.6 Conclusions and Research Needs
18.6.1 Knowledge Gaps
Research to improve the understanding of CRD currently exists in a 
nascent state, because, as noted in the AR5, ‘integrating climate change 
mitigation, climate change adaptation, and sustainable development is 
a relatively new challenge’ (Denton et al., 2014). While a large volume of 
literature has emerged since the AR5 that spans the nexus of sustainable 
development, CRD and climate action, the identified research gaps 
in AR5 (Denton et  al., 2014) continue to be priorities for informing 
CRD. These include enhancing understanding of mainstreaming of 
climate change into institutional decision making, managing risk 
under conditions of uncertainty, catalysing system transitions and 
transformation, and processes for enhancing participation, equity and 
accountability in sustainable development (very high confidence).

The more recent literature adds significant context to the concept 
of CRD, but also introduces broader perspectives regarding its 
significance in the arena of climate action. Hence, concepts that are 
both complementary to, and competitive with, CRD, such as ‘climate 
safe’, ‘climate compatible’ and ‘climate smart’ development (Huxham 
et al., 2015; Kim et al., 2017b; Ficklin et al., 2018; Mcleod et al., 2019) 
(Section 18.1.1). These different framings of the intersection between 
sustainable development and climate action are used in different 
communities of research and practice, which complicates efforts to 
provide clear guidance to decision makers regarding the goals of CRD 
and how best to achieve it. This is attributable in part to persistent 
conceptual confusion and disciplinary divides over more fundamental 
concepts such as resilience and sustainability (Rogers et  al., 2020; 
Zaman, 2021), not to mention contested perspectives regarding

development (Lo et  al., 2020; Song et  al., 2020a; Morton, 2021) 
(medium agreement, medium evidence).
Reconciling different perspectives on CRD is not simply a matter of 
academic debate. Climate action, resilience and sustainable development 
are all active areas of policy and practice with significant economic, 
social, environmental and political implications (Section 18.1.3). Hence, 
enhancing the role of CRD as a practical framework for development 
and a guide for action may necessitate improving the science–policy 
discourse regarding CRD (Winterfeldt, 2013; Jones et al., 2014; Ryan and Bustos, 2019). This includes consideration for risk and science 
communication; decision analysis and decision support systems; and 
mechanisms for knowledge co-production between scientists and 
public policy actors (very high confidence).
In addition, the AR6 WGII report highlights a number of elements 
of CRD that are associated with significant knowledge gaps and

uncertainties. As a result, enhancing the value of CRD as a unifying 
concept in development would benefit from further conceptualisation 
and socialisation of the concept, as well as efforts to address the 
following knowledge gaps:
• The challenges posed by different levels of global warming to 
achieving CRD and the magnitude and nature of the adaptation 
gap (and associated finance needs) that must be addressed to 
enable climate resilience.
• The efficacy of different adaptation, mitigation and sustainable 
development interventions in reducing climate risk and/or 
enhancing opportunities for CRD in the short, medium and long 
term.
• How different CRD pathways can be designed such that they 
illustrate opportunities for the practical pursuit of CRD in a manner 
consistent with principles of inclusion, equity and justice.
• How deliberative, participatory learning can be integrated into 
approaches to CRD to enhance the representation of diverse actors,

forms of knowledge, governance regimes, economic systems and 
models for decision making in CRD.
• The synergies and trade-offs associated with the implementation 
of different policy packages and the design principles and 
development contexts that enhance the ability to successfully 
manage potential trade-offs.
• The limits of incremental system transitions to achieving CRD 
on a timeline that reflects the urgency associated with the Paris 
Agreement and the SDGs.
• The capacity of governments, social institutions and individuals to 
drive large-scale social transformations that open up the solutions 
space for CRD.
• Best practices for avoiding maladaptation and ensuring that 
adaptation interventions are designed so they do not exacerbate 
vulnerability to climate change to support CRD.
18.6.2 Conclusions
The concept of CRD presents an ambitious agenda for actors at 
multiple scales—global to local, particularly in the manner in which it

reframes climate action to integrate a broader set of objectives than 
simply reducing GHG emissions or adapting to the impacts of climate 
change. Specifically, recent literature extends policy goals for climate 
action beyond avoiding dangerous interference with the climate 
system to adopt normative goals of meeting basic human needs, 
eliminating poverty and enabling sustainable development in ways 
that are just and equitable. This creates a policy landscape for climate 
action that is not only richer, but also more complex in that it situates 
responses to climate change squarely within the development arena. 
Current policy goals associated with the Paris Agreement, Sendai 
Framework and the SDGs imply aggressive timetables. Yet, as noted

18
2733Climate Resilient Development Pathways  Chapter 18
in the AR5 and supported by more recent literature (Section 18.2.1), 
the world is neither on track to achieve all of the SDGs nor fulfil the 
Paris Agreement’s objective of limiting the increase in the global 
mean temperature to well below 2°C above pre-industrial levels and 
pursuing efforts to limit warming 1.5°C. (Denton et  al., 2014; IPCC, 
2018a). This places aspirations for CRD in a precarious position. 
Transitions will be necessary across multiple systems (Section 18.1.3). 
While some may be already underway, the pace of those transitions 
must accelerate, and societal transformations may be necessary to 
enable CRD (Sections 18.3, 18.4, Box 18.1).
Given the pace of climate change and the inherent challenge of 
sustainable development, particularly in the face of inevitable 
disruptions and setbacks such as the COVID-19 pandemic (Cross-
Chapter Box  COVID in Chapter 7), the feasibility of achieving CRD is

an open question. Rapid changes will be required to shift public and 
private investments, strengthen institutions and orient them towards 
more sustainable policies and practices, expand the inclusiveness of 
governance and the equity of decision making, and shift societal and 
consumer preferences to more climate-resilient lifestyles. Nevertheless, 
the collective body of recent literature on CRD, system transitions 
and societal transformation, combined with the assessments within 
recent IPCC Special Reports (IPCC, 2018a; IPCC, 2019b; IPCC, 2019d) 
indicate that there are a broad range of opportunities for designing and 
implementing adaptation and mitigation options that enable the climate 
goals in the Paris Agreement to be achieved while enhancing resilience 
and meeting sustainable development objectives. However, options 
should be considered alongside the mechanisms by which societies can 
engage to create the conditions that can support the implementation

of those options (Section 18.4). This includes formal policy mechanisms 
pursued by governments, the catalysation of innovation by private firms 
and entrepreneurship, as well as informal, grassroots interventions by 
civil society. While there is no ‘one-size-fits-all’ solution for CRD that 
will work for all actors at all scales, exploring different pathways by 
which actors can achieve their development and climate goals can make 
valuable contributions to developing effective strategies for CRD.
A fundamental challenge for achieving CRD globally is reconciling 
different perspectives on CRD. As noted in the AR5, ‘as policy makers 
explore what pathways to pursue, they will increasingly face questions 
about managing discourses about what societal objectives to 
pursue’ (Denton et  al., 2014: 1124). Since the AR5, such discourses 
have become prominent in policy debates over climate action and 
sustainable development because of different nations, communities

and subpopulations having different understandings of what 
constitutes CRD. Aggressive efforts to rapidly reduce GHG emissions or 
enhance resilience to climate change, for example, could have negative 
externalities for the development objectives of some actors. This 
potential for trade-offs complicates efforts to build consensus regarding 
what constitutes appropriate climate and development policies and 
practices and by whom. The CRD pathways preferred by one actor are 
likely to be contested by others. This means operationalising concepts 
such as CRD in practice is likely to necessitate ongoing negotiation.
Ultimately, one of the critical developments within the literature is 
the emergence of procedural and distributive justice as key criteria for 
evaluating climate action and CRD more specifically. This trend not only recognises the need to prevent vulnerable human and ecological 
systems from experiencing disproportionate harm from the changing

climate, but also the need to prevent those same systems from being 
harmed by mitigation, adaptation and sustainable development 
policies and practices. Failure to adequately engage with equity 
and justice when designing sustainability transitions could lead 
to maladaptation, aggravated poverty, reinforcement of existing 
inequalities, and entrenched gender bias and exclusion of Indigenous 
and marginalised communities (Jenkins et al., 2018; Fisher et al., 2019; 
Schipper et  al., 2020b). These consequences could ultimately slow, 
rather than accelerate, CRD. Hence, developing programmes and 
practices for prioritising equity in effective transition risk management 
is an important dimension of enabling CRD.
As indicated by the literature assessed within this chapter, keeping 
windows of opportunity open for CRD will necessitate urgent action, 
even under diverse assumptions regarding how future mitigation and 
adaptation interventions evolve. If nations are to collectively limit

warming to well below 2°C, for example, unprecedented emissions 
reductions will be necessary over the next decade (IPCC, 2018a). 
These reductions would necessitate rapid progression of system 
transitions (Section  18.3). If, despite the Paris Agreement, future 
emissions trajectories take the world beyond 2°C, a greater demand 
will be placed on adaptation as a means of enhancing the resilience 
of development. Given the long-lived nature of human systems, and 
the built environment in particular, significant adaptation investments 
would be needed over the near-term to meet this demand. Yet, it 
is important to note that, even in the absence of consideration for 
climate change, substantial development needs exist for communities 
around the world at present. Hence, a robust strategy for the pursuit of 
CRDPs is a near-term focus on portfolios of policies and practices that 
promote human and ecological well-being.

18
2734Chapter 18 Climate Resilient Development Pathways
FAQ 18.1 | What is a climate resilient development pathway?
A pathway is defined in IPCC reports as a temporal evolution of natural and/or human systems towards a 
future state. Pathways can range from sets of scenarios or narratives of potential futures to solution-oriented 
decision-making processes to achieve desirable societal goals. Climate resilient development pathways (CRDPs) are 
therefore trajectories for the pursuit of climate resilient development (CRD) and navigating its complexities. They 
involve ongoing processes that strengthen sustainable development, eradicate poverty and reduce inequalities 
while promoting fair adaptation and mitigation across multiple scales. As the pursuit of CRDPs is contingent on 
achieving larger-scale societal transformation, CRDPs invariably raise questions of ethics, equity and feasibility of

options to drastically reduce emission of greenhouse gasses (mitigation) that limit global warming (e.g., to well 
below 2°C) and achieve desirable and liveable futures and well-being for all.
There in no one, correct pathway for CRD, but rather multiple pathways depending on factors such as the political, 
cultural and economic contexts in which different actors find themselves. Some development pathways are more 
consistent with CRD, while others move society away from CRD. Moreover, CRDPs are not one single decision 
or action. Rather, CRDPs represent a continuum of coherent, consistent decisions, actions and interventions that 
evolve within individual communities, nations, and the world. Different actors, the private sector, and civil society, 
influenced by science, local and Indigenous knowledges, and the media play a role in designing and navigating 
CRD pathways.
While dependent on past patterns of development and their socio-ethical, political, economic, ecological and

knowledge-technology outcomes at any point in time, transformation, ecological tipping points and shocks can 
create sudden shifts and unexpected nonlinear development pathways. Actions taken today can enable or foreclose 
some future potential CRDPs. The differentiated impacts of hurricanes and COVID-19 on nations and communities 
around the world illustrate how the character of societal development such as equity and inclusion have enabled 
some societies to be more resilient than others.
FAQ 18.2 | What is climate resilient development and how can climate change adaptation (measures) contribute to 
achieving this?
Climate resilient development (CRD) is a process of implementing greenhouse gas mitigation and adaptation options 
to support sustainable development for all in ways that support human and planetary health and well-being, equity 
and justice. CRD combines adaptation and mitigation with underlying development choices and everyday actions,

carried out by multiple actors within political, economic, ecological, socio-ethical and knowledge-technology 
arenas. The character of processes within these development arenas are intrinsic to how social choices are made and 
they determine whether development moves society along pathways toward CRD or away. For example, inclusion, 
agency and social justice are qualities within the political arena that underpin actions that enable CRD.
CRD addresses the relationship between GHG emissions, levels of warming and related climate risks. However, CRD 
involves more than just achieving temperature targets. It considers the possible transitions that enable those targets 
to be achieved as well as the evaluation of different adaptation strategies and how the implementation of these 
strategies interact with broader sustainable development efforts and objectives. This interdependence between

patterns of development, climate risk and the demand for mitigation and adaptation action is fundamental to the 
concept of CRD. Therefore, climate change and sustainable development cannot be assessed or planned in isolation 
of one another.
Hence, CRD represents development that deliberately adopts mitigation and adaptation measures to secure a 
safe climate on earth, meet basic needs for each human being, eliminate poverty and enable equitable, just and 
sustainable development. It halts practices causing dangerous levels of global warming. CRD may involve deep 
societal transformation to ensure well-being for all. CRD is now emerging as one of the guiding principles for 
climate policy, both at the international level, reflected in the Paris Agreement (UNFCCC, 2015), and within specific 

18
2735Climate Resilient Development Pathways  Chapter 18
Multiple intertwined climate resilient development pathways
Drought
Heat wave
FamineEcosystem shiftSpecies 
extinction
Pathway not taken
HurricanesCovid 19
Conflict
ovid 19
Flood
INCREASING LY CLIMA TE  RESILIENT  DEVELOPMENT
  
PAST PRESEN T FUTUREEnergy system transition Land and ecosystem transitions Indigene knowledge Inclusion 
Well-being based development Diversity Gender equalityE mpowerment
Quality education Solidarity Social justice and equity Human and planetary healthSAFE CLIMA TE.
DIGNIFIED LIVING ST ANDARDS
FOR ALL
DANGEROUS CLIM ATE
IRRESPONSIBLE CONSUMPTION.
IN AN UNEQUA L WORLDFossil-fuel based consumption Emission and pollution Individualism Exclusion Disempowerment
Environmental degradation Exploitation Social injustice and inequity Nature-society disconnect Marginalisation and discriminationShock

Figure FAQ18.2.1 |  Multiple intertwined climate resilient development pathways. Climate change adaptation is one of several climatic and non-climatic measures carried out through decision making by multiple 
actors that may drive a pathway in a CRD or non-CRD direction. Adaptation, mitigation and sustainable development actions can push a society in a CRD direction, but only if these measures are just and equitable. There are 
multiple simultaneous pathways in the past, present and future. Societies (illustrated as boats) move on different pathways, towards CRD and non-CRD, with some pathways more dominant than others. The direction of pathways 
is emergent, taking place through contestations and social choices, through social transformation as well as through surprises and shocks (illustrated as rocks). Path dependency means it is possible but often turbulent to shift

from a non-CRD to a CRD pathway. Such a shift becomes more difficult as risks/shocks increase (more rocks) and non-CRD processes and outcomes progress, limiting future options. Low CRD processes and outcomes at the 
bottom are characterised by inequity, exclusion, polarisation, environmental and social exploitation, entrenchment of Business-As-Usual, with increasing risks/shocks. High CRD processes and outcomes (at the top of the figure) 
are characterised by equity, solidarity, justice, human well-being, planetary health, stewardship/care and system transitions.FAQ 5.1 (continued)

18
2736Chapter 18 Climate Resilient Development Pathways
FAQ 18.3 | How can different actors across society and levels of government be empowered to pursue climate 
resilient development?
CRD entails trade-offs between different policy objectives. Governments as well as political and economic elites 
may play a key role in defining the direction of development at a national and sub-national scale; but in practice, 
these pathways can be influenced and even resisted by local people, non-governmental organisations (NGOs) and 
civil society.
Given such tensions, contestation and debate are inherent to the definition and pursuit of CRD. An active civil 
society and citizenship create the enabling conditions for deliberation, protest, dissent and pressure, which are 
fundamental for an inclusive participatory process. These enable a multiplicity of actors to engage across multiple

arenas including governmental, economic and financial, political, knowledge, science & technology, and community. 
Decisions and actions may be influenced by uneven interactions among actors, including socio-political relations 
of domination, marginalisation, contestation, compliance and resistance, with diverse and often unpredictable 
outcomes.
In this way, recent social movements and climate protests reflect new modalities of action in response to social, 
economic, and political inaction. The new climate movement, led mostly by youth, seeks science-based policy and, 
more importantly, rejects a reformist stance toward climate action in favour of radical climate action. This is mostly 
pursued through collective disruptive action and non-violent resistance to promote awareness, a regenerative 
culture and ethics of care. These movements have resulted in notable political successes, such as declarations

of climate emergency at the national and local level, as well as in universities. Also, their methods have proven 
effective to end fossil fuel sponsorship.
The success and importance of recent climate movements also suggest a need to rethink the role of science in society. 
On one hand, the new climate movements demanding political action were prompted by the findings of scientific 
reports, mainly the IPCC (2018a) and IPBES (2019) reports. On the other hand, these movements have increased 
public awareness and stimulated public engagement with climate change at unprecedented levels beyond what the 
scientific community can do alone.
FAQ 18.4 | What role do transitions and transformations in energy, urban and infrastructure, industrial, land and 
ocean ecosystems, and in society, play in climate resilient development?
The IPCC SR1.5 report identified transitions in four key systems, including energy, land and ocean ecosystems, urban

and infrastructure, and industry, as being fundamental to the pursuit of CRD. In addition, this report identifies 
societal transitions, in terms of values and worldviews that shape aspirations, lifestyles and consumption patterns, 
as another key component of CRD. Acknowledging societal transitions has implications for how one assesses options 
and values different outcomes from the perspectives of ethics, equity, justice and inclusion. Collectively, these 
system transitions can widen the solution space and accelerate and deepen the implementation of sustainable 
development, adaptation, and mitigation actions by equipping actors and decision-makers with more effective 
and more equitable options. However, the way they are pursued may not necessarily be perceived as ethical or 
desirable to all actors. Moreover, system transitions are necessary precursors for more fundamental climate and

sustainable-development transformations. Yet, these transitions can themselves be outcomes of transformative 
actions.

18
2737Climate Resilient Development Pathways  Chapter 18
FAQ 18.5 | What are success criteria in climate resilient development and how can actors satisfy those criteria?
CRD is not a predefined goal to be achieved at a certain point or stage in the future. It is a constant process of 
evaluating, valuing, acting and adjusting various options for mitigation, adaptation and sustainable development, 
shaped by societal values as well as contestations of those values. Any achievement or success is always a work in 
progress driven by with continuous, directed, intentional actions. These actions will vary according to the priorities 
and needs of each population or system; therefore, specific criteria for, and indicators of, CRD will vary according 
to each specific context. This respect for context ensures the pursuit of CRD prioritizes people, planet, prosperity, 
peace and partnership, per the broad goals of the Agenda 2030 on sustainable development.

If CRD is defined as a process of implementing greenhouse gas mitigation and adaptation options to support 
sustainable development for all, this implies various potential criteria for success. These include the adoption of 
mitigation and adaptation measures to secure a safe climate, meet basic needs, eliminate poverty and enable 
equitable, just and sustainable development for all. Therefore, the 17 United Nations’ SDGs provide a good 
(although limited) measure of progress toward CRD. The SDGs aim at ending poverty and hunger globally and 
protect life on land and underwater until the year 2030. Although there are proven synergies between the SDGs 
and mitigation, there remain synergies between the SDGs and adaptation that need to be explored further.

18
2738Chapter 18 Climate Resilient Development Pathways
Abrahams, D., 2020: Conflict in abundance and peacebuilding in scarcity: 
Challenges and opportunities in addressing climate change and conflict. 
World Dev, 132, 104998, doi:10.1016/j.worlddev.2020.104998.
Abrahams, D. and E. Carr, 2017: Understanding the Connections Between 
Climate Change and Conflict: Contributions From Geography and Political 
Ecology. Curr Clim Change Rep, 3, doi:10.1007/s40641-017-0080-z.
Abson, D.J., et  al., 2017: Leverage points for sustainability transformation. 
Ambio, 46(1), 30–39, doi:10.1007/s13280-016-0800-y.
Abu-Rabia-Queder, S. and A. Morris, 2018: Women in Drylands: Barriers and 
Benefits for Sustainable Livelihood. J. Arid Environ., 149, 1–3, doi:10.1016/j.
jaridenv.2017.11.009.
Acemoglu, D., U. Akcigit, D. Hanley and W. Kerr, 2016: Transition to Clean 
Technology. J. Polit. Econ., 124(1), 52–104, doi:10.1086/684511.

Acevedo, S., et al., 2018: The Effects of Weather Shocks on Economic Activity: 
What are the Channels of Impact? Imf Work. Pap., 18(144), 1. Available at: 
https://www.imf.org/en/Publications/WP/Issues/2018/06/22/The-Effects-
of-Weather-Shocks-on-Economic-Activity-What-are-the-Channels-of-
Impact-45970#: :text=IMF Working Papers&text=In these countries%2C a 
rise,slower investment%2C and poorer health.
Acosta, M., et al., 2019: Discursive translations of gender mainstreaming norms: 
the case of agricultural and climate change policies in Uganda. Womens 
Stud. Int. Forum, 74, 9–19, doi:10.1016/j.wsif.2019.02.010.
Adams, C., T. Ide, J. Barnett and A. Detges, 2018: Sampling bias in climate–
conflict research. Nat. Clim. Chang., 8(3), 200–203, doi:10.1038/s41558-
018-0068-2.
Adenle, A.A., et al., 2017: Managing Climate Change Risks in Africa – A Global 
Perspective. Ecol. Econ., 141, 190–201.
Adzawla, W., S.B. Azumah, P .Y . Anani and S.A. Donkoh, 2019: Gender

perspectives of climate change adaptation in two selected districts of Ghana. 
Heliyon, 5(11), e2854, doi:10.1016/j.heliyon.2019.e02854.
Agarwal, B., 2020: Does group farming empower rural women? Lessons from 
India’s experiments. J Peasant Stud, 47(4), 841–872, doi:10.1080/0306615
0.2019.1628020.
Ahlborg, H., 2017: Towards a conceptualization of power in energy transitions. 
Environ. Innov. Soc. Transit., 25, 122–141, doi:10.1016/j.eist.2017.01.004.
Ahlborg, H., I. Ruiz-Mercado, S. Molander and O. Masera, 2019: Bringing 
Technology into Social-Ecological Systems Research—Motivations for a 
Socio-Technical-Ecological Systems Approach. Sustainability, 11(7), 2009, 
doi:10.3390/su11072009.
Ahmad, B., 2019: An Analysis of Climate Change in Pakistan. Scholars’ Press, 
Chisinau, Republic of Moldova. ISBN 13: 9786138512813.
Åhman, M., L.J. Nilsson and B. Johansson, 2017: Global climate policy and deep 
decarbonization of energy-intensive industries. Clim. Policy, 17(5), 634–649,

doi:10.1080/14693062.2016.1167009.
Aipira, C., A. Kidd and K. Morioka, 2017: Climate change adaptation in pacific 
countries: fostering resilience through gender equality. In: Climate Change 
Adaptation in Pacific Countries [Leal Filho, W.(ed.)]. Springer International 
Publishing, Cham, pp. 225–239. ISBN 978-3319500935 978-3-319-50094-
2).
Ajibade, I. and M. Egge, 2019: SDGs and climate change adaptation in asian 
megacities: synergies and opportunities for transformation. In: Achieving the 
Sustainable Development Goals. Routledge, London. ISBN 978-0429029622.
Alam, K., et al., 2011: The political economy of climate resilient development 
planning in Bangladesh. IDS Bull., 42(3), 52–61, doi:10.1111/j.1759-
5436.2011.00222.x.
Albizua, A., E. Corbera and U. Pascual, 2019: Farmers’ vulnerability to global 
change in Navarre, Spain: large-scale irrigation as maladaptation. Reg. 
Environ. Change, 19(4), 1147–1158, doi:10.1007/s10113-019-01462-2.

Albrecht, G., et  al., 2007: Solastalgia: the distress caused by environmental 
change. Australas Psychiatry, doi:10.1080/10398560701701288.Aldy, J.E., N. Bento and G. Gianfrate, 2021: National climate policies and 
corporate internal carbon pricing. Energy J., 42(5), 87–98.
Aldy, J.E. and G. Gianfrate, 2019: Future-Proof Your Climate Strategy. Harvard 
Business Review, Boston.
Aleluia Reis, L., L. Drouet, R. Van Dingenen and J. Emmerling, 2018: Future global 
air quality indices under different socioeconomic and climate assumptions. 
Sustainability, 10(10), doi:10.3390/su10103645.
Alkire, S. and S. Jahan, 2018: The New Global MPI 2018: Aligning with the 
Sustainable Development Goals. UNDP Human Development Report Office, 
New York.
Allen, C., G. Metternicht and T. Wiedmann, 2018a: Initial progress in 
implementing the Sustainable Development Goals (SDGs): a review of 
evidence from countries. Sustain Sci, 13(5), 1453–1467, doi:10.1007/
s11625-018-0572-3.

Allen, M.R., O.P . Dube, W. Solecki, F . Aragón-Durand, W. Cramer, S. Humphreys, 
M. Kainuma, J. Kala, N. Mahowald, Y . Mulugetta, R. Perez, M. Wairiu, and 
K. Zickfeld, 2018b: Framing and Context. In: Global Warming of 1.5°C. An 
IPCC Special Report on the impacts of global warming of 1.5°C above pre-
industrial levels and related global greenhouse gas emission pathways, in 
the context of strengthening the global response to the. 29–91 pp.
Allwood, G., 2014: Gender mainstreaming and EU climate change policy. Eur. 
Integr. Online Pap. (EIoP), 18, 1–26; doi: 10.1695/2014006.
Allwood, G., 2020: Gender equality in European Union development policy in times 
of crisis. Polit. Stud. Rev., 18(3), 329–345, doi:10.1177/1478929919863224.
Allwood, J., et  al., 2019: Absolute Zero. https://www.repository.cam.ac.uk/
handle/1810/299414. Accessed 2020.
Allwood, J.M., M.F . Ashby, T.G. Gutowski and E. Worrell, 2013: Material

efficiency: providing material services with less material production. Philos. 
Trans. A. Math. Phys. Eng. Sci., 371(1986), doi:10.1098/rsta.2012.0496.
Alston, M., 2014: Gender mainstreaming and climate change. Womens Stud Int 
Forum, 47, 287–294, doi:10.1016/j.wsif.2013.01.016.
Altenburg, T. and D. Rodrik, 2017: Green industrial policy: Accelerating structural 
change towards wealthy green economies. Green Ind. Policy. Available at: 
https://j.mp/2wULvlo .
Alvaredo, F ., et al., 2017: Global inequality dynamics: new findings from WID.
world. Am. Econ. Rev., 107(5), 404–409.
Ambrosio, N., Y .H. Kim, S. Swann and Z. Wang, 2020: Optimizing Community 
Infrastructure. In: Chapter 7 – Addressing Climate Risk in Financial Decision 
Making [Colker, R.(ed.)]. Butterworth-Heinemann, Oxford, UK, pp. 123–142. 
ISBN 978-0128162408.
Aminga, V.M. and F . Krampe, 2020: Climate-related Security Risks and the 
African Union. SIPRI. accessed 2021-08-30. Available at: https://www.sipri.

org/publications/2020/sipri-policy-briefs/climate-related-security-risks-and-
african-union
Enders, J. C., 2016: Theories of Sustainable Development. Routledge, London.
Andersson, J.J., 2019: Carbon Taxes and CO<sub>2</sub> Emissions: Sweden 
as a Case Study. Am. Econ. Journal: Econ. Policy, 11(4), 1–30, doi:10.1257/
pol.20170144.
Andonova, L.B., T.N. Hale and C.B. Roger, 2017: National policy and transnational 
governance of climate change: substitutes or complements? Int Studies Q, 
61(2), 253–268.
Andreucci, D., 2019: Populism, emancipation, and environmental governance: 
insights from Bolivia. Ann. Am. Assoc. Geogr., 109(2), 624–633, doi:10.1080
/24694452.2018.1506696.
Andrijevic, M., et al., 2020a: Overcoming gender inequality for climate resilient 
development. Nat Commun, 11(1), 6261, doi:10.1038/s41467-020-19856-w.
Andrijevic, M., et  al., 2020b: COVID-19 recovery funds dwarf clean energy 
investment needs. Science, 370(6514), 298–300, doi:10.1126/science.
abc9697.

18
2739Climate Resilient Development Pathways  Chapter 18
Ansar, A., B. Caldecott and J. Tilbury, 2013: Stranded assets and the fossil fuel 
divestment campaign: What does divestment mean for the valuation of 
fossil fuel assets? Business. Available at: https://www.semanticscholar.org/
paper/Stranded-assets-and-the-fossil-fuel-divestment-What-Ansar-Caldecot
t/7b3e360b303a7303e80643452d9720e50237abd4
Antal, M. and J. van den Bergh, 2014: Green growth and climate change: 
conceptual and empirical considerations. Clim. Policy, 16, 1–13, doi:10.10
80/14693062.2014.992003.
Antwi-Agyei, P ., A.J. Dougill, T.P . Agyekum and L.C. Stringer, 2018: Alignment 
between nationally determined contributions and the sustainable 
development goals for West Africa. Clim. Policy, 18(10), 1296–1312, doi:10.
1080/14693062.2018.1431199.
Antwi-Agyei, P ., A.J. Dougill and L.C. Stringer, 2017: Assessing coherence 
between sector policies and climate compatible development: opportunities

for triple wins. Sustainability, 9(11), 2130, doi:10.3390/su9112130.
Appiah, D.O. and S.E.A. Gbeddy, 2018: A synthesis of the implementation 
ambivalence of REDD+ in Sub-Saharan Africa and Southeast Asia. For. Soc., 
92–111, doi:10.24259/fs.v2i1.2918.
Araos, M., J. Ford, L. Berrang-ford and R. Biesbroek, 2017: Climate change 
adaptation planning for Global South megacities : the case of Dhaka. J. 
Environ. Policy Plan., 19(6), 1–15.
Ari, İ., 2017: Tthe interconnection between sustainable development goals and 
the climate change negotiations: the Paris Agreement case. Altern. Polit., 
9(Special), 27–45.
Arifeen, A. and I. Nyborg, 2021: How humanitarian assistance practices 
exacerbate vulnerability: Knowledges, authority and legitimacy in disaster 
interventions in Baltistan, Pakistan. Int. J. Disaster Risk Reduct., 54, 102027, 
doi:10.1016/j.ijdrr.2020.102027.
Arku, G. and L. Marais, 2021: Global South Urbanisms and Urban

Sustainability—Challenges and the Way Forward. Front. Sustain. Cities, 
doi:10.3389/frsc.2021.692799.
Arneth, A., F . Denton, F . Agus, A. Elbehri, K. Erb, B. Osman Elasha, M. Rahimi, M. 
Rounsevell, A. Spence, R. Valentini, 2019: Framing and context. In: Climate 
Change and Land: an IPCC special report on climate change, desertification, 
land degradation, sustainable land management, food security, and 
greenhouse gas fluxes in terrestrial ecosystems [Shukla, P . R., J. Skea, E. 
Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P . Zhai, R. 
Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, 
M. Pathak, J. Petzold, J. Portugal Pereira, P . Vyas, E. Huntley, K. Kissick, M. 
Belkacemi and J. Malley (eds.)], In press.
Arneth, A., N. Unger, M. Kulmala and M.O. Andreae, 2009: Clean the Air, Heat 
the Planet? Science, 326(5953), 672–673, doi:10.1126/science.1181568.
Arora-Jonsson, S., 2011: Virtue and vulnerability: Discourses on women, gender

and climate change. Glob. Environ. Chang., 21(2), 744–751, doi:10.1016/j.
gloenvcha.2011.01.005.
Arora-Jonsson, S., 2019: Indigeneity and Climate Justice in Northern Sweden. 
Vol. 9781786997821. eBook ePub: 9781786997852 eBook Kindle: 
9781786997845 Library Edition: 9781786997814.
Arora-Jonsson, S. and B.B. Sijapati, 2018: Disciplining gender in environmental 
organizations: the texts and practices of gender mainstreaming. Gend. Work. 
Organ., 25(3), 309–325, doi:10.1111/gwao.12195.
Arora-Jonsson, S., L. Westholm, B.J. Temu and A. Petitt, 2016: Carbon and cash 
in climate assemblages: the making of a new global citizenship. Antipode, 
48(1), 74–96, doi:10.1111/anti.12170.
Arslan, A., et al., 2015: Climate smart agriculture? Assessing the adaptation 
implications in Zambia. J. Agric. Econ., 66(3), 753–780, doi:10.1111/1477-
9552.12107.
Asefi-Najafabady, S., et  al., 2018: Climate change, population, and poverty:

vulnerability and exposure to heat stress in countries bordering the Great 
Lakes of Africa. Clim. Change, 148(4), 561–573, doi:10.1007/s10584-018-
2211-5.Asfaw, S., G. Pallante and A. Palma, 2018: Diversification strategies and 
adaptation deficit: evidence from rural communities in Niger. World Dev, 
101, 219–234, doi:10.1016/j.worlddev.2017.09.004.
ASSAR, 2018: What global warming of 1.5°C and higher means for Kenya. 
University of Cape Town, (ASSAR), A. a. S. i. S. A. R. Available at: https://www.
weadapt.org/sites/weadapt.org/files/kenya_1.5_infographic_and_climate-
by-zones_table.pdf (accessed 20/01/2022).
Atsiaya, G.O., O.I. Ayuya, L.W. Nakhone and J.K. Lagat, 2019: Drivers and 
responses to climate variability by agro-pastoralists in Kenya: the case of 
Laikipia County. Sn Appl. Sci., 1(8), 827.
Atteridge, A. and E. Remling, 2018: Is adaptation reducing vulnerability or 
redistributing it? WIREs Clim. Chang., 9(1), e500, doi:10.1002/wcc.500.

Auffhammer, M., P . Baylis and C.H. Hausman, 2017: Climate change is projected 
to have severe impacts on the frequency and intensity of peak electricity 
demand across the United States. Proc. Natl. Acad. Sci., 114(8), 1886–1891, 
doi:10.1073/pnas.1613193114.
Aung, T.S., B. Saboori and E. Rasoulinezhad, 2017: Economic growth and 
environmental pollution in Myanmar: an analysis of environmental Kuznets 
curve. Environ. Sci. Pollut. Res., 24(25), 20487–20501, doi:10.1007/s11356-
017-9567-3.
Austin, S., et al., 2015: Public health adaptation to climate change in Canadian 
jurisdictions. IJERPH, 12(1), 623–651.
Avelino, F . and J. Grin, 2017: Beyond deconstruction. a reconstructive 
perspective on sustainability transition governance. Environ. Innov. Soc. 
Transit., 22, 15–25, doi:10.1016/j.eist.2016.07.003.
Avelino, F ., J. Grin, B. Pel and S. Jhagroe, 2016: The politics of sustainability 
transitions. J. Environ. Policy Plan., 18(5), 557–567, doi:10.1080/152390
8X.2016.1216782.

Avelino, F . and J.M. Wittmayer, 2016: Shifting power relations in sustainability 
transitions: a multi-actor perspective. J. Environ. Policy Plan., 18(5), 628–
649, doi:10.1080/1523908X.2015.1112259.
Avila, S., 2018: Environmental justice and the expanding geography of wind 
power conflicts. Sustain Sci, 13(3), 599–616, doi:10.1007/s11625-018-0547-
4.
Avriel-Avni, N. and J. Dick, 2019: Chapter Five – Differing perceptions of 
socio-ecological systems: Insights for future transdisciplinary research. In: 
Advances in Ecological Research [Bohan, D.A. and A.J. Dumbrell(eds.)]. 
Academic Press, Cambridge, MA , pp. 153–190.
Ayers, J.M. and S. Huq, 2009: Supporting adaptation to climate change: what 
role for official development assistance? Dev Policy Rev, 27(6), 675–692, 
doi:10.1111/j.1467-7679.2009.00465.x.
Babcicky, P ., 2013: A conflict-sensitive approach to climate change adaptation. 
Peace Rev., 25(4), 480–488, doi:10.1080/10402659.2013.846131.

Bai, Y ., et  al., 2019: Does climate adaptation of vulnerable households to 
extreme events benefit livestock production? J Clean Prod, 210, 358–365, 
doi:10.1016/j.jclepro.2018.10.250.
Baker, L., P . Newell and J. Phillips, 2014: The political economy of energy 
transitions: the case of South Africa. New Polit. Econ., 19(6), 791–818, doi:1
0.1080/13563467.2013.849674.
Balehey, S., G. Tesfay and M. Balehegn, 2018: Traditional gender inequalities 
limit pastoral women’s opportunities for adaptation to climate change: 
Evidence from the Afar pastoralists of Ethiopia. Pastoralism, 8(1), 23, 
doi:10.1186/s13570-018-0129-1.
Bamzai-Dodson, A., A. E. Cravens, A. A. Wade and R. A. McPherson, 2021: 
Engaging with stakeholders to produce actionable science: a framework and 
guidance. Weather, Climate, and Society, 13(4), 1027–1041.
Bandura, A. and L. Cherry, 2020: Enlisting the power of youth for climate 
change. Am. Psychol., 75(7), 945–951, doi:10.1037/amp0000512.

Banerjee, O., M. Cicowiez, R. Vargas and M. Horridge, 2019: The SEEA-based 
integrated economic-environmental modelling framework: an illustration 
with Guatemala’s forest and fuelwood sector. Environ. Resour. Econ., 72(2), 
539–558.

18
2740Chapter 18 Climate Resilient Development Pathways
Bangura, Y ., 2019: Convergence is not equality. Development & Change, 50(2), 
394–409, doi:10.1111/dech.12489.
Bank of England, 2019: The 2021 biennial exploratory scenario on the financial 
risks from climate change. Available at: https://www.bankofengland.co.uk/
paper/2019/biennial-exploratory-scenario-climate-change-discussion-paper
Baranzini, A., et  al., 2017: Carbon pricing in climate policy: seven reasons, 
complementary instruments, and political economy considerations. WIREs 
Clim. Chang., 8(4), e462, doi:10.1002/wcc.462.
Barata, M. M. L., P . L. Kinney, K. Dear, E. Ligeti, K. L. Ebi, J. Hess, T. Dickinson, A. K. 
Quinn, M. Obermaier, D. Silva Sousa, D. Jack 2018: Urban Health. In: Climate 
Change and Cities: Second Assessment Report of the Urban Climate Change 
Research Network. Cambridge University Press, New York, NY , pp. 363–398.
Bárcena-Martín, E., S. Medina-Claros and S. Pérez-Moreno, 2021: Economic

regulation, opportunity-driven entrepreneurship and gender gap: emerging 
versus high-income economies. Int. J. Entrepreneurial Behav. Res., 27(5), 
1311–1328, doi:10.1108/IJEBR-05-2020-0321.
Barker, A.J. and J. Pickerill, 2020: Doings with the land and sea: Decolonising 
geographies, Indigeneity, and enacting place-agency. Prog Hum Geogr, 
44(4), 640–662, doi:10.1177/0309132519839863.
Barnes, P .W., et  al., 2019: Ozone depletion, ultraviolet radiation, climate 
change and prospects for a sustainable future. Nat. Sustain., 2(7), 569–579, 
doi:10.1038/s41893-019-0314-2.
Barnett, J., 2019: Global environmental change I: Climate resilient peace? Prog 
Hum Geogr, 43(5), 927–936.
Barnett, J., 2020: Global environmental change II: Political 
economies of vulnerability to climate change. Prog Hum Geogr, 
doi:10.1177/0309132519898254.
Barnett, J., et al., 2014: A local coastal adaptation pathway. Nat. Clim. Chang., 
4(12), 1103–1108, doi:10.1038/nclimate2383.

Barnett, J. and C. McMichael, 2018: The effects of climate change on the 
geography and timing of human mobility. Popul. Environ., 39(4), 339–356, 
doi:10.1007/s11111-018-0295-5.
Barnett, J. and S. O’Neill, 2010: Maladaptation. Glob. Environ. Chang., 20, 211–
213, doi:10.1016/j.gloenvcha.2009.11.004.
Barnhardt, R. and O.K. Angayuqaq, 2005: Indigenous knowledge systems 
and alaska native ways of knowing. Anthropol. Educ. Q., 36(1), 8–23, 
doi:10.1525/aeq.2005.36.1.008.
Bartos, M.D. and M.V. Chester, 2015: Impacts of climate change on electric 
power supply in the Western United States. Nat. Clim. Change, 5(8), 748–
752, doi:10.1038/nclimate2648.
Bassett, T.J. and C. Fogelman, 2013: Déjà vu or something new? The adaptation 
concept in the climate change literature. Geoforum, 48, 42–53, doi:10.1016/j.
geoforum.2013.04.010.
Bataille, C., et al., 2018: A review of technology and policy deep decarbonization 
pathway options for making energy-intensive industry production consistent

with the Paris Agreement. J  Clean Prod, 187, 960–973, doi:10.1016/j.
jclepro.2018.03.107.
Batten, S., 2018: Climate change and the macro-economy: a critical review. 
Staff Working Paper, Vol. 706. Bank of England.
Battiston, S., et al., 2017: A climate stress-test of the financial system. Nat. Clim. 
Chang., 7(4), 283–288, doi:10.1038/nclimate3255.
Bauer, N., et al., 2018: Global energy sector emission reductions and bioenergy 
use: overview of the bioenergy demand phase of the EMF-33 model 
comparison. Clim Change, doi:10.1007/s10584-018-2226-y.
Beck, U., 2015: Emancipatory catastrophism: What does it mean 
to climate change and risk society? Curr. Sociol., 63(1), 75–88, 
doi:10.1177/0011392114559951.
Becker, S., T. Blanchet and C. Kunze, 2016: Social movements and urban energy 
policy: Assessing contexts, agency and outcomes of remunicipalisation 
processes in Hamburg and Berlin. Util. Policy, 41, 228–236, doi:10.1016/j.

jup.2016.02.001.Beddoe, R., et al., 2009: Overcoming systemic roadblocks to sustainability: the 
evolutionary redesign of worldviews, institutions, and technologies. Proc. 
Natl. Acad. Sci. U.S.A., 106(8), 2483–2489.
Bell, K., 2013: Post-conventional approaches to gender, climate change and 
social justice. In: Research, Action and Policy: Addressing the Gendered 
Impacts of Climate Change [Alston, M. and K. Whittenbury(eds.)]. Springer 
Netherlands, Dordrecht, pp. 53–61. ISBN 978-9400755185.
Béné, C., et al., 2018: Resilience as a policy narrative: potentials and limits in 
the context of urban planning. Clim. Dev., 10(2), 116–133, doi:10.1080/175
65529.2017.1301868.
Benjaminsen, G. and R. Kaarhus, 2018: Commodification of forest carbon: 
REDD+ and socially embedded forest practices in Zanzibar. Geoforum, 93, 
doi:10.1016/j.geoforum.2018.04.021.
Bento, N. and G. Gianfrate, 2020: Determinants of internal carbon pricing. 
Energy Policy, 143, 111499, doi:10.1016/j.enpol.2020.111499.

Bento, N., G. Gianfrate and M.H. Thoni, 2019: Crowdfunding for sustainability 
ventures. J Clean Prod, 237, 117751, doi:10.1016/j.jclepro.2019.117751.
Berga, L., 2016: The role of hydropower in climate change mitigation 
and adaptation: a review. Engineering, 2(3), 313–318, doi:10.1016/J.
ENG.2016.03.004.
Berglund, O., & Schmidt, D. (2020). A Theory of Change: The Civil Resistance 
Model. Extinction Rebellion and Climate Change Activism, 79–95. 
doi:10.1007/978-3-030-48359-3_6
Berkhout, E., J. Bouma, N. Terzidis and M. Voors, 2018: Supporting local 
institutions for inclusive green growth: developing an evidence gap map. 
NJAS - Wageningen J. Life Sci., 84, 51–71, doi:10.1016/j.njas.2017.10.001.
Bernauer, W. and G. Slowey, 2020: COVID-19, extractive industries, 
and indigenous communities in Canada: Notes towards a political 
economy research agenda. Extr. Ind. Soc., 7(3), 844–846, doi:10.1016/j.
exis.2020.05.012.

Berrang-Ford, L., et  al., 2014: What drives national adaptation? A global 
assessment. Clim Change, 124(1), 441–450, doi:10.1007/s10584-014-1078-
3.
Berry, P .M., et al., 2015: Cross-sectoral interactions of adaptation and mitigation 
measures. Clim Change, 128(3), 381–393, doi:10.1007/s10584-014-1214-0.
Bethel, B.J., Y . Buravleva and D. Tang, 2021: Blue economy and blue activities: 
opportunities, challenges, and recommendations for the Bahamas. Water, 
13(10), 1399, doi:10.3390/w13101399.
Beuchelt, T.D. and L. Badstue, 2013: Gender, nutrition- and climate-smart 
food production: opportunities and trade-offs. Food Sec., 5(5), 709–721, 
doi:10.1007/s12571-013-0290-8.
Beveridge, R. and P . Koch, 2016: The post-political trap? Reflections on politics, 
agency and the city. Urban Stud., doi:10.1177/0042098016671477.
Bezner Kerr, R., et al., 2019: Participatory agroecological research on climate 
change adaptation improves smallholder farmer household food security

and dietary diversity in Malawi. Agric. Ecosyst. Environ., 279, 109–121, 
doi:10.1016/j.agee.2019.04.004.
Bhamidipati, P .L., J. Haselip and U. Elmer Hansen, 2019: How do energy policies 
accelerate sustainable transitions? Unpacking the policy transfer process in 
the case of GETFiT Uganda. Energy Policy, 132, 1320–1332, doi:10.1016/j.
enpol.2019.05.053.
Bhavnani, K.-K., J. Foran, P .A. Kurian and D. Munshi, 2019: Climate futures: 
Re-imagining global climate justice. Zed Books Ltd., London. ISBN 978-
1786997852.
Bherwani, H., S. Gautam and A. Gupta, 2021: Qualitative and quantitative 
analyses of impact of COVID-19 on sustainable development goals (SDGs) in 
Indian subcontinent with a focus on air quality. Int. J. Environ. Sci. Technol., 
18, doi:10.1007/s13762-020-03122-z.
Biagini, B. and A. Miller, 2013: Engaging the private sector in adaptation to 
climate change in developing countries: importance, status, and challenges. 
Clim. Dev., 5(3), 242–252.

Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, 
N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa,

18
2741Climate Resilient Development Pathways  Chapter 18
B. Rinkevich, T. Suga, A. Tagliabue, and P . Williamson, 2019: Changing 
Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special 
Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-
O., D. C. Roberts, V. Masson-Delmotte, P . Zhai, M. Tignor, E. Poloczanska, K. 
Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. 
Weyer (eds.)], In press.
Birkmann, J., et al., 2021: New methods for local vulnerability scenarios to heat 
stress to inform urban planning—case study City of Ludwigsburg/Germany. 
Clim Change, 165(1), 37, doi:10.1007/s10584-021-03005-3.
Bistline, J.E. and S.K. Rose, 2018: Social cost of carbon pricing of power sector 
CO 2 : accounting for leakage and other social implications from subnational 
policies. Environ. Res. Lett., 13(1), 14027, doi:10.1088/1748-9326/aa9c89.
Biswas, A.K. and C. Tortajada, 2016: Water security, climate change and

sustainable development: an introduction. In: Water Security, Climate 
Change and Sustainable Development [Biswas, A.K. and C. Tortajada(eds.)]. 
Springer Singapore, Singapore, pp. 1–5. ISBN 978-9812879745 978-981-
287-976-9).
Bizikova, L., J.-E. Parry, J. Karami and D. Echeverria, 2015: Review of key 
initiatives and approaches to adaptation planning at the national level in 
semi-arid areas. Reg Environ Change, 15(5), 837–850.
Blanco G., R. Gerlagh, S. Suh, J. Barrett, H. C. de Coninck, C. F . Diaz Morejon, 
R. Mathur, N. Nakicenovic, A. Ofosu Ahenkora, J. Pan, H. Pathak, J. Rice, R. 
Richels, S. J. Smith, D. I. Stern, F . L. Toth, and P . Zhou, 2014: Drivers, Trends 
and Mitigation. In: Climate Change 2014: Mitigation of Climate Change. 
Contribution of Working Group III to the Fifth Assessment Report of the 
Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-
Madruga, Y . Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum,

S. Brunner, P . Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von 
Stechow, T. Zwickel and J. C. Minx (eds.)]. Cambridge University Press, 
Cambridge, UK and New York, NY , USA, pp. 351–411. ISBN 9781107058217.
Blanford, G.J., S.K. Rose and M. Tavoni, 2012: Baseline projections of energy 
and emissions in Asia. Energy Econ., 34, 284–S292, doi:10.1016/j.
eneco.2012.08.006.
Bleischwitz, R., et  al., 2018: Extrapolation or saturation – Revisiting growth 
patterns, development stages and decoupling. Glob. Environ. Chang., 48, 
86–96, doi:10.1016/j.gloenvcha.2017.11.008.
Bloemen, P ., M.V.D. Steen and Z.V.D. Wal, 2019: Designing a century ahead: 
climate change adaptation in the Dutch Delta. Policy Soc., 38(1), 58–76, doi
:10.1080/14494035.2018.1513731.
Blühdorn, I. and M. Deflorian, 2021: Politicisation beyond post-politics: new 
social activism and the reconfiguration of political discourse. Soc. Mov Stud., 
20(3), 259–275, doi:10.1080/14742837.2021.1872375.

Blythe, J., et  al., 2018: The Dark Side of Transformation: Latent Risks in 
Contemporary Sustainability Discourse. Antipode, 50(5), 1206–1223, 
doi:10.1111/anti.12405.
Bocquillon, P ., 2018: (De-)Constructing coherence? Strategic entrepreneurs, 
policy frames and the integration of climate and energy policies in the 
European Union. Environ. Policy Gov., 28(5), 339–349, doi:10.1002/eet.1820.
Bodnar, S., 2008: Wasted and Bombed: Clinical Enactments of a Changing 
Relationship to the Earth. Psychoanal. Dialogues., 18(4), 484–512.
Boeckmann, M. and H. Zeeb, 2014: Using a social justice and health framework 
to assess European climate change adaptation strategies. Int. J. Environ. Res. 
Public Health, 11(12), 12389–12411, doi:10.3390/ijerph111212389.
Boermans, M.  A. and R. Galema, 2019: Are pension funds actively 
decarbonizing their portfolios? Ecol. Econ., 161, 50–60, doi:10.1016/j.
ecolecon.2019.03.008.
Bohoslavsky, J.P ., 2019: The impact of economic reforms and austerity measures

on women’s human rights. Center for Women’s Global Leadership – OHCHR. 
Available at: https://www.ohchr.org/EN/Issues/Development/IEDebt/Pages/
ImpactEconomicReformPoliciesWomen.aspx
Bond, S. and J. Barth, 2020: Care-full and just: Making a difference 
through climate change adaptation. Cities, 102, 102734, doi:10.1016/j.
cities.2020.102734.Bosomworth, K., P . Leith, A. Harwood and P .J. Wallis, 2017: What’s the problem 
in adaptation pathways planning? The potential of a diagnostic problem-
structuring approach. Environ. Sci. Policy, 76, 23–28, doi:10.1016/j.
envsci.2017.06.007.
Boulton, E., 2016: Climate change as a ‘hyperobject’: a critical review of 
Timothy Morton’s reframing narrative. WIREs Clim. Chang., 7(5), 772–785, 
doi:10.1002/wcc.410.
Bourgeois, R., 2015: A Glossary of Terms commonly used in Futures Studies. 
Doi: 10.13140/RG.2.1.1600.2008
Bowen, K.J., et al., 2017: Implementing the “Sustainable Development Goals”:

towards addressing three key governance challenges—collective action, 
trade-offs, and accountability. Curr. Opin. Environ. Sustain., 26-27, 90–96, 
doi:10.1016/j.cosust.2017.05.002.
Boyce, J.K., 2018: Carbon pricing: effectiveness and equity. Ecol. Econ., 150, 
52–61, doi:10.1016/j.ecolecon.2018.03.030.
Boyd, J., 2007: Nonmarket benefits of nature: What should be counted in green 
GDP? Ecol. Econ., 61(4), 716–723.
Bradley, K. and J. Hedrén, 2014: Utopian Thought in the Making of Green 
Futures. In: Green Utopianism: Perspectives, Politics and Micro-Practices 
[Bradley, K. & Hedrén, J. (eds.)], pp. 1–20. ISBN 978-0415814447.
Brand, U., C. Görg and M. Wissen, 2020: Overcoming neoliberal globalization: 
social-ecological transformation from a Polanyian perspective and beyond. 
Globalizations, 17(1), 161–176, doi:10.1080/14747731.2019.1644708.
Brandi, C. and A. Dzebo, 2017: The case for connecting the implementation 
of the Paris Climate Agreement and the 2030 Agenda for Sustainable

Development. German Development Institute. Available at: https://www.
die-gdi.de/uploads/media/BP_21.2017.pdf
Brasseur, G. P . and L. Gallardo, 2016: Climate services: Lessons learned and 
future prospects. Earth’s Future, 4(3), 79–89.
Brattland, C. and T. Mustonen, 2018: How traditional knowledge comes to 
matter in atlantic salmon governance in Norway and Finland. Arct. Inst. N. 
Am., 71(4), 375–392, doi:10.14430/arctic4751.
Brattland, C. and S. Nilsen, 2011: Reclaiming indigenous seascapes. Sami place 
names in Norwegian sea charts. Polar Geogr., 34, 275–297, doi:10.1080/10
88937x.2011.644871.
Brechin, S. and M. Espinoza, 2017: A case for further refinement of the Green 
Climate Fund’s 50:50 ratio climate change mitigation and adaptation 
allocation framework: toward a more targeted approach. Clim Change, 142, 
doi:10.1007/s10584-017-1938-8.
Brink, E. and C. Wamsler, 2019: Citizen engagement in climate adaptation

surveyed: The role of values, worldviews, gender and place. J Clean Prod, 
209, 1342–1353, doi:10.1016/j.jclepro.2018.10.164.
Brooks, N., et al., 2014: Tracking Adaptation and Measuring Development: a 
step-by-step guide. International Institute for Environment and Development. 
Available at: https://pubs.iied.org/10100iied
Broto, V.C., E. Boyd and J. Ensor, 2015: Participatory urban planning for climate 
change adaptation in coastal cities: lessons from a pilot experience in 
Maputo, Mozambique. Curr. Opin. Environ. Sustain., 13, 11–18, doi:10.1016/j.
cosust.2014.12.005.
Brown, K., 2013: Global environmental change I: A social turn for resilience? 
Prog Hum Geogr, doi:10.1177/0309132513498837.
Brulle, R.J. and K.M. Norgaard, 2019: Avoiding cultural trauma: climate change 
and social inertia. Env Polit, 28(5), 886–908.
Brundiers, K., 2018: Disasters as opportunities for sustainability: the case 
of Christchurch, Aotearoa New Zealand. Sustain Sci, 13(4), 1075–1091,

doi:10.1007/s11625-017-0523-4.
Brundiers, K., 2020: Leveraging disasters for sustainable development. Environ. 
Sci. Policy Sustain. Dev., 62(1), 15–27, doi:10.1080/00139157.2020.16872
26.
Brundiers, K. and H.C. Eakin, 2018: Leveraging post-disaster windows of 
opportunities for change towards sustainability: a framework. Sustainability, 
10(5), 1390.
Brundtland, C., 1987: Our Common Future. Oxford University Press, Oxford, UK.

18
2742Chapter 18 Climate Resilient Development Pathways
Bryant, L. and B. Garnham, 2015: The fallen hero: masculinity, shame and 
farmer suicide in Australia. Gend. Place Cult., 22(1), 67–82, doi:10.1080/09
66369X.2013.855628.
Brzoska, M. and C. Fröhlich, 2016: Climate change, migration and violent 
conflict: vulnerabilities, pathways and adaptation strategies. Migr. Dev., 5(2), 
190–210, doi:10.1080/21632324.2015.1022973.
Buckwell, A., et  al., 2020: Social benefit cost analysis of ecosystem-based 
climate change adaptations: a community-level case study in Tanna Island, 
Vanuatu. Clim. Dev., 12(6), 495–510, doi:10.1080/17565529.2019.1642179.
Buggy, L. and K.E. McNamara, 2016: The need to reinterpret “community” for 
climate change adaptation: a case study of Pele Island, Vanuatu. Clim. Dev., 
8(3), 270–280, doi:10.1080/17565529.2015.1041445.
Buhaug, H., 2015: Climate–conflict research: some reflections on the way 
forward. WIREs Clim. Chang., 6(3), 269–275, doi:10.1002/wcc.336.

Burch, S., et  al., 2016: Governing and accelerating transformative 
entrepreneurship: exploring the potential for small business innovation on 
urban sustainability transitions. Curr. Opin. Environ. Sustain., 22, 26–32, 
doi:10.1016/j.cosust.2017.04.002.
Burke, M., S. Heft-Neal and E. Bendavid, 2016: Sources of variation in under-5 
mortality across sub-Saharan Africa: a spatial analysis. Lancet Glob. Health, 
4(12), e936–e945, doi:10.1016/S2214-109X(16)30212-1.
Burke, M., S.M. Hsiang and E. Miguel, 2015: Global non-linear effect of 
temperature on economic production. Nature, 527(7577), 235–239, 
doi:10.1038/nature15725.
Burman, A., 2017: The political ontology of climate change: moral meteorology, 
climate justice, and the coloniality of reality in the Bolivian Andes. J. Polit. 
Ecol., 24(1), 921–930, doi:10.2458/v24i1.20974.
Busby, J.W., 2021: Beyond internal conflict: The emergent practice of climate 
security. J Peace Res, 58(1), 186–194, doi:10.1177/0022343320971019.

Busch, T., M. Johnson and T. Pioch, 2020: Corporate carbon performance data: 
Quo vadis? J Ind Ecol, doi:10.1111/jiec.13008.
Busscher, T., M. v. d. Brink and S. Verweij, 2019: Strategies for integrating water 
management and spatial planning: Organising for spatial quality in the 
Dutch “Room for the River” program. J. Flood Risk Manag., 12(1), e12448, 
doi:10.1111/jfr3.12448.
Butler, C.D., 2017: Limits to growth, planetary boundaries, and planetary health. 
Curr. Opin. Environ. Sustain., 25, 59–65, doi:10.1016/j.cosust.2017.08.002.
Butler, J.R.A., et al., 2016: Priming adaptation pathways through adaptive co-
management: Design and evaluation for developing countries. Clim. Risk 
Manag., 12, 1–16.
Byers, E., et  al., 2018: Global exposure and vulnerability to multi-sector 
development and climate change hotspots. Environ. Res. Lett., 13(5), 55012, 
doi:10.1088/1748-9326/aabf45.
Cai, Y . and T.S. Lontzek, 2018: The social cost of carbon with economic and

climate risks. J. Polit. Econ., 127(6), 2684–2734, doi:10.1086/701890.
Calkins, J., 2015: Moving forward after Sendai: how countries want to use 
science, evidence and technology for disaster risk reduction. PLoS Curr, 7, 
doi:10.1371/currents.dis.22247d6293d4109d09794890bcda1878.
Camargo, A. and D. Ojeda, 2017: Ambivalent desires: State formation 
and dispossession in the face of climate crisis. Polit Geogr, 60, 57–65, 
doi:10.1016/j.polgeo.2017.04.003.
Campbell, B.M., et  al., 2016: Reducing risks to food security from climate 
change. Glob Food Sec, 11, 34–43, doi:10.1016/j.gfs.2016.06.002.
Caniglia, G., et  al., 2021: A pluralistic and integrated approach to action-
oriented knowledge for sustainability. Nat. Sustain., 4(2), 93–100, 
doi:10.1038/s41893-020-00616-z.
Cannon, T. and D. Müller-Mahn, 2010: Vulnerability, resilience and development 
discourses in context of climate change. Nat. Hazards, 55(3), 621–635, 
doi:10.1007/s11069-010-9499-4.

Capaldi, C.A., et al., 2015: Flourishing in nature: A review of the benefits of 
connecting with nature and its application as a wellbeing intervention. Intnl. 
J. Wellbeing, 5(4), doi:10.5502/ijw.v5i4.449.Carabine, E., M. Jouanjean and J. Tsui, 2015: Kenya ending drought emergencies 
policy review: Scenarios for building resilience in the ASALs. Oversees 
Development Institute. Available at: https://odi.org/en/publications/
kenya-ending-drought-emergencies-policy-review-scenarios-for-building-
resilience-in-asals/
Carino, J. and L. Tamayo, 2019: Global Report on the Situation of Lands, 
Territories and Resources of Indigenous Peoples. Indigenous Peoples Major 
Group for Sustainable Development, www.indigenouspeoples-sdg.org 
(accessed 20/01/2022).
Carmin, J., N. Nadkarni and C. Rhie, 2012: Progress and challenges in urban 
climate adaptation planning: results of a global. MIT Press, Cambridge, MA.
Carpenter, S.R., C. Folke, M. Scheffer and F .R. Westley, 2019: Dancing on the

volcano: social exploration in times of discontent. Ecol. Soc., 24(1), art23.
Carr, C., 2013: Discourse yes, implementation maybe: an immobility and 
paralysis of sustainable development policy. Eur. Plan. Stud., 22(9), 1824–
1840.
Carr, E., 2019: Properties and projects: reconciling resilience and transformation 
for adaptation and development. World Dev, 122, 70–84, doi:10.1016/j.
worlddev.2019.05.011.
Carr, E.R., 2020: Resilient livelihoods in an era of global transformation. Glob. 
Environ. Chang., 64, 102155, doi:10.1016/j.gloenvcha.2020.102155.
Carr, E.R. and M.C. Thompson, 2014: Gender and climate change adaptation 
in agrarian settings: current thinking, new directions, and research frontiers: 
gender and climate change adaptation in agrarian settings. Geogr. Compass, 
8(3), 182–197. Doi: 10.1111/gec3.12121
Casas Varez, M., 2017: La transversalización del enfoque de género en las 
políticas públicas frente al cambio climático en América Latina. Economic

Commission for Latin America and the Caribbean. Available at: https://www.
cepal.org/es/publicaciones/41101-la-transversalizacion-enfoque-genero-
politicas-publicas-frente-al-cambio
Casey, G., 2019: Energy Efficiency and Directed Technical Change: Implications 
for Climate Change Mitigation. Department of Economics, Williams 
College, https://ideas.repec.org/p/wil/wileco/2019-17.html. Accessed 2020 . 
(Department of Economics Working Papers).
Cash, D.W., et al., 2003: Knowledge systems for sustainable development. Proc. 
Natl. Acad. Sci. U. S. A., 100(14), 8086–8091, doi:10.1073/pnas.1231332100.
Castán Broto, V., et  al., 2020: Spatiotemporal perspectives on urban energy 
transitions: a comparative study of three cities in China. Urban Transform., 
2(1), 11, doi:10.1186/s42854-020-00015-9.
Castañeda Carney, I., et  al., 2020: Gender-based violence and environment 
linkages. IUCN, Gland, Switzerland, ISBN 978-2831720340.

Castells-Quintana, D., M.P . Lopez-Uribe and T.K.J. McDermott, 2018: Adaptation 
to climate change: a review through a development economics lens. World 
Dev, 104I, 183–196.
Catalano, M., L. Forni and E. Pezzolla, 2020: Climate-change adaptation: 
the role of fiscal policy. Resour. Energy Econ., 59, 101111, doi:10.1016/j.
reseneeco.2019.07.005.
Catley, A., J. Lind and I. Scoones, 2013: Pastoralism and development in Africa: 
dynamic change at the margins. Routledge, London. ISBN 978-1136255854.
Cavaliere, P ., 2019: Electrolysis of iron ores: most efficient technologies for 
greenhouse emissions abatement. In: Clean Ironmaking and Steelmaking 
Processes: Efficient Technologies for Greenhouse Emissions Abatement 
[Cavaliere, P .(ed.)]. Springer International Publishing, Cham, pp. 555–576. 
ISBN 978-3030212094.
Ceballos, G., P .R. Ehrlich and R. Dirzo, 2017: Biological annihilation via the 
ongoing sixth mass extinction signaled by vertebrate population losses

and declines. Proc. Natl. Acad. Sci., 114(30), E6089–E6096, doi:10.1073/
pnas.1704949114.
Cervigni, R. and M. Morris, 2016: Confronting Drought in Africa’s Drylands: 
Opportunities for Enhancing Resilience. World Bank; and Agence Française 
de Développement, Washington, DC, ISBN 978-1464808173.
Cervigni, R., et al., 2016: Vulnerability in Drylands Tomorrow: Business as Usual 
Raising Ominous Prospects. World Bank Group.

18
2743Climate Resilient Development Pathways  Chapter 18
Chae, Y ., S.H. Choi and Y .J. Kim, 2020: Climate change policy implications 
of sustainable development pathways in korea at sub-national scale. 
Sustainability, 12(10), doi:10.3390/su12104310.
Chakraborty, R. and P .Y . Sherpa, 2021: From climate adaptation to climate 
justice: critical reflections on the IPCC and himalayan climate knowledges. 
Clim Change, 167, 49, doi:10.1007/s10584-021-03158-1.
Chambers, R., 2012: Revolutions in Development Inquiry, 1st edn., Routledge, 
London. 258 pp.
Chaminade, C. and F . Randelli, 2020: The role of territorially embedded 
innovation ecosystems accelerating sustainability transformations: a case 
study of the transformation to organic wine production in Tuscany (Italy). 
Sustainability, 12(11), 4621.
Chancel, L. and T. Piketty, 2019: Indian income inequality, 1922-2015: 
from British Raj to billionaire Raj? Rev Income Wealth, 65(S1), 33–S62, 
doi:10.1111/roiw.12439.

Chen, A.A., et al., 2020a: Pathways to climate change mitigation and stable 
energy by 100% renewable for a small island: Jamaica as an example. 
Renew. Sustain. Energy Rev., 121, 109671, doi:10.1016/j.rser.2019.109671.
Chen, C., et  al., 2016: Measuring the adaptation gap: A framework for 
evaluating climate hazards and opportunities in urban areas. Environ. Sci. 
Policy, 66, 403–419, doi:10.1016/j.envsci.2016.05.007.
Chen, G., et al., 2020b: Global projections of future urban land expansion under 
shared socioeconomic pathways. Nat Commun, 11(1), 537, doi:10.1038/
s41467-020-14386-x.
Chen, K., et  al., 2018: Future ozone-related acute excess mortality under 
climate and population change scenarios in China: A modeling study. PLoS 
Med, 15(7), e1002598, doi:10.1371/journal.pmed.1002598.
Chen, K., et  al., 2017: Impact of climate change on heat-related mortality 
in Jiangsu Province, China. Environ. Pollut., 224, 317–325, doi:10.1016/j.
envpol.2017.02.011.

Chen, Q. and D. Taylor, 2020: Economic development and pollution emissions 
in Singapore: evidence in support of the environmental Kuznets curve 
hypothesis and its implications for regional sustainability. J Clean Prod, 243, 
118637, doi:10.1016/j.jclepro.2019.118637.
Chen, Y ., et al., 2019: Tele-connecting China’s future urban growth to impacts 
on ecosystem services under the shared socioeconomic pathways. Sci. Total. 
Environ., 652, 765–779, doi:10.1016/j.scitotenv.2018.10.283.
Chenery, H., 1974: Redistribution with growth: Policies to improve income 
distribution in developing countries in the context of economic growth : 
a joint stuI... of Development Studies. University of Sussex, London, ISBN 
978-0199200696. 304 pp.
Chi, Y .F . and J.N. Rauch, 2010: The Plight of Green GDP in China. The Journal of 
Sustainable Development, doi: 10.7916/D8FX794J
Chome, N., 2020: Land, livelihoods and belonging: negotiating change and

anticipating LAPSSET in Kenya’s Lamu county. J. East. African Stud., 14(2), 
310–331.
Chowdhooree, I., 2019: Indigenous knowledge for enhancing community 
resilience: An experience from the south-western coastal region of 
Bangladesh. Int. J. Disaster Risk Reduct., 40, 101259, doi:10.1016/j.
ijdrr.2019.101259.
Chu, E. and K. Michael, 2019: Recognition in urban climate justice: marginality 
and exclusion of migrants in Indian cities. environ urban, 31(1), 139–156, 
doi:10.1177/0956247818814449.
Chu, E.K., 2018: Urban climate adaptation and the reshaping of state–society 
relations: the politics of community knowledge and mobilisation in Indore, 
India. Urban Stud., 55(8), 1766–1782.
Tiam Fook, C., 2017: Transformational processes for community-focused 
adaptation and social change: a synthesis. Clim. Dev., 9(1), 5–21.
Ciwmb, 2003: Plastics White Paper—optimizing plastics use, recycling, and 
disposal in California. California Integrated Waste Management Board.

Available at: https://www2.calrecycle.ca.gov/Publications/Details/1010 
(accessed 20/01/2022).Clapp, C. and J. Sillmann, 2019: Facilitating climate-smart investments. One 
Earth, 1(1), 57–61, doi:10.1016/j.oneear.2019.08.009.
Clark, W.C., et  al., 2016: Boundary work for sustainable development: 
Natural resource management at the Consultative Group on International 
Agricultural Research (CGIAR). Proc. Natl. Acad. Sci. USA, 113(17), 4615–
4622, doi:10.1073/pnas.0900231108.
Clarke L., K. Jiang, K. Akimoto, M. Babiker, G. Blanford, K. Fisher-Vanden, J.-C. 
Hourcade, V. Krey, E. Kriegler, A. Löschel, D. McCollum, S. Paltsev, S. Rose, 
P . R. Shukla, M. Tavoni, B. C. C. van der Zwaan, and D.P . van Vuure, 2014: 
Assessing Transformation Pathways. In: Climate Change 2014: Mitigation of 
Climate Change. Contribution of Working Group III to the Fifth Assessment 
Report of the Intergovernmental Panel on Climate Change [Edenhofer, O.,

R. Pichs-Madruga, Y . Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. 
Baum, S. Brunner, P . Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. 
von Stechow, T. Zwickel and J. C. Minx (eds.)]. Cambridge University Press, 
Cambridge, UK and New York, NY , USA, pp. 413–510. ISBN 9781107058217.
Clay, L., M. Papas, K. Gill and D. Abramson, 2018: Factors associated with 
continued food insecurity among households recovering from hurricane 
Katrina. IJERPH, 15(8), 1647.
Clayton, S., et al., 2015: Psychological research and global climate change. Nat. 
Clim. Chang., 5(7), 640–646.
Clayton, S., C. Manning, K. Krygsman and M. Speiser, 2017: Mental health and 
our changing climate: impacts. Washington, D.C.: American Psychological 
Association, and ecoAmerica. Available at: https://www.apa.org/news/
press/releases/2017/03/mental-health-climate.pdf (accessed 20/01/2022).
Cloutier, G., et al., 2015: Planning adaptation based on local actors’ knowledge

and participation: a climate governance experiment. Clim. Policy, 15(4), 
458–474.
Clucas, B., I.D. Parker and A.M. Feldpausch-Parker, 2018: A systematic review of 
the relationship between urban agriculture and biodiversity. Urban Ecosyst, 
21(4), 635–643, doi:10.1007/s11252-018-0748-8.
Cobb, C.W. and H. Daly, 1989: For the common good: redirecting the economy 
towards community, the environment, and a sustainable future. Beacon 
Press, Boston. 482 pp.
Cohen, M.J., 2020: Does the COVID-19 outbreak mark the onset of a sustainable 
consumption transition? Sustain. Sci. Pract. Policy, 16(1), 1–3, doi:10.1080/
15487733.2020.1740472.
Cole, P . and G. Banks, 2017: Renewable energy programmes in the South 
Pacific – Are these a solution to dependency? Energy Policy, 110, 500–508, 
doi:10.1016/j.enpol.2017.08.048.
Collins, A.M., J.A. Grant and P . Ackah-Baidoo, 2019a: The glocal dynamics of 
land reform in natural resource sectors: Insights from Tanzania. Land Use 
Policy, 81, 889–896.

Collins, S.M., et al., 2019b: ‘I know how stressful it is to lack water!’ Exploring 
the lived experiences of household water insecurity among pregnant and 
postpartum women in western Kenya. Glob Public Health, 14(5), 649–662.
Colloff, M.J., et al., 2021: Adapting transformation and transforming adaptation 
to climate change using a pathways approach. Environ. Sci. Policy, 124, 
163–174, doi:10.1016/j.envsci.2021.06.014.
Collste, D., M. Pedercini and S.E. Cornell, 2017: Policy coherence to achieve 
the SDGs: using integrated simulation models to assess effective policies. 
Sustain Sci, 12(6), 921–931, doi:10.1007/s11625-017-0457-x.
Conway, D., et  al., 2015: Climate and southern Africa’s water–energy–food 
nexus. Nat. Clim. Chang., 5(9), 837–846, doi:10.1038/nclimate2735.
Cormack, Z., 2016: The promotion of pastoralist heritage and alternative 
‘visions’ for the future of Northern Kenya. J. East. African Stud., 10(3), 548–
567.

Cormack, Z. and A. Kurewa, 2018: The changing value of land in Northern Kenya: 
the case of Lake Turkana Wind Power. Crit. African Stud., 10(1), 89–107.
Cornell, S. et al., 2013: Opening up knowledge systems for better responses to 
global environmental change. Environ. Sci. Policy, 28, 60–70.
Costantini, V., A. Markandya, E. Paglialunga and G. Sforna, 2018: Impact and 
distribution of climatic damages: a methodological proposal with a dynamic

18
2744Chapter 18 Climate Resilient Development Pathways
CGE model applied to global climate negotiations. Econ Polit, 35(3), 809–
843, doi:10.1007/s40888-018-0129-z.
Cote, M. and A.J. Nightingale, 2011: Resilience thinking meets social theory: 
Situating social change in socio-ecological systems (SES) research. Prog Hum 
Geogr, doi:10.1177/0309132511425708.
Country, B., et  al., 2018: Intercultural Communication Handbook. http://
bawakacollective.com/handbook/.
Country, B., et  al., 2016: Co-becoming Bawaka:towards a relational 
understanding of place/space. Prog Hum Geogr, 40(4), 455–475, 
doi:10.1177/0309132515589437.
Cretney, R., 2014: Resilience for whom? Emerging critical geographies of 
Socio-ecological resilience. Geogr. Compass, 8(9), 627–640, doi:10.1111/
gec3.12154.
Crippa, M., et al., 2019: Fossil CO2 and GHG emissions of all world countries. 
Publications Office of the European Union, https://publications.jrc.ec.europa.
eu/repository/handle/111111111/57649. Accessed 2020.

Cubillo-Guevara, A.P ., A.L. Hidalgo-Capitán and J.A. Domínguez-Gómez, 2014: 
El pensamiento sobre el Buen Vivir. Entre el indigenismo, el socialismo y el 
posdesarrollismo. Revista Del Clad Reforma Y Democr., 60, 27–58.
Cumming, D.J., G. Leboeuf and A. Schwienbacher, 2017: Crowdfunding 
cleantech. Energy Econ., 65, 292–303, doi:10.1016/j.eneco.2017.04.030.
Cundill, G., et al., 2014: Social Learning for Adaptation: a Descriptive Handbook 
for Practitioners and Action Researchers. IDRC/Rhodes University, Ruliv. 
ISBN 9780868104805.
D’Alessandro, S., A. Cieplinski, T. Distefano and K. Dittmer, 2020: Feasible 
alternatives to green growth. Nat. Sustain., 3(4), 329–335, doi:10.1038/
s41893-020-0484-y.
Dabelko, G., L. Risi and S. Null, 2013: Backdraft: The Conflict Potential of Climate 
Change Adaptation and Mitigation. Woodrow Wilson International Center 
for Scholars, Washington, DC.
Daigneault, A., et  al., 2019: Developing detailed shared socioeconomic

pathway (SSP) narratives for the global forest sector. J. For. Econ., 34(1-2), 
7–45, doi:10.1561/112.00000441.
Daly, H. and J. Cobb, 1989: For the Common Good. Redirecting the Economy 
Toward Community, the Environment and a Sustainable Future. Beacon 
Press, Boston, Massachusetts.
Danilina, V. and Trionfetti, 2019: Green Public Procurement vs Environmental 
Taxation: implications for the EU-MENA environmental policy. Available 
at: https://planbleu.org/en/publications/green-public-procurement-v-s-
environmental-taxation-implications-for-the-eu-mena-environmental-
policy/ (accessed 20/01/2022).
Daron, J., et  al., 2021: Integrating seasonal climate forecasts into adaptive 
social protection in the Sahel. Clim. Dev., 13(6), 543–550, doi:10.1080/17
565529.2020.1825920.
Davelaar, D., 2021: Transformation for sustainability: a deep leverage points 
approach. Sustain Sci, 16(3), 727–747, doi:10.1007/s11625-020-00872-0.

David, M., 2017: Moving beyond the heuristic of creative destruction: Targeting 
exnovation with policy mixes for energy transitions. Energy Res. Soc. Sci., 33, 
138–146, doi:10.1016/j.erss.2017.09.023.
Tàbara, D., et al., 2018: Positive tipping points in a rapidly warming world. Curr. 
Opin. Environ. Sustain., 31, 120–129, doi:10.1016/j.cosust.2018.01.012.
Davidson, K., T.M.P . Nguyen, R. Beilin and J. Briggs, 2019: The emerging addition 
of resilience as a component of sustainability in urban policy. Cities, 92, 1–9, 
doi:10.1016/j.cities.2019.03.012.
Davidsson, Å., 2020: Disasters as an opportunity for improved environmental 
conditions. Int. J. Disaster Risk Reduct., 48, 101590, doi:10.1016/j.
ijdrr.2020.101590.
de Coninck, H., A. Revi, M. Babiker, P . Bertoldi, M. Buckeridge, A. Cartwright, 
W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P . Newman, 
A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018: Strengthening

and Implementing the Global Response. In: Global Warming of 1.5°C. An 
industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate 
change, sustainable development, and efforts to eradicate poverty [Masson-
Delmotte, V., P . Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P . R. Shukla, A. Pirani, 
W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y . 
Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield 
(eds.)]. In press. ISBN 9789291691517.
de Coninck, H. and A. Sagar, 2015: Making sense of policy for climate technology 
development and transfer. Clim. Policy, 15(1), 1–11.
De Juan, A., 2015: Long-term environmental change and geographical patterns 
of  violence in Darfur, 2003–2005. Polit Geogr, 45, 22–33, doi:10.1016/j.
polgeo.2014.09.001.

de la Torre-Castro, M., et  al., 2017: Gender analysis for better coastal 
management – Increasing our understanding of social-ecological seascapes. 
Mar. Policy., 83, 62–74, doi:10.1016/j.marpol.2017.05.015.
de Vries, G.W., W.P .C. Boon and A. Peine, 2016: User-led innovation in civic 
energy communities. Environ. Innov. Soc. Transit., 19, 51–65, doi:10.1016/j.
eist.2015.09.001.
de Witt, A., 2015: Climate change and the clash of Worldviews: an exploration 
of how to move forward in a polarized debate. Zygon, 50(4), 906–921, 
doi:10.1111/zygo.12226.
De Witt, A., J. de Boer, N. Hedlund and P . Osseweijer, 2016: A new tool to map 
the major worldviews in the Netherlands and USA, and explore how they 
relate to climate change. Environ. Sci. Policy, 63, 101–112, doi:10.1016/j.
envsci.2016.05.012.
Deaconu, A., G. Mercille and M. Batal, 2019: The agroecological farmer’s 
pathways from agriculture to nutrition: a practice-based case from Ecuador’s

highlands. Ecol. Food. Nutr., 58(2), 142–165.
Dearing, J.A., et al., 2014: Safe and just operating spaces for regional social-
ecological systems. Glob. Environ. Chang., 28, 227–238.
DeCaro, D.A., C.A. Arnold, E. Frimpong Boamah and A.S. Garmestani, 2017: 
Understanding and applying principles of social cognition and decision 
making in adaptive environmental governance. Ecol. Soc., 22(1), art33.
Dekker, S., 2020: Responding to climate change : the role of local government 
in ireland. Irel. Clim. Crisis, 109–127. Doi: 10.1007/978-3-030-47587-1_7.
Delina, L.L. and B.K. Sovacool, 2018: Of temporality and plurality: an epistemic 
and governance agenda for accelerating just transitions for energy access 
and sustainable development. Curr. Opin. Environ. Sustain., 34, 1–6, 
doi:10.1016/j.cosust.2018.05.016.
Dellink, R., E. Lanzi and J. Chateau, 2019: The sectoral and regional economic 
consequences of climate change to 2060. Environ. Resour. Econ., 72(2),

309–363, doi:10.1007/s10640-017-0197-5.
Dellmuth, L.M., M.-T. Gustafsson, N. Bremberg and M. Mobjörk, 2018: 
Intergovernmental organizations and climate security: advancing the 
research agenda. WIREs Clim. Chang., 9(1), e496, doi:10.1002/wcc.496.
Demaria, F . and A. Kothari, 2017: The post-development dictionary agenda: 
paths to the pluriverse. Third World Q, 38(12), 2588–2599, doi:10.1080/014
36597.2017.1350821.
Demaria, F ., F . Schneider, F . Sekulova and J. Martinez-Alier, 2013: What is 
degrowth? From an activist slogan to a social movement. environ values, 
22(2), 191–215, doi:10.3197/096327113X13581561725194.
Demeritt, D., et  al., 2011: Pathways to Sustainability: perspectives and 
provocations. Environ. Plan. A., 43(5), 1226–1237, doi:10.1068/a227sym.
Dempsey, J., T.G. Martin and U.R. Sumaila, 2020: Subsidizing extinction? 
Conservation Letters, 13(1), e12705, doi:10.1111/conl.12705.
Demuzere, M., et al., 2014: Mitigating and adapting to climate change: multi-

functional and multi-scale assessment of green urban infrastructure. J. 
Environ. Manag., 146, 107–115, doi:10.1016/j.jenvman.2014.07.025.
Deneulin, S. and L. Shahani, 2009: An Introduction to the Human Development 
and Capability Approach: Freedom and Agency. 1st edn. Routledge, London ; 
Sterling, VA : Ottawa, ON. ISBN 978-1844078066 376 pp).
Denton, F ., T.J. Wilbanks, A.C. Abeysinghe, I. Burton, Q. Gao, M.C. Lemos, 
T. Masui, K.L. O’Brien, and K. Warner, 2014: Climate-Resilient Pathways: 
Adaptation, Mitigation, and Sustainable Development. In: Climate Change

18
2745Climate Resilient Development Pathways  Chapter 18
2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral 
Aspects. Contribution of Working Group II to the Fifth Assessment Report of 
the Intergovernmental Panel on Climate Change [Field, C. B., V. R. Barros, D. 
J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y . 
O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P . R. 
Mastrandrea and L. L. White (eds.)]. Cambridge University Press, Cambridge, 
UK and New York, NY , USA, pp. 1101-1131. ISBN 9781107058071.
Díaz, S., et al., 2015: The IPBES conceptual framework—connecting nature and 
people. Curr. Opin. Environ. Sustain., 14, 1–16.
Díaz, S., et  al., 2018: Assessing nature’s contributions to people. Science, 
359(6373), 270–272, doi:10.1126/science.aap8826.
Dietz, S., A. Bowen, C. Dixon and P . Gradwell, 2016: ‘Climate value at risk’

of global financial assets. Nat. Clim. Chang., 6(7), 676–679, doi:10.1038/
nclimate2972.
Diffenbaugh, N. and M. Burke, 2019: Global warming has increased global 
economic inequality. Proc. Natl. Acad. Sci., 116, 201816020, doi:10.1073/
pnas.1816020116.
DiGregorio, M., 2015: Bargaining with disaster: flooding, climate change, and 
Urban growth ambitions in Quy Nhon, Vietnam. pac aff, 88(3), 577–597, 
doi:10.5509/2015883577.
Dimson, E., O. Karakaş and X. Li, 2015: Active ownership. Rev Financ Stud, 
28(12), 3225–3268, doi:10.1093/rfs/hhv044.
Dimson, E., O. Karakaş and X. Li, 2020: Coordinated Engagements. European 
Corporate Governance Institute – Finance Working Paper No. 721/2021. Doi: 
10.2139/ssrn.3209072.
Djoudi, H., et  al., 2016: Beyond dichotomies: Gender and intersecting 
inequalities in climate change studies. Ambio, 45(S3), 248–262.
Doczi, J., 2015: Understanding patterns of climate resilient economic 
development. Available at: https://www.odi.org/publications/10101-

understanding-patterns-climate-resilient-economic-development (accessed 
20/01/2021).
Dodman, D. and D. Mitlin, 2015: The national and local politics of climate 
change adaptation in Zimbabwe. Clim. Dev., 7(3), 223–234.
Dolšak, N. and A. Prakash, 2018: The politics of climate change adaptation. 
Annu. Rev. Environ. Resour., 43(1), 317–341, doi:10.1146/annurev-
environ-102017-025739.
Domingo, A., 2018: Analyzing zombie dystopia as neoliberal scenario: an 
exercise in emancipatory catastrophism. Front. Sociol., 3, 20, doi:10.3389/
fsoc.2018.00020.
Donner, S.D., M. Kandlikar and S. Webber, 2016: Measuring and tracking the 
flow of climate change adaptation aid to the developing world. Environ. Res. 
Lett., 11(5), 54006.
Dornan, M., 2015: Renewable energy development in small island 
developing states of the pacific. Resources, 4(3), 490–506, doi:10.3390/
resources4030490.
Douxchamps, S., et al., 2016: Linking agricultural adaptation strategies, food

security and vulnerability: evidence from West Africa. Reg Environ Change, 
16(5), 1305–1317, doi:10.1007/s10113-015-0838-6.
Dresse, A., I. Fischhendler, J.Ø. Nielsen and D. Zikos, 2018: Environmental 
peacebuilding: towards a theoretical framework. Coop Confl, 
doi:10.1177/0010836718808331.
Dreyer, J.S., 2015: Ubuntu. Int. J. Pract. Theol., 19(1), 189–209, doi:10.1515/
ijpt-2015-0022.
Driver, E., M. Parsons and K. Fisher, 2018: Technically political: The post-
politics(?) of the New Zealand Emissions Trading Scheme. Geoforum, 97, 
253–267, doi:10.1016/j.geoforum.2018.09.023.
Duan, F ., Q. Ji, B.-Y . Liu and Y . Fan, 2018: Energy investment risk assessment for 
nations along China’s Belt & Road Initiative. J Clean Prod, 170, 535–547, 
doi:10.1016/j.jclepro.2017.09.152.
Duku, C., S.J. Zwart and L. Hein, 2018: Impacts of climate change on cropping 
patterns in a tropical, sub-humid watershed. PLoS One, 13(3), e192642,

doi:10.1371/journal.pone.0192642.Dumont, E.S., S. Bonhomme, T.F . Pagella and F .L. Sinclair, 2019: Structured 
stakeholder engagement leads to development of more diverse and inclusive 
agroforestry options. Exp. Agric., 55(S1), 252–274.
Dun, O., C. McMichael, K. McNamara and C. Farbotko, 2020: Investing in home: 
development outcomes and climate change adaptation for seasonal workers 
living between Solomon Islands and Australia. Migr. Dev., 0(0), 1–24, doi:10
.1080/21632324.2020.1837535.
Dzebo, A., et  al., 2017: Exploring connections between the Paris Agreement 
and the 2030 Agenda for Sustainable Development. Stockholm Environment 
Institute. Available at: https://transparency-partnership.net/system/files/
document/SEI_2017_Exploring%20Paris%20Agreement%20and%20
SDG%20connections.pdf (accessed 20/0/2022)
Dzebo, A., H. Janetschek, C. Brandi and G. Iacobuta, 2019: Connections 
between the Paris Agreement and the 2030 Agenda: the case for policy

coherence. Stockholm Environment Institute. Available at: https://www.sei.
org/publications/connections-between-the-paris-agreement-and-the-2030-
agenda/ (accessed 20/01/2021).
Eakin, H.C., M.C. Lemos and D.R. Nelson, 2014: Differentiating capacities as 
a means to sustainable climate change adaptation. Glob. Environ. Chang., 
27(1), 1–8.
Eastin, J., 2018: Climate change and gender equality in developing states. 
World Dev, 107, 289–305.
Ebi, K.L., N.H. Ogden, J.C. Semenza and A. Woodward, 2017: Detecting and 
attributing health burdens to climate change. Environ. Health Perspect., 
125(8), 85004, doi:10.1289/EHP1509.
ECB, 2020: Guide on Climate-Related and Environmental Risks. European 
Central Bank. Available at: https://www.bankingsupervision.europa.eu/
legalframework/publiccons/pdf/climate-related_risks/ssm.202005_draft_
guide_on_climate-related_and_environmental_risks.en.pdf (accessed 
20/01/2022).
Economides, G. and A. Xepapadeas, 2018: Monetary Policy under Climate

Change. CESifo Working Paper, CESifo, Munich, Vol. 7021.
Ehnert, F ., et al., 2018: Urban sustainability transitions in a context of multi-
level governance: A comparison of four European states. Environ. Innov. Soc. 
Transit., 26, 101–116, doi:10.1016/j.eist.2017.05.002.
Eisenmenger, N., et  al., 2020: The Sustainable Development Goals prioritize 
economic growth over sustainable resource use: a critical reflection on the 
SDGs from a socio-ecological perspective. Sustain Sci, 15(4), 1101–1110, 
doi:10.1007/s11625-020-00813-x.
Elliott, A. and J. Cullis, 2017: The importance of the humanities to the climate 
change debate. In: Climate Change and the Humanities: Historical, 
Philosophical and Interdisciplinary Approaches to the Contemporary 
Environmental Crisis [Elliott, A., J. Cullis and V. Damodaran(eds.)]. Palgrave 
Macmillan UK, London, pp. 15–42. ISBN 978-1137551245.
Ellis, N.R. and P . Tschakert, 2019: Triple-wins as pathways to transformation?

A critical review. Geoforum, 103, 167–170, doi:10.1016/j.
geoforum.2018.12.006.
Emmerling, J. and M. Tavoni, 2021: Representing inequalities in integrated 
assessment modeling of climate change. One Earth, 4(2), 177–180, 
doi:10.1016/j.oneear.2021.01.013.
Enns, C., 2018: Mobilizing research on Africa’s development corridors. 
Geoforum, 88, 105–108.
Enns, C. and B. Bersaglio, 2020: On the coloniality of “new” mega-infrastructure 
projects in east africa. Antipode, 52(1), 101–123.
Ensor, J., 2016: Adaptation and resilience in Vanuatu: Interpreting community 
perceptions of vulnerability, knowledge and power for community-based 
adaptation programming. Report by SEI for Oxfam Australia, Carlton. 
Stockhom Environment Institute, Stockholm.
Eriksen, S., R. Grøndahl and A.-M. Sæbønes, 2021a: On CRDP and CRPD: Why 
the rights of persons with disabilities are critical for understanding climate 
resilient development pathways. Lancet Planet. Health.

18
2746Chapter 18 Climate Resilient Development Pathways
Eriksen, S., et  al., 2021b: Adaptation interventions and their effect on 
vulnerability in developing countries: help, hindrance or irrelevance? World 
Dev, 141, 105383, doi:10.1016/j.worlddev.2020.105383.
Eriksen, S.H., L.K. Cramer, I. Vetrhus and P . Thornton, 2019: Can climate 
interventions open up space for transformation? Examining the case of 
climate-smart agriculture (CSA) in Uganda. Front. Sustain. Food Syst., 3.
Eriksen, S.H., A.J. Nightingale and H. Eakin, 2015: Reframing adaptation: the 
political nature of climate change adaptation. Glob. Environ. Chang., 35, 
523–533, doi:10.1016/j.gloenvcha.2015.09.014.
Escobar, A., 1995: Imagining a post-development era. Power Dev., 211–227. 
Doi: 10.2307/466217
Escobar, A., 2016: Thinking-feeling with the earth; territorial struggles and the 
ontological dimension of the epistemologies of the south. AIBR Revista De 
Antropología Iberoamericana, 11(1), 11–32.

Esquivel-Muelbert, A., et al., 2019: Compositional response of Amazon forests 
to climate change. Glob. Change Biol., 25(1), 39–56, doi:10.1111/gcb.14413.
Estoque, R., et  al., 2018: A review of quality of life (QOL) assessments and 
indicators: Towards a “QOL-Climate” assessment framework. Ambio, 48, 
doi:10.1007/s13280-018-1090-3.
Etchart, L., 2017: The role of indigenous peoples in combating climate change. 
Palgrave Commun, 3(1), 1–4, doi:10.1057/palcomms.2017.85.
Ewuoso, C. and S. Hall, 2019: Core aspects of ubuntu: a systematic review. S Afr 
J BL, 12(2), 93–103, doi:10.7196/SAJBL.2019.v12i2.679.
Fadrique, B., et al., 2018: Widespread but heterogeneous responses of Andean 
forests to climate change. Nature, 564(7735), 207–212, doi:10.1038/
s41586-018-0715-9.
Faist, T., 2018: The Socio-natural question: how climate change adds to social 
inequalities. J. Intercult. Stud., 39(2), 195–206, doi:10.1080/07256868.201
8.1446670.

Fankhauser, S. and T. McDermott, 2016: The Economics of Climate Resilient 
Development. Edward Elgar Publishing, Cheltenham, UK, ISBN 978-
1785360312.
Fankhauser, S. and T.K.J. McDermott, 2014: Understanding the adaptation 
deficit: why are poor countries more vulnerable to climate events 
than rich countries? Glob. Environ. Chang., 27, 9–18, doi:10.1016/j.
gloenvcha.2014.04.014.
FAO, 2014: Walking the Nexus Talk: Assessing the Water-Energy-Food Nexus in 
the Context of the Sustainable Energy for All Initiative. Food and Agriculture 
Organization of the United Nations, Rome.
Fao, 2016: Base de datos Género y derecho a la tierra. FAO, Available at: http://
www.fao.org/gender-landrights-database (accessed 20/01/2022).
Farrell, N., 2015: ‘Conscience capitalism’and the neoliberalisation of the non-
profit sector. New Polit. Econ., 20(2), 254-272.
Faruque, M.O., 2017: Neoliberal resource governance and counter-hegemonic

social movement in Bangladesh. Soc. Mov Stud., 16(2), 254–259, doi:10.10
80/14742837.2016.1268957.
Favretto, N., et al., 2018: Links between climate change mitigation, adaptation 
and development in land policy and ecosystem restoration projects: lessons 
from South Africa. Sustainability, 10(3), 779, doi:10.3390/su10030779.
Fazey, I., et al., 2018a: Transformation in a changing climate: a research agenda. 
Clim. Dev., 10(3), 197–217.
Fazey, I., et  al., 2018b: Ten essentials for action-oriented and second order 
energy transitions, transformations and climate change research. Energy 
Res. Soc. Sci., 40, 54–70.
Fazey, I., et al., 2016: Past and future adaptation pathways. Clim. Dev., 8(1), 
26–44.
Fedele, G., et  al., 2019: Transformative adaptation to climate change for 
sustainable social-ecological systems. Environ. Sci. Policy, 101, 116–125, 
doi:10.1016/j.envsci.2019.07.001.
Feodoroff, P ., 2020: Indigenous Female Bodies as Indicators of Change.

Global Report of Indigenous Knowledge and Local Knowledge on Climate 
Change 2020. Available at: https://arcticpassion.eu/blog/Paulina (accessed 
20/01/2022).Feola, G., 2015: Societal transformation in response to global environmental 
change: a review of emerging concepts. Ambio, 44(5), 376–390, doi:10.1007/
s13280-014-0582-z.
Feola, G., O. Koretskaya and D. Moore, 2021: (Un)making in sustainability 
transformation beyond capitalism. Glob. Environ. Chang., 69, 102290, 
doi:10.1016/j.gloenvcha.2021.102290.
Fergnani, A. and Z. Song, 2020: The six scenario archetypes framework: A 
systematic investigation of science fiction films set in the future. Futures, 
124, 102645, doi:10.1016/j.futures.2020.102645.
Fernandez, G. and I. Ahmed, 2019: “build back better” approach to disaster 
recovery: research trends since 2006. Prog. Disaster Sci., 1, 100003, 
doi:10.1016/j.pdisas.2019.100003.
Ferragina, E., A. Arrigoni and T.F . Spreckelsen, 2020: The rising invisible majority.

Rev. Int. Polit. Econ., 0(0), 1–38, doi:10.1080/09692290.2020.1797853.
Few, R., et  al., 2017: Transformation, adaptation and development: 
relating concepts to practice. Palgrave Commun, 3(1), 1–9, doi:10.1057/
palcomms.2017.92.
Ficklin, L., L.C. Stringer, A.J. Dougill and S.M. Sallu, 2018: Climate compatible 
development reconsidered: calling for a critical perspective. Clim. Dev., 
10(3), 193–196, doi:10.1080/17565529.2017.1372260.
Fioramonti, L., L. Coscieme and L.F . Mortensen, 2019: From gross domestic 
product to wellbeing: How alternative indicators can help connect the new 
economy with the Sustainable Development Goals. Anthropocene Rev., 
doi:10.1177/2053019619869947.
Fischedick M., J. Roy, A. Abdel-Aziz, A. Acquaye, J. M. Allwood, J.-P . Ceron, Y . 
Geng, H. Kheshgi, A. Lanza, D. Perczyk, L. Price, E. Santalla, C. Sheinbaum 
and K. Tanaka, 2014: Industry. In: Climate Change 2014: Mitigation of 
Climate Change. Contribution of Working Group III to the Fifth Assessment

Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., 
Cambridge, UK and New York, NY , USA, pp. 739–810. ISBN 9781107058217.
Fischer, J. and M. Riechers, 2019: A leverage points perspective on sustainability. 
People Nat., 1(1), 115–120, doi:10.1002/pan3.13.
Fisher, B. S. et al., 2007: Issues related to mitigation in the long term context. 
In: Climate Change 2007: Mitigation. Contribution of Working Group III to 
the Fourth Assessment Report of the Intergovernmental Panel on Climate 
Change [Metz, B., O. R. Davidson, P . R. Bosch, R. Dave and L. A. Meyer (eds.)]. 
Cambridge University Press, Cambridge, UK and New York, NY , USA, pp. 169-
250. ISBN 9780521880114.

Fisher, E., J. Hellin, H. Greatrex and N. Jensen, 2019: Index insurance and climate 
risk management: addressing social equity. Dev Policy Rev, 37(5), 581–602, 
doi:10.1111/dpr.12387.
Flammer, C., 2020: Green Bonds: Effectiveness and Implications for Public 
Policy. National Bureau of Economic Research, Cambridge, MA, https://
www.nber.org/papers/w25950. Accessed 2020.
Fleurbaey M., S. Kartha, S. Bolwig, Y . L. Chee, Y . Chen, E. Corbera, F . Lecocq, 
W. Lutz, M. S. Muylaert, R. B. Norgaard, C. Oker-eke, and A. D. Sagar, 2014: 
Sustainable Development and Equity. In: Climate Change 2014: Mitigation 
of Climate Change. Contribution of Working Group III to the Fifth Assessment 
Report of the Intergovernmental Panel on Climate Change [Field, C. B., V. R. 
Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, 
K. L. Ebi, Y . O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S.

MacCracken, P . R. Mastrandrea and L. L. White (eds.)]. Cambridge University 
Press, Cambridge, UK and New York, NY , USA.
Folke, C., et  al., 2016: Social-ecological resilience and biosphere-based 
sustainability science. Ecology and Society 21(3):41. Doi: 10.5751/ES-08748-
210341
Ford, J., et al., 2016: Including indigenous knowledge and experience in IPCC 
assessment reports. Nat. Clim. Chang., 64(4), 349.

18
2747Climate Resilient Development Pathways  Chapter 18
Ford, J., W. Vanderbilt and L. Berrang-Ford, 2012: Authorship in IPCC AR5 and 
its implications for content: climate change and Indigenous populations in 
WGII. Clim Change, 113(2), 201–213, doi:10.1007/s10584-011-0350-z.
Ford, J.D., et al., 2015: The status of climate change adaptation in Africa and 
Asia. Reg Environ Change, 15(5), 801–814.
Ford, J.D., et al., 2020: The resilience of Indigenous Peoples to environmental 
change. One Earth, 2, 532–543.
Forsyth, T., 2018: Is resilience to climate change socially inclusive? Investigating 
theories of change processes in Myanmar. World Dev, 111, 13–26, 
doi:10.1016/j.worlddev.2018.06.023.
Foster-Fishman, P .G., B. Nowell and H. Yang, 2007: Putting the system back 
into systems change: a framework for understanding and changing 
organizational and community systems. Am J Community Psychol, 39(3-4), 
197–215, doi:10.1007/s10464-007-9109-0.

Fougère, L. and S. Bond, 2016: Legitimising activism in democracy: a 
place for antagonism in environmental governance. Plan. Theory, 
doi:10.1177/1473095216682795.
Fragkias, M. and C.G. Boone, 2016: Modern political economy, global 
environmental change and urban sustainability transitions. Curr. Opin. 
Environ. Sustain., 22, 63–68, doi:10.1016/j.cosust.2017.04.007.
Frainer, A., et al., 2020: Cultural and linguistic diversities are underappreciated 
pillars of biodiversity. Proc. Natl. Acad. Sci., 117(43), 26539–26543, 
doi:10.1073/pnas.2019469117.
Franco-Torres, M., B.C. Rogers and R. Harder, 2020: Articulating the new urban 
water paradigm. Crit Rev Environ Sci Technol, 0(0), 1–47, doi:10.1080/1064
3389.2020.1803686.
Frank, S., et  al., 2017: Reducing greenhouse gas emissions in agriculture 
without compromising food security? Environ. Res. Lett., 12(10), 105004, 
doi:10.1088/1748-9326/aa8c83.
Frank, T.D., et al., 2019: Global, regional, and national incidence, prevalence, and

mortality of HIV, 1980-2017, and forecasts to 2030, for 195 countries and 
territories: a systematic analysis for the Global Burden of Diseases, Injuries, 
and Risk Factors Study 2017. Lancet HIV, 6(12), e831–e859, doi:10.1016/
s2352-3018(19)30196-1.
Frantzeskaki, N., et  al., 2017: Nature-based solutions accelerating urban 
sustainability transitions in cities: lessons from Dresden, Genk and Stockholm 
cities. In: Nature-Based Solutions to Climate Change Adaptation in Urban 
Areas [Korn, H. & Kabisch, N. (eds).]. Springer, Cham, pp. 65–88.
Fresnillo Sallan, I., 2020: Debt and climate: entangled emergencies derailing 
women’s rights and gender justice. Gend. Dev., 28(3), 499–513, doi:10.108
0/13552074.2020.1838168.
Friedman, S., S. Schmer-Galunder, A. Chen and J. Rye (eds.), 2019: Relating 
Word Embedding Gender Biases to Gender Gaps: A Cross-Cultural Analysis. 
Association for Computational Linguistics, Florence, Italy, 18–24.

Froehlich, H.E., J.C. Afflerbach, M. Frazier and B.S. Halpern, 2019: Blue Growth 
Potential to Mitigate Climate Change through Seaweed Offsetting. Curr. 
Biol., 29(18), 3087–3093.e3083, doi:10.1016/j.cub.2019.07.041.
Fudge, S., M. Peters and B. Woodman, 2016: Local authorities as niche actors: 
the case of energy governance in the UK. Environ. Innov. Soc. Transit., 18, 
1–17. Doi: 10.1016/j.eist.2015.06.004.
Fund for Peace, 2021: Fragile States Index Annual Report 2021. Fund for Peace, 
Peace, F . f., Washington, DC. 51 pp.
Fuso Nerini, F ., et  al., 2018: Mapping synergies and trade-offs between 
energy and the Sustainable Development Goals. Nat. Energy, 3(1), 10–15, 
doi:10.1038/s41560-017-0036-5.
Gaddy, B.E., V. Sivaram, T.B. Jones and L. Wayman, 2017: Venture Capital and 
Cleantech: The wrong model for energy innovation. Energy Policy, 102, 385–
395, doi:10.1016/j.enpol.2016.12.035.
Galvin, K.  A., 2009: Transitions: pastoralists living with change. Annu. Rev.

Anthropol., 38(1), 185–198.
Gambrel, J.C. and P . Cafaro, 2009: The virtue of simplicity. J. Agric. Environ. 
Ethics., 23(1), 85, doi:10.1007/s10806-009-9187-0.Garcia, A., P . Tschakert and N.A. Karikari, 2020: ‘Less able’: how gendered 
subjectivities warp climate change adaptation in Ghana’s Central 
Region. Gend. Place Cult., 27(11), 1602–1627, doi:10.1080/096636
9X.2020.1786017.
Garcia, D.J. and F . You, 2017: Introducing green GDP as an objective to account 
for changes in global ecosystem services Due to Biofuel production. In: 
Computer Aided Chemical Engineering [Espuña, A., M. Graells and L. 
Puigjaner(eds.)]. Elsevier, Amsterdam, pp. 505–510.
Garg, A. and K. Halsnæs, 2007: Sustainable development to enhance 
resilience to climate change vulnerability. From vulnerability to resilience: 
The challenge of adaptation to climate change, 17–25. United Nations 
Environment Programme. Available at: https://www.researchgate.

net/publication/228466265_From_Vulnerability_to_Resilience_The_
Challenge_of_Adaptation_to_Climate_Change (accessed 20/01/2022).
Garschagen, M., et al., 2021: The consideration of future risk trends in national 
adaptation planning: conceptual gaps and empirical lessons. Clim. Risk 
Manag. In press. Doi: 10.1016/j.crm.2021.100357
Gaur, M. and V.R. Squires, 2018: Climate Variability Impacts on Land Use and 
Livelihoods in Drylands. Springer International Publishing, Cham. ISBN 978-
3319566801.
Gay-Antaki, M., 2020: Feminist geographies of climate change: negotiating 
gender at climate talks. Geoforum, 115, 1–10.
Gay-Antaki, M. and D. Liverman, 2018: Climate for women in climate science: 
women scientists and the intergovernmental panel on climate change. Proc. 
Natl. Acad. Sci., 115(9), 2060–2065.
Gazzotti, P ., et al., 2021: Persistent inequality in economically optimal climate 
policies. Nat Commun, 12(1), 3421, doi:10.1038/s41467-021-23613-y.

Gebreyes, M., 2018: ‘Producing’ institutions of climate change adaptation and 
food security in north eastern Ethiopia. NJAS - Wageningen J. Life Sci., 84, 
123–132, doi:10.1016/j.njas.2017.10.007.
Geere, J.-A.L. and P .R. Hunter, 2020: The association of water carriage, water 
supply and sanitation usage with maternal and child health. A combined 
analysis of 49 Multiple Indicator Cluster Surveys from 41 countries. Int J Hyg 
Environ Health, 223(1), 238–247.
Geradts, T.H. and N.M. Bocken, 2019: Driving sustainability-oriented innovation. 
MIT Sloan Manage. Rev., 60(2), 1.
Gernert, M., H. El Bilali and C. Strassner, 2018: Grassroots initiatives as 
Sustainability transition pioneers: implications and lessons for urban food 
systems. Urban Sci., 2(1), 23.
Gerst, M., P . Raskin and J. Rockström, 2013: Contours of a resilient global 
future. Sustainability, 6, 123–135, doi:10.3390/su6010123.
Ghisetti, C., S. Mancinelli, M. Mazzanti and M. Zoli, 2017: Financial barriers and

environmental innovations: evidence from EU manufacturing firms. Clim. 
Policy, 17(sup1), 131–S147, doi:10.1080/14693062.2016.1242057.
Gianfrate, G. and M. Peri, 2019: The green advantage: exploring the convenience 
of issuing green bonds. J  Clean Prod, 219, 127–135, doi:10.1016/j.
jclepro.2019.02.022.
Gibon, T., et  al., 2020: Shades of green: life cycle assessment of renewable 
energy projects financed through green bonds. Environ. Res. Lett., 15, 
104045, doi:10.1088/1748-9326/abaa0c.
Gibson-Graham, J.K., 2006: A Postcapitalist Politics, 1st  edn., University of 
Minnesota Press, Minneapolis, ISBN 978-0816648047. 316 pp.
Gibson-Graham, J.K., 2005: Surplus possibilities: postdevelopment and 
community economies. Singap J Trop Geogr, 26(1), 4–26.
Gielen, D., et  al., 2019: The role of renewable energy in the global energy 
transformation. Energy Strategy Rev., 24, 38–50, doi:10.1016/j.
esr.2019.01.006.
Gill, S.R. and S.R. Benatar, 2020: Reflections on the political economy of

planetary health. Rev. Int. Polit. Econ., 27(1), 167–190, doi:10.1080/0969
2290.2019.1607769.
Gillard, R., et al., 2016: Transformational responses to climate change: beyond 
a systems perspective of social change in mitigation and adaptation. Wiley 
Interdiscip. Rev. Clim. Chang., 7, 251–265.

18
2748Chapter 18 Climate Resilient Development Pathways
Gills, B. and J. Morgan, 2020: Global Climate Emergency: after COP24, climate 
science, urgency, and the threat to humanity. Globalizations, 17(6), 885–
902, doi:10.1080/14747731.2019.1669915.
Gleditsch, N.P . and R. Nordås, 2014: Conflicting messages? The IPCC on 
conflict and human security. Polit Geogr, 43, 82–90, doi:10.1016/j.
polgeo.2014.08.007.
Gliedt, T., C.E. Hoicka and N. Jackson, 2018: Innovation intermediaries 
accelerating environmental sustainability transitions. J  Clean Prod, 174, 
1247–1261.
Goda, T., Ö. Onaran and E. Stockhammer, 2017: Income inequality and wealth 
concentration in the recent crisis. Development & Change, 48(1), 3–27, 
doi:10.1111/dech.12280.
Goddard, G. and M.  A. Farrelly, 2018: Just transition management: Balancing 
just outcomes with just processes in Australian renewable energy transitions. 
Appl Energy, 225, 110–123, doi:10.1016/j.apenergy.2018.05.025.

Godfrey-Wood, R. and O. Naess, 2016: Adapting to climate change: Transforming 
development? Available at: https://bulletin.ids.ac.uk/index.php/idsbo/article/
view/2717 (accessed 21/01/2022).
Goldman, M.J., M.D. Turner and M. Daly, 2018: A critical political ecology of 
human dimensions of climate change: Epistemology, ontology, and ethics. 
WIREs Clim. Chang., 9(4), e526, doi:10.1002/wcc.526.
Gonçalves-Souza, D., P .H. Verburg and R. Dobrovolski, 2020: Habitat loss, 
extinction predictability and conservation efforts in the terrestrial ecoregions. 
Biol. Conserv., 246, 108579, doi:10.1016/j.biocon.2020.108579.
Gonda, N., 2017: Revealing the patriarchal sides of climate change adaptation 
through intersectionality: a case study from Nicaragua. In: Understanding 
Climate Change through Gender Relations [Buckingham, S. & Le Masson, V. 
(eds.)] 173–189. Routledge, London. ISBN 978-1315661605.
Goodrich, C.G., A. Prakash and P .B. Udas, 2019: Gendered vulnerability and

adaptation in Hindu-Kush Himalayas: Research insights. Environ. Dev., 31, 
1–8, doi:10.1016/j.envdev.2019.01.001.
Göpel, M., 2016: The Great Mindshift: How a New Economic Paradigm and 
Sustainability Transformations Go Hand in Hand. Springer, Berlin.
Gorddard, R., et al., 2016: Values, rules and knowledge: adaptation as change 
in the decision context. Environ. Sci. Policy, 57, 60–69.
Görg, C., et  al., 2017: Challenges for social-ecological transformations: 
contributions from social and political ecology. Sustainability, 9, 1045, 
doi:10.3390/su9071045.
Gorissen, L., et  al., 2018: Moving towards systemic change? Investigating 
acceleration dynamics of urban sustainability transitions in the Belgian City 
of Genk. J Clean Prod, 173, 171–185.
Gotham, K.F ., 2016: Antinomies of risk reduction: climate change and the 
contradictions of coastal restoration. Environ. Sociol., 2(2), 208–219, doi:10.
1080/23251042.2016.1177363.

Government of Kenya, 2008: About Vision 2030. Available at: https://vision2030.
go.ke/about-vision-2030/ (accessed 21/01/2022).
Government of Kenya, 2012: Vision 2030 Development Strategy for Northern 
Kenya and other Arid Lands. Available at: https://www.adaconsortium.org/
images/publications/Vision2030-2011.pdf (accessed 21/01/2022).
Government of Kenya, 2016: Brief on LAPSSET corridor project. Available 
at: https://s3-eu-west-1.amazonaws.com/s3.sourceafrica.net/
documents/118442/LAPSSET-Project-Report-July-2016-1.pdf (accessed 
21/01/2022).
Government of Kenya, 2018: Ministry Of Devolution And ASALs, State 
Department For Development Of The Arid And Semi Arid Lands Strategic 
Plan (2018 – 2022). Available at: https://www.asals.go.ke/wp-content/
uploads/2019/05/ASAL-Report-Final-for-printing.pdf (accessed 21/01/2022).
Graham, N.T., et  al., 2018: Water sector assumptions for the shared 
socioeconomic pathways in an integrated modeling framework. Water

Resour. Res., 54(9), 6423–6440, doi:10.1029/2018WR023452.
Gram-Hanssen, I., N. Schafenacker and J. Bentz, 2021: Decolonizing 
transformations through ‘right relations. Sustain Sci, doi:10.1007/s11625-
021-00960-9.Gratani, M., et  al., 2011: Is validation of Indigenous ecological knowledge 
a disrespectful process? A case study of traditional fishing poisons and 
invasive fish management from the wet tropics, Australia. Ecol. Soc., 16(3), 
doi:10.5751/es-04249-160325.
Green, A.S., et al., 2020: An interdisciplinary framework for using archaeology, 
history and collective action to enhance India’s agricultural resilience and 
sustainability. Environ. Res. Lett., 15(10), 105021, doi:10.1088/1748-9326/
aba780.
Green, R., et al., 2013: The effect of rising food prices on food consumption: 
systematic review with meta-regression. BMJ, 346(1), f3703–f3703.
Greiner, C., 2016: Land-use change, territorial restructuring, and economies of

anticipation in dryland Kenya. J. East. African Stud., 10(3), 530–547.
Griscom, B.W., et  al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci., 
114(44), 11645–11650.
Grove, K., 2018: Resilience. Routledge, London.
Grydehøj, A. and I. Kelman, 2017: The eco-island trap: climate change mitigation 
and conspicuous sustainability. Area, 49(1), 106–113.
Guido, Z., C. Knudson, D. Campbell and J. Tomlinson, 2020: Climate information 
services for adaptation: what does it mean to know the context? Clim. Dev., 
12(5), 395-407.
Gumucio, T., M.  A. Alves, N. Orentlicher and M.C. Hernández Ceballo, 2018: 
Analysis of gender research on forest, tree and agroforestry value chains in 
Latin America. For. Trees Livelihoods, 27(2), 69–85.
Gundimeda, H., P . Sukhdev, R.K. Sinha and S. Sanyal, 2007: Natural resource 
accounting for Indian states - Illustrating the case of forest resources. Ecol. 
Econ., 61(4), 635–649, doi:10.1016/j.ecolecon.2006.07.035.

Güneralp, B., et al., 2017: Urbanization in Africa: challenges and opportunities 
for conservation. Environ. Res. Lett., 13(1), 15002, doi:10.1088/1748-9326/
aa94 fe.
Gupta, J. and N. Pouw, 2017: Towards a trans-disciplinary conceptualization 
of inclusive development. Curr. Opin. Environ. Sustain., 24, 96–103. Doi: 
10.1016/j.cosust.2017.03.004
Haasnoot, M., J.H. Kwakkel, W.E. Walker and J. ter Maat, 2013: Dynamic 
adaptive policy pathways: a method for crafting robust decisions for a deeply 
uncertain world. Glob. Environ. Chang., 23(2), 485–498, doi:10.1016/j.
gloenvcha.2012.12.006.
Haines, A., et  al., 2017: Short-lived climate pollutant mitigation and the 
Sustainable Development Goals. Nat. Clim. Chang., 7(12), 863–869, 
doi:10.1038/s41558-017-0012-x.
Hajer, M., et  al., 2015: Beyond cockpit-ism: four insights to enhance the 
transformative potential of the sustainable development goals. Sustainability, 
7(2), 1651–1660.

Hale, T., 2016: “ All hands on deck”: the Paris Agreement and nonstate climate 
action. Glob. Environ. Polit., 16(3), 12–22, doi:10.1162/GLEP_a_00362.
Hall, S.M., 2020: The personal is political: feminist geographies of/in austerity. 
Geoforum, 110, 242–251, doi:10.1016/j.geoforum.2018.04.010.
Hallam, R., 2019: Common sense for the 21st century: only nonviolent rebellion 
can now stop climate breakdown and social collapse. Chelsea Green 
Publishing, Hartford. ISBN 978-1527246744.
Hallegatte, S., C. Brandon, et al., 2018: The economics of (and obstacles to) 
aligning development and climate change adaptation: A World Bank Group 
Contribution to the Global Commission on Adaptation. Available at: https://
gca.org/reports/the-economics-of-and-obstacles-to-aligning-development-
and-climate-change-adaptation/ (accessed 21/01/2022).
Hallegatte, S., J. Rentschler and J. Rozenberg, 2019: Lifelines: The Resilient 
Infrastructure Opportunity. World Bank, Washington, DC, ISBN 978-
1464814303.

Hallegatte, S. and J. Rozenberg, 2017: Climate change through a poverty lens. 
Nat. Clim. Change, 7(4), 250–256, doi:10.1038/nclimate3253.
Han, H. and S.W. Ahn, 2020: Youth mobilization to stop global climate 
change: narratives and impact. Sustainability, 12(10), 4127, doi:10.3390/
su12104127.

18
2749Climate Resilient Development Pathways  Chapter 18
Han, S.-J., 2015: Emancipatory catastrophism from an East Asian perspective: 
feedback from the dialogue organizer. Curr. Sociol., 63(1), 115–120, 
doi:10.1177/0011392114559851.
Hansen, L., 2021: Central Banking Challenges Posed by Uncertain Climate 
Change and Natural Disasters. Available at: https://www.frbsf.org/economic-
research/files/hansen-vsce-061021-paper.pdf (accessed 21/01/2022).
Hansen, L., et  al., 2013: The state of adaptation in the United States: an 
overview. A report for the John D. and Catherine T. MacArthur Foundation. 
Ecoadapt, Bainbridge Island, WA, USA.
Hansen, U.  E. and R. Lema, 2019: The co-evolution of learning mechanisms and 
technological capabilities: Lessons from energy technologies in emerging 
economies. Technol Forecast Soc Change, 140, 241–257, doi:10.1016/j.
techfore.2018.12.007.
Hardee, K., et al., 2018: Family planning and resilience: associations found in

a population, health, and environment (PHE) project in western Tanzania. 
Popul Environ, 40(2), 204–238, doi:10.1007/s11111-018-0310-x.
Hardoy, J., E. Gencer and M. Winograd, 2019: Participatory planning for climate 
resilient and inclusive urban development in Dosquebradas, Santa Ana and 
Santa Tomé. environ urban, 31(1), 33–52, doi:10.1177/0956247819825539.
Harnphatananusorn, S., S. Santipolvut and C. Sonthi, 2019: Concepts and 
empirical calculation of the green GDP for Thailand. Int. J. Green Econ., 13, 
68, doi:10.1504/IJGE.2019.10023001.
Harper, S., J.S. Kaufman and R.S. Cooper, 2017: Declining US life expectancy. 
Epidemiology, 28(6), e54–e56.
Harrington, L.J. and F .E.L. Otto, 2018: Changing population dynamics and 
uneven temperature emergence combine to exacerbate regional exposure 
to heat extremes under 1.5°C and 2°C of warming. Environ. Res. Lett., 13(3), 
34011, doi:10.1088/1748-9326/aaaa99.

Harris, L.M., E.K. Chu and G. Ziervogel, 2018: Negotiated resilience. Resilience, 
6(3), 196–214, doi:10.1080/21693293.2017.1353196.
Harris, P . and L. Clarke, 2017: Maramataka. In: He Whare Hangarau M?ori-
Language, Culture and Technology [Whaanga, H., T.T. Keegan and M. 
Apperley].
Harrison, P ., et  al., 2019: Synthesizing plausible futures for biodiversity and 
ecosystem services in Europe and Central Asia using scenario archetypes. 
Ecol. Soc., 24(2), doi:10.5751/ES-10818-240227.
Harvey, B., L. Cochrane and M.V. Epp, 2019: Charting knowledge co-production 
pathways in climate and development. Environ. Policy Gov., 29(2), 107–117, 
doi:10.1002/eet.1834.
Hasegawa, T., et  al., 2018: Risk of increased food insecurity under stringent 
global climate change mitigation policy. Nat. Clim. Change, 8(8), 699–703, 
doi:10.1038/s41558-018-0230-x.
Hasegawa, T., S. Fujimori, K. Takahashi and T. Masui, 2015: Scenarios for the risk

of hunger in the twenty-first century using shared socioeconomic pathways. 
Environ. Res. Lett., 10(1), 14010, doi:10.1088/1748-9326/10/1/014010.
Haukkala, T., 2018: A struggle for change—the formation of a green-transition 
advocacy coalition in Finland. Environ. Innov. Soc. Transit., 27, 146–156, 
doi:10.1016/j.eist.2017.12.001.
Hay, J.E., P . Hartley and J. Roop, 2019: Climate risk assessments and 
management options for redevelopment of the Parliamentary Complex in 
Samoa, South Pacific. Weather. Clim. Extrem., 25, 100214, doi:10.1016/j.
wace.2019.100214.
Häyhä, T., et  al., 2016: From planetary boundaries to national fair shares of 
the global safe operating space — how can the scales be bridged? Glob. 
Environ. Chang., 40, 60–72.
Healey, P ., 2006: Transforming governance: Challenges of institutional 
adaptation and a new politics of space. Eur. Plan. Stud., 14(3), 299–320, 
doi:10.1080/09654310500420792.

Hegre, H., K. Petrova and N. von Uexkull, 2020: Synergies and trade-offs in 
reaching the sustainable development goals. Sustainability, 12(20), 
doi:10.3390/su12208729.
Heigl, F ., et  al., 2019: Opinion: toward an international definition of citizen 
science. Proc. Natl. Acad. Sci., 116(17), 8089–8092.Heinrichs, H., 2020: Artful sustainability governance—foundational 
considerations on sensory-informed policymaking for sustainable 
development. Sustain. Dev., 28(4), 791–799, doi:10.1002/sd.2029.
Hémous, D., 2016: The dynamic impact of unilateral environmental policies. 
J Int Econ, 103, 80–95, doi:10.1016/j.jinteco.2016.09.001.
Hepburn, C., et al., 2020: Will COVID-19 fiscal recovery packages accelerate or 
retard progress on climate change? Oxf. Rev. Econ. Policy, 36. Doi: 10.1093/
oxrep/graa015.
Herrero, M., et  al., 2016: Climate change and pastoralism: impacts, 
consequences and adaptation: -EN- -FR- Le changement climatique et le

pastoralisme : effets, conséquences et adaptation -ES- Cambio climático y 
pastoreo: efectos directos, repercusiones y adaptación. Revue Sci. Et Tech. De 
L’oie, 35(2), 417–433.
Hewitt, C.D., et  al., 2020: Making society climate resilient: international 
progress under the global framework for climate services. Bull. Am. Meteorol. 
Soc., 101(2), E237–E252.
Heyen, A., L. Hermwille and T. Wehnert, 2017: Out of the comfort zone! 
Governing the exnovation of unsustainable technologies and practices. 
GAIA - Ecol. Perspect. Sci. Soc., 26(4), 326–331, doi:10.14512/gaia.26.4.9.
Hickel, J., 2017: Is global inequality getting better or worse? A critique of the 
World Bank’s convergence narrative. Third World Q, 38(10), 2208–2222, doi
:10.1080/01436597.2017.1333414.
Hickel, J., 2019: Is it possible to achieve a good life for all within planetary 
boundaries? Third World Q, 40(1), 18–35, doi:10.1080/01436597.2018.15
35895.

Hickel, J., et al., 2021: Urgent need for post-growth climate mitigation scenarios. 
Nat. Energy, 6(8), 766–768, doi:10.1038/s41560-021-00884-9.
Hickel, J. and G. Kallis, 2020: Is green growth possible? New Polit. Econ., 25(4), 
469–486, doi:10.1080/13563467.2019.1598964.
Hildingsson, R., A. Kronsell and J. Khan, 2019: The green state and industrial 
decarbonisation. Env Polit, 28(5), 909–928, doi:10.1080/09644016.2018.1
488484.
Hilmi, N., et al., 2017: The effects of energetic transition on economic growth in 
Iran. Marmara İktisat Dergisi, 1(1), 77–93.
Hilmi, N. and A. Safa, 2010: The economic and financial policies to fight against 
climate change: the case of Maghreb. MEEA Online J., 12. Available at: 
https://ecommons.luc.edu/meea/120/.
Hilmi, N., A. Safa, N. Peridy and M. Zemouri, 2015: Taking into account 
sustainable development for MENA countries: The calculation of a modified 
HDI index. Top. Middle eastern North African Econ., 17. Available at: https://

meea.sites.luc.edu/volume17/pdfs/Hilmi-Safa-Peridy-Zemouri.pdf .
Hirpa, F .A., et al., 2018: Finding sustainable water futures in data-sparse regions 
under climate change: Insights from the Turkwel River basin, Kenya. J. Hydrol. 
Reg. Stud., 19, 124–135. Doi: 10.1016/j.ejrh.2018.08.005
Hjerpe, M., S. Storbjörk and J. Alberth, 2015: “There is nothing political in it”: 
triggers of local political leaders’ engagement in climate adaptation. Local 
Environ, 20(8), 855–873, doi:10.1080/13549839.2013.872092.
Hoagland, S.J., 2017: Integrating traditional ecological knowledge with western 
science for optimal natural resource management. IK: Other Ways Knowing, 
3(1), 1–15, doi:10.18113/P8ik359744.
Hoegh-Guldberg, O., R. Cai, E.S. Poloczanska, P .G. Brewer, S. Sundby, K. Hilmi, 
V.J. Fabry, and S. Jung, 2014: The Ocean. In: Climate Change 2014: Impacts, 
Adaptation, and Vulnerability. Part B: Regional Aspects: Working Group II

Contribution to the Fifth Assessment Report of the Intergovernmental Panel 
on Climate Change [Barros, V. R., C. B. Field, D. J. Dokken, M. D. Mastrandrea, 
K. J. Mach, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y . O. Estrada, R. C. Genova, B. 
Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P . R. Mastrandrea and L. L. 
White (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY , 
USA, pp. 1655–1731. ISBN 9781107058163.
Hoegh-Guldberg, O., et al., 2019: The Ocean as a Solution to Climate Change: 
Five Opportunities for Action. Available at: https://www.oceanpanel.org/
climate (accessed 21/01/2022).

18
2750Chapter 18 Climate Resilient Development Pathways
Hoekstra, A., M. Steinbuch and G. Verbong, 2017: Creating agent-based energy 
transition management models that can uncover profitable pathways to 
climate change mitigation. Complexity. Doi: 10.1155/2017/1967645.
Holden, E., et al., 2017: The Imperatives of Sustainable Development. Routledge, 
ISBN 978-0203022177.
Hölscher, K., N. Frantzeskaki and D. Loorbach, 2019: Steering transformations 
under climate change: capacities for transformative climate governance and 
the case of Rotterdam, the Netherlands. Reg Environ Change, 19(3), 791–
805, doi:10.1007/s10113-018-1329-3.
Hölscher, K., J.M. Wittmayer and D. Loorbach, 2018: Transition versus 
transformation: What’s the difference? Environ. Innov. Soc. Transit., 27, 1–3, 
doi:10.1016/j.eist.2017.10.007.
Holvoet, N. and L. Inberg, 2014: Gender sensitivity of sub-saharan africa 
national adaptation programmes of action: findings from a desk review of

31 countries. Clim. Dev., 6(3), 266–276.
Holzer, J.M., N. Carmon and D.E. Orenstein, 2018: A methodology for evaluating 
transdisciplinary research on coupled socio-ecological systems. Ecol Indic, 
85, 808–819, doi:10.1016/j.ecolind.2017.10.074.
Honohan, P ., 2019: Should Monetary Policy Take Inequality and Climate Change 
into Account? Working Paper Series, Peterson Institute for International 
Economics , Accessed 2021-08-26.
Hopkins, P ., 2019: Social geography I: intersectionality. Prog Hum Geogr, 43(5), 
937–947, doi:10.1177/0309132517743677.
Horcea-Milcu, A.-I., et  al., 2019: Values in transformational sustainability 
science: four perspectives for change. Sustain Sci, 14(5), 1425–1437, 
doi:10.1007/s11625-019-00656-1.
Houser, M., R. Gunderson and D. Stuart, 2019: Farmers’ perceptions of climate 
change in context: toward a political economy of relevance. Sociol Ruralis, 
59(4), 789–809, doi:10.1111/soru.12268.

Hov, Ø., et al., 2017: Five priorities for weather and climate research. Nature, 
552(7684), 168–170.
Howells, M., et  al., 2013: Integrated analysis of climate change, land-use, 
energy and water strategies. Nat. Clim. Chang., 3(7), 621–626, doi:10.1038/
nclimate1789.
Howlett, M.P . and K. Saguin, 2018: Policy Capacity for Policy Integration: 
Implications for the Sustainable Development Goals. Available at: https://
www.researchgate.net/profile/Kidjie-Saguin/publication/324660470_
Policy_Capacity_for_Policy_Integration_Implications_for_the_Sustainable_
Development_Goals/links/5b43ec5c458515f71cb88d56/Policy-Capacity-for-
Policy-Integration-Implications-for-the-Sustainable-Development-Goals.pdf  
(accessed 21/01/2022).
Huaman, E.S. and B. Sriraman, 2015: Indigenous Innovation: Universalities and 
Peculiarities. Springer, Berlin, 214. ISBN 978-9463002264.
Huang, P ., V. Castán Broto, Y . Liu and H. Ma, 2018: The governance of

urban energy transitions: A comparative study of solar water heating 
systems in two Chinese cities. J Clean Prod, 180, 222–231, doi:10.1016/j.
jclepro.2018.01.053.
Huang, R. and X. Sun, 2020: Dual mediation and success of environmental 
protests in China: a qualitative comparative analysis of 10 cases. Soc. Mov 
Stud., 19(4), 408–425, doi:10.1080/14742837.2019.1682539.
Huitema, D., et al., 2016: The governance of adaptation: choices, reasons, and 
effects. Introduction to the Special Feature. Ecol. Soc., 21(3), doi:10.5751/
ES-08797-210337.
Hulme, M., 2018: “Gaps” in climate change knowledge. Environ. Humanit., 10, 
330–337, doi:10.1215/22011919-4385599.
Hulme, M., 2019: Climate Emergency Politics Is Dangerous. Issues in Science 
and Technology, 36(1), 23–25.
Human Development Report Office, 2020: Human Development Report 2020: 
The Next Frontier Human development and the Anthropocene. United 
Nations Development Programme, New York, http://hdr.undp.org/sites/

default/files/hdr2020.pdf (accessed 21/01/2022). (412 pp).
Hunt, D., et al., 2012: Scenario archetypes: converging rather than diverging 
themes. Sustainability, 4, 740–772, doi:10.3390/su4040740.Huntington, H.P ., et al., 2017: How small communities respond to environmental 
change: patterns from tropical to polar ecosystems. Ecol. Soc., 22(3).
Huntjens, P . and T. Zhang, 2016: Climate justice: equitable and inclusive 
governance of climate action. Working Paper, Vol. 16. The Hague Institute, 
The Hague, Netherlands.
Hurlbert, M., J. Krishnaswamy, E. Davin, F .X. Johnson, C.F . Mena, J. Morton, S. 
Myeong, D. Viner, K. Warner, A. Wreford, S. Zakieldeen, Z. Zommers, 2019: Risk 
management and decision-making in relation to sustainable development. 
In: Climate Change and Land: an IPCC special report on climate change, 
desertification, land degradation, sustainable land management, food 
security, and greenhouse gas fluxes in terrestrial ecosystems [Shukla, P . R.,

J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, 
P . Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. 
Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P . Vyas, E. Huntley, K. Kissick, 
M. Belkacemi and J. Malley (eds.)], In press.
Huxham, M., et  al., 2015: Applying climate compatible development and 
economic valuation to coastal management: a case study of Kenya’s 
mangrove forests. J. Environ. Manag., 157, 168–181, doi:10.1016/j.
jenvman.2015.04.018.
Ide, T., 2020: The dark side of environmental peacebuilding. World Dev, 127, 
104777, doi:10.1016/j.worlddev.2019.104777.
IEA, 2015: Energy and climate change, world energy outlook special report. 
International Energy Agency, Paris. Available at: https://www.actu-
environnement.com/media/pdf/news-24754-rapport-aie.pdf (accessed 
IEA, 2017: Energy Technology Perspectives 2017. International Energy

Agency (IEA), Available at: https://www.iea.org/reports/energy-technology-
perspectives-2017 (accessed 21/01/2022).
IEA, 2019: Material efficiency in clean energy transitions. International Energy 
Agency (IEA).
IEA, 2020: Global Energy Review 2020: The impacts of the Covid-19 crisis on 
global energy demand and CO2 emissions. International Energy Agency 
(IEA), Paris.
IEA, et  al., 2019: Tracking SDG 7: The Energy Progress Report 2019. https://
trackingsdg7.esmap.org/ (accessed 21/01/2022).
Ikeda, S. and S. Managi, 2019: Future inclusive wealth and human well-being 
in regional Japan: projections of sustainability indices based on shared 
socioeconomic pathways. Sustain Sci, 14(1), 147–158, doi:10.1007/s11625-
018-0589-7.
ILO, 1989: Indigenous and Tribal Peoples Convention, C169. International 
Labour Organisation, Geneva.
IMF , 2017: Reigniting growth in low-income and emerging market economies: 
what role can structural reforms play? World Economic Outlook. Available

at: https://www.imf.org/~/media/Files/Publications/WEO/2019/October/
English/Ch3.ashx (accessed 21/01/2022). .
IMF , 2019: Fiscal Monitor: How to Mitigate Climate Change. International 
Monetary Fund, Washington, DC.
IMF , 2021: Fiscal Policies to Address Climate Change in Asia and the Pacific. 
International Monetary Fund, Washington, DC.
IMF and OECD, 2021: Tax Policy and Climate Change: IMF/OECD Report for 
the G20 Finance Ministers and Central Bank Governors. Available at: https://
www.oecd.org/tax/tax-policy/imf-oecd-g20-report-tax-policy-and-climate-
change.htm (accessed 21/01/2022).
Inaotombi, S. and P .C. Mahanta, 2019: Pathways of socio-ecological resilience 
to climate change for fisheries through indigenous knowledge. Hum. Ecol. 
Risk Assessment: Int. J., 25(8), 2032–2044, doi:10.1080/10807039.2018.1
482197.
Iñigo, E.A. and L. Albareda, 2016: Understanding sustainable innovation as a 
complex adaptive system: a systemic approach to the firm. J Clean Prod,

126, 1–20, doi:10.1016/j.jclepro.2016.03.036.
IPBES, 2019: Summary for policymakers of the global assessment report on 
biodiversity and ecosystem services of the Intergovernmental Science-
Policy Platform on Biodiversity and Ecosystem Services. Secretariat of the

18
2751Climate Resilient Development Pathways  Chapter 18
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem 
Services (IPBES), Bonn, Germany, ISBN 978-3947851133. 56 pp.
IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance 
Climate Change Adaptation. A Special Report of Working Groups I and II of 
the Intergovernmental Panel on Climate Change [Field, C. B., V. Barros, T. F . 
Stocker, Q. Dahe, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. 
Plattner, S. K. Allen, M. Tignor and P . M. Midgley (eds.)]. Cambridge University 
Press, Cambridge, UK and New York, NY , USA, 582 pp. ISBN 9781107025066.
IPCC, 2014a: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part 
B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment 
Report of the Intergovernmental Panel on Climate Change [Barros, V. R., C. 
B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, M. Chatterjee,

K. L. Ebi, Y . O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. 
Press, Cambridge, UK and New York, NY , USA, 688 pp. ISBN 9781107058163.
IPCC, 2014b: Summary for Policymakers. In: Climate Change 2014: Impacts, 
Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. 
Contribution of Working Group II to the Fifth Assessment Report of the 
Intergovernmental Panel on Climate Change [Field, C. B., V. R. Barros, D. J. 
Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y . O. 
Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P . R. 
UK and New York, NY , USA, pp. 1-32. ISBN 9781107058071.
IPCC, 2018a: Global warming of 1.5 C. An IPCC Special Report on the impacts 
of global warming of 1.5°C above pre-industrial levels and related global

greenhouse gas emission pathways, in the context of strengthening the 
global response to the threat of climate change, sustainable development, 
and efforts to eradicate poverty [Masson, D., P . Zhai, H. O. Pörtner, D. Roberts, 
J. Skea, P . R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, 
S. Connors, J. B. R. Matthews, Y . Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. 
Maycock, M. Tignor and Waterfield (eds.)]. Organization, W. M., Geneva, 
Switzerland, 32 pp.
IPCC, 2018b: Summary for Policymakers. In: Global Warming of 1.5°C. An 
the context of strengthening the global response to the threat of climate 
Delmotte, V., P . Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P . R. Shukla, A. Pirani,

IPCC, 2019a: Annex I: Glossary [Weyer, N.M. (ed.)]. In: IPCC Special Report on the 
Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D. C. Roberts, 
V. Masson-Delmotte, P . Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. 
Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In 
press.
IPCC, 2019b: Climate Change and Land: an IPCC special report on climate 
change, desertification, land degradation, sustainable land management, 
food security, and greenhouse gas fluxes in terrestrial ecosystems [Shukla, P . 
R., J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, 
Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P . Vyas, E. Huntley, K. Kissick,

M. Belkacemi and J. Malley (eds.)]. In press.
IPCC, 2019c: Climate Change and Land: IPCC Special Report on Climate Change, 
Desertification, Land Degradation, Sustainable Land Management, Food 
Security, and Greenhouse gas fluxes in Terrestrial Ecosystems [Shukla, P . R., 
J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H. O. Pörtner, D. C. Roberts, 
M. Belkacemi and J. Malley (eds.)]. Available at: https://www.ipcc.ch/srccl-
report-download-page/ (accessed 2019/10/2).
IPCC, 2019d: IPCC Special Report on the Ocean and Cryosphere in a Changing 
Climate [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P . Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. 
Rama and N. M. Weyer (eds.)]. In press.
IPCC, 2019e: Special Report on the Ocean and Cryosphere in a Changing

Climate [Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P . Zhai, M. Tignor, 
E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. 
Rama and N. M. Weyer (eds.)]. Available at: https://www.ipcc.ch/srocc/home/ 
(accessed 2019/10/2).
IPCC, 2019 f: Summary for Policymakers. In: Climate Change and Land: an 
IPCC special report on climate change, desertification, land degradation, 
sustainable land management, food security, and greenhouse gas fluxes in 
terrestrial ecosystems [Shukla, P . R., J. Skea, E. Calvo Buendia, V. Masson-
Delmotte, H.-O. Pörtner, D. C. Roberts, P . Zhai, R. Slade, S. Connors, R. van 
Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. 
Portugal Pereira, P . Vyas, E. Huntley, K. Kissick, M. Belkacemi and J. Malley 
(eds.)]. In press.
IPCC, 2019g: Technical Summary. In: IPCC Special Report on the Ocean and 
Cryosphere in a Changing Climate [Pörtner, H.-O., D. C. Roberts, V. Masson-

Delmotte, P . Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. 
Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In press.
IPCC, 2021a: Climate Change 2021: The Physical Science Basis. Contribution of 
Working Group I to the Sixth Assessment Report of the Intergovernmental 
Panel on Climate Change. [Masson-Delmotte, V., P . Zhai, A. Pirani, S.L. 
Connors, C. Péan, S. Berger, N. Caud, Y . Chen, L. Goldfarb, M.I. Gomis, M. 
Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, 
O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.
IPCC, 2021b: Summary for Policymakers. In: Climate Change 2021: The Physical 
Science Basis. Contribution of Working Group I to the Sixth Assessment Report 
of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P . 
Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y . Chen, L. Goldfarb,

M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. 
Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. 
In Press.
Isgren, E., C.S. Boda, D. Harnesk and D. O’Byrne, 2019: Science has much to 
offer social movements in the face of planetary emergencies. Nat. Ecol. Evol., 
3(11), 1498–1498.
Islam, S., C. Chu and J.C.R. Smart, 2020: Challenges in integrating disaster risk 
reduction and climate change adaptation: Exploring the Bangladesh case. 
Int. J. Disaster Risk Reduct., 47, 101540, doi:10.1016/j.ijdrr.2020.101540.
ITK, 2019: National Inuit Climate Change Strategy, Inuit Tapiriit Kanatami (ITK), 
Ottawa. Accessed 2021-09-02.
https://www.itk.ca/wp-content/uploads/2019/06/ITK_Climate-Change-
Strategy_English.pdf
Ives, C., R. Freeth and J. Fischer, 2019: Inside-out sustainability: the neglect of 
inner worlds. Ambio, 49, doi:10.1007/s13280-019-01187-w.
Ives, C.D., et  al., 2017: Human–nature connection: a multidisciplinary

review. Curr. Opin. Environ. Sustain., 26-27, 106–113, doi:10.1016/j.
cosust.2017.05.005.
Jabeen, H., 2014: Adapting the built environment: the role of gender in shaping 
vulnerability and resilience to climate extremes in Dhaka. environ urban, 
26(1), 147–165, doi:10.1177/0956247813517851.
Jabeen, H. and S. Guy, 2015: Fluid engagements: responding to the co-evolution 
of poverty and climate change in Dhaka, Bangladesh. Habitat Int., 47, 307–
314, doi:10.1016/j.habitatint.2015.02.005.
Jackson, E.A., 2016: Ontological and Epistemological Discourse(s) on 
Sustainable Development: Perspective on Sierra Leone in the Aftermath of 
a Decade of Civil Unrest. Social Science Research Network, Rochester, NY , 
https://papers.ssrn.com/abstract=2811522. Accessed 2020.
Jackson, M., 2014: Composing postcolonial geographies: postconstructivism, 
ecology and overcoming ontologies of critique. Singap J Trop Geogr, 35(1), 
72–87, doi:10.1111/sjtg.12052.

Jackson, T., 2009: Prosperity Without Growth: Economics for a Finite Planet. 
Earthscan, UK, USA, ISBN 978-1-84407-894-3.

18
2752Chapter 18 Climate Resilient Development Pathways
Jackson, T., 2021: Post Growth: Life after Capitalism, 1st edn., Polity, Cambridge, 
UK Medford, MA, USA, ISBN 978-1509542529. 256 pp.
Jafry, T., 2016: Making the case for gender sensitive climate policy – lessons 
from South Asia/IGP . Int. J. Clim. Change Strateg. Manag., 8(4), 559–577, 
doi:10.1108/IJCCSM-04-2015-0049.
Jafry, T., M. Mikulewicz and K. Helwig, 2018: Introduction: justice in the era 
of climate change. In: Routledge Handbook of Climate Justice, pp. 1–9. 
Routledge, London.
Janke, T., 2018: Protecting indigenous cultural expressions in Australia and New 
Zealand: Two decades after the ‘Mataatua Declaration and our culture, our 
future’. Intellect. Prop. Forum: J. Intellect. Ind. Prop. Soc. Aust. N. Z., 114, 
21–30, doi:10.3316/informit.204204436835200.
Jendrzejewski, B., 2020: Bioeconomic modelling – An application of 
environmentally adjusted economic accounts and the computable general

equilibrium model. Land Use Policy, 92, Mar 1;92:104431.
Jenkins, K., 2017: Women anti-mining activists’ narratives of everyday resistance 
in the Andes: staying put and carrying on in Peru and Ecuador. Gend. Place 
Cult., 24(10), 1441–1459, doi:10.1080/0966369X.2017.1387102.
Jenkins, K., B.K. Sovacool and D. McCauley, 2018: Humanizing sociotechnical 
transitions through energy justice: An ethical framework for global 
transformative change. Energy Policy, 117, 66–74, doi:10.1016/j.
enpol.2018.02.036.
Jernnäs, M. and B.-O. Linnér, 2019: A discursive cartography of nationally 
determined contributions to the Paris climate agreement. Glob. Environ. 
Chang., 55, 73–83, doi:10.1016/j.gloenvcha.2019.01.006.
Joakim, E.P ., L. Mortsch and G. Oulahen, 2015: Using vulnerability and resilience 
concepts to advance climate change adaptation. Environ. Hazards, 14(2), 
137–155, doi:10.1080/17477891.2014.1003777.
Johansson, T., A. Patwardhan, N. Nakicenovic and L. Gomez-Echeverri, 2012:

Global Energy Assessment: Toward a Sustainable Future. Cambridge 
University Press, Cambridge UK and New York, NY , USA and the International 
Institute for Applied Systems Analysis, Laxenburg, Austria. ISBN 978-
0521182935.
Johnstone, P . and P . Newell, 2018: Sustainability transitions and the state. 
Environ. Innov. Soc. Transit., 2018 Jun 1;27:72-82.
Jones, L., et al., 2014: Exploring the role of climate science in supporting long-
term adaptation and decision-making in sub-Saharan Africa. CDKN, 2014.
Jordan, J.C., 2019: Deconstructing resilience: why gender and power matter in 
responding to climate stress in Bangladesh. Clim. Dev., 11(2), 167–179, doi:
10.1080/17565529.2018.1442790.
Jørgensen, M.S., U. Jørgensen and J.S. Jensen, 2017: Navigations and 
governance in the Danish energy transition reflecting changing Arenas of 
Development, controversies and policy mixes. Energy Res. Soc. Sci., 33, 173–
185, doi:10.1016/j.erss.2017.09.034.

Jørgensen, U., 2012: Mapping and navigating transitions—the multi-level 
perspective compared with arenas of development. Res Policy, 41(6), 996–
1010, doi:10.1016/j.respol.2012.03.001.
Jost, C., et  al., 2016: Understanding gender dimensions of agriculture and 
climate change in smallholder farming communities. Clim. Dev., 8(2), 133–
144.
Juhola, S., E. Glaas, B.-O. Linnér and T.-S. Neset, 2015: Redefining maladaptation. 
Environ. Sci. Policy, 2016, 135–140, doi:10.1016/j.envsci.2015.09.014.
Kabir, M. and R. Salim, 2019: Valuation of subsoil minerals in Bangladesh: An 
application of the system of environmental-economic accounting. Resour. 
Policy, 62, 193–204.
Kabubo-Mariara, M.J., 2009: Adaptation to Climate Change and Livestock 
Biodiversity: Evidence from Kenya. In: Conserving and Valuing Ecosystem 
Services and Biodiversity: Economic, Social and Institutional Challenges in 
the New Millennium [Ninan, K.N.(ed.)]. Earthscan, pp. 345–369.

Kakenmaster, W., 2019: Articulating resistance: Agonism, radical 
democracy and climate change activism. Millennium, 47(3), 373–397, 
doi:10.1177/0305829819839862.Kalafatis, S., 2017: Identifying the potential for climate compatible development 
efforts and the missing links. Sustainability, 9(9), 1642.
Kallis, G., 2011: In defence of degrowth. Ecol. Econ., 70(5), 873–880.
Kallis, G., 2018: Degrowth. Agenda Publishing, Newcastle upon Tyne, UK, ISBN 
978-1911116806. 176 pp.
Kallis, G., J. Martinez-Alier and R.B. Norgaard, 2009: Paper assets, real debts: An 
ecological-economic exploration of the global economic crisis. Crit. Perspect. 
Int. Bus., 5(1/2), 14–25.
Kamal Uddin, A.M., et  al., 2006: Climate resilient development: country 
framework to mainstream climate risk management and adaptation : 
working paper. Climate Change Cell, Dept. of Environment, Dhaka, ISBN 
978-9843237040.
Kamau, M., P . Chasek and D. O’Connor, 2018: Transforming Multilateral

Diplomacy: The Inside Story of the Sustainable Development Goals, 1st edn., 
Routledge, London New York, ISBN 978-0813350868. 366 pp.
Kandlikar, M., C. Reynolds and A. Grieshop, 2009: A Perspective Paper on 
Black Carbon Mitigation as a Response to Climate Change. Copenhagen 
Consensus Center, 2009.
Karlsson, M., E. Alfredsson and N. Westling, 2020: Climate policy co-benefits: a 
review. Clim. Policy, 20(3), 292–316, doi:10.1080/14693062.2020.1724070.
Kasdan, M., L. Kuhl and P . Kurukulasuriya, 2021: The evolution of transformational 
change in multilateral funds dedicated to financing adaptation to climate 
change. Clim. Dev., 13(5), 427–442, doi:10.1080/17565529.2020.1790333.
Kattumuri, R., D. Ravindranath and T. Esteves, 2017: Local adaptation strategies 
in semi-arid regions: study of two villages in Karnataka, India. Clim. Dev., 
9(1), 36–49.
Kaya, H.O. and M. Chinsamy, 2016: Community-based environmental resource 
management systems for sustainable livelihood and climate change

adaptation: a review of best practices in africa. J. Soc. Sci., 46(2), 123–129, 
doi:10.1080/09718923.2016.11893519.
Kaza, S., L. Yao, P . Bhada-Tata and F .V. Woerden, 2018: What a Waste 2.0: 
A Global Snapshot of Solid Waste Management to 2050. World Bank 
Publications, Washington, DCISBN 978-1464813474. 230 pp.
https://openknowledge.worldbank.org/handle/10986/30317
Keith, H., M. Vardon, J.A. Stein and D. Lindenmayer, 2019: Contribution of 
native forests to climate change mitigation – A common approach to carbon 
accounting that aligns results from environmental-economic accounting 
with rules for emissions reduction. Environ. Sci. Policy, 93, 189–199.
Kenis, A. and M. Lievens, 2016: Imagining the carbon neutral city: the (post)
politics of time and space. Environ. Plan. A., doi:10.1177/0308518X16680617.
Kenis, A. and E. Mathijs, 2012: Beyond individual behaviour change: the role of 
power, knowledge and strategy in tackling climate change. Environ. Educ.

Res., 18(1), 45–65, doi:10.1080/13504622.2011.576315.
Kenis, A. and E. Mathijs, 2014: Climate change and post-politics: repoliticizing 
the present by imagining the future? Geoforum, 52, 148–156, doi:10.1016/j.
geoforum.2014.01.009.
Kermoal, N. and I. Altamirano-Jiménez, 2016: Kermoal and Altamirano-Jiménez, 
2016. Athabasca University Press, Athabasca.
Khanal, U., et  al., 2021: Smallholder farmers’ adaptation to climate change 
and its potential contribution to UN’s sustainable development goals of 
zero hunger and no poverty. J  Clean Prod, 281, 124999, doi:10.1016/j.
jclepro.2020.124999.
Khatri, D.B., 2018: Climate and development at the third pole. https://pub.
epsilon.slu.se/15564/. Accessed 09 Sep 2020.
Kim, Y ., M.V. Chester, D.A. Eisenberg and C.L. Redman, 2019: The infrastructure 
trolley problem: positioning safe-to-fail infrastructure for climate change 
adaptation. Earth’s Future, 7(7), 704–717, doi:10.1029/2019EF001208.

Kim, Y ., et al., 2017a: Fail-safe and safe-to-fail adaptation: decision-making for 
urban flooding under climate change. Clim Change, doi:10.1007/s10584-
017-2090-1.
Kim, Y ., et  al., 2017b: A perspective on climate resilient development and 
national adaptation planning based on USAID’s experience. Clim. Dev., 9(2), 
141–151.

18
2753Climate Resilient Development Pathways  Chapter 18
Kingsborough, A., E. Borgomeo and J.W. Hall, 2016: Adaptation pathways in 
practice: Mapping options and trade-offs for London’s water resources. 
Sustain. Cities Soc., 27, 386–397, doi:10.1016/j.scs.2016.08.013.
Kirchhoff, C.J., M. Carmen Lemos and S. Dessai, 2013: Actionable knowledge 
for environmental decision making: broadening the usability of climate 
science. Annu. Rev. Environ. Resour., 38(1), 393–414, doi:10.1146/annurev-
environ-022112-112828.
Kivimaa, P ., W. Boon, S. Hyysalo and L. Klerkx, 2019a: Towards a typology of 
intermediaries in sustainability transitions: A systematic review and a research 
agenda. Res Policy, 48(4), 1062–1075, doi:10.1016/j.respol.2018.10.006.
Kivimaa, P ., et  al., 2019b: Passing the baton: how intermediaries advance 
sustainability transitions in different phases. Environ. Innov. Soc. Transit., 31, 
110–125, doi:10.1016/j.eist.2019.01.001.

Kivimaa, P . and M. Martiskainen, 2018: Dynamics of policy change and 
intermediation: the arduous transition towards low-energy homes in 
the United Kingdom. Energy Res. Soc. Sci., 44, 83–99, doi:10.1016/j.
erss.2018.04.032.
Kivimaa, P ., E. Primmer and J. Lukkarinen, 2020: Intermediating policy for 
transitions towards net-zero energy buildings. Environ. Innov. Soc. Transit. 
https://doi.org/10.1016/j.eist.2020.01.007
Klein, N., 2020: On fire: the (burning) case for a green new deal. Simon 
& Schuster, ISBN 978-1982129927. https://doi.org/10.3197/09632712
0x15916910310572
Klein, R. J. T. et  al., 2007: Inter-relationships between adaptation and 
mitigation. In: Climate Change 2007: Impacts, Adaptation and Vulnerability. 
Contribution of Working Group II to the Fourth Assessment Report of the 
Intergovernmental Panel on Climate Change [Parry, M. L., O. F . Canziani, J. P . 
Palutikof, P . J. van der Linden and C. E. Hanson (eds.)]. Cambridge University

Press, Cambridge, UK, pp. 745–777. ISBN 9780521880107.
Klepp, S. and J. Herbeck, 2016: The politics of environmental migration and 
climate justice in the Pacific region. J. Hum. Rights Environ., 7, 54–73, 
doi:10.4337/jhre.2016.01.03.
Kleres, J. and Å. Wettergren, 2017: Fear, hope, anger, and guilt in climate 
activism. Soc. Mov Stud., 1–13, doi:10.1080/14742837.2017.1344546.
Klinsky, S., et al., 2017: Why equity is fundamental in climate change policy 
research. Glob. Environ. Chang., 44, 170–173.
Klinsky, S. and H. Winkler, 2018: Building equity in: strategies for integrating 
equity into modelling for a 1.5°C world. Philos. Trans. Royal Soc. A: Math. 
Phys. Eng. Sci., 376(2119), 20160461.
Koch, M., 2020: Structure, action and change: a Bourdieusian perspective on 
the preconditions for a degrowth transition. Sustain. Sci. Pract. Policy, 16(1), 
4–14, doi:10.1080/15487733.2020.1754693.
Kochore, H.H., 2016: The road to Kenya?: Visions, expectations and anxieties

around new infrastructure development in Northern Kenya. J. East. African 
Stud., 10(3), 494–510.
Köhler, J., et al., 2019: An agenda for sustainability transitions research: State 
of the art and future directions. Environ. Innov. Soc. Transit., 31, 1–32, 
doi:10.1016/j.eist.2019.01.004.
Kohler, P ., S. Renggli and C. Lüthi, 2019: WASH and gender in health care 
facilities: the uncharted territory. Health. Care. Women. Int., 40(1), 3–12.
Komendantova, N., et al., 2016: Multi-risk approach and urban resilience. Int. J. 
Disaster Resil. Built Environ., 7, 114–132, doi:10.1108/IJDRBE-03-2015-0013.
Kookana, R.S., et al., 2016: Groundwater scarcity impact on inclusiveness and 
women empowerment: Insights from school absenteeism of female students 
in two watersheds in India. Int. J. Incl. Educ., 20(11), 1155–1171.
Kothari, A., F . Demaria and A. Acosta, 2014: Buen Vivir, degrowth and ecological 
Swaraj: alternatives to sustainable development and the green economy.

Development, 57(3), 362–375, doi:10.1057/dev.2015.24.
Koubi, V., 2019: Climate change and conflict. Annu. Rev. Polit. Sci., 22(1), 343–
360, doi:10.1146/annurev-polisci-050317-070830.
Krampe, F ., E.S. Smith and M.D. Hamidi, 2021: Security implications of climate 
development in conflict-affected states implications of local-level effects of rural hydropower development on farmers in Herat. Polit Geogr, 90, 102454, 
doi:10.1016/j.polgeo.2021.102454.
Kriegler, E., et al., 2018: Pathways limiting warming to 1.5°C: a tale of turning 
around in no time? Philos. Trans. Royal Soc. A: Math. Phys. Eng. Sci., 
376(2119), 20160457, doi:10.1098/rsta.2016.0457.
Kristjanson, P ., et  al., 2017: Addressing gender in agricultural research for 
development in the face of a changing climate: where are we and where 
should we be going? Int. J. Agric. Sustain., 15(5), 482–500.
Krogstrup, S. and W. Oman, 2019: Macroeconomic and Financial Policies for

Climate Change Mitigation: A Review of the Literature. IMF Working Papers, 
Vol. 19. doi:10.5089/9781513511955.001.
Krueger, P ., Z. Sautner and L.T. Starks, Rochester, NY: The Importance of Climate 
Risks for Institutional Investors. Social Science Research Network, https://
papers.ssrn.com/abstract=3235190.Accessed 2019.
Krusell, P . and A. Smith, 2018: Climate Change Around the World. Slides from 
ifo Institute Workshop on “Heterogeneous Agents and the Macroeconomics 
of Climate Change”.
Kubiszewski, I., et  al., 2013: Beyond GDP: Measuring and achieving global 
genuine progress. Ecol. Econ., 93, 57–68.
Kuenkel, P ., 2019: Stewarding Sustainability Transformations: An Emerging 
Theory and Practice of SDG Implementation. Springer International 
Publishing, Switzerland. ISBN 978-3030036904.
Kull, M., 2016: Multi-level governance and structural constructivism–
understanding multilevel politics in the European Union. In: European

Integration and Rural Development. Routledge, London and New York.
Kuokkanen, A., V. Uusitalo and K. Koistinen, 2019: A framework of disruptive 
sustainable innovation: an example of the Finnish food system. Technol. 
Analysis Strateg. Manag., 31(7), 749–764, doi:10.1080/09537325.2018.15
50254.
Kuper, M., H. Amichi and P .-L. Mayaux, 2017: Groundwater use in North Africa 
as a cautionary tale for climate change adaptation. Water Int., 42(6), 725–
740, doi:10.1080/02508060.2017.1351058.
Kuzemko, C., A. Lawrence and M. Watson, 2019: New directions in the 
international political economy of energy. Rev. Int. Polit. Econ., 26(1), 1–24, 
doi:10.1080/09692290.2018.1553796.
Labriet, M., et al., 2015: Worldwide impacts of climate change on energy for 
heating and cooling. Mitig Adapt Strateg Glob Change, 20(7), 1111–1136.
Lacroix, K. and R. Gifford, 2017: Psychological barriers to energy conservation 
behavior: the role of worldviews and climate change risk perception. Environ

Behav, doi:10.1177/0013916517715296.
Lahn, B., 2018: In the light of equity and science: scientific expertise and climate 
justice after Paris. Int. Environ. Agreements: Polit. Law Econ., 18(1), 29–43.
Lahsen, M. and E. Turnhout, 2021: How norms, needs, and power in science 
obstruct transformations towards sustainability. Environ. Res. Lett., 16(2), 
25008, doi:10.1088/1748-9326/abdcf0.
Lam, D.P .M., et  al., 2020: Indigenous and local knowledge in sustainability 
transformations research: a literature review. Ecol. Soc., 25(1), doi:10.5751/
ES-11305-250103.
Lamsal, P ., L. Kumar, K. Atreya and K.P . Pant, 2017: Vulnerability and impacts 
of climate change on forest and freshwater wetland ecosystems in Nepal: A 
review. Ambio, 46(8), 915–930, doi:10.1007/s13280-017-0923-9.
Langlois, A.J., 2014: Social connection & political responsibility: an engagement 
with Iris Marion Young. St Antony’s Int. Rev., 10(1), 43–63.

Larik, J.E., et  al., 2017: Blue growth and sustainable development in Indian 
ocean governance. The hague Inst. Glob. Justice Policy Brief.
Latour, B., D. Milstein, I. Marrero-Guillamón and I. Rodríguez-Giralt, 2018: 
Down to earth social movements: an interview with Bruno Latour. Soc. Mov 
Stud., 17(3), 353–361, doi:10.1080/14742837.2018.1459298.
Lau, J.D., D. Kleiber, S. Lawless and P .J. Cohen, 2021: Gender equality in climate 
policy and practice hindered by assumptions. Nat. Clim. Chang., 11(3), 186–
192, doi:10.1038/s41558-021-00999-7.

18
2754Chapter 18 Climate Resilient Development Pathways
Lawrence, J., et al., 2015: Adapting to changing climate risk by local government 
in New Zealand : institutional practice barriers and enablers. Local Environ, 
20(3), 298–320.
Le Blanc, D., 2015: Towards integration at last? The sustainable development 
goals as a network of targets: the sustainable development goals as a 
network of targets. Sustain. Dev., 23(3), 176–187, doi:10.1002/sd.1582.
Le, T.-H., Y . Chang and D. Park, 2016: Trade openness and environmental quality: 
international evidence. Energy Polii, 92(C), 45–55.
Leal Filho, W., et  al., 2020: COVID-19 and the UN sustainable development 
goals: threat to solidarity or an opportunity? Sustainability, 12(13), 5343, 
doi:10.3390/su12135343.
Lee, J.-Y ., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P . Dunne, F . Engelbrecht, E. 
Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal,

and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and 
Near-Term Information. In: Climate Change 2021: The Physical Science Basis. 
Contribution of Working Group I to the Sixth Assessment Report of the 
Intergovernmental Panel on Climate Change. Cambridge University Press.
Lee, K., N. Gjersoe, S. O’Neill and J. Barnett, 2020: Youth perceptions of climate 
change: a narrative synthesis. WIREs Clim. Chang.
https://doi.org/10.1002/wcc.641
Lee, S.-H. and M. Thiel, 2017: Relations between GDP growth and environmental 
performance using latent growth curve model applied for environmental 
Kuznets curve. Int. J. Sustain. Econ., 9(2), 87–104, doi:10.1504/
IJSE.2017.083362.
Lee, S.A., 2020: Playing the Long Game: The intersection of Climate Change 
Risk and Financial Regulation.
Keynote Remarks at PLI’s 52nd Annual Institute on Securities Regulation.
Leibenluft, J., 2020: Rethinking the Intersection of Climate Policy and the 
Federal Budget. Center for American Progress.

https://www.americanprogress.org/article/rethinking-intersection-climate-
policy-federal-budget/
Leigh, N.G. and H. Lee, 2019: Sustainable and resilient urban water systems: 
the role of decentralization and planning. Sustainability, 11(3), 918, 
doi:10.3390/su11030918.
Lele, S., V. Srinivasan, B.K. Thomas and P . Jamwal, 2018: Adapting to climate 
change in rapidly urbanizing river basins: insights from a multiple-concerns, 
multiple-stressors, and multi-level approach. Water Int., 43(2), 281–304, doi
:10.1080/02508060.2017.1416442.
Lemke, S. and T. Delormier, 2017: Indigenous Peoples’ food systems, nutrition, 
and gender: Conceptual and methodological considerations. Matern. Child 
Nutr., (Suppl 3), doi:10.1111/mcn.12499.
Lemoine, D. and C. Traeger, 2014: Watch your step: optimal policy in a tipping 
climate. Am. Econ. Journal: Econ. Policy, 6, doi:10.1257/pol.6.1.137.
Lenzholzer, S., et  al., 2020: Urban climate awareness and urgency to adapt:

an international overview. Urban Clim., 33, 100667, doi:10.1016/j.
uclim.2020.100667.
Lertzman, R.A., 2010: Myth of apathy: psychoanalytic explorations of 
environmental degradation. Cardiff University, UK, ISBN 978-1073291229.
Lesnikowski, A., et  al., 2017: What does the Paris Agreement mean for 
adaptation? Clim. Policy, 17(7), 825–831.
Lesutis, G., 2020: How to understand a development corridor? The case of 
Lamu Port–South Sudan–Ethiopia-Transport corridor in Kenya. Area, 52(3), 
600–608.
Leventon, J., D.J. Abson and D.J. Lang, 2021: Leverage points for sustainability 
transformations: nine guiding questions for sustainability science and 
practice. Sustain Sci, 16(3), 721–726, doi:10.1007/s11625-021-00961-8.
Ley, D., 2017: Sustainable development, climate change, and renewable energy 
in rural central america. In: Evaluating Climate Change Action for Sustainable 
Development [Uitto, J.I., J. Puri and R.D. van den Berg(eds.)]. Springer

International Publishing, Cham, pp. 187–212. ISBN 978-3319437026.
Ley, D., H.J. Corsair, S. Fuss and C. Singh, 2020: Evaluating the use of renewable 
energy and communal governance systems for climate change adaptation. 
Cent. Eur. Rev. Econ. Manag., 4(1), 53–70, doi:10.29015/cerem.850.Li, D., et  al., 2019a: Challenges and opportunities for the development of 
MEGACITIES. Int. J. Digit. Earth, 12(12), 1382–1395, doi:10.1080/1753894
7.2018.1512662.
Li, L., et al., 2018: Service learning, service climate, and service-based social 
innovation for sustainability. Sustainability, 10(7), 2566.
Li, S., et al., 2017: Integrated modelling of urban spatial development under 
uncertain climate futures: a case study in Hungary. Environ. Model. Softw., 
96, 251–264, doi:10.1016/j.envsoft.2017.07.005.
Li, X., et al., 2019b: Projecting global urban area growth through 2100 based 
on historical time series data and future shared socioeconomic pathways.

Earth’s Future, 7(4), 351–362, doi:10.1029/2019EF001152.
Liem, A., R.B. Natari, Jimmy and B.J. Hall, 2020: Digital health applications in 
mental health care for immigrants and refugees: a rapid review. Telemedicine 
E-health, doi:10.1089/tmj.2020.0012.
Lillebø, A.I., et  al., 2017: How can marine ecosystem services support 
the Blue Growth agenda? Mar. Policy., 81, 132–142, doi:10.1016/j.
marpol.2017.03.008.
Lind, J., 2018: Devolution, shifting centre-periphery relationships and conflict in 
northern Kenya. Polit Geogr, 63, 135–147.
Lindoso, D., et  al., 2018: Harvesting water for living with drought: insights 
from the Brazilian human coexistence with semi-aridity approach towards 
achieving the sustainable development goals. Sustainability, 10(3), 622.
Liobikienė, G. and M. Butkus, 2018: The challenges and opportunities of climate 
change policy under different stages of economic development. Sci. Total 
Environ., 642, 999–1007, doi:10.1016/j.scitotenv.2018.06.140.

Lipper, L., et al., 2014: Climate-smart agriculture for food security. Nat. Clim. 
Chang., 4(12), 1068–1072, doi:10.1038/nclimate2437.
Liu, J.-Y ., et al., 2019: Identifying trade-offs and co-benefits of climate policies 
in China to align policies with SDGs and achieve the 2°C goal. Environ. Res. 
Lett., 14(12), 124070, doi:10.1088/1748-9326/ab59c4.
Lo, A.Y ., S. Liu, L.T.O. Cheung and F .K.S. Chan, 2020: Contested transformations: 
sustainable economic development and capacity for adapting to climate 
change. Ann. Am. Assoc. Geogr., 110(1), 223–241, doi:10.1080/24694452.
2019.1625748.
Lo, K. and V. Castán Broto, 2019: Co-benefits, contradictions, and multi-level 
governance of low-carbon experimentation: Leveraging solar energy for 
sustainable development in China. Glob. Environ. Chang., 59, 101993, 
doi:10.1016/j.gloenvcha.2019.101993.
Lockwood, M., C. Kuzemko, C. Mitchell and R. Hoggett, 2016: Historical

institutionalism and the politics of sustainable energy transitions: a research 
agenda. Environ. Plan. C: Polit. Space, doi:10.1177/0263774X16660561.
Löfmarck, E. and R. Lidskog, 2017: Bumping against the boundary: IPBES 
and the knowledge divide. Environ Sci Policy, 69, 22–28, doi:10.1016/j.
envsci.2016.12.008.
Loorbach, D., N. Frantzeskaki and F . Avelino, 2017: Sustainability transitions 
research: transforming science and practice for societal change. 
Annu. Rev. Environ. Resour., 42(1), 599–626, doi:10.1146/annurev-
environ-102014-021340.
Loorbach, D., et al., 2020: Transformative innovation and translocal diffusion. 
Environ. Innov. Soc. Transit., 35, 251–260, doi:10.1016/j.eist.2020.01.009.
Lopez-Carr, D., Population-health-environment ( phe ) synergies ? Evidence 
from USAID-sponsored programs in african and asian core conservation 
areas. Accessed 2021-08-20.
Lorenzoni, I. and L. Whitmarsh, 2014: Climate change and perceptions,

behaviors, and communication research after the IPCC 5th Assessment 
Report - a WIREs Editorial: Climate change and perceptions, behaviors, and 
communication research. Wiley Interdiscip. Rev. Clim. Chang., 5(6), 703–708.
Lucas, H., et  al., 2017: Critical challenges and capacity building needs for 
renewable energy deployment in Pacific Small Island Developing States 
(Pacific SIDS). Renew. Energy, 107, 42–52, doi:10.1016/j.renene.2017.01.029.
Ludwig, D. and L. Poliseli, 2018: Relating traditional and academic ecological 
knowledge: mechanistic and holistic epistemologies across cultures. Biol. 
Philos., 33(5), 43, doi:10.1007/s10539-018-9655-x.

18
2755Climate Resilient Development Pathways  Chapter 18
Ludwig, F ., E. van Slobbe and W. Cofino, 2014: Climate change adaptation and 
integrated water resource management in the water sector. J. Hydrol. Reg. 
Stud., 518, 235–242, doi:10.1016/j.jhydrol.2013.08.010.
Luederitz, C., D.J. Abson, R. Audet and D.J. Lang, 2017: Many pathways toward 
sustainability: not conflict but co-learning between transition narratives. 
Sustain Sci, 12(3), 393–407, doi:10.1007/s11625-016-0414-0.
Luton, L.S., 2015: Climate scientists and the intergovernmental panel on 
climate change: evolving dynamics of a belief in political neutrality. Adm. 
Theory Prax., 37(3), 144–161, doi:10.1080/10841806.2015.1053360.
Lwasa, S., 2015: A systematic review of research on climate change adaptation 
policy and practice in Africa and South Asia deltas. Reg Environ Change, 
15(5), 815–824.
Lyon, C., 2018: Complexity ethics and UNFCCC practices for 1.5°C climate 
change. Curr. Opin. Environ. Sustain., 31, 48–55.

MacGregor, S., 2003: Feminist perspectives on sustainability. In: UNESCO 
Encyclopedia of Life Support Systems: Introduction to Sustainable 
Development, ed. / David V J Bell; Annie Cheung. Oxford : Eolss Publishers, 
pp. 467–492.
MacGregor, S., 2014: Only resist: feminist ecological citizenship and the post-
politics of climate change. Hypatia, 29(3), 617–633.
MacGregor, S., 2019: Finding transformative potential in the cracks? The 
ambiguities of urban environmental activism in a neoliberal city. Soc. Mov 
Stud., 0(0), 1–17, doi:10.1080/14742837.2019.1677224.
MacGregor, S., 2020: Gender matters in environmental justice. In: 
Environmental Justice [Coolsaet, B.(ed).], 1st edn. Key issues in environment 
and sustainability, Routledge, Abingdon, Oxon ; New York, NY , pp. 234–248. 
ISBN 978-0429029585.
Mach, K.J., et  al., 2019: Climate as a risk factor for armed conflict. Nature, 
571(7764), 193–197, doi:10.1038/s41586-019-1300-6.

Mackey, B. and D. Claudie, 2015: Points of contact: integrating traditional and 
scientific knowledge for Biocultural conservation. Environ. Ethics., (Fall), 
37(3), 341–357.
Maes, M.J.A., K.E. Jones, M.B. Toledano and B. Milligan, 2019: Mapping synergies 
and trade-offs between urban ecosystems and the sustainable development 
goals. Environ. Sci. Policy, 93, 181–188, doi:10.1016/j.envsci.2018.12.010.
Magnan, A.K. and T. Ribera, 2016: Global adaptation after Paris. Science, 
352(6291), 1280–1282.
Magnan, A.K., et  al., 2016: Addressing the risk of maladaptation to climate 
change. WIREs Clim. Chang., 7(5), 646–665, doi:10.1002/wcc.409.
Magnan, A.K., E.L.F . Schipper and V.K.E. Duvat, 2020: Frontiers in climate change 
adaptation science: advancing guidelines to design adaptation pathways. 
Curr. Clim. Change Rep., 6(4), 166–177, doi:10.1007/s40641-020-00166-8.
Mahoney, J.L. and K. Thelen, 2009: A theory of gradual institutional change.

Explain. Institutional Chang. Ambiguity Agency Power, 1–37, doi:10.1017/
CBO9780511806414.003.
Mahony, M. and M. Hulme, 2016: Modelling and the Nation: Institutionalising 
Climate Prediction in the UK, 1988–92. Minerva, 54(4), 445–470, 
doi:10.1007/s11024-016-9302-0.
Mainali, K., et al., 2020: Contrasting responses to climate change at Himalayan 
treelines revealed by population demographics of two dominant species. 
Ecol. Evol., 10(3), 1209–1222, doi:10.1002/ece3.5968.
Major, D.C., M. Lehmann and J. Fitton, 2018: Linking the management of climate 
change adaptation in small coastal towns and cities to the Sustainable 
Development Goals. Ocean. Coast. Manag., 163, 205–208, doi:10.1016/j.
ocecoaman.2018.06.010.
Makuvaro, V., et  al., 2017: Constraints to crop production and adaptation 
strategies of smallholder farmers in semi-arid Central and Western 
Zimbabwe. African Crop. Sci. J., 25(2), 221–235, doi:10.4314/acsj.v25i2.7.

Mallucci, Enrico (2020). “Natural Disasters, Climate Change, and Sovereign 
Risk,” International Finance Discussion Papers 1291. Washington: Board 
of Governors of the Federal Reserve System, https://doi.org/10.17016/
IFDP .2020.1291.Manne, A.S. and R.G. Richels, 1992: Buying Greenhouse Insurance: The 
Economic Costs of CO2 Emission Limits. The MIT Press, Cambridge, Mass, 
194. ISBN 978-0262132800. New ed.
Maor, M., J. Tosun and A. Jordan, 2017: Proportionate and disproportionate 
policy responses to climate change: core concepts and empirical applications. 
J. Environ. Policy Plan., 0(0), 1–13.
Mapfumo, P ., Mtambanengwe, F . and Chikowo, R.2016: Building on indigenous 
knowledge to strengthen the capacity of smallholder farming communities 
to adapt to climate change and variability in southern Africa. Clim. Dev., 8, 
72–81, doi:10.1080/17565529.2014.998604.
Marin, A. and L.O. Naess, 2017: Climate change adaptation through humanitarian

aid? Promises, perils and potentials of the ‘new humanitarianism’. IDS Bull.
https://doi.org/10.19088/1968-2017.150
Markard, J., F .W. Geels and R. Raven, 2020: Challenges in the acceleration of 
sustainability transitions. Environ. Res. Lett.
https://doi.org/10.1088/1748-9326/ab9468
Markkanen, S. and A. Anger-Kraavi, 2019: Social impacts of climate change 
mitigation policies and their implications for inequality. Clim. Policy, 19(7), 
827–844, doi:10.1080/14693062.2019.1596873.
Marmot, M., 2020: The Art of Medicine - Society and the slow burn of inequality. 
Inst. Health Equity, 395, 1413–1414.
https://doi.org/10.1016/s0140-6736(20)30940-5
Marmot, M. and J. Allen, 2020: COVID-19: exposing and amplifying inequalities. 
J Epidemiol Community Health, doi:10.1136/jech-2020-214720.
Marsha, A., et al., 2018: Influences of climatic and population changes on heat-
related mortality in Houston, Texas, USA. Clim Change, 146(3), 471–485, 
doi:10.1007/s10584-016-1775-1.

Martinez-Alier, J., 2021: Mapping ecological distribution conflicts: The EJAtlas. 
Extr. Ind. Soc., doi:10.1016/j.exis.2021.02.003.
Martínez-Fernández, J., I. Banos-González and M.Á. Esteve-Selma, 2021: 
An integral approach to address socio-ecological systems sustainability 
and their uncertainties. Sci. Total. Environ., 762, 144457, doi:10.1016/j.
scitotenv.2020.144457.
Martiskainen, M., et  al., 2020: Contextualizing climate justice activism: 
knowledge, emotions, motivations, and actions among climate 
strikers in six cities. Glob. Environ. Chang., 65, 102180, doi:10.1016/j.
gloenvcha.2020.102180.
Maru, Y .T., et  al., 2014: A linked vulnerability and resilience framework for 
adaptation pathways in remote disadvantaged communities. Glob. Environ. 
Chang., 28, 337–350, doi:10.1016/j.gloenvcha.2013.12.007.
Massarella, K., et al., 2021: Transformation beyond conservation: how critical 
social science can contribute to a radical new agenda in biodiversity

conservation. Curr. Opin. Environ. Sustain., 49, 79–87, doi:10.1016/j.
cosust.2021.03.005.
Massey, E., R. Biesbroek, D. Huitema and A. Jordan, 2014: Climate policy 
innovation: the adoption and diffusion of adaptation policies across europe. 
Glob. Environ. Chang., 29, 434–443.
Material, E., 2019: Industrial Transformation 2050: Pathways to net-zero 
emissions from EU Heavy Industry. https://materialeconomics.com/
publications/industrial-transformation-2050. Accessed 25-04-2019.
Mathai, M.V., J.A.P . de Oliveira and G. Dale, 2018: The rise and flaws of green 
growth. Apn Sci. Bull., doi:10.30852/sb.2018.359.
Matin, N., J. Forrester and J. Ensor, 2018: What is equitable resilience? World 
Dev, 109, 197–205.
Matos, P ., et al., 2019: Modeling the provision of air-quality regulation ecosystem 
service provided by urban green spaces using lichens as ecological indicators. 
Sci. Total. Environ., 665, 521–530, doi:10.1016/j.scitotenv.2019.02.023.

Matsui, K., 2015: Problems of defining and validating traditional knowledge: a 
historical approach. Int. Indig. Policy J., 6(2), 2, doi:10.18584/iipj.2015.6.2.2.
Matthew, R., 2014: Integrating climate change into peacebuilding. Clim 
Change, 123(1), 83–93, doi:10.1007/s10584-013-0894-1.
Mbow, C., C. Rosenzweig, L.G. Barioni, T.G. Benton, M. Herrero, M. Krishnapillai, 
E. Liwenga, P . Pradhan, M.G. Rivera-Ferre, T. Sapkota, F .N. Tubiello, Y . Xu, 2019:

18
2756Chapter 18 Climate Resilient Development Pathways
Food security. In: Climate Change and Land: an IPCC special report on climate 
McCartney, G., et  al., 2017: Regeneration and health: a structured, rapid 
literature review. Public Health, 148, 69–87.  https://doi.org/10.1016/j.
puhe.2017.02.022
McCollum, D.L., et  al., 2018a: Connecting the sustainable development 
goals by their energy inter-linkages. Environ. Res. Lett., 13(3), 33006, 
doi:10.1088/1748-9326/aaafe3.

McCollum, D.L., et al., 2018b: Energy investment needs for fulfilling the paris 
agreement and achieving the sustainable development goals. Nat. Energy, 
3(7), 589–599, doi:10.1038/s41560-018-0179-z.
McGregor, D., S. Whitaker and M. Sritharan, 2020: Indigenous environmental 
justice and sustainability. Curr. Opin. Environ. Sustain., 43, 35–40, 
doi:10.1016/j.cosust.2020.01.007.
Mcleod, E., et al., 2018: Raising the voices of Pacific Island women to inform 
climate adaptation policies. Mar. Policy, 93, 178–185.
Mcleod, E., et al., 2019: Lessons from the pacific islands – adapting to climate 
change by supporting social and ecological resilience. Front. Mar. Sci., 6(289), 
doi:10.3389/fmars.2019.00289.
McManamay, R.A., C.R. Vernon and H.I. Jager, 2021: Global biodiversity 
implications of alternative electrification strategies under the shared 
socioeconomic pathways. Biol. Conserv., 260, 109234, doi:10.1016/j.
biocon.2021.109234.

McNamara, K.E., et al., 2020: An assessment of community-based adaptation 
initiatives in the Pacific Islands. Nat. Clim. Change, 10(7), 628–639, 
doi:10.1038/s41558-020-0813-1.
McPhearson, T. and K. Wijsman, 2017: Transitioning complex urban systems: 
the importance of Urban ecology for sustainability in new York City. In: 
Urban Sustainability Transitions [Frantzeskaki, N., Broto, V.C., Coenen, L. and 
Loorbach, D. (eds.)]. Routledge, London. ISBN 978-1315228389.
McPhetres, J. and M. Zuckerman, 2018: Religiosity predicts negative attitudes 
towards science and lower levels of science literacy. PLoS ONE, 13(11), 
doi:10.1371/journal.pone.0207125.
Meadows, D., 1999: Leverage Points: Places to Intervene in a System. The 
Sustainability Institute. Available at: https://donellameadows.org/archives/
leverage-points-places-to-intervene-in-a-system/ (accessed 31.01.22)
Melica, G., et al., 2018: Multilevel governance of sustainable energy policies:

The role of regions and provinces to support the participation of small local 
authorities in the Covenant of Mayors. Sustain. Cities Soc., 39, 729–739. Doi: 
10.1016/j.scs.2018.01.013.
Meriluoto, T., 2018: ‘The will to not be empowered (according to your 
rules)’: Resistance in Finnish participatory social policy. Crit. Soc. Policy, 
doi:10.1177/0261018318764322.
Mersha, A.A. and F . van Laerhoven, 2019: Gender and climate policy: a 
discursive institutional analysis of Ethiopia’s climate resilient strategy. Reg. 
Environ. Change, 19(2), 429–440.
Metcalf, G.E., 2016: A conceptual framework for measuring the effectiveness of 
green fiscal reforms. Int. J. Green Growth Dev., 2(2), 87–126.
Metcalf, G.E., 2019: On the Economics of a Carbon Tax for the United States. 
Brookings Papers on Economic Activity. Available at: https://www.brookings.
edu/wp-content/uploads/2019/03/On-the-Economics-of-a-Carbon-Tax-for-
the-United-States.pdf (accedded 31.01.22).

Mganga, K.Z., N.K.R. Musimba and D.M. Nyariki, 2015: Combining sustainable 
land management technologies to combat land degradation and improve 
rural livelihoods in semi-arid lands in Kenya. Environ Manage, 56(6), 1538–
1548.Michalena, E. and J.M. Hills, 2018: Paths of renewable energy development in 
small island developing states of the South Pacific. Renew. Sustain. Energy 
Rev., 82, 343–352, doi:10.1016/j.rser.2017.09.017.
Mikulewicz, M., 2018: Politicizing vulnerability and adaptation: on the need 
to democratize local responses to climate impacts in developing countries. 
Clim. Dev., 10(1), 18–34, doi:10.1080/17565529.2017.1304887.
Mikulewicz, M., 2019: Thwarting adaptation’s potential? A critique of resilience 
and climate resilient development. Geoforum, 104, 267–282, doi:10.1016/j.
geoforum.2019.05.010.
Mikulewicz, M. and M. Taylor, 2020: Getting the resilience right: climate change 
and development policy in the ‘African Age’. New Polit. Econ., 25(4), 626–

641, doi:10.1080/13563467.2019.1625317.
Millennium Ecosystem Assessment, 2005: Ecosystems and Human Well-Being: 
Synthesis. Island Press, Washington, D.C.
Miller, C.A., 2017: Incorporating sustainability into climate change adaptation. 
In: Encyclopedia of Sustainable Technologies [Abraham, M.  A.(ed.)]. Elsevier, 
Oxford, pp. 175–182. ISBN 978-0128047927.
Milman, A. and Y . Arsano, 2014: Climate adaptation and development: 
contradictions for human security in Gambella, Ethiopia. Glob. Environ. 
Chang., 29, 349–359, doi:10.1016/j.gloenvcha.2013.11.017.
Mimura, N., R.S. Pulwarty, D.M. Duc, I. Elshinnawy, M.H. Redsteer, H.Q. 
Huang, J.N. Nkem, and R.A. Sanchez Rodriguez, 2014: Adaptation Planning 
and Implementation. In: Climate Change 2014: Impacts, Adaptation, and 
Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working 
Group II to the Fifth Assessment Report of the Intergovernmental Panel on

Climate Change [Field, C. B., V. R. Barros, D. J. Dokken, K. J. Mach, M. D. 
Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y . O. Estrada, R. C. Genova, 
B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P . R. Mastrandrea and L. L. 
USA, pp. 869-898. ISBN 9781107058071.
Minx, J.C., et al., 2017: Learning about climate change solutions in the IPCC and 
beyond. Environ. Sci. Policy, 77, 252–259, doi:10.1016/j.envsci.2017.05.014.
Mirumachi, N., A. Sawas and M. Workman, 2020: Unveiling the security concerns 
of low carbon development: climate security analysis of the undesirable and 
unintended effects of mitigation and adaptation. Clim. Dev., 12(2), 97–109, 
doi:10.1080/17565529.2019.1604310.
Mirzabaev, A., et  al., 2015: Bioenergy, food security and poverty reduction: 
trade-offs and synergies along the water–energy–food security nexus. Water 
Int., 40(5-6), 772–790, doi:10.1080/02508060.2015.1048924.

Mistry, J., B.A. Bilbao and A. Berardi, 2016: Community owned solutions for 
fire management in tropical ecosystems: casestudies from Indigenous 
communities of South. Philos. Trans., 371, 1–10.
Mitchell, D., S. Enemark and P . van der Molen, 2015: Climate resilient urban 
development: why responsible land governance is important. Land Use 
Policy, 48, 190–198, doi:10.1016/j.landusepol.2015.05.026.
Mitchell, T. and S. Maxwell, 2010: Defining Climate Compatible Development. 
CDKR. Available at: https://www.gov.uk/research-for-development-
outputs/defining-climate-compatible-development#:~:text='Climate%20
compatible%20development'%20is%20development,emissions%2C%20
more%20resilient%2C%20future. (accessed 31.01.22).
Mitter, H., et  al., 2020: Shared socio-economic pathways for European 
agriculture and food systems: the Eur-Agri-SSPs. Glob. Environ. Chang., 65, 
102159, doi:10.1016/j.gloenvcha.2020.102159.
Mocca, E. and S. Osborne, 2019: “Solidarity is our weapon”: social Mobilisation

in Scotland in the contest of the post-political condition. Antipode, 51(2), 
620–641, doi:10.1111/anti.12466.
Mogelgaard, K., et al., 2018: From Planning to Action: Mainstreaming Climate 
Change Adaptation Into Development. World Resources Institute. Available 
at: https://www.wri.org/research/planning-action-mainstreaming-climate-
change-adaptation-development (accessed 31.01.22).
Mohammed, R. and M. Scholz, 2017: Adaptation strategy to mitigate the 
impact of climate change on water resources in arid and semi-arid regions: a

18
2757Climate Resilient Development Pathways  Chapter 18
case study. Water Resour. Manag., 31(11), 3557–3573, doi:10.1007/s11269-
017-1685-7.
Montoute, A., D. Mohammed and J. Francis, 2019: Prospects and challenges 
for civil society and climate change engagement in the caribbean: the 
case of trinidad and Tobago. Caribb. Stud., 47(2), 81–109, doi:10.1353/
crb.2019.0015.
Moore, M.-L., D. Riddell and D. Vocisano, 2015: Scaling out, scaling up, scaling 
deep: strategies of non-profits in advancing systemic social innovation. J. 
Corp. Citizsh., 58, 67–84.
Mora, C., et al., 2017: Global risk of deadly heat. Nat. Clim. Change, 7(7), 501–
506, doi:10.1038/nclimate3322.
Mora, O., et al., 2020: Exploring the future of land use and food security: A new 
set of global scenarios. PLoS ONE, 15(7), e235597.
Moran, et  al., 2018: The Intersection of Global Fragility and Climate Risks. 
United States Agency for International Development, Washington, D.C.

Mormina, M., 2019: Science, technology and innovation as social goods for 
development: rethinking research capacity building from Sen’s capabilities 
approach. SCI ENG ETHICS, 25(3), 671–692.
Morote, Á.-F ., J. Olcina and M. Hernández, 2019: The use of non-conventional 
water resources as a means of adaptation to drought and climate change 
in semi-arid regions: south-eastern Spain. Water, 11(1), 93, doi:10.3390/
w11010093.
Morton, T., 2013: Hyperobjects: Philosophy and Ecology after the End of 
the World, 1st  edn., Univ Of Minnesota Press, Minneapolis, ISBN 978-
0816689231. 240 pp.
Morton, T., 2021: Contesting coal, contesting climate: materializing the social 
drama of climate change in Australia and Germany. Environ. Commun., 
15(4), 465–481, doi:10.1080/17524032.2020.1865428.
Mosberg, M., E. Nyukuri and L.O. Naess, 2017: The power of ‘know-who’: 
adaptation to climate change in a changing humanitarian landscape in 
Isiolo, Kenya. IDS Bull., 48(4), doi:10.19088/1968-2017.154.

Moser, S.C. and J.A. Ekstrom, 2011: Taking ownership of climate change: 
participatory adaptation planning in two local case studies from California. 
J Environ Stud Sci, 1(1), 63–74, doi:10.1007/s13412-011-0012-5.
Moser, S., S. Meerow, J. Arnott and E. Jack-Scott, 2019: The turbulent world of 
resilience: interpretations and themes for transdisciplinary dialogue. Clim. 
Change, 153(1), 21–40, doi:10.1007/s10584-018-2358-0.
Mosley, J. and E.E. Watson, 2016: Frontier transformations: development 
visions, spaces and processes in Northern Kenya and Southern Ethiopia. J. 
East. African Stud., 10(3), 452–475.
Mouratiadou, I., et al., 2016: The impact of climate change mitigation on water 
demand for energy and food: an integrated analysis based on the shared 
socioeconomic pathways. Environ. Sci. Policy, 64, 48–58, doi:10.1016/j.
envsci.2016.06.007.
Moyer, J.D. and D.K. Bohl, 2019: Alternative pathways to human development:

assessing trade-offs and synergies in achieving the sustainable development 
goals. Futures, 105, 199–210, doi:10.1016/j.futures.2018.10.007.
Republic of Mozambique and IUCN, 2014: Climate Change and Gender Action 
Plan for the Republic of Mozambique. The Global Gender Office of the 
International Union for Conservation of Nature (IUCN GGO). Available at: 
https://genderandenvironment.org/climate-change-gender-action-plan-
phase-ii-for-the-republic-of-mozambique/ Accessed 2019-09-24.
Mpandeli, S., et  al., 2018: Climate change adaptation through the water-
energy-food nexus in southern Africa. IJERPH, 15(10), 2306, doi:10.3390/
ijerph15102306.
https://www.mrfcj.org/wp-content/uploads/2015/11/MRFCJ-Rights-for-Action-
edition-2.pdf (accessed 31.01.22). (28 pp).
Mullins, J.T. and C. White, 2020: Can access to health care mitigate the effects 
of temperature on mortality? J  Public Econ, 191, 104259, doi:10.1016/j.
jpubeco.2020.104259.

Mulugetta, Y ., E.B. Hagan and D. Kammen, 2019: Energy access for sustainable 
development. Environ. Res. Lett., 14(2), 20201, doi:10.1088/1748-9326/
aaf449.Muok, B., M. Mosberg, S. Eriksen and D. Ochieng, 2021: The politics of forest 
governance in a changing climate: Political reforms, conflict and socio-
environmental changes in Laikipia, Kenya. For. Policy Econ. In press. Doi: 
10.1016/j.forpol.2021.102590
Muratori, M., et al., 2020: EMF-33 insights on bioenergy with carbon capture 
and storage (BECCS). Clim Change, doi:10.1007/s10584-020-02784-5.
Muricho, D.N., D.J. Otieno, W. Oluoch-Kosura and M. Jirström, 2019: Building 
pastoralists’ resilience to shocks for sustainable disaster risk mitigation: 
Lessons from West Pokot County, Kenya. Int. J. Disaster Risk Reduct., 34, 
429–435.
Murphy, B.L., 2011: From interdisciplinary to inter-epistemological approaches: 
confronting the challenges of integrated climate change research. Can.

Geogr., 55(4), 490–509, doi:10.1111/j.1541-0064.2011.00388.x.
Murtinho, F ., 2016: What facilitates adaptation? An analysis of community-
based adaptation to environmental change in the Andes. Int. J. Commons, 
10(1), 119–141, doi:10.18352/ijc.585.
Mustafa, S., A. Estim and R. Shapawi, 2019: Future-Proofing Oceans for 
Food Security and Poverty Alleviation, 1–11. ISBN 978-3319696270. Doi: 
10.1007/978-3-319-71058-7_57-1
Mustonen, T., 2013: Oral histories as a baseline of landscape restoration – 
co-management and watershed knowledge in Jukajoki river. Fennia, 191, 
76–91. Doi: 10.11143/7637
Mustonen, T., 2014: Endemic time-spaces of Finland: Aquatic regimes. Fennia, 
192(2), 120–139.
Mustonen, T., 2018: Meaningful engagement and oral histories of the 
indigenous peoples of the north. Nord. Geogr. Publ., 47(2), 1–38.
Mustonen, T. and P . Feodoroff, 2020: What is a river? A cross-disciplinary 
and indigenous assessment. In: Indigenous Wellbeing and Enterprise Self-

Determination and Sustainable Economic Development [Colborne, R.(ed.)]. 
Mustonen, T. and V. Shadrin, 2020: The River Alazeya: Shifting socio-ecological 
systems connected to a Northeastern Siberian Rive. Arctic Institute of North 
America, Calgary.
Mycoo, M.  A., 2018: Beyond 1.5°C: vulnerabilities and adaptation strategies 
for Caribbean Small Island Developing States. Reg Environ Change. Doi: 
10.1007/s10113-017-1248-8
Mythen, G. and S. Walklate, 2016: Not knowing, emancipatory catastrophism 
and metamorphosis: embracing the spirit of Ulrich beck. Secur. Dialogue, 
47(5), 403–419, doi:10.1177/0967010616651027.
Nabuurs, G.-J., et al., 2017: By 2050 the mitigation effects of EU forests could 
nearly double through climate smart forestry. Forests, 8, 484, doi:10.3390/
f8120484.
Nabuurs, G.-J., et  al., 2019: Next-generation information to support a 
sustainable course for European forests. Nat. Sustain., 2(9), 815–818, 
doi:10.1038/s41893-019-0374-3.

Nachmany, M., S. Fankhauser, J. Setzer and A. Averchenkova, 2017: Global 
trends in climate change legislation and litigation: 2017 update. Available 
at: https://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2017/05/
Global_trends_in_climate_change_legislation_and_litigation_summary_
flyer.pdf (accessed 31.01.22).
Nachmany, M. and J. Setzer, 2018: Policy brief Global trends in climate change 
legislation and litigation: 2018 snapshot. Grantham Research Center 
on Climate Change and the Environment and Center for Climate Change 
Economics and Policy, London.
Naess, L.O., 2013: The role of local knowledge in adaptation to climate change. 
WIREs Clim. Chang., 4(2), 99–106, doi:10.1002/wcc.204.
Naess, L.O., et al., 2015: Climate policy meets national development contexts: 
insights from Kenya and Mozambique. Glob. Environ. Chang., 35, 534–544, 
doi:10.1016/j.gloenvcha.2015.08.015.
Nagoda, S., 2015: New discourses but same old development approaches?

Climate change adaptation policies, chronic food insecurity and development 
interventions in northwestern Nepal. Glob. Environ. Chang., 35, 570–579, 
doi:10.1016/j.gloenvcha.2015.08.014.

18
2758Chapter 18 Climate Resilient Development Pathways
Nagoda, S. and A.J. Nightingale, 2017: Participation and power in climate 
change adaptation policies: vulnerability in food security programs in Nepal. 
World Dev, 100, 85–93, doi:10.1016/j.worlddev.2017.07.022.
Naidoo, R. and B. Fisher, 2020: Reset sustainable development goals for a 
pandemic world. Nature, 583(7815), 198–201, doi:10.1038/d41586-020-
01999-x.
Nakashima, D.J., et al., 2012: Weathering Uncertainty: Traditional Knowledge 
for Climate Change Assessment and Adaptation. UNESCO, and Darwin, UNU, 
Paris.
Nalau, J., et  al., 2018: The role of indigenous and traditional knowledge in 
ecosystem-based adaptation: a review of the literature and case studies 
from the Pacific Islands. Wea. Clim. Soc., 10(4), 851–865, doi:10.1175/
WCAS-D-18-0032.1.
Nalau, J., B.L. Preston and M.C. Maloney, 2015: Is adaptation a local 
responsibility? Environ. Sci. Policy, 48, 89–98.

Naspolini, G.F ., B.S. Ciasca, E.L. La Rovere and A.O. Pereira Jr, 2020: Brazilian 
environmental-economic accounting for water: a structural decomposition 
analysis. J. Environ. Manag., 265, 110508.
Naude, P ., 2019: Decolonising knowledge: can Ubuntu ethics save us from 
coloniality? J. Bus. Ethics., 159(1), 23–37, doi:10.1007/s10551-017-3763-4.
Naylor, A., J. Ford, T. Pearce and J. Van Alstine, 2020: Conceptualizing climate 
vulnerability in complex adaptive systems. One Earth, 2(5), 444–454.
NDRC, 2015: China Belt and Road Initiative. Available at: https://www.cfr.org/
backgrounder/chinas-massive-belt-and-road-initiative (accessed 31.01.22).
NEF , 2016: Happy Planet Index. Available at: https://happyplanetindex.org/wp-
content/themes/hpi/public/downloads/happy-planet-index-briefing-paper.
pdf (accessed 31.01.22).
Newell, P ., 2019: Trasformismo or transformation? The global political economy 
of energy transitions. Rev. Int. Polit. Econ., 26(1), 25–48, doi:10.1080/0969

2290.2018.1511448.
Newell, P ., 2020: Towards a global political economy of transitions: a comment 
on the transitions research agenda. Environ. Innov. Soc. Transit., 34, 344–
345, doi:10.1016/j.eist.2019.10.007.
Newell, P . and R. Lane, 2020: A climate for change? The impacts of climate 
change on energy politics. Camb. Rev. Int. Aff., 33(3), 347–364, doi:10.1080
/09557571.2018.1508203.
Newman, T.P ., E.C. Nisbet and M.C. Nisbet, 2018: Climate change, cultural 
cognition, and media effects: Worldviews drive news selectivity, 
biased processing, and polarized attitudes. Public Underst. Sci., 
doi:10.1177/0963662518801170.
Nhamo, G. and S. Nhamo, 2018: Gender and Geographical Balance: With a 
Focus on the UN Secretariat and the Intergovernmental Panel on Climate 
Change. Gend. Quest., 5(1). Doi: 10.25159/2412-8457/2520
Nhamo, L., B. Ndlela, S. Mpandeli and T. Mabhaudhi, 2020: The water-energy-
food nexus as an adaptation strategy for achieving sustainable livelihoods at

a local level. Sustainability, 12(20), 8582, doi:10.3390/su12208582.
Nielsen, S. and I.R. Skov, 2019: Investment screening model for spatial 
deployment of power-to-gas plants on a national scale – a Danish case. Int J 
Hydrogen Energy, 44(19), 9544–9557, doi:10.1016/j.ijhydene.2018.09.129.
Nightingale, A.J., 2016: Adaptive scholarship and situated knowledges? Hybrid 
methodologies and plural epistemologies in climate change adaptation 
research. Area, 48(1), 41–47, doi:10.1111/area.12195.
Nightingale, A.J., 2017: Power and politics in climate change adaptation efforts: 
Struggles over authority and recognition in the context of political instability. 
Geoforum, 84, 11–20.
Nightingale, A.J., et al., 2020: Beyond Technical Fixes: climate solutions and the 
great derangement. Clim. Dev., 12(4), 343–352.
Nightingale, A.J., T. Böhler, B. Campbell and L. Karlsson, 2019: Background and 
history of sustainability. In: Environment and Sustainability in a Globalizing

World [Nightingale, A.J., T. Böhler, B. Campbell and L. Karlsson (eds).]. 
Routledge, London. pp. 13–34. ISBN 978-0765646446.Nikulina, V., D. Simon, H. Ny and H. Baumann, 2019: Context-adapted urban 
planning for rapid transitioning of personal mobility towards sustainability: 
A systematic literature review. Sustainability, 11(4), 1007.
Nilsson, M. and N. Weitz, 2019: Governing trade-offs and building coherence in 
policy-making for the 2030 agenda. Polit. Gov., 7(4), 254–263, doi:10.17645/
pag.v7i4.2229.
Nilsson, M., et al., 2012: Understanding policy coherence: analytical framework 
and examples of sector–environment policy interactions in the EU. Environ. 
Policy Gov., 22(6), 395–423, doi:10.1002/eet.1589.
Nkemelang, T., M. New and M. Zaroug, 2018: Temperature and precipitation 
extremes under current, 1.5°C and 2.0°C global warming above pre-
industrial levels over Botswana, and implications for climate change 
vulnerability. Environ. Res. Lett., 13(6), 65016.

Nordhaus, W.D. and J. Tobin, 1973: Is growth obsolete? In: The measurement 
of economic and social performance [Moss, M. (eds.)..National Bureau of 
Economic Research, Cambridge, MA. , pp. 509–564.
Novák, A., 2021: Every city needs a Klinika: The struggle for autonomy in the 
post-political city. Soc. Mov Stud., 20(3), 276–291, doi:10.1080/14742837.
2020.1770070.
Nunn, P . and N. Reid, 2016: Aboriginal memories of inundation of the Australian 
coast dating from more than 7000 years ago. Aust. Geogr., 47(1), 11–47, do
i:10.1080/00049182.2015.1077539.
Nyariki, D.M. and D.A. Amwata, 2019: The value of pastoralism in Kenya: 
Application of total economic value approach. Pastoralism, 9(1), 9.
Nyasimi, M., et al., 2018: Inclusion of gender in africa’s climate change policies 
and strategies, 171–185. Springer, Berlin. ISBN 978-3319698373.
Nyberg, G., et al., 2015: Enclosures in West Pokot, Kenya: transforming land, 
livestock and livelihoods in drylands. Pastoralism, 5(1), 25.

O’Brien, K., S. Eriksen, T.H. Inderberg and L. Sygna, 2015: Climate change 
adaptation and development: transforming through paradigms and practics. 
Routledge, New York.
O’Brien, K. and E. Selboe, 2015: Social Transformation: The Real Adaptive 
Challenge, 311–324. Cambridge University Press, Cambridge. ISBN 978-
1139149389.
O’Brien, K.L., 2016: Climate change and social transformations: is it time for a 
quantum leap? WIREs Clim. Chang., 7(5), 618–626, doi:10.1002/wcc.413.
O’Brien, K., 2018: Is the 1.5°C target possible? Exploring the three spheres 
of transformation. Curr. Opin. Environ. Sustain., 31, 153–160, doi:10.1016/j.
cosust.2018.04.010.
O’Brien, K., 2021: Reflecting on the anthropocene: the call for deeper 
transformations. Ambio, 50(10), 1793–1797, doi:10.1007/s13280-020-
01468-9.
O’Neil, T., A. Fleury and M. Foresti, 2017: Gender equality, migration and the 
2030 Agenda for Sustainable Development. Louise Arbour, UN Special 
Representative for International Migration, 45.

O’Neill, B.C., et al., 2017: The roads ahead: narratives for shared socioeconomic 
pathways describing world futures in the 21st century. Glob. Environ. Chang., 
42, 169–180, doi:10.1016/j.gloenvcha.2015.01.004.
O’Neil, T., A. Fleury and M. Foresti, 2017: Women on the move: Gender equality, 
migration and the 2030 Agenda for Sustainable Development. Oversees 
women-on-the-move-migration-gender-equality-and-the-2030-agenda-for-
sustainable-development/ (accessed 31.01.22).
O’Neill, D.W., A.L. Fanning, W.F . Lamb and J.K. Steinberger, 2018: A good life 
for all within planetary boundaries. Nat. Sustain., 1(2), 88–95, doi:10.1038/
s41893-018-0021-4.

Ochieng, J., L. Kirimi and J. Makau, 2017: Adapting to climate variability and 
change in rural Kenya: farmer perceptions, strategies and climate trends. Nat 
Resour Forum, 41(4), 195–208.

18
2759Climate Resilient Development Pathways  Chapter 18
OECD, 2017: Mobilising Investment in Clean Energy Infrastructure. Available 
at: https://www.oecd.org/investment/investment-policy/clean-energy-
infrastructure.htm (accessed 31.01.22).
OECD, 2019a: Better Life Index. Available at: https://www.oecdbetterlifeindex.
org/ (accessed 31.01.22).
OECD, 2019b: Society at a Glance 2019. Available at: https://www.oecd-ilibrary.
org/social-issues-migration-health/society-at-a-glance-2019_soc_glance-
2019-en (accessed 31.01.22).
OECD, 2021: Climate change and long term fiscal sustainability. Available 
at: https://www.oecd.org/gov/budgeting/scoping-paper-on-fiscal-
sustainability-and-climate-change.pdf (accessed 31.01.22).
Ofir, Z., 2021: The DAC Criteria Guidance 2021 - Evaluation for 
transformation. Available at: https://www.oecd.org/dac/evaluation/
daccriteriaforevaluatingdevelopmentassistance.htm (accessed 31.01.22).

Ogar, E., G. Pecl and T. Mustonen, 2020: Science must embrace traditional and 
indigenous knowledge to solve our Biodiversity crisis. One Earth, 162–164, 
doi:10.1016/j.oneear.2020.07.006.
Ojwang, L., et  al., 2017: Assessment of coastal governance for climate 
change adaptation in Kenya. Earth’s Future, 5(11), 1119–1132, 
doi:10.1002/2017EF000595.
Oke, C., et al., 2021: Cities should respond to the biodiversity extinction crisis. 
Npj Urban Sustain., 1(1), 1–4, doi:10.1038/s42949-020-00010-w.
Oleribe, O.O. and S.D. Taylor-Robinson, 2016: Before sustainable 
development goals (SDG): why Nigeria failed to achieve the millennium 
development goals (MDGs). Pan. Afr. Med. J., 24, 156–156, doi:10.11604/
pamj.2016.24.156.8447.
Olmstead, S.M., 2014: Climate change adaptation and water resource 
management: a review of the literature. Energy Econ., 46, 500–509, 
doi:10.1016/j.eneco.2013.09.005.
Olsson, L., M. Opondo, P . Tschakert, A. Agrawal, S.H. Eriksen, S. Ma, L.N. Perch,

and S.A. Zakieldeen, 2014: Livelihoods and Poverty. In: Climate Change 
UK and New York, NY , USA, pp. 793-832. ISBN 9781107058071.
Omukuti, J., 2020: Country ownership of adaptation: Stakeholder influence 
or government control? Geoforum, 113, 26–38, doi:10.1016/j.
geoforum.2020.04.019.
Onarheim, K.H., J.H. Iversen and D.E. Bloom, 2016: Economic benefits of 
investing in women’s health: a systematic review. PLoS ONE, 11(3), e150120, 
doi:10.1371/journal.pone.0150120.

Opiyo, F ., et  al., 2016: Determinants of perceptions of climate change and 
adaptation among Turkana pastoralists in northwestern Kenya. Clim. Dev., 
8(2), 179–189.
Opiyo, F .E., O.V. Wasonga and M.M. Nyangito, 2014: Measuring household 
vulnerability to climate-induced stresses in pastoral rangelands of Kenya: 
Implications for resilience programming. Pastoralism, 4(1), 10.
Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. 
Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F . 
Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and 
Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special 
Weyer (eds.)]. In press.

Orlove, B., R. Shwom, E. Markowitz and S.-M. Cheong, 2020: Climate decision-
making. Annu. Rev. Environ. Resour., 45(1), 271–303, doi:10.1146/annurev-
environ-012320-085130.Ota, T., 2017: Economic growth, income inequality and environment: assessing 
the applicability of the Kuznets hypotheses to Asia. Palgrave Commun, 3(1), 
1–23, doi:10.1057/palcomms.2017.69.
Ott, C. and B. Kiteme, 2016: Concepts and practices for the democratisation of 
knowledge generation in research partnerships for sustainable development. 
Evid. Policy: A J. Res. Debate Pract., 12(3), 405–430, doi:10.1332/17442641
6X14700793045951.
Ott, K.K., 2018: On the political economy of solar radiation management. Front. 
Environ. Sci., 6, doi:10.3389/fenvs.2018.00043.
Owen, G., 2020: What makes climate change adaptation effective? A systematic 
review of the literature. Glob. Environ. Change, 62, 102071, doi:10.1016/j.
gloenvcha.2020.102071.

Paik, S., D.T.P . Le, L.T. Nhu and B.F . Mills, 2020: Salt-tolerant rice variety adoption 
in the mekong river delta: farmer adaptation to sea-level rise. PLoS ONE, 
15(3), e229464, doi:10.1371/journal.pone.0229464.
Palazzo, A., et  al., 2017: Linking regional stakeholder scenarios and shared 
socioeconomic pathways: Quantified West African food and climate futures 
in a global context. Glob. Environ. Chang., 45, 227–242, doi:10.1016/j.
gloenvcha.2016.12.002.
Palermo, V. and Y . Hernandez, 2020: Group discussions on how to implement 
a participatory process in climate adaptation planning: a case study in 
Malaysia. Ecol. Econ., 177, 106791, doi:10.1016/j.ecolecon.2020.106791.
Pant, L.P ., B. Adhikari and K.K. Bhattarai, 2015: Adaptive transition for 
transformations to sustainability in developing countries. Curr. Opin. Environ. 
Sustain., 14, 206–212, doi:10.1016/j.cosust.2015.07.006.
Papoutsi, M., M. Piaszzesi and M. Schneider, 2021: How Unconventional is

Green Monetary Policy? Available at: https://web.stanford.edu/~piazzesi/
How_unconventional_is_green_monetary_policy.pdf (accessed 31.01.22).
Paprocki, K., 2018: Threatening dystopias: development and adaptation regimes 
in Bangladesh. Ann. Am. Assoc. Geogr., 108(4), 955–973, doi:10.1080/2469
4452.2017.1406330.
Parikh, K.S., J.K. Parikh and P .P . Ghosh, 2018: Can India grow and live within a 
1.5 degree CO2 emissions budget? Energy Policy, 120, 24–37.
Parraguez-Vergara, E., et al., 2018: Does indigenous and campesino traditional 
agriculture have anything to contribute to food sovereignty in Latin America? 
Evidence from Chile, Peru, Ecuador, Colombia, Guatemala and Mexico. Int. J. 
Agric. Sustain., 326–341, doi:10.1080/14735903.2018.1489361.
Parry, I., 2021: Implementing the United States’ Domestic and International 
Climate Mitigation Goals: A Supportive Fiscal Policy Approach. IMF Working 
Paper. Doi: 10.5089/9781513571003.001.

Parry, J.-E., 2017: Building a Climate-Resilient City: Transformational adaptation. 
IISD, http://prairieclimatecentre.ca/wp-content/uploads/2017/04/pcc-brief-
climate-resilient-city-transformational-adaptation.pdf. (accessed 31.01.22).
Parsons, M., C. Brown, J. Nalau and K. Fisher, 2018: Assessing adaptive capacity 
and adaptation: insights from Samoan tourism operators. Clim. Dev., 10(7), 
644–663, doi:10.1080/17565529.2017.1410082.
Parsons, M., K. Fisher and J. Nalau, 2016: Alternative approaches to co-design: 
insights from indigenous/academic research collaborations. Curr. Opin. 
Environ. Sustain., 20, 99-105.
Partzsch, L., 2017: ‘Power with’ and ‘power to’ in environmental politics and 
the transition to sustainability. Env Polit, 26(2), 193–211, doi:10.1080/0964
4016.2016.1256961.
Pascual, U., et  al., 2017: Valuing nature’s contributions to people: the IPBES 
approach. Curr. Opin. Environ. Sustain., 26-27, 7–16, doi:10.1016/j.
cosust.2016.12.006.

Pasquini, L., 2020: The urban governance of climate change adaptation in least-
developed African countries and in small cities: the engagement of local 
decision-makers in Dar es Salaam, Tanzania, and Karonga, Malawi. Clim. 
Dev., 12(5), 408–419, doi:10.1080/17565529.2019.1632166.
Paton, F .L., H.R. Maier and G.C. Dandy, 2014: Including adaptation and mitigation 
responses to climate change in a multiobjective evolutionary algorithm 
framework for urban water supply systems incorporating GHG emissions. 
Water Resour. Res., 50(8), 6285–6304, doi:10.1002/2013WR015195.

18
2760Chapter 18 Climate Resilient Development Pathways
Patorniti, N.P ., N.J. Stevens and P .M. Salmon, 2017: A systems approach to city 
design: exploring the compatibility of sociotechnical systems. Habitat Int., 
66, 42–48, doi:10.1016/j.habitatint.2017.05.008.
Patorniti, N.P ., N.J. Stevens and P .M. Salmon, 2018: A sociotechnical systems 
approach to understand complex urban systems: A global transdisciplinary 
perspective. Hum. Factors Ergonomics Manuf. Serv. Ind., 28(6), 281–296, 
doi:10.1002/hfm.20742.
Patterson, J., et  al., 2017: Exploring the governance and politics of 
transformations towards sustainability. Environ. Innov. Soc. Transit., 24, 
1–16, doi:10.1016/j.eist.2016.09.001.
Patterson, J.J., et al., 2018: Political feasibility of 1.5° C societal transformations: 
the role of social justice. Curr. Opin. Environ. Sustain., 31, 1–9.
Patton, M.Q., 2021: Evaluation criteria for evaluating transformation:

implications for the coronavirus pandemic and the global climate emergency. 
Am. J. Eval., 42(1), 53–89, doi:10.1177/1098214020933689.
Pauliuk, S., T. Wang and D.B. Müller, 2013: Steel all over the world: estimating 
in-use stocks of iron for 200 countries. Resour. Conserv. Recycl., 71, 22–30, 
doi:10.1016/j.resconrec.2012.11.008.
Pauw, W.P ., et al., 2018: Beyond headline mitigation numbers: we need more 
transparent and comparable NDCs to achieve the Paris Agreement on climate 
change. Clim Change, 147(1), 23–29, doi:10.1007/s10584-017-2122-x.
PBCSF , 2019: Support for BRI low carbon development: a green finance roadmap. 
Available at: https://www.climateworks.org/wp-content/uploads/2019/08/
BRI_Exec_Summary_v10-screen_pages_lo-1.pdf (accessed 31.01.22).
Pearce, T.D., E.H. Rodríguez, D. Fawcett and J.D. Ford, 2018a: How is Australia 
adapting to climate change based on a systematic review? Sustainability, 
10(9), 3280, doi:10.3390/su10093280.

Pearce, W., M. Mahony and S. Raman, 2018b: Science advice for global 
challenges: learning from trade-offs in the IPCC. Environ. Sci. Policy, 80, 
125–131, doi:10.1016/j.envsci.2017.11.017.
Pearse, R., 2017: Gender and climate change. WIREs Clim. Chang., 8(2), e451, 
doi:10.1002/wcc.451.
Pecl, G.T., et al., 2017: Biodiversity redistribution under climate change: impacts 
on ecosystems and human well-being. Science, 355(6332), doi:10.1126/
science.aai9214.
Pelling, M., 2010: Adaptation to Climate Change: From Resilience to 
Transformation, 1st edn., Routledge, London, UK, ISBN 978-0203889046.
Pelling, M., T. Abeling and M. Garschagen, 2016: Emergence and transition in 
London’s climate change adaptation pathways. J. Extrem. Events, 03(03), 
1650012.
Pelling, M. and M. Garschagen, 2019: Put equity first in climate adaptation. 
Nature, 569(7756), 327–329, doi:10.1038/d41586-019-01497-9.
Pelling, M., et al., 2018: Africa’s urban adaptation transition under a 1.5° climate.

Curr. Opin. Environ. Sustain., 31, 10–15, doi:10.1016/j.cosust.2017.11.005.
Pelling, M., K. O’Brien and D. Matyas, 2015: Adaptation and transformation. 
Clim Change, 133(1), 113–127, doi:10.1007/s10584-014-1303-0.
Pereira, J.J., 2018: Science and technology to enhance disaster resilience in 
a changing climate. In: Science and Technology in Disaster Risk Reduction 
in Asia [Shaw, R., K. Shiwaku and T. Izumi(eds.)] Vol.3, Academic Press, 
Cambridge, MA, pp. 31–38. ISBN 978-0128127117.
Perera, A.T.D., et  al., 2020: Quantifying the impacts of climate change and 
extreme climate events on energy systems. Nat. Energy, 5(2), 150–159, 
doi:10.1038/s41560-020-0558-0.
Perkins, P .E., 2019: Climate justice, commons, and degrowth. Ecol. Econ., 160, 
183–190, doi:10.1016/j.ecolecon.2019.02.005.
Persson, Å., 2019: Global adaptation governance: an emerging but contested 
domain. WIREs Clim. Chang., 10(6), e618, doi:10.1002/wcc.618.

Pervin, M., et al., 2013: A framework for mainstreaming climate resilience into 
development planning. https://pubs.iied.org/pdfs/10050IIED.pdf , (accessed 
31.01.22)..
Peters, H.P ., 2013: Gap between science and media revisited: Scientists as public 
communicators. Proc. Natl. Acad. Sci., 110(Supplement_3), 14102–14109.Peterson, G.D., et  al., 2018: Welcoming different perspectives in IPBES: 
“Nature’s contributions to people” and “Ecosystem services”. Ecol. Soc., 
23(1), doi:10.5751/es-10134-230139.
Petrides, D., A. Papacharalampopoulos, P . Stavropoulos and G. Chryssolouris, 
2018: Dematerialisation of products and manufacturing-generated 
knowledge content: relationship through paradigms. Int. J. Prod. Res., 56(1), 
86–96, doi:10.1080/00207543.2017.1401246.
Phillips, C.A., et al., 2020: Compound climate risks in the COVID-19 pandemic. 
Nat. Clim. Chang., 10(7), 586–588, doi:10.1038/s41558-020-0804-2.
Piggot, G., 2018: The influence of social movements on policies that constrain

fossil fuel supply. Clim. Policy, 18(7), 942–954, doi:10.1080/14693062.201
7.1394255.
Pineda-López, M.R., et al., 2015: The role of women in the forest conservation 
in a Mexican national park: pruning firs for the manufacture of christmas 
wreaths. Hum Ecol, 43(3), 493–501, doi:10.1007/s10745-015-9756-y.
Pinnegar, J.K., et al., 2021: Future Socio-political scenarios for aquatic resources 
in europe: a common framework based on shared-socioeconomic-pathways 
(SSPs). Front. Mar. Sci., 7(1096), doi:10.3389/fmars.2020.568219.
Pirmana, V., A.S. Alisjahbana, R. Hoekstra and A. Tukker, 2019: Implementation 
barriers for a system of environmental-economic accounting in developing 
countries and its implications for monitoring sustainable development goals. 
Sustainability, 11(22), 6417, doi:10.3390/su11226417.
Plank, C., et  al., 2021: Climate policy integration viewed through the 
stakeholders’ eyes: a co-production of knowledge in social-ecological

transformation research. Environ. Policy Gov., 31(4), 387–399, doi:10.1002/
eet.1938.
Plesa, P ., 2019: A theoretical foundation for ecopsychology: looking at 
ecofeminist epistemology. New Ideas Psychol, 52, 18–25, doi:10.1016/j.
newideapsych.2018.10.002.
Popescu, G.H., et al., 2017: Stabilizing valences of an optimum monetary zone 
in a resilient economy—approaches and limitations. Sustainability, 9(6), 
1051, doi:10.3390/su9061051.
Popp, A., et al., 2014: Land-use transition for bioenergy and climate stabilization: 
model comparison of drivers, impacts and interactions with other land use 
based mitigation options. Clim Change, 123(3), 495–509, doi:10.1007/
s10584-013-0926-x.
Porta, D., 2020: Protests as critical junctures: some reflections towards a 
momentous approach to social movements. Soc. Mov Stud., 19(5-6), 556–
575, doi:10.1080/14742837.2018.1555458.
Power, M., et  al., 2016: The political economy of energy transitions in

Mozambique and South Africa: the role of the rising powers. Energy Res. Soc. 
Sci., 17, 10–19, doi:10.1016/j.erss.2016.03.007.
Preston, B.L., L. Rickards, H. Fünfgeld and R.J. Keenan, 2015: Toward reflexive 
climate adaptation research. Curr. Opin. Environ. Sustain., 14, 127–135.
Price, R., 2021: Overview of Political Economy Analysis Frameworks in the Area 
of Climate Governance and Key Issues to Consider. Institute of Development 
Studies. Available at: https://opendocs.ids.ac.uk/opendocs/bitstream/
handle/20.500.12413/16655/1014_PEA_climate_change_frameworks.
pdf?sequence=1&isAllowed=y (Accessed 31.01.22).
Pringle, P . and T. Leiter, 2018: Pitfalls and potential of measuring climate 
change adaptation through adaptation metrics. In: Adaptation metrics: 
Perspectives on measuring, aggregating and comparing adaptation results 
[Christiansen, L. (eds.)]. UNEP DTU Partnership, Copenhagen, Denmark. ISBN 
978-8793458277.

Psaltoglou, A. and E. Calle, 2018: Enhanced connectivity index - A new measure 
for identifying critical points in urban public transportation networks. Int. J. 
Crit. Infrastruct. Prot., 21, 22–32, doi:10.1016/j.ijcip.2018.02.003.
Purdon, M., 2014: The Comparative Turn in Climate Change Adaptation 
and Food Security Governance Research. CCAFS Working Paper no. 92. 
Copenhagen, Denmark: CGIAR Research Program on Climate Change, 
Agriculture and Food Security (CCAFS).
Quader, M.  A., S. Ahmed and R.A.R. Ghazillaa, 2016: Recent progress and 
future trends of CO2 breakthrough iron and steelmaking technologies for

18
2761Climate Resilient Development Pathways  Chapter 18
CO2 mitigation. In: Ironmaking and Steelmaking Processes: Greenhouse 
Emissions, Control, and Reduction [Cavaliere, P .(ed.)]. Springer International 
Publishing, Cham, pp. 373–388. ISBN 978-3319395296.
Rabaey, K., T. Vandekerckhove, A.V. de Walle and D.L. Sedlak, 2020: The third 
route: Using extreme decentralization to create resilient urban water 
systems. Water Res., 185, 116276, doi:10.1016/j.watres.2020.116276.
Radcliffe, S.A., 2014: Gendered frontiers of land control: indigenous territory, 
women and contests over land in Ecuador. Gend. Place Cult., 21(7), 854–
871, doi:10.1080/0966369X.2013.802675.
Ramalho, J., 2019: Worlding aspirations and resilient futures: Framings of risk 
and contemporary city-making in Metro Cebu, the Philippines. Asia Pac 
Viewp, 60(1), 24–36, doi:10.1111/apv.12208.
Ramkissoon, H. and L. Smith, 2014: The Relationship between Environmental

Worldviews, Emotions and Personal Efficacy in Climate Change. International 
Journal of Arts & Sciences, CD-ROM. ISSN: 1944-6934, 7(1):93–109.
Randers, J., et al., 2019: Achieving the 17 sustainable development goals within 
9 planetary boundaries. Glob. Sustain., 2, e24.
Rao, N., 2019: From abandonment to autonomy: Gendered strategies for coping 
with climate change, Isiolo County, Kenya. Geoforum, 102, 27–37, Doi: 
10.1016/j.geoforum.2019.03.017.
Rao, N., et al., 2019a: A qualitative comparative analysis of women’s agency 
and adaptive capacity in climate change hotspots in Asia and Africa. Nat. 
Clim. Chang., 9(12), 964–971, doi:10.1038/s41558-019-0638-y.
Rao, N.D., P . Sauer, M. Gidden and K. Riahi, 2019b: Income inequality projections 
for the shared socioeconomic pathways (SSPs). Futures, 105, 27–39, 
doi:10.1016/j.futures.2018.07.001.
Rasul, G. and B. Sharma, 2016: The nexus approach to water–energy–food

security: an option for adaptation to climate change. Clim. Policy, 16(6), 
682–702, doi:10.1080/14693062.2015.1029865.
Raworth, K., 2017: Doughnut Economics: Seven Ways to Think Like a 21st-
Century Economist. Chelsea Green Publishing, White River Junction, 
Vermont, 320. ISBN 978-1603586740.
Reckien, D., et al., 2018: Equity, environmental justice, and urban climate change. 
In: Climate Change and Cities: Second Assessment Report of the Urban 
Climate Change Research Network [Rosenzweig, C., et al.(ed.)]. Cambridge 
University Press, Cambridge, pp. 173–224. ISBN 978-1316603338.
Reed, M.G., A. Scott, D. Natcher and M. Johnston, 2014: Linking gender, climate 
change, adaptive capacity, and forest-based communities in Canada. Can. J. 
For. Res. , 44(9), 995–1004, doi:10.1139/cjfr-2014-0174.
Reid, R.S., M.  E. Fernández-Giménez and K.  A. Galvin, 2014: Dynamics and 
resilience of rangelands and pastoral peoples around the globe. Annu. Rev. 
Environ. Resour., 39(1), 217–242.

Remling, E., 2021: Climate-related Security Risks in the 2020 Updated Nationally 
Determined Contributions. SIPRI, January, 2021. , Accessed 2021-08-30.
https://www.sipri.org/publications/2021/sipri-insights-peace-and-security/
climate-related-security-risks-2020-updated-nationally-determined-
contributions
Remling, E. and J. Veitayaki, 2016: Community-based action in Fiji’s Gau Island: 
a model for the Pacific? Int. J. Clim. Chang. Strateg. Manag., 8(3), 375–398, 
doi:10.1108/IJCCSM-07-2015-0101.
Repo, P . and K. Matschoss, 2020: Social innovation for sustainability challenges. 
Sustainability, 12(1), 319.
Riahi, K., et al., 2017: The shared socioeconomic pathways and their energy, 
land use, and greenhouse gas emissions implications: an overview. Glob. 
Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
Ribeiro, H.V., D. Rybski and J.P . Kropp, 2019: Effects of changing population 
or density on urban carbon dioxide emissions. Nat Commun, 10(1), 3204,

doi:10.1038/s41467-019-11184-y.
Ribot, J., 2011: Vulnerability before adaptation: toward Transformative 
climate action. Glob. Environ. Chang., 21, 1160–1162, doi:10.1016/j.
gloenvcha.2011.07.008.Richardson, K.J., et al., 2018: Food security outcomes under a changing climate: 
impacts of mitigation and adaptation on vulnerability to food insecurity. 
Clim Change, 147(1), 327–341, doi:10.1007/s10584-018-2137-y.
Riechers, M., Á. Balázsi, M. García-Llorente and J. Loos, 2021: Human-nature 
connectedness as leverage point. Ecosyst. People, 17(1), 215–221, doi:10.1
080/26395916.2021.1912830.
Roberts, D., J. Douwes, C. Sutherland and V. Sim, 2020: Durban’s 100 resilient 
cities journey: governing resilience from within. environ urban, 32(2), 547–
568, doi:10.1177/0956247820946555.
Robinson, S., et  al., 2021: Climate compatible development in practice. Dev. 
Pract., 0(0), 1–11, doi:10.1080/09614524.2021.1907534.
Rochette, A., 2016: Climate change is a social justice issue: The need for a

gender-based analysis of mitigation and adaptation policies in Canada and 
Quebec. J. Environ. Law Pract., 29, 383–410.
Rockström, J., et  al., 2009a: A safe operating space for humanity. Nature, 
461(7263), 472–475, doi:10.1038/461472a.
Rockström, J., et al., 2009b: Planetary boundaries: exploring the safe operating 
space for humanity. Ecol. Soc., 14(2), doi:10.5751/ES-03180-140232.
Roelich, K. and J. Giesekam, 2019: Decision making under uncertainty in 
climate change mitigation: introducing multiple actor motivations, agency 
and influence. Clim. Policy, 19(2), 175–188.
Rogelj, J., et  al., 2010: Copenhagen accord pledges are paltry. Nature, 
464(7292), 1126–1128, doi:10.1038/4641126a.
Rogelj, J., D. Shindell, K. Jiang, S. Fifita, P . Forster, V. Ginzburg, C. Handa, H. 
Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian, and M.V. Vilariño, 
2018: Mitigation Pathways Compatible with 1.5°C in the Context of

Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special 
Report on the impacts of global warming of 1.5°C above pre-industrial 
levels and related global greenhouse gas emission pathways, in the context 
of strengthening the global response to the threat of climate change, 
sustainable development, and efforts to eradicate poverty [Masson-
Rogers, P ., J.J. Bohland and J. Lawrence, 2020: Resilience and values: Global 
perspectives on the values and worldviews underpinning the resilience 
concept. Polit Geogr, 83, 102280, doi:10.1016/j.polgeo.2020.102280.
Roggema, R., T. Vermeend and A.V. Dobbelsteen, 2012: Incremental change, 
transition or transformation? Optimising change pathways for climate

adaptation in spatial planning. Sustainability, 4(10), doi:10.3390/su4102525.
Rohat, G., et  al., 2019: Influence of changes in socioeconomic and climatic 
conditions on future heat-related health challenges in Europe. Glob. Planet. 
Change., 172, 45–59, doi:10.1016/j.gloplacha.2018.09.013.
Romero-Lankao, P ., et al., 2018: Urban transformative potential in a changing 
climate. Nature Clim Change, 8(9), 754–756, doi:10.1038/s41558-018-0264-
0.
Romero Mosquera, M., 2019: Banning plastic straws: the beginning of the war 
against plastics. Environ. Earth Law J. (eelj), 9(1).
Rose, S.K., 2017: Managing Climate Damages: Exploring Trade-offs. Electric 
Power Research Institute, http://web.stanford.edu/group/emf-research/docs/
sm/2019/wk2/Rose.pdf , (accessed 31.01.22).
Rose, S.K., et  al., 2014: Bioenergy in energy transformation and climate 
management. Clim Change, 123(3), 477–493, doi:10.1007/s10584-013-
0965-3.
Rose, S.K. and M. Scott, 2018: Grounding Decisions: A Scientific Foundation

for Companies Considering Global Climate Scenarios and Greenhouse 
Gases. Electric Power Research Institute, https://www.epri.com/research/
products/000000003002014510, (accessed 31.01.22).
Rose, S.K. and M. Scott, 2020: Review of 1.5°C and Other Newer Global Emissions 
Scenarios: Insights for Company and Financial Climate Low-Carbon Transition 
Risk Assessment and Greenhouse Gas Goal Setting. Electric Power Research

18
2762Chapter 18 Climate Resilient Development Pathways
Institute, https://www.epri.com/research/products/000000003002018053, 
(accessed 31.01.22).
Rosenberg, M.N., 2021: What matters? The role of values in transformations 
toward sustainability: a case study of coffee production in Burundi. Sustain 
Sci, doi:10.1007/s11625-021-00974-3.
Rosenzweig, C. and W. Solecki, 2018: Action pathways for transforming cities. 
Nature Clim Change, 8(9), 756–759, doi:10.1038/s41558-018-0267-x.
Rougoor, W. and C. van Marrewijk, 2015: Demography, growth, and global 
income inequality. World Dev., 74(C), 220–232.
Rowell, L. and A. Feldman, 2019: Knowledge democracy and action research. 
Educ. Action Res., 27(1), 1–6, doi:10.1080/09650792.2019.1557456.
Roy, J., P . Tschakert, H. Waisman, S. Abdul Halim, P . Antwi-Agyei, P . Dasgupta, 
B. Hayward, M. Kanninen, D. Liverman, C. Okereke, P .F . Pinho, K. Riahi, 
and A.G. Suarez Rodriguez, 2018: Sustainable Development, Poverty

Eradication and Reducing Inequalities. In: Global Warming of 1.5°C. An 
Rudebusch, G., 2019: Climate change and the Federal Reserve. FRBS Economic 
Letter Available at: https://www.frbsf.org/economic-research/publications/
economic-letter/2019/march/climate-change-and-federal-reserve/ (accessed 
31.01.22).Ruggeri Laderchi, C., R. Saith and F . Stewart, 2003: Does it matter

that we do not agree on the definition of poverty? A comparison of four 
approaches. Oxf. Dev. Stud., 31, 243–274, doi:10.1080/136008103200011
1698.
Ruggiero, S., H. Busch, T. Hansen and A. Isakovic, 2021: Context and agency 
in urban community energy initiatives: an analysis of six case studies 
from the Baltic Sea region. Energy Policy, 148, 111956, doi:10.1016/j.
enpol.2020.111956.
Runhaar, H., 2018: Mainstreaming climate adaptation : taking stock about B 
what works ^ from empirical research worldwide. Reg Environ Change, 18, 
1201–1210.
Ryan, D. and E. Bustos, 2019: Knowledge gaps and climate adaptation policy: 
a comparative analysis of six Latin American countries. Clim. Policy, 19(10), 
1297–1309, doi:10.1080/14693062.2019.1661819.
Ryan, L., S. Moarif, E. Levina and R. Baron, 2011: Energy efficiency policy and 
carbon pricing. IEA Information Paper. Available at: https://www.iea.org/
reports/energy-efficiency-policy-and-carbon-pricing (accessed 31.01.22).

Ryota, N., 2019: A Possible Approach to Fiscal Rules in Small Islands — 
Incorporating Natural Disasters and Climate ChangeRyot. IMF Working 
paper. WP/19/186.
Sachdeva, S., 2016: Religious Identity, Beliefs, and Views about Climate Change. 
Oxford University Press USA, 2016.
Sadoff, C.W., E. Borgomeo and S. Uhlenbrook, 2020: Rethinking water for SDG 
6. Nat. Sustain., 3(5), 346–347, doi:10.1038/s41893-020-0530-9.
Sage, C., C. Kropp and I. Antoni-Komar, 2021: Grassroots initiatives in food 
system transformation: the role of food movements in the second ‘Great 
Transformation’, 1–19. Routledge, ISBN 978-1003131304.
Saj, S., et al., 2017: The way forward: an agroecological perspective for climate-
smart agriculture. Agric. Ecosyst. Environ., 250, 20–24, doi:10.1016/j.
agee.2017.09.003.
Saja, A.M.  A., A. Goonetilleke, M. Teo and A.M. Ziyath, 2019: A critical review 
of social resilience assessment frameworks in disaster management. Int. J.

Disaster Risk Reduct., 35, 101096. 10.1016/j.ijdrr.2019.101096
Sakaguchi, K., A. Varughese and G. Auld, 2017: Climate wars? A systematic 
review of empirical analyses on the links between climate change and 
violent conflict. Int. Stud. Rev., 19(4), 622–645, doi:10.1093/isr/vix022.Salamon, M.K., 2019: Leading the public into emergency mode. Clim. 
Mobilization.
Sánchez, A. and M. Izzo, 2017: Micro hydropower: an alternative for climate 
change mitigation, adaptation, and development of marginalized local 
communities in Hispaniola Island. Clim. Change, 140(1), 79–87, doi:10.1007/
s10584-016-1865-0.
Sanchez Rodriguez, R., D. Ürge-Vorsatz and A.S. Barau, 2018: Sustainable 
development goals and climate change adaptation in cities. Nature Clim 
Change, 8(3), 181–183, doi:10.1038/s41558-018-0098-9.
Sanganyado, E., C. Teta and B. Masiri, 2018: Impact of African traditional 
worldviews on climate change adaptation. Integr Environ Assess Manag, 
14(2), 189–193, doi:10.1002/ieam.2010.

Sarker, S., et al., 2018: From science to action: Exploring the potentials of Blue 
Economy for enhancing economic sustainability in Bangladesh. Ocean. 
Coast. Manag., 157, 180–192, doi:10.1016/j.ocecoaman.2018.03.001.
Satyal, P ., M.F . Byskov and K. Hyams, 2020: Addressing multi-dimensional 
injustice in indigenous adaptation: the case of Uganda’s Batwa community. 
Clim. Dev., 0(0), 1–14, doi:10.1080/17565529.2020.1824888.
Savaget, P ., M. Geissdoerfer, A. Kharrazi and S. Evans, 2019: The theoretical 
foundations of sociotechnical systems change for sustainability: A 
systematic literature review. J  Clean Prod, 206, 878–892, doi:10.1016/j.
jclepro.2018.09.208.
Scarano, F .R., 2017: Ecosystem-based adaptation to climate change: concept, 
scalability and a role for conservation science. Perspect. Ecol. Conserv., 15(2), 
65–73, doi:10.1016/j.pecon.2017.05.003.
Schaeffer, M., et  al., 2015: Mid- and long-term climate projections for

fragmented and delayed-action scenarios. Technol Forecast Soc Change, 90, 
257–268, doi:10.1016/j.techfore.2013.09.013.
Scheffran, J., 2020: Climate extremes and conflict dynamics. In: Climate 
Extremes and Their Implications for Impact and Risk Assessment [Sillmann, 
J., S. Sippel and S. Russo(eds.)]. Elsevier, Amsterdam, pp. 293–315. ISBN 978-
0128148952 Chapter 16). https://doi.org/10.1016/C2017-0-01794-9.
Scherer, L., et  al., 2018: Trade-offs between social and environmental 
sustainable development goals. Environ. Sci. Policy, 90, 65–72, doi:10.1016/j.
envsci.2018.10.002.
Schipper, E.L.F ., 2020: Maladaptation: when adaptation to climate change goes 
very wrong. One Earth, 3(4), 409–414, doi:10.1016/j.oneear.2020.09.014.
Schipper, E.L.F ., et al., 2014: Community-based adaptation to climate change : 
scaling it up. Routledge, Oxfordshire, UK, ISBN 978-0203105061.
Schipper, E.L.F ., et  al., 2020a: Turbulent transformation: abrupt societal

disruption and climate resilient development. Clim. Dev., 0(0), 1–8, doi:10.1
080/17565529.2020.1799738.
Schipper, E.L.F ., et  al., 2020b: Turbulent transformation: abrupt societal 
disruption and climate resilient development. Clim. Dev. https://doi.org/10.1
080/17565529.2020.1799738
Schipper, E.L.F ., et  al., 2020c: The debate: Is global development adapting 
to climate change? World Dev. Perspect., 18, 100205, doi:10.1016/j.
wdp.2020.100205.
Schleussner, C.-F ., et  al., 2021: Pathways of climate resilience over the 21st 
century. Environ. Res. Lett., 16(5), 54058, doi:10.1088/1748-9326/abed79.
Schlosberg, D., L.B. Collins and S. Niemeyer, 2017: Adaptation policy and 
community discourse: risk, vulnerability, and just transformation. Env Polit, 
26(3), 413–437, doi:10.1080/09644016.2017.1287628.
Schmitz, H., 2017: Who drives climate-relevant policies in the rising powers? 
New Polit. Econ., 22(5), 521–540, doi:10.1080/13563467.2017.1257597.

Schoenmaker, D., 2019: Greening monetary policy. https://www.bruegel.
org/2019/02/greening-monetary-policy/. Accessed 29 Apr 2019  .
Schoenmaker, D. and W. Schramade, 2020: Asset Pricing and Sustainability: A 
Teaching Note. Social Science Research Network, Rochester, NY . Available at: 
https://papers.ssrn.com/abstract=3539080.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3539080. Accessed 
2020.

18
2763Climate Resilient Development Pathways  Chapter 18
Schot, J. and W.E. Steinmueller, 2018: Three frames for innovation policy: R&D, 
systems of innovation and transformative change. Res Policy, 47(9), 1554–
1567.
Schwarz, M., C. Nakhle and C. Knoeri, 2020: Innovative designs of building 
energy codes for building decarbonization and their implementation 
challenges. J Clean Prod, 248, 119260, doi:10.1016/j.jclepro.2019.119260.
Scobie, M., 2013: Climate regulation: implications for trade competitiveness 
in caribbean states. In: Climate-Smart Technologies Integrating Renewable 
Energy and Energy Efficiency in Mitigation and Adaptation Responses. 
Springer, Berlin Heidelberg, pp. 33–49.
Scobie, M., 2017: Fossil fuel reform in developing states: the case of Trinidad 
and Tobago, a petroleum producing small island developing state. Energy 
Policy, 104, 265–273, doi:10.1016/j.enpol.2017.02.008.
Scobie, M., J. Skovgaard and H. Asselt, 2018: Actors, frames and contexts in fossil

fuel subsidy reform: the case of Trinidad and Tobago, 283–302.  Cambridge 
University Press, 2018, UK. ISBN 978-1108416795.
Scott, S. and C. Ku, 2018: The UN Security Council and global action on climate 
change. Fac. Scholarsh., 1–24, doi:10.4337/9781785364648.00008.
Scoville-Simonds, M., H. Jamali and M. Hufty, 2020: The hazards of 
mainstreaming: climate change adaptation politics in three dimensions. 
World Dev., 125, 104683, doi:10.1016/j.worlddev.2019.104683.
Segger, M.-C., 2016: Advancing the Paris Agreement on climate change for 
sustainable development. Camb. Int. Law J., 5, 202–237, doi:10.4337/
cilj.2016.02.03.
Sellers, S. and K.L. Ebi, 2018: Climate change and health under the shared 
socioeconomic pathway framework. IJERPH, 15(1), doi:10.3390/
ijerph15010003.
Sen, A., 2000: Development as Freedom. Anchor, New York, ISBN 978-
0385720274. Reprint edition, 384 pp.
Sengers, F ., A.J. Wieczorek and R. Raven, 2019: Experimenting for sustainability

transitions: A systematic literature review. Technol Forecast Soc Change, 
145, 153–164, doi:10.1016/j.techfore.2016.08.031.
Serfilippi, E. and G. Ramnath, 2018: Resilience measurement and conceptual 
frameworks: a review of the literature. Ann. Public Coop. Econ., 89(4), 645–
664.
Settele, J., R. Scholes, R. Betts, S. Bunn, P . Leadley, D. Nepstad, J.T. Overpeck, and 
M. A. Taboada, 2014:Terrestrial and inland water systems. In: Climate Change 
2014: Impacts, Adaptation, and Vulnerability.Part A: Global and Sectoral 
Aspects. Contribution of Working Group II to the Fifth Assessment Report 
of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, 
D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, 
Y .O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P .R. 
Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, 
UK and New York, NY , USA, pp. 271–359. ISBN 978-1107058071.

Setzer, J. and R. Byrnes, 2019: Global trends in climate change litigation: 
2019 snapshot. Grantham Research Institute on Climate Change and the 
Environment and the Centre for Climate Change Economics and Policy, 
www.lse.ac.uk/GranthamInstitute , Accessed 2022-03-16.
Seyfang, G. and A. Smith, 2007: Grassroots innovations for sustainable 
development: Towards a new research and policy agenda. Env Polit, 16(4), 
584–603, doi:10.1080/09644010701419121.
Shaneyfelt, K.M., A.R. Anderson, P . Kumar and W.F . Hunt, 2017: Air quality 
considerations for stormwater green street design. Environ. Pollut., 231, 
768–778, doi:10.1016/j.envpol.2017.08.081.
Sharifi, A., 2020: Trade-offs and conflicts between urban climate change 
mitigation and adaptation measures: A literature review. J Clean Prod, 276, 
122813, doi:10.1016/j.jclepro.2020.122813.
Sharpe, B., et al., 2016: Three horizons: a pathways practice for transformation. 
Ecol. Soc., 21(2), doi:10.5751/ES-08388-210247.

Shaw, J., 2016: Archaeology, climate change and environmental ethics: 
diachronic perspectives on human:non-human:environment worldviews, 
activism and care. World. Archaeol., 48(4), 449–465, doi:10.1080/004382
43.2016.1326754.Sheller, M., 2021: Reconstructing tourism in the Caribbean: connecting 
pandemic recovery, climate resilience and sustainable tourism through 
mobility justice. J. Sustain. Tour., 29(9), 1436–1449, doi:10.1080/0966958
2.2020.1791141.
Shi, J., V.H.M. Visschers and M. Siegrist, 2015: Public perception of climate 
change: the importance of knowledge and cultural worldviews. Risk Analysis, 
35(12), 2183–2201, doi:10.1111/risa.12406.
Shi, L., 2020a: Beyond flood risk reduction: How can green infrastructure 
advance both social justice and regional impact? Socio-ecological Pract. 
Res., 2(4), 311–320, doi:10.1007/s42532-020-00065-0.
Shi, L., 2020b: From progressive cities to resilient cities: lessons from history

for new debates in equitable adaptation to climate change. Urban Aff. Rev., 
doi:10.1177/1078087419910827. 1078087419910827.
Shi, L. and S. Moser, 2021: Transformative climate adaptation in the United 
States: trends and prospects. Science, 372(6549), doi:10.1126/science.
abc8054.
Shiller, R., 2012: Finance and the Good Society. Princeton University Press, 
Princeton, New Jersey.
Shim, Y .-H., 2015: A research methodological debate on emancipatory 
catastrophism: The case of transnational marriage. Curr. Sociol., 63(1), 105–
109, doi:10.1177/0011392114559850.
Shindell, D., et  al., 2017: A climate policy pathway for near- and long-term 
benefits. Science, 356(6337), 493–494, doi:10.1126/science.aak9521.
Shiva, V., 2017: Swaraj. Hart, J. ed., 2017 In: The Wiley Blackwell Companion 
to Religion and Ecology. John Wiley & Sons, Ltd, UK, In, pp. 12–19. ISBN 
978-1118465523.
Shukla, P . R., J. Skea, R. Slade, R. van Diemen, E. Haughey, J. Malley, M. Pathak,

J. Portugal Pereira (eds.), 2019: Technical Summary, 2019. In: Climate Change 
and Land: an IPCC special report on climate change, desertification, land 
degradation, sustainable land management, food security, and greenhouse 
gas fluxes in terrestrial ecosystems [P .R. Shukla, J. Skea, E. Calvo Buendia, V. 
Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P . Zhai, R. Slade, S. Connors, 
R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, 
J. Portugal Pereira, P . Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, 
Sillitoe, P ., 2016: Indigenous studies and engaged anthropology: The 
collaborative moment. Routledge, Oxfordshire, UK.
Silva Rodríguez de San Miguel, J.A., 2018: Gender and water management in 
Mexico. Manag. Environ. Qual. Int. J., 29(5), 842–858, doi:10.1108/MEQ-10-
2017-0112.
Silver, J.J. and L.M. Campbell, 2018: Conservation, development and the 
blue frontier: the Republic of Seychelles’ debt restructuring for marine

conservation and climate adaptation program. Int. Soc. Sci. J., 68(229-230), 
241–256, doi:10.1111/issj.12156.
Silvola, H. and T. Landau, 2021: Sustainable Investing: Beating the Market 
with ESG, 1st  edn., Palgrave Macmillan, Cham, Switzerland, ISBN 978-
3030714888. 266 pp.
Siméant-Germanos, J., 2019: Conceptualizing contexts or contextualizing 
concepts? On some issues of the modeling of relational spaces in the study 
of collective action. Soc. Mov Stud., 0(0), 1–16, doi:10.1080/14742837.201
9.1629899.
Simonee, N. et al., 2021: Sila qanuippa? (how’s the weather?): Integrating Inuit 
Qaujimajatuqangit and environmental forecasting products to support travel 
safety around Pond Inlet, Nunavut in a changing climate. Weather, Climate, 
and Society. doi:10.1175/WCAS-D-20-0174.1.
Simonet, C. and G. Jobbins, 2016: Understanding patterns of climate resilient 
development – the case of Senegal. Overseas Development Institute,

London. Available at: http://prise.odi.org/wp-content/uploads/2016/04/
Understanding-patterns-of-climate-resilient-development-%E2%80%93-
the-case-of-Senegal_Low-Res.pdf.
Singh, C., 2019: Migration as a driver of changing household structures: 
implications for local livelihoods and adaptation. Migr. Dev., 8(3), 301–319, 
doi:10.1080/21632324.2019.1589073.

18
2764Chapter 18 Climate Resilient Development Pathways
Singh, C. et  al., 2018: The utility of weather and climate information for 
adaptation decision-making: current uses and future prospects in Africa and 
India. Clim. Dev., 10(5), 389–405, doi:10.1080/17565529.2017.1318744.
Singh, C., et  al., 2021a: Interrogating ‘effectiveness’ in climate change 
adaptation: 11 guiding principles for adaptation research and practice. Clim. 
Dev., 1–15, doi:10.1080/17565529.2021.1964937.
Singh, C., D. Solomon and N. Rao, 2021b: How does climate change adaptation 
policy in India consider gender? An analysis of 28 state action plans. Clim. 
Policy, 21(7), 958–975, doi:10.1080/14693062.2021.1953434.
Sitas, N., et  al., 2019: Exploring the usefulness of scenario archetypes in 
science-policy processes: experience across IPBES assessments. Ecol. Soc., 
24(3), doi:10.5751/ES-11039-240335.
Sitas, N., O. Selomane, M. Hamann and S.P .S. Gajjar, 2021: Towards equitable

urban resilience in the global south within a context of planning and 
management. In: Urban Ecology in the Global South [Shackleton, C.M., S.S. 
Cilliers, E. Davoren and M.J. du Toit(eds.)]. Springer International Publishing, 
Cham, pp. 325–345. ISBN 978-3030676506.
Slaven, M. and J. Heydon, 2020: Crisis, deliberation, and extinction rebellion. 
Crit. Stud. Secur., 8(1), 59–62.
Slee, B., 2015: Is there a case for community-based equity participation in 
Scottish on-shore wind energy production? Gaps in evidence and research 
needs. Renew. Sustain. Energy Rev., 41, 540–549.
Sloat, L.L., et al., 2020: Climate adaptation by crop migration. Nat Commun, 
11(1), 1243, doi:10.1038/s41467-020-15076-4.
Smit, B., et al., 2001: Adaptation to climate change in the context of sustainable 
development and equity. In: Climate Change 2001: Impacts, Adaptation, and 
Vulnerability. Contribution of Working Group II to the Third Assessment

Report of the Intergovernmental Panel on Climate Change [McCarthy, J.J., 
O.F . Canziani, N.A. Leary, D.J. Dokken and K.S. White(eds.)]. Cambridge 
University Press, Cambridge, UK and New York, NY , USA, pp. 877–912. ISBN 
978-0521807685.
Smith, A. and A. Stirling, 2018: Innovation, sustainability and democracy: an 
analysis of grassroots contributions. J. Self-gov. Manag. Econ., 6(1), 64, 
doi:10.22381/JSME6120183.
Smith, L.T., 2013: Decolonizing methodologies: Research and indigenous 
peoples. Zed Books Ltd.London.
Smith, P ., et al., 2013: How much land-based greenhouse gas mitigation can 
be achieved without compromising food security and environmental goals? 
Glob Change Biol, 19(8), 2285–2302, doi:10.1111/gcb.12160.
Smith, R. and K. McNamara, 2015: Future migrations from Tuvalu and Kiribati: 
exploring government, civil society and donor perceptions. Clim. Dev., 7, doi
:10.1080/17565529.2014.900603.

Smucker, T.A., et  al., 2015: Differentiated livelihoods, local institutions, and 
the adaptation imperative: Assessing climate change adaptation policy in 
Tanzania. Geoforum, 59, 39–50.
Söderholm, P . and J.E. Tilton, 2012: Material efficiency: An economic perspective. 
Resour. Conserv. Recycl., 61, 75–82, doi:10.1016/j.resconrec.2012.01.003.
Soga, M. and K.J. Gaston, 2016: Extinction of experience: the loss of human–
nature interactions. Front. Ecol. Environ., 14(2), 94–101, doi:10.1002/
fee.1225.
Solecki, W., M. Pelling and M. Garschagen, 2017: Transitions between risk 
management regimes in cities. Ecol. Soc., 22, 38, doi:10.5751/ES-09102-
220238.
Solecki, W., et al., 2018: City transformations in a 1.5°C warmer world. Nature 
Clim Change, 8(3), 177–181, doi:10.1038/s41558-018-0101-5.
Soma, K., et al., 2018: Social innovation – A future pathway for Blue growth? 
Mar. Policy., 87, 363–370, doi:10.1016/j.marpol.2017.10.008.

Song, L., et  al., 2020a: Contested energy futures, conflicted rewards? 
Examining low-carbon transition risks and governance dynamics in 
China’s built environment. Energy Res. Soc. Sci., 59, 101306, doi:10.1016/j.
erss.2019.101306.
Song, S., Z. Liu, C. He and W. Lu, 2020b: Evaluating the effects of urban expansion 
on natural habitat quality by coupling localized shared socioeconomic pathways and the land use scenario dynamics-urban model. Ecol Indic, 112, 
106071, doi:10.1016/j.ecolind.2020.106071.
Sørensen, E. and J. Torfing, 2019: Designing institutional platforms and arenas 
for interactive political leadership. Public Manag. Rev., 21(10), 1443–1463, 
doi:10.1080/14719037.2018.1559342.
Sovacool, B.K., 2018: Bamboo beating bandits: conflict, inequality, and 
vulnerability in the political ecology of climate change adaptation in 
Bangladesh. World Dev., 102, 183–194, doi:10.1016/j.worlddev.2017.10.014.

Sovacool, B.K., et al., 2015: The political economy of climate adaptation. Nature 
Clim Change, 5(7), 616–618.
Sovacool, B.K., M. Tan-Mullins, D. Ockwell and P . Newell, 2017: Political 
economy, poverty, and polycentrism in the global environment facility’s 
least developed countries fund (LDCF) for climate change adaptation. Third 
World Q, 38(6), 1249–1271, doi:10.1080/01436597.2017.1282816.
Srivastava, A., Sharma, R.K. and Suresh, A., 2020. Impact of Covid-19 on 
sustainable development goals.  Int. J. Adv. Sci. Technol,  29, pp.253-258.
Stafford-Smith, M., et  al., 2017: Integration: the key to implementing the 
sustainable development goals. Sustain Sci, 12(6), 911–919, doi:10.1007/
s11625-016-0383-3.
Stańczuk-Gałwiaczek, M., K. Sobolewska-Mikulska, H. Ritzema and J.M. 
van Loon-Steensma, 2018: Integration of water management and land 
consolidation in rural areas to adapt to climate change: Experiences from

Poland and the Netherlands. Land Use Policy, 77, 498–511, doi:10.1016/j.
landusepol.2018.06.005.
Starbird, E., M. Norton and R. Marcus, 2016: Investing in family planning: key 
to achieving the sustainable development goals. Glob Health Sci Pract, 4(2), 
191–210, doi:10.9745/GHSP-D-15-00374.
Steelman, T.A., et  al., 2019: Identifying transformational space for 
transdisciplinarity: using art to access the hidden third. Sustain Sci, 14(3), 
771–790, doi:10.1007/s11625-018-0644-4.
Steffen, B., 2018: The importance of project finance for renewable energy 
projects. Energy Econ., 69, 280–294, doi:10.1016/j.eneco.2017.11.006.
Stein, S. and M. Kalina, 2019: Becoming an agricultural growth corridor. Environ. 
Soc., 10(1), 83–100.
Sterk, M., I.  A. van de Leemput and E.T. Peeters, 2017: How to conceptualize and 
operationalize resilience in socio-ecological systems? Curr. Opin. Environ. 
Sustain., 28, 108–113, doi:10.1016/j.cosust.2017.09.003.

Stern, N., 2007: The Economics of Climate Change: The Stern Review. Cambridge 
University Press, Cambridge, ISBN 978-0511817434.
Stevenson, K.T., et al., 2014: Overcoming skepticism with education: interacting 
influences of worldview and climate change knowledge on perceived 
climate change risk among adolescents. Clim Change, 126(3), 293–304, 
doi:10.1007/s10584-014-1228-7.
Stiglitz, J.E., Sen, A. and Fitoussi, J.P ., 2009. Report by the commission on the 
measurement of economic performance and social progress.Stjepanović, 
S., D. Tomic and M. Skare, 2017: A new approach to measuring green GDP: 
a cross-country analysis. Entrepreneurship Sustain. Issues, 4, 574–590, 
doi:10.9770/jesi.2017.4.4(13).
Stjepanović, S., D. Tomic and M. Skare, 2019: Green GDP: an analysis for 
developing and developed countries. E+m Ekon. A Manag., 22, 4–17, 
doi:10.15240/tul/001/2019-4-001.
Stoddard, I., et al., 2021: Three decades of climate mitigation: why haven’t we

bent the global emissions curve? Annu. Rev. Environ. Resour., doi:10.1146/
annurev-environ-012220-011104.
Strazds, L., 2019: Radical hope: Transforming sustainability. J. Sustain. Educ. 
Journal of Sustainability Education, 21, 1–18.
Stuart, D., R. Gunderson and B. Petersen, 2020: The climate crisis as a catalyst 
for emancipatory transformation: An examination of the possible. Int. Sociol., 
35(4), 433–456.
Sugden, F ., et  al., 2014: Agrarian stress and climate change in the eastern 
Gangetic plains: gendered vulnerability in a stratified social formation. Glob. 
Environ. Chang., 29, 258–269, doi:10.1016/j.gloenvcha.2014.10.008.

18
2765Climate Resilient Development Pathways  Chapter 18
Sultana, F ., 2010: Living in hazardous waterscapes: Gendered vulnerabilities 
and experiences of floods and disasters. Environ. Hazards, 9(1), 43–53, 
doi:10.3763/ehaz.2010.SI02.
Sultana, F ., 2021: Climate change, COVID-19, and the co-production of 
injustices: a feminist reading of overlapping crises. Soc. Cult. Geogr., 22(4), 
447–460, doi:10.1080/14649365.2021.1910994.
Sultana, P ., et  al., 2019: Transforming local natural resource conflicts to 
cooperation in a changing climate: Bangladesh and Nepal lessons. Clim. 
Policy, 19(sup1), 94–106, doi:10.1080/14693062.2018.1527678.
Solomon, S., C. Singh and F . Islam, 2021: Examining the outcomes of urban 
adaptation interventions on gender equality using SDG 5. Clim. Dev., 0(0), 
1–12, doi:10.1080/17565529.2021.1939643.
Susskind, L., et al., 2020: Breaking out of carbon lock-in: Malaysia’s path to 
decarbonization. Front. Built Environ., 6, 21, doi:10.3389/fbuil.2020.00021.

Swaffield, J., 2017: Freebies, freedom and fundamental change: resistance to 
neoliberal environmentalism in large “green” corporations. Local Environ, 
22(5), 553–567, doi:10.1080/13549839.2016.1233952.
Swatuk, L.A., et al., 2021: The ‘boomerang effect’: insights for improved climate 
action. Clim. Dev., 13(1), 61–67, doi:10.1080/17565529.2020.1723470.
Swyngedouw, E., 2018: Promises of the Political: Insurgent Cities in a Post-
Political Environment. The MIT Press, Cambridge, Massachusetts, ISBN 978-
0262535656. 232 pp.
Swyngedouw, E., 2019: The perverse lure of autocratic postdemocracy. South 
Atl. Q. , 118(2), 267–286, doi:10.1215/00382876-7381134.
Tan, R.R. and D.C.Y . Foo, 2018: Integrated multi-scale water management as 
a climate change adaptation strategy. Clean Technol. Environ. Policy, 20(6), 
1123–1125, doi:10.1007/s10098-018-1551-1.
Tanaka, K., 2011: Review of policies and measures for energy efficiency 
in industry sector. Energy Policy, 39(10), 6532–6550, doi:10.1016/j.

enpol.2011.07.058.
Tanner, T. and J. Allouche, 2011: Towards a new political economy of climate 
change and development. IDS Bull., 42(3), 1–14, doi:10.1111/j.1759-
5436.2011.00217.x.
Tanner, T., et al., 2019: Influencing resilience: the role of policy entrepreneurs 
in mainstreaming climate adaptation. Disasters, 43(S3), 388–S411, 
doi:10.1111/disa.12338.
Tari, D. and J. Pattison, 2014: Evolving Customary Institutions in the Drylands 
An Opportunity for Devolved Natural Resource Governance in Kenya? Issue 
Paper. IIED, London ISBN 978-1784310424.
Tauginienė, L., et al., 2020: Citizen science in the social sciences and humanities: 
the power of interdisciplinarity. Palgrave Commun, 6(1), 1–11, doi:10.1057/
s41599-020-0471-y.
Taylor, M., 2008: Beyond technology-push and demand-pull: Lessons from 
California’s solar policy. Energy Econ., 30(6), 2829–2854, doi:10.1016/j.
eneco.2008.06.004.
Temmer, J., 2017a: Building a Climate-Resilient City: Agriculture and food

security. IISD, http://prairieclimatecentre.ca/wp-content/uploads/2017/04/
pcc-brief-climate-resilient-city-agriculture-food.pdf. Accessed April 23, 2017
Temmer, J., 2017b: Building a Climate-Resilient City: Disaster preparedness and 
emergency management. IISD, http://prairieclimatecentre.ca/wp-content/
uploads/2017/04/pcc-brief-disaster-mangement-emergency-preparedness.
pdf.
Tengö, M., et  al., 2017: Weaving knowledge systems in IPBES, CBD and 
beyond—lessons learned for sustainability. Curr. Opin. Environ. Sustain., 26-
27, 17–25, doi:10.1016/j.cosust.2016.12.005.
Terry, G., 2009: No climate justice without gender justice: an overview of the 
issues. Gend. Dev., 17(1), 5–18, doi:10.1080/13552070802696839.
Terton, A., 2017: Building a Climate-Resilient City: Urban ecosystems. IISD, 
http://prairieclimatecentre.ca/wp-content/uploads/2017/04/pcc-brief-
climate-resilient-city-urban-ecosystems.pdf. Accessed 13th May 2019.

Thackeray, S.J., et  al., 2020: Civil disobedience movements such as school 
strike for the climate are raising public awareness of the climate change 
emergency. Glob Change Biol, 26(3), 1042–1044.The People’s Republic of China, 2017: Vision for Maritime Cooperation under 
the Belt and Road Initiative.
Thew, H., L. Middlemiss and J. Paavola, 2020: “Youth is not a political position”: 
Exploring justice claims-making in the UN Climate change negotiations. 
Glob. Environ. Chang., 61, 102036.
Thomas, B.K., 2008: Methodological debate in poverty studies: 
towards ‘participatory qual–quant’? Dev. Pract., 18(2), 280–288, 
doi:10.1080/09614520801899218.
Thompson-Hall, M., E.R. Carr and U. Pascual, 2016: Enhancing and expanding 
intersectional research for climate change adaptation in agrarian settings. 
Ambio, 45(3), 373–382, doi:10.1007/s13280-016-0827-0.
Tilleard, S. and J. Ford, 2016: Adaptation readiness and adaptive capacity of

transboundary river basins. Clim Change, 137(3-4), 575–591.
Tobias, J.K. and R.C.A., 2014: “that land means everything to us as 
Anishinaabe….”: environmental dispossession and resilience on the 
north shore of Lake Superior. Health Place, 29, 26–33, doi:10.1016/j.
healthplace.2014.05.008.
Tolliver, C., A.R. Keeley and S. Managi, 2019: Green bonds for the Paris 
Agreement and sustainable development goals. Environ. Res. Lett., 14(6), 
64009, doi:10.1088/1748-9326/ab1118.
Törnberg, A., 2018: Combining transition studies and social movement theory: 
towards a new research agenda. Theor Soc, 47(3), 381–408, doi:10.1007/
s11186-018-9318-6.
Tramel, S., 2018: Convergence as political strategy: social justice movements, 
natural resources and climate change. Third World Q, 39(7), 1290–1307, doi
:10.1080/01436597.2018.1460196.
Tramutola, M.J., 2019: Adpatación al Cambio Climático ¿Con Perspectiva de 
Género? LatinoAdapta, http://www.cambioclimaticoydecisiones.org/wp-

content/uploads/2019/08/PolicyBrief-Genero_5_09_2019.pdf.
Trewin, D. and J. Hall, 2004: Measures of Australia’s progress: key economic, 
social and environment indicators. Oecd Obs., 246-247, 35–37.
Tschakert, P ., et  al., 2016: Tchange: the role of values and visioning 
in transformation science. Curr. Opin. Environ. Sustain., 20, 21–25, 
doi:10.1016/j.cosust.2016.04.003.
Tufa, F ., A. Amsalu and E.B. Zoomers, 2018: Failed promises: governance regimes 
and conflict transformation related to Jatropha cultivation in Ethiopia. Ecol. 
Soc., 23(4), doi:10.5751/ES-10486-230426.
Turnhout, E., et  al., 2021: Enabling transformative economic change in the 
post-2020 biodiversity agenda. CONSERVATION LETTERS, 14(4), e12805, 
doi:10.1111/conl.12805.
TWI, 2019: TWI2050 Report: The Digital Revolution and Sustainable 
Development: Opportunities and Challenges. https://iiasa.ac.at/web/home/
research/twi/Report2019.html.
Ullah, I., H. Fang and K. Jebran, 2019: Do gender diversity and CEO gender

enhance firm’s value? Evidence from an emerging economy. Corporate 
Governance, 20(1), 44–66, doi:10.1108/CG-03-2019-0085.
UNDRR, 2015: Sendai Framework for Disaster Risk Reduction. United Nations 
International Strategy for Disaster Reduction, Geneva, Switzerland, 2015–
2030.
Undrr, 2019: Global Assessment Report on Disaster Risk Reduction. Geneva, 
Switzerland. United Nations Office for Disaster Risk Reduction.
UNEP , 2014: Adaptation Gap Report 2014.
UNEP , 2018a: Adaptation Gap Report 2018.
UNEP , 2018b: Single-Use Plastics: A Roadmap for Sustainability. https://
www.unenvironment.org/resources/report/single-use-plastics-roadmap-
sustainability.
UNEP , 2021: Adaptation Gap Report 2020.
UNESCO, 2019: Resilience in a Time of Uncertainty: Indigenous peoples and 
climate change. https://en.unesco.org/events/resilience-time-uncertainty-
indigenous-peoples-and-climate-change.
UNESCO, 2018: What is Local and Indigenous Knowledge. United Nations

18
2766Chapter 18 Climate Resilient Development Pathways
UNFCCC, 1992: United Nations Framework Convention on Climate Change. 
https://unfccc.int/resource/docs/convkp/conveng.pdf.
UNFCCC, 2010: Cancún Climate Change Conference.
UNFCCC, 2011: Launching the Green Climate Fund. https://unfccc.int/resource/
docs/2011/cop17/eng/09a01.pdf#page=55.
UNFCCC, 2015: Paris Agreement. United Nations. United Nations Framework 
Convention on Climate Change.
UNFCCC, 2017: The China Clean Development Mechanism Fund (CCDMF).
United Nations, 1994: United Nations Convention to Combat Desertification 
in those Countries Experiencing Serious Drought and/or Desertification, 
Particularly in Africa. United Nations, New York, NY .
United Nations, 2007: United Nations Declaration on the Rights of 
Indigenous Peoples. New York, https://www.un.org/development/desa/
indigenouspeoples/wp-content/uploads/sites/19/2018/11/UNDRIP_E_web.
pdf.
United Nations, 2015: Transforming our world: the 2030 Agenda for

Sustainable Development. https://sustainabledevelopment.un.org/post2015/
transformingourworld.
United Nations, 2020: Human Development Report 2020. The Next Frontier: 
Human Development and the Anthropocene. United Nations Development 
Programme.
United Nations, 2021: The Sustainable Development Goals Report. Department 
of Economic and Social Affairs, United Nations, New York, N.Y .
United Nations, 2021: Adaptation Gap Report 2020. https://www.unep.org/
adaptation-gap-report-2020 . (United Nations Environment Programme, 
Programme, U. N. E., Nairobi, Kenya, 99 pp.).
Ura, K. and K. Galay, 2004: Gross National Happiness and Development.
USAID, 2014: Climate resilient development a framework for understanding 
and addressing climate change. https://pdf.usaid.gov/pdf_docs/PBAAA245.
pdf.
USAID, 2018: Climate Risk Profile: Kenya.
USEPA, 2019: Global Greenhouse Gas Emissions Data.
Wuebbles, D.J., et  al. (ed.), 2017: Climate Science Special Report: Fourth

National Climate Assessment, Volume I. Washington, DC, USA. U.S. Global 
Change Research Program, 470 pp.
Vaghefi, N., C. Siwar and S. Aziz, 2015: Green GDP and Sustainable Development 
in Malaysia. Curr. World Environ., 10(1), 1–8.
Valta, J., 2020: Innovation ecosystem perspective accelerating sustainable 
business models. In: Affordable and Clean Energy [Leal Filho, W., A.M. Azul, 
L. Brandli, P .G. Özuyar and T. Wall(eds.)]. Springer International Publishing, 
Cham, pp. 1–11. ISBN 978-3319710570.
van Baalen, S. and M. Mobjörk, 2018: Climate change and violent conflict in 
east Africa: integrating qualitative and quantitative research to probe the 
mechanisms. Int. Stud. Rev., 20(4), 547–575, doi:10.1093/isr/vix043.
Van den Bergh, J.C., 2007: Abolishing GDP.
van Dooren, C., et al., 2018: Unsustainable dietary habits of specific subgroups 
require dedicated transition strategies: Evidence from the Netherlands. Food 
Policy, 79, 44–57, doi:10.1016/j.foodpol.2018.05.002.

van Egmond, N.D. and H.J.M. de Vries, 2011: Sustainability: The search 
for the integral worldview. Futures, 43(8), 853–867, doi:10.1016/j.
futures.2011.05.027.
Van Opstal, M. and J. Hugé, 2013: Knowledge for sustainable development: a 
worldviews perspective. Environ Dev Sustain, 15(3), 687–709, doi:10.1007/
s10668-012-9401-5.
van Puijenbroek, P .J.T.M., A.  F . Bouwman, A.H.W. Beusen and P .L. Lucas, 2014: 
Global implementation of two shared socioeconomic pathways for future 
sanitation and wastewater flows. Water Sci. Technol., 71(2), 227–233, 
doi:10.2166/wst.2014.498.
van Ruijven, B.J., et al., 2014: Enhancing the relevance of shared socioeconomic 
pathways for climate change impacts, adaptation and vulnerability research. 
Clim Change, 122(3), 481–494, doi:10.1007/s10584-013-0931-0.Van Vuuren, D.P ., et al., 2019: Integrated scenarios to support analysis of the 
food–energy–water nexus. Nat. Sustain., 2(12), 1132–1141, doi:10.1038/
s41893-019-0418-8.

Vander Stichele, M., 2020: The Financialised Firm.
Veland, S., I. Gram-Hanssen, D. Maggs and A.H. Lynch, 2021: Can the sustainable 
development goals harness the means and the manner of transformation? 
Sustain Sci, doi:10.1007/s11625-021-01032-8.
Venema, H. and J. Temmer, 2017a: Building a Climate-Resilient City: Electricity 
and information and communication technology infrastructure. IISD, http://
prairieclimatecentre.ca/wp-content/uploads/2017/04/pcc-brief-climate-
resilient-city-electricity-ict.pdf.
Venema, H. and J. Temmer, 2017b: Building a Climate-Resilient City: Water 
supply and sanitation systems. e International Institute for Sustainable 
Development and the University of Winnipeg, , http://prairieclimatecentre.
ca/wp-content/uploads/2017/04/pcc-brief-climate-resilient-city-water-
supply-sanitation.pdf , Accessed 2022-03-16.
Verkerk, P .J., et al., 2020: Climate-smart forestry: the missing link. For. Policy 
Econ., 115, 102164, doi:10.1016/j.forpol.2020.102164.

Vermeulen, S.J., et al., 2018: Transformation in practice: a review of empirical 
cases of transformational adaptation in agriculture under climate change. 
Front. Sustain. Food Syst., 2, doi:10.3389/fsufs.2018.00065.
Vihemäki, H., A. Toppinen and R. Toivonen, 2020: Intermediaries to accelerate 
the diffusion of wooden multi-storey construction in Finland. Environ. Innov. 
Soc. Transit., 36, 433–448, doi:10.1016/j.eist.2020.04.002.
Villamayor-Tomas, S. and G. García-López, 2018: Social movements as key 
actors in governing the commons: Evidence from community-based resource 
management cases across the world. Glob. Environ. Chang., 53, 114–126, 
doi:10.1016/j.gloenvcha.2018.09.005.
Vincent, K. and W. Colenbrander, 2018: Developing and applying a five step 
process for mainstreaming climate change into local development plans: 
A case study from Zambia. Clim. Risk Manag., 21, 26–38, doi:10.1016/j.
crm.2018.04.005.
Vinyeta, K., K. Whyte and K. Lynn, 2016: Climate Change Through an

Intersectional Lens: Gendered Vulnerability and Resilience in Indigenous 
Communities in the United States. Social Science Research Network, 
Rochester, NY . Available at: https://papers.ssrn.com/abstract=2770089.
Visseren-Hamakers, I.J., et al., 2021: Transformative governance of biodiversity: 
insights for sustainable development. Curr. Opin. Environ. Sustain., 53, 20–
28, doi:10.1016/j.cosust.2021.06.002.
Visvizi, A., M.D. Lytras, E. Damiani and H. Mathkour, 2018: Policy making 
for smart cities: innovation and social inclusive economic growth for 
sustainability. J. Sci. Technol. Policy Manag., 9(2), 126–133, doi:10.1108/
JSTPM-07-2018-079.
Vogel, C. and K. O’Brien, 2021: Getting to the heart of transformation. Sustain 
Sci, doi:10.1007/s11625-021-01016-8.
Vogl, V., M. Åhman and L.J. Nilsson, 2018: Assessment of hydrogen direct 
reduction for fossil-free steelmaking. J  Clean Prod, 203, 736–745, 
doi:10.1016/j.jclepro.2018.08.279.

von Stechow, C., et al., 2016: 2°C and SDGs: united they stand, divided they 
fall? Environ. Res. Lett., 11(3), e34022.
von Uexkull, N. and H. Buhaug, 2021: Security implications of climate 
change: a decade of scientific progress. J. Peace Res., 58(1), 3–17, 
doi:10.1177/0022343320984210.
Wada, Y ., et al., 2016: Modeling global water use for the 21st century: the Water 
Futures and Solutions (WFaS) initiative and its approaches. Geosci. Model 
Dev., 9(1), 175–222, doi:10.5194/gmd-9-175-2016.
Waddock, S., 2019: Shaping the shift: shamanic leadership, memes, and 
transformation. J. Bus. Ethics., 155(4), 931–939, doi:10.1007/s10551-018-
3900-8.
Walch, C., 2019: Adaptive governance in the developing world: disaster risk 
reduction in the state of Odisha, India. Clim. Dev., 11(3), 238–252, doi:10.1
080/17565529.2018.1442794.

18
2767Climate Resilient Development Pathways  Chapter 18
Waldherr, A., 2012: The mass media as actors in innovation systems. In: 
Innovation Policy and Governance in High-Tech Industries [Bauer, J., A. Lang 
and V. Schneider(eds.)]. Springer, Berlin Heidelberg, pp. 77–100. ISBN 978-
3642125621 978-3-642-12563-8).
Walsh, C., 2018: 9. Development as Buen Vivir: institutional arrangements and 
(de)colonial entanglements. In: B. Reiter (ed.), Constructing the Pluriverse: 
The Geopolitics of Knowledge. Duke University Press, pp. 184–194. ISBN 
978-1478002017.
Walsh, J.H. and J. Dillard-Wright, 2020: The case for “structural missingness:” 
A critical discourse of missed care. Nurs Philos, 21(1), e12279, doi:10.1111/
nup.12279.
Walshe, R. and A.G. Argumedo, 2016: Ayni, Ayllu, Yanantin and Chanincha: the 
cultural values enabling adaptation to climate change in communities of 
the Potato Park, in the Peruvian Andes. Gaia - Ecol. Perspect. Sci. Soc., 25(3), 
166–173, doi:10.14512/gaia.25.3.7.

Walton, A. A., 2018: Fossil Fuel Divestment: The Power of Positively Deviant 
Leadership for Catalyzing Climate Action and Financing Clean Energy. In: 
Evolving Leadership for Collective Wellbeing (Building Leadership Bridges). 
Emerald Publishing Limited, Bingley, pp. 101–120.
Walton, A. A., 2018: Positive deviance and behavior change: A research methods 
approach for understanding fossil fuel divestment. Energy Research & Social 
Science, 45, 235–249, doi:https://doi.org/10.1016/j.erss.2018.07.003.
Wamsler, C., 2019: Contemplative sustainable futures: the role of individual 
inner dimensions and transformation in sustainability research and 
education. In: Sustainability and the Humanities [Leal Filho, W. and A. 
Consorte McCrea(eds.)]. Springer International Publishing, Cham, pp. 359–
373. ISBN 978-3319953366.
Wamsler, C. and E. Brink, 2018: Mindsets for sustainability: exploring the link 
between mindfulness and sustainable climate adaptation. Ecol. Econ., 151,

55–61, doi:10.1016/j.ecolecon.2018.04.029.
Wang, F ., R. Wang and J. Wang, 2020: Measurement of China’s green GDP and 
its dynamic variation based on industrial perspective. Environ. Sci. Pollut. 
Res. 27, 43813–43828.
Wang, Y ., et al., 2019: Tens of thousands additional deaths annually in cities 
of China between 1.5°C and 2.0°C warming. Nat Commun, 10(1), 3376, 
doi:10.1038/s41467-019-11283-w.
Washington-Ottombre, C. and B.C. Pijanowski, 2013: Rural organizations and 
adaptation to climate change and variability in rural Kenya. Reg Environ 
Change, 13(3), 537–550.
Washington, H. and P . Twomey (eds.), 2016: A Future Beyond Growth: Towards 
a Steady State Economy, 1st edn., Routledge, Oxfordshire, 282 pp.
Watson, E.E., H.H. Kochore and B.H. Dabasso, 2016: Camels and climate 
resilience: adaptation in northern Kenya. Hum Ecol, 44(6), 701–713.
Watts, N., et al., 2019: The 2019 report of The Lancet Countdown on health and

climate change: ensuring that the health of a child born today is not defined 
by a changing climate. Lancet, 394, doi:10.1016/S0140-6736(19)32596-6.
Webb, R., et al., 2018: Sustainable urban systems: Co-design and framing for 
transformation. Ambio, 47(1), 57–77, doi:10.1007/s13280-017-0934-6.
Webber, S., 2016: Climate change adaptation as a growing development 
priority: towards critical adaptation scholarship. Geogr. Compass, 10(10), 
401–413, doi:10.1111/gec3.12278.
Webber, S. and S.D. Donner, 2017: Climate service warnings: cautions about 
commercializing climate science for adaptation in the developing world. 
Wires Clim. Chang., 8(1), e424, doi:10.1002/wcc.424.
Weir, T., 2018: Renewable energy in the Pacific Islands: Its role and status. 
Renew. Sustain. Energy Rev., 94, 762–771, doi:10.1016/j.rser.2018.05.069.
Weir, T. and M. Kumar, 2020: Renewable energy can enhance resilience of small 
islands. Nat Hazards, 104(3), 2719–2725, doi:10.1007/s11069-020-04266-
4.

Welch, D. and L. Yates, 2018: The practices of collective action: Practice theory, 
sustainability transitions and social change. J Theory Soc Behav, 48(3), 288–
305, doi:10.1111/jtsb.12168.Wells, P ., W. Abouarghoub, S. Pettit and A. Beresford, 2020: A socio-technical 
transitions perspective for assessing future sustainability following the 
COVID-19 pandemic. Sustain. Sci. Pract. Policy, 16(1), 29–36, doi:10.1080/
15487733.2020.1763002.
Werners, S.E., et al., 2018: Towards Climate Resilient Development Pathways 
for the People in the Hindu Kush Himalayan Region. HI-AWARE, Kathmandu.
Werners, S.E., et al., 2021: Adaptation pathways: A review of approaches and 
a learning framework. Environ. Sci. Policy, 116, 266–275, doi:10.1016/j.
envsci.2020.11.003.
Westholm, L. and S. Arora-Jonsson, 2015: Defining solutions, finding problems: 
deforestation, gender, and REDD+ in Burkina Faso. Conserv. Soc., 13(2), 
189–199.

Westholm, L. and S. Arora-Jonsson, 2018: What room for politics and change in 
global climate governance? Addressing gender in co-benefits and safeguards. 
Env Polit, 27(5), 917–938, doi:10.1080/09644016.2018.1479115.
Westley, F ., et al., 2011: Tipping toward sustainability: emerging pathways of 
transformation. Ambio, 40(7), 762, doi:10.1007/s13280-011-0186-9.
Westwell, E. and J. Bunting, 2020: The regenerative culture of extinction 
rebellion: self-care, people care, planet care. Env Polit, 29(3), 546–551.
Weyant, J. and E. Kriegler, 2014: Preface and introduction to EMF 27. Clim 
Change, 123(3), 345–352, doi:10.1007/s10584-014-1102-7.
White, D., 2020: Just transitions/design for transitions: preliminary notes on a 
design politics for a green new deal. Capitalism Nat. Social., 31(2), 20–39, 
doi:10.1080/10455752.2019.1583762.
WHO, 2016: Climate And Health Country Profile – 2015: Kenya. World Health 
Organisation, Geneva, Switzerland.

Wiebe, K., et al., 2015: Climate change impacts on agriculture in 2050 under 
a range of plausible socioeconomic and emissions scenarios. Environ. Res. 
Lett., 10(8), 85010, doi:10.1088/1748-9326/10/8/085010.
Wieczorek, A.J., 2018: Sustainability transitions in developing countries: Major 
insights and their implications for research and policy. Environ. Sci. Policy, 
84, 204–216, doi:10.1016/j.envsci.2017.08.008.
Wijsman, K. and M. Feagan, 2019: Rethinking knowledge systems for urban 
resilience: Feminist and decolonial contributions to just transformations. 
Environ. Sci. Policy, 98, 70–76, doi:10.1016/j.envsci.2019.04.017.
Wilhelmi, O.V., P .D. Howe, M.H. Hayden and C.R. O’Lenick, 2021: Compounding 
hazards and intersecting vulnerabilities: experiences and responses 
to extreme heat during COVID-19. Environ. Res. Lett., 16(8), 84060, 
doi:10.1088/1748-9326/ac1760.
Williams, A., J. Dickman and R. Smurthwaite, 2021: Advancing evaluation and

learning on transformational change: lessons from the climate investment 
funds’ transformational change learning partnership. Am. J. Eval., 42(1), 
90–109, doi:10.1177/1098214020970283.
Wilson, N.J., et al., 2019: Water is medicine: Reimagining water security through 
Tr’ondëk Hwëch’in relationships to treated and traditional water sources in 
Yukon. Canada. water, 11(3), 624, doi:10.3390/w11030624.
Wilson, N.J. and J. Inkster, 2018: Respecting water: Indigenous water 
governance, ontologies, and the politics of kinship on the ground. Environ. 
Plan. E: Nat. Space, doi:10.1177/2514848618789378.
Winkler, H., A. Boyd, M. Torres Gunfaus and S. Raubenheimer, 2015: 
Reconsidering development by reflecting on climate change. Int. Environ. 
Agreements: Polit. Law Econ., 15(4), 369–385, doi:10.1007/s10784-015-
9304-7.
Winkler, H. and N.K. Dubash, 2016: Who determines transformational change 
in development and climate finance? Clim. Policy, 16(6), 783–791, doi:10.1
080/14693062.2015.1033674.

v. Winterfeldt, D., 2013: Bridging the gap between science and decision making. 
Proc. Natl. Acad. Sci., 110(Supplement 3), 14055–14061, doi:10.1073/
pnas.1213532110.
Wise, R.M., et  al., 2016: How climate compatible are livelihood adaptation 
strategies and development programs in rural Indonesia? Clim. Risk Manag., 
12, 100–114, doi:10.1016/j.crm.2015.11.001.

18
2768Chapter 18 Climate Resilient Development Pathways
Woiwode, C., 2020: Inner transformation for 21st-century futures: the missing 
dimension in higher education. Environ. Sci. Policy Sustain. Dev., 62(4), 30–
38, doi:10.1080/00139157.2020.1764299.
Wood, B.T., 2017: Socially just Triple-Wins? An Evaluation of Projects that 
Pursue Climate Compatible Development Goals in Malawi. University of 
Leeds, Leeds, UK.
Wood, B.T., C.H. Quinn, L.C. Stringer and A.J. Dougill, 2017: Investigating climate 
compatible development outcomes and their implications for distributive 
justice: evidence from Malawi. Environ Manage, 60(3), 436–453.
World Bank, 2000: World Development Report 2000/2001: Attacking Poverty. 
https://openknowledge.worldbank.org/handle/10986/11856 . World Bank, 
Washington, DC, USA..
World Bank, 2017: Climate Vulnerability Assessment: Making Fiji Climate 
Resistant. World Bank, Washington, DC, USA.
World Bank, 2021: GINI Index. In: The World Development Indicators (WDI).

World Bank, Washington, DC.
World Bank G., 2019: Using Carbon Revenues. https://openknowledge.
worldbank.org/handle/10986/32247, Accessed 2022-03-16.
World Commission on Environment and Development, 1987: Our Common 
Future. Oxford University Press, Oxford, UK.
World Economic Forum, 2019: Innovate Europe: Competing for Global 
Innovation Leadership, Geneva, Switzerland.
Woroniecki, S., 2019: Enabling environments? Examining social co-benefits of 
ecosystem-based adaptation to climate change in Sri Lanka. Sustainability, 
11(3), 772, doi:10.3390/su11030772.
Worrell, E., J. Allwood and T. Gutowski, 2016: The role of material efficiency 
in environmental stewardship. Annu. Rev. Environ. Resour., 41(1), 575–598, 
doi:10.1146/annurev-environ-110615-085737.
Xu, T., 2018: Investigating environmental Kuznets curve in China–aggregation 
bias and policy implications. Energy Policy, 114, 315–322, doi:10.1016/j.
enpol.2017.12.027.

Xu, Y . and V. Ramanathan, 2017: Well below 2°C: Mitigation strategies for 
avoiding dangerous to catastrophic climate changes. Proc. Natl. Acad. Sci., 
114(39), 10315–10323, doi:10.1073/pnas.1618481114.
Xue, W., et  al., 2016: Cultural worldviews and climate change: A view from 
China. Asian J Soc Psychol, 19(2), 134–144, doi:10.1111/ajsp.12116.
Yadav, S.S. and R. Lal, 2018: Vulnerability of women to climate change in arid 
and semi-arid regions: The case of India and South Asia. J. Arid Environ., 149, 
4–17, doi:10.1016/j.jaridenv.2017.08.001.
Yalew, A.W., 2020: Climate Resilient Development. In: Economic Development 
under Climate Change : Economy-Wide and Regional Analysis for Ethiopia 
[Yalew, A.W.(ed.)]. Springer Fachmedien, Wiesbaden, pp. 99–108. ISBN 978-
3658294137.
Yao, M., et  al., 2017: Building regional water-use scenarios consistent with 
global shared socioeconomic pathways. Environ. Process., 4(1), 15–31.

Yohe, G.W., R.D. Lasco, Q.K. Ahmad, N.W. Arnell, S.J. Cohen, C. Hope, A.C. Janetos 
and R.T. Perez, 2007: Perspectives on climate change and sustainability. In: 
Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of 
Working Group II to the Fourth Assessment Report of the Intergovernmental 
Panel on Climate Change [Parry, M.L., O.F . Canziani, J.P . Palutikof, P .J. van der 
Linden and C.E. Hanson(eds.)]. Cambridge University Press, Cambridge, UK, 
pp. 811–841. ISBN 978-0521880107.
Young, I.M., 2003: Political Responsibility and Structural Injustice. University of 
Kansas, Department of Philosophy, Lawrence, Kansas.
Yu, Y ., et al., 2019: National green GDP assessment and prediction for China 
based on a CA-Markov land use simulation model. Sustainability, 11(3), 576.
Yunkaporta, T., 2019: Sand Talk: How Indigenous thinking can save the world. 
Text Publishing, Melbourne.
Yurdakul, M. and H. Kazan, 2020: Effects of eco-innovation on economic

and environmental performance: evidence from Turkey’s manufacturing 
companies. Sustainability, 12(8), 3167, doi:10.3390/su12083167.Zaman, F ., 2021: The role of popular discourse about climate change in disaster 
preparedness: A critical discourse analysis. Int. J. Disaster Risk Reduct., 60, 
102270, doi:10.1016/j.ijdrr.2021.102270.
Zambia, R. o. and IUCN, 2017: Climate Change Gender Action Plan of The 
Republic of Zambia. (International Union for Conservation of Nature Global 
Gender Office (IUCN GGO)).https://portals.iucn.org/union/sites/union/files/
doc/ccgap-zambia-final-web.pdf, Accessed 2019-09-24.
Zavaleta, C., et  al., 2018: Multiple non-climatic drivers of food insecurity 
reinforce climate change maladaptation trajectories among Peruvian 
Indigenous Shawi in the Amazon. PLoS ONE, 13(10), e205714, doi:10.1371/
journal.pone.0205714.
Ziervogel, G., 2019: Building transformative capacity for adaptation planning

and implementation that works for the urban poor: Insights from South 
Africa. Ambio, 48(5), 494–506, doi:10.1007/s13280-018-1141-9.
Ziervogel, G., E. Archer van Garderen and P . Price, 2016a: Strengthening the 
knowledge–policy interface through co-production of a climate adaptation 
plan: leveraging opportunities in Bergrivier Municipality, South Africa. 
environ urban, 28(2), 455–474.
Ziervogel, G., A. Cowen and J. Ziniades, 2016b: Moving from adaptive to 
transformative capacity: building foundations for inclusive, thriving, and 
regenerative urban settlements. Sustainability, 8(9), 955, doi:10.3390/
su8090955.
Ziervogel, G., J. Enqvist, L. Metelerkamp and J. van Breda, 2021: Supporting 
transformative climate adaptation: community-level capacity building and 
knowledge co-creation in South Africa. Clim. Policy, 0(0), 1–16, doi:10.1080
/14693062.2020.1863180.
Zinsstag, J., et al., 2018: Climate change and one health. FEMS Microbiol. Lett., 
365(11), doi:10.1093/femsle/fny085.

Zivkovic, S., 2018: Systemic innovation labs: a lab for wicked problems. Social 
Enterprise Journal, 14 (3), 348–366.
Zummo, L., B. Donovan and K.C. Busch, 2020: Complex influences of 
mechanistic knowledge, worldview, and quantitative reasoning on climate 
change discourse: Evidence for ideologically motivated reasoning among 
youth. J Res Sci Teach, doi:10.1002/tea.21648.

18
2769Climate Resilient Development Pathways  Chapter 18
Cross-Chapter Box FEASIB | Feasibility Assessment of Adaptation Options: An Update of the SR1.5
Authors: Debora Ley (Guatemala/Mexico), Helen Adams (UK), Malcolm Araos (Canada/USA), Ritwika Basu (India/UK), Amir Bazaz (India), 
Luigi Conte (Italy), Katy Davis (UK), Constantino Dockendorff (Chile/Germany), James Ford (UK/Canada), Sabine Fuss (Germany), Elisabeth 
A Gilmore (USA/Canada), Tania Guillén Bolaños (Nicaragua/Germany), Ove Hoegh-Guldberg (Australia), Mark Howden (Australia), 
Bavisha Kalyan (South Africa/USA), Laura Moro (Italy), Anuszka Mosurska (UK/Poland), Reinhard Mechler (Germany), Joana Portugal-
Pereira (Brazil), Aromar Revi (India), Swarnika Sharma (India), Anne J. Sietsma (the Netherlands/UK), Chandni Singh (India), Alessandro 
Triacca (Italy), Bianca van Bavel (Canada/Ireland/UK), Ivan Villaverde Canosa (Spain/UK), Mustafa Babiker (Sudan/Saudi Arabia), Paolo

Bertoldi (Italy), Brett Cohen (South Africa), Annette Cowie (Australia), Kiane de Kleijne (the Netherlands), Jeremy Emmet-Booth (Ireland), 
Amit Garg (India), Gert-Jan Nabuurs (the Netherlands), André Frossard Pereira de Lucena (Brazil), Adrian Leip (Italy/Germany), Lars J. 
Nilsson (Sweden), Pete Smith (UK), Linda Steg (the Netherlands), Masahiro Sugiyama (Japan)
The feasibility assessment (FA) presents a systematic framework to assess adaptation and mitigation options organised 
by system transitions. This Cross-Chapter Box assessed the feasibility of 23 adaptation options across six dimensions: economic, 
technological, institutional, socio-cultural, environmental-ecological, and geophysical to identify factors within each dimension that 
present barriers to the achievement of the option. The results are presented below.
For energy systems transitions, the adaptation options of infrastructure resilience, efficient water use and water management,

and reliable power systems enable energy systems to work during disasters with reduced costs, demonstrating the 
synergistic relationships between mitigation and adaptation (high confidence). There is high confidence in the high feasibility of 
infrastructure resilience and reliable power systems as they enable power systems to provide emergency services during disasters, as well 
as continue these services during recovery periods. New evidence has focused on both options for peri-urban and rural areas through 
distributed generation and isolated renewable energy systems, which also provide multiple social co-benefits (medium confidence).  For 
efficient water use and management, the synergistic potential with mitigation can make processes more efficient and cost effective (high 
confidence). With regards to adaptation feasibility, efficient water use is especially useful in drought-stricken areas and provides better 
water management for multiple uses (high confidence).

There are multiple adaptation options for land and ocean ecosystems. Forest- and biodiversity-based adaptation options 
are generally promoted on the basis of their positive impacts on adaptive and ecological capacities, increased provision 
of ecosystem services and goods, with a particularly strong contribution to carbon sequestration (high confidence).  
However, large afforestation projects and the introduction of non-native and fast-growing vegetation reduce water availability, 
impoverish habitats for wildlife and reduce overall ecological resilience, threatening the achievement of some Sustainable Development 
Goals (SDGs), and potentially leading to maladaptation (high confidence).Over-reliance on forest-based solutions may increase the 
susceptibility to wildfires, with detrimental consequences both for mitigation and adaptation (medium confidence). Over the last

decade, forest- and biodiversity-based solutions have gained considerable political traction and social acceptability (high confidence), 
but in countries with economies highly dependent on the export of agricultural commodities, opportunity costs continue to hinder the 
expansion of these alternatives, particularly against more profitable land uses (high confidence). In such cases, government support 
and innovative financial schemes, including payments for ecosystem services, are fundamental for broader adherence to forest- and 
biodiversity-based options.
Agro-forestry solutions have strong ecological and adaptive co-benefits (high confidence), including improved provision of 
ecosystem services, synergies with the water–energy–land–food nexus, and positive outcomes in agricultural intensification, 
job diversification and household income. While broad inclusion of agro-forestry schemes in countries’ Nationally Determined

Contributions (NDCs) reflect growing international interest in these strategies, insufficient financial support to smallholder farmers 
continues to limit the expansion of agro-forestry initiatives in developing and tropical countries.
Implementing environmentally and biodiversity sensitive coastal defence options—often as part of Integrated Coastal 
Zone Management—is limited by economic, environmental, institutional and social barriers. Successful implementation 
requires a strong socioeconomic framework and can offer diverse social, ecological and economic benefits, as well as 
sequestering carbon (high confidence). There is extensive experience with hard coastal defence structures (e.g., sea walls), which 
can be cost-effective in economic terms, depending on the location (medium confidence); however, they are considered maladaptive and 
unsustainable in some contexts (medium confidence) due to their lack of flexibility or robustness in response to a changing climate, as

well as their carbon-intensiveness and potential ecological impacts (medium confidence).

18
2770Chapter 18 Climate Resilient Development Pathways
There is medium confidence on the feasibility of sustainable aquaculture and fisheries as adaptation options. There are 
financial barriers to implementing sustainable aquaculture and fisheries, even though they can improve employment opportunities, 
especially for local communities (medium confidence). Technical resource availability is still lacking and could represent a barrier to 
implementing sustainable aquaculture and fisheries (medium confidence). Robust institutional and legal frameworks are needed to 
guarantee effective adaptation (high confidence). Sustainable aquaculture and fisheries are highly dependent on healthy and resilient 
ecosystems (high confidence). They can provide diverse ecosystem services and support coastal ecosystems restoration (medium 
There are a range of strategies to improve livestock system efficiency including improved livestock diets, enhanced animal

health, breeding and manure management, and grassland management. This suite of strategies has strong feasibility to build 
resilience while improving incomes (medium confidence) and providing mitigation co-benefits (high confidence). While technological 
and ecological feasibility is high, institutional, market and socio-political acceptability remain significant barriers (medium confidence).
Improving water use efficiency and water resource management under land and ecosystem transitions has high technological 
feasibility (high confidence) with positive resilience-building and socioeconomic co-benefits. However, economic and 
institutional barriers remain and are based on type, scale and location of interventions (medium confidence). Notably, inadequate 
institutional capacities to prepare for changing water availability, especially in the long term, unsustainable and unequal water use and

sharing practices, and fragmented water resource management approaches remain critical barriers to feasibility (high confidence).
Improved cropland management includes agricultural adaptation strategies such as integrated soil management, no/
reduced tillage, conservation agriculture, planting of stress-resistant or early maturing crop varieties, and mulching. 
These strategies have high economic and environmental feasibility (high confidence) and substantial mitigation co-benefits (medium 
confidence). However, high costs, inadequate information and technical know-how, delays between actions and tangible benefits, lack 
of comprehensive policies, fragmentation across different sectors, inadequate access to credit, and unequal access to resources constrain 
technological, institutional and socio-cultural feasibility (medium confidence).
For urban and infrastructure system transitions, sustainable urban planning can support both adaptation and decarbonisation

by mainstreaming climate concerns, including effective land use into urban policies, by promoting resilient and low-carbon 
infrastructure, and by protecting and integrating carbon-reducing biodiversity and ecosystem services into city planning 
(medium confidence). Urban green infrastructure and ecosystem services have high feasibility to support climate adaptation and 
mitigation efforts in cities, for example to reduce flood exposure and attenuate the urban heat island (high confidence). While green 
infrastructure options are cost-effective and provide co-benefits in terms of ecosystem services such as improved air quality or other 
health benefits (high confidence), there remains a need for systematically assessing co-benefits, particularly for flood risk management 
and sustainable material flow analysis. Governments across scales can support urban sustainable water management by undertaking

projects to recycle wastewater and runoff through green infrastructure; enabling greater coherence between urban water and riverine 
basin management; decentralising water systems; supporting networks for sharing best practices in water supply and storm runoff 
treatment to scale sustainable management; and foregrounding equity and justice concerns, especially through participation involving 
informal settlement residents (medium confidence).
Strong and equitable health systems can protect the health of populations in the face of known and unexpected stressors 
(medium confidence). Health and health systems adaptation is feasible where capacity is well developed, and where options align with 
national priorities and engage local and international communities (medium confidence). Socio-cultural acceptability of health and health 
systems adaptation is high and there is significant potential for risk-mitigation and social co-benefits where adaptation addresses the

needs of vulnerable regions and populations (medium confidence). Microeconomic feasibility and socioeconomic vulnerability reduction 
potentials are also high (high confidence), although economic feasibility may pose a significant challenge in low-income settings (medium 
confidence). However, inadequate institutional capacity and resource availability represent major barriers, particularly for health systems 
struggling to manage current health risks (high confidence).
There is strong evidence that disaster risk management (DRM) is highly feasible when supported by strong institutions, 
good governance, local engagement and trust across actors (medium confidence). DRM is constrained by lack of capacity, 
inadequate institutions, limited coordination across levels of government (high confidence), lack of transparency and accountability, and 
poor communication (medium confidence). There is a preference for top-down DRM processes, which can undermine local institutions

and perpetuate uneven power relationships (medium confidence). However, local integration of worldviews, belief systems and local and Cross-Chapter Box FEASIB (continued)

18
2771Climate Resilient Development Pathways  Chapter 18
Indigenous Knowledge into DRM activities can facilitate successful, disability-inclusive and gender-focused DRM (medium confidence). 
Moves towards community-based and ecosystem-based DRM are promising but uneven and may increase vulnerability if they fail to 
address underlying and structural determinants of vulnerability (high confidence).
Climate services that are demand-driven and context-specific (e.g., to a particular crop or agricultural system) build 
adaptation capacity and enable short- and longer-term risk management decisions (high confidence). Metrics to assess the 
economic outcomes of climate services remain insufficient to capture longer-term benefits of interventions (medium confidence). While 
technological capacity and political acceptance is high (medium confidence), institutional barriers, poor fit with user requirements and 
inadequate regional coverage constrain the option’s overall feasibility.

Risk insurance can be a feasible tool to adapt to climate risks and support sustainable development (high confidence). They 
can reduce both vulnerability and exposure, support post-disaster recovery and reduce financial burden on governments, households 
and business. Insurance mechanisms enjoy wide legal and regulatory acceptability among policymakers and are institutionally feasible 
(high confidence). However, socio-cultural and financial barriers make insurance spatially and temporally challenging to implement 
(high confidence), even though it can improve the health and well-being of populations (medium confidence). The risk of generating 
maladaptive outcomes can further limit the uptake of insurance, as it can provide disincentives for reducing risk over the long term 
(medium confidence). Expanding the knowledge base on insurance is fundamental to successfully implement insurance among all

relevant stakeholders. Ensuring equitable access to and benefits from innovative financial products (e.g., loans) is needed to guarantee 
successful uptake of insurance across all the population (high confidence).
Migration has been used by millions around the world to maintain and improve their well-being in the face of changed 
circumstances, often as part of labour or livelihood diversification (very high confidence). Properly supported and, where 
levels of agency and assets are high, migration as a climate response can reduce exposure and socioeconomic vulnerability (medium 
confidence). Households and communities in climate-exposed regions experience a range of intersecting stressors. These households can 
undertake distress migration, which results in negative adaptive and resilience outcomes (high confidence). Outcomes can be improved 
through a systematic examination of the political economy of local and regional sectors that employ precarious communities and by

addressing vulnerabilities that pose barriers to in situ adaptation and livelihood strategies (medium confidence). Migrants and their 
sending and receiving communities can be supported through temporary labour-migration schemes, improving discourses on migration, 
and matching existing migration agreements with development objectives (medium confidence).
Planned relocation and resettlement have low feasibility as climate responses (medium confidence).   Previous disaster- and 
development-related relocation has been expensive, contentious, posed multiple challenges for governments and amplified existing, 
and generated new, vulnerabilities for the people involved (high confidence). Planned relocation will be increasingly required as climate 
change undermines habitability, especially for coastal areas (medium confidence). Full participation of those affected, ensuring human

rights-based approaches, preserving cultural, emotional and spiritual bonds to place, and dedicated governance structures and associated 
funding are associated with improved outcomes (high confidence). Improving the feasibility of planned relocation and resettlement is a 
high priority for managing climate risks (high confidence).
CCB FEASIB.1 Scope
The Paris Climate Agreement marked a significant shift for the IPCC AR6 assessment towards a systematic exploration of climate solutions 
and a suite of linked adaptation and mitigation options (IPCC, 2018b; IPCC, 2019b). This shift was first evidenced in SR1.5, whose plenary-
approved outline sought to define feasibility as ‘referring to the potential for a mitigation or adaptation option to be implemented. Factors 
influencing feasibility are context-dependent, temporally dynamic, and may vary between different groups and actors. Feasibility depends

on geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors that enable or constrain the 
implementation of an option. The feasibility of options may change when different options are combined and increase when enabling 
conditions are strengthened’. Based on this, SR1.5 identified (with high confidence) rapid and far-reaching transitions in four systems: 
energy, land and other ecosystems, urban and infrastructure (including transport and buildings), and industrial systems, are necessary to 
enable pathways to limit average global warming to 1.5°C compared with pre-industrial temperatures (Bazaz et al., 2018; IPCC, 2018b). 
This was deepened for terrestrial systems in SRCCL, while SROCC added additional evidence from ocean and cryosphere systems. The 
assessment also included the interactions between carbon dioxide removal (CDR) and adaptation outcomes: compared with previous

Assessment Reports, it is clear that the ambitious temperature targets agreed upon in Paris in 2015 will require at least some CDR, that 
is all 1.5°C pathways will eventually feature annual removals at gigaton level (Rogelj et al., 2018a). This necessitates assessing the 
interactions of CDR with adaptation.Cross-Chapter Box FEASIB (continued)

18
2772Chapter 18 Climate Resilient Development Pathways
This feasibility assessment (FA) of adaptation options is situated within four system transitions identified in SR1.5 (de Coninck et al., 2018b). 
In this report, feasibility refers to the potential for an adaptation option to be implemented. Twenty-three key adaptation options have 
been identified in AR6, across these system transitions, and mapped against representative key risks at global scale (Chapter 16) (Figure 1).
This cross-chapter box first presents the methodology for the (FA) of adaptation options (Section 2); findings of the FA (Section 3); 
presents synergies and trade-offs (S&Ts) of adaptation for mitigation options and mitigation for adaptations (Section 4); and knowledge 
gaps (Section 5).
Feasibility assessment options mapped against Representative Key Risks (RKR)
Systems transitions 
RKRsEnergy systems 
transitionsLand and ecosystems transitionsUrban and infrastructure 
systems transitionsOverarching

adaptation options
Risk to costal socio -
ecological systems•Coastal defense andhardening
•Sustainable aquaculture
•Social safety nets
•Risk spreading ands haring
•Climate services (including  
EWS)
•Disaster risk management
•Population health andh ealth
systems
•Human migration and 
displacemen t
•Planned relocation and 
resettlemen tRisk to terrestrial and
ocean ecosystems•Integrated coastal zone management 
including wetl and, mangrove 
•Sustainable forest management and
conservation, forestations and
afforestation
•Biodiversity management and
ecosystem connectivity
Risk associated with 
critical physical 
networks, and
services•Resilient power 
•Improved power 
reliability•Green infrastructure and 
•Sustainable l and-use 
land urban planning
standards and equity•Livelihood diversification
Risk to human health
Risk to food security•Improved cropland management 
(including integrated soil management,

conservation agriculture)
•Efficient livestock systems (including 
improved grazing land management)
•Agroforestry
Risk to water security•Improve water use 
efficiency•Water use efficiency and water 
resource management•Sustainable urban water 
Risk to peace and
migration
••
••
••
••
••
••
••
ItIt ttdd
 ttll tt
RisRiskakassossociaciatedtedwiwithth
RiRisktktloliiiving
II dd lldd tt
II tt
 WtWt fffifiii dd tt
 SStiti bbllbb tt
RiRisktktopeace
dand
Figure Cross-Chapter Box FEASIB.1 |  Feasibility assessment option mapped against representative key risks (RKRs)Cross-Chapter Box FEASIB (continued)

18
2773Climate Resilient Development Pathways  Chapter 18
There has been growing research emphasis on synthesising adaptation literature through meta-reviews of adaptation research (Sietsma 
et al., 2021; Berrang-Ford et al. 2021), adaptation readiness (Ford et al., 2015a; Ford et al., 2017), adaptation progress (Araos et al., 
2016a), adaptation barriers and enablers (Biesbroek et al., 2013; Eisenack et al., 2014; Barnett et al., 2015), and adaptation outcomes 
(Owen, 2020) (Cross-Chapter Box ADAPT in Chapter 1). In particular, understanding which adaptation options are effective, to what risks, 
and under what conditions, is particularly challenging given the lack of a clearly defined and globally- agreed- adaptation goals, as well 
as disagreement on the metrics to assess adaptation effectiveness (Berrang-Ford et al., 2019; Singh et al., 2021c) (17.5.2 on Successful

Adaptation). Effectiveness studies often use metrics such as reduced risk exposure, damage costs averted, which lend themselves well to 
infrastructural options (e.g., effectiveness of seawalls in reducing sea level rise [SLR] exposure in coastal cities), but do not translate well 
to ‘soft’ adaptation options such as climate services or changing building codes.
CCB FEASIB.2 Methodology: feasibility assessment of adaptation options across key system transitions
The multi-dimensional feasibility of 23 adaptation options is assessed across six dimensions. This multi-dimensional framework goes 
beyond technical or economic feasibility alone to capture how adaptation is mediated by the political environment, sociocultural norms 
(Evans et al., 2016), cognitive and motivational factors (van Valkengoed and Steg, 2019), economic incentives and benefits (Masud et al., 
2017), and ecological conditions (Biesbroek et al., 2013).

The six feasibility dimensions are underpinned by a set of 20 indicators. Each adaptation option is scored as having robust, medium or 
limited evidence on barriers based on a review of literature published from 2018 onwards (pre-2018 literature is expected to be covered 
by SR1.5 but in some cases pre-2018 literature was added) that reports studies that are 1.5°C-relevant. Further details and motivations 
for this methodology can be found in Singh et al., 2020c.
The scoring process is undertaken by one author and reviewed by at least two more authors to ensure robustness and geographical 
coverage. While the literature does not support an assessment at different temperature levels or an assessment of how feasibility can 
change over time, some examples of these spatial and temporal aspects are detailed below.
CCB FEASIB.3 Findings: feasibility assessment of adaptation options across key system transitions

The following sections outline the findings of a 1.5oC-relevant feasibility assessment of adaptation options by the four system transitions. 
A synoptic summary of the findings of the multi-dimensional feasibility is shown at the end of this section in Figure Cross-Chapter 
Box FEASIB.2. The full line of sight can be found in the Supplementary Material (SM).
CCB FEASIB.3.1 Energy systems transitions
The adaptation options assessed for energy system transitions are resilient power infrastructure; water management, focused on water 
efficiency and cooling, for all types of generation sources; and reliable power systems. Since SR1.5, there has not been significant change 
in the feasibility of the first two options as they continue to be implemented successfully, allowing for power generation to maintain 
or increase its reliability during extreme weather events (high confidence) (Zhang et al., 2018; Ali and Kumar, 2016; DeNooyer et al.,

2016). As in the case of SR1.5, these options are not sufficient for the far-reaching transformations required in the energy sector, which 
tend to focus on technological transitions from a fossil-based to a renewable energy regime (Erlinghagen and Markard, 2012; Muench 
et al., 2014; Brand and von Gleich, 2015; Monstadt and Wolff, 2015; Child and Breyer, 2017; Hermwille et al., 2017). The main difference 
from SR1.5 is that resilient power infrastructure now includes distributed generation utilities, such as microgrids, as there is increasing 
evidence of its role in reducing vulnerability, especially within underserved populations (high confidence).
The option for resilient power infrastructure considers all types generation sources, and transmission and distribution systems. There is 
robust evidence and high agreement for the high feasibility of the economic and technological dimensions as the technologies have

been used and their cost effectiveness is high, although the latter is dependent upon the generation source and location of each specific 
generation plant. There is medium institutional feasibility (medium evidence, medium agreement) as there are insufficient policies for 
resilient infrastructure, although there is high acceptability for these options.
The option of efficient water use and management also has high feasibility for the economic, technological and environmental dimensions 
(robust evidence, high agreement), as this option also has proven that technology and efficient water use can make power generation 
operations more efficient and cost effective as well as have positive effects on the environment, especially in drought-stricken regions. 
There is high political acceptability, existence of water use policies, regulations and supporting institutional frameworks to ensure

compliance (Ali and Kumar, 2016; DeNooyer et al., 2016; Zhang et al., 2018). There is medium evidence and high agreement for the 
medium feasibility of the socio-cultural dimension, especially given the evidence of resilience in distributed generation systems and 
independent microgrids.Cross-Chapter Box FEASIB (continued)

18
2774Chapter 18 Climate Resilient Development Pathways
Since AR5, the reliability of power systems has gained interest because of the numerous service disruptions during extreme weather 
events. As with resilient power systems, there is increasing evidence of the feasibility of increased reliability for both existing power 
plants, independently of the generation source, and for rural landscapes. The option has high confidence (robust evidence, high agreement) 
for the high feasibility of the technological and social dimensions. As with previous options, the technological means exist to create 
redundancy in power generation, transmission and distribution systems and their implementation ensures the continuous functionality 
of emergency services, such as communications, health and water pumping, amongst others, in urban, peri-urban and rural landscapes

(high confidence). There is high feasibility for the economic, technical and socio-cultural dimensions (the latter more prominently for 
decentralised systems), and medium feasibility for institutional and geophysical dimensions.
For the three options, some of the indicators within the institutional, social and geophysical dimensions have limited evidence as they 
have not been the focus of dedicated research. For example, when discussing the social co-benefits of energy reliable systems of efficient 
water use, the literature does not focus on intergenerational or gender issues separately from the broad range of social co-benefits the 
options provide, but, for example, highlight the need for electricity for communications and health centres.
CCB FEASIB.3.2 Land and ecosystems
CCB FEASIB.3.2.1 Coastal defence and hardening
There is robust evidence and medium agreement regarding the feasibility of coastal defence and hardening as adaptation options in some

circumstances, which here includes grey coastal infrastructure. Economic and social factors may limit the feasibility of these options as 
they require large investments (both construction, maintenance and monitoring) (Hamin et al., 2018; Magnan and Duvat, 2018; Morris 
et al., 2018; Morris et al., 2019; Nicholls et al., 2019; Hanley et al., 2020b) (Section CCP2.3). While these costs present challenges for rural 
areas, coastal defence structures may still be cost-effective in other areas, such as those with larger economies (Aerts, 2018; Lincke and 
Hinkel, 2018; Tiggeloven et al., 2020; Vousdoukas et al., 2020; Lima and Coelho, 2021). Strong, transparent and inclusive governance is 
key, suggesting that these measures can occasionally fail to adequately balance competing stakeholder interests. Consequently, they may 
disproportionately benefit wealthier people and exacerbate existing vulnerability of the poor (Kind et al., 2017; O’Donnell, 2019; Ratter

et al., 2019; Siders and Keenan, 2020; Siriwardane-de Zoysa, 2020). They are also potentially maladaptive if they are not flexible or robust 
in response to a changing climate (Antunes do Carmo, 2018; Hamin et al., 2018; Morris et al., 2019; Baills et al., 2020; Foti et al., 2020; 
Hanley et al., 2020b) and can have negative impacts on the local environment, habitats, ecosystem services, and communities (Mills et al., 
2016; Morris et al., 2018; Morris et al., 2019; Foti et al., 2020; Hanley et al., 2020b).
Recent projects have focused on improving adaptability and increasing ecological and social sustainability by combining both hard 
engineering and ‘softer’ nature-based solutions (Morris et al., 2019; Scheres and Schüttrumpf, 2019; Schoonees et al., 2019; Van Loon-
Steensma and Vellinga, 2019; Du et al., 2020; Foti et al., 2020; Winters et al., 2020; Ghiasian et al., 2021; Joy and Gopinath, 2021; Tanaya

et al., 2021; Waryszak et al., 2021). For example, coastal defence might involve a combination of ‘stabilising’ ecosystems (e.g., seagrasses, 
mangroves, salt marshes) and hard human-made structures. Such coastal defence ‘mixed’ structures can be part of an Integrated Coastal 
Zone Management (ICZM) strategy, which is covered as a separate option below.
CCB FEASIB.3.2.2 Sustainable aquaculture
There is medium evidence with medium agreement on the feasibility of sustainable aquaculture as an adaptation measure. Sustainable 
aquaculture (e.g., integrated multi-trophic aquaculture, polyculture, aquaponics, mangrove-integrated culture) can have socioeconomic 
benefits for vulnerable communities and small-scale fisheries (Ahmed, 2018; Blasiak et al., 2019; Mustafa et al., 2021; Thomas et al., 
2021; Xuan et al., 2021). However, caution is important to guarantee that access to fish supply of local and vulnerable communities is

not affected (Chan et al., 2019; Galappaththi et al., 2020). Access to financial resources is often a barrier to implementation, although 
sustainable aquaculture can increase employment opportunities that are increasingly gender equitable (Alleway et al., 2018; Leakhena 
et al., 2018; Valenti et al., 2018; Gopal et al., 2020), as well as increasing the resilience of coastal livelihoods to climate change (Shaffril 
et al., 2017; Blasiak and Wabnitz, 2018). Technological, institutional and socio-cultural factors can form barriers to the feasibility of 
sustainable aquaculture (e.g., Ahmed et al., 2018; Blasiak et al., 2019; Galappaththi et al., 2019; Boyd et al., 2020; Osmundsen et al., 2020; 
Stentiford et al., 2020; Mustapha et al., 2021; Xuan et al., 2021).
Sustainable aquaculture depends on healthy ecosystems (Sampantamit et al., 2020; Stentiford et al., 2020; Qurani et al., 2021). At the

same time, its implementation can increase or regenerate ecosystem services, enhance ecosystems’ adaptive capacity (Shaffril et al., 
2017; Freduah et al., 2018; Custódio et al., 2020; Bricknell et al., 2021; Mustafa et al., 2021) and protect nursery grounds and habitats for 
fish and other important organisms (i.e., many commercial species are associated with mangroves). It may also prevent ecosystem Cross-Chapter Box FEASIB (continued)

18
2775Climate Resilient Development Pathways  Chapter 18
degradation such as deforestation, enhancing land use potential (Ahmed et al., 2018; Stentiford et al., 2020; Turolla et al., 2020; Mustafa 
Environmental and economic aspects are key when assessing the sustainability of aquaculture practices (Ahmed et al., 2018; Aubin et al., 
2019; Bohnes et al., 2019; Galappaththi et al., 2019; Boyd et al., 2020; Galappaththi et al., 2020; Osmundsen et al., 2020; Stentiford 
et al., 2020; Thomas et al., 2021). A global picture of where sustainable aquaculture is possible is needed and desirable (FAO, 2018; 
Galappaththi et al., 2019; Bricknell et al., 2021), yet there are few new references to its physical feasibility. Adaptation options for existing 
sustainable aquaculture need to be developed, along with institutional arrangements such as education and technology transfer, focused

on developing sustainable industries (Section 8.6.2.3). Sustainable agriculture is likely to receive strong support from many countries but 
may also experience resistance for several reasons (e.g., competition with existing industries, debates over tolerance to aesthetic changes 
to coastlines). Literature on this area is growing. Potential barriers at the government and political levels are significant (e.g., Jayanthi 
et al., 2018; Blasiak et al., 2019; Hargan et al., 2020; Osmundsen et al., 2020; Stentiford et al., 2020; Mustafa et al., 2021; Qurani et al., 
2021).
CCB FEASIB.3.2.3 Integrated coastal zone management (ICZM)
ICZM measures such as salt marsh management, re-vegetation of shorelines, community-based coastal adaptation and ecosystem-based 
adaptation were considered in this assessment. There is robust evidence and high agreement that ICZM increases ecological and adaptive

capacity to climate change (Villamizar et al., 2017; Antunes do Carmo, 2018; Hamin et al., 2018; Le Cornu et al., 2018; Propato et al., 2018; 
Romañach et al., 2018; Rosendo et al., 2018; Warnken and Mosadeghi, 2018; Morecroft et al., 2019; Morris et al., 2019; Alves et al., 2020; 
Donatti et al., 2020; Erftemeijer et al., 2020; Foti et al., 2020; Gómez Martín et al., 2020; Hanley et al., 2020b; Jones et al., 2020b; Krauss 
and Osland, 2020; O’Mahony et al., 2020; Perera-Valderrama et al., 2020; Cantasano et al., 2021).
Diverse socioeconomic co-benefits have been identified, including integration of tourism activities, increased educational opportunities for 
the reduction in storm damage, maintenance of ecosystems and their services, increasing adaptive capacities of institutions (Romañach 
et al., 2018; Mestanza-Ramón et al., 2019; Morris et al., 2019; Donatti et al., 2020; Ellison et al., 2020; Erftemeijer et al., 2020; Gómez

Martín et al., 2020; Hanley et al., 2020a; Jones et al., 2020b; Martuti et al., 2020; Perera-Valderrama et al., 2020; Telave and Chandankar, 
2021); as well as environmental and geophysical co-benefits aspects, including mitigation potential and hazard risk reduction (Propato 
et al., 2018; Romañach et al., 2018; Ellison et al., 2020; Erftemeijer et al., 2020; Hanley et al., 2020a; Jones et al., 2020b; Martuti et al., 
2020; Cantasano et al., 2021).
ICZM measures are generally more cost-effective than ‘hard engineering’ measures (Antunes do Carmo, 2018; Morecroft et al., 2019; 
Morris et al., 2019; Donatti et al., 2020; Erftemeijer et al., 2020; Hanley et al., 2020a; Jones et al., 2020b), but implementation pose 
barriers, especially in low-income countries (Lamari et al., 2016; Villamizar et al., 2017; Rosendo et al., 2018; Mestanza-Ramón et al.,

2019; Barragán Muñoz, 2020; Botero and Zielinski, 2020; Caviedes et al., 2020; Martuti et al., 2020; Lin et al., 2021). ICZM implementation 
requires strong institutional frameworks, where all relevant stakeholders (especially representatives of local communities) are part of 
decision-making processes (Pérez-Cayeiro and Chica-Ruiz, 2015; Lamari et al., 2016; Hassanali, 2017; Antunes do Carmo, 2018; Hamin 
et al., 2018; Phillips et al., 2018; Romañach et al., 2018; Rosendo et al., 2018; Warnken and Mosadeghi, 2018; Mestanza-Ramón et al., 
2019; Morecroft et al., 2019; Morris et al., 2019; Walsh, 2019; Barragán Muñoz, 2020; Caviedes et al., 2020; Donatti et al., 2020; Ellison 
et al., 2020; Martuti et al., 2020; O’Mahony et al., 2020; Perera-Valderrama et al., 2020). This aspect is mentioned as a key challenge 
in developing countries (Pérez-Cayeiro and Chica-Ruiz, 2015; Villamizar et al., 2017; Rosendo et al., 2018; Alves et al., 2020). Similarly,

explicitly incorporating gender considerations into ICZM is generally recommended, mainly because women are key knowledge holders in 
coastal communities; however, this is rarely done in practice, which may lead to sub-optimal or unequal outcomes (Nguyen Mai and Dang 
Hoang, 2018; Hoegh-Guldberg et al., 2019; Pearson et al., 2019; Barreto et al., 2020). The perception that building ‘hard’ infrastructure 
(i.e., coastal defence and hardening) is a more efficient way of reducing coastal risk than the implementation of ‘soft’ or nature-based 
solutions (NbS) measures has been challenged in recent studies (Magnan and Duvat, 2018).
CCB FEASIB.3.2.4 Agro-forestry
There is robust evidence and high agreement that agro-forestry systems can increase ecological and adaptive capacity (Schoeneberger 
et al., 2012; Smith et al., 2013a; Minang et al., 2014; Apuri et al., 2018; Kmoch et al., 2018; IPCC, 2019b; Jordon et al., 2020). Benefits

include preservation of ecosystems services, such as water provision and soil conservation, more efficient use of limited land, alleviation 
of land degradation, prevention of desertification and improved agricultural output. Agro-forestry solutions also result in co-benefits in 
the water–energy–land–food nexus, with observed positive outcomes in soil management, crop diversification, water efficiency and 
alternative sources of energy (De Beenhouwer et al., 2013; Elagib and Al-Saidi, 2020). Further, they can have social and economic benefits Cross-Chapter Box FEASIB (continued)

18
2776Chapter 18 Climate Resilient Development Pathways
and positive synergies between adaptation and mitigation (Section 8.6.2.2) (Coulibaly et al., 2017; Hernández-Morcillo et al., 2018; 
Tschora and Cherubini, 2020; Duffy et al., 2021).
When locally adapted to fine-scale ecological and social variation, agro-forestry initiatives can improve household income, and provide 
regular employment and sustainable livelihood to local communities, thereby strengthening peoples’ resilience to cope with adverse 
impacts of changing climate conditions (Coe et al., 2014; Ogada et al., 2020; Sharma et al., 2020; Sollen-Norrlin et al., 2020; Awazi et al., 
2021). However, Cechin et al. (2021) questions the financial viability of agro-forestry systems, especially in the case of smallholders in 
agrarian reform settlements, struggling with high upfront costs. Similarly, insufficient financial support was found to be a major constraint

for the implementation of broader agro-forestry initiatives in Southeast Asia and Africa (Sections 8.5.2 and 8.6.2.1) (Dhyani et al., 2021; 
Williams et al., 2021b).
Over the last decade, agro-forestry schemes have grown in acceptability and political support, most notably observed in their broad 
inclusion in countries’ NDCs and National Adaptation Plans (NAPs). Governance and institutional arrangements, however, have not 
been conducive to broader implementation of agro-forestry initiatives at the landscape level (Dhyani et al., 2021; Williams et al., 2021b). 
Medium evidence with medium agreement suggests that economic and cultural barriers may explain difficulties with the implementation 
of agro-forestry systems (Coe et al., 2014; Quandt et al., 2017; Cedamon et al., 2018; Hernández-Morcillo et al., 2018; Ghosh-Jerath et al., 
2021). Also, unclear land tenure and ownership issues, together with inappropriate mapping and incomplete databases for monitoring

vegetation, continue to hinder the adoption of broader agro-forestry strategies, particularly in remote areas and tropical forests (Martin 
Notably, agro-forestry practices are often part of Indigenous and local Knowledge (Santoro et al., 2020), and so far, most literature 
refers to the evaluation of existing agro-forestry practices or autonomous adaptation, with few studies evaluating the effects of targeted 
interventions, especially in low- and middle-income countries (Miller, 2020; Castle et al., 2021).
CCB FEASIB.3.2.5 Forest-based adaptation, including sustainable forest management, forest conservation and restoration, 
avoided deforestation, reforestation and afforestation
There is robust evidence and medium agreement supporting the overall feasibility of forest-based adaptation options. Regarding its 
economic feasibility, some studies (Nabuurs et al., 2017b; Chow et al., 2019; Seddon et al., 2020a) highlight that the net benefits of

measures such as reforestation, sustainable forest management and ecosystem restoration outweigh the costs of implementation and 
maintenance. Yet, another strand of literature observes that limited access to financial resources is a major constraint to forest-based 
initiatives, especially in the face of upfront investment costs and alternative, more profitable land uses, such as agriculture (Bustamante 
et al., 2019; Ota et al., 2020; Seddon et al., 2020b). In countries with extensive rural areas where forests provide for local communities, 
government support together with private investments and long-term assurances of maintenance, are considered fundamental for the 
long-term viability of forest conservation strategies (Bustamante et al., 2019; Seddon et al., 2020b). In rural areas, smallholders can 
diversify their livelihood and increase household income as a result of improved local forest governance (Bustamante et  al., 2019;

Fleischman et al., 2020; Ota et al., 2020) Similarly, forest and ecosystem restoration has been found to reduce poverty and improve 
social inclusion and participation, given that ecosystems can be managed jointly and in traditional ways (Woroniecki et al., 2019). Robust 
evidence (high agreement) links forest-based adaptation to job creation, improved health and recreational benefits, most notably for 
indigenous, rural and remote communities (Muricho et al., 2019b; Rahman et al., 2019; Ambrosino et al., 2020; Bhattarai, 2020; Ota 
et al., 2020; von Holle et al., 2020; Tagliari et al., 2021). However, Chausson et al. (2020) note that frameworks for assessing the cost-
effectiveness of adaptation strategies continue to be tailored to conventional, engineered interventions, which fail to capture the broader 
array of material and non-material benefits that forest-based interventions might bring.

Forest-based solutions enjoy wide local, regional and international support (Lange et al., 2019; Chausson et al., 2020; Seddon et al., 
2020b), and most countries have a basic regulatory framework for environmental protection. However, lack of institutional capacity, 
deficient inter-agency coordination, and insufficient staff and budget continue to limit broader implementation of forest-based adaptation 
measures. Limited technical capacity, insufficient production and supply of seeds and seedlings, long transport distances and immature 
supply chains have also been identified as significant barriers that hinder the expansion of forest-based initiatives (Bustamante et al., 
2019; Nunes et al., 2020).
There is robust evidence and medium agreement that forest-based solutions support ecosystems’ capacity to adapt to climate change, 
including better regulation of microclimate, increased groundwater recharge, improved quality of air and water, reduced soil erosion,

improved and climate-adapted biodiversity habitats and expansion of biomass, as well as continuous provision of renewable wood Cross-Chapter Box FEASIB (continued)

18
2777Climate Resilient Development Pathways  Chapter 18
products (Nabuurs et al., 2017b; Chow et al., 2019; Lochhead et al., 2019; Shannon et al., 2019; Weng et al., 2019; von Holle et al., 2020; 
Dooley et al., 2021; Forster et al., 2021; Tagliari et al., 2021). In well-designed systems, adaptation and mitigation can then go hand in 
hand, as in climate-smart forestry. What is more, adaptive forest management is already being tested in climate-smart forestry pilots in 
several temperate regions (Nabuurs et al., 2017b). However, large afforestation and non-native monoculture plantations may negatively 
impact non-forest ecosystems, such as grasslands, shrublands and peatlands, their water resources and biodiversity (Seddon et al., 2019; 
Seddon et al., 2020a; Seddon et al., 2020b). Similarly, the International Resource Panel (2019) warns that restoration may also imply 
trade-offs with other ecological and societal goals.

Regarding risk reduction potential, forest-based strategies are found to protect in-land infrastructure from landslides and coastal 
infrastructure from storm surges (Seddon et al., 2020a; Seddon et al., 2020b), together with offering a cheaper solution than engineered 
grey solutions (Chausson et al., 2020). Land availability is a limiting factor for expanding forest-based solutions (Morecroft et al., 2019; 
Ontl et al., 2020). However, there is high agreement and robust evidence that reforestation, environmental conservation and NbS result in 
increased carbon sinks (Griscom et al., 2017b; Nabuurs et al., 2017b; de Coninck et al., 2018b; Fuss et al., 2018; Favretto et al., 2020; Forster 
et al., 2021). Some authors argue that primary ecosystems and native forests contain larger stocks of carbon than tree plantations (Seddon 
et al., 2019; Fleischman et al., 2020; Seddon et al., 2020a), while another strain of literature finds that net sequestration rate is lower in

mature primary forests than in younger managed forests with their associated wood value chains (Cowie et al., 2021; Forster et al., 2021; 
Gundersen et al., 2021). There is robust evidence and high agreement that forest- and ecosystem-based strategies result in hazard risk 
reduction potential. Environmental restoration can be an effective climate change adaptation alternative, reducing susceptibility to extreme 
events, improving ecological capacities and increasing overall ecosystems’ resilience (Chapter 8, Box 9.7) (Nunes et al., 2020). However, too 
much reliance on forests and green alternatives might increase water shortages and wildfires (Seddon et al., 2019; Fleischman et al., 2020).
CCB FEASIB.3.2.6 Biodiversity management and ecosystem connectivity
There is robust evidence and medium agreement supporting the overall feasibility of biodiversity management and ecosystem connectivity

as adaptation options. With respect to its economic feasibility, financial constraints continue to hinder broader implementation of 
biodiversity-based solutions (Lausche et al., 2013; Chausson et al., 2020; Jones et al., 2020a). Seddon et al. (2020a) highlights that only 5% 
of climate finance goes towards adaptation strategies, and only 1% is destined to disaster risk management including NbS and biodiversity 
management. Government support via subsidies and fiscal transfers is critical for broader biodiversity management interventions. In 
addition, REDD+ (Reduced Emissions from Deforestation and Land Degradation) initiatives have been promoted as a profitable mechanism 
to advance biodiversity conservation strategies while reducing carbon emissions. As far as ecosystem connectivity is concerned, its feasibility 
will strongly depend on the existence of a regulatory framework that appropriately balances property rights, environmental regulations and

monetary incentives to ensure landowners’ willingness to participate and maintain ecosystem corridors (Jones et al., 2020b). The demands 
of commodity-based economies, favouring extractive land uses, present serious barriers to upscaling biodiversity-based adaptation 
interventions (Seddon et al., 2020a). In addition, integrated assessments have shown how biodiversity-based solutions can deliver jobs 
from landscape restoration or income from wildlife tourism and how those benefits are fairly distributed (Chausson et al., 2020).
Legal and regulatory instruments are not perceived as major barriers to biodiversity management and ecosystem connectivity projects 
(Lausche et al., 2013; D’Aloia et al., 2019). A challenge that biodiversity-based measures still face is less acceptance among decision 
makers because their efficiency and cost-benefit ratio are difficult to determine and most of the measures are only effective in the long

term (Lange et  al., 2019). Methodologies to determine cost-effectiveness vary substantially between studies, in part because these 
analyses must be tailored to the socio–ecological context to be meaningful for local governance. This makes it challenging to capture 
and synthesise the full economic benefits of biodiversity-based solutions in comparison to alternatives (Chausson et al., 2020). In all, 
biodiversity and nature-based solutions have gained considerable political traction, with the greatest emphasis on the role of ecosystems 
as carbon sinks (Lange et al., 2019; Chausson et al., 2020; Seddon et al., 2020a).
Several social co-benefits are found to follow from biodiversity management strategies, including improved community health, recreational 
activities and eco-tourism, in addition to educational, spiritual and scientific benefits (Lausche et al., 2013; Worboys et al., 2016; Seddon

et al., 2020a). Lavorel et al. (2020) show how the benefits of biodiversity management are co-produced by harnessing ecological and 
social capital to promote resilient ecosystems with high connectivity and functional diversity. Furthermore, Chausson et al. (2020) note 
how properly implemented NBS, including biodiversity management, can strengthen social networks and foster a sense of place, 
supporting virtuous cycles of community engagement to sustain interventions over time.
There is high agreement and robust evidence supporting the ecological capacity enhancement of biodiversity-based and ecosystem 
connectivity strategies (Thompson et al., 2017; Lavorel et al., 2020). Forest management that favours mixed-species rather than non-Cross-Chapter Box FEASIB (continued)Cross-Chapter Box FEASIB (continued)

18
2778Chapter 18 Climate Resilient Development Pathways
native monocultures can promote the resilience of timber production and carbon storage while also benefiting biodiversity (Chausson 
et al., 2020). Similarly, monocultures have been found to impoverish biodiversity and hold less resilient carbon stocks than natural and 
semi-natural forests (Seddon et al., 2020a).
There is a relatively high agreement that ecosystem connectivity has the potential to improve the adaptive capacity of both ecological 
systems and humans. Krosby et al. (2010), for example, found that planting trees in short distances could increase the probability of 
range shifts in species that depend on the habitat those trees provide. Likewise, connectivity conservation has benefits for climate change 
mitigation (Lausche et al., 2013), but empirical evidence of the adaptation benefits for humans is scant. More recently, it has been

found that biodiversity conservation reduces the risk of zoonotic diseases when it provides additional habitats for species and reduces 
the potential contact between wildlife, livestock and humans (Van Langevelde et al., 2020). Ecosystem-based approaches have been 
promoted to address the risk of increased zoonotic diseases, including the conservation of wildlife corridors (Gibb et al., 2020).
Despite abundant literature on the necessity to implement ecosystem connectivity strategies, many policy recommendations are mostly 
discursive and not supported by evidence. There is a lack of specificity when referring to the actors that should intervene in the design, 
implementation and evaluation of policies. What is more, most of the literature comes from the natural sciences and is concerned with 
co-benefits to wildlife and nature, with very little elaboration on the socioeconomic co-benefits for humans.
CCB FEASIB.3.2.7 Improved cropland management

Improved cropland management, which includes agricultural adaptation strategies such as integrated soil management, no/reduced tillage, 
conservation agriculture, planting of stress-resistant or early maturing crop varieties, and mulching, has high economic and environmental 
feasibility (robust evidence, high agreement) (AGEGNEHU and AMEDE, 2017; Lalani et al., 2017; Schulte et al., 2017; Thierfelder et al., 
2017; Aryal et al., 2018a; Mayer et al., 2018; Prestele et al., 2018; Sova et al., 2018; Gonzalez-Sanchez et al., 2019; Lunduka et al., 2019; 
McFadden et al., 2019; Shah and Wu, 2019; TerAvest et al., 2019; Adams et al., 2020; Aryal et al., 2020a; Debie, 2020; Mutuku et al., 2020; 
Somasundaram et  al., 2020; Du et  al., 2021). Despite higher initial costs in some cases, the economic feasibility of improved cropland 
management is high through improved productivity, higher net returns and reduced input costs (Aryal, 2020; Mottaleb et al., 2017; Keil

et al., 2019; Lunduka et al., 2019; McFadden et al., 2019; Parihar et al., 2020). Self-efficacy is shown to be the most important predictor in 
technical and non-technical adaptation behaviour (Zobeidi et al., 2021), while subsidies, extension services, training, commercial custom-hire 
services and strong social connections such as farmer networks are among the factors supporting adoption among farmers (Section 8.5.2.3) 
(Aryal et al., 2015a; Aryal et al., 2015b; Kannan and Ramappa, 2017; Bedeke et al., 2019; Acevedo et al., 2020). In some regions and for some 
practices, technological feasibility is constrained by costs and inadequate information and technical know-how on particular practices and 
their benefits and trade-offs, indicating medium feasibility (Khatri-Chhetri et al., 2016; Bhatta et al., 2017; Dougill et al., 2017; Kannan and 
Ramappa, 2017; Aryal et al., 2018a; Sova et al., 2018; Findlater et al., 2019). Delays between actions and tangible benefits can reduce public

and private acceptability and uptake of improved cropland management practices (e.g., Dougill et al., 2017 in Malawi).
There remain institutional and financial barriers to improved cropland management such as lack of comprehensive policies, inadequate 
mainstreaming into national policy priorities (e.g., Amjath-Babu et al., 2019 and Reddy et al., 2020 in South Asia), fragmentation across 
different sectors (Dougill et al., 2017 in Malawi), and inadequate access to credit (Aryal et al., 2018c in India). Adoption of improved 
cropland management practices is often strongly mediated by gender: structural barriers such as unequal access to land, machinery, 
inputs, and extension and credit services, constrain adoption by female farmers (Aryal et al., 2018b; Aryal et al., 2018c) Mponela et al., 
2016; Van Hulst and Posthumus, 2016; Ntshangase et al., 2018; Somasundaram et al., 2020). Improved cropland management practices

have social and ecological co-benefits in terms of better health, education and food security (Agarwal, 2017; Farnworth et al., 2017; 
Hörner and Wollni, 2020) and better soil health and ecosystem functioning (AGEGNEHU and AMEDE, 2017; Mottaleb et  al., 2017; 
Thierfelder et al., 2017; Zomer et al., 2017; Sarkar et al., 2018; Gonzalez-Sanchez et al., 2019; Shah and Wu, 2019; Du et al., 2020; Mutuku 
et al., 2020; Somasundaram et al., 2020).
There is robust evidence ( medium agreement) that improved cropland management can have mitigation co-benefits but the exact quantity 
of emissions reductions and increased removals depend on agro-ecosystem type, climatic factors and cropping practices (VandenBygaart, 
2016; Han et al., 2018; Mayer et al., 2018; Prestele et al., 2018; Singh et al., 2018a; Sommer et al., 2018; Gonzalez-Sanchez et al., 2019; Ogle 
et al., 2019; Shah and Wu, 2019; Adams et al., 2020; Aryal et al., 2020a; Li et al., 2020; Wang et al., 2020b; Shang et al., 2021).

CCB FEASIB.3.2.8 Efficient livestock systems
Enhancing the production efficiency of livestock systems through, for example, improved livestock diets, enhanced animal health, 
breeding and manure management, can contribute to adaptation and mitigation (Ericksen and Crane, 2018; Accatino et al., 2019; Paul Cross-Chapter Box FEASIB (continued)

18
2779Climate Resilient Development Pathways  Chapter 18
et al., 2020; IPCC WGIII AR6 Section 7.4.3). While the technological and ecological feasibility of improving livestock production systems 
is high (i.e., measures are technically well established, with different options applicable to a range of livestock production systems and 
ecological conditions), there are multiple context-specific barriers to adoption. These include the lack of coordinated policy support 
or governance, potentially high implementation costs and limited access to finance, inadequate advisory, knowledge exchange or 
infrastructural capacity (Escarcha et al., 2018; Paul et al., 2020), the potential land requirements and associated ecological impacts of 
adjusting livestock management, lack of context-specific research (Pardo and del Prado, 2020) and socio-cultural barriers limiting access

by women or low-income groups to better breeds or feed varieties (Luqman et al., 2018; Salmon et al., 2018), as well as women losing 
influence in the household in some contexts when farms intensify (Tavenner and Crane, 2018). In dryland livestock systems in Ethiopia 
and Kenya, Ericksen and Crane (2018) find that low governance capacities to implement improved grazing regimes constrain improved 
grassland management.
CCB FEASIB.3.2.9 Water use efficiency and water resource management
There is high technological feasibility (robust evidence, high agreement) of improving water use efficiency as well as of managing 
water resources at basin and field scales. These approaches include rainwater harvesting, drip irrigation, laser land levelling, drainage 
management and stubble retention (Dasgupta and Roy, 2017; Khatri-Chhetri et al., 2017; Rahman et al., 2017; Adham et al., 2018; Darzi-

Naftchali and Ritzema, 2018; Terêncio et al., 2018; Velasco-Muñoz et al., 2018; Sojka et al., 2019). There is robust evidence (medium 
agreement) that such measures have socioeconomic co-benefits and improve adaptive capacities through improved water supply (e.g., 
through rainwater harvesting, increased infiltration or integrated watershed management) and sustainable water demand management 
(e.g., reduction of evaporation loss). There is medium evidence (high agreement) of the option’s economic feasibility due to water and 
energy cost savings enhanced by low-cost monitoring systems in some cases (Kodali and Sarjerao, 2017; Viani et al., 2017). Implementation 
costs vary widely, with land forming and irrigation infrastructure requiring substantial up-front investment, while mulches and cover 
crops are low-cost practices. Water management and use efficiency is currently constrained by governance and institutional factors

such as inadequate institutional capacities to prepare for changing water availability, especially in the long term; unsustainable and 
unequal water use and sharing practices, particularly across boundaries; and fragmented and siloed resource management approaches 
(Lardizabal, 2015; Margerum and Robinson, 2015; Singh et al., 2020a).
CCB FEASIB.3.2.10 Livelihood diversification
Livelihood diversification is a key coping and adaptation strategy to climatic and non-climatic risks (Gautam and Andersen, 2016; Asfaw 
et al., 2018; Liu, 2015; Goulden et al., 2013; Makate et al., 2016; Orchard et al., 2016; Nyantakyi-Frimpong, 2017; Schuhbauer et al., 2017; 
Kihila, 2018; Radel et al., 2018; Tian and Lemos, 2018; Buechler and Lutz-Ley, 2019; Salam and Bauer, 2020). There is robust evidence  
(medium agreement) that diversifying livelihoods improves incomes and reduces socioeconomic vulnerability, but depending on livelihood

type, opportunities and local context, feasibility changes (Section 8.5.1) (Barrett, 2013; Martin and Lorenzen, 2016; Sina et al., 2019). 
Livelihood diversification has positive and negative outcomes for adaptive capacity, especially in ecologically and resource-stressed regions 
(e.g. Anderson et al., 2017; Woodhouse and McCabe, 2018; Rosyida et al., 2019; Ojea et al., 2020), with diversification predominantly 
out of rural farm-based livelihoods on the rise (Rigg and Oven, 2015; Shackleton et al., 2015; Ober and Sakdapolrak, 2020). Key barriers 
to livelihood diversification include socio-cultural and institutional barriers (including social networks; Goulden et al., 2013) as well as 
inadequate resources and livelihood opportunities that hinder the full adaptive possibilities of existing livelihood diversification practices 
(Shackleton et al., 2015; Nightingale, 2017b; Bhowmik et al., 2021; Rahut et al., 2021). Autonomous diversification in the absence of more

equitable and harmonised efforts at regional and national scales to facilitate sustainable diversification can further skew development 
indicators at the sub-national scale in favour of local elites, increased inequality and environmental degradation (Ford et al., 2014; Wilson, 
2014; Alobo Loison, 2015; Tanner et al., 2015; Gautam and Andersen, 2016; Baird and Hartter, 2017; Torell et al., 2017; Asfaw et al., 2018; 
Woodhouse and McCabe, 2018; Brown et al., 2019; Rosyida et al., 2019; Sani Ibrahim et al., 2019; Ojea et al., 2020; Salam and Bauer, 2020).
CCB FEASIB.3.3 Urban and infrastructure system transitions
CCB FEASIB.3.3.1 Sustainable land use and urban planning
Urban planning is a medium feasibility option to support adaptation by prioritising it in city plans, such as land use planning, transportation 
(Liang et al., 2020), and health and social services (Carter et al., 2015; Araos et al., 2016b); by procuring the design and construction of

resilient infrastructure; by promoting community-based adaptation through community-based design and implementation of adaptation 
activities (Archer, 2016); and by protecting and integrating biodiversity and ecosystem services into city planning. Research since SR1.5 
documents the challenging high costs of infrastructure (Georgeson et al., 2016; Woodruff et al., 2018); potential loss of municipal revenue 
in the case of managed retreat (Shi and Varuzzo, 2020; Siders and Keenan, 2020); and the fraught causal connection between planning Cross-Chapter Box FEASIB (continued)

18
2780Chapter 18 Climate Resilient Development Pathways
and the reduction of socioeconomic vulnerability (Keenan et al., 2018; Anguelovski et al., 2019a; Elliott, 2019; Paganini, 2019; Shokry et al., 
2020). However, adaptation benefits could potentially outweigh costs (Carey, 2020). There is financial viability of green infrastructure 
(Meerow, 2019; Zhang et al., 2019; Van Oijstaeijen et al., 2020; Ossola and Lin, 2021); and availability of technical expertise, although the 
inequitable planning processes and distribution of those resources remains a significant concern (Serre and Heinzlef, 2018; Szewrański 
et al., 2018; Fitzgibbons and Mitchell, 2019; Hasan et al., 2019; Heikkinen et al., 2019; Colven, 2020; Goetz et al., 2020; Goh, 2020).
Structural disincentives and institutional arrangements create challenges for planning even where political willingness may be high

(Di Gregorio et  al., 2019; DuPuis and Greenberg, 2019; Shi, 2019; Zen et  al., 2019; Rasmussen et  al., 2020). Social resistance may 
significantly delay or block progress entirely, as vulnerable communities have responded negatively in cases where adaptive urban and 
land use planning leads to perceived ‘resilience gentrification’ (Keenan et al., 2018; Anguelovski et al., 2019a), if residents do not perceive 
themselves as included in the crafting of plans (Araos, 2020; Rasmussen et al., 2020), if the options such as managed retreat are perceived 
as culturally unacceptable (Ajibade, 2019; Koslov, 2019; Siders, 2019), or if wealthier and advantaged residents benefit from planning 
at the expense of socially vulnerable groups (Chu and Michael, 2018; Chu et al., 2018; Fainstein, 2018; Rosenzweig et al., 2018; Pelling 
and Garschagen, 2019a; Ranganathan and Bratman, 2021). Nonetheless, potential social co-benefits related to health and education

are high (Raymond et al., 2017; Spaans and Waterhout, 2017; Klinenberg, 2018; Keeler et al., 2019; Meerow, 2019). Finally, the option 
is highly feasible in relation to ecological and geophysical characteristics, as urban and land use planning’s primary tool is to shape the 
built environment and natural spaces to protect and reduce the vulnerability of residents.
CCB FEASIB.3.3.2 Green infrastructure and ecosystem services
Urban green infrastructure and ecosystem services have high feasibility to support climate adaptation and mitigation efforts in cities, 
for example to reduce flood exposure and attenuate the urban heat island effect (Perrotti and Stremke, 2018; Belčáková et al., 2019; 
De la Sota et  al., 2019; Stefanakis, 2019). While green infrastructure options are cost-effective and provide co-benefits in terms of 
ecosystem services such as improved air quality or other health benefits (Depietri and McPhearson, 2017; Morris et al., 2018; Reguero

et al., 2018; Escobedo et al., 2019; Filazzola et al., 2019; Hewitt et al., 2020b; Venter et al., 2020; Nieuwenhuijsen, 2021) (robust evidence, 
high agreement), a need remains for systematically assessing co-benefits, particularly for flood risk management (Alves et al., 2019; 
Stefanakis, 2019) and sustainable material flow analysis (Perrotti and Stremke, 2018). Moreover, while once neglected, rapidly increasing 
attention has been paid to the equity and justice dimensions of planning and implementing green infrastructure initiatives, such as 
inclusion of citizens in decision making or the allocation of benefits and impacts of projects (Anguelovski et al., 2019b; Buijs et al., 2019; 
Langemeyer et al., 2020; Venter et al., 2020)
Institutional barriers constrain the feasibility of urban green infrastructure (medium confidence), such as policy resistance to shift priorities

from grey to green infrastructure (e.g., Johns, 2019 in Canada) or siloed governance structures (Willems et al., 2021). Further, social and 
political acceptability of green infrastructure is constrained by lack of confidence in efficacy (Thorne et al., 2018) or issues of accessibility 
(Biernacka and Kronenberg, 2018).
For flood management, a mix of green, blue and grey infrastructures are found effective, with grey infrastructure reducing the risk of 
flooding and green infrastructure yielding multiple co-benefits (Alves et al., 2019; Gu et al., 2019; Webber et al., 2020) but catchment-
wide solutions are advocated as the best performing strategy (Webber et al., 2020). Recognising and addressing a full range of ecosystem 
disturbances and disasters over a larger urban spatial scale (Vargas-Hernández and Zdunek-Wielgołaska, 2021) are crucial for planning

green infrastructure-based solutions. In some cases, low impact development interventions yield effective flood management outcomes 
but are adequate only for small flood peaks (Pour et al., 2020), with the major challenge being identifying best practices. NbS hold 
significant potential to achieve mitigation and adaptation goals in comparison with traditional approaches, but more research is necessary 
to understand their effectiveness, distribution, implementation at scale, cost-benefit and integration with spatial dimensions of planning 
(Davies et al., 2019; Dorst et al., 2019; Zwierzchowska et al., 2019; Hobbie and Grimm, 2020).
CCB FEASIB.3.3.3 Sustainable urban water management (blue infrastructure interventions e.g., lake/river restoration; rainwater 
harvesting)
Governments across scales can support urban sustainable water management with high feasibility by undertaking projects to recycle

wastewater and runoff from higher intensity storms, with implications for decarbonisation and adaptation. Green infrastructure, for 
example, has shown a high potential to reduce water-use footprints and to save potable water for consumption (Liu and Jensen, 2018), 
and contributing to a ‘circular’ water system in cities (Oral et al., 2020). Supportive governance can yield positive outcomes such as 
improved water security (Jensen and Nair, 2019) and there is medium evidence and high agreement that participation, such as involving Cross-Chapter Box FEASIB (continued)

18
2781Climate Resilient Development Pathways  Chapter 18
informal settlement residents in water management can improve social inclusion (Pelling et al., 2018; Williams et al., 2018; Leigh and 
Lee, 2019b; Sletto et al., 2019). Green infrastructure can support the planning of ‘sponge cities’, such as in China, wherein large areas 
of green space, permeable surfaces and sustainable water sourcing combine to purify urban runoff, attenuate peak runoff and conserve 
water for consumption (Chan et al., 2018; Nguyen et al., 2019). Similar approaches in Dutch cities focus on designing and planning for 
the capturing, storing and draining of storm water (Dai et al., 2018). However, some interventions suffer from uncertainties in design, 
planning and financing (Nguyen et al., 2019). As drought becomes more severe in some regions, physical barriers in the form of reduced 
availability of water may become pressing (Singh et al., 2021b).

Deployment of decentralised water management through effective local governance frameworks, is an important water management 
strategy (Herslund and Mguni, 2019; Leigh and Lee, 2019b), but in general, insufficient institutional learning and capacity remains a 
critical barrier for the uptake of sustainable urban water management practices (Krueger et al., 2019a; Adem Esmail and Suleiman, 2020). 
Transnational networks of cities for sharing best practices in water supply and storm runoff treatment also hold the potential to scale 
sustainable management (Feingold et al., 2018). In rapidly growing large urban areas, sustainable water management faces challenges 
of institutional heterogeneity (Chu et al., 2018), scalar mismatch, particularly between river basin and city scales (van den Brandeler 
et al., 2019), and equity and justice concerns (Chu et al., 2018; Pelling et al., 2018). Finally, assessing the vulnerability of urban water

infrastructures at city scale remains an important knowledge gap (Dong et al., 2020).
CCB FEASIB.3.4 Cross-cutting adaptation options
CCB FEASIB.3.4.1 Social safety nets
Social safety nets contribute to meeting development goals (e.g., poverty alleviation, accessible education and health services) and are 
increasingly being reconfigured to build adaptive capacities of the most vulnerable (Coirolo et al., 2013; Aleksandrova, 2020; Bowen et al., 
2020; Fischer, 2020; Mueller et al., 2020). They include a range of policy and market-based instruments such as public works programmes 
and conditional or unconditional cash transfers, in-kind transfers, and insurance schemes (Centre, 2019; Aleksandrova, 2020). While 
there is robust evidence (medium agreement) that social safety nets can build adaptive capacities, reduce socioeconomic vulnerability 
and reduce risk linked to hazards (Fischer, 2020; Mueller et al., 2020), macroeconomic, institutional and regulatory barriers such as

limited state resources, underdeveloped credit and insurance markets, and economic leakages constrain their feasibility (Singh et al., 
2018c; Hansen et al., 2019; Aleksandrova, 2020; Lykke Strøbech and Bordon Rosa, 2020). Social safety nets have strong co-benefits with 
development goals (Section 8.6) (Castells-Quintana et al., 2018b; Ulrichs et al., 2019; Mueller et al., 2020) but these positive outcomes 
are constrained by inadequate regional inclusiveness (e.g., limited access in certain remote, rural areas; Singh et al., 2018b; Aleksandrova, 
2020; Lykke Strøbech and Bordon Rosa, 2020) or focus on rural areas overlooks urban vulnerable groups (Coirolo et al., 2013).
CCB FEASIB.3.4.2 Risk spreading and sharing
There is high confidence on risk spreading and sharing, most commonly arranged through insurance, as an adaptation option, but high to

medium feasibility depending on context (e.g., developed versus developing countries). Technological, economic and institutional feasibility 
is high, as insurance can spread risk, provide a buffer against the impact of climate hazards, support recovery and reduce the financial burden 
on governments, households and businesses (Wolfrom and Yokoi-Arai, 2015; O’Hare et al., 2016; Glaas et al., 2017; Jenkins et al., 2017; Patel 
et al., 2017; Kousky et al., 2021). Insurance can shift the mobilisation of financial resources away from ad hoc post-event payments, where 
funding is often unpredictable and delayed, towards more strategic approaches that are set up in advance of disastrous events (Surminski 
et  al., 2016). By pricing risk, insurance can provide incentives for investments and behaviour that reduce vulnerability and exposure 
(Linnerooth-Bayer and Hochrainer-Stigler, 2015; Shapiro, 2016; Jenkins et al., 2017). Socio-cultural barriers, such as social inclusiveness, socio-

cultural acceptability and gender equity constrains feasibility (Bageant and Barrett, 2017; Budhathoki et al., 2019). Insurance can provide 
disincentives for reducing risk through the transfer of the risk spatially and temporally, distorting incentives for adaptation if the pricing is 
too low (moral hazard) and is often unaffordable, poorly understood, and not widely utilised in developing nations even when subsidised, 
possibly leading to maladaptation (García Romero and Molina, 2015; Joyette et al., 2015; Lashley and Warner, 2015; Jin et al., 2016; Müller 
et al., 2017; Tesselaar et al., 2020). Insurance can reinforce exposure and vulnerability through underwriting a return to the ‘status-quo’ rather 
than enabling adaptive behaviour (e.g., through ‘no-betterment’ principles) (Collier and Cox, 2021). For low-income nations and in the 
absence of global support, insurance shifts responsibility to those least responsible for climate change (Surminski et al., 2016).

CCB FEASIB.3.4.3 Disaster risk management
There is robust evidence (high agreement) that DRM aids adaptation decision making, particularly where it is demand-driven, context-
specific and supported by strong institutions, good governance, strong local engagement and trust across actors (Hasan et al., 2019; Kim Cross-Chapter Box FEASIB (continued)

18
2782Chapter 18 Climate Resilient Development Pathways
and Marcouiller, 2020; Peng et al., 2020; Smucker et al., 2020; Uddin et al., 2020; Webb, 2020; Ali et al., 2021; Anderson and Renaud, 2021; 
Glantz and Pierce, 2021; Ji and Lee, 2021; Villeneuve, 2021). These conditions are rarely met, and therefore DRM is often constrained by 
institutional factors that may even increase vulnerability (Booth et al., 2020; Islam et al., 2020b; Islam et al., 2020c; Marchezini, 2020; 
Goryushina, 2021; Mena and Hilhorst, 2021). The feasibility of DRM continues to be constrained by limited coordination across levels 
of government, lack of transparency and accountability, poor communication and a preference for top-down DRM processes that can 
undermine local institutions and perpetuate uneven power relationships (Atanga, 2020; Booth et al., 2020; Bordner et al., 2020; Bronen

et  al., 2020; Goryushina, 2021; Mena and Hilhorst, 2021; Son et  al., 2021; Yumagulova et  al., 2021). However, local integration of 
worldviews, belief systems and local and Indigenous Knowledge into DRM activities improves feasibility (Bordner et al., 2020; Cuaton and 
Su, 2020; Hosen et al., 2020; Sharma and Sharma, 2021), including disability-inclusive and gender-focused DRM (Ruszczyk et al., 2020; 
Crawford et al., 2021). Data access and availability continues to challenge DRM despite advances in data analytics, especially in rapidly 
growing informal settlements, including population estimates and limited mobility data (Goniewicz and Burkle, 2019; Marchezini, 2020).
Moves towards community-based and ecosystem-based DRMs are promising but uneven (Klein et al., 2019; Seebauer et al., 2019; Almutairi 
et al., 2020; Bordner et al., 2020; Hosen et al., 2020; Murti et al., 2020; Sharma and Sharma, 2021), and may increase vulnerability if they fail

to address underlying, structural determinants of vulnerability, particularly among marginalised groups and by gender (Sections 8.4.4 and 
8.4.5) (Seleka et al., 2017; Hossen et al., 2019; Ramalho, 2019b; Atanga, 2020; Cuaton and Su, 2020; Gartrell et al., 2020; Kenney and Phibbs, 
2020; Khalil et al., 2020; Ngin et al., 2020; Ruszczyk et al., 2020; Webb, 2020; Ali et al., 2021; Geekiyanage et al., 2021; Villeneuve, 2021).
CCB FEASIB.3.4.4 Climate services, including early warning systems
There is robust evidence (high agreement) that climate services aid adaptation decision making and build adaptive capacity, particularly 
where they are demand-driven and context-specific (Vaughan et al., 2018; Bruno Soares and Buontempo, 2019; Daniels et al., 2020; Hewitt 
et al., 2020a; Findlater et al., 2021). Climate service interventions are constrained by low capacity, inadequate institutions, difficulties

in maintaining systems beyond pilot project stage (Vincent et al., 2017; Tall et al., 2018; Bruno Soares and Buontempo, 2019), and poor 
mapping between climate services and existing user capacities and demands (Williams et al., 2020) (robust evidence, high agreement). 
Metrics to assess outcomes of climate services remain project-based and insufficiently capture longer-term economic and non-economic 
benefits of interventions (Tall et al., 2018; Parton et al., 2019; Perrels, 2020). The technical feasibility of climate services is relatively strong 
and growing (Vaughan et al., 2016; Kihila, 2017; Findlater et al., 2021) but they can be made more inclusive by focusing on addressing 
uneven uptake based on location or gender (Amegnaglo et al., 2017; Daly and Dessai, 2018; Tall et al., 2018; Alexander and Dessai, 2019; 
Vaughan et al., 2019; Gumucio et al., 2020) and a more balanced focus on uptake rather than data production alone (Dorward et al.,

2021; Findlater et al., 2021) that values co-production and different knowledge systems (Daniels et al., 2020; Martínez-Barón et al., 2021).
CCB FEASIB.3.4.5 Health and health systems adaptation
Climate change will exacerbate existing health challenges. Strong health systems can protect and promote the health of a population in 
the face of known and unexpected stressors and pressures (Watts et al., 2021), including climate change. The building blocks of strong 
health systems engender climate resilience, strong leadership and governance, and effective coordination across sectors, to prioritise the 
needs of the most vulnerable (Ebi et al., 2020). Options for enhancing current health services include providing access to safe water and 
sanitation, improving food security, enhancing access to essential services such as vaccinations, developing or strengthening integrated

surveillance systems, and changing the timing and location of specific vector-control measures (WHO, 2015; Haines and Ebi, 2019). 
These measures can reduce the health system’s vulnerability to climate change, especially if combined with iterative management that 
incorporates monitoring of (and resilience against) climate change impacts (Hanefeld et al., 2018; Haines and Ebi, 2019; Linares et al., 
2020; Rudolph et al., 2020) (medium evidence, high agreement).
Health systems can provide sufficient and high-quality healthcare to all where capacity is well developed, and where options are aligned 
with national priorities, engage local to international communities, and address the needs of particularly vulnerable regions and 
population groups (Hanefeld et al., 2018; Austin et al., 2019; Nuzzo et al., 2019; Sheehan and Fox, 2020). Microeconomic feasibility and

socioeconomic vulnerability reduction potential are high where a system’s capacity is well developed. Economic feasibility poses a 
significant challenge in low-income settings, with many governments projected to require international climate finance for health systems 
which is not currently available (WHO, 2019; Watts et al., 2021), and where adequate household-level financial security is a cross-cutting 
barrier (Paudel and Pant, 2020). Risk mitigation potential is high where capacity is well developed, for example through technologies to 
monitor and alter environmental conditions (Lock-Wah-Hoon et al., 2020; Kouis et al., 2021; Ligsay et al., 2021). Social co-benefits of 
mainstreaming health and climate change are also present, such as the inclusion of environmental health in medical education curricula 
training programmes (Kligler et al., 2021). There is growing recognition that lack of institutional capacity and low availability of resources

represent major barriers to health system adaptation options, particularly for health systems struggling to manage current health risks Cross-Chapter Box FEASIB (continued)

18
2783Climate Resilient Development Pathways Chapter 18
(Ebi et al., 2018; Brooke-Sumner et al., 2019; Chersich and Wright, 2019; Gilfillan, 2019; Negev et al., 2019; Hussey and Arku, 2020), for

neglected populations (Hanefeld et al., 2018; Negev et al., 2019), and where there are conflicting mandates or poor coordination across ministries (Austin et al., 2019; Fox et al., 2019; Gilfillan, 2019; Kendrovski and Schmoll, 2019; Sheehan and Fox, 2020). Barriers to adapting health systems to climate change include lack of institutional funding, staff and data access (Austin et al., 2019; Schramm et al., 2020; Opoku et al., 2021), inadequate resources for evaluation and management of adaptation (Pascal et al., 2021), competing stakeholder goals and costly technology (Negev et al., 2021). Within the healthcare community, surveillance systems generally lack ways to integrate climate observation data, as well as expertise to critically evaluate these data, limiting their ability to plan and prepare for climate hazards and hospital-associated vulnerabilities (Runkle et al., 2018; Chersich and Wright, 2019; Liao et al., 2019). Although understanding of health vulnerability is growing

(Berry et al., 2018), knowledge on the health effects of climate change among health practitioners remains limited (Ebi et al., 2018; Brooke-Sumner et al., 2019; Chersich and Wright, 2019; Fox et al., 2019; Liao et al., 2019; Albright et al., 2020). Mechanisms to ensure transparency and accountability of implementing, monitoring and evaluating adaptation within the health sector are lacking, across scales and contexts (Gostin and Friedman, 2017; Huynh and Stringer, 2018; Parry et al., 2019).

Much climate-related migration is associated with labour migration. Rural–urban migrant networks are important channels for remittances and knowledge that help build resilience to hazards in sending areas (Bragg et al., 2018; Obokata and Veronis, 2018; Semenza and Ebi, 2019; Maharjan et al., 2020; Porst et al., 2020). Whether migration reduces vulnerability for migrants depends on levels of control over the migration decision and assets such as wealth, and education of the migrant household (Thober et al., 2018; Cattaneo, 2019; Hoffmann et al., 2020; Maharjan et al., 2020; Sedova and Kalkuhl, 2020). Individuals from households of all levels of wealth migrate. However, poorer households do so with lower levels of choice and often more likely under duress, and in these cases, migration can undermine well-being (Suckall et al., 2016; Mallick et al., 2017; Nawrotzki and DeWaard, 2018; Natarajan et al., 2019). In some cases, migration can increase poverty in sending communities (Jacobson

et al., 2019). Women in the sending community can experience an increase or decrease in the vulnerability, depending on the livelihoods people are moving into and existing asset bases (Banerjee et al., 2018; Banerjee et al., 2019b; Goodrich et al., 2019; Maharjan et al., 2020; Rao et al., 2020; Singh and Basu, 2020; Singh et al., 2020b).

Migration has been highly politicised, and climate-related immigration has been conceptualised in public and media discourse as a

potential threat which limits adaptation feasibility (Telford, 2018; Honarmand Ebrahimi and Ossewaarde, 2019; McLeman, 2019; Wiegel et al., 2019; Hauer et al., 2020). Existing international agreements provide potential frameworks for climate-related migration to benefit adaptive capacity and sustainable development (Warner, 2018; Kälin, 2019). However, agreements to facilitate temporary or circular migration and remittances are often informal and limited in scope (Webber and Donner, 2017b; Margaret and Matias, 2020) and migrant receiving areas, particularly urban areas, can be better assisted to prepare for population change (Deshpande et al., 2019; Adger et al., 2020; Hauer et al., 2020). Policies and planning are lacking that would ensure that positive migration outcomes for sending and receiving areas and the migrants themselves (Wrathall et al., 2019; Adger et al., 2020; de Salles Cavedon-Capdeville et al., 2020; Hughes, 2020).

Investing in building in situ adaptive capacity through climate resilient development is a precondition to supporting high agency migration 
(Cundill et. al. 2021). Migration only tends to occur when adaptation in situ has been exhausted and thresholds for living with risk have

been crossed (Sections 8.2.2.1, 8.4.4, 8.4.5) (McLeman, 2018; Adams and Kay, 2019; Semenza and Ebi, 2019). The financial, emotional and social costs of leaving are high (Adams and Kay, 2019; McNamara et al., 2021), there are environmental, health and well-being risks in destination areas (Schwerdtle et al., 2018; Schwerdtle et al., 2020), and existential threats to identity and citizenship (Oakes, 2019; Piguet, 2019; Desai et al., 2021). In receiving areas, without appropriate policies to ensure equitable provision of services, there can be socio-cultural barriers to in-migration where there is the perception of a loss caused by new arrivals, although outcomes are mixed (Koubi et al., 2018; Linke et al., 2018; Spilker et al., 2020; Petrova, 2021).
CCB FEASIB.3.4.7  Planned relocation and resettlement

Few climate-related planned resettlement and relocation initiatives have taken place. However, initial findings, and experience from past development and disaster-related resettlement programmes, show that when implemented in a top-down manner and without the full participation of those affected, resettlement increases vulnerability by undermining livelihoods and negatively impacting health, community cohesion and emotional and psychological well-being (Wilmsen and Webber, 2015; Dannenberg et al., 2019; Piggott-McKellar et al., 2019; Tabe, 2019; Ajibade et al., 2020; Henrique and Tschakert, 2020; Desai et al., 2021). Planned relocation could also redistribute vulnerability for those who do not move (Thomas and Benjamin, 2018; Mach et al., 2019a; Piggott-McKellar et al., 2019; Johnson et al., 2021; Maldonado et al., 2021) and vulnerability generally is reproduced along existing social cleavages often worsening inequality (See and Wilmsen, 2020). Approaches that foreground

participation, non-material and socio-cultural factors, livelihoods and local power dynamics can be addressed and adjusted to prevent planned relocation from reproducing inequality (See and Wilmsen, 2020; Alverio et al., 2021).Cross-Chapter Box FEASIB (continued)Cross-Chapter Box FEASIB (continued)

18
2784Chapter 18 Climate Resilient Development Pathways
Other
cross-cutting
risksPeace and human mobility
Risk spreading and sharin gHealth and health systems adaptation
Human migration sCross-
sectoralLiving standards and equity Livelihoo ddiversiﬁcatio n
Climate servicesPlanned relocation and resettlemen t
Disaster riskmanagemen tComposite
Index
Coastal socio-
ecological systems
Terrestrial and ocean 
Food
security
Critical infrastructure,
networks and servicesForest-based adaptation *
Improved cropland managemen t
Efﬁcient livestock system s
Sustainable land use an durban plannin gCoasta ldefence and hardenin g
Resilien tpower system sImprove wate ruse efﬁcienc yBiodiversity management and ecosystem connectivit y
Gree ninfrastructure and ecosyste mservicesSustainable aquaculture and fisherie sIntegrated coastal zone managemen t

Water security Wate ruse efﬁcienc yand water resource managemen tNear-term climate responses and adaptation optionsRepresentative
key risksSystem
transitions
Land,
ocean and
Urban and
systems
Energy
systemsAgroforestry
Energy reliabilit yStrong
co-benefit
/
Confidence in Composite Feasibility Index
High Medium LowAssessed feasibility level sMultidimensional feasibility and synergies with mitigation of 
climate responses and adaptation options relevant in the 
near-term, at global scale and up to 1.5°C of global warmingFeasibility DimensionsEconomic
Social
Geophysica lEnvironmenta l
na/
na
= Not applicablena
= Insufficient evidence/* including sustainable forest management, forest conservation an d
restoration, reforestation and afforestation
High Medium LowH igh Medium Low
Sustainable urban water management

Figure Cross-Chapter Box FEASIB.2 |  This figure summarizes the assessment results classifying options by System Transitions and Representative 
Key Risks. Each option is assessed across six dimensions: economic, technological, institutional, socio-cultural, environmental and geophysical. Each dimension is assessed 
as high (big circle), medium (medium circle), low (small circle) feasibility, and limited evidence or no evidence (LE/NE, as a dash). Composite feasibility is calculated across 
the six dimensions following the same key as above, with feasibility levels determined by circle size and confidence levels by shades of colour. The last column shows 
options with strong synergies with mitigation, which is then broken down in Fig. CCB FEASIB.3.
There is inadequate institutional capacity to enable movement relocation, with global and national policies identified as too abstract

and lacking guidance on ensuring equity (Mortreux et al., 2018; Kelman et al., 2019; Ajibade et al., 2020; Hauer et al., 2020; Alverio et al., 
2021). Lack of institutional capacity can lead to resettlements being stalled indefinitely. Climate-related resettlement can be facilitated 
by novel institutional structures that expand the definition of disaster to include slow onset events, adaptive management frameworks 
that facilitate a continuum of responses from supporting communities to community relocation and approaches that incorporate existing 
power dynamics (Bronen and Chapin, 2013; See and Wilmsen, 2020). In 2018, the Fiji Government provided a framework for climate 
change-related relocation and equipped communities with rights in the planned relocation process (McMichael and Katonivualiku, 2020). 
However, even with guidelines in place, local socio-cultural dynamics complicate planning, and relocation should take place only after

cost–benefit analysis of all available adaptation options (Jolliffe, 2016; Bronen and Chapin, 2013; Albert et al., 2017; Mortreux et al., 
2018). At a local level, issues around land tenure, a lack of financial support, dedicated governance frameworks and complex planning 
processes delay action (Albert et al., 2017). Funding for climate-related resettlement is currently not readily available, exacerbated by Cross-Chapter Box FEASIB (continued)

18
2785Climate Resilient Development Pathways Chapter 18
a lack of appropriate mechanisms through which to deliver that funding (Boston et al., 2021). For example, planned relocation projects 
cannot access disaster relief funds in the USA because of the slow onset nature of the impacts (Bronen and Chapin, 2013).
Without consultation, relocated people can experience significant financial and emotional distress as cultural and spiritual bonds

to place and livelihoods are disrupted (Neef et al., 2018; Roy et al., 2018b; Piggott-McKellar et al., 2019; Bertana, 2020; McMichael and Katonivualiku, 2020; McMichael et al., 2021; Jain et al. 2021). However, in some places, where climate risks are acute, political acceptance for planned relocation is high (e.g., (McNamara, 2015; Roy et al., 2018b) in Kiribati). Socio-cultural feasibility can be improved by participatory approaches and, where possible, moving within ancestral lands (McNamara, 2015). In this case, voluntary planned relocation can represent the assertion of people living in an area to preserve land and community-based social, cultural and spiritual ties.
A summary of feasible options to enable four 1.5°C-relevant system transitions is presented in Figure Cross-Chapter Box FEASIB.2.
CCB FEASIB.4  Syner gies and trade-offs
The feasibility assessment focuses on individual adaptation options. However, systems transitions necessitate assessing how mitigation

and adaptation options interact to mediate overall feasibility. To capture these linkages, this section reports synergies and trade-offs of (a) adaptation options for mitigation and (b) mitigation options for adaptation (following (de Coninck et al., 2018b) as the outcome of an iterative assessment between WGII and WGIII authors. Also assessed are synergies and trade-offs of adaptation with the SDGs, following(which was done for mitigation alone).
(a) Climate responses and adaptation options and their implications for mitigation
Other
cross-cuttingrisksPeace and human mobility
Risk spreading and sharin gPopulation health andh ealth systems
Human migratio n Cross-
sectoralLiving standards and equity Livelihood diversiﬁcatio n
Climate service sPlanned relocation and resettlement
Disaster riskmanagemen tCoastal socio-
Food
security

networks and servicesForest-based adaptation*
Improved cropland management
Sustainable land use and urbanplannin gCoasta ldefence and hardenin g
Resilient powe rsystemsImprove wate ruse efﬁcienc yBiodiversity management and ecosystem connectivity
Gree ninfrastructure and ecosyste mservicesSustainable aquaculture and fisheriesIntegrated coastal zone managemen t
Water security Wate ruse efﬁciency and wate rresource managementNear-term climate responses and adaptation optionsRepresentative
Land,
ocean and
Urban and
infrastructur e
systems
Energy
systemsAgroforestr y
Energy reliabilit ySynergies
with mitigationTrade-offs
with mitigation
insufficient evidenc enot applicabl e
not applicabl e
Overall confidence Overall strength of synergy/trade-off
High Medium Low High Medium Low N one* Including sustainable forest management,

forest conservation and restoration, avoided 
deforestation, reforestation and afforestation.Sustainable urban water managementCross-Chapter Box FEASIB (continued)

18
2786Chapter 18  Climate Resilient Development P athways
Mitigation optionsSystem
transitionsSynergies
with adaptationTrade-offs
with adaptation
Energy
systemLand and
ecosystem
Urban
system
system
 Cross-
sectoralBiomas scrops for bioenergy, biocha rand other bio-based products
Enhanc ecarbon in agricultural system s
Envelope improvement
Healthy balanced diets, rich in plant based food* an dreduced food wast e
Protect and avoid conversion of forests andother ecosystems**
 Reduce non-CO²emissions from agricultur e
Reduce overconsumption
Reforestation and restoration of othe recosystem s
Sustainable management of forests and othe recosystems
Active and passive managemen tandoperatio n
Change in construction methods and materials
Circular and shared economy
Digitalization
Efﬁcient appliances
Electromobilit y
Flexible comfor trequirement s
Fuel efﬁciency in transport
Heating, ventilation and ai rconditionin g
Integrating sector, strategies and innovations

Renewable energy production
Response option: district heating and cooling networ k
Urban land use and spatial planning
Urban nature-based solutions
Waste prevention, minimization and management
Bioenergy and bioenerg ywith carbon capture and storag e
 CO²capture and storag e
Demand side mitigation
Energy storage for low-carbon grid s
Fossil fuels phase ou t
Hydroelectric powe r
Nuclear
Solar energy
System integratio n
Wind energ y
 CO²capture and utilization
Circular econom y
Electriﬁcation and fuel switchin g
 Industria lCO²capture and storag e
Industrial energy efﬁciency
Materials efﬁciency and demand managemen t
Direct air carbon capture and storag e
Enhanced weathering(b) Mitigation options and their implications for adaptation
not applicable
not applicablenot applicable
insufficient evidencenot applicable
High Medium Low High Medium Low N one* Less animal based.

** e.g. peatlands or natural grasslands .
Figure Cross-Chapter Box FEASIB.3 |  This figure shows a) adaptation options synergies and trade-offs with mitigation and b) mitigation options 
synergies and trade-offs with adaptation. The size of the circle denotes the strength of the synergy or trade-offs with big circles meaning strong synergy or trade-off and small circles denoting a weak synergy or trade-off.Cross-Chapter Box FEASIB (continued)

18
2787Climate Resilient Development Pathways Chapter 18
1:
2:3:4:
5:  well-being6:Cleanwater&sanitation
7:Affordable &cleanener gy
8:Decent work  &economi cgrowth
9:Industry,innov ation&infrastructure
10:Reducedinequal ity11:Sustainabl ecities&commun ities
12:Responsibleconsu mption&production
13:Climateaction
14: Lifebelowwater
15: LifeonlandNopoverty
Zerohunger
Goodhealth&
Quality education16:Peace&justicestrong institutions
17:Partnerships for the Goal s
ty Gender equali
Footnotes: 1 The term 
response is used here 
instead of adaptation  
because some responses, such as retreat, may or may not be considered to be  
adaptation.  
2 Including 
sustainable forest management, forest conservation and restoration, reforestation 
and afforestation.  
3 The 
Sustainable Development Goals (SDGs) are integrated and indivisible,  
and efforts to achieve any goal in isolation may trigger synergies or trade-offs with other SDGs. 
4 Relevant in  
the near-term, at global

scale and up to 1.5°C of  
global warming .Types of relation
Climate services, including Early Warning SystemsForest-based adaptation2
Resilient power systemsAgroforestry
Energy reliabilitySustainable aquaculture and fisheries
Efﬁcient livestock systemsBiodiversity management and ecosystem connectivityIntegrated coastal zone management
Water use efﬁciency and water resource management
Green infrastructure and ecosystem service s
Sustainable land use and urban planning
Planned relocation and resettlementImprove water use efﬁciency
Health and health systems adaptation
Livelihood diversiﬁcation
Human migration3
Risk spreading and sharingCoastal defence and hardening
Cross-
sectoralSystem
Land and 
ocean
Urban and
systems
Energy
systemsRelation with Sustainable Development Goals3, 4
14
++ +
+•1
++
++
++
++
+
+
+
+
•
+
+
+7
+
++
+•
+
+
+
•15
++
++
+++
+
+
+
+
•
••2
+•
++
++
++
+
+

+
•
+
+
+17
+++
+
+12
+
++
++
+
+
+
+
•
•16
++
•
+
•+
•
+With benefits+
Not clear or mixed•With dis-benefits–
Insufficient evidence/Climate responses¹
and adaptation options 4
+
++
+
+
+
+
++
Sustainable urban water management3
++
++
++
+++
+
+
+
+
+
+
+
•
+
+
++
+8
+
++
++
+
+
+
+
•
+
+
•+10
•+
++
++
•
•
+
+
+
+
–
•
+
+–
+11
++
++
++
+
+
+
+
–
•
+
+•
+6
+
++
++
+
+
+
+
+
+
+
•
++5
•+
++
++
••
+
+
+
•
•
•+13Climate responses and adaptation options and their relation with the Sustainable Development Goals
9
•+
+
++
+
+
•
•
++
+
Figure Cross-Chapter Box FEASIB.4 |  This figure summarises the assessment of the nexus of each adaptation option considered in this CCB with the 
17 Sustainable Development Goals (SDGs). SDGs with which there is a nexus are colored and have a + for positive nexus, − for negative nexus and +/− for mixed nexus. Blank cells either don’t have a nexus or there is no or limited evidence of such nexus.
CCB FEASIB.5  Knowledge Gaps

Despite the progress in new evidence since the SR1.5, there remain several knowledge gaps for the assessment of adaptation and 
mitigation options. They are underlying the Figure Cross-Chapter Box FEASIB.2 through the NE (no evidence) or LE (limited evidence).
Within energy system transitions, resilient power infrastructure has knowledge gaps on indicators of transparency and accountability 
potential, socio-cultural acceptability, social and regional inclusiveness, and intergenerational equity.Cross-Chapter Box FEASIB (continued)

18
2788Chapter 18  Climate Resilient Development P athways
Under land and ecosystem system transitions, gaps include limited evidence for some of the institutional and socio-cultural feasibility

dimensions indicators of Integrated Coastal Zone Management. Specifically, there is lack of evidence for transparency and accountability potential and for gender and intergenerational equity. For coastal defence and hardening, there is no or limited evidence on the indicators of employment and productivity enhancement, legal and regulatory acceptability, transparency and accountability potential, social and regional inclusiveness, benefits for gender equity, intergenerational equity and land use change enhancement potential. Sustainable aquaculture has knowledge gaps for the indicators of macroeconomic viability, legal and regulatory acceptability, transparency and accountability potential, social and regional inclusiveness, intergenerational equity and land use change enhancement potential. The geographical feasibility for migration and relocation is still an emerging area of research, however, there is limited evidence to assess this specific dimension.

The options of forest-based adaptation and biodiversity management and ecosystems connectivity have knowledge gaps for the indicators 
of risk mitigation potential, legal and regulatory feasibility, and social and regional inclusiveness. The option of improved cropland management has no or limited evidence for the indicators of legal and regulatory feasibility, transparency and accountability potential and hazard risk reduction potential. The efficient livestock systems option has no evidence for political acceptability and legal and regulatory feasibility, and limited evidence for overall institutional feasibility. Agro-forestry has knowledge gaps for employment and productivity enhancement, transparency and accountability potential and intergenerational equity. There is also limited evidence for the economic and technical feasibility dimensions for ecosystem connectivity.

For urban and infrastructure systems, the option of green infrastructure and ecosystem services has limited evidence for macroeconomic 
viability, employment and productivity enhancement, and political acceptability. Sustainable water management has gaps for macroeconomic viability, employment and productivity enhancement, and transparency and accountability potential.
For cross-cutting options, the main knowledge gaps identified are socio-cultural acceptability for social safety nets. While the evidence on 
resettlement, relocation and migration is large and growing, there is disagreement on several indicators, marking the need for more evidence synthesis. Geophysical feasibility for resettlement, relocation and migration has limited evidence, but is an emerging area of research.
In general, throughout most of the options, there is significantly less literature from the regions of Central and South America, and West

and Central Asia, as compared with other world regions.Cross-Chapter Box FEASIB (continued)Cross-Chapter Box FEASIB (continued)

5SPM
Summary for PolicymakersA: Introduction
This Summary for Policymakers (SPM) presents key findings of the Working Group II (WGII) contribution to the Sixth Assessment Report (AR6) of 
the IPCC1. The report builds on the WGII contribution to the Fifth Assessment Report (AR5) of the IPCC, three Special Reports2, and the Working 
Group I (WGI) contribution to the AR6 cycle.
This report recognizes the interdependence of climate, ecosystems and biodiversity3, and human societies (Figure  SPM.1) and integrates 
knowledge more strongly across the natural, ecological, social and economic sciences than earlier IPCC assessments. The assessment of climate 
change impacts and risks as well as adaptation is set against concurrently unfolding non-climatic global trends e.g., biodiversity loss, overall 
unsustainable consumption of natural resources, land and ecosystem degradation, rapid urbanisation, human demographic shifts, social and 
economic inequalities and a pandemic.

The scientific evidence for each key finding is found in the 18 chapters of the underlying report and in the 7 cross-chapter papers as well as the 
integrated synthesis presented in the Technical Summary (hereafter TS) and referred to in curly brackets {}. Based on scientific understanding, key 
findings can be formulated as statements of fact or associated with an assessed level of confidence using the IPCC calibrated language4. The WGII 
Global to Regional Atlas (Annex I) facilitates exploration of key synthesis findings across the WGII regions.
The concept of risk is central to all three AR6 Working Groups. A risk framing and the concepts of adaptation, vulnerability, exposure, resilience, 
equity and justice, and transformation provide alternative, overlapping, complementary, and widely used entry points to the literature assessed 
in this WGII report.

Across all three AR6 working groups, risk5 provides a framework for understanding the increasingly severe, interconnected and often irreversible 
impacts of climate change on ecosystems, biodiversity, and human systems; differing impacts across regions, sectors and communities; and 
how to best reduce adverse consequences for current and future generations. In the context of climate change, risk can arise from the dynamic 
interactions among climate-related hazards6 (see Working Group I), the exposure7 and vulnerability8 of affected human and ecological systems. 
The risk that can be introduced by human responses to climate change is a new aspect considered in the risk concept. This report identifies 127 
key risks9. {1.3, 16.5}
The vulnerability of exposed human and natural systems is a component of risk, but also, independently, an important focus in the literature.

Approaches to analysing and assessing vulnerability have evolved since previous IPCC assessments. Vulnerability is widely understood to differ 
within communities and across societies, regions and countries, also changing through time.
Adaptation10 plays a key role in reducing exposure and vulnerability to climate change. Adaptation in ecological systems includes autonomous 
adjustments through ecological and evolutionary processes. In human systems, adaptation can be anticipatory or reactive, as well as incremental 
1 Decision IPCC/XLVI-3, The assessment covers scientific literature accepted for publication by 1 September 2021.
2 The three Special Reports are: ‘Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission

pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (SR1.5)’; ‘Climate Change and Land. An IPCC 
Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SRCCL)’; ‘IPCC Special Report 
on the Ocean and Cryosphere in a Changing Climate (SROCC)’.
3 Biodiversity: Biodiversity or biological diversity means the variability among living organisms from all sources including, among other things, terrestrial, marine and other aquatic ecosystems, and the 
ecological complexes of which they are part; this includes diversity within species, between species, and of ecosystems.
4 Each finding is grounded in an evaluation of underlying evidence and agreement. A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high, and typeset in italics,

e.g., medium confidence. The following terms have been used to indicate the assessed likelihood of an outcome or a result: virtually certain 99–100% probability, very likely 90–100%, likely 66–100%, 
as likely as not 33–66%, unlikely 0–33%, very unlikely 0–10%, exceptionally unlikely 0–1%. Assessed likelihood is typeset in italics, e.g., very likely. This is consistent with AR5 and the other AR6 Reports.
5 Risk is defined as the potential for adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems.
6 Hazard is defined as the potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, 
infrastructure, livelihoods, service provision, ecosystems and environmental resources. Physical climate conditions that may be associated with hazards are assessed in Working Group I as climatic 
impact-drivers.

7 Exposure is defined as the presence of people; livelihoods; species or ecosystems; environmental functions, services and resources; infrastructure; or economic, social or cultural assets in places and 
8 Vulnerability in this report is defined as the propensity or predisposition to be adversely affected and encompasses a variety of concepts and elements, including sensitivity or susceptibility to harm and 
lack of capacity to cope and adapt.
9 Key risks have potentially severe adverse consequences for humans and social-ecological systems resulting from the interaction of climate related hazards with vulnerabilities of societies and systems 
exposed.
10 Adaptation is defined, in human systems, as the process of adjustment to actual or expected climate and its effects in order to moderate harm or take advantage of beneficial opportunities. In natural

systems, adaptation is the process of adjustment to actual climate and its effects; human intervention may facilitate this.

6SPM
Human Society
Limits to adaptation
Losses and damagesClimate Change
causes
Human Systems
Societal|E nergy
Industry | Urban, Rural
& InfrastructureFuture Climate Change
Limiting Global Warming
s
ral
Land|Freshwater
Coastal |Ocean
Ecosystems and
their biodiversityEcosystems
including biodiversity
Losses and damages(a) Main interactions and trendsFrom climate risk to climate resilient development:  climate, ecosystems (including biodiversity) and human society as coupled systems
n
s
includ
Lim
Risks
VulnerabilityThe risk propeller shows that risk emerges from the overlap of:
...of human systems, ecosystems and their biodiversityExposur e Climate hazard(s)Governance
Finance
Knowledge and capacity
Catalysing conditions
Technologies From urgent to 
timely actionprovision
Livelihoods,Ecosystem
Servicesadaptto,mitigate impactsimpacts
Ecosystembasedapproaches
conserve,restore
impact

adaptsto,mitigatesGreenhousegasemissionsprovision
Servicesadapts,maladapts,mitigatesimpactsimpacts
conserves,restores
impactsadaptto,mitigate Climate Resilient
Planetary health(b) Options to reduce climate risks and establish resilience
Figure SPM.1 | This report has a strong focus on the interactions among the coupled systems climate, ecosystems (including their biodiversity) and human society. These interactions are the basis of emerging risks 
from climate change, ecosystem degradation and biodiversity loss and, at the same time, offer opportunities for the future. 
(a) Human society causes climate change. Climate change, through hazards, exposure and vulnerability generates impacts and risks that can surpass limits to adaptation and result in losses and damages. Human society can adapt to,

maladapt and mitigate climate change, ecosystems can adapt and mitigate within limits. Ecosystems and their biodiversity provision livelihoods and ecosystem services. Human society impacts ecosystems and can restore and conserve them. 
(b) Meeting the objectives of climate resilient development thereby supporting human, ecosystem and planetary health, as well as human well-being, requires society and ecosystems to move over (transition) to a more resilient state. 
The recognition of climate risks can strengthen adaptation and mitigation actions and transitions that reduce risks. Taking action is enabled by governance, finance, knowledge and capacity building, technology and catalysing conditions. 
Transformation entails system transitions strengthening the resilience of ecosystems and society (Section D). In a) arrow colours represent principle human society interactions (blue), ecosystem (including biodiversity) interactions (green)

and the impacts of climate change and human activities, including losses and damages, under continued climate change (red). In b) arrow colours represent human system interactions (blue), ecosystem (including biodiversity) interactions 
(green) and reduced impacts from climate change and human activities (grey). {1.2, Figure 1.2, Figure TS. 2}

7SPM
Summary for Policymakersand/ or transformational. The latter changes the fundamental attributes of a social-ecological system in anticipation of climate change and its 
impacts. Adaptation is subject to hard and soft limits11.
Resilience12 in the literature has a wide range of meanings. Adaptation is often organized around resilience as bouncing back and returning to 
a previous state after a disturbance. More broadly the term describes not just the ability to maintain essential function, identity and structure, 
but also the capacity for transformation.
This report recognises the value of diverse forms of knowledge such as scientific, as well as Indigenous knowledge and local knowledge in 
understanding and evaluating climate adaptation processes and actions to reduce risks from human-induced climate change. AR6 highlights 
adaptation solutions which are effective, feasible13, and conform to principles of justice14. The term climate justice, while used in different ways in

different contexts by different communities, generally includes three principles: distributive justice which refers to the allocation of burdens and 
benefits among individuals, nations and generations; procedural justice which refers to who decides and participates in decision-making; and 
recognition which entails basic respect and robust engagement with and fair consideration of diverse cultures and perspectives.
Effectiveness refers to the extent to which an action reduces vulnerability and climate-related risk, increases resilience, and avoids maladaptation15.
This report has a particular focus on transformation16 and system transitions in energy; land, ocean, coastal and freshwater ecosystems; urban, 
rural and infrastructure; and industry and society. These transitions make possible the adaptation required for high levels of human health and

well-being, economic and social resilience, ecosystem health17, and planetary health18 (Figure SPM.1). These system transitions are also important 
for achieving the low global warming levels (Working Group III) that would avoid many limits to adaptation11. The report also assesses economic 
and non-economic losses and damages19. This report labels the process of implementing mitigation and adaptation together in support of 
sustainable development for all as climate resilient development20.
Box SPM.1 | AR6 Common Climate Dimensions, Global Warming Levels and Reference Periods
Assessments of climate risks consider possible future climate change, societal development and responses. This report assesses literature 
including that based on climate model simulations that are part of the fifth and sixth Coupled Model Intercomparison Project Phase 
(CMIP5, CMIP6) of the World Climate Research Programme. Future projections are driven by emissions and/or concentrations from

illustrative Representative Concentration Pathways (RCPs)21 and Shared Socioeconomic Pathways (SSPs)22 scenarios, respectively23. 
Climate impacts literature is based primarily on climate projections assessed in AR5 or earlier, or assumed global warming levels, though 
some recent impacts literature uses newer projections based on the CMIP6 exercise. Given differences in the impacts literature regarding 
11  Adaptation limits: The point at which an actor’s objectives (or system needs) cannot be secured from intolerable risks through adaptive actions.  
Hard adaptation limit—No adaptive actions are possible to avoid intolerable risks.  
Soft adaptation limit—Options may exist but are currently not available to avoid intolerable risks through adaptive action.
12 Resilience in this report is defined as the capacity of social, economic and ecosystems to cope with a hazardous event or trend or disturbance, responding or reorganising in ways that maintain their

essential function, identity and structure as well as biodiversity in case of ecosystems while also maintaining the capacity for adaptation, learning and transformation. Resilience is a positive attribute 
when it maintains such a capacity for adaptation, learning, and/or transformation.
13 Feasibility refers to the potential for an adaptation option to be implemented.
14 Justice is concerned with setting out the moral or legal principles of fairness and equity in the way people are treated, often based on the ethics and values of society. Social justice comprises just or 
fair relations within society that seek to address the distribution of wealth, access to resources, opportunity and support according to principles of justice and fairness. Climate justice comprises justice 
that links development and human rights to achieve a rights-based approach to addressing climate change.

15 Maladaptation refers to actions that may lead to increased risk of adverse climate-related outcomes, including via increased greenhouse gas emissions, increased or shifted vulnerability to climate 
change, more inequitable outcomes, or diminished welfare, now or in the future. Most often, maladaptation is an unintended consequence.
16 Transformation refers to a change in the fundamental attributes of natural and human systems.
17 Ecosystem health: a metaphor used to describe the condition of an ecosystem, by analogy with human health. Note that there is no universally accepted benchmark for a healthy ecosystem. Rather, 
the apparent health status of an ecosystem is judged on the ecosystem’s resilience to change, with details depending upon which metrics (such as species richness and abundance) are employed in 
judging it and which societal aspirations are driving the assessment.

18 Planetary health: a concept based on the understanding that human health and human civilisation depend on ecosystem health and the wise stewardship of ecosystems.
19 In this report, the term ‘losses and damages’ refers to adverse observed impacts and/or projected risks and can be economic and/or non-economic.
20 In the WGII report, climate resilient development refers to the process of implementing greenhouse gas mitigation and adaptation measures to support sustainable development for all.
21 RCP-based scenarios are referred to as RCPy, where ‘y’ refers to the level of radiative forcing (in watts per square meter, or W m-2) resulting from the scenario in the year 2100.
22 SSP-based scenarios are referred to as SSPx-y, where ‘SSPx’ refers to the Shared Socioeconomic Pathway describing the socioeconomic trends underlying the scenarios, and ‘y’ refers to the level of 
radiative forcing (in watts per square meter, or W m-2) resulting from the scenario in the year 2100.

23 IPCC is neutral with regard to the assumptions underlying the SSPs, which do not cover all possible scenarios. Alternative scenarios may be considered or developed.

8SPM
Summary for Policymakerssocioeconomic details and assumptions, WGII chapters contextualize impacts with respect to exposure, vulnerability and adaptation as 
appropriate for their literature, this includes assessments regarding sustainable development and climate resilient development. There are 
many emissions and socioeconomic pathways that are consistent with a given global warming outcome. These represent a broad range 
of possibilities as available in the literature assessed that affect future climate change exposure and vulnerability. Where available, WGII 
also assesses literature that is based on an integrative SSP-RCP framework where climate projections obtained under the RCP scenarios 
are analysed against the backdrop of various illustrative SSPs22. The WGII assessment combines multiple lines of evidence including 
impacts modelling driven by climate projections, observations, and process understanding. {1.2, 16.5, 18.2, CCB CLIMATE, WGI AR6

SPM.C, WGI AR6 Box SPM.1, WGI AR6 1.6, WGI AR6 12, AR5 WGI}
A common set of reference years and time periods are adopted for assessing climate change and its impacts and risks: the reference 
period 1850–1900 approximates pre-industrial global surface temperature, and three future reference periods cover the near-term 
(2021–2040), mid-term (2041–2060) and long-term (2081–2100). {CCB CLIMATE}
Common levels of global warming relative to 1850–1900 are used to contextualize and facilitate analysis, synthesis and communication 
of assessed past, present and future climate change impacts and risks considering multiple lines of evidence. Robust geographical 
patterns of many variables can be identified at a given level of global warming, common to all scenarios considered and independent of 
timing when the global warming level is reached. {16.5, CCB CLIMATE, WGI AR6 Box SPM.1, WGI AR6 4.2, WGI AR6 CCB11.1}

WGI assessed the increase in global surface temperature is 1.09 [0.95 to 1.20]24 °C in 2011–2020 above 1850–1900. The estimated 
increase in global surface temperature since AR5 is principally due to further warming since 2003–2012 (+0.19 [0.16 to 0.22] °C).25 
Considering all five illustrative scenarios assessed by WGI, there is at least a greater than 50% likelihood that global warming will reach 
or exceed 1.5°C in the near -term, even for the very low greenhouse gas emissions scenario26. { WGI AR6 SPM A1.2, WGI AR6 SPM B1.3, 
WGI AR6 Table SPM.1, WGI AR6 CCB 2.3}
B: Observed and Projected Impacts and Risks
Since AR5, the knowledge base on observed and projected impacts and risks generated by climate hazards, exposure and vulnerability has 
increased with impacts attributed to climate change and key risks identified across the report. Impacts and risks are expressed in terms of their

damages, harms, economic, and non-economic losses. Risks from observed vulnerabilities and responses to climate change are highlighted. 
Risks are projected for the near-term (2021–2040), the mid (2041–2060) and long term (2081–2100), at different global warming levels and 
for pathways that overshoot 1.5°C global warming level for multiple decades27. Complex risks result from multiple climate hazards occurring 
concurrently, and from multiple risks interacting, compounding overall risk and resulting in risks transmitting through interconnected systems 
and across regions.
24 In the WGI report, square brackets [x to y] are used to provide the assessed very likely range, or 90% interval.
25 Since AR5, methodological advances and new datasets have provided a more complete spatial representation of changes in surface temperature, including in the Arctic. These and other improvements

have also increased the estimate of global surface temperature change by approximately 0.1°C, but this increase does not represent additional physical warming since AR5.
26 Global warming of 1.5°C relative to 1850–1900 would be exceeded during the 21st century under the intermediate, high and very high greenhouse gas emissions scenarios considered in this report 
(SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively). Under the five illustrative scenarios, in the near term (2021–2040), the 1.5°C global warming level is very likely to be exceeded under the very high 
greenhouse gas emissions scenario (SSP5-8.5), likely to be exceeded under the intermediate and high greenhouse gas emissions scenarios (SSP2-4.5 and SSP3-7.0), more likely than not to be exceeded 
under the low greenhouse gas emissions scenario (SSP1-2.6) and more likely than not to be reached under the very low greenhouse gas emissions scenario (SSP1-1.9). Furthermore, for the very low

greenhouse gas emissions scenario (SSP1-1.9), it is more likely than not that global surface temperature would decline back to below 1.5°C toward the end of the 21st century, with a temporary 
overshoot of no more than 0.1°C above 1.5°C global warming.
27 Overshoot: In this report, pathways that first exceed a specified global warming level (usually 1.5°C, by more than 0.1°C), and then return to or below that level again before the end of a specified 
period of time (e.g., before 2100). Sometimes the magnitude and likelihood of the overshoot is also characterized. The overshoot duration can vary from at least one decade up to several decades.Box SPM.1 (continued)

9SPM
Summary for PolicymakersObserved Impacts from Climate Change
28 Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assessment of confidence. {Annex II Glossary, CWGB ATTRIB}
29 Impacts of climate change are caused by slow onset and extreme events. Slow onset events are described among the climatic-impact drivers of the WGI AR6 and refer to the risks and impacts 
associated with e.g., increasing temperature means, desertification, decreasing precipitation, loss of biodiversity, land and forest degradation, glacial retreat and related impacts, ocean acidification, 
sea level rise and salinization (https://interactive-atlas.ipcc.ch).
30 Acute food insecurity can occur at any time with a severity that threatens lives, livelihoods or both, regardless of the causes, context or duration, as a result of shocks risking determinants of food

security and nutrition, and used to assess the need for humanitarian action.B.1 Human-induced climate change, including more frequent and intense extreme events, has caused widespread adverse 
impacts and related losses and damages to nature and people, beyond natural climate variability. Some development and 
adaptation efforts have reduced vulnerability. Across sectors and regions the most vulnerable people and systems are ob-
served to be disproportionately affected. The rise in weather and climate extremes has led to some irreversible impacts as 
natural and human systems are pushed beyond their ability to adapt. (high confidence) (Figure SPM.2) {TS B.1, Figure TS.5, 
1.3, 2.3, 2.4, 2.6, 3.3, 3.4, 3.5, 4.2, 4.3, 5.2, 5.12, 6.2, 7.2, 8.2, 9.6, 9.8, 9.10, 9.11, 10.4, 11.3, 12.3, 12.4, 13.10, 14.4, 14.5, 
15.3, 16.2, CCP1.2, CCP3.2, CCP4.1, CCP5.2, CCP6.2, CCP7.2, CCP7.3, CCB DISASTER, CCB EXTREMES, CCB ILLNESS, CCB 
MIGRATE, CCB NATURAL, CCB SLR}

B.1.1 Widespread, pervasive impacts to ecosystems, people, settlements, and infrastructure have resulted from observed increases in the 
frequency and intensity of climate and weather extremes, including hot extremes on land and in the ocean, heavy precipitation events, 
drought and fire weather (high confidence). Increasingly since AR5, these observed impacts have been attributed28 to human-induced 
climate change particularly through increased frequency and severity of extreme events. These include increased heat-related human 
mortality (medium confidence), warm-water coral bleaching and mortality  (high confidence), and increased drought-related tree 
mortality (high confidence). Observed increases in areas burned by wildfires have been attributed to human-induced climate change 
in some regions (medium to high confidence). Adverse impacts from tropical cyclones, with related losses and damages19, have

increased due to sea level rise and the increase in heavy precipitation (medium confidence). Impacts in natural and human systems 
from slow-onset processes29 such as ocean acidification, sea level rise or regional decreases in precipitation have also been attributed 
to human induced climate change (high confidence). {1.3, 2.3, 2.4, 2.5, 3.2, 3.4, 3.5, 3.6, 4.2, 5.2, 5.4, 5.6, 5.12, 7.2, 9.6, 9.7, 9.8, 9.11, 
11.3, Box 11.1, Box 11.2, Table 11.9, 12.3, 12.4, 13.3, 13.5, 13.10, 14.2, 14.5, 15.7, 15.8, 16.2, CCP1.2, CCP2.2, Box CCP5.1, CCP7.3, 
CCB DISASTER, CCB EXTREME, CCB ILLNESS, WGI AR6 SPM.3, WGI AR6 9, WGI AR6 11.3–11.8, SROCC Chapter 4}
B.1.2 Climate change has caused substantial damages, and increasingly irreversible losses, in terrestrial, freshwater and coastal and open 
ocean marine ecosystems (high confidence). The extent and magnitude of climate change impacts are larger than estimated in previous

assessments (high confidence). Widespread deterioration of ecosystem structure and function, resilience and natural adaptive capacity, 
as well as shifts in seasonal timing have occurred due to climate change (high confidence), with adverse socioeconomic consequences 
(high confidence). Approximately half of the species assessed globally have shifted polewards or, on land, also to higher elevations 
(very high confidence). Hundreds of local losses of species have been driven by increases in the magnitude of heat extremes (high 
confidence), as well as mass mortality events on land and in the ocean (very high confidence) and loss of kelp forests (high confidence). 
Some losses are already irreversible, such as the first species extinctions driven by climate change (medium confidence). Other impacts 
are approaching irreversibility such as the impacts of hydrological changes resulting from the retreat of glaciers, or the changes in

some mountain (medium confidence) and Arctic ecosystems driven by permafrost thaw (high confidence). (Figure SPM.2a). { TS B.1, 
Figure TS.5, 2.3, 2.4, 3.4, 3.5, 4.2, 4.3, 4.5, 9.6, 10.4, 11.3, 12.3, 12.8, 13.3, 13.4, 13.10, 14.4, 14.5, 14.6, 15.3, 16.2, CCP1.2, CCP3.2, 
CCP4.1, CCP5.2, Figure CCP5.4, CCP6.1, CCP6.2, CCP7.2, CCP7.3, CCB EXTREMES, CCB ILLNESS, CCB MOVING PLATE, CCB NATURAL, 
CCB PALEO, CCB SLR, SROCC 2.3}
B.1.3 Climate change including increases in frequency and intensity of extremes have reduced food and water security, hindering efforts 
to meet Sustainable Development Goals (high confidence). Although overall agricultural productivity has increased, climate change 
has slowed this growth over the past 50 years globally (medium confidence), related negative impacts were mainly in mid- and low 
latitude regions but positive impacts occurred in some high latitude regions (high confidence). Ocean warming and ocean acidification

have adversely affected food production from shellfish aquaculture and fisheries in some oceanic regions (high confidence). Increasing 
weather and climate extreme events have exposed millions of people to acute food insecurity30 and reduced water security, with the 
largest impacts observed in many locations and/or communities in Africa, Asia, Central and South America, Small Islands and the Arctic 
(high confidence). Jointly, sudden losses of food production and access to food compounded by decreased diet diversity have increased 
malnutrition in many communities (high confidence), especially for Indigenous Peoples, small-scale food producers and low-income 
households (high confidence), with children, elderly people and pregnant women particularly impacted (high confidence). Roughly half 
of the world’s population currently experience severe water scarcity for at least some part of the year due to climatic and non-climatic

drivers (medium confidence). (Figure SPM.2b) {3.5, 4.3, 4.4, Box 4.1, 5.2, 5.4, 5.8, 5.9, 5.12, 7.1, 7.2, 9.8, 10.4, 11.3, 12.3, 13.5, 14.4, 
14.5, 15.3, 16.2, CCP5.2, CCP6.2}

10SPM
na¹not
assessed
notassessed
Impacts of climate change are observed in many ecosystems and human systems worldwide 
(a) Observed impacts of climate change on ecosystems
Conﬁdence
in attribution
to climate change
High or very high
Medium
Low
(b) Observed impacts of climate change on human systems
Impacts
to human systems
in panel (b)Africa
Biodiversity hotspotsSmall IslandsNorth AmericaAustralasiaAsia
EuropeCentral and
DesertsMountain regionsArctic
Antarctic
Tropical forestsMediterranean regionChanges in
ecosystem structureSpecies 
range shifts
/Changes in timing
(phenology)
Terrestrial Freshwater Ocean Terrestrial Freshwater Ocean Terrestrial Freshwater Ocean Ecosystems
na nanana
nanana
nanana
na naGlobal
Evidence limited, 
insufﬁcient
Not applicablena
not
assessed
Impacts on
water scarcity and food productionImpacts on
health and wellbeingImpacts on 
cities, settlements and infrastructure
Infectious

diseases DisplacementWater
scarcityAgriculture/
crop 
productionFisheries
yields and
productionInland
ﬂooding and
associated 
damagesFlood/storm 
induced
damages in
coastal areasDamages
to key
economic
sectorsHuman
systemsAnimal and
livestock
health and 
productivityDamages
to 
infrastructureMental
healthHeat,
malnutrition
and other
Asia
South AmericaAustralasia
Europe
Mediterranean regionSmall Islands
Cities by the seaArcticNorth America
Mountain regionsAfricaGlobalIncreasing
adverse
impacts
adverse
and positive
impacts
Figure SPM.2 |  Observed global and regional impacts on ecosystems and human systems attributed to climate change. Confidence levels reflect uncertainty 
in attribution of the observed impact to climate change. Global assessments focus on large studies, multi-species, meta-analyses and large reviews. For that reason they can be

assessed with higher confidence than regional studies, which may often rely on smaller studies that have more limited data. Regional assessments consider evidence on impacts 
across an entire region and do not focus on any country in particular. 
(a) Climate change has already altered terrestrial, freshwater and ocean ecosystems at global scale, with multiple impacts evident at regional and local scales where there is 
sufficient literature to make an assessment. Impacts are evident on ecosystem structure, species geographic ranges and timing of seasonal life cycles (phenology) (for methodology 
and detailed references to chapters and cross-chapter papers see SMTS.1 and SMTS.1.1).

11SPM
B.1.4 Climate change has adversely affected physical health of people globally (very high confidence) and mental health of people in the 
assessed regions (very high confidence). Climate change impacts on health are mediated through natural and human systems, including 
economic and social conditions and disruptions (high confidence). In all regions extreme heat events have resulted in human mortality 
and morbidity (very high confidence). The occurrence of climate-related food-borne and water-borne diseases has increased (very high 
confidence). The incidence of vector-borne diseases has increased from range expansion and/or increased reproduction of disease vectors 
(high confidence). Animal and human diseases, including zoonoses, are emerging in new areas (high confidence). Water and food-borne 
disease risks have increased regionally from climate-sensitive aquatic pathogens, including Vibrio spp. ( high confidence), and from toxic

substances from harmful freshwater cyanobacteria (medium confidence). Although diarrheal diseases have decreased globally, higher 
temperatures, increased rain and flooding have increased the occurrence of diarrheal diseases, including cholera (very high confidence) 
and other gastrointestinal infections (high confidence). In assessed regions, some mental health challenges are associated with increasing 
temperatures (high confidence), trauma from weather and climate extreme events (very high confidence), and loss of livelihoods and culture 
(high confidence). Increased exposure to wildfire smoke, atmospheric dust, and aeroallergens have been associated with climate-sensitive 
cardiovascular and respiratory distress (high confidence). Health services have been disrupted by extreme events such as floods (high 
confidence). {4.3, 5.12, 7.2, Box  7.3, 8.2, 8.3, Box  8.6, Figure  8.10, 9.10, Figure  9.33, Figure  9.34, 10.4, 11.3, 12.3, 13.7, 14.4, 14.5,

Figure 14.8, 15.3, 16.2, CCP5.2, Table CCP5.1, CCP6.2, Figure CCP6.3, Table CCB ILLNESS.1}
B.1.5 In urban settings, observed climate change has caused impacts on human health, livelihoods and key infrastructure (high confidence). 
Multiple climate and non-climate hazards impact cities, settlements and infrastructure and sometimes coincide, magnifying damage 
(high confidence). Hot extremes including heatwaves have intensified in cities (high confidence), where they have also aggravated 
air pollution events (medium confidence) and limited functioning of key infrastructure (high confidence). Observed impacts are 
concentrated amongst the economically and socially marginalized urban residents, e.g., in informal settlements (high confidence). 
Infrastructure, including transportation, water, sanitation and energy systems have been compromised by extreme and slow-onset

events, with resulting economic losses, disruptions of services and impacts to well-being (high confidence). {4.3, 6.2, 7.1, 7.2, 9.9, 10.4, 
11.3, 12.3, 13.6, 14.5, 15.3, CCP2.2, CCP4.2, CCP5.2}
B.1.6  Overall adverse economic impacts attributable to climate change, including slow-onset and extreme weather events, have been 
increasingly identified (medium confidence). Some positive economic effects have been identified in regions that have benefited from 
lower energy demand as well as comparative advantages in agricultural markets and tourism (high confidence). Economic damages 
from climate change have been detected in climate-exposed sectors, with regional effects to agriculture, forestry, fishery, energy, 
and tourism (high confidence), and through outdoor labour productivity (high confidence). Some extreme weather events, such as 
tropical cyclones, have reduced economic growth in the short-term (high confidence). Non-climatic factors including some patterns

of settlement, and siting of infrastructure have contributed to the exposure of more assets to extreme climate hazards increasing the 
magnitude of the losses (high confidence). Individual livelihoods have been affected through changes in agricultural productivity, 
impacts on human health and food security, destruction of homes and infrastructure, and loss of property and income, with adverse 
effects on gender and social equity (high confidence). {3.5, 4.2, 5.12, 6.2, 7.2, 8.2, 9.6, 10.4, 13.10, 14.5, Box 14.6, 16.2, Table 16.5, 
18.3, CCP6.2, CCB GENDER, CWGB ECONOMICS}
B.1.7  Climate change is contributing to humanitarian crises where climate hazards interact with high vulnerability (high confidence). Climate 
and weather extremes are increasingly driving displacement in all regions (high confidence), with Small Island States disproportionately 
affected (high confidence). Flood and drought-related acute food insecurity and malnutrition have increased in Africa (high confidence)

and Central and South America (high confidence). While non-climatic factors are the dominant drivers of existing intrastate violent 
conflicts, in some assessed regions extreme weather and climate events have had a small, adverse impact on their length, severity or 
frequency, but the statistical association is weak (medium confidence). Through displacement and involuntary migration from extreme 
weather and climate events, climate change has generated and perpetuated vulnerability (medium confidence). {4.2, 4.3, 5.4, 7.2, 9.8, 
Box 9.9, Box 10.4, 12.3, 12.5, 16.2, CCB DISASTER, CCB MIGRATE}(b) Climate change has already had diverse adverse impacts on human systems, including on water security and food production, health and well-being, and cities, settlements and 
infrastructure. The + and – symbols indicate the direction of observed impacts, with a – denoting an increasing adverse impact and a ± denoting that, within a region or globally, both

adverse and positive impacts have been observed (e.g., adverse impacts in one area or food item may occur with positive impacts in another area or food item). Globally, ‘–’ denotes an 
overall adverse impact; ‘Water scarcity’ considers, e.g., water availability in general, groundwater, water quality, demand for water, drought in cities. Impacts on food production were 
assessed by excluding non-climatic drivers of production increases; Global assessment for agricultural production is based on the impacts on global aggregated production; ‘Reduced 
animal and livestock health and productivity’ considers, e.g., heat stress, diseases, productivity, mortality; ‘Reduced fisheries yields and aquaculture production’ includes marine and 
freshwater fisheries/production; ‘Infectious diseases’ include, e.g., water-borne and vector-borne diseases; ‘Heat, malnutrition and other’ considers, e.g., human heat-related morbidity

and mortality, labour productivity, harm from wildfire, nutritional deficiencies; ‘Mental health’ includes impacts from extreme weather events, cumulative events, and vicarious or 
anticipatory events; ‘Displacement’ assessments refer to evidence of displacement attributable to climate and weather extremes; ‘Inland flooding and associated damages’ considers, 
e.g., river overflows, heavy rain, glacier outbursts, urban flooding; ‘Flood/storm induced damages in coastal areas’ include damages due to, e.g., cyclones, sea level rise, storm surges. 
Damages by key economic sectors are observed impacts related to an attributable mean or extreme climate hazard or directly attributed. Key economic sectors include standard 
classifications and sectors of importance to regions (for methodology and detailed references to chapters and cross-chapter papers see SMTS.1 and SMTS.1.2).

12SPM
Summary for PolicymakersVulnerability and Exposure of Ecosystems and People
31 Governance: The structures, processes and actions through which private and public actors interact to address societal goals. This includes formal and informal institutions and the associated norms, 
rules, laws and procedures for deciding, managing, implementing and monitoring policies and measures at any geographic or political scale, from global to local.
32 Balanced diets feature plant-based foods, such as those based on coarse grains, legumes fruits and vegetables, nuts and seeds, and animal-source foods produced in resilient, sustainable and 
low-greenhouse gas emissions systems, as described in SRCCL.B.2 Vulnerability of ecosystems and people to climate change differs substantially among and within regions (very high 
confidence), driven by patterns of intersecting socioeconomic development, unsustainable ocean and land use, inequity,

marginalization, historical and ongoing patterns of inequity such as colonialism, and governance31 (high confidence). 
Approximately 3.3 to 3.6 billion people live in contexts that are highly vulnerable to climate change (high confidence). 
A high proportion of species is vulnerable to climate change (high confidence). Human and ecosystem vulnerability are 
interdependent (high confidence). Current unsustainable development patterns are increasing exposure of ecosystems 
and people to climate hazards (high confidence). {2.3, 2.4, 3.5, 4.3, 6.2, 8.2, 8.3, 9.4, 9.7, 10.4, 12.3, 14.5, 15.3, CCP5.2, 
CCP6.2, CCP7.3, CCP7.4, CCB GENDER}
B.2.1 Since AR5 there is increasing evidence that degradation and destruction of ecosystems by humans increases the vulnerability of 
people (high confidence). Unsustainable land-use and land cover change, unsustainable use of natural resources, deforestation, loss

of biodiversity, pollution, and their interactions, adversely affect the capacities of ecosystems, societies, communities and individuals 
to adapt to climate change (high confidence). Loss of ecosystems and their services has cascading and long-term impacts on people 
globally, especially for Indigenous Peoples and local communities who are directly dependent on ecosystems, to meet basic needs (high 
confidence). {2.3, 2.5, 2.6, 3.5, 3.6, 4.2, 4.3, 4.6, 5.1, 5.4, 5.5, 5.7, 5.8, 7.2, 8.1, 8.2, 8.3, 8.4, 8.5, 9.6, 10.4, 11.3, 12.2, 12.5, 13.8, 14.4, 
14.5, 15.3, CCP1.2, CCP1.3, CCP2.2, CCP3, CCP4.3, CCP5.2, CCP6.2, CCP7.2, CCP7.3, CCP7.4, CCB ILLNESS, CCB MOVING PLATE, CCB 
SLR}
B.2.2 Non-climatic human-induced factors exacerbate current ecosystem vulnerability to climate change (very high confidence). Globally, 
and even within protected areas, unsustainable use of natural resources, habitat fragmentation, and ecosystem damage by pollutants

increase ecosystem vulnerability to climate change (high confidence). Globally, less than 15% of the land, 21% of the freshwater and 
8% of the ocean are protected areas. In most protected areas, there is insufficient stewardship to contribute to reducing damage from, 
or increasing resilience to, climate change (high confidence). {2.4, 2.5, 2.6, 3.4, 3.6, 4.2, 4.3, 5.8, 9.6, 11.3, 12.3, 13.3, 13.4, 14.5, 15.3, 
CCP1.2, Figure CCP1.15, CCP2.1, CCP2.2, CCP4.2, CCP5.2, CCP6.2, CCP7.2, CCP7.3, CCB NATURAL}
B.2.3  Future vulnerability of ecosystems to climate change will be strongly influenced by the past, present and future development of human 
society, including from overall unsustainable consumption and production, and increasing demographic pressures, as well as persistent 
unsustainable use and management of land, ocean, and water (high confidence). Projected climate change, combined with non-climatic

drivers, will cause loss and degradation of much of the world’s forests (high confidence), coral reefs and low-lying coastal wetlands 
(very high confidence). While agricultural development contributes to food security, unsustainable agricultural expansion, driven in part 
by unbalanced diets32, increases ecosystem and human vulnerability and leads to competition for land and/or water resources (high 
confidence). {2.2, 2.3, 2.4, 2.6, 3.4, 3.5, 3.6, 4.3, 4.5, 5.6, 5.12, 5.13, 7.2, 12.3, 13.3, 13.4, 13.10, 14.5, CCP1.2, CCP2.2, CCP5.2, CCP6.2, 
CCP7.2, CCP7.3, CCB HEALTH, CCB NATURAL}
B.2.4  Regions and people with considerable development constraints have high vulnerability to climatic hazards (high confidence). Global 
hotspots of high human vulnerability are found particularly in West-, Central- and East Africa, South Asia, Central and South America,

Small Island Developing States and the Arctic (high confidence). Vulnerability is higher in locations with poverty, governance challenges 
and limited access to basic services and resources, violent conflict and high levels of climate-sensitive livelihoods (e.g., smallholder 
farmers, pastoralists, fishing communities) (high confidence). Between 2010–2020, human mortality from floods, droughts and storms 
was 15 times higher in highly vulnerable regions, compared to regions with very low vulnerability (high confidence). Vulnerability 
at different spatial levels is exacerbated by inequity and marginalization linked to gender, ethnicity, low income or combinations 
thereof (high confidence), especially for many Indigenous Peoples and local communities (high confidence). Present development 
challenges causing high vulnerability are influenced by historical and ongoing patterns of inequity such as colonialism, especially for

many Indigenous Peoples and local communities (high confidence). {4.2, 5.12, 6.2, 6.4, 7.1, 7.2, Box 7.1, 8.2, 8.3, Box 8.4, Figure 8.6, 
Box 9.1, 9.4, 9.7, 9.9, 10.3, 10.4, 10.6, 12.3, 12.5, Box 13.2, 14.4, 15.3, 15.6, 16.2, CCP6.2, CCP7.4}
B.2.5 Future human vulnerability will continue to concentrate where the capacities of local, municipal and national governments, 
communities and the private sector are least able to provide infrastructures and basic services (high confidence). Under the global 
trend of urbanization, human vulnerability will also concentrate in informal settlements and rapidly growing smaller settlements (high

13SPM
Summary for Policymakersconfidence). In rural areas vulnerability will be heightened by compounding processes including high emigration, reduced habitability and 
high reliance on climate-sensitive livelihoods (high confidence). Key infrastructure systems including sanitation, water, health, transport, 
communications and energy will be increasingly vulnerable if design standards do not account for changing climate conditions (high 
confidence). Vulnerability will also rapidly rise in low-lying Small Island Developing States and atolls in the context of sea level rise and 
in some mountain regions, already characterised by high vulnerability due to high dependence on climate-sensitive livelihoods, rising 
population displacement, the accelerating loss of ecosystem services and limited adaptive capacities (high confidence). Future exposure 
to climatic hazards is also increasing globally due to socioeconomic development trends including migration, growing inequality and

urbanization (high confidence). {4.5, 5.5, 6.2, 7.2, 8.3, 9.9, 9.11, 10.3, 10.4, 12.3, 12.5, 13.6, 14.5, 15.3, 15.4, 16.5, CCP2.3, CCP4.3, 
CCP5.2, CCP5.3, CCP5.4, CCP6.2, CCB MIGRATE}
Risks in the near term (2021–2040)
B.3 Global warming, reaching 1.5°C in the near-term, would cause unavoidable increases in multiple climate hazards and 
present multiple risks to ecosystems and humans (very high confidence). The level of risk will depend on concurrent near-
term trends in vulnerability, exposure, level of socioeconomic development and adaptation (high confidence). Near-term 
actions that limit global warming to close to 1.5°C would substantially reduce projected losses and damages related to 
climate change in human systems and ecosystems, compared to higher warming levels, but cannot eliminate them all 
(very high confidence). (Figure SPM.3, Box SPM.1) {16.4, 16.5, 16.6, CCP1.2, CCP5.3, CCB SLR, WGI AR6 SPM B1.3, WGI AR6 
Table SPM.1}

B.3.1 Near-term warming and increased frequency, severity and duration of extreme events will place many terrestrial, freshwater, coastal 
and marine ecosystems at high or very high risks of biodiversity loss (medium to very high confidence, depending on ecosystem). 
Near-term risks for biodiversity loss are moderate to high in forest ecosystems (medium confidence), kelp and seagrass ecosystems 
(high to very high confidence), and high to very high in Arctic sea-ice and terrestrial ecosystems (high confidence) and warm-water 
coral reefs (very high confidence). Continued and accelerating sea level rise will encroach on coastal settlements and infrastructure 
(high confidence) and commit low-lying coastal ecosystems to submergence and loss (medium confidence). If trends in urbanisation in 
exposed areas continue, this will exacerbate the impacts, with more challenges where energy, water and other services are constrained

(medium confidence). The number of people at risk from climate change and associated loss of biodiversity will progressively increase 
(medium confidence). Violent conflict and, separately, migration patterns, in the near-term will be driven by socioeconomic conditions 
and governance more than by climate change (medium confidence). (Figure SPM.3) {2.5, 3.4, 4.6, 6.2, 7.3, 8.7, 9.2, 9.9, 11.6, 12.5, 13.6, 
13.10, 14.6, 15.3, 16.5, 16.6, CCP1.2, CCP2.1, CCP2.2, CCP5.3, CCP6.2, CCP6.3, CCB MIGRATE, CCB SLR}
B.3.2 In the near term, climate-associated risks to natural and human systems depend more strongly on changes in their vulnerability and 
exposure than on differences in climate hazards between emissions scenarios (high confidence). Regional differences exist, and risks 
are highest where species and people exist close to their upper thermal limits, along coastlines, in close association with ice or seasonal

rivers (high confidence). Risks are also high where multiple non-climate drivers persist or where vulnerability is otherwise elevated 
(high confidence). Many of these risks are unavoidable in the near-term, irrespective of emissions scenario (high confidence). Several 
risks can be moderated with adaptation (high confidence). (Figure SPM.3, Section C) {2.5, 3.3, 3.4, 4.5, 6.2, 7.1, 7.3, 8.2, 11.6, 12.4, 
13.6, 13.7, 13.10, 14.5, 16.4, 16.5, CCP2.2, CCP4.3, CCP5.3, CCB SLR, WGI AR6 Table SPM.1}
B.3.3 Levels of risk for all Reasons for Concern (RFC) are assessed to become high to very high at lower global warming levels than in 
AR5 (high confidence). Between 1.2°C and 4.5°C global warming level very high risks emerge in all five RFCs compared to just two 
RFCs in AR5 (high confidence). Two of these transitions from high to very high risk are associated with near-term warming: risks to

unique and threatened systems at a median value of 1.5 [1.2 to 2.0] °C (high confidence) and risks associated with extreme weather 
events at a median value of 2.0 [1.8 to 2.5] °C (medium confidence). Some key risks contributing to the RFCs are projected to lead to 
widespread, pervasive, and potentially irreversible impacts at global warming levels of 1.5–2°C if exposure and vulnerability are high 
and adaptation is low (medium confidence). Near-term actions that limit global warming to close to 1.5°C would substantially reduce 
projected losses and damages related to climate change in human systems and ecosystems, compared to higher warming levels, but 
cannot eliminate them all (very high confidence). (Figure SPM.3b) {16.5, 16.6, CCB SLR}

14SPM
Summary for PolicymakersMid to Long-term Risks (2041–2100)
33 Numbers of species assessed are in the tens of thousands globally.
34 The term ‘very high risks of extinction’ is used here consistently with the IUCN categories and criteria and equates with ‘critically endangered’.B.4 Beyond 2040 and depending on the level of global warming, climate change will lead to numerous risks to natural and 
human systems (high confidence). For 127 identified key risks, assessed mid- and long-term impacts are up to multiple 
times higher than currently observed (high confidence). The magnitude and rate of climate change and associated risks 
depend strongly on near-term mitigation and adaptation actions, and projected adverse impacts and related losses and 
damages escalate with every increment of global warming (very high confidence). (Figure SPM.3) {2.5, 3.4, 4.4, 5.2, 6.2,

7.3, 8.4, 9.2, 10.2, 11.6, 12.4, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 14.6, 15.3, 16.5, 16.6, CCP1.2, CCP2.2, CCP3.3, CCP4.3, 
CCP5.3, CCP6.3, CCP7.3}
B.4.1  Biodiversity loss and degradation, damages to and transformation of ecosystems are already key risks for every region due to past 
global warming and will continue to escalate with every increment of global warming (very high confidence). In terrestrial ecosystems, 
3 to 14% of species assessed33 will likely face very high risk of extinction34 at global warming levels of 1.5°C, increasing up to 3 to 
18% at 2°C, 3 to 29% at 3°C, 3 to 39% at 4°C, and 3 to 48% at 5°C. In ocean and coastal ecosystems, risk of biodiversity loss ranges 
between moderate and very high by 1.5°C global warming level and is moderate to very high by 2°C but with more ecosystems at high 
and very high risk (high confidence), and increases to high to very high across most ocean and coastal ecosystems by 3°C (medium

to high confidence, depending on ecosystem). Very high extinction risk for endemic species in biodiversity hotspots is projected to at 
least double from 2% between 1.5°C and 2°C global warming levels and to increase at least tenfold if warming rises from 1.5°C to 
3°C (medium confidence). (Figure SPM.3c, d, f) {2.4, 2.5, 3.4, 3.5,12.3, 12.5, Table 12.6, 13.4, 13.10, 16.4, 16.6, CCP1.2, Figure CCP1.6, 
Figure CCP1.7, CCP5.3, CCP6.3, CCB PALEO}
B.4.2 Risks in physical water availability and water-related hazards will continue to increase by the mid- to long-term in all assessed regions, 
with greater risk at higher global warming levels (high confidence). At approximately 2°C global warming, snowmelt water availability 
for irrigation is projected to decline in some snowmelt dependent river basins by up to 20%, and global glacier mass loss of 18 ± 13% 
is projected to diminish water availability for agriculture, hydropower, and human settlements in the mid- to long-term, with these

changes projected to double with 4°C global warming (medium confidence). In Small Islands, groundwater availability is threatened by 
climate change (high confidence). Changes to streamflow magnitude, timing and associated extremes are projected to adversely impact 
freshwater ecosystems in many watersheds by the mid- to long-term across all assessed scenarios (medium confidence). Projected 
increases in direct flood damages are higher by 1.4 to 2 times at 2°C and 2.5 to 3.9 times at 3°C compared to 1.5°C global warming 
without adaptation (medium confidence). At global warming of 4°C, approximately 10% of the global land area is projected to face 
increases in both extreme high and low river flows in the same location, with implications for planning for all water use sectors (medium 
confidence). Challenges for water management will be exacerbated in the near, mid and long term, depending on the magnitude, rate

and regional details of future climate change and will be particularly challenging for regions with constrained resources for water 
management (high confidence). {2.3, 4.4, 4.5, Box 4.2, Figure 4.20, 15.3, CCP5.3, CCB DISASTER, SROCC 2.3}
B.4.3 Climate change will increasingly put pressure on food production and access, especially in vulnerable regions, undermining food security 
and nutrition (high confidence).  Increases in frequency, intensity and severity of droughts, floods and heatwaves, and continued sea 
level rise will increase risks to food security (high confidence) in vulnerable regions from moderate to high between 1.5°C and 2°C 
global warming level, with no or low levels of adaptation (medium confidence). At 2°C or higher global warming level in the mid-term, 
food security risks due to climate change will be more severe, leading to malnutrition and micro-nutrient deficiencies, concentrated

in Sub-Saharan Africa, South Asia, Central and South America and Small Islands (high confidence). Global warming will progressively 
weaken soil health and ecosystem services such as pollination, increase pressure from pests and diseases, and reduce marine animal 
biomass, undermining food productivity in many regions on land and in the ocean (medium confidence). At 3°C or higher global warming 
level in the long term, areas exposed to climate-related hazards will expand substantially compared with 2°C or lower global warming 
level (high confidence), exacerbating regional disparity in food security risks (high confidence). (Figure SPM.3) {1.1, 3.3, 4.5, 5.2, 5.4, 5.5, 
5.8, 5.9, 5.12, 7.3, 8.3, 9.11, 13.5, 15.3, 16.5, 16.6, CCB MOVING PLATE, CCB SLR}

15SPM
Summary for PolicymakersB.4.4 Climate change and related extreme events will significantly increase ill health and premature deaths from the near- to long-term (high 
confidence). Globally, population exposure to heatwaves will continue to increase with additional warming, with strong geographical 
differences in heat-related mortality without additional adaptation (very high confidence). Climate-sensitive food-borne, water-borne, 
and vector-borne disease risks are projected to increase under all levels of warming without additional adaptation (high confidence). In 
particular, dengue risk will increase with longer seasons and a wider geographic distribution in Asia, Europe, Central and South America 
and sub-Saharan Africa, potentially putting additional billions of people at risk by the end of the century (high confidence). Mental health 
challenges, including anxiety and stress, are expected to increase under further global warming in all assessed regions, particularly for

children, adolescents, elderly, and those with underlying health conditions (very high confidence). {4.5, 5.12, Box 5.10, 7.3, Figure 7.9, 
8.4, 9.10, Figure 9.32, Figure 9.35, 10.4, Figure 10.11, 11.3, 12.3, Figure 12.5, Figure 12.6, 13.7, Figure 13.23, Figure 13.24, 14.5, 15.3, 
CCP6.2}
B.4.5 Climate change risks to cities, settlements and key infrastructure will rise rapidly in the mid- and long-term with further global 
warming, especially in places already exposed to high temperatures, along coastlines, or with high vulnerabilities (high confidence). 
Globally, population change in low-lying cities and settlements will lead to approximately a billion people projected to be at risk 
from coastal-specific climate hazards in the mid-term under all scenarios, including in Small Islands (high confidence). The population 
potentially exposed to a 100-year coastal flood is projected to increase by about 20% if global mean sea level rises by 0.15 m relative

to 2020 levels; this exposed population doubles at a 0.75 m rise in mean sea level and triples at 1.4 m without population change 
and additional adaptation (medium confidence). Sea level rise poses an existential threat for some Small Islands and some low-lying 
coasts (medium confidence). By 2100 the value of global assets within the future 1-in-100 year coastal floodplains is projected to 
be between US$7.9 and US$12.7 trillion (2011 value) under RCP4.5, rising to between US$8.8 and US$14.2 trillion under RCP8.5 
(medium confidence). Costs for maintenance and reconstruction of urban infrastructure, including building, transportation, and energy 
will increase with global warming level (medium confidence), the associated functional disruptions are projected to be substantial 
particularly for cities, settlements and infrastructure located on permafrost in cold regions and on coasts (high confidence). {6.2, 9.9,

10.4, 13.6, 13.10, 15.3, 16.5, CCP2.1, CCP2.2, CCP5.3, CCP6.2, CCB SLR, SROCC 2.3, SROCC CCB9}
B.4.6 Projected estimates of global aggregate net economic damages generally increase non-linearly with global warming levels (high 
confidence).35 The wide range of global estimates, and the lack of comparability between methodologies, does not allow for identification 
of a robust range of estimates (high confidence). The existence of higher estimates than assessed in AR5 indicates that global aggregate 
economic impacts could be higher than previous estimates (low confidence).36 Significant regional variation in aggregate economic 
damages from climate change is projected (high confidence) with estimated economic damages per capita for developing countries 
often higher as a fraction of income (high confidence). Economic damages, including both those represented and those not represented

in economic markets, are projected to be lower at 1.5°C than at 3°C or higher global warming levels (high confidence). {4.4, 9.11, 11.5, 
13.10, Box 14.6, 16.5, CWGB ECONOMIC}
B.4.7  In the mid- to long-term, displacement will increase with intensification of heavy precipitation and associated flooding, tropical cyclones, 
drought and, increasingly, sea level rise (high confidence). At progressive levels of warming, involuntary migration from regions with 
high exposure and low adaptive capacity would occur (medium confidence). Compared to other socioeconomic factors the influence of 
climate on conflict is assessed as relatively weak (high confidence). Along long-term socioeconomic pathways that reduce non-climatic 
drivers, risk of violent conflict would decline (medium confidence). At higher global warming levels, impacts of weather and climate 
extremes, particularly drought, by increasing vulnerability will increasingly affect violent intrastate conflict (medium confidence). {TS

B.7.4, 7.3, 16.5, CCB MIGRATE }
35 The assessment found estimated rates of increase in projected global economic damages that were both greater than linear and less than linear as global warming level increases. There is evidence 
that some regions could benefit from low levels of warming (high confidence). {CWGB ECONOMIC}
36 Low confidence assigned due to the assessed lack of comparability and robustness of global aggregate economic damage estimates. {CWGB ECONOMIC}

16SPM
Summary for PolicymakersGlobal and regional risks for increasing levels of global warming
234
1.5
1
0
SSP1-1.9
SSP1-2.6 (shade representin gvery likely range)
SSP2-4.5
SSP3-7.0 (shade representin gvery likely range)
SSP5-8.5
RFC4
Global
aggregate
Unique and
Extreme
weather
singular
events
•••• •••• ••••••• ••• ••••• •• ••••• •• ••• •• ••5
Conﬁdence level
LowV ery highVery high
High
Moderate
•••••
••••
0234
1.5
1••••••
•• •••• •• •
•• ••••• •• ••
•• ••• ••
Warm water
corals(d) Impacts and risks

to ocean ecosystems
Kelp
forestsSeagrass
meadowsEpipelagic Salt
marshesRocky
shoresStructure
changeBiodiversity
lossCarbon
lossWildﬁre
increaseTree
mortality(c) Impacts and risks to terrestrial
          and freshwater ecosystems•••••••••••••• ••• ••••••• ••• •••••• •• ••••• •• ••
•• •• •Global surface temperature change (°C)
* Mortality projections include demographic trends but do not include future efforts to improve air quality that reduce ozone c oncentrations.
0234
1.5
1(e) Climate sensitive health outcomes under three adaptation scenarios
Global surface temperature change (°C) Limited
•••• ••• •
Limited
adaptation•••• ••• ••
Limited
adaptation•••••• •
Limited
•••• ••• ••Heat-related morbidity
and mortalityDengue and other diseases carried
by species of Aedes mosquitoes Malaria Ozone-related mortality *Scenario narratives
Limited adaptation: 
Failure to proactively adapt; 
low investment in health 
systems
Incomplete adaptation: 
Incomplete adaptation

planning; moderate 
investment in health systems
Proactive adaptation: 
Proactive adaptive 
management; higher 
investment in health systems Proactive
••••
Proactive
adaptation••••
Proactive
•••• •••
•••• •••
adaptation•••• ••
adaptation•••• •••
Proactive
adaptation••••5°C 5°C

17SPM
Summary for Policymakers(f) Examples of regional key risks
4
023
1.5
1Global surface temperature change (°C) Sea-ice
from sea-ice
 change in
the Arctic
••••••••
Changes in
ﬁsheries catch
for Pollock
and
Paciﬁc Cod
•• •• •••
Costs
and losses
for key 
•• ••
Changes 
in krill
ﬁsheries
in the
Antarctic
••• •• ••
Sea-ice
dependent
 in the
Antarctic
•• •• ••
0234
1.5
1Global surface temperature change (°C)Cascading
impacts on
cities and
in Australasia
••••••••
Loss and
degradation of
coral reefs in 
Australia
••••••••••
Reduced
viability of
tourism-
related
activities in
North
America
••• ••••
Costs and 
damages
related to
maintenance and
reconstruction of
transportation
infrastructure in
•••• ••
Lyme
disease in
North
America
under
incomplete
scenario
•••• •••
0234
1.5
1Global surface temperature change (°C)Delayed
impacts of
sea level
rise in the
••••••
Food

from crops,
ﬁsheries and
livestock
in Africa
•••• • •••
and
in Africa
••• ••• •••
morbidity
from heat and
disease
in Africa
•••••• ••
0234
1.5
Heat stress ,
mortality
and
morbidity
to people
in Europe
••• ••• ••
Coastal
ﬂooding to
people
and
infrastructures
in Europe
•• •• ••
Water scarcity
to people in
southeastern
Europe
••• ••• ••
and
in the
••• ••• •••
Health and
wellbeing
in the
•• ••• ••Absence of risk diagrams does not imply absence of risks within a 
region. The development of synthetic diagrams for Small Islands, Asia and Central and 
South America was limited due to the paucity of adequately downscaled climate projections, 
with uncertainty in the direction of change, the diversity of climatologies and socioeconomic 
contexts across countries within a region, and the resulting few numbers of impact and risk

projections for different warming levels.
The risks listed are of at least medium conﬁdence level:
Europe - Risks to people, economies and infrastructures due to coastal and inland ﬂooding
- Stress and mortality to people due to increasing temperatures and heat extremes
- Marine and terrestrial ecosystems disruptions
- Water scarcity to multiple interconnected sectors
- Losses in crop production, due to compound heat and dry conditions, and extreme 
weatherSmall
Islands- Loss of terrestrial, marine and coastal biodiversity and ecosystem services
- Loss of lives and assets, risk to food security and economic disruption due to  
destruction of settlements and infrastructure
- Economic decline and livelihood failure of ﬁsheries, agriculture, tourism and from 
biodiversity loss from traditional agroecosystems 
- Reduced habitability of reef and non-reef islands leading to increased displacemen t
- Risk to water security in almost every small island

Africa - Species extinction and reduction or irreversible loss of ecosystems and their 
services, including freshwater, land and ocean ecosystems
- Risk to food security, risk of malnutrition (micronutrient deﬁciency), and loss of  
livelihood due to reduced food production from crops, livestock and ﬁsheries
- Risks to marine ecosystem health and to livelihoods in coastal communities
- Increased human mortality and morbidity due to increased heat and infectious  
diseases (including vector-borne and diarrhoeal diseases)
- Reduced economic output and growth, and increased inequality and poverty rates 
- Increased risk to water and energy security due to drought and heat  Aus-
tralasia- Degradation of tropical shallow coral reefs and associated biodiversity and 
ecosystem service values
- Loss of human and natural systems in low-lying coastal areas due to sea level rise
- Impact on livelihoods and incomes due to decline in agricultural production

- Increase in heat-related mortality and morbidity for people and wildlife
- Loss of alpine biodiversity in Australia due to less snow
Asia - Urban infrastructure damage and impacts on human well-being and health due 
to ﬂooding, especially in coastal cities and settlements
- Biodiversity loss and habitat shifts as well as associated disruptions in 
dependent human systems across freshwater, land, and ocean ecosystems
- More frequent, extensive coral bleaching and subsequent coral mortality 
induced by ocean warming and acidiﬁcation, sea level rise, marine heat waves 
and resource extraction
- Decline in coastal ﬁshery resources due to sea level rise, decrease in 
precipitation in some parts and increase in temperature
- Risk to food and water security due to increased temperature extremes, rainfall 
variability and droughtCentral
and
South
America- Risk to water security
- Severe health effects due to increasing epidemics, in particular vector-borne 
diseases

- Coral reef ecosystems degradation due to coral bleaching
- Risk to food security due to frequent/extreme droughts
- Damages to life and infrastructure due to ﬂoods, landslides, sea level rise, storm 
surges and coastal erosion North 
America- Climate-sensitive mental health outcomes, human mortality and morbidity due 
to increasing average temperature, weather and climate extremes, and 
compound climate hazards
- Risk of degradation of marine, coastal and terrestrial ecosystems, including loss 
of biodiversity, function, and protective services 
- Risk to freshwater resources with consequences for ecosystems, reduced surface 
water availability for irrigated agriculture, other human uses, and degraded 
water quality 
- Risk to food and nutritional security through changes in agriculture, livestock, 
hunting, ﬁsheries, and aquaculture productivity and access
- Risks to well-being, livelihoods and economic activities from cascading and

compounding climate hazards, including risks to coastal cities, settlements and 
infrastructure from sea level rise 
Figure SPM.3 |  Synthetic diagrams of global and sectoral assessments and examples of regional key risks. Diagrams show the change in the levels of impacts and 
risks assessed for global warming of 0–5°C global surface temperature change relative to pre-industrial period (1850–1900) over the range.

18SPM
Complex, Compound and Cascading Risks
B.5 Climate change impacts and risks are becoming increasingly complex and more difficult to manage. Multiple climate 
hazards will occur simultaneously, and multiple climatic and non-climatic risks will interact, resulting in compounding 
overall risk and risks cascading across sectors and regions. Some responses to climate change result in new impacts and 
risks. (high confidence) {1.3, 2.4, Box 2.2, Box 9.5, 11.5, 13.5, 14.6, Box 15.1, CCP1.2, CCP2.2, CCB COVID, CCB DISASTER, 
CCB INTEREG, CCB SRM, }
B.5.1 Concurrent and repeated climate hazards occur in all regions, increasing impacts and risks to health, ecosystems, infrastructure, livelihoods 
and food (high confidence). Multiple risks interact, generating new sources of vulnerability to climate hazards, and compounding overall 
risk (high confidence). Increasing concurrence of heat and drought events are causing crop production losses and tree mortality (high

confidence). Above 1.5°C global warming increasing concurrent climate extremes will increase risk of simultaneous crop losses of maize 
in major food-producing regions, with this risk increasing further with higher global warming levels (medium confidence). Future sea 
level rise combined with storm surge and heavy rainfall will increase compound flood risks (high confidence). Risks to health and food 
production will be made more severe from the interaction of sudden food production losses from heat and drought, exacerbated by 
heat-induced labour productivity losses (high confidence). These interacting impacts will increase food prices, reduce household incomes, 
and lead to health risks of malnutrition and climate-related mortality with no or low levels of adaptation, especially in tropical regions 
(high confidence). Risks to food safety from climate change will further compound the risks to health by increasing food contamination

of crops from mycotoxins and contamination of seafood from harmful algal blooms, mycotoxins, and chemical contaminants (high 
confidence). {Figure TS.10c, 5.2, 5.4, 5.8, 5.9, 5.11, 5.12, 7.2, 7.3, 9.8, 9.11, 10.4, 11.3, 11.5, 12.3, 13.5, 14.5, 15.3, Box 15.1, 16.6, CCP1.2, 
CCP6.2, , WGI AR6 SPM A.3.1, WGI AR6 SPM A.3.2, WGI AR6 SPM C.2.7}
B.5.2 Adverse impacts from climate hazards and resulting risks are cascading across sectors and regions (high confidence), propagating 
impacts along coasts and urban centres (medium confidence) and in mountain regions (high confidence). These hazards and cascading 
risks also trigger tipping points in sensitive ecosystems and in significantly and rapidly changing social-ecological systems impacted 
by ice melt, permafrost thaw and changing hydrology in polar regions (high confidence). Wildfires, in many regions, have affected

ecosystems and species, people and their built assets, economic activity, and health (medium to high confidence). In cities and (a) Global surface temperature changes in °C relative to 1850–1900. These changes were obtained by combining CMIP6 model simulations with observational constraints based 
on past simulated warming, as well as an updated assessment of equilibrium climate sensitivity (Box SPM.1). Changes relative to 1850–1900 based on 20-year averaging periods 
are calculated by adding 0.85°C (the observed global surface temperature increase from 1850–1900 to 1995–2014) to simulated changes relative to 1995–2014. Very likely ranges 
are shown for SSP1-2.6 and SSP3-7.0 (WGI AR6 Figure SPM.8). Assessments were carried out at the global scale for (b), (c), (d) and (e). 
(b) The Reasons for Concern (RFC) framework communicates scientific understanding about accrual of risk for five broad categories. Diagrams are shown for each RFC, assuming

low to no adaptation (i.e., adaptation is fragmented, localized and comprises incremental adjustments to existing practices). However, the transition to a very high risk level has an 
emphasis on irreversibility and adaptation limits. Undetectable risk level (white) indicates no associated impacts are detectable and attributable to climate change; moderate risk 
(yellow) indicates associated impacts are both detectable and attributable to climate change with at least medium confidence, also accounting for the other specific criteria for key 
risks; high risk (red) indicates severe and widespread impacts that are judged to be high on one or more criteria for assessing key risks; and very high risk level (purple) indicates 
very high risk of severe impacts and the presence of significant irreversibility or the persistence of climate-related hazards, combined with limited ability to adapt due to the nature

of the hazard or impacts/risks. The horizontal line denotes the present global warming of 1.09°C which is used to separate the observed, past impacts below the line from the future 
projected risks above it. RFC1: Unique and threatened systems: ecological and human systems that have restricted geographic ranges constrained by climate-related conditions and 
have high endemism or other distinctive properties. Examples include coral reefs, the Arctic and its Indigenous Peoples, mountain glaciers and biodiversity hotspots. RFC2: Extreme 
weather events: risks/impacts to human health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, 
and coastal flooding. RFC3: Distribution of impacts: risks/impacts that disproportionately affect particular groups due to uneven distribution of physical climate change hazards,

exposure or vulnerability. RFC4: Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, lives 
affected, species lost or ecosystem degradation at a global scale. RFC5: Large-scale singular events: relatively large, abrupt and sometimes irreversible changes in systems caused 
by global warming, such as ice sheet disintegration or thermohaline circulation slowing. Assessment methods are described in SM16.6 and are identical to AR5, but are enhanced 
by a structured approach to improve robustness and facilitate comparison between AR5 and AR6. 
Risks for (c) terrestrial and freshwater ecosystems and (d) ocean ecosystems. For c) and d), diagrams shown for each risk assume low to no adaptation. The transition to a very high 
risk level has an emphasis on irreversibility and adaptation limits.

(e) Climate-sensitive human health outcomes under three scenarios of adaptation effectiveness. The assessed projections were based on a range of scenarios, including SRES, 
CMIP5, and ISIMIP , and, in some cases, demographic trends. The diagrams are truncated at the nearest whole ºC within the range of temperature change in 2100 under three SSP 
scenarios in panel (a). 
(f) Examples of regional key risks. Risks identified are of at least medium confidence level. Key risks are identified based on the magnitude of adverse consequences (pervasiveness 
of the consequences, degree of change, irreversibility of consequences, potential for impact thresholds or tipping points, potential for cascading effects beyond system boundaries); 
likelihood of adverse consequences; temporal characteristics of the risk; and ability to respond to the risk, e.g., by adaptation. The full set of 127 assessed global and regional key

risks is given in SM16.7. Diagrams are provided for some risks. The development of synthetic diagrams for Small Islands, Asia and Central and South America were limited by the 
availability of adequately downscaled climate projections, with uncertainty in the direction of change, the diversity of climatologies and socioeconomic contexts across countries 
within a region, and the resulting low number of impact and risk projections for different warming levels. Absence of risks diagrams does not imply absence of risks within a region. 
(Box SPM.1) {Figure TS.4, Figure 2.11, Figure SM3.1, Figure 7.9, Figure 9.6, Figure 11.6, Figure 13.28, 16.5, 16.6, Figure 16.15, SM16.3, SM16.4, SM16.5, SM16.6 (methodologies), 
SM16.7, Figure CCP4.8, Figure CCP4.10, Figure CCP6.5, WGI AR6 2, WGI AR6 SPM A.1.2, WGI AR6 Figure SPM.8}

19SPM
Summary for Policymakerssettlements, climate impacts to key infrastructure are leading to losses and damages across water and food systems, and affect 
economic activity, with impacts extending beyond the area directly impacted by the climate hazard (high confidence). In Amazonia, 
and in some mountain regions, cascading impacts from climatic (e.g., heat) and non-climatic stressors (e.g., land use change) will result 
in irreversible and severe losses of ecosystem services and biodiversity at 2°C global warming level and beyond (medium confidence). 
Unavoidable sea level rise will bring cascading and compounding impacts resulting in losses of coastal ecosystems and ecosystem 
services, groundwater salinisation, flooding and damages to coastal infrastructure that cascade into risks to livelihoods, settlements, 
health, well-being, food and water security, and cultural values in the near to long-term (high confidence). (Figure SPM.3) {Figure TS.10,

2.5, 3.4, 3.5, Box 7.3, Box 8.7, Box 9.4, 11.5, Box 11.1, 12.3, 13.9, 14.6, 15.3, 16.5, 16.6, CCP1.2, CCP2.2, CCP5.2, CCP5.3, CCP6.2, 
CCP6.3, Box CCP6.1, Box CCP6.2, CCB EXTREMES, WGI AR6 Figure SPM.8d}
B.5.3 Weather and climate extremes are causing economic and societal impacts across national boundaries through supply-chains, markets, 
and natural resource flows, with increasing transboundary risks projected across the water, energy and food sectors (high confidence). 
Supply chains that rely on specialized commodities and key infrastructure can be disrupted by weather and climate extreme events. 
Climate change causes the redistribution of marine fish stocks, increasing risk of transboundary management conflicts among fisheries 
users, and negatively affecting equitable distribution of food provisioning services as fish stocks shift from lower to higher latitude regions,

thereby increasing the need for climate-informed transboundary management and cooperation (high confidence). Precipitation and water 
availability changes increases the risk of planned infrastructure projects, such as hydropower in some regions, having reduced productivity 
for food and energy sectors including across countries that share river basins (medium confidence). {Figure TS.10e-f, 3.4, 3.5, 4.5, 5.8, 5.13, 
6.2, 9.4, Box 9.5,14.5, Box 14.5, Box 14.6, CCP5.3, CCB DISASTER, CCB EXTREMES, CCB INTEREG, CCB MOVING PLATE}
B.5.4 Risks arise from some responses that are intended to reduce the risks of climate change, including risks from maladaptation and adverse 
side effects of some emissions reduction and carbon dioxide removal measures (high confidence). Deployment of afforestation of 
naturally unforested land, or poorly implemented bioenergy, with or without carbon capture and storage, can compound climate-related

risks to biodiversity, water and food security, and livelihoods, especially if implemented at large scales, especially in regions with insecure 
land tenure (high confidence). {Box 2.2, 4.1, 4.7, 5.13, Table 5.18, Box 9.3, Box 13.2, CCB NATURAL, CWGB BIOECONOMY}
B.5.5 Solar radiation modification approaches, if they were to be implemented, introduce a widespread range of new risks to people and 
ecosystems, which are not well understood (high confidence). Solar radiation modification approaches have potential to offset warming 
and ameliorate some climate hazards, but substantial residual climate change or overcompensating change would occur at regional 
scales and seasonal timescales (high confidence). Large uncertainties and knowledge gaps are associated with the potential of solar 
radiation modification approaches to reduce climate change risks. Solar radiation modification would not stop atmospheric CO 2

concentrations from increasing or reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). 
{CWGB SRM}
Impacts of Temporary Overshoot
37 In this report, overshoot pathways exceed 1.5°C global warming and then return to that level, or below, after several decades.
38 Despite limited evidence specifically on the impacts of a temporary overshoot of 1.5°C, a much broader evidence base from process understanding and the impacts of higher global warming levels 
allows a high confidence statement on the irreversibility of some impacts that would be incurred following such an overshoot.B.6 If global warming transiently exceeds 1.5°C in the coming decades or later (overshoot)37, then many human and natural 
systems will face additional severe risks, compared to remaining below 1.5°C (high confidence). Depending on the mag-
nitude and duration of overshoot, some impacts will cause release of additional greenhouse gases (medium confidence)

and some will be irreversible, even if global warming is reduced (high confidence). (Box SPM.1, Figure SPM.3) {2.5, 3.4, 
12.3, 16.6, CCB DEEP, CCB SLR}
B.6.1 While model-based assessments of the impacts of overshoot pathways are limited, observations and current understanding of processes 
permit assessment of impacts from overshoot. Additional warming, e.g., above 1.5°C during an overshoot period this century, will 
result in irreversible impacts on certain ecosystems with low resilience, such as polar, mountain, and coastal ecosystems, impacted 
by ice-sheet, glacier melt, or by accelerating and higher committed sea level rise (high confidence).38 Risks to human systems will 
increase, including those to infrastructure, low-lying coastal settlements, some ecosystem-based adaptation measures, and associated 
livelihoods (high confidence), cultural and spiritual values (medium confidence). Projected impacts are less severe with shorter duration

and lower levels of overshoot (medium confidence). {2.5, 3.4, 12.3, 13.2, 16.5, 16.6, CCP1.2, CCP2.2, CCP5.3, CCP6.1, CCP6.2, CCB SLR, 
WGI AR6 SPM B.5, WGI AR6 SPM C.3, SROCC 2.3, SROCC 5.4}

20SPM
Summary for PolicymakersB.6.2  Risk of severe impacts increase with every additional increment of global warming during overshoot (high confidence). In high-carbon 
ecosystems (currently storing 3,000 to 4,000 GtC)39 such impacts are already observed and are projected to increase with every 
additional increment of global warming, such as increased wildfires, mass mortality of trees, drying of peatlands, and thawing of 
permafrost, weakening natural land carbon sinks and increasing releases of greenhouse gases (medium confidence). The resulting 
contribution to a potential amplification of global warming indicates that a return to a given global warming level or below would be 
more challenging (medium confidence). {2.4, 2.5, CCP4.2, WGI AR6 SPM B.4.3, SROCC 5.4}
C: Adaptation Measures and Enabling Conditions
Adaptation, in response to current climate change, is reducing climate risks and vulnerability mostly via adjustment of existing systems. Many

adaptation options exist and are used to help manage projected climate change impacts, but their implementation depends upon the capacity and 
effectiveness of governance and decision-making processes. These and other enabling conditions can also support climate resilient development 
(Section D).
Current Adaptation and its Benefits
39 At the global scale, terrestrial ecosystems currently remove more carbon from the atmosphere (-3.4 ± 0.9 Gt yr-1) than they emit (+1.6 ± 0.7 Gt yr-1), a net sink of -1.9 ± 1.1 Gt yr-1. However, recent 
climate change has shifted some systems in some regions from being net carbon sinks to net carbon sources.
40 Adaptation gaps are defined as the difference between actually implemented adaptation and a societally set goal, determined largely by preferences related to tolerated climate change impacts and

reflecting resource limitations and competing priorities.C.1 Progress in adaptation planning and implementation has been observed across all sectors and regions, generating multiple 
benefits (very high confidence). However, adaptation progress is unevenly distributed with observed adaptation gaps40 (high 
confidence). Many initiatives prioritize immediate and near-term climate risk reduction which reduces the opportunity for 
transformational adaptation (high confidence). {2.6, 5.14, 7.4, 10.4, 12.5, 13.11, 14.7, 16.3, 17.3, CCP5.2, CCP5.4}
C.1.1 Adaptation planning and implementation have continued to increase across all regions (very high confidence). Growing public and 
political awareness of climate impacts and risks has resulted in at least 170 countries and many cities including adaptation in their 
climate policies and planning processes (high confidence). Decision support tools and climate services are increasingly being used

(very high confidence). Pilot projects and local experiments are being implemented in different sectors (high confidence). Adaptation 
can generate multiple additional benefits such as improving agricultural productivity, innovation, health and well-being, food security, 
livelihood, and biodiversity conservation as well as reduction of risks and damages (very high confidence). {1.4, 2.6, 3.5, 3.6, 4.7, 4.8, 
5.4, 5.6, 5.10, 6.4, 7.4, 8.5, 9.3, 9.6, 10.4, 12.5, 13.11, 15.5, 16.3, 17.2, 17.3, 17.5, CCP5.4, CCB ADAPT, CCB NATURAL}
C.1.2 Despite progress, adaptation gaps exist between current levels of adaptation and levels needed to respond to impacts and reduce 
climate risks (high confidence).  Most observed adaptation is fragmented, small in scale, incremental, sector-specific, designed to 
respond to current impacts or near-term risks, and focused more on planning rather than implementation (high confidence). Observed

adaptation is unequally distributed across regions (high confidence), and gaps are partially driven by widening disparities between the 
estimated costs of adaptation and documented finance allocated to adaptation (high confidence). The largest adaptation gaps exist 
among lower income population groups (high confidence). At current rates of adaptation planning and implementation the adaptation 
gap will continue to grow (high confidence). As adaptation options often have long implementation times, long-term planning and 
accelerated implementation, particularly in the next decade, is important to close adaptation gaps, recognising that constraints remain 
for some regions (high confidence). {1.1, 1.4, 5.6, 6.3, Figure 6.4, 7.4, 8.3, 10.4, 11.3, 11.7, 13.11, Box 13.1, 15.2, 15.5, 16.3, 16.5, 
Box 16.1, Figure 16.4, Figure 16.5, 17.4, 18.2, CCP2.4, CCP5.4, CCB FINANCE, CCB SLR}

21SPM
Summary for PolicymakersFuture Adaptation Options and their Feasibility
41 In this report, feasibility refers to the potential for a mitigation or adaptation option to be implemented. Factors influencing feasibility are context-dependent, temporally dynamic, and may vary between 
different groups and actors. Feasibility depends on geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors that enable or constrain the implementation 
of an option. The feasibility of options may change when different options are combined and increase when enabling conditions are strengthened.
42 Effectiveness refers to the extent to which an adaptation option is anticipated or observed to reduce climate-related risk.
43 In this report, the term natural forests describes those which are subject to little or no direct human intervention, whereas the term managed forests describes those where planting or other

management activities take place, including those managed for commodity production.C.2 There are feasible41 and effective42 adaptation options which can reduce risks to people and nature. The feasibility of 
implementing adaptation options in the near-term differs across sectors and regions (very high confidence). The effec-
tiveness of adaptation to reduce climate risk is documented for specific contexts, sectors and regions (high confidence) 
and will decrease with increasing warming (high confidence). Integrated, multi-sectoral solutions that address social in-
equities, differentiate responses based on climate risk and cut across systems, increase the feasibility and effectiveness of 
adaptation in multiple sectors (high confidence). (Figure SPM.4) {Figure TS.6e, 1.4, 3.6, 4.7, 5.12, 6.3, 7.4, 11.3, 11.7, 13.2, 
15.5, 17.6, CCP2.3, CCB FEASIB}
Land, Ocean and Ecosystems Transition

C.2.1 Adaptation to water-related risks and impacts make up the majority of all documented adaptation (high confidence).  For inland 
flooding, combinations of non-structural measures like early warning systems and structural measures like levees have reduced loss 
of lives (medium confidence). Enhancing natural water retention such as by restoring wetlands and rivers, land use planning such 
as no build zones or upstream forest management, can further reduce flood risk (medium confidence). On-farm water management, 
water storage, soil moisture conservation and irrigation are some of the most common adaptation responses and provide economic, 
institutional or ecological benefits and reduce vulnerability (high confidence). Irrigation is effective in reducing drought risk and climate 
impacts in many regions and has several livelihood benefits, but needs appropriate management to avoid potential adverse outcomes,

which can include accelerated depletion of groundwater and other water sources and increased soil salinization (medium confidence). 
Large scale irrigation can also alter local to regional temperature and precipitation patterns (high confidence), including both alleviating 
and exacerbating temperature extremes (medium confidence). The effectiveness of most water-related adaptation options to reduce 
projected risks declines with increasing warming (high confidence). {4.1, 4.6, 4.7, Box 4.3, Box 4.6, Box 4.7, Figure 4.22, Figure 4.28, 
Figure 4.29, Table 4.9, 9.3, 9.7, 11.3, 12.5, 13.1, 13.2, 16.3, CCP5.4}
C.2.2 Effective adaptation options, together with supportive public policies enhance food availability and stability and reduce climate risk for 
food systems while increasing their sustainability (medium confidence). Effective options include cultivar improvements, agroforestry,

community-based adaptation, farm and landscape diversification, and urban agriculture  (high confidence). Institutional feasibility, 
adaptation limits of crops and cost effectiveness also influence the effectiveness of the adaptation options (limited evidence, medium 
agreement). Agroecological principles and practices, ecosystem-based management in fisheries and aquaculture, and other approaches 
that work with natural processes support food security, nutrition, health and well-being, livelihoods and biodiversity, sustainability and 
ecosystem services (high confidence). These services include pest control, pollination, buffering of temperature extremes, and carbon 
sequestration and storage (high confidence). Trade-offs and barriers associated with such approaches include costs of establishment, 
access to inputs and viable markets, new knowledge and management (high confidence) and their potential effectiveness varies by

socioeconomic context, ecosystem zone, species combinations and institutional support (medium confidence). Integrated, multi-sectoral 
solutions that address social inequities and differentiate responses based on climate risk and local situation will enhance food security 
and nutrition (high confidence). Adaptation strategies which reduce food loss and waste or support balanced diets33 (as described in the 
IPCC Special Report on Climate Change and Land) contribute to nutrition, health, biodiversity and other environmental benefits (high 
confidence). {3.2, 4.7, 4.6, Box 4.3, 5.4, 5.5, 5.6, 5.8, 5.9, 5.10, 5.11, 5.12, 5.13, 5.14, Box 5.10, Box 5.13, 6.3, 7.4, 10.4, 12.5, 13.5, 13.10, 
14.5, CCP5.4, CCB FEASIB, CCB HEALTH, CCB MOVING PLATE, CCB NATURAL, CWGB BIOECONOMY}
C.2.3 Adaptation for natural forests43 includes conservation, protection and restoration measures. In managed forests43, adaptation options

include sustainable forest management, diversifying and adjusting tree species compositions to build resilience, and managing 
increased risks from pests and diseases and wildfires. Restoring natural forests and drained peatlands and improving sustainability 
of managed forests, generally enhances the resilience of carbon stocks and sinks. Cooperation, and inclusive decision making, with 
local communities and Indigenous Peoples, as well as recognition of inherent rights of Indigenous Peoples, is integral to successful 
forest adaptation in many areas. (high confidence) {2.6, Box 2.2, 5.6, 5.13, Table 5.23, 11.4, 12.5, 13.5, Box 14.1, Box 14.2, CCP7.5, 
Box CCP7.1, CCB FEASIB, CCB INDIG, CCB NATURAL}

22SPM
Climate responses1
and adaptation options
Climate services, including Early Warning System sResilient power systemsAgroforestry
Energy reliabilitySustainable aquaculture and ﬁsheries
Green infrastructure and ecosystem services
Improve water use efﬁciency
Peace and
human mobilityLiving standards and equityCoastal socio-
ocean ecosystem
services
Food
security
Critical
networks
and services
networks and servicesWater
securityRepresentative
key risks
Cross-

Land and 
ocean
Urban and
systems
Energy
systems
Other
risksHigh
LowMediumDimensions of potential feasibility 
1 The term response is used 
here instead of adaptation 
because some responses, 
such as retreat, may or may 
not be considered to be 
2Including sustainable forest 
management, forest 
conservation and restoration, 
3 Migration, when voluntary, 
safe and orderly, allows 
reduction of risks to climatic 
and non-climatic stressors.Forest-based adaptation2
Planned relocation and resettlement
Feasibility level and
synergies with mitigation
Insufﬁcient evidence/
in potential feasibility and
in synergies with mitigation
MediumHigh
Low
Sustainable urban water managementEconomicInsti-
tutionalTechno-
logical Social
/Geo-
physical
/Environ-
mentalPotential
feasibilitySynergies
with
/not assessed

Footnotes:Dimensions of
potential feasibility(a) Diverse feasible climate responses and adaptation options exist to respond to Representative K ey Risks of climate change, with varying synergies with mitigation
Multidimensional feasibility and synergies with mitigation of climate responses and adaptation options relev ant in the near -term,  at global scale and up to 1.5°C of global w arming
Figure SPM.4 |  (a) Climate responses and adaptation options, organized by System Transitions and Representative Key Risks (RKRs), are assessed for their multidimensional feasibility at global scale, in the 
near term and up to 1.5°C global warming. As literature above 1.5°C is limited, feasibility at higher levels of warming may change, which is currently not possible to assess robustly. Climate responses and adaptation options at global

scale are drawn from a set of options assessed in AR6 that have robust evidence across the feasibility dimensions. This figure shows the six feasibility dimensions (economic, technological, institutional, social, environmental and geophysical) 
that are used to calculate the potential feasibility of climate responses and adaptation options, along with their synergies with mitigation. For potential feasibility and feasibility dimensions, the figure shows high, medium, or low feasibility. 
Synergies with mitigation are identified as high, medium, and low. Insufficient evidence is denoted by a dash. {CCB FEASIB, Table SMCCB FEASIB.1.1, SR1.5 4.SM.4.3}

23SPM
Footnotes: 1 The term response is used here instead of adaptation because some responses, such as retreat, may or may not be considered to be adaptation. 2 Including sustainable forest management, forest 
conservation and restoration, reforestation and afforestation. 3 Migration, when voluntary, safe and orderly, allows reduction of risks to climatic and non-climatic stressors. 4 The Sustainable Development Goals 
(SDGs) are integrated and indivisible, and efforts to achieve any goal in isolation may trigger synergies or trade-offs with other SDGs. 5 Relevant in the near-term, at global scale and up to 1.5°C of global warming.Types of relation
Climate services, including Early Warning System sForest-based adaptation2

Risk spreading and sharingCoastal defence and hardeningObserved relation with
sectors and groups at risk
Ethnic
groupsGender
equityEcosystems
and their
servicesLow-
income
groups
Cross-
Land and 
ocean
Urban and
systems
Energy
systemsRelation with
Sustainable Development Goals4, 5
14 17 34 56 10 28 11 15 17 13 91 21 6
+ ++ + •• ++ + •+•++ ++ + +++ ++
++ ++ + +++++ ++ ++ ++ ++ ++
++ ++ ++ • +++ ++ +++ ++ ++ ++ +
+ •• ++ •+ +•
++ ++ ++ • ++ + + ++
++ ++ ++ +
++ ++ + +
•+++ ++ + ++ +
++ ++ ++ ++ + +++ +++
++ + •• •––• • + ••
•• • + •• • •+
++ + •++ + •++

++ ++ ++ +
++ + •+ ••+ +– • ••
1: No Poverty
2: Zero Hunger
3: Good Health and Well-being
4: Quality Education
5: Gender Equality
7: Affordable and Clean Energy
8: Decent Work and Economic Growth
9: Industry, Innovation and Infrastructure
10: Reducing Inequality
11: Sustainable Cities and Communities
12: Responsible Consumption and Production
13: Climate Action
14: Life Below Water
15: Life On Land
16: Peace, Justice, and Strong Institutions
17: Partnerships for the Goals6: Clean Water and Sanitation–
–
––– –
+
+
+
+
+
+
+
+
++
+ +++++
+
+
+•
•
••••••
•••
•
•
••
•••
•
•
••////
/
/
//
•not assessed
not assessedWith beneﬁts+
Not clear or mixed•With dis-beneﬁts–
Related 
Sustainable Development GoalsConﬁdence level
in type of relation with
LowClimate responses¹
+
Sustainable urban water management ++ ++ + ++

– ++ /not assessed(b) Climate responses and adaptation options have beneﬁts for ecosystems , ethnic group s, gender equity,  low-income groups and the Sustainable Development Goals
Relations of sectors and groups at risk (as observed) and the SDGs (relev ant in the near -term,  at global scale and up to 1.5°C of global w arming) with climate responses and adaptation options
+ + ++

24SPM
C.2.4 Conservation, protection and restoration of terrestrial, freshwater, coastal and ocean ecosystems, together with targeted management 
to adapt to unavoidable impacts of climate change, reduces the vulnerability of biodiversity to climate change (high confidence). The 
resilience of species, biological communities and ecosystem processes increases with size of natural area, by restoration of degraded 
areas and by reducing non-climatic stressors (high confidence). To be effective, conservation and restoration actions will increasingly 
need to be responsive, as appropriate, to ongoing changes at various scales, and plan for future changes in ecosystem structure, 
community composition and species’ distributions, especially as 1.5°C global warming is approached and even more so if it is exceeded 
(high confidence). Adaptation options, where circumstances allow, include facilitating the movement of species to new ecologically

appropriate locations, particularly through increasing connectivity  between conserved or protected areas, targeted intensive 
management for vulnerable species and protecting refugial areas where species can survive locally (medium confidence). {2.3, 2,6, 
Figure 2.1, Table 2.6, 3.3, 3.6, Box 3.4, 4.6, Box 4.6, Box 11.2, 12.3, 12.5, 13.4, 14.7, CCP5.4, CCB FEASIB}
C.2.5  Effective Ecosystem-based Adaptation44 reduces a range of climate change risks to people, biodiversity and ecosystem services with 
multiple co-benefits (high confidence). Ecosystem-based Adaptation is vulnerable to climate change impacts, with effectiveness 
declining with increasing global warming (high confidence). Urban greening using trees and other vegetation can provide local cooling 
(very high confidence). Natural river systems, wetlands and upstream forest ecosystems reduce flood risk by storing water and slowing

water flow, in most circumstances (high confidence). Coastal wetlands protect against coastal erosion and flooding associated with 
storms and sea level rise where sufficient space and adequate habitats are available until rates of sea level rise exceeds natural 
adaptive capacity to build sediment (very high confidence). {2.4, 2.5, 2.6, Table 2.7, 3.4, 3.5, 3.6, Figure 3.26, 4.6, Box 4.6, Box 4.7, 5.5, 
5.14, Box 5.11, 6.3, 6.4, Figure 6.6, 7.4, 8.5, 8.6, 9.6, 9.8, 9.9, 10.2, 11.3, 12.5, 13.3, 13.4, 13.5, 14.5, Box 14.7, 16.3, 18.3, CCP5.4, CCB 
FEASIB.3, CCB HEALTH, CCB MOVING PLATE, CCB NATURAL, CWGB BIOECONOMY}
Urban, Rural and Infrastructure Transition
C.2.6 Considering climate change impacts and risks in the design and planning of urban and rural settlements and infrastructure is critical 
for resilience and enhancing human well-being (high confidence). The urgent provision of basic services, infrastructure, livelihood

diversification and employment, strengthening of local and regional food systems and community-based adaptation enhance lives and 
livelihoods, particularly of low-income and marginalised groups (high confidence). Inclusive, integrated and long-term planning at local, 
municipal, sub-national and national scales, together with effective regulation and monitoring systems and financial and technological 
resources and capabilities foster urban and rural system transition (high confidence). Effective partnerships between governments, civil 
society, and private sector organizations, across scales provide infrastructure and services in ways that enhance the adaptive capacity 
of vulnerable people (medium to high confidence). {5.12, 5.13, 5.14, 6.3, 6.4, Box 6.3, Box 6.6, Table 6.6, 7.4, 12.5, 13.6, 14.5, Box 14.4, 
Box 17.4, CCP2.3, CCP2.4, CCP5.4, CCB FEASIB}

C.2.7 An increasing number of adaptation responses exist for urban systems, but their feasibility and effectiveness is constrained by 
institutional, financial, and technological access and capacity, and depends on coordinated and contextually appropriate responses 
across physical, natural and social infrastructure (high confidence). Globally, more financing is directed at physical infrastructure than 
natural and social infrastructure (medium confidence) and there is limited evidence of investment in the informal settlements hosting 
the most vulnerable urban residents (medium to high confidence). Ecosystem-based adaptation (e.g., urban agriculture and forestry, 
river restoration) has increasingly been applied in urban areas (high confidence). Combined ecosystem-based and structural adaptation 
responses are being developed, and there is growing evidence of their potential to reduce adaptation costs and contribute to flood

control, sanitation, water resources management, landslide prevention and coastal protection (medium confidence). {3.6, Box 4.6, 5.12, 
6.3, 6.4, Table 6.8, 7.4, 9.7, 9.9, 10.4, Table 10.3, 11.3, 11.7, Box 11.6, 12.5, 13.2, 13.3, 13.6, 14.5, 15.5, 17.2, Box 17.4, CCP2.3, CCP 
3.2, CCP5.4, CCB FEASIB, CCB SLR, SROCC SPM}
44 Ecosystem based Adaptation (EbA) is recognised internationally under the Convention on Biological Diversity (CBD14/5). A related concept is Nature-based Solutions (NbS), which includes a broader 
range of approaches with safeguards, including those that contribute to adaptation and mitigation. The term ‘Nature-based Solutions’ is widely but not universally used in the scientific literature. The

term is the subject of ongoing debate, with concerns that it may lead to the misunderstanding that NbS on its own can provide a global solution to climate change.Figure SPM.4 |  (b) Climate responses and adaptation options, organized by System Transitions and Representative Key Risks, are assessed at global scale 
for their likely ability to reduce risks for ecosystems and social groups at risk, as well as their relation with the 17 Sustainable Development Goals (SDGs). 
Climate responses and adaptation options are assessed for observed benefits (+) to ecosystems and their services, ethnic groups, gender equity, and low-income groups, or observed 
dis-benefits (-) for these systems and groups. Where there is highly diverging evidence of benefits/ dis-benefits across the scientific literature, e.g., based on differences between

regions, it is shown as not clear or mixed (•). Insufficient evidence is shown by a dash. The relation with the SDGs is assessed as having benefits (+), dis-benefits (-) or not clear or 
mixed (•) based on the impacts of the climate response and adaptation option on each SDG. Areas not coloured indicate there is no evidence of a relation or no interaction with the 
respective SDG. The climate responses and adaptation options are drawn from two assessments. For comparability of climate responses and adaptation options see Table SM17.5. 
{17.2, 17.5, CCB FEASIB}

25SPM
Summary for PolicymakersC.2.8 Sea level rise poses a distinctive and severe adaptation challenge as it implies dealing with slow onset changes and increased frequency 
and magnitude of extreme sea level events which will escalate in the coming decades (high confidence). Such adaptation challenges 
would occur much earlier under high rates of sea level rise, in particular if low-likelihood, high impact outcomes associated with 
collapsing ice sheets occur (high confidence). Responses to ongoing sea level rise and land subsidence in low-lying coastal cities and 
settlements and small islands include protection, accommodation, advance and planned relocation (high confidence)45. These responses 
are more effective if combined and/or sequenced, planned well ahead, aligned with sociocultural values and development priorities, 
and underpinned by inclusive community engagement processes (high confidence). { 6.2, 10.4, 11.7, Box 11.6, 13.2, 14.5, 15.5, CCP2.3,

CCB SLR, WGI AR6 SPM B.5, WGI AR6 SPM C.3, SROCC SPM C3.2}
C.2.9 Approximately 3.4  billion people globally live in rural areas around the world, and many are highly vulnerable to climate change. 
Integrating climate adaptation into social protection programs, including cash transfers and public works programmes, is highly feasible 
and increases resilience to climate change, especially when supported by basic services and infrastructure. Social safety nets are 
increasingly being reconfigured to build adaptive capacities of the most vulnerable in rural and also urban communities. Social safety 
nets that support climate change adaptation have strong co-benefits with development goals such as education, poverty alleviation, 
gender inclusion and food security. (high confidence) {5.14, 9.4, 9.10, 9.11, 12.5, 14.5, CCP5.4, CCB FEASIB, CCB GENDER}
Energy System Transition

C.2.10 Within energy system transitions, the most feasible adaptation options support infrastructure resilience, reliable power systems 
and efficient water use for existing and new energy generation systems (very high confidence). Energy generation diversification, 
including with renewable energy resources and generation that can be decentralised depending on context (e.g., wind, solar, small 
scale hydroelectric) and demand side management (e.g., storage, and energy efficiency improvements) can reduce vulnerabilities to 
climate change, especially in rural populations (high confidence). Adaptations for hydropower and thermo-electric power generation 
are effective in most regions up to 1.5°C to 2°C, with decreasing effectiveness at higher levels of warming (medium confidence). 
Climate responsive energy markets, updated design standards on energy assets according to current and projected climate change,

smart-grid technologies, robust transmission systems and improved capacity to respond to supply deficits have high feasibility in the 
medium- to long-term, with mitigation co-benefits (very high confidence). {4.6, 4.7, Figure 4.28, Figure 4.29, 10.4, Table 11.8, 13.6, 
Figure 13.16, Figure 13.19, 18.3,CCP5.2, CCP5.4, CCB FEASIB, CWGB BIOECONOMY}
Cross-cutting Options
C.2.11 Strengthening the climate resiliency of health systems will protect and promote human health and well-being (high confidence). There 
are multiple opportunities for targeted investments and finance to protect against exposure to climate hazards, particularly for those 
at highest risk. Heat Health Action Plans that include early warning and response systems are effective adaptation options for extreme 
heat (high confidence) . Effective adaptation options for water-borne and food-borne diseases include improving access to potable

water, reducing exposure of water and sanitation systems to flooding and extreme weather events, and improved early warning systems 
(very high confidence). For vector-borne diseases, effective adaptation options include surveillance, early warning systems, and vaccine 
development (very high confidence). Effective adaptation options for reducing mental health risks under climate change include improving 
surveillance, access to mental health care, and monitoring of psychosocial impacts from extreme weather events (high confidence) . Health 
and well-being would benefit from integrated adaptation approaches that mainstream health into food, livelihoods, social protection, 
infrastructure, water and sanitation policies requiring collaboration and coordination at all scales of governance (very high confidence). 
{5.12, 6.3, 7.4, 9.10, Box 9.7, 11.3, 12.5, 13.7, 14.5, CCB COVID, CCB FEASIB, CCB ILLNESS }

C.2.12  Increasing adaptive capacities minimises the negative impacts of climate-related displacement and involuntary migration for migrants 
and sending and receiving areas (high confidence). This improves the degree of choice under which migration decisions are made, 
ensuring safe and orderly movements of people within and between countries (high confidence). Some development reduces underlying 
vulnerabilities associated with conflict, and adaptation contributes by reducing the impacts of climate change on climate sensitive 
drivers of conflict (high confidence). Risks to peace are reduced, for example, by supporting people in climate-sensitive economic 
activities (medium confidence) and advancing women’s empowerment (high confidence). {7.4, Box 9.8, Box 10.2, 12.5, CCB FEASIB, 
CCB MIGRATE}
45 The term ‘response’ is used here instead of adaptation because some responses, such as retreat, may or may not be considered to be adaptation.

26SPM
Summary for PolicymakersC.2.13 There are a range of adaptation options, such as disaster risk management, early warning systems, climate services and risk spreading 
and sharing that have broad applicability across sectors and provide greater benefits to other adaptation options when combined (high 
confidence). For example, climate services that are inclusive of different users and providers can improve agricultural practices, inform 
better water use and efficiency, and enable resilient infrastructure planning (high confidence). {2.6, 3.6, 4.7, 5.4, 5.5, 5.6, 5.8, 5.9, 5.12, 
5.14, 9.4, 9.8, 10.4, 12.5, 13.11, CCP5.4, CCB FEASIB, CCB MOVING PLATE}
46 Climate literacy encompasses being aware of climate change, its anthropogenic causes and implications.C.3 Soft limits to some human adaptation have been reached, but can be overcome by addressing a range of constraints,

primarily financial, governance, institutional and policy constraints (high confidence). Hard limits to adaptation have been 
reached in some ecosystems (high confidence). With increasing global warming, losses and damages will increase and 
additional human and natural systems will reach adaptation limits (high confidence). {Figure TS.7, 1.4, 2.4, 2.5, 2.6, 3.4, 3.6, 
4.7, Figure 4.30, 5.5, Table 8.6, Box 10.7, 11.7, Table 11.16, 12.5, 13.2, 13.5, 13.6, 13.10, 13.11, Figure 13.21, 14.5, 15.6, 16.4, 
Figure 16.8, Table 16.3, Table 16.4, CCP1.2, CCP1.3, CCP2.3, CCP3.3, CCP5.2, CCP5.4, CCP6.3, CCP7.3, CCB SLR}
C.3.1 Soft limits to some human adaptation have been reached, but can be overcome by addressing a range of constraints, which primarily 
consist of financial, governance, institutional and policy constraints (high confidence). For example, individuals and households in

low-lying coastal areas in Australasia and Small Islands and smallholder farmers in Central and South America, Africa, Europe and Asia 
have reached soft limits (medium confidence). Inequity and poverty also constrain adaptation, leading to soft limits and resulting in 
disproportionate exposure and impacts for most vulnerable groups (high confidence). Lack of climate literacy46 at all levels and limited 
availability of information and data pose further constraints to adaptation planning and implementation (medium confidence). {1.4, 4.7, 
5.4, 8.4, Table 8.6, 9.1, 9.4, 9.5, 9.8, 11.7, 12.5 13.5, 15.3, 15.5, 15.6, 16.4, Box 16.1, Figure 16.8, CCP5.2, CCP5.4, CCP6.3}
C.3.2 Financial constraints are important determinants of soft limits to adaptation across sectors and all regions (high confidence). Although 
global tracked climate finance has shown an upward trend since AR5, current global financial flows for adaptation, including from

public and private finance sources, are insufficient for and constrain implementation of adaptation options especially in developing 
countries (high confidence). The overwhelming majority of global tracked climate finance was targeted to mitigation while a small 
proportion was targeted to adaptation (very high confidence). Adaptation finance has come predominantly from public sources (very 
high confidence). Adverse climate impacts can reduce the availability of financial resources by incurring losses and damages and 
through impeding national economic growth, thereby further increasing financial constraints for adaptation, particularly for developing 
and least developed countries (medium confidence). {Figure TS.7, 1.4, 2.6, 3.6, 4.7, Figure 4.30, 5.14, 7.4, 8.4, Table 8.6, 9.4, 9.9, 9.11, 
10.5, 12.5, 13.3, 13.11, Box 14.4, 15.6, 16.2, 16.4, Figure 16.8, Table 16.4, 17.4, 18.1, CCP2.4, CCP5.4, CCP6.3, CCB FINANCE}

C.3.3 Many natural systems are near the hard limits of their natural adaptation capacity and additional systems will reach limits with 
increasing global warming (high confidence). Ecosystems already reaching or surpassing hard adaptation limits include some warm- 
water coral reefs, some coastal wetlands, some rainforests, and some polar and mountain ecosystems (high confidence). Above 1.5°C 
global warming level, some Ecosystem-based Adaptation measures will lose their effectiveness in providing benefits to people as these 
ecosystems will reach hard adaptation limits (high confidence). (Figure SPM.4) {1.4, 2.4, 2.6, 3.4, 3.6, 9.6, Box 11.2, 13.4, 14.5, 15.5, 
16.4, 16.6, 17.2, CCP1.2, CCP5.2, CCP6.3, CCP7.3, CCB SLR}
C.3.4 In human systems, some coastal settlements face soft adaptation limits due to technical and financial difficulties of implementing

coastal protection (high confidence). Above 1.5°C global warming level, limited freshwater resources pose potential hard limits for 
Small Islands and for regions dependent on glacier and snow-melt (medium confidence). By 2°C global warming level, soft limits are 
projected for multiple staple crops in many growing areas, particularly in tropical regions (high confidence). By 3°C global warming 
level, soft limits are projected for some water management measures for many regions, with hard limits projected for parts of Europe 
(medium confidence). Transitioning from incremental to transformational adaptation can help overcome soft adaptation limits (high 
confidence). {1.4, 4.7, 5.4, 5.8, 7.2, 7.3, 8.4, Table 8.6, 9.8, 10.4, 12.5, 13.2, 13.6, 16.4, 17.2, CCP1.3. Box CCP1.1, CCP2.3, CCP3.3, 
CCP4.4, CCP5.3, CCB SLR}
C.3.5 Adaptation does not prevent all losses and damages, even with effective adaptation and before reaching soft and hard limits. Losses

and damages are unequally distributed across systems, regions and sectors and are not comprehensively addressed by current financial, 
governance and institutional arrangements, particularly in vulnerable developing countries. With increasing global warming, losses and 
damages increase and become increasingly difficult to avoid, while strongly concentrated among the poorest vulnerable populations. 
(high confidence) {1.4, 2.6, 3.4, 3.6, 6.3, Figure 6.4, 8.4, 13.2, 13.7, 13.10, 17.2, CCP2.3, CCP4.4, CCB LOSS, CCB SLR, CWGB ECONOMIC}

27SPM
Summary for PolicymakersAvoiding Maladaptation
47 From AR5, an option that would generate net social and/or economic benefits under current climate change and a range of future climate change scenarios, and represent one example of robust 
strategies.C.4 There is increased evidence of maladaptation15 across many sectors and regions since the AR5. Maladaptive responses 
to climate change can create lock-ins of vulnerability, exposure and risks that are difficult and expensive to change and 
exacerbate existing inequalities. Maladaptation can be avoided by flexible, multi-sectoral, inclusive and long-term plan-
ning and implementation of adaptation actions with benefits to many sectors and systems. (high confidence) {1.3, 1.4, 
2.6, Box 2.2, 3.2, 3.6, 4.6, 4.7, Box 4.3, Box 4.5, Figure 4.29, 5.6, 5.13, 8.2, 8.3, 8.4, 8.6, 9.6, 9.7, 9.8, 9.9, 9.10, 9.11, Box 9.5,

Box 9.8, Box 9.9, Box 11.6, 13.11, 13.3, 13.4, 13.5, 14.5, 15.5, 15.6, 16.3, 17.2, 17.3, 17.4, 17.5, 17.6, CCP2.3, CCP2.3, 
CCP5.4, CCB DEEP, CCB NATURAL, CCB SLR, CWGB BIOECONOMY}
C.4.1 Actions that focus on sectors and risks in isolation and on short-term gains often lead to maladaptation if long-term impacts of 
the adaptation option and long-term adaptation commitment are not taken into account (high confidence). The implementation of 
these maladaptive actions can result in infrastructure and institutions that are inflexible and/or expensive to change (high confidence). 
For example, seawalls effectively reduce impacts to people and assets in the short-term but can also result in lock-ins and increase 
exposure to climate risks in the long-term unless they are integrated into a long-term adaptive plan (high confidence). Adaptation 
integrated with development reduces lock-ins and creates opportunities (e.g., infrastructure upgrading) (medium confidence). {1.4, 3.4,

3.6, 10.4, 11.7, Box 11.6, 13.2, 17.2, 17.5, 17.6, CCP 2.3, CCB DEEP , CCB SLR}
C.4.2 Biodiversity and ecosystem resilience to climate change are decreased by maladaptive actions, which also constrain ecosystem 
services. Examples of these maladaptive actions for ecosystems include fire suppression in naturally fire-adapted ecosystems or hard 
defences against flooding. These actions reduce space for natural processes and represent a severe form of maladaptation for the 
ecosystems they degrade, replace or fragment, thereby reducing their resilience to climate change and the ability to provide ecosystem 
services for adaptation. Considering biodiversity and autonomous adaptation in long-term planning processes reduces the risk of 
maladaptation. (high confidence) {2.4, 2.6, Table 2.7, 3.4, 3.6, 4.7, 5.6, 5.13, Table 5.21, Table 5.23, Box 11.2, 13.2, Box 13.2, 17.2, 17.5, 
CCP5.4}

C.4.3 Maladaptation especially affects marginalised and vulnerable groups adversely (e.g., Indigenous Peoples, ethnic minorities, low-income 
households, informal settlements), reinforcing and entrenching existing inequities. Adaptation planning and implementation that do not 
consider adverse outcomes for different groups can lead to maladaptation, increasing exposure to risks, marginalising people from certain 
socioeconomic or livelihood groups, and exacerbating inequity. Inclusive planning initiatives informed by cultural values, Indigenous 
knowledge, local knowledge, and scientific knowledge can help prevent maladaptation. (high confidence) (Figure SPM.4) {2.6, 3.6, 4.3, 
4.6, 4.8, 5.12, 5.13, 5.14, 6.1, Box 7.1, 8.4, 11.4, 12.5, Box 13.2, 14.4, Box 14.1, 17.2, 17.5, 18.2, 17.2, CCP2.4}
C.4.4 To minimize maladaptation, multi-sectoral, multi-actor and inclusive planning with flexible pathways encourages low-regret47 and

timely actions that keep options open, ensure benefits in multiple sectors and systems and indicate the available solution space for 
adapting to long-term climate change (very high confidence). Maladaptation is also minimized by planning that accounts for the time it 
takes to adapt (high confidence), the uncertainty about the rate and magnitude of climate risk (medium confidence) and a wide range 
of potentially adverse consequences of adaptation actions (high confidence). {1.4, 3.6, 5.12, 5.13, 5.14, 11.6, 11.7, 17.3, 17.6, CCP2.3, 
CCP2.4, CCP5.4, CCB DEEP , CCB SLR}
Enabling Conditions
C.5 Enabling conditions are key for implementing, accelerating and sustaining adaptation in human systems and ecosystems. 
These include political commitment and follow-through, institutional frameworks, policies and instruments with clear 
goals and priorities, enhanced knowledge on impacts and solutions, mobilization of and access to adequate financial re-

sources, monitoring and evaluation, and inclusive governance processes. (high confidence) {1.4, 2.6, 3.6, 4.8, 6.4, 7.4, 8.5, 
9.4, 10.5, 11.4, 11.7, 12.5, 13.11, 14.7, 15.6, 17.4, 18.4, CCP2.4, CCP5.4, CCB FINANCE, CCB INDIG}
C.5.1 Political commitment and follow-through across all levels of government accelerate the implementation of adaptation actions 
(high confidence). Implementing actions can require large upfront investments of human, financial and technological resources 
(high confidence), whilst some benefits could only become visible in the next decade or beyond (medium confidence). Accelerating 
commitment and follow-through is promoted by rising public awareness, building business cases for adaptation, accountability and 
transparency mechanisms, monitoring and evaluation of adaptation progress, social movements, and climate-related litigation in some 
regions (medium confidence). {3.6, 4.8, 5.8, 6.4, 8.5, 9.4, 11.7, 12.5, 13.11, 17.4, 17.5, 18.4, CCP2.4, CCB COVID}

28SPM
Summary for PolicymakersC.5.2 Institutional frameworks, policies and instruments that set clear adaptation goals and define responsibilities and commitments and that 
are coordinated amongst actors and governance levels, strengthen and sustain adaptation actions (very high confidence). Sustained 
adaptation actions are strengthened by mainstreaming adaptation into institutional budget and policy planning cycles, statutory 
planning, monitoring and evaluation frameworks and into recovery efforts from disaster events (high confidence). Instruments that 
incorporate adaptation such as policy and legal frameworks, behavioural incentives, and economic instruments that address market 
failures, such as climate risk disclosure, inclusive and deliberative processes strengthen adaptation actions by public and private actors 
(medium confidence). {1.4, 3.6, 4.8, 5.14, 6.3, 6.4, 7.4, 9.4, 10.4, 11.7, Box 11.6, Table 11.17, 13.10, 13.11, 14.7, 15.6, 17.3, 17.4, 17.5,

17.6, 18.4, CCP2.4, CCP5.4, CCP6.3, CCB DEEP}
C.5.3 Enhancing knowledge on risks, impacts, and their consequences, and available adaptation options promotes societal and policy 
responses (high confidence). A wide range of top-down, bottom-up and co-produced processes and sources can deepen climate 
knowledge and sharing, including capacity building at all scales, educational and information programmes, using the arts, participatory 
modelling and climate services, Indigenous knowledge and local knowledge and citizen science (high confidence). These measures can 
facilitate awareness, heighten risk perception and influence behaviours (high confidence). {1.3, 3.6, 4.8, 5.9, 5.14, 6.4, Table 6.8, 7.4, 
9.4, 10.5, 11.1, 11.7, 12.5, 13.9, 13.11, 14.3, 15.6, 15.6, 17.4, 18.4, CCP2.4.1, CCB INDIG}
C.5.4 With adaptation finance needs estimated to be higher than those presented in AR5, enhanced mobilization of and access to financial

resources are essential for implementation of adaptation and to reduce adaptation gaps (high confidence). Building capacity and 
removing some barriers to accessing finance is fundamental to accelerate adaptation, especially for vulnerable groups, regions and 
sectors (high confidence). Public and private finance instruments include inter alia grants, guarantee, equity, concessional debt, 
market debt, and internal budget allocation as well as savings in households and insurance. Public finance is an important enabler 
of adaptation (high confidence). Public mechanisms and finance can leverage private sector finance for adaptation by addressing 
real and perceived regulatory, cost and market barriers, for example via public-private partnerships (high confidence). Financial and 
technological resources enable effective and ongoing implementation of adaptation, especially when supported by institutions with a

strong understanding of adaptation needs and capacity (high confidence). {4.8, 5.14, 6.4, Table 6.10, 7.4, 9.4, Table 11.17, 12.5, 13.11, 
15.6, 17.4, 18.4, Box 18.9, CCP5.4, CCB FINANCE}
C.5.5 Monitoring and evaluation (M&E) of adaptation are critical for tracking progress and enabling effective adaptation (high confidence). 
M&E implementation is currently limited (high confidence) but has increased since AR5 at local and national levels. Although most of 
the monitoring of adaptation is focused towards planning and implementation, the monitoring of outcomes is critical for tracking the 
effectiveness and progress of adaptation (high confidence). M&E facilitates learning on successful and effective adaptation measures, 
and signals when and where additional action may be needed. M&E systems are most effective when supported by capacities and 
resources and embedded in enabling governance systems (high confidence). {1.4, 2.6, 6.4, 7.4, 11.7, 11.8, 13.2, 13.11, 17.5, 18.4,

CCP2.4, CCB DEEP , CCB ILLNESS, CCB NATURAL, CCB PROGRESS}
C.5.6 Inclusive governance that prioritises equity and justice in adaptation planning and implementation leads to more effective and 
sustainable adaptation outcomes (high confidence). Vulnerabilities and climate risks are often reduced through carefully designed and 
implemented laws, policies, processes, and interventions that address context specific inequities such as based on gender, ethnicity, 
disability, age, location and income (high confidence). These approaches, which include multi-stakeholder co-learning platforms, 
transboundary collaborations, community-based adaptation and participatory scenario planning, focus on capacity-building, and 
meaningful participation of the most vulnerable and marginalised groups, and their access to key resources to adapt (high confidence). 
{1.4, 2.6, 3.6, 4.8, 5.4, 5.8, 5.9, 5.13, 6.4, 7.4, 8.5, 11.8, 12.5, 13.11, 14.7, 15.5, 15.7, 17.3, 17.5, 18.4, CCP2.4, CCP5.4, CCP6.4, CCB

GENDER, CCB HEALTH, CCB INDIG}
D: Climate Resilient Development
Climate resilient development integrates adaptation measures and their enabling conditions (Section C) with mitigation to advance sustainable 
development for all. Climate resilient development involves questions of equity and system transitions in land, ocean and ecosystems; urban 
and infrastructure; energy; industry; and society and includes adaptations for human, ecosystem and planetary health. Pursuing climate resilient 
development focuses on both where people and ecosystems are co-located as well as the protection and maintenance of ecosystem function at 
the planetary scale. Pathways for advancing climate resilient development are development trajectories that successfully integrate mitigation and 
adaptation actions to advance sustainable development. Climate resilient development pathways may be temporarily coincident with any RCP

and SSP scenario used throughout AR6, but do not follow any particular scenario in all places and over all time.

29SPM
Summary for PolicymakersConditions for Climate Resilient Development
D.1 Evidence of observed impacts, projected risks, levels and trends in vulnerability, and adaptation limits, demonstrate that 
worldwide climate resilient development action is more urgent than previously assessed in AR5. Comprehensive, effective, 
and innovative responses can harness synergies and reduce trade-offs between adaptation and mitigation to advance 
sustainable development. (very high confidence) {2.6, 3.4, 3.6, 4.2, 4.6, 7.2, 7.4, 8.3, 8.4, 9.3, 10.6, 13.3, 13.8, 13.10, 14.7, 
17.2, 18.3, Box 18.1, Figure 18.1, Table 18.5}
D.1.1 There is a rapidly narrowing window of opportunity to enable climate resilient development. Multiple climate resilient development 
pathways are still possible by which communities, the private sector, governments, nations and the world can pursue climate resilient

development – each involving and resulting from different societal choices influenced by different contexts and opportunities and 
constraints on system transitions.   Climate resilient development pathways are progressively constrained by every increment of 
warming, in particular beyond 1.5°C, social and economic inequalities, the balance between adaptation and mitigation varying by 
national, regional and local circumstances and geographies, according to capabilities including resources, vulnerability, culture and 
values, past development choices leading to past emissions and future warming scenarios, bounding the climate resilient development 
pathways remaining, and the ways in which development trajectories are shaped by equity, and social and climate justice. (very high 
confidence) {Figure TS.14d, 2.6, 4.7, 4.8, 5.14, 6.4, 7.4, 8.3, 9.4, 9.3, 9.4, 9.5, 10.6, 11.8, 12.5, 13.10, 14.7, 15.3, 18.5, CCP2.3, CCP3.4, 
CCP4.4, CCP5.3, CCP5.4, Table CCP5.2, CCP6.3, CCP7.5}

D.1.2 Opportunities for climate resilient development are not equitably distributed around the world (very high confidence). Climate impacts 
and risks exacerbate vulnerability and social and economic inequities and consequently increase persistent and acute development 
challenges, especially in developing regions and sub-regions, and in particularly exposed sites, including coasts, small islands, deserts, 
mountains and polar regions. This  in turn undermines  efforts to achieve sustainable development, particularly for vulnerable and 
marginalized communities (very high confidence). {2.5, 4.4, 4.7, 6.3, Box 6.4, Figure 6.5, 9.4, Table 18.5, CCP2.2, CCP3.2, CCP3.3, 
CCP5.4, CCP6.2, CCB HEALTH, CWGB URBAN}
D.1.3 Embedding effective and equitable adaptation and mitigation in development planning can reduce vulnerability, conserve and restore 
ecosystems, and enable climate resilient development. This is especially challenging in localities with persistent development gaps

and limited resources (high confidence).  Dynamic trade-offs and competing priorities exist between mitigation, adaptation, and 
development. Integrated and inclusive system-oriented solutions based on equity and social and climate justice reduce risks and enable 
climate resilient development (high confidence). {1.4, 2.6, Box 2.2, 3.6, 4.7, 4.8, Box 4.5, Box 4.8, 5.13, 7.4, 8.5, 9.4, Box 9.3, 10.6, 12.5, 
12.6, 13.3, 13.4, 13.10, 13.11, 14.7, 18.4, CCB DEEP , CCP2, CCP5.4, CCB HEALTH, SRCCL}
Enabling Climate Resilient Development
D.2 Climate resilient development  is enabled  when  governments, civil society and the private sector  make inclusive de-
velopment choices that prioritise risk reduction, equity and justice, and when decision-making processes, finance and 
actions are integrated across governance levels, sectors and timeframes (very high confidence). Climate resilient devel-

opment is facilitated by international cooperation and by governments at all levels working with communities, civil 
society, educational bodies, scientific and other institutions, media, investors and businesses; and by developing partner -
ships with traditionally marginalised groups, including women, youth, Indigenous Peoples, local communities and ethnic 
minorities (high confidence). These partnerships  are most effective when supported by enabling political leadership, 
institutions, resources, including finance, as well as climate services, information and decision support tools (high confi -
dence). (Figure SPM.5) {1.3, 1.4, 1.5, 2.7, 3.6, 4.8, 5.14, 6.4, 7.4, 8.5, 8.6, 9.4, 10.6, 11.8, 12.5, 13.11, 14.7, 15.6, 15.7, 17.4, 
17.6, 18.4 , 18.5, CCP2.4, CCP3.4, CCP4.4, CCP5.4, CCP6.4, CCP7.6, CCB DEEP, CCB GENDER, CCB HEALTH, CCB INDIG, CCB 
NATURAL, CCB SLR}

D.2.1 Climate resilient development is advanced when actors work in equitable, just and enabling ways to reconcile divergent interests, values 
and worldviews, toward equitable and just outcomes (high confidence). These practices build on diverse knowledges about climate 
risk and chosen development pathways account for local, regional and global climate impacts, risks, barriers and opportunities (high 
confidence).  Structural vulnerabilities to climate change can be reduced through carefully designed and implemented legal, policy, and 
process interventions from the local to global that address inequities based on gender, ethnicity, disability, age, location and income 
(very  high confidence). This includes rights-based approaches that focus on capacity-building, meaningful participation of the most 
vulnerable groups, and their access to key resources, including financing, to reduce risk and adapt (high confidence). Evidence shows that

climate resilient development processes link scientific, Indigenous, local, practitioner and other forms of knowledge, and are more effective 
and sustainable because they are locally appropriate and lead to more legitimate, relevant and effective actions (high confidence ).

30SPM
(a) Societal choices about adaptation, 
20222100 &
beyond
IPCC
situation
Development Goals2030(b) Illustrative development pathways
Illustrative climatic or non-climatic shock, e.g. COVID-19, drought or ﬂoods, that disrupts the development pathway
(emissions,
climate change,
CLIMATE RESILIENT DEVELOPMENT          HIGHER LOWER(c) Actions and outcomes
characterizing development pathwaysOpportunities missed for higher climate resilient developmentWell-being
Low global
Low risk                   

High risk                 Sustainable development action        System transitions      Transformation          
        Adaptation        Mitigation    Unsustainable development action          Entrenched systems                              
Maladaptation           Rising emissions                                    
opportunity for higher CRDDimensions that enable actions towards 
Dimensions that result in actions towards 
Community
Political
          Equity and justice          InclusionArenas of engagementArenas of engagement        Ecosystem stewardship        Knowledge diversity

Exclusion       Inequity and injustice         Ecosystem degradation       Singular knowledge      Increasing warming; path dependence and adaptation limits undermine sustainable development

31SPM
Pathways towards climate resilient development overcome  jurisdictional and organizational barriers, and  are founded on  societal 
choices that accelerate and deepen key system transitions  (very high confidence). Planning processes and decision analysis tools 
can help identify ‘low regrets’ options47 that enable mitigation and adaptation in the face of change, complexity, deep uncertainty 
and divergent views (medium confidence). {1.3, 1.4, 1.5, 2.7, 3.6, 4.8, 5.14, 6.4, 7.4, 8.5, 8.6, Box 8.7, 9.4, Box 9.2, 10.6, 11.8, 12.5, 13.11, 14.7, 15.6, 15.7, 17.2–17.6, 18.2–18.4, CCP2.3–2.4, CCP3.4, CCP4.4, CCP5.4, CCP6.4, CCP7.6, CCB DEEP , CCB HEALTH, CCB INDIG, CCB NATURAL, CCB SLR}
D.2.2
 Inclusive governance contributes to more effective and 
enduring adaptation outcomes and enables climate resilient development (high

confidence). Inclusive processes strengthen the ability of governments and other stakeholders to jointly consider factors such as the rate and magnitude of change and uncertainties, associated impacts, and timescales of different climate resilient development pathways given past development choices leading to past emissions and scenarios of future global warming (high confidence).  Associated 
societal choices are made continuously through interactions in arenas of engagement from local to international levels. The quality and outcome of these interactions helps determine whether development pathways shift towards or away from climate resilient development (medium confidence). (Figure SPM.5) {2.7, 3.6, 4.8, 5.14, 6.4, 7.4, 8.5, 8.6, 9.4, 10.6, 11.8, 12.5, 13.11, 14.7, 15.6, 15.7, 17.2–17.6, 18.2, 18.4, CCP2.3–2.4, CCP3.4, CCP4.4, CCP5.4, CCP6.4, CCP7.6, CCB GENDER, CCB HEALTH, CCB INDIG}
D.2.3

Governance for climate resilient development is most effective when supported by formal and informal institutions and practices thatare well-aligned across scales
, sectors, policy domains and timeframes. Governance efforts that advance climate resilient development 
account for the dynamic, uncertain and context-specific nature of climate-related risk, and its interconnections with non-climate risks. Institutions
48 that enable climate resilient development are flexible and responsive to emergent risks and facilitate sustained 
and timely action. Governance for climate resilient development is enabled by adequate and appropriate human and technological resources, information, capacities and finance. (high confidence) {2.7, 3.6, 4.8, 5.14, 6.3, 6.4, 7.4, 8.5, 8.6, 9.4, 10.6, 11.8, 12.5, 13.11, 14.7, 15.6, 15.7, 17.2-17.6, 18.2, 18.4, CCP2.3–2.4, CCP3.4, CCP4.4, CCP5.4, CCP6.4, CCP7.6, CCB DEEP , CCB GENDER, CCB HEALTH, CCB INDIG, CCB NATURAL, CCB SLR}

Climate Resilient Development for Natural and Human Systems
48 Institutions:  Rules, norms and conventions that guide, constrain or enable human behaviours and practices. Institutions can be formally established, for instance through laws and regulations, or 
informally established, for instance by traditions or customs. Institutions may spur, hinder, strengthen, weaken or distort the emergence, adoption and implementation of climate action and climate 
governance.D.3 Inter actions between changing urban form, exposure and vulnerability can create climate change-induced risks and losses 
for cities and settlements. However, the global trend of urbanisation also offers a critical opportunity in the near-term,

to advance climate resilient development (high confidence). Integrated, inclusive planning and investment in everyday decision-making about urban infrastructure, including social, ecological and grey/physical infrastructures, can significantly increase the adaptive capacity of urban and rural settlements. Equitable outcomes contributes to multiple benefits for health and well-being and ecosystem services, including for Indigenous Peoples, marginalised and vulnerable communi-ties (high confidence). Climate resilient development in urban areas also supports adaptive capacity in more rural places through maintaining peri-urban supply chains of goods and services and financial flows (medium confidence). Coastal cities and settlements play an especially important role in advancing climate resilient development (high confidence). {6.2, 6.3, Table 6.6, 7.4, 8.6, Box 9.8, 18.3, CCP2.1. CCP2.2, CCP6.2, CWGB URBAN}Figure SPM.5 |  Climate resilient development (CRD) is the process of

implementing greenhouse gas mitigation and adaptation measures to support sustainable

development. This figure builds on Figure SPM.9 in AR5 WGII (depicting climate resilient pathways) by describing how CRD pathways are the result of cumulative societal choices and actions within multiple arenas. 
Panel (a)
 Societal choices tow
ards higher CRD (green cog) or lower CRD (red cog) result from interacting decisions and actions by diverse government, private sector 
and civil society actors, in the context of climate risks, adaptation limits and development gaps. These actors engage with adaptation, mitigation and development actions in 
political, economic and financial, ecological, socio-cultural, knowledge and technology, and community arenas from local to international levels. Opportunities for climate resilient development are not equitably distributed around the world. 
Panel (b)
 Cumulatively,
 societal choices, which are made continuously, shift global development pathways towards higher (green) or lower (red) climate resilient development.

Past conditions (past emissions, climate change and development) have already eliminated some development pathways towards higher CRD (dashed green line). Panel (c)
 Higher CRD is char
acterised by outcomes that advance sustainable development for all. Climate resilient development is progressively harder to achieve with global 
warming levels beyond 1.5°C. Inadequate progress towards the Sustainable Development Goals (SDGs) by 2030 reduces climate resilient development prospects. There is a

narrowing window of opportunity to shift pathways towards more climate resilient development futures as reflected by the adaptation limits and increasing climate risks, considering the remaining carbon budgets. (Figure SPM.2, Figure SPM.3) {Figure TS.14b, 2.6, 3.6, 7.2, 7.3, 7.4, 8.3, 8.4, 8.5, 16.4, 16.5, 17.3, 17.4, 17.5, 18.1, 18.2, 18.3, 18.4, Box 18.1, Figure 18.1, Figure 18.2, Figure 18.3, CCB COVID, CCB GENDER, CCB HEALTH, CCB INDIG, CCB SLR, WGI AR6 Table SPM.1, WGI AR6 Table SPM.2, SR1.5 Figure SPM.1}

32SPM
Summary for PolicymakersD.3.1 Taking integrated action for climate resilience to avoid climate risk requires urgent decision making for the new built environment 
and retrofitting existing urban design, infrastructure and land use. Based on socioeconomic circumstances, adaptation and 
sustainable development actions will provide multiple benefits including for health and well-being, particularly when supported by 
national governments, non-governmental organisations and international agencies  that work across sectors in partnerships with 
local communities.  Equitable partnerships between local and municipal governments, the private sector, Indigenous Peoples, local 
communities, and civil society can, including through international cooperation, advance climate resilient development by addressing 
structural inequalities, insufficient financial resources, cross-city risks and the integration of Indigenous knowledge and local

knowledge. (high confidence) {6.2, 6.3, 6.4, Table 6.6, 7.4, 8.5, 9.4, 10.5. 12.5, 17.4, Table 17.8, 18.2, Box 18.1, CCP2.4, CCB FINANCE, 
CCB GENDER, CCB INDIG, CWGB URBAN}
D.3.2 Rapid global urbanisation offers opportunities for climate resilient development in diverse contexts from rural and informal settlements 
to large metropolitan areas (high confidence).  Dominant models of energy intensive and market-led urbanisation, insufficient and 
misaligned finance and a predominant focus on grey infrastructure in the absence of integration with ecological and social approaches, 
risks missing opportunities for adaptation and locking in maladaptation (high confidence). Poor land use planning and siloed approaches 
to health, ecological and social planning also exacerbates, vulnerability in already marginalised communities (medium confidence). 
Urban climate resilient development is observed to be more effective if it is responsive to regional and local land use development

and adaptation gaps, and addresses the underlying drivers of vulnerability (high confidence). The greatest gains in well-being can be 
achieved by prioritizing finance to reduce climate risk for low-income and marginalized residents including people living in informal 
settlements (high confidence). {5.14, 6.1, 6.2, 6.3, 6.4, 6.5, Figure 6.5, Table 6.6, 7.4, 8.5, 8.6, 9.8, 9.9, 10.4, Table 17.8, 18.2, CCP2.2, 
CCP5.4, CCB HEALTH, CWGB URBAN}
D.3.3 Urban systems are critical, interconnected sites for enabling climate resilient development, especially at the coast.  Coastal cities and 
settlements play a key role in moving toward higher climate resilient development given firstly, almost 11% of the global population – 
896 million people – lived within the Low Elevation Coastal Zone49 in 2020, potentially increasing to beyond 1 billion people by 2050, 
and these people, and associated development and coastal ecosystems, face escalating climate compounded risks, including sea level

rise. Secondly, these coastal cities and settlements make key contributions to climate resilient development through their vital role in 
national economies and inland communities, global trade supply chains, cultural exchange, and centres of innovation. (high confidence) 
{6.1, 6.2, 6.4, Table 6.6, Box 15.2, SMCCP Table 2.1, CCP2.2, CCP2.4, CCB SLR, XWGB URBAN, SROCC Chapter 4}
49 LECZ, coastal areas below 10 m of elevation above sea level that are hydrologically connected to the sea.
50 Ecosystem integrity refers to the ability of ecosystems to maintain key ecological processes, recover from disturbance, and adapt to new conditions.D.4 Safeguarding biodiversity and ecosystems is fundamental to climate resilient development, in light of the threats climate 
change poses to them and their roles in adaptation and mitigation (very high confidence). Recent analyses, drawing on a

range of lines of evidence, suggest that maintaining the resilience of biodiversity and ecosystem services at a global scale 
depends on effective and equitable conservation of approximately 30% to 50% of Earth’s land, freshwater and ocean 
areas, including currently near-natural ecosystems (high confidence). {2.4, 2.5, 2.6, 3.4, 3.5, 3.6, Box 3.4, 12.5, 13.3, 13.4, 
13.5, 13.10, CCB INDIG, CCB NATURAL}
D.4.1  Building the resilience of biodiversity and supporting ecosystem integrity50 can maintain benefits for people, including livelihoods, 
human health and well-being and the provision of food, fibre and water, as well as contributing to disaster risk reduction and climate 
change adaptation and mitigation. {2.2, 2.5, 2.6, Table 2.6, Table 2.7, 3.5, 3.6, 5.8, 5.13, 5.14, Box 5.11, 12.5, CCP5.4, CCB COVID, CCB 
GENDER, CCB ILLNESS, CCB INDIG, CCB MIGRATE, CCB NATURAL}

D.4.2  Protecting and restoring ecosystems  is  essential for  maintaining and enhancing the resilience of the biosphere (very high 
confidence).  Degradation and loss of ecosystems is also a cause of greenhouse gas emissions and is at increasing risk of being 
exacerbated by climate change impacts, including droughts and wildfire  (high confidence).  Climate resilient development 
avoids adaptation and mitigation measures that damage ecosystems (high confidence).  Documented examples of adverse impacts of 
land-based measures intended as mitigation, when poorly implemented, include afforestation of grasslands, savannas and peatlands, 
and risks from bioenergy crops at large scale to water supply, food security and biodiversity (high confidence). {2.4, 2.5, Box 2.2, 3.4, 
3.5, Box 3.4, Box 9.3, CCP7.3, CCB NATURAL, CWGB BIOECONOMY}

33SPM
Summary for PolicymakersD.4.3  Biodiversity and ecosystem services  have limited capacity to adapt to increasing global warming levels, which will make climate resil-
ient development progressively harder to achieve beyond 1.5°C warming (very high confidence). Consequences of current and future 
global warming for climate resilient development include reduced effectiveness of Ecosystem-based Adaptation and approaches to 
climate change mitigation based on ecosystems and amplifying feedbacks to the climate system (high confidence). {Figure TS.14d, 2.4, 2.5, 2.6, 3.4, Box 3.4, 3.5, 3.6, Table 5.2, 12.5, 13.2, 13.3, 13.10, 14.5, 14.5, Box 14.3, 15.3, 17.3, 17.6, CCP5.3, CCP5.4, CCB EXTREMES, CCB ILLNESS, CCB NATURAL, CCB SLR, SR1.5, SRCCL, SROCC}
Achieving Climate Resilient Development
D.5 It is unequivocal that climate change has already disrupted human and natural systems. Past and current development

trends (past emissions, development and climate change) have not advanced global climate resilient development (very high confidence). Societal choices and actions implemented in the next decade determine the extent to which medium- and long-term pathways will deliver higher or lower climate resilient development (high confidence). Importantly climate resilient development prospects are increasingly limited if current greenhouse gas emissions do not rapidly decline, es-pecially if 1.5°C global warming is exceeded in the near-term (high confidence). These prospects are constrained by past development, emissions and climate change, and enabled by inclusive governance, adequate and appropriate human and technological resources, information, capacities and finance (high confidence). {Figure TS.14d, 1.2, 1.4, 1.5, 2.6, 2.7, 3.6, 4.7, 4.8, 5.14, 6.4, 7.4, 8.3, 8.5, 8.6, 9.3, 9.4, 9.5, 10.6, 11.8, 12.5, 13.10, 13.11, 14.7, 15.3, 15.6, 15.7, 16.2, 16.4, 16.5, 16.6, 17.2–17.6, 18.2–18.5,

CCP2.3–2.4, CCP3.4, CCP4.4, CCP5.3, CCP5.4, Table CCP5.2, CCP6.3, CCP6.4, CCP7.5, CCP7.6, CCB DEEP, CCB HEALTH, CCB INDIG, CCB NATURAL, CCB SLR}

D.5.1
 Climate resilient development is already challenging at current global w
arming levels (high confidence). The prospects for climate
resilient development will be further limited if global warming levels exceeds 1.5°C (high confidence) and not be possible in some regions and sub-regions if the global warming level exceeds 2°C (medium confidence).  Climate resilient development is most

constrained in regions/subregions in which climate impacts and risks are already advanced, including low-lying coastal cities and settlements, small islands, deserts, mountains and polar regions (high confidence). Regions and subregions with high levels of poverty, water, food and energy insecurity, vulnerable urban environments, degraded ecosystems and rural environments, and/or few enabling conditions, face many non-climate challenges that inhibit climate resilient development which are further exacerbated by climate change (high confidence). {Figure TS.14d, 1.2, Box 6.6, 9.3, 9.4, 9.5, 10.6, 11.8, 12.5, 13.10, 14.7, 15.3, CCP2.3, CCP3.4, CCP4.4, CCP5.3, Table CCP5.2, CCP6.3, CCP7.5}
D.5.2
 Inclusive governance
, investment aligned with climate resilient development, access to appropriate technology and rapidly

scaled-up finance, and capacity building of governments at all levels, the private sector and civil society enable climate resilient development. Experience shows that climate resilient development processes are timely, anticipatory, integrative, flexible and action focused. Common goals and social learning build adaptive capacity for climate resilient development. When implementing adaptation and mitigation together, and taking trade-offs into account, multiple benefits and synergies for human well-being as well as ecosystem and planetary health can be realised. Prospects for climate resilient development are increased by inclusive processes involving local knowledge and Indigenous Knowledge as well as processes that coordinate across risks and institutions. Climate resilient development is enabled by increased international cooperation including mobilising and enhancing access to finance, particularly for vulnerable regions, sectors and groups. (high confidence) (Figure SPM.5) {2.7,

3.6, 4.8, 5.14, 6.4, 7.4, 8.5, 8.6, 9.4, 10.6, 11.8, 12.5, 13.11, 14.7, 15.6, 15.7, 17.2–17.6, 18.2–18.5, CCP2.3–2.4, CCP3.4, CCP4.4, CCP5.4, CCP6.4, CCP7.6, CCB DEEP , CCB HEALTH, CCB INDIG, CCB NATURAL, CCB SLR}

D.5.3
 T
he cumulative scientific evidence is unequivocal: Climate change is a threat to human well-being and planetary health. Any further
delay in concerted anticipatory global action on adaptation and mitigation will miss a brief and rapidly closing window of opportunity to secure a liveable and sustainable future for all. (very high confidence) {1.2, 1.4, 1.5, 16.2, Table SM16.24, 16.4, 16.5, 16.6, 17.4, 17.5, 17.6, 18.3, 18.4, 18.5, CCB DEEP , CWGB URBAN, WGI AR6 SPM, SROCC SPM, SRCCL SPM}

40
TSTS.A Introduction
TS.A.1 Background
This technical summary complements and expands the key findings of 
the Working Group (WG) II contribution to the Sixth Assessment Report 
(AR6) presented in the Summary for Policymakers and covers literature 
accepted for publication by 1 September 2021. It provides technical 
understanding and is developed from the key findings of chapters and 
cross-chapter papers (CCPs) as presented in their executive summaries 
and integrates across them. The report builds on the WGII contribution 
to the Fifth Assessment Report (AR5) of the IPCC and three special 
reports of the AR6 cycle providing new knowledge and updates. The 
three special reports are the Special Report on Global Warming of 
1.5°C (2018), an IPCC special report on the impacts of global warming 
of 1.5°C above pre-industrial levels and related global greenhouse 
gas emission pathways in the context of strengthening the global

response to the threat of climate change, sustainable development 
and efforts to eradicate poverty; the Special Report on Climate Change 
and Land, which is concerned with climate change, desertification, 
greenhouse gas fluxes in terrestrial ecosystems (2019); and the Special 
Report on the Ocean and Cryosphere in a Changing Climate (2019). 
The WGII assessment integrates with the WGI (the physical science 
basis) and WGIII (mitigation of climate change) contributions and 
contributes to the Synthesis Report.
The contribution of Working Group II (WGII) to the Sixth Assessment 
Report (AR6) of the IPCC summarizes the current understanding of 
observed climate change impacts on ecosystems, human societies 
and their cities, settlements, infrastructures and industrial systems, as 
well as vulnerabilities and future risks tied to different socioeconomic 
development pathways. The report is set against a current backdrop

of rapid urbanisation, biodiversity loss, a growing and dynamic global 
human population, significant inequality and demands for social justice, 
rapid technological change, continuing poverty, land degradation 
and food insecurity, and risks from shocks such as pandemics and 
increasingly intense extreme events from ongoing climate change. 
The report also assesses existing adaptations and their feasibility and 
limits. Any success of adaptation is dependent on the achieved level of 
mitigation and the transformation of global and regional sustainability 
outlined in the Sustainable Development Goals (SDGs). Accordingly, 
adaptation is essential for climate resilient development. Compared to 
earlier IPCC assessments, this report integrates more strongly across 
the natural, social and economic sciences, highlighting the role of social 
justice and diverse forms of knowledge, such as Indigenous knowledge 
and local knowledge, and reflects the increasing importance of urgent

and immediate action to address climate risk. {1.1.1}
Since AR5, climate action has increased at all levels of governance, 
including among non-governmental organisations, small and large 
jointly provide overarching goals for climate action. The 2030 Agenda 
for Sustainable Development, adopted in 2015 by UN member states, 
sets out 17 SDGs, frames policies for achieving a more sustainable future and aligns efforts globally to prioritise ending extreme poverty, 
protect the planet and promote more peaceful, prosperous and 
inclusive societies. Since AR5, several new international conventions 
have identified climate change adaptation and risk reduction as 
important global priorities for sustainable development, including the 
Sendai Framework for Disaster Risk Reduction (SFDRR), the finance-

oriented Addis Ababa Action Agenda, and the New Urban Agenda. The 
Convention on Biological Diversity and its Aichi targets recognise that 
biodiversity is affected by climate change, with negative consequences 
for human well-being, but biodiversity, through ecosystem services, 
contributes to both climate change mitigation and adaptation. {1.1.2}
TS.A.2 TS Structure of the Report
This technical summary is structured in five sections: Section A 
‘Introduction’, Section B ‘Observed Impacts and Adaptation’, Section 
C ‘Projected Impacts and Risks’, Section D ‘Contribution of Adaptation 
to Solutions’ and Section E ‘Climate Resilient Development’. Each 
section includes several headline statements followed by several 
bullet points providing details about the underlying assessments. All 
findings and figures are supported by and traceable to the underlying 
report, indicated by references {in curly brackets} to relevant sections 
of chapters and cross-chapter papers.

Confidence in the key findings of this assessment is communicated 
using the IPCC calibrated uncertainty language. This calibrated 
language is designed to consistently evaluate and communicate 
uncertainties that arise from incomplete knowledge due to a lack 
of information or from disagreement about what is known or even 
knowable. The IPCC calibrated language uses qualitative expressions 
of confidence based on the robustness of evidence for a finding 
and (where possible) uses quantitative expressions to describe the 
likelihood of a finding. Each finding is grounded in an evaluation of 
underlying evidence and agreement. A level of confidence is expressed 
using five qualifiers, very low, low, medium, high and very high, and 
typeset in italics, for example, medium confidence. The following terms 
have been used to indicate the assessed likelihood of an outcome or 
a result: virtually certain 99–100% probability, very likely 90–100%,

likely 66–100%, as likely as not 33–66%, unlikely 0–33%, very unlikely 
0–10%, exceptionally unlikely 0–1%. Assessed likelihood is typeset in 
italics, for example, very likely. This is consistent with AR5 and the 
other AR6 reports. (Figure TS.1) {1.3.4}
TS.A.3 Key Developments Since AR5
since 1988, has advanced in important ways since AR5. Building on 
a substantially expanded scientific and technical literature, this AR6 
report emphasises at least three broad themes. (Figure TS.2) {1.1.4}
First, this AR6 assessment has an increased focus on risk and solution 
best estimates or most likely outcomes and include high-consequence 
outcomes for which probabilities are low or in some cases unknown.

41
In this report, the risk framing for the first time spans all three 
working groups, includes risks from the responses to climate change, 
considers dynamic and cascading consequences, describes with more 
risks over a range of scenarios. The focus on solutions encompasses the 
interconnections among climate responses, sustainable development 
and transformation—and the implications for governance across scales 
within the public and private sectors. The assessment therefore includes 
climate-related decision-making and risk management, climate resilient 
development pathways, implementation and evaluation of adaptation, 
and also limits to adaptation and loss and damage. Specific focal 
areas reflect contexts increasingly important for the implementation of 
responses, such as cities. {1.3.1, 1.4.4, 16, 17, 18}
Second, emphases on social justice, equity and different forms of

expertise have emerged. As climate change impacts and implemented 
responses increasingly occur, there is heightened awareness of the 
ways that climate responses interact with issues of justice and social progress. In this report, expanded attention is given to inequity in 
climate vulnerability and responses, the role of power and participation 
in processes of implementation, unequal and differential impacts 
and climate justice. The historic focus on scientific literature has also 
been increasingly accompanied by attention to and incorporation of 
Indigenous knowledge, local knowledge, and associated scholars. 
{1.3.2, 1.4.1, 17.5.2}
meeting societal goals. {1.5}
The following overarching conclusions have been derived from the 
whole of the assessment of WGII:
i) The magnitude of observed impacts and projected climate risks 
indicate the scale of decision-making, funding and investment

needed over the next decade if climate resilient development is to 
be achieved.
4. Evaluate conﬁdence based on 
Very low conﬁdenceVery high conﬁdenceHigh agreement
5. Sufﬁcient conﬁdence and quantitative 
or probabilistic evidence? 
Present evidence and agreemen tP resent conﬁdence Present likelihood
It is very lik ely that the number of cold 
days and nights has decreased and the 
number of warm days and nights has 
increased on the global scale. Medium conﬁdence
In many regions, changing preciptiation or 
melting snow and ice are altering hydrological 
systems, affecting w ater resources in terms of 
quantity and quality ( medium conﬁdence).  Behaviour , lifestyle, and culture have a consider-
able inﬂuence on energy use and associated 
emissions, with high mitigation potential in some 
sectors, in particular when complementing

technological and structural change ( medium 
evidence, medium agreement).Virtually certain  that there has been a 
change (99-100% probability)
High conﬁdence
Low conﬁdenceOutcome
probabilit y
Very lik ely
Likely
About as lik ely as not
Unlik ely
Very unlik ely
Exceptionally unlik ely99-100% 
90-100% 
66-100%
33-66% 
0-33% 
0-10%
0-1% Extremely unlik ely 0-5% More lik ely than not >50-100% Extremely lik ely 95-100% Evaluation and communication of degree of certainty in AR5 and AR6 ﬁndings
and scientiﬁc agreementStatistics
Models
Observ ations
3. Sufﬁcient evidence and  
conﬁdence? 
no

Figure TS.1 |  The IPCC AR5 and AR6 framework for applying expert judgement in the evaluation and characterisation of assessment findings. This illustration 
depicts the process assessment authors apply in evaluating and communicating the current state of knowledge. {Figure 1.6}

42
TS
causes
Societal |Energy
s
ral
Land |Freshwater
n
s
includ
Lim
Risks
Finance
impact

Figure TS.2 |  This report has a strong focus on the interactions among the coupled systems climate, ecosystems (including their biodiversity) and human society. These interactions are the basis of emerging risks from 
climate change, ecosystem degradation and biodiversity loss and, at the same time, offer opportunities for the future.

maladapt and mitigate climate change, ecosystems can adapt and mitigate within limits. Ecosystems and their biodiversity provision livelihoods and ecosystem services. Human society impacts ecosystems and can restore and conserve them.
Transformation entails system transitions strengthening the resilience of ecosystems and society (Section E). In a) arrow colours represent principle human society interactions (blue), ecosystem (including biodiversity) interactions (green) and

the impacts of climate change and human activities, including losses and damages, under continued climate change (red). In b) arrow colours represent human system interactions (blue), ecosystem (including biodiversity) interactions (green) 
and reduced impacts from climate change and human activities (grey). {1.2, Figure 1.2}

43
Technical SummaryTSii) Since AR5, climate risks are appearing faster and will get more 
severe sooner (high confidence). Impacts cascade through natural 
and human systems, often compounding with the impacts from 
other human activities. Feasible, integrated mitigation and 
adaptation solutions can be tailored to specific locations and 
monitored for their effectiveness while avoiding conflict with 
sustainable development objectives and managing risks and trade-
offs (high confidence).iii) Available evidence on projected climate risks indicates that 
opportunities for adaptation to many climate risks will likely 
become constrained and have reduced effectiveness should 
1.5°C global warming be exceeded and that, for many locations 
on Earth, capacity for adaptation is already significantly limited. 
The maintenance and recovery of natural and human systems will 
require the achievement of mitigation targets.
Box TS.1 | Core Concepts of the Report

This box provides an overview of key definitions and concepts relevant to the WGII AR6 assessment, with a focus on those updated or 
new since AR5.
Risk in this report is defined as the potential for adverse consequences for human or ecological systems, recognising the diversity of 
values and objectives associated with such systems. In the context of climate change impacts, risks result from dynamic interactions 
between climate-related hazards with the exposure and vulnerability of the affected human or ecological system. In the context of 
climate change responses, risks result from the potential for such responses not to achieve the intended objective(s) or from potential 
trade-offs or negative side-effects. Risk management is defined as plans, actions, strategies or policies to reduce the likelihood and/or 
magnitude of adverse potential consequences, based on assessed or perceived risks. {1.2.1, Annex II: Glossary}

Vulnerability is a component of risk, but also, independently, an important focus. Vulnerability in this report is defined as the propensity 
or predisposition to be adversely affected and encompasses a variety of concepts and elements, including sensitivity or susceptibility 
to harm and lack of capacity to cope and adapt (Annex II: Glossary). Over the past several decades, approaches to analysing and 
assessing vulnerability have evolved. An early emphasis on top-down, biophysical evaluation of vulnerability included—and often started 
with—exposure to climate hazards in assessing vulnerability. From this starting point, attention to bottom-up, social and contextual 
determinants of vulnerability, which often differ, has emerged, although this approach is incompletely applied or integrated across 
contexts. Vulnerability is now widely understood to differ within communities and across societies, also changing through time. In WGII

AR6, assessment of the vulnerability of people and ecosystems encompasses the differing approaches that exist within the literature, 
both critiquing and harmonising them based on available evidence. In this context, exposure is defined as the presence of people; 
livelihoods; species or ecosystems; environmental functions, services and resources; infrastructure; or economic, social or cultural assets 
in places and settings that could be adversely affected. Potentially affected places and settings can be defined geographically, as well as 
more dynamically, for example through transmission or interconnections through markets or flows of people. {1.2.1, Annex II: Glossary}
Adaptation in this report is defined, in human systems, as the process of adjustment to actual or expected climate and its effects in 
order to moderate harm or exploit beneficial opportunities. In natural systems, adaptation is the process of adjustment to actual climate

and its effects; human intervention may facilitate this (see Annex II: Glossary). Adaptation planning in human systems generally entails 
a process of iterative risk management. Different types of adaptation have been distinguished, including anticipatory versus reactive, 
autonomous versus planned and incremental versus transformational adaptation. Adaptation is often seen as having five general stages: 
(a) awareness, (b) assessment, (c) planning, (d) implementation and (e) monitoring and evaluation. Government, non-government, and 
private-sector actors have adopted a wide variety of specific approaches to adaptation that, to varying degrees, conform to these five 
general stages. Adaptation in natural systems includes autonomous adjustments through ecological and evolutionary processes. It also 
involves the use of nature through ecosystem-based adaptation. The role of species, biodiversity and ecosystems in such adaptation

options can range from the rehabilitation or restoration of ecosystems (e.g., wetlands or mangroves) to hybrid combinations of so-
called green and grey infrastructure (e.g., horizontal levees). The WGII AR6 emphasises the assessment of observed adaptation-related 
responses to climate change, governance and decision-making in adaptation and the role of adaptation in reducing key risks and global-
scale reasons for concern, as well as limits to such adaptation. {1.2.1, 17.4}
Resilience in this report is defined as the capacity of social, economic and environmental systems to cope with a hazardous event or trend 
or disturbance, responding or reorganising in ways that maintain their essential function, identity and structure while also maintaining 
the capacity for adaptation, learning and transformation. Resilience is an entry point commonly used, although under a wide spectrum

of meanings. Resilience as a system trait overlaps with concepts of vulnerability, adaptive capacity and, thus, risk, and resilience as a 
strategy overlaps with risk management, adaptation and transformation. Implemented adaptation is often organised around resilience 
as bouncing back and returning to a previous state after a disturbance. {1.2.1, Annex II: Glossary}

44
TSBox TS.2 | AR6 Climate Dimensions, Global Warming Levels and Reference Periods
including that based on climate model simulations that are part of the fifth and sixth Coupled Model Intercomparison Project phase 
(CMIP5, CMIP6) of the World Climate Research Programme. Future projections are driven by emissions and/or concentrations from 
illustrative Representative Concentration Pathways (RCPs)1 and Shared Socio-economic Pathways (SSPs)2 scenarios, respectively3. Climate 
impacts literature is based primarily on climate projections assessed in AR5 or earlier, or assumed global warming levels, though some 
recent impacts literature uses newer projections based on the CMIP6 exercise. Given differences in the impacts literature regarding

socioeconomic details and assumptions, WGII chapters contextualize impacts with respect to exposure, vulnerability and adaptation as 
are analysed against the backdrop of various illustrative SSPs2. The WGII assessment combines multiple lines of evidence including 

SPM.C, WGI AR6 Box SPM.1, WGI AR6 1.6, WGI AR6 12, WGI AR5}

WGI assessed increase in global surface temperature is 1.09 [0.95 to 1.20]4 °C in 2011–2020 above 1850–1900. The estimated increase 
in global surface temperature since AR5 is principally due to further warming since 2003–2012 (+0.19 [0.16 to 0.22]°C).5 Considering 
all five illustrative scenarios assessed by WGI, there is at least a greater than 50% likelihood that global warming will reach or exceed 
1.5°C in the near-term, even for the very low greenhouse gas emissions scenario6. {WGI AR6 SPM A1.2, WGI AR6 SPM B1.3, WGI AR6 
Table SPM.1, WGI AR6 CCB2.3}
1 RCP-based scenarios are referred to as RCPy, where ‘y’ refers to the level of radiative forcing (in watts per square meter, or W m-2) resulting from the scenario in the year 2100.
2 SSP-based scenarios are referred to as SSPx-y, where ‘SSPx’ refers to the Shared Socio-economic Pathway describing the socio-economic trends underlying the scenarios, and ‘y’ refers to the level of

3 IPCC is neutral with regard to the assumptions underlying the SSPs, which do not cover all possible scenarios. Alternative scenarios may be considered or developed.
4 In the WGI report, square brackets [x to y] are used to provide the assessed very likely range, or 90% interval.
5 Since AR5, methodological advances and new datasets have provided a more complete spatial representation of changes in surface temperature, including in the Arctic. These and other improvements 
6 Global warming of 1.5°C relative to 1850–1900 would be exceeded during the 21st century under the intermediate, high and very high greenhouse gas emissions scenarios considered in this report

under the low greenhouse gas emissions scenario (SSP1-2.6) and more likely than not to be reached under the very low greenhouse gas emissions scenario (SSP1-1.9). Furthermore, for the very low 
overshoot of no more than 0.1°C above 1.5°C global warming.TS.B Observed Impacts
This section reports on how worldwide climate change is increasingly

affecting marine, freshwater and terrestrial ecosystems and ecosystem 
services, water and food security, settlements and infrastructure, 
health and well-being, and economies and culture, especially through 
compound stresses and events. It refers to the increasing confidence 
since AR5 that detected impacts are attributable to climate change, 
including the impacts of extreme events. It illustrates how compound hazards have become more frequent in all world regions, with 
widespread consequences. Regional increases in temperature, aridity 
and drought have increased the frequency and intensity of fire. The 
interaction between fire, land use change, particularly deforestation, 
and climate change, is directly impacting human health, ecosystem 
functioning, forest structure, food security and the livelihoods of 
resource-dependent communities.

45
Technical SummaryTSClimate change impacts are concurrent and interact with other 
significant societal changes that have become more salient since AR5, 
including a growing and urbanising global population; significant 
inequality and demands for social justice; rapid technological change; 
continuing poverty, land and water degradation, biodiversity loss; food 
insecurity; and a global pandemic.
Ecosystems and biodiversity
TS.B.1 Climate change has altered marine, terrestrial and fresh -
water ecosystems all around the world (very high confidence). 
Effects were experienced earlier and are more widespread with 
more far-reaching consequences than anticipated (medium 
confidence). Biological responses, including changes in physi -
ology, growth, abundance, geographic placement and shifting 
seasonal timing, are often not sufficient to cope with recent 
climate change (very high confidence). Climate change 
has caused local species losses, increases in disease (high

confidence) and mass mortality events of plants and animals 
(very high confidence), resulting in the first climate-driven 
extinctions (medium confidence), ecosystem restructuring, 
increases in areas burned by wildfire (high confidence) and 
declines in key ecosystem services (high confidence). Climate-
driven impacts on ecosystems have caused measurable eco -
nomic and livelihood losses and altered cultural practices and 
recreational activities around the world (high confidence). 
(Figure  TS.3, Figure  TS.5 ECOSYSTEMS) {2.3.1, 2.3.3, 2.4.2, 
2.4.3, 2.4.4, 2.4.5, 3.2, 3.3.2, 3.3.3, 3.4.2, 3.4.3, Box 3.2, 3.5.3, 
3.5.5, 3.5.6, 4.3.5, 9.6.1, 9.6.3, 10.4.2., 11.3.1, 11.3.2, 11.3.11, 
11.3.2, 11.3.11, 12.3, 13.3.1, 13.4.1, 13.10.1, 14.2.1, 14.5.1, 
14.5.2; 15.3.3., 15.3.4, 16.2.3, CCP1.2.1; CCP1.2.2, CCP1.2.4, 
Box  CCP1.1, CCP3.2.1, CCP4.1.3, CCP5.2.1, CCP5.2.7, CP6.1, 
CCP6.2.1, CCP7.2.1, CCP7.3.2, Table 2.2, Table 2.3, Table 2.S. 1,

CCP5.2.1, CCB EXTREMES, CCB ILLNESS, CCB NATURAL, CCB SLR}
TS.B.1.1 Anthropogenic climate change has exposed ecosystems 
to conditions that are unprecedented over millennia (high 
confidence), which has greatly impacted species on land and 
in the ocean (very high confidence). Consistent with expectations, 
species in all ecosystems have shifted their geographic ranges and 
altered the timing of seasonal events (very high confidence). Among 
thousands of species spread across terrestrial, freshwater and marine 
systems, half to two-thirds have shifted their ranges to higher latitudes 
(very high confidence), and approximately two-thirds have shifted 
towards earlier spring life events (very high confidence) in response 
to warming. The move of diseases and their vectors has brought new 
diseases into the high Arctic and at higher elevations in mountain 
regions to which local wildlife and humans are not resistant (high 
confidence). These processes have led to emerging hybridisation,

competition, temporal or spatial mismatches in predator–prey, insect–
plant and host–parasite relationships and invasion of alien plant pests 
or pathogens (medium confidence). (Figure TS.5 ECOSYSTEMS) {2.4.2, 
2.4.3, 2.5.2, 2.5.4, 2.6.1, 3.2.4, 3.4.2, 3.4.3, 3.5.2, 4.3.5, 9.6.1, 10.4.2, 
11.3.1, 11.3.2; 11.3.11, 12.3.1, 12.3.2, 12.3.7, 13.3.1, 13.4.1, 13.10.2, 
14.5.1, 14.5.2; 15.3.3. 16.2.3, 16.2.3, CCP1.2.1, CCP 1.2.2, CCP1.2.4, 
CCP3.2.1, CCP4.1.3, CCP5.2.1, CCP .5.2.7, CCP6.2.1, CCP7.3.2, CCB 
EXTREMES, CCB ILLNESS, CCB MOVING PLATE}TS.B.1.2 Observed responses of species to climate change 
have altered biodiversity and impacted ecosystem structure 
and resilience in most regions (very high confidence). Range 
shifts reduce biodiversity in the warmest regions and locations as 
adaptation limits are exceeded (high confidence). Simultaneously, 
these shifts homogenise biodiversity (medium confidence) in regions 
receiving climate-migrant species, alter food webs and eliminate the

distinctiveness of communities (medium confidence). Increasing losses 
of habitat-forming species such as trees, corals, kelp and seagrass have 
caused irreversible shifts in some ecosystems and threaten associated 
biodiversity in marine systems (high confidence). Human-introduced 
invasive (non-native) species can reduce or replace native species and 
alter ecosystem characteristics if they fare better than endemic species in 
new climate-altered ecological niches (high confidence). Such invasive 
species effects are most prominent in geographically constrained 
areas, including islands, semi-enclosed seas and mountains, and they 
increase vulnerability in these systems (high confidence). Phenological 
shifts increase the risks of temporal mismatches between trophic levels 
within ecosystems (medium confidence), which can lead to reduced 
food availability and population abundances (medium confidence) and 
can further destabilise ecosystem resilience. (Figure TS.5 ECOSYSTEMS)

{2.4.2, 2.4.3, 2.4.5, Box 2.1, 2.5.4, 3.3.3, 3.4.2, 3.4.3. Box 3.2, Box 3.4, 
3.5.2, 3.5.3, 4.3.5, 9.6.1, 10.4.2, 11.3.1, 11.3.2, 11.3.11, 13.3.1, 13.4.1, 
13.10.2, 14.5.1, 15.3.3, 15.3.4, 15.8, Box CCP1.1, CCP1.2.2, CCP1.2.1, 
CCP3.2.1, CCP5.2.1, CCB EXTREMES}
TS.B.1.3 At the warm (equatorward and lower) edges of 
distribu tions, adaptation limits to human-induced warming have 
led to widespread local population losses (extirpations) that result 
in range contractions (very high confidence). Among land plants and 
animals, local population loss was detected in around 50% of studied 
species and is often attributable to extreme events (high confidence). 
Such extirpations are most common in tropical habitats (55%) and 
freshwater systems (74%), but also high in marine (51%) and terrestrial 
(46%) habitats. Many mountain-top species have suffered population 
losses along lower elevations, leaving them increasingly restricted to

a smaller area and at higher risk of extinction (medium confidence). 
Global extinctions due to climate change are already being observed, 
with two extinctions currently attributed to anthropogenic climate 
change (medium confidence). Climate-induced extinctions, including 
mass extinctions, are common in the palaeo record, underlining the 
potential of climate change to have catastrophic impacts on species and 
ecosystems (high confidence). (Figure TS.5 ECOSYSTEMS) {2.3.1, 2.3.3, 
2.4.2, 2.4.5, 2.5.4, 3.3.3, 3.4.2, 3.4.3, Box 3.2, 9.6.1, 11.3.1, 12.3, 13.4.1, 
CCP1.2.1, CCP5.2.1, CCP5.2.7, CCP7.2.1, CCB EXTREMES, CCB PALEO}
TS.B.1.4 Ecosystem change has led to the loss of specialised 
ecosystems where warming has reduced thermal habitat, as at 
the poles, at the tops of mountains and at the equator, with 
the hottest ecosystems becoming intolerable for many species 
(very high confidence). For example, warming, reduced ice, thawing

permafrost and a changing hydrological cycle have resulted in the 
contraction of polar and mountain ecosystems. The Arctic is showing 
increased arrival of species from warmer areas on land and in the sea, 
with a declining extent of tundra and ice-dependent species, such as 
the polar bear (high confidence). Similar patterns of change in the 
Antarctic terrestrial and marine environment are beginning to emerge,

46
TSna¹not
assessed
not
assessed
not
assessed
not
assessed
Conﬁdence
Medium
Low
Impacts
Antarctic
na nanana
nanana
nanana
not
assessed
Asia
Europe
Mountain regionsImpacts on

crop 
yields and
induced
damages in
to key
economic
livestock
health and 
to 
and other
AfricaGlobalIncreasing
adverse
impacts
adverse
impacts
Figure TS.3 |  Observed global and regional impacts on ecosystems and human systems attributed to climate change. Confidence levels reflect uncertainty in 
attribution of the observed impact to climate change. Global assessments focus on large studies, multi-species, meta-analyses and large reviews. For that reason they can be

across an entire region and do not focus on any country in particular.

47
such as declining ranges of krill and emperor penguins (medium 
confidence). Coral reefs are suffering global declines, with abrupt shifts 
in community composition persisting for years (very high confidence). 
Deserts and tropical systems are decreasing in diversity due to heat 
stress and extreme events (high confidence). In contrast, arid lands are 
displaying varied responses around the globe in response to regional 
changes in the hydrological cycle (high confidence). {2.3.1, 2.3.3, 2.4.2, 
2.4.3, 3.2.2, 3.4.2, 3.4.3, 3.5.3, 9.6.1, 10.4.3, 11.3.2, 11.3.11, 12.3.1, 
CCP1.2.4, CCP3.2.1, CCP3.2.2, CCP4.3.2, CCP5.2.1, CCP6.1, CCP6.2, 
CCB EXTREMES}
TS.B.1.5 Climate change is affecting ecosystem services connected 
to human health, livelihoods and well-being (medium confidence).  
In terrestrial ecosystems, carbon uptake services linked to CO 2 fertilisation 
effects are being increasingly limited by drought and warming and

exacerbated by non-climatic anthropogenic impacts (high confidence) . 
Deforestation, draining and burning of peatlands and tropical forests 
and thawing of Arctic permafrost have already shifted some areas from 
being carbon sinks to carbon sources (high confidence) . The severity and 
outbreak extent of forest insect pests increased in several regions (high 
confidence) . Woody plant expansion into grasslands and savannahs, 
linked to increased CO 2, has reduced grazing land, while invasive grasses 
in semiarid lands increased the risk of fire (high confidence). Coastal 
‘blue carbon’ systems are already impacted by multiple climate and non-
climate drivers (very high confidence). Warming and CO 2 fertilisation 
have altered coastal ecosystem biodiversity, making carbon storage 
or release regionally variable (high confidence). {2.2, Table  2.1, 2.4.2, 
2.4.3, 2.4.4, Box 2.1, 3.4.2, 3.5.3, 3.5.5, Table Box 3.4.2, Box 3.4, 9.6.1,

10.4.3, 11.3.11, 11.3.7, 12.3.3, 12.4, Figure  12.8, Figure  12.9, 13.3.1, 
13.5.1, 14.5.1, 15.3.3, 15.5.6, CCP1.2.2, CCP1.2.4, CCP5.2.1, CCP5.2.3, 
CCP7.3.1, Box CCP7.1}
TS.B.1.6 Human communities, especially Indigenous Peoples and 
those more directly reliant on the environment for subsistence, 
are already negatively impacted by the loss of ecosystem 
functions, replacement of endemic species and regime shifts 
across landscapes and seascapes (high confidence). Indigenous 
knowledge contains unique information sources about past changes 
and potential solutions to present issues (medium confidence). 
Tangible heritage, such as traditional harvesting sites or species and 
archaeological and cultural heritage sites, and intangible heritage, such 
as festivals and rites associated with nature-based activities, endemic knowledge and unique insights about plants and animals, are being 
lost (high confidence). As 80% of the world’s remaining biodiversity

is on Indigenous homelands, these losses have cascading impacts on 
cultural and linguistic diversity and Indigenous knowledge systems, 
food security, health, and livelihoods, often with irreparable damage 
and consequences (medium evidence, high agreement). Cultural losses 
threaten adaptive capacity and may accumulate into intergenerational 
trauma and irrevocable losses of sense of belonging, valued cultural 
practices, identity and home (medium confidence). {2.2, Table  2.1, 
2.6.5, 3.5.6, 4.3.5, 4.3.8, 5.4.2, 6.3.3, Box 9.2, 9.12.1, 11.4.1, 11.4.2, 
12.5.8, 13.8.1, Box  13.2, 14.4, 15.3.4, CCP5.2.5, CCP5.2.7, CCP6.2, 
Box CCP7.1}
TS.B.2 Widespread and severe loss and damage to human and 
natural systems are being driven by human-induced climate 
changes increasing the frequency and/or intensity and/or dura-
tion of extreme weather events, including droughts, wildfires, 
terrestrial and marine heatwaves, cyclones (high confidence)

and flood (low confidence). Extremes are surpassing the resil-
ience of some ecological and human systems and challenging 
the adaptation capacities of others, including impacts with irre-
versible consequences (high confidence). Vulnerable people and 
human systems and climate-sensitive species and ecosystems 
are most at risk (very high confidence). (Figure TS.3) {2.3, 2.3.1, 
2.3.1, 2.3.3, 2.4.2, 2.4.5, 2.6.1, 3.2.2, 3.4.2, 3.4.3, 3.5.2, 3.5.3, 
4.2.4, 4.2.5, 10.1, 11.2, 12.3, 13.1, 14.1, 15.1, 16.2.3, CCB EX-
TREMES, WGI AR6 SPM, WGI AR6 9, SROCC SPM}
TS.B.2.1 Extreme climate events comprising conditions beyond 
which many species are adapted are occurring on all continents, 
with severe impacts (very high confidence). The most severe 
impacts are occurring in the most climate-sensitive species and 
ecosystems, characterised by traits that limit their abilities to regenerate 
between events or to adapt, and those most exposed to climate hazards

(high confidence). Losses of local plant and animal populations have 
been widespread, many associated with large increases in hottest 
yearly temperatures and heatwave events (very high confidence). 
Marine heatwave events have led to widespread, abrupt and extensive 
mortality of key habitat-forming species among tropical corals, kelps, 
seagrasses and mangroves, as well as mass mortality of wildlife species, 
including benthic sessile species (high confidence). On land, extreme 
heat events also have been implicated in the mass mortality of fruit bats (a) Climate change has already altered terrestrial, freshwater and ocean ecosystems at global scale, with multiple impacts evident at regional and local scales where there is 
sufficient literature to make an assessment. Impacts are evident on ecosystem structure, species geographic ranges and timing of seasonal life cycles (phenology) (for methodology

(b) Climate change has already had diverse adverse impacts on human systems, including on water security and food production, health and well-being, and cities, settlements and 
infrastructure. The + and – symbols indicate the direction of observed impacts, with a – denoting an increasing adverse impact and a ± denoting that, within a region or globally, 
both adverse and positive impacts have been observed (e.g., adverse impacts in one area or food item may occur with positive impacts in another area or food item). Globally, ‘–’ 
denotes an overall adverse impact; ‘Water scarcity’ considers, e.g., water availability in general, groundwater, water quality, demand for water, drought in cities. Impacts on food 
production were assessed by excluding non-climatic drivers of production increases; Global assessment for agricultural production is based on the impacts on global aggregated

production; ‘Reduced animal and livestock health and productivity’ considers, e.g., heat stress, diseases, productivity, mortality; ‘Reduced fisheries yields and aquaculture production’ 
includes marine and freshwater fisheries/production; ‘Infectious diseases’ include, e.g., water-borne and vector-borne diseases; ‘Heat, malnutrition and other’ considers, e.g., human 
heat-related morbidity and mortality, labour productivity, harm from wildfire, nutritional deficiencies; ‘Mental health’ includes impacts from extreme weather events, cumulative 
events, and vicarious or anticipatory events; ‘Displacement’ assessments refer to evidence of displacement attributable to climate and weather extremes; ‘Inland flooding and 
associated damages’ considers, e.g., river overflows, heavy rain, glacier outbursts, urban flooding; ‘Flood/storm induced damages in coastal areas’ include damages due to, e.g.,

cyclones, sea level rise, storm surges. Damages by key economic sectors are observed impacts related to an attributable mean or extreme climate hazard or directly attributed. Key 
economic sectors include standard classifications and sectors of importance to regions (for methodology and detailed references to chapters and cross-chapter papers see SMTS.1 
and SMTS.1.2).

48
TSand freshwater fish. (Figure TS.3, Figure TS.5 ECOSYSTEMS) {2.3.1, 2.3.3, 
2.4.2, 2.4.4, 2.6, Table 2.2, Table 2.3, Table 2.S. 1, 3.4.2, 3.4.3, 3.5.2, 
11.3.2, Figure 12.8, 12.4, Table 11.4, 13.3.1, 13.4.1, CCB EXTREMES}
TS.B.2.2 Some extreme events have already emerged which 
exceeded projected global mean warming conditions for 2100, 
leading to abrupt changes in marine and terrestrial ecosystems 
(high confidence). For some forest types an increase in the frequency, 
severity and duration of wildfires and droughts has resulted in abrupt 
and possibly irreversible changes (medium to high confidence). The 
interplay between extreme events, long-term climate trends and other 
human pressures has pushed some climate-sensitive ecosystems towards 
thresholds that exceed their natural regenerative capacity (medium 
to high confidence). Extreme events can alter or impede evolutionary 
responses to climate change and the potential for acclimation to extreme

conditions both on land and in the ocean (medium to high confidence). 
(Figure TS.5 ECOSYSTEMS) {2.3.1, 2.3.3, 2.4.2, 2.4.3, 2.4.5, 2.4.4, 2.6.1, 
3.2.2, 3.2.4, 3.4.2, 4.3.5, Table 3.15, 3.6.3, 11.3.1, 11.3.2, 13.3.1, 13.4.1, 
14.5.1, CCB MOVING PLATE, CCB EXTREMES}
TS.B.2.3 Climate-related extremes have affected the 
productivity of agricultural, forestry and fishery sectors (high 
confidence). Droughts, floods, wildfires and marine heatwaves 
contribute to reduced food availability and increased food 
prices, threatening food security, nutrition and livelihoods of 
millions of people across regions (high confidence). Extreme 
events caused economic losses in forest productivity and crops and 
livestock farming, including losses in wheat production in 2012, 2016 
and 2018, with the severity of impacts from extreme heat and drought 
tripling over the last 50  years in Europe (high confidence). Forests 
were impacted by extreme heat and drought impacting timber sales,

for example, in Europe (high confidence). Marine heatwaves, including 
well-documented events along the west coast of North America 
(2013–2016) and east coast of Australia (2015–2016, 2016–2017 and 
2020), have caused the collapse of regional fisheries and aquaculture 
(high confidence). Human populations exposed to extreme weather 
and climate events are at risk of food insecurity with lower diversity 
in diets, leading to malnutrition and increased risk of disease (high 
confidence). (Figure  TS.6 WATER-FOOD) {2.4.4, 3.2.2, 3.4.2, 3.4.3, 
3.5.3, 4.2.4, 4.2.5, 4.3.1, 5.2.1, 5.4.1, 5.4.2, 5.5.2, 5.8.1, 5.9.1, 5.12.1, 
5.14.2, 5.14.6, 7.2.1, 7.2.2, 7.2.3, 7.2.4, 7.2.5, 9.7, 9.8.2, 9.8.5, 11.3.3, 
11.5.1, 11.8.1, 12.3, Figure  12.7, Figure  12.9, Table  SM12.5, 13.1.1, 
13.3.1, 13.5.1, 13.10.2, 14.5.4, CCB MOVING PLATE, WGI AR6 9}
TS.B.2.4 Extreme climatic events have been observed in all 
inhabited regions, with many regions experiencing unprece-

dented consequences, particularly when multiple hazards 
occur at the same time or within the same space (very high 
confidence). Since AR5, the impacts of climate change and extreme 
weather events such as wildfires, extreme heat, cyclones, storms 
and floods have adversely affected or caused loss and damage to 
human health, shelter, displacement, incomes and livelihoods, security 
and inequality (high confidence). Over 20  million people have been 
internally displaced annually by weather-related extreme events 
since 2008, with storms and floods the most common drivers (high 
confidence). Climate-related extreme events are followed by negative 
impacts on mental health, well-being, life satisfaction, happiness, cognitive performance and aggression in exposed populations (very 
high confidence). (Figure TS.8 HEALTH, Figure TS.10 COMPLEX RISK) 
{2.3.0, 2.3.1, 2.3.3, 4.2.4, 4.2.5, 4.3, 7.1, 7.2.4, 7.2.6, 8.2.1, 8.2.2,

8.3.2, 8.3.3, Box 9.4, Table 9.7, 9.7, 9.9, 9.11, 11.2.1, 11.2.2, 11.3.8, 
Table 11.2, Table 11.3, Box 11.6, Box 9.8, 12.4.7, 13.1, 13.2.1, 13.7.1, 
13.10.2, 14.5.6, 15.1, 15.2.1, 15.3.3, 16.2.3, CCB EXTREMES, CCB 
HEALTH, CCB MIGRATE}
Food systems, food security and forestry
TS.B.3 Climate change is already stressing food and forestry 
systems, with negative consequences for the livelihoods, 
food security and nutrition of hundreds of millions of people, 
especially in low and mid-latitudes (high confidence). The global 
food system is failing to address food insecurity and malnutrition 
in an environmentally sustainable way. (Figure TS.2, Figure TS.3, 
Figure  TS.6 FOOD-WATER, Figure  TS.7 VULNERABILITY) {4.3.1, 
5.4.1, 5.5.1, 5.7.1, 5.8.1, 5.9.1, 5.10.1, 5.11.1, 5.12.1, 6.3.4.7; 
7.2, 9.8.1, 9.8.2, 13.10, 9.8, 10.3.5, 12.3, 13.5.1, 14.5.1, 14.5.4, 
15.3.3, 15.3.4, CCP5.2.3, CCP5.2.5, CCP6.2.7, CCB NATURAL}
TS.B 3.1 Climate change impacts are negatively affecting

agriculture, forestry, fisheries and aquaculture, increasingly 
hindering efforts to meet human needs (high confidence). 
Human-induced global warming has slowed the growth of agricultural 
productivity over the past 50 years in mid and low latitudes (medium 
confidence). Crop yields are compromised by surface ozone (high 
confidence). Methane emissions have negatively impacted crop 
yields by increasing temperatures and surface ozone concentrations 
(medium confidence). Warming is negatively affecting crop and 
grassland quality and harvest stability (high confidence). Warmer and 
drier conditions have increased tree mortality and forest disturbances 
in many temperate and boreal biomes (high confidence), negatively 
impacting provisioning services (medium confidence). Ocean warming 
has decreased sustainable yields of some wild fish populations (high 
confidence) by 4.1% between 1930 and 2010. Ocean acidification 
and warming have already affected farmed aquatic species (high

confidence). (Figure  TS.3, Figure  TS.6 FOOD-WATER) {2.4.3, 2.4.4, 
3.4.2, 3.4.3, 4.3.1, 5.2.1, 5.4.1, 5.5.1, 5.6.1, 5.7.1, 5.8.1, 5.9.1, 9.8.2, 
9.8.5, 11.3.4, 11.3.5, Box 11.3, 13.3.1, 13.5.1, 14.5.1, 14.5.4, 15.3.4, 
CCP5.2.3, CCP5.2.5, CCP6.2.5, CCP6.2.8, CCB MOVING PLATE}
TS.B.3.2 Warming has altered the distribution, growing 
area suitability and timing of key biological events, such as 
flowering and insect emergence, impacting food quality and 
harvest stability (high confidence). There is high confidence that 
climate change is altering the distribution of cultivated and wild 
terrestrial, marine and freshwater species. At higher latitudes, warming 
has expanded the available area but has also altered phenology (high 
confidence), potentially causing plant–pollinator and pest mismatches 
(medium confidence). At low latitudes, temperatures have crossed 
upper tolerance thresholds, more frequently leading to heat stress and/

or shifts in distribution and losses for crops, livestock, fisheries and 
aquaculture (high confidence). {2.4.2, 3.4.2, 3.4.3, 5.4.1, 5.7.4, 5.8.1, 
5.12.3, 9.8.2, 12.3.1, 12.3.2, 12.3.6, 13.5.1, 13.5.1, 14.5.4, CCP5.2.5, 
CCP6.2.5, CCB MOVING PLATE}

49
Technical SummaryTSTS.B.3.3 Climate-related extremes have affected the productivity 
of all agricultural and fishery sectors, with negative consequences 
for food security and livelihoods (high confidence). The frequency 
of sudden food production losses has increased since at least the mid-
20th century on land and sea (medium evidence, high agreement). 
The impacts of climate-related extremes on food security, nutrition 
and livelihoods are particularly acute and severe for people living in 
sub-Saharan Africa, Asia, small islands, Central and South America and 
the Arctic and small-scale food producers globally (high confidence). 
Droughts induced by the 2015–2016 El Niño, partially attributable to 
human influences (medium confidence), caused acute food insecurity 
in various regions, including eastern and southern Africa and the Dry 
Corridor of Central America (high confidence). In the northeast Pacific, a

5-year warm period (2013 to 2017) impacted the migration, distribution 
and abundance of key fish resources (high confidence). Increasing 
variability in grazing systems has negatively affected animal fertility, 
mortality and herd recovery rates, reducing livestock keepers’ resilience 
(medium confidence). (Figure TS.6 FOOD-WATER) {3.5.5, 4.3.1, 5.2.1, 
5.4.1, 5.4.2, 5.5.2, 5.8.1, 5.9.1, 5.12.1, 5.14.2, 5.14.6, 9.8.2, 9.8.5, 
13.5.1, 14.5.4, CCP6.2, CCB MOVING PLATE, WGI AR6 11.2–11.8}
TS.B.3.4 Climate-related emerging food safety risks are 
increasing globally in agriculture and fisheries (high confidence). 
Higher temperatures and humidity caused by climate change increases 
toxigenic fungi on many food crops (very high confidence). Harmful 
algal blooms and water-borne diseases threaten food security and the 
economy and livelihoods of many coastal communities (high confidence). 
Increasing ocean warming and acidification are enhancing movement

and bioaccumulation of toxins and contaminants into marine food webs 
(medium confidence) and with bio-magnification of persistent organic 
pollutants and methyl mercury already affecting fisheries (medium 
confidence). Indigenous Peoples and local communities, especially 
where food safety monitoring is underdeveloped, are among the most 
vulnerable to these risks, in particular in the Arctic (high confidence). 
(Figure TS.8 HEALTH) {3.5.5, 5.8.1, 5.9.1, 5.11.1, 7.2.2, 7.2.4, 14.5.6, 
CCP6.2.8, CCB ILLNESS}
TS.B.3.5 The impacts of climate change on food systems affect 
everyone, but some groups are more vulnerable. Women, the elderly 
and children in low-income households, Indigenous Peoples, minority 
groups, small-scale producers and fishing communities and people in 
high-risk regions more often experience malnutrition, livelihood loss 
and rising costs (high confidence). Increasing competition for critical 
resources, such as land, energy and water, can exacerbate the impacts

of climate change on food security (high confidence). Examples include 
large-scale land deals, water use, dietary patterns, energy crops and 
use of feed crops. (Figure TS.10 COMPLEX RISK) {2.6.5, 4.8.3, 5.4.2, 
5.5.2, 5.9.2, 5.12.2, 5.12.3, 5.13.1, 5.13.3, 5.13.4; 6.3.4, 9.8.1, Box 9.5, 
12.3.1, 12.3.2, 14.5.2, 14.5.4, 14.5.6, 14.5.7, 14.5.8, 14.5.11, Box 14.6, 
15.3.4, CCP5.2.3, CCP5.2.5, CCP6.2.7, CCP6.2.8}
Water systems and water security
TS.B.4 Currently, roughly half of the world’s population are 
experiencing severe water scarcity for at least 1  month yr-1 
due to climatic and other factors (medium confidence). Water 
insecurity is manifested through climate-induced water scar -city and hazards and is further exacerbated by inadequate 
water governance (high confidence). Extreme events and un-
derlying vulnerabilities have intensified the societal impacts 
of droughts and floods, negatively impacted agriculture and

energy production and increased the incidence of water-borne 
diseases. Economic and societal impacts of water insecurity are 
more pronounced in low-income countries than in middle- and 
high-income ones (high confidence). (Figure TS.2, Figure TS.3, 
Figure  TS.6 WATER-FOOD) {Table  2.2, Table  2.3, 2.3.3. 2.4.2, 
2.4.4, 4.1.1, Box 4.1, 4.2.1, 4.2.2, 4.2.3, 4.2.4, 4.2.5, 4.2.6, 4.3.1, 
4.3.2, 4.3.3, 4.3.4, 4.3.5, 4.3.6, 4.3.8, 4.4.4, 5.9.1, 5.12.2, 5.12.3, 
6.2.2, 6.2.3, 7.2.2, 7.2.4, 7.2.5, 7.2.6, 7.2.7, 8.3.2, 8.3.3, 9.7.1, 
9.9.2, Box  9.4, 10.4.1, 10.4.4, Box  10.4, 10.5.4, Boxes 11.1–
11.6, Table 11.2, 11.3, 11.3.1, 11.3.2, 11.4, Table 11.4, 11.3.3, 
11.5.2, Table 11.2a, 11.3.3.1, Box, 11.3, Box 11.4, 12.3, 12.3.1, 
12.3.2, 12.3.6, 12.3.7, 12.4, Table  12.4, 12.5.3.1, Figure  12.7, 
Figure 12.9, Figure 12.10, Figure 12.13, Table SM12.6, 13.3.1, 
13.5.1, 13.6.1, 13.8.1, 13.10.1, 14.5.1–4,, 14.5.6, 14.7, Box 14.7, 
15.3.3, 15.3.4, 16.2.3, CCP1.2.3, CCP3.1.2, CCP3.2.1, CCP5.2.2,

CCP5.2.3, CCP5.2.7, CCP6.2.1, CCP6.2.5, CCP7.2.3, CCB DISAS-
TER, CCB ILLNESS, CCB EXTREMES}
TS.B.4.1 Climate change has intensified the global hydrological 
cycle, causing several societal impacts, which are felt 
disproportionately by vulnerable people (high confidence). 
Human-induced climate change has affected physical aspects of water 
security through increasing water scarcity and exposing more people 
to water-related extreme events like floods and droughts, thereby 
exacerbating existing water-related vulnerabilities caused by other 
socioeconomic factors (high confidence). Many of these changes in 
water availability and water-related hazards can be directly attributed 
to anthropogenic climate change (high confidence). Water insecurity 
disproportionately impacts the poor, women, children, Indigenous 
Peoples and the elderly in low-income countries (high confidence) and 
specific marginal geographies (e.g., small island states and mountain

regions). Water insecurity can contribute to social unrest in regions 
where inequality is high and water governance and institutions are 
weak (medium confidence). (Figure  TS.6 WATER-FOOD, Figure  TS.7 
VULNERABILITY) {2.3.1, 2.3.3, 2.4.4, 4.1.1, 4.2.1, Box 4.1, 4.2.4, 4.3.6, 
5.12.2, 5.12.3, 6.2.2, 6.2.3, 7.2.7, 9.7.1, 10.4.4, 12.5.3.1, 13.8.1, 15.3.3, 
15.3.4, CCP5.2.2, CCB EXTREMES}
TS.B.4.2 Worldwide, people are increasingly experiencing unfa-
miliar precipitation patterns, including extreme precipitation 
events (high confidence). Nearly half a billion people now live in 
areas where the long-term average precipitation is now as high as 
was previously seen in only about 1 in 6 years (medium confidence). 
Approximately 163 million people now live in unfamiliarly dry areas 
(medium confidence) compared to 50 years ago. The intensity of heavy 
precipitation has increased in many regions since the 1950s (high con-

fidence). Substantially more people (around 709 million) live in regions 
where annual maximum 1-d precipitation has increased than in re-
gions where it has decreased (around 86 million) (medium confidence) 
since the 1950s. At the same time, more people (around 700 million) 
have been experiencing longer dry spells than shorter dry spells since 
the 1950s (medium confidence), leading to compound hazards related 
to both warming and precipitation extremes in most parts of the world

50
TS(medium confidence). (Figure TS.6 WATER-FOOD) {2.3.1, 4 .2.2, 4.2.3, 
4.2.6, 4.3.1, 4.3.4, 6.2.2, 9.5.2–6, 13.2, 13.10, CCB EXTREMES}
TS.B.4.3 Glaciers are melting at unprecedented rates, causing 
negative societal impacts among communities that depend 
on cryospheric water resources (high confidence). Over the last 
two decades, the global glacier mass loss rate has been the highest 
since the glacier mass balance measurements began a century ago 
(high confidence). Melting of glaciers, snow decline and thawing of 
permafrost have threatened the water and livelihood security of 
local and downstream communities through changes in hydrological 
regimes and increases in the potential of landslides and glacier lake 
outburst floods. Cryosphere changes have impacted cultural uses 
of water among vulnerable mountain and Arctic communities and 
Indigenous Peoples (high confidence), who have long experienced

historical, socioeconomic and political marginalisation (medium to 
high confidence). Cryosphere change has affected ecosystems, water 
resources, livelihoods and cultural uses of water in all cryosphere-
dependent regions across the world (very high confidence). 
(Figure TS.3) {2.4.3, 2.6.5, 4.2.2, 4.3.8, 4.4.4, 6.2.2, 9.5.8, 10.5.4, 11.3.3, 
10.4.4, Box 10.4, CCP5.2.2, CCP5.2.7, CCP6.2.5, 11.2.1, Table 11.2b, 
Table 11.9, 12.3.2, 12.3.7, Figure 12.9, Figure 12.13, Table SM12.6}
TS.B.4.4 Impacts of droughts and floods have intensified due 
to extreme events and underlying societal vulnerabilities (high 
 confidence). Anthropogenic climate change has led to  increased 
 likelihood, severity and societal impacts of droughts (primarily   
 agricultural and hydrological droughts) in many regions (high 
 confidence) . Between 1970 and 2019, drought-related disaster events 
worldwide caused billions of dollars in economic damages (medium

confidence). Drylands are particularly exposed to climate change related 
droughts (high  confidence). Recent heavy rainfall events that have led to 
 catastrophic flooding were made more likely by anthropogenic climate 
change (high confidence). Observed mortality and losses due to floods 
and droughts are much greater in regions with high vulnerability and 
vulnerable  populations such as the poor, women, children, Indigenous 
Peoples and the elderly due to historical, political and socioeconomic 
inequities (high confidence) . {4.2.4, 4.2.5, 4.3.1, 4.3.2, 6.2.2, 7.2.2, 7.2.4, 
7.2.5, 7.2.6, 11.2.1, 11.2.a, 13.2.1, 14.5.3, 15.3.4, CCP3.1.2, CCP3.2.1, 
8.3.2, 8.3.3, 9.9.2, Box 9.4, 15.3.3, 15.3.4, 16.2.3, CCP5.2.6, CCP7.2.3, 
CCB  DISASTER, CCB EXTREMES}
TS.B.4.5 Climate-induced changes in the hydrological cycle have 
negatively impacted freshwater and terrestrial ecosystems. 
Climate change and changes in land use and water pollution are key

drivers of ecosystem loss and degradation (high confidence), with 
negative impacts observed on culturally significant terrestrial and 
freshwater species and ecosystems in the Arctic, mountain regions 
and other biodiversity hotspots (high confidence). Climate trends and 
extreme events have had major impacts on many natural systems (high 
confidence). For example, periodic droughts in parts of the Amazon 
since the 1990s, partly attributed to climate change, resulted in high 
tree mortality rates and basin-wide reductions in forest productivity, 
momentarily turning Amazon forests from a carbon sink into a net 
carbon source (high confidence). Fire risks have increased due to 
heat and drought conditions in many parts of the world (medium 
confidence). Increased precipitation has resulted in range shifts of species in some regions (high confidence). (Figure TS.10 COMPLEX 
RISK) {2.4.2, 2.4.3, 2.4.4; Table 2.2; Table 2.3, Table SM2.1, 4.3.3, 4.3.4,

4.3.5, 4.3.8, 9.6.1, 11.3.1, 11.3.2, Table 11.2b, Table 11.4, Table 11.6, 
Table 11.9, 12.3, 12.4, Figure 12.7, Figure 12.9, Figure 12.10, 13.3.1, 
14.5.1, 14.5.2, 14.5.3, Box 14.7, CCP1.2.3, CCP5.2.3, CCP6.2.1}
TS.B.4.6 Hydrological cycle changes have impacted food and 
diseases. Climate-induced trends and extremes in the water cycle 
have impacted agricultural production positively and negatively, with 
negative impacts outweighing the positive ones (high confidence). 
Droughts, floods and rainfall variability have contributed to reduced food 
availability and increased food prices, threatening food and nutrition 
security, and the livelihoods of millions globally (high confidence), with 
the poor in parts of Asia, Africa and South and Central America being 
disproportionately affected (high confidence). Drought years have 
reduced thermoelectric and hydropower production by around 4–5%

compared to long-term average production since the 1980s (medium 
confidence), reducing economic growth in Africa and with billions in 
US dollars of existing and planned hydropower infrastructure assets 
in mountain regions worldwide and in Africa exposed to increasing 
hazards (high confidence). Changes in temperature, precipitation and 
water-related disasters are linked to increased incidences of water-
borne diseases such as cholera, especially in regions with limited 
access to safe water, sanitation and hygiene infrastructure (high 
confidence). {4.3.1, 4.3.2, 4.3.3, 4.3.4, 4.3.5,4.3.6, 4.3.8, 5.9.1, 7.2.2, 
9.7.1, Box 9.4, Box 9.5, 9.8.2, 9.10.2, 10.4.1, 11.3.3, Box 11.3, 11.4, 
11.5.2, Table  11.2, Boxes 11.1–11.6, 13.2.1, 13.5.1, 13.6.1, 13.7.1, 
14.5.3, CCP5.2.2}
TS.B.5 Climate change has already harmed human physical 
and mental health (very high confidence). In all regions, health 
impacts often undermine efforts for inclusive development.

Women, children, the elderly, Indigenous People, low-income 
households and socially marginalised groups within cities, set-
tlements, regions and countries are the most vulnerable (high 
confidence). (Figure TS.7 VULNERABILITY , Figure TS.8 HEALTH) 
{2.4.2, 3.4.2, 3.5.3, 3.5.5, 3.5.6, 4.2.5, 4.3.3, Table  4.3, 5.5.2, 
5.11.1, 5.12.3, Box 5.10, 7.2.1, 7.2.2, 7.2.3, 7.2.4, 7.2.5, 7.4.2, 
Box 7.1, Box 7.3, 8.2.1, 8.3.2, 8.3.4, Box 8.6, 9.1.5, 9.8.1, 9.10.1, 
9.10.2, Figure 9.34, Figure 9.33, Box 9.1, 10.4.7, 11.3.6, Box 11.1, 
Table 11.10, 12.3.1, 12.3.2, 12.3.4, 12.3.5, 12.3.6, 12.3.7, 12.3.7, 
12.3.8, Figure 12.4, Figure 12.6, Table 12.1, Table 12.2, Table 12.9, 
Table  12.11, 13.7.1, Figure  13.24, 14.4, 14.5.2, 14.5.4, 14.5.6, 
14.5.7, 14.5.8, Box 14.2, Figure 14.8, 15.3.4, 16.2.3, CCP2.2.2, 
CCP5.1, Table CCP5.1, CCP5.2.3, CCP6.2.6, CCP6.3, CCB DISAS-
TER, Table  CCB DISASTER 4.1,CCB HEALTH, CCB ILLNESS, CCB 
MOVING PLATE, CCB SLR, CWGB URBAN}

TS.B.5.1 Observed mortality from floods, drought and storms 
is 15  times higher for countries ranked as highly vulnerable 
compared to less vulnerable countries in the last decade (high 
confidence). While an increase in drought has been observed in 
almost all continents to different extents, it is particularly the most 
vulnerable regions where such droughts result in relatively high

51
Technical SummaryTSmortality (high confidence). Between 1970 and 2019, 7% of all 
disaster events worldwide were drought related, yet they contributed 
to 34% of disaster-related deaths, mostly in Africa. (Figure  TS.7 
VULNERABILITY) {4.2.5, Table 4.3, 7.2.1, 7.2.3, 7.2.4, 8.3.2, Box 9.1, 
9.10.2, 10.4.7, 12.3.1, 12.3.6, 16.2.3, Table  CCP5.1, CCB DISASTER, 
Table CCB DISASTER 4.1, CCB ILLNESS}
TS.B.5.2 Mental health challenges increase with warming 
temperatures (high confidence), trauma associated with extreme 
weather (very high confidence) and loss of livelihoods and culture 
(high confidence) . Distress sufficient to impair mental health has been 
caused by climate-related ecological grief associated with environmental 
change (e.g., solastalgia) or extreme weather and climate events (very 
high confidence), vicarious experience or anticipation of climate events 
(medium confidence) and climate-related loss of livelihoods and food

insecurity (very high confidence). Vulnerability to mental health effects 
of climate change varies by region and population, with evidence that 
Indigenous Peoples, agricultural communities, first responders, women 
and members of minority groups experience greater impacts (high 
confidence). {7.2.5, 7.4.2, 8.3.4, Box 8.6, 9.10.2, 11.3.6, 13.7.1, 14.5.6, 
Figure 14.8, 15.3.4, CCP5.2.5, CCP6.2.6, CCP6.3}
TS.B.5.3 Increasing temperatures and heatwaves have increased 
mortality and morbidity (very high confidence), with impacts that 
vary by age, gender, urbanisation and socioeconomic factors (very 
high confidence). A significant proportion of warm-season heat-related 
mortality in temperate regions is attributed to observed anthropogenic 
climate change (medium confidence), with fewer data available for 
tropical regions in Africa (high confidence). For some heatwave events 
over the last two decades, associated health impacts have been partially

attributed to observed climate change (high confidence). Highly 
vulnerable groups experiencing health impacts from heat stress include 
anyone working outdoors and, especially, those doing outdoor manual 
labour (e.g., construction work, farming). Potential hours of work lost 
due to heat have increased significantly over the past two decades 
(high confidence). Some regions are already experiencing heat stress 
conditions at or approaching the upper limits of labour productivity 
(high confidence). {7.2.1, 7.2.4 8.2.1, 9.1.5, 9.10.1, Figure 9.34, 10.4.7, 
11.3.6.1, 12.3.1, 12.3.7, 12.3.8, Figure 12.6, Table 12.2, 13.7.1, 14.5.6, 
14.5.8, 16.2.3, CWGB URBAN}
TS.B.5.4 Climate change has contributed to malnutrition in all its 
forms in many regions, including undernutrition, overnutrition 
and obesity, and to disease susceptibility (high confidence), 
especially for women, pregnant women, children, low-income 
households, Indigenous Peoples, minority groups and small-scale

producers (high confidence) . Extreme climate events have been key 
drivers in rising undernutrition of millions of people, primarily in Africa 
and Central America (high confidence). For example, anthropogenic 
warming contributed to climate extremes induced by the 2015–2016 
El Niño, which resulted in severe droughts, leading to an additional 
5.9  million children in 51  countries becoming underweight (high 
confidence). Undernutrition can in turn increase susceptibility to other 
health problems, including mental health problems, and impair cognitive 
and work performance, with resulting economic impacts (very high 
confidence). Children and pregnant women experience disproportionate 
adverse health and nutrition impacts (high confidence). {5.12.3, 7.2.4, 7.2.5, CCP5.2.3, CCP5.2.3.1, 14.4, 14.5.2, 14.5.4, 14.5.6, 14.5.7, 
Figure 14.8, 9.8.1, 9.10.2, 10.4.7, 15.3.4, CCP6.2.6, CCB HEALTH, CCB 
ILLNESS, CCB MOVING PLATE}

TS.B.5.5 Climate-related food safety risks have increased globally 
(high confidence). These risks include Salmonella, Campylobacter and 
Cryptosporidium infections (medium confidence) mycotoxins associated 
with cancer and stunting in children (high confidence) and seafood 
contamination with marine toxins and pathogens (high confidence). 
Climate-related food-borne disease risks vary temporally and are 
preferences (medium confidence), as well as adequate food safety 
monitoring (high confidence). {3.4.2, 3.5.3, 3.5.5, 3.5.6, 5.11.1, Box 5.10, 
7.2.1, 7.2.2, 13.7.1, Figure 13.24, 14.5.6, 15.3.4, CCP6.2.6, CCB SLR}
TS.B.5.6 Higher temperatures combined with land use/land cover 
change are making more areas suitable for the transmission of 
vector-borne diseases (high confidence). More extreme weather 
events have contributed to vector-borne disease outbreaks in humans

through direct effects on pathogens and vectors and indirect effects on 
human behaviour and emergency response destabilisation (medium 
confidence). Climate change and variability are facilitating the spread 
of chikungunya virus in North, Central and South America, Europe 
and Asia (medium to high confidence); tick-borne encephalitis in 
Europe (medium confidence); Rift Valley fever in Africa; West Nile 
fever in southeastern Europe, western Asia, the Canadian prairies 
and parts of the USA (medium confidence); Lyme disease vectors in 
North America (high confidence) and Europe (medium confidence); 
malaria in eastern and southern Africa (high confidence); and dengue 
globally (high confidence). For example, in Central and South America, 
the reproduction potential for the transmission of dengue increased 
between 17% and 80% for the period 1950–1954 to 2016–2021, 
depending on the sub-region, as a result of changes in temperature

and precipitation (high confidence). {2.4.2, 4.3.3, 7.2.1, 7.2.2, 9.10.2, 
10.4.7, Table 11.10, 12.3.1, 12.3.2, 12.3.3, 12.3.5, 12.3.6, Figure 12.4, 
Table 12.9, Table 12.11, Table 12.1, 13.7.1, Figure 13.24, 14.5.6, 15.3.4, 
16.2.3, CCB ILLNESS}
TS.B.5.7 Higher temperatures (very high confidence), heavy 
rainfall events (high confidence) and flooding (medium 
confidence) are associated with increased water-borne diseases,  
particularly diarrhoeal diseases, including cholera (very high confidence) 
and other gastrointestinal infections (high confidence) in high-, middle- 
and low-income countries. Water insecurity and inadequate water, 
sanitation and hygiene increase disease risk (high confidence), stress 
and adverse mental health (limited evidence, medium agreement), 
food insecurity and adverse nutritional outcomes and poor cognitive 
and birth outcomes (limited evidence, medium agreement). {4.3.3, 
7.2.2, Box 7.3, 9.10.1, Figure 9.33, 10.4.7, 11.3.6, 12.3.4, 12.3.5, 13.7.1,

Figure 13.24, 14.5.6, 16.2.3, CCP6.2.6, CCB ILLNESS, CWGB URBAN}
TS.B.5.8 Climate change driven range shifts of wildlife, 
exploitation of wildlife and loss of wildlife habitat quality have 
increased opportunities for pathogens to spread from wildlife to 
human populations, which has resulted in increased emergence of 
zoonotic disease epidemics and pandemics (medium confidence). 
Zoonoses that have been historically rare or never documented in Arctic

52
TSand sub-Arctic regions of Europe, Asia and North America are emerging 
as a result of climate-induced environmental change (e.g., anthrax), 
spreading polewards and increasing in incidence (e.g., tularemia) (very 
high confidence). {2.4.2, 5.5.2, 7.2.2, Box  7.1, 10.4.7, 12.3.1, 12.3.4, 
CCP2.2.2, CCP6.2.6, CCB ILLNESS}
TS.B.5.9 Several chronic, non-communicable respiratory diseases 
are climate-sensitive based on their exposure pathways (e.g., 
dominant driver in all cases. Exposure to wildfires and associated 
smoke has increased in several regions (very high confidence). The 
2019–2020 southeastern Australian wildfires resulted in the deaths 
of 33 people, a further 429 deaths and 3230 hospitalisations due to 
cardiovascular or respiratory conditions and $1.95  billion in health

costs. Spring pollen season start dates in northern mid-latitudes are 
occurring earlier due to climate change, increasing the risks of allergic 
respiratory diseases (high confidence). {2.4.4, 7.2.3, 14.5.6, Box 14.2, 
11.3.6, Box 11.1, 12.3.3, 12.3.4, 12.3.6, 12.3.7, 13.7.1}
TS.B.6 Since AR5 there is increased evidence that climate 
hazards associated with extreme events and variability act as 
direct drivers of involuntary migration and displacement and 
as indirect drivers through deteriorating climate-sensitive live-
lihoods (high confidence). Most climate-related displacement 
and migration occur within national boundaries, with interna-
tional movements occurring primarily between countries with 
contiguous borders (high confidence). Since 2008, an annual 
average of over 20  million people have been internally dis-
placed annually by weather-related extreme events, with 
storms and floods being the most common (high confidence).

{1.1.1, 1.3, 7.2.6, 9.9.2, Box 9.8, Box 10.2, 12.3, 13.8.1, 15.3.4, 
16.2.3, 18.2, CCP3.2, CCB MIGRATE}
TS.B.6.1 The most common climatic drivers for migration and 
displacement are drought, tropical storms and hurricanes, heavy 
rains and floods (high confidence). Extreme climate events act as 
both direct drivers (e.g., destruction of homes by tropical cyclones) and 
indirect drivers (e.g., rural income losses during prolonged droughts) 
of involuntary migration and displacement (very high confidence). 
The largest absolute number of people displaced by extreme weather 
each year occurs in Asia (South, Southeast and East), followed by 
sub-Saharan Africa, but small island states in the Caribbean and 
South Pacific are disproportionately affected relative to their small 
population size (high confidence). {4.3.7, 7.2.6, 9.9.2, Box 9.8, 12.3.1, 
12.3.2, 12.3.3, 12.3.5, 12.5.8, 15.3.4, 16.2.3, CCB MIGRATE}
TS.B.6.2 The impacts of climatic drivers on migration are highly

context-specific and interact with social, political, geopolitical 
and economic drivers (high confidence). Specific climate events 
and conditions cause migration to increase, decrease or flow in new 
directions (high confidence). One of the main pathways for climate-
induced migration is through deteriorating economic conditions and 
livelihoods (high confidence). Climate change has influenced changes 
in temporary, seasonal or permanent migration, often rural to urban or rural to rural, that is associated with labour diversification as a risk-
reduction strategy in Central America, Africa, South Asia and Mexico 
(high confidence). This movement is often followed by remittances 
(medium confidence). However, the same economic losses can also 
undermine household resources and savings, limiting mobility and 
compounding people’s exposure and vulnerability (high confidence). 
{4.3.7, 5.5.4, 7.2.6, 8.2.1, Box  9.8, 12.3.1, 12.3.2, 12.3.3, 12.3.5, 
12.5.8, 13.8.1, CCP5.2.5, CCB MIGRATE}

TS.B.6.3 Outcomes of climate-related migration are highly 
variable, with socioeconomic factors and household resources 
affecting migration success (high confidence). The more agency 
migrants have (i.e., the degree of voluntarity and freedom of movement), 
the greater the potential benefits for sending and receiving areas (high 
agreement, medium evidence). Displacement or low-agency migration is 
associated with poor health, well-being and socioeconomic outcomes for 
migrants and yields fewer benefits to sending or receiving communities 
(high agreement, medium evidence). Involuntary migration occurs 
when adaptation alternatives are exhausted or not viable and reflects 
non-climatic factors that constrain adaptive capacity and create high 
levels of exposure and vulnerability (high confidence). These outcomes 
are also shaped by policy and planning decisions at regional, national 
and local scales that relate to housing, infrastructure, water provisioning,

schools and healthcare to support the integration of migrants into 
receiving communities (high confidence). {4.3.7, 5.5.3, 5.5.4, 5.10.1, 
5.12.2, 7.2.6, 7.2.6, 8.2.1, 9.8.3, Box 8.1, 10.3, Box 12.2, CCB MIGRATE, 
CCB SLR}
TS.B.6.4 Immobility in the context of climatic risk reflects both 
vulnerability and lack of agency, but is also a deliberate choice 
(high confidence). Deliberate or voluntary, immobility represents an 
assertion of the importance of culture, livelihood and sense of place. 
exposed to climatic hazards are not presently commonplace, although 
the need is expected to grow. Existing examples of relocations of 
Indigenous Peoples in coastal Alaska and villages in the Solomon 
Islands and Fiji suggest that relocated people can experience significant 
financial and emotional distress as cultural and spiritual bonds to place 
and livelihoods are disrupted (high confidence). {7.2.6, 13.8.1, 15.3.4,

CCP6.2.5, CCB MIGRATE}
Human vulnerability
TS.B.7 Vulnerability significantly determines how climate 
change impacts are being experienced by societies and com-
munities. Vulnerability to climate change is a multi-dimension-
al, dynamic phenomenon shaped by intersecting historical and 
contemporary political, economic and cultural processes of 
marginalisation (high confidence). Societies with high levels of 
inequity are less resilient to climate change (high confidence). 
(Figure TS.7 VULNERABILITY) {2.6.5, 2.6.7, 5.12.3, 5.13.4, 7.1, 
Box  6.6, 6.4.3.5, 8.2.1, 8.2.2, 8.3.2, 8.3.3, 8.3.4, 13.8.2, 9.8.2, 
9.11.4, Box 9.1, 10.3.3., 12.1.1, 12.2, 12.3, 12.5.5, 12.5.7, Fig-
ure 12.2, 14.4, 16.5.2, CCB COVID, CCB GENDER, CCB ILLNESS}
TS.B.7.1 About 3.3  billion people are living in countries with 
high human vulnerability to climate change (high confidence).

53
Technical SummaryTSApproximately 1.8 billion people reside in regions classified as having 
low vulnerability. Global concentrations of high vulnerability are 
emerging in transboundary areas encompassing more than one country 
as a result of interlinked issues concerning health, poverty, migration, 
conflict, gender inequality, inequity, education, high debt, weak 
institutions, lack of governance capacities and infrastructure. Complex 
human vulnerability patterns are shaped by past developments, such 
as colonialism and its ongoing legacy (high confidence), are worsened 
by compounding and cascading risks (high confidence) and are socially 
differentiated. For example, low-income, young, poor and female-
headed households face greater livelihood risks from climate hazards 
(high confidence). (Figure TS.7 VULNERABILITY) {4.3.1, 5.5.2, 5.12.3, 
5.13.3, Box 5.13, 8.3.2, 8.4.5, Box 9.1, 9.4.1, 9.8.1, 9.11.4, 10.3.3, 12.2, 
12.3, 12.5.5, 12.5.7, Figure 12.2, 14.4}

TS.B.7.2 Climate change is impacting Indigenous Peoples’ ways 
of life (very high confidence), cultural and linguistic diversity 
(medium confidence), food security (high confidence) and health 
and well-being (very high confidence). Indigenous knowledge 
and local knowledge can contribute to reducing the vulnerability of 
communities to climate change (medium to high confidence). Supporting 
Indigenous self-determination, recognising Indigenous Peoples’ rights 
and supporting Indigenous knowledge-based adaptation are critical 
to reducing climate change risks and effective adaptation (very high 
confidence). {1.3.2, 2.6.5, 4.3.8, 4.6.9, 4.8.4, 5.5.2, 5.8.2, 5.10.2, 
5.14.2, 6.4.7, Box 8.7, Box 9.2, 11.4.1, 11.4.2, Table 11.10, Table 11.11, 
Table 11.12, 12.3, 12.4, Figure 12.9, 13.8.1, 13.8.2, Box.14.1, 15.3.4, 
CCP5.2.2, CCP5.2.5, CCP6.2, Box CCP6.2, CCP6.3, CCP6.4}
TS.B.7.3 The intersection of gender with race, class, ethnicity,

sexuality, Indigenous identity, age, disability, income, 
migrant status and geographical location often compounds 
vulnerability to climate change impacts (very high confidence), 
exacerbates inequity and creates further injustice (high 
confidence). There is evidence that present adaptation strategies do 
not sufficiently include poverty reduction and the underlying social 
determinants of human vulnerability such as gender, ethnicity and 
governance (high confidence). {1.2.1, 1.4.1, 4.8.3, 4.8.5, 4.8.6, 4.6.3, 
6.1.5, 6.3, 6.4, Box 9.1, 9.4.1, Box 9.8, 11.7.2, 18.4, 18.5, CCP5.2.7, 
CCB GENDER}
TS.B.7.4 Climate variability and extremes are associated with 
more prolonged conflict through food price spikes, food 
and water insecurity, loss of income and loss of livelihoods 
(high confidence), with more consistent evidence for low-
intensity organised violence within countries than for major 
or international armed conflict (medium confidence). Compared

to other socioeconomic factors, the influence of climate on conflict 
has been assessed as being relatively weak (high confidence) but is 
exacerbated by insecure land tenure, weather-sensitive economic 
activities, weak institutions and fragile governance, poverty and 
inequality (medium confidence). The literature also suggests a 
larger climate-related influence on the dynamics of conflict than on 
the likelihood of initial conflict outbreak (low confidence). There is 
insufficient evidence at present to attribute armed conflict to human-
induced climate change. {4.1, 4.3.1, 4.3.6, 5.8.3, 5.12.4, Box  5.9, 
Box 6.3; Box 9.9; 7.2.7, 12.5.8, 12.7.4, 16.2.3}Cities, settlements and infrastructure 
TS.B.8 Cities and settlements (particularly unplanned and in -
formal settlements and in coastal and mountain regions) have 
continued to grow at rapid rates and remain crucial both as 
concentrated sites of increased exposure to risk and increas -

ing vulnerability and as sites of action on climate change 
(high confidence). More people and key assets are exposed to 
climate-induced impacts, and loss and damage in cities, set -
tlements and key infrastructure since AR5 (high confidence). 
Sea level rise, heatwaves, droughts, changes in runoff, floods, 
wildfires and permafrost thaw cause disruptions of key infra -
structure and services such as energy supply and transmission, 
communications, food and water supply and transport systems 
in and between urban and peri-urban areas (high confidence). 
The most rapid growth in urban vulnerability and exposure 
has been in cities and settlements where adaptive capacity is 
limited, including informal settlements in low- and middle-in -
come communities and in smaller and medium-sized urban 
communities (high confidence). (Figure  TS.9 URBAN) {4.3.4, 
8.2, 8.3, 6.1.4, Box 6.1, 9.9.1, 9.9.2, 10.4.6, 11.6, Table 11.14, 
12.6.1, 13.6.1, 14.5.5, 16.2, 16.5, CCP2.2, CCP5.2.5, CCP5.2.6,

CCP5.2.7, CCP6.2.3, CCP6.2.4, Box CCP6.1, CCP6.2.5, CCP6.3.1, 
Table CCP6.5, Table CCP6.6}
TS.B.8.1 Globally, urban populations grew by more than 
397 million people between 2015 and 2020, with more than 
90% of this growth taking place in less developed regions. 
The most rapid growth in urban vulnerability has been 
in unplanned and informal settlements and in smaller to 
medium urban centres in low- and middle-income nations 
where adaptive capacity is limited (high confidence). Since 
AR5, observed impacts of climate change on cities, peri-urban 
areas and settlements have extended from direct, climate-driven 
impacts to compound, cascading and systemic impacts (high 
confidence). Patterns of urban growth, inequity, poverty, informality 
and precariousness in housing are uneven and shape cities in key 
regions, such as within Africa and Asia. In sub-Saharan Africa, 
about 60% of the urban population lives in informal settlements,

while Asia is home to the largest share of people—529 million—
living in informal settlements. The high degree of informality limits 
adaptation and increases differential vulnerability to climate change 
(high confidence). Globally, exposure to climate-driven impacts such 
as heatwaves, extreme precipitation and storms in combination 
with rapid urbanisation and lack of climate-sensitive planning, 
along with continuing threats from urban heat islands, is increasing 
the vulnerability of marginalised urban populations and key 
infrastructure to climate change, for example, more frequent and/
or extreme rainfall and drought stress existing design and capacity 
of current urban water systems and heighten urban and peri-urban 
water insecurity (high confidence). COVID-19 has had a substantial 
urban impact and generated new climate-vulnerable populations 
(high confidence). (Figure TS.9 URBAN) {4.3.4, 6.1.4 6.2, 6.2.2, 9.9.1,

9.9.3, 10.4.6, 12.4, 12.6.1, 14.5.5, 14.5.6, 17.2.1, CCB COVID}
TS.B.8.2 People, livelihoods, ecosystems, buildings and 
infrastructure within many coastal cities and settlements are

54
TSalready experiencing severe compounding impacts, including 
from sea level rise and climate variability (high confidence). 
Coastal cities are disproportionately affected by interacting, cascading 
and climate-compounding climate- and ocean-driven impacts, in 
part because of the exposure of multiple assets, economic activities 
and large populations concentrated in narrow coastal zones (high 
confidence), with about a tenth of the world’s population and physical 
assets in the Low Elevation Coastal Zone (less than 10 m above sea 
level). Early impacts of accelerating sea level rise have been detected 
at sheltered or subsiding coasts, manifesting as nuisance and chronic 
flooding at high tides, water-table salinisation, ecosystem and 
agricultural transitions, increased erosion and coastal flood damage 
(medium confidence). Coastal settlements with high inequality, for 
example a high proportion of informal settlements, as well as deltaic

cities prone to land subsidence (e.g., Bangkok, Jakarta, Lagos, New 
Orleans, Mississippi, Nile, Ganges-Brahmaputra deltas) and small 
island states are highly vulnerable and have experienced impacts 
from severe storms and floods in addition to, or in combination with, 
those from accelerating sea level rise (high confidence). Currently, 
coastal cities already dependent on extensive protective works face 
the prospects of significantly increasing costs to maintain current 
protection levels, especially if the local sea level rises to the point 
that financial and technical limits are reached; systemic changes, 
such as relocation of millions of people, will be necessary (medium 
confidence). (Figure TS.9 URBAN) {4.3.4, Box 6.3, 6.3.1, 6.4.5, Box 6.4, 
6.4.3, 6.4.5, Figure 6.5, Box 9.8, 10.3.7, 11.7.2, 12.1.1, 13.8.1.1, 15.7, 
CWGB URBAN}
TS.B.8.3 Climate impacts on urban population health, livelihoods 
and well-being are felt disproportionately, with the most

economically and socially marginalised being most affected 
(high confidence). Vulnerabilities vary by location and are shaped 
by intersecting processes of marginalization, including gender, class, 
race, income, ethnic origin, age, level of ability, sexuality and non-
conforming gender orientation (high confidence). (Figure TS.9 URBAN) 
{4.3.4, Box 6.3, 6.3.1, 6.4.5, Box 6.4, 6.4.3, 6.4.5, Figure 6.5, Box 9.8, 
10.3.7, 11.7.2, 12.1.1, 13.8.1.1, 15.7, CWGB URBAN}
TS.B.8.4 Infrastructure systems provide critical services to 
individuals, society and the economy in both urban and rural 
areas; their availability and reliability directly or indirectly 
influence the attainment of all SDGs (high confidence). Due 
to the connectivity of infrastructure systems, climate impacts, such 
as with thawing permafrost or severe storms affecting energy and 
transport networks, can propagate outside the reach of the hazard 
footprint and cause larger impacts and widespread regional disruption

(high confidence). Interdependencies between infrastructure systems 
have created new pathways for compounding climate risk, which 
has been accelerated by trends in information and communication 
technologies, increased reliance on energy, and complex (often global) 
supply chains (high confidence). (Figure TS.10 COMPLEX RISK) {2.3, 
4.6.2, 6.2, 6.3, Box 6.2, 9.7.3, 9.9.3, 9.9.5, 10.4.6, 10.5, 10.6, 11.3.3, 
11.3.5, 11.5.1, Box 11.4, 12.3, 12.5, 13.2, 13.6.1, 13.10.2, Box 14.5, 
14.5.5, 15.3, 16.5.2.3, 16.5.2.4, 16.5.3, 16.5.4, 17.2, 17.5, 18.3, 18.4, 
CCP2.2, CCP4.1, CCP5.3, CCP6.2}Economic sectors
TS.B.9 The effects of climate change impacts have been observed 
across economic sectors, although the magnitude of the damage 
varies by sector and by region (high confidence). Recent extreme 
weather and climate-induced events have been associated with 
large costs through damaged property, infrastructure and supply 
chain disruptions, although development patterns have driven

much of these increases (high confidence). Adverse impacts on 
economic growth have been identified from extreme weather 
events (high confidence) with large effects in developing 
countries (high confidence). Widespread climate impacts have 
undermined economic livelihoods, especially among vulnerable 
populations (high confidence). Climate impacts and projected 
risks have been insufficiently internalised into private- and 
public-sector planning and budgeting practices and adaptation 
finance (medium confidence). (Figure TS.3) {3.5.5, 4.3.1, 4.3.2, 
4.3.4, 6.2.4, 6.4.5, Table  6.11, 8.3.3, 8.3.5, 9.11.1, 9.11.4, 
CCP5.2.7, Box 10.7, 11.5.1, 13.10.1, 13.11.1, Box 14.5, Box 14.6, 
14.5.8, 15.3.4, 16.2.3, CCB FINANCE, CWGB ECONOMIC }
TS.B.9.1 Economic losses of climate change arise from adverse 
impacts on inputs, such as crop yields (very high confidence), 
water availability (high confidence) and outdoor labour 
productivity due to heat stress (high confidence). Greater

economic losses are observed for sectors with high direct climate 
exposure, including regional losses to agriculture, forestry, fisheries, 
energy and tourism (high confidence). Many industrial and service 
sectors are indirectly affected through supply disruptions, especially 
during and following extreme events (high confidence). Costs are also 
incurred from adaptation, disaster spending, recovery and rebuilding 
of infrastructure (high confidence). Estimates of the global effects of 
climate change on aggregate measures of economic performance and 
gross domestic product (GDP) range from negative to positive, in part 
due to uncertainty in how weather variability and climate impacts 
manifest in GDP (high confidence). Climate change is estimated to have 
slowed trends of decreasing economic inequality between developed 
and developing countries (low confidence), with particularly negative 
effects for Africa (medium confidence). {4.2.2, 4.3.1, 4.3.2, 4.7.5, 9.6.3,

9.11.1,, 11.3.4 11.5.2, Box 11.1, 13.6.1, 14.5.1, 14.5.2, 14.5.3, 15.3.3, 
15.3.4, 14.5.8, Box 14.6, Box 14.7, 16.2.3, CCP4.4, CCP4.5, CCP5.2.5, 
CCP6.2.5}
TS.B.9.2 A growing range of economic and non-economic 
losses has been detected and attributed to climate extremes 
and slow-onset events under observed increases in global 
temperatures in both low- and high-income countries (medium 
confidence). Extreme weather events, such as tropical cyclones, 
droughts and severe fluvial floods, have reduced economic growth 
in the short term (high confidence) and will continue to reduce it in 
the coming decades (medium confidence) in both developing and 
industrialised countries. Patterns of development have augmented the 
exposure of more assets to extreme hazards, increasing the magnitude 
of the losses (high confidence). Small Island Developing States have 
reported economic losses and a wide range of damage from tropical

cyclones and increases in sea level rise (high confidence). Wildfires 
partly attributed to climate change have caused substantial economic

55
Technical SummaryTSdamage in recent years in North America, Australia and the Arctic (high 
confidence). {4.2.4, 4.2.5, 4.7.5, 8.2, 8.3.4, 8.4.1, 8.4.5, Box 8.5, 9.11.1, 
Box 10.7, Box 11.1, 11.5.2, Table 11.13, 13.10.1, Box 14.6, 15.7, 15.8, 
16.2.3, 16.5.2, CCB DISASTER, CWGB ECONOMIC}
TS.B.9.3 Economic livelihoods that are more climate sensitive 
have been disproportionately degraded by climate change (high 
confidence). Climate-sensitive livelihoods are more concentrated 
in regions that have higher socioeconomic vulnerabilities and lower 
adaptive capacities, exacerbating existing inequalities (medium 
confidence). Extreme events have also had more pronounced adverse 
effects in poorer regions and on more vulnerable populations (medium 
confidence). These greater economic effects have further reduced the 
ability of these populations to adapt to existing impacts (medium 
confidence). Within populations, the poor, women, children, elderly

and Indigenous populations have been especially vulnerable due to 
a combination of factors, including gendered divisions of paid and/
or unpaid labour (high confidence). {4.3.1, 4.3.8, 8.3.5, 9.1.1, 13.8.1, 
Box 14.6, 16.2.3, CCB GENDER, CWGB ECONOMIC}
TS.B.9.4 Current planning and budgeting practices have given 
insufficient consideration to climate impacts and projected 
risks, placing more assets and people in regions with current 
and projected climate hazards (medium confidence). Existing 
adaptation has prevented greater economic losses (medium 
confidence), yet adaptation gaps remain due to limited financial 
resources, including gaps in international adaptation finance and 
competing priorities in budget allocations (medium confidence). 
Insufficient consideration of these impacts, however, has placed more 
assets in areas that are highly exposed to climate hazards (medium 
confidence). {4.7.1, 6.4.5, Box 8.3, 9.4.1, 10.5, 10.6, 11.8.1, 13.11.1,

Box 14.6, 15.3.3, 16.4.3, CCP5.2.7, CCB FINANCE}
TS.C Projected Impacts and Risks
This section identifies future impacts and risks under different degrees 
of climate change. As a result, 127 key risks have been found across 
regions and sectors. These are integrated as eight overarching risks 
(called Representative Key Risks, RKRs) which relate to low-lying 
coastal systems; terrestrial and ocean ecosystems; critical physical 
infrastructure, networks and services; living standards and equity; 
human health; food security; water security; and peace and migration. 
Risks are projected to become severe with increased warming and under 
ecological or societal conditions of high exposure and vulnerability. The 
intertwined issues of biodiversity loss and climatic change together 
with human demographic changes, particularly rapid growth in low-
income countries, an ageing population in high-income countries 
and rapid urbanisation are seen as core issues in understanding risk

distribution at all scales. {16.5.2, Table 16.A.4, SMTS.2}
TS.C.1 Without urgent and ambitious emissions reductions, 
more terrestrial, marine and freshwater species and ecosystems 
will face conditions that approach or exceed the limits of 
their historical experience (very high confidence). Threats to species and ecosystems in oceans, coastal regions and on land, 
particularly in biodiversity hotspots, present a global risk that 
will increase with every additional tenth of a degree of warming 
(high confidence). The transformation of terrestrial and ocean/
coastal ecosystems and loss of biodiversity, exacerbated by 
pollution, habitat fragmentation and land use changes, will 
threaten livelihoods and food security (high confidence). 
(Figure  TS.5 ECOSYSTEMS) {2.5.1, 2.5.2, 2.5.3, Figure  2.6, 
Figure 2.7, Figure 2.8, 2.5.4, Figure 2.11, Table 2.5, 3.2.4, 3.4.2, 
3.4.3, 4.5.5, 9.6.2, 12.4, 13.10.2, 14.5.1, 14.5.2, 15.3.3, 16.4.2,

16.4.3, CCP1.2.4, CCP5.3.2, CCP5.2.7, CCP 7.3.5}
TS.C.1.1 Near-term warming will continue to cause plants and 
animals to alter their timing of seasonal events (high confidence) 
and to move their geographic ranges (high confidence). Risks 
escalate with additional near-term warming in all regions and domains 
(high confidence). Without urgent and deep emissions reductions, some 
species and ecosystems, especially those in polar and already-warm 
areas, will face temperatures beyond their historical experience in 
coming decades (e.g., >20% of species on some tropical landscapes and 
coastlines at 1.5°C global warming). Unique and threatened ecosystems 
are expected to be at high risk in the very near term at 1.2°C global 
warming levels (very high confidence) due to mass tree mortality, coral 
reef bleaching, large declines in sea-ice-dependent species and mass 
mortality events from heatwaves. Even for less vulnerable species and

systems, projected climate change risks surpass hard limits to natural 
adaptation, increasing species at high risk of population declines 
(medium confidence) and loss of critical habitats (medium to high 
confidence) and compromising ecosystem structure, functioning and 
resilience (medium confidence). At a global warming of 2°C with 
associated changes in precipitation global land area burned by wildfire 
is projected to increase by 35% (medium confidence). (Figure  TS.5 
ECOSYSTEMS) {2.5.1, 2.5.2, 2.5.3, 2.5.4, 2.6.1, Figure  2.6, Figure  2.7, 
Figure 2.8, Figure 2.9, Figure 2.11, Table 2.5, 3.4.2, 3.4.3, 3.5.5, 4.5.5, 
9.6.2, 11.3.1, 11.3.2, 12.3, 13.10.2, 14.5.1, 14.5.2, 15.3.3, 16.4.2, 16.4.3, 
CCP1.2.1, CCP1.2.4, CCP5.3.2, CCP7.3, CCB DEEP , CCB SLR}
TS.C.1.2 Risks to ecosystem integrity, functioning and resilience 
are projected to escalate with every tenth of a degree increase 
in global warming (very high confidence). Beginning at 1.5°C

warming, natural adaptation faces hard limits, driving high risks of 
biodiversity decline, mortality, species extinction and loss of related 
livelihoods (high confidence). At 1.6°C (median estimate), >10% of 
species are projected to become endangered, increasing to >20% 
at 2.1°C, representing severe biodiversity risk (medium confidence). 
These risks escalate with warming, most rapidly and severely in areas 
at both extremes of temperature and precipitation (high confidence). 
With warming of 3°C, >80% of marine species across large parts of the 
tropical Indian and Pacific Ocean will experience potentially dangerous 
climate conditions (medium confidence). Beyond 4°C warming, 
projected impacts expand, including extirpation of approx. 50% of 
tropical marine species (medium confidence) and biome shifts (changes 
in the major vegetation form of an ecosystem) across 35% of global 
land area (medium confidence). These will lead to a shift of much of

the Amazon rainforest to drier and lower-biomass vegetation (medium 
confidence), poleward shifts of boreal forest into treeless tundra across 
the Arctic and upslope shifts of montane forests into alpine grassland

56
TS(high confidence). (Figure TS.5 ECOSYSTEMS) { 2.3.2, 2.5, 2.5.1, 2.5.2, 
2.5.3, 2.5.4, 3.4.2, 3.4.3, 9.6.2, 11.3.1, 11.3.2, 12.3, 13.3.1, 13.4.1, 
13.10.2, 16.4.3, 16.5.2, Figure 2.6, Figure 2.7, Figure 2.8, Figure 2.11, 
Figure 3.18, Table 2.6.7, Box 3.2, 9.6.2, Box 11.2, CCP1.2.1, CCP1.2.2, 
CCP5.3.1, CCP5.3.2.3, CC6P4, CCP7.3, CCB EXTREMES}
TS.C.1.3 Damage and degradation of ecosystems exacerbate 
the projected impacts of climate change on biodiversity (high 
confidence). Space for nature is shrinking as large areas of 
forest are lost to deforestation (high confidence), peat draining 
and agricultural expansion, land reclamation and protection 
structures in urban and coastal settlements (high confidence). 
Currently less than 15% of the land and 8% of the ocean are under some 
form of protection, and enforcement of protection is often weak (high 
confidence). Future ecosystem vulnerability will strongly depend on

developments in society, including demographic and economic change 
(high confidence). Deforestation is projected to increase the threat to 
terrestrial ecosystems, as is increasing the use of hard coastal protection 
of cities and settlements by the sea for coastal ecosystems. Coordinated 
and well-monitored habitat restoration, protection and management, 
combined with consumer pressure and incentives, can reduce non-
climatic impacts and increase resilience (high confidence). Adaptation 
and mitigation options, such as afforestation, dam construction and 
coastal infrastructure placements, can increase vulnerability, compete 
for land and water and generate risks for the integrity and functioning 
of ecosystems (high confidence). {2.2, 2.3, 2.3.1, 2.3.2, 2.4.3, 2.5.4, 
2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.6.6, 2.6.7, Figure 2.1, 3.4.2, 3.5, 3.6.3, 4.5.5, 
9.6.2, 9.6.3, 9.6.4, 9.7.2, 11.3.1, 12.3.3, 12.3.4, 13.3.2, 13.4.2, 13.10.2,

13.11.3, 14.5.2, 14.5.4, CCP5.2.1, CCP5.2.5, CCP5.3.2, CCP5.4.1, CCB 
TS.C.1.4 Changes induced by climate change in the physiology, 
biomass, structure and extent of ecosystems will determine 
their future carbon storage capacity (high confidence). In 
terrestrial ecosystems, the fertilisation effects of high atmospheric 
CO 2 concentrations on carbon uptake will be increasingly saturated 
and limited by warming and drought (medium confidence). Increases 
in wildfires, tree mortality, insect pest outbreaks, peatland drying and 
permafrost thaw (high confidence) all exacerbate self-reinforcing 
feedbacks between emissions from high-carbon ecosystems and 
warming with the potential to turn many ecosystems that are currently 
net carbon sinks into sources (medium confidence). In coastal areas 
beyond 1.5°C warming, blue carbon storage by mangroves, marshes 
and seagrass habitats are increasingly threatened by rising sea levels

and the intensity, duration and extent of marine heatwaves, as well as 
adaptation options (including coastal development) (high confidence). 
Changes in ocean stratification are projected to reduce nutrient supply 
and alter the magnitude and efficiency of the biological carbon pump 
(medium confidence). {2.5.2, 2.5.3, 2.5.4, Figure 2.9, Figure 2.11, 3.2.2, 
3.4.2, 3.4.3, Box  3.4, 9.5.10, 9.6.2, 10.4.2, 10.4.3, 11.3.1, 11.3.4, 
Box  11.5, 12.3.3, 12.3.4, 12.3.5, 12.3.6, Table  12.6, 13.3.1, 14.5.1, 
15.3.3, CCB SLR, CCP1.2.4, CCP1.3, CCP7.3, WGI AR6 5.4}
TS.C.1.5 Extinction risk increases disproportionately from global 
warming of 1.5°C to 3°C and is especially high for endemic 
species and species rendered less resilient by human-induced 
non-climate stressors (very high confidence). The median values for percentage of species at very high risk of extinction are 9% at 
1.5°C, 10% at 2°C, 12% at 3°C, 13% at 4°C and 15% at 5°C (high

confidence), with the likely range of estimates having a maximum of 
14% at 1.5°C and rising to a maximum of 48% at 5°C. Extinction risks 
are higher for species in biodiversity hotspots (medium confidence), 
reaching 24% of species at very high extinction risk above 1.5°C, 
with yet higher proportions for endemic species of 84% in mountains 
(medium confidence) and 100% on islands (medium confidence). 
Thousands of individual populations are projected to be locally lost, 
which will reduce species diversity in some areas where there are no 
species moving in to replace them, for example, in tropical systems 
(high confidence). Novel species interactions at the cold edge of 
species’ distribution may also lead to extirpations and extinctions of 
newly encountered species (low confidence). Palaeo records indicate 
that at extreme warming levels (>5°C), mass extinctions of species 
occur (medium confidence). Among the thousands of species at risk,

many are species of ecological, cultural and economic importance. 
{2.3.1, 2.3.3, 2.5.1, 2.5.2, 2.5.3, 2.5.4, Figure 2.1, Figure 2.6, Figure 2.7, 
Figure 2.8, Figure 2.11, 3.4.2, 3.4.3, 4.5.5, 9.6.2, 13.3.1, 13.4.1, 13.10.1, 
13.10.2, CCP1.2.1¸ CCP1.2.4, CCP5.3.1, CCB PALEO}
TS.C.2 Cumulative stressors and extreme events are projected 
to increase in magnitude and frequency (very high confidence) 
and will accelerate projected climate-driven shifts in eco-
systems and loss of the services they provide to people (high 
confidence). These processes will exacerbate both stress on 
systems already at risk from climate impacts and non-climate 
impacts like habitat fragmentation and pollution (high 
confidence). The increasing frequency and severity of extreme 
events will decrease the recovery time available for ecosystems 
(high confidence). Irreversible changes will occur from the 
interaction of stressors and the occurrence of extreme events

(very high confidence), such as the expansion of arid systems 
or total loss of stony coral and sea ice communities. {2.3, 2.3.1, 
3.2.2, 3.4.2, 3.4.3, 13.3.1, 13.4.1, 13.10.2, 14.5.2, 14.5.5, 14.5.9, 
Box 14.2, Box 14.4}
TS.C.2.1. Ecosystem integrity is threatened by the positive 
feedback between direct human impacts (land use change, 
pollution, overexploitation, fragmentation and destruction) and 
climate change (high confidence). In the case of the Amazon forest, 
this could lead to large-scale ecological transformations and shifts 
from a closed, wet forest into a drier and lower-biomass vegetation 
(medium confidence). If these pressures are not successfully addressed, 
the combined and interactive effects between climate change, 
deforestation and forest degradation, and forest fires are projected 
to lead to a reduction of over 60% of the area covered by forest in 
response to 2.5°C global warming level (medium confidence). Some

habitat-forming coastal ecosystems, including many coral reefs, kelp 
forests and seagrass meadows, will undergo irreversible phase shifts 
due to marine heatwaves with global warming levels >1.5°C and are 
at high risk this century even in <1.5°C scenarios that include periods 
of temperature overshoot beyond 1.5°C (high confidence). Under 
SSP1–2.6, coral reefs are at risk of widespread decline, loss of structural 
integrity and transitioning to net erosion by mid-century due to the 
increasing intensity and frequency of marine heatwaves (very high 
confidence). Due to these impacts, the rate of sea level rise is very likely

57
Technical SummaryTSto exceed that of reef growth by 2050, absent adaptation. In response 
to heatwaves, bleaching of the Great Barrier Reef is projected to occur 
annually if warming increases above 2.0°C, resulting in widespread 
decline and loss of structural integrity (very high confidence). Global 
warming of 3.0°C–3.5°C increases the likelihood of extreme and lethal 
heat events in western and northern Africa (medium confidence) and 
across Asia. Drought risks are projected to increase in many regions 
over the 21st century (very high confidence). {2.5.2, 2.5.4, 3.4.2, 3.4.3, 
9.5.3, 9.10, 10.2.1, 10.3.7, 11.3.1, 11.3.2, Box 11.2, Table 11.14, 13.3.1, 
13.4.1, 14.5.3, Box 14.3, CCP7.3.6}
TS.C.2.2 Pests, weeds and disease occurrence and distribution 
are projected to increase with global warming, amplified by 
climate change induced extreme events (e.g., droughts, floods, 
heatwaves and wildfires), with negative consequences for

ecosystem health, food security, human health and livelihoods 
(medium confidence). Invasive plant species are predicted to expand 
both in latitude and altitude (high confidence). Climatically disrupted 
ecosystems will make organisms more susceptible to disease via 
reduced immunity and biodiversity losses, which can increase disease 
transmission. Risks of climate-driven emerging zoonoses will increase. 
Depending on location and human–wildlife interactions, climate-driven 
shifts in distributions of wild animals increase the risk of emergence of 
novel human infectious diseases, as has occurred with SARS, MERS and 
SARS-CoV-2 (medium confidence). Changes in the rates of reproduction 
and distribution of weeds, insect pests, pathogens and disease vectors 
will increase biotic stress on crops, forests and livestock (medium 
evidence, high agreement). Pest and disease outbreaks will require 
greater use of control measures, increasing the cost of production,

food safety impacts and the risk of biodiversity loss and ecosystem 
impacts. These control measures will become costlier under climate 
change (medium confidence). {2.4.2, 2.5.1, 2.5.2, 3.5.5, 4.2.4, 4.2.5, 
4.3.1, 5.4.1, 5.4.3, 5.5.2, 5.9.4, 5.12, 11.3.1, 13.5.1, 14.5.4, 14.5.6, CCB 
ILLNESS, CCB MOVING PLATE, CCB COVID}
TS.C.2.3 The ability of natural ecosystems to provide carbon stor -
age and sequestration is increasingly impacted by heat, wildfire, 
droughts, loss and degradation of vegetation from land use and 
other impacts (high confidence) . Limiting the global temperature 
increase to 1.5°C, compared to 2°C, could reduce projected permafrost 
CO 2 losses by 2100 by 24.2 GtC (low confidence). A temperature rise of 
4°C by 2100 is projected to increase global burned area 50–70% and 
fire frequency by approx. 30%, potentially releasing 11–200 GtC from 
the Arctic alone (medium confidence). Changes in plankton community

structure and productivity are projected to reduce carbon sequestration 
at depth (low to medium confidence). {2.5.2, 2.5.3, 2.5.4, Figure 2.11, 
Table 2.5, 3.4.2, 3.4.3, 3.4.2, 4.2.4, 13.3.1, 13.4.1, Box 14.7, Box 3.4}
TS.C.2.4 Climate change impacts on marine ecosystems are 
projected to lead to profound changes and irreversible losses 
in many regions, with negative consequences for human ways 
of life, economy and cultural identity (medium confidence). For 
example, by 2100, 18.8% ± 19.0% to 38.9% ± 9.4% of the ocean 
will very likely undergo a change of more than 20  days (advances 
and delays) in the start of the phytoplankton growth period under 
SSP1-2.6 and SSP5-8.5 respectively (low confidence). This altered 
timing increases the risk of temporal mismatches between plankton blooms and fish spawning seasons (medium to high confidence) and 
increases the risk of fish recruitment failure for species with restricted

spawning locations, especially in mid- to high latitudes of the northern 
hemisphere (low confidence) but provide short-term opportunities to 
countries benefiting from shifting fish stocks (medium confidence). 
{3.4.2, 3.4.3, 3.5.6, 5.8.3, 5.9.3, 11.3.1, 13.4.1, 13.5.1, 14.5.2, CCP6.3, 
CCB MOVING SPECIES}
TS.C.2.5 Warming pathways that temporarily increase global 
mean temperature over 1.5°C above pre-industrial for multi-
decadal time spans imply severe risks and irreversible impacts 
in many ecosystems (high confidence). Major risks include loss of 
coastal ecosystems such as wetlands and marshlands from committed 
sea level rise associated with overshoot warming (medium confidence), 
coral reefs and kelps from heat-related mortality and associated 
ecosystem transitions (high confidence), disruption of water flows in 
high-elevation ecosystems from glacier loss and shrinking snow cover, 
and local extinctions of terrestrial species. {2.5, 3.4.2, 3.4.4, 4.7.4,

9.6.2, 12.3, 13.10.2, CCP5.3.1}
Food systems and food security
TS.C.3 Climate change will increasingly add pressure on food 
production systems, undermining food security (high confidence). 
With every increment of warming, exposure to climate hazards 
will grow substantially (high confidence), and adverse impacts 
on all food sectors will become prevalent, further stressing 
food security (high confidence). Regional disparity in risks to 
food security will grow with warming levels, increasing poverty 
traps, particularly in regions characterised by a high level of 
human vulnerability (high confidence). (Figure  TS.4) {4.5.1, 
4.6.1, 5.2.2, 5.4.3, 5.4.4, 5.5.3, 5.8.3, 5.9.3, 5.12.4, 7.3.1, 9.8.2, 
9.8.5, 13.5.1, 14.5.4, 16.5.2, 16.6.3, CCB MOVING PLATE}
TS.C.3.1 Climate change will increasingly add pressure on 
terrestrial food production systems with every increment of 
warming (high confidence). Some current global crop and livestock

areas will become climatically unsuitable depending on the emissions 
scenario (high confidence; 10% globally by 2050, by 2100 over 30% 
under SSP-8.5 versus below 8% under SSP1-2.6). Compared to 1.5°C 
global warming level, 2°C global warming level will even further 
negatively impact food production where current temperatures are 
already high as in lower latitudes (high confidence). Increased and 
potentially concurrent climate extremes will increase simultaneous 
losses in major food-producing regions (medium confidence). The 
adverse effects of climate change on food production will become 
more severe when global temperatures rise by more than 2°C (high 
confidence). At 3°C or higher global warming levels, exposure to 
climate hazards will grow substantially (high confidence), further 
stressing food production, notably in sub-Saharan Africa and South 
and South East Asia (high confidence). (Figure TS.4) {4.5.1, 4.6.1, 5.2.2,

5.4.3, 5.4.4, 5.5.3, 5.8.3, 5.9.3, 5.12.4, 9.8.2, 9.8.5, 11.3.4, 13.5.1, 
14.5.4, 16.5.2, 16.6.3, CCB MOVING PLATE}
TS.C.3.2 Climate change will significantly alter aquatic food 
provisioning services, with direct impacts on food-insecure 
people (high confidence). Global ocean animal biomass will

58
TSGlobal and regional risks for increasing levels of global warming
234
1.5
1
0
SSP1-1.9
SSP2-4.5
SSP5-8.5
RFC4
Global
aggregate
Unique and
Extreme
weather
singular
events
High
Moderate
•••••
••••
0234
1.5
1••••••
•• •••• •• •
•• ••• ••
Warm water

59
Technical SummaryTS(f) Examples of regional key risks
4
023
1.5
from sea ice
 change in
the Arctic
••••••••
Changes in
and
Paciﬁc Cod
•• •• •••
Costs
and losses
for key 
•• ••
Changes 
in krill
ﬁsheries
in the
Antarctic
••• •• ••
Sea ice
dependent
 in the
Antarctic
•• •• ••
0234
1.5
impacts on
cities and
••••••••
Loss and
Australia
Reduced
tourism-
related
North
America
••• ••••
Costs and 
damages
related to
•••• ••
Lyme
disease in
North
America
under
scenario
•••• •••
0234
1.5
impacts of
sea level
rise in the
••••••
Food

livestock
in Africa
•••• • •••
and
in Africa
••• ••• •••
morbidity
disease
in Africa
•••••• ••
0234
1.5
Heat stress,
mortality
and
morbidity
to people
in Europe
••• ••• ••
Coastal
ﬂooding to
people
and
in Europe
•• •• ••
Europe
••• ••• ••
and
in the
••• ••• •••
Health and
wellbeing
in the

Figure TS.4 |  Synthetic diagrams of global and sectoral assessments and examples of regional key risks. Diagrams show the change in the levels of impacts and 

60
TSdecrease by 5.7% ± 4.1% and 15.5% ± 8.5% under SSP1-2.6 and 
SSP5-8.5 respectively by 2080–2099 relative to 1995–2014 (medium 
confidence), affecting food provisioning, revenue value and distribution. 
Catch composition will change regionally, and the vulnerability of 
fishers will partially depend on their ability to move, diversify and 
leverage technology (medium confidence). Global marine aquaculture 
will decline under increasing temperature and acidification conditions 
by 2100, with potential short-term gains for finfish aquaculture in 
some temperate regions and overall negative impacts on bivalve 
aquaculture due to habitat reduction (medium confidence). Changes 
in precipitation, sea level rise, temperature and extreme events will 
negatively affect food provisioning from inland aquatic systems 
(medium confidence), which provide a significant source of livelihoods 
and food for direct human consumption, particularly in Asia and Africa.

{3.4.2, 3.4.3, 3.5.3, 3.6.2, 3.6.3, 5.8.3, 5.9.3, 5.13, 9.8.5, 13.5.1, 14.5.2, 
CCP6.2.3, CCP6.2.4, CCP6.2.5, CCP6.2.6, CCP6.2.8, CCB MOVING 
PLATE, CCB SLR}
TS.C.3.3 Climate change will increasingly add significant 
pressure and regionally different impacts on all components 
of food systems, undermining all dimensions of food security 
(high confidence). Extreme weather events will increase risks 
of food insecurity via spikes in food prices, reduced food diversity 
and reduced income for agricultural and fishery livelihoods (high 
confidence), preventing achievement of the UN SDG 2 (‘Zero Hunger’) 
by 2030 in regions with limited adaptive capacities, including Africa, 
small island states and South Asia (high confidence). With about 2°C 
warming, climate-related changes in food availability and diet quality are estimated to increase nutrition-related diseases and the number of 
undernourished people by 2050, affecting tens (under low vulnerability

and low warming) to hundreds of millions of people (under high 
vulnerability and high warming, i.e., SSP-3-RCP6.0), particularly among 
low-income households in low- and middle-income countries in sub-
Saharan Africa, South Asia and Central America (high confidence), for 
example, between 8 million under SSP1-6.0 to up to 80 million people 
under SSP3-6.0. At 3°C or higher global warming levels, adverse 
impacts on all food sectors will become prevalent, further stressing 
food availability (high confidence), agricultural labour productivity and 
food access (medium confidence). Regional disparity in risks to food 
security will grow at these higher warming levels, increasing poverty 
traps, particularly in regions characterised by a high level of human 
vulnerability (high confidence). {4.5.1, 4.6.1, 5.2.2, 5.4.3, 5.4.4, 5.5.3, 
5.8.3, 5.9.3, 5.12.4, 7.3.1, 9.8.2, 9.8.5, 13.5.1, 14.5.4, 16.5.2, 16.6.3, 
CCB MOVING PLATE}
TS.C.3.4 Climate change is projected to increase malnutrition

through reduced nutritional quality, access to balanced food 
and inequality (high confidence). Increased CO 2 concentrations 
promote crop growth and yield but reduce the density of important 
nutrients in some crops (high confidence) with projected increases in 
undernutrition and micronutrient deficiency, particularly in countries 
that currently have high levels of nutrient deficiency (high confidence) 
and regions with low access to diverse foods (medium confidence). 
Marine-dependent communities, including Indigenous Peoples and 
local peoples, will be at increased risk of malnutrition due to losses of 
seafood-sourced nutrients (medium confidence).  {3.5.3, 5.2.2, 5.4.2, (a) Global surface temperature changes in °C relative to 1850–1900. These changes were obtained by combining CMIP6 model simulations with observational constraints based

on past simulated warming, as well as an updated assessment of equilibrium climate sensitivity (Box TS.2). Changes relative to 1850–1900 based on 20-year averaging periods are 
calculated by adding 0.85°C (the observed global surface temperature increase from 1850–1900 to 1995–2014) to simulated changes relative to 1995–2014. Very likely ranges 
are shown for SSP1-2.6 and SSP3-7.0 (WGI AR6 Figure SPM.8). Assessments were carried out at the global scale for (b), (c), (d) and (e).
(b) The Reasons for Concern (RFC) framework communicates scientific understanding about accrual of risk for five broad categories. Diagrams are shown for each RFC, assuming 
low to no adaptation (i.e., adaptation is fragmented, localized and comprises incremental adjustments to existing practices). However, the transition to a very high risk level has an

very high risk of severe impacts and the presence of significant irreversibility or the persistence of climate-related hazards, combined with limited ability to adapt due to the nature 
of the hazard or impacts/risks. The horizontal line denotes the present global warming of 1.09°C which is used to separate the observed, past impacts below the line from the future

and coastal flooding. RFC3: Distribution of impacts: risks/impacts that disproportionately affect particular groups due to uneven distribution of physical climate change hazards, 
exposure or vulnerability. RFC4: Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, lives

by a structured approach to improve robustness and facilitate comparison between AR5 and AR6. Risks for (c) terrestrial and freshwater ecosystems and (d) ocean ecosystems. 
For (c) and (d), diagrams shown for each risk assume low to no adaptation. The transition to a very high risk level has an emphasis on irreversibility and adaptation limits.
(e) Climate-sensitive human health outcomes under three scenarios of adaptation effectiveness. The assessed projections were based on a range of scenarios, including SRES,

CMIP5, and ISIMIP , and, in some cases, demographic trends. The diagrams are truncated at the nearest whole °C within the range of temperature change in 2100 under three SSP 
scenarios in panel (a).
likelihood of adverse consequences; temporal characteristics of the risk; and ability to respond to the risk, e.g., by adaptation. The full set of 127 assessed global and regional key 
risks is given in SMTS.4 and SM16.7. Diagrams are provided for some risks. The development of synthetic diagrams for Small Islands, Asia and Central and South America were

limited by the availability of adequately downscaled climate projections, with uncertainty in the direction of change, the diversity of climatologies and socio-economic contexts 
across countries within a region, and the resulting low number of impact and risk projections for different warming levels. Absence of risks diagrams does not imply absence 
of risks within a region. (Box TS.2) {Figure 2.11, Figure SM3.1, Figure 7.9, Figure 9.6, Figure 11.6, Figure 13.28, 16.5, 16.6, Figure 16.15, SM16.3, SM16.4, SM16.5, SM16.6 
(methodologies), SM16.7, Figure CCP4.8, Figure CCP4.10, Figure CCP6.5, WGI AR6 2, WGI AR6 SPM A.1.2, WGI AR6 Figure SPM.8}

61
Technical SummaryTS5.4.3, 5.5.2, 5.12.1, 5.12.4, 7.3.1, 9.8.5, 16.5.2, CCP6.2.3, CCP6.2.4, 
CCP6.2.5, CCP6.2.6, CCP6.2.8, CCB MOVING PLATE}
TS.C.3.5 Climate change will further increase pressures on 
those terrestrial ecosystem services which support global food 
production systems (high confidence). Climate change will reduce 
the effectiveness of pollination as species are lost from certain areas, or 
the coordination of pollinator activity and flower receptiveness will be 
disrupted in some regions (high confidence). Greenhouse gas emissions 
will negatively impact air, soil and water quality, exacerbating direct 
climatic impacts on yields (high confidence). {5.4.3, 5.5.3, 5.7.1, 5.7.4, 
5.9.4, 5.10.3, Box 5.3, Box 5.4, 13.10.2, 14.5.4, CCB MOVING PLATE, 
SRCCL}
TS.C.3.6 Climate change will compromise food safety through 
multiple pathways (high confidence).  Higher temperatures and 
humidity will expand the risk of aflatoxin contamination into higher-

latitude regions (high confidence). More frequent  and intense  flood 
events and increased melting of snow and ice will increase food 
contamination (high confidence). Aquatic food safety will decrease 
through increased detrimental impacts from harmful algal blooms 
(high confidence) and human exposure to elevated bioaccumulation 
of persistent organic pollutants and methylmercury (low to medium 
confidence). These negative food safety impacts will be greater without 
adaptation and fall disproportionately on low-income countries and 
communities with high consumption of seafood, including coastal 
Indigenous communities (medium confidence). {3.6.3, 5.4.3, 5.8.1, 
5.8.3, 5.11.1, 5.12.4, Box 5.10, 7.3.1, 14.5.6, CCB ILLNESS}
TS.C.4 Water-related risks are projected to increase at all 
warming levels, with risks being proportionally lower at 1.5°C 
than at higher degrees of warming (high confidence). Regions

and populations with higher exposure and vulnerability are pro-
jected to face greater risks than others (medium confidence). 
Projected changes in the water cycle, water quality, cryosphere 
changes, drought and flood will negatively impact natural and 
human systems (high confidence). {2.5.1, 2.5.2, 2.5.3, 2.5.4, 
2.6.3, 3.5.5, 4.4.1, 4.4.2, 4.4.3, 4.4.4, 4.4.5, 4.4.6, 4.5.1, 4.5.2, 
4.5.3, 4.5.4, 4.5.5, 4.5.6, 4.5.8, 4.6.1, Box  4.1, Box  4.3, 5.4.3, 
5.5.2, 5.8.1, 5.8.2, 5.8.3, 5.9.1, 5.9.3, 5.11.1, 5.11.3, 5.12.3, 5.13, 
6.1, 6.2, 6.3, 6.4, 7.3.1, 8.3, 8.4.4, 9.5.8, 9.5.3, 9.5.4, 9.5.5, 9.5.6, 
9.5.7, 9.7.1, 9.7.2, 10.4.6, 10.4.7, Box  10.2, Box  10.5, 11.2.2, 
11.3.3, 11.3.4, Box 11.3, Box 11.4, 12.3, 13.2.1, 13.2.2, 13.6.2, 
13.10.2, 13.10.3, Box 13.1, 14.5.3, 14.5.5, 14.5.9, 16.5.2, 16.6.1, 
CCP1.2.1, CCP1.2.3.2, CCP2.2, CCP4.2, CCP4.3, CCP5.3.2}
TS.C.4.1 Water-related risks are projected to increase with 
every increment in warming level, and the impacts will be felt

disproportionately by vulnerable people in regions with high 
exposure and vulnerability (high confidence). About 800 million 
to 3  billion people at 2°C and about 4  billion at 4°C warming are 
projected to experience different levels of water scarcity (medium 
confidence), leading to increased water insecurity. At 4°C global 
warming by the end of the century, approximately 10% of the global 
land area is projected to face simultaneously increasing high extreme streamflow and decreasing low extreme streamflow, affecting over 
2.1 billion people (medium confidence). Globally, the greatest risks to 
attaining global sustainability goals come from risks to water security 
(high confidence). {4.4.1, 4.4.3, 4.4.5, 4.5.4, 4.6.1, Box 4.2, 5.8.3, 5.9.3, 
5.13, 8.3, 8.4.4., 9.7.2, 12.3, Table 12.3, 13.2.1, 13.2.2, 13.6.1, 13.10.2, 
15.3.3, 16.6.1, CCB SLR}
TS.C.4.2 Projected cryosphere changes will negatively impact 
water security and livelihoods, with higher severity of risks

at higher levels of global warming (high confidence). Glacier 
mass loss, permafrost thaw and decline in snow cover are projected 
to continue beyond the 21st century (high confidence). Many low-
elevation and small glaciers around the world will lose most of their 
total mass at 1.5°C warming (high confidence). Glaciers are likely to 
disappear by nearly 50% in High Mountain Asia and about 70% in 
Central and Western Asia by the end of the 21st century under the 
medium warming scenario. Glacier lake outburst flood will threaten 
the security of local and downstream communities in High Mountain 
Asia (high confidence). By 2100, annual runoff in one-third of the 56 
large-scale glacierised catchments are projected to decline by over 
10%, with the most significant reductions in Central Asia and the 
Andes (medium confidence). Cryosphere related changes in floods, 
landslides and water availability have the potential to lead to severe

consequences for people, infrastructure and the economy in most 
mountain regions (high confidence). {4.4.2, 4.4.3, 4.5.8, 9.5.8, 10.4.4, 
Box 10.5, 11.2.2, Box 11.6, 14.2, 16.5.2, CCP1.2.3, CCP5.3.1, CCP5.3.2, 
SROCC}
TS.C.4.3 Projected changes in the water cycle will impact 
various ecosystem services (medium confidence). By 2050, 
environmentally critical streamflow is projected to be affected in 
42% to 79% of the world’s watersheds, causing negative impacts 
on freshwater ecosystems (medium confidence). Increased wildfire, 
combined with soil erosion due to deforestation, could degrade water 
supplies (medium confidence). Projected climate-driven water cycle 
changes, including increases in evapotranspiration, altered spatial 
patterns and amount of precipitation, and associated changes in 
groundwater recharge, runoff and streamflow, will impact terrestrial, 
freshwater, estuarine and coastal ecosystems and the transport of

materials through the biogeochemical cycles, impacting humans 
and societal well-being (medium confidence). In Africa, 55–68% 
of commercially harvested inland fish species are vulnerable to 
extinction under 2.5°C global warming by 2071–2100. In Central and 
South America, disruption in water flows will significantly degrade 
ecosystems such as high-elevation wetlands (high confidence). {2.5.1, 
2.5.2, 2.5.3, 2.5.4, 2.6.3, 3.5.5, 3.5.5, 4.4.1, 4.4.3, 4.4.5, 4.4.6, 4.5.4, 
5.4.3, 9.8.5, 11.3.1, 12.3, 14.2.2, 14.5.3, 15.3.3, CCP1.2.1}
TS.C.4.4  Drought risks and related societal damage are 
projected to increase with every degree of warming (medium 
confidence). Under RCP6.0 and SSP2, the population that is projected 
to be exposed to extreme to exceptional low total water storage will 
reach up to 7% over the 21st century (medium confidence). Under 
RCP8.5, aridity zones could expand by one-quarter of the 1990 area 
by 2100. In southern Europe, more than a third of the population

will be exposed to water scarcity at 2°C, and the risk doubles at 3°C, 
with significant economic losses (medium confidence).  Over large

62
TSareas of northern South America, the Mediterranean, western China 
and high latitudes in North America and Eurasia, the frequency of 
extreme agricultural droughts is projected to be 150% to 200% more 
likely at 2°C and over 200% more likely at 4°C (medium confidence). 
Above 2°C, the frequency and duration of meteorological drought are 
projected to double over North Africa, the western Sahel and southern 
Africa (medium confidence). More droughts and extreme fire weather 
are projected in southern and eastern Australia (high confidence) and 
over most of New Zealand (medium confidence).  {4.5.1, 4.6.1, Box 4.1, 
4.4.1, 4.4.1.1, 4.4.4, 4.4.5, 4.5.1, 4.5.4, 4.5.5, 4.6.1, 6.2.2, 6.2.3, 7.3.1, 
9.5.2, 9.5.3, 9.5.6, 9.9.4, 10.4.6; 11.2.2, Box  11.6, 14.5.3, 14.5.5, 
CCP3.3.1, CCP3.3.2, CWGB URBAN}
TS.C.4.5 Flood risks and societal damages are projected to 
increase with every increment of global warming (medium

confidence). The projected increase in precipitation intensity (high 
confidence) will increase rain-generated local flooding (medium 
confidence). Direct flood damage is projected to increase by four to five 
times at 4°C compared to 1.5°C (medium confidence). A higher sea level 
with storm surge further inland may create more severe coastal flooding 
(high confidence). Projected intensifications of the hydrological cycle 
pose increasing risks, including potential doubling of flood risk  and 
1.2- to 1.8-fold increase in GDP loss due to flooding between 1.5°C and 
3°C (medium confidence). Projected increase in heavy rainfall events 
at all levels of warming in many regions in Africa will cause increasing 
exposure to pluvial and riverine flooding  (high confidence), with 
expected human displacement increasing 200% for 1.6°C and 600% 
for 2.6°C. A 1.5°C increase would result in an increase of 100–200% 
in the population affected by floods in Colombia, Brazil and Argentina,

300% in Ecuador and 400% in Peru (medium confidence). In Europe, 
above 3°C global warming level, the costs of damage and people 
affected by precipitation and river flooding may double. {4.4.1, 4.4.4, 
4.5.4, 4.5.5, 6.2.2, 7.3.1, Box 4.1, Box 4.3, 9.5.3, 9.5.4, 9.5.5, 9.5.6, 
9.5.7, 9.7.2, 9.9.4, 10.4.6, Box  10.2, Box  11.4, 12.3, 13.2.1, 13.2.2, 
13.6.2, 13.10.2, Box 13.1, 14.2.2, 14.5.3, CCP2.2, CWGB URBAN}
TS.C.4.6 Projected water cycle changes will impact agriculture, 
energy production and urban water uses (medium confidence). 
Agricultural water use will increase globally as a consequence of 
population increase and dietary changes, as well as increased water 
requirements due to climate change (high confidence). Groundwater 
recharge in some semiarid regions are projected to increase, but 
worldwide depletion of non-renewable groundwater storage will 
continue due to increased groundwater demand (medium to high

confidence). Increased floods and droughts, together with heat stress, 
will have an adverse impact on food availability and prices, resulting 
in increased undernourishment in South and Southeast Asia (high 
confidence). In the Mediterranean and parts of Europe, potential 
reductions of hydropower of up to 40% are projected under 3°C 
warming, while declines below 10% and 5% are projected under 
2°C and 1.5°C warming levels respectively. An additional 350 and 
410  million people living in urban areas will be exposed to water 
scarcity from severe droughts at 1.5°C and 2°C respectively. {2.5.3, 
4.4.1, 4.4.2, 4.5.6, 4.6.1, 5.4.3, 6.2.2, 6.2.4, Box 6.2, 6.3.5, 6.4, 9.7.2, 
10.4.7, 12.3, 13.10.3, 4.5.2, 4.6.1, 11.3.3, 11.3.4, Box  11.3, 12.3, 
14.5.3, 14.5.5, CCP4.2, CCP4.3, CWGB URBAN}Risks from sea level rise
TS.C.5 Coastal risks will increase by at least one order of mag-
nitude over the 21st century due to committed sea level rise

impacting ecosystems, people, livelihoods, infrastructure, food 
security, cultural and natural heritage and climate mitigation 
at the coast. Concentrated in cities and settlements by the sea, 
these risks are already being faced and will accelerate beyond 
2050 and continue to escalate beyond 2100, even if warming 
stops. Historically rare extreme sea level events will occur annu-
ally by 2100, compounding these risks (high confidence). {3.4.2, 
3.5.5, 3.6.3, 9.9.4, Box 11.6, 13.2, Box 13.1, 14.5.2, Box 14.4, 
CCP2.2, CCB SLR}
TS.C.5.1 Under all emissions scenarios, coastal wetlands will 
likely face high risk from sea level rise in the mid-term (medium 
confidence), with substantial losses before 2100. These risks will 
be compounded where coastal development prevents upshore 
migration of habitats or where terrestrial sediment inputs are 
limited and tidal ranges are small (high confidence). Loss of these 
habitats disrupts associated ecosystem services, including wave-energy

attenuation, habitat provision for biodiversity, climate mitigation and 
food and fuel resources (high confidence). Near- to mid-term sea 
level rise will also exacerbate coastal erosion and submersion and 
the salinisation of coastal groundwater, expanding the loss of many 
different coastal habitats, ecosystems and ecosystem services (medium 
confidence). {3.4.2, 3.5.2, 3.5.5, 3.6.3, 9.6.2, 11.3.1, 13.4.1, 13.4.2, 
14.5.2, CCB NATURAL, CCB SLR}
TS.C.5.2 The exposure of many coastal populations and 
associated development to sea level rise is high, increasing risks, 
and is concentrated in and around coastal cities and settlements 
(virtually certain). High population growth and urbanisation in 
low-lying coastal zones will be the major driver of increasing exposure 
to sea level rise in the coming decades (high confidence). By 2030, 108–
116 million people will be exposed to sea level rise in Africa (compared 
to 54 million in 2000), increasing to 190–245 million by 2060 (medium

confidence). By 2050, more than a billion people located in low-lying 
cities and settlements will be at risk from coast-specific climate 
hazards, influenced by coastal geomorphology, geographical location 
and adaptation action (high confidence). {9.9.1, 9.9.4, Box 11.6, 14.5.2, 
Box 14.4, CCP2.2, CCB SLR}
TS.C.5.3 Under all climate and socioeconomic scenarios, low-
lying cities and settlements, small islands, Arctic communities, 
remote Indigenous communities and deltaic communities 
will face severe disruption by 2100, and as early as 2050 in 
many cases (very high confidence). Large numbers of people are 
at risk in Asia, Africa and Europe, while a large relative increase in 
risk occurs in small island states and in parts of North and South 
America and Australasia. Risks to water security will occur as early as 
2030 or earlier for the small island states and Torres Strait Islands in 
Australia and remote Maori communities in New Zealand. By 2100,

compound and cascading risks will result in the submergence of some 
low-lying island states and damage to coastal heritage, livelihoods 
and infrastructure (very high confidence). Sea level rise, combined 
with altered rainfall patterns, will increase coastal inundation and

63
Technical SummaryTSwater-use allocation issues between water-dependent sectors, such 
as agriculture, direct human consumption, sanitation and hydropower 
(medium confidence). {Box 4.2, 5.13, 9.12, 9.9.1, 9.9.4, 11.4.1, 11.4.2, 
Box 11.6, 14.5.2, Box 14.4, CCP2.2, CCB SLR}
TS.C.5.4 Risks to coastal cities and settlements are projected to 
increase by at least one order of magnitude by 2100 without 
significant adaptation and mitigation action (high confidence). 
The population at risk in coastal cities and settlements from a 100-
year coastal flood increases by approx. 20% if the global mean sea 
level rises by 0.15 m relative to current levels, doubles at 0.75 m and 
triples at 1.4 m, assuming present-day population and protection 
height (high confidence). For example, in Europe, coastal flood 
damage is projected to increase at least 10-fold by the end of the 
21st century, and even more or earlier with current adaptation and

mitigation (high confidence). By 2100, 158–510  million people and 
USD7,919–12,739 billion in assets are projected to be exposed to the 
1-in-100-year coastal floodplain under RCP4.5, and 176–880 million 
people and USD8,813–14,178  billion assets under RCP8.5 (high 
confidence).  Projected impacts reach far beyond coastal cities and 
settlements, with damage to ports potentially severely compromising 
global supply chains and maritime trade, with local to global geopolitical 
and economic ramifications (medium confidence). Compounded and 
cascading climate risks, such as tropical cyclone storm surge damage 
to coastal infrastructure and supply chain networks, are expected to 
increase (medium confidence). (Figure TS.9 URBAN) {3.5.5, 3.6.2, 6.2.5, 
6.2.7, 9.9.4, 9.12.2, 11.4, Box  11.4, Box  11.6, Table  11.14, 13.2.1, 
13.2.2, 13.6.2, 13.10.2, Box 13.1, 14.5.5, Box 14.4, Box 14.5, CCP2.2.1, 
CCP2.2.2, CCP6.2.3, CCP6.2.7, CCP6.2.8, BoxCCP6.1, CCB SLR}

TS.C.5.5  Particularly exposed and vulnerable coastal 
communities,   especially those relying on coastal  ecosystems 
for protection or  livelihoods, may face adaptation limits well 
before the end of this century, even at low warming levels (high 
 confidence).  Changes in wave climate superimposed on sea level 
rise will significantly increase coastal flooding (high confidence) and 
erosion of low-lying coastal and reef islands (limited evidence, medium 
agreement). The frequency, extent and duration of coastal flooding will 
significantly increase from 2050 (high confidence), unless coastal and 
marine ecosystems are able to naturally adapt to sea level rise through 
vertical growth and landward migration (low confidence). Permafrost 
thaw, sea level rise, and reduced sea ice protection is projected to 
damage or cause loss to many cultural heritage sites, settlements 
and livelihoods across the Arctic (very high confidence).  Deltaic

cities and settlements characterised by high inequality and informal 
settlements are especially vulnerable (high confidence). Although 
risks are distributed across cities and settlements at all levels of 
economic development, wealthier and more urbanised coastal cities 
and settlements are more likely to be able to limit impacts and risk 
in the near- to mid-term through infrastructure resilience and coastal 
protection interventions, with highly uncertain prospects in many of 
these locations beyond 2100 (high confidence). Prospects for enabling 
and contributing to climate resilient development thus vary markedly 
within and between coastal cities and settlements (high confidence). 
{9.9.4, 11.3.5, Table Box  11.6.1, 12.3, 12.4, Figure  12.7, Figure  12.9, 
Table 12.1, Table SM12.5, 13.2, 15.3.3, CCP2.2.1, CCP2.2.3, CCP2.2.5, 
Table SMCCP2.1}Health and well-being
TS.C.6 Climate change will increase the number of deaths and

the global burden of non-communicable and infectious diseases 
(high confidence). Over nine million climate-related deaths per 
year are projected by the end of the century, under a high 
emissions scenario and accounting for population growth, 
economic development and adaptation. Health risks will be 
differentiated by gender, age, income, social status and region 
(high confidence). {3.5.5, 3.6.2, 4.5.3, 5.12.4, Box  5.10, 6.2.2, 
7.3.1, 8.4.5, 9.10.2, Figure 9.32, Figure 9.35, 10.4.7, Figure 10.11, 
11.3.6, Table  11.14,12.3.2, 12.3.4, 12.3.5, 12.3.6, 12.3.8, 
Figure  12.5, Figure  12.6, 13.7.1, Figure  13.23, Figure  13.24, 
14.5.4, 14.5.6, 15.3.4, 16.5.2, CCP Box 6.2, CCP6.2.6, CCB COVID, 
CCB ILLNESS, CCB MOVING PLATE}
TS.C.6.1 Future global burdens of climate-sensitive diseases and 
conditions will depend on emissions and adaptation pathways 
and the efficacy of public health systems, interventions and 
sanitation (very high confidence). Projections under mid-range

emissions scenarios show an additional 250,000 deaths per year 
by 2050 (compared to 1961–1990) due to malaria, heat, childhood 
undernutrition and diarrhoea (high confidence). Overall, more than half 
of this excess mortality is projected for Africa. Mortality and morbidity 
will continue to escalate as exposures become more frequent and 
intense, putting additional strain on health and economic systems (high 
confidence), reducing capacity to respond, particularly in resource-
poor regions. Vulnerable groups include young children (<5 years old), 
the elderly (>65  years old), pregnant women, Indigenous Peoples, 
those with pre-existing diseases, physical labourers and those in low 
socioeconomic conditions (high confidence). {4.5.3, 7.3.1, 9.10.2, 
12.3.5, 16.5.2, CCB MOVING PLATE}
TS.C.6.2 Climate change is expected to have adverse impacts 
on well-being and to further threaten mental health (very 
high confidence). Children and adolescents, particularly girls, as

well as people with existing mental, physical and medical challenges, 
are particularly at risk (high confidence). Mental health impacts 
are expected to arise from exposure to extreme weather events, 
displacement, migration, famine, malnutrition, degradation or 
destruction of health and social care systems, climate-related economic 
and social losses and anxiety and distress associated with worry about 
climate change (very high confidence). {7.3.1, 11.3.6, 14.5.6, CCP6.2.6, 
Box CCP6.2, CCB COVID}
TS.C.6.3 Increased heat-related mortality and morbidity are 
projected globally (very high confidence). Globally, temperature-
related mortality is projected to increase under RCP4.5 to RCP8.5, even 
with adaptation (very high confidence). Tens of thousands of additional 
deaths are projected under moderate and high global warming 
scenarios, particularly in north, west and central Africa, with up to 
year-round exceedance of deadly heat thresholds by 2100 (RCP8.5)

(high agreement, robust evidence). In Melbourne, Sydney and Brisbane, 
urban heat-related excess deaths are projected to increase by about 
300 yr-1 (low emission pathway) to 600 yr-1 (high emission pathway) 
during 2031–2080 relative to 142 yr-1 during 1971–2020 (high 
confidence). In Europe the number of people at high risk of mortality

64
TSwill triple at 3°C compared to 1.5°C warming, in particular in central 
and southern Europe and urban areas (high confidence). {6.2.2, 7.3.1, 
8.4.5, 9.10.2, Figure  9.32, Figure  9.35, 10.4.7, Figure  10.11, 11.3.6, 
11.3.6, Table 11.14, 12.3.4, 12.3.8, Figure 12.6, 13.7.1, Figure 13.23, 
14.5.6, 15.3.4, 16.5.2}
TS.C.6.4 Climate impacts on food systems are projected to 
increase undernutrition and diet-related mortality and risks 
globally (high confidence). Reduced marine and freshwater fisheries 
catch potential is projected to increase malnutrition in East, West 
and Central Africa (medium to high confidence) and in subsistence-
dependent communities across North America (high confidence). 
By 2050, disability-adjusted life years due to undernutrition and 
micronutrient deficiencies are projected to increase by 10% under 
RCP8.5 (medium evidence, high agreement). These projected changes 
will increase diet-related risk factors and related non-communicable

diseases globally and increase undernutrition, stunting and related 
childhood mortality, particularly in Africa and Asia (high confidence). 
Near-term projections (2030) of undernutrition are the highest for 
children (confidence), which can have lifelong adverse consequences 
for physiological and neurological development as well as for earnings 
capacity. Climate change is projected to put 8  million (SSP1-6.0) 
to 80  million people (SSP3-6.0) at risk of hunger in mid-century, 
concentrated in sub-Saharan Africa, South Asia and Central America 
(high confidence). These climate change impacts on nutrition could 
undermine progress towards the eradication of child undernutrition 
(high confidence). {4.5.3, 5.2.2, 5.12.4, Box 5.10, 7.3.1, 9.8.5, 9.10.2, 
10.4.7, Figure  10.11, 13.7.1, 14.5.6, 15.3.4, CCP6.2, CCB MOVING 
PLATE}
TS.C.6.5 Vector-borne disease transmission is projected to 
expand to higher latitudes and altitudes, and the duration

of seasonal transmission risk is projected to increase (high 
confidence), with the greatest risk under high emissions 
scenarios.  Dengue vector ranges will increase in North America, Asia, 
Europe and sub-Saharan Africa under RCP6 and RCP8.5, potentially 
putting another 2.25 billion people at risk (high confidence). Higher 
incidence rates of Lyme disease are projected for the Northern 
Hemisphere (high confidence). Climate change is projected to increase 
malaria’s geographic distribution in endemic areas of sub-Saharan 
and southern Africa, Asia and South America (high confidence), 
exposing tens of millions more people to malaria, predominately 
in east and southern Africa, and up to hundreds of millions more 
exposed under RCP8.5 (high confidence). {7.3.1, 9.10.2, Figure 9.32, 
10.4.7, Figure 10.11, 11.3.6, 12.3.2, 12.3.5, 12.3.6, Figure 12.5, 13.7.1, 
Figure 13.24, 14.5.6, 15.3.4, CCB ILLNESS}
TS.C.6.6 Higher temperatures and heavy rainfall events are

projected to increase rates of water-borne and food-borne 
diseases in many regions (high confidence). At 2.1°C, thousands 
to tens of thousands of additional cases of diarrhoeal disease are 
projected, mainly in central and east Africa (medium confidence). 
Morbidity from cholera will increase in central and east Africa (medium 
confidence), and increased schistosomiasis risk is projected for eastern 
Africa (high confidence). In Asia and Africa, 1°C warming can cause 
a 7% increase in diarrhoea, an 8% increase in E. coli and a 3% to 
11% increase in deaths (medium confidence). Warming increases the risk of food-borne disease outbreaks, including Salmonella and 
Campylobacter infections (medium confidence). Warming supports 
the growth and geographical expansion of toxigenic fungi in crops 
(medium confidence) and potentially toxic marine and freshwater algae 
(medium confidence). Food safety risks in fisheries and aquaculture

are projected through harmful algal blooms (high confidence), 
pathogens (e.g., Vibrio) (high confidence), and human exposure to 
elevated bioaccumulation of persistent organic pollutants and mercury 
(medium confidence). {3.5.5, 3.6.2, 4.5.3, 5.12.4, Box  5.10, 7.3.1, 
9.10.2, Figure 9.32, 10.4.7, Figure 10.11, 11.3.6, 13.7.1, Figure 13.24, 
14.5.4, 14.5.6, 15.3.4, CCP6.2.6, CCB MOVING PLATE}
TS.C.6.7 The burden of several non-communicable diseases is 
projected to increase under climate change (high confidence). 
Cardiovascular disease mortality could increase by 18.4%, 47.8% and 
69.0% in the 2020s, 2050s and 2080s respectively under RCP4.5, and 
by 16.6%, 73.8% and 134% under RCP8.5 compared to the 1980s 
(high confidence). Future risks of respiratory disease associated with 
aeroallergens and ozone exposure are expected to increase (high 
confidence). {7.3.1, 10.4.7, 11.3.6, 12.3.4, 13.7.1}
TS.C.7 Migration patterns due to climate change are difficult

to project as they depend on patterns of population growth, 
adaptive capacity of exposed populations and socioeconomic 
development and migration policies (high confidence). In many 
regions, the frequency and/or severity of floods, extreme storms 
and droughts is projected to increase in coming decades, es-
pecially under high emissions scenarios, raising future risk of 
displacement in the most exposed areas (high confidence). 
Under all global warming levels, some regions that are pres-
ently densely populated will become unsafe or uninhabitable, 
with movement from these regions occurring autonomously 
or through planned relocation (high confidence). {4.5.7, 7.3.2, 
Box 9.8, 15.3.4, CCB MIGRATE}
TS.C.7.1 Future climate-related migration is expected to vary 
by region and over time, according to future climatic drivers, 
patterns of population growth, adaptive capacity of exposed 
populations and international development and migration

policies (high confidence). Future migration and displacement 
patterns in a changing climate will depend not only on the physical 
impacts of climate change, but also on future policies and planning at 
all scales of governance (high confidence). Projecting the number of 
people migrating due to slow onset events is difficult due to the multi-
causal nature of migration and the dominant role that socioeconomic 
factors have in determining migration responses (high confidence). 
Increased frequency of extreme heat events and long-term increases 
settlements in low latitudes; this, combined with the urban heat island 
effect, may in the long term affect migration patterns in exposed areas, 
especially under high emissions scenarios, but more evidence is needed. 
High emissions/low development scenarios raise the potential for both 
increased rates of migration and displacement and larger involuntary

immobile populations that are highly exposed to climatic risks but lack 
the means of moving to other locations (medium confidence). {4.5.7,

65
Technical SummaryTS7.2.6, 7.3.2, 15.3.4, 4.6.9, 5.14.1, 5.14.2, 7.3.2, 7.4.5, 8.2.1, Box 8.1, 
Box 9.8, CCP 6.3.2, CCB MIGRATE}
TS.C.7.2 Estimates of displacement from rapid-onset extreme 
events exist; however, the range of estimates is large as they 
largely depend on assumptions made about future emissions 
and socioeconomic development trajectories (high confidence). 
Uncertainties about socioeconomic development are reflected in the 
wide range of projected population displacements by 2050 in Central 
and South America, sub-Saharan Africa and South Asia due to climate 
change, ranging from 31 million to 143 million people (high confidence). 
Projections of the number of people at risk of future displacement 
by sea level rise range from tens of millions to hundreds of millions 
by the end of this century, depending on the level of warmings and 
assumptions about exposure (high confidence). (Figure TS.9 URBAN)

{4.5.7, 7.3.2, 7.3.2, 7.3.2, 9.9.4, CCP2.2.1, CCP2.2.2, CCB MIGRATE, 
CCB SLR, Figure AI.42}
TS.C.7.3 As climate risk intensifies, the need for planned 
relocations will increase to support those who are unable to 
move voluntarily (medium confidence). Planned relocation will 
be increasingly required as climate change undermines livelihoods, 
safety and overall habitability, especially for coastal areas and small 
islands (medium confidence). This will have implications for traditional 
livelihood practices, social cohesion and knowledge systems that have 
inherent value as intangible culture as well as introduce new risks for 
communities by amplifying existing and generating new vulnerabilities 
(high confidence). {4.6.8, 15.3.4, 14.4, CCP2.3.5, CCB FEASIB, CCB 
MIGRATE}
TS.C.8 Under an inequality scenario (SSP4) by 2030, the number 
of people living in extreme poverty will increase by 122 million 
from currently around 700 million (medium confidence). Future

climate change may increase involuntary displacement, but 
severe impacts also undermine the capacity of households to use 
mobility as a coping strategy, causing high exposure to climate 
risks, with consequences for basic survival, health and well-
being (high confidence). The COVID-19 pandemic is expected 
to increase the adverse consequences of climate change since 
the financial consequences have led to a shift in priorities and 
constrain vulnerability reduction (medium confidence). {7.3.2, 
8.1.1, 8.3.2, 8.4.4, 8.4.5, 9.11.4, Box  9.8, 16, Table  16.9, CCB 
COVID, CCB ILLNESS, CCB MOVING SPECIES}
TS.C.8.1 Even with current, moderate climate change, vulnerable 
people will experience a further erosion of livelihood security 
that can interact with humanitarian crises, such as displacement 
and involuntary migration (high confidence) and violence and 
armed conflict, and lead to social tipping points (medium 
confidence). Under higher emissions scenarios and increasing

climate hazards, the potential for societal risks also increases 
(medium confidence). Lessons from COVID-19 risk management have 
implications for managing urban climate change risk (limited evidence, 
high agreement). {4.5.1, 4.5.3, 4.5.4, 4.5.7, 4.5.8, 6.1.1, 6.3, 6.4, 8.2.1, 
8.3, 8.4.4, 9.11.4}TS.C.8.2 Indigenous Peoples and local communities will 
experience changes in cultural opportunities (low to medium 
confidence). Cultural heritage is already being impacted by climate 
change and variability, for example in Africa, Small Island Developing 
States and the Arctic, where heritage sites are exposed to future 
climate change risk (high confidence). Coastal erosion and sea level 
rise are projected to affect natural and cultural coastal heritage sites 
spread across 36 African countries and all Arctic nations. Frequent 
drought episodes will lower groundwater tables and gradually expose 
highly valued archaeological sites to salt weathering and degradation.

Coastal inundation and ocean acidification will intensify impacts on 
sacred sites, including burial grounds, and the corrosion of shipwrecks 
and underwater ruins. {3.5.3, 3.5.4, 3.5.5, 3.5.6, 4.5.8, 9.12., 2.1.2, 
11.4.1, 11.4.2, 13.8.1.3, 13.8.2, Box 13.2, 14.4, CCP6.2.7, CCP2.2}
TS.C.8.3 Climate change increases risks of violent conflict, 
primarily intrastate conflicts, by strengthening climate-sensitive 
drivers (medium confidence). Climate change may produce severe 
risks to peace within this century through climate variability and extremes, 
especially in contexts marked by low economic development, high 
economic dependence on climate-sensitive activities, high or increasing 
social marginalisation and fragile governance (medium confidence). The 
largest impacts are expected in weather-sensitive communities with 
low resilience to climate extremes and high prevalence of underlying 
risk factors (medium confidence). Trajectories that prioritise economic

growth, political rights and sustainability are associated with lower 
conflict risk (medium confidence). {4.5.6, 7.3.3, 16.5.2}
Cities, settlements and infrastructure
TS.C.9 Climate change increases risks for a larger number of 
growing cities and settlements across wider areas, especially in 
coastal and mountain regions, affecting an additional 2.5 billion 
people residing in cities mainly in Africa and Asia by 2050 (high 
confidence). In all cities and urban areas, projected risks faced 
by people from climate-driven impacts has increased (high 
confidence). Many risks will not be felt evenly across cities and 
settlements or within cities. Communities in informal settlements 
will have higher exposure and lower capacity to adapt (high 
confidence). Most at risk are women and children who make up 
the majority populations of these settlements (high confidence). 
Risks to critical physical infrastructure in cities can be severe

and pervasive under higher warming levels, potentially resulting 
in compound and cascading risks, and can disrupt livelihoods 
both within and across cities (high confidence). In coastal cities 
and settlements, risks to people and infrastructure will get 
progressively worse in a changing climate, sea level rise and with 
ongoing coastal development (very high confidence). {2.6.5, 6.1, 
6.1.4, 6.2, 9.9.4, 16.5, 14.5.5, Box 14.4, CCP2.2}
TS.C.9.1 An additional 2.5  billion people are projected to 
live in urban areas by 2050, with up to 90% of this increase 
concentrated in the regions of Asia and Africa (high confidence). 
By 2050, 64% and 60% of Asia’s and Africa’s population respectively 
will be urban. Growth is most pronounced in smaller and medium-
sized urban settlements of up to one million people (high confidence). 
{4.5.4, 6.1, 6.1.4, 6.2, 9.9.1, 10.4.6}

66
TSTS.C.9.2 Asian and African urban areas are considered high-risk 
locations from projected climate, extreme events, unplanned 
urbanisation and rapid land use change (high confidence). These 
could amplify pre-existing stresses related to poverty, informality, 
exclusion and governance, such as in African cities (high confidence). 
Climate change increases heat stress risks in cities (high confidence) 
and amplifies the urban heat island across Asian cities at 1.5°C and 
2°C warming levels, both substantially larger than under present 
climates (medium confidence). Urban population exposure to extreme 
heat in Africa is projected to increase from 2 billion person-days per 
year in 1985–2005 to 45  billion person-days by the 2060s (1.7°C 
global warming with low population growth) and to 95 billion person-
days (2.8°C global warming with medium-high population growth) 
(medium confidence). Risks driven by flooding and droughts will also

increase in cities (high confidence). Urban populations exposed to 
severe droughts in West Africa will increase (65.3±34.1  million) at 
1.5°C warming and increase further at 2°C (medium confidence). 
Urban land in flood zones and drylands exposed to high-frequency 
floods is expected to increase by as much as 2600% and 627% 
respectively across East, West and Central Africa by 2030. Higher 
risks from temperature and precipitation extremes are projected for 
almost all Asian cities under RCP8.5 (medium confidence), impacting 
on freshwater availability, regional food security, human health and 
industrial outputs. {4.3.4, 4.3.5, 4.5.4, 6.1, 6.2, Table 6.3, Table 6.4, 
9.9.4, 10.3.7, 10.4.6, 15.3.3, 15.3.4, 15.4.3, CCP2.2, CCP6.2.7, CWGB 
URBAN}
TS.C.9.3 Globally, urban key infrastructure systems are 
increasingly sites of risk creation that potentially drive 
compounding and cascading risks (high confidence). Unplanned

rapid urbanisation is a major driver of risk, particularly where 
increasing climate-driven risks affect key infrastructure and potentially 
result in compounding and cascading risks as cities expand into 
coastal and mountain regions prone to flooding or landslides that 
disrupt transportation networks, or where water and energy resources 
are inadequate to meet the needs of growing settlements (high 
confidence). These infrastructure risks expand beyond city boundaries; 
climate-related transport and energy infrastructure damage is 
projected to be a significant financial burden for African countries, 
reaching tens to hundreds of billions of US dollars under moderate 
and high emissions scenarios (high confidence). Projected changes 
in both the hydrological cycle and the cryosphere will threaten urban 
water infrastructure and resource management in most regions 
(very high confidence). South and Southeast Asian coastal cities can

experience significant increases in average annual economic losses 
between 2005 and 2050 due to flooding, with very high losses in 
east Asian cities under RCP8.5 (high confidence). By 2050, permafrost 
thaw in the pan-Arctic is projected to impact 69% of infrastructure, 
more than 1200 settlements, 36,000 buildings, and 4 million people 
in Europe under RCP4.5. In small islands, degraded terrestrial 
ecosystems decrease resource provision (e.g., potable water) and 
amplify the vulnerability of island inhabitants (high confidence). 
Projections suggest that 350 million (± 158.8 million) more people in 
urban areas will be exposed to water scarcity from severe droughts 
at 1.5°C warming and 410.7 million (± 213.5) at 2°C warming (low 
confidence). {6.2.2, 9.9.4, 10.4.6, 13.6.1, 13.6.2, 13.11.3, 14.5.5, 
CCP2.2, SMCCP2.1}TS.C.9.4 The characteristics of coastal cities and settlements 
means that climate-driven risks to people and infrastructure

in many of them are already high and will get progressively 
worse over the 21st century and beyond (high confidence). 
These risks are driven by disproportionately high exposure of multiple 
assets, economic activities and large coastal populations concentrated 
in narrow coastal zones. Climate change risks, including sea level rise, 
interact in intricate ways with non-climatic drivers of coastal change, 
such as land subsidence, continued infrastructure development in 
coastal floodplains, the rise of asset values and landward development 
adversely impacting coastal ecosystems, to shape future risk in coastal 
settlements (high confidence). (Figure TS.9 URBAN) {3.4.2, 6.2, 6.3, 7.4, 
9.9.4, 10, 11.3.5, Box 11.4, 13.6.1, 14.5.5, Box 14.4, 15.3.4, 15.3.4, 
CCP7.1, CCP2.2, CCP2.3, CCB SLR}
Economic sectors
TS.C.10 Across sectors and regions, market and non-market 
damage and adaptation costs will be lower at 1.5°C compared 
to 3°C or higher global warming levels (high confidence).

Some recent estimates of projected global economic damage 
from climate impacts are higher than previous estimates and 
generally increase with global average temperature (high 
confidence). However, the spread in the estimates of the 
magnitude of this damage is substantial and does not allow for 
robust range to be established (high confidence). Non-market, 
non-economic damage and adverse impacts on livelihoods will 
be concentrated in regions and populations that are already 
more vulnerable (high confidence). Socioeconomic drivers and 
more inclusive development will largely determine the extent 
of this damage (high confidence). {4.4.4, 4.7.5, 9.11.2, 10.4.6, 
11.5.2, 13.10.2, 13.10.3, 14.5.8, Box 14.6, 16.5.2, 16.5.3}
TS.C.10.1 Without limiting warming to 1.5°C global warming 
level, many key risks are projected to intensify rapidly in 
almost all regions of the world, causing damage to assets and 
infrastructure and losses to economic sectors and entailing high

recovery and adaptation costs (high confidence). Severe risks are 
more likely in developing regions that are already hotter and in regions 
and communities with a large portion of the workforce employed 
in highly exposed industries (e.g., agriculture, fisheries, forestry, 
tourism, outdoor labour). In addition to market damage and disaster 
management costs, substantial costs of climate inaction are projected 
for human health (high confidence). At higher levels of warming, climate 
impacts will pose risks to financial and insurance markets, especially if 
climate risks are incompletely internalised (medium confidence), with 
adverse implications for the stability of markets (low confidence). While 
the overall economic consequences are clearly negative, opportunities 
may arise for a few economic sectors and regions, such as from longer 
growing seasons or reduced sea ice, primarily in northern latitudes 
(medium to high confidence). {4.4.4, 4.7.5, 9.11.2, 10.4.6, 11.6, 13.9.2,

13.10.3, 14.5.4, 14.5.5, 14.5.7, 14.5.8, 14.5.9, Box  14.5, Box  14.6, 
16.5.2, 16.5.3, CCP4.2, CCP6.2, CCB INTEREG}
TS.C.10.2 Estimates of global economic damage generally 
increase non-linearity with warming and some are larger than 
previous estimates (high confidence). Some recent estimates have

67
Technical SummaryTSincreased relative to the range reported in AR5, though there is low 
agreement and significant spread within and across methodology types 
(e.g., statistical, structural, meta-analysis), resulting in an inability to 
identify a best estimate or robust range (high confidence). Under high 
warming (>4°C) and limited adaptation, the magnitude of decline in 
annual global GDP in 2100 relative to a non-global-warming scenario 
could exceed economic losses during the Great Recession in 2008–
2009 and the COVID-19 pandemic in 2020. Much smaller effects are 
estimated for less warming, lower vulnerability and more adaptation 
(medium confidence). Regional estimates of GDP damage vary (high 
confidence). Severe risks are more likely in (typically hotter) developing 
countries (medium confidence). For Africa, GDP damage is projected to 
be negative across models and approaches (high confidence). {4.4.4,

4.7.5, 9.11.2, 10.4.6, 13.10.2, 13.10.3, 14.5.8, Box 14.6, 16.5.2, 16.6.3, 
CWGB ECONOMIC}
TS.C.10.3 Even at low levels of warming, climate change will 
disrupt the livelihoods of tens to hundreds of millions of 
additional people in regions with high exposure and vulnerability 
and low adaptation in climate-sensitive regions, ecosystems and 
economic sectors (high confidence). If future climate change under 
adaptation measures, losses and damage will likely be concentrated 
among the poorest vulnerable populations (high confidence). {8.4.5, 
9.11.4, Box 15.2, 16.5.3}
TS.C.10.4 Potential socioeconomic futures, in terms of 
population, economic development and orientation towards 
growth, vary widely and these drivers have a large influence on 
the economic costs of climate change (high confidence). Higher 
growth scenarios along higher warming levels increase exposure to

hazards and assets at risk, such as sea level rise for coastal regions, 
which will have large implications for economic activities, including 
shipping and ports (high confidence). The high sensitivity of developing 
economies to climate impacts will pose increasing challenges to 
economic growth and performance, although projections depend as 
much or more on future socioeconomic development pathways and 
mitigation policies as on warming levels (medium confidence). {9.11.2, 
11.4, 13.2.1, 16.5.3, CCB SLR, CWGB ECONOMIC}
TS.C.10.5 Large non-market and non-economic losses are 
projected, especially at higher warming levels (high confidence). 
This wide range of effects underscore the impact of climate change on 
welfare and the adverse effects on vulnerable populations (medium 
confidence). Including as many of these impacts in decision-making 
as possible, and as part of the social cost of carbon, will improve 
evaluation of the overall and distributional effects of climate

mitigation and adaptation actions as well as in more comprehensively 
internalising climate impacts. {1.5.1, 4.5.8, 4.7.5, 8.4.1, 8.4.5, Map 8.8, 
16.5.2, Box 14.6, CWGB ECONOMIC}
Compound, cascading and transboundary risks
TS.C.11 Compound, cascading risks and transboundary risks give 
rise to new and unexpected types of risks (high confidence). 
They exacerbate existing stressors and constrain adaptation 
options (medium confidence). They are projected to become major threats for many areas, such as coastal cities (medium to 
high confidence). Some compound and cascading impacts occur 
locally, some spread across sectors and socioeconomic and 
natural systems, while others can be driven by events in other 
regions, for instance through trade and flows of commodities 
and goods through supply chain linkages (high confidence). 
(Figure TS.10 COMPLEX RISK) {1.3.1, 2.3, 2.5.5, 6.2, 4.4, 4.5.1, 
11.5.1, Box  11.1, 13.10.3, Figure  14.10, 14.5.4, 11.5.1, 11.6,

Box  11.7, Figure Box  11.1.2, Table  11.14, Box  14.5, CCP2.2.5, 
CCP6.2.3, CCB EXTREMES, CCB INTEREG}
TS.C.11.1 Escalating impacts of climate change on terrestrial, 
freshwater and marine life will further alter the biomass of 
animals (medium confidence), the timing of seasonal ecological 
events (high confidence) and the geographic ranges of 
terrestrial, coastal and ocean taxa (high confidence), disrupting 
life cycles (medium confidence), food webs (medium confidence) 
and ecological connectivity throughout the water column 
(medium confidence). For example, cascading effects on food webs 
have been reported in the Baltic due to detrimental oxygen levels 
(high confidence). (Figure TS.5 ECOSYSTEMS, Figure TS.10 COMPLEX 
RISK) {2.4.3, 2.4.5, 2.5.4, 3.4.2, 3.4.3, 13.3.1, 13.4.1, 14.5.2, CCP2.2, 
CCP5.3.2, WGI AR6 2.3.4}
TS.C.11.2 Climate hazards cause multiple impacts, interacting 
to compound risks to food security, nutrition and human health

(high confidence). Compound risks to health and food systems 
(especially in tropical regions) are projected from simultaneous 
reductions in food production across crops, livestock and fisheries 
(high confidence), heat-related loss of labour productivity in 
agriculture (high confidence), increased heat-related mortality (high 
confidence), contamination of seafood (high confidence), malnutrition 
(high confidence) and flooding from sea level rise (high confidence). 
Malnourished populations will increase through direct impacts on 
food production with cascading impacts on food prices and household 
incomes, reducing access to safe and nutritious food (high confidence). 
Food safety will be undermined from increased food contamination 
for seafood with marine toxins from harmful algal blooms and 
chemical contaminants, worsening health risks (high confidence). 
(Figure TS.10 COMPLEX RISK) {4.5.1, 5.2.2, 5.4.3, 5.8.1, 5.8.3, 5.11.1,

5.12, Figure 5.2, 5.12.4, Box 5.10, 7.3.1, 9.10.2, 9.8.2, 9.8.3, 14.5.6, 
CCP5.2.3, CCP6.2.3, CCB ILLNESS}
TS.C.11.3 Compound hazards increasing with global warming 
include increased frequency of concurrent heatwaves and 
droughts (high confidence), dangerous fire weather (medium 
confidence) and floods (medium confidence), resulting in 
increased and more complex risks to agriculture, water 
resources, human health, mortality, livelihoods, settlements 
and infrastructure. Extreme weather events result in cascading and 
compounding risks that affect health and are expected to increase 
with warming (very high confidence). Compound climate hazards 
can overwhelm adaptive capacity and substantially increase damage 
(high confidence); for example, heat and drought are  projected to 
substantially reduce agricultural production, and although irrigation 
can reduce this risk, its feasibility is limited by drought. (Figure TS.10

COMPLEX RISK) {4.2.5, 6.2.5, 7.1.3, 7.1.4,7.2.2, 7.2.1, 7.2.2, 7.2.3,

68
TS7.2.4, 7.3.1, 7.3.2, 7.3.3, 7.4.1, 7.4.5, 11.5.1, 11.8.1, Box  11.1, 
12.4, 13.3.1, 13.10.2, CCP5.4.6, CCP5.4.3, CCP 6, CCB COVID, CCB 
EXTREMES, CCB HEALTH, WGI AR6 11.8}
TS.C.11.4 Interacting climatic and non-climatic drivers when 
coupled with coastal development and urbanisation  are 
projected to lead to losses for coastal ecosystems and their 
services under all scenarios in the near to mid-term (medium to 
high confidence). The compound impacts of warming, acidification 
and sea level rise are projected to lead to losses for coastal ecosystems 
(medium to high confidence). Fewer habitats, less biodiversity, lower 
coastal protection (medium confidence) and decreased food and water 
security will result (medium confidence), reducing the habitability of 
some small islands (high confidence). (Figure TS.10 COMPLEX RISK) 
{2.3, 2.5.5, 3.4.2, 3.5.2, 3.5.3, 3.5.5, 3.5.6, 3.6.3, 4.5.1, 5.13.6, 6.2,

6.2.6, 6.4.3, 11.3.2, 11.5.1, Box  11.6, 12.4, 12.5.2, 13.5.2, 13.10.2, 
Table  13.12, 15.3.3, 15.3.4, Box  15.5, 16.5.2, CCP1.2.1, CCP1.2.4, 
Box CCP1.1, Table CCP1.1, Figure CCP1.1, Figure CCP1.2, CCP2.2, CCP 
2.2.5, CCB EXTREMES, CCB SLR}
TS.C.11.5 Observed human and economic losses have increased 
since AR5 for urban areas and human settlements arising from 
compound, cascading and systemic events (medium evidence, 
high agreement). Urban areas and their infrastructure are susceptible 
to both compounding and cascading risks arising from interactions 
between severe weather from climate change and increasing 
urbanisation (medium evidence, high agreement). Compound risks 
to key infrastructure in cities have increased from extreme weather 
(medium evidence, high agreement). Losses become systemic when 
they affect entire systems and can even jump from one system to 
another (e.g., drought impacting rural food production contributing to

urban food insecurity) (medium confidence). (Figure TS.10 COMPLEX 
RISK) {6.2.6, 6.2.7, 6.4.3, Figure 6.2, 11.5.1, Box 11.1, 13.9.2, 13.5.2, 
13.10.2, 13.10.3, 14.6.3, CCP2, CCP5.3.2, CWGB URBAN}
TS.C.11.6 Interconnectedness and globalisation establish 
pathways for the transmission of climate-related risks 
across sectors and borders, through trade, finance, food and 
ecosystems (high confidence). Flows of commodities and goods, 
as well as people, finance and innovation, can be driven or disrupted 
by distant climate change impacts on rural populations, transport 
networks and commodity speculation (high confidence). For example, 
Europe faces climate risks from outside the area due to global supply 
chain positioning and shared resources (high confidence). Climate risks 
in Europe also impact finance, food production and marine resources 
beyond Europe (medium confidence).  (Figure TS.10 COMPLEX RISK) 
{1.3.1, 5.13.3, 5.13.5, 6.2.4, 9.9, 13.9.2, 13.5.2, 13.9.2, 13.9.3,

Box 14.5, CCB INTEREG, Figure CCB INTEREG.1}
TS.C.11.7 Arctic communities and Indigenous Peoples face risks 
to economic activities (very high confidence)  as direct and 
cascading impacts of climate change continue to occur at a 
magnitude and pace unprecedented in recent history and much 
faster than projected for other regions (very high confidence).  
Impacts and risks include reduced access to and productivity of future 
fisheries, regional and global food and nutritional security (high 
confidence), local livelihoods, health and well-being (high confidence) and loss to sociocultural assets, including heritage sites in all Arctic 
regions (very high confidence). (Figure TS.10 COMPLEX RISK) {Box 7.1, 
13.8.1, Box 13.2, Figure 13.14, CCP6.2.1, CCP6.2.2, CCP6.2.3, CCP .6.2.4, 
CCP6.2.5, CCP6.3.1, Table CCP6.1, Table CCP6.2, Table CCP6.6}
TS.C.11.8 Indigenous Peoples, traditional communities, small-
holder farmers, urban poor, children and elderly in Amazonia are

burdened by cascading impacts and risks from the compound 
effects of climate and land use change on forest fires in the 
region (high confidence). Deforestation, fires and urbanisation have 
increased the exposure of Indigenous Peoples to respiratory problems, 
air pollution and diseases (high confidence). Amazonian forest fires are 
transboundary and increase systemic losses of wild crops, infrastructure 
and livelihoods, requiring a landscape governance approach (medium 
evidence, high agreement). (Figure TS.10 COMPLEX RISK) {2.4.3, 2.4.4, 
2.5.3, 8.2.1, 8.4.5, Box 8.6, CCP7.2.3, CCP7.3}
TS.C.11.9 Population groups in most vulnerable and exposed 
regions to compound and cascading risks have the most urgent 
need for improved adaptive capacity (high confidence). Regions 
characterised by compound challenges of high levels of poverty, a 
significant number of people without access to basic services, such 
as water and sanitation and wealth and gender inequalities, and

governance challenges are among the most vulnerable regions and 
are particularly located in East, Central and West Africa, South Asia, 
Micronesia and Melanesia and in Central America (high confidence). 
{8.3, 8.4, Box 8.6, CCP5.3.2}
TS.C.11.10 Emergent risks arise from responses to climate 
change, including maladaptation and unintended side effects 
of mitigation, including in the case of afforestation and 
hydropower (very high confidence). Solar radiation modification 
(SRM) approaches attempt to offset warming and ameliorate some 
climate risks but introduce a range of new risks to people and 
ecosystems, which are not well understood (high confidence). {1.3.1, 
3.6.3, 5.13.6, CWGB SRM}
Reasons for concern (RFC)
TS.C.12 More evidence now supports the five major RFCs about 
climate change, describing risks associated with unique and 
threatened systems (RFC1), extreme weather events (RFC2), 
distribution of impacts (RFC3), global aggregate impacts (RFC4)

and   large-scale singular events (RFC5) (high confidence). 
(Figure TS.4, Table TS.1) {16.6.3, Figure 16.15}
TS.C.12.1 Compared to AR5 and SR15, risks increase to high 
and very high levels at lower global warming levels for all five 
RFCs (high confidence), and transition ranges are assigned with 
greater confidence. Transitions from high to very high risk emerge in 
all five RFCs, compared to just two RFCs in AR5 (high confidence). As 
in previous assessments, levels of concern at a given level of warming 
remain higher for RFC1 than for other RFCs. (Table TS.1, TS.AII) {16.6.3, 
Figure 16.15}
TS.C.12.2 Limiting global warming to 1.5°C would ensure risk 
levels remain moderate for RFC3, RFC4 and RFC5 (medium

69
Technical SummaryTSconfidence), but risk for RFC2 would have transitioned to a high 
risk at 1.5°C and RFC1 would be well into the transition to very 
high risk (high confidence). Remaining below 2°C warming (but 
above 1.5°C) would imply that risk for RFC3 through RFC5 would be 
transitioning to high, and risk for RFC1 and RFC2 would be transitioning 
to very high (high confidence). By 2.5°C warming, RFC1 will be at very 
high risk (high confidence), and all other RFCs will have begun their 
transitions to very high risk, with medium confidence for RFC2, RFC3 and 
RFC4, and low confidence for RFC5. (Table TS.1) {16.6.3, Figure 16.15}
TS.C.12.3 While the RFCs represent global risk levels for 
aggregated concerns about ‘dangerous anthropogenic 
interference with the climate system’, they represent a great 
diversity of risks, and in reality, there is not one single dangerous 
climate threshold across sectors and regions.  RFC1, RFC2 and

RFC5 include risks that are irreversible, such as species extinction, 
coral reef degradation, loss of cultural heritage or loss of a small island 
due to sea level rise. Once such risks materialise, the impacts would 
persist even if global temperatures subsequently declined to levels 
associated with lower levels of risk in an ‘overshooting’ scenario, for 
example where temperatures increase over ‘well below 2°C above 
pre-industrial’ for multi-decadal time spans  before decreasing  (high 
confidence). (Figure TS.4, see also TS.C.13) {16.6.3, Figure 16.15}
Temporary overshoot
TS.C.13 Warming pathways that imply a temporary temperature 
increase over ‘well below 2°C above pre-industrial’ for multi-
in many natural and human systems (e.g., glacier melt, loss 
of coral reefs, loss of human lives due to heat) even if the 
temperature goals are reached later (high confidence). {2.5.2, 
2.5.3, 4.6.1}

TS.C.13.1 Projected warming pathways may entail exceeding 
1.5°C or 2°C around mid-century. Even if the Paris temperature 
goal is still reached by 2100, this ‘overshoot’ entails severe risks 
and irreversible impacts on many natural and human systems (e.g., 
glacier melt, loss of coral reefs, loss of human life due to heat) (high 
confidence). {2.5, 3.4, 16.6, WGI AR6 SPM}
TS.C.13.2 Overshoot substantially increases risk of carbon 
stored in the biosphere being released into the atmosphere due 
to increases in processes such as wildfires, tree mortality, insect 
pest outbreaks, peatland drying and permafrost thaw  (high confidence). These phenomena exacerbate self-reinforcing feedbacks 
between emissions from high-carbon ecosystems (which currently store 
around 3030–4090 GtC) and increasing global temperatures. Complex 
interactions of climate change, land use change, carbon dioxide fluxes 
and vegetation changes, combined with insect outbreaks and other

disturbances, will regulate the future carbon balance of the biosphere, 
processes incompletely represented in current Earth system models. 
The exact timing and magnitude of climate–biosphere feedbacks 
and potential tipping points of carbon loss are characterised by large 
uncertainty, but studies of feedbacks indicate increased ecosystem 
carbon losses can cause large future temperature increases (medium 
confidence). {2.5.2, 2.5.2, 2.5.3, Figure  2.10, Figure  2.11, Table  2.4, 
Table 2.5, Table 2.S. 2, Table 2.S. 4, Table 5.4, Figure 5.29, WGI AR6 5.4}
TS.C.13.3 Extinction of species is an irreversible impact of 
climate change whose risk increases sharply with rises in global 
temperature (high confidence). Even the lowest estimates of species 
extinctions (9% lost) are 1000  times the natural background rates 
(medium confidence). Projected species extinctions at future global 
warming levels are consistent with projections from AR4, but assessed

on many more species with much greater geographic coverage and a 
broader range of climate models, giving higher confidence. (see also 
TS.C.1) {2.5.1, Figure 2.6, Figure 2.7, Figure 2.8, CCP1, CCB DEEP}
TS.C.13.4 Solar radiation modification (SRM) approaches have 
the potential to offset warming and ameliorate other climate 
hazards, but their potential to reduce risk or introduce novel 
risks to people and ecosystems is not well understood (high 
confidence). SRM effects on climate hazards are highly dependent 
on deployment scenarios, and substantial residual climate change or 
overcompensating change would occur at regional scales and seasonal 
time scales (high confidence). Due in part to limited research, there is 
low confidence in projected benefits or risks to crop yields, economies, 
human health or ecosystems. Large negative impacts are projected 
from rapid warming for a sudden and sustained termination of SRM

in a high-CO 2 scenario. SRM would not stop CO 2 from increasing in the 
in the literature that for addressing climate change risks SRM is, at 
best, a supplement to achieving sustained net zero or net negative CO 2 
provides a chance for responsibly developing SRM technologies with 
broader public participation and political legitimacy, guarding against 
potential risks and harms relevant across a full range of scenarios. 
{CWGB SRM}
Table TS.1 |  Updated assessment of risk level transitions for the five reasons for concern (RFC) {16.6.3}
RFC Example of impacts (not comprehensive)Updated risk level based on 
observed and modelled impactsWarming level
RFC1 Unique and threatened systems: 
ecological and human systems that have 
restricted geographic ranges constrained by

climate-related conditions and have high 
endemism or other distinctive properties. 
Examples include coral reefs, the Arctic and 
its Indigenous Peoples, mountain glaciers 
and biodiversity hotspots.Coral bleaching, mass tree and animal mortalities, species 
extinction; decline in sea-ice dependent species, range shifts in 
multiple ecosystemsIn transition from moderate to high1.1°C 
Further decline of coral reef (by 70–90% at 1.5°C) and Arctic 
sea-ice dependent ecosystems; insects projected to lose 
>50% climatically determined geographic range 2°C; reduced 
habitability of small islands; increased endemic species 
extinction in biodiversity hotspotsProjected to transition from high to 
very high risk1.2°C–2.0°C 

70
TSRFC Example of impacts (not comprehensive)Updated risk level based on 
RFC2 Extreme weather events: risks/
impacts to human health, livelihoods, assets 
and ecosystems from extreme weather 
events such as heatwaves, heavy rain, 
drought and associated wildfires and coastal 
flooding.Increased heat-related human mortality, wildfires, agricultural 
and ecological droughts, water scarcity; short-term food 
shortages; impacts on food security and safety, price spikes; 
marine heatwaves estimated to double in frequency.In transition to high risk at present1.0°C–1.5°C 
Significant projected increases in fluvial flood frequency and 
resultant risks associated with higher populations; at least 
1 d yr-1 with a heat index above 40.6°C for about 65% of 
megacities at 2.7°C and close to 80% at 4°C; soil moisture 
droughts 2–3 times longer; agricultural and ecological droughts

more widespread; simultaneous crop failure across worldwide 
breadbasket regions; malnutrition and increasing risk of disease.Projected to transition to very high risk 
(new in AR6)1.8°C–2.5°C 
RFC3 Distribution of impacts: risks/
impacts that disproportionately affect 
particular groups, such as vulnerable 
societies and socio-ecological systems, 
including disadvantaged people and 
communities in countries at all levels of 
development, due to uneven distribution of 
physical climate change hazards, exposure 
or vulnerability.Increasing undernutrition, stunting and related childhood 
mortality, particularly in Africa and Asia and disproportionately 
affecting children and pregnant women; distributional impacts 
on crop production and water resourcesCurrent risk level is moderate 1.1°C (high confidence)
Risk of simultaneous crop failure in maize estimated to increase 
from 6% to 40%; increasing flood risk in Asia, Africa, China,

India and Bangladesh; high risks of mortality and morbidity due 
to heat extremes and infectious disease with regional disparitiesProjected to transition to high risk1.5°C–2.0°C 
Much more negative impacts on food security in low to 
mid-latitudes; substantial regional disparity in risks to food 
production; food-related health projected to be negatively 
impacted by 2°C–3°C warming; heat-related morbidity and 
mortality, ozone-related mortality, malaria, dengue, Lyme disease 
and West Nile fever projected to increase regionally and globallyProjected to transition to very high risk2.0°C–3.5°C 
RFC4 Global aggregate impacts: impacts 
to socio-ecological systems that can be 
aggregated globally into a single metric, 
such as monetary damages, lives affected, 
species lost or ecosystem degradation at a 
global scale.Aggregate impacts on biodiversity with damages of global 
significance (e.g., drought, pine bark beetles, coral reef

ecosystems); climate-sensitive livelihoods like agriculture, 
fisheries and forestry would be severely impactedIn transition to moderate risk 1.1°C (medium confidence)
Estimated 10% relative decrease in effective labour at 2°C; 
global exposure to multi-sector risks approximately doubles 
between 1.5°C and 2°C; global population exposed to flooding 
projected to rise by 24% at 1.5°C and by 30% at 2°C warning; 
reduced marine food provisioning, fishery distribution and 
revenue value with projected approximate 13% decline in ocean 
animal biomass.Projected to transition to high risk1.5°C–2.5°C 
Widespread death of trees, damage to ecosystems and reduced 
provision of ecosystem services over temperature range 
2.5°C–4.5°C; projected global annual damages associated with 
sea level rise of USD31,000 billion yr-1 in 2100 for 4°C warming 
scenario.Projected to transition to very high risk 
(new in AR6)2.5°C–4.5°C 
RFC5 Large-scale singular events:

relatively large, abrupt and sometimes 
irreversible changes in systems caused 
by global warming, such as ice sheet 
disintegration or thermohaline circulation 
slowing, sometimes called tipping points or 
critical thresholds.Mass loss from both Antarctic (whether associated with marine 
ice sheet instability or not) and Greenland ice sheets is more 
than seven times higher over the period 2010–2016 than over 
the period 1992–1999 for Greenland and four times higher 
for the same time intervals for Antarctica; in Amazon forest, 
increases in tree mortality and a decline in carbon sink are 
reportedCurrent risk level is moderate 1.1°C (high confidence)
Implications for 2000-year commitments to sea level rise from 
sustained mass loss from both ice sheets as projected by various 
ice sheet models, reaching 2.3–3.1 m at 1.5°C peak warming 
and 2–6 m at 2°C peak warming; risk of savannisation for 
Amazon alone was assessed to lie between 1.5°C and 3°C, with

a median value at 2°CProjected to transition to high risk1.5°C–2.5°C 
Uncertainties in projections of sea level rise at higher levels 
of warming, long-term equilibrium sea level rise of 5–25 m 
at mid-Pliocene temperatures of 2.5°C; potential for Amazon 
forest dieback between 4°C and 5°C; risk of ecosystem carbon 
loss from tipping points in tropical forest and loss of Arctic 
permafrost.Projected to transition to very high risk 
(new in AR6)2.5°C–4°C 

71
Technical SummaryTSTS.D Contribution of Adaptation to Solutions
This section covers climate change adaptation and explains how our 
knowledge of it has progressed since AR5. The section begins with 
an explanation of overall progress on adaptation and the adaptation 
gaps and then discusses limits to adaptation. Maladaptation and the 
underlying evidence base are explained together with the strategies 
available to strengthen the biosphere that can help ecosystems 
function in a changing climate. Different adaptation options across 
water, food, nutrition and ecosystem-based adaptation and other 
nature-based solutions are also discussed and, in particular, the ways 
in which urban systems and infrastructure are coping with adaptation. 
Adaptation to sea level rise is specifically discussed given its global 
impact on coastal areas, while health, well-being, migration and 
conflict are also explained as these warrant additional important

considerations. Justice and equity have a significant impact as well on 
how effective adaptation can be and are discussed as key issues that 
relate to decision-making processes on adaptation and the range of 
enablers that can support adaptation. Lastly, the focus shifts to system 
transitions and transformational adaptation that are needed to move 
climate change adaptation forward in a rapidly warming world.
Adaptation progress and gaps
TS.D.1 Increasing adaptation is being observed in natural 
and human systems (very high confidence), yet the majority 
of climate risk management and adaptation currently being 
planned and implemented are incremental (high confidence). 
There are gaps between current adaptation and the adaptation 
needed to avoid the increase of climate impacts that can be 
observed across sectors and regions, especially under medium 
and high warming levels (high confidence). {4.6.1, 4.6.2, 4.6.3,

4.6.4, 4.6.5, 4.6.6, 4.6.7, 4.6.8, 4.6.9, Box 4.3, Box 4.5, Box 4.6, 
7.4.1, Table 4.8, Figure 4.24, Figure 6.4.3, Figure 6.5, 9.3.1, 9.6.4, 
9.8.3, 9.11.4, 13.2, 13.11, 14.7.1, 16.3, 16.4, 17.2.2, CCP5.2.4, 
CCP5.2.7, CCP7.5.1, CCP7.5.2}
TS.D.1.1 Responses have accelerated in both developed and 
developing regions since AR5, with some examples of regression 
(high confidence). Growing adaptation knowledge in public and 
private sectors, increasing numbers of policy and legal frameworks 
and dedicated spending on adaptation are all clear indications that 
the availability of response options has expanded (high confidence). 
However, observed adaptation in human systems across all sectors 
and regions is dominated by small incremental, reactive changes to 
usual practices often after extreme weather events, while evidence 
of transformative adaptation in human systems is limited (high 
confidence). Droughts, pluvial, fluvial and coastal flooding are the

most common hazards for which adaptation is being implemented, 
and many of these have physical, affordability and social limits (high 
confidence). There is some evidence of global vulnerability reduction, 
particularly for flood risk and extreme heat. {1.4.5, 2.4.2, 2.4.5, 
2.5.4, 2.6.1, 2.6.6, 3.4.2, 3.4.3, 3.6.3, 4.6.1, 4.6.2, 4.6.3, 4.6.4, 4.6.5, 
4.6.6, 4.6.7, 4.6.8, 4.6.9, Box 4.3, Box 4.5, Box 4.6, 7.4.1, Table 4.8, 
Figure 4.24, 11.6, Table 11.14, Box 11.2, 12.12.5, 13.2.2, 13.10, 13.11, 
14.7.1, 15.5.4, 16.3.2, 16.4.2, 12.3, CCB EXTREMES}TS.D.1.2 Current adaptation in natural and managed ecosystems 
includes earlier planting and changes in crop varieties, soil 
improvement and water management for livestock and crops, 
aquaculture, restoration of coastal and hydrological processes, 
introduction of heat- and drought-adapted genotypes into high-
risk populations, increasing the size and connectivity of habitat 
patches, agroecological farming, agroforestry and managed

relocations of high-risk species (medium confidence). These 
measures can increase the resilience, productivity and sustainability of 
both natural and food systems under climate change (high confidence). 
Financial barriers limit the implementation of adaptation options in 
natural ecosystems, agriculture, fisheries, aquaculture and forestry as 
financial strategies are stochastically deployed. Investment in climate 
service provision has benefited the agricultural sector in many regions, 
with limited uptake of climate service information into decision-
making frameworks (medium confidence). {2.6.2, 2.6.3, 2.6.4, 2.6.5, 
2.6.8, 3.6.3, 4.6.2, 4.7.1, Figure 4.23, 5.4.3, 5.5.3, 5.9.4, 5.10.3, 5.14.3, 
9.4, 9.4.4, 9.4.1, 12.5.4, 12.8, 13.5.2, 13.10.2, 14.5.4, 15.5.7, 17.2.1, 
17.5.1, CCP5.2.5, CCP 7.5, CCB NATURAL}
TS.D.1.3 The ambition, scope and progress on adaptation have 
risen among governments at the local, national and international

levels, along with businesses, communities and civil society, but 
many funding, knowledge and practice gaps remain for effective 
implementation, monitoring and evaluation (high confidence). 
There are large gaps in risk management and risk transfer in low-
income contexts, and even larger gaps in conflict-affected contexts 
(high confidence). Adaptive capacity is highly uneven across and 
within regions (high confidence). Current adaptation efforts are not 
expected to meet existing goals (high confidence). {1.1.3, 1.2.1, 1.3.1, 
1.3.2, 1.4.5, 2.6.2, 2.6.3, 2.6.6, 2.6.8, 3.6.3, 4.7.1, 6.1, 6.4.3, Figure 6.5, 
9.1.5, 9.4.1, 9.4.5, 11.7.1, 11.7.2, 13.11.1, 14.7.1, 15.6, 17.2, 17.4.2, 
17.5.1, 17.5.2, CCP7.5, CCB DEEP , CCB NATURAL}
TS.D.1.4 Many cities and settlements have developed adaptation 
plans since AR5, but a limited number of these have been 
implemented so that urban adaptation gaps exist in all world 
regions and for all hazard types (high confidence). Many plans

focus on climate risk reduction, missing opportunities to advance co-
benefits of climate mitigation and sustainable development and risking 
compounding inequality and reduced well-being (medium confidence). 
The largest adaptation gaps exist in projects that manage complex 
risks, for example in the food–energy–water–health nexus or the inter-
relationships of air quality and climate risk (high confidence). Most 
innovation in adaptation has occurred through advances in social 
and ecological infrastructures, including disaster risk management, 
social safety nets and green/blue infrastructure (medium confidence). 
However, most financial investment continues to be directed narrowly 
at large-scale hard engineering projects after climate events have 
caused harm (medium confidence). {4.6.5, 6.3.1, 6.3.2, Figure 6.4, 6.4.3, 
6.4.5, 10.3.7, Table 10.2, 11.3.5, 12.5.5, 13.11, 14.5.5, 14.7.1, 15.3.4, 
17.4.2, CCP2.3, CCP2.4, CCP5.2.7, CCB FINANCE}

72
TS
Projected loss of terrestrial and freshwater 
biodiversity compared to pre-industrial period
+3.0°C
+2.0°C
Percentage of biodiversity loss
>80% 60% 40% 20% 10% 0.5% 0.1%
Percentage of species exposed to potentially 
dangerous climate conditions
+3.0°C
+2.0°C
Percentage of biodiversity exposedProjected changes in global marine species 
richness in 2100 compared to 2006
RCP8.5
≈+4.3°C
RCP4.5
≈(b) With every additional increment of global warming more species will be exposed to potentially dangerous climate conditions 
and more biodiversity will be lost.Species and ecosystems around the world are at increasing risk due to climate change
Marine species richness has been declining in 
equatorial and increasing in higher latitudes since 
the 1950s due to global warming
Marine species richness
for a suite of taxonomic groups based
on 48,661 marine species
-50°
Latitude
0°50°
2000 4000 6000 01955–19741975–19941995–2015(a) Observed impacts of

      on ecosystems
Change in species richness
Gain Loss
-50 -250 <-1,000 >1,000 250 50 0for a suite of taxonomic groups based
on12,796 marine species globally
75–100% 50–75% 0–25% 25–50%
Africa
Antarctic
Tropical forestsMediterranean regionGlobalChanges in
ecosystem
structureTerrestrial
Ocean
nanana
naSpecies 
OceanFreshwater
nanana/
naChanges in
timing
Ocean
na nanana
not
assessedConﬁdence in attribution
Medium
Low

73
Sustainable useProtect
Restore/migrate
Uncertainty in effectiveness
with increasing pressures
Low population growth & demand
for food can make land availalbe
More frequent extreme events 
National & international
policies for protectionMitigation option
designed as NbS
Monitoring & early-warning systems
for climate changeExperiments & pilots can
help design effective actions
Incentives for EbA & NbS
Experiments and pilots
can help design
effective actionsIndigenous
knowledge
practicesEnablers Barriers(c) Example of adaptation actions for ecosystems and biodiversity.
(d) Adaptation pathwa ys for ecosystems .
i. Networks of Protected Areas combined with zoning increase resilience .
ii. Assisted migration and evolution might reduce extirpation and extinction.
iii. Adaptation and mitigation increase space for nature and beneﬁt society.
iv. Ecosystem-based Adaptation (EbA) and Nature-based Solutions (NbS).Strategies
Time with increasing 
population growth

and global warmingiii.
i.iv.Consumer choices reduce demand on managed systems
EbA / NbS
Restoratio nA ssisted evolution
Assisted migration
Identify
refugia
Networks of Protected AreasZoning around Protected Areas
Habitat diversiﬁcation in Protected Areas
Protected Areasii.
i.ii.
Assessment of
implementationAdaptation options can be facilitated by actions 
which increase the solution space such as 
consideration of local knowledge, new regulations 
and incentives but also decrease due to climatic 
and non-climatic stressors and maladaptation.Examples for actionsTerrestrial ecosystems Ocean ecosystems
* Considering  species distribution shifts and other climate change responses  ** Low conﬁdence due to limited evidenceConservation of climatic microrefugia Conservations of marine climate refugia
Assisted reintroduction, translocation 
and migration of speciesAssisted reintroduction, translocation
and migration of species

Adjusting conservation strategies and site
objectives to reﬂect changing species
distributions and habitat characteristicsClimate-adaptive management*
Reducing non-climatic stressors 
to increase resilience  of ecosystemsSustainable harvesting, reducing the ecological
vulnerability of marine ecosystems
Restoration of natural ecological
communities and processesMarine habitat restoration, increasing biodiversity 
Protect, restore or create large areas of natura l
and semi-natural habitatTransboundary marine spatial planning (MSP)
and integrated coastal zone management (ICZM)**
Intensive management for vulnerable speciesExpansion of marine protected areas (MPAs) 
and MPA networks 
Increase habitat connectivity Ecosystem-based managementFreshwater ecosystems
Conservation of climatic microrefugia
Assisted reintroduction, translocation
distributions and habitat characteristics

Reducing non-climatic stressors
to increase resilience of ecosystems
Restoring hydrological processes
of wetlands, rivers and catchments
Protect or restore natural
vegetation cover in catchments
Intensive management for vulnerable species
Increased connectivity in river systems
Conﬁdence in
its effectiveness
in reducing
risks of
High
Medium
Low
Figure TS.5 ECOSYSTEMS  | (a) Left: Observed global and regional impacts on ecosystems and human systems attributed to climate change. Confidence levels reflect uncertainty 
in attribution of the observed impact to climate change. For more details and line of sight to chapters and cross-chapter papers see Figure TS.3a, SMTS.1 and Table SMTS.1. Right: 
Observed species richness across latitude for three historical periods. {3.4.3, Figure 3.18}. (b) Left: Global warming levels (GMST) modelled across the ranges of more than 30,000

marine and terrestrial species. Middle: Global warming levels (GSAT); change indicated by the proportion of species (modelled n=119,813 species globally) for which the climate 
is projected to become unsuitable across their current distributions. Right: Modelled 12,796 marine species globally. {2.5.1, Figure 2.6, 3.4.3, Figure 3.18, Figure 3.20a, CCP1.2.4, 
Figures AI.6, AI.15, AI.16}. (c) {2.6.2, Table 2.6, 3.6.2, Figure 3.24}. (d) Some actions facilitate sustainable use but also increase space for nature. {2.4 2, 2.6.2, 2.6.3, 2.6.5, 2.6.7, 
2.6.8, 3.6.2, 3.6.5, Table 3.30, 5.6.3, Box 5.11, 9.3.1, 9.3.2, 9.6.3, 9.6.4, 9.12 .3, 10.4.2, 10.4.3, 11.3.1, 11 .3.2, 11 .7.3, 12.5. 1, 12. 5.2, 12.5.9, 12.6.1, 13.3.2, 13.4.2, 13.5 
.2, 13.10.2, 14.5.1, 14.5.2, Box 14.2, Box 14.7, 15.5.4, 15.3.3, Table 15 .6, 16.5.2, 16.6.3, CCP1.3, CCP3. 2.2, CCP4.4.1, CCP5 .2.5, CCP5.4.1, CCP6.3.2, CCP7.5, CCP7 .5. 1, 
CCPBox7.1, Table CCP7 .3, CCB EXTREMES, CCB NATURAL}

74
TSClimate change is affecting food security through pervasive water impacts
lts impacts are being felt in every w ater use sector , more so in agriculture which globally consumes over 80% of the total w ater.
(c) Observed and projected impacts from climate change in the w ater cycle for human managed systems and crop yield productivity .Drought related
Attributed to other
causes or unknownOther climate related<1 thousand20 millionMetric
tonnes
of food
loss
2013 1970 1975 1980 1985 1990 1995 2000 2005 2010Food production loss events(a) The frequency of climated-related food production losses in crop s, livestocks, ﬁsheries and aquacultures has been increasin g over 
the last decades.
Moderate-severe
Extreme-exceptionalDroughts
0

2006 2020 2030 2040 2050 2060 2070 2080 2090 2099 2006 2020 2030 2040 2050 2060 2070 2080 2090 2099= Population projections based 
on Shared Socio-Economic 
Pathw ay 2 (SSP2)*(b) By the late 21st century the share of the global land area and population* affected by combinations of agricultural , ecological and 
hydrological droughts is projected to increase substantially.
Impacts
on human
managed
systems
Impacts
on crop
yield
productivityMost regions have already experienced negative 
impacts on the water cycle and agricultural 
productivity. 
Positive Negative Mixed
Conﬁdence in attribution
Observed / Projected*GroundwaterWaSH**
Maize
Rice
Soybean
WheatWater quality
High Low Medium
*Mid-century at RCP4.5 (~2°C Global Warming Level) = Water, sanitation and hygiene**Obs. Proj.Africa
//Obs. Proj.Asia
Obs. Proj.Australasia
/ /
/Obs. Proj.Central
and South
America
//

///Obs. Proj.Europe
/
/
/Obs. Proj.North
America
/Obs. Proj.Small
Islands
//
//
//
//Obs. Proj.Global
/
/
/
= Not observed or insufﬁcient evidence// –

75
Technical SummaryTS(d) Drought is exacerbating water management challenges which vary across regions with respect to anticipated water scarcity 
conditions by 2050.
(e) Water-related adaptation responses.
Current beneﬁcial outcomes, co-beneﬁts with mitigation, and 
maladaptive outcomes of responses and future effectiveness of 
adaptation and residual risk under different levels of global 
warming.
Mitigation co-beneﬁts
Maladpative outcomesImproved outcomesEconomic or ﬁnancial
potential 
to reduce
projected risk
1.52.03.04.0Residual risk
remaining
after
1.52.03.04.0Curren tF uture
Assessment under different
High Low MediumConfidence
High Low MediumStrength of evidence /effectiveness/residual risk
Not observed or
insufﬁcient evidence/
Incon-

clusiveHigh Low Medium Changes in cropping pattern and crop systemsImproved cultivars and agronomic practices
//// Livestock and ﬁshery-related ////
//// Indigenous knowledge and local knowledge based adaptations ////
//// Water, sanitation and hygiene (WASH) related adaptations ////Migration and off-farm diversiﬁcation //// ////
Economic or ﬁnancial incentives //// ////
Training and capacity building //// ////Collective action, policies, institutions /// / ////
Multiple agricultural options ////// /
Index in 2050
(median)Policy challenges
<0.4
present day to 2050
Figure TS.6 FOOD-WATER | (a) {5.4.1.1, Box 5.1, FAQ 5.1, SM5.1, Figure Al.20}. (b) Projected increase in the global share of area and population impacted from droughts.

Changes are calculated based on the RCP6.0 concentration pathway for Terrestrial Water Storage (TWS) droughts, which can be considered to be a combination of agricultural, 
ecological and hydrological droughts. TWS is the sum of continental water stored in canopies, snow and ice, rivers, lakes and reservoirs, wetlands, soil and groundwater. {Figure 4.19; 
4.4.5}. (c) Projected impacts are for RCP4.5 mid 21st century, taking into account adaptation and CO 2 fertilisation for the crop yield productivity {4.3.1, 4.2.7, 4.5.1, Figure 4.2, 
5.5.3, 5.4.1, Figure 5.3, Figure 9.22, 15.3.3, 15.3.4}. (d) Projections used five CMIP5 climate models, three global hydrological models from ISIMIP , and three Shared Socioeconomic 
Pathways (SSPs).{Box 4.1, Figure Box 4.1.1, Figure AI.48}. (e) {4.6.2, Figure 4.29, Figure 4.28, SM4.7, SM4.8, 5.5.4, 5.6.3}.

76
TS
(a) Map of observed human vulnerability based on two comprehensive global indicator -systems using national data,  plus examples of
selected local vulnerable populations and Indigenous Peoples
LowHighObserved human vulnerability differs between and within countries and strongly determines
how climate hazards impact people and societyRelative vulnerability
Low

Island communities | limited land , population growth and coastal ecosystem degradation | 15.3.2Examples of local vulnerable populations | Examples of some aspects of vulnerability | Chapter reference sAfricaNorth America
Central &
1
3
42
5
67
8
9
10
11
1278 91291
10128
3612
113612

77
Africa
 Asia(b) Different aspects and dimensions of vulnerability (regional averages of selected vulnerability indicators)
Europe
Average mortality per hazard event is indicated by size of pie charts. The slice of pie chart shows absolute number of deaths from a particular hazard   (c) Average mortality per hazard event per region between 2010 and 2020:  
Storm Flood Drought Wild Fires HeatNorth America Europe Australasia Asia* South &
Central AmericaSmall Island Africa
The large size of the pie chart and the strong representation of heat w aves is caused by the signi/f_icant number of deaths from a single event in a single

country . This single extreme outlier affected the over all avera ge mortality per event in Asia.*

78
TSmediumhigh
lowConstraints associated with limits to adaptation for regions across all sectors:(d) Constraints that mak e it harder to plan and implement human adaptation
Small islandsCentral &
EuropeNorth
AmericaAsia Australasia Africa
Economics Social|Cultural
Human Capacity
Governace, 
PolicyFinanceInformation,
Awareness &
Figure TS.7 VULNERABILITY | (a) The global map of vulnerability is based on two comprehensive global indicator systems, namely INFORM Risk Index and WorldRiskIndex 
(2019). Climate change hazards and exposure levels are not included in this figure. The relative level of average national vulnerability is shown by the colours. Vulnerability values 
are based on the average of the two indices, classified into 5 classes using the quantile method. A hexagon binning method was used to simplify the global map and enlarge small

states. The map combines information about the level of vulnerability (independent of the population size) with two classes of population density (high density ≥ 20 people/km2 
and low density < 20 people/km2). The selected examples of local vulnerable populations underscore that there are also highly vulnerable populations in countries with overall low 
relative vulnerability {8.3.2, Figure 8.6} (b) This figure shows regional averages for selected aspects of human vulnerability. The indicators are a selection of the indicator systems 
used within the global vulnerability map (panel a). The colours represent the average value of the respective indicator for the regional level; classified into three classes using natural 
breaks. This regional information reveals that within all regions challenges exist in terms of different aspects of vulnerability, however, in some regions these challenges are more

severe and accumulate in multiple-dimensions. For example, the indicator “dependency ratio” measures the ratio of the number of children (0–14 years old) and older persons 
(65 years or over) to the working-age population (15–64 years old). {8.3.2, Figure 8.7} (c) The pie charts show the number of deaths (mortality) per hazard (storm, flood, drought, 
heatwaves and wildfires) event per continental region based on Emergency Events Database (EM-DAT) (Centre for Research on the Epidemiology of Disasters, 2020). The size of the 
pie chart represents the average mortality per hazard event while slices of each pie chart show the absolute number of deaths from each hazard. This reveals that significantly more 
fatalities per hazard (storms, floods, droughts, heatwaves and wildfires) did occur in the past decade in more vulnerable regions, e.g. Africa and Asia. {Figure 8.6} (d) The figure

shows constraints that make it harder to plan and implement human adaptation. Across regions and sectors, the most significant challenges to human adaptation are financial, 
governance, institutional and policy constraints. The ability of actors to overcome these socio-economic constraints largely influences whether additional adaptation is able to be 
implemented and prevent limits to adaptation from being reached. Low: <20% of assessed literature identifies this constraint; Medium: 20–40% of assessed literature identifies 
this constraint; High: >40% of assessed literature identifies this constraint. {9.3, 16.4.3, Figure 16.8}

79
Susceptibility
Political commitment
Socioeconomic conditions
Population health status
Individual factorsSocioeconomic factor s
Growing inequity
Demographic change
Economic growth
Migration and (im)mobility
Urbanization
Science and tech investmentEnvironmental factor s
Biodiversity loss
Desertiﬁcation
Water pollution
Vulnerability and upstream 
determinants of health outcomesExposure
pathwayExample health outcomes
Extreme weather eventsHeat stressAir qualityWater quality
and quantityNutrient dense diets
and food safetyVector distribution
and ecologySocial factors
Injuries, fatalities ,
mental health effectsHeat-related illness and death,  
adverse pregnancy outcomes, 
lost work er productivityExacerbated respiratory 
diseases, allergies , 
cardiov ascular diseaseDiarrheal diseases, 
campylobacteria infections ,
cholera,  cryptosporidiosis ,
algal bloomsMalnutrition,  
salmonella,

foodborne diseasesChikungunya,  dengue , 
hantavirus , Lyme disease , 
malaria,  Rift Valley,  West 
Nile, Zika
Health authorities
Strenghtening health delivery and 
system resilience
Leveraging climate change
speciﬁc funding streamsLeadership and governance
Coherent policies and strategies
Sufﬁcient health workforceCollaborations with other 
sectors, agencies ,
and civil societyService delivery (includes 
climate-smart health programs, 
management of environmental 
determinants of health,  disaster 
risk reductionHealth information systems (includes 
integrated risk monitoring and early 
warning and response systems , 
vulnerability,  capacity,  and adapta-
tion assessments, health component 
of national adaptation plans, health 
and climate research)Environmentally sustainable 
and resilient technologies and 
infrastructurePhysical and mental health 
risks,  displacement,  forced 
migration,  other 
context-speciﬁc risks
Health System
Solution SpaceClimate Resilent

Development Pathways
Fully implementing
climate-resilient health systems
Achieving universal
healthcare coverage 
Achieving net 
zero Greenhouse Gas Emissions
from healthcare systems
Achieving the
Sustainable Development Goals 
Adopting mitigation policies
  and technologies with signiﬁcant 
health co-beneﬁts Climate change and human health and wellbeing:  Risks and responses
CLIMAT E HAZARDS,  VULNERABILITY AND EXPOSURE IMPA CT AND RISKS SOLUTIONS SP ACE AND CLIMA TE RESILIENT DEVELOPMENT PATHWA YS
Multiple socio-economic and environmental factors interact with climate risks to shape human health and wellbeing .
Achieving climate resilient development requires leveraging opportunities in the solution space within health systems and acro ss other sectors .Figure TS.8 HEALTH | Multiple socio-economic and environmental factors interact with climate risks to shape human health and well-being. Achieving climate resilient development requires leveraging opportunities

in the solution space within health systems and across other sectors. {7.1.4, 7.1.6, 7.1.7, 7.2.1, 7.2.2, 7.2.3, 7.2.4, 7.2.5, 7.2.6, 7.2.7, 7.3.1, 7.3.3, 7.4.1, 7.4.2, 7.4.3, 7.4.6, 7.4.7, Box 7.1, Box 7.2, Figure 7.6, Figure 7.7, Figure 7.16, 
Table 7.1,Table 7.3, Table 7.6, Table 7.7, Table 7.8, Table 7.10, Table 7.11, CCB COVID, CCB HEALTH, CCB MIGRATE}

80
TS
Climate change in cities and settlements
(a) Urban poor populations residing in informal settlements are highly vulnerable to climate hazards given their housing characteristics 
and location in marginal lands and high-risk areas .
20100200300380
*Excluding
  Australia and New ZealandWestern Asia
and Northern
AfricaLatin
America 
and the
CaribbeanSub-Saharan
AfricaCentral and
Southern
AsiaEastern and
South-eastern
AsiaAustralia and
New ZealandOceania* Northern
America
and EuropeMillions of people in 2018
Millions of people in 2000
1 day365 daysDays per year 
when air 
temperatur e
pose a risk of 
deathMap data without accounting for heatwaves.(b) Global distribution of population exposed to potentially deadly conditions from extreme temperatures and relative humidity.
Named cities are the largest 15 urban 
areas by population size.
Urban population growthMillions of people
Historical period
1991–2005New YorkNew York

Mexico CityMexico City
SSão Pauloão PauloLagosLagosCairoCairo
KinshasaKinshasaDelhiDelhi
KarachiKarachi DhakaDhaka
MumbaiMumbai KolkataKolkataBeijingBeijingTokyoTokyo
ShanghaiShanghai
ManilaManila
New YorkNew York
MumbaiMumbai KolkataKolkataLahoreLahore TokyoTokyo
ManilaManilaNew York
Mexico City
São PauloLagosCairo
KinshasaDelhi
Karachi Dhaka
Mumbai KolkataBeijingTokyo
Shanghai
Manila
New York
Mumbai KolkataLahore Tokyo
Shanghai
Manila
RCP4.5
≈ +2 °C of global
warming in 2041–20602018 2050
0.8
0.6
0.4
0.21
Oceania* Northern
America
and EuropeAustralia and
New ZealandExpanded insetMillions of people
Africa
and the Caribbean
Europe
North AmericaAsia3,479
million people
Oceania41 million people

Figure TS.9 URBAN | (a) The regions shown are reflecting the original dataset from UN Habitat and vary from IPCC regions. {6.1.4, 9.9.3, 10.4.6, 12.5.5) (b) Heat is a growing 
health risk due to increasing urbanization and rising temperature extremes. Within cities the urban heat island effect elevates temperatures further, with some populations in cities 
being disproportionately at risk including low income communities in informal settlements, children, the elderly, disabled, people who work outdoors and ethnic minorities. The 
data does not consider heatwaves which are also projected to increase and can cause thousands of deaths in higher latitudes. {6.1.4, 7.2.4, 7.3.1, 10.4.6, 13.6.1, Annex I: Global 
to Regional Atlas}

81
(c) Projected number of people at risk of a 100-year coastal ﬂood.
(d) Contributions of urban adaptation options to climate resilient development.
Nature-based solutions and social policy as innovative domains of adaptation show how some of the limitations of grey infrastruc ture can be mediated. A mixture 
of the three categories has considerable future scope in adaptation strategies and building climate resilience in cities and se ttlements.
Grey/Physical Infrastructure
Dikes, seawalls | water storage, 
greywater use | slope revetments 
| air conditioning | passive cooling 
| upgrading transport, energy, 
water & sanitation infrastructure  
| Information & Communication 
Technologies | urban design & 
building regulation sNature-based Solutions 
Urban agriculture | street trees | 
green roofs | parks and open 
space | community gardens | rain 
gardens | bioswales | retention 
ponds | riverbanks | ﬂoodplains 
and watershed restorationPlanning and social policy

Land use planning | social safety 
nets | emergency and disaster 
risk management | health 
services | climate education | 
heritage conservation
Addresses multiple hazards
Reduces systemic vulnerability
Constrains knock-on hazard generation
Constrains transfer of risk to other people and places
Enhances social capital
Enhances livelihood
Enhances health
Flexibility post deployment
Deployable at scale
Beneﬁts adaptation in other infrastructure systems
Economic cost
Reduces poverty and marginality
Inclusive and locally accountable
Enables social transformation
Enables ecological transformationEcological beneﬁt
Climate mitigation co-beneﬁtComponents of climate resilient developmentConﬁdence in
positive contribution  or
negative contribution HighHigh MediumMedium LowLow
Mixed positive/negativeHigh Moderate Small NegligibleContribution to
climate resilient
developmentMillions
of people
at risk
0.010.10151025
0.01%0.1%0.5%1%5%10%Percentage
of region’ s

2021204020602080210021202150
Earlier
riskDelayed
riskPeriod when
risk thresholds
are exceededCalculated for sea level 
rise and population 
change under SSP2-4.5, 
based on current  
protection levels
(c) The size of the circle represents the number of people at risk per IPCC region and the colours show the timing of risk based on projected population change and sea level rise 
under SSP2-4.5. Darker colours indicate earlier in setting risks. The left side of the circles shows absolute projected population at risk and the right side the share of the population 
in percentage. {Figure 13.6, Figure 15.3, Figure CCP2.4, Annex I: Global to Regional Atlas). (d) The figure is based on Table 6.6 which is an assessment of 21 urban adaptation 
mechanisms. Supplementary Material 6.3 provides a detailed analysis including definitions for each component of climate resilient development and the evidences. {6.3.1, 6.3.2, 
6.3.3, Table 6.6, SM6.3}

82
TSCryosphere changes, higher 
summer temperatureLand use change 
pressures
landscape 
changes
Sámi 
peopleRisksExposure
Insect 
harassment 
VulnerabilityNew 
diseases Compound,  cascading and transboundary impacts for humans and ecosystems result from the complex 
interaction of multiple climate hazard s, exposures and vulnerabilities
(a) Risks to livelihoods of Sámi Reindeer Herders in the Arctic (b) Socio-ecological risks to the Amazonia ecosystem
(c) Cascading impacts of climate hazards on food and nutrition (d) Compound risks in coastal and island systems reduce habitabil ity
slow onset and
extreme eventsLand use change
stressors
Drought
Material & Non-material losses
Socio-Ecological Tipping Point
Risks over longer time frame Risks over shorter time frame
HealthFood 
Insecurity Loss of 
biodiversity  
Forest 
dieback
Change in well-being of 
Traditional communities & 
Indigenous P eoples+
Climatic
hazardsMore frequent and intensiﬁed 
heat/droughts, ﬂoods

Reduced
& health
Labour 
capacity
Access to 
foodFood
availability,  
+utilizationClimatic
hazardsNon-
climatic
hazards
Climatic
hazards
Heat
Household 
income
Quality
of life
Food yield 
losses
Malnutri-
diseases
Reef 
submer-
gence
ecosystemWater
resources
Homes 
 assests
Health & 
Economy & 
livelihoodsExtreme Events:
Tropical cyclones, storms, ﬂoodingSlow onset ocean changes 
(Sea surface temperature , acidity,  dissolved oxygen)Slow onset climate changes 
(Sea level rise, coastal erosion)
Small 
Island Reduced 
habitabilityDrought Heat
Non-
climatic
hazards
Forest 
ﬁres
Forest 
Marine 
ecosystemSámi reindeer 
herding
reindeer Psychological 
stress  
Self 
determina-
tion &  
adaptive 
capacity 
hSense of 
place Way of 
life Work load 
& costs 
Conﬂicts 
Culture & 
heritageFood
system
Identity
Liveli-
hood
Cultur e
Place –
Attachment

83
Cascading risks
Exposure
Hazard
Risk
Risks
(e) Urban infrastructure failures cascade risk and loss across and  
 beyond the city (f) Cross-sectoral and transboundary impacts of Australian megaﬁres,  
2019–2020
Health
Emergency 
responsesTourism
Vegeta-
tion 
burned
Urban 
infrastructure & 
energy supplyUrban 
services
Transport 
systems
Social
infra-
structur e
Smoke 
and
ash
Ash glaciers 
New 
Zealand
Economic 
Impacts
Social 
impacts
Loss of 
ecosystem 
services
Information 
technology 
systems
Mass 
wildlife 
mortality
Water 
contami-
nation
Infra-
structur e
Agricultur e
Mega 
wildﬁr e
international
supply chain 
& tradeHazard
ExposureRapid onset events
(ﬂoods, storms, cyclones)
7 Mio ha 
wildﬁre 
Footprint
Financial 
markets 
undirectionalCompound risks
bidirectional
Industry & 
manufac-
turing
Figure TS.10 COMPLEX RISK | Compound, cascading and transboundary impacts for humans and ecosystems result from exposure to the complex interactions

of (1) multiple climatic hazards, including with non-climatic stressors (as seen in panels a, b, c, d), (2) multiple vulnerabilities compounding the effect of risks 
(as seen in panel a, b, c), and (3) multiple impacts/risks that compound and cascade to spread across sectors and boundaries (panels b, c, d, e, f)
(a) Climate and land use change result in cumulative impacts on traditional, semi-nomadic Sámi reindeer herding. Impacts cascade due to a lack of access to key ecosystems, 
lakes and rivers, thereby increasing costs and threatening traditional livelihoods, food security, cultural heritage, and mental health. {Box 7.1, Figure Box 9.7.1, 13.8.1.2, Box 13.2, 
Figure 13.14. Table SM13.7, Figure 16.2, Figure CCP6.7}
(b) Risks compound from deforestation, wildfires, urbanization, and climate change in Amazonia impacts biodiversity, livelihoods, medicinal, spiritual, and cultural sites; increasing

migration patterns, loss of place-based attachments, and culture, causing health problems and mental and emotional distress of vulnerable traditional communities and Indigenous 
People dependent on the forest ecosystem. {Box 8.7, Figure Box 9.7.1, 12.4, Figure 12.11, Table 12.6, Figure 16.2}
(c) Complex pathways from climate hazards to malnutrition in subsistence farming households. The factors involved in and the probable impacts of weather variables on food 
yields and of production on malnutrition. {Figure 1.3, Figure 1.4, 5.2.1, 5.2.2, 5.12.3, 5.12.4, Box 5.10, Figure 5.2, 7.2.2, 7.3.1, Figure Box 9.7.1, 13.5.1, 13.5.2, 13.10.2, 16.5.2, 
16.5.3, Figure 16.2}
(d) Risk compounds and amplifies through cascading effects due to interconnectedness of island systems. Loss of marine, coastal, terrestrial biodiversity and ecosystem services

can cause submergence of reef islands, increase water insecurity, destroy settlements and infrastructure, degrade health and well-being, reduce economy and livelihoods, and result 
in loss of cultural resources and heritage. {15.3.4.9, Figure Box 15.1, Figure 15.5, Figure 16.2}
(e) Climate impacts can cascade through interconnected infrastructure in cities and settlements impacting on social well-being and economic activities, spreading loss and risk 
through lost economic productivity disrupting the distribution of goods and provision of basic services, spreading widely, into rural places and across international borders as supply 
chains, financial investment and remittance flows are disrupted. {6.1.3, 6.2.2, 6.2.4, Figure 6.2, Figure 16.2, Figure CCB INTEREG.1}
(f) Cascading, compounding and transboundary impacts on people’s mortality and physical and mental health, economic activity, built assets, ecosystems and mass species

mortality and with smoke and ash transported to New Zealand affecting air quality and glaciers, arising from the “Black Summer” fires of 2019–2020 which burned over a 
five-month period in eastern and southern Australia. Fire weather is projected to worsen across Australasia. {Figure  1.3, Figure  1.4, 11.3.1.3, Box  11.1, Figure Box  11.1.2, 
Figure 16.2, WGI AR6 Figure SPM.9}

84
TSTS.D.1.5 Systemic barriers constrain the implementation of 
adaptation options in vulnerable sectors, regions and social 
groups (high confidence). Key barriers are limited resources, lack 
of private-sector and citizen engagement, insufficient mobilisation of 
finance (including for research), lack of political leadership, limited 
research and/or slow and low uptake of adaptation science and 
a low sense of urgency. Most of the adaptation options to the key 
risks depend on limited water and land resources (high confidence). 
Governance capacity, financial support and the legacy of past urban 
infrastructure investment constrain how cities and settlements 
are able to adapt (high confidence). Critical urban capacity gaps 
include limited ability to identify social vulnerability and community 
strengths, the absence of integrated planning to protect communities, 
the lack of access to innovative funding arrangements and a limited

capability to manage finance and commercial insurance (medium 
confidence). Prioritisation of options and transitions from incremental 
practices, cultures, norms and belief systems. For example, Africa faces 
severe climate data constraints and inequities in research funding and 
leadership that reduce adaptive capacity (very high confidence)—from 
1990 to 2019 research on Africa received just 3.8% of climate-related 
research funding globally, and 78% of this funding for Africa went 
to European Union- and North America–based institutions and only 
14.5% to African institutions. {3.6.3, 9.1.5, 9.5.1, 9.8.4, 12.5.1, 12.5.5, 
12.5.7, 12.8, 13.11, 14.7.2, 15.6.1, 15.7, CCP7.6, CCB FEASIB}
TS.D.1.6 Insufficient financing is a key driver of adaptation gaps 
(high confidence). Annual finance flows targeting adaptation

for Africa, for example, are billions of US dollars less than the 
lowest adaptation cost estimates for near-term climate change 
(high confidence). Finance has not targeted more vulnerable countries 
and communities. From 2014 to 2018 a greater amount of financial 
commitments to developing countries was in the form of debt rather 
than grants, and—excluding multilateral development banks—only 
51% of commitments targeting adaptation were dispersed (compared 
to 85% for other development projects). Tracked private-sector finance 
for climate change action has grown substantially since 2015, but 
the proportion directed towards adaptation has remained small (high 
confidence); in 2018 contributions were 0.05% of total climate finance 
and 1% of adaptation finance. Globally, private-sector financing of 
adaptation has been limited, especially in developing countries (high 
confidence). {3.6.3, 4.7,4, 4.7.5, 4.8.2, 6.4.5, Table 6.10, 9.4.1, 12.5.4, 
12.5.8, 15.6.3, 17.4.3, CCB FINANCE}

TS.D.1.7 Closing the adaptation gap requires moving beyond 
short-term planning to develop long-term, concerted pathways 
and enabling conditions for ongoing adaptation to ensure 
timely and effective implementation (high confidence). Inclusive, 
equitable and just adaptation pathways are critical for climate resilient 
development. Such pathways require consideration of SDGs, gender 
and Indigenous knowledge and local knowledge and practices. The 
success of adaptation will depend on our understanding of which 
adaptation options are feasible and effective in their local context 
(high confidence). Long lead times for nature-based and infrastructure 
solutions or planned relocation will require implementation in the 
coming decade to reduce risks in time. To close the adaptation gap, political commitment, persistent and consistent action across scales of 
government and upfront mobilisation of human and financial capital

are key (high confidence), even when the benefits are not immediately 
visible. {3.6.5, 4.8, 6.3.5, 11.7, 12.5.7, 13.2.2, 13.8, 13.11, 14.7.2, 15.7, 
CCP2.3, CCP2.4, CCP7.5, CCB DEEP , CCB FEASIB, CCB GENDER}
TS.D.2 There is increasing evidence on limits to adaptation 
which result from the interaction of adaptation constraints 
and the speed of change (high confidence). In some natural 
systems, hard limits have been reached (high confidence) and 
more will be reached beyond 1.5°C (medium confidence). 
Surpassing such hard, evolutionary limits causes local species 
extinctions and displacements if suitable habitats exist (high 
confidence). Otherwise, species’ existence is at very high risk 
(high confidence). In human, managed and natural systems, soft 
limits are already being experienced (high confidence). Financial 
constraints are key determinants of adaptation limits in human 
and managed systems, particularly in low-income settings

(high confidence), while in natural systems key determinants 
for limits are inherent traits of the species or ecosystem (very 
high confidence). (Figure TS.7 VULNERABILITY) {2.4.2, 2.6.1, 3.3, 
3.4.2, 3.4.3, 15.5.4, CCP5.3.2, CCP7.5.2, CCB EXTREMES}
TS.D.2.1 Adaptation limits can be differentiated into hard and 
soft limits. Soft limits are those for which no further adaptation 
options are feasible currently but might become available in the future. 
Hard limits are those for which existing adaptation options will cease 
to be effective and additional options are not possible. Hard limits will 
increasingly emerge at higher levels of warming (high confidence). 
Adaptation limits are shaped by constraints that can or cannot be 
overcome by adaptation actions and by the speed with which climate 
impacts unfold. Evidence and signals of the thresholds at which 
constraints result in limits is still sparse and, in human systems, are

expected to remain contested even with increasing knowledge (high 
confidence). {2.4.2, 2.6.1, 4.7.4, Box  4.2, Box  4.3, 15.3.4, 15.5.4, 
16.4.1, 16.4.2, 16.4.3, CCB EXTREMES}
TS.D.2.2 Limits to adaptation have been observed for terrestrial 
and aquatic species and ecosystems and for some human and 
managed systems in specific geographies such as small island 
states and mountain regions (high confidence). Beginning at 
below 1.5°C, autonomous and evolutionary adaptation responses 
by more terrestrial and aquatic species and ecosystems will face hard 
limits, resulting in species extinctions, loss of ecosystem integrity and 
a resulting loss of livelihoods (high confidence). Examples of hard 
limits being exceeded include observed population losses and species 
extinctions and loss of whole ecosystems from certain locations (e.g., 
irrecoverable loss of tropical coral reefs locally). Large local population 
declines of wild species have already impacted human food sources

and livelihoods (e.g., for Indigenous Arctic communities). Soft limits are 
currently being experienced in particular by individuals, households, cities 
and settlements along the coast and by small-scale farmers (medium 
communities face limits due to financial, institutional and socioeconomic 
constraints and a short timeline for adaptation implementation, reducing

85
Technical SummaryTSthe efficacy of coastal protection and accommodation approaches and 
resulting in loss of life and economic damages (medium confidence). 
{2.4.2, 2.5.4, 2.6.1, 3.4.2, 3.4.3, CCP1, CCP2, CCP6, 4.7.4, Box 4.2, 6.4.4, 
11.3.1, 11.3.2, 11.3.4, 11.3.5, 12.5.1, 13.3.1, 13.4.1, 13.10.2, 15.5.4, 
15.5.6, 16.4.2, 16.4.3, CCP5.2.7, CCP5.3.2}
TS.D.2.3 Limits to adaptation will be reached in more systems, 
including, for example, coastal communities, water security, 
agricultural production and human health, as global warming 
increases (medium confidence). Hard limits beginning at 1.5°C 
are also projected for coastal communities reliant on nature-based 
coastal protection (medium confidence). Adaptation to address the 
risks of heat stress, heat mortality and reduced capacities for outdoor 
work for humans face soft and hard limits across regions that become 
significantly more severe at 1.5°C and are particularly relevant for

regions with warm climates (high confidence). Beginning at 3°C, 
hard limits are projected for water management measures, leading to 
and well-being, economic losses in water and energy-dependent sectors 
and potential migration of communities (medium confidence). Soft and 
hard limits for agricultural production are related to water availability 
and the uptake and effectiveness of climate resilient crops, which 
are constrained by socioeconomic and political challenges (medium 
confidence). In terms of settlements, limits to adaptation are often most 
pronounced in smaller and rapidly growing towns and cities, including 
those without dedicated local government (medium confidence). At 
the same time, legacy infrastructure in large and mega cities, designed 
without taking climate change risk into account, constrains innovation, 
leading to stranded assets and with increasing numbers of people

unable to avoid harm, including heat stress and flooding, without 
transformative adaptation (medium confidence). {2.4.2, 3.4.2, 3.5.5, 
3.6.3, 4.7.4, Box  4.2, Box  4.3, 4.7.2, 4.7.3, 6.4.3, 6.4.5, 6.4.5, 6.4.5, 
Figure 6.4, 16.4.2, 16.4.3, 3.4.3, 11.3.1, 11.3.2 11.3.4, 11.3.5, 11.3.6, 
12.5.1, 12.5.2, 12.5.3, 13.10.2, Box  11.6, Table  14.6, 15.3.3, 15.3.4, 
15.5.4, 16.4.2, 16.4.3, CCP2, CCB ILLNESS, CCB SLR}
TS.D.2.4 Across regions and sectors, the most significant 
determinants of soft limits are financial, governance, 
institutional and policy constraints (high confidence). The ability 
of actors to address these socioeconomic constraints largely influences 
whether additional adaptation can be implemented and prevent soft 
limits from becoming hard limits. Global and regional evidence shows 
that climate impacts may limit the availability of financial resources, 
stunt national economic growth, result in higher levels of losses

and damage and thereby increase financial constraints (medium 
evidence). Information, awareness and technological constraints 
are also high in multiple regions (high confidence). For example, 
awareness of anthropogenic climate change ranges between 23% and 
66% of people across 33 African countries, with low climate literacy 
limiting potential for transformative adaptation (medium confidence). 
(Figure TS.7 VULNERABILITY) {2.3.1, 2.3.2, 2.5.1, 2.6.8, 3.6.3, 4.7.4, 
6.4.4, 9.3.1, 9.4.1, 9.4.5, 12.8, 13.11.1, 14.7.2, 15.6.1, 15.6.3, 16.4.2, 
16.4.3, CCP2, CCP5.4.1, CCP7.5, CCP7.6, CCB EXTREMES}
TS.D.2.5 The potential for reaching adaptation limits fundamen-
tally depends on emissions reductions and mitigating global 
warming (high confidence) . Under all emissions scenarios, climate change reduces capacity for adaptive responses and limits choices 
and opportunities for sustainable development. The ability of actors to

overcome socioeconomic constraints determines whether additional 
adaptation can be implemented and prevent soft limits from becoming 
hard limits (medium confidence). Above 1.5°C of warming, limits to 
adaptation are reported for human and natural systems, including coral 
reefs (high confidence), regional water availability (medium evidence, 
high agreement) and outdoor labour and existing tourism-related 
activities. {1.1.3, 1.5.1, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.6.8, 
3.6.3, 3.6.5, 4.7.1, 4.7.2, Box 4.3, 3.5.2, 3.6.2, 3.6.2, 13.10.2, 14.5.7, 
14.5.8, 15.3.3, 15.3.4, Box  15.1, 16.4, 16.5, 16.6, CCP5.3.2}
TS.D.3 Evidence of maladaptation is increasing in some sectors 
and systems, highlighting how inappropriate responses to climate 
change create long-term lock-in of vulnerability, exposure 
and risks that are difficult and costly to change (very high 
confidence) and exacerbate existing inequalities for Indigenous

Peoples and vulnerable groups, impeding achievement of SDGs, 
increasing adaptation needs and shrinking the solution space 
(high confidence). Decreasing maladaptation requires attention 
to justice and a shift in enabling conditions towards those that 
enable timely adjustments for avoiding or minimising damage 
and for seizing opportunities (high confidence). (Figure TS.11a) 
{1.2.1, 1.3.1, 1.4.2, 2.6, Box 2.2, 3.6.3, Box 4.3, Box 4.5, 4.6.8, 
4.7.1, Figure 4.29, 5.6.3, 5.13.4, 8.4.5, 8.2.1, 8.3.3, 8.4.5, 8.6.1, 
9.7, 9.8, 9.9, 9.10, 9.11, Box  9.8, Box  9.9, Box  11.6, 12.5.3, 
12.5.7, 13.3, 13.4, 13.5, 13.11.3, 14.5.9, 15.5.1, 15.6.5, 16.3.2, 
17.5.1, CCP2.3.2, CCP2.3.6, CCB DEEP, CCB NATURAL, CCB SLR, 
CWGB BIOECONOMY}
TS.D.3.1 Maladaptation has been observed across many regions 
and systems and occurs  for many reasons, including  inade-
quate  knowledge and  short-term, fragmented, single-sector 
and/or non-inclusive governance planning and implementa-

tion (high confidence). Policy decisions that ignore the risks of 
adverse effects can be maladaptive by worsening the impacts of 
and vulnerabilities to climate change (high confidence) . Examples 
include in coastal systems (e.g., sea walls that enable further exposure 
through intensification of developments in low-lying coastal areas), 
urban areas (e.g., inflexible infrastructure in cities and settlements that 
cannot be adjusted easily or affordably for increased heavy rainfall), 
agriculture (e.g., the use of high cost irrigation in areas that are projected 
to have more intense drought conditions), forestry (e.g., planting of 
unsuitable trees species which displace Indigenous Peoples and other 
forest- dependent communities ) and human settlements (e.g., stranded 
assets and stranded vulnerable communities that cannot afford to shift 
away or adapt and require an increase in social safety nets) (high

confidence) . {Box  2.2, 2.6.6, 2.6.5, 3.6.3, Box  4.3, Box  4.5, 4.7.1,  Figure  4.29, 
4.6.8, 5, 5.13.4, 9.7, 9.8, 9.9, 9.10, 9.11, Box  9.8, Box  9.9, Box  11.5, 
Box 11.6, 13.2, 13.3, 13.3.1, 13.4, 13.4.2, 13.5.1, 14.5.9, 15.5.1, 15.5.4, 
15.5.5, 16.3.2, CCP2.4, CCB DEEP , CCB FEASIB, CCB SLR}
TS.D.3.2 Indigenous Peoples and disadvantaged groups, such 
as low-income households and ethnic minorities, are especially 
adversely affected by maladaptation, which often deprives

86
TSthem of food and livelihoods and reinforces and entrenches 
existing inequalities (high confidence). Rights-based approaches 
to adaptation, participatory methodologies and inclusion of local 
and Indigenous knowledge, combined with informed consent, deliver 
mechanisms to avoid these pitfalls (medium confidence). Adaptation 
solutions benefit from engagement with Indigenous and marginalised 
groups, solve past equity and justice issues and offer novel approaches 
(medium confidence). Indigenous knowledge is a powerful tool to 
assess interlinked ecosystem functions across terrestrial, marine and 
freshwater systems, bypassing siloed approaches and sectoral problems 
(high confidence). Lastly, engagement with Indigenous knowledge 
and marginalised groups often offers an intergenerational context for 
adaptation solutions needed to avoid maladaptation (high confidence). 
{2.6.5, 4.6.9, 8.4, 8.4.5, 5.12.8, 5.13.4, 11.4.1, 11.4.2, 12.5.8, 13.8.1,

Box 13.2, 14.4, 14.5.9, 5.13.5, 15.6.5, 18.2.4, CCP5.4.2, Box CCP7.1}
TS.D.3.3 Reliance on hard protection against sea level rise can 
lead to development intensification, which compounds risk and 
locks in exposure of people and assets as socioeconomic and 
governance barriers and technical limits are reached. Avoiding 
maladaptive responses to sea level rise depends on immediate mitigation 
and application of adaptive planning that sets out near-term, low-regret 
actions while keeping open options to account for ongoing committed 
sea level rise (very high confidence). Such forward-looking adaptive 
pathway planning and iterative risk management can address the 
current path dependencies that lead to maladaptation and can enable 
timely adaptation alignment with long implementation lead times, as 
well as addressing uncertainty about rate and magnitude of local sea 
level rise, and ensuring that adaptation will be more effective (medium

confidence) . As sea level rise advances, only avoidance and relocation 
will eliminate coastal risks (high confidence). Other measures only 
delay impacts for a time, increasing residual risk, perpetuating risk and 
creating ongoing legacy effects and inevitable property and ecosystem 
losses (high confidence). While relocation may in the near term appear 
socially unacceptable, economically inefficient or technically infeasible, 
it may become the only feasible option as protection costs become 
unaffordable and technical limits are reached (medium confidence). 
{3.4.2, 3.5.5, 3.6.3, 11.7.3, Box  11.6, 12.5.7, 12.5.8, 13.10, 15.3.4, 
15.5.1, 15.5.2, 15.5.3, CCP2.2.3, CCP4, CCB DEEP , CCB SLR}
TS.D.3.4 Maladaptation can be reduced using the principles of 
recognitional, procedural and distributional justice in decision-
making, responsibly evaluating who is regarded as vulnerable 
and at risk, who is part of decision-making, who is the beneficiary

of adaptation measures and integrated and flexible governance 
mechanisms that account for long-term goals (high confidence). 
Examples include selecting native and appropriate species in habitat 
restoration, monitoring key social and environmental indicators for 
adaptation progress, embedding strong monitoring and evaluation 
processes, considering measures of efficiency and social welfare, 
and social and political drivers and power relationships. Integrated 
approaches, such as the water–energy–food nexus and inter-regional 
considerations of risks can reduce the risk of maladaptation, building 
on existing adaptation strategies, increasing community participation 
and consultation, integration of Indigenous knowledge and local 
7 Ecosystem-based adaptation is defined as the use of ecosystem management activities to increase the resilience and reduce the vulnerability of people and ecosystems to climate change.knowledge, focusing on the most vulnerable small-scale producers,

anticipating risks of maladaptation in decision-making for long-lived 
activities, including infrastructure decisions, and the impact of trade-
offs and co-benefits (high confidence). (Figure TS.11a) {2.6.5, 2.6.6, 
2.6.7, 4.7.6, 4.8, Box 4.8, 5.9.2, Table 5.21, 5.9.2, 5.9.4, 5.13.3, 5.14.2, 
5.13.3, 6.2.7, 7.4.2, 8.2.2, 8.3.3, 8.10, 10.6.3, 11.4, 11.5, 11.7.12, 
15.5.4, Figure 15.7, 17.5.1, 17.5.2, 17.6, CCP1.3, CCP5.4.2, CCP5.4.2, 
CCB INTEREG, CCB NATURAL}
Strengthening the biosphere
TS.D.4 Diverse, self-sustaining ecosystems with healthy bio-
diversity provide multiple contributions to people that are 
essential for climate change adaptation and mitigation, thereby 
reducing risk and increasing societal resilience to future climate 
change (high confidence). Better ecosystem protection and 
management is key to reduce the risks that climate change poses 
to biodiversity and ecosystem services and build resilience; it 
is also essential that climate change adaptation be integrated

into the planning and implementation of conservation and 
environmental management if it is to be fully effective in future 
(high confidence). Risks to ecosystems from climate change can 
be reduced by protection and restoration and also by a range 
of targeted actions to adapt conservation practice to climate 
change (high confidence). Protected areas are key elements of 
adaptation but need to be planned and managed in ways that 
take account of climate change, including shifting species distri-
butions and changes in biological communities and ecosystem 
structure. Adaptation to protect ecosystem health and integrity 
is essential to maintain ecosystem services, including for climate 
change mitigation and the prevention of greenhouse gas 
emissions. (Figure TS.12, Figure TS.5 ECOSYSTEMS) {2.5.4, 2.6.2, 
2.6.3, 2.6.6, 2.6.7, 3.6.2, 3.6.3, 3.6.5, 4.6.6, Box  4.6, 5.14.1, 
12.5.1, 13.3.2, 13.4.2, Box 14.7, 15.5.4, 15.5.6, CCP1, CCP5.4.1, 
CCP5.4.2, CCB NATURAL}

TS.D.4.1 Ecosystem protection and restoration can build resil-
ience of ecosystems and generate opportunities to restore eco-
system services with substantial co-benefits (high confidence) 
and provision of ecosystem-based adaptation.7 Ecosystem-based 
adaptation includes protection and restoration of forests, grasslands, 
peatlands and other wetlands, blue carbon systems (mangroves, salt 
marshes and seagrass meadows), and agroecological farming practices. 
In coastal systems, nature-based solutions, including ecosystem-based 
adaptation, can reduce impacts for human settlements until sea level 
rise results in habitat loss. High rates of warming and drought may 
severely threaten the success of nature-based solutions such as forest 
expansion or peatland restoration. Ecosystem-based adaptation is 
being increasingly advocated in coastal defence against storm surges, 
terrestrial flood regulation, reducing urban heat and restoring natural

fire regimes. Nature-based solutions, including ecosystem-based 
adaptation, can therefore reduce risks for ecosystems and benefit 
people, provided they are planned and implemented in the right way 
and in the right place. For example, coastal wetlands and ecosystems 
can also be seriously damaged by coastal defences designed to protect

87
Technical SummaryTSinfrastructure. {2.6.2, 2.6.3, 2.6.5, 2.6.7, Table 2.7, 3.4.2, 3.5.5, 3.6.2, 
3.6.3, 9.6.3, 9.6.4, 13.2.2, 13.3.2, 13.4.2, 13.5.2, 13.6.1, Box 14.7, CCB 
TS.D.4.2 Increasing the resilience of biodiversity and ecosystem 
services to climate change includes minimising additional stresses 
or disturbances, reducing fragmentation, increasing natural 
habitat extent, connectivity and heterogeneity, maintaining 
taxonomic, phylogenetic and functional diversity and redundancy 
and protecting small-scale refugia where microclimate conditions 
can allow species to persist (high confidence) . In some cases, 
specific management interventions may be possible to reduce risks to 
individual species or biological communities, including translocation or 
manipulating microclimate or site hydrology. Adaptation also includes 
actions to prevent the impacts of extreme events or aid the recovery of

ecosystems following extreme events, such as wildfire, drought or marine 
heatwaves. In some cases, recovery of ecosystems from extreme events 
can be facilitated by removing other human pressures. Understanding the 
characteristics of vulnerable species can assist in early warning systems 
to minimise negative impacts and inform management intervention. 
(Figure TS.5 ECOSYSTEMS) {2.3, 2.3.1, 2.3.2, 2.5.3, 2.5.4, 2.6.2, 2.6.5, 
2.6.7, 2.6.8, Figure 2.1, Table 2.6, Table 2.8, 3.6.3, 3.6.5, 4.6.6, Box 4.6, 
12.5.1, 13.3.2, 13.4.2, 13.10.2, Box 14.7, 15.5.4, CCB EXTREMES, CCB 
FEASIB}
TS.D.4.4 Available adaptation options can reduce risks to 
ecosystems and the services they provide, but they cannot 
prevent all changes and should not be regarded as a substitute 
for reductions in greenhouse gas emissions (high confidence). 
Ambitious and swift global mitigation offers more adaptation 
options and pathways to sustain ecosystems and their services (high

confidence). Even under current climate change, it is necessary to take 
account of climate change impacts, which are already occurring or are 
inevitable, in environmental management to maintain biodiversity and 
ecosystem services (high confidence), and this will become increasingly 
important at higher levels of warming. (Figure TS.5 ECOSYSTEMS) {2.2, 
2.3, 2.4.5, 2.5.1, 2.5.2, 2.5.3, 2.5.4, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 
2.6.6, 2.6.7, 2.6.8, 3.4.2, 3.4.3, 3.5.2, 3.5.3, 3.5.5, 3.6.2, 3.6.3, 3.6.5, 
Figure  3.24, Figure  3.25, 4.6.6, Box  4.6, Box  4.7, 13.4.2, Box  14.7, 
15.5.4, CCP5.4.2, CCB FEASIB, CCB NATURAL}
TS.D.4.5 Ecosystem-based adaptation measures can reduce 
climatic risks to people, including from flood, drought, fire 
and overheating (high confidence). Ecosystem-based adaptation 
approaches are increasingly being used as part of strategies to manage 
flood risk, at the coast in the face of rising sea levels and inland in the

context of more extreme rainfall events (high confidence). Flood-risk 
measures that work with nature by allowing flooding within coastal 
and wetland ecosystems and support sediment accretion can reduce 
costs and bring substantial co-benefits to ecosystems, liveability 
and livelihoods (high confidence). In urban areas, trees and natural 
areas can lower temperatures by providing shade and cooling from 
evapotranspiration (high confidence). Restoration of ecosystems in 
catchments can also support water supplies during periods of variable 
8 Actions to protect, sustainably manage and restore natural or modified ecosystems that address societal challenges effectively and adaptively, simultaneously providing human well-being and 
biodiversity benefits.rainfall and maintain water quality and, combined with inclusive 
water regimes that overcome social inequalities, provide disaster risk 
reduction and sustainable development (high confidence). Restoring

natural vegetation cover and wildfire regimes can reduce risks to 
people from catastrophic fires. Restoration of wetlands could support 
livelihoods and help sequester carbon (medium confidence), provided 
they are allowed accommodation space. Ecosystem-based adaptation 
approaches can be cost effective and provide a wide range of additional 
co-benefits in terms of ecosystem services and biodiversity protection 
and enhancement. (Figure TS.9 URBAN, Figure TS.11a) {2.6.3, 2.6.5, 
2.6.7, Table 2.7, 3.6.2, 3.6.3, 3.6.5, Box 4.6, Box 4.7, 12.5.1, 12.5.3, 
12.5.5, 13.2.2, 13.3.2, 13.6.2, Box  14.7, 15.5.4, Figure  15.7, CCP2, 
CCP5.4.2, CCB NATURAL, CCB SLR}
TS.D.4.6 Ecosystem-based adaptation and other nature-based 
solutions8are themselves vulnerable to climate change impacts 
(very high confidence). Under higher emissions scenarios they will 
increasingly be under threat. Nature-based solutions cannot deliver the

full range of benefits, unless they are based on functioning, resilient 
ecosystems and developed taking account of adaptation principles. 
There is a serious risk that high-carbon ecosystems will become sources 
of greenhouse gas emissions, which makes it increasingly difficult 
to halt anthropogenic climate change without prompt protection, 
restoration, adaptation and mitigation at a global scale. {2.5.2, 2.5.3, 
2.5.4, 2.6.3, 2.6.5, 2.6.6, 2.6.7, 3.6.2, 3.6.3, 3.6.5, Box  4.6, 13.4.2, 
15.3.3, 15.5.4, CCB NATURAL, CCB SLR}
TS.D.4.7 Potential benefits and avoidance of harm are maximised 
when nature-based solutions are deployed in the right places 
and with the right approaches for those areas, with inclusive 
governance (high confidence). Taking account of interdisciplinary 
scientific information, Indigenous knowledge and local knowledge and 
practical expertise is essential to effective ecosystem-based adaptation

(high confidence). There is a large risk of maladaptation where this 
does not happen (medium confidence). For example, naturally treeless 
peatlands can be afforested if they are drained, but this leads to the loss 
of distinctive peatland species as well as high greenhouse gas emissions. 
It is important that nature-based solution approaches to climate change 
mitigation also take account of climate change adaptation if they are to 
remain effective. {1.4.2, 2.2, 2.4.3, 2.4.4, 2.5.2, 2.5.3, 2.6.2, 2.6.3, 2.6.5, 
2.6.6, 2.6.7, Box 2.2, Table 2.6, Table 2.7, 3.6.3, 3.6.5, 4.7.2, Box 4.6, 
5.14.2, 13.4.2, Box 14.7, 15.5.4, CCP1, CCB NATURAL}
Water and food sectors
TS.D.5 Various adaptation options in the water, agriculture 
and food sectors are feasible with several co-benefits (high 
confidence), some of which are effective at reducing climate 
impacts (medium confidence). Adaptation responses reduce 
future climate risks at 1.5°C warming, but effectiveness decreases

above 2°C (high confidence). Resilience is strengthened by eco-
system-based adaptation (high confidence) and sustainable 
resource management of terrestrial and aquatic species (medium 
confidence). Agricultural intensification strategies produce

88
TS
Energy reliabilitySustainable aquaculture and /f_isheries
Water use efﬁciency and water resource managemen t
Peace and
services
Food
security
Critical
networks
key risks
Cross-

Land and 
ocean
Urban and
systems
Energy
systems
Other
risksHigh
Insuf/f_icient evidence/
Con/f_idence level
Low
/Geo-
physical
/Environ-
with

potential feasibility(a) Diverse feasible climate responses and adaptation options exist to respond to Representative Key Risks of climate change, with varying synergies with mitigation
 Multidimensional feasibility and synergies with mitigation of climate responses and adaptation options relev ant in the near -term,  at global scale and up to 1.5°C of global w arming

89

Ethnic
and their
income
groups
Cross-
Land and 
ocean
Urban and
systems
Energy
+ •• ++ •+ +•
++ ++ ++ +
++ ++ + +
•• • + •• • •+

++ ++ ++ +
12: Responsible Consumption and Productio n
–
––– –
+
+
+
+
+
+
+
+
++
+ +++++
+
+
+•
•
••••••
•••
•
•
••
•••
•
•
••////
/
/
//
not assessedWith bene/f_its+
Not clear or mixed•With dis-bene/f_its–
Related 
Sustainable Development GoalsCon/f_idence level
+

– ++ /not assessed(b) Climate responses and adaptation options have bene/f_its for ecosystems , ethnic group s, gender equity,  low-income groups and the Sustainable Development Goals
 Relations of sectors and groups at risk (as observed) and the SDGs (relev ant in the near -term,  at global scale and up to 1.5°C  of global w arming) with climate responses and adaptation options
+ + ++

90
TSbenefits but with trade-offs and negative socioeconomic and 
environmental effects (high confidence). Competition, trade-offs 
and conflict between mitigation and adaptation priorities will in-
crease with climate change impacts (high confidence). Integrated, 
multi-sectoral, inclusive and systems-oriented solutions reinforce 
long-term resilience (high confidence), along with supportive 
public policies (medium confidence). (Figure TS.6 FOOD-WATER, 
Figure TS.11a) {2.6, 4.6.2, 4.7.1, 4.7.4, 4.8, Box 4.3, Figure 4.27, 
Figure 4.29, 5.4.3, 5.4.4, 7.4.2, 1.1, 9.12.4, 12.5.3, 12.5.4, 13.2.2, 
14.4.3, 14.4.4, CCP5.4.2, CCB FEASIB, CCB NATURAL}
TS.D.5.1 There are a range of options for water- and food-related 
adaptation in different sociocultural, economic and geographical 
contexts, with benefits across several dimensions across regions 
(high confidence), including climate risk reduction (medium 
confidence). Frequently documented options include rainwater

harvesting, soil moisture conservation, cultivar improvements, 
community-based adaptation, agricultural diversification, climate 
services and adaptive eco-management in fisheries (high confidence). 
Roughly 25% of assessed water-related adaptations have co-benefits, 
while 33% of the assessed reported current or future maladaptive 
outcomes (high confidence). There is  limited evidence, medium 
agreement  on the institutional feasibility or cost effectiveness of 
adaptation activities or their limits. Integration of Indigenous knowledge 
and local knowledge increase their effectiveness (high confidence). 
(Figure TS.6 FOOD-WATER) {4.6, 4.7.1, 5.4.4, 5.5.4, 5.6.3, 5.8.4, 5.9.4, 
5.10.4, 5.11.4, 5.12.4, 5.14.1, 12.5.3, 12.5.4, 13.2.2, 13.5.2, 13.10.2, 
Figure 13.7, Figure 13.15, 15.5.4, 15.5.6, CCB FEASIB}
TS.D.5.2 The projected future effectiveness of available 
adaptation for agriculture and food systems decreases with

increasing warming (high confidence). Currently known adaptation 
responses generally perform more effectively at 1.5°C than at 2°C or 
more, with increasing risks remaining after adaptation at higher warming 
levels (high confidence). Irrigation expansion will face increasing limits 
due to water availability beyond 1.5°C (medium confidence), with a 
potential doubling of regional risks to irrigation water availability 
between 2°C and 4°C (medium confidence). Negative risks even with 
adaptation will become greater beyond 2°C warming in an increasing 
number of regions (high confidence). (Figure TS.6 FOOD-WATER) {4.6.2, 
4.7.1, 4.7.2, 4.7.3, 5.4.3, 5.4.4, 13.5.1, 13.10.2, 14.5.4, 15.3.4}
TS.D.5.3 Ecosystem-based approaches, agroecology and other 
nature-based solutions in agriculture and fisheries have the 
potential to strengthen resilience to climate change with multiple co-benefits (high confidence); trade-offs and benefits

vary with socioecological context. Options such as ecosystem 
approaches to  fisheries, agricultural diversification,  agroforestry 
and other ecological practices support long-term productivity and 
ecosystem services such as pest control, soil health, pollination and 
buffering of temperature extremes (high confidence), but potential 
and trade-offs vary by socioeconomic context, ecosystem zone, 
species combinations and institutional support (medium confidence). 
Ecosystem-based approaches support food security, nutrition and 
livelihoods when inclusive equitable governance processes are used 
(high confidence). {2.6.3, 3.4.2, 3.5.2, 3.5.3, 3.5.5, 3.6.2, 3.6.3, 3.6.5, 
Figure  3.26, Table  SM3.6, 4.6.6, Box  4.6, 5.4.4, 5.6.3, 5.8.4, 5.9.3, 
5.10.4, 5.14.1, 8.5.2, 8.6.3, 9.6.4, 12.5.1, 12.5.4, 13.3.2, 13.5.2, 14.5.1, 
14.5.2, 14.5.3, 14.5.4, Box  14.7, 16.3.2, CCB FEASIB, CCB MOVING 
PLATE, CCB NATURAL, CWGB BIOECONOMY}
TS.D.5.4 Sustainable resource management in response to

distribution shifts of terrestrial and aquatic species under 
climate change is an effective adaptation option to reduce food 
and nutritional risk, conflict and loss of livelihood (medium 
confidence).  Adaptation options exist to reduce the vulnerability of 
fisheries through better management, governance and socioeconomic 
dimensions (medium confidence) to eliminate overexploitation 
and pollution (high confidence). Indigenous knowledge and local 
knowledge can facilitate adaptation in small-scale fisheries, especially 
when combined with scientific knowledge and utilised in management 
regimes (medium confidence).  Adaptive transboundary governance 
and ecosystem-based management, livelihood diversification, capacity 
development and improved knowledge-sharing will reduce conflict 
and promote the fair distribution of sustainably harvested wild 
products and revenues (medium confidence). {5.8.4, 5.14.3, CCP5.4.2, 

TS.D.5.5 Adaptation options that promote intensification of 
production have been widely adopted in agriculture for climate 
change adaptation, but with potential negative effects (high 
confidence). Agricultural intensification addresses short-term food 
security and livelihood goals but has trade-offs in equity, biodiversity 
and ecosystem services (high confidence) . Irrigation is widely used 
and effective for yield stability, but with several negative outcomes, 
including water demand (high confidence), groundwater depletion (high 
confidence), alteration of local to regional climates (high confidence), 
increasing soil salinity (medium confidence), widening inequalities and 
loss of rural smallholder livelihoods with weak governance (medium 
confidence). Conventional breeding assisted by genomics introduces Figure TS.11 |  (a) Climate responses and adaptation options, organized by System Transitions and Representative Key Risks (RKRs), are assessed for their multidimensional

feasibility at global scale, in the near term and up to 1.5°C global warming. As literature above 1.5°C is limited, feasibility at higher levels of warming may change, which is currently 
not possible to assess robustly. Climate responses and adaptation options at global scale are drawn from a set of options assessed in AR6 that have robust evidence across the 
feasibility dimensions. This figure shows the six feasibility dimensions (economic, technological, institutional, social, environmental and geophysical) that are used to calculate the 
potential feasibility of climate responses and adaptation options, along with their synergies with mitigation. For potential feasibility and feasibility dimensions, the figure shows high, 
medium, or low feasibility. Synergies with mitigation are identified as high, medium, and low. Insufficient evidence is denoted by a dash. {CCB FEASIB, Table SMCCB FEASIB.1.1,

SR1.5 4.SM.4.3} (b) Climate responses and adaptation options, organized by System Transitions and Representative Key Risks, are assessed at global scale for their likely ability to 
reduce risks for ecosystems and social groups at risk, as well as their relation with the 17 Sustainable Development Goals (SDGs). Climate responses and adaptation options are 
assessed for observed benefits (+) to ecosystems and their services, ethnic groups, gender equity, and low-income groups, or observed dis-benefits (-) for these systems and groups. 
Where there is highly diverging evidence of benefits/ dis-benefits across the scientific literature, e.g., based on differences between regions, it is shown as not clear or mixed (•). 
Insufficient evidence is shown by a dash. The relation with the SDGs is assessed as having benefits (+), dis-benefits (-) or not clear or mixed (•) based on the impacts of the climate

response and adaptation option on each SDG. Areas not coloured indicate there is no evidence of a relation or no interaction with the respective SDG. The climate responses and 
adaptation options are drawn from two assessments. For comparability of climate responses and adaptation options see Table SM17.5. {17.2, 17.5, CCB FEASIB}

91
Technical SummaryTStraits that adapt crops to climate change (high confidence).  Genetic 
improvements through modern biotechnology have the potential 
to increase climate resilience in food production systems (high 
confidence), but with biophysical ceilings, and technical, agroecosystem, 
socioeconomic and political variables strongly influence and limit the 
uptake of climate resilient crops, particularly for smallholders (medium 
confidence).{4.6.2, 4.7.1, Box 4.3, 5.4.4, 5.12.5, 5.13.4, 5.14.1, 10.2.2, 
12.5.4, 13.5.1, 13.5.2, 13.5.14, 14.5.4, 15.3.4, 17.5.1}
TS.D.5.6 Integrated and systems-oriented solutions to alleviate 
competition and trade-offs between mitigation and adaptation 
will reinforce long-term resilience and equity in water and 
food systems (high confidence). Large-scale land deals for climate 
mitigation have trade-offs with livelihoods, water and food security (high 
confidence). Afforestation programmes without adequate safeguards

adversely affect Indigenous Peoples’ rights, land tenure and adaptive 
capacity (high confidence). Some mitigation measures, such as carbon 
capture and storage, bio-energy and afforestation, have a high water 
footprint (high confidence). Increased demand for aquaculture, animal 
and marine foods and energy products will intensify competition and 
potential conflict over land and water resources, particularly in low- and 
medium-income countries (high confidence), with negative impacts 
on food security and deforestation (medium confidence). Integrated, 
systems-oriented solutions reduce competition and trade-offs and 
include inclusive governance, behavioural (e.g., healthier diets with 
lower carbon and water footprints) and technical (e.g., novel feeds) 
responses (high confidence).{1.4.2, 2.2, 2.3, 2.5. 2.6, 3.6.3, 4.7.1, 4.7.6, 
Box 4.5, Box 4.8, 5.13.1, 5.13.2, 5.13.3, 5.13.5, 5.13.7, 9.4.3, 12.5.8, 
12.6.2, 14.5.4, 15.5.6, 17.5.1, CCP5.4.2, CWGB BIOECONOMY}

TS.D.5.7 Integrated multi-sectoral strategies that address social 
inequities (e.g., gender, ethnicity) and social protection of low-
income groups will increase the effectiveness of adaptation 
responses for water and food security (high confidence). 
Multiple interacting factors help to ensure that adaptive communities 
have water and food security, including addressing poverty, social 
inequities, violent conflict, provision of social services such as water 
and sanitation, social safety nets and vital ecosystem services. 
Differentiated responses based on water and food security level 
and climate risk increase effectiveness, such as social protection 
programmes for extreme events, medium-term responses such as local 
food procurement for school meals, community seed banks or well 
construction to build adaptive capacity (medium confidence). Longer-
term responses include strengthening ecosystem services, local and

regional markets, enhanced capacity and reducing systemic gender, 
land tenure and other social inequalities as part of a rights-based 
approach (medium confidence). In the urban context, policies that 
account for social inclusion in governance and rights to green urban 
spaces will enhance urban agriculture’s potential for food and water 
security and other ecosystem services. (Figure  TS.6 FOOD-WATER) 
{4.7.1, 4.8.3, Figure 4.27, Figure 4.29, 5.12.5, 5.12.7, 12.5.3, 12.5.4, 
12.5.5, 15.6.5, 17.5.1}
TS.D.5.8 Supportive public policies for transitions to resilient 
water and food systems enhance effectiveness and feasibility in 
ecosystem provisioning services, livelihoods and water and food 
security (medium confidence). Collective efforts across sectors, with the involvement of food producers and water users and including 
Indigenous knowledge and local knowledge, are a pre-condition 
to reaching sustainable water and food systems (high confidence).

Policies that support system transitions include shifting subsidies, 
certification, green public procurement, capacity building, payments 
for ecosystem services and social protection (medium confidence). 
(Figure TS.6 FOOD-WATER) {4.7.1, 4.8.4, 5.4.4, 5.4.4, 5.10.4, 5.12.6, 
5.13.4, 5.14.1, 5.14.2, Box 5.13, 12.5.4, CWGB BIOECONOMY}
TS.D.6 Cities and settlements are crucial for delivering urgent 
climate action. The concentration and interconnection of people, 
infrastructure and assets within and across cities and into rural 
areas drives the creation of risks and solutions at a global scale 
(high confidence). Concentrated inequalities in risk are broken 
through prioritising affordable housing and upgrading of 
informal and precarious settlements, paying special attention 
to including marginalised groups and women (high confidence). 
Such actions are most effective when deployed across grey/

physical infrastructure, nature-based solutions and social policy 
and between local and city-wide or national actions (medium 
confidence). City and local governments remain key actors 
facilitating climate change adaptation in cities and settlements. 
Community-based action is also critical. Multi-level governance 
opens an inclusive and accountable adaptation space across 
scales of decision-making, improving development processes 
through an understanding of social and economic systems, 
planning, experimentation and embedded solutions, including 
processes of social learning. (Figure TS.9 URBAN, Figure TS.11a) 
{4.6.5, 4.7.1, 6.1, 6.2, 6.3, 6.4, 8.5.2, 10.3.6, 10.4.6, 12.5.5, 
13.6.2, 13.11.1, 14.5.5, 15.7, 16.4.2, CWGB URBAN}
TS.D.6.1 Continuing rapid growth in urban populations and 
unmet needs for healthy, decent, affordable and sustainable 
housing and infrastructure represent a global opportunity to 
integrate inclusive adaptation strategies into development

(high confidence). The urban adaptation gap shows that for all world 
regions, current adaptation is unable to resolve risks from current 
climate change associated hazards. Moreover, an additional 2.5 billion 
people are projected to be living in urban areas by 2050, with up to 
90% of this increase concentrated in the regions of Asia and Africa 
(high confidence). Retrofitting, upgrading and redesigning existing 
urban places and infrastructure combined with planning and design 
for new urban infrastructure can utilise existing knowledge on social 
policy, nature-based solutions and grey/physical infrastructure to build 
inclusive processes of adaptation into everyday urban planning and 
development. {4.6.5, 6.1, 6.3, 6.4, 9.9.5, 10.3.4, 12.5.5, 13.6.2, 13.11.3}
TS.D.6.2 Diverse adaptation responses to current and near-
term climate impacts are already under way in many cities and 
settlements in different world regions (very high confidence).

These responses range from hard engineering interventions to 
nature-based solutions, social policy and social safety nets to disaster 
management and capacity building, raising or relocation of settlements 
and combinations of such measures sequenced over time. While many 
more cities have developed adaptation plans since AR5, few of these

92
TS
Erosion
Reduced ﬁsheries catchAgroecology
MangroveSediment provisionHuman well-beingUpward migration of high
lower elevation species
invaded by invasive plants
Reduced
Coastal ﬂoodingIntensive
animal
tourism
tourism
 tourism
 tourism
 touris m
 tourismDiverse, indigenous
Rainwa ter
Water
Sustainablemobilit
yUnsustainable
mobilityLow-emission

Water
security
intact
peatlandsHealthy soilsHealthy soilsLandslides
Coastal erosion wa shed to ocean
 and negatively impact nature and people(b) Human activities that protect,  conserve and restore ecosystems contribute
 to climate resilient developmentEcosystem health inﬂuences prospects for climate resilient development
Ocean acidiﬁcation Overﬁshing
Coral bleaching and degradationMarine Protected Areas (MP As)Sustainable ﬁsheries
SeagrassReef ecosystem

93
Technical SummaryTSplans have been implemented, and of these fewer still are being 
developed and evaluated through consultation and co-production with 
diverse and marginalised urban communities (medium confidence). 
{4.6.5, 6.3.3, 6.3.4, 6.3.5, CCP2.3, CCP2.4, 12.5.5, 13.2.2, 13.6.2, 
13.11.3, 14.5.5, 15.3.4, 15.5.4, 15.6.1, 16.4.2, CCB FEASIB}
TS.D.6.3 Globally, urban adaptation gaps exist for all climate 
change-driven risks, although the limits to adaptation are 
unevenly distributed (medium confidence). Governance capacity, 
financial support and the legacy of past urban infrastructure 
investment constrain how cities and settlements can adapt to key 
climate risks (medium confidence). The gap between what can be 
adapted to and what has been adapted to is uneven; it is larger for 
the poorest 20% of populations than for the wealthiest 20%. The 
adaptation gap is also geographically uneven; it is highest in Africa

(medium confidence). Limits to adaptation are often most pronounced 
in rapidly growing urban areas and smaller settlements, including 
those without dedicated local government. At the same time, legacy 
infrastructure in large and mega cities, designed without taking 
climate change risk into account, and past adaptation decisions 
constrain innovation, leading to stranded assets and with increasing 
numbers of people unable to avoid harm, including heat stress and 
flooding, without transformative adaptation (medium confidence). 
{6.3, 6.4, 12.5.5, 13.2, 13.2.3, 13.6.2, 13.6.2, 13.11.3, Box  14.4, 
CCP2.3.6, CCP2.4, CCP2.5, CWGB URBAN}
TS.D.6.4 The greatest gaps between policy and action are in 
projects to integrate justice concerns into adaptation action, 
address complex interconnected risks where solutions lie 
outside as well as within a city, for example in the food–energy–
water–health nexus, and resolve compound risks such as the

relationships between air quality and climate risk (medium 
confidence). The most critical capacity gaps at the city and community 
levels that hinder adaptation include an ability to identify social 
vulnerability and community strengths and to plan in integrated ways to 
protect communities, alongside the ability to access innovative funding 
arrangements and manage finance and commercial insurance, as well 
as locally accountable decision-making with sufficient access to science, 
technology and local knowledge to support application of adaptation 
solutions at scale. As ecosystems provide important additional benefits 
to human well-being and coastal livelihoods, urban adaptation 
strategies can be developed for settlements and nearby ecosystems; 
combining these with engineering solutions can extend their lifetime 
under high rates of sea level rise (medium confidence). In Central and 
South America, the adoption of nature-based solutions and hybrid

(green-grey) infrastructure are still emerging. Monitoring and evaluation 
frameworks that incorporate questions of justice, ecological health and 
multi-sector considerations can help to move away from more narrow, 
static, indicator-based approaches to adaptation. (high confidence) 
{4.6.5, Box 4.8, 5.12.5, 6.1, 6.3, 6.4, 10.3.4, 12.5.5, 13.6.1, 13.6.2}TS.D.6.5 Key innovations in adaptation in social policy and 
nature-based solutions have not been matched by innovation 
in adaptation finance, which tends to favour established 
mechanisms, often led by grey/physical infrastructure at the 
national scale. Social policy innovations include social safety nets, 
inclusive approaches to disaster risk reduction and the integration 
of climate adaptation into education. Nature-based solutions 
include green and blue infrastructure in and around cities, including 
hinterlands, that increase water access and reduce hazards for cities

and settlements, for example reforestation of hill-slope and coastal 
areas. In Europe, many urban innovations are pilot tested, but their up-
scaling remains challenging. Where inclusive approaches to adaptation 
policy and action are supported, this can enable wider gains of more 
equitable urbanisation (medium confidence). (Figure  TS.9 URBAN) 
{2.6.3, 4.6.5, 4.7.1, 6.3.3, 6.3.5, 6.4.3, 12.5.5, 13.6.2 13.11.3, CCB 
FEASIB, CWGB URBAN}
TS.D.6.6 Many urban adaptation plans focus narrowly on climate 
risk reduction and specific climate-associated risks, missing 
opportunities to advance co-benefits with climate mitigation 
and sustainable development (high confidence). This narrow 
approach limits opportunity for urban and infrastructure adaptation 
to tackle the root causes of inequality and exclusion, especially 
among marginalised groups, including women. Urban adaptation 
measures have many opportunities to contribute to climate resilient

development pathways (medium confidence). They can enhance 
social capital, livelihoods, human and ecological health and contribute 
to low-carbon futures. Urban planning, social policy and nature-
based solutions bring great flexibility with co-benefits for climate 
mitigation and sustainable development. Participatory planning for 
infrastructure provision and risk management in informal, precarious 
and underserved neighbourhoods, the inclusion of Indigenous 
knowledge and local knowledge, and communication and efforts 
to build local leadership especially among women and youth are 
examples of inclusive approaches with co-benefits for equity. Targeted 
development planning across the range of innovation and investment 
in social policy, nature-based solutions and grey/physical infrastructure 
can significantly increase the adaptive capacity of urban settlements 
and cities and their contribution to climate resilient development (high

confidence). (Figure TS.9 URBAN) {4.6.5, 6.1, 6.3, 6.4, Box 6.6, 7.4.1, 
7.4.2, 7.4.3, 10.5, 10.6, 12.5.5, 12.5.7, 13.11.3, 14.5.5, 15.6.1, 15.7, 
CCP5.4.3, CCB COVID, CCB FEASIB}
TS.D.6.7 City and infrastructure planning approaches that 
integrate adaptation into everyday decision-making are 
supported by the 2030 Agenda for Sustainable Development: 
the Paris Agreement, SDGs, New Urban Agenda and Sendai 
Framework for Disaster Risk Reduction. The 2030 Agenda provides 
a global framework for city- and community-level action to align 
Nationally Determined Contributions, national adaptation plans and the Figure TS.12 |  This figure shows the interconnectedness between different ecosystems and system transitions, with human activities in urban, rural and 
coastal locations embedded in ecosystems. Maintaining biosphere integrity is essential for biodiversity, human and societal health and a precondition for climate resilient

development. Panel a) illustrates how adaptation, mitigation and development actions characterised by exploitation and degradation lead to unsustainable development and 
adverse outcomes for human well-being and ecosystem integrity. Panel b) illustrates how adaptation options, implemented in an integrated way with mitigation and development 
and based on ecosystem stewardship, can support climate resilient development (Figure TS.13). The protection or restoration of one or more of these ecosystems also provides 
benefits to the other ecosystems and enhances the services provided that improve livelihoods. Protecting and restoring ecosystem health as a part of societal development and 
through societal choices is a key transformative solution space for climate resilient development {2.5, 2.6, 3.5, 3.6, 4.3, 5.13, 6.3, 7.4, CCP1, CCP3, CCP5, Box 18.5}

94
TSSDGs. City and local action can complement—and at times go further 
than—national and international interventions (high confidence). 
Adaptation policy that focuses on informality and sub-serviced 
or inadequately serviced neighbourhoods and supports inclusive 
urbanisation by considering the social and economic root causes of 
unequal vulnerability and exposure can contribute to the broader goals 
of the 2030 Sustainable Development Agenda and reduce vulnerability 
to non-climatic risks, including pandemic risk (high confidence). More 
comprehensive and clearly articulated global ambitions for city and 
community adaptation will contribute to inclusive urbanisation by 
addressing the root causes of social and economic inequalities that 
drive social exclusion and marginalisation, so that adaptation can 
directly support the 2030 Agenda for Sustainable Development (high 
confidence). {6.1.1, 6.2.3, 6.4.1, Table 6.2, 12.5.5, 12.5.7}

TS.D.7 The ability of societies and ecosystems to adapt to current 
coastal impacts to address present and future coastal risks under 
further acceleration of sea level rise depends on immediate and 
effective mitigation and adaptation actions that keep options 
open to further adapt (high confidence). Adaptation pathways 
break adaptation planning into manageable steps based on near-
term, low-regret actions and aligning adaptation choices with 
societal goals that account for changing risk, interests and values, 
uncertain futures and the long-term commitment to adapting to 
sea level rise (high confidence). In charting adaptation pathways, 
reconciling divergent  interests and values  is a priority (high 
confidence). (Figure TS.9 URBAN) {11.7.3, 13.10, 14.5.2, Box 14.4, 
CCP2.3, CCP2.4, CCB DEEP, CCB SLR}
TS.D.7.1 As the scale and pace of sea level rise accelerates 
beyond 2050, long-term adjustments may in some locations be

beyond the limits of current adaptation options and for some 
species and some locations could be an existential risk in the 
21st century (medium confidence). Nature-based interventions, 
for example wetlands and salt marshes, can reduce impacts and 
costs while supporting biodiversity and livelihoods but have limits 
under high warming levels and rapid sea level rise (high confidence). 
Ecological limits and socioeconomic, financial and governance barriers 
will be reached first and are determined by the type of coastline and 
city or settlement (medium confidence). Accommodation can reduce 
impacts on people and assets but can address only limited sea level 
rise. Considering the long term now will help to avoid maladaptive 
lock-in, to build capacity to act in a timely and pre-emptive manner 
and to reduce risks to ecosystems and people. {3.4.2, 3.6.3, 11.7.3, 
13.2, 14.5.2, 15.3.4, CCP2.3, CCB DEEP , CCB SLR}
TS.D.7.2 Adaptation for coastal ecosystems requires space,

networks and sediment to keep up with sea level rise (high 
confidence). With higher warming, faster sea level rise and increasing 
human pressures due to coastal development, the ability to adapt 
decreases (high confidence). Adaptation options, such as providing 
sufficient space for a coastal system to migrate inland, when combined 
with ambitious and urgent mitigation measures, can reduce impacts, 
but they depend on the type of coastline and patterns of coastal 
development (high confidence). With rapid sea level rise, these options will become insufficient to limit risks for marine ecosystems and 
their services such as food provision, coastal protection and carbon 
sequestration (high confidence). (Figure TS.11a) {3.4.2, 3.5.5, 3.6.3, 
Box 3.4, 14.5.2, CCB SLR}
TS.D.7.3 A wide range of adaptation options exists for reducing 
the ongoing multi-faceted coastal risks in cities and settlements 
(very high confidence). A mix of infrastructure, nature-based,

institutional and sociocultural interventions can best address the 
risks. The options include vulnerability-reducing measures, avoidance 
(e.g., disincentivising developments in high-risk areas and addressing 
existing social vulnerabilities), hard and soft protection (e.g., sea walls, 
coastal wetlands), accommodation (e.g., elevating houses), advance 
(e.g., building up and out to sea) and staged, managed retreat (e.g., 
landward movement of people and development) interventions (very 
high confidence). (Figure TS.9 URBAN) {3.6.2, 3.6.3, 11.3.5, Box 11.6, 
12.5.5, 13.2, 14.5.2, 15.5.1, 15.5.2, 15.5.3, 15.5.4, 15.5.5, 15.5.7, 17.2, 
CCP2.3, CCP2.4, CCB FEASIB, CCB SLR}
TS.D.7.4 Implementation of coastal adaptation can be delayed 
by competing public and private interests, trade-offs among 
development and conservation objectives, legacy development, 
policy inconsistencies, contradictory short- and long-term 
objectives and uncertainties on the timing and scale of impacts

(high confidence). Local government barriers to coastal adaptation 
could lead to courts’ becoming de facto decision makers for local 
adaptation, and this could be compounded by legislative shortcomings 
and fragmentation, insufficient leadership, lack of coordination 
between governance levels and disagreement about financial 
responsibility (high confidence). {11.7.3, 15.5.6, CCP2.4}
TS.D.7.5 Adaptation is costly, but the benefit-to-cost ratio is high 
for urbanised coastal areas with high concentrations of assets 
(high confidence). Protection has a high benefit-cost ratio during the 
21st century but can become unaffordable and insufficient to reduce 
coastal risk (e.g., due to salinisation, drainage of rivers and excess 
water), reaching technical limits (high confidence). Hard protection 
sets up lock-in of assets and people to risks and reaches limits by the 
end of the century or sooner, depending on the scenario, local sea level

rise effects and community tolerance thresholds (high confidence). 
Considering coastal retreat as part of the solution space could lower 
global adaptation costs but would result in large land losses and high 
levels of migration for South and Southeast Asia in particular and in 
relative terms, small island nations would suffer most (high confidence). 
Solutions include disincentivising developments in high-risk areas and 
addressing existing social vulnerabilities now (high confidence). {3.4.2, 
3.5.5, 3.6.3, 5.13.4, 9.4.1, Box 11.6, 13.2, 14.5.3, 15.5.1, 15.5.2, 15.5.3, 
16.5.2, CCP2.3, CCB MIGRATE, CCB NATURAL, CCB SLR}
TS.D.7.6 Prospects for addressing climate change compounded 
coastal hazard risk depend on the extent to which societal 
choices, and associated governance processes and practices, 
address the drivers and root causes of exposure and social 
vulnerability (very high confidence). Many drivers and root

causes of coastal risk are historically and institutionally embedded 
(very high confidence). When national and local authorities work 
with their communities, sustained risk reduction in the exposure and

95
Technical SummaryTSvulnerability of those most at risk is more likely (high confidence). 
Drawing on multiple knowledge systems helps in co-designing and 
co-producing more acceptable, effective and enduring responses. 
Reconciling divergent worldviews, values and interests can unlock the 
productive potential of conflict for transitioning towards pathways 
that foster climate resilient development, generate equitable 
adaptation outcomes and remove governance constraints (high 
confidence). Shared understanding and locally appropriate responses 
are enabled by deliberate experimentation, innovation and social 
learning (medium confidence). External assistance and government 
support can enhance community capabilities to reduce coastal hazard 
risk (high confidence). {15.6.1, 17.2, CCP2.4, Table CCP2.1}
TS.D.7.7 Experience in coastal cities and settlements highlights 
critical enablers for addressing coastal hazard risk compounded

by sea level rise (high confidence). These enablers include building 
and strengthening governance capacity and capabilities to tackle 
complex problems; taking a long-term perspective in making short-
term decisions; enabling more effective coordination across scales, 
sectors and policy domains; reducing injustice, inequity and social 
vulnerability; and unlocking the productive potential of coastal 
conflict while strengthening local democracy (medium evidence, 
high agreement). Flexible options enable responses to be adjusted as 
climate risk escalates and circumstances change, which may increase 
exposure  (medium confidence). Legal and financial provisions can 
enable managed retreat from the most at-risk locations (medium 
confidence) but require coordination, trust and legitimate decisions by 
and across policy domains and sectors (high confidence) that prioritise 
vulnerability, justice and equity (medium confidence). Inclusive,

informed and meaningful deliberation and collaborative problem-
solving depend on safe arenas for engagement by all stakeholders 
(high confidence). {CCP2.4, Table CCP2.1, Table CCP2.2, CCB SLR}
Health, well-being, migration and displacement
TS.D.8 With proactive, timely and effective adaptation, many 
risks for human health and well-being could be reduced and some 
potentially avoided (very high confidence) . Building adaptive 
capacity through sustainable development and encouraging safe 
and orderly movements of people within and between states 
represent key adaptation responses to prevent climate-related 
involuntary migration (high confidence). Reducing poverty, 
inequity and food and water insecurity and strengthening 
institutions in particular reduce the risk of conflict and supports 
climate resilient peace (high confidence) . (Figure TS.8 HEALTH) 
{2.6.4, 4.6.4, Box 4.4, 5.12.5, 5.14, Box 6.3, 7.4.1, 8.4.4, 9.10.3,

10.4.7, 11.3.6, 12.5.6, 12.5.7, Table  12.9, 13.7.2, Figure  13.25, 
14.5.6, Table 14.5, CCB ILLNESS}
TS.D.8.1 National planning on health and climate change is 
advancing, but the comprehensiveness of strategies and plans 
need to be strengthened to reduce future risks, and implement-
ing action on key health and climate change priorities remains 
challenging (high confidence) . The COVID-19 pandemic demon-
strated the value of coordinated planning across sectors, safety nets 
and other capacities in societies to cope with a range of shocks and 
stresses and to alleviate system-wide risks to health (high confidence).  A significant adaptation gap exists for human health and well-being 
and for responses to disaster risks (very high confidence) . Most Nation-
ally Determined Contributions to the Paris Agreement from low- and 
middle-income countries identify health as a priority concern (very high 
confidence) . Effective governance institutions, arrangements, funding

and mandates are key for adaptation to climate-related health risks  
(high confidence) . {4.6.4, 5.12.5, 5.14, 7.4.1, 7.4.2, 7.4.3, Table  7.2, 
9.10.3, 10.4.7.3, 11.3.6, 12.5.6, 13.7.2, CCB ILLNESS, CCB COVID}
TS.D.8.2 Continued investment in general health systems and in 
systems enhancing health protection is an effective adaptation 
strategy in the short to medium term (high confidence). Although 
some mortality and morbidity from climate change are already 
unavoidable, targeted adaptation and mitigation actions can reduce 
risks and vulnerabilities (high confidence). The burden of diseases 
could be reduced and resilience increased through health systems, 
generating awareness of climate change impacts on health (medium 
confidence), strengthening access to water and sanitation (high 
confidence), integrating vector control management approaches (very 
high confidence), expanding existing early-warning monitoring systems

(high confidence), increasing vaccine development and coverage 
(medium confidence), improving the heat resistance of the built 
environment (medium confidence) and building financial safety nets 
(medium confidence). {2.6.4, 4.6.4, 5.12.5, 5.14, 7.4.1, 7.4.2, Table 7.2, 
9.10.3, 10.4.7, 11.3.6, 12.5.6, Table 12.9, 13.7.2, Figure 13.25, 14.5.6, 
Table 14.5, CCP6.2.6, CCB FEASIB, CCB ILLNESS}
TS.D.8.3 Many adaptation measures that benefit health and 
well-being are found in other sectors (e.g., food, livelihoods, 
social protection, water and sanitation, infrastructure) (high 
confidence). Such cross-sectoral solutions include improved air quality 
through renewable energy sources (very high confidence), active 
transport (e.g., walking and cycling) (high confidence) and sustainable 
food systems that lead to healthier diets (high confidence). Heat 
Action Plans have strong potential to prevent mortality from extreme 
heat events and elevated temperature (high confidence). Nature-

based solutions reduce a variety of risks to both physical and mental 
health and well-being (high confidence). For example, integrated 
agroecological food systems offer opportunities to improve dietary 
diversity while building climate-related local resilience to food insecurity 
(high confidence), especially when combined with gender equity and 
social justice. Social policy–based adaptation, including education and 
the adaptation of health systems, offers considerable future scope. The 
greatest gaps between policy and action are in failures to manage 
adaptation of social infrastructure (e.g., community facilities, services 
and networks) and failure to address complex interconnected risks 
for example in the food–energy–water–health nexus or the inter-
relationships of air quality and climate risk (medium confidence). 
{2.6.7, 4.6.4, 4.7.1, 5.12.5, 5.14.1, 6.3.1, 6.4.3, 6.4.5, 6.4.5, 6.4.5, 7.4.2, 
9.10.3, 10.4.7, 11.3.6, 12.5.6, Table 12.9, 13.7.2, Figure 13.25, 14.5.6,

Table 14.5, CCB GENDER, CCB HEALTH, CCB NATURAL}
TS.D.8.4 Despite acknowledgement of the importance of 
health adaptation as a key component, action has been slow 
since AR5 (high confidence). Building climate resilient health 
systems will require multi-sectoral, multi-system and collaborative 
efforts at all governance scales (very high confidence). Globally,

96
TShealth systems are poorly resourced in general, and their capacity to 
particularly inadequate (very high confidence). The health sectors in 
some countries have focused on implementing incremental changes 
to policies and measures to respond to impacts (very high confidence). 
As the likelihood of dangerous risks to human health continues to 
increase, there is a greater need for transformational changes to 
health and other systems (very high confidence). This highlights an 
urgent and immediate need to address the wider interactions between 
and well-being (high confidence). {7.4.1, 7.4.2, 7.4.3, 9.10.3, Box 9.7, 
11.3.6.3, 13.7.2, 14.5.6, CCP6.2.6, Figure CCP6.3}
TS.D.8.5 Financial constraints are the most referenced barrier 
to health adaptation, and therefore scaling up financial

investments remains a key international priority (very high 
confidence). Financial support for health adaptation is currently less 
than 0.5% of overall dispersed multilateral climate finance projects 
(high confidence). This level of investment is insufficient to protect 
human health and health systems from most climate-sensitive health 
risks (very high confidence). Adaptation financing often does not reach 
places where the climate sensitivity of the health sector is greatest 
(high confidence). {7.4.1, 7.4.2, 7.4.3, 9.10.3}
TS.D.8.6 Reducing future risks of involuntary migration and 
displacement due to climate change is possible by improving 
outcomes of existing migration patterns, addressing vulner -
abilities that pose barriers to in situ adaptation and livelihood 
strategies and meeting existing migration agreements and 
development objectives (medium confidence). Properly support-
ed and where levels of agency and assets are high, migration as an

adaptation to climate change can reduce exposure and socioeconomic 
vulnerability (medium confidence). However, migration becomes a risk 
when climate hazards cause an individual, household or community 
to move involuntarily or with low agency (high confidence). Inability 
to migrate (i.e., involuntary immobility) in the face of climate hazards 
is also a potential risk to exposed populations (medium confidence). 
Broad-based institutional and cross-sectoral efforts to build adaptive 
capacity, including meeting the SDGs, reduce future risks of climate-
related involuntary displacement and immobility (medium confidence), 
while policies such as the Global Compact on Safe, Orderly and Reg-
ular Migration (medium confidence) that are aimed at ensuring safe 
and orderly movements of people within and between states are 
potential components of climate resilient development pathways that 
can improve migration as an adaptation. {4.6.8, 7.4.4, 9.3.1, 12.5.8,

CCP5.4.2, CCB FEASIB, CCB MIGRATE}
TS.D.8.7 Improving the feasibility of planned relocation and 
resettlement is a high priority for managing climate risks (high 
confidence). Residents of small island states do not view relocation 
as an appropriate or desirable means of adapting to the impacts of 
climate change (high confidence). Previous disaster- and development-
related relocation has been expensive and contentious, posed multiple 
challenges for governments and amplified existing ones and generated 
new vulnerabilities for the people involved (high confidence).  In 
locations where permanent, government-assisted relocation becomes 
unavoidable, active involvement of local populations in planning and decision-making may lead to more successful outcomes (medium 
confidence). {4.6.8, 7.4.4, 9.3.1, 12.5.8, 15.5.3, CCP5.4.2, CCB FEASIB, 
TS.D.8.8 Meeting SDGs supports adaptive capacity that in turn 
supports individuals, households and community manage climate

risks and supports peace (high confidence). By addressing vulner -
ability, improving livelihoods and strengthening institutions, meeting 
the SDGs reduces the risks of armed conflict and violence (medium 
management and environmental peacebuilding, conflict-sensitive 
adaptation and climate-sensitive peacebuilding and gender-sensitive 
approaches offer potential new avenues to build peace in conflict-
prone regions vulnerable to climate change (medium confidence). 
However, there is currently insufficient evidence on their success and 
further monitoring and evaluation is required. (Figure  TS.11b) {4.8, 
7.4.6, Box 9.9, 16.3.2, CCB GENDER}
Justice, equity and governance
TS.D.9 Adaptation actions consistent with climate justice address 
near- and long-term risks through decision-making processes 
that attend to moral and legal principles of fairness, equity and

responsibility including to historically marginalised communities 
and that distribute benefits, burdens and risks equitably (high 
confidence). Concepts of justice, consent and rights-based deci-
sion-making, together with societal measures of well-being, are 
increasingly used to legitimate adaptation actions and evaluate 
the impacts on individuals and ecosystems, diverse communities 
and across generations (medium confidence). Applying these 
principles as part of monitoring and evaluating the outcomes 
of adaptation, particularly during system transitions, provide a 
basis for ensuring that the distribution of benefits and costs are 
identified (medium confidence). {1.4.1, 4.8, 5.10.4, 5.12.3, 6.1.5, 
6.3.6, 12.5.7, 14.7.2, 17.5.1, CCB FEASIB, CCB GENDER}
TS.D.9.1 Near-term adaptation responses influence future 
inequalities, poverty, livelihood security and well-being (high 
confidence). Adaptation and mitigation approaches that exacerbate

inequitable access to resources and fail to address injustice increase 
suffering, including water and food insecurity and malnutrition rates for 
vulnerable groups that rely directly or indirectly on natural resources for 
their livelihoods (high confidence). {1.4.1, 5.12.3, 5.13.3, 6.3.6, 8.6.2, 
Box 9.3, 12.5.7, 18.1}
TS.D.9.2 Under an inequality scenario (SSP4), the number of 
people living in extreme poverty could increase by more than 
100  million (medium confidence). There is medium evidence 
and low agreement about the adaptation impacts of derivative-
based insurance products. Insurance solutions are difficult for low-
income groups to access (medium confidence). Formal insurance 
policies come with risks when implemented in a stand-alone manner, 
including risks of maladaptation (medium confidence). {5.13.5, 
5.14.1, 9.8.4, 9.11.4}
TS.D.9.3 Climate-induced changes are not experienced equally 
across genders, income levels, classes, ethnicities, ages or

97
Technical SummaryTSphysical abilities (high confidence). Therefore, participation of 
historically excluded groups, such as women, youth and marginalised 
communities (e.g., Indigenous Peoples, ethnic minorities, the disabled 
and low-income households), contributes to more equitable and socially 
just adaptation actions. Adaptation actions do not automatically have 
positive outcomes for gender equality. Understanding the positive and 
negative links of adaptation actions with gender equality goals (i.e., 
SDG 5) is important to ensure that adaptive actions do not exacerbate 
existing gender-based and other social inequalities (high confidence). 
Climate literacy varies across diverse communities, compounding 
vulnerability {2.6.3, 2.6.7, 4.3, 4.6, 4.6.9, 5.12.5, 5.14, 6.4.4, Box 6.1, 
9.4.5, Box 9.1, 12.5.8, 16.1.4, CCB GENDER}
TS.D.9.4 Empowering marginalised communities in the co-pro-
duction of policy at all scales of decision-making advances equi-

table adaptation efforts and reduces the risks of maladaptation 
(high confidence). Recognising Indigenous rights and local knowledge 
in the design and implementation of climate change responses contrib-
utes to equitable adaptation outcomes (high confidence). Indigenous 
knowledge and local knowledge play an important role in finding solu-
tions and often creates critical linkages between cultures, policy frame-
works, economic systems and natural resource management (medium 
confidence). Intergenerational approaches to future climate planning 
and policy will become increasingly important in relation to the manage-
ment, use and valuation of social-ecological systems (high confidence). 
Many regions benefit from the significant diversity of local knowledge 
and systems of production, informed by long-standing experience with 
natural variability, providing a rich foundation for adaptation actions ef-
fective at local scales (high confidence). {2.6.3, 2.6.7, 4.8.3, 4.8.4, 4.8.5,

5.12.5, 6.1, 6.4.1, 8.6.2, 8.6.3, 9.1, 9.12, 11.4.1, 11.4.2, 12.5.7, 12.5.8, 
15.5.4, 15.5.5, 17.5.1, CCP6.3.2, CCP 6.6, CCP6.4.3, CCB NATURAL}
TS.D.9.5 Proactive partnerships of government with the 
community, private sector and national agencies to minimise 
negative social, environmental or economic impacts of economy-
wide transitions are emerging, but their implementation is 
uneven (medium confidence). The greatest gains are achieved 
by prioritising investment to reduce climate risk for low-income and 
marginalised residents, particularly in informal settlements and rural 
communities (high confidence). Some city and local governments 
invest directly in adaptation action and work in partnership with a 
range of agencies. Legislative frameworks will assist business and 
insurance sector investment in key infrastructure to drive adaptive 
action at scale for equitable outcomes (medium confidence). {Box 5.8, 
6.4, 6.4.1, 8.5.2, 8.6.3, 9.4.2, 17.4.3, CCP5.2.4, CCB FINANCE}

TS.D.9.6  Inter-sectional, gender-responsive and inclusive decision-
making can accelerate transformative adaptation over the long 
term to reduce vulnerability (high confidence). Approaches to 
adaptation that address the needs of the most disadvantaged, through 
co-production of knowledge, are more sensitive to diverse community 
priorities and can yield beneficial climate co-adaptation benefits. There 
are gender differences in climate literacy in many regions exacerbating 
vulnerability in agricultural contexts in access to resources and 
opportunities for climate resilient crops (high confidence) {3.6.4, 4.6.5, 
4.8.5, 5.4.4, 5.13.4, Table 5.6, 6.3.6, 9.4.2, 9.4.5, Box 9.2, CCB FEASIB, 
CCB MOVING PLATE}TS.D.9.7 Local leadership, especially among women and youth, 
can advance equity within and between generations (medium 
confidence). Since AR5, social movements, including movements led 
by youth, Indigenous and ethnic communities, have heightened public

awareness about the need for urgent, inclusive action to achieve 
adaptation that can also enhance well-being and advance climate 
justice. {4.8.3, Box  5.13, 6.1.5, 6.2, 6.3.5, 6.4, 6.4.1, 6.4.7, Box  6.6, 
Box 9.1, Box 9.2}
TS.D.9.8. Climate justice initiatives that explicitly address multi-
dimensional inequalities as part of a climate change adaptation 
strategy can reduce inequities in access to resources, assets and 
services as well as participation in decision-making and leadership, and 
are essential to achieving gender and climate justice (high confidence). 
{Box 6.1, Box 9.2, 13.7.2, 13.11.1, CCB GENDER}
Enabling implementation
TS.D.10. Various tools, measures and processes are available that 
can enable, accelerate and sustain adaptation implementation 
(high confidence), in particular when anticipating climate change 
impacts, and empower inclusive decision-making and action 
when they are supported by adaptation finance and leadership

across all sectors and groups in society (high confidence). The 
actions and decisions taken today determine future impacts 
and play a critical role in expanding the solution space for 
future adaptation. Breaking adaptation down into manageable 
steps over time, while acknowledging potential long-term 
adaptation needs and options, can increase the prospect that 
effective adaptation plans will be actioned in timely and 
effective ways by stakeholders, sectors and institutions (high 
confidence). {2.6.7, 3.6.3, 3.6.5, 4.8, 11.7.3, 13.10, 15.3.4, 15.6, 
17.5, CCP2.2.4, , CCB DEEP, CCB NATURAL, CCB SLR}
TS.D.10.1 Institutional frameworks, policies and plans that set 
out adaptation goals, define responsibilities and commitment 
devices, coordinate among actors and build adaptive capacity 
will facilitate sustained adaptation actions (very high 
confidence). Adaptation is considered in the climate policies of 
at least 170  countries. Opportunities exist to integrate adaptation

into institutionalised decision cycles (e.g., budget reforms, statutory 
monitoring and evaluation, election cycles) and during windows of 
opportunity (e.g., recovery after disastrous events, designing new or 
replacing existing critical infrastructure or developing COVID recovery 
projects) (high confidence). Appraisal of adaptation options for policy 
and implementation that considers the risks of adverse effects can 
help prevent maladaptive adaptation and take advantage of possible 
co-benefits (medium confidence). Instruments such as behavioural 
nudges, re-directing subsidies and taxes and the regulation of 
marketing and insurance schemes have proven useful to strengthening 
societal responses beyond governmental actors (medium confidence). 
{1.4.4, 3.6.3, 3.6.5, 4.8.5, 4.8.6, 5.12.6, 5.13.3, 5.13.5, 6.1, 6.2, 6.3, 
6.4, 7.4.1, 7.4.2, 9.4.2, 9.11.5, 10.3.6, 10.5.3, 11.4, 11.7, Table 11.14, 
Table 11.16, 13.5.2, 13.10, 13.11, 14.7.2, 17.3.1, 17.3.2, 17.3.3, 17.4,

17.5.1, 17.6, 18.4, CCP2.4, CCP 2.4.3, CCP5.4.2, CCP6.3, CCP6.4, CCB 
DEEP , CCB INDIG}

98
TSTS.D.10.2 Access to and mobilising adequate financial resources 
for vulnerable regions is an important catalysing factor 
for timely climate resilient development and climate risk 
management (high confidence). Total tracked climate finance has 
increased from USD364  billion yr-1 in 2010/2011 to USD579  billion 
in 2017/2018, with only 4–8% of this allocated to adaptation and 
more than 90% of adaptation finance coming from public sources. 
Developed-country climate finance leveraged for developing countries 
for mitigation and adaptation has shown an upward trend, but it has 
fallen short of the USD100 billion yr-1 2020 target of the Copenhagen 
commitment, and less than 20% has been for adaptation. Estimated 
global and regional costs of adaptation vary widely due to differences in 
are higher than the figures presented in AR5. Median (and ranges)

estimated costs for developing country adaptation from recent studies 
are USD127 (15–411) and USD295 (47–1088) billion yr-1 for 2030 and 
2050 respectively. Examples of estimated regional adaptation include 
USD50 billion yr-1 in Africa for 1.5°C of warming in 2050, increasing to 
USD100–350 billion yr-1 for 4°C of global warming towards the end 
of the century. Increasing public and private finance flows by billions 
of dollars per year, increasing direct access to multilateral funds, 
strengthening project pipeline development and shifting finance 
from readiness activities to project implementation can enhance 
implementation of climate change adaptation and are fundamental 
to achieving climate justice for highly vulnerable countries, including 
small island states and African countries. {3.6.3, 4.8.2, 5.14.2, 9.1.1, 
9.4.1, 13.9.4, 15.6, 15.6.1, 15.6.3, 15.7, 17.4.3, CCB FINANCE}
TS.D.10.3 Decision-support tools and decision-analytic methods

are available and being applied for climate adaptation and climate 
risk management in different contexts (high confidence). 
Integrated adaptation frameworks and decision-support tools that 
anticipate multi-dimensional risks and accommodate community 
values are more effective than those with a narrow focus on single risks 
(medium confidence). Approaches that integrate the adaptation needs 
of multiple sectors such as disaster management, account for different 
risk perceptions and integrate multiple knowledge systems are better 
suited to addressing key risks (medium confidence). Reliable climate 
services, monitoring and early warning systems are the most commonly 
used strategies for managing the key risks, complementing long-term 
investments in risk reduction (high confidence). While these strategies 
are applicable to society as a whole, they need to be tailored to specific 
contexts in order to be adopted effectively. {2.6.7, 3.6.3, 3.6.5, 4.5.5, 5.14.1,

7.2.2, 7.4.1, 7.4.2, 9.5.1, 9.4.3, 9.10.3, 9.11.4, Box 9.2, Box 9.7, 15.5.7, 
17.1.2, 17.2, 17.3.2, 17.4.4, 17.6, 18.4, CCP5.4.1, CCP5.6, CCB DEEP}
TS.D.10.4 Effective management of climate risks is dependent 
on systematically integrating adaptations across interacting 
climate risks and across sectors (very high confidence). Integrated 
pathways for managing climate risks will be most suitable when so-
called ‘low-regret’ anticipatory options are established jointly across 
sectors in a timely manner and are feasible and effective in their local 
context, when path dependencies are avoided so as not to limit future 
options for climate resilient development and when maladaptations 
across sectors are avoided (high confidence). Integration of risks across 
sectors can be assisted by mainstreaming climate considerations 
across institutions and decision-making processes (high confidence). Many forms of climate adaptation are likely to be more effective,

efficient and equitable when organised collectively and with multiple 
objectives. Using different assessment, modelling, monitoring and 
evaluation approaches can facilitate understanding of the societal 
implications of trade-offs. {1.4.2, 2.6, 4.5.1, 4.5.2. 11.3.11, 11.5.1, 
11.5.2, 11.7, 11.7.2, 11.7.3, 13.5.2, 13.10, 13.11.2, 13.11.3, 15.7; 
17.3.1, 17.6, CCP2.3.6, CCP5.4.2, CCB DEEP}
TS.D.10.5 Forward-looking adaptive planning and iterative risk 
management can avoid path dependencies and maladaptation 
and ensure timely action (high confidence). Approaches that break 
down adaptation into manageable steps over time and use pathway 
analyses to determine low-regret actions for the near-term and long-
term options are a useful starting point for adaptation (medium 
confidence). Decision frameworks that consider multiple objectives, 
scenarios, time frames and strategies can avoid privileging some views 
over others and help multiple actors to identify resilient and equitable

solutions to complex, deeply uncertain challenges and explicitly deal 
with trade-offs. Considering socioeconomic developments and climatic 
changes beyond 2100 is particularly relevant for long-lived investment 
decisions such as new harbours, airports, urban expansions and flood 
defences to avoid lock-ins (medium confidence). Monitoring climate 
change, socioeconomic developments and progress on implementation 
is critical for learning about adaptation success and maladaptation 
and to assess whether, when and what further actions are needed for 
informing iterative risk management (high confidence). {1.5.2, 11.7, 
13.2.2, 13.11.1, 17.5.2, CCP2.3.6, CCB DEEP}
TS.D.10.6 Enhancing climate change literacy on impacts and 
possible solutions is necessary to ensure widespread, sustained 
implementation of adaptation by state and non-state actors 
(high confidence). Ways to enhance climate literacy and foster 
behavioural change include access to education and information,

programmes involving the performing and visual arts, storytelling, 
training workshops, participatory three-dimensional modelling, 
climate services and community-based monitoring. The use of 
Indigenous knowledge and local knowledge represents and codifies 
actual experiences and autonomous adaptations and facilitates 
awareness, clarifies risk perception and enhances the understanding 
and adoption of solutions. Narratives can effectively communicate 
climate information and link this to societal goals and the actions 
needed to achieve them (high confidence). {1.2.2, 1.3.2, 1.3.3, 1.5.2, 
5.4.4, 5.5.4, 5.8.4, 5.13.2, 5.14.1, 5.14.2, 9.4.5, 14.3, 15.6.4, 15.6.5}
TS.D.10.7 Political commitment and follow-through across  all 
levels of government are important to accelerate the 
implementation of adequate and timely adaptation actions 
(high confidence). Implementing actions often requires large upfront 
investments of human and financial resources and political capital by

public, private and societal actors, while the benefits of these actions 
may only become visible in the mid to long term (medium confidence). 
Examples that can accelerate adaptation action include accountability 
and transparency mechanisms, monitoring and evaluation of 
adaptation progress, social movements, climate litigation, building 
the economic case for adaptation and increased adaptation finance 
(medium evidence, high agreement). {3.6.3, 3.6.5, 4.8.5, 4.8.6, 4.8.7, 
6.3, 6.4, 7.4.3, 9.4.2, 9.4.4, 11.7, 11.7.3, 11.8.1, 12.5, 12.5.6, 13.11, 
14.6, 15.6, 15.6.3, 17.4.2, 17.5.2, 17.6, 18.4, CCB COVID}

99
Technical SummaryTSSystem transitions and transformational adaptation
TS.D.11 Deep-rooted transformational adaptation opens new 
options for adapting to the impacts and risks of climate change 
(high confidence) by changing the fundamental attributes of 
a system, including altered goals or values and addressing 
the root causes of vulnerability. AR6 focuses on five system 
transitions to a just and climate resilient future: societal, energy, 
land and ocean ecosystems, urban and infrastructure, and 
industrial. These transitions call for transformations in existing 
social and social-technological and environmental systems that 
include shifts in most aspects of society. Managing transition 
risk is a critical element of transforming society, increasingly 
acknowledging the importance of transparent, informed and 
inclusive decision-making and evaluation, including a role for 
Indigenous knowledge and local knowledge. (Figure TS.11a, b)

{1.2.1, 1.4.4, 1.5.1, 3.6.4, 4.7.1, 6.1.1, 6.4, Box 6.6, 11.4, 14.7.2, 
18.3, Figure 18.3, CCB FEASIB}
TS.D.11.1 A sub-set of adaptation options has been implemented 
that cuts across sectors to enable sector-specific adaptation 
responses. These options, such as disaster risk management, climate 
services and risk sharing, increase the feasibility and effectiveness 
of other options by expanding the solution space available (high 
confidence). For example, carefully designed and implemented disaster 
risk management and climate services can increase the feasibility 
and effectiveness of adaptation responses to improve agricultural 
practices, income diversification, urban and critical services and 
infrastructure planning (very high confidence). Risk insurance can be a 
feasible tool to adapt to transfer climate risks and support sustainable 
development (high confidence). They can reduce both vulnerability and 
exposure, support post-disaster recovery and reduce financial burden

on governments, households and business. {3.6.3, 3.6.5, 4.6, 4.7.1, 
5.4.4, 5.6.3, 5.5.4, 5.8.4, 5.9.4, 5.12.4, 5.14.1, 5.14.2, 13.11.2, 14.7.2, 
15.5.7, CCB FEASIB, CCB GENDER, CCB MOVING PLATE}
TS.D.11.2 Transformations for energy include the options of 
efficient water use and water management, infrastructure 
resilience and reliable power systems, including the use of 
intermittent renewable energy sources, such as solar and wind 
energy, with the use of storage (very high confidence). These 
options are not sufficient for the far-reaching transformations required 
in the energy sector, which tend to focus on technological transitions 
from a fossil-based to a renewable energy regime. A resilient power 
infrastructure is considered for energy generation, transmission 
and distribution systems. Distributed generation utilities, such as 
microgrids, are increasingly being considered, with growing evidence 
of their role in reducing vulnerability, especially within underserved

populations (high confidence). Infrastructure resilience and reliable 
power are particularly important in reducing risk in peri-urban and 
rural areas when they are supported by distributed generation of 
renewable energy by isolated systems (high confidence). The option 
for a resilient power infrastructure is considered for all types of 
power generation sources and transmission and distribution systems. 
Efficient water use and water management especially in hydropower 
and combined cycle power plants in drought-prone areas have a 
high feasibility (high confidence) with multiple co-benefits (medium confidence). Water-related adaptation in the energy sector is highly 
effective up to 1.5°C but declines with increasing warming (medium 
confidence). {4.6.2, 4.7.1, 4.7.2, 4.7.3, Figure 4.28, Figure 4.29, 13.6.2, 
15.7, 18.3, CCP5.4.2, CCB FEASIB}
TS.D.11.3 Adaptation options that are feasible and effective to the 
3.4 billion people living in rural areas around the world and who

are especially vulnerable to climate change, include the provision 
of basic services, livelihood diversification and strengthening of 
food systems (high confidence). The vulnerability of rural areas to 
climate risks increases due to the long distances to urban centres and 
the lack of or deficient critical infrastructure such as roads, electricity 
and water. Providing critical infrastructure, including through distributed 
generation power systems through renewable energy, has provided many 
co-benefits (high confidence). Biodiversity management strategies have 
social co-benefits, including improved community health, recreational 
activities and ecotourism, which are co-produced by harnessing ecological 
and social capital to promote resilient ecosystems with high connectivity 
and functional diversity. Strengthening local and regional food systems 
through strategies such as collective trademarks, participatory guarantee

systems and city–rural links build rural livelihoods, resilience and self-
reliance (medium confidence) . Livelihood diversification is a key coping 
and adaptive strategy to climatic and non-climatic risks. There is high 
evidence ( medium agreement) that diversifying livelihoods improves 
incomes and reduces socioeconomic vulnerability, but feasibility changes 
depending on livelihood type, opportunities and local context.  Key 
barriers to livelihood diversification include sociocultural and institutional 
barriers as well as inadequate resources and livelihood opportunities that 
hinder the full adaptive possibilities of existing livelihood diversification 
practices (high confidence). (Figure TS.11b) {4.6.2, 4.7.1, 5, 8, 14.5.9, CCB 
FEASIB}
TS.D.11.4 Adaptation can require system-wide transformation 
of ways of knowing, acting and lesson-drawing to rebalance 
the relation between human and nature (high confidence).  
Indigenous knowledge and local knowledge, ecosystem-based

adaptation and community-based adaptation are often found together 
in effective adaptation strategies and actions and together can generate 
transformative sustainable changes, but they need the resources, legal 
basis and an inclusive decision process to be most effective (medium 
confidence). Governance measures that transparently accommodate 
science and Indigenous knowledge can act as enablers of such co-  
production. {1.3.3, 2.6.5, 2.6.7, 5.14.1, 5.14.2, 6.4.7, 9.12, Box 9.1, 11.3.3, 
11.4.1, 11.4.2, 11.5.1, 11.6, Box 11.3, Box 11.7, 12.5.8, 14.4, Box 14.7, 
15.5.4, 15.5.5, 17.2.2, 17.3.1, 17.4.4, CCP6.3.2, CCP 6.6, CCP6.4.3}
TS.D.11.5 Factors motivating transformative adaptation actions 
include risk perception, perceived efficacy, sociocultural norms 
and beliefs, previous experiences of impacts, levels of education 
and awareness (medium confidence). Risk responsibilities across 
the globe are unclear and unevenly defined (high confidence). In

the face of climate change, assigning risk responsibilities facilitates 
upgrading and supporting adaptation efforts (risk governance). There 
are at least two contrasting approaches for pursuing deliberate 
transformation: one seeking rapid, system-wide change and the other a 
collection of incremental actions that together catalyse desired system 
changes (medium confidence). {1.5.2, 6.4.7, 17.2.1, 17.2.2, CCP5.4.2}

100
TSTS.E Climate Resilient Development
Sustainable development, equity and justice
TS.E.1 Climate resilient development implements greenhouse 
gas mitigation and adaptation options to support sustainable 
development. With accelerated warming and the intensification 
of cascading impacts and compounded risks above 1.5°C 
warming, there is a sharply increasing demand for adaptation 
and climate resilient development linked to achieving 
SDGs and equity and balancing societal  priorities. There is 
only limited  opportunity to widen the remaining solution 
space and take advantage of  many potentially effective, yet 
unimplemented, options for reducing society and ecosystem 
vulnerability  (high  confidence).  (Figure  TS.2, Figure  TS.9 
URBAN, Figure TS.11a, Figure TS.13) {1.2.3, 1.5.1, 1.5.2, 1.5.3, 
2.6.7, 3.6.5, 4.8, Box 4.7, 7.1.5, 7.4.6, 13.10.2, 13.11, 17.2.1, 18.1, 
CCB COVID, CCB FINANCE, CCB HEALTH, CCB NATURAL}

TS.E.1.1  Prevailing development pathways do not advance 
climate resilient development (very high confidence). Societal 
choices in the near term will determine future pathways.  There 
is no single pathway or climate that represents climate resilient 
development for all nations, actors or scales, as well as globally, 
and many solutions will emerge locally and regionally. Global trends 
including rising income inequality, urbanisation, migration, continued 
growth in greenhouse gas emissions, land use change, human 
displacement and reversals of long-term trends toward increased 
life expectancy run counter to the SDGs as well as efforts to reduce 
greenhouse gas emissions and adapt to a changing climate.  With 
progressive climate change,  enabling conditions  will diminish, and 
opportunities for successfully transitioning systems for both mitigation 
and adaptation will become more limited (high  confidence). Investments

in economic recovery from COVID-19 offer opportunities to promote 
climate resilient development (high confidence). (Figure TS.13) {16.6.1, 
17.2.1, 18.2, 18.4, CCP5.4.4, CCB COVID}
TS.E.1.2  System transitions can enable climate resilient devel-
opment when accompanied by appropriate enabling conditions 
and inclusive arenas of engagement (very high confidence). Five 
system transitions are considered: energy, industry, urban and infra-
structure, land and ecosystems, and society. Advancing climate resilient 
development in specific contexts may necessitate simultaneous progress 
on all five transitions. Collectively, these system transitions can widen 
the solution space and accelerate and deepen the implementation 
of sustainable development, adaptation and mitigation actions by 
equipping actors and decision makers with more effective options (high 
confidence). For  example, urban ecological infrastructure linked to an

appropriate land use mix, street connectivity, open and green spaces 
and job-housing proximity provides adaptation and mitigation benefits 
that can aid urban transformation (medium confidence). These system 
transitions are necessary precursors for more fundamental climate 
and sustainable-development transformations but can simultaneously 
be outcomes of transformative actions. Enhancing equity and agency 
are cross-cutting considerations for all five transitions. Such transitions 
can generate benefits across different sectors and regions, provided they are facilitated by appropriate enabling conditions, including ef-
fective governance, policy implementation, innovation and climate and 
development finance, which are currently insufficient (high confidence). 
{3.6.4, 15.7, 18.3, 18.4, Table 18.5, CCB FEASIB, CWGB URBAN}
TS.E.1.3 System transitions are highly feasible. For energy system 
transitions, there is  medium confidence in the high feasibility

of resilient infrastructure and efficient water use for power 
plants and  high confidence in the synergies of this option with 
mitigation. For coastal ecosystem transitions, there is  medium to  high 
confidence that ecosystem conservation and biodiversity management 
are increasing adaptive and ecological capacity with socioeconomic 
co-benefits and positive synergies with carbon sequestration. However, 
opportunity costs can be a barrier. For land ecosystem transitions, there 
is high confidence in the role of agroforestry to increase ecological and 
adaptive capacity, once economic and cultural barriers and potential 
land use change trade-offs are overcome. There is  high confidence in 
improved cropland management and its economic feasibility due to 
improved productivity. For efficient livestock systems, there is  medium 
confidence  in the high technological and ecological feasibility. 
(Figure TS.11a) {CCB FEASIB}

TS.E.1.4 For urban and infrastructure system transitions, there 
is medium confidence  for sustainable land use and urban 
planning. There is  high confidence  in the economic and ecological 
feasibility of green infrastructure and ecosystem services, as well as 
sustainable urban water management, once institutional  barriers in 
the form of limited social and political acceptability are overcome. 
Social safety nets, disaster risk management and climate services and 
population health and health  systems are considered overarching 
adaptation options due to their applicability across all system 
transitions. There is  medium to high confidence in the high feasibility of 
disaster risk management and the use of demand-driven and context-
specific climate services as well as in the socioeconomic feasibility of 
social safety nets. Improving health systems through enhancing access 
to medical services and developing or strengthening surveillance

systems can have high feasibility when there is a robust institutional 
and regulatory framework (high confidence). (Figure  TS.8 HEALTH, 
Figure TS.9 URBAN, Figure TS.11a, Figure TS.13) {6.3, CCB FEASIB}
TS.E.1.5  There are multiple possible pathways by which 
communities, nations and the world can  pursue climate 
resilient development. Moving towards different pathways 
involves confronting complex synergies and trade-offs between 
development pathways and the options, contested values and 
interests that underpin climate mitigation and adaptation 
choices (very high confidence).  Climate resilient development 
pathways are trajectories for the pursuit of climate resilient development 
and navigating its complexities. Different actors, the private sector and 
civil society, influenced by science, local and Indigenous knowledges, 
and the media, are both active and passive in designing and navigating 
climate resilient development pathways. Increasing levels of warming

may narrow the options and choices available for local survival and 
sustainable development for human societies and ecosystems. Limiting 
warming to Paris Agreement goals will reduce the magnitude of climate 
risks to which people, places, the economy and ecosystems will have 
to adapt. Reconciling the costs, benefits and trade-offs associated with

101
Technical SummaryTSadaptation, mitigation and sustainable development interventions 
and how they are distributed among different  populations and 
geographies is essential and challenging but also creates the potential 
to pursue synergies that benefit human and ecological well-being (high 
confidence). {1.2.1, 18.1, 18.4}
TS.E.1.6. Economic sectors and global regions are exposed to 
different opportunities and challenges in facilitating climate 
resilient development, suggesting adaptation and mitigation 
options should be aligned to local and regional context and 
development pathways (very high confidence). Given their current 
state of development, some regions may prioritise poverty and inequality 
reduction and economic development over the near term as a means 
over the long term. In contrast, developed economies with mature 
economies and high levels of resilience may prioritise climate action to

transition their energy systems and reduce greenhouse gas emissions. 
Some interventions may be robust in that they are relevant to a broad 
range of potential development trajectories and could be deployed 
in a flexible manner. However, other types of interventions, such as 
those that  are dependent upon emerging technologies, may require 
a specific set of enhanced enabling conditions or factors, including 
infrastructure, supply chains, international cooperation and education 
and training that currently limit their implementation to certain settings. 
Notwithstanding national and regional differences, development 
practices that are aligned to people, prosperity, partnerships, peace 
and the planet as defined in Agenda 2030 could enable more climate 
resilient development. (high confidence) {18.5, Figure 18.1}
TS.E.1.7 Pursuing climate resilient development involves 
considering a broader range of sustainable development

priorities, policies and practices, as well as enabling societal 
choices to accelerate and deepen their implementation  (very 
high confidence).  Scientific assessments of climate change have 
traditionally framed solutions around the implementation of specific 
adaptation and mitigation options as mechanisms for reducing 
societal priorities and  the role of non-climate policies, social norms, 
lifestyles, power relationships and worldviews in enabling climate action 
and sustainable development. Because climate resilient development 
involves different actors pursuing plural development trajectories  in 
diverse contexts, the pursuit of solutions that are equitable for all 
requires opening the space for engagement and action to a diversity 
of people, institutions, forms of knowledge and worldviews. Through 
inclusive modes of engagement that enhance knowledge sharing and

realise the productive potential of diverse perspectives and worldviews, 
societies could alter institutional structures and arrangements, 
development processes, choices and actions that have precipitated 
dangerous climate change, constrained the achievement of SDGs and 
thus limited pathways to achieving climate resilient development. The 
current decade is critical to charting climate resilient development 
pathways that catalyse the transformation of prevailing development 
practices and offer the greatest promise and  potential for human 
well-being and planetary health (very high confidence). {18.4, Box 18.1}TS.E.2 Climate action and sustainable development are 
interdependent. Pursued in an inclusive and integrated manner, 
they enhance human and ecological well-being. Sustainable 
development is fundamental to capacity for climate action, 
including reductions in greenhouse gas emissions as well as 
enhancing social and ecological resilience to climate change.

Increasing social and gender equity is an integral part of the 
technological and social transitions and transformation towards 
climate resilient development. Such transitions in societal 
systems reduce poverty and enable greater equity and agency 
in decision-making. They often require rights-based approaches 
to protect the livelihoods, priorities and survival of marginalised 
groups including Indigenous Peoples, women, ethnic minorities 
and children (high confidence). {2.6.7, 4.8, 6.3.7, 6.4, 6.4.7, 18.2, 
18.4, CCB NATURAL}
TS.E.2.1 Conditions enabling rapid increases and innovative 
climate responses include experience of extreme events or 
climate education influencing perceptions of urgency, together 
with the actions of catalysing agents such as social movements 
and technological entrepreneurs. People who have experienced 
climate shocks are more likely to implement risk management 
measures (high confidence). Autonomous adaptation is very common

in locations where people are more exposed to extreme events 
and have the resources and the temporal capacity to act on their 
own, for example in remote communities (high confidence).{3.5.2, 
4.2.1, 4.6, 4.7.1, 6.4.7, 8.5.2, 9.4.5, 17.4.5, 18.5}
TS.E.2.2 A range of policies, practices and enabling conditions 
accelerate efforts towards climate resilient development. 
Diverse actors including youth, women, Indigenous communities 
and business leaders are the agents of societal changes and 
transformations that enable climate resilient development 
(high confidence). Greater attention to which actors benefit, fail to 
benefit or are directly harmed by different types of interventions could 
significantly advance efforts to pursue climate resilient development. 
(medium to high confidence). {4.6,  4.7.1,  5.13, 5.14, 6.4.7, 8.4.5, 
9.4.5, 17.4, 18.5}
TS.E.2.3 Climate adaptation actions are grounded in local 
realities so understanding links with SDG 5 on gender

equality ensures that adaptive actions do not worsen existing 
gender and other inequities within society (e.g., leading to 
maladaptation practices)  (high confidence). Adaptation actions 
do not automatically have positive outcomes for gender equality. 
Understanding the positive and negative links of adaptation actions 
with gender equality goals (i.e., SDG 5) is important to ensure that 
adaptive actions do not exacerbate existing gender-based and 
other social inequalities. Efforts are needed to change unequal 
power dynamics and to foster inclusive decision-making for climate 
adaptation to have a positive impact for gender equality  (high 
confidence).  There are very few examples of successful integration 
of gender and other social inequities in climate policies to address 
climate change vulnerabilities and questions of social justice (very 
high confidence). Yet inequities in climate change literacy compounds

women’s vulnerability to climate change through its negative effect 
on climate risk perception {4.8.3, 9.4.5, 16.1.4, 17.5.1, CCB GENDER}

102
TS
20222100 &
beyond
IPCC
situation
Low global
Low risk                   

Community
Political

103
Technical SummaryTSRepresentative Key Risks
General Agriculture WaterB uilt
networks & services
Living standards 
& equity
Food Security
Water Security
Peace & human
mobilityHabitat loss; Fish declines; 
Invasives /circle6/circle6/circle6
Wild harvest declines,
food insecurity /circle6/circle6/circle6
Water competition;
malnutrition /circle6/circle6/circle6
Reduced production (crops, 
livestock, ﬁsheries); woody
plant encroachment; water 
scarcity, competition /circle6/circle6/circle6Ecosystems
Irrigation; Energy /circle6/circle6
Irrigation revenues /circle6/circle6
Irrigation;
Infrastructure;
Cascading risks  /circle6/circle6
Drought; Urbanisation;
Energy  /circle6/circle6
Extreme events; drought;
urban pressure; Poverty  /circle6/circle6Water scarcity; Floods;
Landslides; Poverty /circle6/circle6/circle6Water scarcity; extreme
events; sea level rise /circle6/circle6

Flood damages /circle6/circle6 /circle6
Poverty alleviation; 
Water security; SME 
impacts /circle6/circle6/circle6
Disruption, loss of life;
Cascading risks; Sanitation /circle6/circle6
Water security, sanitation in 
food processing,distribution /circle6/circle6
Water scarcity; Cascading risks 
/circle6/circle6/circle6
Adaptation GapHabitat loss; Extreme
events /circle6/circle6/circle6
Heat stress; ocean acidiﬁca-
tion; Extreme events; 
declining ﬁsheries /circle6/circle6/circle6
Livelihoods dependent on
wild systems (tropics)  
/circle6/circle6/circle6/circle6 /circle6/circle6 highConﬁdence level
medium low
small moderate largeGlobal warming level (GWL) adaptation limited
<1.5°C
Not assessed1.5°C
2°C3°C
>3°C(d)

104
TS
& equity
mobilityNatural forest maintenances 
/circle6/circle6
Social protection /circle6/circle6/circle6
Food supply chains; social
protection /circle6/circle6
Irrigation; Water
management, Land
management; Climate
smart agriculture /circle6/circle6/circle6Ecosystems
Eco-urban infrastructure /circle6/circle6
Inclusive sustainable
development; social
Irrigation; Planting
regimes; Cropping Patterns  /circle6/circle6
Dams; irrigation /circle6/circle6
Opportunities for
movement /circle6/circle6Habitat restoration; Storm
surge protection; EbA /circle6/circle6 /circle6
Risk-sensitive design; water 
and sanitation access /circle6/circle6/circle6
Social protection; Health care 

Water management; Sanitation; 
Social protection; Nature-based
solutions /circle6/circle6
Urban agriculture,
forestry; mental health;
heat management /circle6/circle6
Water management; Integrated 
solutions; Flood management
Support networks; Financial
support; Agency for migrants
Global warming level (GWL) adaptation limited (°C)Maintenance and
restoration /circle6/circle6/circle6
Maintenance and
restoration. Protection of
hotspots. EbA. /circle6/circle6/circle6
Social protection /circle6/circle6/circle6Adaptation potential for Representative Key Risks
medium lowFeasibility
small moderate large<1.5°C
2°C3°C
>3°C

105
Technical SummaryTSLand management
General Agriculture Water Built
Buildings &
Energy
Societal 
(behavioural)(-) Energy (renewable energy 
farms, biofuel crops), Carbon
(afforestation of ancient
grasslands) (+) Carbon
(Protection/restoration of
indigenous vegetation )/circle6/circle6/circle6(-) Carbon (afforestation of
ancient grasslands);
(+) Energy (hydro dams)
/circle6/circle6/circle6(-) Carbon (Afforestation
using water, taking over
ancient grasslands)
(+) Carbon (Forest
restoration & sustainable 
management; Soil
management) /circle6/circle6/circle6
(-) Energy (dams altering
water ﬂows)
/circle6/circle6/circle6(+) Carbon (forest
restoration)
(+/-) Energy (Dams - take
water, store water);
(-) Carbon (afforestation
(-) Energy (Hydropower)
(+) Cross basin risk pooling
/ transfer
(-) Options affected by
social inequality in energy,

water and food security
/circle6/circle6/circle6(+) Carbon (ecosystem
protection & restoration; 
forest sustainability, soil 
management /circle6/circle6/circle6
(+) Carbon (soils-
climate -smart agriculture) 
/circle6/circle6/circle6Ecosystems
–––– –––
–––
+|–+|–
+|–+|– +|–
+ Mitigation measures Importance(+) Energy (Decentralised
& low carbon energy
access) /circle6/circle6/circle6
(+) Energy (transport
diversiﬁcation, ecobuilding)
(-) Energy (Dams divert
water) /circle6/circle6/circle6Potentially interacting mitigation measures
+ +
+
++
+
+
(+) Governance (Indigenous 
systems); land management
(reduce emissions)

106
TS
Coordination and
Institutions and 
decision processes
Finance
Technology, information ,
decision support, climate
services & literacy
Processes for Innovation,
adjustment and learningAdaption Legal
frameworks /circle6/circle6/circle6
Broad-based /circle6/circle6/circle6Engagement Options /circle6/circle6/circle6Multi-level Engagement
/circle6/circle6/circle6Protected areas to
support biodiversity and
Coordination;
Transboundary
agreements /circle6/circle6
Response to extreme
events /circle6/circle6/circle6Integrated approaches
across related sectors
/circle6/circle6/circle6Options to improve
multiple sectors
Tools to address
complexity, uncertainty,
change & contestation /circle6/circle6/circle6
Innovation & planning;
social learning
/circle6/circle6/circle6Stakeholder Engagement 
Ownership; Targeted

projects /circle6/circle6/circle6
Climate services & literacy; 
Planning, Information,
Diverse Knowledges /circle6/circle6/circle6
Options to improve
multiple sectors /circle6/circle6/circle6Ecosystems
Enabling GapPoverty reduction
Cross-sectoral and
transboundary solutionsMulti-level Inclusivity /circle6/circle6/circle6 Inclusivity /circle6/circle6
Adaptation ﬁnance /circle6/circle6/circle6 Adaptation ﬁnance /circle6/circle6/circle6Important enablers considered within sectors
Diversiﬁcation
Importance XX
Largem oderate smalln ot asseessedIntegrated Planning, Options  
across multiple sectors; 
Transboundary and multistake-
holder cooperation; Multilevel
Equity; Beneﬁt sharing /circle6/circle6/circle6Information-technology;
climate services /circle6/circle6/circle6
Drought-resistant crops;
agroforestry, diversiﬁcation 
/circle6/circle6/circle6Early warning systems;
Perception management 
/circle6/circle6/circle6XX
XX
XX
XX
XX

107
Technical SummaryTSFigure TS.13 |  Climate resilient development is the process of implementing greenhouse gas mitigation and adaptation measures to support sustainable 
development. This figure builds on Figure SPM.9 in AR5 WGII (depicting climate resilient pathways) by describing how climate resilient development pathways are the result of 
cumulative societal choices and actions within multiple arenas.
Panel (a) Societal choices towards higher climate resilient development (green cog) or lower climate resilient development (red cog) result from interacting decisions and actions 
by diverse government, private sector and civil society actors, in the context of climate risks, adaptation limits and development gaps. These actors engage with adaptation, 
mitigation and development actions in political, economic and financial, ecological, socio-cultural, knowledge and technology, and community arenas from local to international

levels. Opportunities for climate resilient development are not equitably distributed around the world.
Panel (b) Cumulatively, societal choices, which are made continuously, shift global development pathways towards higher (green) or lower (red) climate resilient development. 
Past conditions (past emissions, climate change and development) have already eliminated some development pathways towards higher climate resilient development (dashed 
green line).
Panel (c) Higher climate resilient development is characterised by outcomes that advance sustainable development for all. Climate resilient development is progressively harder 
to achieve with global warming levels beyond 1.5°C. Inadequate progress towards the Sustainable Development Goals (SDGs) by 2030 reduces climate resilient development 
prospects. There is a narrowing window of opportunity to shift pathways towards more climate resilient development futures as reflected by the adaptation limits and increasing

climate risks, considering the remaining carbon budgets. (Figure TS.3, Figure TS.4) {2.6, 3.6, 7.2, 7.3, 7.4, 8.3, 8.4, 8.5, 16.4, 16.5, 17.3, 17.4, 17.5, 18.1, 18.2, 18.3, 18.4, Box 18.1, 
Figure 18.1, Figure 18.2, Figure 18.3, CCB COVID, CCB GENDER, CCB HEALTH, CCB INDIG, CCB SLR, WGI AR6 Table SPM.1, WGI AR6 Table SPM.2, SR1.5 Figure SPM.1}.
Panel (d) Appropriate choices for fostering climate resilient development pathways involve considering the portfolio of risks, the potential for adaptations to satisfactorily reduce 
risks and not exacerbate others, the potential for mitigation measures to interact with risks and adaptations within and across sectors, and how and whether adaptations can be 
enabled.  The graphic table illustrates a possible assembly (not exhaustive) of these considerations for four sectors (agriculture, water, built environments, ecosystems) in the region

Africa, showing  (i) top panel: the potential for cascading and compounding effects amongst risks within sectors, between sectors and across boundaries and the possible constraints 
for adaptation (at what global warming level might risks become too great for adaptation – cell colour) and the adaptation gap to be filled (cell border) (risks are grouped by 
Representative Key Risks); (ii) second panel: the potential for adaptations to reduce risks, including their feasibility (cell border), their interaction with other adaptations addressing 
the same or interacting risks, and whether they are limited by global warming level (cell colour) (possible adaptations are identified for Representative Key Risks); (iii) third panel: 
the mitigation measures grouped into categories that might interact with risks and adaptations, including showing their importance (cell border) and whether the interaction would

be potentially positive, negative or a mixture of both (cell colour) (note: ‘carbon’ refers to carbon sequestration); (iv) bottom panel: Enabling conditions for sectors grouped into 
categories of enablers common across many sectors, showing their importance (cell border) and how they may be suitable across a number of sectors, along with an assessment of 
the gap in the enabler for satisfactory adaptation (cell colour).  Confidence levels on each cell are indicated as *= low confidence, ** = medium confidence, *** = high confidence. 
(see also SMTS.4, Table SMTS.5) {16.5.2, Table SM16.4}
TS.E.2.4 Gender-sensitive, equity- and justice-based adaptation 
approaches, integration of Indigenous knowledge systems 
within legal frameworks and the promotion of Indigenous 
land tenure rights reduce vulnerability and increase resilience 
(high confidence).  Integrating adaptation into social protection 
programmes can build long-term resilience to climate change (high

confidence). Nevertheless, social protection programmes can increase 
resilience to climate related shocks, even if they do not specifically 
address climate risks (high confidence). Climate adaptation actions 
are grounded in local realities so understanding links with SDGs is 
important to ensure that adaptive actions do not worsen existing 
gender and other inequities within society, leading to maladaptation 
practices  (high confidence).  {3.6.4,  4.8.3, 4.8.4,  9.4.5, Box  9.1, 
Box 9.2, Box 9.7, Box 9.8, Box 9.9, Box 9.10, Box 9.11, 14.4, Box 14.1, 
17.5.1, CCP6.3, Box CCP6.2 CCB GENDER}
TS.E.2.5 Water can be either an enabler or a hindrance to success-
ful adaptation and sustainable development.  Central to equity 
issues about water is that it remains a public good (high con-
fidence). Overcoming institutional and financial constraints (govern-
ance,  institutions, policies), including path dependency, is among the

most important requirements enabling effective adaptation in the water 
sector (high confidence). Water-related challenges, despite reported 
adaptation efforts, indicate limits of adaptation in the absence of water 
neutral mitigation action (medium confidence). For some regions, such 
as small island states, coastal areas and mountainous regions, water 
availability already has the potential to become a hard limit on adapta-
tion (limited evidence,  medium agreement) . (Figure TS.6 FOOD-WATER) 
{4.5.3, 4.5.4, 4.5.5, 4.8, 4.6, 4.7.1, 4.7.2, 4.7.6, 6.4 case study 6.1, 15.3.4, 
CCP5.2.2}TS.E.2.6 Procedural and distributional justice and flexible 
institutions facilitate successful adaptation and minimise 
maladaptive outcomes. Reorienting existing institutions to become 
more flexible (e.g., through capacity building and institutional reform) 
and inclusive is key to building adaptive governance systems that are 
equipped to take long-term decisions (medium confidence). Enhancing

climate governance, institutional capacity and differentiated policies 
and regulation from the local to global scale enables and accelerates 
climate resilient development. Transforming financial systems to 
deliver the SDGs, while accelerating system transitions and addressing 
physical and transition risks, is a precondition. Changes in lifestyles, 
human behaviour and preferences can have a significant impact 
on adaptation implementation, demand and hence emissions and 
decision-making around climate action (high confidence). Additionally, 
the use of customary and traditional justice systems, such as those 
of Indigenous peoples, can enhance the equity of adaptation policy 
processes (high confidence). {4.8,  4.6,8, 5.2.3,  13.8,  15.6.1, 15.6.3, 
15.6.4, 15.6.5, 17.1, 18.4}
TS.E.2.7  Enabling environments for adaptation that support 
equitable sustainable development are essential for those 
with climate-sensitive livelihoods who are often least able to

adapt and influence decision-making (high confidence). Enabling 
environments share common governance characteristics, including 
the meaningful involvement of multiple actors and assets, alongside 
multiple centres of power at different levels that are well integrated, 
vertically and horizontally (high confidence). Enabling conditions 
harness synergies, address moral and ethical choices and divergent 
values and interests and support just approaches to livelihood 
transitions that do not undermine human well-being (medium 
confidence). Climate solutions for health, well-being and the changing

108
TSstructure of communities are complex and closely interconnected and 
call for new approaches to sustainable development that consider 
interactions between climate, human and socioecological systems 
to generate climate resilient development (high confidence). To 
address regionally specific adaptation and developmental needs, five 
key dimensions of climate resilient development are identified for 
solutions, adaptation law and climate services and climate change 
literacy  (high confidence). {4.6,  4.8,  6.4.7,  7.1.7,  8.5.1, 8.5.2, 
8.6.3, 9.4.1, 9.4.2, 9.4.3, 9.4.4, 9.4.5, 17.4}
TS.E.2.8 Prevailing ideologies or worldviews, institutions and 
sociopolitical relations influence development trajectories 
by framing climate narratives and possibilities for action 
(medium confidence). The interplay between worldviews and 
ethics, sociopolitical relations, institutions and human behaviour

influence public engagement by individuals and communities. These 
open up opportunities for meaningful engagement and co-production 
of pathways towards climate resilient development. The urgency 
of climate action is a potential enabler of climate decision-making 
(medium confidence). Perceptions of urgency encourage communities, 
businesses and leaders to undertake climate adaptation and 
mitigation measures more quickly and to prioritise climate action (high 
confidence). {1.1.3, 6.4.3, 17.1, 17.4.5, 18.5}
Enablers of societal resilience
TS.E.3 A focus on climate risk alone does not enable effective 
climate resilience (high confidence).  The integration of 
consideration of non-climatic drivers into adaptation pathways 
can reduce climate impacts across food systems, human 
settlements, health, water, economies and livelihoods (high 
confidence). Strengthened health, education and basic social 
services are vital for improving population well-being and

supporting climate resilient development (high confidence). The 
use of climate-smart agriculture technologies that strengthen 
synergies among productivity and mitigation is growing as 
an important adaptation strategy (high confidence). Pertinent 
information for farmers provided by climate information services 
is helping them to understand the role of climate  compared 
with other drivers in perceived productivity changes (medium 
confidence). Index insurance builds resilience and contributes 
to adaptation both by protecting farmers’ assets in the face 
of major climate shocks, by promoting access to credit and 
by adopting improved  farm technologies and practices (high 
confidence). {3.6.4, 4.6, 4.7.1, 7.4.6, Box 9.1, Box 9.7, Box 9.8, 
Box 9.9, Box 9.10, Box 9.11, 12.5.4}
TS.E.3.1  Societal resilience is strengthened by improving the 
management of environmental resources and ecosystem health, 
boosting adaptive capabilities of individuals and communities

to anticipate future risks and minimise them and  removing 
drivers of vulnerability to bring together gender justice, equity, 
Indigenous and local knowledge systems and adaptation 
planning (very high confidence).  Societal resilience is  founded 
on  strengthening local democracy, empowering citizens to shape 
societal choices to support gender and equity inclusive climate resilient development (very high confidence). {7.4.1, 7.4.2, 7.4.3, 7.4.4, 7.4.5, 
7.4.6, 9.4.5, 13.11.3, 14.4, Box 14.1, 15.5.5, 17.5.1, CCP6.3, CCP6.4, 
Box CCP6.2, CCB GENDER}
TS.E.3.2 Some communities/regions are resilient with strong 
social safety nets and social capital that support responses 
and actions already occurring, but there is limited information 
on the effectiveness of adaptation practices and the scale of 
action needed (high confidence). Among island communities, 
greater insights into which drivers weaken local communities and

Indigenous Peoples’ resilience, together with recognition of the 
sociopolitical contexts within which communities operate, can assist 
in identifying opportunities at all scales to enhance climate adaptation 
and enable action towards climate resilient development pathways 
(medium evidence, high agreement). Adaptation responses to climate-
driven impacts in mountain regions vary significantly in terms of goals 
and priorities, scope, depth and speed of implementation, governance 
and modes of decision-making and the extent of financial and other 
resources to implement them (high confidence). Adaptation in Africa 
has multiple benefits, and most assessed adaptation options have 
medium effectiveness at reducing risks for present-day global warming, 
but their efficacy at future warming levels is largely unknown (high 
confidence). In Australia and New Zealand, a range of incremental 
and transformative adaptation options and pathways is available as

long as enablers are in place to implement them (high confidence). 
Several enablers can be used to improve adaptation outcomes and 
to build resilience (high confidence), including better governance and 
legal reforms; improving justice, equity and gender considerations; 
building human resource capacity; increased finance and risk transfer 
mechanisms; education and awareness programmes; increased access 
to climate information; adequately downscaled climate data; inclusion 
of Indigenous knowledge; and integrating cultural resources into 
decision-making (high confidence). {9.3, 9.6.4, 9.8.3, 9.11.4, 11.7.3, 
14.4, Box 14.1, 15.6.1, 15.6.5, 15.7, 15.6.3, 15.6.4, 15.6.5, CCP5.2.4, 
CCP5.2.7, CCP6.3, CCP6.4, Box CCP6.2, CCB GENDER}
TS.E.3.3 Identifying and advancing synergies and co-benefits of 
mitigation, adaptation and SDGs has occurred slowly and unevenly 
(high confidence).  One area of sustained effort is community-based

adaptation planning actions that have potential to be better integrated 
to enhance well-being and create synergies with the SDG ambitions 
of leaving no one behind (high confidence). Complex trade-offs and 
gaps in alignment between mitigation and adaptation over scale and 
across policy areas where sustainable development is hindered or 
reversed also remain (medium confidence). Globally, decisions about 
key infrastructure systems and urban expansion drive risk creation and 
potential action on climate change (high confidence). {4.7.6, 6.4.1, 6.4.3, 
6.4.4, 6.1, 6.2, 6.2.3, 6.3, 6.3.5.1, 6.4, 7.4.7, 9.3.2, CCB HEALTH, CWGB 
BIOECONOMY}
TS.E.3.4 Indigenous knowledge and local knowledge are 
crucial for social-ecological system resilience (high confidence ). 
Indigenous Peoples have been faced with adaptation challenges for 
centuries and have developed strategies for resilience in changing 
environments that can enrich and strengthen other adaptation

efforts (high confidence). Supporting indigenous self-determination, 
recognising Indigenous Peoples’ rights and supporting Indigenous

109
Technical SummaryTSknowledge-based adaptation can accelerate effective robust climate 
resilient development pathways (very high confidence). Indigenous 
knowledge underpins successful understanding of, responses to and 
governance of climate change risks (high confidence). For example, 
Indigenous knowledge contains resource-use practices and ecosystem 
stewardship strategies that conserve and enhance both wild and 
domestic biodiversity, resulting in terrestrial and aquatic ecosystems 
and species that are often less degraded in Indigenous managed lands 
in other lands  (medium confidence) . Valuing Indigenous  knowledge 
systems is a key component of climate justice (high confidence). {2.6.5, 
2.6.7, 4.8.3,  3.6.3, 3.6.4, 3.6.5,  4.8.4, 4.8.5, 4.8.6, 7.4.7, Box  7.1, 
Box  9.2,  12.5.1, 12.5.8, 12.6.2, 13.2.2,  13.8, 13.11, 14.4, 14.7.3, 
Box 14.1,  CCP5.2.6, CP5.4.2, CCP6.3, CCP6.4, Box CCP6.2, CCB INDIG, 
CCB NATURAL}
TS.E.3.5 Ecosystem-based adaptation reduces  climate

risk across sectors, providing social, economic, health and 
environmental co-benefits (high confidence).  Direct human 
dependence on ecosystem services, ecosystem health, and ecosystem 
protection and restoration, conservation agriculture, sustainable 
land management and integrated catchment management support 
climate resilience. Inclusion of interdisciplinary scientific information, 
Indigenous knowledge and practical expertise is essential to effective 
ecosystem-based adaptation (high confidence), and there is a large 
risk of maladaptation where this does not happen (high confidence). 
(Figure TS.9 URBAN) {1.4.2, 2.2, 2.3, 2.5. 2.6, Table 2.7, 3.6.2, 3.6.3, 
3.6.4, 3.6.5, 4.6.6, Box 4.6, 5.14.2, 7.4.2, 9.6, 9.7, 9.8, 9.9, 9.10, 9.11, 
9.12, CCP1, CCP6.3, CCP6.4, CCB NATURAL}
Ecosystem health and resilience
TS.E.4 Maintaining planetary health is essential for human 
and societal health and a pre-condition for climate resilient

development (very high confidence). Effective ecosystem 
conservation on approximately 30% to 50% of Earth’s land, 
freshwater and ocean areas, including all remaining areas 
with a high degree of naturalness and ecosystem integrity, 
will help protect biodiversity, build ecosystem resilience and 
ensure essential ecosystem services (high confidence). In 
addition to this protection, sustainable management of the rest 
of the planet is also important. The protected area required 
to maintain ecosystem integrity varies by ecosystem type 
and region, and their placement will determine the quality 
and ecological representativeness of the resulting network. 
Ecosystem services that are under threat from a combination 
of climate change and other anthropogenic pressures include 
climate change mitigation, flood-risk management and water 
supply (high confidence). (Figure  TS.12) {2.5.4, 2.6.7, 3.4.2, 
3.4.3, 3.6.3, 3.6.5, 13.3.2, 13.5.2, 13.10.2, CCB NATURAL}

TS.E.4.1 Species conservation is an internationally recognised 
objective in its own right and is also important for human life 
and well-being: there is a strong positive association between 
species diversity and ecosystem health that is essential 
for providing critical regulating services, including climate 
regulation, water provisioning, pest and disease control and 
crop pollination (high confidence). The loss of species also lowers the resilience of the ecosystem as a whole, including its capacity to 
persist through climate change and recover from extreme events (high 
confidence). Species extinction levels that are more than 1000 times 
natural background rates as a result of anthropogenic pressures, and 
climate change will increasingly exacerbate this (high confidence). 
Conservation efforts are more effective when integrated into local 
spatial plans inclusive of adaptation responses, alongside sustainable

food and fiber production systems (high confidence). Strong inclusive 
governance systems and participatory planning processes that support 
equitable and effective adaptation outcomes, are gender sensitive 
and reduce intergroup conflict are required for enhanced ecosystem 
protection and restoration (high confidence). {2.2, 2.5.2, 2.5.3, 2.5.4, 
2.6.1-3, 2.6.5, 2.6.7, Table  2.6, Table  2.7, 3.6.3, 3.6.4, 3.6.5, 5.8.4, 
5.13.5, 5.14.1, 5.14.2, 7.4.7, CCP1, CCB COVID, CCB GENDER, CCB 
ILLNESS, CCB INDIG, , CCB MIGRATE, CCB NATURAL}
TS.E.4.2 Solutions that support biodiversity and the integrity of 
ecosystems deliver essential co-benefits for people including 
livelihoods, food and water security and human health and well-
being (high confidence). Limiting warming to 2°C and protecting 
30% of high-biodiversity regions in Africa, Asia and Latin America 
is estimated to reduce the risk of species extinctions by half (high

confidence). Meeting the increasing needs of the human population 
for food and fibre production requires transformation in management 
regimes to recognise dependencies on local healthy ecosystems, with 
greater sustainability, including through increased use of agroecological 
farming approaches and adaptation to the changing climate (high 
confidence). People with higher levels of contact with nature have 
been found to be significantly happier, healthier and more satisfied 
with their lives (high confidence). Participatory, inclusive governance 
approaches such as adaptive co-management or community-based 
planning, which integrate those groups who rely on these ecosystems 
(e.g., Indigenous Peoples, local communities), support equitable and 
effective adaptation outcomes (high confidence). {2.5.4, 2.6.7, 3.4.2, 
3.4.3, 3.6.3, 3.6.4, 3.6.5, 4.8.5, 4.8.6, 5.8.4, 5.13.5, 5.14.1, 5.14.2, 
17.3.1, 17.3.2, 17.6, CCB NATURAL}

TS.E.4.3 Protecting and building the resilience of ecosystems 
through restoration, in ways which are consistent with sustainable 
development, are essential for effective climate change mitigation 
(high confidence) . Degradation and loss of ecosystems is a major 
cause of greenhouse gas emissions, which is increasingly exacerbated by 
climate change (very high confidence) . Globally, there is a 38% overlap 
between areas of high carbon storage and high intact biodiversity, 
but only 12% of that is protected (high confidence). Addressing this 
gap will require an approach which takes account of human needs, 
particularly food security. Tropical rainforests and global peatlands are 
particularly important carbon stores but are highly threatened by human 
disturbance, land conversion and fire. Climate resilient development 
will require strategies for land-based climate change mitigation to be 
integrated with adaptation, biodiversity and sustainable development

objectives; there is good potential for positive synergies, but also the 
potential for conflict, including with afforestation and bioenergy crops, 
when these objectives are pursued in isolation (high confidence). {2.4.3, 
2.4.4, 2.5.3, 2.6.3, 2.6.5-7, 2.6.7, Box 2.2, 3.4.2, 3.5.5, Box 3.4, CCP7.3.2, 
CCB NATURAL, CWGB BIOECONOMY}

110
TSTS.E.4.4 Adaptive management in response to ecosystem 
change is increasingly necessary, and more so under higher 
emissions scenarios (high confidence). Feedback from monitoring 
and assessments of the changing state of planetary conditions and 
local ecosystems enables proactive adaptation to manage risks and 
minimise impacts (medium confidence). Integrated sectoral approaches 
promoting climate resilience, particularly for addressing the impacts 
of extreme events, are key to effective climate resilient development 
(medium confidence). {2.6.2, 2.6.3, 2.6.6, 2.6.7, 3.4.2, 3.4.3, 3.6.3, 
3.6.5, Box 3.4, 17.3.2, 17.6, CCB EXTREMES, SR1.5, SRCCL, SROCC}
TS.E.4.5 Adaptation cannot prevent all risks to biodiversity and 
ecosystem services (high confidence). Adaptation of conservation 
strategies, by building resilience and planning for unavoidable change, 
can reduce harm but will not be possible in all systems, for example,

fragile ecosystems that reach critical thresholds or tipping points 
such as coral reefs, some forests, sea ice and permafrost systems. 
Conservation and restoration will alone be insufficient to protect 
coral reefs beyond 2030 (high confidence) and to protect mangroves 
beyond the 2040s (high confidence). Deep cuts in emissions will be 
necessary to minimise irreversible loss and damage (high confidence). 
(Figure TS.5 ECOSYSTEMS) {2.5.1, 2.5.2, 2.5.4, 2.6.1, 2.6.6, 3.4.2, 3.4.3, 
3.6.3, Figure 3.26, Table SM3.5, Table SM3.6}
TS.E.5 Governance arrangements and practices are presently 
ineffective to reduce risks, reverse path dependencies and 
maladaptation and facilitate climate resilient development (very 
high confidence). Governance for climate resilient development 
involves diverse societal actors, including the most vulnerable, 
who can work collectively, drawing upon local and Indigenous 
knowledges and science, and are supported by strong political will

and climate change leadership (medium confidence). Governance 
practices will work best when they are coordinated within and 
between multiple scales and levels (institutional, geographical 
and temporal) and sectors, with supporting financial resources, 
are tailored for local conditions, are gender-responsive and 
gender-inclusive and are founded upon enduring institutional and 
social learning capabilities to address the complexity, dynamism, 
uncertainty and contestation that characterise escalating climate 
risk (medium confidence). {1.4.2, 3.6.2, 3.6.3, 4.8, 4.8.1, 4.8.2, 
4.8.3, 4.8.4, 4.8.5, 4.8.6, 4.8.7, 6.4.3, 6.4.4, 9.4.5, 17.4, 17.6}
TS.E.5.1 Prevailing governance efforts have not closed the 
adaptation gap (very high confidence), in part due to the 
complex interconnections between climate and non-climate risk 
and the limits of the predominant development and governance 
practices (high confidence). Institutional fragmentation, under-

resourcing of services, inadequate adaptation funding, uneven 
capability to manage uncertainties and conflicting values and reactive 
governance across competing policy domains collectively lock in 
existing exposures and vulnerabilities, creating barriers and limits to 
adaptation, and undermine climate resilient development prospects 
(high confidence). This is amplified by inequity, poverty, population 
growth and high population density, land use change, especially 
deforestation, soil degradation, biodiversity loss, high dependence of national and local economies on natural resources for production 
of commodities, weak governance, unequal access to safe water and 
sanitation services and a lack of infrastructure and financing, which 
reduce adaptation capacity and deepen vulnerability (high confidence). 
{3.6.3, 3.6.5, 6.4.3, Figure 6.5, 9.4.1, 11.7, Table 11.14, Table 11.16, 
12.1.1, 12.2, 12.3, 12.5.5, 12.5.7, Figure 12.2}

TS.E.5.2 Climate governance arrangements and practices are 
enabled when they are embedded in societal systems that advance 
human well-being and planetary health (very high confidence).  
Collective action and strengthened networked collaboration, more 
inclusive governance, spatial planning and risk-sensitive infrastructure 
delivery will contribute to reducing risks (medium confidence). Enablers 
for climate governance include better practices and legal reforms, 
improving justice, equity and gender considerations, building human 
resource capacity, increased finance and risk transfer mechanisms, 
education and climate change literacy programmes, increased access 
to climate information, adequately downscaled climate data and 
embedding Indigenous knowledge and local knowledge as well as 
integrating cultural resources into decision-making (high confidence). 
{4.8.7, 9.4.5, 15.6.1, 15.6.3, 15.6.4, 15.6.5, 17.4, 17.6}
TS.E.5.3 Climate governance will be most effective when it has

meaningful and ongoing involvement of all societal actors from 
local to global levels (very high confidence). Actors, including 
private-sector businesses, non-governmental organisations, Indigenous 
Peoples, religious groups and social movements, at many scales and in 
many sectors, are adapting already and can take stronger adaptation 
and mitigation actions. Many forms of adaptation are more effective, 
more cost-efficient and more equitable when organised inclusively 
(high confidence). Greater coordination and engagement across levels 
of government, business and community serves to move from planning 
to action and from reactive to proactive adaptation (high confidence). 
Inclusion of all societal actors helps to secure credibility, relevance and 
legitimacy, while fostering commitment and social learning (medium to 
high confidence), as well as equity and well-being, and reduces long-

term vulnerability across scales (high evidence, medium agreement). 
Social movements in many cities, including those led by youth, have 
heightened public awareness about the need for urgent, inclusive 
adaptation that can enhance well-being, foster formal and informal 
cooperation and coherence between different institutions and build 
new adaptive capacities. City and local governments remain key actors 
facilitating climate change adaptation in cities and settlements (medium 
confidence). Private and business investment in key infrastructure, 
housing construction and insurance can drive adaptive action at 
scale but can exclude the priorities of the poor (medium confidence). 
Networked community actions can address neighbourhood-scale 
improvements and vulnerability at scale (very high confidence). {1.4.2, 
3.6.5, 6.1, 6.4, 9.4.5, Box 9.4, 11.4.1, 11.4.2, 14.6.3, Box 14.8, 17.2}
TS.E.5.4 Governance practices for climate resilient development

will be most effective when supported by formal (e.g., the 
law) and informal (e.g., local customs and rituals) institutional 
arrangements providing for ongoing coordination between and 
alignment of local to international arrangements across sectors 
and policy domains (high confidence). Aligned national and

111
Technical SummaryTSinternational legal and policy instruments can support the development 
and implementation of adaptation and climate risk management 
(medium confidence) and reduce exposure to key risks (high confidence). 
Dedicated climate change acts can play a foundational and distinctive 
role in supporting effective climate governance, and are drivers of 
subsequent activity in both developing and developed countries (high 
confidence). The transboundary nature of many climate change risks 
and species responses will require transboundary solutions through 
multi-national or regional governance processes on land (medium 
confidence) and at sea (high confidence). {3.6.5, Table 3.28, 4.6.2, 4.6, 
6.1, 9.4.3, 9.4.4, Box 9.5, 11.7.1, 11.7.3, 17.2.1, 17.3.2, 17.4.2, 17.5.1, 
17.6, 18.4.3, CCP5.4.2, CCP6.3, CCB MOVING PLATE}
TS.E.5.5 Multi-lateral governance efforts can help reconcile 
contested interests, worldviews and values about how to

address climate change (medium confidence). Policy responses 
and strategies that localise development and expand the adaptation 
and mobility options of populations exposed to climatic risks can also 
reduce risks of climate-related conflict and political instability (high 
agreement, medium evidence). Formal institutional arrangements for 
natural resource management can contribute to wider cooperation and 
peacebuilding (high confidence). Reducing vulnerability depends on 
the inclusive engagement of the most vulnerable, is gender-responsive 
and includes key societal actors from civil society, the private sector 
and government, with an especially important role played by local 
government in partnership with local communities. Strong governance 
and gender-sensitive approaches to natural resource management 
reduce the risk of intergroup conflict in climate-disrupted areas 
(medium confidence). {3.6.3, 3.6.4, 3.6.5, 4.8.5, 4.8.6, 4.8.7, 6.1, 7.4.4,

7.4.5, CCB COVID, CCB GENDER, CCB HEALTH, CCB INDIG}
TS.E.5.6 A range of governance processes, practices and tools 
that are applicable across a range of temporal and spatial 
scales are available to support inclusive decision-making for 
adaptation and risk management in diverse settings (high 
confidence). National guidance and laws, policies and regulations, 
decision tools that can be tailored to local circumstances, innovative 
engagement processes and collaborative governance can motivate 
better understanding of climate risk and build climate resilient 
development (high confidence). Collaborative networks and 
institutions, including among local communities and their governing 
authorities, can help resolve conflicts (high confidence). A combination 
of robust climate information, adaptive decision-making under 
uncertainty, land use planning, public engagement and conflict 
resolution approaches can help to address governance constraints to

prepare for climate risks and build adaptive capacity (high confidence). 
New modelling, monitoring and evaluation approaches, alongside 
disruptive technologies, can help understand the societal implications 
of trade-offs and build integrated pathways of low-regret anticipatory 
options, established jointly across sectors in a timely manner, to avoid 
locked-in development pathways (high confidence). {3.6.2, 3.6.3, 3.6.4, 
3.6.5, 5.14.1, 5.14.4, 11.4.1, 11.4.2, 11.7.1, 11.7.3, Box 11.5, 15.5.3, 
15.5.4, 15.6.3, 15.6.4, 15.6.5, 17.3.1, 17.3.2, 17.4.2, 17.4.4, 17.6, 
CCP2.4.3, CCB DEEP , CCB NATURAL, CCB SLR, CWGB BIOECONOMY}Transformation towards climate resilient development
TS.E.6 Accelerating climate change and trends in exposure 
and vulnerability underscore  the need for rapid action on 
the range of transformational approaches  to expand the 
future set of effective, feasible and just solutions (very 
high confidence).  Transformation towards climate resilient

development is advanced most effectively when  actors work 
in inclusive and enabling ways to reconcile divergent interests, 
values and worldviews, building on information and knowledge 
on climate risk and adaptation options derived from different 
knowledge systems (high confidence).  Taking action now 
provides the foundation for adaptation to current and future 
risks, for large-scale mitigation measures and for effective 
outcomes for both.  (Figure  TS.13) {2.6.7, 3.4.2, 3.4.3, 3.6.5, 
7.2.1, 7.3.1, 8.3.3, 8.3.4, 8.4.5, Figure 8.12, 13.3.2, 13.4.2, 13.8, 
13.10.2, 18.3.2, Box 18.1, Figure 18.1, Table 18.5, CCB FEASIB, 
CCB FINANCE, CCB ILLNESS, CCB NATURAL}
TS.E.6.1 Large-scale, transformational adaptation necessitates 
enabling improved approaches to governance and coordination 
across sectors and jurisdictions to avoid overwhelming current 
adaptive capacities and to avoid future maladaptive actions 
(high confidence). Response options in one sector can become

response risks that exacerbate impacts in other sectors. A deliberate 
shift from primarily technological adaptation strategies to those 
that additionally incorporate behavioural and institutional changes, 
adaptation finance, equity and environmental justice and that align 
policy with global sustainability goals will facilitate transformational 
adaptation (high confidence). Application and efficacy testing 
of climate resilient development, or adaptation pathways, show 
promise for implementing transformational approaches (medium 
confidence), including expansion of ecosystem-based adaptation 
approaches. Climate information services that are demand driven 
and context specific, combined with climate change literacy, have 
the potential to improve adaptation responses (high confidence). 
{5.14.3, 9.4.5, 14.7.2, 14.6, 17.6}
TS.E.6.2  Climate resilient development pathways depend on 
how contending societal interests, values and worldviews are

reconciled through inclusive and  participatory interactions 
between governance actors in these arenas of engagement 
(high confidence).  These interactions occur in many different arenas 
(e.g., governmental, economic and financial, political, knowledge, 
science and technology, community) that represent the settings, places 
and spaces in which societal actors interact to influence the nature 
and course of development. For instance, Agenda 2030 highlights 
the importance of multi-level adaptation governance, including non-
state actors from civil society and the private sector. This implies the 
need for wider arenas of engagement for diverse actors to collectively 
solve problems and to unlock the synergies between adaptation and 
mitigation and sustainable development (high confidence). {18.4.3}
TS.E.6.3 Managing transition risk is a critical element of 
transforming society (high confidence). System transitions 
towards climate resilient development pose potential risks

to sectors and regions. This implies managing climate risk in the

112
TSevent that greenhouse gas mitigation efforts over- or underperform. 
In addition, decision makers should be aware of the financial risks 
associated with stranded assets, technology risks and the risks 
to social equity or ecosystem health. By acknowledging, assessing and 
managing such risks, actors will have a greater likelihood of achieving 
success in making development climate resilient. Opportunities exist 
to promote synergies between sustainable development, adaptation 
and mitigation, but trade-offs are likely unavoidable, and managing 
trade-offs and synergies will be important (high confidence). Climate 
resilient development risks and opportunities vary by location with 
uncertainty about global mitigation effort and future climates relevant 
to local planning (high confidence). {4.7.6, 4.8, 17.4, 17.6, 18.4, 18.5}
TS.E.6.4 Prospects for transformation towards climate resilient 
development increase when key governance actors work

together in inclusive and constructive ways to create a set of appropriate enabling conditions (high confidence). These enabling 
conditions include effective governance and information flow, policy 
frameworks that incentivise sustainability solutions, adequate financing 
for adaptation, mitigation and sustainable development, institutional 
capacity, science, technology and innovation, monitoring and evaluation 
of climate resilient development policies, programmes and practices 
and international cooperation. Investment in social and technological 
innovation could generate the knowledge and entrepreneurship needed 
to catalyse system transitions and their transfer. The implementation 
of policies that incentivise the deployment of low-carbon technologies 
and  practices within specific sectors, such as energy, buildings 
and agriculture, could accelerate greenhouse gas mitigation and 
deployment of climate resilient infrastructure in both urban and rural

areas. Civic engagement is an important element of building societal 
consensus and reducing barriers to action on adaptation, mitigation 
and sustainable development (very high confidence). {18.4}
Appendix TS.AI: List and Location of WGII AR6 
Cross-Chapter Boxes (CCBs) and Cross-Working 
Group Boxes (CWGBs)
Host Chapter CCB/CWGB Type/Acronym CCB/CWGB Title
1 CCB CLIMATE AR6 WGI Climate change Projections, Global Warming Levels and WGII Common Climate Dimensions
1 CCB PALEO Observed Vulnerability and Adaptation to Past Climate Changes
1 CCB ADAPT Adaptation Science
1CWGB ATTRIB
(WGI & WGII)Attribution in the IPCC Sixth Assessment Report
2 CCB NATURAL Nature-based Solutions for Climate Change Mitigation and Adaptation
2 CCB EXTREMES Ramifications of Climatic Extremes for Marine, Terrestrial, Freshwater and Polar Natural Systems
2 CCB ILLNESS Human Health, Biodiversity and Climate: Serious Risks Posed by Vector- and Water-borne Diseases
3 CCB SLR Sea Level Rise

4 CCB DISASTER Disasters as the Public Face of Climate Change
5 CCB MOVING PLATE The Moving Plate: Sourcing Food When Species Distributions Change
5CWGB BIOECONOMY
(WGII & WGIII)Mitigation and Adaptation via the Bioeconomy
6CWGB URBAN
(WGII & WGIII)Cities and Climate Change in the Age of the Anthropocene
7 CCB COVID COVID-19
7 CCB MIGRATE Climate-related Migration
7 CCB HEALTH Co-benefits of Climate Solutions for Human Health and Well-being
16 CCB INTEREG Inter-regional Flows of Risks and Responses to Risk
16CWGB SRM
(WGII & WGIII)Solar Radiation Modification
16CWGB ECONOMIC
(WGII & WGIII)Estimating Global Economic Impacts from Climate Change and the Social Cost of Carbon
17 CCB LOSS Loss and Damage
17 CCB DEEP Effective Adaptation and Decision-making under Deep Uncertainties
17 CCB FINANCE Finance for Adaptation and Resilience
17 CCB PROGRESS Approaches and Challenges to Assess Adaptation Progress at the Global Level
18 CCB GENDER Gender, Climate Justice and Transformative Pathways

18 CCB INDIG The Role of Indigenous Knowledge and Local Knowledge in Understanding and Adapting to Climate Change
18 CCB FEASIB Feasibility Assessment of Adaptation Options: an Update of SR1.5C

113
Table TS.AII.1 |  Climate-related representative key risks (RKRs). {16.5, Table 16.6}
Code RKR ScopeSub-section assessment 
of RKR
socioecological systemsRisks to ecosystem services, people, livelihoods and key infrastructure in low-lying coastal areas and 
associated with a wide range of hazards, including sea level change, ocean warming and acidification, 
weather extremes (storms, cyclones) and sea ice loss, for example16.5.2.3.1
RKR-BRisk to terrestrial and ocean 
ecosystemsTransformation of terrestrial and ocean/coastal ecosystems, including change in structure and/or 
functioning and/or loss of biodiversity16.5.2.3.2
RKR-CRisks associated with critical 
physical infrastructure, 
networks and servicesSystemic risks due to extreme events leading to the breakdown of physical infrastructure and networks 
providing critical goods and services16.5.2.3.3

RKR-D Risk to living standardsEconomic impacts across scales, including impacts on GDP , poverty and livelihoods, as well as the 
exacerbating effects of impacts on socioeconomic inequality between and within countries16.5.2.3.4
RKR-E Risk to human health Human mortality and morbidity, including heat-related impacts and vector-borne and water-borne diseases 16.5.2.3.5
RKR-F Risk to food securityFood insecurity and the breakdown of food systems due to climate change effects on land or ocean 
resources16.5.2.3.6
RKR-G Risk to water securityRisk from water-related hazards (floods and droughts) and water quality deterioration; focus on water 
scarcity, water-related disasters and risk to Indigenous and traditional cultures and ways of life16.5.2.3.7
RKR-HRisks to peace and to human 
mobilityRisks to peace within and among societies from armed conflict as well as risks to low-agency human

mobility within and across state borders, including the potential for involuntarily immobile populations16.5.2.3.8Appendix TS.AII: Aggregated Climate Risk 
Assessments in WGII AR6
This supplementary material presents the various aggregated risk 
assessments applied in the WGII AR6. This includes the key risks 
identified by all the chapters and the way they can be clustered into 
Representative Key Risks (RKRs) (Section TS.AII.1), with a summary of 
the severity conditions for these RKRs across climate and development 
pathways, and the interactions among these risks (Section TS.AII.2). 
The assessment of the five Reasons for Concern (RFC), presented in the 
iconic ‘burning embers’, provides a complementary cross-cutting impact 
and risk assessment. This approach is described in Section TS.AII.3, along 
with a comparison with the RKRs (Section TS.AII.4). The burning embers 
for the global and cross-cutting RFCs are complemented by similar

depictions for specific regional and thematic concerns (Section SMTS2.1).
TS.AII.1 Key Risks and Representative Key Risks
Regional and sectoral chapters of this report identified 127 key 
risks that could become severe under particular conditions of 
climate hazards, exposure and vulnerability (Table SMTS.4). These 
key risks are assessed to be potentially severe, that is, relevant to the 
interpretation of dangerous anthropogenic interference (DAI) with the 
climate system, along levels for warming, exposure/vulnerability and 
adaptation. Severity has been assessed looking at the magnitude of 
adverse consequences, the likelihood of adverse consequences, the 
temporal characteristics of the risk and the ability to respond to the 
risks. Key risks cover scales from the local to the global, are especially 
prominent in particular regions or systems and are particularly large for 
vulnerable sub-groups, especially low-income populations, and already

at-risk ecosystems (high confidence). {16.5, Table SM16.4}
These key risks can be represented in eight RKR clusters of 
key risks relating to low-lying coastal systems; terrestrial and 
ocean ecosystems; critical physical infrastructure, networks and services; living standards; human health; food security; water 
security; and peace and mobility (high confidence) (Table TS.
AII.1). The assessment of these RKRs, which is presented in detail in 
Chapter 16, has also been used to organise the synthetic assessment 
of adaptation options in Chapter 17 and is integrated across various 
sections in the TS and SPM. {16.5, SM16.2.1, 17.2.1, 17.5.1}
TS.AII.2 Assessment of Severity Conditions for 
Figure TS.AII.1 presents a synthesis of the severity conditions for RKRs 
by the end of this century. As an illustration of the more specific sets of 
conditions that result in severe risk for a particular RKR, Figure TS.AII.2

provides examples from individual studies of risks to living standards 
and the conditions under which they could become severe in terms of 
aggregate economic output, poverty and livelihoods.
The assessment of RKRs demonstrates that severe risk is rarely driven 
by a single determinant (warming, exposure/vulnerability, adaptation), 
but rather by a combination of conditions that jointly produce the level 
of pervasiveness of consequences, irreversibility, thresholds, cascading 
effects, likelihood of consequences, temporal characteristics of risk 
and systems’ ability to respond (medium to  high confidence). In other 
words, climate risk is not a matter of changing hazards (or climatic 
impact drivers) only but of the confrontation between changing 
hazards and changing socioecological conditions.
For most RKRs, potentially global and systemically pervasive risks 
become severe in the case of high levels of warming, combined with

high exposure/vulnerability, low adaptation or both (high confidence). 
Under these conditions there would be severe and pervasive risks to 
critical infrastructure (high confidence), to human health from heat-
related mortality, to low-lying coastal areas, aggregate economic 
output and livelihoods (all  medium confidence) from armed conflict 
(low confidence) and to various aspects of food security (with different

114
TSlevels of confidence). Severe risks interact through cascading effects, 
potentially causing amplification of RKRs over the course of this 
century (low evidence, high agreement). (Figure  TS.AII.1) {16.5.2, 
16.5.4, Figure 16.10}
For some RKRs, potentially global and systemically pervasive risks 
would become severe even with medium to low warming (i.e., 
1.5°C–2°C) if exposure/vulnerability is high and/or adaptation is low 
(medium to high confidence). Under these conditions there would be 
severe and pervasive risks associated with water scarcity and water-
related disasters (high confidence), poverty, involuntary mobility and 
insular ecosystems and biodiversity hotspots (all  medium confidence). 
{16.5.2}
All potentially severe risks that apply to particular sectors or groups of 
people at more specific regional and local levels require high exposure/
vulnerability or low adaptation (or both), but they do not necessarily

require high warming (high confidence). Under these conditions there 
would be severe, specific risks to low-lying coastal systems, to people 
and economies from critical infrastructure disruption, to economic 
output in developing countries and to livelihoods in climate-sensitive 
sectors from water-borne diseases, especially in children in low- and 
middle-income countries, water-related impacts on traditional ways of 
life and involuntary mobility, for example in small islands and low-
lying coastal areas (medium to high confidence). {16.5.2}
Some severe impacts are already occurring (high confidence) and 
will occur in many more systems before mid-century (medium 
confidence). Tropical and polar low-lying coastal human communities 
are experiencing severe impacts today (high confidence), and abrupt 
ecological changes resulting from mass population-level mortality 
are already being observed following climate extreme events. Some

(medium confidence), for example critical infrastructure affected by 
extreme events (medium confidence). Food security for millions of 
(high confidence). {16.5.2, 16.5.3}
In specific systems already marked by high exposure and vulnerability, 
intensive adaptation efforts will not be sufficient to prevent severe 
risks from occurring under high levels of warming (low evidence, 
medium agreement). This is particularly the case for some ecosystems 
and water-related risks (from water scarcity and to Indigenous and 
traditional cultures and ways of life). {16.5.2, 16.5.3}
Key risks increase the challenges in achieving global sustainability

goals (high confidence). The greatest challenges will be from risks 
to water (RKR-G), living standards (RKR-D), coastal socioecological 
systems (RKR-A) and peace and human mobility (RKR-H). The most 
relevant goals are zero hunger (SDG 2), sustainable cities and 
communities (SDG 11), life below water (SDG 14), decent work and 
economic growth (SDG 8), and no poverty (SDG 1). Priority areas 
for regions are indicated by the intersection of hazards, risks and 
challenges, where, in the near term, challenges to SDGs indicate probable systemic vulnerabilities and issues in responding to climatic 
hazards (high confidence). {16.6.1}
potential to create cascades and then to amplify systemic risks and 
impacts far beyond the level of individual RKRs (medium confidence), 
as also reflected in TS.C.11. These are illustrated in Figure TS.AII.3, 
panel A at the RKR level, and in Figure TS.AII.3, panel B at the key risk 
level.

TS.AII.3 Framework and Approach for Assessment of 
Burning Embers for Reasons for Concern
categories: risk associated with (a) unique and threatened systems, 
(b) extreme weather events, (c) distribution of impacts, (d) global 
aggregate impacts and (e) large-scale singular events. The RFC 
framework was first developed during the Third Assessment Report 
along with a visual representation of these risks as ‘burning embers’ 
figures, and this assessment framework has been further developed 
and updated in subsequent IPCC reports including AR5. RFCs reflect 
risks aggregated globally that together inform the interpretation 
of dangerous anthropogenic interference with the climate system. 
(Figure TS.AII.1) {16.6.2}
The risk transition or ‘ember’ diagram illustrates the progression 
of socioecological risk from climate change as a function of

global temperature change, taking into account the exposure and 
vulnerability of people and ecosystems, as assessed by literature-based 
expert judgement. The definitions of risk levels used to make the expert 
judgements are presented in Table TS.AII.2 {16.6.2}. Further details are 
provided in Section 16.6.3. (Figure TS.4)
TS.AII.4 Relationship between Representative Key Risks 
and Reasons for Concern
The RKRs and RFCs are complementary methods that aggregate 
individual risks in different ways, as displayed in Figure  TS.AII.4. 
They have differences in scale, transitions, timing and treatment of 
vulnerability and adaptation {16.6.2}

115
(a) Low-lying coastal systems
 (g) Water securityA EVC
A(c) Critical infrastructure,
EVC
A EVCA EVC
A EVC
A EVC
EV
AA C
EVC
A EVC
A EVCA EVC(b) Terrestrial and marine ecosystems
A EVC
A EVC
C A EV
A EVCA EVC A EVC
A EVC EVCA EVC
A EVC(f) Food security
A EVC(h) Peace and mobility
Only sets of conditions 
assessed in Chapter 16 are 
reported.
For details and examples, 
see Table 16.A.12 in the 
supplementary information 
associated with Chapter 16.Loss oflives,
livelihoods and
Water
diseases
*Vector-borne
mortality
cultures and
protection and
transport
systems
**
** * *A
**
Impactso f
livelihoods,
economiesDamage and
disruption**
**
**
Loss of
economic
impacts*
**
*
***Losso f

ecosystem
ture/functioning*
/ *
*
**
Involuntary
conﬂicts
****/Increased
ecosystem
A EVC
A EVC*
*
*Synthesis of the severity conditions for Representative Key Risks by the end of this century
Type and 
level of
by end of
this centuryExposure and
(warming)
High
Medium
LowHigh
Medium
Low
Medium
HighScope
*
Risks are severe pervasivel
andeven globally
Speciﬁc
Risksa retoparticular areas ,
sectors or groups of peopleConﬁdence levels
High
Medium
Low
Not fully assessed
Figure TS.AII.1 |  Synthesis of the severity conditions for Representative Key Risks (RKRs) by the end of this century. The figure does not aim to describe severity 
conditions exhaustively for each RKR, but rather to illustrate the risks highlighted in this report (Sections 16.5.2.3.1 to 16.5.2.3.8). Coloured circles represent the levels of warming

(climate), exposure/vulnerability and adaptation that would lead to severe risks for particular key risks and RKRs. Each set of three circles represents a combination of conditions that 
would lead to severe risk with a particular level of confidence, indicated by the number of black dots to the right of the set, and for a particular scope, indicated by the number of 
stars to the left of the set. The two scopes are ‘broadly applicable’, meaning applicable pervasively and even globally, and ‘specific’, meaning applicable to particular areas, sectors 
or groups of people. Details of confidence levels and scopes can be found in Section 16.5.2.3. In terms of severity condition levels (Section 16.5.2.3), for warming levels (coloured 
circles labelled ‘C’ in the figure), high refers to climate outcomes consistent with RCP8.5 or higher, low refers to climate outcomes consistent with RCP2.6 or lower, and medium

refers to intermediary climate scenarios. Exposure-vulnerability levels are determined relative to the range of future conditions considered in the literature. For adaptation, high 
refers to near maximum potential and low refers to the continuation of today’s trends. Despite being intertwined in reality, exposure-vulnerability and adaptation conditions are 
distinguished to help understand their respective contributions to risk severity. {Figure 16.10}

116
TS
Type and 
level of
• Poverty
CCCC
EVEVEVEV
AAAA
• Poverty
as agriculture and fisheries, would be

(7; 11 )C
CEV
EVA
• Poverty
• Poverty
(warming)
High
Medium
C
CEV
EV
EVA
A
AWorld
• Poverty
and fishing face severe livelihood,

1. Acevedo (2017); 2. Baarsch et al. (2020); 3. Burke et al. (2015); 4. Byers et al. (2018); 5. Carleton and Greenstone (2021) ; 6. Hallegatte (2017); 7. Hallegatte and Rozenberg (2017) ;        
High
Medium
Low
Medium
High
Figure TS.AII.2 |  Illustrative examples from individual studies of risks to living standards and the conditions under which they could become severe in terms 
of aggregate economic output, poverty and livelihoods. High, medium and low levels of warming, exposure/vulnerability and adaptation are defined as in Figure TS.AII.1. 
{Figure 16.9}

117
* CIDs are physical climate system conditions (e .g., means, events, extremes) that affect an element of society or ecosystems. lndiced changes are system-depend-
ent and can be detrimenlal , beneﬁcial , neutral , or a mixture of each.  {WGI AR6 SPM}(b) Illustration of interactions at the K ey Risk level (e .g. from ecological risk to k ey dimensions for human societies)
Lives and 
healthN.B.: Anthropogenie drivers are 
panel B,  but contribute to all 
risk considered in this  ﬁgure
economies 
armed 
conﬂictsSpecies extinction
Changes in habitats and 
(all latitudes, land and ocean)
Climatic 
impact-driver s
(CID) *

places, identity,  etc.)Loss of 
ecosystem
Climatic 
(CID) *
Living 
security
Infrastructur eEcosystem
services)
HealthRecent development trends and 
Socioecon.  inequalities
etc.
** As illustrative suggested rather across than RKR comprehensiv e, assessments; and qualitative rather than quantitativeRisk cascades ** Representative K ey Risks 
(a) lnteractions across the eight Representative K ey Risk level
A (Low-lying coasts) B (Ecosystems) C (Infrastructure) D (Living standards)
E (Human health) F (Food security) G (W ater security) H (Peace and human     
mobility)Peace and human
mobility

Figure  TS.AII.3 |  Illustration of some connections across key risks. Panel A describes all the cross-RKR risk cascades that are described in RKR assessments 
(Sections 16.5.2.3.2 to 16.5.2.3.9). Panel B provides an illustration of such interactions at the key risk level, for example from ecological risk to key dimensions for human societies 
(building on Section 16.5.2.2 and Table 16.A.4). The arrows are representative of interactions as qualitatively identified; they do not result from any quantitative modelling exercise. 
{Figure 16.11}

118
TSInterconnections between the Key Risks, Representative Key Risks and the Reasons for Concern
Key Risks 
Examples
Systems
- Risk to low-lying coasts- Risk to terrestrial and ocean ecosystems
Sectors
- Risk to critical infrastructure- Risk to living standards- Risk to human health- Risk to food security- Risk to water security
Topics
Aggregated; Cross-systems/Sectors/Topics; Global
- Risks associated with extreme weather events- Risks associated with the distribution of impacts- Risks associated with global aggregate impacts- Risks associated with large-scale singular eventsCombination of
Key Risks includinginteractions

Figure TS.AII.4 |  Interconnections among key risks, representative key risks and reasons for concern {Figure 16.13}Table TS.AII.4 |  Definition of risk levels for reasons for concern. {Table 16.7}
Undetectable (white) No associated impacts are detectable and attributable to climate change
for key risks
High (red) Severe and widespread impacts that are judged to be high on one or more criteria for assessing key risks
adapt due to the nature of the hazard or impacts/risks

1794
Annex I  GlossaryAI1.5°C pathway See Pathways.
Acceptability of policy or system change The extent to which 
a policy or system change is evaluated unfavourably or favourably, 
or rejected or supported, by members of the general public (public 
acceptability) or politicians or governments (political acceptability). 
Acceptability may vary from totally unacceptable/fully rejected to 
totally acceptable/fully supported; individuals may differ in how 
acceptable policies or system changes are believed to be.
Access to modern energy services Access to clean, reliable 
and affordable energy services for cooking, heating, lighting, 
communications, and productive uses.
Adaptation In human systems, the process of adjustment to actual 
or expected climate and its effects, in order to moderate harm or exploit 
beneficial opportunities. In natural systems, the process of adjustment 
to actual climate and its effects; human intervention may facilitate

adjustment to expected climate and its effects. See also Adaptation 
options,  Adaptive capacity,  and Maladaptive actions (Maladaptation).
Adaptation limits 
The change in climate where adaptation is unable to prevent 
damaging impacts and further risk. Soft limits occur when additional 
adaptation may be possible if constraints are able to be overcome. 
Hard limits occur when no additional adaptation is possible.
Incremental adaptation 
Adaptation that maintains the essence and integrity of a system or 
process at a given scale (Park et al. 2012). In some cases, incremental 
adaptation can accrue to result in transformational adaptation 
(Tàbara et al. 2019; Termeer et al. 2017). Incremental adaptations to 
change in climate are understood as extensions of actions and 
Transformational adaptation 
Adaptation that changes the fundamental attributes of a  social-

ecological system in anticipation of climate change and its impacts.
Adaptation options The array of strategies and measures that 
are available and appropriate for addressing adaptation. They 
include a wide range of actions that can be categorised as structural, 
institutional, ecological or behavioural.
Adaptation pathways See Pathways.
Adaptive capacity The ability of systems, institutions, humans and 
other organisms to adjust to potential damage, to take advantage of 
opportunities, or to respond to consequences (MA 2005).
Adaptive governance See Governance.
Additionality The property of being additional. Mitigation is 
additional if the greenhouse gas emission reductions or removals 
would not have occurred in the absence of the associated policy 
intervention or activity. 
[Note: Additionality is one of several key criteria used to ensure the 
environmental integrity of Offsets (in climate change mitigation)].

See also Greenhouse gas emission metric.Adverse side-effect A negative effect that a policy or measure 
aimed at one objective has on another objective, thereby potentially 
reducing the net benefit to society or the environment. See also 
Co-benefits, Risk, and Trade-off.
Aerosol A suspension of airborne solid or liquid particles, with 
typical particle size in the range of a few nanometres to several 
tens of micrometres and atmospheric lifetimes of up to several 
term aerosol, which includes both the particles and the suspending 
gas, is often used in this report in its plural form to mean ‘aerosol 
particles’. Aerosols may be of either natural or anthropogenic origin 
eruptions. Aerosols can cause an effective radiative forcing directly 
through scattering and absorbing radiation (aerosol–radiation

interaction), and indirectly by acting as cloud condensation nuclei or 
ice nucleating particles that affect the properties of clouds (aerosol–
cloud interaction), and upon deposition on snow- or ice-covered 
surfaces. Atmospheric aerosols may be either emitted as primary 
particulate matter or formed within the atmosphere from gaseous 
precursors (secondary production). Aerosols may be composed of 
sea salt, organic carbon, black carbon (BC), mineral species (mainly 
desert dust), sulphate, nitrate and ammonium or their mixtures. See 
also Short-lived climate forcers (SLCFs).
Afforestation Conversion to forest of land that historically has not 
contained forests. 
[Note: For a  discussion of the term forest and related terms such as 
afforestation, reforestation and deforestation, see the 2006 IPCC Guidelines 
for National Greenhouse Gas Inventories and their 2019 Refinement, and 
information provided by the United Nations Framework Convention on

Climate Change (IPCC 2006, 2019; UNFCCC 2021a,b).] 
See also Deforestation, Reducing Emissions from Deforestation 
and Forest Degradation (REDD+), Reforestation, Anthropogenic 
Removals, and Carbon dioxide removal (CDR).
Agreement In this report, the degree of agreement within the 
scientific body of knowledge on a particular finding is assessed based on 
multiple lines of evidence (e.g., mechanistic understanding, theory, data, 
models, expert judgement) and expressed qualitatively (Mastrandrea 
et al. 2010). See also Confidence,  Likelihood,  and Uncertainty.
Agriculture, Forestry and Other Land Use (AFOLU) In the context 
of national greenhouse gas (GHG) inventories under the  United 
Nations Framework Convention on Climate Change (UNFCCC), 
AFOLU is the sum of the GHG inventory sectors Agriculture and 
Land Use, Land-Use Change and Forestry (LULUCF); see the 2006 
IPCC Guidelines for National GHG Inventories for details. Given the

difference in estimating the ‘ anthropogenic’ carbon dioxide (CO 2 ) 
removals between countries and the global modelling community, the 
land-related net GHG emissions from global models included in this 
report are not necessarily directly comparable with LULUCF estimates 
in national GHG Inventories. See also Land use,  Land-use change and 
forestry (LULUCF) and Land-use change (LUC).
Agroecology The science and practice of applying ecological 
concepts, principles and knowledge (i.e.,  the interactions of, and 
explanations for, the diversity, abundance and activities of organisms) 
to the study, design and management of sustainable agroecosystems.

1795
Glossary Annex IAIIt includes the roles of human beings as a  central organism in 
agroecology by way of social and economic processes in farming systems. Agroecology examines the roles and interactions among all relevant biophysical, technical and socio-economic components of farming systems and their surrounding landscapes (IPBES 2019).
 Degradation of air quality with negative effects 
on human health or the natural or built environment due to 
the 
atmosphere of substances (gases, aerosols) which have a  direct (primary pollutants) or indirect (secondary pollutants) harmful effect. See also Short-lived climate forcers (SLCFs).
Albedo
 T
he proportion of sunlight (solar radiation) reflected by 
a surface or object, often expressed as a percentage. Clouds, snow

and ice usually have high albedo; soil surfaces cover the albedo range from high to low; vegetation in the dry season and/or in arid zones can have high albedo, whereas photosynthetically active vegetation and the ocean have low albedo. The Earth’s planetary albedo changes mainly through changes in cloudiness and of snow, ice, leaf area and land cover.
Anomaly
 T
he deviation of a variable from its value averaged over 
a reference period.Anthropogenic
 Resulting from or produced by human activities
.
 Emissions of 
greenhouse gases (GHGs), 
precursors of GHGs and aerosols caused by human activities. These 
activities include the burning of fossil fuels, deforestation, land use and 
land-use changes (LULUC), livestock production, fertilisation, waste management, and industrial processes. See also Anthropogenic and 
Anthropogenic removals.
Anthropogenic removals
 T
he withdrawal of greenhouse gases 
(GHGs) from the atmosphere as a  result of deliberate human

activities. These include enhancing biological sinks of CO 2 and using 
chemical engineering to achieve long-term removal and storage. Carbon capture and storage (CCS), which alone does not remove CO
2 from the atmosphere, can help reduce atmospheric CO 2 from 
industrial and energy-related sources if it is combined with bioenergy production (BECCS), or if CO
2 is captured from the air directly and 
stored (DACCS). 
[Note: In the 2006 IPCC Guidelines for National GHG Inventories (IPCC 2006), 
which are used in reporting of emissions to the UNFCCC, ‘anthropogenic’ land-related GHG fluxes are defined as all those occurring on ‘managed land’, i.e., ‘where human interventions and practices have been applied to perform production, ecological or social functions’. However, some removals (e.g., removals associated with CO
2 fertilisation and N deposition) are not

considered as ‘anthropogenic’, or are referred to as ‘indirect’ anthropogenic effects, in some of the scientific literature assessed in this report. As a consequence, the land-related net GHG emission estimates from global models included in this report are not necessarily directly comparable with LULUCF estimates in national GHG Inventories.] 
See also Carbon dioxide removal (CDR), Afforestation, Biochar, 
Enhanced weathering, Ocean alkalinisation/Ocean alkalinity enhancement, Reforestation, and Soil carbon sequestration (SCS).
 T
he gaseous envelope surrounding the Earth, divided 
into five layers – the troposphere which contains half of the Earth’s atmosphere, the stratosphere, the mesosphere, the thermosphere,

and the exosphere, which is the outer limit of the atmosphere. The dry atmosphere consists almost entirely of nitrogen (78.1% volume mixing ratio) and oxygen (20.9% volume mixing ratio), together with a number of trace gases, such as argon (0.93 % volume mixing ratio), helium and radiatively active greenhouse gases (GHGs) such as carbon dioxide (CO
2 ) (0.04% volume mixing ratio), methane (CH 4 ), nitrous 
oxide (N 2O) and ozone (O 3 ). In addition, the atmosphere contains the 
GHG w
ater vapour (H 2O), whose concentrations are highly variable 
(0–5% volume mixing ratio) as the sources (evapotranspiration) and sinks (precipitation) of water vapour show large spatio-temporal variations, and atmospheric temperature exerts a strong constraint on the amount of water vapour an air parcel can hold. The atmosphere also contains clouds and aerosols.
Avoid, Shift, Improve (ASI)
 Reducing greenhouse gas
 emissions 
by avoiding the use of an emissions-producing service entirely,

shifting to the lowest-emission mode of providing the service, and/or improving the technologies and systems for providing the service in ways that reduce emissions.
Baseline/reference
 See Reference period
 and Reference scenario.
Baseline period
 See
 Reference period.
Biochar
 Relatively stable
, carbon-rich material produced by heating 
biomass in an oxygen-limited environment. Biochar is distinguished 
from charcoal by its application: biochar is used as a soil amendment with the intention to improve soil functions and to reduce greenhouse gas emissions from biomass that would otherwise decompose rapidly 
(IBI 2018). See also Anthropogenic removals and Carbon dioxide removal (CDR).
 Biodiversity or biological diversity means the 
v
ariability among living organisms from all sources including, among 
other things, terrestrial, marine and other aquatic ecosystems, and the

ecological complexes of which they are part; this includes diversity within species, between species, and of ecosystems (UN  1992). See also Bioenergy and Biomass.
Bioenergy
 Energy derived from any form of 
biomass or its metabolic 
by-products. See also Biofuel.Bioenergy with carbon dioxide capture and storage (BECCS)
  
Carbon 
dioxide capture and storage (CCS) technology applied to 
a bioenergy facility. Note that, depending on the total emissions 
of the BECCS supply chain, carbon dioxide (CO 2 ) can be removed 
from the atmosphere. See also Anthropogenic removals and Carbon 
dioxide removal.
Biofuel
 A fuel
, generally in liquid form, produced from biomass. 
Biofuels include bioethanol from sugarcane, sugar beet or maize, and 
biodiesel from canola or soybeans. See also Bioenergy.
Biogenic carbon emissions
 Carbon 
released as carbon dioxide or 
methane from combustion or decomposition of biomass or biobased 
products.
Biomass

Organic material excluding the material that is fossilised 
or 
embedded in geological formations. Biomass may refer to the mass 
of organic matter in a specific area (ISO 2014). See also Bioenergy 
and Biofuel.

1796
Annex I  GlossaryAITraditional biomass 
The combustion of wood, charcoal, agricultural residues and/or animal dung for cooking or heating in open fires or in inefficient stoves as is common in low-income countries.
Black carbon (BC)
 A relatively pure form of carbon,
 also known as 
soot, arising from the incomplete combustion of fossil fuels, biofuel, 
and biomass. It only stays in the atmosphere for days or weeks. BC 
is a climate forcing agent with strong warming effect, both in the atmosphere and when deposited on snow or ice. See also Aerosol.
 Biologically-driven carbon fluxes and storage in 
marine systems that are amenable to management.
 Coastal blue 
carbon focuses on rooted vegetation in the coastal zone, such as tidal

marshes, mangroves and seagrasses. These ecosystems have high carbon burial rates on a per unit area basis and accumulate carbon in their soils and sediments. They provide many non-climatic benefits and can contribute to ecosystem-based adaptation. If degraded or lost, coastal blue carbon ecosystems are likely to release most of their carbon back to the atmosphere. There is current debate regarding the application of the blue carbon concept to other coastal and non-coastal processes and ecosystems, including the open ocean. See also Sequestration.
 See
 Infrastructure.
Business as usual (BAU)
 T
he term business as usual scenario has 
been used to describe a scenario that assumes no additional policies 
beyond those currently in place and that patterns of socio-economic development are consistent with recent trends. The term is now used less frequently than in the past. See also Reference scenario (under Scenario).
Carbon budget
 Refers

to two concepts in the literature:  
(i) an assessment of carbon cycle sources and sinks on a  global
level, through the synthesis of evidence for fossil fuel and cementemissions, emissions and removals associated with land use and
land-use change, ocean and natural land sources and sinks ofcarbon dioxide (CO
2 ), and the resulting change in atmospheric
CO2 concentration. This is referred to as the global carbon budget;  
(ii) the maximum amount of cumulative net global anthropogenicCO
2 emissions that would result in limiting global warming to a given 
level with a given probability, taking into account the effect of other anthropogenic climate forcers. This is referred to as the Total Carbon Budget when expressed starting from the pre-industrial period, and 
as the Remaining Carbon Budget when expressed from a  recent specified date.
[Note 1: Net anthropogenic CO 2 emissions are anthropogenic CO 2 emissions 
minus anthropogenic CO 2 removals. See also Carbon Dioxide Removal (CDR).

CO2 emissions is reached at the time that annual net anthropogenic CO 2 
emissions reach zero.
Note 3: The degree to which anthropogenic climate forcers other than CO 2 
affect the Total Carbon Budget and Remaining Carbon Budget depends on 
human choices about the extent to which these forcers are mitigated and their resulting climate effects.Note 4: The notions of a Total Carbon Budget and Remaining Carbon Budget are also being applied in parts of the scientific literature and by some entities at regional, national, or sub-national level. The distribution of global budgets across individual different entities and emitters depends strongly on considerations of equity and other value judgements.]
Carbon cycle  T he flow of carbon (in various forms, e.g., as carbon 
dioxide (CO 2 ), carbon in biomass, and carbon dissolved in the ocean 
as carbonate and bicarbonate) through the atmosphere
, hydrosphere,

terrestrial and marine biosphere and lithosphere. In this report, the 
reference unit for the global carbon cycle is GtCO 2 or GtC (one 
Gigatonne = 1 Gt = 1015 grams; 1GtC corresponds to 3.664 GtCO 2).
a  by-product of burning fossil fuels (such as oil, gas and coal), of burning biomass, of land-use changes (LUCs) and of industrial 
processes (e.g., cement production). It is the principal anthropogenic greenhouse gas (GHG) that affects the Earth’s radiative balance. It is the reference gas against which other GHGs are measured and therefore has a global warming potential (GWP) of 1.
Carbon dioxide capture and storage (CCS)
 A 
process in which 
a relatively pure stream of carbon dioxide (CO 2 ) from industrial and  
energy-related sources is separated (captured), conditioned, compressed

and transported to a storage location for long-term isolation from the atmosphere. Sometimes referred to as Carbon Capture and Storage. See also Anthropogenic removals,  Bioenergy with carbon dioxide 
capture and storage (BECCS),  Carbon dioxide capture and utilisation 
(CCU),  Carbon dioxide removal (CDR),  and Sequestration.
Carbon dioxide capture and utilisation (CCU)
 A process in 
which carbon dioxide (CO 2 ) is captured and the carbon then used 
in 
a  product. The climate effect of CCU depends on the product 
lifetime, the product it displaces, and the CO 2 source (fossil, biomass 
or atmosphere). CCU is sometimes referred to as Carbon Dioxide 
Capture and Use, or Carbon Capture and Utilisation. See also Anthropogenic removals, Carbon dioxide capture and storage (CCS), and Carbon dioxide removal (CDR).
 Ant
hropogenic activities removing 
carbon dioxide (CO 2 ) from the atmosphere and durably storing it in

geological, terrestrial, or ocean reservoirs, or in products. It includes 
existing and potential anthropogenic enhancement of biological or geochemical CO
2 sinks and direct air carbon dioxide capture and 
storage (DACCS), but excludes natural CO 2 uptake not directly caused 
by human activities. See also Anthropogenic removals,  Afforestation, 
Biochar,  Bioenergy with carbon dioxide capture and storage (BECCS), 
Carbon dioxide capture and storage (CCS),  Enhanced weathering, 
Ocean alkalinisation/Ocean alkalinity enhancement,  Reforestation, 
and Soil carbon sequestration (SCS).
 Measure of the exclusive total amount of 
emissions of 
carbon dioxide (CO 2 ) that is directly and indirectly 
caused by an activity or is accumulated over the lifecycle stages of 
a product (Wiedmann and Minx 2008).
Household carbon footprint 
T
he carbon footprint of an individual household, inclusive of the 
direct and indirect carbon dioxide (CO 2 ) emissions associated with

home energy use
, transportation, food provision, and consumption 
of other goods and services associated with household expenditures.

1797
Glossary Annex IAICarbon intensity  T he amount of emissions of carbon dioxide 
(CO 2 ) released per unit of another variable such as gross domestic 
product (GDP)
, output energy use or transport.
Carbon leakage
 See 
Leakage.
Carbon neutrality
 Condition in which 
anthropogenic carbon 
dioxide (CO 2 ) emissions associated with a subject are balanced by 
anthropogenic CO 2 removals. The subject can be an entity such as 
a country, an organisation, a district or a commodity, or an activity 
such as a service and an event. Carbon neutrality is often assessed over the lifecycle including indirect (‘scope 3’) emissions, but can also be limited to the emissions and removals, over a  specified period, for which the subject has direct control, as determined by the relevant scheme.
[Note 1: Carbon neutrality and net-zero CO 2 emissions are overlapping concepts. 
The concepts can be applied at global or sub-global scales (e.g.,  regional,

national and sub-national). At a global scale, the terms carbon neutrality and net-zero CO
2 emissions are equivalent. At sub-global scales, net-zero CO 2 
emissions is generally applied to emissions and removals under direct control or territorial responsibility of the reporting entity, while carbon neutrality generally includes emissions and removals within and beyond the direct control or territorial responsibility of the reporting entity. Accounting rules specified by greenhouse gas (GHG) programmes or schemes can have a significant influence on the quantification of relevant CO
2 emissions and removals.
Note 2: In some cases, achieving carbon neutrality may rely on the supplementary use of offsets to balance emissions that remain after actions by the reporting entity are taken into account.]
See also Greenhouse gas neutrality, Land use, land-use change and 
forestry (LULUCF) and Net-zero CO 2 emissions.
Carbon price
 T
he price for avoided or released carbon dioxide

(CO 2 ) or CO 2-equivalent emissions. This may refer to the rate of 
a carbon tax, or the price of emission permits. In many models that are used to assess the economic costs of mitigation, carbon prices are used as a proxy to represent the level of effort in mitigation policies.
Carbon sink
 See 
Sink.
 T
he quantity of carbon in a carbon pool.
Choice architecture
 T
he presentation of choices to consumers, 
and the impact that presentation has on consumer decision-making.Circular economy
 A system with minimal input and operational 
losses of materials and energy through extensive reduce
, reuse, 
recycling, and recovery activities. Ten strategies for circularity include: 
Refuse, Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, Recover.
Cities
 Cities are open systems
, continually exchanging resources, 
products and services, waste, people, ideas, and finances with the

hinterlands and broader world. Cities are complex, self-organising, adaptive, and constantly evolving. Cities also encompass multiple actors with varying responsibilities, capabilities and priorities, as well as processes that transcend the institutional sector-based approach to city administration. Cities are embedded in broader ecological, economic, technical, institutional, legal, and governance structures that enable or often constrain their systemic function, which cannot be separated from wider power relations. Urban processes of physical, social, and economic nature are causally interlinked, with interactions and feedbacks that result in both intended and unintended impacts on emissions. See also City region, Peri-urban areas and Urban.
Citizen science
 A voluntary participation of the public in the collection
 
and/or processing of data as part of a scientific study (Silvertown 2009).City region
 T
he areal extent of an individual city’s material

associations and economic or political influence. The city region 
concept accepts that rural livelihoods and land uses can be incorporated within the functional activities of a city. This will include dormitory settlements, sources for critical inputs of water, some food, and waste disposal. See also Cities, Region and Urban systems.
Climate
 Climate in a 
narrow sense is usually defined as the average 
weather, or more rigorously, as the statistical description in terms of 
the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization (WMO). The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system.
 A change in the state of the 
climate that can be

identified (e.g., by using statistical tests) by changes in the mean and/
or the variability of its properties and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use. Note that the United Nations Framework Convention on Climate Change (UNFCCC), in its Article 1, defines climate change as: ‘a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods’. The UNFCCC thus makes a distinction between climate change attributable to human activities altering the atmospheric composition and climate variability attributable to natural causes.
Climate change commitment
 T

he unavoidable future climate 
change resulting from inertia in the geophysical and socio-economic 
systems. Different types of climate change commitment are discussed in the literature. Climate change commitment is usually quantified in terms of the further change in temperature, but it includes other future changes, for example in the hydrological cycle, in extreme weather events, in extreme climate events, and in sea level.
Zero emissions commitment 
The zero emissions commitment is an estimate of the subsequent global warming that would result after anthropogenic emissions are set to zero. It is determined by both inertia in physical climate system components (ocean, cryosphere, land surface) and carbon cycle inertia. In its widest sense it refers to emissions of each climate forcer including greenhouses gases, aerosols and their precursors. The

climate response to this can be complex due to the different climate response time scale of each climate forcer. A specific sub-category of zero emissions commitment is the Zero CO
2 Emissions Commitment 
which refers to the climate system response to CO 2 emissions after 
setting these to net zero. The CO 2-only definition is of specific use in 
estimating remaining carbon budgets.

1798
Annex I  GlossaryAIClimate extreme (extreme weather or climate event) The 
occurrence of a value of a weather or climate variable above (or below) 
a  threshold value near the upper (or lower) ends of the range of 
observed values of the variable. By definition, the characteristics 
of what is called extreme weather may vary from place to place in an 
absolute sense. When a pattern of extreme weather persists for some 
time, such as a season, it may be classified as an extreme climate 
(e.g., high temperature, drought, or heavy rainfall over a season). For 
Climate finance There is no agreed definition of climate finance. 
The term ‘climate finance’ is applied to the financial resources 
devoted to addressing climate change by all public and private actors

from global to local scales, including international financial flows to 
developing countries to assist them in addressing climate change. 
Climate finance aims to reduce net greenhouse gas emissions and/
or to enhance adaptation and increase resilience to the impacts of 
current and projected climate change. Finance can come from private 
and public sources, channelled by various intermediaries, and is 
delivered by a range of instruments, including grants, concessional 
and non-concessional debt, and internal budget reallocations.
Climate governance See Governance.
Climate justice See Justice.
Climate model A qualitative or quantitative representation of 
the climate system based on the physical, chemical and biological 
climate system can be represented by models of varying complexity; 
that is, for any one component or combination of components

a spectrum or hierarchy of models can be identified, differing in such 
aspects as the number of spatial dimensions, the extent to which 
or the level at which empirical parametrisations are involved. There 
is an evolution towards more complex models with interactive 
chemistry and biology. Climate models are applied as a  research 
tool to study and simulate the climate and for operational purposes, 
including monthly, seasonal and interannual climate predictions. 
See also Simple climate model (SCM) and Emulators.
Climate projection Simulated response of the climate system 
to a  scenario of future emissions or concentrations of greenhouse 
gases (GHGs) and aerosols and changes in land use, generally 
derived using climate models. Climate projections are distinguished 
from climate predictions by their dependence on the emission/
concentration/radiative forcing scenario used, which is in turn based

on assumptions concerning, for example, future socio-economic and 
technological developments that may or may not be realised.
Climate sensitivity The change in the surface temperature 
in response to a  change in the atmospheric carbon dioxide (CO 2 ) 
concentration or other radiative forcing.
Transient climate response (TCR) 
in which atmospheric carbon dioxide (CO 2 ) increases at 1% yr –1 from pre-industrial to the time of a  doubling of atmospheric CO 2 
Transient climate response to cumulative CO2 emissions (TCRE) 
The transient surface temperature change per unit cumulative carbon 
dioxide (CO 2 ) emissions, usually 1000 GtC. TCRE combines both 

Climate services Climate services involve the provision of climate 
information in such a way as to assist decision-making. The service 
on scientifically credible information and expertise, has an effective 
access mechanism, and responds to user needs (Hewitt et al. 2012).
components: the atmosphere, the hydrosphere, the cryosphere, 
the lithosphere and the biosphere and the interactions between them. 
The climate system changes in time under the influence of its own internal 
dynamics and because of external forcings such as volcanic eruptions, 
solar variations, orbital forcing, and anthropogenic forcings such as the 
changing composition of the atmosphere and land-use  change.
Climate variability Deviations of climate variables from a given 
mean state (including the occurrence of extremes, etc.) at all spatial

and temporal scales beyond that of individual weather events. 
Variability may be intrinsic, due to fluctuations of processes internal to 
the climate system (internal variability), or extrinsic, due to variations 
in natural or anthropogenic external forcing (forced  variability). 
See also Climate change.
Co-benefits A positive effect that a policy or measure aimed at 
one objective has on another objective, thereby increasing the total 
benefit to society or the environment. Co-benefits are also referred 
to as ancillary benefits. See also Adverse side-effect and Trade-off.
CO2 equivalent (CO 2-eq) emission The amount of carbon dioxide 
(CO 2 ) emission that would have an equivalent effect on a specified 
key measure of climate change, over a  specified time horizon, as 
an emitted amount of another greenhouse gas (GHG) or a mixture 
of other GHGs. For a mix of GHGs, it is obtained by summing the 
CO2-equivalent emissions of each gas. There are various ways and

gas emission metric). CO 2-equivalent emissions are commonly used 
to imply that these emissions have an equivalent effect across all key 
measures of climate change.
[Note: Under the Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37], 
parties have agreed to use GWP100 values from the IPCC AR5 or GWP100 
values from a  subsequent IPCC Assessment Report to report aggregate 
emissions and removals of GHGs. In addition, parties may use other metrics 
to report supplemental information on aggregate emissions and removals 
of GHGs.]
Concentrations scenario See Scenario.
Conference of the Parties (COP) The supreme body of UN 
conventions, such as the United Nations Framework Convention on

1799
Glossary Annex IAIClimate Change (UNFCCC), comprising parties with a right to vote 
that have ratified or acceded to the convention.
 T
he robustness of a finding based on the type, amount, 
quality and consistency of evidence (e.g., mechanistic understanding, 
theory, data, models, expert judgement) and on the degree of agreement across multiple lines of evidence. In this report, confidence is expressed qualitatively (Mastrandrea et al. 2010).
 A farming system that promotes 
minimum soil disturbance (e
.g.,  by using no till practices), 
maintenance of a permanent soil cover, and diversification of plant 
species. It aims to prevent land degradation and regenerate degraded lands by enhancing biodiversity and natural biological processes above and below the ground surface, that contribute to increased water and nutrient use efficiency and improved and sustained crop production (FAO 2016).
Consumption-based emissions
 Emissions released to the

atmosphere in order to generate the goods and services consumed 
by a 
certain entity (e.g., a person, firm, country, or region). See also 
Production-based emissions.
 T
he ability of people, institutions, organisations, 
and systems, using available skills, values, beliefs, resources, and 
opportunities, to address, manage, and overcome adverse conditions in the short to medium term (UNISDR 2009; IPCC 2012). See also  Resilience.
Cost-benefit analysis
 A 
type of economic evaluation that

compares all monetised negative and positive impacts associated with a given action. Cost-benefit analysis enables comparison of different interventions, investments or strategies, and reveals how a  given investment or policy effort pays off for a particular person, company or country, or at a  global scale. Cost-benefit analyses representing society’s point of view are important for climate change decision-making, but there are difficulties in aggregating costs and benefits across different actors and across time scales. See also Discounting.
Cost-effectiveness analysis (CEA)
 A type of economic ev
aluation 
that compares the costs of different courses of action reaching the 
same outcome. In this report, CEA focuses on comparing the costs of mitigation strategies designed to meet a prespecified climate change 
mitigation goal (e.g., an emission-reduction target or a temperature stabilisation target).
Cumulative emissions
 T
he total amount of emissions released

over a specified period of time. See also Carbon budget and Transient 
climate response to cumulative CO 2 emissions (TCRE).
 Human actions to reduce 
carbon dioxide 
emissions from human activities.Decent Living Standard
 A set of minimal material requirements 
essential for achieving basic human 
well-being including nutrition, 
shelter, basic living conditions, clothing, healthcare, education, and 
mobility (Rao and Baer 2012; Rao and Min 2018; O’Neill et al. 2018).
Decoupling
 Decoupling (in relation to climate change) is where
 
economic growth is no longer strongly associated with another relevant 
indicator such as greenhouse gas emissions. Relative decoupling is where both these indicators grow but the other indicators grow more slowly than the economy. Absolute decoupling is where there is economic growth but there is a decline in the other indicator.
 Conversion of 
forest to non-forest.

for National Greenhouse Gas Inventories and their 2019 Refinement, and information provided by the United Nations Framework Convention on Climate Change (IPCC 2006, 2019; UNFCCC 2021a,b).] 
See also Reducing Emissions from Deforestation and Forest 
Degradation (REDD+).
Deliberative governance
 See 
Governance.
Demand
 Disciplinary approaches use the term in different w
ays. 
In economics, demand by a  consumer is willingness and ability to 
purchase in a marketplace. However, the motivation for purchase may vary and can include economic utility, welfare, Decent standard of 
living (DSL), or for the good/services.
Demand- and supply-side measures
Demand-side measures

Policies and programmes for influencing the demand for goods and/or services. In the energy sector, demand-side mitigation measures aim at reducing the amount of greenhouse gas emissions emitted per unit of energy service used.
Supply-side measures 
Policies and programmes for influencing how a  certain demand for goods and/or services is met. In the energy sector, supply-side mitigation measures aim at reducing the amount of greenhouse gas emissions emitted per unit of energy service produced.
Demand-side management
 See 
Demand-side measures.
 Land degradation
 in arid, semi-arid, and dry 
sub-humid areas resulting from many factors, including climatic 
variations and human activities (UNCCD 1994).
 T
here is a  diversity of approaches for

categorising countries on the basis of their level of development, and for defining terms such as ‘industrialised’, ‘developed’, or ‘developing’. Several categorisations are used in this report: (i) In the United Nations (UN) system, there is no established convention for the designation of developed and developing countries or areas. (ii) The UN Statistics Division specifies developed and developingregions based on common practice. In addition, specific countriesare designated as least developed countries, landlocked developingcountries, Small Island Developing States (SIDS), and transition

economies. Many countries appear in more than one of thesecategories. (iii) The World Bank uses income as the main criterion forclassifying countries as low, lower middle, upper middle, and highincome. (iv) The UN Development Programme (UNDP) aggregatesindicators for life expectancy, educational attainment, and incomeinto a single composite Human Development Index (HDI) to classifycountries as low, medium, high, or very high human development.
Development pathway
 See 
Pathways.

1800
Annex I  GlossaryAIDiet The kinds of food that follow a particular pattern that a person 
or community eats (FAO and Alliance of Bioversity International and 
CIAT, 2021).
Direct air capture (DAC) Chemical process by which a  pure 
carbon dioxide (CO 2 ) stream is produced by capturing CO 2 from the 
ambient air. See also Anthropogenic removals,  Carbon dioxide removal 
(CDR) and Direct air carbon dioxide capture and storage (DACCS).
Direct air carbon dioxide capture and storage (DACCS) Chemical 
process by which carbon dioxide (CO 2) is captured directly from 
the ambient air, with subsequent storage. Also known as direct air 
capture and storage (DACS). See also Anthropogenic removals, 
Carbon dioxide removal (CDR) and Direct air capture (DAC).
Direct and indirect services Direct Services: Services 
(e.g., passenger mobility) required by end-users (consumers). Indirect 
services: Services required (e.g., goods transport, manufacturing) for

provisioning systems of direct services.
Direct emissions Emissions that physically arise from activities 
within well-defined boundaries of, for instance, a  region, an economic 
sector, a company, or a process. See also Indirect emissions.
Disaster A ‘serious disruption of the functioning of a community 
or a society at any scale due to hazardous events interacting with 
losses and impacts’ (UNGA 2016). See also Exposure, Hazard, Risk 
and Vulnerability.
Disaster risk management (DRM) Processes for designing, 
implementing, and evaluating strategies, policies, and measures to 
improve the understanding of current and future disaster risk, foster 
disaster risk reduction and transfer, and promote continuous improvement 
in disaster preparedness, prevention and protection, response, and

recovery practices, with the explicit purpose of increasing human 
security, well-being, quality of life, and sustainable development (SD).
Discount rate See Discounting.
Discounting A mathematical operation that aims to make monetary 
(or other) amounts received or expended at different times (years) 
comparable across time. If the discount rate is positive, future values 
are given less weight than those today. The choice of discount rate(s) 
is debated as it is a judgement based on hidden and/or explicit values.
Disruptive innovation Demand-led technological change that 
leads to significant system change and is characterised by strong 
exponential growth.
Distributive equity See Equity.
ecosystems and the human population (due to low rainfall, high 
temperature, and/or wind).
Ecosystem A functional unit consisting of living organisms, their 
non-living environment and the interactions within and between

them. The components included in a  given ecosystem and its 
spatial boundaries depend on the purpose for which the ecosystem 
is defined: in some cases they are relatively sharp, while in others 
they are diffuse. Ecosystem boundaries can change over time. Ecosystems are nested within other ecosystems and their scale can 
range from very small to the entire biosphere. In the current era, 
most ecosystems either contain people as key organisms, or are 
influenced by the effects of human activities in their environment. 
See also Ecosystem services.
Ecosystem services Ecological processes or functions having 
monetary or non-monetary value to individuals or society at large. 
These are frequently classified as: (i) supporting services such as 
productivity or biodiversity maintenance; (ii) provisioning services 
such as food or fibre; (iii) regulating services such as climate 
regulation or carbon sequestration; and (iv) cultural services such as

tourism or spiritual and aesthetic appreciation. See also Ecosystem 
and Nature’s Contribution to People.
Ecosystem-based adaptation (EBA) The use of ecosystem 
vulnerability of people and ecosystems to climate change (Campbell 
et al. 2009).
Embodied (embedded) [emissions, water, land] The total 
emissions [water use, land use] generated [used] in the production 
of goods and services regardless of the location and timing of those 
emissions [water use, land use] in the production process. This 
includes emissions [water use, land use] within the country used 
to produce goods or services for the country’s own use, but also 
includes the emissions [water use, land use] related to the production 
of such goods or services in other countries that are then consumed 
in another country through imports. Such emissions [water, land] 
are termed ‘embodied’ or ‘embedded’ emissions, or, in some cases,

(particularly with water) as ‘virtual water use’ (Davis and Caldeira 
2010; Allan 2005; MacDonald et al. 2015).
Emission and Socioe-conomic Scenario Ensemble A set 
of modelled emission and socio-economic scenarios collected in 
a  database. The scenarios can come from a  single multi-model 
study with systematic variation of harmonised scenario designs 
(structured  ensemble) or from multiple studies in the literature 
(unstructured ensemble). Depending on the scope of the ensemble, 
variation of the results across the scenarios in the ensemble give 
an indication of the spread of results in the literature (unstructured 
ensemble), or an estimate of uncertainties due to different modelling 
structures and methodologies (structured ensemble).
Emission factor/Emissions intensity A coefficient that quantifies 
the emissions or removals of a gas per unit activity. Emission factors 
are often based on a  sample of measurement data, averaged to

develop a representative rate of emission for a given activity level 
under a given set of operating conditions.
Emission pathways See Pathways.
Emission trajectories A projected development in time of the 
emission of a  greenhouse gas (GHG) or group of GHGs, aerosols, and 
GHG precursors. See also Pathways.
Emissions See Anthropogenic emissions,  Direct emissions,  Cumulative 
emissions,  Indirect emissions,  Consumption-based emissions,  Production-
based emissions and Embodied (embedded) [emissions, water, land].
Emissions scenario See Scenario.

1801
Glossary Annex IAIEmulation Reproducing the behaviour of complex, process-
based models – namely, Earth System Models (ESMs) – via simpler 
approaches, using either emulators or simple climate models (SCMs). 
analytical possibilities, given that ESMs take a lot of computational 
resources for each simulation.
Emulators A broad class of heavily parametrised models (‘simple 
algorithms or other artificial intelligence approaches, designed to 
System Models (ESMs). The main application of emulators is 
to extrapolate insights from ESMs and observational constraints to 
a  larger set of emission scenarios. See also Emulation and Simple 
climate models (SCMs).
Enabling conditions (for adaptation and mitigation options)  
Conditions that enhance the feasibility of adaptation and mitigation

governance, and changes in human behaviour and lifestyles.
Energy access Access to clean, reliable and affordable energy 
services for cooking and heating, lighting, communications, and 
productive uses (with special reference to Sustainable Development 
Goal 7) (AGECC 2010). See also Traditional biomass.
Energy efficiency The ratio of output or useful energy or energy 
services or other useful physical outputs obtained from a  system, 
conversion process, transmission or storage activity to the input 
of energy (measured as kWh kWh–1, tonnes kWh–1 or any other 
physical measure of useful output like tonne-km transported). Energy 
efficiency is often described by energy intensity.
Energy poverty The absence of sufficient choice in accessing 
adequate, affordable, reliable, high quality, safe and environmentally

benign energy services to support economic and human development 
(Reddy 2000). See also Fuel poverty.
Energy security The goal of a  given country, or the global 
community as a  whole, to maintain an adequate, stable and 
predictable energy supply. Measures encompass safeguarding the 
sufficiency of energy resources to meet national energy demand at 
competitive and stable prices and the resilience of the energy supply; 
enabling development and deployment of technologies; building 
sufficient infrastructure to generate, store and transmit energy 
supplies and ensuring enforceable contracts of delivery.
Energy services A benefit or amenity (e.g., mobility, communication, 
thermal comfort) received as a result of energy or other resources use.
natural rate of removal of carbon dioxide (CO 2 ) from the atmosphere 
using silicate and carbonate rocks. The active surface area of these

soil, beaches or the open ocean. See also Anthropogenic removals 
and Carbon dioxide removal (CDR).
variations within the bounds of one or more sources of uncertainty, 
and that when averaged can provide a  more robust estimate 
of underlying behaviour. Ensemble techniques are used by the observational, reanalysis and modelling communities. See also 
Emission and Socio-economic Scenario Ensemble and Integrated 
Assessment Scenario Ensemble.
Enteric fermentation A natural part of the digestion process in 
ruminant animal species (domesticated and wild), such as cattle, 
buffalo, sheep, goats, antelope, etc. Microorganisms (bacteria, 
archaea, fungi, protozoa and viruses) present in the fore-stomach 
(reticulorumen or rumen) breakdown plant biomass to produce 
substrates that can be used by the animal for energy and growth

with methane produced as a by-product. Fermentation end-products 
such as hydrogen, carbon dioxide, formate and methyl-containing 
compounds are important substrates for the production of methane 
by the rumen’s methane-forming archaea (known as methanogens).
Equality A principle that ascribes equal worth to all human beings, 
including equal opportunities, rights, and obligations, irrespective 
of origins.
Inequality 
Uneven opportunities and social positions, and processes of 
discrimination within a  group or society, based on gender, class, 
ethnicity, age, and (dis)ability, often produced by uneven development. 
Income inequality refers to gaps between highest and lowest income 
earners within a country and between countries.
See also Equity and Fairness.
Equity The principle of being fair and impartial, and a  basis for 
understanding how the impacts and responses to climate change, 
including costs and benefits, are distributed in and by society in more

or less equal ways. Often aligned with ideas of equality, fairness 
and justice and applied with respect to equity in the responsibility 
for, and distribution of, climate impacts and policies across society, 
generations, and gender, and in the sense of who participates and 
controls the processes of decision-making.
Distributive equity 
Equity in the consequences, outcomes, costs and benefits of actions 
or policies. In the case of climate change or climate policies for 
different people, places and countries, including equity aspects of 
sharing burdens and benefits for mitigation and adaptation.
Gender equity 
Equity between women and men with regard to their rights, resources 
and opportunities. In the case of climate change, gender equity 
recognises that women are often more vulnerable to the impacts 
of climate change and may be disadvantaged in the process and 
outcomes of climate policy.
Inter-generational equity

Equity between generations. In the context of climate change, inter-
generational equity acknowledges that the effects of past and present 
emissions, vulnerabilities and policies impose costs and benefits for 
people in the future and of different age groups.
establish findings. In this report, the degree of evidence reflects the 
amount, quality and consistency of scientific/technical information on 
which the Lead Authors are basing their findings. See also Agreement, 
Confidence, Likelihood, and Uncertainty.

1802
Annex I  GlossaryAIExergy Capacity of energy flows to perform useful work. Exergy 
is a quality (versatility) indicator of energy flows which ranges from 
low (e.g., low-temperature heat, biomass) to high (e.g., electricity). 
Exergy efficiency describes how much useful work can be performed 
by a  particular energy flow in relation to the thermodynamic 
maximum possible. It can be determined for all energy flows and 
energy conversion steps, also including alternative service delivery 
systems (Grubler et al. 2012).
Exposure The presence of people; livelihoods; species or 
ecosystems; environmental functions, services, and resources; 
Extreme weather event An event that is rare at a  particular 
weather event would normally be as rare as, or rarer than, the 10th

or 90th percentile of a probability density function estimated from 
See also Climate extreme (extreme weather or climate event).
Fairness Impartial and just treatment without favouritism or 
discrimination in which each person is considered of equal worth 
with equal opportunity. See also Equality and Equity.
Feasibility In this report, feasibility refers to the potential for 
a  mitigation or adaptation option to be implemented. Factors 
influencing feasibility are context-dependent, temporally dynamic, 
and may vary between different groups and actors. Feasibility 
depends on geophysical, environmental-ecological, technological, 
economic, socio-cultural and institutional factors that enable or 
constrain the implementation of an option. The feasibility of options 
may change when different options are combined, and increase when

enabling conditions are strengthened. See also Enabling conditions 
(for adaptation and mitigation options).
Final energy The energy delivered to final users (firms, individuals, 
institutions), where it becomes usable energy in supplying energy 
services (e.g., light, heat, mobility). See also Primary energy.
Flexibility (demand and supply) Adjustment of energy load 
characteristics by technical and/or non-technical change to balance 
energy demand and supply.
Flexible governance See Governance.
Flood The overflowing of the normal confines of a stream or other 
for example, during storms and cyclones. Floods include river (fluvial) 
coastal floods, and glacial lake outburst floods (GLOFs).
Food loss and waste ‘The decrease in quantity or quality of

food’. Food waste is part of food loss and refers to discarding or 
alternative (non-food) use of food that is safe and nutritious for 
human consumption along the entire food supply chain, from 
primary production to end household consumer level. Food waste 
is recognised as a distinct part of food loss because the drivers that 
generate it and the solutions to it are different from those of food 
losses (FAO 2015).Food security A situation that exists when all people, at all times, 
have physical, social and economic access to sufficient, safe and 
nutritious food that meets their dietary needs and food preferences 
for an active and healthy life. The four pillars of food security are: 
availability; access; utilisation; and stability. The nutritional dimension 
is integral to the concept of food security (FAO 2009, 2018).
Access
Economic and/or physical access to food. Economic access is 
determined by disposable income, food prices and the provision of

production, stocks and net trade.
Stability
The stability of the other three dimensions over time. Even if individuals’ 
food intake is adequate today, they are still considered food-insecure if 
periodically they have inadequate access to food, risking deterioration 
of their nutrition status. Adverse weather conditions, political instability 
or economic factors (unemployment, rising food prices) may have an 
impact on individuals’ food security status.
good care and feeding practices, food preparation, diet diversity

and intra-household distribution of food. Combined with biological 
Food system All the elements (environment, people, inputs, 
processes, infrastructures, institutions, etc.) and activities that 
relate to the production, processing, distribution, preparation and 
consumption of food, and the output of these activities, including 
socio-economic and environmental outcomes (HLPE 2017). 
[Note: Whilst there is a  global food system (encompassing the totality of 
global production and consumption), each location’s food system is unique, 
being defined by that place’s mix of food produced locally, nationally, 
regionally or globally.]
wide differences in biogeophysical conditions, social structure and 
economics.

[Note: For a discussion of the term forest in the context of National GHG 
inventories, see the 2006 IPCC Guidelines for National GHG Inventories and 
their 2019 Refinement, and information provided by the United Nations 
2021a,b).]
including coal, oil, and natural gas.
Fuel poverty A condition in which a  household is unable to 
guarantee a certain level of consumption of domestic energy services

1803
Glossary Annex IAI(especially heating) or suffers disproportionate expenditure burdens 
to meet these needs. See also Energy poverty.
Fugitive emissions (oil and natural gas systems) The release 
of greenhouse gases that occur during the exploration, processing 
and delivery of fossil fuels to the point of final use. This excludes 
greenhouse gas emissions from fuel combustion for the production 
of useful heat or power. It encompasses venting, flaring, and leaks.
Gender equity See Equity.
Geothermal energy Accessible thermal energy stored in the 
Earth’s interior, in both rock and trapped steam or liquid water 
(hydrothermal resources), which may be used to generate electric 
energy in a thermal power plant, or to supply heat to any process 
requiring it. The main sources of geothermal energy are the residual 
energy available from planet formation and the energy continuously 
generated from radionuclide decay. See also Renewable energy.

Gini coefficient A statistical measure of dispersion in a distribution 
and degree of mathematical measure of inequality. For example, it can be 
used for measuring inequality in income, wealth, carbon emissions, and 
access to well-being defining services. The dimensionless GINI coefficient 
ranges between 0 (absolute equality) and 1 (absolute inequality).
Global carbon budget See Carbon budget.
Global mean surface air temperature (GSAT) Global average of 
near-surface air temperatures over land, oceans and sea ice. Changes 
in GSAT are often used as a measure of global temperature change in 
climate models. See also Global mean surface temperature (GMST).
Global mean surface temperature (GMST) Estimated global 
average of near-surface air temperatures over land and sea ice, and sea 
surface temperature (SST) over ice-free ocean regions, with changes 
normally expressed as departures from a  value over a  specified

reference period. See also Global mean surface air temperature (GSAT).
Global warming Global warming refers to the increase in 
global surface temperature relative to a  baseline reference period, 
averaging over a period sufficient to remove interannual variations 
(e.g., 20 or 30 years). A common choice for the baseline is 1850–1900 
(the earliest period of reliable observations with sufficient geographic 
application. See also Climate change and Climate variability.
radiative forcing following an emission of a  unit mass of a  given 
substance, accumulated over a chosen time horizon, relative to that 
of the reference substance, carbon dioxide (CO 2 ). The GWP thus 
remain in the atmosphere, and their effectiveness in causing radiative 
forcing. See also Greenhouse gas emission metric.

Governance The structures, processes, and actions through which 
private and public actors interact to address societal goals. This 
includes formal and informal institutions and the associated norms, 
rules, laws and procedures for deciding, managing, implementing 
scale, from global to local.Adaptive governance 
governance interactions that seek to maintain a  desired state in 
a social-ecological system.
Climate governance 
The structures, processes, and actions through which private and 
Deliberative governance 
Deliberative governance involves decision-making through inclusive 
public conversation which allows opportunity for developing policy 
options through public discussion rather than collating individual 
preferences through voting or referenda (although the latter

governance mechanisms can also be preceded and legitimated by 
public deliberation processes).
Flexible governance 
Strategies of governance at various levels, which prioritise the use 
of social learning and rapid feedback mechanisms in planning and 
policymaking, often through incremental, experimental and iterative 
management processes.
Multi-level governance 
The dispersion of governance across multiple levels of jurisdiction 
and decision-making, including, global, regional, national and local, 
Participatory governance 
A governance system that enables direct public engagement in 
decision-making using a variety of techniques, for example, referenda, 
community deliberation, citizen juries or participatory budgeting. The 
approach can be applied in formal and informal institutional contexts 
from national to local, but is usually associated with devolved 
decision-making (Fung and Wright 2003; Sarmiento and Tilly 2018).

Governance capacity The ability of governance institutions, 
leaders, and non-state and civil society to plan, coordinate, fund, 
implement, evaluate and adjust policies and measures over the 
short, medium and long term, adjusting for uncertainty, rapid 
change and wide-ranging impacts and multiple actors and demands. 
See also Governance.
Grazing land The sum of rangelands and pastures not considered 
as cropland, and subject to livestock grazing or hay production. It 
includes a  wide range of ecosystems, for example, systems with 
vegetation that fall below the threshold used in the forest land 
category, silvo-pastoral systems, as well as natural, managed 
grasslands and semi-deserts.
Green Climate Fund (GCF) The GCF was established by the 
16th Session of the Conference of the Parties (COP) in 2010 as an 
operating entity of the financial mechanism of the United Nations 
Framework Convention on Climate Change (UNFCCC), in accordance

with Article 11 of the Convention, to support projects, programmes 
and policies and other activities in developing country Parties. The 
Fund is governed by a Board and will receive guidance of the COP . 
See also Climate finance.

1804
Annex I  GlossaryAIGreen infrastructure See Infrastructure.
Greenhouse gas emission metric A simplified relationship used 
to quantify the effect of emitting a unit mass of a given greenhouse 
gas (GHG) on a specified key measure of climate change. A relative 
GHG emission metric expresses the effect from one gas relative to 
the effect of emitting a unit mass of a reference GHG on the same 
measure of climate change. There are multiple emission metrics, 
and the most appropriate metric depends on the application. GHG 
emission metrics may differ with respect to: (i) the key measure of 
climate change they consider; (ii) whether they consider climate 
outcomes for a specified point in time or integrated over a specified 
time horizon; (iii) the time horizon over which the metric is applied; 
(iv) whether they apply to a  single emission pulse, emissions 
sustained over a period of time, or a combination of both; and (v)

whether they consider the climate effect from an emission compared 
to the absence of that emission or compared to a reference emissions 
level or climate state.
[Note: Most relative GHG emission metrics (such as the global warming 
potential (GWP), global temperature change potential (GTP), global damage 
potential, and GWP*), use carbon dioxide (CO 2 ) as the reference gas. 
Emissions of non-CO 2 gases, when expressed using such metrics, are often 
referred to as ‘carbon dioxide equivalent’ emissions. A metric that establishes 
equivalence regarding one key measure of the climate system response 
to emissions does not imply equivalence regarding other key measures. 
The choice of a metric, including its time horizon, should reflect the policy 
objectives for which the metric is applied.]
Greenhouse gas neutrality Condition in which metric-weighted 
anthropogenic greenhouse gas (GHG) emissions associated 
with a  subject are balanced by metric-weighted anthropogenic

GHG removals. The subject can be an entity such as a country, an 
organisation, a district or a commodity, or an activity such as a service 
and an event. GHG neutrality is often assessed over the lifecycle, 
including indirect (‘scope 3’) emissions, but can also be limited to 
the emissions and removals, over a specified period, for which the 
[Note 1: Greenhouse gas neutrality and net-zero greenhouse gas emissions 
are overlapping concepts. The concepts can be applied at global or sub-global 
scales (e.g., regional, national and sub-national). At a global scale, the terms 
greenhouse gas neutrality and net-zero greenhouse gas emissions are 
equivalent. At sub-global scales, net-zero GHG emissions is generally applied

to emissions and removals under direct control or territorial responsibility 
of the reporting entity, while GHG neutrality generally includes emissions 
and removals within and beyond the direct control or territorial responsibility 
of the reporting entity. Accounting rules specified by GHG programmes or 
schemes can have a significant influence on the quantification of relevant 
Note 2: Under the Paris Rulebook (Decision 18/CMA.1, annex, paragraph 37), 
of GHGs.Note 3: In some cases, achieving greenhouse gas neutrality may rely on the 
supplementary use of offsets to balance emissions that remain after actions 
by the reporting entity are taken into account.]

See also Carbon neutrality, Greenhouse gas emission metric, 
Land use, Land-use change and forestry (LULUCF) and Net-zero 
Greenhouse gases (GHGs) Gaseous constituents of the 
atmosphere, both natural and anthropogenic, that absorb and emit 
emitted by the Earth’s surface, by the atmosphere itself, and by 
clouds. This property causes the greenhouse effect. Water vapour 
(H2O), carbon dioxide (CO 2 ), nitrous oxide (N 2O), methane (CH 4 ) and 
ozone (O 3 ) are the primary GHGs in the Earth’s atmosphere. Human-
made GHGs include sulphur hexafluoride (SF 6), hydrofluorocarbons 
(HFCs), chlorofluorocarbons (CFCs) and perfluorocarbons (PFCs); 
Montreal Protocol).
Grey infrastructure See Infrastructure.
Gross domestic product (GDP) The sum of gross value added,

at purchasers’ prices, by all resident and non-resident producers in 
the economy, plus any taxes and minus any subsidies not included 
in the  value of the products in a  country or a  geographic region 
for a  given period, normally one year. GDP is calculated without 
deducting for depreciation of fabricated assets or depletion and 
degradation of natural resources.
Halocarbons A collective term for the group of partially halogenated 
organic species, which includes the chlorofluorocarbons  (CFCs), 
hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), halons, 
methyl chloride and methyl bromide. Many of the halocarbons have 
large global warming potentials. The chlorine and bromine-containing 
halocarbons are also involved in the depletion of the ozone layer.
Human behaviour The responses of persons or groups to 
a particular situation, here likely to relate to climate change. Human 
behaviour covers the range of actions by individuals, communities,

organisations, governments and at the international level.
Human rights Rights that are inherent to all human beings, universal, 
inalienable, and indivisible, typically expressed and guaranteed by 
law. They include the right to life, economic, social, and cultural rights, 
and the right to development and self-determination (OHCHR 2018).
Human security A condition that is met when the vital core of 
human lives is protected, and when people have the freedom and 
capacity to live with dignity. In the context of climate change, the 
vital core of human lives includes the universal and culturally specific, 
material and non-material elements necessary for people to act on 
behalf of their interests and to live with dignity.
Human system Any system in which human organisations 
and institutions play a major role. Often, but not always, the term 
agricultural systems, urban systems, political systems, technological

applied in this report.

1805
Glossary Annex IAIHydropower Power harnessed from the flow of water. See also 
Renewable energy.
Impacts The consequences of realised risks on natural and human 
hazards (including extreme weather/climate events), exposure, and 
vulnerability. Impacts generally refer to effects on lives, livelihoods, 
health and well-being, ecosystems and species, economic, social 
outcomes, and can be adverse or beneficial. See also Adaptation, Loss 
Indigenous knowledge The understandings, skills and philosophies 
developed by societies with long histories of interaction with their 
natural surroundings. For many indigenous peoples, indigenous 
knowledge informs decision-making about fundamental aspects of

life, from day-to-day activities to longer-term actions. This knowledge 
is integral to cultural complexes, which also encompass language, 
systems of classification, resource use practices, social interactions, 
values, ritual and spirituality. These distinctive ways of knowing are 
important facets of the world’s cultural diversity (UNESCO 2018). 
See also Local knowledge.
Indirect emissions Emissions that are a  consequence of the 
activities within well-defined boundaries of, for instance, a  region, an 
economic sector, a company or process, but which occur outside the 
specified boundaries. For example, emissions are described as indirect 
if they relate to the use of heat but physically arise outside the 
boundaries of the heat user, or to electricity production but physically 
arise outside of the boundaries of the power supply sector. See also 
Direct emissions.
Indirect land-use change (iLUC) See Land-use change (LUC).

far-reaching social and economic consequences, beginning in Britain 
during the second half of the 18th century and spreading to Europe 
The industrial revolution marks the beginning of a strong increase 
in the use of fossil fuels, initially coal, and hence emission of carbon 
dioxide (CO 2 ).
Inequality See Equality.
Infrastructure The designed and built set of physical systems and 
corresponding institutional arrangements that mediate between 
people, their communities, and the broader environment to provide 
services that support economic growth, health, quality of life, and 
safety (Chester 2019; Dawson et al. 2018).
Blue infrastructure 
lakes and storm drainage, that provide ecological and hydrological

functions including evaporation, transpiration, drainage, infiltration, 
and temporary storage of runoff and discharge.
Green infrastructure 
features that can provide functions and services including air and water purification, temperature management, floodwater 
management and coastal defence often with co-benefits for people 
and biodiversity. Green infrastructure includes planted and remnant 
native vegetation, soils, wetlands, parks and green open spaces, as 
well as building and street-level design interventions that incorporate 
vegetation (Bobbins and Culwick 2016).
Grey infrastructure 
roads, tracks that underpin energy, transport, communications 
(including digital), built form, water and sanitation and solid waste 
management systems.

The social, cultural, and financial activities and institutions as well as 
associated property, buildings and artefacts and policy domains such 
as social protection, health and education that support well-being 
and public life (Latham and Layton 2019; Frolova et al. 2016).
Institutional capacity Building and strengthening individual 
organisations and providing technical and management training to 
support integrated planning and decision-making processes between 
organisations and people, as well as empowerment, social capital, 
and an enabling environment, including the culture, values and 
power relations (Willems and Baumert 2003). See also Governance 
and Institutions.
Institutions Rules, norms and conventions that guide, constrain 
or enable human behaviours and practices. Institutions can be 
formally  established, for instance through laws and regulations, or 
informally established, for instance by traditions or customs. Institutions

may spur, hinder, strengthen, weaken or distort the emergence, adoption 
and implementation of climate action and climate governance.
[Note: Institutions can also refer to a large organisation.]
See also Institutional capacity.
Integrated assessment A method of analysis that combines 
results and models from the physical, biological, economic and 
social sciences and the interactions among these components in 
a consistent framework to evaluate the status and the consequences 
of environmental change and the policy responses to it. See also 
Integrated assessment model (IAM).
knowledge from two or more domains into a single framework. They 
are one of the main tools for undertaking integrated assessments. 
One class of IAM used with respect to climate change mitigation may 
energy, land use and land-use change; interactions between sectors;

the economy as a whole; associated greenhouse gas (GHG) emissions 
and sinks; and reduced representations of the climate system. This 
the costs associated with climate change impacts, but includes less 
assess impacts and mitigation in a cost–benefit framework and have 
been used to estimate the social cost of carbon. See also Integrated 

1806
Annex I  GlossaryAIIntegrated Assessment Scenario Ensemble A set of modelled 
scenarios from an intercomparison of integrated assessment models 
(IAMs) based on a systematic variation of harmonised scenario designs.
Inter-generational equity See Equity.
Internet of Things (IoT) The network of computing devices 
embedded in everyday objects such as cars, phones and computers, 
connected via the internet, enabling them to send and receive data.
Irreversibility A perturbed state of a dynamical system is defined 
as irreversible on a given time scale if the recovery from this state 
due to natural processes takes substantially longer than the time 
scale of interest. See also Tipping point.
Just transitions A set of principles, processes and practices that 
aim to ensure that no people, workers, places, sectors, countries or 
regions are left behind in the transition from a high-carbon to a low-
carbon economy. It stresses the need for targeted and proactive

measures from governments, agencies, and authorities to ensure that 
any negative social, environmental or economic impacts of economy-
wide transitions are minimised, whilst benefits are maximised for 
those disproportionally affected. Key principles of just transitions 
include: respect and dignity for vulnerable groups; fairness in energy 
access and use, social dialogue and democratic consultation with 
relevant stakeholders; the creation of decent jobs; social protection; 
and rights at work. Just transitions could include fairness in energy, 
land use and climate planning and decision-making processes; 
economic diversification based on low-carbon investments; realistic 
training/retraining programmes that lead to decent work; gender-
specific policies that promote equitable outcomes; the fostering 
of international cooperation and coordinated multilateral actions; 
and the eradication of poverty. Lastly, just transitions may embody

UNFCCC 2016).
Justice Justice is concerned with ensuring that people get what 
is due to them, setting out the moral or legal principles of fairness 
and equity in the way people are treated, often based on the ethics 
and values of society.
Climate justice 
Justice that links development and human rights to achieve a human-
centred approach to addressing climate change, safeguarding the 
rights of the most vulnerable people and sharing the burdens and 
benefits of climate change and its impacts equitably and fairly 
(MRFCJ 2018).
Kaya identity In this identity, global emissions are equal to 
the population size, multiplied by per  capita output (gross world 
product), multiplied by the energy intensity of production, multiplied 
by the carbon intensity of energy.
natural resources (soil, near-surface air, vegetation and other biota,

settlements and infrastructure that operate within that system (FAO 
2007; UNCCD 1994).
Land cover The biophysical coverage of land (e.g., bare soil, rocks, 
forests, buildings and roads or lakes). Land cover is often categorised in broad land-cover classes (e.g., deciduous forest, coniferous forest, 
mixed forest, grassland, bare ground). 
[Note: In some literature, land cover and land use are used interchangeably, 
but the two represent distinct classification systems. For example, the land 
cover class woodland can be under various land uses such as livestock 
grazing, recreation, conservation, or wood harvest.]
Land cover change Change from one land cover class to another, 
due to change in land use or change in natural conditions (Pongratz 
et al. 2018).
Land degradation A negative trend in land condition, caused by 
direct or indirect human-induced processes including anthropogenic

climate change, expressed as long-term reduction or loss of at least 
one of the following: biological productivity, ecological integrity or 
value to humans. 
[Note: This definition applies to forest and non-forest land. Changes in land 
condition resulting solely from natural processes (such as volcanic eruptions) 
are not considered to be land degradation. Reduction of biological productivity 
or ecological integrity or value to humans can constitute degradation, but any 
one of these changes need not necessarily be considered degradation.] 
See also Desertification.
Land degradation neutrality A state whereby the amount and 
quality of land resources necessary to support ecosystem functions 
and services and enhance food security remain stable or increase within 
specified temporal and spatial scales and ecosystems (UNCCD 2020).
Land management Sum of land-use practices (e.g.,  sowing, 
fertilising, weeding, harvesting, thinning, clear-cutting) that take

place within broader land-use categories (Pongratz et al. 2018).
Land management change 
A change in land management that occurs within a  land-use category.
Land potential The inherent, long-term potential of the land to 
sustainably generate ecosystem services, which reflects the capacity 
and resilience of the land-based natural capital, in the face of ongoing 
environmental change (UNEP 2016).
Land rehabilitation Direct or indirect actions undertaken with the 
aim of reinstating a level of ecosystem functionality, where the goal 
is provision of goods and services rather than ecological restoration 
(McDonald et al. 2016).
Land restoration The process of assisting the recovery of land 
from a degraded state (IPBES 2018; McDonald et al. 2016).
to a parcel of land. The term land use is also used in the sense of 
the social and economic purposes for which land is managed

(e.g.,  grazing, timber extraction, conservation and city dwelling). 
In national greenhouse gas (GHG) inventories, land use is classified 
for details (IPCC 2006, 2019)).
Land use, land-use change and forestry (LULUCF) In the 
context of national greenhouse gas (GHG) inventories under

1807
Glossary Annex IAIthe United Nations Framework Convention on Climate Change 
(UNFCCC 2019), LULUCF is a  GHG inventory sector that covers 
anthropogenic emissions and removals of GHG in managed lands, 
excluding non-CO 2 agricultural emissions. Following the 2006 IPCC 
Guidelines for National GHG Inventories and their 2019 Refinement, 
‘anthropogenic’ land-related GHG fluxes are defined as all those 
occurring on ‘managed land’, that is, ‘where human interventions 
and practices have been applied to perform production, ecological 
or social functions’. Since managed land may include carbon dioxide 
(CO 2 ), removals not considered as ‘anthropogenic’ in some of the 
scientific literature assessed in this report (e.g., removals associated 
with CO 2 fertilisation and N deposition), the land-related net GHG 
emission estimates from global models included in this report are not 
necessarily directly comparable with LULUCF estimates in National 
GHG Inventories (IPCC 2006, 2019).

Land-use change (LUC) The change from one land use category 
to another. Note that, in some scientific literature, land-use change 
encompasses changes in land-use categories as well as changes in 
land management. See also Afforestation, Agriculture, Forestry and 
Other Land Use (AFOLU), Deforestation, Land use, land-use change 
and forestry (LULUCF), and Reforestation.
Indirect land-use change (iLUC) 
Land-use change outside the area of focus that occurs as 
a consequence of change in use or management of land within the 
may occur elsewhere to replace the former agricultural production.
Latent heat flux The turbulent flux of heat from the Earth’s surface to 
the atmosphere that is associated with evaporation or condensation of 
water vapour at the surface; a component of the surface energy budget.

Leakage The effects of policies that result in a displacement of the 
environmental impact, thereby counteracting the intended effects of 
the initial policies.
Leapfrogging The ability of developing countries to bypass 
intermediate technologies and jump straight to advanced clean 
technologies.
Lifecycle assessment (LCA) Compilation and evaluation of the 
inputs, outputs and the potential environmental impacts of a product 
or service throughout its lifecycle (ISO 2018).
Likelihood The chance of a  specific outcome occurring, where 
this report using a standard terminology (Mastrandrea et al. 2010). 
See also Agreement, Confidence, Evidence, and Uncertainty.
entitlements and assets to which people have access. Such assets can

be categorised as human, social, natural, physical or financial.
Local knowledge (LK) The understandings and skills developed 
by individuals and populations, specific to the places where they 
live. Local knowledge informs decision-making about fundamental 
aspects of life, from day-to-day activities to longer-term actions. 
This knowledge is a key element of the social and cultural systems 
which influence observations of and responses to climate change; it also informs governance decisions (UNESCO 2018). See also 
Indigenous knowledge.
Lock-in A situation in which the future development of a system, 
including infrastructure, technologies, investments, institutions, and 
behavioural norms, is determined or constrained (‘locked in’) by 
historic developments. See also Path dependence.
greenhouse gases with long atmospheric lifetimes. This set of 
compounds includes carbon dioxide (CO 2 ) and nitrous oxide (N 2O),

together with some fluorinated gases. They have a warming effect on 
climate. These compounds accumulate in the atmosphere at decadal 
to centennial time scales, and their effect on climate hence persists for 
decades to centuries after their emission. On time scales of decades 
to a century already emitted emissions of long-lived climate forcers 
can only be abated by greenhouse gas removal (GGR).
Loss and Damage, and losses and damages Research has taken 
Loss and Damage (capitalised letters) to refer to political debate 
under the United Nations Framework Convention on Climate Change 
(UNFCCC) following the establishment of the Warsaw International 
Mechanism for Loss and Damage in 2013, which is to ‘address loss 
Lowercase letters (losses and damages) have been taken to refer

broadly to harm from (observed) impacts and (projected) risks, and 
can be economic or non-economic (Mechler et al. 2018).
to increased risk of adverse climate-related outcomes, including via 
increased greenhouse gas (GHG) emissions, increased vulnerability 
to climate change, or diminished welfare, now or in the future. 
Malnutrition Deficiencies, excesses, or imbalances in a  person’s 
intake of energy and/or nutrients. The term malnutrition addresses 
three broad groups of conditions: undernutrition, which includes 
wasting (low weight-for-height), stunting (low height-for-age) and 
underweight (low weight-for-age); micronutrient-related malnutrition, 
which includes micronutrient deficiencies (a lack of important vitamins 
diet-related noncommunicable diseases (such as heart disease, stroke,

diabetes and some cancers) (WHO 2018). Micronutrient deficiencies 
Managed forest Forests subject to human interventions (notably 
silvicultural management such as planting, pruning, thinning), timber 
and fuelwood harvest, protection (fire suppression, insect suppression) 
and management for amenity values or conservation, with defined 
geographical boundaries (Ogle et al. 2018). See also Managed land.
[Note: For a  discussion of the term ‘forest’ in the context of National 
GHG inventories, see the 2006 IPCC Guidelines for National GHG Inventories 
(IPCC 2006).]
Managed grassland Grasslands on which human interventions 
are carried out, such as grazing domestic livestock or hay removal.

1808
Annex I  GlossaryAIManaged land In the context of national greenhouse gas (GHG)  
inventories under the United Nations Framework Convention on 
Climate Change (UNFCCC), the 2006 IPCC Guidelines for National 
GHG Inventories (IPCC 2006) defines managed land ‘where human 
interventions and practices have been applied to perform production, 
ecological or social functions’. IPCC (2006) defines anthropogenic GHG 
emissions and removals in the LULUCF sector as all those occurring 
on ‘managed land’. The key rationale for this approach is that the 
preponderance of anthropogenic effects occurs on managed lands. 
[Note: More details can be found in IPCC 2006 Guidelines for National GHG 
Inventories, Volume 4, Chapter 1.]
Market failure When private decisions are based on market prices 
that do not reflect the real scarcity of goods and services but rather 
reflect market distortions, they do not generate an efficient allocation

of resources but cause welfare losses. A market distortion is any event 
in which a market reaches a market clearing price that is substantially 
different from the price that a market would achieve while operating 
under conditions of perfect competition and state enforcement 
of legal contracts and the ownership of private property. Examples of 
factors causing market prices to deviate from real economic scarcity 
are environmental externalities, public goods, monopoly power, 
information asymmetry, transaction costs, and non-rational behaviour.
Material substitution Replacement of one material (including an 
energy carrier used as a feedstock) by another, due to scarcity, price, 
technological change, or because of lower environmental impacts or 
Measurement, Reporting and Verification (MRV)
‘Processes of data collection over time, providing basic datasets, 
including associated accuracy and precision, for the range of

field observations, detection through remote sensing and interviews’ 
(UN-REDD 2009).
Reporting
‘The process of formal reporting of assessment results to the UNFCCC, 
according to predetermined formats and established standards, 
especially the Intergovernmental Panel on Climate Change (IPCC) 
Guidelines and GPG (Good Practice Guidance)’ (UN-REDD 2009).
Verification
‘The process of formal verification of reports, for example, the 
established approach to verify national communications and national 
inventory reports to the UNFCCC’ (UN-REDD 2009).
Megacity Urban agglomerations with 10 million inhabitants or 
more. See also City.
Methane (CH 4) The greenhouse gas (GHG) methane is the major 
Significant anthropogenic emissions also occur as a result of animal 
husbandry and paddy rice production. Methane is also produced

such as in wetlands. Under future global warming, there is potential 
for increased methane emissions from thawing permafrost, wetlands 
and sub-sea gas hydrates. See also Short-lived climate forcers (SLCFs).Migrant Any person who is moving or has moved across an 
international border or within a State away from his/her habitual place 
of residence, regardless of: (1) the person’s legal status; (2) whether 
the movement is voluntary or involuntary; (3) what the causes for the 
movement are; or (4) what the length of the stay is (IOM 2018).
Migration (of humans) Movement of a person or a group of persons, 
either across an international border, or within a State. It is a population 
movement, encompassing any kind of movement of people, whatever 
its length, composition and causes; it includes migration of refugees, 
displaced persons, economic migrants, and persons moving for other

purposes, including family reunification (IOM 2018).
Mitigation (of climate change) A human intervention to reduce 
emissions or enhance the sinks of greenhouse gases.
Mitigation measures In climate policy, mitigation measures are 
technologies, processes or practices that contribute to mitigation, 
for example, renewable energy technologies, waste minimisation 
processes, and public transport commuting practices.
Mitigation option A technology or practice that reduces greenhouse 
gas emissions or enhances sinks.
Mitigation pathways See Pathways.
Mitigation potential The quantity of net greenhouse gas emission 
reductions that can be achieved by a given mitigation option relative 
to specified emission baselines.
[Note: Net greenhouse gas emissions reduction is the sum of reduced emissions 
and/or enhanced sinks.]
See also Sequestration potential.
The mitigation potential constrained by biological, geophysical and

exceed the social costs, taking into account a social discount rate and 
The mitigation potential constrained by biogeophysical limits as well as 
Mitigation scenario See Scenario.
Multi-level governance See Governance.
Narrative See Storyline.
Nature’s contributions to people (NCP) All the contributions, 
both positive and negative, of living nature (i.e.,  diversity of

organisms, ecosystems, and their associated ecological and 
evolutionary processes) to the quality of life for people. Beneficial 
contributions from nature include such things as food provision,

1809
Glossary Annex IAIwater purification, flood control, and artistic inspiration, whereas 
detrimental contributions include disease transmission and predation 
that damages people or their assets. Many NCP may be perceived as 
benefits or detriments depending on the cultural, temporal or spatial 
context (Díaz et al. 2018). See also Ecosystem services.
Nature-based solutions Actions to protect, sustainably manage 
and restore natural or modified ecosystems that address societal 
challenges effectively and adaptively, simultaneously providing 
human well-being and biodiversity benefits (IUCN 2016). See also 
Biodiversity and Ecosystem.
Net negative greenhouse gas emissions A situation of net 
negative greenhouse gas emissions is achieved when metric-
weighted anthropogenic greenhouse gas (GHG) removals exceed 
metric-weighted anthropogenic GHG emissions. Where multiple 
GHG are involved, the quantification of net emissions depends on

the metric chosen to compare emissions of different gases (such 
as global warming potential, global temperature change potential, 
and others, as well as the chosen time horizon). See also Carbon 
dioxide removal (CDR), Greenhouse gas emission metric, Net-zero 
CO2 emissions; Net-zero greenhouse gas emissions, and Negative 
Net-zero CO 2 emissions Condition in which anthropogenic 
carbon dioxide (CO 2 ) emissions are balanced by anthropogenic CO 2 
removals over a specified period.
[Note: Carbon neutrality and net-zero CO 2 emissions are overlapping concepts. 
The concepts can be applied at global or sub-global scales (e.g.,  regional, 
national and sub-national). At a global scale, the terms carbon neutrality and 
net-zero CO 2 emissions are equivalent. At sub-global scales, net-zero CO 2 
emissions is generally applied to emissions and removals under direct control 
or territorial responsibility of the reporting entity, while carbon neutrality

generally includes emissions and removals within and beyond the direct 
control or territorial responsibility of the reporting entity. Accounting rules 
specified by GHG programmes or schemes can have a significant influence on 
the quantification of relevant CO 2 emissions and removals.]
See also Carbon neutrality, Land use, land-use change and forestry 
(LULUCF) and Net-zero greenhouse gas emissions.
Net-zero greenhouse gas emissions Condition in which metric-
weighted anthropogenic greenhouse gas (GHG) emissions are balanced 
by metric-weighted anthropogenic GHG removals over a  specified 
period. The quantification of net-zero GHG emissions depends on the 
GHG emission metric chosen to compare emissions and removals of 
[Note 1: Greenhouse gas neutrality and net-zero GHG emissions are 
overlapping concepts. The concept of net-zero GHG emissions can be applied

at global or sub-global scales (e.g.,  regional, national and sub-national). 
At a global scale, the terms GHG neutrality and net-zero GHG emissions are 
equivalent. At sub-global scales, net-zero GHG emissions is generally applied 
to emissions and removals under direct control or territorial responsibility of 
the reporting entity, while GHG neutrality generally includes anthropogenic 
emissions and anthropogenic removals within and beyond the direct control 
or territorial responsibility of the reporting entity. Accounting rules specified 
by GHG programmes or schemes can have a  significant influence on the 
quantification of relevant emissions and removals.Note 2: Under the Paris Rulebook (Decision 18/CMA.1, annex, paragraph 37), 
emissions and removals of GHGs. In addition, parties may use other metrics

of GHGs.]
See also Greenhouse gas neutrality, Net-zero CO 2 emissions, and 
Land use, land-use change and forestry (LULUCF).
Nitrous oxide (N 2O) The main anthropogenic source of N 2O, 
a greenhouse gas (GHG), is agriculture (soil and animal manure 
treatment, fossil fuel combustion, and chemical industrial processes. 
N2O is also produced naturally from a wide variety of biological sources 
in soil and water, particularly microbial action in wet tropical forests.
Non-overshoot pathways See Pathways.
Ocean alkalinisation/Ocean alkalinity enhancement A proposed 
carbon dioxide removal (CDR) method that involves deposition of 
alkaline minerals or their dissociation products at the ocean surface. 
This increases surface total alkalinity, and may thus increase ocean 
carbon dioxide (CO 2 ) uptake and ameliorate surface ocean acidification.

See also Anthropogenic removals.
Ocean fertilisation A proposed carbon dioxide removal (CDR) 
the near-surface ocean with the aim of sequestering additional CO 2 
from the atmosphere through biological production. Methods include 
the additional carbon needs to reach the deep ocean where it has 
the potential to be sequestered on climatically relevant time scales. 
Offset (in climate policy) The reduction, avoidance or removal of 
a unit of greenhouse gas (GHG) emissions by one entity, purchased 
by another entity to counterbalance a unit of GHG emissions by that 
other entity. Offsets are commonly subject to rules and environmental 
integrity criteria intended to ensure that offsets achieve their stated 
mitigation outcome. Relevant criteria include, but are not limited to,

the avoidance of double counting and leakage, use of appropriate 
baselines, additionality, and permanence or measures to address 
impermanence. See also Greenhouse gas emission metric and 
Carbon neutrality.
Organic farming An agricultural production system that aims 
to utilise natural processes and cycles to limit off-farm and notably 
synthetic inputs, while also aiming to enhance agroecosystems and 
society. Organic farming is often legally defined and governed by 
standards, typically guided by principles outlined by the International 
Federation of Organic Agriculture Movements (IFOAM  – Organics 
International) (IFOAM – Organics International 2014).
Overshoot pathways See Pathways.
Ozone (O 3) The triatomic form of oxygen, and a  gaseous 
atmospheric constituent. In the troposphere, O 3 is created both 
from human activities (e.g.,  smog). Tropospheric O 3 acts as

a greenhouse gas (GHG). In the stratosphere, O 3 is created by the 
interaction between solar ultraviolet radiation and molecular oxygen

1810
Annex I  GlossaryAI(O2). Stratospheric O 3 plays a  dominant role in the stratospheric 
radiative balance. Its concentration is highest in the ozone layer.
Pareto optimum A state in which no one’s welfare can be 
increased without reducing someone else’s welfare.
Participatory governance See Governance.
Particulate matter (PM) Atmospheric aerosols involved in 
air pollution issues. Of greatest concern for health are particles of 
aerodynamic diameter less than or equal to 10 micrometers, usually 
designated as PM10 and particles of diameter less than or equal to 
2.5 micrometers, usually designated as PM2.5.
Path dependence The generic situation where decisions, events, 
or outcomes at one point in time constrain adaptation, mitigation, 
or other actions or options at a later point in time. See also Lock-in.
systems towards a future state. Pathway concepts range from sets

of quantitative and qualitative scenarios or narratives of potential 
scales. See also Scenario and Storyline.
A pathway of emissions of greenhouse gases and other climate 
warming either remaining below 1.5°C or returning to 1.5°C by 
Adaptation pathways 
A series of adaptation choices involving trade-offs between short-
term and long-term goals and values. These are processes of 

Climate-resilient pathways 
with climate change.
Development pathways 
decisions being made and actions being taken at all levels of societal 
structure, as well due to the emergent dynamics within and between 
institutions, cultural norms, technological systems and other drivers 
of behavioural change. 
See also Shifting development pathways (SDPs) and Shifting 
development pathways to sustainability (SDPS).
21st century.
Mitigation pathways
A temporal evolution of a set of mitigation scenario features, such as 
greenhouse gas emissions and socio-economic development.Non-overshoot pathways 
Pathways that stay below a specified concentration, forcing, or global

warming level during a specified period of time (e.g., until 2100).
Pathways that first exceed a  specified concentration, forcing, or 
before the end of a  specified period of time (e.g.,  before 2100). 
Sometimes the magnitude and likelihood of the overshoot is also 
over a period of at least one decade and up to several decades.
active gases, as well as land use/land cover (Moss et al. 2010). The 
word representative signifies that each RCP provides only one of

many possible scenarios that would lead to the specific radiative 
forcing characteristics. The term pathway emphasises that not only 
the long-term concentration levels are of interest, but also the 
trajectory taken over time to reach that outcome (Moss et al. 2010).
extending up to 2100, for which integrated assessment models 
Four  RCPs produced from integrated assessment models were 
selected from the published literature and used in the Fifth IPCC 
Assessment, and are also used in this Assessment for comparison, 
high (>4°C) warming best-estimates by the end of the 21st century:

RCP2.6, RCP4.5 and RCP6.0 and RCP8.5.
approximately 3 W m–2 and then declines to be limited at 2.6 W m–2 
in 2100 (the corresponding Extended Concentration Pathway, or 
ECP , has constant emissions after 2100).
• RCP8.5: One high pathway which leads to >8.5 W m–2 in 2100 (the 
corresponding ECP has constant emissions after 2100 until 2150 
and constant concentrations after 2250).
See also Shared socio-economic pathways (SSPs) (under Pathways).
SSPs have been developed to complement the Representative 
Concentration Pathways (RCPs). By design, the RCP emission and 
concentration pathways were stripped of their association with

a certain socio-economic development. Different levels of emissions 
and climate change along the dimension of the RCPs can hence 
be explored against the backdrop of different socio-economic 
development pathways (SSPs) on the other dimension in a matrix. 
This integrative SSP-RCP framework is now widely used in the climate 
impact and policy analysis literature, where climate projections

1811
Glossary Annex IAIobtained under the RCP scenarios are analysed against the backdrop 
of various SSPs. As several emission updates were due, a  new set 
of emission scenarios was developed in conjunction with the SSPs. 
Hence, the abbreviation SSP is now used for two things: On the one 
hand SSP1, SSP2, …, SSP5 are used to denote the five socio-economic 
within an integrated assessment model. Those SSP  scenarios are 
bare of climate policy assumption, but in combination with so-called 
shared policy assumptions (SPAs), various approximate radiative 
forcing levels of 1.9, 2.6, …, or 8.5 W m–2 are reached by the end of 
the century, respectively.
Transformation pathways 
Trajectories describing consistent sets of possible futures of

greenhouse gas (GHG) emissions, atmospheric concentrations, 
or global mean surface temperatures implied from mitigation and 
adaptation actions associated with a  set of broad and irreversible 
economic, technological, societal, and behavioural changes. This can 
encompass changes in the way energy and infrastructure are used 
and produced, natural resources are managed and institutions are set 
up, and in the pace and direction of technological change.
Peri-urban areas Dynamic transition zones that have intense 
interaction between rural and urban economies, activities, households, 
and lifestyles. Neither fully rural or urban (Seto et al. 2010).
Policies (for climate change mitigation and adaptation)  
national and subnational public agencies, and with the private 
sector. Policies for adaptation and mitigation often take the form of

economic incentives, regulatory instruments, and decision-making 
and engagement processes.
Political economy The set of interlinked relationships between 
people, the state, society and markets as defined by law, politics, 
economics, customs and power that determine the outcome of trade 
and transactions and the distribution of wealth in a country or economy.
Pool, carbon and nitrogen A reservoir in the Earth System where 
elements, such as carbon and nitrogen, reside in various chemical forms 
for a period of time. See also Sequestration,  Sink,  Source and Uptake.
Poverty A complex concept with several definitions stemming from 
different schools of thought. It can refer to material circumstances 
(such as need, pattern of deprivation or limited resources), economic 
conditions (such as standard of living, inequality or economic position) 
and/or social relationships (such as social class, dependency, exclusion, 
lack of basic security or lack of entitlement).

Poverty eradication A set of measures to end poverty in all its 
forms everywhere. See also Sustainable Development Goals (SDGs).
gases (GHGs) or aerosols, but that have an effect on GHG or aerosol 
regulating their production or destruction rates.Pre-industrial (period) The multi-century period prior to the 
period 1850–1900 is used to approximate pre-industrial global mean 
surface temperature (GMST). See also Industrial revolution.
Primary energy The energy that is embodied in resources as they 
exist in nature (e.g.,  coal, biomass uranium, solar radiation, wind, 
ocean currents) (Grubler et al. 2012). 
[Note: Primary energy is defined in several alternative ways. The method 
used in this report is the direct equivalent method, which counts one unit

of secondary energy provided from non-combustible sources as one unit of 
primary energy. For more details on the methodology, see Section 7 in Working 
Group III Annex II.]
See also Final energy.
Primary production The synthesis of organic compounds by plants 
and microbes, on land or in the ocean, primarily by photosynthesis 
using light and carbon dioxide (CO 2 ) as sources of energy and 
carbon respectively. It can also occur through chemosynthesis, using 
chemical energy, for example, in deep sea vents.
Private costs Costs carried by individuals, companies or other 
private entities that undertake an action, whereas social costs include 
additionally the external costs on the environment and on society as 
a whole. Quantitative estimates of both private and social costs may 
be incomplete, because of difficulties in measuring all relevant effects.
Production-based emissions Emissions released to the atmosphere

for the production of goods and services by a certain entity (e.g., a person, 
firm, country, or region). See also Consumption-based emissions.
Projection A potential future evolution of a  quantity or set of 
quantities, often computed with the aid of a model. Unlike predictions, 
future socio-economic and technological developments that may or may 
not be realised. See also Climate projection,  Pathways and Scenario.
Prosumers A consumer that also produces energy and inputs 
energy to the system, for which it is an active agent in the energy 
system and market.
upward, radiative flux (expressed in W m–2) due to a  change in 
an external driver of climate change, such as a  change in the 
concentration of carbon dioxide (CO 2 ), the concentration of volcanic 
aerosols or in the output of the Sun. The stratospherically adjusted

termed the ‘effective radiative forcing’.
Rebound effect Phenomena whereby the reduction in energy 
consumption or emissions (relative to a baseline) associated with the 
implementation of mitigation measures in a jurisdiction is offset to 
some degree through induced changes in consumption, production, 
and prices within the same jurisdiction. The rebound effect is most 
typically ascribed to technological energy efficiency improvements.

1812
Annex I  GlossaryAIReducing Emissions from Deforestation and Forest Degradation 
(REDD+) REDD+ refers to reducing emissions from deforestation; 
reducing emissions from forest degradation; conservation of forest 
carbon stocks; sustainable management of forests; and enhancement 
of forest carbon stocks (see UNFCCC decision 1/CP .16, para. 70).
Reference period A time period of interest, or a period over which 
some relevant statistics are calculated. A reference period can be 
used as a baseline period or as a comparison to a baseline period.
A time period against which differences are calculated (e.g., expressed 
as anomalies relative to a baseline).
Reference scenario See Scenario.
Reforestation Conversion to forest of land that has previously 
contained forests but that has been converted to some other use. 
afforestation, reforestation and deforestation, see the 2006 IPCC Guidelines

information provided by the United Nations Framework Convention on 
See also Anthropogenic removals, Carbon dioxide removal (CDR) 
and Reducing Emissions from Deforestation and Forest Degradation 
(REDD+).
Regenerative agriculture A universally agreed definition of 
this relatively new farming approach has yet to be established, 
but regenerative agriculture broadly refers to the implementation 
of varying combinations of agricultural management practices, to 
ensure the continued restoration and enhancement of soil health, 
biodiversity and ecosystem functioning, in conjunction with profitable 
agricultural production.
Region Land and/or ocean area characterised by specific 
geographical and/or climatological features. The climate of a region 
emerges from a multi-scale combination of its own features, remote

Remaining carbon budget See Carbon budget.
Renewable energy (RE) Any form of energy that is replenished 
by natural processes at a rate that equals or exceeds its rate of use.
Variable renewable energy (VRE) 
Renewable energy sources such as wind and solar energy whose 
output is determined by weather, in contrast to ‘dispatchable’ 
generators that adjust their output as a  reaction to economic 
incentives. Variable renewables have also been termed intermittent, 
fluctuating, or non-dispatchable (Hirth 2013).
Representative Concentration Pathways (RCPs) See Pathways.
Resilience The capacity of interconnected social, economic and 
ecological systems to cope with a hazardous event, trend or disturbance, 
responding or reorganising in ways that maintain their essential 
function, identity and structure. Resilience is a positive attribute when 
it maintains capacity for adaptation, learning and/or transformation

(Arctic Council 2016). See also Hazard,  Risk and Vulnerability.Resource cascade Tracking resource use (materials, energy, water, 
etc.), efficiency and losses through all conversion steps from primary 
resource extraction to various conversion steps, all the way to final 
service delivery.
ecological systems, recognising the diversity of values and objectives 
associated with such systems. In the context of climate change, 
risks can arise from potential impacts of climate change as well as 
human responses to climate change. Relevant adverse consequences 
include those on lives, livelihoods, health and well-being, economic, 
(including ecosystem services), ecosystems and species.
interactions between climate-related hazards with the exposure

to uncertainty in terms of magnitude and likelihood of occurrence, 
changes and human decision-making (see also risk management, 
adaptation and mitigation).
societal objectives, such as the Sustainable Development Goals 
(SDGs) (see also risk trade-off). Risks can arise, for example, from 
uncertainty in implementation, effectiveness or outcomes of climate 
adoption, and system transitions.
See also Hazard and Impacts.
Risk assessment The qualitative and/or quantitative scientific

estimation of risks. See also Risk management and Risk perception.
the likelihood and/or magnitude of adverse potential consequences, 
based on assessed or perceived risks. See also Risk assessment, and 
Risk perception.
Risk perception The subjective judgement that people make about 
the characteristics and severity of a  risk. See also Risk assessment, 
and Risk management.
a countervailing risk is generated (knowingly or inadvertently) by an 
intervention to reduce the target risk (Wiener and Graham 2009).
Sea surface temperature (SST) The subsurface bulk temperature 
in the top few metres of the ocean, measured by ships, buoys and 
drifters. From ships, measurements of water samples in buckets 
were mostly switched in the 1940s to samples from engine intake 
water. Satellite measurements of skin temperature (uppermost layer;

a fraction of a millimetre thick) in the infrared or the top centimetre 
or so in the microwave are also used, but must be adjusted to be 
compatible with the bulk temperature.
based on a  coherent and internally consistent set of assumptions 
about key driving forces (e.g., rate of technological change, prices)

1813
Glossary Annex IAIand relationships. Note that scenarios are neither predictions nor 
forecasts, but are used to provide a  view of the implications of 
developments and actions.
See Reference Scenario (under Scenario).
concentrations of substances that are radiatively active (e.g.,  greenhouse 
gases, aerosols, tropospheric ozone), plus human-induced land cover 
changes that can be radiatively active via albedo changes, and often 
used as input to a climate model to compute climate projections.
of substances that are radiatively active (e.g.,  greenhouse gases 
or aerosols), plus human-induced land-cover changes that can 
be radiatively active via albedo changes, based on a coherent and 
internally consistent set of assumptions about driving forces (such

change, energy and land use) and their key relationships. 
Concentration scenarios, derived from emission scenarios, are often 
system responds to the implementation of mitigation policies 
and measures.
Scenario used as starting or reference point for a comparison between 
[Note 1: In many types of climate change research, reference scenarios 
reflect specific assumptions about patterns of socio-economic development 
and may represent futures that assume no climate policies or specified 
climate policies, for example, those in place or planned at the time a study 
is carried out. Reference scenarios may also represent futures with limited 
or no climate impacts or adaptation, to serve as a point of comparison for

futures with impacts and adaptation. These are also referred to as ‘baseline 
scenarios’ in the literature. 
Note 2: Reference scenarios can also be climate policy or impact scenarios, 
which in that case are taken as a  point of comparison to explore the 
implications of other features, for example, of delay, technological options, 
policy design and strategy or to explore the effects of additional impacts and 
adaptation beyond those represented in the reference scenario. 
a  scenario that assumes no additional policies beyond those currently in 
place, and where patterns of socio-economic development are consistent 
with recent trends. The term is now used less frequently than in the past. 
Note 4: In climate change attribution or impact attribution research reference 
scenarios may refer to counterfactual historical scenarios assuming no

anthropogenic greenhouse gas (GHG) emissions (climate change attribution) 
or no climate change (impact attribution).]Socio-economic scenario 
A scenario that describes a plausible future in terms of population, 
Scenario storyline See Storyline.
Sequestration The process of storing carbon in a carbon pool. See 
also Pool, carbon and nitrogen, Sequestration potential, Sink, Soil 
carbon sequestration (SCS), Source, and Uptake.
Sequestration potential The quantity of greenhouse gases that 
can be removed from the atmosphere by anthropogenic enhancement 
of sinks and stored in a pool. See Mitigation potential for different 
nitrogen, Sequestration, Sink, Source, and Uptake.
Service provisioning Various services (such as illumination and

mobility) can be provided by ‘systems’ through the use of energy, 
materials, and other resources comprising: (i) Resource flows 
(e.g., energy); (ii) Technologies for resource use and energy conversion 
(e.g., vehicles and their engines); and (iii) Social/organisational forms 
of service delivery (e.g.,  publicly owned companies, or privately 
owned companies, e-commerce).
Services Activities that help satisfy human wants or needs. While 
they usually involve relationships between producers and consumers, 
services are less tangible and less storable than goods since they 
represent flows not stocks, and when their regeneration conditions 
are protected they may be reused over time.
Settlements Places of concentrated human habitation. Settlements 
can range from isolated rural villages to urban regions with significant 
global influence. They can include formally planned and informal or 
illegal habitation and related infrastructure. See also Cities and Urban.

Pathways (SSPs).
Shared Socio-economic Pathways (SSPs) See Pathways.
Sharing economy A system which allows people to share goods 
and services by enabling collaborative use, access or ownership.
Shifting development pathways (SDPs) In this report, shifting 
development pathways describes transitions aimed at re-directing 
existing developmental trends. Societies may put in place enabling 
conditions to influence their future development pathways, when 
they endeavour to achieve certain outcomes. Some outcomes may 
be common, while others may be context-specific, given different 
starting points. See also Development pathways (under Pathways), 
and Shifting development pathways to sustainability.
Shifting development pathways to sustainability Shifting 
development pathways to sustainability involves transitions aligned 
with a  shared aspiration in the Sustainable Development Goals

(SDGs) agreed globally, though sustainability may be interpreted 
differently in various contexts as societies pursue a  variety of 
sustainable development objectives. See also Development pathways 
(under Pathways), and Shifting development pathways (SDPs).
Short-lived climate forcers (SLCFs) A set of chemically reactive 
compounds with short (relative to carbon dioxide) atmospheric

1814
Annex I  GlossaryAIlifetimes (from hours to about two decades) but characterised by 
different physiochemical properties and environmental effects. Their 
emission or formation has a  significant effect on radiative forcing 
over a period determined by their respective atmospheric lifetimes. 
Changes in their emissions can also induce long-term climate effects 
via, in particular, their interactions with some biogeochemical cycles. 
SLCFs are classified as direct or indirect, with direct SLCFs exerting 
climate effects through their radiative forcing and indirect SLCFs being 
the precursors of other direct climate forcers. Direct SLCFs include 
methane (CH 4 ), ozone (O 3 ), primary aerosols and some halogenated 
species. Indirect SLCFs are precursors of ozone or secondary aerosols. 
SLCFs can be cooling or warming through interactions with radiation 
and clouds. They are also referred to as near-term climate forcers.

Many SLCFs are also air pollutants. A subset of exclusively warming 
SLCFs is also referred to as short-lived climate pollutants (SLCPs), 
including methane, ozone, and black carbon (BC).
Simple climate model (SCM) A broad class of lower-dimensional 
models of the energy balance, radiative transfer, carbon cycle, or 
a combination of such physical components. SCMs are also suitable 
for performing emulations of climate-mean variables of Earth System 
Models (ESMs), given that their structural flexibility can capture both 
the parametric and structural uncertainties across process-oriented 
ESM responses. They can also be used to test consistency across 
multiple lines of evidence with regard to climate sensitivity ranges, 
transient climate responses (TCRs), transient climate response to 
cumulative CO 2 emissions (TCREs) and carbon cycle feedbacks. 
See also Emulators.

a greenhouse gas, an aerosol or a  precursor of a  greenhouse gas 
from the atmosphere (UNFCCC Article 1.8 (UNFCCC 1992)). See also 
Pool, carbon and nitrogen, Sequestration, Source and Uptake.
Small Island Developing States (SIDS) SIDS, as recognised by 
the United Nations OHRLLS (UN Office of the High Representative for 
the Least Developed Countries, Landlocked Developing Countries and 
Small Island Developing States), are a distinct group of developing 
vulnerabilities (UN-OHRLLS 2011). They were recognised as a special 
case, both for their environment and development, at the Rio Earth 
Summit in Brazil in 1992. Fifty-eight countries and territories are 
presently classified as SIDS by the UN OHRLLS, with 38 being UN 
member states and 20 being non-UN members or associate members 
of the Regional Commissions (UN-OHRLLS 2018).

Smart grids A smart grid uses information and communications 
technology to gather data on the behaviours of suppliers and 
consumers in the production, distribution, and use of electricity. 
Through automated responses or the provision of price signals, this 
information can then be used to improve the efficiency, reliability, 
economics, and sustainability of the electricity network.
Social cost of carbon (SCC) The net present value of aggregate 
climate damages (with overall harmful damages expressed as 
a number with positive sign) from one more tonne of carbon in the form of carbon dioxide (CO 2 ), conditional on a  global emissions 
trajectory over time.
Social costs The full costs of an action in terms of social welfare 
losses, including external costs associated with the impacts of this 
action on the environment, the economy (GDP, employment) and on 
the society as a whole.
Social group A collective of people who share similar characteristics

and collectively may have a sense of unity (Forsyth 2010).
Social identity The portion of an individual’s self-concept derived 
from perceived membership in a  relevant social group (Tajfel and 
Turner 1986).
Social inclusion A process of improving the terms of participation 
in society, particularly for people who are disadvantaged, through 
enhancing opportunities, access to resources, and respect for rights 
(UNDESA 2018).
Social infrastructure See Infrastructure.
Social learning A process of social interaction through which 
people learn new behaviours, capacities, values, and attitudes.
Social-ecological system An integrated system that includes 
human societies and ecosystems, in which humans are part of 
nature. The functions of such a system arise from the interactions and 
interdependence of the social and ecological subsystems. The system’s 
structure is characterised by reciprocal feedbacks, emphasising that

humans must be seen as a part of, not apart from, nature (Berkes and 
Folke 1998; Arctic Council 2016).
Socio-economic scenario See Scenario.
Socio-technical transitions Where technological change is 
associated with social systems and the two are inextricably linked.
Soil carbon sequestration (SCS) Land management changes 
which increase the soil organic carbon content, resulting in a  net 
removal of carbon dioxide (CO 2 ) from the atmosphere. See also 
Anthropogenic removals and Carbon dioxide removal (CDR).
Soil organic carbon Carbon contained in soil organic matter.
Soil organic matter The organic component of soil, comprising 
plant and animal residue at various stages of decomposition, and 
soil organisms.
Solar energy Energy from the Sun. Often the phrase is used to 
mean energy that is captured from solar radiation either as heat, as 
light that is converted into chemical energy by natural or artificial 
photosynthesis, or by photovoltaic panels and converted directly into

electricity. See also Renewable energy.
Solar radiation modification (SRM) Refers to a  range of 
(GHG) mitigation that seek to limit global warming. Most methods 
Source Any process or activity which releases a  greenhouse gas 
(GHG), an aerosol or a  precursor of a  GHG into the atmosphere

1815
Glossary Annex IAI(UNFCCC Article 1.9 (UNFCCC 1992)). See also Sink, Pool, carbon and 
nitrogen, Sequestration, Sequestration Potential and Uptake.
Spill-over effect
 T
he effects of domestic or sector mitigation 
measures on other countries or sectors. Spill-over effects can be 
positive or negative and include effects on trade, (carbon) leakage, transfer of innovations, and diffusion of environmentally sound technology and other issues.
Storyline
 A w
ay of making sense of a situation or a series of events 
through the construction of a set of explanatory elements. Usually,

it is built on logical or causal reasoning. In climate research, the term storyline is used both in connection to scenarios as related to a future trajectory of the climate and human systems or to a weather or climate event. In this context, storylines can be used to describe plural, conditional possible futures or explanations of a  current situation, in contrast to single, definitive futures or explanations.
A narrative description of a  scenario (or family of scenarios), 
highlighting the main scenario characteristics, relationships between key driving forces and the dynamics of their evolution.
 Assets exposed to dev
aluations or conversion to 
‘liabilities’ because of unanticipated changes in their initially expected 
revenues due to innovations and/or evolutions of the business context, including changes in public regulations at the domestic and international levels.
Subnational actors
 State/provincial
, regional, metropolitan and

local/municipal governments as well as non-party stakeholders, 
such as civil society, the private sector, cities and other subnational authorities, local communities and indigenous peoples.
Sufficiency
 A set of measures and daily practices that avoid 
demand for energy,
 materials, land and water while delivering human 
well-being for all within planetary boundaries.Sustainability
 A dynamic process that guarantees the persistence 
of natural and human systems in an equitable manner
.
 Development that meets the 
needs of the present without compromising the ability of future 
generations to meet their own needs (WCED 1987) and balances social
, economic and environmental concerns. See also Development 
pathways and Sustainable Development Goals (SDGs).
 T
he 17 global goals 
for development for all countries established by the United Nations

through a participatory process and elaborated in the 2030 Agenda for Sustainable Development, including ending poverty and hunger; ensuring health and well-being, education, gender equality, clean water and energy, and decent work; building and ensuring resilient and sustainable infrastructure, cities and consumption; reducing inequalities; protecting land and water ecosystems; promoting peace, justice and partnerships; and taking urgent action on climate change. See also Sustainable development.
 T
he stewardship and use 
of forests and forest lands in a  way, and at a  rate, that maintains 
their biodiversity, productivity, regeneration capacity, vitality and 
their  potential to fulfil, now and in the future, relevant ecological, economic and social functions, at local, national, and global levels, and 
that does not cause damage to other ecosystems (Forest Europe 1993).
Sustainable intensification (of agriculture)
 Increasing yields

from the same area of land while decreasing negative environmental 
impacts of agricultural production and increasing the provision of environmental services (CGIAR 2019).
 
[Note: This definition is based on the concept of meeting demand from a finite 
land area, but it is scale-dependent. Sustainable intensification at a  given scale (e.g., global or national) may require a decrease in production intensity at smaller scales and, in particular, places (often associated with previous, unsustainable, intensification) to achieve sustainability (Garnett et al. 2013).]
Sustainable land management  T he stewardship and use of 
land resources, including soils, water, animals and plants, to meet 
changing human needs, while simultaneously ensuring the long-term productive potential of these resources and the maintenance of their environmental functions (WOCAT, no date).
Systems of Innovation (SI)
 T
he set of public and private

sector organisations (i.e.,  formally organised entities such as firms 
and universities; ‘actors’) and institutions, whose activities and interactions generate, modify and deploy new technologies. The SI approach has been used to understand and analyse innovation at the national, regional, and technological levels, and in transnational contexts (Lundvall 1992, 1988).
Technology deployment
 T
he act of bringing technology into effective 
application, involving a set of actors and activities to initiate, facilitate 
and/or support its implementation. See also Technology diffusion.
Technology diffusion
 T
he spread of a technology across different 
groups users/markets over time. See also Technology deployment and 
Technology transfer.
Technology transfer
 T
he exchange of knowledge, hardware and 
associated software, money and goods among stakeholders, which

leads to the spread of technology for adaptation or mitigation. The term encompasses both diffusion of technologies and technological cooperation across and within countries. See also Technology diffusion.
 Association between 
climate variables at widely 
separated, geographically fixed locations related to each other through 
physical processes and oceanic and/or atmospheric dynamical pathways. Teleconnections can be caused by several climate phenomena, such as Rossby wave-trains, mid-latitude jet and storm track displacements, fluctuations of the Atlantic Meridional Overturning Circulation (AMOC),  
fluctuations of the Walker circulation, etc. They can be initiated by modes of climate variability, thus providing the development of remote climate anomalies at various temporal lags.
 Exceedance of a 
specified global warming 
level, followed by a decline to or below that level during a specified

period of time (e.g.,  before 2100). Sometimes the magnitude and likelihood of the overshoot is also characterised. The overshoot duration can vary from one pathway to the next, but in most overshoot pathways  
in the literature and as referred to as overshoot pathways in the AR6, the overshoot occurs over a period of at least one decade and up to several decades.

1816
Annex I  GlossaryAITipping point A critical threshold beyond which a  system 
reorganises, often abruptly and/or irreversibly. See also Irreversibility.
Total carbon budget See Carbon budget.
Trade-off A competition between different objectives within 
a  decision situation, where pursuing one objective will diminish 
achievement of other objective(s). A trade-off exists when a policy 
or measure aimed at one objective (e.g., reducing greenhouse gas 
emissions) reduces outcomes for other objective(s) (e.g.,  biodiversity 
conservation, energy security) due to adverse side effects, thereby 
potentially reducing the net benefit to society or the environment. 
See also Co-benefit.
Transformation A change in the fundamental attributes of natural 
and human systems.
Transformation pathways See Pathways.
Transient climate response (TCR) See Climate sensitivity.
See Climate sensitivity.

Transition The process of changing from one state or condition to 
another in a given period of time. Transition can occur in individuals, 
firms, cities, regions and nations, and can be based on incremental or 
transformative change.
Uncertainty A state of incomplete knowledge that can result from 
a lack of information or from disagreement about what is known or 
even knowable. It may have many types of sources, from imprecision 
in the data to ambiguously defined concepts or terminology, 
incomplete understanding of critical processes, or  uncertain 
projections of human behaviour. Uncertainty can therefore be 
represented by quantitative measures (e.g.,  a  probability density 
function) or by qualitative statements (e.g., reflecting the judgement 
of a team of experts) (Moss and Schneider 2000; Mastrandrea et al. 
2010). See also Confidence and Likelihood.
United Nations Convention to Combat Desertification (UNCCD)

A legally binding international agreement linking environment and 
development to sustainable land management, established in 1994. 
The Convention’s objective is ‘to combat desertification and mitigate 
the effects of drought in countries experiencing drought and/or 
desertification’. The Convention specifically addresses the arid, 
semi-arid and dry sub-humid areas, known as the drylands, and has 
a particular focus on Africa. As of September 2020, the UNCCD had 
197 Parties. See also Desertification, Drought and Land degradation.
(UNFCCC) The UNFCCC was adopted in May 1992 and opened for 
signature at the 1992 Earth Summit in Rio de Janeiro. It entered into 
force in March 1994 and, as of September 2020, had 197 Parties 
objective is the ‘stabilisation of greenhouse gas concentrations in the 
atmosphere at a level that would prevent dangerous anthropogenic

interference with the climate system’ (UNFCCC 1992). The provisions 
of the Convention are pursued and implemented by two further 
Uptake The transfer of substances (such as carbon) or energy 
(e.g.,  heat) from one compartment of a  system to another; for example, in the Earth System from the atmosphere to the ocean 
or to the land. See also Pool, carbon and nitrogen, Sequestration, 
Urban The categorisation of areas as ‘urban’ by government 
statistical departments is generally based either on population 
size, population density, economic base, provision of services, or 
some combination of the above. Urban systems are networks and 
nodes of intensive interaction and exchange including capital, 
culture, and material objects. Urban areas exist on a continuum with 
rural areas and tend to exhibit higher levels of complexity, higher

populations and population density, intensity of capital investment, 
and a preponderance of secondary (processing) and tertiary (service) 
sector industries. The extent and intensity of these features varies 
significantly within and between urban areas. Urban places and 
systems are open with much movement and exchange between 
more rural areas as well as other urban regions. Urban areas can 
be globally interconnected facilitating rapid flows between them – 
of capital investment, of ideas and culture, human migration, and 
disease. See also Cities, Peri-urban areas, and Urbanisation.
Urban heat island The relative warmth of a  city compared with 
surrounding rural areas, associated with heat trapping due to land 
use, the configuration and design of the built environment, including 
street layout and building size, the heat-absorbing properties of urban 
building materials, reduced ventilation, reduced greenery and water

features, and domestic and industrial heat emissions generated directly 
from human activities. See also City region,  Urban, and Urban System.
Urban Systems Urban systems refer to two interconnected systems: 
first, the comprehensive collections of city elements with multiple 
dimensions and characteristics: a) encompass physical, built, socio-
economic-technical, political, and ecological subsystems; b) integrate 
social agent/constituency/processes with physical structure 
and processes; and c) exist within broader spatial and temporal scales 
and governance and institutional contexts; and second, the global 
system of cities and towns. See also City region, and Urban.
Urbanisation Urbanisation is a  multi-dimensional process that 
involves at least three simultaneous changes: (i) land-use change: 
transformation of formerly rural settlements or natural land into urban 
settlements; (ii) demographic change: a shift in the spatial distribution

of a population from rural to urban areas; and (iii) infrastructure change: 
an increase in provision of infrastructure services including electricity, 
sanitation, etc. Urbanisation often includes changes in lifestyle, culture, 
and behaviour, and thus alters the demographic, economic, and social 
structure of both urban and rural areas (Stokes and Seto 2019; Seto 
et al. 2014; UNDESA 2018). See also Urban,  and Urban Systems.
Variable renewable energy (VRE) See Renewable energy.
Vulnerability The propensity or predisposition to be adversely 
affected. Vulnerability encompasses a  variety of concepts and 
Well-being A state of existence that fulfils various human needs, 
including material living conditions, meaningful social and community 
relationships and quality of life, as well as the ability to pursue one’s

goals, to thrive, and feel satisfied with one’s life. Ecosystem well-being 
refers to the ability of ecosystems to maintain their diversity and quality.

1817
Glossary Annex IAIEudaimonic 
Relational well-being concept based on the premise that experiencing life purpose, challenges and growth leads to flourishing, self-realisation, personal expression, and full functioning (Niemiec 2014; Lamb and Steinberger 2017).
Hedonic 
Subjective well-being concept based on the idea that attaining pleasure and avoiding pain leads to happiness (Ryan and Deci 2001).
Wind energy
 Kinetic energy from airflow arising from the uneven 
heating 
of the Earth’s surface. The wind’s kinetic energy is converted 
to mechanical shaft energy and electricity by a  wind turbine, 
a rotating machine. A wind farm, wind project, wind park, or wind power plant is a group of wind turbines interconnected to a common utility system through a system of transformers, distribution lines, and (usually) one substation. See also Renewable energy.
 See 
Climate change commitment.

1823
Definitions, Units and Conventions Annex IIAII
This annex on Definitions, Units and Conventions provides background 
information on material used in the Working Group III contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6 WGIII). The material presented in this annex documents metrics and common datasets that are typically used across multiple chapters of the report. In a few instances there are no updates to what was adopted by WGIII during the production of the Fifth Assessment Report (AR5), in which case this annex refers to Annex II of AR5 (Krey et al. 2014).
The annex comprises four parts: Part I introduces standards, metrics

and common definitions adopted in the report; Part II presents methods to derive or calculate certain quantities and identities used in the report; Part III provides more detailed background information about common data sources; and Part IV presents integrative methodologies used in the assessment. While this structure may help readers to navigate through the annex, it is not possible in all cases to unambiguously assign a certain topic to one of these parts, naturally leading to some overlap between the parts.
Part I: Definitions and Units
A.II.1 Classification Schemes for Countries and Areas

In this report, two different levels of classification are used as a standard to present the results of analysis. The basis for the classification is the UN Statistics Division Standard Country or Area Codes for Statistical Use, also known as the M49 Standard (UNSD 1999). This covers geographical regions and, at the time of the literature cut-off date, identified developed regions, developing regions and least developed countries.
The high-level classification has six categories (Table 1): one covering

North America, Europe, and Australia, Japan and New Zealand, labelled ‘developed countries’, and five covering other countries, all classified as developing using the M49 standard at the cut-off date. The high-level classification is an expansion of the RC5 (Regional Categorisation 5) adopted in AR5 WGIII, with Africa and the Middle East now identified separately. The low-level classification (ten categories) divides developed countries into three geographical regions, and Asia and Pacific into three sub-regions. 
The high- and low-level classification schemes reflect schemes used

in many global models and statistical sources. Where the report synthesises data, only these standard classification schemes have been used. On occasions, the underlying literature may deviate from the standard classification scheme and direct citations may unavoidably refer to alternative classifications. This is dealt with on a case-by-case basis and does not imply any endorsement of the scheme used in the underlying literature by the IPCC or the authors of this report. 
The detailed allocation of countries and areas to the low-level

classification is shown in Section 1.1. Following AR5, the classification scheme deviates from the UN regional classification with the result that Annex I, Annex II and non-Annex I countries as defined under the UN Framework Convention on Climate Change (UNFCCC) are distinguished. Some Annex I countries in Western Asia and countries in Eastern Europe which are not members of the European Union are allocated to Eastern Europe and West-Central Asia (EEA). In AR5, these formed part of the Economies in Transition group. The remainder of Western Asia (non-Annex I) is allocated to the Middle East.
Following the practice of the UN Statistics Division, we note that the

designations employed and the presentation of material in this report do not imply the expression of any opinion by the United Nations, the IPCC or the authors of this report concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The term ‘country’ as used in this material also refers, as appropriate, to territories or areas. 
A.II.1.1  Low Level of Regional Groupings

Africa: Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Côte d’Ivoire, Cabo Verde, Cameroon, the Central African Republic, Chad, the Comoros, the Congo, the Democratic Republic of the Congo, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, the Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, the Niger, Nigeria, Rwanda, São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, the South Sudan, the Sudan, Togo, Tunisia, Uganda, the United Republic of Tanzania, Zambia, Zimbabwe.
Middle East: Bahrain, Islamic Republic of Iran, Iraq, Israel, Jordan, 
Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, the State of Palestine, the Syrian Arab Republic, the United Arab Emirates, Yemen.
Latin America and Caribbean: Antigua and Barbuda, Argentina,

the Bahamas, Barbados, Belize, Plurinational State of Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, the Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Bolivarian Republic of Venezuela.
North America: Canada, the United States of America.Eastern Asia: China, the Republic of Korea, the Democratic People’s 
Republic of Korea, Mongolia.
Southern Asia: Afghanistan, Bangladesh, Bhutan, India, Maldives, 
Nepal, Pakistan, Sri Lanka.
South-East Asia and Pacific: Brunei Darussalam, Cambodia,

Cook  Islands, Fiji, Indonesia, Kiribati, the Lao People’s Democratic Republic, Malaysia, the Marshall Islands, Federated States of Micronesia, Myanmar, Nauru, Niue, Palau, Papua New Guinea, the Philippines, Samoa, Singapore, Solomon Islands, Thailand, Timor-Leste, Tonga, Tuvalu, Vanuatu, Viet Nam.
Europe: Albania, Andorra, Austria, Belgium, Bosnia and Herzegovina, 
Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Monaco, Montenegro, the Netherlands,

1824
Annex II  Definitions , Units and ConventionsAIINorth Macedonia, Norway, Poland, Portugal, Romania, San Marino, 
Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, the 
United Kingdom of Great Britain and Northern Ireland.
Australia, Japan, and New Zealand
Eastern Europe and West-Central Asia: Armenia, Azerbaijan, 
Belarus, Georgia, Kazakhstan, Kyrgyzstan, the Republic of Moldova, 
the Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan.
International Shipping and Aviation
A.II.1.2 High, Low Levels of Regional Groupings
Table 1 | Classification schemes for countries and areas.
WGIII AR6
High Level (6) Low Level (10)
Developed Countries (DEV)North America 
Europe
Australia, Japan and New Zealand
Eastern Europe and West-Central Asia (EEA) Eastern Europe and West-Central Asia
Latin America and Caribbean (LAM) Latin America and Caribbean
Africa (AFR) Africa
Middle East (ME) Middle East
Asia and Pacific (APC)Eastern Asia

South-East Asia and Pacific
A.II.2 Standard Units and Unit Conversions
The following sections introduce standard units and unit conversions 
used throughout this report.
A.II.2.1 Standard Units
Standard units of measurements include Système International (SI) 
units, SI-derived units, and other non-SI units as well the standard 
prefixes for basic physical units. 
Table 2 | Système International (SI) units.
Physical quantity Unit Symbol
Length metre m
Mass kilogram kg
Time second s
Thermodynamic temperature kelvin K
Amount of substance mole mol
1 A measure of aggregate greenhouse gas (GHG) emissions. This report uses the GHG metric Global Warming Potential with a time horizon of 100 years (GWP100); 
for details see Section 8. 
2 The is a unit of measure of freight transport which represents the transport of one tonne of goods (including packaging and tare weights of intermodal transport units)

by a given transport mode (road, rail, air, sea, inland waterways, pipeline etc.) over a distance of one kilometre. The tonne measure here is not the same unit of measure 
as metric tonnes earlier in the third row of Table 4. Table 3 | Special names and symbols for certain SI-derived units.
Physical quantity Unit Symbol Definition
Force Newton N kg m s2
Pressure Pascal Pa kg m–1 s–2 (= N m–2)
Energy Joule J kg m2 s–2
Power Watt W kg m2 s–3 (= J s–1)
Frequency Hertz Hz s–1 (cycles per second)
Ionizing radiation dose sievert Sv J kg–1
Table 4 | Non-SI standard units.
Monetary units Unit Symbol
Currency (market exchange rate, MER) Constant US Dollar 2015 USD2015
Currency (purchasing power parity, PPP)Constant International 
Dollar 2015Int$2015
Emission- and  
climate-related unitsUnit Symbol
Emissions Metric tonnes t
CO2 emissions Metric tonnes CO 2 tCO 2
CO2-equivalent emissions1Metric tonnes 
CO2-equivalenttCO 2-eq
Abatement costs and emissions 
prices/taxesConstant US dollar 2015

per metric tonneUSD2015 t–1
CO2 concentration or mixing ratio 
(μmol mol–1)Parts per million (106) Ppm
CH4 concentration or mixing ratio 
(nmol mol–1)Parts per billion (109) ppb
N2O concentration or mixing ratio 
(nmol mol–1)Parts per billion (109) ppb
Radiative forcing Watts per square meter W/m2
Energy-related units Unit Symbol
Energy Joule J
Electricity and heat generation Watt hours Wh
Power (peak capacity)Watt (Watt thermal, 
Watt electric)W (Wth, We)
Capacity factor Percent %
Technical and economic lifetime Years yr
Specific energy investment costsUS dollar 2015 per kW 
(peak capacity)USD2015/kW
Energy costs (e.g., LCOE) and pricesConstant US dollar 2015 
per GJ or US cents 2015 
per kWhUSD2015/GJ and 
USct2015/kWh
Passenger-distance Passenger-kilometre pkm
Payload-distance2Tonne-kilometre tkm
Land-related units Unit Symbol
Area Hectare ha
Note that all monetary and monetary-related units are expressed in constant US

Dollar 2015 (USD2015) or constant International Dollar 2015 (Int$2015).

1825
Definitions, Units and Conventions Annex IIAIITable 5 | Prefixes for basic physical units.
Multiple Prefix Symbol Fraction Prefix Symbol
1E+21 zeta Z 1E-01 deci d
1E+18 exa E 1E-02 centi c
1E+15 peta P 1E-03 milli m
1E+12 tera T 1E-06 micro μ
1E+09 giga G 1E-09 nano n
1E+06 mega M 1E-12 pico p
1E+03 kilo k 1E-15 femto f
1E+02 hecto h 1E-18 atto a
1E+01 deca da 1E-21 zepto z
A.II.2.2 Physical Units Conversion 
Table 6 | Conversion table for common mass units (IPCC 2001).
To: kg t lt st lb
From: Multiply by:
Kilogram kg 1 1.00E-03 9.84E-04 1.10E-03 2.20E+00
Tonne t 1.00E+03 1 9.84E-01 1.10E+00 2.20E+03
Long ton lt 1.02E+03 1.02E+00 1 1.12E+00 2.24E+03
Short Ton st 9.07E+02 9.07E-01 8.93E-01 1 2.00E+03
Pound lb 4.54E-01 4.54E-04 4.46E-04 5.00E-04 1
Table 7 | Conversion table for common volumetric units (IPCC 2001).
To: gal US gal UK bbl ft3l m3
US gallon gal US 1 8.33E-01 2.38E-02 1.34E-01 3.79E+00 3.80E-03

UK/imperial gallon gal UK 1.20E+00 1 2.86E-02 1.61E-01 4.55E+00 4.50E-03
Barrel bbl 4.20E+01 3.50E+01 1 5.62E+00 1.59E+02 1.59E-01
Cubic foot Ft37.48E+00 6.23E+00 1.78E-01 1 2.83E+01 2.83E-02
Litre L 2.64E-01 2.20E-01 6.30E-03 3.53E-02 1 1.00E-03
Cubic metre M32.64E+02 2.20E+02 6.29E+00 3.53E+01 1.00E+03 1
Table 8 | Conversion table for common energy units (NAS 2007; IEA 2019).
To: TJ Gcal Mtoe Mtce MBtu GWh
Tera joule TJ 1 2.39E+02 2.39E-05 3.41E-05 9.48E+02 2.78E-01
Giga calorie Gcal 4.19E-03 1 1.0E-06 1.43E-07 3.97E+00 1.16E-03
Mega tonne oil equivalent Mtoe 4.19E+04 1.0E+08 1 1.43E+00 3.97E+07 1.16E+04
Mega tonne coal equivalent Mtce 2.93E+04 7.0E+06 7.00E-01 1 2.78E+07 8.14E+03
Million british thermal units MBtu 1.06E-03 2.52E-01 2.52E-08 3.60E-08 1 2.93E-04
Giga watt hours GWh 3.60E+00 8.60E+02 8.60E-05 1.23E-4 3.41E+03 1
In addition to the above physical units, datasets often report carbon

emissions in either units of carbon (C) or carbon dioxide (CO 2). In this 
report we report carbon dioxide (CO 2) emissions where possible, 
using the conversion factor (44/12) to convert from units of C into 
CO2. Finally, we note that the conversion from GJ to kWh is as 
follows: 1 GJ = ~277.78 kWh.Where aggregate greenhouse gas emissions are reported, this report 
uses the Global Warming Potential with a time horizon of 100 years 
(GWP100); for details see Section 8.

1826
Annex II  Definitions , Units and ConventionsAIIA.II.2.3 Monetary Unit Conversion
To achieve comparability across cost und price information from 
different regions, where possible monetary quantities reported in the 
AR6 WGIII have been expressed in constant US Dollar 2015 (USD2015) 
or constant International Dollar 2015 (Int$2015), as suitable. 
To facilitate a consistent monetary unit conversion process, a simple 
and transparent procedure to convert different monetary units from 
the literature to USD2015 is established and described below.
In order to convert from year X local currency unit (LCU x) to 2015 
US Dollars (USD2015) two steps are needed:
1. Inflating or deflating from year X to 2015, and 
2. Converting from LCU to USD.
In practice, the order of applying these two steps will lead to different 
results. In this report, the conversion route adopted is LCU x → 
LCU2015 → USD2015, i.e., national or regional deflators are used

to measure country- or region-specific inflation between year X and 
2015 in local currency, then current (2015) exchange rates are used 
to convert to USD2015. The reason for adopting this route is when 
the economy’s GDP deflator is used to convert to a common base 
year, that is, 2015, it captures the changes in prices of all goods 
and services that the economy produces. To convert from LCU2015 
to USD2015, the official 2015 exchange rates are used. Note that 
exchange rates often fluctuate significantly in the short term.
In order to be consistent with the choice of the World Bank databases 
as the primary source for gross domestic product (GDP) and other 
financial data throughout the report, deflators and exchange rates 
from the World Bank Development Indicators are used.3 
To summarise, the following procedure has been adopted to convert 
monetary quantities reported in LCU x to USD2015:
1. Use the country-/region-specific deflator and multiply with the

deflator value to convert from LCU x to LCU2015. In case national/
regional data are reported in non-LCU units (e.g.,  USD x or Euro x), 
which is often the case in multi-national or global studies, 
apply the corresponding currency deflator to convert to 2015 
currency (i.e., the US deflator and the Eurozone deflator in the 
examples above). 
Example of converting GDP from LCU2010 prices to LCU2015 prices:
GDP2015 (in LCU2015 prices) = GDP2010 (in LCU2010 prices)  
* 	"#$!"#"	%&'	()*+,-./
	"#$!"#$	%&'	()*+,-./ 
2. Use the appropriate 2015 exchange rate to convert from 
LCU2015 to USD2015.
3 For instance, the data for GDP deflators for all countries can be downloaded following this link: https://data.worldbank.org/indicator/NY .GDP .DEFL.ZS?locations=US .Part II: Conventions
A.II.3 Levelised Cost Metrics
Across this report, a number of different metrics to characterise cost 
of climate change mitigation are employed. To facilitate a meaningful

economic comparison across diverse options at the technology level, 
the metric of ‘levelised costs’ is used throughout several chapters of 
this report in various forms. The most used metrics are the levelised 
cost of energy (LCOE), the levelised cost of conserved energy (LCCE), 
and the levelised cost of conserved carbon (LCCC). These metrics are 
used throughout the AR6 WGIII to provide a benchmark for comparing 
different technologies or practices of achieving the respective output. 
Each comes with a set of context-specific caveats that need to be 
taken into account for correct interpretation. Various literature 
sources caution against drawing too strong conclusions from these 
metrics. Annex II in AR5, namely Section A.II.3.1, includes a detailed 
discussion on interpretations and caveats. Below is an introduction to 
each of these metrics and how they are derived. 
A.II.3.1 Levelised Cost of Energy
The levelised cost of energy (LCOE) can be defined as the unique

break-even cost-price where discounted revenues (price x quantities) 
are equal to the discounted net expenses (Moomaw et  al. 2011), 
which is expressed as follows:
(1)
where Et is the energy delivered in year t (might vary from year to 
year), expenses cover all (net) expenses in the year t, i is the discount 
rate and n the lifetime of the project.
solving for LCOE:
(2)
The lifetime expenses comprise investment costs I, operation and 
maintenance cost O&M (including waste management costs), fuel 
costs F, carbon costs C, and decommissioning costs D. In this case, 
levelised cost can be determined by (IEA 2010):
(3)!!
"#$""∗$%&"
(1+*)"=!!
"#$"-./01/1 "
(1+*)" 
!"#$=∑!"#$$'()*+)+"
(1+/)"
∑!
"#$$"
(1+/)" 
!"#$=∑!"#$'"+#&*"++"+""+,"
(1+/)"
∑!
"#$$"∗
(1+/)"

1827
Definitions, Units and Conventions Annex IIAIIAssuming energy E provided annually is constant during the lifetime 
of the project, one can rewrite (3) as follows:
(4)
where !"#=%
1−(1−%)!" 	  is the capital recovery factor and 
NPV the net present value of all lifetime expenditures (Suerkemper 
et al. 2012). 
For the simplified case, where the annual costs are also assumed 
constant over time, this can be further simplified to (O&M costs and 
fuel costs F constants):
(5)
Where I is the upfront investment, O&M are the annual operation 
and maintenance costs, F are the annual fuel costs, and E is the 
annual energy provision. The investment I should be interpreted 
as the sum of all capital expenditures needed to make the 
investment fully operational discounted to t = 0. These might 
include discounted retrofit payments during the project lifetime and 
discounted decommissioning costs at the end of the lifetime. Where

applicable, annual O&M costs have to take into account revenues for 
by-products and existing carbon costs must be added or treated as 
part of the annual fuel costs.
A.II.3.2 Levelised Cost of Conserved Energy
The levelised cost of conserved energy (LCCE) annualises the 
investment and operation and maintenance cost differences between 
a baseline technology and the energy-efficient alternative and divides 
this quantity by the annual energy savings. 
The conceptual formula for LCCE is essentially the same as Equation 
(4) above, with ΔE measuring in this context the amount of energy 
saved annually (Suerkemper et al. 2012):
!""#="%&.()*(,!-./0-1/ 	#34/56/6 )
,#=8559-0:(,!-./0-1/ 	#34/56/6 )
,# 
,# 
(6)
In the case of assumed annually constant O&M costs over the project 
lifetime, one can rewrite (6) as follows:
(7)
where ΔI is the difference in investment costs of an energy saving

measure (e.g., in USD) as compared to a baseline investment; ΔO&M 
is the difference in annual operation and maintenance costs of an 
energy saving measure (e.g., in USD) as compared to the baseline 
in which the energy-saving measure is not implemented; ΔE is the 
annual energy conserved by the measure (e.g., in kWh) as compared to the usage of the baseline technology; and CRF is the capital 
recovery factor depending on the discount rate and the lifetime of 
the measure in years as defined above. It should be stressed once 
more that this equation is only valid if ΔO&M and ΔE are constant 
over the project lifetime. As LCCE are designed to be compared with 
complementary levelised cost of energy supply, they do not include 
the annual fuel cost difference. Any additional monetary benefits that 
are associated with the energy-saving measure must be taken into 
account as part of the O&M difference. 
A.II.3.3 Levelised Cost of Conserved Carbon

The levelised cost of conserved carbon can be used for comparing 
mitigation costs per  unit of avoided carbon emissions and 
comparing  these specific emission reduction costs for different 
options. This concept can be applied to other pollutants.
The conceptual formula for LCCC is similar to Equation (6) above, 
with ΔC is the annual reduction in carbon emissions, which can be 
expressed as follows:
(8)
In the case of assumed annually constant O&M costs over the 
lifetime, one can rewrite (8) as follows:
(9)
where ΔI is the difference in investment costs of a mitigation 
is the difference in annual operation and maintenance costs (e.g., in 
USD) and ΔB denotes the annual benefits, all compared to a baseline 
for which the option is not implemented. Note that annual benefits 
include reduced expenditures for fuels, if the investment project 
reduces emissions via a reduction in fuel use. As such LCCC depend

on energy prices. An important characteristic of this equation is that 
LCCC can become negative if ΔB is bigger than the sum of the other 
two terms in the numerator.
A.II.4 Growth Rates
A.II.4.1 Emissions Growth Rates
In order to ensure consistency throughout the reported growth rates 
for emissions in AR6 WGIII, this section establishes the convention for 
calculating these rates.
The annual growth rate of emissions in percent per year for adjacent 
years is given by:
(10)!"#$=!"#	·	&'(	(*+,-.+/-	012-34-4)
$=6337+.8	(*+,-.+/-	012-34-4 )
$ 
!"#$=!"#	·	&+(&*+#
$ 
!""#="%& · )* + ),&.
)# !"""="$%.'()(+!,-./,0. 	234.56.6 )
+"=8559,/:(+!,-./,0. 	234.56.6 )
+" 
!"""="$%.'()(+!,-./,0. 	234.56.6 )
+" 
!"""="$% · () + (+&-−(/
(" 
!=#$!!(&"−1)−$!!(&")*
$!!(&")∗100

1828
Annex II  Definitions , Units and ConventionsAIIwhere EFF stands for fossil fuel CO 2 emissions, but can also be applied 
to other pollutants. 
When relevant a leap-year adjustment is required in order to 
ensure valid interpretation of annual growth rates in the case 
of adjacent years. A leap-year affects adjacent years growth rate by 
approximately 0.3% yr–1 !1
365&  which causes growth rates to go up 
approximately 0.3% if the first year is a leap year, and down 0.3% 
if the second year is a leap year (Friedlingstein et al. 2019).
The relative growth rate of E FF over time periods of greater than one 
year is derived as follows. 
Starting from:
(11)
solving for r :
(12)
A.II.4.2 Economic Growth Rates
A number of different methods exist for calculating economic growth 
rates (e.g.,  GDP), all of which lead to slightly different numerical 
results. If not stated otherwise, the annual growth rates shown in the 
report are derived using the Log Difference Regression technique or

Geometric Average techniques which can be shown to be equivalent.
The Log Difference Regression growth rate r LD is calculated as follows:
(13)
The Geometric Average growth rate rGEO is calculated as shown below:
 (14)
Other methods that are used to calculate annual growth rates include 
the Ordinary Least Square technique and the Average Annual Growth 
Rate technique.
A.II.5 Trends Calculations Between Years 
and Over Decades
In order to compare or contrast trends between two different years, 
for instance comparing 2000 and 2010 cumulative CO 2 emissions, the 
year 2000 runs from 1st of January to 31st of December and similarly 
the year 2010 runs from 1st of January to 31st of December. In order to undertake a timeseries calculation over a decade, the 
10-year period should be defined as follows: from 1st of January 
2001 to 31st of December 2010, that is 2001–2010.
A.II.6 Primary Energy Accounting
Primary energy accounting methods are used to report primary

energy from non-combustible energy sources, in other words, nuclear 
energy and all renewable energy sources except biomass. Annex II of 
AR5, namely Section A.II.4, includes a detailed discussion of the three 
main methods dominant in the literature. The method adopted in AR6 
is the direct equivalent method which counts one unit of secondary 
energy provided from non-combustible sources as one unit of 
primary energy, that is, 1 kWh of electricity or heat is accounted for 
as 1 kWh = 3.6 MJ of primary energy. This method is mostly used 
in the long-term scenarios literature, including multiple IPCC reports 
(IPCC 1995, Morita et al. 2001, Fisher et al. 2007, Fischedick et al. 
2011), because it deals with fundamental transitions of energy 
systems that rely to a large extent on low-carbon, non-combustible 
energy sources.
A.II.7 The Concept of Risk
The concept of risk is a key aspect of how the IPCC assesses and 
communicates to decision-makers the potential adverse impacts of,

and response options to, climate change. For the AR6 cycle, the definition 
of risk was revised (see below). Authors and IPCC Bureau members 
from all three Working Groups produced a Guidance (Reisinger et al. 
2020) for authors on the concept of risk in order to ensure a consistent 
and transparent application across Working Groups. 
This section summarises this Guidance briefly with a focus on issues 
related to WGIII, in other words, with focus on mitigation. 
A.II.7.1 The Definition of Risk
Definition (see Annex I: Glossary): 
Risk is the potential for adverse consequences for human or ecological 
lives, livelihoods, health and well-being, economic, social and cultural

• In the context of climate change impacts, risks result from dynamic 
and vulnerability of the affected human or ecological system 
to the hazards. Hazards, exposure and vulnerability may each 
be subject to uncertainty in terms of magnitude and likelihood 
of occurrence, and each may change over time and space due to 
socio-economic changes and human decision-making (see also 
risk management, adaptation, mitigation).!!!(#+%)=!!!(#)∗(1+*)" 
!=#$!!(&+()
$!!(&)*"/$
−1 
!!"=##−1 with !=1
$−1&!
"#$∆()*" 
!!"#=#$$
$%%%
$&%
−1

1829
Definitions, Units and Conventions Annex IIAII•In the context of climate change responses , risks result from
the potential for such responses not achieving the intended
objective(s), or from potential trade-offs with, or negative side-effects on, other societal objectives, such as the SustainableDevelopment Goals. Risks can arise for example from uncertaintyin implementation, effectiveness or outcomes of climate policy,climate-related investments, technology development or adoption, and system transitions.
A.II.7.2  T he Definition of Risk Management
Plans, actions, strategies or policies to reduce the likelihood and/or magnitude of adverse potential consequences, based on assessed or perceived risks (see also risk assessment, risk perception, risk transfer).
A.II.7.3  T he Uses of the Term Risk and Risk Management

In this report, with the aim of improving the ability of decision-makers to understand and manage risk, the term is used when considering the potential for adverse outcomes and the uncertainty relating to these outcomes. 
The term risk is not used as a simple substitute for probability or 
chance, to describe physical hazards, or as generic term for ‘anything bad that may happen in future’. While the probability of an adverse outcome does not necessarily have to be quantified, it needs to be characterised in some way to allow a risk assessment to inform responses via risk management.
In the AR6, risk refers to the potential for adverse consequences

only. The term hazard is used where climatic events or trends has an identified potential for having adverse consequences to specific elements of an affected system. The contribution of Working Group I to the AR6 uses the more general term ‘climatic impact driver’ where a specific change in climate could have positive or negative consequences, and where a given climatic change may therefore act as a driver of risk or of an opportunity.
A.II.7.4  Examples of Application in the Context of Mitigation
Climate-related risk to food security arises from multiple drivers that 
include both climate change impacts, responses to climate change and other stressors.
In the context of responses to climate change, drivers of risk include

the demand for land from climate change responses (both adaptation and mitigation), the role of markets (e.g.,  price spikes related to biofuel demand in other countries), governance (how are conflicts about access to land and water resolved) and human behaviour more generally (e.g., trade barriers, dietary preferences).
Given the multitude of drivers, the risk to food security depends on 
assumptions about what drivers of risk are changing and which are assumed to remain constant. Such assumptions are important for analytical robustness and are stated where relevant.
Risk in the Investment and Finance LiteratureThe investment and finance literature and practitioner community

broadly distinguish between ‘physical risk’ and ‘transition risk’. The term ‘physical risk’ generally refers to risks arising from climate change impacts and climate-related hazards, while the term ‘transition risk’ typically refers to risks associated with the transition to a low carbon economy. These two types of risk may interact and create cascading or compounding risks.
Physical RiskIn much of the business and financial literature, the term ‘physical

risk’ relates to those derived from the hazard × exposure × vulnerability framework. Physical risks arise from the potential for climate change impacts on the financial value of assets such as industrial plants or real estate, risks to facilities and infrastructure, impact on operations, water and raw material availability and supply chain disruptions. Physical risks have direct financial consequences for organisations where those risks are realised, as well as up-front insurance and investment related costs and downstream effects for users of relevant goods and services. 
Transition Risk Transition risks typically refer to risks associated with transition

to a low carbon economy, which can entail extensive policy, legal, technology, and market changes to address mitigation and adaptation requirements related to climate change. Depending on the nature, speed, and focus of these changes, transition risks may pose varying levels of financial and reputational risk to organisations. Transition risks, if realised, can result in stranded assets, loss of markets, reduced returns on investment, and financial penalties, as well as adverse outcomes for governance and reputation. 
A key issue is the stranding of assets that may not provide the 
expected financial returns and may end up as large financial liabilities. 
Examples of types of transition risk relating to business, finance 
and investments:
•
Risk related to an asset losing its v
alue: the potential for loss of
investment in infrastructure. 
•Risk related to losing some or all of the principal of an investment
(or invested capital).
•Solvency risk:

the risk from reduction in credit ratings due to
potential adverse consequences of climate change or climate policy. This includes liquidity risk or the risk of not being able to accessfunds. Another example is suffering a downgraded credit rating. 
•
Risk of lower
-than-expected return on investment.
•Liability risk:
 lack of response to climate change creates risk of
liability for failure to accurately assess risk of climate change toinfrastructure and people.

1830
Annex II  Definitions , Units and ConventionsAII• Technology risk: reliance on a particular technology to achieve 
an outcome creates the potential for adverse consequences if the 
technology fails to be developed or deployed.
• Policy risk: changes in policy or regulations in response to climate 
change could result in the loss of value of some assets. 
• Market risk: changes in relative prices from increased prices of CO 2 
for instance, could reduce financial returns and hence increase 
risks to investors. 
• Residual risk: in parts of the financial literature, this concept refers 
to adverse consequences that cannot be quantified in probabilistic 
terms. Note that this is different from how the term ‘residual risk’ is 
generally used in IPCC, especially Working Group II, where it means 
the risk remaining after adaptation and risk reduction efforts.
A.II.8 GHG Emission Metrics
Comprehensive mitigation policy relies on consideration of all

anthropogenic forcing agents, which differ widely in their atmospheric 
lifetimes and impacts on the climate system. GHG emission metrics4 
provide simplified information about the effect that emissions 
of different GHGs have on global temperature or other aspects 
of climate, usually expressed relative to the effect of emitting CO 2. 
An assessment of different GHG emission metrics from a mitigation 
perspective is provided in Cross-Chapter Box  2 and Chapter  2 
Supplementary Material, building on the assessment of GHG emission 
metrics from a physical science perspective in AR6 WGI (Forster et al., 
2021, Section 7.6).
The WGIII contribution to the AR6 reports aggregate emissions and 
removals using updated values for the Global Warming Potential 
with a time horizon of 100 years (GWP100) from AR6 WGI unless 
stated otherwise. These updated GWP100 values reflect updated 
scientific understanding of the response of the climate system to

emissions of different gases, and include a methodological update 
to incorporate climate-carbon cycle feedbacks associated with the 
emission of non-CO 2 gases (Forster et al. 2021). For the second-most 
important anthropogenic greenhouse gas, methane, the updated 
GWP100 value of 27 is similar but slightly lower than the value of 28 
reported in the AR5 without climate-carbon cycle feedbacks. A full 
set of GWP100 values used in this report, based on the assessment 
of WGI (Forster et al. 2021, Section 7.6 and Table 7.SM.7), is provided 
in Table 9.
GWP100 was chosen in the WGIII contribution to the AR6 as the default 
GHG emissions metric for both procedural and scientific reasons.
Procedural reasons are to provide continuity with the use of GWP100 
in past IPCC reports and the dominant use of GWP100 in the literature 
assessed by WGIII, and to match decisions made by Governments as 
part of the Paris Agreement Rulebook. Parties to the Paris Agreement

decided to report aggregated emissions and removals (expressed 
as CO 2-eq) based on the Global Warming Potential with a time 
horizon of 100 years (GWP100), using values from IPCC AR5 or 
from a subsequent IPCC report as agreed upon by the CMA,5 and 
4 Emission metrics also exist for aerosols, but these are not commonly used in climate policy. This assessment focuses on GHG emission metrics only.
5 The CMA is the Conference of the Parties serving as the meeting of the Parties to the Paris Agreement.to account for future nationally determined contributions (NDCs) in 
accordance with this approach. Parties may also report supplemental 
information on aggregate emissions and removals, expressed as 
CO2-eq, using other GHG emission metrics assessed by the IPCC 
(4/CMA.1 and 18/CMA.1: UNFCCC 2019).
Scientific reasons for the use of GWP100 as default GHG emission 
metric in WGIII are that GWP100 approximates the relative 
damages caused by the two most important anthropogenic GHGs

CO2 and CH 4 for social discount rates around 3%. In addition, 
for pathways that limit warming to 2°C (>67%) or lower, using 
GWP100 to inform cost-effective abatement choices between gases 
would achieve these long-term temperature goals at close to least 
global cost within a  few percent (high confidence) (see Cross-
Chapter Box 2 in Chapter 2).
However, all emission metrics have limitations and uncertainties, 
given that they simplify the complexity of the physical climate 
system and its response to past and future GHG emissions. The most 
suitable metric for any given climate policy application, depends on 
judgements about the specific context, policy objectives and the way 
in which a metric would be used.
Wherever emissions, removals and mitigation potentials are 
expressed as CO 2-eq in this report, efforts have been made to 
recalculate those values consistently in terms of GWP100 values from 
AR6 WGI. However, in some cases it was not possible or feasible to

disentangle conclusions from the existing literature into individual 
gases and then re-aggregate those emissions using updated GWP100 
values. The existing literature assessed by WGIII uses a range of 
GWP100 values from previous IPCC reports; for CH 4, these values 
vary between 21 (based on the IPCC Second Assessment Report) 
to 28 or even 34 (based on the IPCC Fifth Assessment Report and 
depending on whether the study included or excluded climate-carbon 
cycle feedbacks). Consistent application of any metric is challenging 
as individual GHG emission species are not always provided in the 
literature assessed by WGIII. Where a full recalculation of CO 2-eq 
emissions or mitigation potentials into GWP100 AR6 values was not 
possible or feasible, and especially if non-CO 2 emissions constitute 
only a minor fraction of total emissions or abatement, individual 
chapters note this inconsistency and provide an indication of the 
potential magnitude of inconsistency.

To further reduce ambiguity regarding actual climate outcomes over 
time from any given set of emissions, the WGIII contribution to the 
AR6 reports emissions and mitigation options for individual gases 
where possible based on the available literature, and reports CO 2-eq 
emissions where this is judged to be policy relevant by author teams 
in addition to, not instead of individual gases.

1831
Definitions, Units and Conventions Annex IIAIITable 9 | GWP100 values and atmospheric lifetimes for a range of GHGs, 
based on AR6 WGI (Forster et al. 2021).
Gas AR6 – GWP100 Lifetime
CO2 1 N/A
CH4 (biogenic) 27.0 11.8
CH4 (fossil – combustion)627.0 11.8
CH4 (fossil – fugitive and process) 29.8 11.8
N2O 273 109
HFC-32 770 5.4
HFC-143a 5807 51
CF4 7379 50,000
C2F6 12,410 10,000
C3F8 9289 2600
C4F10 10,022 2600
C5F12 9218 4100
C6F14 8617 3100
C7F16 8409 3000
c-C4F8 13,902 3000
HFC-125 3744 30
HFC-134a 1526 14
HFC-152a 164 1.6
HFC-227ea 3602 36
HFC-23 14,590 228
HFC-236fa 8689 213
HFC-245fa 962 7.9
HFC-365mfc 913 8.9
HFC-43-10-mee 1599 17
SF6 25,184 3200
NF3 17,423 569
Part III: Emissions Datasets
In this section we report on the historical emissions data used in 
the report (Section  9), the sectoral mapping on emissions sources 
(Section  9.1), the methane emissions sources (Section  9.2), and 
indirect emissions (Section 10).
A.II.9 Historical Data

Historic emissions data for countries, regions and sectors are 
presented throughout the report, but especially in Chapters 2, 
6–7, 9–11, the Technical Summary and Summary for Policymakers. 
To ensure consistency and transparency we use the same emissions 
data across these chapters, with a single methodology, division 
of emissions sources, and following the classification scheme 
of countries and areas in Section 1 above.
6 The biogenic CH 4 GWP100 value applies here, given Tier 1 IPCC CO 2 emissions factors which are based on total carbon content. The associated emissions are estimated 
on the bases of complete (100%) oxidation to CO 2 of carbon contained in combusted mass.Our primary data source is the Emissions Database for Global 
Atmospheric Research (EDGAR) (Crippa et al. 2021, Minx et al. 2021). 
This dataset provides annual CO 2, CH 4, N2O and F-gas emissions on 
a country and emissions source level for the time span 1970 to 2019.

The fossil fuel combustion component of EDGAR is closely linked to 
and sourced from International Energy Agency (IEA 2021) energy 
and emissions estimates. Section  2.2.1 in Chapter  2 of this report 
describes the differences between and coverage of different global 
emissions datasets.
In addition to EDGAR, land-use CO 2 emissions are sourced as the 
mean of three bookkeeping models, in a convention established by 
the Global Carbon Project (Friedlingstein et al. 2020) and consistent 
with the Working Group I approach. The bookkeeping models are 
BLUE (Bookkeeping of Land Use Emissions), Hansis et  al. (2015), 
Houghton and Nassikas (2017) and OSCAR (Gasser et al. 2020).
Global total greenhouse gas emissions reported throughout AR6 
are the sum of EDGAR and land-use CO 2 emissions. Significant 
uncertainties are associated with each gas and emissions source. 
These uncertainties are comprehensively treated in Section 2.2.1 of 
Chapter 2.

A.II.9.1 Mapping of Emission Sources to Sectors
The list below shows how emission sources in EDGAR are mapped to 
sectors throughout the AR6 WGIII. This defines unambiguous system 
boundaries for the sectors as represented in Chapters 6, 7 and 9–11 
in the report and enables a discussion and representation of emission 
sources without double-counting.
Emission sources follows the definitions by the IPCC Task Force on 
National Greenhouse Gas Inventories (TFI) (IPCC 2019). EDGARv6 
identifies each source as either ‘Fossil’ or ‘Bio’. The ‘Bio’ label 
indicates the biomass component of fuel combustion, while ‘Fossil’ 
is the default label for all other emissions sources (including, for 
example, agricultural GHG emissions).

1832
Annex II  Definitions , Units and ConventionsAIITable 10 | Mapping emission sources to sectors.
Chapter title Subsector title EDGAR code IPCC 2019 Gases
AFOLUBiomass burning 
(CO 2, CH 4)4F1 (bio), 4F2 (bio), 4F3 (bio), 4F4 (bio), 4F5 (bio) 3.C.1.b (bio) CH4, N2O
AFOLUEnteric fermentation 
(CH 4)4A1-d (fossil), 4A1-n (fossil), 4A2 (fossil), 4A3 (fossil), 
4A4 (fossil), 4A5 (fossil), 4A6 (fossil), 4A7 (fossil), 
4A8 (fossil)3.A.1.a.i (fossil), 3.A.1.a.ii (fossil), 
3.A.1.b (fossil), 3.A.1.c (fossil), 3.A.1.d (fossil), 
3.A.1.e (fossil), 3.A.1.f (fossil), 3.A.1.g (fossil), 
3.A.1.h (fossil)CH4
AFOLUManaged soils and 
pasture (CO 2, N2O)4D12 (fossil), 4D13 (fossil), 4D14 (fossil), 4D15 (fossil), 
4D2 (fossil), 4D3a (fossil), 4D3b (fossil), 4D4a (fossil), 
4D4b (fossil)3.C.4 (fossil), 3.C.5 (fossil), 3.C.6 (fossil), 
3.C.3 (fossil), 3.C.2 (fossil)N2O, CO 2
AFOLUManure 
(N2O, CH 4)4B1-d (fossil), 4B1-n (fossil), 4B2 (fossil), 4B3 (fossil),

4B4 (fossil), 4B5 (fossil), 4B6 (fossil), 4B7 (fossil), 
4B8 (fossil), 4B9 (fossil)3.A.2.a.i (fossil), 3.A.2.a.ii (fossil), 3.A.2.b (fossil), 
3.A.2.c (fossil), 3.A.2.i (fossil), 3.A.2.d (fossil), 
3.A.2.e (fossil), 3.A.2.f (fossil), 3.A.2.g (fossil), 
3.A.2.h (fossil)CH4, N2O
AFOLURice cultivation 
(CH 4)4C (fossil) 3.C.7 (fossil) CH4
AFOLUSynthetic fertiliser 
application (N 2O)4D11 (fossil) 3.C.4 (fossil) N2O
BuildingsNon-CO 2 
(all buildings)2F3 (fossil), 2F4 (fossil), 2F9a (fossil), 2F9c (fossil) 2.F .3 (fossil), 2.F .4 (fossil), 2.G.2.c (fossil)c-C4F8, C4F10, CF4, HFC-125, 
HFC-227ea, HFC-23, HFC-236fa, 
HFC-134a, HFC-152a
Buildings Non-residential 1A4a (bio), 1A4a (fossil) 1.A.4.a (bio), 1.A.4.a (fossil) CH4, N2O, CO 2
Buildings Residential 1A4b (bio), 1A4b (fossil) 1.A.4.b (bio), 1.A.4.b (fossil) CH4, N2O, CO 2
Energy systemsCoal mining 
fugitive emissions1B1a1 (fossil), 1B1a1r (fossil), 1B1a2 (fossil),

1B1a3 (fossil), 1B1b2 (fossil), 1B1b4 (fossil)1.B.1.a (fossil), 1.B.1.c (fossil) CO2, CH 4
Energy systems Electricity and heat1A1a1 (bio), 1A1a1 (fossil), 1A1a2 (bio), 1A1a2 (fossil), 
1A1a3 (bio), 1A1a3 (fossil), 1A1a4 (bio), 1A1a4 (fossil), 
1A1a5 (bio), 1A1a5 (fossil), 1A1a6 (bio), 1A1a6 (fossil), 
1A1a7 (bio), 1A1a7 (fossil)1.A.1.a.i (bio), 1.A.1.a.i (fossil), 1.A.1.a.ii (bio), 
1.A.1.a.ii (fossil), 1.A.1.a.iii (bio), 
1.A.1.a.iii (fossil)CO2, CH 4, N2O
Energy systemsOil and gas 
fugitive emissions1B2a1 (bio), 1B2a1 (fossil), 1B2a2 (fossil), 
1B2a3-l (fossil), 1B2a4-l (fossil), 1B2a4-t (fossil), 
1B2a5(e) (fossil), 1B2b1 (fossil), 1B2b3 (fossil), 
1B2b4 (fossil), 1B2b5 (fossil), 1B2c (fossil)1.B.2.a.iii.2 (bio), 1.B.2.a.iii.2 (fossil), 
1.B.2.a.iii.3 (fossil), 1.B.2.a.iii.4 (fossil), 
1.B.2.b.iii.2 (fossil), 1.B.2.b.iii.4 (fossil), 
1.B.2.b.iii.5 (fossil), 1.B.2.b.iii.3 (fossil), 
1.B.2.b.ii (fossil), 1.B.2.a.ii (fossil)CO2, CH 4, N2O
Energy systemsOther (energy

systems)1A1c3 (bio), 1A1c3 (fossil), 1A1c4 (bio), 1A1c5 (bio), 
1A1c5 (fossil), 1A4c1 (bio), 1A4c1 (fossil), 1A4d (bio), 
1A4d (fossil), 1B1b3 (bio), 2F8b (fossil), 7A1 (fossil), 
7A2 (fossil), 7B1 (fossil), 7C1 (fossil)1.A.1.c.ii (bio), 1.A.1.c.ii (fossil), 1.A.1.c.i (bio), 
1.A.1.c.i (fossil), 1.A.4.c.i (bio), 1.A.4.c.i (fossil), 
1.A.5.a (bio), 1.A.5.a (fossil), 1.B.1.c (bio), 
2.G.1.b (fossil), 5.B (fossil), 5.A (fossil)CO2, CH 4, N2O, SF6
Energy systems Petroleum refining 1A1b (bio), 1A1b (fossil) 1.A.1.b (bio), 1.A.1.b (fossil) CO2, CH 4, N2O
Industry Cement 2A1 (fossil) 2.A.1 (fossil) CO2
Industry Chemicals1A2c (bio), 1A2c (fossil), 2A2 (fossil), 2A3 (fossil), 
2A4a (fossil), 2A4b (fossil), 2A7a (fossil), 2B1g (fossil), 
2B1s (fossil), 2B2 (fossil), 2B3 (fossil), 2B4a (fossil), 
2B4b (fossil), 2B5a (fossil), 2B5b (fossil), 2B5d (fossil), 
2B5e (fossil), 2B5f (fossil), 2B5g (fossil), 2B5g2 (fossil), 
2B5h1 (fossil), 2E (fossil), 2E1 (fossil), 3A (fossil),

3B (fossil), 3C (fossil), 3D (fossil), 3D1 (fossil), 3D3 (fossil)1.A.2.c (bio), 1.A.2.c (fossil), 2.A.2 (fossil), 
2.A.4.d (fossil), 2.A.4.b (fossil), 2.A.3 (fossil), 
2.B.1 (fossil), 2.B.2 (fossil), 2.B.3 (fossil), 
2.B.5 (fossil), 2.B.8.f (fossil), 2.B.8.b (fossil), 
2.B.8.c (fossil), 2.B.8.a (fossil), 2.B.4 (fossil), 
2.B.6 (fossil), 2.B.9.b (fossil), 2.D.3 (fossil), 
2.G.3.a (fossil), 2.G.3.b (fossil)CH4, N2O, CO 2, c-C4F8, C2F6, 
C3F8, C4F10, C5F12, C6F14, CF4, 
HFC-125, HFC-134a, HFC-143a, 
HFC-152a, HFC-227ea, HFC-32, 
HFC-365mfc, NF3, SF6, HFC-23
Industry Metals1A1c1 (fossil), 1A1c2 (fossil), 1A2a (bio), 1A2a (fossil), 
1A2b (bio), 1A2b (fossil), 1B1b1 (fossil), 2C1a (fossil), 
2C1b (fossil), 2C1d (fossil), 2C2 (fossil), 2C3a (fossil), 
2C3b (fossil), 2C4a (fossil), 2C4b (fossil), 2C5lp (fossil), 
2C5mp (fossil), 2C5zp (fossil)1.A.1.c.i (fossil), 1.A.1.c.ii (fossil), 1.A.2.a (bio), 
1.A.2.a (fossil), 1.A.2.b (bio), 1.A.2.b (fossil),

1.B.1.c (fossil), 2.C.1 (fossil), 2.C.2 (fossil), 
2.C.3 (fossil), 2.C.4 (fossil), 2.C.5 (fossil), 
2.C.6 (fossil)CO2, CH 4, N2O, C2F6, CF4, SF6
Industry Other (industry)1A2d (bio), 1A2d (fossil), 1A2e (bio), 1A2e (fossil), 
1A2f (bio), 1A2f (fossil), 1A2f1 (fossil), 1A2f2 (fossil), 
1A5b1 (fossil), 2F1a (fossil), 2F1b (fossil), 2F1c (fossil), 
2F1d (fossil), 2F1e (fossil), 2F1f (fossil), 2F2a (fossil), 
2F2b (fossil), 2F5 (fossil), 2F6 (fossil), 2F7a (fossil), 
2F7b (fossil), 2F7c (fossil), 2F8a (fossil), 2F9 (fossil), 
2F9d (fossil), 2F9e (fossil), 2F9f (fossil), 2G1 (fossil), 
7B2 (fossil), 7C2 (fossil)1.A.2.d (bio), 1.A.2.d (fossil), 1.A.2.e (bio), 
1.A.2.e (fossil), 1.A.2.f (bio), 1.A.2.f (fossil), 
1.A.2.k (fossil), 1.A.2.i (fossil), 1.A.5.b.iii (fossil), 
2.F .1.a (fossil), NA (fossil), 2.F .5 (fossil), 
2.E.1 (fossil), 2.E.2 (fossil), 2.E.3 (fossil), 
2.G.1.a (fossil), 2.G.2.c (fossil), 2.G.2.b (fossil), 
2.G.2.a (fossil), 2.D.1 (fossil), 5.A (fossil)CH4, N2O, CO 2, HFC-125,

HFC-134a, HFC-143a, HFC-152a, 
HFC-227ea, HFC-236fa, 
HFC-245fa, HFC-32, HFC-365mfc, 
C3F8, C6F14, CF4, HFC-43-10-mee, 
HFC-134, HFC-143, HFC-23, 
HFC-41, c-C4F8, C2F6, NF3, SF6, 
HCFC-141b, HCFC-142b, C4F10
Industry Waste6A1 (fossil), 6B1 (fossil), 6B2 (fossil), 6C (fossil), 
6Ca (bio), 6Cb1 (fossil), 6Cb2 (fossil), 6D (fossil)4.A.1 (fossil), 4.D.2 (fossil), 4.D.1 (fossil), 
4.C.1 (fossil), 4.C.2 (bio), 4.C.2 (fossil), 
4.B (fossil)CH4, N2O, CO 2

1833
Definitions, Units and Conventions Annex IIAIIChapter title Subsector title EDGAR code IPCC 2019 Gases
Transport Domestic Aviation 1A3a (fossil) 1.A.3.a.ii (fossil) CO2, CH 4, N2O
Transport Inland Shipping 1A3d (bio), 1A3d (fossil) 1.A.3.d.ii (bio), 1.A.3.d.ii (fossil) CH4, N2O, CO 2
TransportInternational 
Aviation1C1 (fossil) 1.A.3.a.i (fossil) CO2, CH 4, N2O
Shipping1C2 (bio), 1C2 (fossil) 1.A.3.d.i (bio), 1.A.3.d.i (fossil) CH4, N2O, CO 2
Transport Other (transport)1A3e (bio), 1A3e (fossil), 1A4c2 (fossil), 1A4c3 (bio), 
1A4c3 (fossil)1.A.3.e.i (bio), 1.A.3.e.i (fossil), 1.A.4.c.ii (fossil), 
1.A.4.c.iii (bio), 1.A.4.c.iii (fossil)CH4, N2O, CO 2
Transport Rail 1A3c (bio), 1A3c (fossil) 1.A.3.c (bio), 1.A.3.c (fossil) CH4, N2O, CO 2
Transport Road 1A3b (bio), 1A3b (fossil) 1.A.3.b_RES (bio), 1.A.3.b_RES (fossil) CH4, N2O, CO 2
A.II.9.2 Methane Emissions Sources
In order to identify emission trends and mitigation opportunities

by sector WGIII allocates each emission source to a sector and 
subsequently a subsector (check Section  9 above). These trends 
and mitigation opportunities are, in most cases and whenever possible, 
reported in the native unit of gases as well as in CO 2-eq using IPCC 
AR6 GWP100 values (Section 8). In the case of methane (CH 4), it has 
two different GWP100 values according to its source. The relevant 
sources of methane are: biogenic methane, fossil methane (source: 
combustion) and fossil methane (source: fugitive and process).
The majority of biogenic methane emissions result from the AFOLU 
sector due to livestock and other agricultural practices, but also 
from the energy systems, building, transport and industry (waste) 
sectors. Meanwhile, fossil methane (combustion) emissions result 
from electricity and heat generation in the energy systems sector 
as well as various combustion activities in all other sectors. Finally,

fossil methane (fugitive and process) is emitted from the extraction 
and transportation of fossil fuels (fugitive methane), in addition to 
some activities in the industry sector (fugitive and process methane). 
See Table 12 below for a comprehensive list.
There are two GWP100 values assigned to methane depending 
on its source: a GWP100 value of 27 for biogenic methane and 
fossil methane  (combustion), and a higher GWP100 value of 29.8 
for fossil  methane (fugitive and process), see Table  11 below. The 
difference between these two GWP100 values arises from treatment 
of the effect of methane conversion into CO 2 during its chemical 
decay in the atmosphere. The higher GWP100 value takes account of 
the warming caused by CO 2 that methane decays into, which adds 
to the warming caused by methane itself, while the lower GWP100 
value does not.
In the case of biogenic methane, the correct GWP100 value is always 
the low value irrespective of the specific source. This is because

all CO 2 originated from biomass is either already estimated and 
reported as CO 2 emissions from AFOLU sector, or in the case of short-
rotation biomass, the original removal of CO 2 from the atmosphere is 
not reported and hence neither does the release of CO 2 back into the 
atmosphere need to be reported.
For fossil methane, the correct GWP100 value depends on the source, 
in other words, combustion source vs fugitive and process sources. 
Fossil methane (fugitive and process) should use the higher GWP100 value because CO 2 converted from methane in the atmosphere is not 
estimated anywhere else.
For fossil methane (combustion), despite it being fossil, the correct 
GWP100 value is always the low one, for the dataset reported here. 
This is due to the fact that the emissions data provider EDGAR 
(Section 9) considers a complete oxidation to CO 2 of all the carbon 
contained in the fossil fuel upon combustion, which is then reflected

in the CO 2 emissions factors for the different sources based on the 
carbon content of fuels. In other words, IPCC (IPCC 2019) methods 
and defaults (Tier 1 IPCC CO 2 emissions factors) have been used 
where the associated CO 2 emissions are estimated on the basis of 
complete (100%) oxidation to CO 2 of carbon contained in combusted 
mass, which includes not only CO 2 directly released to the atmosphere 
but also CO 2 generated in the atmosphere from the carbon released 
as methane and converted to CO 2 only subsequently.
There are two exceptions applied to the above categorisation, both 
belong to the industry sector, sector codes 6Cb1 (Waste incineration – 
uncontrolled municipal solid waste (MSW) burning) and 6D (other 
waste). Uncontrolled MSW burning (6Cb1) includes both biogenic 
and fossil material, with incomplete oxidation for this source even 
when the IPCC Tier 1 default emission/oxidation factor is used. The

GWP100 value adopted for this source is the low one, given that 
the fossil-origin methane component is unlikely to be very large. 
The ‘other waste’ (6D) source may also include both biogenic and 
fossil methane. However, it is unclear what type of waste handling 
is included here. Furthermore, the associated CO 2 emissions are not 
estimated. Therefore, the high GWP100 value is used.
In total, the estimation of EDGAR methane emissions in 2019 
using a  GWP100 value of 27 across all related sources results in 
10.2 GtCO 2-eq, compared to 10.6 GtCO 2-eq using the higher GWP100 
value as described. This is primarily driven by the readjustment of 
methane emissions from hard coal mining, gas production, and 
venting and flaring (sectors 1B1a1, 1B2b1 and 1B2c).
Table 11 | Summary of methane GWP100 values in AR6 depending on type 
and source.
CH4 GWP100 value
CH4 (biogenic) 27
CH4 (fossil – combustion) 27
CH4 (fossil – fugitive and process) 29.8

1834
Annex II  Definitions , Units and ConventionsAIITable 12 | Methane sources and types.
Sector code Description Sector Subsector CH4 type
1A1a1 Public Electricity Generation (biomass) Energy systems Electricity and heat CH4 Biogenic
1A1a1 Public Electricity Generation Energy systems Electricity and heat CH4 Fossil (Combustion)
1A1a2 Public Combined Heat and Power gen. (biom.) Energy systems Electricity and heat CH4 Biogenic
1A1a2 Public Combined Heat and Power gen. Energy systems Electricity and heat CH4 Fossil (Combustion)
1A1a3 Public Heat Plants (biomass) Energy systems Electricity and heat CH4 Biogenic
1A1a3 Public Heat Plants Energy systems Electricity and heat CH4 Fossil (Combustion)
1A1a4 Public Electricity Gen. (own use) (biom.) Energy systems Electricity and heat CH4 Biogenic
1A1a4 Public Electricity Generation (own use) Energy systems Electricity and heat CH4 Fossil (Combustion)

1A1a5 Electricity Generation (autoproducers) (biom.) Energy systems Electricity and heat CH4 Biogenic
1A1a5 Electricity Generation (autoproducers) Energy systems Electricity and heat CH4 Fossil (Combustion)
1A1a6 Combined Heat and Power gen. (autopr.) (biom.) Energy systems Electricity and heat CH4 Biogenic
1A1a6 Combined Heat and Power gen. (autoprod.) Energy systems Electricity and heat CH4 Fossil (Combustion)
1A1a7 Heat Plants (autoproducers) (biomass) Energy systems Electricity and heat CH4 Biogenic
1A1a7 Heat Plants (autoproducers) Energy systems Electricity and heat CH4 Fossil (Combustion)
1A1b Refineries (biomass) Energy systems Petroleum refining CH4 Biogenic
1A1b Refineries Energy systems Petroleum refining CH4 Fossil (Combustion)
1A1c1 Fuel combustion coke ovens Industry Metals CH4 Fossil (Combustion)
1A1c2 Blast furnaces (pig iron prod.) Industry Metals CH4 Fossil (Combustion)
1A1c3 Gas works (biom.) Energy systems Other (energy systems) CH4 Biogenic

1A1c3 Gas works Energy systems Other (energy systems) CH4 Fossil (Combustion)
1A1c4 Fuel comb. charcoal production (biom.) Energy systems Other (energy systems) CH4 Biogenic
1A1c5 Other transf. sector (BKB, etc.) (biom.) Energy systems Other (energy systems) CH4 Biogenic
1A1c5 Other transformation sector (BKB, etc.) Energy systems Other (energy systems) CH4 Fossil (Combustion)
1A2a Iron and steel (biomass) Industry Metals CH4 Biogenic
1A2a Iron and steel Industry Metals CH4 Fossil (Combustion)
1A2b Non-ferrous metals (biomass) Industry Metals CH4 Biogenic
1A2b Non-ferrous metals Industry Metals CH4 Fossil (Combustion)
1A2c Chemicals (biomass) Industry Chemicals CH4 Biogenic
1A2c Chemicals Industry Chemicals CH4 Fossil (Combustion)
1A2d Pulp and paper (biomass) Industry Other (industry) CH4 Biogenic
1A2d Pulp and paper Industry Other (industry) CH4 Fossil (Combustion)
1A2e Food and tobacco (biomass) Industry Other (industry) CH4 Biogenic

1A2e Food and tobacco Industry Other (industry) CH4 Fossil (Combustion)
1A2f Other industries (stationary) (biom.) Industry Other (industry) CH4 Biogenic
1A2f Other industries (stationary) (fos.) Industry Other (industry) CH4 Fossil (Combustion)
1A2f1 Off-road machinery: construction (diesel) Industry Other (industry) CH4 Fossil (Combustion)
1A2f2 Off-road machinery: mining (diesel) Industry Other (industry) CH4 Fossil (Combustion)
1A3a Domestic air transport Transport Domestic Aviation CH4 Fossil (Combustion)
1A3b Road transport (incl. evap.) (biom.) Transport Road CH4 Biogenic
1A3b Road transport (incl. evap.) (foss.) Transport Road CH4 Fossil (Combustion)
1A3c Non-road transport (rail, etc.) (biom.) Transport Rail CH4 Biogenic
1A3c Non-road transport (rail, etc.) (fos.) Transport Rail CH4 Fossil (Combustion)
1A3d Inland shipping (biom.) Transport Inland Shipping CH4 Biogenic
1A3d Inland shipping (fos.) Transport Inland Shipping CH4 Fossil (Combustion)

1A3e Non-road transport (biom.) Transport Other (transport) CH4 Biogenic
1A3e Non-road transport (fos.) Transport Other (transport) CH4 Fossil (Combustion)
1A4a Commercial and public services (biom.) Buildings Non-residential CH4 Biogenic
1A4a Commercial and public services (fos.) Buildings Non-residential CH4 Fossil (Combustion)
1A4b Residential (biom.) Buildings Residential CH4 Biogenic
1A4b Residential (fos.) Buildings Residential CH4 Fossil (Combustion)

1835
Definitions, Units and Conventions Annex IIAIISector code Description Sector Subsector CH4 type
1A4c1 Agriculture and forestry (biom.) Energy systems Other (energy systems) CH4 Biogenic
1A4c1 Agriculture and forestry (fos.) Energy systems Other (energy systems) CH4 Fossil (Combustion)
1A4c2 Off-road machinery: agric./for. (diesel) Transport Other (transport) CH4 Fossil (Combustion)
1A4c3 Fishing (biom.) Transport Other (transport) CH4 Biogenic
1A4c3 Fishing (fos.) Transport Other (transport) CH4 Fossil (Combustion)
1A4d Non-specified other (biom.) Energy systems Other (energy systems) CH4 Biogenic
1A4d Non-specified other (fos.) Energy systems Other (energy systems) CH4 Fossil (Combustion)
1A5b1 Off-road machinery: mining (diesel) Industry Other (industry) CH4 Fossil (Combustion)
1B1a1 Hard coal mining (gross) Energy systems Coal mining fugitive emissions CH4 Fossil (Fugitive)

1B1a1r Methane recovery from coal mining Energy systems Coal mining fugitive emissions CH4 Fossil (Fugitive)
1B1a2 Abandoned mines Energy systems Coal mining fugitive emissions CH4 Fossil (Fugitive)
1B1a3 Brown coal mining Energy systems Coal mining fugitive emissions CH4 Fossil (Fugitive)
1B1b1 Fuel transformation coke ovens Industry Metals CH4 Fossil (Fugitive)
1B1b3 Fuel transformation charcoal production Energy systems Other (energy systems) CH4 Biogenic
1B2a1 Oil production (biom.) Energy systems Oil and gas fugitive emissions CH4 Biogenic
1B2a1 Oil production Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2a2 Oil transmission Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2a3-l Tanker loading Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2a4-l Tanker oil transport (crude and NGL) Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)

1B2a4-t Transport by oil trucks Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2a5(e) Oil refineries (evaporation) Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2b1 Gas production Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2b3 Gas transmission Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2b4 Gas distribution Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1B2c Venting and flaring during oil and gas production Energy systems Oil and gas fugitive emissions CH4 Fossil (Fugitive)
1C1 International air transport Transport International Aviation CH4 Fossil (Combustion)
1C2 International marine transport (biom.) Transport International Shipping CH4 Biogenic
1C2 International marine transport (bunkers) Transport International Shipping CH4 Fossil (Combustion)
2B4a Silicon carbide production Industry Chemicals CH4 Fossil (Process)

2B5a Carbon black production Industry Chemicals CH4 Fossil (Process)
2B5b Ethylene production Industry Chemicals CH4 Fossil (Process)
2B5d Styrene production Industry Chemicals CH4 Fossil (Process)
2B5e Methanol production Industry Chemicals CH4 Fossil (Process)
2B5g Other bulk chemicals production Industry Chemicals CH4 Fossil (Process)
2C1d Sinter production Industry Metals CH4 Fossil (Process)
2C2 Ferroy Alloy production Industry Metals CH4 Fossil (Process)
4A1-d Dairy cattle AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A1-n Non-dairy cattle AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A2 Buffalo AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A3 Sheep AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A4 Goats AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A5 Camels and Lamas AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A6 Horses AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4A7 Mules and asses AFOLU Enteric Fermentation (CH 4) CH4 Biogenic

4A8 Swine AFOLU Enteric Fermentation (CH 4) CH4 Biogenic
4B1-d Manure Man.: Dairy Cattle (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B1-n Manure Man.: Non-Dairy Cattle (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B2 Manure Man.: Buffalo (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B3 Manure Man.: Sheep (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B4 Manure Man.: Goats (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B5 Manure Man.: Camels and llamas (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic

1836
Annex II  Definitions , Units and ConventionsAIISector code Description Sector Subsector CH4 type
4B6 Manure Man.: Horses (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B7 Manure Man.: Mules and asses (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B8 Manure Man.: Swine (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4B9 Manure Man.: Poultry (confined) AFOLU Manure management (N 2O, CH 4) CH4 Biogenic
4C Rice cultivation (CH4) AFOLU Rice cultivation (CH 4) CH4 Biogenic
4F1 Field burning of agric. res.: cereals AFOLU Biomass burning (CH 4, N2O) CH4 Biogenic
4F2 Field burning of agric. res.: pulses AFOLU Biomass burning (CH 4, N2O) CH4 Biogenic
4F3 Field burning of agric. res.: tuber and roots AFOLU Biomass burning (CH 4, N2O) CH4 Biogenic
4F4 Field burning of agric. res.: sugar cane AFOLU Biomass burning (CH 4, N2O) CH4 Biogenic
4F5 Field burning of agric. res.: other AFOLU Biomass burning (CH 4, N2O) CH4 Biogenic

6A1 Managed waste disposal on land Industry Waste CH4 Biogenic
6B1 Industrial wastewater Industry Waste CH4 Biogenic
6B2 Domestic and commercial wastewater Industry Waste CH4 Biogenic
6C Waste incineration – hazardous Industry Waste CH4 Fossil (Combustion)
6Ca Waste incineration – biogenic Industry Waste CH4 Biogenic
6Cb1 Waste incineration – uncontrolled MSW burning Industry Waste CH4 Fossil (Combustion)
6Cb2 Waste incineration – other non-biogenic Industry Waste CH4 Fossil (Combustion)
6D Other waste Industry Waste CH4 Fossil (Process)
7A1 Coal fires (underground) Energy systems Other (energy systems) CH4 Fossil (Combustion)
7A2 Oil fires (Kuwait) Energy systems Other (energy systems) CH4 Fossil (Combustion)
A.II.10 Indirect Emissions
Carbon dioxide emissions resulting from fuel combusted to produce 
electricity and heat are traditionally reported in the energy sector. 
An indirect emissions accounting principle allocates these emissions

to the end-use sectors (industry, buildings, transport, and agriculture) 
where the electricity and heat are ultimately consumed. Attributing 
indirect emissions to consuming sectors makes it possible to assess 
the full potential impact of demand-side mitigation actions that 
reduce electricity and heat consumption (de la Rue du Can et al. 2015).
In order to estimate the indirect emissions of sectors and subsectors, 
the CO 2 Emissions from Fuel Combustion dataset of the International 
Energy Agency (IEA 2020a) is used. This database reports direct and 
indirect CO 2 emissions for IEA sectors, which are related to the IPCC 
(IPCC 2019) classification of emissions sources. The IEA adopted 
a new methodology in 2020 that is in line with the methodology used 
in Annex II of the WGIII contribution to AR5 (Krey et al. 2014), namely 
Section A.II.4. The IEA now estimates individual electricity and heat 
specific emission factors and allocates indirect emissions related

to electricity and heat in the sectors where these forms of energy 
are used respectively (IEA 2020b). In order to estimate the share of 
energy input that results in the production of heat from the share that 
results in the production of electricity in Combined heat and Power 
plants, the IEA fixes the efficiency for heat production equal to 90%, 
which is the typical efficiency of a heat boiler and then allocates the 
remaining inputs to electricity production (IEA 2020b). 
The base data for total global, regional and sectoral emissions in this 
report is the EDGAR database (see Section 9). Since there are some 
discrepancies between the electricity and heat emissions totals in 
EDGAR and IEA, we make some adjustments in order to estimate indirect emissions in EDGAR using the IEA data. First, we match the 
sectors in EDGAR and IEA. Second, for each country and emissions 
source available in the IEA database, we take the IEA indirect

emissions value and divide it by the total IEA value for electricity and 
heat. Third, we multiply these values through by the EDGAR value for 
electricity and heat. This procedure ensures that indirect emissions, 
in principle, sum to the correct total (EDGAR) value of electricity and 
heat that we use elsewhere in the reporting. However, total indirect 
emissions still do not sum to the total electricity and heat sector. This 
is due to an incomplete allocation of electricity and heat emissions 
in the IEA dataset, equal to 0.008 GtCO 2 in 2018, or about 0.06% of 
the total electricity and heat generation.
Additionally, a couple of adjustments were made to allocate 
emissions from IEA sector categories to IPCC categories from IPCC 
Task force definition as described in IPCC (2019) Guidelines (see 
Section 9). These include:
• Other non-specified sector: the IEA energy statistics report 
final energy and electricity use for three end-use sectors:

industry, transport, and other. The ‘other’ category is further 
subdivided into agriculture, fishing, commercial and public 
services, residential, and non-specified other. The ‘non-specified 
other’ category includes energy used for agriculture, fishing, 
commercial and public services, and residential sectors that has 
not been allocated to these end-use sectors by the submitting 
countries. In most cases, there is no entry in the non-specified 
other category, indicating that all end-use energy consumption 
has been allocated to other end-use sectors. However, for some 
countries the energy reported in the non-specified other category 
needed to be allocated to the appropriate end-use sectors. 
To perform this allocation, the energy use in the non-specified

1837
other category was allocated to the other end-use sectors based 
on the share of energy allocated to each of these sub-sectors for each region.
•
Other energy industry own use: emissions from this category inthe IEA statistics corresponds to the IPCC Source/Sink categories1A1b and 1A1c (see Section  9) and contains emissions fromfuel 
combusted in energy transformation industries that are not
producing heat and/or power and therefore include oil refineries, coal mining, oil and gas extraction and other energy-producingindustries. These emissions were not reallocated to the end usesectors where final products are ultimately consumed due to thelack of data. 
Finally, it is also worth noting that indirect emissions only cover CO
2 
emissions and that a small portion of non-CO 2 are not included in 
the IEA dataset and therefore have not been allocated to the end use sectors. Non-CO
2 emissions from total electricity and heat generation

represents 0.55% of all GHG emissions from that sector.
Part IV: Assessment Methods
In this section we report on assessment methods adopted in the report. Section 11 describes the methodology adopted for assessing the feasibility of mitigation response options. Section 12 describes the methodology adopted for assessing synergies and trade-offs between mitigation options and the SDGs.
A.II.11  Methodology Adopted for Assessing the
Feasibility of Mitigation Response Options
The feasibility assessment aims to identify barriers and enablers of the deployment of mitigation options and pathways. The assessment organises evidence to support decision making on actions and policies that would improve the feasibility of mitigation options and pathways, by removing relevant barriers and strengthening enablers of change.
A.II.11.1  F easibility of mitigation response options

The sectoral chapters in AR6 WGIII assess six dimensions of feasibility, with each dimension comprising a key set of indicators that can be evaluated by combining various strands of literature (see Table 13). The feasibility of systems-level changes is addressed in Chapter 3 of this report. 
The sectoral chapters in this report assess to what extent the indicators 
in Table 13 would be enablers or barriers to implementation using the following scores (Nilsson et al. 2016):
– 
T
he indicator has a negative impact on the feasibility of the option, 
for example, it is associated with prohibitively high costs, levels
of pollution or land use, or low public or political acceptance.
±
 Mixed evidence: the indicator has mixed positive and negativeimpacts on the feasibility of the option (e
.g., more land use in
some regions, while lower in other regions).
+ The indicator has a positive impact on the feasibility of theoption,
 for example, it is associated with low costs, pollution,

land use, or high public or political acceptance.
0/NA
 T
he indicator does not affect the feasibility of the option/
criterion is not applicable for the option.
NE
 No evidence av
ailable to assess the impact on the feasibility of 
the option.
LE
 Limited evidence av
ailable to assess the impact on the 
feasibility the option.Table 13 | Feasibility dimensions and indicators to assess the barriers and enablers of implementing mitigation options.
Metric Indicators
Geophysical feasibility– Physical potential:
 physical constraints to implementation. 
– Geophysical resource av ailability (including geological storage capacity): availability of resources needed to implementation. 
– Land use:
 claims on land when option would be implemented.
Environmental-ecological feasibility– Air pollution:
 increase or decrease in air pollutants, such as NH 4, CH 4 and fine dust.
– T
oxic waste, mining, ecotoxicity and eutrophication. 
– W

ater quantity and quality: changes in amount of water available for other uses, including groundwater.
– Biodiversity:
 changes in conserved primary forest or grassland that affect biodiversity, and management to conserve and maintain 
land carbon stocks.
Technological feasibility– Simplicity:
 is the option technically simple to operate, maintain and integrate.
– T
echnology scalability: can the option be scaled up, quickly. 
– Maturity and technology readiness:
 R&D and time needed to implement to option.
Economic feasibility– Costs now,
 in 2030 and in the long term, including investment costs, costs in USD tCO 2-eq–1, and hidden costs.
– Employment effects and economic growth.
Socio-cultural feasibility– Public acceptance:
 extent to which the public supports the option and changes behavior accordingly.
– Effects on health and well-being.
 
– Distributional effects:
 equity and justice across groups, regions, and generations, including security of energy, water, food and poverty.

Institutional feasibility– P
olitical acceptance: extent to which politicians and governments support the option.
– Institutional capacity and governance
, cross-sectoral coordination: capability of institutions to implement and handle the option, 
and to coordinate it with other sectors, stakeholder and civil society.
– Legal and administrative capacity:
 extent to which supportive legal and administrative changes can be achieved.

1838
Annex II  Definitions , Units and ConventionsAIIA.II.11.2
 Assessment
Each sectoral chapter assesses to what extent the indicators listed 
above would be an enabler or barrier to the implementation of selected mitigation options, by using the above scores. Then the total number of minus and plus points were computed, relative to the maximum possible number of points, per feasibility dimensions, for each option; a + counts as two plus points, a – as two minus points, and a ± as one plus and one minus point. The resulting scores reveal the extent to which each feasibility dimension enables or inhibits the deployment of the relevant option, and indicates which type of additional effort would be needed to reduce or remove barriers as to improve the feasibility of relevant options.
The assessment is based on the literature, which is reflected in a line

of sight. When appropriate, it is indicated whether the feasibility of an option varies across context (e.g.,  region), scale (e.g.,  small, medium, full scale), time (e.g., implementation in 2030 versus 2050) and warming level (e.g., 1.5°C versus 2°C). 
Synergies and trade-offs may occur between the feasibility

dimensions, and between specific mitigation options. Therefore, Chapters 3 and 4 employ a systems perspective and discuss the feasibility of mitigation scenarios and pathways in the long term and near to mid-term, respectively, on the basis of the feasibility assessments in the sectoral chapters taking into account such synergies and trade-offs. Chapter  5 (demand, services and social aspects of mitigation), Chapter 13 (national and sub-national policies and institutions), Chapter 14 (international cooperation), Chapter 15 (investment and finance) and Chapter  16 (innovation, technology development and transfer) address technological, economic, socio-cultural and institutional enabling conditions that can enhance the feasibility of options and remove relevant barriers.
A.II.12  Methodology Adopted for Assessing Synergies
and Trade-offs Between Mitigation Options 
and the SDGs
Adopting climate mitigation options can generate multiple positive

(synergies) and negative (trade-offs) interactions with sustainable development. Understanding these are crucial for selecting mitigation options and policy choices that maximise the synergies, minimise trade-offs, and potentially offset trade-offs (Roy et  al. 2018). Chapter  5 in the IPCC’s Special Report on Global Warming of 1.5°C examines the synergies and trade-offs of adaptation and mitigation measures with sustainable development and UN’s Sustainable Development Goals (SDGs). Building on this, the sectoral chapters in the WGIII contribution to the AR6 include a qualitative assessment of the synergies and trade-offs between mitigation options in different sectors and the SDGs based on existing literature. All these assessments are collated and presented in Chapter  17 with a supplementary table including the details of the synergies and trade-offs with a line of sight (Section  17.3.3.7, Figure  17.1 and Supplementary Material Table  17.1). The assessment also recognises that

interactions of mitigation options with the SDGs are context-specific and therefore provides a detailed explanation in the supplementary table of Chapter 17.

For the assessment, the mitigation options were shortlisted from 
each of the sectoral chapters. The sectoral chapters assessed the literature in terms of the impacts of each of these mitigation options on the 17 SDGs. The assessment uses three signs: 
+
to denote positive interaction only (synergies),
 
– to denote negative interaction only (trade-offs) and
± to denote mixed interactions
. 
In some cases, where there is gap in literature, these are left 
blank denoting that these impacts have not been assessed in the literature included in the sectoral chapters. To support these signs, brief statements are provided followed by uncertainty qualifiers in the supplementary table of Chapter 17. These uncertainty qualifiers denote the confidence levels (low, medium and high).

1843
Scenarios and Modelling Methods  Annex IIIAIII
Preamble
The use of scenarios and modelling methods are pillars in IPCC Working 
Group III (WGIII) Assessment Reports. Past WGIII assessment report cycles identified knowledge gaps about the integration of modelling across scales and disciplines, mainly between global integrated assessment modelling methods and bottom-up modelling insights of mitigation responses. The need to improve the transparency of model assumptions and enhance the communication of scenario results was also recognised.
This annex on Scenarios and Modelling Methods aims to address some 
of these gaps by detailing the modelling frameworks applied in the WGIII Sixth Assessment Report (AR6) chapters and disclose scenario assumptions and its key parameters. It has been explicitly included in the Scoping Meeting Report of the WGIII contribution to the AR6 and approved by the IPCC Panel at the 46th Session of the Panel.

The annex includes two parts: Part I on Modelling Methods summarises 
methods and tools available to evaluate sectoral, technological and behavioural mitigation responses as well as integrated assessment models (IAMs) for the analysis of ‘whole system’ transformation pathways; Part II on Scenarios sets out the portfolio of climate change scenarios and mitigation pathways assessed in the AR6 WGIII chapters, its underlying principles and interactions with scenario assessments by WGI and WGII.
Part I:  Modelling Methods
A.III.I.1  Overview of Modelling Tools
Modelling frameworks vary vastly among themselves, and several key characteristics can be used as basis for model classification (Scrieciu et al. 2013; Dodds et al. 2015; Hardt and O’Neill 2017; Capellán-Pérez et al. 2020). Broadly, literature characterises models along three dimensions: (i) level of detail and heterogeneity, (ii) mathematical algorithm concepts, and (iii) temporal and spatialsystem boundaries (Krey 2014).

Commonly climate mitigation models are referred to as bottom-up 
and top-down depending upon their degree of detail (van Vuuren et al. 2009). Generally, bottom-up approaches present more

systematic individual technological details about a reduced number of mitigation strategies of a specific sector or sub-sector. These models tend to disregard relations between specific sectors/technologies and miss evaluating interactions with the whole system. On the other hand, top-down approaches present a more aggregated and global analysis, in detriment of less detailed technological heterogeneity. They tend to focus on interactions within the whole system, such as market and policy instrument interactions within the global economy systems. Studies using top-down models are more capable of representing economic structural change than adopting technology-explicit decarbonisation strategies (van Vuuren et al. 2009; Kriegler et al. 2015a). Integrated assessment models (IAMs) typically use a top-down approach to model sectoral mitigation strategies.Although this dichotomic classification has been mentioned in the literature, since the IPCC’s Fifth Assessment Report (AR5), climate

mitigation models have evolved towards a  more hybrid approach incorporating attributes of both bottom-up and top-down

approaches. This is partly due to different modelling communities having different understandings of these two approaches’ principles, which can be misleading.
One of the most basic aspects of a  modelling tool is how it

approaches the system modelled from a  solution perspective. A broad interpretation of mathematical algorithm concepts classifies models as simulation and optimisation models. Simulation models are based on the evaluation of the dynamic behaviour of a system (Lund et al. 2017). They can be used to determine the performance of a system under alternative options of key parameters in a plausible manner. Most often, simulation models require comprehensive knowledge of each parameter, in order to choose a  specific path under several alternatives. On the other hand, optimisation models seek to maximise or minimise a  mathematical objective function under a set of constraints (Baños et al. 2011; Iqbal et al. 2014). Most often, the objective function represents the total cost or revenue of a  given system or the total welfare of a  given society. One major aspect of optimisation models is that the solution is achieved by simultaneously binding a  set of constraints, which can be used to

represent real-life limitations on the system, such as: constraints on flows, resource and technology availability, labour and financial limitations, environmental aspects, and many other characteristics that the model may require (Fazlollahi et al. 2012; Pfenninger et al. 2014; Cedillos Alvarado et al. 2016). Specifically, when modelling climate mitigation responses, limiting carbon budgets is often used to represent future temperature level pathways (Rogelj et al. 2016; Millar et al. 2017; Peters 2018; Gidden et al. 2019).

Another major distinction among modelling tools is related to the

solution methodology from a  temporal perspective. They can have a perfect foresight intertemporal assumption or a recursive-dynamic assumption. Intertemporal optimisation with perfect foresight is an optimisation method for achieving an overall optimal solution over time. It is based on perfect information on all future states of a system and assumptions (such as technology availability and prices) and, as such, today’s and future decisions are made simultaneously, resulting in a single path of optimal actions that lead to the overall optimal solution (Keppo and Strubegger 2010; Gerbaulet et al. 2019). Such a modelling approach can present an optimal trajectory of the set of actions and policies that would lead to the overall first-best solution. However, real-life decisions are not always based on optimal solutions (Ellenbeck and Lilliestam 2019) and, therefore, solutions from perfect foresight models can be challenging to be implemented by policymakers (Pindyck 2013, 2017). For

instance, perfect foresight implies perfect knowledge of the future states of the system, such as future demand for goods and products and availability of production factors and technology.

Recursive-dynamic models, also known as myopic or limited 
foresight models, make decisions over sequential periods of time. For each time step, the solution is achieved without information on future

1844
Annex III Scenarios and Modelling Methods AIII
time steps. Therefore, the solution path is a series of solutions in short 
trajectories that, ultimately, is very unlikely to achieve the overall 
optimal solution over the whole time period considered (Fuso Nerini 
et al. 2017). Nonetheless, the solution represents a set of possible and 
plausible policies and behavioural choices of the agents that could be 
taken in short-term cycles, without perfect information (Heuberger 
et al. 2018; Hanna and Gross 2020). In between, some models 
consider imperfect or adaptive expectations, where economic 
decisions are based on past, current and imperfectly anticipated 
future information (Keppo and Strubegger 2010; Kriegler et al. 2015a; 
Löffler et al. 2019). Modelling tools can also be differentiated by 
their level of representation of economic agents and sectors: they 
can have a  full representation of all agents of the economy and

their interactions with each other (general equilibrium) or focus 
on a more detailed representation of a subset of economic sectors 
and  agents (partial equilibrium) (Cheng et al. 2015; Babatunde  
et al. 2017; Hanes and Carpenter 2017; Sanchez et al. 2018; 
Guedes et al. 2019; Pastor et al. 2019) (Annex III.I.2).
The most basic aspect to differentiate models is their main objective 
function, which includes the detail at which they represent key 
sectors, systems and agents. This affects the decision on methodology 
and other coverage aspects. Several models have been developed for 
different sectoral representation, such as the energy (Annex III.I.3), 
buildings (Annex III.I.4), transport (Annex III.I.5), industry 
(Annex III.I.6) and land-use (Annex III.I.7) models.
Modelling exercises vary considerably in terms of key characteristics, 
including geographical scales, time coverage, environmental 
variables, technologies portfolios, and socio-economic assumptions.

A detailed comparison of key characteristics of global and national 
models used in this report is presented in Annex III.I.9. Geographical 
coverage ranges from sub-national (Cheng et al. 2015; Feijoo et al. 
2018; Rajão et al. 2020), national (Li et al. 2019; Sugiyama et al. 2019; 
Vishwanathan et al. 2019; Schaeffer et al. 2020), regional (Vrontisi 
et al. 2016; Hanaoka and Masui 2020) and global (Gidden et al. 
2018; Kriegler et al. 2018a; McCollum et al. 2018; Rogelj et al. 2019b; 
Drouet et al. 2021) models. Even models with the same geographical 
coverage can still be significantly different from each other, for 
instance, due to the number of regions within the model. Models 
can also have spatially implicit and explicit formulations, which in 
turn can have different spatial resolution. This distinction is especially 
important for land-use models, which account for changes in land 
use and agricultural practices (Annex III.I.7: Land-use modelling).

The time horizon, time steps and time resolution are major aspects 
that differ across models. Model horizon can range from short- to 
long-term, typically reaching from a few years to up until the end 
of the century (Fujimori et al. 2019b; Gidden et al. 2019; Rogelj 
et al. 2019a; Ringkjøb et al. 2020). Time resolution is particularly 
relevant for specific applications, such as power sector models, which 
have detailed representation of power technologies dispatch and 
operation (Soria et al. 2016; Abujarad et al. 2017; Guan et al. 2020).
Life Cycle Assessment (LCA) is an integrated technique to evaluate 
the sustainability of a product throughout its life cycle. It quantifies 
the environmental burdens associated with all stages from the 
extraction of raw materials, through the production of the product itself, its utilisation, and end-life, either via reuse, recycling or 
final disposal (Rebitzer et al. 2004; Finnveden et al. 2009; Guinée

et al. 2011; Curran 2013; Hellweg and Milà i Canals 2014). 
The  environmental impacts covered include all types of loads 
on  the environment through the extraction of natural resources 
and emission of hazardous substances. For this reason, LCA has 
the flexibility to evaluate an entire product system, hence avoiding 
sub-optimisation in a  single process and identifying the products 
and processes that result in the least environmental impact. Thus, 
it allows for the quantification of possible trade-offs between 
different environmental impacts (e.g.,  eliminating air emissions by 
increasing non-renewable energy resources) (Hawkins et al. 2013; 
Nordelöf et al. 2014; Gibon et al. 2017) and/or from one stage to 
other (e.g.,  reuse or recycling a  product to bring it back in at the 
raw material acquisition phase) (Hertwich and Hammitt 2001a,b). It 
gives a holistic view of complex systems and reduces the number of

parameters for which decisions have to be taken, while not glossing 
over technical and economical details. In recent years, LCA has been 
widely used in both retrospective and prospective analysis of product 
chains in various climate mitigation fields, namely comparing existing 
energy technologies with planned alternatives (Cetinkaya et al. 2012; 
Portugal-Pereira et al. 2015), product innovation and development 
(Wender et al. 2014; Portugal-Pereira et al. 2015; Sharp and Miller 
2016), certification schemes (Prussi et al. 2021), or supply chain 
management (Hagelaar 2001; Blass and Corbett 2018).
Two different types of LCA approaches can be distinguished: 
Attributional Life Cycle Assessment (ALCA) and Consequential 
Life Cycle Assessment (CLCA). ALCA aims at describing the direct 
environmental impacts of a  product. It typically uses average 
and historical data to quantify the environmental burden during 
a product’s life cycle, and it tends to exclude market effects or other

indirect effects of the production and consumption of products 
(Baitz 2017). CLCA, on the other hand, focuses on the effects of 
changes due to product life cycle, including both consequences 
inside and outside the product life cycle (Earles and Halog 2011). 
Thus, the system boundaries are generally expanded to represent 
direct and indirect effects of products’ outputs. CLCA tends to 
describe more complex systems than ALCA, which are highly 
sensitive to data assumptions (Plevin et al. 2014; Weidema et al. 
2018; Bamber et al. 2020).
Integrated assessment models (IAMs) are simplified  
representations of complex physical and social systems, focusing 
on the interaction between economy, society and the environment 
(Annex  III.I.9). They represent the coupled energy-economy-land-
climate system to varying degrees. In a  way, IAMs differ among 
themselves on all the topics discussed in this section: significant

variation in geographical, sectoral, spatial and time resolution; they 
rely greatly on socio-economic assumptions; different technological 
representation; partial or general equilibrium assumptions; 
differentiated between perfect foresight or recursive-dynamic 
methodology. The difficulty in fully representing the extent of 
climate damages in monetary terms may be the most important 
and challenging limitation of IAMs and it is mostly directed to cost-
benefit IAMs. However, all categories of IAMs present important 
limitations (Annex III.I.9).

1845
Following this brief synopsis of modelling taxonomies, Section I.2 
details key aspects of economic frameworks and principles used to 
model climate mitigation responses and estimate their costs. Sections 
I.3, I.4, I.5, I.6, and I.7 present key aspects of sectoral modelling 
approaches in energy systems, buildings, transport, industry, and 
land use, respectively. Interactions between WGI climate emulators 
and WGIII mitigation models are described in Section I.8. A review 
of integrated assessment model approaches, their components 
and limitations, is presented in Section I.9. Sections I.10 and I.11 
present comparative tables of key characteristics and measures 
of national and global models that contributed to the AR6 WGIII 
scenario database.
A.III.I.2 Economic Frameworks and Concepts 
Used in Sectoral Models and Integrated 
Assessment Models
Several types of ‘full-economy’ frameworks are used in integrated

assessment models. The general equilibrium framework – often 
referred to as Computable General Equilibrium (CGE) – represents the 
economic interdependencies between multiple sectors and agents, 
and the interaction between supply and demand on multiple markets 
(Robinson et al. 1999). It captures the full circularity of economic 
flows through income and demand relationships and feedbacks 
including the overall balance of payments. Most CGE approaches 
used are neoclassical supply-led models with market clearing 
based on price adjustment. Representative agents usually minimise 
production costs or maximise utility under given production and 
utility function, although optimal behaviours are not a precondition 
per se. Most CGE models also include assumptions of perfect markets 
with full employment of factors although market imperfections and 
underemployment of factors (e.g., unemployment) can be assumed 
(Babiker and Eckaus 2007; Guivarch et al. 2011). CGE frameworks can

either be static or dynamic and represent pathways as a sequence of 
equilibria in the second case.
Macro-econometric frameworks represent similar sectoral 
interdependence with balance of payments as general equilibrium, 
and are sometimes considered a subset of the general equilibrium 
framework. They differ from standard neoclassical CGE models in 
the main aspect that economic behaviours are not micro-founded 
optimising behaviours but are represented by macroeconomic and 
sectoral functions estimated through econometric techniques (Barker 
and Scrieciu 2010). In addition, they usually adopt a  demand-
led post-Keynesian approach where final demand and investment 
determine supply and not the other way around. Prices also do not 
instantaneously clear markets and adjust with lag.
Macro-economic growth frameworks are also full-economy 
approaches derived from aggregated growth models. They are 
based on a  single macroeconomic production function combining

capital, labour and sometimes energy to produce a generic good for 
consumption and investment. They are used as the macroeconomic 
component of cost-benefit IAMs (Nordhaus 1993) and some 
detailed-process IAMs.The disaggregation of economic actors and sectors and the 
representation of their interaction differ across full-economy 
frameworks. A  main distinction is between models based on full 
Social Accounting Matrix (SAM) and aggregated growth approaches. 
On the one hand, SAM-based frameworks – CGE and macro-
econometric – follow a  multi-sectoral approach distinguishing 
from several to a hundred different economic sectors or production 
goods and represent sector-specific value-added, final consumption 
and interindustry intermediary consumption (Robinson 1989). 
They also represent economic agents (firms, households, public 
administration, etc.) with specific behaviours and budget constraints. 
On the other hand, macro-economic growth frameworks are reduced

to a single macro-economic agent producing, consuming and investing 
a  single macroeconomic good without considering interindustry 
relationships. In some detailed process IAMs, the aggregated growth 
approach is combined with a detailed representation of energy supply 
and  demand systems that surmises different economic actors and 
subsectors. However, the energy system is driven by an aggregated 
growth engine (Bauer et al. 2008).
Partial equilibrium frameworks do not cover the full economy 
but only represent a  subset of economic sectors and markets 
disconnected from the rest of the economy. They basically represent 
market balance and adjustments for a subset of sectors under ceteris 
paribus assumptions about other markets (labour, capital, etc.), 
income, and so on, ignoring possible feedbacks. Partial equilibrium 
frameworks are used in sectoral models, as well as to model several 
sectors and markets at the same time – for example, energy and

agriculture markets – in energy system models and some detailed 
process IAMs but still without covering the full economy.
In most models the treatment of economic growth follows Solow 
or Ramsey growth approach based on the evolution through time 
of production factors, endowment and productivity. Classically, 
labour endowment and demography are exogenous, and capital 
accumulates through investment. Partial equilibrium frameworks do 
not model economic growth but use exogenous growth assumptions 
derived from growth models. Factors’ productivity evolution 
is assumed exogenous in most cases that is, general technical 
progress is assumed to be an autonomous process. A few models 
feature endogenous growth aspects where factor productivity 
increases with cumulated macroeconomic investment. Models 
also differ in the content of technical progress and alternatively 
consider unbiased total factor productivity improvement or

labour-specific factor-augmenting productivity. In multi-sectoral 
macroeconomic models, economic growth comes with endogenous 
changes of the sectoral composition of GDP known as structural 
change. Structural change results from the interplay between 
differentiated changes of productivity between sectors and of 
the structure of final demand as income grows (Herrendorf et al. 
2014). If general technical progress is mostly assumed exogenous 
and autonomous at an aggregated level, innovation in relation 
to energy demand and technical systems follow more detailed 
specifications in models. Energy efficiency can be assumed an 
autonomous process at different levels – macroeconomic, sector 
or technology – or energy technical change can be endogenous

1846
and induced as a learning by doing process or as a result of R&D 
investments (learning-by-searching) (Löschel 2002).
Multi-regional models consider interactions between regions through 
trade in energy goods, non-energy goods and services – depending on 
model scope – and emission permits in the context of climate policy. 
For each type of goods, trade is usually represented as a  common 
pool where regions interact with the pool through supply (exports) or 
demand (imports). A few models consider bilateral trade flows between 
regions. Traded goods can be assumed as perfectly substitutable 
between regions of origin (Heckscher-Ohlin assumption), such as is 
often the case for energy commodities, or as imperfectly substitutable 
(e.g., Armington goods) for non-energy goods. The representation of 
trade and capital imbalances at the regional level and their evolution 
through time vary across models and imbalances are either not

considered (regional current accounts are balanced at each point in 
time), or a constraint for intertemporal balance is included (an export 
surplus today will be balanced by an import surplus in the future), 
or else trade imbalances follow other rules such as a  convergence 
towards zero in the long run (Foure et al. 2020).
Strategic interaction can also occur between regions, especially in 
the presence of externalities such as climate change, energy prices 
or technology spillovers. Intertemporal models can include several 
types of strategic interaction: (i) a  cooperative Pareto optimal 
solution where all externalities are internalised and based on the 
maximisation of a global discounted welfare with weighted regional 
welfare (Negishi weights), (ii) a  non-cooperative solution that is 
strategically optimal for each region (Nash equilibrium) (Leimbach 
et al. 2017b), and (iii) partially cooperative solutions (Eyckmans and

Tulkens 2003; Yang 2008; Bréchet et al. 2011; Tulkens 2019), akin to 
climate clubs (Nordhaus 2015).
Models cover different investment flows depending on the 
economic framework used. Partial equilibrium models compute 
energy system and/or sectoral (transport, building, industry, etc.) 
technology-specific investment flows associated with productive 
capacities and equipment. Full-economy models compute both 
energy system and macroeconomic investment, the second being 
used to increase macroeconomic capital stock. Full-economy multi-
sectoral models compute sector-specific (energy and non-energy 
sectors) investment and capital flows with some details about the 
investments goods involved.
Full-economy models differ in the representation of macro-finance. 
In most CGE and macro-economic growth frameworks financial 
mechanisms are only implicit and total financial capacity and 
investment are constrained by savings. Consequently, investment in

a given sector (e.g., low-carbon energy) fully crowds out investment 
in other sectors. In macro-econometric frameworks, macro-finance is 
sometimes explicit, and investments can be financed by credit on top 
of savings, which implies more limited crowding out of investments 
(Mercure et al. 2019). Macro-financial constraints are usually not 
accounted for in partial equilibrium models.
Models compare economic flows over time through discounting. 
Table 5 summarises key characteristics of different models assessed in AR6, including the uses of discounting. In cost-benefit analysis 
(CBA), discounting enables the comparison of mitigation costs and 
climate change damage. In the context of mitigation and in cost-
effectiveness analysis (CEA), discounting allows the comparison of 
mitigation costs over time.
In optimisation models a social discount rate is used to compare costs 
and benefits over time. In the case of partial equilibrium optimisation

models, the objective is typically to minimise total discounted system 
cost. The social discount rate is then an exogenous parameter, 
which can be assumed constant or changing (generally decreasing) 
over time (e.g., Gambhir et al. (2017), where a 5% discount rate is 
used). In the case of intertemporal welfare optimisation models, 
a Ramsey intertemporal optimisation framework is generally used, 
considering a representative agent who decides how to allocate her 
consumption, and hence saving, over time, subject to a  resource 
constraint. Ramsey (1928) shows that the solution must always 
satisfy the Ramsey Equation, which provides the determinants of the 
social discount rate. The Ramsey Equation is given as follows:
𝜌𝜌=𝛿𝛿+𝜂𝜂𝑔𝑔! 
  where 𝜌𝜌 
   is the consumption discount rate (also known as the social 
discount rate), 𝛿𝛿 
   is the utility discount rate (also known as the 
pure time discount rate, or time preferences rate) which is a value

judgement that determines the present value of a  change in the 
utility experienced in the future and hence it is an ethical parameter, 
   is the growth rate of consumption per capita over time, and 𝜂𝜂  is 
the elasticity of marginal utility of consumption, which is also a value 
judgement and hence an ethical parameter. The parameter 𝜂𝜂  is also 
a  measure of risk aversion and of society’s aversion to inequality 
within and across generations. The pure time preference rate is an 
exogenous parameter, but the social discount rate is endogenously 
computed by the model itself and depends on the growth rate 
of consumption per capita over time. Note that more complex 
frameworks disentangle inequality aversion from risk aversion, and 
introduce uncertainty, leading to extensions of the social discount 
rate equation (see, for instance, Gollier 2013).
Discounting is also used for ex post comparison of mitigation cost

pathways across models and scenarios. Values typically used for such 
ex post comparison are 2–5% (e.g., Admiraal et al. 2016). Across this 
report, whenever discounting is used for ex post comparisons, the 
discount rate applied is stated explicitly.
The choice of the appropriate social discount rate (and the appropriate 
rate of pure time preference when applicable) is highly debated 
(e.g., Arrow et al. 2013; Gollier and Hammitt 2014; Polasky and Dampha 
2021) and two general approaches are commonly used. Based on 
ethical principles, the prescriptive approach states that the discount 
rate should reflect how costs and benefits supported by different 
generations should be weighted. The descriptive approach identifies 
the social discount rate to the risk-free rate of return to capital as 
observed in the real economy, which generally yields higher values.
In CBA the choice of discount rate is crucial for the balance of 
mitigation costs and avoided climate damages in the long run

1847
and a lower discount rate yields more abatement effort and lower 
global temperature increases (Stern 2006; Hänsel et al. 2020). 
In CEA, the choice of social discount rate influences the timing of 
emission reductions to limit warming to a given temperature level. 
A  lower discount rate increases short-term emissions reductions, 
lowers temperature overshoot, favours currently available mitigation 
options (energy efficiency, renewable energy, etc.) over future 
deployment of net negative emission options and distributes 
mitigation effort more evenly between generations (Emmerling et al. 
2019; Strefler et al. 2021b).
Outside social discounting for intertemporal optimisation, 
discounting is used in simulation models to compute the life cycle 
costs of investment decisions (e.g., energy efficiency choices, choices 
between different types of technologies based on their levelised

costs). In this case, the discount rate can be interpreted as the 
cost of capital faced by investors. The cost of capital influences the 
merit order of technologies and lower capital cost favours capital-
intensive technologies over technologies with higher variable costs. 
Models can reflect regional, sectoral or technology-specific cost of 
capital – through heterogeneous discount rates for life cycle cost 
estimates in simulation models (Iyer et al. 2015) or as hurdle rates 
in energy optimisation models (Ameli et al. 2021). In some cases, 
simulation models may also produce mitigation pathways following 
the Hotelling principle and assuming that the carbon price rises at the 
social discount rate (e.g., Global Change Assessment Model (GCAM) 
scenarios in the Shared Socio-economic Pathways (SSP) study with 
carbon prices increasing at 5% yearly (Guivarch and Rogelj 2017)).
A.III.I.3 Energy System Modelling
In the literature, the energy system models are categorised based on

different criteria, such as (i) energy sectors covered, (ii) geographical 
coverage, (iii) time resolution, (iv) methodology, and (v) programming 
techniques. In the following sections, examples on different types of 
energy system models applied in Chapter 6 are presented.
A.III.I.3.1 Bottom-up Models
A.III.I.3.1.1 Modelling Electricity System Operation and Planning 
with Large-scale Penetration of Renewables
A number of advanced grid modelling approaches have been 
developed (Sani Hassan et al. 2018), such as robust optimisation 
(Jiang et al. 2012), interval optimisation (Dvorkin et al. 2015), or 
stochastic optimisation (Meibom et al. 2011; Monforti et al. 2014) to 
optimally schedule the operation of the future low-carbon systems 
with high penetration of variable renewable energies. Advanced 
stochastic models demonstrated that this would not only lead to 
significantly higher cost of system management but may eventually

limit the ability of the system to accommodate renewable generation 
(Bistline and Young 2019; Hansen et al. 2019; Perez et al. 2019; 
Badesa et al. 2020). Modelling tools such as European Model for 
Power System Investment with Renewable Energy (EMPIRE) (Skar 
et al. 2016), Renewable Energy Mix for Sustainable Electricity 
Supply (REMix) (Scholz et al. 2017), European Unit Commitment And Dispatch model (EUCAD) (Després 2015), SWITCH (Fripp 2012), GenX 
(TNO 2021), and Python for Power System Analysis (PyPSA) (Brown 
et al. 2018) investigated these issues. SWITCH is a stochastic model, 
in which investments in renewable and conventional power plants 
are optimised over a  multi-year period (Fripp 2012). In GenX the 
operational flexibility as well as capacity planning is optimised from 
a  system-wide perspective (TNO 2021). PyPSA is an optimisation 
model for modern electricity systems, including unit commitment of 
generation plants, renewable sources, storage, and interaction with

other energy vectors (Brown et al. 2018).
Furthermore, advanced modelling tools have been developed for the 
purpose of providing estimations of system-wide inertial frequency 
response that would assist system operators in maintaining adequate 
system inertia (Sharma et al. 2011; Teng and Strbac 2017). These 
innovative models also provide fundamental evidence regarding 
the role and value of advanced technologies and control systems in 
supporting cost-effective operation of future electricity systems 
with very high penetration of renewable generation. In particular, 
the importance of enhancing the control capabilities of renewable 
generation and applying flexible technologies, such as energy 
storage (Hall and Bain 2008; Obi et al. 2017; Arbabzadeh et al. 2019), 
demand-side response, interconnection (Aghajani et al. 2017) and 
transmission grid extensions (Schaber et al. 2012) to provide system 
stability control, is demonstrated through novel system integration

models (Lund et al. 2015; Sinsel et al. 2020).
A novel modelling framework is proposed to deliver inertia and 
support primary frequency control through variable-speed wind 
turbines (Morren et al. 2006) and PVs (Waffenschmidt and Hui 2016; 
Liu et al. 2017), including quantification of the value of this technology 
in future renewable generation-dominated power grids (Chu et al. 
2020). Advanced models for controlling distributed energy storage 
systems to provide an effective virtual inertia have been developed, 
demonstrating the provision of virtual synchronous machine 
capabilities for storage devices with power electronic converters, 
which can support system frequency management following 
disturbances (Hammad et al. 2019; Markovic et al. 2019). Regarding 
the application of interconnection for exchange of balancing services 
between neighbouring power grids, alternative control schemes for 
high-voltage direct current (HVDC) converters have been proposed,

in order to demonstrate that this would reduce the cost of balancing 
(Tosatto et al. 2020).
A.III.I.3.1.2 Modelling the Interaction between 
Different Energy Sectors
Several integrated models have been developed in order to study 
the interaction between different energy vectors and whole-system 
approaches, such as Integrated Energy System Simulation model 
(IESM) (NREL 2020), Integrated Whole-Energy System (IWES) (Strbac 
et al. 2018), UK TIMES (Daly and Fais 2014), and Calliope (Pfenninger 
and Pickering 2018).
IESM is an approach in which the multi-system energy challenge is 
investigated holistically rather than looking at each of the systems in 
isolation. IESM capabilities include co-optimisation across multiple

1848
energy systems, including electricity, natural gas, hydrogen, and water 
systems. These provide the opportunity to perform hydro, thermal, 
and gas infrastructure investment and resource use coordination for 
time horizons ranging from sub-hourly (markets and operations) to 
multi-year (planning) (NREL 2020).
The IWES model incorporates detailed modelling of electricity, gas, 
transport, hydrogen, and heat systems and captures the complex 
interactions across those energy vectors. The IWES model also considers 
the short-term operation and long-term investment timescales (from 
seconds to years) simultaneously, while coordinating operation 
of and investment in local district and national/international level 
energy infrastructures (Strbac et al. 2018).
The UK TIMES Model (‘The Integrated MARKAL-EFOM System’) 
uses linear programming to produce a  least-cost energy 
system, optimised according to a number of user constraints, over

medium- to long-term time horizons. It portrays the UK energy 
system, from fuel extraction and trading to fuel processing and 
transport, electricity generation and all final energy demands 
(Taylor et al. 2014; Daly and Fais 2014). The model generates 
scenarios for the evolution of the energy system based on different 
assumptions around the evolution of demand and future technology 
costs, measuring energy system costs and all greenhouse gases 
(GHGs) associated with the scenario. UKTM is built using the 
TIMES model generator: as a partial equilibrium energy system and 
technologically detailed model, it is well suited to investigate the 
economic, social, and technological trade-offs between long-term 
divergent energy scenarios.
Calliope is an open source Python-based toolchain for developing 
energy system models, focusing on flexibility, and high temporal and 
spatial granularities. This model has the ability to execute many runs

on the same base model, with clear separation of model (data) and 
framework (code) (Pfenninger and Pickering 2018).
A.III.I.3.2 Modelling of Energy Systems in the Context  
of the Economy
To study the impact of low-carbon energy systems on the economy, 
numerous integrated assessment modelling tools (top-down models) 
are applied, such as: General Equilibrium Model for Economy-Energy-
Environment (GEM-E3) (Capros et al. 2013), ENV-Linkages (Burniaux 
and Chateau 2010), and Emissions Prediction and Policy Analysis 
(EPPA) (Chen et al. 2016).
GEM-E3 is a  recursive dynamic computable general equilibrium 
model that covers the interactions between the economy, 
the  energy system and the environment. It is specially designed 
to evaluate energy, climate, and environmental policies. GEM-E3 can 
evaluate consistently the distributional and macro-economic effects 
of policies for the various economic sectors and agents across the 
countries/regions (Capros et al. 2013).

The modelling work based on ENV-Linkages (as a successor to the 
OECD GREEN model) provides insights to policymakers in identifying 
least-cost policies by taking into account environmental issues, such as phasing out fossil fuel subsidies, and climate change mitigation 
(Burniaux and Chateau 2010).
In the EPPA model, different processes (e.g.,  economic and 
technological) which have impacts on the environment from regional 
to global at multiple scales are simulated. The outputs of this 
modelling (e.g., greenhouse gas emissions, air and water pollutants) 
are provided to the MIT Earth System (MESM), which investigates the 
interaction between sub-models of physical, dynamical and chemical 
processes in different systems (Chen et al. 2016).
A.III.I.3.3 Hybrid Models
Hybrid models are a combination of macro-economic models (i.e., top-
down) with at least one energy sector model (i.e., bottom-up) that 
could benefit from the advantages of both mentioned approaches.

In this regard, linking these two models can be carried out either 
manually through transferring the data from one model to the other 
(soft linking), or automatically (hard linking) (Prina et al. 2020). In 
this section, some of these models are presented including World 
Energy Model (WEM) (IEA 2020a) and the National Energy Modelling 
System (NEMS) (Fattahi et al. 2020).
The WEM is a  simulation model covering energy supply, energy 
transformation and energy demand. The majority of the end-use sectors 
use stock models to characterise the energy infrastructure. In addition, 
energy-related CO 2 emissions and investments related to energy 
developments are specified. The model is focused on determining the 
share of alternative technologies in satisfying energy service demand. 
This includes investment costs, operating and maintenance costs, fuel 
costs and in some cases costs for emitting CO 2 (IEA 2020a).
The NEMS is an energy-economy modelling system applied for

the USA through 2030. NEMS projects consider the production, 
import, conversion, consumption, and prices of energy, subject 
to assumptions on macroeconomic and financial factors, world 
energy markets, resource availability and costs, behavioural and 
technological choice criteria, cost and performance characteristics 
of energy technologies, and demographics. NEMS was designed and 
implemented by the Energy Information Administration (EIA) of the 
US Department of Energy. NEMS is used by EIA to project the energy, 
economic, environmental, and security impacts on the United States 
considering alternative energy policies and assumptions related to 
energy markets (Fattahi et al. 2020).
A.III.I.4 Building Sector Models
A.III.I.4.1 Models: Purpose, Scope and Types
GHG emissions and mitigation potentials in the building sector 
are modelled using either a  top-down, a  bottom-up or a  hybrid 
approach (Figure 1).
The top-down models are used for assessing economy-wide

responses of building policies. These models are either economic or 
technological and have low granularity.

1849
The bottom-up models are data intensive and based on microscopic 
data of individual end uses and the characteristics of each component 
of buildings. Bottom-up models can be either physics-based, also 
known as engineering models; data-driven, also known as statistical 
models; or a  combination of both, also known as hybrid bottom-
up models. Bottom-up models are useful to assess the technico-
economic potentials of the overall building stock by extrapolating 
the estimated energy consumption of a representative set of invidual 
buildings (Duerinck et al. 2008; Hall and Buckley 2016; Bourdeau 
et al. 2019).
Hybrid models used for buildings can be either optimisation or 
simulation models (Duerinck et al. 2008; Hall and Buckley 2016; 
Bourdeau et al. 2019) (Figure 1). The latter can also be agent-based 
models and could be combined with building performance models 
to allow for an assessment of occupants’ behaviour (Papadopoulos

and Azar 2016; Sachs et al. 2019a; Niamir et al. 2020). Hybrid models 
are used for exploring the impacts of resource constraints and 
for  investigating the role of specific technological choices as well 
as for analysing the impact of specific building policies.
The use of geographical information systems (GIS) layers (Reinhart 
and Cerezo Davila 2016) combined with machine learning techniques 
(Bourdeau et al. 2019) allows the creation of detailed datasets of 
building characteristics while optimising the computing time, thus, 
leading to a better representation of energy demand of buildings and 
a more accurate assessment of GHG mitigation potential.
A.III.I.4.2 Representation of Energy Demand 
and GHG Emissions
Comprehensive models represent energy demand per energy carrier 
and end use for both residential and non-residential buildings, for 
different countries or sets of countries, further disaggregated across 
urban/rural and income groups. Drivers of energy demand considered

include population, the floor area per capita, appliances ownership 
and to some extent occupants’ behaviour in residential buildings. The former are included in top-down, hybrid and bottom-up models while 
the latter is usually included in bottom-up and agent-based models 
(Niamir et al. 2020; IEA 2021). In non-residential buildings, value 
added is considered among the drivers.
GHG emissions from buildings are usually modelled on the basis of 
the estimated energy demand per energy carrier and appropriate 
emissions factors. The purpose of most building models is to assess 
the impact of mitigation measures on energy demand in the use phase 
of buildings and for a given assumption on the per-capita floor area 
and technological improvement (IEA 2021; Pauliuk et al. 2021b). After 
decades of ignoring material cycles and embodied emissions (Pauliuk 
et al. 2017), a few IAMs are now including material stocks and flows 
(Deetman et al. 2020; IEA 2021; Zhong et al. 2021). However, the

top-down nature of these models and the modelling methodology of 
embodied emissions, which are added onto the emissions estimated 
in the use phase, questions the policy relevance of these estimates. As 
of today, the resource efficiency and climate change (RECC) scenario 
(IRP 2020; Fishman et al. 2021; Pauliuk and Heeren 2021; Pauliuk et al. 
2021b; ) is the only global scenario identified which includes measures 
to limit, in the first place, embodied emissions from buildings. The 
scenario is modelled using the bottom-up ODYM-RECC model.
A.III.I.4.3 Representation of Mitigation Options
The assessment conducted in Chapter  9 was based on the SER 
(Sufficiency, Efficiency, Renewable), framework with sufficiency 
being all the measures and daily practices which avoid, in the first 
place, the demand for energy, materials, water, land and other 
natural resources over the life cycle of buildings and appliances/
equipment, while providing decent living standards for all within the

planetary boundaries. By contrast to efficiency, sufficiency measures 
do not consume energy in the use phase. Efficiency improvement 
of the building envelope and appliances/equipment are the main 
mitigation options considered in the existing models and scenarios. 
They are, usually, combined with market-based and information 
instruments and to some extent with behaviour change. As of today, 
Engineer ingGHG emissions
Botto m-up
Econom ic Technol ogical
StatisticalHybrid 
botto m-up
Top-dow n
Hybrid
Optimization Simulation
Figure 1 | Modelling approaches for GHG emissions used in the building sector.

1850
Grubler et al. (2018), Pauliuk et al. (2021b), Kuhnhenn et al. (2020),  
Millward-Hopkins et al. (2020), Kikstra et al. (2021), and van Vuuren 
et al. (2021) are the only six global models/scenarios to include 
sufficiency measures, out of which detailed data were available only 
for two scenarios (Pauliuk et al. 2021b; van Vuuren et al. 2021).
In total, 931 scenarios were submitted to the AR6 scenario database, 
out of which only two scenarios provided detailed data allowing for 
an assessment of emissions reductions based on the SER framework 
considered in the building chapter. An additional 78 bottom-up 
models/scenarios were gathered (Table 1). Mitigation potentials from 
these scenarios are assessed using either a decomposition analysis 
(Section 9.3) or an aggregation of bottom-up potential estimates for 
different countries into regional and then global figures (Section 9.6).

Scenarios considered in the illustrative mitigation pathways included 
in Chapter 3 were assessed, compared to current policy scenarios. 
The assessment was possible for only the combined direct CO 2 
emissions for both residential and non-residential buildings due to 
lack of data on other gases as well as on indirect and embodied 
emissions. The assessment shows mitigation potentials, compared to 
current policies scenarios, at a global level ranging from 9% to 13% 
by 2030 and from 58% to 89% in 2050 (Figure 2-b).
There are great discrepancies in the projected potentials by the IAMs 
across regions and scenarios. In the deep electrification and high 
renewable scenario, emissions in Africa are projected to increase by 
88% by 2030, followed by a decrease of 97% by 2050, compared to 
current policies scenario. Similarly, in the sustainable development

scenario, emissions in developing Asia are projected, compared to current policies scenario, to increase by 56% by 2030, followed by 
a decrease of 75% by 2050. Such variations in emissions over two 
decades in the developing world raise questions about the policy 
relevance of these scenarios. In developed countries, emissions are 
projected to go down in all regions across all scenarios, except in 
SSP2 scenario in Asia-Pacific, where emissions are projected to 
increase by 18% by 2030 followed by a decrease of 25% by 2050, 
compared to current policies scenario. It is worth noting that, across 
all scenarios, Eastern Asia is the region with the lowest estimated 
mitigation potential compared to the current policies (Figure 2-b).
A.III.I.4.4 Representation of Sustainable 
Development Dimensions
The link to the Sustainable Development Goals (SDGs) is not always 
explicit in buildings models/scenarios. However, some models include

requirements to ensure access to decent living standards for all 
(Grubler et al. 2018); Millward-Hopkins et al. 2020; Kikstra et al. 
2021) or to specifically meet the 2030 SDG 7 goal (IEA 2020a, 2021).
A.III.I.4.5 Models Underlying the Assessment in Chapter 9
The AR6 scenario database received 101 models with a  building 
component, out of which 96 were IAM models and five 
building  specific models. This is equivalent to 931 scenarios. After 
an initial screening, quality control and further vetting to assess 
if they sufficiently represented historical trends and climate goals, 
43  models (42 IAMs and 1 building-specific model) were kept for 
the assessment, thus reducing the number of scenarios to assess 
to 554. The unvetted scenarios are still available in the database. 
–9%
–58%–8%
–82%–13%
–89%
05001000150020002500300035004000
2030 2050 2030 2050 2030 2050 2030 2050

*The percentages correspond to the increase or decrease in relation to the same year with the Current Policies Scenario as a baseline.  CO2 emisisons (direct) MtCO2
NGFS2_Current Policies SSP2_openres_lc_50 SusDev_SDP-PkBudg1000
DeepElec_SSP2_ HighRE_Budg900
a) Global
Figure 2 | GHG emissions reductions in the building sector (direct emissions) in scenarios considered as illustrative mitigation pathways in Chapter 3.

1851
After a  final screening based on the SER (Sufficiency, Efficiency, 
Renewable) framework, only two IAMs were kept. Given the top-
down nature of IAMs and their weaknesses in assessing mitigation 
measures, especially sufficiency measures, 78 bottom-up models with 
technological representation have been included in the assessment 
(Table  1). These additional bottom-up models were not submitted 
to the AR6 scenario database. However, scenario owners supplied 
Chapter 9 with the underlying assumptions and data.
A.III.I.5 Transport Models
A.III.I.5.1 Purpose and Scope of Models
GHG emissions from transport are largely a  function of travel 
demand, transport mode, and transport technology and fuel. 
The purpose of transportation system models is to describe how 
future demand for transport can be fulfilled through different 
modes and technologies under different climate change mitigation

targets or policies. Within a given transport mode, technologies differ 
by efficiency and fuel use.
Common components of transportation energy systems models mirror 
these main drivers of GHG emissions. Most models will also quantify how much movement occurs, or the travel demand associated 
with each mode. Models commonly quantify demand through 
transportation mode (e.g.,  active transit, passenger vehicles, 
trucks, boats, planes, etc.) or how movement occurs (e.g., passenger 
travel distance pkm and freight distance tkm). Higher fidelity models 
provide more nuanced breakdowns of demand by trips of various 
lengths such as short-, medium-, and long-distance trips or by 
region (e.g., kilometres or tkm per region). The scope of the model 
often determines how much information it provides on where and 
when movement occurs. While larger scale models typically provide 
aggregate travel demand, higher resolution travel demand models

can be integrated into transportation system models and provide 
much more information on origin and destination of trips, when and 
where trips occur, and the route of travel taken. This level of detail 
is not often characterised in the output of system models but can be 
employed as a ‘base’ model to determine how travel occurs before 
aggregation (Edelenbosch et al. 2017a; Yeh et al. 2017).
A key distinguishing feature between different model types is how 
they control the above components. Our review of the transport 
energy system models can be broadly divided into three main 
categories: (i) optimisation models, (ii) simulation models, and 
(iii) accounting and exploratory models. 
CO2 emisisons (direct) CO2 
NGFS2_Current Policies SSP2_openres_lc_50 SusDev_SDP-PkBudg1000 DeepElec_SSP2_ HighRE_Budg900
* The percentages correspond to the increase or decrease in relation to the same year with the Current Policies Scenario as a baseline.
–5%
–93%–15%–92% –97%88%

0100200300400500600AFRICA
4%7%
–59%–63%–9%
–64%
0100200300400500600700R10EASTERN ASIA
10%
–26%–14%
–96%–20%
–97%
0100200300400500600R10EUROPE
–81%–75%–19%
–77%–32%
–97%
050100150200250300350R10SOUTHERN ASIA
–5%
46%–26%
–78%–30%
–92%
050100150200R10LATIN_AM
6% 9% –34%
–80%–9%
–92%
050100150200250300350400R10MIDDLE_EAST
–4%
–50%–21%
–92%–19%
–91%
0100200300400500600
18%
–25%–20%
–84%–20%
–92%
020406080100120140160
56%
9%
–4%
68%–75%
–94%
050100150200250300
2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 2030 2050 2030 2050R10_DEVELOPING ASIA R10PAC_OECD R10NORTH_AM
b) Regional
Figure 2 (continued): GHG mitigation potentials of scenarios considered in the illustrative mitigation pathways considered in Chapter 3.

1852
Table 1 | Models underlying the assessment in Chapter 9.
Model name/
institution using  
the modelModel 
descriptionGeographic 
scopeBuilding 
type 
includedEnergy demand Example of publications
World Energy Model 
(WEM)/International 
Energy Agency (IEA)A simulation model 
with detailed 
bottom-up building 
stock modelGlobalResidential and 
non-residentialThe building module includes a stock model with detailed technologies, 
end uses and energy carriers. Activity variables such as floor area and 
appliance ownership are projected by end use. A cost-based approach, 
influenced by policy and other constraints, is used to allocate between 
almost 100 technologies. Energy demand projections are based on country-
level historical data for both residential and non-residential buildings. The 
buildings module is integrated within the wider World Energy Model.IEA (2020a); IEA (2021)
IMAGE 3.2 model/
Netherlands 

Assessment AgencyA modular integrated 
assessment model 
using a simulation 
model for 
energy demandGlobalResidential and 
non-residential 
buildingsEnergy demand is calculated as a function of household expenditure and 
population growth, disaggregating across urban/rural and income groups. 
The model includes a building stock model (residential) with a detailed 
description of end uses, energy carrier use and building technologies for 
both residential and non-residential buildings. A scenario analysis assessing 
assumptions on lifestyle changes has also been conducted.van Vuuren et al. (2021)
Resource Efficiency 
and Climate Change 
(RECC) model. Research 
Institutions: Norwegian 
University of Science 
& Technology and 
University of Freiburg. 
Funding Institutions: 
UNEP and International 
Resource PanelBottom-up building 
stock-flow model 
estimating material 
and energy flows 
housing stock 
growth, driven by 
input parameters of 
population and floor

area per capitaGlobalResidential 
buildingsEnergy demand is calculated the model BuildME, a physical model using 
the EnergyPlus simulation engine, incorporating country/region-specific 
projections of envelope and equipment efficiency.IRP (2020); Fishman et al. (2021); Pauliuk and Heeren (2021); Pauliuk et al. (2021a); 
Pauliuk et al. (2021b)
A total of 77 
bottom-up models 
out of which 67 were 
technology-rich and 
10 sufficiency-focusedBottom-up 
technology-rich 
models with detailed 
building and 
other technology 
stock modelsThree global 
(all sufficiency 
models), 
six regional 
(regions here 
refer to regions 
including 
several 
countries), two 
subnational, 
and the 
rest nationalResidential 
and/or non-
residential 
buildingsIn most cases, energy demand was modelled by multiplying units of energy 
consumption of technologies/product/buildings by stocks of corresponding 
technologies/products and/or buildings at national level. The projected

stocks of buildings and/or technologies/products are modelled based 
on past levels. The potential is demonstrated by replacing the business-
as-usual technologies and practices with demonstrated best available 
or commercially feasible technologies and practices. The studies rely on 
some or all of the following mitigation options: the construction of new 
high-performance buildings using building design, forms, and passive 
construction methods; the thermal efficiency improvement of building 
envelopes of the existing stock; the installation of advanced heating, 
ventilation air conditioning systems, equipment and appliances; the 
exchange of lights, appliances, and office equipment, including ICT, water 
heating, and cooking; active and passive demand-side management 
measures; as well as on-site production and use of renewable energy. Many 
bottom-up studies considered the measures as an integrated package 
due to their technological complementarity and interdependence, rather

than the penetration of individual technologies applied in an incremental 
manner in or to these buildings.Department of Environmental Affairs (2014); Alaidroos and Krarti (2015); de Melo and 
de Martino Jannuzzi (2015); Kusumadewi and Limmeechokchai (2015); Markewitz et 
al. (2015); Prada-Hernández et al. (2015); Csoknyai et al. (2016); Energetics (2016); 
Gagnon et al. (2016); Horváth et al. (2016); Nadel (2016); Oluleye et al. (2016); 
Timilsina et al. (2016); Trottier (2016); Virage-Energie Nord-Pas-de-Calais. (2016); Yeh 
et al. (2016); ADB (2017); Bashmakov (2017); Chaichaloempreecha et al. (2017); Iten 
et al. (2017); Khan et al. (2017); Krarti et al. (2017); Kusumadewi and Limmeechokchai 
(2017); Momonoki et al. (2017); négaWatt (2017); Ploss et al. (2017); Radpour et 
al. (2017); Streicher et al. (2017); Subramanyam et al. (2017a,b); Wakiyama and 
Kuramochi (2017); Wilson et al. (2017); de la Rue du Can et al. (2018); Grubler et al.

(2018); Novikova et al. (2018a,b); Oluleye et al. (2018); Ostermeyer et al.(2018a,b,c); 
Tan et al. (2018); Toleikyte et al. (2018); Yu et al. (2018); Zhou et al. (2018); Bierwirth 
and Thomas (2019); Bürger et al. (2019); Cabrera Serrenho et al. (2019); Colenbrander 
et al. (2019); de la Rue du Can et al. (2019); Dioha et al. (2019); Duscha et al. (2019); 
González-Mahecha et al. (2019); Kamal et al. (2019); Krarti (2019); Kwag et al. 
(2019); Levesque et al. (2019); Minami et al. (2019); Onyenokporo and Ochedi (2019); 
Ostermeyer et al. (2019b); Butler et al. (2020); Filippi Oberegger et al. (2020); Grande-
Acosta and Islas-Samperio (2020); Merini et al. (2020); Millward-Hopkins et al. (2020); 
Roca-Puigròs et al. (2020); Rosas-Flores and Rosas-Flores (2020); Roscini et al. (2020); 
Sugiyama et al. (2020b); Brugger et al. (2021); Calise et al. (2021); Sandberg et al. 
(2021); Xing et al. (2021); Zhang et al. (2020)

1853
i. Optimisation models: Identify least cost pathways to meet 
policy targets (such as CO 2 emission targets of transport modes 
or economy-wide) given constraints (such as rate of adoption 
of vehicle technologies or vehicle efficiency standards). For 
example MessageIX-TransportV5 (Krey et al. 2016) and TIMES 
(Daly et al. 2014).
ii. Simulation models: Simulate behaviour of consumers and 
producers given prices, policies, and other factors by using 
parameters calibrated to historically observed behaviours 
such as demand price elasticity and consumer preferences. For 
example models by Barter et al. (2015), Brooker et al. (2015) and 
Schäfer (2017).
iii. Accounting and exploratory models: Track the outcomes 
(such as resources use and emissions) of key decisions (such 
as the adoption of advanced fuels or vehicle technologies) that 
are based on ‘what-if’ scenarios. The major difference between

accounting models versus optimisation and simulation models is 
that key decision variables such as new technologies adoptions 
typically follow modellers’ assumptions as opposed to being 
determined by mathematical formulations as in optimisation 
and simulation models. See models in Fulton et al. (2009), IEA 
(2020a), Gota et al. (2019) and Khalili et al. (2019).
Due to the model types’ relative strengths and weaknesses, they are 
commonly applied to certain problem types (Table 2). Models can do 
forecasting, which makes projections of how futures may evolve, 
or backcasting, which makes projections of a  future that meets 
a predefined goal such as a policy target of 80% reduction in GHG 
emissions from a historical level by a certain year. Models are often 
also used to explore ‘what-if’ questions, to confirm the feasibility 
of certain assumptions/outcomes, and to quantify the  impacts of 
a change such as a policy under different conditions. Enhancing fuel

efficiency standards, banning internal combustion engines, setting 
fuel quality standards, and the impacts of new technologies are 
typical examples of problem types analysed in energy system models.
While these four model types drive the component dynamics in 
different ways, they commonly include modules that include: 
learning and diffusion (via exogenous, e.g.,  autonomous learning, 
or endogenous learning regarding costs and efficiency: i.e.,  cost 
decreases and/or efficiency increases as a  function of adoption, 
and increased diffusion due to lower costs) (Jochem et al. 2018), 
stock turnover (the performance and characteristics of vehicle 
fleets including survival ages, mileages, fuel economies and loads/
occupancy rates are tracked for each new sales/vehicle stocks), 
consumer choice (theories of how people invest in new technology 
and utilise different modes of transport based on their individual preferences given the characteristics of mode or technology) (Daly

et al. 2014; Schäfer 2017), or other feedback loops (Linton et al. 2015).
IAMs (Krey et al. 2016; Edelenbosch et al. 2017a) are typically 
global  in scope and seek to solve for feasible pathways meeting 
a  global temperature target (Annex III.I.9). This implies finding 
least-cost mitigation options within and across sectors. In contrast, 
global and national transport energy system models (GTEMs/NTEMs) 
typically only assess feasible pathways within the transport sector 
(Yeh et al. 2017). The range of feasible pathways can be determined 
through optimisation, simulation, accounting and exploratory 
methods, as outlined in Table 2. Some GTEMs are linked to an IAMs 
model (Krey et al. 2016; Edelenbosch et al. 2017a; Roelfsema et al. 
2020). The key difference between IAMs and GTEMs or NTEMs is 
whether the transportation system is integrated with the rest of the 
energy systems, specifically regarding energy and fuel production and

use, fuel prices, economic drivers such as GDP , and mitigation options 
given a policy goal. IAMs can endogenously determine these factors 
because the transport sector is just one of many sectors captured by 
the IAM. While this gives IAMs certain advantages, IAMs sacrifice 
resolution and complexity for this broader scope. For example, 
most IAMs lack a sophisticated travel demand model that reflects 
the heterogeneity of demands and consumer preferences, whereas 
GTEM/NTEMs can incorporate greater levels of details regarding 
travel demands, consumer choices, and the details of transport 
policies. Consequently, what GTEMs/NTEMs lack in integration with 
other sectors they make up through more detailed analyses of travel 
patterns, policies, and impacts (Yeh et al. 2017).
Several noteworthy recent active research areas in long-term 
transportation energy systems modelling involves the consideration 
of infrastructure investment and consumer acceptance for non-fossil

fuel vehicles including charging for electric vehicles (Jochem et al. 
2019; Statharas et al. 2021) and refuelling stations for hydrogen 
vehicles (Rose and Neumann 2020); and the greater integration 
of the electric, transport, residential, and industrial sectors in fuel 
production, storage, and utilisation (Bellocchi et al. 2020; Lester et al. 
2020; Olovsson et al. 2021; Rottoli et al. 2021). While national and 
regional transport energy models have the advantage of exploring 
these relationships in greater spatial, temporal, and policy details for 
specific countries or regions (Jochem et al. 2019; Bellocchi et al. 2020; 
Lester et al. 2020; Rottoli et al. 2021; Statharas et al. 2021), the IAMs 
have the advantage of examining these interactions across the entire 
economy at the global level (Brear et al. 2020; Rottoli et al. 2021).
A.III.I.5.2 Inventory of Transportation Models included in AR6

Table 2 | Taxonomy of transport models by method (modelling type) and application (problem type).
Problem Type Optimisation model Simulation model Accounting model Heuristic model
Backcasting  
Forecasting   
Exploring feasibility space   
Impact analysis   

1854
The global/national transport energy system models included in the 
transportation chapter (Chapter 10) are listed below in Table 3.
A.III.I.6 Industry Sector Models
A.III.I.6.1 Types of Industry Sector Models
Industry sector modelling approaches can vary considerably from 
one another. As with other types of models, a key characteristic of 
industry sector models is related to their geographical scope. While 
IAMs are often global in scope, many bottom-up sector models 
are limited to individual countries or regions. The models’ system 
boundaries also differ, with some models fully considering the use of 
energy for feedstock purposes and other models focusing only on the 
use of energy for energetic purposes. Differences between models 
also exist in regard to differentiation between the industry sector and 
the energy transformation sector, concerning, for example, refineries 
and industrial power plants.

A.III.I.6.2 Representation of Demand for Industrial Products
Industry sector models vary in regard to their representation of demand 
for industrial goods or products. A more detailed representation of 
demand in a model allows for a more explicit discussion of different 
types of drivers of industrial demand and therefore a more detailed representation of demand-side strategies such as material recycling, 
longer use of products or sharing of products.
Particularly, demand for industrial products is often considered in 
more detail in bottom-up models of the industry sector than in top-
down models by taking more drivers into account. These drivers can 
be, inter alia, population, gross value added, construction activity, 
transport activity, but also changes in material efficiency, recycling 
rates and scrap rates as well as product use efficiency (e.g., through 
longer use of products or sharing of products) (Fleiter et al. 2018; 
Material Economics 2019; IEA 2020b).

A.III.I.6.3 Representation of Mitigation Options
In most top-down IAMs, some energy-intensive sectors, such as iron 
and steel or cement, are included separately, at least in a generalised 
manner, but typically few if any sector-specific technologies are 
explicitly represented. Instead, energy efficiency improvements in 
the industry sector and its subsectors are often either determined by 
exogenous assumptions or are a function of energy prices. Likewise, 
fuel switching occurs primarily as a  result of changes in relative 
fuel prices, which in turn are influenced by CO 2 price developments.  
In IAMs that include specific technologies, fuel switching can be 
constrained based on the characteristics of those technologies, 
while in IAMs with no technological detail, more generic 
Table 4 | Models underlying specific assessments in Chapter 11.
Model name and 
institution using 
the modelModel descriptionGeo-
graphic 
scopeIndustrial 
sectors included/

distinguishedDemand for industrial productsExamples of 
publications
Industry sector 
model of the 
Energy Technology 
Perspectives model
(IEA)The bottom-up industry sector model is 
one of four soft-linked models making 
up the ETP model. The four models 
are an energy supply optimisation 
model and three end-use sector 
models (transport, industry, buildings). 
Technologies and fuels in the industry 
sector model are chosen based on 
cost optimisation.GlobalAluminium, iron and 
steel, chemical and 
petrochemical, cement, 
pulp and paper and 
other industry sectorsDemand for industrial products is derived based 
on country-level historical data on per capita 
consumption. This per capita consumption is 
projected forward by using population projections 
and industry value-added projections. Demand for 
materials is derived by also taking the build-up 
of material stocks into account.IEA (2020b, 2021)
(IEA)Simulation model consisting inter

alia of technologically detailed 
bottom-up representations 
of several industry sectorsGlobal See ETP model See ETP model IEA (2020a, 2021)
Material Economics 
modelling frameworkModelling tool consisting of several 
separate bottom-up modelsEuropean 
UnionSteel, chemicals 
(plastics and 
ammonia), cementDemand for industrial products is derived based 
on scenarios of future activity levels in key 
segments such as construction, mobility and food 
production. Separate models additionally explore 
opportunities for improving materials efficiency 
and increasing materials circulation.Material 
Economics (2019)Table 3 | GTEM/NTEMs models evaluated in Chapter 10.
Model name Organisation Scope Resolution Period Economy-wide Method
Mobility model (MoMo) International Energy Agency (IEA) Global Country groups 2050 Soft link Accounting model
Global Transportation Roadmap International Council on Clean Transportation (ICCT) Global Country groups 2050 No Accounting model

MESSAGE-Transport V.5International Institute for Applied Systems Analysis 
(IIASA)Global Country groups 2100 Yes Optimisation model
Global Change Assessment 
Model (GCAM)Pacific Northwest National Laboratory (PNNL) Global Country groups 2100 Yes Partial equilibrium model

1855
constraints on fuel switching in the industry sector are embedded 
(Edelenbosch et al. 2017b).
In bottom-up models, individual technological mitigation options are 
represented in detail, especially for energy-intensive sectors such as 
iron and steel, cement and chemicals. Typically, for each considered 
technology, not only specific energy demand but also investment 
and operating costs are included in these models. Investment costs 
can change over time, either based on an exogenous assumption or 
on an endogenised process such as a learning rate. While bottom-
up models often consider technology-specific learning, IAMs cover 
technological progress in a more general way associated to industry 
branches. The uptake of new technologies is typically restricted in 
bottom-up models, for example by assuming a minimum lifetime for 
existing stock or by assuming S-shaped diffusion curves (Fleiter et al.

2018). The industrial sector models included in the industry chapter 
(Chapter 11) are listed in Table 4.
A.III.I.6.4 Limitations and Critical Analysis
Aggregated, top-down models of the industry sector, as used in 
most IAMs, are typically calibrated based on long-term historical 
data, for example on the diffusion of new technologies or on new 
fuels. These models are therefore able to implicitly consider real-life 
restrictions of the whole sector that bottom-up models (with their 
focus on individual technologies) may not fully take into account. 
These restrictions may arise, inter alia, from delays in the construction 
of infrastructure or market actors possessing incomplete information 
about new technologies. Furthermore, as IAMs also model the climate 
system, these models can principally take into account potential 
repercussions of climate change impacts on the growth rate and 
structure of economies.
However, a downside of top-down models is that they are typically

limited in their representation of individual technologies and 
processes in the industry sector and particularly of technology-
driven structural change. This lack of technological detail limits the 
usefulness of these models to analyse technology-specific and sector-
specific mitigation measures and related policies. Top-down models 
also tend to have a relatively aggregated representation of industrial 
energy demand, meaning demand-side mitigations strategies such as 
recycling, product-service efficiency and demand reduction options 
are difficult to assess with these models (Pauliuk et al. 2017).
In contrast, technology-rich bottom-up models allow detailed 
analysis of the potential of new technologies, processes and fuels 
in individual industrial sectors to reduce GHG emissions. Their often-
detailed analysis of the demand side allows demand-side mitigation 
strategies to be evaluated. Furthermore, radical future changes in

technology, climate policy or social norms can more easily be reflected 
in bottom-up models than in top-down models which are calibrated 
on past observations. Both types of models are typically not able to 
account for product substitution (e.g., steel vs plastics) arising from 
changing production cost differentials or changing product quality 
due to new production processes. In principle, technology-rich input-
output models could fill this gap.A.III.I.7 Land-use Modelling
Land use related IAM modelling results as presented in Chapter 7 
are based on comprehensive land-use models (LUMs) that are 
either integrated directly, or through emulators, into the integrated 
assessment framework. Given the increasing awareness of the 
importance of the land use sector to achieve ambitious climate 
mitigation targets, LUMs and their integration into IAM systems was 
one of the key innovations to the integrated assessment over the

past decade to allow for an economy-wide quantification of climate 
stabilisation pathways.
LUMs allow developments in the land-use sector to be projected over 
time and the impacts of mitigation policies on different economic 
(markets, trade, prices, demand, supply, etc.) and environmental 
(land use, emissions, fertiliser, irrigation water use, etc.) indicators 
to be assessed. The following models submitted scenarios to the AR6 
database: AIM (Fujimori et al. 2014, 2017; Hasegawa et al. 2017), 
EPPA (Chen et al. 2016), GCAM (Calvin et al. 2019), IMAGE (Stehfest 
et al. 2014), MERGE, MESSAGE-GLOBIOM (Havlík et al. 2014; Fricko 
et al. 2017; Huppmann et al. 2019), POLES (Keramidas et al. 2017), 
REMIND-MAgPIE (Kriegler et al. 2017; Dietrich et al. 2019), WITCH 
(Emmerling et al. 2016).
A.III.I.7.1 Modelling of Land Use and Land-use Change
LUMs represent different land use activities for managed land 
(agriculture including cropland and pastures, managed forests, and

dedicated energy crops) while natural lands (primary forests, natural 
grasslands, shrubland, savannahs, etc.) act as land reserves that 
can be converted to management depending on other constraints 
(Popp et al. 2014a; Schmitz et al. 2014). Typically, the agricultural 
sector has the greatest level of detail across land use sectors. LUMs 
include different crop and livestock production activities, some even 
at the spatially explicit level and differentiated by production system 
(Havlík et al. 2014; Weindl et al. 2015). Forestry is covered with varying 
degrees of complexity across LUMs. While some models represent only 
afforestation/deforestation activities dynamically, others have detailed 
representation of forest management activities and/or forest industries 
(Lauri et al. 2017). The models endogenously determine the land 
allocation of different land use activities as well as land-use changes 
according to different economic principles (land rent, substitution

elasticities, etc.) and/or considering biophysical characteristics such as 
land suitability (Schmitz et al. 2014; Weindl et al. 2017).
A.III.I.7.2 Demand for Food, Feed, Fibre and Agricultural Trade
LUMs project demand for food, feed, other industrial or energy uses 
for different agriculture and forestry commodities over time. While 
partial equilibrium models typically use reduced-form demand 
functions with greater level of detail at the commodity level, however 
limited agriculture and forestry, Computable General Equilibrium 
(CGE) models represent demand starting from utility functions from 
which it is possible to derive demand functions, and functional forms 
for income and price elasticities, however for a more limited set of 
agricultural and forestry commodities but with full coverage of all 
economic sectors (Valin et al. 2014; von  Lampe et al. 2014). Over

1856
time, demand for food, feed, and other industrial uses is projected 
conditional on population and income growth while bioenergy 
demand is typically informed in partial equilibrium (PE) models by 
linking with IAMs/energy systems models, and is usually endogenous 
in CGE/IAMs (Hasegawa et al. 2020). Depending on the model, 
demand projections are sensitive to price changes (Valin et al. 
2014). International trade is often represented in LUMs using either 
Armington or spatial equilibrium approaches (von Lampe et al. 2014).
A.III.I.7.3 Treatment of Land-based Mitigation Options
Two broad categories of land-based mitigation options are represented 
in LUMs: (i) reduction of GHG (CO 2, CH 4 and N 2O) emissions from 
land use, and (ii) carbon sink enhancement options including biomass 
supply for bioenergy. Each of these categories is underpinned by 
a portfolio of mitigation options with varying degrees of complexity

and parameterisation across LUMs. The representation of mitigation 
measures is influenced on the one hand by the availability of data 
for its techno-economic characteristics and future prospects as well 
as the computational challenge, for example in terms of spatial and 
process detail, to represent the measure, and on the other hand, by 
structural differences and general focus of the different LUMs, and 
prioritisation of different mitigation options by the modelling teams. 
While GHG emission reduction and CO 2 sequestration options such as 
afforestation are typically covered directly in LUMs (Hasegawa et al. 
2021), carbon sequestration from biomass supplied for bioenergy 
with carbon capture and storage (BECCS) is usually not accounted for 
in LUMs but in the energy sector and hence is taken care of directly 
in the IAMs. Yet, LUMs provide estimates of available biomass for 
energy production and the impacts of its production.

A.III.I.7.3.1 Treatment of GHG Emissions Reduction
Agricultural non-CO 2 emissions covered in LUMs include CH 4 from 
enteric fermentation, manure management and cultivation of 
rice paddies, and N 2O emissions from soils (fertiliser and manure 
application, crop residues) and manure management and are based 
on IPCC accounting guidelines (IPCC 2019a). For each of those 
sources, LUMs typically represent a (sub)set of technical, structural 
and demand-side mitigation options. Technical options refer to 
technologies such as anaerobic digesters, feed supplements or 
nitrogen inhibitors that are either explicitly represented (Frank et al. 
2018) or implicitly via the use of marginal abatement cost curves 
(MACC) (Lucas et al. 2007; Beach et al. 2015; Harmsen et al. 2019). 
Emission savings from structural changes refer to more fundamental 
changes in the agricultural sector, for example through international 
trade, production system changes or reallocation and substitution

effects (Havlík et al. 2014). Demand-side options include dietary 
changes and reduction of food waste (Springmann et al. 2016; 
Creutzig et al. 2018; Ritchie et al. 2018; Frank et al. 2019; Mbow 
et al. 2019; Clark et al. 2020; Ivanova et al. 2020; Popp et al. 2010; 
Rosenzweig et al. 2020). For the forest sector, emission reduction 
options are mainly targeting CO 2 from deforestation (Overmars et al. 
2014; Hasegawa et al. 2017; Rochedo et al. 2018; Bos et al. 2020; 
Doelman et al. 2020; Eriksson 2020). Mitigation/restoration options 
for wetlands to reduce emissions from drained organic soils are 
typically not represented in LUMs (Humpenöder et al. 2020).There are significant differences between UNFCCC nationally reported 
GHG inventories and analytical global land use models. According 
to Grassi et al. (2017), this discrepancy results in a  3GtCO 2-eq 
difference in estimates between country reports and global models.

The difference relies on different methods to classify and assess 
managed forests and forest management fluxes (Houghton et al. 
2012; Pongratz et al. 2014; Smith et al. 2014; Tubiello et al. 2015; 
Grassi et al. 2017, 2021). While global models account for GHG 
emissions from indirect human-induced effects and natural effects in 
unmanaged land, countries only consider fluxes of land use and land-
use change in managed land. In order to produce policy-relevant 
land-use model exercises, reconciling these differences is needed by 
harmonising definitions and approaches of anthropogenic land and 
the treatment of indirect environmental change (Grassi et al. 2017).
A.III.I.7.3.2 Treatment of Terrestrial Carbon Dioxide Removal 
Options including Biomass Supply for Bioenergy
Terrestrial carbon dioxide removal options are only partially included 
in LUMs and mostly rely on afforestation and bioenergy with carbon 
capture and storage (BECCS) (Fuss et al. 2014, 2018; Minx et al. 2018;

Smith et al. 2019; Butnar et al. 2020). Especially some nature-based 
solutions (Griscom et al. 2017) such as soil carbon management 
(Paustian et al. 2016), which have the potential to alter the 
contribution of land-based mitigation in terms of timing, potential and 
sustainability consequences, are only recently being implemented in 
LUMs (Frank et al. 2017; Humpenöder et al. 2020). The representation 
of bioenergy feedstocks varies across models but typically LUMs have 
comprehensive representation of a series of crops (starch, sugar, oil, 
wood/lignocellulosic feedstocks) or residues/byproducts that can be 
used for liquid and solid bioenergy production (Hanssen et al. 2019).
A.III.I.7.4 Treatment of Environmental and Socio-economic 
Impacts of Land Use
Aside reporting the implications on agriculture, forestry and other 
land use (AFOLU) GHG emissions, LUMs can provide a  set of 
environmental and socio-economic impact indicators to assess the

quantified climate stabilisation pathways in a  broader sustainable 
development agenda (van Vuuren et al. 2015; Obersteiner et al. 2016; 
van Vuuren et al. 2019; Frank et al. 2021; Soergel et al. 2021). These 
indicators typically span from land use area developments (Popp et al. 
2017; Stehfest et al. 2019), fertiliser use, irrigation water use and 
environmental flows (Bonsch et al. 2015; Pastor et al. 2019; Chang 
et al. 2021; de Vos et al. 2021), and on biodiversity (Leclère et al. 
2020; Marquardt et al. 2021), to market impacts on commodity prices 
and food consumption, or impact on undernourishment (Hasegawa 
et al. 2018; Doelman et al. 2019; Fujimori et al. 2019a; Hasegawa et al. 
2020; Soergel et al. 2021).
A.III.I.8 Reduced Complexity Climate Modelling
Climate model emulators (often referred to as reduced complexity or 
simple climate models) are used to integrate the WGI knowledge of 
physical climate science into the WGIII assessment. Hence, emulators

are used to assess the climate implications of the GHG and other

1857
emissions trajectories that IAMs produce (van Vuuren et al. 2008; 
Rogelj et al. 2011; Clarke et al. 2014; Schaeffer et al. 2015; Rogelj 
et al. 2018a). The IAM literature typically uses one of two approaches: 
comprehensive emulators such as MAGICC (Meinshausen et al. 2011) 
or Hector (Hartin et al. 2015) or minimal complexity representations 
such as the representation used in DICE (Nordhaus 2018), PAGE 
(Yumashev et al. 2019; Kikstra et al. 2021c) and Fund (Waldhoff 
et al. 2014). In physical science research, a wider range of different 
emulators are used (Nicholls et al. 2020b, 2021a).
A key application of emulators within IPCC WGIII is the classification 
of emission scenarios with respect to their global mean temperature 
outcomes (Clarke et al. 2014; Rogelj et al. 2018a). WGIII relies on 
emulators to assess the full range of carbon-cycle and climate 
response uncertainty of thousands of scenarios, as assessed by AR6

WGI. An exercise of such amplitude is currently infeasible with more 
computationally demanding state-of-the-art Earth system models. 
Cross-Chapter Box 7.1 in AR6 WGI documents how emulators used 
in WGIII are consistent with the physical science assessment of WGI 
(Forster et al. 2021).
Previous IPCC Assessment Reports relied either on the climate 
output from each individual IAM (IPCC 2000) or a more streamlined 
approach, where one consistent emulator set-up was used to assess 
all scenarios. For instance, in AR5 and the Special Report on Global 
Warming of 1.5°C (SR1.5), MAGICC was used for scenario classification 
(Clarke et al. 2014; Rogelj et al. 2018a). In recent years, numerous 
other emulators have been developed and increased confidence 
and understanding can thus be gained by combining insights from 
more than one emulator. For example, SR1.5 used MAGICC for its 
scenario classification, with additional insights provided by the FaIR

model (Smith et al. 2018). The SR1.5 experience highlighted that 
the veracity of emulators ‘is a  substantial knowledge gap in the 
overall assessment of pathways and their temperature thresholds’ 
(Rogelj et al. 2018a). Since SR1.5, international research efforts have 
demonstrated tractable ways to compare emulator performance 
(Nicholls et al. 2020b), as well as their ability to accurately represent 
a  set of uncertainty ranges in physical parameters (Nicholls et al. 
2021b), such as those reported by AR6 WGI (Forster et al. 2021).
Finally, the recently developed OpenSCM-Runner package (Nicholls 
et al. 2020a) provides users with the ability to run multiple emulators 
from a  single interface. OpenSCM-Runner has been built in 
collaboration with the WGIII research community and forms part of 
the WGIII assessment (Annex III.II.2.5.1).
1 See the common IAM documentation at www.iamcdocumentation.eu.A.III.I.9 Integrated Assessment Modelling

Process-based integrated assessment models (IAMs) describe the 
coupled energy-land-economy-climate system (Weyant 2009; Krey 
2014; Weyant 2017). They typically capture all greenhouse gas 
(GHG) emissions induced by human activities and, in many cases, 
emissions of other climate forcers like sulphate aerosols. Process-
based IAMs represent most GHG and climate pollutant emissions by 
modelling the underlying processes in energy and land use. Those 
models are able to endogenously describe the change in emissions 
due to changes in energy and land use activities, particularly in 
response to climate action. But IAMs differ in the extent to which 
all emissions and the corresponding sources, processes and activities 
are represented endogenously and, thus, can be subjected to policy 
analysis.1 IAMs also differ regarding the scope of representing carbon 
removal options and their interlinkage with other vital systems such 
as the energy and land-use sectors.

Typically, IAMs consider multi-level systems of global, regional, national 
and local constraints and balance equations for different categories 
such as emissions, material and energy flows, financial flows, and 
land availability that are solved simultaneously. Intertemporal IAMs 
can fully incorporate not only flow constraints that are satisfied in 
each period, but also stock constraints that are aggregated over time 
and require to balance activities over time. Changes of activities, for 
example induced by policies to reduce emissions, are connected to 
a  variety of balance equations and constraints and therefore such 
policies lead to system-wide changes that can be analysed with 
IAMs. Many IAMs also contain gridded components to capture, for 
example, land-use and climate change processes where the spatial 
distribution matters greatly for the dynamics of the system. Processes 
that operate on smaller spatial and temporal scales than resolved

by IAMs, such as temporal variability of renewables, are included by 
parameterisation and statistical modelling approaches that capture 
the impact of these subscale processes on the system dynamics at 
the macro level (Pietzcker et al. 2017).
Global IAMs are used to analyse global emissions scenarios 
extrapolating current trends under a  variety of assumptions and 
climate change action pathways under a variety of global goals. In 
recent years, a  class of national and regional IAMs have emerged 
that describe the coupled energy-land-economy system in a  given 
geography. They typically have higher sectoral, policy and technology 
resolution than global models and make assumptions about boundary 
conditions set by global markets and international policy regimes. 
These IAMs are used to study trends and transformation pathways 
for a given region (Shukla and Chaturvedi 2011; Capros et al. 2014; 
Lucena et al. 2016).

1858
A.III.I.9.1 Types of Integrated Assessment Models
IAMs include a variety of model types that can be distinguished into 
two broad classes (Weyant 2017). The first class comprises cost-
benefit IAMs that fully integrate a  stylised socio-economic model 
with a  reduced-form climate model to simultaneously account for 
the costs of mitigation and the damages of global warming using 
highly aggregate cost functions derived from more detailed models. 
In the model context, these functions do not explicitly represent the 
underlying processes, but map mitigation efforts and temperature to 
costs. This closed-loop approach between climate and socio-economic 
systems enables cost-benefit analysis by balancing the cost of 
mitigation and the benefits of avoided climate damages. This can be 
done in a globally cooperative setting to derive the globally optimal 
climate policy where no region can further improve its welfare without

reducing the welfare of another region (Pareto optimum). Alternatively, 
it can be assumed that nations do not engage in emission mitigation at 
all or mitigate in a non-cooperative way, only considering the marginal 
benefit of their own action (Nash equilibrium).  Also, differing degrees 
of partial cooperation are possible.
The second class of IAMs, called process-based IAMs, focuses on 
the analysis of transformation processes depending on a broad set 
of activities that induce emissions as side effects. They describe the 
interlinkages between economic activity, energy use, land use, and 
emissions with emission reductions and removals as well as broader 
sustainable development targets. GHGs and other climate pollutants 
are caused by a broad range of activities that are driven by socio-
economic developments (Riahi et al. 2017) and also induce broader 
environmental consequences such as land-use change (Popp et al.

2017) and air pollution (Rao et al. 2017b). With few exceptions, these 
models typically do not close the loop with climate change and 
damages that affect the economy, but focus on emission scenarios 
and climate change mitigation pathways. Due to the process-based 
representations of emission sources and alternatives, it is not only 
possible to investigate the implications of policies on GHG emissions, 
but also the trade-offs and synergies with social and environmental 
sustainability criteria (von Stechow et al. 2015) (Annex III.I.9.3). 
The analysis of different cross-sectoral synergies and trade-offs is 
frequently termed a nexus analysis, such as the energy-water-land 
nexus. The analysis can also address socio-economic sustainability 
criteria such as energy access and human health. Process-based IAMs 
are also used to explore the synergies and trade-offs of ‘common, but 
differentiated responsibilities’ by analysing issues of burden sharing,

equity, international cooperation, policy differentiation and transfer 
measures (Tavoni et al. 2015; Fujimori et al. 2016; Leimbach and 
Giannousakis 2019; Bauer et al. 2020b).
There exists a broad range of process-based IAMs that differ regarding 
the economic modelling approaches (Annex III.I.2) as well as the 
methodology and detail of sector representation (Annex  III.I.3–7) 
and how they are interlinked with each other.
This leads to differences in model results regarding global aggregates 
as well as sectoral and regional outputs. Several approaches have 
been used to evaluate the performance of IAMs and understand 
differences in IAM behaviour (Schwanitz 2013; Wilson et al. 2021), including sensitivity analysis (McJeon et al. 2011; Luderer et al. 
2013; Rogelj et al. 2013a; Bosetti et al. 2015; Marangoni et al. 2017; 
Giannousakis et al. 2021), model comparisons (Clarke et al. 2009; 
Kriegler et al. 2014a, 2015a; Riahi et al. 2015; Tavoni et al. 2015;

Kriegler et al. 2016; Riahi et al. 2017; Luderer et al. 2018; Roelfsema 
et al. 2020; Riahi et al. 2021; van Soest et al. 2021), model diagnostics 
(Kriegler et al. 2015a; Wilkerson et al. 2015; Harmsen et al. 2021), and 
comparison with historical patterns (Wilson et al. 2013; van Sluisveld 
et al. 2015; Napp et al. 2017).
A.III.I.9.2 Components of Integrated Assessment Models
A.III.I.9.2.1 Energy-economy Component
Typically, IAMs comprise a  model of energy flows, emissions and 
the associated costs (Krey 2014). The demand for exploring the 
Paris Agreement climate goals led to model developments to make 
the challenges and opportunities of the associated transformation 
pathways more transparent. Since AR5 much progress has been 
achieved to improve the representation of mitigation options in the 
energy supply sector (e.g., renewable energy integration (Pietzcker 
et al. 2017), energy trade (Bauer et al. 2016; McCollum et al. 2016;

Bauer et al. 2017; Jewell et al. 2018), capacity inertia, carbon 
removals, and decarbonisation bottlenecks (Luderer et al. 2018)) and 
technological and behavioural change measures in energy demand 
sectors such as transport (van Sluisveld et al. 2016; Edelenbosch et al. 
2017a; McCollum et al. 2017). An energy sector model can be run as 
a partial equilibrium model using exogenous demand drivers for final 
energy and energy services. These models derive mitigation policy 
costs in terms of additional energy sector costs and area under the 
marginal abatement costs curve.
Energy models can be also embedded into a  broader, long-term 
macroeconomic context in a general equilibrium model (Bauer et al. 
2008; Messner and Schrattenholzer 2000). The demands for final 
energy and energy services are endogenously driven by an economic 
growth model that also endogenises the economic allocation problem 
of macroeconomic resources for the energy sector that crowd out

with alternatives. This allows impact analysis of climate policies on 
economic growth and structural change, investment financing and 
crowding out as well as income distribution and tax revenue recycling 
(Guivarch et al. 2011). Moreover, general equilibrium models also 
derive mitigation costs in terms of GDP losses and consumption 
losses, which comprise the full macroeconomic impacts rather than 
only the narrow energy-related costs (Paltsev and Capros 2013).
A.III.I.9.2.2 Land System Component
In recent years, substantial efforts have been devoted to improve 
and integrate land-use sector models in IAMs (Popp et al. 2014b, 
2017). This acknowledges the importance of land-use GHG 
emissions of the agricultural and forestry sectors as well as the 
role of bioenergy, afforestation and other land-based mitigation 
measures. The integration is particularly important in light of the 
long-term climate goals of the Paris Agreement, for four reasons

(IPCC 2019b). First, the GHG emissions from land-use change 
account for more than 10% of global GHG emissions (Kuramochi

1859
et al. 2020) and some sources of CH 4 and N 2O constitute serious 
mitigation bottlenecks. Second, bioenergy is identified as a  crucial 
primary energy source for low-emission energy  supply and carbon 
removal (Bauer et al. 2020a; Butnar et al. 2020; Calvin et al. 2021). 
Third, land use-based mitigation measures such as afforestation 
and reduced deforestation have substantial mitigation potentials. 
Finally, land-cover changes alter the Earth surface albedo, which 
has implications for regional and global climate. Pursuing the Paris 
Agreement climate goals requires the inclusion of a  broad set of 
options regarding GHG emissions and removals, which will intensify 
the interaction between the energy sector, the economy and the land 
use sector. Consequently, intersectoral policy coordination becomes 
more important and the land-related synergies and trade-offs with

sustainable development targets will intensify (Calvin et al. 2014b; 
Kreidenweis et al. 2016; Frank et al. 2017; van Vuuren et al. 2017a; 
Humpenöder et al. 2018; Bauer et al. 2020d). IAMs used by the IPCC 
in the AR6 have continuously improved the integration of land-use 
models with energy models to explore climate mitigation scenarios 
under varying policy and technology conditions (Rogelj et al. 2018a; 
Smith et al. 2019). However, feedbacks from changes in climate 
variables are not included in the land-use sector models, or are only 
included to a limited degree.
A.III.I.9.2.3 Climate System Component
Reduced complexity climate models (often called simple climate 
models or emulators) are used for communicating WGI physical 
climate science knowledge to the research communities associated 
with other IPCC working groups (Annex III.I.8). They are used by IAMs 
to model the climate outcome of the multi-gas emissions trajectories

that IAMs produce (van Vuuren et al. 2011a). A main application of 
such models is related to scenario classifications in WGIII (Clarke 
et al. 2014; Rogelj et al. 2018a). Since WGIII assesses a large number 
of scenarios, it must rely on the use of these simple climate models; 
more computationally demanding models (as used by WGI) will 
not be feasible to apply. For consistency across the AR6 reports, it 
is important that these reduced-complexity models are up to date 
with the latest assessments from WGI. This relies on calibrating these 
models so that they match, as closely as possible, the assessments 
made by WGI (Annex III.II.2.5.1). The calibrated models can then be 
used by WGIII in various parts of its assessment.
A.III.I.9.3 Representation of Nexus Issues and 
Sustainable Development Impacts in Integrated 
An energy-water-land nexus approach integrates the analysis of 
linked resources and infrastructure systems to provide a consistent

platform for multi-sector decision-making (Howells et al. 2013). Many 
of the IAMs that contributed to the assessment incorporate a nexus 
approach that considers simultaneous constraints on land, water and 
energy, as well as important mutual dependencies (Fricko et al. 2017; 
Fujimori et al. 2017; Calvin et al. 2019; Dietrich et al. 2019; van Vuuren 
et al. 2019). Recently IAMs have also been integrated with life cycle 
assessment tools in assessing climate mitigation policies to better 
understand the relevance of life cycle GHG emissions in cost-optimal 
mitigation scenarios (Portugal-Pereira et al. 2016; Pehl et al. 2017; Arvesen et al. 2018; Tokimatsu et al. 2020). This holistic perspective 
ensures mitigation pathways do not exacerbate challenges for other 
sectors or environmental indicators. At the same time, pathways 
are leveraging potential synergies along the way towards achieving 
multiple goals.
IAMs rely on biophysical models with a relatively high degree of spatial

and temporal resolution to inform coarser-scale economic models of 
the potentials and costs for land, water and energy systems (Johnson 
et al. 2019). IAMs leverage population, GDP and  urbanisation 
projections to generate consistent water, energy and crop demand 
projections across multiple sectors (e.g.,  agriculture, livestock, 
domestic, manufacturing and electricity generation) (Mouratiadou 
et al. 2016). The highly distributed nature of decisions and impacts 
across sectors, particularly for land and water, has been addressed 
using multi-scale frameworks that embed regional and sub-regional 
models within global IAMs (Mosnier et al. 2014; Hejazi et al. 2015; 
Bijl et al. 2018; Portugal-Pereira et al. 2018). These analyses have 
demonstrated how local constraints and policies interact with 
national and international strategies aimed at reducing emissions.
Sustainable development impacts extending beyond climate 
outcomes have been assessed by the IAMs that contributed to the

assessment, particularly in the context of the targets and indicators 
consistent with the Sustainable Development Goals (SDGs). The 
representation of individual SDGs is diverse (Figure  3), and recent 
model development has focused mainly on improving capabilities 
to assess climate change mitigation policy combined with indicators 
for economic growth, resource access, air pollution and land use 
(van Soest et al. 2019). Synergies and trade-offs across sustainable 
development objectives can be quantified by analysing multi-
sector impacts across ensembles of IAM scenarios generated from 
single or multiple models (McCollum et al. 2013; Mouratiadou 
et al. 2016). Modules have also been developed for IAMs with 
the specific purpose of incorporating policies that address non-
climatic sustainability outcomes (Cameron et al. 2016; Fujimori et al. 
2018; Parkinson et al. 2019). Similar features have been utilised to 
incorporate explicit adaptation measures and targeted policies that

balance mitigation goals with other sustainability criteria (Bertram 
et al. 2018; McCollum et al. 2018).
A.III.I.9.4 Policy Analysis with IAMs
A key purpose of IAMs is to provide orientation knowledge for the 
deliberation of future climate action strategies by policymakers, civil 
society and the private sector. This is done by presenting different 
courses of actions (climate change and climate action pathways) 
towards a  variety of long-term climate outcomes under a  broad 
range of assumptions about future socio-economic, institutional 
and technological developments. The resulting climate change and 
climate action pathways can be analysed in terms of their outcomes 
towards a set of societal goals (such as the SDGs) and the resulting 
trade-offs between different pathways. Key trade-offs that have 
been investigated in the IAM literature are between (i) no, moderate, 
and ambitious mitigation pathways (Riahi et al. 2017), (ii) early vs

delayed mitigation action (Riahi et al. 2015; Luderer et al. 2018), 
(iii) global action with a  focus on economic efficiency equalising

1860
IAM representation of individual SDGsa
01234
Representation in IAMs Importance of SDG interactions
Low HighSDG interactions and their representation in IAMsb
Improbable Conceivable PlannedNO POVERTY
GOOD HEALTH 
AND WELL-BEING
QUALITY EDUCATION
GENDER EQUALITY
DECENT WORK AND 
ECONOMIC GROWTH
REDUCED INEQUALITIES
ZERO HUNGER
CLEAN WATER AND 
SANITATION
AFFORDABLE AND 
CLEAN ENERGY
RESPONSIBLE CONSUMPTION
AND PRODUCTION
CLIMATE ACTION
LIFE BELOW WATER
LIFE ON LAND
INDUSTRY, INNOVATION
AND INFRASTRUCTURE
SUSTAINABLE CITIES AND
COMMUNITIES
PEACE, JUSTICE AND 
STRONG INSTITUTIONS
PARTNERSHIPS FOR 
THE GOALS1
3
4
5
8
10
2
6
7
12
13
14
15
11
16
179HUMAN
DEVELOPMENTRESOURCE
USEEARTH
SYSTEMGOVERNANCE &
INFRASTRUCTURE
     AFFECTSmodel suitability to
quantify individual SDG targets 
CLEAN ENERGYCLIMATE ACTION
AND INFRASTRUCTURE5
Implemented

Figure 3 | The representation of Sustainable Development Goals by Integrated Assessment Models.  (a) Individual target coverage from a multi-model survey; 
and (b) SDG interactions and coverage by IAM models according to a combination of expert and model surveys. The strength dimension of SDG interactions is indicated by grey 
shading: darker shades represent strong interactions while white represents no interactions. Orange cells indicate where there is the highest agreement between the importance 
of interactions and model representation, while blue coloured cells show the most important interactions without model representation. Source: van Soest et al. (2019).

1861
marginal abatement costs across countries and sectors vs regionally 
and sectorally fragmented action (Blanford et al. 2014a; Bertram et al. 
2015; Kriegler et al. 2015b, 2018b; Bauer et al. 2020b; Roelfsema 
et al. 2020), (iv) pathways with different emphasis on supply-side vs 
demand-side mitigation measures (Grubler et al. 2018; van Vuuren 
et al. 2018) or more broadly different sustainable development 
strategies (Riahi et al. 2012; van Vuuren et al. 2015; Soergel et al. 
2021), and (v) pathways with different preferences about technology 
deployment, in particular with regard to carbon capture and storage 
(CCS) and carbon dioxide removal (CDR) (Krey 2014; Kriegler et al. 
2014a; Riahi et al. 2015; Strefler et al. 2018; Rose et al. 2020; Luderer 
et al. 2021; Strefler et al. 2021b). Key uncertainties that were explored 
in the IAM literature are between (i) different socio-economic futures

as, for example, represented by the Shared Socio-economic Pathways 
(SSPs) (Bauer et al. 2017; Popp et al. 2017; Riahi et al. 2017), 
(ii)  different technological developments (Bosetti et al. 2015) and 
(iii) different resource potentials (Kriegler et al. 2016).
Policy analysis with IAMs follows the approach that a  baseline 
scenario is augmented by some kind of policy intervention. To 
address the uncertainties in baseline projections, the scientific 
community has developed the Shared Socio-economic Pathways 
(SSPs) that provide a set of vastly different future developments as 
reference cases (Annex III.II.1.3,2). Most scenarios used in AR6 are 
based on the middle-of-the-road reference system (SSP2). Depending 
on the research interest, the baseline can be defined as a no-policy 
baseline or it can include policies that either address GHG emissions 
like the nationally determined contributions (NDCs) or other pre-

existing policies such as energy subsidies and taxes. There is no 
standard definition for baseline scenarios regarding the inclusion of 
policies. The baseline scenario is augmented by additional policies 
like a carbon tax aiming towards a long-term climate goal. Hence, 
the IAM-based policy analysis assumes a reference system like SSP2 
within which policy scenarios are compared with a baseline scenario.
Most policy analysis with process-based IAMs apply a mix of short-
term policy evaluation and long-term policy optimisation. Policy 
evaluation applies an exogenous set of policies such as the stated 
NDCs and evaluates the emission outcomes. Policy optimisation is 
mostly implemented as a  cost-effectiveness analysis: a  long-term 
climate stabilisation target is set to derive the optimal mitigation 
strategy that equalises marginal abatement cost across sectors, GHGs 
and countries. This optimal mitigation strategy can be implemented

by a  broad set of well-coordinated sector-specific policies or by 
comprehensive carbon-pricing policies.
Most commonly, the baseline scenario is either a  no-policy 
baseline or based on the NDCs applying an extrapolation beyond 
2030 (Grant et al. 2020; Roelfsema et al. 2020). The climate policy 
regimes most commonly applied include a  long-term target to be 
reached. The optimal climate strategy can be phased in gradually 
or applied immediately after 2020. It can focus on a global carbon 
price equalising marginal abatement costs across countries or policy 
intensities can vary across countries and sectors in the near to medium 
term. The climate policy regime can include or exclude effort-sharing 
mechanisms and transfers between regions. Also, it can be extended 
to include additional sector policies such as improved forest protection or fossil fuel subsidy removal. If certain technologies or activities are 
related to spill-overs such as technology learning, carbon pricing

might be complemented by technology support (Schultes et al. 2018). 
If carbon-pricing policies are fragmented or delayed, additional and 
early sector policies can help reduce distortions and carbon leakage 
effects (Bauer et al. 2020b). All these variations to the policy regime 
can lead to very different transformation pathways and policy costs, 
which is a core result of the IAM analysis.
By applying sensitivity analysis, IAMs can be used to assess the 
importance of strategically developing new technologies and 
options for mitigation and identifying sticking points in climate 
policy frameworks. The sensitivity analysis evaluates differences 
in outcomes subject to changes in assumptions. For instance, the 
assumption about the timing and costs of CCS and CDR availability 
can be varied (Bauer et al. 2020a). The differences in mitigation 
costs and the transformation pathways support the assessment of 
policy prioritisation by identifying and quantifying crucial levers for

achieving long-term climate mitigation targets such as R&D efforts 
and timing of policies.
A.III.I.9.5 Limitations of IAMs
The application of IAMs and their results for providing knowledge 
on climate change response strategies have been criticised based on 
four arguments (Gambhir et al. 2019; Keppo et al. 2021). First, there 
are concerns that IAMs are missing important dynamics, for example 
with regard to climate damages and economic co-benefits of 
mitigation (Stern 2016), demand-side responses (Wilson et al. 2012), 
bioenergy, land degradation and management (Creutzig et al. 2015; 
IPCC 2019b), carbon dioxide removal (Smith et al. 2016), rapid 
technological progress in the renewable energy sector (Creutzig et al. 
2017), actor heterogeneity, and distributional impacts of climate 
change and climate policy. This has given rise to criticism that 
IAMs lack credibility in a set of crucial assumptions, among which

stands out the critique on the availability of carbon dioxide removal 
technologies (Anderson and Peters 2016; Bednar et al. 2019).
These concerns spur continuous model development and 
improvements in scenario design (Keppo et al. 2021), particularly 
with regard to improved representations of energy demand, 
renewable energy, carbon dioxide removal technologies, and land 
management. IAMs are aiming to keep pace with the development 
of sector-specific models, including latest advances in estimating and 
modelling climate damages (Piontek et al. 2019). In places, where 
dynamic modelling approaches are lacking, scenarios are being used 
to explore relevant futures (Grubler et al. 2018). Moreover, sector-
specific model comparison studies have brought together domain 
experts and modellers to improve model representations in these 
areas (Edelenbosch et al. 2017a; Pietzcker et al. 2017; Bauer et al. 
2020a; Harmsen et al. 2020; Rose et al. 2020). Although most models

are still relying on the concept of a single representative household 
representing entire regions, efforts are under way to better represent 
agent heterogeneity and distributional impacts of climate change 
and climate mitigation policies (Rao et al. 2017a; Peng et al. 2021).

1862
Second, concerns have been raised that IAMs are non-transparent and 
thus make it difficult to grasp the context and meaning of their results 
(Skea et al. 2021). These concerns have facilitated a substantial increase 
in model documentation (see the common IAM documentation at 
www.iamcdocumentation.eu as an entry point) and open-source 
models. Nonetheless, more communication tools and co-production of 
knowledge formats will be needed to contextualise IAM results for users 
(Auer et al. 2021). When projecting over a century, uncertainties are 
large and cannot be ignored. Efforts have been undertaken (Marangoni 
et al. 2017; Gillingham et al. 2018; Harmsen et al. 2021; Wilson et al. 
2021) to diagnose key similarities and differences between models and 
better gauge robust findings from these models and how much they 
depend on key assumptions (such as, for example, long-term growth

of the economy, the monetary implication of climate damages, or the 
diffusion and cost of key mitigation technologies).
Third, there are concerns that IAMs are describing transformative 
change on the level of energy and land use, but are largely silent 
about the underlying socio-cultural transitions that could imply 
restructuring of society and institutions. Weyant (2017) notes the 
inability of IAMs to mimic extreme and discontinuous outcomes 
related to these underlying drivers as one of their major limitations. 
This is relevant when modelling extreme climate damages as well 
as when modelling disruptive changes. Dialogues and collaborative 
work between IAM researchers and social scientists have explored 
ways to bridge insights from the various communities to provide 
a  more complete picture of high-impact climate change scenarios 
and, on the other end, deep transformation pathways (Turnheim 
et al. 2015; Geels et al. 2016; Trutnevyte et al. 2019). The extension

of IAM research to sustainable development pathways is giving rise 
to further inter-disciplinary research on underlying transformations 
towards the Paris climate goals and other sustainable development 
goals (Kriegler et al. 2018c; Sachs et al. 2019b).
Finally, there are concerns that IAM analysis could focus on only 
a subset of relevant futures and thus push society in certain directions 
without sufficient scrutiny (Beck and Mahony 2017). IAMs aim to 
explore a  wide range of socio-economic, technology and policy 
assumptions (Riahi et al. 2017), but it remains a constant challenge to 
capture all relevant perspectives (O’Neill et al. 2020). These concerns 
can be addressed by adopting an iterative approach between 
researchers and societal actors in shaping research questions and 
IAM applications (Edenhofer and Kowarsch 2015). IAM research is 
constantly taking up concerns about research gaps and fills it with 
new pathway research, as, for example, occurred for low energy

demand and limited bioenergy with CCS scenarios (Grubler et al. 
2018; van Vuuren et al. 2018).

1863
A.III.I.10 Key Characteristics of Models that Contributed Mitigation Scenarios to the Assessment2
Table 5 | Comparison of modelling characteristics as stated by contributing modelling teams to the AR6 database.  Attributes include regional scope, sectoral coverage, type of baseline or benchmark setup as a basis for 
mitigation policies comparison, technology diffusion, capital vintaging and ‘sunsetting’ of technologies and variety of discount rates approaches.
Global integrated and energy models National integrated modelsAIM
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMAGE 3.0 & 3.2
IMACLIM 
GCAM 
GENeSYS-MOD 
GMM (Global MARKAL Model) 
McKinsey 1.0
MERGE-ETL 
MESSAGEix-GLOBIOM 1.1
MUSE 1.0
POLES 
PROMETHEUS 
TIAM-ECN 1.1
REmap GRO2020
REMIND 2.1 - MAgPIE 4.2
WEM (World Energy Model)
WITCH 
7see6-20_GB
AIM/Enduse-Japan 
BLUES 2.0
China DREAM 
CONTO-RUS 1.0 
E4SMA-EU-TIMES 1.0 
STEM (Swiss TIMES Energy Systems Model) 
JRC-EU-TIMES 
TIMES-China 2.0

TIMES-France
TIMES_PT 
TIMES-Sweden 2.0
Regional scopeGlobal 
National    
Non-global multi-region  
Sectoral 
coverageFull system (covering all GHGs  
from all sectors)        
Energy        
Buildings      
Transport      
Industry      
Characteristics 
of baseline/
benchmark 
setupWell-functioning markets in equilibrium       
Regulatory and/or pricing policies     
Socioeconomic costs & benefits 
of climate change impacts    
Physical impacts of climate  
change on key processes  
2 The tables are limited to the integrated models that provided the information in response to a survey circulated in 2021, and therefore do not have a comprehensive coverage of all models that submitted scenarios to the AR6 

1864
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMACLIM 
GCAM 
MERGE-ETL 
MUSE 1.0
POLES 
WITCH 
BLUES 2.0
TIMES_PT 
Technology 
diffusionLogit substitution    
Constant elasticity of substitution   
Lowest marginal cost w/ 
expansion constraints    
Technology choice depends  
on agents’ preferences
Technologies w/o constraints or 
marginal cost w/ expansion constraints
Capital 
vintaging and 
“sunsetting” of 
technologiesSingle capital stock with fixed lifetime 
and load factor, early retirement via

reduction in load factor possible 
Capital vintaging with fixed 
lifetime and load factors, early 
retirement of vintages or reduction 
in load factors possible        
Single capital stock with fixed 
lifetime and load factor, without 
early retirement  
Mix of the above for 
different technologies         
Discount ratesAs a property of an intertemporal 
welfare function (social discount rate)   
In an objective function of an 
intertemporal optimization, 
to sum values at different times         
To compute lifecycle costs of 
investment decisions or return on 
investments, in functions representing 
agents investment choices      

1865
Table 6 | Overview of evaluated GHG emissions as stated by contributing modelling teams to the AR6 database: carbon dioxide (CO 2) from energy, industrial processes and land-use change, methane (CH 4) from 
fossil fuel combustion, from fugitive and process activities, and agricultural biogenic fluxes, nitrous oxide (N 2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF 6), sulphur dioxide 
(SO 2), black and organic carbon, and non-methane volatile organic compounds (NMVOC).  Levels of emission factor (EF) evaluation were classified in four categories: linked to explicit technology but for average fuel, linked to the 
evolution of other emissions, dependent on average technology classes, and based on an average activity sector.
Type of GHG emissions evaluation Global integrated and energy models National integrated modelsAIM
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMACLIM 
GCAM 
GENeSYS-MOD

MERGE-ETL 
MUSE 1.0
POLES 
WITCH 
BLUES 2.0
TIMES_PT 
CO2 energy a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a
CO2 industrial processes a d a a a b a e d a a a a a c a a d a b a a a a a a c a e e a a
CO2 land-use change a d a a a b a e e c d a e d e a d a e c e e a e d e e e e e e e
CH4 fossil (combustion) a a a a a b a e e c a a a a c a e a a a e a a a a e e e e e a e
CH4 fossil (fugitive and process) a d a a a b a e e a a a e a c a e c e d e d a a c e e e e e a e
CH4 biogenic a e a a a b a e e a d a e d b a e d e c e a a e d e e e e e a e
N2O a d a a a b a e e a d a a d c a e a e c e a a a a e e e e e a e

HFCs d e e a a e a e e e d d e c d e e e e d e c e a b e e e e e e e
PFCs d e e a a e a e e e d e e c d e e e e d e c e a b e e e e e e e
SF6 d e e a a e a e e e d d e c d e e e e d e c e a b e e e e e e e
SO2 a a e d a e a e e e d a e a e e a a e a e e a e b e e e e e e a
Black carbon a d e d a e a e e e e a e a e e e a e a e e e a e e e e e e e a
Organic carbon a d e d a e a e e e e a e a e e e a e a e e e e e e e e e e e a
Non-methane volatile organic compounds (NMVOC) a a e d a e a e e e d a e a e e a a e a e e a e e e e e e e e a
EF linked to explicit technology w/ or w/o fuel representation a EF linked to evolution of other emissions b Average EF for technology class c EF for sector d
Not represented e

1866
A.III.I.11 Comparison of Mitigation and Removal Measures Represented by Models that Contributed Mitigation Scenarios to the Assessment3
Table 7 | Overview of demand- and supply-side mitigation and removal measures in the energy, transport, building, industry and AFOLU sectors, as stated by contributing modelling teams to the AR6 database.  
Levels of inclusion were classified in two dimensions of explicit versus implicit and endogenous or exogenous. An explicit level suggests that the measure is directly represented in the model, while an implicit level refers to measures that are 
estimated indirectly by a proxy. An endogenous level reflects measures that are included in the dynamics of the model framework, whereas an exogenous level refers to measures that are not part of the model dynamics.
Level of inclusion Global integrated and energy models National integrated modelsAIM
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMACLIM
GCAM

GMM (Global MARKAL Model)
MERGE-ETL
MUSE 1.0
POLES
PROMETHEUS
WITCH
AIM/Enduse-Japan
BLUES 2.0
China DREAM
CONTO-RUS 1.0
E4SMA-EU-TIMES 1.0
STEM (Swiss TIMES 
Energy Systems Model)
JRC-EU-TIMES
TIMES_PT
Energy efficiency improvements in energy end uses A B A C A A C B A A C C A C A A A A A C  B A A B A A A A A A A A
Electrification of transport demand A C A A A A A A A A A A A A A A A A A A  B A A B B A A A A A A A
Electrification of energy demand for buildings A C A C A C A A A A A A A A A A A A A C  B A A B B A A A A A A A
Electrification of industrial energy demand A C A C A C C A A A A A A C A A A A A C  B A A B B A A A A A A A
CCS in industrial process applications A A A A A E A E A A A A A A A A A A A C  B A A E B A A A A A A A

Higher share of useful energy in final energy C B A C A D A D A A C C A A C A C C A C  B A A B B A A A A A A A
Reduced energy and service demand in industry C C A C A C C D C B C C D C C C B C B C  B B A A C C A C C C B A
Reduced energy and service demand in buildings C C D C A D C D C B C C D A A C B C B C  B B A B D C A C C C B B
Reduced energy and service demand in transport C C A C A A A A D B D C D A C C B C B D  B B A B D C A C C C B A
Reduced energy and service demand in international transport C E A C C C C D D B D C D A C C B C B D  B E A B E C E C C C E B
Reduced material demand C B B C C D E E E A E E E E E E B E B E  D B B B D E E C C B B B
Urban form E E B E C D E D E E E E E E E E E E C E  D B B B E E A E E E E D
Switch from traditional biomass and modern fuels B A A B A E A C E B A D A A A A A B A D  B E A B B A A A A E A E
Dietary changes (e.g., reducing meat consumption) B E B A B B A E E A E A E E E E E B E E  E E B E E E E E E E E E

Food processing A E A C B B E E E E A E E E E E E E E E  D E A E E E E E E E E E
Reduction of food waste B E E E B E C E E B E B E E E E E B E E  E E D E E E E E E E E E
Substitution of livestock-based products 
with plant-based productsA E B A B D E E E B E E E E E E E B E E  E E B E E E E E E E E E
3 The tables are limited to the integrated models that provided the information in responses to a survey circulated in 2021, and therefore do not have a comprehensive coverage of all models that submitted scenarios to the AR6 
scenario database.Endogenous A CExplicit Implicit
Exogenous B DExplicit Implicit
Not represented E

1867
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMACLIM
GCAM
MERGE-ETL
MUSE 1.0
POLES
WITCH
BLUES 2.0
TIMES_PT
Supply-side measures   
Decarbonisation of electricity   
Solar PV A A A A A A A A A A A A A A A A A A A A  B A A A A A A A A A A A
Solar CSP E E A E A A A A A A A A A A A A A A A A  B E A A E A A A A A A E
Hydropower A A A A A A B A A A A A A A A A A A A D  B A A A A A A A A A A A
Nuclear energy A A A A A A A A A A A A A A A A A A A A  B A A A A A A A A A A A

Advanced, small modular nuclear reactor designs (SMR) E E E C C E E E A E A E E E C A D E C E  B E E E E A A E E E E E
Fuel cells (hydrogen) E A A A A E A A A A A A A A A A B A A A  B A A B E A A A A A A A
CCS at coal and gas-fired power plants A A A A A A A A A A A A A A A A A A A A  B A A E B A A A A A A A
Ocean energy (incl. tidal and current energy) E E E E C E E A A D A E E A E A A E A E  B A E E E A A A A A A E
High-temperature geothermal heat A E A E C E A A A D A A A A C A A A A E  B A E E B A A A A A A E
Wind (on-shore and off-shore lumped together) A A E A A A E A E E A A E E A A A A A A  B E E A A A A E E E E E
Wind (on-shore and off-shore represented individually) E E A E A A A A A A A A A A A A A E A A  B A A E A A E A A A A A
Bio-electricity, including biomass co-firing, without CCS A A A A A A A A A A A A A A A A A A A A  B A A A B A A A A A A A
Bio-electricity, including biomass co-firing, with CCS A A A A A A A A A A A A A A A A A A A A  B A A E E A A A A A A A

Decarbonisation of non-electric fuels   
1st generation biofuels A E A A A A A E A C A B A A A A A B A A  B A A B E A A A A A A A
2nd generation biofuels (grassy/woody biomass 
to liquids) without CCSA E A A A A A A A C A A A A C A A A A A  B A A E E A A A A A A A
to liquids) with CCSA E A A A A A A A C A A A A C A E A A A  B A A E E A A A A A A A
Solar and geothermal heating A E A E C E E A A C A A E A C A A A A E  B A A B B A A A A A A A
Nuclear process heat E E E E C E E E A E A E E E C E E E A E  B E E E B A A A E E E E
Hydrogen from fossil fuels with CCS E E A A A E C A A A A A A A A A A A A A  B A A E E A A A A A A A
Hydrogen from electrolysis E E A A A E A A A A A A A A A A A A A A  B A A B E A A A A A A A
Hydrogen from biomass without CCS E E A A A A A E A D A A A A A A A A A E  B A A E E A A A E A A A
Endogenous A CExplicit Implicit

1868
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMACLIM
GCAM
MERGE-ETL
MUSE 1.0
POLES
WITCH
BLUES 2.0
TIMES_PT
Hydrogen from biomass with CCS E E A A A E A E A D A A A A A A A A A E  B A A E E A A A E A A A
Algae biofuels without CCS E E E E E E E E E E E E A E E E E E C E  B E E E E E E E E E E E
Algae biofuels with CCS E E E E E E E E E E E E E E E E E E C E  B E E E E E E E E E E E
Power-to-gas, methanisation, synthetic fuels, fed 
with fossil CO 2E E A A C E E A A E A E E A A A A E A E  B A A E E A A A E A A A

Power-to-gas, methanisation, syn-fuels, fed with biogenic or 
atmospheric CO 2E E A E C E E A A E A E E A A A A E A E  B A A E E A A A E A A A
Fuel switching and replacing fossil fuels by electricity in 
end-use sectorsC A A A A A A A A A A A A A A A A A A A  B A A B B A A A A A A A
Other processes   
Substitution of halocarbons for refrigerants and insulation E E E E A E C E E E E D E C C E E E E C  E A E B E E E E E E E E
Reduced gas flaring and leakage in extractive industries C E A B C E C E E A E D E A E B E C A C  E E A B B E E E E E E E
Electrical transmission efficiency improvements, 
including smart gridsE E A C C E E D C E D E E E C B A E A C  D E A B B B C E A E B E
Grid integration of intermittent renewables C E A C A C A C C E C A A A C A A A A A  D A A A E A A C E E A C
Electricity storage C D A A A E A A C A C A A A A A A A A A  B A A A D A A C A A A A
AFOLU measures   
Reduced deforestation, forest protection, avoided

forest conversionA D A A A B A E E A E A E C E B D A E C  E E A E B E E E E E E E
Methane reductions in rice paddies A E A C A C C E E A E A E C E B E C E C  E A A B E E E E E E E E
Livestock and grazing management A E A C A A C E E A E A E C E B E C E C  E A A B D E E E E E E E
Increasing agricultural productivity A C A C A A A E E A E A A C E D D C E E  E E A E D E E E E E E E
Nitrogen pollution reductions A E B C A A A E E A E A E C E D E C E E  E A B B B E E E E E E E
Changing agricultural practices enhancing soil carbon E E E C A E E E E A E E A C E B E E E E  E E A E D E E E E E E E
Agroforestry and silviculture E C A E D E E E E B E E E E E B E E E E  E E A E D E E E E E E E
Land-use planning E D A E B E E E E E E E E E E E E E E E  E E A E B E E E E E E E
Urban and peri-urban agriculture and forestry E E E E D E E E E E E E E E E E E E E E  E E E E E E E E E E E E

1869
C3IAM 2.0
COFFEE 1.1
EPPA 6
IMACLIM
GCAM
MERGE-ETL
MUSE 1.0
POLES
WITCH
BLUES 2.0
TIMES_PT
Fire management and (ecological) pest control C E E E D E D E E E E E E E E E E E E E  E E E E D E E E E E E E
Conservation agriculture E E A E D E E E E E E A E E E D E E E E  E E A E E E E E E E E E
Influence on land albedo of land-use change E E E E A E E E E E E E E E E E E D E E  E E E E E E E E E E E E
Manure management A E E E A C C E E A E A E E E B E C E C  E A A B E E E E E E E B

Reduce food post-harvest losses B D E E D E D E E E E B E E E E E E E E  E E E E E E E E E E E E
Recovery of forestry and agricultural residues E E A E A B A E E E E A E C E E D E E E  E E A E B E E E E E E E
Forest management – increasing forest productivity C E E C C B D E E E E A E C E E D E E C  E E E E D E E E E E E A
Forest management – increasing timber/biomass extraction C E E E C B D E E E E A E C E E D E E C  E E E E D E E E E E E A
Forest management – remediating natural disturbances E E E E B B E E E E E E E E E E E E E C  E E E E D E E E E E E E
Forest management – conservation for carbon sequestration E D E E B B D E E A E A E E E E D E E C  E E E E E E E E E E E E
Carbon dioxide removal   
Bioenergy production with carbon capture and 
sequestration (BECCS)A A A A A A A A A A A A A A A A A A A A  B A A E E A A A A A A A
Direct air capture and storage (DACS) E E A A A E E A A A A E E A A A A A A A  B A A E E E A A A E A E

Mineralisation of atmospheric CO 2 through enhanced 
weathering of rocksE E E E E E E E E C E E E E E E E A E E  E E E E E E E E E E E E
Afforestation/Reforestation A A A A A B A E E C E A E C C B C A E A  E E A E B E E E E E E E
Restoration of wetlands E E E E C E E E E E E E E E E E E E E E  E E E E E E E E E E E E
Biochar E E E E D E E E E E A E E E E E E E E E  E E E E E E A E E E E E
Soil carbon enhancement, enhancing carbon sequestration in 
biota and soilsE E A C D D E E E E A A E C E E E C E E  E E A E E E E E E E E E
Material substitution of fossil CO 2 with bio-CO 2 in 
industrial applicationE E A C A E E E E E A E E E E D E E A E  D E A E E A E E E E E E
Ocean iron fertilisation E E E E E E E E E E E E E E E E E E E E  E E E E E E E E E E E E
Ocean alkalinisation E E E E E E E E E E E E E E E E E E E E  E E E E E E E E E E E E
Carbon capture and usage (CCU)

Bioplastics, carbon fibre and other construction materials E E A A E E C E A D A E E E A E A E A E  E E A E B A E A E E A E

1870
Part II:   Scenarios
A.III.II.1 Overview on Climate Change Scenarios
Scenarios are descriptions of alternative future developments. They 
are used to explore the potential implications of possible future 
developments and how they might depend on alternative courses of 
action. They are particularly useful in the context of deep uncertainty. 
Scenarios are conditional on the realisation of external assumptions 
and can be used to explore possible outcomes under a  variety 
of assumptions.
Future climate change is a  prime example for the application of 
scenarios. It is driven by human activities across the world and 
thus can be altered by human agency. It affects all regions over 
many centuries to come. Humankind’s response to climate change 
touches not only on the way we use energy and land, but also on 
socio-economic and institutional layers of societal development.

Climate change scenarios provide a  central tool to analyse this 
wicked problem.
A.III.II.1.1 Purposes of Climate Change Scenarios
Climate change scenarios are developed for a number of purposes 
(O’Neill et al. 2020). First, they are constructed to explore possible 
climate change futures covering the causal chain from (i) socio-
economic developments to (ii) energy and land use to (iii) greenhouse 
gas emissions to (iv) changes in the atmospheric composition of 
greenhouse gases and short-lived climate forcers and the associated 
radiative forcing to (v) changes in temperature and precipitation 
patterns to (vi) bio-physical impacts of climate change and finally to 
(vii) impacts on socio-economic developments, thus closing the loop. 
Quantitative scenarios exploring possible climate change futures 
are often called climate change projections and climate change 
impact projections.
Second, climate change scenarios are developed to explore pathways

towards long-term climate goals. Goal-oriented scenarios often carry 
the word ‘pathway’ in their name, such as climate change mitigation 
pathway, climate change adaptation pathway, or more generally 
climate change transition or transformation pathway. They are 
sometimes called ‘backcasting’4 scenarios, or ‘short backcasts’, in 
the literature, particularly when contrasted with forecasts (Robinson 
1982). Goal-oriented/backcasting scenarios are inherently normative 
and intricately linked to human intervention. They can be used to 
compare and contrast different courses of actions. For example, 
they are applied in climate change mitigation analysis by comparing 
reference scenarios without or with only moderate climate policy 
intervention, sometimes called baseline scenarios, with mitigation 
pathways that achieve certain climate goals (Grant et al. 2020). 
Transformation pathways to climate goals are examples of

backcasting scenarios. Among other things, they can be used to learn 
about the multi-dimensional trade-offs between raising or lowering 
4 Backcasting is different from hindcasting. Hindcasting refers to testing the ability of a mathematical model to reproduce past events. In contrast, backcasting begins with 
a desired future outcome and calculates a pathway from the present to that outcome consistent with constraints.
5 The terms mitigation/transition/transformation scenarios and mitigation/transition/transformation pathways are used interchangeably, as they refer to goal-oriented scenarios.ambition (Clarke et al. 2014; Schleussner et al. 2016). In addition, 
different transformation pathways to the same goal are often used 
to analyse trade-offs between different routes towards this goal 
(Rogelj et al. 2018a). These scenarios need to be looked at as a set to 
understand attainable outcomes and the trade-offs between them. 
With scenarios, context matters.

Third, climate change scenarios are used to integrate knowledge 
and analysis between the three different climate change research 
communities working on the climate system and its response to 
human interference (linked to WG I of the IPCC), climate change 
impacts, adaptation and vulnerability (linked to WGII) and climate 
change mitigation (linked to WGIII) (O’Neill et al. 2016; IPCC 
2000; van Vuuren et al. 2011b) (Annex III.II.1.3). This involves the 
adoption of common scenario frameworks that allow the consistent 
use of, for example, shared emissions scenarios, socio-economic 
development scenarios and climate change projections (Moss et al. 
2010; Kriegler et al. 2012; van Vuuren et al. 2012; O’Neill et al. 2014; 
van Vuuren et al. 2014). The integrative power of scenarios extends 
beyond the climate change research community into neighbouring 
fields such as the social sciences and ecology (Pereira et al. 2020;

Rosa et al. 2020). To foster such integration, underlying scenario 
narratives have proven extremely useful as they allow researchers to 
develop and link quantitative scenario expressions in very different 
domains of knowledge (O’Neill et al. 2020).
Fourth, climate change scenarios and their assessment aim to inform 
society (Kowarsch et al. 2017; Weber et al. 2018; Auer et al. 2021). 
To achieve this, it is important to connect climate change scenarios 
to broader societal development goals (Riahi et al. 2012; van Vuuren 
et al. 2015; Kriegler et al. 2018c; Soergel et al. 2021) and relate them 
to social, sectoral and regional contexts (Absar and Preston 2015; 
Frame et al. 2018; Kok et al. 2019; Aguiar et al. 2020). To this end, 
scenarios can be seen as tools for societal discourse and decision-
making to coordinate perceptions about possible and desirable 
futures between societal actors (Edenhofer and Kowarsch 2015; Beck 
and Mahony 2017).

A.III.II.1.2 Types of Climate Change Mitigation Scenarios
Different types of climate change scenarios are linked to different 
purposes and knowledge domains and different models are used 
to construct them (Annex III.I). Global reference and mitigation 
scenarios and their associated emissions projections, which are often 
called emission scenarios, and national, sector and service transition 
scenarios are key types of scenarios assessed in the Working Group III 
report. They are briefly summarised below.5
A brief description of the common climate change scenario 
framework with relevance for all three IPCC Working Groups is 
provided in Annex III.II.1.3, and a discussion how the WGI and WGII 
assessments relate to the WGIII scenario assessment is given in 
Annex III.II.2.5.

1871
A.III.II.1.2.1 Global mitigation scenarios
Global mitigation scenarios are mostly derived from global integrated 
assessment models (Annex III.I.9) and have been developed in 
single model studies as well as multi-model comparison studies. 
The research questions of these studies have evolved together with 
the climate policy debate and the knowledge about climate change, 
drivers, and response measures. The assessment of global mitigation 
pathways in the Fifth Assessment Report (AR5) (Clarke et al. 2014) 
was informed, inter alia, by a  number of large-scale multi-model 
studies comparing overshoot and not-to-exceed scenarios for a range 
of concentration stabilisation targets (Energy Modelling Forum (EMF) 
study 22: EMF22) (Clarke et al. 2009), exploring the economics of 
different decarbonisation strategies and robust characteristics of the 
energy transition in global mitigation pathways (EMF27, RECIPE)

(Luderer et al. 2012; Krey and Riahi 2013; Kriegler et al. 2014a), and 
analysing co-benefits and trade-offs of mitigation strategies with 
energy security, energy access, and air quality objectives (Global 
Energy Assessment: GEA) (McCollum et al. 2011; Riahi et al. 2012; 
McCollum et al. 2013; Rao et al. 2013; Rogelj et al. 2013b). They also 
investigated the importance of international cooperation for reaching 
ambitious climate goals (EMF22, EMF27, AMPERE) (Clarke et al. 
2009; Blanford et al. 2014b; Kriegler et al. 2015b), the implications of 
collective action towards the 2°C goal from 2020 onwards vs delayed 
mitigation action (AMPERE, LIMITS) (Kriegler et al. 2014b; Riahi et al. 
2015), and the distribution of mitigation costs and burden-sharing 
schemes in global mitigation pathways (LIMITS) (Tavoni et al. 2014, 
2015). Scenarios from these and other studies were collected in 
a  scenario database supporting the AR5 assessment (Krey et al.

2014). With a shelf life of 8 to 14 years, they are now outdated and 
no longer part of this assessment.
Since AR5, many new studies published global mitigation pathways 
and associated emissions projections. After the adoption of the 
Paris Agreement, several large-scale multi-model studies newly 
investigated pathway limiting warming to 1.5°C (ADVANCE: Luderer 
et al. (2018); CD-LINKS: McCollum et al. (2018a); ENGAGE: Riahi et al. 
(2021); SSPs: Rogelj et al. (2018b)), allowing this report to conduct 
a  robust assessment of 1.5°C pathways. Most scenario studies 
took the hybrid climate policy architecture of the Paris Agreement 
with global goals, nationally determined contributions (NDCs) and 
an increasing number of implemented national climate policies 
as a  starting point, including hybrid studies with participation of 
global and national modelling teams to inform the global stocktake 
(ENGAGE: Fujimori et al. (2021); COMMIT: van Soest et al. (2021); CD-

LINKS: Schaeffer et al. (2020), Roelfsema et al. (2020)). Multi-model 
studies covered a range of scenarios from extrapolating current policy 
trends and the implementation of NDCs, respectively, to limiting 
warming to 1.5°C–2°C with immediate global action and after passing 
through the NDCs in 2030, respectively. These scenarios are used to 
investigate, among others, the end-of-century warming implications 
of extrapolating current policy trends and NDCs (Perdana et al. 2020); 
the ability of the NDCs to keep limiting warming to 1.5ºC–2°C in reach 
(Luderer et al. 2018; Vrontisi et al. 2018; Roelfsema et al. 2020), the 
scope for global accelerated action to go beyond the NDCs in 2030 
(van Soest et al. 2021), and the benefits of early action vs the risk of 
overshoot and the use of net negative CO 2 emissions in the long-term (Bertram et al. 2021; Hasegawa et al. 2021; Riahi et al. 2021). 
Other large-scale multi-model studies looked into specific topics: the

international economic implications of the NDCs in 2030 (EMF36) 
(Böhringer et al. 2021), the impact of mitigating short-lived climate 
forcers on warming and health co-benefits in mitigation pathways 
(EMF30) (Harmsen et al. 2020; Smith et al. 2020b) and the role and 
implications of large-scale bioenergy deployment in global mitigation 
pathways (EMF33) (Bauer et al. 2020a; Rose et al. 2020).
A large variety of recent modelling studies, mostly based on individual 
models, deepened research on a diverse set of questions (Annex III.
II.3.2). Selected examples are the impact of peak vs end-of-century 
targets on the timing of action in mitigation pathways (Rogelj et al. 
2019a; Strefler et al. 2021a); demand-side driven deep mitigation 
pathways with sustainable development co-benefits (Bertram et al. 
2018; Grubler et al. 2018; van Vuuren et al. 2018); synergies and 
trade-offs between mitigation and sustainable development goals

(Fujimori et al. 2020; Soergel et al. 2021); and the integration of 
climate impacts into mitigation pathways (Schultes et al. 2021). 
There have also been a number of recent sectoral studies with global 
integrated assessment models and other global models across all 
sectors, for example the energy sector (IRENA 2020; Kober et al. 
2020; IEA 2021) and transport sector (Edelenbosch et al. 2017a; 
Mercure et al. 2018; Zhang et al. 2018; Fisch-Romito and Guivarch 
2019; Rottoli et al. 2021; Lam and Mercure 2021; Paltsev et al. 
2022). Very recent work investigated the impact of COVID-19 on 
mitigation pathways (Kikstra et al. 2021a) and co-designed global 
scenarios for users in the financial sector (NGFS 2021). In addition 
to these policy-, technology- and sector-oriented studies, a  few 
diagnostic studies developed mitigation scenarios to diagnose model 
behaviour (Harmsen et al. 2021) and explore model harmonisation 
(Giarola et al. 2021).

The scenarios from most of these and many other studies were 
collected in the AR6 scenario database (Annex III.II.3.2) and are 
primarily assessed in Chapter  3 of the report. However sectoral 
chapters have also used the scenarios, including their climate 
mitigation categorisations, to ensure consistent cross-chapter 
treatment. Only a  small fraction of these scenarios were already 
available to the assessment of global mitigation pathways in 
the Special Report on Global Warming of 1.5°C (SR1.5) (Rogelj 
et al. 2018a) and were included in the supporting SR1.5 database 
(Huppmann et al. 2018).
A.III.II.1.2.2 National Transition Scenarios
A large number of transition scenarios is developed on a national/
regional level by national integrated assessment, energy-economy 
or computable general equilibrium models, among others. These 
aim to analyse the implications of current climate plans of countries 
and regions, as well as long-term strategies until 2050 investigating

different degrees of low-carbon development. National/regional 
transition scenarios are assessed in Chapter 4 of the report.
Recent research has focused on several different types of national 
transition scenarios that focus on accelerated climate mitigation 
pathways in the near term to 2050. These include scenarios considered

1872
by the authors as tied to meeting specific global climate goals6 and 
scenarios tied to specific policy targets (e.g.,  carbon neutrality or 
80–95% reduction from a  certain baseline year). A  majority of the 
accelerated national transition modelling studies up to 2050 evaluate 
pathways that the authors consider compatible with a  2°C global 
warming limit, with fewer scenarios defined as compatible with 1.5°C 
global pathways. Regionally, national transition scenarios have centred 
on countries in Asia (particularly in China, India, Japan), in the European 
Union, and in North America, with fewer and more narrowly focused 
scenario studies in Latin America and Africa (Lepault and Lecocq 2021).
A.III.II.1.2.3 Sector Transition Scenarios
There are also a  range of sector transition scenarios, both on the 
global and the country level. These include scenarios for the

transition of the electricity, buildings, industry, transport and AFOLU 
sectors until 2050. Due to the accelerated electrification in mitigation 
pathways, sector coupling plays an increasingly important role to 
overcome decarbonisation bottlenecks, complicating a  separate 
sector-by-sector scenario assessment. Likewise, the energy-water-
land nexus limits the scope of a separate assessment of the energy 
and agricultural sectors. Nevertheless, sector transition scenarios 
play an important role for this assessment as they can usually offer 
much more technology, policy and behaviour detail than integrated 
assessment models. They are primarily assessed in the sector chapters 
of the report. Their projections of emissions reductions in the sectors 
in the near to medium term is used to check the sector dynamics of 
global models in Chapter 3 of the report.
Recent transition scenarios considered overarching accelerated

climate mitigation strategies across multiple sectors, including 
demand reduction, energy efficiency improvement, electrification 
and switching to low-carbon fuels. The sectoral strategies considered 
are often specific to national resource availability, political, economic, 
climate, and technological conditions. Many sectoral transition 
strategies have focused on the energy supply sectors, particularly 
the power sector, and the role for renewable and bio-based fuels in 
decarbonising energy supply and carbon capture and sequestration 
(CCS). Some studies present comprehensive scenarios for both supply-
side and demand-side sectors, including sector-specific technologies, 
strategies, and policies. Nearly all demand sector scenarios have 
emphasised the need for energy efficiency, conservation and 
reduction through technological changes, with a limited number of 
models also exploring possible behavioural changes enabled by new 
technological and societal innovations.

A.III.II.1.2.4 Service Transition Scenarios
A central feature of service transition pathways is a  focus on the 
provision of adequate energy services to provide decent standards 
of living for all as the main scenario objective. Energy services are 
proxies for well-being, with common examples being provision 
of shelter (expressed as m2 per capita), mobility (expressed as 
passenger-kilometres), nutrition (expressed as kCal per capita), and 
6 National emission pathways in the near or mid-term cannot be linked to long-term mitigation goals without making additional assumptions about emissions by other 
countries up to the mid-term, and assumptions by all countries up to 2100 (see Chapter 4, Box 4.1).thermal comfort (expressed as degree-days) (Creutzig et al. 2018). 
Service transition pathways seek to meet adequate levels of such 
services with minimal carbon emissions, using combinations of 
demand- and supply-side options. Ideally this is done by improving

the efficiency of service provision systems to minimise overall 
final energy and resource demand, thereby reducing pressure on 
supply-side and carbon dioxide removal technologies (Grubler 
et al. 2018). Specifically, this includes providing convenient access 
to end-use services (health care, education, communication, etc.), 
while minimising both primary and end-use energy required. 
Service transition pathways provide a compelling scenario narrative 
focused on well-being, resulting in technology and policy pathways 
that give explicit priority to decent living standards. Furthermore, 
more efficient service provision often involves combinations of 
behavioural, infrastructural and technological change, expanding 
the options available to policymakers for achieving mitigation goals 
(van Sluisveld et al. 2016, 2018). These dimensions are synergistic, 
in particular in that behavioural and lifestyle changes often require

infrastructures adequately matching lifestyles. Service transition 
scenarios are primarily assessed in Chapter 5 of the report.
A.III.II.1.3 Scenario Framework for Climate Change Research
A.III.II.1.3.1 History of Scenario Frameworks used by the IPCC
For the first three assessment reports, the IPCC directly commissioned 
emission scenarios with social, economic, energy and partially 
policy aspects as drivers of projected GHG emissions. The first set 
of scenarios, the ‘SA90’ of the IPCC First Assessment Report (IPCC 
1990), had four distinct scenarios, ‘business-as-usual’ and three 
policy scenarios of increasing ambition. The set of ‘IS92’ scenarios 
used in the Second Assessment Report investigated variations of 
business-as-usual scenarios with respect to uncertainties about the 
key drivers of economic growth, technology and population (Leggett 
et al. 1992). The SRES scenarios from the IPCC Special Report on 
Emission Scenarios (SRES) (IPCC 2000) were produced by multiple

modelling organisations and were used in the Third and Fourth 
Assessment reports. Four distinct scenario families were characterised 
by narratives and projections of key drivers like population 
development and economic growth (but no policy measures) 
to examine their influence on a  range of GHG and air pollutant 
emissions. Until the Fourth Assessment Report, the IPCC organised 
the scenario development process centrally. Since then, scenarios are 
developed by the research community and the IPCC limited its role 
to catalysing and assessing scenarios. To shorten development times, 
a parallel approach was chosen (Moss et al. 2010) and representative 
concentration pathways (RCPs) were developed (van Vuuren et al. 
2011b) to inform the next generation of climate modelling for the 
Fifth Assessment Report. RCPs explored four different emissions and 
atmospheric composition pathways structured to result in different

levels of radiative forcing in 2100: 2.6, 4.5, 6.0 and 8.5 W m–2. They 
were used as an input to the Climate Model Intercomparison Project 
Phase 5 (CMIP5) (Taylor et al. 2011) and its results were assessed in 
AR5 (Collins et al. 2013).

1873
A.III.II.1.3.2 Current Scenario Framework and SSP-based 
Emission Scenarios
The current scenario framework for climate change research (Kriegler 
et al. 2014c; O’Neill et al. 2014; van Vuuren et al. 2014) is based on 
the concept of Shared Socio-economic Pathways (SSPs) (Kriegler et al. 
2012; O’Neill et al. 2014). Unlike their predecessor scenarios from 
the SRES (IPCC 2000), their underlying narratives are motivated by 
the purpose of using the framework for mitigation and adaptation 
policy analysis. Hence the narratives are structured to cover the space 
of socio-economic challenges to both adaptation and mitigation. 
They tell five stories of sustainability (SSP1), middle of the road 
development (SSP2), regional rivalry (SSP3), inequality (SSP4) and 
fossil-fuelled development (SSP5) (O’Neill et al. 2017). SSP1, SSP2, 
and SSP3 were structured to explore futures with socio-economic

challenges to adaptation and mitigation increasing from low to high 
with increasing number of SSP . SSP4 was structured to explore a world 
with high socio-economic challenges to adaptation but low socio-
economic challenges to mitigation, while SSP5 explored a world with 
low challenges to adaptation but high challenges to mitigation. The 
five narratives have been translated into population and education 
(Kc and Lutz 2017), economic growth (Crespo Cuaresma 2017; Dellink 
et al. 2017; Leimbach et al. 2017a), and urbanisation projections 
(Jiang and O’Neill 2017) for each of the SSPs.
The SSP narratives and associated projections of socio-economic 
drivers provide the core components for building SSP-based scenario 
families. These basic SSPs are not scenarios or goal-oriented pathways 
themselves (despite carrying ‘pathway’ in the name), but building 
blocks from which to develop full-fledged scenarios. In particular,

their basic elements do not make quantitative assumptions about 
energy and land use, emissions, climate change, climate impacts 
and climate policy. Even though including these aspects in the 
scenario-building process may alter some of the basic elements, such 
as projections of economic growth, the resulting scenario remains 
associated with its underlying SSP . To improve the ability of SSPs 
to capture socio-economic environments, basic SSPs have been 
extended in various ways, including the addition of quantitative 
projections on further key socio-economic dimensions like inequality 
(Rao et al. 2019), governance (Andrijevic et al. 2020b), and gender 
equality (Andrijevic et al. 2020a). Extensions also included spatially 
downscaled projections of, for example, population developments 
(Jones and O’Neill 2016). By now, the SSPs have been widely used in 
climate change research ranging from projections of future climate

change to mitigation, impact, adaptation and vulnerability analysis 
(O’Neill et al. 2020).
The integrated assessment modelling community has used the SSPs to 
provide a set of global integrated energy-land use-emissions scenarios 
(Bauer et al. 2017; Calvin et al. 2017; Fricko et al. 2017; Fujimori 
et al. 2017; Kriegler et al. 2017; Popp et al. 2017; Rao et al. 2017b; 
Riahi et al. 2017; van Vuuren et al. 2017b; Rogelj et al. 2018b) in line 
with the matrix architecture of the scenario framework (van Vuuren 
7 Each SSPx-y combination was calculated by multiple IAMs. The specific scenarios developed by the marker models for the associated SSPs (SSP1: IMAGE; SSP2: MESSAGE-
GLOBIOM; SSP3: AIM; SSP4: GCAM; SSP5: REMIND-MAgPIE) were selected as Tier 1/Tier 2 scenarios for use in CMIP6. Tier 2 variants include SSP7-3.0 with high emissions

of short–lived climate forcers and SSP5-3.4 with high overshoot from following SSP5-8.5 until 2040.et al. 2014) (Figure 4). It is structured along two dimensions: socio-
economic assumptions varied along the SSPs, and climate (forcing) 
outcomes varied along the Representative Concentration Pathways 
(RCPs) (van Vuuren et al. 2011b). To distinguish resulting emission 
scenarios from the original four RCPs (RCP2.6, RCP4.5, RCP6.0, and 
RCP8.5), they are typically named SSPx–y with x = {1,…,5} the SSP 
label and y = {1.9, 2.6, 3.4, 4.5, 6.0, 7.0, 8.5} W  m–2 the nominal 
forcing level in 2100. The four forcing levels that were already 
covered by the original RCPs are bolded here.
The new SSP-based emissions and concentrations pathways provided 
the input for CMIP6 (Eyring et al. 2015; O’Neill et al. 2016) and its 
climate change projections are assessed in AR6 (WGI Cross-chapter 
Box 1.2, WGI Chapter 4). From the original set of more than 100 SSP-

based energy-land use-emissions scenarios produced by six IAMs 
(Figure  4), five Tier 1 scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, 
SSP3-7.0, SSP5-8.5), and four Tier 2 scenarios (SSP4-3.4, SSP4-6.0, 
variants of SSP7-3.0, SSP5-3.4) were selected7 (O’Neill et al. 2016), 
further processed and harmonised with historic emissions and 
land-use change estimates (Gidden et al. 2019; Hurtt et al. 2020), 
and then taken up by CMIP6 models. WGI focuses its assessment 
of CMIP6 climate change projections on the five Tier 1 scenarios 
(WGI Chapter 4), but also uses the Tier 2 scenarios where they allow 
assessment of specific aspects like air pollution. All SSP-based IAM 
scenarios from the original studies are included in the AR6 emissions 
scenario database and are part of the assessment of global mitigation 
pathways in Chapter 3.
IAMs could not identify SSP-based emissions scenarios for all 
combinations of SSPs and RCPs (Riahi et al. 2017; Rogelj et al.

2018b) (Figure 4). The highest emission scenarios leading to forcing 
levels similar to RCP8.5 could only be obtained in a  baseline 
without climate policy in SSP5 (SSP5-8.5). Since by now climate 
policies are implemented in many countries around the world, 
the likelihood of future emission levels as high as in SSP5-8.5 has 
become small (Ho et al. 2019). Baselines without climate policies 
for SSP1 and SSP4 reach up to 6.0–7.0 W m–2, with baselines for 
SSP2 and SSP3 coming in higher at around 7.0 W m–2. On the lower 
end, no 1.5°C (RCP1.9) and likely 2°C scenarios (RCP2.6) could be 
identified for SSP3 due to the lack of cooperative action in this 
world of regional rivalry. 1.5°C scenarios (RCP1.9) could only be 
reached by all models under SSP1 assumptions. Models struggled 
to limit warming to 1.5°C under SSP4 assumptions due to limited 
ability to sustainably manage land, and under SSP5 assumptions 
due to their high dependence on ample fossil fuel resources in the

baseline (Rogelj et al. 2018b).
A.III.II.1.4 Key Design Choices and Assumptions 
in Mitigation Scenarios
The development of a scenario involves design choices, in addition to 
the selection of the model. This section will focus on key choices related 
to scenario design, and the respective socio-economic, technical, and

1874
policy assumptions. Model selection cannot be separated from these 
choices. The various advantages and disadvantages of models are 
described in Annex III, Part I (Modelling Methods).
Target setting: Goal-oriented scenarios in the climate scenario 
literature initially focused on concentration stabilisation but have 
now shifted towards temperature limits and associated carbon 
budgets. In early model intercomparisons, climate targets were 
often specified as a CO 2-equivalent concentration level, for example, 
450ppm CO 2-eq or 550ppm CO 2-eq (Clarke et al. 2009). These 
targets were either applied as not-to-exceed or overshoot targets. 
In the latter case, concentration levels could be returned to the 
target level by 2100. Overshoot was particularly allowed for low 
concentration and temperature targets as many models could not 
find a solution otherwise (Clarke et al. 2009; Blanford et al. 2014b;

Kriegler et al. 2014a; Rogelj et al. 2018b). Bioenergy with carbon 
capture and storage (BECCS) was an important technology that 
facilitated aggressive targets to be met in 2100. Due to its ability 
to remove CO 2 from the atmosphere and produce net negative CO 2 
emissions, it enabled overshoot of the target, leading to a distinctive 
peak-and-decline behaviour in concentration, radiative forcing, and 
temperature (Clarke et al. 2014; Fuss et al. 2014). The mitigation scenarios based on the SSP-RCP framework also applied radiative 
forcing levels in 2100 (Riahi et al. 2017). Temperature targets were 
often implemented by imposing end-of-century carbon budgets, that 
is, cumulative emissions up until 2100. In the case of 2°C pathways, 
those budgets were usually chosen such that the 2°C limit was not 
overshoot with some pre-defined probability (Luderer et al. 2018). 
Arguably, the availability of net negative CO 2 emissions has led to

high levels of carbon dioxide removal (CDR) in the second half of 
the century, although CDR deployment is often already substantial to 
compensate residual emissions (Rogelj et al. 2018a).
Recent literature has increasingly focused on alternative approaches 
such as peak warming or peak CO 2 budget constraints to implement 
targets (Rogelj et al. 2019b; Johansson et al. 2020; Riahi et al. 2021). 
Nevertheless, due to the availability of net negative CO 2 emissions 
and the assumption of standard (exponentially increasing) emissions-
pricing profiles from economic theory, peak and decline temperature 
profiles still occurred in a large number of mitigation pathways in 
the literature even in the presence of peak warming and carbon 
budget targets (Strefler et al. 2021b). This has led to proposals to 
combine peak targets with additional assumptions affecting the 
timing of emissions reductions like a constraint on net negative CO 2

Figure  4 | The SSP/RCP matrix showing the SSPs on the horizontal axis and the forcing levels on the vertical axis.  A  = AIM, G = GCAM; I = IMAGE, 
M = MESSAGE-GLOBIOM, R = REMIND-MAgPIE, W = WITCH]. Not all SSP/RCP combinations are feasible (red triangles), and not all combinations were tried (grey triangles). 
Source: adapted with permission from Figure 5 of Rogelj et al. (2018b). Corresponding scenarios were published in Riahi et al. (2017) and Rogelj et al. (2018b) and included 
in the AR6 scenario database.

1875
emissions (Obersteiner et al. 2018; Rogelj et al. 2019a; Riahi et al. 
2021) and different carbon pricing profiles (Strefler et al. 2021b). 
These proposals are aiming at a stabilisation rather than a peak and 
decline of warming under a given warming limit. However, arguments 
in support of peak and decline warming profiles also exist: the goal of 
hedging against positive feedback loops in the Earth system (Lenton 
et al. 2019) and the aim of increasing the likelihood of staying below 
a  temperature limit towards the end of the century (Schleussner 
et al. 2016). It is also noteworthy that peak and decline temperature 
pathways are connected to achieving net-zero GHG emissions (with 
CO2-eq emissions calculated using GWP100) in the second half of the 
century (Rogelj et al. 2021).
Efficiency considerations: Process-based IAMs typically calculate 
cost-effective mitigation pathways towards a  given target as

a  benchmark case (Clarke et al. 2014). In these pathways, global 
mitigation costs are minimised by exploiting the abatement options 
with the least marginal costs across all sectors and regions at any 
time, implicitly assuming a  globally integrated and harmonised 
mitigation regime. This idealised benchmark is typically compared 
across different climate targets or with reference scenarios 
extrapolating current emissions trends (UNEP 2019). It naturally 
evolves over time as the onset of cost-effective action is being set 
to the immediate future of respective studies. This onset was pushed 
back from 2010–2015 in studies assessed by AR5 (Clarke et al. 2014) 
to the first modelling time step after 2020 in studies assessed by AR6.
The notion of cost-effectiveness is sensitive to economic assumptions in 
the underlying models, particularly concerning the assumptions on pre-
existing market distortions (Guivarch et al. 2011; Clarke et al. 2014; Krey

et al. 2014) and the discount rate on future values. Those assumptions 
are often not clearly expressed. Most models have a discount rate of 
3–5%, though the range of alternatives is larger. Cost-benefit IAMs 
have had a tradition of exploring the importance of discount rates, but 
process-based IAMs have generally not. A lower discount rate brings 
mitigation forward in time and uses less net negative CO 2 emissions 
in cases where target overshoot is allowed (Emmerling et al. 2019; 
Realmonte et al. 2019). While most models report discount rates in 
documentation, there is arguably too little sensitivity analysis of how 
the discount rate affects modelled outcomes.
Cost-effective pathways typically do not account for climate impacts 
below the temperature limit, although recent updates to climate 
damage estimates suggest a  strengthening of near-term action in 
cost-effective mitigation pathways (Schultes et al. 2021). Recently,

the research community has begun to combine mitigation pathway 
analysis with ex post analysis of associated climate impacts and the 
benefits of mitigation (Drouet et al. 2021). Cost-effective pathways 
that tap into least cost abatement options globally without considering 
compensation schemes to equalise the mitigation burden between 
countries are not compatible with equity considerations. There is 
a  large body of literature exploring international burden-sharing 
regimes to accompany globally cost-effective mitigation pathways 
(Tavoni et al. 2015; Pan et al. 2017; van den Berg et al. 2020).
Policy assumptions: Cost-effective mitigation scenarios assume that 
climate policies are globally uniform. There is a substantial literature contrasting these benchmark cases with pathways derived under the 
assumption of regionally fragmented and heterogeneous mitigation 
policy regimes (Blanford et al. 2014b; Kriegler et al. 2015b, 2018b;

Roelfsema et al. 2020; van Soest et al. 2021; Bauer et al. 2020b). 
For example, the Shared Policy Assumptions (Kriegler et al. 2014c) 
used in the SSP-RCP framework allow for some fragmentation of 
policy implementation, and many scenarios follow current policies or 
emission pledges until 2030 before implementing stringent policies 
(Riahi et al. 2015; Vrontisi et al. 2018; Roelfsema et al. 2020). Other 
studies assume a  gradual strengthening of emissions pledges and 
regulatory measures converging to a globally harmonised mitigation 
regime slowly over time (Kriegler et al. 2018b; van Soest et al. 2021). 
With increasing announcements of mid-century strategies and the 
rise of net-zero CO 2 or GHG targets, global mitigation scenario 
analysis has begun to build in nationally-specific policy targets until 
mid-century (NGFS 2021).
Scenarios limiting warming to below 2°C phase in climate policies in 
all regions and sectors. Almost all converge to a harmonised global

mitigation regime before the end of century (with the exception 
of Bauer et al. (2020b)). In practice, policies are often a  mix of 
regulations, standards, or subsidies. Implementing these real-world 
policies can give different outcomes to optimal uniform carbon 
pricing (Mercure et al. 2019). Modelled carbon prices will generally 
be lower when other policies are implemented (Calvin et al. 2014a; 
Bertram et al. 2015). As countries implement more and a diverse set 
of policies, the need to further develop the policy assumptions in 
models is becoming apparent (Grant et al. 2020; O’Neill et al. 2020; 
Keppo et al. 2021).
Socio-economic drivers: Key socio-economic drivers of emission 
scenarios are assumptions on population and economic activity. 
There are other socio-economic assumptions, often included in 
underlying narratives (O’Neill et al. 2017), that strongly affect 
energy demand per capita or unit of GDP and dietary choices (Bauer

et al. 2017; Popp et al. 2017; Grubler et al. 2018; van Vuuren et al. 
2018). The SSPs are often used to help harmonise socio-economic 
assumptions, and further explore the scenario space. Many studies 
focus on the middle-of-the-road SSP2 as their default assumption, 
and many use SSP variations to explore the sensitivity of their results 
to socio-economic drivers (Marangoni et al. 2017; Riahi et al. 2017; 
Rogelj et al. 2017). While the SSPs help harmonisation, they are not 
unique and do not fully explore the scenario space (O’Neill et al. 
2020). A  wider range of narratives describing alternative worlds 
is also conceivable. The sustainability world (SSP1), for example, is 
a world with strong economic growth, but sustainability worlds with 
low growth or even elements of degrowth in developed countries 
could also be explored. Thus, standardisation of scenario narratives 
and drivers has advantages, but can also risk narrowing the scenario

space that is explored by the literature. Consequently, many studies 
in the literature have adopted other socio-economic assumptions, 
for example with regard to population and GDP (Kriegler et al. 
2016; Gillingham et al. 2018) and sustainable development trends 
(Soergel et al. 2021).
Technology availability and costs: Technology assumptions are 
a key component of IAMs, with some models representing hundreds

1876
or thousands of technologies. Despite the importance of technology 
costs (Creutzig et al. 2017), there has been limited comparison of 
technology assumptions across models (Kriegler et al. 2015b; Krey 
et al. 2019). There is, however, a substantial literature on the sensitivity 
of mitigation scenarios to technology assumptions, including 
model comparisons (Kriegler et al. 2014a; Riahi et al. 2015), single-
model sensitivity studies (McJeon et al. 2011; Krey and Riahi 2013; 
Giannousakis et al. 2021) and multi-model sensitivity studies (Bosetti 
et al. 2015). Not only are the initial technology costs important, 
but also how these costs evolve over time either exogenously or 
endogenously. Since IAMs have so many interacting technologies, 
assumptions on one technology can affect the deployment of another. 
For example, limits on solar energy expansion rates, or integration,

may lead to higher levels of deployment for alternative technologies. 
Because of these interactions, it can be difficult to determine what 
factors affect deployment across a range of models.
Within these key scenario design choices, model choice cannot be 
ignored. Not all models can implement aspects of a scenario in the 
same way. Alternative target implementations are difficult for some 
model frameworks, and implementation issues also arise around 
technological change and policy implementation. Certain scenario 
designs may lock out certain modelling frameworks. These issues 
indicate the need for a diversity of scenario designs (Johansson et al. 
2020) to ensure that model diversity can be fully exploited.
It is possible for many assumptions to be harmonised, depending on 
the research question. The SSPs were one project aimed at increasing 
harmonisation and comparability. It is also possible to harmonise 
emission data, technology assumptions, and policies (Giarola et al.

2021). While harmonisation facilitates greater comparability between 
studies, it also limits scenario and model diversity. The advantages 
and disadvantages of harmonisation need to be discussed for 
each model study.
A.III.II.2 Use of Scenarios in the Assessment
A.III.II.2.1 Use of Scenario Literature and Database
The WGIII assessment draws on the full literature on mitigation 
scenarios. To support the assessment, as many mitigation scenarios 
in the literature as possible were collected in a scenario database 
with harmonised output reporting (Annex III.II.3). The collection of 
mitigation pathways in a common database is motivated by a number 
of reasons: First, to establish comparability of quantitative scenario 
information in the literature which is often only sporadically available 
from tables and figures in peer-reviewed publications, reports and 
electronic supplementary information. Moreover, this information

is often reported using different output variables and definitions 
requiring harmonisation. Second, to increase latitude of the 
assessment by establishing direct access to quantitative information 
underlying the scenario literature. Third, to improve transparency 
and reproducibility of the assessment by making the quantitative 
information underlying the scenario figures and tables shown in 
the report available to the readers of AR6. The use of such scenario 
databases in AR5 WGIII (Krey et al. 2014) and SR1.5 (Huppmann et al. 2018) proved its value for the assessment as well as for broad use of 
the scenario information by researchers and stakeholders. This is now 
being continued for AR6.
A.III.II.2.2 Treatment of Scenario Uncertainty
The calls for scenarios issued in preparation of this assessment report 
allowed the collection of a  large ensemble of scenarios, coming 
from many modelling teams using various modelling frameworks

in many different studies. Although a  large ensemble of scenarios 
was gathered, it should be acknowledged that only a portion of the 
full uncertainty space is investigated, and that the distribution of 
the scenarios within the ensemble reflects the context of the studies 
the scenarios were developed in. This introduces ‘biases’ in the 
ensemble, for example, (i) the topics of the scenario studies collected 
in the database determine coverage of the scenario space, with large 
model-comparison studies putting large weight on selected topics 
over less explored topics explored by individual models, (ii)  some 
models are more represented than others, (iii) only ‘optimistic’ 
models (i.e.,  models finding lower mitigation costs) reach the 
lowest mitigation targets (Tavoni and Tol 2010). Where appropriate, 
sampling bias was recognised in the assessment, but formal methods 
to reduce bias were not employed due to conceptual limitations.

Furthermore, although attempts have been made to elicit scenario 
likelihoods from expert knowledge (Christensen et al. 2018), 
scenarios are difficult to associate with probabilities as they typically 
describe a  situation of deep uncertainty (Grübler and Nakicenovic 
2001). This and the non-statistical nature of the scenario ensemble 
collected in the database do not allow a probabilistic interpretation 
of the distribution of output variables in the scenario database. 
Throughout the report, descriptive statistics are used to describe 
the spread of scenario outcomes across the scenarios ensemble. The 
ranges of results and the position of scenarios outcomes relative to 
some thresholds of interest are analysed. In some figures, the median 
of the distribution of results is plotted together with the interquartile 
range and possibly other percentiles (5th-10th-90th-95th) to facilitate 
the assessment of results, but these should not be interpreted in

terms of likelihood of outcomes.
A.III.II.2.3 Feasibility of Mitigation Scenarios
In order to develop feasibility metrics of mitigation scenarios 
(Chapter 3, Section 3.8), the assessment relied on the multidimensional 
feasibility framework developed in Brutschin et al. (2021), considering 
five feasibility dimensions: (i) geophysical, (ii) technological, 
(iii)  economic, (iv) institutional and (v) socio-cultural. For each 
dimension, a set of indicators was developed, capturing not only the 
scale but also the timing and the disruptiveness of transformative 
change (Kriegler et al. 2018b). All AR6 scenarios (C1–C3 climate 
categories) were categorised through this framework to quantify 
feasibility challenges by climate category, time, policy architecture 
and by feasibility dimension, summarised in Figure 3.43 (Chapter 3).
Scenarios were categorised into three levels of concerns: (i) low 
levels of concern where transformation is similar to the past or

identified in the literature as feasible/plausible, (ii) medium levels of

1877
concern that might be challenging but within reach, given certain 
enablers, (iii) high levels of concern representing unprecedented levels of transformation, attainable only under consistent enabling conditions. Indicator thresholds defining these three levels of concern were obtained from the available literature and developed with additional empirical literature. Table  8 summarises the main indicators used and the associated thresholds for medium and high levels of concern. Finally, we aggregated feasibility concerns for each dimension and each decade, employing the geometric mean, a non-compensatory method which limits the degree of substitutability between indicators, and used for example by the United Nations for the Human Development Index (HDI). Alternative aggregation scores such as the counting of scenarios exceeding the thresholds were also implemented.A.III.II.2.4  Illustrative Mitigation P athways

In the IPCC Special Report on Global Warming of 1.5°C (SR1.5), illustrative pathways (IPs) were used in addition to descriptions of the key characteristics of the full set of scenarios in the database to assess and communicate the results from the scenario literature. While the latter express the spread in scenario outcomes highlighting uncertain vs robust outcomes, IPs can be used to contrast different stories of mitigating climate change (Rogelj et al. 2018a).
Following the example of the SR1.5, IPs have also been selected for 
the AR6 of WGIII. In contrast to SR1.5, the selection needed to cover a  larger range of climate outcomes while keeping the number of IPs limited. The selection focused on a range of critical themes that Table 8 | Feasibility dimensions, associated indicators and thresholds for the onset of medium and high concerns about feasibility (Chapter 3. 8).

Indicators Computation Medium High SourceGeophysicalBiomass potentialTotal primary energy generation from biomass 
in a given year100 EJ yr–1245 EJ yr–1Frank et al. (2021); Creutzig et al. (2014)
Wind potential Total secondary energy generation from wind in a given year830 EJ yr
–12000 EJ yr–1Deng et al. (2015); Eurek et al. (2017)
Solar potentialTotal primary energy generation from solar in a given year1600 EJ yr
–150 000 EJ yr–1Rogner et al. (2012); Moomaw et al. (2011)EconomicGDP lossDecadal percentage difference in GDP in mitigation vs baseline scenario5% 10%Analogy to current COVID-19 spending Andrijevic et al. (2020c)
Carbon price Carbon price levels (NPV) and decadal increases USD60 USD120 and 5× Brutschin et al. (2021); OECD (2021)
Energy investmentsRatio between investments in mitigation vs baseline in a given decade1.2 1.5 McCollum et al. (2018)

Stranded coal assetsShare of prematurely retired coal power generation in a given decade20% 50%Brutschin et al. (202)1; Global Energy Monitor (2021)Technological
EstablishedWind/solar scale-upDecadal percentage point increase in the wind/solar share in electricity generation10 pp 20 ppBrutschin et al. (2021); Wilson et al. (2020)
Nuclear scale-upDecadal percentage point increase in the nuclear share in electricity generation5 pp 10 ppBrutschin et al. (2021); Markard et al. (2020); Wilson et al. (2020)New TechnologiesBECCS scale-up Amount of CO 2 captured in a given year 3 GtCO 2 yr–17 GtCO 2 yr–1Warszawski et al. (2021)
Fossil CCS scale-up Amount of CO 2 captured in a given year 3.8 GtCO 2 yr–18.8 GtCO 2 yr–1Budinis et al. (2018)
Biofuels in transport scale-upDecadal percentage point increase in the share of biofuels in the final energy demand of the transport sector5 pp 10 pp Nogueira et al. (2020)

Electricity in transport scale-upDecadal percentage point increase in the share of electricity in the final energy demand of the transport sector10 pp 15 pp Muratori et al. (2021)Socio-culturalTotal/transport/ industry/residential energy demand declineDecadal percentage decrease in demand 10 % 20 % Grubler et al. (2018)
Decline of livestock share in food demandDecadal percentage decrease in the livestock share in total food demand0.5 pp 1 pp Grubler et al. (2018); Bajželj et al. (2014)
Forest cover increase Decadal percentage increase in forest cover 2 % 5 % Brutschin et al. (2021)
Pasture cover decrease Decadal percentage decrease in pasture cover 5 % 10 % Brutschin et al. (2021)InstitutionalGovernance level and decarbonisation rateGovernance levels and per capita CO
2 emission 
reductions over a decade>0.6 and <20%<0.6 and >20%Brutschin et al. (2021); Andrijevic et al. (2020b)

1878
emerged from the AR6 assessment: (i) the level of ambition of climate 
policy, (ii) the different mitigation strategies, (iii) timing of mitigation 
actions, and (iv) the combination of climate policy with sustainable 
development policies. The IPs consist of narratives (Table  9) as 
well as possible quantifications. The IPs are illustrative and denote 
implications of different societal choices for the development of future 
emissions and associated transformations of main GHG-emitting 
sectors. For Chapter  3, for each of the IPs a  quantitative scenario 
was selected from the AR6 scenario database to have particular 
characteristics and from diverse modelling frameworks (Table 10).
In total, two reference pathways with warming above 2°C and five 
Illustrative Mitigation Pathways (IMPs) limiting warming in the

1.5–2°C range were selected. The first reference pathway follows current policies as formulated around 2018 (Current Policies, CurPol) 
through to 2030 and then continues to follow a similar mitigation 
effort to 2100. The associated quantitative scenario (NGFS 2021) 
selected by Chapter 3 leads to about 3°C–4°C warming at the end of 
the century. The second reference pathway follows emission pledges 
to 2030 (NDCs) and then continues with moderate climate action 
over time (Moderate Action, ModAct).
The five IMPs are deep mitigation pathways with warming in the 
1.5°C–2°C range. The first IMP pursues gradual strengthening beyond 
NDC ambition levels until 2030 and then acts to likely limit warming 
to 2°C (Climate Category C3) (IMP-GS) (van Soest et al. 2021) 
(Chapter  3.5.3). Three others follow different mitigation strategies 
focusing on low energy demand (IMP-LD) (Grubler et al. 2018),

Table 9 | Storylines for the two reference pathways and five Illustrative Mitigation Pathways (IMPs) limiting warming to 1.5°C–2°C considered in the report.
General char. Policy Innovation EnergyLand use, food 
biodiversityLifestyle
CurPolContinuation of current 
policies and trendsImplementation of current 
climate policies and 
neglect of stated goals 
and objectives; grey 
COVID-19 recoveryBusiness as usual; slow 
progress in low-carbon 
technologiesFossil fuels remain 
important; lock-inFurther expansion 
of western diets; 
further slow 
expansion of 
agriculture areaDemand will 
continue to grow; 
no significant 
changes in 
current habits
ModActNDCs in 2030 as 
announced in 2020, 
fragmented policy 
landscape; post-2030 
action consistent with 
modest action until 2030Strengthening of policies 
to implement NDCs; 
some further post-2030 
strengthening and mixed 
COVID-19 recoveryModest change 
compared to Cur-PolMostly moving away 
from coal; growth of 
renewables; some lock-in

in fossil investmentsAfforestation/
reforestation policies 
as in NDCsModest change 
Cur-Pol
IMPNegMitigation in all sectors 
includes a heavy reliance 
on carbon dioxide removal 
that results in net negative 
global GHG emissionsSuccessful international 
climate policy regime with 
a focus on a long-term 
temperature goalFurther development 
of CDR optionsHeavy reliance on 
CDR in power sector 
and industry; CDR used 
to compensate fossil 
fuel emissionsAfforestation/
reforestation, 
BECCS, increased 
competition for landNot critical – some 
induced via price 
increases
RenGreater emphasis on 
renewables: rapid 
deployment and 
technology development 
of renewables; 
electrificationSuccessful international 
climate policy regime; 
policies and financial 
incentives favouring 
renewable energyRapid further 
innovative electricity 
policy regimesRenewable energy; 
electrification; sector 
coupling; storage or 
power-to-X technologies;

better interconnectionsService 
provisioning and 
demand changes 
to better adapt to 
high renewable 
LDEfficient resource use 
as well as shifts in 
consumption patterns 
globally, leading to low 
demand for resources, 
while ensuring a high level 
of services and satisfying 
basic needsSocial innovation; 
efficiency; across 
all sectorslow demand for energy, 
while ensuring a high 
level of energy services 
and meeting energy 
needs; modal shifts 
in transport; rapid 
diffusion of best available 
technology in buildings 
and industryLower food and 
waste; less meat-
intensive lifestylesService 
demand changes; 
behavioural 
changes
GSless rapid introduction 
of mitigation measures 
followed by a subsequent 
gradual strengtheningUntil 2030, primarily current 
NDCs are implemented and 
gradually strengthened 
moving gradually towards 
a strong, universal climate 
policy regime post-2030Similar to IMP-Neg, but

with some delaySimilar to IMP-Neg, 
but with some delay
SPShifting the global 
pathway towards 
sustainable development. 
including reduced 
inequality and deep GHG 
emissions reductionSDG policies in 
addition to climate 
policy (poverty reduction; 
environmental protection)low demand for energy, 
needs; renewable energyLower food and 
intensive lifestyles; 
afforestationService 
demand changes

1879
renewable electricity (IMP-Ren) (Luderer et al. 2021) and large-scale 
deployment of carbon dioxide removal measures resulting in net 
negative CO 2 emissions in the second half of the century (IMP-Neg). 
The fifth IMP explicitly pursues a  broad sustainable development 
agenda and follows SSP1 socio-economic assumptions (IMP-SP) 
(Soergel et al. 2021). IMP-LD, IMP-Ren and IMP-SP limit warming to 
1.5°C (>50%) with no or limited overshoot (C1), while IMP-Neg has 
a higher overshoot and only returns to nearly 1.5°C (50% chance) 
by 2100 (close to C2). In addition, two sensitivity cases for IMP-Ren 
and IMP-Neg are considered that limit warming to 2°C (>67%) (C3) 
rather than pursuing limiting warming to 1.5°C.
The IMPs are used in different parts of the report. We just mention 
some examples here. In Chapter  3, they are used to illustrate key 
differences between the mitigation strategies, for instance in terms

of timing and sectoral action. In Chapter  6, Box  6.9 discusses 
the  consequences for energy systems. Chapter  7 discusses some 
of the land-use consequences. In Chapter 8, the implications of the 
IMPs are further explored for urban systems where the elements of 
energy, innovation, policy, land use and lifestyle interact (Chapter 8, 
Sections 8.3 and 8.4). In Chapter 10, the consequences of different 
mitigation strategies for mobility are highlighted in different figures. 
The IMPs are discussed further in Chapter 1, Section 1.3; Chapter 3, 
Section 3.2; and the respective sector chapters.
A.III.II.2.5 Scenario Approaches to Connect WGIII with the WGI 
and WGII assessments
A.III.II.2.5.1 Assessment of WGIII Scenarios Building on WGI 
Physical Climate Knowledge
A transparent assessment pipeline has been set up across WGI and 
WGIII to ensure integration of the WGI assessment in the climate 
assessment of emission scenarios in WGIII. This pipeline consists of

a  step where emissions scenarios are harmonised with historical 
emissions (harmonisation), a  step in which species not reported 
by an IAM are filled in (infilling), and a step in which the emission 
evolutions are assessed with three climate model emulators (Annex III.I.8) calibrated to the WGI assessment. These three steps 
ensure a  consistent and comparable assessment of the climate 
response across emission scenarios from the literature.
Harmonisation: IAMs may use different historical datasets, and 
emission scenarios submitted to the AR6 WGIII scenario database 
(Annex III.II.3) are therefore harmonised against a common source 
of historical emissions. To be consistent with WGI, we use the 
same historical emissions that were used for CMIP6 and RCMIP 
(Gidden et al. 2018; Nicholls et al. 2020b). This dataset comprises 
many different emission harmonisation sources (Velders et al. 2015; 
Gütschow et al. 2016; Le Quéré et al. 2016; van Marle et al. 2017;

Meinshausen et al. 2017; Hoesly et al. 2018), including estimates 
of CO 2 emissions from agriculture, forestry, and land-use change 
(mainly CEDS, (Hoesly et al. 2018)) which are on the lower end of 
historical observation uncertainty as assessed in Chapter  2. The 
harmonisation is performed so that different climate futures resulting 
from two different scenarios are a result of different future emission 
evolutions within the scenarios, not due to different historical 
definitions and starting points. Sectoral CO 2 emissions from energy 
and industrial processes and CO 2 from agriculture, forestry, and 
land-use change were harmonised separately. All other emissions 
species are harmonised based on the total reported emissions per 
species. For CO 2 from energy and industrial processes we use a ratio-
based method with convergence in 2080, in line with CMIP6 (Gidden 
et al. 2018, 2019). For CO 2 from agriculture, forestry, and land-use

change and other emissions species with high historical interannual 
variability, we use an offset method with convergence target 2150, 
to avoid strong harmonisation effects resulting from uncertainties in 
historical observations. For all remaining fluorinated gases (F-gases), 
constant ratio harmonisation is used. For all other emissions species, 
we use the default settings of Gidden et al. (2018, 2019a).
Infilling missing species: Infilling ensures that scenarios include all 
relevant anthropogenic emissions. This reduces the risk of a biased 
climate assessment and is important because not all IAMs report 
all climatically active emission species. Infilling was only performed 
for scenarios where models provided native reporting of energy Table 10 | Quantitative scenario selection to represent the two reference pathways and five Illustrative Mitigation Pathways warming to 1.5°C–2°C

for the assessment in Chapter 3.  These quantitative representations of the IMPs have also been taken up by a few other chapters where suitable. The warming profile of 
IMP-Neg peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. While technically classified as a C3, it exhibits the characteristics of C2 high 
overshoot pathways.
AcronymClimate Category 
(II.3.2)ModelScenario name in the AR6 scenario database 
(III.II.3)Reference
CurPol C7 GCAM 5.3 NGFS2_Current Policies NGFS (2021)
ModAct C6 IMAGE 3.0 EN_INDCi2030_3000f Riahi et al. (2021)
Illustrative Mitigation Pathways (IMPs)
Neg C2* COFFEE 1.1 EN_NPi2020_400f_lowBECCS Riahi et al. (2021)
Ren C1 REMIND-MAgPIE 2.1-4.3 DeepElec_SSP2_ HighRE_Budg900 Luderer et al. (2021)
LD C1 MESSAGEix-GLOBIOM 1.0 LowEnergyDemand_1.3_IPCC Grubler et al. (2018)
GS C3 WITCH 5.0 CO_Bridge van Soest et al. (2021)
SP C1 REMIND-MAgPIE 2.1-4.2 SusDev_SDP-PkBudg1000 Soergel et al. (2021)
Sensitivity cases

Neg-2.0 C3 AIM/CGE 2.2 EN_NPi2020_900f Riahi et al. 2(021)
Ren-2.0 C3 MESSAGEix-GLOBIOM_GEI 1.0 SSP2_openres_lc_50 Guo et al. (2021)

1880
and industrial process CO 2, land use CO 2, CH 4, and N 2O emissions 
to avoid infilling gases that have large individual radiative forcing 
contributions and cannot be infilled with high confidence. Models 
that did not meet this minimum reporting requirement were not 
included in the climate assessment. Infilling is performed following 
the methods and guidelines in Lamboll et al. (2020). Missing species 
are infilled based on the relationship with CO 2 from energy and 
industrial processes as found in the harmonised set of all scenarios 
reported to the WGIII scenario database that pass the vetting 
requirements. To ensure high stability to small changes, we apply 
a Quantile Rolling Window method (Lamboll et al. 2020) for aerosol 
precursor emissions, volatile organic compounds and greenhouse 
gases other than F-gases, based on the quantile of the reported CO 2 
from energy and industrial processes in the database at each time

point. F-fases and other gases with small radiative forcing are infilled 
based on a  pathway with lowest root mean squared difference, 
allowing for consistency in spite of limited independently modelled 
pathways in the database.
WGI-calibrated emulators: Using expert judgement, emulators that 
reproduce the best estimates and uncertainties of the majority of AR6 
WGI assessed metrics are recommended for scenario classification 
use by WGIII (see WGI Cross-Chapter Box  7.1). MAGICC (v7) was 
used for the main scenario classification, with FaIR (v1.6.2) being 
used to provide additional uncertainty ranges on reported statistics to 
capture climate model uncertainty. The WGI emulators’ probabilistic 
parameter ensembles are derived such that they match a range of key 
climate metrics assessed by WGI and the extent to which agreement 
is achieved is evaluated (WGI Cross-Chapter Box 7.1). Of particular 
importance to this evaluation is the verification against the WGI

temperature assessment of the five scenarios assessed in Chapter 4 
of WGI (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). 
The inclusion of the temperature assessment as a benchmark for the 
emulators provides the strongest verification that WGIII’s scenario 
classification reflects the WGI assessment. The comprehensive nature 
of the evaluation is a  clear improvement on previous reports and 
ensures that multiple components of the emulators, from their climate 
response to effective radiative forcing through to their carbon cycles, 
have been examined before they are deemed fit for use by WGIII.
Scenario climate assessment: For the WGIII scenario climate 
assessment, emulators are run hundreds to thousands of times per 
scenario, sampling from an emulator-specific probabilistic parameter 
set, which incorporates carbon cycle and climate system uncertainty in 
line with the WGI assessment (WGI Cross-Chapter Box 7.1). Percentiles

for different output variables provide information about the spread 
in individual variables for a given scenario, but the set of variables 
for a given percentile do not form an internally consistent climate 
change projection. Instead, joint distributions of these parameter sets 
are employed by the calibrated emulators. Consistent climate change 
projections are represented by individual ensemble member runs and 
the whole ensemble of these individual member runs. To facilitate 
analysis, multiple percentiles of these large (hundred to thousand 
member) ensemble distributions of projected climate variables are 
provided in the AR6 scenario database. The emulators provide an 
assessment of global surface air temperature (GSAT) response to 
emission scenarios and its key characteristics like peak warming and year of peak warming, ocean heat uptake, atmospheric CO 2, CH 4 and 
N2O concentrations and effective radiative forcing from a range of

species including CO 2, CH 4, N2O and aerosols for each emissions 
scenario, as well as an estimate of CO 2 and non-CO 2 contributions 
to the temperature increase. The climate emulator’s GSAT projections 
are normalised to match the WGI Chapter 2 assessed total warming 
between 1850–1900 and 1995–2014 of 0.85°C.
The GSAT projections from the emulator runs are used for classifying 
those emissions scenarios in the AR6 database that passed the initial 
vetting and allowed a  robust climate assessment. MAGICC (v7) 
was selected as emulator for the climate classification of scenarios, 
as it happens to be slightly warmer than the other considered 
climate emulator, particularly for the higher and long-term warming 
scenarios – reflecting long-term warming in line with Earth system 
models (ESMs) (WGI Cross-Chapter Box  7.1). This means that 
scenarios identified to stay below a given warming limit with a given 
probability by MAGICC will in general be identified to have this

property by the other emulator as well. There is the possibility that 
the other emulator would classify a  scenario in a  lower warming 
class based on their slightly cooler emulation of the temperature 
response. Unlike during the assessment of the SR1.5 database in the 
IPCC SR1.5 report, the updated versions of FaIR and MAGICC are 
however very close, providing robustness to the climate assessment. 
MAgiCC and FaIR were both used to assess the overall uncertainty 
in the warming response for a single scenario or a set of scenarios, 
including both parametric and model uncertainty. Specifically, the 
5th to 95th percentile range across the two emulators is calculated, 
characterising the joint climate uncertainty range of the two models.
Carbon budgets in WGI and WGIII: The remaining carbon budget 
corresponding to a  certain level of future warming depends on 
non-CO 2 emissions of modelled pathways. Box  3.4 in Chapter  3

highlighted this key uncertainty in estimating carbon budgets. In this 
section (Figure 5), we put this into the context of the dependence 
of carbon budgets on two aspects of the non-CO 2 warming 
contribution: (i) assumptions on historical non-CO 2 emissions and 
how they can impact future non-CO 2 warming estimates relative to 
a recent reference period (2010–2019) (Panel a) and (ii) the scenario 
set underlying estimates of non-CO 2 warming at the time of reaching 
net zero CO 2 (Panel b). Both aspects affect the estimated remaining 
carbon budget by changing the non-CO 2 warming contribution from 
the base year to the time of reaching net zero CO 2. MAGICC7 is 
used in WGI in conjunction with different input files for the historical 
warming. For the reported remaining carbon budget estimates (WGI 
CB) WGI used the non-CO 2 warming contributions from MAGICC7 
in line with Meinshausen et al. (2020) and in line with the CMIP6

GHG concentration projections, while the WGI emulator setup 
in line with WGI Cross-Chapter Box  7.1 was used for the WGIII 
climate assessment. The WGIII assessment uses MAGICC7 in line 
with Nicholls et al. (2021) in line with the emission harmonisation 
process employed in WGIII (see above). The difference in historical 
assumptions changes the estimated non-CO 2 contribution by up to 
about 0.05°C for the lower temperature levels, or slightly more than 
10% of the warming until 1.5°C relative to 2010–2019. For peak 
warming around 2°C relative to pre-industrial levels (about 0.97°C 
warming relative to 2010–2019 in Figure 5 plots), the difference is

1881
offset by the difference arising from using either the SR1.5 or AR6 
scenario databases (see panel (b) in Figure 5).
Estimates of the remaining carbon budget that take into account non-
CO2 uncertainty are not only dependent on historical assumptions, 
but also on future non-CO 2 scenario characteristics, which are 
different across the various scenarios in the AR6 database. In panel 
(b) of Figure 5, we show how the SR15 database of scenarios, which 
was used to inform the WGI remaining carbon budget, differs from 
the larger set considered in the WGIII report (both using MAGICC7 
using input files in line with Nicholls et al. (2021)). Overall, there 
is limited difference in the covered range of non-CO 2 warming at 
different peak surface temperature levels, leading to no clear change 
in estimated carbon budgets compared to SR1.5 based on the full 
scenario database. However, as discussed in Cross-Working Group

Box 1 in Chapter 3, and shown in panel (c) of Figure 5, mitigation 
strategies expressed by both the IAM footprint and scenario design (e.g., dietary change scenarios) can have strong effects on estimated 
carbon budgets for staying below 1.5°C.
A.III.II.2.5.2 Relating the WGII and WGIII Assessments 
by use of Warming Levels
WGII sets out common climate dimensions to help contextualise 
and facilitate consistent communication of impacts and synthesis 
across WGII, as well as to facilitate WGI and WGII integration, with 
the dimensions adopted when helpful and possible across WGII (AR6 
WGII Cross-Chapter Box  1.1). ‘Common climate dimensions’ are 
defined as common global warming levels (GWLs), time periods, and 
levels of other variables as needed by WGII authors (see below for 
a list of variables associated with these dimensions). Projected ranges 
for associated climate variables were derived from the AR6 WGI 
report and supporting resources and help contextualise and inform

the projection of potential future climate impacts and key risks. The 
information enables the mapping of climate variable levels to climate 
−0.20.00.20.4
0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
Peak surf ace temper ature (rel. to 2010−2019) [°C]Non−CO2 w arming (rel. to 2010−2019)
at peak cum ulativ e CO2 emissions [°C]SR1.5 scenar io set with diﬀerent histor ical emissions assumptions .A
at peak cum ulativ e CO2 emissions [°C]WG3 scenar ios vs SR15 scenar ios f or the same climate setup .B
1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5°C 2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2°C

1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5C1.5°C 2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2°CAIM COFFEE GCAM IMA GE MESSA GE-GLOBIOM POLES REMIND-MAgPIE WITCH
0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2 0.4 0.6 0.8 1.0 1.2−0.20.00.20.4
at peak cum ulativ e CO2 emissions [°C]Estimating non−CO2 component of peak w arming by model framework.CModel
AIM
COFFEE
GCAM
IMA GE
MESSA GE-
GLOBIOM
POLES
REMIND-
MAgPIE
WITCHCon/f_igur ation
WG1 carbon b udget setup
WG1 CCB7.1 setup
Scenar io set
AR6 database
SR15 scenar ios
Maxim um cum ulativ e CO2 dur ing 21st centur y in 2100
Scenario set

WG1 carbon b udget setup (net zero CO2 before 2100)
WG1 CCB7.1 setup (net zero CO2 before 2100)
2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2C2CRelative to 
1850-1900
Figure 5 | Comparison of non-CO 2 warming relevant for the derivation of cumulative carbon budgets – and its sensitivity to (a) assumptions on historical 
emissions and (b) the set of investigated scenarios (right).  Panel (c) shows how the relationship across scenarios between peak surface temperature and non-CO 2 
warming and peak cumulative CO 2 is different for modelling frameworks. All dashed regression lines are at the 5th and 95th percentiles, solid lines are a regression at the median.
All panels depict non-CO 2 warming in relation to 2010–2019 at the time of peak cumulative CO 2, using MAGICC7. Scenarios that reach net-zero CO 2 this century are coloured,

with dots in grey indicating scenarios that do not reach net-zero CO 2 but still remain below 2°C median peak warming relative to 2010–2019 levels in this century. The scenario 
set ‘AR6 database’ in (b) includes only scenarios of those model frameworks that are shown in panel (c)) which have a detailed land-use model and enough scenarios to imply 
a relationship.
Panel (a) The WGI remaining carbon budget takes into account the non-CO 2 warming in dependence of peak surface temperatures via a regression line approach (lighter 
blue-coloured solid line). For the same scenario set, with historical emissions assumptions as used in Cross-Chapter Box 7.1 (darker blue-coloured solid line), a relationship is 
found with a difference of approximately 0.05°C.
Panel (b) The WGIII database of scenarios tends to imply very similar non-CO 2 warming at peak cumulative CO 2 to the SR15 scenario database, especially around 1.5°C above

pre-industrial (0.43°C above 2010–2019 levels), though with slightly lower non-CO 2 warming for higher peak temperatures.
Panel (c) Regressions at the 5th, 50th, and 95th percentiles indicate a model framework footprint affecting the relationship between peak warming and non-CO 2 warming at 
peak cumulative CO 2.

1882
projections by WGI (AR6 WGI Table SPM.1) and vice versa, with 
ranges of results provided to characterise the physical uncertainties 
relevant to assessing climate impacts risk. Common socio-economic 
dimensions are not adopted in WGII due to a  desire to draw on 
the full literature, inform the broad ranges of relevant possibilities 
(climate, development, adaptation, mitigation), and be flexible. 
The impacts literature is wide-ranging and diverse, with a fraction 
based on global socio-economic scenarios. WGII’s approach allows 
chapters and cross-chapter boxes to assess how impacts and ranges 
depend on socio-economic factors affecting exposure, vulnerability, 
and adaptation independently, as appropriate for their literature. For 
example, WGII Chapter  16 assesses how Representative Key Risks 
vary under low vs high exposure/vulnerability conditions by drawing 
on impact literature based on Shared Socio-economic Pathways

(SSPs). In general, WGII chapters, when possible and conducive 
with their literature, used GWLs or climate projections based on 
Representative Concentration Pathways or SSPs to communicate 
information and facilitate integration and synthesis, with impacts 
results characterised according to other drivers when possible and 
relevant, such as socio-economic conditions.
In the context of common climate dimensions, WGII considers common 
projected GWL ranges by time period, the timing for when GWLs 
might be reached, and projected continental-level result ranges for 
select temperature and precipitation variables by GWL (average 
and  extremes), as well as sea surface temperature changes by 
GWL and ocean biome. Where available, WGII considers the assessed 
WGI ranges as well as the raw CMIP5 and CMIP6 climate change 
projections (ranges and individual projections) from Earth system 
models (Hauser et al. 2019). With WGII’s climate impacts literature

based primarily on climate projections available at the time of AR5 
(CMIP5) and earlier, or assumed temperature levels, it was important to 
be able to map climate variable levels to climate projections of different 
vintages and vice versa. WGII’s common GWLs are based on AR6 WGI’s 
proposed ‘Tier 1’ dimensions of integration range – 1.5, 2.0, 3.0, and 
4.0˚C (relative to the 1850 to 1900 period), which are simply proposed 
common GWLs to facilitate integration across and within WGs (WGI 
Chapter 1). Within WGII, GWLs facilitate comparison of climate states 
across climate change projections, assessment of the full impacts 
literature, and cross-chapter comparison. Across AR6, GWLs facilitate 
integration across Working Groups of climate change projections, 
climate change risks, adaptation opportunities, and mitigation.
For facilitating integration with WGIII, GWLs need to be related to 
WGIII’s classification of mitigation efforts by temperature outcome.

WGII’s Chapter 3 groups full century emissions projections resulting from 
a large set of assessed mitigation scenarios into temperature classes 
(Chapter 3, Sections 3.2 and 3.3, Annex III.II.2.5.1, and Annex III.II.3.2.1). 
Scenarios are classified by median peak global mean temperature 
increase since 1850–1900 in the bands <2°C, 2°C–2.5°C, 2.5°C–3°C, 
3°C–4°C, and >4°C, with the range below 2°C broken out in greater 
detail using estimates of warming levels at peak and in 2100 for which 
the warming response is projected to be likely higher (33th percentile), 
as likely higher as lower (median), and likely lower (67th percentile) 
(Chapter 3, Section 3.2 and Annex III.II.3.2.1). WGII’s common GWLs 
8 https://data.ece.iiasa.ac.at/ar6/.and WGIII’s global warming scenario classes are relatable but differ 
in several important ways. While GWLs represent temperature change 
that occurs at some point in time, emissions scenarios in a temperature

class result in an evolving warming response over time. The emissions 
scenario warming also has a likelihood attached to the warming level 
at any point in time, that is, actual warming outcomes can be lower 
or higher than median warming projections within the range of the 
estimated uncertainty. Thus, multiple WGII results across GWLs will be 
relevant to any particular WGIII emissions pathway, including at the 
peak temperature level.
However, socio-economic conditions are an important factor 
defining both impacts exposure, vulnerability, and adaptation, 
as well as mitigation opportunity and costs, that needs special 
considerations. The WGIII scenario assessment is using additional 
classifications relating to, inter alia, near-term policy developments, 
technology availability, energy demand, population and economic 
growth (Chapter 3, Section 3.3 and Annex III.II.3.2.2), and a set of 
illustrative mitigation pathways with varying socio-techno-economic

assumptions (Annex III.II.2.4, Chapter  3, Section  3.2). Synthesising 
WGII assessments of climate change impacts and WGIII assessments 
of climate change mitigation efforts for similar GWLs/global warming 
scenario classes would have to address how socio-techno-economic 
conditions affect impacts, adaptation, and mitigation outcomes. 
Furthermore, a synthesis of mitigation costs and mitigation benefits in 
terms of avoided climate change impacts would require a framework 
that ensures consistency in socio-economic development assumptions 
and emissions and adaptation dynamics and allows for consideration 
of benefits and costs along the entire pathway (O’Neill et al. 2020) 
(Cross-Working Group Box 1 in Chapter 3).
A.III.II.3 WGIII AR6 Scenario Database
[Note: The scenario numbers documented in this section refer to all 
scenarios that were submitted and not retracted by the literature 
acceptance deadline of 11 October 2021, and that fulfilled the

requirement of being supported by an eligible literature source. Not 
all those scenarios were used in the assessment, for example some 
did not pass the vetting process as documented in II.3.1.]
As for previous IPCC reports of Working Group III, including the 
Special Report on Global Warming of 1.5°C (SR1.5) (Huppmann 
et al. 2018; Rogelj et al. 2018a) and the Fifth Assessment Report 
(AR5) (Clarke et al. 2014; Krey et al. 2014), quantitative information 
on mitigation pathways is collected in a  dedicated AR6 scenario 
database8 to underpin the assessment.
By the time of the AR6 literature acceptance deadline of IPCC WGIII 
(11 October 2021) the AR6 scenario database comprised 191 unique 
modelling frameworks (including different versions and country 
setups) from 95+ model families – of which 98 were globally 
comprehensive, 71 national or multi-regional, and 20 sectoral 
models – with in total 3,131 scenarios, summarised in Tables 11–17

1883
(global mitigation pathways), Table  18 (national and regional 
mitigation pathways) and Table 19 (sector transition pathways).
A.III.II.3.1 Process of Scenario Collection and Vetting
To facilitate the AR6 assessment, modelling teams were invited to 
submit their available emissions scenarios to a web-based database 
hosted by the International Institute for Applied Systems Analysis 
(IIASA).9 The co-chairs of Working Group III as well as a  range of 
scientific institutions, including the Integrated Assessment Modelling 
Consortium (IAMC), University of Cape Town and the Centre 
International de Recherche sur l’Environnement et le Développement 
(CIRED), supported the open call for scenarios which was subdivided 
into four dedicated calls:
i. a call for global long-term scenarios to underpin the assessment 
in Chapter  3 as well as facilitating integration with sectoral 
Chapters 6, 7, 8, 9, 10 and 11,

ii. a call for short- to medium-term scenarios at the national and 
regional scales underpinning the assessment in Chapter 4, and
iii. a call for building-focused scenarios to inform the assessment 
in Chapter 9, and
iv. a call for transport-focused scenarios to inform the 
assessment in Chapter 10.
A common data reporting template with a defined variable structure 
was used and all teams were required to register and submit detailed 
model and scenario metadata. Scenarios were required to come from 
a formal quantitative model and the scenarios must be published in 
accordance with IPCC literature requirements. The calls for scenarios 
9 https://data.ene.iiasa.ac.at/ar6/#/about.were open for a period of 22 months (September 2019 to July 2021), 
with updates possible until October 2021 in line with the literature 
acceptance deadline. The data submission process included various 
quality control procedures to increase accuracy and consistency in

reporting. Additional categorisation and processing of metadata over 
the full database provided a wide range of indicators and categories 
that were made centrally available to authors of the report to 
enhance consistency of the assessment, such as: climate, policy and 
technology categories; characteristics about emissions, energy, socio-
economics and carbon sequestration; metadata such as literature 
references, model documentation and related projects.
For all scenarios reporting global data, a  vetting process was 
undertaken to ensure that key indicators were within reasonable 
ranges for the baseline period – primarily for indicators relating to 
emissions and the energy sector (Table 11). As part of the submission 
process, model teams were contacted individually with information 
on the vetting outcome with regard to their submitted scenarios, 
giving them the opportunity to verify the reporting of their data.

Checks on technology-specific variables for nuclear, solar and wind 
energy, and CCS, screen not only for accuracy with respect to recent 
developments, but also indicate reporting errors relating to different 
primary energy accounting methods. While the criteria ranges appear 
to be large, the focus of these scenarios is the medium to long term 
and there is also uncertainty in the historical values. For vetting of the 
Illustrative Mitigation Pathways, the same criteria were used, albeit 
with narrower ranges (Table  11). Selected future values were also 
vetted and the result of the vetting reported to authors, but not used 
as exclusion criterion. Where possible the latest values available were 
used, generally 2019, and if necessary extrapolated to 2020 as most 
Table  11 | Summary of the vetting criteria and ranges applied to the global scenarios for the climate assessment and preliminary screening for 
Illustrative Mitigation Pathways.

Reference value Range (IP range) Pass Fail Not reported
Historical emissions (sources: EDGAR v6 IPCC and CEDS, 2019 values)
CO2 total (EIP + AFOLU) emissions 44,251 MtCO 2 yr–1±40% (±20%) 1848 23 395
CO2 EIP emissions 37,646 MtCO 2 yr–1±20% (±10%) 2162 55 49
CH4 emissions 379 MtCH 4 yr–1±20% (±20%) 1651 139 476
CO2 emissions EIP 2010–2020 % change – +0 to +50% 1742 74 450
CCS from energy 2020 – 0-250 (100) MtCO 2 yr–11624 77 565
Historical energy production (sources: IEA 2019; IRENA; BP; EMBERS; trends extrapolated to 2020)
Primary energy (2020, IEA) 578 EJ ±20% (±10%) 1813 73 380
Electricity: nuclear (2020, IEA) 9.77 EJ ±30% (±20%) 1603 266 397
Electricity: solar and wind (2020. IEA, IRENA, BP, EMBERS). 8.51 EJ ±50% (±25%) 1459 377 430
Overall 1686 580 –
Future criteria (not used for exclusion in climate assessment but flagged to authors as potentially problematic)
No net negative CO 2 emissions before 2030 CO2 total in 2030 >0 1867 4 395

CCS from energy in 2030 <2000 MtCO 2 yr–11518 183 565
Electricity from nuclear in 2030 <20 EJ yr–11595 274 397
CH4 emissions in 2040 100–1000 MtCH 4 yr–11775 15 476
Rows do not sum to the same total of scenarios as not all scenarios reported all variables. EIP stands for energy and industrial process emissions.

1884
models report only at five- to 10-year intervals. 2020 as reported in 
most scenarios collected in the database does not include the impact 
of the COVID-19 pandemic.
Almost three-quarters of submitted global scenarios passed the 
vetting. The remaining quarter comprised a fraction of scenarios 
that were rolled over from the SR1.5 database, and were no longer 
up to date with recent developments (excluding the COVID-19 
shock). This included scenarios that started stringent mitigation 
action already in 2015. Other scenarios were expected to deviate 
from historical trends due to their diagnostic design. All historical 
criteria for reported variables needed to be met in order to 
pass the vetting.
2266 global scenarios were submitted to the scenario database that 
fulfilled a  minimum requirement of reporting at least one global 
emission or energy variable covering multiple sectors. 1686 global

scenarios passed the vetting criteria described in Table  11. These 
scenarios were subsequently flagged as meeting minimum quality 
standards for use in long-term scenarios assessment. Additional criteria for inclusion in the Chapter  3 climate assessment are 
described in Annex III.II.3.2.1.
A.III.II.3.2 Global Pathways
Scenarios were submitted by both individual studies and model inter-
comparisons. The main model inter-comparisons submitting scenarios 
are shown in Table  12. Model inter-comparisons have a  shared 
experimental design and assess research questions across different 
modelling platforms to enable more structured and systematic 
assessments. The model comparison projects thus help to understand 
the robustness of the insights.
The number of submitted scenarios varies considerably by study, 
for example from 10 to almost 600 scenarios for the model inter-
comparison studies (Table 12). The number of scenarios also varies

substantially by model (Table 15), highlighting the fact that the global 
scenario set collected in the AR6 scenario database is not a statistical 
sample (Section II.2.2).
Table 12 | Model inter-comparison studies that submitted global scenarios to the AR6 scenario database and for which at least one scenario passed the 
vetting.  Scenario counts refer to all scenarios submitted by a study (in brackets), those that passed vetting (centre) and those that passed the vetting and received a climate 
assessment (left).
Project Description Publication year Key references WebsiteNumber of 
scenarios
SSP model-
comparisonThe SSPs are part of a new framework that 
the climate change research community has 
adopted to facilitate the integrated analysis 
of future climate impacts, vulnerabilities, 
adaptation, and mitigation (II.1.3)2017 / 2018Riahi et al. (2017); Rogelj 
et al. (2018b)https://tntcat.iiasa.ac.at/SspDb 70 / 77 (126)
ADVANCEDeveloped a new generation of advanced

IAMs and applied the improved models to 
explore different climate mitigation policy 
options in the post-Paris framework2018Luderer et al. (2018); 
Vrontisi et al. (2018)http://www.fp7-advance.eu/ 37 / 40 (72)
Industry sector study 2017 Edelenbosch et al. (2017b) http://www.fp7-advance.eu/ 0 / 6 (6)
CD-LINKSExploring the complex interplay between 
climate action and development, while 
simultaneously taking both global and 
national perspectives and thereby informing 
the design of complementary climate-
development policies2018McCollum et al. (2018); 
Roelfsema et al. (2020)https://www.cd-links.org/ 41 / 52 (77)
COMMITExploring new climate policy scenarios at 
the global level and in different parts of 
the world2021 van Soest et al. (2021) https://themasites.pbl.nl/commit/ 41 / 59 (68)
ENGAGEExploring new climate policy scenarios at 
the world2021 Riahi et al. (2021) http://www.engage-climate.org/ 591 / 591 (603)

EMF30Energy Modelling Forum study into the role 
of non-CO 2 climate forcers2020Smith et al. (2020a); 
Harmsen et al. (2020)https://emf.stanford.edu/projects/
emf-30-short-lived-climate-forcers-
air-quality61 / 69 (149)
EMF33Energy Modelling Forum study into the role 
of bioenergy2020Rose et al. (2020); Bauer 
et al. (2020a)https://emf.stanford.edu/projects/
emf-33-bio-energy-and-land-use67 / 68 (173)
EMF36Energy Modelling Forum study into the role 
of carbon pricing and economic implications 
of NDCs2021 Böhringer et al. (2021)https://emf.stanford.edu/projects/
emf-36-carbon-pricing-after-paris-
carpri0 / 305 (320)

1885
scenarios
NGFSStudy for scenario-based financial risk 
assessment with details on impacts, and 
sectoral and regional granularity2021 NGFS (2020, 2021)https://www.ngfs.net/ngfs-
scenarios-portal24 / 24 (24) 
2 / 2 (2)10
PARIS REINFORCEStudy on the long-term implications of 
current policies and NDCs2020 Perdana et al. (2020) https://paris-reinforce.eu 3 / 25 (39)
PARIS REINFORCEStudy with a focus on harmonising 
socio-economics and techno-economics 
in baselines2021 Giarola et al. (2021) https://paris-reinforce.eu 0 / 8 (16)
CLIMACAP-LAMPStudy on the role of climate change 
mitigation in Latin America2016van der Zwaan et al. 
(2016)n.a. 0 / 10 (22)
Total 937 / 1336 (1697)
 
Table 13 | Single-model studies that submitted global scenarios to the AR6 scenario database and for which at least one scenario passed the vetting.

Scenario counts refer to all scenarios submitted by a  study (in brackets), those that passed vetting (centre) and those that passed the vetting and received a  climate 
Title of study Literature reference11Number of scenarios
Quantification of an efficiency–sovereignty trade-off in climate policy Bauer et al. (2020b) 4 / 4 (4)
Transformation and innovation dynamics of the energy-economic system within climate and sustainability limits Baumstark et al. (2021) 18 / 18 (18)
Tracing international migration in projections of income and inequality across the Shared Socio-economic Pathways Benveniste et al. (2021) 0 / 10 (10)
Targeted policies can compensate most of the increased sustainability risks in 1.5°C mitigation scenarios Bertram et al. (2018) 3 / 3 (12)
Long term, cross country effects of buildings insulation policies Edelenbosch et al. (2021) 0 / 8 (8)
The role of the discount rate for emission pathways and negative emissions Emmerling et al. (2019) 4 / 4 (28)

Studies with the EPPA model on the costs of low-carbon power generation, the cost and deployment of CCS, the 
economics of BECCS, the global electrification of light duty vehicles, the 2018 food, water, energy and climate outlook 
and the 2021 global change outlookReilly et al. (2018); Morris et al. 
(2019, 2021); Smith et al. (2021); 
Fajardy et al. (202)1; Paltsev et al. 
(2021, 2022)7 / 7 (10)
Transportation infrastructures in a low carbon world: An evaluation of investment needs and their determinantsFisch-Romito and Guivarch 
(2019)0 / 24 (32)
Measuring the sustainable development implications of climate change mitigation Fujimori et al. (2020) 5 / 5 (5)
How uncertainty in technology costs and carbon dioxide removal availability affect climate mitigation pathways Giannousakis et al. (2021) 9 / 9 (9)
A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative 
emission technologiesGrubler et al. (2018) 1 / 1 (1)

Global Energy Interconnection: A scenario analysis based on the MESSAGEix-GLOBIOM Model Guo et al. (2021) 20 / 20 (20)
Climate–carbon cycle uncertainties and the Paris Agreement Holden et al. (2018) 0 / 5 (5)
Ratcheting ambition to limit warming to 1.5 °C – trade-offs between emission reductions and carbon dioxide removal Holz et al. (2018) 6 / 6 (6)
Peatland protection and restoration are key for climate change mitigation Humpenöder et al. (2020) 0 / 3 (3)
Energy Technology Perspectives 2020 IEA (2020b) 0 / 1 (1)
World Energy Outlook 2020 – Analysis – IEA IEA (2020a) 0 / 1 (1)
Net Zero by 2050 – A Roadmap for the Global Energy Sector IEA (2021) 0 / 1 (1)
Global Renewables Outlook: Energy transformation 2050 IRENA (2020) 0 / 2 (2)
Climate mitigation scenarios with persistent COVID-19-related energy demand changes Kikstra et al. (2021a) 19 / 19 (19)
Global anthropogenic emissions of particulate matter including black carbon Klimont et al. (2017) 0 / 2 (2)

Global energy perspectives to 2060 – WEC’s World Energy Scenarios 2019 Kober et al. (2020) 0 / 4 (4)
Prospects for fuel efficiency, electrification and fleet decarbonisation Kodjak and Meszler (2019) 0 / 4 (4)
Short term policies to keep the door open for Paris climate goals Kriegler et al. (2018b) 18 / 18 (18)
Deep decarbonisation of buildings energy services through demand and supply transformations in a 1.5°C scenario Levesque et al. (2021) 4 / 4 (4)
Designing a model for the global energy system – GENeSYS-MOD: An application of the Open-Source Energy 
Modelling System (OSeMOSYS)Löffler et al. (2017) 0 / 1 (1)
Impact of declining renewable energy costs on electrification in low emission scenarios Luderer et al. (2021) 8 / 8 (8)
10 The first NGFS scenario publication in 2020 comprised 15 scenarios from the literature and 2 newly developed scenarios. The 15 scenarios are also contained in the 
database under their original study name.

11 Publication date of scenarios coincides with year of publication.

1886
A.III.II.3.2.1 Climate Classification of Global Pathways
The global scenarios underpinning the assessment in Chapter 3 have 
been classified, to the degree possible, by their warming outcome. 
The definition of the climate categories and the distribution of 
scenarios in the database across these categories is shown in 
Table 14 (Chapter 3, Section 3.2). The first four of these categories 
correspond to the ones used in the IPCC SR1.5 (Rogelj et al. 2018a) 
while the latter four have been added as part of the AR6 to capture 
a broader range of warming outcomes.For inclusion in the climate assessment, in addition to passing the 
vetting (Section II.3.1), scenarios needed to run until the end of 
century and report as a  minimum CO 2 (total and for energy and 
industrial processes (EIP)), CH 4 and N 2O emissions to 2100. Where 
CO2 for AFOLU was not reported, the difference between total and

EIP in 2020 must be greater than 500 MtCO 2. Of the total 2266 
global scenarios submitted, 1574 could be assessed in terms of 
their associated climate response, and 1202 of those passed the 
vetting process.Title of study Literature reference11Number of scenarios
The road to achieving the long-term Paris targets: energy transition and the role of direct air capture Marcucci et al. (2017) 1 / 1 (3)
The transition in energy demand sectors to limit global warming to 1.5°C Méjean et al. (2019) 0 / 3 (27)
Deep mitigation of CO 2 and non-CO 2 greenhouse gases toward 1.5°C and 2°C futures Ou et al. (2021) 34 / 35 (36)
Alternative electrification pathways for light-duty vehicles in the European transport sector Rottoli et al. (2021) 8 / 8 (8)
Economic damages from on-going climate change imply deeper near-term emission cuts Schultes et al. (2021) 24 / 24 (24)
A sustainable development pathway for climate action within the UN 2030 Agenda Soergel et al. (2021) 8 / 8 (8)

Delayed mitigation narrows the passage between large-scale CDR and high costs Strefler et al. (2018) 7 / 7 (7)
Alternative carbon price trajectories can avoid excessive carbon removal Strefler et al. (2021b) 9 / 9 (9)
Carbon dioxide removal technologies are not born equal Strefler et al. (2021a) 8 / 8 (8)
The Impact of U.S. Re-engagement in Climate on the Paris Targets van de Ven et al. (2021) 0 / 10 (10)
The 2021 SSP scenarios of the IMAGE 3.2 modelMüller-Casseres et al. (2021); 
van Vuuren et al. (2014, 2021)40 / 40 (40)
Pathway comparison of limiting global warming to 2°C Wei et al. (2021) 0 / 5 (5)
Total 265 / 350 (421)
Table 14 | Classification of global pathways into warming levels using MAGICC (Chapter 3, Section 3.2).
Description DefinitionScenarios
Passed vetting All
C1: Limit warming to 1.5°C (>50%)
with no or limited overshootReach or exceed 1.5°C during the 21st century with a likelihood of ≤67%, 
and limit warming to 1.5°C in 2100 with a likelihood >50%.

Limited overshoot refers to exceeding 1.5°C by up to about 0.1°C and for 
up to several decades.97 160
C2: Return warming to 1.5°C (>50%) 
after a high overshootExceed warming of 1.5°C during the 21st century with a likelihood of >67%,  
and limit warming to 1.5°C in 2100 with a likelihood of >50%.
High overshoot refers to temporarily exceeding 1.5°C global warming 
by 0.1°C–0.3°C for up to several decades.133 170
C3: Limit warming to 2°C (>67%)Limit peak warming to 2°C throughout the 21st century with a likelihood 
of >67%.311 374
C4: Limit warming to 2oC (>50%)Limit peak warming to 2°C throughout the 21st century with a likelihood 
of >50%.159 213
C5: Limit warming to 2.5°C (>50%)Limit peak warming to 2.5°C throughout the 21st century with a likelihood 
of >50%.212 258
C6: Limit warming to3°C (>50%)Limit peak warming to 3°C throughout the 21st century with a likelihood 
of >50%.97 129

C7: Limit warming to 4°C (>50%)Limit peak warming to 4°C throughout the 21st century with a likelihood 
of >50%.164 230
C8: Exceed warming of 4°C (≥50%) Exceed warming of 4°C during the 21st century with a likelihood of ≥50%. 29 40
No climate assessment Scenario time horizon <2100; insufficient emissions species reported. 484 692
Total: 1686 2266

1887
Table 15 | Global scenarios by modelling framework and climate category. Table includes number of scenarios that passed all vetting checks and number of all 
scenarios that received a climate categorisation (in brackets, including those not passing vetting). Unique model versions have been grouped into modelling frameworks for 
presentation in this table.12 For a full list of unique model versions, please see the AR6 scenario database.
Model 
groupC1: Limit 
to 1.5°C 
(>50%) 
with no or 
limited OSC2: Return 
to 1.5°C 
(>50%) 
after high 
OSC3: Limit to 
2°C (>67%)C4: Limit to 
2°C (>50%)C5: Limit 
to 2.5°C 
(>50%)C6: Limit 
to 3.0°C 
(>50%)C7: Limit 
to 4.0°C 
(>50%)C8: Exceed 
4.0°C 
(≥50%)No climate 
assessmentTotal with climate 
categorisation
AIM/
CGE+Hub4 (18) 3 (7) 17 (37) 8 (23) 13 (23) 4 (7) 6 (32) – (8) 7 (7) 55 (155)
C-ROADS 3 (3) 2 (2) 1 (1) 6 (6)
COFFEE 1 (1) 4 (7) 14 (16) 15 (22) 21 (24) 9 (11) 1 (3) 65 (84)

DNE21+ – (4) – (7) – (10) – (3) – (4) – (8) 9 (10) – (36)
EPPA 1 (3) 3 (4) 1 (1) 2 (2) 7 (10)
En-ROADS – (2) – (1) – (3)
GCAM 6 (10) 6 (9) 13 (17) 9 (16) 6 (13) – (1) 4 (6) 1 (1) 18 (63) 45 (73)
GCAM-PR – (1) 1 (3) 2 (3) 13 (14) 3 (7)
GEM-E3 2 (2) 10 (10) 12 (12) 6 (6) 5 (5) 3 (3) 3 (3) 4 (11) 41 (41)
GRAPE-15 – (1) – (7) – (8) – (2) – (18)
IMAGE 7 (16) 9 (9) 34 (34) 18 (18) 22 (22) 16 (16) 34 (34) 2 (2) 2 (2) 142 (151)
MERGE-ETL – (1) 1 (1) – (1) 1 (3)
MESSAGE – (1) – (4) – (3) – (1) – (1) – (9)
MESSAGE-
GLOBIOM20 (20) 43 (48) 59 (61) 39 (40) 57 (59) 20 (22) 28 (33) – (1) 266 (284)
POLES 4 (14) 10 (15) 26 (26) 24 (26) 20 (21) 11 (12) 19 (23) 1 (1) 114 (137)
REMIND 13 (15) 12 (19) 34 (39) 1 (1) 7 (8) 6 (6) 22 (24) 9 (9) 104 (121)
REMIND-
MAgPIE28 (36) 32 (33) 50 (50) 15 (15) 27 (27) 13 (13) 26 (26) 2 (2) 193 (202)
TIAM-ECN 20 (20) 6 (6) 10 (10) 4 (4) 5 (5) – (13) 45 (45)
TIAM-UCL – (4) – (1) – (2) – (7)
TIAM-
WORLD– (3) – (2) – (4) – (2) – (9)

WITCH 5 (13) 1 (9) 29 (35) 14 (16) 24 (24) 9 (9) 4 (4) 4 (4) 90 (114)
WITCH-
GLOBIOM4 (5) 1 (1) 2 (9) – (4) – (8) – (7) 8 (15) 10 (10) 25 (59)
Total 97 (160) 133 (170) 311 (374) 159 (213) 212 (258) 97 (129) 164 (230) 29 (40) 54 (124) 1202 (1574)
12 Scenario numbers by modelling framework combine submissions from different model versions of the same model (indicated by version number or project name in the AR6 
scenario database). For the AIM, MESSAGE and REMIND modelling frameworks, the grouping covers the following distinct models (including different versions):
AIM/CGE+Hub: AIM/CGE, AIM/Hub
MESSAGE: MESSAGE, MESSAGE-Transport
MESSAGE-GLOBIOM: MESSAGE-GLOBIOM, MESSAGEix-GLOBIOM.
REMIND: REMIND, REMIND-H13, REMIND-Buildings, REMIND-Transport, REMIND_EU

1888
Table 16 | Global scenarios by modelling framework that were not included in the climate assessment due to a time horizon shorter than 2100 or 
a limited reporting of emissions species that did not include CO 2 (total emissions or emissions from energy and industry), CH 4 or N 2O. Unique model 
versions have been grouped into modelling frameworks for presentation in this table.13 For a full list of unique model versions, please see the AR6 scenario database.
Model framework Time horizon Passed vetting Total
BET 2100 0 16
C-GEM 2030 32 32
C3IAM 2100 5 14
CGE-MOD 2030 32 32
DART 2030 17 32
E3ME 2050 10 10
EC-MSMR 2030 32 32
EDF-GEPA 2030 32 32
EDGE-Buildings 2100 8 8
ENV-Linkages 2060 7 15
ENVISAGE 2030 32 32
FARM 2100 0 13
GAINS 2050 2 2
GEMINI-E3 2050 6 6
GENeSYS-MOD 2050 1 1
Global TIMES 2050 0 14
GMM 2060 4 4
Global Transportation Roadmap 2050 4 4
ICES 2030/2050 32 43
IEA ETP 2070 1 1
IEA WEM 2050 2 2
IRENA REmap GRO2020 2050 2 2

IMACLIM 2050/2080 30 68
IMACLIM-NLU 2100 1 3
LUT-ESTM 2050 0 1
MAgPIE 2100 3 3
MIGRATION 2100 10 10
MUSE 2100 5 11
McKinsey 2050 0 3
PROMETHEUS 2050 7 7
SNOW 2030 32 32
TEA 2030 32 32
TIAM-Grantham 2100 17 19
WEGDYN 2030 32 32
Total 430 568
13 Scenario numbers by modelling framework combine submissions from different model versions of the same model (indicated by version number or project name in the AR6 
scenario database).

1889
Changes in climate classification of scenarios since SR1.5: Since 
the definition of warming classes was unchanged from SR1.5 for the 
lower range of scenarios limiting warming to 2°C or lower, changes 
in overall emissions characteristics of scenarios in these classes 
from SR1.5 to AR6 would need to come from the substantially 
larger ensemble of deep mitigation scenarios collected in the AR6 
database compared to the SR1.5 database and from updates in the 
methodology of the climate assessment. Updates since SR1.5 include 
the methodology for infilling and harmonisation and the use of 
an updated climate emulator (MAGICC v7) to provide consistency 
with AR6 WGI assessment (Annex III.II.2.5.1). Out of the full set of 
SR1.5, 57% of the 411 scenarios that were represented with global 
temperature assessments in SR1.5 also have been assessed in AR6. 
Some SR1.5 scenarios could not be taken on board since they are

outdated (too early emissions reductions) and failed the vetting or do 
not provide sufficient information/data to be included in AR6.
Comparison between SR1.5 and AR6 scenarios and associated 
climate responses are shown in Figure 6, bottom panel. We show that 
changes in the climate assessment pipeline are minor compared to 
climate model uncertainty ranges in WGI (in the order of 0.1°C), but 
show considerable variation due to different scenario characteristics. 
The updated harmonisation and infilling together have a  small 
cooling effect compared to raw modelled emissions for the subset of 
95 scenarios in C1, C2, and C3 that also were assessed in SR15 (SR1.5 
Chapter 2, Table 2.4). This is due to both applying more advanced 
harmonisation methods consistent with the CMIP6 harmonisation 
used for WGI, and changing the historical harmonisation year from 
2010 to 2015. Together with the update in the climate emulator,

we find that the total AR6 assessment is remarkably consistent 
with SR1.5, albeit slightly cooler (in the order of 0.05°C at peak 
temperature, 0.1°C in 2100).
The lowest temperature category (C1, limiting warming to 1.5°C with 
no or low overshoot) used for classifying the most ambitious climate 
mitigation pathways in the literature, indicates that emissions are on 
average higher in AR6 in the near term (e.g., 2030) and the time of 
net-zero CO 2 is later by about five years compared to SR1.5 (Figure 6, 
middle panel). These differences can in part be ascribed to the fact 
that historical emissions in scenarios, especially among those that 
passed the vetting, have risen since SR1.5 in line with inventories. 
This increase has moved the attainable near-term emissions 
reductions upwards. As a result, the scenarios in the lowest category 
have also a lower probability of staying below 1.5°C peak warming. 
Using the WGI emulators, we find that the median probability of

staying below 1.5°C in the lowest category (C1) has dropped from 
about 46% in the SR1.5 scenarios to 38% among the AR6 scenarios. 
Note that the likelihood of the SR1.5 scenarios limiting warming to 
1.5°C with no or limited overshoot has changed from 41% in SR1.5 
to 46% in AR6 due to the updated climate assessment using the WGI 
AR6 climate emulator. Within C1, the vast majority of scenarios that 
were submitted to AR6 but were not assessed in SR1.5 have median 
peak temperatures close to 1.6°C. The AR6 scenarios in the lowest 
category show higher emissions and have a lower chance of keeping 
warming below 1.5°C, as indicated by the panels showing the 
distribution of peak warming and exceedance probability in AR6 vs 
SR1.5, with for instance C1 median peak temperature warming going from 1.55°C in SR1.5 (1.52°C if reassessed with AR6 assessment 
pipeline) to 1.58°C in AR6.
A.III.II.3.2.2 Policy Classification of Global Scenarios

Global scenarios were also classified based on their assumptions 
regarding climate policy. This information can be deduced from 
study protocols or the description of scenario designs in the 
published literature. It has also been elicited as meta-information 
for scenarios that were submitted to the AR6 database. There are 
multiple purposes for a policy classification, including controlling for 
the level of near-term action (Chapter 3, Section 3.5) and estimating 
costs and other differences between two policy classes (Chapter 3, 
Section  3.6). Policy classes can be combined with climate classes, 
for example to identify scenarios that follow the NDC until 2030 and 
limit warming to 2°C (>67%).
Table  17 presents the policy classification that was chosen for 
this assessment and the distribution of scenarios across the policy 
classes. There is a top-level distinction between diagnostic scenarios, 
scenarios from cost-benefit analyses, scenarios without globally

coordinated action, scenarios with immediate such action, and hybrid 
scenarios that move to globally coordinated action after a period of 
diverse and uncoordinated national action. On the second hierarchy 
level, scenarios are classified along distinctive features of scenarios 
in each class. Scenarios without globally coordinated action are 
often used as reference scenarios and come as baselines without 
climate policy efforts, as an extrapolation of current policy trends 
or as implementation and extrapolation of NDCs (Grant et al. 2020). 
Scenarios that act immediately to limit warming to some level can 
be distinguished by whether or not they include transfers to reflect 
equity considerations (Tavoni et al. 2015; Bauer et al. 2020c; van den 
Berg et al. 2020) or by whether or not they assume additional policies 
augmenting a  global carbon price (Soergel et al. 2021). Scenarios 
that delay globally coordinated action until 2030 can differ in their

assumptions about the level of near-term action (Roelfsema et al. 
2020; van Soest et al. 2021).
To identify the policy classification of each global scenario in the 
AR6 database, classes are first assigned via text pattern matching 
on all the metadata collected when submitting the scenarios to the 
database. The algorithm first looks for keywords and text patterns 
to establish whether a  scenario represents a  global, fragmented, 
diagnostic or CBA policy setup. Then it looks for evidence on 
the presence of specific regional policies, delayed actions and 
transfers of permits. Eventually the different pieces of evidence 
are harmonised into a  single policy categorisation decision. The 
process has been calibrated on the best-known scenarios belonging 
to the larger model intercomparison projects, and fine-tuned 
on the other scenarios via further validation against the related 
literature, consistency checks on reported emission and carbon

price trajectories, exchanges with modellers and supervision 
by the involved IPCC authors. If the information available is 
enough to identify a policy category number but not sufficient for 
a subcategory, then only the number is retained (e.g., P2 instead 
of P2a/b/c). A suffix added after P0 further qualifies a diagnostic 
scenario as one of the other policy categories.

1890
C1 C2 C3
2100 Full-century 2100 Full-century 2100 Full-century0.000.250.500.751.00Exceedance probabilityAR6 temperature outcomes of SR15 scenarios compared to AR6 scenarios.
Probability of exceeding 1.5C in 2100 and full-century
C1 C3
2030 2040 2050 2030 2040 2050-10000010000200003000040000CO2 emissions (MtCO2/yr)CO2 emissions levels.
C1 C3
2030 2040 2050 2030 2040 2050-100-500Percentage reduction (%) compared to 2019
C1 C3
Full-century Full-century1.41.51.61.71.8GSAT compared to pre-industrial (K)Distribution of peak temperatures.
C1C3
2040 2060 2080 2100Timing of net-zero CO2 emissions
Climate emulator Harmonization of emissions Infilling of emissions Total
2100 Full-century peak 2100 Full-century peak 2100 Full-century peak 2100 Full-century peak-0.2-0.10.00.10.2Difference in median GSAT (K)AR6 category
C1 (n=27)
C2 (n=35)
C3 (n=33)Change in temperature attributable to changes in assessment methods.
Temperature category of same

scenario in SR15
Below 1.5C
1.5C low overshoot
1.5C high overshoot
Lower 2C
Higher 2CModelled CO2 emissions reductions.
AR6
AR6 and SR15 overlap
SR15Scenario set with AR6 C1-C3
temperature assessment
AR6 (n=541)
AR6 and SR15 overlap (n=95)
SR15 (n=127)
AR6
SR15Scenario set with AR6 C1&C3
AR6 (n=408)
SR15 (SR15 temperatures) (n=91)
SR15
SR15 (AR6 temperatures) (n=91)
AR6 and SR15 overlap (n=60)
446
0
53
1679
0
0AR6 scenario set
likely below 2C or lower
SR15 scenario set
likely below 2C or
lowerScenarios that were not
likely below 2C in SR15 
but are in AR6Scenarios that are 
likely below 2C or 
lower in both SR15 
and AR6
C1
0 20Categories of scenarios that were
assessed both in AR6 and SR15
C2
C3
assessed both in AR6 and SR1510 10 30
10 30

Figure 6 | Comparing multiple characteristics of scenarios underlying SR1.5 Table 2.4 to the AR6 assessment. 
Top row: The probability of exceeding 1.5C for scenarios using the AR6 climate assessment pipeline for C1, C2, and C3. All scenarios in AR6 that pass vetting 
requirements and get climate classification C1, C2, or C3, are labelled as ‘AR6’ (n=541). The scenarios that are both in the AR6 database (passing the vetting) and were used 
for SR1.5 Table 2.4, and are classified as C1, C2 and C3 using the AR6 assessment, are labelled as ‘AR6 and SR1.5 overlap’ (n=95). ‘SR1.5 (n=127)’ shows all SR1.5 scenarios 
(except five that were not resubmitted for the AR6 report), including those that failed AR6 vetting, that are classified C1, C2, C3 with the updated AR6 temperature assessment. 
Dashed lines indicate cut-off temperature exceedance probabilities that align with AR6 category definitions. The violin area is proportional to the number of scenarios. Coloured

lines indicate the 25th and 75th percentiles, while the dashed black line indicates the median. The insets in each figure show how the temperature category classifications have 
changed from SR1.5 to AR6 for those scenarios that are in both databases.
Middle row: Characteristics of CO 2 emissions pathways and the distribution of median peak temperature assessments for C1 and C3. From left to right: (i) 
Change in CO 2 emissions levels and reductions in 2030, 2040 and 2050 between the AR6 (n=408), AR6 and SR1.5 overlap (n=60) and SR15 sets (n=91). (ii) distribution of 
scenarios with different median peak temperature scenario outcomes for C1 and C3 for AR6 and SR1.5 (both with AR6 temperature assessment as a solid line and with SR1.5 
temperature assessment as a dashed line, with median in yellow). (iii) Year of net-zero CO 2 for C1 and C3 for AR6 and SR1.5. Within C3, 27 AR6 scenarios and 2 SR1.5 scenarios

with no net-zero year before 2100 have not been visualised. The violin area is proportional to the number of scenarios. Coloured lines indicate the 25th and 75th percentiles, 
while the dashed black line indicates the median.
Bottom row: Change in median global mean surface air temperature (GSAT) between the AR6 and SR1.5 climate assessments for both 2100 values and 
peak temperature values during the 21st century. Positive values indicate that the temperature assessment is higher for the same scenario than the SR1.5 climate 
assessment. From left to right: (i) the effect of using MAGICCv7 calibrated to the WGI assessment compared with MAGICC6 as used in SR1.5; (ii) the effect of more advanced 
emissions harmonisation methods; (iii) the effect of more advanced emissions infilling methods; (iv) the total effect which is the sum of the three components. Boxplots show 
the median and interquartile range, with the whiskers indicating the 95% range.

1891
A.III.II.3.3 National and Regional Pathways
National and regional pathways have been collected in the AR6 
scenario database to support the Chapter 4 assessment. In total more 
than 500 pathways for 24 countries/regions have been submitted to 
the AR6 scenario database by integrated assessment, energy-economic 
and computable general equilibrium modelling research teams. This 
represents a  limited sample of the overall literature on mitigation 
pathways at the national level. The majority of these pathways originate from a  set of larger model intercomparison projects, JMIP/EMF35 
(Sugiyama et al. 2020a) focusing on Japan, CD-LINKS (Roelfsema et al. 
2020; Schaeffer et al. 2020), COMMIT (van Soest et al. 2021), ENGAGE 
(Fujimori et al. 2021), and Paris Reinforce (Perdana et al. 2020; Nikas 
et al. 2021) each covering several countries/regions from the following: 
Australia, Brazil, China, EU, India, Indonesia, Japan, Korea, Russia,

Thailand, USA, Vietnam. The remaining pathways stem from individual 
modelling studies that submitted scenarios to the database (Table 18).Table  17 | Policy classification of global scenarios.  If the total for a  class exceeds the sum of the subclasses, there are scenarios in the class that could not be 
assigned to a subclass.
Class Definition Number of scenarios
Passed vetting, with 
climate assessmentPassed vetting All
P0 Diagnostic scenario 73 99 138
P1 No globally coordinated climate policy and either 207 500 632
P1a  –no climate mitigation efforts 72 124 179
P1b  –current national mitigation efforts 51 59 72
P1c  –NDCs 56 160 184
P1d  –other policy assumptions 24 153 189
P2Globally coordinated climate policies with immediate  
(i.e., before 2030) action and579 634 992
P2a  –without any transfer of emission permits 403 435 610
P2b  –with transfers 70 70 143
P2c  –with additional policy assumptions 45 55 83
P3Globally coordinated climate policies with delayed

(i.e., from 2030 onwards or after 2030) action, preceded by341 451 502
P3a  –no mitigation commitment or current national policies 3 7 9
P3b  –NDCs 322 426 464
P3c  –NDCs and additional policies 16 18 29
P4 Cost-benefit analysis 2 2 2
Total 1202 1686 2266
Country/
regiona Model CP NDC Other Total
ARG IMACLIM-ARG 1 2 3
AUS TIMES-Australia 1 7 8
BRA BLUES-Brazil 2 2 15 19
BRA COPPE_MSB-Brazil 8 8
BRA IMACLIM-BRA 5 5
CHE STEM-Switzerland 1 11 12
CHN AIM/Hub-China 1 1 7 9
CHN C3IAM 3 11 14
CHN DREAM-China 1 1
CHN GENeSYS-MOD-CHN 3 3
CHN IPAC-AIM/technology-China 1 1 11 13
CHN PECE-China 2 2
CHN TIMES-Australia 1 1
CHN TIMES-China 1 2 8 11
ECU ELENA-Ecuador 2 2Country/
ETH TIAM-ECN ETH 1 1 2
EU E4SMA-EU-TIMES 1 1
EU eTIMES-EU 23 23
EU JRC-EU-TIMES 8 8
EU PRIMES 2 2 9 13
EU REMIND_EU 9 9
FRA TIMES-France 8 8
GBR 7see 11 11
IDN AIM/Hub-Indonesia 2 2
IDN DDPP Energy 4 4
IND AIM/Enduse India 1 1 5 7
IND AIM/Hub-India 1 1 7 9
IND MARKAL-INDIA 2 3 13 18

JPN AIM/CGE-Enduse-Japan 6 6
JPN AIM/Enduse-Japan 3 3 69 75Table 18 | National and regional mitigation pathways by modelling framework, region and scenario type.

1892
Country/
JPN AIM/Hub-Japan 1 2 42 45
JPN DNE21-Japan 1 30 31
JPN DNE21+ V.14 (national) 1 1 4 6
JPN IEEJ-Japan 1 34 35
KEN TIAM-ECN KEN 1 1 2 4
KOR AIM/CGE-Korea 1 1 6 8
KOR AIM/Hub-Korea 1 1 7 9
MDG TIAM-ECN MDG 1 2 3
MEX GENeSYS-MOD-MEX 4 4Country/
PRT TIMES-Portugal 1 3 4
RUS RU-TIMES 1 1 4 6
SWE TIMES-Sweden 4 4
THA AIM/Hub-Thailand 1 2 19 22
USA GCAM-USA 2 2 9 13
USA RIO-USA 12 12
VNM AIM/Hub-Vietnam 1 2 14 17
ZAF TIAM-ECN AFR 4 4
Total 29 39 466 534
a Countries are abbreviated by their ISO 3166-1 alpha-3 letter codes. EU denotes the European Union.
Notes: CP = current policies, NDC = implementation of Nationally Determined Contributions by 2025/30, Other = all other scenarios.
A.III.II.3.4 Sector Transition Pathways
Sectoral transition pathways based on the AR6 scenario database 
are addressed in a number of Chapters, primarily Chapter 6 (Energy

systems), Chapter  7 (AFOLU), Chapter  9 (Buildings), Chapter  10 
(Transport) and Chapter  11 (Industry). These analyses cover both 
contributions from global IAMs and from sector-specific models 
with regional or global coverage. The assessments cover a  variety of perspectives, including long-term global and macro-region 
trends for the sectors, sectoral analysis of the Illustrative Pathways, 
and comparison of the scenarios between full-economy IAMs and 
sector-specific models on shorter time horizons. These perspectives 
have a  bi-directional utility – to understand how well IAMs are 
representing sectoral trends from more granular models, and to 
position sectoral models in the context of full-economy transitions to 
verify consistency with different climate outcomes.
Table 19 | Overview of how models and scenarios were used in sectoral chapters.  All scenario and model counts listed in the table are contained in the AR6

scenario database, with the exception of Chapter 9 (Buildings), which supplemented its dataset with a large number of scenarios separately pulled from the sectoral literature. 
Scenario counts represents unique model-scenario combinations in the database.
SectorNumber of 
modelsNumber of scenariosKey 
sectionsKey perspectives
Energy systems 
(Chapter 6)12
18
13476
536
7766.6
6.7
6.7.1Regional and global energy system characteristics along mitigation pathways and at net-
zero emissions specifically: CO 2 and GHG emissions; energy resource shares; electricity 
and hydrogen shares of final energy; energy intensity; per-capita energy use; peak 
emissions; energy investments
AFOLU 
(Chapter 7)11
14
13
3384
572
559
47.5.1
7.5.2
7.5.4
7.5.5Regional and global GHG emissions and land use dynamics; economic mitigation 
potential for different GHGs; integrated mitigation pathways
Buildings 
(Chapter 9)80
(of which 2 are 
in AR6 scenario 
database)82
(of which 4 are in AR6

scenario database)9.3, 9.6A mixture of top-down and bottom-up models. The former were either national, regional 
or global while the latter were global only with a breakdown per end use, building type, 
technologies and energy carrier
Transport 
(Chapter 10)24 1210 10.7Global and regional transport demand, activity, modes, vehicles, fuels,  
and mitigation options
Industry 
(Chapter 11)14 508 11.4.2 Global final energy use, CO 2 emissions, carbon sequestration, fuel shares
Note 1: The number of models and scenarios reported in the table cannot be summed across chapters, as there is considerable overlap in selected model-scenario combinations 
across chapters, depending on the filtering processes used for relevant analyses. Moreover, the numbers in the table – and certainly not their sum – are not intended to match 
those reported for the global pathways assessed by Chapter 3 in Section II.3.2.

Note 2: Numbers shown in the model-count column are arrived at through the authors’ best judgement. This has to do with the overlapping nature of unique model versions 
(within a given model family) as models evolve over time. In this case, model versions with substantial overlap were considered the same model, whereas model versions 
that differ significantly were counted as unique. For example, MESSAGEix-GLOBIOM 1.0 and MESSAGEix-GLOBIOM_1.1 are counted as the same model, while MESSAGEix-
GLOBIOM 1.0 and MESSAGE are counted as different. If instead counting all model versions uniquely, then the following counts would apply to each chapter: Energy systems 
(30/38/29), AFOLU (18/27/25/4), Buildings (80), Transport (50), Industry (32).
Note 3: The Transport chapter figures in Chapter 10, Section 10.7, are produced from the final AR6 scenario database by the code accompanying this report. The set of model and

scenario names appearing in each plot or figure of Section 10.7 varies, depending on whether particular submissions to the database included the specific variables appearing 
in that plot. Authors advise inspecting the data files accompanying each figure for the set of models/scenarios specific to that figure, or running the code against the final 
database snapshot to reproduce the figures in question.

153
Introduction and Framing Chapter 11
Global greenhouse gas (GHG) emissions continued to rise to 
2019: the aggregate reductions implied by current Nationally 
Determined Contributions (NDCs) to 2030 would still make 
it impossible to limit warming to 1.5°C with no or limited 
overshoot, and would only be compatible with likely limiting 
warming below 2°C if followed by much steeper decline, hence 
limiting warming to either level implies accelerated mitigation 
actions at all scales (robust evidence,  high agreement). Since 
the IPCC’s Fifth Assessment Report (AR5), important changes that 
have emerged include the specific objectives established in the Paris 
Agreement of 2015 (for temperature, adaptation and finance), rising 
climate impacts, and higher levels of societal awareness and support for 
climate action. The growth of global GHG emissions has slowed over the 
past decade, and delivering the updated NDCs to 2030 would turn this

into decline, but the implied global emissions by 2030 exceed pathways 
consistent with 1.5°C by a large margin, and are near the upper end of 
the range of modelled pathways which keep temperatures likely limit 
warming to 2°C (with >65% probability). Continuing investments in 
carbon-intensive activities at scale will heighten the multitude of risks 
associated with climate change and impede societal and industrial 
transformation towards low-carbon development. Meeting the long-
term temperature objective in the Paris Agreement therefore implies 
a rapid turn to an accelerating decline of GHG emissions towards ‘net 
zero’, which is implausible without urgent and ambitious action at all 
scales. The unprecedented COVID-19 pandemic has had far-reaching 
impacts on the global economic and social system, and recovery will 
present both challenges and opportunities for climate mitigation. {1.2, 
1.3, 1.5, 1.6, Chapters 3 and 4}
While there are some trade-offs, effective and equitable

climate policies are largely compatible with the broader goal 
of sustainable development and efforts to eradicate poverty 
as enshrined in the 17 Sustainable Development Goals (SDGs) 
(robust evidence, high agreement). Climate mitigation is one 
of many goals that societies pursue in the context of sustainable 
development, as evidenced by the wide range of the SDGs. Climate 
mitigation has synergies and/or trade-offs with many other SDGs. 
There has been a strong relationship between development and GHG 
emissions, as historically both per capita and absolute emissions 
have risen with industrialisation. However, recent evidence shows 
countries can grow their economies while reducing emissions. 
Countries have different priorities in achieving the SDGs and reducing 
emissions as informed by their respective national conditions and 
capabilities. Given the differences in GHG emissions contributions, 
degree of vulnerabilities and impacts, as well as capacities within and

between nations, equity and justice are important considerations for 
effective climate policy and for securing national and international 
support for deep decarbonisation. Achieving sustainable global 
development and eradicating poverty as enshrined in the 17 SDGS 
would involve effective and equitable climate policies at all levels 
from local to global scale. Failure to address questions of equity 
and justice over time can undermine social cohesion and stability. 
International cooperation can enhance efforts to achieve ambitious 
global climate mitigation in the context of sustainable development. 
{1.4, 1.6, Chapters 2, 3, 4, 5, 13 and 17}The transition to a  low-carbon economy depends on 
a wide range of closely intertwined drivers and constraints, 
including policies and technologies where notable advances 
over the past decade have opened up new and large-scale 
opportunities for deep decarbonisation, and for alternative

development pathways which could deliver multiple social and 
developmental goals (robust evidence, medium agreement). 
Drivers for and constraints against low-carbon societal transition 
comprise economic and technological factors (the means by which 
services such as food, heating and shelter are provided and for whom, 
the emissions intensity of traded products, finance, and investment), 
socio-political issues (political economy, equity and fairness, 
social innovation and behaviour change), and institutional factors 
(legal framework and institutions, and the quality of international 
cooperation). In addition to being deeply intertwined all the factors 
matter to varying degrees, depending on the prevailing social, 
economic, cultural and political context. They often exert both push 
and pull forces at the same time, within and across different scales. 
The development and deployment of innovative technologies and

systems at scale are important for achieving deep decarbonisation. 
In recent years, the cost of several low-carbon technologies has 
declined sharply, alongside rapid deployment. Over 20 countries 
have also sustained emission reductions, and many more have 
accelerated energy efficiency and/or land-use improvements. Overall, 
however, the global contribution is so far modest, at a few billion 
tonnes (tCO2-eq) of avoided emissions annually. {1.3, 1.4, Chapters 2, 
4, 13 and 14}
Accelerating mitigation to prevent dangerous anthropogenic 
interference within the climate system will require the 
integration of broadened assessment frameworks and tools 
that combine multiple perspectives, applied in a  context of 
multi-level governance (robust evidence, medium agreement). 
Analysing a  challenge on the scale of fully decarbonising our 
economies entails integration of multiple analytic frameworks. 
Approaches to risk assessment and resilience, established across

IPCC Working Groups, are complemented by frameworks for probing 
the challenges in implementing mitigation. Aggregate frameworks 
include cost-effectiveness analysis towards given objectives, and 
cost-benefit analysis, both of which have been developing to take 
fuller account of advances in understanding risks and innovation, 
the dynamics of emitting systems and of climate impacts, and 
welfare economic theory including growing consensus on long-term 
discounting. Ethical frameworks consider the fairness of processes 
and outcomes which can help ameliorate distributional impacts 
across income groups, countries and generations. Transition and 
transformation frameworks explain and evaluate the dynamics of 
transitions to low-carbon systems arising from interactions amongst 
levels, with inevitable resistance from established socio-technical 
structures. Psychological, behavioural and political frameworks 
outline the constraints (and opportunities) arising from human

psychology and the power of incumbent interests. A comprehensive 
understanding of climate mitigation must combine these multiple 
frameworks. Together with established risk frameworks, collectively 
these help to explain potential synergies and trade-offs in mitigation, 
imply a  need for a  wide portfolio of policies attuned to different

154
Chapter 1  Intr oduction and Framing1actors and levels of decision-making, and underpin Just Transition 
strategies in diverse contexts. {1.2.2, 1.7, 1.8}
The speed, direction and depth of any transition will be 
determined by choices in the, environmental, technological, 
economic, socio-cultural and institutional realms (robust 
evidence, high agreement). Transitions in specific systems can be 
gradual or rapid and disruptive. The pace of a transition can be impeded 
by ‘lock-in’ generated by existing physical capital, institutions, and 
social norms. The interaction between power, politics and economy 
is central in explaining why broad commitments do not always 
translate to urgent action. At the same time, attention to and support 
for climate policies and low-carbon societal transition has generally 
increased, as the impacts have become more salient. Both public and 
private financing and financial structures strongly affect the scale

and balance of high- and low-carbon investments. COVID-19 has 
strained public finances, and integrating climate finance into ongoing 
recovery strategies, nationally and internationally, can accelerate the 
diffusion of low-carbon technologies and also help poorer countries 
to minimise future stranded assets. Societal and behavioural norms, 
regulations and institutions are essential conditions to accelerate 
low-carbon transitions in multiple sectors, whilst addressing 
distributional concerns endemic to any major transition. {1.3.3, 1.4, 
1.8, Chapters 2, 4 and 15, and Cross-Chapter Box 1 in this chapter}
Achieving the global transition to a  low-carbon, climate-
resilient and sustainable world requires purposeful and 
increasingly coordinated planning and decisions at many 
scales of governance including local, sub-national, national 
and global levels (robust evidence, high agreement). 
Accelerating mitigation globally would imply strengthening policies

adopted to date, expanding the effort across options, sectors, and 
countries, and broadening responses to include more diverse actors 
and societal processes at multiple – including international – levels. 
Effective governance of climate change entails strong action across 
multiple jurisdictions and decision-making levels, including regular 
evaluation and learning. Choices that cause climate change as well 
as the processes for making and implementing relevant decisions 
involve a range of non-nation state actors such as cities, businesses, 
and civil society organisations. At global, national and sub-national 
levels, climate change actions are interwoven with and embedded 
in the context of much broader social, economic and political goals. 
Therefore, the governance required to address climate change has to 
navigate power, political, economic, and social dynamics at all levels 
of decision-making. Effective climate-governing institutions,  and

openness to experimentation on a  variety of institutional 
arrangements, policies and programmes can play a  vital role in 
engaging stakeholders and building momentum for effective climate 
action. {1.4, 1.9, Chapters 8, 15 and 17}

155
Introduction and Framing Chapter 111.1 Introduction
This report (AR6 WGIII) aims to assess new literature on climate 
mitigation including implications for global sustainable development. 
In this Sixth Assessment Cycle the IPCC has also published three 
Special Reports,1 all of which emphasise the rising threat of climate 
change and the implications for more ambitious mitigation efforts 
at all scales. At the same time, the Paris Agreement (PA) and the UN 
2030 Agenda for Sustainable Development with its 17 Sustainable 
Development Goals (SDGs), both adopted in 2015, set out a globally 
agreed agenda within which climate mitigation efforts must be 
located. Along with a better understanding of the physical science 
basis of climate change (AR6 WGI), and vulnerabilities, impacts, 
and adaptation (AR6 WGII), the landscape of climate mitigation has 
evolved substantially since the Fifth Assessment Report (AR5).
Since (IPCC 2014a), climate mitigation policies around the world

have grown in both number and shape (Chapter 13). However, while 
the average rate of annual increase of CO 2 emissions has declined 
(Section 1.3.2), GHG emissions globally continued to rise, underlining 
the urgency of the mitigation challenge (Chapters  2 and 3). Over 
20  countries have cut absolute emissions alongside sustained 
economic growth, but the scale of mitigation action across countries 
remains varied and is generally much slower than the pace required 
to meet the goals of the Paris Agreement (Sections 1.3.2 and 2.7.2). 
Per capita GHG emissions between countries even at similar stages 
of economic development (based on GDP per capita) vary by a factor 
of three (Figure 1.6) and by more than two on consumption basis 
(Section 2.3).
The Special Report on Global Warming of 1.5°C (SR1.5) underlined 
that humanity is now living with the ‘unifying lens of the 
Anthropocene’ (IPCC 2018a, pp. 52–53), that requires a sharpened

focus on the impact of human activity on the climate system and the 
planet more broadly given ‘planetary boundaries’ (Steffen et al. 2015) 
including interdependencies of climate change and biodiversity 
(Dasgupta 2021). Recent literature assessed by Working Groups I and 
II of this AR6 underlines the urgency of climate action as cumulative 
CO2 emissions, along with other greenhouses gases (GHGs), drives 
the temperature change. Across AR6, global temperature changes 
are defined relative to the period 1850–1900, as in SR1.5 and 
collaboration with WGI enabled the use of AR6-calibrated emulators 
to assure consistency across the three Working Groups. The remaining 
‘carbon budgets’ (see Annex I: Glossary) associated with 1.5°C and 
2°C temperature targets equate to about one (for 1.5°C) to three 
(for 2°C) decades of current emissions, as from 2020, but with 
significant variation depending on multiple factors including other

gases (Figure 2.7, and Cross-Working Group Box 1 in Chapter 3). For 
an outline of the WGIII approach to mitigation scenarios, emission 
pathways implied by the Paris goals, and the timing of peak and ‘net 
zero’ (see Glossary and FAQ 1.3), see Section 1.5 and Chapter 3.
1 These are the Special Report on Global Warming of 1.5°C (SR1.5) (IPCC 2018b); the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC 
2019b); and the Special Report on Climate Change and Land (SRCCL) (IPCC 2019c).Strong differences remain in responsibilities for, and capabilities to, 
take climate action within and between countries. These differences, 
as well as differences in the impact of climate change, point to the 
role of collective action in achieving urgent and ambitious global 
climate mitigation in the context of sustainable development, with 
attention to issues of equity and fairness as highlighted in several 
chapters of the report (Chapters 4, 5, 14, 15 and 17).

Innovation and industrial development of key technologies in 
several relevant sectors have transformed prospects for mitigation 
at much lower cost than previously assessed (Chapters  2 and 
6–12). Large reductions in the cost of widely available renewable 
energy technologies, along with energy efficient technologies and 
behavioural changes (Chapters 5 and 9–11), can enable societies to 
provide services with much lower emissions. However, there are still 
significant differences in the ability to access and utilise low-carbon 
technologies across the world (Chapters 4, 15 and 16). New actors, 
including cities, businesses, and numerous non-state transnational 
alliances have emerged as important players in the global effort to 
tackle climate change (Chapters 13–16).
Along with continued development of concepts, models and 
technologies, there have been numerous insights from both the 
successes and failures of mitigation action that can inform future

policy design and climate action. However, to date, policies and 
investments are still clearly inadequate to put the world in line with 
the PA’s aims (Chapters 13 and 15).
The greater the inertia in emission trends and carbon-intensive 
investments, the more that CO 2 will continue to accumulate (Hilaire 
et al. 2019; IPCC 2019a). Overall, the literature points to the need for 
a more dynamic consideration of intertwined challenges concerning 
the transformation of key GHG-emitting systems: to minimise 
the trade-offs, and maximise the synergies, of delivering deep 
decarbonisation whilst enhancing sustainable development.
This chapter introduces readers to the AR6 WGIII Report and provides 
an overview of progress and challenges, in three parts. Part  A 
(1.1–1.5) introduces the climate mitigation challenge, provides key 
findings and developments since previous assessment, and reviews 
the main drivers for, and constraints against accelerated climate

action. Part B (1.6–1.8) provides an assessment of the key frameworks 
for understanding the climate mitigation challenge covering broad 
approaches such as sustainable development and more specific 
economic, political and ethical framings. Part C  (1.9–1.12) briefly 
highlights the role of governance for steering and coordinating efforts 
to accelerate globally effective and equitable climate mitigation, 
notes the gaps in knowledge that have been identified in the process 
of assessment, and provides a road map to the rest of the report.

156
Chapter 1  Intr oduction and Framing11.2  Previous Assessments
1.2.1 Key Findings from Previous Assessment Reports
Successive WGIII IPCC assessments have emphasised the importance 
of climate mitigation along with the need to consider broader societal 
goals especially sustainable development. Key insights from AR5 and 
the subsequent three Special Reports (IPCC 2018b, 2019b, 2019c) are 
summarised below.
The AR5 projected that in baseline scenarios (i.e., based on prevailing 
trends without explicit additional mitigation efforts), agriculture, 
forestry and other land use (AFOLU) would be the only sector where 
emissions could fall by 2100, with some CO 2 removal (IPCC 2014b, 
p. 17). Direct CO 2 emissions from energy were projected to double 
or even triple by 2050 (IPCC 2014b, p. 20) due to global population 
and economic growth, resulting in global mean surface temperature 
increases in 2100 from 3.7°C to 4.8°C compared to pre-industrial

levels. The AR5 noted that mitigation effort and the costs associated 
with ambitious mitigation differ significantly across countries, and in 
‘globally cost-effective’ scenarios, the biggest reductions (relative to 
projections) occur in the countries with the highest future emissions 
in the baseline scenarios (IPCC 2014b, p. 17). Since most physical 
capital (e.g.,  power plants, buildings, transport infrastructure) 
involved in GHG emissions is long-lived, the timing of the shift in 
investments and strategies will be crucial (IPCC 2014b, p. 18).
A key message from recent Special Reports is the urgency to mitigate 
GHG emissions in order to avoid rapid and potentially irreversible 
changes in natural and human systems (IPCC 2018b, 2019b, 2019c). 
Successive IPCC reports have drawn upon increasing sophistication 
of modelling tools to project emissions in the absence of ambitious 
decarbonisation action, as well as the emission pathways that meet

long-term temperature targets. The SR1.5 examined pathways limiting 
warming to 1.5°C, compared to the historical baseline of 1850–1900, 
finding that ‘in pathways with no or limited overshoot of 1.5°C, 
global net anthropogenic CO 2 emissions decline by about 45% from 
2010 levels by 2030, reaching net zero around 2050’ (2045–2055 
interquartile range); with ‘overshoot’ referring to higher temperatures, 
then brought down by 2100 through ‘net negative’ emissions. It 
found this would require rapid and far-reaching transitions in energy, 
land, urban and infrastructure (including transport and buildings), 
and industrial systems (high confidence) (IPCC 2018b).
The SR1.5 found that the Nationally Determined Contributions 
(NDCs) as declared under the Paris Agreement (PA) would not limit 
warming to 1.5°C; despite significant updates to NDCs in 2020/21, 
this remains the case, although delivery of these more ambitious 
NDCs would somewhat enhance the prospects for staying below 2°C

(Section 1.3.3).
The AR5 WGIII and the Special Reports analysed economic costs 
associated with climate action. The estimates vary widely depending 
on the assumptions made as to how ordered the transition is, 
temperature target, technology availability, and the metric or model 
used, among others (Chapter 6). Modelled direct mitigation costs of 
pathways to 1.5°C, with no/limited overshoot, span a wide range, but were typically three to four times higher than in pathways to 
2°C (high confidence), before taking account of benefits, including 
significant reduction in loss of life and livelihoods, and avoided 
climate impacts (IPCC 2018b).
Successive IPCC reports highlight a  strong connection between 
climate mitigation and sustainable development. Climate 
mitigation and adaptation goals have synergies and trade-offs 
with efforts to achieve sustainable development, including poverty 
eradication. A comprehensive assessment of climate policy therefore

involves going beyond a  narrow focus on specific mitigation and 
adaptation options to incorporate climate issues into the design of 
comprehensive strategies for equitable sustainable development. At 
the same time, some climate mitigation policies can run counter to 
sustainable development and eradicating poverty, which highlights 
the need to consider trade-offs alongside benefits. Examples include 
synergies between climate policy and improved air quality, reducing 
premature deaths and morbidity (IPCC 2014b, Figure SPM.6) (AR6 
WGI Sections 6.6.3 and 6.7.3), but there would be trade-offs if policy 
raises net energy bills, with distributional implications. The Special 
Report on Climate Change and Land (SRCCL) also emphasises 
important synergies and trade-offs, bringing new light on the link 
between healthy and sustainable food consumption and emissions 
caused by the agricultural sector. Land-related responses that

contribute to climate change adaptation and mitigation can also 
combat desertification and land degradation, and enhance food 
security (IPCC 2019a).
Previous Assessment Reports (ARs) have detailed the contribution 
of various sectors and activities to global GHG emissions. When 
indirect emissions (mainly from electricity, heat and other energy 
conversions) are included, the four main consumption (end-use) 
drivers are industry, AFOLU, buildings and transport  (Figure  2.14), 
though the magnitude of these emissions can vary widely between 
countries. These – together with the energy and urban systems which 
feed and shape end-use sectors – define the sectoral chapters in this 
AR6 WGIII report.
Estimates of emissions associated with production and transport 
of internationally traded goods were first presented in AR5 WGIII, 
which estimated the ‘embodied emission transfers’ from upper-
middle-income countries to industrialised countries through trade at

about 10% of CO 2 emissions in each of these groups (IPCC 2014a, 
Figure TS.5). The literature on this and discussion on their accounting 
has grown substantially since then (Chapters 2 and 8).
The atmosphere is a shared global resource and an integral part of 
the ‘global commons’. In the depletion/restoration of this resource, 
myriad actors at various scales are involved, for instance, individuals, 
communities, firms and states. Inter alia, international cooperation to 
tackle ozone depletion and acid rain offer useful examples. The AR5 
noted that greater cooperation would ensue if policies are perceived 
as fair and equitable by all countries along the spectrum of economic 
development – implying a need for equitable sharing of the effort. 
A  key takeaway from AR5 is that climate policy involves value 
judgement and ethics. (IPCC 2014a Box TS.1: ‘People and countries 
have rights and owe duties towards each other. These are matters

157
Introduction and Framing Chapter 11of justice, equity, or fairness. They fall within the subject matter of 
moral and political philosophy, jurisprudence, and economics.’ p. 37). 
International cooperation and collective action on climate change 
alongside local, national, regional and global policies will be crucial 
to solve the problem, and this report notes cooperative approaches 
beyond simple ‘global commons’ framings (Chapters 13 and 14).
The AR5 (all Working Group reports) also underlined that climate 
policy inherently involves risk and uncertainty (in nature, economy, 
society and individuals). To help evaluate responses, there exists 
a  rich suite of analytical tools, for example, cost-benefit analysis, 
cost-effectiveness analysis, multi-criteria analysis, expected utility 
theory, and catastrophe and risk models. All have pros and cons, and 
have been further developed in subsequent literature and in AR6 
(Sections 1.2.2 and 1.7).

Recent assessments (IPCC 2014a, 2018b) began to consider the role 
of individual behavioural choices and cultural norms in driving energy 
and food patterns. Notably, SR1.5 (Section 4.4.3) outlined emerging 
evidence on the potential for changes in behaviour, lifestyle and 
culture to contribute to decarbonisation (and lower the cost); for the 
first time, AR6 devotes a whole chapter (Chapter 5) to consider these 
and other underlying drivers of energy demand, food choices and 
social aspects.
1.2.2 Developments in Climate Science, 
Impacts and Risk
The assessment of the Physical Science Basis (IPCC AR6 WGI) 
documents sustained and widespread changes in the atmosphere, 
cryosphere, biosphere and ocean, providing unequivocal evidence of 
a  world that has warmed, associated with rising atmospheric CO 2 
concentrations reaching levels not experienced in at least the last 
2  million years. Aside from temperature, other clearly discernible,

human-induced changes beyond natural variations include 
declines in Arctic Sea ice and glaciers, thawing of permafrost, and 
a  strengthening of the global water cycle (AR6 WGI SPM A.2, B.3 
and B.4). Oceanic changes include rising sea level, acidification, 
deoxygenation, and changing salinity (WGI SPM B.3). Over land, in 
recent decades, both frequency and severity have increased for hot 
extremes but decreased for cold extremes; intensification of heavy 
precipitation is observed in parallel with a  decrease in available 
water in dry seasons, along with an increased occurrence of weather 
conditions that promote wildfires.
In defining the objective of international climate negotiations as 
being to ‘prevent dangerous anthropogenic interference’ (UNFCCC 
1992, Art. 2), the UNFCCC underlines the centrality of risk framing 
in considering the threats of climate change and potential response 
measures. Against the background of ‘unequivocal’ (AR4) evidence of

human-induced climate change, and the growing experience of direct 
impacts, the IPCC has sought to systematise a robust approach to risk 
and risk management.In AR6 the IPCC employs a  common risk framing across all three 
working groups and provides guidance for more consistent and 
transparent usage (AR6 WGI Cross-Chapter Box 3 in Chapter 1; AR6 
WGII Section  1.4.1; IPCC risk guidance). AR6 defines risk as ‘the 
potential for adverse consequences for human or ecological systems, 
recognising the diversity of values and objectives associated with 
such systems’ (Annex I), encompassing risks from both potential 
impacts of climate change and human responses to it (Reisinger et al. 
2020). The risk framing includes steps for identifying, evaluating, 
and prioritising current and future risks; for understanding the 
interactions among different sources of risk; for distributing effort 
and equitable sharing of risks; for monitoring and adjusting actions

over time while continuing to assess changing circumstances; and 
for communications among analysts, decision-makers, and the public.
Climate change risk assessments face challenges including a tendency 
to mischaracterise risks and pay insufficient attention to the potential 
for surprises (Weitzman 2011; Aven and Renn 2015; Stoerk et al. 
2018). Concepts of resilience and vulnerability provide overlapping, 
alternative entry points to understanding and addressing the societal 
challenges caused and exacerbated by climate change (AR6 WGII, 
Section 1.2.1).
The AR6 WGII devotes a full chapter (Chapter 17) to ‘Decision-Making 
Options for Managing Risk’, detailing the analytic approaches 
and drawing upon the Cynefin classification of known, knowable, 
complex and chaotic systems (Section 17.3.1). With deep uncertainty, 
risk management often aims to identify specific combinations of 
response actions and enabling institutions that increase the potential

for favourable outcomes despite irreducible uncertainties (AR6 WGII 
Chapter  17 Cross-Chapter Box DEEP; also Marchau et al. (2019); 
Doukas and Nikas (2020)).
Literature trying to quantify the cost of climate damages has continued 
to develop. Different methodologies systematically affect outcomes, 
with recent estimates based on empirical approaches – econometric 
measurements based on actual impacts – ‘categorically higher than 
estimates from other approaches’ (AR6 WGII, Cross-Working Group 
Box ECONOMIC in Chapter  16, and Section  16.6.2). This, along 
with other developments strengthen foundations for calculating 
a ‘social cost of carbon’. This informs a common metric for comparing 
different risks and estimating benefits compared to the costs of GHG 
reductions and other risk-reducing options (Section 1.7.1); emissions 
mitigation itself also involves multiple uncertainties, which alongside 
risks can also involve potential opportunities (Section 1.7.3).

Simultaneously, the literature increasingly emphasises the 
importance of multi-objective risk assessment and management 
(e.g., representative key risks in AR6 WGII Chapter 16), which may 
or may not correlate with any single estimate of economic value 
(AR6 WGII, Section  1.4.1; IPCC risk guidance). Given the deep 
uncertainties and risks, the goals established (notably in the Paris 
Agreement and SDGs) reflect negotiated outcomes informed by the 
scientific assessment of risks.

158
Chapter 1  Intr oduction and Framing11.3 The Multilateral Context, Emissions 
Trends and Key Developments
Since AR5, there have been notable multilateral efforts which help 
determine the context for current and future climate action. This 
section summarises key features of this evolving context.
1.3.1 The 2015 Agreements
In 2015 the world concluded four major agreements that are very 
relevant to climate action. These include: the Paris Agreement under 
the 1992 United Nations Framework Convention on Climate Change 
(UNFCCC), the UN agreements on Disaster Risk Reduction (Sendai) 
and Finance for Development (Addis Ababa), and the Sustainable 
Development Goals (SDGs).
The Paris Agreement (PA). The Paris Agreement aims to ‘hold the 
increase to 1.5°C above pre-industrial levels’ (UNFCCC 2015),

alongside goals for adaptation (IPCC AR6 WGII), and ‘aligning 
financial flows’ (see ‘finance goal’, below) , so as ‘to strengthen the 
global response to the threat of climate change, in the context of 
sustainable development and efforts to eradicate poverty.’
The Paris Agreement is predicated on encouraging progressively 
ambitious climate action from all countries on the basis of Nationally 
Determined Contributions (Clémençon 2016; Rajamani 2016). The 
NDC approach requires countries to set their own level of ambitions 
for climate change mitigation but within a collaborative and legally 
binding process to foster ambition towards the agreed goals (Bodansky 
2016; Falkner 2016a). The PA entered into force in November 2016 
and as of February 2021 it already had 190 Parties (out of 197 Parties 
to the UNFCCC).
The PA also underlines ‘the principle of common but differentiated 
responsibilities and respective capabilities, in the light of different

national circumstances’ (PA Art. 2, para. 2), and correspondingly 
that ‘developed country Parties should continue taking the lead by 
undertaking economy-wide absolute emission reductions’. It states 
that developing country Parties should continue enhancing their 
mitigation efforts, and are encouraged to move over time towards 
economy-wide emission reduction or limitation targets in the light of 
different national circumstances.
In order to achieve the its long term temperature goal, the Paris 
Agreement aims ‘to achieve a  balance between anthropogenic 
the second half of this century’ (PA Art. 4 para. 1). The PA provides for 
five-yearly stocktakes in which Parties have to take collective stock 
on progress towards achieving its purposes and its long-term goal 
in the light of equity and available best science (PA Art. 14). The first 
global stocktake is scheduled for 2023 (PA Art. 14, para. 3).

The Paris Agreement’s finance goal aims to make ‘finance flows 
consistent with a pathway towards low greenhouse gas emissions and climate-resilient development’ (PA Art. 2.1C). In keeping with 
the acknowledged context of global sustainable development and 
poverty eradication, and the corresponding aims of aligning finance 
and agreed differentiating principles as indicated above, ‘…the 
developed country parties are to assist developing country parties 
with financial resources’ (PA Art. 9). The Green Climate Fund (GCF), 
an operating entity of the UNFCCC Financial Mechanism to finance 
mitigation and adaptation efforts in developing countries (GCF 2020), 
was given an important role in serving the Agreement and supporting 
PA goals. The GCF gathered pledges worth USD10.3 billion, from 
developed and developing countries, regions, and one city (Paris) 
(Antimiani et al. 2017; Bowman and Minas 2019). Financing has 
since increased but remains short of the goal to mobilise USD100

billion by 2020 (Chapter 15).
Initiatives contributing to the Paris Agreement goals include the 
Non-State Actor Zone for Climate Action (NAZCA: now renamed as 
Global Climate Action) portal, launched at COP20 (December 2014) 
in Lima, Peru, to support city-based actions for mitigating climate 
change (IISD 2015) and Marrakech Partnership for Global Climate 
Action which is a  UNFCCC-backed series of events intended to 
facilitate collaboration between governments and the cities, regions, 
businesses and investors that must act on climate change.
Details of the Paris Agreement, evaluation of the Kyoto Protocol, and 
other key multilateral developments since AR5 that are relevant to 
climate mitigation including the CORSIA aviation agreement adopted 
under ICAO, the IMO shipping strategy, and the Kigali Amendment to 
the Montreal Protocol on hydrofluorocarbons (HFCs), are discussed 
in Chapter 14.
SDGs. In September 2015, the UN endorsed a universal agenda –

‘Transforming our World: the 2030 Agenda for Sustainable 
Development’. The agenda adopted 17 non-legally-binding SDGs 
and 169 targets to support people, peace, prosperity, partnerships 
and the planet. While climate change is explicitly listed as SDG 13, 
the pursuit of the implementation of the UNFCCC is relevant for 
a number of other goals including SDG 7 (clean energy for all), SDG 
9  (sustainable industry), and SDG 11 (sustainable cities), SDG 12 
(responsible consumption and production) as well as those relating 
to life below water (SDG 14) and on land (SDG 15) (Biermann et al. 
2017). Mitigation actions could have multiple synergies and trade-
offs across the SDGs (Pradhan et al. 2017) (Chapter  17) and their 
net effects depend on the pace and magnitude of changes, the 
specific mitigation choices and the management of the transition. 
This suggests that mitigation must be pursued in the broader context 
of sustainable development as explained in Section 1.6.

Finance. The Paris Agreement’s finance goal (above) reflects 
a  broadened focus, beyond the costs of climate adaptation and 
mitigation, to recognising that a  structural shift towards low-
carbon climate-resilient development pathways requires large-scale 
investments that engage the wider financial system (Sections 15.1 
and 15.2.4). The SR1.5 report estimated that 1.5°C pathways would 
require increased investment of 0.5–1% of global GDP between now 
and 2050, which is up to 2.5% of global savings/investment over 
the period. For low- and middle-income countries, SDG-compatible

159
Introduction and Framing Chapter 11infrastructure investments in the most relevant sectors are estimated 
to be around 4–5% of their GDP , and ‘infrastructure investment 
paths compatible with full decarbonisation in the second half of 
the century need not cost more than more-polluting alternatives’ 
(Rozenberg and Fay 2019).
The parallel 2015 UN Addis Ababa Conference on Finance for 
Development, and its resulting Action Agenda, aims to ‘address 
the challenge of financing …  to end poverty and hunger, and to 
achieve sustainable development in its three dimensions through 
promoting inclusive economic growth, protecting the environment, 
and promoting social inclusion.’ The Conference recognises the 
significant potential of regional cooperation and provides a forum for 
discussing the solutions to common challenges faced by developing 
countries (Section 15.6.4).
Alongside this, private and blended climate finance is increasing

but is still short of projected requirements consistent with Paris 
Agreement goals (Section 15.3.2.1). The financing gap is particularly 
acute for adaptation projects, especially in vulnerable developing 
countries. From a  macro-regulatory perspective, there is growing 
recognition that substantial financial value may be at risk from 
changing regulation and technology in a low-carbon transition, with 
potential implications for global financial stability (Section 15.6.3). 
To date, the most significant governance development is the 
Financial Stability Board’s Task Force on Climate-related Financial 
Disclosures (TCFD) and its recommendations that investors and 
companies consider climate change risks in their strategies and 
capital allocation, so investors can make informed decisions (TCFD 
2018), welcomed by over 500 financial institutions and companies 
as signatories, albeit with patchy implementation (Sections  1.4. 
4 and 15.6.3).

Talanoa Dialogue and Just Transition. As mandated at Paris 
COP21 and launched at COP23, the ‘Talanoa Dialogue’ (UNFCCC 
2018a) emphasised holistic approaches across multiple economic 
sectors for climate change mitigation. At COP24 also, the Just 
Transition Silesia Declaration, focusing on the need to consider 
social aspects in designing policies for climate change mitigation 
was signed by 56 heads of state (UNFCCC 2018b). This underlined 
the importance of aiming for Just Transitions in reducing emissions, 
at the same time preserving livelihoods and managing economic 
risks for countries and communities that rely heavily on emissions-
intensive technologies for domestic growth (Markkanen and Anger-
Kraavi 2019), and for maintaining ecosystem integrity through 
nature-based solutions.
2 Indeed, cooling effects of anthropogenic aerosols (organic carbon, black carbon, sulphates, nitrates), which are also important components of local air pollution (Myhre

et al. 2013) (AR6 WGI SPM D1.7) may in global average be of similar magnitude to warming from methane at present. Mitigation which reduces such aerosol masking 
could thereby increase global temperatures, and reducing methane emissions would offset this much more rapidly than reducing CO 2 because of its relatively short lifetime, 
with the combined effects which could counterbalance each other (AR6 WGI SPM D1.7). Methane is thus particularly important in determining whether or when 1.5˚C is 
reached for example.
3 With some exclusions for countries which were very small or undergoing economic collapse: fossil-fuel-and industry (CO 2-FFI) emissions in 2018 were below 2008 levels in 
32 developed countries, but only in 24 when including other GHGs. Reductions were by less than 10% in half these countries. Data from Chapter 2: see Section 2.2.3, as

analysed in Lamb et al. (2021). An earlier study found 18 developed countries that had reduced CO 2-FFI emissions over 2005–2015 (Le Quéré et al. 2019). Decomposition 
analysis of national trends in Xia et al. (2021), identified 23 industrialised countries (UNFCCC Annex I) with CO 2-FFI emissions in 2017 lower than in 2000 (Figure 1.3), 
of which 22 had increased GDP over the period. The previously rising trend of ‘outsourced/embodied emissions’ associated with goods imported into developed countries 
peaked in 2006, but detailed data on this are only available for CO 2-FFI up to 2018 (Section 2.3). See Chapter 3 for reduction rates associated with 1.5°C and 2°C.1.3.2 Global and Regional Emissions
Global GHG emissions have continued to rise since AR5, though 
the average rate of emissions growth slowed, from 2.4% (from 
2000–2010) to 1.3% for 2010–2019 (Figure  1.1). After a  period 
of exceptionally rapid growth from 2000 as charted in AR5, global

fossil fuel- and industry-related (FFI) CO 2 emissions almost plateaued 
between 2014 and 2016 (while the global economy continued to 
expand (World Bank 2020), but increased again over 2017–19, the 
average annual growth rate for all GHGs since 2014 being around 
0.8% yr–1 (IPCC/EDGAR emissions database; see also Chapter  11, 
Figure 11.2)). Important driving factors include population and GDP 
growth, as illustrated in panels (b) and (c) of Figure 1.1 respectively. 
The pause in emissions growth reflected the interplay of strong energy 
efficiency improvements and low-carbon technology deployment, but 
these did not expand fast enough to offset the continued pressures 
for overall growth at global level (UNEP 2018a; IEA 2019a). However, 
since 2013/14, the decline in global emissions intensity (GHG/
GDP) has accelerated somewhat, and global emissions growth has 
averaged slightly slower than population growth (Figure 1.1d), which

if sustained would imply a peak of global CO 2 (GHG) emissions per 
capita, at about 5 tCO 2 per person (7 tCO 2-eq per person) respectively.
Due to its much shorter lifetime, methane has a  disproportionate 
impact on near-term temperature, and is estimated to account for 
almost a third of the warming observed to date (AR6 WGI SPM; AR6 
WGIII Chapter 2, Figure 2.4). Methane reductions could be particularly 
important in relation to near- and medium-term temperatures, 
including through counteracting the impact of reducing short-lived 
aerosol pollutants which have an average cooling effect.2
The land-use component of CO 2 emissions has different drivers and 
particularly large uncertainties (Figures 2.2 and 2.5), hence is shown 
separately. Also, compared to AR5, new evidence showed that the 
AFOLU CO 2 estimates by the global models assessed in this report 
are not necessarily comparable with national GHG inventories, 
due to different approaches to estimate the ‘anthropogenic’

CO2 sink. Possible ways to reconcile these discrepancies are 
discussed in Chapter 7.
Regional trends have varied. Emissions from most countries continued 
to grow, but in absolute terms, 32 countries reduced energy and 
industry CO 2 emissions for at least a decade, and 24 reduced overall 
GHG (CO 2-eq) emissions over the same period, but only half of them 
by more than 10% over the period in each case (Chapter 2).3 In total,

160
Chapter 1  Intr oduction and Framing1
012345678
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019Global population (billions)(b) Trends in global population
020406080100120140
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019Global Gross Domestic Product 
(trillion USD2017 international PPP) (c) Trends in global gross domestic product (GDP)
010203040506070GHG emissions (GtCO 2-eq yr–1)
Year(a) Trends in global greenhouse gas emissions
00.10.20.30.40.50.60.7
0123456789
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Emissions intensity (tCO2-eq USD–1)Per-capita emissions (tCO 2-eq per person)
Year(d) Trends of emissions per capita (columns) and per unit GDP (line)2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Emissions intensity 
(tCO 2-eq USD–1)F-gas CO 2-FFI
N2OCH4
CO2-LULUCF

Figure 1.1 | Global emission trends since 2000 by groups of gases: absolute, per capita, and intensity.  Note: shows CO 2 from fossil fuel combustion and industrial 
processes (FFI); CO 2 from agriculture, forestry and other land use (AFOLU); methane (CH 4); nitrous oxide (N 2O); fluorinated gases (F-gases). Gases reported in GtCO 2-eq 
converted based on AR6 global warming potentials with a 100-year time horizon (GWP100).

161
Introduction and Framing Chapter 11developed country emissions barely changed from 2010, whilst those 
from the rest of the world grew.Figure  1.2 shows the distribution of regional emissions (a) per 
capita and (b) per GDP based on purchasing power parity (GDP ppp) 
of different country groupings in 2019. Plotted against population 
and GDP respectively, the area of each block is proportional to the 
(a) Distribution of regional emissions (territorial, 2019): 
CO 2-FFI (bottom-bar above x-axis, darker), plus non-CO 2 
GHGs (top bar, lighter), plus CO 2-LULUCF (top-most or 
below-axis (negative) bars)
–50510152025
365
730
1095
1460
1826
2191
2556
2921
3287
3652
4017
4382
4748
5113
5478
5843
6209
6574
6939
7304North America: 12.1%
Eastern Europe and 
West-Central Asia: 6.1%
Middle East: 5.6%Australia, Japan and New Zealand: 3.5%
Eastern Asia: 
27.2%Latin America and 
Caribbean: 10.3%
South-East Asia 
and Paciﬁc: 9.2%
Europe: 
8.8%

Southern Asia: 8.4%Africa: 8.8%tonnes CO 2-eq per capita
Cumulative population in million(b) Distribution of regional emissions (territorial, 2019): 
below-axis (negative) bars)kgCO 2-eq per USD GDP PPP
Cumulative GDP PPP (constant 2017 international USD) in trillion USD–0.100.10.20.30.40.50.60.70.80.9
0 10 20 30 40 50 60 70 80 90 100 110 120North America: 
12.1%Eastern Europe and 
West Central Asia: 6.1%-Australia, Japan 
and New Zealand: 
3.5%Africa: 
8.8%Middle East: 5.6%
Europe: 8.8%Latin America and 
Southern Asia: 8.4%Eastern Asia: 
27.2%South-East Asia 
and Paciﬁc: 9.2% 
(c) Distribution of regional emissions (consumption-based
footprint, 2018): CO 2-FFI onlytonnes CO 2 per capita(d) Distribution of regional emissions (consumption-based
footprint, 2018): CO 2-FFI onlykgCO 2-eq per USD GDP PPP

Cumulative GDP PPP (constant 2017 international USD) in trillion USD0 10 20 30 40 50 60 70 80 90 100 110 12002468101214161820North America: 18.9%
West-Central Asia: 5.3%
Australia, Japan and 
New Zealand: 5.1%
29.1%Europe: 
14.3%Latin American and 
Caribbean: 5.4%
and Paciﬁc: 4.9%
Southern Asia: 8.2%
Africa: 3.1%365
730
1095
1460
1826
2191
2556
2921
3287
3652
4017
4382
4748
5113
5478
5843
6209
6574
6939
7304
Cumulative population in millionMiddle East: 5.6%
00.050.10.150.20.250.30.350.40.45
North 
America: 
18.9%Eastern Europe and 
West-Central Asia: 5.3% 
Australia, Japan 
5.1%Africa: 3.1%
Middle East: 5.6%
Europe: 14.3%Latin America and 
Southern Asia: 8.2%Eastern Asia: 
29.1%South-East Asia 
and Paciﬁc: 4.9% 
Figure 1.2 | Distribution of regional greenhouse gas (GHG) emissions for 10 broad global regions according to territorial accounting (panels (a) and (b),

GHG emissions) and consumption-based accounting (panels (c) and (d), CO 2-FFI emissions only). GHG emissions are categorised into: fossil fuel and industry 
(CO 2-FFI); land use, land-use change and forestry (CO 2-LULUCF); and other greenhouse gases (methane, nitrous oxide and F-gas – converted to 100-year global warming 
potentials). Per-capita GHGs for territorial (panel a) and CO 2-FFI emissions vs population for consumption-based accounting (panel c). Panels (b) and (d): GHG emissions 
per unit GDP ppp vs GDP ppp, weighted with purchasing power parity for territorial accounting (panel b), CO 2-FFI emissions per unit GDP ppp for consumption-based accounting 
(panel d). The area of the rectangles refers to the total emissions for each regional category, with the height capturing per-capita emissions (panels a and c) or emissions per

unit GDP ppp (panels (b) and (d)), and the width proportional to the population of the regions and GDP ppp. Emissions from international aviation and shipping (2.4% of the total 
GHG emissions) are not included.

162
Chapter 1  Intr oduction and Framing1region’s emissions. Compared to the equivalent presentations in 
2004 (AR4 WGIII Figure SPM.3) and 2010 (AR5 WGIII Figure  1.8), 
East Asia now forms substantially the biggest group, whilst at about 
8 tCO 2-FFI (/10 tCO 2-eq all GHGs) per person, its emissions per capita 
remain about half that of North America. In contrast, a third of the 
world’s population, in Southern Asia and Africa, emit on average 
under 2  (2.5 tCO 2-eq) per person, little more than in the previous 
assessments. Particularly for these regions, there continue to be 
substantial differences in GDP , life expectancy and other measures of 
well-being (Figure 1.6).
Emissions per unit GDP are much less diverse than per capita and 
have also converged significantly. Poorer countries tend to show 
higher energy/emissions per unit GDP partly because of higher 
reliance on basic industries, and this remains the case, though in 
general their energy/GDP has declined faster.

Many developed country regions are net importers of energy-
intensive goods, and emissions are affected by the accounting of such 
‘embodied emissions’. Panels (c) and (d) show results (only available 
for CO 2-FFI, to 2018) on the basis of consumption footprints which 
include emissions embodied in traded goods. This makes modest 
changes to the relative position of different regions (for further 
discussion see Section 2.3).
While extreme poverty has fallen in more than half of the world’s 
economies in recent years, nearly one fifth of countries faced poverty 
rates above 30% in 2015 (below USD1.90 a  day), reflecting large 
income inequality (Laborde Debucquet and Martin 2017; Rozenberg 
and Fay 2019). Diffenbaugh and Burke (2019) find that global 
warming already has increased global economic inequality, even 
if between-country inequalities have decreased over recent decades. 
The distributional implications between regional groups in the Shared

Socio-economic Pathways (SSPs) diverge according to the scenario 
(Frame et al. 2019).
4 Continually updated information on net zero commitments is available at https://www.zerotracker.net.An important recent development has been commitments by many 
countries, now covering a large majority of global emissions, to reach 
net zero CO 2 or greenhouse gas emissions (Chapter 3).4 Furthermore, 
globally, net zero targets (whether CO 2 or GHG) have been adopted 
by about 823 cities and 101 regions (Chapter 8).
1.3.3 Some Other Key Trends and Developments
The COVID-19 pandemic profoundly impacted economy and human 
society, globally and within countries. As detailed in Cross-Chapter 
Box  1  in this chapter, some of its impacts will be long-lasting, 
permanent even, and there are also lessons relevant to climate change. 
The direct impact on emissions projected for rest of this decade are 
modest, but the necessity for economic recovery packages creates

a central role for government-led investment, and may change the 
economic fundamentals involved for some years to come.
The COVID-19 aftermath consequently also changes the economic 
context for mitigation (Sections  15.2 and 15.4). Many traditional 
forms of economic analysis (expressed as general equilibrium) 
assume that available economic resources are fully employed, 
with limited scope for beneficial economic ‘multiplier effects’ of 
government-led investment. After COVID-19 however, no country is 
in this state. Very low interest rates amplify opportunities for large-
scale investments which could bring ‘economic multiplier’ benefits, 
especially if they help to build the industries and infrastructures for 
further clean growth (Hepburn et al. 2020). However, the capability 
to mobilise low-interest finance varies markedly across countries and 
large public debts – including bringing some developing countries

close to default – undermine both the political appetite and feasibility 
of large-scale clean investments. In practice the current orientation of 
COVID-19 recovery packages is very varied, pointing to a very mixed 
picture about whether or not countries are exploiting this opportunity 
(Cross-Chapter Box 1 in this chapter).
Cross-Chapter Box 1 | The COVID-19 Crisis: Lessons, Risks and Opportunities for Mitigation
Authors: Diana Ürge-Vorsatz (Hungary), Lilia Caiado Couto (Brazil), Felix Creutzig (Germany), Dipak Dasgupta (India), Michael 
Grubb (United Kingdom), Kirsten Halsnæs (Denmark), Şiir Kılkış (Turkey), Alexandre Köberle (Brazil/United Kingdom), Silvia Kreibiehl 
(Germany), Jan Christoph Minx (Germany), Peter Newman (Australia), Chukwumerije Okereke (Nigeria/United Kingdom)
The COVID-19 pandemic triggered the deepest global economic contraction as well as CO 2 emission reductions since the Second

World War (Le Quéré et al. 2020b), (AR6 WGI, Box  6.1) (Section  2.2.2.1). While emissions and most economies are expected to 
rebound in 2021–2022 (IEA 2021), some impacts of the pandemic (e.g., aspects of economy, finance and transport-related emission 
drivers) may last far longer. COVID-19 pushed more than 100 million people back into extreme poverty, and reversed progress towards 
some other SDGs including health, life expectancy and child literacy (UN DESA 2021). Health impacts and the consequences of deep 
economy-wide shocks may last many years even without significant future recurrence (Section 15.6.3). These changes, as well as the 
pandemic response actions, bring both important risks as well as opportunities for accelerating mitigation (Chapters 1, 5, 10 and 15).

163
Introduction and Framing Chapter 11Cross-Chapter Box 1 (continued)
Lessons. Important lessons can be drawn from the pandemic to climate change including the value of forward-looking risk management, 
the role of scientific assessment, preparatory action and international process and institutions (Chapter 5 and Section 1.3). There had 
been long-standing warnings of pandemic risks and precursors – with both pandemic and climate risks being identified by social 
scientists as ‘uncomfortable knowledge’ or ‘unknown knowns’, which tend to be marginalised in practical policy (Rayner 2012; 
Sarewitz 2020). This echoes long-standing climate literature on potential ‘high impact’ events, including those perceived as low 
probability (Dietz 2011; Weitzman 2011). The costs of preparatory action, mainly in those countries that had suffered from earlier 
pandemics were negligible in comparison, suggesting the importance not just of knowledge but its effective communication and

embodiment in society (Chapter 5). Klenert et al. (2020) offer five early lessons for climate policy, concerning: the cost of delay; the bias 
in human judgement; the inequality of impacts; the need for multiple forms of international cooperation; and finally, ‘transparency in 
value judgements at the science–policy interface’.
Emissions and behavioural changes. Overall, global CO 2 FFI emissions declined by about 5.8% (5.1–6.3%) from 2019 to 2020, or 
about 2.2 (1.8–2.4) GtCO 2 in total (Section 2.2.2). Analysis from previous economic crises suggest significant rebound in emissions 
without policy-induced structural shifts (Jaeger et al. 2020) (Section 2.2.2.1 and Figure 2.5). Initial projections suggest the COVID 
aftermath may reduce emissions by 4–5% over 2025–2030 (Shan and Et.al 2020; Reilly et al. 2021), below a ‘no-pandemic’ baseline. 
The long-term impacts on behaviour, technology and associated emissions remain to be seen, but may be particularly significant in

transport – lockdowns reduced mobility-related emissions, alongside two major growth areas: electronic communications replacing 
many work and personal travel requirements (Chapter 10 and Section 4.4.3.4); and revitalised local active transport and e-micromobility 
(Earley and Newman 2021). Temporary ‘clear skies’ may also have raised awareness of the potential environment and health 
co-benefits of reduced fossil fuel use particularly in urban areas (Section 8.7), with evidence also indicating that air pollution itself 
amplified vulnerability to COVID-19 (Gudka et al. 2020; Wu et al. 2020). The significant impacts on passenger aviation are projected 
to extend not just through behavioural changes, but also fleet changes from retiring older planes, and reduced new orders indicating 
expectations of reduced demand and associated GHG emissions until 2030 (Sections 5.1.2 and 10.5) (AR6 WGI Box 6.1 in Chapter 6).

However, air cargo has recovered more rapidly (IATA 2020), possibly enhanced by online ordering.
Fiscal, growth and inequality impacts. Aspects of the global and regional economic crises from COVID-19 may prevail much 
longer than the crisis itself, potentially compromising mitigation. Most countries have undertaken unprecedented levels of short-term 
public expenditures. The International Monetary Fund (IMF) projects sovereign debt to GDP to have increased by 20% in advanced 
economies and 10% in emerging economies by the end of 2021 (IMF 2020). This is likely to slow economic growth, and may squeeze 
financial resources for mitigation and relevant investments for many years to come (Sections 15.2.3 and 15.6.3). COVID-19 further 
lowered interest rates which should facilitate low-carbon investment, but pandemic responses have increased sovereign debt across

countries in all income bands (IMF 2021), and, particularly in some developing economies and regions, it has caused debt distress 
(Bulow et al. 2021), widening the gap in developing countries’ access to capital (Hourcade et al. 2021b) (Section 15.6.3). After decades 
of global progress in reducing poverty, COVID-19 has pushed hundreds of millions of people below poverty thresholds and raises the 
spectre of intersecting health and climate crises that are devastating for the most vulnerable (Section 5.1.2 and Box 5.1). Like those 
of climate change, pandemic impacts fall heavily on disadvantaged groups, exacerbate the uneven distribution of future benefits, 
amplify existing inequities, and introduce new ones. Increased poverty also hinders efforts towards sustainable low-carbon transitions 
(Section 1.6).
Impacts on profitability and investment. COVID-19-induced demand reduction in electricity disproportionally affected coal power

plants, whilst transport reduction most affected oil (IEA 2020a). This accelerated pre-existing decline in the relative profitability of 
most fossil fuel industries (Ameli et al. 2021). Renewables were the only energy sector to increase output (IEA 2020a). Within the 
context of a wider overall reduction in energy investment this prompted a substantial relative shift towards low-carbon investment 
particularly by the private sector (IEA 2020b; Rosembloom and Markard 2020) (Sections 15.2.1, 15.3.1 and 15.6.1).
Post-pandemic recovery pathways provide an opportunity to attract finance into accelerated and transformative low-
carbon public investment (Sections 15.2 and 15.6.3). In most countries, COVID-19 has increased unemployment and/or state-
supported employment. There is a profound difference between short-term ‘bail outs’ to stem unemployment, and the orientation of

new public investment. The public debt is mirrored by large pools of private capital. During deep crises like that of COVID-19, economic 
multipliers of stimulus packages can be high (Hepburn et al., 2020), so much so that fiscal injections can then generate multipliers 
from 1.5 to 2.5, weakening the alleged crowding-out effect of public stimulus  (Auerbach and Gorodnichenko 2012; Blanchard and 
Leigh 2013) (Section 15.2.3).

164
Chapter 1  Intr oduction and Framing1Cross-Chapter Box 1 (continued)
Recovery packages are motivated by assessments of the macroeconomic effectiveness (‘multipliers’) of public spending in ways that 
can crowd-in and revive private investment (Hepburn et al. 2020). There are clear reasons why a low-carbon response can create more 
enduring jobs, better aligned to future growth sectors: by also crowding-in and reviving private investment (e.g., from capital markets 
and institutional investors, including the growing profile of environmental, social and governance (ESG) and green bond markets 
(Section 15.6)), this can boost the effectiveness of public spending (IMF 2020). Stern and Valero (2021) argue that investment in low-
carbon innovation and its diffusion, complemented by investments in sustainable infrastructure, are key to shaping environmentally 
sustainable and inclusive growth in the aftermath of the COVID-19 pandemic crisis. This would be the case both for high-income

economies on the global innovation frontier, and to promote sustainable development in poorer economies.
A study with a global general equilibrium model (Liu et al. 2021) finds that because the COVID-19 economic aftermath combines 
negative impacts on employment and consumption, a shift from employment and consumption taxes to carbon- or other resource-
related taxes would enhance GDP by 1.7% in 2021 relative to ‘no policy’, in addition to reducing CO 2 and other pollutants. A post-
Keynesian model of wider ‘green recovery’ policies (Pollitt et al. 2021) finds a short-run benefit of around 3.5% GDP (compared to 
‘no policy’), and even about 1% above a recovery boosted by cuts in consumption taxes, the latter benefit sustained through 2030 – 
outperforming an equivalent conventional stimulus package while reducing global CO 2 emissions by 12%.
Orientation of recovery packages. The large public spending on supporting or stimulating economies, exceeding USD12 trillion

by October 2020, dwarfs clean-energy investment needs and hence could either help to solve the combined crises, or result in high-
carbon lock-in (Andrijevic et al. 2020). The short-term ‘bail outs’ to date do not foster climate-resilient long-term investments and have 
not been much linked to climate action, (Sections 15.2.3 and 15.6.3): in the G20 counties, 40% of energy-related support spending 
went to the fossil fuel industry compared to 37% on low-carbon energy (EPT 2020). Recovery packages are also at risk of being 
‘colourless’ (Hepburn et al., 2020), though some countries and regions have prioritised green stimulus expenditures for example as 
part of a ‘Green New Deal’ (Rochedo et al. 2021) (Sections 13.9.6 and 15.6.3).
Integrating analyses. The response to COVID-19 also reflects the relevance of combining multiple analytic frameworks spanning

economic efficiency, ethics and equity, transformation dynamics, and psychological and political analyses (Section 1.7). As with climate 
impacts, not only has the global burden of disease been distributed unevenly, but capabilities to prevent and treat disease were 
asymmetrical and those in greatest vulnerability often had the least access to human, physical, and financial resources (Ruger and 
Horton 2020). ‘Green’ versus ‘brown’ recovery has corresponding distributional consequences between these and ‘green’ producers, 
suggesting need for differentiated policies with international coordination (Le Billon et al. 2021). This illustrates the role of Just 
Transition approaches to global responses including the value of integrated, multi-level governance (Sections 1.7, 4.5 and 17.1).
Crises and opportunities: the wider context for mitigation and transformation. The impacts of COVID-19 have been

devastating in many ways, in many countries, and may distract political and financial capacity away from efforts to mitigate climate 
change. Yet, studies of previous post-shock periods suggest that waves of innovation that are ready to emerge can be accelerated 
by crises, which may prompt new behaviours, weaken incumbent (‘meso-level’) systems, and prompt rapid reforms (Roberts and 
Geels 2019a) (Section 1.6.5). Lessons from the collective effort to ‘flatten the curve’ during the pandemic, illustrating aspects of 
science–society interactions for public health in many countries, may carry over to climate mitigation, and open new opportunities 
(Section 5.1.2). COVID-19 appears to have accelerated the emergence of renewable power, electromobility and digitalisation (Newman 
2020) (Sections 5.1.2, 6.3 and 10.2). Institutional change is often very slow but major economic dislocation can create significant

opportunities for new ways of financing and enabling ‘leapfrogging’ investment to happen (Section 10.8). Given the unambiguous 
risks of climate change, and consequent stranded asset risks from new fossil fuel investments (Box 6.11), the most robust recoveries 
are likely to be those which emerge on lower carbon and resilient pathways (Obergassel et al. 2021). Noting the critical global post-
COVID-19 challenge as the double impact of heightened credit risk in developing countries, along with indebtedness in developed 
countries, Hourcade et al. (2021a) estimate that a ‘multilateral’ sovereign guarantee structure to underwrite low-carbon investments 
could leverage projects up to 15 times its value, contributing to shifting development pathways consistent with the SDGs and Paris goals.
COVID- 19 can thus be taken as a reminder of the urgency of addressing climate change, a warning of the risk of future stranded assets

(Rempel and Gupta 2021) (Chapter 17), but also an opportunity for a cleaner recovery.

165
Introduction and Framing Chapter 11In addition to developments in climate science, emissions, the 
international agreements in 2015, and the recent impact of 
COVID-19, a few other key developments have strong implications 
for climate mitigation.
Cheaper renewable energy technologies. Most striking, the cost 
of solar photovoltaic (PV) has fallen by a  factor of 5  to 10 in the 
decade since the IPCC Special Report on Renewable Energy (IPCC 
2011a) and other data inputting to the AR5 assessments. The SR1.5 
reported major cost reductions, the IEA (2020) World Energy Outlook 
described PV as now ‘the cheapest electricity in history’ for projects 
that ‘tap low cost finance and high quality resources.’ Costs and 
deployment both vary widely between different countries (Chapters 6, 
9 and 12) but costs are still projected to continue falling (Vartiainen 
et al. 2020). Rapid technological developments have occurred in

many other low-carbon technologies including batteries and electric 
vehicles (Section 1.4.3), IT and related control systems, with progress 
also where electrification is not possible (Chapters 2, 6 and 11).
Civil society pressures for stronger action. Civic engagement 
increased leading up to the Paris Agreement (Bäckstrand and 
Lövbrand 2019) and after. Youth movements in several countries 
show young people’s awareness about climate change, evidenced 
by the school strikes for the climate (Hagedorn et al. 2019; Buettner 
2020; Thackeray et al. 2020; Walker 2020). Senior figures across many 
religions (Francis 2015; IFEES 2015) stressed the duty of humanity 
to protect future generations and the natural world, and warned 
about the inequities of climate change. Growing awareness of local 
environmental problems such as air pollution in Asia and Africa 
(Karlsson et al. 2020), and the threat to indigenous people’s rights

and existence has also fuelled climate activism (Etchart 2017). Grass-
roots movements (Cheon and Urpelainen 2018; Fisher et al. 2019), 
build political pressure for accelerating climate change mitigation, 
as does increasing climate litigation (Setzer and Vanhala 2019) 
(Chapters 13 and 14).
Climate policies also encounter resistance. However, there 
are multiple sources of resistance to climate action in practice. 
Corporations and trade associations often lobby against measures 
they deem detrimental (Section 1.4.6). The emblematic ‘yellow vest’ 
movement in France was triggered by higher fuel costs as a result of 
a CO 2 tax hike (Lianos 2019; Driscoll 2021), though it had broader 
aspect of income inequality and other social issues. There is often 
a  mismatch between concerns on climate change and people’s 
willingness to pay for mitigation. For example, whilst most Americans 
believe climate change is happening, 68% said in a  survey they

would oppose climate policies that added just USD10 per month 
to electricity bills (EPIC et al. 2019), and worry about energy costs 
can eclipse those about climate change elsewhere (Poortinga et al. 
2018) (Chapter 13).
Global trends contrary to multilateral cooperation. State-
centred politics and geopolitical/geo-economic tensions seem to 
have become more prominent across many countries and issues (WEF 
2019). In some cases, multilateral cooperation could be threatened 
by trends such as rising populism, nationalism, authoritarianism 
and growing protectionism (Abrahamsen et al. 2019), making it more difficult to tackle global challenges including protecting the 
environment (Schreurs 2016; Parker et al. 2017; WEF 2019).
Transnational alliances. Partly countering this trend, cities, 
businesses and a  wide range of other non-state actors also have 
emerged with important international networks to foster mitigation.

City-based examples include the Cities Alliance in addressing climate 
change, Carbon Neutral Cities Alliance and the Covenant of Mayors 
(Chapter 8); there are numerous other alliances and networks such as 
those in finance (Chapter 15) and technology (Chapter 16), amongst 
many others (Chapters 13 and 14).
Finally, under the Paris Agreement process, during 2020/21, many 
countries strengthened their Nationally Determined Contributions 
(NDCs). Including updates until October 2021, these would imply 
global GHG emissions declining by 2030 to between 1–4% below 
2019 levels (unconditional NDCs), or 4–10% (for NDCs conditional 
on international support) (Table  4.3). This is a  significant change 
but would still not be compatible with 1.5°C pathways, and even if 
delivered in full, to limit warming to 2°C (>67%), emissions would 
have to fall very rapidly after 2030 (Section 3.2.5).
Thus, developments since AR5 highlight the complexity of the

mitigation challenge. There is no far-sighted, globally optimising 
decision-maker and indeed climate policymaking at all levels is 
subject to conflicting pressures in multiple ways. The next section 
overviews the drivers and constraints.
1.4 Drivers and Constraints of Climate 
Mitigation and System Transitions/
This section provides a brief assessment of key factors and dynamics 
that drive, shape and/or limit climate mitigation in (i) economic 
factors: which include sectors and services, trade and leakage, 
finance and investment, and technological innovation; (ii) socio-
political issues: which include political economy, social innovation, 
and equity and fairness; and (iii) institutional factors, which 
comprise policy, legal frameworks and international cooperation.
The AR5 introduced six ‘enabling conditions’ for shifting development 
pathways which are presented in Chapter 4 of this report and some

of which overlap with the drivers reviewed here. However, the 
terminology of drivers and constraints have been chosen here to 
reflect the fact that each of these factors can serve as an enabling 
condition or a constraint to ambitious climate action depending on 
the context and how they are deployed. Often one sees the factors 
exerting both push and pull forces at the same time in the same 
and across different scales. For example, finance and investments can 
serve as a barrier or an enabler to climate action (Battiston et al. 2021). 
Similarly, political economy factors can align in favour of ambitious 
climate action or act in ways that inhibit strong cooperation and 
low-carbon transition. The other key insight from the assessment of 
the  system drivers and constraints undertaken below is that none 
of the factors or conditions by themselves is more or less important 
than the others. In addition to being deeply intertwined all the factors

166
Chapter 1  Intr oduction and Framing1matter in different measures with each exacting more or less force 
depending on the prevailing social, economic, cultural and political 
context. Often achieving accelerated mitigation would require effort 
to bring several of the factors in alignment in and across multiple 
levels of political or governance scales.
1.4.1 Services, Sectors and Urbanisation
Human activities drive emissions primarily through the demand 
for a wide range of services such as food, shelter, heating/cooling, 
goods, travel, communication, and entertainment. This demand 
is fulfilled by various activities often grouped into sectors such as 
agriculture, industry and commerce. The literature uses a wide range 
of sectoral definitions to organise data and analysis (Chapter  2). 
Energy sectors are typically organised into primary energy producers, 
energy transformation processes (such as power generation and fuel

refining), and major energy users such as buildings, industry and 
transport (Chapters 2 and 5). Other research (Chapter 8) organises 
data around interacting urban and rural human activities. Land-
based activities can be organised into agriculture, forestry and 
other land-use (AFOLU), or land use, land-use change and forestry 
(LULUCF) (Chapter 7). Each set of sectoral definitions and analysis 
offers its own insights.
Sectoral perspectives help to identify and understand the drivers of 
emissions, opportunities for emissions mitigation, and interactions 
with resources, other goals and other sectors, including the 
co-evolution of systems across scales (Kyle et al. 2016; Moss et al. 
2016; Mori et al. 2017; IPBES 2019). Interactions between sectors 
and agents pursuing multiple goals is a  major theme pervading 
this assessment.
The ‘nexus’ between energy, water, and land – all key contributors 
to human well-being – also helps to provide, regulate and support

ecosystem and cultural services (Bazilian et al. 2011; Ringler et al. 
2013; Smajgl et al. 2016; Albrecht et al. 2018; Brouwer et al. 2018; 
D’Odorico et al. 2018; Van Vuuren et al. 2019), with important 
implications for cities in managing new systems of transformation 
(Thornbush et al. 2013; Wolfram et al. 2016) (Chapter  8).  Other 
important nexuses shaping our planet’s future (Fajardy et al. 2018) 
include agriculture, forestry, land use and ecosystem services 
(Chazdon 2008; Settele et al. 2016; Torralba et al. 2016; Nesshöver 
et al. 2017; Keesstra et al. 2018).
Historically, energy-related GHG emissions were considered 
a  by-product of the increasing scale of human activity, driven by 
population size, economic activity and technology. That simple notion 
has evolved greatly over time to become much more complex and 
diverse, with increasing focus on the provision of energy services 
(Cullen and Allwood 2010; Bardi et al. 2019; Brockway et al. 2019;

Garrett et al. 2020). The demand for agricultural products has 
historically driven conversion of natural lands (land-use change). 
AFOLU along with food processing accounts for 21–37% of total net 
anthropogenic GHG emissions (SRCCL SPM A3).5
5 AFOLU accounted for about 13% of CO 2, 44% of CH 4 and 82% of N 2O global anthropogenic GHG emissions in 2007–2016.Continued growth in population and income are expected to 
continue driving up demand for goods and services (Chapters  2, 
3 and 5), with an important role for urbanisation which is proceeding 
at an unprecedented speed and scale. In the last decade, the urban 
population grew by 70 million people each year, or about 1.3 million 
people per week, with urban area expanding by about 102 km2 per 
day (Chapter  8). Urban areas account for most (45–87%) of the 
global carbon footprint (8.1) and the strong and positive correlation 
between urbanisation and incomes means higher consumption

from urban lifestyles will continue driving direct and indirect GHG 
emissions. Cities provide a conduit to many of the services such as 
transportation, housing, water, food, medical care and recreation, 
and other services and urban carbon emissions are driven not only 
by population and income but also by the form and structure of 
urban areas (Sections  8.1 and 8.3–8.6). This creates opportunities 
for decarbonisation through urban planning and purposeful 
‘experimentation’ (Newman et al. 2017) (Chapter 8).
Human needs and wants evolve over time making the transition 
toward climate and sustainable development goals either more 
or less difficult. For example, changes in the composition of goods 
consumed, such as shifting diets toward a more vegetarian balance, 
can reduce land-use emissions without compromising the quality of 
life (Stehfest et al. 2009; Gough 2017; van Vuuren et al. 2018; van den 
Berg et al. 2019; Hargreaves et al. 2021; SRCCL SPM B2.3).

Human behaviour and choices, including joint achievement of wider 
social goals, will play an important part in enabling or hindering 
climate mitigation and sustainable development (Shi et al. 2016), 
for example, shifting passenger transportation preferences in ways 
that combine climate, health and sustainable development goals 
(Romanello et al. 2021).
1.4.2 Trade, Consumption and Leakage
Emissions associated with international trade account for 20–33 % of 
global emissions, as calculated using multi-regional input-output 
analysis (Wiedmann and Lenzen 2018). Whether international trade 
drives an increase or decrease in global GHG emissions depends on 
the emissions intensity of traded products as well as the influence 
of trade on relocation of production, with studies reaching diverse 
conclusions about the net effect of trade openness on CO 2 emissions 
(Section  2.4.5). Tariff reduction of low-carbon technologies could

facilitate effective mitigation (de Melo and Vijil 2014; Ertugrul et al. 
2016; Islam et al. 2016; WTO 2016).
The magnitude of carbon leakage (see Glossary) caused by unilateral 
mitigation in a fragmented climate policy world depends on trade 
and substitution patterns of fossil fuels and the design of policies 
(IPCC 2014a, Box 5.4), but its potential significance in trade-exposed 
energy-intensive sectors (Bauer et al. 2013; Carbone and Rivers 2017; 
Naegele and Zaklan 2019) can make it an important constraint on 
policy. See Section 13.6.6.1 in Chapter 13 for channels and evidence. 
Akimoto et al. (2018) argue that differences in marginal abatement

167
Introduction and Framing Chapter 11costs of NDCs could cause carbon leakage in energy-intensive, trade-
exposed sectors, and could weaken effective global mitigation.
Policy responses to cope with carbon leakage include border carbon 
adjustment (BCAs) and differentiated carbon taxes (Liu et al. 
2020). Some BCA options focusing on levelling the cost of carbon 
paid by consumers on products could be designed in line with the 
WTO (Ismer et al. 2016), while others may not be (Mehling et al. 
2019). All proposals could involve difficulty of tracing and verifying 
the carbon content of inputs (Onder 2012; Denis-Ryan et al. 2016). 
An international consensus and certification practice on the carbon 
content would help to overcome WTO compatibility (Holzer 2014). 
See Chapter 13 and Mehling et al. (2019) on the context of trade 
law and the PA.
Official inventories report territorial emissions, which do not 
consider the impacts embodied in imports of goods. Global supply

chains undoubtedly lead to a  growth in trade volumes (Federico 
and Tena-Junguito 2017), alternative methods have been suggested 
to account for emissions associated with international trade, such 
as shared responsibility (Lenzen et al. 2007), technology-adjusted 
consumption-based accounting (Kander et al. 2015), value-
added-based responsibility (Piñero et al. 2019) and exergy-based 
responsibility based on thermodynamics (Khajehpour et al. 2019). 
Consumption-based emissions (i.e., attribution of emissions related 
to domestic consumption and imports to final destination) are not 
officially reported in global emissions datasets but data has improved 
(Tukker and Dietzenbacher 2013; Afionis et al. 2017). This analysis 
has been used extensively for consumption-based accounting of 
emissions, and other environmental impacts (Wiedmann and Lenzen 
2018; Malik et al. 2019) (Section 2.3).
Increasing international trade has resulted in a general shifting of

fossil fuel-driven emissions-intensive production from developed 
to developing countries (Arto and Dietzenbacher 2014; Malik and 
Lan 2016), and between developing countries (Zhang et al. 2019). 
High-income developed countries thus tend to be net importers of 
emissions, whereas low/middle-income developing countries net 
exporters (Peters et al. 2011) (Figure 1.2c, d). This trend is shifting, 
with a  growth in trade between non-OECD countries (Meng et al. 
2018; Zhang et al. 2019), and a  decline in emissions intensity of 
traded goods (Wood et al. 2020b).
The Paris Agreement primarily deals with national commitments 
relating to domestic emissions and removals, hence emissions 
from international aviation and shipping are not covered. Aviation 
and shipping accounted for approximately 2.7% of greenhouse 
gas emissions in 2019 (before COVID-19); see Section  10.5.2 for 
discussion. In addition to CO 2 emissions, aircraft-produced contrail

cirrus clouds, and emissions of black carbon and short-lived aerosols 
(e.g., sulphates) from shipping are especially harmful for the Arctic 
(Section 10.8 and Box 10.6).1.4.3 Technology
The rapid developments in technology over the past decade enhance 
potential for transformative changes, in particular to help deliver 
climate goals simultaneously with other SDGs.
The fall in renewable energy costs alongside rapid growth in capacity 
(Figure  1.3; see also Figures  6.8 and 6.11 in Chapter  6) has been 
accompanied by varied progress in many other technology areas 
such as electric vehicles, fuel cells for both stationary and mobile 
applications (Dodds 2019), thermal energy (Chapter 6), and battery 
and other storage technologies (Freeman et al. 2017) (Chapters 6, 
9 and 12; Figure TS.7). Nuclear contributions may be enhanced by 
new generations of reactors (e.g., Generation III) and small modular 
reactors (Knapp and Pevec 2018) (Chapter 6).

Large-scale hydrogen developments could provide a complementary 
energy channel with long-term storage. Like electricity, hydrogen 
(H2) is an energy vector with multiple potential applications, 
including in industrial processes such as steel and non-metallic 
materials production (Chapter  11), for long-range transportation 
(Chapter 10), and low-temperature heating in buildings (Chapter 9). 
Emissions depend on how it is produced, and deploying H 2 delivery 
infrastructure economically is a challenge when the future scale of 
hydrogen demand is so uncertain (Chapter 6). H 2 from natural gas 
with CO 2 capture and storage (CCS) may help to kick-start the H 2 
economy (Sunny et al. 2020).
CO2-based fuels and feedstocks such as synthetic methane, methanol, 
diesel, jet fuel and other hydrocarbons, potentially from carbon 
capture and utilisation (CCU), represent drop-in solutions with limited 
new infrastructure needs (Artz et al. 2018; Bobeck et al. 2019; Yugo

and Soler 2019) (Chapter 10). Deployment and development of CCS 
technologies (with large-scale storage of captured CO 2) have been 
much slower than projected in previous assessments (IEA 2019b; 
Page et al. 2019) (Chapter 11).
Potential constraints on new energy technologies may include their 
material requirements, notably rare earth materials for electronics 
or lithium for batteries (Wanger 2011; Flexer et al. 2018), stressing 
the importance of recycling (IPCC 2011b; Rosendahl and Rubiano 
2019). Innovation is enabling greater recycling and reuse of energy-
intensive materials (Shemi et al. 2018), and introducing radically new 
and more environmentally friendly materials, however, still not all 
materials can be recycled (Allwood 2014).
By sequestering carbon in biomass and soils, soil carbon 
management, and other terrestrial strategies could offset hard-to-
reduce emissions in other sectors. However, large-scale bioenergy

deployment could increase risks of desertification, land degradation, 
and food insecurity (IPCC 2019a), and higher water withdrawals 
(Hasegawa et al. 2018; Fuhrman et al. 2020), though this may be 
at least partially offset by innovation in agriculture, diet shifts and 
plant-based proteins contributing to meeting demand for food, feed, 
fibre and bioenergy (or bioenergy with carbon capture and storage 
(BECCS) with CCS) (Havlik et al. 2014; Popp et al. 2017; Köberle et al. 
2020) (Chapters 5 and 7).

168
A broad class of more speculative technologies propose to counteract 
effects of climate change by removing CO 2 from the atmosphere 
(CDR), or by directly modifying the Earth’s energy balance at a large 
scale (solar radiation modification or SRM). CDR technologies 
include ocean iron fertilisation, enhanced weathering and ocean 
alkalinisation (Council 2015a), along with direct air carbon capture 
and storage (DACCS). They could potentially draw down atmospheric 
CO2 much faster than the Earth’s natural carbon cycle, and reduce 
reliance on biomass-based removal (Köberle 2019; Realmonte et al. 
2019), but some present novel risks to the environment and DACCS 
is currently more expensive than most other forms of mitigation (Fuss 
et al. 2018) (Cross-Chapter Box  8  in Chapter  12). Solar radiation 
modification (SRM) could potentially cool the planet rapidly at low 
estimated direct costs by reflecting incoming sunlight (Council 2015b),

but entails uncertain side effects and thorny international equity and 
governance challenges (Netra et al. 2018; Florin et al. 2020; National 
Academies of Sciences 2021) (Chapter 14). Understanding the climate 
response to SRM remains subject to large uncertainties (AR6 WGI). 
Some literature uses the term ‘geoengineering’ for both CDR or SRM 
when applied at a planetary scale (Shepherd 2009; GESAMP 2019). 
In this report, CDR and SRM are discussed separately, reflecting their 
very different geophysical characteristics.
Large improvements in information storage, processing, and 
communication technologies, including artificial intelligence, will 
affect emissions. They can enhance energy-efficient control, reduce 
transaction costs for energy production and distribution, improve 
demand-side management (DSM) (Raza and Khosravi 2015), 
and reduce the need for physical transport (Smidfelt Rosqvist and 
Winslott Hiselius 2016) (Chapters  5, 6  and 9–11). However, data

centres and related IT systems (including blockchain), are electricity-intensive and will raise demand for energy (Avgerinou et al. 2017) – 
cryptocurrencies may be a major global source of CO 2 if the electricity 
production is not decarbonised (Mora et al. 2018)  – and there is 
also a  concern that Information technologies can compound and 
exacerbate current inequalities (Chapters  5, 16 and Cross-Chapter 
Box 11 in Chapter 16). IT may affect broader patterns of work and 
leisure (Boppart and Krusell 2020), and the emissions intensity of 
how people spend their leisure time will become more important 
(Chapters  5 and 9). Because higher efficiency tends to reduces 
costs, it often involves some ‘rebound’ offsetting at least some of 
the emission savings (Sudbury and Hutchinson 2016; Belkhir and 
Elmeligi 2018; Cohen and Cavoli 2019).
Technology can enable both emissions reductions and/or increased 
emissions (Chapter 16). Governments play an important role in most

major innovations, in both ‘technology-push’ (Mazzucato 2013) and 
induced by ‘demand-pull’ (Grubb et al. 2021a), so policy is important 
in determining its pace, direction and utilisation (Roberts and Geels 
2019a) (Sections 1.7.1 and 1.7.3). Overall, the challenge will be to 
enhance the synergies and minimise the trade-offs and rebounds, 
including taking account of ethical and distributional dimensions 
(Gonella et al. 2019).
1.4.4 Finance and Investment
Finance is both an enabler and a constraint on mitigation, and since 
AR5, attention to the financial sector’s role in mitigation has grown. 
This is partly in the context of the Paris Agreement finance articles and 
the Green Climate Fund, the pledge to mobilise USD100 billion yr–1  
by 2020, and the Addis Abbaba Action Agenda (Section  1.3.1). Fossil fuel cost range
Solar PV capacity
Solar PV LCOE
0100200300400500600700
050100150200250300350400
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023

Electricity generation (GW)LCOE (USD2019 MWh–1)Wind energy
Fossil fuel cost range
Offshore wind installed capacityOffshore wind LCOE
Onshore wind LCOE
Auctioned offshore
wind priceOnshore wind installed capacity
 2010
 2011
 2012
 2013
 2014
 2015
 2016
 2017
 2018
 2019
Electricity generation (GW)LCOE (USD2019 MWh–1)Solar photovoltaic
Figure 1.3 | Cost reductions and adoption in solar photovoltaic and wind energy.  Fossil fuel Levelised Cost of Electricity (LCOE) is indicated by blue shading at 
USD50–177 MWh–1 (IRENA 2020b). Source: data from IRENA (2021a,b).

169
Introduction and Framing Chapter 11However, there is a  persistent but uncertain gap in mitigation 
finance (Cui and Huang 2018) (Table 15.15.1), even though tracked 
climate finance overwhelmingly goes toward mitigation compared 
to adaptation (UNEP 2020) (Section 15.3; Working Group II). Green 
bond issuance has increased recently in parallel with efforts to reform 
the international financial system by supporting development of local 
capital markets (Section 15.6.4).
Climate finance is a multi-actor, multi-objective domain that includes 
central banks, commercial banks, asset managers, underwriters, 
development banks, and corporate planners. Climate change 
presents both risks and opportunities for the financial sector. The 
risks include physical risks related to the impacts of climate change 
itself; transition risks related to the exposure to policy, technology 
and behavioural changes in line with a low-carbon transition; and

liability risks from litigation for climate-related damages (Box 15.2). 
These could potentially lead to stranded assets (the loss of economic 
value of existing assets before the end of their useful lifetimes (Bos 
and Gupta 2019) (Sections 6.7 and 15.6.3). Such risks continue to 
be underestimated by financial institutions (Section  15.6.1). The 
continuing expansion of fossil fuel infrastructure and insufficient 
transparency on how these are valued raises concerns that systemic 
risk may be accumulating in the financial sector in relation to 
a  potential low-carbon transition that may already be under way 
(Battiston et al. 2017) (Section  15.6.3). The Financial Stability 
Board’s Taskforce on Climate-related Financial Disclosures’ (TCFD) 
recommendations on  transparency aim to ensure that investors 
and companies consider climate change risks in their strategies and 
capital allocation (TCFD 2018). This is helping ‘investors to reassess

core assumptions’ and may lead to ‘significant’ capital reallocation 
(Fink 2020). However, metrics and indicators of assets risk exposure 
are inadequate (Monasterolo 2017; Campiglio et al. 2018) and 
transparency alone is insufficient to drive the required asset 
reallocation in the absence of clear regulatory frameworks (Ameli 
et al. 2020; Chenet et al. 2021). A  coalition of central banks have 
formed the Network for Greening the Financial Sector, to support 
and advance the transformation of the financial system (Allen et al. 
2020; NGFS 2020), with some of them conducting climate-related 
institutional stress tests.
Governments cannot single-handedly fund the transition 
(Section  15.6.7), least of all in low-income developing countries 
with large sovereign debt and poor access to global financial 
markets. Long-term sources of private capital are required to close 
the financing gap across sectors and geographies (Section 15.6.7).

Future investment needs are greatest in emerging and developing 
economies (Section 15.5.2) which already face higher costs of capital, 
hindering capacity to finance a  transition (Buhr et al. 2018; Ameli 
et al. 2020). Requisite North–South financial flows are impeded by 
both geographic and technological risk premiums (Iyer et al. 2015), 
and the COVID-19 pandemic has further compromised the ability 
of developing and emerging economies to finance development 
activities or attract additional climate finance from developed 
countries (Section 15.6.3, and Cross-Chapter Box 1 in this chapter). 
Climate-related investments in developing countries also suffer from 
structural barriers such as sovereign risk and exchange rate volatility 
(Farooquee and Shrimali 2016; Guzman et al. 2018) which affect not only climate-related investment but investment in general (Yamahaki 
et al. 2020) including in needed infrastructure development (Gray and

Irwin 2003). A Green Climate Fund (GCF) report notes the paradox 
that USD14 trillion of negative-yielding debt in OECD countries might 
be expected to flow to much larger low-carbon, climate-resilient 
investment opportunities in developing countries, but ‘this is not 
happening’ (Hourcade et al. 2021b).
There is often a  disconnect between stated national climate 
ambition and finance flows, and overseas direct investment (ODI) 
from donor countries may be at odds with national climate pledges 
such as NDCs. One report found funds supported by foreign state-
owned enterprises into 56 recipient countries in Asia and Africa in 
2014–2017 went mostly to fossil fuel-based projects not strongly 
aligned with low-carbon priorities of recipient countries’ NDCs (Zhou 
et al. 2018). Similarly, Steffen and Schmidt (2019) found that even 
within multilateral development banks, ‘public- and private-sector 
branches differ considerably’, with public-sector lending used mainly

in non-renewable and hydropower projects. Political leadership is 
therefore essential to steer financial flows to support low-carbon 
transition (Section  15.6). Voituriez et al. (2019) identify significant 
mitigation potential if financing countries simply applied their own 
environmental standards to their overseas investments.
1.4.5 Political Economy
The politics of interest (most especially economic interest) of key 
actors at sub-national, national and global levels can be important 
determinants of climate (in)action (O’Hara 2009; Lo 2010; Tanner 
and Allouche 2011; Sovacool et al. 2015; Lohmann 2017; Clapp et al. 
2018; Newell and Taylor 2018; Lohmann 2019). Political economy 
approaches can be crudely divided into ‘economic approaches to 
politics’, and those used by other social scientists (Paterson and 
P-Laberge 2018). The former shows how electoral concerns lead 
to weak treaties (Battaglini and Harstad 2016) and when policy

negotiations cause status-quo biases and the use of inefficient 
policy instruments (Austen-Smith et al. 2019) or delays and excessive 
harmonisation (Harstad 2007). The latter emphasises the central 
role of structures of power and production, and a  commitment to 
economic growth and capital accumulation in relation to climate 
action, given the historically central role of fossil fuels to economic 
development and the deep embedding of fossil energy in daily life 
(Newell and Paterson 2010; Huber 2012; Di Muzio 2015; Malm 2015).
The economic centrality of fossil fuels raises obvious questions 
regarding the possibility of decarbonisation. Economically, this 
is well understood as a problem of decoupling. But the constraint is 
also political, in terms of the power of incumbent fossil fuel interests 
to block initiatives towards decarbonisation (Jones and Levy 2009; 
Newell and Paterson 2010; Geels 2014). The effects of climate policy

are key considerations in deciding the level of policy ambition and 
direction and strategies of states (Lo 2010; Alam et al. 2013; Ibikunle 
and Okereke 2014), regions (Goldthau and Sitter 2015), and business 
actors (Wittneben et al. 2012), and there is a  widespread cultural 
assumption that continued fossil fuel use is central to this (Strambo 
and Espinosa 2020). Decarbonisation strategies are often centred

170
Chapter 1  Intr oduction and Framing1around projects to develop new sources of economic activity: carbon 
markets creating new commodities (Newell and Paterson 2010); 
investment generated in new urban infrastructure (Whitehead 2013); 
and/or innovations in a range of new energy technologies (Fankhauser 
et al. 2013; Lachapelle et al. 2017; Meckling and Nahm 2018).
One factor limiting the ambition of climate policy has been the ability 
of incumbent industries to shape government action on climate 
change (Newell and Paterson 1998; Jones and Levy 2009; Geels 2014; 
Breetz et al. 2018). Incumbent industries are often more concentrated 
than those benefiting from climate policy and lobby more effectively 
to prevent losses than those who would gain (Meng and Rode 2019). 
Drawing upon wider networks (Brulle 2014), campaigns by oil and 
coal companies against climate action in the United States of America 
and Australia are perhaps the most well known and largely successful

of these (Pearse 2017; Brulle et al. 2020; Mildenberger 2020; Stokes 
2020), although similar dynamics have been demonstrated in Brazil 
and South Africa (Hochstetler 2020), Canada (Harrison 2018), 
and Norway and Germany (Fitzgerald et al. 2019), for example. 
In other contexts, resistance by incumbent companies is more 
subtle but nevertheless has weakened policy design on emissions 
trading systems (Rosembloom and Markard 2020), and limited the 
development of alternative-fuelled automobiles (Levy and Egan 
2003; Wells and Nieuwenhuis 2012).
The interaction of politics, power and economics is central in explaining 
why countries with higher per-capita emissions, which logically have 
more opportunities to reduce emissions, in practice often take the 
opposite stance, and conversely, why some low-emitting countries 
may find it easier to pursue climate action because they have fewer 
vested interests in high-carbon economies. These dynamics can

arise from the vested interest of state-owned enterprises (SOEs) 
(Wittneben et al. 2012; Polman 2015; Wright and Nyberg 2017), the 
alignment and coalitions of countries in climate negotiations (Gupta 
2016; Okereke and Coventry 2016), and the patterns of opposition 
to or support for climate policy among citizens (Baker 2015; Swilling 
et al. 2016; Heffron and McCauley 2018; Ransan-Cooper et al. 2018; 
Turhan et al. 2019).
1.4.6 Equity and Fairness
Equity and fairness can serve as both drivers and barriers to climate 
mitigation at different scales of governance. Literature regularly 
highlights equity and justice issues as critical components in local 
politics and international diplomacy regarding all SDGs, such as goals 
for no poverty, zero hunger, gender equality, affordable clean energy, 
reducing inequality, but also for climate action (SDG 13) (Marmot and 
Bell 2018; Spijkers 2018). Equity issues help explain why it has proved

hard to reach more substantive global agreements, as it is hard to 
agree on a level of greenhouse gas (GHG) mitigation (or emissions) 
and how to distribute mitigation efforts among countries (Kverndokk 
2018) for several reasons. First, an optimal trade-off between 
mitigation costs and damage costs of climate change depends on 
ethical considerations, and simulations from integrated assessment 
models using different ethical parameters producing different optimal 
mitigation paths (IPCC 2018b) (Section 3.6.1.2). Second, treaties that are considered unfair may be hard to implement (Klinsky et al. 2017; 
Liu et al. 2017). Lessons from experimental economics show that 
people may not accept a distribution that is considered unfair, even if 
there is a cost of not accepting (Gampfer 2014). As equity issues are 
important for reaching deep decarbonisation, the transition towards 
sustainable development (Evans and Phelan 2016; Heffron and

McCauley 2018; Okereke 2018) depends on taking equity seriously 
in climate policies and international negotiations (Okereke and 
Coventry 2016; Klinsky et al. 2017; Martinez et al. 2019).
Climate change and climate policies affect countries and people 
differently. Low-income countries tend to be more dependent 
on primary industries (agriculture and fisheries, etc.) than richer 
countries, and their infrastructure may be less robust to tackle more 
severe weather conditions. Within a country, the burdens may not be 
equally distributed either, due to policy measures implemented and 
from differences in vulnerability and adaptive capacity following from 
e.g. income and wealth distribution, race and gender. For instance, 
unequal social structures can result in women being more vulnerable 
to the effects of climate change compared to men, especially in poor 
countries (Arora-Jonsson 2011; Jost et al. 2016; Rao et al. 2019).

Costs of mitigation also differ across countries. Studies show there 
are large disparities of economic impacts of NDCs across regions, 
and also between relatively similar countries when it comes to the 
level of development, due to large differences in marginal abatement 
costs for the emission-reduction goal of NDCs (Fujimori et al. 2016; 
Hof et al. 2017; Akimoto et al. 2018; Evans &  Gabbatiss 2019). 
Equalising the burdens from climate policies may give more support 
for mitigation policies (Maestre-Andrés et al. 2019).
Taking equity into account in designing an international climate 
agreement is complicated as there is no single universally accepted 
equity criterion, and countries may strategically choose a  criterion 
that favours them (Lange et al. 2007, 2010). Still, several studies 
analyse the consequences of different social preferences in designing 
climate agreements, such as, for instance, inequality aversion,

sovereignty and altruism (Anthoff et al. 2010; Kverndokk et al. 2014).
International transfers from rich to poor countries to support 
mitigation and adaptation activities may help with equalising 
burdens, as agreed upon in the UNFCCC (1992) (Chapters  14 and 
15), such that they may be motivated by strategic as well as equity 
reasons (Kverndokk 2018) (Section 1.4.4).
1.4.7 Social Innovation and Behaviour Change
Social and psychological factors affect both perceptions and 
behaviour (Weber 2015; Whitmarsh et al. 2021). Religion, values, 
culture, gender, identity, social status and habits strongly influence 
individual behaviours and choices, and therefore sustainable 
consumption (Sections  1.6.3.1 and 5.2). Identities can provide 
powerful attachments to consumption activities and objects that 
inhibit shifts away from them (Brekke et al. 2003; Bénabou and 
Tirole 2011; Stoll-Kleemann and Schmidt 2017; Ruby et al. 2020).

Consumption is a  habit-driven and social practice rather than 
simply a  set of individual decisions, making shifts in consumption

171
Introduction and Framing Chapter 11harder to pursue (Evans et al. 2012; Shove and Spurling 2013; Kurz 
et al. 2015; Warde 2017; Verplanken and Whitmarsh 2021). Finally, 
shifts towards low-carbon behaviour are also inhibited by social-
psychological and political dynamics that cause individuals to ignore 
the connections from daily consumption practices to climate change 
impacts (Norgaard 2011; Brulle and Norgaard 2019).
As a notable example, plant-based alternatives to meat could reduce 
emissions from diets (Eshel et al. 2019; Willett et al. 2019). However, 
diets are deeply entrenched in cultures and identities, and hard to 
change (Fresco 2015; Mylan 2018). Changing diets also raises cross-
cultural ethical issues, in addition to meat’s role in providing nutrition 
(Plumwood 2004). Henceforth, some behaviours that are harder to 
change will only be transformed by the transition itself: triggered by 
policies, the transition will bring about technologies that, in turn, will

entrench new sustainable behaviours.
Behaviour can be influenced through a number of mechanisms besides 
economic policy and regulation, such as information campaigns, 
advertising and ‘nudging’. Innovations and infrastructure also impact 
behaviour, as with bicycle lanes to reduce road traffic. Wider social 
innovations also have indirect impacts. Education is increasing 
across the world, and higher education will have impacts on fertility, 
consumption and the attitude towards the environment (Osili and 
Long 2008; Hamilton 2011; McCrary and Royer 2011). Reducing 
poverty and improvements in health and reproductive choice will also 
have implications for fertility, energy use and consumption globally. 
Finally, social capital and the ability to work collectively may have 
large consequences for mitigation and the ability to adapt to climate 
change (Adger 2009; IPCC 2014a Section 4.3.5).
1.4.8 Policy Impacts
Transformation to different systems will hinge on conscious policy to

change the direction in which energy, land use, agriculture and other 
key sectors develop (Bataille et al. 2016) (Chapters 13 and 16). Policy 
plays a  central role in in land-related systems (Chapter  7), urban 
development (Chapter  8), improving energy efficiency in buildings 
(Chapter 9) and transport/mobility (Chapter 10), and decarbonising 
industrial systems (Chapter 11).
Policy has been and will be central not only because GHG emissions 
are almost universally under-priced in market economies (Stern and 
Stiglitz 2017; World Bank 2019), and because of inadequate economic 
incentives to innovation (Jaffe et al. 2005), but also due to various 
delay mechanisms (Karlsson and Gilek 2020) and multiple sources 
of path-dependence and lock-in to existing systems (Section 1.8.2), 
including: ‘Infrastructure developments and long-lived products 
that lock societies into GHG-intensive emissions pathways may be 
difficult or very costly to change, reinforcing the importance of early

6 Linking estimated policy impacts to trends is complex, and as yet very tentative. An important factor is that many mitigation policies involve investments in low-carbon or 
energy-efficient technology, the savings from which persist. As a purely illustrative example: the annual increase in global emissions during 2000–2010 averaged around 
1 GtCO 2-eq yr–1, but with large fluctuations. If policies by 2010 reduced the annual increase in that year by 100 MtCO 2-eq (0.1 GtC0 2-eq) below what it would otherwise 
have been, this is hard to discern. But if these savings sustain, and in each subsequent year, policies cut another 100 MtCO 2-eq off the annual increase compared to the 
previous year, global emissions after a decade would be around 5 GtCO 2-eq yr–1 below what they would have been without any such policies, and on average close to

stabilising. However each step would be difficult to discern in the noise of annual fluctuations.action for ambitious mitigation (robust evidence, high agreement).’ 
(AR5 WGIII p.18).
Many hundreds of policies have been introduced explicitly to mitigate 
GHG emissions, improve energy efficiency or land use, or to foster 
low-carbon industries and innovation, with demonstrable impact. 
The role of policy to date has been most evident in energy efficiency 
(Sections 5.4 and 5.6) and electricity (Chapter 6). The IPCC Special 
Report on Renewable Energy already found that: ‘Government policies 
play a crucial role in accelerating the deployment of RE technologies’ 
(IPCC 2011a, p. 24). Policy packages since then have  driven rapid 
expansion in renewables capacity and cost reductions (e.g., through 
the German Energiewende), and emission reductions from electricity 
(most dramatically with the halving of CO 2 emissions from the UK

power sector, driven by multiple policy instruments and regulatory 
changes), as detailed in Chapter 6 (Section 6.7.5).
Chapter 13 charts the international evolution of policies and many 
of the lessons drawn. Attributing the overall impact on emissions 
is complex, but an emerging literature of several hundred papers 
indicates impacts on multiple drivers of emissions. Collectively, 
policies are likely to have curtailed global emissions growth by 
several GtCO 2-eq annually already by the mid-2010s (Cross-Chapter 
Box 10 in Chapter 14). This suggests initial evidence that policy has 
driven some decoupling (Figure 1.1d) and started to ‘bend the curve’ 
of global emissions, but more specific attribution to observed trends 
is not as yet possible.6
However, some policies (e.g.,  subsidies to fossil fuel production or 
consumption) increase emissions, whilst others (e.g.,  investment 
protection) may constrain efforts at mitigation. Also, wider economic

and developmental policies have important direct and indirect 
impacts on emissions. Policy is thus both a driver and a constraint 
on mitigation.
Synergies and trade-offs arise partly because of the nexus of GHG 
emissions with other adverse impacts (e.g., local air pollution) and 
critical resources (e.g., water and food) (Conway et al. 2015; Andrews-
Speed and Dalin 2017), which also imply interacting policy domains.
The literature shows increasing emphasis on policy packages, 
including those spanning the different levels of niche/behaviour; 
existing regimes governing markets and public actors; and strategic 
and landscape levels (Section 1.7.3). Chapters 13, 16 and 17 appraise 
policies for transformation in the context of sustainable development, 
indicating the importance of policy as a  driver at multiple levels 
and across many actors, with potential for benefits as well as costs 
at many levels.
National-level legislation may be particularly important to the credibility

and long-term stability of policy to reduce the risks, and hence cost,

172
Chapter 1  Intr oduction and Framing1of finance (Chapters 13 and 15), and for encouraging private-sector 
innovation at scale (Chapter 16), for example, if it offers greater stability 
and mid-term predictability for carbon prices; Nash and Steurer (2019) 
find that seven national climate change acts in European countries all 
act as ‘living policy processes, though to varying extents’.
The importance of policy at multiple levels does not lessen the 
importance of international policy, for reasons including long-term 
stability, equity, and scope, but examples of effective implementation 
policy at international levels remain fewer and governance weaker 
(Chapter 14).
1.4.9 Legal Framework and Institutions
Institutions are rules and norms held in common by social actors 
that guide, constrain and shape human interaction  (IPCC 2018b). 
Institutions can be formal, such as laws and policies, or informal, 
such as norms and conventions.  Institutions can both facilitate or

constrain climate policymaking and implementation in multiple 
ways. Institutions set the economic incentives for action or inaction 
on climate change at national, regional and individual levels (Dorsch 
and Flachsland 2017; Sullivan 2017).
Institutions entrench specific political decision-making processes, 
often empowering some interests over others, including powerful 
interest groups who have vested interests in maintaining the current 
high-carbon economic structures (Okereke and Russel 2010; Wilhite 
2016; Engau et al. 2017); see also Section  1.4.6 and Chapter  13 
on the sub-national and national governance challenges including 
coordination, mediating politics and strategy setting.
Some suggest that societal transformation towards a  low-carbon future 
requires new politics that involves thinking in intergenerational time 
horizons, as well as new forms of partnerships between private and 
public actors (Westman and Broto 2018), and associated institutions

and social innovations to increase involvement of non-state actors in 
climate governance (Fuhr et al. 2018). However literature is divided 
as to how much democratisation of climate politics, with greater 
emphasis on equity and community participation, would advance 
societal transformation in the face of climate change (Stehr 2005), 
or may actually hinder radical climate action in some circumstances 
(Povitkina 2018).
Since 2016, the number of climate litigation cases has increased 
rapidly. The UN Environment Programme’s Global Climate Litigation 
Report: 2020 Status Review (UNEP 2020) noted that between 
March 2017 and 1 July 2020, the number of cases nearly doubled with 
at least 1550 climate cases filed in eight countries. Several important 
cases such as Urgenda Foundation vs The State of the Netherlands 
(‘Urgenda’) and Juliana et al. vs United States (‘Juliana’) have had 
ripple effects, inspiring other similar cases (Lin and Kysar 2020).

Numerous international climate governance initiatives engage 
national and sub-national governments, NGOs and private 
corporations, constituting a ‘regime complex’ (Raustiala and Victor 
2004; Keohane and Victor 2011). They may have longer-run and second-order effects if commitments are more precise and binding 
(Kahler 2017). However, without targets, incentives, defined baselines 
or monitoring, reporting, and verification, they are not likely to fill the 
‘mitigation gap’ (Michaelowa and Michaelowa 2017).
1.4.10 International Cooperation
Tackling climate change is often mentioned as an important reason 
for strong international cooperation in the 21st century (Falkner 
2016; Keohane and Victor 2016; Bodansky et al. 2017; Cramton 
et al. 2017b). Mitigation costs are borne by countries taking action, 
while the benefits of reduced climate change are not limited to 
them, being in economic terms ‘global and non-excludable’. Hence

anthropogenic climate change is typically seen as a global commons 
problem (Falkner 2016; Wapner and Elver 2017). Moreover, the belief 
that mitigation will raise energy costs and may adversely affect 
competitiveness creates incentives for free riding, where states avoid 
taking their fair share of action (Barrett 2005; Keohane and Victor 
2016). International cooperation has the potential to address these 
challenges through collective action (Tulkens 2019) and international 
institutions offer the opportunity for actors to engage in meaningful 
communication and exchange of ideas about potential solutions 
(Cole 2015). International cooperation is also vital for the creation 
and diffusion of norms and the framework for stabilising expectations 
among actors (Pettenger 2016).
Some key roles of the UNFCCC have been detailed by its former 
heads (Kinley et al. 2021). In addition to specific agreements (most 
recently the PA) it has enhanced transparency through reporting

and data, and generated or reinforced several important norms for 
global climate action including the principles of equity, common 
but differentiated responsibility and respective capabilities, and the 
precautionary principles for maintaining global cooperation among 
states with unevenly distributed emissions sources, climate impacts, 
and varying mitigation costs across countries (Keohane and Victor, 
2016). In addition to formal negotiations, the annual Conference of 
the Parties (COPs) have increased awareness, and motivated more 
ambitious actions, sometimes through the formation of ‘coalitions 
of the willing’, for example. It provides a  structure for measuring 
and monitoring action towards a global goal (Milkoreit and Haapala 
2019). International cooperation (including the UNFCCC) can also 
promote technology development and transfer and capacity building; 
mobilise finance for mitigation and adaptation; and help address

concerns on climate justice (Okereke and Coventry 2016; Chan et al. 
2018) (Chapters 14–16).
A common criticism of international institutions is their limited 
(if any) powers to enforce compliance (Zahar 2017). As a global legal 
institution, the PA has little enforcement mechanism (Sindico 2015), 
but enforcement is not a  necessary condition for an instrument 
to be legally binding (Bodansky 2016; Rajamani 2016). In reality 
implementation of specific commitments tends to be high once 
countries have ratified and a  treaty or an agreement is in force 
(Bodansky 2016; Rajamani 2016). Often, the problem is not so much 
of ‘power to enforce compliance or sanction non-compliance’, but 
the level of ambition (Chapter 14).

173
Introduction and Framing Chapter 11However, whilst in most respects a driver, international cooperation 
has also been characterised as ‘organised hypocrisy’ where 
proclamations are not matched with corresponding action (Egnell 
2010). Various reasons for inadequate progress after 30 years of 
climate negotiations, have been identified (Stoddard et al. 2021). 
International cooperation can also seem to be a barrier to ambitious 
action when negotiation is trapped in ‘relative-gains’ calculus, in 
which countries seek to game the regime or gain leverage over 
one another (Purdon 2017), or where states lower ambition to the 
‘least common dominator’ to accommodate participation of the least 
ambitious states (Falkner 2016). Geden (2016) and Dubash (2020) 
offer more nuanced assessments.
International collaboration works best if an agreement can be made 
self-reinforcing with incentives for mutual gains and joint action

(Barrett 2016; Keohane and Victor 2016), but the structure of the 
climate challenge makes this hard to achieve. The evidence from the 
Montreal Protocol on ozone-depleting substances and from the Kyoto 
Protocol on GHGs, is that legally binding targets have been effective 
in that participating Parties complied with them (Shishlov et al. 2016; 
Albrecht and Parker 2019), and (for Kyoto) these account for most of 
the countries that have sustained emission reductions for at least the 
past 10 to 15 years (Sections 1.3.2 and 2.2). However, such binding 
commitments may deter participation if there are no clear incentives 
to sustain participation and especially if other growing emitters are 
omitted by design, as with the Kyoto Protocol. Consequently the USA 
refused to ratify (and Canada withdrew), particularly on the grounds 
that developing countries had no targets; with participation in Kyoto’s 
second period commitments declining further, the net result was

limited global progress in emissions under Kyoto (Bodansky 2016; 
Okereke and Coventry 2016; Scavenius and Rayner 2018) despite full 
legal compliance in both commitment periods (Chapter 14).
The negotiation of the Paris Agreement was thus done in the context 
of serious questions about how best to structure international 
climate cooperation to achieve better results. This new agreement 
is designed to sidestep the fractious bargaining which characterised 
international climate cooperation (Marcu 2017). It contains a  mix 
of hard, soft and non-obligations, the boundaries between which 
are blurred, but each of which plays a  distinct and valuable role 
(Rajamani 2016). The provisions of the PA could encourage flexible 
responses to changing conditions, but limit assurances of ambitious 
national commitments and their fulfilment (Pickering et al. 2018). The 
extent to which this new arrangement will drive ambitious climate 
policy in the long run remains to be seen (Chapter 14).

Whilst the PA abandoned common accounting systems and time 
frames, outside of the UNFCCC many other platforms and metrics for 
comparing mitigation efforts have emerged (Aldy 2015). Countries 
may assess others’ efforts in determining their actions through multiple 
platforms including the Climate Change Cooperation Index (C3-I), 
Climate Change Performance Index (CCPI), Climate Laws, Institutions 
and Measures Index (CLIMI) (Bernauer and Böhmelt 2013) and 
Energy Transition Index (Singh et al. 2019). International cooperative 
initiatives between and among non-state (e.g.,  business, investors 
and civil society) and sub-national (e.g., city and state) actors have 
also been emerging, taking the forms of public-private partnerships, private-sector governance initiatives, NGO transnational initiatives, 
and sub-national transnational initiatives (Bulkeley and Schroeder 
2012; Hsu et al. 2018). Literature is mostly positive about the role

of these transnational initiatives in facilitating climate action across 
scales although criticism and caution about their accountability and 
effectiveness remain (Chan et al. 2016; Michaelowa and Michaelowa 
2017; Roger et al. 2017; Widerberg and Pattberg 2017) (Chapter 14).
1.5 Emissions Scenarios and Illustrative 
Mitigation Pathways (IMPs)
Scenarios are a powerful tool for exploring an uncertain future world 
against the background of alternative choices and development. 
Scenarios can be constructed using both narrative and quantitative 
methods. When these two methods are combined they provide 
complementary information and insights. Quantitative and narrative 
models are frequently used to represent scenarios to explore choices 
and challenges. The IPCC has a long history of assessing scenarios 
(Nakicenovic et al. 2000; van Vuuren et al. 2011, 2014) (see also AR6 
WGI Section 1.6 for a history of scenarios within the IPCC). This WGIII

assessment employs a  wide range of qualitative and quantitative 
scenarios including quantitative scenarios developed through a wide 
and heterogeneous set of tools ranging from spreadsheets to complex 
computational models (Annex III: Scenarios and Modelling Methods 
provides further discussion and examples of computational models).
The concept of an illustrative pathway (IP) was introduced in 
the IPCC Special Report on Global Warming of 1.5ºC (IPCC 2018b) 
to highlight a  subset of the quantitative scenarios, drawn from 
a larger pool of published literature, with specific characteristics that 
would help represent some of the key findings emerging from the 
assessment in terms of different strategies, ambitions and options 
available to achieve the Paris goals.
Integrated assessment models (IAMs) are the primary tools for 
quantitatively evaluating the technological and macroeconomic 
implications of decarbonisation, particularly for global long-term

pathways. They broadly divide into ‘stylised aggregate benefit-cost 
models’, and more complex ‘detailed process’ IAMs (Weyant 2017), 
often mirroring the benefit-cost and cost-effective approaches 
outlined in Section  1.7.1, with more detailed classification in, for 
example, Nikas et al. (2019). IAMs embody a number of structural 
and socio-demographic assumptions and include multiple modelling 
approaches, ranging from economic optimising behaviour to 
simulation (see Annex III). Detailed process models can include 
energy system models used to analyse decarbonisation and ‘net zero’ 
scenarios by international agencies (e.g., IEA 2020a).
Calculating cost-effective trajectories towards given goals typically 
involves detailed process IAMs. Often these calculate the dynamic 
portfolio of technologies consistent with a  given climate target. 
Some track records of technology forecasting in IAMs are outlined 
in Section  2.5.4, and Box  16.1. Climate targets may be imposed

in models in a variety of ways that include, but are not limited to, 
constraints on emissions or cumulated emissions (carbon budgets), 
and the pricing of emissions. The time-path of mitigation costs

174
Chapter 1  Intr oduction and Framing1calculated through these models may be translated into ‘shadow 
prices’ that (like the social cost of carbon; SCC) offer a benchmark 
to assess the cost-effectiveness of investments, as used by some 
governments and companies (Section 1.8.2).
Scenarios in the IPCC and AR6. For AR6, WGIII received submissions 
of more than 2500 model-based scenarios published in the scientific 
literature. Such scenarios, which explore different possible evolutions 
of future energy and land use (with or without climate policy) and 
associated emissions, are made available through an interactive 
AR6 scenario database. The main characteristics of pathways in 
relation to ‘net zero’ emissions and remaining ‘carbon budgets’ 
are summarised in Box 3.5 in Chapter 3. The warming contribution 
of CO 2 is very closely related to cumulative CO 2 emissions, but the 
remaining ‘carbon budget’ for a  given warming depends strongly

inter alia on emissions of other GHGs; for targets below 2°C this 
may affect the corresponding ‘carbon budget’ by about ±220 GtCO 2, 
compared to central estimates of around 500 GtCO 2 (for 1.5°C) and 
1350 GtCO 2 (for 2°C) (AR6 WGI, Table SPM.2) (Cross-Working Group 
Box 1 in Chapter 3).
Pathways and ‘net zero’. The date at which the world needs 
aggregate emissions to reach net zero for Paris-consistent 
temperature goals depends both on progress in reducing non-CO 2 
GHG emissions and near-term progress in reducing CO 2 emissions. 
Faster progress in the near term extends the date at which net 
zero must be reached, while conversely, slower near-term progress 
brings the date even closer to the present. Some of the modelled 
1.5°C pathways with limited overshoot cut global CO 2 emissions in 
half until 2030, which allows for a more gradual decline thereafter,

reaching net zero CO 2 after 2050; also, net zero GHGs occurs later, with remaining emissions of some non-CO 2 GHGs compensated by 
‘net negative’ CO 2 (see Glossary and FAQ 1.3, and Cross-Chapter 
Box 3 in Chapter 3).
Drawing from the scenarios database, five Illustrative Mitigation 
Pathways (IMPs) were defined for this report (Figure  3.5 and 
Table  1.1). These are introduced here, with a  more complete 
description and discussion provided in Section  3.2.5. These IMPs 
were chosen to illustrate key themes with respect to mitigation 
strategies across the entire WGIII assessment. The IMPs embody 
both a  storyline, which describes in narrative form the key socio-
economic characteristics of that scenario, and a  quantitative 
illustration providing numerical values that are internally consistent 
and comparable across chapters of this report. Quantitative IMPs 
can be associated directly with specific human activities and provide

a quantitative point of reference that links activities in different parts 
of socio-economic systems. Some parts of the report draw on these 
quantitative scenarios, whilst others use only the narratives. No 
assessment of the likelihood of each IMP has been made (as they 
reflect both human choice and deep uncertainty).
The IMPs are organised around two dimensions: the level of 
ambition consistent with meeting Paris goals, and the scenario 
features (Figure 1.4). The IMPs explore different pathways potentially 
consistent with meeting the long-term temperature goals of the Paris 
Agreement. As detailed in Section 3.2.5 and in Chapter 4, a pathway 
of Gradual Strengthening of current policies (IMP-GS) to 2030, if 
followed by very fast reductions, may stay below 2°C. The IMP-NEG 
pathway, with somewhat deeper emission cutbacks to 2030, might 
enable 1.5°C to be reached but only after significant overshoot, 
through the subsequent extensive use of CDR in the energy and 
CurPol
ModAct

IMP-GS
IMP-LD IMP-SP
Gradual
(GS)Accelerated
Renewables
(REN)Low Energy
Demand
(LD)Emphasis on
Net Negative
EmissionsFocus on Shifting 
PathwaysIncreasing level of ambition
Scenario featuresIMPs consistent with Paris objectivesScenarios fo r
compari son
IMP-RenIMP-Neg
Figure 1.4 | Illustrative Mitigation Pathways (IMPs) used in AR6: illustration of key features and levels of ambition.

175
the industry sectors to achieve net negative global emissions, as 
discussed in Chapters 3, 6, 7, 10 and 12.
Three other IMPs illustrate different features of technology scenarios 
with more short-term rapid emission reductions, which could deliver outcomes compatible with the temperature range in the Paris Agreement without large overshoot. Based on the assessment in Section 5.3.3, one key mitigation strategy would be to rely on the opportunities for reducing demand (IMP-LD). Chapters 6 and 7–11 show how energy systems based on accelerated deep renewable energy penetration and electrification can also provide a pathway to deep mitigation (IMP-REN). Chapters 3, 4 and 17 provide insights into how shifting development pathways can lead to deep emission reductions and achieve sustainable development goals (IMP-SP).
These pathways can be implemented with different levels of ambition,

that can be measured through the classes (C) of temperature levels from the scenarios database, see Chapter  3  (Table  3.2). In the IMP framework, Section  3.2.5 presents and explores quantitative scenarios that can limit warming to 1.5°C (with a probability of 50% or greater, i.e., C1 for the illustrated quantification of LD, SP and REN, and C2 for NEG scenario), along with other GS pathways which keep warming below 2°C with a  probability of 67% or greater (C3). In addition to these primary IMPs, the full scenario database contains sensitivity cases that explore alternative warming levels.
In addition to the IMPs two additional scenarios were selected, which

illustrate the consequences of current policies and pledges. Current Policies (CurPol) explores the consequences of continuing along the path of implemented climate policies in 2020 and only a  Gradual Strengthening after that, drawing on numerous such scenarios in the literature. Moderate Action (ModAct) explores the impact of implementing NDCs to 2030, but without further strengthening: both result in global mean temperature above 2°C.  They provide benchmarks against which to compare the IMPs.
Table 1.1 summarises the main storyline elements of the reference 
scenarios and each IMP .
What the IMPs do and don’t do.  The IMPs are, as their name 
implies, a set of scenarios meant to illustrate some important themes

that run through the entire WGIII assessment. They illustrate that the climate outcomes that individuals and society will face in the century ahead depend on individual and societal choices. In addition, they illustrate that there are multiple ways to successful achievement of Paris long-term temperature goals.
IMPs are not intended to be comprehensive. They are not intended 
to illustrate all possible themes in this report. They do not, for example, attempt to illustrate the range of alternative socio-economic pathways against which efforts to implement Paris goals may be set, or to reflect variations in potential regional development pathways. They do not explore issues around income distribution or environmental justice, but assume implicitly that where and how

action occurs can be separated from who pays, in ways to adequately address such issues. They are essentially pathways of technological evolution and demand shifts reflecting broad global trends in social choice. The IMPs do not directly assess issues of realisation linked to the ‘drivers and constraints’ summarised in our previous section, and the quantifications use, for the most part, models that are grounded mainly in the Aggregate Economics Frameworks (Section 7.1). As such they reflect primarily the geophysical, economic and technological Dimensions of Assessment, but can be assessed in relation to the full set of Feasibility criteria (Section 1.8.1).
Together the IMPs provide illustrations of potential future 
developments that can be shaped by human choices, including: Table 1.1 | Illustrative Mitigation Pathways used in AR6.
Scenarios Full name Main policy characteristics

CurPol Current PoliciesImplementation of current climate policies (mostly as reported in Nationally Determined Contributions (NDCs)), neglecting 
stated subsequent goals and objectives (e.g., for 2030); only Gradual Strengthening after 2030; grey COVID recovery.
ModAct Moderate ActionImplementation of current policies and achievement of 2030 NDCs, with further strengthening post-2030. Similarly to the situation implied by the diversity of NDCs (both policies and pledges), a fragmented policy landscape remains; mixed COVID recovery.
IMPs1.5°C/ <2°CGS Gradual StrengtheningUntil 2030, primarily current NDCs are implemented; after that a strong universal regime leads to coordinated and rapid decarbonisation actions.

NegNet Negative EmissionsSuccessful international climate policy regime reduces emissions below ModAct or GS to 2030, but with a focus on the long-term temperature goal, negative emissions kick in at growing scales thereafter, so that mitigation in all sectors also includes a growing and ultimately large reliance on negative emissions, with large ‘net global negative’ after 2050 to meet 1.5˚C after significant overshoot.
Ren RenewablesSuccessful international climate policy regime with immediate action, particularly policies and incentives (including international finance) favouring renewable energy; less emphasis on negative-emission technologies. Rapid deployment and innovation of renewables and systems; electrification of all end use.

LD Low DemandSuccessful international climate policy regime with immediate action on the demand side; policies and financial incentives favouring reduced demand that in turn leads to early emission reductions; this reduces the decarbonisation effort on the supply side.
SP Shifting PathwaysSuccessful international climate policy regime with a focus on additional SDG policies aiming, for example, at poverty reduction and broader environmental protection. Major transformations shift development towards sustainability and reduced inequality, including deep GHG emissions reduction.

176
Chapter 1  Intr oduction and Framing1Where are current policies and pledges leading? What is needed to 
reach specific temperature goals under varying assumptions? What 
are the consequences of different strategies to meet climate targets 
(i.e., demand-side strategy, a renewable energy strategy or a strategy 
with a role for net negative emissions)? What are the consequences 
of delay? What are the implications for other SDGs of various climate 
mitigation pathways?
1.6 Achieving Mitigation in the Context 
of Sustainable Development
This chapter now sets out approaches to understanding the 
mitigation challenge, working from its broad location in the context 
of wider aspirations for sustainable development, then identifying 
specific analytic approaches, before summarising the corresponding 
main dimensions used for the assessment of options and pathways 
in much of the report.
1.6.1 The Climate Change and 
Development Connection

Climate change mitigation is one of many goals that societies pursue 
in the context of sustainable development, as evidenced by the wide 
range of the Sustainable Development Goals (SDGs). Climate change 
and sustainable development, as well as development more broadly, 
are interwoven along multiple and complex lines of relationship 
(Okereke et al. 2009; Fankhauser and McDermott 2016; Okereke 
and Massaquoi 2017; Gomez-Echeverri 2018a), as highlighted in 
several previous IPCC reports (IPCC 2007, 2011a, 2014a, 2018b, 
2019a). With its significant negative impact on natural systems, 
food security and infrastructure, loss of lives and territories, species 
extinction, conflict health, among several other risks, climate change 
poses a serious threat to development and wellbeing in both rich and 
poor countries (IPCC 2007, 2011a, 2014a, 2018b, 2019b). Without 
serious efforts at mitigation and adaptation, climate change could

push millions further into poverty and limit the opportunities for 
economic development (Chapters 4 and 17). It follows that ambitious 
climate mitigation is necessary to secure a safe climate within which 
development and well-being can be pursued and sustained.
At the same time, rapid and large-scale economic development (which 
has in the past driven climate change through land-use change and 
dependence on fossil fuels), is widely seen as needed to improve 
global well-being and lift millions especially in low- and middle-
income countries out of poverty (Chen et al. 2017; Mugambiwa and 
Tirivangasi 2017; Lu et al. 2019; Baarsch et al. 2020) (Figure  1.6). 
This strand of literature emphasises the importance of economic 
growth including for tackling climate change itself, pointing to the 
relationship between economic development and climate resilience 
as well as the role of industry-powered technologies such as electric

vehicles in reducing GHG levels and promoting well-being (Heinrichs 
et al. 2014; Kasztelan 2017). Yet, others argue that the character 
of social and economic development produced by the nature of 
capitalist society (Pelling and Manuel-Navarrete 2011; Koch 2012; 
Malm 2016) is ultimately unsustainable.There are at least two major implications of the very close link 
between climate change and development as outlined above. The 
first is that the choice of development paths made by countries and 
regions have significant consequences for GHG emissions and efforts 
to combat climate change (Chapters 2, 3, 4, 5 and 14). The second is 
that climate mitigation at local, national and global levels cannot be 
effectively achieved by a narrow focus on ‘climate-specific’ sectors, 
actors and policies, but rather through a much broader attention to 
the mix of development choices and the resulting development paths 
and trajectories (O’Neill et al. 2014) (Chapters 4, 6 and 10).

As a key staple of IPCC reports and the global climate policy landscape 
(IPCC 2007, 2014b; van Vuuren et al. 2017; Gidden et al. 2019; 
Quilcaille et al. 2019) (Chapter  2), integrated assessment models 
and global scenarios (such as the Shared Socio-economic Pathways – 
SSPs) highlight the interaction between development paths, climate 
change and emission stabilisation (Section 3.6). The close links are 
also recognised in the PA (Section 1.3.1).
The impact of climate change in limiting well-being is most acutely 
felt by the world’s poorest people, communities, and nations, who 
have the smallest carbon footprint, constrained capacity to respond 
and limited voice in important decision-making circles (Okereke and 
Ehresman 2015; Tosam and Mbih 2015; Mugambiwa and Tirivangasi 
2017). The wide variation in the contribution to, and impact of climate 
change within and across countries makes equity, inequality, justice,

and poverty eradication, inescapable aspects of the relationship 
between sustainable development and climate change (Okereke 
and Coventry 2016; Klinsky et al. 2017; Reckien et al. 2017; Bos 
and Gupta 2019; Kayal et al. 2019; Diffenbaugh and Burke 2019; 
Baarsch et al. 2020). This underpins the conclusion, as commonly 
expressed, that climate action needs to be pursued in the context of 
sustainable development, equity and poverty eradication (Smit et al. 
2001; Tschakert and Olsson 2005; IPCC 2014a, 2018b; Klinsky and 
Winkler 2014).
1.6.2 Concepts and Frameworks for Integrating 
Climate Mitigation and Development
At one level, sustainable development can be seen as a  meta 
framework for integrating climate action with other global 
sustainability goals (Casadio Tarabusi and Guarini 2013; Antal and 
Van Den Bergh 2016). Fundamentally, the concept of sustainable 
development underscores the interlinkages and interdependence

of human and natural systems and the need to balance economic, 
social, and environmental (including climate pollution) aspects in 
development planning and processes (Nunan 2017; Gomez-Echeverri 
2018b; Zhenmin and Espinosa 2019).
Despite the appeal of the concept, tensions remain over the 
interpretation and practical application, with acute disagreements 
regarding what the balancing entails in real life, how to measure 
well-being, which goals to set, and the means through which such 
goals might be pursued (Arrow et al. 2011; Dasgupta et al. 2015; 
Michelsen et al. 2016; Okereke and Massaquoi 2017; UNEP 2018b; 
Haberl et al. 2019; Shang et al. 2019; Sugiawan et al. 2019).

177
Introduction and Framing Chapter 11Moreover, countries differ enormously in their respective situation 
regarding their development path  – a condition which affects 
their capability, goals, priorities and approach to the pursuit of 
sustainability (Shi et al. 2016; Ramos-Mejía et al. 2018; Okereke et al. 
2019). Most of the literature recognises that despite its limitations, 
sustainable development with its emphasis on integrating social, 
economic and environmental goals, provides a more comprehensive 
approach to the pursuit of planetary health and human well-being. 
Sustainable development is then not a static objective but a dynamic 
framework for measuring human progress (Costanza et al. 2016; 
Fotis and Polemis 2018), relevant for all countries even if different 
groups of nations experience the challenge of sustainability in 
different ways.
Much like sustainable development, concepts like low-carbon 
development (Mulugetta and Urban 2010; Yuan et al. 2011; Wang

et al. 2017; Tian et al. 2019), climate-compatible development 
(CCD) (Mitchell and Maxwell 2010; Tompkins et al. 2013; Stringer 
et al. 2014; Bickersteth et al. 2017) and more recently climate-
resilient development (CRD) (Fankhauser and McDermott 2016; 
Henly-Shepard et al. 2018; IPCC 2018b) have all emerged as 
ideas, tools and frameworks, intended to bring together the goals 
of climate mitigation and the SDGs, as well as development more 
broadly. Figure  1.5 suggests that the prospects for realising 
a climate-resilient and equitable world are enhanced by a process 
of transformation and development trajectories that seek to limit 
global warming while also achieving the SDGs. The SDGs represent 
medium-term goals, and long-term sustainability requires continued effort to keep the world along a climate-resilient development path. 
A  key feature of development or transformation pathways that 
achieve a climate-resilient world is that they maximise the synergies

and minimise the trade-offs between climate mitigation and other 
sustainable development goals (Klausbruckner et al. 2016; Thornton 
and Comberti 2017; Wüstemann et al. 2017; Dagnachew et al. 2018; 
Fuso Nerini et al. 2018; Mainali et al. 2018). Crucially, the nature of 
trade-offs and timing of related decisions will vary across countries 
depending on circumstances including the level of development, 
capability and access to resources (Cross-Chapter Box  5, Shifting 
Development Paths to Increase Sustainability, in Chapter 4).
Other concepts such as ‘Doughnut Economics’ (Raworth 2018), 
ecological modernisation, and mainstreaming are also used to 
convey ideals of development pathways that take sustainability, 
climate mitigation, and environmental limits seriously (Dale et al. 
2015a). Mainstreaming focuses on incorporating climate change 
into national development activities, such as the building of 
infrastructure (Wamsler and Pauleit 2016; Runhaar et al. 2018). The

‘green economy’ and green growth – growth without undermining 
ecological systems, partly by gaining economic value from cleaner 
technologies and systems and is inclusive and equitable in its 
outcomes – has gained popularity in both developed and developing 
countries as an approach for harnessing economic growth to address 
environmental issues (Bina 2013; Georgeson et al. 2017; Capasso 
et al. 2019; Song et al. 2020; Hao et al. 2021). However, critics argue 
that green economy ultimately emphasises economic growth to the 
detriment of other important aspects of human welfare such as social 
Figure  1.5 | A climate-resilient and equitable world requires limiting global warming while achieving the Sustainable Development Goals (SDGs).  
Source: IPCC (2018b).

178
Chapter 1  Intr oduction and Framing1justice (Death 2014; Adelman 2015; Kamuti 2015), and challenge 
the central idea that it is possible to decouple economic activity 
and growth (measured as GDP increment) from increasing use of 
biophysical resources (raw materials, energy) (Jackson and Victor 
2019; Parrique et al. 2019; Haberl et al. 2020; Hickel and Kallis 2020; 
Vadén et al. 2020).
Literature on degrowth, post growth, and post development 
questions the sustainability and imperative of more growth 
especially in already industrialised countries and argues that 
prosperity and the ‘Good Life’ are not immutably tied to economic 
growth (Asara et al. 2015; Escobar 2015; Latouche 2018; Kallis 
2019) (Section  5.2.1). The concept of Just Transition also stresses 
the need to integrate justice concerns so as to not impose hardship 
on already marginalised populations within and between countries 
(Evans and Phelan 2016; Goddard and Farrelly 2018; Heffron and

McCauley 2018; Smith, Jackie and Patterson 2018; McCauley 
and Heffron 2018) (Section 1.7.2). The key insight is that pursuing 
climate goals in the context of sustainable development requires 
holistic thinking including on how to measure well-being, serious 
consideration of the notion of ecological limits, at least some level 
of decoupling and certainly choices and decision-making approaches 
that exploit and maximise the synergy and minimise the trade-off 
between climate mitigation and other sustainable development 
goals. It also requires consideration of equity and justice within 
and between countries. However, ideas of a synergistic relationship 
between development and climate mitigation can sometimes offer 
limited practical guidelines for reconciling the tensions that are often 
present in practical policymaking (Ferguson et al. 2014; Dale et al. 
2015b; Kasztelan 2017; Kotzé 2018).
1.6.3 Climate Mitigation, Equity and the Sustainable 
Development Goals (SDGs)

Climate action can be conceptualised as both a  stand-alone and 
cross-cutting issue in the 2030 SDGs (Makomere and Liti Mbeva 
2018), given that several of the other goals such as ending poverty 
(SDG 1), zero hunger (SDG 2), good health and well-being (SDG 3), 
and affordable and clean energy (SDG 7), among many others, are 
related to climate change (Figure 3.39).
In addition to galvanising global collective action, the SDGs provide 
concrete themes, targets and indicators for measuring human 
progress to sustainability (Kanie and Biermann 2017). The SDGs also 
provide a basis for exploring the synergies and trade-offs between 
sustainable development and climate change mitigation (Pradhan 
et al. 2017; Fuso Nerini et al. 2018; Mainali et al. 2018; Makomere 
and Liti Mbeva 2018). Progress to date (Sachs et al. 2016) shows 
fulfilling SDGs is a challenge for all groups of countries – developed

7 The Historical Index of Human Development (HIHD) emulates the widely used Human Development Index (HDI) as they both summarise in indexes the key human 
development dimensions consisting of a healthy life, knowledge and a decent standard of living. HDI is based on: life expectancy, expected years of schooling of children, 
the mean years of schooling of the adult population, and gross national income (GNI) per capita adjusted for purchasing power; the HIHD is based on: life expectancy 
at birth, adult literacy rates, educational enrolment rates, and GDP per capita, and is used in Figure 1.6 because it is available for a longer time series (Prados de la 
Escosura 2015).
8 Based on global population projections of between 8 and 8.5 billion people in 2030, and GHG emissions levels from the C1, C2 and C3 categories of scenarios in 
Table 3.2 and Box 3.7.and developing  – even though the challenge differs between 
countries and regions (Pradhan et al. 2017).

Historically, the industrialisation associated with economic 
development has involved a strong relationship with GHG emissions 
(Section 5.2.1). Figure 1.6 shows per-capita GHG emissions on the 
vertical axis and Historical Index of Human Development (HIHD) 
levels (Prados de la Escosura 2015) on the horizontal axis.7 The 
grey line shows historic global average GHG emissions per capita 
and levels of human development over time, from 1870 to 2014. 
The current positions of different regions are shown by bubbles, 
with sizes representing total GHG emissions. Figure 1.6 also shows 
the estimated position of the SDGs zone for the year 2030, and 
a  ‘sustainable development corridor’ as countries reach towards 
higher HDI and lower emissions. To fulfil the SDGs, including SDG 13 
(climate action), the historic relationship needs to change.
The top of the SDG zone is situated around the global per-capita GHG 
emissions level of 5 tCO 2-eq required for the world to be path towards

fulfilling the Paris Agreement.8 The horizontal position of the SDG 
zone is estimated based on the HIHD levels (Prados de la Escosura 
2015) of countries that have been shown to either have achieved, 
or have some challenges, when it comes to SDG 3, SDG 4 and SDG 
8 (Sachs et al. 2016), as these SDGs are related to the constituent 
parts of the HIHD.  Beyond 2030, the sustainable development 
corridor allows for increasing levels of human development while 
lowering per-capita GHG emissions.
Figure 1.6 shows that at present, regions with HIHD levels of around 
0.5 all have emissions at or above about 5 tCO 2-eq per capita (even 
more so on a  consumption footprint basis; see Figure  1.1c,d), but 
there are wide variations within this. Indeed, there are regions with 
HIHD levels above 0.8 which have GHG per-capita emissions lower 
than several with HIHD levels of around 0.5. The mitigation challenge 
involves countries at many different stages of development seeking

paths towards higher welfare with low emissions.
From Figure  1.6, there are two distinct dimensions to sustainable 
development pathways for fulfilling the SDGs. In terms of per-capita 
GHG emissions (the vertical), some regions have such low levels that 
they could increase and still be below the global average required 
in 2030 for the world to be on path to fulfil the Paris Agreement. 
Meanwhile, other regions with high per-capita GHG emissions would 
require a  rapid transformation in technologies and practices. It is 
against this background that Dubash (2019) emphasises placing the 
need for urgent action on climate change in the context of domestic 
political priorities and the institutions within which national 
frameworks are crystallised.

179
Concerns over equity in the context of growing global inequality 
and very tight remaining global carbon budgets have motivated 
an emphasis on equitable access to sustainable development 
(Peters et al. 2015; Kartha et al. 2018b; Matthews et al. 2019; van den 
Berg et al. 2019). This literature emphasises the need for less developed 
countries to have sufficient room for development while addressing 
climate change (Winkler et al. 2013; Pan et al. 2014; Gajevic Sayegh 
2017; Robinson and Shine 2018; Warlenius 2018). Meanwhile, many countries reliant on fossil fuels, related technologies and economic 
activities, are eager to ensure tax revenues are maintained, workers 
and industries have income and justice is embedded in the economic 
transformations required to limit GHG emissions (Cronin et al. 2021).
Correlation between CO 2 emission intensity, or absolute emission 
and gross domestic product growth, is not rigid, unambiguous and

deterministic (Ojekunle et al. 2015), but the extent to which SDGs Developed Countries
Eastern Europe and West Central Asia Latin America and CaribbeanMiddle East Asia and Paciﬁc
Africa and Middle East
–505101525
20GHG emissions per capita (tonnes CO 2-eq)
0.1 1.0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Development level (Historical Index of Human Development)Net negative emissions0.0Historical global per capita emissions 
and development from 1870 to 2014Historical global per capita emissions 
and development from 1870 to 2014Australia and 
USA and 
Canada
Eurasia
Middle 
East
Northern and 
Southern and 
Eastern EuropeJapan
South AmericaSouth-East Asia
Meso AmericaSouthern and Middle Africa
India and Sri LankaCaribbeanEastern Africa
Rest of Southern AsiaSustainable Development CorridorSustainable Development Corridor
(1870)Sustainable 
Development PathwaysSustainable 

PathwaysSustainable 
Pathways
Figure 1.6 | Sustainable development pathways towards fulfilling the Sustainable Development Goals (SDGs).  The graph shows global average per-capita 
GHG emissions (vertical axis) and relative ‘Historic Index of Human Development’ (HIHD) levels (horizontal) have increased globally since the industrial revolution (grey line). 
The bubbles on the graph show regional per-capita GHG emissions and human development levels in the year 2015, illustrating large disparities. Pathways towards fulfilling the 
Paris Agreement (and SDG 13) involve global average per-capita GHG emissions below about 5 tCO 2-eq by 2030. Likewise, to fulfil SDGs 3, 4 and 8, HIHD levels (see footnote 7) 
need to be at least 0.5 or greater. This suggests a ‘sustainable development zone’ for year 2030 (in pale brown); the in-figure text also suggests a ‘sustainable development

corridor’, where countries limit per-capita GHG emissions while improving levels of human development over time. The emphasis of pathways into the sustainable development 
zone differ (dashed brown arrows), but in each case transformations are needed in how human development is attained while limiting GHG emissions.

180
Chapter 1  Intr oduction and Framing1and economic growth expectations can be fulfilled while decoupling 
GHG emissions remains a  concern (Haberl et al. 2020; Hickel and 
Kallis 2020). Below some thresholds of absolute poverty, more 
consumption is necessary for development to lead to well-being 
(Section  5.2.1.1), which may not be the case at higher levels of 
consumption (Lamb and Steinberger 2017; Steinberger et al. 2020) 
(Section 1.7.2).
In conclusion, achieving climate stabilisation in the context of 
sustainable development and efforts to eradicate poverty requires 
collective action and exploiting synergies between climate action 
and sustainable development, while minimising the impact of trade-
offs (Najam 2005; Okereke and Massaquoi 2017; Makomere and Liti 
Mbeva 2018; Dooley et al. 2021). It also requires a focus on equity 
considerations to avoid climate-induced harm, as well as unfairness 
that can result from urgent actions to cut emissions (Pan et al.

2014; Robiou du Pont et al. 2017; Kartha et al. 2018a). This is ever 
more important as the diminishing carbon budget has intensified 
debates on which countries should have the greatest claim to the 
‘remaining space’ for emissions (Raupach et al. 2014) or production 
(McGlade and Ekins 2015), amplified by persistent concerns over 
the insufficiency of support for means of implementation, to support 
ambitious mitigation efforts (Pickering et al. 2015; Weikmans and 
Roberts 2019).
1.7 Four Analytic Frameworks 
for Understanding Mitigation 
Response Strategies
Climate change is unprecedented in its scope (sectors, actors and 
countries), depth (major transformations) and time scales (over 
generations). As such, it creates unique challenges for analysis. It has 
been called ‘the greatest market failure in history’ (Stern 2007); the 
‘perfect moral storm’ (Gardiner 2006) and a ‘super wicked problem’ 
(Lazarus 2009; Levin et al. 2012) – one which appears difficult to solve

through the traditional tools and assumptions of social organisation 
and analysis.
To complement the extensive literature on risks and decision-
making under uncertainty reviewed in AR6 WGII (notably, 
Chapter  19), this section summarises insights and developments 
in key analytic frameworks and tools particularly relevant to 
understanding specific mitigation strategies, policies and other 
actions, including explaining the observed if limited progress 
to date. Organised partly as reflected in the quotes above, these 
include aggregated (principally, economic) frameworks to evaluate 
system-level choices; ethical perspectives on values and equity 
including stages of development and distributional concerns; and 
transition frameworks which focus on the processes and actors 
involved in major technological and social transitions. These need 
to be complemented by a  fourth set of approaches which shine 
more light on psychological/behavioural and political factors. All

these frameworks are relevant, and together they point to the 
multiple perspectives and actions required if the positive drivers 
of emission reduction summarised in Section 4 are to outweigh the 
barriers and overcome the constraints.1.7.1 Aggregated Approaches: Economic Efficiency 
and Global Dynamics of Mitigation
Some of the most established and influential approaches to 
understanding the aggregate causes and consequences of climate 
change and mitigation across societies, draw upon economic 
theories and modelling to generate global emission pathways in 
the absence of climate policies and to study alternative mitigation 
pathways (described in detail in Section 3.2.5, and Appendix 3). The 
underlying economic concepts aggregate wealth or other measures 
of welfare based on utilitarian ethical foundations, and in most 
applications, a  number of additional assumptions detailed in AR5 
(Chapters 2 and 3).
1.7.1.1 Cost-benefit Analysis and Cost-effectiveness Analysis

Such global aggregate economic studies coalesce around two main 
questions. One, as pioneered by Nordhaus (1992, 2008) attempts to 
monetise overall climate damages and mitigation costs so as to strike 
a ‘cost-benefit optimum’ pathway. More detailed and empirically-
grounded ‘cost-effectiveness analysis’ explores pathways that would 
minimise mitigation costs (Ekholm 2014; IPCC 2014a Section  2.5; 
Weyant 2017) for given targets (e.g.,  as agreed in international 
negotiations, see Section 3.2 in Chapter 3). Both approaches recognise 
that resources are limited and climate change competes with other 
priorities in government policymaking, and are generally examined 
with some form of Integrated Assessment Model (IAM) (Section 1.5 
and Appendix III). Depending on the regional disaggregation of the 
modelling tools used and on the scope of the analyses, these studies 
may or may not address distributional aspects within and across

nations associated with climate policies (Bauer et al. 2020).
For at least 10 to 15 years after the first computed global cost-benefit 
estimate (Nordhaus 1992), the dominant conclusions from these 
different approaches seemed to yield very different recommendations, 
with cost-benefit studies suggesting lenient mitigation compared 
to the climate targets typically recommended from scientific risk 
assessments (Weyant 2017). Over the past 10 to 15 years, literature 
has made important strides towards reconciling these two approaches, 
both in the analytic methods and the conclusions arising.
Damages and risks. Incorporating impacts which may be extremely 
severe but are uncertain (known as ‘fat tails’ (Weitzman 2009, 
2011)), strengthens the economic case for ambitious action to avoid 
risks of extreme climate impacts (Ackerman et al. 2010; Fankhauser 
et al. 2013; Dietz and Stern 2015). The salience of risks has also been

amplified by improved understanding of climate ‘tipping points’ 
(Lontzek et al. 2015; Lenton et al. 2019); valuations should reflect 
that cutting emissions reduces not only average expected damages, 
but also the risk of catastrophic events (IWG 2021).
Discounting. The role of time discounting in weighting future climate 
change impacts against today’s costs of mitigating emissions has been 
long recognised (Weitzman 1994, 2001; Nordhaus 2007; Stern 2007; 
Dasgupta 2008). Its importance is underlined in analytical Integrated 
Assessment Models (IAMs) (Golosov et al. 2014; van  den Bijgaart 
et al. 2016; van der Ploeg and Rezai 2019) (Annex III). Economic

181
Introduction and Framing Chapter 11literature suggests applying risk-free, public, and long-term interest 
rates when evaluating overall climate strategy (Weitzman 2001; 
Dasgupta 2008; Arrow et al. 2013; Groom and Hepburn 2017). Expert 
elicitations indicate values around 2% (majority) to 3% (Drupp et al. 
2018). This is lower than in many of the studies reviewed in earlier 
IPCC assessments, and many IAM studies since, and by increasing the 
weight accorded to the future would increase current ‘optimal effort’. 
The US Interagency Working Group on the Social Cost of Carbon used 
3% as its central value (IAWG 2016; Li and Pizer 2018; Adler et al. 
2017). Individual projects may require specific risk adjustments.
Distribution of impacts. The economic damages from climate 
change at the nationally aggregated and sub-national level are very 
diverse (Moore et al. 2017; Ricke et al. 2018; Carleton et al. 2020). 
A ‘global damage function’ necessarily implies aggregating impacts

across people and countries with different levels of income, and over 
generations, a process which obscures the strategic considerations 
that drive climate policymaking (Keohane and Oppenheimer 2016). 
Economics acknowledges there is no single, objectively defined 
‘social welfare function’ (IPCC 1995, 2014a). This applies also to 
the distribution of responses: both underline the relevance of equity 
(next section) and global negotiations to determine national and 
collective objectives.
Obvious limitations arise from these multiple difficulties in assessing 
an objective, globally acceptable single estimate of climate change 
damages (e.g., Arrow et al. 2013; Pindyck 2013; Auffhammer 2018; 
Stern et al. 2021), with some arguing that agreement on a specific 
value can never be expected (Rosen and Guenther 2015; Pezzey 2018). 
A  new generation of cost-benefits analysis, based on projections 
of actual observed damages, results in stronger mitigation efforts

as optimal (Glanemann et al. 2020; Hänsel et al. 2020). Overall, 
the combination of improved damage functions with the wider 
consensus on low discount rates (as well as lower mitigation costs 
due to innovation) has increasingly yielded ‘optimal’ results from 
benefit-cost studies in line with the range established in the Paris 
Agreement (Cross-Working Group Box 1 in Chapter 3).
Hybrid cost-benefit approaches that extend the objective of the 
optimisation beyond traditional welfare, adding some form of 
temperature targets as in Llavador et al. (2015) and Held (2019) also 
represent a step in bridging the gap between the two approaches and 
result in proposed strategies much more in line with those coming 
from the cost-effectiveness literature. Approaching from the opposite 
side, cost-effectiveness studies have looked into incorporating 
benefits from avoided climate damages, to improve the assessment 
of net costs (Drouet et al. 2021).

Cost-benefit IAMs utilise damage functions to derive a social cost of 
CO2 emissions’ (SCC  – the additional cost to society of a  pulse 
of  CO 2 emissions). One review considered that ‘the best estimate’ 
of the optimal (near-term) level ‘still ranges from a few tens to a few 
hundreds of dollars per ton of carbon’ (Tol 2018), with various recent 
studies in the hundreds, taking account of risks (Taconet et al. 2019), 
learning (Ekholm 2018) and distribution (Ricke et al. 2018). In addition 
to the importance of uncertainty/risk, aggregation, and realistic 
damage functions as noted, on which some progress has been made, some reviews additionally critique how IAMs represent abatement 
costs in terms of energy efficiency and innovation (e.g., Farmer et al. 
2015; Rosen and Guenther 2015; Keen 2021) (Sections  1.7.3 and 
1.7.4). IAMs may better reflect associated ‘rebound’ at system level 
(Saunders et al. 2021), and inefficient implementation would raise

mitigation costs (Homma et al. 2019); conversely, co-benefits – most 
extensively estimated for air quality, valued at a  few tens of USD 
per tCO 2-eq across 16 studies (Karlsson et al. 2020) – complement 
global with additional local benefits (Table 1.2).
Whereas many of these factors affect primarily cost-benefit 
evaluation, discounting also determines the cost-effective trajectory: 
Emmerling et al. (2019) find that, for a  remaining budget of 
1000 GtCO 2, reducing the discount rate from 5% to 2% would more 
than double current efforts, limit ‘overshoot’, greatly reduce a late 
rush to negative emissions, and improve intergenerational justice by 
more evenly distributing policy costs across the 21st century.
1.7.1.2 Dynamic Efficiency and Uncertainty
Care is required to clarify what is optimised (Dietz and Venmans 
2019). Optimising a  path towards a  given temperature goal by 
a fixed date (e.g., 2100) gives time-inconsistent results backloaded to

large, last-minute investment in carbon dioxide removal (CDR). ‘Cost-
effective’ optimisations generate less initial effort than equivalent 
cost-benefit models (Dietz and Venmans 2019; Gollier 2021) as they 
do not incorporate benefits of reducing impacts earlier.
‘Efficient pathways’ are affected by inertia and innovation. Inertia 
implies amplifying action on long-lived investments and infrastructure 
that could otherwise lock-in emissions for many decades (Vogt-Schilb 
et al. 2018; Baldwin et al. 2020). Chapter 3 (Section 3.5) discusses 
interactions between near-, medium- and long-term actions in 
global pathways, particularly vis-à-vis inertia. Also, to the extent 
that early action induces low-carbon innovation, it ‘multiplies’ the 
optimal effort (for given damage assumptions), because it facilitates 
subsequent cheaper abatement. For example, a ‘learning-by-doing’ 
analysis concludes that early deployment of expensive PV was of net

global economic benefit, due to induced innovation (Newbery 2018).
Research thus increasingly emphasises the need to understand 
climate transformation in terms of dynamic, rather than static, 
efficiency (Gillingham and Stock 2018). This means taking account of 
inertia, learning and various additional sources of ‘path-dependence’. 
Including induced innovation in stylised IAMs can radically change 
the outlook (Acemoglu et al. 2012, 2016), albeit with limitations 
(Pottier et al. 2014); many more detailed-process IAMs now do 
include endogenous technical change (as reviewed in Yang et al. 
2018 and Grubb et al. 2021b) (Annex III).
These dynamic and uncertainty effects typically justify greater upfront 
effort (Kalkuhl et al. 2012; Bertram et al. 2015), including accelerated 
international diffusion (Schultes et al. 2018), and strengthen optimal 
initial effort in cost-benefit models (Baldwin et al. 2020; Grubb et al.

2021b). Approaches to risk premia common in finance would similarly 
amplify the initial mitigation effort, declining as uncertainties reduce 
(Daniel et al. 2019).

182
Chapter 1  Intr oduction and Framing11.7.1.3 Disequilibrium, Complex Systems  
and Evolutionary Approaches
Other approaches to aggregate evaluation draw on various branches 
of intrinsically non-equilibrium theories (e.g.,  Chang 2014). These 
including long-standing theories from the 1930s (e.g., Schumpeter 
1934; Keynes 1936) to understand situations of structurally 
underemployed resources, potential financial instabilities (Minsky 
1986), and related economic approaches which emphasise time 
dimensions (e.g.,  recent reviews in Legrand and Hagemann 2017; 
Stern 2018). More recently developing have been formal economic 
theories of endogenous growth building on, for example, Romer 
(1986), and developments of Schumpeterian creative destruction 
(Aghion et al. 2021) and evolutionary economic theories which 
abandon any notion of full or stable resource utilisation even 
as a  reference concept (Nelson and Winter 1982; Freeman and

Perez 1988; Carlsson and Stankiewicz 1991; Freeman and Louçã 
2001; Perez 2001).
The latter especially are technically grounded in complex system 
theories (e.g.,  Arthur 1989, 1999; Beinhocker 2007; Hidalgo and 
Hausmann 2009). These take inherently dynamic views of economies 
as continually evolving systems with continuously unfolding and 
path-dependent properties, and emphasise uncertainty in contrast to 
any predictable or default optimality. Such approaches have been 
variously applied in policy evaluation (Walton 2014; Moore et al. 
2018), and specifically for global decarbonisation (e.g., Barker and 
Crawford-Brown 2014) using global simulation models. Because 
these have no natural reference ‘least lost’ trajectory, they illustrate 
varied and divergent pathways and tend to emphasise the diversity 
of possibilities and relevant policies, particularly linked to innovation 
and potentially ‘sensitive intervention points’ (Farmer et al. 2019)

(Section  1.7.3). They also illustrate that different representations 
of innovation and financial markets together can explain why 
estimated impacts of mitigation on GDP can differ very widely 
(potentially even in sign), between different model types (Chapter 15, 
Section 15.6.3 and Box 15.7).
1.7.2 Ethical Approaches
Gardiner’s (2011) book on climate change as ‘The Perfect Moral 
Storm’ identified three ‘tempests’. Its global dimension, in a world 
of sovereign states which have only fragmentary responsibility 
and control, makes it ‘difficult to generate the moral consideration 
and necessary political will’. Its impacts are intergenerational but 
future generations have no voice in contemporary affairs, the usual 
mechanism for addressing distributional injustices, amplified by 
the intrinsic inequity of wealthy big emitters impacting particularly 
poorer victims. He argues that these are exacerbated by a  third, 
theoretical failure to acknowledge a  central need for ‘moral

sensitivity, compassion, transnational and transgenerational care, 
and other forms of ethical concern to rise to the surface’ to help 
guide effective climate action. As noted in Section 1.4.6, however, 
equity and ethics are both a driver of and constraint on mitigation.1.7.2.1 Ethics and Values
A large body of literature examines the critical role of values, ethics, 
attitudes, and behaviours as foundational frames for understanding 
and assessing climate action, sustainable development and societal 
transformation ( IPCC 2014a Chapter 3). Most of this work is offered 
as a  counterpoint or critique to mainstream literature’s focus on 
the safeguarding of economic growth of nations, corporations 
and individuals (Castree 2017; Gunster 2017). These perspectives 
highlight the dominance of economic utilitarianism in western 
philosophical thought as a key driver for unsustainable consumption 
and global environmental change (Hoeing et al. 2015; Popescu 2016).

Entrenching alternative values that promote deep decarbonisation, 
environmental conservation and protection across all levels of society 
is then viewed as foundational component of climate-resilient and 
sustainable development and for achieving human rights, and a safe 
climate world (Evensen 2015; Jolly et al. 2015; Popescu 2016; Tàbara 
et al. 2019). The UN Human Rights Office of the High Commissioner 
has highlighted the potentially crucial role of human rights in relation 
to climate change (UNHCR 2018). While acknowledging the role of 
policy, technology, and finance, the ‘managerialist’ approaches, that 
emphasise ‘technical governance’ and fail to challenge the deeper 
values that underpin society, may not secure the deep change 
required to avert dangerous climate change and other environmental 
challenges (Hartzell-Nichols 2014; Steinberger et al. 2020).
Social justice perspectives emphasise the distribution of

responsibilities, rights, and mutual obligations between nations 
in navigating societal transformations (Gawel and Kuhlicke 2017; 
Leach et al. 2018; Patterson et al. 2018). Current approaches to 
climate action may fail to match what is required by science because 
they tend to circumvent constraints on human behaviour, especially 
constraints on economic interest and activity. Related literature 
explores governance models that are centred on environmental limits, 
planetary boundaries and the moral imperative to prioritise the poor 
in earth systems governance (Carley and Konisky 2020; Kashwan 
et al. 2020), with emphasis on trust and solidarity as foundations 
for global cooperation on climate change (Jolly et al. 2015). A key 
obstacle is that the economic interests of states tend to be stronger 
than the drivers for urgent climate action (Bain 2017).
Short-term interests of stakeholders are acknowledged to impede 
the reflection and deliberation needed for climate mitigation

and adaptation planning (Hackmann 2016; Sussman et al. 2016; 
Schlosberg et al. 2017; Herrick 2018). Situationally appropriate 
mitigation and adaptation policies at both national and international 
level may require more ethical self-reflection (Herrick 2018), including 
self-transcendent values such as universalism and benevolence, 
and moderation which are positively related to pro-environmental 
behaviours (Jonsson and Nilsson 2014; Katz-Gerro et al. 2015; Braito 
et al. 2017; Howell and Allen 2017).
Another strong theme in the literature concerns recognition of 
interdependence including the intimate relationship between 
humans and the non-human world (Hannis 2016; Gupta and Racherla 
2018; Howell and Allen 2017), with such ecological interdependence

183
Introduction and Framing Chapter 11offered as an organising principle for enduring transformation 
to sustainability. A  key policy implication of this is moving away 
from valuing nature only in market and monetary terms to strongly 
incorporating existential and non-material value of nature in natural-
resource accounting (Neuteleers and Engelen 2015; Shackleton et al. 
2017; Himes-Cornell et al. 2018). There has been increasing attention 
on ways to design climate policy frameworks to help reconcile 
ecological virtue (with its emphasis on the collective) with individual 
freedoms and personal autonomy (Kasperbauer 2016; Nash et al. 2017; 
Xiang et al. 2019). In such a framework, moderation, fairness, and 
stewardship are all understood and promoted as directly contributing 
to the ‘good life’. Such approaches are deemed vital to counteract 
tendencies to ‘free ride’, and to achieve behavioural changes often 
associated with tackling climate change (Section 5.2.1).

Some literature suggests that attention to emotions, especially 
with regards to climate communication, could help societies and 
individuals act in ways that focus less on monetary gain and more 
on climate and environmental sustainability (Bryck and Ellis 2016; 
Chapman et al. 2017; Nabi et al. 2018; Zummo et al. 2020).
1.7.2.2 Equity and Representation: International Public 
Choice Across Time and Space
Equity perspectives highlight three asymmetries relevant for climate 
change (Okereke and Coventry 2016; Okereke 2017) (Section 1.4.6). 
Asymmetry in contribution highlights different contributions to climate 
change both in historical and current terms, and applies both within 
and between states as well as between generations (Caney 2016; 
Heyward and Roser 2016). Asymmetry in impacts highlights the fact 
that the damages will be borne disproportionately across countries, 
regions, communities, individuals and gender; moreover, it is often

those that have contributed the least that stand to bear the greatest 
impact of climate change (IPCC 2014a; Shi et al. 2016). Asymmetry in 
capacity highlights differences of power between groups and nations 
to participate in climate decision and governance, including the 
capacity to implement mitigation and adaptation measures.
If attention is not paid to equity, efforts designed to tackle climate 
change may end up exacerbating inequities among communities and 
between countries (Heffron and McCauley 2018). The implication is 
that to be sustainable in the long run, mitigation involves a central 
place for consideration of justice, both within and between countries 
(Chapters 4 and 14). Arguments that the injustices following from 
climate change are symptomatic of a more fundamental structural 
injustice in social relations, are taken to imply a need to address the 
deeper inequities within societies (Routledge et al. 2018).

differently, with the poor likely to be more affected (Section 1.6.1). 
Ideas of Just Transitions (outlined in Section  1.8.2.) often have 
a  national focus in the literature, but also imply that mitigation 
should not increase the asymmetries between rich and poor countries, 
implying a  desire for transitions which seek to reduce (or at least 
avoid adverse) distributional affects. Thus, it comes into play in the 
timing of zero emissions (Chapters 3 and 14). International climate 
finance in which rich countries finance mitigation and adaptation in poor countries is also essential for reducing the asymmetries between 
rich and poor countries (Section 1.6.3 and Chapter 15).
Equity across generations  – the distribution between the present 
and future generations  – also matters. One aspect is discounting 
(Section  1.7.1). Another approach has been to study the burdens

on each generation following from the transition to low-carbon 
economies (IPCC 2014a Chapter 3) (Cross-Working Group Box 3 in 
Chapter  12). Suggestions include shifting more investments into 
‘natural capital’, so that future generations will inherit less physical 
capital but a better environment, or financing mitigation efforts today 
using governmental debt redeemed by future generations (Heijdra 
et al. 2006; Broome 2012; Karp and Rezai 2014; Hoel et al. 2019).
1.7.3 Transition and Transformation Processes
This report uses the term transition as the process, and transformation 
as the overall change or outcome, of large-scale shifts in technological, 
economic and social systems, called socio-technical systems in 
the innovation literature. Typically, new technologies, ideas and 
associated systems initially grow slowly in absolute terms, but may 
then ‘take-off’ in a phase of exponential growth as they emerge from

a  position of niche into mainstream diffusion, as indicated by the 
‘S-curve’ growth in Figure  1.7 (lower panel). These dynamics arise 
from interactions between innovation (in technologies, companies 
and other organisations), markets, infrastructure and institutions, 
at multiple levels (Geels et al. 2017; Kramer 2018). Consequently, 
interdisciplinary perspectives are needed (Turnheim et al. 2015; 
Geels et al. 2016; Hof et al. 2020). Beyond aggregated economic 
perspectives on dynamics (Section  1.7.1.2), these emphasise the 
multiple actors and processes involved.
Technological Innovation Systems (TIS) frameworks (Section  16.4) 
focus on processes and policies of early innovation and ‘emergence’, 
which combine experimentation and commercialisation, involving 
Strategic Niche Management (Rip and Kemp 1998; Geels and Raven 
2006). Literatures on the wider processes of transition highlight 
different stages (e.g.,  Cross-Chapter Box  12 in Chapter  16) and

scales across three main levels, most generally termed micro, meso 
and macro (Rotmans et al. 2001).
The widely-used Multi-Level Perspective or MLP (Geels 2002) identifies 
the meso level as the established ‘socio-technical (ST) regime’, 
a set of interrelated sub-systems which define rules and regulatory 
structures around existing technologies and practices. The micro level 
is an ecosystem of varied niche alternatives, and overlaying the ST 
regime is a macro ‘landscape’ level. Transitions often start with niche 
alternatives (Grin et al. 2010; Köhler et al. 2019), which may break 
through to wider diffusion (second stage in Figure 1.8), especially if 
external landscape developments ‘create pressures on the regime that 
lead to cracks, tensions and windows of opportunity’ (Rotmans et al. 
2001; Geels 2010); an example is climate change putting sustained 
pressure on current regimes of energy production and consumption

(Kuzemko et al. 2016). There are continual interactions between 
landscape, regime and niches, with varied implications for Transition 
Management (Rotmans et al. 2001; Loorbach 2010).

184
R&D, demand-pull, infrastructure and industrial development
Established technologies
Prices, taxes, market structures,
planning and regulation
STRATEGIC INVESTMENT
Standards, engagement and dissemination programmes
NORMS AND BEHAVIOUR
MARKET
MACRO
MESO
MICRO
LANDSCAPE
SOCIO-
TECHINCAL
REGIME
NICHE
Unsuccessful technology
Niche alternatives
Policy pillars
(relative importance at different stages)
Stages of
Spread of technology/behaviour/idea
Time
Reconﬁguration
EMERGENCEBREAKTHROUGH
& DIFFUSIONMATURATION
Figure 1.7 | Transition dynamics: levels, policies and processes.  Note: the lower panel illustrates growth of innovative technologies or practices, which if successful 
emerge from niches into an S-shape dynamic of exponential growth. The diffusion stage often involves new infrastructure and reconfiguration of existing market and regulatory

structures (known in the literature as the ‘socio-technical regime’). During the phase of more widespread diffusion, growth levels off to linear, then slows as the industry and 
market matures. The processes displace incumbent technologies/practices which decline, initially slowly but then at an accelerating pace. Many related literatures identify three 
main levels with different characteristics, most generally termed micro, meso and macro. Transitions can be accelerated by policies appropriately targeted, which may be similarly 
grouped and sequenced (upper panel) in terms of three corresponding pillars of policy (Section 1.7.3): generally all are relevant, but their relative importance differs according 
to the stage of the transition.

185
Introduction and Framing Chapter 11In contrast to standard economic metrics of marginal or 
smooth change (e.g.,  elasticities), transition theories emphasise 
interdisciplinary approaches and the non-linear dynamics, social, 
economic and environmental aspects of transitions to sustainability 
(Cherp et al. 2018; Köhler et al. 2018). This may explain persistent 
tendencies to underestimate the exponential pace of change now 
being observed in renewable electricity (Chapters  2 and 6) and 
emerging in mobility (Chapter 10).
Recent decades have seen parallel broadening of economic 
perspectives and theories. Building also on the New Institutional 
Economics literatures, Building on the New Institutional Economics 
literature (Williamson 2000), Grubb et al. (2014, 2015) classify these 
into three ‘domains of economic decision-making’ associated with 
different branches of economic theory, respectively (i) behavioural

and organisational; (ii) neoclassical and welfare; and (iii) evolutionary 
and institutional. Like MLP , these are related to different social and 
temporal scales, as applied also in studying the ‘adaptive finance’ 
in UK electricity transition (Hall et al. 2017). There are significant 
differences but these approaches all point to understanding the 
characteristics of different actors, notably, individuals/local actors; 
larger corporate organisations (public or private); and (mainly) public 
authorities, each with different decision-making characteristics.
Sustainability may require purposeful actions at the different levels 
to foster the growth of sustainable technologies and practices, 
including support for niche alternatives (Grin et al. 2010). The middle 
level (established ‘socio-technical regime’) tends to resist major 
change, reforms generally involve pressures from the other two 
levels. Thus, transitions can be accelerated by policies appropriately

targeting relevant actors at the different levels (Köhler et al. 2019), 
the foundations for ‘three pillars of policy’ (Grubb et al. 2014), which 
logically evolve in the course of transition (Figure 2.6a). Incumbent 
industries have to adapt if they are to thrive within the growth of 
new systems. Policy may need to balance existing socio-technical 
systems with strategic investment and institutional development 
of the emerging niches (e.g., the maintenance of energy provision 
and energy security with the development of renewables), and help 
manage declining industries (Koasidis et al. 2020).
There is usually a  social dimension to such transitions. Key 
elements include capacity to transform (Folke et al. 2010), planning, 
and interdisciplinarity (Woiwode 2013). The Second World War 
demonstrated the extent to which crises can motivate (sometimes 
positive) change across complex social and technical systems,

including industry, and agriculture which then doubled its productivity 
over 15 years (Roberts and Geels 2019b). In practice, climate change 
may involve a combination of (reactive) transformational adaptation, 
and (proactive) societal transformation (Feola 2015), the latter 
seen as reorientation (including values and norms) in a sustainable 
direction (Section 5.4), including, for example, ‘democratisation’ in 
energy systems (Sorman et al. 2020). Business change management 
principles could be relevant to support positive social change (Stephan 
et al. 2016). Overall, effective transitions rest on appropriate enabling 
conditions, which can also link socio-technical transitions to broader 
development pathways (Cross-Chapter Box 12 in Chapter 16).Transition theories tend to come from very different disciplines and 
approaches compared to either economics or other social sciences, 
with less quantification, notwithstanding evolutionary and complex

system models (Section  1.7.1.3). However, a  few distinct types of 
quantitative models of ‘socio-technical energy transition’ (Li et al. 
2015) have emerged. For policy evaluation, transitions can be viewed 
as processes in which dynamic efficiency (Section 1.7.2) dominates 
over static allocative efficiency, with potential ‘positive intervention 
points’ (Farmer et al. 2019). Given inherent uncertainties, there are 
obvious risks (e.g., Alic and Sarewitz 2016). All this may make an 
evaluation framework of risks and opportunities more appropriate 
than traditional cost-benefit (Mercure et al. 2021), and (drawing on 
lessons from renewables and electric vehicles) create foundations 
for sector-based international ‘positive sum cooperation’ in climate 
mitigation (Sharpe and Lenton 2021).
1.7.4 Approaches From Psychology and Politics 
of Changing Course
The continued increase in global emissions to 2019, despite three

decades of scientific warnings of ever-greater clarity and urgency, 
motivates growing attention in the literature to the psychological 
‘faults of our rationality’ (Bryck and Ellis 2016), and the political 
nature of climate mitigation.
1.7.4.1 Psychological and Behavioural Dimensions
The AR5 emphasised that decision processes often include both 
deliberate (‘calculate the costs and benefits’) and intuitive thinking, 
the latter utilising emotion- and rule-based responses that are 
conditioned by personal past experience, social context, and cultural 
factors (e.g.,  Kahneman 2003), and that laypersons tend to judge 
risks differently than experts  – for example, ‘intuitive’ reactions 
are often characterised by biases to the status quo and aversion to 
perceived risks and ambiguity (Kahneman and Tversky 1979). Many 
of these features of human reasoning create ‘psychological distance’ 
from climate change (Spence et al. 2012; Marshall 2014). These can

impede adequate personal responses, in addition to the collective 
nature of the problem, where such problems can take the form of 
‘uncomfortable knowledge’, neglected and so becoming ‘unknown 
knowns’ (Sarewitz 2020). These decision processes, and the 
perceptions that shape them, have been studied through different 
lenses from psychology (Weber 2016) to sociology (Guilbeault 
et al. 2018), and media studies (Boykoff 2011). Karlsson and Gilek 
(2020) identify science denialism and ‘decision thresholds’ as key 
mechanisms of delay.
Experimental economics (Allcott 2011) also helps explain why 
cost-effective energy efficiency measures or other mitigation 
technologies are not taken up as fast or as widely as the benefits 
might suggest, including procrastination and inattention, as 
‘we often resist actions with clear long-term benefits if they are 
unpleasant in the short run’ (Allcott and Mullainathan 2010). 
Incorporating behavioural and social dynamics in models is required

particularly to better represent the demand side (Nikas et al. 2020), 
for example, Safarzyńska (2018) demonstrates how behavioural

186
Chapter 1  Intr oduction and Framing1factors change responses to carbon pricing relative to other 
instruments. A  key perspective is to eschew ‘either/or’ between 
economic and behavioural frameworks, as the greatest effects 
often involve combining behavioural dimensions (e.g.,  norms, 
social influence networks, convenience and quality assurance) with 
financial incentives and information (Stern et al. 2010). Randomised, 
controlled field trials can help predict the effects of behavioural 
interventions (Levitt and List 2009; McRae and Meeks 2016; Gillan 
2017). Chapter 5 explores both positive and negative dimensions 
of behaivour in more depth, including the development of norms 
and interactions with the wider social context, with emphasis upon 
the services associated with human well-being, rather than the 
economic activities per se.
1.7.4.2 Socio-political and Institutional Approaches
Political and institutional dynamics shape climate change responses in

important ways, not least because incumbent actors have frequently 
blocked climate policy (Section  1.4.5). Institutional perspectives 
probe networks of opposition (Brulle 2019) and emphasise that 
their ability to block – as well as the ability of others to foster low-
carbon transitions  – are structured by specific institutional forms 
across countries (Lamb and Minx 2020). National institutions 
have widely been developed to promote traditionally fossil fuel-
based sectors like electricity and transport as key to economic 
development, contributing to carbon lock-in (Seto et al. 2016) and 
inertia (Rosenschöld et al. 2014).
The influence of interest groups on policymaking varies across 
countries. Comparative political economy approaches tend to 
find that countries where interests are closely coordinated by 
governments (‘coordinated market economies’) have been able to 
generate transformative change more than those with a more arms-

length, even combative relationship between interest groups and 
governments (‘liberal market economies’) (Lachapelle and Paterson 
2013; Ćetković and Buzogány 2016; Zou et al. 2016; Meckling 2018). 
‘Developmental states’ often have the capacity for strong intervention 
but any low-carbon interventions may be overwhelmed by other 
pressures and very rapid economic growth (Wood et al. 2020a).
Institutional features affecting climate policy include levels and 
types of democracy (Povitkina 2018), electoral systems, or levels 
of institutional centralisation (federal vs unitary states, presidential 
vs parliamentary systems) (Lachapelle and Paterson 2013; Steurer 
and Clar 2018; Clulow 2019). Countries that have constructed 
an overarching architecture of climate governance institutions 
(e.g.,  cross-department and multi-level coordination, and semi-
autonomous climate agencies), are more able to develop the strategic 
approaches to climate governance needed to foster transformative

change (Dubash 2021).
Access of non-governmental organisations (NGOs) to policy 
processes enables new ideas to be adopted, but too close an NGO-
government relation may stifle innovation and transformative action 
(Dryzek et al. 2003). NGO campaigns on fracking (Neville et al. 
2019) or divestment (Mangat et al. 2018) have raised attention to 
ideas such as ‘stranded assets’ in policy arenas (Green 2018; Piggot 2018; Newell et al. 2020; Paterson 2021). Attempts to depoliticise 
climate change may narrow the space for democratic participation 
and contestation, thus impacting policy responses (Swyngedouw 
2010, 2011; Kenis and Lievens 2014). Some institutional innovations 
have more directly targeted enhanced public deliberation and 
participation, notably in citizens’ climate assemblies (Howarth et al. 
2020) and in the use of legal institutions to litigate against those 
opposing climate action (Peel and Osofksy 2020). This literature

shows that transformative pathways are possible within a  variety 
of institutional settings, although institutional innovation will be 
necessary everywhere to pursue zero carbon transitions (Section 4.4, 
Chapter 13 and Cross-Chapter Box 12).
Balancing the forces outlined in Section  4.6 in Chapter  4  typically 
involves building coalitions of actors who benefit economically from 
climate policy (Levin et al. 2012). Policy stability is critical to enabling 
long-term investments in decarbonisation (Rietig and Laing 2017; 
Rosenbloom et al. 2018). Policy design can encourage coalitions 
to form that sustain momentum by supporting further policy 
development to accelerate decarbonisation (Roberts et al. 2018), for 
example, by generating concentrated benefits to coalition members 
(Bernstein and Hoffmann 2018; Meckling 2019; Millar et al. 2020), as 
with renewable feed-in tariffs (FiTs) in Germany (Michaelowa et al. 
2018). Coalitions may also be sustained by overarching framings,

especially to involve actors (e.g.,  NGOs) for whom the benefits of 
climate policy are not narrowly economic. However, policy design 
can also provoke coalitions to oppose climate policy, as in the FiT 
programme in Ontario (Stokes 2013; Raymond 2020) or the yellow 
vest protests against carbon taxation in France (Berry and Laurent 
2019). The Just Transitions frame can thus also be understood in 
terms of coalition-building, as well as ethics, as the pursuit of low-
carbon transitions which spread the economic benefits broadly, 
through ‘green jobs’, and the redistributive policies embedded in 
them both nationally and globally (Healy and Barry 2017; Winkler 
2020). Appropriate policy design will be different at different stages 
of the transition process (Meckling et al. 2017; Breetz et al. 2018).
Integration. Politics is ultimately the way in which societies make 
decisions  – which in turn, reflect diverse forces and assumed

frameworks. Effective policy requires understandings which combine 
economic efficiency, ethics and equity, the dynamics and processes 
of large-scale transitions, and the role of psychology and politics. 
No one framework is adequate to such a  broad-ranging goal, nor 
are single tools. Chapter  13 (Figure  13.6) presents a  ‘framing’ 
table for policy instruments depending on the extent to which they 
focus on mitigation per se or wider socio-economic development, 
and whether they aim to shift marginal incentives or drive larger 
transitions. Holistic analysis needs to bridge modelling, qualitative 
transition theories illuminated by case studies, and practice-based 
action research (Geels et al. 2016).
These analytic frameworks also point to arenas of potential 
synergies and trade-offs (when broadly known), and opportunities 
and risks (when uncertainties are greater), associated with 
mitigation. This offers theoretical foundations for mitigation

strategies which can also generate co-benefits. Climate policy 
may help to motivate policies with beneficial synergies (such as

187
the consumer cost savings from energy efficiency, better forest 
management, transitions to cleaner vehicles) and opportunities 
(such as stimulating innovation), by focusing on options for 
which the positives outweigh the negatives, or can be made to 
be, through smart policy (e.g., Karlsson et al. 2020). More broadly, 
climate concerns may help to attract international investment, and 
help overcoming bureaucratic or political obstacles to better policy, 
and support synergies between mitigation, adaptation, and other 
SDGs, a  foundation for shifting development pathways towards 
sustainability (Chapter 17 and Section 1.6.1).
1.8 Feasibility and Multi-dimensional 
Assessment of Mitigation
1.8.1 Building on the SR1.5 Assessment Framework: 
Feasibility and Enabling Conditions
While previous ARs dealt with the definition of alternative mitigation 
pathways mostly exploring the technological potentials, the latest

research focused on what kind of mitigation pathways are feasible 
in a broader sense, underlining the multi-dimensional nature of the 
mitigation challenge. Building on frameworks introduced by Majone 
(1975) and Gilabert and Lawford-Smith (2012), SR1.5 introduced 
multi-dimensional approaches to analysing ‘feasibility’ and ‘enabling 
conditions’, which AR6 develops and applies broadly in relation to 
six ‘dimensions of feasibility assessment’ (Figure 1.8). Two reflect the 
physical environment:
• Geophysical, not only the global risks from climate change 
but also, for technology assessment, the global availability of 
critical resources.
• Environmental and ecological, including local environmental 
constraints and co-benefits of different technologies and pathways.
The other four dimensions correspond broadly to the four analytic 
frameworks outlined in Section 1.7:
• Economic, particularly aggregate economic and financial

indicators, and SDGs reflecting different stages and goals of 
• Socio-cultural, including particularly ethical and justice 
dimensions, and social and cultural norms.
• Technological, including innovation needs and transitional 
dynamics associated with new and emergent technologies and 
associated systems.• Institutional and political, including political acceptability, legal 
and administrative feasibility, and the capacity and governance 
requirements at different levels to deliver sustained mitigation in 
the wider context of sustainable development.
The AR6 emphasises that all pathways involve different challenges 
and require choices to be made. Continuing ‘business as usual’ is 
still a  choice, which in addition to the obvious geophysical risks, 
involves not making the best use of new technologies, risks of 
future stranded assets, greater local pollution, and multiple other 
environmental threats.

The dimensions as listed provide a  basis for this assessment both 
in the sectoral chapters (6–11), providing a common framework for 
cross-sectoral assessment detailed further in Chapter 12, and in the 
evaluation of global pathways (Section 3.2). More specific indicators 
under each of these dimensions offer consistency in assessing the 
challenges, choices, and enabling requirements facing different 
aspects of mitigating climate change.
Figure 1.8 also illustrates variants on these dimensions appropriate 
for evaluating domestic and international policies (Chapters 13 and 
14). The SR1.5 (Section 4.4) also introduced a framework of ‘Enabling 
Conditions for systemic change’, which as illustrated also has key 
dimensions in common with those of our feasibility assessment. In 
AR6 these enabling conditions are applied particularly in the context 
of shifting development pathways (Chapter 4.4).
Some fundamental criteria may span across several dimensions.

Most obviously, issues of ethics and equity are intrinsic to the 
economic, socio-cultural (values, including intergenerational justice) 
and institutional (e.g.,  procedural justice) dimensions. Geopolitical 
issues could also clearly involve several dimensions, for example, 
concerning the politics of international trade, finance and resource 
distribution (economic dimension); international versus nationalistic 
identity (socio-cultural); and multilateral governance (institutional).
In this report, chapters with a strong demand-side dimension also 
suggest a simple policy hierarchy, reflecting that avoiding wastage – 
demands superfluous to human needs and wants  – can carry 
benefits across multiple indicators. Consequently, Chapters 5 and 10 
organise key actions in a hierarchy of Avoid (unnecessary demand) – 
Shift (to less resource-intensive modes)  – Improve (technologies 
for existing modes), with a  closely-related policy hierarchy in

Chapter 9 (buildings).Table 1.2 | Potential for net co-benefits arising from synergies and trade-offs, opportunities and risks.
Positives Negatives
Broadly known (e.g., air pollution, distributional). Synergies Trade-offs
Deep uncertainties (e.g., radical innovations). Opportunities Risks
Select options with maximum synergies, and foster 
and exploit opportunities.Ameliorate trade-offs (e.g., revenue redistribution), 
and minimise or allocate risks appropriately.
Net co-benefits from appropriate mitigation choices

188
1.8.2 Illustrations of Multi-dimensional Assessment: 
Lock-in, Policies and ‘Just Transition’
The rest of this section illustrates briefly how such multi-dimensional 
assessment, utilising the associated analytic frameworks, can shed 
light on a  few key issues which arise across many chapters of 
Carbon Lock-in. The continued rise of global emissions reflects in 
part the strongly path-dependent nature of socio-economic systems, 
which implies a historic tendency to ‘carbon lock-in’ (Unruh 2000). 
An interdisciplinary review (Seto et al. 2016) identifies a dozen main 
components organised into four types, across the relevant dimensions 
of assessment as summarised in Table 1.3.
Along with the long lifetime of various physical assets detailed 
in AR5, AR6 underlines the exceptional degree of path-dependence 
in urban systems (Chapter 8) and associated buildings (Chapter 9)

and transport (Chapter 10) sectors, but it is a feature across almost 
all the major emitting sectors. The (typically expected) operating 
lifetimes of existing carbon-emitting assets would involve anticipated 
emissions (often but inaccurately called ‘committed’ emissions in the 
literature), substantially exceeding the remaining carbon budgets 
associated with 1.5°C pathways (Chapter  2.7). Ongoing GHG-
intensive investments, including those from basic industrialisation in 
poorer countries, are adding to this.
9 Beyond GHG externalities, Stern (2015) lists such market failures as: inadequate R&D; failures in risk/capital markets; network effects creating coordination failures; wider 
information failures; and co-benefits.The fact that investors anticipate a  level of fossil fuel use that is 
not compatible with severe climate constraints creates a clear risk 
of ‘stranded assets’ facing these investors (Box  6.2), and others

who depend on them, which itself raises issues of equity. A multi-
dimensional/multi-framework assessment helps to explain why 
such investments have continued, even in rich countries, and the 
consequent risks, and the complexity of shifting such investments 
in all countries. It may also inform approaches that could exploit 
path-dependence in clean energy systems, if there is sufficient 
investment in building up the low-GHG industries, infrastructures 
and networks required.
Carbon pricing. Appraisal of policy instruments also requires such 
multi-dimensional assessment. Stern’s (2007) reference to climate 
change as ‘the greatest market failure in history’ highlights that 
damages inflicted by climate change are not properly costed in 
most economic decision-making. Economic perspectives emphasise 
the value of removing fossil fuel subsidies, and pricing emissions to 
‘internalise’ in economic decision-making the ‘external’ damages

imposed by GHG emissions, and/or to meet agreed goals. Aggregate 
economic frameworks generally indicate carbon pricing (on principles 
which extend to other gases) as the most cost-effective way to 
reduce emissions, notwithstanding various market failures which 
complicate this logic.9 The High-Level Commission on carbon pricing 
(Stern and Stiglitz 2017) estimated an appropriate range as USD40–
80 tCO 2 in 2020, rising steadily thereafter. In practice the extent and 
level of carbon pricing implemented to date is far lower than this or Physical potentials; use of scarce 
(geo) physi cal resour ces; land use
Air qualit y; ecotoxicity; water
quant ity and qualit y; biodiversity
Financi al implications; employment 
effects and e conom ic growth
Simplicity; sca labilit y; maturity
and te chnol ogy readi ness
Political accept ance ; institutional  
capaci ty; coor dination across sectors 
and stakehol ders; legal  and 
adm inistrative capaci tyPublic accept ance ; effects on heal th

and well-bei ng; distributional  effectsPolicy packages including 
climate and deve lopm ent 
Finance  
Lifestyle and behavi our
norms and choices Technol ogy and 
innova tion 
Governance  and 
institutional  capaci ty Emissions and e nvironm ental
co-beneﬁ ts
Econom ic effectiveness
Distributional  effects 
Socio-cu ltural co-beneﬁ ts 
and other side effects Transf ormative potential 
Institutional  requi rementsFeasibility assessm ent
(Chapters 3, 6–12)Enab ling condi tions National and sub-national
Policy and In ternational
Cooper ation Assessm ent
(Chapters 13, 14)
Econom ic
Socio-cul tural
and polit icalGeophysi cal
Environm ental
and ecol ogical
Technol ogical
including infrastructureDimen sions
Figure 1.8 | Feasibility and related dimensions of assessment.

189
than most economic analyses now recommend (Section 3.6.1), and 
nowhere is carbon pricing the only instrument deployed.
A socio-cultural and equity perspective emphasises that the faith in 
and role of markets varies widely between countries – many energy 
systems do not in fact operate on a basis of competitive markets – 
and that because market-based carbon pricing involves large revenue 
transfers, it must also contend with major distributional effects and 
political viability (Prinn et al. 2017; Klenert et al. 2018), both domestic 
(Chapter 13) and international (Chapter 14). A major review (Maestre-
Andrés et al. 2019) finds persistent distributional concerns (rich 
incumbents have also been vocal in using arguments about impacts 
on the poor (Rennkamp 2019)), but suggests these may be addressed 
by combining redistribution of revenues with support for low-carbon 
innovation. Measures could include redistributing the tax revenue to

favour of low-income groups or differentiated carbon taxes (Metcalf 
2009; Klenert and Mattauch 2016; Stiglitz 2019), including ‘dual 
track’ approaches (van den Bergh et al. 2020). To an extent though, 
all these depend on levels of trust, and institutional capacity.
Technological and transitions perspectives in turn find carbon-
pricing incentives may only stimulate incremental improvements, 
but other instruments may be much more effective for driving 
deeper innovation and transitions (Chapters 14, 15 and 16), whilst 
psychological and behavioural studies emphasise many factors 
beyond only pricing (Sections  5.4.1 and 5.4.2). In practice, a  wide 
range of policy instruments are used (Chapter 13).
Finally, in economic theory, negotiations on a common carbon price 
(or other common policies) may have large benefits (less subject to 
‘free riding’) compared to a  focus on negotiating national targets 
(Cramton et al. 2017a). The fact that this has never even been

seriously considered (outside some efforts in the EU) may reflect the 
exceptional sovereignty sensitivities around taxation and cultural 
differences around the role of markets. However, carbon-pricing 
concepts can be important outside of the traditional market (‘tax 
or trading’) applications. A  ’social cost of carbon’ can be used to evaluate government and regulatory decisions, to compensate for 
inadequate carbon prices in actual markets, and by companies to 
reflect the external damage of their emissions and strategic risks of 
future carbon controls (Zhou and Wen 2020). An agreed ‘social value 
of mitigation activities’ could form a basic index for underwriting risks 
in low-carbon investments internationally (Hourcade et al. 2021a).
Thus, practical assessment of carbon pricing inherently needs multi-
dimensional analysis. The realities of political economy and lobbying 
have to date severely limited the implementation of carbon pricing

(Mildenberger 2020), leading some social scientists to ask ‘Can we 
price carbon?’ (Rabe 2018). Slowly growing adoption (World Bank 
2019) suggests ‘yes’, but only through complex evolution of efforts: 
a study of 66 implemented carbon-pricing policies show important 
effects of regional clustering, international processes, and seizing 
political windows of opportunity (Skovgaard et al. 2019).
Just Transitions. Finally, whilst ‘transition’ frameworks may 
explain potential dynamics that could transform systems, a  multi-
dimensional/multi-framework assessment underlines the motivation 
for Just Transitions (Sections 1.6.2.3 and 4.5). This can be defined as 
a transition from a high-carbon to a low-carbon economy which is 
considered sufficiently equitable for the affected individuals, workers, 
communities, sectors, regions and countries (Jasanoff 2018; Newell 
and Mulvaney 2013). As noted, sufficient equity is not only an ethical

issue but an enabler of deeper ambition for accelerated mitigation 
(Klinsky and Winkler 2018; Urpelainen and Van de Graaf 2018; 
Hoegh-Guldberg et al. 2019). Perception of fairness influences the 
effectiveness of cooperative action (Winkler et al. 2018), and this can 
apply to affected individuals, workers, communities, sectors, regions 
and countries (Newell and Mulvaney 2013; Jasanoff 2018).
A Just Transitions framing can also enable coalitions which integrate 
low-carbon transformations with concerns for climate adaptation 
(Patterson et al. 2018). All this explains the emergence of ‘Just 
Transition Commissions’ in several of the more ambitious developed 
countries and complex social packages for coal phase-out in Europe Table 1.3 | Carbon lock-in – types and key characteristics.  Source: adapted from Seto et al. (2016).
Lock-in type Key characteristics

Economic –Large investments with long lead times and sunk costs, made on the basis of anticipated use of resources, capital, and equipment to pay back the 
investment and generate profits.
 –Initial choices account for private but not social costs and benefits.
Socio-cultural, 
equity and behaviour –Lock-in through social structure (e.g., norms and social processes).
 –Lock-in through individual decision-making (e.g., psychological processes).
 –Single, calculated choices become a long string of non-calculated and self-reinforcing habits.
 –Interrupting habits is difficult but possible (e.g., family size, thermostat setting) to change.
 –Individuals and communities become dependent on the fossil fuel economy, meaning that change may have adverse distributional impacts.
and infrastructure –Learning-by-doing and scale effects, including the cumulative nature of innovation, reinforces established technologies.

–Interaction of technologies and networks (physical, organisational, financial) on which they depend.
 –Random, unintentional events including network and learning-effect final outcomes (e.g., lock-in to the QWERTY keyboard).
Institutional and political –Powerful economic, social, and political actors seek to reinforce the status quo that favours their interests.
 –Laws and Institutions, including regulatory structures, are designed to stabilise and lock-in a desired trajectory, and also to provide long-term 
predictability (socio-technical regimes in transition theories).
 –Beneficial and intended outcomes for some actors.
 –Not random chance but intentional choice (e.g., support for renewable electricity in Germany) can develop political consistencies that reinforce 
a direction of travel.

190
Chapter 1  Intr oduction and Framing1(Sovacool et al. 2019; Green and Gambhir 2020) (Section 4.5), as well 
as reference to the concept in the PA and its emphasis in the Talanoa 
Dialogue and Silesia Declaration (Section 1.2.2).
Whilst the broad concepts of Just Transitions have roots going 
back decades, its specific realisation in relation to climate change 
is of course complex: Section  4.5 identifies at least eight distinct 
elements proposed in the literature, even before considering the 
international dimensions.
1.9 Governing Climate Change
Previous sections have highlighted the multiple factors that drive 
and constrain climate action, the complex interconnection between 
climate mitigation and other societal objectives, and the diversity 
of analytical frames for interpreting these connections. Despite 
the complexities, there are signs of progress including increased 
societal awareness, change in social attitudes, policy commitments

by a  broad range of actors and sustained emission reductions in 
some jurisdictions. Nevertheless, emission trends at the global level 
remains incompatible with the goals agreed in the Paris Agreement. 
Fundamentally, the challenge of how best to urgently scale up and 
speed up the climate-mitigation effort at all scales – from local to 
global  – to the pace needed to address the climate challenge is 
that of governance understood as ‘modes and mechanisms to steer 
society’ (Jordan et al. 2015). The concept of governance encompasses 
the ability to plan and create the organisations needed to achieve 
a desired goal (Güney 2017) and the process of interaction among 
actors involved in a common problem for making and implementing 
decisions (Kooiman 2003; Hufty 2012).
Climate change governance has been projected as conscious 
transformation at unprecedented scale and speed involving 
a  contest of ideas and experimentation across scales of authority

and jurisdiction (Hildén et al. 2017; Kivimaa et al. 2017; Laakso et al. 
2017; Gordon 2018; van der Heijden 2018). Yet, there remains a sense 
that achieving the urgent transition to a low-carbon, climate-resilient 
and sustainable world requires significant innovation in governance 
(Hoffmann 2011; Stevenson and Dryzek 2013; Aykut 2016).
Starting from an initial focus on multilateral agreements, climate 
change governance has long evolved into a  complex polycentric 
structure that spans from the global to national and sub-national 
levels, with ‘multiple parallel initiatives involving a range of actors at 
different levels of governance’ (Okereke et al. 2009) and relying on 
both formal and informal networks and policy channels (Bulkeley et al. 
2014; Jordan et al. 2015). At the international level, implementation 
of the Paris Agreement and the UNFCCC more broadly is proceeding 
in parallel with other activities in an increasingly diverse landscape

of loosely coordinated institutions, constituting ‘regime complex’ 
(Keohane and Victor 2011), and new cooperative efforts demonstrate 
an evolution in the shifting authority given to actors at different 
levels of governance (Chan et al. 2018).
Multi-level governance has been used to highlight the notion that the 
processes involved in making and implementing decisions on climate change are no longer the exclusive preserve of government actors 
but rather involve a range of non-nation state actors such as cities, 
businesses, and civil society organisations (IPCC 2014a; Bäckstrand 
et al. 2017; Jordan et al. 2018) (Chapter 13, and Sections 13.3.1 and 
13.5.2). Increased multi-level participation of sub-national actors, 
along with a  diversity of other transnational and non-state actors 
has helped to facilitate increased awareness, experimentation, 
innovation, learning and achieving benefits at multiple scales. Multi-

level participation in governance systems can help to build coalitions 
to support climate change mitigation policies (Roberts et al. 2018) 
and fragmentation has the potential to take cooperative and even 
synergistic forms (Biermann et al. 2009).
However, there is no guarantee that multi-level governance can 
successfully deal with complex human-ecological systems (York 
et al. 2005; Biermann et al. 2017; Di Gregorio et al. 2019). Multi-
level governance can contribute to an extremely polarised discussion 
and policy blockage rather than enabling policy innovation (Fisher 
and Leifeld 2019). A fragmented governance landscape may lead to 
coordination and legitimacy gaps undermining the regime (Nasiritousi 
and Bäckstrand 2019). The realities of the ‘drivers and constraints’ 
detailed in Section 4, the ‘glocal’ nature of climate change, the divided 
authority in world politics, diverse preferences of public and private

entities across the spectrum, and pervasive suspicions of free riding, 
imply the challenge as how to incrementally deepen cooperation in 
a polycentric global system, rather than seeking a single, integrated 
governance (Keohane and Victor 2016).
Crucially, climate governance takes place in the context of embedded 
power relations, operating in global, national and local contexts. 
Effective rules and institutions to govern climate change are more 
likely to emerge where and when power structures and interests 
favour action. However widespread and enduring cooperation can 
only be expected when the benefits outweigh the cost of cooperation 
and when the interests of key actors are sufficiently aligned (Barrett 
1994; Finus and Rübbelke 2008; Victor 2011; Mainali et al. 2018; 
Tulkens 2019). Investigating the distribution and role of hard and 
soft power resources, capacities and power relations within and 
across different jurisdictional levels is therefore important to uncover

hindrances to effective climate governance (Marquardt 2017). 
Institutions at international and national levels are also critical as 
they have the ability to mediate the power and interest of actors, and 
sustain cooperation based on equity and fair rules and outcomes. 
Governance, in fact, helps to align and moderate the interests of 
actors as well as to shift perceptions, including the negative, burden-
sharing narratives that often accompany discussion about climate 
action, especially in international negotiations. It is also useful for 
engaging the wider public and international networks in imagining 
low-carbon societies (e.g.,  Levy and Spicer 2013; Milkoreit 2017; 
Nikoleris et al. 2017; Wapner and Elver 2017; Bengtsson Sonesson 
et al. 2019; Fatemi et al. 2020). Experimentation also represents an 
important source of governance innovation and capability formation, 
linked to global knowledge and technology flows, which could

reshape emergent socio-technical regimes and so contribute to 
alternative development pathways (Berkhout et al. 2010; Roberts 
et al. 2018; Turnheim et al. 2018; Lo and Castán Broto 2019).

191
Introduction and Framing Chapter 111.10 Conclusions
Global conditions have changed substantially since the IPCC’s Fifth 
Assessment Report in 2014. The Paris Agreement and the SDGs 
provided a new international context, but global intergovernmental 
cooperation has been under intense stress. Growing direct impacts 
of climate change are unambiguous and movements of protest and 
activism  – in countries and transnational organisations at many 
levels – have grown. Global emissions growth had slowed but not 
stopped up to 2018/19, albeit with more diverse national trends. 
Growing numbers of countries have adopted ‘net zero’ CO 2 and/
or GHG emission goals and decarbonisation or low-carbon growth 
strategies, but the current NDCs to 2030 collectively would barely 
reduce global emissions below present levels (Section  1.3.3). An 
unfolding technology revolution is making significant contributions 
in some countries, but as yet its global impact is limited. Global

climate change can only be tackled within, and if integrated with, the 
wider context of sustainable development, and related social goals 
including equity concerns. Countries and their populations have 
many conflicting priorities. Developing countries in particular have 
multiple urgent needs associated with earlier stages of sustainable 
development as reflected in the non-climate SDGs. Developed 
countries are amongst the most unsustainable in terms of overall 
consumption, but also face social constraints particularly arising from 
distributional impacts of climate policies.
The assessment of the key drivers for, and barriers against mitigation 
undertaken in this chapter underscore the complexity and multi-
dimensional nature of climate mitigation. Historically, much of the 
academic analysis of mitigating climate change, particularly global 
approaches, has focused on modelling costs and pathways, and 
discussion about ‘optimal’ policy instruments. Developments since

AR5 have continued to highlight the role of a wide range of factors 
intersecting the political, economic, social and institutional domains. 
Yet despite such complexities, there are signs of progress emerging 
from years of policy effort in terms of technology, social attitudes, 
and emission reductions in some countries, with tentative signs of 
impact on the trajectory of global emissions. The challenge remains 
how best to urgently scale up and speed up the climate mitigation 
effort at all scales – from local to global – to achieve the level of 
mitigation needed to address the problem as indicated by climate 
science. A related challenge is how to ensure that mitigation effort 
and any associated benefits of action are distributed fairly within and 
between countries and aligned to the overarching objective of global 
sustainable development. Lastly, globally effective and efficient 
mitigation will require international cooperation especially in the

realms of finance and technology.
Multiple frameworks of analytic assessment, adapted to the 
realities of climate change mitigation, are therefore required. We 
identified four main groups. Aggregate economic frameworks  – 
including environmental costs or goals, and with due attention to 
implied behavioural, distributional and dynamic assumptions – can 
provide insights about trade-offs, cost-effectiveness and policies for 
delivering agreed goals. Ethical frameworks are equally essential 
to inform both international and domestic discourse and decisions, 
including the relationship with international (and intergenerational) responsibilities, related financial systems, and domestic policy design 
in all countries. Explicit frameworks for analysing transition and 
transformation across multiple sectors need to draw on both socio-
technical transition literatures, and those on social transformation. 
Finally, literatures on psychology, behaviour and political sciences

can illuminate obstacles that have impeded progress to date and 
suggest ways to overcome them.
No single analytical framework, or single discipline, on its own can 
offer a  comprehensive assessment of climate change mitigation. 
Together they point to the relevance of growing literatures and 
discourses on Just Transitions, and the role of governance at multiple 
levels. Ultimately all these frameworks are needed to inform the 
decisions required to deepen and connect the scattered elements 
of progress to date, and hence accelerate progress towards agreed 
goals and multiple dimensions of climate change mitigation in the 
context of sustainable development.
1.11 Knowledge Gaps
Despite huge expansion in the literature (Callaghan et al. 2020), 
knowledge gaps remain. Modeling still struggles to bring together 
detailed physical and economic climate impacts and mitigation, 
with limited representation of financial and distributional dynamics.

There are few interdisciplinary tools which apply theories of 
transition and transformation to questions of economic and social 
impacts, compounded by remaining uncertainties concerning the 
role of new technological sets, international instruments, policy and 
political evaluation.
One scan of future research needs suggests three priority areas (Roberts 
et al. 2020): (i) human welfare-focused development (e.g., reducing 
inequality); (ii) how the historic position of states within international 
power relations conditions their ability to respond to climate change; 
(iii) transition dynamics and the flexibility of institutions to drive 
towards low-carbon development pathways. There remain gaps in 
understanding how international dynamics and agreements filter 
down to affect constituencies and local implementation. Literature 
on the potential for supply-side agreements, in which producers 
agree to restrict the supply of fossil fuels (e.g., Asheim et al. 2019) is

limited but gaining increasing academic attention.
Nature is under pressure both at land and at sea, as demonstrated by 
declining biodiversity (IPBES 2019). Climate policies could increase 
the pressure on land and oceans (IPCC 2019c,b), with insufficient 
attention to relationships between biodiversity and climate 
agreements and associated policies. IPBES aims to coordinate with 
the IPCC more directly, but literature will be required to support 
these reports.
Compounding these gaps is the fact that socially oriented, agriculture-
related options, where human and non-human systems intersect most 
obviously, remain under-researched (e.g., Balasubramanya and Stifel 
2020). Efforts to engage with policies here, especially framed around 
ecosystem services, have often neglected their ‘practical fitness’ in

192
Chapter 1  Intr oduction and Framing1favour of focusing on their ‘institutional fitness’, which needs to be 
addressed in future research (Stevenson et al. 2021).
The relative roles of short-term mitigation policies and long-term 
investments, including government and financial decision-making 
tools, remains inadequately explored. Strategic investments may 
include city planning, public transport, EV-charging networks, and 
CCU/CCS.  Understanding how international treaties can increase 
incentives to make such investments is all the more salient in the 
aftermath of COVID-19, on which research is necessarily young but 
rapidly growing. Finally, the economic, institutional and political 
strategies to close the gap between NDCs, actual implementation, 
and mitigation goals – informed by the PA and the UNFCCC Global 
Stocktake – require much further research.
1.12 Roadmap to the Report
This Sixth Assessment Report covers mitigation in five main parts

(Figure 1.9), namely: introduction and frameworks; emission trends, 
scenarios and pathways; sectors; institutional dimensions including 
national and international policy, financial and technological 
mitigation drivers; and conclusions.
Chapters 2 to 5 cover the big picture trends, drivers and projections 
at national and global levels. Chapter  2  analyses emission trends 
and drivers to date. Chapter 3 presents long-term global scenarios, 
including the projected economic and other characteristics of 
mitigation through to the balancing of sources and sinks through 
the second half of this century, and the implications for global 
temperature change and risks. Chapter 4 explores the shorter-term 
prospects including NDCs, and the possibilities for accelerating 
mitigation out to 2050 in the context of sustainable development 
at the national, regional and international scales. Chapter 5, a new 
chapter for IPCC Assessments, focuses upon the role of services and

derived demand for energy and land use, and the social dimensions.
Chapters 6 to 12 examine sectoral contributions and possibilities for 
mitigation. Chapter 6 summarises characteristics and trends in the 
energy sector, specifically supply, including the remarkable changes 
in the cost of some key technologies since AR5. Chapter 7 examines 
the roles of AFOLU, drawing upon and updating the recent Special 
Report, including the potential tensions between the multiple 
uses of land. Chapter  8  presents a  holistic view of the trends 
and pressures of  urban systems, as both a  challenge and an 
opportunity for mitigation. Chapters  9 and 10 then examine two 
sectors which entwine with, but go well beyond, urban systems: 
buildings (Chapter  9) including construction materials and zero-
carbon buildings; and transport (Chapter  10), including shipping 
and aviation and a  wider look at mobility as a  general service. 
Chapter  11 explores the contribution of industry, including supply

chain developments, resource efficiency/circular economy, and the 
cross-system implications of decarbonisation for industrial systems. 
Finally, Chapter 12 takes a cross-sectoral perspective and explores 
cross-cutting issues like the interactions of biomass energy, food and 
land, and carbon dioxide removal.Four chapters then review thematic issues in implementation and 
governance of mitigation. Chapter  13 explores national and sub-
national policies and institutions, bringing together lessons of policies 
examined in the sectoral chapters, as well as insights from service 
and demand-side perspectives (Chapter 5), along with governance 
approaches and capacity-building, and the role and relationships of 
sub-national actors. Chapter 14 then considers the roles and status 
of international cooperation, including the UNFCCC agreements 
and international institutions, sectoral agreements and multiple 
forms of international partnerships, and the ethics and governance

challenges of solar radiation modification. Chapter  15 explores 
investment and finance, including current trends, the investment 
needs for deep decarbonisation, and the complementary roles of 
public and private finance. This includes climate-related investment 
opportunities and risks (e.g.,  ‘stranded assets’), linkages between 
finance and investments in adaptation and mitigation; and the 
impact of COVID-19. A new chapter on innovation (Chapter 16) looks 
at technology development, accelerated deployment and global 
diffusion as systemic issues that hold potential for transformative 
changes, and the challenges of managing such changes at multiple 
levels including the role of international cooperation.
Finally, Chapter  17 considers accelerating the transition in the 
context of sustainable development, including practical pathways 
for joint responses to climate change and sustainable development 
challenges. This includes major regional perspectives, mitigation-

adaptation interlinkages, and enabling conditions including the roles 
of technology, finance and cooperation for sustainable development.

193
IPCC
AR6
WGIIIChapter 1
Introduction and framingChapter 17
Accelerating the transition
in the context of
Chapters 13–16
Institutional national,
sub-national
and international
ﬁnancial and
technological
driversChapters 2–5
Emissions trends,
drivers and
pathways
Chapters 6–12
Sectors
(Energy, Agriculture/forestry/land,
Urban systems, Buildings, Transport,
Industry, and Cross-sectoral perspectives)
Figure 1.9 | The structure of the AR6 mitigation report.

194
Chapter 1  Intr oduction and Framing1Frequently Asked Questions (FAQs)
FAQ 1.1 |  W hat is climate change mitigation?
Climate change mitigation refers to actions or activities that limit emissions of greenhouse gases (GHGs) from entering the 
atmosphere and/or reduce their levels in the atmosphere. Mitigation includes reducing the GHGs emitted from energy production and use (e.g., that reduces use of fossil fuels), and land use, and methods to mitigate warming, for example, by carbon sinks which remove emissions from the atmosphere through land-use or other (including artificial) mechanisms (Sections 12.3 and 14.4.5; see AR6 WGI for physical science, and WGIII Chapter 7 for AFOLU mitigation).
The ultimate goal of mitigation is to preserve a biosphere which can sustain human civilisation and the complex of ecosystem

services which surround and support it. This means reducing anthropogenic GHG emissions towards net zero to limit the warming, with global goals agreed in the Paris Agreement. Effective mitigation strategies require an understanding of mechanisms that underpin release of emissions, and the technical, policy and societal options for influencing these.
FAQ 1.2 |  W hich greenhouse gases (GHGs) are relevant to which sectors?
Anthropogenic GHGs such as carbon dioxide (CO 2), methane (CH 4), nitrous oxide (N 2O), and fluorinated gases (e.g.,  hydrofluorocarbons, 
perfluorocarbons, sulphur hexafluoride) are released from various sources. CO 2 makes the largest contribution to global GHG 
emissions, but some have extremely long atmospheric lifetimes extending to tens of thousands of years, such as F-gases (Chapter 2).
Different combinations of gases are emitted from different activities. The largest source of CO 2 is combustion of fossil fuels in energy

conversion systems like boilers in electric power plants, engines in aircraft and automobiles, and in cooking and heating within 
homes and businesses (approximately 64% of emissions, Figure SPM.2). Fossil fuels are also a major source of methane (CH 4), 
the second biggest contributor to global warming. While most GHGs come from fossil fuel combustion, about one quarter comes from land-related activities like agriculture (mainly CH
4 and N 2O) and deforestation (mainly CO 2), with additional emissions from 
industrial processes (mainly CO 2, N2O and F-gases), and municipal waste and wastewater (mainly CH 4) (Chapter 2). In addition to 
these emissions, black carbon – an aerosol that is, for example, emitted during incomplete combustion of fossil fuels – contributes to warming of the Earth’s atmosphere, whilst some other short-lived pollutants temporarily cool the surface (IPCC AR6 WGI Section 6.5.4.3).

FAQ 1.3 |  W hat is the difference between ‘net zero emissions’ and ‘carbon neutrality’?
Annex I (Glossary) states that ‘carbon neutrality and net zero CO 2 emissions are overlapping concepts’ which ‘can be applied at the 
global or sub-global scales (e.g., regional, national and sub-national)’. At the global scale the terms are equivalent. At sub-global scales, net zero CO
2 typically applies to emissions under direct control or territorial responsibility of the entity reporting them 
(e.g., a country, district or sector); while carbon neutrality is also applied to firms, commodities and activities (e.g., a service or an event) and generally includes emissions and removals beyond the entity’s direct control or territorial responsibility, termed ‘Scope 3’ or ‘value chain emissions’ (Bhatia et al. 2011).
This means the emissions and removals that should be included are wider for ‘neutrality’ than for net zero goals, but also that offset

mechanisms could be employed to help achieve neutrality through abatement beyond what is possible under the direct control of the entity. Rules and environmental integrity criteria are intended to ensure additionality and avoid double counting of offsets consistent with ‘neutrality’ claims (see ‘carbon neutrality’ and ‘offset’ in Glossary, for detail and a list of criteria).
While the term ‘carbon’ neutrality in this report is defined as referring specifically to CO
2 neutrality, use of this term in practice can 
be ambiguous, as some users apply it to neutrality of all GHG emissions. GHG neutrality means an entity’s gross emissions of all 
GHG must be balanced by the removal of an equivalent amount of CO 2 from the atmosphere. This requires the selection of a suitable 
metric that aggregates emissions from non-CO 2 gases, such as the commonly used GWP100 metric (for a discussion of GHG metrics, 
see AR6 WGI Box 1.3 and Cross-Chapter Box 2 in Chapter 2 of this report).

217
Emissions Trends and Drivers Chapter 22
Global net anthropogenic greenhouse gas (GHG) emissions 
during the last decade (2010–2019) were higher than at any 
previous time in human history (high confidence).  Since 2010, 
GHG emissions have continued to grow, reaching 59 ± 6.6 GtCO 2-eq 
in 2019,1 but the average annual growth in the last decade 
(1.3%, 2010–2019) was lower than in the previous decade (2.1%, 
2000–2009) (high confidence). Average annual GHG emissions were 
56 ± 6.0 GtCO 2-eq yr –1 for the decade 2010–2019 growing by about 
9.1 GtCO 2-eq yr –1 from the previous decade (2000–2009)  – the 
highest decadal average on record (high confidence). {2.2.2,  Table 2.1, 
Figure 2.2, Figure 2.5}
Emissions growth has varied, but persisted across all groups of 
GHGs (high confidence).  The average annual emission levels of the 
last decade (2010–2019) were higher than in any previous decade for 
each group of GHGs (high confidence). In 2019, CO 2 emissions were

45 ± 5.5 GtCO 2,2 CH 4 11 ± 3.2 GtCO 2-eq, N 2O 2.7 ± 1.6 GtCO 2-eq and 
fluorinated gases (F-gases: HFCs, PFCs, SF 6, NF 3) 1.4 ± 0.41 GtCO 2-eq. 
Compared to 1990, the magnitude and speed of these increases 
differed across gases: CO 2 from fossil fuel and industry (FFI) grew by 
15 GtCO 2-eq yr –1 (67%), CH 4 by 2.4 GtCO 2-eq yr –1 (29%), F-gases 
by 0.97 GtCO 2-eq yr –1 (254%), and N 2O by 0.65  GtCO 2-eq  yr –1 
(33%). CO 2 emissions from net land use, land-use change and 
forestry (LULUCF) have shown little long-term change, with large 
uncertainties preventing the detection of statistically significant 
trends. F-gases excluded from GHG emissions inventories such as 
chlorofluorocarbons and hydrochlorofluorocarbons are about the 
same size as those included (high confidence). {2.2.1, 2.2.2, Table 2.1, 
Figures 2.2, 2.3 and 2.5}
Globally, gross domestic product (GDP) per  capita and 
population growth remained the strongest drivers of CO 2

emissions from fossil fuel combustion in the last decade 
(robust evidence, high agreement). Trends since 1990 continued 
in the years 2010 to 2019 with GDP per capita and population growth 
increasing emissions by 2.3% and 1.2% yr –1, respectively. This 
growth outpaced the reduction in the use of energy per unit of GDP 
(–2% yr –1, globally) as well as improvements in the carbon intensity 
of energy (–0.3% yr –1) (high confidence). {2.4.1, Figure 2.16}
The global COVID-19 pandemic led to a  steep drop in CO 2 
emissions from fossil fuel and industry (high confidence). 
Global CO 2-FFI emissions dropped in 2020 by about 5.8% (5.1–6.3%) 
or about 2.2 (1.9–2.4) GtCO 2 compared to 2019. Emissions, however, 
have rebounded globally by the end of December 2020 (medium 
confidence). {2.2.2, Figure 2.6}
1 Emissions of GHGs are weighed by global warming potentials with a 100-year time horizon (GWP100) from the Sixth Assessment Report (Forster et al. 2021). GWP100 is

commonly used in wide parts of the literature on climate change mitigation and is required for reporting emissions under the United Nations Framework Convention on 
Climate Change (UNFCCC). All metrics have limitations and uncertainties. (Cross-Chapter Box 2 in Chapter 2 and Annex II, Part II, Section 8).
2 In 2019, CO 2 from fossil fuel and industry (FFI) were 38 ± 3.0 Gt, CO 2 from net land use, land-use change and forestry (LULUCF) 6.6 ± 4.6 Gt.Cumulative net CO 2 emissions of the last decade (2010–2019) 
are about the same size as the remaining carbon budget for 
keeping warming to 1.5°C (medium confidence).  Cumulative 
net CO 2 emissions since 1850 are increasing at an accelerating rate: 
about 62% of total cumulative CO 2 emissions from 1850 to 2019 
occurred since 1970 (1500 ± 140 GtCO 2); about 43% since 1990 
(1000 ± 90 GtCO 2); and about 17% since 2010 (410 ± 30 GtCO 2). For 
comparison, the remaining carbon budget for keeping warming to

1.5°C with a 67% (50%) probability is about 400 (500) ± 220 GtCO 2 
(medium confidence). {2.2.2, Figure  2.7; AR6 WGI 5.5; AR6 WGI 
Table 5.8}
A growing number of countries have achieved GHG emission 
reductions longer than 10 years – a few at rates that are broadly 
consistent with climate change mitigation scenarios that limit 
warming to well below 2°C (high confidence). There are at least 
18 countries that have reduced CO 2 and GHG emissions for longer than 
10 years. Reduction rates in a few countries have reached 4% in some 
years, in line with rates observed in pathways that limit warming to 
2°C (>67%). However, the total reduction in annual GHG emissions of 
these countries is small (about 3.2 GtCO 2-eq yr –1) compared to global 
emissions growth observed over the last decades. Complementary 
evidence suggests that countries have decoupled  territorial 
CO2  emissions from GDP , but fewer have decoupled consumption-

based emissions from GDP . This decoupling has mostly occurred in 
countries with high per capita GDP and high per capita CO 2 emissions. 
{2.2.3, 2.3.3, Figure 2.11, Table 2.3, Table 2.4}
Consumption-based CO 2 emissions in Developed Countries 
and the Asia and Pacific region are higher than in other 
regions (high confidence). In Developed Countries, consumption-
based CO 2 emissions peaked at 15 GtCO 2 in 2007, declining to about 
13 GtCO 2 in 2018. The Asia and Pacific region, with 52% of current 
global population, has become a major contributor to consumption-
based CO 2 emission growth since 2000 (5.5% yr –1 for 2000–2018); 
it exceeded the Developed Countries region, which accounts for 16% 
of current global population, as the largest emitter of consumption-
based CO 2. {2.3.2, Figure 2.14}
Carbon intensity improvements in the production of traded 
products have led to a net reduction in CO 2 emissions embodied 
in international trade (robust evidence, high agreement).

A  decrease in the carbon intensity of traded products has offset 
increased trade volumes between 2006 and 2016. Emissions embodied 
in internationally traded products depend on the composition of the 
global supply chain across sectors and countries and the respective 
carbon intensity of production processes (emissions per  unit of 
economic output). {2.3, 2.4}

218
Chapter 2  Emissions Trends and Drivers2
Developed Countries tend to be net CO 2 emission importers, 
whereas developing countries tend to be net emission exporters 
(robust evidence, high agreement).  Net CO 2 emission  transfers 
from developing to Developed Countries via global supply chains 
have decreased between 2006 and 2016. Between 2004 and 2011, 
CO2 emission embodied in trade between developing countries have 
more than doubled (from 0.47 to 1.1 Gt) with the centre of trade 
activities shifting from Europe to Asia. {2.3.4, Figure 2.15}
Emissions from developing countries have continued to grow, 
starting from a  low base of per  capita emissions and with 
a lower contribution to cumulative emissions than Developed 
Countries (robust evidence, high agreement). Average 2019 
per  capita CO 2-FFI emissions in three developing regions  – Africa 
(1.2  tCO 2 per  capita), Asia and Pacific (4.4 tCO 2 per  capita), and

Latin America and Caribbean (2.7 tCO 2 per capita) – remained less 
than half that of Developed Countries (9.5 tCO 2 per capita) in 2019. 
CO2-FFI emissions in the three developing regions together grew by 
26% between 2010 and 2019, compared to 260% between 1990 
and 2010, while in Developed Countries emissions contracted by 
9.9% between 2010 and 2019, and by 9.6% between 1990 and 
2010. Historically, the three developing regions together contributed 
28% to cumulative CO 2-FFI emissions between 1850 and 2019, 
whereas Developed Countries contributed 57% and Least-Developed 
Countries contributed 0.4%. {2.2.3, Figures 2.9 and 2.10}
Globally, GHG emissions continued to rise across all sectors 
and subsectors; most rapidly in transport and industry (high 
confidence). In 2019, 34% (20 GtCO 2-eq) of global GHG emissions 
came from the energy sector, 24% (14 GtCO 2-eq) from industry, 22% 
(13 GtCO 2-eq) from agriculture, forestry and other land use (AFOLU),

15% (8.7 GtCO 2-eq) from transport and 5.6% (3.3 GtCO 2-eq) from 
buildings. Once indirect emissions from energy use are considered, 
the relative shares of industry and buildings emissions rise to 34% 
and 16%, respectively. Average annual GHG emissions growth during 
2010 to 2019 slowed compared to the previous decade in energy 
supply (from 2.3% to 1.0%) and industry (from 3.4% to 1.4%, direct 
emissions only), but remained roughly constant at about 2% per year 
in the transport sector (high confidence). Emission growth in AFOLU 
is more uncertain due to the high share of CO 2-LULUCF emissions 
(medium confidence). {2.4.2, Figure 2.13, Figures 2.16 to 2.21}
Average annual growth in GHG emissions from energy supply 
decreased from 2.3% for 2000–2009 to 1.0% for 2010–2019 
(high confidence). This slowing of growth is attributable to further 
improvements in energy efficiency (annually, 1.9% less energy per unit 
of GDP was used globally between 2010 and 2019). Reductions in

global carbon intensity by –0.2% yr –1 contributed further – reversing 
the trend during 2000 to 2009 (+0.2% yr –1) (medium confidence). 
These carbon intensity improvements were driven by fuel switching 
from coal to gas, reduced expansion of coal capacity, particularly in 
Eastern Asia, and the increased use of renewables. {2.2.4, 2.4.2.1, 
Figure 2.17}
3 Decent Living Standards (DLS) – a benchmark of material conditions for human well-being – overlaps with many Sustainable Development Goals (SDGs). Minimum 
requirements of energy use consistent with enabling well-being for all is between 20 and 50 GJ per capita yr –1 depending on the context. {5.2.2, 5.2.2, Box 5.3, Figure 5.6}GHG emissions in the industry, buildings and transport 
sectors continue to grow, driven by an increase in the global 
demand for products and services (high confidence). These final 
demand sectors make up 44% of global GHG emissions, or 66% when

the emissions from electricity and heat production are reallocated as 
indirect emissions to related sectors, mainly to industry and buildings. 
Emissions are driven by the large rise in demand for basic materials 
and manufactured products, a global trend of increasing floor space 
per capita, building energy service use, travel distances, and vehicle 
size and weight. Between 2010 and 2019, domestic and international 
aviation were particularly fast growing at average annual rates of 
+3.3% and +3.4%. Global energy efficiencies have improved in 
all three demand sectors, but carbon intensities have not. {2.2.4; 
Figures 2.18 to 2.20}
Providing access to modern energy services universally would 
increase global GHG emissions by, at most, a  few percent 
(medium confidence).  The additional energy demand needed 
to support decent living standards3 for all is estimated to be well 
below current average energy consumption (medium evidence,  high

agreement). More equitable income distributions can reduce carbon 
emissions, but the nature of this relationship can vary by level of income 
and development (limited evidence,  medium agreement). {2.4.3}
Evidence of rapid energy transitions exists, but only at 
sub-global scales (medium evidence, medium agreement).  
Emerging evidence since the Fifth Assessment Report of the 
Intergovernmental Panel on Climate Change (IPCC AR5) on past 
energy transitions identifies a growing number of cases of accelerated 
technology diffusion at sub-global scales and describes mechanisms 
by which future energy transitions may occur more quickly than 
those in the past. Important drivers include technology transfer 
and cooperation, intentional policy and financial support, and 
harnessing synergies among technologies within a sustainable energy 
system perspective (medium evidence,  medium agreement). A  fast 
global low-carbon energy transition enabled by finance to facilitate

low-carbon technology adoption in developing, and particularly in 
least-developed countries, can facilitate achieving climate stabilisation 
targets (robust evidence,  high agreement). {2.5.2, Table 2.5}
Multiple low-carbon technologies have shown rapid progress 
since AR5 – in cost, performance, and adoption – enhancing 
the feasibility of rapid energy transitions (robust evidence, 
high agreement).  The rapid deployment and cost decrease of 
modular technologies like solar, wind, and batteries have occurred 
much faster than anticipated by experts and modelled in previous 
mitigation scenarios (robust evidence, high agreement). The political, 
economic, social, and technical feasibility of solar energy, wind energy 
and electricity storage technologies has improved dramatically over 
the past few years. In contrast, the adoption of nuclear energy and 
carbon capture and storage (CCS) in the electricity sector has been

slower than the growth rates anticipated in stabilisation scenarios. 
Emerging evidence since AR5 indicates that small-scale technologies 
(e.g., solar, batteries) tend to improve faster and be adopted more

219
quickly than large-scale technologies (nuclear, CCS) (medium 
evidence, medium agreement). {2.5.3, 2.5.4, Figures 2.22 and 2.23}
Robust incentives for investment in innovation, especially 
incentives reinforced by national policy and international 
agreements, are central to accelerating low-carbon 
technological change (robust evidence, medium agreement).  
Policies have driven innovation, including instruments for technology 
push (e.g.,  scientific training, research and development) and 
demand pull (e.g.,  carbon pricing, adoption subsidies), as well as 
those promoting knowledge flows and especially technology transfer. 
The magnitude of the scale-up challenge elevates the importance of 
rapid technology development and adoption. This includes ensuring 
participation of developing countries in an enhanced global flow of 
knowledge, skills, experience, and equipment. Also, technology itself

requires strong financial, institutional, and capacity-building support 
(robust evidence, high agreement). {2.5.4, 2.5, 2.8}
The global wealthiest 10% contribute about 36–45% of 
global GHG emissions (robust evidence, high agreement). 
The  global  10% wealthiest consumers live in all continents, with 
two-thirds in high-income regions and one-third in emerging 
economies (robust evidence, medium agreement). The lifestyle 
consumption emissions of the middle-income and poorest citizens 
in emerging economies are between 5 and 50 times below their 
counterparts in high-income countries (medium evidence, medium 
agreement). Increasing inequality within a  country can exacerbate 
dilemmas of redistribution and social cohesion, and affect the 
willingness of rich and poor to accept lifestyle changes for mitigation 
and policies to protect the environment (medium evidence, medium 
agreement) {2.6.1, 2.6.2, Figure 2.25}
Estimates of future CO 2 emissions from existing fossil fuel

infrastructures already exceed remaining cumulative net CO 2 
emissions in pathways limiting warming to 1.5°C with no or 
limited overshoot (high confidence).  Assuming variations in 
historical patterns of use and decommissioning, estimated future 
CO2 emissions from existing fossil fuel infrastructure alone are 
660 (460–890) GtCO 2 and from existing and currently planned 
infrastructure 850 (600–1100) GtCO 2. This compares to overall 
cumulative net CO 2 emissions until reaching net zero CO 2 of 510 
(330–710) Gt in pathways that limit warming to 1.5°C with no or 
limited overshoot, and 890 (640–1160) Gt in pathways that limit 
warming to 2°C (<67%) (high confidence). While most future CO 2 
emissions from existing and currently planned fossil fuel infrastructure 
are situated in the power sector, most remaining fossil fuel CO 2 
emissions in pathways that limit warming to 2°C (<67%) and below 
are from non-electric energy – most importantly from the industry

and transportation sectors (high confidence). Decommissioning and 
reduced utilisation of existing fossil fuel installations in the power 
sector as well as cancellation of new installations are required to 
align future CO 2 emissions from the power sector with projections 
in these pathways (high confidence). {2.7.2, 2.7.3, Figure  2.26, 
Table 2.6, Table 2.7}A broad range of climate policies, including instruments like 
carbon pricing, play an increasing role in GHG emissions 
reductions. The literature is in broad agreement, but the 
magnitude of the reduction rate varies by the data and 
methodology used, country, and sector (robust evidence, 
high agreement).  Countries with a lower carbon pricing gap (higher 
carbon price) tend to be less carbon intensive (medium confidence). 
{2.8.2, 2.8.3}
Climate-related policies have also contributed to decreasing 
GHG emissions. Policies such as taxes and subsidies for clean 
and public transportation, and renewable policies have

reduced GHG emissions in some contexts (robust evidence, 
high agreement).  Pollution control policies and legislations that 
go beyond end-of-pipe controls have also had climate co-benefits, 
particularly if complementarities with GHG emissions are considered 
in policy design (medium evidence, medium agreement). Policies on 
AFOLU and sector-related policies such as afforestation can have 
important impacts on GHG emissions (medium evidence, medium 
agreement). {2.8.4}

220
As demonstrated by the contribution of Working Group I to the Sixth 
Assessment Report of the Intergovernmental Panel on Climate Change 
(AR6 WGI) (IPCC 2021a), greenhouse gas4 (GHG) concentrations 
in the atmosphere and annual anthropogenic GHG  emissions 
continue  to grow and have reached a  historic high, driven mainly 
by continued fossil fuels use (Jackson et al. 2019; Friedlingstein et al. 
2020; Peters et  al. 2020). Unsurprisingly, a  large volume of new 
literature has emerged since AR5 on the trends and underlying drivers 
of anthropogenic GHG emissions. This chapter provides a structured 
assessment of this new literature and establishes the most important 
thematic links to other chapters in this report.
4 Greenhouse gases are gaseous constituents of the atmosphere that absorb and emit radiation at specific wavelengths within the spectrum of radiation emitted by the

Earth’s surface, by the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapour (H 2O), carbon dioxide (CO 2), nitrous oxide (N 2O), 
methane (CH 4), and ozone (O 3) are the primary GHGs in the Earth’s atmosphere. Human-made GHGs include sulphur hexafluoride (SF 6), hydrofluorocarbons (HFCs), 
chlorofluorocarbons (CFCs), and perfluorocarbons (PFCs); see Annex I: Glossary.While AR5 has mostly assessed GHG emissions trends and drivers 
between 1970 and 2010, this assessment focuses on the period 
1990–2019 with the main emphasis on changes since 2010. 
Compared to Chapter  5 in the contribution of WG III to AR5 
(Blanco  et  al. 2014), the scope of the present chapter is broader. 
It presents the historical background of global progress in climate 
change mitigation for the rest of the report and serves as a starting 
point for the assessment of long-term as well as near- and medium-
term mitigation pathways in Chapters 3 and 4, respectively. It also

provides a systemic perspective on past emissions trends in different 
sectors of the economy (Chapters 6–12), and relates GHG emissions 
trends to past policies (Chapter  13) and observed technological 
development (Chapter  16). There is also a  greater focus on the 
analysis of consumption-based sectoral emissions trends, empirical 
Chapters 3, 4, 6–11
Chapters 3, 4, 
6–11, 16Chapters 3, 4, 6–11
Chapters 3, 5, 
7–12
Chapters 4–11, 
13, 14, 17Chapters 6–11 Chapters 4, 82.2 
Past and present trends 
of territorial GHG 
emissions2.3 
Past and present trends of 
consumption-based CO2 
emissions
2.7 
Emissions associated with 
existing and planned 
long-lived infrastructure2.6 
Behavioural choices 
and lifestyles2.5 
Technological change 
and innovation2.4 
Economic drivers and 
their trends by regions 
and sectors
2.8 
Climate and non-climate 
policies and measures and 
their impacts on emissions

Figure 2.1 | Chapter 2 road map and linkages to other chapters. Black arrows show the causal chain driving emissions. Blue lines indicate key linkages to other chapters 

221
evidence of  emissions consequences of behavioural choices and 
lifestyles, and the social aspects of mitigation (Chapter  5). Finally, 
a  completely new section discusses the mitigation implications of 
existing and planned long-lived infrastructure and carbon lock-in.
Figure  2.1 presents the road map of this chapter. It is a  simplified 
illustration of the causal chain driving emissions along the black 
arrows. It also highlights the most important linkages to other 
chapters in this volume (blue lines). The logic of the figure is that the 
main topic of this chapter is GHG emissions trends (discussed only 
in this chapter at such level of detail), hence they are at the top of 
the figure in grey-outlined boxes. The secondary theme is the drivers 
behind these trends, depicted in the second line of grey-outlined 
boxes. Four categories of drivers highlight key issues and guide readers

to chapters in which more details are presented. Finally, in addition 
to their own motivations and objectives, climate and non-climate 
policies and measures shape the aspirations and activities of actors in 
the main driver categories, as shown in the grey-outlined box below.
Accordingly, the grey-outlined boxes at the top of Figure 2.1 show 
that the first part of the chapter presents GHG emissions from two 
main perspectives: their geographical locations; and the places where 
goods are consumed and services are utilised. A complicated chain of 
drivers underlie these emissions. They are linked across time, space, 
and various segments of the economy and society in complex non-
linear relationships. Sections shown in the second row of grey-outlined 
boxes assess the latest literature and improve the understanding of 
the relative importance of these drivers in mitigating GHG emissions. 
A huge mass of physical capital embodying immense financial assets

and potentially operating over a  long lifetime produces vast GHG 
emissions. This long-lived infrastructure can be a significant hindrance 
to fast and deep reductions of emissions; it is therefore also shown as 
an important driver. A large range of economic, social, environmental, 
and other policies has been shaping these drivers of GHG emissions 
in the past and are anticipated to influence them in the future, as 
indicated by the grey-outlined policies box and its manifold linkages. 
As noted, blue lines show linkages of sections to other chapters that 
discuss these drivers and their operating mechanisms in detail.
2.2 Past and Present Trends of Territorial 
GHG Emissions
Total anthropogenic greenhouse gas (GHG) emissions as discussed 
in this chapter comprise CO 2 emissions from fossil fuel combustion 
and industrial (FFI) processes,5 net CO 2 emissions from land 
use, land-use change, and forestry (CO 2-LULUCF) (often named

FOLU  – forestry and other land-use  – in previous IPCC reports), 
methane (CH 4), nitrous oxide (N 2O) and fluorinated gases (F-gases) 
comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), 
sulphur hexafluoride (SF 6) as well as nitrogen trifluoride (NF 3). 
There are other major sources of F-gas emissions that are regulated 
under the Montreal Protocol such as chlorofluorocarbons (CFCs) 
and hydrochlorofluorocarbons (HCFCs) that also have considerable 
warming impacts (Figure  2.4), however they are not considered 
5 Industrial processes relate to CO 2 releases from fossil fuel oxidation and carbonate decomposition.here. Other substances, including ozone and aerosols, that further 
contribute climate forcing are only treated very briefly, but a  full 
chapter is devoted to this subject in the Working Group I contribution 
to AR6 (Szopa et al. 2021a; 2021b).
A growing number of global GHG emissions inventories have

become available since AR5 (Minx et al. 2021). However, only a few 
are comprehensive in their coverage of sectors, countries and gases – 
namely EDGAR (Emissions Database for Global Atmospheric Research) 
(Crippa et al. 2021), PRIMAP (Potsdam Real-time Integrated Model 
for probabilistic Assessment of emissions Paths) (Gütschow et  al. 
2021a), CAIT (Climate Analysis Indicators Tool) (WRI 2019) and CEDS 
(A Community Emissions Data System for Historical Emissions) (Hoesly 
et al. 2018). None of these inventories presently cover CO 2-LULUCF , 
while CEDS excludes F-gases. For individual gases and sectors, 
additional GHG inventories are available, as shown in Figure 2.2, but 
each has varying system boundaries leading to important differences 
between their respective estimates (Section 2.2.1). Some inventories 
are compiled bottom-up, while others are produced synthetically 
and are dependent on other inventories. A more comprehensive list

and discussion of different datasets is provided in the Chapter  2 
Supplementary Material (2.SM.1) and in Minx et al. (2021).
Across this report, version 6 of EDGAR (Crippa et al. 2021) provided 
by the Joint Research Centre of the European Commission, is used 
for a  consistent assessment of GHG emissions trends and drivers. 
It covers anthropogenic releases of CO 2-FFI, CH 4, N2O, and F-gas 
(HFCs, PFCs, SF 6, NF 3) emissions by 228 countries and territories and 
across five sectors and 27 subsectors. EDGAR is chosen because it 
provides the most comprehensive global dataset in its coverage 
of sources, sectors and gases. For transparency, and as part of the 
uncertainty assessment, EDGAR is compared to other global datasets 
in Section 2.2.1 as well as in the Chapter 2 Supplementary Material 
(2.SM.1). For individual country estimates of GHG emissions, it may 
be more appropriate to use inventory data submitted to the United

Nations Framework Convention on Climate Change (UNFCCC) under 
the common reporting format (CRF) (UNFCCC 2021). However, these 
inventories are only up to date for Annex I countries and cannot be 
used to estimate global or regional totals. As part of the regional 
analysis, a comparison of EDGAR and CRF estimates at the country-
level is provided, where the latter is available (Figure 2.9).
Net CO 2-LULUCF estimates are added to the dataset as the average 
of estimates from three bookkeeping models of land-use emissions 
(Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020) 
following the Global Carbon Project (Friedlingstein et al. 2020). This 
is different to AR5, where land-based CO 2 emissions from forest fires, 
peat fires, and peat decay, were used as an approximation of the 
net-flux of CO 2-LULUCF (Blanco et al. 2014). Note that the definition 
of CO 2-LULUCF emissions by global carbon cycle models, as used

here, differs from IPCC definitions (IPCC 2006) applied in national 
greenhouse gas inventories (NGHGI) for reporting under the climate 
convention (Grassi et al. 2018, 2021) and, similarly, from estimates 
by the Food and Agriculture Organization of the United Nations (FAO) 
for carbon fluxes on forest land (Tubiello et al. 2021). The conceptual

222
difference in approaches reflects different scopes. We use the 
global carbon cycle models’ approach for consistency with Working 
Group I (Canadell et  al. 2021) and to comprehensively distinguish 
natural from anthropogenic drivers, while NGHGI generally report 
as anthropogenic all CO 2 fluxes from lands considered managed 
(Section  7.2.2). Finally, note that the CO 2-LULUCF estimate from 
bookkeeping models as provided in this chapter is indistinguishable 
from the CO 2 from agriculture, forestry and other land use (AFOLU) 
as reported in Chapter 7, because the CO 2 emissions component from 
agriculture is negligible.
The resulting synthetic dataset used here has undergone additional 
peer review and is publicly available (Minx et al. 2021). Comprehensive 
information about the dataset as well as underlying uncertainties 
(including a  comparison with other datasets) can be found in the

Supplementary Material to this chapter and in Minx et al. (2021).
In this chapter and the report as a  whole, different GHGs are 
frequently converted into common units of CO 2 equivalent (CO 2-eq) 
emissions using 100-year global warming potentials (GWP100) from 
AR6 WGI (Forster et al. 2021a). This reflects the dominant use in the 
scientific literature and is consistent with decisions made by Parties 
to the Paris Agreement for reporting and accounting of emissions 
and removals (UNFCCC 2019). Other GHG emissions metrics exist, 
all of which, like GWP100, are designed for specific purposes and 
have limitations and uncertainties. The appropriate choice of GHG 
emissions metrics depends on policy objective and context (Myhre 
et  al. 2013; Kolstad et  al. 2015). A  discussion of GHG metrics is 
provided in a Cross-Chapter Box later in the chapter (Cross-Chapter 
Box  2) and at length in the Chapter  2 Supplementary Material.

Throughout the chapter GHG emissions are reported (in GtCO 2-eq) at 
two significant digits to reflect prevailing uncertainties in emissions 
estimates. Estimates are subject to uncertainty, which we report for 
a 90% confidence interval.
2.2.1 Uncertainties in GHG Emissions
Estimates of historical GHG emissions  – CO 2, CH 4, N 2O and 
F-gases – are uncertain to different degrees. Assessing and reporting 
uncertainties is crucial in order to understand whether available 
estimates are sufficiently robust to answer policy questions  – for 
example, if GHG emissions are still rising, or if a country has achieved 
an emission reduction goal (Marland 2008). These uncertainties can 
be of scientific nature, such as when a  process is not sufficiently 
understood. They also arise from incomplete or unknown parameter 
information (e.g.,  activity data, or emission factors), as well as 
estimation uncertainties from imperfect modelling techniques. There

are at least three major ways to examine uncertainties in emission 
estimates (Marland et al. 2009): (i) by comparing estimates made by 
independent methods and observations (e.g., comparing atmospheric 
measurements with bottom-up emissions inventory estimates) 
(Saunois et al. 2020; Petrescu et al. 2020a and 2020b; Tian et al. 2020); 
(ii) by comparing estimates from multiple sources and understanding 
sources of variation (Macknick 2011; Andres et  al. 2012; Andrew 
2020; Ciais et al. 2021); and (iii) by evaluating estimates from a single 
source (Hoesly and Smith 2018), for instance via statistical sampling across parameter values (e.g.,  Monni et  al. 2007; Robert J. Andres 
et al. 2014; Tian et al. 2019; Solazzo et al. 2021).
Uncertainty estimates can be rather different depending on the 
method chosen. For example, the range of estimates from multiple 
sources is bounded by their interdependency; they can be lower than

true structural plus parameter uncertainty, or than estimates made 
by independent methods. In particular, it is important to account for 
potential bias in estimates, which can result from using common 
methodological or parameter assumptions, or from missing sources 
(systemic bias). It is further crucial to account for differences in 
system boundaries – that is, which emissions sources are included 
in a  dataset and which are not, otherwise direct comparisons 
can exaggerate uncertainties (Macknick 2011; Andrew 2020). 
Independent top-down observational constraints are, therefore, 
particularly useful to bound total emission estimates, but are not 
yet capable of verifying emission levels or trends (Petrescu et  al. 
2021a, 2021b). Similarly, uncertainties estimates are influenced by 
specific modelling choices. For example, uncertainty estimates from 
studies on the propagation of uncertainties associated with key input

parameters (activity data, emissions factors) following the IPCC 
Guidelines (IPCC 2006) are strongly determined by assumptions on 
how these parameters are correlated between sectors, countries, 
and regions (Janssens-Maenhout et  al. 2019; Solazzo et  al. 2021). 
Assuming (full) covariance between source categories, and therefore 
dependence between them, increases uncertainty estimates. 
Estimates allowing for some covariance as in Sollazo et al. (2021) 
also tend to yield higher estimates than the range of values from 
ensemble of dependent inventories (Saunois et al. 2016, 2020).
For this report, a  comprehensive assessment of uncertainties is 
provided in the Supplementary Material (2.SM.2) to this chapter 
based on Minx et al. (2021). The uncertainties reported here combine 
statistical analysis, comparisons of global emissions inventories 
and an expert judgement of the likelihood of results lying outside 
a  defined confidence interval, rooted in an understanding gained

from the relevant literature. This literature has improved considerably 
since AR5, with a growing number of studies that assess uncertainties 
based on multiple lines of evidence (Saunois et  al. 2016, 2020; 
Tian et al. 2020; Petrescu et al. 2021a, 2021b).
To report the uncertainties in GHG emissions estimates, a  90% 
confidence interval (5th–95th percentile) is adopted – that is, there 
is a 90% likelihood that the true value will be within the provided 
range if the errors have a  Gaussian distribution, and no bias is 
assumed. This is in line with previous reporting in IPCC AR5 (Blanco 
et al. 2014; Ciais et al. 2014). Note that national emissions inventory 
submissions to the UNFCCC are requested to report uncertainty using 
a  95% confidence interval. The use of this broader uncertainty 
interval implies, however, a  relatively high degree of knowledge 
about the uncertainty structure of the associated data, particularly

regarding the distribution of uncertainty in the tails of the probability 
distributions. Such a high degree of knowledge is not present over all 
regions, emission sectors and species considered here.
Based on the assessment of relevant uncertainties above, a constant, 
relative, global uncertainty estimates for GHGs are applied at a 90%

223
(a) Annual global CO2-FFI emissions
(b) Annual global CO2-LULUCF emissions
(c) Annual global CH4 emissions
(d) Annual global N2O emissions10
8
6
4
2
0
–2
1970 1980 1990 2000 2010 2020Gt CO2
Blue
DGVM mean
OSCAR
HN
FAO Tier 1
NGHGI
400
1970 1980 1990 2000 2010 2020Mt CH4CEDS (1)
EDGAR v6.0* (1)
CEDS 2021-04-21 (1)
PRIMAP-Hist TP v2.3 (1)
GAINS (1)
EPA 2019 (1)
PRIMAP-Hist CR v2.3 (1)
FAO (2)
14
12
10
8
6
4
2
0
1970 1980 1990 2000 2010 2020Mt N2OGCP v2020 (1)
GAINS (1)
FAO (2)40
30
20
10
0
1970 1980 1990 2000 2010 2020Gt CO2EIA v2021 (2)
PRIMAP-Hist v2.3 CR (1,3)
PRIMAP-Hist v2.3 TP (1,3)
CEDS 2021_04_21 (1,3)
EDGAR v6.0_FT2020 (1,3)
CDIAC v2020 (2,4)
GCP v2021 (2,4)
IEA v2021 (2)
BP v2021 (2)Includes Energy sector plus:
(1) all IPPU
(2) some fossil fuels in IPPU
(3) agricultural CO2
(4) cement
(1) All anthropogenic sources

(2) Agricultural sources only
Figure 2.2 | Estimates of global anthropogenic greenhouse gas emissions from different data sources 1970–2019.

224
 confidence interval that range from relatively low values for CO 2-FFI 
(±8%), to intermediate values for CH 4 and F-gases (±30%), to higher 
values for N 2O (±60%) and CO 2-LULUCF (±70%). Uncertainties 
for aggregated total GHG emissions in terms of CO 2-eq emissions 
are calculated as the square root of the squared sums of absolute 
uncertainties for individual gases (taking F-gases together), using 
GWP100 to weight emissions of non-CO 2 gases but excluding 
uncertainties in the metric itself.
This assessment of uncertainties is broadly in line with AR5 WGIII 
(Blanco et al. 2014), but revises individual uncertainty judgements in line with the more recent literature (Saunois et  al. 2016, 2020; 
Janssens-Maenhout et al. 2019; Friedlingstein et al. 2020; Tian et al. 
2020; Solazzo et al. 2021) as well as the underlying synthetic analysis 
provided here (e.g.,  Figures 2.2 and 2.3 in this chapter; and Minx

et  al. 2021). As such, reported changes in these estimates do not 
reflect changes in the underlying uncertainties, but rather a change 
in expert judgement based on an improved evidence base in the 
scientific literature. Uncertainty estimates for CO 2-FFI and N 2O remain 
unchanged compared to AR5. The change in the uncertainty estimates 
for CH 4 from 20% to 30% is justified by larger uncertainties reported 
for EDGAR emissions (Janssens-Maenhout et al. 2019; Solazzo et al. Figure 2.2 (continued): Estimates of global anthropogenic greenhouse gas emissions from different data sources 1970–2019.  Panel (a): CO 2 FFI emissions 
from: EDGAR – Emissions Database for Global Atmospheric Research (this dataset) (Crippa et al. 2021); GCP – Global Carbon Project (Friedlingstein et al. 2020; Andrew and 
Peters 2021); CEDS – Community Emissions Data System (Hoesly et al. 2018; O’Rourke et al. 2021); CDIAC Global, Regional, and National Fossil-Fuel CO 2 Emissions (Gilfillan

et al. 2020); PRIMAP-hist – Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (Gütschow et al. 2016, 2021b); EIA – Energy Information 
Administration International Energy Statistics (EIA 2021); BP – BP Statistical Review of World Energy (BP 2021); IEA – International Energy Agency (IEA 2021a, 2021b); 
IPPU refers to emissions from industrial processes and product use. Panel (b): Net anthropogenic CO 2-LULUCF emissions from: BLUE – Bookkeeping of land-use emissions 
(Hansis et al. 2015; Friedlingstein et al. 2020); DGVM-mean – multi-model mean of CO 2-LULUCF emissions from dynamic global vegetation models (Friedlingstein et al. 2020); 
OSCAR – an earth system compact model (Friedlingstein et al. 2020; Gasser et al. 2020); HN – Houghton and Nassikas Bookkeeping Model (Houghton and Nassikas 2017;

Friedlingstein et al. 2020); for comparison, the net CO 2 flux from FAOSTAT (FAO Tier 1) is plotted, which comprises net emissions and removals on forest land and from net 
forest conversion (FAOSTAT 2021; Tubiello et al. 2021), emissions from drained organic soils under cropland/grassland (Conchedda and Tubiello 2020), and fires in organic soils 
(Prosperi et al. 2020), as well as a net CO 2 flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC, which include land-use 
change, and fluxes in managed lands (Grassi et al. 2021). Panel (c): Anthropogenic CH 4 emissions from: EDGAR (above); CEDS (above); PRIMAP-hist (above); GAINS – The 
Greenhouse gas – Air pollution Interactions and Synergies Model (Höglund-Isaksson et al. 2020); EPA-2019: Greenhouse gas emission inventory (US-EPA, 2019); FAO –FAOSTAT

inventory emissions (Tubiello et al. 2013; Tubiello 2018; FAOSTAT 2021); Panel (d): Anthropogenic N 2O emissions from: GCP – global nitrous oxide budget (Tian et al. 2020); 
CEDS (above); EDGAR (above); PRIMAP-hist (above); GAINS (Winiwarter et al. 2018); EPA-2019 (above); FAO (above). Differences in emissions across different versions of the 
EDGAR dataset are shown in the Supplementary Material (Figure 2.SM.2). Source: Minx et al. (2021).
1980 1990 2000 20100.20.40.60.81.01.21.41.6
1980 1990 2000 20100123456789
HFCs + PFCs + SF6 (+NF3) 
HFCs + PFCs + SF6 (EDGAR v5FT; Olivier and Peters, 2020) HFCs + PFCs + SF6 + NF3  (WMO, 2018) HCFC-22, -141b, -142b (WMO, 2018)
CFC-11, -12, -113 (WMO, 2018) CO2-equivalent emissions (Gt yr–1)HFCs + PFCs + SF6 (+NF3)  CFCs + HCFCs
Figure  2.3 | Comparison between top-down estimates and bottom-up EDGAR inventory data on GHG emissions for 1980–2016. Left panel: Total

GWP100-weighted emissions based on IPCC AR6 (Forster et al. 2021a) of F-gases in Olivier and Peters (2020) [EDGARv5FT] (dark-red dotted line, excluding C 4F10, C5F12, 
C6F14 and C 7F16) and EDGARv6 (bright red dashed line) compared to top-down estimates based on AGAGE and NOAA data from WMO (2018) (blue lines; Engel and Rigby 
(2018); Montzka and Velders (2018)). Right panel: Top-down aggregated emissions for the three most abundant CFCs (–11, –12 and –113) and HCFCs (–22, –141b, 
–142b) not covered in bottom-up emissions inventories are shown in dark blue and yellow. For top-down estimates the shaded areas between two respective lines represent 
1σ uncertainties. Source: Minx et al. (2021).

225
2021) as well as the wider literature (Kirschke et al. 2013; Tubiello 
et  al. 2015; Saunois et  al. 2016, 2020). As AR6  – in contrast to 
AR5  – uses CO 2-LULUCF data from global bookkeeping models, 
the respective uncertainty estimate is based on the reporting in the 
underlying literature (Friedlingstein et al. 2020) as well as Working 
Group I (Canadell et al. 2021). The 70% uncertainty value is at the 
higher end of the range considered in AR5 (Blanco et al. 2014).
Finally, for F-gas emissions top-down atmospheric measurements 
from the 2018 World Meteorological Organization’s (WMO) Scientific 
Assessment of Ozone Depletion (Engel and Rigby 2018; Montzka and 
Velders 2018) are compared to the data used in this report (Crippa 
et al. 2021; Minx et al. 2021) as shown in Figure 2.3. Due to the general 
absence of natural F-gas fluxes, there is a sound understanding of global

and regional F-gas emissions from top-down estimates of atmospheric 
measurements with small and well-understood measurement, lifetime 
and transport model uncertainties (Engel and Rigby 2018; Montzka 
and Velders 2018). However, when species are aggregated into total 
F-gas emissions, EDGARv6.0 emissions are around 10% lower than the 
WMO 2018 values throughout, with larger differences for individual 
F-gas species, and further discrepancies when comparing to older 
EDGAR versions. Based on this, the overall uncertainties for aggregate F-gas emissions is judged conservatively at 30%  – 10  percentage 
points higher than in AR5 (Blanco et al. 2014).
Aggregate uncertainty across all GHGs is approximately ±11% 
depending on the composition of gases in a particular year. AR5 applied 
a  constant uncertainty estimates of ±10% for total GHG emissions. 
The upwards revision applied to the uncertainties of CO 2-LULUCF , 
CH4 and F-gas emissions therefore has a limited overall effect on the

assessment of GHG emissions.
GHG emissions metrics such as GWP100 have their own uncertainties, 
which has been largely neglected in the literature so far. Minx et al. 
(2021) report the uncertainty in GWP100 metric values as ±50% for 
methane and other short-lived climate forcers (SLCFs), and ±40% 
for  non-CO 2 gases with longer atmospheric lifetimes (specifically, 
those with lifetimes longer than 20 years). If uncertainties in GHG 
metrics are considered, and are assumed independent (which may lead 
to an underestimate) the overall uncertainty of total GHG emissions 
in 2019 increases from ±11% to ±13%. Metric uncertainties are not 
further considered in this chapter, but are referred to in Cross-Chapter 
Box 2 in this chapter, and Chapter 2 Supplementary Material on GHG 
metrics (2.SM.3).
1.6 1.0
0.8
0.6
0.4
0.2
1970 2010 2000 1990 1980
1970 2010 2000 1990 19801.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
–0.80.4
0.2
0.0
–0.2
–0.4
–0.6
–0.80.4
0.2
0.0
–0.2
–0.4

–0.60.0(a) GHG contributions to warming from 1750 (b) GHG contributions to 1750–2018 warming
(c) SLCF contributions to warming from 1750Contributions to warming from 1750 (°C) Contributions to warming from 1750 (°C)(d) SLCF contributions to 1750–2018 warmingF-gases other
BC
CO
NOx
NH3
OC
NMVOCs
SOx
netF-gases Kyoto/Paris
N2O
CH4
CO2-FFI
Figure 2.4 | Contribution of different GHGs to global warming over the period 1750 to 2018.  Top row: contributions estimated with the FaIR reduced-complexity 
climate model. Major GHGs and aggregates of minor gases as a timeseries in (a) and as a total warming bar chart with 90% confidence interval added in (b). Bottom row: 
contribution from short-lived climate forcers as a time series in (c) and as a total warming bar chart with 90% confidence interval added in (d). The dotted line in (c) gives the

net temperature change from short-lived climate forcers other than CH 4. F-Kyoto/Paris includes the gases covered by the Kyoto Protocol and Paris Agreement, while F-other 
includes the gases covered by the Montreal Protocol but excluding the HFCs. Source: Minx et al. (2021).

226
The most appropriate metric to aggregate GHG emissions depends 
on the objective (Cross-Chapter Box 2). One such objective can be to 
understand the contribution of emissions in any given year to warming, 
while another can be to understand the contribution of cumulative 
emissions over an extended time period to warming. In Figure 2.4 the 
modelled warming from emissions of each gas or group of gases is 
also shown – calculated using the reduced-complexity climate model 
Finite Amplitude Impulse Response (FaIR) model v1.6, which has been 
calibrated to match several aspects of the overall WGI assessment 
(Forster et al. 2021a; specifically Cross-Chapter Box 7 in Chapter 10 
therein). Additionally, its temperature response to emissions with 
shorter atmospheric lifetimes such as aerosols, methane or ozone 
has been adjusted to broadly match those presented in Szopa et al. 
(2021a). There are some differences in actual warming compared to

the GWP100 weighted emissions of each gas (Figure 2.4), in particular 
a greater contribution from CH 4 emissions to historical warming. This is consistent with warming from CH 4 being short-lived and hence 
having a  more pronounced effect in the near-term during a  period 
of rising emissions. Nonetheless, Figure 2.4 highlights that emissions 
weighted by GWP100 do not provide a  fundamentally different 
information about the contribution of individual gases than modelled 
actual warming over the historical period, when emissions of most 
GHGs have been rising continuously, with CO 2 being the dominant 
and CH 4 being the second most important contributor to GHG-induced 
warming. Other metrics such as GWP* (or GWP star) (Cain et  al. 
2019) offer an even closer resemblance between cumulative CO 2-eq 
emissions and temperature change. Such a  metric may be more 
appropriate when the key objective is to track temperature change 
when emissions are falling, as in mitigation scenarios.

Cross-Chapter Box 2 | GHG Emissions Metrics
Authors: Andy Reisinger (New Zealand), Alaa Al Khourdajie (United Kingdom/Syria), Kornelis Blok (the Netherlands), Harry Clark 
(New Zealand), Annette Cowie (Australia), Jan S. Fuglestvedt (Norway), Oliver Geden (Germany), Veronika Ginzburg (the Russian 
Federation), Céline Guivarch (France), Joanna I. House (United Kingdom), Jan Christoph Minx (Germany), Rachid Mrabet (Morocco), 
Gert-Jan Nabuurs (the Netherlands), Glen P . Peters (Norway/Australia), Keywan Riahi (Austria), Roberto Schaeffer (Brazil), Raphael Slade 
(United Kingdom), Anders Hammer Strømman (Norway), Detlef P . van Vuuren (the Netherlands)
Comprehensive mitigation policy relies on consideration of all anthropogenic forcing agents, which differ widely in their atmospheric 
lifetimes and impacts on the climate system. GHG emission metrics6 provide simplified information about the effects that emissions

of different GHGs have on global temperature or other aspects of climate, usually expressed relative to the effect of emitting CO 2 
(see emission metrics in Annex I: Glossary). This information can inform prioritisation and management of trade-offs in mitigation 
policies and emission targets for non-CO 2 gases relative to CO 2, as well as for baskets of gases expressed in CO 2-eq. This assessment 
builds on the evaluation of GHG emission metrics from a physical science perspective by WGI (Forster et al. 2021b). For additional 
details and supporting references, see Chapter 2 Supplementary Material (2.SM.3) and Annex II.8.
The global warming potential (GWP) and the global temperature change potential (GTP) were the main metrics assessed in AR5 (Myhre 
et al. 2013; Kolstad et al. 2014). The GWP with a lifetime of 100 years (GWP100) continues to be the dominant metric used in the scientific

literature on mitigation assessed by WGIII. The assessment by WGI (Forster et al. 2021) includes updated values for these metrics based 
on updated scientific understanding of the response of the climate system to emissions of different gases, including changing background 
concentrations. It also assesses new metrics published since AR5. Metric values in AR6 include climate-carbon cycle feedbacks by default; 
this provides an important update and clarification from AR5 which reported metric values both with and without such feedbacks.
The choice of metric, including time horizon, should reflect the policy objectives for which the metric is applied (Plattner et al. 2009). 
Recent studies confirm earlier findings that the GWP is consistent with a cost-benefit framework (Kolstad et al. 2014), which implies 
weighting each emission based on the economic damages that this emission will cause over time, or conversely, the avoided damages

from avoiding that emission. The GWP time horizon can be linked to the discount rate used to evaluate economic damages from each 
emission. For methane, GWP100 implies a social discount rate of about 3–5% depending on the assumed damage function, whereas 
GWP20 implies a much higher discount rate, greater than 10% (medium confidence) (Mallapragada and Mignone 2019; Sarofim and 
Giordano 2018). The dynamic GTP is aligned with a cost-effectiveness framework, as it weights each emission based on its contribution 
to global warming in a specified future year (e.g., the expected year of peak warming for a given temperature goal). This implies 
a shrinking time horizon and increasing relative importance of SLCF emissions as the target year is approached (Johansson 2011; 
Aaheim and Mideksa 2017). The GTP with a static time horizon (e.g., GTP100) is not well-matched to either a cost-benefit or a cost-

effectiveness framework, as the year for which the temperature outcome is evaluated would not match the year of peak warming, nor 
the overall damages caused by each emission (Edwards and Trancik 2014; Strefler et al. 2014; Mallapragada and Mignone 2017).
6 Emission metrics also exist for aerosols, but these are not commonly used in climate policy. This assessment focuses on GHG emission metrics only.

227
Cross-Chapter Box 2 (continued)
A number of studies since AR5 have evaluated the impact of various GHG emission metrics and time horizons on the global economic 
costs of limiting global average temperature change to a pre-determined level (e.g. Strefler et al. 2014; Harmsen et al. 2016; Tanaka 
et al. 2021) (see 2.SM.3 for additional detail). These studies indicate that, for mitigation pathways that limit warming to 2°C (<67%) 
above pre-industrial levels or lower, using GWP100 to inform cost-effective abatement choices between gases would achieve such 
long-term temperature goals at close to least global cost within a few percent (high confidence). Using the dynamic GTP instead of 
GWP100 could reduce global mitigation costs by a few percent in theory (high confidence), but the ability to realise those cost savings

depends on the temperature limit, policy foresight and flexibility in abatement choices as the weighting of SLCF emissions increases 
over time (medium confidence) (van den Berg et al. 2015; Huntingford et al. 2015). Similar benefits as for the dynamic GTP might be 
obtained by regularly reviewing and potentially updating the time horizon used for GWP in light of actual emissions trends compared 
to climate goals (Tanaka et al. 2020).
The choice of metric and time horizon can affect the distribution of costs and the timing of abatement between countries and sectors 
in cost-effective mitigation strategies. Sector-specific lifecycle assessments find that different emission metrics and different time 
horizons can lead to divergent conclusions about the effectiveness of mitigation strategies that involve reductions of one gas but an 
increase of another gas with a different lifetime (e.g., Tanaka et al. 2019). Assessing the sensitivity of conclusions to different emission

metrics and time horizons can support more robust decision-making (Levasseur et al. 2016; Balcombe et al. 2018) (see 2.SM.3 for 
details). Sectoral and national perspectives on GHG emission metrics may differ from a global least-cost perspective, depending on 
other policy objectives and equity considerations, but the literature does not provide a consistent framework for assessing GHG 
emission metrics based on equity principles.
Literature since AR5 has emphasised that the GWP100 is not well-suited to estimating the warming effect at specific points in time 
from sustained SLCF emissions (e.g., Allen et al. 2016; Cain et al. 2019; Collins et al. 2019). This is because the warming caused by 
an individual SLCF emission pulse diminishes over time and hence, unlike CO 2, the warming from SLCF emissions that are sustained 
over multiple decades to centuries depends mostly on their ongoing rate of emissions rather than their cumulative emissions. Treating

all gases interchangeably based on GWP100 within a stated emissions target therefore creates ambiguity about actual global 
temperature outcomes (Fuglestvedt et al. 2018; Denison et al. 2019). Supplementing economy-wide emission targets with information 
about the expected contribution from individual gases to such targets would reduce the ambiguity in global temperature outcomes.
Recently developed step/pulse metrics such as the combined global temperature change potential (CGTP) (Collins et al. 2019) and 
GWP* (Allen et al. 2018; Cain et al. 2019) recognise that a sustained increase/decrease in the rate of SLCF emissions has a similar 
effect on global surface temperature over multiple decades as a one-off pulse emission/removal of CO 2. These metrics use this 
relationship to calculate the CO 2 emissions or removals that would result in roughly the same temperature change as a sustained

change in the rate of SLCF emissions (CGTP) over a given time period, or as a varying time series of CH 4 emissions (GWP*). From 
a mitigation perspective, these metrics indicate greater climate benefits from rapid and sustained methane reductions over the next 
few decades than if such reductions are weighted by GWP100, while conversely, sustained methane increases have greater adverse 
climate impacts (Collins et al. 2019; Lynch et al. 2020). The ability of these metrics to relate changes in emission rates of short-lived 
gases to cumulative CO 2 emissions makes them well-suited, in principle, to estimating the effect on the remaining carbon budget from 
more, or less, ambitious SLCF mitigation over multiple decades compared to a given reference scenario (high confidence) (Collins et al. 
2019; Forster et al. 2021).
The potential application of GWP* in wider climate policy (e.g., to inform equitable and ambitious emission targets or to support

sector-specific mitigation policies) is contested, although relevant literature is still limited (Rogelj and Schleussner 2019, 2021; 
Schleussner et al. 2019; Allen et al. 2021; Cain et al. 2021). Whereas GWP and GTP describe the marginal effect of each emission 
relative to the absence of that emission, GWP* describes the equivalent CO 2 emissions that would give the same temperature 
change as an emissions trajectory of the gas considered, starting at a (user-determined) reference point. The warming based on 
those cumulative CO 2-equivalent emission at any point in time is relative to the warming caused by emissions of that gas before the 
reference point. Because of their different focus, GWP* and GWP100 can equate radically different CO 2 emissions to the same CH 4 
emissions: rapidly declining CH 4 emissions have a negative CO 2-warming-equivalent value based on GWP* (rapidly declining SLCF

emissions result in declining temperature, relative to the warming caused by past SLCF emissions at a previous point in time) but a 
positive CO 2-equivalent value based on GWP or GTP (each SLCF emission from any source results in increased future radiative forcing 
and global average temperature than without this emission, regardless of whether the rate of SLCF emissions is rising or declining). 
The different focus in these metrics can have important distributional consequences, depending on how they are used to inform 
emission targets (Lynch et al. 2021; Reisinger et al. 2021), but this has only begun to be explored in the scientific literature.

228
A key insight from WGI is that, for a  given emissions scenario, different metric choices can alter the time at which net zero 
GHG emissions are calculated to be reached, or whether net zero GHG emissions are reached at all (2.SM.3). From a mitigation 
perspective, this implies that changing GHG emission metrics but retaining the same numerical CO 2-equivalent emissions targets 
would result in different climate outcomes. For example, achieving a balance of global anthropogenic GHG emissions and removals, as 
stated in Article 4.1 of the Paris Agreement could, depending on the GHG emission metric used, result in different peak temperatures 
and in either stable, or slowly or rapidly declining temperature after the peak (Allen et al. 2018; Fuglestvedt et al. 2018; Tanaka and 
O’Neill 2018; Schleussner et al. 2019). A fundamental change in GHG emission metrics used to monitor achievement of existing

emission targets could therefore inadvertently change their intended climate outcomes or ambition, unless existing emission targets 
are re-evaluated at the same time (very high confidence).
The WGIII contribution to AR6 reports aggregate emissions and removals using updated GWP100 values from AR6 WGI unless stated 
otherwise. This choice was made on both scientific grounds (the alignment of GWP100 with a cost-benefit perspective under social 
discount rates and its performance from a global cost-effectiveness perspective) and for procedural reasons, including continuity 
with past IPCC reports and alignment with decisions under the Paris Agreement Rulebook (Annex II.8). A key constraint in the choice 
of metric is also that the literature assessed by WGIII predominantly uses GWP100 and often does not provide sufficient detail 
on emissions and abatement of individual gases to allow translation into different metrics. Presenting such information routinely

in mitigation studies would enable the application of more diverse GHG emission metrics in future assessments to evaluate their 
contribution to different policy objectives.
All metrics have limitations and uncertainties, given that they simplify the complexity of the physical climate system and its response 
to past and future GHG emissions. No single metric is well-suited to all applications in climate policy. For this reason, the WGIII 
contribution to AR6 reports emissions and mitigation options for individual gases where possible; CO 2-equivalent emissions are 
reported in addition to individual gas emissions where this is judged to be policy-relevant. This approach aims to reduce the ambiguity 
regarding mitigation potentials for specific gases and actual climate outcomes over time arising from the use of any specific GHG 
emission metric.
2.2.2 Trends in the Global GHG Emissions Trajectories 
and Short-lived Climate Forcers
2.2.2.1 Anthropogenic Greenhouse Gas Emissions Trends

Global GHG emissions continued to rise since AR5, but the rate of 
emissions growth slowed (high confidence). GHG emissions reached 
59 ± 6.6 GtCO 2-eq in 2019 (Table  2.1 and Figure  2.5). In 2019, 
CO2  emissions from the FFI were 38 (±3.0) Gt, CO 2 from LULUCF 
6.6 ± 4.6 Gt, CH 4 11 ± 3.2 GtCO 2-eq, N 2O 2.7 ± 1.6 GtCO 2-eq and 
F-gases 1.4 ± 0.41 GtCO 2-eq. There is high confidence that average 
annual GHG emissions for the last decade (2010–2019) were the 
highest on record in terms of aggregate CO 2-eq emissions, but low 
confidence for annual emissions in 2019 as uncertainties are large 
considering the size and composition of observed increases in the 
most recent years (UNEP 2020a; Minx et al. 2021).
GHG emissions levels in 2019 were higher compared to 10 and 
30  years earlier (high confidence): about 12% (6.5 GtCO 2-eq) 
higher than in 2010 (53 ± 5.7 GtCO 2-eq) (the last year of AR5 
reporting) and about 54% (21 GtCO 2-eq) higher than in 1990

(38 ± 4.8 GtCO 2-eq) (the baseline year of the Kyoto Protocol and 
frequent nationally determined contribution (NDC) reference). GHG 
emissions growth slowed compared to the previous decade (high 
confidence): From 2010 to 2019, GHG emissions grew on average 
by about 1.3% per year compared to an average annual growth of 2.1% between 2000 and 2009. Nevertheless the absolute increase 
in average annual GHG emissions for 2010–2019 compared to 
2000–2009 was 9.1 GtCO 2-eq and, as such, the largest observed in 
the data since 1970 (Table 2.1) – and most likely in human history 
(Friedlingstein et al. 2020; Gütschow et al. 2021b). Decade-by-decade 
growth in average annual GHG emissions was observed across all 
(groups of) gas as shown in Table 2.1, but for N 2O and CO 2-LULUCF 
emissions this is much more uncertain.
Reported total annual GHG emission estimates differ between 
the WGIII contributions in AR5 (Blanco et  al. 2014) and AR6

(this chapter) mainly due to differing global warming potentials (high 
confidence). For the year 2010, total GHG emissions were estimated 
at 49 ± 4.9 GtCO 2-eq in AR5 (Blanco et al. 2014), while we report 
53 ± 5.7 GtCO 2-eq here. However, in AR5 total GHG emissions were 
weighted based on GWP100 values from IPCC’s Second Assessment 
Report. Applying those GWP values to the 2010 emissions from AR6 
yields 50 GtCO 2-eq (Forster et al. 2021a). Hence, observed differences 
are mainly due to the use of most recent GWP values, which have 
higher warming potentials for methane (29% higher for biogenic and 
42% higher for fugitive methane) and 12% lower values for nitrous 
oxide (Cross-Chapter Box 2 in this chapter).
Emissions growth has been persistent but varied in pace across gases. 
The average annual emission levels of the last decade (2010–2019) 
were higher than in any previous decade for each group of GHGs:

229
CO2, CH 4, N2O, and F-gases (high confidence). Since 1990, CO 2-FFI 
have grown by 67% (15 GtCO 2-eq), CH 4 by 29% (2.4 GtCO 2-eq), 
and N 2O by 33% (0.65 GtCO 2-eq), respectively (Figure 2.5). Growth 
in fluorinated gases (F-gas) has been by far the highest with about 
254% (1.0 GtCO 2-eq), but it occurred from low levels. In 2019, 
total F-gas levels were no longer negligible with a share of 2.3% of 
global GHG emissions. Note that the F-gases reported here do not 
include CFCs and HCFCs, which are groups of substances regulated under the Montreal Protocol. The aggregate CO 2-eq emissions of 
HFCs, HCFCs and CFCs were each approximately equal in 2016, 
with a  smaller contribution from PFCs, SF 6, NF 3 and some more 
minor F-gases. Therefore, the GWP-weighted F-gas emissions 
reported here (HFCs, PFCs, SF 6, NF 3), which are dominated by the 
HFCs, represent less than half of the overall CO 2-eq F-gas emissions

in 2016 (Figure 2.3).Table 2.1 | Total anthropogenic GHG emissions (GtCO 2-eq yr –1) 1990–2019. CO 2 from fossil fuel combustion and industrial processes (FFI); CO 2 from Land Use, Land 
Use Change and Forestry (LULUCF); methane (CH 4); nitrous oxide (N 2O); fluorinated gases (F-gases: HFCs, PFCs, SF 6, NF 3). Aggregate GHG emissions trends by groups of gases 
reported in GtCO 2-eq converted based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (AR6). Uncertainties are 
reported for a 90% confidence interval. Source: Minx et al. (2021).
Average annual emissions (GtCO 2-eq)
CO2 FFI CO2 LULUCF CH4 N2O Fluorinated gases GHG
2019 38 ± 3.0 6.6 ± 4.6 11 ± 3.2 2.7 ± 1.6 1.4 ± 0.41 59 ± 6.6
2010–2019 36 ± 2.9 5.7 ± 4.0 10 ± 3.0 2.6 ± 1.5 1.2 ± 0.35 56 ± 6.0
2000–2009 29 ± 2.4 5.3 ± 3.7 9.0 ± 2.7 2.3 ± 1.4 0.81 ± 0.24 47 ± 5.3
1990–1999 24 ± 1.9 5.0 ± 3.5 8.2 ± 2.5 2.1 ± 1.2 0.49 ± 0.15 40 ± 4.9

1990 23 ± 1.8 5.0 ± 3.5 8.2 ± 2.5 2.0 ± 1.2 0.38 ± 0.11 38 ± 4.8
38Gt
+0.7% yr  –1
+2.1% yr  –1
+1.3% yr  –1 42Gt 53Gt 59Gt
CO2 from fossil 
fuel and industry 
(CO2-FFI)Net CO2 from land 
use, land-use 
change, forestry 
(CO2-LULUCF)Methane (CH4)Nitrous 
oxide (N2O)Fluorinated 
gases (F-gases)(a) Global net anthropogenic GHG emissions 1990–2019 
(c) Global anthropogenic GHG emissions and uncertainties by gas – relative to 1990GHG emissions (GtCO2-eq yr  –1) GHG emissions (%)
02019 
emissions 
(GtCO2-eq)1990–2019 
increase
(GtCO2-eq)Emissions 
in 2019, 
relative 
to 1990 (%)
CO2-FFI 38 ± 3 15 167
CO2-LULUCF 6.6 ± 4.6 1.6 133
CH4 11 ± 3.2 2.4 129
N2O 2.7 ± 1.6 0.65 133
F-gases 1.4 ± 0.41 0.97 354
Total 59 ± 6.6 21 154
The solid line indicates central estimate of emissions trends. The shaded area indicates the uncertainty range.0102030405060
1990 2000 2010 2019
50100150200250CO2-LULUCF
1990 2019CH4
1990 2019CO2-FFI
1990 2019N2O
1990 2019F-gases
0
1990 2019100200300500
40021%
13%
59%2%
5%

20%
12%
61%2%
5%
18%
10%
65%2%
4%
18%
11%
64%1%
5%59.6 62.3 56.9
AR6* AR5* AR2 AR5(b) Evolution of GWP-100 metric 
values across assessments
(* including feedbacks)Emissions of greenhouse gases have continued to increase since 1990, at varying rates
59.5
Figure 2.5 | Total anthropogenic GHG emissions (GtCO 2-eq yr –1) 1990–2019.  CO2 from fossil fuel combustion and industrial processes (FFI); net CO 2 from land use, 
land use change and forestry (LULUCF); methane (CH 4); nitrous oxide (N 2O); fluorinated gases (F-gases: HFCs, PFCs, SF 6, NF 3). Panel (a): Aggregate GHG emissions trends 
by groups of gases reported in GtCO 2-eq converted based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. 
Panel (b):  Waterfall diagrams juxtaposes GHG emissions for the most recent year (2019) in CO 2 equivalent units using GWP100 values from the IPCC’s Second, Fifth, and Sixth

Assessment Reports, respectively. Error bars show the associated uncertainties at a 90% confidence interval. Panel (c): individual trends in CO 2-FFI, CO 2-LULUCF , CH 4, N2O and 
F-gas emissions for the period 1990–2019, normalised to 1 in 1990. Source: data from Minx et al. (2021).

230
The only exception to these patterns of GHG emissions growth is net 
anthropogenic CO 2-LULUCF emissions, where there is no statistically 
significant trend due to high uncertainties in estimates (Figures 2.2 and 
2.5; Chapter 2 Supplementary Material). While the average estimate 
from the bookkeeping models report a  slightly increasing trend in 
emissions, NGHGI and FAOSTAT estimates show a slightly decreasing 
trend, which diverges in recent years (Figure 2.2). Similarly, trends in 
CO2-LULUCF estimates from individual bookkeeping models differ: 
while two models (BLUE and OSCAR) show a sustained increase in 
emissions levels since the mid-1990s, emissions from the third model 
(Houghton and Nassikas (HN)) declined (Figure 2.2 in this chapter; 
Friedlingstein et al. 2020). Differences in accounting approaches and 
their impacts CO 2 emissions estimates from land use is covered in

Chapter  7 and in the Chapter  2 Supplementary Material (2.SM.2). 
Note that anthropogenic net emissions from bioenergy are covered 
by the CO 2-LULUCF estimates presented here.
The CO 2-FFI share in total CO 2-eq emissions has plateaued at about 
65% in recent years and its growth has slowed considerably since 
AR5 (high confidence). The CO 2-FFI emissions grew at 1.1% during 
the 1990s and 2.5% during the 2000s. For the last decade (2010s) – 
not covered by AR5 – this rate dropped to 1.2%. This included a short 
period between 2014 and 2016 with little or no growth in CO 2-FFI 
emissions, mainly due to reduced emissions from coal combustion (Jackson et al. 2016; Qi et al. 2016; Peters et al. 2017a; Canadell et al. 
2021). Subsequently, CO 2-FFI emissions started to rise again (Peters 
et al. 2017b; Figueres et al. 2018; Peters et al. 2020).
Starting in the spring of 2020 a major break in global emissions 
trends was observed due to lockdown policies implemented in

response to the COVID-19 pandemic (high confidence) (Forster 
et al. 2020; Le Quéré et al. 2020, 2021; Z. Liu et al. 2020b; Bertram 
et  al. 2021). Overall, global CO 2-FFI emissions are estimated 
to have declined by 5.8% (5.1%–6.3%) in 2020, or about 2.2 
(1.9–2.4)  GtCO 2 in total (Friedlingstein et  al. 2020; Z. Liu et  al. 
2020b; BP 2021; Crippa et al. 2021; IEA 2021a). This exceeds any 
previous global emissions decline since 1970, both in relative 
and absolute terms (Figure 2.6). Daily emissions, estimated based 
on activity and power-generation data, declined substantially 
compared to 2019 during periods of economic lockdown, 
particularly in April 2020 – as shown in Figure 2.6 – but rebounded 
by the end of 2020 (medium confidence) (Le Quéré et  al. 2020, 
2021; Z. Liu et  al. 2020b). Impacts were differentiated by sector, 
with road transport and aviation particularly affected. Inventories 
estimate the total power sector CO 2 reduction from 2019 to 2020

at 3% (IEA 2021a) and 4.5% (Crippa et al. 2021). Approaches that 
predict near real-time estimates of the power sector reduction are 
more uncertain and estimates range more widely, between 1.8% 
1973 Oil Crisis
−0.3%1991 Soviet Union dissolusion
−0.3%2008 Global 
ﬁnancial crisis
−1%2020 COVID−19 
pandemic
−5.1% (EDGAR)
−5.6% (GCB)
−6.3% (BP)
−5.8% (IEA)
−6.0% (CM)
10203040
1970 1980 1990 2000 2010 2020
Aviation Industry Land transport Power Residential
JanApr JulOct JanApr JulOct JanApr JulOct JanApr JulOct JanApr JulOct−10.0−7.5−5.0−2.50.02.5 
Total
Jan Apr JulOct−20−100(a) Global CO2 emissions and the impact of economic and geopolitical events
(b) Daily CO2 emissions in 2020 versus 2019 and the impact of COVID-19 lockdown measuresCO2 emissions (GtCO2 yr–1) Change in global daily 
CO2 emissions (MtCO2 day–1)EDGAR Le Quéré et al. (2021) Carbon Monitor (CM)

Figure 2.6 | Global CO 2 emissions from fossil fuel combustion and industry (FFI) in 2020 and the impact of COVID-19. Panel (a) depicts CO 2-FFI emissions 
over the past five decades (GtCO 2 yr –1). The single year declines in emissions following major economic and geopolitical events are shown, as well as the decline recorded 
in five different datasets for emissions in 2020 (COVID-19) compared to 2019 (no COVID-19). Panel (b) depicts the change in global daily carbon emissions (MtCO 2 per day) 
in 2020 compared to 2019, showing the impact of COVID-19 lockdown policies. Source: Friedlingstein et al. (2020), Le Quéré et al. (2020), Carbon Monitor (Liu et al. 2020b), 
BP (2021), Crippa et al. (2021), IEA (2021a).

231
(Le Quéré et al. 2020, 2021), 4.1% (Z. Liu et al. 2020b) and 6.8% 
(Bertram et  al. 2021); the latter taking into account the over-
proportional reduction of coal generation due to low gas prices 
and merit order effects. Due to the very recent nature of this event, 
it remains unclear what the exact short- and long-term impacts on 
future global emissions trends will be.
From 1850 until around 1950, anthropogenic CO 2 emissions 
were mainly (>50%) from land use, land-use change and forestry 
(Figure 2.7). Over the past half-century CO 2 emissions from LULUCF 
have remained relatively constant around 5.1 ± 3.6 GtCO 2 but with 
a large spread across estimates (Le Quéré et al. 2018a; Friedlingstein 
et al. 2019, 2020). By contrast, global annual FFI-CO 2 emissions have 
continuously grown since 1850, and since the 1960s from a decadal 
average of 11 ± 0.9 GtCO 2 to 36 ± 2.9 GtCO 2 during 2010–2019 
(Table 2.1).

Cumulative CO 2 emissions since 1850 reached 2400 ± 240 GtCO 2 
in 2019 (high confidence).7 More than half (62%) of total emissions 
from 1850 to 2019 occurred since 1970 (1500 ± 140 GtCO 2), 
about 42% since 1990 (1000 ± 90 GtCO 2) and about 17% since 
2010 (410  ±  30  GtCO 2) (Friedlingstein et  al. 2019; Friedlingstein 
et al. 2020; Canadell et al. 2021) (Figure 2.7). Emissions in the last 
decade are about the same size as the remaining carbon budget of 
400 ± 220 (500, 650) GtCO 2 for limiting global warming to 1.5°C 
and between one-third and half the 1150 ± 220 (1350, 1700) GtCO 2 
for limiting global warming below 2°C with a  67% (50%, 33%) 
probability, respectively (medium confidence) (Canadell et al. 2021). 
7 For consistency with WGI, uncertainties in this paragraph are reported at a 68% confidence interval. This reflects the difficulty in the WGI context of characterising the

uncertainty in the CO 2 fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty of updating 
the emissions from land-use change.At current (2019) levels of emissions, it would only take 8 (2–15) 
and 25 (18–35)  years to emit the equivalent amount of CO 2 
for a  67th  percentile 1.5°C and 2°C remaining carbon budget, 
respectively. Related discussions of carbon budgets, short-term 
ambition in the context of Nationally Determined Contributions 
(NDCs), pathways to limiting warming to well below 2°C and carbon 
dioxide removals are mainly discussed in Chapters 3, 4, and 12, but 
also Section 2.7 of this chapter.
Even when taking uncertainties into account, historical emissions 
between 1850 and 2019 constitute a  large share of total carbon 
budgets from 2020 onwards for limiting warming to 1.5°C with 
a 50% probability as well as for limiting warming to 2°C with a 67%

probability. Based on central estimates only, historical cumulative 
net CO 2 emissions between 1850–2019 amount to about four fifths 
of the total carbon budget for a 50% probability of limiting global 
warming to 1.5°C (central estimate about 2900 GtCO 2), and to 
about two thirds of the total carbon budget for a 67% probability 
to limit global warming to 2°C (central estimate about 3550 GtCO 2). 
The carbon budget is the maximum amount of cumulative net 
global anthropogenic CO 2 emissions that would result in limiting 
global warming to a given level with a given likelihood, taking into 
account the effect of other anthropogenic climate forcers. This is 
referred to as the total carbon budget when expressed starting from 
the pre-industrial period, and as the remaining carbon budget when 
expressed from a  recent specified date. The total carbon budgets 
reported here are the sum of historical emissions from 1850 to 2019

and the remaining carbon budgets from 2020 onwards, which extend 
93%31%
68%34%
13%
49%32%
14%
33%
18%4%
33%
1%
18%
29%
15%3 Gt 6 Gt 12 Gt 28 Gt 43 Gt
Cement
Coal GasFlaring Oil
LULUCFCO2 emissions (GtCO2 yr–1)(a) Long term trend of anthropogenic CO 2 emissions sources
40
30
20
10
0
1850 1900 1950 1990 2019
CO2 emissions (GtCO2)4000
3000
2000
1000
0
1850–
19891990–
20092010–
20191.5°C
budget2°C
budget(b) Historic emissions vs. 
future carbon budgets
1400
± 195620
± 60410
± 30500
± 2201200
± 220
Figure 2.7 | Historic anthropogenic CO 2 emission and cumulative CO 2 emissions (1850–2019) as well as remaining carbon budgets for limiting warming 
to 1.5°C and 2°C. Panel (a) shows historic annual anthropogenic CO 2 emissions (GtCO 2 yr –1) by fuel type and process. Panel (b) shows historic cumulative anthropogenic 
CO2 emissions for the periods 1850–1989, 1990–2009, and 2010–2019 as well as remaining future carbon budgets as of 1 January 2020 to limit warming to 1.5°C and 2°C

at the 67th percentile of the transient climate response to cumulative CO 2 emissions. The whiskers indicate a budget uncertainty of ±220 GtCO 2-eq for each budget and the 
aggregate uncertainty range at one standard deviation for historical cumulative CO 2 emissions, consistent with Working Group 1. Sources: Friedlingstein et al. (2020) and 
Canadell et al. (2021).

232
until global net zero CO 2 emissions are reached. Uncertainties for 
total carbon budgets have not been assessed and could affect the 
specific calculated fractions (IPCC 2021 [Working Group 1 SPM], 
Canadell et al., 2021 [Working Group 1 Ch5]).
Comparisons between historic GHG emissions and baseline 
projections provide increased evidence that global emissions are 
not tracking high-end scenarios (Hausfather and Peters 2020), and 
rather followed ‘middle-of-the-road’ scenario narratives in the earlier 
series, and by combinations of ‘global-sustainability’ and ‘middle-of-
the-road’ narratives in the most recent series (IPCC Special Report 
on Emissions Scenarios (SRES) and Shared Socioeconomic Pathways 
(SSP)-baselines) (Pedersen et al. 2020; Strandsbjerg Tristan Pedersen 
et al. 2021). As countries increasingly implement climate policies and 
technology costs continue to evolve, it is expected that emissions

will continually shift away from scenarios that assume no climate 
policy but remain insufficient to limit warming to below 2°C (Vrontisi 
et  al. 2018; Hausfather and Peters 2020; Roelfsema et  al. 2020; 
UNEP 2020b).
The literature since AR5 suggests that compared to historical trends 
baseline scenarios might be biased towards higher levels of fossil 
fuel use compared to what is observed historically (Cross-Chapter 
Box  1 in Chapter  1; Ritchie and Dowlatabadi 2017, 2018; Ritchie 
2019; Creutzig et al. 2021;). Ritchie and Dowlatabadi (2017) show 
that per-capita primary energy consumption in baseline scenarios tends to increase at rates faster than those observed in the long-term 
historical evidence – particularly in terms of coal use. For example, 
SSP5 envisions a six-fold increase in per capita coal use by 2100 – 
against flat long-term historical observations  – while the most 
optimistic baseline scenario SSP1-Sustainability is associated with

coal consumption that is broadly in line with historical long-term 
trends (Ritchie and Dowlatabadi 2017). In contrast, models have 
struggled to reproduce historical upscaling of wind and solar and 
other granular energy technologies (Wilson et al. 2013; van Sluisveld 
et al. 2015; Creutzig et al. 2017; Shiraki and Sugiyama 2020; Sweerts 
et al. 2020; Wilson et al. 2020b).
2.2.2.2 Other Short-lived Climate Forcers (SLCFs)
There are other emissions with shorter atmospheric lifetimes that 
contribute to climate changes. Some of them (aerosols, sulphur 
emissions or organic carbon) reduce forcing, while others  – such 
as black carbon, carbon monoxide or non-methane volatile organic 
compounds (NMVOC)  – contribute to warming (Figure  2.4) as 
assessed in WGI (Forster et al. 2021c; Szopa et al. 2021a). Many of 
these other SLCFs are co-emitted during combustion processes in 
power plants, cars, trucks, airplanes, but also during wildfires and

household activities such as traditional cooking with open biomass 
burning. As these co-emissions have implications for net warming, 
they are also considered in long-term emission reduction scenarios 
as covered in the literature (Harmsen et al. 2020; Rauner et al. 2020b; 
Figure 2.8 | Air pollution emissions by major sectors from CEDS (1970–2019) and EDGAR (1970–2015) inventories.  Source: Crippa et al. (2019a, 2018); 
O’Rourke et al. (2020); McDuffie et al. (2020).

233
Smith et al. 2020; Vandyck et al. 2020) as well as Chapter 3 of this 
report. These air pollutants are also detrimental to human health 
(e.g.,  Lelieveld et  al. 2015, 2018; Vohra et  al. 2021). For example, 
Lelieveld et  al. (2015) estimate a  total of 3.3 (1.6–4.8) million 
premature deaths in 2010 from outdoor air pollution. Reducing 
air pollutants in the context of climate policies therefore leads to 
substantial co-benefits of mitigation efforts (Von Stechow et al. 2015; 
Rao et al. 2017; Lelieveld et al. 2019; Rauner et al. 2020a). Here we 
briefly outline the major trends in emissions of SLCFs.
Conventional air pollutants that are subject to significant emission 
controls in many countries include sulphur dioxide (SO 2), nitrogen 
oxides (NO x), black carbon (BC) and carbon monoxide (CO). From 
2015 to 2019, global SO 2 and NOx emissions declined, mainly due 
to reductions in energy systems (Figure 2.8). Reductions in BC and

CO emissions appear to have occurred over the same period, but 
trends are less certain due to the large contribution of emissions 
from poorly quantified traditional biofuel use. Emissions of CH 4, OC 
and NMVOC have remained relatively stable in the past five years. 
OC and NMVOC may have plateaued, although there is additional 
uncertainty due to sources of NMVOCs that may be missing in current 
inventories (McDonald et al. 2018).
2.2.3 Regional GHG Emissions Trends
Regional contributions to global GHG emissions have shifted since 
the beginning of the international climate negotiations in the 1990s 
(high confidence). As shown in Figure  2.9, developed countries 
(North America, Europe, and Australia, Japan, New Zealand) as 
a group have not managed to reduce GHG emissions substantially, 
with fairly stable levels at about 15 GtCO 2-eq yr –1 between 1990 
and 2010, while countries in Asia and Pacific (Eastern Asia, Southern

Asia, and South-East Asia and Pacific) have rapidly increased their 
share of global GHG emissions  – particularly since the 2000s 
(Jackson et al. 2019; Peters et al. 2020; UNEP 2020c; Crippa et al. 
2021; IEA 2021b).
Most global GHG emission growth occurred in Asia and Pacific, which 
accounted for 77% of the net 21 GtCO 2-eq increase in GHG emissions 
since 1990, and 83% of the net 6.5 GtCO 2-eq increase since 2010.8 
Africa contributed 11% of GHG emissions growth since 1990 (2.3 
GtCO 2-eq) and 10% (0.7 GtCO 2-eq) since 2010. The Middle East 
contributed 10% of GHG emissions growth since 1990 (2.1 GtCO 2-
eq) and also 10% (0.7 GtCO 2-eq) since 2010. Latin America and the 
Caribbean contributed 11% of GHG emissions growth since 1990 
(2.2 GtCO 2-eq), and 5% (0.3 GtCO 2-eq) since 2010. Two regions, 
Developed Countries, and Eastern Europe and West Central Asia, 
reduced emissions overall since 1990, by –1.6  GtCO 2-eq and –0.8

GtCO 2-eq, respectively. However, emissions in the latter started 
to grow again since 2010, contributing to 5% of the global GHG 
emissions change (0.3 GtCO 2-eq).
8 Note that GHG emissions from international aviation and shipping could not be attributed to individual regions, while CO 2 emissions from AFOLU could not be attributed 
to individual countries. Change in GHG emissions that can be easily assigned to regions is 20.3 of 20.8 GtCO 2-eq for 1990–2019 and 6.3 of 6.5 GtCO 2-eq for 2010–2019.
9 In all cases, constraining countries within the emissions range to those larger than 1 million population.Average annual GHG emission growth across all regions slowed 
between 2010 and 2019 compared to 1990–2010, with the 
exception of Eastern Europe and West Central Asia. Global 
emissions changes tend to be driven by a  limited number of 
countries, principally the G20 Group (Friedlingstein et  al. 2020; 
UNEP 2020c; Xia et al. 2021). For instance, the slowing of global

GHG emissions between 2010 and 2019, compared to the previous 
decade, was primarily triggered by substantial reductions in GHG 
emissions growth in China. Ten countries jointly contributed about 
75% of the net 6.5 GtCO 2-eq yr–1 increase in GHG emissions during 
2010–2019, of which two countries contributed more than 50% 
(Figure 2.9) (see also Minx et al., 2021; Crippa et al., 2021).
GHG and CO 2-FFI levels diverge starkly between countries and regions 
(high confidence) (Jackson et  al. 2019; Friedlingstein et  al. 2020; 
UNEP 2020c; Crippa et  al. 2021). Developed Countries sustained 
high levels of per capita CO 2-FFI emissions at 9.5 tCO 2 per capita in 
2019 (but with a wide range of 1.9–16 tCO 2 per capita). This is more 
than double that of three developing regions: 4.4 (0.3–12.8) tCO 2 
per  capita in Asia and Pacific; 1.2  (0.03–8.5) tCO 2 per  capita in 
Africa; and 2.7 (0.3–24) tCO 2 per capita in Latin America.9 Per capita

CO2-FFI emissions were 9.9 (0.89–15) tCO 2 per  capita in Eastern 
Europe and West Central Asia, and 8.6 (0.36–38) tCO 2 per capita in 
the Middle East. CO 2-FFI emissions in the three developing regions 
together grew by 26% between 2010 and 2019, compared to 260% 
between 1990 and 2010, while in Developed Countries emissions 
contracted by 9.9% between 2010–2019 and by 9.6% between 
1990–2010.
Least-Developed Countries and Small Island Developing States 
contributed only a negligible proportion of historic GHG emissions 
growth and have the lowest per capita emissions. As of 2019 Least 
Developed Countries contribute 3.3% of global GHG emissions, 
excluding LULUCF CO 2, despite making up 13% of the global 
population. Small Island Developing States contributed 0.6% of 
global GHG emissions in 2019, excluding LULUCF CO 2, with 0.9% 
of the global population. Since the start of the industrial revolution in 
1850 up until 2019, Least Developed Countries contributed 0.4% of

total cumulative CO 2 emissions, while Small Island Developing States 
contributed 0.5% (Figure  2.10). Conversely, Developed Countries 
have the highest share of historic cumulative emissions (Rocha 
et  al. 2015; Gütschow et  al. 2016; Matthews 2016), contributing 
approximately 57% (Figure 2.10), followed by Asia and Pacific (21%), 
Eastern Europe and West Central Asia (9%), Latin America and the 
Caribbean (4%), the Middle East (3%), and Africa (3%). Developed 
Countries still have the highest share of historic cumulative emissions 
(45%) when CO 2-LULUCF emissions are included, which typically 
account for a higher proportion of emissions in developing regions 
(Figure 2.10).

234
Years: 2010−2019 Years: 2010−2019
United KingdomFranceGermanyUnited StatesJapanAustraliaSouth AfricaMexicoRussian FederationCanadaBrazilKorea, Rep.Iran, Islamic Rep.ChinaSaudi ArabiaIndiaPakistanTurkeyIndonesiaVietnam
−2 0 2 4
% yr–1GtCO2-eq yr–1
tCO2eq cap–1kgCO2-eq USD–1(b) Average annual GHG emissions change
Year: 2019 Year: 2019(d) GHG emissions per capitaUnited StatesUnited KingdomGermanyJapanFranceAustraliaSouth AfricaMexicoCanadaKorea, Rep.BrazilPakistanTurkeySaudi ArabiaIran, Islamic Rep.VietnamIndonesiaIndiaChina
0 1 2(c) Change in annual GHG emission level
PakistanIndiaIndonesiaVietnamBrazilMexicoFranceUnited KingdomTurkeyJapanGermanyChinaSouth AfricaIran, Islamic Rep.Korea, Rep.Russian FederationUnited StatesCanadaSaudi ArabiaAustralia
0 5 10 15 20United KingdomFranceGermanyJapanTurkeyUnited StatesMexicoKorea, Rep.IndonesiaIndiaBrazilCanadaAustraliaSaudi ArabiaPakistanRussian FederationVietnamChinaSouth AfricaIran, Islamic Rep.

0.00 0.25 0.50 0.75 1.00(e) GHG emissions intensityRussian Federation(a) Global net anthropogenic GHG emissions by region (1990–2019)
2000 1990 2010 2019102030
0405060GHG emissions (GtCO2-eq yr–1)
Eastern AsiaNorth AmericaLatin America and CaribbeanSouth-East Asia and PaciﬁcAfricaSouthern AsiaEuropeEastern Europe and West-Central AsiaMiddle EastAustralia, Japan and New ZealandInternational shipping and aviation
27%
13%16%24%12%
18%19%14%10%
10%11%11%9%
7%7%7%9%
7%8%8%8%
7%8%8%
16%14%3%5%2%2%2%2%
5%
8%4%7%5%4%5%3%
6%
13%10%8%38 Gt 42 Gt 53 Gt 59 Gt
Figure 2.9 | Change in regional GHGs from multiple perspectives and their underlying drivers.

235
Figure  2.9 (continued): Change in regional GHGs from multiple perspectives and their underlying drivers.  Panel (a): Regional GHG emissions trends 
(in GtCO 2-eq yr –1) for the time period 1990–2019. GHG emissions from international aviation and shipping are not assigned to individual countries and shown separately. 
Panels (b) and (c): Changes in GHG emissions for the 20 largest emitters (as of 2019) for the post-AR5 reporting period 2010–2019 in relative (% annual change) and 
absolute terms (GtCO 2-eq). Panels (d) and (e):  GHG emissions per capita and per GDP in 2019 for the 20 largest emitters (as of 2019). GDP estimated using constant 
international purchasing power parity (USD2017). Emissions are converted into CO 2-equivalents based on global warming potentials with a 100-year time horizon (GWP100)

from the IPCC Sixth Assessment Report (Forster et al. 2021a). The black dots represent the emissions data from UNFCCC-CRFs (2021) that were accessed through Gütschow 
et al. (2021a). Net LULUCF CO 2 emissions are included in panel (a), based on the average of three bookkeeping models (Section 2.2), but are excluded in panels (b–e) due to 
a lack of country resolution.
8 billion 38 GtCO2 34 GtCO2 52 GtCO2-eq 58 GtCO2-eq
24%
8% 8% 9% 8%9%
5% 5% 6% 9%5%
15%19% 13%12%3%
6%6%6% 6%8%
5%5%7%10%8%
10%14%10%8%4%
7%6%
7%7%19% 33%30%
29%27%2%4%5%
4% 4%17%4%3%
6%9%2411
GtCO2-eq1648
GtCO2-eq
4% 4%8%2%23%
27%2%3%11%
4%16%
23%10% 11%12% 15%4%5%7% 3%(a) Current population and 
emissions by region(b) Historical cumulative
emissions by region
0%25%50%75%100%
1.1% 13.4% 0.9% 3.3%
0.5% 0.9% 0.6%0.4%
0.5%
(2019)Consumption
CO2-FFI
(2018)Production
CO2-FFI
(2019)Production
GHG, excl.
LULUCF
GHG, incl.
LULUCF
CO2, excl.
LULUCF
(1850−2019)Production
CO2, incl.

LULUCF
(1850−2019)Small Island Developing
States (SIDS)Least Developed
Countries (LDCs)Africa
Australia, Japan,
West Central Asia
Europe
and Caribbean
Southern AsiaSouth-East Asia
and Paciﬁc
N/A
N/AN/A
N/A N/A
Figure 2.10 | Different perspectives on historic emissions and equity. Panel (a) shows the regional proportion (%) of total global population or emissions in 2018 or 
2019, for five categories: population (persons); consumption-based CO 2-FFI emissions (GtCO 2); production-based CO 2-FFI emissions (GtCO 2); production-based GHG emissions 
excluding CO 2-LULUCF (GtCO 2-eq); and production-based GHG emissions including CO 2-LULUCF (GtCO 2-eq). Panel (b) shows the regional proportion (%) of total cumulative 
production-based CO 2 emissions from 1850 to 2019, including and excluding CO 2-LULUCF (GtCO 2). In the lower panels, the proportion of each population or emissions

category attributable to Least-Developed Countries and Small Island Developing States (SIDS) are shown, where available (CO 2-LULUCF data is not available for these regions). 
GHG emissions are converted into CO 2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report 
(Forster et al. 2021a). Source: data from Friedglinstein et al. (2020).

236
A growing number of countries have reduced CO 2 and GHG emissions 
for longer than 10 years (high confidence) (Le Quéré et  al. 2019; 
Burck et al. 2021; Lamb et al. 2021a; Wu et al. 2021). Data up to 2018 
indicates that about 24 countries have reduced territorial CO 2 and GHG 
emissions (excluding LULUCF CO 2), as well as consumption-based CO 2 
emissions, for at least 10 years (Lamb et al. 2021a). Uncertainties in 
emissions levels and changes over time prevents a precise assessment 
of reductions in some cases. Of these 24  countries, 12 peaked 
emissions in the 2000s; six have sustained longer reductions since 
the 1970s; and six are former members of the Eastern Bloc, where 
emissions dropped rapidly in the 1990s and continued declining 
at a  slower pace thereafter. Country emissions reductions have 
been driven by both climate and non-climate policies and factors, 
including structural changes. To date, most territorial emissions

reductions were realised in the electricity and heat sector, followed 
by industry and buildings, while in many cases transport emissions 
have increased since countries reached their overall emissions peak 
(Climate Transparency 2021; Lamb et al. 2021a). One estimate of the 
total reduction in annual GHG emissions – from peak years to 2018 – 
sums to 3.2 GtCO 2-eq across all decarbonising countries (Lamb et al. 
2021a). These reductions have therefore been far outweighed by 
recent emissions growth. However, climate policy related reductions 
may be even larger when compared against a counterfactual case of 
emissions growth across different sectors (Eskander and Fankhauser 
2020) (Cross-Chapter Box 1 in Chapter 1; Section 2.8).
The recent (2010–2019) emissions changes of some countries are 
in line with pathways that limit warming to below 2°C (<67%) 
(e.g., –4% average annual reductions) (Figure 2.10). Overall, there are

first country cases emerging that highlight the feasibility of sustained 
emission reductions outside of periods of economic disruption 
(Lamb et al. 2021a). However, such pathways will need to be taken by many more countries to keep the goals of the Paris Agreement 
in reach (Höhne et  al. 2020; Roelfsema et  al. 2020; Kriegler et  al. 
2018a; den Elzen et al. 2019) as analysed by Chapter 4 of this report. 
Moreover, observed reductions are not yet consistent and long-term, 
nor achieved across all sectors, nor fully aligned with country NDC 
targets (Le Quéré et al. 2019; Lamb et al. 2021a; den Elzen et al. 2019; 
Burck et al. 2021; Climate Transparency 2021).
2.2.4 Sectoral GHG Emissions Trends
In 2019, 34% (20 GtCO 2-eq) of the 59 GtCO 2-eq GHG emissions 
came from the energy sector, 24% (14 GtCO 2-eq) from industry, 
22% (13 GtCO 2-eq) from AFOLU, 15% (8.7 GtCO 2-eq) from transport 
and 6% (3.3 GtCO 2-eq) from buildings (Figure 2.12). The relative size

of each sector depends on the exact definition of sector boundaries 
(de la Rue du Can et  al. 2015; Lamb et  al. 2021b). The largest 
individual subsector contributing to global GHG emissions in 2019 
was electricity and heat generation at 14 GtCO 2-eq. This subsector 
can be reallocated to consuming sectors as indirect (scope  2) 
emissions to emphasise the role of final energy demand and demand-
side solutions in climate change mitigation (Creutzig et  al. 2018) 
(Chapter 5). This increases the emission share of the industry sector 
to 34% and of the buildings sector to 16%.
Average annual GHG emissions growth has been fastest in the 
transport sector with about 1.8% for the most recent period 
2010–2019, followed by direct emissions in the industry sector (1.4%) 
and the energy sector (1%) (Figure 2.13). This is different to growth 
patterns observed in the previous decade as reported in AR5 (IPCC 
2014a; Blanco et  al. 2014). Between 2000 and 2009 fastest GHG

emissions growth was observed for industry with 3.4% followed by 
Average annual emissions change (2010−2019)Scenario 
reduction rates 
(2020−2040)
Australia,
Japan, New
ZealandMiddle
EastEastern
Europe
and West 
Central
AsiaEurope Southern
AsiaAfrica South-East
Asia and
PaciﬁcLatin
America
and
CaribbeanNorth
AmericaEastern
AsiaC3a:
Likely
below
2°C with
action
starting
in 2020C1: 1.5°C
with no
or limited
overshoot−1001020Average annual GHG
emissions growth rates (%)
Figure 2.11 | Recent average annual GHG emissions changes of countries (left panel) versus rates of reduction in 1.5°C and 2°C mitigation scenarios. 
Scenario data is taken from Chapter 3 of this report with the scenario categories defined and summarised in Table 3.2 in Chapter 3. Emissions are converted into CO 2-equivalents 
based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (Forster et al. 2021a). Circles indicate countries (left panel)

or individual scenarios (right panel), the former scaled by total emissions in 2019. Horizontal lines indicate the region average emissions change (left panel), or scenario category 
average emissions change (right panel). Source: data from Minx et al. (2021).

237
the energy sector with 2.3%. GHG emissions growth in the transport 
sector has been stable across both periods at about 1.8%, while direct 
building emissions growth averaged below 1% during 2010–2019. 
Ranking of high-emitting subsectors by direct emissions highlights 
the importance of CO 2 emissions from LULUCF (6.6 GtCO 2-eq; but 
with low confidence in magnitude and trend), road transport (6.1 
GtCO 2-eq), metals (3.1 GtCO 2-eq), and other industry (4.4 GtCO 2-eq). 
Overall, some of the fastest growing sources of subsector emissions 
from 2010 to 2019 have been international aviation (+3.4%),10 
domestic aviation (+3.3%), inland shipping (+2.9%), metals (+2.3%), 
international shipping (+1.7%), and road transport (+1.7%).
10 Note that this does not include the additional warming impacts from aviation due to short-lived climate forcers, which are assessed in Chapter 10 (Section 10.5).Direct emissions by sector (59 GtCO2-eq)

Direct+indirect emissions by sector (59 GtCO2-eq) Agriculture, forestry and 
other land use (AFOLU) 22%Industry
24%Electricity+heat
23%Transport
15%Buildings
6%Other energy
10%
Electricity+heat by sector
Agriculture, forestry and 
other land use (AFOLU) 22%Industry 
34%Transport 
15%Other energy
12%Buildings 
16%
– Inland shipping 0.3%
– Rail 0.4%
– Domestic aviation 0.7%
– Other (transport) 0.9%
– International aviation 
1.1%
– International shipping 
1.3%
– Road 10%– Biomass burning 
(CO 2, CH 4) 0.1%
– Synthetic fertiliser 
application (N 2O) 0.75%
– Manure management 
(N2O, CH 4) 0.7%
– Rice cultivation (CH 4) 1.7%
– Managed soils and pasture 
(CO 2, N 2O) 2.5%
– Enteric fermentation (CH 4) 5%
– LULUCF CO 2 11%– Cement (process only) 2.6%
– Waste 3.9%
– Chemicals 6.3%
– Metals 7.8%
– Other (industry) 13%– Petroleum 
reﬁning 1.1%
– Coal mining fugitive 
emissions 2.2%
– Oil and gas fugitive 
emissions 4.4%
– Other (energy systems) 
4.7%– Non-CO 2 (all buildings) 
0.1%

– Non-residential 5.9%
– Residential 11%– Energy systems 8.5%
– Industry 43.0%
– AFOLU 0.0%
– Transport 1.6%
– Buildings 46.9%
Direct Direct Direct Direct Direct Indirect IndirectIndirect Indirect
Figure 2.12 | Total anthropogenic direct and indirect GHG emissions for the year 2019 (in GtCO 2-eq) by sector and subsector.  Direct emissions estimates 
assign emissions to the sector in which they arise (scope 1 reporting). Indirect emissions – as used here – refer to the reallocation of emissions from electricity and heat to the 
sector of final use (scope 2 reporting). Note that cement refers to process emissions only, as a lack of data prevents the full reallocation of indirect emissions to this sector. 
More comprehensive conceptualisations of indirect emissions including all products and services (scope 3 reporting) are discussed in Section 2.3 of this chapter. Emissions are

converted into CO 2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Percentages may not add 
up to 100 across categories due to rounding at the second significant digit. Source: based on Lamb et al. (2021b); data: Minx et al. (2021).

238
31%
21%
27%
13%
8%33%
20%
25%
15%
7%35%
24%
22%
14%
6%34%
24%
22%
15%
6%+0.7% yr–1+2.1% yr–1+1.3% yr–138 Gt 42 Gt 53 Gt 59 Gt
0102030405060
1990 2000 2010 2019GHG emissions (GtCO2-eq yr–1) GHG emissions (GtCO2-eq yr–1)Energy systems
Industry
AFOLU
Transport
Buildingsa. Trends in global GHG emissions by sector
A. Paciﬁc Africa E. Asia Eurasia Europe Latin Am. Middle E. N. America S. Asia S.E. Asia
1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019051015b. Regional GHG emissions trends by sector
Years: 2010−2019 ResidentialOther (industry)Other (energy systems)Enteric Fermentation (CH4)Managed soils and pasture (CO2, N2O)Electricity & heatCoal mining fugitive emissionsOil and gas fugitive emissionsCementWasteChemicalsRoadMetalsLULUCF (CO2)
0.0 0.5 1.0 1.5 2.0 2.5
%c. Avg. annual GHG emissions 
growth by subsector

Years: 2010−2019 Other (energy systems)Managed soils and pasture (CO2, N2O)ResidentialCoal mining fugitive emissionsCementEnteric Fermentation (CH4)Oil and gas fugitive emissionsOther (industry)WasteChemicalsMetalsRoadElectricity & heatLULUCF (CO2)
0.0 0.5 1.0
GtCO2-eq yr–1d. Absolute GHG emissions 
Figure 2.13 | Total annual anthropogenic GHG emissions by major economic sector and their underlying trends by region. Panel (a): Trends in total annual 
anthropogenic GHG emissions (in GtCO 2-eq yr –1) by major economic sector. Panel (b): Trends in total annual anthropogenic GHG emissions (in GtCO 2-eq yr –1) by major 
economic sector and region. Panels c and d: Largest subsectoral changes in GHG emissions for the reporting period 2010–2019 in relative (% annual change) and absolute 
terms (GtCO 2-eq yr –1). Emissions are converted into CO 2-equivalents based on global warming potentials with a  100-year time horizon (GWP100) from the IPCC Sixth

Assessment Report. Source: based on Lamb et al. (2021b); Data: Crippa et al. (2021); Minx et al. (2021).

239
2.3 Past and Present Trends of Consumption-
based CO 2 Emissions (CBEs) and 
Emissions Embodied in Trade
2.3.1 Scope, Variability and Uncertainty of CBEs
Consumption is increasingly met by global supply chains often 
involving large geographical distances and causing emissions in 
producing countries (Hubacek et  al. 2014, 2016; Wiedmann and 
Lenzen 2018). Therefore, accounting for emissions from production 
along the entire supply chain to fulfil final demand,  – so-called 
consumption-based emissions (CBEs), – is necessary to understand 
why emissions occur and to what extent consumption choices and 
associated supply chains contribute to total emissions, and ultimately 
how to influence consumption to achieve climate mitigation targets 
and environmental justice (Vasconcellos 2020).
Production-based emissions (PBEs) and territorial emissions 
resulting from the production and consumption of goods and

services within a  region (for both domestic use and export) are 
often used by authorities to report carbon emissions (Peters 2008) 
(Section  2.2). PBEs also include emissions from international 
activities (e.g.,  international aviation/shipping and non-resident 
activities), which are excluded from territorial emissions (Karstensen 
et al. 2018; Shan et al. 2018). In contrast, CBEs refer to emissions 
along the entire supply chains induced by consumption, irrespective 
of the place of production (Liu et al. 2015b). This reflects a shared 
understanding that a wider system boundary going beyond territorial 
emissions is important to avoid outsourcing of pollution and to 
achieve global decarbonisation. CBEs allow for the identification of 
new policy levers through information on a country’s trade balance 
of embodied emissions, households’ carbon implications of their

lifestyle choices, companies’ upstream emissions as input for supply chain management, and cities’ footprints outside their administrative 
boundaries (Davis and Caldeira 2010; Feng et al. 2013). Kander et al. 
(2015) proposed a technology-adjusted consumption-based emission 
accounting (TCBA) approach to address the issue of carbon intensity 
in exports. TCBA incorporates emissions embodied in trade but also 
adjusted for differences in carbon efficiency in exports of different 
countries. Unlike PBEs, there are no internationally agreed approaches 
to calculate CBEs, making it a major drawback for mainstreaming the 
use of this indicator in policymaking.
There are other proposed emission accounting approaches used 
in different circumstances. Historical cumulative emissions (HCEs) 
are used when analysing countries’ historic contribution to 
emissions and responsibility for emission reduction. HCEs account 
for a  country’s cumulative past emissions, which may be different

from the country’s  current annual emissions (Botzen et  al. 2008; 
Ritchie 2019), but are sensitive to the choice of cut-off period. For 
example, the USA and EU-27 countries plus the UK contributed 
respectively 13.3% and 8.7% to global PBEs in 2019 (Crippa et al. 
2020), however, they emitted around 25% and 22% of global 
historical PBEs since 1751 (Ritchie 2019). Extraction-based emissions 
(EBEs) accounting allocates all emissions from burning fossil fuels 
throughout the supply chains to the country where the fuels were 
extracted (Steininger and Schinko 2015). EBEs can be calculated 
by multiplying primary energy extraction of fossil fuels with their 
respective carbon content (Erickson and Lazarus 2013). Another 
approach for accounting emissions is income-based emission (IBE), 
which traces emissions throughout all supply chains and allocates 
emissions to primary inputs (e.g., capital and labour). In other words,

IBEs investigate a  country’s direct and indirect downstream GHG 
emissions enabled by its primary inputs (Liang et al. 2017a). All these 
approaches provide complementary information and different angles 
to assigning responsibility for emissions reductions.
Box 2.1 | Policy Applications of Consumption-based Emissions
Consumption-based emissions provide additional or complementary information to production-based emissions that can be used for 
a variety of policy applications. These include:
• Complementary national-level emissions accounting and target or budget setting
• Raising awareness and increasing understanding of the GHG effects of consumption
• Accounting for and understanding of distributional and responsibility issues in GHG emissions mitigation, both nationally 
and internationally
• Incentives to change consumption patterns or reduce consumption (e.g., through taxation policies)
• Accounting for and understanding of carbon leakage and emissions embodied in trade*

• International emissions trading schemes or linked national schemes
• Trade policies addressing emissions embodied in trade and international supply chains (e.g., border tax adjustments and clean 
technology transfers, carbon offsetting or financing, etc.)
• Including embodied emissions in product performance standards and labelling
• Policies of public and private procurement
• Agreements with international suppliers
• Discussing the climate impacts of lifestyles and inequalities in consumption and associated emissions.

240
The dominant method for calculating nations’ CBEs is global multi-
region input-output (GMRIO) analysis (Wiedmann and Lenzen 
2018). Other frequently used approaches include analysing bilateral 
trade flows of products and their lifecycle emission factors (Sato 
2014). Generally, the uncertainties associated with CBEs depends 
on the choice of the dataset/model used for calculation, which 
differs according to: (i) the national economic and trade data 
used; (ii) the emissions data used; (iii) the sector or product-level 
aggregation; (iv) the regional aggregation; (v) the conceptual scope 
(e.g., residential vs territorial accounting principle); and (vi) the model 
construction techniques, which include table-balancing algorithms 
and ways of dealing with missing or conflicting data (Moran and 
Wood 2014; Owen, 2017; Wieland et  al. 2018; Wood et  al. 2018, 
2019). When excluding systematic error sources, research has shown

that the stochastic relative standard deviation (RSD) of total national 
CBEs is not significantly different to that from PBEs accounts and in 
the region of 5–15% (Wood et al. 2018, 2019).
Six global accounts for consumption-based GHG emissions at the 
country level are widely used (Table  2.2). Each dataset has been 
constructed by different teams of researchers, covering different time 
periods and containing CBEs estimates for different sets of countries 
and regions (Owen 2017).
Wood et al. (2019) present a comprehensive and systematic model 
intercomparison and find a  variation of 5–10% for both PBE and 
CBE accounts of major economies and country groups (e.g., EU-28, 
OECD). The estimates for the USA were the most closely aligned, 
with 3.7% RSD. For smaller countries, variability is in the order 
of 20–30% and can reach more than 40% in cases of very small,

highly trade-exposed countries such as Singapore and Luxembourg (Wood  et  al.  2019). It  is recommended that CBEs results for such 
countries be interpreted with care.
Overall, production accounts showed a slightly higher convergence 
(8% average of RSD) than consumption-based accounts (12%). 
The variation across model results can be approximately halved, 
when normalising national totals to one common value for 
a  selected base year. The difference between PBEs result variation 
(4% average RSD after normalisation) and CBEs results (7%) remains 
after normalisation.
In general, the largest contributors to uncertainty of CBEs results 
are – in descending order of priority – the total of territorial GHG 
emission accounts, the allocation of emissions to economic sectors, 
the total and composition of final demand, and lastly the structure 
of the economy. Harmonising territorial emissions across GMRIO 
datasets is the single most important factor that reduces uncertainty

by about 50% (Tukker et al. 2020). More work is required to optimise 
or even institutionalise the compilation of multi-region input-output 
data and models to enhance the accuracy of consumption-based 
accounting (Tukker et al. 2018; Wood et al. 2018).
2.3.2 Trends in Global and Regional CBEs Trajectories
In comparison to territorial emissions discussed in Section  2.2, 
Figure 2.14 shows the trends of global and regional CBEs from 1990 
to 2018. This section uses the PBEs and CBEs data from the Global 
Carbon Budget 2020 (Friedlingstein et al. 2020), which are slightly 
different from the PBEs used in Section 2.2. The Global Carbon Budget 
only includes CO 2 emissions from fossil fuels and cement production.Box 2.1 (continued)
The points above are based on a synopsis of studies (Steininger et al. 2014; Afionis et al. 2017; Hubacek et al. 2017b; Wang and Zhou 
2018; Bolea et al. 2020).

* Note, however, that comparing embodied emissions in trade between countries is further complicated by the fact that emission intensities differ 
across countries. Approaches to adjust for these differences and facilitate comparisons have been suggested (Kander et al. 2015; Baumert et al. 2019; 
Dietzenbacher et al. 2020; Jakob 2021). Many different approaches on how to share responsibility between producers and consumers have been 
proposed in designing effective integrated global climate policies (Liu and Fan 2017; Khajehpour et al. 2019; Jakob et al. 2021). Ultimately, assigning 
responsibility is normative.
Table 2.2 | Features of six global datasets for consumption-based emissions accounts.
Name of consumption-based account datasets (and references) Years availableNumber of  
countries/regionsNumber of sectors
Eora (Lenzen et al. 2013); (https://worldmrio.com) 1990–2015 190 Varies from 25 to >500

EXIOBASE (Stadler et al. 2018); (https://www.exiobase.eu) 1995–2016 49 200 products and 163 industries
GTAP (Peters, et al. 2011b; Aguiar et al. 2019); (https://www.gtap.agecon.purdue.edu) 2004, 2007, 2011, 2014 140 57
OECD/ICIO (Yamano and Guilhoto, 2020); (http://oe.cd/io-co2) 1995–2015 67 36
WIOD (Dietzenbacher et al. 2013; Timmer et al. 2015); (http://wiod.org) 2000–2014 44 56
Global Carbon Budget (Friedlingstein et al. 2020) 1990–2018 118 N/A

241
(a) CB−CO 2 (GtCO 2 yr–1)
2%17%57%11%4%3%7%
2%22%56%6%5%3%7%
2%22%56%5%5%3%7%
3%26%50%5%5%4%7%
3%33%42%5%5%5%7%
3%37%37%5%5%5%7%
3%39%35%5%5%5%8%23 Gt 23 Gt 25 Gt 29 Gt 33 Gt 35 Gt 36 Gt
010203040
1990 1995 2000 2005 2010 2015 2018
510
1990 1995 2000 2005 2010 2015 2018(b) CB−CO 2 (tCO 2 cap–1)
Africa
Developed CountriesAsia and PaciﬁcLatin America 
and CaribbeanYears: 1990−2018Russian FederationGermanyUnited KingdomItalyFranceJapanUnited StatesCanadaMexicoAustraliaTurkeyBrazilKorea, Rep.Iran, Islamic Rep.Saudi ArabiaIndiaChinaIndonesia
0 100 200 300
Years: 1990−2018Russian FederationGermanyUnited KingdomItalyFranceJapanCanadaAustraliaMexicoTurkeyBrazilKorea, Rep.Saudi ArabiaIndonesiaIran, Islamic Rep.United StatesIndiaChina
0.0 2.5 5.0
Year: 2018IndiaIndonesiaBrazilMexicoTurkeyChinaFranceItalyUnited KingdomIran, Islamic Rep.Russian FederationJapanGermanyKorea, Rep.AustraliaCanadaUnited StatesSaudi Arabia
0 5 10 15(c) CB−CO 2 growth (%)

(d) CB−CO 2 growth (GtCO 2 yr–1)
(e) CB−CO 2 per capita (tCO 2 cap–1)
West-Central AsiaRest of WorldMiddle East
Figure 2.14 | Consumption-based CO 2 emissions trends for the period 1990–2018.  The CBEs of countries are collected from the Global Carbon Budget 2020 
(Friedlingstein et al. 2020). Source: this figure is modified based on Hubacek et al. (2021).

242
The two panels at left in Figure  2.14 show total and per  capita 
CBEs for six regions. The three panels on the right show additional 
information for the 18 top-emitting countries with the highest 
CBEs in 2018. In  Developed Countries, consumption-based CO 2 
emissions peaked at 15 GtCO 2 in 2007 with a  subsequent 16% 
decline until 2016 (to 12.7  GtCO 2) and a  slight rebound of 1.6% 
until 2018 (to 12.9  GtCO 2). Asia and Pacific has been a  major 
contributor to consumption-based CO 2 emissions growth since 2000 
and exceeded Developed Countries as the global largest emissions 
source in 2015. From 1990 to 2018, the average growth rate of Asia 
and Pacific was  4.8% per  year, while in other regions emissions 
declined by –1.1%–4.3% per  year on average. In 2018, 35% of 
global consumption-based CO 2 emissions were from Developed 
Countries and 39% from Asia and Pacific, 5% from Latin American

and Caribbean, 5% from Eastern Europe and West Central Asia, 5% 
from Middle East, and 3% from Africa (Hubacek et al. 2021). Global 
CBEs kept growing over the period with a short-lived decline in 2008 
due to the global financial crisis. In 2020, lockdowns associated with 
COVID-19 significantly reduced global emissions (Section  2.2.2), 
including CBEs (Shan et al. 2021a).2.3.3 Decoupling of Emissions from Economic Growth
There has been a long-standing discussion on whether environmental 
impacts such as carbon emissions and use of natural resources can 
be decoupled from economic growth. It is controversial whether 
absolute decoupling can be achieved at a  global scale (Ward et 
al. 2016; Hickel and Kallis 2020; Haberl et al. 2020). However, 
a number of studies found that it is feasible to achieve decoupling 
at the national level, and they have explored the reasons for such 
decoupling (Schandl et  al. 2016; Ward et  al. 2016; Deutch 2017;

Roinioti and Koroneos 2017; Vadén et al. 2020; Habimana Simbi et al. 
2021; Shan et al. 2021b).
Table  2.3 shows the extent of decoupling of CBEs and GDP 
of countries based on CBEs from the Global Carbon Budget 
(Friedlingstein et  al. 2020) and GDP data from the World Bank. 
Table 2.4 also presents countries’ degree of decoupling of PBEs and 
GDP . These data allow a comparison of decoupling between GDP 
and both PBEs and CBEs. 
Table 2.3 | Country groups with different degree of CBE–GDP decoupling from 2015 to 2018.
Number of countriesAbsolute decoupling Relative decoupling No decoupling Economic recession
23 67 19 6
CBEs (gigatonnes)Total 5.40 25.33 1.93 0.85
Global share 16.1% 75.6% 5.8% 2.5%
PBEs (gigatonnes)Total 4.84 25.73 2.16 0.84
Global share 14.4% 76.6% 6.4% 2.5%
Population (million)Total 625 5195 768 270
Global share 9.1% 75.7% 11.2% 3.9%
GDP (billion)Total 19,891 54,240 2300 2997
Global share 25.0% 68.3% 2.9% 3.8%

Per capita GDP (1000 USD2010)Average 31.45 16.29 6.57 17.78
Median 23.55 8.03 2.56 13.12
Max 110.70 79.23 63.93 33.11
Min 1.31 0.49 0.52 5.80
Per capita CBEs (tonnes)Average 10.27 5.30 4.47 12.55
Median 8.87 4.13 1.67 11.33
Max 37.95 17.65 25.35 23.21
Min 0.64 0.09 0.18 2.33
CBE intensity  
(tonnes per 1000 USD2010)Average 0.45 0.50 0.93 0.66
Median 0.36 0.42 0.62 0.69
Max 1.16 2.41 4.10 1.22
Min 0.11 0.10 0.28 0.21
Per capita PBEs (tonnes)Average 8.20 4.36 5.32 14.15
Median 6.79 3.02 1.19 13.22
Max 19.58 20.13 39.27 27.24
Min 0.49 0.09 0.08 2.23
PBE intensity  
(tonnes per 1000 USD2010)Average 0.42 0.40 0.94 0.75
Median 0.28 0.31 0.58 0.68
Max 1.57 1.47 4.83 1.80
Min 0.10 0.05 0.16 0.20
Note: CBEs are obtained from the Global Carbon Budget 2020 (Friedlingstein et al. 2020), GDP and population are from the World Bank. One country (Venezuela) does not have

GDP data after 2015, so the degree of decoupling was only calculated for 115 countries. This table is modified from Hubacek et al. (2021).

243
Absolute decoupling refers to a decline of emissions in absolute terms 
or as being stable while GDP grows (i.e., a decoupling index11 greater 
than 1); relative decoupling refers to growth of emissions being lower 
than growth of GDP (a decoupling index between 0 and 1); and no 
decoupling, which refers to a situation where emissions grow to the 
same extent or faster than GDP (a decoupling index of less than 0) 
(Wu et al. 2018).
During the most recent three-year period from 2015 to 2018, 23  countries 
(or 20% of the 116 sample countries) have achieved absolute 
decoupling of CBEs and GDP , while 32 countries (or 28%) achieved 
absolute decoupling of PBEs and GDP: 14 of them (e.g., the UK, Japan, 
11 The decoupling index can be calculated based on changes of a country’s GDP and CO 2 emissions (Akizu-Gardoki et al. 2018; Wu et al. 2018). See the equation below.

DI refers to decoupling index; G 1 refers to the GDP of reporting year while G 0 refers to the base year; E 1 refers to emissions of the reporting year while E 0 refers to emissions 
of the base year.and the Netherlands) also decoupled PBEs and GDP . Countries with 
absolute decoupling of CBEs tend to achieve decoupling at relatively 
high levels of economic development and high per capita emissions. 
Most of EU and North American countries are in this group. Decoupling 
was not only achieved by outsourcing carbon-intensive production, but 
also improvements in production efficiency and energy mix, leading 
to a  decline of emissions. Structural Decomposition Analysis shows 
that the main driver for decoupling has been a  reduction in carbon 
intensity (i.e., change in energy mix and energy efficiency) from both 
domestic production and imports (Hubacek et  al. 2021). Similarly, 
Wood et al. (2020b) found that EU countries have reduced their overall

consumption-based GHG emissions by 8% between 1995 and 2016, 
!"=∆%%−∆(%
∆%%=)%!−%"
%"−(!−("
("*%!−%"
%"+  Table 2.4 | Country groups with different degree of PBE–GDP decoupling from 2015 to 2018.
32 41 36 6
CBEs (gigatonnes)Total 6.41 23.43 2.83 0.85
Global share 19.1% 69.9% 8.4% 2.5%
PBEs (gigatonnes)Total 5.33 24.36 3.04 0.84
Global share 15.9% 72.6% 9.1% 2.5%
Population (million)Total 857 4518 1213 270
Global share 12.5% 65.9% 17.7% 3.9%
GDP (billion)Total 27091 45255 4086 2997
Global share 34.1% 57.0% 5.1% 3.8%
Per capita GDP (1000 USD2010)Average 28.83 19.53 6.00 17.78
Median 26.36 12.04 3.64 13.12
Max 79.23 110.70 63.93 33.11
Min 1.09 0.57 0.49 5.80
Per capita CBEs (tonnes)Average 7.70 6.98 3.99 12.55
Median 6.78 6.00 1.95 11.33
Max 23.22 37.95 25.35 23.21
Min 0.43 0.09 0.18 2.33
CBEs intensity 
(tonnes per 1000 USD2010)Average 0.41 0.50 0.77 0.66
Median 0.31 0.44 0.52 0.69

Max 2.41 1.68 4.10 1.22
Min 0.12 0.10 0.20 0.21
Per capita PBEs (tonnes)Average 6.02 5.69 4.33 14.15
Median 5.36 4.88 1.67 13.22
Max 20.13 16.65 39.27 27.24
Min 0.30 0.09 0.01 2.23
PBEs intensity 
(tonnes per 1000 USD2010)Average 0.33 0.45 0.71 0.75
Median 0.20 0.31 0.44 0.68
Max 1.47 1.76 4.83 1.80
Min 0.05 0.10 0.13 0.20
Note: CBEs are obtained from the Global Carbon Budget 2020 (Friedlingstein et al. 2020), GDP and population are from the World Bank. One country (Venezuela) does not 
have GDP data after 2015, so the degree of decoupling was only calculated for 115 countries. In order to be consistent with the results of CBEs, we calculate the decoupling 
of PBE until 2018.

244
mainly due to the use of more efficient technology. The literature also 
shows that changes in the structure of economy with a shift to tertiary 
sectors of production may contribute to such decoupling (Kanitkar et 
al. 2015; Jiang et al. 2021).
A total of 67 (or 58%) countries, including China and India, have 
relatively decoupled GDP and CBEs between 2015 and 2018, 
reflecting a slower growth in emissions than GDP . It is worth noting 
that the USA shows relative decoupling of emissions (both CBEs and 
PBEs) and GDP over the most recent period, although it strongly 
decoupled economic growth from emissions between 2005 and 
2015. Thus decoupling can be temporary and countries’ emissions 
may again increase after a period of decoupling.
Another 19 (or 16%) countries, such as South Africa and Nepal, 
have experienced no decoupling between GDP and CBEs from 2015 
to 2018, meaning the growth of their GDP is closely tied with the

consumption of emission-intensive goods. As a  result, a  further 
increase of GDP in these countries will likely lead to higher emissions, 
if they follow the historical trend without substantive improvement 
in efficiency of production and energy use.
It is important to note that a country’s degree of decoupling changes 
over time. For example, 32 countries achieved absolute decoupling 
from 2010 to 2015 but only 10 of them remained decoupled over the 
next three years. More importantly, although absolute decoupling has 
reduced annual emissions, the remaining emissions are still contributing 
to an increase in atmospheric carbon concentration. Absolute decoupling is not sufficient to avoid consuming the remaining CO 2 
emission budget under the global warming limit of 1.5°C or 2°C and to 
avoid climate breakdown (Stoknes and Rockström 2018; Hickel and 
Kallis 2020). Even if all countries decouple in absolute terms this might

still not be sufficient and thus can only serve as one of the indicators 
and steps toward fully decarbonising the economy and society.
2.3.4 Emissions Embodied in Trade (EET)
As global trade patterns have changed over recent decades, so have 
emissions embodied in trade (EET) (Jiang & Green 2017). EET refers 
to emissions associated with production of traded goods and services 
and is equal to the difference between PBEs and CBEs (Wiebe and 
Yamano 2016). EET includes two parts: emissions embodied in 
imports (EEI); and emissions embodied in exports (EEE). For a given 
country or region with CBEs higher than PBEs, it is a net importer 
with a higher EEI than EEE, and vice versa.
EET have been rising faster since the 1980s due to an increase in 
trade volume (Xu and Dietzenbacher 2014; Wood et al. 2018). CO 2 
emissions from the production of internationally traded products 
peaked in 2006 at about 26% of global CO 2 emissions. Since then,

international CO 2 emissions transfers declined but are likely to 
remain an important part of the climate policy agenda (Wood et al. 
2020a). About 24% of global economic output and 25% of global 
CO2 emissions are embodied in the international trade of goods and 
services as of 2014 (Hubacek et al. 2021).
481216
CO2 emissions (GtCO2/yr–1)
Net Import
Net Export
Consumption-based
Production-based
Transfer of Embodied CO 2Attribution Principle
Developed Countries
Eastern Europe and West-Central AsiaLatin America and CaribbeanAsia and Paciﬁc
Africa
Figure 2.15 | Total annual CO 2 emissions for 116 countries by global region based on consumption- and production-based emissions. The shaded areas 
are the net CO 2 trade balances (differences) between each of the regions. Yellow shading indicates that the region is a net importer of embodied CO 2 emissions, leading to

consumption-based emission estimates that are higher than traditional territorial emission estimates. Blue shading indicates the reverse. Production-based emissions are 
collected from EDGAR and consumption-based emissions from the Global Carbon Budget 2020 (Friedlingstein et al. 2020). Source: this figure is modified based on Hubacek 
et al. (2021).

245
2.3.4.1 Net Emission Transfers
Located downstream in global supply chains, developed countries 
(mostly in Western Europe and North America) tend to be net 
emission importers, that is, EEI are larger than EEE. For example, over 
40% of national CO 2 footprints in France, Germany, Italy, and Spain 
are from imports (Fan et al. 2017). Developing countries tend to be 
net emission exporters with higher PBEs than their CBEs (Peters et al. 
2011a), especially for Asia and Pacific (as shown in Figure 2.15). That 
is to say, there is a net emission transfer and outsourcing of carbon-
intensive production from developed to developing economies via 
global trade (Jiang et al. 2018), mainly caused by cheap labour costs 
(Tate and Bals 2017) and cheap raw materials (Mukherjee 2018). 
Increasing openness to trade (Fernández-Amador et  al. 2016) and 
less stringent environmental legislation (acting as so-called pollution

havens) are also possible reasons (Hoekstra et al. 2016; Malik and 
Lan 2016; Banerjee and Murshed 2020).
Net emissions transferred between developing and developed 
countries peaked at 7.3% of global CO 2 emissions in 2006 and then 
subsequently declined (Wood et  al. 2020a). The main reason for 
the decline was an improvement in the carbon intensity of traded 
products, rather than a decline in trade volume (Wood et al. 2020a). 
Despite continued improvements, developing economies tend to 
have higher emission intensity than developed economies due to 
less efficient technologies and a carbon-intensive fuel mix (Jiang and 
Guan 2017).
2.3.4.2 Geographical Shifts of Emissions Embodied in Trade
With the rapid growth of developing countries, the geographical 
centre of global trade as well as emissions embodies in trade is 
changing. The fast growth of Asian countries is shifting the global 
trade centre from Europe to Asia (Zhang et al. 2019). Asian exports in

monetary units increased by 235% from 1996 to 2011, and its share 
in global exports increased from 25% to 46%, whereas Europe’s 
share in global exports decreased from 51% in 1996 to 39% in 2011. 
After 2011, global trade has stalled, but Asia’s share of global exports 
further increased to 42% in 2020 (UNCTAD 2021).
In addition to changes in trade volume, trading patterns have also 
been changing significantly in Asian countries. These countries are 
replacing traditional trading hubs (such as Russia and Germany) due 
to the fast growth in trade flows, especially with countries of the 
Global South (Zhang et al. 2019). The largest geographical shifts in 
trade-embodied emissions between 1995 and 2011 occurred in high-
tech, electronics, and machinery (Malik and Lan 2016; Jiang et  al. 
2018). For example, China is shifting its exports to include more 
low-carbon and higher value-added goods and services. As a result, 
China’s exported emissions declined by 20% from 2008 to 2015

(Mi et al. 2018).
Developing countries are increasingly playing an important role 
in global trade. EET between developing countries, so-called 
South-South trade, has more than doubled between 2004 (0.47 Gt) 
and 2011 (1.11 Gt), which is seen as a reflection of a new phase of globalisation (Meng et al. 2018). Developing countries, therefore, 
have gained importance as global suppliers of goods and services and 
have also become more relevant as global consumers as they grow 
their domestic demand (Fernández-Amador et al. 2016). Since 2014, 
CO2 emission transfer between developing countries has plateaued 
and then slightly declined and seems to have stabilised at around the 
same level of transfers between non-OECD and OECD countries at 
around 2.4 GtCO 2 yr –1 (Wood et al. 2020a). In both cases, a decrease 
in carbon intensity of trade just about offset increased trade volumes 
(Wood et al. 2020a).
2.4 Economic Drivers and Their Trends 
by Regions and Sectors

This section provides a  summary of the main economic drivers of 
GHG emissions (mostly territorial) by regions and sectors, including 
those that are more indirect drivers related to economic activity, such 
as inequality and rapid urbanisation. Trade as a driver of global GHG 
emissions is described in the Chapter  2 Supplementary Material. 
Socio-demographic drivers are described in Section  2.6. The Kaya 
decomposition presented in this section is based on the International 
Energy Agency (IEA) and Emissions Database for Global Atmospheric 
Research (EDGAR) v6 databases and tracks global, regional, and 
sectoral GHG emissions from 1990 to 2019 (Crippa et  al. 2021; 
IEA 2021c; Lamb et  al. 2021b; Minx et  al. 2021). It shows main 
contributors to GHG emissions as independent factors, although 
these factors also interact with each other.
2.4.1 Economic Drivers at Global and Regional Levels
Economic growth (measured as GDP) and its main components  –

GDP per  capita and population growth  – remained the strongest 
drivers of GHG emissions in the last decade, following a long-term 
trend (robust evidence, high agreement) (Liddle 2015; Malik et al. 
2016; Sanchez and Stern 2016; Chang et al. 2019; Dong et al. 2019; 
Liobikiene and Butkus 2019; Liu et al. 2019a; Mardani et al. 2019; Pan 
et al. 2019; Dong et al. 2020; Parker and Bhatti 2020; Xia et al. 2021). 
Globally, GDP per  capita remained by far the strongest upward 
driver, increasing almost in tandem with energy consumption and 
CO2 emissions up until 2015, after which some modest decoupling 
occurred (Deutch 2017; Wood et al. 2018) (Section 2.3.3). The main 
counteracting, yet insufficient, factor that led to emissions reductions 
was decreased energy use per  unit of GDP in almost all regions 
(–2.0% yr –1 between 2010 and 2019 globally) (see also Lamb et al. 
2021b) (Figure  2.16) (robust evidence, high agreement). These

reductions in energy intensity are a result of technological innovation, 
structural changes, regulation, fiscal support, and direct investment, 
as well as increased economic efficiency in underlying sectors (Yao 
et al. 2015; Sanchez and Stern 2016; Chang et al. 2019; Dong et al. 
2019a; Mohmmed et al. 2019; Stern 2019; Azhgaliyeva et al. 2020; 
Goldemberg 2020; Gao et al. 2021; Liddle and Huntington 2021; Liu 
et al. 2019b; Xia et al. 2021).

246
The decades-long trend that efficiency gains were outpaced by 
an increase in worldwide GDP (or income) per  capita continued 
unabated in the last 10 years (robust evidence, high agreement) 
(Wiedmann et al. 2020; Xia et al. 2021). In addition, the emissions-
reducing effects of energy efficiency improvements are diminished 
by the energy rebound effect, which has been found in several 
studies to largely offset any energy savings (robust evidence, high 
agreement) (Rausch and Schwerin 2018; Colmenares et  al. 2020; 
Stern 2020; Brockway et al. 2021; Bruns et al. 2021). The rebound 
effect is discussed extensively in Section 9.9.2.A significant decarbonisation of the energy system was only 
noticeable in North America, Europe and Eurasia. Globally, the 
amount of CO 2 per  unit of energy used has practically remained 
unchanged over the last three decades (Tavakoli 2018; Chang et al.

2019), although it is expected to decrease more consistently in 
the future (Xia et al. 2021). Population growth has also remained 
a  strong and persistent upward driver in almost all regions 
(+1.2%  yr –1 globally from 2010 to 2019) (Lamb et  al. 2021) 
(Figure 2.16), although per capita emission levels are very uneven 
across world regions. Therefore, modest population increases in 
38 Gt 42 Gt 53 Gt 59 Gt
6%15%22%24%34%+0.7% yr–1+2.1% yr–1+1.3% yr–1
0204060
1990 2000 2010 2019GHG emissions (GtCO2-eq yr–1)  (a) Total global GHG emission trends by sectors
WorldS.E. AsiaSouth AsiaN. AmericaMiddle EastLatin Am.EuropeEurasiaEast AsiaAfricaAustralia, Japan,
9%3%
27%
6%
9%
10%
5%
12%
9%8%% of tot. sector
GHG emissions
010 20 30 40(b) Emissions by region in 2019
(all GHGs, incl. LULUCF CO2, excl. Indirect CO2)
Per capita GHG emissions
(tCO2-eq per capita)
5 0 20 10 15+2.3%−0.6%
+2.4%
+0.6%
−1.7%
+0.2%
+2.3%
−1.1%
+4.5%+4.4%
+1.1%−0%+0.9%
−0.6%
−0.6%
−0.8%
−0.4%
−0.3%
−1.3%

+1.6%+0.6%
−0.3%−0.8%−2.7%
−3.4%
−0.9%
−2.7%
−0.8%
+0.2%
−2%
−2.1%−2.3%
−2%+2.4%−1.5%
+3%
+1.2%
−0.9%
+0.6%
+2.6%
+0.2%
+2.9%+3.8%
+1.4%+0.8%+1.2%
+6.1%
+1.6%
+1.5%
+0.3%
+0.2%
+1.5%
+3.8%+4.9%
+2.3%+2.4%+0.1%
+0.5%
+0.5%
+0.3%
+1.1%
+2.3%
+0.7%
+1.1%+1.2%
+1.2%CO2 Carbon
intensityEnergy
cons.GDP/
pop.Pop.(c) Average annual change in Kaya factors 2010−2019
(CO2 only, excl. LULUCF CO2, excl. indirect CO2)Energy systems
AFOLU
TransportIndustry
Buildings
Growing (> +1% yr–1) Stable (~0% yr–1)
Declining (< −1% yr–1)World average
Figure 2.16 | Trends and drivers of global GHG emissions, including: (a) trends of GHG emissions by sectors 1990–2019; (b) share of total and per capita 
GHG emissions by world region in 2019; and (c) Kaya decomposition of CO 2 emissions drivers. The Kaya decomposition is based on the equation F = P(G/P)

(E/G)(F/E), where F is CO 2 emissions, P is population, G/P is GDP per capita, E/G is the energy intensity of GDP and F/E is the carbon intensity of energy. The indicated annual 
growth rates are averaged across the years 2010–2019 (in panel (c), these are for fossil fuel CO 2 emissions only, in order to ensure compatibility with underlying energy data). 
Note that the energy consumption by itself (primary energy supply) is not part of the decomposition, but is listed here for comparison with the Kaya factors. Source: data from 
Crippa et al. (2021), IEA (2021c), Minx et al. (2021).

247
wealthy countries may have a similar impact on emissions as high 
population increases in regions with low per capita emission levels.
Developing countries remained major accelerators of global 
CO2 emissions growth since 2010, mostly driven by increased 
consumption and production, in particular in East Asia (robust 
evidence, high agreement) (Jiborn et al. 2020). While energy intensity 
declined to a  similar extent in countries of the Organisation for 
Economic Co-operation and Development (OECD) and non-OECD 
countries over the last 30 years, economic growth has been much 
stronger in non-OECD countries (González-Torres et al. 2021). This led 
to an average annual growth rate of 2.8% of CO 2 emissions in these 
countries, whereas they decreased by 0.3% yr –1 in OECD countries 
(UNEP 2019). The majority of developed economies reduced both 
production-based and consumption-based CO 2 emissions modestly

(Jiborn et al. 2020; Xia et al. 2021). This was due to slower economic 
growth, increased energy efficiency (less energy per unit of GDP), fuel 
switching from coal to gas (mostly in North America) (Wang et al. 
2020b), and the use of less and cleaner energy from renewables in 
Europe (Peters et al. 2017; Karstensen et al. 2018; Chang et al. 2019; 
Wood et al. 2019c).
Economic growth as the main driver of GHG emissions is particularly 
strong in China and India (robust evidence, high agreement) (Liu et al. 
2019b; Ortega-Ruiz et al. 2020; Z. Wang et al. 2020b; Yang et al. 2020; 
Zheng et  al. 2020; Xia et  al. 2021), although both countries show 
signs of relative decoupling because of structural changes (Marin 
and Mazzanti 2019). A change in China’s production structure (with 
relatively less heavy industry and lower-carbon manufacturing) and 
consumption patterns (i.e., the type of goods and services consumed) 
has become the main moderating factor of emissions after 2010,

while economic growth, consumption levels, and investment remain 
the dominating factors driving up emissions (Wang and Jiang 2019; 
Jiborn et  al. 2020; Zheng et  al. 2020). In India, an expansion of 
production and trade as well as a higher energy intensity between 
2010 and 2014 caused increased emissions (Kanitkar et  al. 2015; 
Wang and Zhou 2020; Z. Wang et al. 2020b).
2.4.2 Sectoral Drivers
GHG emissions continued to rise since 2010 across all sectors 
and subsectors, most rapidly in electricity production, industry, 
and transport. Decarbonisation gains from improvements in energy 
efficiency across different sectors and worldwide have been largely 
wiped out by increases in demand for goods and services. Prevailing 
consumption patterns have also tended to aggravate energy use 
and emissions, with the long-term trend led by developed regions. 
Decarbonisation trends in some developed regions are limited in 
size and geographically. Globally, there are enormous unexploited

mitigation potentials from adopting best available technologies.
The following subsections discuss main emissions drivers by sector. 
More detailed analyses of sectoral emissions and mitigation options 
are presented in Chapters 6–11.2.4.2.1 Energy Systems
Global energy system emissions growth has slowed down in recent 
years, but global oil and gas use was still growing (Jackson et  al. 
2019) and the sector remained the single largest contributor to global 
GHG emissions in 2019 with 20 GtCO 2-eq (34%) (high confidence) 
(Figure  2.17). Most of the 14 GtCO 2-eq from electricity and heat 
generation (23% of global GHG emissions in 2019) were due to 
energy use in industry and in buildings, making these two sectors 
also prominent targets for mitigation (Davis et al. 2018; Crippa et al. 
2019) (see subsections 2.4.2.2 and 2.4.2.3 below).
Growth in CO 2 emissions from energy systems has closely tracked 
rising GDP per  capita globally (Lamb et  al. 2021b), affirming the

substantial literature describing the mutual relationship between 
economic growth and demand for energy and electricity (robust 
evidence, high agreement) (Khanna and Rao 2009; Stern, 2011). 
This relationship has played out strongly in developing regions, 
particularly in Asia, where a massive scale up of energy supply has 
accompanied economic growth – with average annual increases of 
energy demand between 3.8–4.3% in 2010–2019 (Figure 2.17). The 
key driver for slowing the growth of energy systems CO 2 emissions 
has been declining energy intensities in almost all regions. Annually, 
1.9% less energy per unit of GDP was used globally between 2010 
and 2019.
The carbon intensity of power generation varies widely between 
(and also within) regions (Chapter  6). In North America, a  switch 
from coal to gas for power generation (Peters et al. 2017, 2020; Feng 
2019; Mohlin et al. 2019) as well as an overall decline in the share

of fossil fuels in electricity production (from 66% in 2010 to 59% in 
2018) (Mohlin et al. 2019) has decreased carbon intensity and CO 2 
emissions. Since 2007, Europe’s carbon intensity improvements have 
been driven by the steady expansion of renewables in the share of 
electricity generation (medium evidence, high agreement) (Peters 
et al. 2017, 2020; Le Quéré et al. 2019; Rodrigues et al. 2020). Some 
studies attribute these effects to climate policies, such as the carbon 
floor price in the UK, the EU emissions trading scheme, and generous 
renewable energy subsidies across the continent (Dyrstad et  al. 
2019; H. Wang et  al. 2020). South-East Asian developed countries 
and Australia, Japan and New Zealand stand out in contrast to other 
developed regions, with an increase of regional carbon intensity of 
1.8 and 1.9% yr –1, respectively (Figure 2.17). Generally, the use of 
natural gas for electricity production is growing strongly in most

countries and gas has contributed to the largest increase in global 
fossil CO 2 emissions in recent years (Jackson et  al. 2019; Peters 
et al. 2020). Furthermore, gas brings the risk of increased methane 
(CH 4) emissions from fugitive sources, as well as large cumulative 
emissions over the lifetime of new gas power plants that may erase 
early carbon intensity reductions (Shearer et al. 2020).
The growth of emissions from coal power slowed after 2010, and 
even declined between 2011 and 2019, primarily due to a slowdown 
of economic growth and fewer coal capacity additions in China 
(Friedlingstein et al. 2019; Peters et al. 2020). Discussions of a global 
‘peak coal’, however, may be premature, as further growth was 
observed in 2019 (Friedlingstein et al. 2019; Peters et al. 2020). Large

248
ongoing and planned capacity increases in India, Turkey, Indonesia, 
Vietnam, South Africa, and other countries has become a driver of 
thermal coal use after 2014 (UNEP 2017; Edenhofer et  al. 2018; 
Steckel et al. 2019).
2.4.2.2 Industry Sector
When indirect emissions from electricity and heat production are 
included, industry becomes the single highest emitting sector of 
GHGs (20.0 GtCO 2-eq in 2019) (high confidence). Facilitated by 
globalisation, East Asia has been the main source and primary driver 
of global industry emissions growth since 2000 (robust evidence, 
high agreement) (Lamb et  al. 2021). However, while East Asia 
has emitted 45% of the world’s industry GHG emissions in 2019, 
a remarkable decrease of 5.0% yr –1 in energy intensity and 1.6% in carbon intensity helped to stabilise direct industrial CO 2 emissions in 
this region (–0.3% yr –1 between 2010 and 2019; Figure 2.18). Direct

industry CO 2 emissions have also declined in Latin America, Europe 
and Australia, Japan and New Zealand, and – to a smaller extent – 
in North America. In all other regions, they were growing  – most 
rapidly in southern Asia (+4.3% annually for direct CO 2 emissions 
since 2010) (Figure 2.18).
The main global driver of industry emissions has been a massive rise 
in the demand for products that are indirectly used in production, 
such as cement, chemicals, steel, aluminium, wood, paper, plastics, 
lubricants, fertilisers, and so on. This demand was driven by economic 
growth, rising affluence, and consumption, as well as a  rapid rise 
in urban populations and associated infrastructure development 
(robust evidence, high agreement) (Krausmann et al. 2018). There is 010
520GHG emissions (GtCO 2-eq yr–1)  
% of tot. sector
GHG emissionsPer capita GHG emissions
(tCO2-eq per capita)CO2 Carbon
cons.GDP/
pop.Pop.
0

Declining (< −1% yr–1)World average(a) Total global GHG emission trends by sectors
(b) Emissions by region in 2019
(all GHGs, incl. LULUCF CO2, excl. indirect CO2)(c) Average annual change in Kaya factors 2010−2019
(CO2 only, excl. LULUCF CO2, excl. indirect CO2)15
6%4%
34%
9%
8%
4%
9%
13%
6%8%
20 40 0 5 10 15 20 25+1.5%−0.3%
+3.9%
−0.7%
−3.2%
+0.2%
+3.1%
−2.9%
+5.8%+4.6%
+1.2%−0.6%+1.9%
−0.4%
−0.8%
−1.5%
−0%
+0%
−2.4%
+1.8%+0.8%
−0.2%−1.1%−3.5%
−2.1%
−2%
−3.5%
−1.1%
+0.6%
−2.6%
−1%−2.3%
−1.9%+2.1%−2.2%
+4.3%
+0%
−1.6%
+0.2%
+3.1%
−0.4%
+3.9%+3.8%
+6.1%
+1.6%
+1.5%
+0.3%
+0.2%
+1.5%
+0.5%
+0.5%
+0.3%
+1.1%
+2.3%
+0.7%
+1.2%12 Gt 14 Gt 18 Gt 20 Gt
6%8%69%+1.3% yr–1+2.3% yr–1+1% yr–1
Electricity and heat
Other (energy systems)
Coal mining fugitive emissionsOil and gas fugitive emissions
Petroleum reﬁning

1990 2000 2010 201913%
3%
Figure 2.17 | Trends and drivers of global energy sector emissions (see Figure 2.16 caption for details) with energy measured as primary energy supply.

249
strong evidence that the growing use of concrete, steel, and other 
construction materials is particularly tightly coupled to these drivers 
(Pauliuk et al. 2013; Cao et al. 2017; Krausmann et al. 2017; Plank 
et al. 2018; Haberl et al. 2020). Per capita stocks of cement and steel 
show a typical pattern of rapid take-off as countries urbanise and 
industrialise, before slowing down to low growth at high levels of 
GDP . Hence, in countries that have recently been industrialising and 
urbanising  – that is Eastern, Southern and South-Eastern Asia  – 
a particularly strong increase of emissions from these subsectors can 
be observed. Selected wealthy countries seem to stabilise at high 
per capita levels of stocks, although it is unclear if these stabilisations 
persist and if they result in significant absolute reductions of material 
use (Wiedenhofer et  al. 2015; Cao et  al. 2017; Krausmann et  al.

2018). Opportunities for prolonging lifetimes and improving end of life recycling in order to achieve absolute reductions in extraction 
activities are as yet unexploited (Krausmann et al. 2017; Zink and 
Geyer, 2017).
On the production side, improvements in the efficiency of material 
extraction, processing, and manufacturing have reduced industrial 
energy use per unit of output (J. Wang et al. 2019). These measures, 
alongside improved material substitution, lightweight designs, 
extended product and servicing lifetimes, improved service efficiency, 
and increased reuse and recycling will enable substantial emissions 
reductions in the future (Hertwich et al. 2019). In absence of these 
improvements in energy intensity, the growth of population and 
GDP per  capita would have driven the industrial CO 2 emissions 
to rise by more than 100% by 2017 compared with 1990, instead Chemicals
Waste 
(tCO 2-eq per capita)CO 2 Carbon

cons.GDP/
pop.Pop.
Declining (< −1% yr–1)World average(a) Industry global GHG emission trends by subsectors
(all GHGs, incl. indirect CO2)(c) Average annual change in Kaya factors 2010−2019
(CO2 only, excl. indirect CO2)
+0.8%+1.2%
+6.1%
+1.6%
+1.5%
+0.3%
+0.2%
+1.5%
+0.5%
+0.5%
+0.3%
+1.1%
+2.3%
+0.7%
+1.2%Indirect CO2
MetalsOther (industry)
Cement
+1.5%−0.8%
−0.3%
+2.7%
−1.2%
−1.8%
+0.8%
−0.4%
+2.8%+4.3%
+0.3%+0.5%+0.2%
−1.6%
+2%
−0.7%
−1.1%
−0.6%
−0.4%
−0.7%−0.7%
−0.7%−2.2%−2.3%
−5%
−1.5%
−2.4%
−2%
−1.1%
−2.2%
−1.4%−1.2%
−2.3%+1%−1.1%
+1.3%
+0.6%
−0.6%
−0.6%
+1.4%
+0%
+3.5%+5%
+1%4%3%
45%
6%
9%
6%
5%
8%
5%9%
0 20 40 60 0 5 10 158%12%14%16%22%30%

05101520GHG emissions (Gt CO 2-eq yr–1)11 Gt 12 Gt 18 Gt 20 Gt+0.5% yr–1+3.1% yr–1+1.3% yr–1
Figure  2.18 | Trends and drivers of global industry sector emissions (see Figure  2.16 caption for details) with energy measured as total final 
energy consumption.

250
of 56% (Lamb et al. 2021b). Nonetheless, many studies point to deep 
regional differences in efficiency levels and large globally unexploited 
potentials to improve industrial energy efficiency by adopting best 
available technologies and practices for metal, cement, and chemical 
production (Gutowski et  al. 2013; Schulze et  al. 2016; Hernandez 
et al. 2018; Talaei et al. 2018).
2.4.2.3 Buildings Sector
Global direct and indirect GHG emissions from the buildings sector 
reached 9.7 GtCO 2-eq in 2019, or 16% of global emissions). Most of 
these emissions (66%, or 6.4 GtCO 2-eq) were upstream emissions 
from power generation and commercial heat (Figure  2.19). The 
remaining 33% (3.3 GtCO 2-eq) of emissions were directly produced in buildings, for instance by gas and coal boilers, and cooking and 
lighting devices that burn kerosene, biomass, and other fuels (Lamb 
et al. 2021). Residential buildings accounted for the majority of this

sector’s emissions (64%, 6.3 GtCO 2-eq, including both direct and 
indirect emissions), followed by non-residential buildings (35%, 
3.5 GtCO 2-eq) (high confidence).
Global buildings sector GHG emissions increased by 0.7% yr –1 
between 2010 and 2019 (Figure 2.19), growing the most in absolute 
terms in East and South Asia, whereas they declined the most in 
Europe, mostly due to the expansion of renewables in the energy 
sector and increased energy efficiency (Lamb et  al. 2021). North 
America has the highest per capita GHG emissions from buildings 
and the second highest absolute level after East Asia (Figure 2.19).
Non-residential
Non-CO2 (all buildings) 
cons.GDP/
pop.Pop.
Declining (< −1% yr–1)World average(a) Buildings global GHG emission trends by subsectors

+6.1%
+1.6%
+1.5%
+0.3%
+0.2%
+1.5%
+0.5%
+0.5%
+0.3%
+1.1%
+2.3%
+0.7%
+1.2%Residential (indirect CO2)
ResidentialNon-residential (indirect CO2)
5%5%
26%
8%
13%
3%
7%
20%
5%7%
0 10 20 30 40 0 2 4 60369GHG emissions (Gt CO 2-eq yr–1)6.4 Gt 7.5 Gt 9.2 Gt 9.7 Gt+1.6% yr–1+1.8% yr–1+0.7% yr–1
1990 2000 2010 20190%9%24%26%40%
+5.2%−1.2%
+0.7%
+4%
−2.4%
−0.2%
+1.4%
+0.3%
+0.9%+3.3%
+0.4%+2.6%−0.3%
−2%
+1.7%
−1.1%
−1.2%
−1.3%
−0.1%
+2.8%+1.1%
−0.6%−0.7%−2.2%
−3.7%
+0.1%
−3.1%
−0.4%
+0.3%
−1.8%
−6.6%−3.9%
−2.4%+2.5%−0.9%
+2.7%
+2.2%
−1.3%
+1%
+2.8%
+0.4%
−1.9%+2.1%
+0.9%
Figure  2.19 | Trends and drivers of global buildings sector emissions (see Figure  2.16 caption for details) with energy measured as total final

251
Rising wealth has been associated with more floor space being 
required to service growing demand in the retail, office, and hotel 
sectors (medium evidence, high agreement) (Daioglou et  al. 2012; 
Deetman et  al. 2020). In addition, demographic and social factors 
have driven a cross-national trend of increasing floor space per capita. 
As populations age and decrease in fertility, and as individuals seek 
greater privacy and autonomy, households declined in size, at least 
before the COVID-19 pandemic (Ellsworth-Krebs 2020). These factors 
led to increased floor space per capita, even as populations stabilise. 
This in turn is a  key driver for building sector emissions, because 
building characteristics such as size and type, rather than occupant 
behaviour, tend to explain the majority of energy use within dwellings 
(Guerra Santin et  al. 2009; Ürge-Vorsatz et  al. 2015; Huebner and 
Shipworth 2017) (Chapter 9).

Energy activity levels further drive regional differences. In Eurasia, 
Europe and North America, thermal demands for space heating 
dominate building energy use, at 66%, 62% and 48% of residential 
energy demand, respectively (IEA 2020a). In contrast, cooking has 
a much higher share of building energy use in regions of the Global 
South, including China (Cao et al. 2016). And, despite temperatures 
being on average warmer in the Global South, electricity use for 
cooling is a more prominent factor in the Global North (Waite et al. 
2017). This situation is changing, however, as rapid income growth 
and demographic changes in the Global South enable households to 
heat and cool their homes (Ürge-Vorsatz et al. 2015, 2020).
Steady improvements in building energy intensities across regions can 
be attributed to baseline improvements in building fabrics, appliance 
efficiencies, energy prices, and fuel shifts. Many countries have

adopted a mix of relevant policies, such as energy labelling, building 
energy codes, and mandatory energy performance requirements 
(Nie and Kemp 2014; Nejat et  al. 2015; Economidou et  al. 2020). 
Efforts towards building refurbishments and retrofits have also 
been pursued in several nations, especially for historical buildings in 
Europe, but evidence suggests that the recent retrofit rates have not 
made a significant dent on emissions (Corrado and Ballarini 2016). 
The Chinese central government launched various policies, including 
command and control, economic incentives, and technology measures, 
but a big gap remains between the total rate of building green retrofit 
in the nation and the future retrofit potential (G. Liu et  al. 2020a, 
2020b). Still, one major global factor driving down energy intensities 
has been the global transition from inefficient coal and biomass use in 
buildings for heating and cooking, towards natural gas and electricity,

in part led by concerted policy action in Asian countries (Ürge-Vorsatz 
et al. 2015; Kerimray et al. 2017; Thoday et al. 2018). As developing 
countries construct new buildings, there is sizable potential to reduce 
and use less carbon-intensive building materials and adopt building 
designs and standards that lower lifecycle buildings energy use and 
allow for passive comfort. Chapter 9 describes the mitigation options 
of the buildings sector.
2.4.2.4 Transport Sector
With a steady, average annual growth of +1.8% yr –1 between 2010 
and 2019, global transport GHG emissions reached 8.9  GtCO 2-eq 
in 2019 and accounted for 15% of all direct and indirect emissions (Figure  2.20). Road transport passenger and freight 
emissions represented by far the largest component and source of 
this growth (6.1 GtCO 2-eq, 69% of all transport emissions in 2019) 
(high confidence). National plus international shipping and aviation 
emissions together accounted for 2.0 GtCO 2-eq or 22% of the

sector’s total in 2019. North America, Europe and Eastern Asia stand 
out as the main regional contributors to global transport emissions 
and together account for 50% of the sector’s total.
The proportion of total final energy used in transport (28%) and 
its fast expansion over time weighs heavily on climate mitigation 
efforts, as 92% of transport energy comes from oil-based fuels (IEA 
2020b). These trends situate transport as one of the most challenging 
sectors for climate change mitigation – no country has so far been 
able to realise significant emissions reductions in the sector. North 
America’s absolute and per  capita transport emissions are the 
highest amongst world regions, but those of South, South-East and 
East Asia are growing the fastest (high confidence) (between +4.6% 
and +5.2% yr –1 for CO 2 between 2010 and 2019) (Figure 2.20).
More so than any other sector, transport energy use has tracked GDP

per capita growth (Figure 2.20), (Lamb et al. 2021). With the exception 
of road gasoline demand in OECD countries, the demand for all road 
fuels generally increases at least as fast as the rate at which GDP 
per  capita increases (Liddle and Huntington 2020). Developments 
since 1990 continue a historical trend of increasing travel distances 
and a shift from low- to high-speed transport modes that goes along 
with GDP growth (Schäfer et  al. 2009; Gota et  al. 2019). Modest 
improvements in energy efficiency have been realised between 2010 
and 2019, averaging –1.5% yr –1 in energy intensity globally, while 
carbon intensities of the transport sector have remained stable in all 
world regions (Figure  2.20). Overall, global increases in passenger 
and freight travel activity levels have outpaced energy efficiency and 
fuel economy improvements, continuing a  long-term trend for the 
transport sector (medium evidence, high agreement) (Gucwa and

Schäfer 2013; Grübler 2015; McKinnon 2016).
Despite some policy achievements, energy use in the global transport 
system remains to the present deeply rooted in fossil fuels (robust 
evidence, high agreement) (Figueroa et al. 2014; IEA 2019). In part 
this is due to the increasing adoption of larger, heavier combustion-
based vehicles in some regions, which have tended to far outpace 
electric and hybrid vehicle sales (Chapter 10). Yet, stringent material 
efficiency and lightweight design of passenger vehicles alone would 
have the potential to cut cumulative global GHG emissions until 
2060 by 16–39 GtCO 2-eq (Pauliuk et al. 2021).
While global passenger activity has expanded in all world regions, 
great disparities exist between low- and high-income regions, and 
within countries between urban and rural areas (ITF 2019). While 
private car use is dominant in OECD countries (EC 2019), the growth 
of passenger-km (the product of number of travellers and distance

travelled) has considerably slowed there, down to an increase of 
just 1% yr –1 between 2000 and 2017 (SLoCaT 2018) (Chapter 10). 
Meanwhile, emerging economies in the Global South are becoming 
more car-dependent, with rapidly growing motorisation, on-demand 
private transport services, urban sprawl, and the emergence of local

252
automotive production, while public transport struggles to provide 
adequate services (Dargay et  al. 2007; Hansen and Nielsen 2017; 
Pojani and Stead 2017).
Freight travel activity grew across the globe by 68% in the last 
two decades, driven by global GDP increases, together with the 
proliferation of online commerce and rapid (i.e.,  same-day and 
next-day) delivery (SLoCaT 2018). Growth has been particularly rapid 
in heavy-duty road freight transport.
While accounting for a small share of total GHG emissions, domestic 
and international aviation have been growing faster than road 
transport emissions, with average annual growth rates of +3.3% and 
+3.4%, respectively, between 2010 and 2019 (Crippa et  al. 2021; Minx et al. 2021;). Energy efficiency improvements in aviation were 
considerably larger than in road transport, but were outpaced by 
even larger increases in activity levels (SLoCaT 2018; Lee et al. 2021) 
(Chapter 10).

2.4.2.5 AFOLU Sector
GHG emissions from agriculture, forestry and other land use 
(AFOLU) reached 13 GtCO 2-eq globally in 2019 (medium confidence) 
(Figure 2.21). AFOLU trends, particularly those for CO 2-LULUCF , are 
subject to a high degree of uncertainty (Section 2.2.1). Overall, the 
AFOLU sector accounts for 22% of total global GHG emissions, and 
in several regions – Africa, Latin America, and South-East Asia – it 
is the single largest emitting sector, which is also significantly International aviation
Domestic aviation 
cons.GDP/
pop.Pop.
Declining (< −1% yr–1)World average(a) Transport global GHG emission trends by subsectors

+6.1%
+1.6%
+1.5%
+0.3%
+0.2%
+1.5%
+0.5%
+0.5%
+0.3%
+1.1%
+2.3%
+0.7%
International shippingRoad
Other (transport)
Inland shipping
Rail
0 2 4 64%4%
14%
4%
13%
7%
5%
23%
5%5%
0 10 20 30 40GHG emissions (GtCO 2-eq yr–1)5.1 Gt 6.3 Gt 7.6 Gt 8.9 Gt+2.1% yr–1+1.7% yr–1+1.8% yr–1
1990 2000 2010 20191%2%6%4%7%9%69%2%
0.02.55.07.510.0
+3.8%−0.5%
+4.7%
+1%
+0.5%
+1.2%
+2.6%
+0.6%
+4.6%+5.2%
+1.8%+0%+0%
−0.3%
+0.2%
−0.1%
−0.4%
−0%
−0.2%
−0.5%−0.1%
−0.2%+0.5%−1.7%
−1.5%
−1.2%
−1.2%
+0.2%
+0.1%
−1.4%
+0.1%−0.9%
−1.5%+3.7%−0.5%
+5%
+0.9%
+0.6%
+1.5%
+2.6%
+0.8%
+5.1%+5.3%
+1.9%
Figure  2.20 | Trends and drivers of global transport sector emissions (see Figure  2.16 caption for details) with energy measured as total final 

253
affected itself by climate change (AR6 WGI Chapters 8, 11, and 12; 
and AR6 WGII Chapter 5). Latin America has the highest absolute and 
per capita AFOLU GHG emissions of any world region (Figure 2.21). 
CO2 emissions from land-use change and CH 4 emissions from 
enteric fermentation together account for 74% of sector-wide 
GHGs. Note that CO 2-LULUCF estimates included in this chapter are 
not necessarily comparable with country GHG inventories, due to 
different approaches to estimating anthropogenic CO 2 sinks (Grassi 
et al. 2018) (Chapter 7).Unlike all other sectors, AFOLU emissions are typically higher in 
developing compared to developed regions (medium confidence). 
In  Africa, Latin America, and South-East Asia, CO 2 emissions 
associated with land-use change and management predominate, 
dwarfing other AFOLU and non-AFOLU sources and making AFOLU 
the single largest sector with more than 50% of emissions in these

regions (Lamb et  al. 2021b). Land-use and land-management 
emissions are associated with the expansion of agriculture into 
carbon-dense tropical forest areas (Vancutsem et  al. 2021), where 
large quantities of CO 2 emissions result from the removal and burning 
of biomass and draining of carbon rich soils (Pearson  et  al.  2017; 
Rice cultivation (CH 4)
Manure management (N2O, CH4) 
(tCO2-eq per capita)GHG GHG 
intensityLand
efﬁciencyAgricultural
outputPopulation
Declining (< −1% yr–1)World average(a) AFOLU global GHG emission trends by subsectors
(all GHGs, incl. LULUCF CO 2)(c) Average annual change in Kaya factors 2010−2019
(GHG excl. LULUCF CO2)
New ZealandLULUCF (CO 2)
Managed soils and pasture 
(CO2, N2O)Enteric fermentation (CH4)

Synthetic fertiliser application 
(N2O)
Biomass burning (CH4, N2O)
20%2%
9%
3%
3%
26%
1%
4%
22%10%
0 10 20 30 40
 0 2 4 6GHG emissions (GtCO 2-eq yr–1)10 Gt 10 Gt 11 Gt 13 Gt–0.5% yr–1+1.4% yr–1+1.6% yr–1
1990 2000 2010 20193%3%8%11%23%51%
051015
1%
+2%−0.1%
−0.4%
+1.4%
+0.6%
+0.8%
−0.6%
+0.8%
+0.6%+1%+1.6%+0.6%
−0.6%
+1%
+0.8%
+0.6%
−0.6%
+0.9%
+0.6%+0.6%−2.3%−0.4%
−1.7%
−5.4%
−1.3%
−3.3%
−1.9%
−1.1%
−2.2%−2%+0.2%−0.4%
+1.4%
+5.7%
+0.7%
+2.5%
+0.1%
+0.4%
+1.3%+1.1%+2.6%+0.2%
+0.6%
+0.5%
+0.4%
+1%
+1.9%
+0.7%
+1.2%+1.3%
+0.7%−0.1% −2.5% +2.1% +1.2%
Figure 2.21 | Trends and drivers of global AFOLU sector emissions: (a) trends of GHG emissions by subsectors 1990–2019; (b) share of total sector and 
per capita GHG emissions by world region in 2019; and (c) Kaya decomposition of GHG emissions drivers.  Based on the equation H=P(A/P)(L/A)(H/L), where

P is population, A/P is agricultural output per capita, L/A is the land required per unit of agricultural output (land efficiency), and H/L is GHG emissions per unit of land (GHG 
intensity) (Hong et al. 2021). GHG emissions H comprise agricultural CH 4 and N 2O emissions from EDGAR v6.0. The indicated annual growth rates are averaged across the years 
2010–2019 – LULUCF CO 2 emissions are excluded in panel (c). (Note: due to different datasets, the population breakdown for AFOLU emissions is slightly different than that 
in the other sector figures above).

254
IPCC 2018; Hong  et  al. 2021). Ruminant livestock rearing takes 
place on vast tracts of pasture land worldwide, contributing to 
large quantities of CH 4 emissions from enteric fermentation in Latin 
America (0.8 GtCO 2-eq in 2018), Southern Asia (0.6 GtCO 2-eq), and 
Africa (0.5 GtCO 2-eq), while also playing a sizable role in the total 
AFOLU emissions of most other regions (Lamb et al. 2021b).
In all regions, the amount of land required per unit of agricultural 
output has decreased significantly from 2010 to 2019, with a global 
average of –2.2% yr –1 (land efficiency metric in Figure  2.21). This 
reflects agricultural intensification and technological progress. 
However, in most regions this was mirrored by an increase in output 
per capita, meaning that absolute GHG emissions in most regions 
increased over the last decade. A significant increase in total AFOLU 
emissions occurred in Africa, driven by both increased GHG emissions

per unit of land and increased populations (Figure 2.21).
The AFOLU sector and its emissions impacts are closely tied to 
global supply chains, with countries in Latin America and South-East 
Asia using large portions of their land for agricultural and forestry 
products exported to other countries (Chapter 7). The strong increases 
in production per capita and associated GHG emissions seen in these 
regions are at least partly attributable to growing exports and not 
national food system or dietary changes. At the same time, efforts to 
promote environmental sustainability in regions such as the EU and 
the USA (but also fast-growing emerging economies such as China) 
can take place at the cost of increasing land displacement elsewhere 
to meet their own demand (Meyfroidt et  al. 2010; Yu et  al. 2013; 
Creutzig et al. 2019).
Global diets are a  key driver of production per  capita, and thus 
land pressure and AFOLU emissions (Chapter  7). As per  capita

incomes rise and populations urbanise, traditional, low-calorie diets 
that emphasise starchy foods, legumes, and vegetables transition 
towards energy-intensive products such as refined sugars, fats, oils, 
and meat (Pradhan et al. 2013; Tilman and Clark 2014). At a certain 
point in national development, affluence and associated diets thus 
override population growth as the main driver of AFOLU emissions 
(Kastner et  al. 2012). Very high calorie diets have high total GHG 
emissions per  capita (Heller and Keoleian 2015) and are common 
in the developed world (Pradhan et  al. 2013). Over the last few 
decades, a  ‘westernisation’ of diets has also been occurring in 
developing countries (Pradhan et al. 2013). Low- and middle-income 
countries such as India, Brazil, Egypt, Mexico, and South Africa 
have experienced a  rapid dietary shift towards western-style diets 
(De Carvalho et al. 2013; Pradhan et al. 2013; Popkin 2015). Another

driver of higher food requirements per capita is food waste, which 
has increased more or less continuously since the 1960s in all regions 
but Europe (Porter and Reay 2016).
2.4.3 Poverty and Inequality
Increasing economic inequality globally has given rise to concern 
that unequal societies may be more likely to pollute and degrade 
their environments (Masud et al. 2018; Chancel 2020; Hailemariam 
et al. 2020; Millward-Hopkins and Oswald 2021). The nature of this relationship has important implications for the design of income 
redistribution policies aiming to reduce inequalities (Section  2.6 
presents evidence on how affluence and high consumption relate to 
emissions). Income inequality and carbon intensity of consumption 
differs across countries and individuals (Baležentis et  al. 2020) 
(Section  2.3.3). Reduced income inequality between nations can 
reduce emissions intensity of global income growth, if energy 
intensity reductions from income growth in some nations offset

increases in energy and emissions from higher growth in other 
nations (Rao and Min 2018). Increasing income inequality between 
individuals can translate into larger energy and emissions inequality 
if higher incomes are spent on more energy-intensive consumption 
and affluent lifestyles (Oswald et  al. 2020; Wiedmann et  al. 2020) 
(Section 2.6).
Literature shows that more equitable income distributions can 
improve environmental quality, but the nature of this relationship 
can vary by level of development (low evidence, medium agreement) 
(Knight et al. 2017; Chen et al. 2020; Hailemariam et al. 2020; Huang 
and Duan 2020; Liobikienė and Rimkuvienė 2020; Rojas-Vallejos 
and Lastuka 2020; Uddin et  al. 2020). Differences in the energy 
and carbon intensities of consumption and the composition of 
consumption baskets across populations and nations matter for 
emissions. (Jorgenson et al. 2016; Grunewald et al. 2017). There is

evidence to suggest that more equal societies place a higher value 
on environmental public goods (Baumgärtner et  al. 2017; Drupp 
et  al. 2018). Additional research shows that reducing top income 
inequality in OECD countries can reduce carbon emissions and 
improve environmental quality (Hailemariam et  al. 2020) and that 
the effect of wealth inequality, measured as the wealth share of 
the top decile, on per capita emissions in high-income countries, is 
positive (Knight et al. 2017). Evidence from 40 sub-Saharan African 
countries suggests that a  rise in income inequality contributed to 
increasing CO 2 emissions between 2010 and 2016, controlling for 
other drivers such as economic growth, population size, and inflation 
(Baloch et al. 2020).
The key development objective of eradicating extreme poverty 
(Chakravarty and Tavoni 2013; Hubacek et al. 2017a; Malerba 2020) 
and providing universal access to modern energy services (Pachauri

et al. 2013, 2018; Pachauri 2014; Singh et al. 2017) only marginally 
affects GHG emissions (medium evidence, high agreement). Shifts 
from biomass to more efficient energy sources and collective 
provisioning systems for safe water, health, and education are 
associated with reduced energy demand (Baltruszewicz et al. 2021). 
Efforts to alleviate multi-dimensional poverty by providing minimum 
decent living standards universally, however, may require more energy 
and resources. Recent estimates of the additional energy needed are 
still within bounds of projections of energy demand under climate 
stabilisation scenarios (Hubacek et  al. 2017a, 2017b; Rao et  al. 
2019; Pascale et al. 2020; Kikstra et al. 2021). Bottom-up estimates 
suggest that achieving decent living standards requires 13–40  GJ 
per capita annually, much less than the current world average energy 
consumption of 80 GJ per  capita in 2020 (Millward-Hopkins et  al.

2020) (medium evidence, high agreement). Aggregate top-down 
estimates suggest that achieving a  high Human Development 
Index (HDI) score above 0.8 requires energy consumption between

255
30–100  GJ per  capita yr –1 (Lamb and Rao 2015). There is some 
evidence, however, of a  decoupling between energy consumption 
and HDI over time (Akizu-Gardoki et  al. 2018). The emissions 
consequences of poverty alleviation and decent living also depend on 
whether improvements in well-being occur via energy- and carbon-
intensive industrialisation or low-carbon development (Semieniuk 
and Yakovenko 2020; Fu et al. 2021; Huang and Tian 2021).
2.4.4 Rapid and Large-scale Urbanisation 
as a Driver of GHG Emissions
Economic growth and urbanisation go hand in hand and are both 
influencing GHG emissions. However, the exact role of urban 
development in driving emissions is multi-faceted and heterogeneous, 
depending on development status and other regional factors (medium 
evidence,  high agreement) (Jorgenson et al. 2014; Lamb et al. 2014; 
Liddle and Lung 2014; Creutzig et al. 2015; Pincetl 2017; Azizalrahman

and Hasyimi 2019; Muñoz et al. 2020). This calls for a differentiated 
assessment. This section assesses the process of rapid urban growth 
in developing countries and how emissions change over time when 
cities’ urban populations and infrastructure expand at fast speed and 
at a massive scale (Seto et al. 2017; Elmqvist et al. 2021). To distinguish, 
Section 2.6 includes the carbon footprint of urban lifestyles and the 
difference in emissions profiles between already urbanised and less 
urbanised areas. Chapter  8 deals with urban strategies for climate 
change mitigation.
Urban development is most significant and rapid in developing and 
transition countries, accompanied by a substantial migration of rural 
populations to urban areas (Apergis and Li 2016; Azizalrahman and 
Hasyimi 2019; Z. Wang et al. 2019) and associated impacts on land 
use (Richardson et  al. 2015). If the trend of developing countries 
following infrastructure stock patterns in industrialised nations

continues until 2050, this could cause approximately 350 GtCO 2 
from the production of materials (Müller et al. 2013). This would be 
equivalent to 70% of the 500 GtCO 2 estimated remaining carbon 
budget from the beginning of 2020 to limit global warming to 1.5°C 
with a likelihood of 50% (IPCC 2021b).
In many developing countries across the world, the process of urban 
expansion leads to higher per  capita consumption-based GHG 
emissions (medium evidence, high agreement) (Jorgenson et al. 2014; 
Yao et al. 2015; Zhang et al. 2016; Wood et al. 2018a; Muñoz et al. 
2020). The high disparity between rural and urban personal carbon 
footprints in these countries (Wiedenhofer et al. 2017) (Section 2.6) 
means that migration to urban areas increases overall emissions as 
levels of income and expenditure rise, leading to further economic 
growth and infrastructure development in urban areas (Müller 
et al. 2013; Li et al. 2015; Wang and Yang 2016; Zhang et al. 2016;

Wiedenhofer et al. 2017; Cetin and Bakirtas 2019; Fan et al. 2019; 
Li and Zhou 2019; Xia et al. 2019; Sarkodie et al. 2020).
For total production-based emissions in general, urbanisation is 
thought to have a smaller effect than changes in population, GDP 
per capita, and energy and emissions intensities, which are all more 
influential (Lin et al. 2017). Another driver of urban emissions is rising ambient air temperature caused by urban land expansion, which 
will likely drive a  substantive increase in air conditioning use and 
cold storage for food (Huang et al. 2019). Specific emission drivers, 
however, depend on city- and place-specific circumstances such 
as income, household size, density, or local climate (Baiocchi et al. 
2015; H. Wang et al. 2019). Geographical factors, urban form, and 
transport/fuel costs are dependent on each other, and, together with 
economic activity, have been found to explain 37% of urban direct

energy use and 88% of urban transport energy use in a global sample 
of 274 cities (Creutzig et al. 2015).
2.5 Technological Change is Key 
to Reducing Emissions
Technological change for climate change mitigation involves 
improvement in and adoption of technologies, primarily those 
associated with energy production and use. Technological change 
has had a  mitigating effect on emissions over the long term and 
is central to efforts to achieving climate goals (high confidence). 
Progress since AR5 shows that multiple low-carbon technologies are 
improving and falling in cost (high confidence); technology adoption 
is reaching substantial shares, and small-scale technologies are 
particularly promising on both (medium confidence). Faster adoption 
and continued technological progress can play a  crucial role in 
accelerating the energy transition. However, the historical pace of 
technological change is still insufficient to catalyse a complete and

timely transition to a low-carbon energy system: technological change 
needs to accelerate (high confidence). This section assesses the role of 
technological change in driving emissions reductions and the factors 
that drive technological change, with an emphasis on the speed of 
transitions. Incentives and support for technological change affect 
technology outcomes (Sivaram et al. 2018; Wilson et al. 2020a). Work 
since AR5 has focused on evaluating the effectiveness of policies: 
those that accelerate technological change by enhancing knowledge 
(technology push) and those that increase market opportunities 
for successful technologies (demand pull) (Nemet 2013); as well 
as the importance of tailoring support to country contexts (Barido 
et al. 2020; Rosenbloom et al. 2020), including the limits of carbon-
pricing policies to date (Lilliestam et  al. 2020). Section  2.8 and 
Chapter 13 describe how these polices affect emissions; Chapter 14

and Cross-Chapter Box 12 in Chapter 16 discuss transition dynamics; 
and Chapter 16 provides a more detailed assessment of the evolution 
and mitigation impacts of technology development, innovation, 
and transfer.
2.5.1 Technological Change Has Reduced Emissions
Technological change that facilitates efficient energy utilisation from 
production to its final conversion into end-use services is a critical 
driver of carbon emissions reductions (high confidence). Technological 
change can facilitate stringent mitigation, but it can also reduce these 
effects by changing consumer behaviour, such as through rebound 
effects (Section 2.6 and Chapter 16). AR6 includes an entire chapter 
on innovation, technology development, and transfer (Chapter 16). 
A  focus gained in this section is the extent to which aligned,

256
credible, and durable policies can accelerate technological change 
factors to put emissions reductions on a trajectory compatible with 
reaching United Nations Framework Convention on Climate Change 
(UNFCCC) goals.
Technological change has facilitated the provision of more diverse 
and efficient energy services (heating, cooling, lighting, and mobility) 
while generating fewer emissions per  unit of service. As seen in 
Section  2.4, in Kaya identity terms (Lima et  al. 2016) (see ‘Kaya 
identity’ in Glossary): population and economic growth are factors 
that have increased emissions, while technological change has 
reduced emissions (Peters et  al. 2017). These Kaya statistics show 
that, while technological change can facilitate the transition to 
a low-carbon economy, it needs to proceed at a much faster pace 
than historical trends (Peters et al. 2017).
Multiple challenges exist in accelerating the past rate of

technological change. First, an array of physical assets in the energy 
system are long-lived and thus involve substantial committed carbon 
(Section 2.7) (Knapp 1999; Cui et al. 2019). A process of ‘exnovation’, 
accelerating the phase-out of incumbent technology through 
intentional policy (such as by pricing carbon), provides a means to 
address long lifetimes (Davidson 2019; Rosenbloom and Rinscheid 
2020). Second, countries may not have the capacity to absorb the 
flows of ideas and research results from international knowledge 
spillovers due to weak infrastructure, limited research capacity, lack 
of credit facilities (Chapter 15, Section 15.5), and other barriers to 
technology transfer (Adenle et  al. 2015). In a  developing country 
context, processes of innovation and diffusion need to include 
competence-building systems (Lema et  al. 2015; Perrot and Sanni 
2018; Stender et al. 2020). Third, public policy is central to stimulating

technological change to reduce emissions; policy depends on creating 
credible expectations of future market opportunities (Alkemade and 
Suurs 2012), but the historical evidence shows that, despite recent 
progress, policies related to energy and climate over the long term 
have been inconsistent (Taylor 2012; Nemet et al. 2013; Koch et al. 
2016). Bolstering the credibility and durability of policies related 
to low-carbon technology are crucial to accelerating technological 
change and inducing the private sector investment required (Helm 
et al. 2003; Habermacher et al. 2020).2.5.2 A Low-carbon Energy Transition Needs 
to Occur Faster Than Previous Transitions
An illuminating debate on the possibility of faster transitions has 
emerged since AR5  – with diverging assumptions about future 
technological change at the core of the discourse (Bazilian et  al. 
2020; Lu and Nemet 2020). Table 2.5 summarises these arguments.
2.5.2.1 Energy Transitions Can Occur Faster Than in the Past

Recent studies have identified examples supporting fast energy 
transitions (Sovacool 2016; Bond et  al. 2019; Reed et  al. 2019). 
One describes five rapid national-scale transitions in end-use 
technologies, including lighting in Sweden, cook-stoves in China, 
liquefied petroleum gas stoves in Indonesia, ethanol vehicles in 
Brazil, and air conditioning in the USA (Sovacool 2016). Adoption of 
electric vehicles in Norway and in cities in China have also been rapid 
(Rietmann and Lieven 2019; Li et al. 2020; Fridstrøm 2021). Examples 
in energy supply, include electrification in Kuwait, natural gas in the 
Netherlands, nuclear electricity in France and Sweden, combined 
heat and power in Denmark, renewable energy in Uruguay, and coal 
retirements in Ontario, Canada (Qvist and Brook 2015). Reasons 
that these exemplars could be applied more broadly in the future 
include: growing urgency on climate change, shifting motivation

from price response to proactive resource scarcity, and an increase in 
the likelihood of technological breakthroughs (medium confidence) 
(Sovacool 2016; Bazilian et  al. 2020). The emergence of smaller 
unit scale, granular technologies (described below) also creates the 
potential for faster system change (Trancik 2006; Grubler et al. 2018; 
Wilson et al. 2020a). Energy service prices and government actions 
that affect demand are critical to the speed and extent of energy 
transitions (Kramer and Haigh 2009). Reasons scholars consider for 
expecting a fast transition include: intentional policy and alignment 
with goals; globalisation which diversifies sources and integrates 
supply chains; collective action via the Paris Agreement; as well 
as bottom-up grassroots movements and private sector initiatives 
(Kern and Rogge 2016). Political support for change can also speed 
transitions (Burke and Stephens 2017; Stokes and Breetz 2018), as

can the credibility of transition-related targets (Li and Pye 2018; 
Rogge and Dütschke 2018).
Table 2.5 | Summary of reasons to expect a fast energy transition/slow transition.
Fast transition Slow transition
Evidentiary basis Technology and country cases over 50 years Historical global system over 200 years
Systems Complementary technologies enable integration Difficult integration with existing infrastructure
Economics Falling costs of nascent technologyMature incumbent technologies
Upfront costs and capital constraints
TechnologyDigitalisation and global supply chains
More abundant innovation
Granular technologyLong lifetimes of capital stock
Difficult to decarbonise sectors
ActorsProactive efforts for transition
Bottom-up public concern
Mobilised low-carbon interest groupsRisk-averse adopters
Attributes do not appeal to consumers
Rent-seeking by powerful incumbents
Governance Leaders catalyse faster change Collective action problems

257
The important role of leader countries is often missed when looking 
only at global aggregates (Meckling and Hughes 2018); leaders 
accumulate important knowledge, provide scaled market, and set 
positive examples for followers (medium confidence) (Schwerhoff 
2016; Buchholz et  al. 2019). In recent years, the conception of 
where leadership, climate-relevant innovation, and technology 
transfer originate has shifted to considering more meaningfully 
direct South-South and South-North forms of technology transfer, 
flows of capital, drivers for market access, origins of innovation, 
and other forms of cooperation (Urban 2018; Köhler et al. 2019). 
Recent evidence shows that South-South trade is enabling clean 
technology transfer (Gosens 2020). Leaders can initiate a process 
of ‘catalytic cooperation’ in which they overcome collective action 
problems and stimulate rapid change (Hale 2018). Similarly,

‘sensitive intervention points’  – targeted support of social 
movements, technologies, or policies themselves – can lead to rapid 
and self-sustaining change (Farmer et  al. 2019), such as support 
for photovoltaics in Germany in the 2000s and student climate 
activism in Europe in  2019. The focus on leadership, catalysts, 
and intervention points reflects a  systemic view of transitions 
that emphasises interactions and interdependence (Geels 2018; 
Meckling and Hughes 2018). Technological change has been at the 
core of transitions, but is best understood as part of a system in 
which social aspects are crucial (medium confidence) (Cherp et al. 
2018; Köhler et al. 2019; Overland and Sovacool 2020).
2.5.2.2 Reasons Why Transitions Will Occur 
at Historical Rates of Change
Recent work has also reasserted previous claims that the speed 
of a  low-carbon transition will follow historical patterns (low 
confidence). Broad transitions involve technological complexity,

time-consuming technological development, risk-averse adopters, 
high upfront costs, and low immediate individual adoption benefits, 
attributes that are not all present in the examples of rapid change 
described above (Grubler et al. 2016). Additional factors that slow 
transitions include: the need for the transition to occur globally, thus 
requiring nations with unequal economic resources and development 
circumstances to engage in near-universal participation; slow 
progress in recent decades; intermittence of renewables, and the time 
involved in building supporting infrastructure (Smil 2016); difficulty 
in decarbonising transportation and industry (Rissman et al. 2020); 
and material resource constraints (Davidsson et al. 2014).
2.5.3 Improvements in Technologies 
Enable Faster Adoption
Since AR5, multiple low-carbon technologies have shown dramatic 
improvement, particularly solar photovoltaic (PV), wind, and 
batteries (high confidence). The observed pace of these changes

and the likelihood of their continuation support the arguments in 
the previous section that future energy transitions are likely to occur 
more quickly than in the past (medium confidence).2.5.3.1 Technological Change Has Produced 
Dramatic Cost Reductions
A wide array of technologies shows long-term improvements in 
performance, efficiency, and cost. Among the most notable are 
solar PV, wind power, and batteries (high confidence) (Chapters 6 
and 16). The dynamics for PVs are the most impressive, having fallen 
in cost by a factor of 10,000 from the first commercial application 
on a  satellite in 1958 (Maycock and Wakefield 1975) to power 
purchase agreements signed in 2019 (IRENA 2020). Wind has been 
on a  nearly as steep trajectory (Wiser and Bolinger 2019) as are 
lithium-ion battery packs for electric vehicles (Nykvist and Nilsson 
2015; Service 2019). The future potential for PV and batteries seems 
especially promising given that neither industry has yet begun to

adopt alternative materials with attractive properties as the cost 
reductions and performance improvements associated with the 
current generation of each technology continue (medium confidence) 
(Kwade et al. 2018). A key challenge is improving access to finance, 
especially in developing country contexts, where the costs of financing 
are of crucial importance (Creutzig et al. 2017; Schmidt 2019).
2.5.3.2 Technological Change has Accelerated Since AR5
Figure  2.22 shows changes in the costs of four dynamic energy 
technologies. One can see rapid changes since AR5, cost data for 
which ended in 2010. Solar PV is by far the most dynamic technology, 
and its cost since AR5 has continued on its steep decline at about the 
same rate of change as before AR5, but now costs are well within 
the range of fossil fuels (high confidence) (Chapter  6). Very few 
concentrating solar power (CSP) plants had been built between the 
1980s and 2012. Since AR5, 4GW have been built and costs have

fallen by half. Onshore wind has continued its pace of cost reductions 
such that it is well within the range of fossil fuels. Offshore wind has 
changed the most since AR5. Whereas costs were increasing before 
AR5, they have decreased by 50% since. None of these technologies 
shows indications of reaching a limit in their cost reductions. Crucial 
to their impact will be extending these gains in the electricity and 
transportation sectors to the industrial sector (Davis et al. 2018).
2.5.3.3 Granular Technologies Improve Faster
The array of evidence of technology learning that has accumulated 
both before and since AR5 (Thomassen et al. 2020) has prompted 
investigations about the factors that enable rapid technology 
learning. From the wide variety of factors considered, unit size has 
generated the strongest and most robust results. Smaller unit sizes, 
sometimes referred to as ‘granularity’, tend to be associated with

faster learning rates (medium confidence) (Sweerts et  al. 2020; 
Wilson et al. 2020). Examples include solar PV, batteries, heat pumps, 
and to some extent wind power. The explanatory mechanisms 
for these observations are manifold and well established: more 
iterations are available with which to make improvements (Trancik 
2006); mass production can be more powerful than economies of 
scale (Dahlgren et al. 2013); project management is simpler and less 
risky (Wilson et al. 2020); the ease of early retirement can enable 
risk-taking for innovative designs (Sweerts et  al. 2020); and they 
tend to be less complicated (Malhotra and Schmidt 2020; Wilson

258
et al. 2020). Small technologies often involve iterative production 
processes with many opportunities for learning by doing, and have 
much of the most advanced technology in the production equipment 
than in the product itself. In contrast, large unit scale technologies – 
such as full-scale nuclear power, carbon capture and storage (CCS), 
low-carbon steel making, and negative emissions technologies such 
as bioenergy with carbon capture and storage (BECCS) – are often 
primarily built on site and include thousands to millions of parts, 
such that complexity and system integration issues are paramount 
(Nemet 2019). Despite the accumulating evidence of the benefits 
of granularity, these studies are careful to acknowledge the role 
of other factors in explaining learning. In a  study of 41  energy 
technologies (Figure 2.23), unit size explained 22% of the variation 
in learning rates (Sweerts et al. 2020) and a study of 31 low-carbon

technologies showed that unit size explained 33% (Wilson et  al. 
2020). Attributing that amount of variation to a  single factor is 
rare in studies of technological change. The large residual has 
motivated studies, which find that small-scale technologies provide opportunities for rapid change, but they do not make rapid change 
inevitable; a  supportive context, including supportive policy and 
complementary technologies, can stimulate more favourable 
technology outcomes (high confidence).
There is also evidence that small technologies not only learn 
but become adopted faster than large technologies (medium 
confidence) (Wilson et al. 2020b). Some of the mechanisms related 
to the adoption rate difference are associated with cost reductions; 
for example, smaller, less lumpy investments involve lower risk 
for adopters (Dahlgren et al. 2013; Wilson et al. 2020b). The shorter 
lifetimes of small technologies allow users to take advantage of new

performance improvements (Knapp 1999) and access a  large set 
of small adopters (Finger et al. 2019). Other mechanisms for faster 
adoption are distinctly related to markets: modular technologies can 
address a wide variety of niche markets (Geels 2018) with different 
willingness to pay (Nemet 2019) and strategically find protected 
niches while technology is maturing (Coles et al. 2018).
Adoption (millions of EVs)Cost (USD 2020 MWh–1)
Li-on battery packs (USD2020 kWh–1)
Market cost
Adoption (note different scales) Fossil fuel cost (2020)AR5 (2010)Batteries for passenger 
electric vehicles (EVs) Photovoltaics (PV)Concentrating 
solar power (CSP) Onshore wind Offshore windThe unit costs of some forms of renewable energy and of batteries for passenger EVs have fallen, 
and their use continues to rise.
12001600
800
400
0 0 0150300450
2000 2020600
150300450600
2000 2020 2000 2010 2010 2010 20200
2000 2020Adoption (GW)
0200400600800
2000 2020 20100

2000 2020 2010 2010
2000 2020 2010010203040
2000 2020 201002468
2000 2020 2010
Share of electricity 
produced in 2020: 3%
produced in 2020: 6%
produced in 2020: <1%
Share of passenger 
vehicle ﬂeet in 2020: 1%
Figure  2.22 | Unit cost reductions and use in some rapidly changing mitigation technologies.  The top panel shows global costs per  unit of energy 
(USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range 
between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding to 
USD55–148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. For batteries,

costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs per MWh of 
electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made. However, it 
does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that may modify the 
overall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each technology, in GW 
of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5.

Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production 
(for PV , onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, 
for the same amount of installed capacity, wind produces about twice as much electricity as solar PV . {2.5, 6.4} Renewable energy and battery technologies were selected as 
illustrative examples because they have recently shown rapid changes in costs and adoption, and because consistent data are available. Other mitigation options assessed 
in the report are not included as they do not meet these criteria.

259
2.5.4 Rapid Adoption Accelerates Energy Transitions
The transition to a  more sustainable energy system depends not 
just on improvement in technologies, but also on their widespread 
adoption. Work since AR5 has also substantiated the bidirectional 
causal link between technology improvement and adoption. Cost 
reductions facilitate adoption, which generates opportunities for 
further cost reductions through a  process of learning by doing 
(medium confidence). The rate of adoption is thus closely related to 
the speed at which an energy transition is possible.
Results of integrated assessment models (IAMs) show that scale-up 
needs are massive for 2°C scenarios. Using logistic growth rates of 
energy shares as in previous work (Wilson 2012; Cherp et al. 2021), 
most of these technologies include annual adoption growth rates of 
20% in the 2020s and 2030s, and are in line with recent adoption

of wind and solar. However, it is important to realise that IAMs 
include faster adoption rates for some mitigation technologies than 
for others (Peters et al. 2017). Growth rates in IAMs for large-scale 
CCS – biomass, coal, and gas – are between 15–30% (25th and 75th 
percentiles) (Figure 2.24). So few plants have been built that there 
is little historical data to analyse expected growth; with only two 
full-scale CCS power plants built and a 7% growth rate, if including 
industrial CCS. In contrast, IAMs indicate that they expect much 
lower rates of growth in future years for the technologies that have 
been growing fastest in recent years (wind and solar), without strong 
evidence for why this should occur.The overall pattern shows that IAMs expect growth in small-scale 
renewables to fall to less than half of their recent pace, and large-
scale CCS to more than double from the limited deployment assessed 
(high confidence). The emerging work since AR5 showing the rapid

adoption and faster learning in small-scale technologies should 
prompt a keener focus on what technologies the world can depend 
on to scale up quickly (Grubb et al. 2021). The scenario results make 
it quite clear that climate stabilisation depends on rapid adoption of 
low-carbon technologies throughout the 2020–2040 period.
Figure 2.23 | Learning rates for 41 energy demand, supply, and storage technologies.  Source: Sweerts et al. (2020).

260
2.6 Behavioural Choices and Lifestyles
2.6.1 Introduction
This section synthesises how behavioural choices, lifestyles, and 
consumption preferences affect energy use and emissions. Household 
consumption is the largest component of a country’s gross domestic 
product (GDP) and the main contributor to greenhouse gas (GHG) 
emissions through direct energy consumption for heating and 
cooling or private transportation, and indirectly through carbon 
emitted during production of final consumption items. There is 
great variation in individual, group and household behaviour and 
consumption patterns within and between countries and over time. 
A  number of factors affect people’s consumption patterns and 
associated carbon emissions, such as: socio-demographics; socio-
economic status; infrastructure and access to public services; the 
regulatory framework; availability, affordability and accessibility of

more or less sustainable choices on markets; and individual values 
and preferences (Dietz et al. 2009).
Carbon footprints vary between and within countries and show an 
uneven distribution because of differences in development levels, 
economic structure, economic cycle, available public infrastructure, 
climate and residential lifestyles (Bruckner et  al. 2021). Similar 
emission characteristics can also be found within a country – see, 
for China; Feng et al. (2013); for the USA: Pizer et al. (2010); Feng 
et  al. (2013); Miehe et  al. (2016); Hubacek et  al. (2017b); Wang 
et al. (2018); for Brazil: Sanches-Pereira et al. (2016); and for Latin 
American countries: Zhong et al. (2020).In western countries, the largest contribution to the household 
carbon footprint is from transportation, housing, and consumption 
of food (Druckman and Jackson 2015). The joint contribution 
of these three items varies in different countries, depending on

consumption patterns, and account for 58.5%, on average, in EU25 
countries (Tukker and Jansen 2006). However, different countries, 
and regions within countries, may have different emission patterns 
due to differences in income, lifestyle, geography, infrastructure, 
political and economic situation. For example, the main contributors 
to the average US household is private transport (19.6%), followed 
by electricity (14.8%) and meat (5.2%) (Jones and Kammen 2011), 
while UK households have 24.6% emissions on energy and housing, 
13.7% emissions on food, and 12.2% emissions on consumables 
(Gough et al. 2011). A study of 49 Japanese cities found that energy 
(31%), food (27%), and accommodation (15%) were the largest 
sources of household emissions (Long et al. 2017). An investigation 
of Japan’s household emissions found that energy, food, and utility 
are the three main emissions sources, but their shares are dependent

on age (Shigetomi et al. 2014). See Section 12.4 (Chapter 12) and 
Box 5.4 (Chapter 5) for a more in-depth discussion on food systems 
and dietary shifts towards lower emission food.
In terms of rapidly growing economies, China is the most extensively 
researched country. China’s household emissions were primarily 
derived from electricity and coal consumption, as well as residents’ 
consumption of emission-intensive products, such as housing (33.4%), 
food (23.6%), private transportation and communication (14.8%) 
(Wang et al. 2018). Space heating was the largest contributor among 
various daily energy uses in northern cities (Yang and Liu 2017). In 
comparison, Indonesian rural households have a  larger emission 
Figure 2.24 | Growth of key technologies (2020–2040) in Paris-consistent mitigation scenarios compared to historical growth. Comparisons of historical

growth (grey bars) to growth in 2020–2040 mitigation scenarios (dots). Values on the vertical axis are logistic annual growth rates for share of each technology in electricity 
supply. Horizontal arrangement of dots within technology categories indicates the count of scenarios at each growth rate. Source: data on scenarios from Chapter 3; historical 
data from BP (2021).

261
Country (sorted by increasing per capita GDP)
DEV EEA LAM APC ME AF
1 10 100Norway
Denmark
Sweden
USA
Ireland
Austria
Finland
Belgium
Germany
France
Cyprus
Spain
Greece
Slovenia
Portugal
Malta
Czech Republic
Slovakia
Estonia
Croatia
Hungary
Poland
Lithuania
Latvia
Turkey
Romania
Bulgaria
Serbia
Bosnia and Herzegovina
Albania
Ukraine
Azerbaijan
Macedonia, FYR
Armenia
Kyrgyz Republic
Tajikistan
Brazil
Mexico
Colombia
Peru
Jamaica
El Salvador
Guatemala
Honduras
Bolivia
Nicaragua
Maldives
Thailand
China
Fiji
Indonesia
Sri Lanka
Mongolia
Bhutan
Papua New Guinea
India
Vietnam
Lao PDR
Pakistan
Cambodia
Nepal
Iraq
Jordan
Yemen, Rep.
Gabon
Mauritius
Namibia
Swaziland
Cabo Verde
Congo, Rep.
Morocco
Egypt, Arab. Rep.
Nigeria
Zambia
Djibouti
Ghana
Côte d’Ivoire
Mauritania
Lesotho
São Tomé and Principe
Cameroon
Senegal
Kenya
Chad
Benin

Tanzania
Mali
Uganda
Gambia, The
Rwanda
Togo
Malawi
Sierra Leone
Niger
Ethiopia
Liberia
Burundi
Per capita carbon footprint (tCO2/pop)Income category: Average pc footprint Lowest
LowestLow
LowMiddle
MiddleHigher Highest
High
Figure 2.25 | Carbon footprints per capita income and expenditure category for 109 countries ranked by per capita income (consumption-based emissions).

262
share on food and a much smaller share on services and recreation 
than urban households (Irfany and Klasen 2017). Urban Indonesian 
households have a much larger share of transport-related emissions 
(Irfany and Klasen 2017). Analysis from the Philippines shows that, on 
average, households in urban areas emit twice as much as rural ones 
because of much lower direct energy use in homes and for transport 
in rural areas (Serino 2017). In other emerging economies, such 
as India, Brazil, Turkey and South Africa, a high share of transport-
related carbon emissions among urban middle- and high-income 
households is evident (Huang and Tian 2021).
2.6.2 Factors Affecting Household Consumption 
Patterns and Behavioural Choices
Households’ carbon emissions are closely linked to activities and 
consumption patterns of individuals and as a group in households. 
Individual and group behaviour, in turn, is shaped by economic,

technological, and psychological factors, social contexts (such as 
family ties, friends and peer pressure) and cultural contexts (social 
identity, status, and norms) as well as the natural environment 
(number of hot and cold days) and physical infrastructure, or 
geography (Jorgenson et  al. 2019). For example, a  city with an 
excellent bicycle infrastructure will make it safer and easier for 
citizens to become highly mobile by using their bikes; a city that 
has less density and is dominated by automobile infrastructure 
induces more people to travel by car (Chapters 8 and 10). 
As  a  consequence, many climate relevant consumption acts are 
not consciously decided on or deliberately made part of a lifestyle, 
but are strongly influenced by the factors listed above. Chapter 5 
provides a  more in-depth discussion on behavioural drivers and 
examples of behavioural interventions and policies that can be 
used to reduce emissions.

Demographic characteristics such as age, sex, and education 
constitute an important set of determinants influencing emissions 
patterns. People of different genders have different consumption 
patterns. For example, men tend to consume more food (especially 
meat) than women, leading to higher food-related emissions. Also, 
men spend more money on vehicles and driving (Wang et al. 2018). 
Similar evidence has been found in Germany, Greece, Norway, and 
Sweden, where men’s energy use is 8%, 39%, 6%, and 22% higher 
than women’s, respectively (Räty and Carlsson-Kanyama 2010).
Income. Due to the differences that shape individuals’ consumption 
patterns, there are enormous differences in the associated carbon 
footprints – with income being one of the most important predictors. 
Globally, households with income in the top 10% – income higher 
than USD23.03 purchasing power parity (PPP) per capita per day – 
are responsible for 34–45% of GHG emissions, while those in the

bottom 50% – income less than USD2.97 PPP per capita per day – are responsible for only 13–15% of emissions, depending on the study 
(Chancel and Piketty 2015; Hubacek et al. 2017b) (Figure 2.25). The 
average carbon footprint of the high household incomes is more than 
an order of magnitudes larger than that of the lowest expenditure 
group (Feng et al. 2021). For example, Zhang et al. (2016) analysed 
the impact of household consumption across different income 
households on CO 2 emissions in China and concluded that the impact 
on CO 2 emissions generated by urban households’ consumption is 
1.8 times as much as that of rural ones. High-income households 
have higher emissions related to transport and entertainment – such 
as recreational expenditure, travel, and eating out – than low-income 
households. Low-income households tend to have a  larger share 
on necessities such as fuel for heating and cooking (Kerkhof et al.

2009). Figure 2.25 shows the carbon footprint per capita ranked by 
per capita income.
Age. The effect of population ageing on emissions is contested in 
the literature. Ageing when accompanied by shrinking household 
size and more energy-intensive consumption and activity patterns 
results in increased emissions. However, an ageing labour force can 
also dampen economic growth and result in less energy-intensive 
activity such as driving, which decreases emissions (Liddle and 
Lung 2010; Liddle 2011). Ageing of the population characterises the 
demographic transition in both developed and developing countries. 
The implications of ageing for emissions depend on labour force 
participation of the elderly and differences in the consumption and 
investment patterns of different age groups (O’Neill et  al. 2012). 
Analysis using panel macro data from OECD countries suggests that 
shifts in age and cohort composition have contributed to rising GHG

emissions since the 1960s (Menz and Welsch 2012; Nassen 2014). 
Household-level data over time for the USA provides evidence 
that residential energy consumption increases over the lifetime 
of household members, largely due to accompanying changes in 
household size (Estiri and Zagheni 2019). Similar insights emerge 
from Japan, where analysis shows that those in their 70s or older, 
a group that is growing in size in Japan, have higher emissions than 
other age groups (Shigetomi et al. 2014, 2018, 2019). Recent analysis 
from China suggests that the shift to smaller and ageing households 
is resulting in higher carbon emissions because of the accompanying 
time-use and consumption shifts (Yu et al. 2018; Li and Zhou 2019). 
An increase in the dependency ratio  – that is, the proportion of 
children aged under 15 and people over 65 relative to the working-
age population  – in other analyses, has been shown to  lead to

reduced CO 2 emissions in China (Wei et al. 2018; Li and Zhou 2019). 
Implications of the nature of this relationship are important to policy 
discussions of working hours and retirement age that are likely to 
have an influence on emissions. For example, children and youth 
tend to emit more education-related emissions than adults (Han 
et al. 2015). Older people tend to have higher emissions related to 
heating and cooling being more sensitive to temperature (Meier and 
Rehdanz 2010).Figure 2.25 (continued): Carbon footprints per capita income and expenditure category for 109 countries ranked by per capita income (consumption-
based emissions).  Notes: countries and income categories are dependent on data availability. Light blue dots represent income quintiles (lowest, low, middle, higher, 
and highest) of EU countries and the USA. Yellow dots are for the developing country group provided by the World Bank for four expenditure categories: lowest, low, middle

and higher (Hubacek et al. 2017b). Dark blue diamonds represent average per capita carbon footprints. Countries are ranked from the lowest per capita income (bottom) to 
the highest income (top) within each country group. Countries are grouped using the IPCC’s six high-level classification categories. Footprint values for higher income groups 
in the World Bank data are less reliable.

263
Household size. Per capita emissions tend to decrease with family 
size, as living together becomes more energy efficient (Qu et  al. 
2013). The household size in most countries is decreasing (Liu et al. 
2011), but the degree differs across countries – for example, there is 
a higher decrease rate in China than in Canada and the UK (Maraseni 
et al. 2015). The evidence shows that shifts to smaller households are 
associated with larger per-capita footprints (Liddle and Lung 2014; 
Underwood and Zahran 2015; Ivanova et al. 2017; Wiedenhofer et al. 
2018), at least in developed countries (Meangbua et al. 2019).
Urban living. The carbon footprint of individuals and households 
is also significantly influenced by urban-rural differences (Ivanova 
et al. 2018; Wiedenhofer et al. 2018). In some cases, the difference 
can be explained by the effect of locational and spatial configuration

characteristics, such as levels of compactness/density, centrality, 
proximity and ease of access to services. In all these parameters, urban 
areas score higher compared with rural or peri-urban (outlying and 
suburban) areas, thus influencing household emissions in different 
ways. Urban households tend to have higher emissions than rural 
households (O’Neill et al. 2010; Liu et al. 2011), but with adifferent 
energy and consumption structures. For example, rural households 
have more diverse energy inputs, such as biomass, biogas, solar, wind, 
small hydro and geothermal in addition to coal (Maraseni et al. 2016).
In terms of indirect emissions, urban households have more service-
related emissions  – such as from education and entertainment  – 
than rural households, while rural households tend to have higher 
emissions related to food consumption or transportation (Büchs and 
Schnepf 2013; Maraseni et al. 2016) but this is strongly dependent

on the specific situation of the respective country, as in poorer 
regions, rural transport might be mainly based on public transport 
with lower carbon emissions per capita. Centrality and location also 
play a  role on the level of urban household emissions. Studies on 
US  households  found that residents in the urban core have 20% 
lower household emissions than residents in suburbs, which show 
a large range of household emissions (from –50% to +60%) (Kahn 
2000; Jones and Kammen 2014). Higher population density tends to 
be associated with lower per capita emissions (Liddle and Lung 2014; 
Liu et al. 2017).
Location choices are a significant contributor to household emissions. 
Suburbanites tend to own larger, spacious homes with larger heating 
and cooling requirements. Commuting distance and access to public 
transportation, recreation areas, city centres, public services, and 
shops are other important neighbourhood-specific determinants

of carbon emissions (Baiocchi et  al. 2010) (see more on this in 
Chapters 8 and 10).
Time use. A  study on the emissions implications of time use 
(Wiedenhofer et  al. 2018) found that the most carbon-intensive 
activities are personal care, eating and drinking and commuting. 
Indirect emissions are also high for repairs and gardening. In 
contrast, home-based activities, such as sleep and resting, cleaning 
and socialising at home, have low carbon intensities per  hour of 
time use. The same study also found that households in cities and 
areas with higher incomes tend to substitute personal activities 
for contracted services, thus shifting away from households to the service sector (Wiedenhofer et  al. 2018). Improvements in the 
efficiency of time or resource use are diminished by rebound effects 
that have been shown to reduce emissions savings by 20–40% on 
average (Gillingham et  al. 2015), while other authors argue that,

potentially, the size of the rebound effect could be larger (Saunders 
2015) (see more coverage of the rebound effect in Chapters 9 and 
16). Lifestyle shifts brought about by using information technologies 
and socio-technological changes are inducing alterations in people’s 
daily activities and time-use patterns.
The reduction of working hours is increasingly discussed as an 
approach to improve well-being and reduce emissions (Fitzgerald 
et al. 2015, 2018; Melo et al. 2018; Wiedenhofer et al. 2018; Smetschka 
et  al. 2019). For instance, analysis of differences in working hours 
across the USA for the period 2007–2013 shows that there is a strong 
positive relationship between carbon emissions and working hours. 
This relationship holds, even after controlling for other differences in 
political, demographic and economic drivers of emissions (Fitzgerald 
et  al. 2018). In other analyses, this relationship is seen to hold in

both developed and developing countries (Fitzgerald et  al. 2015). 
One recent study, however, finds evidence of nonlinear relationships 
between working time and environmental pressure in EU15 countries 
between 1970 and 2010, in cases where non-work time is spent 
instead in carbon-intensive leisure activities (Shao and Shen 2017).
Social norms. Evidence from experiments in the US shows that 
social norms cannot only help in reducing a  household’s absolute 
level of electricity use but also shift the time of use to periods when 
more renewable electricity is in the system (Horne and Kennedy, 
2017). Analysis from Sweden shows that adoption of sustainable 
innovations like solar panels is influenced by perceived behaviour 
and expectations of others (Palm, 2017). Similar conclusions emerge 
from analysis in the Netherlands on the adoption of electric vehicles 
and smart energy systems (Noppers et al. 2019).
Broader contextual factors and cultural trends towards consumerism,

individualisation and defining self-worth through conspicuous 
consumption can drive emissions up (Chancel and Piketty, 2015). 
However, cohort and generational shifts can drive emissions down. 
For instance, evidence, from millennials in the OECD shows that fewer 
younger people have driving licenses compared to older generations 
(Kuhnimhof et al. 2012). Similar, findings are evident from analysis for 
the US, where changing attitudes, decreased employment and rising 
virtual mobility explain decreased travel by Millennials (McDonald, 
2015). Analysis for France shows that baby boomers are higher 
emitters than other generations (Chancel, 2014). A change in social 
norms is taking place with the spread of the sharing economy by 
which consumers share or borrow goods from other consumers. 
Sharing opportunities are more advanced within the mobility sector 
(Greenblatt and Shaheen, 2015). Successful car and bike sharing

have rapidly expanded in countries such as China, Indonesia, Mexico, 
Brazil and Turkey. Technology and data advances are currently barriers 
to spreading of sharing in low- and lower middle-income cities but 
the potential offered by these technologies to allow poor countries 
to leapfrog to more integrated, efficient, multimodal transport 
systems is important (Yanocha et  al. 2020). Despite this potential 
it is unclear how much shared mobility contributes to transport

264
decarbonisation or to make it worse as it takes away riders from 
public transit (ITF , 2019). The evidence so far shows that the potential 
positive impacts of shared mobility with pooled rides in lowering 
travel costs, abating congestion, and reduced GHG emissions have 
not materialised to date (Merlin, 2019) (Chapter 5).
Education and environmental knowledge. A positive relationship 
was found between general and carbon-specific knowledge and 
the attitude towards carbon-specific behaviours in US consumers 
(Polonsky et  al. 2012). One example, pertaining to students, 
found that the gain of environmental knowledge resulted in more 
environmentally favourable attitude among these high school 
students (Bradley et  al. 1999). A  comparison across states in the 
USA, for example, shows that environmental awareness can be 
a mitigating factor of territorial GHG emissions (Dietz et al. 2015).

A 1% increase in ‘environmentalism’ – defined as the ‘environmental 
voting record of the state’s Congressional delegation’ (Dietz et  al. 
2015) – leads to a 0.45% decrease in emissions.
Environmental knowledge is not always directly translating into 
decreased ecological footprint (Csutora 2012). While pro-environmental 
action is lagging behind, research shows that this is not caused by 
people undervaluing the environment, but rather by people structurally 
underestimating how much others care (Bouman and Steg 2019). Other 
evidence shows that there are multiple causal pathways through which 
a more educated population can effect emissions, some of which may 
be positive and others negative (Lutz et al. 2019). A more educated 
population is more productive and can drive higher economic growth 
and therefore emissions (Lenzen and Cummins 2013). Moreover, 
education that is designed to specifically inform decision makers of

the impacts of their decisions and provide behavioural nudges can be 
a way to reduce emissions (Duarte et al. 2016).
Status competition. As part of a  larger consumer society and 
consumer culture, based on consumer-oriented lifestyles, products 
frequently provide a  source for identity and fulfilment (Stearns 
2001; Baudrillard 2017; Jorgenson et  al. 2019). People pursue 
cultural constructs such as status, comfort, convenience, hygiene, 
nutrition, and necessity. Consumption is, by and large, not an end 
in itself but a  means to achieve some other end, and those ends 
are diverse and not necessarily connected to one another (Wilk 
2010). This shows that consumption patterns cannot be sufficiently 
understood without also considering the context – for example, the 
cultural and social contexts leading to status competition and status-
related consumption (Veblen 2009; Ehrhardt-Martinez, K. et al. 2015; 
Wilk 2017). Status seeking can work to reduce emissions when

‘green products’ such as an electric car or photovoltaics on the roof 
become a sign for high-status (Griskevicius et al. 2010). It also can 
work to increase emissions through visible and high-carbon intensive 
consumption items, such a larger homes, fuel-inefficient sport utility 
vehicles (SUVs), and long-distance vacations (Schor 1998), driven 
by a  notion of having ‘to keep up with the Joneses’ (Hamilton 
2011). This can lead to formation of new habits and needs, where 
products and services become normalised and are quickly perceived 
as needed, reinforced through social networks and advertisement, 
making it psychologically easy to convert a luxury item to a perceived 
necessity (Assadour 2012). For example, the share of adults who consider a microwave a necessity was about one-third in 1996 but 
had increased to more than two-thirds in 2006, but retreated in 
importance during the recession years 2008–2009 (Morin and Taylor

2009). Similar ups and downs have been observed for television 
sets, air conditioning, dishwashers or clothes dryers. (Druckman and 
Jackson 2009). Basic needs and luxury items are subject to change 
over one’s lifetime and in relation to others (Horowitz 1988). This 
shows that the boundaries of the public’s luxury-versus-necessity 
perceptions are malleable (Morin and Taylor 2009).
Inequality. Global inequality within and between countries has 
shifted over the last decade’s expanding consumption and consumer 
culture (Castilhos and Fonseca 2016; Alvaredo et al. 2018; Short and 
Martínez 2020). The rise of income of middle-class in countries, mostly 
in Asia – for example, China, India, Indonesia and Vietnam – and the 
stagnating incomes of the middle classes in developed economies 
reduced between countries’ income differences; meanwhile, the 
population under extreme poverty (a threshold of USD1.9 per person 
per day) is now concentrated in Sub-Saharan Africa and South Asia

(Milanović 2016). A  major gap between top and bottom incomes 
occurred in parallel within countries. Since 1980, the top 1% richest 
individuals in the world captured twice as much growth as the 
bottom 50% individuals (Friedman and Savage 2017; Alvaredo et al. 
2018). The influence of these dual inequality trends on lifestyles, 
new consumption patterns and carbon emissions at regional, local 
and global scale are large and have led to the fastest growth of 
global carbon emissions, in particular, for fast emerging economies 
(Sections 2.2. and 2.3). Emissions remain highly concentrated, with 
the top 10% per capita emitters contributing to between 35–45% 
of global emissions, while the bottom 50% emitters contribute to 
13–15% of global emissions (Hubacek et  al. 2017a). Furthermore, 
the top 1% of income earners by some estimates could have an 
average carbon footprint 175 times that of an average person in the

bottom 10% (Otto et al. 2020). The top 10% high emitters live in all 
continents, and one-third of them live in emerging countries (Chancel 
and Piketty 2015; Hubacek et al. 2017a; Semieniuk and Yakovenko 
2020). Mitigation pathways need to consider how to minimise the 
impacts of inequality on climate change and the different mechanisms 
and effects coming into play between the inequality of income and 
emissions (Baek and Gweisah 2013; Berthe and Elie 2015; Hao et al. 
2016; Grunewald et al. 2017) (Section 2.4.3).
Inequality trends catalyse impact at a  demand level, mobilising 
rapid lifestyles changes, symbolic consumption and ideals of 
material improvements and upward mobility (Castilhos et al. 2017) 
and emulation of high-carbon emissions intensive lifestyle of the 
wealthy (Gough 2017). Decoupling energy use and emissions from 
income growth and the decarbonisation of energy services have 
not counteracted these trends (Section 2.4.1). Alternative options to

deal with carbon inequality, such as sharing global carbon emissions 
among high emitters (Chakravarty et  al. 2009; Chakravarty and 
Tavoni 2013) or addressing the discourse of income distribution 
and the carbon intensity of high emitters lifestyles (Hubacek et al., 
2017b; Gössling 2019; Otto et al. 2019) are met with caution that 
such alternatives may necessitate hard-to-implement institutional 
changes (Semieniuk and Yakovenko 2020). Growing inequality within 
countries may make recomposition of emission intensive consumption

265
more difficult and, it may also exacerbate redistribution and social 
cohesion dilemmas (Gough 2017; Römpke et  al. 2019). Climate 
mitigation action has different motivational departures in unequal 
context. An emerging global ‘middle class’ strengthens consumption 
at the margin as evidence by first-time purchases of white goods 
with likely impacts on energy demand (Wolfram et  al. 2012), and 
with a warming climate, the increased use of air conditioning (Davis 
and Gertler 2015). Inequality may affect the willingness of rich and 
poor to pay for environmental goods or accept policies to protect 
the environment (Baumgärtner et  al. 2017). Unequal departure 
for action is strongly manifested in cities of all sizes in developing 
countries with low-income urban residents hardest hit in lock-in 
situations such as lack of access to transportation and jobs (Altshuler 
2013; Mattioli 2017), lack of green spaces (Joassart-Marcelli et al.

2011), poor access to waste collection (King and Gutberlet 2013) 
and to energy and clean water provision. The exacerbation of 
these conditions constrains the feasibility for achieving emissions 
reductions through lifestyle or behavioural changes alone (Baiocchi 
et al. 2010; Oxfam 2015). High inequality limits mitigation efforts and 
conversely, advancing mitigation should not contribute to deepen 
existing inequalities (Rao and Min 2018; Saheb et  al. 2019). It is 
critically important to account for varying demands and affordability 
across heterogeneous household groups in access to quality energy, 
education, health, decent jobs and services, while recomposing 
consumption and balancing societal trade-offs via policies to boost 
the inclusion of low-income and energy-poor population groups 
(Pachauri et al. 2013). Further, there is a need to reduce inequalities 
and improve the capabilities people have to live the lives they value

(Sen 1999; Gough et al. 2011; Gough, 2017; Aranoff et al. 2019).
2.7 Emissions Associated With Existing 
and Planned Long-lived Infrastructure
2.7.1 Introduction: Clarification of Concepts
Carbon lock-in can be understood as inertia in a system that limits 
the rate of transformation by a path-dependent process (Seto et al. 
2016). For example, long lifetimes of infrastructures such as power 
plants, roads, buildings or industrial plants may influence the rate 
of transformation substantially and lock societies into carbon-
intensive lifestyles and practices for many decades (Unruh 2000, 
2002; Unruh and Carrillo-Hermosilla 2006; Grubler 2012; Seto et al. 
2016; Sovacool 2016). Infrastructure stock evolution depends on 
technological and economic factors, but also on institutional and 
behavioural ones that are often mutually reinforcing. That is, physical 
infrastructure such as the built environment of urban areas can shape

people’s behaviour and practices, which in turn change the demand 
for such infrastructure and lock-in energy demand patterns (Banister 
et al. 1997; Makido et al. 2012; Creutzig et al. 2016; Seto et al. 2016; 
Shove and Trentmann 2018).
There is a broad literature on carbon lock-in related to infrastructure 
that has analysed different geographical scales and sectors, with 
a  strong focus on the power sector (Fisch-Romito et  al. 2020). 
Available quantifications differ in the time frames of analysis that can 
be classified as backward-looking, static for a given year, or forward-looking using scenarios (Fisch-Romito et  al. 2020). Quantifications 
also differ in the indicators used to describe carbon lock-in. Literature 
has assessed how delays in climate policy affect the evolution of 
fossil-fuel infrastructure stock in the short term (Bertram et al. 2015; 
Kefford et  al. 2018; McGlade et  al. 2018), overall mitigation costs

(Riahi et al. 2015; Luderer et al. 2016), or the transition risks from 
premature retirements or underutilisation of existing assets (Iyer 
et al. 2015; Johnson et al. 2015; Lane et al. 2016; Luderer et al. 2016; 
Farfan and Breyer 2017; van Soest et al. 2017; Kefford et al. 2018; Cui 
et al. 2019; Fofrich et al. 2020; Malik et al. 2020; H. Wang et al. 2020; 
Pradhan et al. 2021). Only a few authors have relied on indicators 
related to institutional factors such as technology scale or employment 
(Erickson et al. 2015; Spencer et al. 2018). Complementary literature 
has explored how the sheer size of the world’s fossil fuel reserves (and 
resources) and owners’ financial interests could contribute to supply-
side dynamics that sustain the use of fossil fuels (Jewell et al. 2013; 
Jakob and Hilaire 2015; McGlade and Ekins 2015; Bauer et al. 2016; 
Heede and Oreskes 2016; Welsby et al. 2021).
One way of quantifying potential carbon lock-in is to estimate the

future CO 2 emissions from existing and planned infrastructure (Davis 
et al. 2010; Davis and Socolow 2014) based on historic patterns of 
use and decommissioning. Such estimates focus on CO 2 emissions 
from operating infrastructure and do not comprise any upstream 
or downstream emissions across the lifecycle, which are provided 
elsewhere in the literature (Müller et al. 2013; Creutzig et al. 2016; 
Krausmann et al. 2020; Fisch-Romito 2021). Estimates tend to focus 
on energy, while other areas, such as the agricultural sector are 
usually not covered. Another strand of literature quantifies lock-in by 
estimating fossil-fuel related CO 2 emissions that are hard to avoid in 
future scenarios using integrated assessment models (IAMs) (Kriegler 
et al. 2018b; Luderer et al. 2018). The remainder of this chapter will 
assess potential carbon lock-in through those two related strands 
2.7.2 Estimates of Future CO 2 Emissions 
From Long-lived Infrastructures

Table  2.6 summarises studies that apply an accounting approach 
based on plant-level data to quantify future CO 2 emissions from long-
lived fossil fuel infrastructure (Davis et al. 2010; Davis and Socolow 
2014; Rozenberg et  al. 2015; Edenhofer et  al. 2018; Pfeiffer et  al. 
2018; Cui et al. 2019; Smith et al. 2019; Tong et al. 2019; Pradhan 
et al. 2021). Differences between studies arise in the scope of the 
infrastructure covered (including resolution), the inclusion of new 
infrastructure proposals, the exact estimation methodology applied 
as well as their assessments of uncertainties. Other studies provide 
analysis with a sectoral focus (Bullock et al. 2020; Vogl et al. 2021) or 
with a regional focus on the power sector (Shearer et al. 2017, 2020; 
González-Mahecha et al. 2019; Grubert 2020; Tao et al. 2020).
Assuming variations in historic patterns of use and decommissioning, 
comprehensive estimates of cumulative future CO 2 emissions from

current fossil fuel infrastructures are 720 (550–910) GtCO 2 (Smith 
et al. 2019) and 660 (460–890) (high confidence) (Tong et al. 2019) 
(Table  2.6 and Figure  2.26). This is about the same size as the

266
Table 2.6 | Comparing cumulative future CO 2 emissions estimates from existing and proposed long-lived infrastructures by sector.  Future CO 2 emissions estimates are reported from the ‘year of dataset’. Note that, in some 
cases, the totals may not correspond to the sum of underlying sectors due to rounding (based on Tong et al. 2019). Initial estimates of future CO 2 emissions from fossil fuel infrastructures by Davis et al. (2010) are considerably lower than more 
recent estimates by Smith et al. (2019) and Tong et al. (2019) due to substantial growth in fossil energy infrastructure, as represented by more recent data. Estimates presented here are rounded to two significant digits.
Davis et al.
(2010)Davis and Socolow
(2014)Rozenberg et al. 
(2015)Edenhofer et al. 
(2018)Pfeiffer et al.
(2018)Smith et al.
(2019)Tong et al. (2019) Cui et al. (2019)
GtCO 2Year of 
datasetGtCO 2Year of 
datasetGtCO 2Year of

dataset
ExistingElectricity 220 2009 310 2012 – – – – 310 2016350
(260–450)2009*360
(240–490)2018 – –
Coal 2009 210 2012 – – 190 2016 220 2016 – –260
(180–360)2018 340 2017
Gas, oil, and other fuels 2009 100 2012 – – – – 88 2016 – –98
(65–140)2018 – –
Industry 100 2009 – – – – – –150
(120–190)2009160
(110–220)2017 – –
Transport 120 2009 – – – – – –92
(73–110)201764
(53–75)2017 – –
Residential, commercial, and 
other energy53 2009 – – – – – –120
(91–160)2009*74
(52–110)2018 – –
All sectors500
(280–700)660
(370–890)2013 – – – –720
(550–910)–660
(460–890)– – –
ProposedElectricity – – – – 270 2016 – –190
(140–230)2018 – –
Coal – – 150 2016 210 2016 – –97
(74–120)2018 180 2017
Gas, oil, and other fuels – – – – 60 2016 – –91
(68–110)2018 – –
All sectors + proposed electricity850
(600–1100)

267
overall cumulative net CO 2 emissions until reaching net zero CO 2 of 
510 (330–710) Gt in pathways that limit warming to 1.5°C with no 
or limited overshoot (Chapter  3). About 50% of cumulative future 
CO2 emissions from current fossil fuel infrastructures come from the 
power sector and 70% of these (or about 40% of the total) are from 
coal plants only. Like global annual CO 2 emissions (Friedlingstein 
et  al. 2020; Peters et  al. 2020), future CO 2 emissions  from fossil 
fuel infrastructures have increased over time  – that is, future CO 2 
emissions from fossil fuel infrastructure additions in a  given year 
still outgrow ‘savings’ from infrastructure retirements (Davis and 
Socolow 2014; Tong et  al. 2019). This could add further inertia to 
the system as it may require more and faster retirement of fossil-
fuel based infrastructures later, and lead to higher costs for meeting

climate goals (e.g., Bertram et al. 2015; Johnson et al. 2015).
Estimates of total cumulative future CO 2 commitments from proposed 
infrastructure focus only on the power sector due to data availability 
(Table 2.6 and Figure 2.26). Infrastructure proposals can be at various 
stages of development involving very different probabilities of 
implementation. About one-third of the currently proposed projects 
are more probable as they are already under construction (Cui 
et al. 2019). Pfeiffer et al. (2018) and Tong et al. (2019) assess the 
cumulated CO 2 emissions from proposed infrastructure in the entire 
power sector at 270 GtCO 2 and 190 GtCO 2 respectively. Estimates of 
CO2 emissions implications for new coal power infrastructure plans 
are more frequent (Edenhofer et  al. 2018; Pfeiffer et  al. 2018; Cui 
et al. 2019; Tong et al. 2019) ranging between 100 and 210 GtCO 2. 
Differences across estimates of future CO 2 emissions from proposed

power infrastructure mostly reflect substantial cancellations of coal 
infrastructure proposals in 2017 and 2018 (Tong et al. 2019).The global estimate of future CO 2 emissions from current and 
planned fossil-fuel infrastructures is 850 (600–1100) GtCO 2 (Tong 
et al. 2019). This already exceeds total cumulative net CO 2 emissions 
in pathways that limit warming to 1.5°C with no or limited overshoot 
(see above). It is about the same size as the total cumulative net 
CO2 emissions of 890 (640–1160) GtCO 2 from pathways that limit 
warming to 2°C (<67%) (Chapter  3). Hence, cumulative net CO 2 
emissions to limit warming to 2°C (<67%) or lower could already 
be exhausted by current and planned fossil fuel infrastructure (high 
confidence) even though this estimate only covers a fraction of all 
infrastructure developments over the 21st  century as present in 
mitigation pathways, does not cover all sectors (e.g., AFOLU) and

does not include currently infrastructure development plans in 
transport, buildings, and industry due to a lack of data.
Hence, the Paris climate goals could move out of reach unless there are 
dedicated efforts for early decommissioning, and reduced utilisation of 
existing fossil fuel infrastructures, cancellation of plans for new fossil 
fuel infrastructures, or compensation efforts by removing some of the 
CO2 emissions from the atmosphere (Cui et al. 2019; Smith et al. 2019; 
Tong et al. 2019; Pradhan et al. 2021). For example, Fofrich et al. (2020) 
suggest in a multi-model study that coal and gas power infrastructure 
would need to be retired 30 (19–34) and 24 (21–26) years earlier 
than the historical averages of 39 and 36 years when following 1.5°C 
pathways and 23 (11–33) and 19 (11–16) years earlier when following 
2°C pathways. Cui et al. (2019) arrive at more conservative estimates 
for coal power plants, but only consider the existing and currently

proposed capacity. Premature retirement of power plants pledged 
by members of the Powering Past Coal Alliance would cut emissions 
by 1.6 GtCO 2, which is 150 times less than future CO 2 emissions from 
existing coal power plants (Jewell et al. 2019).
92
300358Cumulative emissions (GtCO2)
800
All sectors
w/o electricity1000
600
400
200
0
Electricity Proposed
coalProposed
gas and oil
1.5°C scenarios
2.0°C scenarios890
5101200
MAFOECD 1990
LAMEIT
Asia
97
Figure 2.26 | Future CO 2 emissions from existing and currently planned fossil fuel infrastructure in the context of Paris carbon budgets in GtCO 2 based 
on historic patterns of infrastructure lifetimes and capacity utilisation.  Future CO 2 emissions estimates of existing infrastructure for the electricity sector as well as all 
other sectors (industry, transport, buildings, other fossil fuel infrastructures) and of proposed infrastructures for coal power as well as gas and oil power. Grey bars on the right

depict the range (5th–95th percentile) in overall cumulative net CO 2 emissions until reaching net zero CO 2 in pathways that limit warming to 1.5°C with no or limited overshoot 
(1.5°C scenarios), and in pathways that limit warming to 2°C (<67%) (2°C scenarios). Source: based on Edenhofer et al. (2018) and Tong et al. (2019).

268
Few quantifications of carbon lock-in from urban infrastructure, in 
particular urban form, have been attempted, in part because they also 
relate to behaviours that are closely tied to routines and norms that 
co-evolve with ‘hard infrastructures’ and technologies, as well as ‘soft 
infrastructure‘ such as social networks and markets (Seto et al. 2016). 
There are some notable exceptions providing early attempts (Guivarch 
and Hallegatte 2011; Driscoll 2014; Seto et al.2014; Lucon et al. 2014; 
Erickson and Tempest 2015; Creutzig et al. 2016). Creutzig et al. (2016) 
attempt a synthesis of this literature and estimate the total cumulative 
future CO 2 emissions from existing urban infrastructure at 210 Gt, and 
from new infrastructures at 495 Gt for the period 2010–2030.
2.7.3 Synthesis – Comparison with Estimates 
of Residual Fossil Fuel CO 2 Emissions
A complementary strand of literature uses IAMs to assess the cumulative

gross amount of unabated CO 2 emissions from fossil fuels across 
decarbonisation pathways that are not removed from the system, even 
under strong (short- and long-term) climate policy ambitions. Lower 
bound estimates for such a  minimum amount of unabated residual 
CO2 emissions across the 21st century that is not removed from the 
system, even under very ambitious climate policy assumptions, may 
be around 600–700 GtCO 2 (Kriegler et al. 2018b). This range increases 
to 650–1800 GtCO 2 (Table  2.7) as soon as a  broader set of policy 
assumptions are considered, including delayed action in scenarios that 
limit warming to 1.5°C and 2°C respectively (Luderer et al. 2018).Notably, the lower end of residual fossil fuel emissions in IAM scenarios 
(Luderer et al. 2018) is remarkably similar to global estimates from 
the accounting studies of the previous section, as shown in Table 2.6. 
Yet, there are important conceptual and interpretative differences

that are also reflected in the very different distribution of reported 
future CO 2 emissions attached to current and future fossil fuel 
infrastructures (Table  2.7). Accounting studies start from granular, 
plant-based data for existing fossil fuel infrastructure and make 
statements about their future CO 2 emissions, assuming variations 
of historic patterns of use and decommissioning. Expansions to the 
future are limited to proposals for new infrastructures that we know 
of today. Scenario studies quantifying residual fossil fuel emissions 
start from aggregate infrastructure descriptions, but dynamically 
update those through new investment decisions in each time step 
across the 21st  century based on the development of energy and 
energy service demands, as well as technology availability, guided by 
defined climate policy goals (or their absence).
In accounting studies, estimates of future CO 2 emissions from current

fossil fuel infrastructures are dominated by the power sector with 
its large fossil fuel capacities. In contrast, scenario studies highlight 
residual emissions from non-electric energy  – particularly in the 
transport and industry sectors. Fossil-fuel infrastructure in the power 
sector can be much more easily retired than in those sectors, where 
there are fewer and more costly alternatives. IAMs therefore account 
for continued investments into fossil-based energy technologies 
in areas with limited decarbonisation potential, such as some areas of 
Table 2.7 | Residual (gross) fossil fuel emissions (GtCO 2) in climate change mitigation scenarios strengthening mitigation action after 2020 (‘early 
strengthening’), compared to scenarios that keep Nationally Determined Contribution (NDC) ambition level until 2030 and only strengthen thereafter.

Cumulative gross CO 2 emissions from fossil fuel and industry until reaching net zero CO 2 emissions are given in terms of the mean as well as minimum and maximum (in 
parentheses) across seven participating models: AIM/CGE, GCAM, IMAGE, MESSAGES, POLES, REMIND, WITCH. Scenario design prescribes a harmonised, global carbon price 
in line with long-term carbon budget. Delay scenarios follow the same price trajectory, but 10 years later. Carbon dioxide removal requirements represent ex-post calculations 
that subtract gross fossil fuel emissions from the carbon budget associated with the respective long-term warming limit. We take the carbon budget for limiting warming to 
1.5°C with a 50% probability and to 2°C with a 67% probability (Canadell et al. 2021). Hence, carbon dioxide removal (CDR) requirements reflect a minimum amount of CDR 
for a given mitigation trajectory. Results are reported at two significant digits. Sources: Luderer et al. (2018); Tong et al. (2019).

Future CO 2 emissions from existing 
and planned fossil fuel infrastructure 
(accounting studies)Residual fossil fuel emissions – cumulative gross CO 2 emissions from fossil fuel and industry 
until reaching net zero CO 2 emissions (in GtCO 2)
Tong et al. (2019) Early strengthening from (2020) Delayed strengthening from 2030
GtCO 2 Year Well below 2°C Below 1.5°C in 2100 Well below 2°C Below 1.5°C in 2100Existing and 
proposedElectricity550
(380–730)2018
Existing AND future instalmentsElectricity180
(140–310)130
(90–160)250
(220–340)200
(190–230)
Non-electric supply Non-electric supply100
(42–130)59
(27–83)120
(55–150)75
(40–100)ExistingIndustry160
(110–220)2017 Industry260
(160–330)140
(86–180)290
(200–370)200
(130–250)
Transportation64
(53–75)2017 Transportation310
(190–370)170
(110–220)310
(250–400)200
(140–260)
Buildings74
(52–110)2018 Buildings110
(75–110)58
(35–77)120
(80–150)73
(51–93)
All sectors and 
proposed electricity850
(600–1100)All sectors (2021 – 
net zero CO 2)960

(730–1100)570
(400–640)1100
(900–1200)770
(590–860)
All sectors (2021–2100)1300
(970–1500)850
(650–1100)1400
(1200–1600)1000
(860–1300)
Implied minimum 
requirement for carbon 
dioxide removal until 2100150
(0–350)350
(150–600)250
(50–450)500
(360–800)

269
transportation (in particular aviation, shipping and road-based freight) 
or some industrial processes (such as cement production or feedstocks 
for chemicals). This explains the key discrepancies observable in 
Table 2.7. Therefore, our overall assessment of these available lines 
of evidence strongly emphasises the importance of decommissioning, 
reduced utilisation of existing power sector infrastructure, as well as 
continued cancellation of new power sector infrastructures in order 
to limit warming to well below 2°C (high confidence) (Kriegler et al. 
2018b; Luderer et al. 2018; Chen et al. 2019; Cui et al. 2019; Fofrich 
et al. 2020). This is important as the power sector is comparatively easy 
to decarbonise (IPCC 2014a; Krey et al. 2014; Davis et al. 2018; Méjean 
et al. 2019) and it is crucial to make space for residual emissions from 
non-electric energy end uses that are more difficult to mitigate (high

confidence). Any further delay in climate policy substantially increases 
carbon lock-in and mitigation challenges as well as a dependence on 
carbon dioxide removal technologies for meeting the Paris climate 
goals (Kriegler et al. 2018b; Luderer et al. 2018).
2.8 Climate and Non-Climate Policies 
and Measures and their Impacts 
on Emissions
2.8.1 Introduction
The key to achieving climate change mitigation targets includes 
crafting environmentally effective, economically efficient and socially 
equitable policies. For the purposes of this section, policies are defined 
broadly as actions to guide decisions to reach explicit goals and, 
accordingly, climate (mitigation) policies are the ones whose primary 
objective is to reduce GHG emissions. They include a range of domains 
from economic and institutional to research and development (R&D) 
and social policies, and are implemented by various instruments 
(e.g.,  market-based and regulatory in the economic domain) and

measures (e.g.,  legal provisions and governance arrangements in 
the institutional domain) (Chapter  13, and see ‘mitigation policies’ 
in Glossary). Yet GHG emissions are also affected by policies enacted in 
various social, economic and environmental areas to pursue primarily 
non-climatic objectives. This section presents succinct assessments of 
the outcomes and effectiveness of a few selected policy instruments 
applied in the last two decades that target climate protection 
(Sections  2.8.2 and 2.8.3) and GHG emissions impacts of selected 
other policies primarily aimed at improvements in environmental 
quality and natural resource management (Section 2.8.4).12
It is rather difficult, though not impossible, to discern the genuine 
impacts of climate and non-climate policies on GHG emissions. 
Most current and past policies target only a  small part of global 
emissions in a  limited geographical area and/or from a  small

number of economic sectors. However, in addition to the targeted 
12 This section only reviews the emission impacts of selected policy instruments. Other important aspects such as equity and cost-effectiveness are assessed in Chapter 13, 
presenting comprehensive evaluations of policies and measures.
13 Refer to Chapter 13 on policies and institutions for a detailed discussion of emissions leakages and complex interactions from policy mixes.
14 The OECD (2018) measures carbon prices using the effective carbon rate (ECR), which is the sum of three components: specific taxes on fossil fuels; carbon taxes; and prices 
of tradable emissions permits. The carbon pricing gap measures the difference between actual ECRs and benchmark rates. The carbon pricing gap indicates the extent to 
which polluters do not pay for the damage from carbon emissions.region or sector, policies and measures tend to affect GHG emissions 
in other parts of the world. Emissions leakage is the key channel

by which such phenomena and complex interactions occur.13 
Uncertainties in impacts, synergies, and trade-offs between policies 
and measures also complicate the evaluation of emissions impacts. 
These make it challenging to identify the impacts of any specific 
policy or measure on emissions of any specific region or sector. 
Rigorous statistical analyses are necessary for building strong 
empirical evidence, but the experience with climate-related policy 
experiments to date is limited.
2.8.2 Comprehensive Multinational Assessments
Comprehensive multinational evaluations with wider regional and 
sectoral coverage enable the assessment of emissions impacts 
without distortions from emissions leakage. Among the wide range 
of climate policy instruments, pricing carbon – such as a carbon tax or 
an emissions trading system – has been one of the most widely used 
and effective options to reduce GHG emissions (robust evidence,

high agreement). In a comparison of 142 countries with and without 
carbon pricing, countries with a  carbon price show annual CO 2 
emission growth rates of 2 percentage points lower than countries 
without such policies (Best et  al. 2020). A  more comprehensive 
evaluation of carbon prices shows that countries with a lower carbon 
pricing gap (a higher carbon price) tend to be more carbon-efficient, 
that is, they have a lower carbon intensity of GDP (OECD 2018).14 
An empirical analysis of the effects of environmental regulation and 
innovation on the carbon emissions of OECD countries during the 
period 1999–2014 indicates that a 1% increase in environmentally 
friendly patents reduced carbon emissions by 0.017%, and a  1% 
increase in environmental tax revenue per  capita reduced carbon 
emissions by 0.03% (Hashmi and Alam 2019).
Domestic and international climate legislation have also contributed 
to the reduction of GHG emissions. An empirical analysis of legislative

activity in 133 countries over the period 1999–2016 based on panel 
data indicates that each new law reduced annual CO 2 emissions 
per unit of GDP by 0.78% nationally in the first three years, and by 
1.79% beyond three years. Additionally, climate laws as of 2016 
were associated with an annual reduction in global CO 2 emissions 
of 5.9 GtCO 2 and 38 GtCO 2 cumulatively since 1999 (Eskander and 
Fankhauser 2020). It is notable that 36 countries that accepted legally 
binding targets under the Kyoto Protocol all complied (Shishlov et al. 
2016). It is impossible to disentangle precisely the contribution of 
individual mitigation policies, but it is clear that the participating 
countries, especially those in the OECD, did make substantial policy 
efforts with material impact (Grubb 2016). An ex-post evaluation 
shows a significant impact of the Protocol on emissions reductions 
(Maamoun 2019).

270
Renewable energy policies, such as Renewable Portfolio Standards 
and Feed-in-Tariff, have played an essential role in the massive 
expansion of renewable energy capacities, another key driver of GHG 
emissions reductions (robust evidence, high agreement). Drivers of 
decreasing CO 2 emissions seen in a group of 18 developed economies 
that decarbonised over the period 2005–2015 are the displacement 
of fossil fuels by renewable energy and decreases in energy use 
(Le Quéré et al. 2019). Renewable energy policies both at the EU and 
member states level have played an essential role in abating GHG 
emissions (ICF International 2016).
2.8.3 National, Sectoral, and Cross-sectoral Policies
2.8.3.1 National and Regional Carbon Pricing
Carbon prices  – such as carbon taxes and GHG emissions trading 
schemes (ETSs)  – are among the most widely used climate policy 
instruments across the globe, together with technology support

instruments (IRENA 2018). As of May 2020, there were 61 carbon 
pricing schemes in place or scheduled for implementation, consisting 
of 31 ETSs and 30 carbon tax regimes, covering 12 GtCO 2-eq or 
about 22% of annual global GHG emissions (World Bank 2020). The 
performance of carbon pricing in practice varies by countries and 
sectors, and depends on the policy environment (robust evidence, 
The European Union Emissions Trading Scheme (EU ETS), the longest-
standing regional climate policy instrument to date, has reduced 
emissions, though the estimates of the amount vary by study, by 
country, and by sector; ranging from 3–28% (McGuinness and 
Ellerman 2008; Ellerman et  al. 2010; Abrell et  al. 2011; Anderson 
and Di Maria 2011; Egenhofer et al. 2011; Petrick and Wagner 2014; 
Arlinghaus 2015; Martin et al. 2016). The EU ETS avoided emitting 
about 1.2 GtCO 2 between 2008 and 2016 (3.8%), almost half of

what EU governments promised to reduce under their Kyoto Protocol 
commitments (Bayer and Aklin 2020).
China’s emission trading pilots have resulted in a decline in carbon 
intensity in the pilot provinces by adjusting the industrial structure 
(Zhou et  al. 2019). The Regional Greenhouse Gas Initiative (RGGI) 
in the USA has induced leakage in emissions through increases in 
electricity generation in surrounding non-RGGI areas, but it has led 
to the reduction of emissions by way of changes in the fuel mix from 
coal to gas (Fell and Maniloff 2018). Actual emissions declined in six 
of the 10 ETSs for which data is available, although other factors, 
such as the 2009 recession, have had significant impacts on those 
emissions as well (Haites et al. 2018).
The evidence of environmental effectiveness of carbon taxes in 
Western European countries is varied depending on country and 
study (robust evidence, high agreement). A  significant impact is

found in Finland but insignificant impacts are found in Denmark and 
the Netherlands, and there are mixed results for Sweden (Lin and Li 
2011; Brännlund et al. 2014). Only six of the 17 taxes, where data are 
available, have reduced actual emissions subject to the tax. Tax rates 
tend to be too low in many cases and the scale and frequency of the rate changes has not been sufficient to stimulate further emissions 
reductions (Haites et al. 2018).
2.8.3.2 Selected Sectoral Climate Policy Instruments
Many governments have implemented sector-specific policies, in 
addition to nationwide measures, to reduce GHG emissions (high 
confidence). Examples of sectoral climate policies include carbon taxes 
on transportation fuels, low-carbon fuel standards, and regulation of 
coal power generation.
The implementation of a carbon tax and value-added tax on gasoline 
and diesel in Sweden resulted in significant reductions of CO 2 
emissions in the transportation sector (Shmelev and Speck 2018;

Andersson 2019). An assessment of a variety of carbon tax schemes 
across various sectors in the EU shows a  negative relationship 
between CO 2 emissions and a CO 2 tax (Hájek et al. 2019). In British 
Columbia (Canada), the carbon tax resulted in a decrease in demand 
for gasoline and a reduction in total GHG emissions (not exclusive to 
the transportation sector) estimated to be between 5–15% (Murray 
and Rivers 2015; Rivers and Schaufele 2015). The Low Carbon Fuel 
Standard in California has contributed to reducing carbon emissions 
in the transportation sector by approximately 9.85–13.28% during 
1997–2014 (Huseynov and Palma 2018).
The power sector typically accounts for a large portion of countries’ 
CO2 emissions. Market-based regulation and government subsidies in 
China contributed to improving operational efficiency and reducing 
emissions (Zhao et al. 2015). In addition, the implementation of ultra-
low emission standards has also resulted in a significant reduction in

emissions from China’s power plants (Tang et al. 2019). Mandatory 
climate and energy policies, including the California Global Warming 
Solutions Act, reduced CO 2 emissions by 2.7–25% of the average 
state-level annual emissions from the power sector over the period 
1990–2014 in the USA. Mandatory GHG registry/reporting, electric 
decoupling and a public benefit fund have been effective in further 
decreasing power sector emissions in the USA (Martin and Saikawa 
2017). In the UK electricity sector, a carbon price floor, combined with 
electricity market reform (competitive auctions for both firm capacity 
and renewable energy), displaced coal, whose share fell from 46% in 
1995 to 7% in 2017, halving CO 2 emissions, while renewables grew 
from under 4% in 2008 to 22% by 2017 (Grubb and Newbery 2018). 
See Chapter 13 for more information.
An alternative approach to a carbon tax is an indirect emissions tax 
on fuels such as an excise tax, or on vehicles, based on the expected

CO2 intensity of new passenger vehicles. Vehicle purchase taxes 
can result in a  reduction in GHG emissions through reducing the 
CO2 emissions intensity of vehicles, while also discouraging new 
vehicle purchases (Aydin and Esen 2018). For example, a vehicle tax 
policy in Norway resulted in a reduction of average CO 2 intensity 
per  kilometre of 7.5 gCO 2  km–1 (Ciccone 2018; Steinsland et  al. 
2018). Despite such evidence, studies of carbon pricing find that 
additional policies are often needed to stimulate sufficient emissions 
reductions in transportation (medium confidence) (Tvinnereim and 
Mehling 2018).

271
Electric vehicles (EVs) powered by clean electricity can reduce GHG 
emissions, and such policies are important for spurring adoption of 
such vehicles (Kumar and Alok 2020; Thiel et al. 2020). The extent to 
which EV deployment can decrease emissions by replacing internal 
combustion engine-based vehicles depends on the generation 
mix of the electric grid (Abdul-Manan 2015; Nichols et  al. 2015; 
Canals Casals et al. 2016; Hofmann et al. 2016; Choi et al. 2018; 
Teixeira and Sodré 2018) although, even with current grids, EVs 
reduce emissions in almost all cases (Knobloch et al. 2020). Policy 
incentives for EV adoption can be an effective mechanism to 
increase EV sales (Langbroek et al. 2016) and may include discounts, 
purchase subsidies, regulations, and government leadership  
(medium confidence) (Bakker and Jacob Trip 2013; Silvia and Krause 
2016; Teixeira and Sodré 2018; Qiu et al. 2019; Santos and Davies

2020). The presence of charging infrastructure and publicly available 
charging increases the adoption rate of EVs (Vergis and Chen 
2015; Javid et al. 2019). A comparison of EV adoption rates across 
30 countries shows a positive correlation between charging stations 
and EV market share (Sierzchula et al. 2014). A rollout of 80,000 DC 
fast chargers across the USA is estimated to have resulted in a 4% 
reduction in emissions compared to a baseline of no additional fast 
chargers (Levinson and West 2018). More recently, bans on internal 
combustion engine vehicles have provided a  much more direct 
approach to stimulating the adoption of EVs and its supporting 
infrastructure; however, the efficacy of such measures depends on 
enforcement (Plötz et al. 2019).
Public transit can reduce vehicle travel and lower GHG emissions by 
reducing the number of trips taken by private vehicles and the length 
of those trips (medium confidence). Changes to the operation of

public transportation systems (such as density of bus stops, distance 
from stops to households, duration and frequency of trip times, and 
lowering ridership costs) can result in a  mode shift from private 
car trips to public transit trips (Cats et al. 2017; Choi 2018; Carroll 
et  al. 2019). These changes in the public transit system operation 
and network optimisation have been shown to have reduced GHG 
emissions in cases such as San Francisco, where the cost optimisation 
of the transit network was estimated to decrease emissions by 
a  factor of three (Cheng et  al. 2018) and Barcelona, where the 
optimisation of the urban bus system was estimated to reduce GHG 
emissions by 50% (Griswold et al. 2017). For every 1% increase in 
investment in transit services and transit-oriented design, there is 
an estimated 0.16% reduction in private vehicle kilometres travelled 
per capita (McIntosh et al. 2014).
Bike- and car-sharing programmes can reduce GHG emissions

(medium confidence). Albeit a study of eight cities in the USA with 
larger bike share systems and higher ridership found that their 
potential to reduce total emissions is limited to <0.1% of total GHG 
emissions from the transportation sectors of these cities (Kou et al. 
2020). The emissions reductions effects of car-sharing programmes 
depends on the specifics of programmes: the mode shift from public 
transit to car-sharing services can outweigh the decreases in GHG 
emissions associated with a  reduced number of cars on the road 
(Jung and Koo 2018), whereas car-sharing programmes with EV 
fleets may reduce GHG emissions (Luna et al. 2020).2.8.4 Emission Impacts of Other Related Policies
Policies other than those intended directly to mitigate GHGs can 
also influence these emissions. Policies to protect the stratospheric 
ozone layer is a  case in point. Implementing the Montreal 
Protocol and its amendments, emissions of controlled ozone-

depleting substances (ODSs) (those covered by the protocol) 
declined to a very low level of about 1.4 GtCO 2-eq yr –1 by 2010, 
avoiding GHG emissions of an estimated 13.3–16.7 GtCO 2-eq yr –1 
(9.7–12.5 GtCO 2-eq yr –1 when accounting for the ozone depletion 
and hydrofluorocarbons (HFCs) offsets) (Velders et  al. 2007). Yet 
fluorinated gases (F-gases), the substances introduced to substitute 
ODSs are also potent GHGs. See Section 2.2 for emissions data, and 
Chapter 13 on current policies to mitigate HFCs and other F-gases. 
GHG implications of two other categories of non-climate policies 
are briefly assessed in this section.
2.8.4.1 Co-impacts of Air Quality, Sector-specific 
and Energy Policies on Climate Mitigation
Co-impacts of local or regional air pollution abatement policies for 
climate mitigation are widely studied in the literature. Cross-border 
externalities of air pollution have also made these a focus of several

international agreements (Mitchell et  al. 2020). Evaluating the 
effectiveness of such treaties and policies is difficult because deriving 
causal inferences and accurate attribution requires accounting 
for several confounding factors, and direct and indirect spillovers 
(Isaksen 2020). Nevertheless, several studies assess the effectiveness 
of such treaties and regulations (De Foy et al. 2016; Li et al. 2017a, 
2017b; Morgenstern 2018; Mardones and Cornejo 2020). However, 
there is little ex-post empirical analysis and a  greater focus on 
ex-ante studies in the literature.
At a  local scale, air pollutants are often co-emitted with GHGs in 
combustion processes. Many air quality policies and regulations focus 
on local pollution from specific sources that can potentially either 
substitute or complement global GHG emissions in production and 
generation processes. Also, policies that reduce certain air pollutants,

such as sulphur dioxide (SO 2), have a  positive radiative forcing 
effect (Navarro et al. 2016). The evidence on individual air pollution 
control regulation and policies for GHG emissions is therefore mixed 
(medium evidence, medium agreement). Evidence from the USA 
suggests that increased stringency of local pollution regulation had 
no statistically detectable co-benefits or costs on GHG emissions 
(Brunel and Johnson 2019). Evidence from China suggests that the 
effectiveness of policies addressing local point sources differed from 
those of non-point sources and the co-benefits for climate are mixed, 
though policies addressing large industrial point sources have been 
easier to implement and have had significant impact (Huang and 
Wang 2016; Xu et al. 2016; van der A et al. 2017; Dang and Liao 2019; 
Fang et al. 2019; Yu et al. 2019). Legislation to reduce emissions of 
air pollutants in Europe have significantly improved air quality and

health but have had an unintended warming effect on the climate 
(Turnock et al. 2016).

272
Often, the realisation of potential co-benefits depends on the 
type of pollutant addressed by the specific policy, and whether 
complementarities between local pollution and global GHG emissions 
are considered in policy design (medium evidence,  high agreement) 
(Rafaj et al. 2014; Li et al. 2017a). Effective environmental regulations 
that also deliver co-benefits for climate mitigation require integrated 
policies (Schmale et  al. 2014; Haines et  al. 2017). Uncoordinated 
policies can have unintended consequences and even increase 
emissions (Holland et al. 2015). Many studies suggest that policies that 
target both local and global environmental benefits simultaneously 
may be more effective (medium evidence,  medium agreement) 
(Klemun et  al. 2020). Furthermore, air pollution policies aimed at 
inducing structural changes – for example, closure of polluting coal 
power plants or reducing motorised miles travelled – are more likely

to have potential positive spillover effects for climate mitigation, as 
compared to policies incentivising end-of-pipe controls (Wang 2021).
Other policies that typically have potential co-benefits for climate 
mitigation include those specific to certain sectors and are discussed 
in Chapters 5–11. Examples of such policies include those that 
encourage active travel modes, which have been found to have 
ancillary benefits for local air quality, human health, and GHG 
emissions (Fujii et al. 2018). Policies to reduce energy use through 
greater efficiency have also been found to have benefits for air quality 
and the climate (robust evidence, medium agreement) (Tzeiranaki 
et al. 2019; Bertoldi and Mosconi 2020). Important air quality and 
climate co-benefits of renewable or nuclear energy policies have also 
been found (medium evidence, medium agreement) (Lee et al. 2017; 
Apergis et al. 2018; Sovacool and Monyei 2021).

Policies specific to other sectors, such as encouraging green building 
design, can also reduce GHG emissions (Eisenstein et  al. 2017). 
Evidence from several countries also shows that replacing polluting 
solid biomass cooking with cleaner gas-burning or electric alternatives 
have strong co-benefits for health, air quality, and climate change 
(robust evidence,  high agreement) (Anenberg et al. 2017; Singh et al. 
2017; Tao et al. 2018).
2.8.4.2 Climate Impacts of Agricultural, Forestry, 
Land Use, and AFOLU-related Policies
Policies on agriculture, forestry, and other land use (AFOLU), and 
AFOLU sector-related policies have had a  long history in many 
developing and developed countries. Co-impacts of these policies 
on the climate have been only marginally studied, although their 
impacts might be quite important because the AFOLU sector is 
responsible for 22% of total GHG emissions (robust evidence, high 
agreement). The results of afforestation policies around the world

and the contribution to CCS are also important.
Private and governmental policies can have a major impact on the 
climate. Experience indicates that ‘climate proofing’ a policy is likely 
to require some stimulus, resources, and expertise from agencies 
or organisations from outside the country. Stimulus and support 
for adaptation and mitigation can come from the UN system and 
from international development institutions (FAO 2009). These 
findings are also valid for small/organic farmers vis-à-vis large-scale agro-industry. For example, small/medium and environmentally 
concerned farmers in Europe are often asking for more policies 
and regulations, and see it as necessary from a climate perspective, 
and also to maintain competitiveness relative to large agro-industrial 
complexes. Therefore, the need for governmental support for small 
producers in regulations encompasses all AFOLU sectors.
Forestry case: zero deforestation

Forest is generally defined as land spanning more than 0.5 hectares 
with trees higher than 5 metres and a canopy cover of more than 
10%, or trees able to reach these thresholds in situ (FAO 1998). 
Zero-deforestation (i.e.,  both gross and net zero deforestation) 
initiatives generate results at multiple levels (Meijer 2014). Efforts 
to achieve zero-deforestation (and consequently emissions) are 
announced by non-governmental organisations (NGOs), companies, 
governments, and other stakeholder groups. NGOs engage through 
their campaigning, but also propose tools and approaches for 
companies (Leijten et al. 2020). The extent to which companies can 
actually monitor actions conducive to zero-deforestation pledges 
depends on their position in the supply chain. Beyond the business 
practices of participating companies, achieving long-term positive 
societal impacts requires upscaling from supply chains towards 
landscapes, with engagement of all stakeholders, and in particular

small producers. The various success indicators for zero deforestation 
mirror the multiple levels at which such initiatives develop: progress 
towards certification, improved traceability, and legality are apparent 
output measures, whereas direct-area monitoring and site selection 
approaches target the business practices themselves.
Such efforts have led to the development of the High Carbon 
Stock (HCS) approach that combines carbon stock values with the 
protection of HCS areas (including peatlands and riparian zones) and 
areas important for the livelihoods of local communities (Rosoman 
et  al. 2017). Long-term positive impacts, however, will need to be 
assessed with hindsight and focus on national and global statistics. 
Successful initiatives targeting zero deforestation at jurisdictional 
level would also need to improve the enforcement of forest laws and 
regulations (EII 2015; Meyer and Miller 2015).
Large-scale agribusiness, banks, and consumer goods companies

dominate supply chain-focused zero-deforestation initiatives, but 
only the producers, including local communities and smallholders, can 
change the production circumstances (TFD 2014). Producers shoulder 
much of the burden for meeting environmental requirements of 
pledges. And local communities and small producers are vulnerable 
to being cut out when supply chains reorient. The zero-deforestation 
pledges do not always devise programmes for introducing new 
sourcing strategies, and governments may have an important 
contribution to make, particularly in safeguarding the interests of 
small producers.
Other than in Brazil and Indonesia, beyond individual supply chains, 
there is still little evidence on positive results of zero-deforestation 
commitments, as information available for companies to judge 
their progress is scarce. Moreover, many zero-deforestation pledges 
set targets to be achieved by 2020 or 2030, and, consequently,

273
many companies have not yet reported publicly on their progress. 
Similarly, only a  few governments have yet shown progress in 
reducing deforestation, but the New York Declaration on Forests, 
the Sustainable Development Goals (SDGs) and the Paris Agreement 
were adopted relatively recently. The effectiveness of private-sector 
zero-deforestation pledges depends on the extent to which they 
can be supported by governmental action and foster a cooperative 
environment with the engagement of all stakeholders. Where the 
pledges are coordinated with regulation, multi-stakeholder dialogues, 
and technical and financial support, a true paradigm shift becomes 
possible. Many governments are still building the capacity to improve 
overall forest governance, but implementing ambitious international 
targets is likely to depend on technical and major financial support 
that has not yet been mobilised.
2.9 Knowledge Gaps

• Global GHG emissions estimates are published less frequently 
and with greater reporting lags than, for example, CO 2 from fossil 
fuel and industry. Data quality and reporting frequency remains 
an issue, particularly in developing countries where the statistical 
infrastructure is not well developed. Efforts to compile a global 
GHG emissions inventory by country, sector, and across time, 
that is annually updated based on the best-available inventory 
information, similar to ongoing activities for carbon dioxide 
(CO 2), methane (CH 4) or nitrous oxide (N 2O), could fill this 
gap. Uncertainties and their methodological treatment in GHG 
emissions estimates are still not comprehensively understood.
• There is a more fundamental data gap for F-gas emissions, where 
data quality in global inventories is poor due to considerable 
gaps in the underlying activity data – particularly in developing 
countries. Comprehensive tracking of fluorinated gases (F-gas)

emissions would also imply the inclusion of other gases not 
covered under the Paris Agreement, such as chlorofluorocarbons, 
hydrochlorofluorocarbons and others.
• Currently, despite advances in terms of data availability, sectoral 
and spatial resolution, the results in consumption-based emission 
estimates are dependent on the database used, the level of 
sectoral aggregation and country resolution. More fine-grained 
data at spatial resolution as well as the product level would 
support exploring the mitigation options at the sub-national 
level, companies and households.
• Consumption-based emission accounts suffer from lack of 
quantification of uncertainties at the subnational level and 
especially in data-scarce environments, such as for developing 
countries. A better understanding of drivers that caused decoupling 
of emissions at the national and especially sub-national level are 
important to explore.
• Understanding how socio-economic drivers modulate emission

mitigation is crucial. Technological improvements (e.g., improved 
energy or land-use intensity of the economy) have shown 
a persistent pattern over the last few decades, but gains have been 
outpaced by increases in affluence (GDP per capita) and population 
growth, leading to continued emissions growth. Therefore the 
key gap in knowledge is how these drivers of emissions can be 
mitigated by demand management, alternative economic models, population control and rapid technological transition to different 
extents and in different settings. More research on decoupling 
and sustainability transformations would help to answer these 
questions. Key knowledge gaps also remain in the role of trade – in 
particular, how supporting low-carbon technologies in developing 
and exporting countries can counteract the upward-driving effect 
of trade, and how to achieve decoupling without outsourcing 
emissions to others and often to less developed regions.

• Understanding of how inequality affects emissions is in a nascent 
stage. Less is known about the causal mechanisms by which different 
dimensions of inequality – such as income, socio-economic, spatial, 
socio-cultural-gender and ethnicity – affect emissions. In particular, 
limited knowledge exists on the linkages between dimensions 
of inequality other than income or wealth and emissions arising 
from different service demands. Research gaps are apparent on 
how inequalities in living standards relate to emissions and how 
changes in inequalities between genders, social groups, and other 
marginalised communities impact emissions trends.
• Digitalisation of the economy is often quoted as providing new 
mitigation opportunities, but knowledge and evidences are yet 
limited – such as understanding of the role of smart apps and the 
potential and influence of disruptive technologies at the demand 
and supply side on GHG emissions.

• Despite growing evidence of technological progress across 
a variety of mitigation areas and the availability of increasingly 
precise datasets, knowledge gaps remain on technological 
change and innovation and evidence on speed of transitions 
to clarify what would make them fast or slow. Innovation is an 
inherently uncertain process and there will always be imperfect 
ex ante knowledge on technological outcomes and their effects 
on mitigation. The extent to which a low-carbon transition can 
proceed faster than historical examples is crucial to aid future 
mitigation. That depends on a better understanding of the speed 
of building, updating and replacing infrastructure. Additionally, 
how and whether financing for low-carbon technology 
investment in low- and middle-income countries can be delivered 
at low-cost and sustained over time are important questions. The 
emerging findings that small-scale technologies learn faster and

are adopted more quickly need to be tested against a broader set 
of cases, and in particular against the large dispersion in data.
• Future CO2 emissions from existing and planned infrastructure is not 
well understood and quantified outside the power sector. Further 
integration of bottom-up accounting and scenario approaches 
from integrated assessment seems promising. Comprehensive 
assessments of hard-to-abate residual fossil fuel emissions and 
their relationship to CO 2 removal activities are lacking, but will be 
important for informing net-zero emissions strategies.
• Empirical evidence of emission impacts from climate policies, 
including carbon pricing, is not sufficient for unambiguous 
attribution assessment, mainly due to the limited experience 
with climate-related policy experiments to date. More attention 
to the methodology for comprehensive evaluation of climate 
policies and measures, such as effective carbon rates is apparent.

Key knowledge gaps also exist on ex-post evaluations of climate 
and non-climate policies and measures for their impact on 
emissions, particularly at the global scale, considering national 
circumstances and priorities.

274
Frequently Asked Questions (FAQs)
FAQ 2.1 |  Ar e emissions still increasing or are they falling?
Global greenhouse gas (GHG) emissions continued to rise and reached 59 ± 6.6 GtCO 2-eq in 2019, although the rate of growth 
has fallen compared to the previous decade. However, emissions were higher than at any point in human history before. Emissions 
were around 12% and 54% higher than in 2010 and 1990, respectively. Average annual GHG emissions for 2009–2019 were higher 
compared to the periods 2000–2009 and 1990–1999, respectively. GHG emissions growth slowed since 2010: while average annual GHG emissions growth was 2.1% for 2000–2010, it was only 1.3% for 2010–2019. In order to stop the temperature increase, however, net emissions must be zero.
FAQ 2.2 |   Ar e there countries that have reduced emissions and grown economically 
at the same time?

About 24 countries have reduced territorial CO 2 and GHG emissions for more than 10 years. Uncertainties in emission levels and 
changes over time prevent a precise assessment in some country cases. In the short observation period of 2010–2015, 43 out of 166 countries have achieved absolute decoupling of consumption-based CO
2 emissions from economic growth, which means that 
these countries experienced GDP growth while their emissions have stabilised or declined. A group of developed countries, such as some EU countries and the USA, and some developing countries, such as Cuba, have successfully achieved an absolute decoupling of consumption-based CO
2 emissions and GDP growth. Decoupling has been achieved at various levels of per capita income and 
per capita emissions. Overall, the absolute reduction in annual emissions achieved by some countries has been outweighed by growth in emissions elsewhere in the world.

FAQ 2.3 |   How much time do we have to act to k eep global warming below 1.5 degrees?
If global CO 2 emissions continue at current rates, the remaining carbon budget for keeping warming to 1.5°C will likely be 
exhausted before 2030. Between 1850 and 2019, total cumulative CO 2 emissions from the fossil fuel industry (FFI) and agriculture, 
forestry, and other land use (AFOLU) were 2400 (±240 GtCO 2). Of these, about 410 ± 30 GtCO 2 were added since 2010. This is 
about the same size as the remaining carbon budget for keeping global warming to 1.5°C and between one-third and one-half of the 1150 ± 220 (1350, 1700) GtCO
2 for limiting global warming below 2°C with a 67% (50%, 33%) probability, respectively 
(Canadell et al. 2021). At current (2019) rates of emissions, it would only take 8 (2–15) and 25 (18–35) years to emit the equivalent amount of CO
2 for a 67th percentile 1.5°C and 2°C remaining carbon budget, respectively. This highlights the dependence of 1.5°C

pathways on the availability of substantial CO 2 removal capacities, as discussed in Chapters 3, 4, and 12, but also Section 2.7 of 
this chapter.

298
Chapter 3  Mitigation P athways Compatible with Long-term Goals3
Chapter 3 assesses the emissions pathways literature in order 
to identify their key characteristics (both in commonalities and 
differences) and to understand how societal choices may steer 
the system into a particular direction (high confidence). More 
than 2000 quantitative emissions pathways were submitted to the 
IPCC’s Sixth Assessment Report AR6 scenarios database, out of which 
1202 scenarios included sufficient information for assessing the 
associated warming consistent with WGI. Five Illustrative Mitigation 
Pathways (IMPs) were selected, each emphasising a different scenario 
element as its defining feature: heavy reliance on renewables (IMP-
Ren), strong emphasis on energy demand reductions (IMP-LD), 
extensive use of carbon dioxide removal (CDR) in the energy and 
the industry sectors to achieve net negative emissions (IMP-Neg),

mitigation in the context of broader sustainable development (IMP-
SP), and the implications of a less rapid and gradual strengthening of 
near-term mitigation actions (IMP-GS). {3.2, 3.3}
Pathways consistent with the implementation and 
extrapolation of countries’ implemented policies until the end 
of 2020 see greenhouse gas (GHG) emissions reaching 54–
61 GtCO 2-eq yr–1 by 2030 and to 47–67 GtCO 2-eq yr–1 by 2050, 
leading to a median global warming of 2.2°C to 3.5°C by 2100 
(medium confidence). These pathways consider policies at the time 
that they were developed. The Shared Socio-economic Pathways 
(SSPs) permit a more systematic assessment of future GHG emissions 
and their uncertainties than was possible in AR5. The main emissions 
drivers include growth in population, reaching  8.5–9.7 billion by 
2050, and an increase in global GDP of 2.7–4.1% per year between 
2015 and 2050. Final energy demand in the absence of any new

climate policies is projected to grow to around 480–750 EJ yr–1 in 
2050 (compared to around 390 EJ in 2015) (medium confidence). The 
highest emissions scenarios in the literature result in global warming 
of >5°C by 2100, based on assumptions of rapid economic growth 
and pervasive climate policy failures (high confidence). {3.3} 
Many pathways in the literature show how to limit global 
warming compared to pre-industrial times to 2°C (>67%) with 
no overshoot or to limit warming to 1.5°C (>50%) with no or 
limited overshoot. The likelihood of limiting warming to 1.5°C 
with no or limited overshoot has dropped in AR6 compared 
to the Special Report on Global Warming of  1.5°C (SR1.5) 
because global GHG emissions have risen since the time SR1.5 
was published, leading to higher near-term emissions (2030) 
and higher cumulative CO 2 emissions until the time of net zero 
(medium confidence). Only a small number of published pathways

limit global warming to 1.5°C without overshoot over the course of 
the 21st century. {3.3, Annex III.II.3}
1 Immediate action in modelled global pathways refers to the adoption between 2020 and at latest before 2025 of climate policies intended to limit global warming to 
a given level. Modelled pathways that limit warming to 2°C (>67%) based on immediate action are summarised in category C3a in Table SPM.2. All assessed modelled 
global pathways that limit warming to 1.5°C (>50%) with no or limited overshoot assume immediate action as defined here (Category C1 in Table SPM.2).
2 NDCs announced prior to COP26 refer to the most recent nationally determined contributions submitted to the UNFCCC up to the literature cut-off date of this report, 
11 October 2021, and revised NDCs announced by China, Japan and the Republic of Korea prior to October 2021 but only submitted thereafter.Cost-effective mitigation pathways assuming immediate

action1 to limit warming to 2°C (>67%) are associated with 
net global GHG emissions of 30–49 GtCO 2-eq yr–1 by 2030 
and 14–26 GtCO 2-eq yr–1 by 2050 (medium confidence). This 
corresponds to reductions, relative to 2019 levels, of 13–45% by 
2030 and 52–76% by 2050. Pathways that limit global warming to 
below 1.5°C with no or limited overshoot require a further acceleration 
in the pace of the transformation, with net GHG emissions typically 
around 21–36 GtCO 2-eq yr–1 by 2030 and 1–15 GtCO 2-eq yr–1 by 
2050; thus, reductions of 34–60% by 2030 and 73–98% by 2050 
relative to 2019 levels. {3.3}
Pathways following Nationally Determined Contributions 
(NDCs) announced prior to COP262 until 2030 reach annual 
emissions of 47–57 GtCO 2-eq by 2030, thereby making it 
impossible to limit warming to  1.5°C with no or limited 
overshoot and strongly increasing the challenge to limit 
warming to 2°C (>67%) (high confidence). A  high overshoot

of  1.5°C increases the risks from climate impacts and increases 
the dependence on large-scale carbon dioxide removal from the 
atmosphere. A  future consistent with NDCs announced prior to 
COP26 implies higher fossil fuel deployment and lower reliance on 
low-carbon alternatives until 2030, compared to mitigation pathways 
with immediate action to limit warming to 2°C (>67%) or lower. To 
limit warming to 2°C (>67%) after following the NDCs to 2030, the 
pace of global GHG emission reductions would need to accelerate 
rapidly from 2030 onward: to an average of 1.4–2.0 GtCO 2-eq yr–1  
between 2030 and 2050, which is around two-thirds of the global 
CO2 emission reductions in 2020 due to the COVID-19 pandemic, 
and around 70% faster than in immediate action pathways  that 
limit warming to 2°C (>67%). Accelerating emission reductions after 
following an NDC pathway to 2030 would be particularly challenging 
because of the continued buildup of fossil fuel infrastructure that

would be expected to take place between now and 2030. {3.5, 4.2}
Pathways accelerating actions compared to NDCs announced 
prior to COP26 that reduce annual GHG emissions to 
48  (38–52) GtCO 2-eq by 2030, or  2–9 GtCO 2-eq below 
projected emissions from fully implementing NDCs announced 
prior to COP26, reduce the mitigation challenge for limiting 
warming to 2°C (>67%) after 2030 (medium confidence).  
The accelerated action pathways are characterised by a  global, 
but regionally differentiated, roll out of regulatory and  pricing 
policies. Compared to NDCs, they see less fossil fuels  and more 
low-carbon fuels until 2030, and narrow, but do not close the gap 
to pathways assuming immediate global action using all available 
least-cost abatement options. All delayed or accelerated action 
pathways  that limit warming to 2°C (>67%) converge to a global 
mitigation regime at some point after 2030 by putting a significant

value on reducing carbon and other GHG emissions in all sectors 
and regions. {3.5}

299
Mitigation Pathways Compatible with Long-term Goals Chapter 33
Mitigation pathways limiting warming to 1.5°C (>50%) with 
no or limited overshoot reach 50% reductions of CO 2 in the 
2030s, relative to 2019, then reduce emissions further to 
reach net zero CO 2 emissions in the 2050s. Pathways  limiting 
warming to 2°C (>67%) reach 50% reductions in the 2040s 
and net zero CO 2 by 2070s (medium confidence). {3.3, Cross-
Chapter Box 3 in this chapter}
Peak warming in mitigation pathways is determined by the 
cumulative net CO 2 emissions until the time of net zero CO 2 and 
the warming contribution of other GHGs and climate forcers 
at that time (high confidence). Cumulative net CO 2 emissions 
from 2020 to the time of net zero CO 2 are 510 (330–710) GtCO 2 
in pathways that limit warming to 1.5°C (>50%) with no or limited 
overshoot and 890 (640–1160) GtCO 2 in pathways limiting warming 
to 2°C (>67%). These estimates are consistent with the assessment

of remaining carbon budgets by WGI after adjusting for differences 
in peak warming levels. {3.3, Box 3.4}
Rapid reductions in non-CO 2 GHGs, particularly methane, 
would lower the level of peak warming (high confidence). 
Residual non-CO 2 emissions at the time of reaching net zero 
CO2 range between 5 and 11 GtCO 2-eq yr–1 in pathways limiting 
warming to 2°C   (>67%) or lower. Methane (CH 4) is reduced by 
around 19% (4–46%) in 2030 and 45% (29–64%) in 2050, relative 
to 2019. Methane emission reductions in pathways limiting warming 
to  1.5°C (>50%) with no or limited overshoot are substantially 
higher by 2030, 34% (21–57%), but only moderately so by 2050, 
51% (35–70%). Methane emissions reductions are thus attainable 
at relatively lower GHG prices but are at the same time limited in 
scope in most  1.5°C–2°C pathways. Deeper methane emissions 
reductions by 2050 could further constrain the peak warming. N 2O 
emissions are reduced too, but similar to CH 4, emission reductions

saturate for more stringent climate goals. In the mitigation pathways, 
the emissions of cooling aerosols are reduced due to reduced use of 
fossil fuels. The overall impact on non-CO 2-related warming combines 
these factors. {3.3}
Net zero GHG emissions imply net negative CO 2 emissions at 
a level compensating residual non-CO 2 emissions. Only 30% of 
the pathways limiting warming to 2°C (>67%) or lower reach 
net zero GHG emissions in the 21st century (high confidence). 
In those pathways reaching net zero GHGs, it is achieved around 
10 to 40 years later than for net zero CO 2 (medium confidence). 
The reported quantity of residual non-CO 2 emissions depends on 
accounting: the choice of GHG metric. Reaching and sustaining global 
net zero GHG emissions, measured in terms of GWP-100, results in 
a gradual decline of temperature (high confidence). {Cross-Chapter 
Box 2 in Chapter 2, 3.3, Cross-Chapter Box 3 in this chapter}
Pathways limiting warming to 2°C (>67%) or lower exhibit

substantial reductions in emissions from all sectors (high 
confidence). Projected CO 2 emissions reductions between 2019 
and 2050 in 1.5°C (>50%) pathways with no or limited overshoot 
are around 77% (31–96%) for energy demand, 115% (90–167%) 
for energy supply, and 148% (94–387%) for agriculture, forestry 
and other land use (AFOLU). In pathways limiting warming to 2°C (>67%), projected CO 2 emissions are reduced between 2019 and 
2050 by around 49% for energy demand, 97% for energy supply, and 
136% for AFOLU (medium confidence). {3.4}
Delaying or sacrificing emissions reductions in one sector 
or region involves compensating reductions in other sectors or 
regions if warming is to be limited (high confidence). Mitigation 
pathways show differences in the timing of decarbonisation and 
when net zero CO 2 emissions are achieved across sectors and 
regions. At the time of global net zero CO 2 emissions, emissions in 
some sectors and regions are positive while others are negative;

the ordering depends on the mitigation options available, the cost 
of those options, and the policies implemented. In cost-effective 
mitigation pathways, the energy-supply sector typically reaches net 
zero CO 2 before the economy as a whole, while the demand sectors 
reach net zero CO 2 later, if ever (high confidence). {3.4}
Pathways limiting warming to 2°C (>67%) or lower involve 
substantial reductions in fossil fuel consumption and a near 
elimination of the use of coal without carbon capture and 
storage (CCS) (high confidence). These pathways show an increase 
in low-carbon energy, with 88% (69–97%) of primary energy coming 
from these sources by 2100. {3.4}
Stringent emissions reductions at the level required for 
2°C (>67%) or lower are achieved through increased direct 
electrification of buildings, transport, and industry, resulting 
in increased electricity generation in all pathways (high 
confidence). Nearly all electricity in pathways limiting warming to

2°C (>67%) or lower is from low- or no-carbon technologies, with 
different shares of nuclear, biomass, non-biomass renewables, and 
fossil CCS across pathways. {3.4}
The measures required to limit warming to 2°C (>67%) or 
lower can result in large-scale transformation of the land 
surface (high confidence). Pathways limiting warming to 2°C 
(>67%) or lower are projected to reach net zero CO 2 emissions in 
the AFOLU sector between the 2020s and 2070, with an increase of 
forest cover of about 322 million ha (–67 to 890 million ha) in 2050 
in pathways limiting warming to 1.5°C (>50%) with no or limited 
overshoot. Cropland area to supply biomass for bioenergy (including 
bioenergy with carbon capture and storage  – BECCS) is around 
199  (56–482) million ha in 2050 in pathways limiting warming 
to 1.5°C (>50%) with no or limited overshoot. The use of bioenergy 
can lead to either increased or reduced emissions, depending on the

scale of deployment, conversion technology, fuel displaced, and how/
where the biomass is produced (high confidence). {3.4}
Anthropogenic land CO 2 emissions and removals in Integrated 
Assessment Model (IAM) pathways cannot be directly 
compared with those reported in national GHG inventories 
(high confidence). Methodologies enabling a  more like-for-like 
comparison between models’ and countries’ approaches would 
support more accurate assessment of the collective progress achieved 
under the Paris Agreement. {3.4, 7.2.2.5}

300
Pathways that  limit warming to 2°C (>67%) or lower involve 
some amount of CDR to compensate for residual GHG  emissions 
remaining after substantial direct emissions reductions in all 
sectors and regions (high confidence). CDR deployment in 
pathways serves multiple purposes: accelerating the pace of emissions 
reductions, offsetting residual emissions, and creating the option for 
net negative CO 2 emissions in case temperature reductions need to 
be achieved in the long term (high confidence). CDR options in the 
pathways are mostly limited to BECCS, afforestation and direct air 
carbon capture and storage (DACCS). CDR through some measures in 
AFOLU can be maintained for decades but not in the very long term 
because these sinks will ultimately saturate (high confidence). {3.4} 
Mitigation pathways show reductions in energy demand 
relative to reference scenarios, through a  diverse set of

demand-side interventions (high confidence). Bottom-up 
and non-IAM studies show significant potential for demand-side 
mitigation. A stronger emphasis on demand-side mitigation implies 
less dependence on CDR and, consequently, reduced pressure on 
land and biodiversity. {3.4, 3.7}
Limiting warming requires shifting energy investments away 
from fossil fuels and towards low-carbon technologies (high 
confidence). The bulk of investments are needed in medium- and 
low-income regions. Investment needs in the electricity sector are on 
average 2.3 trillion USD2015 yr–1 over 2023 to 2052 for pathways 
that limit warming to 1.5°C (>50%) with no or limited overshoot, 
and 1.7 trillion USD2015 yr–1 for pathways that limit warming to 2°C 
(>67%). {3.6.1}
Pathways limiting warming to 2°C (>67%) require more rapid 
near-term transformations and are associated with higher 
upfront transition costs, but meanwhile bring long-term gains

for the economy as well as earlier benefits in avoided climate 
change impacts (high confidence). This conclusion is independent 
of the discount rate applied, though the modelled cost-optimal 
balance of mitigation action over time does depend on the discount 
rate. Lower discount rates favour earlier mitigation, reducing reliance 
on CDR and temperature overshoot. {3.6.1, 3.8}
Mitigation pathways that limit warming to 2°C (>67%) entail 
losses in global GDP with respect to reference scenarios 
of between  1.3% and  2.7% in 2050; and in pathways that 
limit warming to 1.5°C (>50%) with no or limited overshoot, 
losses are between  2.6% and  4.2%. Yet, these estimates do 
not account for the economic benefits of avoided climate 
change impacts (medium confidence). In mitigation pathways 
that limit warming to 2°C (>67%), marginal abatement costs 
of carbon are about 90  (60–120) USD2015 tCO 2 in 2030 and 
about 210 (140–340) USD2015 tCO 2 in 2050; in pathways that

limit warming to  1.5°C (>50%) with no or limited overshoot, 
they are about 220 (170–290) USD2015 tCO 2 in 2030 and about 
630 (430–990) USD2015 tCO 2 in 2050.3 {3.6.1}
3 Numbers in parenthesis represent the interquartile range of the scenario samples.The global benefits of pathways limiting warming to 2°C 
(>67%) outweigh global mitigation costs over the 21st century, 
if aggregated economic impacts of climate change are at the 
moderate to high end of the assessed range, and a  weight 
consistent with economic theory is given to economic impacts 
over the long term. This holds true even without accounting for 
benefits in other sustainable development dimensions or non-
market damages from climate change (medium confidence). 
The aggregate global economic repercussions of mitigation 
pathways include the macroeconomic impacts of investments in 
low-carbon solutions and structural changes away from emitting 
activities, co-benefits and adverse side effects of mitigation,

(avoided) climate change impacts, and (reduced) adaptation costs. 
Existing quantifications of global aggregate economic impacts show 
a strong dependence on socio-economic development conditions, as 
these shape exposure and vulnerability and adaptation opportunities 
and responses. (Avoided) impacts for poorer households and 
poorer countries represent a  smaller share in aggregate economic 
quantifications expressed in GDP or monetary terms, whereas their 
well-being and welfare effects are comparatively larger. When 
aggregate economic benefits from avoided climate change impacts 
are accounted for, mitigation is a welfare-enhancing strategy (high 
confidence). {3.6.2}
The economic benefits on human health from air quality 
improvement arising from mitigation action can be of the 
same order of magnitude as mitigation costs, and potentially 
even larger (medium confidence). {3.6.3}
Differences between aggregate employment in mitigation

pathways compared to reference scenarios are relatively 
small, although there may be substantial reallocations across 
sectors, with job creation in some sectors and job losses in 
others (medium confidence). The net employment effect (and its 
sign) depends on scenario assumptions, modelling framework, and 
modelled policy design. Mitigation has implications for employment 
through multiple channels, each of which impacts geographies, 
sectors and skill categories differently (medium confidence). {3.6.4}
The economic repercussions of mitigation vary widely across 
regions and households, depending on policy design and level 
of international cooperation (high confidence). Delayed global 
cooperation increases policy costs across regions, especially in those 
that are relatively carbon intensive at present (high confidence). 
Pathways with uniform carbon values show higher mitigation costs 
in more carbon-intensive regions, in fossil fuel exporting regions

and in poorer regions (high confidence). Aggregate quantifications 
expressed in GDP or monetary terms undervalue the economic effects 
on households in poorer countries; the actual effects on welfare and 
well-being are comparatively larger (high confidence). Mitigation 
at the speed and scale required to limit warming to 2°C (>67%) or 
lower implies deep economic and structural changes, thereby raising 
multiple types of distributional concerns across regions, income 
classes and sectors (high confidence). {3.6.1, 3.6.4}

301
The timing of mitigation actions and their effectiveness 
will have significant consequences for broader sustainable 
development outcomes in the longer term (high confidence). 
Ambitious mitigation can be considered a precondition for achieving 
the Sustainable Development Goals (SDGs), especially for vulnerable 
populations and ecosystems with little capacity to adapt to climate 
impacts. Dimensions with anticipated co-benefits include health, 
especially regarding air pollution, clean energy access and water 
availability. Dimensions with potential trade-offs include food, 
employment, water stress, and biodiversity, which come under 
pressure from large-scale CDR deployment, energy affordability/
access, and mineral-resource extraction (high confidence). {3.7}
Many of the potential trade-offs of mitigation measures 
for other sustainable development outcomes depend on

policy design and can thus be compensated or avoided with 
additional policies and investments or through policies that 
integrate mitigation with other SDGs (high confidence). 
Targeted SDG policies and investments, for example in the areas 
of healthy nutrition, sustainable consumption and production, and 
international collaboration, can support climate change mitigation 
policies and resolve or alleviate trade-offs. Trade-offs can be addressed 
by complementary policies and investments, as well as through the 
design of cross-sectoral policies integrating mitigation with 
the Sustainable Development Goals of health, nutrition, sustainable 
consumption and production, equity and biodiversity. {3.7}
Decent living standards, which encompass many SDG 
dimensions, are achievable at lower energy use than previously 
thought (high confidence). Mitigation strategies that focus on 
lower demands for energy and land-based resources exhibit reduced

trade-offs and negative consequences for sustainable development 
relative to pathways involving either high emissions and climate 
impacts or those with high consumption and emissions that are 
ultimately compensated by large quantities of BECCS. {3.7}
Different mitigation pathways are associated with different 
feasibility challenges, though appropriate enabling conditions 
can reduce these challenges (high confidence). Feasibility 
challenges are transient and concentrated in the next two to 
three decades (high confidence).   They are multidimensional, 
context-dependent and malleable to policy, technological and 
societal trends. {3.8} 
Mitigation pathways are associated with significant 
institutional and economic feasibility challenges rather than 
technological and geophysical feasibility challenges (medium 
confidence). The rapid pace of technological development and 
deployment in mitigation pathways is not incompatible with

historical records. Institutional capacity is rather a key limiting factor 
for a successful transition. Emerging economies appear to have the 
highest feasibility challenges in the short to medium term. {3.8} 
Pathways relying on a broad portfolio of mitigation strategies 
are more robust and resilient (high confidence). Portfolios of 
technological solutions reduce the feasibility risks associated with 
the low-carbon transition. {3.8}

302
3.1 Introduction
3.1.1 Assessment of Mitigation Pathways and 
Their Compatibility With Long-term Goals
Chapter 3 takes a long-term perspective on climate change mitigation 
pathways. Its focus is on the implications of long-term targets for the 
required short- and medium-term system changes and associated 
greenhouse gas (GHG) emissions. This focus dictates a more global 
view and on issues related to path-dependency and up-scaling 
of mitigation options necessary to achieve different emissions 
trajectories, including particularly deep mitigation pathways that 
require rapid and fundamental changes.
Stabilising global average-temperature change requires reducing 
CO2 emissions to net zero. Thus, a central cross-cutting topic within 
the chapter is the timing of reaching net zero CO 2 emissions and 
how a  ‘balance between anthropogenic emissions by sources

and removals by sinks’ could be achieved across time and space. This 
includes particularly the increasing body of literature since the IPCC 
Special Report on Global Warming of 1.5°C (SR1.5) which focuses on 
net zero CO 2 emissions pathways that avoid temperature overshoot 
and hence do not rely on net negative CO 2 emissions. The chapter 
conducts a systematic assessment of the associated economic costs 
as well as the benefits of mitigation for other societal objectives, 
such as the Sustainable Development Goals (SDGs). In addition, the 
chapter builds on SR1.5 and introduces a new conceptual framing for 
the assessment of possible social, economic, technical, political, and 
geophysical ‘feasibility’ concerns of alternative pathways, including 
the enabling conditions that would need to fall into place so that 
stringent climate goals become attainable.
The structure of the chapter is as follows: Section  3.2 introduces

different types of mitigation pathways as well as the available 
modelling. Section 3.3 explores different emissions trajectories given 
socio-economic uncertainties and consistent with different long-term 
climate outcomes. A central element in this section is the systematic 
categorisation of the scenario space according to key characteristics of 
the mitigation pathways (including e.g., global average-temperature 
change, socio-economic development, technology assumptions, etc.). 
In addition, the section introduces selected Illustrative Mitigation 
Pathways (IMPs) that are used across the whole report. Section 3.4 
conducts a sectoral analysis of the mitigation pathways, assessing 
the pace and direction of systems changes across sectors. Among 
others, this section aims at the integration of the sectoral information 
across AR6 WGIII chapters through a comparative assessment of the 
sectoral dynamics in economy-wide systems models compared to

the insights from bottom-up sectoral models (from Chapters 6 to 11). 
Section 3.5 focuses on the required timing of mitigation actions, and 
the implication of near-term choices for the attainability of a range 
of long-term climate goals. After having explored the underlying 
systems transitions and the required timing of the mitigation actions, 
Section 3.6 assesses the economic implications, mitigation costs and 
benefits; and Section  3.7 assesses related co-benefits, synergies, 
and possible trade-offs for sustainable development and other 
societal (non-climate) objectives. Section 3.8 assumes a central role 
in the chapter and introduces a multidimensional feasibility metric that permits the evaluation of mitigation pathways across a range 
of feasibility concerns. Finally, methods of the assessment and 
knowledge gaps are discussed in Section 3.9, followed by Frequently 
Asked Questions (FAQs).
3.1.2 Linkages to Other Chapters in the Report

Chapter 3 is linked to many other chapters in the report. The most 
important connections exist with Chapter  4 on mitigation and 
development pathways in the near to mid-term; with the sectoral 
chapters (Chapters  6–11); with the chapters dealing with cross-
cutting issues (Chapters 12 and 17, e.g., feasibility); and finally also 
with AR6 WGI and WGII.
Within the overall framing of the AR6 report, Chapter 3 and Chapter 4 
provide important complementary views of the required systems 
transitions across different temporal and spatial scales. While 
Chapter 3 focuses on the questions concerning the implications of the 
long-term objectives for the medium-to-near-term transformations, 
Chapter 4 comes from the other direction, and focuses on current 
near-term trends and policies (such as the Nationally Determined 
Contributions  – NDCs) and their consequences with regards to 
GHG emissions. The latter chapter naturally focuses much more

on the regional and national dimensions, and the heterogeneity of 
current and planned policies. Bringing together the information from 
these two chapters enables the assessment of whether current and 
planned actions are consistent with the required systems changes for 
the long-term objectives of the Paris Agreement.
Important other linkages comprise the collaboration with the 
‘sectoral’ Chapters 6 to 11 to provide an integrated cross-sectoral 
perspective. This information (including information also from 
the sectoral chapters) is taken up ultimately also by Chapter 5 on 
demand/services and Chapter 12 for a further assessment of sectoral 
potential and costs.
Linkages to other chapters exist also on the topic of feasibility, which 
are informed by the policy, the sectoral and the demand chapters, 
the technology and finance chapters, as well as Chapter  4 on 
national circumstances.
Close collaboration with WGI permitted the use of AR6-calibrated

emulators, which assure full consistency across the different 
working groups. Linkages to WGII concern the assessment of 
macroeconomic benefits of avoided impacts that are put into the 
context of mitigation costs as well as co-benefits and trade-offs for 
3.1.3 Complementary Use of Large Scenario Ensembles 
and a Limited Set of Illustrative Mitigation 
Pathways (IMPs)
The assessment of mitigation pathways explores a  wide scenario 
space from the literature within which seven Illustrative Pathways 
(IPs) are explored. The overall process is indicated in Figure 3.5a.

303
For a comprehensive assessment, a large ensemble of scenarios is 
collected and made available through an interactive AR6 Scenarios 
Database4. The collected information is shared across the chapters of 
AR6 and includes more than 3000 different pathways from a diverse 
set of studies. After an initial screening and quality control, scenarios 
were further vetted to assess if they sufficiently represented historical 
trends (Annex III.II.3.1). Subsequently, the climate consequences of 
each scenario were assessed using the climate emulator (leading to 
further classification). The assessment in Chapter 3 is, however, not 
limited to the scenarios from the database, and wherever necessary 
other literature sources are also assessed in order to bring together 
multiple lines of evidence.
In parallel, based on the overall AR6 assessment, seven illustrative

pathways (IP) were defined representing critical mitigation strategies 
discussed in the assessment. The seven pathways are composed of 
two sets: (i) one set of five Illustrative Mitigation Pathways (IMPs) and 
(ii) one set of two reference pathways illustrative for high emissions. 
The IMPs are on the one hand representative of the scenario spac but 
also help to communicate archetypes of distinctly different systems 
transformations and related policy choices. Subsequently, seven 
scenarios were selected from the full database that fitted these 
storylines of each IP best. For these scenarios more strict vetting 
criteria were applied. The selection was done by first applying specific 
filters based on the storyline followed by a final selection (Box 3.1 
and Figure 3.5a).
3.2 Which Mitigation Pathways are 
Compatible With Long-term Goals?
3.2.1 Scenario and Emission Pathways
Scenario and emission pathways are used to explore possible

long-term trajectories, the effectiveness of possible mitigation 
strategies, and to help understand key uncertainties about the 
future. A  scenario is an integrated description of a possible future 
of the human–environment system (Clarke et al. 2014), and could 
be a qualitative narrative, quantitative projection, or both. Scenarios 
4 Available at: https://doi.org/10.5281/zenodo.5886911. All figures and tables in this chapter source data from the AR6 Scenarios Database, unless otherwise stated.typically capture interactions and processes that change key driving 
forces such as population, GDP , technology, lifestyles, and policy, and 
the consequences on energy use, land use, and emissions. Scenarios 
are not predictions or forecasts. An emission pathway is a modelled 
trajectory of anthropogenic emissions (Rogelj et al. 2018a) and, 
therefore, a part of a scenario.
There is no unique or preferred method to develop scenarios, and

future pathways can be developed from diverse methods, depending 
on user needs and research questions (Turnheim et al. 2015; 
Trutnevyte et al. 2019a; Hirt et al. 2020). The most comprehensive 
scenarios in the literature are qualitative narratives that are translated 
into quantitative pathways using models (Clarke et al. 2014; Rogelj 
et al. 2018a). Schematic or illustrative pathways can also be used 
to communicate specific features of more complex scenarios (Allen 
et al. 2018). Simplified models can be used to explain the mechanisms 
operating in more complex models (e.g.,  Emmerling et al. 2019). 
Ultimately, a diversity of scenario and modelling approaches can lead 
to more robust findings (Schinko et al. 2017; Gambhir et al. 2019).
3.2.1.1 Reference Scenarios
It is common to define a reference scenario (also called a baseline 
scenario). Depending on the research question, a reference scenario 
could be defined in different ways (Grant et al. 2020): (i) a hypothetical

world with no climate policies or climate impacts (Kriegler 
et al. 2014b), (ii) assuming current policies or pledged policies are 
implemented (Roelfsema et al. 2020), or (iii) a mitigation scenario 
to compare sensitivity with other mitigation scenarios (Kriegler et al. 
2014a; Sognnaes et al. 2021).
No-climate-policy reference scenarios have often been compared 
with mitigation scenarios (Clarke et al. 2014). A  no-climate-policy 
scenario assumes that no future climate policies are implemented, 
beyond what is in the model calibration, effectively implying that 
the carbon price is zero. No-climate-policy reference scenarios 
have a  broad range depending on socio-economic assumptions 
and model characteristics, and consequently are important when 
assessing mitigation costs (Riahi et al. 2017; Rogelj et al. 2018b). As 
Box 3.1 | Illustrative Mitigation Pathways (IMPs)

The literature shows a wide range of possible emissions trajectories, depicting developments in the absence of new climate policies or 
showing pathways consistent with the Paris Agreement. From the literature, a set of five Illustrative Mitigation Pathways (IMPs) was 
selected to denote implications of choices on socio-economic development and climate policies, and the associated transformations of 
the main GHG-emitting sectors (Figure 3.5b). The IMPs include a set of transformative pathways that illustrate how choices may lead 
to distinctly different transformations that may keep temperature increase to below 2°C (>67%) or 1.5°C. These pathways illustrate 
the implications of a focus on renewable energy such as solar and wind; reduced energy demand; extensive use of CDR in the energy 
and the industry sectors to achieve net negative emissions and reliance on other supply-side measures; strategies that avoid net

negative carbon emissions, and gradual strengthening. In addition, one IMP explores how climate policies consistent with keeping 
limit warming to 1.5C (>50%) can be combined with a broader shift towards sustainable development. These IMPs are used in various 
chapters, exploring for instance their implications for different sectors, regions, and innovation characteristics (Figure 3.5b).

304
countries move forward with climate policies of varying stringency,  
no-climate-policy baselines are becoming increasingly hypothetical 
(Hausfather and Peters 2020). Studies clearly show current policies 
are having an effect, particularly when combined with the declining 
costs of low-carbon technologies (IEA 2020a; Roelfsema et al. 2020; 
Sognnaes et al. 2021; UNEP 2020), and, consequently, realised 
trajectories begin to differ from earlier no-climate-policy scenarios 
(Burgess et al. 2020). High-end emission scenarios, such as RCP8.5 and 
SSP5-8.5, are becoming less likely with climate policy and technology 
change (Box  3.3), but high-end concentration and warming levels 
may still be reached with the inclusion of strong carbon or climate 
feedbacks (Hausfather and Peters 2020; Pedersen et al. 2020).
3.2.1.2 Mitigation Scenarios
Mitigation scenarios explore different strategies to meet climate goals

and are typically derived from reference scenarios by adding climate 
or other policies. Mitigation pathways are often developed to meet 
a predefined level of climate change, often referred to as a backcast. 
There are relatively few IAMs that include an endogenous climate 
model or emulator due to the added computational complexity, 
though exceptions do exist. In practice, models implement climate 
constraints by either iterating carbon-price assumptions (Strefler 
et al. 2021b) or by adopting an associated carbon budget (Riahi 
et al. 2021). In both cases, other GHGs are typically controlled by 
CO2-equivalent pricing. A large part of the AR5 literature has focused 
on forcing pathways towards a target at the end of the century (van 
Vuuren et al. 2007, 2011; Clarke et al. 2009; Blanford et al. 2014; Riahi 
et al. 2017), featuring a  temporary overshoot of the warming and 
forcing levels (Geden and Löschel 2017). In comparison, many recent

studies explore mitigation strategies that limit overshoot (Johansson 
et al. 2020; Riahi et al. 2021). An increasing number of IAM studies 
also explore climate pathways that limit adverse side effects with 
respect to other societal objectives, such as food security (van Vuuren 
et al. 2019; Riahi et al. 2021) or larger sets of sustainability objectives 
(Soergel et al. 2021a).
3.2.2 The Utility of Integrated Assessment Models
Integrated Assessment Models (IAMs) are critical for understanding 
the implications of long-term climate objectives for the required 
near-term transition. For doing so, an integrated systems perspective 
including the representation of all sectors and GHGs is necessary. 
IAMs are used to explore the response of complex systems in a formal 
and consistent framework. They cover a broad range of modelling 
frameworks (Keppo et al. 2021). Given the complexity of the systems 
under investigation, IAMs necessarily make simplifying assumptions

and therefore results need to be interpreted in the context of these 
assumptions. IAMs can range from economic models that consider 
only carbon dioxide emissions through to detailed process-based 
representations of the global energy system, covering separate 
regions and sectors (such as energy, transport, and land use), all GHG 
emissions and air pollutants, interactions with land and water, and 
a reduced representation of the climate system. IAMs are generally 
driven by economics and can have a variety of characteristics such as 
partial-, general- or non-equilibrium; myopic or perfect foresight; be based on optimisation or simulation; have exogenous or endogenous 
technological change amongst many other characteristics. IAMs take 
as input socio-economic and technical variables and parameters 
to represent various systems. There is no unique way to integrate 
this knowledge into a model, and due to their complexity, various

simplifications and omissions are made for tractability. IAMs 
therefore have various advantages and disadvantages which need 
to be weighed up when interpreting IAM outcomes. Annex  III.I 
contains an overview of the different types of models and their 
key characteristics.
Most IAMs are necessarily broad as they capture long-term dynamics. 
IAMs are strong in showing the key characteristics of emission 
pathways and are most suited to questions related to short- versus 
long-term trade-offs, key interactions with non-climate objectives, 
long-term energy and land-use characteristics, and implications of 
different overarching technological and policy choices (Clarke et al. 
2014; Rogelj et al. 2018a). While some IAMs have a  high level of 
regional and sectoral detail, for questions that require higher levels 
of granularity (e.g., local policy implementation) specific region and 
sector models may be better suited. Utility of the IAM pathways

increases when the quantitative results are contextualized through 
qualitative narratives or other additional types of knowledge 
to provide deeper insights (Geels et al. 2016a; Weyant 2017; 
Gambhir et al. 2019).
IAMs have a  long history in addressing environmental 
problems, particularly in the IPCC assessment process (van Beek 
et al. 2020). Many policy discussions have been guided by IAM-based 
quantifications, such as the required emission reduction rates, net 
zero years, or technology deployment rates required to meet certain 
climate outcomes. This has led to the discussion about whether IAM 
scenarios have become performative, meaning that they act upon, 
transform or bring into being the scenarios they describe (Beck and 
Mahony 2017, 2018). Transparency of underlying data and methods 
is critical for scenario users to understand what drives different 
scenario results (Robertson 2020). A number of community activities

have thus focused on the provision of transparent and publicly 
accessible databases of both input and output data (Riahi et al. 2012; 
Huppmann et al. 2018; Krey et al. 2019; Daioglou et al. 2020), as well 
as the provision of open-source code, and increased documentation 
(Annex III.I.9). Transparency is needed to reveal conditionality of results 
on specific choices in terms of assumptions (e.g., discount rates) and 
model architecture. More detailed explanations of underlying model 
dynamics would be critical to increase the understanding of what 
drives results (Bistline et al. 2020; Butnar et al. 2020; Robertson 2020).
Mitigation scenarios developed for a  long-term climate constraint 
typically focus on cost-effective mitigation action towards a  long-
term climate goal. Results from IAM as well as sectoral models depend 
on model structure (Mercure et al. 2019), economic assumptions 
(Emmerling et al. 2019), technology assumptions (Pye et al. 2018),

climate/emissions target formulation (Johansson et al. 2020), and 
the extent to which pre-existing market distortions are considered 
(Guivarch et al. 2011). The vast majority of IAM pathways do not 
consider climate impacts (Schultes et al. 2021). Equity hinges upon 
ethical and normative choices. As most IAM pathways follow the

305
cost-effectiveness approach, they do not make any additional equity 
assumptions. Notable exceptions include Tavoni et al. (2015), Pan et al. 
(2017), van den Berg et al. (2020), and Bauer et al. (2020). Regional 
IAM results therefore need to be assessed with care, considering that 
emissions reductions are happening where it is most cost-effective, 
which needs to be separated from who is ultimately paying for 
the mitigation costs. Cost-effective pathways can provide a  useful 
benchmark, but may not reflect real-world developments (Calvin 
et al. 2014a; Trutnevyte 2016). Different modelling frameworks may 
lead to different outcomes (Mercure et al. 2019). Recent studies have 
shown that other desirable outcomes can evolve with only minor 
deviations from cost-effective pathways (Bauer et al. 2020; Neumann 
and Brown 2021). IAM and sectoral models represent social, political,

and institutional factors only in a rudimentary way. This assessment is 
thus relying on new methods for the ex post assessment of feasibility 
concerns (Jewell and Cherp 2020; Brutschin et al. 2021). A literature is 
emerging that recognises and reflects on the diversity and strengths/
weaknesses of model-based scenario analysis (Keppo et al. 2021).
The climate constraint implementation can have a meaningful impact 
on model results. The literature so far includes many temperature 
overshoot scenarios with heavy reliance on long-term CDR and net 
negative CO 2 emissions to bring back temperatures after the peak 
(Rogelj et al. 2019b; Johansson et al. 2020). New approaches have 
been developed to avoid temperature overshoot. The new generation 
of scenarios show that CDR is important beyond its ability to reduce 
temperature, but is essential also for offsetting residual emissions to 
reach net zero CO 2 emissions (Rogelj et al. 2019b; Johansson et al.

2020; Riahi et al. 2021; Strefler et al. 2021b).
Many factors influence the deployment of technologies in the 
IAMs. Since AR5, there has been fervent debate on the large-scale 
deployment of bioenergy with carbon capture and storage (BECCS) in 
scenarios (Fuss et al. 2014; Geden 2015; Anderson and Peters 2016; 
Smith et al. 2016; van Vuuren et al. 2017; Galik 2020; Köberle 2019). 
Hence, many recent studies explore mitigation pathways with limited 
BECCS deployment (Grubler et al. 2018; van Vuuren et al. 2019; 
Riahi et al. 2021; Soergel et al. 2021a). While some have argued that 
technology diffusion in IAMs occurs too rapidly (Gambhir et al. 2019), 
others argued that most models prefer large-scale solutions resulting 
in a  relatively slow phase-out of fossil fuels (Carton 2019). While 
IAMs are particularly strong on supply-side representation, demand-
side measures still lag in detail of representation despite progress

since AR5 (Grubler et al. 2018; Lovins et al. 2019; van den Berg et al. 
2019;  O’Neill et al. 2020b; Hickel et al. 2021; Keyßer and Lenzen 
2021). The discount rate has a  significant impact on the balance 
between near-term and long-term mitigation. Lower discount rates 
<4% (than used in IAMs) may lead to more near-term emissions 
reductions – depending on the stringency of the target (Emmerling 
et al. 2019; Riahi et al. 2021). Models often use simplified policy 
assumptions (O’Neill et al. 2020b) which can affect the deployment 
of technologies (Sognnaes et al. 2021). Uncertainty in technologies 
can lead to more or less short-term mitigation (Grant et al. 2021; 
Bednar et al. 2021). There is also a recognition to put more emphasis 
on what drives the results of different IAMs (Gambhir et al. 2019) 
and suggestions to focus more on what is driving differences in result

across IAMs (Nikas et al. 2021). As noted by Weyant (2017, p. 131), ‘IAms can provide very useful information, but this information needs 
to be carefully interpreted and integrated with other quantitative and 
qualitative inputs in the decision-making process.’
3.2.3 The Scenario Literature and Scenario Databases
IPCC reports have often used voluntary submissions to a  scenario 
database in its assessments. The database is an ensemble of opportunity, 
as there is not a well-designed statistical sampling of the hypothetical 
model or scenario space: the literature is unlikely to cover all possible 
models and scenarios, and not all scenarios in the literature are 
submitted to the database. Model intercomparisons are often the core 
of scenario databases assessed by the IPCC (Cointe et al. 2019; Nikas 
et al. 2021). Single-model studies may allow more detailed sensitivity 
analyses or address specific research questions. The scenarios that are

organised within the scientific community are more likely to enter the 
assessment process via the scenario database (Cointe et al. 2019), 
while scenarios from different communities, in the emerging literature, 
or not structurally consistent with the database may be overlooked. 
Scenarios in the grey literature may not be assessed even though they 
may have greater weight in a policy context.
One notable development since AR5 is the Shared Socio-economic 
Pathways (SSPs), conceptually outlined in Moss et al. (2010) and 
subsequently developed to support integrated climate research across 
the IPCC Working Groups (O’Neill et al. 2014). Initially, a set of SSP 
narratives were developed, describing worlds with different challenges 
to mitigation and adaptation (O’Neill et al. 2017a): SSP1 (sustainability), 
SSP2 (middle of the road), SSP3 (regional rivalry), SSP4 (inequality) 
and SSP5 (rapid growth). The SSPs have now been quantified in terms

of energy, land-use, and emission pathways (Riahi et al. 2017), for 
both no-climate-policy reference scenarios and mitigation scenarios 
that follow similar radiative-forcing pathways as the Representative 
Concentration Pathways (RCPs) assessed in AR5 WGI. Since then the 
SSPs have been successfully applied in thousands of studies (O’Neill 
et al. 2020b) including some critiques on the use and application of 
the SSP framework (Pielke and Ritchie 2021; Rosen 2021). A selection 
of the quantified SSPs are used prominently in AR6 WGI as they were 
the basis for most climate modelling since AR5 (O’Neill et al. 2016). 
Since 2014, when the first set of SSP data was made available, there 
has been a divergence between scenario and historic trends (Burgess 
et al. 2020). As a result, the SSPs require updating (O’Neill et al. 2020b). 
Most of the scenarios in the AR6 database are SSP-based and consider 
various updates compared to the first release (Riahi et al. 2017).

3.2.4 The AR6 Scenario Database
To facilitate this assessment, a  large ensemble of scenarios has 
been collected and made available through an interactive AR6 
WGIII scenario database. The collection of the scenario outputs 
is coordinated by Chapter  3 and expands upon the IPCC SR1.5 
scenario explorer (Huppmann et al. 2018; Rogelj et al. 2018a). 
A  complementary database for national pathways has been 
established by Chapter 4. Annex III.II.3 contains full details on how 
the scenario database was compiled.

306
The AR6 scenario database contains 3131 scenarios (Figure  3.5a). 
After an initial screening and quality control, scenarios were further 
vetted to assess if they sufficiently represented historical trends 
(Annex III.II.3.1). Of the initial 2266 scenarios with global scope, 
1686 scenarios passed the vetting process and are assessed in this 
chapter. The scenarios that did not pass the vetting are still available in 
the database. The vetted scenarios were from over 50 different model 
families, or over 100 when considering all versions of the same family 
(Figure 3.1). The scenarios originated from over 15 different model intercomparison projects, with around one-fifth originating from 
individual studies (Figure 3.2). Because of the uneven distribution of 
scenarios from different models and projects, uncorrected statistics 
from the database can be misleading.

Each scenario with sufficient data is given a  temperature 
classification using climate model emulators. Three emulators were 
used in the assessment: FAIR (Smith et al. 2018), CICERO-SCM 
(Skeie et al. 2021), MAGICC (Meinshausen et al. 2020). Only the 300
250
200
150
100
50
0Number of scenarios from each model family
Vetted scenarios in database (n=1686)
REMINDMESSAGEIMAGE POLES WITCH GCAMCOFFEEAIMTIAM
GEM-E3C-GEM
CGE-MOD EC-MSMR EDF-GEPA ENVISAGE ICES-EMF
SNOW_GL_HH_v1TEA
WEGDYN IMACLIMOthers
Figure 3.1 | Scenario counts from each model family defined as all versions under the same model’s name.
600
500
400
300
200
100
0Number of scenarios from each project
ENGAGEEMF36SSP
EMF30 EMF33COMMIT CD-LINKS ADVANCE
PARIS REINFORCENGFSOthers
Figure 3.2 | Scenario counts from each named project.

307
Table 3.1 | Classification of emissions scenarios into warming levels using MAGICC
Category Description WGI SSP WGIII IP/IMP Scenarios
C1: Limit warming to 1.5°C (>50%)  
with no or limited overshootReach or exceed 1.5°C during the 21st century with a likelihood of ≤67%, and limit 
warming to 1.5°C in 2100 with a likelihood >50%. 
Limited overshoot refers to exceeding 1.5°C by up to about 0.1°C and for up to several decades.SSP1-1.9IMP-SP , 
IMP-LD,  
IMP-Ren97
C2: Return warming to 1.5°C (>50%)  
after a high overshootExceed warming of 1.5°C during the 21st century with a likelihood of >67%, and limit 
warming to 1.5°C in 2100 with a likelihood of >50%.
High overshoot refers to temporarily exceeding 1.5°C global warming by 0.1°C–0.3°C for 
up to several decades.IMP-Nega 133
C3: Limit warming to 2°C (>67%) Limit peak warming to 2°C throughout the 21st century with a likelihood of >67%. SSP1-2.6 IMP-GS 311

C4: Limit warming to 2°C (>50%) Limit peak warming to 2°C throughout the 21st century with a likelihood of >50%. 159
C5: Limit warming to 2.5°C (>50%) Limit peak warming to 2.5°C throughout the 21st century with a likelihood of >50%. 212
C6: Limit warming to 3°C (>50%) Limit peak warming to 3°C throughout the 21st century with a likelihood of >50%. SSP2-4.5 ModAct 97
C7: Limit warming to 4°C (>50%) Limit peak warming to 4°C throughout the 21st century with a likelihood of >50%. SSP3-7.0 CurPol 164
C8: Exceed warming of 4°C (≥50%) Exceed warming of 4°C during the 21st century with a likelihood of ≥50%. SSP5-8.5 29
C1, C2, C3: limit warming to 2°C 
(>67%) or lowerAll scenarios in Categories C1, C2 and C3 541
a The Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative

emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the 
characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.
results of MAGICC are shown in this chapter as it adequately covers the 
range of outcomes. The emulators are calibrated against the behaviour 
of complex climate models and observation data, consistent with the 
outcomes of AR6 WGI (Cross-Chapter Box 7.1). The climate assessment 
is a three-step process of harmonisation, infilling and a probabilistic 
climate model emulator run (Annex  III.II.2.5). Warming projections 
until the year 2100 were derived for 1574 scenarios, of which 1202 
passed vetting, with the remaining scenarios having insufficient 
information (Figure 3.3 and Table 3.1). For scenarios that limit warming

to 2°C or lower, the SR1.5 classification was adopted in AR6, with 
more disaggregation provided for higher warming levels (Table 3.1). These choices can be compared with the selection of common global 
warming levels (GWLs) of 1.5°C, 2°C, 3°C and 4°C to classify climate 
change impacts in the WGII assessment.
In addition to the temperature classification, each scenario is assigned 
to one of the following policy categories: (P0) diagnostic scenarios – 99 
of 1686 vetted scenarios; (P1) scenarios with no globally coordinated 
policy (500) and (P1a) no climate mitigation efforts  – 124, (P1b) 
current national mitigation efforts – 59, (P1c) Nationally Determined 
Contributions (NDCs) – 160, or (P1d) other non-standard assumptions – 
153; (P2) globally coordinated climate policies with immediate 500
450
400
350
300
250
150200
100
50
0Number of scenarios in each climate category
Vetted scenarios in database (n=1686) ...with warming estimates (n=1202)
97
C1: limit warming

to 1.5°C (>50%) 
with no or 
limited overshootC2: return 
warming to 
1.5°C (>50%) 
after a high 
overshootC3: limit 
warming to 
2°C (>67%)C4: limit 
warming to 
2°C (>50%)C5: limit 
warming to 
2.5°C (>50%)C6: limit 
warming to 
3°C (>50%)C7: limit 
warming to 
4°C (>50%)C8: exceed 
warming of 
4°C (≥50%)133311
159212
97164
29484
None
Figure 3.3 | Of the 1686 scenarios that passed vetting, 1202 had sufficient data available to be classified according to temperature, with an uneven 
distribution across warming levels.

308
7 8 9 10 11 12 13
Population in 2100 (billion)0500CountSSP1 SSP2 SSP3 SSP4 SSP5
0 500 1000 1500
Final energy in 2100 (EJ)0100200300Count
 200 300 400 500 600 700 800 900 1000 1100
Gross Domestic Product in 2100 (trillion USD2010 yr–1)0100200CountSSP1 SSP2 SSP3 SSP4 SSP5
Primary energy in 2100 (EJ)050100Count
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Carbon capture and storage cumulative (GtCO2)050100Count
0 100 200 300 400 500 600 700 800 900 1000
Fossil fuels in 2100 (EJ)050100Count
Carbon dioxide removal (BECCS) Cumulative (GtCO2)050100Count
Non-biomass renewables in 2100 (EJ)050100Count
Carbon dioxide removal (DACCS) Cumulative (GtCO2)050100Count
0 50 100 150 200 250 300 350 400 450 500
Bioenergy in 2100 (EJ)050100150Count

Carbon dioxide removal (land use) cumulative (GtCO2)050100Count
Nuclear in 2100 (EJ)050100150Count
Figure 3.4 | Histograms for key categories in the AR6 scenario database.  Only scenarios that passed vetting are shown. For population and GDP , the SSP input data are also shown. The grey shading represents the 0–100% range 
(light grey), 25–75% range (dark grey), and the median is a black line. The figures with white areas are outside of the scenario range, but the axis limits are retained to allow comparability with other categories. Each sub-figure potentially has 
different x- and y-axis limits. Each figure also potentially contains different numbers of scenarios, depending on what was submitted to the database. Source: AR6 scenarios database.

309
action (634) and (P2a) without any transfer of emission permits – 435, 
(P2b) with transfers – 70; or (P2c) with additional policy assumptions – 
55; (P3) globally coordinated climate policies with delayed (i.e., from 
2030 onwards or after 2030) action (451), preceded by (P3a) no 
mitigation commitment or current national policies – 7, (P3b) NDCs – 
426, (P3c) NDCs and additional policies – 18; (P4) cost-benefit analysis 
(CBA)  –  2. The policy categories were identified using text pattern 
matching on the scenario metadata and calibrated on the best-known 
scenarios from model intercomparisons, with further validation against 
the related literature, reported emission and carbon price trajectories, 
and exchanges with modellers. If the information available is enough 
to qualify a policy category number but not sufficient for a subcategory, 
then only the number is retained (e.g., P2 instead of P2a/b/c). A suffix

added after P0 further qualifies a diagnostic scenario as one of the other 
policy categories. To demonstrate the diversity of the scenarios, the vetted 
scenarios were classified into different categories along the dimensions 
of population, GDP , energy, and cumulative emissions (Figure 3.4). The 
number of scenarios in each category provides some insight into the 
current literature, but this does not indicate a higher probability of that 
category occurring in reality. For population, the majority of scenarios 
are consistent with the SSP2 ‘middle of the road’ category, with very 
few scenarios exploring the outer extremes. GDP has a slightly larger 
variation, but overall most scenarios are around the SSP2 socio-
economic assumptions. The level of CCS and CDR is expected to change 
depending on the extent of mitigation, but there remains extensive use 
of both CDR and CCS in scenarios. CDR is dominated by bioenergy with

CCS (BECCS) and sequestration on land, with relatively few scenarios 
using direct air capture with carbon storage (DACCS) and even less 
with enhanced weathering (EW) and other technologies (not shown). 
In terms of energy consumption, final energy has a much smaller range 
than primary energy as conversion losses are not included in final 
energy. Both mitigation and reference scenarios are shown, so there is 
a broad spread in different energy carriers represented in the database. 
Bioenergy has a number of scenarios at around 100 EJ, representing 
a constraint used in many model intercomparisons.
3.2.5 Illustrative Mitigation Pathways
Successive IPCC Assessment Reports (ARs) have used scenarios to 
illustrate key characteristics of possible climate (policy) futures. In AR5 
four RCPs made the basis of climate modelling in WGI and WGII, with 
WGIII assessing over 1000 scenarios spanning those RCPs (Clarke et al. 
2014). Of the over 400 scenarios assessed in SR1.5, four scenarios

were selected to highlight the trade-off between short-term emission 
reductions and long-term deployment of BECCS (Rogelj et al. 2018a), 
referred to as ‘Illustrative Pathways’ (IPs). AR6 WGI and WGII rely on 
the scenarios selected for CMIP6, called ScenarioMIP (O’Neill et al. 
2016), to assess warming levels. In addition to the full set of scenarios, 
AR6 WGIII also uses selected Illustrative Mitigation Pathways (IMPs).
In WGIII, IMPs were selected to denote the implications of different 
societal choices for the development of future emissions and 
associated transformations of main GHG-emitting sectors (Figure 3.5a 
and Box 3.1). The most important function of the IMPs is to illustrate 
key themes that form a  common thread in the report, both with 
a storyline and a quantitative illustration. The storyline describes the key characteristics that define an IMP . The quantitative versions of 
the IMPs provide numerical values that are internally consistent and

comparable across chapters of the report. The quantitative IMPs have 
been selected from the AR6 scenario database. No assessment of the 
likelihood of each IMP has been made.
The selected scenarios (IPs) are divided into two sets (Figures  3.5 
and 3.6): two reference pathways illustrative of high emissions and five 
Illustrative Mitigation Pathways (IMPs). The narratives are explained 
in full in Annex III.II.2.4. The two reference pathways explore the 
consequences of current policies and pledges: Current Policies (CurPol) 
and Moderate Action (ModAct). The CurPol pathway explores the 
consequences of continuing along the path of implemented climate 
policies in 2020 and only a  gradual strengthening after that. The 
scenario illustrates the outcomes of many scenarios in the literature 
that project the trend from implemented policies until the end of 
2020. The ModAct pathway explores the impact of implementing 
the Nationally Determined Contributions (NDCs) as formulated in

2020 and some further strengthening after that. In line with current 
literature, these two reference pathways lead to an increase in global 
mean temperature of more than 2°C (Section 3.3).
The Illustrative Mitigation Pathways (IMPs) properly explore different 
pathways consistent with meeting the long-term temperature goals 
of the Paris Agreement. They represent five different pathways that 
emerge from the overall assessment. The IMPs differ in terms of their 
focus, for example, placing greater emphasis on renewables (IMP-Ren), 
deployment of carbon dioxide removal that results in net negative 
global GHG emissions (IMP-Neg), and efficient resource use and 
shifts in consumption patterns, leading to low demand for resources, 
while ensuring a high level of services (IMP-LD). Other IMPs illustrate 
the implications of a  less rapid introduction of mitigation measures 
followed by a subsequent gradual strengthening (IMP-GS), and how

shifting global pathways towards sustainable development, including 
by reducing inequality, can lead to mitigation (IMP-SP) In the IMP 
framework, IMP-GS is consistent with limiting warming to 2°C (>67%) 
(C3), IMP-Neg shows a strategy that also limits warming to 2°C (>67%) 
but returns to nearly 1.5°C (>50%) by the end of the century (hence 
indicated as C2*). The other variants that can limit warming to 1.5°C 
(>50%) (C1) were selected. In addition to these IMPs, sensitivity cases 
that explore alternative warming levels (C3) for IMP-Neg and IMP-Ren  
are assessed (IMP-Neg-2.0 and IMP-Ren-2.0).
The IMPs are selected to have different mitigation strategies, which 
can be illustrated looking at the energy system and emission pathways 
(Figure 3.7 and Figure 3.8). The mitigation strategies show the different 
options in emission reduction (Figure 3.7). Each panel shows the key 
characteristics leading to total GHG emissions, consisting of residual

(gross) emissions (fossil CO2 emissions, CO 2 emissions from industrial 
processes, and non-CO 2 emissions) and removals (net land-use change, 
bioenergy with carbon capture and storage  – BECCS, and direct air 
carbon capture and storage – DACCS), in addition to avoided emissions 
through the use of carbon capture and storage on fossil fuels. The 
IMP-Neg and IMP-GS scenarios were shown to illustrate scenarios 
with a significant role of CDR. The energy supply (Figure 3.8) shows 
the phase-out of fossil fuels in the IMP-LD, IMP-Ren and IMP-SP cases, 
but a less substantial decrease in the IMP-Neg case. The IMP-GS case

310
Different routesLevel of ambition
Baseline Gradual 
StrengtheningFocus on 
RenewablesFocus on 
Low DemandExtensive use of 
net negative 
emissionsShifting 
PathwaysIMP-SPAR6 WGIII scenario database
Modelling 
teamsAR6 WGIII  
scenario database
Initial 
vettingClimate 
emulators
IP 
vettingIPs storylinesIllustrative pathways2266 scenarios 1686 scenarios
Vetting criteria:
Historic trends
Near-term plausibilityVetting criteria:
Coherence with historic trends
1202 scenariosIPCC author 
teamC1: limit warming to 1.5°C (>50%)
       with no or limited overshoot
C2: return warming to 1.5°C (>50%) 
      after a high overshoot
C3: limit warming to 2°C (>67%)
C4: limit warming to 2°C (>50%)
C5: limit warming to 2.5°C (>50%)
C6: limit warming to 3°C (>50%)
C7: limit warming to 4°C (>50%)
C8: exceed warming of 4°C (≥50%)
IMPs
b)a)
CurPol 
ﬁlters
CurPol 
(C7)ModAct
ﬁlters
ModAct
(C6)GS
ﬁlters
GS 
(C3)Neg

ﬁlters
Neg 
(C2*)
Neg2.0 
(C3)Ren 
ﬁlters
Ren
(C1)
Ren2.0 
(C3)LD
ﬁlters
LD 
(C1)SP
ﬁlters
SP 
(C1)
IMP-Neg
IMP-LDIMP-GS Ren2.0
IMP-RenNeg2.0CurPol
ModAct
Figure 3.5 | (a) Process for creating the AR6 scenario database and selecting the illustrative (mitigation) pathways.  The compiled scenarios in the AR6 
scenarios database were vetted for consistency with historical statistics and subsequently a temperature classification was added using climate model emulators. The illustrative 
(mitigation) pathways were selected from the full set of pathways based on storylines of critical mitigation strategies that emerged from the assessment. (b) An overview of 
the Illustrative Pathways selected for use in IPCC AR6 WGIII, consisting of pathways illustrative of higher emissions, Current Policies (CurPol) and Moderate Action (ModAct),

and Illustrative Mitigation Pathways (IMPs): gradual strengthening of current policies (IMP-GS), extensive use of net negative emissions (IMP-Neg), renewables (IMP-Ren), 
low demand (IMP-LD), and shifting pathways (IMP-SP). The Ren2.0 and Neg2.0 scenarios are alternative scenarios to the IMPs. These pathways are based on renewables and 
extensive use of negative emissions, respectively, but leading to temperature levels comparable to the C3 category and have sometimes been used for comparison.

311
–200/uni00A020/uni00A040/uni00A060/uni00A080/uni00A0
–20020/uni00A040/uni00A060/uni00A080/uni00A0
Modelled mitigation pathways that limit warming to 1.5° C, and 2° C, involve dee p, rapid and 
sustained emissions r eduction s.
a. Net global GHG emissions b. Net global CO 2 emissions
Year of net-zero GHG emissions Year of net-zero CO   emissions2
C3
C1C3
C1
All climate categories 
(very likely range)
Implemented policies and 2030 pledges
(very likely range)CurPol (C7)
ModAct (C6)
IMP-GS (C3)
IMP-Neg (C2)
Limit warming to 2°C (>67%) (C3)
Limit warming to 1.5°C (>50%)
with no or limited overshoot (C1)
(very likely range)IMP-LD (C1)
IMP-Ren (C1)
IMP-SP (C1)Past emissions (2000–2015)
Percentile of 2100 emission level:
95th
Median
5th75th
25th
Model range for 2015 emissions
Past GHG emissions and uncertainty 
for 2015 and 2019 (dot indicates the median)
2100 2080 2060 2040 2020 2000

2100 2080 2060 2040 2020 20002100 2080 2060 2040 2020 2000
CO2 
comparisonGHG 
comparisonGtCO 2 yr  –1GtCO2-eq yr  –1
–100%–50%Modelled
2019 level+50%
NDC range
Figure 3.6 | Overview of the net CO 2 emissions and Kyoto greenhouse gas (GHG) emissions for each Illustrative Mitigation Pathway (IMP).

312
–200
2020
2040
2060
2080
2100
2020
2040
2060
2080
2100
2020
2040
2060
2080
21002020
2040
2060
2080
2100
2020
2040
2060
2080
2100
2020
2040
2060
2080
2100
2020
2040
2060
2080
2100204060
–200204060GtCO2-eq yr–1GtCO2-eq yr–1CurPol ModAct IMP-GS
IMP-Neg IMP-Ren IMP-LD IMP-SPNon CO2
Fossil CO2Avoided emissions FFCCS
Industry
DACCS
BECCSLUCNet GHG
Net CO2
Figure 3.7 | The residual fossil fuel and industry emissions, carbon dioxide removal (CDR) {LUC, DACCS, BECCS}, and non-CO 2 emissions (using AR6 
GWP-100) for each of the seven illustrative pathways (IPs).  Fossil CCS is also shown, though this does not lead to emissions to the atmosphere (Section 3.2.5).
0200
2020
2040
2060
2080
2100
2020
2040
2060
2080
2100
2020
2040
2060
2080
21002020
2040
2060
2080
2100
2020
2040
2060
2080
2100
2020
2040
2060
2080
2100
2020
2040
2060
2080
2100400800
6001000
0200400800
6001000EJ yr–1EJ yr–1CurPol ModAct IMP-GS

IMP-Neg IMP-Ren IMP-LD IMP-SPBiomass (non-traditional)
FossilRenewables (non-biomass)
Biomass (traditional)
NuclearTotal
Figure 3.8 | The energy system in each of the illustrative pathways (IPs).

313
needs to make up its slow start by (i)  rapid reductions mid-century 
and (ii) massive reliance on net negative emissions by the end of the 
century. The CurPol and ModAct cases both result in relatively high 
emissions, showing a  slight increase and stabilisation compared to 
current emissions, respectively.
3.3 Emission Pathways, Including Socio-
economic, Carbon Budget and Climate 
Responses Uncertainties
3.3.1 Socio-economic Drivers of Emissions Scenarios
Greenhouse gas (GHG) emissions mainly originate from the use and 
transformation of energy, agriculture, land use (change) and industrial 
activities. The future development of these sources is  influenced 
by trends in socio-economic development, including  population, 
economic activity, technology, politics, lifestyles, and climate policy. 
Trends for these factors are not independent, and scenarios provide

a consistent outlook for these factors together (Section 3.2). Marangoni 
et al. (2017) show that in projections, assumptions influencing 
energy intensity (e.g., structural change, lifestyle and efficiency) and 
economic growth are the most important determinants of future 
CO2 emissions from energy combustion. Other critical factors include 
technology assumptions, preferences, resource assumptions and 
policy (van Vuuren et al. 2008). As many of the factors are represented 
differently in specific models, the model itself is also an important 
factor  – providing a  reason for the importance of model diversity 
(Sognnaes et al. 2021). For land use, Stehfest et al. (2019) show that 
assumptions on population growth are more dominant given that 
variations in per capita consumption of food are smaller than for 
energy. Here, we only provide a brief overview of some key drivers. We 
focus first on so-called reference scenarios (without stringent climate

policy) and look at mitigation scenarios in detail later. We use the SSPs 
to discuss trends in more detail. The SSPs were published in 2017, and 
by now, some elements will have to be updated (O’Neill et al. 2020b). 
Still, the ranges represent the full literature relatively well.
Historically, population and GDP have been growing over time. 
Scenario studies agree that further global population growth 
is likely up to 2050, leading to a  range of possible outcomes of 
around 8.5–11 billion people (Figure 3.9a). After 2050, projections 
show a much wider range. If fertility drops below replacement levels, 
a decline in the global population is possible (as illustrated by SSP1 
and SSP5). This typically includes scenarios with rapid development 
and investment in education. However, median projections mostly 
show a  stabilisation of the world population (e.g.,  SSP2), while 
high-end projections show a continued growth (e.g., SSP3). The UN

Population Prospects include considerably higher values for both 
the medium projection and the high end of the range than the SSP 
scenarios (KC and Lutz 2017; UN 2019). The most recent median 
UN projection reaches almost 11 billion people in 2100. The key 
differences are in Africa and China: here, the population projections 
are strongly influenced by the rate of fertility change (faster drop in 
SSPs). Underlying these differences, the UN approach is more based 
on current demographic trends while the SSPs assume a  broader 
range of factors (including education) driving future fertility.Economic growth is even more uncertain than the population projections 
(Figure  3.9c). The average growth rate of GDP was about  2.8% per 
year (constant USD) in the 1990–2019 period (The World Bank 2021). 
In 2020, the COVID-19 crisis resulted in a  considerable drop in GDP 
(estimated around  4–5%) (IMF 2021). After a  recovery period, most

economic projections assume growth rates to converge back to previous 
projections, although at a lower level (IMF 2021; OECD 2021) (see also 
Box  3.2). In the long term, assumptions on future growth relate to 
political stability, the role of the progress of the technology frontier and 
the degree to which countries can catch up (Johansson et al. 2013). The 
SSP scenarios cover an extensive range, with low per-capita growth in 
SSP3 and SSP4 (mostly in developing countries) and rapid growth in 
SSP1 and SSP5. At the same, however, also scenarios outside the range 
have some plausibility – including the option of economic decline (Kallis 
et al. 2012) or much faster economic development (Christensen et al. 
2018). The OECD long-term projection is at the global level reasonably 
consistent with SSP2. Equally important economic parameters include 
income distribution (inequity) and the type of growth (structural 
change,  i.e.,  services vs manufacturing industries). Some projections

(like SSP1) show a considerable convergence of income levels within 
and across countries, while in other projections, this does not occur 
(e.g., SSP3). Most scenarios reflect the suggested inverse relationship 
between the assumed growth rate for income and population growth 
(Figure  3.9e). SSP1 and SSP5 represent examples of scenarios with 
relatively low population increase and relatively high-income increase 
over the century. SSP3 represents an example of the opposite – while 
SSP2 and SSP4 are placed more in the middle. Nearly all scenarios 
assessed here do not account for climate impacts on growth (mostly 
for methodological reasons). As discussed in Section 3.5 these impacts 
can be considerable. An emerging area of literature emphasises the 
possibility of stabilisation (or even decline) of income levels in developed 
countries, arguing that such a trend would be preferred or even needed 
for environmental reasons (Anderson and Larkin 2013; Hickel and Kallis

2020; Kallis et al. 2020; Hickel et al. 2021; Keyßer and Lenzen 2021) (see 
also Chapter 5). Such scenarios are not common among IAM outcomes, 
that are more commonly based on the idea that decarbonisation can 
be combined with economic growth by a combination of technology, 
lifestyle and structural economic changes. Still, such scenarios could 
result in a dramatic reduction of energy and resource consumption.
Scenarios show a range of possible energy projections. In the absence 
of climate policy, most scenarios project the final energy demand 
to continue to grow to around 650–800 EJ yr–1 in 2100 (based on 
the AR6 Scenarios Database, Figure  3.9b). Some projections show 
a very high energy demand up to 1000 EJ yr–1 (comparable to SSP5). 
The scenario of the IEA lies within the SSP range but near the SSP1 
projection. However, it should be noted that the IEA scenario includes 
current policies (most reference scenarios do not) and many scenarios

published before 2021 did not account for the COVID-19 crisis. Several 
researchers discuss the possibility of decoupling material and energy 
demand from economic growth in the literature, mainly in developed 
countries (Kemp-Benedict 2018) (decoupling here refers to either 
a much slower increase in demand or even a decrease). In the scenario 
literature, this is reflected by scenarios with very low demand for final 
energy based on increased energy efficiency and less energy-intensive 
lifestyles (e.g., SSP1 and the LED scenario) (Grubler et al. 2018; van 
Vuuren et al. 2018). While these studies show the feasibility of such

314
5–95th percentile
baseline scenarios
SSP2SSP1
SSP4SSP3
Reference ReferenceSSP5 Historical
2000 2050 21006810121416
2000 2050 210040060080010001200
2000 2050 210000.5M1M
2000 2050 21002500300035004000
0.0% 0.2% 0.4% 0.6% 0.8%0.0%1.0%2.0%3.0%
2000 2050 210040004500500055006000
Population growth (average, yearly)billion
EJ yr–1billion USD2010 yr–1
kcal capita–1
 day–1Per capita GDP growth (average, yearly)
million haPopulation (a)
(c)
(e)(b)
(d)
(f)Final energy
GDP | PPP Food demand
Per capita GDP vs population growth Land | Agriculture
Figure 3.9 | Trends in key scenario characteristics and driving forces as included in the SSP scenarios (showing 5–95th percentiles of the reference 
scenarios as included in the database in grey shading).  Reference (dotted lines) refers to the UN low-, medium- and high-population scenarios (UN 2019), the OECD

long-term economic growth scenario (OECD 2021), the scenarios from the IEA’s World Energy Outlook (IEA 2019), and the scenarios in the FAO assessment (FAO 2018).

315
pathways, their energy efficiency improvement rates are considerably 
above the historic range of around 2% (Gütschow et al. 2018; Jeffery et 
al. 2018; Vrontisi et al. 2018; Haberl et al. 2020; Roelfsema et al. 2020; 
Giarola et al. 2021; Höhne et al. 2021; IEA 2021a; Höhne et al. 2021; 
Sognnaes et al. 2021). These scenarios also show clear differences in 
food consumption and the amount of land used for agriculture. Food 
demand in terms of per-capita caloric intake is projected to increase in 
most scenarios (Figure 3.9d). However, it should be noted that there 
are large differences in dietary composition across the scenarios (from 
more meat-intensive in scenarios such as SSP5 to a decrease in meat 
consumptions in other scenarios such as SSP1). Land-use projections 
also depend on assumed changes in yield and the population scenarios 
(Figure 3.9f). Typically, changes in land use are less drastic than some

other parameters (in fact, the  5–95th percentile database range 
is almost stable). Agriculture land is projected to increase in SSP3, SSP2, and SSP4 – it is more-or-less stable in SSP5 and is projected to 
decline in SSP1.
3.3.2 Emission Pathways and Temperature Outcomes
3.3.2.1 Overall Mitigation Profiles and 
Temperature Consequences
Figure  3.10 shows the GHG and CO 2 emission trajectories for 
different temperature categories as defined in Section  3.2 (the 
temperature levels are calculated using simple climate models, 
consistent with the outcomes of the recent WGI assessment, Cross-
Chapter Box 7.1). It should be noted that most scenarios currently in 
the literature do not account for the impact of COVID-19 (Box 3.2). 
2050 2100 2050 2100−20020406080100120140160Total emissions in all scenariosGlobal emissions (GtCO2-eq yr–1)Greenhouse gas emissions
 Only CO2C1: limit warming to 1.5°C (>50%) 
with no or limited overshoot

after a high overshoot
IMP-GSCurPol
ModAct
IMP-Ren
IMP-LDSSP2-45
SSP4-60
SSP3-70
SSP5-85SSP1-19
SSP1-26
SSP4-34Full Scenario range
ModAct
IMP-Ren
IMP-LD
IMP-Neg
IMP-Neg
Figure 3.10 | Total emissions profiles in the scenarios based on climate category for GHGs (AR6 GWP-100) and CO 2. The Illustrative mitigation pathways (IMPs)  
are also indicated.

316
The higher categories (C6 and C7) mostly included scenarios with 
no or modest climate policy. Because of the progression of climate 
policy, it is becoming more common that reference scenarios 
incorporate implemented climate policies. Modelling studies typically 
implement current or pledged policies up until 2030 (Vrontisi et al. 
2018; Roelfsema et al. 2020; Sognnaes et al. 2021) with some studies 
focusing also on the policy development in the long term (Höhne 
et al. 2021; IEA  2021a; Jeffery et al. 2018; Gütschow et al. 2018).  
Based on the assessment in Chapter 4, reference pathways consistent 
with the implementation and trend from implemented policies until 
the  end of 2020 are associated with increased GHG emissions 
from 59 (53–65) GtCO 2-eq yr–1 in 2019 to 54–60 GtCO 2-eq yr–1 by 
2030 and to 47–67 GtCO 2-eq yr–1 by 2050 (Figure  3.6). Pathways

with these near-term emissions characteristics lead to a  median 
global warming of 2.2°C to 3.5°C by 2100 (see also further in this 
section). These pathways consider policies at the time that they 
were developed. A recent model comparison that harmonised socio-
economic, technological, and policy assumptions (Giarola et al. 2021) 
found a  2.2°C–2.9°C median temperature rise in 2100 for current 
and stated policies, with the results sensitive to the model used and 
the method of implementing policies (Sognnaes et al. 2021). Scenario 
inference and construction methods using similar policy assumptions 
lead to a median range of 2.9°C–3.2°C in 2100 for current policies 
and 2.4°C–2.9°C in 2100 for 2030 pledges (Höhne et al. 2021). The 
median spread of 1°C across these studies (2.2°C–3.2°C) indicates 
the deep uncertainties involved with modelling temperature 
outcomes of 2030 policies through to 2100 (Höhne et al. 2021).

The lower categories include increasingly stringent assumed climate 
policies. For all scenario categories, except the highest category, emissions peak in the 21st century. For the lowest categories, 
the emissions peak is mostly before 2030. In fact, for scenarios 
in the  category that avoids temperature overshoot for the  1.5°C 
scenario (C1 category), GHG emissions are reduced already to almost 
zero around the middle of the century. Typically, CO 2 emissions reach 
net zero about 10 to 40 years before total GHG emissions reach net 
zero. The main reason is that scenarios reduce non-CO 2 greenhouse 
gas emissions less than CO 2 due to a  limited mitigation potential 
(Section 3.3.2.2). Figure 3.10 also shows that many scenarios in the 
literature with a temperature outcome below 2°C show net negative 
emissions. There are, however, also exceptions in which more 
immediate emission reductions limits the need for CDR. The IMPs

illustrate alternative pathways to reach the C1–C3 temperature levels.
Figure 3.11 shows the possible consequences of the different scenario 
categories for global mean temperature calculated using a reduced 
complexity model (RCM) calibrated to the IPCC AR6 WGI assessment 
(see Annex III.II.2.5 of this report and Cross-Chapter Box 7.1 in 
AR6 WGI report). For the C5–C7 categories (containing most of the 
reference and current policy scenarios), the global mean temperature 
is expected to increase throughout the century (and further increase 
will happen after 2100 for C6 and C7). While warming would more 
likely than not  be in the range from 2.2°C to 3.5°C, warming up to 5°C 
cannot be excluded. The highest emissions scenarios in the literature 
combine assumptions about rapid long-term economic growth 
and pervasive climate policy failures, leading to a reversal of some 
recent trends (Box 3.3). For the categories C1–C4, a peak in global

mean temperature is reached mid-century for most scenarios in the 
database, followed by a small (C3/C4) or more considerable decline 
(C1/C2). There is a clear distinction between the scenarios with no or Box 3.2 | Impact of COVID-19 on Long-term Emissions
The reduction in CO 2 emissions of the COVID-19 pandemic in 2020 was estimated to be about 6% (Section 4.2.2.4 and Table 4.SM.2) 
lower than 2019 levels (Forster et al. 2020; Friedlingstein et al. 2020; Liu et al. 2020c; BP 2021; Crippa et al. 2021; IEA 2021; Le Quéré 
et al. 2021). Near-real-time monitoring estimates show a rebound in emissions levels, meaning 2021 emissions levels are expected 
to be higher than 2020 (Le Quéré et al. 2021). The longer-term effects are uncertain but so far do not indicate a clear structural 
change for climate policy related to the pandemic. The increase in renewable shares in 2020 could stimulate a further transition,

but slow economic growth can also slow down (renewable) energy investments. Also, lifestyle changes during the crisis can still 
develop in different directions (working from home, but maybe also living further away from work). Without a major intervention, 
most long-term scenarios project that emissions will start to follow a similar pathway as earlier projections (although at a reduced 
level) (IEA 2020b; Kikstra et al. 2021a; Rochedo et al. 2021). If emissions reductions are limited to only a short time, the adjustment 
of pathways will lead to negligible outcomes in the order of 0.01K (Forster et al. 2020; Jones et al. 2021). At the same time, however, 
the large amount of investments pledged in the recovery packages could provide a  unique opportunity to determine the long-
term development of infrastructure, energy systems and land use (Andrijevic et al. 2020b; Hepburn et al. 2020; Pianta et al. 2021).

Near-term alternative recovery pathways have been shown to have the potential to influence carbon-price pathways, and energy 
investments and electrification requirements under stringent mitigation targets (Bertram et al. 2021; Kikstra et al. 2021a; Pollitt et al. 
2021; Rochedo et al. 2021; Shan et al. 202). Most studies suggest a noticeable reduction in 2030 emissions. However, much further 
reductions would be needed to reach the emission levels consistent with mitigation scenarios that limit warming to 2°C (>67%) 
or lower (see Chapter 4). At the moment, the share of investments in greenhouse gas reduction is relatively small in most recovery 
packages, and no structural shifts for climate policies are observed linked to the pandemic. Finally, most of the scenarios analysed 
in this Chapter do not include the 2020 emissions reduction related to the COVID-19 pandemic. The effect of the pandemic on the

pathways will likely be very small. The assessment of climate mitigation pathways in this chapter should be interpreted as being 
almost exclusively based on the assumption of a fast recovery with limited persistent effects on emissions or structural changes.

317
Box 3.3 | The Likelihood of High-end Emissions Scenarios
At the time the Representative Concentration Pathways (RCPs) were published, they included three scenarios that could represent 
emission developments in the absence of climate policy: RCP4.5, RCP6 and RCP8.5, described as, respectively, low, medium and high-
end scenarios in the absence of strong climate policy (van Vuuren et al. 2011). RCP8.5 was described as representative of the top 5% 
scenarios in the literature. The SSPs-based set of scenarios covered the RCP forcing levels, adding a new low scenario (at 1.9 W m–2). 
Hausfather and Peters (2020) pointed out that since 2011, the rapid development of renewable energy technologies and emerging 
climate policy have made it considerably less likely that emissions could end up as high as RCP8.5. Still, emission trends in developing

countries track RCP8.5 Pedersen et al. (2020), and high land-use emissions could imply that emissions would continue to do so in the 
future, even at the global scale (Schwalm et al. 2020). Other factors resulting in high emissions include higher population or economic 
growth as included in the SSPs (Section 3.3.1) or rapid development of new energy services. Climate projections of RCP8.5 can also 
result from strong feedbacks of climate change on (natural) emission sources and high climate sensitivity (AR6 WGI Chapter 7), and 
therefore their median climate impacts might also materialise while following a lower emission path (e.g., Hausfather and Betts 2020). 
The discussion also relates to a more fundamental discussion on assigning likelihoods to scenarios, which is extremely difficult given 
the deep uncertainty and direct relationship with human choice. However, it would help to appreciate certain projections (e.g., Ho

et al. 2019). All in all, this means that high-end scenarios have become considerably less likely since AR5 but cannot be ruled out. It 
is important to realise that RCP8.5 and SSP5-8.5 do not represent a typical ‘business-as-usual’ projection but are only useful as high-
end, high-risk scenarios. Reference emission scenarios (without additional climate policy) typically end up in the C5–C7 categories 
included in this assessment.
C1: limit warming to 1.5°C (>50%) 
(a) Median global warming across scenarios in categories C1 to C8 (b) Peak and 2100 global warming across 
scenario categories, IMPs and SSPx-y scenarios 
considered by AR6 WGIGlobal warming relative to 1850–1900 (°C)
Scenario categories, IMPs and SSPx-y scenariosC1 C2C3 C4C5C6C8
2020 2030 2040 2050 2060 2070 2080 2090 21000123456
0123456

C1C2C3C4C5C6C7C8Scenario range within category: 
5–95% across medians of scenarios 
Median within category
IMP
SSPx-y
ﬁlled: Peak warming (over the 21st century) 
open: 2100 warmingClimate & scenario uncertainty: 
5–95% across scenarios
of 5–95% 2100 warmingScenario range within
category: 5–95% across
medians of scenarios
IMP-SPIMP-LDIMP-RenSSP1-1.9IMP-NegIMP-GSSSP1-2.6ModActSSP2-4.5CurPolSSP3-7.0SSP5-8.5
C7
Figure 3.11 | Global mean temperature outcome of the ensemble of scenarios included in the climate categories C1–C8 (based on a reduced complexity 
model – RCM – calibrated to the WGI assessment, both in terms of future and historic warming).  The left panel shows the ranges of scenario uncertainty (shaded

area) with the P50 RCM probability (line). The right panel shows the P5 to P95 range of combined RCM climate uncertainty (C1–C8 is explained in Table 3.1) and scenario 
uncertainty, and the P50 (line).

318
limited overshoot (typically <0.1°C, C1) compared to those with high 
overshoot (C2): in emissions, the C1 category is characterised by steep 
early reductions and a relatively small contribution of net negative 
emissions (like IMP-LD and IMP-Ren) (Figure  3.10). In addition to 
the temperature caused by the range of scenarios in each category 
(main panel), climate uncertainties also contribute to a  range of 
temperature outcomes (including uncertainties regarding the carbon 
cycle, climate sensitivity, and the rate of change, see AR6 WGI). The 
bars on the right of Figure 3.11 show the uncertainty range for each category (combining scenario and climate uncertainty). While the C1 
category more likely than not limits warming to 1.5°C (>50%) by the 
end of the century, even with such a scenario, warming above 2°C 
cannot be excluded (95th percentile). The uncertainty range for the

highest emission categories (C7) implies that these scenarios could 
lead to a warming above 6°C.
3.3.2.2 The Role of Carbon Dioxide and Other 
5–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85-95%5–15%all85–95%
C1 C2 C3 C4 C5 C6 C7−200204060801005–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85–95%5–15%all85–95%
C1 C2 C3 C4 C5 C6 C7010002000300040005000Net emissions in 2100 (GtCO2-eq )
Cumulative CO2 emissions (2020–2100, GtCO2)a. Contribution of various gases in CO2
    equivalents, in 2100 (median values)b. Sources and sinks of cumulative CO2 emissionsMtCH4 yr –1
MtN2O yr –1
0100200 /uni00A0300 /uni00A0400 /uni00A0500 /uni00A0
0 5 /uni00A010 /uni00A015 /uni00A020 /uni00A0c. Net global CH4 emissions d. Net global N2O emissions 
2100 2080 2060 2040 2020 2000 2100 2080 2060 2040 2020 2000GtCO2-eq yr –1
051015
GtCO2-eq yr –1
023
145

(very likely range)CO2
CH4
N2O
OtherAFOLU (pos)
Energy (pos)
AFOLU (neg)
Energy (neg)
CurPol (C7)
IMP-Neg (C2)Limit warming to 2°C (>67%) (C3)
95th
Median
5th75th
25th
Figure 3.12 | (a) The role of CO 2 and other greenhouse gases.  Emission in CO 2-eq in 2100 (using AR6 GWP-100) (other = halogenated gases) and (b) cumulative CO 2 
emissions in the 2020–2100 period. Panels (c) and (d) show the development of CH 4 and N 2O emissions over time. Energy emissions include the contribution of BECCS. For both

energy and AFOLU sectors, the positive and negative values represent the cumulated annual balances. In both panels, the three bars per scenario category represent the lowest 5–15th 
percentile, the average value and the highest 5–15th percentile. These illustrate the range of scenarios in each category. The definition of C1–C7 can be found in Table 3.1.

319
The trajectory of future CO 2 emissions plays a critical role in mitigation, 
given CO 2 long-term impact and dominance in total greenhouse gas 
forcing. As shown in Figure 3.12, CO 2 dominates total greenhouse 
gas emissions in the high-emissions scenarios but is also reduced 
most, going from scenarios in the highest to lower categories. In C4 
and below, most scenarios exhibit net negative CO 2 emissions in the 
second half of the century compensating for some of the residual 
emissions of non-CO 2 gases as well as reducing overall warming 
from an intermediate peak. Still, early emission reductions and further 
reductions in non-CO 2 emissions can also lead to scenarios without 
net negative emissions in 2100, even in C1 and C3 (shown for the 
85–95th percentile). In C1, avoidance of significant overshoot implies 
that immediate gross reductions are more relevant than long-term

net negative emissions (explaining the lower number than in C2) but 
carbon dioxide removal (CDR) is still playing a role in compensating 
for remaining positive emissions in hard-to-abate sectors.
CH4 and N 2O emissions are also reduced from C7 to C1, but this mostly 
occurs between C7 and C5. The main reason is the characteristics 
of abatement potential: technical measures can significantly reduce 
CH4 and N 2O emissions at relatively low costs to about 50% of the 
current levels (e.g., by reducing CH 4 leaks from fossil fuel production 
and transport, reducing landfill emissions gazing, land management 
and introducing measures related to manure management, see also 
Chapter 7 and 11). However, technical potential estimates become 
exhausted even if the stringency of mitigation is increased (Harmsen 
et al. 2019a,b; Höglund-Isaksson et al. 2020). Therefore, further 
reduction may come from changes in activity levels, such as switching

to a less meat-intensive diet, therefore reducing livestock (Stehfest 
et al. 2009; Willett et al. 2019; Ivanova et al. 2020) (Chapter 7). Other 
non-CO 2 GHG emissions (halogenated gases) are reduced to low 
levels for scenarios below 2.5°C.
Short-lived climate forcers (SLCFs) also play an important role in climate 
change, certainly for short-term changes (AR6 WGI, Figure  SPM.2) 
(Shindell et al. 2012). These forcers consist of (i) substances contributing 
to warming, such as methane, black carbon and tropospheric ozone, 
and (ii) substances contributing to cooling (other aerosols, such as 
related to sulphur emissions). Most SLCFs are also air pollutants, and 
reducing their emissions provides additional co-benefits (Shindell et al. 
2017a,b; Hanaoka and Masui 2020). In the case of the first group, 
emission reduction thus leads to both air pollution and climate benefits. 
For the second, group there is a possible trade-off (Shindell and Smith

2019; Lund et al. 2020). As aerosol emissions are mostly associated 
with fossil fuel combustion, the benefits of reducing CO 2 could, in 
the short term, be reduced as a result of lower aerosol cooling. There 
has been an active discussion on the exact climate contribution of 
SLCF-focused policies in the literature. This discussion partly emerged 
from different assumptions on possible reductions in the absence of 
ambitious climate policy and the uncertain global climate benefit from 
aerosol (black carbon) (Rogelj et al. 2014). The latter is now assessed to 
be smaller than originally thought (Takemura and Suzuki 2019; Smith 
et al. 2020b) (see also AR6 WGI Section 6.4). Reducing SLCF emissions 
is critical to meet long-term climate goals and might help reduce 
the rate of climate change in the short term. Deep SLCF emission 
reductions also increase the remaining carbon budget for a  specific 
temperature goal (Rogelj et al. 2015a; Reisinger et al. 2021) (Box 3.4).

A more detailed discussion can be found in AR6 WGI Chapters 5 and 6.For accounting of emissions and the substitution of different gases as 
part of a  mitigation strategy, typically, emission metrics are used to 
compare the climate impact of different gases. Most policies currently 
use Global Warming Potentials (GWPs) with a  100-year time horizon 
as this is also mandated for emissions reporting in the Paris Rulebook 
(for a  wider discussion of GHG metrics, see Box 2.1 in Chapter  2 of 
this report, and AR6 WGI, Chapter 7, Section 7.6). Alternative metrics 
have also been proposed, such as those using a shorter or longer time 
horizon, or those that focus directly on the consequences of reaching 
a certain temperature target (Global Temperature Change Potential – 
GTP), allowing a more direct comparison with cumulative CO 2 emissions 
(Allen et al. 2016; Lynch et al. 2020) or focusing on damages (Global 
Damage Potential) (an overview is given in Chapter 2, and Cross-Chapter

Box 3 in Chapter 3). Depending on the metric, the value attributed to 
reducing short-lived forcers such as methane can be lower in the near 
term (e.g., in the case of GTP) or higher (GWP with a short reference 
period). For most metrics, however, the impact on mitigation strategies is 
relatively small, among others, due to the marginal abatement cost curve 
of methane (low costs for low-to-medium mitigation levels; expensive 
for high levels). The timing of reductions across different gases impacts 
warming and the co-benefits (Harmsen et al. 2016; Cain et al. 2019). 
Nearly all scenarios in the literature use GWP-100 in cost-optimisation, 
reflecting the existing policy approach; the use of GWP-100 deviates 
from cost-optimal mitigation pathways by at most a  few percent for 
temperature goals that limit warming to 2°C (>67%) or lower (Box 2.1).
Cumulative CO 2 emissions and temperature goals
The dominating role of CO 2 and its long lifetime in the atmosphere

and some critical characteristics of the Earth System implies that 
there is a  strong relationship between cumulative CO 2 emissions 
and temperature outcomes (Allen et al. 2009; Matthews et al. 2009; 
Meinshausen et al. 2009; MacDougall and Friedlingstein 2015). This 
is illustrated in Figure 3.13, which plots the cumulative CO 2 emissions 
against the projected outcome for global mean temperature, both until 
peak temperature and through to end of century (or 2100). The deviations 
from a linear relationship in Figure 3.13 are mostly caused by different 
non-CO 2 emission and forcing levels (see also Rogelj et al. 2015b). This 
means that  reducing non-CO 2 emissions can play an important role 
in limiting peak warming: the smaller the residual non-CO 2 warming, 
the larger the carbon budget. This impact on carbon budgets can be 
substantial for stringent warming limits. For 1.5°C pathways, variations 
in non-CO 2 warming across different emission scenarios have been

found to vary the remaining carbon budget by approximately 220 GtCO 2 
(AR6 WGI Chapter 5, Section 5.5.2.2). In addition to reaching net zero 
CO2 emissions, a strong reduction in methane emissions is the most 
critical component in non-CO 2 mitigation to keep the Paris climate goals 
in reach (Collins et al. 2018; van Vuuren et al. 2018) (see also AR6 WGI, 
Chapters 5, 6 and 7). It should be noted that the temperature categories 
(C1–C7) generally aligned with the horizontal axis, except for the end-
of-century values for C1 and C2 that coincide.

320
Box 3.4 | Consistency of Remaining Carbon Budgets in the WGI Assessment and Cumulative 
CO2 Emissions in WGIII Mitigation Pathways
The WGI assessment has shown that the increase in global mean temperature has a  near-linear relationship with cumulative 
CO2 emissions (Chapter 5, Section 5.5, Box 5.3 of AR6 WGI report). Consistently, WGI has confirmed that net zero CO 2 emissions are 
required to halt CO 2-induced warming. This permits the estimation of carbon budgets consistent with specific temperature goals. In 
Chapter 3, we present the temperature outcomes and cumulative CO 2 emissions associated with different warming levels for around 
1200 scenarios published in the literature and which were classified according to different warming levels (Section 3.2 and Annex III.
II.3.2). In this box, we discuss the consistency of the assessments presented here and in IPCC AR6 WGI. The box summarises how

the remaining carbon budgets assessed by AR6 WGI relate to the remaining cumulative CO 2 emissions until the time of net zero CO 2 
emissions in mitigation pathways (Tables 3.2 and SPM.1) assessed by AR6 WGIII.
In its assessment, AR6 WGI uses a framework in which the various components of the remaining carbon budget are informed by 
various lines of evidence and assessed climate system characteristics. The AR6 WGIII, instead, uses around 1200 emission scenarios 
with estimated warming levels that cover the scenario range presented in AR6 WGI but also contain many more intermediate 
projections with varying emission profiles and a combination of CO 2 emissions and other greenhouse gases. In order to assess their 
climate outcomes, climate model emulators are used. The emulators are reduced complexity climate models that are provided by AR6 
WGI, and which are calibrated to the AR6 WGI assessment of future warming for various purposes (a detailed description of the use of

climate model emulators in the AR6 WGI and WGIII assessments can be found in Cross-Chapter Box 7.1 in the AR6 WGI report, with 
the connection of WGI and WGIII discussed in Annex III.2.5.1).
Remaining carbon budgets estimated by AR6 WGI
The AR6 WGI estimated the remaining carbon budgets from their assessment of (i) the transient climate response to cumulative 
emissions of carbon dioxide (TCRE), and estimates of (ii) the historical human-induced warming, (iii) the temperature change after 
reaching net zero CO 2 emissions, (iv) the contribution of future non-CO 2 warming (derived from the emissions scenarios assessed in 
the Special Report on 1.5°C Warming using WGI-calibrated emulators), and (v) the Earth System feedbacks (AR6 WGI Chapter 5.5, 
Box 5.2).  For a given warming level, AR6 WGI assessed the remaining carbon budget from the beginning of 2020 onwards. These are

650/500/400 GtCO 2 for limiting warming to 1.5°C with 33%/50%/ 67% chance and 1350/1150 GtCO 2 for limiting warming to 2°C 
with 50%/67% chance. The estimates are subject to considerable uncertainty related to historical warming, future non-CO 2 forcing, 
and poorly quantified climate feedbacks. For instance, variation in non-CO 2 emissions across scenarios are estimated to either increase 
or decrease the remaining carbon budget estimates by 220 GtCO 2. The estimates of the remaining carbon budget assume that non-
CO2 emissions are reduced consistently with the tight temperature targets for which the budgets are estimated.
Cumulative CO 2 emissions until net zero estimated by AR6 WGIII
The AR6 WGIII provides estimates of cumulative net CO 2 emissions (from 2020 inclusive) until the time of reaching net zero CO 2 emissions 
(henceforth called ‘peak cumulative CO 2 emissions’) and until the end of the century for eight temperature classes that span a range of

warming levels. The numbers can be found in Table 3.2 (330–710 GtCO 2 for C1; 530–930 for C2; and 640–1160 for C3).
Comparing the AR6 WGI remaining carbon budgets and remaining cumulative CO 2 emissions of the AR6 WGIII scenarios
A comparison between AR6 WGI and WGIII findings requires recognising that, unlike in WGI, cumulative emissions in WGIII are not 
provided for a specific peak-warming threshold or level but are instead provided for a set of scenarios in a category, representing 
a  specific range of peak-temperature outcomes (for instance the C4 category contains scenarios with a  median peak warming 
anywhere between approximately 1.8°C and up to 2°C). When accounting for this difference, the AR6 WGI and WGIII findings are 
very consistent for temperature levels below 2°C. Figure 1 compares the peak temperatures and associated cumulative CO 2 emissions

(i.e., peak cumulative CO 2 emissions) for the WGIII scenarios to the remaining carbon budgets assessed by WGI. This shows only minor 
differences between the WGI and WGIII approaches.
After correcting for the categorisation, some (small) differences between the AR6 WGI and WGIII numbers arise from remaining 
differences between the outcomes of the climate emulators and their set-up (IPCC AR6 WGI Cross-Chapter Box 7.1) and the differences 
in the underlying scenarios. Moreover, the WGI assessment estimated the non-CO 2 warming at the time of net zero CO 2 emissions 
based on a relationship derived from the SR1.5 scenario database with historical emission estimates as in Meinshausen et al. (2020) 
(AR6 WGI Chapter 5). The WGIII assessment uses the same climate emulator with improved historical emissions estimates (Nicholls 
et al. 2021) (AR6 WGI Cross-Chapter Box  7.1). Annex III.II.2.5.1 further explores the effects of these factors on the relationship

between non-CO 2 warming at peak cumulative CO 2 and peak surface temperature.

321
Estimates of the remaining carbon budgets thus vary with the assumed level of non-CO 2 emissions, which are a function of policies 
and technology development. The linear relationship used in the AR6 WGI assessment between peak temperature and the warming 
as a result of non-CO 2 emissions (based on the SR1.5 data) is shown in the right panel of Figure 2 (dashed line). In the AR6 WGIII 
approach, the non-CO 2 warming for each single scenario is based on the individual scenario characteristics. This is shown in the same 
figure by plotting the outcomes of scenario outcomes of a range of models (dots). The lines show the fitted data for individual models, 
emphasising the clear differences across models and the relationship with peak warming (policy level). In some scenarios, stringent 
non-CO 2 emission reductions provide an option to reach more stringent climate goals with the same carbon budget. This is especially

the case for scenarios with a very low non-CO 2 warming, for instance, as a result of methane reductions through diet change. The 
left panel shows how these differences impact estimates of the remaining carbon budget. While the AR6 scenarios database includes 
a broad range of non-CO 2 emission projections the overall range is still very consistent with the WGI relationship and the estimated 
uncertainty with a ±220 GtCO 2 range (see also Figure 5 in Annex III.II.2.5.1).
Overall, the slight differences between the cumulative emissions in AR6 WGIII and the carbon budget in AR6 WGI are because the 
non-CO 2 warming in the WGIII scenarios is slightly lower than in the SR1.5 scenarios that are used for the budget estimates in WGI 
(Annex III.2.5.1). In addition, improved consistency with Cross-Chapter Box 7.1 in Chapter 7, AR6 WGI results in a non-CO 2-induced 
temperature difference of about about 0.05K between the assessments. Recalculating the remaining carbon budget using the WGI

methodology combined with the full AR6 WGIII scenario database results in a reduction of the estimated remaining 1.5°C carbon 
budget by about 100 GtCO 2 (–20%), and a reduction of about 40 GtCO 2 (–3%) for 2°C. Accounting also for the categorisation effect, 
the difference between the WGI and WGIII estimates is found to be small and well within the uncertainty range (Figure 1). This means 
that the cumulative CO 2 emissions presented in WGIII and the WGI carbon budgets are highly consistent.
A detailed comparison of the impact of different assessment steps (i.e., the new emulators, scenarios, and harmonisation methods), 
has been made and is presented in Figure 6 in Annex III.II.3.2 .Box 3.4 (continued)
Peak cumulative CO2 emissions from beginning of 2020 (GtCO2)Median peak warming rel. 1850-1900 ( ºC)
33-percentile (lower) peak warming rel. 1850-1900 ( ºC)

67-percentile (upper) peak warming rel. 1850-1900 ( ºC)a) ... 50% chance b) ... 33% chance c) ... 67% chanceCumulative emissions to limit peak warming at various levels with a .... 
C1a
C1b
C2
C3
C4
C5AR6 scenarios
WG1 remaining
carbon budget
(incl. non-CO2uncertainty)Max. cumulative
CO2 emissions
(adjusted to
0.1C intervals)
0 500 1000 1500 2000 25001.21.31.41.51.61.71.81.92.02.12.2
Box 3.4, Figure 1 | Cumulative CO 2 emissions from AR6 scenario categories (coloured dots), adjusted for distinct 0.1°C warming levels (black 
bars) in comparison to the WGI remaining carbon budgets (grey bars).  The cumulative carbon emissions for the AR6 scenarios are shown for the median 
peak warming (a), the 33rd-percentile peak warming (b) and the upper 67th-percentile peak warming (c) calculated with the WGI-calibrated emulator MAGICC7

(IPCC AR6 WGI, Cross-Chapter Box 7.1). The adjustment to the nearest 0.1°C intervals is made using AR6 WGI TCRE (at the relevant percentile, e.g., the 67th-percentile 
TCRE is used to adjust the 67th-percentile peak warming), with the 5–95% range of adjusted scenarios provided by the black bar. The AR6 WGI remaining carbon 
budget is shown, including the WGI estimate of at least a ±220 GtCO 2 uncertainty due to non-CO 2 emissions variations across scenarios (grey bars). For median 
peak warming (panel  a) projections below 2°C relative to 1850–1900, the AR6 WGIII assessment of cumulative carbon emissions tends to be slightly smaller 
than the remaining carbon budgets provided by WGI but well within the uncertainties. Note that only a few scenarios in WGIII limit warming to below 1.5°C with 
a 50% chance, thus statistics for that specific threshold have low confidence.

322
The concept of a finite carbon budget means that the world needs to get to net zero CO 2, no matter whether global warming is 
limited to 1.5°C or well below 2°C (or any other level). Moreover, exceeding the remaining carbon budget will have consequences by 
overshooting temperature levels. Still, the relationship between the timing of net zero and temperature targets is a flexible one, as 
discussed further in Cross-Chapter Box 3 in this chapter. It should be noted that the national-level inventory as used by UNFCCC for 
the land use, land-use change and forestry sector is different from the overall concept of anthropogenic emissions employed by IPCC 
AR6 WGI. For emissions estimates based on these inventories, the remaining carbon budgets must be correspondingly reduced by 
approximately 15%, depending on the scenarios (Grassi et al., 2021) (Chapter 7).

One of the uncertainties of the remaining carbon budget is the level of non-CO 2 emissions which is a function of policies and technology 
development. This represents a point of leverage for policies rather than an inherent geophysical uncertainty. Stringent non-CO 2 emission 
reductions hence can provide – to some degree – an option to reach more stringent climate goals with the same carbon budget.Box 3.4 (continued)
500 1470
0.00.5
1.5°1.0
0 1000 2000 3000 4000
Carbon budget [GtCO2]Peak surface temperature target (rel. to 2010−2019) [°C]Carbon budget non-CO2 uncertainty depends
to a large extent on mitigation strategies(a) (b)
0.00.10.20.30.4
0.50 0.75 1.00 1.25
Peak surface temperature (rel. to 2010−2019) [°C]Non−CO2 warming (rel. to 2010−2019) at peak cumulative CO2 emissions [°C]Non-CO2 warming contribution in WGIII models 
compared to WGI assumed relationship
Min−max rangeModelWGI Chapter 5 non-CO2 uncertainty range around the median

of the model regression linesWGI Chapter 5 setup
AIM
COFFEEGCAM
IMAGEMESSAGE−GLOBIOM
POLESREMIND−MAgPIE
WITCH
Median 33rd−66th percentile 17th−83rd percentile280 11802.0°
Box 3.4, Figure 2 | (a) Differences in regressions of the relationship between peak surface temperature and associated cumulative CO 2 emissions 
from 2020 derived from scenarios of eight integrated assessment model frameworks.  The coloured lines show the regression at median for scenarios of 
the eight modelling frameworks, each with more than 20 scenarios in the database and a detailed land-use representation. The red dotted lines indicate the non-CO 2 
uncertainty range of AR6 WGI Chapter 5 (±220 GtCO 2), here visualised around the median of the eight model framework lines. Carbon budgets from 2020 until 1.5°C 
(0.43K above 2010–2019 levels) and 2.0°C (0.93K above 2010–2019 levels) are shown for minimum and maximum model estimates at the median, rounded to the

nearest 10 GtCO 2. Panel (b) shows the relationship between the estimated non-CO 2 warming in mitigation scenarios that reach net zero and the associated peak 
surface temperature outcomes. The coloured lines show the regression at median for scenarios of the eight modelling frameworks with more than 20 scenarios in the 
database and a detailed land-use representation. The black dashed line indicates the non-CO 2 relationship based on the scenarios and climate emulator setup as was 
assessed in AR6 WGI Chapter 5.

323
The near-linear relationship implies that cumulative CO 2 emissions 
are critically important for climate outcomes (Collins et al. 2013). 
The maximum temperature increase is a  direct function of the 
cumulative emissions until net zero CO 2 emissions is reached 
(the emission budget) (Figure  3.13, left side). The end-of-century 
temperature correlates well with cumulative emissions across 
the century (right panel). For long-term climate goals, positive 
emissions in the first half of the century can be offset by net 
removal of CO 2 from the atmosphere (net negative emissions) at 
the cost of a  temporary overshoot of the target (Tokarska et al. 
2019). The bottom panels of Figure 3.13 show the contribution of 
net negative CO 2 emissions.
Focusing on cumulative emissions, the right-hand panel of 
Figure  3.12b shows that for high-end scenarios (C6–C7), most

emissions originate from fossil fuels, with a  smaller contribution 
from net deforestation. For C5 and lower, there is also a  negative 
contribution to emissions from both AFOLU emissions and energy systems. For the energy systems, these negative emissions originate 
from bioenergy with carbon capture and storage (BECCS), while 
for AFOLU, they originate from reforestation and afforestation. For 
C3–C5, reforestation has a  larger CDR contribution than BECCS, 
mostly due to considerably lower costs (Rochedo et al. 2018). For C1 
and C2, the tight carbon budgets imply in many scenarios more CDR 
use (Riahi et al. 2021). Please note that net negative emissions are not 
so relevant for peak-temperature targets, and thus the C1 category, 
but CDR can still be used to offset the remaining positive emissions 
(Riahi et al. 2021). While positive CO 2 emissions from fossil fuels are 
significantly reduced, inertia and hard-to-abate sectors imply that

in many C1–C3 scenarios, around 800–1000 GtCO 2 of net positive 
cumulative CO 2 emissions remain. This is consistent with literature 
estimates that current infrastructure is associated with 650 GtCO 2 
(best estimate) if operated until the end of its lifetime (Tong et al. 
2019). These numbers are considerably above the estimated carbon 
budgets for  1.5°C estimated in AR6 WGI, hence explaining CDR 
reliance (either to offset emissions immediately or later in time).
0 2000 4000 6000 80001°C2°C3°C4°C5°C6°C
0 1000 20001°C1.5°C2°C2.5°C
0 1000 20001°C1.5°C2°C2.5°CCurPol
ModAct
IMP-GS
IMP-Neg-2.0
IMP-Ren-2.0
IMP-Neg
IMP-Ren
IMP-LD
IMP-SP
Total net neg. (GtCO2): 
< 1
1 to 100
100 to 200
200 to 400
> 4000.60.811.21.41.61.822.2
non-CO2 forcing in 2100 (W m– 2)
Cumulative emissions 2020 until peak (GtCO2) Cumulative emissions 2020–2100 (GtCO2)  
Cumulative emissions 2020 until peak (GtCO2)   Cumulative emissions 2020–2100 (GtCO2)  Peak warming

Warming 2100Peak warming
Warming 2100Peak warming (p66) Warming 2100 (p66)
C1C2
C3C4C5C6C7
C1C2
C1C2
C3C4C5
C1C2
C3C4C5
Figure 3.13 | The near-linear relationship between cumulative CO 2 emissions and temperature.  The left panel shows cumulative emissions until net zero emission 
is reached. The right panel shows cumulative emissions until the end of the century, plotted against peak and end-of-century temperature, respectively. Both are shown as 
a function of non-CO 2 forcing and cumulative net negative CO 2 emissions. Position temperature categories (circles) and IPs are also indicated, including two 2°C sensitivity 
cases for Neg (Neg-2.0) and Ren (Ren-2.0).

324
Creating net negative emissions can thus be an important part of 
a mitigation strategy to offset remaining emissions or compensate 
for emissions earlier in time. As indicated above, there are different 
ways to potentially achieve this, including reforestation and 
afforestation and BECCS (as often covered in IAMs) but also soil 
carbon enhancement, direct air carbon capture and storage (DACCS) 
and ocean alkalinisation (Chapter 12). Except for reforestation, these 
options have not been tested at large scale and often require more  R&D. 
Moreover, the reliance on CDR in scenarios has been discussed given 
possible consequences of land use related to biodiversity loss and 
food security (BECCS and afforestation), the reliance on uncertain 
storage potentials (BECCS and DACCS), water use (BECCS), energy 
use (DACCS), the risks of possible temperature overshoot and the

consequences for meeting Sustainable Development Goals (SDGs) 
(Anderson and Peters 2016; Smith et al. 2016; Venton 2016; Peters 
and Geden 2017; van Vuuren et al. 2017; Honegger et al. 2021). In 
the case of BECCS, it should be noted that bioenergy typically is 
associated with early-on positive CO 2 emissions and net negative 
effects are only achieved in time (carbon debt), and its potential is 
limited (Cherubini et al. 2013; Hanssen et al. 2020); most IAMs have 
only a very limited representation of these time dynamics. Several 
scenarios have therefore explored how reliance on net negative CO 2 
emissions can be reduced or even avoided by alternative emission 
strategies (Grubler et al. 2018; van Vuuren et al. 2018) or early 
reductions by more stringent emission reduction in the short term 
(Rogelj et al. 2019b; Riahi et al. 2021). A more in-depth discussion 
of land-based mitigation options can be found in Chapter 7. It needs

to be emphasised that even in strategies with net negative CO 2 
emissions, the emission reduction via more conventional mitigation 
measures (efficiency improvement, decarbonisation of energy supply) 
is much larger than the CDR contribution (Tsutsui et al. 2020).
3.3.2.3 The Timing of Net Zero Emissions
In addition to the constraints on change in global mean temperature, 
the Paris Agreement also calls for reaching a  balance of sources 
and sinks of GHG emissions (Art. 4). Different interpretations of the 
concept related to balance have been published (Rogelj et al. 2015c; 
Fuglestvedt et al. 2018). Key concepts include that of net zero CO 2 
emissions (anthropogenic CO 2 sources and sinks equal zero) and net 
zero greenhouse gas emissions (see Annex I: Glossary, and Box 3.3). 
The same notion can be used for all GHG emissions, but here 
ranges also depend on the use of equivalence metrics (Box  2.1). 
Moreover, it should be noted that while reaching net zero CO 2

emissions typically coincides with the peak in temperature increase; 
net zero GHG emissions (based on GWP-100) imply a  decrease in 
global temperature (Riahi et al. 2021) and net zero GHG emissions 
typically require negative CO 2 emissions to compensate for the 
remaining emissions from other GHGs. Many countries have started 
to formulate climate policy in the year that net zero emissions (either 
CO2 or all greenhouse gases) are reached – although, at the moment, 
formulations are often still vague (Rogelj et al. 2021). There has 
been increased attention on the timing of net zero emissions in the 
scientific literature and ways to achieve it.Figure  3.14 shows that there is a  relationship between the 
temperature target, the cumulative CO 2 emissions budget, and the net 
zero year for CO 2 emissions (panel a) and the sum of greenhouse 
gases (panel b) for the scenarios published in the literature. In other 
words, the temperature targets from the Paris Agreement can, to

some degree, be translated into a  net-zero emission year (Tanaka 
and  O’Neill 2018). There is, however, a  considerable spread. In 
addition to the factors influencing the emission budget (AR6 WGI 
and Section  3.3.2.2), this is influenced by the emission trajectory 
until net zero is reached, decisions related to temperature overshoot 
and non-CO 2 emissions (especially for the moment CO 2 reaches net 
zero emissions). Scenarios with limited or no net negative emissions 
and rapid near-term emission reductions can allow small positive 
emissions (e.g., in hard-to-abate-sectors). They may therefore have 
a later year that net zero CO 2 emissions are achieved. High emissions 
in the short term, in contrast, require an early net zero year.
For the scenarios in the C1 category (limit warming to 1.5°C (>50% 
with no or limited overshoot, the net zero year for CO 2 emissions 
is typically around 2035–2070. For scenarios in C3 (limiting

warming to 2°C (>67%)), CO 2 emissions reach net zero around 
after 2050. Similarly, also the years for net zero GHG emissions 
can be calculated (see Fig 3.14b. The GHG net zero emissions year 
is typically around 10–40 years later than the carbon neutrality. 
Residual non-CO 2 emissions at the time of reaching net zero CO 2 
range between 5–11 GtCO 2-eq in pathways that limit warming to 
2°C (>67%) or lower. In pathways limiting warming to 2°C (>67%), 
methane is reduced by around 19% (3–46%) in 2030 and 46% (29–
64%) in 2050, and in pathways limiting warming to 1.5°C (>50%) 
with no or limited overshoot by around 34% (21–57%) in 2030 and 
a  similar 51% (35–70%) in 2050. Emissions-reduction potentials 
assumed in the pathways become largely exhausted when limiting 
warming to 2°C (>50%). N 2O emissions are reduced too, but similar 
to CH 4, emission reductions saturate for stringent climate goals. 
In the mitigation pathways, the emissions of cooling aerosols are

reduced due to reduced use of fossil fuels. The overall impact on non-
CO2-related warming combines these factors.
In cost-optimal scenarios, regions will mostly achieve net zero 
emissions as a function of options for emission reduction, CDR, and 
expected baseline emission growth (van Soest et al. 2021b). This 
typically implies relatively early net zero emission years in scenarios 
for the Latin America region and relatively late net zero years for 
Asia and Africa (and average values for OECD countries). However, an 
allocation based on equity principles (such as responsibility, capability 
and equality) might result in different net zero years, based on the 
principles applied – with often earlier net zero years for the OECD 
(Fyson et al. 2020; van Soest et al. 2021b). Therefore, the emission 
trajectory until net zero emissions is a critical determinant of future 
warming (Section 3.5). The more CO 2 is emitted until 2030, the less

CO2 can be emitted after that to stay below a warming limit (Riahi 
et al. 2015). As discussed before, also non-CO 2 forcing plays a key 
role in the short term.

325
Cross-Chapter Box 3 | Understanding Net Zero CO 2 and Net Zero GHG Emissions
Authors: Elmar Kriegler (Germany), Alaa Al Khourdajie (United Kingdom/Syria), Edward Byers (Austria/Ireland), Katherine Calvin (the 
United States of America), Leon Clarke (the United States of America), Annette Cowie (Australia), Navroz Dubash (India), Jae Edmonds 
(the United States of America), Jan  S. Fuglestvedt (Norway), Oliver Geden (Germany), Giacomo Grassi (Italy/European Union), 
Anders Hammer Strømman (Norway), Frank Jotzo (Australia), Alexandre Köberle (Brazil/United Kingdom), Franck Lecocq (France), 
Yun Seng Lim (Malaysia), Eric Masanet (the United States of America), Toshihiko Masui (Japan), Catherine Mitchell (United Kingdom), 
Gert-Jan Nabuurs (the Netherlands), Anthony Patt (the United States of America/Switzerland), Glen  P . Peters (Norway/Australia),

Andy  Reisinger (New Zealand), Keywan Riahi (Austria), Joeri Rogelj (United Kingdom/Belgium), Yamina Saheb (France/Algeria), 
Jim Skea (United Kingdom), Detlef P . van Vuuren (the Netherlands), Harald Winkler (Republic of South Africa)
This Cross-Chapter Box surveys scientific, technical and policy aspects of net zero carbon dioxide (CO 2) and net zero greenhouse gas 
(GHG) emissions, with a focus on timing, the relationship with warming levels, and sectoral and regional characteristics of net zero 
emissions. Assessment of net zero GHG emissions additionally requires consideration of non-CO 2 gases and choice of GHG emission 
metrics used to aggregate emissions and removals of different GHGs (Cross-Chapter Box 2 in Chapter 2 and Cross-Chapter Box 7 in 
Chapter 10). The following considers net zero CO 2 and GHG emissions globally, followed by regional and sectoral dimensions.

Reaching net zero CO 2 emissions globally is necessary for limiting global warming to any level. At the point of net zero 
CO2, the amount of CO 2 human activity is putting into the atmosphere equals the amount of CO 2 human activity is removing from 
the atmosphere (see Annex I: Glossary). Reaching and sustaining net zero CO 2 emissions globally stabilizes CO 2-induced warming. 
Reaching net zero CO 2 emissions and then moving to net negative CO 2 emissions globally leads to a peak and decline in CO 2-induced 
warming (AR6 WGI Sections 5.5 and 5.6).
Limiting warming to 1.5°C (>50%) or to 2°C (>67%) requires deep, rapid, and sustained reductions of other greenhouse 
gases including methane alongside rapid reductions of CO 2 emissions to net zero. This ensures that the warming contributions 
from non-CO 2 forcing agents as well as from CO 2 emissions are both limited at low levels. The AR6 WGI estimated remaining carbon

budgets until the time of reaching net zero CO 2 emissions for a range of warming limits, taking into account historical CO 2 emissions 
and projections of the warming from non-CO 2 forcing agents (Box 3.4 in Section 3.3, AR6 WGI Section 5.5).
The earlier global net zero CO 2 emissions are reached, the lower the cumulative net amount of CO 2 emissions and 
human-induced global warming, all else being equal (Figure 1a in this Cross-Chapter Box). For a given net zero date, a variation 
in the shape of the CO 2 emissions profile can lead to a variation in the cumulative net amount of CO 2 emissions until the time of net 
zero CO 2 and as a result to different peak-warming levels. For example, cumulative net CO 2 emissions until the time of reaching 
net zero CO 2 will be smaller, and peak warming lower, if emissions are reduced steeply and then more slowly compared to reducing

emissions slowly and then more steeply (Figure 1b in this Cross-Chapter Box).0 1000 2000204020502060207020802090≥2100
0 1000 2000C1: limit warming to 
1.5°C (>50%) with no 
or limited overshoot
C2: return warming 
to 1.5°C (>50%) after 
a high overshoot
C3: limit warming 
to 2°C (>67%)
C4: limit warming 
to 2°C (>50%)
C5: limit warming 
to 2.5°C (>50%)
IMP-GS
IMP-Neg
IMP-Ren
IMP-LD Cumulative emissions 2020 until peak (GtCO2) Cumulative emissions 2020 until peak (GtCO2) Net zero yeara. Net zero CO2b. Net zero Kyoto Gases
Figure 3.14 | Net zero year for CO 2 and all GHGs (based on AR6 GWP100) as a function of remaining carbon budget and temperature outcomes (note 
that scenarios that stabilise (near) zero are also included in determining the net zero year).

326
Cross-Chapter Box 3 (continued)
–1001020304050
2000 2020 2060 2100 2040 20800.511.52
2000 2020 2060 2100 2040 2080[°C]–1001020304050
2000 2020 2060 2100 2040 2080–1001020304050
0.511.52(a) Net global CO 2 emissions Median global warming from 1850–1990
(b) 
(c) 
[GtCO2/yr  –1] [GtCO2/yr  –1] [GtCO2/yr  –1]2000 2020 2060 2100 2040 2080
[°C]
2000 2020 2060 2100 2040 2080[°C]REMIND-MAgPIE PEP-1p5C-full-eff GEM-E3 EN-NPi2020-800f GEM-E3 EN-NPi2020-1400f
IMP-LD GEM-E3 EN-NPi2020-500f AIM/CGE EN-INDCi2030-800f
REMIND-MAgPIE NGFS2 Delayed Transition GEM-E3 EN-NPi2020-1000 POLES EN-NPi2020-800For a given warming limit, more rapid 
near-term emission reductions allow 
reaching net zero CO 2 at a later point in timeIf net zero CO 2 dates are about the same, 
more ambitious emissions reductions in the
near term lead to lower peak warmingThe time by when net zero CO 2 is reached is

a good proxy for the level of peak warming 
if the shape of emissions proﬁles is similar
Cross-Chapter Box 3, Figure 1 | Selected global CO 2 emissions trajectories with similar shape and different net zero CO 2 date (a), different shape 
and similar net zero CO 2 date (b), and similar peak warming, but varying shapes and net zero CO 2 dates (c). Funnels show pathways limiting warming 
to 1.5°C (>50%) with no or limited overshoot (light blue) and limiting warming to 2°C (>67%) (beige). Historic CO 2 emissions from Section 2.2 (EDGAR v6).

327
Net zero CO 2 emissions are reached between 2050–2055 (2035–2070) in global emissions pathways limiting warming 
to 1.5°C (>50%) with no or limited overshoot, and between 2070–2075 (2055–…) in pathways limiting warming to 2°C 
(>67%) as reported in the AR6 scenarios database (median five-year interval and 5–95th percentile ranges).5 The variation of 
non-CO 2 emissions in 1.5°C–2°C pathways varies the available remaining carbon budget which can move the time of reaching net 
zero CO 2 in these pathways forward or backward.6 The shape of the CO 2 emissions reduction profile also affects the time of reaching 
net zero CO 2 (Figure 1c in this Cross-Chapter Box). Global emission pathways that more than halve CO 2 emissions from 2020 to 2030 
can follow this rapid reduction by a more gradual decline towards net zero CO 2 and still limit warming to 1.5°C with no or limited

overshoot, reaching the point of net zero after 2050. The literature since SR1.5 included a larger fraction of such pathways than were 
available at the time of SR1.5. This is the primary reason for the small backward shift in the median estimate of reaching global net 
zero CO 2 emissions in 1.5°C pathways collected in the AR6 scenario database compared to SR1.5. This does not mean that the world 
is assessed to have more time to rapidly reduce current emissions levels compared to SR1.5. The assessment of emissions reductions 
by 2030 and 2040 in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot has not changed substantially. It only 
means that the exact timing of reaching net zero CO 2 after a steep decline of CO 2 emissions until 2030 and 2040 can show some 
variation, and the SR1.5 median value of 2050 is still close to the middle of the current range (Figure 1c in this Cross-Chapter Box).

Pathways following emissions levels projected from the implementation of Nationally Determined Contributions (NDCs) 
announced prior to COP26 until 2030 would result in substantially (>0.1°C) exceeding 1.5°C. They would have to reach 
net zero CO 2 around 5–10 years later7 than in pathways with no or limited overshoot in order to reach the net negative emissions 
that would then be required to return warming to 1.5°C (>50%) after a high overshoot by 2100. Those high overshoot pathways 
have higher transient warming and higher reliance on net negative CO 2 emissions towards the end of the 21st century. As they need 
to reach net zero CO 2 emissions in only limited amount of time but from much higher 2030 emissions levels, their post-2030 CO 2 
emissions reduction rates are substantially higher (by around 30%) than in pathways limiting warming to 1.5°C with no or limited 
overshoot. (Section 3.5).

Pathways following emissions levels projected from the implementation of NDCs announced prior to COP26 until 
2030 would have to reach net zero CO 2 around 5 years earlier8 than cost-effective pathways that limit warming to 2°C 
(>67%). While cost-effective pathways take around 50–55 years to reach net zero CO 2 emissions, those pathways would only have 
35–40 years left for transitioning to net zero CO 2 from 2030 onwards, close to the transition times that 1.5°C pathways are faced with 
today. Current CO 2 emissions and 2030 emission levels projected under the NDCs announced prior to COP26 are in a similar range 
(Sections 3.5 and 4.2).
Net zero greenhouse gas (GHG) emissions
The amount of CO 2-equivalent emissions and the point when net zero GHG emissions are reached in multi-GHG emissions 
pathways depends on the choice of GHG emissions metric. Various GHG emission metrics are available for this purpose.9

GWP-100 is the most commonly used metric for reporting CO 2-equivalent emissions and is required for emissions reporting under the 
Rulebook of the Paris Agreement. (Cross-Chapter Box 2 in Chapter 2, Annex I and Annex II.9)
5 A small fraction of pathways in the AR6 scenarios database that limit warming to 2°C (7% for C3 and 14% for C4) do not reach net zero CO 2 emissions during the 21st 
century. This is not inconsistent with the fundamental scientific requirement to reach net zero CO 2 emissions for a stable climate, but reflects that in some pathways, 
concurrent reductions in non-CO 2 emissions temporarily compensate for ongoing warming from CO 2 emissions. These would have to reach net zero CO 2 emissions 
eventually after 2100 to maintain these warming limits. For the two classes of pathways, the 95th percentile cannot be deduced from the scenario database as more 
than 5% of them do not reach net zero CO 2 by 2100.

6 The AR6 WGI Section 5.5 estimates a variation of the remaining carbon budget by ±220 GtCO 2 due to variations of the non-CO 2 warming contribution in 1.5°C–2°C 
pathways. This translates to a shift of the timing of net zero CO 2 by about ±10 years, assuming global CO 2 emissions decrease linearly from current levels of around 
40 GtCO 2 to net zero.
7 Pathways following emissions levels of NDCs announced prior to COP26 to 2030 and then returning warming to 1.5°C (>50%) after high overshoot by 2100 reach net 
zero during 2055–2060 (2045–2070) (median five-year interval and 5–95th percentile range).
8 Pathways that follow emission levels projected from the implementation of NDCs announced prior to COP26 until 2030 and that still limit warming to 2°C (>67%) reach 
net zero CO 2 emissions during 2065–2070 (2055–2090) compared with 2070–2075 (2055–…) in cost-effective pathways acting immediately to likely limit warming to

2°C (median five-year interval and 5–95th percentile range). See Footnote 5 for the lack of 95th percentile (Section 3.3 and Table 3.2).
9 Defining net zero GHG emissions for a basket of greenhouse gases (GHGs) relies on a metric to convert GHG emissions including methane (CH 4), nitrous oxide (N 2O), 
fluorinated gases (F-gases), and potentially other gases, to CO 2-equivalent emissions. The choice of metric ranges from global warming potentials (GWPs) and global 
temperature change potentials (GTP) to economically oriented metrics. All metrics have advantages and disadvantages depending on the context in which they are used 
(Cross-Chapter Box 2 in Chapter 2).Cross-Chapter Box 3 (continued)

328
For most choices of GHG emissions metric, reaching net zero GHG emissions requires net negative CO 2 emissions in order 
to balance residual CH 4, N2O and F-gas emissions. Under foreseen technology developments, some CH 4, N2O and F-gas emissions 
from, for example, agriculture and industry, will remain over the course of this century. Net negative CO 2 emissions will therefore be 
needed to balance these remaining non-CO 2 GHG emissions to obtain net zero GHG emissions at a point in time after net zero CO 2 
has been reached in emissions pathways. Both the amount of net negative CO 2 emissions and the time lag to reaching net zero GHG 
depend on the choice of GHG emission metric.
Reaching net zero GHG emissions globally in terms of GWP-100 leads to a reduction in global warming from an earlier

peak. This is due to net negative CO 2 emissions balancing the GWP-100-equivalent emissions of short-lived GHG emissions, which by 
themselves do not contribute to further warming if sufficiently declining (Fuglestvedt et al. 2018; Rogelj et al. 2021). Hence, 1.5°C–2°C 
emissions pathways in the AR6 scenario database that reach global net zero GHG emissions in the second half of the century show 
warming being halted at some peak value followed by a gradual decline towards the end of the century (AR6 WGI Chapter 1, Box 1.4).
Global net zero GHG emissions measured in terms of GWP-100 are reached between 2095 and 2100 (2050–…)10 in 
emission pathways limiting warming to 1.5°C (>50%) with no or limited overshoot (median and 5–95th percentile). 
Around 50% of pathways limiting warming to 1.5°C (>50%) with no or limited overshoot and 70% of pathways limiting warming

to 2°C (>67%) do not reach net zero GHG emissions in terms of GWP-100 before 2100. These pathways tend to show less reduction 
in warming after the peak than pathways that reach net zero GHG emissions. For the subset of pathways that reach net zero GHG 
emissions before 2100, including around 90% of pathways that return warming to 1.5°C after a high overshoot (>0.1°C) by 2100, 
the time lag between reaching net zero CO 2 and net zero GHG is 12–14 (7–39) years and the amount of net negative CO 2 emissions 
deployed to balance non-CO 2 emissions at the time of net zero GHG is around -7 (–10 to –4) GtCO 2 (range of medians and lowest 
5th to highest 95 percentile across the four scenario classes that limit median warming to 2°C or lower) (Section 3.3 and Table 3.2).
Sectoral and regional aspects of net zero
The timing of net zero CO 2 or GHG emissions may differ across regions and sectors. Achieving net zero emissions globally

implies that some sectors and regions must reach net zero CO 2 or GHG ahead of the time of global net zero CO 2 or GHG if 
others reach it later. Similarly, some sectors and regions would need to achieve net negative CO 2 or GHG emissions to compensate 
for continued emissions by other sectors and regions after the global net zero year. Differences in the timing to reach net zero 
emissions between sectors and regions depend on multiple factors, including the potential of countries and sectors to reduce GHG 
emissions and undertake carbon dioxide removal (CDR), the associated costs, and the availability of policy mechanisms to balance 
emissions and removals between sectors and countries (Fyson et al. 2020; Strefler et al. 2021a; van Soest et al. 2021b). A lack of such 
mechanisms could lead to higher global costs to reach net zero emissions globally, but less interdependencies and institutional needs

(Fajardy and Mac Dowell 2020). Sectors will reach net zero CO 2 and GHG emissions at different times if they are aiming for such 
targets with sector-specific policies or as part of an economy-wide net zero emissions strategy integrating emissions reductions and 
removals across sectors. In the latter case, sectors with large potential for achieving net negative emissions would go beyond net zero 
to balance residual emissions from sectors with low potential, which in turn would take more time compared to the case of sector-
specific action. Global pathways project global AFOLU emissions to reach global net zero CO 2 the earliest, around 2030 to 2035 in 
pathways to limit warming to 2°C (>67%) or lower, by rapid reduction of deforestation and enhancing carbon sinks on land, although 
net zero GHG emissions from global AFOLU are typically reached 30 years later, if at all. The ability of global AFOLU CO 2 emissions to

reach net zero as early as in the 2030s in modelled pathways hinges on optimistic assumptions about the ability to establish global 
cost-effective mechanisms to balance emissions reductions and removals across regions and sectors. These assumptions have been 
challenged in the literature and the Special Report on Climate Change and Land (IPCC SRCCL).
The adoption and implementation of net zero CO 2 or GHG emission targets by countries and regions also depends on 
equity and capacity criteria. The Paris Agreement recognises that peaking of emissions will occur later in developing countries 
(Art. 4.1). Just transitions to net zero CO 2 or GHG could be expected to follow multiple pathways, in different contexts. Regions may 
decide about net zero pathways based on their consideration of potential for rapid transition to low-carbon development pathways, 
the capacity to design and implement those changes, and perceptions of equity within and across countries. Cost-effective pathways

from global models have been shown to distribute the mitigation effort unevenly and inequitably in the absence of financial support 
mechanisms and capacity building (Budolfson et al. 2021), and hence would require additional measures to become aligned with 
10 The 95th percentile cannot be deduced from the scenario database as more than 5% of pathways do not reach net zero GHG by 2100 (Section 3.3 and Table 3.2.), hence 
denoted by -….Cross-Chapter Box 3 (continued)

329
equity considerations (Fyson et al. 2020; van Soest et al. 2021b). Formulation of net zero pathways by countries will benefit from clarity 
on scope, roadmaps and fairness (Rogelj et al. 2021; Smith 2021). Achieving net zero emission targets relies on policies, institutions 
and milestones against which to track progress. Milestones can include emissions levels, as well as markers of technological diffusion.
The accounting of anthropogenic carbon dioxide removal on land matters for the evaluation of net zero CO 2 and net zero 
GHG strategies. Due to the use of different approaches between national inventories and global models, the current net CO 2 emissions 
are lower by 5.5 GtCO 2, and cumulative net CO 2 emissions in modelled 1.5°C–2°C pathways would be lower by 104–170 GtCO 2, if carbon

dioxide removals on land are accounted based on national GHG inventories. National GHG inventories typically consider a much larger 
area of managed forest than global models, and on this area additionally consider the fluxes due to human-induced global environmental 
change (indirect effects) to be anthropogenic, while global models consider these fluxes to be natural. Both approaches capture the same 
land fluxes, only the accounting of anthropogenic vs natural emissions is different. Methods to convert estimates from global models 
to the accounting scheme of national GHG inventories will improve the use of emission pathways from global models as benchmarks 
against which collective progress is assessed. (Section 7.2.2.5).
Net zero CO 2 and carbon neutrality have different meanings in this assessment, as is the case for net zero GHG and 
GHG neutrality. They apply to different boundaries in the emissions and removals being considered. Net zero (GHG or CO 2) refers

to emissions and removals under the direct control or territorial responsibility of the reporting entity. In contrast, (GHG or carbon) 
neutrality includes anthropogenic emissions and anthropogenic removals within and also those beyond the direct control or territorial 
responsibility of the reporting entity. At the global scale, net zero CO 2 and carbon neutrality are equivalent, as is the case for net zero 
GHG and GHG neutrality. The term ‘climate neutrality’ is not used in this assessment because the concept of climate neutrality is 
diffuse, used differently by different communities, and not readily quantified.Cross-Chapter Box 3 (continued)
Table  3.2 summarises the key characteristics for all temperature 
categories in terms of cumulative CO 2 emissions, near-term 
emission reductions, and the years of peak emission and net 
zero CO 2 and GHG emissions. The table shows again that many 
pathways in the literature limit global warming to 2° C (>67%)

or limit warming to  1.5°C (>50%) with no or limited overshoot 
compared to pre-industrial levels. Cumulative net CO 2 emissions 
from the year 2020 until the time of net zero CO 2 in pathways 
that limit warming to 1.5°C (>50%) with no or limited overshoot 
are 510 (330–710) GtCO 2 and in pathways that limit warming 
to 2°C (>67%), 890 (640–1160) GtCO 2 (see also Cross-Chapter 
Box 3 in this chapter). Mitigation pathways that limit warming to 
2°C (>67%) compared to pre-industrial levels are associated with 
net global GHG emissions of 44 (32–55) GtCO 2-eq yr–1 by 2030 
and 20 (13–26) GtCO 2-eq yr–1 in 2050. These correspond to GHG 
emissions reductions of 21% (1–42%) by 2030, and 64% (53–77%) 
by 2050 relative to 2019 emission levels. Pathways that limit 
global warming to  1.5°C (>50%) with no or limited overshoot 
require a  further acceleration in the pace of the transformation, 
with GHG emissions reductions of 43% (34–60%) by 2030 and

84% (73–98%) in 2050 relative to modelled 2019 emission levels. 
The likelihood of limiting warming to below  1.5°C (>50%) with 
no or limited overshoot of the most stringent mitigation pathways 
in the literature (C1) has declined since SR1.5. This is because 
emissions have risen since 2010 by about 9 GtCO 2 yr–1, resulting 
in relatively higher near-term emissions of the AR6 pathways by 
2030 and slightly later dates for reaching net zero CO 2 emissions 
compared to SR1.5.Given the larger contribution of scenarios in the literature that aim 
to reduce net negative emissions, emission reductions are somewhat 
larger in the short term compared to similar categories in the IPCC 
SR1.5. At the same time, the year of net zero emissions is somewhat 
later (but only if these rapid, short-term emission reductions are 
achieved). The scenarios in the literature in C1–C3 show a peak in 
global emissions before 2025. Not achieving this requires a  more

rapid reduction after 2025 to still meet the Paris goals (Section 3.5).

330
Table 3.2 | GHG, CO 2 emissions and warming characteristics of different mitigation pathways submitted to the AR6 scenarios database and as categorised in the climate assessment.
p50 [p5–p95]aGHG emissions 
Gt CO 2-eq/yr gGHG emissions 
reductions from 2019 
% hEmissions milestones i,jCumulative CO 2 
emissions 
Gt CO 2 mCumulative 
net-negative 
CO2 emissions 
Gt CO 2Global mean 
changes 50% 
probability n 
°CLikelihood of peak global 
warming staying below (%) oTime when specific global 
warming levels are reached (with 
a 50% probability)
Category  
b, c, d 
[# path-
ways]Category/ 
subset labelWG I SSP 
& WG III 
IPs/IMPs 
alignment 
e, f2030 2040 2050 2030 2040 2050Peak CO 2 
emissions  
(% peak 
before 2100)Peak GHG 
emissions  
(% peak 
before 2100)Net-zero CO 2 
(% net-zero 
pathways)Net-zero 
GHGs k, l  
pathways)2020 to 
net-zero 
CO22020–
2100Year of net-
zero CO 2 to 
2100at peak

warming2100 <1.5°C <2°C <3°C 1.5°C 2°C 3°C
Modelled global emissions pathways 
categorised by projected global warming 
levels (GWL). Detailed likelihood definitions 
are provided in SPM Box1.  
The five illustrative scenarios (SSPx-yy) 
considered by AR6 WGI and the Illustrative 
(Mitigation) Pathways assessed in WGIII are 
aligned with the temperature categories 
and are indicated in a separate column. 
Global emission pathways contain regionally 
differentiated information. This assessment 
focuses on their global characteristics.Projected median annual 
GHG emissions in the 
year across the scenarios, 
with the 5th–95th 
percentile in brackets.
Modelled GHG  
emissions in 2019:  
55 [53–58] Gt CO 2-eq.Projected median GHG 
emissions reductions of 
pathways in the year 
across the scenarios 
compared to modelled 
2019, with the 5th–95th 
percentile in brackets. 
Negative numbers 
indicate increase in 
emissions compared 
to 2019.Median 5-year intervals at which

projected CO 2 & GHG emissions 
peak, with the 5th–95th percentile 
interval in square brackets. 
Percentage of peaking pathways 
is denoted in round brackets. 
Three dots (…) denotes emissions 
peak in 2100 or beyond for 
that percentile.Median 5-year intervals at 
which projected CO 2 & GHG 
emissions of pathways in this 
category reach net-zero, with 
the 5th–95th percentile interval 
in square brackets. Percentage 
of net zero pathways is denoted 
in round brackets. 
Three dots (…) denotes 
net zero  not reached for 
that percentile.Median cumulative net 
CO2 emissions across 
the projected scenarios 
in this category until 
reaching net-zero or until 
2100, with the 5th–95th 
percentile interval in 
square brackets.Median 
cumulative 
CO2 emissions      
between the 
year of net-zero 
CO2 and 2100. 
More net-
negative results 
in greater 
declines 
after peak.Projected 
of pathways in 
this category (50% 
probability across

the range of climate 
uncertainties), 
1850–1900, at 
peak warming 
and in 2100, for 
the median value 
and the 5th–95th 
square brackets.Median likelihood that the 
projected pathways in this 
category stay below a given 
global warming level, with the 
5th–95th percentile interval 
in square brackets.Median 5-year intervals at which 
specific global warming levels are 
reached (50% probability), with 
in square brackets.  Percentage 
of  pathways is denoted in 
round brackets. 
Three dots (…) denotes temperature 
does not exceed the GWL by 2100 
for that percentile.
C1 [97] limit warming 
to 1.5°C 
(>50%) with 
no or limited 
overshoot 31
[21–36]17
[6–23]9
[1–15]43
[34–60]69
[58–90]84
[73–98]
2020–2025 (100%)
[2020–2025]2050–2055 
(100%)
[2035–2070]2095–2100 
(52%)
[2050–…]510
[330–710]320
[–210–570]–220
[–660-–20]1.6
[1.4–1.6]1.3
[1.1–1.5]38
[33–58]90
[86–97]100
[99–100]2030–2035 
(91%)

[2030–…]…–… (0%)
[…–…]…–… (0%)
[…–…]
C1a [50]… with net-
zero GHGsSSP1-1.9,  
IMP-SP
IMP-LD33
[22–37]18
[6–24]8
[0–15]41
[31–59]66
[58–89]85
[72–100]2070–2075 
(100%)
[2050–2090]550
[340–760]160
[–220–620]–360
[–680-–140]1.6
[1.4–1.6]1.2
[1.1–1.4]38
[34–60]90
[85–98]100
(90%)
[…–…]
C1b [47]… without 
net-zero 
GHGsIMP-Ren29
[21–36]16
[7–21]9
[4–13]48
[35–61]70
[62–87]84
[76–93]…–… (0%)
[…–…]460
[320–590]360
[10–540]–60
[–440–0]1.6
[1.5–1.6]1.4
[1.3–1.5]37
[33–56]89
[87–96]100
(91%)
[…–…]
C2 [133]return 
warming to 
after a high 
overshootIMP-Neg
42
[31–55]25
[17–34]14
[5–21]23
[0–44]55
[40–71]75
[62–91]2020–2025 (100%)2055–2060 
(100%)
[2045–2070]2070–2075 
(87%)
[2055–…]720
[530–930]400
[–90–620]–360
[–680-–60]1.7
[1.5–1.8]1.4
[1.2–1.5]24
[15–42]82
[71–93]100
(100%)
[…–…] [2020–2030] [2020–2025]
C3 [311]limit warming

to 2°C (>67%)44
[32–55]29
[20–36]20
[13–26]21
[1–42]46
[34–63]64
[53–77]2020–2025 (100%)2070–2075 
(93%)
[2055–…]…–… (30%)
[2075–…]890
[640–1160]800
[510–1140]–40
[–290–0]1.7
[1.6–1.8]1.6
[1.5–1.8]20
[13–41]76
[68–91]99
[98–100]2030–2035 
(100%)
C3a [204]… with action 
starting in 
2020SSP1-2.640
[30–49]29
[21–36]20
[14–27]27
[13–45]47
[35–63]63
[52–76]2020–2025 (100%)
[2020–2025]          2070–2075 
(91%)
[2055–…]…–… (24%)
[2080–…]860
[640–1180]790
[480–1150]–30
[–280–0]1.7
[1.5–1.8]21
[14–42]78
[69–91]100
(100%)
[2030–
2040]…–… (0%)
[…–…]

331
emissions 
Category  
b, c, d 
[# path-
& WG III 
IPs/IMPs 
alignment 
emissions  
(% peak 
emissions  
(% peak 
GHGs k, l  
net-zero 
CO22020–
zero CO 2 to 
2100at peak 
levels (GWL). Detailed likelihood definitions

to 2019.Median 5-year intervals at which 
is denoted in round brackets.

More net-
in greater 
declines 
probability across 
and the 5th–95th

C3b [97]… NDCs until 
2030IMP-GS52
[47–56]29
[20–36]18
[10–25]5
[0–14]46
[34–63]68
[56–82]
[2020–2030]2065–2070 
(97%)
[2055–2090]…–… (41%)
[2075–…]910
[720–1150]800
[560–1050]–60
[–300–0]1.8
[1.5–1.7]17
[12–35]73
[67–87]99
[98–99]2030–2035 
(100%)
[2030–
2035]…–… (0%)
[…–…]
C4 [159]limit warming 
to 2°C 
(>50%)50
[41–56]38
[28–44]28
[19–35]10
[0–27]31
[20–50]49
[35–65]2080–2085 
(86%)
[2065–…]…–… (31%)
[2075–…]1210
[970–1490]1160

[700–1490]–30
[–390–0]1.9
[1.7–2.0]1.8
[1.5–2.0]11
[7–22]59
[50–77]98
[95–99]2030–2035 
(100%)
[2030–
[…–…]
C5 [212]limit warming 
to 2.5°C 
(>50%)52
[46–56]45
[37–53]39
[30–49]6
[–1–18]18
[4–33]29
[11–48]…–… (41%)
[2080–…]…–… (12%)
[2090–…]1780
[1400–2360]1780
[1260–2360]0
[–160–0]2.2
[1.9–2.5]2.1
[1.9–2.5]4
[0–10]37
[18–59]91
[83–98]2030–2035 
(100%)
[2030–
2035]2060–2065 
(99%)
[2050–
2095]…–… (0%)
[…–…]
C6 [97]limit warming 
to 3°C 
(>50%)SSP2-4.5
Mod-Act54
[50–62]53
[48–61]52
[45–57]2
[–10–11]3
[–14–14]5
[–2–18] 
2030–2035 
(96%) 
2020–2025 
(97%)
no net-zero no net-zero2790
[2440–3520]
no net-zerotemperature 
does not 
peak by 
21002.7
[2.4–2.9]0
[0–0]8
[2–18]71
[53–88]2030–2035 
(100%)
[2030–
2035]2050–2055 
(100%)
[2045–
2060]…–… (0%)
[…–…]
[2020–2090]
C7 [164]limit warming 
to 4°C 
(>50%)SSP3-7.0
Cur-Pol62
[53–69]67
[56–76]70
[58–83]–11
[–18–3]–19
[–31–1]–24
[–41––2] 
2085–2090 
(57%) 
2090–2095 
(56%)4220
[3160–5000]3.5
[2.8–3.9]0
[0–0]0
[0–2]22

[7–60]2030–2035 
(100%)
[2030–
2035]2045–2050 
(100%)
[2040–
2055]2080–2085 
(100%)
[2070–
2100][2040–…]
C8 [29]exceed 
warming of 
4°C (≥50%)SSP5-8.5            71
[69–81]80
[78–96]88
[82–112]–20
[–34-
–17]–35
[–65-
–29]–46
[–92-
–36]2080–2085 (90%)
[2070–…]5600
[4910–7450]4.2
[3.7–5.0]0
[0–0]0
[0–0]4
[0–11]2030–2035 
(100%)
[2030–
2035]2040–2045 
(100%)
[2040–
2050]2065–2070 
(100%)
[2060–
2075]Table 3.2 (continued):

332
Table 3.2 (continued):
a Values in the table refer to the 50th and [5th–95th] percentile values across the pathways falling within a given category as defined in Box SPM.1. For emissions-related 
columns these values relate to the distribution of all the pathways in that category. Harmonised emissions values are given for consistency with projected global warming 
outcomes using climate emulators. Based on the assessment of climate emulators in AR6 WGI (WG1 Chapter 7, Box 7.1), two climate emulators are used for the probabilistic 
assessment of the resulting warming of the pathways. For the ‘Temperature change’ and ‘Likelihood’ columns, the single upper-row values represent the 50th percentile 
across the pathways in that category and the median [50th percentile] across the warming estimates of the probabilistic MAGICC climate model emulator. For the bracketed

ranges, the median warming for every pathway in that category is calculated for each of the two climate model emulators (MAGICC and FaIR). Subsequently, the 5th and 
95th percentile values across all pathways for each emulator are calculated. The coolest and warmest outcomes (i.e., the lowest p5 of two emulators, and the highest p95, 
respectively) are shown in square brackets. These ranges therefore cover both the uncertainty of the emissions pathways as well as the climate emulators’ uncertainty.
b For a description of pathways categories see Box SPM.1 and Table 3.1.
c All global warming levels are relative to 1850–1900. (See footnote n below and Box SPM.145 for more details.)
d C3 pathways are sub-categorised according to the timing of policy action to match the emissions pathways in Figure SPM.4. Two pathways derived from a cost-benefit

analysis have been added to C3a, whilst 10 pathways with specifically designed near-term action until 2030, whose emissions fall below those implied by NDCs announced 
prior to COP26, are not included in either of the two subsets.
e Alignment with the categories of the illustrative SSP scenarios considered in AR6 WGI, and the Illustrative (Mitigation) Pathways (IPs/IMPs) of WGIII. The IMPs have 
common features such as deep and rapid emissions reductions, but also different combinations of sectoral mitigation strategies. See Box SPM.1 for an introduction of the 
IPs and IMPs, and Chapter 3 for full descriptions. {3.2, 3.3, Annex III.II.2.4}
f The Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative 
emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the

g The 2019 range of harmonised GHG emissions across the pathways [53–58 GtCO 2-eq] is within the uncertainty ranges of 2019 emissions assessed in Chapter 2 
[53–66 GtCO 2-eq].49 (Figure SPM.1, Figure SPM.2, Box SPM.1) 
h Rates of global emission reduction in mitigation pathways are reported on a pathway-by-pathway basis relative to harmonised modelled global emissions in 2019 
rather than the global emissions reported in SPM Section B and Chapter 2; this ensures internal consistency in assumptions about emission sources and activities, as well 
as consistency with temperature projections based on the physical climate science assessment by WGI.49 {Annex III.II.2.5}. Negative values (e.g., in C7, C8) represent an 
increase in emissions.

i Emissions milestones are provided for five-year intervals in order to be consistent with the underlying five-year time-step data of the modelled pathways. Peak emissions 
(CO 2 and GHGs) are assessed for five-year reporting intervals starting in 2020. The interval 2020–2025 signifies that projected emissions peak as soon as possible between 
2020 and at latest before 2025. The upper five-year interval refers to the median interval within which the emissions peak or reach net zero. Ranges in square brackets 
underneath refer to the range across the pathways, comprising the lower bound of the 5th percentile five-year interval and the upper bound of the 95th percentile five-year 
interval. Numbers in round brackets signify the fraction of pathways that reach specific milestones.
j Percentiles reported across all pathways in that category include those that do not reach net zero before 2100 (fraction of pathways reaching net zero is given in round

brackets). If the fraction of pathways that reach net zero before 2100 is lower than the fraction of pathways covered by a percentile (e.g., 0.95 for the 95th percentile), 
the percentile is not defined and denoted with ‘…’. The fraction of pathways reaching net zero includes all with reported non-harmonised, and/or harmonised emissions 
profiles that reach net zero. Pathways were counted when at least one of the two profiles fell below 100 MtCO 2 yr–1 until 2100.
k The timing of net zero is further discussed in SPM C2.4 and Cross-Chapter Box 3 in Chapter 3 on net zero CO 2 and net zero GHG emissions.
l For cases where models do not report all GHGs, missing GHG species are infilled and aggregated into a Kyoto basket of GHG emissions in CO 2-eq defined by the 100-year 
global warming potential. For each pathway, reporting of CO 2, CH 4, and N 2O emissions was the minimum required for the assessment of the climate response and the

assignment to a climate category. Emissions pathways without climate assessment are not included in the ranges presented here. {See Annex III.II.2.5 }
m Cumulative emissions are calculated from the start of 2020 to the time of net zero and 2100, respectively. They are based on harmonised net CO 2 emissions, ensuring 
consistency with the WGI assessment of the remaining carbon budget.50 {Box 3.4}
n Global mean temperature change for category (at peak, if peak temperature occurs before 2100, and in 2100) relative to 1850–1900, based on the median global 
warming for each pathway assessed using the probabilistic climate model emulators calibrated to the AR6 WGI assessment.12 (See also Box SPM.1) {Annex III.II.2.5; 
WGI Cross-Chapter Box 7.1} 
o Probability of staying below the temperature thresholds for the pathways in each category, taking into consideration the range of uncertainty from the climate model

emulators consistent with the AR6 WGI assessment. The probabilities refer to the probability at peak temperature. Note that in the case of temperature overshoot 
(e.g., category C2 and some pathways in C1), the probabilities of staying below at the end of the century are higher than the probabilities at peak temperature.
3.3.2.4 Mitigation Strategies
Detailed sectoral implications are discussed in Section  3.4 and 
Chapters 5–11 (see also Table 3.3). The stringency of climate policy 
has clear implications for mitigation action (Figure 3.15). There are 
a number of important commonalities of pathways limiting warming 
to 2°C (>67%) or lower: for instance, they all rely on significant 
improvement of energy efficiency, rapid decarbonisation of supply 
and, many of them, CDR (in energy supply or AFOLU), either in terms 
of net negative emissions or to compensate residual emissions. 
Still, there are also important differences and the (IMPs) show how

different choices can steer the system into alternative directions with 
different combinations of response options. For decarbonisation of 
energy supply many options exist, including CCS, nuclear power, and 
renewables (Chapter 6). In the majority of the scenarios reaching low 
GHG targets, a considerable amount of CCS is applied (Figure 3.15d). The share of renewables is around 30–70% in the scenarios that 
limit warming to 2°C (>67%) and clearly above 40% for scenarios 
that limit warming   1.5°C (>50%) (panel  c). Scenarios have been 
published with 100% renewable energy systems even at a  global 
scale, partly reflecting the rapid progress made for these technologies 
in the last decade (Creutzig et al. 2017; Jacobson et al. 2018; Breyer 
and Jefferson 2020). These scenarios do not show in the graph due to 
a lack of information from non-energy sources. There is a debate in 
the literature on whether it is possible to achieve a 100% renewable

energy system by 2050 (Brook et al. 2018). This critically depends on 
assumptions made on future system integration, system flexibility, 
storage options, consequences for material demand and the ability 
to supply high-temperature functions and specific mobility functions 
with renewable energy. The range of studies published showing 
100% renewable energy systems show that it is possible to design 
such systems in the context of energy system models (Hong et al.

333
2014a,b; Lehtveer and Hedenus 2015a,b; Pfenninger and Keirstead 
2015; Sepulveda et al. 2018; Zappa et al. 2019; IEA 2021b) (see also 
Box  6.6 on 100% renewables in net zero CO 2 systems). Panels e 
and f, finally, show the contribution of CDR – both in terms of net 
negative emissions and gross CDR. The contribution of total CDR 
obviously exceeds the net negative emissions. It should be noted that 
while a majority of scenarios rely on net negative emissions to reach 
stringent mitigation goals – this is not the case for all of them.
The spread shown in Figure  3.15 implies different mitigation 
strategies that could all lead to emissions levels consistent with the 
Paris Agreement (and reach zero emissions). The IMPs illustrate some options for different decarbonisation pathways with heavy reliance 
on renewables (IMP-Ren), strong emphasis on energy-demand 
reductions (IMP-LD), widespread deployment of CDR methods

coupled with CCS (BECCS and DACCS) (IMP-Neg), mitigation in 
the context of sustainable development (IMP-SP) (Figure 3.16). For 
example, in some scenarios, a small part of the energy system is still 
based on fossil fuels in 2100 (IMP-Neg), while in others, fossil fuels 
are almost or completely phased out (IMP-Ren). Nevertheless, in all 
scenarios, fossil fuel use is greatly reduced and unabated coal use 
is completely phased out by 2050. Also, nuclear power can be part 
of a mitigation strategy (however, the literature only includes some 
scenarios with high-nuclear contributions, such as Berger et al. 2017). 0 2000 4000 6000 8000−0.200.20.40.60.811.2
0 2000 4000 6000 8000−0.200.20.40.60.811.2
0 2000 4000 6000 800000.20.40.60.81
0 2000 4000 6000 80000102030
0 2000 4000 6000 800005001000
0 2000 4000 6000 800005001000CurPol
ModAct
IMP-GS
Neg-2.0
Ren-2.0
IMP-Neg
IMP-Ren
IMP-LD
IMP-SPC1: limit 
warming to 
with no or 
limited 
overshoot
C2: return 
warming to

overshoot
C3: limit 
warming to 2°C 
(>67%)
C4: limit 
(>50%)
C5: limit 
warming to 
2.5°C (>50%)
C6: limit 
warming to 3°C 
(>50%)
C7: limit 
warming to 4°C 
(>50%)
C8: exceed 
warming of 4°C 
(≥50%)Cum. CO 2 2020–2100 (GtCO  2) Cum. CO2  2020–2100 (GtCO2 )
Cum. CO 2 2020–2100 (GtCO2) Cum. CO 2  2020–2100 (GtCO2 )
Cum. CO2  2020–2100 (GtCO2) Cum. CO2  2020–2100 (GtCO2) Ratio 2050/2020
Ratio 2050/2020Share
GtCO 2 Net neg. 2020–2100 (GtCO 2)
Gross neg. 2020–2100 (GtCO 2) Carbon intensity (2050/2020) Energy intensity (2050/2020)
Share renewables in 2050 CCS in 2050
Total net negative (2020–2100) Total gross negative (2020–2100)(a) (b)
(c) (d)
(e) (f)
Figure 3.15 | Characteristics of scenarios as a function of the remaining carbon budget (mean decarbonisation rate is shown as the average reduction 
in the period 2010–2050 divided by 2010 emissions).  The categories C1–C7 are explained in Table 3.1.

334
2020
2050
2100
0
100
200
300
400
500
600
700
800
900
1000
2020
2050
2100
2020
2050
2100
2020
2050
2100
2020
2050
2100
2020
2050
2100
2020
2050
2100a. IMP characteristics: primary energy
CurPol ModAct IMP-GS IMP-Neg IMP-Ren IMP-LD IMP-SPPrimary Energy (EJ yr–1)Non-Biomass Renewables
Nuclear
Biomass with CCS
Biomass without CCS
Gas with CCS
Gas without CCS
Oil with CCS
Oil without CCS
Coal with CCS
Coal without CCSGtCO2-eq yr  –1
–100102030405060b. Sectoral GHG emissions at the time of net-zero CO2 emissions  
(compared to modelled 2019 emissions)
IMP-GS
IMP-Neg
IMP-LD
IMP-SP
IMP-Ren
Sources
Sinks
At time of net-zero CO2 2019Non-CO2 from all sectorsLULUCFEnergy supply (neg.)Energy supply (pos.)TransportIndustryBuildings
Figure 3.16 | Primary energy use and net emissions at net zero year for the different IMPS.  Source: AR6 Scenarios Database.
This is explored further in Section 3.5. The different strategies are also

clearly apparent in the way they scenarios reach net zero emissions. 
While IMP-GS and IMP-Neg rely significantly on BECCS and DACCS, 
their use is far more restricted in the other IMPs. Consistently, in these 
IMPs residual emissions are also significantly lower.Mitigation pathways also have a regional dimension. In 2010, about 
40% of emissions originated from the Developed Countries and Eastern 
Europe and West Central Asia regions. According to the projections 
shown in Figure 3.17, the share of the latter regions will further increase 
to about 70% by 2050. In the scenarios in the literature, emissions are 
typically almost equally reduced across the regions.

335
3.3.3 Climate Impacts on Mitigation Potential
At the moment, climate change impact on mitigation potential is hardly 
considered in model-based scenarios. While a detailed overview of 
climate impacts is provided in IPCC AR6 WGII and Section 3.6 discusses 
the economic consequences, here we concentrate on the implications 
for mitigation potential. Climate change directly impacts the carbon 
budget via all kinds of feedbacks – which is included in the ranges 
provided for the carbon budget (e.g., 300–900 GtCO 2 for 17th–83rd 
percentile for not exceeding 1.5°C; see AR6 WGI Chapter 5, 2021). 
Climate change, however, alters the production and consumption 
of energy (Section 6.5). An overview of the literature is provided by 
Yalew et al. (2020). In terms of supply, impacts could influence the 
cooling capacity of thermal plants, the potential and predictability of

renewable energy, and energy infrastructure (van Vliet et al. 2016; 
Turner et al. 2017; Cronin et al. 2018a; Lucena et al. 2018; Yalew 
et al. 2020; Gernaat et al. 2021). Although the outcomes of these 
studies differ, they seem to suggest that although impacts might be 
relatively small at the global scale, they could be substantial at the 
regional scale (increasing or decreasing potential). Climate change 
can also impact energy demand, with rising temperatures resulting 
in decreases in heating demand and increases in cooling demand 
(Isaac and van Vuuren 2009; Zhou et al. 2014; Labriet et al. 2015; 
McFarland et al. 2015; Auffhammer et al. 2017; Clarke et al. 2018; 
van Ruijven et al. 2019; Yalew et al. 2020). As expected, the increase 
in cooling demand dominates the impact in warm regions and 
decreases in heating demand in cold regions (Isaac and van Vuuren 
2009; Zhou et al. 2014; Clarke et al. 2018). Globally, most studies

show a net increase in energy demand at the end of the century due 
to climate impacts (Isaac and van Vuuren 2009; Clarke et al. 2018; 
van Ruijven et al. 2019); however, one study shows a net decrease 
(Labriet et al. 2015). Only a  few studies quantify the combined 
impacts of climate change on energy supply and energy demand 
(McFarland et al. 2015; Mima and Criqui 2015; Emodi et al. 2019; 
11 The countries and areas classification in this figure deviate from the standard classification scheme adopted by AR6 WGIII as set out in Annex II.I.1.Steinberg et al. 2020). These  studies show increases in electricity 
generation in the USA (McFarland et al. 2015; Steinberg et al. 2020) 
and increases in CO 2 emissions in Australia (Emodi et al. 2019) or the 
USA (McFarland et al. 2015).
Climate change can impact the potential for AFOLU mitigation 
action by altering terrestrial carbon uptake, crop yields and 
bioenergy potential (Chapter  7). Carbon sequestration in forests

may be positively or adversely affected by climate change 
and  CO 2 fertilisation. On the one hand, elevated CO 2 levels and 
higher temperatures could enhance tree growth rates, carbon 
sequestration, and timber and biomass production (Beach et al. 
2015; Kim et al. 2017; Anderegg et al. 2020). On the other hand, 
climate change could lead to greater frequency and intensity of 
disturbance events in forests, such as fires, prolonged droughts, 
storms, pests and diseases (Kim et al. 2017; Anderegg et al. 2020). 
The impact of climate change on crop yields could also indirectly 
impact the availability of land for mitigation and AFOLU emissions 
(Calvin et al. 2013; Bajželj and Richards 2014; Kyle et al. 2014; Beach 
et al. 2015; Meijl et al. 2018). The impact is, however, uncertain, as 
discussed in AR6 WGII Chapter 5. A few studies estimate the effect 
of climate impacts on AFOLU on mitigation, finding increases in

carbon prices or mitigation costs by 1–6% in most scenarios (Calvin 
et al. 2013; Kyle et al. 2014).
In summary, a  limited number of studies quantify the impact of 
climate on emissions pathways. The most important impact in 
energy systems might be through the impact on demand, although 
climate change could also impact renewable mitigation potential – 
certainly at the local and regional scale. Climate change might be 
more important for land-use related mitigation measures, including 
afforestation, bioenergy and nature-based solutions. The net effect of 
changes in climate and CO 2 fertilisation are uncertain but could be 
substantial (Chapter 7).Africa and 
Asia Paciﬁc
Developed 
Countries
Latin America 
Eastern 
Europe and 
West Central 
Asia
2020 C1 C2 C3 C4 C5 C6 C7Emissions (Kyoto Gases, GtCO 2-eq)
2050
Figure 3.1711 | Emissions by region (including 5–95th percentile range).  Source: AR6 Scenarios Database.

336
3.4 Integrating Sectoral Analysis 
Into Systems Transformations
This section describes the role of sectors in long-term emissions 
pathways (Table  3.3). We discuss both sectoral aspects of IAM 
pathways and some insights from sectoral studies. Sectoral studies 
typically include more detail and additional mitigation options 
compared to IAMs. However, sectoral studies miss potential 
feedbacks and cross-sectoral linkages that are captured by IAMs. 
Additionally, since IAMs include all emissions sources, these models 
can be used to identify pathways to particular climate goals. In such 
pathways, emissions are balanced across sectors typically based 
on relative marginal abatement costs; as a result, some sectors are 
sources and some are sinks at the time of net zero CO 2 emissions. 
For these reasons, the mitigation observed in each sector in an IAM

may differ from the potential in sectoral studies. Given the strengths 
and limitations of each type of model, IAMs and sectoral models are 
complementary, providing different perspectives.3.4.1 Cross-sector Linkages
3.4.1.1 Demand and Supply Strategies
Most IAM pathways rely heavily on supply-side mitigation 
strategies, including fuel switching, decarbonisation of fuels, and 
CDR (Creutzig et al. 2016; Bertram et al. 2018; Rogelj et al. 2018b; 
Mundaca et al. 2019). For demand-side mitigation, IAMs incorporate 
changes in energy efficiency, but many other demand-side options 
(e.g.,  behaviour and lifestyle changes) are often excluded from 
models (van Sluisveld et al. 2015; Creutzig et al. 2016; van den Berg 
et al. 2019; Wilson et al. 2019). In addition, this mitigation is typically 
price-driven and limited in magnitude (Yeh et al. 2017; Luderer 
et al. 2018; Wachsmuth and Duscha 2019; Sharmina et al. 2020). In 
contrast, bottom-up modelling studies show considerable potential

for demand-side mitigation (Creutzig et al. 2016; Yeh et al. 2017; 
Mundaca et al. 2019; Wachsmuth and Duscha 2019) (Chapter  5), 
which can slow emissions growth and/or reduce emissions (Creutzig 
et al. 2016; Samadi et al. 2017).
A small number of mitigation pathways include stringent demand-
side mitigation, including changes in thermostat set points 
(van Sluisveld et al. 2016; van Vuuren et al. 2018), more efficient or 
smarter  appliances (van Sluisveld et al. 2016; Grubler et al. 2018; 
Napp  et al. 2019), increased recycling or reduced industrial goods 
(Liu  et al. 2018; van  Sluisveld et al. 2016; Grubler et al. 2018; 
van  de  Ven et al. 2018; Napp et al. 2019), telework and travel 
avoidance (Grubler et al. 2018; van de Ven et al. 2018), shifts to public 
transit (van  Sluisveld et al. 2016; Grubler et al. 2018; van Vuuren 
et al. 2018), reductions in food waste (van de Ven et al. 2018) and 
less meat-intensive diets (Liu et al. 2018; van de Ven et al. 2018;

van Vuuren et al. 2018). These pathways show reduced dependence 
on CDR and reduced pressure on land (Grubler et al. 2018; Rogelj et al. 
2018a; van de Ven et al. 2018; van Vuuren et al. 2018) (Section 5.3.3). 
However, the representation of these demand-side mitigation 
options in IAMs is limited, with most models excluding the costs of 
such changes (van Sluisveld et al. 2016), using stylised assumptions 
to represent them (van den Berg et al. 2019), and excluding rebound 
effects (Krey et al. 2019; Brockway et al. 2021). Furthermore, there 
are questions about the achievability of such pathways, including 
whether the behavioural changes included are feasible (Azevedo 
et al. 2021) and the extent to which development and demand can 
be decoupled (Steckel et al. 2013; Brockway et al. 2021; Keyßer and 
Lenzen 2021; Semieniuk et al. 2021).
Figure 3.18 shows indicators of supply- and demand-side mitigation 
in the IMPs, as well as the range across the database. Two of these

IMPs (IMP-SP, IMP-LD) show strong reductions in energy demand, 
resulting in less reliance on bioenergy and limited CDR from 
energy supply. In contrast, IMP-Neg has higher energy demand, 
depending more on bioenergy and net negative CO 2 emissions from 
energy supply.Table 3.3 | Section 3.4 structure, definitions, and relevant chapters.
Section Sector What is includedRelevant 
chapter(s)
3.4.1 Cross-sectorSupply and demand, bioenergy, 
timing of net zero CO 2, other 
interactions among sectorsChapters 5, 
12
3.4.2 Energy supplyEnergy resources, transformation 
(e.g., electricity generation, 
refineries, etc.)Chapter 6
3.4.3 BuildingsaResidential and commercial 
buildings, other non-specified bChapter 9
3.4.4 TransportationaRoad, rail, aviation, and shipping Chapter 10
3.4.5 IndustryaIndustrial energy use and 
industrial processesChapter 11
3.4.6 AFOLUAgriculture, forestry, and other 
land useChapter 7
3.4.7 Other CDRCDR options not included in 
individual sectors (e.g., direct air

carbon capture and sequestration, 
enhanced weathering)Chapter 12
a Direct energy use and direct emissions only; emissions do not include those 
associated with energy production.
b Other non-specified fuel use, including military. Some models report this category in 
the buildings sector, while others report it in the ‘Other’ sector.

337
3.4.1.2 Sectoral Emissions Strategies and the Timing  
of Net Zero
Mitigation pathways show differences in the timing of decarbonisation 
(Figure 3.20) and the timing of net zero (Figure 3.19) across sectors 
and regions (high confidence); the timing in a given sector depends 
on the cost of abatement in it, the availability of CDR options, the 
scenario design, near-term emissions levels, and the amount of non-
CO2 abatement (Yeh et al. 2017; Emmerling et al. 2019; Rogelj et al. 
2019a,b; Johansson et al. 2020; Azevedo et al. 2021; Ou et al. 2021; 
van Soest et al. 2021b) (Cross-Chapter Box 3 in this chapter). However, 
delaying emissions reductions, or more limited emissions reductions 
in one sector or region, involves compensating reductions in other 
sectors or regions if warming is to be limited (high confidence) (Price 
and Keppo 2017; Grubler et al. 2018; Rochedo et al. 2018; van Soest 
et al. 2021b).

At the time of net zero global CO 2 emissions, emissions in some 
sectors are positive and some negative. In cost-effective mitigation 
pathways, the energy supply sector typically reaches net zero CO 2 
before the economy as a whole, while the demand sectors reach net 
zero CO 2 later, if at all (Pietzcker et al. 2014; Price and Keppo 2017; 
Luderer et al. 2018; Rogelj et al. 2018a,b; Méjean et al. 2019; Azevedo 
12 Unless otherwise specified, the values in parentheses in Section 3.4 from this point forward indicate the 5–95th percentile range.et al. 2021) (Section 6.7). CO 2 emissions from transport, industry, and 
buildings are positive, and non-CO 2 GHG emissions are also positive 
at the time of global net zero CO 2 emissions (Figure 3.20).
So, while pathways indicate some flexibility in emissions 
reductions across sectors, all pathways involve substantial CO 2 
emissions reductions in all sectors and regions (high confidence)

(Luderer et al. 2018; Rogelj et al. 2018a,b; Méjean et al. 2019; 
Azevedo et al. 2021). Projected CO 2 emissions reductions between 
2019 and 2050 in  1.5°C (>50%) pathways with no or limited 
overshoot are around 77% for energy demand, with a  5–95% 
range of 31–96%,12 115% for energy supply (90–167%), and 
148% for AFOLU (94–387%). In pathways that limit warming to 
2°C (>67%), projected CO 2 emissions are reduced between 2019 
and 2050 by around 49% for energy demand, 97% for energy 
supply, and 136% for AFOLU (Sections 3.4.2–3.4.6). Almost 75% 
of GHG reductions at the time of net zero GHG are from the 
energy system, 13% are from AFOLU CO 2, and 13% from non-CO 2 
(Figure 3.21). These reductions are achieved through a variety of 
sectoral strategies, illustrated in Figure 3.21 (Figure 3.21b), and 
described in Sections 3.4.2 to 3.4.7; the primary strategies include 
declines in fossil energy, increases in low-carbon energy use, and 
CDR to address residual emissions.

0100200300400
2025 2050 2075 2100
YearEJ yr–1Building Final Energy
Energy Supply CO2 Emissions
YearEJ yr–1Transport Final Energy
YearEJ yr–1Industrial Final Energy
020,000
–20,00040,00060,000
YearMtCO2 yr–1
YearEJ yr–1Biomass Consumption Category
Illustrative PathwayC1: limit warming to 1.5°C (>50%) 
C2: return warming to 1.5°C (>50%)
Reference
IMP-Neg
CurPol
IMP-SPModAct
IMP-LD
IMP-Ren
IMP-GS(a) (b) (c)
(d) (e)
Figure 3.18 | Indicators of demand and supply-side mitigation in the Illustrative Pathways (lines) and the 5–95% range of Reference, 1.5°C and 2°C 
scenarios (shaded areas).

338
Table 3.4 | Energy and emissions  characteristics of the pathways by climate category for 2030, 2050, 2100.  Source: AR6 scenarios database.
p50
(p5–p95)aGlobal Mean Surface Air 
Temperature changeLow-carbon share 
of Primary Energy d, e
[%]
2020 = 16 (12–18)Energy & Industrial 
Processes Index
2020 = 100Final energy demand
[EJ/yr]
2020 = 419 (367–458)Final energy intensity of 
GDP Index
2020 = 100Electricity share in final 
energy
[%]
2020 = 20 (18–25)CO2 intensity of electricity
[Mt CO 2/TWh]
2020 = 469 (419–538)Non-energy GHG emissions
[Gt CO 2-eq]
2020 = 18 (15–21)Fossil CCS (2100)
[Gt CO 2]
2020 = 0 (0–0)
Category [# 
pathways] 
b, cCategory/ 
subsetWG1 SSP 
& IPs 
alignment2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 21002020–
2100
to 1.5°C 
overshootIMP-SD, 
IMP-LD,  
IMP-Ren,

SSP1-1.9 32 68 75 65 8 –3 399 410 612 71 46 26 27 52 66 99 –5 –4 10 5 2 1 2 3 196
(17–48) (25–86) (19–98) (49–75) (–8–24) (–20–8) (293–447) (325–540) (321–818) (59–81) (34–60) (14–45) (23–35) (40–64) (50–78) (4–215) (–66–11) (–104–1) (5–13) (1–9) (–2–9) (0–5) (0–13) (0–16) (3–882)
C2 [133]return warming 
overshootIMP-Neg24 57 86 79 18 –14 458 442 675 76 44 23 25 45 61 218 0 –1 13 6 1 0 3 1 280
(11–35) (19–77) (25–97) (66–94) (2–37) (–25–0) (372–504) (345–561) (415–819) (64–88) (35–63) (15–45) (20–29) (34–56) (49–73) (99–353) (–75–16) (–118–3) (10–19) (2–9) (–7–7) (0–4) (0–13) (0–16) (7–831)
C3 [311]limit warming 
to 2°C (>67%)24 51 73 84 31 –1 446 448 625 77 50 26 24 42 60 248 5 –8 12 7 5 0 3 5 266
(16–32) (29–75) (34–94) (70–95) (9–47) (–19–8) (356–491) (344–540) (421–788) (65–88) (36–62) (18–41) (20–29) (30–54) (43–72) (93–375) (–72–51) (–105–5) (6–18) (3–12) (–1–8) (0–3) (0–12) (0–15) (7–773)
C3a [204]… with action

starting in 2020SSP2-2.621 39 71 92 45 –3 459 489 641 76 45 22 23 35 56 322 24 –14 13 9 2 0 2 6 279
(14–24) (24–63) (34–91) (80–100) (26–64) (–21–9) (379–497) (362–601) (450–796) (71–87) (39–65) (19–41) (19–28) (23–44) (44–69) (227–381) (–48–112) (–117–7) (8–19) (3–12) (–1–9) (0–2) (0–9) (0–16) (7–684)
C3b [97]… NDCs  
until 2030IMP-GS21 31 67 92 66 9 466 519 680 77 51 23 23 32 53 341 107 –3 15 10 4 0 1 5 200
(12–24) (22–44) (42–84) (84–102) (50–84) (–13–32) (389–499) (435–585) (383–812) (74–88) (45–66) (18–40) (19–28) (19–41) (40–65) (257–418) (14–208) (–73–34) (10–19) (5–15) (–1–11) (0–1) (0–7) (0–15) (5–730)
to 2°C (>50%)20 25 47 94 82 47 467 551 701 79 55 26 23 29 48 354 216 28 17 13 8 0 0 4 47
(11–23) (14–36) (28–65) (87–101) (67–92) (21–78) (410–508) (471–632) (432–910) (75–89) (50–70) (20–42) (19–28) (19–38) (30–56) (257–469) (69–317) (–20–166) (11–20) (9–17) (2–12) (0–0) (0–4) (0–16) (0–536)
C5 [212]limit warming

to 2.5°C (>50%)17 19 29 98 94 73 492 599 804 85 64 33 24 29 41 414 311 185 19 19 16 0 0 0 0
(11–21) (8–29) (8–51) (91–101) (80–101) (56–106) (434–540) (513–701) (557–983) (76–91) (54–76) (27–48) (20–28) (23–35) (29–50) (311–538) (130–499) (12–461) (13–24) (14–25) (9–26) (0–0) (0–2) (0–8) (0–221)
to 3°C (>50%)SSP2-4.5 13 13 29 102 106 91 540 696 941 89 73 47 26 31 43 463 425 189 20 21 20 0 0 0 0
Mod-Act (11–17) (9–20) (14–45) (99–103) (104–109) (87–95) (413–574) (504–856) (692–
1136)(88–92) (64–79) (25–51) (22–30) (28–35) (35–50) (372–514) (352–484) (142–441) (19–25) (20–29) (13–31) (0–0) (0–0) (0–2) (0–38)
to 4°C (>50%)SSP3-7.0 32 68 75 65 8 –3 399 410 612 71 46 26 27 52 66 99 –5 –4 10 5 2 1 2 3 196
Cur-Pol (17–48) (25–86) (19–98) (49–75) (–8–24) (–20–8) (293–447) (325–540) (321–818) (59–81) (34–60) (14–45) (23–35) (40–64) (50–78) (4–215) (–66–11) (–104–1) (5–13) (1–9) (–2–9) (0–5) (0–13) (0–16) (3–882)
C8 [29]exceed warming

of 4°C (≥50%)SSP5-8.5 24 57 86 79 18 –14 458 442 675 76 44 23 25 45 61 218 0 –1 13 6 1 0 3 1 280
a Values in the table refer to the 50th and (5–95th) percentile values.
b  See category descriptions in Table 3.1.
c The warming profile of IMP-Neg peaks around 2060 and declines thereafter to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as a C3, it strongly exhibits the characteristics of C2 high-overshoot scenarios. 
d Primary Energy as calculated in ‘Direct Equivalent’ terms according to IPCC reporting conventions.
e Low-carbon energy here defined to include: renewables (including biomass, solar, wind, hydro, geothermal, ocean); fossil fuels when used with CCS; and, nuclear power.

339
2020s
2030s
2040s
2050s
2060s
2070s
2080s
2090s
2100s
NeverC4: limit warming 
to 2°C (>50%)C3: limit warming 
to 2°C (>67%)C2: return warming to 
1.5°C (>50%) after 
a high overshootC1: limit warming to 
1.5°C (>50%) with 
no or limited overshootC8: exceed warming
of 4°C (≥50%)C7: limit warming 
to 4°C (>50%)C6: limit warming 
to 3°C (>50%)C5: limit warming 
to 2.5°C (>50%)Total
AFOLU
Energy supply
Industry
Transport
Buildings
Total
AFOLU
Industry
Transport
Buildings
Total
AFOLU
Industry
Transport
Buildings
Total
AFOLU
Industry
Transport
Buildings0%25%50%75%100%
SectorPercentage of scenariosYear of Net
Zero CO2
Figure 3.19 | Decade in which sectoral CO 2 emissions first reach net negative values.  Each panel is a different temperature level. The colours indicate the decade

in which CO 2 emissions go negative; the y-axis indicates the share of scenarios achieving net zero in that decade. Only scenarios that pass the vetting criteria are included 
(Section 3.2). Scenarios achieving net zero prior to 2020 are excluded.
–10 0 10 20
Non-CO2
Total CO2
Transport
Industry
Buildings
AFOLU
Non-CO2
Total CO2
Transport
Industry
Buildings
AFOLUNon-CO2
Total CO2
Transport
Industry
Buildings
AFOLU
Non-CO2
Total CO2
Transport
Industry
Buildings
AFOLU
Non-CO2
Total CO2
Transport
Industry
Buildings
AFOLUGtCO2-eq yr–1Annual Emissions: 2050 Annual Emissions: 2100–20 –10 0 10
–5 –10 0 5 10Annual Emissions: Global Net Zero CO2 Year–500 0 500 1000Cumulative Emissions (2020–2100)
0 400 800 1200Cumulative Emissions 
(2020-Net Zero CO2 Year)
Category

C3: limit warming to 2°C (>67%)GtCO2(a) (b)
(d) (e)(c)
Figure 3.20 | Greenhouse gas (GHG) emissions, including CO 2 emissions by sector and total non-CO 2 GHGs in 2050 (top left), 2100 (top middle), year of 
global net zero CO 2 (top right), cumulative CO 2 emissions from 2020–2100 (bottom left), and cumulative CO 2 emissions from 2020 until the year of net 
zero CO 2 for scenarios that limit warming to below 2°C.  Scenarios are grouped by their temperature category. ‘Industry’ includes CO 2 emissions associated with industrial 
energy use only; sectors shown in this figure do not necessarily sum to total CO 2. In this, and other figures in Section 3.4, unless stated otherwise, only scenarios that pass the 
vetting criteria are included (Section 3.2). Boxes indicate the interquartile range, the median is shown with a horizontal black line, while vertical lines show the 5–95% interval.

340
In the context of mitigation pathways, only a  few studies have 
examined solar radiation modification (SRM), typically focusing on 
Stratospheric Aerosol Injection (Arinoa et al. 2016; Emmerling and 
Tavoni 2018a,b; Heutel et al. 2018; Helwegen et al. 2019; Rickels 
et al. 2020; Belaia et al. 2021). These studies find that substantial 
mitigation is required to limit warming to a given level, even if SRM 
is available (Moreno-Cruz and Smulders 2017; Emmerling and Tavoni 
2018b; Belaia et al. 2021). SRM may reduce some climate impacts, 
reduce peak temperatures, lower mitigation costs, and extend the 
time available to achieve mitigation; however, SRM does not address 
ocean acidification and may involve risks to crop yields, economies, 
human health, or ecosystems (AR6 WGII Chapter 16; AR6 WGI TS and 
Chapter 5; SR1.5 SPM; and Cross-Working Group Box 4 in Chapter 14

of this report). There are also significant uncertainties surrounding 
SRM, including uncertainties on the costs and risks, which can 
substantially alter the amount of SRM used in modelled pathways 
(Tavoni et al. 2017; Heutel et al. 2018; IPCC 2018; Helwegen 
et al. 2019; NASEM 2021). Furthermore, the degree of international 
cooperation can influence the amount of SRM deployed in scenarios, 
with uncoordinated action resulting in larger SRM deployment and 
consequently larger risks/impacts from SRM (Emmerling and Tavoni 
2018a). Bridging research and governance involves consideration 
of the full range of societal choices and ramifications (Sugiyama 
et al. 2018). More information on SRM, including the caveats, risks, 
uncertainties, and governance issues is found in AR6 WGI Chapter 4; 
AR6 WGIII Chapter 14; and Cross-Working Group Box 4 in Chapter 14 
of this report.3.4.1.3 Linkages Among Sectors
Mitigation in one sector can be dependent upon mitigation in

another sector, or may involve trade-offs between sectors. Mitigation 
in energy demand often includes electrification (Pietzcker et al. 2014; 
Luderer et al. 2018; Sharmina et al. 2020; DeAngelo et al. 2021), 
however such pathways only result in reduced emissions if  the 
electricity sector is decarbonised (Zhang and Fujimori 2020) 
(Chapter  12). Relatedly, the mitigation potential of some sectors 
(e.g., transportation) depends on the decarbonisation of liquid fuels, 
for example, through biofuels (Pietzcker et al. 2014; Wise et al. 2017; 
Sharmina et al. 2020) (Chapter 12). In other cases, mitigation in one 
sector results in reduced emissions in another sector. For example, 
increased recycling can reduce primary resource extraction; planting 
trees or green roofs in urban areas can reduce the energy demand 
associated with space cooling (Chapter 12).
Mitigation in one sector can also result in additional emissions

in  another. One example is electrification of end use which can 
result in increased emissions from energy supply. However, one 
comparitively well-researched example of this linkage is bioenergy. 
An increase in demand for bioenergy within the energy system has 
the potential to influence emissions in the AFOLU sector through the 
intensification of land and forest management and/or via land-use 
change (Daioglou et al. 2019; Smith et al. 2019; Smith et al. 2020a; 
IPCC 2019a). The effect of bioenergy and BECCS on mitigation 
depends on a variety of factors in modelled pathways. In the energy 
system, the emissions mitigation depends on the scale of deployment, 
the conversion technology, and the fuel displaced (Calvin et al. 2021). 0%  20%  40%  60%  80%  100%  % of modelled 2019 emissionsContributions to r eaching net zer o GHG emissions (for all scenarios r eaching net-zer o GHGs)
Direct
Indirect
2019Contributions 
by sector (CO2)Total direct
and indirect

energy (CO2)LULUCF (CO2) 
and non-CO2
Direct:
Non-CO2 from
all sectorsLULUCFEnergy Supply (neg.)Energy Supply (pos .)TransportIndustryBuildings
Total direct 
energy emissionsTotal indirect 
energy emissions 
(equals sum 
of energy supply 
emissions)Indirect:(a) (b) (c) (d)
Figure 3.21 | Left panel: Greenhouse gas (GHG) emissions reductions from 2019 by sector at the year of net zero GHG for all scenarios that reach net zero 
GHG.  Emissions reductions by sector for direct (demand) and indirect (upstream supply) are shown as the percent of total GHG reductions. Right panel: key indicators in 2050 
for the IMPs. Definitions of significant and very significant are defined relative to 2019 and vary between indicators, as follows: fossil energy (significant >10%, very significant 
>50%), renewables (>150 EJ yr–1, >200 EJ yr–1), bioenergy (>100%, >200%), BECCS (>2.0 GtCO 2 yr–1, >3.5 GtCO 2 yr–1), AFOLU (>100% decline, >130% decline), energy

crops (>150 million ha, >400 million ha), forest (>5% increase, >15% increase). Source: AR6 Scenarios Database.

341
Limiting or excluding bioenergy and/or BECCS increases mitigation 
cost and may limit the ability of a model to reach a low warming 
level (Edmonds et al. 2013; Calvin et al. 2014b; Luderer et al. 2018; 
Muratori et al. 2020). In AFOLU, bioenergy can increase or decrease 
terrestrial carbon stocks and carbon sequestration, depending on the 
scale, biomass feedstock, land management practices, and prior land 
use (Calvin et al. 2014c; Wise et al. 2015; IPCC 2019a; Smith et al. 
2019, 2020a; Calvin et al. 2021).
Pathways with very high biomass production for energy use typically 
include very high carbon prices in the energy system (Popp et al. 
2017; Rogelj et al. 2018b), little or no land policy (Calvin et al. 2014b), 
a high discount rate (Emmerling et al. 2019), and limited non-BECCS 
CDR options (e.g.,  afforestation, DACCS) (Chen and Tavoni 2013; 
Calvin et al. 2014b; Marcucci et al. 2017; Realmonte et al. 2019;

Fuhrman et al. 2020). Higher levels of bioenergy consumption are 
likely to involve trade-offs with mitigation in other sectors, notably 
in construction (i.e., wood for material and structural products) and 
AFOLU (carbon stocks and future carbon sequestration), as well as 
trade-offs with sustainability (Section  3.7) and feasibility concerns 
(Section 3.8). Not all of these trade-offs are fully represented in all 
IAMs. Based on sectoral studies, the technical potential for bioenergy, 
when constraints for food security and environmental considerations 
are included, are 5–50 EJ yr–1 and 50–250 EJ yr–1 in 2050 for residues 
and dedicated biomass production systems, respectively (Chapter 7). 
Bioenergy deployment in IAMs is within the range of these potentials, with between 75 and 248 EJ yr–1 in 2050 in pathways that limit 
warming to 1.5°C with no or limited overshoot. Finally, IAMs do not 
include all potential feedstock and management practices, and have

limited representation of institutions, governance, and local context 
(Brown et al. 2019; Butnar et al. 2020; Calvin et al. 2021).
The inclusion of CDR options, like BECCS, can affect the timing of 
emissions mitigation in IAM scenarios, that is, delays in mitigations 
actions are compensated by net negative emissions in the second half 
of the century. However, studies with limited net negative emissions 
in the long term require very rapid declines in emissions in the near 
term (van Vuuren et al. 2017). Especially in forest-based systems, 
increased harvesting of forests can perturb the carbon balance of 
forestry systems, increasing emissions for some period; the duration 
of this period of increased emissions, preceding net emissions 
reductions, can be very variable (Mitchell et al. 2012; Lamers and 
Junginger 2013; Röder et al. 2019; Hanssen et al. 2020; Cowie et al. 
2021). However, the factors contributing to differences in recovery

time are known (Mitchell et al. 2012; Zanchi et al. 2012; Lamers and 
Junginger 2013; Laganière et al. 2017; Röder et al. 2019). Some studies 
that consider market-mediated effects find that an increased demand 
for biomass from forests can provide incentives to maintain existing 
forests and potentially to expand forest areas, providing additional 
carbon sequestration as well as additional biomass (Dwivedi et al. 
2014; Kim et al. 2018; Baker et al. 2019; Favero et al. 2020). However, 
these responses are uncertain and likely to vary geographically.
40080012001600
2030 2050 2100
YearEJ yr–1Total Primary Energy
Year050010001500EJ yr–1Fossil
YearEJ yr–1Coal without CCS
02505007501000
YearEJ yr–1Non-Biomass Renewables
Year0100200300400500EJ yr–1BiomassCategory

C8: exceed warming of 4°C (≥50%)(a) (b)
(d) (e)(c)
Figure 3.22 | Primary energy consumption across scenarios: total primary energy (a), fossil fuels (b), coal without CCS (c), non-biomass renewables (d), 
and biomass (e).  Scenarios are grouped by their temperature category. Primary energy is reported in direct equivalent, where one unit of nuclear or non-biomass renewable 
energy output is reported as one unit of primary energy. Not all subcategories of primary energy are shown.

342
3.4.2 Energy Supply
Without mitigation, energy consumption and supply emissions 
continue to rise (high confidence) (Kriegler et al. 2016; Bauer et al. 
2017; Riahi et al. 2017; Mcjeon et al. 2021) (Section 6.7). While the 
share of renewable energy continues to grow in reference scenarios, 
fossil fuel accounts for the largest share of primary energy (Bauer 
et al. 2017; Price and Keppo 2017; Riahi et al. 2017). In scenarios 
that limit warming to 2°C or lower, transition of the energy-supply 
sector to a  low- or no-carbon system is rapid (Rogelj et al. 2016, 
2018b; Grubler et al. 2018; Luderer et al. 2018; van Vuuren et al. 
2018). CO 2 emissions from energy supply reach net zero around 
2041 (2033–2057) in pathways limiting warming to 1.5°C (>50%) 
with no or limited overshoot and around 2053 (2040–2066) in 
pathways  that limit warming to 2°C (>67%). Emissions reductions

continue, with emissions reaching –7.1 GtCO 2 yr–1  (–15 to 
–2.3 GtCO 2 yr–1) in 2100 in all pathways that  limit warming to 2°C 
(>67%) or lower.
All pathways that limit warming to 2°C (>67%) or lower show 
substantial reductions in fossil fuel consumption and a near elimination 
of the use of coal without CCS (high confidence) (Bauer et al. 2017; 
van Vuuren et al. 2018; Grubler et al. 2018; Luderer et al. 2018; Rogelj 
et al. 2018a,b;  Azevedo et al. 2021; Mcjeon et al. 2021; Welsby et al. 
2021) (Figure 3.22). In these pathways, the use of coal, gas and oil is 
reduced by 90%, 25%, and 41%, respectively, between 2019 and 2050 
and 91%, 39%, and 78% between 2019 and 2100; coal without CCS is further reduced to 99% below its 2019 levels in 2100. These pathways 
show an increase in low-carbon energy, with 88% (69–97%) of primary 
energy from low-carbon sources in 2100, with different combinations 
of low-carbon fuels (e.g., non-biomass renewables, biomass, nuclear,

and CCS) (Rogelj et al. 2018a,b; van Vuuren et al. 2018) (Sections 3.4.1 
and 6.7). Across all pathways that limit warming to 2°C and below, 
non-biomass renewables account for 52% (24–77%) of primary 
energy in 2100 (Creutzig et al. 2017; Pietzcker et al. 2017; Rogelj et al. 
2018b) (Chapter 6 and Figure 3.22). There are some studies analysing 
the potential for 100% renewable energy systems (Hansen et al. 2019); 
however, there are a range of issues around such systems (Box 6.6).
Stringent emissions reductions at the level required to limit warming 
to 2°C (>67%) or 1.5°C are achieved through increased electrification 
of end use, resulting in increased electricity generation in all 
pathways (high confidence) (Rogelj et al. 2018a; Azevedo et al. 2021) 
(Figure 3.23). Nearly all electricity in pathways likely to limit warming 
to 2°C and below is from low- or no-carbon fuels (Rogelj et al. 2018a; 
Azevedo et al. 2021), with different shares of nuclear, biomass, non-

biomass renewables, and fossil CCS across pathways. Low-emissions 
scenarios also show increases in hydrogen use (Figure 3.23).
3.4.3 Buildings
Global final energy use in the building sector increases in all 
pathways as a result of population growth and increasing affluence 200
0400600800
YearEJ yr–1Electricity
20406080100
Year%Share of low-c arbon electricity
YearEJ yr–1Hydrogen
Category
C1: limit warming to 1.5°C (>50%) with no or limited overshoot
C2: return warming to 1.5°C (>50%) after a high overshoot
C8: exceed warming of 4°C (≥50%)(a)
(c)(b)
Figure 3.23 | Electricity (top left), share of low-carbon electricity (top right), and hydrogen (bottom left) production across all scenarios, grouped by the

categories introduced in Section 3.2. Low carbon includes non-biomass renewables, biomass, nuclear, and CCS.

343
(Figure 3.24). There is very little difference in final energy intensity 
for the buildings sector across scenarios. Direct CO 2 emissions 
from the buildings sector vary more widely across temperature 
stabilisation levels than energy consumption. In 2100, scenarios 
above 3°C [C7–C8] still show an increase of CO 2 emissions from 
buildings around 29% above 2019, while all scenarios likely to limit 
warming to 2°C and below have emission reductions of around 
85% (8–100%). Carbon intensity declines in all scenarios, but much 
more sharply as the warming level is reduced.
In all scenarios, the share of electricity in final energy use increases, 
a trend that is accelerated by 2050 for the scenarios likely to limit 
warming to 2°C and below (Figure 3.23). By 2100, the low-warming 
scenarios show large shares of electricity in final energy consumption 
for buildings. The opposite is observed for gases.

While several global IAM models have developed their buildings 
modules considerably over the past decade (Daioglou et al. 2012; 
Knobloch et al. 2017; Clarke et al. 2018; Edelenbosch et al. 2021; 
Mastrucci et al. 2021), the extremely limited availability of key 
sectoral variables in the AR6 scenarios database (such as floor space 
and energy use for individual services) prohibit a detailed analysis 
of sectoral dynamics. Individual studies in the literature often 
focus on single aspects of the buildings sector, though collectively 
providing a  more comprehensive overview (Edelenbosch et al. 
13 2019 values are from model results and interpolated from other years when not directly reported.2020; Ürge-Vorsatz et al. 2020). For example, energy demand is 
driven by economic development that fulfills basic needs (Mastrucci 
et al. 2019; Rao et al. 2019a), but also drives up floor space in 
general (Daioglou et al. 2012; Levesque et al. 2018; Mastrucci et al.

2021) and ownership of energy-intensive appliances such as air 
conditioners (Isaac and van Vuuren 2009; Colelli and Cian 2020; 
Poblete-Cazenave et al. 2021). These dynamics are heterogeneous 
and lead to differences in energy demand and emission mitigation 
potential across urban/rural buildings and income levels (Krey et al. 
2012; Poblete-Cazenave et al. 2021). Mitigation scenarios rely on 
fuel switching and technology (Knobloch et al. 2017; Dagnachew 
et al. 2020), efficiency improvement in building envelopes 
(Levesque et al. 2018; Edelenbosch et al. 2021) and behavioural 
changes (van  Sluisveld et al. 2016; Niamir et al. 2018, 2020). 
The in-depth dynamics of mitigation in the building sector are 
explored in Chapter 9.
3.4.4 Transport
Reference scenarios show growth in transport demand, particularly 
in aviation and freight (Yeh et al. 2017; Sharmina et al. 2020; 
Müller-Casseres et al. 2021b). Energy consumption continues to be

dominated by fossil fuels in reference scenarios, with some increases 
in electrification (Yeh et al. 2017; Edelenbosch et al. 2020; Yeh et al. 0123
YearIndex ( 2019 = 1)Final Energy
0.00.51.01.52.02.5
YearIndex ( 2019 = 1)CO2 Emissions
0.000.250.500.751.00
YearIndex ( 2019 = 1)Energy Intensity
0255075100
Year% of ﬁnal energyShare of Electricity0.00.51.01.5
YearIndex ( 2019 = 1)Carbon Intensity
0204060
Year% of ﬁnal energyShare of GasesCategory
C1: limit 
warming to 
with no or 
limited 
overshoot
C2: return 
warming to 
overshoot
C3: limit 
(>67%)
C4: limit 
(>50%)
C5: limit 
warming to 
C6: limit 
(>50%)
C7: limit 
(>50%)
C8: exceed 
(≥50%)(a) (b) (c)
(d) (e) (f)

Figure 3.24 | Buildings final energy (a), CO 2 emissions (b), carbon intensity (c), energy intensity (d), share of final energy from electricity (e), and share 
of final energy from gases (f).  Energy intensity is final energy per unit of GDP . Carbon intensity is CO 2 emissions per EJ of final energy. The first four indicators are indexed 
to 2019,12 where values less than 1 indicate a reduction.

344
2017). CO 2 emissions from transport increase for most models in 
reference scenarios (Yeh et al. 2017; Edelenbosch et al. 2020).
The relative contribution of demand-side reduction, energy- efficiency 
improvements, fuel switching, and decarbonisation of fuels, varyies 
by model, level of mitigation, mitigation options available, and 
underlying socio-economic pathway (Longden 2014; Wise et al. 2017; 
Yeh et al. 2017;  Luderer et al. 2018; Yeh et al. 2017; Edelenbosch 
et al. 2020; Müller-Casseres et al. 2021a,b). IAMs typically rely on 
technology-focused measures like energy- efficiency improvements 
and fuel switching to reduce carbon emissions (Pietzcker et al. 2014; 
Edelenbosch et al. 2017a; Yeh et al. 2017; Zhang et al. 2018a,b; Rogelj 
et al. 2018b; Zhang et al. 2018a,b;  Sharmina et al. 2020). Many 
mitigation pathways show electrification of the transport system

(Luderer et al. 2018; Pietzcker et al. 2014; Longden 2014; Luderer 
et al. 2018; Zhang et al. 2018a); however, without decarboniszation 
of the electricity system, transport electrification can increase 
total energy system emissions (Zhang and Fujimori 2020). A small 
number of pathways include demand-side mitigation measures in 
the transport sector; these studies show reduced carbon prices and 
reduced dependence on CDR (Grubler et al. 2018; Méjean et al. 
2019; van de Ven et al. 2018; Zhang et al. 2018c; Méjean et al. 2019) 
(Section 3.4.1).
14 2019 values are from model results and interpolated from other years when not directly reported.
15 Some of these models are treated as global transport energy sectoral models (GTEMs) in Chapter 10.Across all IAM scenarios assessed, final energy demand for transport 
continues to grow, including in many stringent mitigation pathways 
(Figure  3.25). The carbon intensity of energy declines substantially

by 2100 in likely 2°C (>67%) and below scenarios, leading to 
substantial declines in transport  sector CO 2 emissions with increased 
electrification of the transport system (Figure 3.23).
The transport sector has more detail than other sectors in many 
IAMs (Edelenbosch et al. 2020); however, there is considerable 
variation across models. Some models (e.g.,  GCAM, IMAGE, 
MESSAGE-GLOBIOM) represent different transport modes with 
endogenous shifts across modes as a  function of income, price, 
and modal speed (Edelenbosch et al. 2020).15 However, IAMs, 
including those with detailed transport, exclude several supply-side 
(e.g.,  synthetic fuels) and demand-side (e.g.,  behaviour change, 
reduced shipping, telework and automation) mitigation options 
(Pietzcker et al. 2014; Creutzig et al. 2016; Mittal et al. 2017; Davis 
et al. 2018; Köhler et al. 2020; Mittal et al. 2017; Gota et al. 2019; 
Wilson et al. 2019; Creutzig et al. 2016; Köhler et al. 2020; Sharmina

et al. 2020; Pietzcker et al. 2014; Lefèvre et al. 2021; Müller-Casseres 
et al. 2021a,b).1
023
0204060
Year% of ﬁnal energyShare of Electricity0.00.51.01.52.02.5
01020304050
Year% of ﬁnal energyShare of Hydrogen0.000.250.500.751.001.25
0204060
Year% of ﬁnal energyShare of BiofuelsCategory
C1: limit 
warming to 
with no or 
limited 
overshoot
C2: return 
warming to 
overshoot
C3: limit 
(>67%)
C4: limit 
(>50%)
C5: limit 
warming to 
C6: limit 
(>50%)
C7: limit 
(>50%)
C8: exceed 
(≥50%)(a) (b) (b)
(d) (e) (f)

Figure 3.25 | Transport final energy (a), CO 2 emissions (b), carbon intensity (cand share of final energy from electricity (d), hydrogen (e), and biofuels (f).  
See Chapter 10 for a discussion of energy intensity. Carbon intensity is CO 2 emissions per EJ of final energy. The first three indicators are indexed to 2019,13, where values less 
than 1 indicate a reduction.

345
As a result of these missing options and differences in how mitigation 
is implemented, IAMs tend to show less mitigation than the potential 
from national transport/energy models (Wachsmuth and Duscha 
2019; Gota et al. 2019; Yeh et al. 2017; Gota et al. 2019; Wachsmuth 
and Duscha 2019; Edelenbosch et al. 2020). For the transport sector 
as a whole, studies suggest a mitigation potential of 4–-5 GtCO 2 per 
year in 2030 (Edelenbosch et al. 2020) with complete decarbonization 
decarbonisation possible by 2050 (Gota et al. 2019; Wachsmuth and 
Duscha 2019). However, in the  scenarios assessed in this chapter that 
limit warming to below 1.5°C (>50%) with no or limited overshoot, 
transport sector CO 2 emissions are reduced by only 59% (28–% 
to 81%) in 2050 compared to 2015. IAM pathways also show less 
electrification than the potential from other studies; pathways that

limit warming to 1.5°C with no or limited overshoot show a median 
of 25% (7– to 43%) of final energy from electricity in 2050, while the 
IEA NZE scenario includes 45% (IEA 2021a).
16 2019 values are from model results and interpolated from other years when not directly reported.3.4.5 Industry
Reference scenarios show declines in energy intensity, but increases 
in final energy use in the industrial sector (Edelenbosch et al. 
2017b). These scenarios show increases in CO 2 emissions both for 
the total industrial sector (Edelenbosch et al. 2017b, 2020; Luderer 
et al. 2018) and individual subsectors such as cement and iron and 
steel (van Ruijven et al. 2016; van Sluisveld et al. 2021) or chemicals 
(Daioglou et al. 2014; van Sluisveld et al. 2021).
In mitigation pathways, CO 2 emissions reductions are achieved 
through a  combination of energy savings (via energy-efficiency 
improvements and energy conservation), structural change, fuel

switching, and decarbonisation of fuels (Edelenbosch et al. 2017b, 
2020; Grubler et al. 2018; Luderer et al. 2018). Mitigation pathways 
show reductions in final energy for industry compared to the baseline 
(Edelenbosch et al. 2017b; Luderer et al. 2018; Edelenbosch et al. 
2020) and reductions in the carbon intensity of the industrial sector 
through both fuel switching and the use of CCS (van Ruijven et al. 
2016; Edelenbosch et al. 2017b, 2020; Luderer et al. 2018; Paltsev 
et al. 2021; van Sluisveld et al. 2021). The mitigation potential differs 
depending on the industrial subsector and the availability of CCS, 
with larger potential reductions in the steel sector (van Ruijven 
et al. 2016) and cement industry (Sanjuán et al. 2020) than in the 
0123
0
–1 –1123
012

Year% of ﬁnal energyShare of Electricity
010203040
C1: limit 
warming to 
with no or 
limited 
overshoot
C2: return 
warming to 
overshoot
C3: limit 
(>67%)
C4: limit 
(>50%)
C5: limit 
warming to 
C6: limit 
(>50%)
C7: limit 
(>50%)
C8: exceed 
(d) (e) (f)
Figure 3.26 | Industrial final energy, including feedstocks (a), CO 2 emissions (b), carbon intensity (c), energy intensity (d), share of final energy from 
electricity (e), and share of final energy from gases (f).  Energy intensity is final energy per unit of GDP . Carbon intensity is CO 2 emissions per EJ of final energy. The first

four indicators are indexed to 2019,15 where values less than 1 indicate a reduction. Industrial sector CO 2 emissions include fuel combustion emissions only.

346
chemicals sector (Daioglou et al. 2014). Many scenarios, including 
stringent mitigation scenarios, show continued growth in final 
energy; however, the carbon intensity of energy declines in all 
mitigation scenarios (Figure 3.26).
The representation of the industry sector is very aggregated in most 
IAMs, with only a small subset of models disaggregating key sectors 
such as cement, fertiliser, chemicals, and iron and steel (Daioglou 
et al. 2014; Edelenbosch et al. 2017b;  Pauliuk et al. 2017; Napp 
et al. 2019; van Sluisveld et al. 2021). IAMs often account for both 
energy combustion and feedstocks (Edelenbosch et al. 2017b), but 
IAMs typically ignore material flows and miss linkages between 
sectors (Pauliuk et al. 2017; Kermeli et al. 2019). By excluding 
these processes, IAMs misrepresent the mitigation potential of the 
industry sector, for example by overlooking mitigation from material

efficiency and circular economies (Sharmina et al. 2020), which can 
have substantial mitigation potential (Sections 5.3.4 and 11.3).
Sectoral studies indicate a large mitigation potential in the industrial 
sector by 2050, including the potential for net zero CO 2 emissions 
for steel, plastics, ammonia, and cement (Section  11.4.1). Detailed 
industry sector pathways show emissions reductions between 
39% and 94% by mid-century compared to the present day17 
(Section  11.4.2) and a  substantial increase in direct electrification 
(IEA 2021a). IAMs show comparable mitigation potential to sectoral 
17 Some studies calculate emissions reductions in 2050 compared to 2014, while others note emissions reductions in 2060 relative to 2018.studies with median reductions in CO 2 emissions between 2019 and 
2050 of 70% in scenarios likely to limit warming to 2°C (>67%) 
and below and a maximum reduction of 96% (Figure 3.26). Some

differences between IAMs and sectoral models can be attributed to 
differences in technology availability, with IAMs sometimes including 
more technologies (van Ruijven et al. 2016) and sometimes less 
(Sharmina et al. 2020).
3.4.6 Agriculture, Forestry and Other Land Use (AFOLU)
Mitigation pathways show substantial reductions in CO 2 emissions, 
but more modest reductions in AFOLU CH 4 and N 2O emissions 
(high confidence) (Popp et al. 2017; Roe et al. 2019; Reisinger et al. 
2021) (Figure  3.27). Pathways limiting warming to likely 2°C or 
lower are projected to reach net zero CO 2 emissions in the AFOLU 
sector around 2033 (2024–2060); however, AFOLU CH 4 and N 2O 
emissions remain positive in all pathways (Figure  3.27). While 
IAMs include many land-based mitigation options, these models 
exclude several options with large mitigation potential, such as 
biochar, agroforestry, restoration/avoided conversion of coastal

wetlands, and restoration/avoided conversion of peatland (IPCC 
2019a; Smith et al. 2019) (Chapter  7 and Section  3.4). Sectoral 
studies show higher mitigation potential than IAM pathways, as 
100
0200300400
Year% reduction from 2019CO2 Emissions
–80–4004080
Year% reduction from 2019N2O Emissions
–500
–252575
50
Year% reduction from 2019CH4 Emissions  Category
(b) (c)
Figure 3.27 | Reduction in AFOLU GHG emissions from 2019.  The AFOLU CO 2 estimates in this figure are not necessarily comparable with country GHG inventories 
(see Chapter 7).

347
these studies include more mitigation options than IAMs (medium 
confidence) (Chapter 7).
Limiting warming to likely 2°C (>67%) or lower can result in 
large-scale transformation of the land surface (high confidence) 
(Popp et al. 2017; Rogelj et al. 2018a,b; Brown et al. 2019; Roe 
et al. 2019). The scale of land transformation depends, inter alia, 
on the temperature goal and the mitigation options included (Popp 
et al. 2017; Rogelj et al. 2018a; IPCC 2019a). Pathways with more 
demand-side mitigation options show less land transformation 
than those with more limited options (Grubler et al. 2018; van 
Vuuren et al. 2018; IPCC 2019a). Most of these pathways show 
increases in forest cover, with an increase of 322 million ha   
(–67 to 890 million ha) in 2050 in pathways that limit warming 
to  1.5°C (>50%) with no or limited overshoot, whereas bottom-
up models portray an economic potential of 300–500 million ha of

additional forest (Chapter 7). Many IAM pathways also include large 
amounts of energy cropland area, to supply biomass for bioenergy and 
BECCS, with 199 (56–482) million ha in 2050 in pathways that limit 
warming to 1.5°C (>50%) with no or limited overshoot. Large land 
transformations, such as afforestation/reforestation and widespread 
planting of energy crops, can have implications for biodiversity and 
sustainable development (Sections 3.7, 7.7.4 and 12.5).
Delayed mitigation has implications for land-use transitions 
(Hasegawa et al. 2021a). Delaying mitigation action can result in 
a temporary overshoot of temperature and large-scale deployment 
of CDR in the second half of the century to reduce temperatures from their peak to a given level (Smith et al. 2019; Hasegawa et al. 2021a). 
IAM pathways rely on afforestation and BECCS as CDR measures, 
so delayed mitigation action results in substantial land-use change

in the second half of the century with implications for sustainable 
development (Hasegawa et al. 2021a) (Section  3.7). Shifting to 
earlier mitigation action reduces the amount of land required for 
this, though at the cost of larger land-use transitions earlier in the 
century (Hasegawa et al. 2021a). Earlier action could also reduce 
climate impacts on agriculture and land-based mitigation options 
(Smith et al. 2019).
Some AFOLU mitigation options can enhance vegetation and 
soil carbon stocks such as reforestation, restoration of degraded 
ecosystems, protection of ecosystems with high carbon stocks and 
changes to agricultural land management to increase soil carbon 
(high confidence) (Griscom et al. 2017; de Coninck et al. 2018; Fuss 
et al. 2018; Smith et al. 2019) (AR6 WGIII Chapter 7). The time scales 
associated with these options indicate that carbon sinks in terrestrial 
vegetation and soil systems can be maintained or enhanced so as to

contribute towards long-term mitigation (high confidence); however, 
many AFOLU mitigation options do not continue to sequester 
carbon indefinitely (Fuss et al. 2018; de Coninck et al. 2018; IPCC 
2019a) (AR6 WGIII Chapter  7). In the very long term (the latter 
part of the century and beyond), it will become more challenging 
to continue to enhance vegetation and soil carbon stocks, so that 
the associated carbon sinks could diminish or even become sources 
(high confidence) (de  Coninck et al. 2018; IPCC 2019a) (AR6 WGI 
Chapter 5). Sustainable forest management, including harvest and –50005001000Change in land from 2019 (million ha)
–1000–5000500Change in land from 2019 (million ha)
–500–2500250500Change in land from 2019 (million ha)2030 2050 2100
YearForest
YearNon-Energy Crops
0200400600800Change in land from 2019 (million ha)2030 2050 2100
YearPasture
YearEnergy CropsCategory
C1: limit warming 
with no or limited 
overshoot

C2: return 
warming to 1.5°C 
(>50%) after a 
high overshoot
C6: limit warming 
to 3°C (>50%)
C7: limit warming 
to 4°C (>50%)
C8: exceed 
(≥50%)(a) (b)
(c) (d)
Figure 3.28 | Change in land cover from 2019 in million hectares.  Positive values indicate an increase in area.

348
forest regeneration, can help to remediate and slow any decline in the 
forest carbon sink, for example by restoring degraded forest areas, 
and so go some way towards addressing the issue of sink saturation 
(IPCC 2019) (AR6 WGI Chapter 5; and Chapter 7 in this report). The 
accumulated carbon resulting from mitigation options that enhance 
carbon sequestration (e.g., reforestation, soil carbon sequestration) is 
also at risk of future loss due to disturbances (e.g., fire, pests) (Boysen 
et al. 2017; de Coninck et al. 2018; Fuss et al. 2018; Smith et al. 2019; 
IPCC 2019a;  Anderegg et al. 2020) (AR6 WGI Chapter 5). Maintaining 
the resultant high vegetation and soil carbon stocks could limit future 
land-use options, as maintaining these carbon stocks would require 
retaining the land use and land-cover configuration implemented to 
achieve the increased stocks.

Anthropogenic land CO 2 emissions and removals in IAM pathways 
cannot be directly compared with those reported in national 
GHG inventories (high confidence)  (Grassi et al. 2018, 2021) 
(Section  7.2).   Due to differences in  definitions  for the area  of 
managed forests and which emissions and removals are considered 
anthropogenic, the reported anthropogenic land CO 2  emissions 
and removals differ by about  5.5 GtCO 2  yr–1 between IAMs, 
which rely on bookkeeping approaches  (e.g.,  Houghton and 
Nassikas 2017), and national GHG inventories (Grassi et al. 
2021). Such  differences  in  definitions  can alter the  reported  time 
at which  anthropogenic  net zero  CO 2 emissions  are  reached  for 
a  given emission scenario. Using national inventories would lead 
to an earlier reported time of net zero (van Soest et al. 2021b) or 
to lower calculated cumulative emissions until the time of net zero 
(Grassi et al. 2021) as compared to IAM pathways. The numerical

differences are purely due to differences in the conventions applied 
for reporting  the  anthropogenic  emissions and do not have any 
implications for the underlying land-use changes or mitigation 
measures in the pathways. Grassi et al. (Grassi et al. 2021) 
offer a  methodology for adjusting to reconcile these differences 
and enable a more accurate assessment of the collective progress 
achieved under the Paris Agreement (Chapter 7 and Cross-Chapter 
Box 6 in Chapter 7).
3.4.7 Other Carbon Dioxide Removal Options
This subsection includes other CDR options not discussed in the 
previous subsections, including direct air carbon capture and storage (DACCS), enhanced weathering (EW), and ocean-based approaches, 
focusing on the role of these options in long-term mitigation 
pathways, using both IAMs (Chen and Tavoni 2013; Marcucci 
et al. 2017; Rickels et al. 2018; Fuhrman et al. 2019, 2020, 2021; 
Realmonte et al. 2019; Akimoto et al. 2021; Strefler et al. 2021a) and

non-IAMs (Fuss et al. 2013; González and Ilyina 2016; Bednar et al. 
2021; Shayegh et al. 2021). There are other options discussed in the 
literature, such as methane capture (Jackson et al. 2019), however, 
the role of these options in long-term mitigation pathways has not 
been quantified and is thus excluded here. Chapter 12 includes a more 
detailed description of the individual technologies, including their 
costs, potentials, financing, risks, impacts, maturity and upscaling.
Very few studies and pathways include other CDR options (Table 3.5). 
Pathways with DACCS include potentially large removal from DACCS 
(up to 37 GtCO 2 yr–1 in 2100) in the second half of the century 
(Chen and Tavoni 2013; Marcucci et al. 2017; Realmonte et al. 2019; 
Fuhrman et al. 2020, 2021; Shayegh et al. 2021; Akimoto et al. 2021) 
and reduced cost of mitigation (Bistline and Blanford 2021; Strefler 
et al. 2021a). At large scales, the use of DACCS has substantial

implications for energy use, emissions, land, and water; substituting 
DACCS for BECCS results in increased energy usage, but reduced 
land-use change and water withdrawals (Fuhrman et al., 2020, 
2021) (Chapter 12.3.2; AR6 WGI Chapter 5). The level of deployment 
of DACCS is sensitive to the rate at which it can be scaled up, the 
climate goal or carbon budget, the underlying socio-economic 
scenario, the availability of other decarbonisation options, the cost 
of DACCS and other mitigation options, and the strength of carbon-
cycle feedbacks (Chen and Tavoni 2013; Fuss et al. 2013; Honegger 
and Reiner 2018; Realmonte et al. 2019; Fuhrman et al. 2020; Bistline 
and Blanford 2021; Fuhrman et al. 2021; Strefler et al. 2021a) (AR6 
WGI Chapter  5). Since DACCS consumes energy, its effectiveness 
depends on the type of energy used; the use of fossil fuels would 
reduce its sequestration efficiency (Creutzig et al. 2019; NASEM 2019;

Babacan et al. 2020). Studies with additional CDR options in addition 
to DACCS (e.g., enhanced weathering, BECCS, afforestation, biochar, 
and soil carbon sequestration) find that CO 2 removal is spread across 
available options (Holz et al. 2018; Strefler et al. 2021a). Similar to 
DACCS, the deployment of deep-ocean storage depends on cost and 
the strength of carbon-cycle feedbacks (Rickels et al. 2018).Table 3.5 | Carbon dioxide removal in assessed pathways.  Scenarios are grouped by temperature categories, as defined in Section 3.2.4. Quantity indicates the median 
and 5–95th percentile range of cumulative sequestration from 2020 to 2100 in GtCO 2. Count indicates the number of scenarios with positive values for that option. Source: 
AR6 Scenarios Database.
CDR optionC1: Limit warming to 1.5°C (>50%) 
with no or limited overshootC2: Return warming to 1.5°C 
(>50%) after a  high overshootC3: Limit warming to 2°C (>67%)
Quantity Count Quantity Count Quantity Count

CO2 removal on managed land including 
Afforestation/Reforestation1262 (17–397) 64 330 (28–439) 82 209 (20–415) 196
BECCS 334 (32–780) 91 464 (226–842) 122 291 (174–653) 294
Enhanced weathering 0 (0–47) 2 0 (0–0) 1 0 (0–0) 1
DACCS 30 (0–308) 31 109 (0 – 539) 24 19 (0–253) 91
1 Cumulative CDR from AFOLU cannot be quantified precisely because models use different reporting methodologies that in some cases combine gross emissions and removals, 
and use different baselines.

349
3.5 Interaction Between Near-, Medium- and 
Long-term Action in Mitigation Pathways
This section assesses the relationship between long-term climate 
goals and short- to medium-term emissions reduction strategies 
based on the mitigation pathway literature. After an overview of 
this relationship (Section 3.5.1), it provides an assessment of what 
currently planned near-term action implies for limiting warming 
to  1.5°C–2°C (Section  3.5.2), and to what extent pathways with 
accelerated action beyond current NDCs can improve the ability to 
keep long-term targets in reach (Section 3.5.3).
The assessment in this section shows that if mitigation ambitions 
in NDCs announced prior to COP262,18 are followed until 2030, 
leading to estimated emissions of 47–57 GtCO 2-eq in 203019 
(Section  4.2.2), it is no longer possible to limit warming to  1.5°C 
(>50%) with no or limited overshoot (high confidence). Instead, it

would entail high overshoot (typically >0.1°C) and reliance on net 
negative CO 2 emissions with uncertain potential to return warming 
to 1.5°C (>50%) by the end of the century. It would also strongly 
increase mitigation challenges to limit warming to 2°C (>67%) 
(high confidence). GHG emissions reductions would need to abruptly 
increase after 2030 to an annual average rate of  1.4–2.0 GtCO 2-
eq during the period 2030–2050, around 70% higher than in 
mitigation pathways assuming immediate action1 to limit warming 
to 2°C (>67%). The higher post-2030 reduction rates would have 
to be obtained in an environment of continued buildup of fossil fuel 
infrastructure and less development of low-carbon alternatives until 
2030. A  lock-in to fossil fuel-intensive production systems (carbon 
lock-in) will increase the societal, economic and political strain of 
a rapid low-carbon transition after 2030 (high confidence).
The section builds on previous assessments in the IPCC’s Fifth

Assessment Report (Clarke et al. 2014) and the IPCC Special Report 
on 1.5°C Warming (Rogelj et al. 2018a). The literature assessed in these 
two reports has focused on delayed action until 2030 in the context of 
limiting warming to 2°C (den Elzen et al. 2010; van Vuuren and Riahi 
2011; Luderer et al. 2013, 2016; Rogelj et al. 2013a; Kriegler et al. 2015; 
Riahi et al. 2015) and 1.5°C (Rogelj et al. 2013b; Luderer et al. 2018; 
Strefler et al. 2018). Here we provide an update of these assessments 
drawing on the most recent literature on global mitigation pathways. 
New studies have focused, inter alia, on  constraining near-term 
developments by peak warming limits (Rogelj et al. 2019b; Riahi et al. 
2021; Strefler et al. 2021b) and updating assumptions about near- and  
medium-term emissions developments based on national plans 
and long-term strategies (Roelfsema et al. 2020) (Section 4.2). Several 
studies have explored new types of pathways with accelerated action

bridging between current policy plans and the goal of limiting warming 
below 2°C (Kriegler et al. 2018a; van Soest et al. 2021a) and looked at 
hybrid international policy regimes to phase in global collective action 
(Bauer et al. 2020).
18 Original NDCs refer to those submitted to the UNFCCC in 2015 and 2016. See Section 4.2.
19 In this section, the emissions range associated with NDCs announced prior to COP26 (or original NDCs) refer to the combined emissions ranges from the two 
cases of implementing only the unconditional elements of NDCs announced prior to COP26 (50–57 GtCO 2-eq) and implementing both unconditional and conditional 
elements of NDCs announced prior to COP26 (47–55 GtCO 2-eq), if not specified otherwise.3.5.1 Relationship Between Long-term Climate Goals 
and Near- to Medium-term Emissions Reductions
The close link between cumulative CO 2 emissions and warming has 
strong implications for the relationship between near-, medium-,

and long-term climate action to limit global warming. The AR6 
WGI Assessment has estimated a  remaining carbon budget of 
500 (400) GtCO 2 from the beginning of 2020 onwards for staying 
below  1.5°C with 50% (67%) likelihood, subject to additional 
uncertainties about historic warming and the climate response, 
and variations in warming from non-CO 2 climate forcers (Canadell and 
Monteiro 2019) (AR6 WGI Chapter 5, Section 5.5). For comparison, if 
current CO 2 emissions of more than 40 GtCO 2 are keeping up until 
2030, more than 400 GtCO 2 will be emitted during 2021–2030, 
already exhausting the remaining carbon budget for 1.5°C by 2030.
The relationship between warming limits and near-term action is 
illustrated in Figure 3.29, using a set of 1.5°C–2°C scenarios with 
different levels of near-term action, overshoot and non-CO 2 warming 
contribution from a recent study (Riahi et al. 2021). In general, the 
more CO 2 is emitted until 2030, the less CO 2 can be emitted thereafter

to stay within a remaining carbon budget and below a warming limit. 
Scenarios with immediate action to observe the warming limit give 
the longest time to exhaust the associated remaining carbon budget 
and reach net zero CO 2 emissions (see light blue lines in Figure 3.29 
and Cross-Chapter Box 3 in this chapter). In comparison, following 
projected NDC emissions until 2030 would imply a more pronounced 
drop in emissions from 2030 levels to net zero to make up for the 
additional near-term emissions (see orange lines in Figure  3.29). 
If  such a  drop does not occur, the remaining carbon budget is 
exceeded and net negative CO 2 emissions are required to return 
global mean temperature below the warming limit (see black lines in 
Figure 3.29) (Clarke et al. 2014; Fuss et al. 2014; Rogelj et al. 2018a).
also affected by the warming contribution of non-CO 2 greenhouse

gases and other short-lived climate forcers (Section  3.3; AR6 WGI 
Section  6.7). The estimated budget values for limiting warming 
to  1.5°C–2°C already assume stringent reductions in non-CO 2 
greenhouse gases and non-CO 2 climate forcing as found in 1.5°C–2°C 
pathways (Section 3.3 and Cross-Working Group Box 1 in this chapter; 
AR6 WGI Section  5.5 and Box  5.2 in Chapter  5). Further variations 
in non-CO 2 warming observed across  1.5°C–2°C pathways can vary 
the median estimate for the remaining carbon budget by 220 GtCO 2 
(AR6 WGI Section 5.5). In 1.5°C–2°C pathways, the non-CO 2 warming 
contribution differs strongly between the near, medium and long term. 
Changes to the atmospheric composition of short-lived climate forcers 
(SLCFs) dominate the warming response in the near term (AR6 WGI 
Section 6.7). CO 2 reductions are combined with strong reductions in air 
pollutant emissions due to rapid reduction in fossil fuel combustion and

in some cases the assumption of stringent air quality policies (Rao et al. 
2017b; Smith et al. 2020c). As air pollutants exert a net-cooling effect,

350
their reduction drives up non-CO 2 warming in the near term, which 
can be attenuated by the simultaneous reduction of methane and 
black carbon (Shindell and Smith 2019; Smith et al. 2020b) (AR6 WGI 
Section 6.7). After 2030, the reduction in methane concentrations and 
associated reductions in tropospheric ozone levels tend to dominate 
so that a peak and decline in non-CO 2 forcing and non-CO 2-induced 
warming can occur before net zero CO 2 is reached (Figure 3.29) (Rogelj 
et al. 2018a). The more stringent the reductions in methane and other 
short-lived warming agents such as black carbon, the lower this peak 
and the earlier the decline of non-CO 2 warming, leading to a reduction 
of warming rates and overall warming in the near to medium term 
(Harmsen et al. 2020; Smith et al. 2020b). This is important for keeping 
warming below a tight warming limit that is already reached around

mid-century as is the case in  1.5°C pathways (Xu and Ramanathan 2017). Early and deep reductions of methane emissions, and other 
short-lived warming agents such as black carbon, provide space for 
residual CO 2-induced warming until the point of net zero CO 2 emissions 
is reached (see purple lines in Figure 3.29). Such emissions reductions 
have also been advocated due to co-benefits for, for example, reducing 
air pollution (Rao et al. 2016; Shindell et al. 2017a, 2018; Shindell and 
Smith 2019; Rauner et al. 2020a; Vandyck et al. 2020).
The relationship between long-term climate goals and near-term 
action is further constrained by social, technological, economic and 
political factors (Cherp et al. 2018; van Sluisveld et al. 2018b; Aghion 
et al. 2019; Mercure et al. 2019; Trutnevyte et al. 2019b; Jewell and 
Cherp 2020). These factors influence path dependency and transition

speed (Pahle et al. 2018; Vogt-Schilb et al. 2018). While detailed 2000 2020 2040 2060 2080 2100-20-1001020304050[GtCO2 yr–1]
[GtCO2][W m–2]
[ºC]2a) Global CO2 emissions 
2000 2020 2040 2060 2080 21000200400600800100012001400b) Cumulated CO2 emissions from 2020
2000 2020 2040 2060 2080 210000.20.40.60.81c) Effective non-CO2 forcing
2000 2020 2040 2060 2080 21000.60.811.21.41.61.822.2d) Global mean temperature increase (p67)
Immediate action within CO2 budget
2030 NDC within CO2 budget
2030 NDC with budget overshoot
2030 NDC with higher CO2 budget
Figure 3.29 | Illustration of emissions and climate response in four mitigation pathways with different assumptions about near-term policy developments, 
global warming limit and non-CO 2 warming contribution drawn from Riahi et al.  (2021). Shown are (a) CO 2 emissions trajectories, (b) cumulative CO 2 emissions,

(c) effective non-CO 2 radiative forcing, and (d) the resulting estimate of the 67th percentile of global mean temperature response relative to 1850–1900. Light blue lines 
show a scenario that acts immediately on a remaining carbon budget of 900 GtCO 2 from 2020 without allowing net negative CO 2 emissions (i.e., temporary budget overshoot) 
(COFFEE 1.1, Scenario EN_NPi2020_900). Orange and black lines show scenarios drawn from the same model that follow the NDCs until 2030 and thereafter introduce action 
to stay within the same budget – in one case excluding net negative CO 2 emissions like before (orange lines; COFFEE 1.1., Scenario EN-INDCi2030_900) and in the other 
allowing for a temporary overshoot of the carbon budget until 2100 (black lines; COFFEE 1.1., Scenario EN-INDCi2030_900f). Light blue lines describe a scenario following the

NDCs until 2030, and then aiming for a higher budget of 2300 GtCO 2 without overshoot (AIM/CGE 2.2, Scenario EN-INDCi2030_1200). It is drawn from another model which 
projects a lower anthropogenic non-CO 2 forcing contribution and therefore achieves about the same temperature outcome as the other two non-overshoot scenarios despite 
the higher CO 2 budget. Grey funnels include the trajectories from all scenarios that limit warming to 2°C (>67%) (category C3). Historical CO 2 emissions until 2019 are from 
Chapter SM.2.1 EDGAR v6.0.

351
integrated assessment modelling of global mitigation pathways 
accounts for technology inertia (Bertram et al. 2015a; Mercure et al. 
2018) and technology innovation and diffusion (Wilson et al. 2013; 
van Sluisveld et al. 2018a; Luderer et al. 2021), there are limitations 
in capturing socio-technical and political drivers of innovation, 
diffusion and transition processes (Gambhir et al. 2019; Köhler et al. 
2019; Hirt et al. 2020; Keppo et al. 2021). Mitigation pathways show 
a  wide range of transition speeds that have been interrogated in 
the context of socio-technical inertia (Gambhir et al. 2017; Kefford 
et al. 2018; Kriegler et al. 2018a; Brutschin et al. 2021) vs accelerating 
technological change and self-enforcing socio-economic developments 
(Creutzig et al. 2017; Zenghelis 2019) (Section 3.8). Diagnostic analysis 
of detailed IAMs found a lag of 8–20 years between the convergence

of emissions pricing and the convergence of emissions response after 
a period of differentiated emission prices (Harmsen et al. 2021). This 
provides a  measure of the inertia to changing policy signals in the 
model response. It is about half the time scale of 20–40 years observed 
for major energy transitions (Grubb et al. 2021). Hence, the mitigation 
pathways assessed here capture socio-technical inertia in reducing 
emissions, but the limited modelling of socio-political factors may alter 
the extent and persistence of this inertia.
3.5.2 Implications of Near-term Emission Levels for 
Keeping Long-term Climate Goals Within Reach
The implications of near-term climate action for long-term climate 
outcomes can be explored by comparing mitigation pathways with 
different near-term emissions developments aiming for the same 
climate target (Riahi et al. 2015; Vrontisi et al. 2018; Roelfsema 
et al. 2020). A particular example is the comparison of cost-effective

pathways with immediate action to limit warming to 1.5°C–2°C with 
mitigation pathways pursuing more moderate mitigation action 
until 2030. After the adoption of the Paris Agreement, near-term 
action was often modelled to reflect conditional and unconditional 
elements of originally submitted NDCs (2015–2019) (Fawcett et al. 
2015; Fujimori et al. 2016a; Kriegler et al. 2018a; Vrontisi et al. 
2018; Roelfsema et al. 2020). The most recent modelling studies 
also include submission of updated NDCs or announcements of 
planned updates in the first half of 2021 (Network for Greening 
the Financial System 2021; Riahi et al. 2021). Emissions levels 
under NDCs announced prior to COP26 are assessed to range 
between 47–57 GtCO 2-eq in 2030 (Section  4.2.2). This assessed 
range corresponds well to 2030 emissions levels in 2°C mitigation 
pathways in the literature that are designed to follow the original 
or updated NDCs until 2030.20 For the 139 scenarios of this kind

that are collected in the AR6 scenario database and that still 
limit warming to 2°C (>67%), the 2030 emissions range is 53 
(45–58) GtCO 2-eq (based on native model reporting) and 52.5 
(47–56.5) GtCO 2-eq, respectively (based on harmonised emissions 
data for climate assessment (Annex III.2.5.1); median and 5–95th 
percentile). This close match allows a  robust assessment of the 
implications of implementing NDCs announced prior to COP26 for 
20 The intended design of mitigation pathways in the literature can be deduced from underlying publications and study protocols. This information was collected as part 
of this assessment to establish a categorisation of policy assumptions underpinning the mitigation pathways collected in the AR6 scenario database (Section 3.2 and 
Annex III.II.3.2.2).post-2030 mitigation efforts and warming outcomes based on the 
literature and the AR6 scenarios database. 
Without a  strengthening of policies beyond those that are

implemented by the end of 2020, GHG emissions are projected 
to rise beyond 2025, leading to a median global warming of 3.2 
[2.2 to  3.5] °C by 2100. Modelled pathways that are consistent 
with NDCs announced prior to COP26 until 2030 and assume no 
increase in ambition thereafter have lower emissions, leading to 
a median global warming of 2.8°C [2.1–3.4°C] by 2100.
The assessed emission ranges from implementing the unconditional 
(unconditional and conditional) elements of NDCs announced prior to 
COP26 implies an emissions gap to cost-effective mitigation pathways 
of 19–26 (16–23) GtCO 2-eq in 2030 for limiting warming to 1.5°C 
(>50%) with no or limited overshoot and 10–16 (6–14) GtCO 2-eq in 
2030 for limiting warming to 2°C (>67%) (Cross-Chapter Box 4 in 
Chapter 4). The emissions gap gives rise to a number of mitigation 
challenges (Kriegler et al. 2013a, 2018a,b; Luderer et al. 2013, 2018; 
Rogelj et al. 2013a; Fawcett et al. 2015; Riahi et al. 2015; Fujimori

et al. 2016b; Strefler et al. 2018; Winning et al. 2019; SEI et al. 2020; 
UNEP 2020): (i) larger transitional challenges post-2030 to still 
remain under the warming limit, in particular higher CO 2 emissions 
reduction rates and technology transition rates required during 
2030–2050; (ii)  larger lock-in into carbon-intensive infrastructure 
and increased risk of stranded fossil fuel assets (Section  3.5.2.2); 
and (iii) larger reliance on CDR to reach net zero CO 2 more rapidly 
and compensate excess emissions in the second half of the century 
(Section  3.5.2.1). All these factors exacerbate the socio-economic 
strain of implementing the transition, leading to an increased risk 
of overshooting the warming and a  higher risk of climate change 
impacts (Drouet et al. 2021).
The challenges are illustrated in Table 3.6 and Figure 3.30, surveying 
global mitigation pathways in the literature that were collected in 
the AR6 scenarios database. There is a clear trend of increasing peak

warming with increasing 2030 GHG emission levels (Figure 3.30a,b). 
In particular, there is no mitigation pathway designed to follow the 
NDCs until 2030 that can limit warming to 1.5°C (>50%) with no or 
limited overshoot. Our assessment confirms the finding of the IPCC  
Special Report on Global Warming of  1.5°C (Rogelj et al. 2018) for 
the case of NDCs announced prior to COP26 that pathways following 
the NDCs until 2030 ‘would not limit global warming to 1.5°C, even if 
supplemented by very challenging increases in the scale and ambition 
of emissions reductions after 2030’ (SR1.5 SPM). This assessment is now 
more robust than in SR1.5 as it is based on a larger set of 1.5°C–2°C 
pathways with better representation of current trends and plans 
covering a  wider range of post-2030 emissions developments. In 
particular, a  recent multi-model study limiting peak cumulative CO 2 
emissions for a wide range of carbon budgets and immediate vs NDC-

type action until 2030 established a feasibility frontier for the existence 
of such pathways across participating models (Riahi et al. 2021).

352
20 30 40 50 60
GHG emissions in 2030 [GtCO 2-eq] GHG emissions in 2030 [GtCO 2-eq]
GHG emissions in 2030 [GtCO 2-eq] GHG emissions in 2030 [GtCO 2-eq]1.41.61.82[oC]a) Median peak warming
20 30 40 50 601.41.61.82[oC]b) p67 peak warming
20 30 40 50 60-2-10[GtCO2 yr-1]c) Annual avg. change CO2 emissions 2030-50
20 30 40 50 6020202040206020802100d) Year of net zero CO2 emissions
20 30 40 50 600200400600800[GtCO2]e) Net negative CO2 emissions until 2100
20 30 40 50 600200400600g kWh-1f) Carbon intensity electricity generation 2030
20 30 40 50 60050100150[%]g) Coal use in 2030 (relative to 2019)
20 30 40 50 60050100150[%]h) Oil and gas use in 2030 (relative to 2019)
IMP-GS
IMP-Neg
IMP-LDIMP-Ren
IMP-SDP

Figure 3.30 | Relationship between level of global GHG emissions in 2030 and selected indicators as listed in the panel titles for scenarios collected 
in the AR6 scenario database.  Emissions data based on harmonised emissions used for the climate assessment. All scenarios that limit warming to 2°C (>67%) or lower 
are coloured blue or red (see p67 peak warming in panel (b)). The large majority of blue-coloured scenarios act immediately on the temperature target, while red-coloured 
scenarios depict all those that were designed to follow the NDCs or lesser action until 2030 and orange-coloured scenarios comprise a small set of pathways with additional 
regulatory action beyond NDCs (Section 3.5.3). Grey-coloured scenarios do not limit warming to 2°C (>67%) due to temporary overshoot or towards the end of the century.

Large markers denote the five Illustrative Mitigation Pathways (IMPs) (legend in Panel (h); Section 3.2). Shaded yellow areas depict the estimated range of 2030 emissions from 
NDCs announced prior to COP26 (Section 4.2.2). Dotted lines are inserted in some panels to highlight trends in the dependency of selected output variables on 2030 GHG 
emissions levels (Section 3.5.2).

353
Table 3.6 | Comparison of key scenario characteristics for five scenario classes (see Table 3.2): (i) immediate action to limit warming to 1.5°C (>50%) with 
no or limited overshoot, (ii) near team action following the NDCs until 2030 and returning warming to below 1.5°C (>50%) by 2100 after a high overshoot, 
(iii) immediate action to limit warming to 2°C (>67%), (iv) near term action following the NDCs until 2030 followed by post-2030 action to limit warming to 
2°C (>67%).  Also shown are the characteristics for (v) the combined class of all scenarios that limit warming to 2°C (>67%). The classes (ii) and (iv) comprise the large majority of 
scenarios indicated by red dots, and the classes (i) and (iii) comprise the scenarios depicted by blue dots in Figure 3.30. Shown are median and interquartile ranges (in brackets) for

selected global indicators. Emissions ranges are based on harmonized emissions data for the climate assessment with the exception of land use CO 2 emissions for which uncertainty 
in historic estimates is large. Numbers are rounded to the nearest 5, with the exception of cumulative CCS, BECCS, and net negative CO 2 emissions rounded to the nearest 10.
Global indicators1.5°C1.5°C (>50%) by 
21002°C (>67%)
Immediate action, 
overshoot  
(C1, 97 scenarios)NDCs until 2030, with 
overshoot before 
2100 (subset of 42 
scenarios in C2)Immediate action 
(C3a, 204 scenarios)NDCs until 2030 
(C3b; 97 scenarios)All (C3; 311 
scenarios)
Change in GHG emissions in 2030 (% rel to 2019) –45 (–50,–40) –5 (–5,0) –25 (–35,–20) –5 (–10,0) –20 (–30,–10)
 in 2050 (% rel to 2019) –85 (–90,–80) –75 (–85,–70) –65 (–70,–60) –70 (–70,–60) –65 (–70,–60)
Change in CO 2 emissions in 2030 (% rel to 2019) –50 (–60,–40) –5 (–5,0) –25 (–35,–20) –5 (–5,0) –20 (–30,–5)

in 2050 (% rel to 2019) –100 (–105,–95) –85 (–95,–80) –70 (–80,–65) –75 (–80,–65) –75 (–80,–65)
Change in net land use CO 2 emissions in 2030 (% rel to 2019) –100 (–105,–95) –30 (–60,–20) –90 (–105,–75) –20 (–80,–20) –80 (–100,–30)
 in 2050 (% rel to 2019) –150 (–200,–100) –135 (–165,–120) –135 (–185,–100) –130 (–145,–115) –135 (–180,–100)
Change in CH 4 emissions in 2030 (% rel to 2019) –35 (–40,–30) –5 (–5,0) –25 (–35,–20) –10 (–15,–5) –20 (–25,–10)
 in 2050 (% rel to 2019) –50 (–60,–45) –50 (–60,–45) –45 (–50,–40) –50 (–65,–45) –45 (–55,–40)
Cumulative CCS until 2100 (GtCO 2) 670 (520,900) 670 (540,860) 610 (490,900) 530 (440,720) 590 (480,820)
 of which BECCS (GtCO 2) 330 (250,560) 370 (280,590) 350 (240,450) 270 (240,400) 290 (240,430)
Cumulative net negative CO 2 emissions until 2100 (GtCO 2) 220 (70,430) 380 (300,470) 30 (0,130) 60 (20,210) 40 (10, 180)

Change in primary energy from coal in 2030 (% rel to 2019) –75 (–80,–65) –10 (–20,–5) –50 (–65,–35) –15 (–20,–10) –35 (–55,–20)
 in 2050 (% rel to 2019) –95 (–100,–80) –90 (–100,–85) –85 (–100,–65) –80 (–90,–70) –85 (–95,–65)
Change in primary energy from coal without CCS in 2030 
(% rel to 2019)–75 (–80,–65) –10 (–20,–10) –50 (–65,–35) –15 (–20,–10) –35 (–55,–20)
 in 2050 (% rel to 2019) –100 (–100,–95) –95 (–100,–95) –95 (–100,–90) –90 (–95,–85) –95 (–100,–90)
Change in primary energy from oil in 2030 (% rel to 2019) –10 (–25,0) 5 (5,10) 0 (–10,10) 10 (5,10) 5 (0,10)
 in 2050 (% rel to 2019) –60 (–75,–40) –50 (–65,–35) –30 (–45,–15) –40 (–55,–20) –30 (–50,–15)
Change in primary energy from oil without CCS in 2030 
(% rel to 2019)–5 (–20,0) 5 (5,10) 0 (–10,10) 10 (5,10) 5 (–5,10)
 in 2050 (% rel to 2019) –60 (–75,–45) –50 (–65,–30) –30 (–45,–15) –40 (–55,–20) –35 (–50,–15)
Change in primary energy from gas in 2030 (% rel to 2019) –10 (–30,0) 15 (10,25) 10 (0,15) 15 (10,15) 10 (0,15)

in 2050 (% rel to 2019) –45 (–60,–20) –45 (–55,–30) –10 (–35,15) –30 (–45,–5) –15 (–40,10)
Change in primary energy from gas without CCS in 2030 
(% rel to 2019)–20 (–30,–5) 15 (10,25) 5 (–5,10) 15 (10,15) 10 (0,15)
 in 2050 (% rel to 2019) –70 (–80,–60) –60 (–70,–50) –35 (–50,–20) –40 (–60,–35) –40 (–55,–20)
Change in primary energy from nuclear in 2030 (% rel to 2019) 40 (10,70) 10 (0,25) 35 (5,50) 10 (0,30) 25 (0,45)
 in 2050 (% rel to 2019) 90 (15,295) 100 (45,130) 85 (30,200) 75 (30,120) 80 (30,140)
Change in primary energy from modern biomass in 2030 
(% rel to 2019)75 (55,130) 45 (20,75) 60 (35,105) 45 (20,80) 55 (35,105)
 in 2050 (% rel to 2019) 290 (215,430) 230 (170,420) 240 (130,355) 260 (95,435) 250 (115,405)
Change in primary energy from non–biomass renewables 
in 2030 (% rel to 2019)225 (155,270) 100 (85,145) 150 (115,190) 115 (85,130) 130 (90,170)
 in 2050 (% rel to 2019) 725 (545,950) 665 (535,925) 565 (415,765) 625 (545,700) 605 (470,735)

Change in carbon intensity of electricity in 2030 (% rel to 2019) –75 (–80,–70) –30 (–40,–30) –60 (–70,–50) –35 (–40,–30) –50 (–65,–35)
 in 2050 (% rel to 2019) –100 (–100,–100) –100 (–100,–100) –95 (–100,–95) –100 (–100,–95) –95 (–100,–95)
Change in carbon intensity of non–electric final energy 
consumption in 2030 (% rel to 2019)–15 (–15,–10) 0 (–5,0) –10 (–10,–5) 0 (–5,5) –5 (–10,0)
 in 2050 (% rel to 2019) –50 (–55,–40) –35 (–40,–30) –30 (–35,–25) –30 (–40,–20) –30 (–35,–20)

354
The 2030 emissions levels in the NDCS announced prior to COP26 
also tighten the remaining space to limit warming to 2°C (>67%). As 
shown in Figure 3.30b, the 67th percentile of peak warming reaches 
values above 1.7°C in pathways with 2030 emissions levels in this 
range. To still limit warming to 2°C (>67%), the global post-2030 GHG 
emission reduction rates would need to be abruptly raised in 2030 
from 0–0.7 GtCO 2-eq yr–1 to an average of 1.4–2.0 GtCO 2-eq yr–1 
during the period 2030–2050 (Figure  3.30c), around 70% higher 
than in immediate mitigation pathways confirming findings in 
the literature (Winning et al. 2019). Their average reduction rate 
of 0.6–1.4 GtCO 2 yr–1 would already be unprecedented at the global 
scale and, with a  few exceptions, national scale for an extended 
period of time (Riahi et al. 2015). For comparison, the impact of 
COVID-19 on the global economy is projected to have lead to

a decline of around 2.5–3 GtCO 2 of global CO 2 emissions from fossil 
fuels and industry in 2020 (Friedlingstein et al. 2020) (Section 2.2).
The increased post-2030 transition challenge in mitigation pathways 
with moderate near-term action is also reflected in the timing of 
reaching net zero CO 2 emissions (Figure  3.30d and Cross-Chapter 
Box 3 in this chapter). As 2030 emission levels and the cumulated 
CO2 emissions until 2030 increase, the remaining time for dropping 
to net zero CO 2 and staying within the remaining carbon budget 
shortens (Figure  3.29). This gives rise to an inverted  v-shape of 
the lower bound on the year of reaching net zero as a function of 
2030 emissions levels. Reaching low emissions in 2030 facilitates 
reaching net zero early (left leg of the inverted v), but staying high 
until 2030 also requires reaching net zero CO 2 faster to compensate 
for higher emissions early on (right leg of the inverted v). Overall,

there is a considerable spread of the timing of net zero CO 2 for any 
2030 emissions level due to variation in the timing of spending the 
remaining carbon budget and the non-CO 2 warming contribution 
(Cross-Chapter Box 3 in this chapter).
There is also a  profound impact on the underlying transition of 
energy and land use (Figure  3.30f–h and Table  3.6). Scenarios 
following NDCs until 2030 show a much smaller reduction in fossil 
fuel use, a slower growth in renewable energy use, and a  smaller 
reduction in CO 2 and CH 4 land-use emissions in 2030 compared to 
immediate action scenarios. This is then followed by a much faster 
reduction of land-use emissions and fossil fuels, and a larger increase 
of nuclear energy, bioenergy and non-biomass renewable energy 
during the medium term in order to get close to the levels of the 
immediate action pathways in 2050. This is combined with a larger 
amount of net negative CO 2 emissions that are used to compensate

the additional emissions before 2030. The faster transition during 
2030–2050 is taking place from a greater investment in fossil fuel 
infrastructure and lower deployment of low-carbon alternatives in 
2030, adding to the socio-economic challenges to realise the higher 
transition rates (Section 3.5.2.2). Therefore, these pathways also show 
higher mitigation costs, particularly during the period 2030–2050, 
than immediate action scenarios (Section  3.6.1 and Figure  3.34d) 
(Liu et al. 2016; Kriegler et al. 2018a; Vrontisi et al. 2018). Given 
these circumstances and the fact the modelling of socio-political and 
institutional constraints is limited in Integrated Assessment Models 
(IAMs) (Gambhir et al. 2019; Köhler et al. 2019; Hirt et al. 2020; Keppo 
et al. 2021), the feasibility of realising these scenarios is assessed to be lower (Gambhir et al. 2017; Napp et al. 2017; Brutschin et al. 2021) 
(cf. Section 3.8), increasing the risk of an overshoot of climate goals.

3.5.2.1 Overshoot and Net Negative CO 2 Emissions
If near- to medium-term emissions developments deplete the 
remaining carbon budget, the associated warming limit will be 
overshot. Some pathways that return warming to 1.5°C (>50%) by 
the end of the century show mid-century overshoots of up to 1.8°C 
median warming. The overshoot tends to be higher, the higher the 
2030 emissions. Mitigation pathways with 2030 emissions levels in 
the NDCS announced prior to COP26 consistently overshoot 1.5°C 
by  0.15°C–0.3°C. This leads to higher risks from climate change 
impacts during the time of overshoot compared to pathways that limit 
warming to 1.5°C (>50%) with no or limited overshoot (Schleussner 
et al. 2016a; Mengel et al. 2018; Hofmann et al. 2019; Lenton et al. 
2019; Tachiiri et al. 2019;  Drouet et al. 2021). Furthermore, even if 
warming is reversed by net negative emissions, other climate changes 
such as sea level rise would continue in their current direction for

decades to millennia (AR6 WGI Sections 4.6 and 5.6).
Returning warming to lower levels requires net negative CO 2 
emissions in the second half of the century (Clarke et al. 2014; 
Fuss et al. 2014; Rogelj et al. 2018a). The amount of net negative 
CO2 emissions in pathways limiting warming to 1.5°C–2°C climate 
goals varies widely, with some pathways not deploying net negative 
CO2 emissions at all and others deploying up to –600 to –800 GtCO 2. 
The amount of net negative CO 2 emissions tends to increase with 
2030 emissions levels (Figure 3.30e and Table 3.6). Studies confirmed 
the ability of net negative CO 2 emissions to reduce warming, but 
pointed to path dependencies in the storage of carbon and heat 
in the Earth System and the need for further research particularly 
for cases of high overshoot (Zickfeld et al. 2016, 2021; Keller et al. 
2018a,b; Tokarska et al. 2019). The AR6 WGI assessed the reduction 
in global surface temperature to be approximately linearly related to

cumulative CO 2 removal and, with lower confidence, that the amount 
of cooling per unit CO 2 removed is approximately independent of the 
rate and amount of removal (AR6 WGI TS.3.3.2). Still there remains 
large uncertainty about a potential asymmetry between the warming 
response to CO 2 emissions and the cooling response to net negative 
CO2 emissions (Zickfeld et al. 2021). It was also shown that warming 
can adversely affect the efficacy of carbon dioxide removal measures 
and hence the ability to achieve net negative CO 2 emissions 
(Boysen et al. 2016).
Obtaining net negative CO 2 emissions requires massive deployment 
of carbon dioxide removal (CDR) in the second half of the century, on 
the order of 220 (160–370) GtCO 2 for each 0.1°C degree of cooling 
(based on the assessment of the likely range of the transient response 
to cumulative CO 2 emissions in AR6 WGI Section 5.5 in Chapter 5, 
not taking into account potential asymmetries in the temperature

response to CO 2 emissions and removals). CDR is assessed in detail 
in Section  12.3 of this report (see also Cross-Chapter Box  8 in 
Chapter 12). Here we only point to the finding that CDR ramp-up 
rates and absolute deployment levels are tightly limited by techno-
economic, social, political, institutional and sustainability constraints 
(Smith et al. 2016; Boysen et al. 2017; Fuss et al. 2018, 2020; Nemet

355
et al. 2018; Hilaire et al. 2019; Jia et al. 2019) (Section 12.3). CDR 
therefore cannot be deployed arbitrarily to compensate any degree 
of overshoot. A fraction of models was not able to compute pathways 
that would follow the mitigation ambition in unconditional and 
conditional NDCs until 2030 and return warming to below 1.5°C by 
2100 (Luderer et al. 2018; Roelfsema et al. 2020; Riahi et al. 2021). 
There exists a  three-way trade-off between near-term emissions 
developments until 2030, transitional challenges during 2030–50, 
and long-term CDR deployment post-2050 (Sanderson et al. 2016; 
Holz et al. 2018; Strefler et al. 2018). For example, Strefler et al. (2018) 
find that if CO 2 emission levels stay at around 40 GtCO 2 until 2030, 
within the range of what is projected for NDCs announced prior 
to COP26, rather than being halved to 20 GtCO 2 until 2030, CDR

deployment in the second half of the century would have to increase 
by 50–100%, depending on whether the 2030–2050 CO 2 emissions 
reduction rate is doubled from 6% to 12% or kept at 6% yr–1. This 
three-way trade-off has also been identified at the national level 
(Pan et al. 2020).
In addition to enabling a  temporary budget overshoot by net 
negative CO 2 emissions in the second half of the century, CDR can 
also be used to compensate  – on an annual basis  – residual CO 2 
emissions from sources that are difficult to eliminate and to reach 
net zero CO 2 emissions more rapidly if deployed before this point 
(Kriegler et al. 2013b; Rogelj et al. 2018a). This explains its continued 
deployment in pathways that exclude overshoot and net negative 
CO2 emissions (Riahi et al. 2021). However, given the time scales that 
would likely be needed to ramp-up CDR to gigatonne scale (Nemet 
et al. 2018), it can be expected to only make a limited contribution to

reaching net zero CO 2 as fast as possible. In the vast majority (95%) 
of 1.5°C–2°C mitigation pathways assessed in this report, cumulative 
CDR deployment did not exceed 100 GtCO 2 until mid-century. This 
adds to the risk of excessively relying on CDR to compensate for 
weak mitigation action until 2030 by either facilitating massive 
net CO 2 emissions reduction rates during 2030–2050 or allowing 
a high temporary overshoot of 1.5°C until the end of the century. If 
international burden-sharing considerations are taken into account, 
the CDR penalty for weak action could increase further, in particular 
for developed countries (Fyson et al. 2020). Further assessment 
of CDR deployment in  1.5°C–2°C mitigation pathways is found 
in Section 3.4.7.
3.5.2.2 Carbon Lock-in and Stranded Assets
There already exists a substantial and growing carbon lock-in today, 
as measured by committed emissions associated with existing long-

lived infrastructure (Section  2.7 and Figure  2.31). If existing fossil 
fuel infrastructure would continue to be operated as historically, it 
would entail CO 2 emissions exceeding the carbon budget for 1.5°C 
(Section  2.7.2 and Figure  2.32). However, owner-operators and 
societies may choose to retire existing infrastructure earlier than 
in the past, and committed emissions are thus contingent on the 
competitiveness of non-emitting alternative technologies and 
climate policy ambition. Therefore, in mitigation pathways, some 
infrastructure may become stranded assets. Stranded assets have 
been defined as ‘assets that have suffered from unanticipated or premature write-downs, devaluations or conversion to liabilities’ 
(Caldecott 2017).
A systematic map of the literature on carbon lock-in has synthesized 
quantification of stranded assets in the mitigation pathways 
literature, and showed that (i) coal power plants are the most

exposed to risk of  becoming stranded, (ii) delayed mitigation 
action increases stranded assets, and (iii) sectoral distribution and 
the amount of  stranded assets differ between countries (Fisch-
Romito et al. 2021). There is high agreement that existing fossil fuel 
infrastructure would need to be retired earlier than historically, used 
less, or retrofitted with CCS, to stay within the remaining carbon 
budgets of limiting warming to 1.5°C or 2°C (Johnson et al. 2016; 
Kefford et al. 2018; Pfeiffer et al. 2018; Cui et al. 2019; Fofrich et al. 
2020; Rogelj et al. 2018a). Studies estimate that cumulative early 
retired power plant capacities by 2060 can be up to 600 GW for gas 
and 1700 GW for coal (Iyer et al. 2015a; Kefford et al. 2018), that 
only 42% of the total capital stock of both operating and planned 
coal-fired powers plants can be utilised to be compatible with the 
2°C target (Pfeiffer et al. 2018), and that coal-fired power plants in

scenarios consistent with keeping global warming below 2°C or 1.5°C 
retire one to three decades earlier than historically has been the case 
(Cui et al. 2019; Fofrich et al. 2020). After coal, electricity production 
based on gas is also projected to be phased out, with some capacity 
remaining as back-up (van Soest et al. 2017a). Kefford et al. (2018) 
find USD541 billion worth of stranded fossil fuel power plants could 
be created by 2060, with China and India the most exposed.
Some publications have suggested that stranded long-lived assets 
may be even more important outside of the power sector. While 
stranded power sector assets by 2050 could reach up to USD1.8 trillion 
in scenarios consistent with a 2°C target, Saygin et al. (2019) found 
a range of USD5–11 trillion in the buildings sector. Muldoon-Smith 
and Greenhalgh (2019) have even estimated a potential value at risk 
for global real estate assets up to USD21 trillion. More broadly, the set

of economic activities that are potentially affected by a low-carbon 
transition is wide and includes also energy-intensive industries, 
transport and housing, as reflected in the concept of climate policy 
relevant sectors introduced in Battiston et al. (2017). The sectoral 
distribution and amount of stranded assets differ across countries 
(Fisch-Romito et al. 2021). Capital for fossil fuel production and 
distribution represents a larger share of potentially stranded assets in 
fossil fuel-producing countries such as the United States and Russia. 
Electricity generation would be a larger share of total stranded assets 
in emerging countries because this capital is relatively new compared 
to its operational lifetime. Conversely, buildings could represent 
a  larger part of stranded capital in more developed countries and 
regions such as the USA, EU or even Russia because of high market 
value and low turnover rate.
Many quantitative estimates of stranded assets along mitigation

pathways have focused on fossil fuel power plants in pathways 
characterised by mitigation ambition until 2030 corresponding to the 
NDCs followed by strengthened action afterwards to limit warming 
to 2°C (>67%) or lower (Bertram et al. 2015a; Iyer et al. 2015b; 
Lane  et al. 2016; Farfan and Breyer 2017; van Soest et al. 2017a; 
Kriegler et al. 2018a; Luderer et al. 2018; Cui et al. 2019; Saygin et al.

356
2019; SEI et al. 2020). Pathways following NDCs announced prior 
to COP26 until 2030 do not show a significant reduction of coal, oil 
and gas use (Figure 3.30f–h and Table 3.6) compared to immediate 
action pathways. Stranded coal power assets are evaluated to be 
higher by a factor of two to three if action is strengthened after 2030 
rather than now (Iyer et al. 2015b; Cui et al. 2019). There is high 
agreement that the later climate policies are implemented, the higher 
the expected stranded assets and the societal, economic and political 
strain of strengthening action. Associated price increases for carbon-
intensive goods and transitional macro-economic costs have been 
found to scale with the emissions gap in 2030 (Kriegler et al. 2013a). 
At the aggregate level of the whole global economy, Rozenberg et al. 
(2015) showed that each year of delaying the start of mitigation

decreases the required CO 2 intensity of new production by 20–50 
gCO 2 per USD. Carbon lock-in can have a long-lasting effect on future 
emissions trajectories after 2030. Luderer et al. (2018) compared 
cost-effective pathways with immediate action to limit warming 
to  1.5°C–2°C with pathways following the NDCs until 2030 and 
adopting the pricing policy of the cost-effective pathways thereafter, 
and found that the majority of additional CO 2 emissions from carbon 
lock-in occur after 2030, reaching a cumulative amount of 290 (160–
330) GtCO 2 by 2100 (Section 2.7.2). Early action and avoidance of 
investments in new carbon-intensive assets can minimise these risks.
The risk of stranded assets has implications for workers depending 
on those assets, asset owners, assets portfolio managers, financial 
institutions and the stability of the financial system. Chapter  6 
assesses the risks and implications of stranded assets for energy

systems (Section 6.7.3 and Box 6.11) and fossil fuels (Section 6.7.4). 
The implications of stranded assets for inequality and Just Transition 
are assessed in Chapter 17 (Section 17.3.2.3). Chapter 15 assesses 
the literature on those implications for the financial system as well 
as on coping options (Sections 15.5.2 and 15.6.1).
On the other hand, mitigation, by limiting climate change, reduces 
the risk of destroyed or stranded assets from the physical impacts of 
climate change on natural and human systems, from more frequent, 
intense or extended extreme events and from sea level rise (O’Neill 
et al. 2020a). The literature on mitigation pathways rarely includes an 
evaluation of stranded assets from climate change impacts. Unruh 
(2019) suggest that these are the real stranded assets of carbon lock-
in and could prove much more costly.
3.5.3 Global Accelerated Action Towards Long-term 
Climate Goals
A growing literature explores long-term mitigation pathways with

accelerated near-term action going beyond the NDCs (Graichen 
et al. 2017; Jiang et al. 2017; Kriegler et al. 2018a; Roelfsema et al. 
2018; Fekete et al. 2021; van Soest et al. 2021a). Global accelerated 
action pathways are designed to transition more gradually from 
implemented policies and planned implementation of NDCs onto 
a  1.5°C–2°C pathway and at the same time alleviate the abrupt 
transition in 2030 that would be caused by following the NDCs until 
2030 and strengthening towards limiting warming to 2°C thereafter 
(Section 3.5.2). Therefore, they have sometimes been called bridging scenarios/pathways in the literature (IEA 2011; Spencer et al. 
2015; van Soest et al. 2021a). They rely on regionally differentiated 
regulatory and pricing policies to gradually strengthening regional 
and sectoral action beyond the mitigation ambition in the NDCs. 
There are limitations to this approach. The tighter the warming

limit, the more likely it is that disruptive action becomes inevitable 
to achieve the speed of transition that would be required (Kriegler 
et al. 2018a). Cost-effective pathways already have abrupt shifts in 
deployments, investments and prices at the time a stringent warming 
limit is imposed, reflecting the fact that the overall response to 
climate change has so far been misaligned with long-term climate 
goals (Fawcett et al. 2015; Rogelj et al. 2016; Schleussner et al. 2016b; 
Geiges et al. 2020). Disruptive action can help to break lock-ins and 
enable transformative change (Vogt-Schilb et al. 2018).
The large literature on accelerating climate action was assessed in 
the IPCC Special Report on Global Warming of  1.5°C (de Coninck 
et al. 2018) and is taken up in this report primarily in Chapters 4, 13, 
and 14. Accelerating climate action and facilitating transformational 
change requires a perspective on socio-technical transitions (Geels

et al. 2016a; Geels et al. 2016b; Geels 2020), a portfolio of policy 
instruments to manage technological and environmental change 
(Fischer and Newell 2008; Goulder and Parry 2008; Acemoglu et al. 
2012, 2016), a  notion of path dependency and policy sequencing 
(Pierson 2000; Meckling et al. 2017; Pahle et al. 2018) and the 
evolvement of polycentric governance layers of institutions and 
norms in support of the transformation (Dietz et al. 2003; Leach et al. 
2007; Messner 2015). This subsection is focused on an assessment 
of the emerging quantitative literature on global accelerated action 
pathways towards 1.5°C–2°C, which to a large extent abstracts from 
the underlying processes and uses a number of stylised approaches 
to generate these pathways. A representative of accelerated action 
pathways has been identified as one of the Illustrative Mitigation 
Pathways (IMPs) in this assessment (IMP-GS, Figure 3.31).
One approach relies on augmenting initially moderate emissions-

pricing policies with robust anticipation of ratcheting up climate 
action in the future (Spencer et al. 2015). If announcements of 
strong future climate policies are perceived to be credible, they 
can help to prevent carbon lock-in as investors anticipating high 
future costs of GHG emissions would reduce investment into fossil 
fuel infrastructure, such as coal power plants (Bauer et al. 2018b). 
However, the effectiveness of such announcements strongly 
hinges on their credibility. If investors believe that policymakers 
could drop them if anticipatory action did not occur, they may not 
undertake such action.
Another approach relies on international cooperation to strengthen 
near-term climate action. These studies build on international climate 
policy architectures that could incentivise a coalition of like-minded 
countries to raise their mitigation ambition beyond what is stated in 
their NDC (Graichen et al. 2017). Examples are the idea of climate

clubs characterised by harmonised carbon and technology markets 
(Nordhaus 2015; Keohane et al. 2017; Paroussos et al. 2019; Pihl 
2020) and the Powering Past Coal Alliance (PPCA) (Jewell et al. 2019). 
Paroussos et al. (2019) find economic benefits of joining a climate 
club despite the associated higher mitigation effort, in particular due

357
2030 2050 21001.51.61.71.81.92[oC]a) Global mean temperature increase (p67)
2030 2050510501005001000[$/tCO2]b) Carbon price
2030 2050 21000204060[GtCO2-eq/yr]c) Global GHG emissions
2030 2050-3-2.5-2-1.5-1-0.500.5[GtCO2-eq/yr]d) Annual average change GHG emissions
2030 2050 2100-20-10010203040[GtCO2/yr]e) Global CO2 emissions
2020-2030 2030-2050-20-15-10-505[EJ/yr]f) Annual average change in fossil fuel use
2030 2050 21000100200300400[MtCH4/yr]g) Global CH4 emissions
2020-2030 2030-20500246810[EJ/yr]h) Annual average change in low carbon electricity
NDC
Accelerated
Immediate
Figure 3.31 | Comparison of (i) pathways with immediate action to limit warming to 2°C (>67%) (Immediate, light blue), (ii) pathways following the 
NDCs until 2030 and limiting warming to 2°C (>67%) thereafter (NDC; orange), and (iii) pathways accelerating near-term action until 2030 beyond NDC

ambition levels and limiting warming to 2°C (>67%) thereafter (accelerated) for selected indicators as listed in the panel titles, based on pathways from 
van Soest et al.  (2021a). Low-carbon electricity comprises renewable and nuclear power. Indicator ranges are shown as box plots (full range, interquartile range, and median) 
for the years 2030, 2050 and 2100 (absolute values) and for the periods 2020–2030, 2030–2050 (change indicators). Ranges are based on nine models participating in van 
Soest et al. (2021a) with only seven models reporting emissions and climate results and eight models reporting carbon prices. The purple dot denotes the Illustrative Mitigation 
Pathway GS that was part of the study by van Soest et al.

358
to access to technology and climate finance. Graichen et al. (2017) 
find an additional reduction of  5–11 GtCO 2-eq compared to the 
mitigation ambition in the NDCs from the successful implementation 
of international climate initiatives. Other studies assess benefits from 
international transfers of mitigation outcomes (Stua 2017; Edmonds 
et al. 2021). Edmonds et al. (2021) find economic gains from sharing 
NDC emissions-reduction commitments compared to purely domestic 
implementation of NDCs. If reinvested in mitigation efforts, the study 
projects an additional reduction of 9 billion tonnes of CO 2 in 2030.
The most common approach relies on strengthening regulatory policies 
beyond current policy trends, also motivated by the finding that such 
policies have so far been employed more often than comprehensive 
carbon pricing (Kriegler et al. 2018a; Roelfsema et al. 2018; Fekete et al.

2021; IEA 2021a; van Soest et al. 2021a). Some studies have focused 
on generic regulatory policies such as low-carbon support policies, 
fossil fuel-sunset policies, and resource-efficiency policies (Bertram 
et al. 2015b; Hatfield-Dodds et al. 2017). Bertram et al. (2015b) found 
that a moderate carbon price combined with a coal moratorium and 
ambitious low-carbon support policies can limit efficiency losses until 
2030 if emissions pricing is raised thereafter to limit warming to 2°C. 
They also showed that all three components are needed to achieve this 
outcome. Hatfield-Dodds et al. (2017) found that resource efficiency 
can lower 2050 emissions by an additional 15–20% while boosting 
near-term economic growth. The International Energy Agency (IEA 
2021a) developed a detailed net zero scenario for the global energy 
sector characterised by a  rapid phase-out of fossil fuels, a  massive 
clean energy and electrification push, and the stabilisation of energy

demand, leading to 10 GtCO 2 lower emissions from energy use in 2030 
than in a scenario following the announced pledges.
The Paris Agreement has spurred the formulation of NDCs for 2030 
and mid-century strategies around the world (cf. Chapter 4). This is 
giving researchers a  rich empirical basis to formulate accelerated 
policy packages taking national decarbonisation pathways as 
a starting point (Graichen et al. 2017; Jiang et al. 2017; van Soest 
et al. 2017b; Waisman et al. 2019). The concept is to identify good 
practice policies that had demonstrable impact on pushing low-
carbon options or reducing emissions in a  country or region and 
then consider a wider roll out of these policies taking into account 
regional specificities (den Elzen et al. 2015; Fekete et al. 2015, 2021; 
Kriegler et al. 2018a; Kuramochi et al. 2018; Roelfsema et al. 2018). 
A challenge for this approach is to account for the fact that policy

effectiveness varies with different political environments in different 
geographies. As a result, a global roll out of good practice policies to 
close the emissions gap will still be an idealised benchmark, but it is 
useful to understand how much could be gained from it.
Accelerated action pathways derived with this approach show 
considerable scope for narrowing the emissions gap between 
pathways reflecting the ambition level of the NDCs and cost-
effective mitigation pathways in 2030. Kriegler et al. (2018a) find 
around 10 GtCO 2-eq lower emissions compared to original NDCs 
from a global roll out of good practice plus net zero policies and 
a  moderate increase in regionally differentiated carbon pricing. 
Fekete et al. (2021) show that global replication of sector progress 
in five major economies would reduce GHG emissions in 2030 by about 20% compared to a  current policy scenario. These findings 
were found in good agreement with a  recent model comparison

study based on results from nine integrated assessment models 
(IAMs) (van Soest et al. 2021a). Based on these three studies, 
implementing accelerated action in terms of a  global roll out of 
regulatory and moderate pricing policies is assessed to lead to 
global GHG emissions of 48 (38–52) GtCO 2-eq in 2030 (median 
and 5–95th percentile based on 10 distinct modelled pathways). This 
closes the implementation gap for the NDCs, and in addition falls 
below the emissions range implied by implementing unconditional 
and conditional elements of NDCs by  2–9 GtCO 2-eq. However, it 
does not close the emissions gap to immediate action pathways 
that limit warming to 2°C (>67%), and, based on our assessment in 
Section 3.5.2, emission levels above 40 GtCO 2-eq in 2030 still have 
a very low prospect for limiting warming to 1.5°C (>50%) with no 
or limited overshoot.
Figure  3.31 shows the intermediate position of accelerated action

pathways derived by van Soest et al. (2021a) between pathways 
that follow the NDCs until 2030 and immediate action pathways 
limiting warming to 2°C (>67%). Accelerated action is able to reduce 
the abrupt shifts in emissions, fossil fuel use and low-carbon power 
generation in 2030 and also limits peak warming more effectively 
than NDC pathways. But primarily due to the moderate carbon price 
assumptions (Figure 3.31b), the reductions in emissions and particular 
fossil fuel use are markedly smaller than what would be obtained in 
the case of immediate action. The assessment shows that accelerated 
action until 2030 can have significant benefits in terms of reducing the 
mitigation challenges from following the NDCs until 2030. But putting 
a significant value on GHG emissions reductions globally remains a key 
element of moving onto  1.5°C–2°C pathways. The vast majority of 
pathways that limit warming to 2°C (>67%) or lower, independently

of their differences in near-term emission developments, converge to 
a global mitigation regime putting a significant value on GHG emission 
reductions in all regions and sectors.
3.6 Economics of Long-term Mitigation 
and Development Pathways, Including 
Mitigation Costs and Benefits
A complete appraisal of economic effects and welfare effects at 
different temperature levels would include the macroeconomic 
impacts of investments in low-carbon solutions and structural 
change away from emitting activities, co-benefits and adverse side 
effects of mitigation, (avoided) climate damages, as well as (reduced) 
adaptation costs, with high temporal, spatial and social heterogeneity 
using a  harmonised framework. If no such complete appraisal in 
a harmonised framework exists, key elements are emerging from the 
literature, and assessed in the following subsections: on aggregated 
economy-wide global mitigation costs (Section  3.6.1), on the

economic benefits of avoiding climate impacts (Section  3.6.2), on 
economic benefits and costs associated with mitigation co-benefits 
and co-harms (Section  3.6.3) and on the distribution of economic 
implications between economic sectors and actors (Section 3.6.4).

359
Box 3.5 | Concepts and Modelling Frameworks Used for Quantifying Macroeconomic Effects 
of Mitigation
Most studies that have developed mitigation pathways have used a  cost-effectiveness analysis (CEA) framework, which aim to 
compare the costs of different mitigation strategies designed to meet a given climate change mitigation goal (e.g., an emission-
reduction target or a temperature stabilisation target) but does not represent economic impacts from climate change itself, nor the 
associated economic benefits of avoided impacts. Other studies use modelling frameworks that represent the feedback of damages 
from climate change on the economy in a cost-benefit analysis (CBA) approach, which balances mitigation costs and benefits. This 
second type of study is represented in Section 3.6.2.
The marginal abatement cost of carbon, also called carbon price, is determined by the mitigation target under consideration: it

describes the cost of reducing the last unit of emissions to reach the target at a given point in time. Total macroeconomic mitigation 
costs (or gains) aggregate the economy-wide impacts of investments in low-carbon solutions and structural changes away from 
emitting activities. The total macroeconomic effects of mitigation pathways are reported in terms of variations in economic output 
or consumption levels, measured against a  reference scenario, also called baseline, at various points in time or discounted over 
a given time period. Depending on the study, the reference scenario reflects specific assumptions about patterns of socio-economic 
development and assumes either no-climate policies or the climate policies in place or planned at the time the study was carried 
out. When available in the AR6 scenarios database, this second type of reference scenario, with trends from implemented policies

until the end of 2020, has been chosen for computation of mitigation costs. In the vast majority of studies that have produced the 
body of work on the cost of mitigation assessed here, and in particular in all studies that have submitted global scenarios to the AR6 
scenarios database except (Schultes et al. 2021), the feedbacks of climate change impacts on the economic development pathways 
are not accounted for. This omission of climate impacts leads to overly optimistic economic projections in the reference scenarios, in 
particular in reference scenarios with no or limited mitigation action where the extent of global warming is the greatest. Mitigation 
cost estimates computed against no or limited policy reference scenarios therefore omit economic benefits brought by avoided climate 
change impact along mitigation pathways, and should be interpreted with care (Grant et al. 2020). When aggregate economic benefits

from avoided climate change impacts are accounted for, mitigation is a welfare-enhancing strategy (Section 3.6.2).
If GDP or consumption in mitigation pathways are below the reference scenario levels, they are reported as losses or macroeconomic 
costs. Such cost estimates give an indication of how economic activity slows relative to the reference scenario; they do not necessarily 
describe, in absolute terms, a reduction of economic output or consumption levels relative to previous years along the pathway. Aggregate 
mitigation costs depend strongly on the modelling framework used and the assumptions about the reference scenario against which 
mitigation costs are measured, in particular whether the reference scenario is, or not, on the efficiency frontier of the economy. If the 
economy is assumed to be at the efficiency frontier in the reference scenario, mitigation inevitably leads to actual costs, at least in

the short-run until the production frontier evolves with technical and structural change. Starting from a reference scenario that is not 
on the efficiency frontier opens the possibility to simultaneously reduce emissions and obtain macroeconomic gains, depending on the 
design and implementation of mitigation policies. A number of factors can result in reference scenarios below the efficiency frontier, for 
instance distorting labour taxes and/or fossil fuel subsidies, misallocation or under-utilisation of production factors such as involuntary 
unemployment, imperfect information or non-rational behaviours. Although these factors are pervasive, the modelling frameworks used 
to construct mitigation pathways are often limited in their ability to represent them (Köberle et al. 2021).
The absolute level of economic activity and welfare also strongly depends on the socio-economic pathway assumptions regarding,

inter alia, evolutions in demography, productivity, education levels, inequality, and technical change and innovation. The GDP or 
consumption indicators reported in the database of scenarios, and synthesized below, represent the absolute level of aggregate 
economic activity or consumption but do not reflect welfare and well-being (Roberts et al. 2020), that notably depend on human-
needs satisfaction, distribution within society and inequality (Section 3.6.4).
Chapter 1 and  Annex III.I give further details of the economic concepts and modelling frameworks, including their limitations, used 
in this report, respectively.3.6.1 Economy-wide Implications of Mitigation
3.6.1.1 Global Economic Effects of Mitigation and Carbon 
Values in Mitigation Pathways

360
Estimates for the marginal abatement cost of carbon in mitigation 
pathways vary widely, depending on the modelling framework used 
and socio-economic, technological and policy assumptions. However, 
it is robust across modelling frameworks that the marginal abatement 
cost of carbon increases for lower temperature categories, with 
a higher increase in the short term than in the longer term (Figure 3.32, 
left panel) (high confidence). The marginal abatement cost of carbon 
increases non-linearly with the decrease of CO 2 emissions level, but 
the uncertainty in the range of estimates also increases (Figure 3.33). 
Mitigation pathways with low-energy consumption patterns exhibit 
lower carbon values (Méjean et al. 2019; Meyer et al. 2021). In the 
context of the COVID-19 pandemic recovery, Kikstra et al. (2021a) also 
show that a  low-energy-demand recovery scenario reduces carbon

prices for a 1.5°C-consistent pathway by 19% compared to a scenario 
with energy demand trends restored to pre-pandemic levels.For optimisation modelling frameworks, the time profile of marginal 
abatement costs of carbon depends on the discount rate, with lower 
discount rates implying higher carbon values in the short term but 
lower values in the long term (Emmerling et al. 2019) (see also 
‘Discounting’ in Annex I: Glossary, and Annex III.I.2). In that case, the 
discount rate also influences the shape of the emissions trajectory, 
with low discount rates implying more emissions reduction in the 
short term and, for low-temperature categories, limiting CDR and 
temperature overshoot.
Pathways that correspond to NDCs announced prior to COP26 
in 2030 and strengthen action after 2030 imply higher marginal 
abatement costs of carbon in the longer run than pathways 
with stronger immediate global mitigation action (Figure  3.32b) 
(high confidence).2030104
103
102
101
16
5
4
3
2

2050 2050
year year2100USD2015 tCO2C1: limit 
warming to 
with no or 
limited 
overshoot
C2: return 
warming to 
overshoot
C3: limit 
(>67%)
C4: limit 
(>50%)
C5: limit 
warming to 
C6: limit 
(>50%)(a) (b)
Figure 3.32 | Marginal abatement cost of carbon in 2030, 2050 and 2100 for mitigation pathways with immediate global mitigation action (a), and ratio 
in 2050 between pathways that correspond to NDCs announced prior to COP26 in 2030 and strengthen action after 2030 and pathways with immediate 
global mitigation action, for C3 and C4 temperature categories (b).
10 –5 0 5 15 20 25 30 35 10 15 20 25 30 35 40 40 45 50700 2,500
2,000
05001,0001,500600
500
400
300
200
100
0
Global CO2 emissions in 2030 (GtCO2 yr–1) Global CO2 emissions in 2050 (GtCO2 yr–1)Marginal abatement cost in 2050 (UD$2015/tCO2)Marginal abatement cost in 2030 (UD$2015/tCO2)Category
C1: limit warming to 1.5°C

(>50%) with no or limited 
overshoot
C2: return warming to 
1.5°C (>50%) after a high 
overshoot
C3: limit warming to 2°C 
(>67%)
C4: limit warming to 2°C 
(>50%)
C5: limit warming to 2.5°C 
(>50%)
C6: limit warming to 3°C 
Figure 3.33 | Marginal abatement cost of carbon with respect to CO 2 emissions for mitigation pathways with immediate global mitigation action, in 
2030 (a) and 2050 (b).

361
Aggregate economic activity and consumption levels in mitigation 
pathways are primarily determined by socio-economic development 
pathways but are also influenced by the stringency of the mitigation 
goal and the policy choices to reach the goal (high confidence). 
Mitigation pathways in temperature categories C1 and C2 entail 
losses in global consumption with respect to their baselines  – not 
including benefits of avoided climate change impacts nor co-benefits 
or co-harms of mitigation action – that correspond to an annualised 
reduction of consumption growth by 0.04 (median value) (interquartile 
range [0.02–0.06]) percentage points over the century. For pathways in 
temperature categories C3 and C4 this reduction in global consumption 
growth is  0.03 (median value) (interquartile range [0.01–0.05]) 
percentage points over the century. In the majority of studies that

focus on the economic effects of mitigation without accounting for 
climate damages, global economic growth and consumption growth 
is reduced compared to baseline scenarios (that omit damages from 
climate change), but mitigation pathways do not represent an absolute 
decrease of economic activity level (Figure 3.34b,c).
However, the possibility for increased economic activity following 
mitigation action, and conversely the risk of large negative economic 
effects, are not excluded. Some studies find that mitigation increases 
the speed of economic growth compared to baseline scenarios (Pollitt 
and Mercure 2018; Mercure et al. 2019). These studies are based on 
a  macroeconomic modelling framework that represent baselines below the efficiency frontier, based on non-equilibrium economic 
theory, and assume that mitigation is undertaken in such a  way 
that green investments do not crowd out investment in other parts

of the economy – and therefore offers an economic stimulus. In the 
context of the recovery from the COVID-19 crisis, it is estimated that 
a green investment push would initially boost the economy while also 
reducing GHG emissions (IMF 2020; Pollitt et al. 2021). Conversely, 
several studies find that only a  GDP non-growth/degrowth or post-
growth approach enable reaching climate stabilisation below 2°C 
(Hardt and  O’Neill 2017;  D’Alessandro et al. 2020; Hickel and Kallis 
2020; Nieto et al. 2020), or to minimise the risks of reliance on high 
energy-GDP decoupling, large-scale CDR and large-scale renewable 
energy deployment (Keyßer and Lenzen 2021). Similarly, feedbacks of 
financial system risk amplifying shocks induced by mitigation policy 
and lead to a higher impact on economic activity (Stolbova et al. 2018).
Mitigation costs increase with the stringency of mitigation (Hof 
et al. 2017; Vrontisi et al. 2018) (Figure 3.34b,c), but are reduced when

energy demand is moderated through energy efficiency and lifestyle 
changes (Fujimori et al. 2014; Bibas et al. 2015; Liu et al. 2018; Méjean 
et al. 2019), when sustainable transport policies are implemented 
(Zhang et al. 2018c), and when international technology cooperation is 
fostered (Schultes et al. 2018; Paroussos et al. 2019). Mitigation costs 
also depend on assumptions on availability and costs of technologies 
(Clarke et al. 2014; Bosetti et al. 2015; Dessens et al. 2016; Creutzig et al. 3.0 10
8
6
4
22.5
2.0
1.5
1.0
0.5
0.0
012
10
8
6
4
20%
%
562030 2050
Year Year2100 2100
2030 500 3500 3000 2500 2000 1500 1000 2050
Year Cumultative CO2 emissions (Gt) from 2020 to year of net zero emissionsDiscounted consumption loss (%) w 3% disc.21004
3
2
1
0%Category
overshoot
C2: return 
C5: limit warming

C8: exceed 
(≥50%)
Unclassiﬁed(a) (b)
(c) (d)
Immediate action
Weaker short-term 
action
Figure 3.34 | (a) Mean annual global consumption growth rate over 2020–2100 for the mitigation pathways in the AR6 scenarios database. (b) Global GDP 
loss compared to baselines (not accounting for climate change damages) in 2030, 2050 and 2100 for mitigation pathways with immediate global action. (c) Total discounted 
consumption loss (with a 3% discount rate) in mitigation scenarios with respect to their corresponding baseline (not accounting for climate change damages) as a function of 
cumulative CO 2 emissions until date of net zero CO 2. (d) Comparison of GDP losses compared to baselines (not accounting for climate change damages) in 2030, 2050 and 
2100 for pairs of scenarios depicting immediate action pathways and delayed action pathways. Source: AR6 Scenarios Database.

362
2018; Napp et al. 2019; Giannousakis et al. 2021), on the representation 
of innovation dynamics in modelling frameworks (Hoekstra et al. 
2017; Rengs et al. 2020) (Chapter 16), as well as the representation 
of investment dynamics and financing mechanisms (Iyer et al. 2015c; 
Mercure et al. 2019; Battiston et al. 2021). In particular, endogenous 
and induced innovation reduce technology costs over time, create 
path dependencies and reduce the macroeconomic cost of reaching 
a mitigation target (Section 1.7.1.2). Mitigation costs also depend on 
socio-economic assumptions (Hof et al. 2017; van Vuuren et al. 2020).
Mitigation pathways with early emissions reductions represent higher 
mitigation costs in the short-run but bring long-term gains for the 
economy compared to delayed transition pathways (high confidence). 
Pathways with earlier mitigation action bring higher long-term GDP

than pathways reaching the same end-of-century temperature with 
weaker early action (Figure 3.34d). Comparing counterfactual history 
scenarios, Sanderson and  O’Neill (2020) also find that delayed 
mitigation action leads to higher peak costs. Rogelj et al. (2019b) 
and Riahi et al. (2021) also show that pathways with earlier timing 
of net zero CO 2 lead to higher transition costs but lower long-term 
mitigation costs, due to dynamic effects arising from lock-in avoidance 
and learning effects. For example, Riahi et al.(2021) find that for a 2°C 
target, the GDP losses (compared to a  reference scenario without 
impacts from climate change) in 2100 are 5–70% lower in pathways 
that avoid net negative CO 2 emissions and temperature overshoot 
than in pathways with overshoot. Accounting also for climate change 
damage, van der Wijst et al. (2021a) show that avoiding net negative 
emissions leads to a small increase in total discounted mitigation costs

over 2020–2100, between 5% and 14% in their medium assumptions, 
but does not increase mitigation costs when damages are high and 
when using a low discount rate, and becomes economically attractive 
if damages are not fully reversible. The modelled cost-optimal balance 
of mitigation action over time strongly depends on the discount rate 
used to compute or evaluate mitigation pathways: lower discount 
rates favour earlier mitigation, reducing both temperature overshoot 
and reliance on net negative carbon emissions (Emmerling et al. 2019; Riahi et al. 2021). Mitigation pathways with weak early action 
corresponding to NDCs announced prior to COP26 in 2030 and 
strengthening action after 2030 to reach end-of-century temperature 
targets imply limited mitigation costs in 2030, compared to immediate 
global action pathways, but faster increase in costs post-2030, with 
implications for intergenerational equity (Aldy et al. 2016; Liu et al.

2016; Vrontisi et al. 2018). Emissions trading policies reduce global 
aggregate mitigation costs, in particular in the context of achieving 
NDCs (Fujimori et al. 2015, 2016a; Böhringer et al. 2021; Edmonds 
et al. 2021), and change the distribution of mitigation costs between 
regions and countries (Section 3.6.1.2).
3.6.1.2  Regional Mitigation Costs and Effort-sharing Regimes
The economic repercussions of mitigation policies vary across 
countries (Aldy et al. 2016; Hof et al. 2017): regional variations 
exist in institutions, economic and technological development, and 
mitigation opportunities. For a globally uniform carbon price, carbon-
intensive and energy-exporting countries bear the highest economic 
costs because of a  deeper transformation of their economies and 
of trade losses in the fossil markets (Stern et al. 2012; Tavoni et al. 
2015; Böhringer et al. 2021). This finding is confirmed in Figure 3.35. 
Since carbon-intensive countries are often poorer, uniform global

carbon prices raise equity concerns (Tavoni et al. 2015). On the other 
hand, the climate economic benefits of mitigating climate change 
will be larger in poorer countries (Cross-Working Group Box 1 in this 
chapter). This reduces policy regressivity but does not eliminate it 
(Taconet et al. 2020; Gazzotti et al. 2021). Together with co-benefits, 
such as health benefits of improved air quality, the economic benefits 
of mitigating climate change are likely to outweigh mitigation costs 
in many regions (Li et al. 2018, 2019; Scovronick et al. 2021).
Regional policy costs depend on the evaluation framework (Budolfson 
et al. 2021), policy design, including revenue recycling, and on 
international coordination, especially among trade partners. By 
fostering technological change and finance, climate cooperation can 
C3: limit warming to 2°C (>67%)Immediate action

Weaker short-term action8863 8863 8763 8863 8863 8863 8356 8863 886305101520 17.5
0.02.55.07.510.012.515.0
EU NAM PAC CHN LAM IND AFR REF MEA EU NAM PAC CHN LAM IND AFR REF MEA
GDP Loss MER %GDP losses (%)(b) R10 scenarios – Timing of Action – C3 (a)
Figure 3.35 | a: regional mitigation costs in the year 2050 (expressed as GDP losses between mitigation scenarios and corresponding baselines, not 
accounting for climate change damages), under the assumption of immediate global action with uniform global carbon pricing and no international 
transfers, by climate categories for the 2°C (>67%) and 1.5°C (>50%) (with and without overshoot) categories. Right panel: policy costs in 2050 (as in 
panel a) for 2°C (>67%) climate category C3 for scenario pairs that represent either immediate global action (‘immediate’) or delayed global action (‘delayed’) with weaker 
action in the short term, strengthening to reach the same end-of-century temperature target.

363
generate economic benefits, both in large developing economies such 
as China and India (Paroussos et al. 2019) and industrialised regions 
such as Europe (Vrontisi et al. 2020). International coordination is 
a major driver of regional policy costs. Delayed participation in global 
mitigation efforts raises participation costs, especially in carbon-
intensive economies (Figure 3.35a. Trading systems and transfers can 
deliver cost savings and improve equity (Rose et al. 2017a). On the 
other hand, measures that reduce imports of energy-intensive goods 
such as carbon-border tax adjustment may imply costs outside of 
the policy jurisdiction and have international equity repercussions, 
depending on how they are designed (Böhringer et al. 2012, 2017; 
Cosbey et al. 2019) (Section 13.6.6).
An equitable global emission-trading scheme would require very 
large international financial transfers, in the order of several

hundred billion USD per year (Tavoni et al. 2015; Bauer et al. 2020; 
van den Berg et al. 2020). The magnitude of transfers depends on the 
stringency of the climate goals and on the burden-sharing principle. 
Some interpretations of equitable burden sharing compliant with 
the Paris Agreement leads to negative carbon allowances for 
developed countries and some developing countries by mid-century 
(van  den  Berg et al. 2020), more stringent than cost-optimal 
pathways. International transfers also depend on the underlying 
socio-economic development (Leimbach and Giannousakis 2019), 
as these drive the mitigation costs of meeting the Paris Agreement (Rogelj et al. 2018b). By contrast, achieving equity without 
international markets would result in a  large discrepancy in 
regional carbon prices, up to a factor of 100 (Bauer et al. 2020). The 
efficiency-sovereignty trade-off can be partly resolved by allowing 
for limited differentiation of regional carbon prices: moderate

financial transfers substantially reduce inefficiencies by narrowing 
the carbon price spread (Bauer et al. 2020).
3.6.1.3 Investments in Mitigation Pathways
Figures  3.36 and  3.37 show increased investment needs in the 
energy sector in lower temperature categories, and a  major shift 
away from fossil fuel generation and extraction towards electricity, 
including for system enhancements for electricity transmission, 
distribution and storage, and low-carbon technologies. Investment 
needs in the electricity sector are  2.3 trillion USD2015 yr–1 
over 2023–2050 on average for C1 pathways, 2 trillion USD 
for C2 pathways,  1.7  trillion  USD for C3,  1.2 trillion USD for C4 
and  0.9–1.1 billion USD for C5/C6/C7 (mean values for pathways 
in each temperature category). The regional pattern of power 
sector investments broadly mirrors the global picture. However, the 
bulk of investment requirements are in medium- and low-income

regions. These results from the AR6 scenarios database corroborate 
the findings from McCollum et al. (2018a), Zhou et al. (2019) and 
Bertram et al. (2021).
48
94
205
101
143
64
62
539
76
153
78
116
47
38
4
50
100
221
112
154
66
66
729
69
159
76
107
46
4052
94
207
99
142
63
66
552
96
207
101
143
64
69
552
96
207
101
143
64
70
5
50
81
157
67
111
45
51
552
96
207
101
143
64
70
5
29
59
130
80
112
46
39
6 050010001500
Solar Wind Storage T&D Nuclear Hydro Bio Geothermal Fossil Extraction
VariableAverage yearly investments until 2050 (billion USD)Category
C1: limit 
warming to 
with no or 
limited 
overshoot
C2: return 
warming to 
overshoot
C3: limit 
(>67%)
C4: limit 
(>50%)
C5: limit 
warming to 
C6: limit 
(>50%)
C7: limit 
(>50%)
C8: exceed 
(≥50%)

Figure 3.36 | Global average yearly investments from 2023–2052 for nine electricity supply subcomponents and for extraction of fossil fuels (in billion 
USD2015), in pathways by temperature categories.  T&D: transmission and distribution of electricity. Bars show the median values (number of pathways at the bottom), 
and whiskers show the interquartile ranges.

364
Average yearly investments until 2050 (bn US$)
243766245316205
243766235315175243766245316205
17334523521495253767245316205
243766235315175253767245316205
17334523521495
253767245316205
17334523521495ME DEV EEAAFR APC LAM
Solar Wind T&D Extr. Solar Wind T&D Extr. Solar Wind T&D Extr.Solar Wind T&D Extr. Solar Wind T&D Extr. Solar Wind T&D Extr.050100150200
050100150175
125
75
250100200300400500
0100200300400450
350
250
150
50050
25100125
75
Variable
Category
C4: limit warming to 2°C (>50%)C5: limit warming to 2.5°C (>50%)

Figure  3.37 | Average yearly investments from 2023–2052 for the four subcomponents of the energy system representing the larger amounts 
(in billion USD2015), by aggregate regions, in pathways by temperature categories.  T&D: transmissions and distribution of electricity. Extr.: extraction of fossil fuels. 
Bars show the median values (number of pathways at the bottom), and whiskers show the interquartile ranges. For definition of regional classifications used see Annex II Table 1.
In the context of the COVID-19 pandemic recovery, Kikstra et al. 
(2021a) show that a low-energy-demand recovery scenario reduces 
energy investments required until 2030 for a  1.5°C consistent 
pathway by  9% (corresponding to reducing total required energy 
investment by USD1.8 trillion) compared to a scenario with energy 
demand trends restored to pre-pandemic levels.

Few studies extend the scope of the investment needs quantification 
beyond the energy sector. Fisch-Romito and Guivarch (2019) 
and Ó  Broin and Guivarch (2017) assess investment needs for 
transportation infrastructures and find lower investment needs in 
low-carbon pathways, due to a  reduction in transport activity and 
a  shift towards less road construction, compared to high-carbon pathways. Rozenberg and Fay (2019) estimate the funding needs 
to close the service gaps in water and sanitation, transportation, 
electricity, irrigation, and flood protection in thousands of scenarios, 
showing that infrastructure investment paths compatible with full 
decarbonisation in the second half of the century need not cost more 
than more-polluting alternatives. Investment needs are estimated 
between 2% to 8% of GDP , depending on the quality and quantity of 
services targeted, the timing of investments, construction costs, and 
complementary policies.

Chapter 15 also reports investment requirements in global mitigation 
pathways in the near term, compares them to recent investment 
trends, and assesses financing issues.

365
Cross-Working Group Box 1 | Economic Benefits from Avoided Climate Impacts Along Long-
term Mitigation Pathways
Authors: Céline Guivarch (France), Steven Rose (the United States of America), Alaa Al Khourdajie (United Kingdom/Syria), 
Valentina Bosetti (Italy), Edward Byers (Austria/Ireland), Katherine Calvin (the United States of America), Tamma Carleton (the United 
States of America), Delavane Diaz (the United States of America), Laurent Drouet (France/Italy), Michael Grubb (United Kingdom), 
Tomoko Hasegawa (Japan), Alexandre C. Köberle (Brazil/United Kingdom), Elmar Kriegler (Germany), David McCollum (the United 
States of America), Aurélie Méjean (France), Brian O’Neill (the United States of America), Franziska Piontek (Germany), Julia Steinberger 
(United Kingdom/Switzerland), Massimo Tavoni (Italy)

Mitigation reduces the extent of climate change and its impacts on ecosystems, infrastructure, and livelihoods. This box summarises 
elements from the AR6 WGII report on aggregate climate change impacts and risks, putting them into the context of mitigation 
pathways. AR6 WGII provides an assessment of current lines of evidence regarding potential climate risks with future climate change, 
and therefore, the avoided risks from mitigating climate change. Regional and sectoral climate risks to physical and social systems are 
assessed (AR6 WGII Chapters 2–15). Over 100 of these are identified as Key Risks (KRs) and further synthesised by WGII Chapter 16 
into eight overarching Representative Key Risks (RKRs) relating to low-lying coastal systems; terrestrial and ocean ecosystems; critical 
physical infrastructure, networks and services; living standards; human health; food security; water security; and peace and mobility

(AR6 WGII Section  16.5.2). The RKR assessment finds that risks increase with global warming level, and also depend on socio-
economic development conditions, which shape exposure and vulnerability, and adaptation opportunities and responses. ‘Reasons 
For Concern’, another WGII aggregate climate-impacts risk framing, are also assessed to increase with climate change, with increasing 
risk for unique and threatened systems, extreme weather events, distribution of impacts, global aggregate impacts, and large-scale 
singular events (AR6 WGII Chapter  16). For human systems, in general, the poor and disadvantaged are found to have greater 
exposure level and vulnerability for a given hazard. With some increase in global average warming from today expected regardless 
of mitigation efforts, human and natural systems will be exposed to new conditions and additional adaptation will be needed (AR6

WGII Chapter 18). The range of dates for when a specific warming level could be reached depends on future global emissions, with 
significant overlap of ranges across emissions scenarios due to climate system response uncertainties (AR6 WGI Tables 4.2 and 4.5). 
The speed at which the climate changes is relevant to adaptation timing, possibilities, and net impacts.
The AR6 WGII also assesses the growing literature estimating the global aggregate economic impacts of climate change and the social 
cost of carbon dioxide and other greenhouse gases (AR6 WGII Cross-Working Box ECONOMIC: Estimating Global Economic Impacts 
from Climate Change and the Social Cost of Carbon in AR6 WGII Chapter  16). The former represents aggregate estimates that 
inform assessment of the economic benefits of mitigation. This literature is characterised by significant variation in the estimates,

including for today’s level of global warming, due primarily to fundamental differences in methods, but also differences in impacts 
included, representation of socio-economic exposure, consideration of adaptation, aggregation approach, and assumed persistence 
of damages. The AR6 WGII’s assessment identifies different approaches to quantification of aggregated economic impacts of climate 
change, including: physical modelling of impact processes, such as projected mortality rates from climate risks such as heat, vector- or 
waterborne diseases that are then monetised; structural economic modelling of impacts on production, consumption, and markets for 
economic sectors and regional economies; and statistical estimation of impacts based on observed historical responses to weather 
and climate. The AR6 WGII finds that variation in estimated global economic impacts increases with warming in all methodologies,

indicating higher risk in terms of economic impacts at higher temperatures (high confidence). Many estimates are non-linear with 
marginal economic impacts increasing with temperature, although some show declining marginal economic impacts with temperature, 
and functional forms cannot be determined for all studies. The AR6 WGII’s assessment finds that the lack of comparability between 
methodologies does not allow for identification of robust ranges of global economic impact estimates (high confidence). Further, AR6 
WGII identifies evaluating and reconciling differences in methodologies as a research priority for facilitating use of the different lines 
of evidence (high confidence). However, there are estimates that are higher than AR5, indicating that global aggregate economic 
impacts could be higher than previously estimated (low confidence due to the lack of comparability across methodologies and lack of 
robustness of estimates) (AR6 WGII Cross-Working Box ECONOMIC).

Conceptually, the difference in aggregate economic impacts from climate change between two given temperature levels represents 
the aggregate economic benefits arising from avoided climate change impacts due to mitigation action. A subset of the studies whose 
estimates were evaluated by AR6 WGII (5 of 15) are used to derive illustrative estimates of aggregate economic benefits in 2100 arising 3.6.2 Economic Benefits of Avoiding Climate Change Impacts

366
Cross-Working Group Box 1 (continued)
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
Global temperature change above pre-industrial% loss in global GDP
a) Global aggregate economic impact estimates by global 
    warming level
B18 H17 K19 T19 B18 H17 T19 B18 H17 T19 B18 H17 P18 T1905101520
StudiesBeneﬁts in % global GDPb) Global aggregate economic beneﬁts of mitigation from avoided climate change impacts in 2100
P18: Pretis et al. (2018), B18: Burke et al. (2018), K19: Khan et al. (2019), T19: Takakura et al. (2019), H17: Howard & Sterner (2017)
3°C 4°C
Mitigating from 4°C
to below 2°CMitigating from 3°C
to below 2°CMitigating from 
above 2°C
below 2°C
to below 1.5°Cbelow 
1.5°Cbelow 
2°Cabove 
2°C
Study
B18
H17
K19P18
T19Baseline
Other
SSP1
SSP2SSP3
SSP4
SSP5

Cross-Working Group Box  1, Figure  1 | Global aggregate economic benefits of mitigation from avoided climate change impacts in 2100 
corresponding to shifting from a higher temperature category (4°C (3.75°C–4.25°C), 3°C (2.75°C–3.25°C), or above 2°C (2°C–2.5°C), to below 
2°C (1.5°C–2°C), as well as from below 2°C to below 1.5°C (1°C–1.5°C)), from the five studies discussed in the text.  Panel (a) is adapted from AR6 
WGII Cross-Working Group Box ECONOMIC, Figure 1, showing global aggregate economic impact estimates (% global GDP loss relative to GDP without additional 
climate change) by temperature change level. All estimates are shown in grey. Estimates used for the computation of estimated benefits in 2100 in panel (b) are 
coloured for the selected studies, which provide results for different temperature change levels. See the AR6 WGII AR6 WGII Cross-Working Group Box ECONOMIC for

discussion and assessment of the estimates in panel (a) and the differences in methodologies. For B18 and T19, median estimates in the cluster are considered. Shape 
distinguishes the baseline scenarios. Temperature ranges are highlighted. HS17 estimates are based on their preferred model –50th percentile of non-catastrophic 
damage. Panel (b) shows the implied aggregate economic benefits in 2100 of a lower temperature increase. Economic benefits for point estimates are computed as 
a difference, while economic benefits from the curve HS17 are computed as ranges from the segment differences.
from avoided climate change (Howard and Sterner 2017; Burke et al. 2018; Pretis et al. 2018; Kahn et al. 2019; Takakura et al. 2019). 
Burke et al. (2018), Pretis et al. (2018) and Kahn et al. (2019) are examples of statistical estimations of historical relationships between

temperature and economic growth, whereas Takakura et al. (2019) is an example of structural modelling, which evaluates selected 
impact channels (impacts on agriculture productivity, undernourishment, heat-related mortality, labour productivity, cooling/heating 
demand, hydro-electric and thermal power generation capacity and fluvial flooding) with a general equilibrium model. Howard and 
Sterner (2017) and Rose et al. (2017b) estimate damage functions that can be used to compute the economic benefits of mitigation 
from avoiding a given temperature level for a lower one. Howard and Sterner (2017) estimate a damage function from a meta-
analysis of aggregate economic impact studies, while Rose et al. (2017b) derive global functions by temperature and socio-economic 
drivers from stylised aggregate cost-benefit-analysis (CBA) integrated assessment models (IAMs) using diagnostic experiments. Cross-

Working Group Box 1, Figure 1 summarises the global aggregate economic benefits in 2100 of avoided climate change impacts from 
individual studies corresponding to shifting from a higher temperature category (above 3°C, below 3°C or below 2.5°C) to below 2°C, 
as well as from below 2°C to below 1.5°C. Benefits are positive and increase with the temperature gap for any given study, and this 
result is robust across socio-economic scenarios. The Figure provides evidence of a wide range of quantifications, and illustrates the 
important differences associated with methods. Panel a puts the studies used to calculate aggregate economic benefits arising from 
avoided impacts into the context of the broader set of studies assessed in WGII (Section 16.6.2 of this report, AR6 WGII Cross-Working 
Group Box ECONOMIC,). However, economic benefits in 2100 arising from avoided impacts cannot be directly computed from damage

estimates across this broader set of studies, due to inconsistencies – different socio-economic assumptions, scenario designs, and 
counterfactual reference scenarios across studies. Furthermore, these types of estimates cannot be readily compared to mitigation 
cost estimates. The comparison would require a framework that ensures consistency in assumptions and dynamics and allows for 
consideration of benefits and costs along the entire pathway.
Aggregate benefits from avoided impacts expressed in GDP terms, as in Figure 1, do not encompass all avoided climate risks, adaptation 
possibilities, and do not represent their influence on well-being and welfare (AR6 WGII Cross-Working Group Box ECONOMIC). 
Methodological challenges for economic impact estimates include representing uncertainty and variability, capturing interactions and 
spillovers, considering distributional effects, representing micro- and macro-adaptation processes, specifying non-gradual damages

and non-linearities, and improving understanding of potential long-run growth effects. In addition, the economic benefits aggregated

367
Cost-benefit analyses (CBA) aim to balance all costs and benefits 
in a  unified framework (Nordhaus, 2008). Estimates of economic 
benefits from avoided climate change impacts depend on the types 
of damages accounted for, the assumed exposure and vulnerability 
to these damages as well as the adaptation capacity, which in 
turn are based on the development pathway assumed (Cross-
Working Group Box 1 in this chapter). CBA IAMs raised criticism, in 
particular for omitting elements of dynamic realism, such as inertia, 
induced innovation and path dependence, in their representation of 
mitigation (Grubb et al. 2021), and for underestimating damages 
from climate change, missing non-monetary damages, the uncertain 
and heterogeneous nature of damages and the risk of catastrophic 
damages (Stern 2013, 2016; Diaz and Moore 2017; NASEM 2017; 
Pindyck 2017; Stoerk et al. 2018; Stern and Stiglitz 2021). Emerging

literature has started to address those gaps, and integrated into cost-
benefit frameworks the account of heterogeneity of climate damage 
and inequality (Dennig et al. 2015; Budolfson et al. 2017; Fleurbaey 
et al. 2019; Kornek et al. 2021), damages with higher persistence, 
including damages on capital and growth (Moyer et al. 2014; Dietz 
and Stern 2015; Moore and Diaz 2015; Guivarch and Pottier 2018; 
Ricke et al. 2018; Piontek et al. 2019), risks of tipping points (Cai et al. 
2015, 2016; Lontzek et al. 2015; Lemoine and Traeger 2016; van der 
Ploeg and de Zeeuw 2018; Cai and Lontzek 2019; Nordhaus 2019; 
Yumashev et al. 2019; Taconet et al. 2021) and damages to natural 
capital and non-market goods (Tol 1994; Sterner and Persson 2008; 
Bastien-Olvera and Moore 2020; Drupp and Hänsel 2021).
Each of these factors, when accounted for in a  CBA framework, 
tends to increase the welfare benefit of mitigation, thus leading

to stabilisation at a  lower temperature in optimal mitigation 
pathways. The limitations in CBA modelling frameworks remain 
significant, their ability to represent all damages incomplete, and 
the uncertainty in estimates remains large. However, emerging 
evidence suggests that, even without accounting for co-benefits 
of mitigation on other sustainable development dimensions (see 
Section 3.6.3 for further details about on co-benefits), global benefits 
of pathways that limit warming to 2°C outweigh global mitigation 
costs over the 21st century: depending on the study, the reason for 
this result lies in assumptions of economic damages from climate 
change in the higher end of available estimates (Moore and Diaz 
2015; Ueckerdt et al. 2019; Brown and Saunders 2020; Glanemann 
et al. 2020), in the introduction of risks of tipping points (Cai and Lontzek 2019), in the consideration of damages to natural capital 
and non-market goods (Bastien-Olvera and Moore 2020) or in the

combination of updated representations of carbon cycle and climate 
modules, updated damage estimates and/or updated representations 
of economic and mitigation dynamics (Dietz and Stern 2015; Hänsel 
et al. 2020; Wei et al. 2020; van der Wijst et al. 2021b). In the studies 
cited above that perform a sensitivity analysis, this result is found to 
be robust to a wide range of assumptions on social preferences (in 
particular, on inequality aversion and pure rate-of-time preference) 
and holds except if assumptions of economic damages from climate 
change are in the lower end of available estimates and the pure rate-
of-time preference is in the higher range of values usually considered 
(typically above 1.5%). However, although such pathways bring net 
benefits over time (in terms of aggregate discounted present value), 
they involve distributional consequences and transition costs (Brown 
et al. 2020; Brown and Saunders 2020) (Sections 3.6.1.2 and 3.6.4).

The standard discounted utilitarian framework dominates CBA, thus 
often limiting the analysis to the question of discounting. CBA can be 
expanded to accommodate a wider variety of ethical values to assess 
mitigation pathways (Fleurbaey et al. 2019). The role of ethical values 
with regard to inequality and the situation of the worse off (Adler 
et al. 2017), risk (van den Bergh and Botzen 2014; Drouet et al. 2015), 
and population size (Scovronick et al. 2017; Méjean et al. 2020) has 
been explored. In most of these studies, the optimal climate policy is 
found to be more stringent than the one obtained using a standard 
discounted utilitarian criterion.
Comparing economic costs and benefits of mitigation raises a number 
of methodological and fundamental difficulties. Monetising the full 
range of climate change impacts is extremely hard, if not impossible 
(AR6 WGII Chapter 16), as is aggregating costs and benefits over time

and across individuals when values are heterogeneous (Chapter 1; 
AR5 WGIII Chapter 3). Other approaches should thus be considered 
in supplement for decision-making (Chapter 1 and Section 1.7), in 
particular cost-effectiveness approaches that analyse how to achieve 
a defined mitigation objective at least cost or while also reaching 
other societal goals (Koomey 2013; Kaufman et al. 2020; Köberle 
et al. 2021; Stern and Stiglitz 2021). In cost-effectiveness studies 
too, incorporating benefits from avoided climate damages influences 
the results and leads to more stringent mitigation in the short term 
(Drouet et al. 2021; Schultes et al. 2021).at the global scale provide limited insights into regional heterogeneity. Global economic impact studies with regional estimates 
find large differences across regions in absolute and percentage terms, with developing and transitional economies typically more

vulnerable. Furthermore, (avoided) impacts for poorer households and poorer countries can represent a smaller share in aggregate 
quantifications expressed in GDP terms or monetary terms, compared to their influence on well-being and welfare (Hallegatte et al. 
2020; Markhvida et al. 2020). Finally, as noted by AR6 WGII, other lines of evidence regarding climate risks, beyond monetary estimates, 
should be considered in decision-making, including Key Risks and Reasons for Concern.Cross-Working Group Box 1 (continued)

368
3.6.3 Aggregate Economic Implication of Mitigation 
Co-benefits and Trade-offs
Mitigation actions have co-benefits and trade-offs with other 
sustainable development dimensions (Section  3.7) beyond climate 
change, which imply welfare effects and economic effects, as well 
as other implications beyond the economic dimension. The majority 
of quantifications of mitigation costs and benefits synthesized 
in Sections  3.6.1 and  3.6.2 do not account for these economic 
benefits and costs associated with co-benefits and trade-offs along 
mitigation pathways.
Systematic reviews of the literature on co-benefits and trade-offs 
from mitigation actions have shown that only a  small portion of 
articles provide economic quantifications (Deng et al. 2017; Karlsson 
et al. 2020). Most economic quantifications use monetary valuation 
approaches. Improved air quality, and associated health effects,

are the co-benefit category dominating the literature (Markandya 
et al. 2018; Vandyck et al. 2018; Scovronick et al. 2019; Howard et al. 
2020; Karlsson et al. 2020b; Rauner et al. 2020a,b), but some studies 
cover other categories, including health effects from diet change 
(Springmann et al. 2016b) and biodiversity impacts (Rauner et al. 
2020a). Regarding health effects from air quality improvement and 
from diet change, co-benefits are shown to be of the same order of 
magnitude as mitigation costs (Thompson et al. 2014; Springmann 
et al. 2016a,b; Markandya et al. 2018; Scovronick et al. 2019b; 
Howard et al. 2020; Rauner et al. 2020a,b; Liu et al. 2021; Yang 
et al. 2021). Co-benefits from improved air quality are concentrated 
sooner in time than economic benefits from avoided climate change 
impacts (Karlsson et al. 2020), such that when accounting both 
for positive health impacts from reduced air pollution and for the

negative climate effect of reduced cooling aerosols, optimal GHG 
mitigation pathways exhibit immediate and continual net economic 
benefits (Scovronick et al. 2019a). However, AR6 WGI Chapter  6 
(Section 6.7.3) shows a delay in air pollution reduction benefits when 
they come from climate change mitigation policies compared with air 
pollution reduction policies.
Achieving co-benefits is not automatic but results from coordinated 
policies and implementation strategies (Clarke et al. 2014; McCollum 
et al. 2018a). Similarly, avoiding trade-offs requires targeted policies 
(van Vuuren et al. 2015; Bertram et al. 2018). There is limited 
evidence of such pathways, but the evidence shows that mitigation 
pathways designed to reach multiple Sustainable Development 
Goals instead of focusing exclusively on emissions reductions, 
result in limited additional costs compared to the increased benefits 
(Cameron et al. 2016; McCollum et al. 2018b; Fujimori et al. 2020a;

Sognnaes et al. 2021).
3.6.4 Structural Change, Employment and 
Distributional Issues Along Mitigation Pathways
Beyond aggregate effects at the economy-wide level, mitigation 
pathways have heterogeneous economic implications for different 
sectors and different actors. Climate-related factors are only one 
driver of the future structure of the economy, of the future of employment, and of future inequality trends, as overarching trends in 
demographics, technological change (innovation, automation, etc.), 
education and institutions will be prominent drivers. For instance, 
Rao et al. (2019b) and Benveniste et al. (2021) have shown that 
income inequality projections for the 21st century vary significantly, 
depending on socio-economic assumptions related to demography, 
education levels, social public spending and migrations. However, 
the sections below focus on climate-related factors, both climate-
mitigation actions themselves and the climate change impacts

avoided along mitigation pathways, effects on structural change, 
including employment, and distributional effects.
3.6.4.1  Economic Structural Change and Employment 
in Long-term Mitigation Pathways
Mitigation pathways entail transformation of the energy sector, with 
structural change away from fossil energy and towards low-carbon 
energy (Section 3.3), as well as broader economic structural change, 
including industrial restructuring and reductions in carbon-intensive 
activities in parallel to extensions in low-carbon activities.
Mitigation affects work through multiple channels, which impacts 
geographies, sectors and skill categories differently (Fankhaeser 
et al. 2008; Bowen et al. 2018; Malerba and Wiebe 2021). Aggregate 
employment impacts of mitigation pathways mainly depend on 
the aggregate macroeconomic effect of mitigation (Sections  3.6.1 
and  3.6.2) and of mitigation policy design and implementation

(Freire-González 2018) (Section 4.2.6.3). Most studies that quantify 
overall employment implications of mitigation policies are conducted 
at the national or regional scales (Section 4.2.6.3), or sectoral scales 
(e.g., see Chapter 6 for energy sector jobs). The evidence is limited 
at the multinational or global scale, but studies generally find 
small differences in aggregate employment in mitigation pathways 
compared to baselines: the sign of the difference depends on the 
assumptions and modelling frameworks used and the policy design 
tested, with some studies or policy design cases leading to small 
increases in employment (Chateau and Saint-Martin 2013; Pollitt 
et al. 2015; Barker et al. 2016; Garcia-Casals et al. 2019; Fujimori 
et al. 2020a; Vrontisi et al. 2020; Malerba and Wiebe 2021) and other 
studies or policy design cases leading to small decreases (Chateau 
and Saint-Martin 2013; Vandyck et al. 2016). The small variations in

aggregate employment hide substantial reallocation of jobs across 
sectors, with jobs creation in some sectors and jobs destruction in 
others. Mitigation action through thermal renovation of buildings, 
installation and maintenance of low-carbon generation, and the 
expansion of public transit lead to job creation, while jobs are lost 
in fossil fuel extraction, energy supply and energy-intensive sectors 
in mitigation pathways (von Stechow et al. 2015, 2016; Barker et al. 
2016; Fuso Nerini et al. 2018; Perrier and Quirion 2018; Pollitt and 
Mercure 2018; Dominish et al. 2019; Garcia-Casals et al. 2019). In 
the energy sector, job losses in the fossil fuel sector are found to 
be compensated by gains in wind and solar jobs, leading to a net 
increase in energy sector jobs in 2050 in a  mitigation pathway 
compatible with stabilisation of the temperature increase below 
2°C (Pai et al. 2021). Employment effects also differ by geographies,

with energy-importing regions benefiting from net job creations but 
energy-exporting regions experiencing very small gains or suffering

369
from net job destruction (Barker et al. 2016; Pollitt and Mercure 2018; 
Garcia-Casals et al. 2019; Malerba and Wiebe 2021). Coal phase-out 
raises acute issues of just transition for the coal-dependent countries 
(Spencer et al. 2018; Jakob et al. 2020) (Section 4.5 and Box 6.2).
Mitigation action also affects employment through avoided climate 
change impacts. Mitigation reduces the risks to human health and 
associated impacts on labour and helps protect workers from the 
occupational health and safety hazards imposed by climate change 
(Kjellstrom et al. 2016, 2018, 2019; Levi et al. 2018; Day et al. 2019) 
(AR6 WGII Chapter 16).
3.6.4.2 Distributional Implications of Long-term 
Mitigation Pathways
Mitigation policies can have important distributive effects between 
and within countries, either reducing or increasing economic 
inequality and poverty, depending on policy instruments’ design

and implementation (see Section  3.6.1.2 for an assessment of 
the distribution of mitigation costs across regions in mitigation 
pathways; Sections 3.7 and 4.2.2.6, and Box 3.6 for an assessment of 
the fairness and ambition of NDCs; and Section 4.5 for an assessment 
of national mitigation pathways along the criteria of equity, including 
Just Transition, as well as Section 17.4.5 for equity in a Just Transition). 
For instance, emissions taxation has important distributive effects, 
both between and within income groups (Cronin et al. 2018b; Klenert 
et al. 2018; Pizer and Sexton 2019; Douenne 2020; Steckel et al. 
2021). These effects are more significant in some sectors, such as 
transport, and depend on country-specific consumption structures 
(Dorband et al. 2019; Fullerton and Muehlegger 2019; Ohlendorf 
et al. 2021). However, revenues from emissions taxation can be used 
to lessen their regressive distributional impacts or even turn the

policy into a  progressive policy reducing inequality and/or leading 
to gains for lower-income households (Cameron et al. 2016; Jakob 
and Steckel 2016; Fremstad and Paul 2019; Fujimori et al. 2020b; 
Böhringer et al. 2021; Budolfson et al. 2021; Soergel et al. 2021b; 
Steckel et al. 2021). Mitigation policies may affect the poorest 
through effects on energy and food prices (Hasegawa et al. 2015; 
Fujimori et al. 2019). Markkanen and Anger-Kraavi (2019) and Lamb 
et al. (2020) synthesize evidence from the existing literature on social 
co-impacts of climate change mitigation policy and their implications 
for inequality. They show that most policies can compound or 
lessen inequalities depending on contextual factors, policy design 
and policy implementation, but that negative inequality impacts of 
climate policies can be mitigated (and possibly even prevented), 
when distributive and procedural justice are taken into consideration

in all stages of policymaking, including policy planning, development 
and implementation, and when focusing on the carbon intensity 
of lifestyles, sufficiency and equity, well-being and decent living 
standards for all (Section 13.6).
Mitigation pathways also affect economic inequalities between 
and within countries, and poverty, through the reduction of climate 
change impacts that fall more heavily on low-income countries, 
communities and households, and exacerbate poverty (AR6 WGII 
Chapters  8 and 16). Higher levels of warming are projected to 
generate higher inequality between countries as well as within them (AR6 WGII Chapter 16). Through avoiding impacts, mitigation thus 
reduces economic inequalities and poverty (high confidence).
A few studies consider both mitigation policies’ distributional impacts 
and avoided climate change impacts on inequalities along mitigation 
pathways. Rezai et al. (2018) find that unmitigated climate change

impacts increase inequality, whereas mitigation has the potential 
to reverse this effect. Considering uncertainty in socio-economic 
assumptions, emission pathways, mitigation costs, temperature 
response, and climate damage, Taconet et al. (2020) show that the 
uncertainties associated with socio-economic assumptions and 
damage estimates are the main drivers of future inequalities between 
countries and that in most cases mitigation policies reduce future 
inequalities between countries. Gazzotti et al. (2021) show that 
inequality persists in 2°C-consistent pathways due to regressivity 
of residual climate damages. However, the evidence on mitigation 
pathways’ implications for global inequality and poverty remains 
limited, and the modelling frameworks used have limited ability to 
fully represent the different dimensions of inequality and poverty and 
all the mechanisms by which mitigation affects inequality and poverty

(Rao et al. 2017a; Emmerling and Tavoni 2021; Jafino et al. 2021).
3.7 Sustainable Development, 
Mitigation and Avoided Impacts
3.7.1 Synthesis Findings on Mitigation 
and Sustainable Development
Rapid and effective climate mitigation is a  necessary part of sustainable 
development (high confidence) (Cross-Chapter Box 5 in Chapter 4), 
but the latter can only be realised if climate mitigation becomes 
integrated with sustainable development policies (high confidence). 
Targeted policy areas must include healthy nutrition, sustainable 
consumption and production, inequality and poverty alleviation, 
air quality and international collaboration (high confidence). 
Lower energy demand enables synergies between mitigation and 
sustainability, with lower reliance on CDR (high confidence).
This section covers the long-term interconnection of sustainable 
development and mitigation, taking forward the holistic vision of 
sustainable development described in the SDGs (Brandi 2015; Leal

Filho et al. 2018). Recent studies have explored the aggregated 
impact of mitigation for multiple sustainable-development 
dimensions (Hasegawa et al. 2014; Bertram et al. 2018; Fuso Nerini 
et al. 2018; Grubler et al. 2018; McCollum et al. 2018b; Soergel et al. 
2021a; van Vuuren et al. 2019). For instance, Figure  3.38 shows 
selected mitigation co-benefits and trade-offs based on a subset 
of models and scenarios, since so far many IAMs do not have 
a comprehensive coverage of SDGs (Rao et al. 2017a; van Soest et al. 
2019). Figure 3.38 shows that mitigation likely leads to increased 
forest cover (SDG 15  – life on land) and reduced mortality from 
ambient PM2.5 pollution (SDG 3  – good health and well-being) 
compared to reference scenarios. However, mitigation policies 
can also cause higher food prices and an increased population at 
risk of hunger (SDG 2  – zero hunger) and relying on solid fuels 
(SDG 3 – good health and well-being; and SDG 7 – affordable and

370
clean energy) as side effects. These trade-offs can be compensated 
through targeted support measures and/or additional sustainable 
development policies (Cameron et al. 2016; Bertram et al. 2018; 
Fujimori et al. 2019; Soergel et al. 2021a).
The synthesis of the interplay between climate mitigation and 
sustainable development is shown in Figure  3.39. Panel a  shows 
the reduction in population affected by climate impacts at  1.5°C 
compared to 3°C according to sustainability domains (Byers et al. 
2018). Reducing warming reduces the population impacted by all 
impact categories shown (high confidence). The left panel does not 
take into account any side effects of mitigation efforts or policies to 
reduce warming: only reductions in climate impacts. This underscores 
that mitigation is an integral basis for comprehensive sustainable 
development (Watts et al. 2015).

Panels b and c of Figure  3.39 show the effects of  1.5°C 
mitigation  policies compared to current national policies: narrow mitigation policies (averaged over several models, middle panel), 
and policies integrating sustainability considerations (right panel of 
Figure  3.39, based on the Illustrative Mitigation Pathway ‘Shifting 
Pathways’ (IMP-SP) (Soergel et al. 2021a)). Note that neither middle 
nor right panels include climate impacts.
Areas of co-benefits include human health, ambient air pollution 
and other specific kinds of pollution, while areas of trade-off include 
food access, habitat loss and mineral resources (medium confidence). 
For example, action consistent with 1.5°C in the absence of energy-
demand reduction measures require large quantities of CDR, which, 
depending on the type used, are likely to negatively impact both food 
availability and areas for biodiversity (Fujimori et al. 2018; Ohashi 
et al. 2019; Roelfsema et al. 2020).

Mitigation to 1.5°C reduces climate impacts on sustainability (left). 
Policies integrating sustainability and mitigation (right) have far 
fewer trade-offs than narrow mitigation policies (middle).
Population relying on solid fuels (million) Premature deaths from PM2.5 (2015 = 1)Forest cover (million ha)(a)
(d) (e)(b) (c) Food prices (2015 = 1) Population at risk of hunger (million)
2020 2030 2040 2050 2020 2030 2040 20502020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 205002505007501000
1.0
0.01.21.41.6
0.500.751.001.251.501.753500400045005000
200030003500
2500
1500
1000Scenario type
reference
1.5°C
IMP-SP (1.5°C + SD)n = 8 n = 8 n = 7
n = 5 n = 7
Boxes show median (thick line) 
and interquartile range across 
scenarios; whiskers extend to 
the minimum/maximum values.
‘n’ is the number of scenarios 
per boxplot in each panel.
Figure 3.38 | Effect of climate change mitigation on different dimensions of sustainable development: shown are mitigation scenarios compatible with

the 1.5°C target (blue) and reference scenarios (yellow). Blue box plots contain scenarios that include narrow mitigation policies from different studies (see below). This 
is compared to a sustainable development scenario (SP, Soergel et al. (2021a), grey diamonds) integrating mitigation and SD policies (e.g., zero hunger in 2050 by assumption). 
Scenario sources for box plots: single scenarios from: (i) Fujimori et al. (2020a); (ii) Soergel et al. (2021a); multi-model scenario set from CD-LINKS (McCollum et al. 2018b; 
Fujimori et al. 2019; Roelfsema et al. 2020). For associated methods, see also Cameron et al. (2016) and Rafaj et al. (2021). The reference scenario for Fujimori et al. (2020a) 
is no-policy baseline; for all other studies, it includes current climate policies. In the ‘Food prices’ and ‘Risk of hunger’ panels, scenarios from CD-LINKS include a price cap of

USD200 tCO 2-eq for land-use emissions (Fujimori et al. 2019). For the other indicators, CD-LINKS scenarios without price cap (Roelfsema et al. 2020) are used due to SDG 
indicator availability. In the ‘Premature deaths’ panel, a well-below 2°C scenario from Fujimori et al. (2020a) is used in place of a 1.5°C scenario due to data availability, and 
all scenarios are indexed to their 2015 values due to a spread in reported levels between models. SDG icons were created by the United Nations.

371
–100%
–50%
0%
50%
100%
150%
200%
400%
600%
Risk of hunger
Premature deaths 
PM 2.5
Human toxicity
N-fertiliser use
Water withdrawals 
for irrigation
Population relying 
upon solid fuels
Fossil-based energy
Mineral resources
Marine ecotoxicity
Marine eutrophication
F
orest cover
–100%
–80%
–60%
–40%
–20%
0%
Crop yield losses
Nitrate leaching
Groundwater stress
Water stress
Cooling 
degree days
Risk to 
power plants
Habitat 
–100%
–50%
0%
(a) Avoided climate impacts
(b) Effects of narrow 
mitigation policies
(c) Effects of sustainable 
development policies
Change relative to 3°C
Change relative 
to current policies
Hunger
Health
Water
Energy
 (land)
 (water)
Res’
C&P
Beneﬁt
Harm
PM 2.5

Energy services
(Dev. Asia & Africa)
Food waste
Carbonate saturation
of oceans (aragonite)
Forest cover
Figure 3.39 | Sustainable development effects of mitigation to 1.5°C. Panel (a): benefits of mitigation from avoided impacts. Panel (b): sustainability co-benefits 
and trade-offs of narrow mitigation policies (averaged over multiple models). Panel (c): sustainability co-benefits and trade-offs of mitigation policies integrating Sustainable 
Development Goals. Scale: 0% means no change compared to 3°C (left) or current policies (middle and right). Blue values correspond to proportional improvements, red values 
to proportional worsening. Note: only the left panel considers climate impacts on sustainable development; the middle and right panels do not. ‘Res’ C&P’ stands for Responsible

Consumption and Production (SDG 12). Data are from Byers et al. (2018) (left), SP/Soergel et al. (2021a) (right). Methods used in middle panel: for biodiversity, Ohashi et al. 
(2019); for ecotoxicity and eutrophication, Arvesen et al. (2018) and Pehl et al. (2017); for energy access, Cameron et al. (2016). ‘Energy services’ on the right is a measure of 
useful energy in buildings and transport. ‘Food prices’ and ‘Risk of hunger’ in the middle panel are the same as in Figure 3.38.

372
3.7.1.1 Policies Combining Mitigation  
and Sustainable Development
These findings indicate that holistic policymaking integrating 
sustainability objectives alongside mitigation will be important in 
attaining Sustainable Development Goals (van Vuuren et al. 2015, 
2018; Bertram et al. 2018; Fujimori et al. 2018; Hasegawa et al. 
2018; Liu et al. 2020a; Honegger et al. 2021; Soergel et al. 2021a). 
Mitigation policies which target direct sector-level regulation, early 
mitigation action, and lifestyle changes have beneficial sustainable 
development outcomes across air pollution, food, energy and water 
(Bertram et al. 2018).
These policies include ones around stringent air quality (Kinney 2018; 
Rafaj et al. 2018; Soergel et al. 2021a); efficient and safe demand-
side technologies, especially cook stoves (Cameron et al. 2016); 
lifestyle changes (Bertram et al. 2018; Grubler et al. 2018; Soergel

et al. 2021a); industrial and sectoral policy (Bertram et al. 2018); 
agricultural and food policies (including food waste) (van Vuuren 
et al. 2019; Soergel et al. 2021a); international cooperation (Soergel 
et al. 2021a); as well as economic policies described in Section 3.6. 
Recent research shows that mitigation is compatible with reductions 
in inequality and poverty (Box 3.6).Lower demand  – for example, for energy and land-intensive 
consumption such as meat  – represents a  synergistic strategy for 
achieving ambitious climate mitigation without compromising 
Sustainable Development Goals (high confidence) (Bertram et al. 
2018; Grubler et al. 2018; van Vuuren et al. 2018; Kikstra et al. 2021b; 
Soergel et al. 2021a). This is especially true for reliance on BECCS 
(Hickel et al. 2021; Keyßer and Lenzen 2021). Options that reduce 
agricultural demand (e.g., dietary change, reduced food waste) can 
have co-benefits for adaptation through reductions in demand for

land and water (Bertram et al. 2018; Grubler et al. 2018; IPCC 2019a; 
Soergel et al. 2021a).
While the impacts of climate change on agricultural output are 
expected to increase the population at risk of hunger, there is 
evidence suggesting population growth will be the dominant 
driver of hunger and undernourishment in Africa in 2050 (Hall et al. 
2017). Meeting SDG 5, relating to gender equality and reproductive 
rights, could substantially lower population growth, leading to 
a  global population lower than the 95% prediction range of the 
UN projections (Abel et al. 2016). Meeting SDG 5 (gender equality, 
including via voluntary family planning (O’Sullivan 2018)) could thus 
minimise the risks to SDG 2 (zero hunger) that are posed by meeting 
SDG 13 (climate action).
Box 3.6 | Poverty and Inequality
There is high confidence (medium evidence, high agreement) that the eradication of extreme poverty and universal access to energy

can be achieved without resulting in significant GHG emissions (Tait and Winkler 2012; Chakravarty and Tavoni 2013; Pachauri et al. 
2013; Pachauri 2014; Rao 2014; Hubacek et al. 2017b; Poblete-Cazenave et al. 2021). There is also high agreement in the literature 
that a focus on well-being and decent living standards for all can reduce disparities in access to basic needs for services concurrently 
with climate mitigation (Section 5.2). Mitigation pathways in which national redistribution of carbon-pricing revenues is combined with 
international climate finance, achieve poverty reduction globally (Fujimori et al. 2020b; Soergel et al. 2021b). Carbon-pricing revenues 
in mitigation pathways consistent with limiting temperature increase to 2°C could also contribute to finance investment needs for 
basic infrastructure (Jakob et al. 2016) and the achievement of the SDGs (Franks et al. 2018).

Several studies conclude that reaching higher income levels globally, beyond exiting extreme poverty, and achieving more qualitative 
social objectives and well-being, are associated with higher emissions (Ribas et al. 2017, 2019; Hubacek et al. 2017b; Fischetti 2018; 
Scherer et al. 2018). Studies give divergent results on the effect of economic inequality reduction on emissions, with either an increase 
or a decrease in emissions (Berthe and Elie 2015; Lamb and Rao 2015; Grunewald et al. 2017; Hubacek et al. 2017a,b; Jorgenson et al. 
2017; Knight et al. 2017; Mader 2018; Rao and Min 2018; Liu et al. 2019; Sager 2019; Baležentis et al. 2020; Liobikienė 2020; Liobikienė 
and Rimkuvienė 2020; Liu et al. 2020b; Millward-Hopkins and Oswald 2021). However, the absolute effect of economic inequality 
reduction on emissions remains moderate, under the assumptions tested. For instance, Sager (2019) finds that a full redistribution

of income leading to equality among US households in a counterfactual scenario for 2009 would raise emissions by 2.3%; and Rao 
and Min (2018) limit to 8% the maximum plausible increase in emissions that would accompany the reduction of the global Gini 
coefficient from its current level of 0.55 to a level of 0.3 by 2050. Similarly, reduced income inequality would lead to a global energy-
demand increase of 7% (Oswald et al. 2021). Reconciling mitigation and inequality reduction objectives requires policies that take into 
account both objectives at all stages of policymaking (Markkanen and Anger-Kraavi 2019), including focusing on the carbon intensity 
of lifestyles (Scherer et al. 2018), attention to sufficiency and equity (Fischetti 2018), and targeting the consumption of the richest and 
highest-emitting households (Otto et al. 2019).
In modelled mitigation pathways, inequality in per-capita emissions between regions are generally reduced over time, and the

reduction is generally more pronounced in lower-temperature pathways (Box 3.6, Figure 1). Already in 2030, if NDCs from the Paris 
Agreement, announced prior to COP26, are fully achieved, inequalities in per-capita GHG emissions between countries would be 
reduced (Benveniste et al. 2018).

373
The remainder of this section covers specific domains of sustainable 
development: food (Section  3.7.2), water (Section  3.7.3), energy 
(Section  3.7.4), health (Section  3.7.5), biodiversity (Section  3.7.6) 
and multi-sector  – cities, infrastructure, industry, production and 
consumption (Section  3.7.7). These represent the areas with the 
strongest research connecting mitigation to sustainable development. 
The links to individual SDGs are given within these sections. Each 
domain covers the benefits of avoided climate impacts and the 
implications (synergies and trade-offs) of mitigation efforts.
3.7.2 Food
The goal of SDG 2 is to achieve ‘zero-hunger’ by 2030. According 
to the UN (2015), over 25% of the global population currently 
experience food insecurity and nearly 40% of these experience severe 
food insecurity, a  situation worsened by the COVID-19 pandemic

(Paslakis et al. 2021).3.7.2.1 Benefits of Avoided Climate Impacts Along 
Climate change will reduce crop yields, increase food insecurity, and 
negatively influence nutrition and mortality (high confidence) (AR6 
WGII Chapter 5). Climate mitigation will thus reduce these impacts, 
and hence reduce food insecurity (high confidence). The yield 
reduction of global food production will increase food insecurity and 
influence nutrition and mortality (Hasegawa et al. 2014; Springmann 
et al. 2016a). For instance, Springmann et al. (2016a) estimate that 
climate change could lead to 315,000–736,000 additional deaths by 
2050, though these could mostly be averted by stringent mitigation 
efforts. Reducing warming reduces the impacts of climate change, 
including extreme climates, on food production and risk of hunger 
(Hasegawa et al. 2014, 2021b).
3.7.2.2 Implications of Mitigation Efforts Along Pathways
Recent studies explore the effect of climate change mitigation on

agricultural markets and food security (Havlík et al. 2014; Hasegawa 
et al. 2018; Doelman et al. 2019; Fujimori et al. 2019). Mitigation 
policies aimed at achieving  1.5°C–2°C, if not managed properly, Box 3.6 (continued)
20
051015tCO2-eq yr–1
2030 2050
YearC1: limit warming 
overshoot
C2: return 
C8: exceed 
(≥50%)
Box 3.6, Figure 1 | Difference in per-capita emissions of Kyoto gases between the highest emitting and the lowest emitting of the 10 regions, 
in 2030 and 2050, by temperature category of pathways.
Through avoiding impacts of climate change, which fall more heavily on low-income countries, communities and households, and 
exacerbate poverty, mitigation reduces inequalities and poverty (Section 3.6.4.2).

374
could negatively affect food security through changes in land and 
food prices (high confidence), leading to increases in the population 
at risk of hunger by 80–280 million people compared to baseline 
scenarios. These studies assume uniform carbon prices on AFOLU 
sectors (with some sectoral caps) and do not account for climate 
impacts on food production.
Mitigating climate change while ensuring that food security is 
not adversely affected requires a range of different strategies and 
interventions (high confidence). Fujimori et al. (2018) explore possible 
economic solutions to these unintended impacts of mitigation 
(e.g.,  agricultural subsidies, food aid, and domestic reallocation of 
income) with an additional small (<0.1%) change in global GDP . 
Targeted food-security support is needed to shield impoverished and 
vulnerable people from the risk of hunger that could be caused by the

economic effects of policies narrowly focussed on climate objectives. 
Introducing more biofuels and careful selection of bioenergy 
feedstocks could also reduce negative impacts (FAO, IFAD, UNICEF , 
WFP and WHO, 2017). Reconciling bioenergy demands with food and 
biodiversity, as well as competition for land and water, will require 
changes in food systems  – agricultural intensification, open trade, 
less consumption of animal products and reduced food losses – and 
advanced biotechnologies (Henry et al. 2018; Xu et al. 2019).
There are many other synergistic measures for climate mitigation and 
food security. Agricultural technological innovation can improve the 
efficiency of land use and food systems, thus reducing the pressure 
on land from increasing food demand (Foley et al. 2011; Popp et al. 
2014; Obersteiner et al. 2016; Humpenöder et al. 2018; Doelman 
et al. 2019). Furthermore, decreasing consumption of animal

products could contribute to SDG 3.4 by reducing the risk of non-
communicable diseases (Garnett 2016).
Taken together, climate changes will reduce crop yields, increase food 
insecurity and influence nutrition and mortality (high confidence) 
(see 3.7.2.1). However, if measures are not properly designed, mitigating 
climate change will also negatively impact on food consumption and 
security. Additional solutions to negative impacts associated with 
climate mitigation on food production and consumption include 
a transition to a sustainable agriculture and food system that is less 
resource intensive, more resilient to a  changing climate, and in line 
with biodiversity and social targets (Kayal et al. 2019).
3.7.3 Water
Water is relevant to SDG 6 (clean water and sanitation), SDG 15 (life 
on land ), and SDG Targets 12.4 and 3.9 (water pollution and health). 
This section discusses water quantity, water quality, and water-

related extremes. See Section 3.7.5 for water-related health effects.
3.7.3.1 Benefits of Avoided Climate Impacts Along 
Global precipitation, evapotranspiration, runoff and water availability 
increase with warming (Hanasaki et al. 2013; Greve et al. 2018) 
(AR6 WGII Chapter 4). Climate change also affects the occurrence of and exposure to hydrological extremes (high confidence) (Arnell 
and Lloyd-Hughes 2014; Asadieh and Krakauer 2017; Dottori 
et al. 2018; Naumann et al. 2018; IPCC 2019a; Do et al. 2020) (AR6 
WGII Chapter 4). Climate models project increases in precipitation 
intensity (high confidence), local flooding (medium confidence), and 
drought risk (very high confidence) (Arnell and Lloyd-Hughes 2014; 
Asadieh and Krakauer 2017; Dottori et al. 2018; IPCC 2019a) (AR6 
WGII Chapter 4).
The effect of climate change on water availability and hydrological 
extremes varies by region (high confidence) due to differences in the

spatial patterns of projected precipitation changes (Hanasaki et al. 
2013; Schewe et al. 2014; Schlosser et al. 2014; Asadieh and Krakauer 
2017; Dottori et al. 2018; Naumann et al. 2018; Koutroulis et al. 2019) 
(AR6 WGII Chapter 4). Global exposure to water stress is projected to 
increase with increased warming, but increases will not occur in all 
regions (Hanasaki et al. 2013; Schewe et al. 2014; Arnell and Lloyd-
Hughes 2014; Gosling and Arnell 2016; IPCC 2019a).
Limiting warming could reduce water-related risks (high confidence) 
(O’Neill et al. 2017b; Byers et al. 2018; Hurlbert et al. 2019) (AR6 WGII 
Chapter  4) and the population exposed to increased water stress 
(Hanasaki et al. 2013; Arnell and Lloyd-Hughes 2014; Schewe et al. 
2014; Gosling and Arnell 2016; IPCC 2019a).
The effect of climate change on water depends on the climate model, 
the hydrological model, and the metric (high confidence) stress 
Hanasaki et al. (2013); Arnell and Lloyd-Hughes (2014); Schewe

et al. (2014); Schlosser et al. (2014); Gosling and Arnell (2016); 
IPCC (2019a).
However, the effect of socio-economic development could be larger 
than the effect of climate change (high confidence) (Arnell and Lloyd-
Hughes 2014; Schlosser et al. 2014; Graham et al. 2020).
Climate change can also affect water quality (both thermal and 
chemical) (Liu et al. 2017), leading to increases in stream temperature 
and nitrogen loading in rivers (Ballard et al. 2019).
3.7.3.2 Implications of Mitigation Efforts Along Pathways
The effects of mitigation on water demand depends on the mitigation 
technologies deployed (high confidence) (Chaturvedi et al. 2013a,b; 
Hanasaki et al. 2013; Kyle et al. 2013; Hejazi et al. 2014; Bonsch et al. 
2016; Jakob and Steckel 2016; Mouratiadou et al. 2016; Fujimori 
et al. 2017; Maïzi et al. 2017; Bijl et al. 2018; Cui et al. 2018; Graham 
et al. 2018; Parkinson et al. 2019). Some mitigation options could

increase water consumption (volume removed and not returned) 
while decreasing withdrawals (total volume of water removed, 
some of which may be returned) (Kyle et al. 2013; Fricko et al. 2016; 
Mouratiadou et al. 2016; Parkinson et al. 2019). Bioenergy and 
BECCS can increase water withdrawals and water consumption (high 
confidence) (Chaturvedi et al. 2013a; Kyle et al. 2013; Hejazi et al. 
2014; Bonsch et al. 2016; Jakob and Steckel 2016; Mouratiadou et al. 
2016; Fujimori et al. 2017; Maïzi et al. 2017; Séférian et al. 2018; 
Yamagata et al. 2018; Parkinson et al. 2019) (AR6 WGII Chapter 4). 
DACCS (Fuhrman et al. 2020) and CCS (Kyle et al. 2013; Fujimori

375
et al. 2017) could increase water demand; however, the implications 
of CCS depend on the cooling technology and when capture occurs 
(Magneschi et al. 2017; Maïzi et al. 2017; Giannaris et al. 2020). 
Demand-side mitigation (e.g., dietary change, reduced food waste, 
reduced energy demand) can reduce water demand (Bajželj et al. 
2014; Aleksandrowicz et al. 2016; Green et al. 2018; Springmann 
et al. 2018). Introducing specific measures (e.g., environmental flow 
requirements, improved efficiency, priority rules) can reduce water 
withdrawals (Bertram et al. 2018; Bijl et al. 2018; Parkinson et al. 2019).
The effect of mitigation on water quality depends on the mitigation 
option, its implementation, and the aspect of quality considered 
(high confidence) (Ng et al. 2010; Flörke et al. 2019; Sinha et al. 
2019; Smith et al. 2019; Fuhrman et al. 2020; Karlsson et al. 2020; 
McElwee et al. 2020).
3.7.4 Energy

Energy is relevant to SDG 7 (affordable and clean energy). Access 
to sufficient levels of reliable, affordable and renewable energy is 
essential for sustainable development. Currently, over 1 billion people 
still lack access to electricity (Ribas et al. 2019).
3.7.4.1 Benefits of Avoided Climate Impacts Along 
Climate change alters the production of energy through changes 
in temperature (hydropower, fossil fuel, nuclear, solar, bioenergy, 
transmission and pipelines), precipitation (hydropower, fossil fuel, 
nuclear and bioenergy), windiness (wind and wave), and cloudiness 
(solar) (high confidence). Increases in temperature reduce efficiencies 
of thermal power plants (e.g.,  fossil fuel and nuclear plants) with 
air-cooled condensers by  0.4–0.7% per °C increase in ambient 
temperature (Cronin et al. 2018a; Simioni and Schaeffer 2019; 
Yalew, S.G. et al. 2020). Potentials and costs for renewable energy

technologies are also affected by climate change, though with 
considerable regional variation and uncertainty (Gernaat et al. 2021). 
Biofuel yields could increase or decrease depending on the level of 
warming, changes in precipitation, and the effect of CO 2 fertilisation 
(Calvin et al. 2013; Kyle et al. 2014; Gernaat et al. 2021). Coastal 
energy facilities could potentially be impacted by sea level rise 
(Brown et al. 2014).
The energy sector uses large volumes of water (Fricko 
et al. 2016), making it highly vulnerable to climate change (Tan and 
Zhi 2016) (high confidence). Thermoelectric and hydropower sources 
are the most vulnerable to water stress (van Vliet et al. 2016). 
Restricted water supply to these power sources can affect grid security 
and affordable energy access (Koch et al. 2014; Ranzani et al. 2018; 
Zhang et al. 2018d).The hydropower facilities from high mountain 
areas of Central Europe, Iceland, Western USA/Canada, and Latin

America (Hock et al. 2019), as well as Africa and China (Bartos and 
Chester 2015; Gaupp et al. 2015; Tarroja et al. 2016; Conway et al. 
2017; Byers et al. 2018; Eyer and Wichman 2018; Ranzani et al. 2018; 
Savelsberg et al. 2018; Zhang et al. 2018d; Zhou et al. 2018; Wang 
et al. 2019) have experienced changes in seasonality and availability.3.7.4.2 Implications of Mitigation Efforts Along Pathways
Extending energy access to all in line with SDG7 is compatible 
with strong mitigation consistent with the Paris Agreement (high 
confidence). The Low Energy Demand (LED) scenario projects that 
these twin goals can be achieved by relying heavily on energy 
efficiency and rapid social transformations (Grubler et al. 2018). 
The IEA’s Sustainable Development Scenario (IEA 2020a) achieves 
development outcomes but with higher average energy use, and 
bottom-up modelling suggests that decent living standards could be 
provided to all in 2040–2050 with roughly 150 EJ, or 40% of current

final energy use (Millward-Hopkins et al. 2020; Kikstra et al. 2021b). 
The trade-offs between climate mitigation and increasing energy 
consumption of the world’s poorest are negligible (Rao and Min 
2018; Scherer et al. 2018).
The additional energy demand to meet the basic cooling requirement 
in the Global South is estimated to be much larger than the 
electricity needed to provide basic residential energy services 
universally via clean and affordable energy, as defined by SDG 7 (IEA 
2019; Mastrucci et al. 2019) (high confidence). If conventional air-
conditioning systems are widely deployed to provide cooling, energy 
use could rise significantly (van Ruijven et al. 2019; Bezerra et al. 
2021; Falchetta and Mistry 2021), thus creating a positive feedback 
further increasing cooling demand. However, the overall emissions 
are barely altered by the changing energy demand composition with 
reductions in heating demand occurring simultaneously (Isaac and

van Vuuren 2009; Labriet et al. 2015; McFarland et al. 2015; Clarke 
et al. 2018). Some mitigation scenarios show price increases of clean 
cooking fuels, slowing the transition to clean cooking fuels (SDG 7.1) 
and leaving a billion people in 2050 still reliant on solid fuels in South 
Asia (Cameron et al. 2016).
In contrast, future energy infrastructure could improve reliability, thus 
lowering dependence on high-carbon, high-air pollution back-up diesel 
generators (Farquharson et al. 2018) that are often used to cope with 
unreliable power in developing countries (Maruyama Rentschler et al. 
2019). There can be significant reliability issues where mini-grids are 
used to electrify rural areas (Numminen and Lund 2019). A  stable, 
sustainable energy transition policy that considers national sustainable 
development in the short and long term is critical in driving a transition 
to an energy future that addresses the trilemma of energy security,

equity, and sustainability (La Viña et al. 2018).
3.7.5 Health
SDG 3 (good health and well-being) aims to ensure healthy lives and 
promote well-being for all at all ages. Climate change is increasingly 
causing injuries, illnesses, malnutrition, threats to mental health 
and well-being, and deaths (AR6 WGII Chapter 7). Mitigation policies 
and technologies to reduce GHG emissions are often beneficial 
for human health on a  shorter time scale than benefits in terms 
of slowing climate change (Limaye et al. 2020). The financial value 
of health benefits from improved air quality alone is projected to 
exceed the costs of meeting the goals of the Paris Agreement 
(Markandya et al. 2018).

376
3.7.5.1 Benefits of Avoided Climate Impacts Along 
The human health chapter of the WGII contribution to the AR6 
concluded that climate change is increasingly affecting a  growing 
number of health outcomes, with negative net impacts at the global 
scale and positive impacts only in a few limited situations. There are 
few estimates of economic costs of increases in climate-sensitive 
health outcomes. In the USA in 2012, the financial burden in terms 
of deaths, hospitalisations, and emergency department visits for 
ten climate-sensitive events across 11 states were estimated to be 
10 (2.7–24.6) billion USD2018 (Limaye et al. 2019).
3.7.5.2 Implications of Mitigation Efforts Along Pathways
Transitioning toward equitable, low-carbon societies has multiple 
co-benefits for health and well-being (AR6 WGII Chapter 7). Health 
benefits can be gained from improvements in air quality through

transitioning to renewable energy and active transport (e.g., walking 
and cycling); shifting to affordable low-meat, plant-rich diets; and 
green buildings and nature-based solutions, such as green-and-blue 
urban infrastructure, as shown in Figure 3.40 (Iacobucci 2016).The avoided health impacts associated with climate change mitigation 
can substantially offset mitigation costs at the societal level (Ščasný 
et al. 2015; Schucht et al. 2015; Chang et al. 2017; Markandya et al. 
2018). Models of health co-benefits show that a 1.5°C pathway could 
result in 152 million ± 43 million fewer premature deaths worldwide 
between 2020 and 2100 in comparison to a  business-as-usual 
scenario, particularly due to reductions in exposure to PM2.5 (Shindell 
et al. 2018; Rauner et al. 2020a; Rafaj et al. 2021). Some of the most 
substantial health, well-being, and equity benefits associated with 
climate action derive from investing in basic infrastructure: sanitation,

clean drinking water, clean energy, affordable healthy diets, clean 
public transport, and improved air quality from transformative 
solutions across economic sectors including agriculture, energy, 
transport and buildings (Chang et al. 2017).
The health co-benefits of the NDCs for 2040 were compared for 
two scenarios, one consistent with the goal of the Paris Agreement 
and the SDGs and the other also placing health as a  central focus 
of the policies (i.e., health in all climate policies scenario) (Hamilton 
et al. 2021), for Brazil, China, Germany, India, Indonesia, Nigeria, 
South Africa, the UK, and the USA. Modelling of the energy, food 
and agriculture, and transport sectors, and associated risk factors 
Figure 3.40 | Diagram showing the co-benefits between health and mitigation.  Source:  with permission from Iacobucci 2016.

377
related to mortality, suggested the sustainable pathways scenario 
could result in annual reductions of 1.18 million air pollution-related 
deaths, 5.86 million diet-related deaths, and 1.15 million deaths due to 
physical inactivity. Adopting the more ambitious health in all climate 
policies scenario could result in further reductions of 462,000 annual 
deaths attributable to air pollution, 572,000 annual deaths attributable 
to diet, and 943,000 annual deaths attributable to physical inactivity. 
These benefits were attributable to the mitigation of direct GHG 
emissions and the commensurate actions that reduce exposure to 
harmful pollutants, as well as improved diets and safe physical activity.
Cost-benefit analyses for climate mitigation in urban settings that do 
not account for health may underestimate the potential cost savings 
and benefits (Hess et al. 2020). The net health benefits of controlling

air pollution as part of climate mitigation efforts could reach trillions 
of dollars annually, depending on the air quality policies adopted 
globally (Markandya et al. 2018; Scovronick et al. 2019b). Air pollution 
reductions resulting from meeting the Paris Agreement targets 
were estimated to provide health co-benefits-to-mitigation ratios 
of between 1.4 and 2.5 (Markandya et al. 2018). In Asia, the benefit of 
air pollution reduction through mitigation measures was estimated to 
reduce premature mortality by 0.79 million, with an associated health 
benefit of USD2.8 trillion versus mitigation costs of USD840 billion, 
equating to 6% and 2% of GDP , respectively (Xie et al. 2018). Similarly, 
stabilising radiative forcing to  3.4 W  m–2 in South Korea could 
cost USD1.3–8.5 billion in 2050 and could lead to a USD23.5 billion 
cost reduction from the combined benefits of avoided premature 
mortality, health expenditures, and lost work hours (Kim et al. 2020).

The health co-benefits related to physical exercise and reduced air 
pollution largely offset the costs of implementing low-CO 2-emitting 
urban mobility strategies in three Austrian cities (Wolkinger et al. 2018).
Just in the USA, over the next 50 years, a  2°C pathway could 
prevent roughly  4.5 million premature deaths, about  3.5 million 
hospitalisations and emergency room visits, and approximately 
300 million lost workdays (Shindell 2020). The estimated yearly 
benefits of USD700 billion were more than the estimated cost of the 
energy transition.
3.7.6 Biodiversity (Land and Water)
Biodiversity covers life below water (SDG 14) and life on land 
(SDG  15). Ecosystem services are relevant to the goals of zero 
hunger (SDG 2), good health and well-being (SDG 3), clean water 
and sanitation (SDG 6) and responsible consumption and production 
(SDG 12), as well as being essential to human existence (IPBES 2019).
3.7.6.1 Benefits of Avoided Climate Impacts Along 

Terrestrial and freshwater aquatic ecosystems
Climate change is a major driver of species extinction and terrestrial 
and freshwater ecosystems destruction (high confidence) (AR6 WGII 
Chapter 2). Analysis shows that approximately half of all species with 
long-term records have shifted their ranges in elevation and about two thirds have advanced their timing of spring events (Parmesan and 
Hanley 2015). Under 3.2°C warming, 49% of insects, 44% of plants 
and 26% of vertebrates are projected to be at risk of extinction. At  
2°C, this falls to 18% of insects, 16% of plants  and  8%  of  vertebrates  and 
at 1.5°C, to 6% of insects, 8% of plants and 4% of vertebrates (Warren 
et al. 2018). Incidents of migration of invasive species, including pests 
and diseases, are also attributable to climate change, with negative 
impacts on food security and vector-borne diseases. Moreover, if 
climate change reduces crop yields, cropland may expand – a primary

driver of biodiversity loss – in order to meet food demand (Molotoks 
et al. 2020). Land restoration and halting land degradation under all 
mitigation scenarios has the potential for synergy between mitigation 
Marine and coastal ecosystems
Marine ecosystems are being affected by climate change and growing 
non-climate pressures including temperature change, acidification, 
land-sourced pollution, sedimentation, resource extraction and 
habitat destruction (high confidence) (Bindoff et al. 2019; IPCC 2019b). 
The impacts of climate drivers and their combinations vary across 
taxa (AR6 WGII Chapter 3). The danger or warming and acidification 
to coral reefs, rocky shores and kelp forests is well established 
(high confidence) (AR6 WGII Chapter 3). Migration towards optimal 
thermal and chemical conditions (Burrows et al. 2019) contributes to 
large-scale redistribution of fish and invertebrate populations, and

major impacts on global marine biomass production and maximum 
sustainable yield (Bindoff et al. 2019).
3.7.6.2 Implications of Mitigation Efforts Along Pathways
Mitigation measures have the potential to reduce the progress 
of negative impacts on ecosystems, although it is unlikely that all 
impacts can be mitigated (high confidence) (Ohashi et al. 2019). 
The specifics of mitigation achievement are crucial, since large-
scale deployment of some climate mitigation and land-based CDR 
measures could have deleterious impacts on biodiversity (Santangeli 
et al. 2016; Hof et al. 2018).
Climate change mitigation actions to reduce or slow negative impacts 
on ecosystems are likely to support the achievement of SDGs 2, 3, 6, 
12, 14 and 15. Some studies show that stringent and constant GHG 
mitigation practices bring a net benefit to global biodiversity even 
if land-based mitigation measures are also adopted (Ohashi et al. 
2019), as opposed to delayed action which would require much more

widespread use of BECCS. Scenarios based on demand reductions 
of energy and land-based production are expected to avoid many 
such consequences, due to their minimised reliance on BECCS (Conijn 
et al. 2018; Grubler et al. 2018;  Bowles et al. 2019; Soergel et al. 
2021a). Stringent mitigation that includes reductions in demand for 
animal-based foods and food waste could also relieve pressures on 
land use and biodiversity (high confidence), both directly by reducing 
agricultural land requirements (Leclère et al. 2020) and indirectly by 
reducing the need for land-based CDR (van Vuuren et al. 2018).
As environmental conservation and sustainable use of the Earth’s 
terrestrial species and ecosystems are strongly related, recent studies

378
have evaluated interconnections among key aspects of land and show 
a pathway to the global sustainable future of land (Popp et al. 2014; 
Erb et al. 2016; Obersteiner et al. 2016; Humpenöder et al. 2018). Most 
studies agree that many biophysical options exist to achieve global 
climate mitigation and sustainable land use in future. Conserving 
local biodiversity requires careful policy design in conjunction with 
land-use regulations and societal transformation in order to minimise 
the conversion of natural habitats.
3.7.7 Cities and Infrastructure
This subsection focuses upon SDG 9 (industry, innovation and 
infrastructure) and SDG 11 (sustainable cities and communities).
3.7.7.1 Benefits of Avoided Climate Impacts Along 
By 2100, urban population will be almost double and more urban 
areas will be built (Jiang and O’Neill 2017), although COVID-19 may

modify these trends (Kii 2021). Urbanisation will amplify projected air 
temperature changes in cities, including amplifying heatwaves (AR6 
WGI Chapter 10, Box 10.3). Benefits of climate mitigation in urban 
areas include reducing heat, air pollution and flooding. Industrial 
infrastructure and production-consumption supply networks also 
benefit from avoided impacts.
3.7.7.2 Implications of Mitigation Efforts Along Pathways
Many co-benefits to urban mitigation actions (Chapter  8, 
Section  8.2.1) improve the liveability of cities and contribute to 
achieving SDG 11. In particular, compact urban form, efficient 
technologies and infrastructure can play a valuable role in mitigation 
by reducing energy demand (Creutzig et al. 2016; Güneralp et al. 
2017), thus averting carbon lock-in, while reducing land sprawl and 
hence increasing carbon storage and biodiversity (D’Amour et al. 
2017). Benefits of mitigation include air quality improvements from

decreased traffic and congestion when private vehicles are displaced 
by other modes; health benefits from increases in active travel; and 
lowered urban heat island effects from green-blue infrastructures 
(Section 8.2.1).
However, increasing urban density or enlarging urban green spaces 
can increase property prices and reduce affordability (Section 8.2.1). 
Raising living conditions for slum dwellers and people living in 
informal settlements will require significant materials and energy; 
however, regeneration can be conducted in ways that avoid carbon-
intense infrastructure lock-in (Chapters 8 and 9). Cities affect other 
regions through supply chains (Marinova et al. 2020).
Sustainable production, consumption and management of natural 
resources are consistent with, and necessary for, mitigation 
(Chapters 5 and 11). Demand-side measures can lower requirements 
for upstream material and energy use (Chapter  5). In terms of

industrial production, transformational changes across sectors will 
be necessary for mitigation (Sections 11.3 and 11.4).Addressing multiple SDG arenas requires new systemic thinking 
in the areas of governance and policy, such as those proposed by 
Sachs et al. (2019).
3.8 Feasibility of Socio/Techno/Economic 
The objective of this section is to discuss concepts of feasibility in 
the context of the low-carbon transition and pathways. We aim to 
identify drivers of low-carbon scenarios feasibility and to highlight 
enabling conditions which can ameliorate feasibility concerns.
3.8.1 Feasibility Frameworks for the Low-carbon 
Transition and Scenarios
Effectively responding to climate change and achieving sustainable 
development requires overcoming a series of challenges to transition 
away from fossil-based economies. Feasibility can be defined in many 
ways (Chapter 1). The political science literature (Majone 1975a,b;

Gilabert and Lawford-Smith 2012) distinguishes the feasibility of 
‘what’ (i.e., emission reduction strategies), ‘when and where’ (i.e., in 
the year 2050, globally) and ‘whom’ (i.e.,  cities). It distinguishes 
desirability from political feasibility (von Stechow et al. 2015): the 
former represents a normative assessment of the compatibility with 
societal goals (i.e., SDGs), while the latter evaluates the plausibility of 
what can be attained given the prevailing context of transformation 
(Nielsen et al. 2020). Feasibility concerns are context and time 
dependent and malleable: enabling conditions can help overcome 
them. For example, public support for carbon taxes has been hard to 
secure but appropriate policy design and household rebates can help 
dissipate opposition (Murray and Rivers 2015; Carattini et al. 2019).
Regarding scenarios, the feasibility ‘what’ question is the one most 
commonly dealt with in the literature, though most of the studies

have focused on expanding low-carbon system, and yet political 
constraints might arise mostly from phasing out fossil fuel-based 
ones (Spencer et al. 2018; Fattouh et al. 2019). The ‘when and where’ 
dimension can also be related to the scenario assessment, but only 
insofar that the models generating them can differentiate time and 
geographical contextual factors. Distinguishing mitigation potential 
by regional institutional capacity has a significant influence on the 
costs of stabilising climate (Iyer et al. 2015c). The ‘whom’ question 
is the most difficult to capture by scenarios, given the multitude 
of actors involved as well as their complex interactions. The focus of 
socio-technical transition sciences on the co-evolutionary processes 
can shed light on the dynamics of feasibility (Nielsen et al. 2020).
The when-where-whom distinction allows depicting a  feasibility 
frontier beyond which implementation challenges prevent mitigation

action (Jewell and Cherp 2020). Even if the current feasibility frontier 
appears restraining in some jurisdictions, it is context-dependent and 
dynamic as innovation proceeds and institutional capacity builds up 
(Nielsen et al. 2020). The question is whether the feasibility frontier 
can move faster than the pace at which the carbon budget is being

379
exhausted. Jewell et al. (2019) show that the emission savings from 
the pledges of premature retirement of coal plants is 150 times less 
than globally committed emissions from existing coal power plants. 
The pledges come from countries with high institutional capacity 
and relatively low shares of coal in electricity. Other factors currently 
limiting the capacity to steer transitions at the necessary speed 
include the electoral-market orientation of politicians (Willis 2017), 
the status-quo orientation of senior public officials (Geden 2016), 
path dependencies created by ‘instrument constituencies’ (Béland 
and Howlett 2016), or the impacts of deliberate inconsistencies 
between talk, decisions and actions in climate policy (Rickards et al. 
2014). All in all, a  number of different delay mechanisms in both 
science and policy have been identified to potentially impede climate

goal achievement (Karlsson and Gilek 2020) (Chapter 13).
In addition to its contextual and dynamic nature, feasibility is a multi-
dimensional concept. The IPCC SR1.5 distinguishes six dimensions 
of feasibility: geophysical, environmental-ecological, technological, 
economic, socio-cultural and institutional. At the individual option 
level, different mitigation strategies face various barriers as well as 
enablers (see Chapter 6 for the option-level assessment). However, 
a systemic transformation involves interconnections of a wide range 
of indicators. Model-based assessments are meant to capture the 
integrative elements of the transition and of associated feasibility 
challenges. However, the translation of model-generated pathways 
into feasibility concerns (Rogelj et al. 2018b) has developed 
only recently. Furthermore, multiple forms of knowledge can be 
mobilised to support strategic decision-making and complement

scenario analysis (Turnheim and Nykvist 2019). We discuss both 
approaches next.
3.8.2 Feasibility Appraisal of Low-carbon Scenarios
Evaluating the feasibility of low-carbon pathways can take different 
forms. In the narrowest sense, there is feasibility pertaining the 
reporting of model-generated scenarios: here an infeasible scenario 
is one which cannot meet the constraints embedded implicitly or 
explicitly in the models which attempted to generate it. Second, 
there is a  feasibility that relates to specific elements or overall 
structure characterising the low-carbon transition compared to some 
specified benchmark.
3.8.2.1  Model Solvability
In order to be generated, scenarios must be coherent with 
the constraints and assumptions embedded in the models 
(i.e.,  deployment potential of given technologies, physical and 
geological limits) and in the scenario design (i.e.,  carbon budget). 
Sometimes, models cannot solve specific scenarios. This provides

a  first, coarse indication of feasibility concerns. Specific vetting 
criteria can be imposed, such as carbon-price values above which 
scenarios should not be reported, as in Clarke et al. (2009). However, 
model solvability raises issues of aggregation in model ensembles. 
Since model solving is not a random process, but a function of the 
characteristics of the models, analysing only reported outcomes leads 
to statistical biases (Tavoni and Tol 2010).Although model-feasibility differs distinctly from feasibility in the 
real world, it can indicate the relative challenges of low-carbon 
scenarios  – primarily when performed in a  model ensemble of 
sufficient size. Riahi et al. (2015) interpreted infeasibility across 
a large number of models as an indication of increased risk that the 
transformation may not be attainable due to technical or economic 
concerns. All models involved in a model comparison of 1.5°C targets

(Rogelj et al. 2018b) (Table S1) were able to solve under favourable 
underlying socio-economic assumptions (SSP1), but none for the 
more challenging SSP3. This interpretation of feasibility was used 
to highlight the importance of socio-economic drivers for attaining 
climate stabilisation. Gambhir et al. (2017) constrained the models 
to historically observed rates of change and found that it would 
no longer allow to solve for 2°C, highlighting the need for rapid 
technological change.
3.8.2.2 Scenario Feasibility
Evaluating the feasibility of scenarios involves several steps 
(Figure  3.41). First, one needs to identify which dimensions of 
feasibility to focus on. Then, for each dimension, one needs to 
select relevant indicators for which sufficient empirical basis exists 
and which are an output of models (or at least of a  sufficient 
number of them). Then, thresholds marking different levels of 
feasibility concerns are defined based on available literature,

expert elicitations and empirical analysis based on appropriately 
chosen historical precedents. Finally, scenario feasibility scores are 
obtained for each indicator, and where needed aggregated up in 
time or dimensions, as a  way to provide an overall appraisal of 
feasibility trade-offs, depending on the timing, disruptiveness and 
scale of transformation.
Most of the existing literature has focused on the technological 
dimensions, given the technology focus of models and the ease of 
comparison. The literature points to varied findings. Some suggest that 
scenarios envision technological progress consistent with historical 
benchmarks (Wilson et al. 2013; Loftus  et al. 2015). Others that 
scenarios exceed historically observed rates of low-carbon technology 
deployment and of energy demand transformation globally (van der 
Zwaan et al. 2013; Napp et al. 2017; Cherp et al. 2021; Semieniuk 
et al. 2021), but not for all countries (Cherp et al. 2021). The reason for

these discrepancies depends on the unit of analysis and the indicators 
used. Comparing  different kinds of historical indicators, (van Sluisveld 
et al. 2015) find that indicators that look into the absolute change of 
energy systems remain within the range of historical growth frontiers 
for the next decade, but increase to unprecedented levels before mid-
century. Expert assessments provide another way of benchmarking 
scenarios, though they have shown to be systematically biased 
(Wiser et al. 2021) and to underperform empirical methods (Meng 
et al. 2021). van Sluisveld et al. (2018a) find that scenarios and 
experts align for baseline scenarios but differ for low-carbon ones. 
Scenarios rely more on conventional technologies based on existing 
infrastructure (such as nuclear and CCS) than what is forecasted by 
experts. Overall, the technology assessment of the feasibility space 
highlights that Paris-compliant transformations would have few

380
Step 1
dimensions
geophysical
economic
socio-culturalStep 2
Indicators
For each dimension, 
selection of relevant 
indicators measuring 
decadal changes 
(among indicators 
available or 
computable based on 
scenario set)Step 3
Thresholds
Categorisation of 
level of feasibility 
concern for each 
indicator in each 
decade based on 
thresholds deﬁned 
based on the literature 
and available 
empirical data
– 3 high
– 2 medium
– 1 lowStep 4
Aggregation 
(geometric mean)
Aggregation within 
each dimension
Aggregation across 
dimensions at different 
points in time
dimensions and 
across timeallows assessing 
tradeoffs among 
feasibility dimensions
allows assessing 
the timing and 
disruptiveness of 
the transformation
the scale of the 

Figure 3.41 | Steps involved in evaluating the feasibility of scenarios.  Source: adapted with permission from Brutschin et al. 2021.
Benchmarking to available evidence
Trajectories from scenario data
Unprecedented:
Unprecedented and 
speculative rate of 
Best case scenario:
Could be plausibly 
extrapolated based 
on the current state 
of knowledge
Plausible:
Documented in 
the literature
TimeFeasibility dimensionsInstitutional
Economic
GeophysicalSpeed of mitigation compared 
with institutional capacity
Energy demand changes
Dietary changes
Wind, solar, nuclear
BECCS
Fossil CCS
GDP loss
Carbon Price 
Investments
Stranded coal assets
Biomass potential
Wind potential
Solar potential
Figure  3.42 | Example of multi-dimensional feasibility analysis and indicators used in the IPCC AR6 scenarios.  The approach defines relevant indicators

characterising the key dimensions of feasibility. Indicators capture the timing, scale and disruptiveness challenges. Low-, medium- and high-feasibility concerns are defined 
based on historical trends and available literature. Details about indicator and threshold values can be found in Annex III.II.2.3.

381
Ø
*
+
*
+
Ø
*
+
Ø
*
*
Ø
Ø
+
+
Figure 3.43 | Feasibility characteristics of the Paris-consistent scenarios in the AR6 scenarios database : Feasibility corridors for the AR6 scenarios 
database, applying the methodology by (Brutschin et al.  2021).  (a) The fraction of scenarios falling within three categories of feasibility concerns (plausible, best case, 
unprecedented), for different times (2030, 2050, 2100), different climate categories consistent with the Paris Agreement and five dimensions. (b) Composite feasibility score 
(obtained by geometric mean of underlying indicators) over time for scenarios with immediate and delayed global mitigation efforts, for different climate categories (C1, C2, C3. 
Note: no C1 scenario has delayed participation). (c) The fraction of scenarios which in any point in time over the century exceed the feasibility concerns, for C1 and C3 climate

categories. Overlayed are the Illustrative Mitigation Pathways (IMP-LP, IMP-SP, IMP-Ren: C1 category; IMP-Neg, IMP-GS: C3 category).

382
Recent approaches have addressed multiple dimensions of feasibility, 
an important advancement since social and institutional aspects 
are as, if not more, important than technology ones (Jewell and 
Cherp 2020). Feasibility corridors of scenarios based on their scale, 
rate of change and disruptiveness have been identified (Kriegler 
et al. 2018b; Warszawski et al. 2021). The reality check shows that 
many  1.5°C-compatible scenarios violate the feasibility corridors. 
The ones that didn’t are associated with a greater coverage of the 
available mitigation levers (Warszawski et al. 2021).
Brutschin et al. (2021) proposed an operational framework covering 
all six dimensions of feasibility. They developed a  set of multi-
dimensional metrics capturing the timing, disruptiveness and the scale 
of the transformative change within each dimension (as in Kriegler

et al. 2018b). Thresholds of feasibility risks of different intensity are 
obtained through the review of the relevant literature and empirical 
analysis of historical data. Novel indicators include governance 
levels (Andrijevic et al. 2020a). The 17 bottom-up indicators are then 
aggregated up across time and dimension, as a  way to highlight 
feasibility trade-offs. Aggregation is done via compensatory 
approaches such as the geometric mean. This is employed, for 
instance, for the Human Development Index. A conceptual example 
of this approach as applied to the IPCC AR6 scenarios database is 
shown in Figure 3.42 and further described in the Annex III.II.2.3.
In Figure  3.43, we show the results of applying the methodology 
of Brutschin et al. (2021) to the AR6 scenarios database. The 
charts highlight the dynamic nature of feasibility risks, which are 
mostly concentrated in the decades before mid-century except

for geophysical risks driven by CO 2 removals later in the century. 
Different dimensions pose differentiated challenges: for example, 
institutional feasibility challenges appear to be the most relevant, in 
line with the qualitative literature. Thus, feasibility concerns might be 
particularly relevant in countries with weaker institutional capacity. 
Figure 3.43 also highlights the key roles of policy and technology as 
enabling factors. In particular (panel b), internationally coordinated 
and immediate emission reductions allow to smooth out feasibility 
concerns and reduce long-term challenges compared to delayed 
policy action, as a  result of a  more gradual transition and lower 
requirements of CO 2 removals. For the same climate objective, 
different Illustrative Mitigation Pathways entail somewhat different 
degrees and distributions of implementation challenges (panel c).
3.8.3 Feasibility in Light of Socio-technical Transitions

The limitations associated with quantitative low-carbon transition 
pathways stem from a  predominant reliance on techno-economic 
considerations with a  simplified or non-existent representation 
of the socio-political and institutional agreement. Accompanying 
the required deployment of low-carbon technologies will be the 
formation of new socio-technical systems (Bergek et al. 2008). With 
a  socio-technical system being defined as a  cluster of elements 
comprising of technology, regulation, user practices and markets, 
cultural meaning, infrastructure, maintenance networks, and supply 
networks (Hofman et al. 2004; Geels and Geels 2005); the inter-
relationship between technological systems and social systems must be comprehensively understood. It is of vital importance that the 
process of technical change must be considered in its institutional 
and social context so as to ascertain potential transition barriers

which in turn provide an indication of pathway feasibility. In order 
to address the multitudinous challenges associated with low-
carbon transition feasibility and governance, it has been opined 
that the robustness of evaluating pathways may be improved by the 
bridging of differing quantitative-qualitative analytical approaches 
(Haxeltine et al. 2008; Foxon et al. 2010; Hughes 2013; Wangel et al. 
2013; Li et al. 2015; Turnheim et al. 2015; Geels et al. 2016a,b, 2020; 
Moallemi et al. 2017; De Cian et al. 2020; Li and Strachan 2019). The 
rationale for such analytical bridging is to rectify the issue that in 
isolation each disciplinary approach can only generate a fragmented 
comprehension of the transition pathway with the consequence 
being an incomplete identification of associated challenges in terms 
of feasibility. Concerning low-carbon transition pathways generated 
by IAMs, it has been argued that a comprehensive analysis should

include social scientific enquiry (Geels et al. 2016a, 2020; van Sluisveld 
et al. 2018b). The normative analysis of IAM pathways assists in the 
generation of a vision or the formulation of a general plan with this 
being complemented by socio-technical transition theory (Geels et al. 
2016a). Such an approach thereby allowing for the socio-political 
feasibility and the social acceptance and legitimacy of low-carbon 
options to be considered. Combining computer models and the multi-
level perspective can help identify ‘transition bottlenecks’ (Geels 
et al. 2020). Similarly, increased resolution of integrated assessment 
models’ actors has led to more realistic narratives of transition in terms 
of granularity and behaviour (McCollum et al. 2017; van  Sluisveld 
et al. 2018b). Increased data availability of actual behaviour from 
smart technology lowers the barriers to representing behavioural 
change in computer simulations, and thus better represents crucial

demand-side transformations (Creutzig et al. 2018). Increasing the 
model resolution is a meaningful way forward. However, integrating 
a much broader combination of real-life aspects and dynamics into 
models could lead to an increased complexity that could restrict them 
to smaller fields of applications (De Cian et al. 2020).
Other elements of feasibility relate to social justice, which could be 
essential to enhance the political and public acceptability of the 
low-carbon transition. Reviewing the literature, one study finds that 
employing social justice as an orienting principle can increase the 
political feasibility of low-carbon policies (Patterson et al. 2018). 
Three elements are identified as key: (i) protecting vulnerable people 
from climate change impacts, (ii) protecting people from disruptions 
of transformation, (iii) enhancing the process of envisioning and 
implementing an equitable post-carbon society.
3.8.4 Enabling Factors

There is strong agreement that the climate policy institutional 
framework as well as technological progress have a profound impact 
on the attainability of low-carbon pathways. Delaying international 
cooperation reduces the available carbon budget and locks into 
carbon-intensive infrastructure exacerbating implementation 
challenges (Keppo and Rao 2007; Bosetti et al. 2009; Boucher et al. 
2009; Clarke et al. 2009; Krey and Riahi 2009; van Vliet et al. 2009;

383
Knopf et al. 2011; Jakob et al. 2012; Luderer et al. 2013; Rogelj 
et al. 2013a; Aboumahboub et al. 2014; Kriegler et al. 2014a; Popp 
et al. 2014; Riahi et al. 2015; Gambhir et al. 2017; Bertram et al. 
2021). Similarly, technological availability influences the feasibility 
of climate stabilisation, though differently for different technologies 
(Kriegler et al. 2014a; Iyer et al. 2015a; Riahi et al. 2015).
One of the most relevant factors affecting mitigation pathways and 
their feasibility is the rate and kind of socio-economic development. 
For example, certain socio-economic trends and assumptions 
about  policy effectiveness preclude achieving stringent mitigation 
futures (Rogelj et al. 2018b). The risk of failure increases markedly 
in high-growth, unequal and/or  energy-intensive worlds such as 
those characterised by the shared socio-economic pathways SSP3,

SSP4 and SSP5. On the other hand, socio-economic development 
conducive to mitigation relieves the energy sector transformation 
from relying on large-scale technology development: for example, 
the amount of biomass with CCS in SSP1 is one third of that in SSP5. 
The reason why socio-economic trends matter so much is that they 
both affect the CO 2 emissions in counterfactual scenarios as well 
as the mitigation capacity (Riahi et al. 2017; Rogelj et al. 2018b). 
Economic growth assumptions are the most important determinant 
of scenario emissions (Marangoni et al. 2017). Degrowth and post-
growth scenarios have been suggested as valuable alternatives to 
be considered (Hickel et al. 2021; Keyßer and Lenzen 2021), though 
substantial challenges remain regarding political feasibility (Keyßer 
and Lenzen 2021).
The type of policy instrument assumed to drive the decarbonisation 
process also plays a vital role for determining feasibility. The majority

of scenarios exploring climate stabilisation pathways in the past have 
focused on uniform carbon pricing as the most efficient instrument 
to regulate emissions. However, carbon taxation raises political 
challenges (Beiser-McGrath and Bernauer 2019) (Chapters  13 and 
14). Carbon pricing will transfer economic surplus from consumers 
and producers to the government. Losses for producers will be highly 
concentrated in those industries possessing fixed or durable assets 
with ‘high asset specificity’ (Murphy 2002; Dolphin et al. 2020). These 
sectors have opposed climate jurisdictions (Jenkins 2014). Citizens 
are sensitive to rising energy prices, though revenue recycling can 
be used to increase support (Carattini et al. 2019). A recent model 
comparison project confirms findings from the extant literature: 
using revenues to reduce pre-existing capital or, to a lesser extent, 
labour taxes, reduces policy costs and eases distributional concerns

(Barron et al. 2018; Mcfarland et al. 2018).
Nonetheless, winning support will require a mix of policies which go 
beyond carbon pricing, and include subsidies, mandates and feebates 
(Jenkins 2014; Rozenberg et al. 2018). More recent scenarios take 
into account a more comprehensive range of policies and regional 
heterogeneity in the near to medium term (Roelfsema et al. 2020). 
Regulatory policies complementing carbon prices could reduce 
the implementation challenges by increasing short-term emission 
reduction, though they could eventually reduce economic efficiency 
(Bertram et al. 2015b; Kriegler et al. 2018a). Innovation policies 
such as subsidies to R&D have been shown to be desirable due to innovation market failures, and also address the dynamic nature of 
political feasibility (Bosetti et al. 2011).
3.9 Methods of Assessment and Gaps 
in Knowledge and Data
3.9.1 AR6 Mitigation Pathways
The analysis in this chapter relies on the available literature as well

as an assessment of the scenarios contained in the AR6 scenarios 
database. Scenarios were submitted by research and other institutions 
following an open call (Annex III.II.3.1). The scenarios included in the 
AR6 scenarios database are an unstructured ensemble, as they are 
from multiple underlying studies and depend on which institutions 
chose to submit scenarios to the database. As noted in Section 3.2, 
they do not represent the full scenario literature or the complete set 
of possible scenarios. For example, scenarios that include climate 
change impacts or economic degrowth are not fully represented, 
as these scenarios, with a  few exceptions, were not submitted 
to the database. Additionally, sensitivity studies, which could 
help elucidate model behaviour and drivers of change, are mostly 
absent from the database – though examples exist in the literature 
(Marangoni et al. 2017).
The AR6 scenarios database contains 3131 scenarios of which

2425 with global scope were considered by this chapter, generated 
by almost 100 different model versions, from more than 50 model 
families. Of the 1686 vetted scenarios, 1202 provided sufficient 
information for a climate categorisation. Around 46% of the pathways 
are consistent with an end-of-century temperature of at least likely 
limiting warming to below 2°C (>67%). There are many ways of 
constructing scenarios that limit warming to a particular level and 
the choice of scenario construction has implications for the timing of 
both net zero CO 2 and GHG emissions and the deployment of CDR 
(Emmerling et al. 2019; Rogelj et al. 2019b; Johansson et al. 2020). 
The AR6 scenarios database includes scenarios where temperature 
is temporarily exceeded (40% of all scenarios in the database 
have median temperature in 2100 that is 0.1°C lower than median 
peak temperature). Climate stabilisation scenarios are typically

implemented by assuming a carbon price rising at a particular rate 
per year, though that rate varies across model, scenario, and time 
period. Standard scenarios assume a  global single carbon price to 
minimise policy costs. Cost-minimising pathways can be reconciled 
with equity considerations through posterior international transfers. 
Many scenarios extrapolate current policies and include non-market, 
regulatory instruments such as technology mandates.
Scenarios are not independent of each other and not representative 
of all possible outcomes, nor of the underlying scenario generation 
process; thus, the statistical power of the database is limited. 
Dependencies in the data-generation process originate from various 
sources. Certain model groups, and types, are over-represented. For 
example, eight model teams contributed 90% of scenarios. Second, 
not all models can generate all scenarios, and these differences are

not random, thereby creating selection bias (Tavoni and Tol 2010).

384
Third, there are strong model dependencies: the modelling scientific 
community shares code and data, and several IAMs are open-source. 
3.9.2 Models Assessed in This Chapter
The models assessed in this chapter differ in their sectoral coverage 
and the level of complexity in each sector. Models tend to have more 
detail in their representation of energy supply and transportation, 
than they do for industry (Section 3.4 and Annex III.I). Some models 
include detailed land-use models, while others exclude land models 
entirely and use supply curves to represent bioenergy potential 
(Bauer et al. 2018a). IAMs do not include all mitigation options 
available in the literature (Rogelj et al. 2018b; Smith et al. 2019). 
For example, most IAM pathways exclude many granular demand-
side mitigation options and land-based mitigation options found in 
more detailed sectoral models; additionally, only a  few pathways

include CDR options beyond afforestation/reforestation and BECCS. 
Section 3.4 and Chapter 12 include some results and comparisons 
to non-IAM models (e.g.,  bottom-up studies and detailed sectoral 
models). These sectoral studies often include a  more complete set 
of mitigation options but exclude feedbacks and linkages across 
sectors which may alter the mitigation potential of a given sector. 
There is an increasing focus in IAM studies on SDGs (Section 3.7), 
with some studies reporting the implications of mitigation pathways 
on SDGs (e.g.,  Bennich et al. 2020) and others using achieving 
SDGs as a constraint on the scenario itself (van Vuuren et al. 2015; 
Soergel et al. 2021a). However, IAMs are still limited in the SDGs they 
represent, often focusing on energy, water, air pollution and land. 
On the economic side, the majority of the models report information 
on marginal costs (i.e.,  carbon price). Only a  subset provides full

economic implications measured by either economic activity or 
welfare. Also often missing, is detail about economic inequality 
within countries or large aggregate regions.
For further details about the models and scenarios, see Annex III.

385
FAQ 3.1 |  Is it possible to stabilise warming without net negative CO 2 and GHG emissions?
Yes. Achieving net zero CO 2 emissions and sustaining them into the future is sufficient to stabilise the CO 2-induced warming signal 
which scales with the cumulative net amount of CO 2 emissions. At the same time, the warming signal of non-CO 2 GHGs can be 
stabilised or reduced by declining emissions that lead to stable or slightly declining concentrations in the atmosphere. For short-lived 
GHGs with atmospheric lifetimes of less than 20 years, this is achieved when residual emissions are reduced to levels that are lower 
than the natural removal of these gases in the atmosphere. Taken together, mitigation pathways that bring CO 2 emissions to net 
zero and sustain it, while strongly reducing non-CO 2 GHGs to levels that stabilise or decline their aggregate warming contribution,

will stabilise warming without using net negative CO 2 emissions and with positive overall GHG emissions when aggregated using 
GWP-100. A considerable fraction of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and limit warming 
to 2°C (>67%), respectively, do not or only marginally (<10 GtCO 2 cumulative until 2100) deploy net negative CO 2 emissions (26% 
and 46%, respectively) and do not reach net zero GHG emissions by the end of the century (48% and 70%, respectively). This is no 
longer the case in pathways that return warming to 1.5°C (>50%) after a high overshoot (typically >0.1°C). All of these pathways 
deploy net negative emissions on the order of 360 (60–680) GtCO 2 (median and 5–95th percentile) and 87% achieve net negative 
GHGs emissions in AR6 GWP-100 before the end of the century. Hence, global net negative CO 2 emissions, and net zero or net

negative GHG emissions, are only needed to decline, not to stabilise global warming. The deployment of carbon dioxide removal 
(CDR) is distinct from the deployment of net negative CO 2 emissions, because it is also used to neutralise residual CO 2 emissions to 
achieve and sustain net zero CO 2 emissions. CDR deployment can be considerable in pathways without net negative emissions and 
all pathways limiting warming to 1.5°C use it to some extent.
FAQ 3.2 |   How can net zero emissions be achieved and what are the implications of net zero 
emissions for the climate?
Halting global warming in the long term requires, at a minimum, that no additional CO 2 emissions from human activities are added 
to the atmosphere (i.e., CO 2 emissions must reach ‘net’ zero). Given that CO 2 emissions constitute the dominant human influence 
on global climate, global net zero CO 2 emissions are a prerequisite for stabilising warming at any level. However, CO 2 is not the

only greenhouse gas that contributes to global warming and reducing emissions of other greenhouse gases (GHGs) alongside 
CO2 towards net zero emissions of all GHGs would lower the level at which global temperature would peak. The temperature 
implications of net zero GHG emissions depend on the bundle of gases that is being considered, and the emissions metric used to 
calculate aggregated GHG emissions and removals. If reached and sustained, global net zero GHG emissions using the 100-year 
Global Warming Potential (GWP-100) will lead to gradually declining global temperature.
Not all emissions can be avoided. Achieving net zero CO 2 emissions globally therefore requires deep emissions cuts across all sectors 
and regions, along with active removal of CO 2 from the atmosphere to balance remaining emissions that may be too difficult, too 
costly, or impossible to abate at that time. Achieving global net zero GHG emissions would require, in addition, deep reductions of

non-CO 2 emissions and additional CO 2 removals to balance remaining non-CO 2 emissions.
Not all regions and sectors must reach net zero CO 2 or GHG emissions individually to achieve global net zero CO 2 or GHG emissions, 
respectively; instead, positive emissions in one sector or region can be compensated by net negative emissions from another sector 
or region. The time each sector or region reaches net zero CO 2 or GHG emissions depends on the mitigation options available, the 
cost of those options, and the policies implemented (including any consideration of equity or fairness). Most modelled pathways 
that likely limit warming to 2°C (>67%) above pre-industrial levels and below use land-based CO 2 removal such as afforestation/
reforestation and BECCS to achieve net zero CO 2 and net zero GHG emissions even while some CO 2 and non-CO 2 emissions continue

to occur. Pathways with more demand-side interventions that limit the amount of energy we use, or where the diet that we consume 
is changed, can achieve net zero CO 2, or net zero GHG emissions with less carbon dioxide removal (CDR). All available studies 
require at least some kind of carbon dioxide removal to reach net zero; that is, there are no studies where absolute zero GHG or 
even CO 2 emissions are reached by deep emissions reductions alone.
Total GHG emissions are greater than emissions of CO 2 only; reaching net zero CO 2 emissions therefore occurs earlier, by up to 
several decades, than net zero GHG emissions in all modelled pathways. In most modelled pathways that likely limit warming to 2°C 
(>67%) above pre-industrial levels and below in the most cost-effective way, the agriculture, forestry and other land-use (AFOLU) 
and energy supply sectors reach net zero CO 2 emissions several decades earlier than other sectors; however, many pathways show

much reduced, but still positive, net GHG emissions in the AFOLU sector in 2100.

386
FAQ 3.3 | How plausible ar e high emissions scenarios, and how do they inform policy?
IAMs are used to develop a wide range of scenarios describing future trajectories for greenhouse gas emissions based on a wide set

of assumptions regarding socio-economic development, technological changes, political development and climate policy. Typically, the IAM-based scenarios can be divided into (i) reference scenarios (describing possible trajectories in the absence of new stringent climate policies) and (ii) mitigation scenarios (describing the impact of various climate policy assumptions). Reference scenarios typically result in high emissions and, subsequently, high levels of climate change (in the order of 2.5°C–4°C during the 21st century). The purpose of such reference scenarios is to explore the consequences of climate change and act as a reference for mitigation scenarios. The possible emission levels for reference scenarios diverge from stabilising and even slowly declining emissions (e.g., for current policy scenarios or SSP1) to very high emission levels (e.g., SSP5 and RCP8.5). The latter leads to nearly 5°C of warming by the end of the century for medium climate sensitivity. Hausfather and

Peters (2020) pointed out that since 2011, the rapid development of renewable energy technologies and emerging climate policy have made it considerably less likely that emissions could end up as high as RCP8.5. This means that reaching emissions levels as high as RCP8.5 has become less likely. Still, high emissions cannot be ruled out for many reasons, including political factors and, for instance, higher than anticipated population and economic growth. Climate projections of RCP8.5 can also result from strong feedbacks of climate change on (natural) emission sources and high climate sensitivity (AR6 WGI Chapter 7). Therefore, their median climate impacts might also materialise while following a lower emission path (e.g., Hausfather and Betts 2020). All in all, this means that high-end scenarios have become considerably less likely since AR5 but cannot be ruled out. High-end scenarios (like RCP8.5) can be very useful to explore high-end risks of climate change but are not typical

‘business-as-usual’ projections and should therefore not be presented as such.

411
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44
This chapter focuses on accelerating mitigation and on shifting 
development pathways to increased sustainability, based on literature 
particularly at national scale. While previous WGIII assessments have 
discussed mitigation pathways, focus on development pathways is 
more recent. The timeframe is the near term (now up to 2030) to 
mid-term (2030 to 2050), complementing Chapter 3 on the long term 
(from 2050 onward).
An emissions gap persists, exacerbated by an implementation 
gap, despite mitigation efforts including those in near-universal 
nationally determined contributions (NDCs). The ‘emissions gap’ 
is understood as the difference between the emissions with NDCs in 
2030, and mitigation pathways consistent with the temperature goals. 
In general, the term ‘implementation gap’ refers to the difference 
between goals on paper and how they are achieved in practice. In

this report, the term refers to the gap between mitigation pledges 
contained in national determined contributions, and the expected 
outcome of existing policies. There is considerable literature on 
country-level mitigation pathways, including but not limited to NDCs. 
Country distribution of this literature is very unequal (robust evidence, 
high agreement). Current policies lead to median global greenhouse 
gas (GHG) emissions of 57 GtCO 2-eq with a full range of 52–60 by 
2030. NDCs with unconditional and conditional elements1 lead to 
53 (50–57) and 50 (47–55) GtCO 2-eq, respectively (medium evidence, 
medium agreement) (Table 4.3). This leaves estimated emissions gaps 
in 2030 between projected outcomes of unconditional elements of 
NDCs and emissions in scenarios that limit warming to 1.5°C (>50%) 
with no or limited overshoot of 19–26 GtCO 2-eq, and 10–16 GtCO 2-eq 
for scenarios that limit warming to 2°C (>67%) with immediate action.

When conditional elements of NDCs are included, these gaps narrow 
to 16–23 GtCO 2-eq and 6–14 GtCO 2-eq, respectively. {Cross-Chapter 
Box 4, Figure 1}
Studies evaluating up to 105 updated NDCs submitted by 
October 2021 indicate that emissions in conditional NDCs 
have been reduced by 4.5 (2.7–6.3) GtCO 2-eq, but only closes 
the emission gaps by about one-third to 2°C and about 20% 
to 1.5°C compared to the original NDCs submitted in 2015/16 
(medium evidence, medium agreement). The magnitude of these 
emission gaps calls into question whether current development 
pathways and efforts to accelerate mitigation are adequate to achieve 
the Paris mitigation objectives. In addition, an implementation gap 
exists between the projected emissions of ‘current policies’ and 
the projected emissions resulting from the implementation of the 
unconditional and conditional elements of NDCs, and is estimated to 
be around and 7 GtCO 2-eq in 2030, respectively (medium evidence,

medium agreement), with many countries requiring additional 
policies and associated climate action to meet their autonomously 
determined mitigation targets as specified under the first NDCs 
(limited evidence). There is, furthermore, a  potential difference 
between mitigation targets set in NDCs ex ante and what is achieved 
ex post. A  limited number of studies assess the implementation 
gaps of conditional NDCs in terms of finance, technology and 
1 See Section 4.2.1 for description of ‘unconditional’ and ‘conditional’ elements of NDCs. capacity building support. The disruptions triggered by the COVID-19 
epidemic increase uncertainty over range of projections relative 
to pre-COVID-19 literature. As indicated by a  growing number of 
studies at the national and global level, how large near- to mid-term 
emissions implications of the COVID-19 pandemic are, to a  large 
degree depends on how stimulus or recovery packages are designed. 
{4.2, 4.2.2.5, Cross-Chapter Box 4}

Given the gaps, there is a  need to explore accelerated 
mitigation (relative to NDCs and current policies). There is 
increasing understanding of the technical content of accelerated 
mitigation pathways, differentiated by national circumstances, with 
considerable though uneven literature at country-level (medium 
evidence, high agreement). Transformative technological and 
institutional changes for the near term include demand reductions 
through efficiency and reduced activity, rapid decarbonisation of the 
electricity sector and low-carbon electrification of buildings, industry 
and transport (robust evidence, medium agreement). A  focus on 
energy use and supply is essential, but not sufficient on its own – 
the land sector and food systems deserve attention. The literature 
does not adequately include demand-side options and systems 
analysis, and captures the impact from non-CO 2 GHGs with medium 
confidence. Countries and regions will have different starting points

for transition pathways. Some factors include climate conditions 
resulting in different heating and cooling needs, endowments with 
different energy resources, patterns of spatial development, and 
political and economic conditions. {4.2.5}
Accelerated mitigation alone may run into obstacles. If such 
obstacles are rooted in underlying structural features of society, then 
transforming such structures helps remove obstacles, which amounts 
to shifting development pathways. Various actors have developed an 
increasing number of mitigation strategies up to 2050 (mid-term). 
A growing number of such strategies aim at net zero GHG or CO 2 
emissions, but it is not yet possible to draw global implications due 
to the limited size of sample (medium evidence, low agreement). 
Non-state actors are also engaging in a  wide range of mitigation 
initiatives. When adding up emission reduction potentials, sub-
national and non-state international cooperative initiatives could

reduce up to about 20 GtCO 2-eq in 2030 (limited evidence, medium 
agreement). Yet perceived or real conflicts between mitigation and 
other Sustainable Development Goals (SDGs) can impede such 
action. If undertaken without precaution, accelerated mitigation is 
found to have significant implications for development objectives and 
macroeconomic costs at country level. For example, most country-
level mitigation modelling studies in which GDP is an endogenous 
variable report negative impacts of mitigation on GDP in 2030 and 
2050, relative to the reference. In all reviewed studies, however, 
GDP continues to grow even with mitigation (robust evidence, high 
agreement). The literature finds that employment effect of mitigation 
policies tends to be limited on aggregate, but can be significant 
at sectoral level (limited evidence, medium agreement). Detailed 
design of mitigation policies is critical for distributional impacts and

avoiding lock-in (robust evidence, high agreement), though further 
research is needed in that direction. {4.2.3, 4.2.4, 4.2.6}

412
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4Shifting development pathways towards sustainability 
offers ways to (i) broaden the range of levers and enablers 
that a  society can use to provide enabling conditions and 
accelerate mitigation; and (ii) increase the chances of 
advancing at the same time towards mitigation and towards 
other development goals. The way countries develop determines 
their capacity to accelerate mitigation and achieve other sustainable 
development objectives simultaneously (medium-robust evidence, 
medium agreement). Yet meeting ambitious mitigation and 
development goals cannot be achieved through incremental change, 
hence the focus on shifting development pathways (robust evidence, 
medium agreement). Though development pathways result from the 
actions of a wide range of actors, it is possible to shift development 
pathways through policies and enhancing enabling conditions (limited

evidence, medium agreement). For example, policies such as those 
listed in Table 4.12 are typically associated with broader objectives 
than greenhouse gas mitigation. They are generally conceived and 
implemented in the pursuit of overall societal development objectives, 
such as job creation, macroeconomic stability, economic growth, and 
public health and welfare. In some countries, such policies are framed 
as part of a just transition. However, they can have major influence 
on mitigative capacity, and hence can be seen as tools to broaden 
mitigation options, as illustrated by the Illustrative Mitigation 
Pathway ‘Shifting Pathways’ (medium evidence, medium agreement). 
There are practical options to shift development pathways in ways 
that advance mitigation and other sustainable development 
objectives, supporting political feasibility, increase resources to 
meet multiple goals, and reduce emissions (limited evidence, high

agreement). Concrete examples assessed in this chapter include high 
employment and low emissions structural change, fiscal reforms for 
mitigation and social contract, combining housing policies to deliver 
both housing and transport mitigation, and change economic, social 
and spatial patterns of development of the agriculture sector provide 
the basis for sustained reductions in emissions from deforestation. 
These examples differ by context. Examples in other chapters include 
transformations in energy, urban, building, industrial, transport, and 
land-based systems, changes in behaviour and social practices, as well 
as transformational changes across whole economies and societies. 
Coordinated policy mixes would need to coordinate multiple actors – 
individuals, groups and collectives, corporate actors, institutions and 
infrastructure actors – to deepen decarbonisation and shift pathways 
towards sustainability. Shifts in one country may spill over to other

countries. Shifting development pathways can jointly support 
mitigation and adaptation. Some studies explore the risks of high 
complexity and potential delay attached to shifting development 
pathways. {4.3, 4.3.1, 4.3.2, 4.4.2, 4.4.3, 4.4.1.7–4.4.1.10, Figure 4.7, 
Cross-Chapter Box 5, 5.8, Box 6.2, 8.2, 8.3.1, 8.4, 9.8.1, 9.8.2, 10.4.1, 
Cross-Chapter Box 5, Cross-Chapter Box 7, Cross-Chapter Box 12}
The literature identifies a broad set of enabling conditions that 
can both foster shifting development pathways and accelerated 
mitigation, along five categories (medium evidence, high 
agreement). Policy integration is a necessary component of shifting 
development pathways, addressing multiple objectives. To this aim, 
mobilising a range of policies is preferable to single policy instruments 
(robust evidence, high agreement). Governance for climate mitigation

and shifting development pathways is enhanced when tailored to national and local contexts. Improved institutions and governance 
enable ambitious climate action and help bridge implementation 
gaps (medium evidence, high agreement). Given that strengthening 
institutions may be a  long term endeavour, it needs attention in 
the near ter  m. Accelerated mitigation and shifting development 
pathways necessitates both redirecting existing financial flows from 
high- to low-emissions technologies and systems and to provide 
additional resources to overcome current financial barriers (robust 
evidence, high agreement). Opportunities exist in the near term to 
close the finance gap. At the national level, public finance for actions 
promoting the SDG agenda helps broaden the scope of mitigation 
(medium evidence, medium agreement). Changes in behaviour and 
lifestyles are important to move beyond mitigation as incremental 
change, and when supporting shifts to more sustainable development

pathways will broadening the scope of mitigation (medium evidence, 
medium agreement). The direction of innovation matters (robust 
evidence, high agreement). The necessary transformational changes 
are likely to be more acceptable if rooted in the development 
aspirations of the economy and society within which they take place. 
{4.4.1, 4.4.1.2, 4.4.1.3, 4.4.1.4, 4.4.1.5, 4.4.1.6, Figure 4.8, 15.2.2}
Equity can be an important enabler of deeper ambition for 
accelerated mitigation, dealing with the distribution of costs and 
benefits and how these are shared as per social contracts, national 
policy and international agreements. Transition pathways have 
distributional consequences such as large changes in employment 
and economic structure (robust evidence, high agreement). In that 
regard, the just transition concept has become an international focal 
point tying together social movements, trade unions, and other key

stakeholders to ensure equity is better accounted for in low-carbon 
transitions. Effectiveness of cooperative action and the perception of 
fairness of such arrangements are closely related, in that pathways 
that prioritise equity and allow broad stakeholders participation can 
enable broader consensus for the transformational change implied 
by deeper mitigation efforts (robust evidence, medium agreement). 
Hence, equity is a  concept that is instrumentally important. 
{4.5, Figure 4.9}
In sum, this chapter suggests that the immediate tasks are 
to broaden and deepen mitigation in the near term if the global 
community is to deliver emission reductions at the scale required 
to keep temperature well below 2°C and pursue efforts at  1.5°C. 
Deepening mitigation means more rapid decarbonisation. Shifting 
development pathways to increased sustainability (SDPS) broadens 
the scope of mitigation. Putting the enabling conditions above in

place supports both. Depending on context, some enabling conditions 
such as shifting behaviour may take time to establish, underscoring 
the importance of early action. Other enabling conditions, such as 
improved access to financing, can be put in place in a relatively short 
time frame, and can yield results rapidly.
Accelerating mitigation: The literature points to well-understood 
policy measures and technologies for accelerating mitigation, though 
the balance depends on country specificities: (i) decarbonising 
electricity supply to produce net zero CO2, including renewable 
energy, (ii) radically more efficient use of energy than today; 
(iii) electrification of end-uses including transport; (iv) dramatically

413
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44lower use of fossil fuels than today; (v) converting other uses to low- 
or zero-carbon fuels (e.g., hydrogen, bioenergy, ammonia) in hard-
to-decarbonise sectors; (vi) promote bioenergy, demand reduction, 
dietary changes, and policies, incentives, and rules for mitigation in 
the land sector; and (vii) setting and meeting ambitious targets to 
reduce methane and other short-lived climate forcers. Charting just 
transitions to net zero may provide a vision, which policy measures 
can help achieve. Though there is increasing experience with 
pricing carbon directly or indirectly, decision-makers might consider 
a broader toolbox of enablers and levers that is available in domains 
that have not traditionally been considered climate policy. {4.5, 
Annex II.IV.11}
Broadening opportunities by focusing on development pathways 
and considering how to shift them: Some of the policy measures may

yield rapid results, whereas other, larger transformations may take 
longer. If we are to overcome obstacles, a near-term priority is to put 
in place the enabling conditions to shifting development pathways 
to increased sustainability. Learning from the examples above, 
focusing on SDPS also provides a broader set of tools to accelerating 
mitigation and achieve other sustainable development goals. 
Consider climate whenever you make choices about development, 
and vice versa. {4.4.1}

414
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term44.1 Introduction
The recent IPCC Report on Global Warming of 1.5°C (SR1.5) made 
clear that the next three decades are critical if we are to achieve 
the long-term mitigation goal of the Paris Agreement (IPCC 2018a). 
The present chapter assesses the literature on mitigation and 
development pathways over that timeframe, in the near (up to 2030) 
and mid-term (up to 2050).
It considers three questions: (i) Where are we heading now? That 
is, what is the current state of affairs with respect to climate 
mitigation and how did we get here? (ii) Where do we want to go? 
For example, what state of affairs would meet the objectives of the 
Paris Agreement and achieving the Sustainable Development Goals 
(SDGs)? and (iii) How do we bring about this shift? In other words, 
what interventions are at societies’ disposal to bring about the 
necessary change in an equitable manner?

Where are we heading now? Despite the drop in emissions due to 
the COVID-19 crisis, the gap between projected emissions based on 
Nationally Determined Contributions (NDCs) in 2030 and emissions 
pathways compatible with the long term temperature goal set in the 
Paris Agreement remains large (Section  4.2.2). In addition to this 
persistent emissions gap, we face an implementation gap, as current 
policies are insufficient to achieve mitigation targets in NDCs, and 
sufficient international support is not yet available to developing 
countries who have requested and quantified support needs. 
Continuing along a development pathway characterised by the same 
underlying drivers, structural obstacles and insufficient enabling 
conditions that led to high emissions will not address the problem 
The analysis of the gap is conducted together with Chapter 3 (Cross-
Chapter Box  4  in this chapter). Chapter  3  is working backward,

assessing mitigation in the long term (beyond 2050 up to 2100) to 
draw the near- and mid-term implications of long-term temperature 
and mitigations goals. Chapter  4, on the other hand, works 
forward from current and planned mitigation (including NDCs) 
(Sections 4.2.1 and 4.2.2) and from current development paths to 
assess the implications for near- and mid-term greenhouse gases 
(GHG) emissions and development goals. Some countries, regions, 
cities, communities and non-state actors are taking leadership in 
implementing more ambitious action (Section  4.2.3). This chapter 
also assesses national low emission development strategies 
(Section 4.2.4).
Where do we want to go? Technical alternatives and policy options 
exist to bridge the emissions and implementation gaps, and the 
literature illustrates these with a wide range of accelerated techno-
economic pathways that deepen decarbonisation closer to the pace 
and scale required (Section  4.2.5), and examines their impacts on

other development objectives (Section 4.2.6). In practice, however, 
scaling up at the broader, deeper, and faster level required to meet 
climate goals while advancing other development objectives regularly 
faces prohibitive obstacles (Section 4.2.7). Mitigation policies grafted 
on to existing development pathways are unlikely to achieve rapid 
and deep emission reductions.Secondly, even if carefully designed, climate policies to accelerate 
mitigation may have adverse consequences for other development 
objectives. As a complement to mitigation action, taking action to 
shift development pathways towards sustainability broadens the 
range of mitigation options, while increasing the possibility to meet 
other development priorities at the same time (medium evidence, 
Development pathways and shifting them to increased sustainability 
are introduced in Chapter  1, and constitute a  thread throughout 
the report (see ‘development pathways’ in Annex  I: Glossary).

The AR6 WGII Report highlights the related concept of climate 
resilient development pathways (AR6 WGII, Chapter  18). Cross-
Chapter Box  5  in this chapter  – on shifting sustainable pathway 
towards sustainability  – elaborates on the concept. The influence 
of development pathways on emissions and mitigative capacity is 
discussed in Chapter  2. Chapter  3  assesses modelling of shifts in 
development pathways, illustrated by the illustrative mitigation 
pathway called ‘shifting pathways’. The importance of behavioural 
change as societies make decisions that intentionally shift their 
future development pathway is emphasised in Chapter 5. The systems 
Chapters (6–12) take sectoral perspectives, while pathways that are 
sustainable are the specific focus of Chapter 17.
How can one shift development pathway and accelerate 
mitigation? The literature does not provide a  complete handbook 
for shifting development pathways and accelerating mitigation.

The literature does, however, shed light on some of the underlying 
dynamics. Shifting development pathways can be necessitated by the 
existence of pervasive obstacles that prove prohibitive to reaching 
mitigation and other development objectives (Section 4.2.7). Deliberate 
measures taken to facilitate the shifting of development pathways 
and accelerated mitigation involve putting in place key enabling 
conditions that help overcome those obstacles (Figure 4.6) – improving 
governance and institutional capacity, fostering behavioural change 
and technological innovation, designing and implementing adequate 
policy, and finance. Just transitions, while they will differ by context, 
are critical to identifying and avoiding or addressing inequitable 
distributive consequences (robust evidence,  high agreement).
Enabling conditions necessary to accelerate mitigation and shift 
development pathways are discussed in depth in Chapters  5, 13,

14, 15 and 16. In addition, Chapters  13 and 14 detail the policy 
instruments that could help shift development pathways and 
accelerate the scale and pace of mitigation, while Chapter 4 describes 
those in broad strategies terms. Chapter  13 adds more texture 
on  institutional and governance machinery; policy choice, 
design and implementation; as well as policy formulation processes, 
actors and structure across scales.
Since development pathways and mitigation options depend to 
large extent on national objectives and circumstances, this chapter 
is primarily concerned with literature at national level (or in the case 
of the European Union, at regional level), while Chapter 3 is primarily 
concerned with literature at global scale. The national scale selected 
in this chapter requires attention as national mitigation pathways 
cannot be linked directly to global mitigation goals (Box 4.2). This

415
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44chapter is also concerned mostly with economy-wide development 
and mitigation pathways, as distinct from detailed sectoral work that 
is assessed in the systems Chapters 6 to 12. The present chapter also 
assesses literature on non-state action.
Chapter 4 draws on five major strands of literature: (i) an emerging 
literature on development pathways  – conceptual, empirical, and 
model-based, including at the national and sub-national scales; 
(ii)  a  rapidly expanding, model-based, literature on mitigation 
pathways in the near- and mid-term (Lepault and Lecocq 2021); 
(iii)  studies of NDCs and mid-century strategies; (iv) a  broader 
literature on transformation and shifts in development pathways, 
including from non-climate literatures; and (v) a significant literature 
on equity, including just transitions. This is supported by a database

of country-level mitigation scenarios at country level assembled for 
the preparation of this chapter (Annex III, Table I.10 and I.11).
The chapter builds on past IPCC reports. In AR5, all mitigation 
pathways were assessed in a  single chapter (Clarke et al. 2014), 
which focused mostly on the long term. IPCC Special Report on 
Global Warming of 1.5°C (SR1.5) included a chapter on mitigation 
pathways compatible with the temperature goal in the Paris 
Agreement (Rogelj et al. 2018a), mostly at the global level. It also 
considered strengthening mitigation (de Coninck et al. 2018) in the 
context of poverty, inequality and sustainable development (Roy et al. 
2018). Development pathways have also been explored, albeit less 
frequently, in past IPCC reports starting with the Special Report on 
Emissions Scenarios (Nakicenovic et al. 2000). Some early framing of 
development pathways was included in the Third Assessment Report

(Banuri et al. 2001), further developed in the Fourth Assessment 
Report (Sathaye et al. 2007). An extended discussion of climate 
change and equity was conducted in AR5 (Fleurbaey et al. 2014).
Chapter  4  examines mitigation within the broader context of 
development pathways, and examines how shifting development 
pathways can have a  major impact on mitigative capacity and 
broadening mitigation options. It is organised as follows.
Section 4.2 demonstrates that collective mitigation actions fall short 
of pathways that keep in reach the Paris temperature goals in the long 
term. Section 4.3 introduces development pathways (given its relative 
novelty in IPCC assessments), considers the implications of mitigation 
for development and vice versa, and articulates an approach on both 
accelerating mitigation and shifting development pathways.
Section  4.4 discusses how to shift development pathway and 
accelerate the scale and pace of mitigation, what levers are available

to policymakers, and how policies may intersect with adaptation 
goals. It points out that development pathways also drive adaptation 
and adaptative capacity, and discusses various risks associated with 
shifting development pathways and accelerated mitigation strategies.
Finally, equity and just transitions are recurring themes in the 
chapter, specifically in relation to accelerating mitigation and shifting 
development pathways toward sustainability. In Section  4.2.2.7, 
equity is discussed in the context of Parties’ assertions regarding 
the fairness of their NDCs, alongside reflections from academic scholarship on the ethical underpinnings of these assertions and of 
various quantitative analyses of equitable effort-sharing. Section 4.2.6 
discusses certain distributional implications of domestic mitigation 
efforts, such as shifts in employment. Sections  4.2.7 and  4.3 note 
the relevance of potential distributional impacts as an obstacle to

climate action, as well as the inequitable distribution of decision-
making authority. Finally, Section  4.5 recognises the structural 
relationship between equity and climate, explores just transitions as 
an international focal point tying together social movements, trade 
unions, and other stakeholders, and thus an instrumental role in 
establishing consensus.
4.2 Accelerating Mitigation Actions  
Across Scales
4.2.1 Mitigation Targets and Measures in Nationally 
Determined Contributions
A central instrument of the Paris Agreement is the NDCs, submitted by 
each country, and reflecting national efforts to reduce GHG emissions 
and build resilience to the impacts of climate change. Every five years, 
collective progress will be compared against long-term goals of the 
Paris Agreement. Considering the outcome of a  global stocktake, 
countries will prepare subsequent NDCs, showing progression in their 
ambition and enhancing international cooperation (UNFCCC 2015a).

Prior to COP21, in 2015, most countries submitted their Intended 
Nationally Determined Contributions (INDCs), which included 
mitigation targets for 2025 or 2030. INDCs become first NDCs on 
ratification and/or after national governments’ revision, and by 
11 October 2021, the official NDC registry contained 194 first NDCs 
with 105 new and updated NDCs from 132 Parties to the Paris 
Agreement, covering 53% of the total global emissions in 2019 
of 52.4  GtCO 2-eq without land use, land-use change and forestry 
(LULUCF), and 13 second NDCs. Most of the Parties that submitted 
new or updated NDCs have demonstrated increased ambition in 
addressing climate change. Moreover, though some countries have 
not submitted their updated NDCs yet, they have already announced 
their updated NDC goals somewhere. Countries will take the first 
stock in 2023 based on their progression towards achieving the 
objectives of Paris Agreement (UNFCCC 2015a, 2018a; SB Chairs 
2021) (Section 14.3.2.5).

Submitted NDCs vary in content, scope and background assumptions. 
First NDCs contain mitigation targets, and in many cases also 
provisions about adaptation. The mitigation targets range from 
economy-wide absolute emission reduction targets to strategies, 
plans and actions for low-emission development. Baseline years vary 
from 1990 to 2015 and in almost all NDCs the targeted time frame is 
2030, with a few specified periods of until 2025, 2035, 2040 or 2050. 
Around 43% of the mitigation targets in first NDCs are expressed in 
terms of deviation below business-as-usual by a specified target year, 
either for the whole economy or for specific sectors, while around 
35% include fixed-level targets (either reductions or limitations 
compared to base years), and another 22% refer to intensity targets 
(in terms of GHG, CO 2 or energy) or policies and measures, with an

416
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4increasing number of Parties moving to absolute emission reduction 
targets in their new or updated NDCs (UNFCCC 2016a, 2021). Some 
developing countries’ NDCs include unconditional elements, while 
others include conditional ones, the latter with higher ambition if 
finance, technology and capacity building support from developed 
countries is provided (UNFCCC 2016a).2 In some NDCs, the additional 
mitigation is quantified, in others not (Figure 14.2).
Most first NDCs cover all specific sectors, including LULUCF , and 
communicate specific targets for individual sub-sectors to support 
their overall mitigation targets. Concrete actions and priority areas 
are more detailed in the energy sector, with increased share of 
renewable energies and energy efficiency being highlighted in the 
majority of NDCs. Given the uncertainty behind LULUCF emission

and removal accounting (Grassi et al. 2017; Jian et al. 2019), several 
countries state that their accounting framework will only be defined 
in later NDCs. The GHG included and the global warming potentials 
(GWPs) used to aggregate emissions also vary across NDCs. Most 
countries only refer to carbon dioxide, methane and nitrous oxide 
emissions aggregated based on IPCC AR2 or AR4 metrics, while few 
NDCs also include fluorinated gases and use IPCC AR5 GWPs. The 
shares of Parties that indicate possible use of at least one type of 
voluntary cooperation and set qualitative limits on their use have 
both nearly doubled in new or updated NDCs.
There is considerable literature on country-level mitigation 
pathways, including but not limited to NDCs. Country distribution 
of this literature is very unequal (robust evidence,  high agreement). 
In particular, there is a growing literature on (I)NDCs, with a wide 
scope which includes estimate of emissions levels of NDCs

(Section  4.2.2.2); alignment with sustainable development goals 
(Caetano et al. 2020; Campagnolo and Davide 2019; Fuso Nerini 
et al. 2019; Antwi-Agyei et al. 2018); ambition (Höhne et al. 2018; 
Vogt-Schilb and Hallegatte 2017; Hermwille et al. 2019); energy 
development (Scott et al. 2018); and the legality of downgrading 
NDCs (Rajamani and Brunnée 2017). Other studies note that many 
NDCs contain single-year mitigation targets, and suggest that 
a multi-year trajectory is important for more rigorous monitoring 
(Elliott et al. 2017; Dagnet et al. 2017).
The literature also points out that beyond the ‘headline numbers’, 
information in (I)NDCs is difficult to analyse (Pauw et al. 2018). 
Information for ‘clarity, transparency and understanding’ is to be 
communicated with NDCs, although initial guidance was not specific 
(UNFCCC 2014). While the adoption of the Paris rule-book provided 
some greater specificity (UNFCCC 2018b,c), the information included

in the NDCs remains uneven. Many NDCs omit important mitigation 
sectors and do not adequately provide details on costs and financing 
of implementation (Pauw et al. 2018). Countries are also invited to 
explain how their NDCs are fair and ambitious, though the way this 
has been done so far has been criticised as insufficiently rigorous 
(Winkler et al. 2018).
2 ‘Unconditional’ NDCs refer to abatement efforts pledged without any conditions (this terminology is used by the literature, not by the Paris Agreement). They are based 
mainly on domestic abatement actions, although countries can use international cooperation to meet their targets. ‘Conditional’ NDCs require international cooperation, 
for example bilateral agreements under article 6, financing or monetary and/or technological transfers (14.3.2).4.2.2 Aggregate Effects of NDCs and Other 
Mitigation Efforts Relative to Long-term 
Mitigation Pathways
4.2.2.1 Introduction

Near-term mitigation targets submitted as part of NDCs to the 
UNFCCC, as well as currently implemented policies, provide a basis 
for assessing potential emissions levels up to 2030 at the national, 
regional and global level. The following sections present an evaluation 
of the methods used for assessing projected emissions under NDCs 
and current policies (Section  4.2.2.2), and the results of these 
assessments at global, regional and national level assessing a broad 
available literature based on first NDC submissions from 2015/16 
and pre-COVID economic projections (Section 4.2.2.3). The impacts 
of the COVID-19 pandemic and related government responses on 
emissions projections are then discussed in Section 4.2.2.4 and the 
implications of updated NDCs submitted in 2020/21 on emissions 
follow in Section  4.2.2.5. Section  4.2.2.6 presents an assessment 
of the so-called ‘implementation gap’ between what currently

implemented policies are expected to deliver and what the ambitions 
laid out under the full implementation of the NDCs are projected to 
achieve. Finally, a comparison of ambitions across different countries 
or regions (Section  4.2.2.7) is presented and the uncertainties of 
projected emissions associated with NDCs and current policies are 
estimated, including a discussion of measures to reduce uncertainties 
in the specification of NDCs (Section 4.2.2.8).
The literature reviewed in this section includes globally comprehensive 
assessments of NDCs and current policies, both peer-reviewed and 
non-peer-reviewed (but not unpublished model results) as well as 
synthesis reports by the UNFCCC Secretariat, government reports 
and national studies.
The aggregate effects of NDCs provide information on where 
emissions might be in 2025/2030, working forward from their recent 
levels. Chapter 3 of this report works backwards from temperature

goals, defining a  range of long-term global pathways consistent 
with 1.5°C, 2°C and higher temperature levels. By considering the 
two together, it is possible to assess whether NDCs are collectively 
consistent with 1.5°C, 2°C and other temperature pathways (Cross-
Chapter Box 4 in this chapter).
4.2.2.2 Methods to Project Emissions Under NDCs 
and Current Policies
A variety of different methods are used to assess emissions 
implications of NDCs and current policies over the time horizon to 
2025 or 2030. Some of these projections were explicitly submitted as 
part of an official communication to UNFCCC (e.g., Biennial Report, 
Biennial Update Reports or National Communications) while the 
majority is from independent studies.

417
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44Methods that are used in independent studies (but that can also 
underlie the official communications) can broadly be separated 
into two groups:
1. system modelling studies which analyse policies and targets 
in a  comprehensive modelling framework such an integrated 
assessment, energy systems or integrated land-use model to 
project emissions (or other indicators) of mitigation targets in 
NDCs and current policies, either at the national or global scale 
(noting some differences in the systems); and
2. hybrid approaches that typically start out with emissions 
pathways as assessed by other published studies (e.g., the IEA 
World Energy Outlook, national emissions pathways such as 
those specified in some NDCs) and use these directly or apply 
additional modifications to them.
System modelling studies are conducted at global, regional and 
national scales. Global models provide an overview, are necessary

for assessment of global phenomena (e.g.,  temperature change), 
can integrate climate models and trade effects. National models 
typically include more details on sectors, technology, behaviour and 
intersectoral linkages, but often use simplifying assumptions for 
international trade (e.g., the Armington elasticity approach). Critically, 
they can also better reflect local socio-economic and political 
conditions and their evolution (i.e., national development pathways). 
A variety of modelling paradigms are found, including optimisation 
and simulation models, myopic and with foresight, monolithic and 
modular (Annex III: Scenarios and Modelling Methods).
Among the hybrid approaches, three broader categories can be 
distinguished, (i) direct use of official emission projection as part of 
submitted NDC or other communication to UNFCCC, (ii) historical 
trend extrapolation of emissions based on inventory data, possibly 
disaggregated by sector and emission species, and (iii) use of

Reference/Business-As-Usual pathways from an independent 
published study (e.g.,  IEA WEO). In all cases, the reductions are 
then estimated on top of the resulting emission trajectory. Note 
that globally comprehensive studies may vary the approach used 
depending on the country.
Beyond the method applied, studies also differ in a  number of 
dimensions, including (i) their spatial resolution and coverage, 
(ii) their sectoral resolution and coverage, (iii) the GHGs that are 
included in the assessment, the GWPs (or other metrics) to aggregate 
them, the emissions inventory (official vs independent inventory 
data) and related accounting approaches used as a  starting point 
for the projections, (iv) the set of scenarios analysed (Reference/
Business-As-Usual, Current Policies, NDCs, etc.), and (v) the degree to 
which individual policies and their impact on emissions are explicitly 
represented (Table 4.1).
First, the studies are relevant to different spatial levels, ranging

from macro-scale regions with globally comprehensive coverage to 
national level (Section 4.2.2.3) and sub-national and company level in 
a few cases (Section 4.2.3). It is important to recognise that globally 
comprehensive studies typically resolve a limited number of countries 
individually, in particular those that contribute a high share to global emissions, but have poor resolution of remaining countries or regions, 
which are assessed in aggregate terms. Conversely, studies with high 
resolution of a particular country tend to treat interactions with the 
global scale in a limited way. The recent literature includes attempts 
to provide a composite global picture from detailed national studies 
(Bataille et al. 2016a; Deep Decarbonization Pathways Project 2015; 
Roelfsema et al. 2020).
A second dimension in which the studies are different is their 
comprehensiveness of covering different emitting sectors. Some 
studies focus on the contribution of a  single sector, for example

the agriculture, forestry and other land use (AFOLU) sector (Fyson 
and Jeffery 2019; Grassi et al. 2017) or the energy system (including 
both energy supply and demand sectors), to emission reductions as 
specified in the NDC. Such studies give an indication of the importance 
of a given sector to achieving the NDC target of a country and can be 
used as a benchmark to compare to comprehensive studies, but adding 
sectoral contributions up represents a methodological challenge.
Third, GHG coverage is different across studies. Some focus on CO2 
only, while others take into account the full suite of Kyoto gases 
(CO 2, CH 4, N2O, HFCs, PFCs and SF 6). For the latter, different metrics 
for aggregating GHGs to a  CO 2-equivalent metric are being used, 
typically GWP 100 from different IPCC assessments (Table 4.1).
Fourth, studies typically cover a set of scenarios, though how these 
scenarios are defined varies widely. The literature reporting IAM

results often includes Nationally Determined Contribution (NDC), 
which are officially communicated, and Current Policies (CP) as 
interpreted by modellers. Studies based on national modelling, by 
contrast, tend to define scenarios reflecting very different national 
contexts. In both cases, modellers typically include so-called No 
Policy Baseline scenarios (alternatively referred to as Reference 
or Business-as-Usual scenarios) which do not necessarily reflect 
currently implemented policies and thus are not assessed as reference 
pathways (Section  4.2.6.1). There are also various approaches to 
considering more ambitious action compared to the CP or NDC 
projections that are covered in addition.
Fifth, studies differ in the way they represent policies (current or 
envisioned in NDCs), depending on their internal structure. For 
example, a subsidy to energy efficiency in buildings may be explicitly 
modelled (e.g.,  in a  sectoral model that represents household

decisions relative to building insulation), represented by a  proxy 
(e.g.,  by an exogenous decrease in the discount rate households 
use to make choices), or captured by its estimated outcome (e.g., by 
an exogenous decrease in the household demand for energy, say in an 
energy system model or in a compact CGE). Detailed representations 
(such as the former example) do not necessarily yield more accurate 
results than compact ones (the latter example), but the set of 
assumptions that are necessary to represent the same policy will be 
very different.
Finally, policy coverage strongly varies across studies with some just 
implementing high level targets specified in policy documents and 
NDCs while others represent the policies with the largest impact 
on emissions and some looking at very detailed measures and

418
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4policies at sub-national level. In addition, in countries with rapidly 
evolving policy environments, slightly different cut-off dates for the 
policies considered in an emission projection can make a significant 
difference for the results (Dubash et al. 2018).
The challenges described above are dealt with in the assessment 
of quantitative results in Section 4.2.2.3 by (i) comparing national 
studies with country-level results from global studies to understand 
systematic biases; (ii) comparing economy-wide emissions (including 
AFOLU) as well as energy-related emissions; (iii) using different 
emission metrics including CO 2 and Kyoto GHG emissions where 
the latter have been harmonised to using AR6 GWP100 metrics; and 
(iv) tracking cut-off dates of implemented policies and NDCs used 
in different references (Table 4.SM.1). The most notable differences

in quantitative emission estimates related to current policies 
and NDCs relate to the COVID-19 pandemic and its implications and 
to the updated NDCs mostly submitted since early 2020 which are 
separately dealt with in Sections 4.2.2.4 and 4.2.2.5, respectively.
In addition to assessing the emissions outcomes of NDCs, some 
studies report development indicators, by which they mean a wide 
diversity of socio-economic indicators (Jiang et al. 2013; Chai and 
Xu 2014; Delgado et al. 2014; La Rovere et al. 2014a; Zevallos 
et al. 2014; Benavides et al. 2015; Altieri et al. 2016; Bataille et al. 
2016a; Zou et al. 2016; Paladugula et al. 2018; Parikh et al. 2018; 
Yang et al. 2021), share of low-carbon energy (Bertram et al. 2015; 
Riahi et al. 2015), renewable energy deployment (Roelfsema et al. 
2018), production of fossil fuels (SEI et al. 2020) or investments into 
low-carbon mitigation measures (McCollum et al. 2018) to track 
progress towards long-term temperature goals.

4.2.2.3 Projected Emissions Under NDCs and Current 
Policies by 2025/2030
The emissions projections presented in this section relate to the first 
NDCs, as communicated in 2015 and 2016, and on which an extensive 
literature exists. New and updated NDCs, mostly submitted since the 
beginning of 2020, are dealt with in Section  4.2.2.5. Similarly, 
the implications of COVID-19 and the related government responses 
on emissions projections is specifically dealt with in Section 4.2.2.4.
Table 4.1 presents the evidence base for the assessment of projected 
emissions of original NDCs and current policies until 2030. It covers 
31 countries and regions responsible for about 82% of global GHG 
emission (excluding FOLU CO 2 emissions) and draws quantitative 
estimates from more than 40 studies (Table  4.SM.1 in the 
Supplementary Material to this chapter). The table allows comparing 
emission projections from national and globally comprehensive

studies as well as official communications by countries to the 
UNFCCC at the national/regional level. The global aggregates 
presented in Table  4.1 derive from globally comprehensive studies 
only and are not the result of aggregating country projections up 
3 Note that the statistical metrics reported are slightly different across the reports. For example, IPCC SR1.5 reported the 25th to 75th percentile range while the UNEP 
Emissions Gap Report uses median and 10th to 90th percentile ranges. In addition, this report applies 100-year GWPs from AR6 to aggregate across different GHG 
emission species, whereas 100-year GWPs from AR4 were applied in IPCC SR1.5 and UNEP 2020a. The application of AR6 GWPs on average leads to increase of estimates 
by about 1.3% and ranges are wider due to the difference in statistical error metrics.to the global level. As different studies report different emission 
indicators, the table includes four different indicators: CO 2 and GHG

emissions, including or excluding AFOLU emissions. Where possible, 
multiple indicators are included per study.
Globally comprehensive studies
The UNFCCC Secretariat has assessed the aggregate effect of NDCs 
multiple times. The first report considered the intended NDCs in 
relation to 2°C (UNFCCC 2015b), whereas the second considered 
NDCs also in relation to 1.5°C (UNFCCC 2016b). New submissions 
and updates of NDCs in 2020/21 are assessed in Section  4.2.2.5. 
A number of globally comprehensive studies (den Elzen et al. 2016; 
Luderer et al. 2016; Rogelj et al. 2016, 2017; Vandyck et al. 2016; 
Rose et al. 2017; Baumstark et al. 2021) which estimate aggregate 
emissions outcomes of NDCs and current policies have previously 
been assessed in Cross-Chapter-Box 11 of IPCC SR1.5.
According to the assessment in this report, studies projecting 
emissions of current policies based on pre-COVID assumptions lead 
to median global GHG emissions of 60 GtCO 2-eq with a full range

of 54–68 by 2030 and original unconditional and conditional NDCs 
submitted in 2015/16 to 57 (49–63) and 54 (50–60) GtCO 2-eq, 
respectively (robust evidence, medium agreement) (Table  4.1). 
Globally comprehensive and national-level studies project emissions 
of current policies and NDCs to 2025 and 2030 and, in general, are 
in good agreement about projected emissions at the country level.
These estimates are close to the ones provided by the IPCC 
SR1.5, Cross-Chapter-Box  11, and the UNEP emissions gap 
report (UNEP 2020a).3
National studies
A large body of literature on national and regional emissions 
projections, including official communications of as part of the 
NDC submissions and independent studies exist. A  subset of this 
literature provides quantitative estimates for the 2030 timeframe. 
As highlighted in Section 4.2.1, the number of independent studies 
varies considerably across countries with an emphasis on the largest

emitting countries. This is reflected in Table 4.1 (see also Table 4.SM.1). 
Despite smaller differences between globally comprehensive 
and national studies for a  few countries, there is generally good 
agreement between the different types of studies, providing evidence 
that these quantitative estimates are fairly robust.
Sectoral studies
Sectoral studies are essential to understand the contributions of 
concrete measures of NDCs and current policies. For example, 
approximately 98% of NDCs include the energy sector in their 
mitigation contributions, of which nearly 50% include a  specific

419
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44Table 4.1 | Assessment of projected 2030 emissions of current policies based on pre-COVID assumptions and original NDCs submitted in 2015/16 for 
28 individual countries/regions and the world.  The table compares projected emissions from globally comprehensive studies, national studies and, when available, official 
communications to UNFCCC using different emission sources (fossil fuels, AFOLU sector) and different emission metrics (CO 2, Kyoto GHGs). The comparison allows identifying 
potential biases across the ranges and median estimates projected by the different sets of studies.
RegionaGHG 
share 
[%]bTypec # 
estimatesdCurrent Policies 2030 emissions NDC 2030 emissions (conditional/unconditional)
CO2 only  
[GtCO 2] 
median  
(min–max)fKyoto GHGse  
[GtCO 2-eq] 
median  
(min–max)fCO2 only  
[GtCO 2] 
median  
(min–max)fKyoto GHGse 
 [GtCO 2-eq] 
median  
(min–max)f

incl. AFOLUgfossil fuels incl. AFOLUgincl. AFOLUgfossil fuels incl. AFOLUg
World 100 global 93 43 (38–51) 37 (33–45) 60 (54–68)40 (35–45)/ 
37 (35–39)32 (26–39)/ 
31 (27–37)54 (50–60)/ 
57 (49–63)
CHN 27global 76 12 (9.7–15) 11 (8.4–14) 15 (12–18) – /11 (9.8–13) – /8.8 (6.9–13) – /14 (13–16)
national 13 12 (12–12) 11 (9.2–13) 15 (13–15) – /12 (11–12) – /11 (10–11) – /15 (13–16)
USAh12global 71 4.9 (4.4–6.6) 4.6 (3.5–6.5) 5.9 (4.9–6.6) – /3.8 (3.3–4.1) – /3.9 (3.1–5.3) – /4.6 (4–5.1)
national 5 4.1 4.5 (4.1–4.9) 5.9 (5.2–6.7) – /3.4 – /3.5 – /4.3
EUi8.1global 24 2.7 (2.1–3.5) 2.6 (2.1–3.3) 3.4 (2.6–4.7) – /2.6 (2.1–2.8) – /2.4 (2.1–2.7) – /3.2 (2.6–3.7)
national 3 3.1 2.6 – /2.5
official 3 3.2 (2.8–3.7)
IND 7.1global 79 3.7 (3–4.5) 3.2 (2.5–4.5) 4.7 (4.1–6.4) 3.3 (3.1–4.4)/43.3 (2.4–5.6)/3.8 
(2.9–5.6)5 (4.2–6.4)/5.8 
(4.9–6.1)
national 9 3.4 (3.3–4) 3.4 (2.9–3.9) 5.5 (5–5.7) 3.4 (3.2–3.6)/3.2 3.4 (3.2–3.5)/2.9 5.1/4.9

RUS 4.5global 66 1.7 (0.84–2) 1.6 (1.5–2) 2.3 (1.6–3.3) – /1.7 (0.85–1.9) – /1.6 (1.2–1.9) – /2.6 (1.9–3.1)
national 6 1.5 (1.5–1.5) 2.6 – /1.5 (1.5–1.5) – /2.5
official 2 2.1 – /2.7
BRA 2.5global 69 1.1 (0.79–1.7) 0.5 (0.28–1.1) 1.8 (1.4–2.7) – /0.94 (0.52–1.5)– /0.38 
(0.097–0.86)– /1.3 (1.2–2.5)
national 4 0.59 0.47 1.8 – /0.51 – /0.47 – /1.2
official 1 – /1.2
JPN 2.4global 66 1.2 (0.94–1.3) 1.1 (0.67–1.3) 1.2 (0.95–1.3) – /1 (0.9–1.2) – /0.83 (0.65–1.2) – /1 (0.95–1.2)
national 16 1.1 (1.1–1.6) 1.1 (1.1–1.5) 1.3 (1.2–1.7) – /0.93 (0.91–1.2) – /0.93 (0.87–1.1) – /1 (1–1.3)
official 1 – /1
IDN 2.2global 25 1.1 (0.79–2) 0.62 (0.51–0.89) 1.7 (1.4–2.4)0.93 (0.76–
1.4)/0.990.53 (0.45–
0.66)/0.68 
(0.6–0.77)1.8 (1.3–2.1)/2.1 
(1.5–2.2)
official 2 1.9 (1.8–1.9)/2.2
CAN 1.5global 67 0.58 (0.4–0.8) 0.43 (0.38–0.72) 0.68 (0.51–1)– /0.43 
(0.34–0.67)– /0.43 
(0.31–0.64)– /0.53 
(0.49–0.82)
national 2 0.54 0.71 – /0.41 – /0.54
official 2 0.67

MEX 1.5global 31 0.61 (0.54–1.3) 0.48 (0.3–0.56) 0.82 (0.72–1.7)0.54 (0.48–
1)/0.460.43 (0.27–
0.54)/0.33 
(0.26–0.42)0.65 (0.62–
1.4)/0.73 
(0.63–0.79)
official 2 0.62/0.76
SAU 1.5 global 6 0.7 (0.57–0.82) 0.61 (0.48–0.74) 1 (0.7–1.1) 0.7 (0.58–0.82)/ –0.62 (0.49–0.74)/ 
–0.83 (0.7–0.96)/ –
KOR 1.4global 64 0.69 (0.55–0.76) 0.67 (0.42–0.91) 0.72 (0.68–0.81) – /0.57 (0.5–0.65) – /0.4 (0.26–0.61) – /0.57 (0.5–0.69)
national 4 0.78 (0.75–0.81) 0.73 (0.7–0.76) 0.86 (0.83–0.89)– /0.62 
(0.51–0.72)– /0.58 
(0.49–0.67)– /0.68 (0.56–0.8)
official 1
AUS 1.1global 16 0.42 (0.34–0.49) 0.34 (0.28–0.46) 0.54 (0.46–0.69)– /0.36 
(0.28–0.43)– /0.3 (0.24–0.41)– /0.44 
(0.39–0.52)
national 3 0.55
official 2 0.52 (0.51–0.52)
TUR 1.1global 18 0.44 (0.44–0.49) 0.4 (0.34–0.43) 0.6 (0.51–0.83)– /0.44 
(0.44–0.49)– /0.4 (0.27–0.43) – /0.94 (0.55–1)
official 1 – /0.93

420
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4RegionaGHG 
share 
CO2 only  
[GtCO 2] 
median  
median  
[GtCO 2] 
median  
median  
ZAF 1.1global 26 0.49 (0.35–0.62) 0.36 (0.23–0.56) 0.64 (0.45–0.85) – /0.4 (0.27–0.55)– /0.35 
(0.21–0.44)0.41/0.58 
(0.39–0.65)
official 1– /0.52 
(0.41–0.64)
VNM 0.92global 2 0.61/0.77
national 4 0.36 0.280.32 (0.28–
0.36)/0.360.26 (0.24–
0.28)/0.28
GBR 0.86 global 4 0.37 0.33 (0.3–0.37) – /0.37 – /0.33 (0.3–0.37)
FRA 0.85 global 4 0.22 0.32 (0.24–0.4) – /0.22 – /0.32 (0.24–0.4)
THA 0.84global 5 0.41 (0.41–0.41) 0.44/0.47
national 3 0.43 0.4 0.58 0.35/0.36 0.32/0.34 0.43/0.46

ARG 0.76global 22 0.33 (0.17–0.52) 0.2 (0.15–0.35) 0.51 (0.33–0.75)0.25 (0.17–
0.46)/0.250.21 (0.18–
0.23)/0.15 
(0.14–0.16)0.39 (0.32–
0.69)/0.51 
(0.33–0.52)
national 2 0.42 (0.41–0.43) – /0.19
official 2 0.4/0.52
KAZ 0.71 global 3 0.45 0.28/0.32
UKR 0.52 global 2 0.42 (0.42–0.42) – /0.54
PHL 0.48 global 3 0.24 0.082/ –
COL 0.4 global 5 0.23 (0.23–0.23)0.26 (0.26–
0.26)/0.29 
(0.29–0.29)
ETH 0.31 global 5 0.022 0.23 (0.19–0.27) – /0.0230.16 (0.15–0.16)/ 
–
MAR 0.21 global 5 0.11 (0.087–0.13)0.13 (0.1–
0.15)/0.13 
(0.1–0.15)
KEN 0.18 global 5 0.022 0.13 (0.11–0.14) – /0.0230.11 (0.11–0.11)/ 
–
SWE 0.13 global 4 –0.0120.03 (0.029–
0.031)– /–0.012– /0.03 
(0.028–0.032)
PRT 0.12global 2 0.045 0.036 – /0.045 – /0.036
national 1 – /0.023
CHE 0.094global 1 – /0.026
national 1 0.027 0.025
MDG 0.065global 1 0.033/ –
national 3 0.071 0.00590.07 (0.068–
0.071)/ –0.0043 (0.0026–
0.0059)/ –

Notes: a Countries are abbreviated by their ISO 3166-1 alpha-3 letter codes. EU denotes the European Union. b 2018 Share of global Kyoto GHG emissions, excluding FOLU 
emissions, based on 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). c Type distinguishes between independent globally comprehensive studies 
(that also provide information at the country/region level), independent national studies and official communications via Biennial Reports, Biennial Update Reports or National 
Communications. d Different estimates from one study (e.g., data from multiple models or minimum and maximum estimates) are counted individually, if available. e GHG 
emissions expressed in CO 2-eq emission using AR6 100-year GWPs (see Section 2.2.2 for a discussion of implications for historical emissions). GHG emissions from scenario

data is recalculated from individual emission species using AR6 100-year GWPs. GHG emissions from studies that do provide aggregate GHG emissions using other GWPs 
are rescaled using 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). f If more than one value is available, a median is provided and the full range of 
estimates (in parenthesis). To avoid a bias due to multiple estimates provided by the same model, only one estimate per model, typically the most recent update, is included 
in the median estimate. In the full range, multiple estimates from the same model might be included, in case these reflect specific sensitivity analyses of the ‘central estimate’ 
(e.g., Baumstark et al. 2021; Rogelj et al. 2017). g Note that AFOLU emissions from national GHG inventories and global/national land use models are generally different

due to different approaches to estimate the anthropogenic CO 2 sink (Grassi et al. 2018, 2021) (Section 7.2.3 and Cross-Chapter Box 6 in Chapter 7). h The estimates for 
USA are based on the first NDC submitted prior to the withdrawal from the Paris Agreement, but not including the updated NDC submitted following its re-entry. i The EU 
estimates are based on the 28 member states up until 31 January 2020, i.e., including UK.

421
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44target for the share of renewables, and about 5% aim at increasing 
nuclear energy production (Stephan et al. 2016). Transport is covered 
explicitly in 75% of NDCs, although specific targets for the sector 
exist in only 21% of NDCs (PPMC and SLoCaT 2016). Measures or 
targets for buildings are referred to explicitly in 27% of NDCs (GIZ 
2017). Additionally, 36% of NDCs include targets or actions that are 
specific to the agriculture sector (FAO 2016). LULUCF (mitigation) is 
included in 80% of all submitted NDCs, while 59% include adaptation 
and 29% refer to REDD+.
Greater sectoral expertise and involvement will be critical to 
accomplishing development and climate goals due to enhanced 
availability of information and expertise on specific sectoral options, 
greater ease of aligning the NDCs with sectoral strategies, and greater 
awareness among sector-level decision-makers and stakeholders

(Fekete et al. 2015; NDC Partnership 2017). Sector-specific studies 
are assessed in the sectoral Chapters (6 to 11) of this report.
4.2.2.4 Estimated Impact of COVID-19 and Governmental 
Responses on Emissions Projections
The impacts of COVID-19 and national governments’ economic 
recovery measures on current (Section 2.2.2) and projected emissions 
of individual countries and globally under current policies scenarios 
until 2030 may be significant, although estimates are highly uncertain 
and vary across the few available studies. The analyses published to 
date (October 2021) are based on limited information about how 
COVID-19 has affected the economy and hence GHG emissions 
across countries so far in 2020, and also based on assumptions about 
COVID-19’s longer term impact. Moreover, the comparison of pre- 
and post-COVID-19 projections captures the impact of COVID-19 as 
well as other factors such as the consideration of recently adopted

policies not related to COVID-19, and methodological changes.
Across different studies (Kikstra et al. 2021; IEA 2020; Dafnomilis 
et al. 2021; Pollitt et al. 2021; UNEP 2020a; Climate Action Tracker 
2020; Keramidas et al. 2021; Dafnomilis et al. 2020), the impact of the 
general slowdown of the economy due to the COVID-19 pandemic 
and its associated policy responses would lead to a  reduced 
estimate of global GHG emissions in 2030 of about 1 to 5 GtCO 2-eq, 
equivalent to  1.5–8.5%, compared to the pre-COVID-19 estimates (Table  4.SM.2). Nascimento et al. (2021) analyse the impacts of 
COVID-19 on current policy emission projections for 26 countries and 
regions and find a large range of emission reduction – between –1% 
and –21% – across these.
As indicated by a  growing number of studies at the national and 
global level, how large near- to mid-term emissions implications 
of the COVID-19 pandemic are to a large degree depends on how

stimulus or  recovery packages are designed (Forster et al. 2020; 
Gillingham et al. 2020; IEA 2020; Le Quéré et al. 2020; Malliet 
et al. 2020; Wang  et al. 2020; Obergassel et al. 2021; Pollitt et al. 
2021; UNEP 2020a).
Four studies (Climate Action Tracker 2021; den Elzen et al. 2021; JRC 
2021; Riahi et al. 2021) provide an update of the current policies 
assessment presented in Section 4.2.2.3 by taking into account the 
effects of COVID-19 as well as potential updates of policies. The 
resulting GHG emissions in 2030 are estimated to be 57 GtCO 2-eq 
with a full range of 52 to 60 GtCO 2-eq (Table 4.2). This is a reduction 
of about 3 GtCO 2-eq or 5% compared to the pre-COVID estimates 
from Section 4.2.2.3.
4.2.2.5 Estimated Impact of New and Updated NDCs 
on Emissions Projections
The number of studies estimating the emissions implications of new 
and updated NDCs and announced mitigation pledges that can be used

for the quantitative assessment is limited to four (Table 4.3) (Climate 
Action Tracker 2021; den Elzen et al. 2021; Meinshausen et al. 2021; 
JRC 2021). One other study includes a limited number of NDC updates 
(Riahi et al. 2021) and another (UNFCCC 2021) excludes LULUCF 
emissions. They are therefore not directly comparable to the other two. 
In addition, the UNEP Emissions Gap Report 2021 (UNEP 2021) in itself 
is assessment of almost the same studies included here. The evidence 
base for the updated NDC assessment is thus considerably smaller 
compared to that of the assessment of emissions implications of original 
NDCs presented in Section 4.2.2.3. However, it is worthwhile to note 
that the earlier versions of the studies summarised in Table 4.2 and 
Table 4.3 are broadly representative for the emissions range implied by 
the pre-COVID-19 current policies and original NDCs of the full set of 
studies shown in Table 4.1, therefore building confidence in estimates.

Table 4.2 | Projected global GHG emissions of current policies by 2030.
Study Cut-off dateKyoto GHGsa [GtCO 2-eq] 
median (min–max)b References
Climate Action Tracker 8/2020 54 (52–56) Climate Action Tracker (2021)
PBL 11/2020 58 den Elzen et al. (2021); Nascimento et al. (2021)
JRC – GECO 12/2019 57 JRC (2021)
ENGAGEc7/2019 57 (52–60) Riahi et al. (2021)
Totald57 (52–60)
Notes: a GHG emissions expressed in CO 2-eq emission using AR6 100-year GWPs. GHG emissions from studies that provide aggregate GHG emissions using other GWPs are 
rescaled using 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). b If a range is available from a study, a median is provided in addition to the range. 
c Range includes estimates from four models: GEM-E3, MESSAGEix-GLOBIOM, POLES, REMIND-MAgPIE, based on sensitivity analysis. d To avoid a bias due to multiple estimates

provided by the same model, only one estimate per model, typically the most recent update, is included in the median estimate for the total.

422
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4An additional challenge lies in the fact that these studies do not all 
apply the same cut-off date for NDC updates, potentially leading 
to larger systematic deviations in the resulting emission estimates. 
Another complication is the fact that publicly announced mitigation 
pledges on global 2030 emissions that have not been officially 
submitted to the UNFCCC NDC registry yet, have been included in 
several of the studies to anticipate their impact on emission levels 
(see notes to Table 4.3). In addition to the updates of NDC targets, 
most of the new studies also include impacts of COVID-19 on future 
emission levels (as discussed in Section 4.2.2.4) which may have led 
to considerable downward revisions of emission trends unrelated to 
NDCs. Table 4.3 presents the emission estimates of the four studies 
that form the basis of the quantitative assessment presented here

and three other studies to compare with.
Comparing the emission levels implied by the new and updated 
NDCs as shown in Table  4.3 with those estimated by the original 
NDCs from the same studies (as included in Table 4.1), a downward 
revision of 3.8 (3.0–5.3) GtCO 2-eq of the central unconditional NDC 
estimates and of 4.5 (2.7–6.3) GtCO 2-eq of the central conditional 
NDC estimate emerges (medium evidence, medium agreement). The 
emissions gaps between temperature limits and new and updated 
NDCs are assessed in Cross-Chapter Box 4 below. New and updated 
unconditional NDCs reduce the median gap with emissions pathways 
that limit warming to 2°C (>67%) in 2030 by slightly more than 20%, 
from a median gap of 17 GtCO 2-eq (9–23) to 13 (10–16). New and 
updated conditional NDCs reduce the median gap with emissions 
pathways that limt warming to 2°C (>67%) in 2030 by about one 
third, from 14  GtCO 2-eq (10–20) to 9  (6–14). New and updated

that limit warming to 1.5°C (>50%) with no or limited overshoot in 
2030 by about 15%, from a median gap of 27 GtCO 2-eq (19–32) to 22 GtCO 2-eq (19–26). New and updated conditional NDCs reduce the 
median gap with emissions pathways that limit warming to 1.5°C 
(>50%) with no or limited overshoot in 2030 by about 20%, from 
a  median gap of 24  GtCO 2-eq (20–29) to 19  GtCO 2-eq (16–23). 
Box 4.1 discusses the adaptation gap.
Globally, the implementation gap between projected emissions of 
current policies and the unconditional and conditional new and 
updated NDCs is estimated to be around 4 and 7 GtCO 2-eq in 2030, 
respectively (medium evidence, medium agreement) (Tables  4.2 
and  4.3), with many countries requiring additional policies and 
associated climate action to meet their mitigation targets as 
specified under the NDCs (limited evidence) (Section  4.2.2.6). It

should be noted that the implementation gap varies considerably 
across countries, with some having policies in place estimated to be 
sufficient to achieve the emission targets their NDCs, some where 
additional policies may be required to be sufficient, as well as 
differences between the policies in place and action on the ground.
4.2.2.6 Tracking Progress in Implementing 
and Achieving NDCs
Under the Enhanced Transparency Framework, countries will transition 
from reporting biennial reports (BRs) and biennial update reports 
(BURs) to reporting biennial transparency reports (BTRs) starting, at 
the latest, by December 2024. Each Party will be required to report 
information necessary to track progress made in implementing and 
achieving its NDC under the Paris Agreement (UNFCCC 2018b). Thus, 
no official data exists yet on tracking progress of individual NDCs.
Meanwhile, there is some literature at global and national level that

aims at assessing whether countries are on track or progressing Table 4.3 | Projected global GHG emissions of new and updated NDCs by 2030.
StudyCut-off 
dateKyoto GHGsa [GtCO 2-eq]
References Historical Median (min–max)b 2030
2015 2019 Unconditional NDCs Conditional NDCs
Climate Action Trackerc5/2021 51 52 50 47 Climate Action Tracker (2021)
PBLd9/2021 52 54 53 (51–55) 52 (49–53) den Elzen et al. (2021); Nascimento et al. (2021)
JRC – GECOe10/2021 51 48 JRC (2021)
Meinshausen et al.f10/2021 54 56 55 (54–57) 53 (52–55) Meinshausen et al. (2021)
Totalg53 (50–57) 50 (47–55)
Other studies for comparison
UNEP EGRh 9/2021 53 (50–55) 50 (47–53) UNEP (2017a)
UNFCCC Secretariati 7/2021 57 (55–58) 54 (52–56) UNFCCC (2021)
ENGAGEj 3/2021 51 (49–53) Riahi et al. (2021)
Notes: a GHG emissions expressed in CO 2-eq emission using AR6 100-year GWPs. GHG emissions from studies that provide aggregate GHG emissions using other GWPs are

rescaled using 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). Note that due to slightly different system boundaries across historical emission 
datasets as well as data uncertainties (Chapter 2, SM2.2) relative change compared to historical emissions should be calculated vis-à-vis the historical emissions data used by 
a particular study. b If a range is available from a study, a median is provided in addition to the range. c Announced mitigation pledges on global 2030 emissions of China and 
Japan included. d Announced mitigation pledges of China, Japan, Republic of Korea included. e Announced mitigation pledge of Korea not included. f Announced mitigation 
pledges of China and Republic of Korea not included, emissions from international aviation and shipping not included. g Ranges across four studies are calculated using the

median and the full range including the minimum and maximum of studies if available. h UNEP EGR 2021 estimate listed for comparison, but since largely relying on the same 
studies not included in range estimate. i NDCs submitted until 30 July included, announcements not included, excluding LULUCF emissions. j NDC updates of Brazil, EU and 
announcement of China included as a sensitivity analysis compared to original NDCs.

423
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44towards implementing their NDCs and to which degree the NDCs 
collectively are sufficient to reach the temperature targets of the 
Paris agreement (Rogelj et al. 2016; Quéré et al. 2018; Höhne et al. 
2018; Roelfsema et al. 2020; den Elzen et al. 2019; Höhne et al. 2020). 
Most of these studies focus on major emitters such as G20 countries 
and with the aim to inform countries to strengthen their ambition 
regularly, for example, through progress of NDCs and as part of the 
global stocktake (Höhne et al. 2018; Peters et al. 2017). However, 
a  limited number of studies assess the implementation gaps of 
conditional NDCs in terms of finance, technology and capacity 
building support. Some authors conclude that finance needed to 
fulfil conditional NDCs exceeds available resources or the current 
long-term goal for finance (USD100 billion yr–1) (Pauw et al. 2019);

others assess financial resources needed for forest-related activities 
(Kissinger et al. 2019) (Section 15.4.2). The literature suggests that 
consistent and harmonised approach to track progress of countries 
towards their NDCs would be helpful (Peters et al. 2017; Höhne et al. 
2018; den Elzen et al. 2019), and negotiations on a common tabular 
format are expected to conclude during COP26 in November 2021.
With an implementation gap in 2030 of 4  to 7  GtCO 2-eq 
(Section 4.2.2.5), many countries will need to implement additional 
policies to meet their self-determined mitigation targets as specified 
under the NDCs. Studies that assess the level of projected emissions 
under current policies indicate that new policies (that have been 
implemented since the first assessment of the NDCs in 2015 and are 
thus covered in more recent projections) have reduced projections, 
by about two GtCO 2-eq since the adoption of the Paris Agreement

in 2015 to 2019 (Climate Action Tracker 2019; UNEP 2020a; 
den Elzen et al. 2019).
4.2.2.7 Literature on Fairness and Ambition of NDCs
Most countries provided information on how they consider their 
NDCs to be fair and ambitious in the NDCs submitted to UNFCCC and 
many of these NDCs refer to specific national circumstances such as 
social, economic and geographical factors when outlining why they 
are fair and ambitious. Further, several Parties provided information 
on specific criteria for evaluating fairness and ambition, including 
criteria relating to: responsibility and capability; share of emissions; 
development and/or technological capacity; mitigation potential; cost 
of mitigation actions; the degree of progression or stretching beyond 
the current level of effort; and the link to objectives and global goals 
(UNFCCC 2016a).
According to its Article 2.2, the Paris Agreement will be implemented 
to reflect equity and the principle of common but differentiated

responsibilities and respective capabilities, in the light of different 
national circumstances, the latter clause being new, added to 
the UNFCCC principle (Voigt and Ferreira 2016; Rajamani 2017). 
Possible different interpretations of equity principles lead to different 
assessment frameworks (Lahn and Sundqvist 2017; Lahn 2018).
Various assessment frameworks have been proposed to analyse 
fair share ranges for NDCs. The literature on equity frameworks 
including quantification of national emissions allocation is assessed 
in section 4.5 (Sections 13.4.2, 14.3.2 and 14.5.3). Recent literature has assessed equity, analysing how fairness is expressed in NDCs 
in a  bottom-up manner (Mbeva and Pauw 2016; Cunliffe et al. 
2019; Winkler et al. 2018). Some studies compare NDC ambition 
level with different effort sharing regimes and which principles are 
applied to various countries and regions (Peters et al. 2015; Pan 
et al. 2017; Robiou Du Pont et al. 2017; Holz et al. 2018; Robiou

du Pont and Meinshausen 2018; van den Berg et al. 2019). Others 
propose multi-dimensional evaluation schemes for NDCs that 
combine a  range of indicators, including the NDC targets, cost-
effectiveness compared to global models, recent trends and policy 
implementation into consideration (Aldy et al. 2017; Höhne et al. 
2018). Yet other literature evaluates NDC ambition against factors 
such as technological progress of energy efficiency and low-carbon 
technologies (Jiang et al. 2017; Kuramochi et al. 2017; Wakiyama 
and Kuramochi 2017), synergies with adaptation plans (Fridahl 
and Johansson 2017), the obligations to deploy carbon dioxide 
removal technologies like bioenergy with carbon capture and 
storage (BECCS) in the future implied by their near-term emission 
reductions where they are not reflected on in the first NDCs (Peters 
and Geden 2017; Fyson et al. 2020; Pozo et al. 2020; Mace et al. 
2021). Others identify possible risks of unfairness when applying

GWP* as emissions metric at national scale (Rogelj and Schleussner 
2019). A recent study on national fair shares draws on principles of 
international environmental law, excludes approaches based on cost 
and grandfathering, thus narrowing the range of national fair shares 
previously assessed, and apply this to the quantification of national 
fair share emissions targets (Rajamani et al. 2021).
4.2.2.8 Uncertainty in Estimates
There are many factors that influence the global aggregated effects of 
NDCs. There is limited literature on systematically analysing the impact 
of uncertainties on the NDC projections with some exception (Rogelj 
et al. 2017; Benveniste et al. 2018). The UNEP Gap Report (UNEP 
2017a) discusses uncertainties of NDC estimates in some detail. The 
main factors include variations in overall socio-economic development; 
uncertainties in GHG inventories; conditionality; targets with ranges or 
for single years; accounting of biomass; and different GHG aggregation

metrics (e.g., GWP values from different IPCC assessments). In addition, 
when mitigation effort in NDCs is described as measures that do only 
indirectly translate into emission reductions, assumptions necessary 
for the translation come into play (Doelle 2019). For a more elaborate 
discussion of uncertainties in NDCs (Section 14.3.2).
Some studies assume successful implementation of all of the NDCs’ 
proposed measures, sometimes including varying assumptions to 
account for some of the NDC features which are subject to assumed 
conditions related to finance and technology transfer. Countries ‘shall 
pursue domestic mitigation measures’ under Article 4.2 of the Paris 
Agreement (UNFCCC 2015a), but they are not legally bound to the 
result of reducing emissions (Winkler 2017a). Some authors consider 
this to be a  lack of a  strong guarantee that mitigation targets in 
NDCs will be implemented (Nemet et al. 2017). Others point to

growing extent of national legislation to provide a  legal basis for 
action (Iacobuta et al. 2018) (Section 13.2). These factors together 
with incomplete information in NDCs mean there is uncertainty 
about the estimates of anticipated 2030 emission levels.

424
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4The aggregation of targets results in large uncertainty (Rogelj et al. 
2017; Benveniste et al. 2018). In particular, clarity on the contributions 
from the land use sector to NDCs is needed ‘to prevent high 
LULUCF uncertainties from undermining the strength and clarity of 
mitigation in other sectors’ (Fyson and Jeffery 2019). Methodological 
differences in the accounting of the LULUCF anthropogenic CO 2 
sink between scientific studies and national GHG inventories (as 
submitted to UNFCCC) further complicate the comparison and aggregation of emissions of NDC implementation (Grassi et al. 2018, 
2021) (Section  7.2.3 and Cross-Chapter Box  6  in Chapter  7). This 
uncertainty could be reduced with clearer guidelines for compiling 
future NDCs, in particular when it comes to mitigation efforts not 
expressed as absolute economy-wide targets (Doelle 2019), and

explicit specification of technical details, including energy accounting 
methods, harmonised emission inventories (Rogelj et al. 2017) and 
finally, increased transparency and comparability (Pauw et al. 2018).
Cross-Chapter Box 4 | Comparison of NDCs and current policies with the 2030 GHG Emissions 
from Long-term Temperature Pathways
Authors: Edward Byers (Austria/Ireland), Michel den Elzen (the Netherlands), Céline Guivarch (France), Volker Krey (Germany/Austria), 
Elmar Kriegler (Germany), Franck Lecocq (France), Keywan Riahi (Austria), Harald Winkler (South Africa)
The Paris Agreement (PA) sets a long-term goal of holding the increase of global average temperature to ‘well below 2°C above pre-
industrial levels’ and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels. This is underpinned by 
the ‘aim to reach global peaking of greenhouse gas emissions as soon as possible’ and ‘achieve a balance between anthropogenic

emissions by sources and removals by sinks of GHG in the second half of this century’ (UNFCCC 2015a). The PA adopts a bottom-up 
approach in which countries determine their contribution to reach the PA’s long-term goal. These national targets, plans and measures 
are called ‘nationally determined contributions’ or NDCs.
a. Global GHG emissions b. 2030 c. 2050 d. 2100
102030
0
–10 –1040506070
102030
04050607080 80GHG emissions (GtCO2-eq yr –1)
2020 2025 2015 2010 2030 2035 2040 2045 2050
Trend from implemented policiesModelled pathways:
Limit warming to 2°C (>67%) or return warming to 
1.5°C (>50%) after a high overshoot, NDCs until 2030
Limit warming to 1.5°C (>50%) with no or limited overshoot
Past GHG emissions and uncertainty for 2015 and 2019
(dot indicates the median)Limit warming to 2°C (>67%)Percentile:
95th
Median
5th75th
25th
Policy 
assessments 
for 2030
Policies implemented by the end of 2020Policy assessments for 2030:
NDCs prior to COP26,
including conditional elements

NDCs prior to COP26, 
unconditional elements  
88
Cross-Chapter Box 4, Figure 1 | Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) and 
projected emission outcomes from near-term policy assessments for 2030 (Panel b).

425
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44Cross-Chapter Box 4 (continued)
Cross-Chapter Box 4, Figure 1 (continued): Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) 
and projected emission outcomes from near-term policy assessments for 2030 (Panel b).
Panel a shows global GHG emissions over 2015–2050 for four types of assessed modelled global pathways: 
 – Trend from implemented policies: Pathways with projected near-term GHG emissions in line with policies implemented until the end of 2020 and 
extended with comparable ambition levels beyond 2030 (29 scenarios across categories C5–C7, Table SPM.2).
 – Limit to 2°C (>67%) or return warming to 1.5°C (>50%) after a high overshoot, NDCs until 2030: Pathways with GHG emissions until 2030 
associated with the implementation of NDCs announced prior to COP26, followed by accelerated emissions reductions likely to limit warming

to 2°C (C3b, Table SPM.2) or to return warming to 1.5°C with a probability of 50% or greater after high overshoot (subset of 42 scenarios from 
C2, Table SPM.2).
 – Limit to 2°C (>67%) with immediate action: Pathways that limit warming to 2°C (>67%) with immediate action after 2020 (C3a, Table SPM.2).
 – Limit to 1.5°C (>50%) with no or limited overshoot: Pathways limiting warming to 1.5°C with no or limited overshoot (C1, Table SPM.2 C1). All 
these pathways assume immediate action after 2020.
Past GHG emissions for 2010–2015 used to project global warming outcomes of the modelled pathways are shown by a black line4 and past global 
GHG emissions in 2015 and 2019 as assessed in Chapter 2 are shown by whiskers.
Panels b, c and d show snapshots of the GHG emission ranges of the modelled pathways in 2030, 2050, and 2100, respectively. Panel b also

shows projected emissions outcomes from near-term policy assessments in 2030 from Chapter 4.2 (Tables 4.2 and 4.3; median and full range). GHG 
emissions are in CO 2-equivalent using GWP100 from AR6 WGI. {3.5, 4.2, Table 4.2, Table 4.3, Cross-Chapter Box 4 in Chapter 4}
The NDCs are a central instrument of the PA to achieve its long-term goal. It thus combines a global goal with a country-driven 
(bottom-up) instrument to a hybrid climate policy architecture to strengthen the global response to climate change. All signatory 
countries committed to communicating nationally determined contributions including mitigation targets, every five years. While the 
NDCs mostly state targets, countries are also obliged to pursue domestic mitigation measures to achieve the objectives. The literature 
examines the emissions outcome of the range of policies implemented to reach these targets.
Emissions gap

A comparison between the projected emission outcomes of current policies, the NDCs (which include unconditional and conditional 
elements, Section 4.2.1) and mitigation pathways acting immediately, i.e. from 2020 onwards, on reaching different temperature 
goals in the long-term (Section  3.3.3) allows identifying different ‘emission gaps’ in 2030 (Cross-Chapter Box  4, Figure  1). First, 
the  implementation gap between ‘current policies’ and unconditional and conditional NDCs is estimated to be around 4  and 
7  GtCO 2-eq in 2030, respectively (Section  4.2.2 and Tables  4.2 and  4.3). Second, the comparison of unconditional (conditional) 
NDCs and long-term mitigation pathways that limit warming to 2°C (>67%) or lower gives rise to a 2030 median emissions gap 
of 19–26  GtCO 2-eq (16–23  GtCO 2-eq) for limiting end-of-century warming to  1.5°C (>50%) with no or limited overshoot and

10–16 GtCO 2-eq (6–14 GtCO 2-eq) for limiting warming to 2°C (>67%).5 GHG emissions of NDCs are broadly consistent with 2030 
emission levels of cost-effective long-term pathways staying below 2.5°C (scenarios category C5, Table 3.2, Chapter 3).
Other ‘gap indicators’
Beyond the quantification of different GHG emissions gaps, there is an emerging literature that identifies gaps between current 
policies, NDCs and long-term temperature in terms of other indicators, including for example the deployment of low-carbon energy 
sources, energy efficiency improvements, fossil fuel production levels or investments into mitigation measures (Roelfsema et al. 2020; 
McCollum et al. 2018; SEI et al. 2020).
A 2030 gap in the contribution of low-carbon energy sources to the energy mix in 2030 between current policies and cost-effective 
long-term temperature pathways is calculated to be around 7percentage-points (2°C) and 13percentage-points (1.5°C) by Roelfsema

et al. (Roelfsema et al. 2020). The same authors estimate an energy intensity improvement gap 10% and 18% for 2030 between current 
policies pathways and 2°C and 1.5°C pathways, respectively. SEI et al. (2020) estimates the ‘fossil fuel production gap’, by which they 
mean ‘the level of countries’ planned fossil fuel production expressed in their carbon content to be 120% and 50% higher compared 
to the fossil fuel production consistent with 1.5°C and 2°C pathways, respectively, as assessed in IPCC SR1.5 (Rogelj et al. 2018a). 
4 See Box SPM.1 for a description of the approach to project global warming outcomes of modelled pathways and its consistency with the climate assessment in AR6 WGI.
5 The emission gap ranges provided here is calculated as the difference between minimum and maximum emissions estimates of NDCs and the median of the 1.5°C 
and 2°C pathways.

426
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term4
4.2.3 Mitigation Efforts in Sub-national and Non-state 
Action Plans and Policies
The decision adopting the Paris Agreement stresses the importance 
of ‘stronger and more ambitious climate action’ by non-government 
and sub-national stakeholders, ‘including civil society, the private 
sector, financial institutions, cities and other sub-national authorities, 
local communities and indigenous peoples’ (UNFCCC 2015a). The 
Marrakech Partnership for Global Action, launched in the 2016 
UNFCCC Conference of Parties by two ‘high-level champions,’ further 
formalised the contributions of non-government and sub-national 
actors taking action through seven thematic areas (e.g.,  energy, 
human settlements, industry, land-use, etc.) and one cross-cutting 
area (resilience). Since then, non-state actors, for example, companies 
and civil society, and sub-national actors, such as cities and regions,

have emerged to undertake a  range of largely voluntary carbon 
mitigation actions (Hsu et al. 2018, 2019) both as individual non-state 
actors (NSAs in the following) and through national and international 
cooperative initiatives, or ICIs (Hsu et al. 2018). ICIs take a variety of 
forms, ranging from those that focus solely on non-state actors to 
those that engage national and even local governments. They can 
also range in commitment level, from primarily membership-based 
initiatives that do not require specific actions to those that require members to tackle emissions reductions in specific sectors or aim for 
transformational change.
Quantification of the (potential) impact of these actions is still 
limited. Almost all studies estimate the potential impact of the 
implementation of actions by NSAs and ICIs, but do not factor in 
that they may not reach their targets. The main reason for this is that 
there is very limited data currently available from individual actors

(e.g., annual GHG inventory reports) and initiatives to assess their 
progress towards their targets. A few studies have attempted to assess 
progress of initiatives by looking into the initiatives’ production of 
relevant outputs (Chan et al. 2018). Quantification does not yet cover 
all commitments and only a selected number of ICIs are analysed in 
the existing literature. Most of these studies exclude commitments 
that are not (self-)identified as related to climate change mitigation, 
those that are not connected to international networks, or those that 
are communicating in languages other than English.
Non state action could make significant contributions to achieving 
the Paris climate goals (limited evidence, high agreement). However, 
efforts to measure the extent to which non-state and sub-national 
actors go beyond national policy are still nascent (Hsu et al. 2019; 
Kuramochi et al. 2020) and we do not fully understand the extent Cross-Chapter Box 4 (continued)

The methodology used for this estimation is very similar to how emissions gaps are derived (SEI et al. 2019). The gap of global 
annual average investments in low-carbon energy and energy efficiency in 2030 between following current policy on the one 
hand and achieving the NDCs, the 2°C and 1.5°C targets on the other hand, is estimated to be approximately USD 130, 320, or 
480 billion per year (McCollum et al. 2018).
It is important to note that such comparisons are less straight forward as the link between long-term temperature goals and these 
indicators is less pronounced compared to the emission levels themselves; they are therefore associated with greater uncertainty 
compared to the emissions gap.
Box 4.1 | Adaptation gap and NDCs
NDCs have been an important driver of national adaptation planning, with cascading effects on sectors and sub-national action,

especially in developing countries. Yet, only 40 developing countries have quantifiable adaptation targets in their current NDCs; 
49 countries include quantifiable targets in their national legislation (UNEP 2018a).
Working Group II contribution to this Assessment finds that the overall extent of adaptation-related responses in human systems is 
low (high confidence) and that there is limited evidence on the extent to which adaptation-related responses in human systems are 
reducing climate risk (O’Neill et al. 2020). Thus there is an adaptation gap (UNEP 2018a), and bridging that gap requires enablers 
including institutional capacity, planning and investment (UNEP 2016). Estimates of adaptation costs vary greatly across studies. Recent 
studies based on climate change under RCP8.5 report adaptation costs for developing countries of up to 400 billion (300 billion in

RCP2.6) USD2005 in 2030 (New et al. 2020). Of the NDCs submitted in 2015, 50 countries estimated adaptation costs of USD39 billion 
annually. Both public and private finance for adaptation is increasing, but remains insufficient and constitutes a small fraction (4–8%) 
of total climate finance which is mostly aimed at mitigation. The pledge of developed countries of mobilising finance for developing 
countries to address adaptation needs globally as part of the Paris Agreement are insufficient. By 2030 the adaptation needs are 
expected to be three to six times larger than what is pledged, further increasing towards 2050 (UNEP 2016; New et al. 2020).

427
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44Table 4.4 | Emissions reduction potential for sub-national and non-state international cooperative initiatives by 2030.
Sector Leading actor Name Scale Target(s)2030 emissions reduction potential 
compared to no policy, current 
policies or NDC baseline  
(GtCO 2-eq yr–1)Membership 
assumptions
Min Max
Energy efficiencyIntergovernmental 
(UNEP)United for Efficiency (U4E)Global (focus 
on developing 
countries)Members to adopt policies for energy-efficient appliances 
and equipment0.6 1.25 Current membership
Energy efficiency IntergovernmentalSuper-efficient Equipment 
and Appliance Deployment 
(SEAD) InitiativeGlobalMembers to adopt current policy best practices for energy efficiency 
product standards0.5 1.7 (excl. China) Current membership
Buildings Business Architecture 2030Global (focus on 
North America)New buildings and major renovations shall be designed to meet

an energy consumption performance standard of 70% below 
the regional (or country) average/median for that building type 
and to go carbon-neutral in 20300.2 0.2 Current membership
Transport Business (aviation sector)Collaborative Climate 
Action Across the Air 
Transport World (CAATW)GlobalTwo key objectives: (i) 2% annual fuel efficiency improvement 
through 2050, (ii) stabilise net carbon emissions from 20200.3 0.6 Current membership
Transport Business Lean and Green EuropeMember companies to reduce CO 2 emissions from logistics 
and freight activity by at least 25% over a five-year period0.02 0.02 Current membership
Transport HybridGlobal Fuel Economy  
Initiative (GFEI)GlobalHalve the fuel consumption of the LDV fleet in 2050  
compared to 20050.5 1.0 Current membership
Transport Business Below50 LCTPi aGlobalReplace 10% of global transportation fossil fuel use with low-carbon 
transport fuels by 20300.5 0.5Scaled-up  
global potential
Renewable

energyBusinessEuropean Technology 
& Innovation Platform 
Photovoltaic (ETIP PV)EuropeSupply 20% of electricity from solar Photovoltaic PV technologies 
by 20300.2 0.5 Current membership
Renewable 
energyIntergovernmental 
(African Union)Africa Renewable Energy 
Initiative (AREI)AfricaProduce 300 gigawatt (GW) of electricity for Africa by 2030 
from clean, affordable and appropriate forms of energy0.3 0.8 Current membership
Renewable 
energyHybridGlobal Geothermal 
Alliance (GGA)GlobalAchieve a five-fold growth in the installed capacity for 
geothermal power generation and a more than two-fold 
growth in geothermal heating by 20300.2 0.5 Targeted capacity
Renewable 
energyBusiness REscale LCTPi aGlobalSupport deployment of 1.5 TW of additional renewable energy 
capacity by 2025 in line with the IEA’s 2°C scenario5 5Scaled-up  
Renewable 
energyBusiness RE100 initiative Global2,000 companies commit to source 100% of their electricity

from renewable sources by 20301.9 4 Targeted membership
Forestry HybridBonn Challenge/Governors’ 
Climate and Forests Task Force 
(GCFTF)/New York Declaration 
on Forests (NYDF)GlobalEnd forest loss by 2030 in member countries and restore 
150 million hectares of deforested and degraded lands by 2020 
and an additional 200 million hectares by 20303.8 8.8Scaled-up  
Non-CO 2 
emissionsGovernmentClimate & Clean Air  
Coalition (CCAC)GlobalMembers to implement policies that will deliver substantial short-
lived climate pollutants (SLCP) reductions in the near to medium-
term (i.e., by 2030) for HFCs and methane1.4 3.8 Current membership

428
Min Max
Non-CO 2 
emissionsIntergovernmental  
(World Bank)Zero Routine Flaring Global Eliminate routine flaring no later than 2030 0.4 0.4 Current membership
Multisectoral Cities and regions Under2 Coalition GlobalLocal governments (220 members) aim to limit their GHG emissions 
by 80 to 95% below 1990 levels by 20504.6 5 Current membership
Multisectoral Cities and regionsGlobal Covenant of Mayors 
for Climate & Energy (GCoM)Global Member cities have a variety of targets (+9,000 members) 1.4 1.4 Current membership
Multisectoral Cities and regionsC40 Cities Climate Leadership 
Group (C40)Global94 member cities have a variety of targets, aiming for 1.5°C 
compatibility by 2050. The network carries two explicit goals:

(i) to have every C40 city develop a climate action plan before 
the end of 2020 (Deadline 2020), which is to  ‘deliver action 
consistent with the objectives of the Paris Agreement’  
and (ii) to have cities achieve emissions neutrality by 20501.5 3 Current membership
Agriculture BusinessClimate Smart Agriculture 
(CSA) LCTPi aGlobalReducing agricultural and land-use change emissions from 
agriculture by at least 50% by 2030 and 65% by 2050. 
24 companies and 15 partners3.7 3.7Scaled-up global 
potential
Multisectoral BusinessScience Based Targets  
initiative (SBTi)GlobalBy 2030, 2000 companies have adopted a science-based target 
in line with a 2°C temperature goal2.7 2.7 Targeted membership
Source: Hsu et al. (2020). Note a As of December 2020 most of the Low Carbon Technology Partnerships (LCTPi) initiatives are defunct, except the Climate Smart Agriculture programme.

429
to which ambitious action by non-state actors is additional to what 
national governments intend to do. Sub-national and non-state 
climate action may also have benefits in reinforcing, implementing, 
or piloting national policy, in place of or in addition to achieving 
additional emissions reductions (Broekhoff et al. 2015; Heidrich et al. 
2016; Hsu et al. 2017).
Quantification of commitments by individual NSAs are limited 
to date. Attempts to quantify aggregate effects in 2030 of 
commitments by individual non-state and sub-national actors are 
reported by (Hsu et al. 2019; Kuramochi et al. 2020). Kuramochi 
et al. (2020) estimate potential mitigation by more than  1,600 
companies, around 6,000 cities and many regions (cities assessed 
have a  collective population of 579 million, and regions 514 
million). Individual commitments by these sub-national regions,

cities and companies could reduce GHG emissions in 2030 by 1.2 
to 2.0 GtCO 2-eq yr–1 compared to current national policies scenario 
projections, reducing projected emissions by 3.8–5.5% in 2030, if 
commitments are fully implemented and do not lead to weaker 
mitigation actions by others (Figure 4.1 left). In several countries, 
NSA commitments could potentially help meet or exceed national 
mitigation targets.
Quantification of potential emission reductions from international 
cooperative initiatives have been assessed in several studies, and 
recently synthesised (Hsu et al. 2020; Lui et al. 2021), with some initiatives reporting high potential. In Table 4.4 and Figure 4.1, we 
report estimates of the emissions reductions from 19 distinct sub-
national and non-state initiatives to mitigate climate change. The 
table shows wide ranges of potential mitigation based on current, 
target or potential membership, as well as a  wide diversity of

actors and membership assumptions. Current membership reflects 
the number of non-state or sub-national actors that are presently 
committed to a  particular initiative; while targeted or potential 
membership represents a  membership goal (e.g.,  increasing from 
100 to 200 members) that an initiative may seek to achieve (Kuramochi 
et al. 2020). When adding up emission reduction potentials, sub-
national and non-state international cooperative initiatives could 
reduce up to about 20 Gt of CO 2-eq in 2030 (limited evidence, medium 
agreement). Chapter 8 also presents data on the savings potential of 
cities and it suggests that these could reach 2.3 GtCO 2-eq annually 
by 2030 and 4.2 GtCO 2-eq annually for 2050.
Non-state action may be broader than assessed in the literature 
so far, though subject to uncertainty. The examples in Table  4.4 
and Figure 4.1 do not include initiatives that target the emissions 
from religious organisations, colleges and universities, civic and

cultural groups, and, to some extent, households, and in this sense 
may underestimate sub-national potential for mitigating emissions, 
rather than overestimate it. That said, the estimates are contingent 
on assumptions that sub-national and non-state actors achieve 
commitments – both with respect to mitigation and in some cases 
Figure 4.1 | Emissions reduction potential for non-state and sub-national actors by 2030.  Source: data in left panel from Hsu et al. (2020), right panel from 
Lui et al. (2020).

430
membership – and that these actions are not accounted for in nor 
lead to weakening of national actions.
Care is to be taken not to depict these efforts as additional to action 
within national NDCs, unless this is clearly established (Broekhoff 
et al. 2015). There are potential overlaps between individual NSAs 
and ICIs, and across ICIs. Kuramochi et al. (2020) propose partial 
and conservative partial effect methods to avoid double counting 
when comparing ambition, a matter that merits further attention. As 
the diversity of actions increased, the potential to count the same 
reductions multiple times increases.
Equally important to note here is that none of the studies reviewed 
in Figure  4.1 quantified the potential impact of financial sector 
actions, for example, divestment from emission intensive activities 
(Section 15.3 has a more detailed discussion of how financial actors

and instruments are addressing climate change). Moreover, only 
a limited number of studies on the impact of actions by diverse actors 
go beyond 2050 (Table 4.4), which may reflect analysts’ recognition 
of the increasing uncertainties of longer time horizons. Accurate 
accounting methods can help to avoiding counting finance multiple 
times, and methods across mitigation and finance would consider 
counting carbon market flows and the tons reduced. As Table  4.4 
and Figure  4.1 indicate, activities by businesses have potential to 
significantly contribute to global mitigation efforts. For example, 
the SBTi (Science Based Targets initiative) encourages companies to 
pledge to reduce their emissions at rates which according to SBTi 
would be compatible with global pathways to well below 2°C or 1.5°C, 
with various methodologies being proposed (Andersen et al. 2021; 
Faria and Labutong 2019). Readers may note, however, that the link

between emissions by individual actors and long-term temperature 
goals cannot be inferred without additional assumptions (Box 4.2). In 
the energy sector, some voluntary initiatives are also emerging to stop 
methane emissions associated with oil and gas supply chains. The Oil 
and Gas Methane Partnership (OGMP) is a voluntary initiative lead 
by the Climate and Clean Air Coalition, which has recently published 
a  comprehensive framework for methane detection, measurement 
and reporting (UNEP 2020b).
Initiatives made up of cities and sub-national regions have an 
especially large potential to reduce emissions, due to their inclusion 
of many actors, across a range of different geographic regions, with 
ambitious emissions reduction targets, and these actors’ coverage of 
a large share of emissions (Kuramochi et al. 2020). Hsu et al. (2019) 
find largest potential in that area. Several sub-national regions like 
California and Scotland have set zero emission targets (Höhne et al.

2019), supported by short- and medium-term interim goals (Scottish 
Government 2020b; State of California 2018). Sharing of effort across 
global and sub-global scales has not been quantified, though one 
study suggests that non-state actors have increasingly adopted 
more diverse framings, including vulnerability, human rights and 
transformational framings of justice (Shawoo and McDermott 2020). 
Initiatives focused on forestry have high emissions reduction potential 
due to the current high deforestation rates, and due to the ambitious 
targets of many of these forestry initiatives, such as the New York 
Declaration on Forests’ goal to end deforestation by 2030 (Höhne 
et al. 2019; Lui et al. 2021), although the Initiative acknowledges that insufficient progress has to-date been made towards this goal 
(NYDF Assessment Partners 2020). On the other hand, uncertainties 
in global forest carbon emissions (and therefore potential reductions)

are high and despite a multitude of initiatives in the sector, actually 
measured deforestation rates have not declined since the initiative 
was announced in 2014 (Sections 7.2 and 7.3.1). Moreover, not all 
initiatives are transparent about how they plan to reach their goals 
and may also rely on offsets.
Initiatives focused on non-CO2 emissions, and particularly on 
methane, can achieve sizable reductions, in the order of multiple 
GtCO 2-eq yr–1 (Table  4.4). The Global Cement and Concrete 
Association (formerly the Cement Sustainability Initiative), has 
contributed to the development of consistent energy and emissions 
reporting from member companies. The CSI also suggested 
possible approaches to balance GHG mitigation and the issues of 
competitiveness and leakage (Cook and Ponssard 2011). The member 
companies of the GCCA (CSI) have become better prepared for future 
legislation on managing GHG emissions and developed management

competence to respond to climate change compared to non-member 
companies in the cement sector (Busch et al. 2008; Global Cement 
and Concrete Association 2020). Accordingly, the cement industry 
has developed some roadmaps to reach net zero GHG around 2050 
(Sanjuán et al. 2020).
It is also important to note that individual NSAs and ICIs that commit 
to GHG mitigation activities are often scarce in many crucial and 
‘hard-to-abate’ sectors, such as iron and steel, cement and freight 
transport (Chapters 10 and 11). Sub-national and non-state action 
efforts could help these sectors meet an urgent need to accelerate 
the commercialisation and uptake of technical options to achieve low 
zero emissions (Bataille 2020).
4.2.4 Mid-century Low-emission Strategies 
at the National Level
An increasing amount of literature describes mitigation pathways 
for the mid-term (up to 2050). We assess literature reflecting on the 
UNFCCC process (Section 4.2.4.1), other official plans and strategies

(Section  4.2.4.2) and academic literature on mid-century low-
emission pathways at the national level (Section 4.2.4.3). After the 
Paris Agreement and the IPCC SR1.5 Report, the number of academic 
papers analysing domestic emission pathways compatible with 
the 1.5°C limit has been increasing. Governments have developed 
an increasing number of mitigation strategies up to 2050. Several 
among these strategies aim at net zero CO 2 or net zero GHG, but it is 
not yet possible to draw global implications due to the limited size of 
sample (limited evidence, limited agreement).

431
4.2.4.1 GHG Mitigation Target Under UNFCCC 
and Paris Agreement
The Paris Agreement requests that Parties should strive to formulate 
and communicate long-term low GHG development strategies by 
2020. (Note that by ‘long-term’, the UNFCCC means 2050, which is 
the end point of the ‘mid-term’ horizon range in the present report.) 
6 Specifying gases aids clarity, see Cross-Chapter Boxes 2 and 3 in chapters 2 and 3, respectively. Some countries refer to net zero GHG emissions as ‘climate neutrality’ or 
‘carbon neutrality’; the more precise terms are used where supported by the information assessed in this report.As of August 25, 2021, 31 countries and the European Union had 
submitted low-emissions development strategies (LEDS) (Table 4.5).
By 2018, most long-term strategies targeted 80% emissions reduction 
in 2050 relative to a reference (1990, 2000 or 2005). After IPCC SR1.5

was published, the number of the countries aiming at net zero CO 2 or 
GHG emissions has been increasing.6Box 4.2 | Direct Links Between an Individual Actor’s Mitigation Efforts in the Near Term and 
Global Temperature Goals in the Long Term Cannot be Inferred: Making direct links requires 
clear distinctions of spatial and temporal scales (Robertson 2021; Rogelj et al. 2021) and 
explicit treatment of ethical judgements made (Klinsky et al. 2017a; Holz et al. 2018; Klinsky 
and Winkler 2018; Rajamani et al. 2021)
The literature frequently refers to national mitigation pathways up to 2030 or 2050 using long-term temperature limits in the Paris 
Agreement (i.e., ‘2°C’ or ‘1.5°C scenario’). Without additional information, such denomination is incorrect. Working Group I reaffirmed 
‘with high confidence the AR5 finding that there is a near-linear relationship between cumulative anthropogenic CO 2 emissions and

the global warming they cause’ (WGI SPM AR6). It is not the function of any single country’s mitigation efforts, nor any individual 
actor’s. Emission pathways of individual countries or sectors in the near to mid-term can only be linked to a long-term temperature 
with additional assumptions specifying (i) the GHG emissions and removals of other countries up the mid-term; and (ii) the GHG 
emissions and removals of all countries beyond the near and mid-term. For example, a national mitigation pathway can be labelled 
‘2°C compatible’ if it derives from a global mitigation pathway consistent with 2°C via an explicit effort sharing scheme across 
countries (Sections 4.2.2.6 and 4.5).
Table 4.5 | Countries having submitted long-term low-GHG emission development strategy (as of 25 August 2021).
Country Date submitted GHG reduction target
USA Nov. 16, 2016 80% reduction of GHG in 2050 compared to 2005 level
Mexico Nov. 16, 2016 50% reduction of GHG in 2050 compared to 2000 level

Canada Nov. 17, 2016 80% reduction of GHG in 2050 compared to 2005 level
GermanyNov. 17, 2016
Rev. Apr. 26, 2017
Rev. May 4, 2017GHG neutrality by 2050
(Old target: 80–95% reduction of GHG in 2050 compared to 1990 level)
FranceDec. 28, 2016
Rev. Apr. 18, 2017
Rev. Feb. 8, 2021Achieving net zero GHG emissions by 2050
(Old target: 75% reduction of GHG in 2050 compared to 1990 level)
Benin Dec. 12, 2016 Resilient to climate change and low-carbon intensity by 2025
Czech Republic Jan. 15, 2018 80% reduction of GHG in 2050 compared to 1990 level
UK April 17, 2018 80% reduction of GHG in 2050 compared to 1990 level
Ukraine July 30, 2018 66–69% reduction of GHG in 2050 compared to 1990 level
Republic of the 
Marshall IslandsSept. 25, 2018 Net zero GHG emissions by 2050
Fiji Feb. 25, 2019 Net zero carbon by 2050 as central goal, and net negative emissions in 2041 under a Very High Ambition scenario

Japan June 26, 2019 80% reduction of GHG in 2050, and decarbonised society as early as possible in the 2nd half of 21st century
Portugal Sept. 20, 2019 Carbon neutrality by 2050
Costa Rica Dec. 12, 2019 Decarbonised economy with net zero emissions by 2050
European Union March 6, 2020 Net zero GHG emissions by 2050
Slovakia March 30, 2020Climate neutrality by 2050, with decarbonisation targets implying reduction of at least 90% compared to 1990  
(not taking into account removals)
Singapore March 31, 2020Halving emissions from its peak to 33 MtCO 2-eq by 2050, with a view to achieving net zero emissions as soon as viable in the second half 
of the century

432
4.2.4.2 Other National Emission Pathways to Mid-century
At the 2019 Climate Action Summit, 77 countries indicated their 
aim to reach net zero CO 2 emissions by 2050, more the number 
of countries having submitted LEDS to the UNFCCC. Table 4.6 lists 
the countries that have a national net zero by 2050 target in laws, 
strategies or other documents (The Energy and Climate Intelligence 
Unit 2019). Bhutan and Suriname already have achieved net negative 
emissions. France second ‘low-carbon national strategy’ adopted in 
2020 has an objective of GHG neutrality by 2050. Net zero is also the 
basis of the recent revision of the official notional price of carbon for 
public investment in France (Quinet et al. 2019). The Committee on 
Climate Change of the UK analyses sectoral options and concludes 
that delivering net zero GHG by 2050 is technically feasible but

highly challenging (Committee on Climate Change 2019). For 
Germany, three steps to climate neutrality by 2050 are introduced: 
first, a  65% reduction of emissions by 2030; second, a  complete 
switch to climate-neutral technologies, leading to a  95% cut in 
emissions, all relative to 1990 levels by 2050; and third balancing 
of residual emissions through carbon capture and storage (Prognos 
et al. 2020). In addition to the countries in Table 4.6, EU reported 
the net zero GHG emission pathways by 2050 under Green Deal 
(European Commission 2019). China and South Korea, have made 
announcements of carbon neutrality before 2060 and net zero GHG 
emission by 2050, respectively (UN 2020a,b). In the case of Japan, 
the new target to net zero GHG emission by 2050 was announced 
in 2020 (UN 2020c). As of August 25, 2021, a total 121 countries 
participate in the ‘Climate Ambition Alliance: Net Zero 2050’,

together with businesses, cities and regions.Country Date submitted GHG reduction target
South Africa Sep. 23, 2020 Net zero carbon economy by 2050
Finland Oct. 5, 2020 Carbon neutrality by 2035; 87.5–90% reduction of GHG in 2050 to 1990 level (excluding land use sector)
Norway Nov. 25, 2020 Being a low-emission society by 2050
Latvia Dec. 9, 2020 Climate neutrality by 2050 (non-reducible GHG emissions are compensated by removals in the LULUCF sector)
Spain Dec. 10, 2020 Climate neutrality by 2050
Belgium Dec. 10, 2020Carbon neutrality by 2050 (Walloon Region); Full climate neutrality (Flemish Region), and the European target of carbon neutrality by 2050 
(Brussels-Capital Region)
Austria Dec. 11, 2020 Climate-neutral by no later than 2050
Netherlands Dec. 11, 2020 Reduction of GHG emissions by 95% by 2050 compared to 1990 level.
Sweden Dec. 11, 2020 Zero net emissions of GHG into the atmosphere latest by 2045
Denmark Dec. 30, 2020 Climate neutrality by 2050

Republic of Korea Dec. 30, 2020 Carbon neutrality by 2050
Switzerland Jan. 28, 2021 2050 net zero GHG
Guatemala July 6, 2021 59% reduction of projected emissions by 2050
Indonesia July 22, 2021 540 MtCO 2-eq by 2050, and with further exploring opportunity to rapidly progress towards net zero emission in 2060 or sooner
Slovenia Aug. 23, 2021 Net zero emissions or climate neutrality by 2050
 ‘rev.’ = ‘date revised’
Table 4.6 | Countries with a national net zero CO 2 or GHG target by 2050 
(as of 25 August 2021).
CountryTarget 
yearTarget 
statusSource
Suriname Achieved Suriname INDC
Bhutan AchievedRoyal Government of Bhutan National 
Environment Commission
Germany 2045 In Law KSG
Sweden 2045 In Law Climate Policy Framework
European 
Union2050 In Law European Climate Law
Japan 2050 In LawJapan enshrines PM Suga’s 2050 carbon 
neutrality promise into law
United 
Kingdom2050 In Law The Climate Change Act
France 2050 In Law Energy and Climate Law

Canada 2050 In LawCanadian Net Zero Emissions  
Accountability Act
Spain 2050 In Law New Law
Denmark 2050 In Law The Climate Act
New Zealand 2050 In Law Zero Carbon Act
Hungary 2050 In Law Climate Ambition Alliance: Net Zero 2050
Luxembourg 2050 In Law Climate Ambition Alliance: Net Zero 2050
South Korea 2050Proposed 
LegislationSpeeches and Statements by the President
Ireland 2050Proposed 
LegislationClimate Action and Low Carbon 
Development (Amendment) Bill 2021
Chile 2050Proposed 
LegislationChile charts path to greener, fairer future
Fiji 2050Proposed 
LegislationDraft Climate Law
Note: In addition to the above list, the numbers of ‘In Policy Document’ and ‘Target 
Under discussion’ as Target status are 37 countries and 79 countries, respectively.

433
4.2.4.3 Mid-century Low Emission Strategies at the National 
Level in the Academic Literature
Since the 2000s, an increasing number of studies have quantified 
the emission pathways to mid-century by using national scale 
models. In the early stages, the national emission pathways were 
mainly assessed in the developed countries such as Germany, UK, 
France, the Netherlands, Japan, Canada, and USA. For example, the 
Enquete Commission in Germany identified robust and sustainable 
80% emission reduction pathways (Deutscher Bundestag 2002). In 
Japan, 2050 Japan Low-Carbon Society scenario team (2008) assessed 
the 70% reduction scenarios in Japan, and summarised the necessary 
measures to ‘Dozen Actions towards Low-Carbon Societies’.
Among developing countries, China, India, South Africa assessed 
their national emission pathways. For example, detailed analysis was

undertaken to analyse pathways to China’s goal for carbon neutrality 
(EFC 2020). In South Africa, a  Scenario Building Team (2007) 
quantified the Long Term Mitigation Scenarios for South Africa.
Prior to COP21, most of the literature on mid-century mitigation 
pathways at the national level was dedicated to pathways compatible 
with a  2°C limit (see Box  4.2 for a  discussion on the relationship 
between national mitigation pathways and global, long-term targets). 
After COP21 and the IPCC SR1.5, literature increasingly explored just 
transition to net zero emissions around 2050. This literature reflects 
on low-emissions development strategies (cognate with SDPS, 
Section 4.3.1) and policies to get to net zero CO 2 or GHG emissions 
(Garg and Waisman 2021) (Cross-Chapter Box 5 in this chapter).Figure 4.2 provides a snapshot of this literature. For a selected set of 
countries, it shows the mid-century emission pathways at national

scale that have been registered in the International Institute for 
Applied Systems Analysis (IIASA) national mitigation scenario 
database built for the purpose of this Report (Annex III.3.3). Overall, 
the database contains scenarios for 50 countries. Total GHG emission 
are the most comprehensive information to assess the pathways on 
climate mitigation actions, but energy-related CO 2 emissions are the 
most widely populated data in the scenarios. As a result, Figure 4.2 
shows energy-related CO 2 emission trajectories. Scenarios for EU 
countries show reduction trends even in the reference scenario, 
whereas developing countries and non-European developed 
countries such as Japan and USA show emissions increase in the 
reference. In most countries plotted on Figure  4.2, studies have 
found that reaching net zero energy related CO 2 emissions by 2050 is 
feasible, although the number of such pathways is limited.
The literature underlines the differences induced by the shift from

‘2°C scenarios’ (typically assumed to imply mitigation in 2050 
around 80% relative to 1990) to ‘1.5°C scenarios’ (typically assumed 
to imply net zero CO 2 or GHG emissions in 2050) (Box 4.2). For Japan, 
Oshiro et al. (2018) shows the difference between the implications of 
a 2°C scenario (80% reduction of CO 2 in 2050) and a 1.5°C scenario 
(net zero CO 2 emission in 2050), suggesting that for a net zero CO 2 
emission scenario, BECCS is a key technology. Their sectoral analysis 
aims in 2050 at negative CO 2 emissions in the energy sector, and 
near-zero emissions in the buildings and transport sectors, requiring 
energy efficiency improvement and electrification. To do so, drastic 
mitigation is introduced immediately, and, as a result, the mitigation 
Table 4.7 | Examples of research projects on country-level mitigation pathways in the near to medium-term under the multi-national analyses.
Project name Features

DDPP (Deep Decarbonisation Pathways Project)16 countries participated and estimated the deep decarbonisation pathways from the viewpoint of each country’s perspective 
using their own models (Waisman et al. 2019).
COMMIT (Climate Policy assessment and 
Mitigation Modelling to Integrate national 
and global Transition pathways)This research project assessed the country contributions to the target of the Paris Agreement (COMMIT 2019).
MAPS (Mitigation Action Plans and Scenarios)The mitigation potential and socio-economic implications in Brazil, Chile, Colombia and Peru were assessed (Delgado et al. 
2014; Zevallos et al. 2014; Benavides et al. 2015; La Rovere et al. 2018). The experiences of the MAPS programme suggests that 
co-production of knowledge by researchers and stakeholders strengthens the impact of research findings, and in depth studies of 
stakeholder engagement provide lessons (Boulle et al. 2015; Raubenheimer et al. 2015; Kane and Boulle 2018), which can assist

building capacity for long-term planning in other contexts (Calfucoy et al. 2019).
CD-LINKS (Linking Climate and Development 
Policies – Leveraging International Networks 
and Knowledge Sharing)The complex interplay between climate action and development at both the global scale and some national perspectives were 
explored. The climate policies for G20 countries up to 2015 and some levels of the carbon budget are assessed for short-term 
and long-term, respectively (Rogelj et al. 2017).
APEC Energy Demand and Supply OutlookTotal 21 APEC countries assessed a 2°C scenario scenario which follows the carbon emissions reduction pathway included  
in the IEA Energy Technology Perspectives (IEA 2017) by using the common framework (APERC 2019).
Low-Carbon Asia Research ProjectThe low-carbon emission scenarios for several countries and cities in Asia were assessed by using the same framework (Matsuoka

et al. 2013). The mitigation activities were summarised into 10 actions toward Low Carbon Asia to show a guideline to plan and 
implement the strategies for an LCS in Asia (Low-Carbon Asia Research Project 2012).
CLIMACAP–LAMPThis is an inter-model comparison exercise that focused on energy and climate change mitigation in Latin America  
(Clarke et al. 2016).
DDPP-LAC (Latin American Deep Decarbonisation 
Pathways project)Six countries in Latin America analysed the activities in agriculture, forestry and other land use (AFOLU) commonly  
(Bataille et al. 2020).
MILES (Modelling and Informing Low-Emission 
Strategies)This is an international research project which covers five countries and one region in order to build capacity and knowledge on 
low-emissions development strategies both at a national and global level, by investigating the concrete implications of INDCs for 
the low-carbon transformation by and beyond 2030 (Spencer et al. 2015).

434
target of Japan’s current NDC is considered not sufficient to achieve 
a 1.5°C scenario. Jiang et al. (2018) also show the possibility of net 
negative emissions in the power sector in China by 2050, indicating 
that biomass energy with carbon capture and storage (CCS) must be 
adopted on a large scale by 2040. Samadi et al. (2018) indicate the 
widespread use of electricity-derived synthetic fuels in end-use sectors 
as well as behavioural change for the 1.5°C scenario in Germany.In addition to those analyses, Vishwanathan et al. (2018b), Chunark 
and Limmeechokchai (2018) and Pradhan et al. (2018b) build national 
scenarios in India, Thailand and Nepal, respectively, compatible 
with a global 1.5°C. Unlike the studies mentioned in the previous 
paragraph, they translate the 1.5°C goal by introducing in their model 
a carbon price trajectory estimated by global models as sufficient to

achieve the 1.5°C target. Because of the high economic growth and 
Figure 4.2 | Energy related CO 2 emission pathways to mid-century from existing studies.  Source of the historical data: Greenhouse Gas Inventory Data of UNFCCC 
(https://di.unfccc.int/detailed_data_by_party)

435
increase of GHG emissions in the reference case, CO 2 emissions in 
2050 do not reach zero. Finally, the literature also underlines that 
to achieve a 1.5°C target, mitigation measures relative to non-CO 2 
emissions become important, especially in developing countries 
where the share of non-CO 2 emissions is relatively high. (La Rovere 
et al. 2018) treat mitigation actions in AFOLU sector.
Chapter 3 reported on multi-model analyses, comparison of results 
using different models, of global emissions in the long term. At the 
national scale, multi-model analyses are still limited, though such 
analyses are growing as shown in Table 4.7. By comparing the results 
among different models and different scenarios in a  country, the 
uncertainties on the emission pathways including the mitigation 
measures to achieve a given emission target can be assessed.

Another type of multi-model analysis is international, in other 
words, different countries join the same project and use their own 
national models to assess a  pre-agreed joint mitigation scenario. 
By comparing the results of various national models, such projects 
help highlight specific features of each country. More robust 
mitigation measures can be proposed if different types of models 
participate. These activities can also contribute to capacity building 
in developing countries.
4.2.5 What Is to Be Done to Accelerate Mitigation?
4.2.5.1 Overview of Accelerated Mitigation Pathways
The literature reports an increasing number of accelerated mitigation 
pathways that are beyond NDCs in different regions and countries. 
There is increasing understanding of the technical content of such 
pathways, though the literature remains limited on some dimensions, 
such as demand-side options, systems analysis, or mitigation of

AFOLU non-CO 2 GHGs. The present section describes insights from 
this literature.
Overall, the literature shows that pathways considered consistent 
with below 2°C (>67%) or 1.5°C (Box 4.2) – including inter alia 
80% reduction of GHG emissions in 2050 relative to 1990 or 
100% renewable electricity scenarios  – are technically feasible 
(Lund and Mathiesen 2009; Mathiesen et al. 2011; Esteban and 
Portugal-Pereira 2014; Young and Brans 2017; Esteban et al. 2018; 
Child et al. 2019; Hansen et al. 2019). They entail increased end-
use energy efficiency, significant increases in low-carbon energy, 
electrification, other new and transformative technologies in 
demand sectors, adoption of carbon capture and sequestration 
(CCS) to reduce gross emissions, and contribution to net negative 
emissions through carbon dioxide removal (CDR) and carbon sinks. 
For these pathways to be realised, the literature assumes higher 
carbon prices, combined in policy packages with a range of other

policy measures.
The most recent literature also reflects on accelerated mitigation 
pathways aiming at reaching net zero CO 2 emissions or net zero 
GHG emissions by 2050 (Section 4.2.4 and Table 4.6; see Glossary 
entries on ‘net zero CO 2 emissions’ and ‘net zero GHG emissions’). Specific policies, measures and technologies are needed to reach 
such targets. These include, broadly, decarbonising electricity supply, 
including through low-carbon energy, radically more efficient use of 
energy than today; electrification of end-uses (including transport/
electric vehicles); dramatically lower use of fossil fuels than today; 
converting other uses to low- or zero-carbon fuels (e.g., hydrogen, 
bioenergy, ammonia) in hard-to-decarbonise sectors; and setting 
ambitious targets to reduce methane and other short-lived climate 
Accelerated mitigation pathways differ by countries, depending inter 
alia on sources of emissions, mitigation opportunities and economic

context. In China, India, Japan and other Southeast Asian countries, 
more aggressive action related to climate change is also motivated 
by regional concerns over health and air quality related to air 
pollutants and SLCFs (Ashina et al. 2012; Aggarwal 2017; Kuramochi 
et al. 2017; Xunzhang et al. 2017; Dhar et al. 2018; Jiang et al. 2018; 
Oshiro et al. 2018; China National Renewable Energy Centre 2019; 
Energy Transitions Commission and Rocky Mountain Institute 2019; 
Khanna et al. 2019). Studies of accelerated mitigation pathways 
in North America tend to focus on power sector and imported fuel 
decarbonisation in the US , and on electrification and demand-side 
reductions in Canada (Vaillancourt et al. 2017; Hodson et al. 2018; 
Victor et al. 2018; Bahn and Vaillancourt 2020; Hammond et al. 2020; 
Jayadev et al. 2020). In Latin America, many pathways emphasise 
supply-side mitigation measures, finding that replacing thermal

power generation and developing bioenergy (where resources are 
available) utilisation offers the greatest mitigation opportunities 
(Herreras Martínez et al. 2015; Nogueira de Oliveira et al. 2016; 
Arango-Aramburo et al. 2019; Delgado et al. 2020; Lap et al. 2020). 
The European Union member states (EU-28) recently announced 
2050 climate neutrality goal is explored by pathways that emphasise 
complete substitution of fossil fuels with electricity generated by 
low-carbon sources, particularly renewables; demand reductions 
through efficiency and conservation, and novel fuels and end-use 
technologies (Prognos et al. 2020). The limited literature so far on 
Africa’s future pathways suggest those could be shaped by increasing 
energy access and mitigating the air pollution and health effects 
of relying on traditional biomass use, as well as cleaner expansion of 
power supply alongside end-use efficiency improvements (Hamilton

and Kelly 2017; Oyewo et al. 2019, 2020; Ven et al. 2019; Wright et al. 
2019; Forouli et al. 2020).
Though they differ across countries, accelerated mitigation pathways 
share common characteristics as follows. First, energy  efficiency, 
conservation, and reducing energy use in all energy demand 
sectors (buildings, transport, and industry) are included in nearly 
all literature that addresses future demand growth (Ashina et al. 
2012; Saveyn et al. 2012; Schmid and Knopf 2012; Chiodi et al. 2013; 
Deetman et al. 2013; Jiang et al. 2013; Thepkhun et al. 2013; Schiffer 
2015; Altieri et al. 2016; Jiang et al. 2016; McNeil et al. 2016; 
Nogueira de Oliveira et al. 2016; Chilvers et al. 2017; Elizondo et al. 
2017; Fragkos et al. 2017; Jacobson et al. 2017, 2019; Kuramochi 
et al. 2017; Oshiro et al. 2017a; Ouedraogo 2017; Shahiduzzaman 
and Layton 2017; Vaillancourt et al. 2017; Hanaoka and Masui 2018; 
Hodson et al. 2018; Lee et al. 2018; Lefèvre et al. Oshiro et al. 2018;

2018; Capros et al. 2019; Dioha et al. 2019; Duscha et al. 2019;

436
Khanna et al. 2019; Kato and Kurosawa 2019; Nieves et al. 2019; 
Sugiyama et al. 2019; Zhou et al. 2019; Dioha and Kumar 2020).
Similarly, electrification of industrial processes (up to 50% for EU and 
China) and transport (e.g., 30–60% for trucks in Canada), buildings, 
and district heating and cooling are commonplace (Ashina et al. 2012; 
Massetti 2012; Saveyn et al. 2012; Chiodi et al. 2013; Deetman et al. 
2013; Fragkos et al. 2017; Oshiro et al. 2017b; Vaillancourt et al. 2017; 
Xunzhang et al. 2017; Jiang et al. 2018; Mittal et al. 2018; Oshiro et al. 
2018; Capros et al. 2019; Zhou et al. 2019; Hammond et al. 2020).
Third, lower emissions sources of energy, such as nuclear, renewables, 
and some biofuels, are seen as necessary in all pathways. However, 
the extent of deployment depends on resource availability. Some 
countries have set targets of up to 100% renewable electricity, while

others such as Brazil rely on increasing biomass up to 40–45% of 
total or industry energy consumption by 2050.
Fourth, CCS and CDR are part of many of the national studies reviewed 
(Ashina et al. 2012; Massetti 2012; Jiang et al. 2013; Thepkhun 
et al. 2013; Herreras Martínez et al. 2015; van der Zwaan et al. 
2016; Chilvers et al. 2017; Solano Rodriguez et al. 2017; Xunzhang 
et al. 2017; Kuramochi et al. 2018; Mittal et al. 2018; Oshiro et al. 
2018; Roberts et al. 2018b; Vishwanathan et al. 2018b; Kato and 
Kurosawa 2019). CCS helps reduce gross emissions but does not 
remove CO 2 from the atmosphere, unless combined with bioenergy 
(BECCS). CO 2 removal from sources with no identified mitigation 
measures is considered necessary to help achieve economy-wide 
net negative emissions (Massetti 2012; Deetman et al. 2013; Solano 
Rodriguez et al. 2017).
Each option is assessed in more detail in the following sections.4.2.5.2 Accelerated Decarbonisation of Electricity Through

Renewable Energy
Power generation could decarbonise much faster with scaled up 
deployment of renewable energy and storage. Both technologies are 
mature, available, and fast decreasing in costs, more than for many 
other mitigation options. Models continuously underestimate the 
speed at which renewables and storage expand. Higher penetration of 
renewable energy in the power sector is a common theme in scenarios. 
Some studies provide cost optimal electricity mix under emission 
constraints, while others explicitly explore a  100%  renewables or 
100% emission free electricity sector (Box 4.3).
Figure  4.3 shows an increasing share of renewable electricity in 
most countries historically, with further increases projected in many 
decarbonisation pathways. Targets for very high shares of renewable 
electricity generation – up to 100% – are shown for a number of 
countries, with the global share projected to range from 60% to

70% for 1.5°C with no overshoot (C0) to below 2°C (C4) scenarios. 
Countries and states that have set 100% renewables targets include 
Scotland for 2020 (Scottish Government 2021), Austria (2030), 
Denmark (2035) and California (2045) (Figure 4.3).
While 100% renewable electricity generation by 2050 is found 
to be feasible, it is not without issues. For example, (Jacobson 
et al. 2017, 2019) find it feasible for 143 countries with only a 9% 
average increase in economic costs (considering all social costs) if 
annual electricity demand can be reduced by 57%. Others state that 
challenges exist with speed of expansion, ensuring sufficient supply 
at all times or higher costs compared to other alternatives (Clack 
et al. 2017). In-depth discussion of net zero electricity systems can be 
found in Section 6.6.
Box 4.3 | Examples of High-renewable Accelerated Mitigation Pathways

Many accelerated mitigation pathways include high shares of renewable energy, with national variations. In Europe, some argue 
that the EU 2050 net zero GHG emissions goal can be met with 100% renewable power generation, including use of renewable 
electricity to produce hydrogen, biofuels (including imports), and synthetic hydrocarbons, but will require significant increases in 
transmission capacity (Duscha et al. 2019; Zappa et al. 2019). Capros et al. (2019) explore a 1.5°C compatible pathway that includes 
85% renewable generation, with battery, pumped hydro, and chemical storage for variable renewables. High-renewable scenarios 
also exist for individual Member States. In France, for example, Krakowski et al. (2016) propose a 100% renewable power generation 
scenario that relies primarily on wind (62%), solar PV (26%) and oceans (12%). To reach this aim, integration into the European grid is

of vital importance (Brown et al. 2018). While debated, incremental costs could be limited regardless of specific assumptions of future 
costs of individual technologies (Shirizadeh et al. 2020). In Germany, similarly, 100% renewable electricity systems are found feasible 
by numerous studies (Oei et al. 2020; Thomas Klaus et al. 2010; Wuppertal-Institut 2021; Hansen et al. 2019).
In South Africa, it is found that long-term mitigation goals could be achieved with accelerated adoption of solar PV and wind 
generation, if the electricity sector decarbonises by phasing-out coal entirely by 2050, even if CCS is not feasible before 2025 (Altieri 
et al. 2015; Beck et al. 2013). Abundant solar PV and wind potential, coupled with land availability suggest that more than 75% of 
power generation could ultimately originate from solar PV and wind (Oyewo et al. 2019; Wright et al. 2019).

For the US, share of renewables in power generation in 2050 in accelerated mitigation scenarios vary widely, 40% in (Hodson et al. 
2018; Jayadev et al. 2020), more than half renewable and nuclear in (Victor et al. 2018) to 100% in Jacobson et al. (2017, 2019).

437
Box 4.3 (continued)
Under cost optimisation scenarios for Brazil, electricity generation, which is currently dominated by hydropower, could reach 100% 
by adding biomass (Köberle et al. 2020). Other studies find that renewable energy, including biomass, could account for more than 
30% of total electricity generation (Nogueira de Oliveira et al. 2016; Portugal-Pereira et al. 2016).
In Colombia, where hydropower resources are abundant and potential also exist for solar and wind, a deep decarbonisation pathway 
would require 57% renewable power generation by 2050 (Arango-Aramburo et al. 2019) while others find 80% would be possible 
(Delgado et al. 2020).
In Asia, Japan could have up to 50% variable renewable electricity supply to reduce CO 2 emissions by 80% by 2050 in some of 
its deep mitigation scenarios ( Kato and Kurosawa 2019; Sugiyama et al. 2019; Ju et al. 2021; Shiraki et al. 2021; Silva Herran and

Fujimori 2021). One view of China’s 1.5°C pathway includes 59% renewable power generation by 2050 (Jiang et al. 2018). One view 
of India’s 1.5°C pathway also includes 52% renewable power generation, and would require storage needs for 35% of generation 
(Parikh et al. 2018).
Figure 4.3 | Historical and projected levels and targets for the share of renewables in electricity generation.  Sources: IEA energy balances for past trends, IPCC 
AR6 scenario dataset including national model and regional versions in global models (10th to 90th percentile of 1.5°C with no overshoot (C0) to below 2°C (C4) scenarios), 
national/regional sources.

438
4.2.5.3 Bioenergy Plays Significant Role in Resource Abundant 
Countries in Latin America and Parts of Europe
Bioenergy could account for up to 40% of Brazil’s total final energy 
consumption, and a  60% share of fuel for light-duty vehicles by 
2030 (Lefèvre et al. 2018), and is considered most cost-effective 
in transport and industrial applications (Lap et al. 2020). BECCS in 
the power sector is also considered cost-effective option for supply-
side mitigation (Borba et al. 2012; Herreras Martínez et al. 2015; 
Bioenergy also plays a prominent role in some EU countries’ deep 
decarbonisation strategies. Domestic biomass alone can help 
Germany meet its 95% CO 2 reduction by 2050 goal, and biomass 
and CCS together are needed to reduce CO 2 by 80% by 2050 in the 
Netherlands (Mikova et al. 2019). Studies suggest that mitigation

efforts in France include biofuels and significant increases in biomass 
use, including up to 45% of industry energy by 2050 for its net GHG 
neutrality goal (Doumax-Tagliavini and Sarasa 2018; Capros et al. 
2019). Increased imports may be needed to meet significant increases 
in EU’s bioenergy use, which could affect energy security and the 
sustainability of bioenergy production outside of the EU (Mandley 
et al. 2020; Daioglou et al. 2020).
While BECCS is needed in multiple accelerated mitigation pathways, 
large-scale land-based biological CDR may not prove as effective as 
expected, and its large-scale deployment may result in ecological 
and social impacts, suggesting it may not be a viable carbon removal 
strategy in the next 10–20 years (Vaughan and Gough 2016; Boysen 
et al. 2017; Dooley and Kartha 2018). The effectiveness of BECCS 
could depend on local contexts, choice of biomass, fate of initial 
aboveground biomass and fossil-fuel emissions offsets  – carbon

removed through BECCS could be offset by losses due to land-use 
change (Harper et al. 2018; Butnar et al. 2020; Calvin et al. 2021). 
Large-scale BECCS may push planetary boundaries for freshwater use, 
exacerbate land-system change, significantly alter biosphere integrity 
and biogeochemical flows (Heck et al. 2018; Fuhrman et al. 2020; 
Stenzel et al. 2021; Ai et al. 2021). (Sections 7.4 and 12.5) 
4.2.5.4 CCS May Be Needed to Mitigate Emissions From the 
Remaining Fossil Fuels That Cannot Be Decarbonised, 
but the Economic Feasibility of Deployment 
Is Not Yet Clear
CCS is present in many accelerated mitigation scenarios in the 
literature. In Brazil, (Nogueira de Oliveira et al. 2016) consider 
BECCS and CCS in hydrogen generation more feasible than CCS in 
thermal power plants, with costs ranging from USD70–100 per tCO 2. 
Overall, (van der Zwaan et al. 2016) estimate that 33–50% of total 
electricity generation in Latin America could be ultimately covered

by CCS. In Japan, CCS and increased bioenergy adoption plus waste-
to-energy and hydrogen-reforming from fossil fuel are all considered 
necessary in the power sector in existing studies, with potential up 
to 200 MtCO 2 yr–1 (Ashina et al. 2012; Oshiro et al. 2017a; Kato 
and Kurosawa 2019; Sugiyama et al. 2021). In parts of the EU, 
after 2030, CCS could become profitable with rising CO 2 prices 
(Schiffer 2015). CDR is seen as necessary in some net GHG neutrality pathways (Capros et al. 2019) but evidence on cost-effectiveness is 
scarce and uncertain (European Commission 2013). For France and 
Sweden, (Millot et al. 2020) include CCS and BECCS to meet net zero 
GHG emissions by 2050. For Italy, (Massetti 2012) propose a zero-
emission electricity scenario with a combination of renewable and 
coal, natural gas, and BECCS.
In China, an analysis concluded that CCS is necessary for remaining 
coal and natural gas generation out to 2050 (Jiang et al. 2018; Energy

Transitions Commission and Rocky Mountain Institute 2019). Seven 
to 10 CCS projects with installed capacity of 15 GW by 2020 and 
total CCS investment of 105 billion RMB (2010 RMB) are projected 
to be needed by 2050 under a 2°C compatible pathway according to 
(Jiang et al. 2013, 2016; Lee et al. 2018). Under 1.5°C pathway, an 
analysis found China would need full CCS coverage of the remaining 
12% of power generation from coal and gas power and 250 GW of 
BECCS (Jiang et al. 2018). Combined with expanded renewable and 
nuclear development, total estimated investment in this study is 5% 
of China’s total GDP in 2020,  1.3% in 2030, and  0.6% in 2050 
(Jiang et al. 2016).
Views regarding feasibility of CCS can vary greatly for the same 
country. In the case of India’s electricity sector for instance, some 
studies indicate that CCS would be necessary (Vishwanathan et al. 
2018a), while others do not – citing concerns around its feasibility

due to limited potential sites and issues related to socio-political 
acceptance  – and rather point to very ambitious increase in 
renewable energy, which in turn could pose significant challenges 
in systematically integrating renewable energy into the current 
energy systems (Viebahn et al. 2014; Mathur and Shekhar 2020). 
Some limitations of CCS, including uncertain costs, lifecycle and net 
emissions, other biophysical resource needs, and social acceptance 
are acknowledged in existing studies (Viebahn et al. 2014; Jacobson 
2019; Mathur and Shekhar 2020; Sekera and Lichtenberger 2020).
While national mitigation portfolios aiming at net zero emissions 
or lower will need to include some level of CDR, the choice of 
methods and the scale and timing of their deployment will depend 
on the ambition for gross emission reductions, how sustainability and 
feasibility constraints are managed, and how political preferences

and social acceptability evolve (Cross-Chapter Box 8). Furthermore, 
mitigation deterrence may create further uncertainty, as anticipated 
future CDR could dilute incentives to reduce emissions now (Grant 
et al. 2021), and the political economy of net negative emissions has 
implications for equity (Mohan et al. 2021).
4.2.5.5 Nuclear Power Is Considered Strategic for Some 
Countries, While Others Plan to Reach Their Mitigation 
Targets Without Additional Nuclear Power
Nuclear power generation is developed in many countries, though 
larger-scale national nuclear generation does not tend to associate 
with significantly lower carbon emissions (Sovacool et al. 2020). 
Unlike other energy sources such as wind and PV solar, levelised 
costs of nuclear power has been rising in the last decades (Grubler 
2010; Gilbert et al. 2017; Portugal-Pereira et al. 2018). This is mainly 
due to overrun of overnight construction costs related to delays in

439
project approvals and construction, and more stringent passive 
safety measures, which increases the complexity of systems. After 
the Fukushima Daiichi accident in Japan, nuclear programs in several 
countries have been phased out or cancelled (Carrara 2020; Huenteler 
et al. 2012; Kharecha and Sato 2019; Hoffman and Durlak 2018). 
Also the compatibility of conventional prresurised water reactors and 
boiling water reactors with large proportion of renewable energy in 
the grid it is yet to be fully understood.
Accelerated mitigation scenarios offer contrasting views on the 
share of nuclear in power generation. In the USA, (Victor et al. 2018) 
build a scenario in which nuclear contributes 23% of CO 2 emission 
reductions needed to reduce GHG emissions by 80% from 2005 levels 
by 2050. Deep power sector decarbonisation pathways could require

a two-folded increase in nuclear capacity according to (Jayadev et al. 
2020) for the USA, and nearly a ten-fold increase for Canada, but 
may be difficult to implement (Vaillancourt et al. 2017). For China to 
meet a 1.5°C pathway or achieve carbon neutrality by 2050, nuclear 
may represent 14–28% of power generation in 2050 according to 
(Jiang et al. 2018; China National Renewable Energy Centre 2019; 
Energy Transitions Commission and Rocky Mountain Institute 2019). 
For South Korea, Hong et al. (2014) and Hong and Brook (2018) find 
that increasing nuclear power can help complement renewables in 
decarbonising the grid. Similarly, India has put in place a three-stage 
nuclear programme which aims to enhance nuclear power capacity 
from the current level of 6  GW to 63 GW by 2032, if fuel supply 
is ensured (GoI 2015). Nuclear energy is also considered necessary 
as part of accelerated mitigation pathways in Brazil, although it is

not expected to increase significantly by 2050 even under stringent 
low-carbon scenarios (Lucena et al. 2016). France developed its 
nuclear strategy in response to energy security concerns after the 
1970s oil crisis, but has committed to reducing nuclear’s share of 
power generation to 50% by 2035 (Millot et al. 2020). Conversely, 
some analysis find deep mitigation pathways, including net zero GHG 
emissions and 80–90% reduction from 2013 levels, feasible without 
additional nuclear power in EU-28 and Japan respectively, but 
assuming a combination of bio- and novel fuels and CCS or land-use 
based carbon sinks (Kato and Kurosawa 2019; Duscha et al. 2019).
Radically more efficient use of energy than today, including electricity, 
is a complementary set of measures, explored in the following.
4.2.5.6 Efficient Cooling, SLCFs and Co-benefits
In warmer climate regions undergoing economic transitions, 
improving the energy efficiency of cooling and refrigeration equipment

is often important for managing peak electricity demand and can 
have co-benefits for climate mitigation as well as SLCF reduction, as 
expected in India, Africa, and Southeast Asia in the future.
Air conditioner adoption is rising significantly in low- and middle-
income countries as incomes rise and average temperatures increase, 
including in Southeast Asian countries such as Thailand, Indonesia, 
Vietnam, and the Philippines, as well as Brazil, Pakistan, Bangladesh, 
and Nigeria (Biardeau et al. 2020). Cooling appliances are expected 
to increase from  3.6 billion to  9.5 billion by 2050, though up to 
14 billion could be required to provide adequate cooling for all (Birmingham Energy Institute 2018). Current technology pathways are 
not sufficient to deliver universal access to cooling or meet the 2030 
targets under the SDGs, but energy efficiency, including in equipment 
efficiency like air conditioners, can reduce this demand and help limit

additional emissions that would further exacerbate climate change 
(Biardeau et al. 2020; Dreyfus et al. 2020; UNEP and IEA 2020). Some 
countries (India, South Africa) have started to recognise the need for 
more efficient equipment in their mitigation strategies (Altieri et al. 
2016; Ouedraogo 2017; Paladugula et al. 2018).
One possible synergy between SLCF and climate change mitigation 
is the simultaneous improvement in energy efficiency in refrigeration 
and air-conditioning equipment during the hydrofluorocarbon (HFC) 
phase-down, as recognised in the Kigali Amendment to the Montreal 
Protocol. The Kigali Amendment and related national and regional 
regulations are projected to reduce future radiative forcing from 
HFCs by about half in 2050 compared to a scenario without any HFC 
controls, and to reduce future global average warming in 2100 from 
a baseline of 0.3°C–0.5°C to less than 0.1°C, according to a recent

scientific assessment of a  wide literature (World Meteorological 
Organization 2018). If ratified by signatories, the rapid phase-down 
of HFCs under the Kigali Amendment is possible because of extensive 
replacement of high-global warming potential (GWP) HFCs with 
commercially available low-GWP alternatives in refrigeration and 
air-conditioning equipment. Each country’s choices of alternative 
refrigerants will likely be determined by energy efficiency, costs, 
and refrigerant toxicity and flammability. National and regional 
regulations will be needed to drive technological innovation and 
development (Polonara et al. 2017).
4.2.5.7 Efficient Buildings, Cooler in Summer, Warmer 
in Winter, Towards Net Zero Energy
Most accelerated mitigation pathway scenarios include significant 
increase in building energy efficiency. Countries in cold regions, 
in particular, often focus more on building sector GHG emissions 
mitigation measures such as improving building envelopes and home

appliances, and electrifying space heating and water heating.
For example, scenarios for Japan project continued electrification of 
residential and commercial buildings to 65% and 79% respectively 
by 2050 to reach 70–90% CO 2 reduction from 2013 levels (Kato 
and Kurosawa 2019). Similarly, a  mitigation pathway for China 
compatible with 1.5°C would require 58% to 70% electrification of 
buildings according to (Jiang et al. 2018; China National Renewable 
Energy Centre 2019E; nergy Transitions Commission and Rocky 
Mountain Institute 2019). For the EU-28 to reach net carbon 
neutrality, complete substitution of fossil fuels with electricity (up 
to 65% share), district heating, and direct use of solar and ambient 
heat are projected to be needed for buildings, along with increased 
use of solar thermal and heat pumps for heating (Duscha et al. 
2019). In the UK and Canada, improved insulation to reduce energy 
demand and efficient building appliances and heating systems are

important building strategies needed to reduce emissions to zero 
by 2050 (Vaillancourt et al. 2017; Chilvers et al. 2017; Roberts 
et al. 2018a). In Ireland, achieving 80–95% emissions reduction 
below 1990 levels by 2050 also requires changes in building energy

440
technology and efficiency, including improving building envelopes, 
fuel switching for residential buildings, and replacing service-sector 
coal use with gas and renewables according to (Chiodi et al. 2013). 
In South Africa, improving industry and building energy efficiency is 
also considered a key part of mitigation strategies (Altieri et al. 2016; 
Ouedraogo 2017).
In addition, an increasing number of countries have set up net zero 
energy building targets (Table 4.8) (Höhne et al. 2020). Twenty-seven 
countries have developed roadmap documents for NZEBs, mostly 
in developed countries in Europe, North America, and Asia-Pacific, 
focusing on energy efficiency and improved insulation and design, 
renewable and smart technologies (Mata et al. 2020). The EU, Japan 
and the USA (the latter for public buildings only) have set targets 
for shifting new buildings to 100% near-zero energy buildings by

2030, with earlier targets for public buildings. Scotland has a similar 
target for 2050 (Höhne et al. 2020). Technologies identified as 
needed for achieving near-zero energy buildings vary by region, but 
include energy-efficient envelope components, natural ventilation, 
passive cooling and heating, high performance building systems, 
air heat recovery, smart and information and communication 
technologies, and changing future heating and cooling supply fuel 
mixes towards solar, geothermal, and biomass (Mata et al. 2020). 
Sub-national regions in Spain, USA, Germany, and Mexico have set 
local commitments to achieving net zero carbon new buildings by 
2050, with California having the most ambitious aspirational target 
of zero net energy buildings for all new buildings by 2030 (Höhne 
et al. 2020). The EU is also targeting the retrofitting of 3% of existing 
public buildings to zero-energy, with emphasis on greater thermal

insulation of building envelopes (Höhne et al. 2020; Mata et al. 
2020). China’s roadmaps have emphasised insulation of building 
envelope, heat recovery systems in combination with renewable 
energy, including solar, shallow geothermal, and air source heat 
pumps (Mata et al. 2020).
4.2.5.8 Electrifying Transport
Electrification of transport in tandem with power sector 
decarbonisation is expected to be a  key strategy for deep CO 2 
mitigation in many countries. Passenger transport and light duty 
freight can already be electrified, but electrifying heavy-duty road 
transport and fuel switching in aviation and shipping are much more 
difficult and have not been addressed in most of the recent research.
In Germany, widespread electrification of private vehicles is expected 
by 2030 (Schmid and Knopf 2012) while for the EU-28, 50% overall transport electrification (excluding feedstock) and 75% electrification 
of road transport is needed to reach net carbon neutrality according

to (Duscha et al. 2019). In addition, novel fuels such as hydrogen, 
synthetic hydrocarbons and sustainable biogenic fuels are needed 
to decarbonise aviation and water transport to achieve net carbon 
neutrality (Duscha et al. 2019).
In India, electrification, hydrogen, and biofuels are key to 
decarbonising the transport sector (Dhar et al. 2018; Mittal et al. 
2018; Vishwanathan et al. 2018b; Mathur and Shekhar 2020). Under 
a 1.5°C scenario, nearly half of the light-duty passenger vehicle stock 
needs to be electrified according to (Parikh et al. 2018). In China, 
a  1.5°C-compatible pathway would require electrification of two-
fifths of transport (Jiang et al. 2018; China National Renewable 
Energy Centre 2019).
Similarly, in Canada, electrification of 59% of light-duty trucks and 
23% of heavy-duty trucks are needed as part of overall strategy 
to reduce CO 2 emissions by 80% by 2050. In addition, hydrogen is 
expected to play a major role by accounting for nearly one-third of

light-duty trucks, 68% of heavy-duty trucks, and 33% of rail by 2050 
according to Hammond et al. (2020).
4.2.5.9 Urban Form Meets Information Technology
Beyond technological measures, some densely populated countries 
including Germany, Japan, and India are exploring using information 
technology/internet of things (IOT) to support mode-shifting and 
reduce mobility demand through broader behaviour and lifestyle 
changes (Ashina et al. 2012; Canzler and Wittowsky 2016; Aggarwal 
2017; Dhar et al. 2018; Vishwanathan et al. 2018b). In Japan, 
accelerated mitigation pathways consider the use of information 
technology and internet of things (IoT) to transform human 
behaviour and transition to a sharing economy (Ashina et al. 2012; 
Oshiro et al. 2017a, 2018). In Germany, one study points to including 
electromobility information and communication technologies in 
the transport sector as key (Canzler and Wittowsky 2016) while

another emphasise shifting from road to rail transport, and reduced 
distances travelled as other possible transport strategies (Schmid and 
Knopf 2012). India’s transport sector strategies also include use of 
information technology and the internet, a  transition to a  sharing 
economy, and increasing infrastructure investment (Dhar et al. 2018; 
Vishwanathan et al. 2018b). Behaviour and lifestyle change along 
with stakeholder integration in decision-making are considered 
key to implementing new transport policies (Aggarwal 2017; 
Dhar et al. 2018).Table 4.8 | Targets by countries, regions, cities and businesses on decarbonising the building sector.
Countries Sub-national Regions Cities Businesses
Shift to 100% (near-)zero energy buildings for new buildings 3 6 >28 >44
Fully decarbonise the building sector 1 6 >28 >44
Phase out fossil fuels (for example, gas) for residential heating 1 – >3
Increase the rate of zero-energy renovations 1 (public buildings)

Source: Höhne et al. (2020), supplementary information. https://newclimate.org/ambitiousactions .

441
4.2.5.10 Industrial Energy Efficiency
Industrial energy efficiency improvements are considered in nearly 
all countries but for countries where industry is expected to continue 
to be a  key sector, new and emerging technologies that require 
significant  R&D investment, such as hydrogen and CCS, make 
ambitious targets achievable.
In China, for example, non-conventional electrical and renewable 
technologies, including low-grade renewable heat, biomass use for 
high-temperature heat in steel and cement sectors, and additional 
electrification in glass, food and beverage, and paper and pulp 
industries, are part of scenarios that achieve 60% reduction in 
national CO 2 emission by 2050 (Khanna et al. 2019; Zhou et al. 
2019), in addition to increased recycled steel for electric arc furnaces 
and direct electrolysis or hydrogen-based direct reduction of iron

and CCS utilisation in clinker and steel-making (Jiang et al. 2018; 
China National Renewable Energy Centre 2019). Similarly, in India, 
(Vishwanathan and Garg 2020) point to the need for renewable 
energy and CCS to decarbonise the industrial sector. In EU-28, net 
CO2 neutrality can only be reached with 92% reduction in industrial 
emissions relative to 1990, through electrification, efficiency 
improvement and new technologies such as hydrogen-based direct 
reduction of steel, low-carbon cement and recycling (Duscha et al. 
2019). Both China and EU see 50% of industry electrification by 2050 
as needed to meet 1.5°C and net carbon neutrality pathways (Jiang 
et al. 2018; Capros et al. 2019).
Aggressive adoption of technology solutions for power sector 
decarbonisation coupled with end-use efficiency improvements 
and low-carbon electrification of buildings, industry and transport 
provides a pathway for accelerated mitigation in many key countries,

but will still be insufficient to meet zero emission/1.5°C goals for all 
countries. Although not included in a majority of the studies related to 
pathways and national modelling analysis, energy demand reduction 
through deeper efficiency and other measures such as lifestyle 
changes and system solutions that go beyond components, as well 
as the co-benefits of the reduction of short-lived pollutants, needs to 
be evaluated for inclusion in future zero emission/1.5°C pathways.
4.2.5.11 Lowering Demand, Downscaling Economies
Studies have identified socio-technological pathways to help achieve 
net zero CO 2 and GHG targets at national scale, that in aggregate are 
crucial to keeping global temperature below agreed limits. However, 
most of the literature focuses on supply-side options, including carbon 
dioxide removal mechanisms (BECCS, afforestation, and others) that 
are not fully commercialised (Cross-Chapter Box  8  in Chapter  12).

Costs to research, deploy, and scale up these technologies are often 
high. Recent studies have addressed lowering demand through 
energy conversion efficiency improvements, but few studies have 
considered demand reduction through efficiency (Grubler et al. 2018) 
and the related supply implications and mitigation measures.
Five main drivers of long-term energy demand reduction that can 
meet the  1.5°C target include quality of life, urbanisation, novel 
energy services, diversification of end-user roles, and information innovation (Grubler et al. 2018). A  Low Energy Demand scenario 
requires fundamental societal and institutional transformation from 
current patterns of consumption, including: decentralised services 
and increased granularity (small-scale, low-cost technologies to 
provide decentralised services), increased use value from services 
(multi-use vs single use), sharing economies, digitalisation, and 
rapid transformation driven by end-user demand. This approach

to transformation differs from the status quo and current climate 
change policies in emphasising energy end-use and services first, 
with downstream effects driving intermediate and upstream 
structural change.
Radical low-carbon innovation involves systemic, cultural, and policy 
changes and acceptance of uncertainty in the beginning stages. 
However, the current dominant analytical perspectives are grounded 
in neoclassical economics and social psychology, and focus primarily 
on marginal changes rather than radical transformations (Geels 
et al. 2018). Some literature is beginning to focus on mitigation 
through behaviour and lifestyle changes, but specific policy measures 
for supporting such changes and their contribution to emission 
reductions remain unclear (Section 4.4.2 and Chapter 5).
4.2.5.12 Ambitious Targets to Reduce Short-lived Climate 
Forcers, Including Methane
Recent research shows that temperature increases are likely to

exceed 1.5°C during the 2030s and 2°C by mid-century unless both 
CO2 and short-lived climate forcers (SLCFs) are reduced (Shindell et al. 
2017; Rogelj et al. 2018a). Because of their short lifetimes (days to 
a  decade and a  half), SLCFs can provide fast mitigation, potentially 
avoiding warming of up to 0.6°C at 2050 and up to 1.2°C at 2100 
(Ramanathan and Xu 2010; Xu and Ramanathan 2017). In Asia 
especially, co-benefits of drastic CO 2 and air pollution mitigation 
measures reduce emissions of methane, black carbon, sulphur dioxide, 
nitrogen oxide, and fine particulate matter by approximately 23%, 
63%, 73%, 27%, and 65% respectively in 2050 as compared to 
2010 levels. Including the co-benefits of reduction of climate forcing 
adds significantly to the benefits reducing air pollutants (Hanaoka 
and Masui 2018).
To achieve net zero GHG emissions implies consideration of targets 
for non-CO 2 gases. While methane emissions have grown less rapidly

than CO 2 and F-gases since 1990 (Chapter 2), the literature urges 
action to bring methane back to a pathway more in line with the Paris 
goals (Nisbet et al. 2020). Measures to reduce methane emissions 
from anthropogenic sources are considered intractable – where they 
sustain livelihoods  – but also becoming more feasible, as studies 
report the options for mitigation in agriculture without undermining 
food security (Wollenberg et al. 2016; Frank et al. 2017; Nisbet et al. 
2020). The choice of emission metrics has implications for SLCF (Cain 
et al. 2019) (Cross-Chapter Box 2 in Chapter 2). Ambitious reductions 
of methane are complementary to, rather than substitutes for, 
reductions in CO 2 (Nisbet et al. 2020).
Rapid SLCF reductions, specifically of methane, black carbon, and 
tropospheric ozone have immediate co-benefits including meeting 
sustainable development goals for reducing health burdens of

442
household air pollution and reversing health- and crop-damaging 
tropospheric ozone (Jacobson 2002, 2010). SLCF mitigation measures 
can have regional impacts, including avoiding premature deaths in 
Asia and Africa and warming in central and northern Asia, southern 
Africa, and the Mediterranean (Shindell et al. 2012). Reducing 
outdoor air pollution could avoid 2.4 million premature deaths and 
52 million tonnes of crop losses for four major staples (Haines et al. 
2017). Existing research emphasises climate and agriculture benefits 
of methane mitigation measures with relatively small human health 
benefits (Shindell et al. 2012). Research also predicts that black 
carbon mitigation could substantially benefit global climate and 
human health, but there is more uncertainty about these outcomes 
than about some other predictions (Shindell et al. 2012). Other

benefits to SLCF reduction include reducing warming in the critical 
near term, which will slow amplifying feedbacks, reduce the risk 
of non-linear changes, and reduce long-term cumulative climate 
impacts – like sea-level rise – and mitigation costs (Hu et al. 2017; 
UNEP and WMO 2011; Rogelj et al. 2018a; Xu and Ramanathan 
2017; Shindell et al. 2012).
4.2.5.13 System Analysis Solutions Are Only Beginning to 
Be Recognised in Current Literature on Accelerated 
Mitigation Pathways, and Rarely Included in Existing 
National Policies or Strategies
Most models and studies fail to address system impacts of widespread 
new technology deployment, for example: (i) material and resources 
needed for hydrogen production or additional emissions and energy 
required to transport hydrogen; or (ii) materials, resources, grid 
integration, and generation capacity expansion limits of a  largely 
decarbonised power sector and electrified transport sector. These

impacts could limit regional and national scale-ups.
Systemic solutions are also not being sufficiently discussed, such 
as low-carbon materials; light-weighting of buildings, transport, 
and industrial equipment; promoting circular economy, recyclability 
and reusability, and addressing the food-energy-water nexus. 
These solutions reduce demand in multiple sectors, improve overall 
supply chain efficiency, and require cross-sector policies. Using 
fewer building materials could reduce the need for cement, steel, 
and other materials and thus the need for production and freight 
transport. Concrete can also be produced from low-carbon cement, 
or designed to absorb CO 2 from the atmosphere. Few regions 
have developed comprehensive policies or strategies for a  circular 
economy, with the exception of the EU and China, and policies in 
the EU have only emerged within the last decade. While China’s 
circular economy policies emphasises industrial production, water,

pollution and scaling-up in response to rapid economic growth and 
industrialisation, EU’s strategy is focused more narrowly on waste 
and resources and overall resource efficiency to increase economic 
competitiveness (McDowall et al. 2017).
Increased bioenergy consumption is considered in many 1.5°C and 2°C 
scenarios. System thinking is needed to evaluate bioenergy’s viability 
because increased demand could affect land and water availability, 
food prices, and trade (Sharmina et al. 2016). To adequately address 
the water-energy-food nexus, policies and models must consider interconnections, synergies, and trade-offs among and within sectors, 
which is currently not the norm (Section 12.4).
A systems approach is also needed to support technological 
innovation. This includes recognising unintended consequences 
of political support mechanisms for technology adoption and 
restructuring current incentives to realise multi-sector benefits. It also

entails assimilating knowledge from multiple sources as a basis for 
policy and decision-making (Hoolohan et al. 2019).
Current literature does not explicitly consider systematic, physical 
drivers of inertia, such as capital and infrastructure needed to support 
accelerated mitigation (Pfeiffer et al. 2018). This makes it difficult to 
understand what is needed to successfully shift from current limited 
mitigation actions to significant transformations needed to rapidly 
achieve deep mitigation.
4.2.6 Implications of Accelerated Mitigation 
for National Development Objectives
4.2.6.1 Introduction
This section examines how accelerated mitigation may impact 
the realisation of development objectives in the near- and mid-
term. It focuses on three objectives discussed in the literature, 
sustaining economic growth (Section 4.2.6.2), providing employment 
(Section  4.2.6.3), and alleviating poverty and ensuring equity

(Section 4.2.6.4). It complements similar review performed at global 
level in Section 3.6. For a comprehensive survey of research on the 
impact of mitigation in other areas (including air quality, health, and 
biodiversity), see Karlsson et al. (2020).
4.2.6.2 Mitigation and Economic Growth  
in the Near- and Mid-term
A significant part of the literature assesses the impacts of mitigation 
on GDP , consistent with policymakers’ interest in this variable. 
It must be noted upfront that computable equilibrium models, on 
which our assessments are mostly based, capture the impact of 
mitigation on GDP and other core economic variables while typically 
overlooking other effects that may matter (like improvements in air 
quality). Second, even though GDP (or better, GDP per capita) is not 
an indicator of welfare (Fleurbaey and Blanchet 2013), changes in 
GDP per capita across countries and over time are highly correlated

with changes in welfare indicators in the areas of poverty, health, 
and education (Gable et al. 2015). The mechanisms linking mitigation 
to GDP outlined below would remain valid even with alternative 
indicators of well-being (Section  5.2.1). Third, another stream of 
literature criticises the pursuit of economic growth as a goal, instead 
advocating a range of alternatives and suggesting modelling of post-
growth approaches to achieve rapid mitigation while improving social 
outcomes (Hickel et al. 2021). In the language of the present chapter, 
these alternatives constitute alternative development pathways.
Most country-level mitigation modelling studies in which GDP is an 
endogenous variable report negative impacts of mitigation on GDP

443
in 2030 and 2050, relative to the reference (robust evidence, high 
agreement), for example (Nong et al. 2017) for Australia, (Chen et al. 
2013) for Brazil, (Dai et al. 2016; Li et al. 2017; Dong et al. 2018; Mu 
et al. 2018a; Zhao et al. 2018; Cui et al. 2019) for China, (Álvarez-
Espinosa et al. 2018) for Colombia, (Fragkos et al. 2017) for the EU, 
(Mittal et al. 2018) for India, (Fujimori et al. 2019) for Japan, (Veysey 
et al. 2014) for Mexico, (Pereira et al. 2016) for Portugal, (Alton et al. 
2014; van Heerden et al. 2016) for South Africa, (Chunark et al. 2017) 
for Thailand, (Acar and Yeldan 2016) for Turkey, (Roberts et al. 2018b) 
for the UK, (Zhang et al. 2017; Chen and Hafstead 2019) for USA, 
(Nong 2018) for Vietnam (Figure  4.4). The downward relationship 
between mitigation effort and emissions is strong in studies up to 2030, much weaker for studies looking farther ahead. In all reviewed

studies, however, GDP continues to grow even with mitigation. It 
may be noted that none of the studies assessed above integrates the 
benefits of mitigation in terms of reduced impacts of climate change 
or lower adaptation costs. This is not surprising since these studies 
are at national or regional scale and do not extend beyond 2050, 
whereas the benefits depend on global emissions and primarily occur 
after 2050. Discussion on reduced impacts is provided in Section 3.6.2 
and Cross-Working Group Box 1 in Chapter 3.
Two major mechanisms interplay to explain the impact of 
mitigation on GDP . First, the carbon constraint imposes reduced 
Table 4.9 | Examples of country-level modelling studies finding positive short-term outcome of mitigation on GDP relative to baseline.
Reference Country/region Explanation for positive outcome of mitigation on GDP
Antimiani et al. (2016) European Union GDP increases relative to reference only in the scenario with global cooperation on mitigation.

Willenbockel et al. (2017) KenyaThe mitigation scenario introduces cheaper (geothermal) power generation units than in BAU (in which thermal increases).  
Electricity prices actually decrease.
Siagian et al. (2017) Indonesia Coal sector with low productivity is forced into BAU. Mitigation redirects investment towards sectors with higher productivity.
Blazquez et al. (2017) Saudi ArabiaRenewable energy penetration assumed to free oil that would have been sold at publicly subsidised price on the domestic market 
to be sold internationally at market price.
Wei et al. (2019) ChinaAnalyse impacts of feed-in tariffs to renewables, find positive short-run impacts on GDP; public spending boost activity in the RE 
sector. New capital being built at faster rate than in reference increases activity more than activity decreases due to lower public 
spending elsewhere.

Gupta et al. (2019) IndiaSavings adjust to investment and fixed unemployment is considered target of public policy, thereby limiting impact of mitigation 
on GDP relative to other economic variables (consumption, terms of trade).
Huang et al. (2019) China Power generation plan in the baseline is assumed not cost minimising.–20%–18%–16%–14%–12%–10%–8%–6%–4%–2%0%2%4%6%8%GDP variation relative to reference (%)
–90% –80% –70% –60% –50% –40% –30% –20% –10% 0%
CO2 emissions change relative to reference (%)2031–2050 Up to 2030
Figure 4.4 | GDP against emissions in country-level modelling studies, in variations relative to reference.

444
use of a production factor (fossil energy), thus reducing GDP . In the 
simulations, the mechanism at work is that firms and households 
reduce their use of GHG-intensive goods and services in response 
to higher prices due to reduced fossil energy use. Second, additional 
investment required for mitigation partially crowds out productive 
investment elsewhere (Fujimori et al. 2019), except in Keynesian 
models in which increased public investment actually boosts GDP 
(Pollitt et al. 2015; Landa Rivera et al. 2016; Bulavskaya and Reynès 
2018). Magnitude and duration of GDP loss depend on the stringency 
of the carbon constraint, the degree of substitutability with less-GHG-
intensive goods and services, assumptions about costs of low-carbon 
technologies and their evolution over time (e.g., Duan et al. 2018; van 
Meijl et al. 2018; Cui et al. 2019) and decisions by trading partners,

which influence competitiveness impacts for firms (Alton et al. 2014; 
Fragkos et al. 2017) (high evidence, high agreement).
In the near term, presence of long-lived emissions intensive capital 
stock, and rigidities in the labour market (Devarajan et al. 2011) and 
other areas may increase impacts of mitigation on GDP . In the mid-
term, on the other hand, physical and human capital, technology, 
institutions, skills or location of households and activities are more 
flexible. The development of renewable energy may help create more 
employment and demands for new skills, particularly in the high-skill 
labour market  (Helgenberger, S. et al., 2019). In addition, cumulative 
mechanisms such as induced technical change or learning by doing 
on low-emissions technologies and process may reduce the impacts 
of mitigation on GDP .
Country-level studies find that the negative impacts of mitigation 
on GDP can be reduced if pre-existing economic or institutional

obstacles are removed in complement to the imposition of the 
carbon constraint (robust evidence, high agreement). For example, 
if the carbon constraint takes the form of a carbon tax or of permits 
that are auctioned, the way the proceeds from the tax (or the 
revenues from the sales of permits) are used is critical for the overall 
macroeconomic impacts (Chen et al. 2013). (For a detailed discussion of different carbon pricing instruments, including the auctioning of 
permits, see Section 13.6.3).
Figure 4.5 shows that depending on the choice of how to implement 
a  carbon constraint, the same level of carbon constraint can yield 
very different outcomes for GDP . The potential for mitigating GDP 
implications of mitigation through fiscal reform is discussed in 
Section 4.4.1.8.
More generally, mitigation costs can be reduced by proper policy 
design if the economy initially is not on the efficiency frontier (Grubb

2014), defined as the set of configurations within which the quality 
of the environment and economic activity cannot be simultaneously 
improved given current technologies – such improvements in policy 
design may include reductions in distortionary taxes. Most of the 
studies which find that GDP increases with mitigation in the near 
term precisely assume that the economy is initially not on the 
frontier. Making the economy more efficient – in other words, lifting 
the constraints that maintain the economy in an interior position – 
creates opportunities to simultaneously improve economic activity 
and reduce emissions. Table 4.9 describes the underlying assumptions 
in a selection of studies.
Finally, marginal costs of mitigation are not always reported in studies 
of national mitigation pathways. Comparing numbers across countries 
is not straightforward due to exchange rate fluctuations, differing 
assumptions by modellers in individual country studies, etc. The

database of national mitigation pathways assembled for this Report – 
which covers only a  fraction of available national mitigation 
studies in the literature – shows that marginal costs of mitigation 
are positive, with a median value of 101 USD2010 tCO2–1 in 2030, 
244 in 2040 and 733 in 2050 for median mitigation efforts of 21%, 
46% and 76% relative to business-as-usual respectively. Marginal 
costs increase over time along accelerated mitigation pathways, as 
constraints become tighter, with a non-linearity as mitigation reaches 
80% of reference emissions or more. Dispersion across and within  –8%–6%–4%–2%0%2%4%
Alton et al. 2014 –
South AfricaDevarajan et al. 
2011 – South AfricaFernandez
Daignault 2018 –
New ZealandGlomsrod et al. 
2016 – ChinaNong 2018 –
VietnamAsakawa et al.
2021 – JapanVariations in GDP in 2030
Figure 4.5 | Illustrative ranges of variations in GDP relative to reference in 2030 associated with introduction of carbon constraint, depending on

modality of policy implementation. Source: based on Alton et al. (2014); Devarajan et al. (2011); Fernandez and Daigneault (2018); Glomsrød et al. (2016); Nong (2018); 
Asakawa et al. (2021). Stringency of carbon constraint is not comparable across the studies.

445
countries is high, even in the near term but increases notably in the 
mid-term (medium evidence, medium agreement).
4.2.6.3 Mitigation and Employment in the Short- 
and Medium-term
Numerous studies have analysed the potential impact of carbon 
pricing on labour markets. Chateau et al. (2018) and OECD (2017a) find 
that the implementation of green policies globally (defined broadly as 
policies that internalise environmental externalities through taxes and 
other tools, shifting profitability from polluting to green sectors) need 
not harm total employment, and that the broad skill composition (low, 
high- and medium-skilled jobs) of emerging and contracting sectors 
is very similar, with the largest shares of job creation and destruction 
at the lowest skill level. To smoothen the labour market transition, 
they conclude that it may be important to reduce labour taxes, to

compensate vulnerable households, and to provide education and 
training programs, the latter making it easier for labour to move to 
new jobs. Consistent with this, other studies that simulate the impact 
of scenarios with more or less ambitious mitigation policies (including 
100% reliance on renewable energy by 2050) find relatively small 
(positive or negative) impacts on aggregate global employment that 
are more positive if labour taxes are reduced but encompass substantial 
losses for sectors and regions that today are heavily dependent on 
fossil fuels (Arndt et al. 2013; Huang et al. 2019; Vandyck et al. 2016; 
Jacobson et al. 2019). Among worker categories, low-skilled workers 
tend to suffer wage losses as they are more likely to have to reallocate, 
something that can come at a cost in the form of a wage cut (assuming 
that workers who relocate are initially less productive than those who 
already work in the sector). The results for alternative carbon revenue

recycling schemes point to trade-offs: a reduction in labour taxes often 
leads to the most positive employment outcomes while lump-sum 
(uniform per-capita) transfers to households irrespective of income 
yield a more egalitarian outcome.
The results from country-level studies using CGE models tend be similar 
to those at global level. Aggregate employment impacts are small and 
may be positive especially if labour taxes are cut, see for example, 
Telaye et al. (2019) for Ethiopia,(Kolsuz and Yeldan (2017) for Turkey, 
Fragkos et al. (2017) for the EU, and Mu et al. (2018b) for China. On the 
other hand, sectoral reallocations away from fossil-dependent sectors 
may be substantial, see for example, Alton et al. (2014) for South Africa 
or Huang et al. (2019) for China. Targeting of investment to labour-
intensive green sectors may generate the strongest employment gains, 
see, for example, Perrier and Quirion (2018) for France, van Meijl

et al. (2018) for the Netherlands, and Patrizio et al. 2(018) for the USA. 
Changes in skill requirements between emerging and declining sectors 
appear to be quite similar, involving smaller transitions than during the 
IT revolution (Bowen et al. 2018).
In sum, the literature suggests that the employment impact of 
mitigation policies tends to be limited on aggregate, but can 
be significant at the sectoral level (medium evidence, medium 
agreement) and that cutting labour taxes may limit adverse effects on 
employment (limited evidence, medium agreement). Labour market 
impacts, including job losses in certain sectors, can be mitigated 
by  equipping workers for job changes via education and training, and by reducing labour taxes to boost overall labour demand (Stiglitz 
et al. 2017) (Section 4.5).
Like most of the literature on climate change, the above studies 
do not address gender aspects. These may be significant 
since the employment shares for men and women vary across

sectors and countries.
4.2.6.4  Mitigation and Equity in the Near and Mid-term
Climate mitigation may exacerbate socio-economic pressures on 
poorer households (Jakob et al. 2014). First, the price increase in 
energy-intensive goods and services – including food (Hasegawa et al. 
2018)  – associated with mitigation may affect poorer households 
disproportionally (Bento 2013), and increase the number of energy-
poor (Berry 2019). Second, the mitigation may disproportionally 
affect low-skilled workers (see previous section). Distributional 
issues have been identified not only with explicit price measures 
(carbon tax, emission permits system, subsidy removal), but also with 
subsidies for renewables (Borenstein and Davis 2016), and efficiency 
and emissions standards (Davis and Knittel 2019; Bruegge et al. 
2019; Levinson 2019; Fullerton and Muehlegger 2019).
Distributional implications, however, are context specific, depending

on consumption patterns (initially and ease of adjusting them in 
response to price changes) and asset ownership (see for example 
analysis of energy prices in Indonesia by Renner et al. 2019). In 
an analysis of the distributional impact of carbon pricing based 
on household expenditure data for 87 low- and middle-income 
countries, Dorband et al. (2019) find that, in countries with a per-
capita income of up to USD15,000 per capita (purchasing power parity 
(PPP) adjusted), carbon pricing has a progressive impact on income 
distribution and that there may be an inversely U-shaped relationship 
between energy expenditure shares and per-capita income, rendering 
carbon pricing regressive in high-income countries, in other words, in 
countries where the capacity to pursue compensatory policies tends 
to be relatively strong.
The literature finds that the detailed design of mitigation policies 
is critical for their distributional impacts (robust evidence, high

agreement). For example, Vogt-Schilb et al. (2019) suggest to turn 
to cash transfer programs, established as some of the most efficient 
tools for poverty reduction in developing countries. In an analysis 
of Latin America and the Caribbean, they find that allocation of 
30% of carbon revenues would suffice to compensate poor and 
vulnerable households on average, leaving the rest for other uses. 
This policy tool is not only available in countries with relatively high 
per-capita incomes: in Sub-Saharan Africa, where per-capita incomes 
are relatively low, cash transfer programs have been implemented in 
almost all countries (Beegle et al. 2018, p. 57), and are found central 
to the success of energy subsidy reforms (Rentschler and Bazilian 
2017). In the same vein, Böhringer et al. (2021) finds that recycling 
of revenues from emissions pricing in equal amounts to every 
household appeals as an attractive strategy to mitigate regressive

effects and thereby make stringent climate policy more acceptable 
on societal fairness grounds. However, distributional gains from such 
recycling may come at the opportunity cost of not reaping efficiency

446
gains from reductions in the taxes that are most distortionary 
(Goulder et al. 2019).
Distributional concerns related to climate mitigation are also prevalent 
in developed countries, as demonstrated, for instance, by France’s 
recent yellow-vest movement, which was ignited by an increase 
in carbon taxes. It exemplifies the fact that, when analysing the 
distributional effects of carbon pricing, it is not sufficient to consider 
vertical redistribution (i.e., redistribution between households at 
different incomes levels but also horizontal redistribution (i.e., 
redistribution between households at similar incomes which is due 
to differences in terms of spending shares and elasticities for fuel 
consumption). Compared to vertical redistribution, it is more difficult 
to devise policies that effectively address horizontal redistribution 
(Cronin et al. 2019; Pizer and Sexton 2019; Douenne 2020). However,

it has been shown ex post that transfer schemes considering income 
levels and location could have protected or even improved the 
purchasing power of the bottom half of the population (Bureau et al. 
2019). Investments in public transportation may reduce  horizontal 
redistribution if it makes it easier for households to reduce fossil 
fuel consumption when prices increase (see Sections  4.4.1.5 
and 4.4.1.9). Similarly, in relation to energy use in housing, policies 
that encourage investments that raise energy efficiency for low-
income households may complement or be an alternative to taxes 
and subsidies as a means of simultaneously mitigating and reducing 
fuel poverty (Charlier et al. 2019). From a  different angle, public 
acceptance of the French increase in the carbon tax could also have 
been enhanced via a public information campaign could have raised 
public acceptance of the carbon tax increase (Douenne and Fabre

2020). (See Section 4.4.1.8 for a discussion of this and other factors 
that influence public support for carbon taxation.)
4.2.7 Obstacles to Accelerated Mitigation and 
How Overcoming Them Amounts to Shifts 
in Development Pathways
As outlined in Sections 4.2.3, 4.2.4, 4.2.5 and 4.2.6 there is improved 
understanding since AR5 of what accelerated mitigation would 
entail in the coming decades. A  major finding is that accelerated 
mitigation pathways in the near to mid-term appear technically and 
economically feasible in most contexts. Chapter 4, however, cannot 
stop here. Section  4.2.2 has documented an important policy gap for current climate pledges, and Cross-Chapter Box 4 in this chapter 
shows an even larger ambition gap between current pledges and what 
would be needed in the near term to be on pathways consistent with 
below 2°C, let alone 1.5°C. In other words, while the implementation 
of mitigation policies to achieve updated NDC almost doubles the

mitigation efforts, and notwithstanding the widespread availability 
of the necessary technologies, this doubling of effort merely narrows 
the gap to pathways consistent with 2°C by at most 20%.
Obstacles to the implementation of accelerated mitigation pathways 
can be grouped in four main categories (Table  4.10). The first set 
of arguments can be understood through the lens of cost-benefit 
analysis of decision-makers, as they revolve around the following 
question: Are costs too high relative to benefits? More precisely, are 
the opportunity costs – in economics terms, what is being forfeited by 
allocating scarce resources to mitigation – justified by the benefits for 
the decision-maker (whether individual, firm, or nation)? This first set 
of obstacles is particularly relevant because accelerated mitigation 
pathways imply significant effort in the short-run, while benefits in 
terms of limited warming accrue later and almost wholly to other

actors. However, as discussed in Sections 3.6 and 4.2.6, mitigation 
costs for a  given mitigation target are not carved in stone. They 
strongly depend on numerous factors, including the way mitigation 
policies have been designed, selected, and implemented, the 
processes through which markets have been shaped by market actors 
and institutions, and nature of socially- and culturally-determined 
influences on consumer preferences. Hence, mitigation choices that 
might be expressed straightforwardly as techno-economic decisions 
are, at a deeper level, strongly conditioned by underlying structures 
of society.
A second set of likely obstacles in the short-term to accelerated 
mitigation revolves around undesirable distributional consequences, 
within and across countries. As discussed in Section  4.2.6.3, the 
distributional implications of climate policies depend strongly on their 
design, the way they are implemented, and on the context into which

they are inserted. Distributional implications of climate policies have 
both ethics and equity dimensions, to determine what is desirable/
acceptable by a given society in a given context, notably the relative 
power of different winners and losers to have their interests taken 
into account, or not, in the relevant decision-making processes. Like 
costs, distributional implications of accelerated mitigation are rooted 
in the underlying socio-political-institutional structures of a society.
Table 4.10 | Objections to accelerated mitigation and where they are assessed in the WG3 report.
Category Main dimensionsLocation in AR6 WGIII report where 
objection is assessed and solutions 
are discussed
Costs of mitigationMarginal, sectoral or macroeconomic costs of mitigation too high; scarce resources could/
should be used for other development priorities; mitigation benefits are not worth the costs 
(or even non-existent); lack of financingSections 3.6, 4.2.6, 12.2; Chapter 15, Chapter 17

Distributional 
implicationsRisk of job losses; diminished competitiveness; inappropriate impact on poor/vulnerable people; 
negative impact on vested interestsSection 4.5; Chapter 5, Chapter 13, Chapter 14
Lack of technology Lack of suitable technologies; lack of technology transfer; unfavourable socio-political environment Section 4.2.5, Chapter 16
Unsuitable ‘structures’Inertia of installed capital stock; inertia of socio-technical systems; inertia to behaviour change; 
unsuitable institutionsSection 3.5; Chapter 5, Chapter 13

447
A third set of obstacles are about technology availability and 
adoption. Lack of access even to existing cost-effective mitigation 
technologies remains an important issue, particularly for many 
developing countries, and even in the short-term. Though it relates 
most directly to techno-economic costs, technology availability raises 
broader issues related to the socio-technical systems within which 
innovation and adoption are embedded, and issues of technology 
availability are inherently issues of systemic failure (Section  16.3). 
The underlying legal, economic and social structures of the economy 
are central to the different stages of socio-transition processes 
(Cross-Chapter Box 12 in Chapter 16).
The last set of obstacles revolves around the unsuitability of 
existing structures to accelerated mitigation. We include here all 
forms of established structures, material (e.g.,  physical capital) or

not (institutions, social norms, patterns of individual behaviour), 
that are potentially long-lived and limit the implementation of 
accelerated mitigation pathways. Typically, such structures exist for 
reasons other than climate change and climate mitigation, including 
the distribution of power among various actors. Modifying them in 
the name of accelerated climate mitigation thus requires to deal with 
other non-climate issues as well. For example, resolving the landlord-
tenant dilemma, an institutional barrier to the deployment of energy efficiency in building, opens fundamental questions on private 
property in buildings.
A common thread in the discussion above is that the obstacles to 
accelerated mitigation are to a large degree rooted in the underlying 
structural features of societies. As a  result, transforming those 
underlying structures can help to remove those obstacles, and 
thus facilitate the acceleration of mitigation. This remark is all the

more important that accelerated mitigation pathways, while very 
different across countries, all share three characteristics: speed of 
implementation, breadth of action across all sectors of the economy, 
and depth of emission reduction achieving more ambitious targets. 
Transforming those underlying structures amounts to shifting 
a  society’s development pathway (Figure  4.6). In the following 
Sections  3 and  4, we argue that it is thus necessary to recast 
accelerated mitigation in the broader context of shifting development 
pathways, and that doing so opens up additional opportunities to 
(i)  overcome the obstacles outlined above, and also (ii) combine 
climate mitigation with other development objectives.Strengthening 
governance and 
institutional capacityAligning technology 
and innovation 
systemsAligning ﬁnance 
and investment 
institutionsFacilitating 
behaviour change
Measures to enable shift in development pathwayConstrained 
mitigation 
policies
Broader 

policies
Improved enabling conditionsPoor enabling conditions
Reduced barriers, inertia and resistanceInadequate mitigation outcomes
Enhanced mitigation outcomesBARRIERS
RESISTANCEINERTIA
Figure 4.6 | Obstacles to mitigation (top panel) and measures to remove these obstacles and enable shift in development pathways (lower panel).

448
4.3 Shifting Development Pathways
4.3.1 Framing of Development Pathways
4.3.1.1 What are Development Pathways?
The term development pathway is defined in various ways in the 
literature, and these definitions invariably refer to the evolution 
over time of a society’s defining features. A society’s development 
pathway can be described, analysed, and explained from a variety 
of perspectives, capturing a  range of possible features, trends, 
processes, and mechanisms. It can be examined in terms of specific 
quantitative indicators, such as population, urbanisation level, 
life expectancy, literacy rate, GDP , carbon dioxide emission rate, 
average surface temperature, etc. Alternately, it can be described 
with reference to trends and shifts in broad socio-political or cultural 
features, such as democratisation, liberalisation, colonisation,

globalisation, consumerism, etc. Or, it can be described in a way that 
highlights and details a particular domain of interest; for example, 
as an ‘economic pathway’, ‘technological pathway’, ‘demographic 
pathway’, or others. Any such focused description of a pathway is 
more limited, by definition, than the general and encompassing 
notion of a development pathway.
Development pathways represent societal evolution over time, and 
can be assessed retrospectively and interpreted in a historical light, or 
explored prospectively by anticipating and assessing alternative future 
pathways. Development pathways, and prospective development 
pathways in particular, can reflect societal objectives, as in ‘low-emission 
development pathways’, ‘climate-resilient development pathways’, 
‘sustainable development pathways’, ‘inclusive development pathway’, 
and as such can embed normative assumptions or preferences, or

can reflect potential dystopian futures to be avoided. A  national development plan (Section  4.3.2) is a  representation of a  possible 
development pathway for a given society reflecting its objectives, as 
refracted through its development planning process.
One approach for exploring shifts in future development pathways 
is through scenarios. Some examples of scenario exercises in the 
literature are provided in Table 4.11.
Different narratives of development pathways can have distinct 
and even competing focuses such as economic growth, shifts in 
industrial structure, technological determinism, and can embody 
alternative framings of development itself (from growth to 
well-being, see Chapter  5), and of sustainable development in 
particular (Sections 1.6 and 17.1). Scenario exercises are structured 
undertakings to explore alternative future development pathways, 
often drawing on stakeholder input and accepting the deep and

irreducible uncertainty inherent in societal development into the 
future (Schweizer and Kriegler 2012; Kahane 2012; Raskin and Swart 
2020). The results of scenario explorations, including modelling 
exercises, thus help clarify the characteristics of a particular future 
pathway, in light of a  particular set of assumptions and choice of 
indicators for assessment. Processes of developing scenarios can 
inform choices by decision makers of various kinds.
Scenarios are useful to clarify societal objectives, understand 
constraints, and explore future shifts. Scenario exercises are effective 
when they enable multi-dimensional assessment, and accommodate 
divergent normative viewpoints (Kowarsch et al. 2017). Such processes 
might take into account participants’ explicit and implicit priorities, 
values, disciplinary backgrounds, and world views. The process of 
defining and describing a society’s development pathway contributes

to the ongoing process of understanding, explaining and defining the 
historical and contemporary meaning and significance of a society. 
Table 4.11 | Prospective development pathways at global, national and local scale.
Scale Process and publication Description of development pathways
GlobalIPCC Special Report on Emission Scenarios 
(Nakicenovic et al. 2000)Four different narrative storylines describing relationships between driving forces and the evolution of emission 
scenarios over the 21st century.
GlobalShared Socio-economic Pathways (SSPs)
(Riahi et al. 2017; O’Neill et al. 2017)Five narratives describing alternative socio-economic developments, including sustainable development, regional 
rivalry, inequality, fossil-fuelled development, and middle-of-the-road development, using alternative long-term 
projections of demographics, human development, economy and lifestyle, policies and institutions, technology, 
and environment and natural resources.

GlobalIncome inequality projections for SSPs 
(Rao et al. 2019)Alternative development pathways that explore several drivers of rising or falling inequality.
GlobalFutures of Work
(World Economic Forum 2018)Eight possible visions of the future of work in the year 2030, based on different combinations of three core 
variables: the rate of technological change and its impact on business models, the evolution of learning among 
the current and future workforce, and the magnitude of labour mobility across geographies – all of which are likely 
to strongly influence the nature of work in the future.
NationalMont Fleur Scenarios
(Galer 2004)Four socio-political scenarios intended to explore possible futures of a newly post-apartheid South Africa, 
which included three dark prophecies and one bright vision which reportedly influenced the new leadership.
NationalMitigation Action Plans and Scenarios (MAPS)

(Winkler et al. 2017; Raubenheimer et al. 2015)Mitigation and development-focused scenarios for Brazil, Chile, Peru, and Colombia, entailing linked sectoral 
and economy modelling including socio-economic implications, combined with intensive stakeholder engagement.
NationalDeep Decarbonisation Pathways  
(Bataille et al. 2016a; Waisman et al. 2019)Mitigation-focused scenarios for sixteen countries from each country’s perspective, carried out by local institutes 
using national models. The common method is a tool for decision-makers in each context to debate differing 
concrete visions for deep decarbonisation, seek consensus on near-term policy packages, with aim to contribute 
to long-term global decarbonisation.
LocalNew Lenses on Future Cities
(Shell Global 2014)Six city archetypes used to create scenarios to help understand how cities could evolve through more sustainable

urbanisation processes and become more efficient, while coping with major development challenges in the past.

449
The imagination of facilitated stakeholder process combined with the 
rigour of modelling helps improve understanding of constraints, trade-
offs, and choices. ‘Scenario analysis offers a structured approach for 
illuminating the vast range of possibilities. A scenario is a story, told 
in words and numbers, describing the way events might unfold. If 
constructed with rigor and imagination, scenarios help us to explore 
where we might be headed, but more, offering guidance on how to 
act now to direct the flow of events toward a desirable future’  (Raskin 
et al. 2002). Scenario processes are valuable for the quantitative and 
qualitative insights they can provide, and also for the role they can 
play in providing a forum and process by which diverse institutions 
and even antagonistic stakeholders can come together, build trust, 
improve understanding, and ultimately converge in their objectives

(Kane and Boulle 2018; Dubash 2021).
4.3.1.2 Shifting Development Pathways
Development pathways evolve as the result of the countless decisions 
and actions at all levels of societal structure, as well due to the emergent dynamics within and between institutions, cultural norms, 
socio-technological systems, and the biogeophysical environment. 
Society can choose to make decisions and take actions with the 
shared intention of influencing the future development pathway 
toward specific agreed objectives.
The SDGs provide a lens on diverse national and local development 
objectives. Humankind currently faces multiple sustainability 
challenges that together present global society with the challenge 
of assessing, deliberating, and attempting to bring about a viable, 
positive future development pathway. Ecological sustainability 
challenges include reducing GHG emissions, protecting the ozone 
layer, controlling pollutants such as aerosols and persistent organics,

managing nitrogen and phosphorous cycles, etc. (Steffen et al. 2015), 
which are necessary to address the rising risks to biodiversity and 
ecosystem services on which humanity depends (IPBES 2019a). 
Socio-economic sustainability challenges include conflict, persistent 
poverty and deprivation, various forms of pervasive and systemic 
discrimination and deprivation, and socially corrosive inequality. 
Very low emissions
Most SDGs
Low emissions
Many SDGs
High emissions
Few SDGs
Very high emissions
Very few SDGsOutputs
Decision makers
Policymakers, 
organisations 
and individuals
Decision points
Decisions on a broad 
range of development 
choices (not only climate 
policy choices)Tools
Some development 
pathways will provide 
more policy tools to 
accelerate mitigation 
and achieve other SDGs
Time
Figure 4.7 | Shifting development pathways to increased sustainability: choices by a wide range of actors at key decision points on development

pathways can reduce barriers and provide more tools to accelerate mitigation and achieve other Sustainable Development Goals.

450
The global adoption of the SDGs and their underlying indicators (UN 
2017, 2018 and 2019) reflect a  negotiated prioritisation of these 
common challenges.
Figure 4.7 illustrates the process of shifting development pathways. 
The lines illustrate different possible development pathways through 
time, some of which (shown here toward the top of the figure) 
remove obstacles to the adoption and effective implementation of 
sustainable development policies, and thus give access to a  rich 
policy toolbox for accelerating mitigation and achieving SDGs. Other 
development pathways (shown here toward the bottom of the 
figure) do not overcome, or even reinforce the obstacles to adopting 
and effectively implementing sustainable development policies, 
and thus leave decision-makers with more limited policy toolbox 
(Section 4.2.7 and Figure 4.6). A richer tool box enables faster, deeper 
and broader mitigation.

The development pathways branch and branch again, signifying 
how a  diversity of decision-makers (policymakers, organisations, 
investors, voters, consumers, etc.) are continuously making choices 
that influence which of many potential development pathways 
society follows. Some of these choices fall clearly within the domain 
of mitigation policy. For example, what level carbon price, if any, 
should be imposed? Should fossil fuel subsidies be removed? Most 
decisions, of course, fall outside the direct domain of mitigation 
policy. Shifting development pathways toward sustainability involves 
this broader realm of choices beyond mitigation policy per se, and 
requires identifying those choices that are important determinants of 
the existing obstacles to accelerating mitigation and meeting other 
SDGs. Addressing these choices coherently shifts the development 
pathway away from a continuation of existing trends.
4.3.1.3 Expanding the Range of Policies and Other 
Mitigative Options

Shifting development pathways aims to influence the ultimate drivers 
of emissions (and development generally), such as the systemic and 
cultural determinants of consumption patterns, the political systems 
and power structures that govern decision-making, the institutions 
and incentives that guide and constrain socio-technical innovation, 
and the norms and information platforms that shape knowledge and 
discourse, and culture, values and needs (Raskin et al. 2002). These 
ultimate drivers determine the mitigative capacity of a society.
Decision-makers might usefully consider a broader palette of policies 
and measures as part of an overall strategy to meet climate goals and 
other sustainable development goals (Section 4.3.2 and Table 4.12). 
This is consistent with the fact that mitigation is increasingly 
understood to be inseparable from broader developmental goals, 
which can be facilitated by policy coherence and integration with

broader objectives and policies sectorally and societally. This is 
supported by other observations that mitigation measures based 
on conventional climate policy instruments, such as emissions taxes 
or permits, price incentives such as feed-in tariffs for low-carbon 
electricity generation, and fuel economy standards, and building 
codes, which aim to influence the proximate drivers of emissions alone will not achieve the long-term goals of the Paris Agreement 
(Méjean et al. 2015; Rogelj et al. 2016; IPCC 2018a; UNEP 2018). 
An approach of shifting development pathways to increased 
sustainability (SDPS) broadens the scope for mitigation.
4.3.1.4 An Approach of SDPS Helps Manage Trade-offs 
Between Mitigation and Other SDGs
Beyond removing structural obstacles to accelerated mitigation, 
broadening the approach to policies that facilitate shifts in 
development pathways also helps manage the potential trade-offs 
between mitigation and other development objectives discussed

in Section 4.2.7.
Systematic studies of the 17 SDGs have found the interactions 
among them to be manifold and complex (Nilsson et al. 2016; 
Pradhan et al. 2017; Weitz et al. 2018; Fuso Nerini et al. 2019). 
Addressing them calls for interventions affecting fundamental, 
interconnected, structural features of global society (International 
Panel on Social Progress 2018; TWI2050 – The World in 2050 2018), 
such as to our physical infrastructure (e.g., energy, water, industrial, 
urban infrastructure) (Waage et al. 2015; Adshead et al. 2019; 
Chester 2019; Mansell et al. 2019; Thacker et al. 2019; ), our societal 
institutions (e.g.,  educational, public health, economic, innovation, 
and political institutions) (Ostrom 2010; Kläy et al. 2015; Messner 
2015; Sachs et al. 2019), and behavioural and cultural tendencies 
(e.g.,  consumption patterns, conventional biases, discriminatory 
interpersonal and intergroup dynamics, and inequitable power

structures) (Esquivel 2016; Sachs et al. 2019). These observations 
imply that attempt to address each SDG in isolation, or as 
independent technical challenges, would be insufficient, as would 
incremental, marginal changes. In contrast, effectively addressing 
the SDGs is likely to mean significant disruption of long-standing 
trends and transformative progress to shift development pathways 
to meet al. the SDGs, including climate action, beyond incremental 
changes targeted at addressing mitigation objectives in isolation. 
In other words, mitigation conceived as incremental change is not 
enough. Transformational change has implications for equity in its 
multiple dimensions ( Steffen and Stafford Smith 2013; Klinsky et al. 
2017a; Leach et al. 2018) including just transitions (Section 4.5).
Working Group II examines climate resilient development pathways 
(CRDP)  – continuous processes that imply deep societal changes

and/or transformation, so as to strengthen sustainable development, 
efforts to eradicate poverty and reduce inequalities while promoting 
fair and cross-scalar capacities for adaptation to global warming and 
reduction of GHG emissions in the atmosphere. Transformative action 
in the context of CRDP specifically concerns leveraging change in 
the five dimensions of development (people, prosperity, partnership, 
peace, planet) (AR6 WGII, Chapter 18).
Section  4.3.2 provides more details on the way development 
pathways influence emissions and mitigative capacity. Section 4.3.3 
provides examples of shifts in development pathways, as well as 
of policies that might facilitate those. Cross-Chapter Box  5  in this 
chapter details the links between SDPS and sustainability.

451
4.3.2 Implications of Development Pathways 
for Mitigation and Mitigative Capacity
4.3.2.1 Countries Have Different Development Priorities
At the global level, the SDGs adopted by all the United Nations Member 
States in 2015 are delineated with a view to end poverty, protect the 
planet and ensure that all people enjoy peace and prosperity by 2030. 
The 17 SDGs are integrated and imply that development must balance 
social, economic and environmental sustainability.
While all countries share the totality of the SDGs, development 
priorities differ across countries and over time. These priorities are 
strongly linked to local contexts, and depend on which dimensions 
of improvements in the well-being of people are considered 
the most urgent.
Development priorities are reflected in the decisions that actors 
within societies make, such as policy choices by governments and

parliaments at all levels, votes over competing policy platforms by 
citizens, or selection of issues that non-state actors push for. Multiple 
objectives range from poverty eradication to providing energy access, 
addressing concerns of inequality, providing education, improving 
health, cleaning air and water, improving connectivity, sustaining 
growth and providing jobs, among others. For example, eradicating 
poverty and reducing inequality is a key development priority across 
many countries, such as Brazil (Grottera et al. 2017), Indonesia 
(Irfany and Klasen 2017), India (GoI 2015), South Africa (Winkler 
2018) and other low- and middle-income countries (Dorband et al. 
2019). Reducing inequality relates not only to income, but also to 
other dimensions such as in access to energy services (Tait 2017), 
gender, education, racial and ethnic profiles (Andrijevic et al. 2020), 
and thereby assumes relevance in both developing and developed

countries. The development priorities of many poor countries and 
communities with low capacities to adapt, has been focused more 
on reducing poverty, providing basic infrastructure, education and 
improving health, rather than on mitigation (Chimhowu et al. 2019).
4.3.2.2 The Nature of National Development 
Plans Is Changing
Governments are increasingly resorting to the development of 
national plans to build institutions, resources, and risk/shock 
management capabilities to guide national development. The 
number of countries with a  national development plan has more 
than doubled, from about 62 in 2006 (World Bank 2007) to 134 
plans published between 2012 and 2018 (Chimhowu et al. 2019). 
The comeback of planning may be linked to increased consideration 
given to sustainability, which is by construction forward-looking and 
far ranging, and therefore requires state and civil society to prepare 
and implement plans at all levels of governance. Governments

are increasingly engaging in the development and formulation of 
national plans in an organised, conscious and continual attempt to 
select the best available alternatives to achieve specific goals.
A systematic assessment of 107 national development plans and 
10 country case studies provides useful insights regarding the type and content of the plans (Chimhowu et al. 2019). development plans 
are increasingly focusing on mobilising action across multiple actors 
and multiple dimensions to enhance resilience and improve the 
ability to undertake stronger mitigation actions. Various initiatives 
such as the World Summit for Children in 1990; the Heavily Indebted 
Poor Country initiative that started offering debt relief in exchange 
for commitments by beneficiary states to invest in health, education, 
nutrition and poverty reduction in 1996; and push towards 
Comprehensive Development Frameworks seem to have catalysed 
the development of national actions plans across countries to

estimate, measure and track investments and progress towards SDGs.
The most recent development plans also tend to differ from the 
earlier ones in terms of their approach. Complexity science has over 
the years argued for new forms of planning based on contingency, 
behaviour change, adaptation and constant learning (Colander and 
Kupers 2016; Ramalingam, 2013), and new plans have increasingly 
focused on increasing resilience of individuals, organisations and 
systems (Hummelbrunner and Jones, 2013). Finally, alongside short-
term (typically five year) plans with operational purpose, countries 
have also expressed visions of their development pathways over 
longer time horizons, via, for example, Voluntary National Reviews 
submitted in the context of the UN High Level Political Forum on 
Sustainable Development.
National development plans are also increasingly more holistic 
in their approach, linking closely with SDGs and incorporating

climate action in their agendas. For instance, the Low Carbon 
Development Initiative (LCDI), launched in 2017 by the Government 
of Indonesia, seeks to identify the development policies that can help 
Indonesia achieve multiple (social, economic, and environmental) 
goals simultaneously along with preserving and improving the 
country’s  natural resources (Bappenas 2019). Likewise, Nepal’s 
Fifteenth Plan (five-year) recognises the need for climate mitigation 
and adaptation and corresponding access to international finance 
and technologies. The plan suggests mobilisation of foreign aid in the 
climate change domain in line with Nepal’s priorities and its inclusion 
in the country’s climate-friendly development programs as the key 
opportunities in this regard (Nepal 2020).
China’s development plans have evolved over time from being 
largely growth oriented, and geared largely towards the objectives 
of addressing poverty, improving health, education and public

well-being to also including modernisation of agriculture, industry 
and infrastructure, new forms of urbanisation and a clear intent of 
focusing on innovation and new drivers of development (Central 
Compilation & Translation Press 2016). China’s 14th Five Year Plan 
not only seeks to promote high quality development in all aspects 
and focus on strengthening the economy in the global industrial 
chain, but also includes a vision of an ‘ecological civilisation’, which 
had been developed (CPC-CC 2015) and analysed earlier (He 2016; 
Xiao and Zhao 2017). It seeks to enhance China’s climate pledge to 
peak CO2 emissions by 2030 and achieve carbon neutrality by 2060 
through more vigorous policies and measures. Development plans 
tie in multiple development priorities that evolve and broaden over 
time as societies develop, as exemplified inter alia by the history of 
development plans in India (Box 4.4).

452
4.3.2.3 Development Pathways Shape Emissions 
and Capacities to Mitigate
Analysis in the mitigation literature often frames mitigation policy 
as having development co-benefits, the main objective being climate 
stabilisation. This misses the point that development drives emissions, 
and not vice versa, and it is the overall development approach and 
policies that determine mitigation pathways (Munasinghe 2007). 
A large body of literature supports the fact that development pathways 
have direct and, just as importantly, indirect implications for GHG 
emissions (Nakicenovic et al. 2000; Winkler 2017b), through multiple 
channels, such as the nature of economic activity, spatial patterns of 
development, degree of inequality, and population growth.
Economic structure: Chapter  2  notes that overall, affluence 
(GDP per capita), economic growth and population growth have

remained the main upward drivers of CO 2 emissions from fossil-
fuel combustion in the past decade, with energy efficiency the 
main countervailing force (Lin and Liu 2015; Wang and Feng 2017) 
(Section  2.4). A  major component of the development pathway of 
a country is precisely the nature of the economic activities on which 
the country relies (e.g.,  agriculture and mining, heavy industry, 
services, high-tech products, etc.) as well as the way it articulates its 
economy with the rest of the World (e.g., export-led growth vs import 
substitution strategies). Hence, the development pathway ultimately 
drives the underlying structure of the economy, and to a large degree 
the relationship between activity and GHG emissions.
At country level, however, the picture is more nuanced. Both India 
and China show signs of relative decoupling between GDP and 
emissions because of structural change (Chen et al. 2018a). Sumabat

et al. (2016) indicate that economic growth had a negative impact on 
CO2 emissions in Philippines. Baek and Gweisah (2013) find that CO 2 emissions tend to drop monotonously as incomes increased. Lantz 
and Feng (2006) also indicate that per capita GDP is not related 
to CO 2 emissions in Canada. Other studies point to an emerging 
consensus that the relationship between CO 2 emissions and economic 
indicators depends on the level of development of countries (Nguyen 
and Kakinaka 2019; Sharma 2011). While some literature indicates 
that absolute decoupling of economic growth and GHG emissions 
has occurred in some countries (Le Quéré et al. 2019), a  larger 
systematic review found limited evidence of this (Haberl et al. 2020).
Looking ahead, choices about the nature of economic activities are 
expected to have significant implications for emissions. For example, 
a development pathway that focuses on enhancing economic growth

based on manufacturing is likely to lead to very different challenges 
for mitigation compared to one that focuses on services-led growth. 
(Quéré et al. 2018) find that choices about whether or not to export 
offshore oil in Brazil will have significant implications for the country’s 
GHG emissions. Similarly, in China, transforming industrial structure 
towards tertiary sectors (Kwok et al. 2018) and restructuring exports 
towards higher value-added products (Wu et al. 2019) are expected 
to have significant implications for GHG emissions.
Spatial patterns of development: Chapter  2  notes that rapid 
urbanisation in developing and transition countries leads to increased 
CO2 emissions, the substantial migration of rural populations to urban 
areas in these countries being the main factor leading to increased 
levels of income and expenditure of new urban dwellers which in turn 
leads to increased personal carbon footprints and overall emissions

(Section  2.4). Urbanisation, and more broadly spatial patterns of 
development, are in turned driven to a large part by development 
choices, such as, inter alia, spatial provision of infrastructure and 
services, choices regarding the agriculture and forestry sector, 
land-use policies, support to regional/local development, among Box 4.4 | India’s National Development Plan
India’s initial national development plans focused on improving the living standards of its people, increasing national income and 
food self-sufficiency. Accordingly, there was a thrust towards enhancing productivity of the agricultural and industrial sectors. While 
the main focus was on maintaining high economic growth and industrial productivity, poverty eradication, employment and inclusive 
growth remained important priorities. The National Action Plan on Climate Change with eight National Missions focusing on mitigation

as well as adaptation was launched in 2008 integrating climate change considerations in planning and decision-making (MoEF 2008). 
The 12th Five-Year Plan (2012–2017) also brought in a focus on sustainability and mentioned the need for faster, sustainable and 
inclusive growth. The National Institution for Transforming India (NITI Aayog) was set up in 2017 replacing the erstwhile Planning 
Commission, with a renewed focus towards bringing innovation, technology, enterprise and efficient management together at the 
core of policy formulation and implementation. However, while India has moved away from its Five-Year Plans, decision-making is 
more dynamic, with a  number of sector-specific initiatives and targets focused on integrating sustainability dimensions through 
a series of policies and measures supporting resource efficiency, improved energy access, infrastructure development, low-carbon

options and building resilient communities, among other objectives (MoEFCC 2018, 2021). India’s overall development pathway 
currently has a strong focus on achieving robust and inclusive growth to ensure balanced development across all regions and states 
and across sectors. There is a thrust on embracing new technologies while fostering innovation and upskilling, modernisation of 
agriculture, improving regional and interpersonal equity, bridging the gap between public and private sector performance, by focusing 
on efficient delivery of public services, rooting out corruption and black economy, formalising the economy and expanding the tax 
base, improving the ease of doing business, nursing the stressed commercial banking sector back to a healthy state, and stopping 
leakages through direct benefit transfers, among other measures (GoI 2015, 2018; MoEFCC 2021).

453
others (World Bank 2009). For example, Dorin (2017) points out 
that if agriculture sectors in Africa and India follow the same development path that developed countries have followed in the past, namely increased labour productivity through enlargement and robotisation of farms, then unprecedented emigrations of rural workers towards cities or foreign countries will ensue, with large-scale social, economic and environmental consequences. Looking ahead, a development pathway that encourages concentrated influx of people to large urban centres will lead to very different energy and infrastructure consumption patterns than a pathway that prioritises the development of smaller, self-contained towns and cities.
Degree of inequality: Chapter  2  notes that while eradicating

extreme poverty and providing universal access to modern energy services to poor populations across the globe has negligible implications for emissions growth, existing studies on the role of poverty and inequality as drivers of GHG emissions provide limited evidence that under certain contexts greater inequality can lead to a deterioration in environmental quality and may be associated with higher GHG emissions (Section 2.4). In fact, factors affecting household consumption-based emissions include household size, age, education attainment, employment status, urban vs rural location and housing stock (Druckman and Jackson 2015). There is evidence to indicate that at the household level, the increase in emissions from additional consumption of the lower income households could be larger than the reduction in emissions from the drop in consumption from the high income households (Sager 2019). Accordingly, as countries seek to fulfil the objective of reducing inequality, there are

Since reducing inequality, as noted above, is globally one of the 
main development priorities, a  large body of literature focuses on the compatibility of climate change mitigation and reduction in economic inequality (Baek and Gweisah 2013; Auffhammer and Wolfram 2014; Berthe and Elie 2015; Hao et al. 2016; Grunewald et al. 2017; Wiedenhofer et al. 2017). However, the use of narrow approaches or simple methods of studying the relationships of income inequality and emissions by looking at correlations, may miss important linkages. For example, the influence of inequality on social values such as status and civic mindedness and non-political interests that shape environmental policy can influence overall consumption and its environmental impacts (Berthe and Elie 2015). Moreover, inequalities may also be reflected in gender, education, racial and ethnic profiles and could accordingly be associated with the level of emissions and mitigation prospects (Andrijevic et al. 2020).

Report (Box 3.1 and Section 3.2.5) provide another example of how development pathways influence mitigative capacity. Precisely, IMP1.5-SP (Shifting Pathways) and 1.5-Ren (Renewables) lead to the same long-term temperature, but differ in underlying socio-economic conditions. The former is based on Shared Socio-economic Pathway (SSP) 1  (sustainable development), whereas the latter is based on SSP2 (middle of the road). Comparing 1.5-Ren to 1.5-SP can thus be interpreted as a numerical translation of trying the reach the same long-term temperature goal without and with shifting development pathways towards sustainability. Data shows that the global price of carbon necessary to remain on target is 40–50% lower in the latter relative to the former, thus indicating that mitigation is cheaper with a  shift in development pathway towards sustainability. Other cost indicators (e.g. consumption loss or GDP loss) tell the same story. Since both IMPs were computed using the same underlying

In sum, development pathways can lead to different emission levels 
and different capacities and opportunities to mitigate (medium evidence, high agreement). Thus, focusing on shifting development pathways can lead to larger systemic sustainability benefits.
4.3.2.4  Integrating Mitigation Considerations Requires 
Non-marginal Shifts in Development P
athways
Concerns about mitigation are already being introduced in national

development plans, as there is evidence that development strategies and pathways can be carefully designed so as to align towards multiple priorities and achieve greater synergistic benefits. For example, India’s solar programme is a key element in its NDC that can in the long run, not only provide energy security and contribute to mitigation, but can simultaneously contribute to economic growth, improved energy access and additional employment opportunities, if appropriate policies and measures are carefully planned and implemented. However, the environmental implications of the transition need to be carefully examined with regard to the socio-economic implications in light of the potential of other alternatives like green hydrogen, nuclear or carbon capture, use and storage (CCUS). Similarly, South Africa National Development Plan (2011) also integrates transition to low-carbon as part of the country development objectives (Box 4.5).

Box 4.5 | South Africa’s National Development Plan
South Africa adopted its first National Development Plan (NDP) in 2011 (NPC 2011), the same year in which the country adopted climate policy (RSA 2011) and hosted COP17 in Durban. Chapter 5 of the NDP addresses environmental sustainability in the context of development planning, and specifically ‘an equitable transition to a low-carbon economy’ (NPC 2011). The chapter refers explicitly to the need for a just transition, protecting the poor from impacts and any transitional costs from emissions-intensive to low-carbon. The plan proposes several mitigation measures, including a carbon budgeting approach, reference to Treasury’s carbon tax, use of various low-carbon options while maintaining energy security, and the integrated resource plan for electricity. The NDP refers to coal in several chapters, in some places suggesting additional investment (including new rail lines to transport coal and coal to liquids),

454
Looking ahead, given that different development pathways can lead 
to different levels of GHG emissions and to different capacities and 
opportunities to mitigate, there is increasing research on how to make 
development pathways more sustainable. Literature is also focusing 
on the need for a ‘new normal’ as a  system capable of achieving 
higher quality growth while addressing multiple development 
objectives by focusing on ‘innovative development pathways’.
Literature suggests that if development pathways are to be changed 
to address the climate change problem, choices that would need to 
be made about development pathways would not be marginal (Stern 
2009), and would require a new social contract to address a complex 
set of inter-linkages across sectors, classes and the whole economy 
(Winkler 2017b). Shifting development pathways necessitates

planning in a  holistic manner, rather than thinking about discrete 
and isolated activities and actions to undertake mitigation. Further, 
the necessary transformational changes can be positive if they are 
rooted in the development aspirations of the economy and society in 
which they take place (Dubash 2012; Jones et al. 2013), but they can 
also lead to carbon colonialism if the transformations are imposed by 
Northern donors or perceived as such.
Accordingly, influencing a  societies’ development pathways draws 
upon a  broader range of policies and other efforts than narrowly 
influencing mitigation pathways, to be able to achieve the multiple 
objectives of reducing poverty, inequality and GHG emissions. The 
implications for employment, education, mobility, housing and many 
other development aspects must be integrated and new ways of 
looking at development pathways which are low carbon must be 
considered (Bataille et al. 2016b; Waisman et al. 2019). For instance,

job creation and education are important elements that could play 
a key role in reducing inequality and poverty in countries like South 
Africa and India (Winkler et al. 2015; Rao and Min 2018) while these 
also open up broader opportunities for mitigation.
4.3.2.5 New Tools Are Needed to Pave and Assess 
Relative to the literature on mitigation pathways described in 4.2.5 
and in  4.3.3, the literature on development pathways is limited. 
The climate research community has developed the Shared Socio-
economic Pathways (SSPs) that link several socio-economic drivers 
including equity in relation to welfare, resources, institutions, 
governance and climate mitigation policies in order to reflect 
many of the key development directions (O’Neill et al. 2014). In most modelling exercises however, development remains treated 
as an exogenous input. In addition, models may capture only 
some dimensions of development that are relevant for mitigation

options, thereby not capturing distributional aspects and not 
allowing consistency checks with broader developmental goals 
(Valadkhani et al. 2016). Quantitative tools for assessing mitigation 
pathways could be more helpful if they could provide information 
on a  broader range of development indicators, and could model 
substantively different alternative development paths, thereby 
providing information on which levers might shift development in 
a more sustainable direction.
Doing so requires new ways of thinking with interdisciplinary 
research and use of alternative frameworks and methods suited to 
deeper understanding of change agents, determinants of change 
and adaptive management among other issues (Winkler 2018). 
This includes, inter alia, being able to examine enabling conditions 
for shifting development pathways (Section  4.4.1); re-evaluating 
the neo-classical assumptions within most models, both on the

functioning of markets and on the behaviour of agents, to better 
address obstacles on the demand side, obstacles on the supply 
side and market distortions (Ekholm et al. 2013; Staub-Kaminski 
et al. 2014; Grubb et al. 2015) improving representation of issues 
related with uncertainty, innovation, inertia and irreversibility 
within the larger development contexts, including energy access 
and security; improving the representation of social and human 
capital, and of social, technological and governance innovations 
(Pedde et al. 2019).
Tools have been developed in that direction, for example in the 
Mitigation Action Plans and Scenarios (MAPS) community (La 
Rovere et al. 2014b), but need to be further mainstreamed in the 
analysis. Back-casting is often a  preferred modelling approach for 
assessment aiming to align national development goals with global 
climate goals like CO 2 stabilisation. Back-casting is a  normative

approach where modellers construct desirable futures and specify 
upfront targets and then find out possible pathways to attain these 
targets (IPCC et al. 2001). Use of approaches like back-casting are 
useful not only in incorporating the long term national development 
objectives in the models, but also evaluating conflicts and synergies 
more effectively (van der Voorn et al. 2020). In back casting, the long-
term national development objectives remain the key benchmarks 
guiding the model dynamics and the global climate goal is interfaced 
to realise the co-benefits. The models then delineate the roadmap 
of national actions such that the national goals are achieved with Box 4.5 (continued)
in others decommissioning coal-fired power ‘procuring at least 20,000 MW of renewable electricity by 2030, importing electricity 
from the region, decommissioning 11,000 MW of ageing coal-fired power stations and stepping up investments in energy-efficiency’

(NPC 2011: p. 46). Reference to environmental sustainability is not limited to Chapter 5 – the introductory vision statement includes 
acknowledgement ‘that each and every one of us is intimately and inextricably of this earth with its beauty and life-giving sources; 
that our lives on earth are both enriched and complicated by what we have contributed to its condition’ (NPC 2011: p. 21); and the 
overview of the plan includes a section on climate change, addressing both mitigation and adaptation.

455
a  comprehensive understanding of the full costs and benefits of 
low-carbon development (often including the costs of adaptation 
and impacts from residual climate change). Back-casting modelling 
exercises show that aligning development and climate actions could 
result in much lower ‘social cost of carbon’ (Shukla et al. 2008). 
Back-casting does not aim to produce blueprints. Rather, it indicates 
the relative feasibility and the social, environmental, and political 
implications of different development and climate futures on the 
assumption of a clear relationship between goal setting and policy 
planning (Dreborg 1996). Accordingly, back-casting exercises are 
well suited for preparing local specific roadmaps like for cities (Gomi 
et al. 2010, 2011).
4.3.3 Examples of Shifts in Development Pathways and 
of Supporting Policies
As noted in Section  4.3.1, policy approaches that include

a broader range of instruments and initiatives would impact more 
fundamentally on the actors, institutions and structures of societies 
and the dynamics among them, aiming to alter the underlying drivers 
of emissions, opening up a wider range of mitigation opportunities 
and potential in the process of achieving societal development goals. 
While the evolution of these drivers is subject to varied influences 
and complex interactions, there are policy measures by which 
decision-makers might influence them. Table  4.12 provides some 
examples of policy measures that can affect key drivers (shown in 
the row headings).
Table 4.12 | Examples of policies that can help shift development pathways.
Drivers Examples of policy measures
Behaviour –Progressive taxation
 –Ecological tax reform
 –Regulation of advertisement
 –Investment in public transit
 –Eco-labelling
and institutions –Campaign finance laws
 –Regulatory transparency

–Commitment to multilateral environmental governance
 –Public investment in education and R&D
 –Public-service information initiatives
 –Public sector commitment to science-based decision-making
 –Anti-corruption policies
Innovation –Investment in public education
 –Public sector R&D support
 –Fiscal incentives for private investments in public goods
 –International technology development and transfer initiatives
Finance and 
investment –International investment treaties support common objectives
 –Litigation and liability regulations
 –Reform of subsidies and other incentives not aligned with
 –Insurance sector and pension regulation
 –Green quantitative easing
 –Risk disclosurePolicies such as those listed in Table 4.12 are typically associated with 
broader objectives than GHG mitigation. They are generally conceived 
and implemented in the pursuit of overall societal development 
objectives, such as job creation, macroeconomic stability, economic

growth, and public health and welfare. However, they can have major 
influence on mitigative capacity, and hence can be seen as necessary 
tools if mitigation options are to be significantly broadened and 
accelerated (medium evidence, medium agreement). The example of 
the UK shows how accelerated mitigation through dietary changes 
require a wide set of efforts to shift underlying drivers of behaviour. 
In this case, multiple forces have interacted to lead to reduced meat 
consumption, including health attitudes, animal welfare concerns, 
and an increasing focus on climate and other environmental 
impacts of livestock production, along with corporate investment 
in market opportunities, and technological developments in meat 
alternatives (Box 5.5).
Other historic cases that are unrelated to recent mitigation efforts 
might be more appropriate examples of major socio-technical shifts 
that were largely driven by intentional, coherent intentional policy

initiatives across numerous domains to meet multiple objectives. The 
modernisation of agriculture in various national contexts fits such 
a  mold. In the USA, for example, major government investments 
in agricultural innovation through the creation of agricultural 
universities and support for research provided advances in the 
technological basis for modernisation. A  network of agricultural 
extension services accelerated the popularization and uptake of 
modern methods. Infrastructure investments in irrigation and drainage 
made production more viable, and investment in roadways and rail 
for transport supported market formation. Agricultural development 
banks made credit available, and government subsidies improved 
the profitability for farmers and agricultural corporations. Public 
campaigns were launched to modify food habits (Ferleger 2000).
Further examples of SDPS across many different systems and sectors 
are elaborated across this report. Concrete examples assessed in

this chapter include high employment and low emissions structural 
change, fiscal reforms for mitigation and social contract, combining 
housing policies to deliver both housing and transport mitigation, 
and change economic, social and spatial patterns of development 
of the agriculture sector provide the basis for sustained reductions 
in emissions from deforestation (Sections  4.4.1.7–4.4.1.10). These 
examples differ by context. Examples in other chapters include 
transformations in energy, urban, building, industrial, transport, 
and land-based systems, changes in behaviour and social practices, 
as well as transformational changes across whole economies and 
societies (Cross-Chapter Box 5 in this chapter, Section 5.8, Box 6.2, 
Sections 8.2, 8.3.1, 8.4, 9.8.1, 9.8.2 and 10.4.1, and Cross-Chapter 
Box 12 in Chapter 16). These examples and others can be understood 
in the context of an explanation of the concept of SDPS, and how to

shifting development pathways (Cross-Chapter Box 5 in this chapter).

456
Cross-Chapter Box 5 | Shifting Development Pathways to Increase Sustainability and Broaden 
Mitigation Options
Authors: Franck Lecocq (France), Harald Winkler (Republic of South Africa), Mustafa Babiker (Sudan/Saudi Arabia), Brett Cohen 
(Republic of South Africa), Heleen de Coninck (the Netherlands), Dipak Dasgupta (India), Navroz K. Dubash (India), María Josefina 
Figueroa Meza (Venezuela/Denmark), Michael Grubb (United Kingdom), Kirsten Halsnæs (Denmark), Şiir Kılkış (Turkey), William Lamb 
(Germany/United Kingdom), Sebastian Mirasgedis (Greece), Sudarmanto Budi Nugroho (Indonesia), Chukwumerije Okereke (Nigeria/
United Kingdom), Minal Pathak (India), Joyashree Roy (India/Thailand), Ambuj Sagar (India), Yamina Saheb (France/Algeria), 
Priyadarshi  Shukla (India), Jim Skea (United Kingdom), Youba Sokona (Mali), Julia Steinberger (United Kingdom/Switzerland),

Mariama Williams (Jamaica/the United States of America)
1. What do we mean by development pathways?
In the present report, development pathways refer to patterns of development resulting from multiple decisions and choices made 
by many actors in the national and global contexts. Each society whether in the Global North or the Global South follows its own 
pattern of development (Figure 1.6). Development pathways can also be described at smaller scales (e.g., for regions or cities). By 
extension, the concept can also be applied to sectors and systems (e.g., the development pathway of the agricultural sector or of 
industrial systems).
2. Why do development pathways matter in a report about mitigation?
2a. Past development pathways determine both today’s GHG emissions and the set of opportunities to reduce emissions
Development pathways drive GHG emissions for a large part (Sections 2.4, 2.5 and 2.6). For example, different social choices and

policy packages with regard to land use and associated rents will result in human settlements with different spatial patterns, different 
types of housing markets and cultures, and different degrees of inclusiveness, and thus different demand for transport services and 
associated GHG emissions (Sections 8.3.1 and 10.2.1).
There is compelling evidence to show that continuing along existing development pathways is unlikely to achieve rapid and deep 
emission reductions (robust evidence, medium agreement). For example, investments in long-lived infrastructure, including energy 
supply systems, could lock-in high emissions pathways and risk making deep decarbonisation and sustainable policies more 
difficult and expensive.
Development pathways also determine the set of tools available to mitigate climate change (Figure 4.7). For example, the capacity 
of households to move closer to their workplace, in response to, for example, a price signal on carbon and thus on gasoline, depends

on rents, which themselves depend on the spatial patterns of development of human settlements (Section 8.3.1). Said differently, 
mitigation costs depend on past development choices. Similarly, development pathways determine the enablers and levers available 
for adaptation (AR6 WGII, Chapter 18) and for achieving other SDGs.
In the absence of shifts in development pathways, conventional mitigation policy instruments (e.g., carbon tax, emission quotas, 
technological norms, etc.) may not be able to limit emissions to a degree sufficient for deep decarbonisation or only at very high 
economic and social costs.
Policies to shift development pathways, on the contrary, make mitigation policies more effective. For example, policies that prioritise 
non-car transit, or limit rents close to work places would make it easier for households to relocate in response to a price signal on 
transport, and thus makes the same degree of mitigation achievable at lower economic and social cost.

2b. Shifting development pathways broadens the scope for synergies between development objectives and mitigation
Second, societies pursue a variety of development objectives, of which protecting the Earth’s climate is part. The SDGs provide a global 
mapping of these goals. Absent climate mitigation, our collective ability to achieve the SDGs in 2030 and to sustain them beyond 2030 
is likely to be compromised, even if adaptation measures are put in place (AR6 WGII).
There are many instances in which reducing GHG emissions and moving towards the achievement of other development objectives 
can go hand in hand, in the near-, mid- and long-term (Sections 3.7, 6.7.7, 7.6.5, 8.2, 9.8, 10.1.1, 11.5.3 and 17.3, and Figures 3.40 
and 12.1). For example, transitions from coal-based power to lower-emissions electricity generation technologies and from internal 
combustion engine to lower-carbon transport has large mitigation potential and direct benefits for health through reduction in local

air pollution (Box 6.2 and Section 10.4.1). Energy efficiency in buildings and energy poverty alleviation through improved access to 
clean fuels also delivers significant health benefits (Sections 9.8.1 and 9.8.2).

457
Cross-Chapter Box 5 (continued)
Careful design of mitigation policies is critical to achieving these synergies (Section 13.8). Integrated policies can support the creation 
of synergies between climate change goals and other SDGs. For example, when measures promoting walkable urban areas are 
combined with electrification and clean renewable energy, there are several co-benefits to be attained (Figure SPM.8 and Section 5.2). 
These include reduced pressures on agricultural land from reduced urban growth, health co-benefits from cleaner air and benefits from 
enhanced mobility (Sections 8.2, 8.4 and 4.4.1.9).
Policy design can also manage trade-offs, for example through policy measures as part of just transitions (Section 17.4). However, 
even with good policy design, decisions about mitigation actions, and the timing and scale thereof, may entail trade-offs with the

achievement of other national development objectives in the near-, mid- and long term. In the near term, for example, regulations may 
ban vehicles from city centres to reduce congestion and local air pollution, but reduce mobility and choice. Increasing green spaces 
within cities without caps on housing prices may involve trade-offs with affordable housing and push low income residents outside 
the city (Section 8.2.2). In the mid- and long-term, large-scale deployment of biomass energy raises concerns about food security and 
biodiversity conservation (Sections 3.7.1, 3.7.5, 7.4.4, 9.8.1, 12.5.2 and 12.5.3). Conflicts between mitigation and other development 
objectives can act as an impediment to climate action (Section 13.8). Climate change is the result of decades of unsustainable energy 
production, land-use, production and consumption patterns, as well as governance arrangements and political economic institutions

that lock in resource-intensive development patterns (robust evidence, high agreement). Reframing development objectives and 
shifting development pathways towards sustainability can help transform these patterns and practices, allowing space for transitions 
transforming unsustainable systems (medium evidence, high agreement) (Chapter 17, Executive Summary).
Prioritising is one way to manage trade-offs, addressing some national development objectives earlier than others. Another way 
is to adopt policy packages aimed at shifting development pathways towards sustainability as they expand the range of tools available 
to simultaneously achieve multiple development objectives, including mitigation. In the city example of Section 2a, a carbon tax alone 
would run counter to other development objectives if it made suburban households locked into high emissions transport modes poorer

or if it restricted mobility choices, in particular for low- and middle-income households. Policy packages combining affordable housing 
and provision of safe low-carbon mobility could both facilitate equitable access to housing (a major development objective in many 
countries) and make it easier to mitigate by shifting the urban development pathway.
Similarly, a fundamental shift in the service provision that helps reduce energy demand (Chapter 5), driven by targeted policies, 
investment and enabling socio-cultural and behavioural change, would reduce pressure on supply side mitigation need, hence limiting 
pressure on water and food and the achievement of associated SDGs. Some studies assume Western European lifestyle as a reference 
for the Global North and an improvement in the living standard for the Global South to reduce energy demand and emissions (Grubler

et al. 2018), while others explore a transformative change in the Global North to achieve a decent living standard for all (Bertram et al. 
2018; Millward-Hopkins et al. 2020) (Section 3.7.8). For example, in the UK, interaction between multiple behavioural, socio-cultural, 
and corporate drivers including NGO campaigns, social movements and product innovations resulted in an observed decline in meat 
consumption (Sections 5.4 and 5.6.4).
3. What does shifting development pathways towards sustainability entail?
Shifting development pathways towards sustainability implies making transformative changes that disrupt existing developmental 
trends. Such choices would not be marginal (Stern 2009), but include technology adoption, infrastructure availability and use, and 
socio-behavioural factors (Chapter 5).
These include creating new infrastructure, sustainable supply chains, institutional capacities for evidence-based and integrated

decision-making, financial alignment towards low-carbon socially responsible investments, just transitions and shifts in behaviour 
and norms to support shifts away from fossil-fuel consumption (Green and Denniss 2018). Adopting multi-level governance modes, 
tackling corruption where it inhibits shifts to sustainability, and improving social and political trust are also key for aligning and 
supporting long-term environmentally just policies and processes.
Shifting development pathways entails fundamental changes in energy, urban, building, industrial, transport, and land-based systems. 
It also requires changes in behaviour and social practices. Overcoming inertia and locked-in practices may face considerable opposition 
(Geels et al. 2017) (Section  5.4.5). The durability of carbon intensive transport modes and electricity generating infrastructures

increase the risk of lock-in to high emissions pathways, as these comprise not just consumer practices, but sunk costs in infrastructure, 
supporting institutions and rules (Seto et al. 2016; Mattioli et al. 2020). Shifting investments towards low-GHG solutions requires 
a combination of conducive public policies, attractive investment opportunities, as well as the availability of financing to enable such 
a transition (Section 15.3).

458
4. How to shift development pathways?
Shifting development paths is complex. If history is any guide, practices that can easily supplant existing systems and are clearly 
profitable move fastest (Griliches 1957). Changes that involve ‘dissimilar, unfamiliar and more complex science-based components’ 
take more time, acceptance and legitimation and involve complex social learning (Conley and Udry 2010), even when they promise 
large gains (Pezzoni et al. 2019).
Yet despite the complexities of the interactions that result in patterns of development, history also shows that societies can influence 
the direction of development pathways based on choices made by decision-makers, citizens, the private sector and social stakeholders. 
For example, fundamentally different responses to the first oil shock shifted then-comparable economies on to different energy sector

development and economic pathways in the 1970s and 80s (Sathaye et al. 2009). More recent examples have shown evidence of 
voluntary transitions for example, advanced lighting in Sweden, improved cook-stoves in China, liquefied petroleum gas stoves in 
Indonesia or ethanol vehicles in Brazil (Sovacool 2016).
There is no one-size-fits-all recipe for shifting development pathways. However, the following insights can be drawn from past 
experience and scenarios of possible future development pathways (Section  4.4.1). For example, policies making inner-urban 
neighbourhoods more accessible and affordable reduce transport costs for low- and middle-income households, and also reduce 
transport emissions (Section 4.4.1.9). Shifts in development pathways result from both sustained political interventions and bottom-
up changes in public opinion. No single sector or policy action is enough to achieve this. Coordinated policy mixes would need to

coordinate multiple actors – in other words, individuals, groups and collectives, corporate actors, institutions and infrastructure actors – 
to deepen decarbonisation and shift pathways towards sustainability (Pettifor 2020). One example was the liquefied petroleum gas 
(LPG) Subsidy (‘Zero Kero’) Program in Indonesia which harnessed creative policy design to shift to cleaner energy by overcoming 
existing private interests. The objective of decreasing fiscal expenditures on domestic kerosene subsidies by replacing it with LPG was 
achieved by harnessing distribution networks of existing providers supported by government subsidised provision of equipment and 
subsidised pricing (Cross-Chapter Box 9 in Chapter 13).
Shifts in one country may spill over to other countries. Collective action by individuals as part of formal social movements or informal 
lifestyle changes underpins system change (Sections 5.2.3, 5.4.1, 5.4.5.3 and 13.5).

Sectoral transitions that aspire to shift development pathways often have multiple objectives, and deploy a diverse mix or package of 
policies and institutional measures (Figure 13.6). Context specific governance conditions can significantly enable or disable sectoral 
transitions, and play a determinative role in whether a sectoral transition leads to a shift in development pathway. For example, 
if implemented policies to tackle fuel poverty target the most socially vulnerable households, this can help address barriers poor 
households face in undertaking building retrofits. In the EU-28, it has been shown that accelerated energy efficiency policies coupled 
with strong social policies targeting the most vulnerable households, can help reduce the energy demand in residential sector, and 
deliver additional co-benefits of avoided premature deaths and reduced health impacts (Section 9.8.2).

Literature suggests that through equitable resource distribution, high levels of human development can be provided at moderate 
energy and carbon levels by changing consumption patterns and redirecting systems in the direction of more sustainable resource use, 
suggesting that a special effort can be made in the near term for those on higher incomes who account for a disproportionate fraction 
of global emissions (Millward-Hopkins et al. 2020; Hickel et al. 2021) (Section 5.2.2 and Figure 5.14).
The necessary transformational changes are likely to be more acceptable if rooted in the development aspirations of the economy and 
society within which they take place (Dubash 2012; Jones et al. 2013) and may enable a new social contract to address a complex set 
of inter-linkages across sectors, classes and the whole economy (Fleurbaey et al. 2018).
Taking advantage of windows of opportunity and disruptions to mindsets and socio-technical systems could advance deeper

transformations. These might include the globally declining costs of renewables (Figure 1.7, Section 2.2.5 and Box 16.2), emerging 
social norms for climate mitigation (Green and Denniss 2018), or the COVID-19 pandemic, all of which might be harnessed to centre 
political action on protecting human and planetary health (Büchs et al. 2020), but if not handled carefully could also risk undermining 
the support for transformation.

459
In sum, development pathways unfold over time in response to 
complex dynamics among various drivers and diverse actors with 
varying interests and motivations (high agreement, robust evidence). 
The way countries develop determines the nature and degree of the 
obstacles to accelerating mitigation and achieving other sustainable 
development objectives (medium-robust evidence, medium 
agreement). Meeting ambitious mitigation and development goals 
cannot be achieved through incremental change (robust evidence, 
medium agreement). Shifting development pathways thus involves 
designing and implementing policies where possible to intentionally 
enhance enabling conditions and reduce obstacles to desired 
outcomes (medium evidence, medium agreement).
Section  4.4 elaborates mechanisms through which societies can 
develop and implement policies to substantially shift development

pathways toward securing shared societal objectives. Such policies 
entail overcoming obstacles (Section 4.2.7) by means of favourable 
enabling conditions: governance and institutions, behaviour, 
innovation, policy and finance. These enabling conditions are 
amenable to intentional change – to greater or lesser degrees and 
over longer or shorter time scales – based on a range of possible 
measures and processes (Section 4.4).4.4 How to Shift Development Pathways 
and Accelerate the Pace and 
Scale of Mitigation
4.4.1 Approaches, Enabling Conditions and Examples
4.4.1.1 Framing the Problem
What have we learned so far? As highlighted above, despite 30 years 
of UNFCCC and growing contributions by non-state actors, the 
emissions gap keeps growing (Sections 4.2.2 and 4.2.3). Mitigation 
conceived as incremental change is not enough. Meeting ambitious 
mitigation goals entails rapid, non-marginal changes in production

and consumption patterns (Sections 4.2.4 and 4.2.5). Taking another 
approach, we have seen in Section  4.3 that shifting development 
pathways broadens the scope for mitigation (Sections  4.3.1 
and 4.3.2) and offers more opportunities than mitigation alone to 
combine mitigation with the realisation of other SDGs (Section 4.3.1 
and Cross-Chapter Box 5 in this chapter).
A practical way forward is to combine shifting development 
pathways and accelerating mitigation (medium evidence, high 
agreement). This means introducing multi-objective policy packages 
and sequences with climate and development components that 
both target mitigation directly and create the conditions for shifts in 
development pathways that will help accelerate further mitigation 
down the line, and meet other development objectives. Since 
development pathways result from myriad decisions from multiple 
actors (Section 4.3.1), coordination across countries and with non-

state actors is essential.Cross-Chapter Box 5 (continued)
5. How can shifts in development pathways be implemented by actors in different contexts?
Shifting development pathways to increased sustainability is a  shared aspiration. Yet since countries differ in starting points 
(e.g., social, economic, cultural, political) and history, they have different urgent needs in terms of facilitating the economic, social, 
and environmental dimensions of sustainable development and, therefore, give different priorities (Sections 4.3.2 and 17.4). The 
appropriate set of policies to shift development pathways thus depends on national circumstances and capacities.
In some developed countries and communities, affluence leads to high levels of consumption and emissions across sectors (Mazur 
and Rosa 1974; Wiedmann et al. 2020). For some countries, reducing consumption can reduce emissions without compromising on

wellbeing. However, some developing countries still face the challenge of escaping ‘middle-income traps’ (Agénor and Canuto 2015), 
as labour-saving technological change and globalisation have limited options to develop via the manufacturing sector (Altenburg 
and Rodrik 2017). In least developed countries, infrastructure, industry, and public services are still being established, posing both 
a challenge to financial support to deploy technologies, and large opportunities to support accelerating low-to-zero carbon options – 
especially in terms of efficient and sufficient provision (Millward-Hopkins et al. 2020). Availability of capital, or lack thereof, is a critical 
discriminant across countries and requires international cooperation (Section 15.2.2).
Shifting development pathways towards sustainability needs to be supported by global partnerships to strengthen suitable capacity,

technological innovation (Section  16.6), and financial flows (Sections  14.4.1, 15.2.4). The international community can play 
a particularly key role by helping ensure the necessary broad participation in climate-mitigation efforts, including by countries at 
different development levels, through sustained support for policies and partnerships that support shifting development pathways 
towards sustainability while promoting equity and being mindful of different transition capacities (Sections  4.3.2, 16.5, 16.6, 
14.4 and 17.4).

460
The literature does not provide a handbook on how to accomplish the 
above. However, analysis of past experience as well as understanding 
of how societies function yield insights that the present section aims 
at presenting. Human history has seen multiple transformation of 
economies due to path-breaking innovations (Michaelowa et al. 
2018), like the transformation of the energy system from traditional 
biomass to fossil fuels or from steam to electricity (Fouquet 2010, 
2016a; Sovacool 2016). Fouquet (2016b) and Smil (2016) argue 
that even the most rapid global transformations have taken several 
decades. Enabling transformational change implies to create now 
the conditions that lead to that transformation (Díaz et al. 2019). 
The starting point is that there is no single factor determining such 
a transformation. Rather a range of enabling conditions can combine

in a co-evolutionary process. Amongst the conditions that have been 
cited in the literature are higher levels of innovation, multilevel 
governance, transformative policy regimes or profound behavioural 
transformation (Rockström et al. 2017; IPCC 2018a; Geels et al. 2018; 
Kriegler et al. 2018). It might be possible to put in place some of 
the above conditions rapidly, while others may take longer, thereby 
requiring an early start.
The present chapter uses the set of enabling conditions identified in 
the IPCC SR1.5 report, namely policy, governance and institutional 
capacity, finance, behaviour and lifestyles and innovation and 
technology (de Coninck et al. 2018). As Figure  4.8 illustrates, 
public policies are required to foster both accelerating mitigation 
and shifting development pathways. They are also vital to guide 
and provide the other enabling conditions (compare Table  4.12). 
Improved governance and enhanced institutional capacity facilitate

the adoption of policies that accelerate mitigation and shift 
development pathways, with the potential to achieve multiple 
mitigation and development objectives. Finance is required both to 
accelerate mitigation and to shift development pathways. Chapter 15 argues that near term actions to shift the financial system over the 
next decade (2021–2030) are critically important and feasible, and 
that the immediate post-COVID recovery opens up opportunities 
to scale up financing from billions to trillions (Mawdsley 2018) 
(Section 15.6.7). As discussed in Section 4.2.5, accelerated mitigation 
pathways encompass both rapid deployment of new technologies 
such as CCS or electric vehicles, as well as changes in consumption 
patterns: rapid deployment of mitigation technology and behaviour 
change are thus two enabling conditions to accelerated mitigation. 
Dynamics of deployment of technologies are relatively well known,

pointing to specific, short-term action to accelerate innovation and 
deployment (Cross-Chapter Box 12 in Chapter 16), whereas dynamics 
of collective behaviour change is less well understood. Arguably, the 
latter also facilitates shifting development pathways.
Individual enabling conditions are discussed at length in 
Chapter  5  (behaviour change), 13 (policies, governance and 
institutional capacity), 15 (finance) and 16 (innovation). The purpose 
of the discussion below is to draw operational implications from 
these chapters for action, taking into account the focus of the present 
Chapter on action at the national level in the near- and mid-term, and 
its special emphasis on shifting development pathways in addition to 
accelerated mitigation.
The rest of the Section is organised as follows. Policy packages 
that combine climate and development policies are first 
discussed  (Section  4.4.1.2). The next sections are dedicated to

the conditions that facilitate shifts in development pathways 
and accelerated mitigation: governance and institutions 
(Section  4.4.1.3), financial resources (Section  4.4.1.4), behaviour 
change (Section  4.4.1.5) and innovation  (Section  4.4.1.6). Four 
examples of how climate and development policies can be combined 
to shift pathways and accelerate mitigation are then presented 
Accelerating 
mitigationShifting development 
pathways towards 
sustainabilityGovernance and institutional capacity
Policy packages, including climate and 
innovationBehaviour 
changeFinanceFacilitates
Is required for
Figure 4.8 | Enabling conditions for accelerating mitigation and shifting development pathways towards sustainability.

461
(Sections 4.4.1.7, 4.4.1.8, 4.4.1.9 and 4.4.1.10). Section 4.4.2 focuses 
specifically on how shifts in development pathways can deliver both 
mitigation and adaptation. Finally, Section  4.4.3 discusses risks 
and uncertainties associated with combining shifting development 
pathways and accelerating mitigation.
4.4.1.2 Policy Packages That Include Climate 
and Development Policies
Although many transformations in the past have been driven by 
the emergence and diffusion of an innovative technology, policy 
intervention was frequent, especially in the more rapid ones 
(Michaelowa et al. 2018; Grubb et al. 2021). Likewise, it is not 
expected that spontaneous behaviour change or market evolution 
alone yield the type of transformations outlined in the accelerated 
mitigation pathways described in Section  4.2.5, or in the shifts in 
development pathways described in Section 4.3.3. On the contrary,

stringent temperature targets imply bold policies in the short term 
(Rockström et al. 2017; Kriegler et al. 2018) to enforce effective 
existing policy instruments and regulations, as well as to reform or 
remove harmful existing policies and subsidies (Díaz et al. 2019).
Policy integration, addressing multiple objectives, is an essential 
component of shifting development pathways and accelerating 
mitigation (robust evidence, high agreement). A shift in development 
pathways that fosters accelerated mitigation may best be achieved 
through integrated actions that comprise policies in support of 
the broader SDG agenda, based on country-specific priorities 
(Sections  4.3.2, 13.8 and 13.9). These may include for example, 
fiscal policies, or integrating industrial (Nilsson et al. 2021) and 
energy policies (Fragkos et al. 2021) with climate policies. Similarly, 
sectoral transitions that aspire to shifting development pathways

towards sustainability often have multiple objectives, and deploy 
a  diverse mix or package of policies and institutional measures 
(Cross-Chapter Box 5).
Because low-carbon transitions are political processes, analyses 
are needed of policy as well as for policy (Section  13.6). Political 
scientists have developed a number of theoretical models that both 
explain policy-making processes and provide useful insights for 
influencing those processes. Case studies of successes and failures 
in sustainable development and mitigation offer equally important 
insights. Both theoretical and empirical analysis reinforce the 
argument that single policy instruments are not sufficient (robust 
evidence, high agreement). Policymakers might rather mobilise 
a range of policies, such as financial instruments (taxes, subsidies, 
grants, loans), regulatory instruments (standards, laws, performance 
targets) and processual instruments (demonstration projects, network

management, public debates, consultations, foresight exercises, 
roadmaps) (Voß et al. 2007). Policies can be designed to focus on 
limiting or phasing out high-carbon technology. The appropriate mix is 
likely to vary between countries and domains, depending on political 
cultures and stakeholder configurations (Rogge and Reichardt 2016), 
but is likely to include a combination of: (i) standards, nudges and 
information to encourage low-carbon technology adoption and 
behavioural change; (ii) economic incentives to reward low-carbon 
investments; (iii) supply-side policy instruments including for fossil fuel production (to complement demand-side climate policies) 
and (iv) innovation support and strategic investment to encourage 
systemic change (Grubb 2014). These approaches can be mutually 
reinforcing. For example, carbon pricing can incentivise low-carbon 
innovation, while targeted support for emerging niche technologies

can make them more competitive encourage their diffusion and 
ultimately facilitate a  higher level of carbon pricing. Similarly, the 
success of feed-in tariffs in Germany only worked as well as it did 
because it formed part of a broader policy mix including ‘supply-push’ 
mechanisms such as subsidies for research and ‘systemic measures’ 
such as collaborative research projects and systems of knowledge 
exchange (Rogge et al. 2015).
4.4.1.3 Governance and Institutional Capacity
Governance for climate mitigation and shifting development pathways 
is enhanced when tailored to national and local contexts. Improved 
institutions and governance enable ambitious climate action and help 
bridge implementation gaps (medium evidence, high agreement). 
Improving institutions involve a  broad range of stakeholders and 
multiple regional and temporal scales. It necessitates a  credible 
and trusted process for reconciling perspectives and balancing

potential side-effects, managing winners and losers and adopting 
compensatory measures to ensure an inclusive and just transition 
(Newell and Mulvaney 2013; Miller and Richter 2014; Gambhir et al. 
2018; Diffenbaugh and Burke 2019), managing the risk of inequitable 
or non-representative power dynamics and avoiding regulatory 
capture by special interests (Helsinki Design Lab 2011; Boulle et al. 
2015; Kahane 2012).
Long experience of political management of change demonstrates 
that managing such risks is not easy, and requires sufficiently strong 
and competent institutions (Stiglitz 1998). For example, shift away 
from fossil fuel-based energy economy could significantly disrupt the 
status quo, leading to a stranding of financial and capital assets and 
shifting of political-economic power. Ensuring the decision-making 
process is not unduly influenced by actors with much to lose is key 
to managing a  transformation. Effective governance, as noted in

Chapter 13, requires establishing strategic direction, coordination of 
policy responses, and mediation among divergent interests. Among 
varieties of climate governance, which institutions emerge is path-
dependent, based on the interplay of national political institutions, 
international drivers, and bureaucratic structures (Dubash 2021). 
Focused national climate institutions to address these challenges 
are more likely to emerge, persist and be effective when they 
are consistent with a  framing of climate change that has broad 
national political support (medium evidence, medium agreement) 
(Sections 4.5, 13.2 and 13.5).
Innovative governance approaches can help meet these challenges 
(Clark et al. 2018; Díaz et al. 2019). Enabling multilevel governance  – 
i.e., better alignment across governance scales – and coordination 
of international organisations and national governments can 
help accelerate a  transition to sustainable development and deep

decarbonisation (Tait and Euston-Brown 2017; Michaelowa and 
Michaelowa 2017; Ringel 2017; Revi 2017; Cheshmehzangi 2016; 
IPCC 2018a). Participatory and inclusive governance  – partnerships

462
between state and non-state actors, and concerted effort across 
different stakeholders are crucial in supporting acceleration (Burch 
et al. 2014; Hering et al. 2014; Roberts 2016; Figueres et al. 2017; 
Clark et al. 2018; Leal Filho et al. 2018; Lee et al. 2018). So do 
partnerships through transnational climate governance initiatives, 
which coordinate nation states and non-state actors on an 
international scale (Hsu et al. 2018). Although they are unlikely 
to close the gap of the insufficient mitigation effort of national 
governments (Michaelowa and Michaelowa 2017) (Section  4.2.3), 
they help building confidence in governments concerning climate 
policy and push for more ambitious national goals (UNEP 2018b).
Meeting these challenges also requires enhanced institutional 
capacity and enhanced institutional mechanisms to strengthen the 
coordination between multiple actors, improve complementarities

and synergies between multiple objectives (Rasul 2016; Ringel 2017; 
Liu et al. 2018) and pursue climate action and other development 
objectives in an integrated and coherent way (Von Stechow et al. 
2016; McCollum et al. 2018; Rogelj et al. 2018b; Roy et al. 2018; Fuso 
Nerini et al. 2019), particularly in developing countries (Adenle et al. 
2017; Rosenbloom 2017). Institutional capacities to be strengthened 
include vertical collaboration and interaction within nation states 
and horizontal collaboration (e.g.,  transnational city networks) for 
the development and implementation of plans, regulations and 
policies. More specifically capacities include: capacity for knowledge 
harnessing and integration (from multiple perspectives); for 
integrated policy design and implementation (Scott 2017); for long-
term planning (Lecocq et al. 2021) for monitoring and review process; 
for coordinating multi-actor processes to create synergies and avoid

trade-offs. As a result, institutions that enable and improve human 
capacities and capabilities are a major driver of transformation. To 
this extent, promoting education, health care and social safety, also 
are instrumental to undertake climate change mitigation and cope 
with environmental problems (Winkler et al. 2007; Sachs et al. 2019). 
Given that strengthening institutions may be a long-term endeavour, 
it needs attention in the near term.
4.4.1.4 Channelling Financial Resources
Accelerated mitigation and shifting development pathways 
necessitate both redirecting existing financial flows from high- to 
low-emissions technologies and systems and providing additional 
resources (robust evidence, high agreement). An example is changes 
in investments from fossil fuels to renewable energy, with pressures 
to disinvest in the former while increasing levels of ‘green finance’ 
(Sections  6.7.4 and 15.5). While some lower-carbon technologies

have become competitive (Sections 1.4.3 and 2.5), support remains 
needed for the low-emissions options have higher costs per unit of 
service provided than high-emission ones. Lack of financial resources 
is identified as a major barrier to the implementation of accelerated 
mitigation and of shifts in development pathways. Overcoming this 
obstacle has two major components. One relates to private capital. 
The other to public finance.
There is substantial amount of research on the redirection of private 
financial flows towards low-carbon investment and the role of financial regulators and central banks, as detailed in Chapter  15. 
Financial systems are an indispensable element of a  systemic 
transition (Fankhauser et al. 2016; Naidoo 2020). Policy frameworks 
can redirect financial resources towards low-emission assets 
and services (UNEP 2015), mainstreaming climate finance within 
financial and banking system regulation, and reducing transaction

costs for bankable mitigation technology projects (Mundaca et al. 
2013; Brunner and Enting 2014; Yeo 2019). Shifts in the financial 
system to finance climate mitigation and other SDGs can be achieved 
by aligning incentives and investments with multiple objectives 
(UNEP Inquiry 2016).
Different approaches have been explored to improve such alignment 
(Section  15.6), from national credit policies to directly green 
mainstream financial regulations (e.g., through modifications in the 
Basel rules for banks). For all approaches, an essential precondition 
is to assess and monitor the contribution of financial flows to climate 
and sustainability goals, with better metrics that clearly link with 
financial activity (Chenet et al. 2019). Enabling the alignment of 
investment decision-making with achieving climate and broader 
sustainability goals includes acknowledgment and disclosure of 
climate-change related risk and of risks associated with mitigation

in financial portfolios. Current disclosures remain far from the 
scale the markets need to channel investment to sustainable and 
resilient solutions (UNEP - Finance Initiative 2020; Clark et al. 2018; 
Task Force on Climate-Related Financial Disclosures 2019; IPCC 
2018b). Disclosure, however, is not enough (Ameli et al. 2020). 
In addition, climate targets can be translated into investment 
roadmaps and financing needs for financial institutions, both at 
national and international level. Financing needs are usable for 
financial institutions, to inform portfolio allocation decisions and 
financing priorities (Chenet et al. 2019). At the international level, 
for example, technology roadmaps for key sectors can be translated 
into investment roadmaps and financing needs, as shown by existing 
experiences in energy and industrial sectors (IEA 2015; IEA and 
WBSCD 2018; Chenet et al. 2019).
The transition from traditional public climate finance interventions

to the market-based support of climate mitigation (Bodnar et al. 2018) 
demands innovative forms of financial cooperation and innovative 
financing mechanisms to help de-risk low-emission investments 
and support new business models. These financial innovations may 
involve sub-national actors like cities and regional governments 
in raising finance to achieve their commitments (Cartwright 2015; 
CCFLA 2017). Moreover, public-private partnerships have proved to 
be an important vehicle for financing investments to meet the SDGs, 
including economic instruments for financing conservation (Sovacool 
2013; Díaz et al. 2019).
Overall, early action is needed to overcome barriers and to adjust the 
existing incentive system to align national development strategies 
with climate and sustainable development goals in the medium-
term. Steckel et al. (2017) conclude that climate finance could 
become a  central pillar of sustainable development by reconciling

the global goal of cost-efficient mitigation with national policy 
priorities. Without a more rapid, scaled redeployment of financing,

463
in development trajectories that hinder the realisation of the global 
goals will be locked in (Zadek and Robins 2016). Investment might 
be designed to avoid trading off the Paris goals against other SDGs, 
as well as those that simultaneously reduce poverty, inequality, and 
emissions (Fuso Nerini et al. 2019).
At the national level, it is also essential to create public fiscal space 
for actions promoting the SDG agenda and thereby broadening the 
scope of mitigation (medium evidence, medium agreement). To do 
so, pricing carbon – either through tax payments based on the level 
of emissions or cap-and-trade systems that limit total allowable 
emissions – is an efficient means of discouraging carbon emissions 
throughout an economy (both in consumption and production) while 
simultaneously encouraging a switch to non-carbon energy sources 
and generating revenues for prioritised actions (Section  13.6.3).

Regarding to levels, the High-Level Commission on Carbon Prices 
concluded that ‘carbon-price level consistent with achieving the Paris 
temperature target is at least USD40–80 tCO 2–1 by 2020 and USD50–
100 tCO 2–1 by 2030, provided a supportive policy environment is in 
place’ (CPLC 2017; Wall Street Journal 2019). National level models 
yield median carbon values of carbon values of USD733 tCO 2–1 in 
2050 along accelerated mitigation pathways (Section 4.2.6), while 
global models find a median value of USD578 tCO 2–1 for pathways 
that reach net zero CO 2 between 2045 and 2055 [interquartile range 
USD405–708] (Section 3.6.1).
Carbon pricing, however, is designed to reduce its fiscal base. 
Fiscal space may therefore also need to stem from other sources, 
although fiscal reforms are complex endeavours (Section 4.4.1.8). For 
countries at lower income levels, foreign aid can make an important 
contribution to the same agenda (Kharas and McArthur 2019). It may

also be noted that, according to estimates at the global level, military 
spending amounted to USD1.748 trillion in 2012 (the last year with 
data), a figure that corresponded to 2.3% of GDP , 55% of government 
spending in education, and was 13 times the level of net ODA (World 
Bank 2020; SIPRI 2020). Given this, moderate reductions in military 
spending (which may involve conflict resolution and cross-country 
agreements on arms limitations) could free up considerable resources 
for the SDG agenda, both in the countries that reduce spending and 
in the form of ODA. The resolution of conflicts within and between 
countries before they become violent would also reduce the need for 
public and private spending repairing human and physical damage. 
The fact that civil wars are common in the countries that face the 
severest SDG challenges underscores the importance of this issue 
(Collier 2007, pp.17–37).
4.4.1.5 Changing Behaviour and Lifestyles

Changes in behaviour and lifestyles are important to accelerated 
mitigation. Most global mitigation pathways that limit warming to 
2°C (>67%) or lower assume substantial behavioural and societal 
change and low-carbon lifestyles (de Coninck et al. 2018; IPCC 
2018a; Luderer et al. 2018a) (see also Section 3.3.1 in this report; 
and Table  4.9 and Figure  4.3 in IPCC SR1.5). Chapter  5  concludes 
that behavioural changes within transition pathways offer Gigaton-
scale CO 2 savings potential at the global level, an often overlooked 
strategy in traditional mitigation scenarios.Individual motivation and capacity are impacted by different factors 
that go beyond traditional social, demographic and economic 
predictors. However, it is unclear to what extent behavioural factors 
(i.e., cognitive, motivational and contextual aspects) are taken into 
account in policy design (Dubois et al. 2019; Mundaca et al. 2019). 
In fact, while economic policies play a significant role in influencing

people’s decisions and behaviour, many drivers of human behaviour 
and values work largely outside the market system (Winkler et al. 
2015; Díaz et al. 2019) as actors in society, particularly individuals, do 
not respond in an economically ‘rational’ manner based on perfect-
information cost-benefit analyses (Runge 1984; Shiller 2019). Rather, 
compelling narratives can drive individuals to adopt new norms and 
policies. And norms can be more quickly and more robustly shifted 
by proposing and framing policies designed with awareness of how 
framings interact with individual cognitive tendencies (van der Linden 
et al. 2015). Transformative policies are thus much more likely to be 
successfully adopted and lead to long-term behavioural change if 
designed in accordance with principles of cognitive psychology (van 
der Linden et al. 2015), and with the deep understanding of decision-
making offered by behavioural science (UNEP 2017b). Similarly, given

that present bias – being motivated by costs and benefits that take 
effect immediately than those delivered later  – significantly shapes 
behaviour, schemes that bring forward distant costs into the present or 
that upfront incentives have proved to be more effective (Zauberman 
et al. 2009; van den Broek et al. 2017; Safarzyńska 2018). Overall, 
transformational strategies that align mitigation with subjective life 
satisfaction, and build societal support by positive discourses about 
economic, social, and cultural benefits of low-carbon innovations, 
promises far more success than targeting mitigation alone (WBGU 
2011; Asensio and Delmas 2016; Geels et al. 2017).
Climate actions are related to knowledge but even strongly to 
motivational factors (Hornsey et al. 2016; Bolderdijk et al. 2013; 
Boomsma and Steg 2014), which explains the gap between awareness 
and action (Ünal et al. 2018). Social influences, particularly from peers,

affect people’s engagement in climate action (Schelly 2014). Role 
models appear to have a solid basis in people’s everyday preferences 
(WBGU 2011). Social norms can reinforce individuals’ underlying 
motivations and be effective in encouraging sustainable consumption 
patterns, as many examples offered by behavioural science illustrate. 
Social networks also influence and spread behaviours (Service et al. 
2014; Clayton et al. 2015; Farrow et al. 2017; Shah et al. 2019). These 
social influences can be harnessed by climate policy.
Collective action by individuals as part of formal social movements 
or informal lifestyle movements underpins system change (robust 
evidence, high agreement) (Sections 5.4 and 5.5). Organisations are 
comprised of individuals, but also become actors in their own right. 
Recent literature has considered the role of coalitions and social 
movements in energy democracy and energy transitions towards

sustainability (Hess 2018). Other scholars have examined the role of 
women in redistributing power, both in the sense of energy transition 
and in terms of gender relations (Allen et al. 2019; Routledge et al. 
2018). Mitigation and broader sustainable development policies that 
facilitate active participation by stakeholders can build trust, forge 
new social contracts, and contribute to a  positive cycle building 
climate governance capacity (Section 5.2.3).

464
However, behavioural change not embedded in structural change 
will contribute little to climate change mitigation, suggesting that 
behavioural change is not only a  function of individual agency 
but also depends on other enabling factors, such as the provision 
of infrastructure and institutions (Section  5.4). Successful shifts 
towards public transport, for example, involve technologies (buses, 
trams), infrastructure (light rail, dedicated bus lanes), regulations 
(operational licenses, performance contracts), institutions (new 
organisations, responsibilities, oversight), and high-enough density, 
which in turn depends on such choices as housing or planning 
policies (Section 4.4.1.9).
4.4.1.6 Fostering Technological Innovation
As outlined in Section 4.2.5, rapid, large-scale deployment of improved 
low-carbon technology is a  critical component of accelerated

mitigation pathways. As part of its key role in technological 
change, R&D can make a crucial contribution to accelerated mitigation 
up to 2030 and beyond, among other things by focusing on closing 
technology gaps that stand in the way of decarbonising today’s high 
emitting sectors. Such sectors include shipping, trucking, aviation 
and heavy industries like steel, cement and chemicals. More broadly, 
it is increasingly clear that digital changes are becoming a key driving 
force in societal transformation (Tegmark 2017). Digitalisation is not 
only an ‘instrument’ for resolving sustainability challenges, it is also 
a fundamental driver of disruptive, multiscalar change (Sachs et al. 
2019) that amounts to a shift in development pathway. Information 
and communication technologies, artificial intelligence, the internet 
of things, nanotechnologies, biotechnologies, robotics, are not usually 
categorised as climate technologies, but have a potential impact on

GHG emissions (OECD 2017b) (Cross-Chapter Box 11 in Chapter 16).
The direction of innovation matters (robust evidence, high agreement). 
The research community has called for more ‘responsible innovation’ 
(Pandza and Ellwood 2013), ‘open innovation’ (Rauter et al. 2019), 
‘mission-oriented’ innovation (Mazzucato and Semieniuk 2017), 
‘holistic innovation’ (Chen et al. 2018b), ‘next-generation innovation 
policy’ (Kuhlmann and Rip 2018) or ‘transformative innovation’ (Schot 
and Steinmueller 2018) so that innovation patterns and processes 
are commensurate to our growing sustainability challenges. There 
is a  growing recognition that new forms of innovation can be 
harnessed and coupled to climate objectives (Fagerberg et al. 2016; 
Wang et al. 2018). As such, innovation and socio-technical change 
can be channelled to intensify mitigation via ‘deliberate acceleration’ 
(Roberts et al. 2018a) and ‘coalition building’ (Hess 2018).

Innovation goes beyond technology. For example, decarbonisation in 
sectors with long lived capital stock (such as heavy industry, buildings, 
transport infrastructure) entail technology, policy and financing 
innovations (Bataille 2020). Similarly, expanding the deployment 
of  photovoltaics can draw upon policies that support specific 
technical innovations (e.g., to improve photovoltaics efficiency), or 
innovations in regulatory and market regimes (e.g.,  net-metering), 
to innovations in social organisation (e.g.,  community-ownership). 
System innovation is a core focus of the transitions literature (Grin 
et al. 2010; Markard et al. 2012; Geels et al. 2017). Accelerating low-carbon transitions not only involves a shift of system elements but 
also underlying routines and rules, and hence transitions shift the 
directionality of innovation. They hence concern the development 
of a  new paradigm or regime that is more focused on solving

sustainability challenges that cannot be solved within the dominant 
regime they substitute (Cross-Chapter Box 12 in Chapter 16).
Several studies have pointed at the important possible contributions 
of grassroots innovators for the start-up of sustainability transitions 
(Seyfang and Smith 2007; Seyfang et al. 2014; Smith et al. 2016). In 
particular, a range of studies have shown that users can play a variety 
of roles in promoting system innovation: shielding, nurturing (including 
learning, networking and visioning) and empowering the niches 
in relation to the dominant system and regime (Schot et al. 2016; 
Randelli and Rocchi 2017; Meelen et al. 2019). More fundamentally, 
innovation regimes can be led and guided by markets driven by 
monetisable profits (as much of private sector led technological 
innovation of patentable  intellectual property), or prioritise social 
returns (e.g.,  innovation structures such as innovation prizes, public

sector innovation, investments in human capital, and socially-beneficial 
intellectual property regimes). In both cases, public policies can play 
a key role by providing resources and favourable incentives (IEA 2020). 
Chapter 16 provides more details on ways to foster innovation.
4.4.1.7 Example: Structural Change Provides a Way to Keep 
Jobs and Mitigate
Developing countries have experienced a period of rapid economic 
growth in the past two decades. Patterns of growth have differed 
markedly across regions, with newly emerging East Asian economies 
building on transition to manufacturing – as China has done in the 
past – while Latin American countries tend to transition directly from 
primary sector to services (Rodrik 2016), and African countries tend 
to rely on productivity improvements in the primary sectors (Diao 
et al. 2019). Yet many countries still face the challenge of getting out 
of the ‘middle-income trap’ (Agénor and Canuto 2015), as labour-

saving technological change and globalisation have limited options 
to develop via the manufacturing sector (Altenburg and Rodrik 2017).
Looking ahead, several studies have illustrated how structural 
change towards sustainability could lead to reduced emissions 
intensity and higher mitigative capacity. In China, for example, 
the shift away from heavy industry (to light industry and services) 
has already been identified as the most important force limiting 
emissions growth (Guan et al. 2018), and as a major factor for future 
emissions (Kwok et al. 2018).
Overall, Altenburg et al. (2017) argue that reallocation of capital 
and labour from low- to high-productivity sectors – in other words, 
structural change  – remains a  necessity, and that it is possible to 
combine it with reduced environmental footprint (including, but not 
limited to, mitigation). They argue that this dual challenge calls for 
structural transformation policies different from those implemented

in the past, most importantly through a  ‘systematic steering of 
investment behaviour in a socially agreed direction’ and encompassing 
policy coordination (limited evidence, medium agreement).

465
In order to permit progress on their SDG agendas, it is essential 
that countries develop visions of their future decarbonised sectoral 
production structure, including its ability to generate growth in 
incomes, employment and foreign exchange earnings. as well as the 
related spatial distribution of production, employment, and housing. 
To this extent, governance and institutional capacity matter, such as 
availability of tools to support long-term planning. A sectoral structure 
that permits strong growth is essential given strong associations 
between growth in per-capita incomes and progress on most SDGs 
(including those related to poverty; health; education; and access to 
water, sanitation, electricity, and roads; but not income equality), in 
part due to the fact that higher incomes provide both households 
and governments with resources that at least in part would be used

to promote SDGs (Gable et al. 2015).
The future viability of sectors will depend on the extent to which 
they can remain profitable while relying on lower-carbon energy. 
The challenge to identify alternative sectors of growth is particularly 
acute for countries that today depend on oil and natural gas for 
most of their foreign exchange and government revenues (Mirzoev 
et al. 2020). Changes in economic structure will also have gender 
implications since the roles of men and women vary across sectors. 
For example, in many developing countries, sectors in which women 
play a  relatively important role, including agriculture and unpaid 
household services like collection of water and fuel wood, may be 
negatively affected by climate change (Roy 2018). It may thus be 
important to take complementary actions to address the gender 
implications of changes in economic structure.
Given strong complementarities between policies discussed above,

an integrated policy approach is crucial. For example, as suggested, 
the actions that influence the pace at which GHG emissions can be 
cut with political support may depend on taxation (including carbon 
taxes), investments in infrastructure, spending on R&D, changes in 
income distribution (influenced by transfers), and communication. 
In this light, it is important to consider the demands that alternative 
policy packages put on government policy-making efficiency and 
credibility as well as the roles of other enabling conditions. In fact, 
plans to undertake major reforms may provide governments with 
impetus to accelerate the enhancement of their capacities as part 
of the preparations (Karapin 2016; Withana 2016; Jakob et al. 2019).
4.4.1.8 Example: Embedding Carbon Finance in Broader 
Fiscal Reforms Offers a Way to Mitigate and Rethink 
the Social Contract
In many countries, fiscal systems are currently under stress to provide

resources for the implementation of development priorities, such as, 
for example, providing universal health coverage and other social 
services (Meheus and McIntyre 2017) or sustainably funding pension 
systems in the context of aging populations (Asher and Bali 2017; 
Cruz-Martinez 2018). Overall, Baum et al. (2017) argue that low-
income countries are likely not to have the fiscal space to undertake 
the investment entailed in reaching the SDGs. To create additional 
fiscal space, major options include improving tax recovery, reducing 
subsidies and levying additional taxes.Mitigation offers an opportunity to create additional fiscal space, and 
thus to serve the objectives outlined above, by creating a new source 
of revenue for the government via carbon taxation or emissions 
permit auctioning and by reducing existing expenditures via reduction 
in subsidies to fossil-fuel. The 1991 tax reform in Sweden is an early

example in which environmental taxation (including, but not limited 
to, fossil fuel taxation) was introduced as part of a package primarily 
aimed at lowering the marginal tax rates (more than 80% at the 
time), at reducing other taxes, while keeping most of the welfare 
state. To do so, the tax base was broadened, including through 
environmental and carbon taxation (Sterner 2007). Once in place, the 
carbon tax rate was substantially ramped up over time, and its base 
broadened (Criqui et al. 2019).
The future potential for using carbon taxation as a way to provide 
space for fiscal reform has been highlighted in the so-called ‘green 
fiscal reform’ literature (Vogt-Schilb et al. 2019). The potential is 
large, since only 13% of global GHG emissions were covered by 
carbon pricing schemes in 2019 (Watts et al. 2019) and since many 
countries price carbon negatively by subsidising fossil fuel use, thus 
generating effects that are the opposite of those that positive carbon

prices hope to promote. In 2018, the global subsidy value amounted 
to USD427 billion, some 10 times the payment for carbon use (Watts 
et al. 2019). However, the size of the potential for creating fiscal 
space varies strongly across countries given differences in terms of 
current carbon prices and fuel subsidies.
The limited adoption of and political support for carbon pricing 
may be explained by the fact that most of the gains occur in the 
future and depend on actions across the globe, making them 
seem abstract and unpredictable, whereas the costs in the form of 
higher carbon prices are immediate (Karapin 2016). Furthermore, 
the links between carbon pricing and emissions may not be clear 
to the public who, in addition, may not trust that the government 
will use budgetary savings according to stated plans. The latter may 
be due to various factors, including a history of limited government 
commitment and corruption (Withana 2016; Chadwick 2017; 
Maestre-Andrés et al. 2019).

The literature reports limited systematic evidence based on ex post 
analysis of the performance of carbon pricing  – carbon taxes and 
greenhouse gas (GHG) emissions trading systems (ETSs) (Haites 
2018). Performance assessment is complicated by the effect of other 
policies and exogenous factors. Haites (2018) suggests that since 
2008, other policies have probably contributed more to emission 
reductions than carbon taxes, and most tax rates are too low to 
achieve mitigation objectives. Emissions under ETSs have declined, 
with the exception of four systems without emissions caps (ibid). 
Every jurisdiction with an ETS and/or carbon tax also has other 
policies that affect its GHG emissions.
To help policymakers overcome obstacles, research has reviewed the 
international experience from carbon pricing reforms. Elimination 
of fossil fuel subsidies, equivalent to the elimination of negative 
carbon prices, have been more successful when they have included

466
support, accompanied by a strong communications component that 
explains the measures and stresses their benefits (Withana 2016; 
Rentschler and Bazilian 2017; Maestre-Andrés et al. 2019).
Part of the losses (and related calls for compensation or exemptions) 
due to carbon pricing are related to the fact that it hurts the 
competitiveness of sectors that face imports from countries with 
lower carbon prices, leading to ‘carbon leakage’ if carbon-intensive 
production (and related jobs) migrates from countries with relatively 
high carbon prices. Some research suggests that evidence that 
a border carbon tax (or adjustment), set on the basis of the carbon 
content of the import, including a downward adjustment on the basis 
of any carbon payments (taxes or other) already made before entry, 
could reduce carbon leakage while also raising additional revenue

and encouraging carbon pricing in the exporting country (Withana 
2016; Cosbey et al. 2019).
The timing of carbon pricing reforms is also important: they are more 
likely to succeed if they exploit windows of opportunity provided 
by events that raise awareness of the costs of carbon emissions 
(like bouts of elevated local air pollution or reports about the role 
of emissions in causing global warming), as well as momentum 
from climate actions by other countries and international climate 
agreements (Karapin 2016; Jakob et al.2019). It is also important 
to consider the level of international prices of carbon energy: when 
they are low, consumer resistance would be smaller since prices 
will remain relatively low, though the tax may become more visible 
when energy prices increase again. As part of ongoing efforts 
to accelerate mitigation, such tax hikes may be crucial to avoid 
a  slow down in the shift to renewable energy sources (Withana

2016; Rentschler and Bazilian 2017). In countries that export 
carbon energy, carbon taxation may run into additional resistance 
from producers.
There is also considerable literature providing insights on the political 
and social acceptability of carbon taxes, suggesting for example 
that political support may be boosted if the revenue is recycled to 
the tax payers or earmarked for areas with positive environmental 
effects (e.g., Bachus et al. (2019) for Belgium, and Beiser-McGrath 
and Bernauer (2019) for Germany and the USA), as well as on 
the difficulties associated with political vagaries (and economic 
consequences thereof) associated with the introduction of such 
instruments (Pereira et al. 2016). Similarly, ‘best practices’ have been 
drawn from past experience on fossil-fuel subsidy reforms (Rentschler 
and Bazilian 2017; Sovacool 2017). Specific policies, however, 
depend on societal objectives, endowments, structure of production,

employment, and trade, and institutional structure (including the 
functioning of markets and government capacity) (Kettner et al. 
2019). As noted in Section 4.2.6, macroeconomic analysis finds that 
the overall economic implications of carbon pricing differ markedly 
depending on the way the proceeds from carbon pricing are used, 
and thus on the way the fiscal system is reformed, with potential for 
double dividend if the proceeds from the tax are used to repeal the 
most distortive taxes in the economy.
In the context of this section on development pathways, it is 
worth emphasising that potential revenues drawn from the climate mitigation component of the fiscal reform varies strongly 
with the context, and may not be sufficient to address the other 
objectives pursued. Even if the carbon price is high, the revenue 
it generates may be moderate as a share of GDP and eventually it 
will be zero if emissions are eliminated. For example, Jakob et al.

(2016) find that the carbon pricing revenues that most countries 
in Sub-Saharan Africa could expect to generate only would meet 
a  small part of their infrastructure spending needs. In Sweden, 
the country with the highest carbon tax rate in the world, the tax 
has not been a  significant part of total tax revenues. Moreover, 
emissions from sectors covered by the tax have shrunk and, 
as a  result, the revenues from the tax, as a  share of GDP , have 
also declined, from a peak of 0.93% in 2004, when the rate was 
USD109 per metric tonne of CO2, to 0.48% in 2018, when the rate 
had reached USD132 (Jonsson et al. 2020; Statistics Sweden 2020). 
This means that governments that want to avoid a decline in the 
GDP share for total tax revenues over time would have to raise 
the intake from other taxes. However, it is here important to note 
that domestic tax hikes are likely to involve trade-offs since, at the 
same time as the spending they fund may provide various benefits,

they may also reduce the capacity of households and the private 
sector to consume and invest, something that may reduce growth 
over time and reduced resources for spending in support of human 
development (Lofgren et al. 2013). It is also worth emphasising that 
restructuring of the fiscal system amount to changes in the social 
contract of the society (Combet and Hourcade 2017 and 2014), and 
thus represents a major economic and social decision.
4.4.1.9 Example: Combining Housing Policies With Carbon 
Taxation Can Deliver Both Housing and Mitigation 
in the Transport Sector
The spatial distribution of households and firms across urban and rural 
areas is a central characteristic of development pathways. Patterns of 
urbanisation, territorial development, and regional integration have 
wide-ranging implications for economic, social and environmental 
objectives (World Bank 2009). Notably, choices regarding spatial 
forms of development have large-scale implications for demand for

transportation and associated GHG emissions.
Exclusionary mechanisms such as decreasing accessibility and 
affordability of inner-urban neighbourhoods is a  major cause 
of suburbanisation of low- to middle-income households 
(e.g., Hochstenbach and Musterd 2018). Suburbanisation, in turn, is 
associated with higher transportation demand (Bento et al. 2005) 
and higher carbon footprints for households (Jones and Kammen 
2014). Similarly, other studies find a significant positive link between 
housing prices and energy demand (Lampin et al. 2013).
Reducing emissions from transport in cities through traditional climate 
policy instruments (e.g., through a carbon tax) is more difficult when 
inner-urban neighbourhoods are less accessible and less affordable, 
because exclusionary mechanisms act as a  countervailing force to 
the rising transportation costs induced by the climate policy, pushing 
households outwards rather than inwards. Said differently, the costs

of mitigating intra-city transportation emissions are higher when 
inner-urban housing prices are higher (Lampin et al. 2013).

467
This suggests that policies making inner-urban neighbourhoods more 
accessible and more affordable can open up broader opportunities 
for suburban households to relocate in the face of increasing 
transportation costs. This is particularly important for low- and 
middle-income households, who spend a  greater portion of their 
income on housing and transportation, and are more likely to be 
locked into locations that are distant from their jobs. Making inner-
urban neighbourhoods more accessible and more affordable has the 
potential to reduce both the social costs (e.g.,  households feeling 
helpless in front of rising fuel prices) and the economic costs of 
mitigation policies – as a lower price of carbon is likely to achieve 
the same amount of emission reductions since households have more 
capacities to adjust.
Making inner-cities neighbourhoods more accessible and more

affordable is a complex endeavour (Benner and Karner 2016). At the 
same time, it is already a policy objective in its own right in many 
countries, independent of the climate mitigation motivation, for 
a  range of social, health and economic reasons. Revenues derived 
from climate policies could provide additional resources to support 
such programs, as some climate policy already have provisions to use 
their revenues towards low-income groups (Karner and Marcantonio 
2018). The mitigation benefits of keeping inner-cities more accessible 
and affordable for low- and middle-income households often remains 
out of, or is only emerging in the debates surrounding the planning 
of fast-developing cities in many developing countries (IADB 2012; 
Grant 2015; Khosla and Bhardwaj 2019). Finally, from a  political 
economy perspective, it is also interesting to note that (Bergquist et 
al. 2020) find higher support for climate policy packages in the USA

when affordable housing programs are included.
In addition, investment in infrastructure is critical to the development 
of decarbonised economic structures that generate growth, 
employment, and universal access to a wide range of services that 
are central to the SDG agenda: transportation, water, sanitation, 
electricity, flood protection, and irrigation. For low- and middle-
income countries, annual costs of reaching these goals by 2030 and 
putting their economies on a path toward decarbonisation may range 
between 2% and 8% of GDP , with the level depending on spending 
efficiency. Notably, these costs need not exceed those of more 
polluting alternatives (Rozenberg and Fay 2019). For transportation, 
this involves a shift toward more public transportation (rail and bus), 
and decarbonised electricity for vehicles, combined with land-use 
policies that densify cities and reduce distances between homes and 
jobs. By influencing the spatial distribution of households and firms

and the organisation of transportation, infrastructure has a strong 
bearing on GHG emissions and the costs of providing services to 
different populations. Depending on country context, the private 
sector may play a  particularly important role in the financing of 
infrastructure (World Bank 2009; Klein 2015).
Many investments in infrastructure and sectoral capital stocks have 
long lifetimes. Given this, it may be important to make sure that 
today’s investments be fully decarbonised at the start or that they 
later can be converted to zero carbon. Today’s investments in electric 
vehicles in settings where electricity is produced with fossil fuels is an example of convertible investments – they will be decarbonised 
once electricity production has switched to renewable energies. For 
capital stocks that cannot be decarbonised, countries may face costs 
of decommissioning well before the end of their useful lifetimes, 
especially when it is needed to respect country commitments to

future full decarbonisation.
4.4.1.10 Example: Changing Economic, Social and Spatial 
Patterns of Development of the Agriculture Sector 
Provide the Basis for Sustained Reductions in 
Emissions From Deforestation
A growing literature assesses co-benefits of sectoral policies that 
lead to decarbonisation and simultaneously promote economic 
development, improve living standards, reduce inequality, and create 
job opportunities (Maroun and Schaeffer 2012; Bataille et al. 2016b; 
Pye et al. 2016; Bataille et al. 2018; La Rovere et al. 2018; Richter 
et al. 2018; Waisman et al. 2019). While this may be particularly 
challenging in developing countries, given large populations still 
lacking basic needs, previous development paths show that finding 
synergies in development and climate objectives in the AFOLU 
sector is possible. One example is Brazil, which has arguably shifted 
its development pathway to reduce emissions and make progress

towards several SDGs, though progress is not linear. Over the past 
two decades, Brazil had made remarkable progress in implementing 
a sequence of policies across multiple sectors. This policy package 
simultaneously increased minimum wages of low income families, 
achieved universal energy access, and raised the quality of life and 
well-being for the large majority of the population (Da Silveira 
Bezerra et al. 2017; Grottera et al. 2017, 2018; La Rovere et al. 2018). 
This led to significant social benefits, reduction of income inequality 
and poverty eradication (Da Silveira Bezerra et al. 2017; Grottera 
et al. 2017), reflected in a decrease of the Gini coefficient and a rise 
in the human development index (La Rovere 2017).
Regulatory instruments were used to limit deforestation rates, 
together with implemented economic instruments that provided 
benefits to those protecting local ecosystems and enhancing land-
based carbon sinks (Nunes et al. 2017; Bustamante et al. 2018;

Soterroni et al. 2018, 2019). In parallel, public policies reinforced 
environmental regulation and command-and-control instruments 
to limit deforestation rates and implemented market-based 
mechanisms to provide benefits to those protecting local ecosystems 
and enhancing land-based carbon sinks (Sunderlin et al. 2014; 
Nunes et al. 2017; Hein et al. 2018; Simonet et al. 2019). The private 
sector, aligned with public policies and civil society, implemented the 
Amazon Soy Moratorium, a voluntary agreement that bans trading of 
soybeans from cropland associated with cleared Amazon rainforest 
and blacklists farmers using slave labour. This was achieved without 
undermining production of soybean commodities (Soterroni et al. 
2019). As a result, between 2005 and 2012, the country halved its 
GHG emissions and reduced the rate of deforestation by 78% (INPE 
2019a,b). This example shows that development delivering well-
being can be accompanied by significant mitigation. A  long-term

and strategic vision was important in guiding enabling policies 
and mechanisms.

468
In more recent years, some of these shifts in Brazil’s development 
pathways were undone. Political changes have redefined 
development priorities, with higher priority being given to 
agricultural development than climate change mitigation. The current 
administration has reduced the power of environmental agencies and 
forestry protection laws (including the forest code), while allowing 
the expansion of cropland to protected Amazon rainforest areas 
(Ferrante and Fearnside 2019; Rochedo et al. 2018). As a result, in 
2020, deforestation exceeded 11,000 km2, and reached the highest 
rate in the last 12 years (INPE 2020). The literature cautions that, 
if current policies and trends continue, the Amazon may reach an 
irreversible tipping point beyond which it will be impossible to 
remediate lost ecosystems and restore carbon sinks and indigenous 
people knowledge (Lovejoy and Nobre 2018; INPE 2019a; Nobre

2019). In addition, fossil fuel subsidies and other fiscal support of 
increased exploitation of oil resources may create carbon lock-ins 
that further inhibit low-carbon investments (Lefèvre et al. 2018).
Brazil’s progress in mitigation depended significantly on reduced 
deforestation in the past. If deforestation rates keep on rising, 
mitigation efforts would need to shift to the energy sector. However, 
according to Rochedo et al. (2018), mitigation costs in the energy 
sector in Brazil are three times the costs of reducing deforestation 
and increasing land-based carbon sinks. Further mitigation strategies 
may depend on CCS in Brazil as elsewhere (Herreras Martínez 
et al. 2015; Nogueira de Oliveira et al. 2016), though the economic 
feasibility of deployment is not yet clear (Section 4.2.5.4).
4.4.2 Adaptation, Development Pathways  
Mitigation actions are strongly linked to adaptation. These 
connections come about because mitigation actions can be adaptive

(e.g.,  some agroforestry projects) but also through policy choices 
(e.g.,  climate finance is allocated among adaptation or mitigation 
projects) and even biophysical links (e.g.,  climate trajectories, 
themselves determined by mitigation, can influence the viability of 
adaptation projects). As development pathways shape the levers and 
enablers available to a society (Section 4.3.1, Figure 4.7), a broader 
set of enabling conditions also helps with adaptation (medium 
Previous assessments have consistently recognised this linkage. The 
Paris Agreement includes mitigation and adaptation as key areas of 
action, through NDCs and communicating adaptation actions and 
plans. The Agreement explicitly recognises that mitigation co-benefits 
resulting from adaptation can count towards NDC targets. The IPCC 
Fifth Assessment Report (IPCC 2014) emphasised that sustainable 
development is helpful in going beyond a narrow focus on separate

mitigation and adaptation options and their specific co-benefits. 
The IPCC Special Report on climate change and land addresses 
GHG emissions from land-based ecosystems with a  focus on the 
vulnerability of land-based systems to climate change. The report 
identifies the potential of changes to land use and land management 
practices to mitigate and adapt to climate change, and to generate 
co-benefits that help meet other SDGs (Jian et al. 2019).A substantial literature detailing trade-offs and synergies between 
mitigation and adaptation exists and is summarised in the IPCC SR1.5 
including energy system transitions; land and ecosystem transitions 
(including addressing food system efficiency, sustainable agricultural 
intensification, ecosystem restoration); urban and infrastructure 
system transitions (including land use planning, transport systems, 
and improved infrastructure for delivering and using power);

industrial system transitions (including energy efficiency, bio-based 
and circularity, electrification and hydrogen, and industrial carbon 
capture, utilisation and storage (CCUS); and carbon dioxide removal 
(including bioenergy with CCS, afforestation and reforestation, 
soil carbon sequestration, and enhanced weathering) (IPCC 2018: 
Table  4.SM.5.1). Careful design of policies to shift development 
pathways towards sustainability can increase synergies and manage 
trade-offs between mitigation and adaptation (robust evidence, 
This section examines how development pathways can build greater 
adaptive and mitigative capacity, and then turns to several examples 
of mitigation actions with implications for adaptation where there 
is a notable link to development pathways and policy choices. These 
examples are in the areas of agriculture, blue carbon and terrestrial 
ecosystem restoration.
4.4.2.1 Development Pathways can Build Greater Capacity for

Both Adaptation and Mitigation
Previous IPCC assessments have reflected on making development 
more sustainable (IPCC et al. 2001; Sathaye et al. 2007; Fleurbaey 
et al. 2014). Other assessments have highlighted how ecosystem 
functions can support sustainable development and are critical to 
meeting the goals of the Paris Agreement (IPBES 2019b). IPCC SR1.5 
found that sustainable development pathways to  1.5°C broadly 
support and often enable transformations and that ‘sustainable 
development has the potential to significantly reduce systemic 
vulnerability, enhance adaptive capacity, and promote livelihood 
security for poor and disadvantaged populations (high confidence)’ 
(IPCC 2018b: Section  5.3.1). With careful management, shifting 
development pathways can build greater adaptive and mitigative 
capacity, as further confirmed in recent literature (Schramski et al. 
2018; Harvey et al. 2014; Ebi et al. 2014; Rosenbloom et al. 2018;

Antwi-Agyei et al. 2015; Singh 2018; IPBES 2019b). The literature 
points to the challenge of design of specific policies and shifts 
in development pathways to achieve both mitigation and 
adaptation goals.
Governance and institutional capacity
Governance and institutional capacity necessary for mitigation 
actions also enables effective adaptation actions. Implementation of 
mitigation and adaptation actions can, however, encounter different 
sets of challenges. Mitigation actions requiring a  shift away from 
established sectors and resources (e.g., fossil fuels) entail governance 
challenges to overcome vested interests (Piggot et al. 2020; SEI 
et al. 2020). Mitigation-focused initiatives from non-state actors 
tend to attain greater completion than adaptation-focused initiatives 
(NewClimate Institute et al. 2019).

469
Behaviour and lifestyles
On the level of individual entities, adaptation is reactive to current 
or anticipated environmental changes but mitigation is undertaken 
deliberately. Chapter  5  considers behavioural change, including 
the reconsideration of values and what is meant by well-being, 
and reflecting on a range of actors addressing both adaptation and 
mitigation. Shifting development pathways may be disruptive (Cross-
Chapter Box  5), and there may be limits to propensity to change. 
Some studies report that climate change deniers and sceptics can be 
induced to undertake pro-environmental action if those actions are 
framed in terms of societal welfare, not climate change (Bain et al. 
2012; Hornsey et al. 2016). Concrete initiatives to change behaviour 
and lifestyles include the Transition Town movement, which seeks 
to implement a just transition – both in relation to adaptation and

mitigation – in specific localities (Roy et al. 2018).
Finance
Finance and investment of mitigation actions must be examined in 
conjunction with funding of adaptation actions, due to biophysical 
linkages and policy trade-offs (Box  15.1). Most climate funding 
supports mitigation efforts, not adaptation efforts (Buchner et al. 
2019) (Halimanjaya and Papyrakis 2012). Mitigation projects are 
often more attractive to private capital (Abadie et al. 2013; Buchner 
et al. 2019). Efforts to integrate adaptation and mitigation in climate 
change finance are limited (Kongsager et al. 2016; Locatelli et al. 2016) 
There is a perception that integration of mitigation and adaptation 
projects would lead to competition for limited finance available for 
adaptation (Locatelli et al. 2016). Long-standing debates (Ayers and 
Huq 2009; Smith et al. 2011) whether development finance counts 
as adaptation funding remain unresolved. See Chapter 15 for more

in-depth discussion relating investment in funding mitigation and 
adaptation actions.
Innovation and technologies
Systems transitions that address both adaptation and mitigation 
include the widespread adoption of new and possibly disruptive 
technologies and practices and enhanced climate-driven innovation 
(IPCC 2018a). See Chapter  16 for an in-depth discussion of 
innovation and technology transfer. The literature points to trade-
offs that developing countries face in investing limited resources 
in research and development, though finding synergies in relation 
to agriculture (Adenle et al. 2015). Other studies point to difference 
in technology transfers for adaptation and mitigation (Biagini 
et al. 2014). Adaptation projects tend to use existing technologies 
whereas mitigation climate actions are more likely to rely on novel 
technologies. Innovations for mitigation are typically technology 
transfers from developed to less-developed countries (Biagini

et al. 2014), however this so-called North-South technology transfer 
pathway is not exclusive (Biagini et al. 2014), and is increasingly 
challenged by China’s global role in implementing mitigation actions 
(Chen 2018; Urban 2018). Indigenous knowledge can be a unique 
source for techniques for adaptation (Nyong et al. 2007) and may be 
favoured over externally generated knowledge (Tume et al. 2019).Policy
Adaptation-focused pathways might reduce inequality, if adequate 
support is available and well-distributed (Pelling and Garschagen 
2019). Some studies suggest that cities might plan for possible 
synergies in adaptation and mitigation strategies, currently done 
independently (Grafakos et al. 2019). The literature suggests that 
cities might identify both mitigation and adaptation as co-benefits of 
interventions targeted at developmental goals (Dulal 2017).
4.4.2.2 Specific Links Between Mitigation and Adaptation

Mitigation actions can be adaptive and vice-versa. In particular, 
many nature-based solutions (NBS) for climate mitigation 
are  adaptive (medium evidence,  medium agreement). Multiple 
NBS are being pursued under current development pathways 
(Chapter  7), but shifting to sustainable development pathways 
may enable a wider set of nature-based mitigation solutions with 
adaptation benefits. An example of this would be a shift to more 
sustainable diets through guidelines, carbon taxes, or investment 
in  R&D of animal product substitutes (Figure  13.2) which could 
reduce pressure on land and allow for implementation of multiple 
NBS. Many of these solutions are consistent with meeting other 
societal goals, including biodiversity conservation and other 
sustainable development goals (Griscom et al. 2017; Fargione 
et al. 2018; Tallis et al. 2018). However, there can be synergies 
and trade-offs in meeting a  complex set of sustainability goals

(e.g., biodiversity, Section 7.6.5 and 3.1.5).
Development is a  key factor leading to land degradation in many 
parts of the world (IPBES 2019b). Shifting development pathways to 
sustainability can include restoration and protection of ecosystems, 
which can enhance capacity for both mitigation and adaptation 
actions (IPBES 2019b).
In this section, we explore mitigation actions related to sustainable 
agriculture, coastal ecosystems (‘blue carbon’), and restoration and 
protection of some terrestrial ecosystems. These mitigation actions 
are exemplary of trade-offs and synergies with adaptation, sensitivity 
to biophysical coupling, and linkages to development pathways. 
Other specific examples can be found in Chapters 6 to 11.
Farming system approaches can benefit mitigation and adaptation
Farming system approaches can be a  significant contributor to 
mitigation pathways. These practices (which are not mutually 
exclusive) include agroecology, conservation agriculture,

integrated production systems and organic farming (Box  7.5). 
Such methods have potential to sequester significant amounts of 
soil carbon (Section 7.4.3.1) as well as reduce emissions from on-
field practices such as rice cultivation, fertilizer management, and 
manure management (Section 7.4.3) with total mitigation potential 
of 3.9 ± 0.2 GtCO 2-eq yr–1 (Chapter 7). Critically, these approaches 
may have significant benefits in terms of adaptation and other 
development goals.

470
Farming system approaches to agricultural mitigation have a wide 
variety of co-benefits and trade-offs. Indeed, there are conceptual 
formulations for these practices in which the co-benefits are more of 
a focus, such as climate-smart agriculture (CSA) which ties mitigation 
to adaptation through its three pillars of increased productivity, 
mitigation, and adaptation (Lipper et al. 2014). The ‘4 per 1000’ goal 
to increase soil carbon by 0.4% per year (Soussana et al. 2019) is 
compatible with the three pillars of CSA. Sustainable intensification, 
a framework which centers around a need for increased agricultural 
production within environmental constraints also complements CSA 
(Campbell et al. 2014). The literature reports examples of mitigation 
co-benefits of adaptation actions, with evidence from various regions 
(Thornton and Herrero 2015; Thornton et al. 2018) (Chapter 7).

Conservation agriculture, promoted for improving agricultural soils 
and crop diversity (Powlson et al. 2016) can help build adaptive 
capacity (Smith et al. 2017; Pradhan et al. 2018a) and yield 
mitigation co-benefits through improved fertiliser use or efficient use 
of machinery and fossil fuels (Harvey et al. 2014; Cui et al. 2018; 
Pradhan et al. 2018a).
There is a  complex set of barriers to implementation of farming-
system approaches for climate mitigation (Section 7.6.4), suggesting 
a  need for deliberate shifts in development pathways to achieve 
significant progress in this sector. The link between NDCs and 
mitigation in the land use sector can provide impetus for such policies. 
For example, there are multiple agricultural mitigation options that 
southeast Asian countries could use to meet NDCs that would have 
an important adaptive impact (Amjath-Babu et al. 2019).
Some agricultural practices considered sustainable have trade-offs,

and their implementation can have negative effects on adaptation or 
other ecosystem services. Fast-growing tree monocultures or biofuel 
crops may enhance carbon stocks but reduce downstream water 
availability and decrease availability of agricultural land (Windham-
Myers et al. 2018; Kuwae and Hori 2019). In some dry environments 
similarly, agroforestry can increase competition with crops and 
pastures, decreasing productivity, and reduce catchment water yield 
(Schrobback et al. 2011).
Agricultural practices can adapt to climate change while decreasing 
CO2 emissions on the farm field. However, if such a practice leads 
to lower yields, interconnections of the global agricultural system 
can lead to land use change elsewhere and a  net increase in 
GHG emissions (Erb et al. 2016). Implementation of sustainable 
agriculture can increase or decrease yields depending on context 
(Pretty et al. 2006).
Blue carbon and mitigation co-benefits of adaptation actions

The Paris Agreement recognises that mitigation co-benefits resulting 
from Parties’ adaptation actions and/or economic diversification 
plans can contribute to mitigation outcomes (UNFCCC 2015a: 
Article 4.7). Blue carbon refers to biologically-driven carbon flux or 
storage in coastal ecosystems such as seagrasses, salt marshes, and 
mangroves (Wylie et al. 2016; Fennessy et al. 2019; Fourqurean et al. 2012; Tokoro et al. 2014) (see Cross-Chapter Box 8 on blue carbon as 
a storage medium and removal process).
Restoring or protecting coastal ecosystems is a  mitigation action 
with synergies with adaptation and development. Such restoration 
has been described as a ‘no regrets’ mitigation option in the Special 
Report on the Ocean and Cryosphere in a Changing Climate (Bindoff 
et al. 2019) and advocated as a climate solution at national scales 
(Bindoff et al. 2019; Taillardat et al. 2018; Fargione et al. 2018) and 
global scales (Howard et al. 2017). On a  per-area basis, carbon

stocks in coastal ecosystems can be higher than in terrestrial forests 
(Howard et al. 2017), with below-ground carbon storage up to 1000 
tC ha–1 (McLeod et al. 2011; Crooks et al. 2018; Bindoff et al. 2019). 
Overall, coastal vegetated systems have a  mitigation potential of 
around 0.5% of current global emissions, with an upper limit of less 
than 2% (Bindoff et al. 2019).
Restoration or protection of coastal ecosystems is an important 
adaptation action with multiple benefits, with bounded global 
mitigation benefits (Gattuso et al. 2018; Bindoff et al. 2019). Such 
restoration/preservation reduces coastal erosion and protects from 
storm surges, and otherwise mitigates impacts of sea level rise and 
extreme weather along the coast line (Siikamäki et al. 2012; Romañach 
et al. 2018; Alongi 2008). Restoration of tidal flow to coastal wetlands 
inhibits methane emissions which occur in fresh and brackish water 
(Kroeger et al. 2017) (Section 7.4.2.8 describes a more inclusive set

of ecosystem services provided by coastal wetlands). Coastal habitat 
restoration projects can also provide significant social benefits in the 
form of job creation (through tourism and recreation opportunities), 
as well as ecological benefits through habitat preservation (Edwards 
et al. 2013; Sutton-Grier et al. 2015; Sutton-Grier and Moore 2016; 
Wylie et al. 2016; Kairo et al. 2018; Bindoff et al. 2019).
Coastal ecosystem-based mitigation can be cost-effective, but 
interventions should be designed with care. One concern is to 
assure that actions remain effective at higher levels of climate 
change (Alongi 2015; Bindoff et al. 2019). Also, methane emissions 
from ecosystems may partially reduce the benefit of the carbon 
sequestration (Rosentreter et al. 2018) depending on the salinity 
(Poffenbarger et al. 2011; Kroeger et al. 2017). As the main driver 
of mangrove forest loss is aquaculture/agriculture (Thomas et al.

2017), there may be entrenched interests opposing restoration and 
protection actions.
Restoration and protection of terrestrial ecosystems
Restoration of terrestrial landscapes can be a  direct outcome of 
development pathways, and can be critical to achieving a variety of 
SDGs (especially 1, 2, 6, 8, 13, 15)  (Vergara et al. 2016; Lapola et al. 
2018) although it also presents risks and can have trade-offs with 
other SDGs (Cao et al. 2010; Dooley and Kartha 2018). Landscape 
restoration is nearly always a mitigation action, and can also provide 
adaptive capacity. While policy in Brazil has tended to focus on the 
Amazon as a carbon sink, the mitigation co-benefits of ecosystem-
based adaptation actions have been highlighted in the literature 
(Locatelli et al. 2011; Di Gregorio et al. 2016). A study of potential

471
restoration of degraded lands in Latin America (Vergara et al. 2016) 
indicates that substantial benefits for mitigation, adaptation, and 
economic development accrue after several years, underscoring 
a reliance on deliberate development choices. In agricultural contexts, 
restoration is a development choice that can enhance adaptive and 
mitigative capacity via impact on farmer livelihoods.
Preventing degradation of landscapes can support both mitigation 
and adaptation (IPCC 2019). Restoration of ecosystems is associated 
with improved water filtration, groundwater recharge and flood 
control and multiple other ecosystem services (Ouyang et al. 2016).
Restoration projects must be designed with care. There can be trade-
offs in addition to the synergies noted above (Section  7.6.4.3). 
Restorations may be unsuccessful if not considered in their socio-

economic context (Lengefeld et al. 2020; Iftekhar et al. 2017; Jellinek 
et al. 2019). Restoration projects for mitigation purposes can be 
more effective if done with adaptation in mind (Gray et al. 2011) as 
a changing climate may render some mitigation actions biophysically 
infeasible (Arneth et al. 2021). Landscape restoration projects 
intended for CDR may underperform due to future release of stored 
carbon, or deferral of storage until after irreversible climate change 
effects (e.g. extinctions) (Dooley and Kartha 2018).
Afforestation plans have received substantial attention as a climate 
mitigation action, with ongoing unresolved debate on the feasibility 
and trade-offs of such plans. Such afforestation programs can fail 
for biophysical reasons (Fleischman et al. 2020) (Section  7.4.2.2) 
but also lack of consideration of socioeconomic and development 
contexts (Fleischman et al. 2020).
4.4.3 Risks and Uncertainties

Shifting development pathways and accelerating mitigation are 
complex endeavours that carry risks. Some of these risks can be easily 
captured by quantitative models. Others are better understood via 
qualitative approaches, such as qualitative narrative storylines (told 
in words) and methods mixing qualitative and quantitative models 
(Kemp-Benedict 2012; Hanger-Kopp et al. 2019). The following 
outline key risks and relevant hedging strategies identified in 
4.4.3.1 Actions by Others Not Consistent 
With Domestic efforts
The international context is a major source of uncertainty for national-
level planning, especially for small- or medium-sized open economies, 
because the outcome of domestic choices may significantly depend 
on decisions made by other countries and actor, over which national 
governments have limited or no control (Lachapelle and Paterson 
2013). Availability of foreign financial resources in countries with

limited domestic savings (Baum et al. 2017) and availability of 
technology transfers (Glachant and Dechezleprêtre 2017) are some 
examples. Other external decisions with significant bearing on 
domestic action include mitigation policies in other countries (Dai et al. 2017), and especially in major trading partners, the lack of 
which can result in competitive disadvantage for sectors exposed to 
international competition (Alton et al. 2014). The international prices 
of the key commodities (notably energy), goods and services are 
important, notably when shifting development pathway is based on 
structural change (e.g.,  Willenbockel et al. 2017 for Ghana and Kenya).
Remedies include first devising policy packages that are, to the 
extent possible, robust to uncertainty regarding external decisions. 
For example, mitigation in the building sector is considered less 
problematic for competitiveness since the construction sector is

less exposed to international competition. Remedies also include 
securing international cooperation to reduce the uncertainty 
that domestic decision-makers face about the international 
context. Shifting investments towards low-GHG solutions requires 
a  combination of conducive public policies, attractive investment 
opportunities and financing of transitions (Section 15.6), which can 
enable shifting development pathways. Cooperation can generate 
positive spill overs through technology diffusion (Section  13.6.6). 
Third, cooperation is not limited to governments. As discussed in 
Section 4.2.3, international cooperative initiatives among non-state 
actors (cities, economic branches, etc.) can also provide know-how, 
resources and stable cooperative frameworks that reduce uncertainty 
for individual actors (Section 14.5.5).
4.4.3.2 Parts of Complex Policy Packages Fail
As outlined in the examples in Section  4.4.1 above, shifting

development pathways and accelerating mitigation are complex 
endeavours, on which there is limited experience and know-how 
from the past. An uncertainty is that parts of these policy packages 
may fail, in other words, under-deliver relative to the amount of 
mitigation and of transformations initially expected. For example, 
France has failed to meet its 2015–2018 carbon budget as housing 
retrofitting programs, in particular, have failed to deliver the expected 
amount of emission reductions (Haut Conseil pour le Climat 2019). 
There are two main options to tackle this risk. The first is to build 
in redundancy. The second is to anticipate that some parts of the 
policies will inevitably fail, and build-in monitoring and corrective 
mechanisms in a sequential decision-making process. To this regard, 
building institutions that can properly monitor, learn from and 
improve over time is critical (Nair and Howlett 2017).
4.4.3.3 New Information Becomes Available

The science on climate change, its impacts and the opportunities to 
mitigate is continuously being updated. Even though decisions are no 
longer made ‘in a sea of uncertainty’ (Lave 1991), we know that new 
information will come over time, that may have significant bearing on 
the design and objectives of policies to shift development pathways 
and accelerate mitigation. New information may come from climate 
sciences (e.g.,  updated GWP values or available carbon budgets) 
(Quéré et al. 2018), impact sciences (e.g.,  re-evaluation of climate 
impacts associated with given emission pathways) (Ricke et al. 2018) 
or mitigation sciences (e.g.,  on availability of given technologies) 
(Lenzi et al. 2018; Giannousakis et al. 2020).

472
At the same time, economic and social systems are characterised by 
high degree of inertia, via long-lived capital stock or urban forms 
(Lecocq and Shalizi 2014), or more broadly mutually reinforcing 
physical, economic, and social constraints (Seto et al. 2016) that 
may lead to carbon lock-ins (Erickson et al. 2015). Risks associated 
with long-lasting fossil-fuel power plants have been the object of 
particular attention. For example, Pfeiffer et al. (2018) estimate 
that even if the current pipeline of power plants was cancelled, 
about 20% of the existing capacity might be stranded to remain 
compatible with 1.5°C or 2°C pathways – implying that additional 
capital accumulation would lead to higher sunk costs associated with 
stranded assets (Ansar et al. 2013; Johnson et al. 2015; Kriegler et al. 
2018; Luderer et al. 2018b).
In the presence of uncertainty and inertia (or irreversibilities),

hedging strategies may be considered, that include selection of 
risk-hedging strategies and processes to adjust decisions as new 
information becomes available. The notion of hedging against risks 
is also prominent in the adaptation literature, as exemplified by 
the terminology of ‘climate resilient development’ (Fankhauser and 
McDermott 2016) (AR6 WGII, Chapter18). There is also a  growing 
literature on hedging strategies for individual actors (e.g.,  firms 
or investors) in the face of the uncertainties associated with 
mitigation (e.g.,  policy uncertainty or the associated carbon price 
uncertainty; e.g., Andersson et al. 2016 or Morris et al. 2018). On the 
other hand, there is often limited discussion of uncertainty and of 
its implication for hedging strategies in the accelerated mitigation 
pathway literature. Exceptions include (Capros et al. 2019), who elicit 
‘no-regret’ and ‘disruptive’ mitigation options for the EU through

a detailed sensitivity analysis, and (Watson et al. 2015) who discuss 
flexible strategies for the UK energy sector transition in the face of 
multiple uncertainties.
4.4.3.4 Black Swans (Such as the COVID-19 Crisis)
As the current COVID-19 crisis demonstrates, events happen that 
can derail the best-laid plans. Unexpected events beyond the range 
of human experience until then are called ‘black swans’, given the 
expectation that all swans are white. The only point to note here is 
that such events may also provide opportunities. In the COVID-19 
case, for example, the experience of conducting many activities on-
line, which reduces emissions from transport, may leave an imprint 
on how some of these activities are carried out in the post-COVID-19 
world. Similarly, reduced air pollution seen during the pandemics may 
increase support for mitigation and strengthen the case for climate 
action. However, the emissions implications of recovery packages

depend on choosing policies that support climate action while 
addressing the socio-economic implications of COVID-19 (Hepburn 
et al. 2020). Governments may be in a  stronger position to do so 
due to their pivotal role in assuring the survival of many businesses 
during the pandemics. Given the magnitude of recovery packages 
and their implications (Pollitt et al. 2021), choosing the direction of 
recovery packages amounts to choosing a  development pathway 
(Cross-Chapter Box 1 in Chapter 1).4.4.3.5 Transformations Run Into Oppositions
As noted above, shifting development pathways and accelerating 
mitigation involve a  broad range of stakeholders and decision-
makers, at multiple geographical and temporal scales. They 
require a  credible and trusted process for reconciling perspectives 
and balancing potential side-effects, managing winners and losers 
and implementing compensatory measures to ensure an inclusive

just transition (Newell and Mulvaney 2013; Miller and Richter 2014; 
Gambhir et al. 2018; Diffenbaugh and Burke 2019). Such processes 
are designed to manage the risk of inequitable or non-representative 
power dynamics (Helsinki Design Lab 2011; Boulle et al. 2015; 
Kahane 2012). More generally, stakeholder processes can be subject 
to regulatory capture by special interests, or outright opposition 
from a  variety of stakeholders. Information asymmetry between 
government and business may shape the results of consultative 
processes. Long experience of political management of change 
demonstrates that managing such risks is not easy, and requires 
sufficiently strong and competent institutions (Stiglitz 1998). The 
next section on Just Transition (Section 4.5) addresses this issue.
4.5 Equity, Including Just Transitions
Equity is an ethical and at times economic imperative, but it is 
also instrumentally an enabler of deeper ambition for accelerated

mitigation (Hoegh-Guldberg et al. 2019). The literature supports 
a range of estimates of the net benefits – globally or nationally – of 
low-carbon transformation, and it identifies a number of difficulties 
in drawing definitive quantitative conclusions (e.g.,  comparisons 
of costs and benefits among different actors, the existence of non-
economic impacts, comparison across time, uncertainty in magnitude) 
(Section 3.6). One of the most important of these dimensions is the 
distributional consequences of mitigation, as well as a range of equity 
considerations arising from the uncertainty in net benefits, as well 
as from the distribution of costs and benefits among winners and 
losers (Rendall 2019; Caney 2016; Lahn and Bradley 2016; Lenferna 
2018a; Kartha et al. 2018b; Robiou Du Pont et al. 2017). Some equity 
approaches are even just seeking corrective justice including for 
historical emissions (Adler 2007). For an assessment of literature on

fairness in NDCs, see Section 4.2.2.7.
Equity issues are often discussed in the literature via frameworks that 
are well-founded in the ethical literature and that have a  strong bearing 
on effort-sharing, but have not yet been quantitatively modelled 
and expressed in the form of an emissions allocation  quantified 
framework. These include, for example, ethical perspectives based 
in human rights (Johl and Duyck 2012), human capabilities (Klinsky 
et al. 2017b), environmental justice (Mohai et al. 2009; Schlosberg 
2009), ecological debt (Srinivasana et al. 2008; Warlenius et al. 2015), 
transitional justice (Klinsky 2017; Klinsky and Brankovic 2018), and 
planetary boundaries (Häyhä et al. 2016).

473
While there is extensive literature on equity frameworks for national 
emissions allocations (CSO Equity Review 2015, 2017, 2018; Holz et al. 
2018; Kemp-Benedict et al. 2018; Robiou du Pont and Meinshausen 
2018; Fyson et al. 2020; Pozo et al. 2020; Pye et al. 2020), such 
studies have tended to focus on allocation of a global carbon budget 
among countries based on quantified equity frameworks. The implicit 
normative choices made in these analysis have limitations (Kartha 
et al. 2018a). Moreover, there are many ethical parameters that could 
be introduced to enrich the existing quantitative frameworks, such 
as progressivity (Holz et al. 2018), consumption-based accounting 
(Afionis et al. 2017), prioritarianism (Adler and Treich 2015), and a right 
to development (Moellendorf 2020). Introducing these ethical frames 
into conventional quantification approaches generally implies greater

allocations for poorer and lower-emitting populations, suggesting that 
the approaches that are typically highlighted in emissions allocation 
analyses tends to favour wealthier and higher-emitting countries. 
Broader, more inclusive sharing of costs and burdens is seen as a way 
to enhance equity in procedures and outcomes.
Ultimately, equity consequences depend on how costs and benefits 
are initially incurred and how they are shared as per social contracts 
(Combet and Hourcade 2017), national policy, and international 
agreements. The literature suggests a  relationship between the 
effectiveness of cooperative action and the perception of fairness of 
such arrangements. Winkler et al. (2018) demonstrate that countries 
have put forward a  wide variety of indicators and approaches for 
explaining the fairness and ambition of their NDCs, reflecting the 
broader range of perspectives found in the moral philosophical 
literature cited above. Mbeva and Pauw (2016) further find that

adaptation and financing issues take on greater salience in the 
national perspectives reflected in the NDCs.
Topics of equity and fairness have begun to receive a greater amount 
of attention within the energy and climate literature, namely through 
the approaches of gender and race (Pearson et al. 2017; Lennon 2017; 
Allen et al. 2019), climate justice (Roberts and Parks 2007; Routledge 
et al. 2018) (Roberts &  Parks, 2006; Routledge et al. 2018), and 
energy justice (Sovacool and Dworkin 2014). While such approaches 
frequently envision justice and equity as an ethical imperative, justice 
also possesses the instrumental value of enabling deeper and more 
socially acceptable mitigation efforts (Klinsky and Winkler 2018).
A concrete focal point on these issues has been that of ‘just transition’. 
Getting broad consensus for the transformational changes entailed 
in moving from a  high- to a  low-carbon economy means ‘leaving

no one behind’, in other words, ensuring (sufficiently) equitable 
transition for the relevant affected individuals, workers, communities, 
sectors, regions and countries (Newell and Mulvaney, 2013; Jasanoff 
2018). The concept of a ‘just transition’ owes its origin to the USA 
trade union movement of the 1980s. The earliest version of a just 
transition was called the ‘Superfund for Workers’ modelled on the 
1980 Superfund program that designed federal funds for the clean-
up of toxic substances from chemicals, mining and energy production 
(Stevis and Felli 2015). It was further taken up, for example in the 
collaboration of the International Trade Union Confederation (ITUC), 
the International Labour Organization (ILO) and the UN Environment 
Programme (UNEP) in promoting ‘green jobs’ as integral elements of a  just transition (ILO 2015; Rosemberg 2010). In recent years 
the concept of a ‘just transition’ has gained increased traction, for

example incorporated in the outcome of the Rio+20 Earth Summit 
and more recently recognised in the preamble of the Paris Agreement, 
which states ‘the imperative of a just transition of the workforce and 
the creation of decent work and quality jobs in accordance with 
nationally defined development priorities’ (UNFCCC 2015a). Some 
heads of state and government signed a Solidarity and Just Transition 
Silesia Declaration first introduced at COP24 in Poland (HoSG 2018).
The literature identifies targeted and proactive measures from 
governments, agencies, and authorities to ensure that any negative 
social, environmental or economic impacts of economy-wide transitions 
are minimised, while benefits are maximised for those disproportionally 
affected (Healy and Barry 2017). While the precise definition varies by 
source, core elements tend to include: (i) investments in establishing 
low-emission and labour-intensive technologies and sectors (Mijn

Cha et al. 2020); (ii) research and early assessment of the social and 
employment impacts of climate policies (Green and Gambhir 2020; 
Mogomotsi et al. 2018); (iii) social dialogue and democratic consultation 
of social partners and stakeholders (Swilling and Annecke 2012; Smith 
2017); (iv) the creation of decent jobs; active labour markets policies; 
and rights at work (ILO 2015; UNFCCC 2016c); (v) fairness in energy 
access and use (Carley and Konisky 2020); (vi) economic diversification 
based on low-carbon investments; (vii) realistic training/retraining 
programs that lead to decent work; (viii) gender specific politics 
that promote equitable outcomes (Allwood 2020); (ix) the  fostering 
of international cooperation and coordinated multilateral actions 
(Lenferna 2018b; Newell and Simms 2020); (x) redressing of past 
harms and perceived injustices (Setzer and Vanhala 2019; UNHRC 
2020); and (xi) consideration of inter-generational justice concerns,

such as the impacts of policy decisions on future generations (Newell 
and Mulvaney, 2013).
A just transition could therefore entail that the state intervenes more 
actively in the eradication of poverty, and creates jobs in lower-
carbon sectors, in part to compensate for soon-to-be abandoned 
fossil-fuel-based sectors, and that governments, polluting industries, 
corporations and those more able to pay higher associated taxes 
pay for transition costs, provide a welfare safety net and adequate 
compensation for people, communities, places, and regions that 
have been impacted by pollution, marginalised or negatively 
impacted by a transition from a high- to low-carbon economy and 
society (Muttitt and Kartha 2020; Le Billon and Kristoffersen 2020; 
Kartha et al. 2018b). Reducing climate impacts is another important 
dimension of equity, in that the poor who are least responsible 
for climate change are most vulnerable to its impacts (AR6 WGII,

Chapter 8). Focusing on financial losses alone however can obscure 
an important distinction between losses incurred by corporations 
and states and losses experienced by workers and communities. 
Processes established in the name of a  just transition are also at 
risk of being co-opted by incumbent interests and powerful/wealthy 
agents (Green and Gambhir, 2020). Policy interventions associated 
with good governance, democratic oversight, and legal recourse can 
help overcome attempted co-optation of just transition, or use of 
COVID-19 recovery packages for continued carbon lock-in (Hepburn 
et al. 2020; SEI et al. 2020).

474
The just transition concept has thus become an international focal 
point tying together social movements, trade unions, and other key 
transitions and to seek to protect workers and communities. It also 
forms a central pillar of the growing movement for a ‘Green New 
Deal’ – a roadmap for a broad spectrum of policies, programs, and 
legislation that aims to rapidly decarbonise the economy while 
significantly reducing economic inequality (Allam et al. 2021; Galvin 
and Healy 2020). The US Green New Deal Resolution (Ocasio-Cortez 
2019) for example, positions structural inequality, poverty mitigation, 
and a just transition at its centre. The European Green Deal proposed 
in 2019 (European Commission 2019), including a  €100  billion 
‘Just Transition Mechanism’ to mitigate the social effects of

transitioning away from jobs in fossil-based industries. National 
level green new deals with strong just transition components have 
been proposed in South Korea, Australia, Spain, UK, Puerto Rico, 
Canada, as well as regional proposals across Latin America and the 
Caribbean (Pollin 2020).A just transition at national, regional and local scales can help to 
ensure that workers, communities, frontline communities and 
the energy-poor are not left behind in the transition. Moreover, 
a  just transition necessitates that rapid decarbonisation does not 
perpetuate asymmetries between richer and poorer states and 
people (UNHRC 2020). Alliances around a just transition in countries 
across the world take many forms (Box 4.6).
As Figure 4.9 shows, no fewer than seven national commissions or task 
forces on a just transition existed as of 2020 as well as seven other 
sets of national policies and a  multitude of other actors, networks,

and movements. For instance, the German phase-out of coal subsidies 
involved a  savings package for unemployed miners. Subsidy reform 
packages introduced by Iran, Namibia, the Philippines, Turkey, and 
United Kingdom provide similar compensating measures to affected 
groups (Sovacool 2017). Spain’s just transition plan for coal miners 
includes early retirement, redundancy packages, silicosis compensation, 
retraining for green jobs, and priority job placement for former miners.Box 4.6 | Selected Organisations and Movements Supporting a Just Transition
• 350.org (global)
• Asian Pacific Forum on Women, Law and Development 
(Asia Pacific)
• Blue Green Alliance (USA)
• Beyond Coal campaign (USA)
• Central Única dos Trabalhadores (Brazil)
• Climate Action Network (global)
• Climate Justice Alliance (USA)
• Cooperation Jackson (USA)
• Dejusticia (Colombia)
• Deutscher Gewerkschaftsbund (German Trade Union 
Confederation, Germany)
• DiEM25 (pan-European)
• European Union

• European Trade Union Confederation (EU)
• Grassroots Global Justice (USA)
• IndustriALL Global Union (global)
• Indigenous Environmental Network (USA)
• International Labor Organization (global)
• ITUC-affiliated Just Transition Centre (global)
• ITUC-affiliated Just Transition Centre (Americas)
• Just Transition Alliance (USA)
• Just Transition Centre (global)• Just Transition Fund (USA)
• Kentuckians for the Commonwealth (USA)
• Labor Network for Sustainability (USA)
• Latrobe Valley Authority (Australia)
• Movement Generation (USA)
• NAACP (USA)
• National Union of Mineworkers of South Africa 
(South Africa)
• Pan African Climate Justice Alliance (Africa)
• Post Petroleum Transitions Roundtable 
(Mesa de Transición Post Petrolera) (Argentina)
• Powering Past Coal Alliance (global)
• Right to the city alliance (USA)
• Sierra Club (USA)
• Sunrise Movement (USA)
• The Leap Manifesto (Canada)
• The Trade Unions for Energy Democracy Initiative 
(global)

• Trade Union Confederation of the Americas (TUCA)
• Transition Towns Movement (UK)
• Women’s Environment and Development 
Organization (global)

475
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44Canada: Task Force 
on Just Transition for 
Canadian Coal Power 
Workers
Spain: Framework 
Agreement for a Just 
Transition on Coal Mining 
Development UK: Scottish Just Transition 
CommissionFrance: 2018 Ecological 
Transition Contracts 
programme
Greece: National Just 
Transition Fund for Lignite 
areasFinland: Working group to 
ensure a fair and just 
transition and acceptability 
of climate measures Czech Republic: Czech 
Coal Commission
Slovakia: Transformation 
Action Plan of coal region 
Upper NitraChina: Mine closure 
provisions in the 13th Five 
Year Plan for Coal Industry 
Development, 2016–2020
Poland:  The 1998 
Mining Social Package 
and Special Privileges 
for the mining 
communesGermany: German 
Commission on Growth, 
Structural Change and 
Employment (German Coal 
Commission)
United States: Partnership 
for Opportunity and 
Workforce and Economic 
Revitalisation Plan

(POWER+)Costa Rica: National 
Decarbonisation Plan 
2018–2050
Ghana: The National 
Dialogue on Decent Work 
and ‘Just Transition’ to a 
Sustainable Economy and 
Society
South Africa: National 
Planning Just Transition 
Dialogue + Presidential 
Climate CommissionIreland: Just Transition 
Fund IrelandItaly: Enel’s Just Transition 
Framework and Futur-e 
projectNew Zealand: ‘Just 
Transitions Unit’ within the 
ministry of Business, 
Innovation and 
Employment (MBIE)(a) Just Transition commissions, task forces and dialogues
CA
US
CR
SACH
AUS
NZSPFRGERCZSLFI
PO
GRIT
GHUKIE
Australia: La Trobe Valley 
Authority
(b) Eur opean Gr een Deal – J ust Transitions Fund
Silesia,  Lower Silesia,  Greater 
Poland,  Lesser P olandBrandenburg,  Saxony , Saxony 
Anhalt,  North Rhine-W estphalia
Moravia-Silesia,  Usti,  Karlovy Vary
Jiu ValleyWestern Macedonia Upper Nitra Asturias , Aragón,  Castilla-y-León 
Zasavska,  Savinjsk o-Šaleška
Midlands(c) Platform for coal r egions in tr ansition

Figure 4.9 | Just Transitions around the world, 2020.  Panel (a) shows commissions, task forces, dialogues behind a just transition in many countries (Schweitzer and 
Tonn 2003; Thalmann 2004; Harrison 2013; Galgóczi 2014; Mendoza 2014; Adeoti et al. 2016; Ng et al. 2016; Gass and Echeverria 2017; Snell 2018; ILO 2018; Ministry of 
Employment and Labour Relations of Ghana 2018; Szpor, A. and Ziółkowska 2018; van Asselt and Moerenhout 2018; Bankwatch 2019; Commission on Growth Structural 
Change and Employment 2019; European Union 2019, 2020; Galgóczi 2019; Government of Canada 2019; Government of Costa Rica 2019; NPC (National Planning 
Commission) 2019; Ministry of Business Innovation & Employment New Zealand 2019; Piggot et al. 2019; Popp 2019; Strambo et al. 2019; Government of Spain 2019; Finnish 
Government 2020; Scottish Government 2020; White House 2016; Mijn Cha et al. 2020); panel (b) shows the funds related to the Just Transition within the European Union

Green Deal, and panel (c) shows the European Union’s Platform for Coal Regions in Transition.

476
Chapter 4 Mitigation and Development Pathways in the Near to Mid-term44.6 Knowledge Gaps
This section summarises knowledge gaps that require further research:
• Literature on mitigation pathways at the national level remains 
skewed towards large emitters. Many low-income countries 
have very few or no studies at all (Lepault and Lecocq 2021) 
(Section  4.2) (Annex III). Development of new studies and 
inclusion of associated scenarios in updated mitigation national 
mitigation pathway database would enhance understanding of 
mitigation at national level.
• Ex ante and ex post analysis of mitigation action and of 
mitigation  plans by non-state actors, and their relationship 
with mitigation action and plans by governments is limited 
(Section 4.2.3).
• System analysis solutions are only beginning to be recognised in 
current literature on deep mitigation pathways, and rarely 
included in existing national policies or strategies (Section 4.2.5).

• While the technology elements of accelerated mitigation 
pathways at national level are generally well documented, 
studies of the economic and social implications of such pathways 
remain scarce (Section 4.2.6).
• Literature on the implication of development choices for 
emissions and for capacity to mitigate is limited (Section 4.3.2). 
In particular, more contributions from the research community 
working on development issues would be very useful here.
• Literature describing shifts in development pathways, and the 
conditions for such shifts (based on past experience or on models) 
remains scarce (Sections 4.3.1, 4.3.3 and 4.4.1). Studying shifts 
in development pathways requires new ways of thinking with 
interdisciplinary research and use of alternative frameworks 
and methods suited for understanding of change agents, 
determinants of change and adaptive management among other 
issues (Winkler 2018). Research is not only expected to produce

knowledge and boost innovation, but also to help identify 
transformation pathways and to enlighten public debate and 
public decision-making on related political choices.
• Other research gaps concern the open ocean and blue carbon. 
There is limited knowledge about quantification of the blue 
carbon stocks. Research is required into what happens if the 
sequestration capacity of the ocean and marine ecosystems 
is damaged by climate change to the tipping point until the 
sink becomes an emitter, and on how to manage blue carbon 
(Section 4.4.2).
• Knowledge is limited on: (i) linking equity frameworks on 
mitigation with adaptation and most importantly with loss and 
damage, (ii) applying ethical parameters to enrich many of the 
existing quantitative frameworks, to assess fairness and ambition 
of NDCs; (iii) extending equity frameworks to quantify equitable 
international support, as the difference between equity-based 
national emissions scenarios and national domestic emissions

477
Mitigation and Development Pathways in the Near to Mid-term  Chapter 44Frequently Asked Questions (FAQs)
FAQ 4.1 |  What is to be done over and above countries’ existing pledges under the Paris 
Agreement to keep global warming well below 2°C?
Current pledges and efforts under the PA aimed at keeping global warming below 2°C are not enough, falling short by 14 to 
23 GtCO 2-eq (Cross-Chapter Box 4 in this Chapter). There is a further shortfall of about 4 to 7 GtCO 2-eq in 2030 if the conditions 
are not fulfilled for those Parties that have made their pledges with conditions for support (Section 4.2.2.3). To cover up for these 
shortfalls will require taking actions across all sectors that can substantially reduce GHG emissions. Examples of such actions 
include shifting to low- or zero-emission power generation, such as renewables; changing food systems, such as diet changes away

from land-intensive animal products; electrifying transport and developing ‘green infrastructure’, such as building green roofs, or 
improving energy efficiency by smart urban planning, which will change the layout of many cities. Because these different actions 
are connected, it means all relevant companies, industries and stakeholders would need to be involved to increase the support 
and chance of successful implementation (Section 4.2.5). The deployment of low-emission technology depends upon economic 
conditions (e.g., employment generation or capacity to mobilise investment), but also on social/cultural conditions (e.g., awareness 
and acceptability) and institutional conditions (e.g., political support and understanding), and the provision of relevant enabling 
conditions (Section  4.4.1). Encouraging stronger and more ambitious climate action by non-government and sub-national

stakeholders, as well as international cooperative initiatives (ICIs) could make significant contributions to emissions reduction 
FAQ 4.2 |  What is to be done in the near term to accelerate mitigation and shift 
development pathways?
Increasing speed of implementation, breadth of action across all sectors of the economy, and depth of emission reduction faces 
important obstacles, that are rooted in the underlying structure of societies (Section 4.2.7). Addressing these obstacles amounts to 
shifting away from existing developmental trends (i.e., shifting development pathways, Cross-Chapter Box 5). This can be done by 
strengthening governance and institutional capacity, aligning technology and innovation systems with low-carbon development, 
facilitating behaviour change and providing adequate finance within the context of multi-objective policy packages and sequences

(Section  4.4.1). Shifting development pathways towards sustainability broadens the scope for, and is thus a  complement to, 
accelerated mitigation (Section 4.3).
FAQ 4.3 |  Is it possible to accelerate mitigation in the near term while there are so many other 
development priorities? (Education, health, employment, etc.)
It is possible to accelerate mitigation while addressing other developmental priorities by implementing measures that simultaneously 
address both climate and development goals. Casting mitigation in the broader context of development pathways provides additional 
opportunities to articulate both (Section 4.3.1.4). Policies such as progressive taxation, investment in public transport, regulatory 
transparency, commitment to multilateral environmental governance, fiscal incentives for private investments, international 
technology development and transfer initiatives, and risk disclosure and efforts to improve underlying enabling conditions (improving

governance and institutional capacity, fostering behavioural change and technological innovation, and provision of finance) address 
multiple objectives beyond mitigation, such as job creation, macroeconomic stability, economic growth, public health and welfare, 
providing energy access, providing formal housing, and providing mobility. How we manage our land and agriculture, growing cities, 
transport needs, our industries, and the way people are trained and employed all impact on GHG emissions and the options we 
have to reduce them. In turn, reducing GHG emissions can also contribute to reducing poverty, preventing hunger, improving health 
and wellbeing, and providing clean water and clean energy. Implementing right policies and investments can help to address the 
challenges of how to reduce emissions without constraining development. For example, in land use, widespread planting of a single

tree species or crops for bioenergy (organic matter turned into renewable energy) could affect food and water supplies. Therefore, if 
bioenergy is to be relied upon to offset emissions, the right policies and investments are needed (see also Chapter 17).

505
Demand, Services and Social Aspects of Mitigation Chapter 55Executive Summary
Assessment of the social science literature and regional case studies 
reveals how social norms, culture, and individual choices interact with 
infrastructure and other structural changes over time. This provides 
new insight into climate change mitigation strategies, and how 
economic and social activity might be organised across sectors to 
support emission reductions. To enhance well-being, people demand 
services and not primary energy and physical resources per  se. 
Focusing on demand for services and the different social and political 
roles people play broadens the participation in climate action.
Potential of Demand-side Actions and Service 
Provisioning Systems
Demand-side mitigation and new ways of providing services 
can help avoid, shift, and improve final service demand. Rapid 
and deep changes in demand make it easier for every sector

to reduce greenhouse gas (GHG) emissions in the short and 
medium term (high confidence). {5.2, 5.3}
The indicative potential of demand-side strategies to reduce 
emissions of direct and indirect CO 2 and non-CO 2 GHG emissions 
in three end-use sectors (buildings, land transport, and food) is 
40–70% globally by 2050 (high confidence). Technical mitigation 
potentials compared to the 2050 emissions projection of two scenarios 
consistent with policies announced by national governments until 2020 
amount to 6.8 GtCO 2 for building use and construction, 4.6 GtCO 2 for 
land transport and 8.0 GtCO 2-eq for food demand,  and amount to 
4.4 GtCO 2 for industry. Mitigation strategies can be classified as Avoid-
Shift-Improve (ASI) options, that reflect opportunities for socio-cultural, 
infrastructural, and technological change. The greatest ‘Avoid’ potential 
comes from reducing long-haul aviation and providing short-distance

low-carbon urban infrastructures. The greatest ‘Shift’ potential would 
come from switching to plant-based diets. The greatest ‘Improve’ 
potential comes from within the building sector, and in particular 
increased use of energy-efficient end-use technologies and passive 
housing. {5.3.1, 5.3.2, Figure  5.7, Figure  5.8, Table  5.1, Chapter  5 
Supplementary Material II, Table 5.SM.2}
Socio-cultural and lifestyle changes can accelerate climate change 
mitigation (medium confidence). Among 60 identified actions that 
could change individual consumption, individual mobility choices have 
the largest potential to reduce carbon footprints. Prioritising car-free 
mobility by walking and cycling and adoption of electric mobility could 
save 2 tCO 2-eq cap–1 yr –1. Other options with high mitigation potential 
include reducing air travel, heating and cooling set-point adjustments, 
reduced appliance use, shifts to public transit, and shifting consumption

towards plant-based diets. {5.3.1, 5.3.1.2, Figure 5.8}
Leveraging improvements in end-use service delivery through 
behavioural and technological innovations, and innovations 
in market organisation, leads to large reductions in upstream 
resource use (high confidence). Analysis of indicative potentials 
range from a factor 10- to 20-fold improvement in the case of available 
energy (exergy) analysis, with the highest improvement potentials at the end-user and service-provisioning levels. Realisable service-level 
efficiency improvements could reduce upstream energy demand by 
45% in 2050. {5.3.2, Figure 5.10}
Alternative service provision systems, for example those 
enabled through digitalisation, sharing economy initiatives 
and circular economy initiatives, have to date made a limited 
contribution to climate change mitigation (medium confidence). 
While digitalisation through specific new products and applications

holds potential for improvement in service-level efficiencies, without 
public policies and regulations, it also has the potential to increase 
consumption and energy use. Reducing the energy use of data centres, 
networks, and connected devices is possible in managing low-carbon 
digitalisation. Claims on the benefits of the circular economy for 
sustainability and climate change mitigation have limited evidence. 
{5.3.4, 5.3.4.1, 5.3.4.2, Figure 5.12, Figure 5.13}
Social Aspects of Demand-side Mitigation Actions
Decent living standards and well-being for all are achievable 
through the implementation of high-efficiency low demand 
mitigation pathways (medium confidence). Decent living standards 
(DLS) – a benchmark of minimum material conditions for human well-
being  – overlaps with many Sustainable Development Goals (SDGs). 
Minimum requirements of energy use consistent with enabling 
well-being for all is between 20 and 50 GJ per person per year (cap–1 yr –1)

depending on the context. {5.2.2.1, 5.2.2.2, Box 5.3}
Providing better services with less energy and resource 
input has high technical potential and is consistent with 
providing well-being for all (medium confidence). Assessment 
of 19 demand-side mitigation options and 18 different constituents 
of well-being show that positive impacts on well-being outweigh 
negative ones by a factor of 11. {5.2, 5.2.3, Figure 5.6}
Demand-side mitigation options bring multiple interacting 
benefits (high confidence). Energy services to meet human needs 
for nutrition, shelter, health, and so on are met in many different ways, 
with different emissions implications that depend on local contexts, 
cultures, geography, available technologies, and social preferences. 
In the near term, many less-developed countries and poor people 
everywhere require better access to safe and low-emissions energy 
sources to ensure decent living standards and increase energy savings

from service improvements by about 20–25%. {5.2, 5.4.5, Figure 5.3, 
Figure 5.4, Figure 5.5, Figure 5.6, Box 5.2, Box 5.3}
Granular technologies and decentralised energy end use, 
characterised by modularity, small unit sizes and small unit 
costs, diffuse faster into markets and are associated with 
faster technological learning benefits, greater efficiency, more 
opportunities to escape technological lock-in, and greater 
employment (high confidence) . Examples include solar photovoltaic 
systems, batteries, and thermal heat pumps. {5.3, 5.5, 5.5.3}
Wealthy individuals contribute disproportionately to higher 
emissions and have a high potential for emissions reductions

506
Chapter 5 Demand, Services and Social Aspects of Mitigation 5while maintaining decent living standards and well-being 
(high confidence). Individuals with high socio-economic status 
are capable of reducing their GHG emissions by becoming role 
models of low-carbon lifestyles, investing in low-carbon businesses, 
and advocating for stringent climate policies. {5.4.1, 5.4.3, 5.4.4, 
Figure 5.14}
Demand-side solutions require both motivation and capacity 
for change (high confidence). Motivation by individuals or 
households worldwide to change energy consumption behaviour is 
generally low. Individual behavioural change is insufficient for climate 
change mitigation unless embedded in structural and cultural change. 
Different factors influence individual motivation and capacity for 
change in different demographics and geographies. These factors go 
beyond traditional socio-demographic and economic predictors and 
include psychological variables such as awareness, perceived risk,

subjective and social norms, values, and perceived behavioural control. 
Behavioural nudges promote easy behaviour change, for example 
‘Improve’  actions such as making investments in energy efficiency, 
but fail to motivate harder lifestyle changes (high confidence). {5.4}
Meta-analyses demonstrate that behavioural interventions, 
including the way choices are presented to consumers,1 work 
synergistically with price signals, making the combination 
more effective (medium confidence). Behavioural interventions 
through nudges, and alternative ways of redesigning and motivating 
decisions, alone provide small to medium contributions to reduce 
energy consumption and GHG emissions. Green defaults, such as 
automatic enrolment in ‘green energy’ provision, are highly effective. 
Judicious labelling, framing, and communication of social norms can 
also increase the effect of mandates, subsidies, or taxes. {5.4, 5.4.1, 
Table 5.3a, Table 5.3b}

Coordinated change in several domains leads to the 
emergence of new low-carbon configurations with cascading 
mitigation effects (high confidence). Demand-side transitions 
involve interacting and sometimes antagonistic processes on the 
behavioural, socio-cultural, institutional, business, and technological 
dimensions. Individual- or sectoral-level change may be stymied by 
reinforcing social, infrastructural, and cultural lock-ins. Coordinating 
the way choices are presented to end users and planners, physical 
infrastructures, new technologies and related business models can 
rapidly realise system-level change. {5.4.2, 5.4.3, 5.4.4, 5.4.5, 5.5}
Cultural change, in combination with new or adapted 
infrastructure, is necessary to enable and realise many ‘Avoid’ 
and ‘Shift’ options (medium confidence). By drawing support 
from diverse actors, narratives of change can enable coalitions to 
form, providing the basis for social movements to campaign in favour

of (or against) societal transformations. People act and contribute to 
climate change mitigation in their diverse capacities as consumers, 
citizens, professionals, role models, investors, and policymakers. 
{5.4, 5.5, 5.6}
1 The way choices are presented to consumers is known as ‘choice architecture’ in the field of behavioural economics.Collective action as part of social or lifestyle movements 
underpins system change (high confidence). Collective action 
and social organising are crucial to shift the possibility space of public 
policy on climate change mitigation. For example, climate strikes have 
given voice to youth in more than 180 countries. In other instances, 
mitigation policies allow the active participation of all stakeholders, 
resulting in building social trust, new coalitions, legitimising change, 
and thus initiate a positive cycle in climate governance capacity and 
policies. {5.4.2, Figure 5.14}
Transition pathways and changes in social norms often start

with pilot experiments led by dedicated individuals and 
niche groups (high confidence). Collectively, such initiatives 
can find entry points to prompt policy, infrastructure, and policy 
reconfigurations, supporting the further uptake of technological and 
lifestyle innovations. Individuals’ agency is central as social change 
agents and narrators of meaning. These bottom-up socio-cultural 
forces catalyse a  supportive policy environment, which enables 
changes. {5.5.2}
The current effects of climate change, as well as some mitigation 
strategies, are threatening the viability of existing business 
practices, while some corporate efforts also delay mitigation 
action (medium confidence). Policy packages that include job 
creation programmes help to preserve social trust, livelihoods, 
respect, and dignity of all workers and employees involved. Business 
models that protect rent-extracting behaviour may sometimes delay

political action. Corporate advertisement and marketing strategies 
may also attempt to deflect corporate responsibility to individuals 
or aim to appropriate climate care sentiments in their own brand 
building. {5.4.3, 5.6.4}
Middle actors – professionals, experts, and regulators – play 
a  crucial, albeit underestimated and underutilised, role in 
establishing low-carbon standards and practices (medium 
confidence). Building managers, landlords, energy efficiency 
advisers, technology installers, and car dealers influence patterns 
of mobility and energy consumption by acting as middle actors or 
intermediaries in the provision of building or mobility services and 
need greater capacity and motivation to play this role. {5.4.3}
Social influencers and thought leaders can increase the 
adoption of low-carbon technologies, behaviours, and lifestyles 
(high confidence). Preferences are malleable and can align with

a cultural shift. The modelling of such shifts by salient and respected 
community members can help bring about changes in different 
service provisioning systems. Between 10% and 30% of committed 
individuals are required to set new social norms. {5.2.1, 5.4}
Preconditions and Instruments to Enable 
Demand-side Transformation
Social equity reinforces capacity and motivation for mitigating 
climate change (medium confidence). Impartial governance

507
Demand, Services and Social Aspects of Mitigation Chapter 55such as fair treatment by law and order institutions, fair treatment 
by gender, and income equity, increases social trust, thus enabling 
demand-side climate policies. High status (often high carbon) item 
consumption may be reduced by taxing absolute wealth without 
compromising well-being. {5.2, 5.4.2, 5.6}
Policies that increase the political access and participation 
of women, racialised, and marginalised groups increase the 
democratic impetus for climate action (high confidence) . 
Including more differently situated knowledge and diverse perspectives 
makes climate mitigation policies more effective. {5.2, 5.6}
Carbon pricing is most effective if revenues are redistributed 
or used impartially (high confidence). A carbon levy earmarked 
for green infrastructures or saliently returned to taxpayers 
corresponding to widely accepted notions of fairness increases the

political acceptability of carbon pricing. {5.6, Box 5.11}
Greater contextualisation and granularity in policy approaches 
better addresses the challenges of rapid transitions towards 
zero-carbon systems (high confidence). Larger systems take 
more time to evolve, grow, and change compared to smaller ones. 
Creating and scaling up entirely new systems takes longer than 
replacing existing technologies and practices. Late adopters tend to 
adopt faster than early pioneers. Obstacles and feasibility barriers 
are high in the early transition phases. Barriers decrease as a result 
of technical and social learning processes, network building, scale 
economies, cultural debates, and institutional adjustments. {5.5, 5.6}
The lockdowns implemented in many countries in response 
to the COVID-19 pandemic demonstrated that behavioural 
change at a massive scale and in a short time is possible (high 
confidence). COVID-19 accelerated some specific trends, such as

increased uptake of urban cycling. However, the acceptability of 
collective social change over a longer term towards less resource-
intensive lifestyles depends on social mandate building through 
public participation, discussion and debate over information provided 
by experts, to produce recommendations that inform policymaking. 
{Box 5.2}
Mitigation policies that integrate and communicate with the 
values people hold are more successful (high confidence). Values 
differ between cultures. Measures that support autonomy, energy 
security and safety, equity and environmental protection, and fairness 
resonate well in many communities and social groups. Changing from 
a commercialised, individualised, entrepreneurial training model to an 
education cognisant of planetary health and human well-being can 
accelerate climate change awareness and action. {5.4.1, 5.4.2}
Changes in consumption choices that are supported by 
structural changes and political action enable the uptake of

low-carbon choices (high confidence). Policy instruments applied 
in coordination can help to accelerate change in a consistent desired 
direction. Targeted technological change, regulation, and public 
policy can help in steering digitalisation, the sharing economy, and 
circular economy towards climate change mitigation. {5.3, 5.6}Complementarity in policies helps in the design of an optimal 
demand-side policy mix (medium confidence). In the case of 
energy efficiency, for example, this may involve CO 2 pricing, standards 
and norms, and information feedback. {5.3, 5.4, 5.6}

508
Chapter 5 Demand, Services and Social Aspects of Mitigation 55.1 Introduction
The Sixth Assessment Report of the IPCC (AR6), for the first time, 
features a  chapter on demand, services, and social aspects of 
mitigation. It builds on the AR4 and AR5, which linked behaviour and 
lifestyle change to mitigating climate change (IPCC 2007; Roy and Pal 
2009; IPCC 2014a), the Global Energy Assessment (Roy et al. 2012), 
and the AR5, which identified sectoral demand-side mitigation options 
across chapters (IPCC 2014a; IPCC 2014b; Creutzig et al. 2016b). The 
literature on the nature, scale, implementation and implications of 
demand-side solutions, and associated changes in lifestyles, social 
norms, and well-being, has been growing rapidly (Creutzig et  al. 
2021a) (Box 5.2). Demand-side solutions support near-term climate 
change mitigation (Méjean et al. 2019; Wachsmuth and Duscha 2019) 
and include consumers’ technology choices, behaviours, lifestyle

changes, coupled with production-consumption infrastructures and 
systems, service provision strategies, and associated socio-technical 
transitions. This chapter’s assessment of the social sciences (also see 
Chapter 5 Supplementary Material I) reveals that social dynamics at 
different levels offer diverse entry points for acting on and mitigating 
climate change (Jorgenson et al. 2018).
Three entry points are relevant for this chapter. First, well-designed 
demand for services scenarios are consistent with adequate levels 
of well-being for everyone (Rao and Baer 2012; Grubler et al. 2018; 
Mastrucci et al. 2020; Millward-Hopkins et al. 2020), with high and/
or improved quality of life (Max-Neef 1995), improved levels of 
happiness (Easterlin et al. 2010) and sustainable human development 
(Arrow et al. 2013; Dasgupta and Dasgupta 2017).
Second, demand-side solutions support staying within planetary 
boundaries (Haberl et  al. 2014; Matson et  al. 2016; Hillebrand

et al. 2018; Andersen and Quinn 2020; UNDESA 2020; Hickel et al. 
2021; Keyßer and Lenzen 2021). Demand side solutions entail 
fewer environmental risks than many supply-side technologies (Von 
Stechow et al. 2016). Additionally they make carbon dioxide removal 
technologies, such as bioenergy with carbon capture and storage 
(BECCS) less relevant (Van Vuuren et al. 2018) but modelling studies 
(Grubler et al. 2018; Hickel et al. 2021; Keyßer and Lenzen 2021) still 
require ecosystem-based carbon dioxide removal. In the IPCC’s Special 
Report on Global Warming of 1.5°C (SR1.5) (IPCC 2018), four stylised 
scenarios have explored possible pathways towards stabilising global 
warming at 1.5°C (IPCC 2014a, Figure SPM.3a) (Figure 5.1) One of 
these scenarios, LED-19, investigates the scope of demand-side 
solutions (Figure 5.1). The comparison of scenarios reveals that such 
low energy demand pathways eliminate the need for technologies

with high uncertainty, such as BECCS. Third, interrogating demand 
for services from the well-being perspective also opens new avenues 
for assessing mitigation potentials (Brand-Correa and Steinberger 
2017; Mastrucci and Rao 2017; Rao and Min 2018a; Mastrucci 
and Rao 2019; Baltruszewicz et  al. 2021). Arguably, demand-side 
interventions often operate institutionally or in terms of restoring 
natural functioning and have so far been politically sidelined but 
COVID-19 revealed interesting perspectives (Box 5.2). Such demand-
side solutions also support near-term goals towards climate change 
mitigation and reduce the need for politically challenging high global 
carbon prices (Méjean et al. 2019) (Box 5.11). The well-being focus emphasises equity and universal need satisfaction, compatible with 
progress towards meeting the Sustainable Development Goals 
(SDGs) (Lamb and Steinberger 2017).
The requisites for well-being include collective and social interactions

as well as consumption-based material inputs. Moreover, rather than 
material inputs per se, people need and demand services for dignified 
survival, sustenance, mobility, communication, comfort and material 
well-being (Nakićenović et al. 1996b; Johansson et al. 2012; Creutzig 
et al. 2018). These services may be provided in many different context-
specific ways using physical resources (biomass, energy, materials, 
etc.) and available technologies (e.g.,  cooking tools, appliances). 
Here we understand demand as demand for services (often requiring 
material input), with particular focus on services that are required for 
well-being (such as lighting, accessibility, shelter, etc.), and that are 
shaped by culturally and geographically differentiated social aspects, 
choice architectures and the built environment (infrastructures).
Focusing on demand for services broadens the climate solution space 
beyond technological switches confined to the supply side, to include

solutions that maintain or improve well-being related to nutrition, 
shelter and mobility while (sometimes radically) reducing energy and 
material input levels (Creutzig et  al. 2018; Cervantes Barron 2020; 
Baltruszewicz et al. 2021; Kikstra et al. 2021b). This also recognises 
that mitigation policies are politically, economically and socially 
more feasible, as well as more effective, when there is a  two-way 
alignment between climate action and well-being (OECD 2019a). 
There is medium evidence and high agreement that well-designed 
Rao et al. 2019b; Millward-Hopkins et al. 2020; Kikstra et al. 2021b), 
with high and/or improved quality of life (Max-Neef 1995; Vogel et al. 
2021) and improved levels of happiness (Easterlin et  al. 2010) and 
sustainable human development (Gadrey and Jany-Catrice 2006;

Arrow et al. 2013; Dasgupta and Dasgupta 2017). While demand for 
services is high as development levels increase, and related emissions 
are growing in many countries (Yumashev et al. 2020; Bamisile et al. 
2021), there is also evidence that provisioning systems delink services 
provided from emissions (Conte Grand 2016; Patra et  al. 2017; 
Kavitha et  al. 2020). Various mitigation strategies, often classified 
into Avoid-Shift-Improve (ASI) options, effectively reduce primary 
energy demand and/or material input (Haas et al. 2015; Haberl et al. 
2017; Samadi et al. 2017; Hausknost et al. 2018; Haberl et al. 2019; 
Van den Berg et al. 2019; Ivanova et al. 2020). Users’ participation in 
decisions about how services are provided, not just their technological 
feasibility, is an important determinant of their effectiveness and 
sustainability (Whittle et al. 2019; Vanegas Cantarero 2020).
Sector-specific mitigation approaches (Chapters 6–11) emphasise

the potential of mitigation via improvements in energy- and 
materials-efficient manufacturing (Gutowski et  al. 2013; Gramkow 
and Anger-Kraavi 2019; Olatunji et al. 2019; Wang et al. 2019), new 
product design (Fischedick et  al. 2014), energy-efficient buildings 
(Lucon et al. 2014), shifts in diet (Bajželj et al. 2014; Smith et al. 2014), 
transport infrastructure design (Sims et al. 2014), and compact urban 
forms (Seto et  al. 2014). In this chapter, service-related mitigation 
strategies are categorised as ‘Avoid’, ‘Shift’, or ‘Improve’ options to

509
Demand, Services and Social Aspects of Mitigation Chapter 55show how mitigation potentials, and social groups who can deliver 
them, are much broader than usually considered in traditional sector-
specific presentations. ASI originally arose from the need to assess 
the staging and combinations of inter-related mitigation options 
in the provision of transportation services (Hidalgo and Huizenga 
2013). In the context of transportation services, ASI seeks to mitigate 
emissions through  avoiding as much transport service demand as 
possible (e.g., through telework to eliminate commutes, mixed-use 
urban zoning to shorten commute distances), shifting remaining 
demand to more efficient modes (e.g.,  bus rapid transit replacing 
passenger vehicles), and improving the carbon intensity of modes 
utilised (e.g., electric buses powered by renewables) (Creutzig et al. 
2016a). This chapter summarises ASI options and potentials across

sectors and generalises the definitions. ‘Avoid’ refers to all mitigation 
options that reduce unnecessary (in the sense of being not required 
to deliver the desired service output) energy consumption by 
redesigning service provisioning systems; ‘Shift’ refers to the switch 
to already existing competitive efficient technologies and service 
provisioning systems; and ‘Improve’ refers to improvements in 
efficiency in existing technologies. The Avoid-Shift-Improve framing 
operates in three domains: Socio-cultural, where social norms, 
culture, and individual choices play an important role – a category 
especially, but not only, relevant for ‘Avoid’ options; Infrastructure, 
which provides the cost and benefit landscape for realising options 
and is particularly relevant for ‘Shift’ options; and Technologies, 
especially important for the ‘Improve’ options.
‘Avoid’, ‘Shift’, and ‘Improve’ choices will be made by individuals and

households, instigated by salient and respected role models and novel 
social norms, but will require support by adequate infrastructures designed by urban planners and building and transport professionals, 
corresponding investments, and a  political culture supportive of 
mitigation action. This is particularly true for many ‘Avoid’ and ‘Shift’ 
decisions that are difficult because they encounter psychological 
barriers of breaking routines, habits and imagining new lifestyles 
and the social costs of not conforming to society (Kaiser 2006). 
Simpler ‘Improve’ decisions like energy efficiency investments, on 
the other hand, can be triggered and sustained by traditional policy 
instruments, complemented by behavioural nudges.
A key concern about climate change mitigation policies is that 
they may reduce quality of life. Based on growing literature, in 
this chapter we adopt the concept of decent living standards (DLS,

explained further in relation to other individual and collective well-
being measures and concepts in the Social Science Primer, Chapter 5 
Supplementary Material I) as a universal set of service requirements 
essential for achieving basic human well-being. DLS includes the 
dimensions of nutrition, shelter, living condition, clothing, health care, 
education, and mobility (Frye et al. 2018; Rao and Min 2018b). DLS 
provides a fair, direct way to understand the basic low-carbon energy 
needs of society and specifies the underlying minimum material and 
energy requirements. This chapter also comprehensively assesses 
related well-being metrics that result from demand-side action, 
observing overall positive effects (Section 5.3). Similarly, ambitious 
low-emissions demand-side scenarios suggest that well-being 
could be maintained or improved while reducing global final energy 
demand, and some current literature estimates that it is possible

to meet decent living standards for all within the 2°C warming 
window (Grubler et al. 2018; Burke 2020; Keyßer and Lenzen 2021) 
(Section 5.4). A key concern here is how to blend new technologies 
BECCS400 Gt C-budget
AfforestationDecarbonisation
01002003004005006007008009001000
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10,000Energy demand (avg. 2020–2100) EJ yr–1
Cumulative emission reduction (2020–2100) GtCO 2SSP5
SSP2
SSP1
LED
Figure 5.1 | Low Energy Demand Scenario needs no BECCS and needs less decarbonisation effort.  Dependence of the size of the mitigation effort to reach a 1.5°C 
climate target (cumulative GtCO 2 emission reduction 2020–2100 by option) as a function of the level of energy demand (average global final energy demand 2020–2100 in 
EJ yr –1) in baseline and corresponding 1.5°C scenarios (1.9 W m–2 radiative forcing change) based on the IPCC Special Report on Global Warming of 1.5°C (data obtained from

the Scenario Explorer database, LED baseline emission data obtained from authors). In this figure an example of remaining carbon budget of 400 Gt has been taken from Rogelj 
et al. (2019) for illustrative purposes. 400 Gt is also the number given in Table SPM.2 (IPCC 2021, p. 29) for a probability of 67% to limit global warming to 1.5°C.

510
Chapter 5 Demand, Services and Social Aspects of Mitigation 5with social change to integrate Improving ways of living, Shifting 
modalities and Avoiding certain kinds of emissions altogether 
(Section 5.6).
Social practice theory emphasises that material stocks and social 
relations are key in forming and maintaining habits (Reckwitz 2002; 
Haberl et al. 2021). This chapter reflects these insights by assessing 
the role of infrastructures and social norms in GHG emission-intensive 
or low-carbon lifestyles (Section 5.4).
A core operational principle for sustainable development is equitable 
access to services to provide well-being for all, while minimising 
resource inputs and environmental and social externalities/trade-
offs, underpinning the Sustainable Development Goals (Princen 
2003; Lamb and Steinberger 2017; Dasgupta and Dasgupta 2017). 
Sustainable development is not possible without changes in consumption patterns within the widely recognised constraints of

planetary boundaries, resource availability, and the need to provide 
decent living standards for all (Langhelle 2000; Toth and Szigeti 
2016; O’Neill et  al. 2018). Inversely, reduced poverty and higher 
social equity offer opportunities for delinking demand for services 
from emissions, for example via more long-term decision-making 
after having escaped poverty traps and by reduced demand for 
non-well-being-enhancing status consumption (Nabi et  al. 2020; 
Ortega-Ruiz et al. 2020; Parker and Bhatti 2020; Teame and Habte 
2020) (Section 5.3).
Throughout this chapter we discuss how people can realise various 
opportunities to reduce GHG emission-intensive consumption 
(Sections 5.2 and 5.3), and act in various roles (Section  5.4), 
within an enabling environment created by policy instruments and 
infrastructure that build on social dynamics (Section 5.6).
Box 5.1 | Bibliometric Foundation of Demand-side Climate Change Mitigation

A bibliometric overview of the literature found 99,065 academic peer-reviewed papers identified with 34 distinct search queries 
addressing relevant content of this chapter (Creutzig et al. 2021a). The literature is growing rapidly (15% yr –1) and the literature body 
assessed in the AR6 period (2014–2020) is twice as large as all literature published before.
Box 5.1, Figure 1 | Map of the literature on demand, services and social aspects of climate change mitigation.  Dots show document positions obtained 
by reducing the 60-dimensional topic scores to two dimensions aiming to preserve similarity in overall topic score. The two axes therefore have no direct interpretation 
but represent a reduced version of similarities between documents across 60 topics. Documents are coloured by query category. Topic labels of the 24 most relevant

topics are placed in the centre of each of the large clusters of documents associated with each topic. % value in caption indicates the proportion of studies in each 
‘relevance’ bracket. Source: reused with permission from Creutzig et al. (2021a).

511
Demand, Services and Social Aspects of Mitigation Chapter 55Section 5.2 provides evidence on the links among mitigation and well-
being, services, equity, trust, and governance. Section 5.3 quantifies 
the demand-side opportunity space for mitigation, relying on the 
Avoid-Shift-Improve framework. Section  5.4 assesses the relevant 
contribution of different parts of society to climate change mitigation. 
Section  5.5 evaluates the overall dynamics of social transition processes while Section 5.6 summarises insights on governance and 
policy packages for demand-side mitigation and well-being. A Social 
Science Primer (Chapter  5 Supplementary Material I) defines and 
discusses key terms and social science concepts used in the context 
of climate change mitigation.Box 5.1 (continued)
A large part of the literature is highly repetitive and/or includes no concepts or little quantitative or qualitative data of relevance to

this chapter. For example, a systematic review on economic growth and decoupling identified more than 11,500 papers treating this 
topic, but only 834 of those, that is, 7%, included relevant data (Wiedenhofer et al. 2020). In another systematic review, assessing 
quantitative estimates of consumption-based solutions (Ivanova et al. 2020), only 0.8% of papers were considered after consistency 
criteria were enforced. Altogether, we relied on systematic reviews wherever possible. Other important papers were not captured by 
systematic reviews but are included in this chapter through expert judgement. Based on topical modelling and relevance coding of 
resulting topics, the full literature body can be mapped into two dimensions, where spatial relationships indicate topical distance 
(Box  5.1, Figure  1). The interpretation of topics demonstrates that the literature organises in four clusters of high relevance for

demand-side solutions (housing, mobility, food, and policy), whereas other clusters (nature, energy supply) are relatively less relevant. 
Box 5.2 | COVID-19, Service Provisioning and Climate Change Mitigation
There is now high evidence and high agreement that the COVID-19 pandemic has increased the political feasibility of large-scale 
government actions to support the services for provision of public goods, including climate change policies. Many behavioural changes 
due to COVID-19 reinforce sufficiency and emphasis on solidarity, economies built around care, livelihood protection, collective action, 
and basic service provision, linked to reduced emissions.
COVID-19 led to direct and indirect health, economic, and confinement-induced hardships and suffering, mostly for the poor, and 
reset habits and everyday behaviours of the well-off too, enabling a reflection on the basic needs for a good life. Although COVID-19

and climate change pose different kinds of threats and therefore elicit different policies, there are several lessons from COVID-19 for 
advancing climate change mitigation (Klenert et al. 2020; Manzanedo and Manning 2020; Stark 2020). Both crises are global in scale, 
requiring holistic societal response; governments can act rapidly, and delay in action is costly (Bouman et al. 2020a; Klenert et al. 2020). 
The pandemic highlighted the role of individuals in collective action and many people felt morally compelled and responsible to act for 
others (Budd and Ison, 2020). COVID-19 also taught the effectiveness of rapid collective action (physical distancing, wearing masks, 
etc.) as contributions to the public good. The messaging about social distancing, wearing masks and handwashing during the pandemic 
called attention to the importance of effective public information (e.g., also about reducing personal carbon footprints), recognising that

rapid pro-social responses are driven by personal and socio-cultural norms (Bouman et al. 2020a; Sovacool et al. 2020a). In contrast, low 
trust in public authorities impairs the effectiveness of policies and polarises society (Bavel et al. 2020; Hornsey 2020).
During the shutdown, emissions declined relatively most in aviation, and absolutely most in car transport (Le Quéré et al. 2020, 
Sarkis et al. 2020), and there were disproportionally strong reductions in GHG emissions from coal (Bertram et al. 2021) (Chapter 2). 
At their peak, CO 2 emissions in individual countries decreased by 17% on average (Le Quéré et al. 2020). Global energy demand was 
projected to drop by 5% in 2020, energy-related CO 2 emissions by 7%, and energy investment by 18% (IEA 2020a). COVID-19 shock 
and recovery scenarios project final energy demand reductions of 1–36 EJ yr−1 by 2025 and cumulative CO 2 emission reductions of

14–45 GtCO 2 by 2030 (Kikstra et al. 2021a). Plastics use and waste generation increased during the pandemic (Klemeš et al. 2020; 
Prata et al. 2020). Responses to COVID-19 had important connections with energy demand and GHG emissions due to quarantine and 
travel restrictions (Sovacool et al. 2020a). Reductions in mobility and economic activity reduced energy use in sectors such as industry 
and transport, but increased energy use in the residential sector (Diffenbaugh et al. 2020). COVID-19 induced behavioural changes 
that may translate into new habits, some beneficial and some harmful for climate change mitigation. New digitally-enabled service 
accessibility patterns (videoconferencing, telecommuting) played an important role in sustaining various service needs while avoiding 
demand for individual mobility. However, public transit lost customers to cars, personalised two wheelers, walking and cycling, while

suburban and rural living gained popularity, possibly with long-term consequences. Reduced air travel, pressures for more localised

512
Chapter 5 Demand, Services and Social Aspects of Mitigation 55.2 Services, Well-being and Equity 
in Demand-side Mitigation
As outlined in section 5.1, mitigation, equity and well-being go hand 
in hand to motivate actions. Global, regional, and national actions 
and policies that advance inclusive well-being and build social trust 
strengthen governance. There is high evidence and high agreement that 
demand-side measures cut across all sectors, and can bring multiple 
benefits (Mundaca et al. 2019; Wachsmuth and Duscha 2019; Geels 
2020; Niamir et al. 2020b; Garvey et al. 2021; Roy et al. 2021). Since 
effective demand requires affordability, one of the necessary conditions 
for acceleration of mitigation through demand-side measures is wide 
and equitable participation from all sectors of society. Low-cost low-
emissions technologies, supported by institutions and government 
policies, can help meet service demand and advance both climate

and well-being goals (Steffen et  al. 2018a; Khosla et  al. 2019). This 
section introduces metrics of well-being and their relationship to GHG 
emissions, and clarifies the concept of service provisioning.
5.2.1 Metrics of Well-being and their Relationship 
to Greenhouse Gas Emissions
There is high evidence and high agreement in the literature that 
human well-being and related metrics provide a societal perspective which is inclusive, compatible with sustainable development, and 
generates multiple ways to mitigate emissions. Development targeted 
to basic needs and well-being for all entails less carbon intensity than 
GDP-focused growth (Rao et al. 2014; Lamb and Rao 2015).
Current socioeconomic systems are based on high-carbon economic 
growth and resource use (Steffen et  al. 2018b). Several systematic 
reviews confirm that economic growth is tightly coupled with 
increasing CO 2 emissions (Ayres and Warr 2005; Tiba and Omri

2017; Mardani et  al. 2019; Wiedenhofer et  al. 2020) although the 
level of emissions depends on inequality (Baležentis et al. 2020; Liu 
et al. 2020b), and on geographic and infrastructural constraints that 
force consumers to use fossil fuels (Pottier et  al. 2021). Different 
patterns emerge in the causality of the energy–growth nexus: 
(i) energy consumption causes economic growth; (ii) growth causes 
energy consumption; (iii) bidirectional causality; and (iv) no significant 
causality (Ozturk 2010). In a systematic review, Mardani et al. (2019) 
found that in most cases, energy use and economic growth have 
a  bidirectional causal effect, indicating that as economic growth 
increases, further CO 2 emissions are stimulated at higher levels; in turn, 
measures designed to lower GHG emissions may reduce economic 
growth. However, energy substitution and efficiency gains may offer 
opportunities to break the bidirectional dependency (Komiyama

2014; Brockway et  al. 2017; Shuai et  al. 2019). Worldwide trends 
reveal that at best only relative decoupling (resource use grows at Box 5.2 (continued)
food and manufacturing supply chains (Hobbs 2020; Nandi et al. 2020; Quayson et al. 2020), and governments’ revealed willingness 
to make large-scale interventions in the economy also reflect sudden shifts in service provisions and GHG emissions, some likely to be 
lasting (Aldaco et al. 2020; Bilal et al. 2020; Boyer 2020; Hepburn et al. 2020; Norouzi et al. 2020; Prideaux et al. 2020; Sovacool et al. 
2020a). If changes in some preference behaviours, for example for larger homes and work environments to enable home working and 
online education, lead to sprawling suburbs or gentrification with linked environmental consequences, this could translate into long-
term implications for climate change (Beaunoyer et al. 2020; Diffenbaugh et al. 2020). Recovering from the pandemic by adopting low

energy demand practices – embedded in new travel, work, consumption and production behaviour and patterns – could reduce carbon 
prices for a 1.5°C consistent pathway by 19%, reduce energy supply investments until 2030 by USD1.8 trillion, and lessen pressure on 
the upscaling of low-carbon energy technologies (Kikstra et al. 2021a).
COVID-19 drove hundreds of millions of people below poverty thresholds, reversing decades of poverty reduction accomplishments (Krieger 
2020; Mahler et al. 2020; Patel et al. 2020; Sumner et al. 2020) and raising the spectre of intersecting health and climate crises that are 
devastating for the most vulnerable (Flyvbjerg 2020; Phillips et al. 2020). Like those of climate change, pandemic impacts fall heavily on 
disadvantaged groups, exacerbate the uneven distribution of future benefits, amplify existing inequities, and introduce new ones (Beaunoyer

et al. 2020; Devine-Wright et al. 2020). Addressing such inequities is a positive step towards the social trust that leads to improved climate 
policies as well as individual actions. Increased support for care workers and social infrastructures within a solidarity economy is consistent 
with lower-emission economic transformation (Shelley 2017; Di Chiro 2019; Pichler et al. 2019; Smetschka et al. 2019).
Fiscally, the pandemic may have slowed the transition to a sustainable energy world: governments redistributed public funding to combat 
the disease, adopted austerity and reduced capacity. Of nearly 300 policies implemented to counteract the pandemic, the vast majority are 
related to rescue, including worker and business compensation, and only 4% of these focus on green policies with potential to reduce GHG 
emissions in the long term; some rescue policies also assist emissions-intensive business (Hepburn et al. 2020; Leach et al. 2021). However,

climate investments can double as the basis of the COVID-19 recovery (Stark 2020), with policies focused on both economic multipliers and 
climate impacts, such as clean physical infrastructure, natural capital investment, clean research and development (R&D) and education 
and training (Hepburn et al. 2020). This requires attention to investment priorities, including often-underprioritised social investment, given 
how inequality intersects with, and is a recognised core driver of, environmental damage and climate change (Millward-Hopkins et al. 2020).

513
Demand, Services and Social Aspects of Mitigation Chapter 55a slower pace than GDP) was the norm during the twentieth century 
(Jackson 2009; Krausmann et  al. 2009; Ward et  al. 2016; Jackson 
2016), while absolute decoupling (when material use declines as GDP 
grows) is rare, observed only during recessions or periods of low or 
no economic growth (Heun and Brockway 2019; Hickel and Kallis 
2019; Vadén et al. 2020; Wiedenhofer et al. 2020). Recent trends in 
OECD countries demonstrate the potential for absolute decoupling of 
economic growth not only from territorial but also from consumption-
based emissions (Le Quéré et al. 2019), albeit at scales insufficient for 
mitigation pathways (Vadén et al. 2020) (Chapter 2).
Energy demand and demand for GHG-intensive products increased 
from 2010 until 2020 across all sectors and categories. 2019 witnessed 
a reduction in energy demand growth rate to below 1% and 2020 
an overall decline in energy demand, with repercussions for energy

supply disproportionally affecting coal via merit order effects (Bertram 
et al. 2021) (Cross-Chapter Box 1 in Chapter 1). There was a slight 
but significant shift from high-carbon beef consumption to medium-
carbon intensive poultry consumption. Final energy use in buildings 
grew from 118 EJ in 2010 to around 128 EJ in 2019 (increased about 
8%). The highest increase was observed in non-residential buildings, 
with a 13% increase against 8% in residential energy demand (IEA 
2019a). While electricity accounted for one-third of building energy 
use in 2019, fossil fuel use also increased at a marginal annual average 
growth rate of 0.7% since 2010 (IEA 2020a). Energy-related CO 2 
emissions from buildings have risen in recent years after flattening 
between 2013 and 2016. Direct and indirect emissions from electricity 
and commercial heat used in buildings rose to 10 GtCO 2 in 2019, the 
highest level ever recorded. Several factors have contributed to this

rise, including growing energy demand for heating and cooling with 
rising air conditioner ownership and extreme weather events. A critical 
issue remains how comfortable people feel with temperatures 
they will be exposed to in the future and this depends on physical, 
psychological and behavioural factors (Singh et al. 2018; Jacobs et al. 
2019). Literature now shows high evidence and high agreement  
around the observation that policies and infrastructure interventions 
that lead to change in human preferences are more valuable for 
climate change mitigation. In economics, welfare evaluations are 
predominantly based on the preference approach. Preferences 
are  typically assumed to be fixed, so that only changes in relative 
prices will reduce emissions. However, as decarbonisation is a societal 
transition, individuals’ preferences do shift and this can contribute 
to climate change mitigation (Gough 2015). Even if preferences are

assumed to change in response to policy, it is nevertheless possible 
to evaluate policy, and demand-side solutions, by approaches to well-
being and welfare that are based on deeper concepts of preferences 
across disciplines (Roy and Pal 2009; Fleurbaey and Tadenuma 2014; 
Komiyama 2014; Dietrich and  List 2016; Mattauch and Hepburn 
2016). In cases of past societal transitions, such as smoking reduction, 
there is evidence that societies guided the processes of shifting 
preferences, and values changed along with changing relative prices 
(Nyborg  and  Rege 2003; Stuber et  al. 2008; Brownell and Warner 
2009). Further evidence on changing preferences in consumption 
choices pertinent to decarbonisation includes Grinblatt et al. (2008) 
and Weinberger and Goetzke (2010) for mobility; Erb et  al. (2016), 
Muller  et  al. (2017),  and Costa and Johnson (2019) for diets; and

Baranzini et al. (2017) for solar panel uptake. If individuals’ preferences and values change during a transition to the low-carbon economy, then 
this overturns conclusions on what count as adequate or even optimal 
policy responses to climate change mitigation in economics (Jacobsen 
et al. 2012; Schumacher 2015; Dasgupta et al. 2016; Daube and Ulph 
2016; Ulph and Ulph 2021). In particular, if policy instruments, such as 
awareness campaigns, infrastructure development or education, can 
change people’s preferences, then policies or infrastructure provision – 
socially constrained by deliberative decision making – which change 
both relative prices and preferences, are more valuable for mitigation 
than previously thought (Creutzig et al. 2016b; Mattauch et al. 2016; 
Mattauch et  al. 2018). The provisioning context of human needs is 
participatory, so transformative mitigation potential arises from social 
as well as technological change (Lamb and Steinberger 2017). Many

dimensions of well-being and ‘basic needs’ are social, not individual, in 
character (Schneider 2016), so extending well-being and DLS analysis 
to emissions also involves understanding individual situations in social 
contexts. This includes building supports for collective strategies to 
reduce emissions (Chan et al. 2019), going beyond individual consumer 
choice. Climate policies that affect collective behaviour fairly are the 
most acceptable policies across political ideologies (Clayton 2018); 
thus collective preferences for mitigation are synergistic with evolving 
policies and norms in governance contexts that reduce risk, ensure 
social justice and build trust (Atkinson et  al. 2017; Cramton et  al. 
2017; Milkoreit 2017; Tvinnereim et al. 2017; Smith and Reid 2018; 
Carattini et al. 2019).
Because of data limitations, which can make cross-country 
comparisons difficult, health-based indicators and in particular life

expectancy (Lamb et  al. 2014) have sometimes been proposed as 
quick and practical ways to compare local or national situations, 
climate impacts, and policy effects (Decancq et al. 2009; Sager 2017; 
Burstein et al. 2019). A number of different well-being metrics are 
valuable in emphasising the constituents of what is needed for 
a  decent life in different dimensions (Lamb and Steinberger 2017; 
Porter et al. 2017; Smith and Reid 2018). The SDGs overlap in many 
ways with such indicators, and the data needed to assess progress 
in meeting the SDGs is also useful for quantifying well-being (Gough 
2017). For the purposes of this chapter, indicators directly relating 
GHG emissions to well-being for all are particularly relevant.
Well-being can be categorised either as ‘hedonic’ or ‘eudaimonic’. 
Hedonic well-being is related to a  subjective state of human 
motivation, balancing pleasure over pain, and has gained influence

in psychology assessing ‘subjective well-being’, assuming that the 
individual is motivated to enhance personal freedom, self-preservation 
and enhancement (Sirgy 2012; Brand-Correa and Steinberger 2017; 
Lamb and Steinberger 2017; Ganglmair-Wooliscroft and Wooliscroft 
2019). Eudaimonic well-being focuses on the individual in the broader 
context, associating happiness with virtue (Sirgy 2012), allowing for 
the creation of social institutions and political systems and considering 
their ability to enable individuals to flourish. Eudaimonic analysis 
supports numerous development approaches (Fanning and O’Neill 
2019) such as the capabilities (Sen 1985), human needs (Doyal and 
Gough 1991; Max-Neef et al. 1991) and models of psychosocial well-
being (Ryan and Deci 2001). Measures of well-being differ somewhat 
in developed and developing countries (Sulemana et al. 2016; Ng and 
Diener 2019); for example, food insecurity, associated everywhere

514
Chapter 5 Demand, Services and Social Aspects of Mitigation 5with lower subjective well-being, is more strongly associated with 
poor subjective well-being in more-developed countries (Frongillo 
et al. 2019); in wealthier countries, the relationship between living in 
rural areas is less strongly associated with negative well-being than 
in less-developed countries (Requena 2016); and income inequality 
is negatively associated with subjective well-being in developed 
countries, but positively so in less-developed countries (Ngamaba 
et al. 2018). This chapter connects demand-side climate mitigation 
options to multiple dimensions of well-being, going beyond the 
single dimensional metric of GDP which is at the core of IAMs. Many 
demand side-mitigation solutions generate positive and negative 
impacts on wider dimensions of human well-being which are not 
always quantifiable (medium evidence, medium agreement).
5.2.1.1 Services for Well-being

Well-being needs are met through services. Provision of services 
associated with low energy demand is a key component of current 
and future efforts to reduce carbon emissions. Services can be 
provided in various culturally-appropriate ways, with diverse 
climate implications. There is high evidence and high agreement 
in the literature that many granular service provision systems can 
make ‘demand’ more flexible, provide new options for mitigation, 
support access to basic needs, and enhance human well-being. 
Energy services offer an important lens to analyse the relationship 
between energy systems and human well-being (Jackson and 
Papathanasopoulou 2008; Druckman and Jackson 2010; Mattioli 
2016; Walker et al. 2016; Fell 2017; Brand-Correa et al. 2018; King 
et al. 2019; Pagliano and Erba 2019; Whiting et al. 2020). Direct and 
indirect services provided by energy, rather than energy itself, deliver 
well-being benefits (Kalt et al. 2019). For example, illumination and

transport are intermediary services in relation to education, health 
care, meal preparation, sanitation, and so on, which are basic human 
needs. Sustainable consumption and  production revolve around 
‘doing more and better with the same’ and thereby increasing well-
being from economic activities ‘by reducing resource use, degradation 
and pollution along the whole lifecycle, while increasing quality 
of life’ (UNEP 2010). Although energy is required for delivering 
human development by supporting access to basic needs (Lamb 
and Rao 2015; Lamb and Steinberger 2017), a reduction in primary 
energy use and/or shift to low-carbon energy, if associated with 
the maintenance or improvement of services, can not only ensure 
better environmental quality but also directly enhance well-being 
(Roy et al. 2012). The correlation between human development and 
emissions is not necessarily coupled in the long term, which implies

there is a need to prioritise human well-being and the environment 
over economic growth (Steinberger et al. 2020). At the interpersonal 
and community levels, cultural specificities, infrastructure, norms, and 
relational behaviours differ (Box 5.3). For example, demand for space 
heating and cooling depends on building materials and designs, 
urban planning, vegetation, clothing and social norms as well as 
geography, incomes, and outside temperatures (Brand-Correa et al. 
2018; Campbell et al. 2018; Ivanova et al. 2018; IEA 2019b; Dreyfus 
et al. 2020). In personal mobility, different variable needs satisfiers 
(e.g.,  street space allocated to cars, buses or bicycles) can help 
satisfy human needs, such as accessibility to jobs, health care, and 
education. Social interactions and normative values play a  crucial role in determining energy demand. Hence, demand-side and service-
oriented mitigation strategies are most effective if geographically

and culturally differentiated (Niamir et al. 2020a).
Decent living standards (DLS) serves as a socio-economic benchmark 
as it views human welfare not in relation to consumption but 
rather in terms of services which together help meet human needs 
(e.g.,  nutrition, shelter, health, etc.), recognising that these service 
needs may be met in many different ways (with different emissions 
implications) depending on local contexts, cultures, geography, 
available technologies, social preferences, and other factors. Therefore, 
one key way of thinking about providing well-being for all with low 
carbon emissions centres around prioritising ways of providing 
services for DLS in a  low-carbon way (including choices of needs 
satisfiers, and how these are provided or made accessible). They may 
be supplied to individuals or groups or communities, both through 
formal markets and/or informally, for example by collaborative

work, in coordinated ways that are locally appropriate, designed and 
implemented in accordance with overlapping local needs.
The most pressing DLS service shortfalls, as shown in Figure 5.2, lie 
in the areas of nutrition, mobility, and communication. Gaps in regions 
such as Africa and the Middle East are accompanied by current levels 
of service provision in the highly industrialised countries at much 
higher than DLS levels for the same three service categories. The lowest 
population quartile by income worldwide faces glaring shortfalls 
in housing, mobility, and nutrition. Meeting these service needs 
using low-emissions energy sources is a top priority. Reducing GHG 
emissions associated with high levels of consumption and material 
throughput by those far above DLS levels has potential to address 
both emissions and inequality in energy and emission footprints 
(Otto et al. 2019). This, in turn, has further potential benefits; under

the conditions of ‘fair’ income reallocation to public services, this can 
reduce national carbon footprint by up to 30% while allowing the 
consumption of those at the bottom to increase (Millward-Hopkins 
and Oswald 2021). The challenge then is to address the upper limits of 
consumption. When consumption only just supports the satisfaction 
of basic needs, any decrease causes deficiencies in human-need 
satisfaction. This is quite unlinke the case of consumption that 
exceeds the limits of basic needs, in which deprivation causes 
a  subjective discomfort (Brand-Correa et  al. 2020). Therefore, to 
collectively remain within environmental limits, the establishment of 
minimum and maximum standards of consumption, or sustainable 
consumption corridors, (Wiedmann et al. 2020) has been suggested, 
depending on the context. In some countries, carbon-intensive ways 
of satisfying human needs have been locked-in, for example via car-

dependent infrastructures (Jackson and Papathanasopoulou 2008; 
Druckman and Jackson 2010; Mattioli 2016; King et al. 2019), and 
both infrastructure reconfiguration and adaptation are required to 
organise need satisfaction in low-carbon ways (see also Section 10.2).
There is high evidence and high agreement in the literature that vital 
dimensions of human well-being correlate with consumption, but 
only up to a threshold. High potential for mitigation lies in using low-
carbon energy for new basic needs satisfaction while cutting emissions 
of those whose basic needs are already met (Grubler et  al.  2018; 
Rao and Min 2018b; Rao et al. 2019b; Millward-Hopkins et al. 2020;

515
Demand, Services and Social Aspects of Mitigation Chapter 550 1000 2000 3000Food cal day–1 cap–1
0 20 40 60m2 cap–1Housing
0 20 40Pass-km day–1 cap–1MobilityCommunication
(internet)Device access cap–1
Decent Living Standards (DLS) 
threshold (Rao et al. 2019)Global average
0 10 20 30 
m2 cap–1
Top 10%
Bottom 10%
Netherlands IndiaTop 25%
Bottom 25%Housing
0 20 40Mobility
0 1000 2000cal day–1 cap–1 
Pass-km day–1 cap–1Food (other) Food (meat) 0 250 500 750cal/day/capita
Africa and 
Middle EastAsia and
Developing PaciﬁcDeveloped
CountriesEastern Europe and 
West-Central AsiaLatin America 
and CaribbeanGlobalGJ cap–1 yr–1a. Across country heterogeneity (annual 
average per capita energy consumption).b. Within country heterogeneity in service levels as a function of income differences for 
the Netherlands (bottom and 10% of incomes) and India (bottom and top 25% of incomes).
c. Globally averaged “decent living energy” (DLE) per capita scenario in 2050 by region.

The major contributors to DLE are nutrition and mobility at ~3 GJ cap–1 yr–1 each.*Top 10%
Bottom 10%
Bottom 25%
Top 10%
Bottom 10%
Bottom 25%
Top 10%
Bottom 10%
Bottom 25%
Asia and Developing Paciﬁc
Africa and Middle EastDeveloped Countries
Latin America and CaribbeanEastern Europe and West-Central Asia
Education
HealthcareOther (power supply and retail 
and freight activities)
Communication and informationMobilityClothing
Hygiene
Shelter and living conditions
Nutrition
*Calculation is based on 
  Millward-Hopkins et al. (2020)0.0 0.2 0.4
Figure 5.22 | Heterogeneity in access to and availability of services for human well-being within and across countries.  Panel (a)  Across-country differences 
in panel (a) food (meat and other), (b) housing, (c) mobility, (d) communication (mobile phones and high-speed internet access). Variation in service levels across countries within

a region is shown as error bars (black). Values proposed as decent standards of living threshold (Rao et al. 2019b) are shown as red dashed lines. Global average values are 
shown as blue dashed lines. Panel (b) Within-country differences in service levels as a function of income differences for the Netherlands (bottom and top 10% of incomes) and 
India (bottom and top 25% of incomes) (Grubler et al. 2012b) (data update 2016). Panel (c)  Decent living energy (DLE) scenario using global, regional and DLS dimensions for 
final energy consumption at 149 EJ (15.3 GJ cap–1 yr –1) in 2050 (Millward-Hopkins et al. 2020), requiring advanced technologies in all sectors and radical demand-side changes. 
Values are shown for five world regions based on the AR6 WGIII Regional breakdown. We use passenger kilometres per day per capita (km day–1 cap–1) as a metric for mobility

only as a reference, however, transport and social inclusion research suggest the aim is to maximise accessibility and not travel levels or travelled distance.
2 The countries and areas classification in this figure deviate from the standard classification scheme adopted by WGIII as set out in Annex II, section 1.

516
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Keyßer and Lenzen 2021). Decent living standards indicators serve as 
tools to clarify this socio-economic benchmark and identify well-being 
for all compatible mitigation potential. Energy services provisioning 
opens up avenues of efficiency and possibilities for decoupling energy 
services demand from primary energy supply, while needs satisfaction 
leads to the analysis of the factors influencing the energy demand 
associated with the achievement of well-being (Brand-Correa and 
Steinberger 2017; Tanikawa et  al. 2021). Vital dimensions of well-
being correlate with consumption, but only up to a threshold: decent 
living energy thresholds range from about 13 to 18.4 GJ cap–1 yr –1 of 
final energy consumption but the current consumption ranges from 
under 5 GJ cap–1 yr –1 to over 200 GJ cap–1 yr–1 (Millward-Hopkins 
et  al. 2020), thus a  mitigation strategy that protects minimum

levels of essential-goods service delivery for DLS, but critically 
views consumption beyond the point of diminishing returns of 
needs satisfaction, is able to sustain well-being while generating 
emissions reductions (Goldemberg et  al. 1988; Jackson and Marks 
1999; Druckman and Jackson 2010; Girod and De Haan 2010; Vita 
et  al. 2019a; Baltruszewicz et  al. 2021). Such relational dynamics 
are relevant both within and between countries, due to variances 
in income levels, lifestyle choice (see also Section 5.4.4), geography, resource assets and local contexts. Provisioning for human needs 
is recognised as participatory and inter-relational; transformative 
mitigation potential can be found in social as well as technological 
change (Mazur and Rosa 1974; Goldemberg et al. 1985; Lamb and 
Steinberger 2017; O’Neill et al. 2018; Hayward and Roy 2019; Vita 
et al. 2019a). More equitable societies which provide DLS for all can

devote attention and resources to mitigation (Richards 2003; Dubash 
2013; Rafaty 2018; Oswald et  al. 2021). For further exploration of 
these concepts, see Chapter 5 Supplementary Material I.
5.2.2 Inequity in Access to Basic Energy 
Use and Services
5.2.2.1 Variations in Access to Needs-satisfiers 
for Decent Living Standards
There is very high evidence and very high agreement that globally, 
there are differences in the amount of energy that societies require 
to provide the basic needs for everyone. At present nearly one-
third of the world’s population are ‘energy poor’, facing challenges 
Energy use (GJ cap–1 yr–1  )Income category for 
Developing and Least 
Developed CountriesIncome category for 
Developed Developing Least Developed Country
0 25 50 75 100 125 150 175 200 225 250 275JapanUnited KingdomGermanyNorwayUnited States
BrazilChinaIndiaSouth AfricaMexico
SenegalBangladeshCambodiaEthiopiaNepalHighest
High
Middle
Low
Lowest
High
Middle
Low
Lowest

Figure 5.3 | Energy use per capita per year of three groups of countries ranked by socio-economic development and displayed for each country based 
on four or five different income groups (according to data availability) as well as geographical representation.  The final energy use for decent living standards 
(20–50 GJ cap–1 yr –1) (Rao et al. 2019b) is indicated in the blue column as a reference for global range, rather than dependent on each country. Source: data based on Oswald 
et al. (2020).

517
Demand, Services and Social Aspects of Mitigation Chapter 55in both access and affordability, that is, more than 2.6 billion 
people have little or no access to energy for clean cooking. About 
1.2 billion lack energy for cleaning, sanitation and water supply, 
lighting, and basic livelihood tasks (Sovacool and Drupady 2016; 
Rao and Pachauri 2017).The current per capita energy requirement 
to provide a  decent standard of living range from around 5 to 
200 GJ cap–1 yr –1 (Steckel et al. 2013; Lamb and Steinberger 2017; 
Rao et al. 2019b; Millward-Hopkins et al. 2020), which shows the 
level of inequality that exists; this depends on the context, such 
as geography, culture, infrastructure or how services are provided 
(Brand-Correa et  al. 2018) (Box  5.3). However, through efficient 
technologies and radical demand-side transformations, the final 
energy requirements for providing DLS by 2050 is estimated at 
15.3  GJ cap–1 yr –1 (Millward-Hopkins et  al. 2020). Recent DLS

estimates for Brazil, South Africa, and India are in the range between 
15 and 25 GJ cap–1 yr –1 (Rao et al. 2019b).The most gravely energy 
poor are often those living in informal settlements, particularly 
women, in sub-Saharan Africa and developing Asia, whose 
socially-determined responsibilities for food, water, and care are 
highly labour-intensive and made more intense by climate change (Guruswamy 2016; Wester et al. 2019). In Brazil, India and South 
Africa, where inequality is extreme (Alvaredo et al. 2018) mobility 
(51–60%), food production and preparation (21–27%) and housing 
(5–12%) dominate total energy needs (Rao et al. 2019b). Minimum 
requirements of energy use consistent with enabling well-being for 
all is between 20 and 50 GJ cap–1 yr –1 depending on context (Rao 
et al. 2019b). Inequality in access to and availability of services for 
human well-being varies in extreme degree across countries and 
income groups. In developing countries, the bottom 50% receive

about 10% of the energy used in land transport and less than 5% 
in air transport, while the top 10% use about 45% of the energy 
for land transport and around 75% for air transport (Oswald et al. 
2020). Within-country analysis shows that particular groups in 
China – women born in the rural West with disadvantaged family 
backgrounds – face unequal opportunities for energy consumption 
(Shi 2019). Figure 5.3 shows the wide variation across world regions 
in people’s access to some of the basic material prerequisites for 
meeting DLS, and variations in energy consumption, providing 
a starting point for comparative global analysis.
Box 5.3 | Inequities in Access to and Levels of End-use Technologies and Infrastructure Services
Acceleration in mitigation action needs to be understood from a societal perspective. Technologies, access and service equity factors

sometimes change rapidly. Access to technologies, infrastructures and products, and the services they provide, are essential for raising 
global living standards and improving human well-being (Alkire and Santos 2014; Rao and Min 2018b). Yet access to and levels 
of service delivery are distributed extremely inequitably as of now. How fast such inequities can be reduced by granular end-use 
technologies is illustrated by the cellphone (households with mobiles), comparing the situation between 2000 and 2018. In this 
eighteen-year period, cellphones changed from a very inequitably-distributed technology to one with almost universal access, bringing 
accessibility benefits especially to populations with very low disposable income and to those whose physical mobility is limited (Porter 
2016). Every human has the right to a dignified decent life, to live in good health and to participate in society. This is a daunting

challenge, requiring that in the next decade governments build out infrastructure to provide billions of people with access to a number 
of services and basic amenities in comfortable homes, nutritious food, and transit options (Rao and Min 2018b). For a long time, this 
challenge was thought to also be an impediment to developing countries’ participation in global climate mitigation efforts. However, 
recent research shows that this need not be the case (Millward-Hopkins et al. 2020; Rao et al. 2019b).
Several of the Sustainable Development Goals (SDGs) (UN 2015) deal with providing access to technologies and service infrastructures 
to the share of population so far excluded, showing that the UN 2030 Agenda has adopted a multidimensional perspective on poverty. 
Multidimensional poverty indices, such as the Social Progress Indicator and the Individual Deprivation Measure, go beyond income

and focus on tracking the delivery of access to basic services by the poorest population groups, both in developing countries (Fulton 
et al. 2009; Alkire and Santos 2014; Alkire and Robles 2017; Rao and Min 2018b), and in developed countries (Townsend 1979; 
Aaberge and Brandolini 2015; Eurostat 2018). At the same time, the SDGs, primarily SDG 10 on reducing inequalities within and 
among countries, promote a more equitable world, both in terms of inter- as well as intra-national equality.
Access to various end-use technologies and infrastructure services features directly in the SDG targets and among the indicators 
used to track their progress (UN 2015; UNESC 2017): Basic services in households (SDG 1.4.1), Improved water sources (SDG 6.1.1); 
Improved sanitation (SDG 6.1.2); Electricity (SDG 7.1.1); Internet – fixed broadband subscriptions (SDG 17.6.2); Internet – proportion

of population using (SDG 17.8.1). Transport (public transit, cars, mopeds or bicycles) and media technologies (mobile phones, TVs, 
radios, PCs, Internet) can be seen as proxies for access to mobility and communication, crucial for participation in society and the 
economy (Smith et al. 2015). In addition, SDG 10 is a more conventional income-based inequality goal, referring to income inequality 
(SDG 10.1), social, economic and political inclusion of all (SDG 10.2.), and equal opportunities and reduced inequalities of outcome 
(SDG 10.3).

518
Chapter 5 Demand, Services and Social Aspects of Mitigation 5
5.2.2.2 Variations in Energy Use
through equitable distribution, well-being for all can be assured at 
the lowest-possible energy consumption levels (Steinberger and 
Roberts 2010; Oswald et al. 2020) by reducing emissions related to 
consumption as much as possible, while assuring DLS for everyone 
(Annecke 2002; de Zoysa 2011; Ehrlich and Ehrlich 2013; Spangenberg 
2014; Toroitich and Kerber 2014; Kenner 2015; Toth and Szigeti 2016; 
Smil 2017; Otto et al. 2019; Baltruszewicz et al. 2021). For example, 
at similar levels of human development, per capita energy demand in the US was 63% higher than in Germany (Arto et al. 2016); those 
patterns are explained by context in terms of various climate, cultural 
and historical factors influencing consumption. Context matters even 
in within-country analysis, for example, electricity consumption in

the US shows that efficiency innovations do exert positive influence 
on savings of residential energy consumption, but the relationship 
is mixed; on the contrary, affluence (household income and home 
size) and context (geographical location) drive resource utilisation 
significantly (Adua and Clark 2019); affluence is central to any future 
prospect in terms of environmental conditions (Wiedmann et  al. 
2020). In China, inequality of energy consumption and expenditure Box 5.3 (continued)
Box 5.3, Figure 1 | International inequality in access and use of goods and services.  Upper panel: International Lorenz curves and Gini coefficients 
accounting for the share of population living in households without access (origin of the curves on the y-axis), multiple ownership not considered. Lower panel: Gini, 
number of people without access, access rates and coverage in terms of share of global population and number of countries included. *Reduced samples lead to

underestimation of inequality. A sample, for example, of around 80% of world population (taking the same 43 countries as for mobiles and cars) led to a lower Gini 
of around 0.48 (–0.04) for electricity. The reduced sample was kept for mobiles in 2018 to allow for comparability with 2000. Source: Zimm (2019).0102030405060708090100
0 10 20 30 40 50 60 70 80 90 100Cumulative percentage of consumption
Cumulative percentage of global populationEquality line
Mobiles 2000, 0.84
Mobiles 2018, 0.14
TVs 2018, 0.24
Electricity 2018, 0.52
PCs 2019, 0.66
Cars 2014, 0.77
Internet 2019, 0.78
0.78 2019 3.7 48.8 99.3Technology/infrastructure Gini Year bn %
Mobiles* 0.84 2000
Mobiles* 0.14 2018
TVs* 0.24 2018
Electricity (kWh) 0.52 2018
PCs 0.66 2019
Cars* 0.77 2014
International bandwidth (bits/sec)4.0
0.8
1.6
0.6
4.6
4.282.0
13.7
24.1
8.7
60.5
73.380.4
78.3
89.8
95.9
98.0
78.943 ITU+/WBWDI/WPDS+
43 ITU+/WBWDI/WPDS+
86 ITU+/WBWDI/WPDS+
142 WB WDI/IEA
183 ITU/WBWDI/WPDS+
44
197aPEW/WBWDI

ITU/WBWDIworld population countries included 
%Source Population without access Coverage

519
Demand, Services and Social Aspects of Mitigation Chapter 55
Figure 5.4 | Improving services for well-being is possible, often at huge margin, at a given (relatively low) level of energy use.  Source: reused with permission 
from Vogel et al. (2021).

520
Chapter 5 Demand, Services and Social Aspects of Mitigation 5varies highly depending on the energy type, end-use demand and 
climatic region (Wu et al. 2017).
Consumption is energy- and materials-intensive and expands along 
with income. About half of the energy used in the world is consumed 
by the richest 10% of people, most of whom live in developed 
countries, especially when one includes the energy embodied in 
the goods they purchase from other countries and the structure of 
consumption as a function of income level (Arto et al. 2016; Wolfram 
et  al. 2016; Santillán Vera et  al. 2021). International trade plays 
a central role, being responsible for shifting burdens in most cases 
from low-income developing countries producers to high-income 
developed countries as consumers (Wiedmann et  al. 2020). China 
is the largest exporter to the EU and United States, and accounts 
for nearly half and 40% of their imports in energy use respectively

(Wu et al. 2019). Wealthy countries have exported or outsourced their 
climate and energy crisis to low- and middle-income countries (Baker 
2018), exacerbated by intensive international trade (Steinberger 
et  al. 2012; Scherer et  al. 2018). Therefore, issues of total energy 
consumption are inseparably related to the energy inequity among 
the countries and regions of the world.
Within the energy use induced by global consumer products, 
household consumption is the biggest contributor, contributing to 
around three-quarters of the global total (Wu et al. 2019). A more 
granular analysis of household energy consumption reveals that 
the lowest two quintiles in countries with average annual income 
below USD15,000 cap–1 yr –1 consume less energy than the 
international energy requirements for DLS (20–50 GJ cap–1); 77% of 
people consume less than 30 GJ cap–1 yr –1 and 38% consume less 
than 10 GJ cap–1 yr –1 (Oswald et al. 2020). Many energy-intensive

goods have high price elasticity (>1.0), implying that growing 
incomes lead to over-proportional growth of energy footprints in 
these consumption categories. Highly unequally distributed energy 
consumption is concentrated in the transport sector, ranging from 
vehicle purchase to fuels, and most unequally in package holidays 
and aviation (Gössling 2019; Oswald et al. 2020).
Socio-economic dynamics and outcomes affect whether provisioning 
of goods and services is achieved at low energy demand levels 
(Figure  5.4). Specifically, multivariate regression shows that public 
service quality, income equality, democracy, and electricity access 
enable higher need satisfaction at lower energy demand, whereas 
extractivism and economic growth beyond moderate levels of 
affluence reduce need satisfaction at higher energy demand (Vogel 
et  al. 2021). Altogether, this demonstrates that at a  given level of 
energy provided, there is large scope to improve service levels for

well-being by modifying socio-economic context without increasing 
energy supply (Figure 5.4).
5.2.2.3 Variations in Consumption-based Emissions
The carbon footprint of a  nation is equal to the direct emissions 
occurring due to households’ transport, heating and cooking, as well 
as the impact embodied in the production of all consumed goods 
and services (Wiedmann and Minx 2008; Davis and Caldeira 2010; 
Hübler 2017; Vita et al. 2019a). There are large differences in carbon footprints between the poor and the rich. As a result of energy use 
inequality, the lowest global emitters (the poorest 10% in developing 
countries) in 2013 emitted about 0.1  tCO 2  cap–1  yr–1, whereas the 
highest global emitters (the top 1% in the richest countries) emitted 
about 200–300 tCO 2 cap–1 yr–1 (World Bank 2019). The poorest 50% 
of the world’s population are responsible for only about 10% of total 
lifetime consumption emissions, in contrast about 50% of the world’s

GHG emissions can be attributed to consumption by the world’s richest 
10%, with the average carbon footprint of the richest being 175 times 
higher than that of the poorest 10% (Chancel and Piketty 2015). This 
richest 10% consumed the global carbon budget by nearly 30% during 
the period 1990–2015 (Kartha et al. 2020; Gore 2020). While mitigation 
efforts often focus on the poorest, the lifestyle and consumption 
patterns of the affluent often influence the growing middle class (Otto 
et  al. 2019). Across EU countries, only 5% of households are living 
within 1.5°C climate limits and the top 1% emit more than 22 times 
the target on average, with land and air transport being particular 
characteristics of the highest-emitting countries (Ivanova and Wood 
2020).
In low-income nations – which can exhibit per-capita carbon footprints 
30 times lower than wealthy nations (Hertwich and Peters 2009) – 
emissions are predominantly domestic and driven by provision of

essential services (shelter, low-meat diets, clothing). Per capita carbon 
footprints average 1.6 tonnes per year for the lowest income category, 
then quickly increase to 4.9 and 9.8 tonnes for the two middle-income 
categories and finally to an average of 17.9 tonnes for the highest 
income category. Global CO 2 emissions remain concentrated: the top 
10% of emitters contribute about 35–45% of the total, while the 
bottom 50% contribute just 13–15% of global emissions (Chancel and 
Piketty 2015; Hubacek et al. 2017). In wealthy nations, services such as 
private road transport, frequent air travel, private jet ownership, meat-
intensive diets, entertainment and leisure add significant emissions, 
while a considerable fraction of the carbon footprint is imported from 
abroad, embedded in goods and services (Hubacek et al. 2017).
High-income households consume and demand energy at an order of 
magnitude greater than what is necessary for DLS (Oswald et al. 2020).

Energy-intensive goods, such as package holidays, have a  higher 
income elasticity of demand than less energy-intensive goods like 
food, water supply and housing maintenance, which results in high-
income individuals having much higher energy footprints (Oswald et al. 
2020). Evidence highlights highly unequal GHG emissions in aviation: 
only 2–4% of the global population flew internationally in 2018, with 
1% of the world population emitting 50% of CO 2 from commercial 
aviation (Gössling and Humpe 2020). Some individuals may add more 
than 1600 tCO 2 yr –1 individually by air travel (Gössling 2019).
The food sector dominates in all income groups, comprising 28% 
of households’ carbon footprint, with cattle and rice the major 
contributors (Scherer et al. 2018); food also accounts for 48% and 
70% of household impacts on land and water resources respectively, 
and consumption of meat, dairy, and processed food rise fast

asincomes increase (Ivanova et al. 2016). Roughly 20–40% of food 
produced worldwide is lost to waste before it reaches the market, 
or is wasted by households, the energy embodied in wasted food was 
estimated at around 36 EJ yr –1, and during the period 2010–2016

521
Demand, Services and Social Aspects of Mitigation Chapter 55global food loss and waste equalled 8–10% of total GHG emissions 
(Godfray and Garnett 2014; Springmann et  al. 2018; Mbow et  al. 
2019). Global agri-food supply chains are crucial in the variation 
of per  capita food consumption-related-GHG footprints, mainly in 
the case of red meat and dairy (Kim et al. 2020) since the highest 
per capita food-consumption-related GHG emissions do not correlate 
perfectly with the income status of countries. Thus, it is also crucial 
to focus on high-emitting individuals and groups within countries, 
rather than only those who live in high-emitting countries, since the 
top 10% of emitters live on all continents and one-third of them are 
from the developing world (Chakravarty et al. 2009; Pan et al. 2019).
The environmental impact of increasing equity across income 
groups can be either positive or negative (Hubacek et al. 2017; Rao

and Min 2018a; Scherer et al. 2018; Millward-Hopkins et al. 2020). 
Projections for achieving equitable levels of service provision globally 
predict large increases in global GHG emissions and demand for key 
resources (Blomsma and Brennan 2017), especially in passenger 
transport, which is predicted to increase nearly three-fold between 
2015 and 2050, from 44 trillion to 122 trillion passenger -kilometres 
(OECD 2019a), and associated infrastructure needs, increasing freight 
(Murray et al. 2017), increasing demand for cooling (IEA 2018), and 
shifts to carbon-intensive high-meat diets (OECD/FAO 2018).
Increasing incomes for all to attain DLS raises emissions and energy 
footprints, but only slightly (Chakravarty et  al. 2009; Jorgenson 
et al. 2016; Scherer et al. 2018; Millward-Hopkins et al. 2020; Oswald 
et  al. 2020; Oswald et  al. 2021). The amount of energy needed for 
a high global level of human development is dropping (Steinberger and

Roberts 2010) and could by 2050 be reduced to 1950 levels (Millward-
Hopkins et al. 2020) requiring a massive deployment of technologies 
across the different sectors as well as demand-side reduction 
consumption. The consumption share of the bottom half of the world’s 
population represents less than 20% of all energy footprints, which is 
less than what the top 5% of people consume (Oswald et al. 2020).
Income inequality itself also raises carbon emissions (Hao et al. 2016; 
Sinha 2016; Uzar and Eyuboglu 2019; Baloch et  al. 2020; Oswald 
et al. 2020; Wiedmann et al. 2020; Vogel et al. 2021). Wide inequality 
can increase status-based consumption patterns, where individuals 
spend more to emulate the standards of the high-income group (the 
Veblen effect); inequality also diminishes environmental efforts by 
reducing social cohesion and cooperation (Jorgenson et al. 2017) and 
finally, inequality also operates by inducing an increase in working

hours that leads to higher economic growth and, consequently, 
higher emissions and ecological footprint, so working time reduction 
is key for policy to both reduce emissions and protect employment 
(Fitzgerald et al. 2015; Fitzgerald et al. 2018).
5.2.3 Equity, Trust, and Participation  
There is high evidence and high agreement in literature that socio-
economic equity builds not only well-being for all, but also trust and 
effective participatory governance, which in turn strengthen demand-
side climate mitigation. Equity, participation, social trust, well-being, governance and mitigation are parts of a  continuous interactive 
and self-reinforcing process (Figure 5.5). Chapter 5 Supplementary 
Material I (Section  5.SM.1) contains more detail on these links, 
drawing from social science literature.
Economic growth in equitable societies is associated with lower 
emissions than in inequitable societies (McGee and Greiner 2018),

and income inequality is associated with higher global emissions 
(Ravallion et al. 1997; McGee and Greiner 2018; Rao and Min 2018c; 
Diffenbaugh and Burke 2019; Fremstad and Paul 2019; Liu and Hao 
2020). Relatively slight increases in energy consumption and carbon 
emissions produce great increases in human development and well-
being in less-developed countries, and the amount of energy needed 
for a high global level of human development is dropping (Steinberger 
and Roberts 2010). Equitable and democratic societies which provide 
high quality public services to their population have high well-being 
outcomes at lower energy use than those which do not, whereas 
those which prioritise economic growth beyond moderate incomes 
and extractive sectors display a reversed effect (Vogel et al. 2021).
Well-designed climate mitigation policies ameliorate constituents of 
well-being (Creutzig et al. 2021b). The study shows that of all demand-

side option effects on well-being, 79% are positive, 18% are neutral (or 
not relevant or specified), and only 3% are negative (high confidence) 
(Creutzig et  al. 2021b) (Figure  5.6). Figure  5.6 illustrates that active 
mobility (cycling and walking), efficient buildings and prosumer 
choices of renewable technologies have the most encompassing 
beneficial effects on well-being, with no negative outcomes detected. 
Urban and industry strategies are highly positive overall for well-
being, but they will also reshape supply-side businesses with transient 
intermediate negative effects. Shared mobility, like all the others, 
has overall highly beneficial effects on well-being, but also displays 
a few negative consequences, depending on implementation, such as 
a minor decrease in personal security for patrons of ride-sourcing.
Well-being improvements are most notable in health, air, and energy 
(high confidence). These categories are also most substantiated

in the literature, often under the framing of co-benefits. In many 
cases, co-benefits outweigh the mitigation benefits of specific GHG 
emission reduction strategies. Food (medium confidence), mobility 
(high confidence), and water (medium confidence) are further 
categories where well-being is improved. Mobility has entries 
with highest well-being rankings for teleworking, compact cities, 
and urban system approaches. Effects on well-being in water and 
sanitation mostly come from buildings and urban solutions. Social 
dimensions, such as personal security, social cohesion, and especially 
political stability, are less predominantly represented. An exception 
is economic stability, suggesting that demand-side options generate 
stable opportunities to participate in economic activities (high 
confidence). Although the relation between demand-side mitigation 
strategies and the social aspects of human well-being is important,

this has been less reflected in the literature so far, and hence the 
assessment finds more neutral/unknown interactions (Figure 5.6).
Policies designed to foster higher well-being for all via climate mitigation 
include reducing emissions through wider participation in climate 
action, building more effective governance for improved mitigation,

522
Chapter 5 Demand, Services and Social Aspects of Mitigation 5and including social trust, greater equity, and informal-sector support 
as integral parts of climate policies. Public participation facilitates 
social learning and people’s support of and engagement with climate 
change priorities; improved governance is closely tied to effective 
climate policies (Phuong et al. 2017). Better education, health care, 
valuing of social diversity, and reduced poverty – characteristics of 
more equal societies – all lead to resilience, innovation, and readiness 
to adopt progressive and locally-appropriate mitigation policies, 
whether high-tech or low-tech, centralised or decentralised (Tanner 
et al. 2009; Lorenz 2013; Chu 2015; Cloutier et al. 2015; Mitchell 2015; 
Martin and Shaheen 2016; Vandeweerdt et al. 2016; Turnheim et al. 
2018). Moreover, these factors are the ones identified as enablers of 
high need satisfaction at lower energy use (Vogel et al. 2021).

There is less policy lock-in in more equitable societies (Seto et al. 2016). 
International communication, networking, and global connections 
among citizens are more prevalent in more equitable societies, and 
these help spread promising mitigation approaches (Scheffran et al. 
2012). Climate-related injustices are addressed where equity is 
prioritised (Klinsky and Winkler 2014). Thus, there is high confidence 
in the literature that addressing inequities in income, wealth, and 
DLS not only raises overall well-being and furthers the SDGs but also 
improves the effectiveness of climate change mitigation policies. For 
example, job creation, retraining for new jobs, local production of livelihood necessities, social provisioning, and other positive steps 
toward climate mitigation and adaptation are all associated with 
more equitable and resilient societies (Okvat and Zautra 2011; Bentley 
2014; Klinsky et al. 2016; Roy et al. 2018a). At all scales of governance,

the popularity and sustainability of climate policies requires attention 
to the fairness of their health and economic implications for all, 
and participatory engagement across social groups – a responsible 
development framing (Cazorla and Toman 2001; Dulal et  al. 2009; 
Chuku 2010; Shonkoff et al. 2011; Navroz 2019; Hofstad and Vedeld 
2020; Muttitt and Kartha 2020; Roy and Schaffartzik 2020; Temper 
et  al. 2020; Waller et  al. 2020). Far from being secondary or even 
a  distraction from climate mitigation priorities, an equity focus is 
intertwined with mitigation goals (Klinsky et al. 2016). Demand-side 
climate mitigation options have pervasive ancillary, equity-enhancing 
benefits, for example for health, local livelihoods, and community forest 
resources (Chhatre and Agrawal 2009; Garg 2011; Shaw et al. 2014; 
Serrao-Neumann et al. 2015; Klausbruckner et al. 2016; Salas and Jha 
2019) (Figure  5.6). Limiting climate change risks is fundamental to

collective well-being (Max-Neef et al. 1989; Yamin et al. 2005; Nelson 
et al. 2013; Gough 2015; Gough 2017; Pecl et al. 2017; Tschakert et al. 
2017). Section 5.6 discusses well-designed climate policies more fully, 
with examples. Rapid changes in social norms which are underway 
and which underlie socially-acceptable climate policy initiatives are 
discussed in section 5.4.Well-being for all
Well-being is 
protected in 
equitable societies 
More social trust 
leads to equity, 
and vice versaEquitable 
societies use 
energy and 
resources more 
efﬁciently1
Social trust 
aids policy 
implementationWell-designed climate 
mitigation policies  
contribute to dimensions 
of well-being
Equity strengthens 
involves understanding 
and engagement with 
policies including 
climate responsesEffective 
governance 
fosters 
well-being 
for all
Reduced emissions through wider 
participation in climate actionEquity, sufﬁciency and 
decent living standards

Effective governance More trust2
3
456
7
8
Figure 5.5 | Well-being, equity, trust, governance and climate mitigation: positive feedbacks.  Well-being for all, increasingly seen as the main goal of sustainable 
economies, reinforces emissions reductions through a network of positive feedbacks linking effective governance, social trust, equity, participation and sufficiency. This diagram 
depicts relationships noted in this chapter text and explained further in the Social Science Primer (Chapter 5 Supplementary Material I). The width of the arrows corresponds to 
the level of confidence and degree of evidence from recent social sciences literature.

523
Demand, Services and Social Aspects of Mitigation Chapter 55Figure 5.6 | Two-way link between demand-side climate mitigation strategies and multiple dimensions of human well-being and SDGs.  All demand-side mitigation strategies improve well-being in sum, though not 
necessarily in each individual dimension. Incumbent business (in contrast to overall economic performance) may be challenged. Source: Creutzig et al. (2021b).
High positive impact [+3] 
Medium positive impact [+2] Low positive impact [+1] 
Overall neutralNo impact
Low negative impact [–1] Medium negative impact [–2] 
Confidence level SDGs 2 6 7,11 3 6 7 11 11 4 1,2,8,10 5,10,16 5,16 10,16 11,16 8 9,12
Mitigation strategies/ 
Well-being dimensions
Food
Water
Air
Health
Sanitation
Energy
Shelter
Mobility
Education 
Communication 
Social 
protection
Participation 
Personal 
Security 
Social cohesion
Political 
stability
Economic 
stability
Material 
provision

Sufficiency (adequate floor space, etc.)[+1] [+2] [+2] [+3] [+1] [+3] [+1] [+1] [+1] [+2] [+1] [+1] [+2] [+2] [+2] 
••• •••• ••••• ••••• • •••• • •• •• ••• •• •• ••••• •••• ••••
Efficiency[+2] [+2] [+3/–1] [+3/–1] [+1] [+3] [+2] [+1] [+1] [+1] [+1] [+2/–1] [+2] [+2/–1] 
• •••• •••• ••••• • •••• •••• ••• ••• •••• ••• •••• ••••• ••••
Lower carbon and renewable energy[+2/–1] [+2/–1] [+3] [+3] [+3] [+1] [+1] [+1] [+2] [+1] [+1] [+2/–1] [+2/–1] [+2] 
••• •••• ••••• ••••• •••• ••• ••• ••• ••• ••• ••• •••• ••••• ••••
Food waste[+1] [+2] [+2] [+2] [+1] [+1] [+1] [–1/+1] [+1] [+1] [+1] 
••• •••• •••• ••• •• •••• •• ••• ••• • ••
Over-consumption[+1] [+1/–1] [+1/–1] [+3] [+1/–1] [+2] [+1] 
• • • •••• • •••• •
Plant based diets[+2] [+2] [+3] [+3] [–1] [+3] [+1] [–1] [+2] 
••• •••• ••••• ••• ••• ••••• •••• • •
Teleworking and online 
education system[+1] [+3] [+2] [+2] [+1] [+2] [–1] [+2] [+1] [+2] [+1/–1] [+2] [+2] [+2] 
•• •••• •••• •••• •• •••• ••• •••• •••• •••• •••• •••• ••• •••

Non-motorised transport [+2] [+1] [+1] [+3] [+2] [+3] [+1] [+3] [+1] [+1] [+2] [+2] [+2] [+2] 
•• •• ••••• ••••• •••• ••••• •••• ••• ••• •• •••• ••• •• •••
Shared mobility[+1] [+3] [+2] [+1] [+2] [+1] [+2] [+1] [+1/–1] [+1/–1] [–1] [+2] [+2] 
•• ••• •••• ••• •••• ••• ••• ••• ••• •••• •••• •••• ••••
Electric vehicles (EVs)[+1] [+2] [+1] [+1] [+3] [+2] [+3] [+2] [+2] [–1] 
••• •••• •••• •••• •••• •••• ••••• ••• •••• ••
Compact city[+2/–1] [+1] [+2/–1] [+3/–1] [+1] [+3/–1] [–1] [+3] [+1] [+1/–1] [+2] [+1] [+1] [+1/–1] [+1] [+1] 
••• •• ••• •••• •• ••••• ••••• ••••• ••••• ••• •• •• •••• ••••• •••• ••
Circular and shared economy [+2] [+1] [+2] [+2] [+3] [+2/–1] [+3] [+1] [+1] [+1] [+1] [+2] [+1] [+1] [+2] [+3] 
•••• ••• ••• ••• ••• ••• ••••• •••• •••• ••• ••• •••• •• •• •• •••
Systems approach in urban 
policy and practice[+1] [+2] [+2] [+3] [+1] [+3] [+2] [+3] [+1] [–1] [+1] [+2] [+1] [+1] [+3] 
••• ••• ••• ••• ••• ••• ••• ••• •• •• ••• • •• •• •••••

Nature-Based Solutions[+2] [+1/–1] [+3/–1] [+3] [+1] [+3] [+1/–1] [+1] [+2] [+2] [+3] [+1] [+2/–2] [+3] [+1] 
••• ••••• •••• ••••• ••• ••• ••• ••• •••• •• •• ••• ••• •••• ••
Using less material by design[+2] [+2] [+3] [+2] [+2] [+3] [+2] [+2] [+1] [+2] [+1] [+1] [+1] [+1] [+1] [+2] [+3] 
•• ••• ••• •• ••• •••• •••• •••• •• ••• •• ••• •• •• •• ••• ••
Product life extension[+2] [+2] [+3] [+2] [+2] [+3] [+2] [+2] [+1] [+2] [+1] [–1] [+1] [+1] [+1] [+2] [+3] 
•• ••• ••• •• ••• •••• •••• •••• •• ••• •• •••• •• •• •• ••• ••
Energy efficiency[+2] [+2] [+3] [+1] [+2] [+3] [+2] [+2] [+1] [+2] [+2] [+2] [+1] [+1] [+2] [+2] 
•• ••• ••• •• ••• •••• •••• •••• •• ••• •••• ••• •• •• ••• ••
Circular economy [+2] [+2] [+3] [+1] [+2] [+3] [+2] [+2] [+1] [+2] [+1] [+1] [+2] [+1] [+2] [+3] 
••• ••• ••• •• ••• •••• •••• •••• •• ••• •• ••• •• •• ••• ••

524
Chapter 5 Demand, Services and Social Aspects of Mitigation 5The distinction between necessities and luxuries helps to frame 
a  growing stream of social sciences literature with climate policy 
relevance (Arrow et al. 2004; Ramakrishnan and Creutzig 2021). Given 
growing public support worldwide for strong sustainability, sufficiency, 
and sustainable consumption, changing demand patterns and 
reduced demand are accompanying environmental and social benefits 
(Jackson 2008; Fedrigo et al. 2010; Schroeder 2013; Figge et al. 2014; 
Spangenberg and Germany 2016; Spengler 2016; Burke 2020; Mont 
et al. 2020). Beyond a threshold, increased material consumption is not 
closely correlated with improvements in human progress (Frank 1999; 
Kahneman and Deaton 2010; Steinberger and Roberts 2010; Roy et al. 
2012; Oishi et al. 2018; Xie et al. 2018; Vita et al. 2019b; Wang et al. 2019; 
Vita et al. 2020). Policies focusing on the ‘super-rich’, also called the

‘polluter elite’, are gaining attention for moral or norms-based as well 
as emissions-control reasons (Kenner 2019; Otto et al. 2019; Pascale 
et al. 2020; Stratford 2020) (Section 5.2.2.3). Conspicuous consumption 
by the wealthy is the cause of a large proportion of emissions in all 
countries, related to expenditures on such things as air travel, tourism, 
large private vehicles and large homes (Brand and Boardman 2008; 
Roy and Pal 2009; Roy et al. 2012; Brand and Preston 2010; Gore 2015; 
Hubacek et al. 2017; Jorgenson et al. 2017; Sahakian 2018; Gössling 
2019; Kenner 2019; Lynch et al. 2019; Osuoka and Haruna 2019).
Since no country now meets its citizens’ basic needs at a  level of 
resource use that is globally sustainable, while high levels of life 
satisfaction for those just escaping extreme poverty require even 
more resources, the need for transformative shifts in governance and 
policies is large (O’Neill et al. 2018; Vogel et al. 2021).

Inequitable societies use energy and resources less efficiently. 
Higher income inequality is associated with higher carbon emissions, 
at least in developed countries (Grunewald et al. 2011; Golley and 
Meng 2012; Chancel et al. 2015; Grunewald et al. 2017; Jorgenson 
et  al. 2017; Sager 2017; Klasen 2018; Liu et  al. 2019); reducing 
inequality in high-income countries helps to reduce emissions (Klasen 
2018). There is high agreement in the literature that alienation or 
distrust weakens collective governance and fragments political 
approaches towards climate action (Smit and Pilifosova 2001; Adger 
et al. 2003; Hammar and Jagers 2007; Van Vossole 2012; Bulkeley 
and Newell 2015; Smith and Howe 2015; ISSC et al. 2016; Alvaredo 
et al. 2018; Smith and Mayer 2018; Fairbrother et al. 2019; Hayward 
and Roy 2019; Kulin and Johansson Sevä 2019; Liao et al. 2019).
Populism and politics of fear are less prevalent under conditions of

more income equality (Chevigny 2003; Bryson and Rauwolf 2016; 
O’Connor 2017; Fraune and Knodt 2018; Myrick and Evans Comfort 
2019). Ideology and other social factors also play a role in populist 
climate scepticism, but many of these also relate to resentment of 
elites and desire for engagement (Swyngedouw 2011; Lockwood 
2018; Huber et al. 2020). ‘Climate populism’ movements are driven by 
an impetus for justice (Beeson 2019; Hilson 2019). When people feel 
powerless and/or that climate change is too big a problem to solve 
because others are not acting, they may take less action themselves 
(Williams and Jaftha 2020). However, systems for benefit-sharing 
can build trust and address large-scale ‘commons dilemmas’, in the 
context of strong civil society (Barnett 2003; Mearns and Norton 2009; 
Inderberg et al. 2015; Sovacool et al. 2015; Hunsberger et al. 2017; Soliev and Theesfeld 2020). Leadership is also important in fostering

environmentally-responsible group behaviours (Liu and Hao 2020).
In some less-developed countries, higher income inequality may in 
fact be associated with lower per capita emissions, but this is because 
people who are excluded by poverty from access to fossil fuels must 
rely on biomass (Klasen 2018). Such energy poverty – the fact that 
millions of people do not have access to energy sources to help meet 
human needs  – implies the opposite of development (Guruswamy 
2010; Guruswamy 2020). In developing countries, livelihood 
improvements do not necessarily cause increases in emissions (Peters 
et al. 2012; Reusser et al. 2013; Creutzig et al. 2015a; Chhatre and 
Agrawal 2009; Baltruszewicz et  al. 2021) and poverty alleviation 
causes negligible emissions (Chakravarty et  al. 2009). Greater 
equity is an important step towards sustainable service provisioning 
(Godfray et al. 2018; Dorling 2019; Timko 2019).
As discussed in Section 5.6, policies to assist the low-carbon energy

transition can be designed to include additional benefits for income 
equality, besides contributing to greater energy access for the poor 
(Burke and Stephens 2017; Frank 2017; Healy and Barry 2017; 
Sen 2017; Chapman et  al. 2018; La Viña et  al. 2018; Chapman 
and Fraser 2019; Piggot et al. 2019; Sunderland et al. 2020). Global 
and intergenerational climate inequities impact people’s well-being, 
which affects their consumption patterns and political actions 
(Albrecht et al. 2007; Fritze et al. 2008; Gori-Maia 2013; Clayton et al. 
2015; Pizzigati 2018) (Box 5.4).
Consumption reductions, both voluntary and policy-induced, 
can have positive and double-dividend effects on efficiency as 
well as reductions in energy and materials use (Mulder et al. 
2006; Harriss and Shui 2010; Figge et  al. 2014; Grinde 
et al. 2018; Spangenberg and Lorek 2019; Vita et al. 2020). Less 
waste, better emissions control and more effective carbon policies lead

to better governance and stronger democracies. Systems-dynamics 
models linking strong emissions-reducing policies and strong social 
equity policies show that a low-carbon transition in conjunction with 
social sustainability is possible, even without economic growth (Kallis 
et al. 2012; Jackson and Victor 2016; Stuart et al. 2017; Chapman and 
Fraser 2019; D’Alessandro et al. 2019; Gabriel and Bond 2019; Huang 
et al. 2019; Victor 2019). Such degrowth pathways may be crucial in 
combining technical feasibility of mitigation with social development 
goals (Hickel et al. 2021; Keyßer and Lenzen 2021).
Multi-level or polycentric governance can enhance well-being and 
improve climate governance and social resilience, due to varying 
adaptive, flexible policy interventions at different times and scales 
(Kern and Bulkeley 2009; Lidskog and Elander 2009; Amundsen 
et al. 2010; Keskitalo 2010; Lee and Koski 2015; Jokinen et al. 2016;

Lepeley 2017; Marquardt 2017; Di Gregorio et al. 2019). Institutional 
transformation may also result from socio-ecological stresses that 
accompany climate change, leading to more effective governance 
structures (David Tàbara et  al. 2018; Patterson and Huitema 2019; 
Barnes et  al. 2020). An appropriate, context-specific mix of options 
facilitated by policies can deliver both higher well-being and reduced 
disparity in access to basic needs for services concurrently with climate 
mitigation (Thomas and Twyman 2005; Mearns and Norton 2009;

525
Demand, Services and Social Aspects of Mitigation Chapter 55Klinsky and Winkler 2014; Lamb et  al. 2014; Lamb and  Steinberger 
2017). Hence, nurturing equitable human well-being through provision 
of decent living standards for all goes hand in hand with climate change 
mitigation (ISSC et al. 2016; OECD 2019a). There is high confidence in 
the literature that addressing inequities in income, wealth, and DLS not 
only raises overall well-being and furthers the SDGs but also improves 
the effectiveness of climate change mitigation policies.
Participatory governance involves understanding and 
engagement with policies, including climate policies. Greater 
public participation in climate policy processes and governance, by 
increasing the diversity of ideas and stakeholders, builds resilience 
and allows broader societal transformation towards systemic change, 
even in complex, dynamic and contested contexts (Dombrowski 2010;

Wise et al. 2014; Haque et al. 2015; Jodoin et al. 2015; Mitchell 2015; 
Kaiser 2020; Alegria 2021). This sometimes involves complex policy 
discussions that can lead to governance innovations, also influencing 
social norms (Martinez 2020). A specific example are citizen assemblies, 
deliberating public policy challenges, such as climate change (Devaney 
et al. 2020). Activist climate movements are changing policies as well as 
normative values (Section 5.4 and the Social Science Primer, Chapter 5 
Supplementary Material I). Environmental justice and climate justice 
activists worldwide have called attention to the links between economic 
and environmental inequities, collected and publicised data about 
them, and demanded stronger mitigation (Goodman 2009; Schlosberg 
and Collins 2014; Jafry 2019; Cheon 2020). Youth climate activists, and 
Indigenous leaders, are also exerting growing political influence towards 
mitigation (Helferty and Clarke 2009; White 2011; Powless 2012;

Petheram et al. 2015; UN 2015; Curnow and Gross 2016; Grady-Benson 
and Sarathy 2016; Claeys and Delgado Pugley 2017; O’Brien et al. 2018; 
Rowlands and Gomez Peña 2019; Bergmann and Ossewaarde 2020; 
Han and Ahn 2020; Nkrumah 2021). Indigenous resurgence (activism 
fuelled by ongoing colonial social and environmental injustices, land 
claims, and deep spiritual and cultural commitment to environmental protection) not only strengthens climate leadership in many countries, 
but also changes broad social norms by raising knowledge of 
Indigenous governance systems which supported sustainable lifeways 
over thousands of years (Wildcat 2014; Chanza and De Wit 2016; 
Whyte 2017; Whyte 2018, Temper et al. 2020). Related trends include 
recognition of the value of traditional ecological knowledge, Indigenous 
governance principles, decentralisation, and appropriate technologies 
(Lange et al. 2007; Goldthau 2014; Whyte 2017).

Social trust aids policy implementation. More equal societies display 
higher trust, which is a key requirement for successful implementation 
of climate policies (Rothstein and Teorell 2008; Carattini et al. 2015; 
Klenert et al. 2018; Patterson et al. 2018). Inter-personal trust among 
citizens often promotes pro-environment behaviour by influencing 
perceptions (Harring and Jagers 2013), enhancing cooperation, and 
reducing free-riding and opportunistic behaviour (Gür 2020). Individual 
support for carbon taxes and energy innovations falls when collective 
community support is lacking (Bolsen et  al. 2014; Smith and Mayer 
2018; Simon 2020). Social trust has a  positive influence on civic 
engagement among local communities, NGOs, and self-help groups for 
local clean cooking fuel installation (Nayak et al. 2015).
Section  5.6 includes examples of climate mitigation policies and 
policy packages which address the interrelationships shown in

Figure 5.5. Improving well-being for all through climate mitigation 
includes emissions-reduction goals in policy packages that ensure 
equitable outcomes, prioritise social trust-building, support wide 
public participation in climate action including within the informal 
sector, and facilitate institutional change for effective multi-level 
governance, as integral components of climate strategies. This 
strategic approach, and its feasibility of success, rely on complex 
contextual factors that may differ widely, especially between the 
Global North and Global South (Atteridge et al. 2012; Patterson et al. 
2018; Jewell and Cherp 2020; Singh et al. 2020; Singh et al. 2021).
Box 5.4 | Gender, Race, Intersectionality and Climate Mitigation
There is high evidence and high agreement that empowering women benefits both mitigation and adaptation, because women

prioritise climate change in their voting, purchasing, community leadership, and work, both professionally and at home (high evidence, 
high agreement). Increasing voice and agency for those marginalised in intersectional ways by indigeneity, race, ethnicity, dis/ability, 
and other factors has positive effects for climate policy (high evidence, high agreement).
Climate change affects people differently along all measures of difference and identity, which have intersectional impacts linked to 
economic vulnerability and marginalisation (Morello Frosch et al. 2009; Dankelman 2010; Habtezion 2013; Godfrey and Torres 2016; 
Walsh 2016; Flatø et al. 2017; Goodrich et al. 2019; Perkins 2019; Gür 2020). Worldwide, racialised and Indigenous people bear the 
brunt of environmental and climate injustices through geographic location in extraction and energy ‘sacrifice zones’, areas most

impacted by extreme weather events, and/or through inequitable energy access (Aubrey 2019; Jafry 2019; Gonzalez 2020; Lacey-
Barnacle et al. 2020; Porter et al. 2020; Temper et al. 2020) Disparities in climate change vulnerability not only reflect pre-existing 
inequalities, they also reinforce them. For example, inequities in income and in the ownership and control of household assets, 
familial responsibilities due to male out-migration, declining food and water access, and increased disaster exposure can undermine 
women’s ability to achieve economic independence, enhance human capital, and maintain physical and mental health and well-being 
(Chandra et al. 2017; Eastin 2018; Das et al. 2019). Studies during the COVID-19 crisis have found that, in general, women’s economic 
and productive lives have been affected disproportionately to men’s (Alon et al. 2020; ILO 2020). Women have less access to social

protections and their capacity to absorb economic shocks is very low, so they face a ‘triple burden’ during crises – including those

526
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Box 5.4 (continued)
resulting from climate change – and this is heightened for women in the less-developed countries and for those who are intersectionally 
vulnerable (Coates et al. 2020; McLaren et al. 2020; Wenham et al. 2020; Azong and Kelso 2021; Erwin et al. 2021; Maobe and Atela 
2021; Nicoson 2021; Sultana 2021; Versey 2021). Because men currently hold the majority of energy-sector jobs, energy transition 
will impact them economically and psychologically; benefits, burdens and opportunities on both the demand and supply sides of the 
mitigation transition have a range of equity implications (Pearl-Martinez and Stephens 2017; Standal et al. 2020; Mang-Benza 2021). 
Mitigating gendered climate impacts requires addressing inequitable power relations throughout society (Wester and Lama 2019).

Women’s well-being and gender-responsive climate policy have been emphasised in international agreements including the Paris Agreement 
(UNFCCC 2015), Convention on the Elimination of all Forms of Discrimination Against Women General Recommendation 37 (Vijeyarasa 
2021), and the 2016 Decision 21/CP .22 on Gender and Climate Change (UNFCCC 2016; Larson et al. 2018). Increasing the participation 
of women and marginalised social groups, and addressing their special needs, helps to meet a range of SDGs, improve disaster and crisis 
response, increase social trust, and improve climate mitigation policy development and implementation (Alber 2009; Whyte 2014; Elnakat 
and Gomez 2015; Salehi et al. 2015; Buckingham and Kulcur 2017; Cohen 2017; Kronsell 2017; Lee and Zusman 2019).
Women have a key role in the changing energy economy due to their demand for and end use of energy resources in socially-gendered

productive roles in food production and processing, health, care, education, clothing purchases and maintenance, commerce, and 
other work, both within and beyond the home (Räty and Carlsson-Kanyama 2009; Oparaocha and Dutta 2011; Bob and Babugura 
2014; Macgregor 2014; Perez et al. 2015; Bradshaw 2018; Clancy and Feenstra 2019; Clancy et al. 2019; Fortnam et al. 2019; Rao 
et al. 2019a; Quandt 2019; Horen Greenford et al. 2020; Johnson 2020). Women’s work and decision-making are central in the food 
chain and agricultural output in most developing countries, and in household management everywhere. Emissions from cooking fuels 
can cause serious health damage, and unsustainable extraction of biofuels can also hurt mitigation (Bailis et al. 2015), so considering 
health, biodiversity and climate tradeoffs and co-benefits is important (Rosenthal et al. 2018; Aberilla et al. 2020; Mazorra et al. 2020).

Policies on energy use and consumption are often focused on technical issues related to energy supply, thereby overlooking demand-
side factors such as household decision-making, unpaid work, livelihoods and care (Himmelweit 2002; Perch 2011; Fumo 2014; Hans 
et al. 2019; Huyer and Partey 2020). Such gender-blindness represents the manifestation of wider issues related to political ideology, 
culture and tradition (Carr and Thompson 2014; Thoyre 2020; Perez et al. 2015; Fortnam et al. 2019).
Women, and all those who are economically and/or politically marginalised, often have less access to energy and use less, not just 
because they may be poorer but case studies show because their consumption choices are more ecologically inclined and their energy 
use is more efficient (Lee et al. 2013; Permana et al. 2015; Li et al. 2019). Women’s carbon footprints are about 6–28% lower than

men’s (with high variation across countries), mostly based on their lower meat consumption and lower vehicle use (Isenhour and 
Ardenfors 2009; Räty and Carlsson-Kanyama 2009; Räty and Carlsson-Kanyama 2010; Barnett et al. 2012; Medina and Toledo-Bruno 
2016; Ahmad et al. 2017; Fernström Nåtby and Rönnerfalk 2018; Li et al. 2019). Gender-based income redistribution in the form of pay 
equity for women could reduce emissions if the redistribution is revenue neutral (Terry 2009; Dengler and Strunk 2018). Also, advances 
in female education and reproductive health, especially voluntary family planning, can contribute greatly to reducing world population 
growth (Abel et al. 2016; Dodson et al. 2020).
Carbon emissions are lower per capita in countries where women have more political ‘voice’, controlling for GDP per capita and a range of 
other factors (Ergas and York 2012). While most people recognise that climate change is happening (Lewis et al. 2018; Ballew et al. 2019),

climate denialism is more prevalent among men (McCright and Dunlap 2011; Anshelm and Hultman 2014; Nagel 2015; Jylhä et al. 2016), 
while women are more likely to be environmental activists, and to support stronger environmental and climate policies (Stein 2004; 
McCright and Xiao 2014, Whyte 2014). Racialised groups are more likely to be concerned about climate change and to take political 
action to support climate mitigation policies (Leiserowitz and Akerlof 2010; Godfrey and Torres 2016; Schuldt and Pearson 2016; Pearson 
et al. 2017; Ballew et al. 2020; Johnson 2020). This underscores the important synergies between equity and mitigation. The contributions 
of women, racialised people, and indigenous people, who are socially positioned as those first and most affected by climate change – and 
therefore experts on appropriate climate responses – are substantial (Dankelman and Jansen 2010; Wickramasinghe 2015; Black 2016;

Vinyeta et al. 2016; Pearse 2017). Equitable power, participation, and agency in climate policymaking is hence an effective contribution for 
improving governance and decision-making on climate change mitigation (Reckien et al. 2017; Collins 2019). Indigenous knowledge is an 
important source of guidance for biodiversity conservation, impact assessment, governance, disaster preparedness and resilience (Salick 
and Ross 2009; Green and Raygorodetsky 2010; Speranza et al. 2010; Mekuriaw Bizuneh 2013; Mekuriaw 2017), and women are often 
the local educators, passing on and utilising traditional and indigenous knowledge (Ketlhoilwe 2013; Onyige 2017; Azong et al. 2018).
Higher female political participation, controlled for other factors, leads to higher stringency in climate policies, and results in lower 
GHG emissions (Cook et al. 2019). Gender equity is also correlated with lower per capita CO 2-eq emissions (Ergas and York 2012).

527
Demand, Services and Social Aspects of Mitigation Chapter 555.3 Mapping the Opportunity Space
Reducing global energy demand and resource inputs while improving 
well-being for all requires an identification of options, services and 
pathways that do not compromise essentials of a  decent living. To 
identify such a solution space, this section summarises socio-cultural, 
technological and infrastructural interventions through the Avoid-
Shift-Improve concept. ASI (Section  5.1) provides a  categorisation 
of options aimed at continuously eliminating waste in the current 
systems of service provision (Section 5.3.1.1). It also concisely presents 
demand-side options to reduce GHG emissions by individual choices 
which can be leveraged by supporting policies, technologies and 
infrastructure. Two key concepts for evaluating the efficiency of service 
provision systems are: resource cascades and exergy. These concepts 
provide powerful analytical lenses through which to identify and

substantially reduce energy and resource waste in service provision 
systems, both for decent living standards (Section 5.3.2) and higher 
well-being levels. They typically focus on end-use conversion and 
service delivery improvements as the most influential opportunities 
for system-wide waste reductions. Review of the state of modelling 
low energy and resource demand pathways in long-term climate 
mitigation scenarios (recognising the importance of such scenarios for 
illuminating technology and policy pathways for more efficient service 
provision) and summary of the mitigation potentials estimated from 
relevant scenarios to date are in Section 5.3.3. Finally, it reviews the 
role of three megatrends that are transforming delivery of services 
in innovative ways  – digitalisation, the sharing economy, and the 
circular economy (Section 5.3.4). The review of megatrends makes an 
assessment highlighting the potential risks of rebound effects, and

even accelerated consumption; it also scopes for proactive and vigilant 
policies to harness their potential for future energy and resource 
demand reductions, and, conversely, avoiding undesirable outcomes.
5.3.1 Efficient Service Provision
This section organises demand reductions under the ASI framework. 
It presents service-oriented demand-side solutions consistent with 
decent living standards (Creutzig et al. 2018) (Table 5.1). The sharing 
economy, digitalisation, and the circular economy can all contribute to ASI strategies, with the circular economy tentatively more on the 
supply side, and the sharing economy and digitalisation tentatively 
more on the demand side (Section 5.3.4). These new service delivery 
models go beyond sectoral boundaries (IPCC sector chapter boundaries 
are explained in Chapter  12) and take advantage of technological 
innovations, design concepts, and innovative forms of  cooperation,

cutting across sectors to contribute to systemic changes worldwide. 
Some of these changes can be realised in the short term, such as 
energy access, while others may take a longer period, such as radical 
and systemic eco-innovations like shared electric autonomous vehicles. 
It is important to understand benefits and distributional impacts of 
these systemic changes.
5.3.1.1 Integration of Service Provision Solutions 
with Avoid-Shift-Improve Framework
Assessment of service-related mitigation options within the ASI 
framework is aided by decomposition of emissions intensities into 
explanatory contributing factors, which depend on the type of 
service delivered. Table 5.1 shows ASI options in selected sectors and 
services. It summarises resource, energy, and emissions intensities 
commonly used by type of service (Cuenot et  al. 2010; Lucon et  al. 
2014; Fischedick et al. 2014). Also relevant are the concepts of service

provision adequacy (Arrow et al. 2004; Samadi et al. 2017), establishing 
the extents to which consumption levels exceed (e.g.,  high-calorie 
diets contributing to health issues (Roy et  al. 2012); excessive food 
waste) or fall short (e.g., malnourishment) of service level sufficiency 
(e.g.,  recommended calories) (Millward-Hopkins et  al. 2020); and 
service level efficiency (e.g., effect of occupancy on the energy intensity 
of public transit passenger-km travelled (Schäfer and Yeh 2020). 
Service-oriented solutions are discussed in Table 5.1. Implementation 
of these solutions requires combinations of institutional, infrastructural, 
behavioural, socio-cultural, and business changes which are mentioned 
in Section 5.2 and discussed in Section 5.4.
Opportunities for avoiding waste associated with the provision of 
services, or avoiding overprovision of or excess demand for services, 
exist across multiple service categories. ‘Avoid’ options are relevant

in all end-use sectors, namely, teleworking and avoiding long-haul 
flights, adjusting dwelling size to household size, and avoiding short-Box 5.4 (continued)
In societies where women have more economic equity, their votes push political decision-making in the direction of environmental 
and sustainable development policies, less high-emission militarisation, and more emphasis on equity and social policies such as via 
wealth and capital gains taxes (Ergas and York 2012; Resurrección 2013; UNEP 2013; Glemarec et al. 2016; Bryan et al. 2018; Crawford 
2019). Changing social norms on race and climate are linked and policy-relevant (Benegal 2018; Elias et al. 2018; Slocum 2018; Gach 
2019; Wallace-Wells 2019; Temple 2020; Drolet 2021). For all these reasons, climate policies are strengthened by including more 
differently-situated knowledge and diverse perspectives, such as feminist expertise in the study of power (Bell et al. 2020; Lieu et al.

2020); clarifying equity goals (e.g., distinguishing among ‘reach, ‘benefit’, and ‘empowerment’; obtaining disaggregated data and 
using clear empirical equity measures; and confronting deeply-ingrained inequities in society (Lau et al. 2021). Inclusivity in climate 
governance spans mitigation–adaptation, supply–demand and formal–informal sector boundaries in its positive effects (Morello 
Frosch et al. 2009; Dankelman 2010; Bryan and Behrman 2013; Habtezion 2013; Godfrey and Torres 2016; Walsh 2016; Flatø et al. 
2017; Wilson et al. 2018; Goodrich et al. 2019; Perkins 2019; Bell et al. 2020; Gür 2020).

528
Chapter 5 Demand, Services and Social Aspects of Mitigation 5lifespan products and food waste. Cities and built environments can 
play an additional role. For example, more compact designs and higher 
accessibility reduce travel demand and translate into lower average floor 
space and corresponding heating/cooling and lighting demand, and thus 
reductions of between 5% to 20% of GHG emissions of end-use sectors 
(Creutzig et al. 2021b). Avoidance of food loss and wastage – which 
equalled 8–10% of total anthropogenic GHG emissions from 2010–2016 
(Mbow et al. 2019), while millions suffer from hunger and malnutrition – 
is a  prime example (Chapter  12). A key challenge in meeting global 
nutrition services is therefore to avoid food loss and waste while 
simultaneously raising nutrition levels to equitable standards globally. 
Literature results indicate that in developed economies, consumers are 
the largest source of food waste, and that behavioural changes such as

meal planning, use of leftovers, and avoidance of over-preparation can 
be important service-oriented solutions (Gunders et al. 2017; Schanes 
et al. 2018), while improvements to expiration labels by regulators would 
reduce unnecessary disposal of unexpired items (Wilson et al. 2017) and 
improved preservation in supply chains would reduce spoilage (Duncan 
and Gulbahar 2019). Around 931 million tonnes of food waste was 
generated in 2019 globally, 61% of which came from households, 26% 
from food service and 13% from retail.Demand-side mitigations are achieved through changing Socio-
cultural factors, Infrastructure use and Technology adoption by various 
social actors in urban and other settlements, food choice and waste 
management (high confidence) (Figure  5.7). In all sectors, end-use 
strategies can help reduce the majority of emissions, ranging from 
28.7% (4.4 GtCO 2) emission reductions in the industry sector, to 44.2%

(8.0 GtCO 2-eq)  in the food sector, to 66.75% (4.6 GtCO 2) emission 
reductions in the land transport sector, and 66% (6.8  GtCO 2) in the 
buildings sector. These numbers are median estimates and represent 
benchmark accounting. Estimates are approximations, as they are 
simple products of individual assessments for each of the three options 
listed above. If interactions were taken into account, the full mitigation 
potentials may be higher or lower, independent of relevant barriers 
to realising the median potential estimates. See more in Chapter  5 
Supplementary Material II, Table 5.SM.2.
The technical mitigation potential of food loss and waste reductions 
globally has been estimated at 0.1–5.8 GtCO 2-eq (high confidence) 
(Poore and Nemecek 2018; Smith, et al. 2019) (Section 7.4.5, Figure 5.7 
and Table 12.3). Coupling food waste reductions with dietary shifts

can further reduce energy, land, and resource demand in upstream Table 5.1 | Avoid-Shift-Improve options in selected sectors and services.  Many options, such as urban form and infrastructures, are systemic, and influence several 
sectors simultaneously. Linkages to concepts presented in sectoral chapters are indicated in parentheses in the first column. Source: adapted from Creutzig at al. (2018).
ServiceEmission 
decomposition factorsAvoid Shift Improve
Mobility
[passenger-km]
(Chapters 8, 10, 11, 16)kgCO 2 = (passenger km)* 
(MJ pkm–1)*(kgCO 2 MJ–1)Innovative mobility to reduce 
passenger-km:
Integrate transport and land-use planning
Smart logistics
Teleworking
Compact cities
Fewer long-haul flights
Local holidaysIncreased options for 
mobility MJ pkm–1:
Modal shifts, from car to cycling, 
walking, or public transit
Modal shift from air travel 
to high-speed railInnovation in equipment design 
MJ pkm–1 and CO 2-eq MJ–1:
Lightweight vehicles
Hydrogen vehicles
Electric vehicles

Eco-driving
Shelter
[square metres]
(Chapters 8, 9, 11)kgCO 2 = (square metres)* 
(tonnes material m–2)* 
(kg CO 2 tonne material–1)Innovative dwellings to reduce 
square metres:
Smaller decent dwellings
Shared common spaces
Multigenerational housingMaterials-efficient housing 
tonnes material m–2:
Less material-intensive dwelling designs
Shift from single-family to multi-family 
dwellingsLow emission dwelling design 
kgCO 2 tonne–1 material:
Use wood as material
Use low-carbon production 
processes for building materials 
(e.g., cement and steel)
Thermal comfort
[indoor temperature]
(Chapters 9, 16)kgCO 2 = (Δ°C m3 to 
warm or cool) (MJ m–3)* 
(kgCO 2 MJ–1)Choice of healthy indoor 
temperature Δ°C m3:
Reduce m2 as above
Change temperature set-points
Change dress code
Change working timesDesign options to reduce 
MJ Δ°C–1 m–3:
Architectural design (shading, natural 
ventilation, etc.)New technologies to reduce 
MJ Δ°C–1 m–3 and kgCO 2 MJ–1:
Solar thermal devices
Improved insulation

Heat pumps
District heating
Goods
[units]
(Chapters 11, 12)kgCO 2 = (product units)* 
(kg material product–1)* 
(kgCO 2 kg material–1)More service per product:
Reduce consumption quantities
Long lasting fabric, appliances
Sharing economyInnovative product design 
kg material product–1:
Materials-efficient product designsChoice of new materials 
kgCO 2 kg material–1:
Use of low-carbon materials
New manufacturing processes 
and equipment use
Nutrition
[calories consumed]
(Chapters 6, 12)kgCO 2-eq =  
(calories consumed)* 
(calories produced 
calories consumed–1)* 
(kgCO 2-eq calorie 
produced–1)Reduce calories produced/
calories consumed and optimise 
calories consumed:
Keep calories in line with daily needs 
and health guidelines
Reduce waste in supply chain 
and after purchaseAdd more variety in food plate to 
reduce kgCO 2-eq cal–1 produced:
Dietary shifts from ruminant meat and 
dairy to other protein sources while 
maintaining nutritional qualityReduce kgCO 2-eq cal–1 produced:

Improved agricultural practices
Energy efficient food processing
Lighting
[lumens]
(Chapters 9, 16)kgCO 2 = lumens* 
(kWh lumen–1)* 
(kgCO 2 kWh–1)Minimise artificial lumen demand:
Occupancy sensors
Lighting controlsDesign options to increase 
natural lumen supply:
Architectural designs with 
maximal daylightingDemand innovation lighting 
technologies kWh lumens–1 and 
power supply kgCO 2 kWh–1:
LED lamps

529
Demand, Services and Social Aspects of Mitigation Chapter 55food provision systems, leading to substantial GHG emissions benefits. 
The estimated technical potential for GHG emissions reductions 
associated with shifts to sustainable healthy diets is 0.5–8 GtCO 2-eq 
(high confidence) (Smith et al. 2013; Jarmul et al. 2020; Creutzig et al. 
2021b) (Figure 5.7, Table 12.2). Current literature on health, diets, and 
emissions indicates that sustainable food systems providing healthy 
diets for all are within reach but require significant cross-sectoral 
action, including improved agricultural practices, dietary shifts among 
consumers, and food waste reductions in production, distribution, 
retail, and consumption (Erb et al. 2016; Muller et al. 2017; Graça et al. 
2019; Willett and al. 2019) (Table 12.9).
Reduced food waste and dietary shifts have highly relevant 
repercussions in the land-use sector that underpin the high GHG

emission reduction potential. Demand-side measures lead to 
changes in consumption of land-based resources and can save GHG 
emissions by reducing or improving management of residues  or 
making land areas available for other uses such as afforestation 
or bioenergy production (Smith et  al. 2013; Hoegh-Guldberg et  al. 
2019). Deforestation is the second-largest source of anthropogenic 
greenhouse gas emissions, caused mainly by expanding forestry and 
agriculture, and in many cases this agricultural expansion is driven 
by trade demand for food. For example, across the tropics, cattle and 
oilseed products account for half the deforestation carbon emissions, 
embodied in international trade to China and Europe (Creutzig 
et al. 2019a; Pendrill et al. 2019). Benefits from shifts in diets and 
resulting lowered land pressure are also reflected in reductions of 
land degradation and emissions.
Increased demand for biomass can increase the pressure on forest

and conservation areas (Cowie et al. 2013) and poses a heightened 
risk for biodiversity, livelihoods, and intertemporal carbon balances 
(Lamb et  al. 2016; Creutzig et  al. 2021c), requiring policy and 
regulations to ensure sustainable forest management, which 
depends on forest type, region, climate, and ownership. This suggests 
that demand-side actions hold sustainability advantages over the 
intensive use of bioenergy and BECCS, but also enable land use for 
bioenergy by saving agricultural land for food.
In the transport sector, ASI opportunities exist at multiple levels, 
comprehensively summarised in Bongardt et  al. (2013), Sims 
et  al. (2014), and Roy et  al. (2021) (Chapter  10). Modelling based 
on a  plethora of bottom-up insights and options reveals that 
a  balanced portfolio of ASI policies brings global transport sector 
emissions in line with global warming of not more than 1.5°C (Gota 
et  al. 2019). For example, telework may be a  significant lever for

avoiding road transport associated with daily commutes, achievable 
through digitalisation, but its savings depend heavily on the modes, 
distances, and types of office use avoided (Hook et  al. 2020) and 
whether additional travel is induced due to greater available time 
(Mokhtarian 2002) or vehicle use by other household members (Kim 
et al. 2015; de Abreu e Silva and Melo 2018). More robustly, avoiding 
kilometres travelled through improved urban planning and smart 
logistical systems can lead to fuel, and, hence, emissions savings 
(Creutzig et  al. 2015a; IEA 2016; IEA 2017a; Wiedenhofer et  al. 
2018), or through avoiding long-haul flights (IEA 2021). For example, 
reallocating road and parking space to exclusive public transit lanes, protected bike lanes and pedestrian priority streets can reduce 
vehicle kilometres travelled in urban areas (ITF 2021). At the vehicle 
level, lightweighting strategies (Fischedick et al. 2014) and avoiding

inputs of carbon-intensive materials into vehicle manufacturing can 
also lead to significant emissions savings through improved fuel 
economy (Das et al. 2016; Hertwich et al. 2019; IEA 2019b).
Figure 5.7 shows socio-cultural factors can contribute up to 15% to land 
transport GHG emissions reduction by 2050, with 5% as our central 
estimate. Active mobility, such as walking and cycling, has 2–10% 
potential in GHG emissions reduction. Well designed teleworking 
policies can reduce transport-related GHG emissions by at least 1%. 
A  systematic review demonstrates that 26 of 39  studies identified 
suggest that teleworking reduces energy use, induced mainly by distance 
travelled, and only eight studies suggest that teleworking increases or 
has a neutral impact on energy use (Hook et al. 2020). Infrastructure 
use (specifically urban planning and shared pooled mobility) has about 
20–50% (on average) potential in land transport GHG emissions

reduction, especially via redirecting the ongoing design of existing 
infrastructures in developing countries, and with 30% as our central 
estimate (Section  5.3.4.2). Technology adoption, particularly banning 
combustion and diesel engines and 100% EV targets (and other zero-
carbon fuels, especially in freight) and efficient lightweight cars, can 
contribute to between 30% and 70% of GHG emissions reduction from 
land transport in 2050, with 50% as our central estimate (see Chapter 5 
Supplementary Material II, Table 5.SM.2 and Chapter 10, Sections 10.4 
and 10.7), consistent with scenario modelling (Figure  10.27) and 
based on rapid reduction in the GHG emission footprint of vehicle 
production. These numbers are consistent with the end of fossil fuel-
based new cars in 2035 in major economies and of 100% of vehicles 
being zero-emission vehicles in 2050. Other economies that display 
vehicles obtained on second hand markets may phase out fossil fuel

cars only after 2050, hence limiting the overall mitigation potential of 
electric vehicles to well below 100% in 2050. Higher energy use and 
CO2-footprint in BEV production compared to ICE production are to 
be met with more rapid decarbonisation of the industry sector and by 
the reduced need for overall vehicle stock, due to socio-cultural and 
infrastructure measures. Ehrenberger et  al. (2021) shows that the 
development of technologies, fleets, and their use are decisive factors in 
reducing the use of fossil energies, resulting in 26–65% CO 2 emissions 
reduction potential until 2040 for the case of Germany. Electric vehicles 
can be used to provide new shared services. In this case, reductions of 
CO2 emissions of close to 20% can be obtained in a scenario where 
20% of car trips and all bus feeder trips are replaced, but considerably 
higher reductions are possible when shared pooled mobility replaces 
private vehicle trips in urban areas (ITF 2017b, ITF 2017d). A study

shows that ICE vehicles reduce CO 2 emissions to 60% or 80% of 
current emissions levels by 2050 (Hill et al. 2019). Similarly, the power 
grid decarbonisation is assumed to improve to either 50% or 80% over 
current rates, with 80% being the expected decarbonisation and 50% 
a  more conservative estimate. Each possibility for EV adoption rate, 
ICE efficiency improvement, and power decarbonisation is combined 
(Hill et al. 2019). Beyond consuming less energy, EVs enable greater 
use of low-carbon and renewable energy sources than is possible for 
conventional petroleum-based fuels. These technical advantages lead 
to the potential for greatly reducing petroleum use, air pollution and 
carbon emissions. International collaboration could better leverage

530
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Demand-side mitigation can be achieved through changes in socio-cultural factors, infrastructure 
design and use, and end-use technology adoption by 2050.
15
10
5
0GtCO2-eq yr  –1
Direct reduction of food 
related emissions, excluding
reforestation of freed up landAFOLU
End-use
sectors
Services for
3 1 The presentation of choices to consumers, and the impact of that presentation on consumer decision-making. 
2 Load management refers to demand-side ﬂexibility that cuts across all sectors and can be achieved through incentive design like time of use pricing/monitoring 
by artiﬁcial intelligence, diversiﬁcation of storage facilities, etc.
The impact of demand-side mitigation on electricity sector emissions depends on the baseline carbon intensity of electricity supply, which is scenario dependent.Emissions that cannot be 
avoided or reduced through 
demand-side options are 
assumed to be addressed

by supply-side optionsTotal emissions 2050
Infrastructure useSocio-cultural factors
End-use technology 
adoptionIndustryAdd. electriﬁcation
BuildingsLand transport
Load managementc. Electricity: indicative impacts
of change in service demand
Additional emissions from increased 
electricity generation to enable the 
end-use sectors’ substitution of electricity 
for fossil fuels, e.g. via heat pumps and 
electric cars {Table SM5.3; 6.6}Additional electriﬁcation (+60%)
Industry
Land transport
Buildings
Load management2
Reduced emissions through demand-side 
mitigation options (in end-use sectors: 
buildings, industry and land transport) 
which has potential to reduce 
electricity demand3Demand-side 
measures 
–73%a. Nutrition 
NutritionFood
Socio-cultural factors
Dietary shift (shifting to balanced, 
sustainable healthy diets), 
avoidance of food waste
and over-consumption
Infrastructure use
Choice architecture1 and 
information to guide dietary

choices; ﬁnancial incentives; 
waste management; 
recycling infrastructure
End-use technology adoption
Currently estimates are not 
available (for lab-based meat and 
similar options – no quantitative 
literature available, overall potential 
considered in socio-cultural factors)b. Manufactured products, mobility, shelter 
Manufactured products Mobility ShelterLand transport Aviation Shipping Industry Buildings
Shift in demand towards 
sustainable consumption, 
such as intensive use 
of longer-lived 
repairable productsTeleworking or 
telecommuting; 
active mobility 
through walking 
and cyclingAvoid long-haul 
ﬂights; shift to 
trains wherever 
possibleSocial practices 
resulting in energy 
saving; lifestyle and 
behavioural changesSocio-cultural factors
Networks established 
for recycling, repurposing, 
remanufacturing and 
reuse of metals, plastics 
and glass; labelling 
low-emissions materials 
and productsPublic transport; 
shared mobility; 
compact cities;

spatial planningCompact cities; 
rationalisation of living 
ﬂoor space; architectural 
design; urban planning 
(e.g., green roof, cool 
roof, urban green 
spaces etc.)Infrastructure use
Green procurement to 
access material-efﬁcient 
products and services; 
access to energy-efﬁcient 
and CO 2 neutral materialsElectric vehicles; 
shift to more 
efﬁcient vehiclesEnergy-efﬁcient 
building envelopes 
and appliances; 
shift to renewablesEnd-use technology adoption
Total emissions 2050: Mean IEA-STEPS IP_ModActCurrently not 
applicableCurrently not 
applicable
Adoption of
energy-efﬁcient 
technologies; 
with improved 
aerodynamics    Adoption of 
technology/
systems 
Figure 5.7 | Demand-side mitigation options and indicative potentials.  Demand-side mitigation response options related to demand for services have been categorised

into three broad domains: ‘socio-cultural factors’, associated with individual choices, behaviour and lifestyle change, social norms and culture; ‘infrastructure use’, related to 
the design and use of supporting hard and soft infrastructure that enables changes in individual choices and behaviour; and ‘end-use technology adoption’, which refers to the 
uptake of technologies by end users. Demand-side mitigation is a central element of the IMP-LD and IMP-SP scenarios (Section 3.3). Food (nutrition) demand-side potentials 
in 2050 assessment is based on bottom-up studies and estimated following the 2050 baseline for the food sector presented in peer-reviewed literature (more information in 
Chapter 5 Supplementary Material II and Chapter 7, Section 7.4.5). Industry (manufactured products), land transport, aviation and shipping (mobility), and buildings (shelter)

assessment of potentials for total emissions in 2050 are estimated based on approximately 500 bottom-up studies representing all global regions (detailed list is in Table 5.
SM.2). Baseline is provided by the sectoral mean GHG emissions in 2050 of the two scenarios consistent with policies announced by national governments until 2020. The 
heights of the coloured columns represent the potentials represented by the median value. These are based on a range of values available in the case studies from literature 
shown in Chapter 5 Supplementary Material II. The range is shown by the dots connected by dotted lines representing the highest and the lowest potentials reported in the 
literature. The demand-side potential of socio-cultural factors in food has two parts.The median value of direct emissions (mostly non-CO 2) reduction through socio-cultural

factors is 1.9 GtCO 2-eq without considering land-use change through reforestation of freed up land. If changes in land-use patterns enabled by this change in food demand 
are considered, the indicative potential could reach 7 GtCO 2-eq. The ‘electricity’ panel presents how sectoral demand-side mitigation options (industry, transport and buildings) 
can change demand on the electricity distribution system. Electricity accounts for an increasing proportion of final energy demand in 2050 (‘additional electrification’ bar) 
in line with multiple bottom-up studies (detailed list is in Table 5.SM.3) and Chapter 6 (Section 6.6). These studies are used to compute the impact of end-use electrification 
which increases overall electricity demand. Some of the projected increase in electricity demand can be avoided through demand-side mitigation options in the domains of

socio-cultural factors and infrastructure use strategies in end-use electricity use in buildings, industry and land transport found in literature based on bottom-up assessments 
(Section 5.3 and Chapter 5 Supplementary Material II).

531
Demand, Services and Social Aspects of Mitigation Chapter 55existing efforts to promote zero-emission vehicles. The establishment 
of a zero-emission vehicle deployment target and an electric mobility 
target for 2035 would help in establishing a common long-term global 
electric-drive vision (Lutsey 2015).
Socio-cultural factors such as avoiding long-haul flights and shifting 
to train wherever possible can contribute between 10% and 40% 
to aviation GHG emissions reduction by 2050 (Figure 5.7). Maritime 
transport (shipping) emits around 940 MtCO 2 annually and is 
responsible for about 2.5% of global GHG emissions (IMO 2020). 
Technology measures and management measures, such as slow 
steaming, weather routing, contra-rotating propellers, and propulsion 
efficiency devices can deliver more fuel savings between 1% and 
40% than the investment required (Bouman et al. 2017) (Chapter 5, 
Supplementary Material II, Table 5.SM.2).

In the buildings sector, avoidance strategies can occur at the end 
use or individual building operation level. End-use technologies and 
strategies such as the use of daylighting (Bodart and De Herde 2002) 
and lighting sensors can avoid demand for lumens from artificial 
light, while passive houses, thermal mass, and smart controllers can 
avoid demand for space conditioning services. Eliminating standby 
power losses can avoid energy wasted for no useful service in many 
appliances and devices, which may reduce household electricity use 
by up to 10% (Roy et al. 2012). At the building level, smaller dwellings 
can reduce overall demand for lighting and space conditioning 
services, while smaller dwellings, shared housing, and building lifespan 
extension can all reduce the overall demand for carbon-intensive 
building materials such as concrete and steel (Material Economics 
2018; Hertwich et al. 2019; IEA 2019b; Pauliuk et al. 2021). Emerging

strategies for materials efficiency, such as 3D printing to optimise the 
geometries and minimise the materials content of structural elements, 
may also play a key role if thermal performance and circularity can be 
improved (Mahadevan et al. 2020; Adaloudis and Bonnin Roca 2021). 
Several scenarios estimate an ‘Avoid’ potential in the building sector, 
which includes reducing waste in superfluous floor space, heating and 
IT equipment, and energy use, of between 10% and 30%, in one case 
even by 50% (Nadel and Ungar 2019) (Chapter 9).
Socio-cultural factors and behavioural and social practices in energy 
saving, like adaptive heating and cooling by changing temperature, 
can contribute about 15% to GHG emissions reduction in the buildings 
sector by 2050 (Figure  5.7). Infrastructure use such as compact city 
and  urban planning interventions, living floor space rationalisation, 
and access to low-carbon architectural design has about 20% potential

in building sector GHG emissions reduction. Technology adoption, 
particularly access to energy efficient technologies, and installation of 
renewable energy technologies can contribute between 30% and 70% 
to GHG emissions reduction in the buildings sector (Chapters 8 and 9 
and Chapter 5 Supplementary Material II, Table 5.SM.2).
Service efficiency strategies are emerging to avoid materials demand 
at the product level, including dematerialisation strategies for various 
forms of packaging (Worrell and Van Sluisveld 2013) and the concept 
of ‘products as services’, in which product systems are designed and 
maintained for long lifespans to provide a marketable service (Oliva 
and Kallenberg 2003), thereby reducing the number of products sold and tonnes of materials needed to provide the same service to 
consumers, consistent with circular economy and materials efficiency 
principles (Chapter 11). Successful examples of this approach have

been documented for carpets (Stubbs and Cocklin 2008), copiers 
(Roy 2000), kitchens (Liedtke et al. 1998), vehicles (Williams 2006; 
Ceschin and Vezzoli 2010) and more (Roy 2000).
‘Shift’ strategies unique to the service-oriented perspective generally 
involve meeting service demands at much lower lifecycle energy, 
emissions, and resource intensities (Roy and Pal 2009), through such 
strategies as shifting from single-family to multi-family dwellings 
(reducing the materials intensity per unit floor area (Ochsendorf et al. 
2011)), shifting from passenger cars to rail or bus (reducing fuel, 
vehicle manufacturing, and infrastructure requirements (Chester and 
Horvath 2009)), shifting materials to reduce resource and emissions 
intensities (e.g., low-carbon concrete blends (Scrivener and Gartner 
2018)) and shifting from conventional to additive manufacturing 
processes to reduce materials requirements and improve end-use 
product performance (Huang et al. 2016, 2017).

An important consideration in all ASI strategies is the potential for 
unintended rebound effects (Sorrell et al. 2009; Brockway et al. 2021) 
as indicated in Figures 5.8, 5.12, and 5.13a, which must be carefully 
avoided through various regulatory and behavioural measures 
(Santarius et  al. 2016). In many developing country contexts, 
rebound effects can help in accelerated provision of  affordable 
access to modern energy and a minimum level of per capita energy 
consumption (Saunders et  al. 2021; Chakravarty and Roy 2021). 
Extending the lifespan of energy inefficient products may lead to net 
increases in emissions (Gutowski et al. 2011), whereas automated 
car sharing may reduce the number of cars manufactured at the 
expense of increased demand for passenger kilometres due to lower 
travel opportunity cost (Wadud et al. 2016) (Section 5.3.2).
Avoiding short lifespan products in favour of products with longer

lifespan as a socio-cultural factor; and infrastructure use measures such 
as increasing the re-usability and recyclability of products’ components 
and materials, and adopting materials-efficient services and CO2-
neutral materials, have about 29% indicative potential by 2050. 
(Chapter 11 and Chapter 5 Supplementary Material II, Table 5.SM.2).
In summary, sector-specific demand-side mitigation options reflect the 
important role of socio-cultural, technological and infrastructural factors 
and the interdependence among them (Figure 5.7). The assessment in 
Figure 5.7 shows that by 2050 high emission reduction potential can be 
realised with demand-side actions alone, which can be complementary 
to supply-side interventions, with considerable impact by reducing the 
need for capacity addition on the electricity supply system. Integrated 
cross-sectoral actions shown through sector coupling is also important 
for investment decision-making and policy framing going beyond

sector boundaries (high evidence and high agreement).
5.3.1.2 Household Consumption Options 
to Reduce GHG Emissions
A systematic review of options to reduce the GHG emissions 
associated with household consumption activities identified

532
Chapter 5 Demand, Services and Social Aspects of Mitigation 56,990 peer-reviewed journal papers, with 771 options that were 
aggregated into 61  consumption option categories (Ivanova et  al. 
2020) (Figure 5.8). Consistently with previous research (Herendeen 
and Tanaka 1976; Pachauri and Spreng 2002; Pachauri 2007; Ivanova 
et  al. 2016), a  hierarchical list of mitigation options emerges. 
Choosing low-carbon options, such as car-free living, plant-based diets with no or very little animal products, low-carbon sources of 
electricity and heating at home, as well as local holiday plans, can 
reduce an individual’s carbon footprint by up to 9 tCO 2-eq. Realising 
these options requires substantial policy support to overcome 
infrastructural, institutional and socio-cultural lock-in (Sections 5.4 
and 5.6).
Less car transportOne less ﬂight (medium return)One less ﬂight (long return)Live car-free
Less transport by air

Fuel efﬁcient drivingFood waste reductionLess living space/co-housingTelecommutingNo pets
Less packaging
Less processed food/alcoholLower room temperatureFood sufﬁciencyLess animal productsHot water saving
Fewer purchases/durable items
Less paperBio-plastics/less plasticFewer appliancesLess energy use (clothing)Less textilesAvoid Mitigation potential (tCO 2-eq cap–1) Improve Mitigation potential (tCO 2-eq cap–1)
0 1 2 3 4 5Improved cooking equipmentHeat pumpRefurbishment and renovationRenewable electricity
Renewable-based heating
Shift to a smaller carProduce own foodShift to PHEV/HEVProduce renewable electricityPassive house
Better thermal insulation
Food waste managementBetter use of appliancesEnergy and material efﬁciencyMore efﬁcient appliancesSmart metering
Low-carbon construction
RecycleGreen roofsRecycled materials
0 1 2 3 4 5
Shift Mitigation potential (tCO 2-eq cap–1)
0 1 2 3 4 5Vegetarian dietSustainable diet (unspeciﬁed)Vegan dietShift to public transport

Shift to lower carbon meats
Car-pooling/sharingRegional/local foodMediterranean and similarShift to active transportOrganic food
Service/sharing economy
Walk instead of busPartial shift to dairy/plants/ﬁshSeasonal/fresh foodNutrition guidelines dietEat out eco-friendly0 1 2 3 4 5 –3 –2 –1
Mean
Estimates25th Percentile (Q1)
Median75th Percentile (Q3)
Interquartile range (IQR)Maximum 
(Q3+IQR×1.5)Minimum 
(Q1–IQR×1.5)
0 4 –2 2
Figure 5.8 | Synthesis of 60 demand-side options ordered by the median GHG mitigation potential found across all estimates from the literature.  The grey 
crosses are averages. The boxes represent the 25th percentile, median and 75th percentiles of study results. The whiskers or dots show the minimum and maximum mitigation 
potentials of each option. Negative values (in the red area) represent the potentials for backfire due to rebound, i.e., a net increase of GHG emissions due to adopting the option. 
Source: with permission from Ivanova et al. (2020).

533
Demand, Services and Social Aspects of Mitigation Chapter 555.3.2 Technical Tools to Identify Avoid-Shift-
Improve Options
Service delivery systems to satisfy a  variety of service needs 
(e.g., mobility, nutrition, thermal comfort, etc.) comprise a series of 
interlinked processes to convert primary resources (e.g., coal, minerals) into useable products (e.g.,  electricity, copper wires, lamps, light 
bulbs). It is useful to differentiate between conversion and processing 
steps ‘upstream’ of end users (mines, power plants, manufacturing 
facilities) and ‘downstream’, that is, those associated with end-users, 
including service levels, and direct well-being benefits for people (Kalt 
et al. 2019). Illustrative examples of such resource processing systems 
c)a)
b)
Ore and scrap inputs Crude steel Steel in purchased products Recovered post use scap0%25%50%75%100%
Water Crop Food Nutrition0%25%50%75%100%

Primary energy Final energy Useful energy ServiceTotalResidential/CommercialIndustry (excl. non-energy uses)
TransportSector511 EJ
378 EJ
208 EJ
72 EJ
Figure 5.9 | Resource processing steps and efficiency cascades (in percentage of primary resource inputs [vertical axis] remaining at respective steps 
until ultimate service delivery) for illustrative global service delivery systems for energy (panel (a), disaggregated into three sectoral service types 
and the aggregate total), food (panel (b), water use in agriculture and food processing, delivery and use), and materials (panel (c), example steel).  The 
aggregate efficiencies of service delivery chains is with 13–17% low. Source: TWI2050 (2018).

534
Chapter 5 Demand, Services and Social Aspects of Mitigation 5and associated conversion losses drawn from the literature are 
shown in Figure 5.9, in the form of resource processing cascades for 
energy (direct energy conversion efficiencies (Nakićenović et al. 1993; 
De Stercke 2014)), water use in food production systems (water use 
efficiency and embodied water losses in food delivery and consumption 
(Lundqvist et al. 2008; Sadras et al. 2011)), and materials (Ayres and 
Simonis 1994; Fischer-Kowalski et al. 2011), using the example of steel 
manufacturing, use and recycling at the global level (Allwood and 
Cullen 2012). Invariably, conversion losses along the entire service 
delivery systems are substantial, ranging from 83% (water) to 86% 
(energy) and 87% (steel) of primary resource inputs (TWI2050 2018). 
In other words, only between 14 to 17% of the harnessed primary

resources remain at the level of ultimate service delivery.Examples of conversion losses on the supply side of resource processing 
systems include, for instance: for energy, electricity generation (global 
output/input conversion efficiency of electric plants of 45% as shown 
in energy balance statistics (IEA 2020b)); for water embodied in food, 
irrigation water use efficiency (some 40% (Sadras et al. 2011)) and 
calorific conversion efficiency (food calories in to food calories out) 
in meat production of 60% (Lundqvist et al. 2008), or for materials, 
globally only 47% of primary iron ore extracted and recovered steel 
scrap end up as steel in purchased products, (i.e.,  a  loss of 57%) 
(Allwood and Cullen 2012).
A substantial part of losses happens at the end-use point and in final 
service delivery (where losses account for 47% to 60% of aggregate 
systems losses for steel and energy respectively, and 23% in the case

of water embodied in food). The efficiency of service delivery (Brand-
Correa and Steinberger 2017) has usually both a  technological 
component (efficiency of end-use devices such as cars, light bulbs) 
and a behavioural component (i.e., how efficiently end-use devices 
are used, e.g.,  load factors) (Dietz et  al. 2009; Laitner et  al. 2009; 
Norton 2012; Kane and Srinivas 2014; Ehrhardt-Martinez 2015; 
Thaler 2015; Lopes et al. 2017). Using the example of mobility, where 
service levels are usually expressed by passenger-km, service delivery 
efficiency is thus a function of the fuel efficiency of the vehicle and 
its drivetrain (typically only about 20%–25% for internal combustion 
engines, but close to 100% for electric motors) plus how many 
passengers the vehicle actually transports (load factor, typically as 
low as 20–25%, i.e. one passenger per vehicle that could seat four 
to five), that is, an aggregate end-use efficiency of between 4–6%

only. Aggregated energy end-use efficiencies at the global level are 
estimated as low as 20% (De Stercke 2014), 13% for steel (recovered 
post-use scrap) (Allwood and Cullen 2012), and some 70% for 
food (including distribution losses and food waste of some 30%) 
(Lundqvist et al. 2008).
To harness additional gains in efficiency by shifting the focus in service 
delivery systems to the end user can translate into large upstream 
resource reductions. For each unit of improvement at the end-use point 
of the service delivery system (examples shown in Figure 5.9), primary 
resource inputs are reduced between a factor of 6 to 7 units (water, 
steel, energy) (TWI2050 2018). For example, reducing energy needs 
for final service delivery equivalent to 1 EJ, reduces primary energy 
needs by some 7 EJ. There is thus high evidence and high agreement 
in the literature that the leverage effect for improvements in end-
use service delivery efficiency through behavioural, technological,

and market organisational innovations is very large, ranging from 
a factor 6 to 7 (resource cascades) to up to a factor 10 to 20 (exergy 
analysis), with the highest improvement potentials at the end-user 
and service provisioning levels (for systemic reviews see Nakićenović 
et al. (1996a), Grubler et al. (2012b), and Sousa et al. (2017)). Also, the 
literature shows high agreement that current conversion efficiencies 
are invariably low, particularly for those components at the end-
use and service-delivery back end of service provisioning systems. 
It also suggests that efficiencies might actually be even lower 
than those revealed by direct input-output resource accounting, 
as discussed above (Figure  5.9). Illustrative exergy efficiencies of 
entire national or global service delivery systems range from 2.5% 
(USA (Ayres 1989)) to 5% (OECD average (Grubler  et  al. 2012b)) 
Improve
Shift Shift
Avoid(c) Achieving a Low Demand scenario by 2050
Utilities, 
equipment

manufacturing, 
public sector 
(regulation)* Primary energy 
demand growth 
due to transport 
electriﬁcation* Primary energy 
electriﬁcation
End users, 
manufacturers, 
(regulation, 
incentives)End users, service 
companies, 
transport
operators, public 
sector (standards)Consumers, 
(investments, 
welfare policies)0100200300400500EJ world energy demand2020 primary
Energy 511 EJ *Primary
Energy Final 
EnergyUseful
EnergyServices
Delivered
2050 LED 
Services 279 EJActorsBuilding shells, 
transport choice, 
Behavioural 
changeBuilding shells, 
changeIncreased services 
(to meet SDGs) m2 
dwellings, public 
services, pass-km, 
Policies, 
behavioursIncreased services 
Policies, 
behaviours
Improved service provisioning systems enable increases in 
service levels and at the same time a reduction in upstream

energy demand by 45%.Global south Global north
Materials Buildings Transport
Vehicles, 
appliances, 
Adoption 
decisionsVehicles, 
Adoption 
decisionsEfﬁciency 
Improvements 
& decisions:
Power plants, 
fuel production, 
Investments 
decisions
Figure  5.10 | Realisable energy efficiency improvements by region and 
by end-use type between 2020 and 2050 in an illustrative Low Energy 
Demand scenario (in EJ).  Efficiency improvements are decomposed by respective 
steps in the conversion chain from primary energy to final, and useful, energy, and to 
service delivery, and disaggregated by region (developed and developing countries) 
and end-use type (buildings, transport, materials). Improvements are dominated by 
improved efficiency in service delivery (153 EJ) and by more efficient end-use energy 
conversion (134 EJ). Improvements in service efficiency in transport shown here are

conservative in this scenario but could be substantially higher with the full adoption 
of integrated urban shared mobility schemes. Increases in energy use due to increases 
in service levels and system effects of transport electrification (grey bars on top of first 
pair in the bar charts) that counterbalance some of the efficiency improvements are 
also shown. Examples of options for efficiency improvements and decision involved 
(grey text in the chart), the relative weight of generic demand-side strategies (Avoid-
Shift-Improve blue arrows), as well as prototype actors involved, are also illustrated. 
Data source: Figure 5.9 and Grubler et al. (2018).

535
Demand, Services and Social Aspects of Mitigation Chapter 55and 10% (global (Nakićenović et al., 1996)). Studies that adopt more 
restricted systems boundaries, either leaving out upstream resource 
processing/conversion or conversely end-use and service provision, 
show typical exergetic efficiencies between 15% (city of Geneva 
(Grubler et al. 2012a)) to below 25% (Japan, Italy, and Brazil, albeit 
with incomplete systems coverage that miss important conversion 
losses (Nakićenović et al. 1996b)). These findings are confirmed by 
more recent exergy efficiency studies that also include longitudinal 
time trend analysis (Cullen and Allwood 2010; Brockway et al. 2014; 
Serrenho et  al. 2014; Brockway et  al. 2015; Guevara et  al. 2016). 
Figure 5.10 illustrates how energy demand reductions can be realised 
by improving the resource efficiency cascades shown in Figure 5.9.
5.3.3 Low Demand Scenarios
Long-term mitigation scenarios play a crucial role in climate policy

design in the near term, by illuminating transition pathways, 
interactions between supply-side and demand-side interventions, 
their timing, and the scales of required investments needed to 
achieve mitigation goals (Chapter  3). Historically, most long-term 
mitigation scenarios have taken technology-centric approaches 
with heavy reliance on supply-side solutions and the use of carbon 
dioxide removal, particularly in 1.5°C scenarios (Rogelj et al. 2018). 
Comparatively less attention has been paid to deep demand-side 
reductions incorporating socio-cultural change and the cascade 
effects (Section 5.3.2) associated with ASI strategies, primarily due to 
limited past representation of such service-oriented interventions in 
long-term integrated assessment models (IAMs) and energy systems 
models (ESMs) (Grubler et  al. 2018; van de Ven et  al. 2018; Napp 
et al. 2019). There is ample evidence of savings from sector- or issue-

specific bottom-up studies (Section 5.3.1.2). However, these savings 
typically get lost in the dominant narrative provided by IAMs and 
ESMs and in their aggregate-level evaluations of combinations of ASI 
and efficiency strategies. As a result, their interaction effects do not 
typically get equal focus alongside supply-side and carbon dioxide 
removal options (Samadi et al. 2017; Van Vuuren et al. 2018; Van den 
Berg et al. 2019).
In response to 1.5°C ambitions, and a  growing desire to identify 
participatory pathways with less reliance on carbon dioxide removal 
which has high uncertainty, some recent IAM and ESM mitigation 
scenarios have explored the role of deep demand-side energy and 
resource use reduction potentials at global and regional levels. 
Table 5.2 summarises long-term scenarios that aimed to: minimise 
service-level energy and resource demand as a  central mitigation 
tenet; specifically evaluate the role of behavioural change and ASI

strategies; and/or achieve a carbon budget with limited or no carbon 
dioxide removal. From assessment of this emerging body of literature, 
several general observations arise and are presented below.
First, socio-cultural changes within transition pathways can offer 
gigatonne-scale CO 2 savings potential at the global level, and 
therefore represent a substantial overlooked strategy in traditional 
mitigation scenarios. Two lifestyle change scenarios conducted 
with the IMAGE IAM suggested that behaviour and cultural 
changes such as heating and cooling set-point adjustments, shorter showers, reduced appliance use, shifts to public transit, less meat-
intensive diets, and improved recycling can deliver an additional 
1.7 Gt and 3 GtCO 2 savings in 2050, beyond the savings achieved 
in traditional technology-centric mitigation scenarios for the 2°C 
and 1.5°C ambitions, respectively (van Sluisveld et  al. 2016; Van 
Vuuren et al. 2018). In its Sustainable Development Scenario, the

IEA’s behavioural change and resource efficiency wedges deliver 
around 3 GtCO 2-eq reduction in 2050, combined savings, roughly 
equivalent to those of solar PV that same year (IEA 2019a). In 
Europe, a  Global Change Assessment Model (GCAM) scenario 
evaluating combined lifestyle changes such as teleworking, travel 
avoidance, dietary shifts, food waste reductions, and recycling 
reduced cumulative EU  27 CO 2 emissions 2011–2050 by up 
to 16% compared to an SSP2 baseline (van de Ven et  al. 2018). 
Also in Europe, a  multi-regional input-output analysis suggested 
that adoption of low-carbon consumption practices could reduce 
carbon footprints by 25%, or 1.4 Gt (Moran et al. 2020). A global 
transport scenario suggests that transport sector emissions can 
decline from business-as-usual 18 GtCO 2-eq to 2 GtCO 2-eq if ASI 
strategies are deployed (Gota et  al. 2019), a  value considerably 
below the estimates provided in IAM scenarios that have limited or

no resolution in ASI strategies (Chapter 10).
The IEA’s Net-Zero Emissions by 2050 (NZE) scenario, in which 
behavioural changes lead to 1.7 GtCO 2 savings in 2030, expresses the 
substantial mitigation opportunity in terms of low-carbon technology 
equivalencies: to achieve the same emissions reductions, the global 
share of EVs in the NZE would have to increase from 20% to 45% by 
2030 or the number of installed heat pumps in homes would have to 
increase from 440 to 660 million by 2030 (IEA 2021).
In light of the limited number of mitigation scenarios that represent 
socio-behavioural changes explicitly, there is medium evidence in the 
literature that such changes can reduce emissions at regional and global 
levels, but high agreement within that literature that such changes 
hold up to gigatonne-scale CO 2 emissions reduction potentials.
Second, pursuant to the ASI principle, deep demand reductions 
require parallel pursuit of behavioural change and advanced energy-

efficient technology deployment; neither is sufficient on its own. The 
LED scenario (Figure 5.10) combines behavioural and technological 
change consistent with numerous ASI strategies that leverage 
digitalisation, sharing, and circular economy megatrends to deliver 
decent living standards while reducing global final energy demand 
in 2050 to 245 EJ (Grubler et  al. 2018). This value is 40% lower 
than final energy demand in 2018 (IEA 2019a), and a lower 2050 
outcome than other IAM/ESM scenarios with primarily technology-
centric mitigation approaches (Teske et al. 2015; IEA 2017b). In the 
IEA’s B2DS scenario, Avoid/Shift in the transport sector accounts 
for around 2 GtCO 2-eq yr –1 in 2060, whereas parallel vehicle 
efficiency improvements increase the overall mitigation wedge to 
5.5 GtCO 2-eq yr –1 in 2060 (IEA 2017b). Through a combination of 
behavioural change and energy-efficient technology adoption, the 
IEA’s NZE requires only 340 EJ of global final energy demand with

universal energy access in 2050, which is among the lowest of IPCC 
net zero SR1.5 scenarios (IEA 2021).

536
Table 5.2 | Summary of long-term scenarios with elements that aimed to minimise service-level energy and resource demand.
Global scenarios
#Scenario
[Temp]IAM/
ESMFinal 
energyFocused demand reduction element(s)
Baseline 
scenarioMitigation potentialc
Scope SectorsaKey demand reduction measures considered (A, S, I)bCO2 (Gt)Final 
energyPrimary 
energy
1Lifestyle change 
scenario [2°C]IMAGE – Whole scenario R, T, IA: set-points, smaller houses, reduced shower times, wash temperatures, 
standby loss, reduced car travel, reduced plastics
S: from cars to bikes, rail
I: improved plastic recycling2°C technology-
centric scenario 
in 20501.9 – –
2Sustainable 
Development scenario 
[1.8°C]World 
Energy 
Model 
(WEM)398 EJ 
in 2040Behavioural change 
wedge and resource 
efficiency wedgeT, IS: shifts from cars to mass transit, building lifespan extension, 
materials-efficient construction, product reuse

I: improved recyclingStated policies 
in 20503 – –
3Beyond 2 Degrees 
scenario [1.75°C]ETP-TIMES377 EJ 
in 2050Transport Avoid/Shift 
wedge and material 
efficiency wedgeT, IA: shorter car trips, optimised truck routing and utilisation
S: shifts from cars to mass transit
I: plastics and metal recycling, production yield improvementsStated policies 
in 20602.8 – –
4Lifestyle change 
scenario [1.5°C]IMAGE322 EJ 
in 2050Whole scenario R, C, T, IA: set-points, reduced appliance use
S: from cars to mass transit, less meat-intensive diets, cultured meat
I: best available technologies across sectors1.5°C technology-
in 20503.1 – –
5Low Energy Demand 
scenario [1.5°C]MESSAGE245 EJ 
in 2050Whole scenarioR, C, T, 
I, FA: device integration, telework, shared mobility, material efficiency, 
dematerialisation, reduced paper
S: multi-purpose dwellings, healthier diets
I: best available technologies across sectorsFinal energy 
in 2020– 179 EJ –
6Advanced Energy

[R]evolution–279 EJ 
in 2050Whole scenario R, C, T, IS: shifts from cars to mass transit
I: best available technologies across sectorsContinuation of 
current trends and 
policies in 2050– 260 EJ –
7Limited BECCS – 
lifestyle change [1.5°C]IMAGE – Whole scenario R, C, T, FA: set-points, reduced appliance use
in 20502.2 Gt – 82 EJ
8 Lifestyle scenario [1.5°C] AIM374 EJ 
in 2050Whole scenario T, I, FA: reduced transport services demand, reduced demand 
for industrial goods
S: less meat-intensive diets1.5°C supply 
technology-centric 
scenario in 2050– 42 EJ –
9Transport scenario 
[1.5°C]Bottom-up 
construction– Whole scenario TA: multiple options
S: multiple options
I: multiple options89% vs BAU: 
16GtCO 2– –
10Net Zero Emissions 
2050 scenarioWorld 
Energy 
Model

(WEM)– Behaviour change wedge R, TA: set-points, line drying, reduced wash temperatures, telework, 
reduced air travel
S: shifts to walking, cycling
I: eco-drivingStated policies 
in 20302 – –
11Decent living with 
minimum energyBottom-up 
construction149 EJ 
in 2050Whole scenario R, T, I, FA: activity levels for mobility, shelter, nutrition, etc., consistent with 
S: shifts away from animal-based foods, shifts to public transit, etc.
I: energy efficiency consistent with best available technologiesIEA Stated Policies 
Scenario in 2050– 75% –
12Net-Zero Emissions by 
2050 Scenario (NZE)Hybrid 
model based 
on WEM and 
ETP-TIMES340 EJ in 
2050Behavioural 
change reductionsR, C, T, IA: heating, air conditioning, and hot water set-points, reduce 
international flights, line drying, vehicle light-weighting, 
materials-efficient construction, building lifespan extension
S: shifts from regional flights to high-speed rail, cars to walking, 
cycling or public transport,

I: eco-driving, plastics recyclingStated policies 
in 20502.6 37 EJ

537
Demand, Services and Social Aspects of Mitigation Chapter 55Global scenarios
#Scenario
ESMFinal 
Baseline 
energy
Regional scenarios
13 Urban mitigation wedge –540 EJ in 
global cities 
in 2050Whole scenario R, C, TA: reduced transport demand
S: mixed-use developments
I: vehicle efficiency, building codes and retrofitsCurrent trends 
to 2050– 180 EJ –
14France 2072 collective 
societyTIMES-Fr4.2 EJ in 
France in 
2072Whole scenario R, TA: less travel by car and plane, longer building and device lifespans, 
less spending
S: shared housing, shifts from cars to walking, biking, mass transitFinal energy 
in 2014– 1.7 EJ –
15EU 27 lifestyle change – 
enthusiastic profileGCAM – Whole scenario R, T, FA: telework, avoid short flights, closer holidays, food waste reduction, 
car sharing, set-points

S: vegan diet, shifts to cycling and public transit
I: eco-driving, composting, paper, metal, plastic, and glass recyclingSSP2, cumulative 
emissions 
2011–205016% – –
16Europe broader regime 
change scenarioIMAGE35 EJ in EU 
in 2050Whole scenario R, TA: reduced passenger and air travel, smaller dwellings, fewer appliances, 
reduced shower times, set points, avoid standby losses
S: car sharing, shifts to public transit
I: best available technologiesSSP2 in 2050 – 10 EJ –
17 EU Carbon-CAPEXIOBASE 
3 MRIO– Whole scenario R, T, F90 demand-side behaviour change opportunities spanning A-S-I including 
changes to consumption patterns, reducing consumption, and switching 
to using goods with lower-carbon production and low-carbon use phases.Present day 
footprint1.4 – –
18France ‘négawatt’ 
scenarioBottom-up 
constructionSufficiency wedgeR, C, T, 
I, FA: increase building capacity utilisation, reduced appliance use,

car sharing, telework, reduced goods consumption, less packaging
S: shifts to attached buildings; shifts from cars and air to public transit 
and active mobility, car sharing, freight shifts to rail and water, shifts 
away from animal proteins
I: reduced speed limits, vehicle efficiency, increased recyclingBusiness as 
usual in 2050 
(~2,300 TWh 
primary energy)– – ~500 TWh
19The Netherlands 
household energy 
behavioural changesBENCH-NLD 
agent-based 
model–Individual energy 
behavioural changes 
and social dynamics; 
considering carbon pricingRA: reduce energy consumption through changing lifestyle, habits 
and consumption patterns
S: to green energy provider; investment in solar PVs (prosumers)
I: investment in insulation and energy-efficient appliancesSSP2 in 2030 50% – –
20The Netherlands 
behavioural changes and 
social dynamicsRA: reduce energy consumption

S: investment in solar PVs (prosumers)
I: investment in insulation and energy-efficient appliancesSSP2 in 2050 56% 51–71%
21Spain household energy 
behavioural changesBENCH-ESP 
I: investment in insulation and energy-efficient appliancesSSP2 in 2050 44% 16–64%
22A Societal 
Transformation Scenario 
for Staying Below 1.5°CGlobal 
calculator187 EJ 
in 2050Whole scenario R,C,I,F A: reduce energy, material and land use consumption n/aDown to 
9.1 GtCO 2 
in 2050
Sources: a van Sluisveld et al. (2016); b IEA (2019a); c IEA (2017b); d Van Vuuren et al. (2018); e Grubler et al. (2018); f Teske et al. (2015); g Esmeijer et al. (2018): h Liu et al. (2018); i Gota et al. (2019); j IEA (2020a); k Millward-Hopkins et al. (2020);

l IEA (2021); m Creutzig et al. (2015b); n Millot et al. (2018); o van de Ven et al. (2018); p van Sluisveld et al. (2018); q Moran et al. (2020); r négawatt Association (2018); s Niamir et al. (2020c); t, u Niamir et al. (2020a); v Kuhnhenn et al. (2020).
a R = residential (Chapters 8, 9); C = commercial (Chapters 8, 9), T = transport (Chapters 8, 10), I = industry (Chapter 11), F = food (Chapters 6, 12).
b A= Avoid; S = Shift, I = Improve, BAU = business as usual.
c Relative to indicated baseline scenario value in stated year.

538
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Third, low demand scenarios can reduce both supply-side capacity 
additions and the need for carbon capture and removal technologies 
to reach emissions targets. Of the scenarios listed in Table 5.2, one 
(LED-MESSAGE) reaches 2050 emissions targets with no carbon 
capture or removal technologies (Grubler et  al. 2018), whereas 
others report significant reductions in reliance on bioenergy with 
carbon capture and storage (BECCS) compared to traditional 
technology-centric mitigation pathways (Liu et al. 2018; Van Vuuren 
et al. 2018; Napp et al. 2019), with the IEA’s NZE notably requiring 
the least carbon dioxide removal (1.8 Gt in 2050) and primary 
bioenergy (100  EJ in 2050) compared to IPCC net zero SR1.5 
scenarios (IEA 2021).
Fourth, the costs of reaching mitigation targets may be lower when 
incorporating ASI strategies for deep energy and resource demand

reductions. The TIAM-Grantham low demand scenarios displayed 
reduction in mitigation costs (0.87–2.4% of GDP), while achieving 
even lower cumulative emissions to 2100 (228 to ~475 GtCO 2) 
than its central demand scenario (741 to 1066 GtCO 2), which had 
a cost range of (2.4–4.1% of GDP) (Napp et al. 2019). The GCAM 
behavioural change scenario concluded that domestic emission 
savings would contribute to reducing the costs of achieving the 
internationally agreed climate goal of the EU by 13.5% to 30% (van 
de Ven et al. 2018). The AIMS lifestyle case indicated that mitigation 
costs, expressed as global GDP loss, would be 14% lower than the 
SSP2 reference scenario in 2100, for both 2°C and 1.5°C mitigation 
targets (Liu et  al. 2018). These findings mirror earlier AIM results, 
which indicated lower overall mitigation costs for scenarios focused 
on energy service demand reductions (Fujimori et al. 2014). In the 
IEA’s NZE, behavioural changes that avoid energy and resource

demand save USD4 trillion (cumulatively 2021–2050) compared to 
if those emissions reductions were achieved through low-carbon 
electricity and hydrogen deployment (IEA 2021).
Based on the limited number of long-term mitigation scenarios that 
explicitly represent demand reductions enabled by ASI strategies, 
there is medium evidence but with high agreement within the 
literature that such scenarios can reduce dependence on supply-side 
capacity additions and carbon capture and removal technologies, 
with opportunites for lower overall mitigation costs.
If the limitations within most IAMs and ESMs regarding non-inclusion 
of granular ASI strategy analysis can be addressed, it will expand 
and improve long-term mitigation scenarios (Van den Berg et  al. 
2019). These include broader inclusion of mitigation costs for 
behavioural interventions (van Sluisveld et al. 2016), much greater 
incorporation of rebound effects (Krey et al. 2019), including from

improved efficiencies (Brockway et al. 2021) and avoided spending 
(van de Ven et al. 2018), improved representation of materials cycles 
to assess resource cascades (Pauliuk et al. 2017), broader coverage 
of behavioural change (Samadi et  al. 2017; Saujot et  al. 2020), 
improved consideration of how economic development affects 
service demand (Semieniuk et al. 2021), explicit representation of 
intersectoral linkages related to digitalisation, sharing economy, 
and circular economy strategies (Section  5.3.4), and institutional, 
political, social, entrepreneurial, and cultural factors (van Sluisveld 
et al. 2018). Addressing the current significant modelling limitations will require increased investments in data generation and 
collection, model development, and inter-model comparisons, with 
a  particular focus on socio-behavioural research, which has been 
underrepresented in mitigation research funding to date (Overland 
and Sovacool 2020).

COVID-19 interacts with demand-side scenarios (Box 5.2). Energy 
demand will mostly likely be reduced between 2020 and 2030 
compared to the default pathway, and if recovery is steered 
towards low energy demand, carbon prices for a 1.5°C-consistent 
pathway will be reduced by 19%, energy supply investments until 
2030 will be reduced by USD1.8 trillion, and the pressure to rapidly 
upscale renewable energy technologies will be softened (Kikstra 
et al. 2021a).
5.3.4 Transformative Megatrends
The sharing economy, the circular economy, and digitalisation have 
all received much attention from the research, advocacy, business 
models and policy communities as potentially transformative trends 
for climate change mitigation (IEA 2017a; Material Economics 2018; 
TWI2050 2019). All are essentially emerging and contested concepts 
(Gallie 1955) that have the common goal of increasing convenience 
for users and rendering economic systems more resource efficient,

but which exhibit variability in the literature on their definitions and 
system boundaries. Historically, both sharing and circular economies 
have been commonplace in developing countries, where reuse, repair, 
and waste scavenging and recycling comprise the core of informal 
economies facilitated by human interventions (Wilson et  al. 2006; 
Asim et al. 2012; Pacheco et al. 2012). Digitalisation is now propelling 
sharing and circular economy concepts in developed and developing 
countries alike (Roy et al. 2021), and the three megatrends are highly 
interrelated, as seen in Figure  5.11. For example, many sharing 
economy concepts rely on corporate or, to lesser degree, non-profit 
digital platforms that enable efficient information and opportunity 
sharing, thus making it part of the digitalisation trend. Parts of 
the sharing economy are also included in some circular economy 
approaches, as shared resource use renders utilisation of material

more efficient. Digital approaches to material management also 
support the circular economy, such as through waste exchanges 
and industrial symbiosis. Digitalisation aims more broadly to deliver 
services in more efficient, timely, intelligent, and less resource-
intensive ways (i.e., by moving bits and not atoms), through the use 
of increasingly interconnected physical and digital systems in many 
facets of economies. With rising digitalisation also comes the risk of 
increased electricity use to power billions of devices and the internet 
infrastructure that connects them, as well as growing quantities of 
e-waste, presenting an important policy agenda for monitoring and 
balancing the carbon and resource costs and benefits of digitalisation 
(Malmodin and Lundén 2018; TWI2050 2019). Rebound effects 
and instigated consumption of digitalisation are risking to lead to 
a  net increase in GHG emissions (Belkhir and Elmeligi 2018). The

determinants and possible scales of mitigation potentials associated 
with each megatrend are discussed below.

539
Demand, Services and Social Aspects of Mitigation Chapter 555.3.4.1 Digitalisation
In the context of service provision, there are numerous opportunities 
for consumers to buy, subscribe to, adopt, access, install or use 
digital goods and services (Wilson et al. 2020b). Digitalisation has 
opened up new possibilities across all domains of consumer activity, 
from travel and retail to domestic living and energy use. Digital 
platforms allow surplus resources to be identified, offered, shared, 
transacted and exchanged (Frenken 2017). Real-time information 
flows on consumers’ preferences and needs mean service provision 
can be personalised, differentiated, automated, and optimised 
(TWI2050 2019). Rapid innovation cycles and software upgrades 
drive continual improvements in performance and responsiveness 
to consumer behaviour. These characteristics of digitalisation enable 
new business models and services that affect both service demand,

from shared ride-hailing (ITF 2017a) to smart heating (IEA 2017a), 
and how services are provisioned, from online farmers’ markets 
(Richards and Hamilton 2018) to peer-to-peer electricity trading to 
enable distributed power systems (Morstyn et al. 2018).
In many cases, digitalisation provides a ‘radical functionality’ that 
enables users to do or accomplish something that they could not 
do before (Nagy et al. 2016). Indeed the consumer appeal of digital 
innovations varies widely, from choice, convenience, flexibility and 
control to relational and social benefits (Pettifor and Wilson 2020). Reviewing over 30 digital goods and services for mobility, food 
buying and domestic living, Wilson et al. (2020b) also found shared 
elements of appeal across multiple innovations including (i) making 
use of surplus, (ii) using not owning, (iii) being part of wider 
networks, and (iv) exerting greater control over service provisioning

systems. Digitalisation thus creates a  strong value proposition 
for certain consumer niches. Concurrent diffusion of many digital 
innovations amplifies their disruptive potential (Schuelke-Leech 
2018; Wilson et al. 2019b). Besides basic mobile telephone service 
for communication, digital innovations have been primarily geared to 
population groups with high purchasing power, and too little to the 
needs of poor and vulnerable people.
The long-term sustainability implications of digitalised services hinge 
on four factors: (i) the direct energy demands of connected devices 
and the digital infrastructures (i.e., data centres and communication 
networks) that provide necessary computing, storage, and 
communication services (Section 9.4.6); (ii) the systems-level energy 
and resource efficiencies that may be gained through the provision 
of digital services (Wilson et al. 2020b); (iii) the resource, material, 
and waste management requirements of the billions of ICT devices

that comprise the world’s digital systems (Belkhir and Elmeligi 2018; 
Malmodin and Lundén 2018) and (iv) the magnitude of potential 
rebound effects or induced energy demands that might unleash 
unintended and unsustainable demand growth, such as autonomous Past Present EmergingPersonal  comput ers
Fixed line internet
Cellular voice phones
Equipment cont rols
Energ y management syst emsUbiquitous devices
Wireless/mobile  internet
Smart phones
Internet o f things
Telework
Cryptocurrenci esIndust ry 4.0
Artiﬁcial intelligence
Autonomous vehicles
Distributed manuf acturing
Myri ad blockchai n applica tionsInformal and co mmuni ty-based 
shari ng of clothing, goods, shel terSharing economyCircular economy
DigitalisationSuppl y chain tracking
3D-printed structures
Smart ma terials
Automated disassemblyWaste scavengi ng
Loca l repai r/reuse
Loca l by-product re cyclin g
Post-consu mer recyclin g
P2P shari ng of clothing, 
goods, shel ter

Ride-hailing/ ride shari ngRadically shared mobilit y
Food shari ng
Freight co-l oading
Crowd-shippingDigitally enabled transformations
Digital waste exchanges
Addi tive manuf acturing
Advanced process cont rols
Failure diagnost ics
Just-in-time product ion
Decent job opportunities
Risk of growth in electricity demand, resource demand, and e-waste
Figure 5.11 | The growing nexus between digitalisation, the sharing economy, and the circular economy in service delivery systems.  While these trends 
started mostly independently, rapid digitalisation is creating new synergistic opportunities with systemic potential to improve the quality of jobs, particularly in developing 
economies. Widespread digitalisation may lead to net increases in electricity use, demand for electronics manufacturing resources, and e-waste, all of which must be monitored 
and managed via targeted policies.

540
Chapter 5 Demand, Services and Social Aspects of Mitigation 5vehicles inducing more frequent and longer journeys due to 
reduced travel costs (Wadud et al. 2016). Estimating digitalisation’s 
direct energy demand has historically been hampered by lack of 
consistent global data on IT device stocks, their power consumption 
characteristics, and usage patterns, for both consumer devices and the 
data centres and communication networks behind them. As a result, 
quantitative estimates vary widely, with literature values suggesting 
that consumer devices, data centres, and data networks account for 
anywhere from 6% to 12% of global electricity use (Gelenbe and 
Caseau 2015; Cook et  al. 2017; Malmodin and Lundén 2018). For 
example, within the literature on data centres, top-down models that 
project energy use on the basis of increasing demand for internet 
services tend to predict rapid global energy use growth, (Andrae and

Edler 2015; Belkhir and Elmeligi 2018; Liu et  al. 2020a), whereas 
bottom-up models that consider data centre technology stocks and 
their energy efficiency trends tend to predict slower but still positive 
growth (Shehabi et  al. 2018; Hintemann and Hinterholzer 2019; Malmodin 2020; Masanet et al. 2020). Yet there is growing concern 
that remaining energy efficiency improvements might be outpaced 
by rising demand for digital services, particularly as data-intensive 
technologies such as artificial intelligence, smart and connected 
energy systems, distributed manufacturing systems, and autonomous 
vehicles promise to increase demand for data services even further in 
the future (TWI2050 2019; Masanet et al. 2020; Strubell et al. 2020). 
Rapid digitalisation is also contributing to an expanding e-waste 
problem, estimated to be the fastest growing domestic waste stream 
globally (Forti et al. 2020).
As digitalisation proliferates, an important policy objective is

therefore to invest in data collection and monitoring systems and 
energy demand models of digitalised systems to guide technology 
and policy investment decisions for addressing potential direct 
energy demand growth (IEA 2017a) and potentially concomitant 
growth in e-waste.
100%
–100%
–50%
0%
50%
14
12
10
0
2
4
8
6
Home energy management systems [Energy]Disaggregated feedback [Energy]3D printed structures [CO 2]3D printed structures [Energy]
Smart lighting [Energy]
Meal kits [Energy]Meal kits [CO 2]Food gamiﬁcation Food gamiﬁcation [CO 2]Smart heating [Energy]
Digital hubs for local food [CO 2]
e-publications [Energy]e-music [Energy]e-video [CO 2]e-video [Energy]
e-publications [CO 2]
3D printing for lightweight aircraft [Energy]
Telework [CO 2]Telework [Energy]Virtual meetings [Energy]Virtual meetings [CO 2]3D printing for lightweight aircraft [CO 2]
Telework [Activity]
Shelter
Nutrition
Education and
Entertainment
Mobility

Figure 5.12 | Studies assessing net changes in CO 2 emissions, energy use, and activity levels indicate mitigation potentials for numerous end-user-
oriented digitalisation solutions, but also risk of increased emissions due to inefficient substitutions, induced demand, and rebound effects.  90 studies 
were assessed with 207 observations (indicated by vertical bars) including those based on empirical research, attributional and consequential lifecycle assessments, and techno-
economic analyses and scenarios at different scales, which are not directly comparable but are useful for indicating the directionality and determinants of net emissions, energy, 
and activity effects. Sources: Erdmann and Hilty (2010); Gebler et al. (2014); Huang et al. (2016); Verhoef et al. (2018); Alhumayani et al. (2020); Court and Sorrell (2020); Hook 
et al. (2020); IEA (2020a); Saade et al. (2020); Torres-Carrillo et al. (2020); Wilson et al. (2020c); Yao et al. (2020); Muñoz et al. (2021).

541
Demand, Services and Social Aspects of Mitigation Chapter 55However, the net systems-level energy and resource efficiencies 
gained through the provision of digital services could play 
an important role in dealing with climate change and other 
environmental challenges (Masanet and Matthews 2010; Melville 
2010; Elliot 2011; Watson et  al. 2012; Gholami et  al. 2013; Añón 
Higón et  al. 2017). As shown in Figure  5.12, assessments of 
numerous digital service opportunities for mobility, nutrition, 
shelter, and education and entertainment suggest that net emissions 
benefits can be delivered at the systems level, although these effects 
are highly context dependent. Importantly, evidence of potential 
negative outcomes due to rebound effects, induced demand, or life-
cycle trade-offs can also be observed. For example, telework has 
been shown to reduce emissions where long and/or energy-intensive 
commutes are avoided, but can lead to net emissions increases in

cases where greater non-work vehicle use occurs or only short, low-
emissions commutes (e.g., via public transit) are avoided (Hook et al. 
2020; IEA 2020a; Viana Cerqueira et al. 2020). Similarly, substitution 
of physical media by digital alternatives may lead to emissions 
increases where greater consumption is fuelled, whereas a  shift 
to 3D printed structures may require more emissions-intensive 
concrete formulations or result in reduced thermal energy efficiency, 
leading to life-cycle emissions increases (Mahadevan et  al. 2020; 
Yao et al. 2020).
Furthermore, digitalisation, automation and artificial intelligence, 
as general-purpose technologies, may lead to a  plethora of new 
products and applications that are likely to be efficient on their own 
but that may also lead to undesirable changes or absolute increases 
in demand for products (Figure 5.12). For example, last-mile delivery 
in logistics is both expensive and cumbersome. Battery-powered

drones enable a delivery of goods at similar lifecycle emissions to 
delivery vans (Stolaroff et al. 2018). At the same time, drone delivery 
is cheaper in terms of time (immediate delivery) and monetary costs 
(automation saves the highest-cost component: personnel) (Sudbury 
and Hutchinson 2016). As a  result, demand for package delivery 
may increase rapidly. Similarly, automated vehicles reduce the costs 
of time, parking, and personnel, and therefore may dramatically 
increase vehicle mileage (Wadud et al. 2016; Cohen and Cavoli 2019). 
On-demand electric scooters offer mobility access preferable to 
passenger cars, but can replace trips otherwise taken on public transit 
(de Bortoli and Christoforou 2020) and can come with significant 
additional energy requirements for night-time system rebalancing 
(Hollingsworth et  al. 2019; ITF 2020). The energy requirements of 
cryptocurrencies is also a  growing concern, although considerable

uncertainty exists surrounding the energy use of their underlying 
blockchain infrastructure (Vranken 2017; de Vries 2018; Stoll et al. 
2019). For example, while it is clear that the energy requirements 
of global Bitcoin mining have grown significantly since 2017, recent 
literature indicates a wide range of estimates for 2020 (47 TWh to 
125 TWh) due to data gaps and differences in modelling approaches 
(Lei et al. 2021). Initial estimates of the computational intensity of 
artificial intelligence algorithms suggest that energy requirements 
may be enormous without concerted effort to improve efficiencies, 
especially on the computational side (Strubell et al. 2020). Efficiency 
gains enabled by digitalisation, in terms of reduced GHG emissions 
or energy use per service unit, may be overcompensated by activity/
scale effects.Maximising the mitigation potential of digitalisation trends 
involves diligent monitoring and proactive management of both

direct and indirect demand effects, to ensure that a proper balance 
is maintained. Direct energy demand can be managed through 
continued investments in, and incentives for, energy-efficient data 
centres, networks, and end-use devices (Masanet et  al. 2011; 
Avgerinou et al. 2017; IEA 2017a; Koronen et al. 2020). Shifts to low-
carbon power are a particularly important strategy being undertaken 
by data centre and network operators (Cook et al. 2014; Huang et al. 
2020), which might be adopted across the digital device spectrum 
as a  proactive mitigation strategy where data demands outpace 
hardware efficiency gains, which may be approaching limits in the 
near future (Koomey et  al. 2011). Most recently, data centres are 
being investigated as a  potential resource for demand response 
and load balancing in renewable power grids (Koronen et al. 2020; 
Zheng et al. 2020), while a large bandwidth for improving software 
efficiency has been suggested for overcoming slowing hardware

efficiency gains (Leiserson et al. 2020). Ensuring efficiency benefits of 
digital services while avoiding potential rebound effects and demand 
surges will require early and proactive public policies to avoid excess 
energy use (TWI2050 2019; WBGU 2019), which will also necessitate 
investments in data collection and monitoring systems to ensure that 
net mitigation benefits are realised and that unintended consequences 
can be identified early and properly managed (IEA 2017a).
Within a small but growing body of literature on the net effects of 
digitalisation, there is medium evidence that digitalised consumer 
services can reduce overall emissions, energy use, and activity levels, 
with medium agreement on the scale of potential savings, with the 
important caveat that induced demand and rebound effects must be 
managed carefully to avoid negative outcomes.
5.3.4.2 The Sharing Economy
Opportunities to increase service per  product include peer-to-peer

based sharing of goods and services such as housing, mobility, and 
tools. Hence, consumable products become durable goods delivering 
a ‘product service’, which potentially could provide the same level 
of service with fewer products (Fischedick et al. 2014).The sharing 
economy is an old practice of sharing assets between many without 
transferring ownership, which has been made new through focuses 
on sharing underutilised products and assets in ways that promote 
flexibility and convenience, often in a highly developed context via 
gig economy or online platforms. However, the sharing economy 
offers the potential to shift from ‘asset-heavy’ ownership to ‘asset-
light’ access, especially in developing countries (Retamal 2019). 
General conclusions on the sharing economy as a  framework for 
climate change mitigation are challenging and are better broken 
down to specific subsystems (Mi and Coffman 2019) (Chapter  5 
Supplementary Material I, 5.SM.4.3).
Shared mobility

Shared mobility is characterised by the sharing of an asset 
(e.g.,  a  bicycle, e-scooter, vehicle), and the use of technology 
(i.e.,  apps and the Internet) to connect users and providers. It 
succeeded by identifying market inefficiencies and transferring

542
Chapter 5 Demand, Services and Social Aspects of Mitigation 5control over transactions to consumers. Even though most 
shared mobility providers operate privately, their services can be 
considered as part of a  public transport system in so far as it is 
accessible to most transport users and does not require private 
asset ownership. Shared mobility reduces GHG emissions if it 
substitutes for more GHG-intensive travel (usually private car 
travel) (Martin and Shaheen 2011; Shaheen and Chan 2016; Santos 
2018; Axsen  and Sovacool 2019; Shaheen and  Cohen 2019), and 
especially if it changes consumer behaviour in the long run ‘by 
shifting personal transportation choices from ownership to demand-
fulfilment’ (Mi and Coffman 2019).
Demand is an important driver for energy use and emissions because 
decreased cost of travel time by sharing an asset (e.g., a vehicle) could 
lead to an increase in emissions, but a high level of vehicle sharing

could reduce negative impacts associated with this (Brown and 
Dodder 2019). One example is the megacity Kolkata, India, which 
has as many as twelve different modes of public transportation 
that co-exist and offer means of mobility to its 14 million citizens 
(Box 5.8). Most public transport modes are shared mobility options 
ranging from sharing between two people in a rickshaw or between 
a  few hundred in metro or suburban trains. Sharing also happens 
informally as daily commuters avail shared taxis and neighbours 
borrow each other’s car or bicycle for urgent or day trips.
Shared mobility using private vehicle assets is categorised into four 
models (Santos 2018): peer-to-peer platforms where individuals can 
rent the vehicle when not in use (Ballús-Armet et al. 2014); short-
term rental managed and owned by a  provider (Enoch and Taylor 
2006; Schaefers et  al. 2016; Bardhi and Eckhardt 2012); Uber-like 
ridehailing services (Wallsten 2015; Angrist et  al. 2017); and ride

pooling using private vehicles shared by passengers to a common 
destination (Liyanage et  al. 2019; Shaheen and Cohen 2019). The 
latest model  – ride pooling  – is promising in terms of congestion 
and per capita CO 2 emissions reductions and is a common practice 
in developing countries, however it is challenging in terms of waiting 
and travel time, comfort, and convenience, relative to private cars 
(Santos 2018; Shaheen and Cohen 2019). The other three models 
often yield profits to private parties, but remain mostly unrelated 
to reduction in CO 2 emissions (Santos 2018). Shared travel models, 
especially Uber-like models, are criticised because of the flexibilisation 
of labour, especially in developing countries, in which unemployment 
rates and unregulated labour markets lay a foundation of precarity 
that lead many workers to seek out wide-ranging means towards 
patching together a  living (Ettlinger 2017; Wells et  al. 2020).

Despite the advantages of shared mobility, such as convenience and 
affordability, consumers may also perceive risk formed by possible 
physical injury from strangers or unexpected poor service quality 
(Hong et al. 2019).
From a mitigation perspective, the current state of shared mobility 
looks at best questionable (Fishman et al. 2014; Ricci 2015; Martin 
2016; Zhang and Mi 2018; Creutzig et al. 2019b; Mi and Coffman 
2019; Zhang et al. 2019). Transport entrepreneurs and government 
officials often conflate ‘smart’ and ‘shared’ vehicles with 
‘sustainable’ mobility, a conflation not withstanding scrutiny (Noy 
and Givoni 2018). Surveys demonstrate that many users take free-floating car sharing instead of public transit, rather than to 
replace their private car (Herrmann et al. 2014); while in the United 
States, ride-hailing and sharing data indicate that these services 
have increased road congestion and lowered transit ridership, with

an insignificant change in vehicle ownership, and may further lead to 
net increases in energy use and CO 2 emissions due to deadheading 
(Diao et  al. 2021; Ward et  al. 2021). If substitution effects and 
deadheading, which is the practice of allowing employees of 
a common carrier to use a vehicle as a non-revenue passenger, are 
accounted for, flexible motor-cycle sharing in Djakarta, Indonesia, 
is at best neutral to overall GHG emissions (Suatmadi et al. 2019). 
Passenger surveys conducted in Denver, Colorado, US, indicated 
that around 22% of all trips travelled with Uber and Lyft would 
have been travelled by transit, 12% would have walked or biked, 
and another 12% of passengers would not have travelled at all 
(Henao and Marshall 2019).
Positive effects can be realised directly in bike sharing due to its 
very low marginal transport emissions. For example, in 2016, bike 
sharing in Shanghai, China, reduced CO 2 emissions by 25 ktCO 2, with

additional benefits to air quality (Zhang and Mi 2018). However, 
bike-sharing can also increase emissions from motor vehicle usage 
when inventory management is not optimised during maintenance, 
collection, and redistribution of dock-less bikes (Fishman et al. 2014; 
Zhang et al. 2019; Mi and Coffman 2019).
Shared mobility scenarios demonstrate that GHG emission reduction 
can be substantial when mobility systems and digitalisation are 
regulated. One study modelled that ride pooling with electric cars 
(6 to 16 seats), which shifts the service to a more efficient transport 
mode, improves its carbon intensity by cutting GHG emissions by 
one-third (International Transport Forum 2016). Another study found 
that shared autonomous taxis had the potential to reduce per-mile 
GHG emissions to 63–82% below those of projected hybrid vehicles 
in 2030, 87% to 94% lower than a  privately owned, gasoline-
powered vehicle in 2014 (Greenblatt and Saxena 2015). This also

realises 95% reduction in space required for public parking; and 
total vehicle kilometres travelled would be 37% lower than the 
present day, although each vehicle would travel ten times the total 
distance of current vehicles (International Transport Forum 2016). 
Studies of Berlin, Germany, and Lisbon, Portugal, demonstrate that 
sharing strategies could reduce the number of cars by more than 
90%, also saving valuable street space for human-scale activity 
(Bischoff and Maciejewski 2016; Martinez and Viegas 2017; 
Creutzig et al. 2019b). The impacts will depend on sharing levels – 
concurrent or sequential – and the future modal split among public 
transit, automated electric vehicles fleets, and shared or pooled 
rides. Evidence from attributional lifecycle assessments (LCAs) of 
ride-hailing, whether Uber-like or by taxi, suggests that the key 
determinants of net emissions effects are average vehicle occupancy

and vehicle powertrain, with high-occupancy and electric drivetrain 
cars delivering the greatest emissions benefits, even rivalling 
traditional metro/urban rail and bus options (Figure  5.13b). It is 
possible that shared automated electric vehicle fleets could become 
widely used without many shared rides, and single- or even zero-
occupant vehicles will continue to be the majority of vehicle trips. 
It is also feasible that shared rides could become more common,

543
Demand, Services and Social Aspects of Mitigation Chapter 55if automation makes route deviation more efficient, more cost 
effective, and more convenient, increasing total travel substantially 
(Wadud et  al. 2016). Car sharing with automated vehicles could 
even worsen congestion and emissions by generating additional 
travel demand (Rubin et  al. 2016). Travel time in autonomous 
vehicles can be used for other activities but driving and travel costs 
are expected to decrease, which most likely will induce additional 
demand for auto travel (Moeckel and Lewis 2017) and could even 
create incentives for further urban sprawl. More generally, increased 
efficiency generated by big data and smart algorithms may generate 
rebound effects in demand and potentially compromise the public 
benefits of their efficiency promise (Gossart 2015).
In many countries, shared mobility and ride pooling are often the 
norm. Here the challenge is to improve service quality to keep users

in shared mobility and public transport (Box  5.8). A key barrier in 
cities like Nairobi, Kenya, is the lack of public involvement of users 
and sustainability experts in designing transport systems, leaving 
planning to transport engineers, and thus preventing inclusive shared 
mobility system design (Klopp 2012).
Altogether, travel behaviour, business models, and especially public 
policy will be key components in determining how impacts of 
pooling and shared automated electric vehicles unfold (Shaheen 
and Cohen 2019). Urban-scale governance of smart mobility holds 
potential for prioritising public transit and the use of public spaces 
for human activities, managing the data as a  digital sustainable 
commons (e.g., via the installation of a Central Information Officer, 
as in Tel Aviv, Israel), and managing the social and environmental 
risks of smart mobility to realise its benefits (Creutzig et al. 2019b). 
Pricing of energy use and GHG emissions will be helpful to achieve

these goals. The governance of shared mobility is complicated, as 
it involves many actors, and is key to realising wider benefits of 
shared mobility (Akyelken et al. 2018). New actors, networks and 
technologies enabling shared mobility are already fundamentally 
challenging how transport is governed worldwide. This is not 
a debate about state versus non-state actors but instead about the 
role the state takes within these new networks to steer, facilitate, 
and also reject different elements of the mobility system (Docherty 
Shared accommodation
In developing countries and in many student accommodations 
globally, shared accommodation allows affordable housing for 
a large part of the population. For example, living arrangements are 
built expressly around the practice of sharing toilets, bathrooms and 
kitchens. While the sharing of such facilities does connote a lower 
level of service provision and quality of life, it provides access

for a  consumer base with very low and unreliable incomes. Thus, 
sharing key facilities can help guarantee the provision of affordable 
housing (Gulyani et  al. 2018). In developed countries, large-scale 
developments are targeting students and ‘young professionals’ 
by offering shared accommodation and services. Historically 
shared accommodation has been part of the student life due to its 
flexible and affordable characteristics. However, the expansion of 
housing supply through densification can use shared facilities as an instrument to ‘commercialize small housing production, while 
housing affordability and accessibility are threatened’ (Uyttebrouck 
et al. 2020).
With respect to travel accommodation, several models are emerging 
in which accommodation is offered to, or shared with, travellers 
by private individuals organised by business-driven or non-profit 
online platforms. Accommodation sharing includes peer-to-peer,

ICT-enabled, short-term renting, swapping, borrowing or lending 
of existing privately-owned lodging facilities (Möhlmann 2015; 
Voytenko Palgan et al. 2017).
With shared accommodation services via the platform economy, 
there may be risks of negative sustainability effects, such as rebound 
effects caused by increased travel frequency (Tussyadiah and Pesonen 
2016). This is particularly a problem if apartments are removed from 
long-term rental markets, thus indirectly inducing construction 
activities, with substantial GHG emissions of their own. However, if 
a host shares their accommodation with a guest, the use of some 
resources, such as heating and lighting, is shared, thereby leading to 
more efficient resource use per capita (Chenoweth 2009; Voytenko 
Palgan et al. 2017). Given the nascence of shared accommodation 
via the platform economy, quantifications of its systems-level energy 
and emissions impacts are lacking in the literature, representing an

important area for future study.
Mitigation potentials of sharing economy strategies
Sharing economy initiatives play a  central role in enabling 
individuals to share underutilised products. While the literature on 
the net effects of sharing economy strategies is still limited, available 
studies have presented different mitigation potentials to date, as 
shown in Figure 5.13. For many sharing economy strategies, there is 
a risk of negative rebound and induced demand effects, which may 
occur by changing consuming patterns, for example if savings from 
sharing housing are used to finance air travel. Thus, the mitigation 
potentials of sharing economy strategies will depend on stringent 
public policy and consumer awareness that reins in runaway 
consumption effects. Shared economy solutions generally relate to 
the ‘Avoid’ and ‘Shift’ strategies (Sections 5.1 and 5.3.2). On the one 
hand, they hold potential for providing similar or improved services

for well-being (mobility, shelter) at reduced energy and resource 
input, with the proper policy signals and consumer responses. On 
the other hand, shared economy strategies may increase emissions, 
for example shared mobility may shift activity away from public 
transit and lead to lower vehicle occupancy, deadheading, and use 
of inefficient shared vehicles (Jones and Leibowicz 2019; Merlin 
2019; Bonilla-Alicea et  al. 2020; Ward et  al. 2021). Similarly to 
digitalisation, there is medium evidence that the sharing economy 
can reduce overall emissions, energy use, and activity levels, with 
medium agreement on the scale of potential savings if induced 
demand and rebound effects can be carefully managed to avoid 
negative outcomes.

544
The circular economy
While the demand for energy and materials will increase until 2060 
following the traditional linear model of production and consumption, 
resulting in serious environmental consequences (OECD 2019b), the 
circular economy (CE) provides strategies for reducing societal needs 
for energy and primary materials to deliver the same level of service 
with lower environmental impacts. The CE framework embodies 
multiple schools of thought with roots in a number of related concepts 
(Blomsma and Brennan 2017; Murray et al. 2017), including cradle 
to cradle (McDonough and Braungart 2002), performance economy 
(Stahel 2016), biomimicry (Benyus 1997), green economy (Loiseau 
et al. 2016) and industrial ecology (Saavedra et al. 2018). As a result, there are also many definitions of CE: a systematic literature review 
identified 114 different definitions (Kirchherr et  al. 2017). One of

the most comprehensive models is suggested by the Netherlands 
Environmental Assessment Agency (Potting et al. 2018), which defines 
ten strategies for circularity: Refuse (R0), Rethink (R1), Reduce (R2), 
Reuse (R3), Repair (R4), Refurbish (R5), Remanufacture  (R6), 
Repurpose (R7), Recycle (R8), and Recover energy (R9). Overall, 
the definition of CE is contested, with varying boundary conditions 
chosen. As illustrated in Figure 5.11, the CE overlaps with both the 
sharing economy and digitalisation megatrends.
In line with the principles of SDG 12 (responsible consumption 
and production), the essence of building a CE is to retain as much 
0 50 100 150 200 250ICE
HEV
PHEV
BEV
FCEV
HEV
PHEV
BEV
BEV (2 packs)
FCEV
ICE
HEV
PHEV
BEV
FCEV
Private bike
Shared bike
Metro/urban train
Bus – ICEPrivate car Taxi Ridesourcing Public/other
GHG emissions per pkm (gCO 2/pkm)–100% 0%
5 4 3 2 1Car-sharing [A]Car-sharing [E]Bikesharing [CO 2e]More intense use [CO 2e]

Car-sharing [CO 2e]
P2P sharing mobility [A]P2P accommodation [CO 2e]P2P second hand trading [CO 2e]P2P second hand trading [A]Ridesharing [CO 2e]
P2P sharing mobility [CO 2e]
Recycling [CO 2e]Reuse [CO 2e]Light-weighting [CO 2e]Material substitution [CO 2e]Lifetime prolongation [CO 2e]
Remanufacturing [CO 2e]Sharing economy Circular economy
30 20 0 10Car occupancy (passengers)b) a)
Figure 5.13 | (a) Published estimates from 72 studies with 185 observations (indicated by vertical bars) of the relative mitigation potential of different 
shared and circular economy strategies, demonstrating limited observations for many emerging strategies, a wide variance in estimated benefits for 
most strategies, and within the sharing economy, risk of increased emissions due to inefficient substitutions, induced demand, and rebound effects.

Mitigation potentials are conditional on corresponding public policy and/or regulation. (b) Attributional LCA comparisons of ridesharing mobility options, which highlight the 
large effects of vehicle occupancy and vehicle technology on total CO 2 emissions per passenger-km and the preferability of high-occupancy and non-ICE configurations for 
emissions reductions compared to private cars. Also indicated are possible emissions increases associated with shared car mobility when it substitutes for non-motorised and 
public transit options. BEV = battery electric vehicle; FCEV = fuel cell electric vehicle; HEV = hybrid electric vehicle; ICE = internal combustion engine; PHEV = plug-in hybrid 
electric vehicle. Sources: data from Jacobson and King (2009); Firnkorn and Müller (2011); Baptista et al. (2014); Liu et al. (2014); Namazu and Dowlatabadi (2015); Nijland

et al. (2015); IEA (2016); Koh (2016); Martin and Shaheen (2016); Rabbitt and Ghosh (2016); Bruck et al. (2017); Bullock et al. (2017); Clewlow and Mishra (2017); Fremstad 
(2017); ITF (2017a,b,c); Nasir et al. (2017); Nijland and van Meerkerk (2017); Rademaekers et al. (2017); Skjelvik et al. (2017); Yin et al. (2017); Campbell (2018); Favier et al. 
(2018); Ghisellini et al. (2018); Hopkinson et al. (2018); IEA (2018); ITF (2018); Lokhandwala and Cai (2018); Makov and Font Vivanco (2018); Malmqvist et al. (2018); Material 
Economics (2018); Nasr et al. (2018); Yu et al. (2018); Zhang and Mi (2018); Brambilla et al. (2019); Brütting et al. (2019); Buyle et al. (2019); Castro and Pasanen (2019); 
Coulombel et al. (2019); Eberhardt et al. (2019); IEA (2019b); ITF (2019); Jones and Leibowicz (2019); Ludmann (2019); Merlin (2019); Nußholz et al. (2019); Bonilla-Alicea

et al. (2020); Cantzler et al. (2020); Churkina et al. (2020); Gallego-Schmid et al. (2020); Hertwich et al. (2020); ITF (2020a,b); Liang et al. (2020); Miller (2020); Wilson et al. 
(2020c); Yan et al. (2020); Cordella et al. (2021); Diao et al. (2021); Pauliuk et al. (2021); Ward et al. (2021); Wolfram et al. (2021).

545
Demand, Services and Social Aspects of Mitigation Chapter 55value as possible from products and components when they reach 
the end of their useful life in a  given application (Lewandowski 
2016; Lieder and Rashid 2016; Stahel 2016; Linder and Williander 
2017). This requires an integrated approach during the design 
phase that, for example, extends product usage and ensures 
recyclability after use (de Coninck et  al. 2018). While traditional 
‘Improve’ strategies tend to focus on direct energy and carbon 
efficiency, service-oriented strategies focus on reducing lifecycle 
emissions through harnessing the leverage effect (Creutzig et  al. 
2018). The development of closed-loop models in service-oriented 
businesses can increase resource and energy efficiency, reducing 
emissions and contributing to climate change mitigation goals at 
national, regional, and global levels (Johannsdottir 2014; Korhonen 
et  al. 2018). Key examples include remanufacturing of consumer

products to extend lifespans while maintaining adequate service 
levels (Klausner et  al. 1998), reuse of building components to 
reduce demand for primary materials and construction processes 
(Shanks et al. 2019), and improved recycling to reduce upstream 
resource pressures (IEA 2019b; IEA 2017b).
Among the many schools of thought on the CE and climate change 
mitigation, two different trends can be distinguished from the 
literature to date. First, there are publications, many of them not peer-
reviewed, that eulogise the perceived benefits of the CE, but in many 
cases stop short of providing a quantitative assessment. Promotion 
of CE from this perspective has been criticised as a greenwashing 
attempt by industry to avoid serious regulation (Isenhour 2019). 
Second, there are more methodologically rigorous publications, 
mostly originating in the industrial ecology field, but sometimes 
investigating only limited aspects of the CE (Bocken et al. 2017; Cullen

2017; Goldberg 2017). Conclusions on CE’s mitigation potential 
also differ, with diverging definitions of the CE. A systematic review 
identified 3,244 peer-reviewed articles addressing CE and climate 
change, but only 10% of those provide insights on how the CE can 
support mitigation, and most of them found only small potentials 
to reduce GHG emissions (Cantzler et al. 2020). Recycling is the CE 
category most investigated, while reuse and reduce strategies have 
seen comparatively less attention (Cantzler et  al. 2020). However, 
mitigation potentials were also context- and material-specific, as 
illustrated by the ranges shown in Figure 5.13a.
There are three key concerns relating to the effectiveness of the 
CE concept. First, many proposals on the CE insufficiently reflect 
on thermodynamic constraints that limit the potential of recycling 
from both mass conservation and material quality perspectives or 
ignore the considerable amount of energy needed to reuse materials

(Cullen 2017). Second, demand for materials and resources will likely 
outpace efficiency gains in supply chains, becoming a key driver of 
GHG emissions and other environmental problems, rendering the 
CE alone an insufficient strategy to reduce emissions (Bengtsson 
et  al. 2018). In fact, the empirical literature points out that only 
6.5% of all processed materials (4 Gt yr –1) globally originate from 
recycled sources (Haas et  al. 2015). The low degree of circularity 
is explained by the high proportion of processed materials (44%) 
used to provide energy, thus not available for recycling; and the 
high rate of net additions to stocks of 17 Gt yr –1. As long as long-
lived material stocks (e.g., in buildings and infrastructure) continue to grow, strategies targeting end-of-pipe materials cannot keep 
pace with primary materials demand (Krausmann et  al. 2017; 
Haas et al. 2020). Instead, a significant reduction of societal stock

growth, and decisive eco-design, are suggested to advance the CE 
(Haas et al. 2015). Third, cost-effectiveness underlying CE activities 
may concurrently also increase energy intensity and reduce labour 
intensity, causing systematically undesirable effects. To a  large 
extent, the distribution of costs and benefits of material and 
energy use depend on institutions in order to include demand-side 
solutions. Thus, institutional conditions have an essential role to 
play in setting rules differentiating profitable from nonprofitable 
activities in CE (Moreau et al. 2017). Moreover, the prevalence of 
CE practices such as reuse, refurbishment, and recycling can differ 
substantially between developed and developing economies, 
leading to highly context-specific mitigation potentials and policy 
approaches (McDowall et al. 2017).
One report estimates that the CE can contribute to more than 
6 GtCO 2 emission reductions in 2030, including strategies such as

material substitution in buildings (Blok et al. 2016). Reform of the tax 
system towards GHG emissions and the extraction of raw materials 
substituting taxes on labour is a key precondition to achieve such 
a  potential. Otherwise, rebound effects tend to take back a  high 
share of marginal CE efforts. A 50% reduction of GHG emissions 
in industrial processes, including the production of goods in steel, 
cement, plastic, paper, and aluminium, from 2010 until 2050, is 
impossible to attain only with reuse and radical product innovation 
strategies, but will need to also rely on the reduction of primary input 
(Allwood et al. 2010).
CE strategies generally correspond to the ‘Avoid’ strategy for 
primary materials (Sections 5.1 and 5.3.2). CE strategies in industrial 
settings improve well-being mostly indirectly, via the reduction of 
environmental harm and climate impact. They can also save monetary 
resources of consumers by reducing the need for consumption. It may

seem counterintuitive, but reducing consumers’ need to consume 
a particular product or service (e.g., reducing energy consumption) 
may increase consumption of another product or service (e.g., travel) 
associated with some type of energy use, or lead to greater 
consumption if additional secondary markets are created. Hence, 
carbon emissions could rise if the rebound effect is not considered 
(Chitnis et al. 2013; Zink and Geyer 2017).
Looking at ‘Shift’ strategies (Sections 5.1 and 5.3.2), the role of 
individuals as consumers and users has received less attention than 
other aspects of the CE (e.g., technological interventions as ‘Improve’ 
strategies and waste minimisation as ‘Avoid’ strategies) within 
mainstream debates to date. One explanation is that CE has roots 
in the field of industrial ecology, which has historically emphasised 
materials systems more than the end user. By shifting this perspective 
from the supply side to the demand side in the CE, users are, for

the most part, discussed as social entities that now must form new 
relations with businesses to meet their needs. That is, the demand-
side approach largely replaces the concept of a consumer with that 
of a user, who must either accept or reject new business models for 
service provision, stimulated by the pushes and pulls of prices and 
performance (Hobson 2019).

546
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Relevant contributions to climate change mitigation at gigatonne scale 
by the CE will remain out of scope if decision-makers and industry fail 
to reduce primary inputs (high confidence). Systemic (consequential) 
analysis is required to avoid the risk that scaling effects negate 
efficiency gains; such analysis is however rarely applied to date. For 
example, material substitution or refurbishment of buildings brings 
risk of increasing emissions despite improving or avoiding current 
materials (Castro and Pasanen 2019; Eberhardt et al. 2019). Besides, 
CE concepts that extend the lifetime of products and increase the 
fraction of recycling are useful but are both thermodynamically 
limited and will remain relatively small in scale as long as demand 
for primary materials continues to grow, and scale effects dominate. 
In spite of presenting a large body of literature on CE in general, only

a small but growing body of literature exists on the net effects of its 
strategies from a quantitative perspective, with key knowledge gaps 
remaining on specific CE strategies. There is medium evidence that 
the CE can reduce overall emissions, energy use, and activity levels, 
with medium evidence that the sharing economy can reduce overall 
emissions, energy use, and activity levels, with medium agreement on 
the scale of potential savings.
5.4 Transition Toward High Well-being 
and Low-carbon-demand Societies
Demand-side mitigation involves individuals (e.g.,  consumption 
choices), culture (e.g.,  social norms, values), corporate 
(e.g.,  investments), institutions (e.g.,  political agency), and 
infrastructure change (high evidence, high agreement). These five 
drivers of human behaviour either contribute to the status quo 
of a  global high-carbon, consumption- and GDP growth-oriented

economy or help generate the desired change to a  low-carbon energy-services, well-being, and equity-oriented economy (Jackson 
2016; Cassiers et  al. 2018; Yuana et  al. 2020; Nielsen et al. 2021) 
(Figure 5.14). Each driver has novel implications for the design and 
implementation of demand-side mitigation policies. They show 
important synergies, making energy demand mitigation a  dynamic 
problem where the packaging and/or sequencing of different policies 
play a role in their effectiveness, demonstrated in Sections 5.5 and 
5.6. The Social Science Primer (Chapter 5 Supplementary Material I) 
describes theory and empirical insights about the interplay between 
individual agency, the social and physical context of demand-side 
decisions in the form of social  roles and norms, infrastructure and 
technological constraints and affordances, and other formal and 
informal institutions. Incremental interventions on all five fronts

change social practices, affecting simultaneously energy and well-
being (Schot and Kanger 2018). Transformative change will require 
coordinated use of all five drivers, as described in Figure 5.14 and, 
using novel insights about behaviour change for policy design and 
implementation (high evidence,  high agreement). In particular, socio-
economic factors, such as equity, public service quality, electricity 
access and democracy are found to be highly significant in enabling 
need satisfaction at low energy use, whereas economic growth beyond 
moderate incomes and extractive economic activities are observed to 
be prohibiting factors (Vogel et al. 2021).
5.4.1 Behavioural Drivers
Behaviour change by individuals and households requires both 
motivation to change and capacity for change (option availability/
knowledge; material/cognitive resources to initiate and maintain 
change) (Moser and Ekstrom 2010; Michie et al. 2011) and is best

seen as part of more encompassing collective action. Motivation for 
Demand side mitigation is about more than behavioural change. Reconﬁguring the way services are provided while simultaneously changing social 
norms and preferences will help reduce emissions and access. Transformation happens through societal, technological and institutional changes. 
A: Providing decent living 
standards through low-carbon 
infrastructures 
SI: reduce primary demand via 
efﬁcient technology adoption
A: By reducing demand
SI: additional demand  
reduction/waste reduction
Unequal consumption 
and greenhouse gas 
emissionsAvoid-Shift-Improve 
decisions across 
different income groupsConsumption 
of bottom 90% 48%
Consumption
of top 10%37%
Consumption of top 1% 15%
Incentives and nudgesSustainable infrastructures
Social trust
Governing digitalization, 
urbanization and other 
transformative changesCitizensInvestor
Consumer
Role model

ProfessionalCollective action by various social actors to tilt the balance
Digniﬁed living standards for all, 
stabilized temperaturesGlobal warmingPolitical culture
Responsible investment
Responsible consumption
Introduce novel social norms
Plan infrastructuresTilting the balance towards less resource intensive service provisioning
Figure 5.14 | Role of people, demand-side action and consumption in reversing a planetary trajectory to a warming Earth towards effective climate 
change mitigation and dignified living standards for all.

547
Demand, Services and Social Aspects of Mitigation Chapter 55change for collective good comes from economic, legal, and social 
incentives, and regard for deeper intrinsic value of concern for others 
over extrinsic values. Capacity for change varies; people in informal 
settlements or rural areas are incapacitated by socio-political realities 
and have limited access to new energy-service options.
Motivation and effort required for behaviour change increase from 
‘Improve’ to ‘Shift’ to ‘Avoid’ decisions. ‘Improve’ requires changes in 
personal purchase decisions, ‘Shift’ involves changes in behavioural 
routines, ‘Avoid’ also involves changes in deeper values or mindsets. 
People set easy goals for themselves and more difficult ones for 
others (Attari et al. 2016) and underestimate the energy savings of 
behaviour changes that make a large difference (Attari et al. 2010). 
Most personal actions taken so far have small mitigation potential

(recycling, ecodriving), and people refrain from options advocated 
more recently with high impact (less flying, living car free) (Dubois 
et al. 2019).As individuals pursue a  broad set of goals and use calculation-, 
emotion-, and rule-based processes when they make energy decisions, 
demand-side policies can use a  broad range of behavioural tools 
that complement subsidies, taxes, and regulations (Chakravarty and 
Roy 2016; Mattauch et  al. 2016; Niamir 2019) (high evidence, high 
agreement). The provision of targeted information, social advertisements, 
and influence of trusted in-group members and/role models or admired 
role models like celebrities can be used to create better climate change 
knowledge and awareness (Niamir 2019; Niamir et al. 2020b; Niamir 
et  al. 2020c). Behavioural interventions like communicating changes 
in social norms can accelerate behaviour change by creating tipping 
points (Nyborg et al. 2016). When changes in energy-demand decisions

(such as switching to a plant-based diet, (Box 5.5)) are motivated by the 
creation and activation of a social identity consistent with this and other 
behaviours, positive spillover can accelerate behaviour change (Truelove 
et al. 2014), both within a domain or across settings, for example from 
work to home (Maki and Rothman 2017).
Box 5.5 | Dietary Shifts in UK Society Towards Lower-emission Foods
Meat eating is declining in the UK, alongside a shift from carbon-intensive red meat towards poultry. This is due to the interaction of 
behavioural, socio-cultural and organisational drivers (Vinnari and Vinnari 2014). Reduced meat consumption is primarily driven by 
issues of personal health and animal welfare, instead of climate or environment concerns (Latvala et al. 2012; Dibb and Fitzpatrick 
2014; Hartmann and Siegrist 2017; Graça et al. 2019). Social movements have promoted shifts to a vegan diet (Morris et al. 2014;

Laestadius et al. 2016) yet their impact on actual behaviour is the subject of debate (Taufik et al. 2019; Harguess et al. 2020; Sahakian 
et al. 2020). Companies have expanded new markets in non-meat products (MINTEL 2019). Both corporate food actors and new 
entrants offering more innovative ‘meat alternatives’ view consumer preferences as an economic opportunity, and are responding 
by increasing the availability of meat replacement products. No significant policy change has taken place in the UK to enable dietary 
shift (Wellesley and Froggatt 2015); however the Climate Change Committee has recommended dietary shift in the Sixth Carbon 
Budget (Climate Change Committee 2020), involving reduced consumption of high-carbon meat and dairy products by 20% by 
2030, with further reductions in later years in order to reach net zero GHG emissions by 2050. Agricultural policies serve to support

meat production with large subsidies that lower production cost and effectively increase the meat intensity of diets at a population 
level (Simon 2003; Godfray et al. 2018). Deeper, population-wide reductions in meat consumption are hampered by these lock-in 
mechanisms which continue to stabilise the existing meat production-consumption system. The extent to which policymakers are 
willing to actively stimulate reduced meat consumption thus remains an open question (Godfray et al. 2018). See more in Chapter 5 
Supplementary Material I, Section 5.SM.6.4.
People’s general perceptions of climate risks, first covered in AR5, 
motivate behaviour change; more proximate and personal feelings of 
being at risk triggered by extreme weather and climate-linked natural 
disasters will increase concern and willingness to act (Bergquist et al. 
2019), though the window of increased support is short (Sisco 
et al. 2017). 67% of individuals in 26 countries see climate change as

a major threat to their country, an increase from 53% in 2013, though 
29% also consider it a minor or no threat (Fagan and Huang 2019). 
Concern that the COVID-19 crisis may derail this momentum due 
to a finite pool of worry (Weber 2006) appears to be unwarranted: 
Americans’ positions on climate change in 2020 matched high levels 
of concern measured in 2019 (Leiserowitz et al. 2020). Younger, female, 
and more educated individuals perceive climate risks to be larger 
(Weber 2016; Fagan and Huang 2019). Moral values and political 
ideology influence climate risk perception and beliefs about the 
outcomes and effectiveness of climate action (Maibach et al. 2011). Motivation for demand-side solutions can be increased by focusing 
on personal health or financial risks and benefits that clearly matter 
to people (Petrovic et al. 2014). Consistent with climate change as 
a normally distant, non-threatening, statistical issue (Gifford 2011;

Fox-Glassman and Weber 2016), personal experience with climate-
linked flooding or other extreme weather events increases perceptions 
of risk and willingness to act (Weber 2013; Atreya and Ferreira 2015; 
Sisco et  al. 2017) when plausible mediators and moderators are 
considered Brügger et al. (2021), confirmed in all 24 countries studied 
by Broomell et al. (2015). Discounting the future matters (Hershfield 
et al. 2014): across multiple countries, individuals more focused on 
future outcomes are more likely to engage in environmental actions 
(Milfont et al. 2012).
There is medium evidence and high agreement that demographics, 
values, goals, personal and social norms differentially determine

548
Chapter 5 Demand, Services and Social Aspects of Mitigation 5ASI behaviours, in the Netherlands and Spain (Abrahamse and Steg 
2009; Niamir 2019; Niamir et al. 2020b), the OECD (Ameli and Brandt 
2015), and 11 European countries (Mills and Schleich 2012; Roy et al. 
2012). Education and income increase ‘Shift’ and ‘Improve’ behaviour, 
whereas personal norms help to increase the more difficult ‘Avoid’ 
behaviours (Mills and Schleich 2012). Socio-demographic variables 
(household size and income) predict energy use, but psychological 
variables (perceived behavioural control, perceived responsibility) 
predict changes in energy use; younger households are more likely 
to adopt ‘Improve’ decisions, whereas education increases ‘Avoid’ decisions (Ahmad et  al. 2015). In India and developing countries, 
‘Avoid’ decisions are made by individuals championing a  cause, 
while ‘Improve’ and ‘Shift’ behaviour are increased by awareness

programmes and promotional materials highlighting environmental 
and financial benefits (Chakravarty and Roy 2016; Roy et al. 2018a). 
Cleaner cookstove adoption Box  5.6), a  widely studied ‘Improve’ 
solution in developing countries (Nepal et al. 2010; Pant et al. 2014), 
goes up with income, education, and urban location. Female education 
and investments in reproductive health are evident measures to 
reduce world population growth (Abel et al. 2016).
Box 5.6 | Socio-behavioural Aspects of Deploying Cookstoves
Universal access to clean and modern cooking energy could cut premature deaths from household air pollution by two-thirds, while 
reducing forest degradation and deforestation and contributinh to the reduction of up to 50% of CO 2 emissions from cooking (relative 
to baseline by 2030) (IEA 2017c; Dagnachew et al. 2019). However, in the absence of policy reform and substantial energy investments,

2.3 billion people will have no access to clean cooking fuels such as biogas, LPG, natural gas or electricity in 2030 (IEA 2017c). 
Studies reveal that a combination of drivers influence adoption of new cookstove appliances, including affordability, behavioural and 
cultural aspects (lifestyles, social norms around cooking and dietary practices), information provision, availability, aesthetic qualities 
of the technology, perceived health benefits, and infrastructure (spatial design of households and cooking areas). The increasing 
efficiency improvements in electric cooking technologies could enable households to shift to electrical cooking at mass scale. The use 
of pressure cookers and rice cookers is now widespread in South Asia and beginning to penetrate the African market as consumer 
attitudes are changing towards household appliances with higher energy efficiencies (Batchelor et al. 2019). There are shifts towards

electric and LPG stoves in Bhutan (Dendup and Arimura 2019), India (Pattanayak et al. 2019), Ecuador (Martínez et al. 2017; Gould 
et al. 2018) and Ethiopia (Tesfamichael et al. 2021); and improved biomass stoves in China (Smith et al. 1993). Significant subsidy, 
information (Dendup and Arimura 2019), social marketing and availability of technology in the local markets are some of the key 
policy instruments helping to adopt improved cookstoves (Pattanayak et al. 2019). There is no one-size-fits-all solution to household 
air pollution – different levels of shift and improvement occur in different cultural contexts, indicating the importance of socio-cultural 
and behavioural aspects in shifts in cooking practices. See more in Chapter 5 Supplementary Material I, Section 5.SM.6.2.
There is high agreement in the literature that the updating of educational 
systems from a commercialised, individualised, entrepreneurial training

model to an education cognisant of planetary health and human well-
being can accelerate climate change awareness and action (Mendoza 
and Roa 2014; Dombrowski et  al. 2016) (Supplementary Material I 
Chapter 5).
There is high evidence and high agreement that people’s core values 
affect climate-related decisions and climate policy support by shaping 
beliefs and identities (Dietz 2014; Steg 2016; Hayward and Roy 
2019). People with altruistic and biospheric values are more likely to 
act on climate change and support climate policies than those with 
hedonic or egoistic values (Taylor et al. 2014), because these values 
are associated with higher awareness and concern about climate 
change, stronger belief that personal actions can help mitigate 
climate change, and stronger feelings of responsibility for taking 
climate action (Dietz 2014; Steg 2016). Research also suggest that 
egalitarian, individualistic, and hierarchical worldviews (Wildavsky

and Dake 1990) have their role, and that successful solutions 
require policy-makers of all three worldviews to come together and 
communicate with each other (Chuang et al. 2020).Core values also influence which costs and benefits are considered 
(Hahnel et al. 2015; Gölz and Hahnel 2016; Steg 2016). Information 
provision and appeals are thus more effective when tailored to those 
values (Bolderdijk et al. 2013; Boomsma and Steg 2014), as implemented 
by the energy cultures framework (Stephenson et al. 2015; Klaniecki 
et  al. 2020). Awareness, personal norms, and perceived behavioural 
control predict willingness to change energy-related behaviour above 
and beyond traditional socio-demographic and economic predictors 
(Schwartz 1977; Ajzen 1985; Stern 2000), as do perceptions of self-
efficacy (Bostrom et al. 2019). However, such motivation for change 
is often not enough, as actors also need capacity for change and help

to overcome individual, institutional and market barriers (Young et al. 
2010; Bray et al. 2011; Carrington et al. 2014).
Table  5.4 describes common obstacles to demand-side energy 
behaviour change, from loss aversion to present bias (for more detail 
see Chapter 5 Supplementary Material I). Choice architecture refers 
to interventions (‘nudges’) that shape the choice context and how 
choices are presented, with seemingly-irrelevant details (e.g., option 
order or labels) often more important than option price (Thaler and 
Sunstein 2009). There is high evidence and high agreement that 
choice architecture nudges shape energy decisions by capturing 
deciders’ attention; engaging their desire to contribute to the social

549
Demand, Services and Social Aspects of Mitigation Chapter 55good; facilitating accurate assessment of risks, costs, and benefits; 
and making complex information more accessible (Yoeli et al. 2017; 
Zangheri et  al. 2019). Climate-friendly choice architecture includes 
the setting of proper defaults, the salient positioning of green options 
(in stores and online), forms of framing, and communication of social 
norms (Johnson et al. 2012). Simplifying access to greener options (and 
hence lowering effort) can promote ASI changes (Mani et al. 2013). 
Setting effective ‘green’ defaults may be the most effective policy to 
mainstream low-carbon energy choices (Sunstein and Reisch 2014), 
adopted in many contexts (Jachimowicz et  al. 2019) and deemed 
acceptable in many countries (Sunstein et al. 2019). Table 5.3a lists 
how often different choice-architecture tools were used in many 
countries over the past 10 years to change ASI behaviours, and how

often each tool was used to enhance an economic incentive. These 
tools have been tested mostly in developed countries. Reduction 
in energy use (typically electricity consumption) is the most widely 
studied behaviour (because metering is easily observable). All 
but one tool was applied to increase this ‘Avoid’ behaviour, with 
demand-side reductions from 0% to up to 20%, with most values 
below 3% (see also meta-analyses by Hummel and Maedche (2019); 
Nisa et al. (2019); van der Linden and Goldberg (2020); Stankuniene 
et al. (2020); and Khanna et al. (2021). Behavioural, economic, and 
legal instruments are most effective when applied as an internally 
consistent ensemble where they can reinforce each other, a concept referred to as ‘policy packaging’ in transport policy research (Givoni 
2014). A meta-analysis, combining evidence of psychological and 
economic studies, demonstrates that feedback, monetary incentives

and social comparison operate synergistically and are together more 
effective than the sum of individual interventions (Khanna et al. 2021). 
The same meta-analysis also shows that combined with monetary 
incentives, nudges and choice architecture can reduce global GHG 
emissions from household energy use by 5–6% (Khanna et al. 2021).
Choice architecture has been depicted as an anti-democratic attempt 
at manipulating the behaviour of actors without their awareness or 
approval (Gumbert 2019). Such critiques ignore the fact that there 
is no neutral way to present energy-use-related decisions, as every 
presentation format and choice environment influences choice, 
whether intentionally or not. Educating households and policy makers 
about the effectiveness of choice architecture and adding these 
behavioural tools to existing market- and regulation-based tools in 
a  transparent and consultative way can provide desired outcomes

with increased effectiveness, while avoiding charges of manipulation 
or deception. People consent to choice-architecture tools if their 
use is welfare-enhancing, policymakers are transparent about 
their goals and processes, public deliberation and participation are 
encouraged, and the choice architect is trusted (Sunstein et al. 2019).
Table 5.3a | Inventory of behavioural interventions experimentally tested to change energy behaviours.
tool
# of papers
# in developed 
countries
# in other 
countries Energy demand behaviour
Avoid
Shift
Improve
Economic 
incentive
Set the proper 
defaults27 26 1Carbon Offset Programme (3) 
Löfgren et al. (2012); Araña and León (2013)
Energy Source (4) 
Kaiser et al. (2020); Wolske et al. (2020)*
Energy Use (16) 
Jachimowicz et al. (2019); Nisa et al. (2019); Grilli and Curtis (2021)*
Investment in Energy Efficiency (7) 
Theotokis and Manganari (2015); Ohler et al. (2020)
Mode of Transportation (1) 
Goodman et al. (2013)11 12 9 6

Reach out 
during 
transitions10 9 1Energy Use (4) 
Verplanken (2006); Jack and Smith (2016); Iweka et al. (2019)*
Investment in Energy Efficiency (4) 
Gimpel et al. (2020)
Mode of Transportation (2) 
Verplanken et al. (2008)1 3 7 1
Provide timely 
feedback and 
reminders256 246 10Energy Use (252) 
Darby (2006); Buckley (2019)* 
Abrahamse et al. (2005); Fischer (2008); Steg (2008); Faruqui et al. (2010); Delmas et al. (2013); 
McKerracher and Torriti (2013); Karlin et al. (2015); Andor and Fels (2018); Bergquist et al. (2019); 
Iweka et al. (2019); Nisa et al. (2019); Zangheri et al. (2019); Ahir and Chakraborty (2021); Grilli 
and Curtis (2021); Khanna et al. (2021)*
Mode of Transportation (3) 
Steg (2008); Sanguinetti et al. (2020)*244 6 7 33

550
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Behavioural 
tool
# of papers
countries
# in other 
Avoid
Shift
Improve
Economic 
incentive
Make 
intuitive and 
easy to access247 235 12Energy Source (3) 
Havas et al. (2015); Jagger et al. (2019)
Energy Use (202) 
Henryson et al. (2000); Darby (2006); Carlsson-Kanyama and Lindén (2007); Chen et al. (2017); 
Iwafune et al. (2017); Burkhardt et al. (2019); Henry et al. (2019); Wong-Parodi et al. (2019); Mi et al. (2020); 
Stojanovski et al. (2020) 
[Abrahamse et al. (2005); Ehrhardt-Martinez and Donnelly (2010); Delmas et al. (2013); Andor and 
Fels (2018); Bergquist et al. (2019); Buckley (2019); Iweka et al. (2019); Nisa et al. (2019); Zangheri et al. 
(2019); Wolske et al. (2020); Ahir and Chakraborty (2021); Grilli and Curtis (2021); Khanna et al. (2021)]*
Investment in Energy Efficiency (30) 
Larrick and Soll (2008); Steg (2008); Andor and Fels (2018)*

Mode of Transportation (19) 
Steg (2008); Pettifor et al. (2017)*197 38 24 33
Make 
behaviour 
observable 
and provide 
recognition58 53 5Energy Use (24) 
Abrahamse et al. (2005); Delmas et al. (2013); Bergquist et al. (2019); Iweka et al. (2019); Nisa et al. (2019); 
Grilli and Curtis (2021)*
Pettifor et al. (2017)*
Mode of Transportation (4) 
Pettifor et al. (2017)*27 28 5 6
Communicate 
a norm138 131 7Energy Source (1) 
Hafner et al. (2019)
Energy Use (116) 
Nolan et al. (2008); Ayers and Forsyth (2009); Allcott (2011); Costa and Kahn (2013); Allcott and Rogers (2014)  
Abrahamse et al. (2005); Abrahamse and Steg (2013); Delmas et al. (2013); Andor and Fels (2018); 
Bergquist et al. (2019); Buckley (2019); Iweka et al. (2019); Nisa et al. (2019); Ahir and Chakraborty (2021); 
Khanna et al. (2021)*
Investment in Energy Efficiency (15) 
Pettifor et al. (2017); Niamir et al. (2020b); Grilli and Curtis (2021)*
Mode of Transportation (7)

Bamberg et al. (2007); Bergquist et al. (2019)*106 21 16 15
Reframe 
consequences 
in terms people 
care about74 68 6Energy Source (5) 
Wolske et al. (2018); Hafner et al. (2019); Grilli and Curtis (2021)*
Energy Use (47) 
Abrahamse et al. (2005); Darby (2006); Delmas et al. (2013); Chen et al. (2017); Eguiguren-Cosmelli (2018); 
Bergquist et al. (2019); Ghesla et al. (2020); Mi et al. (2020); Khanna et al. (2021)*
Investment in Energy Efficiency (22) 
Andor and Fels (2018);* Forster et al. (2021)
Nepal et al. (2010); Mattauch et al. (2016)41 18 19 18
Obtain 
a commitment52 47 5Energy Source (1) 
Jagger et al. (2019)
Ghesla et al. (2020); Abrahamse et al. (2005); Steg (2008); Delmas et al. (2013); Andor and Fels (2018); 
Iweka et al. (2019); Nisa et al. (2019); Grilli and Curtis (2021); Khanna et al. (2021)*
Investment in Energy Efficiency (1) 
Steg (2008)*
Mode of Transportation (5) 
Matthies et al. (2006); Steg (2008)*45 4 4 10

Note: Papers in this review of behavioural interventions to reduce household energy demand were collected through a systemic literature search up to August 2021. Studies 
are included in the reported counts if they are (i) experimental, (ii) peer-reviewed or highly cited reports, (iii) the intervention is behavioural, and (iv) the targeted behaviour is 
household energy demand. 559 papers are included in the review. Each paper was coded for: type of behavioural intervention, country of study, energy demand behaviour 
targeted, whether the target is an ‘Avoid’, ‘Shift’, or ‘Improve’ behaviour, and whether the intervention includes an economic incentive. Some papers do not report all elements. 
The energy demand behaviour column provides the count of papers that focus on each behaviour type (in parentheses after the behaviour). The citations that follow are not

exhaustive but exemplify papers in the category, selected for impact, range, and recency. The asterisk (*) indicates references that are meta-analyses or systematic reviews. 
Papers within meta-analyses and systematic reviews that meet the inclusion criteria are counted individually in the total counts. The full reference list is available at https://osf.
io/9463u/.

551
Demand, Services and Social Aspects of Mitigation Chapter 55Table 5.3b | Summary of effects of behavioural interventions in Table 5.3a.
Behavioural toolResults
(expressed in household energy savings, unless otherwise stated)Results summary
Set proper defaultMeta-analyses find a medium to strong effect of defaults on environmental behaviour. 
Jachimowicz et al. (2019) report a strong average effect of defaults on environmental 
behaviour (Cohen’s d = 0.75, confidence interval 0.39–1.12), though not as high as for 
consumer decisions. They find that defaults, across domains, are more effective when they 
reflect an endorsement (recommendation by a trusted source) or endowment (reflecting 
the status quo). Nisa et al. (2019)* report a medium average effect size (Cohen’s d = 0.35; 
range 0.04–0.55).
Reach out during 
transitionsThe few interventions that focus on transitions and measure behaviour change (rather

than energy savings) report mixed, moderate effect sizes. People were unwilling to change 
their behaviour if they were satisfied with current options (Mahapatra and Gustavsson 
2008). Iweka et al. (2019) find that effective messages can prompt habit disruption.
Timely feedback 
and remindersThe average effects of meta-analyses of feedback interventions on household energy 
use reductions range from 1.8% to 7.7%, with large variations (Delmas et al. 2013; 
Buckley 2019; Nisa et al. 2019; Buckley 2020; Ahir and Chakraborty 2021; Khanna et al. 
2021). The same is true for two literature reviews (Abrahamse et al. 2005; Bergquist 
et al. 2019). Most studies find a 4–10% average reduction during the intervention; 
some studies find a non-significant result (Dünnhoff and Duscha 2008) or a negative 
reduction (Winett et al. 1978).
Real-time feedback is most effective, followed by personalised feedback (Buckley 2019;

Buckley 2020). A review by Darby et al. (2006) finds direct feedback (from the meter or 
display monitor) is more effective than indirect feedback (via billing) (5–15% savings vs 
0–10% savings). Feedback effects (Cohen’s d = 0.241) are increased when combined with 
a monetary incentive (Cohen’s d = 0.96) and with a social comparison and a monetary 
incentive (Cohen’s d = 0.714) (Khanna et al. 2021).
Sanguinetti et al. (2020) find that onboard feedback results in a 6.6% improvement in 
the fuel economy of cars (Cohen’s d: 0.07, [range 0.05–0.08]).
small medium large

552
and reminders 
(continued)The effectiveness of feedback from in home displays is highly studied. Two reviews find 
them to have result in a 2–14% energy saving (Ehrhardt-Martinez and Donnelly 2010; 
Faruqui et al. 2010). A meta-analysis by McKerracher and Torriti (2013) finds a smaller 
range of results, with 3–5% energy savings.
Make information 
intuitive and easy 
to accessMeta-analyses of information interventions on household energy use find average energy 
savings between 1.8–7.4% and Cohen’s d effect sizes between 0.05 and 0.30 (Delmas 
et al. 2013; Buckley 2019; Nisa et al. 2019);* Buckley 2020; Nemati and Penn 2020; Ahir 
and Chakraborty 2021; Khanna et al. 2021). Study quality affects the measured effect –

small sample sizes, shorter measurement windows, and self-selection are correlated with 
larger effects (Nisa et al. 2019; Nemati and Penn 2020). RCTs have a smaller effect size, 
5.2% savings (95% confidence interval [range 0.5% –9.5%]) (Nemati and Penn 2020).
Information combined with comparative feedback is more effective than information 
alone (d = .34 vs. 30 (Khanna et al. 2021); 8.5% vs 7.4% (Delmas et al. 2013). Monetary 
incentives make information interventions more effective (Khanna et al. 2021).
Energy efficiency labeling has a heterogenous effect on investment in energy efficiency 
(Abrahamse et al. 2005; Andor and Fels 2018). Efficiency labels on houses lead to higher 
price mark ups (Jensen et al. 2016) and house prices (Brounen and Kok 2011). Energy star 
labels lead to significantly higher willingness to pay for refrigerators (Houde et al. 2013), 
but energy and water conservation varies by appliance from 0–23% (Kurz et al. 2005).

A meta-analysis of interventions to increase alternative fuel vehicle adoption find a small 
effect (d = .20–.28) (Pettifor et al. 2017).

553
Demand, Services and Social Aspects of Mitigation Chapter 55Behavioural toolResults
Make behaviour 
observable and 
provide recognitionMaking behaviour observable and providing recognition lead to 6–7% energy savings 
(Winett et al. 1978; Handgraaf et al. 2013; Nemati and Penn 2020) and a large effects 
size (Cohen’s d = 0.79-1.06); (Nisa et al. 2019*). Community-wide interventions result 
in 1–27% energy savings (Iweka et al. 2019).
Neighbourhood social influence has a small (d = .28) effect on alternative fuel vehicle 
adoption (Pettifor et al. 2017).
a normThe effect of social norm information on household energy savings ranges from  
1.7–11.5% (Delmas et al. 2013; Buckley 2020) and Cohen’s d from 0.08–0.32, 
(Abrahamse and Steg 2013; Bergquist et al. 2019; Khanna et al. 2021); (Nisa et al. 2019)* 
with similar effects on choice of mode of transportation. Pettifor et al. (2017) report

a small effect (d = .20–.28) on selecting a more energy efficient car.
The OPOWER study (Allcott 2011), prototypical for the impact of social norms on household 
energy consumption, finds 2% reduction in long-term energy use and 11–20% energy 
reduction in the short run (Allcott 2011; Ayres et al. 2013; Costa and Kahn 2013; Allcott 
and Rogers 2014). Impact decays over time (Allcott and Rogers 2012). Norm interventions 
are less effective for low energy users (Schultz et al. 2007; Andor et al. 2020). Moral 
licensing and negative spillover can reduce the overall positive feedback of normative 
feedback (Tiefenbeck et al. 2013).
Interventions are more effective when the norm is implicitly inducted, in individual 
countries, and when people care about the norm (Nolan et al. 2008; Bergquist et al. 2019; 
Khanna et al. 2021). Descriptive norm interventions (social comparisons) are more effective 
when communicated online,by email or through in-home displays compared to billing

letters (Andor and Fels 2018), when the reference group is more specific (Shen et al. 2015). 
Dolan and Metcalfe (2013) find conservation increased from 4% to 11% when energy 
savings tips are added.

554
Reframe 
consequences in 
terms people care 
aboutA meta-analysis by Khanna et al. ( 2021) finds a small and variable effect of 
motivational interventions that reframe consequences (Cohen’s d = [0–0.423]). Effects 
are larger when reframing is combined with monetary incentives and feedback (d = .96). 
Darby et al. (2006) report 10–20% savings for US pay-as-you-go systems. Providing 
lifecycle cost information increases likelihood of purchasing eco-innovative products 
(Kaenzig and Wüstenhagen 2010). Long term (10-year) operating cost information leads 
to higher willingness to pay for energy efficiency compared to short-term (1-year) cost 
information (Heinzle and Wüstenhagen 2012). Monetary information increases the success 
of energy reduction interventions (Newell and Siikamäki 2014; Andor and Fels 2018).

Reframing interventions are more effective when combined with feedback (d = .24–.96) 
and with social comparisons and feedback (d = .42) (Khanna et al. 2021).
Obtain 
a commitmentCommitment and goal interventions result in significant energy reduction in half of studies 
(Abrahamse et al. 2005; Andor and Fels 2018; Nisa et al. 2019*). Nisa et al. (2019) report 
a moderate average effect (Cohen’s d = 0.34, [0.11–0.66]). When results are significant, 
the energy savings are around 10% (Andor and Fels 2018). Self-set goals perform better 
than assigned goals (van Houwelingen and van Raaij 1989; McCalley and Midden 2002; 
Andor and Fels 2018) and reasonable goals perform better than unreasonably high or 
low goals (van Houwelingen and van Raaij 1989; Abrahamse et al. 2007; Harding and 
Hsiaw 2014). Interventions are more effective when the commitment is public (Pallak 
and Cummings 1976) and when combined with information and rewards (Slavin et al. 
1981; Völlink and Meertens 1999).

Note: The second column describes the effects of each of the eight behavioural tools. The third column plots the results of meta-analyses and reviews that focus on each tool. Effects are reported as described in the referenced paper, either as 
percentage of energy saved (dotted box) or by the effect size, measured as Cohen’s d (dashed box).
*Two responses to Nisa et al. (2019) challenge their conclusion that behavioural interventions have a small impact on household energy use (Stern 2020; van der Linden and Goldberg, 2020). We report the raw data collected and used in 
Nisa et al. (2019). Our data summary supports the arguments by Stern (2020) and van der Linden and Goldberg (2020) that interventions should be evaluated in combination, as well as individually, and that the results are highly sensitive 
to the chosen estimator.
a Range reported as 95% confidence interval of results used in the meta-analysis or review.

b Range reported as all results included in the meta-analysis or review.
c No range reported.
d Range indicates the reported results within a meta-analysis; this applies when multiple intervention types in a meta-analysis are classified as a single behavioural tool.small medium large

555
Demand, Services and Social Aspects of Mitigation Chapter 555.4.2 Socio-cultural Drivers of Climate Mitigation
Collective behaviours and social organisation are part of everyday 
life, and feeling part of active collective action renders mitigation 
measures efficient and pervasive (Climact 2018). Social and cultural 
processes play an important role in shaping what actions people 
take on climate mitigation, interacting with individual, structural, 
institutional and economic drivers (Barr and Prillwitz 2014). Just like 
infrastructure, social and cultural processes can ‘lock in’ societies to 
carbon-intensive patterns of service delivery. They also offer potential 
levers to change normative ideas and social practices in order to 
achieve extensive emissions cuts (high confidence) (Table 5.4).
In terms of cultural processes, we can distinguish two levels of 
analysis: specific meanings associated with particular technologies

or practices, and general narratives about climate change mitigation. 
Specific meanings (e.g.,  comfort, status, identity and agency) are 
associated with many technologies and everyday social practices that 
deliver energy services, from driving a car to using a cookstove (high 
evidence, high agreement) (Section 5.5). Meanings are symbolic and 
influence the willingness of individuals to use existing technologies 
or shift to new ones (Wilhite and Ling 1995; Wilhite 2009; Sorrell 
2015). Symbolic motives are more important predictors of technology 
adoption than instrumental motives (Steg 2005; Noppers et al. 2014; 
Noppers et  al. 2015; Noppers et  al. 2016) (see case study on app 
cabs in Kolkata, India (Box 5.8)). If an individual’s pro-environmental 
behaviour is associated with personal meaning than it also increases 
subjective well-being (Zawadzki et al. 2020). Status consciousness is 
highly relevant in GHG emission-intensive consumption choices (cars,

houses). However, inversely framing energy-saving behaviour as high 
status is a promising strategy for emission reduction (Ramakrishnan 
and Creutzig 2021).
At a  broader level, narratives about climate mitigation circulate within 
and across societies, as recognised in SR1.5, and are broader than the 
meanings associated with specific technologies (high evidence, high 
agreement). Narratives enable people to imagine and make sense 
of the future through processes of interpretation, understanding, 
communication and social interaction (Smith et  al. 2017). Stories 
about climate change are relevant for mitigation in numerous ways. 
They can be utopian or dystopian (e.g.,  The great derangement by 
Amitav Ghosh) (Ghosh 2016), for example presenting apocalyptic 
stories and imagery to capture people’s attention and evoke emotional 
and behavioural response (O’Neill and Smith 2014). Reading climate 
stories has been shown to cause short-term influences on attitudes

towards climate change, increasing the belief that climate change is 
human caused and increasing its issue priority (Schneider-Mayerson 
et al. 2020). Climate narratives can also be used to justify scepticism 
of science, drawing together coalitions of diverse actors into social 
movements that aim to prevent climate action (Lejano and Nero 
2020). Narratives are also used in integrated assessment and energy 
system models that construct climate stabilisation scenarios, for 
example in the choice of parameters, their interpretation and model 
structure (Ellenbeck and Lilliestam 2019). One important narrative 
choice of many models involves framing climate change as market 
failure (which leads to the result that carbon pricing is required). While such a choice can be justified, other model framings can be 
equally justified (Ellenbeck and Lilliestam 2019).
Power and agency shape which climate narratives are told and how 
prevalent they are (O’Neill and Smith 2014; Schneider-Mayerson

et al. 2020). For example, narratives have been used by indigenous 
communities to imagine climate futures divergent from top-down, 
government-led narratives (Streeby 2018). The uptake of new climate 
narratives is influenced by political beliefs and trust. Policymakers can 
enable emissions reduction by employing narratives that have broad 
societal appeal, encourage behavioural change and complement 
regulatory and fiscal measures (Terzi 2020). Justice narratives may 
not have universal appeal: in a  UK study, justice narratives 
polarised individuals along ideological lines, with lower support 
amongst individuals with right-wing beliefs; by contrast, narratives 
centred on saving energy, avoiding waste and patriotic values were 
more widely supported across society (Whitmarsh and Corner 2017). 
More research is needed to assess if these findings are prevalent in 
diverse socio-cultural contexts, as well as the role played by social

media platforms to influence emerging narratives of climate change 
(Pearce et al. 2019).
Trust in organisations is a key predictor of the take-up of novel energy 
services (Lutzenhiser 1993), particularly when financial incentives 
are high (Stern et al. 1985; Joskow 1995). Research has shown that 
if there is low public trust in utility companies, service delivery by 
community-based non-profit organisations in the US (Stern et  al. 
1985) or public/private partnerships in Mexico (Friedmann and 
Sheinbaum 1998), offer more effective solutions, yet only if public 
trust is higher in these types of organisations. UK research shows 
that acceptance of shifts to less resource-intensive service provision 
(e.g., more resource-efficient products, extending product lifetimes, 
community schemes for sharing products) varies depending on factors 
including trust in suppliers and manufacturers, affordability, quality 
and hygiene of shared products, and fair allocation of responsibilities

(Cherry et  al. 2018). Trust in other people plays an important role 
in the sharing economy (Li and Wang 2020), for example predicting 
shifts in transport mode, specifically car sharing involving rides with 
strangers (Acheampong and Siiba 2019) (Section 5.3.4.2).
Action on climate mitigation is influenced by our perception of 
what other people commonly do, think or expect, known as social 
norms (high evidence, high agreement) (Cialdini 2006) (Table 5.3), 
even though people often do not acknowledge this (Nolan et  al. 
2008; Noppers et al. 2014). Changing social norms can encourage 
societal transformation and social tipping points to address climate 
mitigation (Nyborg et al. 2016; Otto et al. 2020). Providing feedback 
to people about how their own actions compare to others’ can 
encourage mitigation (Delmas et  al. 2013), although the overall 
effect size is not strong (Abrahamse and Steg 2013). Trending 
norms are behaviours that are becoming more popular, even if

currently practised by a minority. Communicating messages that the 
number of people engaging in a mitigation behaviour (e.g., giving 
a financial donation to an environmental conservation organisation) 
is increasing – a simple low-cost policy intervention – can encourage 
shifts to the targeted behaviour, even if the effect size is relatively 
small (Mortensen et al. 2019).

556
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Socially comparative feedback seems to be more effective when 
people strongly identify with the reference group (De Dominicis 
et al. 2019). Descriptive norms (perceptions of behaviours common 
in others) are more strongly related to mitigation actions when 
injunctive norms (perceptions of whether certain behaviours are 
commonly approved or disapproved) are also strong, when people 
are not strongly personally involved with mitigation topics (Göckeritz 
et  al. 2010), when people are currently acting inconsistently with 
their preferences, when norm-based interventions are supported by 
other interventions and when the context supports norm-congruent 
actions (Miller and Prentice 2016). A descriptive norm prime (‘most 
other people try to reduce energy consumption’) together with 
injunctive norm feedback (‘you are very good at saving energy’) 
is a  very effective combination to motivate further energy savings

(Bonan et al. 2020). Second-order beliefs (perceptions of what others 
in the community believe) are particularly important for leveraging 
descriptive norms (Jachimowicz et al. 2018).
Behavioural contagion, which describes how ideas and behaviours 
often spread like infectious diseases, is a  major contributor to the 
climate crisis (Sunstein 2019). But harnessing contagion can also 
mitigate warming. Carbon-heavy consumption patterns have become 
the norm only in part because we’re not charged for environmental 
damage we cause (Pigou 1920). The deeper source of these patterns 
has been peer influence (Frank 1999), because what we do influences 
others. A rooftop solar installation early in the adoption cycle, for 
example, spawns a copycat installation in the same neighbourhood 
within four months, on average. With such installations thus doubling 
every four months, a  single new order results in 32 additional

installations in just two years. And contagion doesn’t stop there, since 
each family also influences friends and relatives in distant locations.
Harnessing contagion can also underwrite the investment necessary 
for climate stability. If taxed more heavily, top earners would spend 
less, shifting the frames of reference that shape spending of those just 
below, and so on – each step simultaneously reducing emissions and 
liberating resources for additional green investment (Frank  2020). 
Many resist, believing that higher taxes would make it harder to 
buy life’s special extras. But that belief is a cognitive illusion (Frank 
2020). Acquiring special things, which are inherently in short supply, 
requires outbidding others who also want them. When top tax rates 
rise in tandem, relative bidding power is completely unchanged, so 
the same penthouse apartments would end up in the same hands 
as before. More generally, behavioural contagion is important to

leverage all relevant social tipping points for stabilising Earth’s 
climate (Otto et al. 2020).
For new climate policies and mitigation technologies to be rapidly 
and extensively implemented, they must be socially acceptable to 
those who are directly impacted by those policies and technologies 
(medium evidence, high agreement). Policies that run counter to 
social norms or cultural meanings are less likely to be effective in 
reducing emissions (Demski et  al. 2015; Perlaviciute et  al. 2018; 
Roy et  al. 2018b). More just and acceptable implementation of 
renewable energy technologies requires taking account of the 
cultural meanings, emotional attachments and identities linked 
to particular landscapes and places where those technologies are proposed (Devine-Wright  2009) and enabling fairness in how 
decisions are taken and costs and benefits distributed (Wolsink 
2007). This is important for achieving the goal of SDG 7 (increased

use of renewable energy resources) in developing countries while 
achieving energy justice (Calzadilla and Mauger 2017). ‘Top-down’ 
imposition of climate policies by governments can translate into local 
opposition when perceived to be unjust and lacking transparency 
(high evidence, high agreement). Policymakers can build trust and 
increase the legitimacy of new policies by implementing early 
and extensive public and stakeholder participation, avoiding ‘Nimby’ 
(Not In My Back Yard) assumptions about objectors and adopting 
‘Just Transition’ principles (Owens 2000; Wolsink 2007; Wüstenhagen 
et al. 2007; Dietz and Stern 2008; Devine-Wright 2011; Heffron and 
McCauley 2018). Participatory mechanisms that enable deliberation 
by a representative sample of the public (Climate Assembly UK 2020) 
can inform policymaking and increase the legitimacy of new and 
difficult policy actions (Dryzek et al. 2019).
Collective action by civil society groups and social movements can

work to enable or constrain climate mitigation. Civil society groups 
can advocate policy change, provide policy research and open up 
opportunities for new political reforms (high evidence, high agreement) 
as recognised in previous IPCC reports (IPCC 2007). Grassroots 
environmental initiatives, including community energy groups, are 
collective responses to, and critiques of, normative ways that everyday 
material needs (e.g., food, energy, making) are produced, supplied and 
circulated (Schlosberg and Coles 2016). Such initiatives can reconcile 
lower carbon footprints with higher life satisfaction and higher 
incomes (Vita et al. 2020). Local initiatives such as Transition Towns 
and community energy projects can lead to improvements in energy 
efficiency, ensure a decent standard of living and increase renewable 
energy uptake, while building on existing social trust, and, in turn, 
building social trust and initiating engagement, capacity building, and

social capital formation (Hicks and Ison 2018). Another example are 
grassroot initiatives that aim to reduce food loss and waste, even as 
overall evidence on their effectiveness remains limited (Mariam et al. 
2020). However, community energy initiatives are not always inclusive 
and require policy support for widespread implementation across all 
socio-economic groups (Aiken et al. 2017). In addition, more evidence 
is required of the impacts of community energy initiatives (Creamer 
et al. 2018; Bardsley et al. 2019).
Civil society social movements are a  primary driver of social and 
institutional change (high evidence, high agreement) and can be 
differently positioned as, on the one hand, ‘insider’ social movements 
(e.g., World Wildlife Fund) that seek to influence existing state 
institutions through lobbying, advice and research and, on the 
other hand, ‘outsider’ social movements (e.g., Rising Tide, Extinction 
Rebellion) that advocate radical reform through protests and

demonstrations (Newell 2005; Caniglia et al. 2015). Civil society social 
movements frame grievances that resonate with society, mobilise 
resources to coordinate and sustain mass collective action, and 
operate within  – and seek to influence  – external conditions that 
enable or constrain political change (Caniglia et  al. 2015). When 
successful, social movements open up windows of opportunity 
(so called ‘Overton Windows’) to unlock structural change (high 
evidence, high agreement) (Szałek 2013; Piggot 2018).

557
Demand, Services and Social Aspects of Mitigation Chapter 55Climate social movements advocate new narratives or framings 
for climate mitigation (e.g.,  ‘climate emergency’) (della Porta 
and Parks 2014); criticise positive meanings associated with high 
emission technologies or practices (see case studies on diet and 
solar PV, (Boxes 5.5 and 5.7)); show disapproval for high-emission 
behaviours (e.g., through ‘flight shaming’); model behaviour change 
(e.g., shifting to veganism or public transport – see case study on 
mobility in Kolkata, India (Box 5.8)); demonstrate against extraction 
and use of fossil fuels (Cheon and Urpelainen 2018); and aim to 
increase a sense of agency amongst certain social groups (e.g., young 
people or indigenous communities) that structural change is possible. 
Climate strikes have become internationally prevalent, for example 
the September 2019 strikes involved participants in more than

180 countries (Rosane 2019; Fisher and Nasrin 2020; Martiskainen 
et  al. 2020). Enabled by digitalisation, these have given voice to 
youth on climate (Lee et al. 2020) and created a new cohort of active 
citizens engaged in climate demonstrations (Fisher 2019). Research 
on bystanders shows that marches increase positive beliefs about 
marchers and collective efficacy (Swim et al. 2019).
Countermovement coalitions work to oppose climate mitigation (high 
confidence). Examples include efforts in the US to oppose mandatory 
limits on carbon emissions supported by organisations from the coal and electrical utility sectors (Brulle 2019). There is evidence that 
US opposition to climate action by carbon-connected industries is 
broad-based, highly organised, and matched with extensive lobbying 
(Cory et al., 2021). Social movements can also work to prevent policy 
changes, for example in France the Gilet Jaunes objected to increases

in fuel costs on the grounds that they unfairly distributed the costs 
and benefits of price rises across social groups, for example between 
urban, peri-urban and rural areas (Copland 2019).
Religion could play an important role in enabling collective action 
on climate mitigation by providing cultural interpretations of change 
and institutional responses that provide resources and infrastructure 
to sustain collective actions (Roy et  al. 2012; Haluza-DeLay 2014; 
Caniglia et  al. 2015; Hulme 2015). Religion can be an important 
cultural resource towards sustainability at individual, community and 
institutional levels (Ives and Kidwell 2019), providing leverage points 
for inner transformation towards sustainability (Woiwode et  al. 
2021). Normative interpretations of climate change for and from 
religious communities are found in nearly every geography, and often 
observe popular movements for climate action drawing on religious

symbols or metaphors (Jenkins et al. 2018). This suggests the value for 
policymakers of involving religious constituencies as significant civil 
society organisations in devising and delivering climate responses.
Box 5.7 | Solar PV and the Agency of Consumers
As an innovative technology, solar PV was strongly taken up by consumers (Nemet 2019). Several key factors explain its success. First, 
modular design made it applicable to different scales of deployment in different geographical contexts (e.g., large-scale grid-connected 
projects and smaller-scale off-grid projects) and allowed its application by companies taking advantage of emerging markets (Shum 
and Watanabe 2009). Second, culturally, solar PV symbolised an environmentally progressive technology that was valued by users 
(Morris and Jungjohann 2016). Large-scale adoption led to policy change (i.e., the introduction of feed-in tariffs that guaranteed

a financial return) that in turn enabled improvements to the technology by companies. Over time, this has driven large-scale reductions 
in cost and increase in deployment worldwide. The relative importance of drivers varied across contexts. In Japan, state subsidies were 
lower yet did not hinder take-up because consumer behaviour was motivated by non-cost symbolic aspects. In Germany, policy change 
arose from social movements that campaigned for environmental conservation and opposed nuclear power, making solar PV policies 
politically acceptable. In summary, the seven-decade evolution of solar PV shows an evolution in which the agency of consumers has 
consistently played a key role in multiple countries, such that deriving 30–50% of global electricity supply from solar is now a realistic 
possibility (Creutzig et al. 2017). See more in Chapter 5 Supplementary Material I, 5.SM.6.1.
5.4.3 Business and Corporate Drivers
Businesses and corporate organisations play a  key role in the

mitigation of global warming, through their own commitments 
to zero-carbon footprints (Mendiluce 2021), decisions to invest 
in researching and implementing new energy technologies and 
energy-efficient measures, and the supply-side interaction with 
changing consumer preferences and behaviours, such as via 
marketing. Business models and strategies work both as a barrier 
to and an accelerator of decarbonisation. Still existing locked-
in infrastructures and business models advantages fossil fuel 
industry over renewable and energy efficient end use industry 
(Klitkou et al. 2015). The fossil fuel energy generation and delivery 
system therefore epitomises a  barrier to the acceptance and implementation of new and cleaner renewable energy technologies 
(Kariuki 2018). A good number of corporate agents have attempted 
to derail climate change mitigation by targeted lobbying and doubt-
inducing media strategies (Oreskes and Conway 2011). A number of

corporations that are involved in both upstream and downstream 
supply chains of fossil fuel companies make up the majority of 
organisations opposed to climate action (Dunlap and McCright 
2015; Brulle 2019; Cory et  al. 2021). Corporate advertisement 
and brand-building strategies also attempt to deflect corporate 
responsibility to individuals, and/or to appropriate climate care 
sentiments in their own brand building; climate change mitigation 
is uniquely framed through choice of products and consumption, 
avoiding the notion of the political collective action sphere (Doyle 
2011; Doyle et al. 2019).

558
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Business and corporations are also agents of change towards 
decarbonisation, as demonstrated in the case of PV and battery 
electric cars (Teece 2018). Beyond new low-carbon technologies, 
strong sustainability business models are characterised by identifying 
nature as the primary stakeholder, strong local anchorage, the creation 
of diversified income sources, and deliberate limitations on economic 
growth (Brozovic 2019). However, such business models are difficult 
to maintain if generally traditional business models, which require 
short-term accounting, prevail.
Liability of fossil fuel business models and insurance against climate 
damages are key concerns of corporations and business. Limitations 
and regulation on GHG emissions will compel reductions in demand for 
fossil fuel companies’ products (Porter and Kramer 2006). According 
to a  report by the Advisory Scientific Committee of the European

Systemic Risk Board, insurance industries are very likely to incur losses 
due to liability risks (ESRB 2016). The divestment movement adds 
additional pressure on fossil fuel related investments (Braungardt 
et al. 2019), even though fossil fuel financing remains resilient (Curran 
2020). Companies, businesses and organisations, especially those 
in the carbon-intensive energy sector, might face liability claims for 
their contribution to climate change. A late transition to a low-carbon 
economy would exacerbate the physical costs of climate change on 
governments, businesses and corporations (ESRB 2016).
Despite the seemingly positive roles that businesses and corporate 
organisations tend to play towards sustainable transitions, there is 
a need to highlight the dynamic relationship between sustainable and 
unsustainable trends (Antal et al. 2020), or example, the production 
of sport utility vehicles (SUVs) in the automobile market at the

same time that car manufacturers are producing electric vehicles. 
An analysis of the role of consumers as drivers of unsustainability 
for businesses and corporate organisations is very important here as 
this trend will offset the sustainability progress being made by these 
businesses and organisations (Antal et al. 2020).
Professional actors, such as building managers, landlords, energy 
efficiency advisers, technology installers and car dealers, influence 
patterns of mobility and energy consumption (Shove 2003) by acting 
as ‘middle actors’ (Janda and Parag 2013; Parag and Janda 2014) 
or intermediaries in the provision of building or mobility services (Grandclément et al. 2015; De Rubens et al. 2018). Middle actors can 
bring about change in several different directions, be it, upstream, 
downstream or sideways. They can redefine professional ethics 
around sustainability issues, and, as influencers on the process of

diffusion of innovations (Rogers 2003), professionals can enable or 
obstruct improvements in efficient service provision or shifts towards 
low-carbon technologies (e.g.,  air and ground source heat pumps, 
solar hot water, underfloor heating, programmable thermostats, and 
mechanical ventilation with heat recovery) and mobility technologies 
(e.g., electric vehicles).
5.4.4 Institutional Drivers
The allocation of political power to incumbent actors and coalitions 
has contributed to lock-in of particular institutions, stabilising the 
interests of incumbents through networks that include policymakers, 
bureaucracies, advocacy groups and knowledge institutions 
(high agreement, high evidence). There is high evidence and high 
agreement that institutions are central in addressing climate change 
mitigation. Indeed, social provisioning contexts, including equity, 
democracy, public services and high quality infrastructure, are

found to facilitate high levels of need satisfaction at lower energy 
use, whereas economic growth beyond moderate incomes and 
dependence on extractive industries inhibit it (Vogel et  al. 2021). 
They shape and interact with technological systems (Unruh 2000; 
Foxon et  al. 2004; Seto et  al. 2014) and represent rules, norms 
and conventions that organise and structure actions (Vatn 2015) 
and help create new path dependency or strengthen existing path 
dependency (Mattioli et al. 2020) (see case studies in Boxes 5.5 to 
5.8 and Chapter 5 Supplementary Material I). These drive behaviour 
of actors through formal (e.g.,  laws, regulations, and standards) 
or informal (e.g.,  norms, habits, and customs) processes, and can 
create constraints on policy options (Breukers and Wolsink 2007). 
For example, the car-dependent transport system is maintained by 
interlocking elements and institutions, consisting of (i) the automotive

industry; (ii) the provision of car infrastructure; (iii)  the political 
economy of urban sprawl; (iv)  the provision of public transport; 
(v) cultures of car consumption (Mattioli et al. 2020). The behaviour 
of actors, their processes and implications on policy options and 
decisions are discussed further in Section 5.6.
Box 5.8 | Shifts from Private to Public Transport in an Indian Megacity
In densely populated, fast-growing megacities, policymakers face the difficult challenge of preventing widespread adoption of petrol 
or diesel fuelled private cars as a mode of transport. The megacity of Kolkata in India provides a useful case study. As many as twelve 
different modes of public transportation, each with its own system structure, actors and meanings, co-exist and offer means of 
mobility to its 14 million citizens. Most of the public transport modes are shared mobility options, ranging from sharing between two

people in a rickshaw or a few hundred in metro or sub-urban trains. Sharing also happens informally as daily commuters avail shared 
taxis and neighbours borrow each other’s car or bicycle for urgent or day trips.

559
Demand, Services and Social Aspects of Mitigation Chapter 555.4.5 Technological and Infrastructural Drivers
Technologies and infrastructures shape social practices and their 
design matters for effective mitigation measures (high evidence, 
high agreement). There are systemic interconnections between 
infrastructures and practices (Cass et al. 2018; Haberl et al. 2021), 
and their intersection explains their relevance (Thacker et al. 2019). 
The design of a new electricity system to meet new emerging demand 
based on intermittent renewable sources can lead to a  change 
in consumption habits and the adaption of lifestyles compliant 
with more power supply interruption (Maïzi et al. 2017; Maïzi and 
Mazauric 2019). The quality of the service delivery impacts directly 
the potential user uptake of low-carbon technologies among rural 
households. In the state of Himachal Pradesh in India, a shift from 
LPG to electricity among rural households, with induction stoves,

has been successful due to the availability of stable and continuous 
electricity, which has been difficult to achieve in any other Indian 
state (Banerjee et al. 2016). In contrast, in South Africa, people who 
were using electricity earlier are now adopting LPG to diversify the 
energy source for cooking due to high electricity tariffs and frequent 
blackouts (Kimemia and Annegarn 2016) (Box  5.5 and Chapter  5 
Supplementary Material I).
From a  welfare point of view, infrastructure investments are not 
constrained by revealed or stated preferences (high evidence, 
high agreement). Preferences change with social and physical 
environment, and infrastructure interventions can be justified by objective measures, such as public health and climate change 
mitigation, not only given preferences (high agreement,  high 
evidence). Specifically, there is a case for more investment in low-
carbon transport infrastructure than assumed in environmental

economics as it induces low-carbon preferences (Creutzig et  al. 
2016a; Mattauch et  al. 2016; Mattauch et  al. 2018). Changes in 
infrastructure provision for active travel may contribute to uptake 
of more walking and cycling (Frank et  al. 2019). These effects 
contribute to higher uptake of low-carbon travel options, albeit 
the magnitude of effects depends on design choices and context 
(Goodman et  al. 2013; Goodman et  al. 2014; Song et  al. 2017; 
Javaid et  al. 2020; Abraham et  al. 2021). Infrastructure is thus 
not only required to make low-carbon travel possible but can 
also be a pre-condition for the formation of low-carbon mobility 
preferences (see case study in Box 5.8).
The dynamic interaction of habits and infrastructures also predict 
CO2-intensive choices. When people move from a  city with good 
public transport to a  car-dependent city, they are more likely to 
own fewer vehicles due to learned preferences for lower levels of

car ownership (Weinberger and Goetzke 2010). When individuals 
moving to a  new city with extensive public transport were given 
targeted material about public transport options, the modal share 
of public transport increased significantly (Bamberg et  al. 2003). 
Similarly, an exogenous change to route choice in public transport 
makes commuters change their habitual routes (Larcom et al. 2017).Box 5.8 (continued)
A key role is played by the state government, in collaboration with other stakeholders, to improve the system as whole and formalise 
certain semi-formal modes of transport. An important policy consideration has been to make Kolkata’s mobility system more efficient 
(in terms of speed, reliability and avoidance of congestion) and sustainable through strengthening coordination between different 
mode-based regimes (Ghosh 2019) and more comfortable with air conditioned space in a hot and humid climate (Roy et al. 2018b).

Policymakers have introduced multiple technological, behavioural and socio-cultural measures to tackle this challenge. New buses 
have been purchased by public authorities (Ghosh and Schot 2019). These have been promoted to middle class workers in terms of 
modernity, efficiency and comfort, and implemented using premium fares. Digitalisation and the sharing economy have encouraged 
take-up of shared taxi rides (‘app cabs’), being low cost and fast, but also influenced by levels of social trust involved in rides with 
strangers (Acheampong and Siiba 2019; Ghosh and Schot 2019). Rickshaws have been improved through use of LNG and cycling 
has been banned from busy roads. These measures contributed positively to halving greenhouse gas emissions per unit of GDP tin 
one decade within the Kolkata metropolitan area, with potential for further reduction (Colenbrander et al. 2016). However, social

movements have opposed some changes due to concerns about social equity, since many of the new policies cater to middle class 
aspirations and preferences, at the cost of low-income and less privileged communities.
To conclude, urban mobility transitions in Kolkata show interconnected policy, institutional and socio-cultural drivers for socio-
technical change. Change has unfolded in complex interactions between multiple actors, sustainability values and megatrends, where 
direct causalities are hard to identify. However, the prominence of policy actors as change agents is clear as they are changing 
multiple regimes from within. The state government initiated infrastructural change in public bus systems, coordinated with private 
and non-governmental actors such as auto-rickshaw operators and app cab owners, who hold crucial agency in offering public

transport services in the city. The latter can directly be attributed to the global momentum of mobility-as-a-service platforms, at the 
intersection of digitalisation and sharing economy trends. More thoughtful action at a policy level is required to sustain and coordinate 
the diversity of public transport modes through infrastructure design and reflect on the overall direction of change (Roy et al. 2018b; 
Schot and Steinmueller 2018). See more in Chapter 5 Supplementary Material I, Section 5.SM.6.3.

560
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Table 5.4 | Main features, insights, and policy implications of five drivers of decision and action.  Entries in each column are independent lists, not intended to 
line up with each other.
DriverHow does driver contribute 
to status quo bias?What needs to change?Driver’s policy 
implicationsExamples
Behavioural –Habits and routines formed under different 
circumstances do not get updated
 –Present bias penalises upfront costs and 
discourages energy efficiency investments
 –Loss aversion magnifies the costs 
of change
 –When climate change is seen as distant, 
it is not feared
 –Nuclear power and accident potential 
score high on psychological dread –New goals (sustainable lifestyle)
 –New capabilities (online real-time 
communication)
 –New resources (increased education)
 –Use of full range of incentives and mechanisms 
to change demand-side behaviour –Policies need to be 
context specific and 
coordinate economic,

legal, social, and 
infrastructural tools 
and nudges
 –Relate climate action 
to salient local risks 
and issues –India’s new LPG scale up 
policy uses insights about 
multiple behavioural drivers 
of adoption and use
 –Rooftop solar adoption 
expanded in Germany, when 
feed-in tariffs removed risk 
from upfront-cost recovery
 –Nuclear power policies 
in Germany post Fukushima 
affected by emotional factors
Socio-cultural –Cultural norms (e.g., status, comfort, 
convenience) support existing behaviour
 –Lack of social trust reduces willingness 
to shift behaviour (e.g., adopt car sharing)
 –Fear of social disapproval decreases 
willingness to adopt new behaviours
 –Lack of opportunities to participate 
in policy create reactance against 
‘top-down’ imposition
 –Unclear or dystopian narratives of climate 
response reduce willingness to change and 
to accept new policies and technologies –Create positive meanings and norms around 
low-emission service delivery (e.g., mass transit)

–Community initiatives to build social trust 
and engagement, capacity building, and social 
capital formation
 –Climate movements that call out the 
insufficient, highly problematic state of delayed 
climate action
 –Public participation in policymaking and 
technology implementation that increases trust, 
builds capacity and increases social acceptance
 –Positive narratives about possible futures that 
avoid emissions (e.g., emphasis upon health 
and slow/active travel) –Embed policies in 
supportive social norms
 –Support collective 
action on climate 
mitigation to create 
social trust 
and inclusion
 –Involve arts and 
humanities to 
create narratives 
for policy process –Communicate descriptive 
norms to electricity 
end users
 –Community energy initiative
 –REScoop
 –Fridays For Future
Business and 
corporate –Lock-in mechanisms that make incumbent 
firms reluctant to change: core capabilities, 
sunk investments in staff and factories,

stranded assets –New companies (like car-sharing companies, 
renewable energy start-ups) that pioneer new 
business models or energy service provisions –Influence consumer 
behaviour via 
product innovation
 –Provide capital for clean 
energy innovation –Electrification of transport 
opens up new markets for 
more than a hundred million 
new vehicles
Institutional –Lock-in mechanisms related to power 
struggles, lobbying, political economy –New policy instruments, policy discussions, 
policy platforms, implementation agencies, 
including capacity –Feed-in tariffs and 
other regulations that 
turn energy consumers 
into prosumers –Mobility case study, India’s 
LPG policy sequence
Infrastructural –Various lock-in mechanisms such as sunk 
investments, capabilities, embedding 
in routines/lifestyles –Many emerging technologies, which are initially 
often more expensive, but may benefit from 
learning curves and scale economies that 
drive costs down –Systemic governance 
to avoid rebound

effects –Urban walking and 
bike paths
 –Stable and continuous 
electricity supply fostering 
induction stoves
5.5 An Integrative View on Transitioning
5.5.1 Demand-side Transitions as 
Multi-dimensional Processes
Several integrative frameworks including social practice theory 
(Røpke 2009; Shove and Walker 2014), the energy cultures framework 
(Stephenson et  al. 2015; Jürisoo et  al. 2019) and socio-technical 
transitions theory (McMeekin and Southerton 2012; Geels et al. 2017) 
conceptualise demand-side transitions as multi-dimensional and 
interacting processes (high evidence, high agreement). Social practice 
theory emphasises interactions between artefacts, competences, 
and cultural meanings (Røpke 2009; Shove and Walker 2014). The 
energy cultures framework highlights feedbacks between materials, 
norms, and behavioural practices (Stephenson et  al. 2015; Jürisoo

et al. 2019). Socio-technical transitions theory addresses interactions between technologies, user practices, cultural meanings, business, 
infrastructures, and public policies (McMeekin and Southerton 2012; 
Geels et  al. 2017) and can thus accommodate the five drivers of 
change and stability discussed in Section 5.4.
Section  5.4 shows with high evidence and high agreement that 
the relative influence of different drivers varies between demand-
side solutions. The deployment of ‘Improve’ options like LEDs and 
clean cookstoves mostly involves technological change, adoption by 
consumers who integrate new technologies in their daily life practices 
(Smith et  al. 1993; Sanderson and Simons 2014; Franceschini and 
Alkemade 2016), and some policy change. Changes in meanings 
are less pertinent for those ‘Improve’ options that are primarily 
about technological substitution. Other ‘Improve’ options, like clean 
cookstoves, involve both technological substitution and changes in

561
Demand, Services and Social Aspects of Mitigation Chapter 55Deployment of ‘Shift’ options like enhanced public transport involves 
substantial behavioural change and transitions to new or expanded 
provisioning systems, which may include new technologies (buses, 
trams), infrastructures (light rail, dedicated bus lanes), institutions 
(operational licences, performance contracts), financial arrangements, 
and new organisations (with particular responsibilities and oversight) 
(high evidence, high agreement) (Deng and Nelson 2011; Turnheim 
and Geels 2019). Changes in cultural meanings can facilitate ‘Shift’ 
options. Shifts towards low-meat diets, for instance, are motivated 
by costs and by beliefs about the undesirability of meat that relate 
more to issues like health, nutrition and animal welfare than climate 
change (De Boer et al. 2014; Mylan 2018).
‘Avoid’ options that reduce service levels (e.g.,  sufficiency or

downshifting) imply very substantial behavioural and cultural 
changes that may not resonate with mainstream consumers (Dubois 
et  al. 2019). Other ‘Avoid’ options like teleworking also require 
changes in cultural meanings and beliefs (about the importance 
of supervision, coaching, social contacts, or office politics), as well 
as changes in behaviour, institutions, business, and technology 
(including good internet connections and office space at home). 
Because these interconnected changes were not widespread, 
teleworking remained stuck in small niches and did not diffuse widely 
before the COVID-19 crisis (Hynes 2014; Hynes 2016; Belzunegui-
Eraso and Erro-Garcés 2020; Stiles 2020). As preferences change, 
new infrastructures and social settings can also elicit new desires 
associated with emerging low-energy demand service provisioning 
systems (Section 5.4.5).
Demand-side transitions involve interactions between radical social

or technical innovations (such as the Avoid-Shift-Improve options 
discussed in Section  5.3) and existing socio-technical systems, 
energy cultures, and social practices (high evidence, high agreement) 
(Stephenson et al. 2015; Geels et al. 2017). Radical innovations such 
as teleworking, plant-based burgers, car sharing, vegetarianism, or 
electric vehicles initially emerge in small, peripheral niches (Kemp 
et  al. 1998; Schot and Geels 2008), constituted by R&D projects, 
technological demonstration projects (Borghei and Magnusson 2016; 
Rosenbloom et al. 2018b), local community initiatives or grassroots 
projects by environmental activists (Hargreaves et al. 2013a; Hossain 
2016). Such niches offer protection from mainstream selection 
pressures and nurture the development of radical innovations (Smith 
and Raven 2012). Many low-carbon niche innovations, such as 
those described in Section 5.3, face uphill struggles against existing

socio-technical systems, energy cultures, and social practices that 
are stabilised by multiple lock-in mechanisms (high evidence, high 
agreement) (Klitkou et  al. 2015; Seto et  al. 2016; Clausen et  al. 
2017; Ivanova et al. 2018). Demand-side transitions therefore do not 
happen easily and involve interacting processes and struggles on the 
behavioural, socio-cultural, institutional, business and technological 
dimensions (Nikas et al. 2020) (Section 5.4).
5.5.2 Phases in Transitions
Transitions often take several decades, unfolding through several 
phases. Although there is variability across innovations, sectors, and countries, the transitions literature distinguishes four phases, 
characterised by generic core processes and challenges: (i) emergence, 
(ii) early adaptation, (i) diffusion, (iv) stabilisation (high confidence) 
(Rotmans et  al. 2001; Markard et  al. 2012; Geels et  al. 2017) 
(Cross-Chapter Box 12 in Chapter 16). These four phases do not imply

that transitions are linear, teleological processes, because set-backs 
or reversals may occur as a result of learning processes, conflicts, or 
changing coalitions (very high confidence) (Geels and Raven 2006; 
Messner 2015; Davidescu et al. 2018). There is also no guarantee that 
technological, social, or business model innovations progress beyond 
the first phase.
In the first phase, radical innovations emerge in peripheral niches, 
where researchers, inventors, social movement organisations or 
community activists dedicate time and effort to their development (high 
confidence) (Kemp et al. 1998; Schot and Geels 2008). Radical social, 
technical and business model innovations are initially characterised by 
many uncertainties about technical performance, consumer interest, 
institutions and cultural meanings. Learning processes are therefore 
essential and can be stimulated through R&D, demonstration projects, 
local community initiatives or grassroots projects (Borghei and

Magnusson 2016; Hossain 2016; Rosenbloom et al. 2018b; van Mierlo 
and Beers 2020). Typical challenges are fragmentation and high rates 
of project failure (den Hartog et al. 2018; Dana et al. 2021), limited 
funding (Auerswald and Branscomb 2003), limited consumer interest, 
and socio-cultural acceptance problems due to being perceived as 
strange or unfamiliar (Lounsbury and Glynn 2001).
In the second phase, social or technical innovations are appropriated 
or purchased by early adopters, which increases visibility and may 
provide a small but steady flow of financial resources (high evidence, 
high agreement) (Zimmerman and Zeitz 2002; Dewald and Truffer 
2011). Learning processes, knowledge sharing and codification 
activities help stabilise the innovation, leading to best practice 
guidelines, standards, and formalised knowledge (high evidence, high 
agreement) (Raven et al. 2008; Borghei and Magnusson 2018). User

innovation may lead to the articulation of new routines and social 
practices, often in tandem with the integration of new technologies 
into people’s daily lives (Nielsen et  al. 2016; Schot et  al. 2016). 
Radical innovations remain confined to niches in the second phase 
because adoption is limited to small, dedicated groups (Schot et al. 
2016), innovations are expensive or do not appeal to wider groups, 
or because complementary infrastructure are missing (Markard and 
Hoffmann 2016).
In the third phase, radical innovations diffuse into wider communities 
and mainstream markets. Typical drivers are performance 
improvements, cost reductions, widespread consumer interest, 
investments in infrastructure and complementary technologies, 
institutional support and strong cultural appeal (high evidence, high 
agreement) (Wilson 2012; Markard and Hoffmann 2016; Malone 
et al. 2017; Raven et al. 2017; Kanger et al. 2019). The latter may

be related to wider cultural shifts such as increased public attention 
to climate change and new framings like ‘climate emergency’ which 
gained traction before the Covid-19 pandemic (Bouman et al. 2020b). 
These concerns may not last, however, since public attention typically 
follows cycles (Downs 1972; Djerf-Pierre 2012).

562
Chapter 5 Demand, Services and Social Aspects of Mitigation 5This phase often involves multiple struggles: economic competition 
between low-carbon innovations and existing technologies and 
practices, business struggles between incumbents and new entrants 
(Hockerts and Wüstenhagen 2010), cultural and framing struggles in 
public opinion arenas (Kammermann and Dermont 2018; Rosenbloom 
2018; Hess 2019a), and political struggles over adjustments in policies 
and institutions, which shape markets and innovations (Meadowcroft 
2011; Roberts and Geels 2019). The lock-in mechanisms of existing 
practices and systems tend to weaken in the third phase, either 
because competing innovations erode their economic viability, 
cultural legitimacy or institutional support (Turnheim and Geels 2012; 
Roberts 2017; Kuokkanen et  al. 2018; Leipprand and Flachsland 
2018) or because exogenous shocks and pressures disrupt the status 
quo (Kungl and Geels 2018; Simpson 2019).

In the fourth phase, the diffusing innovations replace or substantially 
reconfigure existing practices and systems, which may lead to the 
downfall or reorientation of incumbent firms (Bergek et  al. 2013; 
McMeekin et  al. 2019). The new system becomes institutionalised 
and anchored in professional standards, technical capabilities, 
infrastructures, educational programmes, regulations and institutional 
logics, user habits, and views of normality, which create new lock-ins 
(Galaskiewicz 1985; Shove and Southerton 2000; Barnes et al. 2018).
‘Avoid’, ‘Shift’ and ‘Improve’ options vary with regard to the four 
transition phases. Incremental ‘Improve’ options, such as energy-
efficient appliances or stand-alone insulation measures, are not 
transitions but upgrades of existing technologies. They have 
progressed furthest since they build on existing knowledge and do 
not require wider changes (Geels et al. 2018). Some radical ‘Improve’

options, which have a different technological knowledge base, are 
beginning to diffuse, moving from phase two to three in multiple 
countries. Examples are electric vehicles, light-emitting diodes (LED), 
or passive house designs (Franceschini and Alkemade 2016; Berkeley 
et al. 2017). Many ‘Shift’ and ‘Avoid/Reduce’ options like heat pumps, 
district heating, passive house designs, compact cities, less meat 
initiatives, flight and car use reduction have low momentum in most 
countries, and are mostly in the first phase of isolated initiatives 
and projects (Bergman 2013; Morris et al. 2014; Bows-Larkin 2015; 
Bush et al. 2016; Kivimaa and Martiskainen 2018; Hoolohan et al. 
2018). Structural transitions in Dutch cities, Copenhagen, and 
more recently Paris, however, demonstrate that transitions towards 
low-carbon lifestyles, developed around cycling, are possible 
(Colville-Andersen  2018). Low-carbon demand-side transitions are

often still in early phases (high evidence, high agreement).
5.5.3 Feasible Rate of Change
Transitional change is usually slow in the first and second transition 
phases, because experimentation, social and technological learning, 
and stabilisation processes take a  long time, often decades, and 
remain restricted to small niches (high confidence) (Wilson 2012; 
Bento 2013; Bento et al. 2018b). Transitional change accelerates in 
the third phase, as radical innovations diffuse from initial niches into 
mainstream markets, propelled by the self-reinforcing mechanisms 
discussed above. The rate of adoption (diffusion) of new practices, processes, artefacts, and behaviours is determined by a wide range 
of factors at the macro- and micro-scales, which have been identified 
by several decades of diffusion research in multiple disciplines 
(Mansfield 1968; Martino et  al. 1978; Davis 1979; Mahajan et  al. 
1990; Ausubel 1991; Grubler 1991; Feder and Umali 1993; Bayus

1994; Comin and Hobijn 2003; Rogers 2003; Van den Bulte and 
Stremersch 2004; Meade and Islam 2006; Peres et al. 2010).
Diffusion rates are determined by two broad categories of variables: 
those intrinsic to the technology, product or practice under consideration 
(typically performance, costs, benefits), and those intrinsic to the 
adoption environment (e.g., socio-economic and market characteristics).
Despite differences, the literature offers three robust conclusions 
on acceleration (high evidence, high agreement): First, size matters. 
Acceleration of transitions is more difficult for social, economic, or 
technological systems of larger size (in terms of number of users, 
financial investments, infrastructure, powerful industries) (Wilson 
2009; Wilson 2012). Size also matters at the level of the systems 
component involved in a  transition. Components with smaller 
unit-scale (‘granular’ and thus relatively cheap), such as light

bulbs or household appliances, turn over much faster (often within 
a decade) than large-scale, capital-intensive lumpy technologies and 
infrastructures (such as transport systems) where rates of change 
typically involve several decades, even up to a century (Grubler 1991; 
Leibowicz 2018). Also, the creation of entirely new systems (diffusion) 
takes longer time than replacements of existing technologies or 
practices (substitution) (Grübler et al. 1999); and late adopters tend 
to adopt faster than early pioneers (Wilson 2012; Grubler 1996).
Arguments about scale in the energy system date back at least to 
the 1970s when Schumacher, Lovins and others argued the case for 
smaller-scale, distributed technologies (Schumacher 1974; Lovins 
1976; Lovins 1979). In Small is Profitable Lovins and colleagues 
evidenced over 200 reasons why decentralised energy resources, from 
distributed generation to end-use efficiency, made good business

sense in addition to their social, human-centred benefits (Lovins et al. 
2003). More recent advances in digital, solar and energy storage 
technologies have renewed technical and economic arguments in 
favour of adopting decentralised approaches to decarbonisation 
(Cook et al. 2016; Jain et al. 2017; Lovins et al. 2018). Smaller-scale 
technologies from microprocessors to solar panels show dramatically 
faster cost and performance improvement trajectories than large-scale 
energy supply facilities (Trancik 2014; Sweerts et al. 2020, Creutzig 
et al. 2021) (Figure 5.15). Analysing the performance of over 80 energy 
technologies historically, Wilson et al. (2020a) found that smaller scale, 
more ‘granular’ technologies are empirically associated with faster 
diffusion, lower investment risk, faster learning, more opportunities to 
escape lock-in, more equitable access, more job creation, and higher 
social returns on innovation investment. These advantages of more

granular technologies are consistent with accelerated low-carbon 
transformation (Wilson et al. 2020a).
Second, complexity matters, which is often related to unit scale (Ma et al. 
2008). Acceleration is more difficult for options with higher degrees of 
complexity (e.g., carbon capture, transport and storage, or a hydrogen 
economy) representing higher technological and investment risks that

563
Demand, Services and Social Aspects of Mitigation Chapter 55can slow down change. Options with lower complexity are easier to 
accelerate because they involve less experimentation and debugging 
and require less adoption efforts and risk.
Third, agency, structure and meaning can accelerate transitions. 
The creation and mobilisation of actor coalitions is widely seen as 
important for acceleration, especially if these involve actors with 
technical skills, financial resources and political capital (Kern and 
Rogge 2016; Hess 2019b; Roberts and Geels 2019). Changes in policies and institutions can also accelerate transitions, especially if 
these create stable and attractive financial incentives or introduce 
technology-forcing standards or regulations (Brand et al. 2013; Kester 
et al. 2018; Roberts et al. 2018). Changes in meanings and cultural 
norms can also accelerate transitions, especially when they affect 
consumer practices, enhance social acceptance, and create legitimacy

for stronger policy support (Lounsbury and Glynn 2001; Rogers 2003; 
Buschmann and Oels 2019). Adoption of most advanced practices 
can support leapfrogging of polluting technologies (Box 5.9).
Figure 5.15 | Demand technologies show high learning rates.  Learning from small-scale granular technologies outperforms learning from larger supply-side technologies. 
Line is linear fit of log unit size to learning rate for all 41 technologies plotted. Source: Creutzig et al. (2021); based on Sweerts et al. (2020).
Box 5.9 | Is Leapfrogging Possible?
The concept of leapfrogging emerged in development economics (Soete 1985), energy policy (Goldemberg 1991) and environmental 
regulation (Perkins 2003, which provides a  first critical review of the concept), and refers to a  development strategy that skips 
traditional and polluting development in favour of the most advanced concepts. For instance, in rural areas without telephone

landlines or electricity access (cables), a direct shift to mobile telephony or distributed, locally-sourced energy systems is promoted, or 
economic development policies for pre-industrial economies forego the traditional initial emphasis on heavy industry industrialisation, 
instead focusing on services like finance or tourism. Often leapfrogging is enabled by learning and innovation externalities where 
improved knowledge and technologies become available for late adopters at low costs. The literature highlights many cases of 
successful leapfrogging but also highlights limitations (Watson and Sauter 2011); with example case studies for China (Gallagher 
2006; Chen and Li-Hua 2011); Mexico (Gallagher and Zarsky 2007); and Japan and Korea (Cho et al. 1998). Increasingly the concept 
is being integrated into the literature of low-carbon development, including innovation and technology transfer policies (Pigato et al.

2020), highlighting in particular the importance of contextual factors of successful technology transfer and leapfrogging including: 
domestic absorptive capacity and technological capabilities (Cirera and Maloney 2017); human capital, skills, and relevant technical 
know-how (Nelson and Phelps 1966); the size of the market (Keller 2004); greater openness to trade (Sachs and Warner 1995; Keller 
2004); geographical proximity to investors and financing (Comin et al. 2012); environmental regulatory proximity (Dechezleprêtre 
et al. 2015); and stronger protection of intellectual property rights (Dechezleprêtre et al. 2013; Dussaux et al. 2017). The existence 
of a technological potential for leapfrogging therefore needs to be considered within a wider context of social, institutional, and 
economic factors that influence whether leapfrogging potentials can be realised (high evidence, high agreement).

564
Chapter 5 Demand, Services and Social Aspects of Mitigation 5There are also some contentious topics in the debate on accelerated 
low-carbon transitions. First, while acceleration is desirable to 
mitigate climate change, there is a  risk that accelerating change 
too much may short-cut crucial experimentation and social and 
technological learning in ‘formative phases’ (Bento 2013; Bento et al. 
2018b) and potentially lead to a pre-mature lock-in of solutions that 
later turn out to have negative impacts (Cowan 1990; Cowan 1991) 
(high evidence, medium agreement).
Second, there is an ongoing debate about the most powerful leverage 
points and policies for speeding up change in social and technological 
systems. Farmer et  al. (2019) suggested ‘sensitive intervention 
points’ for low-carbon transitions, but do not quantify the impacts 
on transformations. Grubler et al. (2018) proposed an end-user and 
efficiency-focused strategy to achieve rapid emission reductions

and quantified their scenario with a leading IAM. However, discussion 
of the policy implications of such a  strategy have only just started 
(Wilson et al. 2019a), suggesting an important area for future research.
The last contentious issue is if policies can or should substitute for 
lack of economic or social appeal of change or for technological risks. 
Many large-scale supply-side climate mitigation options, such as CCS 
or nuclear power, involve high technological risks, critically depend 
on a stable carbon price, and are controversial in terms of social and 
environmental impacts (Sovacool et al. 2014; Smith et al. 2016; Wilson 
et al. 2020a) (high evidence,  medium agreement). There is continuing 
debate if and how policies could counterbalance these impacts in 
order to accelerate transitions (Nordhaus 2019; Lovins 2015). Some 
demand-side options like large-scale public transport infrastructures 
such as ‘Hyperloop’ (Decker et al. 2017) or concepts such as the Asian

Super Grid (maglev fast train coupled with superconducting electricity 
transmission networks) (AIGC 2017) may face similar challenges, which 
adds weight and robustness to those demand-side options that are 
more decentralised, granular in scale, and provide potential tangible 
consumer benefits besides being low-carbon (like more efficient 
buildings and appliances, ‘soft’ urban mobility options (walking and 
cycling), digitalisation, among others (Grubler et al. 2018)).A robust conclusion from this review is that there are no generic 
acceleration policies that are independent from the nature of 
what changes, by whom and how. Greater contextualisation and 
granularity in policy approaches is therefore important to address 
the challenges of rapid transitions towards zero-carbon systems 
(high evidence, high agreement).
5.6 Governance and Policy
5.6.1 Governing Mitigation: Participation 
and Social Trust
In demand-side mitigation, governance is key to drive the

multidimensional changes needed to meet service needs within 
a society that provide people with a decent living while increasingly 
reducing resource and energy input levels (Rojas-Rueda et  al. 2012; 
Batchelor et al. 2018; OECD 2019a). Impartial governance, understood 
as equal treatment of everyone by the rule of law, creates social trust 
and is thus a  key enabler of inclusive and participatory demand-
side climate policies (Rothstein 2011). Inclusive and broad-based 
participation itself also leads to greater social trust and thus is also 
a key enabler of demand-side climate mitigation (Section 5.2). Higher 
social trust and inclusive participatory processes also reduce inequality, 
restrain opportunistic behaviour and enhance cooperation (Drews and 
van den Bergh 2016; Gür 2020) (Section 5.2). Altogether, broad-based 
participatory processes are central to the successful implementation of 
climate policies (Rothstein and Teorell 2008; Klenert et al. 2018) (high

evidence, medium agreement). A culture of cooperation feeds back to 
increase social trust and enables action that reduce GHG emissions 
(Carattini et al. 2015; Jo and Carattini 2021), and requires including 
explicit consideration of the informal sector (Box 5.10). More equitable 
societies also have the institutional flexibility to allow for mitigation 
to advance faster, given their readiness to adopt locally-appropriate 
mitigation policies; they also suffer less from policy lock-in (Tanner 
et al. 2009; Lorenz 2013; Chu 2015; Cloutier et al. 2015; Martin 2016; 
Seto et al. 2016; Vandeweerdt et al. 2016; Turnheim et al. 2018).
Box 5.10 | The Informal Sector and Climate Mitigation
The informal economy represents a large and growing portion of socio-economic activities (Charmes 2016; Muchie et al. 2016; Mbaye 
and Gueye 2018), including much of the work done by women worldwide. It accounts for an estimated 61% of global employment

in the world; 90% in developing countries, 67% in emerging countries, and 18% in developed countries (Berik 2018), representing 
roughly 30% of GDP across a range of countries (Durán Heras 2012; Narayan 2017). Due to its importance, policies which support 
informal-sector climate mitigation activities may be extremely efficient (Garland 2015). For example, environmental and energy taxes 
may have negative gross costs when the informal sector dominates economic activity since these taxes indirectly tax the informal 
sector; informal production may substitute for energy-intensive goods, with strong welfare-enhancing effects (Bento et al. 2018a). The 
informal sector can assemble social and financial capital, create jobs, and build low-carbon local economies (Ruzek 2015). Constraints 
on small and informal-sector firms’ ability to build climate resilience include financial and data barriers, limited access to information

technology, and policy exclusion (Kraemer-Mbula and Wunsch-Vincent 2016; Crick et al. 2018a; Crick et al. 2018b).
Informal-sector innovation is often underrated. It gives marginalised people access to welfare-enhancing innovations, building on 
alternative knowledge and socially-embedded reciprocal exchange (Jaffe and Koster 2019; Sheikh 2019; Sheikh and Bhaduri 2020). 
Large improvements in low-emission, locally-appropriate service provision are possible by facilitating informal-sector service providers’

565
Demand, Services and Social Aspects of Mitigation Chapter 555.6.2 Policies to Strengthen Avoid-Shift-Improve
There is high untapped potential of demand-side mitigation options 
if considered holistically within the domains of Avoid-Shift-Improve 
(Sections 5.3 and 5.4, Tables 5.1, 5.2, and 5.3a,b). Within the demand-
side mitigation options opportunity space, policies currently focus 
more on efficiency and ‘Improve’ options and relatively less on 
‘Shift’ and ‘Avoid’ options (Dubois et al. 2019; Moberg et al. 2019). 
Current demand-side policies are fragmented, piecemeal and too 
weak to drive demand-side transitions commensurate with 1.5°C or 
2°C climate goals (Wilson et al. 2012; Fawcett et al. 2019; Mundaca 
et  al. 2019; Moberg et  al. 2019) (high evidence, high agreement). 
However, increasingly policy mix in a number of countries has seen 
a rise in prohibitions on fossil fuel use as a way to weaken lock-ins,

for example, on fossil fuel heating in favour of low-carbon alternatives 
(Rosenbloom et al. 2020). Policies that are aimed at behaviour and 
lifestyle changes carry a perception of political risks for policymakers, 
which may explain why policy instruments focus more on information 
provision and adoption of incentives than on regulation and investment 
(Rosenow et al. 2017; Moberg et al. 2019). Acceleration of demand-
side transitions would thus require both a broadening of demand-side 
options and the creation of comprehensive and targeted policy mixes 
(Kern et al. 2017; Rosenow et al. 2017; IPCC 2018) that strengthen the 
five drivers of decision and action identified in Section 5.4, Table 5.4 
and in Tables 5.5–5.7 (high evidence, high agreement). Demand-side 
transitions in developing and emerging economies would also require 
stronger administrative capacity as well as technical and financial 
support (UN-Habitat 2013; Creutzig et al. 2016b).

Systematic categorisation of demand-side policy options in different 
sectors and services through the Avoid-Shift-Improve framework 
enables identification of major entry points and possible associated social struggles to overcome for the policy instruments/interventions 
as discussed below.
5.6.2.1 ‘Avoid’ Policies
There is high evidence and high agreement that ‘Avoid’ policies 
that affect lifestyle changes offer opportunities for cost-effective 
reductions in energy use and emissions, but would need to overcome 
political sensitivities around government efforts to shape and modify 
individual-level behaviour (Rosenow et al. 2017; Grubb et al. 2020) 
(Table 5.5). These policies include ways to help avoid travel growth 
through integrated city planning or building retrofits to help avoid 
demand for transport, heating or cooling (Bakker et al. 2014; Lucon 
et  al. 2014; de Feijter et  al. 2019), which interact with existing

infrastructure. Dense pedestrianised cities and towns and medium-
density transit corridors are better placed to implement policies for 
car reductions than ‘sprawled’ cities characterised by low-density, 
auto-dependent and separated land uses (Seto et al. 2014; Newman 
and Kenworthy 2015; Newman et al. 2017; Bakker et al. 2014).
Cities face pressing priorities like poverty reduction, meeting basic 
services and building human and institutional capacity. These are met 
with highly accessible walkable and cyclable cities, connected with 
public transit corridors, enabling equal accessibility for all citizens, 
and enabling a high level of service provisioning (UN-Habitat 2013; 
Creutzig et al. 2016b). Infrastructure development costs less than for 
car dependent cities. However, it requires a mindset shift for urban 
and transport planners (medium evidence, high agreement).
Policies that support the avoidance of higher-emission lifestyles

and improve well-being are facilitated by the introduction of smart 
technologies, infrastructures and practices (Amini et al. 2019). They Box 5.10 (continued)
access to low-energy technologies (while taking care not to additionally burden the unpaid and marginalised), through such means 
as education, participatory governance, government policies to assist the informal sector, social services, health care, credit provision, 
and removing harmful policies and regulatory silos. The importance of the informal economy, especially in low-income countries, 
opens many possibilities for new approaches to decent living standards service provision along with climate resilience (Rynikiewicz 
and Chetaille 2006; Backstränd et al. 2010; Porio 2011; Kriegler et al. 2014; Taylor and Peter 2014; Brown and McGranahan 2016; 
Chu 2016; Satterthwaite et al. 2018; Boran 2019; Hugo and du Plessis 2019; Schröder et al. 2019; Javaid et al. 2020).

Public information and understanding of the CO 2-eq emissions implied by consumption patterns can unleash great creativity for meeting 
service needs fairly and with lower emissions (Darier and Schüle 1999; Sterman and Sweeney 2002; Lorenzoni et al. 2007; Billett 2010; 
Marres 2011; Zapico Lamela et al. 2011; Polonsky et al. 2012; Williams et al. 2019). Community-based mapping, social learning, green 
infrastructure development, and participatory governance facilitate such information-sharing (Tauhid and Zawani 2018; Mazeka et al. 
2019; Sharifi 2020), strengthening mitigation policies (Loiter and Norberg-Bohm 1999; Stokes and Warshaw 2017; Zhou et al. 2019).
Since informal settlements are usually dense, upgrading them supports low-carbon development pathways which leapfrog less-efficient 
housing, transport and other service provision, using locally-appropriate innovations (Satterthwaite et al. 2018). Examples of informal-

sector mitigation include digital banking in Africa; mobility in India using collective transport; food production, meal provision, and 
reduction of food waste in Latin America (e.g., soup kitchens in Brazil, community kitchens in Lima, Peru); informal materials recycling, 
space heating and cooling, and illumination (Hordijk 2000; Baldez 2003; Maumbe 2006; Gutberlet 2008; Chaturvedi and Gidwani 
2011; Nandy et al. 2015; Rouse and Verhoef 2016; Ackah 2017).

566
Chapter 5 Demand, Services and Social Aspects of Mitigation 5include regulations and measures for investment in high-quality ICT 
infrastructure and regulations to restrict number plates, as well as 
company policy around flexible working conditions (Lachapelle et al. 
2018; Shabanpour et  al. 2018). Working-from-home arrangements 
may advantage certain segments of society such as male, older, 
higher-educated and highly-paid employees, potentially exacerbating 
existing inequalities in the labour market (Lambert et  al. 2020; Bonacini et al. 2021). In the absence of distributive or other equity-
based measures, the potential gains in terms of emissions reduction 
may therefore be counteracted by the cost of increasing inequality. 
This potential growth in inequality is likely to be more severe in poorer 
countries that will additionally suffer from a  lack of international 
funding for achieving the SDGs (high evidence, medium agreement) 
(Barbier and Burgess 2020; UN 2020).

Table 5.5 | Examples of policies to enable ‘Avoid’ options.
Mitigation option Perceived struggles to overcome Policy to overcome struggles (Incentives)
Reduce passenger km –Existing paradigms and planning practices and car 
dependency (Rosenow et al. 2017; Grubb et al. 2020)
 –Financial and capacity barrier in many developing countries
 –Status dimension of private cars –Integrated city planning to avoid travel growth, car reduction, building retrofits to avoid 
heating or cooling demand (Bakker et al. 2014; Lucon et al. 2014; de Feijter et al. 2019)
 –Public-private partnership to overcome financial barrier (Roy et al. 2018b) (Box 5.8)
 –Taxation of status consumption; reframing of low-carbon transport as high status 
(Hoor 2020; Ramakrishnan and Creutzig 2021)
Reduce/Avoid 
food wasteLittle visible political and social momentum to prevent food 
waste in the Global NorthStrengthen national nutrition guidelines for health safety; improve education/awareness

on food waste; policies to eliminate ambiguous food labelling include well-defined 
and clear date labelling systems for food (Wilson et al. 2017); policies to support R&D 
to improve packaging to extend shelf life (Thyberg and Tonjes 2016); charging according 
to how much food households throw away
Reduce size 
of dwellingsSize of dwellings getting larger in many countriesCompact city design, taxing residential properties with high per capita area, progressive 
taxation of high status consumption (Ramakrishnan and Creutzig 2021)
heating, cooling and 
lighting in dwellingsChange in individual behaviour in dress codes and working timesTemperature set point as norm; building energy codes that set building standards; 
bioclimatic and/or zero emissions buildings; cities and buildings that incorporate features 
like daylighting and increased building depth, height, and compactness (Steemers 2003; 
Creutzig et al. 2016a)
Sharing economy 
for more service

per productLack of inclusivity and involvement of users in design. Digital 
divide, unequal access and unequal digital literacy (Pouri and 
Hilty 2018). Political or power relations among actors involved 
in the sharing economy (Curtis and Lehner 2019)Lower prices for public parking, and subsidies towards the purchase of electric vehicles 
for providers of electric vehicle sharing services (Jung and Koo 2018)
5.6.2.2 ‘Shift’ Policies
As indicated in Table  5.6, ‘Shift’ policies have various forms 
such as the demand for low-carbon materials for buildings and 
infrastructure in manufacturing and services and shift from meat-
based protein, mainly beef, to plant-based diets of other protein 
sources (high evidence, high agreement) (Springmann et al. 2016a; Ritchie et al. 2018; Willett et al. 2019). Governments also play a direct 
role beyond nudging citizens with information about health and well-
being. While the effectiveness of these policies on behaviour change

overall may be limited (Pearson-Stuttard et al. 2017; Shangguan et al. 
2019), there is some room for policy to influence actors upstream, 
such as industry and supermarkets, which may give rise to longer-
term, structural change.
Table 5.6 | Examples of policies to enable ‘Shift’ options.
More walking, less 
car use, train rather 
air travelAdequate infrastructure may be absent, speed a part of modern life –Congestion charges (Pearson-Stuttard et al. 2017; Shangguan et al. 
2019); deliberate urban design including cycling lanes, shared 
micromobility, and extensive cycling infrastructure; synchronised/
integrated transport system and timetable
 –Fair street space allocation (Creutzig et al. 2020)
Multifamily housingZonings that favour single family homes have been dominant in planning 
(Hagen 2016)Taxation, relaxation of single-family zoning policies and land use 
regulation (Geffner 2017)

Shifting from meat 
to other proteinMinimal meat required for protein intake, especially in developing countries for 
population suffering from malnutrition and when plant-based protein is lacking 
(Garnett 2011; Sunguya et al. 2014; Behrens et al. 2017; Godfray et al. 2018); 
dominance of market-based instruments limits governments’ role to nudging 
citizens with information about health and well-being, and point-of-purchase 
labelling (Pearson-Stuttard et al. 2017; Shangguan et al. 2019) –Tax on meat/beef in wealthier countries and/or households (Edjabou 
and Smed 2013; Säll and Gren 2015)
 –Nationally recommended diets (Garnett 2011; Sunguya et al. 2014; 
Behrens et al. 2017; Godfray et al. 2018)
Material-efficient 
product design, 
packagingResistance by architects and builders who might perceive risks with lean 
designs. Cultural and social norms. Policy measures not keeping up with

changes on the ground such as increased consumption of packagingEmbodied carbon standards for buildings (IEA 2019c)
Architectural design 
with shading and 
ventilationLack of education, awareness and capacity for new thinking, local air pollutionIncentives for increased urban density and incentives to encourage 
architectural forms with lower surface-to-volume ratios and increased 
shading support (Creutzig et al. 2016a)

567
Demand, Services and Social Aspects of Mitigation Chapter 55Mobility services is one of the key areas where a combination of 
market-based and command-and-control measures have been 
implemented to persuade large numbers of people to get out 
of their automobiles and take up public transport and cycling 
alternatives (Gehl et  al. 2011). Congestion charges are often 
complemented by other measures, such as company subsidies for 
bicycles, to incentivise the shift to public mobility services. Attracting 
people to public transport requires sufficient spatial coverage of 
transport with adequate level of provision, and good quality service 
at affordable fares (Sims et  al. 2014; Moberg et  al. 2019) (high 
evidence, high agreement). Cities such as Bogota, Colombia, Buenos 
Aires, Argentina, and Santiago, Chile, have seen rapid growth 
of cycling, resulting in a  six-fold increase in cyclists (Pucher and 
Buehler 2017). Broadly, the history and type of city determines how

quickly the transition to public modes of transport can be achieved. 
For example, cities in developed countries enjoy an advantage in 
that there is a network of high-quality public transport predating the 
advent of automobiles, whereas cities in less developed countries 
are latecomers to large-scale network infrastructure (UN-Habitat 
2013; Gota et al. 2019).
5.6.2.3 ‘Improve’ Policies
‘Improve’ policies focus on the efficiency and enhancement of 
technological performance of services (Table 5.7). In mobility services, ‘Improve’ policies aim at improving vehicles, comfort, fuels, transport 
operations and management technologies; and in buildings, they 
include policies for improving efficiency of heating systems and 
retrofitting existing buildings. Efficiency improvements in electric 
cooking appliances, together with the ongoing decrease in prices of 
renewable energy technologies, are opening policy opportunities to

support households to adopt electrical cooking at mass scale (medium 
evidence, medium agreement) (IEA 2017c; Puzzolo et  al. 2019). 
These actions towards cleaner energy for cooking often come with 
cooking-related reduction of GHG emissions, even though the extent 
of the reductions is highly dependent on context and technology and 
fuel pathways (high evidence, high agreement) (Martínez et al. 2017; 
Mondal et  al. 2018; Rosenthal et  al. 2018; Serrano-Medrano et  al. 
2018; Dagnachew et al. 2019) (Box 5.6).
Table  5.7 highlights the significant progress made in the uptake of 
the electrical vehicle (EV) in Europe, driven by a  suite of incentives 
and policies. Increased activity in widening electric vehicle use is also 
occurring in developing countries. The Indian Government’s proposal to 
reach the target of a 100% electric vehicle fleet by 2030 has stimulated 
investment in charging infrastructure that can facilitate diffusion of

larger EVs (Dhar et al. 2017). Although the proposal was not converted 
into a  policy, India’s large and growing two-wheeler market has 
benefitted from the policy attention on EVs, showing a  significant 
potential for increasing the share of electric two- and three-wheelers 
Table 5.7 | Examples of policies to enable ‘Improve’ options
Mitigation option Perceived struggles to overcomePolicy to overcome struggles
(Incentives)
Lightweight vehicles, 
hydrogen cars, 
electric vehicles, 
ecodrivingAdequate infrastructure may be absent, speed a part of modern lifeMonetary incentives and traffic regulations favouring electric vehicles; investment 
in public charging infrastructure; car purchase tax calculated by a combination of 
weight, CO 2 and NO x emissions (Haugneland and Kvisle 2015; Globisch et al. 2018; 
Gnann et al. 2018; Lieven and Rietmann 2018; Rietmann and Lieven 2019)
Use low-carbon 
materials in 
dwelling designManufacturing and R&D costs, recycling processes and aesthetic

performance (Orsini and Marrone 2019). Access to secondary 
materials in the building sector (Nußholz et al. 2019)Increasing recycling of construction and demolition waste; incentives must be 
available to companies in the waste collection and recovery markets to offer 
recovered material at higher value (Nußholz et al. 2019)
Better insulation 
and retrofitting –Policies to advance retrofitting and GHG emission reductions 
in buildings are laden with high expectations since they are 
core components of politically ambitious city climate targets 
(Haug et al. 2010)
 –Building owners’ to implement measures identified 
in auditing results
 –Lack of incentive for building owners to invest in higher 
efficiency than required norms (Trencher et al. 2016)Grants and loans through development banks, building and heating system labels, 
and technical renovation requirements to continuously raise standards (Ortiz et al.

2019; Sebi et al. 2019); disclosure of energy use, financing and technical assistance 
(Sebi et al. 2019)
Widen low-carbon 
energy accessAccess to finance, capacity, robust policies, affordability for poor 
households for off-grid solutions until recently (Rolffs et al. 2015; 
Fuso Nerini et al. 2018; Mulugetta et al. 2019)Feed-in tariffs and auctions to stimulate investment. Pay-as-you-go end-user 
financing scheme where customers pay a small up-front fee for the equipment, 
followed by monthly payments, using mobile payment system (Rolffs et al. 2015; 
Yadav et al. 2019)
Improve illumination-
related emissionLack of supply-side solutions for low-carbon electricity provisionBuilding energy codes that set building standards; grants and other incentives 
for R&D
Improve efficiency of 
cooking appliancesReliability of power in many countries is not guaranteed; electricity 
tariff is high in many countries; cooking appliances are mostly

imported using scarce foreign currencyDriven by a combination of government support for appliance purchases, 
shifting subsidies from kerosene or LPG to electricity; community-level consultation 
and awareness campaigns about the hazards associated with indoor air pollution 
from the use of fuelwood, coal and kerosene, as well as education on the multiple 
benefits of electric cooking (Martínez-Gómez et al. 2016; Yangka and Diesendorf 
2016; Martínez et al. 2017; Gould and Urpelainen 2018; Dendup and Arimura 2019; 
Pattanayak et al. 2019)
Shift to LED lampsPeople spend increasing amounts of time indoors, with heavy 
dependence on and demand for artificial lighting (Ding et al. 2020)Government incentives, utility incentive (Bertoldi et al. 2021). EU bans on directional 
and non-directional halogen bulbs (Franceschini et al. 2018)
Solar water heatingDominance of incumbent energy source i.e., electricity; cheap 
conventional energy; high initial investment costs and long payback

(Joubert et al. 2016)Subsidy for solar heaters (Li et al. 2013; Bessa and Prado 2015; Sgouridis et al. 2016)

568
Chapter 5 Demand, Services and Social Aspects of Mitigation 5in the short term (Ahmad and Creutzig 2019). Similar opportunities 
exist for China, where e-bikes have replaced car trips and are reported 
to act as intermediate links in multimodal mobility (Cherry et al. 2016).
In recent years, policy interest has arisen to address the energy access 
challenge in Africa using low-carbon energy technologies to meet 
energy for poverty reduction and climate action simultaneously 
(Rolffs et al. 2015; Fuso Nerini et al. 2018; Mulugetta et al. 2019). This 
aspiration has been bolstered on the technical front by significant 
advances in appliance efficiency such as light-emitting diode (LED) 
technology, complemented by the sharp reduction in the cost of 
renewable energy technologies, and largely driven by market-
stimulating policies and public R&D to mitigate risks (high evidence, 
high agreement) (Alstone et al. 2015; Zubi et al. 2019).
5.6.3 Policies in Transition Phases

Demand-side policies tend to vary for different transition phases 
(high evidence, high agreement) (Roberts and Geels 2019; Sandin et  al. 2019). In the first phase, which is characterised by the 
emergence or introduction of radical innovations in small niches, 
policies focus on: (i) supporting R&D and demonstration projects 
to enable learning and capability developments, (ii) nurturing 
the building of networks and  multi-stakeholder interactions, and 
(iii)  providing future orientation through visions or targets (Brown 
et  al. 2003; López-García et  al. 2019; Roesler and Hassler 2019). 
In the second phase, the policy emphasis shifts towards upscaling 
of experiments, standardisation, cost reduction, and the creation 
of early market niches (Borghei and Magnusson 2018; Ruggiero et al. 
2018). In  the third and later phases, comprehensive policy mixes 
are used to stimulate mass adoption, infrastructure creation, social

acceptance and business investment (Fichter and Clausen 2016; 
Geels et al. 2018; Strauch 2020). In the fourth phase, transitions can 
also be stimulated through policies that weaken or phase out existing 
regimes, such as removing inefficient subsidies (for cheap petrol or 
fuel oil) that encourage wasteful consumption, increasing taxes on 
carbon-intensive products and practices (Box 5.11), or substantially 
tightening regulations and standards (Kivimaa and Kern 2016; David 
2017; Rogge and Johnstone 2017).
Box 5.11 | Carbon Pricing and Fairness
Whether the public supports specific policy instruments for reducing greenhouse gas emissions is determined by cultural and political 
world views (Cherry et al. 2017; Kotchen et al. 2017; Alberini et al. 2018) and national positions in international climate negotiations, 
with major implications for policy design. For example, policy proposals need to circumvent ‘solution aversion’: that is, individuals are

more doubtful about the urgency of climate change mitigation if the proposed policy contradicts their political worldviews (Campbell 
and Kay 2014). While there are reasons to believe that carbon pricing is the most efficient way to reduce emissions, a recent literature – 
focusing on populations in Western Europe and North America and carbon taxes – documents that efficiency features alone is not 
what makes citizens like or dislike carbon pricing schemes (Kallbekken et al. 2011; Carattini et al. 2017; Klenert et al. 2018).
Citizens tend to ignore or doubt the idea that pricing carbon emissions reduces GHG emissions (Kallbekken et al. 2011; Douenne 
and Fabre 2019; Maestre-Andrés et  al. 2019). Further, citizens have fairness concerns about carbon pricing (Büchs and Schnepf 
2013; Douenne and Fabre 2019; Maestre-Andrés et al. 2019), even if higher carbon prices can be made progressive by suitable use

of revenues (Rausch et al. 2011; Williams et al. 2015; Klenert and Mattauch 2016). There are also non-economic properties of policy 
instruments that matter for public support: Calling a carbon price a ‘CO 2 levy’ alleviates solution aversion (Kallbekken et al. 2011; 
Carattini et al. 2017). It may be that the word ‘tax’ evokes a feeling of distrust in government and fears of high costs, low benefits 
and distributional effects (Strand 2020). Trust in politicians is negatively correlated with higher carbon prices (Hammar and Jagers 
2006; Rafaty 2018) and political campaigns for a carbon tax can lower public support for them (Anderson et al. 2019). Few developing 
countries have adopted carbon taxes, probably due to high costs, relatively low benefits, and distributional effects (Strand 2020).
To address these realities regarding support for carbon pricing, some studies have examined whether specific uses of the revenue

can increase public support for higher carbon prices (Carattini et al. 2017; Beiser-McGrath and Bernauer 2019). Doubt about the 
environmental effectiveness of carbon pricing may be alleviated if revenue from carbon pricing is earmarked for specific uses 
(Kallbekken et al. 2011; Carattini et al. 2017) and higher carbon prices may then be supported (Beiser-McGrath and Bernauer 2019). 
This is especially the case for using the proceeds on ‘green investment’ in infrastructure or energy efficiency programmes (Kotchen 
et al. 2017). Further, returning the revenues to individuals in a salient manner may increase public support and alleviate fairness 
proposals, given sufficient information (Carattini et al. 2017; Klenert et al. 2018). Perceived fairness is one of the strongest predictors 
of policy support (Jagers et al. 2010; Whittle et al. 2019).

569
Demand, Services and Social Aspects of Mitigation Chapter 555.6.4 Policy Sequencing and Packaging 
to Strengthen Enabling Conditions
Policy coordination is critical to manage infrastructure interdependence 
across sectors, and to avoid trade-off effects (Raven and Verbong 2007; 
Hiteva and Watson 2019), specifically requiring the consideration of 
interactions among supply-side and demand-side measures (high 
evidence,  high agreement) (Kivimaa and Virkamäki 2014; Rogge and 
Reichardt 2016; de Coninck et al. 2018; Edmondson et al. 2019). For 
example, the amount of electricity required for cooking can overwhelm 
the grid which can lead to failure, causing end-users to shift back to 
traditional biomass or fossil fuels (Ateba et al. 2018; Israel-Akinbo et al. 
2018); thus grid stability policies need to be undertaken in conjunction.
Policymakers operate in a  politically dynamic national and 
international environment, and their policies often reflect their

contextual situations and constraints with regards to climate-related 
reforms (Levin et al. 2012; Copland 2019), including differentiation 
between developed and developing countries (high evidence, high 
agreement) (Beer and Beer 2014; Roy et al. 2018c). Variables such 
as internal political stability, equity, informality (Box 5.10), macro-
economic conditions, public debt, governance of policies, global 
oil prices, quality of public services, and the maturity of green 
technologies play important roles in determining policy directions.
Sequencing policies appropriately is a  success factor for climate 
policy regimes (high evidence,  high agreement). In most situations 
policy measures require a preparatory phase that prepares the ground 
by lowering the costs of policies, communicating the costs and 
benefits to citizens, and building coalitions for policies, thus reducing 
political resistance (Meckling et  al. 2017). This policy sequencing

aims to incrementally relax or remove barriers over time to enable 
significant cumulative increases in policy stringency and create 
coalitions that support future policy development (Pahle et al. 2018). 
German policies on renewables began with funding for research, 
design and development (RD&D), then subsidies for demonstration 
projects during the 1970s and 1980s, and continued to larger-scale 
projects such as ‘Solar Roofs’ programmes in the 1990s, including 
scaled-up feed-in tariffs for solar power (Jacobsson and Lauber 2006). 
These policies led to industrial expansion in wind and solar energy 
systems, giving rise to powerful renewables interest coalitions that 
defend existing measures and lend political support for further action. 
Policy sequencing has also been deployed to introduce technology 
bans and strict performance standards with a  view to eliminating 
emissions as the end goal, and may involve simultaneous support

for low-carbon options while deliberately phasing out established 
technological regimes (Rogge and Johnstone 2017).
As a key contending policy instrument, carbon pricing also requires 
embedding into policy packages (high evidence,  medium agreement) . 
Pricing may be regressive and perceived as additional costs by 
households and industry, making investments in green infrastructure 
politically unfeasible, as examples from France and Australia show 
(Copland 2019; Douenne and Fabre 2020). Reforms that would 
push up household energy expenses are often left aside for fear of 
how citizens, especially the poor, would react or cope with higher 
bills (high evidence,  medium agreement) (Martinez and Viegas 2017; Tesfamichael et al. 2021). This makes it important to precede 
carbon pricing with investments in renewable energy and low-
carbon transport modes (Biber et al. 2017; Tvinnereim and Mehling 
2018), and especially support for developing countries by building up

low-carbon energy and mobility infrastructures and technologies, thus 
reducing resistance to carbon pricing (Creutzig 2019). Additionally, 
carbon pricing receives higher acceptance if fairness and distributive 
considerations are made explicit in revenue distribution (Box 5.11).
The effectiveness of a policy package is determined by design decisions 
as well as the wider governance context that include the political 
environment, institutions for coordination across scales, bureaucratic 
traditions, and judicial functioning (high evidence,  high agreement) 
(Howlett and Rayner 2013; Rogge and Reichardt 2013; Rosenow et al. 
2016). Policy packages often emerge through interactions between 
different policy instruments as they operate in either complementary 
or contradictory ways, resulting from conflicting policy goals 
(Cunningham et al. 2013; Givoni et al. 2013). An example includes the 
acceleration in shift from traditional biomass to the adoption of modern

cooking fuel for 80 million households in rural India over a very short 
period of four years (2016–2020), which employed a comprehensive 
policy package including financial incentives, infrastructural support 
and strengthening of the supply chain to induce households to shift 
towards a clean cooking fuel from the use of biomass (Kumar 2019). 
This was operationalised by creating a LPG supply chain by linking 
oil and gas companies with distributors to assure availability, and 
create infrastructure for local storage along with an improvement of 
the rural road network, especially in the rural context (Sankhyayan 
and Dasgupta 2019). State governments initiated separate policies to 
increase the distributorship of LPG in their states (Kumar et al. 2016). 
Similarly, policy actions for scaling up electric vehicles need to be 
well designed and coordinated where EV policy, transport policy and 
climate policy are used together, working on different decision points

and different aspects of human behaviour (Barton and Schütte 2017). 
The coordination of the multiple policy actions enables co-evolution 
of multiple outcomes that involve shifting towards renewable energy 
production, improving access to charging infrastructure, carbon 
pricing and other GHG measures (Wolbertus et al. 2018).
Design of policy packages should consider not only policies that 
support low-carbon transitions but also those that challenge existing 
carbon-intensive regimes, generating not just policy ‘winners’ but 
also ‘losers’ (high evidence, high agreement) (Carley and Konisky 
2020). The winners include low-carbon innovators and entrepreneurs, 
while the potential losers include incumbents with vested interests 
in sustaining the status quo (Mundaca et al. 2018; Monasterolo and 
Raberto 2019). Low-carbon policy packages would benefit from 
looking beyond climate benefits to include non-climate benefits

such as health benefits, fuel poverty reductions and environmental 
co-benefits (Ürge-Vorsatz et  al. 2014; Sovacool et  al. 2020b). The 
uptake of decentralised energy services using solar PV in rural 
areas in developing countries is one such example where successful 
initiatives are linked to the convergence of multiple policies that 
include import tariffs, research incentives for R&D, job creation 
programmes, policies to widen health and education services, and 
strategies for increased safety for women and children (Kattumuri 
and Kruse 2019; Gebreslassie 2020).

570
Chapter 5 Demand, Services and Social Aspects of Mitigation 5The energy-efficient lighting transition in Europe represents a good 
case of the formation of policy coalitions that led to the development of 
policy packages. As attention to energy efficiency in Europe increased 
in the 1990s, policymakers attempted to stimulate energy-saving 
lamp diffusion through voluntary measures. But policies stimulated 
only limited adoption. Consumers perceived compact fluorescent 
lamps (CFLs) as giving ‘cold’ light, being unattractively shaped, taking 
too long to achieve full brightness, unsuitable for many fixtures, and 
unreliable (Wall and Crosbie 2009). Still, innovations by major CFL 
and LED multinationals continued. Increasing political attention to 
climate change and criticisms from environmental NGOs (e.g. WWF , 
Greenpeace) strengthened awareness about the inefficiency of 
incandescent light bulbs (ILBs), which led to negative socio-cultural

framings that associated ILBs  with energy waste (Franceschini and 
Alkemade 2016). The combined pressures from the lighting industry, 
NGOs and member states led the European Commission to introduce 
the 2009 ban of ILBs of more than 80W, progressing to lower-wattage 
bans in successive years. While the ILB ban initially mainly boosted 
CFL diffusion, it also stimulated LED uptake. LED prices decreased 
quickly by more than 85% between 2008 and 2012 (Sanderson 
and Simons 2014), because of scale economies, standardisation 
and commoditisation of LED chip technology, and improved 
manufacturing techniques. Because of further rapid developments to 
meet consumer tastes, LEDs came to be seen as the future of domestic 
lighting (Franceschini et  al. 2018). Acknowledging these changing 
views, the 2016 and 2018 European bans on directional and non-
directional halogen bulbs explicitly intended to further accelerate the

LED transition and reduce energy consumption for residential lighting.
In summary, more equitable societies are associated with high levels 
of social trust and enable actions that reduce GHG emissions. To this 
end, people play an important role in the delivery of demand-side 
mitigation options within which efficiency and ‘Improve’ options 
dominate. Policies that are aimed at behaviour and lifestyle changes 
come with political risks for policymakers. However, the potential 
exists for broadening demand-side interventions to include ‘Avoid’ 
and ‘Shift’ policies. Longer term thinking and implementation that 
involves careful sequencing of policies as well as designing policy 
packages that address multiple co-benefits would be critical to 
manage interactions among supply-side and demand-side options 
to accelerate mitigation.
5.7 Knowledge Gaps
Knowledge gap 1: Better metric to measure actual 
human well-being
Knowledge on climate action that starts with the social practices

and how people live in various environments, cultures, contexts and 
attempts to improve their well-being, is still in its infancy. In models, 
climate solutions remain supply-side oriented, and evaluated against 
GDP , without acknowledging the reduction in  well-being  due 
to  climate impacts. GDP is a  poor metric of human well-being, 
and climate policy evaluation requires better grounding in relation 
to decent living standards and/or similar benchmarks. Actual 
solutions will invariably include demand, service provisioning and end use. Literature on how gender, informal economies mostly in 
developing countries, and solidarity and care frameworks translate 
into climate action, but also how climate action can improve the life 
of marginalised groups, remains scarce. The working of economic 
systems under a well-being-driven rather than GDP-driven paradigm 
requires better understanding.
Knowledge gap 2: Evaluation of climate implications 
of the digital economy

The digital economy, as well as shared and circular economy, is 
emerging as a  template for great narratives, hopes and fears. Yet, 
there are few systematic evaluations of what is already happening 
and what can govern it towards a better narrative. Research needs 
to better gauge energy trends for rapidly evolving systems like data 
centres, increased use of social media and influence of consumption 
and choices, AI, blockchain; and implications of digital divides among 
social groups and countries on well-being. Governance decisions on 
AI, indirectly fostering either climate harming or climate mitigating 
activities remain unexplored. Better integration of mitigation models 
and consequential lifecycle analysis is needed for assessing how 
digitalisation, shared economy and circular economy change material 
and energy demand.
Knowledge gap 3: Scenario modelling of services
Scenarios start within parameter-rich models carrying more than

a  decade-long legacy of supply-side technologies that are not 
always gauged in recent technological developments. Service 
provisioning systems are not explicitly modelled, and diversity in 
concepts and patterns of lifestyles rarely considered. A new class of 
flexible and  modular models with focus on services and activities, 
based on a variety of data sources including big data collected and 
compiled, is needed. There is scope for more sensitivity analysis 
on two aspects to better guide further detailed studies on societal 
response to policy. These aspects need to explore which socio-
behavioural aspects and/or organisation changes has the biggest 
impact on energy/emissions reductions, and on the scale for take-
back effects, due to interdependence on inclusion or exclusion 
of groups of people. Models mostly consider behavioural change 
free, and don’t account for how savings due to ‘Avoid’ measures 
may be re-spent. Most quantitatively measurable service indicators,

for example passenger-kilometres travelled or tonne-kilometres 
of freight transport are also inadequate to measure services in the 
sense of well-being contributions. More research is needed on how 
to measure, for example, accessibility, social inclusion etc. Otherwise, 
services will also be poorly represented in scenarios.
Knowledge gap 4: Dynamic interaction between 
individual, social, and structural drivers of change
Better understanding is required on: (i) more detailed causal 
mechanisms in the mutual interactions between individual, social, 
and structural drivers of change and how these vary over time, that

571
Demand, Services and Social Aspects of Mitigation Chapter 55is, what is their relative importance in different transition phases; 
(ii) how narratives associated with specific technologies, group 
identities, and climate change influence each other and interact over 
time to enable and constrain mitigation outcomes; (iii) how social 
media influences the development and impacts of narratives about 
low-carbon transitions; (iv) the effects of social movements (for 
climate justice, youth climate activism, fossil fuel divestment, and 
climate action more generally) on social norms and political change, 
especially in less developed countries; (v) how existing provisioning 
systems and social practices destabilise through the weakening of 
various lock-in mechanisms, and resulting deliberate strategies for 
accelerating demand-side transitions; (vi) a dynamic understanding 
of feasibility, which addresses the dynamic mechanisms that lower

barriers or drive mitigation options over the barriers; (vii) how 
shocks like prolonged pandemic impact willingness and capacity to 
change and their permanency for various social actors and country 
contexts. The debate on the most powerful leverage points and 
policies for speeding up change in social and technological systems 
need to be resolved with more evidence. Discussion on the policy 
interdependence and implications of end-user and efficiency focused 
strategies have only just started suggesting an important area for 
future research.

572
Chapter 5 Demand, Services and Social Aspects of Mitigation 5Frequently Asked Questions (FAQs)
FAQ 5.1 | What can every person do to limit warming to 1.5°C?
People can be educated through knowledge transfer so they can act in different roles, and in each role everyone can contribute to 
limit global warming to 1.5°C. Citizens with enough knowledge can organise and put political pressure on the system. Role models 
can set examples to others. Professionals (e.g., engineers, urban planners, teachers, researchers) can change professional standards 
in consistency with decarbonisation; for example urban planners and architects can design physical infrastructures to facilitate low-
carbon mobility and energy use by making walking and cycling safe for children. Rich investors can make strategic plans to divest 
from fossils and invest in carbon-neutral technologies. Consumers, especially those in the top 10% of the world population in terms

of income, can limit consumption, especially in mobility, and explore the good life consistent with sustainable consumption.
Policymakers support individual actions in certain contexts, not only by economic incentives, such as carbon pricing, but also by 
interventions that understand complex decision-making processes, habits, and routines. Examples of such interventions include, 
but are not limited to, choice architectures and nudges that set green options as default, shift away from cheap petrol or gasoline, 
increasing taxes on carbon-intensive products, or substantially tightening regulations and standards to support shifts in social 
norms, and thus can be effective beyond the direct economic incentive.
FAQ 5.2 | How does society perceive transformative change?
Humaninduced global warming, together with other global trends and events, such as digitalisation and automation, and the

COVID-19 pandemic, induce changes in labour markets, and bring large uncertainty and ambiguity. History and psychology reveal 
that societies can thrive in these circumstances if they openly embrace uncertainty on the future and try out ways to improve life. 
Tolerating ambiguity can be learned, for example by interacting with history, poetry and the arts. Sometimes religion and philosophy 
also help.
As a key enabler, novel narratives created in a variety of ways, such as by advertising, images and the entertainment industry, 
help to break away from the established meanings, values and discourses and the status quo. For example, discourses that frame 
comfortable public transport services to avoid stress from driving cars on busy, congested roads help avoid car driving as a status 
symbol and create a  new social norm to shift to public transport. Discourses that portray plant-based protein as healthy and

natural promote and stabilise particular diets. Novel narratives and inclusive processes help strategies to overcome multiple barriers. 
Case studies demonstrate that citizens support transformative changes if participatory processes enable a design that meets local 
interests and culture. Promising narratives specify that even as speed and capabilities differ, humanity embarks on a joint journey 
towards well-being for all and a healthy planet.
FAQ 5.3 | Is demand reduction compatible with growth of human well-being?
There is a growing realisation that mere monetary value of income growth is insufficient to measure national welfare and individual 
well-being. Hence, any action towards climate change mitigation is best evaluated against a set of indicators that represent a broader 
variety of needs to define individual well-being, macroeconomic stability, and planetary health. Many solutions that reduce primary

material and fossil energy demand, and thus reduce GHG emissions, provide better services to help achieve well-being for all.
Economic growth measured by total or individual income growth is a main driver of GHG emissions. Only a few countries with low 
economic growth rates have reduced both territorial and consumption-based GHG emissions, typically by switching from fossil fuels 
to renewable energy and by reduction in energy use and switching to low/zero carbon fuels, but until now at insufficient rates and 
levels for stabilising global warming at 1.5°C. High deployment of low/zero carbon fuels and associated rapid reduction in demand 
for and use of coal, gas, and oil can further reduce the interdependence between economic growth and GHG emissions.

615
Energy Systems  Chapter 66Executive Summary
Warming cannot be limited to well below 2°C without rapid 
and deep reductions in energy system carbon dioxide (CO 2) 
and greenhouse gas (GHG) emissions.  In scenarios limiting 
warming to 1.5°C (>50%) with no or limited overshoot (2°C (>67%) with action starting in 2020), net energy system CO
(interquartile range) fall by 87–97% (60–79%) in 2050. In 2030, in scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot, net CO
2 and GHG emissions fall by 35–51% and 38–52% 
respectively. In scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot (2°C (>67%)), net electricity sector CO
2 
emissions reach zero globally between 2045 and 2055 (2050 and 2080). (high confidence) {6.7}
Limiting warming to well below 2°C will require substantial

energy system changes over the next 30 years. This includes reduced fossil fuel consumption, increased production from low- and zero-carbon energy sources, and increased use of electricity and alternative energy carriers. Coal consumption without carbon capture and storage (CCS) falls by 67–82% (interquartile range) in 2030 in scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot. Oil and gas consumption fall more slowly. Low-carbon sources produce 93–97% of global electricity by 2050 in scenarios that limit warming to 2°C (>67%) with action starting in 2020. In scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot (2°C (>67%) with action starting in 2020), electricity supplies 48–58% (36–47%) of final energy in 2050, up from 20% in 2019. (high confidence) {6.7}
Net-zero energy systems will share common characteristics,

but the approach in every country will depend on national circumstances. Common characteristics of net-zero energy systems will include: (i) electricity systems that produce no net CO
2 or remove 
CO2 from the atmosphere; (ii) widespread electrification of end 
uses, including light-duty transport, space heating, and cooking; (iii) substantially lower use of fossil fuels than today; (iv)  use ofalternative energy carriers such as hydrogen, bioenergy, and ammoniato substitute for fossil fuels in sectors less amenable to electrification;(v) more efficient use of energy than today; (vi) greater energy systemintegration across regions and across components of the energysystem;  and (vii) use of CO
2 removal (e.g.,  direct air carbon capture 
and storage (DACCS) and bioenergy with carbon capture and storage (DACCS, BECCS)) to offset any residual emissions. (high confidence) {6.6}
Energy demands and energy sector emissions have continued

to rise. From 2015 to 2019, global final energy consumption grew by 6.6%, CO
2 emissions from the global energy system grew by 
4.6%, and total GHG emissions from energy supply rose by 2.7%. Methane emissions, mainly fugitive emissions from oil, gas, and coal, accounted for 18% of GHG emissions in 2019. Coal electricity capacity grew by 7.6% between 2015 and 2019, as new builds in some countries offset declines in others. Total consumption of oil and oil products increased by 5%, and natural gas consumption grew by 15%. Declining energy intensity in almost all regions has been balanced by increased energy consumption. (high confidence) {6.3}Prices have dropped rapidly over the last five years for several key energy system mitigation options, notably solar photovoltaics (PV), wind power, and batteries.  From 2015 to

2020, the prices of electricity from PV and wind dropped 56% and 45%, respectively, and battery prices dropped by 64%. Electricity from PV and wind is now cheaper than electricity from fossil sources in many regions, electric vehicles are increasingly competitive with internal combustion engines, and large-scale battery storage on electricity grids is increasingly viable. (high confidence) {6.3, 6.4}
Global wind and solar PV capacity and generation have 
increased rapidly.  Solar PV grew by 170% (to 680 TWh); wind

grew by 70% (to 1420 TWh) from 2015 to 2019. Policy, societal pressure to limit fossil generation, low interest rates, and cost reductions have all driven wind and solar PV deployment. Solar PV and wind together accounted for 21% of total low-carbon electricity generation and 8% of total electricity generation in 2019. Nuclear generation grew 9% between 2015 and 2019 and accounted for 10% of total generation in 2019 (2790 TWh); hydroelectric power grew by 10% and accounted for 16% (4290 TWh) of total generation. In total, low- and zero-carbon electricity generation technologies produced 37% of global electricity in 2019. (high confidence) {6.3, 6.4}
If investments in coal and other fossil infrastructure continue,

energy systems will be locked in to higher emissions, making it harder to limit warming to well below 2°C. Many aspects of the energy system  – physical infrastructure; institutions, laws, and regulations; and behaviour – are resistant to change or take many years to change. New investments in coal-fired electricity without CCS are inconsistent with limiting warming to well below 2°C. (high confidence) {6.3, 6.7}
Limiting warming to well below 2°C will strand fossil-related 
assets, including fossil infrastructure and unburned fossil fuel resources.  The economic impact of stranded assets could amount to 
trillions of dollars. Coal assets are most vulnerable over the coming decade; oil and gas assets are more vulnerable toward mid-century. CCS can allow fossil fuels to be used longer, reducing potential stranded assets. (high confidence) {6.7}
A low-carbon energy transition will shift investment patterns 
and create new economic opportunities.  Total energy investment

needs will rise, relative to today, over the next decades, if warming is limited to 2°C (>67%) or lower. These increases will be far less pronounced, however, than the reallocations of investment flows that are likely to be seen across sub-sectors, namely from fossil fuels (extraction, conversion, and electricity generation) without CCS and toward renewables, nuclear power, CCS, electricity networks and  storage, and end-use energy efficiency. A significant and growing share of investments between now and 2050 will be made in emerging economies, particularly in Asia. (high confidence) {6.7}

616
Chapter 6 Energy Systems6Climate change will affect many future local and national low-
carbon energy systems. The impacts, however, are uncertain, 
particularly at the regional scale. Climate change will alter 
hydropower production, bioenergy and agricultural yields, thermal 
power plant efficiencies, and demands for heating and cooling, and 
it will directly impact power system infrastructure. Climate change 
will not affect wind and solar resources to the extent that it would 
compromise their ability to reduce emissions. (high confidence) {6.5}
Electricity systems powered predominantly by renewables will 
be increasingly viable over the coming decades, but it will be 
challenging to supply the entire energy system with renewable 
energy. Large shares of variable solar PV and wind power can 
be incorporated in electricity grids through batteries, hydrogen, 
and other forms of storage; transmission; flexible non-renewable

generation; advanced controls; and greater demand-side responses. 
Because some applications (e.g.,  air travel) are not currently 
amenable to electrification, 100% renewable energy systems would 
likely need to include alternative fuels such as hydrogen or biofuels. 
Economic, regulatory, social, and operational challenges increase 
with higher shares of renewable electricity and energy. The ability to 
overcome these challenges in practice is not fully understood. (high 
confidence) {6.6}
Multiple energy supply options are available to reduce 
emissions over the next decade.  Nuclear power and hydropower 
are already established technologies. Solar PV and wind are now 
cheaper than fossil-generated electricity in many locations. Bioenergy 
accounts for about a tenth of global primary energy. Carbon capture 
is widely used in the oil and gas industry, with early applications in 
electricity production and biofuels. It will not be possible to widely

deploy all of these and other options without efforts to address the 
geophysical, environmental-ecological, economic, technological, 
socio-cultural, and institutional factors that can facilitate or hinder 
their implementation. (high confidence) {6.4}
Some mitigation options can provide more immediate and cost-
effective emissions reductions than others, but a comprehensive 
approach will be required over the next 10  years to limit 
warming to well below 2°C. There are substantial, cost-effective 
opportunities to reduce emissions rapidly in several sectors, including 
electricity generation and light-duty transportation. But near-term 
reductions in these sectors will not be sufficient to limit warming to 
well below 2°C. A broad-based approach across the energy sector will 
be necessary to reduce emissions over the next 10 years and to set 
the stage for still deeper reductions beyond 2030. (high confidence) 
{6.4, 6.6, 6.7}

Enhanced integration across energy system sectors and across 
scales will lower costs and facilitate low-carbon energy 
system transitions. Greater integration between the electricity 
sector and end use sectors can facilitate integration of variable 
renewable energy (VRE) options. Energy systems can be integrated 
across district, regional, national, and international scales. (high 
confidence) {6.4, 6.6}The viable speed and scope of a  low-carbon energy system 
transition will depend on how well it can support sustainable 
development goals (SDGs) and other societal objectives. 
Energy systems are linked to a range of societal objectives, including 
energy access, air and water pollution, health, energy security, 
water security, food security, economic prosperity, international 
competitiveness, employment. These linkages and their importance 
vary among regions. Energy sector mitigation and efforts to achieve 
SDGs generally support one another, though there are important

region-specific exceptions. (high confidence) {6.1, 6.7}
The economic outcomes of low-carbon transitions in some 
sectors and regions may be on a  par with, or superior to 
those of an emissions-intensive future. Cost reductions in key 
technologies, particularly in electricity and light-duty transport, have 
increased the economic attractiveness of near-term low-carbon 
transitions. Long-term mitigation costs are not well understood and 
depend on policy design and implementation, and the future costs 
and availability of technologies. Advances in low-carbon energy 
resources and carriers such as next-generation biofuels, hydrogen 
produced from electrolysis, synthetic fuels, and carbon-neutral 
ammonia would substantially improve the economics of net-zero 
energy systems. (medium confidence) {6.4, 6.7}

617
Energy Systems  Chapter 666.1 Introduction
The global energy system is the largest source of CO 2 emissions 
(Chapter 2). Reducing energy sector emissions is therefore essential 
to limit warming. The energy systems of the future will be very different from those of today if the world successfully limits warming 
to well below 2°C. Energy will be provided, converted, and used in 
different ways than it is today (Figure 6.1). Achieving and responding 
to these changes presents an impressive range of challenges 
and opportunities.
Coal
162
Transport
121Oil
191
140
Residential 
and 
120Electricity 39 EJ
Heat 6 EJIndustry
162Electricity 37 EJ
Heat 6 EJ
Hydro
15
Biomass
57Solar
4Wind
5
Geothermal
– other 1Nuclear
10
Primary supply: 585 EJ Transformation Final consumption: 448 EJ
Non-energy use 
and non-speciﬁed
45Global energy use, 2019
102
4943
46
244422532
19 4182
29
4112
111Reﬁnery 189
Power plants 206
2912
1116
329
8
5
Coal
2
Transport
99Oil
28

and 
154Electricity 141 EJ
Heat 5 EJIndustry
134Electricity 7 EJ
Heat 76 EJ
20
Hydro
24
Biomass
101Solar
261
Wind
104Geothermal
– other 10
Nuclear
2Power
plants 
and heat
425Primary supply: 554 EJ Final consumption: 387 EJGlobal energy use, 2060, Scenario IMP-REN-2.0
Hydrogen 63
Reﬁnery
100
3939
395
16 6 727
7
16581468355
218 2
6
Figure 6.1 | Global energy flows within the 2019 global energy system (top panel) and within two illustrative future, net-zero CO 2 emissions global 
energy systems (bottom panels).

618
Chapter 6 Energy Systems6Within this context, this chapter has two main objectives. First, 
it aims to assess specific, individual mitigation options in energy 
supply, energy transformation, and energy transportation and 
transmission. This assessment is complementary to a set of chapters 
that explore mitigation options in agriculture, forestry, and other land 
uses (Chapter 7), urban systems and other settlements (Chapter 8), 
buildings (Chapter 9), transport (Chapter 10), industry (Chapter 11), 
and cross-sectoral perspectives (Chapter  12). Second, this chapter 
aims to assess system-level mitigation opportunities and challenges 
across the entirety of energy systems. These systems include energy 
supply, transformation, transmission, storage, transportation, and 
end uses. They also include the societal systems that interact with 
the physical energy system. As energy systems become increasingly 
integrated and interconnected, a  system-wide perspective is

necessary for understanding mitigation opportunities and challenges.
Within this context, this chapter addresses six topics, each of which 
is addressed in a separate section. First, Section 6.2 defines the scope 
of the energy system. Section 6.3 then discusses the recent trends 
in energy systems that might exert the most significant influence 
on energy system evolution and options for reducing emissions. 
Section  6.4 assesses the status and potential of individual energy 
supply, transformation, storage, transportation and transmission, 
and integration mitigation options in the energy sector. Section 6.5 
explores how climate change might affect energy systems and 
alter potential energy system mitigation options and strategies. 
Section  6.6 identifies key characteristics of net-zero energy 
systems – those that emit very little or no CO 2. Section 6.7 explores

transition pathways toward and through net-zero energy systems. Across all of these sections, the chapter aims to explore the ways 
that energy sector mitigation options and strategies interact with 
Sustainable Development Goals (SDGs) and other societal and 
environmental goals.
6.2 The Scope of the Energy System 
and its Possible Evolution
For this chapter, energy systems are defined broadly to include both 
physical and societal elements. The physical infrastructure includes 
all the infrastructure and equipment used to extract, transform, 
transport, transmit, and convert energy to provide energy services. 
In addition to the physical system, a broad range of societal systems 
and dynamics are relevant to the energy system. Human societies 
use energy to transport themselves and the goods that they use and 
consume, to heat, cool, and light their homes, to cook their food, 
and to produce goods and services. Energy systems are therefore

tied to the systems involved in the provision of these various goods 
and services. All energy users engage in the operation of energy 
systems by demanding energy at particular times and in particular 
forms. They can adjust their behaviour and demands, for example, by 
using less energy or by changing when they use energy. Consumers 
can invest in equipment that reduces their energy needs, and they 
can invest in technologies that transform energy (e.g., rooftop solar) 
or store energy (e.g.,  batteries). Firms and governments invest in 
equipment to produce, transform, and transport energy such as 
power plants, refineries, electric transmission lines, and oil tankers. 
All aspects of energy systems are governed by laws, regulations, and Coal
82
Transport
139Oil
124
and 
120Electricity 85 EJ
Heat 12 EJIndustry
238Electricity 151 EJ
Heat 24 EJ
100
Hydro
30
Biomass
156
Solar
91Wind
93Geothermal
28

Primary supply: 705 EJ Final consumption: 508 EJ
Power
plants 
and heat
470Global energy use, 2070, Scenario IMP-NEG-2.0
Reﬁnery
15418
64
9
7175
26
3
41
14
5
479714
1
13
94739756 107
11
Figure 6.1 (continued): Global energy flows within the 2019 global energy system (top panel) and within two illustrative future, net-zero CO 2 emissions 
global energy systems (bottom panels).  Source: IEA, AR6 Scenarios Database. Flows below 1 EJ are not represented. Agricultural energy and energy own use are included 
in industry. Captured methane is included in natural gas supply where appropriate. The illustrative net-zero scenarios correspond to the years in which net energy system CO 2 
emissions reach zero – 2060 in IMP-Ren and 2070 in IMP-Neg-2.0. Source: data from IMP-Ren: Luderer et al. (2022); IMP-Neg-2.0: Riahi, K. et al. (2021).

619
Energy Systems  Chapter 66actual institutions that reside within businesses and governments 
at all levels. This includes, for example, rules for trading emissions 
permits, deciding when particular electricity generation technologies 
might come online, water management and related environmental 
rules that define the availability of hydropower or influence water 
availability for cooling power plants, regulations for injecting CO 2 
into underground reservoirs or disposing of nuclear waste, and even 
company policies regarding work hours or teleworking, which can 
have important implications for energy demand profiles. Many people 
are employed in the energy sector, and energy system mitigation will 
eliminate some jobs while creating others.
This broader view of energy systems is essential for understanding 
energy system mitigation, as these broader societal and institutional 
factors can have an important influence on energy system

transformations and the potential to rapidly reduce energy CO 2 
emissions. Energy system mitigation is as much about the challenges 
of societal change as it is about the challenges of changes in 
physical infrastructure, technologies, and operations. While this 
chapter does not attempt to draw a specific boundary around all the 
different systems that interact with the energy system, it frequently 
explores these broader system interactions when assessing different 
mitigation options and strategies.
There is no single spatial scale at which energy systems might be 
defined and assessed. They can be assessed at the scales of homes, 
cities, states or provinces, countries, regions, or the entire world. 
These different scales are frequently both distinct with their own 
internal dynamics yet  al.o connected to one another. This chapter 
most frequently assesses energy systems from the country and 
global perspective.
Because the energy system is so complex, it can be hard to define

particular parts of it precisely, and there may be competing definitions in the literature. For the purposes of this chapter, ‘energy supply‘ 
encompasses all primary energy, conversion, and transmission 
processes with the exception of those that use final energy to 
provide energy services in the end-use sectors (transport, buildings, 
industry and agriculture). The ‘energy system‘ includes energy end 
uses sectors along with energy supply. ‘Low-emissions‘ is used for 
energy technologies that produce little CO 2 or no CO 2 or that remove 
CO2 from the atmosphere. Similarly, ‘low-carbon‘ transitions is used 
to describe transitions that limit likely to 2°C (>67%) or below. 
‘Net-zero‘ energy systems refer to those that produce very little or no 
CO2 or may even sequester CO 2 from the atmosphere.
6.3 Recent Energy System Trends 
and Developments
Global energy sector emissions continue to grow but at 
a decreasing rate

Current energy sector emissions trends, if continued, will not limit 
global temperature change to well below 2°C (high confidence). 
Global energy system fossil fuel CO 2 emissions grew by 4.6% between 
2015 and 2019 (1.1% yr –1), reaching 38 GtCO 2 yr –1 and accounting 
for approximately two-thirds of annual global anthropogenic GHG 
emissions. In 2020, with the worldwide COVID-19 pandemic, energy 
sector CO 2 emissions dropped by roughly 2 GtCO 2 yr –1 (Figure  6.2). 
However global energy-related CO 2 emissions are projected to rebound 
by nearly 5% in 2021, approaching the 2018–19 peak (IEA 2021d).
Coal was the single largest contributor to energy sector CO 2 emissions 
between 2015 and 2019, accounting for about 44% of energy sector 
CO2 emissions in 2019. Oil accounted for about 34% and natural gas 
accounted for about 22% of energy sector CO 2 emissions. Coal, oil 
and natural gas CO 2 emissions grew respectively by 1.2%, 2% and 
–2500–2000–1500–1000–50005001000150020002500

0500010,00015,00020,00025,00030,00035,00040,000
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Total emissions Yearly changeMtCO2
Annual change MtCO2
Figure 6.2 | Global energy sector fossil fuel CO 2 emissions and annual change 2000–2019 (MtCO 2 yr –1). Source: adapted from Minx et al. (2021a); Crippa et al. (2021).

620
Chapter 6 Energy Systems612.7% (annual rates of 0.31%, 0.5% and 3%) (Figure  6.3). The 
electricity sector remains the single largest source of energy sector 
CO2 emissions, accounting for about 36% in 2019, followed by 
industry at 22% and transport (excluding international shipping and 
aviation transport) at about 18% (Figure 6.3). Shipping and aviation 
accounted for a  little over 3%. These proportions have remained 
relatively unchanged over the last decade. Recent trends reinforce 
the near-term challenges facing energy sector mitigation – electricity 
sector emissions continue to rise despite rapid deployment of wind 
and solar power (see below); transportation emissions continue to 
rise, and petroleum remains the dominant fuel, despite advances in 
batteries and electric cars (see below). Some specific sectors, such as 
shipping and aviation, may present longer-term challenges.
Energy supply GHG emissions, including CO 2 and non-CO 2

greenhouse gases, reached 20 GtCO 2-eq yr –1 in 2019, rising by 
2.7% between 2015 and 2019 (0.66% yr –1). Approximately 18% of energy supply emissions were non-CO 2 emissions. Electricity and 
heat contributed approximately 69% of total energy supply GHG 
emissions in 2019 (Figure 6.3). This growth has occurred despite the 
high penetration of solar PV and wind power, particularly in Asia and 
developed countries.
Fugitive emissions from fossil fuel production, primarily methane, 
accounted for about 18% of sector supply emissions in 2019, 
with 2.6  Gt CO 2-eq yr –1 linked to oil and gas production and 
1.3 GtCO 2-eq yr –1 to coal mining (Crippa et al. 2021). Oil and gas 
operations produced 2.9 GtCO 2-eq yr –1 in 2019 (82 Mt yr –1 as 
methane), split roughly equally between the two (IEA 2020a). There 
remains a high degree of uncertainty in methane emissions estimates 
from oil and gas operations despite the emergence of new data from

satellites and other measurement campaigns. According to a recent 
study (Hmiel et al. 2020), methane emissions are underestimated by 
about 25 to 40%.
Other
Oil and gas 
fugitive emissions
Petroleum reﬁningCoal, peat and oil shale
Oil
EurasiaAfrica
EuropeAsia and Paciﬁc
South Asia Eastern AsiaOther sectors
Power industryBuildings
Other industrial 
combustion
Transport 
(excl. international)International aviation 
and shipping
Coal mining 
Other (including 
bio-energy systems)036912151821
2000 2010 2019GtCO2-eq
Europe
Eastern AsiaNorth America
036912151821
2000 2005 2010 2015 2018 2019Energy emissions (GtCO2-eq)0510152025303540
0510152025303540
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019Energy emissions (GtCO2)
2000 2003 2006 2009 2012 2015 2018Energy emissions (GtCO2)(a) Global energy sector CO2 emissions by fuel (b) Global energy sector CO2 emissions by sector

(c) Global energy supply GHG emissions by sector (d) Global energy supply GHG emissions by region
Figure 6.3 | Global energy sector CO 2 emissions and global energy supply GHG emission.  Source: Panel (a): data from IEA (2020a); other panels: data from Crippa 

621
Energy Systems  Chapter 66+0.6%+2.7%+1.8%
−1.1%
−0.4%
+1.5%
+0.6%
−0.6%+0.1%
−2.8%
+1.4%+1%+1.5%
−0.7%−0.2%
+5.3%
+1%+5.3%
+0.4%
−1.4%+1.2%
+0.1%−0%
+2.4%
−3.6%
+1.6%
+0.3%
−2.6%−1.2%
−3.2%
+1.4%
+0.7%
−2.9%
−2.6%
−2.4%
+2.3%
+1.2%+1.4%
+0.1%
−2.1%+6.3%
+0.3%+4.1%
+0.4%
−2.8%
+0.4%
+1%+1%
+1%
−1.3%
+3.9%
+1.2%+5.9%
+2.3%
−1.5%
Southern Asia WorldMiddle East North America South−East Asia and Developing PaciﬁcEurasia Europe Latin America and CaribbeanAfrica Asia−Paciﬁc Developed Eastern Asia
1990 2000 2010 2018 1990 2000 2010 20181990 2000 2010 2018 1990 2000 2010 2018 1990 2000 2010 20181990 2000 2010 2018 1990 2000 2010 2018 1990 2000 2010 20181990 2000 2010 2018 1990 2000 2010 2018 1990 2000 2010 2018246
1.01.52.02.5
2460.81.01.21.41.6
0.500.751.001.251.50
0.751.001.251.50
0.91.21.51.80.81.21.62.0
0.70.91.11.3
1234
12345CO2
Energy GDP
GDP POPCO2 energy
POP
Figure 6.4 | Drivers of greenhouse gas emissions across selected regions.  Source: Lamb et al. 2021.

622
Chapter 6 Energy Systems6Increasing global energy sector GHG emissions have been driven by 
rising emissions in some large developing and emerging countries; 
however, per  capita emissions in these countries remain well below 
those in developed countries (Yu et al. 2019). From 2015 to 2019, Eastern 
Asia, Southern Asia, and South-East Asia energy sector CO 2 emissions 
grew by 2.4% yr –1, 2.6% yr –1, and 5.1% yr –1, respectively. The relative 
and absolute shares of Europe and North America have continued to 
decline, partly due to the growth in other countries (Figure 6.3).
Despite the declining energy intensity, global energy system CO 2 
emissions have closely tracked GDP per capita (Figure 6.4). This is 
especially true in the Asian economies, which have experienced 
rapid GDP per  capita growth in the past decades and a  massive 
rise in energy demand. Similarly, emissions have declined in times 
of economic downturns – for example, in Eurasia in the 1990s and

globally in 2009 and 2020. Population growth has also contributed to 
emissions growth globally and in most regions, particularly Africa, but 
the effect of population growth has been less than that of economic 
growth. Since 2015, energy intensity has been declining (IEA 2020b), 
limiting the impact of economic and population growth. However, 
there is no region where this factor alone would have been sufficient 
to decrease CO 2 emissions from the energy system. In Europe and 
North America, the only two regions where emissions decreased 
meaningfully since 2010, a steady decrease in the carbon intensity 
of energy was a significant downward driver. The reduction in carbon 
intensity in the EU is due primarily to the increase of renewable 
electricity production coupled with the low levels of fossil fuel-based 
production in the energy mix (Dyrstad et al. 2019).
Global energy production and demand continue to grow, but 
at a declining rate

Recent changes in the energy system can be viewed within the context 
of longer-term trends in energy supply and use. Over the last decade, there has been a significant increase in the total primary energy supply 
(TPES) and major changes in energy sources. From 2015 to 2019, TPES 
grew by 6.6% (1.6% yr –1) from 569 EJ yr –1 to 606 EJ yr –1. Natural gas 
consumption grew most quickly during this period, at 3.5% yr –1. Coal, 
oil and oil products grew at annual rates of 0.23% yr –1 and 0.83% yr –1, 
respectively. In 2019, the shares of coal, oil, and natural gas in global 
TPES were 27%, 31% and 23%, representing only a  modest shift 
from 2015, when the shares were 28%, 32% and 22%, respectively. 
Renewables, excluding hydropower, grew at an annual rate of 12% yr –1 
during this period; however, their share remains marginal in 2019, 
with just 2.2% of the TPES compared to 1.5% in 2015 (Figure 6.5). 
Bioenergy (including traditional bioenergy) accounted for 9.4% of the

TPES, a similar share compared with 2015.
The total final energy consumption (TFC) grew by 6.6% (1.6% yr –1) 
from 2015 to 2019, rising from 392 EJ yr –1 to 418 EJ yr –1. This 
is a  slower growth rate than the previous decade (2.8% yr –1) 
(Figure 6.5). In 2019, oil products used for transportation accounted 
for 41% of TFC. The penetration of non-fossil fuels is still marginal 
despite the significant growth of electric vehicles in recent years. 
Coal still accounted for 9.5% of TFC in 2019, dropping from 11.7% in 
2015. Coal is mainly used as a primary energy source in industry and, 
to a lesser extent, in the residential sector. The share of electricity 
increased modestly, from 18.6% in 2015 to 20.2% in 2019, reflecting 
increasing access in developing countries and increasing use of 
electricity for a  wide variety of end uses in the residential sector 
(Box 6.1). Heat accounts for approximately 3% of TFC, used mainly in

industry and the residential sector. Biofuels and waste accounted for 
10.4% of TFC in 2019, only modestly changed compared with 2015.
There are important differences in fuel use across countries. While 
developed countries almost exclusively use modern fuels, many 
countries still obtain a  significant fraction of their energy from 
traditional bioenergy (fuelwood and charcoal). Traditional bioenergy 
Coal
HydroNatural gas
Oil and oil products Biofuels and waste Heat ElectricityWind, solar, other renewablesNuclearPrimary energy (EJ)
Final energy (EJ)
050100150200250300350400450
2000 2005 2010 2015 2018 20190100200300400500600
2000 2005 2010 2015 2018 2019
Coal Oil and oil products
Natural gas Biofuels and waste
Figure 6.5 | World total primary energy supply (TPES) (EJ) and total final energy consumption (TFC) 2000–2019. Primary energy in this figure is based on IEA

accounting methods and not direct equivalents for several energy sources. Final energy does not include industry own use and losses. Source: adapted from IEA world energy 
balances, Minx et al. (2021b) database for IPCC.

623
Energy Systems  Chapter 66(fuelwood and charcoal) is particularly important in sub-Saharan 
countries and some Asian countries such as India, particularly in the 
residential sector for cooking. Africa is still characterised by a high share 
of traditional bioenergy in TPES and TFC. In 2019, biomass and waste 
in Africa accounted for 44% of the TPES. The global average was 9.4%.
Asia has been particularly important in TFC growth since 2015. 
In 2019, Eastern Asia accounted for more 24% of TFC (1.52% annual 
growth from 2015). In contrast, TFC has increased by only 0.58% in 
Europe and 1.24% in North America. Despite an increase of 2.05% 
over the same period, Africa’s TFC remains relatively low (6.1% of 
global TFC), particularly in sub-Saharan countries. Approximately 
860  million people, mostly in sub-Saharan Africa and some Asian 
countries, lacked access to electricity and about 2.65 billion to clean-
cooking facilities in 2018 (IEA 2019a). Achieving universal energy

access (SDG 7) will require energy transitions in the domestic sector, 
including new developments in off-grid energy technologies, emphasis 
on rationalising energy subsidies, and increasing efforts to address 
health concerns related to the use of traditional fuels (Box 6.1).
Non-climate factors continue to drive energy systems changes
While energy system changes are vital to climate mitigation, recent energy 
system changes have arisen in response to a much broader set of factors. 
Important factors include economic growth, energy access, energy justice, 
energy security, air pollution, technological progress in low-emissions 
technologies, local job creation. Several of these are discussed here.
Energy access. Between 2000 and 2019, the proportion of the 
population with access to electricity increased from 73% to 90% (IEA 
2020c). Although most of those people gaining access to energy have 
gained access to fossil fuel-based electricity, an increasing number

are gaining access to electricity from renewable sources. Low-
emissions, decentralised systems are proving a cost-effective way to 
provide electricity in rural areas (Scott et al. 2016; Muchunku et al. 
2018; IEA 2019b), although the use of diesel generators continues 
in some remote areas. Between 2000 and 2019 the proportion of 
the population with access to clean cooking (modern fuels and/or 
improved biomass cookstoves) rose from 52% to 66%.Energy security. The ability of countries to maintain access to 
reliable and affordable energy resources continues to shape energy 
policy. Energy security is perceived as a national security issue and 
often prioritised over climate concerns (Nyman 2018). The linkage 
between climate and energy security is now widely recognised 
(Blarke and Lund 2007; Toke and Vezirgiannidou 2013; La Viña et al. 
2018; World Energy Council 2020; Fu et  al. 2021; United Nations 
2021). Approaches to energy security are frequently driven by the

scope of domestic energy resources. For example, energy security 
concerns have led to continued reliance on domestic coal production 
and consumption (Jakob et  al. 2020) and increased investment in 
domestic renewable generation (Konstantinos and Ioannidis 2017). 
Liquefied natural gas (LNG) Importers have diversified their sources 
as reliance on LNG has increased (Vivoda 2019).
Air pollution. The energy system is an important source of air 
pollution, including both indoor and outdoor air pollution. Efforts 
to address air pollution in several countries and regions (the USA, 
Mexico, China, India, European Union, Africa, Southeast Asia, among 
others) have had an importance influence on energy system changes 
(Bollen and Brink 2014; Fang et al. 2019). Policies aimed at controlling 
nitrogen oxides (NO x) and sulphur dioxide (SO 2) emissions have 
driven emissions abatement efforts and coal fleet retirements (Singh 
and Rao 2015; Drake and York, 2021). In some places, the prospect

of reducing local air pollution remains more salient to policymakers 
and the public than climate mitigation when deciding to tighten 
regulations on coal use (Brauers and Oei 2020).
Technology and costs. Costs for renewable technologies have fallen 
significantly in recent years, driving significant changes in electricity 
production and transportation (see below). These advances are not 
divorced from climate and other environmental concerns (Kuik, 
Branger and Quirion 2019; Timilsina and Shah 2020). Recent advances 
in PV cells, for example, can be traced in part to aggressive deployment 
policies spurred by energy security, climate, and other environmental 
concerns (Kreuz and Müsgens 2017) (Sections 6.3.5 and 6.4.2). 
The falling costs of batteries, manly Li-ion batteries, has boosted the 
competitiveness of electric vehicles (Nykvist et al. 2015) (Section 6.3.7).
Box 6.1 | Energy Access, Energy Systems, and Sustainability

Successful mitigation must work in tandem with fundamental development goals such as access to modern forms of energy. In many 
developing countries, access to electricity, clean cooking fuels, and modern and efficient energy remain an essential societal priority. 
This is particularly true in sub-Saharan Africa and several Asian countries. SDG 7 on universal access to modern energy includes targets 
on modern energy services, renewable energy, and energy efficiency, which implies a profound transformation of the current energy 
systems. Although there are different definitions of energy access, the ultimate goal is universal access to clean and modern fuels.
Despite progress in some countries such as India, Bangladesh and Kenya, 860 million people were without access to electricity in 2018, 
compared with 1.2 billion in 2010. About 2.65 billion households were cooking with solid fuels, distributed across Asia and Africa

(IEA et al. 2020). Around 850 million people in sub-Saharan Africa relied on traditional biomass (firewood and charcoal) for cooking, 
and 60 million relied on kerosene and coal to meet their energy needs (IEA 2018a). Air pollution was likely responsible for 1.1 million 
deaths across Africa in 2019 (Fisher et al. 2021). It has been estimated that 2.2 billion people will still be dependent on inefficient 
and polluting energy sources for cooking by 2030, mainly in Asia and Sub-Saharan Africa, and 650 million people are likely to remain 
without access to electricity in 2030, 90% of whom will reside in Sub-Saharan Africa (IEA et al. 2020).

624
Chapter 6 Energy Systems6There have been initial efforts to phase out coal but only 
modest declines in use
Global coal consumption has been declining, with small fluctuations, 
since it peaked in 2013 (IEA 2020d). Coal is faring differently across 
regions. Coal use has been decreasing in the OECD regions, particularly 
in the USA and the European Union (EU), while remaining mostly flat 
in China after a period of growth, and it is continuing to increase 
in other major developing Asian economies (IEA 2020d). Trends in 
the electricity sector, where most coal is being consumed, are similar. 
Growth in coal-fired electricity generation capacity in the Asia Pacific 
region has offset retirements in North America and Europe (Jakob 
et al. 2020; Global Energy Monitor et al., 2021).
Reductions in coal consumption have been driven in large part by 
non-climate factors, most notably environmental regulations to 
address air pollution, rapidly declining costs of renewables, and lower

natural gas prices, especially inexpensive unconventional gas in the 
USA. (Culver and Hong 2016; Diluiso et al.2021; Vinichenko et al. 2021). 
Older coal-fired power plants that cannot meet new environmental 
regulations, or have become unprofitable or uncompetitive, have 
been closed in many regions. Moreover, coal power expansion has 
slowed down in Asia, as countries have suspended and cancelled new projects for reasons such as overcapacity, environmental constraints, 
and the development of renewables (Box 6.2).
Different regions have replaced retired coal with different energy 
sources. Old coal fleets have been replaced approximately half by 
gas and half by renewables in the USA, mainly by renewables in the 
EU, and by advanced coal plants and renewables in Asia (EMBER 
2020). Replacing old coal with new coal facilities is inconsistent with 
limiting warming to 2°C or below (high confidence) (Pfeiffer et al. 
2016, 2018; Smith et al. 2019; Tong et al. 2019) (Section 6.7.4).

Major coal-consuming countries with abundant coal reserves remain 
far from phasing out coal (Edenhofer et al. 2018; Spencer et al. 2018). 
In most developing countries with large coal reserves, coal use has 
been increasing to support energy security and because it is perceived 
to have lower costs than alternatives (Steckel et  al. 2015; Kalkuhl 
et al.2019). However, coal faces increasing business risks from the 
decreasing costs of alternative, low-emissions energy sources and 
increasing focus on air pollution and other environmental impacts 
from coal mining and use (Garg et al. 2017; Sovacool et al. 2021). 
Continued coal builds, mostly in developing countries, will increase 
the risks of stranded assets (Farfan Orozco 2017; Cui et  al. 2019; 
Saygin et al. 2019) (Box 6.13).Box 6.1 (continued)
Overall Energy 
Access Index
Community 
Buildings 
IndexEducation 
Facilities 
IndexHealth 
IndexStreet 
Lighting 
IndexPublic 
Ofﬁces
IndexHH
Electricity 
IndexHH
Heating

IndexHH
Cooking
IndexIndex of Access to Energy 
for Community FacilitiesIndex of Access to Energy 
for Productive EngagementsIndex of Household 
Access to Energy
Box 6.1, Figure 1 | Measuring access to energy.  Source: with permission from ESMAP-World Bank 2015.
Research indicates that decentralised and on-grid renewables are likely the least cost options to provide universal access to electricity 
by 2030 (Section 6.4.2). Natural gas, LPG, and improved biomass cookstoves are the most important options for cooking. Universal 
access to electricity and clean cooking requires a rapid shift from traditional biomass to cleaner fuels and/or clean cooking technologies 
(IEA et  al. 2020). It has been estimated that the provision of electricity and clean cooking for all would require USD786 billion 
in cumulative investment to 2030, equal to 3.4% of total energy sector investment over the period (IEA 2017).

Even without universal access to modern energy, increased access will substantially affect energy systems, particularly electricity 
systems through the deployment of renewable energy, LPG, and biomass supply chains. Universal access for households, however, 
will have a minimal impact on global energy demand; it has been estimated that universal access for household will increase energy 
demand by 0.2% in 2030 (37 Mtoe yr –1) relative to a future without any change in access to modern energy (IEA 2017).

625
Energy Systems  Chapter 66Economic, social, and employment impacts of accelerated coal 
phase-outs tend to be significant in coal-dependent regions. 
Tailored reemployment has been used to support coal transitions in 
some regions. Although some estimates show higher employment 
opportunities from low-carbon energy (Garrett-Peltier 2017), 
results vary across regions. Moreover, even with a  net increase in 
total employment, in the long run, renewable jobs are often located outside of coal regions and require different skill sets from the coal 
industry (Spencer et al. 2018). In a broader sense, achieving a ‘just 
transition’ also requires managing the impacts on regional economic 
development for coal-dependent communities and the effects of 
higher energy prices for consumers and energy-intensive industries 
through a comprehensive policy package (Green and Gambhir 2020; 
Jakob et al. 2020) (Box 6.2).
Box 6.2 | Status and Challenges of a Coal Phase-out

Limiting global warming to 2°C or below requires a rapid shift away from unabated coal consumption – coal without CCS – in the 
energy system by 2050 (IPCC 2018a; Section 6.7; Chapter 3). This will require cancellation of new coal power projects and accelerated 
retirement of existing coal plants (Edenhofer et al. 2018; Kriegler et al. 2018; Pfeiffer et al. 2018; Smith et al. 2019; Tong et al. 2019). 
To limit warming to 2°C or lower, and without new builds, existing coal plants will need to retire 10 to 25 years earlier than the 
historical average operating lifetime. Completing all planned projects will further reduce the viable lifetime of all plants by 5 to 
10 years if warming is to be limited to 2°C or lower (Cui et al. 2019). Phasing-out coal in the next few decades will present economic, 
social, and security challenges. These will vary across regions based on the characteristics of existing coal infrastructure, the availability

of alternatives, economic development, and technological and institutional lock-in (Jakob et al. 2020).
Box 6.2, Figure 1 | Retirement of coal-fired power plants to limit warming to 1.5°C and 2°C or lower. (a) Historical facility age at retirement, (b) the 
vintage year of existing units, (c) global coal capacity under different plant lifetimes, compared to capacity levels consistent with a well-below 2°C (green) and 1.5°C 
(blue) pathway assuming no new coal plants, and (d) and assuming plants currently under construction come online as scheduled, but those in planning or permitting 
stages are not built. Source: with permission from Cui et al. (2019).

626
Chapter 6 Energy Systems6Box 6.2 (continued)
Between 2015 and 2019, global coal power capacity grew by 146 GW, or 7.6%, as new builds offset retirements in some countries 
(Global Energy Monitor et al. 2021). Meanwhile, an increasing number of countries and regions have committed to or operationalised 
coal phase-outs (Jewell et al. 2019; Watts et al. 2019; Littlecott et al. 2021). Actions are being taken by various international and 
sub-national actors, including national and sub-national governments, public and private power companies, and financial institutions 
and pension funds that have committed not to fund new coal or coal-based infrastructure (yan Nie et al. 2016; Buckley 2019; Auger 
et al. 2021). Although these initial efforts are not yet sufficient in limiting warming to 1.5°C, and most have occurred in regions with 
older coal fleets, these examples provide insight into possible coal phase-out strategies (Spencer et al. 2018) and help identify the

mechanisms driving the move away from coal, such as market, technology, policy, or other societal objectives. They also enable better 
understanding of the possible character of oil and gas phase-downs that would ultimately be needed to limit warming two well below 
2°C (Section 6.7.4) (Raimi et al. 2019).
Europe. Several European countries are part of the Powering Past Coal Alliance (PPCA) and have committed to phase out unabated 
coal on or before 2030 (Jewell et al. 2019). Because these countries represent a small share of global coal generation capacity and 
have mostly ageing coal plants, they tend to face fewer changes in phasing out coal. The effectiveness of PPCA in countries with 
younger coal fleets has thus been questioned (Jewell et al. 2019; Blondeel et al. 2020). Germany recently joined the PPCA and has 
committed to phase out unabated coal by 2038. As part of its commitment to phase out coal, Germany is implementing a set of

measures that include compensation for power plant closures, labour market measures for coal workers, and substantial support 
of structural change in coal-mining regions. Poland, another coal-heavy country in Europe, has not indicated a coal phase-out target 
and faces substantial challenges (Whitley et  al. 2017; Antosiewicz et  al. 2020). European efforts to phase out coal indicate that 
appropriate financial instruments are needed (Rentier et al. 2019), and a just transition for workers are important to gain broad public 
support and help those most affected by the phase-out (Johnstone and Hielscher 2017; Osička et al. 2020).
North America. Coal use has been declining in North America. In the USA, the primary driver has been the availability of cheap 
shale gas and ageing coal fleets. Coal use in the USA has dropped by over 50% since 2008 (EIA 2019). The recently announced

Nationally Determined Contribution (NDC) by the Biden Administration sets a 100% carbon-free electricity goal by 2035 (The White 
House 2021), indicating a phase-out not only of unabated coal electricity generation, but also of natural gas generation. As one of the 
two founding countries of the PPCA, Canada has committed to phasing out unabated coal power by 2030 (Government of Canada 
2018). Declining coal use in both the USA and Canada has decreased GHG emissions, local air pollutants, and cooling water use 
(Harris et al. 2015; Kondash et al. 2019). However, there have been concerns about social and economic consequences, particularly at 
the local level. For instance, the USA has lost about 50,000 coal mining jobs between 2011 and 2021 (US Bureau of Labor Statistics, 
2021), with significant regional and economic inequities (Bodenhamer 2016; Abraham 2017; Greenberg 2018). Comprehensive social

programmes, such as retirement compensation, training for reemployment, and business support for economic diversification, have 
been suggested as means to support a just transition (Homagain et al. 2015; Patrizio et al. 2018; Grubert 2020).
Asia. After a period of rapid growth, coal expansion has slowed in Asia, but it still the primary driver of the global increase in coal 
demand (IEA 2020e). China’s coal consumption reached a plateau under policy efforts during the 13th Five-Year Plan (2016–2020), 
and new coal plants are being built at a slower rate than previously. Both China and India have suspended and cancelled many new 
coal power projects and retired a small set of old, dirty, inefficient coal plants (CEA 2019; Global Energy Monitor et al. 2021). These 
efforts are largely due to non-climate reasons, such air pollution and health (Singh and Rao 2015; Gass et al. 2016; Peng et al. 2018;

Malik et al. 2020), overcapacity (Blondeel and Van de Graaf 2018), and rural electrification and renewable investments (Aklin et al. 
2017; Thapar et al. 2018). However, as new builds offset retirements, coal generation capacity has continued to grow in both countries 
since 2015 (Global Energy Monitor et al. 2021). Other fast-growing Southeast Asian countries, such as Indonesia, Vietnam, and the 
Philippines have experienced strong growth in coal use (IEA 2020b), but an increasing number of new coal power projects are being 
cancelled (Littlecott et al. 2021). Coal projects in these countries are decreasingly likely to proceed because they rely on international 
financing, and China, Japan, USA, and other G7 countries have pledged to end overseas coal financing (Schiermeier 2021).
Africa. New coal power projects in Africa have been declining since 2016, with only South Africa and Zimbabwe currently building

new coal plants and several others with planned projects (Littlecott et al. 2021). However, these projects also largely depend on 
international financing and are thus less likely to be implemented (see above). In South Africa, employment in the coal mining 
sector has dropped by almost half since the 1980s and has been estimated to fall from 77,000 today to 22,000 to 42,000 by 2050 
(Cock 2019; Strambo et al. 2019). Policy and financial support are essential to ensure a sustainable transition for these workers 
(Swilling et al. 2016).

627
Energy Systems  Chapter 66Solar and wind energy have grown dramatically, but global 
shares remain low relative to other sources
Global PV and wind electric capacities grew 170% and 70%, respectively, 
between 2015 and 2019. Total solar and wind capacities in 2019 were 
609 GW and 623 GW (Figure 6.6) and generation was 680 TWh yr –1 
and 1420 TWh yr –1. The combined share of solar and wind in the total 
global electricity generation in 2019 was around 8% (5.5% wind, 2.5% 
solar), up from around 5% in 2015 (IEA 2021a). Since 2015, the cost 
of solar PVs has declined by over 60%. Offshore wind costs have fallen 
by 32%, and onshore wind costs have fallen by 23% (Section 6.4). PV 
was around 99% of total solar capacity  in 2019; onshore wind was 
about 95% of total wind capacity. Concentrating solar power (CSP) 
deployment has also continued to grow, but it remains far below PV. 
Prior to 2010, 50% of all wind capacity was in Europe, but since then,

capacity growth in Asia (led by China), has surpassed the growth in 
Europe. As a consequence, Europe’s share in global solar capacity has 
declined from 74% in 2010 to 24% in 2019. Asia’s share in wind and 
solar capacity in 2019 was 41% and 56%, followed by Europe (31% 
and 24%) and North America (20% and 12%) (IRENA 2020a, 2021a).
Although the shares of wind and solar remain low in the global total 
electricity generation, recent growth rates signal the potential for 
these technologies to support substantial mitigation. The prospects 
for a continuation of recent growth rates will depend on meeting key 
challenges such as rapidly integrating wind and solar into electricity 
grids (Section 6.6.2, Box 6.8) and retiring fossil power plants (see above).
Low-carbon energy sources beyond wind and solar have 
continued to grow
Low-carbon energy sources such as nuclear, hydropower, bioenergy,

geothermal, marine, and fossil or bioenergy with carbon capture, use and storage (CCUS) have continued to grow since 2015 (IEA 2017, 
2021a). Hydroelectric power grew from 3890 TWh yr –1 (14.0 EJ yr –1) 
in 2015 to 4290 TWh yr –1 (15.5 EJ yr –1) in 2019, or 10.3%; nuclear 
power grew from 2570 TWh yr –1 (9.3 EJ yr –1) to 2790 TWh yr –1 
(10.1 EJ yr –1), or 8.6%. Hydroelectric and nuclear shares in global 
total electricity generation remained around 16% and 10%, 
respectively (IEA 2017, 2021a). Global biofuels production grew 
from 3.2 EJ yr –1 to 4.0 EJ yr –1 from 2015 to 2019 (IEA 2017, 2021a). 
Bioenergy accounted for 2.4% of electricity generation in 2019. 
Geothermal energy sources produced 92 TWh  yr –1 (0.33  EJ  yr –1) 
of electricity in 2019, up from 80 TWh yr –1 (0.28 EJ yr –1) in 2015 
(IEA 2017, 2021a). At present, there are 28 commercially operating 
CCUS facilities with a CO 2 removal capacity of around 40 million

tonnes yr –1 (Mtpa). Only two of these are associated with 
electricity production: the majority are in industrial applications – 
37  commercial projects, accounting for about 75 Mtpa, are in 
various stages of development and construction (Global CCS 
Institute 2020). The share of marine energy in global electricity 
generation has remained at approximately 1 TWh yr –1 since 2015. 
In total, low- and zero-carbon electricity generation technologies 
produced 37% of global electricity in 2019.
Battery prices have dropped substantially, spurring deployment 
in electricity and transportation
Recent years have seen a  rapid decline in the cost of energy 
storage, particularly batteries (Section 6.4.4). The price of lithium-
ion batteries (LIBs) has declined by 97% in the past three decades, 
and by 90% in the past decade alone (IEA 2021a; Ziegler and 
Trancik 2021). These declines have important implications for the 
energy systems, most notably in supporting increased deployment

of variable renewable energy (VRE) generation and electrification 
of the vehicle fleet.
Wind Solar % of solar and wind in total electricty generation0%2%4%6%8%10%
  0  100  200  300  400  500  600  700
2015 2016 2017 2018 2019
Share in total electricity generationCumulative installed capacity (GW)
Year
Figure  6.6 | Global solar and wind electricity installed capacities (GW) from 2015–2019 and their combined share in total electricity generation. 
Source: data from IEA (2021a) and IRENA (2021).

628
Chapter 6 Energy Systems6Battery electricity storage has emerged as important for supporting 
the flexibility of electricity systems as they accommodate rising shares 
of VRE. Although pumped-storage hydropower systems accounted 
for 160 GW, or over 90%, of total energy storage capacity in 2019 
(IEA 2020c), battery energy storage systems, led by LIB technology, 
have accounted for over 90% of new capacity addition since 2015 
(IRENA 2019a). In 2019, 10 GW of batteries were connected at the 
grid and consumer level, rising from 0.6 GW in 2015 (IEA WEO 2019; 
IEA 2020c).
In California in the USA, legislation was passed to procure around 
1.3 GW energy storage (excluding pumped storage) by 2020. One of 
the largest utility-scale battery storage facilities (300 MW) recently 
went online in California (Vistra Corp. 2021). Other major projects 
are in Florida in the USA (409 MW), London in the UK (320 MW), 
Lithuania (200 MW), Australia (150 MW), Chile (112 MW) and

Germany (90 MW), (IRENA 2019a; ARENA 2020; Katz 2020).
The drop in battery prices has also had important implications in the 
transportation sector. Automotive LIB production rose from around 
40 GWh in 2015 to 160 GWh in 2020 (32%). The stock of battery 
electric vehicles (BEVs) grew from around 0.7 million in 2015 to 
4.8 million in 2019 (IEA 2020d). The number of publicly accessible 
vehicle chargers reached 1.3 million in 2020, 30% of which were fast 
chargers. The average battery size of BEVs reached 67 kWh in 2019 
due to consumer preferences and government incentives for long-
range vehicles (Agency 2020; IEA 2021b).
The energy policy landscape continues to evolve
The current energy sector policy landscape consists of policy mixes 
or policy packages, including regulatory, market-based and other 
approaches. These mixes have evolved over time and include many 
sectoral but also some economy-wide policy instruments, such as 
carbon pricing subsidies.

Governments have chosen a  mix of policies and institutional 
mechanisms that consists of regulatory instruments, like efficiency 
and technology standards, economic instruments (e.g.,  carbon 
pricing, subsidies) (Bertram et al. 2015; Martin and Saikawa 2017) 
and other policies, such as government interventions to provide 
infrastructure, information policies, and voluntary actions by non-
government actors (Somanathan et  al. 2014). In recent years, 
regulatory instruments to promote low-carbon infrastructure have 
gained traction in developing countries (Finon 2019). The choice 
of policies has depended on institutional capacities, technological 
maturity and other developmental priorities of governments. For 
example, governments have favoured regulatory instruments over 
economic instruments when there has been sufficient institutional 
capacity to implement and monitor the regulations and standards 
(Hughes and Urpelainen 2015). Furthermore, institutional capacity

has also determined the extent of implemented measures (Adenle 
et al. 2017). Market conditions and technological maturity are other 
important determinants of policy mixes being deployed in the energy 
sector. For example, subsidies for mitigation like feed-in-tariffs 
have worked best when the technologies are in nascent stages of 
development (Gupta et al. 2019a).On the other hand, market-based instruments like emissions rading 
schemes (ETS) and auctions coupled with a  regulatory framework 
have been a favourable strategy for more mature technologies (Polzin 
et al. 2015; Kitzing et al. 2018). FIT, tax incentives, and renewable 
portfolio standards – despite potentially substantial programme costs 
(Andor and Voss 2016; Abrell et al. 2019) – have played a significant 
role in attracting foreign direct investments in the renewable 
sector (Wall et  al. 2019). Subsidies and carbon pricing have also 
played an important role in mainstreaming these renewable energy

sources (Best and Burke 2018). Recently, subsidy-free investments 
in renewables, such as wind offshore (Jansen et  al. 2020), backed 
by power purchase agreements, have gained momentum (Frankfurt 
School-UNEP Centre and BNEF 2020). Similar considerations apply 
for policy mixes targeted to other sectors – for example, transport 
and buildings.
The role of carbon pricing is still limited though increasing. Different 
measures have been suggested to improve the performance of 
the ETS, such as ‘price floors and caps’ and other carbon pricing 
schemes (Campiglio 2016; Bataille et  al. 2018; Goulder and 
Morgenstern 2018). In 2020, 61 regional, national and sub-national 
carbon pricing instruments, representing 22% of the global GHG 
emissions, were in action or scheduled for implementation (World 
Bank 2019). Over 51% of emissions covered are priced at less than 
USD10  per  tCO2-eq. At present, however, only 5% of the global

emissions covered under carbon pricing initiatives are consistent 
with the range of carbon prices that have been suggested as 
needed to limit warming to well below 2°C (Stiglitz and Stern 2017). 
Most of the carbon pricing schemes have taken place in the OECD 
countries. The limited application of carbon pricing instruments 
in developing, and emerging economies may be due to political 
economy constraints (Campiglio 2016; Finon 2019). Carbon pricing 
had a  sizeable impact on emissions  – for example, the EU ETS 
impacts emissions from electricity in Germany (Schäfer 2019) and 
manufacturing in France (Colmer et al. 2020), respectively. Emissions 
reductions could be increased with higher carbon prices and without 
free allocation of allowances.
In the absence of a  global comprehensive carbon price, regional 
regulatory policies for fossil fuels supply and key demand sectors 
like transport, industry and buildings (Chapters 9–11), coupled with

regional carbon pricing instruments, were implemented to help 
initiate the climate actions consistent with the Paris Agreement 
(Kriegler et  al. 2018). However, differences in the stringency of 
climate regulation have triggered fear that regulation reduces the 
competitiveness of industries in regulated countries and leads to 
industry relocation and ‘carbon leakage‘ (Schenker et  al. 2018). 
In recent years, however, there is little evidence of carbon leakage 
(Naegele and Zaklan 2019; Schäfer 2019), and even positive effects 
of carbon pricing on efficiency have been observed (e.g., Löschel et al. 
2019, for German manufacturing firms, and Germeshausen 2020 
for German power plants). However, with asymmetric rising carbon 
prices, discussions about specific policy mechanisms to address 
carbon leakage like carbon border adjustments (Cosbey et al. 2019) 
were amplified. Furthermore, multiple policies – often implemented

by different governmental levels (national vs sub-national)  – 
interacted with each other and thereby affected their environmental

629
Energy Systems  Chapter 66and economic effectiveness. Recent examples include interactions of 
ETS with renewable support policies (e.g. Boehringer and Behrens 
2015; Del Rio 2017), energy efficiency policies (e.g. Wiese et al. 2018) 
or electricity market reform (e.g. Teng et al. 2017), respectively.
Apart from explicit carbon pricing, various implicit carbon pricing 
mechanisms, such as fossil fuel taxes and removal of fossil fuel subsidies (Box  6.3) and regulatory instruments, are used by many 
countries as part of their climate policies. In addition, public provision 
and procurement of low-carbon infrastructure and technologies 
such as energy-efficient devices, renewable energy, and upgrades 
in electricity grids through state-sponsored institutions and public-
private partnerships have played an important role in low-carbon 
development (e.g., Baron 2016).
Box 6.3 | Energy Subsidies

Energy subsidies continue to be widely applied. Global fossil fuel subsidies represent more than half of total energy subsidies with 
predominantly adverse environmental, economic, and social effects (high confidence).
Energy subsidies can be defined as policy measures in the energy sector to lower the prices for consumers, raise the prices for producers, 
or reduce energy production costs (IEA 1999). There are subsidies for fossil fuels, renewables, and energy efficiency measures. The 
majority of the renewable subsidies are generation-based incentives for solar, wind or biomass in the form of feed-in-tariffs (Chapter 13), 
with total annual renewable subsidy estimates of about USD150 billion yr –1 globally (IEA 2018b). Estimates of fossil fuel subsidies 
can vary by an order of magnitude. For the year 2017, the IEA estimated fossil fuel subsidies of USD300 billion using IEA’s pre-tax,

price-gap method (IEA 2018b), while the International Monetary Fund (IMF) included unpriced externalities in calculating subsidies of 
USD5.2 trillion or 6.5% of global GDP (Coady et al. 2017, 2019; World Bank 2019). It has been estimated that the amount spent on 
fossil fuel subsidies was around double the amount of subsidies spent on renewables (IEA 2018b). There are adverse environmental, 
economic and social consequences of fossil fuel subsidies (Rentschler and Bazilian 2017). More than 75% of the distortions created by 
fossil fuel subsidies are domestic, and studies indicate that reforming them can have substantial in-country benefits (Coady et al. 2017, 
2019). Some of the G20 countries have implemented subsidy reforms based on low oil prices (Jewell et al. 2018).
Fossil fuel subsidies most commonly pursue non-climate objectives, for example, enhanced access to energy sources (high confidence).

In some cases, these energy access subsidies have helped extend modern energy sources to the poor (Kimemia and Annegarn 2016) 
and thereby contribute to SDG 7. However, the subsidies have proven to be regressive in most cases, with little benefit reaching the 
poor (Lockwood 2015). For example, Indonesia has introduced LPG subsidies for cooking. The kerosene-to-LPG conversion programme 
(‘Zero Kero’) was launched in 2007 and provided mainly households with free initial LPG equipment and LPG at a low subsidised price 
(Imelda et al. 2018b; Thoday et al. 2018). Besides the national government, provincial governments and industry played a crucial role 
in implementation. Overall, the LPG conversion programme in Indonesia reduced cooking kerosene use (Andadari et al. 2014; Imelda 
et al. 2018b) and GHG emissions (Permadi et al. 2017) with positive health effects (Imelda et al. 2018b; Thoday et al. 2018). However,

the programme is generally viewed as regressive and has failed to reduce traditional solid fuel use (Andadari et al. 2014; Toft 2016; 
Thoday et al. 2018). Furthermore, even if the programme decreased GHG emissions relative to continued kerosene use, these subsidies 
are still targeted at fossil fuels and contribute to GHG emissions.
India started a large LPG programme in 2015 that provided a capital cost subsidy to poor households (e.g., Gould 2018; Jose et al. 
2018; Kar et al. 2019). While the programme has increased adoption of LPG in India (e.g., Sharma et al. 2019), it has not yet achieved 
a sustained use of LPG and replacement of solid fuels for cooking, amplifying the need for complementary policy measures (Gould 
2018; Kar et al. 2019; Mani et al. 2020). The climate impacts of switching from biomass to LPG depend on the degree of biomass 
combustion in stoves and the extent to which biomass originates from non-renewable sources (Singh and Rao 2015; Jose et al. 2018).

Barriers to increasing LPG use for cooking further included abundance of solid fuels at zero (monetary) costs (Mani et al. 2020) as well 
as benefits of solid fuels, such as maintaining the traditional taste of food and space heating in colder seasons (Gould 2018; Sharma 
6.4 Mitigation Options
6.4.1 Elements of Characterisation
This section characterises energy system mitigation options and 
discusses which factors enable and inhibit their implementation. We touch on a broad range of factors that may enable and inhibit the 
implementation of mitigation options by considering six dimensions 
that affect their feasibility (Table 6.1 and Annex II.11). The assessment 
aims to identify which mitigation options can be readily implemented 
and which face barriers that would need to be overcome before they 
can be deployed at scale.

630
Chapter 6 Energy Systems6Table 6.1 | Dimensions and indicators to assess the barriers and enablers of implementing mitigation options in low-carbon energy systems.
Geophysical: Are the required resources available? –Physical potential: physical constraints to implementation
 –Geophysical resources (including geological storage capacity): availability of resources needed for implementation
 –Land use: claims on land where an option would be implemented
Environmental-ecological: What are the 
wider environmental and ecological impacts 
of the option? –Air pollution: increase or decrease in air pollutants, such as NH 4, CH 4 and fine dust
 –Toxic waste, ecotoxicity and eutrophication
 –Water quantity and quality: changes in the amount of water available for other uses
 –Biodiversity: changes in conserved primary forest or grassland that affect biodiversity, and management to conserve and maintain 
land carbon stocks
Technological: Can the required technology

be upscaled soon? –Simplicity: is the option technically simple to operate, maintain and integrate?
 –Technology scalability: can the option be scaled up technically?
 –Maturity and technology readiness: research and development (R&D) and time needed to implement the option
Economic: What economic conditions can support 
or inhibit the implementation of the option? –Costs in 2030 and in the long term: investment costs, costs in USD tCO 2-eq–1
 –Employment effects and economic growth: decrease or increase in jobs and economic welfare
Socio-cultural: What social conditions 
could support or inhibit acceptance, adoption, 
and use of the option before 2030? –Public acceptance: the extent to which the public supports the option and will change their behaviour accordingly
 –Effects on health and well-being
 –Distributional effects: equity and justice across groups, regions, and generations, including energy, water, and food security 
and poverty
Institutional: What institutional conditions

could support or inhibit the implementation 
of the option? –Political acceptance: the extent to which politicians support the option
 –Institutional capacity and governance, cross-sectoral coordination: capability of institutions to implement and handle the option
 –Legal and administrative capacity
6.4.2 Energy Sources and Energy Conversion
6.4.2.1 Solar Energy
Solar photovoltaic (PV) is increasingly competitive with other forms of 
electricity generation, and is the low-cost option in many applications 
(high confidence). Costs have declined by 62% since 2015 (high 
confidence) and are anticipated to decline by an additional 16% by 
2030 if current trends continue (low confidence, medium evidence). 
Key areas for continued improvement are grid integration and non-
module costs for rooftop systems (high confidence). Most deployment 
is now utility-scale (high confidence). Global future potential is not 
limited by solar irradiation, but by grid integration needed to address

its variability, as well as access to finance, particularly in developing 
The global technical potential of direct solar energy far exceeds that of any 
other renewable energy resource and is well beyond the total amount of 
energy needed to support ambitious mitigation over the current century 
(high confidence). Estimates of global solar resources have not changed 
since the IPCC’s Fifth Assessment Report (AR5) (Lewis 2007; Besharat 
et al. 2013) even as precision and near-term forecasting have improved 
(Diagne et al. 2013; Abreu et al. 2018). Approximately 120,000 TW of 
sunlight reaches the Earth’s surface continuously, almost 10,000 times 
average world energy consumption; factoring in competition for land 
use leaves a technical potential of about 300 PWh yr –1 (1080 EJ yr –1) 
for solar PV, roughly double current consumption (Dupont et al. 2020). 
The technical potential for concentrating solar power (CSP) is estimated

to be 45–82 PWh yr –1 (162–295 EJ yr –1) (Dupont et al. 2020). Areas 
with the highest solar irradiation are: western South America; northern, 
eastern and southwestern Africa; and the Middle East and Australia 
(Figure 6.7) (Prăvălie et al. 2019).
In many parts of the world, the cost of electricity from PV is below 
the  cost of electricity generated from fossil fuels; in some, it is below  the operating costs of electricity generated from fossil fuels 
(high confidence). The weighted average cost of PV in 2019 was 
USD68 MWh–1, near the bottom of the range of fossil fuel prices (IRENA 
2019b). The cost of electricity from PV has fallen by 89% since 2000 and 
69% since AR5, at a rate of –16% per year. The 5:95 percentile range 
for PV in 2019 was USD52–190 MWh–1 (IRENA 2021b). Differences 
in solar insolation, financing costs, equipment acquisition, installation 
labour, and other sources of price dispersion explain this range (Nemet

et  al. 2016; Vartiainen et  al. 2020) and scale. For example, in India, 
rooftop installations cost 41% more than utility-scale installations, and 
commercial-scale costs are 39% higher than utility-scale. Significant 
differences in regional cost persist (Kazhamiaka et al. 2017; Vartiainen 
et al. 2020), with particularly low prices in China, India, and parts of 
Europe. Globally, the range of global PV costs is quite similar to the 
range of coal and natural gas prices.
PV costs (Figure 6.8) have fallen for various reasons: lower silicon 
costs, automation, lower margins, automation, higher efficiency, 
and a  variety of incremental improvements (Fu et  al. 2018; Green 
2019) (Chapter  16). Increasingly, the costs of PV electricity are 
concentrated in the installation and related ‘soft costs’ (marketing, 
permitting) associated with the technology rather than in the 
modules themselves, which now account for only 30% of installed

costs of rooftop systems (O’Shaughnessy et al. 2019; IRENA 2021b). 
Financing costs are a  significant barrier in developing countries 
(Ondraczek et al. 2015) and growth there depends on access to low-
cost finance (Creutzig et al. 2017).
CSP costs have also fallen, albeit at about half the rate of PV: –9% yr –1 
since AR5. The lowest prices for CSP are now competitive with more 
expensive fossil fuels, although the average CSP cost is above the 
range for fossil-based power generation. Other data sources put 
recent CSP costs at USD120 MWh–1, in the middle of the fossil range 
(Lilliestam et al. 2020). Continuing the pace of change since AR5 will 
make CSP competitive with fossil fuels in sunny locations, although

631
Energy Systems  Chapter 66
Global horizontal irradiation [kWh m–2 day–1]
1 8 7 6 5 4 3 2
Figure 6.7 | Distribution of the daily mean global horizontal irradiation (GHI, kWh m–2 day–1). Source: Global Solar Atlas (ESMAP 2019).
0100200
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020300400500LCOE (USD2020 MWh–1)post-
AR5post-
AR5PV
Fossil
0100200300400500CSP
Figure 6.8 | Levelised costs of electricity (LCOE) of solar energy technologies 2000–2020.  Range of fossil fuel LCOE indicated as dashed lines USD50–177 MWh–1. 
Linear fit lines were applied to data for AR4–AR5 and post-AR5 (2012). Yellow dots are capacity-weighted global averages for utility-scale installations. The blue area shows 
the range between the 5th and 95th percentile in each year. Data: IRENA (2021b).

632
Chapter 6 Energy Systems6it will be difficult for CSP to compete with PV and even hybrid 
PV-battery systems. CSP electricity can be more valuable, however, 
because CSP systems can store heat longer than PV battery systems.
The share of total costs of PV-intensive electricity systems attributed 
to integration costs has been increasing but can be reduced by 
enhancing grid flexibility (high confidence) (Sections 6.4.3 and 6.6, 
and Box 6.8). The total costs of PV include grid integration, which 
varies tremendously depending on PV’s share of electricity, other 
supply sources like wind, availability of storage, transmission capacity, 
and demand flexibility (Heptonstall and Gross 2020). Transmission 
costs can add USD1–10 MWh–1 or 3–33% to the cost of utility-scale 
PV (Gorman et al. 2019). Distributed (rooftop) PV involves a broader 
set of grid integration costs – including grid reinforcement, voltage 
balancing and control, and impacts on other generations – and has

a larger range of integration costs from USD2–25 MWh–1, which is 
–3% to +37% (Hirth et al. 2015; Wu et al. 2015; Gorman et al. 2019). 
Other meta-analyses put the range at USD1–7 MWh–1 in the USA 
(Luckow et al. 2015.; Wiser et al. 2017), while a comprehensive study 
put the range at USD12–18 MWh–1 for up to 35% renewables and 
USD25–46 MWh–1 above 35% renewables (Heptonstall and Gross 
2020). Increased system flexibility can reduce integration costs of 
solar energy (Wu et al. 2015) including storage, demand response, 
sector-coupling (Brown et  al. 2018; Bogdanov et  al. 2019), and 
increase complementarity between wind and solar (Heide et al. 2010) 
(Sections 6.4.3 and 6.4.4).
Since solar PV panels have very low operating costs, they can, at 
high penetrations and in the absence of adequate incentives to shift 
demand, depress prices in wholesale electricity markets, making it 
difficult to recoup investment, and potentially reducing incentives

for new installations (Hirth 2013; Millstein et al. 2021). Continued 
cost reductions help address this issue of value deflation, but only 
partially. Comprehensive solutions depend on adding transmission 
and storage (Das et al. 2020) and, more fundamentally, adjustments 
to electricity market design (Roques and Finon 2017; Bistline and 
Young 2019).
The most important ways to minimise PV’s impact on the environment 
lie in recycling materials at end of life and making smart land-use 
decisions (medium confidence). A comprehensive assessment of PV’s 
environmental impacts requires lifecycle analysis (LCA) of resource 
depletion, land-use, ecotoxicity, eutrophication, acidification, ozone, 
and particulates, among other things (Mahmud et  al. 2018). LCA 
studies show that solar PVs produce far less CO 2 per unit of electricity 
than fossil generation, but PV CO 2 emissions vary due to the carbon 
intensity of manufacturing energy and offset electricity (Grant and

Hicks 2020). Concerns about systemic impacts, such as reducing the 
Earth’s albedo by covering surfaces with dark panels, have shown 
to be trivial compared to the mitigation benefits (Nemet 2009) 
(Box  6.7). Even though GHG LCA estimates span a  considerable 
range of 9–250 gCO 2 kWh–1 (de Wild-Scholten 2013; Kommalapati 
et  al. 2017), recent studies that reflect higher efficiencies and 
manufacturing improvements find lower lifecycle emissions, 
including a  range of 18–60 gCO 2 kWh–1 (Wetzel and Borchers 
2015) and central estimates of 80 gCO 2 kWh–1 (Hou et  al. 2016), 
50 gCO 2 kWh–1 (Nugent and Sovacool 2014), and 20 gCO 2 kWh–1 (Louwen et al. 2016). These recent values are an order of magnitude 
lower than coal, and natural gas and further decarbonisation of the 
energy system will make them lower still. Thin films and organics 
produce half the lifecycle emissions of silicon wafer PV, mainly 
because they use less material (Lizin et al. 2013; Hou et al. 2016). Novel

materials promise even lower environmental impacts, especially with 
improvements to their performance ratios and reliability (Gong et al. 
2015; Muteri et al. 2020). Higher efficiencies, longer lifetimes, sunny 
locations, less carbon-intensive manufacturing inputs, and shifting to 
thin films could reduce future lifecycle impacts.
Another environmental concern with large PV power plants is the 
conversion of land to collect solar energy (Hernandez et al. 2015). 
Approximately 2 hectares of land are needed for 1 MW of solar 
electricity capacity (Perpiña Castillo et al. 2016; Kabir et al. 2018); 
at 20% efficiency, a  square of PV panels of 550  km by 550  km, 
comprising 0.2% of Earth’s land area, could meet global energy 
demand. Land conversion can have local impacts, especially near 
cities and where land used for solar competes with alternative 
uses, such as agriculture. Large installations can also adversely 
impact biodiversity (Hernandez et  al. 2014), especially where the

above-ground vegetation is cleared and soils are typically graded. 
Landscape fragmentation creates barriers to the movement of 
species. However, a  variety of means have emerged to mitigate 
land use issues. Substitution among renewables can reduce land 
conversion (Tröndle 2020). Solar can be integrated with other uses 
through ‘agrivoltaics’ (the use of land for both agriculture and solar 
production) (Dupraz et al. 2011) by, for example, using shade-tolerant 
crops (Dinesh and Pearce 2016). Combining solar and agriculture can 
also create income diversification, reduced drought stress, higher 
solar output due to radiative cooling, and other benefits (Elamri et al. 
2018; Hassanpour Adeh et al. 2018; Barron-Gafford et al. 2019). PV 
installations floating on water also avoid land-use conflicts (Sahu 
et al. 2016; Lee et al. 2020), as does dual-use infrastructure, such as 
landfills (Jäger-Waldau 2020) and reservoirs where evaporation can 
also be reduced (Farfan and Breyer 2018).

Material demand for PV will likely increase substantially to limit 
warming to well below 2°C, but PV materials are widely available, 
have possible substitutes, and can be recycled (medium confidence) 
(Box  6.4). The primary materials for PV are silicon, copper, glass, 
aluminium, and silver – the costliest being silicon, and glass being 
the most essential by mass, at 70%. None of these materials is 
considered to be either critical or potentially scarce (IEA 2020e). Thin-
film cells, such as amorphous silicon, cadmium telluride and copper 
indium gallium diselenide (CIGS), use far less material (though they 
use more glass), but account for less than 10% of the global solar 
market. Other thin-films, such as those based on perovskites, organic 
solar cells, or earth-abundant, non-toxic materials such as kesterites, 
either on their own, or layered on silicon, could further reduce 
material use per energy produced (Box 6.4).

After a typical lifetime of 30 years of use, PV modules can be recycled to 
prevent environmental contamination from the toxic materials within 
them, reusing valuable materials and avoiding waste accumulation. 
Recycling allows the reuse of nearly all – 83% in one study – of the 
components of PV modules, other than plastics (Ardente et al. 2019)

633
Energy Systems  Chapter 66and would add less than 1% to lifecycle GHG emissions (Latunussa 
et al. 2016). Glass accounts for 70% of the mass of a solar cell and is 
relatively easy to recycle. Recycling technology is advancing, but the 
scale and share of recycling is still small (Li et al. 2020d). By 2050, 
however, end-of-life PV could total 80 MT and comprise 10% of 
global electronic waste (Stolz and Frischknecht 2017), although most 
of it is glass. IEA runs a programme to enable PV recycling by sharing 
best practices to minimise recycling lifecycle impacts. Ensuring 
that a substantial amount of panels are recycled at end of life will 
likely require policy incentives, as the market value of the recovered 
materials, aside from aluminium and copper, is likely to be too low 
to justify recycling on its own (Deng et al. 2019). A near-term priority 
is maximising the recovery of silver, silicon, and aluminium, the most 
valuable PV material components (Heath et al. 2020).

Many alternative PV materials are improving in efficiency and stability, 
providing longer-term pathways for continued PV costs reductions 
and better performance (high confidence). While solar PV based on 
semi-conductors constructed from wafers of silicon still captures 
90% of the market, new designs and materials have the potential to 
reduce costs further, increase efficiency, reduce resource use, and open 
new applications. The most significant technological advance within 
silicon PV in the past 10 years has been the widespread adoption 
of the passivated emitter and rear cell (PERC) design (Green 2015), 
which now accounts for the majority of production. This advance 
boosts efficiency over traditional aluminium backing by increasing 
reflectivity within the cell and reducing electron-hole recombination 
(Blakers 2019). Bifacial modules increase efficiency by using reflected 
light from the ground or roof on the backside of modules (Guerrero-

Lemus et al. 2016). Integrating PV into buildings can reduce overall 
costs and improve building energy performance (Shukla et al. 2016). 
Concentrating PV uses lenses or mirrors that collect and concentrate 
light onto high efficiency PV cells (Li et al. 2020a). Beyond crystalline 
silicon, thin films of amorphous silicon, cadmium telluride, and 
copper indium gallium selenide (among others) have the potential 
for much lower costs while their efficiencies have increased (Green 
et al. 2019). Perovskites, inexpensive and easy to produce crystalline 
structures, have increased in efficiency by a factor of six in the past 
decade; the biggest challenge is light-induced degradation as well as 
finding lead-free efficient compounds, or establishing lead recycling 
at the end of the lifecycle of the device (Petrus et al. 2017; Chang 
et al. 2018; Wang et al. 2019b; Zhu et al. 2020). Organic solar cells 
are made of carbon-based semiconductors like the ones found in the

displays made from organic light emitting diodes (OLEDs) and can be 
processed in thin films on large areas with scalable and fast coating 
processes on plastic substrates. The main challenges are raising the 
efficiency and improving their lifetime (Ma et al. 2020; Riede et al. 
2021). Quantum dots, spherical semi-conductor nanocrystals, can be 
tuned to absorb specific wavelengths of sunlight, giving them the 
potential for high efficiency with very little material use (Kramer et al. 
2015). A common challenge for all emerging solar cell technologies 
is developing the corresponding production equipment. Hybrids 
of silicon with layers of quantum dots and perovskites have the 
potential to take advantage of the benefits of all three, although 
those designs require that these new technologies have stability and 
scale that match those of silicon (Chang et al. 2017; Palmstrom et al.

2019). This broad array of alternatives to making PV from crystalline silicon offer realistic potential for lower costs, reduced material use, 
and higher efficiencies in future years (Victoria et al. 2021).
Besides PV, alternative solar technologies exist, including CSP , 
which can provide special services in high-temperature heat and 
diurnal storage, even if it is more costly than PV and its potential for 
deployment is limited. CSP uses reflective surfaces, such as parabolic 
mirrors, to focus sunlight on a receiver to heat a working fluid, which 
is subsequently transformed into electricity (Islam et al. 2018). Solar 
heating and cooling are also well established technologies, and 
solar  energy can be utilised directly for domestic or commercial 
applications such as drying, heating, cooling, and cooking (Ge et al. 
2018). Solar chimneys, (still purely conceptual), heat air using large 
transparent greenhouse-like structures and channel the warm air

to turbines in tall chimneys (Kasaeian et  al. 2017). Solar energy 
can also be used to produce solar fuels, for example, hydrogen or 
synthetic gas (syngas) (Montoya et  al. 2016; Nocera 2017; Detz 
et al. 2018). In addition, research proceeds on space-based solar PV, 
which takes advantage of high insolation and a  continuous solar 
resource (Kelzenberg et al. 2018), but faces the formidable obstacle 
of developing safe, efficient, and inexpensive microwave or laser 
transmission to the Earth’s surface (Yang et al. 2016). CSP is the most 
widely adopted of these alternative solar technologies.
Like PV, CSP facilities can deliver large amounts of power (up to 
200 MW per unit) and maintain substantial thermal storage, which 
is valuable for load balancing over the diurnal cycle (McPherson 
et al. 2020). However, unlike PV, CSP can only use direct sunlight, 
constraining its cost-effectiveness to North Africa, the Middle East,

Southern Africa, Australia, the Western USA, parts of South America 
(Peru, Chile), and the Western part of China (Deng et  al. 2015; 
Dupont et al. 2020). Parabolic troughs, central towers and parabolic 
dishes are the three leading solar thermal technologies (Wang et al. 
2017d). Parabolic troughs represented approximately 70% of new 
capacity in 2018 with the balance made up by central tower plants 
(Islam et al. 2018). Especially promising research directions are on 
tower-based designs that can achieve high temperatures, useful 
for industrial heat and energy storage (Mehos et  al. 2017), and 
direct steam generation designs (Islam et  al. 2018). Costs of CSP 
have fallen by nearly half since AR5 (Figure 6.8) albeit at a slower 
rate than PV. Since AR5, almost all new CSP plants have storage 
(Figure 6.9) (Thonig 2020).
Solar energy elicits favourable public responses in most countries 
(high confidence) (Mcgowan and Sauter 2005; Ma et  al. 2015;

Hanger et  al. 2016; Bessette and Arvai 2018; Jobin and Siegrist 
2018; Roddis et al. 2019; Hazboun and Boudet 2020). Solar energy is 
perceived as clean and environmentally friendly with few downsides 
(Faiers and Neame 2006; Whitmarsh et al. 2011b). Key motivations 
for homeowners to adopt PV systems are expected financial gains, 
environmental benefits, the desire to become more self-sufficient, 
and peer expectations (Korcaj et al. 2015; Vasseur and Kemp 2015; 
Palm 2017). Hence, the observability of PV systems can facilitate 
adoption (Boudet 2019). The main barriers to the adoption of solar PV 
by households are its high upfront costs, aesthetics, landlord-tenant 
incentives, and concerns about performance and reliability (Faiers 
and Neame 2006; Whitmarsh et al. 2011b; Vasseur and Kemp 2015).

634
Chapter 6 Energy Systems66.4.2.2 Wind Energy
Wind power is increasingly competitive with other forms of electricity 
generation and is the low-cost option in many applications (high 
confidence). Costs have declined by 18% and 40% on land and 
offshore since 2015 (high confidence), and further reductions can be 
expected by 2030 (medium confidence). Critical areas for continued 
improvement are technology advancements and economies of scale 
(high confidence). Global future potential is primarily limited by 
onshore land availability in wind power-rich areas, lack of supporting 
infrastructure, grid integration, and access to finance (especially in 
developing countries) (high confidence).Energy from wind is abundant, and the estimated technical potentials 
surpass the total amount of energy needed to limit warming 
to well below 2°C (high confidence). Recent global estimates 
of potentially exploitable wind energy resource are in the range of

557–717 PWh  yr –1 (2005–2580 EJ yr –1) (Eurek et  al. 2017; Bosch 
et al. 2017, 2018; McKenna et al. 2022), or 20–30 times the 2017 
global electricity demand. Studies have suggested that ‘bottom-
up’ approaches may overestimate technical potentials (Miller et al. 
2015; Kleidon and Miller 2020). But even in the most conservative 
‘top-down’ approaches, the technical wind potential surpasses the 
amount needed to limit warming to well below 2°C (Bosch et al. 2017; 
Eurek et al. 2017; Volker et al. 2017). The projected climate change   046810121416Storage capacity (h)
2
–2
1980 1985 1995 2000 2005
Year operational2010 2015 2020 1990
Trough Tower Fresnel
Figure 6.9 | CSP plants by storage capacity in hours (vertical), year of installation (horizontal), and size of plant in MW (circle size).  Since AR5, almost all 
new CSP plants have storage (Thonig 2020). Source: with permission from https://csp.guru/metadata.html .

635
Energy Systems  Chapter 66mitigation from wind energy by 2100 ranges from 0.3°C–0.8°C 
depending on the precise socio-economic pathway and wind energy 
expansion scenario followed (Barthelmie and Pryor 2021). Wind 
resources are unevenly distributed over the globe and by time of 
the year (Petersen and Troen 2012), but potential hotspots exist on 
every continent (Figure 6.10) as expressed by the wind power density 
(a quantitative measure of wind energy available at any location). 
Technical potentials for onshore wind power vary considerably, often 
because of inconsistent assessments of suitability factors (McKenna 
et al. 2020). The potential for offshore wind power is larger than for 
onshore because offshore wind is stronger and less variable (Bosch 
et  al. 2018). Offshore wind is more expensive, however, because 
of higher costs for construction, maintenance, and transmission. 
Wind power varies at a range of time scales, from annual to sub-

seconds; the effects of local short-term variability can be offset by 
power plant control, flexible grid integration, and storage (Barra 
et al. 2021) (Section 6.4.3). In some regions, interannual variations in 
wind energy resources could be important for optimal power system 
design (Wohland et al. 2019a; Coker et al. 2020).
Wind power cost reductions (Figure 6.11) are driven mainly by larger 
capacity turbines, larger rotor diameters and taller hub heights  – 
larger swept areas increase the energy captured and the capacity 
factors for a given wind speed; taller towers provide access to higher 
wind speeds (Beiter et  al. 2021). All major onshore wind markets 
have experienced rapid growth in both rotor diameter (from 81.2 m in 2010 to 120 m in 2020) (IRENA 2021b), and average power 
ratings (from 1.9 MW in 2010 to 3 MW in 2020). The generation 
capacity of offshore wind turbines grew by a  factor of 3.7 in less 
than two decades, from 1.6 MW in 2000 to 6 MW in 2020 (Wiser

et  al. 2021). Floating foundations could revolutionise offshore 
wind power by tapping into the abundant wind potential in deeper 
waters. This technology is particularly important for regions where 
coastal waters are too deep for fixed-bottom wind turbines. Floating 
wind farms potentially offer economic and environmental benefits 
compared with fixed-bottom designs due to less-invasive activity on 
the seabed during installation, but the long-term ecological effects 
are unknown and meteorological conditions further offshore and 
in deeper waters are harsher on wind turbine components (IRENA 
2019c). A radical new class of wind energy converters has also been 
conceived under the name of airborne wind energy systems that can 
harvest strong, high-altitude winds (typically between 200–800m), 
which are inaccessible by traditional wind turbines (Cherubini et al. 
2015). This technology has seen development and testing of small 
devices (Watson et al. 2019).

Wind capacity factors have increased over the last decade (Figure 6.11). 
The capacity factor for onshore wind farms increased from 27% in 2010 
to 36% in 2020 (IRENA 2021a). The global average offshore capacity 
factor has decreased from a peak of 45% in 2017. This has been driven 
by the increased share of offshore development in China, where projects 
are often near-shore and use smaller wind turbines than in Europe 
Wind power density (100 m) [Wm–2]
0 200 400 600 800 1,000
Figure 6.10 | Mean wind power density [W m–2] at 100 m above ground level over land and within 100 km of the coastline.  Source: Global Wind Atlas, 
available at: https://globalwindatlas.info/.

636
Chapter 6 Energy Systems6(IRENA 2021b). Improvements in capacity factors also come from 
increased functionality of wind turbines and wind farms. Manufactures 
can adapt the wind turbine generator to the wind conditions. Turbines 
for windy sites have smaller generators and smaller specific capacity 
per  rotor area, and therefore operate more efficiently and reach full 
capacity for a longer time period (Rohrig et al. 2019).
Electricity from onshore wind is less expensive than electricity 
generated from fossil fuels in a  growing number of markets (high 
confidence). The global average LCOE onshore declined by 38% from 
2010 to 2020 (Figure 6.11), reaching USD0.039 kWh–1. However, the 
decrease in cost varies substantially by region. Since 2014, wind costs 
have declined more rapidly than the majority of experts predicted 
(Wiser et al. 2021). New modelling projects onshore wind LCOE of 
USD.037 kWh–1 by 2030 (Junginger et  al. 2020a), and additional

reductions of 37–39% have been predicted by 2050 (Wiser et  al. 
2021). The future cost of offshore wind is more uncertain because other aspects besides increases in capacity factors influence the cost 
(Junginger et al. 2020b).
The cost of the turbine (including the towers) makes up the largest 
component of wind LCOE. Total installed costs for both onshore 
and offshore wind farms have decreased since 2015 (Figure 6.11), 
but the total installed costs for onshore wind projects are very 
site- and market-specific, as reflected in the range of LCOEs. China, 
India, and  the USA have experienced the largest declines in total 
installed costs. In 2020, typical country-average total installed 
costs were around USD1150 kW–1 in China and India, and between 
USD1403–2472 kW–1 elsewhere (IRENA 2021b). Total installed costs 
of offshore wind farms declined by 12% between 2010 and 2020. 
But, because some of the new offshore wind projects have moved

to deeper waters and further offshore, there are considerable year-
to-year variations in their price (IRENA 2021b). Projects outside 
China in recent years have typically been built in deeper waters 3500
3000
2500
2000
1500
1000
500
0
010002000Cost [2020USD kW–1]Total installed cost Capacity factorOnshore wind energy, 2010–2020
Offshore wind energy, 2010–2020Levelised cost of electricity
Total installed cost Capacity factor Levelised cost of electricity
300040005000600070008000201019391851
16591647
1491
2012 2014 2016
Year Year Year
Year Year Year2018 2020
2010 2012 2014 2016 2018 2020
CF [%]55
50
45
40
35
30
25
20
55
50
45
40
35
30
25
202010 2012 2014 2016 2018 2020
LCOE [2020USD kW–1]Cost [2020USD kW–1]
CF [%]
LCOE [2020USD kW–1]0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
0.30
0.25
0.20
0.15
0.10
0.05
0.002010 2012 2014 2016 2018 2020
2010 2012 2014 2016 2018 202028
272932360.083
0.079 0.063
0.057
0.045
0.171
0.1390.142
0.114
0.0935390
50415323
4735

37233845
4245
42
Figure 6.11 | Global weighted average total installed costs, capacity factors, and LCOE for onshore (top) and offshore (bottom) wind power of existing 
power plants per year (2010–2020).  The shaded area represents the 5th and 95th percentiles, and the red dashed line represents the fossil fuel cost range. Source:  with 
permission from IRENA (2021a).

637
Energy Systems  Chapter 66(10–55 m) and up to 120 km offshore, compared to around 10 m in 
2001–2006, when distances rarely exceeded 20 km. With the shift to 
deeper waters and sites further from ports, the total installed costs of 
offshore wind farms rose, from an average of around USD2500 kW–1 
in 2000 to around USD5127 kW–1 by 2011–2014, before falling 
to around USD3185 kW–1 in 2020 (IRENA 2020a). The full cost of 
wind power includes the transmission and system integration costs 
(Sections 6.4.3 and 6.4.6). A new technology in development is 
the co-location of wind and solar PV power farms, also known as 
hybrid power plants. Co-locating wind, solar PV, and batteries can 
lead to synergies in electricity generation, infrastructure, and land 
usage, which may lower the overall plant cost compared to single 
technology systems (Lindberg et al. 2021).
Wind power plants pose relatively low environmental impact, but

sometimes locally significant ecological effects (high confidence). The 
environmental impact of wind technologies, including CO 2 emissions, 
is concentrated in the manufacturing, transport, and building stage 
and in disposal as the end-of-life of wind turbines is reached (Liu and 
Barlow 2017; Mishnaevsky 2021). The operation of wind turbines 
produces no waste or pollutants. The LCA for wind turbines is strongly 
influenced by the operating lifetime, quality of wind resources, 
conversion efficiency, and size of the wind turbines (Kaldellis and 
Apostolou 2017; Laurent et  al. 2018). All wind power technologies 
repay their carbon footprint in less than a year (Bonou et al. 2016).
Wind farms can cause local ecological impacts, including on animal 
habitat and movements, biological concerns, bird and bat fatalities 
from collisions with rotating blades, and health concerns (Morrison 
and Sinclair 2004). The impacts on animal habitats and collisions can

be resolved or reduced by selectively stopping some wind turbines 
in high-risk locations, often without affecting the productivity of 
the wind farm (de Lucas et al. 2012). Many countries now require 
environmental studies of impacts of wind turbines on wildlife prior to project development, and, in some regions, shutdowns are 
required during active bird migration (de Lucas et al. 2012). Offshore 
wind farms can also impact migratory birds and other sea species 
(Hooper et al. 2017). Floating foundations pose lower environmental 
impacts at build stage (IRENA 2019c), but their cumulative long-term 
impacts are unclear (Goodale and Milman 2016). Recent studies find 
weak associations between wind farm noise and measures of long-
term human health (Poulsen et al. 2018a, b, 2019a, b).
Public support for onshore and particularly offshore wind energy 
is generally high, although people may oppose specific wind farm 
projects (high confidence) (e.g.,  Bell et  al. 2005; Batel and Devine-

Wright 2015; Rand and Hoen 2017; Steg 2018). People generally 
believe that wind energy is associated with environmental benefits 
and that it is relatively cheap. Yet, some people believe wind turbines 
can cause noise and visual aesthetic pollution, threaten places of 
symbolic value (Devine-Wright and Wiersma 2020; Russell et al. 2020), 
and have adverse effects on wildlife (Bates and Firestone 2015), which 
challenges public acceptability (Rand and Hoen 2017). Support for 
local wind projects is higher when people believe fair decision-making 
procedures have been implemented (Dietz and  Stern  2008; Aitken 
2010a). Evidence is mixed whether distance from wind turbines or 
financial compensation increases public acceptability of wind turbines 
(Cass et al. 2010; Rand and Hoen 2017; Rudolph et al. 2018; Hoen 
et al. 2019). Offshore wind farms projects have higher public support, 
but can also face resistance (Bidwell 2017; Rudolph et al. 2018).

Common economic barriers to wind development are high initial 
cost of capital, long payback periods, and inadequate access to 
capital. Optimal wind energy expansion is most likely to occur in 
the presence of a political commitment to establish, maintain, and 
improve financial support instruments, technological efforts to 
support a  local supply chains, and grid investments integrate VRE 
electricity (Diógenes et al. 2020).
Box 6.4 | Critical Strategic Minerals and a Low-carbon Energy System Transition
The secure supply of many metals and minerals (e.g., cobalt, copper, lithium, and rare earth elements (REEs)) is critical to supporting 
a low-emissions energy system transition (Sovacool et al. 2020). A low-carbon energy system transition will increase the demand for 
these minerals to be used in technologies like wind turbines, PV cells, and batteries (World Bank 2020). Reliance on these minerals has

raised questions about possible constraints to a low-carbon energy system transition, including supply chain disruptions (Chapter 10.6). 
Concerns have also been raised about mining for these materials, which frequently results in severe environmental impacts (Sonter 
et al. 2020), and metal production itself is energy-intensive and difficult to decarbonise (Sovacool et al. 2020).
Wind energy depends on two critical REEs – neodymium and dysprosium – used in magnets in high-performance generators (Pavel 
et al. 2017; Li et al. 2020b). Silicon-wafer-based solar PV, which accounted for 95% of PV production in 2020, does not use REEs 
but utilises aluminium, copper, and silver (IEA 2021a). Lithium, nickel, cobalt, and phosphorous are used in batteries. Many critical 
minerals are used in EVs, including aluminium and copper in manufacturing the necessary EV charging infrastructure, and neodymium 
in permanent magnet motors.

These strategic minerals are found in a limited number of countries, and concerns have been raised that geopolitical factors could 
disrupt the supply chain necessary for a low-carbon energy system transition. However, excluding cobalt and lithium, no single country 
holds more than a third of the world reserves. The known supply of some strategic minerals is still close to 600 years at current levels 
of demand (BP 2020), but increased demand would cut more quickly into supplies.

638
Chapter 6 Energy Systems66.4.2.3 Hydroelectric Power
Hydropower is technically mature, proved worldwide as a  primary 
source of renewable electricity, and may be used to balance 
electricity supply by providing flexibility and storage. The LCOE of 
hydropower is lower than the cheapest new fossil fuel-fired option. 
However, the future mitigation potential of hydropower depends on 
minimising environmental and social impacts during the planning 
stages, reducing the risks of dam failures, and modernising the ageing 
hydropower fleet to increase generation capacity and flexibility 
(high confidence).
Estimates of global gross theoretical available hydropower potential 
varies from 31–128 PWh yr –1 (112–460 EJ yr –1), exceeding total 
electricity production in 2018 (Banerjee et  al.  2017; BP 2020; IEA 2021d). This potential is distributed over 11.8 million locations 
(Figure  6.12), but many of the locations cannot be developed for

(current) technical, economic, or political reasons. The estimated 
technical potential of hydropower is 8–30 PWh yr –1 (29–108 EJ yr –1), 
and its estimated economic potential is 8–15 PWh yr –1 (29–54 EJ yr –1) 
(Zhou et  al. 2015; van Vliet et  al. 2016c). Actual hydropower 
generation in 2019 was 4.2 PWh (15.3 EJ), providing about 16% of 
global electricity and 43% of global electricity from renewables (BP 
2020; IEA 2020f; Killingtveit 2020). Asia holds the largest hydropower 
potential (48%), followed by South America (19%) (Hoes et al. 2017).
Hydropower is a mature technology with locally adapted solutions 
(high confidence) (Zhou et  al. 2015; Killingtveit 2020). The peak 
efficiency of hydroelectric plants is greater than 85%. Hydropower 
plants without storage or with small storage typically produce Box 6.4 (continued)
There are alternatives to the strategic minerals currently used to support a low-carbon transition. Wind turbines can be manufactured

without permanent magnets to reduce the need for strategic minerals, but the production costs are higher, and their efficiency is 
reduced (Månberger and Stenqvist 2018). Alternatives to silicon, such as thin films, could be used to produce PVs. Thin-films use much 
less material than silicon-based PV, but they contain other potentially critical metals like tellurium, cadmium, and gallium. Alternatives 
to lithium-ion batteries, such as sodium-ion batteries, are becoming more practical and feasible (Sovacool et al. 2020).
Gross hydropower potential [GWh yr–1]
1 10 100 1000 10,000
Figure 6.12 | Global map of gross hydropower potential distribution [GWh yr –1]. Source: data from Hoes et al. (2017).

639
Energy Systems  Chapter 66a few kWs to 10 MWs (examples of plants producing higher amounts 
do exist), and are useful for providing electricity at a  scale from 
households to small communities (El Bassam et  al. 2013; Towler 
2014). However, hydropower plants without or with small storage 
may be susceptible to climate variability, especially droughts, when 
the amount of water may not be sufficient to generate electricity 
(Premalatha et al. 2014) (Section 6.5).
Hydropower plants with storage may produce 10 GW, reaching over 
100 TWh yr –1 (0.36 EJ yr –1), but generally require large areas. Pumped 
storage hydropower stores energy by pumping water to higher 
reservoirs during low-demand periods (Killingtveit 2020). The storage 
in hydropower systems provides flexibility to compensate for rapid 
variations in electricity loads and supplies. The regulating characteristics 
of the storage play an important role in assuring continuity of energy

supply from renewable sources (Yang et al. 2018b).
Hydropower is one of the lowest-cost electricity technologies 
(Mukheibir 2013; IRENA 2021b). Its operation and maintenance 
costs are typically 2–2.5% of the investment costs per kW yr –1 for 
a lifetime of 40–80 years (Killingtveit 2020). Construction costs are 
site-specific. The total cost for an installed large hydropower project 
varies from USD10,600–804,500 kW–1 if the site is located far 
away from transmission lines, roads, and infrastructure. Investment 
costs increase for small hydropower plants and may be as high as 
USD100,000 kW–1or more for the installation of plants of less than 
1 MW – 20% to 80% more than for large hydropower plants (IRENA 
2015). During the past 100 years, total installed costs and LCOE have 
risen by a few percent, but the LCOE of hydropower remains lower 
than the cheapest new fossil fuel-fired option (IRENA 2019b, 2021).
Hydroelectric power plants may pose serious environmental and

societal impacts (high confidence) (McCartney 2009). Dams may lead 
to fragmentation of ecological habitats because they act as barriers 
for migration of fish and other land and water-borne fauna, sediments, 
and water flow. These barriers can be mitigated by sediment passes 
and fish migration aids, and with provision of environmental flows. 
Below dams, there can be considerable alterations to vegetation, 
natural river flows, retention of sediments and nutrients, and water 
quality and temperature. Construction of large reservoirs leads to loss 
of land, which may result in social and environmental consequences. 
Minimising societal and environmental impacts requires taking 
into account local physical, environmental, climatological, social, 
economic, and political aspects during the planning stage (Killingtveit 
2020). Moreover, when large areas of land are flooded by dam 
construction, they generate GHGs (Prairie et al. 2018; Phyoe and Wang

2019; Maavara et al. 2020). On the other hand, hydropower provides 
flexible, competitive low-emission electricity, local economic benefits 
(e.g., by increasing irrigation and electricity production in developing 
countries), and ancillary services such as municipal water supply, 
irrigation and drought management, navigation and recreation, 
and flood control (IRENA 2021b). However, the long-term economic 
benefits to communities affected by reservoirs are a subject of debate 
(de Faria et al. 2017; Catolico et al. 2021).
Public support for hydroelectric energy is generally high (Steg 
2018), and higher than support for coal, gas, and nuclear. Yet, public support for hydro seems to differ for existing and new projects (high 
confidence). Public support is generally high for small- and medium-
scale hydropower in regions where hydropower was historically used 
(Gormally et al. 2014). Additionally, there is high support for existing

large hydropower projects in Switzerland (Rudolf et al. 2014; Plum 
et al. 2019), Canada (Boyd et al. 2019), and Norway (Karlstrøm and 
Ryghaug 2014), where it is a  trusted and common energy source. 
Public support seems lower for new hydropower projects (Hazboun 
and Boudet 2020), and the construction of new large hydropower 
plants has been met with strong resistance in some areas (Vince 
2010; Bronfman et al. 2015). People generally perceive hydroelectric 
energy as clean and a  non-contributor to climate change and 
environmental pollution (Kaldellis et  al. 2013). For example, in 
Sweden, people believed that existing hydropower projects have as 
few negative environmental impacts as solar, and even less than wind 
(Ek 2005). However, in areas where the construction of new large-
scale hydroelectric energy is met with resistance, people believe that 
electricity generation from hydro can cause environmental, social,

and personal risks (Bronfman et al. 2012; Kaldellis et al. 2013).
The construction time of hydroelectric power plants is longer than 
many other renewable technologies, and that construction time 
may be extended by the additional time it takes to fill the reservoir. 
This extended timeline can create uncertainty in the completion 
of the project. The uncertainty is due to insecurity in year-to-year 
variations in precipitation and the water inflows required to fill 
reservoirs. This is especially critical in the case of trans-boundary 
hydroelectric power plants, where filling up the reservoirs can have 
large implications on downstream users in other nations. As a result 
of social and environmental constraints, only a  small fraction of 
potential economic hydropower projects can be developed, especially 
in developed countries. Many developing countries have major 
undeveloped hydropower potential, and there are opportunities to

develop hydropower combined with other economic activities such as 
irrigation (Lacombe et al. 2014). Competition for hydropower across 
country borders can lead to conflict, which could be exacerbated if 
climate alters rainfall and streamflow (Ito et al. 2016).
6.4.2.4 Nuclear Energy
Nuclear power can deliver low-carbon energy at scale (high 
confidence). Doing so will require improvements in managing 
construction of reactor designs that hold the promise of lower costs 
and broader use (medium confidence). At the same time, nuclear 
power continues to be affected by cost overruns, high upfront 
investment needs, challenges with final disposal of radioactive 
waste, and varying public acceptance and political support levels 
There are sufficient resources for substantially increasing nuclear 
deployment (medium confidence). Estimates for identified uranium 
resources have been increasing steadily over the years. Conventional

uranium resources have been estimated to be sufficient for over 
130 years of supply at current levels of use; 100 years were estimated 
in 2009 (Hahn 1983; NEA/IAEA 2021). In the case of future uranium 
resource scarcity, thorium or recycling of spent fuel might be used 
as alternatives. Interest in these alternatives has waned with better

640
Chapter 6 Energy Systems6understanding of uranium deposits, their availability, and low prices 
(IAEA 2005; OECD NEA 2015).
There are several possible nuclear technology options for the period 
from 2030 to 2050 (medium confidence). In addition to electricity, 
nuclear can also be used to produce low-carbon hydrogen and 
freshwater (Kavvadias and Khamis 2014; Kayfeci et al. 2019).
• Large reactors. The nuclear industry has entered a new phase of 
reactor construction, based on evolutionary designs. These reactors 
achieve improvements over previous designs through small to 
moderate modifications, including improved redundancy, increased 
application of passive safety features, and significant improvements 
to containment design to reduce the risk of a major accident (MIT 
2018). Examples include European  – EPR, Korean  – APR1400, 
USA – AP1000, Chinese – HPR1000 or Russian – VVER-1200.
• Long-term operation (LTO) of the current fleet. Continued

production from nuclear power will depend in part on life 
extensions of the existing fleet. By the end of 2020, two-
thirds of nuclear power reactors will have been operational for 
over 30  years. The design lifetime of most of existing reactors 
is 30–40  years. Engineering assessments have established 
that reactors can operate safely for longer if key replaceable 
components (e.g.,  steam generator, mechanical and electrical 
equipment, instrumentation and control parts) are changed or 
refurbished (IAEA 2018). The first lifetime extension considered 
in most of the countries typically is 10–20 years (IEA 2020j).
• Small modular reactors (SMR). There are more than 70 SMR 
designs at different stages of consideration and development, 
from the conceptual phase to licensing and construction of first-
of-a-kind facilities (IAEA 2020). Due to smaller unit sizes, the 
SMRs are expected to have lower total investment costs, although

the cost per unit of generation might be higher than conventional 
large reactors (Mignacca and Locatelli 2020). Modularity and off-
site pre-production may allow greater efficiency in construction, 
shorter delivery times, and overall cost optimisation (IEA 2019c). 
SMR designs aim to offer an increased load-following capability 
that makes them suitable to operate in smaller systems and in 
systems with increasing shares of VRE sources. Their market 
development by the early 2030s will strongly depend on the 
successful deployment of prototypes during the 2020s.
Nuclear power costs vary substantially across countries (high 
confidence). First-of-a-kind projects under construction in Northern 
America and Europe have been marked by delays and costs overruns 
(Berthelemy and Rangel 2015). Construction times have exceeded 
13–15 years and cost has surpassed three to four times initial budget 
estimates (IEA 2020j). In contrast, most of the recent projects in

Eastern Asia (with construction starts from 2012) were implemented 
within five to six years (IAEA 2021). In addition to region-specific 
factors, future nuclear costs will depend on the ability to benefit from 
the accumulated experience in controlling the main drivers of cost. 
These cost drivers fall into four categories: design maturity; project 
management; regulatory stability and predictability; and multi-unit 
and series effects (NEA 2020). With lessons learned from first-of-a-kind 
projects, the cost of electricity for new builds are expected to be in the 
range of USD42–102 MWh–1 depending on the region (IEA 2020j).Lifetime extensions are significantly cheaper than new builds and cost 
competitive with other low-carbon technologies. The overnight cost of 
lifetime extensions is estimated in the range of USD390–630 kWe–1 
for Europe and North America, and the LCOE in the range of 
USD30–36 MWh–1 for extensions of 10–20 years (IEA 2020j).

Cost-cutting opportunities, such as design standardisation and 
innovations in construction approaches, are expected to make SMRs 
competitive against large reactors by 2040 (Rubio and Tricot 2016) 
(medium confidence). As SMRs are under development, there is 
substantial uncertainty regarding the construction costs. Vendors 
have estimated first-of-a-kind LCOEs at USD131–190 MWh–1. Effects 
of learning for nth-of-a-kind SMR are anticipated to reduce the first-
of-a-kind LCOE by 19–32%.
Despite low probabilities, the potential for major nuclear accidents 
exists, and the radiation exposure impacts could be large and long-
lasting (Steinhauser et  al. 2014). However, new reactor designs 
with passive and enhanced safety systems reduce the risk of such 
accidents significantly (high confidence). The (normal) activity of 
a nuclear reactor results in low volumes of radioactive waste, which 
requires strictly controlled and regulated disposal. On a global scale,

roughly 421 kt of spent nuclear fuel have been produced since 
1971 (IEA 2014). Out of this volume, 2–3% is high-level radioactive 
waste, which presents challenges in terms of radiotoxicity and decay 
longevity, and ultimately entails permanent disposal.
Nuclear energy is found to be favourable regarding land occupation 
(Cheng and Hammond 2017; Luderer et  al. 2019) and ecological 
impacts (Brook and Bradshaw 2015; Gibon et  al. 2017). Similarly, 
bulk material requirements per  unit of energy produced are low 
(e.g., aluminum, copper, iron, rare earth metals) (Vidal et al. 2013; 
Luderer et  al. 2019). Water-intensive inland nuclear power plants 
may contribute to localised water stress and competition for water 
uses. The choice of cooling systems (closed-loop instead of once-
through) can significantly moderate withdrawal rates of freshwater 
(Meldrum et al. 2013; Fricko et al. 2016; Mouratiadou et al. 2016; Jin

et al. 2019). Reactors situated on the seashore are not affected by 
water scarcity issues (Abousahl et al. 2021). Lifecycle analysis (LCA) 
studies suggest that the overall impacts on human health (in terms 
of disability adjusted life years (DALYs)) from the normal operation 
of nuclear power plants are substantially lower than those caused 
by fossil fuel technologies and are comparable to renewable energy 
sources (Treyer et al. 2014; Gibon et al. 2017).
Nuclear power continues to suffer from limited public and political 
support in some countries (high confidence). Public support for 
nuclear energy is consistently lower than for renewable energy and 
natural gas, and in many countries as low as support for energy from 
coal and oil (Corner et al. 2011; Pampel 2011; Hobman and Ashworth 
2013). The major nuclear accidents (i.e., Three Mile Island, Chernobyl, 
and Fukushima) decreased public support (Poortinga et  al. 2013;

Bird et al. 2014). The public remains concerned about the safety risks 
of nuclear power plants and radioactive materials (Pampel 2011; 
Bird  et  al. 2014; Tsujikawa et  al. 2016). At the same time, some 
groups see nuclear energy as a reliable energy source, beneficial for 
the economy and helpful in climate change mitigation. Public support

641
Energy Systems  Chapter 66for nuclear energy is higher when people are concerned about energy 
security, including concerns about the availability of energy and high 
energy prices (Groot et  al. 2013; Gupta et  al. 2019b), and when 
they expect local benefit (Wang et  al. 2020c). Public support also 
increases when trust in managing bodies is higher (de Groot and 
Steg 2011). Similarly, transparent and participative decision-making 
processes enhance perceived procedural fairness and public support 
(Sjoberg 2004).
Because of the sheer scale of the investment required (individual 
projects can exceed USD10 billion in value), nearly 90% of nuclear 
power plants under construction are run by state-owned or controlled 
companies, with governments assuming significant part of the risks 
and costs. For countries that choose nuclear power in their energy 
portfolio, stable political conditions and support, clear regulatory

regimes, and adequate financial framework are crucial for successful 
and efficient implementation.
Many countries have adopted technology-specific policies for 
low-carbon energy courses, and these policies influence the 
competitiveness of nuclear power. For example, feed-in-tariffs and 
feed-in premiums for renewables widely applied in the EU (Kitzing 
et al. 2012) or renewable portfolio standards in the USA (Barbose 
et al. 2016) impact wholesale electricity price (leading occasionally to 
low or even negative prices), which affects the revenues of existing 
nuclear and other plants (Bruninx et al. 2013; Newbery et al. 2018; 
Lesser 2019).
Nuclear power’s long-term viability may hinge on demonstrating to 
the public and investors that there is a long-term solution to spent 
nuclear fuel. Evidence from countries steadily progressing towards 
first final disposals – Finland, Sweden and France – suggests that 
broad political support, coherent nuclear waste policies, and a well-

managed, consensus-based decision-making process are critical 
for accelerating this process (Metlay 2016). Proliferation concerns 
surrounding nuclear power are related to fuel cycle (i.e., uranium 
enrichment and spent fuel processing). These processes are 
implemented in a  very limited number of countries following 
strict national and internationals norms and rules, such as the 
International Atomic Energy Agency (IAEA) guidelines, treaties and 
conventions. Most of the countries that might introduce nuclear 
power in the future for their climate change mitigation benefits 
do not envision developing their own full fuel cycle, significantly 
reducing any risks that might be linked to proliferation (IAEA 
2014, 2019).6.4.2.5 Carbon Dioxide Capture, Utilisation and Storage
Since AR5, there have been increased efforts to develop novel 
platforms that reduce the energy penalty associated with CO 2 capture, 
develop CO 2 utilisation pathways as a substitute to geologic storage,

and establish global policies to support CCS (high confidence). CCS 
can be used within electricity and other sectors. While it increases the 
cost of electricity, CCS has the potential to contribute significantly to 
low-carbon energy system transitions (IPCC 2018).
The theoretical global geologic storage potential is about 
10,000 GtCO 2, with more than 80% of this capacity existing in saline 
aquifers (medium confidence). Not all the storage capacity is usable 
because geologic and engineering factors limit the actual storage 
capacity to an order of magnitude below the theoretical potential, 
which is still more than the CO 2 storage requirement through 2100 
to limit temperature change to 1.5°C (Martin-Roberts et  al. 2021) 
(high confidence). One of the key limiting factors associated with 
geologic CO 2 storage is the global distribution of storage capacity 
(Table  6.2). Most of the available storage capacity exists in saline

aquifers. Capacity in oil and gas reservoirs and coalbed methane 
fields is limited. Storage potential in the USA alone is >1000 GtCO 2, 
which is more than 10% of the world total (NETL 2015). The Middle 
East has more than 50% of global enhanced oil recovery potential 
(Selosse and Ricci 2017). It is likely that oil and gas reservoirs will 
be developed as geologic sinks before saline aquifers because of 
existing infrastructure and extensive subsurface data (Alcalde et al. 
2019; Hastings and Smith 2020). Notably, not all geologic storage is 
utilisable. In places with limited geologic storage, international CCS 
chains are being considered, where sources and sinks of CO 2 are 
located in two or more countries (Sharma and Xu 2021). For economic 
long-term storage, the desirable conditions are a depth of 800–3000 
m, thickness of greater than 50 m and permeability greater than 
500 mD (Chadwick et al. 2008; Singh et al. 2021). Even in reservoirs

with large storage potential, the rate of injection might be limited 
by the subsurface pressure of the reservoir (Baik et al. 2018). It is 
estimated that geologic sequestration is reliable with overall leakage 
rates at <0.001% yr –1 (Alcalde et al. 2018). In many cases, geological 
storage resources are not located close to CO 2 sources, increasing 
costs and reducing viability (Garg et al. 2017a).
CO2 utilisation (CCU) – instead of geologic storage – could present 
an alternative method of decarbonisation (high confidence). The 
global CO 2 utilisation potential, however, is currently limited to 
1–2 GtCO 2 yr –1 for use of CO 2 as a feedstock (Hepburn et al. 2019; 
Table 6.2 | Geologic storage potential across underground formations globally.  These represent order-of-magnitude estimates. Data: Selosse and Ricci (2017).
Reservoir type Africa Australia Canada China CSA EEU FSU India MEA Mexico ODA USA WEU
Enhanced oil recovery 3 0 3 1 8 2 15 0 38 0 1 8 0

Depleted oil and gas fields 20 8 19 1 33 2 191 0 252 22 47 32 37
Enhanced coalbed methane recovery 8 30 16 16 0 2 26 8 0 0 24 90 12
Deep saline aquifers 1000 500 667 500 1000 250 1000 500 500 250 1015 1000 250
CSA: Central and South America, EEU: Eastern Europe, FSU: Former Soviet Union, MEA: Middle East, ODA: Other Asia (except China and India), WEU: Western Europe.

642
Chapter 6 Energy Systems6Kätelhön et  al. 2019) but could increase to 20 GtCO 2 by the mid-
century (medium confidence). CCU involves using CO 2 as a feedstock 
to synthesise products of economic value and as substitute to fossil 
feedstock. However, several CO 2 utilisation avenues might be limited 
by energy availability. Depending on the utilisation pathway, the 
CO2 may be considered sequestered for centuries (e.g.,  cement 
curing, aggregates), decades (plastics), or only a  few days or 
months (e.g., fuels) (Hepburn et al. 2019). Moreover, when carbon-
rich fuel end-products are combusted, CO 2 is emitted back into the 
atmosphere. Because of the presence of several industrial clusters 
(regions with high density of industrial infrastructure) globally, 
a  number of regions demonstrate locations where CO 2 utilisation 
potential could be matched with large point sources of CO 2 (Wei 
The technological development for several CO 2 utilisation pathways

is still in the laboratory, prototype, and pilot phases, while others have 
been fully commercialised (such as urea manufacturing). Technology 
development in some end uses is limited by purity requirements 
for CO 2 as a feedstock. The efficacy of CCU processes depends on 
additional technological constraints such as CO 2 purity and pressure 
requirements. For instance, urea production requires CO 2 pressurised 
to 122 bar and purified to 99.9%. While most utilisation pathways 
require purity levels of 95–99%, algae production may be carried out 
with atmospheric CO 2 (Voldsund et al. 2016; Ho et al. 2019).
Existing post-combustion approaches relying on absorption are 
technologically ready for full-scale deployment (high confidence). 
More novel approaches using membranes and chemical looping that 
might reduce the energy penalty associated with absorption are in 
different stages of development – ranging from laboratory phase to

prototype phase (Abanades et al. 2015) (high confidence). There has 
been significant progress in post-combustion capture technologies 
that used absorption in solvents such as monoethanolamine (MEA). 
There are commercial-scale application of solvent-based absorption 
at two electricity generating facilities – Boundary Dam since 2015 
and Petra Nova (temporarily suspended) since 2017, with capacities 
of 1 and 1.6  MtCO 2 yr –1 respectively (Mantripragada et  al. 2019; 
Giannaris et al. 2020a). Several second- and third-generation capture 
technologies are being developed with the aim of not just lowering 
costs but also enhancing other performance characteristics such 
as improved ramp-up and lower water consumption. These include 
processes such as chemical looping, which also has the advantage 
of being capable of co-firing with biomass with a better efficiency 
(Bhave et al. 2017; Yang et al. 2019). Another important technological

development is the Allam cycle, which utilises CO 2 as a working fluid and operates based on oxy-combustion capture. Applications using 
the Allam Cycle can deliver net energy efficiency greater than 50% 
and nearly 100% CO 2 capture, but they are quite sensitive to oxygen 
and CO 2 purity needs (Scaccabarozzi et al. 2016; Ferrari et al. 2017).
CO2 capture costs present a  key challenge, remaining higher than 
USD50 tCO 2–1 for most technologies and regions; novel technologies 
could help reduce some costs (high confidence). The capital cost of 
a coal or gas electricity generation facility with CCS is almost double 
that of one without CCS (Rubin et al. 2015; Zhai and Rubin 2016; 
Bui et al. 2018). Additionally, the energy penalty increases the fuel 
requirement for electricity generation by 13–44%, leading to further 
cost increases (Table 6.3).
In addition to reductions in capture costs, other approaches to reduce

CCS costs rely on utilising the revenues from co-products such as oil, 
gas, or methanol, and on clustering of large-point sources to reduce 
infrastructure costs. The potential for such reductions is limited in 
several regions due to low sink availability, but it could jump-start initial 
investments (medium confidence). Injecting CO 2 into hydrocarbon 
formations for enhanced oil or gas recovery can produce revenues and 
lower costs (Edwards and Celia 2018). While enhanced oil recovery 
potential is <5% of the actual CCS needs, they can enable early pilot 
and demonstration projects (Núñez-López and Moskal 2019; Núñez-
López et al. 2019). Substantial portions of CO 2 are effectively stored 
during enhanced oil recovery (Menefee and Ellis 2020; Sminchak et al. 
2020). By clustering together of several CO 2 sources, overall costs may 
be reduced by USD10 tCO 2–1 (Abotalib et al. 2016; Garg et al. 2017a), 
but geographical circumstances determine the prospects of these

cost reductions via economies of scale. The major pathways for CO 2 
utilisation via methanol, methane, liquid fuel production, and cement 
curing have costs greater than USD500 tCO 2–1 (Hepburn et al. 2019). 
The success of these pathways therefore depends on the value of such 
fuels and on the values of other alternatives.
The public is largely unfamiliar with carbon capture, use and storage 
technologies (L’Orange Seigo et al. 2014; Tcvetkov et al. 2019) (high 
confidence), and many people may not have formed stable attitudes 
and risk perceptions regarding these technologies (Daamen et  al. 
2006; Jones et al. 2015; Van Heek et al. 2017) (medium confidence). 
In general, low support has been reported for CCS technologies (Allen 
and Chatterton 2013; Demski et  al. 2017). When presented with 
neutral information on CCS, people favour other mitigation options 
such as renewable energy and energy efficiency (de Best-Waldhober

et al. 2009; Scheer et al. 2013; Karlstrøm and Ryghaug 2014). Although 
few totally reject CCS, specific CCS projects have faced strong local 
Table 6.3 | Costs and efficiency parameters of CCS in electric power plants.  Data: Muratori et al. (2017a).
Capital cost [USD kW–1] Efficiency [%] CO2 capture cost [USD tCO 2–1] CO2 avoided cost [USD tCO 2–1]
Coal (steam plant) + CCS 5800 28% 63 88
Coal (IGCC) + CCS 6600 32% 61 106
Natural gas (CC) + CCS 2100 42% 91 33
Oil (CC) + CCS 2600 39% 105 95
Biomass (steam plant) + CCS 7700 18% 72 244
Biomass (IGCC) + CCS 8850 25% 66 242

643
Energy Systems  Chapter 66resistance, which has contributed to the cancellation of CCS projects 
(Terwel et al. 2012; L’Orange Seigo et al. 2014). Communities may 
also consider CCU to be lower-risk and view it more favourably than 
CCS (Arning et al. 2019).
CCS requires considerable increases in some resources and chemicals, 
most notably water. Power plants with CCS could shut down 
periodically due to water scarcity. In several cases, water withdrawals 
for CCS are 25–200% higher than plants without CCS (Rosa et al. 
2020b; Yang et al. 2020) due to energy penalty and cooling duty. The 
increase is slightly lower for non-absorption technologies. In regions 
prone to water scarcity such as the Southwestern USA or Southeast 
Asia, this may limit deployment and result in power plant shutdowns 
during the summer months (Liu et al. 2019b; Wang et al. 2019c). The 
water use could be managed by changing heat integration strategies

and implementing reuse of wastewater (Magneschi et  al. 2017; 
Giannaris et al. 2020b).
Because CCS always adds cost, policy instruments are required for it 
to be widely deployed (high confidence). Relevant policy instruments 
include financial instruments such as emission certification and 
trading, legally enforced emission restraints, and carbon pricing 
(Haszeldine 2016; Kang et al. 2020). There are some recent examples 
of policy instruments specifically focused on promoting CCS. The 
recent 45Q tax credits in the USA offer nationwide tax credits for CO2 capture projects above USD35–50 tCO 2–1 which offset CO 2 
capture costs at some efficient plants (Esposito et al. 2019). Similarly, 
California’s low-carbon fuel standard offers benefits for CO 2 capture 
at some industrial facilities such as biorefineries and refineries (Von 
Wald et al. 2020).
6.4.2.6 Bioenergy
Bioenergy has the potential to be a  high-value and large-scale

mitigation option to support many different parts of the energy 
system. Bioenergy could be particularly valuable for sectors with 
limited alternatives to fossil fuels (e.g.,  aviation, heavy industry), 
production of chemicals and products, and, potentially, in carbon 
dioxide removal (CDR) via BECCS or biochar. While traditional 
biomass and first-generation biofuels are widely used today, the 
technology for large-scale production from advanced processes is not 
competitive, and growing dedicated bioenergy crops raises a broad 
set of sustainability concerns. Its long-term role in low-carbon 
energy systems is therefore uncertain (high confidence). (Note that 
this section focuses on the key technological developments for 
deployment of commercial bioenergy.)
Bioenergy is versatile: technology pathways exist to produce multiple 
energy carriers from biomass – electricity, liquid fuels, gaseous fuels, 
hydrogen, and solid fuels – as well as other value-added products

Figure 6.13 | Costs and potential for different CO 2 utilisation pathways.  Source: with permission from Hepburn et al. (2019).

644
Chapter 6 Energy Systems6(high confidence). Different chemical and biological conversion 
pathways exist to convert diverse biomass feedstocks into multiple 
final energy carriers (Figure 6.14). Currently, biomass is mostly used 
to produce heat, or for cooking purposes (traditional biomass), 
electricity, or first-generation sugar-based biofuels (e.g.,  ethanol 
produced via fermentation), as well as biodiesel produced from 
vegetable oils and animal fats. Electricity generated from biomass 
contributes about 3% of global generation. Tens of billions of gallons 
of first-generation biofuels are produced per  year. The processing 
requirements (drying, dewatering, pelletising) of different feedstocks 
for producing electricity from biomass are energy-intensive, and when 
utilising current power plants, the efficiency is around 22%, with an 
increase up to 28% with advanced technologies (Zhang et al. 2020).
Scaling up bioenergy use will require advanced technologies such

as gasification, Fischer-Tropsch processing, hydrothermal liquefaction 
(HTL), and pyrolysis. These pathways could deliver several final 
energy carriers starting from multiple feedstocks, including forest 
biomass, dedicated cellulosic feedstocks, crop residues, and wastes 
(Figure  6.14). While potentially cost-competitive in the future, 
pyrolysis, Fischer-Tropsch, and HTL are not currently cost-competitive 
(IEA 2018c; Molino et al. 2018; Prussi et al. 2019), and scaling-up 
these processes will require robust business strategies and optimised 
use of co-products (Lee and Lavoie 2013). Advanced biofuels 
production processes are at the pilot or demonstration stage and will require substantial breakthroughs or market changes to become 
competitive. Moreover, fuels produced from these processes require 
upgrading to reach ‘drop-in’ conditions – that is, conditions in which 
they may be used directly consistent with current standards in

existing technologies (van Dyk et al. 2019). Additional opportunities 
exist to co-optimise second-generation biofuels and engines (Ostadi 
et  al. 2019; Salman et  al. 2020). In addition, gaseous wastes, or 
high-moisture biomass, such as dairy manure, wastewater sludge 
and organic municipal solid waste (MSW) could be utilised to 
produce renewable natural gas. Technologies for producing biogas 
(e.g.,  digestion) tend to be less efficient than thermochemical 
approaches and often produce large amounts of CO 2, requiring the 
produced fuels to undergo significant upgrading (Melara et al. 2020).
A major scale-up of bioenergy production will require dedicated 
production of advanced biofuels. First-generation biofuels produced 
directly from food crops or animal fats have limited potential 
and lower yield per  land area than advanced biofuels. Wastes 
and  residues (e.g.,  from agricultural, forestry, animal manure 
processing) or biomass grown on degraded, surplus, and marginal

land can provide opportunities for cost-effective and sustainable 
bioenergy at significant but limited scale (Morris et al. 2013; Saha 
and Eckelman 2018; Fajardy and Mac Dowell 2020; Spagnolo et al. 
2020). Assessing the potential for a major scale-up of purpose-grown 
bioenergy is challenging due to its far-reaching linkages to issues 
Forest biomass
– Forest management
– Manufacturing residue
– Shrub and chapparal
Gaseous waste + 
high-moisture biomass
– Wastewater sludge
– Algae
– Landﬁll/dairy gasMunicipal solid waste
– Dry waste
– Green waste
– Food wasteCellulosic biomass
– Dedicated biomass
– Crop residueGasiﬁcation
Combustion
Pyrolysis
Hydrothermal
liquefaction
Biogas
utilisationHydrogenKey end product
Compatible with CCS
Suitable for soil carbon
sequestrationGrid electricity Liquid fuels Biochar RNG
Figure 6.14 | Range of advanced bioenergy conversion pathways (excluding traditional biomass, direct heat generation, first-generation biofuels, and

non-energy products) based on feedstock, targeted end product, and compatibility with carbon dioxide removal (CDR) via carbon capture and storage 
(CCS) and soil carbon sequestration.  Source: modified with permission from Baker et al. (2020).

645
Energy Systems  Chapter 66beyond the energy sector, including competition with land for food 
production and forestry, water use, impacts on ecosystems, and land-
use change (IPCC 2020; Roe et al. 2021) (Chapter 12). These factors, 
rather than geophysical characteristics, largely define the potential 
for bioenergy and explain the difference in estimates of potential in 
the literature. Biomass resources are not always in close proximity 
to energy demand, necessitating additional infrastructure or means 
to  transport biomass or final bioenergy over larger distances and 
incur additional energy use (Baik et al. 2018; Singh et al. 2021).
An important feature of bioenergy is that it can be used to remove 
carbon from the atmosphere by capturing CO 2 in different parts of 
the conversion process and then permanently storing the CO 2 (BECCS 
or biochar) (Smith et al. 2016; Fuss et al. 2018) (Chapters 3 and 7, 
and Section 12.5). Some early opportunities for low-cost BECCS are

being utilised in the ethanol sector but these are applicable only in 
the near-term at the scale of ≤100 MtCO 2 yr –1 (Sanchez et al. 2018). 
Several technological and institutional barriers exist for large-scale 
BECCS implementation, including large energy requirements for 
CCS, limit and cost of biomass supply and geologic sinks for CO 2 
in several regions, and cost of CO 2 capture technologies (high 
confidence). Besides BECCS, biofuels production through pyrolysis 
and hydrothermal liquefaction creates biochar, which could also be 
used to store carbon as 80% of the carbon sequestered in biochar 
will remain in the biochar permanently (Chapter 7). In addition to its 
ability to sequester carbon, biochar can be used as a soil amendment 
(Wang et al. 2014b).
First-generation bioenergy is currently competitive in some markets 
though, on average, its costs are higher than other forms of final energy. 
Bioenergy from waste and residues from forestry and agriculture is

also currently competitive, but the supply is limited (Aguilar et  al. 
2020). These costs are context-dependent, and regions having large 
waste resources are already producing low-cost bioenergy (Jin and 
Sutherland 2018). In the future, technology costs are anticipated 
to decrease, but bioenergy produced through cellulosic feedstocks 
may remain more expensive than fossil alternatives. Large-scale 
deployment of early opportunities, especially in the liquid fuel 
sector, may reduce the technological costs associated with biomass 
conversion (IEA 2020g). At the same time, the cost of feedstocks 
may rise as bioenergy requirements increase, especially in scenarios 
with large bioenergy deployment (Muratori et al. 2020). The costs of 
bioenergy production pathways are highly uncertain (Table 6.4).• Electricity. The costs of baseload electricity production with 
biomass are higher than corresponding fossil electricity production

with and without CCS, and are likely to remain as such without 
carbon pricing (Bhave et al. 2017). The additional cost associated 
with CO 2 capture are high for conventional solvent-based 
technologies. However, upcoming technologies such as chemical 
looping are well-suited to biomass and could reduce CCS costs.
• Hydrogen. The costs of hydrogen production from biomass are 
somewhat higher than, but comparable, to that produced by 
natural gas reforming with CCS. Further, the incremental costs for 
incorporating CCS in this process are less than 5% of the levelised 
costs in some cases, since the gasification route creates a high-
purity stream of CO 2 (Muratori et al. 2017a; Sunny et al. 2020). While 
these processes have fewer ongoing prototypes/demonstrations, 
the costs of biomass-based hydrogen (with or without CCS) are 
substantially cheaper than that produced from electrolysis utilising 
solar/wind resources (Kayfeci et al. 2019; Newborough and Cooley

2020), even though electrolysis costs are dropping.
• Liquid biofuels. First-generation sugar-based biofuels 
(e.g., ethanol produced via fermentation) or biodiesel produced 
from vegetable oils and animal fats, are produced in several 
countries at large scale and costs competitive with fossil fuels. 
However, supply is limited. The costs for second-generation 
processes (Fischer-Tropsch and cellulosic ethanol) are higher in 
most regions (Li et al. 2019). Technological learning is projected 
to reduce these costs by half (IEA 2020g).
Large-scale bioenergy production will require more than wastes/
residues and cultivation on marginal lands, which may raise conflicts 
with SDGs relevant to environmental and societal priorities (Heck et al. 
2018; Gerten et al. 2020) (Chapter 12). These include competition with 
food crops, implications for biodiversity, potential deforestation to 
support bioenergy crop production, energy security implications from

bioenergy trade, point-of-use emissions and associated effects on 
air quality, and water use and fertiliser use (Fajardy and Mac Dowell 
2018; Fuss et  al. 2018; Tanzer and Ramírez 2019; Brack and King 
2020). Overall, the environmental impact of bioenergy production at 
scale remains uncertain and varies by region and application.
Alleviating these issues would require some combination of increasing 
crop yields, improving conversion efficiencies, and developing 
advanced biotechnologies for increasing the fuel yield per tonne of 
feedstock (Henry et al. 2018). Policy structures would be necessary to 
Table 6.4 | The costs of electricity generation, hydrogen production, and second-generation liquid fuels production from biomass in 2020.  These costs are 
adapted from Bhave et al. (2017), Daioglou et al. (2020), NREL (2020a, 2020b), Witcover and Williams (2020), and Lepage et al. (2021).
Unit Low Median High
Bioelectricity with CCS USD MWh–174 86 160

Bioelectricity without CCS USD MWh–166 84 112
Biohydrogen with CCSaUSD kg–11.63 2.37 2.41
Biohydrogen without CCSaUSD kg–11.59 1.79 2.37
Liquid biofuels with CCS USD gge–11.34 4.20 7.85
Liquid biofuels without CCS USD gge–11.15 4.00 7.60
a Using cellulosic feedstocks.

646
Chapter 6 Energy Systems6retain biodiversity, manage water use, limit deforestation and land-
use change emissions, and ultimately optimally integrate bioenergy 
with transforming ecosystems. Large-scale international trade of 
biomass might be required to support a global bioeconomy, raising 
questions about infrastructure, logistics, financing options, and global 
standards for bioenergy production and trade (Box 6.10). Additional 
institutional and economic barriers are associated with accounting of 
carbon dioxide removal, including BECCS (Fuss et al. 2014; Muratori 
et al. 2016; Fridahl and Lehtveer 2018).
Lifecycle emissions impacts from bioenergy are subject to large 
uncertainties and could be incompatible with net-zero emissions 
in some contexts. Due to the potentially large energy conversion 
requirements and associated GHG emissions (Chapters 7 and 12), 
bioenergy systems may fail to deliver near-zero emissions depending

on operating conditions and regional contexts (Elshout et  al. 2015; 
Daioglou et al. 2017; Staples et al. 2017; Hanssen et al. 2020; Lade et al. 
2020). As a result, bioenergy carbon neutrality is debated and depends 
on factors such as the source of biomass, conversion pathways and 
energy used for production and transport of biomass, and land-use 
changes, as well as assumed analysis boundary and considered time scale (Zanchi et  al. 2012; Wiloso et  al. 2016; Booth 2018; Fan et  al. 
2021). Similarly, the lifecycle emissions of BECCS remain uncertain and 
will depend on how effectively bioenergy conversion processes are 
optimised (Fajardy and Mac Dowell 2017; Tanzer and Ramírez 2019).
Acceptability of bioenergy is relatively low compared to other 
renewable energy sources like solar and wind (Poortinga et al. 2013; 
Ma et al. 2015; Peterson et al. 2015; EPCC 2017) and comparable 
to natural gas (Scheer et al. 2013). People also know relatively little

about bioenergy compared to other energy sources (Whitmarsh 
et al. 2011a; EPCC 2017) and tend be be more ambivalent towards 
bioenergy compared to other mitigation options (Allen and 
Chatterton 2013). People evaluate biomass from waste products 
(e.g., food waste) more favourably than grown-for-purpose energy 
crops, which are more controversial (Plate et al. 2010; Demski et al. 
2015). The most pressing concerns for use of woody biomass are air 
pollution and loss of local forests (Plate et al. 2010). Various types 
of bioenergy additionally raise concerns about landscape impacts 
(Whitmarsh et  al. 2011a) and biodiversity (Immerzeel et  al. 2014). 
Moreover, many people do not see biomass as a renewable energy 
source, possibly because it involves burning of material.
Box 6.5 | Methane Mitigation Options for Coal, Oil, and Gas
Methane emissions mainly from coal, oil, and gas currently represent in 2019 about 18% of energy supply sector greenhouse gas

(GHG) emissions and 90% of global energy supply non-CO 2 emissions in 2019 (Minx et al. 2021b). While approximately 80% of 
the lifecycle methane emissions in the coal sector occur during underground mining, oil and gas emissions are spread throughout 
upstream, midstream, and downstream stages (Alvarez et al. 2018; IPCC 2019). For this reason, methane reductions from coal mining 
can be accomplished through coal mine methane recovery (where methane and coal are recovered simultaneously) and from the 
ventilation air, which can cumulatively reduce methane emissions by 50–75% (Zhou et al. 2016; Singh and Hajra 2018). Governments 
incentivise such operations through a number of emissions trading and offset programmes (Haya et al. 2020). Methane emissions in 
the oil and gas sector can be reduced by leak detection and repair, relevant across varying time scales (hours to decades) and regional

scopes (component/facility level to continental) (Fox et al. 2019). Around 50% of the methane emitted from oil and gas infrastructure 
can be mitigated at net-negative costs; that is, the market price of the recovered methane is higher than the mitigation costs (IEA 
2021e). As CO 2 emissions are reduced and fossil fuel consumption decreases, methane emissions associated with these supply chains 
are anticipated to decline (Section 6.7). That said, substantial ‘legacy’ methane emissions – methane leaks after abandonment – will 
remain, even if a complete fossil fuel phase-out takes place. These legacy emissions are estimated to be less than 1–4% of overall 
methane emissions across all fossil fuel sources (Kholod et al. 2020; Williams et al. 2021b). Even without a complete phase-out, 50–80% 
of methane emissions from coal, oil and gas could be avoided with currently available technologies at less than USD50 tCO 2-eq–1

(Harmsen et al. 2019; Höglund-Isaksson et al. 2020). Methane recovery from abandoned coal mines could offset most project costs 
(Singh and Sahu 2018). For abandoned oil and gas wells, low plugging costs could be offset through methane recovery, while high 
plugging costs would likely require some market or policy support (Kang et al. 2019).
6.4.2.7 Fossil Energy
Fossil fuels could play a role in climate change mitigation if strategically 
deployed with CCS (high confidence). On the one hand, the primary 
mechanism for reducing emissions is to eliminate the unabated 
fossil fuel use. On the other hand, fossil energy combined with CCS 
provides a means of producing low-carbon energy while still utilising 
the available base of fossil energy worldwide and limiting stranded 
assets. While Section 6.4.2.5 discusses the important aspects of CCS with fossil fuels, this section aims to elucidate the feasibility criteria 
around these fuels itself.

Fossil fuel reserves have continued to rise because of advanced 
exploration and utilisation techniques (high confidence). A fraction of 
these available reserves can be used consistent with mitigation goals 
when paired with CCS opportunities in close geographical proximity 
(high confidence). Based on continued exploration, the fossil fuel 
resource base has increased significantly; for example, a 9% increase

647
Energy Systems  Chapter 66in gas reserves and 12% in oil reserves was observed in the USA 
between 2017 and 2018. This increase is a  result of advanced 
exploration techniques, which are often subsidised (Lazarus and van 
Asselt 2018; MA et al. 2018). Fossil reserves are distributed unevenly 
throughout the globe. Coal represents the largest remaining resource 
(close to 500 ZJ). Conventional oil and gas resources are an order 
of magnitude smaller (15–20 ZJ each). Technological advances have 
increased the reserves of unconventional fossil in the last decade. 
Discovered ultimate recoverable resources of unconventional oil and 
gas are comparable to conventional oil and gas (Fizaine et al. 2017).
It is unlikely that resource constraints will lead to a  phase-out of 
fossil fuels, and instead, such a phase-out would require policy action. 
Around 80% of coal, 50% of gas, and 20% of oil reserves are likely to 
remain unextractable under 2°C constraints (McGlade and Ekins 2015;

Pellegrini et al. 2020). Reserves are more likely to be utilised in a low-
carbon transition if they can be paired with CCS. Availability of CCS 
technology not only allows continued use of fossil fuels as a capital 
resource for countries but also paves the way for CDR through BECCS 
(Haszeldine 2016; Pye et al. 2020). While the theoretical geologic CO 2 
sequestration potential is vast, there are limits on how much resource 
base could be utilised based on geologic, engineering, and source-sink 
mapping criteria (Budinis et al. 2017).
Technological changes have continued to drive down fossil fuel 
extraction costs. Significant decarbonisation potential also exists 
via diversification of the fossil fuel uses beyond combustion (high 
evidence). The costs of extracting oil and gas globally have gone 
down by utilising hydraulic fracturing and directional drilling for 
resources in unconventional reservoirs (Wachtmeister and Höök

2020). Although the extraction of these resources is still more 
expensive than those derived from conventional reservoirs, the large 
availability of unconventional resources has significantly reduced 
global prices. The emergence of liquefied natural gas (LNG) markets 
has also provided opportunities to export natural gas significant 
distances from the place of production (Avraam et  al. 2020). The 
increase in availability of natural gas has been accompanied by an 
increase in the production of natural gas liquids as a co-product to oil 
and gas. Over the period from 2014 to 2019, exports of natural gas 
liquids increased by 160%. Natural gas liquids could potentially be 
a lower-carbon alternative to liquid fuels and hydrocarbons. On the 
demand side, natural gas can be used to produce hydrogen using 
steam methane reforming, which is a technologically mature process 
(Sections 6.4.4 and 6.4.5). When combined with 90% CO 2 capture,

the costs of producing hydrogen are around USD1.5–2 kg(H 2)–1 
(Collodi et al. 2017; Newborough and Cooley 2020), considerably less 
than hydrogen produced via electrolysis.
Significant potential exists for gasifying deep-seated coal deposits 
in situ to produce hydrogen. Doing so reduces fugitive methane 
emissions from underground coal mining. The integration costs 
of this process with CCS are less than with natural gas reforming. 
The extent to which coal gasification could be compatible with 
low-carbon energy would depend on the rate of CO 2 capture and 
the ultimate use of the gas (Verma and Kumar 2015). Similarly, for 
ongoing underground mining projects, coal mine methane recovery 
can be economic for major coal producers such as China and India. Coal mine methane and ventilation air methane recovery can reduce 
the fugitive methane emissions by 50–75% (Zhou et al. 2016; Singh 
and Sahu 2018).
The cost of producing electricity from fossil sources has remained

roughly the same with some regional exceptions while the costs 
of producing transport fuels has gone down significantly (high 
confidence). The cost of producing electricity from fossil fuels has 
remained largely static, with the exception of some regional changes, 
for example, a 40% cost reduction in the USA for natural gas (Rai et al. 
2019), where the gas wellhead price has declined by almost two-thirds 
due to large reserves. Similarly, the global price of crude oil has declined 
from almost USD100 bbl–1 to USD55 bbl–1 in the last five years.
The energy return of investment (EROI) is a useful indicator of full 
fossil lifecycle costs. Fossil fuels create significantly more energy 
per unit energy invested – or in other words have much larger EROI – 
than most cleaner fuels such as biomass or electrolysis-derived 
hydrogen, where intensive processing reduces EROI (Hall et  al. 
2014). That said, recent years have seen a decrease in fossil EROI,

especially as underground coal mining still represents a substantial 
portion of global production. Exploitation of unconventional gas 
reservoirs is also energy intensive and has led to a reduction in EROI. 
The primary energy EROI of fossil fuels has converged at about 30, 
which represents a 20-point decrease from the 1995 value for coal 
(Brockway et  al. 2019). When processing and refining stages are 
considered, these EROI values further decrease.
Several countries have large reserves of fossil fuels. Owing to climate 
constraints, these may become stranded, causing considerable 
economic impacts (high confidence) (Sections 6.7.3 and 6.7.4, and 
Box  6.13). While global fossil energy resources are greater than 
600 ZJ, more than half of these resources would likely be unburnable, 
even in the presence of CCS (McGlade and Ekins 2015; Pye et  al. 
2020). This would entail a significant capital loss for the countries

with large reserves. The total amount of stranded assets in such 
a case would amount to USD1–4 trillion at present value (Box 6.13).
Apart from CO 2 emissions and air pollutants from fossil fuel 
combustion, other environmental impacts include fugitive methane 
leakages and implications to water systems. While the rate of 
methane leakage from unconventional gas systems is uncertain, their 
overall GHG impact is less than coal (Tanaka et al. 2019; Deetjen and 
Azevedo 2020). The stated rate of leakage in such systems ranges from 
1–8%, and reconciling different estimates requires a  combination 
of top-down and bottom-up approaches (Zavala-Araiza et  al. 
2015; Grubert and Brandt 2019). Similarly, for coal mining, fugitive 
methane emissions have grown, despite some regulations on the 
degree to which emission controls must be deployed. Recent IPCC 
inventory guidance also notes considerable CO 2 emissions resulting

from spontaneous combustion of the coal surface, and accounting for 
these emissions will likely increase the overall lifecycle emissions by 
1–5% (IPCC 2019; Singh 2019; Fiehn et al. 2020).
Another key issue consistently noted with unconventional wells (both 
oil and gas, and coalbed methane) is the large water requirements 
(Qin et  al. 2018). The overall water footprint of unconventional

648
Chapter 6 Energy Systems6reservoirs is higher than conventional reservoirs because of higher 
lateral length and fracturing requirements (Scanlon et  al. 2017; 
Kondash et al. 2018). Moreover, produced water from such formations 
is moderately to highly brackish, and treating such waters has large 
energy consumption (Bartholomew and Mauter 2016; Singh and 
Colosi 2019).
Oil and coal consistently rank among the least preferred energy 
sources in many countries (high confidence). The main perceived 
advantage of fossil energy is the relatively low costs, and emphasising 
these costs might increase acceptability somewhat (Pohjolainen et al. 
2018; Boyd et al. 2019; Hazboun and Boudet 2020). Acceptability of 
fossil fuels is, on average, similar to acceptability of nuclear energy, 
although evaluations are less polarised. People evaluate natural 
gas as somewhat more acceptable than other fossil fuels, although 
they generally oppose hydraulic fracturing (Clarke et al. 2016). Yet,

natural gas is evaluated as less acceptable than renewable energy 
sources, although evaluations of natural gas and biogas are similar 
(Liebe and Dobers 2019; Plum et  al. 2019). Acceptability of fossil 
energy tends to be higher in countries and regions that strongly 
rely on them for their energy production (Pohjolainen et  al. 2018; 
Boyd et al. 2019). Combining fossil fuels with CCS can increase their 
acceptability (Van Rijnsoever et al. 2015; Bessette and Arvai 2018). 
Some people seem ambivalent about natural gas, as they perceive 
both benefits (e.g., affordability, less carbon emissions than coal) and 
disadvantages (e.g., finite resource, contributing to climate change) 
(Blumer et al. 2018).
Fossil fuel subsidies have been valued in the order of USD0.5–5 trillion 
annually by various estimates which have the tendency to introduce 
economic inefficiency within systems (Jakob et al. 2015; Merrill et al. 
2015) (high confidence). Subsequent reforms have been suggested

by different researchers who have estimated reductions in CO 2 
emissions may take place if these subsidies are removed (Mundaca 
2017). Such reforms could create the necessary framework for enhanced investments in social welfare – through sanitation, water, 
clean energy – with differentiating impacts (Edenhofer 2015).
6.4.2.8 Geothermal Energy
Geothermal energy is heat stored in the Earth’s subsurface and 
is a  renewable resource that can be sustainably exploited. The 
geophysical potential of geothermal resources is 1.3 to 13 times 
the  global electricity demand in 2019 (medium confidence). 
Geothermal energy can be used directly for various thermal 
applications, including space heating and industrial heat input, 
or converted to electricity depending on the source temperature 
(Limberger et al. 2018; Moya et al. 2018; REN21 2019).
Suitable aquifers underlay 16% of the Earth’s land surface and store 
an estimated 110,000–1,400,000 PWh (400,000–1,450,000 EJ) that

could theoretically be used for direct heat applications. For electricity 
generation, the technical potential of geothermal energy is estimated 
to be between 30 PWh yr –1 (108 EJ yr –1) (to 3  km depth) and 
300 PWh yr –1 (1080 EJ yr –1) (to 10 km depth). For direct thermal uses, 
the technical potential is estimated to range from 2.7–86 PWh yr –1 
(9.7–310 EJ yr –1) (IPCC 2011). Despite the potential, geothermal 
direct heat supplies only 0.15% of the annual global final energy 
consumption. The technical potential for electricity generation, 
depending on the depth, can meet one third to almost three times 
the global final consumption  – based on International Energy 
Agency (IEA) database for IPCC. The mismatch between potential 
and developed geothermal resources is caused by high upfront costs, 
decentralised geothermal heat production, lack of uniformity among 
geothermal projects, geological uncertainties, and geotechnical risks

(IRENA 2017a; Limberger et al. 2018). A limited number of countries 
have a long history in geothermal. At least in two countries (Iceland 
and New Zealand), geothermal accounts for 20–25% of electricity 
generation (Pan et  al. 2019; Spittler et  al. 2020). Furthermore, in 
Iceland approximately 90% of the households are heated with 
Total installed cost Capacity factorGeothermal energy, 2010–2020
Levelised cost of electricity
9000
70008000
6000
5000
4000
3000
2000
1000
0COST [2020USD kW–1]
CF [%]100
95
90
85
80
75
70
65
60
LCOE [2020USD kW–1]0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
2010 2012 2014 2016
Year Year Year2018 2020 2010 2012 2014 2016 2018 2020 2010 2012 2014 2016 2018 20205317
361337164221446887
0.085
0.0670.071 0.072 0.071838485 85
83
Figure 6.15 | Global weighted average total installed costs, capacity factors and levelised costs of electricity (LCOE) for geothermal power per year

(2010–2020).  The shaded area represents the 5% and 95% percentiles. Source: with permission from IRENA (2021a).

649
Energy Systems  Chapter 66geothermal energy. In Kenya, as of July 2019, geothermal accounted 
for 734 MW effective capacity spread over 10 power plants and 
approximately one third of the total installed capacity (Kahlen 2019).
There are two main types of geothermal resources: convective 
hydrothermal resources, in which the Earth’s heat is carried by 
natural hot water or steam to the surface; and hot, dry rock 
resources, in which heat cannot be extracted using water or steam, 
and other methods must be developed. There are three basic types 
of geothermal power plants: (i) dry steam plants use steam directly 
from a  geothermal reservoir to turn generator turbines; (ii) flash 
steam plants take high-pressure hot water from deep inside the Earth 
and convert it to steam to drive generator turbines; and (iii) binary 
cycle power plants transfer the heat from geothermal hot water to 
another liquid. Many of the power plants in operation today are dry

steam plants or flash plants (single, double and triple) harnessing 
temperatures of more than 180°C.
However, medium temperature fields are increasingly used for 
electricity generation or combined heat and power. The use of medium 
temperature fields has been enabled through the development of 
binary cycle technology, in which a geothermal fluid is used via heat 
exchangers. Increasing binary generation technologies are now being 
utilised instead of flash steam power plants. This will result in almost 
100% injection and essentially zero GHG emissions, although GHG 
emissions from geothermal power production are generally small 
compared to traditional baseload thermal energy power generation 
facilities (Fridriksson et al. 2016).
Additionally, new technologies are being developed like Enhanced 
Geothermal Systems (EGS), which is in the demonstration stage 
(IRENA 2018), deep geothermal technology, which may increase the

prospects for harnessing the geothermal potential in a large number 
of countries, or shallow-geothermal energy, which represents 
a promising supply source for heating and cooling buildings (Narsilio 
and Aye 2018). Successful large-scale deployment of shallow 
geothermal energy will depend not only on site-specific economic 
performance but also on developing suitable governance frameworks 
(Bloemendal et al. 2018; García-Gil et al. 2020). Technologies for direct 
uses like district heating, geothermal heat pumps, greenhouses, and 
other applications, are widely used and considered mature. Given 
the limited number of plants commissioned, economic indicators 
(Figure 6.15) vary considerably depending on site characteristics.
Public awareness and knowledge of geothermal energy is relatively low  
(high confidence). Geothermal energy is evaluated as less acceptable 
than other renewable energy sources such as solar and wind, but is

preferred over fossil and nuclear energy, and in some studies, over 
hydroelectric energy (high confidence) (Pellizzone et  al. 2015; Steel 
et al. 2015; Karytsas et al. 2019; Hazboun and Boudet 2020). Some 
people are concerned about the installation of geothermal facilities 
close to their homes, similar to solar and wind projects (Pellizzone 
et al. 2015). The main concerns about geothermal energy, particularly 
for large-scale, high-temperature geothermal power generation plants, 
involve water usage, water scarcity, and seismic risks of drilling (Dowd 
et  al. 2011). Moreover, noise, smell and damages to the landscape 
have been reasons for protests against specific projects (Walker 1995). However, with the implementation of modern technologies, geothermal 
presents fewer adverse environmental impacts. At the same time, 
people perceive geothermal energy as relatively environmentally 
friendly (Tampakis et al. 2013).
6.4.2.9 Marine Energy

The ocean is a vast source of energy (Hoegh-Guldberg et al. 2019). 
Ocean energy can be extracted from tides, waves, ocean thermal 
energy conversion (OTEC), currents, and salinity gradients (Bindoff 
et al. 2019). Their technical potentials, without considering possible 
exclusion zones, are explored below. Tidal energy, which uses 
elevation differences between high and low tides, appears in two 
forms: potential energy (rise and fall of the tide); and current energy 
(from tidal currents). The global technically harvestable tidal power 
from areas close to the coast is estimated as about 1.2 PWh yr –1 
(4.3 EJ yr –1) (IRENA 2020b). The potential for tidal current energy is 
estimated to be larger than that for tidal range or barrage (Melikoglu 
2018). Ocean wave energy is abundant and predictable and can 
be extracted directly from surface waves or pressure fluctuations 
below the surface (Melikoglu 2018). Its global theoretical potential

is 29.5 PWh yr –1 (106 EJ yr –1),which means that wave energy alone 
could meet  all global energy demand (Mørk et  al. 2010; IRENA 
2020b). The temperature gradients in the ocean can be exploited to 
produce energy, and its total estimated available resource could be 
up to 44.0 PWh yr –1 (158 EJ yr –1) (Rajagopalan and Nihous 2013). 
Salinity gradient energy, also known as osmotic power, has a global 
theoretical potential of over 1.6 PWh yr –1 (6.0 EJ yr –1) (IRENA 
2020b). The greatest advantage of most marine energy, excluding 
wave energy, is that their sources are highly regular and predictable, 
and energy can be furthermore generated both day and  night. 
An  additional use of sea water is to develop lower-cost district 
cooling systems near the sea (Hunt et al. 2019). The greatest barrier 
to most marine technology advances is the relatively high upfront 
costs, uncertainty on environmental regulation and impact, need for

investments and insufficient infrastructure (Kempener and Neumann 
2014a, b). There are also concerns about technology maturity and 
performance; thus, not all have the potential to become economically 
viable (IRENA 2020b).
6.4.2.10 Waste-to-Energy
Waste-to-energy (WTE) is a strategy to recover energy from waste 
in a form of consumable heat, electricity, or fuel (Zhao et al. 2016). 
Thermal (incineration, gasification, and pyrolysis) and biological 
(anaerobic digestion and landfill gas to energy) technologies are 
commonly used (Ahmad et  al. 2020). When WTE technologies 
are equipped with proper air pollution reduction facilities they can 
contribute to clean electricity production and reduction of GHG 
emissions. However, if not properly operated, they can exacerbate 
air quality issues.
In 2019, there were more than 1,200 WTE incineration facilities 
worldwide, with estimated capacity of 310 million tonnes per  year 
(UNECE 2020). It is estimated that treatment of a  minimum of

261  million tonnes/year of waste could produce 283 TWh (1 EJ) 
of power and heat by 2022 (Awasthi et al. 2019). Incineration plants

650
Chapter 6 Energy Systems6can reduce the mass of waste by 70–80% and the volume of waste by 
80–90% (Haraguchi et al. 2019). Incineration technology can reduce 
water and soil pollution (Gu et  al. 2019). However, if not properly 
handled, dust, and gases such as SO 2, HCL, HF , NO 2, and dioxins in the 
flue gases can harm the environment (Mutz et  al. 2017). Anaerobic 
digestion technology has a  positive environmental impact and the 
ability to reduce GHG emissions (Ayodele et  al. 2018; Cudjoe et  al. 
2020). The by-product of the anaerobic digestion process could be used 
as a nutrient-rich fertiliser for enhancing soil richness for agricultural 
purposes (Wainaina et  al. 2020). Due to the potential negative 
impacts on domestic environment and residents’ health, WTE projects 
such as incineration encounter substantial opposition from the local 
communities in which they are located (Baxter et al. 2016; Ren et al.

2016). Therefore, for WTE to be deployed more widely, policies would 
need to be tailored with specific guidelines focused on mitigating 
emissions, which may have an adverse effect on the environment.
Depending on the origin of the waste used, the integration of WTE 
and carbon capture and storage (CCS) could enable waste to be a net-
zero or even net negative emissions energy source (Kearns 2019; 
Wienchol et al. 2020). For example, in Europe only, the integration 
of CCS with WTE facilities has the potential to capture about 60 to 
70 million tonnes of carbon dioxide annually (Tota et al. 2021).
Waste-to-energy is an expensive process compared to other energy 
sources such as fossil fuels and natural gas (Mohammadi and 
Harjunkoski 2020). However, the environmental and economic 
benefits make its high financial costs justifiable. In 2019, the global 
WTE market size was valued at USD31 billion, and it is predicted to 
experience 7.4% annual growth until 2027 (UNECE 2020).

6.4.3 Energy System Integration
Greenhouse gases are emitted across all economic activities. 
Therefore, cost-effective decarbonisation requires a  ‘system of 
systems’ approach that considers the interaction between different 
energy sectors and systems. Flexibility technologies and advanced 
control of integrated energy systems (e.g., considering the interaction 
between electricity, heating/cooling, gas/hydrogen, transport sectors) 
could reduce energy infrastructure investments substantially in future 
low-carbon energy systems (Strbac et al. 2015b; Jacobson et al. 2019).
The electricity grid will serve as a  backbone of future low-carbon 
energy systems. Integration of large amounts of VRE generation 
(Hansen et al. 2019), particularly wind and solar generation (Bistline 
and Young 2019; Perez et al. 2019), presents economic and technical 
challenges to electricity system management across different time 
scales from sub-seconds, hours, days, seasons, to multiple years.

Furthermore, electrification of segments of the transport and heat 
sectors could disproportionately increase peak demand relative to 
supply (Bistline et al. 2021). Increases in peak demand may require 
reinforcing network infrastructures and generation in the historical 
passive system operation paradigm (Strbac et al. 2020).
These challenges to electricity system management can be addressed 
through system integration and a  digitalised control paradigm involving advanced information and communication technologies. 
Real-time maintenance of supply-demand balance and sufficient 
flexibility technologies such as electricity storage, flexible demand, 
and grid forming converters (Strbac et  al. 2015a; López Prol and 
Schill 2021) would be increasingly valuable for incorporating larger 
amounts of VRE generation. This flexibility will be particularly 
important to deal with sudden losses of supply, for example, due to

a failure of a large generator or interconnector or a rapid increase 
in demand (Teng et al. 2017; Chamorro et al. 2020).
The transition to a  digitalised-based electricity system control 
paradigm would facilitate radical changes in the security of supply, 
moving from the traditional approach of redundancy in assets to 
a  smart control paradigm. Advanced control and communication 
systems can significantly reduce the electricity system investment 
and operation costs (Harper et al. 2018; Münster et al. 2020).
6.4.3.1 Importance of Cross-sector Coupling for Cost-effective 
Energy System Decarbonisation
Integrated whole-system approaches can reduce the costs of 
low-carbon energy system transitions (high confidence). A lack 
of flexibility in the electricity system may limit the cost-effective 
integration of technologies as part of broader net-zero energy 
systems. At the same time, the enormous latent flexibility hidden

in heating and cooling, hydrogen, transport, gas systems, and other 
energy systems provides opportunities to take advantage of synergies 
and to coordinate operations across systems (Martin et  al. 2017; 
Zhang et al. 2018; Martinez Cesena and Mancarella 2019; Pavičević 
et al. 2020; Bogdanov et al. 2021) (Figure 6.16).
Sector coupling can significantly increase system flexibility, driven 
by the application of advanced technologies (Clegg and Mancarella 
2016; Heinen et al. 2016; Bogdanov et al. 2019; Solomon et al. 2019; 
Zhang et al. 2019b; Zhang and Fujimori 2020; Zhao et al. 2021). For 
example, district heating infrastructure can generate both heat and 
power. Cooling systems and electrified heating systems in buildings 
can provide flexibility through preheating and precooling via thermal 
energy storage (Z. Li et al. 2016; G. Li et al. 2017). System balancing 
services can be provided by electric vehicles (EVs) based on vehicle-

to-grid concepts and deferred charging through smart control of 
EV batteries without compromising customers’ requirements for 
transport (Aunedi and Strbac 2020).
Hydrogen production processes (power-to-gas and vice versa) 
and hydrogen storage can support short-term and long-term 
balancing in the energy systems and enhance resilience (Stephen 
and Pierluigi 2016; Strbac et  al. 2020). However, the economic 
benefits of flexible power-to-gas plants, energy storage, and other 
flexibility technological and options will depend on the locations of 
VRE sources, storage sites, gas, hydrogen, and electricity networks 
(Jentsch et al. 2014; Heymann and Bessa 2015; Ameli et al. 2020). 
Coordinated operation of gas and electricity systems can bring 
significant benefits in supplying heat demands. For example, 
hybrid heating can eliminate investment in electricity infrastructure 
reinforcement by switching to heat pumps in off-peak hours and

gas boilers in peak hours (Fischer et al. 2017; Dengiz et al. 2019;

651
Energy Systems  Chapter 66Bistline et al. 2021). The heat required by direct air carbon capture 
and storage (DACCS) could be effectively supplied by inherent 
heat energy in nuclear plants, enhancing overall system efficiency 
(Realmonte et al. 2019).
Rather than incremental planning, strategic energy system planning 
can help minimise long-term mitigation costs (high confidence). 
With a whole-system perspective, integrated planning can consider 
both short-term operation and long-term investment decisions, 
covering infrastructure from local to national and international, 
while meeting security of supply requirements and incorporating 
the flexibility provided by different technologies and advanced 
control strategies (Zhang et  al. 2018; O’Malley et  al. 2020; Strbac 
et al. 2020). Management of conflicts and synergies between local 
district and national level energy system objectives, including 
strategic investment in local hydrogen and heat infrastructure, can

drive significant whole-system cost savings (Zhang et al. 2019b; Fu 
et  al. 2020). For example, long-term planning of the offshore grid 
infrastructure to support offshore wind development, including 
interconnection between different countries and regions, can provide 
significant savings compared to a short-term incremental approach 
in which every offshore wind farm is individually connected to the 
onshore grid (E3G 2021).
6.4.3.2 Role of Flexibility Technologies
Flexibility technologies  – including energy storage, demand-side 
response, flexible/dispatchable generation, grid-forming converters, 
and transmission interconnection  – as well as advanced control 
systems – can facilitate cost-effective and secure low-carbon energy 
systems (high confidence). Flexibility technologies have already been implemented, but they can be enhanced and deployed more 
widely. Due to their interdependencies and similarities, there can

be both synergies and conflicts for utilising these flexibility options 
(Bistline et al. 2021). It will therefore be important to coordinate the 
deployment of the potential flexibility technologies and smart control 
strategies. Important electricity system flexibility options include 
the following:
• Flexible/dispatchable generation. Advances in generation 
technologies, for example, gas/hydrogen plants and nuclear 
plants, can enable them to provide flexibility services. These 
technologies would start more quickly, operate at lower power 
output, and make faster output changes, enabling more secure 
and cost-effective integration of VRE generation and end-
use electrification. There are already important developments 
in increasing nuclear plants flexibility (e.g.,  in France (Office 
of Nuclear Energy 2021)) and the development of small 
modular reactors, which could support system balancing (FTI 
Consulting 2018).
• Grid-forming converters (inverters). The transition from

conventional electricity generation, applying mainly synchronous 
machines to inverter-dominated renewable generation, creates 
significant operating challenges. These challenges are mainly 
associated with reduced synchronous inertia, system stability, 
and ‘black start’ capability. Grid-forming converters will be 
a  cornerstone for the control of future electricity systems 
dominated by VRE generation. These converters will address 
critical stability challenges, including the lack of system inertia, 
frequency and voltage regulation, and black start services 
while reducing or eliminating the need to operate conventional 
generation (Tayyebi et al. 2019).Transport Building stock Industrial
Reuse 
of heatEnd-useTransport/
storageSources
Power
Gas/hydrogen
StorageInternational
Storage Thermal
ServicesRES/Nuclear Fossils Liquid fuels
Figure 6.16 | Interaction between different energy sectors.  Source: extracted with permission from Münster et al. (2020).

652
Chapter 6 Energy Systems6• Interconnection. Electricity interconnections between different 
regions can facilitate more cost-effective renewable electricity 
deployment. Interconnection can enable large-scale sharing of 
energy and provide balancing services. Backup energy carriers 
beyond electricity, such as ammonia, can be shared through 
gas/ammonia/hydrogen-based interconnections, strengthening 
temporal coupling of multiple sectors in different regions 
(Bhagwat et al. 2017; Brown et al. 2018) (Section 6.4.5).
• Demand-side response. Demand-side schemes  – including, 
for example, smart appliances, EVs, and building-based thermal 
energy storage (Heleno et  al. 2014)  – can provide flexibility 
services across multiple time frames and systems. Through 
differentiation between essential and non-essential needs during 
emergency conditions, smart control of demands can significantly 
enhance system resilience (Chaffey 2016).

• Energy storage. Energy storage technologies (Section 6.4.4) can 
act as both demand and generation sources. They can provide 
services such as system balancing, various ancillary services, 
and network management. Long-duration energy storage can 
significantly enhance the utilisation of renewable energy sources 
and reduce the need for firm low-carbon generation (Sepulveda 
et al. 2021).
6.4.3.3 Role of Digitalisation and Advanced Control Systems
A digitalised energy system can significantly reduce energy 
infrastructure investments while enhancing supply security and 
resilience (high confidence) (Andoni et  al. 2019; Strbac et  al. 
2020). Significant progress has been made in the development of 
technologies essential for the transition to a digitalised energy control 
paradigm, although the full implementation is still under development. 
Electrification and the increased integration of the electricity system 
with other systems will fundamentally transform the operational and

planning paradigm of future energy infrastructure. A fully intelligent 
and sophisticated coordination of the multiple systems through 
smart control will support this paradigm shift. This shift will provide 
significant savings through better utilisation of existing infrastructure 
locally, regionally, nationally, and internationally. Supply system 
reliability will be enhanced through advanced control of local 
infrastructure (Strbac et  al. 2015a). Furthermore, this paradigm 
shift offers the potential to increase energy efficiency through 
a  combination of technologies that gather and analyse data and 
consequently optimise energy use in real-time.
The transition to advanced data-driven control of energy system 
operations (Cremer et  al. 2019; Sun et  al. 2019a) will require 
advanced information and communication technologies and 
infrastructure, including the internet, wireless networks, computers, 
software, middleware, smart sensors, internet of things components,

and dedicated technological developments (Hossein Motlagh et al. 
2020). The transition will raise standardisation and cyber-security 
issues, given that digitalisation can become a single point of failure 
for the complete system (Ustun and Hussain 2019; Unsal et al. 2021). 
Implementing peer-to-peer energy trading based on blockchain is 
expected to be one of the key elements of next-generation electricity 
systems (Qiu et  al. 2021). This trading will enable consumers to drive system operation and future design, increasing overall system 
efficiency and security of supply while reducing emissions without 
sacrificing users’ privacy (Andoni et al. 2019; Ahl et al. 2020). When 
deployed with smart contracts, this concept will be suitable for 
energy systems involving many participants, where a prerequisite is 
digitalisation (e.g., smart meters, end-use demand control systems) 
(Juhar and Khaled 2018; Teufel et al. 2019).
6.4.3.4 System Benefits of Flexibility Technologies

and Advanced Control Systems
New sources of flexibility and advanced control systems provide 
a significant opportunity to reduce low-carbon energy system costs 
by enhancing operating efficiency and reducing energy infrastructure 
and low-carbon generation investments, while continuing to meet 
security requirements (high confidence). In the USA, for example, one 
study found that flexibility in buildings alone could reduce US CO 2 
emissions by 80 Mt yr –1 and save USD18 billion yr –1 in electricity 
system costs by 2030 (Satchwell et al. 2021). Key means for creating 
savings are associated with the following:
• Efficient energy system operation. Flexibility technologies 
such as storage, demand-side response, interconnection, and 
cross-system control will enable more efficient, real-time 
demand and supply balancing. This balancing has historically 
been provided by conventional fossil-fuel generation (Nuytten 
et al. 2013).

• Savings in investment in low-carbon/renewable generation 
capacity. System flexibility sources can absorb or export surplus 
electricity, thus reducing or avoiding energy curtailment and 
reducing the need for firm low-carbon capacity such as nuclear 
and fossil-fuel plants with CCS (Newbery et al. 2013; Solomon 
et  al. 2019). For example, one study found that flexibility 
technologies and advanced control systems could reduce the 
need for nuclear power by 14 GW and offshore wind by 20 GW in 
the UK’s low-carbon transition (Strbac et al. 2015b).
• Reduced need for backup capacity. System flexibility can 
reduce energy demand peaks, reducing the required generation 
capacity to maintain the security of supply, producing significant 
savings in generation investments (Strbac et al. 2020).
• Deferral or avoidance of electricity network reinforcement/
addition. Flexibility technologies supported by advanced 
control systems can provide significant savings in investment

in electricity network reinforcement that might emerge from 
increased demand, for example, driven by electrification of 
transport and heat sectors. Historical network planning and 
operation standards are being revised considering alternative 
flexibility technologies, which would further support cost-
effective integration of decarbonised transport and heat sectors 
(Strbac et al. 2020).
6.4.4 Energy Storage for Low-carbon Grids
Energy storage technologies make low-carbon electricity systems more 
cost-effective, allowing VRE technologies to replace more expensive 
firm low-carbon generation technologies (Carbon Trust  2016) and

653
Energy Systems  Chapter 66reducing investment costs in backup generation, interconnection, 
transmission, and distribution network upgrades (high confidence). 
Energy system decarbonisation relies on increased electrification 
(Section  6.6.2.3). Meeting increasing demands with variable 
renewable sources presents challenges and could lead to costly 
infrastructure reinforcements. Energy storage enables electricity from 
variable renewables to be matched against evolving demands across 
both time and space, using short-, medium- and long-term storage of 
excess energy for delivery later or at a different location. In 2017, an 
estimated 4.67 TWh (0.017 EJ) of electricity storage was in operation 
globally (IRENA 2017b). If the integration of renewables is doubled 
from 2014 levels by 2030, the total capacity of global electricity 
storage could triple, reaching 11.89–15.27 TWh (0.043–0.055 EJ) 
(IRENA 2017b).
Energy storage technologies can provide a  range of different grid

services (Table 6.5). Energy storage enhances security of supply by 
providing real-time system regulation services (voltage support, 
frequency regulation, fast reserve, and short-term reserve). A greater 
proportion of variable renewable sources reduces system inertia, 
requiring more urgent responses to changes in system frequency, 
which rapid response storage technologies can provide (stability 
requires responses within sub-second time scale for provision 
of frequency and voltage control services). Energy storage also 
provides intermittent renewable sources with flexibility, allowing 
them to contribute a  greater proportion of electrical energy and 
avoiding curtailment (capacity firming). Investment costs in backup 
generation, interconnection, transmission, and distribution network upgrades can thus be reduced (upgrade deferral), meaning that 
less low-carbon generation will need to be built while still reducing

emissions. In the event of an outage, energy storage reserves can keep 
critical services running (islanding) and restart the grid (black start). 
The ability to store and release energy as required provides a range 
of market opportunities for buying and selling of energy (arbitrage).
No single, sufficiently mature energy storage technology can 
provide all the required grid services – a portfolio of complementary 
technologies working together can provide the optimum solution 
(high confidence). Different energy storage technologies can 
provide these services and support cost-effective energy system 
decarbonisation (Carbon Trust 2016). To achieve very low-carbon 
systems, significant volumes of storage will be required (Strbac 
et  al. 2015a; Section  6.4.3.2). There are few mature global supply 
chains for many of the less-developed energy storage technologies. 
This means that, although costs today may be relatively high,

there are significant opportunities for future cost reductions, both 
through technology innovation and through manufacturing scale. 
Adding significant amounts of storage will reduce the price variation 
and, therefore, the profitability of additional and existing storage, 
increasing investment risk.
Energy storage extends beyond electricity storage and includes 
technologies that can store energy as heat, cold, and both liquid and 
gaseous fuels. Energy storage is a conversion technology, enabling 
energy to be converted from one form to another. This diversification 
improves the overall resilience of energy systems, with each system Table 6.5 | Suitability of low-carbon energy storage technologies, in terms of the grid services they can provide, and overall features such as technology 
maturity: where Low represents an emerging technology; Med represents a maturing technology; and High a fully mature technology.  The opportunity for

the cost of a technology to reduce over the next decade is represented by Low, Med and High and the lifetime of installations by: Long, for projects lasting more than 25 years; 
Med for those lasting 15–25 years; Short, for those lasting less than 15 years. 
Suitability factor PHS CAES LAES TES FES LiB Scap RFB PtX RHFC
Upgrade deferral          
Energy arbitrage        
Capacity firming         
Seasonal storage   
Stability       
Frequency regulation         
Voltage support         
Black start       
Short-term reserve       
Fast reserve        
Islanding       
Uninterruptible power supply     
Maturity High High Med Low High Med Low Low Low Low
Opportunity to reduce costs Low Low Low Med Med High High High Med High
Lifetime Long Long Long Long Med Short Med Med Med Short
Roundtrip efficiency 60–80% 30–60% 55–90% 70–80% 90% >95% >95% 80–90% 35–60% <30%

Note: PHS – Pumped Hydroelectric Storage; CAES – Compressed Air Energy Storage; LAES – Liquid Air Energy Storage; TES – Thermal Energy Storage; FES – Flywheel Energy 
Storage; LIB – Li-ion Batteries; Scap – Supercapacitors; RFB – Redox Flow Batteries; RHFC – Reversible Hydrogen Fuel Cells; PtX – Power to fuels. Source: PHS – Barbour et al. 
2016, Yang 2016, IRENA 2017b; CAES – Luo et al. 2014, Brandon et al. 2015, IRENA 2017b; LAES – Luo et al. 2014, Highview 2019; TES – Brandon et al. 2015, Gallo et al. 
2016, Smallbone et al. 2017; FES – IRENA 2017b, Yulong et al. 2017; LIB – IRENA 2015b, Hammond and Hazeldine 2015, Nykvist and Nilsson 2015, Staffell, I. and Rustomji, 
M. et al. 2016, IRENA 2017b, Schmidt et al. 2017c, May et al. 2018; Scap – Brandon et al. 2015, Gur 2018; RFB – IRENA 2017b; RHFC – IEA 2015, Gur 2018.

654
Chapter 6 Energy Systems6being able to cover supply shortfalls in the others. For example, 
storage can support the electrification of heating or cooling, as well 
as transport through electric vehicles, powered by batteries or by fuel 
cells. Storage significantly reduces the need for costly reinforcement 
of local distribution networks through smart charging schemes and 
the ability to flow electricity back to the grid (e.g., through vehicle-
to-grid). By capturing otherwise wasted energy streams, such as heat 
or cold, energy storage improves the efficiency of many systems, such 
as buildings, data centres and industrial processes.
6.4.4.1 Energy Storage Technologies
Pumped hydroelectric storage (PHS). PHS makes use of 
gravitational potential energy, using water as the medium. Water 
is pumped into an elevated reservoir using off-peak electricity and 
stored for later release when electricity is needed. These closed-loop

hydropower plants have been in use for decades and account for 97% 
of worldwide electricity storage capacity (IRENA 2017b; IEA 2018b).  
PHS is best suited to balancing daily energy needs at a large scale, 
and advances in the technology now allow rapid response and power 
regulation in both generating and pumping mode (Valavi and Nysveen 
2018; Dong et al. 2019; Kougias et al. 2019). The construction itself 
can cause disruption to the local community and environment (Hayes 
et al. 2019), the initial investment is costly, and extended construction 
periods delay return on investment (Section  6.4.2.3). In addition, 
locations for large-scale PHS plants are limited.
Advanced pump-turbines are being developed, allowing both 
reversible and variable-speed operation, supporting frequency control 
and grid stability with improved round-trip efficiencies (Ardizzon 
et al. 2014). New possibilities are being explored for small-scale PHS

installations and expanding the potential for siting (Kougias et  al. 
2019). For example, in underwater PHS, the upper reservoir is the 
sea, and the lower is a hollow deposit at the seabed. Seawater is 
pumped out of the deposit to store off-peak energy and re-enters through turbines to recharge it (Kougias et al. 2019). Using a similar 
concept, underground siting in abandoned mines and caverns could 
be developed reasonably quickly (IEA 2020h). Storage of energy as 
gravitational potential can also be implemented using materials 
other than water, such as rocks and sand. Pumped technology is 
a mature technology (Rehman et al. 2015; Barbour et al. 2016) and 
can be important in supporting the transition to future low-carbon 
electricity grids (IHA 2021).
Batteries. There are many types of batteries, all having unique 
features and suitability, but their key feature is their rapid response 
time. A rechargeable battery cell is charged by using electricity to

drive ions from one electrode to another, with the reverse occurring 
on discharge, producing a  usable electric current (Crabtree et  al. 
2015). While lead-acid batteries (LABs) have been widely used for 
automotive and grid applications for decades (May et al. 2018), LIBs 
are increasingly being used in grid-scale projects (Crabtree et  al. 
2015), displacing LABs. The rapid response time of batteries makes 
them suitable for enhanced frequency regulation and voltage support, 
enabling the integration of variable renewables into electricity grids 
(Strbac and Aunedi 2016). Batteries can provide almost all electricity 
services, except for seasonal storage. LIBs, in particular, can store 
energy and power in small volumes and with low weight, making 
them the default choice for EVs (Placke et al. 2017). EV batteries are 
expected to form a distributed storage resource as this market grows, 
both impacting and supporting the grid (Staffell and Rustomji 2016).

Drawbacks of batteries include relatively short lifespans and the 
use of hazardous or costly materials in some variants. While LIB 
costs are decreasing (Schmidt et al. 2017; Vartiainen et al. 2020), 
the risk of thermal runaway, which could ignite a fire (Gur 2018; 
Wang et al. 2019a), concerns about long-term resource availability 
(Olivetti et  al. 2017; Sun et  al. 2017), and concerns about global 
cradle-to-grave impacts (Peters et al. 2017; Kallitsis et al. 2020) need 
to be addressed.
Table 6.6 | Technical characteristics of a selected range of battery chemistries, categorised as those which precede LIBs (white background), LIBs (yellow 
background) and post LIBs (blue background).  
Battery type Technology maturity Lifespan (cycles) Energy density (Wh L–1)Specific energy (Wh kg–1)Price (USD kWh–1) in 2017
Lead acid High 300–800 e102–106 e38–60 e70–160 e
Ni MH High 600–1200 e220–250 e42–110 e210–365 e
Ni Cd High 1350 b100 b60 b700

High-temperature Na batteries High 1000 e150–280 h80–120 a315–490 h
LIB state of the art High 1000–6000 e200–680 c110–250 c176 f
LIB energy-optimised Under development 600–850 c300–440 c
Classic Li Metal (CLIM) Under development 800–1050 c420–530 c
Metal Sulphur (Li S) Near commercialisation 100–500 e350–680 c, h360–560 c, h36–130 e
Metal Sulphur (Na S) Under development 5000–10,000 h
Metal Air (Li/air) Under development 20–100 e470–900 d70–200 e
Metal Air (Zn/air) Under development 150–450 e200–410 d70–160 e
Na ion Under development 500 g600 g
All-solid-state Under development 278–479 c
Redox Under development >12,000–14,000 j15–25 j10–20 j66 j
Note: With the exception of the All-solid-state batteries, all use liquid electrolytes. Source: a Mahmoudzadeh et al. 2017; b Manzetti and Mariasiu 2015; c Placke et al. 2017; 
d Nykvist and Nilsson 2015; e Cano et al. 2018; f Bloomberg Energy Finance, 2019; g You and Manthiram 2017; h Fotouhi et al. 2017; i IRENA 2017b; j Yang et al. 2020.

655
Energy Systems  Chapter 66The superior characteristics of LIBs will keep them the dominant choice 
for EV and grid applications in the medium term (high confidence). 
There are, however, several next-generation battery chemistries 
(Placke et  al. 2017), which show promise (high  confidence). 
Cost reductions through economies of scale are a  key area for 
development. Extending the life of the battery can bring down overall 
costs and mitigate the environmental impacts (Peters et al. 2017). 
Understanding and controlling battery degradation is therefore 
important. The liquid, air-reactive electrolytes of conventional LIBs 
are the main source of their safety issues (Janek and Zeier 2016; Gur 
2018), so all-solid-state batteries, in which the electrolyte is a solid, 
stable material, are being developed. They are expected to be safe, 
be durable, and have higher energy densities (Janek and Zeier 2016). 
New chemistries and concepts are being explored, such as lithium-

sulphur batteries to achieve even higher energy densities (Van 
Noorden 2014; Blomgren 2017) and sodium chemistries because 
sodium is more abundant than lithium (Hwang et  al. 2017). Cost-
effective recycling of batteries will address many sustainability issues 
and prevent hazardous and wasteful disposal of used batteries 
(Harper et  al. 2019). Post-LIB chemistries include metal sulphur, 
metal-air, metal ion (besides lithium) and all-solid-state batteries.
Compressed air energy storage (CAES). With CAES, off-peak 
electricity is used to compress air in a  reservoir  – either in salt 
caverns for large-scale or in high-pressure tanks for smaller-scale 
installations. The air is later released to generate electricity. While 
conventional CAES has used natural gas to power compression, 
new low-carbon CAES technologies, such as isothermal or adiabatic 
CAES, control thermal losses during compression and expansion 
(Wang et  al. 2017c). Fast responses and higher efficiencies occur

in small-scale CAES installations, scalable to suit the application 
as a  distributed energy store, offering a  flexible, low-maintenance 
alternative (Luo et al. 2014; Venkataramani et al. 2016).
CAES is a mature technology in use since the 1970s. Although CAES 
technologies have been developed, there are not many installations at 
present (Wang et al. 2017b; Blanc et al. 2020). While the opportunities 
for CAES are significant, with a global geological storage potential of 
about 6.5 PW (Aghahosseini and Breyer 2018), a significant amount 
of initial investment is required. Higher efficiencies and energy 
densities can be achieved by exploiting the hydrostatic pressure of 
deep water to compress air within submersible reservoirs (Pimm 
et al. 2014). CAES is best suited to bulk diurnal electricity storage for 
buffering VRE sources and services, which do not need a very rapid 
response. In contrast to PHS, CAES has far more siting options and 
poses few environmental impacts.

Liquid air energy storage (LAES). LAES uses electricity to 
liquefy air by cooling it to   –196°C and storing it in this condensed 
form (largely liquid nitrogen) in large, insulated tanks. To release 
electricity, the ‘liquid air’ is evaporated through heating, expanding 
to drive gas turbines. Low-grade waste heat can be utilised, providing 
opportunities for integrating with industrial processes to increase 
system efficiency. There are clear, exploitable synergies with the 
existing liquid gas infrastructure (Peters and Sievert 2016).LAES provides bulk daily storage of electricity, with the additional 
advantage of being able to capture waste heat from industrial 
processes. This technology is in the early commercial stage (Brandon 
et al. 2015; Regen 2017). Advances in whole systems integration can be 
developed to integrate LAES with industrial processes, making use of 
their waste heat streams. LAES uniquely removes contaminants in the

air and could potentially incorporate CO 2 capture (Taylor et al. 2012).
Thermal energy storage (TES). TES refers to a  range of 
technologies exploiting the ability of materials to absorb and store 
heat or cold, either within the same phase (sensible TES), through 
phase changes (latent TES), or through reversible chemical reactions 
(thermochemical TES). Pumped Thermal Energy Storage (PTES), 
a hybrid form of TES, is an air-driven electricity storage technology 
storing both heat and cold in gravel beds, using a reversible heat-
pump system to maintain the temperature difference between the 
two beds and gas compression to generate and transfer heat (Regen 
2017). TES technologies can store both heat and cold energy for long 
periods, for example, in underground water reservoirs for balancing 
between seasons (Dahash et al. 2019; Tian et al. 2019), storing heat 
and cold to balance daily and seasonal temperatures in buildings and

reducing heat build-up in applications generating excessive waste 
heat, such as data centres and underground operations.
TES can be much cheaper than batteries and has the unique ability to 
capture and reuse waste heat and cold, enabling the efficiency of many 
industrial, buildings, and domestic processes to be greatly improved 
(high confidence). Integration of TES into energy systems is particularly 
important, as the global demand for cooling is expected to grow (Elzinga 
et al. 2014; Peters and Sievert 2016).  Sensible TES is well developed 
and widely used; latent TES is less developed with few applications. 
Thermochemical TES is the least developed, with no application yet 
(Prieto et  al. 2016; Clark et  al. 2020). The potential for high-density 
storage of industrial heat for long periods in thermochemical TES 
(Brandon et  al. 2015) is high, with energy densities comparable to 
that of batteries (Taylor et al. 2012), but material costs are currently

prohibitive, ranging from hundreds to thousands of dollars per tonne.
Flywheel energy storage (FES). Flywheels are charged by 
accelerating a rotor/flywheel. Energy is stored in the spinning rotor’s 
inertia which is only decelerated by friction (minimised by magnetic 
bearings in a vacuum), or by contact with a mechanical, electric motor. 
They can reach full charge very rapidly, their state of charge can be 
easily determined (Amiryar and Pullen 2017), and they operate over 
a  wide range of temperatures. While they are more expensive to 
install than batteries and supercapacitors, they last a long time and 
are best suited to stationary grid storage, providing high power for 
short periods (minutes). Flywheels can be used in vehicles, but not as 
the primary energy source.
Flywheels are a relatively mature storage technology but not widely 
used, despite their many advantages over electrochemical storage 
(Dragoni 2017). Conventional flywheels require costly, high tensile

strength materials, but high-energy flywheels, using lightweight 
rotor materials, are being developed (Hedlund et al. 2015; Amiryar 
and Pullen 2017).

656
Chapter 6 Energy Systems6Supercapacitors  – also known as ultracapacitors or double 
layer capacitors (Scap). Supercapacitors consist of a  porous 
separator sandwiched between two electrodes, immersed in 
a  liquid electrolyte (Gur 2018). When a  voltage is applied across 
the electrodes, ions in the electrolyte form electric double layers at 
the  electrode surfaces, held by electrostatic forces. This structure 
forms a capacitor, storing electrical charge (Brandon et al. 2015; Lin 
et al. 2017) and can operate from –40°C to 65°C.
Supercapacitors can supply high peaks of power very rapidly for 
short periods (seconds up to minutes) and are able to fulfil the 
grid requirements for frequency regulation, but they would need 
to be hybridised with batteries for automotive applications. Their 
commercial status is limited by costly materials and additional 
power electronics required to stabilise their output (Brandon et al.

2015). Progress in this area includes the development of high-energy 
supercapacitors, LIB-supercapacitor devices (Gonzalez et  al. 2016), 
and cheaper materials (Wang et al. 2017a), all providing the potential 
to improve the economic case for supercapacitors, either by reducing 
manufacturing costs or extending their service portfolio.
Redox flow batteries (RFB). Redox flow batteries use two separate 
electrolyte solutions, usually liquids, but solid or gaseous forms may 
also be involved, stored in separate tanks, and pumped over or through 
electrode stacks during charge and discharge, with an ion-conducting 
membrane separating the liquids. The larger the tank, the greater the 
energy storage capacity, whereas more and larger cells in the stack 
increase the power of the flow battery. This decoupling of energy from 
power enables RFB installations to be uniquely tailored to suit the 
requirements of any given application. There are two commercially

available types today: vanadium and zinc bromide, and both operate 
at near ambient temperatures, incurring minimal operational costs.
RFBs respond rapidly and can perform all the same services as LIBs, 
except for onboard electricity for EVs. Lower cost chemistries are 
emerging, to enable cost-effective bulk energy storage (Brandon 
et al. 2015). A new membrane-free design eliminates the need for 
a separator and also halves the system requirements, as the chemical 
reactions can coexist in a single electrolyte solution (Navalpotro et al. 
2017; Arenas et al. 2018).
Power to fuels (PtX) (see also Section 6.4.3.1). The process of using 
electricity to generate a gaseous fuel, such as hydrogen or ammonia, 
is termed power-to-gas (PtG/P2G) (IEA 2020h). When injected into 
the existing gas infrastructure (Section 6.4.5), it has the added benefit 
of decarbonising gas (Brandon et al. 2015). Electricity can be used 
to generate hydrogen, which is then converted back into electricity

using combined-cycle gas turbines that have been converted to run 
on hydrogen. For greater compatibility with existing gas systems 
and appliances, the hydrogen can be combined with captured 
carbon dioxide to form methane and other synthetic fuels (Thema 
et al. 2019), however, methane has high global warming potential 
and its supply chain emissions have been found to be significant 
(Balcombe et al. 2013).
PtX can provide all required grid services, depending on how it is 
integrated. However, a  significant amount of PtX is required for storage to produce electricity again (Bogdanov et al. 2019) due to 
the low roundtrip efficiency of converting electricity to fuel and 
back again. However, portable fuels (hydrogen, methane, ammonia, 
synthetic hydrocarbons) are useful in certain applications, for 
example, in energy systems lacking the potential for renewables. 
The high energy density of chemical storage is essential for more

demanding applications, such as transporting heavy goods and 
heating or cooling buildings (IEA 2020h). Research is needed into 
more efficient and flexible electrolysers which last longer and cost 
less (Brandon et al. 2015).
Hydrogen and reversible hydrogen fuel cells (H/RHFC). Hydrogen 
is a flexible fuel with diverse uses, capable of providing electricity, 
heat, and long-term energy storage for grids, industry, and transport, 
and has been widely used industrially for decades (Section 6.4.5.1). 
Hydrogen can be produced in various ways and stored in significant 
quantities in geological formations at moderate pressures, often 
for long periods, providing seasonal storage (Gabrielli et al. 2020). 
A core and emerging implementation of PtX is hydrogen production 
through electrolysers. Hydrogen is a carbon-free fuel holding three 
times the energy of an equivalent mass of gasoline but occupying 
a larger volume. An electrolyser uses excess electricity to split water

into hydrogen and oxygen through the process of electrolysis. A fuel 
cell performs the reverse process of recombining hydrogen and 
oxygen back into water, converting chemical energy into electricity 
(Elzinga et  al. 2014). Reversible hydrogen fuel cells (RHFCs) can 
perform both functions in a single device, however, they are still in 
the pre-commercial stage, due to prohibitive production costs.
Hydrogen can play an important role in reducing emissions and has 
been shown to be the most cost-effective option in some cases, as it 
builds on existing systems (Staffell et al. 2018). Fuel cell costs need to 
be reduced and the harmonies between hydrogen and complementary 
technologies, such as batteries, for specific applications need to be 
explored further. Hydrogen can provide long-duration storage to 
deal with prolonged extreme events, such as very low output of 
wind generation, to support resilience of future low-carbon energy

systems. Research in this technology focuses on improving roundtrip 
efficiencies, which can be as high as 80% with recycled waste heat 
and in high-pressure electrolysers, incorporating more efficient 
compression (Matos et al. 2019). Photo-electrolysis uses solar energy 
to directly generate hydrogen from water (Amirante et al. 2017).
6.4.4.2 Societal Dimensions of Energy Storage
Public awareness and knowledge about electricity storage 
technologies, their current state, and their potential role in future 
energy systems is limited (Jones et  al. 2018). For instance, people 
do not perceive energy system flexibility and storage as a significant 
issue, or assume storage is already taking place. Public perceptions 
differ across storage technologies. Hydrogen is considered a modern 
and clean technology, but people also have safety concerns. 
Moreover, the public is uncertain about hydrogen storage size and 
the possibility of storing hydrogen in or near residential areas (Eitan

and Fischhendler 2021). Battery storage both on the household and 
community level was perceived as slightly positive in one study in the 
UK (Ambrosio-Albala et al. 2020). However, financial costs are seen

657
Energy Systems  Chapter 66as a main barrier. The potential of EV batteries to function as flexible 
storage is limited by the current numbers of EV owners and concerns 
that one’s car battery might not be fully loaded when needed.
6.4.5 Energy Transport and Transmission
The linkage between energy supply and distribution, on the one hand, 
and energy use on the other is facilitated by various mechanisms 
for transporting energy. As the energy system evolves, the way that 
energy is transported will also evolve.
6.4.5.1 Hydrogen: Low-carbon Energy Fuel
Hydrogen is a  promising energy carrier for a  decarbonised world 
(Box 6.9). It  can be utilised for electricity, heat, transport, industrial 
demand, and energy storage (Abdin et  al. 2020). In low-carbon 
energy systems, hydrogen is expected to be utilised in applications 
that are not as amenable to electrification, such as a  fuel for 
heavy-duty road transport and shipping, or as a chemical feedstock

(Schemme et  al. 2017; Griffiths et  al. 2021). Hydrogen could also 
provide low-carbon heat for industrial processes or be utilised for 
direct reduction of iron ore (Vogl et al. 2018). Hydrogen could replace 
natural gas-based electricity generation (do Sacramento et al. 2013) 
in certain regions and support the integration of variable renewables 
into electricity systems by providing a means of long-term electricity 
storage. Hydrogen-based carriers, such as ammonia and synthetic 
hydrocarbons, can likewise be used in energy-intensive industries 
and the transport sector (Schemme et  al. 2017; IRENA 2019b) 
(e.g.,  synthetic fuels for aviation). These hydrogen-based energy 
carriers are easier to store than hydrogen. At present hydrogen has 
limited applications – mainly being produced onsite for the creation 
of methanol and ammonia (IEA 2019c), as well as in refineries.Low- or zero-carbon produced hydrogen is not currently competitive

for large-scale applications, but it is likely to have a significant role 
in future energy systems, due to its wide-range of applications 
(high confidence). Key challenges for hydrogen are: (i) cost-effective 
low/zero carbon production; (ii) delivery infrastructure cost; 
(iii)  land area (i.e., ‘footprint’) requirements of hydrogen pipelines, 
compressor stations, and other infrastructure; (iv) challenges in using 
existing pipeline infrastructure; (v) maintaining hydrogen purity; 
(vi) minimising hydrogen leakage; and (vii) the cost and performance 
of end uses. Furthermore, it is necessary to consider the public 
perception and social acceptance of hydrogen technologies and their 
related infrastructure requirements (Iribarren et al. 2016; Scott and 
Powells 2020).
Hydrogen production. Low- or zero-carbon hydrogen can be 
produced from multiple sources. While there is no consensus on 
the hydrogen production spectrum, ‘blue’ hydrogen (Goldmann and

Dinkelacker 2018) generally refers to hydrogen produced from natural 
gas combined with CCS through processes such as steam methane 
reforming (SMR) (Sanusi and Mokheimer 2019) and advanced gas 
reforming (Zhou et  al. 2020). Low-carbon hydrogen could also be 
produced from coal coupled with CCS (Hu et al. 2020) (Table 6.7). 
Current estimates are that adding CCS to produce hydrogen from 
SMR will add on average 50% on the capital cost, 10% to fuel, and 
100% to operating costs. For coal gasification, CCS will add 5% 
to the capital and fuel costs and 130% to operating costs (Staffell 
et al. 2018; IEA 2019d). Further, biomass gasification could produce 
renewable hydrogen, and when joined with CCS could provide 
negative carbon emissions. ‘Green’ hydrogen (Jaszczur et al. 2016) 
is most often referred to as hydrogen produced from zero-carbon 
electricity sources such as solar power and wind power (Schmidt et al. 
2017) (Table 6.8). Nuclear power could also provide clean hydrogen,

via electrolysis or thermochemical water splitting (EERE 2020). 
Table 6.7 | Key performance and cost characteristics of different non-electric hydrogen production technologies, including carbon capture and storage (CCS).
TechnologyLHV efficiency (%)
Carbon intensity (kgCO 2 (kgH 2)–1)Cost estimates* (USD (kgH 2)–1)
Current Long-term Current Long-term
Steam methane reforming (SMR) 65 e74 e,f1.0–3.6 e,i1.0–2.7 a,b,c,d,e1.5–2.6 e
Advanced gas reforming – 81–84 e,f0.9–2.9 e1.3–2.1 e1.2–3.4 e,f
Hydrogen from coal gasification 54 e54(5)2.1–5.5 e,i1.8–3.1 a,b,c,d,e2.4–3.3 e
Hydrogen from biomass gasification 53.6 g40–60 ePotential to achieve negative emission e,h4.9 e2.9–5.9 e,f
Source: a CSIRO 2021; b IEA 2020; c IRENA 2019; d Hydrogen Council 2020; e CCC 2018; f BEIS 2021; g Ishaq et al. 2021; h Al-Mahtani et al. 2021; i IEA 2019.
* USD per GBP exchange rate: 0.72 (August 2021); LHV: Lower Heating Values; Long-term refers to 2040 and 2050 according to different references.

Table 6.8 | Efficiency and cost characteristics of electrolysis technologies for hydrogen production.  
TechnologyLHV efficiency (%) CAPEX (USD kW e–1) Cost estimates*,† (USD (kgH 2)–1)
Current Long-term b,e,f,hCurrent gLong-term gCurrent Long-term
Alkaline Electrolysers 58–77 a,b,e,f,h70–82 500–1400 200–700 2.3–6.9 a,b,c,e0.9–3.9 c,e
Polymer electrolyte membrane (PEM) 54–72 a,b,e,f,h67–82 1100–1800 200–900 3.5–9.3 a,d,e,f2.2–7.2 e,f
Solid oxide electrolyser cell (SOEC) 74–81 b,f,h77–92 2800–5600 500–1000 4.2 e2.6–3.6 e
Source: a CSIRO 2021; b IEA 2020; c IRENA 2019; d Hydrogen Council 2020; e CCC 2018; f BEIS 2021; g IEA 2019; h Christensen 2020.
* USD per GBP exchange rate: 0.72 (August 2021); † The cost of hydrogen production from electrolysers is highly dependent on the technology, source of electricity, and 
operating hours, and some values provided are based on the assumptions made in the references.

658
Chapter 6 Energy Systems6Hydrogen can even be produced by pyrolysis of methane (Sánchez-
Bastardo et al. 2020) – sometimes called ‘turquoise’ hydrogen, solar 
thermochemical water splitting, biological hydrogen production 
(cyanobacteria) (Velazquez Abad and Dodds 2017) – and microbes 
that use light to make hydrogen (under research) (EIA 2020).
Hydrogen energy carriers. Hydrogen can be both an energy 
carrier itself, be converted further into other energy carriers (such 
as synthetic fuels) and be a  means of transporting other sources 
of energy. For example, hydrogen could be transported in its native 
gaseous form or liquefied. Hydrogen can also be combined with 
carbon and transported as a synthetic hydrocarbons (Gumber and 
Gurumoorthy 2018) (IRENA 2019d) as well as be transported via 
liquid organic hydrogen carriers (LOHCs) or ammonia (IRENA 
2019d). For synthetic hydrocarbons such as methane or methanol 
to be considered zero carbon, the CO 2 used to produce them would

need to come from the atmosphere either directly through DACCS 
or indirectly through BECCS (IRENA 2019b). LOHCs are organic 
substances in liquid or semi-solid states, which store hydrogen 
based on reversible catalytic hydrogenation and de-hydrogenation 
of carbon double bounds (Niermann et  al. 2019; Rao and Yoon 
2020). Hydrogen produced from electrolysis could also be seen as 
an electricity energy carrier. This is an example of the PtX processes 
(Section 6.4.4), entailing the conversion of electricity to other energy 
carriers for subsequent use.
Ammonia is a  promising cost-effective hydrogen carrier (Creutzig 
et  al. 2019). Onsite generation of hydrogen for the production of 
ammonia already occurs today, and the ammonia (NH 3) could be 
subsequently ‘cracked’ (with a  15–25% energy loss) to reproduce 
hydrogen (Hansgen et  al. 2010; Montoya et  al. 2015; Bell and 
Torrente-Murciano 2016). Because the energy density of ammonia

is 38% higher than liquid hydrogen (Osman and Sgouridis 2018), it 
is potentially a  suitable energy carrier for long-distance transport 
and storage (Salmon et  al. 2021). Moreover, ammonia is more 
easily condensable (liquefied at 0.8 MPa, 20°C), which provides 
economically viable hydrogen storage and supply systems. 
Ammonia production and transport are also established industrial 
processes (about 180 MMT yr –1) (Valera-Medina et  al. 2017), and 
hence ammonia is considered to be a  scalable and cost-effective 
hydrogen-based energy carrier. At present, most ammonia is used 
in fertilisers (about 80%), followed by many industrial processes, 
such as the manufacturing of mining explosives and petrochemicals 
(Jiao and Xu 2018). In contrast to hydrogen, ammonia can be used 
directly as a fuel without any phase change for internal combustion 
engines, gas turbines, and industrial furnaces (Kobayashi et al. 2019). 
Ammonia can also be used in low- and high-temperature fuel cells

(Lan and Tao 2014), whereby both electricity and hydrogen can be 
produced without any nitrogen oxide (NO x) emissions. Furthermore, 
ammonia provides the flexibility to be dehydrogenated for hydrogen-
use purposes. Ammonia is considered a  carbon-free sustainable 
fuel for electricity generation, since in a complete combustion, only 
water and nitrogen are produced (Valera-Medina et al. 2017). Like 
hydrogen, ammonia could facilitate management of VRE, due to its 
cost-effective grid-scale energy storage capabilities. In this regard, 
production of ammonia via hydrogen from low- or zero-carbon 
generation technologies along with ammonia energy recovery technologies (Afif et  al. 2016) could play a  major role in forming 
a hydrogen and/or ammonia economy to support decarbonisation. 
However, there are serious concerns regarding the ability to safely 
use ammonia for all these purposes, given its toxicity  – whereas 
hydrogen is not considered toxic.

In general, challenges around hydrogen-based energy carriers  – 
including safety issues around flammability, toxicity, storage, and 
consumption – require new devices and techniques to facilitate their 
large-scale use. Relatively high capital costs and large electricity 
requirements are also challenges for technologies that produce 
hydrogen energy carriers. Yet, these energy carriers could become 
economically viable through the availability of low-cost electricity 
generation and excess of renewable energy production (Daiyan et al. 
2020). A key challenge in use of ammonia is related to the significant 
amount of NO x emissions, which is released from nitrogen and 
oxygen combustion, and unburned ammonia. Both have substantial 
air pollution risks, which can result in lung and other injuries, and 
can reduce visibility (EPA 2001). Due to the low flammability of 
hydrogen energy carriers such as liquefied hydrogen (Nilsson et al.

2016) and ammonia (Li et al. 2018), a stable combustion (Lamas and 
Rodriguez 2019; Zengel et al. 2020) in the existing gas turbines is not 
currently feasible. In recent developments, however, the proportion of 
hydrogen in gas turbines has been successfully increased, and further 
development of gas turbines may enable them to operate on 100% 
hydrogen by 2030 (Pflug et al. 2019).
Long-distance hydrogen transport. Hydrogen can allow regional 
integration and better utilisation of low- or zero-carbon energy 
sources (Boxes 6.9 and 6.10). Hydrogen produced from renewables 
or other low-carbon sources in one location could be transported for 
use elsewhere (Philibert 2017; Ameli et al. 2020). Depending on the 
distance to the user and specific energy carrier utilised (e.g., gaseous 
hydrogen or LOHC), various hydrogen transport infrastructures, 
distribution systems, and storage facilities would be required (Hansen 
2020; Schönauer and Glanz 2021) (Figure 6.17).

Hydrogen can be liquefied and transported at volume over the ocean 
without pressurisation. This requires a temperature of –253°C and is 
therefore energy-intensive and costly (Niermann et al. 2021). Once 
it reaches its destination, the hydrogen needs to be re-gasified, 
adding further cost. A demonstration project is under development 
exporting liquid hydrogen from Australia to Japan (Yamashita et al. 
2019). Hydrogen could also be transported as ammonia by ocean in 
liquid form. Ammonia is advantageous because it is easier to store 
than hydrogen (Zamfirescu and Dincer 2008; Soloveichik 2016; Nam 
et al. 2018). Liquid ammonia requires temperatures below –33°C and 
is therefore more straightforward and less costly to transport than 
liquefied hydrogen and even liquefied natural gas (Singh and Sahu 
2018). A project exporting ammonia from Saudi Arabia to Japan is 
under consideration (Nagashima 2018). LOHCs could also be used

to transport hydrogen at ambient temperature and pressure. This 
advantageous property of LOHCs makes them similar to oil products, 
meaning they can be transported in existing oil infrastructure 
including oil tankers and tanks (IEA 2019; Niermann et  al. 2019). 
A project is under development to export hydrogen from Brunei to 
Japan using LOHCs (Kurosaki 2018).

659
Energy Systems  Chapter 66Intra-regional hydrogen transportation. Within a country or region, 
hydrogen would likely be pressurised and delivered as compressed 
gas. About three times as much compressed hydrogen by volume is 
required to supply the same amount of energy as natural gas. Security 
of supply is therefore more challenging in hydrogen networks than 
in natural gas networks. Storing hydrogen in pipelines (linepack) 
would be important to maintaining security of supply (Ameli et  al. 
2017, 2019). Due to the physics of hydrogen, in most cases exiting 
gas infrastructure would need to be upgraded to transport hydrogen. 
Transporting hydrogen in medium- or high-pressure networks most 
often would require reinforcements in compressor stations and 
pipeline construction routes (Dohi et al. 2016). There are several recent 
examples of efforts to transport hydrogen by pipeline. For example,

in the Iron Mains Replacement Programme in the UK, the existing low-pressure gas distribution pipes are being converted from iron to 
plastic (Committee on Climate Change 2018). In the Netherlands, an 
existing low-pressure 12 km natural gas pipeline has been used for 
transporting hydrogen (Dohi et al. 2016).
To bypass gas infrastructure in transporting hydrogen, methane can 
be transported using the existing gas infrastructure, while hydrogen 
can be produced close to the demand centres. This approach will only 
make sense if the methane is produced in a manner that captures 
carbon from the atmosphere and/or if CCS is used when the methane 
is used to produce hydrogen.
Bulk hydrogen storage. Currently, hydrogen is stored in bulk in 
chemical processes such as metal and chemical hydrides as well as Production Utilisation Transport/storage
Hydrogen production through electrolysis processes driven by:
• Renewable energy resources (wind, solar)
• Tidal lagoon
• Nuclear

Non-electric hydrogen production, such as:
• SMR/ATR with CCS
• Biomass/coal gasiﬁcation with CCSLiqueﬁed hydrogen 
carriers (e.g., hydrogen, 
ammonia, LOHCs)
Gas infrastructure
By road (e.g., trucks)
Inter-seasonal storageShort-term storage
Gas-ﬁred and 
CHP plantsCommercial/residentialIndustrials Transport 
(e.g., fuel cell vehicles, aviation)Hydrogen/ammonia export
Figure 6.17 | Hydrogen value chain.  Hydrogen can be produced by various means and input and fuel sources. These processes have different emissions 
implications. Hydrogen can be transported by various means and in various forms, and it can be stored in bulk for longer-term use. It also has multiple potential end uses. 
CHP: Combined heat and power.

660
Chapter 6 Energy Systems6in geologic caverns (Andersson and Grönkvist 2019; Caglayan et al. 
2019) (e.g.,  salt caverns operate in Sweden) (Elberry et  al. 2021). 
There are still many challenges, however, due to salt or hard rock 
geologies, large size, and minimum pressure requirements of the sites 
(IEA 2019c). Consequently, alternative carbon-free energy carriers, 
which store hydrogen, may become more attractive (Lan et al. 2012; 
Kobayashi et al. 2019).
6.4.5.2 Electricity Transmission
Given the significant geographical variations in the efficiency of 
renewable resources across different regions and continents, electricity 
transmission could facilitate cost-effective deployment of renewable 
generation, enhance resilience and security of supply, and increase 
operational efficiency (high confidence). The diurnal and  seasonal 
characteristics of different renewable energy sources such as wind, 
solar, and hydropower can vary significantly by location. Through

enhanced electricity transmission infrastructure, more wind turbines 
can be deployed in areas with high wind potential and more solar 
panels in areas with larger solar irradiation. Increases in electricity 
transmission and trade can also enhance operational efficiency and 
reduce or defer the need for investment in peaking plants, storage, or 
other load management techniques needed to meet security of supply 
requirements associated with localised use of VRE sources. Increased 
interconnectivity of large-scale grids also allows the aggregation of 
‘smart grid’ solutions such as flexible heating and cooling devices 
for flexible demand in industrial, commercial, and domestic sectors 
(Hakimi et al. 2020) and EVs (Muratori and Mai 2020; Li et al. 2021). 
In general, interconnection is more cost-optimal for countries that are 
geographically close to each other and can benefit from the diversity 
of their energy mixes and usage (Schlachtberger et al. 2017). Such

developments are not without price, however, and among other 
concerns, raise issues surrounding land use, public acceptance, 
and resource acquisition for materials necessary for renewable 
developments (Capellán-Pérez et al. 2017; Vakulchuk et al. 2020).
A number of studies have demonstrated the cost benefits of 
interconnected grids in a range of geographical settings, including 
across the USA (Bloom et  al. 2020), Europe (Newbery et  al. 2013; 
Cluet et al. 2020), between Australia and parts of Asia (Halawa et al. 
2018), and broader global regions, for example between the Middle 
East and Europe or North Africa and Europe (Tsoutsos et al. 2015). 
While there is growing interest in interconnection among different 
regions or continents, a broad range of geopolitical and socio-techno-
economic challenges would need to be overcome to support this 
level of international cooperation and large-scale network expansion 
(Bertsch et al. 2017; Palle 2021).

Status of electricity transmission technology. Long-distance 
electricity transmission technologies are already available. High 
voltage alternating current (HVAC), high-voltage direct current 
(HVDC), and ultra HVDC (UHVDC) technologies are well-established 
and widely used for bulk electricity transmission (Alassi et al. 2019). 
HVDC is used with underground cables or long-distance overhead 
lines (typically voltages between 100–800 kV) (Alassi et  al. 2019) 
where HVAC is infeasible or not economic. A project development 
agreement, worth approximately USD17 billion, was signed in January 2021 that would connect 10 GW of PVs in the north of Australia 
via a 4500 km 3 GW HVDC cable to Singapore, suggesting that this 
would be cost effective (Sun Cable 2021). In September 2019, the 
Changji-Guquan ±1,100 kV UHVDC transmission project built by 
State Grid Corporation of China was officially completed and put 
into operation. The transmission line is able to transmit up to 12 GW

over 3341 km (Pei et al. 2020). This is the UHVDC transmission project 
with the highest voltage level, the largest transmission capacity, and 
the longest transmission distance in the world (Liu 2015).
Other technologies that could expand the size of transmission 
corridors and/or improve the operational characteristics include low-
frequency AC transmission (LFAC) (Y . Tang et al. 2021; Xiang et al. 
2021) and half-wave AC transmission (HWACT) (Song et  al. 2018; 
Xu et al. 2019). LFAC is technically feasible, but the circumstances 
in which it is the best economic choice compared to HVDC or 
HVAC still needs to be established (Xiang et  al. 2016). HWACT is 
restricted to very long distances, and it has not been demonstrated 
in practice, so its feasibility is unproven. There are still a number of 
technological challenges for long-distance transmission networks 
such as protection systems for DC or hybrid AC-DC networks (Chaffey

2016; Franck C. et al. 2017), improvement in cabling technology, and 
including the use of superconductors and nanocomposites (Ballarino 
et al. 2016; Doukas 2019), which require advanced solutions.
Challenges, barriers, and recommendations. The main challenge 
to inter-regional transmission is the absence of appropriate market 
designs and regulatory and policy frameworks. In addition, there 
are commercial barriers for further enhancement of cross-border 
transmission. The differing impacts of cross-border interconnections 
on costs and revenues for generation companies in different regions 
could delay the development of these interconnectors. It is not 
yet clear how the investment cost of interconnections should be 
allocated and recovered, although there is growing support for 
allocating costs in accordance with the benefits delivered to the 
market participants. Increased cross-border interconnection may 
also require new business models which provide incentives for

investment and efficient operation, manage risks and uncertainties, 
and facilitate coordinated planning and governance (Poudineh and 
Rubino 2017).
Optimising the design and operation of the interconnected 
transmission system, both onshore and offshore grids, also requires 
more integrated economic and reliability approaches (Moreno et al. 
2012) to ensure the optimal balance between the economics and the 
provision of system security while maximising the benefits of smart 
network technologies.
A wide range of factors, including generation profiles, demand 
profiles circuit losses, reliability characteristics, and maintenance, as 
well as the uncertainties around them will need to be considered in 
designing and operating long-distance transmission systems if they 
are to be widely deployed (Djapic et al. 2008; Du 2009; De Sa and Al 
Zubaidy 2011; E3G 2021). Public support for extending transmission 
systems will also be crucial, and studies indicate that such support is

661
Energy Systems  Chapter 666.4.6 Demand-side Mitigation Options from 
an Energy Systems Perspective
Demand-side measures are fundamental to an integrated approach 
to low-carbon energy systems (high confidence). Mitigation options, 
such as wind parks, CCS, and nuclear power plants, may not be 
implemented when actors oppose these options. Further, end 
users, including consumers, governments, businesses and industry, 
would need to adopt the relevant options, and then use these as 
intended; user adoption can be a key driver to scale up markets for 
low-carbon technologies. This section discusses which factors shape 
the likelihood that end users engage in relevant mitigation actions, 
focusing on consumers; strategies to promote mitigation actions are 
discussed in Section 6.7.6.1.
A wide range of actions of end users would reduce carbon emissions 
in energy systems (Abrahamse et  al. 2007; Dietz 2013; Hackmann 
et al. 2014; Creutzig et al. 2018; Grubler et al. 2018), including:

• use of low-carbon energy sources and carriers. Actors can produce 
and use their own renewable energy (e.g., install solar PV, solar 
water heaters, heat pumps), buy shares in a renewable energy 
project (e.g., wind shares), or select a renewable energy provider.
• adoption of technologies that support flexibility in energy use and 
sector coupling, thereby providing flexibility services by balancing 
demand and renewable energy supply. This would reduce the 
need to use fossil fuels to meet demand when renewable energy 
production is low and put less pressure on deployment of low-
emission energy supply systems. Examples are technologies to 
store energy (e.g., batteries and EVs) or that automatically shift 
appliances on or off (e.g., fridges, washing machines).
• adoption of energy-efficient appliances and systems and increase 
of resource efficiency of end uses so that less energy is required 
to provide the same service. Examples are insulating buildings,

and passive or energy-positive buildings.
• change behaviour to reduce overall energy demand or to match 
energy demand to available energy supplies. Examples include 
adjusting indoor temperature settings, reducing showering 
time, reducing car use or flying, and operating appliances when 
renewable energy production is high.
• purchase and use products and services that are associated with low 
GHG emissions during their production (e.g., reduce dairy and meat 
consumption) or for transporting products (e.g.,  local products). 
Also, end users can engage in behaviour supporting a  circular 
economy, by reducing waste (e.g.,  of food), sharing products 
(e.g., cars, equipment), and refurbishing products (e.g., repair rather 
than buying new products) so that fewer new products are used.
Various factors shape whether such mitigation actions are 
feasible and considered by end users, including contextual factors, 
individual abilities, and motivational factors. Mitigation actions can

be facilitated and encouraged by targeting relevant barriers and 
enablers (Section 6.7.6.1).
Contextual factors, such as physical and climate conditions, 
infrastructure, available technology, regulations, institutions, culture, 
and financial conditions define the costs and benefits of mitigation options that enable or inhibit their adoption (high confidence). 
Geographic location and climate factors may make some technologies, 
such as solar PV or solar water heaters, impractical (Chang et al. 2009). 
Culture can inhibit efficient use of home heating or PV (Sovacool and 
Griffiths 2020), low-carbon diets (Dubois et al. 2019), and advanced 
fuel choices (Van Der Kroon et  al. 2013). Also, favourable financial 
conditions promote the uptake of PV (Wolske and Stern 2018), good 
facilities increase recycling (Geiger et al. 2019), and vegetarian meal 
sales increase when more vegetarian options are offered.
Mitigation actions are more likely when individuals feel capable to

adopt them (Pisano and Lubell 2017; Geiger et al. 2019), which may 
depend on income and knowledge. Low-income groups may lack 
resources to invest in refurbishments and energy-efficient technology 
with high upfront costs (Chang et al. 2009; Andrews-Speed and Ma 
2016; Wolske and Stern 2018). Yet, higher-income groups can afford 
more carbon-intensive lifestyles (Golley and Meng 2012; Frederiks 
et al. 2015; Wiedenhofer et al. 2017; Namazkhan et al. 2019; Santillán 
Vera and de la Vega Navarro 2019; Mi et al. 2020). Knowledge of the 
causes and consequences of climate change and of ways to reduce 
GHG emissions is not always accurate, but lack of knowledge is not 
a main barrier to mitigation actions (Boudet 2019).
Motivation to engage in mitigation action, reflecting individuals’ 
reasons for actions, depends on general goals that people strive for 
in their life (i.e., values). People who strongly value protecting the

environment and other people are more likely to consider climate 
impacts and to engage in a wide range of mitigation actions than 
those who strongly value individual consequences of actions, such 
as pleasure and money (Taylor et al. 2014; Steg 2016). Values affect 
which types of costs and benefits people consider and prioritise 
when making choices, including individual, affective, social, and 
environmental costs and benefits (Gowdy 2008; Steg 2016).
First, people are more likely to engage in mitigation behaviour 
(i.e., energy savings, energy efficiency, resource efficiency in buildings, 
low-carbon energy generation) when they believe such behaviour has 
more individual benefits than costs (Harland et al. 1999; Steg and Vlek 
2009; Kastner and Stern 2015; Korcaj et al. 2015; Kardooni et al. 2016; 
Kastner and Matthies 2016; Wolske et al. 2017), including financial 
benefits, convenience, comfort, autonomy, and independence in

energy supply (Wolske and Stern 2018). Yet, financial consequences 
seem less important for decisions to invest in energy-efficiency and 
renewable energy production than people indicate (Zhao et al. 2012).
Second, people are less likely to engage in mitigation behaviours that 
are unpleasurable or inconvenient (Steg 2016), and more likely to do 
so when they expect to derive positive feelings from such actions 
(Smith et al. 1994; Pelletier et al. 1998; Steg 2005; Carrus et al. 2008; 
Brosch et al. 2014; Taufik et al. 2016). Positive feelings may be elicited 
when behaviour is pleasurable, but also when it is perceived as 
meaningful (Bolderdijk et al. 2013; Taufik et al. 2015).
Third, social costs and benefits can affect climate action (Farrow et al. 
2017), although people do not always recognise this (Nolan et al. 2008; 
Noppers et al. 2014). People engage more in mitigation actions when 
they think others expect them to do so and when others act as well

662
Chapter 6 Energy Systems6(Harland et al. 1999; Nolan et al. 2008; Rai et al. 2016). Being part of 
a group that advocates mitigation encourages such actions (Biddau 
et al. 2016; Fielding and Hornsey 2016; Jans et al. 2018). Talking with 
peers can reduce uncertainties and confirm benefits about adoption 
of renewable energy technology (Palm 2017), and peers can provide 
social support (Wolske et al. 2017). People may engage in mitigation 
actions when they think this would signal something positive about 
them (Milinski et  al. 2006; Griskevicius et  al. 2010; Noppers et  al. 
2014; Kastner and Stern 2015). Social influence can also originate 
from political and business leaders (Bouman and Steg 2019); GHG 
emissions are lower when legislators have strong environmental 
records (Jensen and Spoon 2011; Dietz et al. 2015).
Fourth, mitigation actions, including saving energy and hot water, 
limiting meat consumption, and investing in energy efficiency,

resource efficiency in buildings, and renewable energy generation 
are more likely when people care more strongly about others and the 
environment (Steg et al. 2015; Van Der Werff and Steg 2015; Wolske 
et  al. 2017). People across the world generally strongly value the 
environment (Steg 2016; Bouman and Steg 2019), suggesting that 
they are motivated to mitigate climate change. The more individuals 
are aware of the climate impact of their behaviour, the more they 
think their actions can help reduce such impacts, which strengthens their moral norms to act accordingly, and promotes mitigation 
actions (Steg and de Groot 2010; Jakovcevic and Steg 2013; Chen 
2015; Wolske et al. 2017).
Initial mitigation actions can encourage engagement in other 
mitigation actions when people experience that such actions are 
easy and effective (Lauren et al. 2016), and when initial actions make 
them realise they are a pro-environmental person, motivating them

to engage in more mitigation actions so as to be consistent (van der 
Werff et al. 2014; Lacasse 2015, 2016; Peters et al. 2018). This implies 
it would be important to create conditions that make it likely that 
initial mitigation actions motivate further actions.
6.4.7 Summary of Mitigation Options
Designing feasible, desirable, and cost-effective energy sector 
mitigation strategies requires comparison between the different 
mitigation options. One such metric is the cost of delivering one unit 
of energy, for example, the levelised cost, or USD MWh–1, of electricity 
produced from different sources. Levelised costs of electricity (LCOE) 
are useful because they normalise the costs per  unit of service 
provided. While useful in characterising options in broad strokes, 
Fossil fuel LCOE range in circa 2020
Solar PV − Utility
Solar PV − Other
CSP
Wind − Onshore
Wind − Offshore
Nuclear
Hydro
NGCC with CCS
Coal
Bio-electricity
with CCS

electricitycirca 2020
>2020 to 2040
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
circa 2020
EIA IEA IRENA NREL Other
Region Global North America Europe Asia South America0.30
0.25
0.20
0.15
0.10
0.05
02020USD kWh–1
Figure 6.18 | Range of LCOE (in USD kWh–1) from recent studies for different electricity-generating technologies circa 2020 and in the future between 
2020–2040.  LCOEs are primarily taken from recent studies, because the costs of some technologies are changing rapidly. To make the figure more tractable across the studies, 
we highlight the data from IEA WEO 2020 STEPS scenario in yellow (IEA 2020), the EIA AEO 2021 in light blue (EIA 2021), NREL ATB 2021 in brown, (NREL 2021), and

IRENA’s 2020 Renewable Power Generation Costs in dark blue (IRENA 2021). All other studies are shown in light grey markers. Marker shapes identify the regions included in 
the studies. Studies that included several regions are labelled as global. Only sources that provided LCOEs are included. Ranges for studies frequently reflect variations among 
regional estimates. Studies that are shown as a mid-point and a solid line represent studies that reported either a median or an average, and that had either a confidence 
interval or a minimum and a maximum reported. Dashed lines with markers at the end represent the range of values reported in studies that had several point estimates for 
either different regions or used different assumptions. All estimates were converted to USD2020. The publication year was used if no USD year was provided. Some studies

included transmissions costs, and some of the CCS studies included storage and sequestration costs, while others did not. Vertical axis is capped at USD2020 0.30 kWh–1, but 
some estimates for hydro, geothermal, natural gas and bioelectricity were higher than 0.30. The grey horizontal band denotes the range of fossil fuel electricity LCOEs circa 2020.

663
Energy Systems  Chapter 66it is important to acknowledge and understand several caveats 
associated with these metrics, particularly when applied globally. 
They may be constructed with different discount rates; they require 
information on energy input costs for options that require energy 
inputs (e.g.,  fossil electricity generation, biofuels); they depend on 
local resource availability, for example, solar insolation for solar 
power, wind classes for wind power, and rainfall and streamflow for 
hydropower; and actual implementation costs may include additional 
elements, for example, the costs of managing electricity grids heavily 
dependent on VRE electricity sources. These complicating factors vary 
across regions, some depend strongly on the policy environment in 
which mitigation options are deployed, and some depend on how 
technologies are constructed and operated.
The literature provides multiple LCOE estimates for mitigation options

today and in the future (see Table  6.9 for electricity  generation 
options). LCOE ranges for low- and zero-carbon electricity technologies 
overlap with LCOE’s of fossil generation without CCS. For example, 
LCOEs for utility solar and wind today and in the future overlap with 
those of new coal and gas without CCS (IEA WEO 2020; Lazard, 2020; 
NREL 2021) (Figure 6.18). Some of the overlap stems from differences 
in assumptions or regional conditions that apply to all technologies 
(e.g.,  variations in assumed discount rates), but the overlap also 
reflects the fact that low- and zero-carbon electricity generation 
options are, and will be, less expensive than emitting options in 
many regions. Future cost projections also illustrate that several 
technologies are anticipated to experience further cost declines over 
the coming decades, reinforcing the increasingly competitiveness of 
low- and zero-carbon electricity. For example, IEA’s LCOEs estimates

for offshore wind halve between 2020 and 2040 in several regions 
(IEA WEO 2020).
A more direct metric of mitigation options is the cost to reduce 
one tonne of CO 2 or equivalent GHGs, or USD tCO 2-eq–1 avoided. 
In  addition to the comparison challenges noted above, this metric 
must account for the costs and emissions of the emitting options that 
are being displaced by the low-carbon option. Assumptions about the 
displaced option can lead to very different mitigation cost estimates 
(Table  6.9). Despite these challenges, these metrics are useful for 
identifying broad trends and making broad comparisons, even from 
the global perspective in this assessment. But local information will 
always be critical to determine which options are most cost-effective 
in any specific applications.The feasibility and desirability of mitigation options extends well 
beyond the market economic costs of installation and operation

(Section  6.4.1). Figure  6.19 summarises the barriers and enablers 
for implementing different mitigation options in energy systems. The 
feasibility of different options can be enhanced by removing barriers 
and/or strengthening enablers of the implementation of the options. 
The feasibility of options may differ across context (e.g., region), time 
(e.g., 2030 versus 2050), scale (e.g., small versus large) and the long-
term warming goal (e.g., 1.5°C versus 2°C).
6.5 Climate Change Impacts 
on the Energy System
6.5.1 Climate Impacts on the Energy System
Many components of the energy system are affected by individual 
weather events and climate conditions (Table  6.10). In addition, 
a range of compounding effects can be anticipated, as the complex, 
interconnected climate and energy systems are influenced by multiple 
weather and climate conditions. This raises the question of whether 
the energy system transformation needed to limit warming will be 
impacted by climate change.

The impacts of climate change on the energy system can be divided 
into three areas: impacts on the energy supply; impacts on energy 
consumption; and impacts on energy infrastructure. The rest of this 
section focuses on how the future changes in climate drivers might 
affect the ability of the energy system transformation needed to 
mitigate climate change. The discussion of energy infrastructure in 
this section is limited to electricity system vulnerability.Table 6.9 | Examples of cost of mitigation for selected electricity options.  Results represent variations in mitigation options and displaced fossil generation. LCOEs 
are illustrative, but consistent with recent estimates. Negative values mean that the mitigation option is cheaper than the displaced option, irrespective of emissions benefits. 
NGCC: natural gas combined cycle.
Baseline
New coal Existing coal New NGCC Existing NGCC
Baseline emissions rate (tCO 2 MWh–1) 0.8 0.9 0.34 0.42

LCOEs (USD2020  kWh–1) 0.065 0.041 0.044 0.028
Utility scale solar PV 
(poor resource site)0.100 USD44 tCO 2-eq–1USD66 tCO 2-eq–1USD165 tCO 2-eq–1USD171 tCO 2-eq–1
(good resource site)0.035 –38 USD tCO 2-eq–1–7 USD tCO 2-eq–1–26 USD tCO 2-eq–1USD17 tCO 2-eq–1

664
Chapter 6 Energy Systems6
Figure 6.19 | Summary of the extent to which different factors would enable or inhibit the deployment of mitigation options in energy systems.  Blue bars indicate the extent to which the indicator enables the 
implementation of the option (E) and orange bars indicate the extent to which an indicator is a barrier (B) to the deployment of the option, relative to the maximum possible barriers and enablers assessed. An X signifies that the indicator 
is not applicable or does not affect the feasibility of the option, while a forward slash indicates that there is no or limited evidence whether the indicator affects the feasibility of the option. The shading indicates the level of confidence, with 
darker shading signifying higher levels of confidence. Appendix II provides an overview of the factors affecting the feasibility of options and how they differ across context (e.g., region), time (e.g., 2030 versus 2050), and scale (e.g., small

versus large), and includes a line of sight on which the assessment is based. The assessment method is explained in Annex II.11.
Geophysical Environmental-Ecological Technological Economic Socio-Cultural InstitutionalPhysical potential
Geophysical resources
Land use
Toxic waste, ecotoxicity 
eutrophication
Water quantity and quality
Simplicity 
Technological scalability
Maturity and technology 
readiness
Costs in 2030 and long term
Effects on employment and 
economic growth
Public acceptance 
Effects on health & wellbeing
Distributional effects
Political acceptance
Institutional capacity, governance 
and cross-sectoral coordination
Legal and administrative capacity
E B E B E B E B E B E B E B E B E B E B E B E B E B E B E B E B E B E B
Hydroelectric power
Nuclear
Carbon dioxide capture, utilization, and storage
Bioenergy
Fossil fuel phaseout
Energy storage for low-carbon grids
System integration

Low Confidence level barriers: B = BarriersE = Enablers
MediumLow Confidence level enablers: Medium High
HighStrength of enablers and barriers
0 50 100 Not applicableLimited or no evidence

665
Energy Systems  Chapter 66Table 6.10 | Relevance of the key climatic impact drivers (and their respective changes in intensity, frequency, duration, timing, and spatial extent) for major categories of activities in the energy sector.  
The climate impact drivers (CIDs) are identified in Table 12.1 in Chapter 12 of WGI AR6 report. The relevance is assessed as: positive/negative (+ or –), or both (±). D&O: Design and Operation; CF: Capacity Factor.
Energy 
sector Energy activity
Mean air temperature
Cold spell
Frost
Heavy precipitation and 
pluvial flood
Landslide
Aridity
Hail

HydropowerResources (dammed)
D&O (dammed)
Resources (undammed)
D&O (undammed)
Wind powerCapacity factors
D&O (onshore)
D&O (offshore)
Solar powerCF (PV)
CF (CSP)
D&O
Ocean energy Resources
Bio-energy Resources
Thermal 
power plants 
(incl nuclear)Efficiency
CCS Efficiency
Energy 
consumptionHeating
Cooling
Electric power 
systemD&O
Relevance of the climate impact driver:  Negative Positive Positive or negative

666
Chapter 6 Energy Systems66.5.2 Impacts on Energy Supply
The increased weather dependency of future low-carbon electricity 
systems amplifies the possible impacts of climate change (Staffell 
and Pfenninger 2018). However, globally climate change impacts 
on electricity generation  – including hydro, wind and solar power 
potentials  – should not compromise climate mitigation strategies 
(high confidence). Many of the changes in the climate system will 
be geographically complex at the regional and local levels. Thus, 
regionally climate change impacts on electricity generation could 
be significant. Climate change impacts on bioenergy potentials 
are more uncertain because of uncertainties associated with the 
crop response to climate change, future water availability and crop 
deployment. Climate change can reduce the efficiency of thermal 
power generation and increase the risk of power plant shutdowns 
during droughts. The potential additional cooling water needs of CCS

can increase these risks.
6.5.2.1 Hydropower
The impacts of climate change on hydropower will vary by region. 
High latitudes in the northern hemisphere are anticipated to 
experience increased runoff and hydropower potential. For other 
regions, studies find both increasing and decreasing runoff and 
hydropower potential. Areas with decreased runoff are anticipated 
to experience reduced hydropower production and increased water 
conflict among different economic activities (high confidence).
Hydropower production is directly related to the availability of water. 
Changes in runoff and its seasonality and changes in temperature 
and precipitation intensity will influence hydroelectricity production (IHA 2019). In general, increased precipitation will increase water 
availability and hydropower production. Increased precipitation 
intensity, however, may impact on the integrity of dam structures 
and affect power production by increasing debris accumulation and

vegetation growth. Additionally, increased precipitation intensity 
results in the silting of the reservoirs or increases the amount of 
water spilt, resulting in erosion (Schaeffer et  al. 2012; IHA 2019). 
Climate change will likely lead to higher air temperatures, resulting 
in more surface evaporation, less water storage, and loss of 
equipment efficiency (Ebinger and Vergara 2011; Mukheibir 2013; 
Fluixá-Sanmartín et  al. 2018; Hock et  al. 2019). Climate change 
may alter the demands for water use by other sectors that often 
rely on stored water in multi-purpose reservoirs, and may therefore 
generate conflicts over water use. The increased need for water 
for irrigation and/or industry can affect the availability of water for 
hydropower generation (Spalding-Fecher et  al. 2016; Solaun and 
Cerdá 2017). Higher temperatures increase glacier melt, increasing 
water availability for hydropower while the glaciers exist. Changes

in the timing of snow and ice melt may require upgrading in storage 
capacity and adaptation of the hydropower plant management for 
fully exploiting the increase in water availability.
The conclusions regarding climate change impacts on hydropower 
vary due to differences in modelling assumptions and methodology, 
such as choice of the climate and hydrological models, choice of 
metrics (e.g.,  projected production vs hydropower potential), level 
of  modelling details between local and global studies, reservoir 
operation assumptions. Also important is how hydropower production 
matches up with other reservoir purposes, accounting for other water 
and energy users, and how the competing uses are impacted by 
climate change (van Vliet et al. 2016b; Turner et al. 2017). Nonetheless, 
< –60 –60 to –40 –40 to –20 –20 to –5 –5 to +5 +5 to +20
+20 to +40 +40 to +60 >+602050s
2080sRCP2.6 RCP8.5
Gross hydropower potential changes (%)

Figure 6.20 | Global spatial patterns of changes in gross hydropower potential based on climate forcing from five climate models.  Changes are shown for 
the 2050s (upper) and the 2080s (lower) for the low-emission scenario (RCP2.6; left) and highest emission scenario (RCP8.5; right) scenarios relative to the control period 
(1971–2000). Source: data from van Vliet et al. (2016b).

667
Energy Systems  Chapter 66analyses consistently demonstrate that the global impact of climate 
change on hydropower will be small, but the regional impacts will be 
larger, and will be both positive and negative (Figure 6.20). Gross global 
hydropower potential in the 2050s has been estimated to slightly 
decrease (Hamududu and Killingtveit 2012) between 0.4% (for the 
low-emission scenario) and 6.1% (for the highest-emission scenario) 
for the 2080s compared to 1971–2000 (van Vliet et al. 2016a).
Regional changes in hydropower are estimated from 5–20% 
increases for most areas in high latitudes (van Vliet et  al. 2016b; 
Turner et al. 2017) to decreases of 5–20% in areas with increased 
drought conditions (Cronin et al. 2018). Models show a consistent 
increase in streamflow and hydropower production by 2080 in 
high latitudes of the northern hemisphere and parts of the tropics 
(Figure 6.20) (e.g., central Africa and southern Asia) while decreasing

in the USA, southern and central Europe, Southeast Asia and southern 
South America, Africa and Australia (van Vliet et  al. 2016c,a). 
Decreases in hydropower production are indicated for parts of North 
America, central and southern Europe, the Middle East, central Asia 
and Southern South America. Studies disagree on the changes in 
hydropower production in China, central South America, and partially 
in southern Africa (Hamududu and Killingtveit 2012; van Vliet et al. 
2016b; Solaun and Cerdá 2019; Fan et al. 2020).
6.5.2.2 Wind Energy
Climate change will not substantially impact future wind resources 
and will not compromise the ability of wind energy to support low-
carbon transitions (high confidence). Changing wind variability may 
have a small-to-modest impact on backup energy and storage needs 
(low confidence); however, current evidence is largely from studies 
focused on Europe.
Long-term global wind energy resources are not expected to

substantially change in future climate scenarios (Karnauskas 
et  al. 2018; Pryor et  al. 2020; Yalew et  al. 2020). However, recent 
research has indicated consistent shifts in the geographic position of 
atmospheric jets in the high-emission scenarios (Harvey et al. 2014), 
which would decrease wind power potentials across the Northern 
Hemisphere mid-latitudes and increase wind potentials across 
the tropics and the Southern Hemisphere. However, the climate 
models used to make these assessments differ in how well they can 
reproduce the historical wind resources and wind extremes, which 
raises questions about the robustness of their predictions of future 
wind resources (Pryor et al. 2020).
There are many regional studies on changes in wind resources from 
climate change. For Europe, there is medium evidence and moderate 
agreement that wind resources are already increasing and will 
continue to increase in Northern Europe and decrease in Southern

Europe (Carvalho et al. 2017; Devis et al. 2018; Moemken et al. 2018). 
For North America, the various studies have low agreement for the 
changes in future wind resources in part because the year-to-year 
variations in wind resources are often larger than the future change 
due to climate change (Johnson and Erhardt 2016; Chen 2020; 
Costoya et al. 2020; Wang et al. 2020b). Studies show increases in 
future wind resources in windy areas in South America (Ruffato-Ferreira et al. 2017; de Jong et al. 2019). No robust future changes 
in wind resources have been identified in China (Xiong et al. 2019). 
However, none of the global or regional studies of the effects of climate 
change on wind resources considers the fine-scale dependence of 
wind resources on the topography and wind direction (Sanz Rodrigo 
et al. 2016; Dörenkämper et al. 2020) or the effect of expanding wind 
energy exploitation (Volker et al. 2017; Lundquist et al. 2019). There

is limited evidence that extreme wind speeds, which can damage 
wind turbines, will increase due to climate change (Pes et al. 2017; 
Pryor et  al. 2020). Nevertheless, projected changes in Europe and 
North America – regions where the most extensive analysis has been 
undertaken – are expected to be within the estimates embedded in 
the design standards of wind turbines (Pryor and Barthelmie 2013).
Future wind generation in Europe could decrease in summer and 
autumn, increasing in winter in northern-central Europe but decreasing 
in southernmost Europe (Carvalho et al. 2017). Towards 2100, intra-
annual variations increase in most of Europe, except around the 
Mediterranean area (Reyers et al. 2016), but this may reflect natural 
multi-decadal variability (Wohland et  al. 2019b). Wind speeds may 
become more homogeneous over large geographical regions in 
Europe due to climate change, increasing the likelihood of large areas

experiencing high or low wind speeds simultaneously (Wohland et al. 
2017). These changes could result in fewer benefits in the transmission 
of wind generation between countries and increased system integration 
costs. Europe could require a modest increase (up to 7%) in backup 
energy towards the end of the 21st century due to more homogeneous 
wind conditions over Europe (Wohland et al. 2017; Weber et al. 2018). 
However, other studies report that the impact of climate change is 
substantially smaller than interannual variability, with no significant 
impact on the occurrence of extreme low wind production events 
in Europe (Van Der Wiel et al. 2019). If European electricity systems 
are designed to manage the effects of existing weather variability on 
wind power, they can likely also cope with climate change impacts 
on wind power (Ravestein et al. 2018). Changes in wind-generation 
variability caused by climate change are also reported for North

America (Haupt et al. 2016; Losada Carreño et al. 2018), with modest 
impacts on electricity system operation (Craig et al. 2019).
6.5.2.3 Solar Energy
Climate change is not expected to substantially impact global solar 
insolation and will not compromise the ability of solar energy to 
support low-carbon transitions (high confidence). Models show 
dimming and brightening in certain regions, driven by cloud, 
aerosol and water vapour trends (Chapter 12 of IPCC AR6 WGI). The 
increase in surface temperature, which affects all regions, decreases 
solar power output by reducing the PV panel efficiency. In  some 
models and climate scenarios, the increases in solar insolation 
are counterbalanced by reducing efficiency due to rising surface 
air temperatures, which increase significantly in all models and 
scenarios (Jerez et al. 2015; Bartók et al. 2017; Emodi et al. 2019). 
Increases in aerosols would reduce the solar resource available and

add to maintenance costs (Chapter 12 of IPCC AR6 WGI).
In many emission scenarios, the effect on solar PV from temperature-
induced efficiency losses is smaller than the effect expected from

668
Chapter 6 Energy Systems6changes on solar insolation due to variations in water vapour and 
clouds in most regions. Also, future PV technologies will likely 
have higher efficiency, which would offset temperature-related 
declines (Müller et  al. 2019). Cloud cover is projected to decrease 
in the subtropics (around –0.05% per year), including parts of North 
America, vast parts of Europe and China, South America, South 
Africa and Australia (medium agreement, medium evidence). Thus, 
models project modest (<3%) increases in solar PV by the end of 
the century for southern Europe, northern and southern Africa, 
Central America, and the Caribbean (Emodi et al. 2019). There are 
several studies projecting decreasing solar production, but these 
are generally influenced by other factors, for example, increasing air 
pollution (Ruosteenoja et al. 2019). The multi-model means for solar 
insolation in regional models decrease 0.60 W m–2 per decade from

2006 to 2100 over most of Europe (Bartók et al. 2017), with the most 
significant decreases in the Northern countries (Jerez et al. 2015).
6.5.2.4 Bioenergy
Climate change can affect biomass resource potential directly, via 
changes in the suitable range (i.e., the area where bioenergy crops 
can grow) and/or changes in yield, and indirectly, through changes 
in land availability. Increases in CO 2 concentration increase biomass 
yield; climate changes (e.g., temperature, precipitation, and so on) 
can either increase or decrease the yield and suitable range.
Climate change will shift the suitable range for bioenergy towards 
higher latitudes, but the net change in the total suitable area is 
uncertain (high confidence). Several studies show northward shifts 
in the suitable range for bioenergy in the northern hemisphere (Tuck 
et al. 2006; Barney and DiTomaso 2010; Bellarby et al. 2010; Hager 
et al. 2014; Wang et al. 2014a; Preston et al. 2016; Conant et al. 2018;

Cronin et  al. 2018), but the net effect of climate change on total 
suitable area varies by region, species, and climate model (Barney 
and DiTomaso 2010; Hager et al. 2014; Wang et al. 2014a).
The effect of climate change on bioenergy crop yields will vary across 
region and feedstock (high confidence); however, in general, yields 
will decline in low latitudes (medium confidence) and increase in 
high latitudes (low confidence) (Haberl et al. 2010; Cosentino et al. 
2012; Preston et  al. 2016; Cronin et  al. 2018; Mbow et  al. 2019). 
However, the average change in yield varies significantly across 
studies, depending on the feedstock, region, and other factors 
(Beringer et al. 2011; Kyle et al. 2014; Mbow et al. 2019; Dolan et al. 
2020). Only a few studies extend the modelling of climate change 
impacts on bioenergy to quantify the effect on bioenergy deployment 
or its implications on the energy system (Calvin et al. 2013, 2019;

Kyle et al. 2014; Thornton et al. 2017). These studies find that changes 
in deployment are of the same sign as changes in yield; that is, if 
yields increase, then deployment increases.
Some of the uncertainty in the sign and magnitude of the impacts 
of climate change on bioenergy potential is due to uncertainties 
in CO 2 fertilisation (the increase in photosynthesis due to increases in 
atmospheric CO 2 concentration) (Haberl et al. 2011; Bonjean Stanton 
et al. 2016; Cronin et al. 2018; Solaun and Cerdá 2019; Yalew et al. 
2020). For example, earlier studies found that, without CO 2 fertilisation, climate change will reduce global bioenergy potential by about 16%; 
with CO 2 fertilisation, however, climate change increases this potential 
by 45% (Haberl et al. 2011). However, newer studies in the USA find 
little effect of CO 2 fertilisation on switchgrass yield (Dolan et al. 2020). 
There is also a considerable uncertainty across climate and crop models

in estimating bioenergy potential (Hager et al. 2014).
6.5.2.5 Thermal Power Plants
The operation of thermal power plants will be affected by climate 
change, deriving from changes in the ambient conditions like 
temperature, humidity and water availability (Schaeffer et al. 2012) 
(high confidence). Changes in ambient temperature have relatively 
small impacts on coal-fired and nuclear power plants (Rankine 
cycle); however, gas-fired power plants (Brayton or combined-cycle) 
may have their thermal efficiency and power output significantly 
decreased (De Sa and Al Zubaidy 2011; Schaeffer et  al. 2012). 
Droughts decrease potential cooling water for thermal power plants 
and increase the probability of water outlet temperatures exceeding 
regulatory limits, leading to lower production or shutdowns. Thermal 
power utilisation has been reported to be, on average, 3.8% lower 
during drought years globally (van Vliet et  al. 2016c), and further

significant decreases in available thermal power plant capacity due 
to climate change are projected (Koch et  al. 2014; van Vliet et  al. 
2016b; Yalew et  al. 2020). An increase in climate-related nuclear 
power disruptions has been reported in the past decades globally 
(Ahmad 2021).
Carbon capture may increase cooling water usage significantly, 
especially in retrofits, with up to 50% increase in water usage for coal-
fired power plants globally, depending on the CCS technology (Rosa 
et al. 2020) (Section 6.4). In Asia, planned coal capacity is expected to 
be vulnerable to droughts, sea level rise, and rising air temperatures, 
and this may be exacerbated by incorporating carbon capture (Wang 
et al. 2019c). Recently, however, studies have proposed designs of 
CCS with a minimal increase in water requirements (Magneschi et al. 
2017; Mikunda et al. 2021).
Older thermal power plants can be retrofitted to mitigate climate

impacts by altering and redesigning the cooling systems (Westlén 
2018), although the costs for these solutions may be high. For example, 
dry cooling may be used instead of once-through cooling; however, 
it lowers thermal efficiency and would leave plants vulnerable to 
ambient temperature increase (Ahmad 2021). Closed-circuit cooling 
is much less sensitive to water temperature than once-through 
cooling (Bonjean Stanton et  al. 2016). Modifying policies and 
regulation of water and heat emissions from power plants may also 
be used to mitigate plant reliability problems induced by climate 
change (Eisenack 2016; Mu et al. 2020), albeit with potential impacts 
for other water users and ecology. Improvements in water use and 
thermal efficiencies and the use of transmission capabilities over 
large geographical regions to mitigate risks on individual plants are 
also possible mitigation options (Miara et al. 2017).

669
Energy Systems  Chapter 666.5.3 Impacts on Energy Consumption
Heating demand will decrease, and cooling demand will increase 
in response to climate change. Peak load may increase more than 
energy consumption, and the changing spatial and temporal load 
patterns can impact transmission and needs for storage, demands-
side management, and peak-generating capacity (high confidence).
Climate change will decrease heating demands, especially in cold 
regions, and it will increase cooling demands, especially in warm 
regions (Yalew et  al. 2020). Recent studies report significant net 
impacts, with the commercial and industrial sectors and substantial 
air condition penetration driving an increase in energy demand 
(Davis and Gertler 2015; Levesque et  al. 2018; De Cian and Sue 
Wing 2019; van Ruijven et al. 2019; Yalew et al. 2020). For example, 
globally, De Cian and Sue Wing (2019) found a 7–17% increase in 
energy consumption due to climate change in 2050, with the range

depending on the climate change scenario. The overall effects of 
climate change on building energy consumption are regionally 
dependent. For example, Zhang et  al. (2019) find that reduced 
heating will outweigh increased cooling in the residential buildings 
in Europe, but the reverse will be true in China.While many studies have focused on energy consumption, climate 
extremes are expected to alter peak energy demands, with the 
potential for blackouts, brownouts, and other short-term energy system 
impacts (Yalew et al. 2020). For example, peak energy demand during 
heatwaves can coincide with reduced transmission and distribution 
capacity at higher temperatures. In large cities, extreme heat events 
increase cooling degree days significantly, with the urban heat island 
effect compounding the impact (Morakinyo et  al. 2019). One study 
found that total electricity consumption at the end of the century in the 
USA could increase on average by 20% during summer months and

decrease on average by 6% in the winter (Ralston Fonseca et al. 2019). 
While the average increase in consumption is modest, climate change 
is projected to have severe impacts on the frequency and intensity of 
peak electricity loads (Auffhammer et al. 2017). Bartos et al. (2016) find 
that peak per-capita summertime load in the USA may rise by 4.2–15% 
by mid-century. Efficient cooling technologies and other demand-side 
measures can limit cooling energy loads during periods of particularly 
high demand (IEA 2018; Dreyfus et al. 2020).
Box 6.6 | Energy Resilience
In February 2021, the state of Texas was hit by three major storms and suffered significant scale power outages. More than 4.5 million 
homes and businesses on the Texas electric grid were left without electricity for days, limiting the ability to heat homes during 
dangerously low temperatures and leading to food and clean water shortages (Busby et al. 2021). The Texas and other events – for

example, Typhoon Haiyan in Southeast Asia in 2013; the Australian bush fires in 2019–2020; forest fires in 2018 in California; water 
shortages in Cape Town, South Africa in 2018 and the western USA during 2021 – raise the question of whether future low-carbon 
energy systems will be more or less resilient than those of today.
Some characteristics of low-carbon energy systems will make them less resilient. Droughts reduce hydroelectric electricity generation 
(Gleick 2016; van Vliet et al. 2016c); wind farms do not produce electricity in calm conditions or shut down in very strong winds 
(Petersen and Troen 2012); solar PV generation is reduced by clouds and is less efficient under extreme heat, dust storms, and wildfires 
(Perry and Troccoli 2015; Jackson and Gunda 2021). In addition, the electrification of heating will increase the weather dependence 
of electricity consumption (Staffell and Pfenninger 2018; Gea-Bermúdez et al. 2021). Non-renewable generation, for example, from

nuclear and fossil power plants, are also vulnerable to high temperatures and droughts as they depend on water for cooling (Cronin 
et al. 2018; Ahmad 2021).
But some aspects of low-carbon energy systems will make them more resilient. Wind and solar farms are often spread geographically, 
which reduces the chances of being affected by the same extreme weather event (Perera et al. 2020). The diversification of energy 
sources, in which each component has different vulnerabilities, increases resilience. Less reliance on thermal electricity generation 
technologies will reduce the risks of curtailment or efficiency losses from droughts and heat waves (Lohrmann et  al. 2019). 
More generally, increased electricity system integration and flexibility (Section 6.4.3) and weatherisation of generators increases 
electricity system resilience (Busby et al. 2021; Heffron et al. 2021). Likewise, local district micro-grids with appropriate enabling

technologies (e.g., distributed generation, energy storage, greater demand-side participation, electric vehicles) may ensure access 
to electricity during major long-duration power outage events and radically enhance the resilience of supply of essential demand 
(Stout et al. 2019).

670
Chapter 6 Energy Systems66.5.4 Impacts on Electricity System Vulnerability
While long-term trends are important for electricity system planning, 
short-term effects associated with loss of power can be disruptive and 
lead to significant economic losses along with cascading impacts on 
health and safety. Extreme weather and storms threaten the electricity 
system in different ways, affecting system resilience, reliability, and 
adequacy (Moreno-Mateos et al. 2020). The implications of climate 
change for electricity system vulnerability will depend on the degree 
to which climate change alters the frequency and intensity of extreme 
weather events. The complex compounding effects of simultaneous 
events (e.g., high winds and lightning occurring at the same time) are 
not well understood.
High wind speeds can shear lines through mechanical failure 
or cause lines to collide, causing transient events (Panteli and 
Mancarella 2015; Yalew et  al. 2020). Hurricane conditions can

damage electricity system infrastructures, including utility-scale 
wind and solar PV plants. Electricity systems may experience high 
demand when lines are particularly at risk from mechanical failure 
from wind and storm-related effects. However, except for medium 
evidence of increases in heavy precipitation associated with tropical 
cyclones, there is limited evidence that extreme wind events will 
increase in frequency or intensity in the future (Kumar et al. 2015; 
Pryor et al. 2020).
Wildfires pose a  significant threat to electricity systems in dry 
conditions and arid regions (Dian et al. 2019). With climate change, 
wildfires will probably become more frequent (Flannigan et al. 2013) 
and more difficult to address, given that they frequently coincide with 
dry air and can be exacerbated by high winds (Mitchell 2013).Lightning can cause wildfires or common-mode faults on electricity 
systems associated with vegetation falling on power substations or

overhead lines but is more generally associated with flashovers and 
overloads (Balijepalli et al. 2005). Climate change may change the 
probability of lightning-related events (Romps et al. 2014).
Snow and icing can impact overhead power lines by weighing 
them down beyond their mechanical limits, leading to collapse and 
cascading outages (Feng et al. 2015). Snow can also lead to flashovers 
on lines due to wet snow accumulation on insulators (Yaji et al. 2014; 
Croce et al. 2018) and snow and ice can impact wind turbines (Davis 
et  al. 2016). Climate change will lower the risk of snow and ice 
conditions (McColl et al. 2012), but there is still an underlying risk 
of sporadic acute cold conditions such as those associated with the 
winter storms in Texas in 2021 (Box 6.6).
Flooding poses a threat to the transmission and distribution systems 
by inundating low-lying substations and underground cables. Coastal

flooding also poses a threat to electricity system infrastructure. Rising 
sea levels from climate change and associated storm surge may also 
pose a  significant risk for coastal electricity systems (Entriken and 
Lordan 2012).
Temperature increases influence electricity load profiles and 
electricity generation, as well as potentially impact supporting 
information and communication infrastructure. Heat can pose direct 
impacts to electricity system equipment such as transformers. Referred 
to as ‘solar heat faults’, they occur under high temperatures and low 
wind speeds and can be exacerbated by the urban heat island effect 
(McColl et al. 2012). Increasing temperatures affect system adequacy 
by reducing electric transmission capacity, simultaneously increasing 
peak load due to increased air conditioning needs (Bartos et al. 2016).
Box 6.7 | Impacts of Renewable Energy Production on Climate

While climate change will affect energy systems (Section 6.5), the reverse is potentially also true: increasing the use of renewable energy 
sources could affect local climate. Large solar PV arrays and hydroelectric dams darken the land surface, and wind turbines extract the wind’s 
kinetic energy near the Earth’s surface. Their environmental impacts of renewable energy production are mostly confined to areas close 
to the production sources and have been shown to be trivial compared to the mitigation benefits of renewable energy (high confidence).
Solar energy. Observations and model simulations have addressed whether large-scale solar PV power plants can alter the local 
and regional climate. In rural areas at the local scale, large-scale solar PV farms change the surface characteristics and affect air 
temperatures (Taha 2013). Measurements in rural Arizona, USA show local night-time temperatures 3°C–4°C warmer at the PV

farm than surroundings (Barron-Gafford et al. 2016). In contrast, measurements in urban settings show that solar PV panels on 
roofs provide a cooling effect (Taha 2013; Ma et al. 2017). On the regional scale, modelling studies suggest cooling in urban areas 
(0.11–0.53°C) and warming in rural areas (up to 0.27°C) (Millstein and Menon 2011). Global climate model simulations show that 
solar panels induce regional cooling by converting part of the incoming solar energy to electricity (Hu et al. 2016). However, converting 
the generated electricity to heat in urban areas increases regional and local temperatures, compensating for the cooling effect.
Wind energy. Surface temperature changes in the vicinity of wind farms have been detected (Smith et al. 2013; Lee and Lundquist 
2017; Takle et al. 2019; Xia et al. 2019) in the form of night-time warming. Data from field campaigns suggest that a ‘suppression

of cooling’ can explain the observed warming (Takle et  al. 2019). Regional and climate models have been used to describe the 
interactions between turbines and the atmosphere and find minor impacts (Vautard et al. 2014). More sophisticated models confirm 
the local warming effect of wind farms but report that the impact on the regional area is slight and occasional (Wang et al. 2019d). 
Wind turbines alter the transport and dissipation of momentum near the surface but do not directly impact the Earth’s energy balance

671
Energy Systems  Chapter 666.6 Key Characteristics of Net-zero 
Energy Systems
6.6.1 What is a Net-zero Energy System?
Limiting warming to well below 2°C requires that CO 2 emissions 
from the energy sector be reduced to near zero or even below 
zero (Section  6.7; Chapter  3). Policies, technologies, behaviours, 
investments, and other factors will determine the speed at which 
countries transition to net-zero energy systems – those that emit very 
little or no emissions. An understanding of these future energy systems 
can help to chart a course toward them over the coming decades.This section synthesises current understanding of net-zero energy 
systems. Discussions surrounding efforts to limit warming are 
frequently communicated in terms of the point in time at which net 
anthropogenic CO 2 emissions reach zero, accompanied by substantial 
reductions in non-CO 2 emissions (IPCC 2018, Chapter 3). Net-zero GHG 
goals are also common, and they require net-negative CO 2 emissions

to compensate for residual non-CO 2 emissions. Economy-wide CO 2 and 
GHG goals appear in many government and corporate decarbonisation 
strategies, and they are used in a variety of ways. Most existing carbon-
neutrality commitments from countries and sub-national jurisdictions 
aim for economies with very low emissions rather than zero emissions. 
Offsets, carbon dioxide removal (CDR) methods, and/or land sink 
assumptions are used to achieve net-zero goals (Kelly Levin et al. 2020).Box 6.7 (continued)
(Fischereit et al. 2021). However, the secondary modifications to the energy and water exchanges have added implications for the 
climate system (Jacobson and Archer 2012).
Hydropower. The potential climate impacts of hydropower concentrate on the GHG emissions from organic matter decomposition 
when the carbon cycle is altered by the flooding of the hydroelectric power plant reservoir (Ocko and Hamburg 2019), but emissions

from organic matter decomposition decrease over time. The darker surface of the reservoir, compared to the lighter surrounding land 
may counterbalance part of the reduced GHG emissions by hydropower production (Wohlfahrt et al. 2021). However, these impacts 
vary significantly among facilities due to the surrounding land properties and the area inundated by the reservoir.
CO2 emissions in energy system net-zero year (GtCO2 yr–1)–10 –5 0 10 5CDR from 
bioenergy 
with CCSResidual 
emissions from
energy and 
industrial 
processes>2.0°C2.0°C1.5°C
Net emissions
from agriculture
and land use
Figure 6.21 | Residual emissions and carbon dioxide removal (CDR) when global energy and industrial CO 2 emissions reach net-zero.  Residual emissions and 
CDR in net-zero scenarios from the AR6 Scenarios Database show global differences across warming levels (light blue = scenarios that limit warming to 1.5°C (>50%) with no

or limited overshoot and scenarios that return warming to 1.5°C (>50%) after a high overshoot; yellow = scenarios that limit warming to 2°C (>67%) and scenarios that limit 
warming to 2°C (>50%); dark blue = scenarios that limit warming to 2.5°C (>50%), scenarios that limit warming to 3°C (>50%), scenarios that limit warming to 4°C (>50%), 
and scenarios that exceed warming of 4°C (≥50%)). In each case, the boxes show the 25th to 75th percentile ranges, and whiskers show the 5th and 95th percentiles. Lines 
and circles within the boxes denote the median and mean values, respectively.

672
Chapter 6 Energy Systems6Precisely describing a  net-zero energy system is complicated by the 
fact that different scenarios attribute different future CO 2 emissions 
to the energy system, even under scenarios where economy-wide CO 2 
emissions reach net zero. It is also complicated by the dependence 
of energy system configurations on unknown future conditions such 
as population and economic growth, and technological change. The 
energy system is not the only source or sink of CO 2 emissions. Terrestrial 
systems may store or emit carbon, and CDR options like BECCS or 
DACCS can be used to store CO 2, relieving pressure on the energy 
system (Chapter 3). The location of such CDR options is ambiguous, as it 
might be deployed within or outside of the energy sector (Figure 6.21), 
and many CDR options, such as DACCS, would be important energy 
consumers (Bistline and Blanford 2021a) (Section  6.6.2). If CDR 
methods are deployed outside of the energy system (e.g., net negative

agriculture, forestry, and land-use CO 2 emissions), it is possible for the 
energy system to still emit CO 2 but have economy-wide emissions of 
zero or below. When global energy and industrial CO 2 emissions reach 
net zero, the space remaining for fossil energy emissions is determined 
by deployment of CDR options (Figure 6.21).
This section focuses on energy systems that produce very little or no 
CO2 emissions, referred to in this chapter as ‘net-zero energy systems’. 
While energy systems may not reach net zero concurrently with 
economy-wide CO 2 or GHG emissions, they are a useful benchmark 
for planning a path to net zero. Note that the focus here is on energy 
systems with net-zero CO 2 emissions from fossil fuel combustion 
and industrial processes, but the lessons will be broadly applicable 
to net-zero GHG energy systems as well. Net-zero GHG energy 
systems would incorporate the major efforts made to reduce non-CO 2

emissions (e.g., CH 4 from oil, gas and coal as discussed in Section 6.4) 
and would also need to incorporate more CDR to compensate for 
remaining non-CO 2 GHG emissions. Energy sector emissions in many 
countries may not reach net zero at the same time as global energy 
system emissions (Figure 6.25 and Cross-Chapter Box 3 in Chapter 3).
6.6.2 Configurations of Net-zero Energy Systems
Net-zero energy systems entail trade-offs across economic, 
environmental, and social dimensions (Davis et  al. 2018). Many 
socio-economic, policy, and market uncertainties will also influence 
the configuration of net-zero energy systems (Smith et  al. 2015; 
van Vuuren et  al. 2018; Bistline et  al. 2019; Krey et  al. 2019; 
Azevedo et al. 2021, Pye et al. 2021). There are reasons that countries 
might focus on one system configuration versus another, including 
cost, resource endowments, related industrial bases, existing 
infrastructure, geography, governance, public acceptance, and other

policy priorities (Section 6.6.4 and Chapter 18 of WGII).
Explorations of net-zero energy systems have been emerging in the 
detailed systems modelling literature (Azevedo et  al. 2021; Bistline 
2021b). Reports associated with net-zero economy-wide targets for 
countries and sub-national entities typically do not provide detailed 
roadmaps or modelling but discuss high-level guiding principles, though 
more detailed studies are emerging at national levels (Capros et al. 2019; 
Wei et al. 2020; Duan et al. 2021; Williams et al. 2021a). Most analysis 
has focused on identifying potential decarbonisation technologies and pathways for different sectors, enumerating opportunities and barriers 
for each, their costs, highlighting robust insights, and characterising key 
uncertainties (Davis et al. 2018; Hepburn et al. 2019).
The literature on the configuration of net-zero energy systems is 
limited in a few respects. On the one hand, there is a robust integrated

assessment literature that provides characterisations of these systems 
in broad strokes (the AR6 database), offering internally consistent 
global scenarios to link global warming targets to regional/national 
goals. All integrated assessment scenarios that discuss net-zero 
energy system CO 2 emissions provide high-level characterisations of 
net-zero systems. Because these characterisations have less temporal, 
spatial, technological, regulatory, and societal detail, however, they 
may not consider the complexities that could ultimately influence 
regional, national, or local pathways. High-fidelity models and 
analyses are needed to assess the economic and environmental 
characteristics and the feasibility of many aspects of net-zero or 
net-negative emissions energy systems (high confidence) (Blanford 
et al. 2018; Bistline and Blanford 2020). For example, evaluating the 
competitiveness of electricity sector technologies requires temporal,

spatial, and technological detail to accurately represent system 
investments and operations (Collins et al. 2017; Santen et al. 2017; 
Helistoe et al. 2019; Bistline 2021c; Victoria et al. 2021).
Configurations of net-zero energy systems will vary by region but 
are likely to share several common characteristics (high confidence) 
(Figure 6.22). We focus on seven of those common characteristics in 
the remainder of this subsection.
6.6.2.1 Limited and/or Targeted Use of Fossil Fuels
Net-zero energy systems will use far less fossil fuel than today (high 
confidence). The precise quantity of fossil fuels will largely depend 
on the relative costs of such fuels, electrification, alternative fuels, 
and CDR (Section 6.6.2.4) in the energy system (high confidence). All 
of these are affected by regional differences in resources (McGlade 
and Ekins 2015), existing energy infrastructure (Tong et  al. 2019), 
demand for energy services, and climate and energy policies. Fossil

fuel use may persist, for example, if and where the costs of such 
fuels and the compensating carbon management (e.g.,  CDR, CCS) 
are less than non-fossil energy. For most applications, however, it is 
likely that electrification (McCollum et al. 2014; Madeddu et al. 2020; 
Zhang and Fujimori 2020) or use of non-fossil alternative fuels (Zeman 
and Keith 2008; Graves et al. 2011; Hänggi et al. 2019; Ueckerdt et al. 
2021) will prove to be the cheapest options. Most residual demand 
for fossil fuels is likely to predominantly be petroleum and natural 
gas given their high energy density (Davis et al. 2018), while demand 
for coal in net-zero energy systems is likely to be very low (Luderer 
et al. 2018; Jakob et al. 2020, Section 6.7.4) (high confidence).
There is considerable flexibility regarding the overall quantity of 
liquid and gaseous fuels that will be required in net-zero energy 
systems (high confidence) (Figure 6.22 and Section 6.7.4). This will be

determined by the relative value of such fuels as compared to systems 
which rely more or less heavily on zero-emissions electricity. In turn, 
the share of any fuels that are fossil or fossil-derived is uncertain 
and will depend on the feasibility of CCS and CDR technologies and

673
present(a)
0 0.5 1.0 2.5 2.0 1.5 3.0
Per-capita carbon dioxide removal(CDR) from BECCS (t-CO
2 per person)2.0
1.5
0.5
03.0
Per-Capita Residual Emissions from
Energy/Industry (t-CO2/person)
(d)
0.5 1.0 2.0 2.5 1.5Emissions intensity of ﬁnal energy (MtCO2 EJ–1)
10
02030
Final energy intensity of economic
activity (EJ trillion USD2010–1) 50
401.02.5
(e)
2040 2060 20802030Peak emissions year 2020
2015
201020252035
Net-zero emissions year21002040(c)
0 20 60 120 8090Electricity share of ﬁnal energy (%)70
60
50
3080
Per-capita ﬁnal energy (GJ person–1)40
40 100(b)
0 20 40 100 80 60100Share of renewable primary energy
from non-biomass renewables (%)60
40
20
080
Share of Primary Energy from Renewables (%)net negative
net positive
50
Energy intensityEnergy resources
more solar, wind, hydro
more biomass
ElectriﬁcationNet energy and industry emissions
Emissions trajectory
1.5°C
2.0°C
>2.0°C
Unspeciﬁedn=357

Figure 6.22 | Characteristics of global net-zero energy systems when global energy and industrial CO 2 emissions reach net-zero.

674
Chapter 6 Energy Systems6long-term sequestration as compared to alternative, carbon-neutral 
fuels. Moreover, to the extent that physical, biological, and/or socio-
political factors limit the availability of CDR (Smith et al. 2015; Field 
and Mach 2017), carbon management efforts may prioritise residual 
emissions related to land use and other non-energy sources.
6.6.2.2 Zero or Negative CO 2 Emissions from Electricity
Net-zero energy systems will rely on decarbonised or net-negative 
CO2 emissions electricity systems, due to the many lower-cost options 
for producing zero-carbon electricity and the important role of end-
use electrification in decarbonising other sectors (high confidence).
There are many possible configurations and technologies for zero- or net-
negative-emissions electricity systems (high confidence). These systems 
could entail a  mix of variable renewables, dispatchable  renewables 
(e.g.,  biomass, hydropower), other firm, dispatchable (‘on-demand’)

low-carbon generation (e.g., nuclear, CCS-equipped capacity), energy 
storage, transmission, carbon removal options (e.g., BECCS, DACCS), 
and demand management (Luderer et  al. 2017; Bistline et  al. 2018; 
Jenkins et al. 2018b; Bistline and Blanford 2021b). The marginal cost of 
deploying electricity sector mitigation options increases as electricity 
emissions approach zero; in addition, the most cost-effective mix of 
system resources changes as emissions approach zero and, therefore, 
so do the implications of electricity sector mitigation for sustainability 
and other societal goals (Mileva et  al. 2016; Bistline et  al. 2018; 
Sepulveda et al. 2018; Jayadev et al. 2020; Cole et al. 2021). Key factors 
influencing the electricity mix include relative costs and system benefits, 
local resource bases, infrastructure availability, regional integration 
and trade, co-benefits, societal preferences and other policy priorities,

all of which vary by country and region (Section 6.6.4). Many of these 
factors depend on when the net-zero point is reached (Figure 6.22).
Based on their increasing economic competitiveness, VRE technologies, 
especially wind and solar power, will likely comprise large shares of 
many regional generation mixes (high confidence) (Figure  6.22). 
While wind and solar will likely be prominent electricity resources, 
this does not imply that 100% renewable energy systems will be 
pursued under all circumstances, since economic and operational 
challenges increase nonlinearly as shares approach 100% (Box 6.8) 
(Frew et al. 2016; Imelda et al. 2018b; Shaner et al. 2018; Bistline and 
Blanford 2021a; Cole et  al. 2021). Real-world experience planning 
and operating regional electricity systems with high instantaneous 
and annual shares of renewable generation is accumulating, but 
debates continue about how much wind and solar should be included

in different systems, and the cost-effectiveness of mechanisms for 
managing variability (Box 6.8). Either firm, dispatchable generation 
(including nuclear, CCS-equipped capacity, dispatchable renewables 
such as geothermal, and fossil units run with low capacity factors 
and CDR to balance emissions) or seasonal energy storage (alongside other balancing resources discussed in Box 6.8) will be needed to 
ensure reliability and resource adequacy with high percentages of 
wind and solar (Jenkins et al. 2018b; Dowling et al. 2020; Denholm 
et  al. 2021) though each option involves uncertainty about costs, 
timing, and public acceptance (Albertus et al. 2020).
Electricity systems require a range of different functional roles – for 
example, providing energy, capacity, or ancillary services. As a result, 
a  range of different types of generation, energy storage, and 
transmission resources may be deployed in these systems (Baik et al.

2021). There are many options for each of these roles, each with their 
strengths and weaknesses (Sections 6.4.3 and 6.4.4), and deployment 
of these options will be influenced by the evolution of technological 
costs, system benefits, and local resources (Fell and Linn 2013; Hirth 
2015; Bistline et al. 2018; Mai et al. 2018; Veers et al. 2019).
System management is critical for zero- or negative-emissions electricity 
systems. Maintaining reliability will increasingly entail system planning 
and operations that account for characteristics of supply- and demand-
side resources (Hu et al. 2018). Coordinated planning and operations will 
likely become more prevalent across portions of the electricity system 
(e.g., integrated generation, transmission, and distribution planning), 
across sectors, and across geographies (EPRI 2017; Konstantelos et al. 
2017; Chan et al. 2018; Bistline and Young 2019) (Section 6.4.3).
Energy storage will be increasingly important in net-zero energy

systems, especially in systems with shares of VRE (high confidence). 
Deployment of energy storage will vary based on the system benefits 
and values of different options (Arbabzadeh et al. 2019; Denholm and 
Mai 2019). Diurnal storage options like lithium-ion batteries have 
different value than storing and discharging electricity over longer 
periods through long-duration energy storage with less frequent 
cycling, which require different technologies, supporting policies, 
and business models (Gallo et  al. 2016; Blanco and Faaij  2017; 
Albertus  et  al. 2020; Dowling et  al. 2020; Sepulveda et  al. 2021) 
(Section 6.4.4). The value of energy storage varies with the level of 
deployment and on the competitiveness of economic complements 
such as VRE options (Mileva et al. 2016; Bistline and Young 2020) and 
substitutes such as flexible demand (Brown et al. 2018; Merrick et al. 
2018), transmission (Schlachtberger et  al. 2017; Brown et  al. 2018;

Merrick et  al. 2018; Bistline and Young 2019), trade (Bistline et  al. 
2020b), dispatchable generators (Hittinger and Lueken 2015; Gils 
et al. 2017; Arbabzadeh et al. 2019), direct air capture (DAC) (Daggash 
et al. 2019), and efficiencies in system operations (Tuohy et al. 2015).
The approach to other sectors could impact on electricity sector planning, 
and the role of some technologies (e.g.,  hydrogen, batteries, CCS) 
could depend on deployment in other sectors. CCS offers opportunities 
for CO2 removal when fuelled with syngas or biomass containing 
carbon captured from the atmosphere (Hepburn et al. 2019); however, Figure 6.22 (continued): Characteristics of global net-zero energy systems when global energy and industrial CO 2 emissions reach net-zero.  Scenarios 
reaching net-zero emissions show differences in residual emissions and carbon removal (a), energy resources (b), electrification (c), energy intensity (as measured here by

energy GDP–1) (d), and emissions trajectory (e), particularly with respect to warming levels (light blue = scenarios that limit warming to 1.5°C (>50%) with no or limited 
overshoot and scenarios that return warming to 1.5°C (>50%) after a high overshoot; yellow = scenarios that limit warming to 2°C (>67%) and scenarios that limit warming 
to 2°C (>50%); dark blue = scenarios that limit warming to 2.5°C (>50%), scenarios that limit warming to 3°C (>50%), scenarios that limit warming to 4°C (>50%), and 
scenarios that exceed warming of 4°C (≥50%); grey = unspecified warming). Points represent individual scenarios from the AR6 Scenarios Database, with probability density 
distributions shown along each axis for each warming level (colours corresponding to warming levels) and for all scenarios (black).

675
Energy Systems  Chapter 66concerns about lifecycle environmental impacts, uncertain costs, and 
public acceptance are potential barriers to widespread deployment 
(Section 6.4.2). It is unclear whether CDR options like BECCS will be 
included in the electricity mix to offset continued emissions in other 
parts of the energy system or beyond (MacDowell et al. 2017; Bauer 
et al. 2018a; Luderer et al. 2018). Some applications may also rely on power to fuels (PtX) electricity conversion to create low-emissions 
synthetic fuels (Sections 6.6.2.6, 6.4.4, and 6.4.5), which could impact 
on electricity system planning and operations. Additionally, if DAC 
technologies are used, electricity and heat requirements to operate 
DAC could impact electricity  system investments and operations 
(Realmonte et al. 2019; Bistline and Blanford 2021a).
Box 6.8 | 100% Renewables in Net-zero Energy Systems

The decreasing cost and increasing performance of renewable energy has generated interest in the feasibility of providing nearly all energy 
services with renewables. Renewable energy includes wind power, solar power, hydroelectric power, bioenergy, geothermal energy, tidal 
power, and ocean power. There are two primary frames around which 100% renewable energy systems are discussed: 100% renewable 
electricity systems and 100% renewable energy systems, considering not only electricity but all aspects of the energy system.
It is technically feasible to use very high renewable shares (e.g., above 75% of annual regional generation) to meet hourly electricity 
demand under a range of conditions, especially when VRE options, notably wind and solar, are complemented by other resources (high 
confidence). There are currently many grids with high renewable shares and large anticipated roles for VRE sources, in particular wind

and solar (Section 6.4), in future low-carbon electricity systems. An increasingly large set of studies examines the feasibility of high 
renewable penetration and economic drivers under different policy, technology, and market scenarios (Cochran et al. 2014; Deason 
2018; Jenkins et al. 2018b; Bistline et al. 2019; Hansen et al. 2019; Dowling et al. 2020; Blanford et al. 2021; Denholm et al. 2021). 
High wind and solar penetration involves technical and economic challenges due to their unique characteristics such as spatial and 
temporal variability, short- and long-term uncertainty, and non-synchronous generation (Cole et al. 2017). These challenges become 
increasingly important as renewable shares approach 100% (Sections 6.6.2.2 and 6.4.3).
There are many balancing options in systems with very high renewables (Milligan et al. 2015; Jenkins et al. 2018b; Mai et al. 2018; 
Bistline 2021a; Denholm et al. 2021).

• Energy storage. Energy storage technologies like batteries, pumped hydro, and hydrogen can provide a  range of system 
services (Balducci et al. 2018; Bistline et al. 2020a) (Section 6.4.4). Lithium-ion batteries have received attention as costs fall and 
installations increase, but very high renewable shares typically entail either dispatchable generation or long-duration storage in 
addition to short-duration options (Jenkins et al. 2018b; Arbabzadeh et al. 2019; Schill 2020). Energy storage technologies are 
part of a broad set of options (including synchronous condensers, demand-side measures, and even inverter-based technologies 
themselves) for providing grid services (Castillo and Gayme 2014; EPRI 2019a).
• Transmission and trade. To balance differences in resource availability, high renewable systems will very likely entail investments 
in transmission capacity (Mai et al. 2014; Macdonald et al. 2016; Pleßmann and Blechinger 2017; Zappa et al. 2019) (Section 6.4.5)

and changes in trade (Abrell and Rausch 2016; Bistline et al. 2019). These increases will likely be accompanied by expanded 
balancing regions to take advantage of geographical smoothing.
• Dispatchable (‘on-demand’) generation. Dispatchable generation could include flexible fossil units or low-carbon fuels such 
as hydrogen with lower minimum load levels (Denholm et al. 2018; Bistline 2019), renewables like hydropower, geothermal, or 
biomass (Hirth 2016; Hansen et al. 2019), or flexible nuclear (Jenkins et al. 2018a). The composition depends on costs and other 
policy goals, though in all cases, capacity factors are low for these resources (Mills et al. 2020).
• Demand management: Many low-emitting and high-renewables systems also utilise increased load flexibility in the forms of 
energy efficiency, demand response, and demand flexibility, utilising newly electrified end uses such as electric vehicles to shape

demand profiles to better match supply (Ameli et al. 2017; Hale 2017; Brown et al. 2018; Imelda et al. 2018a; Bistline 2021a).
• Sector coupling: Sector coupling includes increased end-use electrification and PtX electricity conversion pathways, which may 
entail using electricity to create synthetic fuels such as hydrogen (Davis et al. 2018; Ueckerdt et al. 2021) (Sections 6.4.3, 6.4., 
6.4.5, 6.6.4.3, and 6.6.4.6).
Deployment of integration options depends on their relative costs and value, regulations, and electricity market design. There is 
considerable uncertainty about future technology costs, performance, availability, scalability, and public acceptance (Kondziella and 
Bruckner 2016; Bistline et al. 2019). Deploying balanced resources likely requires operational, market design, and other institutional 
changes, as well as technological changes in some cases (Denholm et al. 2021; Cochran et al. 2014). Mixes will differ based on

resources, system size, flexibility, and whether grids are isolated or interconnected.

676
Chapter 6 Energy Systems66.6.2.3 Widespread Electrification of End Uses
Net-zero energy systems will rely more heavily on increased use of 
electricity (electrification) in end uses (high confidence). The literature 
on net-zero energy systems almost universally calls for increased 
electrification (Sugiyama 2012; Williams et  al. 2012; Kriegler et  al. 
2014a; Williams et  al. 2014; Rogelj et  al. 2015a; Sachs et  al. 2016; 
Luderer et  al. 2018; Sven et  al. 2018; Schreyer et  al. 2020). At least 
30% of the global final energy needs are expected to be served by 
electricity, with some estimates suggesting upwards of 80% of 
total energy use being electrified (Figure  6.22, panel c). Increased 
electrification is especially valuable in net-zero energy systems in 
tandem with decarbonised electricity generation or net-negative 
emissions electricity generation (Section 6.5.4.2). Flexible electric loads 
(electric vehicles, smart appliances) can in turn facilitate incorporation

of VRE electricity options, increase system flexibility, and reduce needs 
for grid storage (Section 6.4.3) (Mathiesen et al. 2015; Lund et al. 2018).
Several end uses, such as passenger transportation (light-duty electric 
vehicles, two and three wheelers, buses, rail) as well as building 
energy uses (lighting, cooling) are likely to be electrified in net-
zero energy  systems (high confidence). Variations in projections of 
electrification largely result from differences in expectations about 
the ability and cost-competitiveness of electricity to serve other end 
uses such as non-rail freight transport, aviation, and heavy industry 
(McCollum et al. 2014; Bataille et al. 2016; EPRI 2018; Breyer et al. 2019) (Section  6.5.4.4), especially relative to biofuels and hydrogen (‘low-
carbon fuels’) (McCollum et al. 2014; Sachs et al. 2016; Rockström et al. 
2017), the prospects for which are still quite uncertain (Section 6.4).

The emergence of CDR technologies and the extent to which they 
allow for residual emissions as an alternative to electrification will also 
affect the overall share of energy served by electricity (Section 6.6.2.7).
Regions endowed with cheap and plentiful low-carbon electricity 
resources (wind, solar, hydropower) are likely to emphasise 
electrification, while those with substantial bioenergy resources 
or availability of other liquid fuels might put less emphasis on 
electrification, particularly in hard-to-electrify end uses (medium 
confidence). For example, among a group of Latin American countries, 
relative assumptions about liquid fuels and electricity result in an 
electrification range of 28–82% for achieving a  net-zero energy 
system (Bataille et  al. 2020). Similarly, the level of penetration of 
biofuels that can substitute for electrification will depend on regional 
circumstances such as land-use constraints, competition with food,

and sustainability of biomass production (Section 6.6.2.4).
Electrification of most buildings services, with the possible exception 
of space heating in extreme climates, is expected in net-zero energy 
systems (high confidence) (Chapter 9). Space cooling and water heating 
are expected to be largely electrified. Building electrification is expected 
to rely substantially on heat pumps, which will help lower emissions 
both through reduced thermal requirements and higher efficiencies Box 6.8 (continued)
Although there are no technical upper bounds on renewable electricity penetration, the economic value of additional wind and 
solar capacity typically decreases as their penetration rises, creating economic challenges at higher deployment levels (Hirth 2013; 
Gowrisankaran et al. 2016; Cole et al. 2021; Denholm et al. 2021; Millstein et al. 2021). The integration options above, as well as changes

to market design, can mitigate these challenges but likely will not solve them, especially since these options can exhibit declining value 
themselves (De Sisternes et al. 2016; Bistline 2017; Denholm and Mai 2019) and may be complements or substitutes to each other.
Energy systems that are 100% renewable (including all parts of the energy sector, and not only electricity generation) raise a range 
of technological, regulatory, market, and operational challenges that make their competitiveness uncertain (high confidence). 
These systems require decarbonising all electricity, using this zero-carbon electricity broadly, and then utilising zero-carbon energy 
carriers for all end uses not served by electricity, for example, air travel, long-distance transport, and high-temperature process heat. 
Broader questions emerge regarding the attractiveness of supplying all energy, and not just electricity, with renewables (Figure 6.22).

Integrated assessment and energy systems research suggest large roles for renewables, but energy and electricity shares are far from 
100%, even with stringent emissions reductions targets and optimistic assumptions about future cost reductions (Bauer et al. 2018; 
Bistline et al. 2018; Jenkins et al. 2018b; Huntington et al. 2020) (Section 6.7.1). Scenarios with 100% renewable energy systems 
are an emerging subset in the decarbonisation literature, especially at regional levels (Hansen et al. 2019; Denholm et al. 2021). 
Many 100% renewables studies focus more heavily on electrification for decarbonising end uses, and include less biofuels and 
hydrogen than the broader literature on deep decarbonisation (Bauer et al. 2018a). These studies typically assume a constrained set of 
available technologies to demonstrate the technical feasibility of very high renewable systems and do not optimise to find least-cost,

technology-neutral decarbonisation pathways, and many 100% renewables studies focus on the electricity sector or a limited number 
of sectors (Jenkins et al. 2018a; Hansen et al. 2019). In addition to renewables, studies broadly agree that including additional low-
carbon options – including not only low-carbon electricity but also targeted use of fossil fuels with and without CCS (Section 6.6.2.1) 
and alternative fuels for sectors that are difficult to electrify (Section 6.6.2.4) – can lower the cost of decarbonisation, even with very 
high shares of renewables (Figure 6.22). However, there is disagreement about the magnitude of cost savings from larger portfolios, 
which depend on context- and scenario-specific assumptions about technologies, markets, and policies.

677
Energy Systems  Chapter 66(Mathiesen et al. 2015; Sven et al. 2018; Rissman et al. 2020). The level of 
electrification for heating will depend on the trade-offs between building 
or household level heat pumps versus more centralised district heating 
network options (Mathiesen et al. 2015; Brown et al. 2018), as well as 
the cost and performance of heat pumps in more extreme climates and 
regional grid infrastructure (EPRI 2018; Waite and Modi 2020).
A significant share of transportation, especially road transportation, 
is expected to be electrified in net-zero energy systems (high 
confidence). In road transportation, two- and three-wheelers, 
light-duty vehicles (LDVs), and buses, are especially amenable to 
electrification, with more than half of passenger LDVs expected to be 
electrified globally in net-zero energy systems (medium confidence) 
(Fulton et al. 2015; Sven et al. 2018; Khalili et al. 2019; Bataille et al.

2020). Long-haul trucks, large ships, and aircraft are expected to be 
harder to electrify without technological breakthroughs (Fulton et al. 
2015; Mathiesen et al. 2015), although continued improvements in 
battery technology may enable electrification of long-haul trucks 
(Nykvist and Olsson 2021) (Chapter 10). Due to the relative ease of 
rail electrification, near complete electrification of rail and a  shift 
of air and truck freight to rail is expected in net-zero energy systems 
(Fulton et al. 2015; Rockström et al. 2017; Sven et al. 2018; Khalili 
et al. 2019). The degree of modal shifts and electrification will depend 
on local factors such as infrastructure availability and location 
accessibility. Due to the challenges associated with electrification of 
some transport modes, net-zero energy systems may include some 
residual emissions associated with the freight sector that are offset 
through CDR technologies (Muratori et al. 2017b), or reliance on low

and zero-carbon fuels instead of electrification.
A non-trivial number of industry applications could be electrified as 
a part of a net-zero energy system, but direct electrification of heavy 
industry applications such as cement, primary steel manufacturing, 
and chemical feedstocks is expected to be challenging (medium 
confidence) (Davis et al. 2018; Philibert 2019; Madeddu et al. 2020; 
van Sluisveld et  al. 2021). Process and boiler heating in industrial 
facilities are anticipated to be electrified in net-zero energy systems. 
Emissions intensity reductions for cement and concrete production 
can be achieved through the use of electrified cement kilns, while 
emissions associated with steel production can be reduced through 
the use of an electric arc furnace (EAF) powered by decarbonised 
electricity (Rissman et  al. 2020). Electricity can also be used to 
replace thermal heat such as resistive heating, EAFs, and laser

sintering (Madeddu et  al. 2020; Rissman et  al. 2020). One study 
found that as much as 60% of the energy end-use in European 
industry could be met with direct electrification using existing and 
emerging technologies (Madeddu et al. 2020). Industry electrification 
for different regions will depend on the economics and availability 
of alternative emissions mitigation strategies such as carbon neutral 
fuels and CCS (Davis et al. 2018; Madeddu et al. 2020).
6.6.2.4 Alternative Fuels in Sectors not Amenable 
to Electrification
Net-zero energy systems will need to rely on alternative fuels – notably 
hydrogen or biofuels  – in several sectors that are not amenable to 
electricity and otherwise hard to decarbonise (medium confidence).  Useful carbon-based fuels (e.g.,  methane, petroleum, methanol), 
hydrogen, ammonia, or alcohols can be produced with net-zero CO 2 
emissions and without fossil fuel inputs (Sections 6.4.4 and 6.4.5). For

example, liquid hydrocarbons can be synthesised via hydrogenation 
of non-fossil carbon by processes such as Fischer-Tropsch (MacDowell 
et  al. 2017) or by conversion of biomass (Tilman et  al. 2009). The 
resulting energy-dense fuels can serve applications that are difficult to 
electrify, but it is not clear if and when the combined costs of obtaining 
necessary feedstocks and producing these fuels without fossil inputs 
will be less than continuing to use fossil fuels and managing the 
related carbon through, for example, CCS or CDR (Ueckerdt et al. 2021).
CO2 emissions from some energy services are expected to be particularly 
difficult to cost-effectively avoid, among them: aviation; long-distance 
freight by ships; process emissions from cement and steel production; 
high-temperature heat (e.g.,  >1000°C); and electricity reliability in 
systems with high penetration of variable renewable energy sources 
(NAS) (Davis et al. 2018; Luderer et al. 2018; Sepulveda et al. 2018;

Chiaramonti 2019; Bataille 2020; Madeddu et al. 2020; Rissman et al. 
2020; Thiel and Stark 2021). The literature focused on these services and 
sectors is growing, but remains limited, and provides minimal guidance 
on the most promising or attractive technological options and systems 
for avoiding these sectors’ emissions. Technological solutions do exist, 
but those mentioned in the literature are prohibitively expensive, 
exist  only at an early stage, and/or are subject to much broader 
concerns about sustainability (e.g., biofuels) (Davis et al. 2018).
Liquid biofuels today supply about 4% of transportation energy 
worldwide, mostly as ethanol from grain and sugar cane and biodiesel 
from oil seeds and waste oils (Davis et al. 2018). These biofuels could 
conceivably be targeted to difficult-to-electrify sectors, but face 
substantial challenges related to their lifecycle carbon emissions, 
cost, and further scalability (Tilman et  al. 2009; Staples et  al. 2018),

(Section 6.4.2). The extent to which biomass will supply liquid fuels or 
high temperature heat for industry in a future net-zero energy system will 
thus depend on advances in conversion technology that enable use of 
feedstocks such as woody crops, agricultural residues, algae, and wastes, 
as well as competing demands for bioenergy and land, the feasibility 
of other sources of carbon-neutral fuels, and integration of bioenergy 
production with other objectives, including CDR, economic development, 
food security, ecological conservation, and air quality (Fargione 2010; 
Williams and Laurens 2010; Creutzig et al. 2015; Chatziaras et al. 2016; 
Laurens 2017; Lynd 2017; Bauer et  al. 2018a, b; Strefler et  al. 2018; 
Muratori et al. 2020b; Fennell et al. 2021) (Section 6.4.2.6).
Costs are the main barrier to synthesis of net-zero emissions fuels 
(high confidence), particularly costs of hydrogen (a constituent of 
hydrocarbons, ammonia, and alcohols) (Section 6.4.5). Today, most

hydrogen is supplied by steam reformation of fossil methane (CH 4 
into CO 2 and H 2) at a cost of 1.30– USD1.50 kg–1 (Sherwin 2021). 
Non-fossil hydrogen can be obtained by electrolysis of water, at 
current costs of USD5–7 kgH 2–1 (assuming relatively low electricity 
costs and high utilisation rates) (Graves et  al. 2011; DOE 2020a; 
Newborough and Cooley 2020; Peterson et al. 2020). At these costs 
for electrolytic hydrogen, synthesised net-zero emissions fuels would 
cost at least USD1.6 per litre of diesel equivalent (or USD6 gallon–1 
and USD46  GJ–1, assuming non-fossil carbon feedstock costs

678
Chapter 6 Energy Systems6of USD100 per  tonne of CO 2 and low process costs of USD0.05 
litre–1 or USD1.5 GJ–1). Similar calculations suggest that synthetic 
hydrocarbon fuels could currently avoid CO 2 emissions at a cost of 
USD936–1404 tonne–1 (Ueckerdt et al. 2021). However, economies 
of scale are expected to bring these costs down substantially in the 
future (IRENA 2020c; Ueckerdt et  al. 2021), and R&D efforts are 
targeting 60–80% reductions in costs (to less than USD2 kg–1 (H2)–1) 
possibly by use of less mature but promising technologies such as 
high-temperature electrolysis and thermochemical water splitting 
(Kuckshinrichs et al. 2017; Pes et al. 2017; Schmidt et al. 2017; Saba 
et al. 2018; DOE, 2018, 2020b). Technologies capable of producing 
hydrogen directly from water and sunlight (photoelectrochemical 
cells or photocatalysts) are also under development, but are at an early stage (Nielander et al. 2015; DOE 2020a). High hydrogen

production efficiencies have been demonstrated, but costs, capacity 
factors, and lifetimes need to be improved in order to make such 
technologies feasible for net-zero emissions fuel production at scale 
(McKone et al. 2014; DOE 2020a; Newborough and Cooley 2020).
The carbon contained in net-zero emissions hydrocarbons must have 
been removed from the atmosphere either through DAC, or, in the case 
of biofuels, by photosynthesis (which could include CO 2 captured from 
the exhaust of biomass or biogas combustion) (Zeman and Keith 2008; 
Graves et al. 2011). A number of different groups are now developing 
DAC technologies, targeting costs of USD100 per tonne of CO 2 or less 
(Darton and Yang 2018; Keith et al. 2018; Fasihi et al. 2019).
e-H2NH 3
CxHyOz
N. NuclearK. Hydropower/
pumped storageA. Demand for
highly reliable
electricity
E. Geologic
storageC. Demand for aviation, 
long-distance transport,
D. Ammonia
plant
L. Electrolysis
R. Compressed
air energy

storageG. Cement
and steel
w/ captureB. Demand for
structural
materials
F. Synthetic
gas/liquids
J. Direct
air captureI. Biomass
gas/liquidsCO 2O2H2O
Air
N2
H. Direct
solar fuels
H2OO2
M. Natural gas/
biomass/
syngas
w/ capture
S. Wind
O. Solar
Q. Other centralized storage
(e.g., thermal, batteries)P. Hydrogen/
synthetic gasCO 2Essential
energy services
CO 2
CO 2
Figure 6.23 | Schematic of net-zero emissions energy system, including methods to address difficult-to-electrify sectors.  Source: with permission from Davis 

679
Energy Systems  Chapter 666.6.2.5 Using Less Energy and Using It More Efficiently
Demand-side or demand reduction strategies include technology 
efficiency improvements, strategies that reduce energy consumption 
or demand for energy services (such as reducing the use of personal 
transportation, often called ‘conservation’) (Creutzig et al. 2018), and 
strategies such as load curtailment.
Net-zero energy systems will use energy more efficiently than 
those of today (high confidence). Energy efficiency and energy 
use reduction strategies are generally identified as being flexible and cost-effective, with the potential for large-scale deployment 
(Chapters 5, 9, 10, and 11). For this reason, existing studies find that 
energy efficiency and demand reduction strategies will be important 
contributors to net-zero energy systems (Creutzig et al. 2018; Davis 
et al. 2018; DeAngelo et al. 2021). Lower demand reduces the need 
for low-carbon energy or alternative fuel sources.

Characterising efficiency of net-zero energy systems is problematic 
due to measurement challenges (high confidence). Efficiency itself 
is difficult to define and measure across full economies (Saunders 
et al. 2021). There is no single definition of energy efficiency and the Box 6.9 | The Hydrogen Economy
The phrase ‘hydrogen economy’ is often used to describe future energy systems in which hydrogen plays a prominent role. These 
future energy systems would not use hydrogen for all end uses; they would use hydrogen to complement other energy carriers, mainly 
electricity, where hydrogen might have advantages. Hydrogen could provide long-term electricity storage to support high-penetration 
of intermittent renewables and could enable trading and storage of electricity between different regions to overcome seasonal or 
production capability differences (Dowling et al. 2020; Sepulveda et al. 2021). It could also be used in lieu of natural gas for peaking

generation, provide process heat for industrial needs, or be used in the metal sector via direct reduction of iron ore (Chapter 11). Clean 
hydrogen could be used as a feedstock in the production of various chemicals and synthetic hydrocarbons. Finally, hydrogen-based 
fuel cells could power vehicles. Recent advances in battery storage make electric vehicles the most attractive alternative for light-duty 
transport. However, fuel cell technology could complement electric vehicles in supporting the decarbonisation of heavy-duty transport 
segments (e.g., trucks, buses, ships, and trains) (Chapter 10).
Hydrogen production costs have historically been prohibitive, but recent technological developments are bringing costs down. These 
developments include improvements in hydrogen production technologies in terms of efficiency and capital costs (e.g., steam methane 
reforming) (Alrashed and Zahid 2021; Boretti and Banik 2021) and the emergence of alternative production technologies such as

electrolysers (Dawood et al. 2020). These technological changes, along with decreasing costs of renewable power, are increasing 
the viability of hydrogen. Other improvements in hydrogen-based technologies are also emerging quickly. Gas turbines now run on 
blended fuels containing 5–95% hydrogen by volume (GE 2020) and could operate entirely on hydrogen by 2030 (Pflug et al. 2019). 
Fuel cell costs have decreased by 80–95% since the early 2000s, while power density and durability have improved (Jouin et al. 2016; 
IEA 2019e; Kurtz et al. 2019).
For hydrogen to support decarbonisation, it will need to be produced from zero-carbon or extremely low-carbon energy sources. 
One such production category is ‘green hydrogen’. While there is no unified definition for green hydrogen, it can be produced by 
the electrolysis of water using electricity generated without carbon emissions (such as renewables). Hydrogen can also be produced

through biomass gasification with carbon capture and storage (BECCS), leading to negative carbon emissions (Arnaiz del Pozo et al. 
2021). Additionally, ‘blue hydrogen’ can be produced from natural gas through the process of auto-thermal reforming (ATR) or steam 
methane reforming, combined with CCS technology that would absorb most of the resulting CO 2 (80–90%).
However, the potential role of hydrogen in future energy systems depends on more than just production methods and costs. For 
some applications, the competitiveness of hydrogen also depends on the availability of the infrastructure needed to transport and 
deliver it at relevant scales (Lee et al. 2021). Transporting hydrogen through existing gas pipelines is generally not feasible without 
changes to the infrastructure itself (Gumber and Gurumoorthy 2018; Muratori et al. 2018). Existing physical barriers, such as steel

embrittlement and degradation of seals, reinforcements in compressor stations, and valves, require retrofitting during the conversion 
to H 2 distribution or new dedicated pipelines to be constructed (Dohi et al. 2016). The capacity to leverage and convert existing 
gas infrastructure to transport hydrogen will vary regionally, but in many cases could be the most economically viable pathway 
(Cerniauskas et al. 2020; Brändle et al. 2021; Brooks 2021; Wettengel 2021). Hydrogen could also be transported as liquid gas or as 
liquid organic hydrogen carriers such as ammonia, for which industry knowledge exists (Demir et al. 2018; Wulf et al. 2018; Hong et al. 
2021). Additionally, improvements in fuel cell technologies are needed to make hydrogen-based transport economically viable. There 
are also safety concerns associated with the flammability (Nilsson et al. 2017) and storage (Andersson and Grönkvist 2019; Caglayan 
et al. 2019) of hydrogen which will need to be considered.

680
Chapter 6 Energy Systems6definition understandably depends on the context used (Patterson 
1996), which ranges from device-level efficiency all the way to the 
efficient use of energy throughout an economy. Broadly, energy-
efficient strategies allow for the same level of services or output 
while using less energy. At the level of the entire economy, measures 
such as primary or final energy per capita or per GDP are often used 
as a  proxy for energy efficiency; these measures reflect not only 
efficiency, but also many other factors such as industrial structure, 
endowed natural resources, consumer preferences, policies, and 
regulations. Energy efficiency and other demand-side strategies 
represent such a large set of technologies, strategies, policies, market 
and consumers’ responses and policies that aggregate measures can 
be difficult to define (Saunders et al. 2021).
Measurement issues notwithstanding, virtually all studies that

address net-zero energy systems assume improved energy intensity 
in the future (high confidence). The overall efficiency outcomes and 
the access to such improvements across different nations, however, 
are not clear. Energy consumption will increase over time – despite 
energy efficiency improvements  – due to population growth and 
development (DeAngelo et al. 2021).
A study (DeAngelo et  al. 2021) reviewed 153 integrated asset 
management scenarios that attain net-zero energy sector CO 2 
emissions and found that, under a scenario with net-zero emissions: 
global final energy per  capita lies between 21–109 GJ per  person 
(median: 57), in comparison to 2018 global final energy use of 
55  GJ per  person; many countries use far more energy per  capita 
than today as their incomes increase; global final energy use 
per unit of economic output ranges from 0.7–2.2 EJ per trillion USD 
(median: 1.5), in comparison to 5 EJ per trillion USD in 2018; and

the median final energy consumption is 529 EJ. By comparison, final 
energy consumption would be 550 EJ if current energy consumption 
per capita continued under a future population of 10 billion people. 
Across all scenarios, total final energy consumption is higher today 
than in the year in which net-zero emissions are attained, and 
regionally, only the OECD+EU and Eurasia have lower median total 
final energy than in 2010.
Net-zero energy systems will be characterised by greater efficiency 
and more efficient use of energy across all sectors (high confidence). 
Road transportation efficiency improvements will require a shift from 
liquid fuels (Chapters 5 and 10). Emissions reductions will come 
from a transition to electricity, hydrogen, or synthetic fuels produced 
with low-carbon energy sources or processes. Vehicle automation, 
ride-hailing services, online shopping with door delivery services, 
and new solutions like last mile delivery with drones may result in

increased service share. Lighter vehicles, a shift to public transit, and 
incorporation of two- and three-wheelers will be features of a net-
zero energy system (Chapter  10). Teleworking and automation of 
work may provide reductions in driving needs. Other sectors, such 
as air travel and marine transportation may rely on alternative fuels 
such as biofuels, synthetic fuels, ammonia, produced with zero carbon 
energy source (Section 6.6.2.4).
Under net-zero energy systems, buildings would by characterised 
by improved construction materials, an increase in multi-family dwellings, early retirement of inefficient buildings, smaller floor areas, 
and smart controls to optimise energy use in the building, namely for 
heating, cooling, LED lighting, and water heating (Chapter 9). End 
uses would utilise electricity, or potentially hydrogen, produced from 
zero-carbon sources. The use of electricity for heating and cooking

may often be a less efficient process at converting primary energy to 
energy services than using natural gas, but using natural gas would 
require CDR in order to be considered net-zero emissions. Changes in 
behaviour may modestly lower demand. Most economies would have 
buildings with more efficient technologies powered by zero-carbon 
electricity, and developing economics would shift from biomass 
to electricity, raising their energy consumption as population and 
wealth increase under net-zero energy systems.
Industry has seen major efficiency improvements in the past, but 
many processes are now close to their thermodynamic limits. 
Electrification and breakthrough processes (such as producing steel 
with electricity and hydrogen), using recycled materials, using heat 
more efficiently by improving thermal insulation, and using waste 
heat for heat pumps, as well using advanced sensors, monitoring, 
and visualisation and communication technologies may provide

further efficiency improvements (Chapter 11).
6.6.2.6 Greater Reliance on Integrated Energy 
System Approaches
Energy systems integration refers to connected planning and 
operations across energy carriers, including electricity, fuels, 
and  thermal resources. Coordinated planning could be important 
in lowering system costs, increasing reliability, minimising 
environmental impacts, and ensuring that costs of R&D and 
infrastructure account for not just current needs but also for those 
of future energy systems (Section  6.4.3). Integration includes not 
only the physical energy systems themselves but also simultaneous 
societal objectives (e.g., sustainable development goals), innovation 
processes (e.g.,  coordinating R&D to increase the likelihood of 
beneficial technological spillovers), and other institutional and 
infrastructural transformations (Sachs et  al. 2019). Given system 
variability and differences in regional resources, there are economic

and technical advantages to greater coordination of investments 
and policies across jurisdictions, sectors, and levels of government 
(Schmalensee and Stavins 2017). Coordinated planning and 
operations can improve system economics by sharing resources, 
increasing the utilisation of capital-intensive assets, enhancing the 
geographical diversity of resource bases, and smoothing demand. 
But integration could require regulatory and market frameworks 
to facilitate and appropriate price signals to align incentives and to 
coordinate investments and operations.
Carbon-neutral energy systems are likely to be more interconnected 
than those of today (high confidence). The many possible feedstocks, 
energy carriers, and interconversion processes imply a greater need 
for the integration of production, transport, storage, and consumption 
of different fuels (Davis et al. 2018). For instance, electrification is 
expected to play an important role in decarbonising light-duty

vehicles (Chapter 10, Section 6.4.3), yet the electricity and transport 
sectors have few direct interactions today. Systems integration and

681
Energy Systems  Chapter 66sectoral coupling are increasingly relevant to ensure that net-zero 
energy systems are reliable, resilient, and affordable (EPRI 2017; 
Martin et al. 2017; Buttler and Spliethoff 2018; O’Malley et al. 2020). 
Deep decarbonisation offers new opportunities and challenges for 
integrating different sectors as well as supply- and demand-side 
options. For instance, increasing electrification will change daily 
and seasonal load shapes, and end-use flexibilities and constraints 
could impact the desirability of different supply-side technologies 
(Brown et al. 2018; EPRI 2019b). The feasibility of net-zero energy 
system configurations could depend on demonstrating cross-sector 
benefits like balancing VRE sources in the electricity sector, and on 
offering the flexibility to produce multiple products. For instance, 
low-emissions synthetic fuels could help to bridge stationary and 
mobile applications, since fuel markets have more flexibility than

instantaneously balanced electricity markets due to the comparative 
ease and cost of large-scale, long-term storage of chemical fuels 
(Davis et al. 2018).
There are few detailed archetypes of integrated energy systems that 
provide services with zero- or net-negative CO 2 emissions (such as 
Jacobson et  al. 2019), so there is considerable uncertainty about 
integration and interactions across parts of the system. Although 
alternate configurations, trade-offs, and pathways are still being 
identified, common elements include fuels and processes like zero- 
or negative-CO 2 electricity generation and transmission, hydrogen 
production and transport, synthetic hydrocarbon production 
and transport, ammonia production and transport, and carbon 
management, where linkages across pathways could include the use 
of electricity to produce hydrogen via electrolysis (Smith et al. 2016; 
Moore 2017; Davis et al. 2018; Jenkins et al. 2018b; Shih et al. 2018;

van Vuuren et al. 2018). Linked analytical frameworks are increasing 
being used to understand the potential role for system coupling with 
greater temporal resolution, spatial resolution, and heterogeneity 
of consumer and firm decisions (Bohringer and Rutherford 2008; 
Bistline and de la Chesnaye 2017; Collins et al. 2017; Gerboni et al. 
2017; Santen et al. 2017; Pye et al. 2021).
Challenges associated with integrating net-zero energy systems 
include rapid technological change, the importance of behavioural 
dimensions in domains with limited experience and data, policy 
changes and interactions, and path dependence. Technological cost 
and public acceptance will influence the degree of integration. Sectoral 
pathways will likely be adaptive and adjust based on the resolution of 
uncertainties over time, and the relative competitiveness will evolve 
as the technological frontier evolves, which is a  complex and path-

dependent function of deployment, R&D, and inter-industry spillovers. 
Supply-side options interact with demand-side measures in increasingly 
integrated energy systems (Sorrell 2015; van Vuuren et al. 2018).
6.6.2.7 Carbon Dioxide Removal
While CDR is likely necessary for net-zero energy systems, the scale 
and mix of strategies is unclear –nonetheless some combination of 
BECCS and DACCS are likely to be part of net-zero energy systems 
(high confidence). Studies indicate that energy-sector CDR may 
potentially remove 5–12 GtCO 2 annually globally in net-zero energy 
systems (Fuss et al. 2018) (Figure 6.22; Section 6.7; Chapter 12). CDR is not intended as a replacement for emissions reduction, but rather as 
a complementary effort to offset residual emissions from sectors that 
are not decarbonised and from other low-carbon technologies such as 
fossil CCS (McLaren et al. 2019; Gaffney et al. 2020; Iyer et al. 2021).
CDR covers a  broad set of methods and implementation options

(Chapters 7 and 12). The two CDR methods most relevant to the energy 
sector are BECCS, which is used to produce energy carriers, and DACCS 
which is an energy user (Smith et al. 2016; Singh and Colosi 2021). 
BECCS has value as an electricity generation technology, providing 
firm, dispatchable power to support electricity grids with large amounts 
of VRE sources, and reducing the reliance on other means to manage 
these grids, including electricity storage (Mac Dowell et  al. 2017; 
Bistline and Blanford 2021a). BECCS may also be used to produce 
liquid fuels or gaseous fuels, including hydrogen (Section  6.4.2.6) 
(Muratori et  al. 2020b). For instance, CO 2 from bio-refineries could 
be captured at <USD45 tCO 2–1 (Sanchez et al. 2018). Similarly, while 
CO2 capture is expensive in the electricity sector, its integration with 
hydrogen via biomass gasification can be achieved at an incremental 
capital cost of 3–35% (Muratori et al. 2020b) (Section 6.4). As with all

uses of bioenergy, linkages to broad sustainability concerns may limit 
the viable development, as will the presence of high-quality geologic 
sinks in close proximity (Melara et al. 2020).
DACCS offers a modular approach to CDR (Creutzig et al. 2019), but 
it could be a significant consumer of energy. DAC could also interact 
with other elements of the energy systems as the captured CO 2 
could be reused to produce low-carbon methanol and other fuels 
(Hoppe et al. 2018; Realmonte et al. 2019; Zhang and Fujimori 2020). 
DACCS might also offer an alternative for use of excess electricity 
produced by variable renewables (Wohland et  al. 2018), though 
there are uncertainties about the economic performance of this 
integrated approach.
6.6.3 The Institutional and Societal Characteristics 
of Net-zero Energy Systems
The transition to net-zero energy systems is not just technological; 
it requires shifts in institutions, organisations, and society more

generally. As such, it involves institutional changes alongside 
changes in supply, technology, or markets (Andrews-Speed 2016, 
Pai et al. 2021). Institutional relationships between governments and 
energy sector actors (e.g.,  consumers, electricity companies) affect 
the nature of net-zero systems, as these entities may collaborate 
on or dispute net-zero goals and measures to achieve them. For 
example, following the Fukushima disaster, Japan placed emphasis 
on government-utility-public cooperation on use of nuclear power as 
a means of reducing carbon emissions (Sklarew 2018). Institutions 
are instrumental in shaping net-zero energy systems in multiple ways, 
complemented by and interacting with the behaviours of actors and 
policy regimes in these systems (Figure 6.24).
One level of institutional interactions reflects embedded institutions, 
norms, beliefs, and ideas that would need to change to support net-zero

energy systems. This applies, for example, to the objectives of modern 
economies and the potentially contradictory dynamics embedded in

682
Chapter 6 Energy Systems6the concept of ‘green growth’ (Stegemann and Ossewaarde 2018; 
Stoknes and Rockström 2018). The institutional environment  – the 
political and legal systems that govern exchanges and protect property 
rights – would also need to be different in net-zero energy systems. 
In this setting, changing regulations or subsidies that continue to 
favour carbon-intensive systems over the technologies of a net-zero 
energy system might prove difficult (Sovacool 2017). More generally, 
net-zero energy systems will need new regulatory frameworks to 
undertake new challenges, from managing a  more interconnected 
grid to adequately governing underground storage of CO 2. Institutions 
may also govern specific transactions, such as firms or networks that 
supply energy fuels or services. Current actors are typically resistant 
to disruptions, even if such disruptions may broadly benefit society 
(Kungl 2015; Schmid et al. 2017; Mori 2018).

For example, one energy system characterised by differentiated 
institutional interactions is the USA, where delivery of liquid fuels 
is lightly regulated, while electricity delivery is closely regulated 
(Dworkin et  al. 2013). Reforming this two-pronged system for 
decarbonisation would require four types of institutional change: 
(i) changes to the control systems that coordinate generation and 
transmission through a  pyramidal architecture for the operational 
control, dispatch, and delivery of electricity with a primary emphasis 
on reliability; (ii) changes to the financing of central-station power 
plants through long-term bonds, as valued by Wall Street ratings 
analysts; (iii) changes to the structure of investor-owned utilities 
that attract private investors who expected decades of technological 
stability to yield long-term, low-risk revenues; and (iv) changes to 
regulations to restructure and limit excessive returns and easy

entry of new retail competitors, all recognising local and national 
concerns through state and federal regulatory agencies. The example 
shows how decision-making and the infrastructures involved are 
layered, and can create ‘nested hierarchies’ where institutions fulfil 
multiple roles for energy governance or regulation simultaneously (Stern et al. 2016b). Internationally and across different parts of the 
energy system, institutional challenges such as these could become 
even more stark and complex (Van de Graaf 2013).
6.6.4 Regional Circumstances and Net-zero 
Countries have flexibility to pursue options that make the most sense 
for their national circumstances (Figure 6.25). They may emphasise 
supply transformation over demand reduction; deploy different 
resources; engage at different levels in international energy trade; 
support different energy industries; focus on different energy carriers

(e.g., electricity, hydrogen); or focus more on distributed or integrated 
systems, among others. Many factors may influence the long-term 
net-zero energy systems that are appropriate for any country’s 
national circumstances, including the following.
Future technology. Technological transitions have often been driven 
by the relative merits of different technology options. Recent trends in 
the use of PV cells, wind power, and in batteries, for example, have been 
spurred by their increasing economic competitiveness (Section 6.3). 
Yet future technology cannot be fully predicted, so it provides only 
a partial guide today for charting a path toward future systems.
Indigenous energy resources. Countries may emphasise 
approaches that take advantage of indigenous energy resources 
such as solar power, wind, hydroelectric resources, land for bioenergy 
crops, CO 2 storage capability, or fossil resources to be used with CCS.

Countries with less abundant resources may put greater emphasis 
on demand reductions and regional integration. Countries with 
resource bases that are easily tradeable, like low-carbon electricity or 
bioenergy, may choose to trade those resources rather than use them 
domestically (Box 6.10, Section 6.4.3, 6.4.5).4. Behaviours:
The actual transactions which determine prices 
and output quantities2. Institutional environment:
Political, economic and legal systems; 
government structures; property rights
3. Institutions which govern transactions:
Firms, bureaus, markets, hybrids, networks. 
Policies, laws and policy instruments1. Embedded institutions:
Norms, beliefs, ideas
Figure 6.24 | A four-level framework for institutional change.  The diagram depicts three levels of institutions (1–3) which collectively govern actor behaviours (4). 
Source: with permission from Andrews-Speed (2016).

683
Energy Systems  Chapter 66Regional climate. Climate influences heating and cooling demand, 
both of which influence countries’ energy demands and energy 
infrastructure to meet those demands (Section 6.5). In addition to 
daily demand profiles, heating and cooling are seasonal, influencing 
which energy sources may serve these loads and the seasonal 
storage they require. Cooling is almost entirely served by electricity 
today, and heating has commonly been served by non-electric fuels. 
In low-carbon energy systems, heating may be increasingly served 
by electricity (Section 6.6.4), meaning that the influence of regional 
climate may be strongest on countries’ electricity systems.Current energy system configuration. Future sectoral energy 
demands and the potential for demand-side transformation are 
partially determined by existing infrastructure (e.g., building stocks, 
transport infrastructure). Countries with less developed or growing

energy systems will have more flexibility to create the systems that 
best match their long-term goals, but there may be substantial 
challenges in transitioning directly to the most advanced low-carbon 
technology options, and countries may have different capacities to 
absorb technology from other countries.net negative
net positiveNet energy and industry emissions
(d)
0 1 1.5 3 2 440Emissions intensity of
ﬁnal energy (MtCO2 EJ–1)
20
10
03070
activity (EJ trillion USD2010–1)(b)
0 20 40 100 80 60100Share of renewable energy
from non-Biomass renewables (%)60
40
20
080
Share of primary energy
from renewables (%)(a)
0 1 3 4 2 5Per capita carbon dioxide removal 
(CDR) from BECCS (tCO2 per person)
6
4
2
0810
Per-capita residual emissions from
energy/industry (tCO2 per person)12
6
(c)
0 160 120 80100Electricity share of ﬁnal energy (%)70
60
50
2080
Per capita ﬁnal energy (GJ person–1)40
40 0.590
3050
5060
3.5 2.5Energy intensityn=219Energy resources

Electriﬁcation
n=216n=218
n=215
E. Europe + Russia OECD + EU Africa Latin America Middle East Asia
Figure 6.25 | Characteristics of regional energy systems and emissions when global energy and industrial CO 2 emissions reach net-zero.  Regional differences 
are shown for: (a) residual emissions and carbon removal; (b) energy resources; (c) electrification; and (d) energy intensity. Distributions of scenarios are shown along each axis 
for each region. Colour scheme is shown in (a). Points represent individual scenarios from the AR6 Scenarios Database (R6 regions dataset).

684
Chapter 6 Energy Systems6Regional integration. Regional integration will allow countries 
to bridge energy gaps using external linkages, including regional 
electricity integration and trade in hydrogen, biomass, and other fuels. 
Countries with greater integration can rely more heavily on imports 
and may therefore rely less on indigenous resources (Box 6.10).
Societal preferences. Citizens in every country have preferences for 
certain technological options or mitigation approaches over others 
that will influence energy system choices. The public generally prefers 
a  future energy system based largely on renewables. Preferences 
for non-renewable energy differ across regions and groups. For 
example, studies have found that people in the UK, Germany, the 
Netherlands, and Switzerland prefer renewable energy and personal 
energy efficiency and savings to nuclear, fossil fuels and CCS (Jones 
et al. 2012; Scheer et al. 2013; Demski et al. 2017; Bessette and Arvai

2018; Steg 2018; Volken et al. 2018). Studies have found that people 
with higher education levels, higher incomes, females, and liberals 
prefer renewables to fossil fuels and nuclear (Van Rijnsoever et al. 
2015; Bertsch et  al. 2016; Blumer et  al. 2018; Jobin et  al. 2019). 
The  willingness to pay for renewable electricity differs by source 
(Ma et al. 2015; Sundt and Rehdanz 2015).Technological leadership, economic opportunities, and growth. 
Countries may emphasise technologies in which they intend to have 
technological leadership and a competitive advantage. These could 
emerge over time or be based on current areas of opportunity or 
leadership. Industrial policy will influence future energy system as 
technological choices can benefit or hamper incumbents or new 
market actors.
Energy security. Countries emphasising import security will tend 
to rely more heavily on indigenous resources (Section  6.3). Some

indigenous resources may raise security of supply issues that will 
influence energy system configurations. Bioenergy and hydropower, 
for example, can be subject to import climate risks (Section 6.5), and 
significant integration of VRE technologies will influence electricity 
system infrastructure and management (Section 6.6.2, Box 6.8).
Other factors. Countries will consider a  wide range of other 
factors in building toward low-carbon energy systems. Population 
density, for example, will influence building and transportation 
energy demands;  economic transitions will influence industrial 
energy  demands. Societal priorities beyond climate, notably SDGs 
may influence technology choices and types of energy systems 
(Sections 6.3 and 6.7.7).
Box 6.10 | Regional Integration of Energy Systems
Energy systems are linked across countries in many ways: countries transport crude oil across the ocean in supertankers, pipelines

carry oil and natural gas across country boundaries, electric power lines cross country boundaries, and countries trade industrial 
commodities that carry embodied energy or that are essential inputs to mitigation technologies. Future systems will generate electricity 
using different mixes of technologies, produce and transport different carriers (e.g., hydrogen or biofuels), and use far less fossil fuel, 
among other major changes. Important examples include electricity, hydrogen, and biomass.
Electricity system integration. Net-zero energy systems will rely more heavily on electricity generated from low-emissions 
technologies. Given the significant variations in the location of low-carbon electricity resources and the temporal variability of some 
renewable electricity sources, notably solar and wind power, regional electricity grids could reduce overall costs of net-zero energy

systems (Section 6.4.5). Furthermore, electricity transmission interconnections could significantly reduce local energy balancing costs 
and investment in peaking plants needed to meet security of supply requirements, and it could increase system resilience, especially in 
the case of extreme events such as heat waves or cold spells (Fasihi and Bogdanov 2016). Important challenges to regional electricity 
integration include geopolitical concerns from cross-border trade and societal and technological challenges associated with building 
new transmission lines.
Hydrogen trade. Hydrogen may play an important role in future net-zero energy systems, particularly in applications where electricity 
is not economically advantageous (Box 6.9). Hydrogen can be used to decarbonise regions in which it is produced, and it can also be 
transported long distances to facilitate decarbonisation of sectors distant from sources of low-cost supply. Methods of long-distance,

high-volume hydrogen transport could include liquid storage, chemical carriers, and gaseous delivery via pipelines (Section 6.4.5). 
In net-zero systems with substantial wind and solar power generation, hydrogen can be generated through electrolysis and then 
shipped to other locations. Important challenges to hydrogen trade include cost-effective low-carbon production, cost of delivery 
infrastructure, storage, and end-use technology costs and safety.
Trade in biomass. Biomass may also play an important role in net-zero energy systems (Section 6.6.4, Chapter 3). Large-scale bioenergy 
production and consumption is likely to trigger global biomass trade. Global bioenergy trade volumes presently exceed 1 EJ yr –1, 
of which 60% is directly traded for energy purposes (Proskurina et al. 2019a). Established trade mechanisms include wood pellet 
transport, ethanol, and biodiesel (Proskurina et al. 2019b). In a net-zero global energy system, bioenergy trade could be greater than

current trade of coal or natural gas, but less than that of petroleum (Sharmina et al. 2017; Mandley et al. 2020). Some studies indicate

685
Energy Systems  Chapter 666.7 Low-carbon Energy System Transitions 
in the Near and MediumTerm
6.7.1 Low-carbon Energy System Transition Pathways
6.7.1.1 Energy System Emissions
Without additional efforts to reduce emissions, it is very unlikely 
that energy system CO 2 emissions will decrease sufficiently to limit 
warming to well below 2°C (high confidence). Scenarios assuming 
improvements in technology but no additional climate policies 
beyond those in place today provide a  benchmark for comparison 
against energy-related CO 2 emissions in mitigation scenarios 
(Figure  6.26). Emissions in these reference scenarios increase 
through 2050 but span a broad range (Riahi et al. 2017; Wei et al. 
2018) (Chapter 3, Figure 3.16). The highest emission levels are about 
four times current emissions; the lowest are modestly below today’s 
emissions. Emissions in these scenarios increase in most regions, but 
they diverge significantly across regions (Bauer et  al. 2017). Asia

and the Middle East and Africa account for the majority of increased 
emissions across these scenarios (Figure 6.27). While it is unlikely that 
there will be no new climate policies in the future, these scenarios nonetheless support the conclusion that the energy sector will not 
be decarbonised without explicit policy actions to reduce emissions.
Warming cannot be limited to well below 2°C without rapid and 
deep reductions in energy system GHG emissions (high confidence). 
Energy sector CO 2 emissions fall by 87–97% (interquartile range) 
by 2050 in scenarios limiting warming to 1.5°C (>50%) with no or 
limited overshoot and 60–79% in scenarios limiting warming to 2°C 
(>67%) with action starting in 2020 (Figure 6.26). Energy sector GHG 
emissions fall by 85–95% (interquartile range) in scenarios limiting 
warming to 1.5°C (>50%) with no or limited overshoot, and 62–78% 
in scenarios limiting warming to 2°C (>67%) with action starting in

2020 (Figure 6.26). In 2030, in scenarios limiting warming to 1.5°C 
(>50%) with no or limited overshoot, net CO 2 and GHG emissions 
fall by 35–51% and 38–52% respectively.  Key characteristics of 
emissions pathways  – the year of peak emissions, the year when 
net emissions  reach zero, and the pace of emissions reductions  – 
vary widely across countries and regions. These differences arise 
from differences in economic development, demographics, resource 
endowments, land use, and potential carbon sinks (Schaeffer, 
et  al. 2020; Schreyer, et  al. 2020; van Soest, Heleen et  al. 2021) 
(Figure 6.27, Figure 6.28, Box 6.11). If countries do not move quickly Box 6.10 (continued)
that Latin America and Africa could become key exporting regions, with the EU, the USA, and East Asia emerging as key importers 
(Alsaleh and Abdul-Rahim 2018; Rentizelas et al. 2019). Studies have found that net bioenergy exports could be as high as 10% of

GDP for some Latin American countries, while other regions like the EU may be faced with burgeoning import reliance (Daioglou et al. 
2020b; Mahlknecht et al. 2020). In addition to challenges associated with bioenergy production (Section 6.4 and Chapter 7), important 
challenges to biomass trade include differences in sustainability criteria and land/biomass definitions in different jurisdictions, and 
difficulties in establishing consistent monitoring and auditing systems (Lamers et al. 2016).
2030 2040 2050Emissions relative to 2020 (%)
–20020406080100120GHG
Limit warming to 1.5°C (>50%) with no/limited overshoot
Return warming to 1.5°C (>50%) after a high overshootLimit warming to 2°C (>67%),/uni00A0with action starting in 2020
Limit warming to 2°C (>67%),/uni00A0with NDCs until 20302030 2040 2050Emissions relative to 2020 (%)
–20020406080100120CO2
0102030405060708090100CH4

Figure 6.26 | Projected energy sector GHG emissions for the 1.5°C scenarios (without and with overshoot), and likely below 2°C scenarios (without 
and with delayed policy action) during 2020–2050 (Source: AR6 Scenarios Database). Boxes indicate 25th and 75th percentiles, while whiskers indicate 5th and 95th 
percentiles. GHG emissions are inclusive of energy sector CO 2, CH 4, N2O emissions and 80% of global HFC emissions. Number of model-scenario combinations in AR6 Scenarios 
Database: limit warming to 1.5°C (>50%) with no or limited overshoot: 77; return warming to 1.5°C (>50%) after a high overshoot: 110; limit warming to 2(C (>67%) with 
action starting in 2020: 164; limit warming to 2°C (>67%) with NDCs until 2030: 97.

686
Chapter 6 Energy Systems62030 2040 2050Emissions relative to 2020 (%)
–50050100150200250R6AFRICA
–20020406080100120R6ASIA
–100–50050100150R6LAM
020406080100140
120R6MIDDLE_EAST
–20020406080100R6OECD+EU
–40–20020406080140
120
100R6REF
Figure 6.27 | Net regional (R6) CO 2 emissions from energy across scenarios that limit/return warming to 1.5°C (>50%) with no or limited/after a high 
overshoot, and scenarios that limit warming to 2°C (>67%) with action starting in 2020 or with NDCs until 2030, during 2020–2050 (Source: AR6 Scenarios

Database). Boxes indicate 25th and 75th percentiles, while whiskers indicate 5th and 95th percentiles. Most mitigation scenarios are based on a cost-minimising framework 
that does not consider historical responsibility or other equity approaches.
Figure 6.28 | The timing of net-zero emissions for full economy greenhouse gases (GHGs), energy sector CO 2, and electricity sector CO 2. Boxes indicate 
25th and 75th percentiles, centre black line is the median, while whiskers indicate 1.5x the inter-quartile range. The vertical dashed lines represent the median point at which 
emissions in the scenarios have dropped by 95% (pink) and 97.5% (purple), respectively. Dots represent individual scenarios. The fraction indicates the number of scenarios 
reaching net-zero by 2100 out of the total sample. Source: AR6 Scenario Database.

687
Energy Systems  Chapter 66to reduce emissions – if reductions are delayed – a more rapid energy 
transition will subsequently be required to limit warming to 2°C or 
lower (Rogelj et al. 2015a, 2018a; IPCC 2018).
The timing of net-zero energy system emissions varies substantially 
across scenarios. In scenarios limiting warming to 1.5°C (>50%) with 
no or limited overshoot (2°C (>67%)), the energy system reaches 
net-zero CO 2 emissions (interquartile range) from 2060 onwards 
(2080–). (Figure 6.28). However, net emissions reach near-zero more 
quickly. For example, in scenarios limiting warming to 1.5°C (>50%) 
with no or limited overshoot (2°C (>67%)) net energy system CO 2 
emissions drop by 95% between 2056 and 2075 (2073 and 2093). 
Net full economy GHG emissions reach zero more slowly than net 
CO2 emissions. In some scenarios, net energy system CO 2 and total 
GHG emissions do not reach zero this century, offset by CDR in 
other sectors.

The timing of emissions reductions will vary across the different 
parts of the energy sector (Figure 6.28). To decarbonise most cost-
effectively, global net CO 2 emissions from electricity generation 
will likely reach zero before the rest of the energy sector (medium 
confidence). In scenarios limiting warming to 1.5°C (>50%) with no or 
limited overshoot (2°C (>67%)), net electricity sector CO 2 emissions (interquartile range) reach zero globally between 2044 and 2055 
(2052 and 2078) (Figure 6.28). It is likely to be less costly to reduce 
net CO 2 emissions close to or below zero in the electricity sector than 
in other sectors, because there are relatively more low-emissions 
options in electricity. Sectors such as long-distance transport, air 
transport, and process heat are anticipated to face greater challenges 
to decarbonisation than the electricity sector (Clark and Herzog 2014; 
Rogelj et al. 2015b, 2018b; IPCC 2018; Luderer et al. 2018).

In addition, there are potential options to remove CO 2 from the 
atmosphere in the electricity sector, notably BECCS, which would 
allow electricity sector emissions to drop below zero. Without CDR 
options, electricity sector emissions may not fall all the way to zero. 
If CDR is accomplished in other sectors and not in electricity, some 
fossil fuel plants may still lead to positive net electricity sector CO 2 
emissions, even in net-zero economies (Bistline and Blanford 2021b; 
Williams et al. 2021a).
We lack sufficient understanding to pin down precise dates at which 
energy system CO 2 emissions in individual countries, regions, or 
sectors will reach net zero. Net-zero timing is based on many factors 
that are not known today or are bound up in development of key 
technologies, such as energy storage, bioenergy, or hydrogen. Some 

–60–40–20020406080100120Electricity
020406080100120140Transport
050100150200250Industry
020406080100120140Residential and commercial
Figure 6.29 | Reductions in CO 2 emissions relative to 2020 levels for scenarios that limit/return warming to 1.5°C (>50%) with no or limited/after a high, 
overshoot, and scenarios that limit warming to 2°C (>67%), with action starting in 2020 or NDCs until 2030, during 2030–2050.  Boxes indicate 25th and 
75th percentiles while whiskers indicate 5th and 95th percentiles. Source: AR6 Scenarios Database.

688
Chapter 6 Energy Systems6countries have low-carbon resource bases that could support deep 
emissions reductions, while others do not. Timing is also affected 
by the availability of CDR options, whether these options are in the 
energy sector or elsewhere, and the discount rate used to assess 
strategies (Bednar et  al. 2019; Emmerling et  al. 2019). Moreover, 
while many scenarios are designed to minimise global mitigation 
costs, many other frameworks exist for allocating mitigation effort 
across countries (van den Berg et al. 2019) (Chapter 4).
6.7.1.2 Low-carbon Energy Transition Strategies
There are multiple technological routes to reduce energy system 
emissions (Section 6.6). Here we discuss three of these: (i) decarbonising 
primary energy and electricity generation; (ii) switching to electricity, 
bioenergy, hydrogen, and other fuels produced from low-carbon 
sources; and (iii) limiting energy use through improvement of

efficiency and conservation. CDR is discussed in Section 6.7.1.3 Fossil 
fuel transitions are discussed in Section 6.7.4.
Decarbonising primary energy and electricity generation. 
Limiting warming to well below 2°C requires a  rapid and dramatic 
increase in energy produced from low- or zero-carbon sources (high confidence). Low- and zero-carbon technologies produce 74–82% 
(interquartile range) of primary energy in 2050 in scenarios limiting 
warming to 1.5°C (>50%) with no or limited overshoot and 55–68% 
in scenarios limiting warming to 2°C (>67%) (Figure 6.29). The share 
of low-carbon technologies in global primary energy supply today is 
below 20% (Chapter 3, Section 6.3, and Figure 6.29). The percentage 
of low- and zero-carbon energy will depend in part on the evolution of 
energy demand – the more that energy demand grows, the more energy 
from low- and zero-carbon sources will be needed, and the higher the 
percentage of total primary energy these sources will represent.

Low- and zero-carbon sources produce 97–99% of global electricity 
in 2050 in scenarios limiting warming to 1.5°C (>50%) with no or 
limited overshoot and 93–97% in scenarios limiting warming to 2°C 
(>67%) (Figure 6.29) (medium confidence). Decarbonising electricity 
generation, in tandem with increasing use of electricity (see below), 
is an essential near-term strategy for limiting warming. The increase 
in low- and zero-carbon electricity will occur while electricity demand 
grows substantially. Studies have projected that global electricity 
demand will roughly double by 2050 and quadruple to quintuple by 
2100 irrespective of efforts to reduce emissions (Bauer et al. 2017; 
Luderer et al. 2017; IEA 2019a).
2020 2040 2050Share (%)
0102030405070
6090
80100Low-carbon energy in primary energy

Limit warming to 2°C (>67%),/uni00A0with NDCs until 20302020 2040 2050Share (%)
051015202530Bioenergy in primary energy
020406080100Low-carbon energy in electricity
020
1040
3060
5080
7090Solar and wind in electricity
2020 2040 2050Share (%)CCS in electricity
2020 20402030 2030 2030
2030 2030 2030 2050Share (%)Nuclear in electricity
Figure 6.30 | Shares of low-carbon energy (all sources except unabated fossil fuels) and bioenergy (including both traditional and commercial biomass) 
in total primary energy, and solar+wind, CCS and nuclear in electricity for scenarios that limit/return warming to 1.5°C (>50%) with no or limited/after 
a high, overshoot, and scenarios that limit warming to 2°C (>67%), with action starting in 2020 or NDCs until 2030, during 2030–2050 (Source: AR6 
Scenarios Database). Boxes indicate 25th and 75th percentiles while whiskers indicate 5th and 95th percentiles.

689
Energy Systems  Chapter 66Renewable energy, especially generation from solar and wind, is 
likely to have an important role in many low-carbon electricity 
systems. The contributions of wind and solar electricity will depend 
on their levelised costs relative to other options, integration costs, 
system value, and the ability to integrate variable resources into the 
grid (Section 6.6). Electric sector technology mixes will vary by region 
but will typically include additional resources such as hydropower, 
nuclear power, fossil generation with CCS, energy storage resources, 
and geothermal energy, among others. Contributions of different 
options vary widely across scenarios based on different assumptions 
about these factors (Figure 6.30).Nonetheless, it is likely that wind and solar will dominate low-carbon 
generation and capacity growth over the next couple of decades due 
to supporting policies in many countries, and due to their significant

roles in early electric sector decarbonisation, alongside reductions in 
coal generation (Bistline and Blanford 2021b; Pan et al. 2021). Clean 
firm technologies play important roles in providing flexibility and on-
demand generation for longer durations, though deployment of these 
technologies is typically associated with deeper decarbonisation levels 
(e.g., beyond 70–80% reductions), which are likely to be more important 
after 2030 in many regions, and with more limited CDR deployment 
(Baik et al. 2021; Bistline and Blanford 2021a; Williams et al. 2021a).
Box 6.11 | Illustrative Low-carbon Energy System Transitions
There are multiple possible strategies to transform the energy system to reach net-zero CO 2 emissions and to limit warming to 2°C 
(>67%) or lower. All pathways rely on the strategies for net-zero CO 2 energy systems highlighted in Section 6.6.2, but they vary in the

emphasis that they put on different aspects of these strategies and the pace at which they approach net-zero emissions. The pathway 
that any country or region might follow will depend on a wide variety of factors (Section 6.6.4), including, for example, resource 
endowments, trade and integration with other countries and regions, carbon sequestration potential, public acceptability of various 
technologies, climate, the nature of domestic industries, the degree of urbanisation, and the relationship with other societal priorities 
such as energy access, energy security, air pollution, and economic competitiveness. The Illustrative Mitigation Pathways presented in 
this box demonstrate four distinct strategies for energy system transformations and how each plays out for a different region, aligned 
with global strategies that would limit warming to 2.0°C (>67%) or to 1.5°C (>50%). Each pathway represents a very different vision

of a net-zero energy system. Yet, all these pathways share the common characteristic of a dramatic system-wide transformation over 
the coming decades. 
Box 6.11, Figure 1 | Illustrative Mitigation Pathway 2.0-Neg: Latin America & Caribbean (LAM) in a scenario that limits warming to 2°C (>67%) 
(LAM net-zero economy 2040–2045, net-zero energy system 2045–2050).  Supply-side focus with growing dependency on carbon dioxide removal and 
agriculture, forestry and other land-use (AFOLU), thus achieves net-zero CO 2 relatively early.Biomass
Fossil without CCSNon-biomass renewables
Fossil with CCSNuclearTrade+Other Electricity
Heat
HydrogenGasesFinal energy per capita
Final energy | GDP PPP
Liquids
Solids201050
40
30EJ yr–1
EJ yr–1
GJ per capita
GJ USD2010–1
20
10
050
40
30
20
10
0150 12
0246810 125
100
75
50
25
0
2020 2030 2040 2050 2010 2020 2030 2040 2050 2010 2020 2030 2040 2050Primary energy Secondary energy carriers Final energy

690
Chapter 6 Energy Systems6Box 6.11 (continued)
Box 6.11, Figure 2 | Illustrative Mitigation Pathway 1.5-Renewables: Africa (AF) in a scenario that limts warming to 1.5°C (>50%) (AF net-zero 
economy, 2055–2060, AF net-zero energy system 2055–2060).  Rapid expansion of non-biomass renewables, high electrification, and a fossil fuel phase-out.
Box 6.11, Figure 3 | Illustrative Mitigation Pathway 1.5-Low Demand: Developed Countries (DEV) in a scenario that limits warming to 1.5°C 
(>50%) (DEV net-zero economy, 2055–2060, net-zero energy system 2075–2080).  Major reduction of energy demand, high electrification, and gradual 
fossil fuel phase-out.Final energy per capita
Final energy GDP PPP201040
30EJ yr–1
EJ yr–1
GJ/US$201020
10
040
30
20
10
0150 12
100
75
50
25
0
Biomass

Heat
HydrogenGases
Liquids
Solids
Final energy per capita
Final energy GDP PPP2010200
150EJ yr–1
EJ yr–1
100
50
0200
150
100
50
0150 12
100
75
50
25
0
2020 2030 2040 2050 2010 2020 2030 2040 2050 2010 2020 2030 2040 2050Primary Energy Secondary energy carriers Final energy
Biomass
Heat
Liquids
Solids

691
Energy Systems  Chapter 66Switching to low-carbon energy carriers. Switching to energy 
carriers produced from low-carbon sources will be an important 
strategy for energy sector decarbonisation. Accelerated electrification 
of end uses such as light duty transport, space heating, and cooking 
is a critical near-term mitigation strategy (Sugiyama 2012; Zou et al. 
2015; Rockström et al. 2017; IEA 2019f; Waisman et al. 2019; B. Tang 
et al. 2021). Electricity supplies 48–58% (interquartile range) of the 
global final energy demand by 2050 in scenarios limiting warming to 
1.5°C (>50%) with no or limited overshoot and 36–47% in scenarios 
limiting warming to 2°C (>67%) (Figure 6.29). Globally, the current 
level of electrification is about 20%.Indirect electrification encompasses the use of electricity to 
produce hydrogen and synthetic fuels (efuels or power fuels). The 
extent of indirect electrification of final energy will depend on

resource endowments and other regionally specific circumstances. 
Although indirect electrification is less efficient compared to direct 
electrification, it allows low-carbon fuels to be imported from regions 
with abundant low-carbon electricity generation resources (Fasihi 
and Bogdanov 2016; Lehtveer et al. 2019; Fasihi and Breyer 2020) 
(Box 6.10 on regional integration).Box 6.11 (continued)
Box 6.11, Figure 4 | Illustrative Mitigation Pathway 1.5-Shifting Pathways:  Asia and Pacific (APC) in a scenario that limits warming to 1.5°C 
(>50%) (APC net-zero economy, 2075–2080, net-zero energy system 2090–2095).  Renewables, high electrification, fossil fuel phase-out and low 
agriculture, forestry and other land-use (AFOLU) emissions. Reaches net-zero CO 2 relatively late.
Box 6.11, Table 1 | Summary of selected Illustrative Mitigation Pathways energy system characteristics in 2050 for the chosen regions.
Energy 
sector CO 2 
Reduction 
2020–
2050Energy 
intensityVariable 
renewable

generationLow-carbon 
capacity 
additionsCO2 removal 
BECCS, 
AFOLU, TotalGDP per capita Year net-zero CO 2 emissions
%MJ/PPP 
USD2010EJ yr –1 (%) GW yr –1GtCO 2 yr –1 PPP USD2010 
per personFull 
economyEnergy 
sectorElectricity
Region 2050 2020 2050 2020 2050 2020 2050 2050 2020 2050
IMP-
NegLAM 124 3 2.10.5 
(9)7.7 
(53)15.4 21.5 1.1, 0.2, 1.9 12,952 24,8602040–
20452045–
20502025– 
2030
IMP-
RenAF 85 7.6 1.90.1 
(5)18 
(84)5 217 0.1, 0, 0.1 2965 85212055–
20602055–
20602025– 
2030
IMP-
LDDEV 92 3.1 0.94.6 
(13)37 
(72)52 188 0, 0.6, 0.6 42,945 61,2912055–
20602075–
20802045– 
2050
IMP-
SPAPC 76 3.8 1.13  
(7)91 
(79)123 603 0.1, 0.4, 0.4 10,514 37,1802075–
20802085–
20902085– 
20902010250
200
150
100
50
0250
200
150
100
50
0150 12
100
75
50
25
0
Final energy GDP PPPEJ yr–1
EJ yr–1

Biomass
Heat
Liquids
Solids

692
Chapter 6 Energy Systems6While electrifying end uses is a  key decarbonisation strategy, 
some end uses such as long-distance transport (freight, aviation, 
and shipping) and energy-intensive industries will be harder to 
electrify. For these sectors, alternative fuels or energy carriers such 
as biofuels, hydrogen, ammonia or synthetic methane, may be 
needed (Section 6.6 and Box 6.9). Most scenarios find that hydrogen 
consumption will grow gradually, becoming more valuable when the 
energy system has become predominantly low-carbon (Figure 6.31).
Reducing energy demand. Energy service demand is expected 
to continue to increase with growth of the economy, but there is 
great uncertainty about how much it will increase (Bauer et al. 2017; 
Riahi et al. 2017; Yu et al. 2018). Given the need to produce low-
carbon energy, the scale of energy demand is a critical determinant 
of the mitigation challenge (Riahi et al. 2012). Higher energy demand

calls for more low-carbon energy and increases the challenge; 
lower energy demand reduces the need for low-carbon sources 
and therefore can ease a  low-carbon transition. Recent studies 
have shown that tempering the growth of energy demand, while 
ensuring services and needs are still satisfied, can materially affect 
the need for technological CDR (Section 6.7.1.3) (Grubler et al. 2018; 
van Vuuren et al. 2018). Two of the Illustrative Mitigation Pathways 
(IMP-SP , IMP-LD) feature substantially lower final energy demand across buildings, transport, and industry than most other pathways in 
the literature. In some cases, energy demand levels are lower in 2050 
(and later) than in 2019. These lower demands result in less reliance 
on bioenergy and a more limited role for CDR (Figure 3.18).
6.7.1.3 Technology Options to Offset Residual Emissions
CDR technologies can offset emissions from sectors that are difficult

to decarbonise (Section 6.6), altering the timeline and character of 
energy sector transitions. A number of studies suggest that CDR is 
no longer a choice, but rather a necessity to limit warming to 1.5°C 
(Rogelj et  al. 2015a; Detz et  al. 2018; Luderer et  al. 2018; Strefler 
et al. 2018; van Vuuren et al. 2018). The reliance on CDR varies across 
scenarios and is tightly linked to future energy demand and the rate 
of emission reductions in the next two decades: deeper near-term 
emissions reductions will reduce the need to rely on CDR to constrain 
cumulative CO 2 emissions. Some studies have argued that only with 
a transition to lower energy demands will it be possible to largely 
eliminate the need for engineered CDR options (Grubler et al. 2018; 
van Vuuren et al. 2018). Overall, the amount of CDR will depend on 
CO2 capture costs, lifestyle changes, reduction in non-CO 2 GHGs, and 
utilisation of zero-emission end-use fuels (Muratori et al. 2017; van

Vuuren et al. 2018).Limit warming to 1.5°C (>50%) with no/limited overshoot
Limit warming to 2°C (>67%),/uni00A0with NDCs until 20302020 2040 2050 2030
2020 2040 2050 20302020 2040 2050 2030
2020 2040 2050 2030Share (%)
010203040506070Electricity in ﬁnal energy
05101520253035404550Electricity in ﬁnal energy
(transport)
01020304050607080Electricity in ﬁnal energy
(industry)
0102030405060708090100Electricity in ﬁnal energy 
(residential and commercial)Share (%)
0123456789Hydrogen in ﬁnal energy
051015202530Hydrogen in ﬁnal energy
0246810121416Hydrogen in ﬁnal energy
00.511.522.533.544.5Hydrogen in ﬁnal energy
(residential and commercial)

Figure 6.31 | Shares of electricity and hydrogen in final energy in scenarios that limit/return warming to 1.5°C (>50%) with no or limited/after a high, 
overshoot, and scenarios that limit warming to 2°C (>67%), with action starting in 2020 or NDCs until 2030, during 2030–2050 (Source: AR6 Scenarios 
Database). Boxes indicate 25th and 75th percentiles while whiskers indicate 5th and 95th percentiles.

693
Energy Systems  Chapter 66There is substantial uncertainty about the amount of CDR that might 
ultimately be deployed. In most scenarios that limit warming to 1.5°C, 
CDR deployment is fairly limited through 2030 at less than 1 GtCO 2 yr –1. 
The key projected increase in CDR deployment (BECCS and DAC only) 
occurs between 2030 and 2050, with annual CDR in 2050 projected 
at 2.5–7.5 GtCO 2 yr –1 in 2050 (interquartile range) in scenarios 
limiting warming to 1.5°C (>50%) with limited or no overshoot, and 
0.7–1.4 GtCO 2 yr –1 in 2050 in scenarios limiting warming to 2°C 
(>67%) with action starting in 2020. This characteristic of scenarios 
largely reflects substantial capacity addition of BECCS power plants. 
BECCS is also deployed in multiple ways across sectors. For instance, 
the contribution (interquartile range) of BECCS to electricity is 1–5% 
limited overshoot, and 0–5% in scenarios that limit warming to 2°C

(>67%) with action starting in 2020. The contribution (interquartile 
range) of BECCS to liquid fuels is 9–21% in 2050 in scenarios limiting warming to 1.5°C (>50%) with no or limited overshoot and 2–11% 
in scenarios that limit warming to 2°C (>67%) with action starting in 
2020. Large-scale deployment of CDR allows flexibility in timing of 
emissions reduction in hard-to-decarbonise sectors.
CDR will influence the potential fossil-related stranded assets 
(Box 6.13). Availability of low-cost CDR can help reduce premature 
retirement for some fossil fuel infrastructure. CDR can allow countries 
to reach net-zero emissions without phasing out all fossil fuels. 
Specific infrastructure could also be extended if it is used to burn 
biomass or other non-emitting sources. For example, existing coal-
fired power plants, particularly those with CCS, could be co-fired with 
biomass (Woolf et al. 2016; Lu et al. 2019; Pradhan et al. 2021). In

many scenarios, energy sector CDR is deployed to such an extent that 
energy sector CO 2 emissions become negative in the second half of 
the century (Chapter 3).
Box 6.12 | Taking Stock of the Energy System Transition
The Global Stocktake is a regularly occurring process under the UN Framework Convention on Climate Change (UNFCCC) in which 
efforts will be made to understand progress on, among other things, global mitigation. Collective progress of countries towards the 
Paris Agreement goal will be assessed and its outcome will inform Parties in updating and enhancing their Nationally Determined 
Contributions (NDCs). This box explores potential indicators to understand energy system mitigation progress.
CO2 emissions from fuel combustion are the bottom line on energy system progress. Beyond CO 2 emissions, primary energy demand 
by energy sources, final energy consumption by sectors, and total electricity demand provide a first order assessment of energy system

transitions. The year at which CO 2 emissions peak is also important. The Kaya Identity can be used to decompose energy system CO 2 
emissions into carbon intensity of the energy system (CO 2 emissions from fossil-fuel combustion and industry divided by energy 
use), energy intensity (energy use divided by economic output), and economic output. The impacts of energy and climate policy are 
reflected in the changes of carbon intensity and energy intensity. Carbon intensity captures decarbonisation of energy supply systems, 
for example, through fuel switching from fossil fuels to non-fossil fuels, upscaling of low-carbon energy sources, and deploying carbon 
dioxide removal technologies. The carbon intensity of electricity is specifically important, given the role of the electricity sector in near-
term mitigation. Economy-wide energy intensity represents efforts of demand-side energy, such as energy conservation, increase of

energy performance of technologies, structural change of economy, and development of efficient urban infrastructure.
Beyond these aggregate indicators, a second order assessment would capture more details, such as the electrification rate, share of 
renewables, nuclear, CCS or other low-carbon technologies in electricity generation, land area used for energy production, and the 
number of EVs or PHEVs. Consumption of coal, oil and gas captures the underlying factors of CO 2 emissions. The emphasis of these 
indicators could differ across countries in the context of national specific circumstances. Technology- or project-based statistics are 
also useful to check the progress of the low-carbon transition, for example, the number of CCS facilities.
A critical challenge in the assessment of energy sector progress is how to measure societal, institutional, and political progress. These

factors are difficult to quantify, yet they are fundamental determinants of the ability to reduce emissions. Public opinion, special 
interest politics, implications of mitigation for employment, energy subsidies, and energy policies are all critical indicators of progress. 
In addition, while much of the literature focuses on national-level action, mitigation is increasingly being led by cities, states, provinces, 
businesses, and other sub-national or non-national actors. Understanding the progress of these actors will be critical to assess energy 
system mitigation progress. New research is needed to better assess these ‘societal’ indicators and the role of non-national actors.
6.7.2 Investments in Technology and Infrastructure
Total global energy investment was roughly USD1940 billion yr –1 
in 2019 (IEA 2021f). This total can be broken down into the 
following main categories: fossil-related energy supply, including oil, gas, and coal extraction and fossil electricity generation

(USD990 billion  yr –1); renewable electricity, primarily solar and 
wind (USD340 billion yr –1); nuclear energy (USD40 billion yr –1); 
electricity networks (USD270 billion yr –1); and end-use energy 
efficiency (USD270 billion yr –1).

694
Chapter 6 Energy Systems6Energy investment needs are projected to rise, according to 
investment-focused scenario studies found in the literature (McCollum 
et  al. 2018a; Zhou et  al. 2019; Bertram et  al. 2021). While these 
increases are projected to occur in emissions-intensive pathways as 
well as low-carbon pathways, they are projected to be largest in low-
carbon pathways. Average annual global energy investments over 
the 2016–2050 period range (across six models) from USD2100 to 
4100 billion yr –1 in pathways limiting warming to 2°C (>67%) and 
from USD2400 to 4700 billion yr –1 in pathways limiting warming to 
1.5°C (>50%) with no or limited overshoot (McCollum et al. 2018). 
Whatever the scenario, a significant and growing share of investments 
between now and 2050 will be channelled toward infrastructure build-
out in emerging economies, particularly in Asia (Zhou et al. 2019).
More widespread electrification of buildings, transport, and industry

means particularly substantial investment in the electricity system. 
According to C1–C3 pathways in the IPCC’s Sixth Assessment 
Report (AR6 Scenarios Database), such investments could be at the 
following average annual levels (inter-quartile range, USD2015) over the 2023–2052 timeframe: USD1670 to 3070 billion yr –1 (C1), 
USD1600 to 2780 billion yr –1 (C2), and USD1330 to 2680 billion yr –1 
(C3) (see also Section 3.6.1.3).
Beyond these sector-wide numbers, a  key feature of stringent 
mitigation pathways is a  pronounced reallocation of investment 
flows across sub-sectors, namely from unabated fossil fuels 
(extraction, conversion, and electricity generation) and toward 
renewables, nuclear power, CCS, electricity networks and storage, 
and end-use energy efficiency (McCollum et  al. 2018a; Bertram 
et al. 2021; IEA 2021f) (Figure 6.32). Investments in solar, wind, and 
electricity transmission, distribution, and storage increase the most

in mitigation scenarios. Up to 2050, the bulk of these investments 
are made in OECD and Asian countries (Figure  6.33). While fossil 
fuel extraction investments exhibit a marked downscaling across all 
regions, compared to reference scenarios, the declines are especially 
strong in the Middle East, Reforming Economies of Eastern Europe 
and the Former Soviet Union (REF), and OECD.C1: limit warming to 1.5°C (>50%) with no or limited overshoot
C3: limit warming to 2°C (>67%)201903006009001200
Solar Wind Storage T&D Nuclear Hydro Bio Geothermal Fossil ExtractionAverage annual investments, 2023−2052 (USD billion yr–1)  
Category
Figure  6.32 | Global average annual investments from 2023 to 2052 (undiscounted, in USD billion yr –1) for electricity supply sub-sectors and for 
extraction of fossil fuels in scenarios that limit warming to 2°C (>67%) or lower (C1-C3) (Source: AR6 Scenarios Database and Chapter 3). Historical

investments are also shown for comparison (Source: IEA 2021; approximations are made for hydro and geothermal based on available data; solar and wind values are for 2020). 
T&D: transmission and distribution of electricity. Bars show median values across models-scenarios, and whiskers the interquartile ranges. See Chapters 3 and 15 for additional 
information on investments and finance.

695
Energy Systems  Chapter 66Investments into end-use energy efficiency are projected to also 
be substantial in mitigation pathways, potentially upwards of 
several hundred USD billion yr –1 on average to 2050, compared to 
USD270  billion yr –1 in 2019 (McCollum et  al. 2018a; IEA 2021f). 
However, the literature is inconsistent in how demand-side 
investments are calculated, as boundary conditions are less clear 
than for energy supply investments. Taking a broader definition can 
result in estimates that are an order-of-magnitude higher, meaning 
as large or larger than supply-side investments (Grubler et al. 2012; 
IEA 2021f).
Increasing low-carbon investment primarily requires shifting existing 
capital investment through regulation and incentives as well as 
removing existing investment barriers (McCollum et al. 2018; Hafner 
et al. 2020; Ameli, N. et al. 2021). While there is a considerable amount 
of capital in the world, it is not always available to those wishing

to invest in certain projects. Total annual global investment in fixed capital was USD22.4 trillion in 2021, over an order-of-magnitude 
larger than energy sector investment (World Bank 2021).
Future investment patterns will vary by region, as they do now, due to 
differences in risk profiles, resource endowments and economic and 
governance structures (Fizaine et al. 2016; Zhou et al. 2019; Ameli, N. 
et  al. 2021). In rapidly growing countries, investments to support 
a low-carbon energy system transition will be integrated with those 
needed to meet rapidly increasing energy demands, irrespective of 
whether efforts are made to reduce emissions. In less rapidly growing 
countries (Sun et al. 2019), investments will focus on transitioning 
current energy systems to low-carbon configurations. Most current 
energy investments are concentrated in high- and upper-middle-
income countries (IEA 2021f), but this will change as investment

needs continue to grow in today’s lower-middle- and low-income 
countries (McCollum et al. 2018a; Zhou et al. 2019; Bertram et al. 
2021; IEA 2021f).C1: limit warming to 1.5°C (>50%) with no or limited overshoot
C3: limit warming to 2°C (>67%)2015Average annual investments, 2023−2052 (USD billion yr–1)
 
CategoryR6MIDDLE_EAST R6OECD90+EU R6REFR6AFRICA R6ASIA R6LAM
Extr. Solar T&D Wind Extr. Solar T&D Wind Extr. Solar T&D Wind0255075100
02550751000100200300400500
01002003000306090
0255075100125
Figure 6.33 | Regional average annual investments from 2023 to 2052 (undiscounted, in USD billion yr –1) for four of the largest sub-sectors of the energy 
system in scenarios that limit warming to 2°C (>67%) or lower (C1–C3) (Source: AR6 Scenarios Database and Chapter 3). Historical investments are also

shown for comparison (Source: IEA, 2016). T&D: transmission and distribution of electricity. Extr.: extraction of fossil fuels. Bars show median values across models-scenarios, 
and whiskers the inter-quartile ranges. See Chapters 3 and 15 for additional information on investments and finance.

696
Chapter 6 Energy Systems66.7.3 Energy System Lock-in and Path Dependence
Path dependence refers to resistance to change due to favourable 
socio-economic conditions with existing systems; decisions made in 
the past unduly shape future trajectories. Carbon lock-in is a specific 
type of path dependence (Seto et al. 2016). Given that energy system mitigation will require a major course change from recent history, 
lock-in is an important issue for emission reductions in the energy 
sector. While lock-in is typically expressed in terms of physical 
infrastructure that would need to be retired early to reach mitigation 
goals, it involves a much broader set of issues that go beyond physical 
systems and into societal and institutional systems (Table 6.11).
Table 6.11 | Lock-in types and typical mechanisms. Source: Kotilainen et al. 2020), Reproduced under Creative Commons 4.0 International Licence.
Type Primary lock-in mechanisms References

Technological (and infrastructural) –Economies of scale
 –Economies of scope
 –Learning effects
 –Network externalities
 –Technological interrelatedness –Arthur (1994); Hughes (1994); Klitkou et al (2015)
 –David (1985); Panzar and Willig (1981)
 –Arthur (1994)
 –David (1985); Katz and Shapiro (1986)
 –Arrow (1962); Arthur (1994); David (1985); Van den Bergh and Oosterhuis (2008)
Institutional –Collective action
 –Complexity and opacity of politics
 –Differentiation of power and institutions
 –High density of institutions
 –Institutional learning effects
 –Vested interests –Seto et al (2016)
 –Foxon (2002); Pierson (2000)
 –Foxon (2002)
 –Pierson (2000)
 –Foxon (2002); Boschma (2005)
 –Boschma (2005)
Behavioural –Habituation
 –Cognitive switching costs
 –Increasing informational returns –David (1985); Barnes et al. (2004); Zauberman (2003); Murray and Haubl (2007)
 –Zauberman (2003); Murray and Haubl (2007); Van den Bergh and Oosterhuis (2008)

6.7.3.1 Societal and Institutional Inertia
A combination of factors – user, business, cultural, regulatory, and 
transnational  – will hinder low-carbon energy transitions. Strong 
path dependencies, even in early formative stages, can have lasting 
impacts on energy systems, producing inertia that cuts across 
technological, economic, institutional and political dimensions (high 
confidence) (Rickards et al. 2014; Vadén et al. 2019) (Chapter 5).
Energy systems exemplify the ways in which massive volumes of 
labour, capital, and effort become sunk into particular institutional 
configurations (Bridge et al. 2013, 2018). Several embedded factors 
affect large-scale transformation of these systems and make 
technological diffusion a complex process:
• User environments affect purchase activities and can involve 
the integration of new technologies into user practices and the 
development of new preferences, routines, habits and evenvalues 
(Kanger et al. 2019).

• Business environments can shape the development of industries, 
business models, supply and distribution chains, instrument 
constituencies and repair facilities (Béland and Howlett 2016).
• Culture can encompass the articulation of positive discourses, 
narratives, and visions that enhance cultural legitimacy and 
societal acceptance of new technologies. Regulatory embedding 
can capture the variety of policies that shape production, markets 
and use of new technologies.
• Transnational community can reflect a shared understanding 
in a  community of global experts related to new technologies 
that transcends the borders of a single place, often a country.
While low-carbon innovation involves systemic change (Geels 
et  al. 2018), these are typically less popular than energy supply innovations among policymakers and the wider public. Managing 
low-carbon transitions is therefore not only a  techno-managerial 
challenge (based on targets, policies, and expert knowledge), but

also a broader political project that involves the building of support 
coalitions that include businesses and civil society (moderate 
Low-carbon transitions involve cultural changes extending beyond 
purely technical developments to include changes in consumer 
practices, business models, and organisational arrangements. The 
development and adoption of low-carbon innovations will therefore 
require sustained and effective policies to create appropriate 
incentives and support. The implementation of such policies entails 
political struggles because actors have different understandings and 
interests, giving rise to disagreements and conflicts.
Such innovation also involves pervasive uncertainty around technical 
potential, cost, consumer demand, and social acceptance. Such 
uncertainty carries governance challenges. Policy approaches facing 
deep uncertainty must protect against and/or prepare for unforeseeable

developments, whether it is through resistance (planning for the 
worst possible case or future situation), resilience (making sure you 
can recover quickly), or adaptation (changes to policy under changing 
conditions). Such uncertainty can be hedged in part by learning by 
firms, consumers, and policymakers. Social interactions and network 
building (e.g., supply and distribution chains, intermediary actors) and 
the articulation of positive visions, such as in long-term, low-emission 
development strategies, all play a crucial role. This uncertainty extends 
to the impacts of low-carbon innovations on energy demand and 
other variables, where unanticipated and unintended outcomes are 
the norm. For instance, rapid investments in public transport networks 
could restrict car ownership from becoming common in developing 
countries (Du and Lin 2017).

697
Energy Systems  Chapter 666.7.3.2 Physical Energy System Lock-In
Current investments in fossil infrastructure have committed 
500–700  GtCO 2 of emissions, creating significant risks for limiting 
warming to 1.5°C (Callaghan 2020) (high confidence). These 
current investments combined with emissions from proposed fossil 
infrastructure exceed the emissions required to limit warming to 
1.5°C (medium confidence). Existing coal- and gas-fired electricity 
generation accounts for 200–300 GtCO 2 of committed emissions. 
Emissions from coal generation are larger than for gas plants  (Smith 
et al. 2019; Tong et al. 2019). The lifetime of coal-fired power plants 
is 25–50 years, creating long-lasting risks to climate goals (Erickson 
and Tempest 2015). Gas-fired power plants are younger on average 
than coal-fired power plants. Industry sector lock-in amounts for 
more than 100 GtCO 2, while buildings and transport sector together

contribute another 50–100 GtCO 2 (Erickson and Tempest 2015).
Lock-in is also relevant to fossil resources. Both coal and gas 
exploration continue, and new permits are being issued, which may 
cause economic (Erickson et al. 2018) as well as non-economic issues 
(Boettcher et al. 2019).
The nature of lock-in varies across the energy system. For example, lock-
in in urban and transport sectors is different from the electricity sector. 
Broadly, urban environments involve infrastructural, institutional, and 
behavioural lock-in (Ürge-Vorsatz et al. 2018). Addressing lock-in in 
these sectors requires action by multiple stakeholders and is unlikely 
with just technological evolution (Table 6.11).
Committed carbon emissions are unevenly distributed. The 
disproportionate high share of committed emissions in emerging 
economies is the result of rapid growth in recent years, which has led 
to a comparably young fossil infrastructure with substantial remaining

life (Shearer et al. 2017). Mature industrialised countries tend to have 
older infrastructures, part of which will be up for retirement in the near future (Tong et al. 2019). Coal-fired power plants currently planned or 
under construction are associated with 150–300 GtCO 2, of which about 
75% and about 10% are located in Asia and the OECD respectively 
(Edenhofer et al. 2018; Pfeiffer et al. 2018). If implemented, these new 
fleets will further shorten all coal plants’ lifetimes by another 10 years 
for meeting climate goals (Cui et al. 2019).
Despite the imperative to reduce use of fossil fuels and the multiple 
health and other benefits from closing coal-based infrastructure 
(Portugal-Pereira et al. 2018; Liu et al. 2019a; Karlsson et al. 2020; 
Rauner et al. 2020; Cui et al. 2021), coal power plants have continued 
to be commissioned globally (Jewell et al. 2019; Jakob et al. 2020), 
most notably in Asian countries. Gas power plants also continue to

be built. In many regions, new fossil electricity generation exceeds 
needed capacity (Shearer et al. 2017).
Existing policies and the NDCs are insufficient to prevent an 
increase in fossil infrastructure and associated carbon lock-in (high 
confidence) (Bertram et  al. 2015; Johnson et  al. 2015). Current 
investment decisions are critical because there is limited room 
within the carbon budget required to limit warming to well below 
2°C (Kalkuhl et al. 2019; Rosenbloom 2019). Delays in mitigation 
will increase carbon lock-in and could result in large-scale stranded 
assets if stringency is subsequently increased to limit warming 
(Box 6.11). Near-term implementation of stringent GHG mitigation 
policies are likely to be most effective in reducing carbon lock-in 
(Haelg et al. 2018). Near-term mitigation policies will also need to 
consider different energy transition strategies as a result of different 
resources and carbon budgets between countries (Lucas 2016; Bos

and Gupta 2018).
Near-term policy choices are particularly consequential for fast-
growing economies. For example, Malik et  al. (2020) found that 
133 to 227 GW of coal capacity would be stranded after 2030 if 
India were to delay ambitious mitigation through 2030 and then 
pursue an ambitious, post-2030 climate strategy. Cui et  al. (2021) 
identified 18% of old, small, inefficient coal plants for rapid near-
term retirement in China to help achieve air quality, health, water, 
and other societal goals and a feasible coal phase-out under climate 
goals. Comparable magnitudes of stranded assets may also be 
created in Latin America when adding all announced, authorised, 
and procured power plants up to 2060 (González-Mahecha et  al. 
2019). Options to reduce carbon lock-in include reducing fossil fuels 
subsidies (Box  6.3), building CCS-ready facilities, or ensuring that 
facilities are appropriately designed for fuel switching (Budinis et al.

2018). Substantial lock-in may necessitate considerable deployment 
of CDR to compensate for high cumulative emissions.
Past and present energy sector investments have created 
technological, institutional, and behavioural path dependencies 
aligned towards coal, oil, and natural gas (high confidence). In 
several emerging economies, large projects are planned that address 
poverty reduction and economic development. Coal infrastructure 
may be the default choice for these investments without policies to 
invest in low-carbon infrastructure instead (Joshua and Alola 2020; 
Steckel et al. 2020). Path dependencies frequently have sustainability 
implications beyond carbon emissions. (Box 6.2 and Section 6.7.7). 
Figure 6.34 | Annual emissions from existing, proposed, and future energy 
system infrastructure.  Source: with permission from Tong et al. 2019.

698
Chapter 6 Energy Systems6There are several SDG co-benefits associated with decarbonisation of 
energy systems (Section 6.7.7) (Sörgel et al. 2021). For example, coal mining communities frequently experience significant health and 
economic burdens from resource extraction.
Box 6.13 | Stranded Assets
Limiting warming to 2°C (>67%) or lower will result in stranded assets (high confidence). Stranded assets can be broadly defined as assets 
that ‘suffer from unanticipated or premature write-offs, downward revaluations or [conversion] to liabilities’. Stranded assets may create 
risks for financial market stability and macro-economic stability (Battiston et al. 2017; Mercure et al. 2018; Sen and von Schickfus 2020), 
and they will result in a rapid loss of wealth for the owners of affected assets (Vogt-Schilb and Hallegatte 2017; Ploeg and Rezai 2020).
There are two types of stranded assets: fossil-fuel resources that cannot be burned; and premature retirement of fossil infrastructure

(e.g., power plants). About 30% of oil, 50% of gas, and 80% of coal reserves will remain unburnable if warming is limited to 2°C 
(Meinshausen et al. 2009; Leaton 2011; Leaton Ranger 2013; McGlade and Ekins 2015; Bauer et al. 2016; IRENA 2017b; Pye et al. 
2020) (high confidence). Significantly more reserves are expected to remain unburned if warming is limited to 1.5°C. Countries with 
large oil, gas, and coal reserves are most at risk (Caldecott et al. 2017; Ansari and Holz 2020).
About 200 GW of fossil fuel electricity generation per year will likely need to be retired prematurely after 2030 to limit warming to 2°C, 
even if countries achieve their Nationally Determined Contributions (NDCs) (medium confidence) (Iyer et al. 2015; Johnson et al. 2015; 
Fofrich et al. 2020). Limiting warming to 1.5°C will require significantly more rapid premature retirement of electricity generation

capacity (Binsted et al. 2020). Coal- and gas-fired power plants will likely need to retire about 25 years earlier than in the past to limit 
warming to 2°C, and 30 years earlier to limit warming to 1.5°C (Cui et al. 2019; Fofrich et al. 2020). Coal-fired power plants are at 
significantly greater risk of stranding compared with gas-fired and oil-fired plants (Iyer et al. 2015; Johnson et al. 2015; Fofrich et al. 
2020). The risks of stranded power plants are greatest in countries with newer fossil infrastructure.
If warming is limited to 2°C, the discounted economic impacts of stranded assets, including unburned fossil reserves, could be as high 
as USD1–4 trillion from 2015 through 2050 (USD10–20 trillion in undiscounted terms) (medium confidence) (IRENA, 2017c; Mercure 
et al. 2018). About 40% of these impacts correspond to unburned fossil reserves (IRENA 2017b). If warming is limited to 1.5°C, the

economic impacts of stranded assets are expected to be significantly higher (Binsted et al. 2020).
Stronger near-term mitigation will reduce premature retirements of fossil infrastructure, because more rapid mitigation will decrease 
new builds of fossil infrastructure that might later be stranded (Johnson et al. 2015; Bertram et al. 2018) (high confidence). For example, 
if warming is limited to 2°C, strengthening the NDC pledges beyond their 2015 levels could decrease stranded electricity sector assets 
by more than 50% (Iyer et al. 2015). By contrast, if countries fail to meet their NDCs and continue to build fossil infrastructure, 
mitigation will need to be accelerated beyond 2030, resulting up to double the amount of stranded electricity generation capacity (Iyer 
et al. 2015). This corresponds to a total undiscounted cost of about USD2 trillion from electricity infrastructure alone, from the period

2015 to 2050 (IRENA 2017). CCS (6.4) could potentially help reduce hundreds of gigawatts stranded power plant capacity along with 
other fossil-based capital (Clark and Herzog 2014; Iyer et al. 2017; Fan et al. 2018).
6.7.4 Fossil Fuels in a Low-carbon Transition
Global fossil fuel use will need to decline substantially by 2050 to 
limit warming to 2°C (>67%), and it must decline substantially by 
2030 to limit warming to 1.5°C (>50%) with no or limited overshoot 
(high confidence). Failing to reduce global fossil fuel use below 
today’s levels by 2030 will make it more challenging to limit warming 
to below 2°C (>67%). (high confidence). Fossil fuel use declines by 
260–330 EJ (52–73% from 2020 levels, interquartile range) through 
2050 in scenarios that limit warming to 1.5°C (>50%) with no or 
limited overshoot, and 124–231 EJ (24–51% reduction compared 
to 2020 levels) in scenarios that limit warming to 2°C (>67%) with

action starting in 2020. This will require a significant reduction in coal, 
oil and gas investments. Fossil fuels account for about 80% of primary 
energy today. In scenarios limiting warming to 1.5°C (>50%) with limited or no overshoot, fossil energy provides 59–69% (interquartile 
range) of primary energy in 2030 and 25–40% primary energy in 
2050 (AR6 Scenarios Database). In scenarios limiting warming to 2°C 
(>67%) with action starting in 2020, fossil energy provides 71–75% 
(interquartile range) primary energy in 2030 and 41–57% primary 
energy in 2050 (AR6 Scenarios Database). The timeline for reducing 
production and usage varies across coal, oil, and gas due to their 
differing carbon intensities and uses.
Global coal consumption without CCS needs to be largely eliminated 
by 2040–2050 to limit warming to 1.5°C (>50%), and 2050–2060 
to limit warming to 2°C (>67%) (high confidence). New investments 
in coal-fired electricity without CCS are inconsistent with limiting

warming to 2°C (>67%) or lower (high confidence) (Edenhofer et al. 
2018; Pfeiffer et al. 2018; Spencer et al. 2018; Cui et al. 2019). Coal

699
Energy Systems  Chapter 66consumption declines 130 EJ yr –1 to 140 EJ yr –1 in 2050 (78–99% 
compared to 2020 levels, interquartile range) in scenarios limiting 
warming to 1.5°C (>50%) with no or limited overshoot and 118 EJ yr –1 
to 139 EJ yr –1 (65% to 98% compared to 2020 levels) in scenarios 
limiting warming to 2°C (>67%) with action starting in 2020. Coal 
consumption without CCS falls by 67% to 82% (interquartile range) in 
2030 in scenarios limiting warming to 1.5°C (>50%) with no or limited 
overshoot. Studies indicate that coal use may decline substantially in 
the USA and Europe over the coming decade, based on the increasing 
competitiveness of low-carbon sources and near-term policy actions 
(Grubert and Brandt 2019; Oei et  al. 2020). In several developing 
economies, the relative youth of the coal-fired electricity fleet will make 
a complete phase-out before 2050 difficult (Garg and Shukla 2009;

Jewell et  al. 2016). There are considerable differences in projected 
coal phase-out timelines in major Asian economies. Some studies 
suggest that coal may continue to be a part of the Chinese energy mix 
composing around one-third of the total primary energy consumption 
by 2050, even if emissions are reduced by 50% by 2030 (He et al. 
2020). Others indicate that a strategic transition would decrease the 
risk of stranded assets and enable a  near-complete phase-out by 
2050 (Wang et al. 2020a; Cui et al. 2021). This would entail prioritising earlier retirements of plants based on technical (efficiency), economic 
(profitability, local employment) and environmental considerations 
(e.g., water scarcity for cooling).
Natural gas may remain part of energy systems through mid-century, 
both for electricity generation and use in industry and buildings, and 
particularly in developed economies, even if warming is limited to

2°C (>67%) or lower (medium confidence). The decline in natural gas 
use from 2020 to 2050 is 38 EJ yr –1 to 78 EJ yr –1 (21–62% decline 
from 2020 levels, interquartile range) in scenarios limiting warming 
to 1.5°C (>50%) with no or limited overshoot and –22 EJ yr –1 to 
46  EJ yr –1 (–14% to 36% decline from 2020 levels, interquartile 
range) in scenarios limiting warming to 2°C (>67%) with action 
starting in 2020. Scenarios indicate that gas use in electricity will 
likely peak around 2035 and 2050 if warming is limited to 1.5°C 
(>50%) with limited or no overshoot or to 2°C (>67%) with action 
starting in 2020, respectively. There is variability in the role gas would 
play in future scenarios based on national climate commitments and 
availability of cheap renewables (Malik et  al. 2020; Vishwanathan 
and Garg 2020; Vrontisi et al. 2020). Note that these differences are 
not only present in the electricity sector but also in other end uses.
1.5°C without overshoot

1.5°C with overshootLikely below 2°C
Likely below 2°C with delay2020 2040 2050 2030 2020 2040 2050 2030Primary energy relative to 2020 (%)Coal without CCS Gas without CCS Oil Total fossilPrimary energy relative to 2020 (%)Total fossil without CCS Coal with CCS Gas with CCS
2030 2040 2050020406080100120 Primary energy relative to 2020 (%)Primary energy relative to 2020 (%) Primary energy (EJ)
Primary energy relative to 2020 (%) Primary energy (EJ)2030 2040 2050020406080100120
2030 2040 2050020406080100120140
2030 2040 2050
020406080100120
2030 2040 20500102030405060708090
Figure  6.35 | Global fossil fuel pathways for scenarios that limit/return warming to 1.5°C (>50%) with no or limited/after a  high, overshoot, and 
scenarios that limit warming to 2°C (>67%), with action starting in 2020 or NDCs until 2030, during 2030–2050. Boxes indicate 25th and 75th percentiles

while whiskers indicate 5th and 95th percentiles. Results for total consumption are expressed as a percentage relative to 2020 consumption. Results for fossil energy with CCS 
are expressed in total energy consumption. Oil use with CCS is not shown here as it remains below 5% of total use. Source: AR6 Scenarios Database.

700
Chapter 6 Energy Systems6While oil use is anticipated to decline substantially, due to changes 
in the transport sector, its use will likely continue through the mid-
century, even if warming is limited to 2°C (>67%) or lower (medium 
confidence). Oil use declines by 73 EJ yr –1 to 145 EJ yr –1 (30–78% 
from 2020 levels, interquartile range) in scenarios that limit warming 
to 1.5°C (>50%) with no or limited overshoot and 26 EJ yr –1 to 
86 EJ yr –1 (14–45% from 2020 levels) by 2050 in scenarios that limit 
warming to 2°C (>67%) with action starting in 2020. While oil use 
is anticipated to decline immediately in scenarios limiting warming 
to 1.5°C (>50%), it is likely to continue to be used through 2050. Oil 
use continues to be a significant source of transport fuels in most 
scenarios limiting warming to 2°C (Welsby et al. 2021). Oil use may 
drop to about half of current levels as a transport fuel by 2050 if

warming is limited to 2°C, because of the availability of other options 
(biofuels, green hydrogen) and rapid deployment of EVs (Feijoo et al. 
2020). In the absence of rapid transport electrification, the decline 
is slower with some studies projecting peak oil use around 2035 
(Delgado et al. 2020; Pan et al. 2020).
There is a  lack of consensus about how CCS might alter fossil 
fuel transitions for limiting warming to 2°C (>67%) or lower. CCS 
deployment will increase the shares of fossil fuels associated with 
limiting warming, and it can ease the economic transition to a low-
carbon energy system (Muratori et al. 2016; Marcucci et al. 2019). While 
some studies find a significant role for fossil fuels with CCS by 2050 
(Koelbl et al. 2014; Eom et al. 2015; Vishwanathan and Garg 2020), 
others find that retirement of unabated coal far outpaces the 
deployment of coal with CCS (Budinis et al. 2018; Xie et al. 2020; 
McJeon et  al. 2021) Moreover, several studies also project that,

with availability of CO 2 capture technology, BECCS might become 
significantly more appealing than fossil CCS, even before 2050 
(Muratori et al. 2017; Luderer et al. 2018b).
6.7.5 Policy and Governance
Policy and governance frameworks are essential for shaping near- 
and medium-term low-emissions energy system transitions (high 
confidence). While policy interventions are necessary to achieve 
low-carbon energy system transitions, appropriate governance 
frameworks are crucial to ensure policy implementation (high 
confidence). The policy environment in energy transition pathways 
relate to climate policy goals, the characteristics of the policy regimes 
and measures to reach the policy goals including implementation 
limits and obstacles, and the timing of the climate instrument 
(Kriegler et al. 2014b).
The literature discusses a  broad set of policy approaches. Environmental 
economics focuses mainly on market-based approaches as the least-

cost policy to achieve emission reductions (Kube et al. 2018). Many 
countries, however, have implemented policy mixes with a diverse 
set of complementary policies to achieve energy and climate policy 
targets. One example is the German Energiewende, which includes 
substantial support for renewables, an action plan for energy 
efficiency, and phase-out processes for nuclear- and coal-based 
power generation next to carbon pricing (Löschel et al. 2019). The 
halving of CO 2 emissions in UK power generation reflects multiple policies, particularly within the UK’s Climate Change Act 2008 (Grubb 
and Newbery 2018). More generally, the implementation of the NDCs 
under the Paris Agreement are all characterised by diverse climate 
policy mixes.
These policy mixes (or policy packages) are shaped by different 
factors, including policy goals and objectives (including political, 
social and technological influences), multiple market, governance

or behavioural failures or previous policy choices of earlier policy 
eras (Rogge 2017). When pursuing multiple policy goals or targeting 
some type of imperfection, well designed policy mixes can, in 
principle, reduce mitigation costs (Corradini et al. 2018) or address 
distributional concerns, especially vulnerable populations. For 
example, the interaction between carbon pricing and the support 
for clean energy technologies in the EU clean low-carbon strategy for 
2050 can reduce mitigation costs and allow for the early adoption of 
more stringent climate targets (Vandyck et al. 2016). Policy efforts to 
promote adoption of low-carbon technologies are more successful if 
they focus not only on economic incentives but include behavioural 
interventions that target relevant cognitive and motivational 
factors (Mundaca et  al. 2019; Khanna et  al. 2021) (Section  6.7.6). 
Overlapping nudges might not necessarily lead to lower effectiveness 
(Brandon et al. 2019).

Well-designed policy mixes can support the pursuit of multiple 
policy goals, target effectively different types of imperfections and 
framework conditions and take into account the technological, 
economical, and societal situation (high confidence). Accounting for 
the different development stages of new technologies will enhance 
low-emissions transitions (Graaf and Sovacool 2020). For prototype 
technologies and technologies in the demonstration phase, research 
subsidies and demonstration projects are most important. For 
technologies experiencing early adoption, infrastructure development 
and strengthening of markets are increasingly important, while 
retiring or repurposing of existing assets is important for mature 
technologies (IEA 2020h) Effective policy mixes will address different 
market frictions and deal with various uncertainties, for example, 
those pertaining to technological, climate, and socio-economic

developments (Aldy 2020), but also with respect to outcomes of 
individual policies (e.g.,  Borenstein et  al. 2019). Therefore, policy 
mixes may balance the trade-off between stability and the flexibility 
to change individual policies (Gawel and Lehmann 2019) and the 
policy mix over time (Rayner et al. 2017). Some policy instruments 
may become feasible over time, for example, as technological 
advancements reduce the transaction costs of comprehensive market-
based approaches (Andoni et al. 2019; Di Silvestre et al. 2020), or 
as weakened barriers to stringency enable policy sequencing (Pahle 
et al. 2018). Energy system policy mixes often include sector-specific 
regulation. Compared to economy-wide approaches, sectoral policies 
may be able to directly target specific sectors or mitigation options. 
However, uncoordinated implementation or limited coordination 
across sectors may lead to efficiency losses (e.g. Rosendahl et al. 2017).

These losses also depend on other policies, such as pre-existing taxes 
(Goulder et al. 2016; Marten et al. 2018) or research and development 
policies (Acemoglu et al. 2016). Moreover, unilateral policies – those 
taken by individual countries in the absence of coordination with 
other countries – could raise carbon leakage risks, while balancing

701
Energy Systems  Chapter 66potential issues of (industrial) competitiveness (Martin et al. 2014; 
Rosendahl et al. 2017). Energy leakage may become more important 
during low-carbon energy systems. Numerous studies have identified 
pathways for carbon leakage in electricity markets with incomplete 
emission markets (Caron et  al. 2015; Murray and Maniloff 2015; 
Thurber et al. 2015; Duan et al. 2017; Fell and Maniloff 2017; Qian 
et al. 2018). Well-designed policy mixes will need to target the whole 
lifecycle or value chains, for example, through policies on limiting 
fossil fuel extraction (Asheim et al. 2019), or they will need to include 
measures to limit carbon leakage (e.g. Cosbey et al. 2019).
Interactions between policy measures including their scope, 
stringency, and timing, influence the costs of reducing emissions 
(Corradini et  al. 2018). In particular, some policy instruments may 
lead to lock-in effects (Section 6.7.3), compete with other regulations

(Graaf and Sovacool 2020), or trigger negative policy interactions 
(Perino 2015; Jarke-Neuert and Perino 2020). Existing policy mixes 
often reflect different political economy constraints, and sometimes 
not well coordinated goals. The resulting policy mixes are often 
economically inefficient. However, comprehensive evaluation of 
policy mixes requires a broader set of criteria that reflect different 
considerations, such as broader goals (e.g., SDGs) and the feasibility 
of policies (high confidence).
Policy mixes might rather emerge piece-by-piece over time out of 
individual policy interventions rather than be designed as a whole from 
the outset (Howlett 2014; Rogge 2017) and may reflect differences 
across jurisdictions and sectors (Howlett 2014). For example, taking 
into account country-specific objectives, failures, and limitations, 
carbon prices may be only one part of a  broader policy mix, and 
thereby may not be uniform across countries (Bataille 2020). This lack

of consistency makes it more difficult to assess economic outcomes 
since costs of complementary policies are often less visible and are 
often targeted at high-cost mitigation options (Borenstein et al. 2019).
Effective assessment of policy mixes requires comprehensive, validated 
international data, methodologies, and indicators. Existing policy 
mixes are difficult to evaluate because they target multiple objectives, 
and the evaluation must consider various criteria (Chapter  13 and 
Section  6.7.7), such as environmental and economic effectiveness, 
distributional effects, transformative potential, institutional 
requirements, and feasibility. Economic outcomes depend on policy 
goals and implementation. Existing studies on policy mixes suggest 
the benefits of a  comprehensive approach (Rosenow et  al. 2017), 
while also highlighting that an ‘excessive’ number of instruments 
may reduce overall effectiveness (Costantini et al. 2017). Combining

environmental regulation and innovation policies may be of particular 
importance to tackle both emissions and innovation market failures 
(Fabrizi et al. 2018). The consistency and credibility of policy mixes is 
positively associated with green innovation (Rogge and Schleich 2018).
Potential future policies are difficult to evaluate due to methodological 
challenges (high confidence). Recent model-based analyses of future 
policy mixes based on ‘current policy scenarios’ try to implement 
existing policies besides explicit or implicit carbon prices (den Elzen 
et al. 2016; Rogelj et al. 2016; van Soest et al. 2017; Roelfsema et al. 
2020). Many assessments of future low-carbon energy transitions are still based on cost-optimal evaluation frameworks and include only 
limited analysis of interactions between policy measures. Hence they 
are often not describing real-world energy transitions properly, but 
rather differences in implied carbon prices, constraints in technology

deployment, and timing of policies (Trutnevyte 2016).
6.7.6 Behaviour and Societal Integration
Members of societies, including individuals, civil society, and 
businesses, will all need to engage with, and be affected by, low-
carbon energy system transitions (high confidence). This raises 
questions about the extent to which different strategies and policy 
would effectively promote mitigation behaviours and the factors that 
increase the social acceptability of mitigation options, policies, and 
system changes.
6.7.6.1 Strategies to Encourage Climate Mitigation Actions
Climate policy will be particularly effective if it targets key factors 
inhibiting, enabling, and motivating mitigation behaviours. As 
barriers differ across mitigation options, regions, and groups, tailored 
approaches are more effective (Grubb et al. 2017). When people face 
important barriers to change (e.g., high costs, legal barriers), policy 
would be needed make low-carbon actions more attractive, or to

make high-carbon actions less attractive. As people generally face 
multiple barriers for change, combinations of policies would be more 
effective (Rosenow et al. 2017).
Financial incentives can motivate mitigation actions (Santos 2008; 
Thøgersen 2009; Bolderdijk et  al. 2011; Eliasson 2014; Maki et  al. 
2016), particularly when actions are costly (Mundaca 2007). 
In many countries, more residential solar PV were installed after the 
introduction of favourable financial schemes such as feed-in-tariffs, 
federal income tax credits, and net metering (Wolske and Stern 
2018). Similarly, many programs have promoted the installation of 
lower-carbon household options such as heat pumps, district heating, 
or solar water heaters across Europe, the Asia-Pacific and Africa (Hu 
et  al. 2012; Sovacool and Martiskainen 2020; Ahmed et  al. 2021). 
Yet, financial incentives may underperform expectations when 
other factors are overlooked. For example, people may not respond

to financial incentives when they do not trust the organisation 
sponsoring the programme, or when it takes too much effort to 
receive the incentive (Mundaca 2007; Stern et al. 2016a). Financial 
incentives are more effective if combined with strategies addressing 
non-financial barriers.
Communicating financial consequences of behaviour seems less 
effective than emphasising social rewards (Handgraaf et al. 2013) or 
benefits of actions for people (e.g., public health, comfort) and the 
environment (Bolderdijk et al. 2013; Asensio and Delmas 2015, 2016; 
Schwartz et al. 2015; Ossokina 2020). Financial appeals may have 
limited effects because they reduce people’s focus on environmental 
consequences, weaken intrinsic motivation to engage in mitigation 
actions, provide a licence to pollute (Agrawal et al. 2015; Bolderdijk 
and Steg 2015; Schwartz et al. 2015), and because pursuing small

702
Chapter 6 Energy Systems6financial gains is perceived not worth the effort (Bolderdijk et  al. 
2013; Dogan et al. 2014).
Providing information on the causes and consequences of climate 
change or on effective mitigation actions increases people’s 
knowledge and awareness, but generally does not promote mitigation 
actions by individuals (Abrahamse et  al. 2005) or organisations 
(Anderson and Newell 2004). Fear-inducing representations of 
climate change may inhibit action when they make people feel 
helpless (O’Neill and Nicholson-Cole 2009). Energy-related advice 
and feedback can promote energy savings, load shifting in electricity 
use and sustainable travel, particularly when framed in terms of 
losses rather than gains (Gonzales et al. 1988; Wolak 2011; Bradley 
et al. 2016; Bager and Mundaca 2017). Also, credible and targeted 
information at the point of decision can promote action (Stern et al. 
2016a). Information is more effective when delivered by a trusted

source, such as peers (Palm 2017), advocacy groups (Schelly 2014), 
and community organisations (Noll et al. 2014), and when tailored 
to actors’ personal situations and core values (Daamen et al. 2001; 
Abrahamse et al. 2007; Bolderdijk et al. 2013; Boomsma and Steg 
2014; Wolsko et al. 2016; van den Broek et al. 2017). This explains why 
home energy audits promoted energy savings (Delmas et al. 2013; 
Alberini and Towe 2015), and investments in resource efficiency and 
renewable energy generation (Kastner and Stern 2015).
Energy use feedback can promote energy saving behaviour within 
households (Fischer 2008; Grønhøj and Thøgersen 2011; Delmas 
et al. 2013; Karlin et al. 2015; Zangheri et al. 2019) and at work (Young 
et al. 2015), particularly when provided in real time or immediately 
after the action so that people learn the impact of different actions 
(Abrahamse et al. 2005; Faruqui et al. 2009; Delmas et al. 2013; Yu et al.

2015; Stern et al. 2016a; Tiefenbeck et al. 2016). Energy labels (Banerjee 
and Solomon 2003; Stadelmann 2017), visualisation techniques (Pahl 
et al. 2016), and ambient persuasive technology (Midden and Ham 
2012) can encourage energy savings as they immediately make sense 
and hardly require users’ conscious attention. Feedback can make 
people aware of their previous mitigation behaviours, which can 
strengthen their environmental self-identity, and motivate them to 
engage in other mitigation actions, to act in line with their self-image 
(Van der Werff et al. 2014).
Social influence approaches that communicate what other people 
do or think can encourage mitigation actions (Clayton et al. 2015), 
as can social models of desired actions (Osbaldiston and Schott 
2012; Abrahamse and Steg 2013; Sussman and Gifford 2013; Wolske 
et al. 2020). Feedback on one’s own energy use relative to others 
can be effective (Nolan et al. 2008; Allcott 2011; Schultz et al. 2015),

although not always, and effect sizes are small (Abrahamse and Steg 
2013) compared to other types of feedback (Karlin et al. 2015).
Interventions that capitalise on people’s motivation to be consistent 
can promote mitigation actions (Steg 2016). Examples are commitment 
strategies where people pledge to act (Abrahamse and Steg 2013; 
Lokhorst et al. 2013), implementation intentions where they additionally 
explicate how and when they will perform the relevant action and how 
they would cope with possible barriers (Bamberg 2000, 2002; Rees 
et al. 2018), and hypocrisy-related strategies that make people aware of inconsistencies between their attitudes and behaviour (Osbaldiston 
and Schott 2012).
Bottom-up approaches can promote mitigation action (Abrahamse 
and Steg 2013). Indeed, community energy initiatives can encourage 
members’ low-carbon behaviour (Middlemiss 2011; Seyfang and 
Haxeltine 2012; Abrahamse and Steg 2013; Sloot et  al. 2018).

Organisations can promote mitigation behaviour among their employees 
and customers by communicating their mission and strategies to 
mitigate climate change (Ruepert et al. 2017; van der Werff et al. 2021).
Default options, where a preset choice is implemented if users do not 
select another option, can promote mitigation actions such as energy 
savings, green electricity uptake, and meat-free options (Pichert and 
Katsikopoulos 2008; Bessette et al. 2014; Campbell-Arvai et al. 2014; 
Kunreuther and Weber 2014; Ölander and Thøgersen 2014; Ebeling 
and Lotz 2015; Liebe et al. 2018; Liebe et al. 2021).
6.7.6.2 Acceptability of Policy, Mitigation Options 
and System Changes
Public acceptability reflects the extent to which the public 
evaluates climate policy, mitigation options, and system changes 
(un)favourably, which can shape, enable, or prevent low-carbon 
energy system transitions. Public acceptability of policy and mitigation

options is higher when people expect these have more positive and 
less negative consequences for self, others, and the environment 
(Perlaviciute and Steg 2014; Demski et al. 2015; Drews and Van den 
Bergh 2016). Public opposition may result when a culturally valued 
landscape is affected by renewable energy development (Warren 
et al. 2005; Devine-Wright and Howes 2010), particularly when place-
based identities are threatened (Devine-Wright 2009, 2013; Boudet 
2019). Acceptability can increase after a policy or change has been 
implemented and the consequences appear to be more positive than 
expected (Schuitema et al. 2010; Eliasson 2014; Weber 2015; Carattini 
et al. 2018); effective policy trials can thus build public support.
Next, climate policy and low-carbon options are evaluated as more 
fair and acceptable when costs and benefits are distributed equally, 
and when nature, the environment and future generations are

protected (Schuitema et al. 2011; Drews and Van den Bergh 2016). 
Compensating affected groups for losses due to policy or systems 
changes enhanced public acceptability in some cases (Perlaviciute and 
Steg 2014), but people may disagree on which compensation would 
be worthwhile (Aitken 2010b; Cass et al. 2010), on the distribution 
of compensation (Devine-Wright and Sherry-Brennan 2019; Leer 
Jørgensen et al. 2020), or feel they are being bribed (Cass et al. 2010; 
Perlaviciute and Steg 2014). Pricing policies are more acceptable 
when revenues are earmarked for environmental purposes (Steg 
et  al. 2006; Sælen and Kallbekken 2011) or redistributed towards 
those affected (Schuitema and Steg 2008).
Climate policy and mitigation options, such as renewable energy 
projects, are also perceived as more fair and acceptable when the 
public (Dietz 2013; Bidwell 2014; Bernauer et  al. 2016b) or public 
society organisations (Terwel et al. 2010; Bernauer et al. 2016b) could

participate in the decision-making (Arvai 2003; Devine-Wright 2005;

703
Energy Systems  Chapter 66Terwel et al. 2012; Walker and Baxter 2017; Perlaviciute and Squintani 
2020). People are more motivated to participate in decision-making 
on local projects than on national or general policy goals (Perlaviciute 
and Squintani 2020). Public acceptability is also higher when people 
can influence major rather than only minor decisions, particularly 
when trust in responsible parties is low (Liu et  al. 2019a). Public 
participation can enhance the quality and legitimacy of decisions by 
including local knowledge and views that may otherwise be missed 
(Dietz 2013; Bidwell 2016).
Public support is higher when people trust responsible parties 
(Perlaviciute and Steg 2014; Drews and Van den Bergh 2016; Michaels 
and Parag 2016; Jiang et al. 2018; Liu et al. 2019a). Public support 
for unilateral climate policy is rather strong and robust (Bernauer 
et al. 2016a), even in the absence of reciprocal commitments by other 
states (Bernauer and Gampfer 2015).

Public acceptability of climate policy and low-carbon options differs 
across individuals. Climate policy and low-carbon options are more 
acceptable when people strongly value protecting other people and 
the environment, and support egalitarian worldviews, left-wing or 
green political ideologies, while acceptability is lower when people 
strongly endorse self-centred values, and support individualistic 
worldviews (Dietz et  al. 2007; Perlaviciute and Steg 2014; Drews 
and Van den Bergh 2016). Similarly, public decision-makers support 
climate policy more when they endorse environmental values 
(Nilsson et al. 2016). Climate and energy policy is more acceptable 
when people are more concerned about climate change (Hornsey 
et  al. 2016), when they believe their actions would help mitigate 
climate change, and feel responsible to mitigate climate change (Steg 
2005; Eriksson et al. 2006; Jakovcevic and Steg 2013; Drews and Van 
den Bergh 2016; Kim and Shin 2017; Ünal et al. 2019).

6.7.7 The Costs and Benefits of Low-carbon 
Energy System Transitions in the Context 
The attractiveness of energy sector mitigation ultimately depends 
on the way that it provides benefits and reduces the costs for the 
many different priorities that societies value (Yang et al. 2018a; Wei 
et al. 2018, 2020). While costs and benefits of climate mitigation are 
often considered in the context of pure economic outcomes  – for 
example, GDP effects or changes in value of consumption  – costs 
and benefits should be viewed with a  broader lens that accounts 
for the many ways that the energy system interacts with societal 
priorities (Karlsson et al. 2020). Climate mitigation is not separate 
from countries’ broader growth and development strategies, but 
rather as a key element of those strategies.
Cost reductions in key technologies, particularly in electricity and 
light-duty transport, have increased the economic attractiveness of

near-term low-carbon energy system transitions (high confidence). 
The near-term, economic outcomes of low-carbon energy system 
transitions in some sectors and regions may be on par with or superior 
to those of an emissions-intensive future (high confidence). Even in 
cases when system costs are higher for low-carbon transitions, these transitions may still be economically favourable when accounting 
for health impacts and other co-benefits (Gielen et  al. 2019). Past 
assessments have quantified the aggregate economic costs for 
climate change mitigation using different metrics, for example, 
carbon prices, GDP losses, investments in energy infrastructure, and 
energy system costs. Assessments of mitigation costs from integrated 
assessment and energy system models vary widely. For example, 
scenarios include carbon prices in 2030 of less than USD20 tCO 2–1, 
but also more than USD400 tCO 2–1 depending on the region, sector

boundary, and methodology (e.g., Bauer et al. 2016; Brouwer et al. 
2016; Oshiro et al. 2017; Vaillancourt et al. 2017; Chen et al. 2019). 
Those arise both from different methodologies (Guivarch and Rogelj 
2017) and assumptions about uncertainties in key factors that drive 
costs (Meyer et al. 2021).
Recent developments, however, raise the prospect that economic 
outcomes could be substantially superior to prior estimates, 
particularly if key technologies continue to improve rapidly. In some 
regions and circumstances, particularly in the electricity sector, near-
term mitigation may lead to superior economic outcomes than 
continuing to invest in and utilise emissions-intensive infrastructure 
(e.g. Brown et  al. 2017; Kumar et  al. 2020). Given the importance 
of electricity decarbonisation in near-term mitigation strategies 
(Section  6.7.1), decreasing costs of solar PV, wind power, and 
batteries to support their integration, have an outsized influence on

near-term economic outcomes from mitigation. At the same time, 
economic outcomes may vary across regions depending, among 
other things, on the characteristics of the current energy systems, 
energy resources, and needs for integrating VRE technologies.
The long-term economic characteristics of low-emissions energy 
system transitions are not well understood, and they depend on 
policy design and implementation along with future costs and 
availability of technologies in key sectors (e.g., process heat, long-
distance transport), and the ease of electrification in end-use sectors 
(high confidence). The long-term aggregate economic outcomes from 
a low-emissions future are not likely to be substantially worse than 
in an emissions-intensive future and may prove superior (Child et al. 
2019, Farmer et al. 2020; Bogdanov et al. 2021) (medium confidence). 
For the whole economy, the interquartile range of estimated 
mitigation costs is between 140 USD2015 and 340 USD2015 tCO 2–1

in 2050 in scenarios limiting warming to 2°C (>67%) and between 
430 USD2015 and 990 USD2015 tCO 2–1 in scenarios limiting 
warming to 1.5°C (>50%) with no or limited overshoot (Chapter 3). 
For energy sectors in various regions and globally, different 
scenarios show a  wide range of implied carbon prices in 2050 to 
limit warming to 1.5°C, from below USD50 tCO 2–1 to more than 
USD900 tCO 2–1 (Brouwer et al. 2016; Rogelj et al. 2018a). Mitigation 
costs for scenarios limiting warming to 2°C (>67%) were 3–11% in 
consumption losses in AR5, but the median in newer studies is about 
3% in GDP losses (Su et al. 2018; Gambhir et al. 2019).
Estimates of long-run mitigation costs are highly uncertain and 
depend on various factors. Both faster technological developments 
and international cooperation are consistently found to improve 
economic outcomes (Paroussos et al. 2019). Long-term mitigation is 
likely to be more challenging than near-term mitigation because low-

704
Chapter 6 Energy Systems6cost opportunities get utilised first and later efforts would require 
mitigation in more challenging sectors (Section  6.6). Advances in 
low-carbon energy resources and carriers such as next-generation 
biofuels, hydrogen produced from electrolysis, synthetic fuels, and 
carbon-neutral ammonia would substantially improve the economics 
of net-zero energy systems (high confidence). Current estimates of 
cumulative mitigation costs are comparably high for developing 
countries, amounting to up to 2–3% of GDP , indicating difficulties 
for mitigation without adequate support from developed countries 
(Dorband et  al. 2019; Fujimori et  al. 2020). In scenarios involving 
large amounts of stranded assets, the overall costs of low-carbon 
transitions also include the additional costs of early retirements 
(Box 6.11).
Focusing only on aggregate economic outcomes neglects distributional 
impacts, impacts on broader SDGs, and other outcomes of broad

societal importance. Strategies to increase energy efficiency and 
energy conservation are, in most instances, mutually reinforcing with 
strategies to support sustainable development. Improving efficiency 
and energy conservation will promote sustainable consumption 
and production of energy and associated materials (SDG 12) (high 
confidence). Contrastingly, successful implementation of demand-
side options requires sustainable partnerships (SDG 17) between 
different actors in energy systems, for example, governments, utilities, 
distributors, and consumers. Many authors have argued that energy 
efficiency has a large untapped potential in both supply and demand 
(Lovins 2018; Méjean et  al. 2019). For example, improved fossil 
power plant efficiency has been estimated to lower the costs of CCS 
from USD80–100 tCO 2–1 for a subcritical plant to <USD40 tCO 2–1 for 
a high-efficiency plant (Hu and Zhai 2017; Singh et al. 2017). This

could enhance energy access and affordability. Eliminating electricity transmission losses has been estimated to mitigate 500  MtCO 2 
per  year globally (Surana and Jordaan 2019). For several other 
options, such as methane mitigation from the natural gas sector, the 
costs of infrastructure refurbishing could be offset with the value of 
the recovered natural gas (Kang et al. 2019).
Efficient end-use technologies are likely to be particularly cost-
effective in developing countries where new infrastructure is 
rapidly getting built and there is an opportunity to create positive 
path dependencies (Section  6.7.3). Aside from reducing energy 
consumption, efficient end-use technologies reduce resource 
extraction, for example, fossil fuel extraction or mining for materials 
used in wind turbines or solar PV cells (Luderer et  al. 2019). 
Reduced resource extraction is an important precursor to SDG 12 
on sustainable consumption and production of minerals. End-use

efficiency strategies also reduce the need for, and therefore SDG trade-
offs associated with, CDR towards the end of the century and avoid 
temperature overshoot (van Vuuren et al. 2018). But fully leveraging 
the demand-side efficiency would entail behavioural changes and 
thus rely on strong partnerships with communities (SDG  17). For 
instance, approaches that inform households of the economic value 
of conservation strategies at home could be particularly useful 
(Niamir et al. 2018). Improved energy efficiency is interlinked with 
higher economic growth in Africa (Lin and Abudu 2020; Ohene-Asare 
et al. 2020). An important distinction here between SDGs focusing 
on infrastructural and behavioural interventions is the temporal 
context. Improving building heat systems or the electricity grid with 
reduced T&D losses would provide climate mitigation with one-time 
investments and minor maintenance over decades. On the other

hand, behavioural changes would be an ongoing process involving 
sustained, long-term societal interactions.
Figure 6.36 | The relationship between total per capita energy use, rate of electrification and human development index (HDI).  Improved efficiency has 
lowered the energy demand required for meeting a threshold HDI during 2012–2017.

705
Energy Systems  Chapter 66Increasing electrification will support and reduce the costs of key 
elements of human development, such as education, health, and 
employment (high confidence). Greater access to electricity might 
offer greater access to irrigation opportunities for agricultural 
communities (Peters and Sievert 2016) which could have the potential 
for increasing farmer incomes in support of SDG 1. Coordinated 
electrification policies also improve enrolment for all forms of 
education (Kumar and Rauniyar 2018; López-González et  al. 2020). 
Empirical evidence from India suggests that electrification reduced 
the time for biomass collection, and thus increased the time children 
have available for schooling (SDGs 4 and 5) (Khandker et al. 2014). 
Reduced kerosene use in developing countries has improved indoor air 
quality (SDG 3) (Barron and Torero 2017; Lewis and Severnini 2020). 
These positive linkages between climate change mitigation and other

goals have improved perceptions of solar PV among the public and 
policymakers. ‘Goodwill’ towards solar PV is the highest among all the 
major mitigation options considered in this chapter (Section 6.4.2).
Past trends have also indicated that, in some Asian countries, 
electrification has been obtained at lower income levels as compared 
to developed countries (Rao and Pachauri 2017), with corresponding 
impacts for development goals. For example, a human development 
index (HDI) greater than 0.7 (Figure  6.36) which signifies high 
development is now possible at close to 30 GJ yr –1 per  person. 
This was attainable only at the energy consumption of 50 GJ yr –1 
per person in preceding decades.
Electrification also improves energy efficiency, with corresponding 
implications for development goals. For example, the availability of 
electric cooking may reduce the cooking primary energy requirement 
considerably compared to traditional stoves (Yang and Yang 2018;

Batchelor et al. 2019; Khan and Alam 2020) while also promoting 
improved indoor air quality (SDG 3). Similarly, PV-powered irrigation 
and water pumping reduces pumping energy demands, which has the added advantage of promoting SDG 6 on clean water (Rathore 
et al. 2018; Elkadeem et al. 2019).
Phasing out fossil fuels in favour of low-carbon sources is likely to 
have considerable SDG benefits, particularly if trade-offs such as 
unemployment to fossil fuel workers are minimised (high confidence). 
A phase-out of coal (Box 6.2) will support SDGs 3, 7 and 14, but it is 
also anticipated to create large job losses if not properly managed. 
At the same time, there are large potential employment opportunities 
that may be created in alternative sectors such as renewables and 
bioenergy for both skilled and unskilled workers. ‘Sustainable 
transition’ pathways have indicated a  complete fossil phase-out 
which could entail numerous other co-benefits. For instance, fossil

fuels are estimated to generate only 2.65 jobs per million USD as 
compared to projected 7.49 from renewables (Garrett-Peltier 2017). 
Similar synergies may also emerge for nuclear power in the long 
term, though the high costs create trade-offs in developing country 
contexts (Agyekum et al. 2020; Castor et al. 2020). While bioenergy 
production may create jobs, it may also be problematic for SDG 2 on 
zero hunger by affecting the supplies and prices of food. Phasing out 
of fossil fuels will also improve air quality (SDG 3) and premature 
deaths by reducing PM2.5 emissions, (He et al. 2020; Li et al. 2020c). 
Energy transitions from fossil fuels to renewables, as well as within 
fossil fuels (coal to gas switching), are already occurring in some 
regions, spurred by climate concerns, health concerns, market 
dynamics, or consumer choice (e.g., in the transport sector).
CDR and CCS can create significant land and water trade-offs (high

confidence). For large-scale CDR and CCS deployment to not conflict 
with development goals requires efforts to reduce implications on 
water and food systems. The water impacts of carbon capture are 
large, but these impacts can be strategically managed (Magneschi 
et al. 2017; Liu et al. 2019a; Realmonte et al. 2019; Giannaris et al. 
2020c). In addition, high-salinity brines are produced from geologic 
carbon storage, which may be a synergy or trade-off depending on 
Figure 6.37 | Nature of the interactions between SDG 7 (Energy) and the non-energy SDGs. Source: McCollum et al. 2018c, reproduced under Creative Commons 
3.0 Licence.

706
Chapter 6 Energy Systems6the energy intensity of the treatment process and the reusability 
of the treated waters (Klapperich et  al. 2014; Arena et  al. 2017); 
if the produced brine from geologic formations can be treated 
via desalination technologies, there is an opportunity to keep the 
water intensity of electricity as constant (Section  6.4.2.5). Both 
implications of CCS and CDR are related to SDG 6 on clean water. 
CDR discussions in the context of energy systems frequently pertains 
to BECCS which could affect food prices based on land management 
approaches (Daioglou et  al. 2020a). Several CDR processes also 
require considerable infrastructure refurbishment and electrification 
to reduce upstream CO 2 emissions (Singh and Colosi 2021). Large-
scale CDR could also open the potential for low-carbon transport and 
urban energy (by offsetting emissions in these sectors) use that would 
create synergies with SDG 11 (sustainable cities and communities).

Effective siting of CDR infrastructure therefore requires consideration 
of trade-offs with other priorities. At the same time, several SDG 
synergies have also been reported to accompany CCS projects, such 
as with reduced air pollution (SDG 3) (Mikunda et al. 2021).
Greater energy system integration (Sections 6.4.3 and 6.6.2) 
would enhance energy-SDG synergies while eliminating trade-offs 
associated with deploying mitigation options (high confidence). 
Energy system integration strategies focus on codependence of 
individual technologies in ways that optimise system performance. 
Accordingly, they can improve economic outcomes and reduce 
negative implications for SDGs. For example, VRE electricity options 
raise intermittency concerns and hydrogen can be expensive due 
to the costs of electricity. Both are relevant to SDG 7 on affordable 
and reliable energy access. Routing excess solar generation during 
daytime for hydrogen production will improve grid stability as lower

hydrogen costs (Tarroja et al. 2015). Due to the varying patterns of 
solar and wind energy, these two energy sources could be operated 
in tandem, thus reducing the material needs for their construction 
and for storage, thus promoting SDG 12 on sustainable production 
(Weitemeyer et  al. 2015; Wang et  al. 2019d). For CCS facilities, 
co-firing of fossil fuels and biomass could enable a  more gradual, 
near-term low-carbon transition (Lu et al. 2019). This could enable 
early retirements (associated with SDG 1) while also providing air 
pollution reductions (associated with SDG 3).
Overall, the scope for positive interactions between low-carbon 
energy systems and SDGs is considerably larger than the trade-offs 
(Figure 6.37) (McCollum et al. 2018b). Some critical trade-offs include 
impact to biodiversity due to large-scale mineral mining needed for 
renewable infrastructure (Sonter et al. 2020).

707
Energy Systems  Chapter 66Frequently Asked Questions
FAQ 6.1 | Will energy systems that emit little or no CO 2 be different than those of today?
Low-carbon energy systems will be similar to those of today in that they will provide many of the same services as today – for example, 
heating and cooling homes, travelling to work or on vacation, transporting goods and services, and powering manufacturing. But 
future energy systems may be different in that people may also demand new services that aren’t foreseen today, just as people now 
use energy for many information technology uses that were not anticipated 50 years ago. More importantly, low-carbon energy 
systems will be different in the way that energy is produced, transformed, and used to provide these services. In the future, almost 
all electricity will be produced from sources that emit little or no CO 2, such as solar power, wind power, nuclear power, bioenergy,

hydropower, geothermal power, or fossil energy in which the CO 2 is captured and stored. Electricity, hydrogen, and bioenergy will 
be used in many situations where fossil fuels are used today, for example, in cars or heating homes. And energy is likely to be used 
more efficiently than today, for example, through more efficient cars, trucks, and appliances, buildings that use very little energy, 
and greater use or public transportation. All of these changes may require new policies, institutions, and even new ways for people 
to live their lives. And fundamental to all of these changes is that low-carbon energy systems will use far less fossil fuel than today.
FAQ 6.2 |  Can renewable sources provide all the energy needed for energy systems that emit 
little or no CO 2?
Renewable energy technologies harness energy from natural sources that are continually replenished, for example, from the sun

(solar energy), the wind (wind energy), plants (bioenergy), rainfall (hydropower), or even the ocean. The energy from these sources 
exceeds the world’s current and future energy needs many times. But that does not mean that renewable sources will provide all 
energy in future low-carbon energy systems. Some countries have a lot of renewable energy, whereas others do not, and other 
energy sources, such as nuclear power or fossil energy in which CO 2 emissions are captured and stored (carbon dioxide capture 
and storage, or CCS) can also contribute to low-carbon energy systems. The energy from sources such as solar energy, wind energy, 
and hydropower can vary throughout the day or over seasons or years. All low-carbon energy sources have other implications for 
people and countries, some of which are desirable, for example, reducing air pollution or making it easy to provide electricity in

remote locations, and some of which are undesirable, for example decreasing biodiversity or mining of minerals to produce low-
emissions technologies. For all of these reasons, it is unlikely that all low-carbon energy systems around the world will rely entirely 
on renewable energy sources.
FAQ 6.3 | What are the most important steps to decarbonise the energy system?
To create a low-carbon energy system, emissions must be reduced across all parts of the system, and not just one or two. This 
means, for example, reducing the emissions from producing electricity, driving cars, hauling freight, heating and cooling buildings, 
powering data centres, and manufacturing goods. There are more opportunities to reduce emissions over the next decade in some 
sectors compared to others. For example, it is possible to substantially reduce electricity emissions over the next decade by investing

in low-carbon electricity sources, while at the same time halting the construction of new coal-fired power plants, retiring existing 
coal-fired power plants or retrofitting them with carbon capture and storage (CCS), and limiting the construction of new gas-fired 
power plants. There are also opportunities to increase the number of electric cars, trucks, and other vehicles on the road, or to use 
electricity rather than natural gas or coal to heat homes. And across the whole energy system, emissions can be reduced by using 
more efficient technologies. While these and other actions will be critical over the coming decade, it is also important to remember 
that the low-carbon energy transition needs to extend for many decades into the future to limit warming. This means that it is 
important now to improve and test options that could be useful later on, for example, producing hydrogen from low-carbon sources 
or producing bioenergy from crops that require less land than today.

750
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Executive Summary
The Agriculture, Forestry and Other Land Use1 (AFOLU) sector 
encompasses managed ecosystems and offers significant 
mitigation opportunities while delivering food, wood and 
other renewable resources as well as biodiversity conservation, 
provided the sector adapts to climate change. Land-based 
mitigation measures represent some of the most important options 
currently available. They can both deliver carbon dioxide removal 
(CDR) and substitute for fossil fuels, thereby enabling emissions 
reductions in other sectors. The rapid deployment of AFOLU measures 
is essential in all pathways staying within the limits of the remaining 
budget for a  1.5°C target (high confidence). Where carefully and 
appropriately implemented, AFOLU mitigation measures are uniquely 
positioned to deliver substantial co-benefits and help address many 
of the wider challenges associated with land management. If AFOLU

measures are deployed badly then, when taken together with the 
increasing need to produce sufficient food, feed, fuel and wood, 
they may exacerbate trade-offs with the conservation of habitats, 
adaptation, biodiversity and other services. At the same time the 
capacity of the land to support these functions may be threatened by 
climate change itself (high confidence). {IPCC AR6 WGI, Figure SPM.7; 
IPCC AR6 WGII, 7.1, 7.6}
The AFOLU (managed land) sector, on average, accounted for 
13–21% of global total anthropogenic greenhouse gas (GHG) 
emissions in the period 2010–2019 (medium confidence). At the 
same time managed and natural terrestrial ecosystems were 
a carbon sink, absorbing around one third of anthropogenic CO 2 
emissions (medium confidence). Estimated anthropogenic net 
CO2 emissions from AFOLU (based on book-keeping models) result in 
a net source of +5.9 ± 4.1 GtCO 2 yr–1 between 2010 and 2019 with 
an unclear trend. Based on FAOSTAT or national GHG inventories,

the net CO 2 emissions from AFOLU were 0.0 to +0.8 GtCO 2 yr–1 
over the same period. There is a  discrepancy in the reported CO 2 
AFOLU emissions magnitude because alternative methodological 
approaches that incorporate different assumptions are used. If the 
managed and natural responses of all land to both anthropogenic 
environmental change and natural climate variability, estimated to 
be a  gross sink of –12.5 ± 3.2 GtCO 2 yr–1 for the period 2010–2019, 
are included with land use emissions, then land overall, constituted 
a net sink of –6.6 ± 5.2 GtCO 2 yr–1 in terms of CO 2 emissions (medium 
confidence). {7.2, 7.2.2.5, Table 7.1; IPCC AR6 WGI}
AFOLU CO 2 emissions fluxes are mainly driven by land use 
change (CO 2 LULUCF), and account for about half of total 
net AFOLU emissions. The rate of deforestation has generally 
declined, while global tree cover and global forest growing 
stock levels are likely increasing (medium confidence).

There are substantial regional differences, with losses of carbon 
generally observed in tropical regions and gains in temperate and 
1 Global databases make different choices about which emissions and removals occurring on land are considered anthropogenic. Currently, net CO 2 land fluxes from land 
reported by global book-keeping models used here differ from those from the aggregate global net emissions based on national GHG inventories. This difference, which has 
been considered in the literature, mainly reflects differences in how anthropogenic forest sinks and areas of managed land are defined. Other reasons for this difference, 
which are more difficult to quantify, can arise from the limited representation of land management in global models and varying levels of accuracy and completeness of 
estimated LULUCF fluxes in national GHG inventories. Neither method is inherently preferable. This chapter reports estimates from different databases and approaches, but

uses CO 2 LULUCF from book-keeping models to report overall emissions to ensure consistency and comparability across chapters.boreal regions. Agricultural methane (CH 4) and nitrous oxide (N 2O) 
emissions are estimated to average 157 ± 47.1 MtCH 4 yr–1 and 
6.6  ± 4.0 MtN 2O yr–1 or 4.2 ± 1.3 and 1.8 ± 1.1 GtCO 2-eq yr–1 
(using IPCC AR6 GWP100 values for CH 4 and N 2O) respectively 
between 2010 and 2019. AFOLU CH 4 emissions continue to increase 
(high confidence), the main source of which is enteric fermentation 
from ruminant animals (high confidence). Similarly, AFOLU N 2O 
emissions are increasing, dominated by agriculture, notably from 
manure application, nitrogen deposition, and nitrogen fertiliser use 
(high confidence). In addition to being a source and sink for GHG 
emissions, land plays an important role in climate through albedo 
effects, evapotranspiration and volatile organic compounds (VOCs) 
and their mix, although the combined role in total climate forcing is

unclear and varies strongly with bioclimatic region and management 
type. {2.4.2.5, 7.2, 7.2.1, 7.2.3, 7.3}
The AFOLU sector offers significant near-term mitigation 
potential at relatively low cost but cannot compensate for 
delayed emission reductions in other sectors (high evidence, 
medium agreement). The AFOLU sector can provide 20–30% 
(interquartile range) of the global mitigation needed for a 1.5°C or 
2°C pathway towards 2050 (robust evidence, medium agreement), 
though there are highly variable mitigation strategies for how AFOLU 
potential can be deployed for achieving climate targets. The estimated 
likely economic (<USD100 tCO 2-eq–1) AFOLU sector mitigation 
potential is 8 to 14 GtCO 2-eq yr–1 between 2020 and 2050, with 
the bottom end of this range representing the mean from integrated 
assessment models (IAMs) and the upper end representing the mean 
estimate from global sectoral studies. The economic potential is about

half of the technical potential from AFOLU, and about 30–50% could 
be achieved under USD20 tCO 2-eq–1. The implementation of robust 
measurement, reporting and verification processes is paramount to 
improving the transparency of net-carbon-stock changes per land 
unit to prevent misleading assumptions or claims on mitigation. 
{7.1, 7.4, 7.5}
Between 2020 and 2050, mitigation measures in forests and 
other natural ecosystems provide the largest share of the 
economic (up to USD100 tCO 2-eq–1) AFOLU mitigation potential, 
followed by agriculture and demand-side measures (high 
confidence). In the global sectoral studies, the protection, improved 
management, and restoration of forests, peatlands, coastal wetlands, 
savannas and grasslands have the potential to reduce emissions and/
or sequester 7.3 mean (3.9–13.1 range) GtCO 2-eq yr–1. Agriculture 
provides the second largest share of the mitigation potential, with

4.1 (1.7–6.7) GtCO 2-eq yr–1 (up to USD100 tCO 2-eq–1) from cropland 
and grassland soil carbon management, agroforestry, use of biochar, 
improved rice cultivation, and livestock and nutrient management. 
Demand-side measures including shifting to sustainable healthy diets, 
reducing food waste, and building with wood and biochemicals and 
bio-textiles have a mitigation potential of 2.2 (1.1–3.6) GtCO 2-eq yr–1.

751
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Most mitigation options are available and ready to deploy. Emissions 
reductions can be unlocked relatively quickly, whereas CDR needs 
upfront investment. Sustainable intensification in agriculture, shifting 
diets, and reducing food waste could enhance efficiencies and reduce 
agricultural land needs, and are therefore critical for enabling 
supply-side measures such as reforestation, restoration, as well as 
decreasing CH 4 and N 2O emissions from agricultural production. In 
addition, emerging technologies (e.g.,  vaccines or inhibitors) have 
the potential to substantially increase CH 4 mitigation potential 
beyond current estimates. AFOLU mitigation is not only relevant in 
countries with large land areas. Many smaller countries and regions, 
particularly with wetlands, have disproportionately high levels of 
AFOLU mitigation potential density. {7.4, 7.5}
The economic and political feasibility of implementing AFOLU

mitigation measures is hampered by persistent barriers. 
Assisting countries to overcome barriers will help to achieve 
significant short-term mitigation (medium confidence). 
Finance forms a critical barrier to achieving these gains as currently 
mitigation efforts rely principally on government sources and 
funding mechanisms which do not provide sufficient resources to 
enable the economic potential to be realised. Differences in cultural 
values, governance, accountability and institutional capacity are also 
important barriers. Climate change could also emerge as a barrier to 
AFOLU mitigation, although the IPCC AR6 WGI contribution to AR6 
indicated that an increase in the capacity of natural sinks may occur, 
despite changes in climate (medium confidence). The continued loss 
of biodiversity makes ecosystems less resilient to climate change 
extremes and this may further jeopardise the achievement of the 
AFOLU mitigation potentials indicated in this chapter (IPCC AR6 WGII

and IPBES) (high confidence). {7.4, 7.6; IPCC AR6 WGI, Figure SPM.7}
Bioenergy and other bio-based options represent an 
important share of the total mitigation potential. The range 
of recent estimates for the technical bioenergy potential 
when constrained by food security and environmental 
considerations is 5–50 and 50–250 EJ yr–1 by 2050 for residues 
and dedicated biomass production system respectively. These 
estimates fall within previously estimated ranges (medium 
agreement). Poorly planned deployment of biomass production 
and afforestation options for in-forest carbon sequestration may 
conflict with environmental and social dimensions of sustainability 
(high confidence). The global technical CDR potential of BECCS by 
2050 (considering only the technical capture of CO 2 and storage 
underground) is estimated at 5.9 mean (0.5–11.3) GtCO 2 yr–1, of 
which 1.6 (0.8–3.5) GtCO 2 yr–1 is available at below USD100 tCO 2–1

(medium confidence). Bioenergy and other bio-based products 
provide additional mitigation through the substitution of fossil fuels 
fossil-based products (high confidence). These substitution effects 
are reported in other sectors. Wood used in construction may reduce 
emissions associated with steel and concrete use. The agriculture 
and forestry sectors can devise management approaches that 
enable biomass production and use for energy in conjunction with 
the production of food and timber, thereby reducing the conversion 
pressure on natural ecosystems (medium confidence). {7.4}The deployment of all land-based mitigation measures can 
provide multiple co-benefits, but there are also risks and 
trade-offs from misguided or inappropriate land management 
(high confidence). Such risks can best be managed if 
AFOLU mitigation is pursued in response to the needs and 
perspectives of multiple stakeholders to achieve outcomes 
that maximise synergies while limiting trade-offs (medium

confidence). The results of implementing AFOLU measures are 
often variable and highly context specific. Depending on local 
conditions (e.g.,  ecosystem, climate, food system, land ownership) 
and management strategies (e.g.,  scale, method), mitigation 
measures have the potential to positively or negatively impact 
biodiversity, ecosystem functioning, air quality, water availability 
and quality, soil productivity, rights infringements, food security, and 
human well-being. Mitigation measures addressing GHGs may also 
affect other climate forcers such as albedo and evapotranspiration. 
Integrated responses that contribute to mitigation, adaptation, and 
other land challenges will have greater likelihood of being successful 
(high confidence); measures which provide additional benefits to 
biodiversity and human well-being are sometimes described as 
‘Nature-Based Solutions’. {7.1, 7.4, 7.6}
AFOLU mitigation measures have been well understood

for decades but deployment remains slow and emissions 
trends indicate unsatisfactory progress despite beneficial 
contributions to global emissions reduction from forest-
related options (high confidence). Globally, the AFOLU sector has 
so far contributed modestly to net mitigation, as past policies have 
delivered about 0.65 GtCO 2 yr–1 of mitigation during 2010–2019 
or 1.4% of global gross emissions (high confidence). The majority 
(>80%) of emission reduction resulted from forestry measures (high 
confidence). Although the mitigation potential of AFOLU measures 
is large from a biophysical and ecological perspective, its feasibility 
is hampered by lack of institutional support, uncertainty over long-
term additionality and trade-offs, weak governance, fragmented 
land ownership, and uncertain permanence effects. Despite these 
impediments to change, AFOLU mitigation options are demonstrably 
effective and with appropriate support can enable rapid emission

reductions in most countries. {7.4, 7.6}
Concerted, rapid and sustained effort by all stakeholders, from 
policy makers and investors to land owners and managers is 
a pre-requisite to achieving high levels of mitigation in the 
AFOLU sector (high confidence). To date USD0.7 billion yr–1 is 
estimated to have been spent on AFOLU mitigation. This is well short 
of the more than USD400 billion yr–1 that is estimated to be necessary 
to deliver the up to 30% of global mitigation effort envisaged in 
deep mitigation scenarios (medium confidence). This estimate of the 
global funding requirement is smaller than current subsidies provided 
to agriculture and forestry. Making this funding available would 
require a change in flows of money and determination of who pays. 
A gradual redirection of existing agriculture and forestry subsidies 
would greatly advance mitigation. Effective policy interventions 
and national (investment) plans as part of Nationally Determined

Contributions (NDCs), specific to local circumstances and needs, are

752
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7urgently needed to accelerate the deployment of AFOLU mitigation 
options. These interventions are effective when they include funding 
schemes and long-term consistent support for implementation with 
governments taking the initiative together with private funders and 
non-state actors. {7.6}
Realising the mitigation potential of the AFOLU sector depends 
strongly on policies that directly address emissions and drive 
the deployment of land-based mitigation options, consistent 
with carbon prices in deep mitigation scenarios (high 
confidence). Examples of successful policies and measures include 
establishing and respecting tenure rights and community forestry, 
improved agricultural management and sustainable intensification, 
biodiversity conservation, payments for ecosystem services, improved 
forest management and wood chain usage, bioenergy, voluntary 
supply chain management efforts, consumer behaviour campaigns,

private funding and joint regulatory efforts to avoid, for example, 
leakage. The efficacy of different policies, however, will depend on 
numerous region-specific factors. In addition to funding, these factors 
include governance, institutions, long-term consistent execution of 
measures, and the specific policy setting (high confidence). {7.6}
There is a discrepancy, equating to 5.5 GtCO 2 yr–1 between 
alternative methods of accounting for anthropogenic land 
CO2 fluxes. Reconciling these methods greatly enhances the 
credibility of AFOLU-based emissions offsetting. It would also 
assist in assessing collective progress in a  global stocktake 
(high confidence). The principal accounting approaches are 
national GHG inventories (NGHGI) and global modelling approaches. 
NGHGI, based on IPCC guidelines, consider a  much larger area of 
forest to be under human management than global models. NGHGI 
consider the fluxes due to human-induced environmental change on

this area to be anthropogenic and are thus reported. Global models,2 
in contrast, consider these fluxes to be natural and are excluded from 
the total reported anthropogenic land CO 2 flux. To enable a like-with-
like comparison, the remaining cumulative global CO 2 emissions 
budget can be adjusted (medium confidence). In the absence of 
these adjustments, collective progress would appear better than it is. 
{Cross-Chapter Box 6 in this chapter, 7.2}
Addressing the many knowledge gaps in the development 
and testing of AFOLU mitigation options can rapidly advance 
the likelihood of achieving sustained mitigation (high 
confidence). Research priorities include improved quantification 
of anthropogenic and natural GHG fluxes and emissions modelling, 
better understanding of the impacts of climate change on the 
mitigation potential, permanence and additionality of estimated 
mitigation actions, and improved (real time and cheap) measurement,

reporting and verification. There is a need to include a greater suite of 
mitigation measures in IAMs, informed by more realistic assessments 
that take into account local circumstances and socio-economic 
factors and cross-sector synergies and trade-offs. Finally, there is 
a  critical need for more targeted research to develop appropriate 
country-level, locally specific, policy and land management response 
options. These options could support more specific NDCs with 
2 Bookkeeping models and dynamic global vegetation models.AFOLU measures that enable mitigation while also contributing 
to biodiversity conservation, ecosystem functioning, livelihoods 
for millions of farmers and foresters, and many other Sustainable 
Development Goals (SDGs) (high confidence). {7.7}

753
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.1 Introduction
7.1.1 Key Findings from Previous Reports
Agriculture, Forestry and Other Land Uses (AFOLU) is unique due 
to its capacity to mitigate climate change through greenhouse gas 
(GHG) emission reductions, as well as enhance removals (IPCC 
2019). However, despite the attention on AFOLU since early 1990s 
it was reported in the IPCC Special Report on Climate Change and 
Land (SRCCL) as accounting for almost a quarter of anthropogenic 
emission (IPCC, 2019), with three main GHGs associated with AFOLU; 
carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2O). Overall 
emission levels had remained similar since the publication of AR4 
(Nabuurs et al. 2007). The diverse nature of the sector, its linkage 
with wider societal, ecological and environmental aspects and the 
required coordination of related policy, was suggested to make 
implementation of known and available supply- and demand-side

mitigation measures particularly challenging (IPCC 2019). Despite 
such implementation barriers, the considerable mitigation potential 
of AFOLU as a sector on its own and its capacity to contribute to 
mitigation within other sectors was emphasised, with land-related 
measures, including bioenergy, estimated as capable of contributing 
between 20% and 60% of the total cumulative abatement to 2030 
identified within transformation pathways (IPCC 2018). However, 
the vast mitigation potential from AFOLU initially portrayed in 
literature and in Integrated Assessment Models (IAMs), as explored 
in the IPCC Special Report on Climate Change of 1.5°C (SR1.5), is 
being questioned in terms of feasibility (Roe et al. 2021) and a more 
balanced perspective on the role of land in mitigation is developing, 
while at the same time, interest by private investors in land-based 
mitigation is increasing fast.
The SRCCL (IPCC 2019) outlined with medium evidence and medium

agreement that supply-side agriculture and forestry measures 
had an economic (at USD100 tCO 2-eq–1) mitigation potential of 
7.2–10.6 GtCO 2-eq–1 in 2030 (using GWP100 and multiple IPCC values 
for CH 4 and N 2O) of which about a third was estimated as achievable at 
<USD20 tCO 2-eq–1. Agricultural measures were reported as sensitive 
to carbon price, with cropland and grazing land soil organic carbon 
management having the greatest potential at USD20 tCO 2-eq–1 and 
restoration of organic soils at USD100 tCO 2-eq–1. Forestry measures 
were less sensitive to carbon price, but varied regionally, with 
reduced deforestation, forest management and afforestation having 
the greatest potential depending on region. Although demand-side 
measures related to food could in theory make a large contribution 
to mitigation, in reality the contribution has been very small. Overall, 
the dependency of mitigation within AFOLU on a  complex range

of factors, from population growth, economic and technological 
developments, to the sustainability of mitigation measures and 
impacts of climate change, was suggested to make realisation highly 
challenging (IPCC 2019).
Land can only be part of the solution alongside rapid emission 
reduction in other sectors (IPCC 2019). It was recognised that land 
supports many ecosystem services on which human existence, well-
being and livelihoods ultimately depend. Yet over-exploitation of land 
resources was reported as driving considerable and unprecedented rate of biodiversity loss, and wider environmental degradation (IPBES 
2019b; IPCC 2019). Urgent action to reverse this trend was deemed 
crucial in helping to accommodate the increasing demands on land 
and enhance climate change adaptation capacity. There was high 
confidence that global warming was already causing an increase in 
the frequency and intensity of extreme weather and climate events,

impacting ecosystems, food security, disturbances and production 
processes, with existing (and new) carbon stocks in soils and biomass 
at serious risk. The impact of land cover on regional climate (through 
biophysical effects) was also highlighted, although there was no 
confidence regarding impacts on global climate.
Since the IPCC Fifth Assessment Report (AR5), the share of AFOLU 
to anthropogenic GHG emissions had remained largely unchanged 
at 13–21% of total GHG emissions (medium confidence), though 
uncertainty in estimates of both sources and sinks of CO 2, 
exacerbated by difficulties in separating natural and anthropogenic 
fluxes, was emphasised. Models indicated land (including the natural 
sink) to have very likely provided a net removal of CO 2 between 2007 
and 2016. As in AR5, land cover change, notably deforestation, was 
identified as a  major driver of anthropogenic CO 2 emissions while 
agriculture was a major driver of the increasing anthropogenic CH 4

and N 2O emissions.
In terms of mitigation, without reductions in overall anthropogenic 
emissions, increased reliance on large-scale land-based mitigation 
was predicted, which would add to the many already competing 
demands on land. However, some mitigation measures were 
suggested to not compete with other land uses, while also having 
multiple co-benefits, including adaptation capacity and potential 
synergies with some Sustainable Development Goals (SDGs). As 
in AR5, there was large uncertainty surrounding mitigation within 
AFOLU, in part because current carbon stocks and fluxes are unclear 
and subject to temporal variability. Additionally, the non-additive 
nature of individual measures that are often inter-linked and the highly 
context specific applicability of measures, causes further uncertainty. 
Many AFOLU measures were considered well-established and some 
achievable at low to moderate cost, yet contrasting economic drivers,

insufficient policy, lack of incentivisation and institutional support to 
stimulate implementation among the many stakeholders involved, 
in regionally diverse contexts, was recognised as hampering 
realisation of potential.
None the less, the importance of mitigation within AFOLU was 
highlighted in all IPCC reports, with modelled scenarios demonstrating 
the considerable potential role and land-based mitigation forming an 
important component of pledged mitigation in Nationally Determined 
Contributions (NDCs) under the Paris Agreement. The sector was 
identified as the only one in which large-scale carbon dioxide removal 
(CDR) may currently and at short term be possible (e.g.,  through 
afforestation/reforestation or soil organic carbon management). This 
CDR component was deemed crucial to limit climate change and its 
impacts, which would otherwise lead to enhanced release of carbon 
from land. However, the SRCCL emphasised that mitigation cannot

be pursued in isolation. The need for integrated response options, 
that mitigate and adapt to climate change, but also deal with land 
degradation and desertification, while enhancing food and fibre

754
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7security, biodiversity and contributing to other SDGs has been made 
clear (IPCC 2019; IPBES 2019a; IPBES-IPCC 2021).  
7.1.2 Boundaries, Scope and Changing Context 
of the Current Report
This chapter assesses GHG fluxes between land and the atmosphere 
due to AFOLU, the associated drivers behind these fluxes, 
mitigation response options and related policy, at time scales of 
2030 and 2050. Land and its management has important links 
with other sectors and therefore associated chapters within this 
report, notably concerning the provision of food, feed, fuel or fibre 
for human consumption and societal well-being (Chapter  5), for 
bioenergy (Chapter 6), the built environment (Chapter 9), transport 
(Chapter  10) and industry (Chapter  11). Mitigation within these sectors may in part, be dependent on contributions from land and 
the AFOLU sector, with interactions between all sectors discussed

in Chapter 12. This chapter also has important links with IPCC AR6 
WGII regarding climate change impacts and adaptation. Linkages 
are illustrated in Figure 7.1.
As highlighted in both AR5 and the SRCCL, there is a  complex 
interplay between land management and GHG fluxes as illustrated in 
Figure 7.2, with considerable variation in management regionally, as 
a result of geophysical, climatic, ecological, economic, technological, 
institutional and socio-cultural diversity. The capacity for land-
based mitigation varies accordingly. The principal focus of this 
chapter is therefore, on evaluating regional land-based mitigation 
potential, identifying applicable AFOLU mitigation measures, 
estimating associated costs and exploring policy options that could 
enable implementation.
WGII: Impacts: Ecosystems Chapter, Food and Fibre Chapter, Regional Chapters 
Ch. 3 & 4: Long-term and N ear-term Mitigation and Pathways
Ch. 13–17: 
Policies, Investment, 
Cooperation,

Innovation, SDGsCh. 6: 
Energy 
(Supply of Biomass)Ch. 5: 
Demands,
Food and Fibre
Ch. 8: 
Urban 
(City Cooling)Ch. 7: 
AFOLU
Ch. 11: 
Industry: Raw MaterialsCh. 10: 
Transport: BiofuelsCh. 9: 
Buildings: Building 
With Wood
Ch. 12: Cross Sectorial
Figure 7.1 | Linkage between Chapter 7 and other chapters within this report, as well as to IPCC AR6 WGII. Mitigation potential estimates in this chapter consider 
potential emission reductions and removals only within the AFOLU sector itself, and not the substitution effects from biomass and bio-based products in sectors such as Energy, 
Transport, Industry, Buildings, nor biophysical effects of, for example, cooling of cities. These are covered in their respective chapters.

755
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Mitigation measures are broadly categorised as those relating 
to (i)  forests and other ecosystems (ii) agriculture (iii) biomass 
production for products and bioenergy and (iv) demand-side levers. 
Assessment is made in the context that land-mitigation is expected to 
contribute roughly 25% of the 2030 mitigation pledged in Nationally 
Determined Contributions (NDCs) under the Paris Agreement (Grassi 
et al. 2017), yet very few countries have provided details on how this 
will be achieved. In light of AR5 and the SRCCL findings, that indicate 
large land-based mitigation potential, considerable challenges to its 
realisation, but also a clear nexus at which humankind finds itself, 
whereby current land management, driven by population growth 
and consumption patterns, is undermining the very capacity of land, 
a finite resource, to support wider critical functions and services on

which humankind depends. Mitigation within AFOLU is occasionally 
and wrongly perceived as an opportunity for in-action within other 
sectors. AFOLU simply cannot compensate for mitigation shortfalls 
in other sectors. As the outcomes of many critical challenges (UNEP 
2019), including biodiversity loss (IPBES 2019a) and soil degradation 
(FAO and ITPS 2015), are inextricably linked with how we manage 
land, the evaluation and assessment of AFOLU is crucial. This chapter 
aims to address three core topics:
1. What is the latest estimated (economic) mitigation potential 
of AFOLU measures according to both sectoral studies and 
integrated assessment models, and how much of this may be 
realistic within each global region?
2. How do we realise the mitigation potential, while minimising 
trade-offs and risks and maximising co-benefits that can enhance 
food and fibre security, conserve biodiversity and address other

land challenges?3. How effective have policies been so far and what additional 
policies or incentives might enable realisation of mitigation 
potential and at what costs?
This chapter first outlines the latest trends in AFOLU fluxes and 
the methodology supporting their estimation (Section  7.2). Direct 
and indirect drivers behind emission trends are discussed in 
Section 7.3. Mitigation measures, their costs, co-benefits, trade-offs, 
estimated regional potential and contribution within integrated 
global mitigation scenarios, is presented in Sections  7.4 and 7.5 
respectively. Assessment of associated policy responses and links 
with SDGs are explored in Section 7.6. The chapter concludes with 
gaps in knowledge (Section 7.7) and frequently asked questions.
7.2 Historical and Current Trends in 
GHG Emission and Removals; Their 
Uncertainties and Implications for 
Assessing Collective Climate Progress
The biosphere on land and in wetlands is a source and sink of CO 2

and CH 4, and a source of N 2O due to both natural and anthropogenic 
processes that happen simultaneously and are therefore difficult 
to disentangle (IPCC 2010; Angelo and Du Plesis 2017; IPCC 2019). 
AFOLU is the only GHG sector to currently include anthropogenic sinks. 
A range of methodological approaches and data have been applied to 
estimating AFOLU emissions and removals, each developed for their 
own purposes, with estimates varying accordingly. Since the SRCCL (Jia 
et al. 2019), emissions estimates have been updated (Sections 7.2.2 and 
7.2.3), while the assessment of biophysical processes and short-lived 
land-us e changesCO 2; N 2O; NO xCombus tionAuto and heteroroph ic respirat ion
CH 4; N 2O Litter
Wood and 
agricultural
C products 
poolRespirationSubstitu tion
Phot osynthes is CO2 uptake
RespirationBiomass C pool
Bioenergy
Forest and 
managementForest and 
managem entPhotosynthesis
CO 2 uptakeCO 2 CO 2 CO2

Soil and pe at C poo l 
Figure 7.2 | Summarised representation of interactions between land management, its products in terms of food and fibre, and land–atmospheric GHG 
fluxes.  For legibility reasons only a few of the processes and management measures are depicted.

756
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7climate forcers (Section 7.2.4) is largely unchanged. Further progress 
has been made on the implications of differences in AFOLU emissions 
estimates for assessing collective climate progress (Section 7.2.2.2 and 
Cross-Chapter Box 6 in this chapter).
7.2.1 Total Net GHG Flux from AFOLU
National greenhouse gas inventory (NGHGI) reporting following the 
IPCC 1996 guidelines (IPCC 1996), separates the total anthropogenic 
AFOLU flux into: (i) net anthropogenic flux from Land Use, Land-
Use Change, and Forestry (LULUCF) due to both change in land 
cover and land management; and (ii) the net flux from Agriculture. 
While fluxes of CO 2 (Section 7.2.2) are predominantly from LULUCF 
and fluxes of CH 4 and N 2O (Section 7.2.3) are predominantly from 
agriculture, fluxes of all three gases are associated with both sub-
sectors. However, not all methods separate them consistently

according to these sub-sectors, thus here we use the term AFOLU, separate by gas and implicitly include CO 2 emissions that stem from 
the agriculture part of AFOLU, though these account for a relatively 
small portion.
Total global net anthropogenic GHG emissions from AFOLU were 
11.9 ± 4.4 GtCO 2-eq yr–1 on average over the period 2010–2019, 
around 21% of total global net anthropogenic GHG emissions 
(Table 7.1 and Figure 7.3, using the sum of bookkeeping models for 
the CO 2 component). When using FAOSTAT/NGHGIs CO 2 flux data, 
then the contribution of AFOLU to total emissions amounts to 13% of 
global emissions.
This AFOLU flux is the net of anthropogenic emissions of CO 2, CH 4 and 
N2O, and anthropogenic removals of CO 2. The contribution of AFOLU 
to total emissions varies regionally with highest in Latin America and 
Caribbean with 58% and lowest in Europe and North America with each 
7% (Chapter 2, Section 2.2.3). There is a discrepancy in the reported

CO2 AFOLU emissions magnitude because alternative methodological 
10 Gt 10 Gt 11 Gt 13 Gt
1%
3%
3%
8%
11%
23%
51%
−0.5%/yr +1.4%/yr +1.6%/yr
051015
1990 2000 2010 2019 GHG Emissions (GtCO2-eq yr–1) GHG Emissions (GtCO2-eq yr–1)LULUCF (CO2)
Enteric Fermentation (CH4)
Managed soils and pasture (CO2, N2O)
Rice cultivation (CH4)
Manure management (N2O, CH4)
Synthetic fertilizer application (N2O)
Biomass burning (CH4, N2O)a. AFOLU global trends in GHG emissions and removals
Africa AU, JP, NZ E. Asia E.E. & W.C. Asia Europe Latin Am. Middle E. N. America S. Asia S.E. Asia
1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 1990200020102019 19902000201020190123b. AFOLU regional trends in GHG emissions and removals
Figure 7.3 | Subdivision of the total AFOLU emissions from Table 7.1 by activity and gas for the period 1990 to 2019.  Positive values are emissions from land

to atmosphere, negative values are removals. Panel A shows emissions divided into major activity and gases. Note that ‘biomass burning’ is only the burning of agriculture 
residues in the fields. The indicated growth rates between 1990–2000, 2000–2010, 2010–2019 are annualised across each time period. Panel B illustrates regional emissions 
in the years 1990, 2000, 2010, 2019 AFOLU CO 2 (green shading) represents all AFOLU CO 2 emissions. It is the mean from three bookkeeping models (Hansis et al. 2015; 
Houghton and Nassikas 2017; Gasser et al. 2020) as presented in the Global Carbon Budget (Friedlingstein et al. 2020) and is not directly comparable to LULUCF in NGHGIs 
(Section 7.2.2). Data on CH 4 and N 2O emissions are from the EDGAR database (Crippa et al. 2021). See Sections 7.2.2 and 7.2.3 for comparison of different datasets. All values 
expressed are as CO 2-eq with GWP100 values: CH 4 = 27, N 2O = 273.

757
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Table  7.1 | Net anthropogenic emissions (annual averages for 2010–2019a) from Agriculture, Forestry and Other Land Use (AFOLU).  For context, the 
net flux due to the natural response of land to climate and environmental change is also shown for CO 2 in column E. Positive values represent emissions, negative values 
represent removals.
Anthropogenic Natural responseNatural and 
anthropogenic
Gas UnitsAFOLU Net 
emissions hNon-AFOLU 
GHG 
emissions d, fTotal net 
emissions (AFOLU 
+ non-AFOLU)
by gasAFOLU as 
a % of total net 
emissions by gasNatural land sinks 
including natural response 
of land to anthropogenic 
environmental change 
and climate variability eNet land- 
atmosphere CO 2 flux 
(i.e., anthropogenic 
AFOLU + natural fluxes 
across entire land surface
A B C = A+B D = (A/C) *100 E F=A+E
CO2 GtCO 2-eq yr–15.9 ± 4.1 b, f (book-
keeping models, managed

soils and pasture).
0 to 0.8 (NGHGI/ 
FAOSTAT data)36.2 ± 2.9 42.0 ± 29.0 14% –12.5 ± 3.2 –6.6 ± 4.6
CH4MtCH 4 yr–1157.0 ± 47.1 c207.5 ± 62.2 364.4 ± 109.3 – i
GtCO 2-eq yr–14.2 ± 1.3 g5.9 ± 1.8 10.2 ± 3.0 41%
N2OMtN 2O yr–1 6.6 ± 4.0 c2.8 ± 1.7 9.4 ± 5.6
GtCO 2-eq yr–11.8 ± 1.1 g0.8 ± 0.5 2.6 ± 1.5 69%
Total jGtCO 2-eq yr–111.9 ± 4.4
(CO 2 component 
based on book-keeping 
models, managed 
soils and pasture)44 ± 3.4 55.9 ± 6.1 21%
a Estimates are given until 2019 as this is the latest date when data are available for all gases, consistent with Chapter 2, this report. Positive fluxes are emission from land to 
the atmosphere. Negative fluxes are removals.
b Net anthropogenic flux of CO 2 are due to land-use change such as deforestation and afforestation and land management, including wood harvest and regrowth, peatland 
drainage and fires, cropland and grassland management. Average of three bookkeeping models (Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020),

complemented by data on peatland drainage and fires from FAOSTAT (Prosperi et al. 2020) and GFED4s (van der Werf et al. 2017). Bookkeeping based CO 2-LULUCF emissions 
(5.7±4.0) are consistent with AR6 WGI and Chapter 2 of this report. The value of 5.9(±4.1) includes CO 2 emissions from urea application to managed soils and pasture. 
Comparisons with other estimates are discussed in 7.2.2. Based on NGHGIs and FAOSTAT, the range is 0 to 0.8 GtCO 2 yr–1.
c CH4 and N 2O emission estimates and assessed uncertainty of 30 and 60% respectively, are based on Emissions Database for Global Atmospheric Research (EDGAR) data 
(Crippa et al. 2021) in accordance with Chapter 2, this report (Sections 2.2.1.3 and 2.2.1.4). Both FAOSTAT (Tubiello 2019; USEPA 2019; FAO 2021a) and the USA EPA (USEPA 
2019) also provide data on agricultural non-CO 2 emissions, however, mean global CH 4 and N 2O values considering the three databases are within the uncertainty bounds of

EDGAR. EDGAR only considers agricultural and not overall AFOLU non-CO 2 emissions. Agriculture is estimated to account for approximately 89 and 96% of total AFOLU CH 4 
and N 2O emissions respectively. See Section 7.2.3 for further discussion.
d Total non-AFOLU emissions are the sum of total CO 2-eq emissions values for energy, industrial sources, waste and other emissions with data from the Global Carbon Project 
for CO 2, including international aviation and shipping, and from the PRIMAP database for CH 4 and N 2O averaged over 2007–2014, as that was the period for which data 
were available.
e The modelled CO 2 estimates include natural processes in vegetation and soils and how they respond to both natural climate variability and to human-induced environmental 
changes, for example, the response of vegetation and soils to environmental changes such as increasing atmospheric CO 2 concentration, nitrogen deposition, and climate

change (indirect anthropogenic effects) on both managed and unmanaged lands. The estimate shown represents the average from 17 Dynamic Global Vegetation Models with 
1SD uncertainty (Friedlingstein et al. 2020).
f The NGHGIs take a different approach to calculating ‘anthropogenic’ CO 2 fluxes than the models (Section 7.2.2). In particular the sinks due to environmental change (indirect 
anthropogenic fluxes) on managed lands are generally treated as anthropogenic in NGHGIs and non-anthropogenic in models such as bookkeeping and IAMs. A reconciliation 
of the results between IAMs and NGHGIs is presented in Cross-Chapter Box 6 in this chapter. If applied to this table, it would transfer approximately –5.5 GtCO 2 yr–1 (a sink) 
from Column E (which would become –7.0 GtCO 2 yr–1) to Column A (which would then be 0.4 GtCO 2 yr–1).

g All values expressed in units of CO 2-eq are based on IPCC AR6 100-year Global Warming Potential (GWP100) values with climate-carbon feedbacks (CH 4 = 27, N 2O = 273) 
(Chapter 2, Supplementary Material 2.SM.3; IPCC AR6 WGI Section 7.6).
h For assessment of cross-sector fluxes related to the food sector, see Chapter 12.
i While it is acknowledged that soils are a natural CH 4 sink (Jackson et al. 2020) with soil microbial removals estimated to be 30 ± 19 MtCH 4 yr–1 for the period 2008–2017 
(according to bottom-up estimates), natural CH 4 sources are considerably greater (371 (245–488) MtCH 4 yr–1) resulting in natural processes being a net CH 4 source (IPCC AR6 
WGI Section 5.2.2). The soil CH 4 sink is therefore omitted from Column E.
j Total GHG emissions concerning non-AFOLU sectors and all sectors combined (Columns B and C) include fluorinated gases in addition to CO 2, CH 4 and N 2O. Therefore, total 
values do not equal the sum of estimates for CO 2, CH 4 and N 2O.

758
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7approaches that incorporate different assumptions are used 
(Section 7.2.2.2). While there is low agreement in the trend of global 
AFOLU CO 2 emissions over the past few decades (Section 7.2.2), they 
have remained relatively constant (medium confidence) (Chapter 2). 
Average non-CO 2 emission (aggregated using GWP100 IPCC AR6 
values) from agriculture have risen from 5.2 ± 1.4 GtCO 2-eq yr–1 for 
the period 1990 to 1999, to 6.0 ± 1.7 GtCO 2-eq yr–1 for the period 
2010 to 2019 (Crippa et al. 2021) (Section 7.2.3).
To present a fuller understanding of land–atmosphere interactions, 
Table  7.1 includes an estimate of the natural sink of land to 
atmospheric CO 2 (Jia et al. 2019) (IPCC AR6 WGI Chapter 5). Land 
fluxes respond naturally to human-induced environmental change 
(e.g.,  climate change, and the fertilising effects of increased 
atmospheric CO 2 concentration and nitrogen deposition), known as

‘indirect anthropogenic effects’, and also to ‘natural effects’ such 
as climate variability (IPCC 2010) (Table 7.1 and Section 7.2.2). This 
showed a removal of –12.5 ± 3.2 GtCO 2 yr–1 (medium confidence) 
from the atmosphere during 2010–2019 according to global dynamic 
global vegetation model (DGVM) models (Friedlingstein et al. 2020) 
31% of total anthropogenic net emissions of CO 2 from all sectors. 
It is likely that the NGHIs and FAOSTAT implicitly cover some part 
of this sink and thus provide a net CO 2 AFOLU balance with some 
5 GtCO 2 lower net emissions than according to bookkeeping models, 
with the overall net CO 2 value close to being neutral. Model results and atmospheric observations concur that, when combining both 
anthropogenic (AFOLU) and natural processes on the entire land 
surface (the total ‘land–atmosphere flux’), the land was a  global 
net sink for CO 2 of –6.6 ± 4.6 GtCO 2 yr–1 with a range for 2010 to

2019 from –4.4 to –8.4 GtCO 2 yr–1. (Rödenbeck et al. 2003, 2018; 
Chevallier et al. 2005; Feng et al. 2016; van der Laan-Luijkx et al. 
2017; Niwa et al. 2017; Patra et al. 2018). The natural land sink is 
highly likely to be affected by both future AFOLU activity and climate 
change (IPCC AR6 WGI Box 5.1 and Figure SPM. 7), whereby under 
more severe climate change, the amount of carbon stored on land 
would still increase although the relative share of the emissions that 
land takes up, declines.
7.2.2 Flux of CO 2 from AFOLU,  
and the Non-anthropogenic Land Sink
7.2.2.1 Global Net AFOLU CO 2 Flux
Comparison of estimates of the global net AFOLU flux of CO 2 
from diverse approaches (Figure  7.4) show differences on the 
order of several GtCO 2 yr–1. When considering the reasons for the 
differences, and an approach to reconcile them (Grassi et al. 2021) 
(Section 7.2.2.3), there is medium confidence in the magnitude of

the net AFOLU CO 2 flux. There is a discrepancy in the reported CO 2 
–6
1960 1970 1980 1990 2000 2010
YearNet CO2 emissions (GtCO2 yr–1)
2020–4–20246810
 DGVM mean
FAOSTATnet ﬂux
NGHGInet ﬂux
EOforest data
Bookkeeping models
Bookkeeping Min + Max
Figure 7.4 | Global net CO 2 flux due to AFOLU estimated using different methods for the period 1960 to 2019 (GtCO 2 yr–1). Positive numbers represent 
emissions. Light-blue line: The mean from 17 DGVMs all using the same driving data under TrendyV9 used within the Global Carbon Budget 2020 and including different 
degrees of management (Bastos et al. 2020; Friedlingstein et al. 2020). Brown line: Data downloaded 6 June 2021 from FAOSTAT (FAO 2021b; http://www.fao.org/faostat/) 
comprising: net emissions from (i) forest land converted to other land, (ii) net emissions from organic soils in cropland, grassland and from biomass burning, including peat

fires and peat draining (Prosperi et al. 2020) and (iii) net emissions from forest land remaining forest land, which includes managed forest lands (Tubiello et al. 2020). 
Yellow  line: Net flux estimate from National Greenhouse Gas Inventories (NGHGI) based on country reports to the UNFCCC for LULUCF (Grassi et al. 2021) which include 
land-use change, and flux in managed lands. Red EO line: The 2001–2019 average net CO 2 flux from non-intact forest-related emissions and removals based on ground 
and Earth Observation data (EO) (Harris et al. 2021). Data to mask non-intact forest were used in the tropics (Turubanova et al. 2018) and extra-tropics (Potapov et al. 2017). 
Dark  blue line: the mean estimate and minimum and maximum (dark-blue shading) from three bookkeeping models (Hansis et al. 2015; Houghton and Nassikas 2017;

Gasser et al. 2020). These include land cover change (e.g., deforestation, afforestation), forest management including wood harvest and land degradation, shifting cultivation, 
regrowth of forests following wood harvest or abandonment of agriculture, grassland management, agricultural management. Emissions from peat burning and draining are 
added from external datasets (see text). Both the DGVM and bookkeeping global data is available at: https://www.icos-cp.eu/science-and-impact/global-carbon-budget/2020 
(accessed on 4 October 2021). Data consistent with IPCC AR6 WGI Chapter 5. Dotted lines denote the linear regression from 2000 to 2019. Trends are statistically significant 
(P <0.05) with exception for the NGHGI trend (P <0.01).

759
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77AFOLU emissions magnitude because alternative methodological 
approaches that incorporate different assumptions are used 
(Section  7.2.2.2). While the mean of the bookkeeping and DGVM 
model’s show a  small increase in global CO 2 net emissions since 
year 2000, individual models suggest opposite trends (Friedlingstein 
et al. 2020). The latest FAOSTAT and NGHGI estimates show a small 
reduction in net emission. Overall, the trends are unclear.Regionally (Figure  7.5), there is high confidence of net emissions 
linked to deforestation in Latin America, Africa and South-East Asia 
from 1990 to 2019. There is medium confidence in trends indicating 
a  decrease in net emissions in Latin America since 2005 linked to 
reduced gross deforestation emissions, and a small increase in net 
emissions related to increased gross deforestation emissions in Africa

since 2000 (Figure 7.5). There is high confidence regarding the net  Bookkeeping models
Bookkeeping Min + MaxDGVM mean
NGHGInet ﬂuxSouthern Asia Africa
1990 1995 2000 2005
Years2010 2015 2020 1990 1995 2000 2005
Years2010 2015 2020
Net CO2 emissions (GtCO2 yr–1)4
3
2
1
0
–1Net CO2 emissions (GtCO2 yr–1)4
3
2
1
0
–1
Australia, Japan and New Zealand South-East Asia and Paciﬁc Middle East
Years2010 2015 2020Net CO2 emissions (GtCO2 yr–1)4
3
2
1
0
3
2
1
0
–1
3
2
1
0
–11990 1995Eurasia Europe North America
2000 2005
Net CO2 emissions (GtCO2 year–1)4
3
2
1
0
–1Net CO2 emissions (GtCO2 year–1)4
3
2
1
0
–1
3
2
1
0
–1
Eastern Asia Latin America and Caribbean

3
2
1
0
3
2
1
0
–1
Figure 7.5 | Regional net flux of CO 2 due to AFOLU estimated using different methods for the period 1990–2019 (GtCO 2 yr–1). Positive numbers represent 
emissions. The upper-central panel depicts the world map shaded according to the IPCC AR6 regions corresponding to the individual graphs.  For each regional panel; 
brown  line: Total net flux data from FAOSTAT (Tubiello et al. 2020); yellow line: Net emissions estimates from National Greenhouse Gas Inventories based on country 
reports to the UNFCCC for LULUCF (Grassi et al. 2021); dark-blue line: The mean estimate and minimum and maximum (dark-blue shading) from three bookkeeping models. 
(Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020). Regional estimates from bookkeeping models are available at: https://zenodo.org/record/5548333#.

YVwJB2LMJPY (Minx et al. 2021). See the legend in Figure 7.4 for a detailed explanation of flux components for each dataset.

760
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7AFOLU CO 2 sink in Europe due to forest regrowth and known other 
sinks in managed forests, and medium confidence of a net sink in 
North America and Eurasia since 2010.
7.2.2.2 Why Do Various Methods Deliver 
Difference in Results?
The processes responsible for fluxes from land have been divided 
into three categories (IPCC 2006, 2010): (i) the direct human-induced 
effects due to changing land cover and land management; (ii) the 
indirect human-induced effects due to anthropogenic environmental 
change, such as climate change, CO 2 fertilisation, nitrogen deposition, 
and so on; and (iii) natural effects, including climate variability and 
a background natural disturbance regime (e.g., wildfires, windthrows, 
diseases or insect outbreaks).
Global models estimate the anthropogenic land CO 2 flux considering 
only the impact of direct effects, and only those areas that were subject

to intense and direct management such as clear-cut harvest. It is 
important to note, that DGVMs also estimate the non-anthropogenic 
land CO 2 flux (Land Sink) that results from indirect and natural 
effects (Table 7.1). In contrast, estimates of the anthropogenic land 
CO2 flux in NGHGIs (LULUCF) include the impact of direct effects and, 
in most cases, of indirect effects on a much greater area considered 
‘managed’ than global models (Grassi et al. 2021).
The approach used by countries follows the IPCC methodological 
guidance for NGHGIs (IPCC 2006, 2019). Since separating direct, 
indirect and natural effects on the land CO 2 sink is impossible with 
direct observation such as national forest inventories (IPCC 2010), 
upon which most NGHGIs are based, the IPCC adopted the ‘managed 
land’ concept as a  pragmatic proxy to facilitate NGHGI reporting. 
Anthropogenic land GHG fluxes (direct and indirect effects) are 
defined as all those occurring on managed land, that is, where

production, ecological or social functions (IPCC 2006, 2019). GHG 
fluxes from unmanaged land are not reported in NGHGIs because 
they are assumed to be non-anthropogenic. Countries report NGHGI 
data with a range of methodologies, resolution and completeness, 
dependent on capacity and available data, consistent with IPCC 
guidelines (IPCC 2006, 2019) and subject to an international review 
or assessment processes.
The FAOSTAT approach is conceptually similar to NGHGIs. FAOSTAT 
data on forests are based on country reports to FAO-FRA 2020 
(FAO 2020a), and include changes in biomass carbon stock in 
‘forest land’ and ‘net forest conversions’ in five-year intervals. 
‘Forest land’ may include unmanaged natural forest, leading to 
possible overall overestimation of anthropogenic fluxes for both 
sources and sinks, though emissions from deforestation are likely 
underestimated (Tubiello et al. 2020). FAOSTAT also estimate

emissions from forest fires and other land uses (organic soils), 
following IPCC methods (Prosperi et al. 2020). The FAO-FRA 2020 
(FAO 2020b) update leads to estimates of larger sinks in Russia 
since 1991, and in China and the USA from 2011, and larger 
deforestation emissions in Brazil and smaller in Indonesia than 
FRA 2015 (FAO 2015; Tubiello et al. 2020).The bookkeeping models by Houghton and Nassikas (2017), Hansis 
et al. (2015), and Gasser et al. (2020) and the DGVMs used in the 
Global Carbon Budget (Friedlingstein et al. 2020) use either the LUH2 
dataset (Hurtt et al. 2020), HYDE (Goldewijk et al. 2017), FRA 2015 
(FAO 2015) or a combination. The LUH2 dataset includes a new wood 
harvest reconstruction, new representation of shifting cultivation, 
crop rotations, and management information including irrigation and 
fertiliser application. The area of forest subject to harvest in LUH2 
is much less than the area of forest considered ‘managed’ in the

NGHGIs (Grassi et al. 2018). The model datasets do not yet include 
the FAO FRA 2020 update (FAO 2020a). The DGVMs consider CO 2 
fertilisation effects on forest growth that are sometimes confirmed 
from the ground-based forest inventory networks (Nabuurs et al. 
2013) and sometimes not at all (van der Sleen et al. 2015).
Further, the DGVMs and bookkeeping models do not include a wide 
range of practices which are implicitly covered by the inventories; for 
example: forest dynamics (Pugh et al. 2019; Le Noë et al. 2020), forest 
management including wood harvest (Nabuurs, et al. 2013; Arneth 
et al. 2017), agricultural and grassland practices (Pugh et al. 2015; 
Sanderman et al. 2017; Pongratz et al. 2018); or, for example, fire 
management (Andela et al. 2017; Arora and Melton 2018).
Increasingly, higher emissions estimates are expected from DGVMs 
compared to bookkeeping models, because DGVMs include a  loss 
of additional sink capacity of 3.3 ± 1.1 GtCO 2 yr–1 on average

over 2009–2018, which is increasing with larger climate and CO 2 
impacts (Friedlingstein et al. 2020). This arises because the DGVM 
methodological setup requires a  reference simulation including 
climate and environmental changes but without any land-use change 
such as deforestation, so DGVMs implicitly include the sink capacity 
forests would have developed in response to environmental changes 
on areas that in reality have been cleared (Gitz and Ciais 2003; 
Pongratz et al. 2014) (IPCC AR6 WGI Chapter 5).
Carbon emissions from peat burning have been estimated based 
on the Global Fire Emission Database (GFED4s; van der Werf et al. 
2017). These were included in the bookkeeping model estimates and 
added 2.0 GtC over 1960–2019 (e.g.,  causing the peak in South-
East Asia in 1998) (Figure  7.5). Within the Global Carbon Budget 
(Friedlingstein et al. 2020), peat drainage from agriculture accounted 
for an additional 8.6 GtC from 1960–2019 according to FAOSTAT

(Conchedda and Tubiello, 2020) used by two of the bookkeeping 
models (Hansis et al. 2015; Gasser et al. 2020).
Remote-sensing products provide valuable spatial and temporal 
land-use and biomass data globally (including in remote areas), at 
potentially high spatial and temporal resolutions, that can be used to 
calculate CO 2 fluxes, but have mostly been applied only to forests at 
the global or even regional scale. While such data can strongly support 
monitoring reporting and verification, estimates of forest carbon 
fluxes directly from Earth Observation (EO) data vary considerably 
in both their magnitude and sign (i.e., whether forests are a  net 
source or sink of carbon). For the period 2005–2017, net tropical 
forest carbon fluxes were estimated as –0.4 GtCO 2 yr–1 (Fan et al. 
2019); 0.58 GtCO 2 yr–1 (Grace et al. 2014); 1.6 GtCO 2yr–1 (Baccini 
et al. 2017) and 2.87 GtCO 2 yr–1 (Achard et al. 2014). Differences

761
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77can in part be explained by spatial resolution of the datasets, the 
definition of ‘forest’ and the inclusion of processes and methods used 
to determine degradation and growth in intact and secondary forests, 
or the changes in algorithm over time (Palahí et al. 2021). A recent 
global study integrated ground observations and remote sensing 
data to map forest-related GHG emissions and removals at a high 
spatial resolution (30 m spatial scale), although it only provides an 
average estimate of annual carbon loss over 2001–2019 (Harris 
et al. 2021). The estimated net global forest carbon sink globally was 
–7.66 GtCO 2 yr−1, being –1.7 GtCO 2yr−1 in the tropics only.
Remote sensing products can help to attribute changes to 
anthropogenic activity or natural inter-annual climate variability 
(Fan et al. 2019; Wigneron et al. 2020). Products with higher spatial

resolution make it easier to determine forest and carbon dynamics 
in relatively small-sized managed forests (e.g., Y . Wang et al. 2020; Heinrich et al. 2021; Reiche et al. 2021). For example, secondary 
forest regrowth in the Brazilian Amazon offset 9 to 14% of gross 
emissions due to deforestation1 (Aragão et al. 2018; Silva Junior 
et al. 2021). Yet disturbances such as fire and repeated deforestation 
cycles due to shifting cultivation over the period 1985 to 2017, were 
found to reduce the regrowth rates of secondary forests by 8 to 55% 
depending on the climate region of regrowth (Heinrich et al. 2021).
7.2.2.3 Implications of Differences in AFOLU CO 2 Fluxes 
Between Global Models and National Greenhouse 
Gas Inventories (NGHGIs), and Reconciliation
There is about 5.5 GtCO 2 yr–1 difference in the anthropogenic 
AFOLU estimates between NGHGIs and global models (this number 
relates to an IAMs comparison for the period 2005–2015  – see

Cross-Chapter Box  6 in this chapter; for comparison with other 
✔
✔
✔ ✔✔✔
✔
Direct human-
induced effects
Indirect human-
induced effects;
Natural effectsDirect human-
Natural effectsa) ‘Anthropogenic CO 2 ﬂux’ conceptual inconsistency problem
DGVMs
+≠
=Bookkeeping, DGVMs, IAMs
Bookkeeping, IAMs
‘Land Use’Adjusted models’ result 
(NGHGI-comparable)NGHGIs
‘Land Use’ 
Anthropogenic CO 2 ﬂux deﬁned as arising 
from land-use change, harvest and regrowth
✔ =  Considered in the comparison = Fluxes considered by each respective method‘LULUCF’
Anthropogenic CO 2 ﬂux deﬁned as occurring 
in areas deﬁned as ‘managed’
‘Land Sink’
CO 2 ﬂux associated to the natural response 
of any land to environmental change
Managed land Unmanaged landManaged land Unmanaged land Managed land Unmanaged land
Managed land Unmanaged land Managed land Unmanaged landb) Solution via disaggregation of DGVM results

Figure 7.6 | Main conceptual differences between global models (bookkeeping models, IAMs and DGVMs) and NGHGIs definitions of what is considered 
the ‘anthropogenic’ land CO 2 flux, and proposed solution (from Grassi et al.  2021). (a) Differences in defining the anthropogenic land CO 2 flux by global models 
(‘land use’) and NGHGIs (‘LULUCF’), including the attribution of processes responsible for land fluxes (IPCC 2006; 2010) in managed and unmanaged lands. The anthropogenic 
land CO 2 flux by global models typically includes only the CO 2 flux due to ‘direct effects’ (land-use change, harvest, regrowth). By contrast, most NGHGIs consider anthropogenic 
all fluxes occurring in areas defined as ‘managed’, including also the sink due to ‘indirect effects’ (climate change, atmospheric CO 2 increase, N deposition etc.) and due to

‘natural effects’ (climate variability, background natural disturbances). (b) Proposed solution to the inconsistency, via disaggregation of the ‘Land Sink’ flux from DGVMs into 
CO2 fluxes occurring in managed and in unmanaged lands. The sum of ‘land use’ flux (direct effects from bookkeeping models or IAMs) and the ‘Land Sink’ (indirect effects from 
DGVMs) in managed lands produces an adjusted global model CO 2 flux which is conceptually more comparable with LULUCF fluxes from NGHGIs. Note that the figure may in 
some cases be an oversimplification, in other words, not all NGHGIs include all recent indirect effects.

762
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7models see Figure 7.4). Reconciling the differences, in other words, 
making estimates comparable, can build confidence in land-related 
CO2 estimates, for example for the purpose of assessing collective 
progress in the context of the Global Stocktake (Cross-Chapter Box 6 
in this chapter). The difference largely results from greater estimated 
CO2 in NGHGIs, mostly occurring in forests (Grassi et al. 2021). 
This difference is potentially a consequence of: (i) simplified and/or 
incomplete representation of management in global models (Popp 
et al. 2017; Pongratz et al. 2018), for example, concerning impacts of 
forest management in biomass expansion and thickening (Nabuurs 
et al. 2013; Grassi et al. 2017), (ii) inaccurate and/or incomplete 
estimation of LULUCF fluxes in NGHGIs (Grassi et al. 2017), especially 
in developing countries, primarily in non-forest land uses and in soils,

and (iii) conceptual differences in how global models and NGHGIs 
define ‘anthropogenic’ CO 2 flux from land (Grassi et al. 2018). The 
impacts of (i) and (ii) are difficult to quantify and result in uncertainties 
that will decrease slowly over time through improvements of both 
models and NGHGIs. By contrast, the inconsistencies in (iii) and its 
resulting biases were assessed as explained below.Since changing the NGHGIs’ approach is impractical, an interim 
method to translate and adjust the output of global models was 
outlined for reconciling a bookkeeping model and NGHGIs (Grassi 
et al. 2018). More recently, an improved version of this approach 
has been applied to the future mitigation pathways estimated 
by IAMs (Grassi et al. 2021), with the implications for the Global 
Stocktake discussed in Cross-Chapter Box  6 in this chapter. This 
method implies a post-processing of current global models’ results 
that addresses two components of the conceptual differences in

the ‘anthropogenic’ CO 2 flux; (i) how the impact of human-induced 
environmental changes (indirect effects) are considered, and (ii) the 
extent of forest area considered ‘managed’. Essentially, this approach 
adds DGVM estimates of CO 2 fluxes due to indirect effects from 
countries’ managed forest area (using non-intact forest area maps 
as a proxy) to the original global models’ anthropogenic land CO 2 
fluxes (Figure 7.6).
Cross-Chapter Box 6 | Implications of Reconciled Anthropogenic Land CO 2 Fluxes for Assessing 
Collective Climate Progress in the Global Stocktake
Authors: Giacomo Grassi (Italy/European Union), Joeri Rogelj (Belgium/Austria), Joanna I. House (United Kingdom), Alexander Popp 
(Germany), Detlef van Vuuren (the Netherlands), Katherine Calvin (the United States of America), Shinichiro Fujimori (Japan), Petr 
Havlík (Austria/the Czech Republic), Gert-Jan Nabuurs (the Netherlands)

The Global Stocktake aims to assess countries’ collective progress towards the long-term goals of the Paris Agreement in the light 
of the best available science. Historic progress is assessed based on NGHGIs, while expectations of future progress are based on 
country climate targets (e.g., NDCs for 2025 or 2030 and long-term strategies for 2050). Scenarios consistent with limiting warming 
well-below 2°C and 1.5°C developed by IAMs (Chapter 3) are expected to play a key role as benchmarks against which countries’ 
aggregated future mitigation pledges will be assessed. This, however, implies that estimates by IAMs and country data used to 
measure progress are comparable.
In fact, there is about 5.5 GtCO 2 yr–1 difference during 2005–2015 between global anthropogenic land CO 2 net flux estimates of IAMs 
and aggregated NGHGIs, due to different conceptual approaches to what is ‘anthropogenic’. This approach and its implications when

comparing climate targets with global mitigation pathways are illustrated in this Box Figure 1a–e.
By adjusting the original IAM output (Cross-Chapter Box  6, Figure  1a) with the indirect effects from countries’ managed forest 
(Cross-Chapter Box 6, Figure 1b, estimated by DGVMs, see also Figure 7.6), NGHGI-comparable pathways can be derived (Cross-
Chapter Box 6, Figure 1c). The resulting apparent increase in anthropogenic sink reflects simply a reallocation of a CO 2 flux previously 
labelled as natural, and thus does not reflect a  mitigation action. These changes do not affect non-LULUCF emissions. However, 
since the atmosphere concentration is a combination of CO 2 emissions from LULUCF and from fossil fuels, the proposed land-related 
adjustments also influence the NGHGI-comparable economy-wide (all sector) CO 2 pathways (Cross-Chapter Box 6, Figure 1d).

This approach does not imply a change in the original decarbonisation pathways, nor does it suggest that indirect effects should be 
considered in the mitigation efforts. It simply ensures that a like-with-like comparison is made: if countries’ climate targets use the 
NGHGI definition of anthropogenic emissions, this same definition can be applied to derive NGHGI-comparable future CO 2 pathways. 
This would have an impact on the NGHGI-comparable remaining carbon  or GHG budget (i.e., the allowable emissions until net zero 
CO2 or GHG emissions consistent with a certain climate target). For example, for SSP2-1.9 and SSP2-2.6 (representing pathways in 
line with 1.5°C and well-below 2°C limits under SSP2 assumptions), carbon budget is 170 GtCO 2-eq lower than the original remaining 
carbon budget according to the models’ approach (Cross-Chapter Box 6, Figure 1e). Similarly, the remaining carbon (or GHG) budgets

in Chapter 3 (this report), as well as the net zero carbon (or GHG) targets, could only be used in combination with the definition of 
anthropogenic emissions as used by the IAMs (Cross-Chapter Box 3 in Chapter 3). In the absence of these adjustments, collective 
progress would appear better than it is.

763
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Cross-Chapter Box 6 (continued)
The UNEP’s annual assessment of the global 2030 ‘emission gap’ between aggregated country NDCs and specific target mitigation 
pathways (UNEP 2020), is only affected to a limited degree. This is because some estimates of global emissions under the NDCs 
already use the same land-use definitions as the IAM mitigation pathways (Rogelj et al. 2017), and because historical data of global 
NDC estimates is typically harmonised to the historical data of global mitigation pathway projections (Rogelj et al. 2011). This latter 
procedure, however, is agnostic to the reasons for the observed mismatch, and often uses a constant offset. The adjustment described 
here allows this mismatch to be resolved by drawing on a scientific understanding of the underlying reasons, and thus provides a more 
informed and accurate basis for estimating the emission gap.

The approach to deriving a NGHGI-comparable emission pathways presented here can be further refined with improved estimates of 
the future forest sink. Its use would enable a more accurate assessment of the collective progress achieved and of mitigation pledges 
under the Paris Agreement.
–8–6–4–202468
2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100LULUCF: original IAM pathways Anthropogenic CO2 ﬂux (GtCO2 yr–1) GtCO2 yr–1LULUCF: original and NGHGI-comparable 
pathways (panel a + panel b)(b) (a) (c)
(d) (e)                                                                         Adjustment of cumulated IAM ﬂuxes from 2021 
until carbon neutrality or 2100Global CO2 emissions with LULUCF: 
original and NGHGI-comparable pathways   
2030405060708090100Indirect effects in non-intact forest (proxy for 
countries’ managed forest)
GtCO2% of original 
remaining carbon 
budget
SSP2-1.9 –104 –19%
SSP2-2.6 –170 –18%

SSP2-3.4 –266 –16%
SSP2-4.5 –366 –15%
SSP2-6.0 –475 –12%
SSP2-BaselineNGHGI
SSP2-1.9
SSP2-2.6
SSP2-3.4
SSP2-4.5
SSP2-6.0
SSP2-BaselineOriginal IAM pathways
NGHGI-comparable pathways–454 –9%
2000 2020 2040 2060 2080 2100–10010
Cross-Chapter Box 6, Figure 1 | Impact on global mitigation pathways of adjusting the modelled anthropogenic land CO 2 fluxes to be comparable 
with National Greenhouse Gas Inventories (NGHGIs) (from Grassi et al. 2021). (a) The mismatch between global historical LULUCF CO 2 net flux from 
NGHGIs (black), and the original (un-adjusted) modelled flux historically and under future mitigation pathways for SSP2 scenarios from Integrated Assessment Models 
(IAMs, Chapter 3). (b) Fluxes due to indirect effects of environmental change on areas equivalent to countries’ managed forest (i.e., those fluxes generally considered

‘anthropogenic’ by countries and ‘natural’ by global models). (c) Original modelled (solid line) LULUCF mitigation pathways adjusted to be NGHGI-comparable 
(dashed line), for example, by adding the indirect effects in panel b. The indirect effects in panel b decline over time with increasing mitigation ambition, mainly 
because of the weaker CO 2 fertilisation effect. In panel c, the dependency of the adjusted LULUCF pathways on the target becomes less evident after 2030, because 
the indirect effects in countries’ managed forest (which are progressively more uncertain with time, as highlighted by the grey areas) compensate the effects of the 
original pathways. (d) NGHGI-comparable pathways for global CO 2 emissions from all sectors including LULUCF (obtained by combining global CO 2 pathways without 
LULUCF – where no adjustment is needed – and the NGHGI-comparable CO 2 pathways for LULUCF (Gütschow et al. 2019; Grassi et al. 2017). (e) Cumulative impact

of the adjustments from 2021 until net zero CO 2 emissions or 2100 (whatever comes first) on the remaining carbon budget.

764
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.2.3 CH4 and N 2O Flux From AFOLU
Trends in atmospheric CH 4 and N 2O concentrations and the 
associated sources, including land and land use are discussed in 
Sections 5.2.2 and 5.2.3 of the IPCC AR6 WGI. Regarding AFOLU, the 
SRCCL and AR5 (Jia et al. 2019; Smith et al. 2014) identified three 
global non-CO 2 emissions data sources: EDGAR (Crippa et al. 2021), 
FAOSTAT (FAO 2021a; Tubiello, 2019) and the USA EPA (USEPA 2019). 
Methodological differences have been previously discussed (Jia et al. 
2019). In accordance with Chapter 2, this report, EDGAR data are 
used in Table 7.1 and Figure 7.3. It is important to note that in terms of 
AFOLU sectoral CH 4 and N 2O emissions, only FAOSTAT provides data 
on AFOLU emissions, while EDGAR and USEPA data consider just the 
agricultural component. However, the mean of values across the three 
databases for both CH 4 and N 2O, fall within the assessed uncertainty

bounds (30 and 60% for CH 4 and N 2O respectively, Section  2.2.1, 
in this report) of EDGAR data. NGHGIs annually submitted to the 
UNFCCC (Section 7.2.2.3) provide national AFOLU CH 4 and N 2O data, 
as included in the SRCCL (Jia et al. 2019). Aggregation of NGHGIs to 
indicate global emissions must be considered with caution, as not all countries compile inventories, nor submit annually. Additionally, 
NGHGIs may incorporate a  range of methodologies for CH 4 and 
N2O accounting (e.g., van der Weerden et al. 2016; Ndung’u et al. 
2019; Thakuri et al. 2020), making comparison difficult. The analysis 
of complete AFOLU emissions presented here, is based on FAOSTAT 
data. For agricultural specific discussion, analysis considers EDGAR, 
FAOSTAT and USEPA data.
7.2.3.1 Global AFOLU CH 4 and N 2O Emissions
Using FAOSTAT data, the SRCCL estimated average CH 4 emissions 
from AFOLU to be 161.2 ± 43 MtCH 4 yr–1 for the period 2007–2016,

representing 44% of total anthropogenic CH 4 emissions, with 
agriculture accounting for 88% of the AFOLU component (Jia 
et al. 2019). The latest data (FAO 2021a, 2020b) highlight a trend of 
growing AFOLU CH 4 emissions, with a 10% increase evident between 
1990 and 2019, despite year-to-year variation. Forestry and other land 
use (FOLU) CH 4 emission sources include biomass burning on forest 
land and combustion of organic soils (peatland fires) (FAO 2020c). 
The agricultural share of AFOLU CH 4 emissions remains relatively 
Enteric fermentation
Manure management
Rice cultivation
Agricultural biomass burning
N2O
CH4020406080100120140160CH4 emissions (Mt)
012345678N2O emissions (Mt)Agricultural soils
Agricultural biomass burning1990–1999
2000–2009
2010–2015
USEPA1990–1999
2000–2009
2010–2019
FAOSTAT1990–1999
2000–2009
2010–2019
EDGAR01234567GHG emissions 
(GtCO2-eq)

Figure 7.7 | Estimated global mean agricultural CH 4 (top), N 2O (middle) and aggregated CH 4 and N 2O (using CO 2-eq according to GWP100 AR6 values).  
(Bottom) emissions for three decades according to EDGAR v6.0 (Crippa et al. 2021), FAOSTAT (FAO 2021a) and USEPA (USEPA 2019) databases. Latest 
versions of databases indicate historic emissions to 2019, 2019 and 2015 respectively, with average values for the post–2010 period calculated accordingly. For CH 4, emissions 
classified as ‘Other Ag.’ within USEPA data, are re-classified as ‘Agricultural Biomass Burning’. Despite CH 4 emissions from agricultural soils also being included, this category 
was deemed to principally concern biomass burning on agricultural land and classified accordingly. For N 2O, emissions classified within EDGAR as direct and indirect emissions

from managed soils, and indirect emissions from manure management are combined under ‘Agricultural Soils’. Emissions classified by FOASTAT as from manure deposition and 
application to soils, crop residues, drainage of organic soils and synthetic fertilisers are combined under ‘Agricultural Soils’, while emissions reported as ‘Other Ag.’ under USEPA 
data are re-classified as ‘Agricultural Biomass Burning’.

765
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77unchanged, with the latest data indicating agriculture to have 
accounted for 89% of emissions on average between 1990 and 2019. 
The SRCCL reported with medium evidence and high agreement that 
ruminants and rice production were the most important contributors 
to overall growth trends in atmospheric CH 4 (Jia et al. 2019). The latest 
data confirm this in terms of agricultural emissions, with agreement 
between databases that agricultural CH 4 emissions continue to 
increase and that enteric fermentation and rice cultivation remain 
the main sources (Figure  7.7). The proportionally higher emissions 
from rice cultivation indicated by EDGAR data compared to the other 
databases, may result from the use of a Tier 2 methodology for this 
source within EDGAR (Janssens-Maenhout et al. 2019).
The SRCCL also noted a  trend of increasing atmospheric N 2O 
concentration, with robust evidence and high agreement that

agriculture accounted for approximately two-thirds of overall global 
anthropogenic N 2O emissions. Average AFOLU N 2O emissions were 
reported to be 8.7 ± 2.5 MtN 2O yr–1 for the period 2007–2016, 
accounting for 81% of total anthropogenic N 2O emissions, with 
agriculture accounting for 95% of AFOLU N 2O emissions (Jia et al. 
2019). A  recent comprehensive review confirms agriculture as the 
principal driver of the growing atmospheric N 2O concentration (Tian 
et al. 2020). The latest FAOSTAT data (FAO 2020b, 2021a) document 
a 25% increase in AFOLU N 2O emissions between 1990 and 2019, with the average share from agriculture remaining approximately 
the same (96%). Agricultural soils were identified in the SRCCL and 
in recent literature as a dominant emission source, notably due to 
nitrogen fertiliser and manure applications to croplands, and manure 
production and deposition on pastures (Jia et al. 2019; Tian et al.

2020). There is agreement within latest data that agricultural soils 
remain the dominant source (Figure 7.7).
Aggregation of CH 4 and N 2O to CO 2 equivalence (using GWP100 IPCC 
AR6 values), suggests that AFOLU emissions increased by 15% between 
1990 and 2019, though emissions showed trend variability year to year. 
Agriculture accounted for 91% of AFOLU emissions on average over 
the period (FAO 2020b, 2021a). EDGAR (Crippa et al. 2021), FAOSTAT 
(FAO 2021a) and USEPA (USEPA 2019) data suggest aggregated 
agricultural emissions (CO 2-eq) to have increased since 1990, by 19% 
(1990–2019), 15% (1990–2019) and 21% (1990–2015) respectively, 
with all databases identifying enteric fermentation and agricultural 
soils as the dominant agricultural emissions sources.
7.2.3.2 Regional AFOLU CH 4 and N 2O Emissions
FAOSTAT data (FAO 2020b, 2021a) indicate Africa (+44%), followed 
by Southern Asia (+29%) to have the largest growth in AFOLU

CH4 emissions between 1990 and 2019 (Figure  7.8). Eurasia was 
0.00.20.40.60.81.01.21.41.61.8
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
Africa A. Paciﬁc E. Asia Eurasia Europe L. America Middle E. N. America S-E Asia S. AsiaN2O emissions (Mt)05101520253035CH4 emissions (Mt)Agriculture: rice cultivation Agriculture: manure managementAgriculture: enteric fermentation Agriculture: biomass burning FOLU
Agriculture: soils
Figure 7.8 | Estimated average AFOLU CH 4 (top) and N 2O (bottom) emissions for three decades according to FAOSTAT data by ten global regions, with 
disaggregation of agricultural emissions (FAO 2020b; 2021a).  Note for N 2O: emissions from manure deposition and application to soils, crop residues and synthetic

766
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7characterised by notable emission reductions (–58%), principally 
as a result of a sharp decline (–63%) between 1990 and 1999. The 
average agricultural share of AFOLU emissions between 1990 and 
2019 ranged from 66% in Africa to almost 100% in the Middle East.
In agreement with AR5 (Smith et al. 2014), the SRCCL identified 
Asia as having the largest share (37%) of emissions from enteric 
fermentation and manure management since 2000, but Africa to have 
the fastest growth rate. Asia was identified as responsible for 89% of 
rice cultivation emissions, which were reported as increasing (Jia et al. 
2019). Considering classification by ten IPCC regions, data suggest 
enteric fermentation to have dominated emissions in all regions since 
1990, except in South-East Asia and Pacific, where rice cultivation 
forms the principal source (FAO 2021; USEPA 2019). The different

databases broadly indicate the same regional CH 4 emission trends, 
though the indicated absolute change differs due to methodological 
differences (Section  7.2.3.1). All databases indicate considerable 
emissions growth in Africa since 1990 and that this region recorded 
the greatest regional increases in emissions from both enteric 
fermentation and rice cultivation since 2010. Additionally, FAOSTAT 
data suggest that emissions from agricultural biomass burning 
account for a notably high proportion of agricultural CH 4 emissions 
in Africa (Figure 7.8).
The latest data suggest growth in AFOLU N 2O emissions in most 
regions between 1990 and 2019, with Southern Asia demonstrating 
highest growth (+74%) and Eurasia, greatest reductions (–51%), 
the latter mainly a  result of a  61% reduction between 1990 and 
2000 (FAO 2020b, 2021a). Agriculture was the dominant emission 
source in all regions, its proportional average share between 1990

and 2019 ranging from 87% in Africa, to almost 100% in the Middle 
East (Figure 7.8).
The SRCCL provided limited discussion on regional variation in 
agricultural N 2O emissions but reported with medium confidence 
that certain regions (North America, Europe, East and South Asia) 
were notable sources of grazing land N 2O emissions (Jia et al. 
2019). The AR5 identified Asia as the largest source and as having 
the highest growth rate of N 2O emissions from synthetic fertilisers 
between 2000 and 2010 (Smith et al. 2014). Latest data indicate 
agricultural N 2O emission increases in most regions, though 
variation between databases prevents definitive conclusions on 
trends, with Africa, Southern Asia, and Eastern Asia suggested to 
have had greatest growth since 1990 according to EDGAR (Crippa 
et al. 2021), FAOSTAT (FAO 2021a) and USEPA (USEPA 2019) data 
respectively. However, all databases indicate that emissions declined

in Eurasia and Europe from 1990 levels, in accordance with specific 
environmental regulations put in place since the late 1980s (Tubiello 
2019; European Environment Agency 2020; Tian et al. 2020), but 
generally suggest increases in both regions since 2010.
7.2.4 Biophysical Effects and Short-lived 
Climate Forcers
Despite new literature, general conclusions from the SRCCL 
and WGI-AR6 on biophysical effects and short-lived climate forcers remain the same. Changes in land conditions from land 
cover  change  or  land  management jointly affect water, energy, 
and aerosol fluxes (biophysical fluxes) as well as GHG fluxes 
(biogeochemical fluxes) exchanged between the land and atmosphere 
(high agreement, robust evidence) (Anderson et al. 2011; O’Halloran 
et al. 2012; Alkama and Cescatti 2016; Naudts et al. 2016; Erb et al. 
2017). There is high confidence that changes in land condition do 
not just have local impacts but also have non-local impacts in

adjacent and more distant areas (Pielke et al. 2011; Mahmood et al. 
2014) which may contribute to surpassing climate tipping points 
(Nepstad et al. 2008; Brando et al. 2014). Non-local impacts may 
occur through: GHG fluxes and subsequent changes in radiative 
transfer, changes in atmospheric chemistry, thermal, moisture and 
surface pressure gradients creating horizontal transport (advection) 
(de  Vrese et al. 2016; Davin and de Noblet-Ducoudré 2010) and 
vertical transport (convection and subsidence) (Devaraju et al. 2018). 
Although regional and global biophysical impacts emerge from 
model simulations (Davin and de Noblet-Ducoudré 2010; de Vrese 
et al. 2016; Devaraju et al. 2018), especially if the land condition 
has changed over large areas, there is very low agreement on the 
location, extent and characteristics of the non-local effects across 
models. Recent methodological advances, empirically confirmed 
changes in temperature and precipitation owing to distant changes

in forest cover (Cohn et al. 2019; Meier et al. 2021).
Following changes in land conditions, CO 2, CH 4 and N 2O fluxes are 
quickly mixed into the atmosphere and dispersed, resulting in the 
biogeochemical effects being dominated by the biophysical effects at 
local scales (high confidence) (Y . Li et al. 2015; Alkama and Cescatti 
2016). Afforestation/reforestation (Lejeune et al. 2018; Strandberg 
and Kjellström 2019), urbanisation (Li and Bou-Zeid 2013) and 
irrigation (Mueller et al. 2016 and Thiery et al. 2017) modulate the 
likelihood, intensity, and duration of many extreme events including 
heatwaves (high confidence) and heavy precipitation events (medium 
confidence) (Haberlie et al. 2015). There is high confidence and high 
agreement that afforestation in the tropics (Perugini et al. 2017), 
irrigation (Alter et al. 2015; Mueller et al. 2016) and urban greening 
result in local cooling, high agreement and medium confidence on

the impact of tree growth form (deciduous vs evergreen) (Naudts 
et al. 2016; Luyssaert et al. 2018 and Schwaab et al. 2020), and 
low agreement on the impact of wood harvest, fertilisation, tillage, 
crop harvest, residue management, grazing, mowing, and fire 
management on the local climate.
Studies of biophysical effects have increased since AR5 reaching high 
agreement for the effects of changes in land condition on surface 
albedo (Leonardi et al. 2015). Low confidence remains in proposing 
specific changes in land conditions to achieve desired impacts on 
local, regional and global climates due to: a poor relationship between 
changes in surface albedo and changes in surface temperature (Davin 
and de Noblet-Ducoudré 2010), compensation and feedbacks among 
biophysical processes (Bonan 2016; Kalliokoski et al. 2020), climate 
and seasonal dependency of the biophysical effects (Bonan 2016), 
omittance of short-lived chemical forcers (Unger 2014; Kalliokoski

et al. 2020), and study domains often being too small to document 
possible conflicts between local and non-local effects (Swann et al. 
2012; Hirsch et al. 2018).

767
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.3 Drivers
Since AR5 several global assessments (IPBES 2018a; NYDF Assessment 
Partners 2019; UNEP 2019; IPCC 2019) and studies (e.g., Tubiello 2019; 
Tian et al. 2020) have reported on drivers (natural and anthropogenic 
factors that affect emissions and sinks of the land-use sector) behind 
AFOLU emissions trends, and associated projections for the coming 
decades. The following analysis aligns with the drivers typology used 
by IPBES (2019b) and the Global Environmental Outlook (UNEP 2019). 
Drivers are divided into direct drivers resulting from human decisions 
and actions concerning land use and land-use change, and indirect 
drivers that operate by altering the level or rate of change of one or 
more direct drivers. Although drivers of emissions in agriculture and 
FOLU are presented separately, they are interlinked, operating in many 
complex ways at different temporal and spatial scales, with outcomes

depending on their interactions. For example, deforestation in tropical 
forests is a  significant component of sectorial emissions. A  review 
of deforestation drivers’ studies published between 1996 and 2013, 
indicated a wide range of factors associated with deforestation rates 
across many analyses and studies, covering different regions (Busch 
and Ferretti-Gallon 2017) (Figure 7.9). Higher agricultural prices were 
identified as a  key driver of deforestation, while law enforcement, 
area protection, and ecosystem services payments were found to be 
important drivers of reduced deforestation, while timber activity did 
not show a consistent impact.7.3.1 Anthropogenic Direct Drivers: Deforestation, 
Conversion of Other Ecosystems, and 
Land Degradation
The global forest area in 2020 is estimated at 4.1 billion ha, 
representing 31% of the total land area (FAO 2020a). Most forests 
are situated in the tropics (45%), followed by boreal (27%),

temperate (16%) and subtropical (11%) domains. Considering 
regional distribution of global forest area, Europe and the Russian 
Federation accounts for 25%, followed by South America (21%), 
North and Central America (19%), Africa (16%), Asia (15%) and 
Oceania (5%). However, a significant share (54%) of the world’s 
forest area concerns five countries – The Russian Federation, Brazil, 
Canada, the USA and China (FAO 2020a). Forest loss rates differ 
among regions though the global trend is towards a net forest loss 
(UNEP 2019). The global forest area declined by about 178 Mha in 
the 30 years from 1990 to 2020 (FAO 2020a). The rate of net forest 
loss has decreased since 1990, a  result of reduced deforestation 
in some countries and forest gains in others. The annual net loss 
of forest area declined from 7.8 Mha in 1990–2000, to 5.2 Mha 
in 2000–2010, to 4.7 Mha in 2010–2020, while the total growing 
stock in global forests increased (FAO 2020a). The rate of decline

in net forest loss during the last decade was due mainly to an 
increase in the rate of forest gain (i.e., afforestation and the natural 
expansion of forests).
–9 –8 –7 –6 –5 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9Higher agricultural priceRural income supportNearer to agricultureGreater populationNearer to urban areaNearer to roadsGreater soil suitabilityAgricultural activityNearer to cleared landNearer to waterTimber activityGreater educationMore secure land tenureCommunity forestryWetterHigher timber priceLarger property sizeGreater povertyOlder populationSteeper slopePresence of indigenous peoplesHigher elevationPayments (PES)Protected areaLaw enforcementLess deforestation More deforestation Not consistent
For each category of explanatory variables (left-hand side), the meta-analysis determined whether the driver variables in that 
category were consistently associated with higher rates of deforestation, lower rates of deforestation, or neither (not consistent).

For example, a ratio of –4x indicates that a variable is associated with less deforestation four times as often as it is associated with 
more deforestation.
Figure 7.9 | Association of driver variables with more or less deforestation.  Source: reproduced with permission from Busch and Ferretti-Gallon (2017).

768
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Globally, the area of the more open, other wooded land is also of 
significant importance, with almost 1 billion hectares (FAO 2020a). 
The area of other wooded land decreased by 30.6 Mha between 
1990 and 2020 with larger declines between 1990–2000 (FAO 
2020a). There are still significant challenges in monitoring the 
area of other wooded land, largely associated with difficulties in 
measuring tree-canopy cover in the range of 5–10%.The global area 
of mangroves, one of the most productive terrestrial ecosystems 
(Neogi 2020a), has also experienced a significant decline (Thomas 
et al. 2017; Neogi 2020b), with a decrease of 1.0 Mha between 1990 
and 2020 (FAO 2020a) due to agriculture and aquaculture (Bhattarai 
2011; Ajonina et al. 2014; Webb et al. 2014; Giri et al. 2015; Thomas 
et al. 2017; Fauzi et al. 2019). Some relevant direct drivers affecting

emissions and removal in forests and other ecosystems are discussed 
in proceeding sections.
7.3.1.1 Conversion of Natural Ecosystems to Agriculture
Previous IPCC reports identify land-use change as an important 
driver of emissions and agriculture as a key driver of land-use change, 
causing both deforestation and wetland drainage (P .  Smith et al. 
2019a). The AR5 reported a  trend of declining global agricultural 
land area since 2000 (Smith et al. 2014). The latest data (FAO 2021b) 
indicate a 2% reduction in the global agricultural area between 2000 
and 2019 (Figure 7.10). This area includes (though is not limited to) 
land under permanent and temporary crops or pasture, temporary 
fallow and natural meadows and pasture utilised for grazing or 
agricultural purposes (FAO 2021b), although the extent of land 
used for grazing may not be fully captured (Fetzel et al. 2017). Data 
indicate changes in how agricultural land is used. Between 2000

and 2019, the area classified as permanent meadow and pasture 
decreased (–6%) while cropland area (under arable production 
and temporary crops) increased (+2%). A key driver of this change 
has been a  general trend of intensification, including in livestock 
production (Barger et al. 2018; OECD/FAO 2019; UNEP 2019), 
whereby less grazing land is supporting increasing livestock numbers 
in conjunction with greater use of crops as livestock feed (Barger 
et al. 2018). The share of feed crops, such as maize and soybean, of 
global crop production is projected to grow as the demand for animal 
feed increases with further intensification of livestock production 
(OECD/FAO 2019). Despite increased demand for food, feed, fuel 
and fibre from a  growing human population (FAO 2019b), global 
agricultural land area is projected to remain relatively stable during 
the next decade, with increases in production expected to result from 
agricultural intensification (OECD/FAO 2019).

Despite a decline in global agricultural area, the latest data document 
some regional expansion between 2000 and 2019, specifically in 
Africa (+3%) and Asia and the Pacific (+1%). Agricultural area 
declined in all other regions, notably in developed countries (–9%), 
due to multiple factors including among others, urbanisation (see 
Section 7.3.1.2).7.3.1.2 Infrastructure Development and Urbanisation
Although built-up areas (defined as cities, towns, villages and human 
infrastructure) occupy a  relatively small fraction of land (around 
1% of global land), since 1975 urban clusters (i.e., urban centres as 
well as surrounding suburbs) have expanded approximately 2.5 times 
(UNEP 2019; Chapter 8, this report). Regional differences are striking. 
Between 1975 and 2015, built-up areas doubled in size in Europe 
while urban population remained relatively constant. In Africa built-
up areas grew approximately fourfold, while urban population

tripled (UNEP 2019). Trends indicate that rural-to-urban migration 
will continue and accelerate in developing countries increasing 
environmental pressure in spite of measures to mitigate some of 
the impacts (e.g., by preserving or enhancing natural systems within 
cities, for example, lakes or natural and urban green infrastructures 
(UNEP 2019). If current population densities within cities remain 
stable, the extent of built-up areas in developed countries is expected 
to increase by 30% and triple in developing countries between 2000 
and 2050 (Barger et al. 2018).
Urban expansion leads to landscape fragmentation and urban sprawl 
with effects on forest resources and land use (Ünal et al. 2019) while 
interacting with other drives. For example, in the Brazilian Amazon, 
the most rapid urban growth occurs within cities that are located 
near rural areas that produce commodities (minerals or crops) and 
are connected to export corridors (Richards and VanWey 2015).

Urbanisation, coastal development and industrialisation also play 
crucial roles in the significant loss of mangrove forests (Hirales-Cota 
2010; Richards and Friess 2016; Rivera-Monroy et al. 2017). Among 
infrastructural developments, roads are one of the most consistent 
and most considerable factors in deforestation, particularly in tropical 
frontiers (Pfaff et al. 2007; Rudel et al. 2009; Ferretti-Gallon and 
Busch 2014). The development of roads may also bring subsequent 
impacts on further development intensity due to increasing economic 
activities (see Chapter 8) mostly in the tropics and subtropics, where 
the expansion of road networks increases access to remote forests 
that act as refuges for biodiversity (Campbell et al. 2017) (Box 7.1). 
Logging is one of the main drivers of road construction in tropical 
forests (Kleinschroth and Healey 2017) which leads to more severe 
long-term impacts that include increased fire incidence, soil erosion,

landslides, and sediment accumulation in streams, biological 
invasions, wildlife poaching, illicit land colonisation, illegal logging 
and mining, land grabbing and land speculation (Laurance et al. 
2009; Alamgir et al. 2017).

769
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Box 7.1 | Case Study: Reducing the Impacts of Roads on Deforestation
Summary
Rapidly expanding roads, particularly in tropical regions, are linked to forest loss, degradation, and fragmentation because the land 
becomes more generally accessible. Increase of land values of areas adjacent to roads also drives speculation and deforestation 
related to land tenure (Fearnside 2015). If poorly planned, infrastructure can facilitate fires, illegal mining, and wildlife poaching with 
consequences for GHG emissions and biodiversity conservation. However, some initiatives are providing new approaches for better 
planning and then limit environmental and societal impacts.
Although the number and extent of protected areas has increased markedly in recent decades (Watson et al. 2014), many other

indicators reveal that nature is in broad retreat. For example, the total area of intact wilderness is declining rapidly worldwide (Watson 
et al. 2016), 70% of the world’s forests are now less than 1 km from a forest edge (Haddad et al. 2015), the extent of tropical forest 
fragmentation is accelerating exponentially (Taubert et al. 2018). One of the most direct and immediate driver of deforestation and 
biodiversity decline is the dramatic expansion of roads and other transportation infrastructure (Laurance et al. 2014a; Laurance 
and Arrea 2017; Alamgir et al. 2017).
Case description
From 2010 to 2050, the total length of paved roads is projected to increase by 25 million km (Dulac 2013) including large infrastructure-
expansion schemes in Asia (Laurance and Arrea 2017; Lechner et al. 2018) and in South America (Laurance et al. 2001; Killeen 2007), 
as well as widespread illegal or unplanned road building (Laurance et al. 2009; Barber et al. 2014). For example, in the Amazon,

95% of all deforestation occurs within 5.5 km of a road, and for every km of legal road there are nearly three km of illegal roads 
(Barber et al. 2014).
Interactions and limitations
More than any other proximate factor, the dramatic expansion of roads is determining the pace and patterns of habitat disruption and 
its impacts on biodiversity (Laurance et al. 2009; Laurance and Arrea 2017). Much road expansion is poorly planned. Environmental 
Impact Assessments (EIAs) for roads and other infrastructure are typically too short term and superficial to detect rare species or 
assess long-term or indirect impacts of projects (Flyvbjerg 2009; Laurance and Arrea 2017). Another limitation is the consideration 
of each project in isolation from other existing or planned developments (Laurance et al. 2014b). Hence, EIAs alone are inadequate 
for planning infrastructure projects and assessing their broader environmental, social, and financial impacts and risks (Laurance et al.

2015a; Alamgir et al. 2017, 2018).
Lessons
The large-scale, proactive land-use planning is an option for managing the development of modern infrastructure. Approaches such 
as the ‘Global Roadmap’ scheme (Laurance and Balmford 2013; Laurance et al. 2014a) Strategic Environmental Assessments (Fischer 
2007) can be used to evaluate the relative costs and benefits of infrastructure projects, and to spatially prioritise land uses to optimise 
human benefits while limited new infrastructure in areas of intact or critical habitats. For example, the Global Roadmap strategy has 
been used in parts of South-East Asia (Sloan et al. 2018), Indochina (Balmford et al. 2016), and sub-Saharan Africa (Laurance et al. 
2015b) to devise land-use zoning that can help optimise the many risks and rewards of planned infrastructure projects.
7.3.1.3 Extractive Industry Development
The extent and scale of mining is growing due to increased global

demand (UNEP 2019). Due to declining ore grades, more ore needs 
to be processed to meet demand, with extensive use of open cast 
mining. A  low-carbon future may be more mineral intensive with, 
for example, clean energy technologies requiring greater inputs in 
comparison to fossil-fuel-based technologies (Hund et al. 2020). 
Mining presents cumulative environmental impacts, especially in 
intensively mined regions (UNEP 2019). The impact of mining on 
deforestation varies considerably across minerals and countries. 
Mining causes significant changes to the environment, for example, 
through mining infrastructure establishment, soil erosion, urban expansion to support a  growing workforce and development of 
mineral commodity supply chains (Sonter et al. 2015). The increasing 
consumption of gold in developing countries, increased prices, and 
uncertainty in financial markets is identified as driving gold mining 
and associated deforestation in the Amazon region (Alvarez-Berrios

and Mitchell Aide 2015; Dezécache et al. 2017; Asner and Tupayachi 
2017; Espejo et al. 2018). The total estimated area of gold mining 
throughout the region increased by about 40% between 2012 and 
2016 (Asner and Tupayachi 2017). In the Brazilian Amazon, mining 
significantly increased forest loss up to 70 km beyond mining lease 
boundaries, causing 11,670 km2 of deforestation between 2005 and 
2015, representing 9% of all Amazon forest loss during this time 
(Sonter et al. 2015).

770
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Mining is also an important driver of deforestation in African and Asian 
countries. In the Democratic Republic of Congo, where the second-
largest area of tropical forest in the world occurs, mining-related 
deforestation exacerbated by violent conflict (Butsic et al. 2015). 
In India, mining has contributed to deforestation at a district level, 
with coal, iron and limestone having had the most adverse impact 
on forest area loss (Ranjan 2019). Gold mining is also identified as 
a driver of deforestation in Myanmar (Papworth et al. 2017).
7.3.1.4 Fire Regime Changes
Wildland fires account for approximately 70% of the global biomass 
burned annually (van der Werf et al. 2017) and constitute a  large 
global source of atmospheric trace gases and aerosols (Gunsch et al. 
2018) (IPCC WGI AR6). Although fires are part of the natural system, 
the frequency of fires has increased in many areas, exacerbated by

decreases in precipitation, including in many regions with humid and 
temperate forests that rarely experience large-scale fires naturally. 
Natural and human-ignited fires affect all major biomes, from 
peatlands through shrublands to tropical and boreal forests, altering 
ecosystem structure and functioning (Argañaraz et al. 2015; Nunes 
et al. 2016; Remy et al. 2017; Mancini et al. 2018; Aragão et al. 2018; 
Engel et al. 2019; Rodríguez Vásquez et al. 2021). However, the degree 
of incidence and regional trends are quite different and a study over 
14 years indicated, on average, the largest fires in Australia, boreal 
North America and Northern Hemisphere Africa (Andela et al. 2019). 
More than half of the terrestrial surface of the Earth has fire regimes 
outside the range of natural variability, with changes in fire frequency 
and intensity posing major challenges for land restoration and 
recovery (Barger et al. 2018). In some ecosystems, fire prevention

might lead to accumulation of large fuel loads that enable wildfires 
(Moreira et al. 2020a).
About 98 Mha of forest and savannahs are estimated to have been 
affected by fire in 2015 (FAO and UNEP 2020). Fire is a prevalent 
forest disturbance in the tropics where about 4% of the total forest 
and savannah area in that year was burned and more than two-
thirds of the total area affected was in Africa and South America; 
mostly open savanna types (FAO and UNEP 2020). Fires have many 
different causes, with land clearing for agriculture the primary driver 
in tropical regions, for example, clearance for industrial oil-palm and 
paper-pulp plantations in Indonesia (Chisholm et al. 2016), or for 
pastures in the Amazon (Barlow et al. 2020). Other socio-economic 
factors are also associated with wildfire regimes such as land-use 
conflict and socio-demographic aspects (Nunes et al. 2016; Mancini 
et al. 2018). Wildfire regimes are also changing by the influence

of climate change, with wildfire seasons becoming longer, wildfire 
average size increases in many areas and wildfires occurring in areas 
where they did not occur before (Jolly et al. 2015; Artés et al. 2019). 
Human influence has likely increased fire weather in some regions of 
all inhabited continents (IPCC AR6 WGI Technical Summary) and, in  
the last years, fire seasons of unprecedented magnitude occurred 
in diverse regions as California (Goss et al. 2020), the Mediterranean 
basin (Ruffault et al. 2020), Canada (Kirchmeier -Young et al. 2019) 
with unprecedented fires in British Columbia in 2021, the Arctic and 
Siberia (McCarty et al. 2020), Brazilian Amazon (Silva et al. 2021) 
and Pantanal (Leal Filho et al. 2021), Chile (Bowman et al. 2019) and Australia (Ward et al. 2020; Gallagher et al. 2021). Lightning plays 
an important role in the ignition of wildfires, with the incidence of 
lightning igniting wildfires predicted to increase with rises in global

average air temperature (Worden et al. 2017).
7.3.1.5 Logging and Fuelwood Harvest
The area of forest designated for production has been relatively 
stable since 1990. Considering forest uses, about 30% (1.2 billion ha) 
of all forests is used primarily for production (wood and non-wood 
forest products), about 10% (424 Mha) is designated for biodiversity 
conservation, 398 Mha for the protection of soil and water, and 
186 Mha is allocated for social services (recreation, tourism, education 
research and the conservation of cultural and spiritual sites) (FAO and 
UNEP 2020). While the rate of increase in the area of forest allocated 
primarily for biodiversity conservation has slowed in the last ten years, 
the rate of increase in the area of forest allocated for soil and water 
protection has grown since 1990, and notably in the last ten years. 
Global wood harvest (including from forests, other wooded land and 
trees outside forests) was estimated to be almost 4.0 billion m3 in

2018 (considering both industrial roundwood and fuelwood) (FAO, 
2019). Overall, wood removals are increasing globally as demand for, 
and the consumption of wood products grows annually by 1% in line 
with growing populations and incomes with this trend expected to 
continue in coming decades. When done in a sustainable way, more 
regrowth will occur and is stimulated by management, resulting in 
a net sink. However illegal and unsustainable logging (i.e., harvesting 
of timber in contravention of the laws and regulations of the country 
of harvest) is a global problem with significant negative economic 
(e.g.,  lost revenue), environmental (e.g.,  deforestation, forest 
degradation, GHG emissions and biodiversity losses) and social 
impact (e.g.,  conflicts over land and resources, disempowerment 
of local and indigenous communities) (World Bank 2019). Many 
countries around the world have introduced regulations for the

international trade of forest products to reduce illegal logging, with 
significant and positive impacts (Guan et al. 2018).
Over-extraction of wood for timber and fuelwood is identified as 
an important driver of mangrove deforestation and degradation 
(Bhattarai 2011; Ajonina et al. 2014; Webb et al. 2014; Giri et al. 2015; 
Thomas et al. 2017; Fauzi et al. 2019). Unsustainable selective logging 
and over-extraction of wood is a  substantial form of forest and 
mangrove degradation in many tropical and developing countries, 
with emissions associated with the extracted wood, incidental 
damage to the surrounding forest and from logging infrastructure 
(Bhattarai 2011; Ajonina et al. 2014; Webb et al. 2014; Pearson 
et al. 2014, Giri et al. 2015; Thomas et al. 2017; Fauzi et al. 2019). 
Traditional fuelwood and charcoal continue to represent a dominant 
share of total wood consumption in low-income countries (Barger

et al. 2018). Regionally, the percentage of total wood harvested used 
as fuelwood varies from 90% in Africa, 62% in Asia, 50% in South 
America to less than 20% in Europe, North America and Oceania. 
Under current projections, efforts to intensify wood production in 
plantation forests, together with increases in fuel-use efficiency and 
electrification, are suggested to only partly alleviate the pressure on 
native forests (Barger et al. 2018). Nevertheless, the area of forest 
under management plans has increased in all regions since 2000

771
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77by 233 Mha (FAO 2020e). In regions representing the majority of 
industrial wood production, forests certified under sustainable forest 
management programmes accounted for 51% of total managed 
forest area in 2017, an increase from 11% in 2000 (ICFPA 2021).
7.3.2 Anthropogenic Direct Drivers – Agriculture
7.3.2.1 Livestock Populations and Management
Enteric fermentation dominates agricultural CH 4 emissions 
(Section  7.2.3) with emissions being a  function of both ruminant 
animal numbers and productivity (output per animal). In addition 
to enteric fermentation, both CH 4 and N 2O emissions from manure 
management (i.e., manure storage and application) and deposition 
on pasture, make livestock the main agricultural emissions source 
(Tubiello 2019). The AR5 reported increases in populations of all 
major livestock categories between the 1970s and 2000s, including

ruminants, with increasing numbers directly linked with increasing 
CH4 emissions (Smith et al. 2014). The SRCCL identified managed 
pastures as a disproportionately high N 2O emissions source within 
grazing lands, with medium confidence that increased manure 
production and deposition was a key driver (Jia et al. 2019). The latest 
data (FAO 2021c) indicate continued global livestock population 
growth between 1990 and 2019 (Figure  7.10), including increases 
of 18% in cattle and buffalo numbers, and 30% in sheep and goat 
numbers, corresponding with CH 4 emission trends. Data also indicate 
increased productivity per animal for example, average increases of 
16% in beef, 17% in pig meat and 70% in whole (cow) milk per 
respective animal between 1990 and 2019 (FAO 2021c). Despite these 
advances leading to reduced emissions per unit of product (calories, 
meat and milk) (FAO 2016; Tubiello 2019), increased individual

animal productivity generally requires increased inputs (e.g.,  feed) 
and this generates increased emissions (Beauchemin et al. 2020). 
Manipulation of livestock diets, or improvements in animal genetics 
or health may counteract some of this. In addition, the production of 
inputs to facilitate increased animal productivity, may indirectly drive 
further absolute GHG emissions along the feed supply chain.
Although there are several potential drivers (McDermott et al. 2010; 
Alary et al. 2015), increased livestock production is principally in 
response to growth in demand for animal-sourced food, driven by 
a growing human population (FAO, 2019) and increased consumption 
resulting from changes in affluence, notably in middle-income 
countries (Godfray et al. 2018). Available data document increases 
in total meat and milk consumption by 24 and 22% respectively 
between 1990 and 2013, as indicated by average annual per capita

supply (FAO 2017a). Updated data indicate that trends of increasing 
consumption continued between 2014 and 2018 (FAO 2021d). 
Sustained demand for animal-sourced food is expected to drive 
further livestock sector growth, with global production projected to 
expand by 14% by 2029, facilitated by maintained product prices and 
lower feed prices (OECD/FAO 2019).7.3.2.2 Rice Cultivation
In addition to livestock, both AR5 and the SRCCL identified paddy 
rice cultivation as an important emissions source (Smith et al. 2014), 
with medium evidence and high agreement that its expansion is 
a key driver of growing trends in atmospheric CH 4 concentration (Jia 
et al. 2019). The latest data indicate the global harvested area of rice 
to have grown by 11% between 1990 and 2019, with total paddy 
production increasing by 46%, from 519 Mt to 755 Mt (FAO 2021c). 
Global rice production is projected to increase by 13% by 2028 
compared to 2019 levels (OECD/FAO 2019). However, yield increases

are expected to limit cultivated area expansion, while dietary shifts 
from rice to protein as a result of increasing per capita income, is 
expected to reduce demand in certain regions, with a slight decline 
in related emissions projected to 2030 (USEPA 2019).
Between 1990 and 2019, Africa recorded the greatest increase 
(+160%) in area under rice cultivation, followed by Asia and the 
Pacific (+6%), with area reductions evident in all other regions 
(FAO 2021c) broadly corresponding with related regional CH 4 
emission (Figures  7.3 and 7.8). Data indicate the greatest growth 
in consumption (average annual supply per capita) between 1990 
and 2013 to have occurred in Eastern Europe and West Central Asia 
(+42%) followed by Africa (+25%), with little change (+1%) observed 
in Asia and the Pacific (FAO 2017a). Most of the projected increase 
in global rice consumption is in Africa and Asia (OECD/FAO 2019).
7.3.2.3 Synthetic Fertiliser Use

Both AR5 and the SRCCL described considerable increases in global 
use of synthetic nitrogen fertilisers since the 1970s, which was 
identified to be a major driver of increasing N 2O emissions (Jia et al. 
2019). The latest data document a 41% increase in global nitrogen 
fertiliser use between 1990 and 2019 (FAO 2021e) corresponding 
with associated increased N 2O emissions (Figure  7.3). Increased 
fertiliser use has been driven by pursuit of increased crop yields, with 
for example, a 61% increase in average global cereal yield per hectare 
observed during the same period (FAO 2021c), achieved through 
both increased fertiliser use and varietal improvements. Increased 
yields are in response to increased demand for food, feed, fuel 
and fibre crops which in turn has been driven by a growing human 
population (FAO, 2019), increased demand for animal-sourced food 
and bioenergy policy (OECD/FAO 2019). Global crop production is

projected to increase by almost 15% over the next decade, with low 
income and emerging regions with greater availability of land and 
labour resources expected to experience the strongest growth, and 
account for about 50% of global output growth (OECD/FAO 2019). 
Increases in global nitrogen fertiliser use are also projected, notably 
in low income and emerging regions (USEPA 2019).

772
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)702468101214161820
01002003004005006007008009001000110012001300
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
1990–1999
2000–2009
2010–2019
Africa Asia and
PaciﬁcDeveloped
CountriesEastern Europe and
West-Central AsiaLatin America
and CaribbeanMiddle East
Poultry (billion heads)Livestock (million heads)0102030405060708090100
0200400600800100012001400160018002000220024002600
Fertiliser use  (million tonnes)Area of land use (million hectares)050100150
0200040006000
1990–1999 2000–2009 2010–2019
(Million tonnes)(Million hectares)Global trends
0102030
0100020003000
(Billion heads)(Million heads)Global trendsForest Cropland Permanent meadows and pasture Total agricultural area N fertiliser use
Horses, mules, asses, camels Cattle and buffaloes Pigs Sheep and goats Poultry

Figure 7.10 | Trends in average global and regional land area under specific land uses (FAO 2021b), inorganic nitrogen fertiliser use (FAO 2021e) (top) 
and number of livestock (FAO 2021c) (bottom) for three decades.  For land use classification ‘cropland’ represents the FAOSTAT category ‘arable land’ which includes 
land under temporary crops, meadow, pasture and fallow. ‘Forest’ and ‘permanent meadow and pasture’ follow FAOSTAT categories.

773
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.3.3 Indirect Drivers
The indirect drivers behind how humans both use and impact 
natural resources are outlined in Table 7.2. Specifically; demographic, economic and cultural, scientific and technological, and institutional 
and governance drivers. These indirect drivers not only interact with 
each other at different temporal and spatial scales but are also subject 
to impacts and feedbacks from the direct drivers (Barger et al. 2018).
Table 7.2 | Indirect drivers of anthropogenic land and natural resource use patterns.
DemographyGlobal and regional trends in population growth: There was a 43% increase in global population between 1990 and 2018. The greatest 
growth was observed in Africa and the Middle East (+104%) and least growth in Eastern Europe and West-Central Asia (+7%) (FAO 2019b).
Global and regional projections: Population is projected to increase by 28% between 2018 and 2050 reaching 9.7 billion (FAO 2019).

The world’s population is expected to become older, more urbanised and live in smaller households (UNEP 2019).
Human migration: Growing mobility and population are linked to human migration, a powerful driver of changes in land and resource use 
patterns at decadal time scales, with the dominant flow of people being from rural areas to urban settlements over the past few decades, 
notably in the developing world (Adger et al. 2015; Barger et al. 2018).
Economic development  
and cultural factorsChanges in land use and management come from individual and social responses to economic opportunities (e.g., demand for a particular 
commodity or improved market access), mediated by institutions and policies (e.g., agricultural subsidies and low-interest credit or 
government-led infrastructure projects) (Barger et al. 2018).
Projections on consumption: If the future global population adopts a per capita consumption rate similar to that of the developed

world, the global capacity to provide land-based resources will be exceeded (Barger et al. 2018). Economic growth in the developing world 
is projected to double the global consumption of forest and wood products by 2030, with demand likely to exceed production in many 
developing and emerging economies in Asia and Africa within the next decade (Barger et al. 2018).
Global trade: Market distorting agricultural subsidies and globalisation increases pressure on land systems and functions, with global 
trade and capital flow influencing land use, notably in developing countries (Furumo and Aide 2017; Yao et al. 2018; Pendrill et al. 2019a; 
UNEP 2019, OECD/FAO 2019). Estimates suggest that between 29 and 39% of emissions from deforestation in the tropics resulted from 
the international trade of agricultural commodities (Pendrill et al. 2019a).

Science and technologyTechnological factors operates in conjunction with economic drivers of land use and management, whether through intensified farming 
techniques and biotechnology, high-input approaches to rehabilitating degraded land (e.g., Lin et al. 2017; Guo et al. 2020) or through new 
forms of data collection and monitoring (e.g., Song et al. 2018; Thyagharajan and Vignesh 2019; Arévalo et al. 2020). 
Changes in farming and forestry systems: Changes can have both positive and negative impacts regarding multiple factors, including GHG 
emission trends. Fast advancing technologies shape production and consumption, and drive land-use patterns and terrestrial ecosystems at 
various scales. Innovation is expected to help drive increases in global crop production during the next decade (OECD/FAO 2019). For example, 
emerging gene editing technologies, may advance crop breeding capabilities, though are subject to biosafety, public acceptance and regulatory

approval (Jaganathan et al. 2018; Chen et al. 2019; Schmidt et al. 2020). Technological changes were significant for the expansion of soybean 
in Brazil by adapting to different soils and photoperiods (Abrahão and Costa 2018). In Asia, technological development changed agriculture 
with significant improvements in production and adaptation to climate change (Thomson et al. 2019; Giller and Ewert 2019; Anderson et al. 
2020; Cassman and Grassini 2020). Developments such as precision agriculture and drip irrigation have facilitated more efficient agrochemical 
and water use (UNEP 2019).
Research and development are central to forest restoration strategies that have become increasingly important around the world as costs 
vary depending on methods used, from natural regeneration with native tree species to active restoration using site preparation and planting

(Löf et al. 2019). In addition, climate change poses the challenge about tree species selection in the future. Innovations in the forest sector 
also form the basis of a bioeconomy associated with bioproducts and new processes (Verkerk et al. 2020) (Cross-Working Group Box 3 
in Chapter 12).
Emerging mitigation technologies: Chemically synthesised methanogen inhibitors for ruminants are expected to be commercially 
available in some countries within the next two years and have considerable CH 4 mitigation potential (McGinn et al. 2019; Melgar et al. 
2020; Beauchemin et al. 2020; Reisinger et al. 2021) (Section 7.4.3). There is growing literature (in both academic and non-academic spheres) 
on the biological engineering of protein. Although in its infancy and subject to investment, technological development, regulatory approval 
and consumer acceptance, it is suggested to have the potential to disrupt current livestock production systems and land use (Stephens et al.

2018; Ben-Arye and Levenberg 2019; RethinkX 2019; Post et al. 2020). The extent to which this is possible and the overall climate benefits 
are unclear (Lynch and Pierrehumbert 2019; Chriki and Hocquette 2020).  
Institutions and governanceInstitutional factors often moderate the relevance and impact of changes in economic and demographic variables related to resource 
exploitation and use. Institutions encompass the rule of law, legal frameworks and other social structures (e.g., civil society networks and 
movements) determining land management (e.g., formal and informal property rights, regimes and their enforcement); information and 
knowledge exchange systems; local and traditional knowledge and practice systems (Barger et al. 2018).
Land rights: Land tenure often allows communities to exercise traditional governance based on traditional ecological knowledge, devolved

and dynamic access rights, judicious use, equitable distribution of benefits (Mantyka-Pringle et al. 2017; Wynberg 2017; Thomas et al. 2017), 
biodiversity (Contreras-Negrete et al. 2014) and fire and grazing management (Levang et al. 2015; Varghese et al. 2015).
Agreements and Finance: Since AR5, global agreements were reached on climate change, sustainable development goals, and the 
mobilisation of finance for development and climate action. Several countries adopted policies and commitments to restore degraded 
land (Barger et al. 2018). The UN Environment Programme (UNEP) and the Food and Agriculture Organization of the UN (FAO), launched 
the UN Decade on Ecosystem Restoration (https://www.decadeonrestoration.org/).
Companies have also made pledges to reduce impacts on forests and on the rights of local communities as well as eliminating deforestation

from their supply chains. The finance sector, a crucial driver behind action (Section 7.6, Box 7.12), has also started to make explicit 
commitments to avoiding environmental damage (Barger et al. 2018) and net zero targets (Forest Trends Ecosystem Marketplace 2021), 
though investment is sensitive to market outlook.

774
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.4 Assessment of AFOLU Mitigation 
Measures Including Trade-
offs and Synergies
AFOLU mitigation or land-based climate change mitigation (used in 
this chapter interchangeably) are a variety of land management or 
demand management practices that reduce GHG emissions and/or 
enhance carbon sequestration within the land system (i.e., in forests, 
wetlands, grasslands, croplands and pasturelands). If implemented 
with benefits to human well-being and biodiversity, land-based 
mitigation measures are often referred to as nature-based solutions 
and/or natural climate solutions (Glossary). Measures that result in 
a net removal of GHGs from the atmosphere and storage in either 
living or dead organic material, or in geological stores, are known 
as CDR, and in previous IPCC reports were sometimes referred to as 
greenhouse gas removal (GGR) or negative emissions technologies

(NETs) (Rogelj et al. 2018a; Jia et al. 2019). This section evaluates 
current knowledge and latest scientific literature on AFOLU 
mitigation measures and potentials, including land-based CDR 
measures. Section 7.4.1 provides an overview of the approaches for 
estimating mitigation potential, the co-benefits and risks from land-
based mitigation measures, estimated global and regional mitigation 
potential and associated costs according to literature published over 
the last decade. Subsequent subsections assess literature on 20 key 
AFOLU mitigation measures specifically providing:
• A description of activities, co-benefits, risks and implementation 
opportunities and barriers.
• A summary of conclusions in the IPCC Fifth Assessment Report 
(AR5) and IPCC Special Reports (Special Report on Climate 
Change of 1.5°C (SR1.5), Special Report on the Ocean and 
Cryosphere in a Changing Climate (SROCC) and Special Report 
on Climate Change and Land (SRCCL)).

• An overview of literature and developments since the AR5 and 
IPCC Special Reports.
• An assessment and conclusion based on current evidence.
Measures are categorised as supply-side activities in: (i) forests and 
other ecosystems (Section  7.4.2); (ii) agriculture (Section  7.4.3); 
(iii) bioenergy and other land-based energy technologies (Section 7.4.4); 
as well as (iv) demand-side activities (Section 7.4.5 and Figure 7.11). 
Several information boxes are dispersed within the section and 
provide supporting material, including case studies exploring a range 
of topics from climate-smart forestry in Europe (Box 7.2), agroforestry 
in Brazil (Box 7.3), climate-smart village approaches (Box 7.4), farm 
systems approaches (Box  7.5), mitigation within Indian agriculture 
(Box 7.6), and bioenergy and BECCS mitigation calculations (Box 7.7). 
Novel measures, including enhanced weathering and novel foods are 
covered in Chapter  12, this report. In addition, as mitigation within

AFOLU concerns land management and use of land resources, AFOLU 
measures impact other sectors. Accordingly, AFOLU measures are 
also discussed in other sectoral chapters within this report, notably 
demand-side solutions (Chapter  5), bioenergy and bioenergy with 
carbon capture and storage (BECCS) (Chapter  6), the use of wood 
products and biomass in buildings (Chapter  9), and CDR measures, 
food systems and land related impacts, risks and opportunities of 
mitigation measures (Chapter 12).7.4.1 Introduction and Overview 
of Mitigation Potential
7.4.1.1 Estimating Mitigation Potentials
Mitigation potentials for AFOLU measures are estimated by 
calculating the scale of emissions reductions or carbon sequestration 
against a counterfactual scenario without mitigation activities. The 
types of mitigation potential estimates in recent literature include: 
(i) technical potential (the biophysical potential or amount possible

with current technologies); (ii) economic potential (constrained by 
costs, usually by a  given carbon price (Table  7.3); (iii) sustainable 
potential (constrained by environmental safeguards and/or natural 
resources, e.g., limiting natural forest conversion), and (iv) feasible 
potential (constrained by environmental, socio-cultural, and/or 
institutional barriers), however, there are no set definitions used in 
literature. In addition to types of mitigation estimates, there are two 
AFOLU mitigation categories often calculated: supply-side measures 
(land management interventions) and demand-side measures 
(interventions that require a change in consumer behaviour).
Two main approaches to estimating mitigation potentials include: 
(i) studies on individual measures and/or sectors  – henceforth 
referred to as sectoral assessments, and (ii) integrated assessment 
models (IAM). Sectoral assessments include studies focusing on one

activity (e.g.,  agroforestry) based on spatial and biophysical data, 
as well as econometric and optimisation models for a  sector, for 
example, the forest or agriculture sector, and therefore cover a large 
suite of practices and activities while representing a broad body of 
literature. Sectoral assessments, however, rarely capture cross-sector 
interactions or impacts, making it difficult to completely account for 
land competition, trade-offs, and double counting when aggregating 
sectoral estimates across different studies and methods (Smith et al. 
2014; Jia et al. 2019). On the other hand, IAMs assess the climate 
impact of multiple and interlinked practices across sectors and 
therefore, can account for interactions and trade-offs (including land 
competition, use of other resources and international trade) between 
them. However, the number of land-based measures used in IAMs 
are limited compared with the sectoral portfolio (Figure 7.11). The

resolution of land-based measures in IAMs are also generally coarser 
compared to some sectoral estimates, and as such, may be less 
robust for individual measures (Roe et al. 2021). Given the differences 
between and strengths and weaknesses of the two approaches, it is 
helpful to compare the estimates from both. We combine estimates 
from both approaches to establish an updated range of global land-
based mitigation potential.
For the 20 land-based mitigation measures outlined in this section, 
the mitigation potential estimates are largely derived from sectoral 
approaches, and where data is available, are compared to IAM 
estimates. Integrated assessment models and the emissions 
trajectories, cost-effectiveness and trade-offs of various mitigation 
pathways are detailed in Section  7.5.  It should be noted that the 
underlying literature for sectoral as well as IAM mitigation estimates 
consider GWP100 IPCC AR5 values (CH 4 = 28, N 2O = 265) as well

as GWP100 IPCC AR4 values (CH 4 = 25, N 2O = 298) to convert CH 4 
and N 2O to CO 2-eq. Where possible, we note the various GWP100

775
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77values (in IAM estimates, and the wetlands and agriculture sections), 
however in some instances, the varying GWP100  values used across 
studies prevents description of non-CO 2 gases in native units as well 
as conversion to AR6 GWP100 (CH 4 = 27, N 2O = 273) CO 2-eq values 
to aggregate sectoral assessment estimates.
7.4.1.2 Co-benefits and Risks
Land interventions have interlinked implications for climate mitigation, 
adaptation, food security, biodiversity, ecosystem services, and other 
environmental and societal challenges (Section 7.6.5). Therefore, it 
is important to consider the net effect of mitigation measures for 
achieving both climate and non-climate goals (Section 7.1).
While it is helpful to assess the general benefits, risks and 
opportunities possible for land-based mitigation measures 
(L.G. Smith et al. 2019), their efficacy and scale of benefit or risk

largely depends on the type of activity undertaken, deployment 
strategy (e.g.,  scale, method), and context (e.g.,  soil, biome, 
climate, food system, land ownership) that vary geographically 
and over time (robust evidence,  high agreement)  (L.G. Smith et al. 
2019; P .  Smith et al. 2019a; Hurlbert et al. 2019) (Section  12.5). 
Impacts of land-based mitigation measures are therefore highly 
context specific and conclusions from specific studies may not be 
universally applicable. If implemented at appropriate scales and 
in a  sustainable manner, land-based mitigation practices have 
the capacity to reduce emissions and sequester billions of tonnes 
of carbon from the atmosphere over coming decades, while also 
preserving or enhancing biodiversity, water quality and supply, air 
quality, soil fertility, food and wood security, livelihoods, resilience 
to droughts, floods and other natural disasters, and positively 
contributing to ecosystem health and human well-being (high

confidence) (Toensmeier 2016; Karlsson et al. 2020).
Overall, measures in the AFOLU sector are uniquely positioned to 
deliver substantial co-benefits. However, the negative consequences 
of inappropriate or misguided design and implementation of 
measures may be considerable, potentially impacting for example, 
mitigation permanence, longevity, and leakage, biodiversity, wider 
ecosystem functioning, livelihoods, food security and human well-
being (Section  7.6) (AR6 WGII, Box  2.2). Land-based mitigation 
may also face limitations and trade-offs in achieving sustained 
emission reductions and/or removals due to other land challenges 
including climate change impacts. It is widely recognised that land-
use planning that is context-specific, considers other sustainable 
development goals, and is adaptable over time can help achieve 
land-based mitigation that maximises co-benefits, avoids or limits 
trade-offs, and delivers on international policy goals including the

SDGs, Land Degradation Neutrality, and Convention on Biological 
Diversity (Section 7.6; Chapter 12).
Potential co-benefits and trade-offs are outlined for each of the 
20 land-based mitigation measures in the proceeding sub-sections 
and summarised in Figure 7.12. Section 7.6.5. discusses general links 
with ecosystem services, human well-being and adaptation, while 
Chapter 12 (Section 12.5) provides an in-depth assessment of the land 
related impacts, risks and opportunities associated with mitigation options across sectors, including positive and negative effects on 
land resources, water, biodiversity, climate, and food security.
7.4.1.3 Overview of Global and Regional Technical and 
Economic Potentials in AFOLU
IPCC AR5 (2014). In the AR5, the economic mitigation potential 
of supply-side measures in the AFOLU sector was estimated 
at 7.18–10.60 GtCO 2-eq yr–1 in 2030 with carbon prices up to 
USD100 tCO 2-eq–1, about a  third of which could be achieved at

<USD20 tCO 2-eq–1 (medium evidence, medium agreement) (Smith 
et al. 2014). The AR5 provided a  summary table of individual 
AFOLU mitigation measures, but did not conduct a  detailed 
assessment for each.
IPCC SRCCL (2019). The SRCCL assessed the full range of technical, 
economic and sustainability mitigation potentials in AFOLU for the 
period 2030–2050 and identified reduced deforestation and forest 
degradation to have greatest potential for reducing supply-side 
emissions (0.4 to 5.8 GtCO 2-eq yr–1) (high confidence) followed by 
combined agriculture measures, 0.3 to 3.4 GtCO 2-eq yr–1 (medium 
confidence) (Jia et al. 2019). For the demand-side estimates, shifting 
towards healthy, sustainable diets (0.7 to 8.0 GtCO 2-eq yr–1) (high 
confidence) had the highest potential, followed by reduced food loss 
and waste (0.8 to 4.5 GtCO 2-eq yr–1) (high confidence). Measures 
with greatest potential for CDR were afforestation/reforestation

(0.5 to 10.1 GtCO 2-eq yr–1) (medium confidence), soil carbon 
sequestration in croplands and grasslands (0.4 to 8.6 GtCO 2-eq yr–1) 
(medium confidence) and BECCS (0.4 to 11.3 GtCO 2-eq yr–1) (medium 
confidence). The SRCCL did not explore regional potential, associated 
feasibility nor provide detailed analysis of costs.
IPCC AR6. This assessment concludes the likely range of global land-
based mitigation potential is approximately 8–14 GtCO 2-eq yr–1 
between 2020–2050 with carbon prices up to USD100 tCO 2-eq–1, 
about half of the technical potential (medium evidence, medium 
agreement). About 30–50% could be achieved <USD20 tCO 2-eq–1 
(Table  7.3). The global economic potential estimates in this 
assessment are slightly higher than the AR5 range. Since AR5, there 
have been numerous new global assessments of sectoral land-based 
mitigation potential (Fuss et al. 2018; Griscom et al. 2017, 2020; Roe 
et al. 2019; Jia et al. 2019; Griscom et al. 2020; Roe et al. 2021) as well

as IAM estimates of mitigation potential (Riahi et al. 2017; Popp et al. 
2017; Rogelj et al. 2018a; Frank et al. 2019; Johnston and Radeloff 
2019; Baker et al. 2019), expanding the scope of AFOLU mitigation 
measures included and substantially improving the robustness and 
spatial resolution of mitigation estimates. A  recent development 
is an assessment of country-level technical and economic 
(USD100 tCO 2-eq–1) mitigation potential for 20  AFOLU measures, 
including for demand-side and soil organic carbon sequestration in 
croplands and grasslands, not estimated before (Roe  et al. 2021). 
Estimates on costs, feasibility, sustainability, benefits, and risks 
have also been developed for some mitigation measures, and they 
continue to be active areas of research. Developing more refined 
sustainable potentials at a country-level will be an important next 
step. Although most mitigation estimates still do not consider the

impact of future climate change, there are some emerging studies

776
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7that do (Sonntag et al. 2016; Doelman et al. 2019). Given the IPCC 
WG1 finding that the land sink is continuing to increase although 
its efficiency is decreasing with climate change, it will be critical to 
better understand how future climate will affect mitigation potentials, 
particularly from CDR measures.
Across global sectoral studies, the economic mitigation potential 
(up to USD100 tCO 2-eq–1) of supply-side measures in AFOLU for the 
period 2020–2050 is 11.4 mean (5.6–19.8 full range) GtCO 2-eq yr–1, about 50% of the technical potential of 24.2 (4.9–58) GtCO 2-eq yr–1 
(Table 7.3). Adding 2.1 GtCO 2-eq yr–1 from demand-side measures 
(accounting only for diverted agricultural production to avoid double 
counting with land-use change effects), total land-based mitigation 
potential up to USD100 tCO 2-eq–1 is 13.6 (6.7–23.4) GtCO 2-eq yr–1. 
This estimate aligns with the most recent regional assessment (Roe

et al. 2021), which found the aggregate global mitigation potential 
of supply and demand-side measures to be 13.8 ± 3.1 GtCO 2-eq yr–1 
up to USD100 tCO 2-eq–1 for the period 2020–2050. Across integrated 
Table 7.3 | Estimated annual mitigation potential (GtCO 2-eq yr–1) in 2020–2050 of AFOLU mitigation options by carbon price.  Estimates reflect sectoral 
studies based on a comprehensive literature review updating data from (Roe et al. 2019) and integrated assessment models using the IPCC AR6 database (Section 7.5). 
Values represent the mean, and full range of potential. Sectoral mitigation estimates are averaged for the years 2020–2050 to capture a wider range of literature, and the 
IAM estimates are given for 2050 as many model assumptions delay most land-based mitigation to mid-century. The sectoral potentials are the sum of global estimates for

the individual measures listed for each option. IAM potentials are given for mitigation options with available data; for example, net land-use CO 2 for total forests and other 
ecosystems, and land sequestration from A/R, but not reduced deforestation (protect). Sectoral estimates predominantly use GWP100 IPCC AR5 values (CH 4 = 28, N 2O = 265), 
although some use GWP100 IPCC AR4 values (CH 4 = 25, N 2O = 298); and the IAMs use GWP100 IPCC AR6 values (CH 4 = 27, N 2O = 273). The sectoral and IAM estimates 
reflected here do not account for the substitution effects of avoiding fossil fuel emissions nor emissions from other more energy intensive resources/materials. For example, 
BECCS estimates only consider the carbon dioxide removal (CDR) via geological storage component and not potential mitigation derived from the displacement of fossil fuel

use in the energy sector. Mitigation potential from substitution effects are included in the other sectoral chapters like energy, transport, buildings and industry. The total AFOLU 
sectoral estimate aggregates potential from agriculture, forests and other ecosystems, and diverted agricultural production from avoided food waste and diet shifts (excluding 
land-use impacts to avoid double counting). Because of potential overlaps between measures, sectoral values from BECCS and the full value chain potential from demand-side 
measures are not summed with AFOLU. IAMs account for land competition and resource optimisation and can therefore sum across all available categories to derive the total 
AFOLU potential. Key: ND = no data; Sectoral = as assessed by sectoral literature review; IAM = as assessed by integrated assessment models; EJ = exajoule primary energy.
Mitigation option Estimate type<USD20 
tCO 2-eq–1<USD50 
tCO 2-eq–1<USD100 
tCO 2-eq–1 Technical

Agriculture totalSectoral 0.9 (0.5–1.4) 1.6 (1–2.4) 4.1 (1.7–6.7) 11.2 (1.6–28.5)
IAM 0.9 (0–3.1) 1.3 (0–3.2) 1.8 (0.7–3.3) ND
Agriculture – Carbon sequestration 
(Soil carbon management in croplands and grasslands, 
agroforestry, and biochar) Sectoral 0.5 (0.4–0.6) 1.2 (0.9–1.6) 3.4 (1.4–5.5) 9.5 (1.1–25.3)
IAM ND ND ND ND
Agriculture – Reduce CH 4 and N 2O emissions 
(Improve enteric fermentation, manure management, 
nutrient management, and rice cultivation)Sectoral 0.4 (0.1–0.8) 0.4 (0.1–0.8) 0.6 (0.3–1.3) 1.7 (0.5–3.2)
Forests and other ecosystems totalSectoral 2.9 (2.2–3.5) 3.1 (1.4–5.1) 7.3 (3.9–13.1) 13 (5–29.5)
IAM 2.4 (0–10.5) 3.3 (0–9.9) 4.2 (0–12.1) ND
Forests and other ecosystems – Protect 
(Reduce deforestation, loss and degradation of peatlands, 
coastal wetlands, and grasslands)Sectoral 2.3 (1.7–2.9) 2.4 (1.2–3.6) 4.0 (2.5–7.4) 6.2 (2.8–14.4)
Forests and other ecosystems – Restore

(Afforestation, reforestation, peatland restoration, coastal 
wetland restoration)Sectoral 0.15 0.7 (0.2–1.5) 2.1 (0.8–3.8) 5 (1.1–12.3)
IAM (A/R) 0.6 (0.2–6.5) 0.6 (0.01–8.3) 0.7 (0.07–6.8) ND
Forests and other ecosystems – Manage 
(Improve forest management, fire management)Sectoral 0.4 (0.3–0.4) ND 1.2 (0.6–1.9) 1.8 (1.1–2.8)
Demand-side measures 
(Shift to sustainable healthy diets, reduce food waste, and 
enhanced and improved use of wood products) 
* For all three only the direct avoided emissions; land-use 
effects are in measures aboveSectoral ND ND 2.2 (1.1–3.6)* 4.2 (2.2–7.1)*
BECCS 
(Only the CDR component, for example, the geological 
storage. Substitution effects are accounted in other 
sectoral chapters e.g: Energy (ch 6), Transport (ch 10))Sectoral ND ND 1.6 (0.5–3.5) 5.9 (0.5–11.3)
IAM 0.08 (0–0.7) 0.5 (0–6) 1.8 (0.2–9.9) ND
Bioenergy from residues Sectoral ND ND ND Up to 57 EJ yr–1
TOTAL AFOLU

(Agriculture, forests and other ecosystems, diverted 
agricultural production from demand-side)Sectoral 3.8 (2.7–4.9) 4.3 (2.3–6.7) 13.6 (6.7–23.4) 28.4 (8.8–65.1)
TOTAL AFOLU 
(Agriculture, forests and other ecosystems, BECCS)IAM 3.4 (0–14.6) 5.3 (0.6–19.4) 7.9 (4.1–17.3) ND

777
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77assessment models (IAMs), the economic potential for land-based 
mitigation (Agriculture, LULUCF and BECCS) for USD100 tCO 2-eq–1 
is 7.9 mean (4.1–17.3 range) GtCO 2-eq yr–1 in 2050 (Table  7.3). 
We add the estimate for BECCS here to provide the full land-
based potential, as IAMs optimise land allocation based on costs, 
which displaces land-based CDR activities for BECCS.  Combining 
both IAM and sectoral approaches, the likely range is therefore 
7.9–13.6 (rounded to 8–14) GtCO 2-eq yr–1 up to USD100 tCO 2-eq–1 
between 2020–2050. Considering both IAM and sectoral economic 
potential estimates, land-based mitigation could have the capacity 
to make the AFOLU sector net negative in GHG emissions from 2036 
(Figure 7.12), although there are highly variable mitigation strategies 
for how AFOLU potential can be deployed for achieving climate 
targets (Illustrative Mitigation Pathways in Section 7.5.5). Economic

potential estimates, which reflect a public willingness to pay, may be 
more relevant for policy making compared with technical potentials 
which reflect a  theoretical maximum that may not be feasible 
or sustainable.
Among the mitigation options, the protection, improved management, 
and restoration of forests and other ecosystems (wetlands, savannas 
and grasslands) have the largest potential to reduce emissions 
and/or sequester carbon at 7.3 (3.9–13.1) GtCO 2-eq yr–1 (up to 
USD100 tCO 2-eq–1), with measures that ‘protect’ having the single 
highest total mitigation and mitigation densities (mitigation per 
area) in AFOLU (Table 7.3 and Figure 7.11). Agriculture provides the 
second largest share of mitigation, with 4.1 (1.7–6.7) GtCO 2-eq yr–1 
potential (up to USD100 tCO 2-eq–1), from soil carbon management 
in croplands and grasslands, agroforestry, biochar, rice cultivation, 
and livestock and nutrient management (Table 7.3 and Figure 7.11).

reducing food waste, and improving wood products can mitigate 
2.2 (1.1–3.6) GtCO 2-eq yr–1 when accounting only for diverted 
agricultural production from diets and food waste to avoid double 
counting with measures in forests and other ecosystems (Table 7.3 
and Figure 7.11). The potential of demand-side measures increases 
three-fold, to 6.5 (4–9.5) GtCO 2-eq yr–1 when accounting for the 
entire value chain including land-use effects, but would overlap with 
other measures and is therefore not additive.
Most mitigation options are available and ready to deploy. 
Emissions reductions can be unlocked relatively quickly, whereas 
CDR need upfront investment to generate sequestration over 
time. The protection of natural ecosystems, carbon sequestration 
in agriculture, sustainable healthy diets and reduced food waste 
have especially high co-benefits and cost efficiency. Avoiding the

conversion of carbon-rich primary peatlands, coastal wetlands and 
forests is particularly important as most carbon lost from those 
ecosystems are irrecoverable through restoration by the 2050 
timeline of achieving net zero carbon emissions (Goldstein et al. 
2020). Sustainable intensification, shifting diets, reducing food waste 
could enhance efficiencies and reduce agricultural land needs, and 
are therefore critical for enabling supply-side measures such as 
reduced deforestation, restoration, as well as reducing N 2O and CH 4 
emissions from agricultural production – as seen in the Illustrative 
Mitigation Pathway (IMP-SP) (Section  7.5.6). Although agriculture 
measures that reduce non-CO 2, particularly of CH 4, are important for near-term emissions reductions, they have less economic potential 
due to costs. Demand-side measures may be able to deliver non-CO 2 
emissions reductions more cost efficiently.
Regionally, economic mitigation potential up to USD100 tCO 2-eq–1

is estimated to be greatest in tropical countries in Asia and Pacific 
(34%), Latin America and the Caribbean (24%), and Africa and the 
Middle East (18%) because of the large potential from reducing 
deforestation and sequestering carbon in forests and agriculture 
(Figure  7.11). However, there is also considerable potential in 
Developed Countries (18%) and more modest potential in Eastern 
Europe and West-Central Asia (5%). These results are in line with the 
IAM regional mitigation potentials (Figure 7.11). The protection of 
forests and other ecosystems is the dominant source of mitigation 
potential in tropical regions, whereas carbon sequestration 
in  agricultural land and demand-side measures are important in 
Developed Countries and Asia and Pacific. The restoration and 
management of forests and other ecosystems is more geographically 
distributed, with all regions having significant potential. Regions 
with large livestock herds (Developed Countries, Latin America) and

rice paddy fields (Asia and Pacific) have potential to reduce CH 4. As 
expected, the highest total potential is associated with countries and 
regions with large land areas, however when considering mitigation 
density (total potential per hectare), many smaller countries, 
particularly those with wetlands have disproportionately high levels 
of mitigation for their size (Roe et al. 2021). As global commodity 
markets connect regions, AFOLU measures may create synergies and 
trade-offs across the world, which could make national demand-side 
measures for example, important in mitigating supply-side emissions 
elsewhere (Kallio et al. 2018).
Although economic potentials provide more realistic, near-term 
climate mitigation compared to technical potentials, they still do not 
account for feasibility barriers and enabling conditions that vary by 
region and country. For example, according to most models, including 
IAMs, avoided deforestation is the cheapest land-based mitigation

option (Table 7.3, Sections 7.5.3 and 7.5.4), however implementing 
interventions aimed at reducing deforestation (including REDD+) 
often have higher transaction and implementation costs than 
expected due to various barriers and enabling conditions (Luttrell 
et al. 2018) (Section  7.6). The feasibility of implementing AFOLU 
mitigation measures, including those with multiple co-benefits, 
depends on varying economic, technological, institutional, socio-
cultural, environmental and geophysical barriers (high confidence) 
(L.G.  Smith et al. 2019). The section for each individual mitigation 
measure provides an overview of co-benefits and risks associated 
with the measure and Section 7.6.6 outlines key enabling factors and 
barriers for implementation.

778
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Forests and other ecosystems: Manage
Forests and other ecosystems: Protect
Forests and other ecosystems: RestoreAgriculture: Reduce emissionsAgriculture: Sequester carbon
Demand-side: Diets and food loss/waste
BECCST: Technical potential
M: IAM potential (USD100 tCO2-eq)E: Economical potential (USD100 tCO2-eq)Biodiversity
Water
Air qualityLivelihoods
ResilienceSoil Co-beneﬁt only
Both co-beneﬁt and risk
Risk onlyForests and other ecosystems
Protect Manage RestoreAgriculture
Sequester carbon Reduce emissionsDemand-side Bioenergy
Regional mitigation potential (GtCO2-eq yr–1)
Data adapted from Roe et al. 2021 and AR6 IAM databaseGlobal mitigation potential (GtCO2-eq yr–1) 
Data based on AR6 literature reviewAFOLU global mitigation measures 
and potential co-beneﬁts and risks
T
E
T
EM
TLatin America 
and CaribbeanT
E
M
West-Central AsiaE
MDeveloped 
CountriesT
E
MAsia and PaciﬁcT
E
M

Africa and 
Middle EastT
TE
MReduce deforestation and degradation
Reduce conversion of coastal wetlands
Reduce degradation and conversion of peatlands
Reduce degradation and conversion of grasslands and savanna
Improve forest management
Fire management (forest and grassland/savanna ﬁres)
Afforestation, reforestation and forest ecosystem restoration
Coastal wetland restoration
Soil carbon management in croplands
Soil carbon management in grasslands
Biochar application
Crop nutrient management 
Improve rice management
Bioenergy and BECCS
Reduce food loss and waste
Shift to sustainable healthy diets
Improve use of wood products
0
02 4 6 8 10
2 4 6 8 10T
E
T
E
M
T
E
T
E
M
T
E
T
E
T
E
T
E
T
E
M
T
E
M
T
E
M
T
E
M
T
E
M
T
E
T
E
M
T
E
MT
E
T
E
T
E
Figure 7.11 | Global and regional mitigation potential (GtCO 2-eq yr–1) in 2020–2050 for 20 land-based measures.

779
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.4.2 Forests and Other Ecosystems
7.4.2.1 Reduce Deforestation and Degradation
Activities, co-benefits, risks and implementation opportunities 
and barriers. Reducing deforestation and forest degradation 
conserves existing carbon pools in forest vegetation and soil by 
avoiding tree cover loss and disturbance. Protecting forests involves 
controlling the drivers of deforestation (such as commercial and 
subsistence agriculture, mining, urban expansion) and forest 
degradation (such as overharvesting including fuelwood collection, 
poor harvesting practices, overgrazing, pest outbreaks, and extreme 
wildfires), as well as by establishing well designed, managed 
and funded protected areas (Barber et al. 2014), improving law enforcement, forest governance and land tenure, supporting 
community forest management and introducing forest certification 
(P .  Smith et al. 2019a). Reducing deforestation provides numerous

and substantial co-benefits, preserving biodiversity and ecosystem 
services (e.g., air and water filtration, water cycling, nutrient cycling) 
more effectively and at lower costs than afforestation/reforestation 
(Jia et al. 2019). Potential adverse side effects of these conservation 
measures include reducing the potential for agriculture land 
expansion, restricting the rights and access of local people to forest 
resources, or increasing the dependence of local people to insecure 
external funding. Barriers to implementation include unclear land 
tenure, weak environmental governance, insufficient funds, and 
increasing pressures associated to agriculture conversion, resource 
exploitation and infrastructure development (Sections 7.3 and 7.6).H4
N2O
=  Net AFOLU (anthropogenic) emissionsNatural 
CO2sink
A B=  Net AFOLU (anthropogenic) emissions and the natural land CO2 sink–20–15–10–5051015Projected mitigation1990
2000
20051995CH4
N2O2010
2015
2025
2030
2035
2040
2045Net land-

use CO2 
ﬂuxIAMs
+
BECCS+
BECCS and
demand-side
measures
Natural
CO2 sinkHistoric ﬂuxes
C7 – 3°C
Under current
policy A
B
GtCO2-eq
2050 2020
Figure 7.12 | Historic land sector GHG flux estimates and illustrative AFOLU mitigation pathways to 2050, based on data presented in Sections 7.2, 7.4 
and 7.5. Historic trends consider both A anthropogenic (AFOLU) GHG fluxes (GtCO 2-eq yr–1) according to FAOSTAT (FAO 2021a; 2021b) and B the estimated natural land CO 2 
sink according to (Friedlingstein et al. 2020). Note that for the anthropogenic net land CO 2 flux component, several approaches and methods are described within the literature 
(Section 7.2.2) with a wide range in estimates. For clarity, only one dataset (FAOSTAT) is illustrated here. It is not intended to indicate preference for one particular method

over others. Historic flux trends are illustrated to 2019, the latest year for which data is available. Projected economic mitigation potential (at costs of up to USD100 tCO 2-eq–1) 
includes estimates from IAMs and sectoral studies (Table 7.3). The ‘sectoral studies’ are disaggregated into several cumulative parts: first ‘sectoral studies’ involves measures 
in agriculture, forests and other ecosystems, then an additional BECCS share (‘+ BECCS’), then the additional effect of demand-side measures and BECCS (‘+BECCS and 
demand-side measures’). The latter only accounting for diverted agricultural production to avoid double counting. Projected mitigation assumes adoption of measures to achieve 
increasing, linear mitigation, reaching average annual potential in 2050, although this does not reflect deployment rates for most measures. For illustrative purposes, a pathway

to projected emissions in 2050 according to a scenario of current policy (C7  – above 3.0°C – Model: GCAM 5.3) is additionally included for reference.Figure 7.11 (continued): Global and regional mitigation potential (GtCO 2-eq yr–1) in 2020–2050 for 20 land-based measures.  (a) Global estimates represent 
the mean (bar) and full range (error bars) of the economic potential (up to USD100 tCO 2-eq–1) based on a comprehensive literature review of sectoral studies (references are 
outlined in the sub-section for each measure in Sections 7.4.2–7.4.5). Potential co-benefits and trade-offs for each of the 20 measures are summarised in icons. (b) Regional 
estimates illustrate the mean technical (T) and economic (E) (up to USD100 tCO 2-eq–1) sectoral potential based on data from (Roe et al. 2021). IAM economic potential (M) 
(USD100 tCO 2-eq–1) data is from the IPCC AR6 database.

780
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC 
and SRCCL); mitigation potential, costs, and pathways. Reducing 
deforestation and forest degradation represents one of the most effective 
options for climate change mitigation, with technical potential estimated 
at 0.4–5.8 GtCO 2 yr–1 by 2050 (high confidence) (SRCCL, Chapters 2 and 
4, and Table 6.14). The higher technical estimate represents a complete 
halting of land-use conversion in forests and peatland forests (i.e., 
assuming recent rates of carbon loss are saved each year) and includes 
vegetation and soil carbon pools. Ranges of economic potentials for 
forestry ranged in AR5 from 0.01–1.45 GtCO 2 yr–1 for USD20 tCO 2–1 
to 0.2–13.8 GtCO 2 yr–1 for USD100 tCO 2–1 by 2030 with reduced 
deforestation dominating the forestry mitigation potential LAM and 
MAF , but very little potential in OECD-1990 and EIT (IPCC AR5).

Developments since AR5 and IPCC Special Reports (SR1.5, 
SROCC and SRCCL). Since the SRCCL, several studies have provided 
updated and convergent estimates of economic mitigation potentials 
by region (Busch et al. 2019; Griscom et al. 2020; Austin et al. 2020; 
Roe et al. 2021). Tropical forests and savannas in Latin America 
provide the largest share of mitigation potential (3.9 GtCO 2 yr–1 
technical, 2.5 GtCO 2 yr–1 at USD100 tCO 2–1) followed by South-East 
Asia (2.2 GtCO 2 yr–1 technical, 1.5 GtCO 2 yr–1 at USD100 tCO 2–1) and 
Africa (2.2 GtCO 2 yr–1 technical, 1.2 GtCO 2 yr–1 at USD100 tCO 2–1) 
(Roe et al. 2021). Tropical forests continue to account for the 
highest rates of deforestation and associated GHG emissions. While 
deforestation shows signs of decreasing in several countries, 
in others, it continues at a  high rate or is increasing (Turubanova 
et al. 2018). Between 2010–2020, the rate of net forest loss was

4.7 Mha yr–1 with Africa and South America presenting the largest 
shares (3.9 Mha and 2.6 Mha, respectively) (FAO 2020a).
A major uncertainty in all studies on avoided deforestation potential 
is their reliance on future reference levels that vary across studies and 
approaches. If food demand increases in the future, for example, the 
area of land deforested will likely increase, suggesting more technical 
potential for avoiding deforestation. Transboundary leakage due to 
market adjustments could also increase costs or reduce effectiveness 
of avoiding deforestation (e.g., Ingalls et al. 2018; Gingrich et al. 2019). 
Regarding forest regrowth, there are uncertainties about the time for 
the secondary forest carbon saturation (Houghton and Nassikas 2017; 
Zhu et al. 2018). Permanence of avoided deforestation may also be 
a  concern due to the impacts of climate change and disturbance of 
other biogeochemical cycles on the world’s forests that can result in

future potential changes in terrestrial ecosystem productivity, climate-
driven vegetation migration, wildfires, forest regrowth and carbon 
dynamics (Ballantyne et al. 2012; Kim et al. 2017b; Lovejoy and Nobre 
2018; Aragão et al. 2018).
Critical assessment and conclusion. Based on studies since 
AR5, the technical mitigation potential for reducing deforestation 
and degradation is significant, providing 4.5 (2.3–7) GtCO 2 yr–1 
globally by 2050, of which 3.4 (2.3–6.4) GtCO 2 yr–1 is available at 
below USD100 tCO 2–1 (medium confidence) (Figure  7.11). Over the 
last decade, hundreds of subnational initiatives that aim to reduce 
deforestation related emissions have been implemented across the 
tropics (Section  7.6). Reduced deforestation is a  significant piece of 
the NDCs in the Paris Agreement (Seddon et al. 2020) and keeping 
the temperature below 1.5°C (Crusius 2020). Conservation of forests provides multiple co-benefits linked to ecosystem services, biodiversity

and sustainable development (Section  7.6.). Still, ensuring good 
governance, accountability (e.g., enhanced monitoring and verification 
capacity; Bos 2020), and the rule of law are crucial for implementing 
forest-based mitigation options. In many countries with the highest 
deforestation rates, insecure land rights often are significant barriers for 
forest-based mitigation options (Gren and Zeleke 2016; Essl et al. 2018).
7.4.2.2 Afforestation, Reforestation and Forest 
Ecosystem Restoration
and barriers. Afforestation and reforestation (A/R) are activities that 
convert land to forest, where reforestation is on land that has previously 
contained forests, while afforestation is on land that historically has 
not been forested (Box  7.2). Forest restoration refers to a  form of 
reforestation that gives more priority to ecological integrity as well,

even though it can still be a managed forest. Depending on the location, 
scale, and choice and management of tree species, A/R activities have 
a wide variety of co-benefits and trade-offs. Well-planned, sustainable 
reforestation and forest restoration can enhance climate resilience 
and biodiversity, and provide a variety of ecosystem services including 
water regulation, microclimatic regulation, soil erosion protection, 
as well as renewable resources, income and livelihoods (Locatelli 
et al. 2015; Stanturf et al. 2015; Ellison et al. 2017; Verkerk et al. 2020). 
Afforestation, when well planned, can help address land degradation 
and desertification by reducing runoff and erosion and lead to cloud 
formation however, when not well planned, there are localised trade-
offs such as reduced water yield or biodiversity (Teuling et al. 2017; 
Ellison et al. 2017). The use of non-native species and monocultures may 
have adverse impacts on ecosystem structure and function, and water

availability, particularly in dry regions (Ellison et al. 2017). A/R activities 
may change the surface albedo and evapotranspiration regimes, 
producing net cooling in the tropical and subtropical latitudes for local 
and global climate and net warming at high latitudes (Section 7.4.2). 
Very large-scale implementation of A/R may negatively affect food 
security since an increase in global forest area can increase food prices 
through land competition (Kreidenweis et al. 2016).
Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC 
and SRCCL); mitigation potential, costs, and pathways. The  AR5 
did not provide a new specification of A/R potential, but referred to 
IPCC AR4 mostly for forestry measures (Nabuurs et al. 2007). The AR5 
did view the feasible A/R potential from a diets change scenario that 
released land for reforestation and bioenergy crops. The AR5 provided 
top-down estimates of costs and potentials for forestry mitigation

options  – including reduced deforestation, forest management, 
afforestation, and agroforestry, estimated to contribute between 1.27 
and 4.23 GtCO 2 yr–1 of economically viable abatement in 2030 at 
carbon prices up to USD100 tCO 2-eq–1 (Smith et al. 2014).
The SRCCL remained with a  reported wide range of mitigation 
potential for A/R of 0.5–10.1 GtCO 2 yr–1 by 2050 (medium confidence) 
(Kreidenweis et al. 2016; Griscom et al. 2017; Hawken 2017; Fuss et al. 
2018; Roe et al. 2019) (SRCCL Chapters 2 and 6). The higher estimate 
represents a technical potential of reforesting all areas where forests 
are the native cover type (reforestation), constrained by food security 
and biodiversity considerations, considering above and below-ground

781
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77carbon pools and implementation on a  rather theoretical maximum 
of 678 Mha of land (Griscom et al. 2017; Roe et al. 2019). The lower 
estimates represent the minimum range from an Earth System Model 
and a  sustainable global CDR potential (Fuss et al. 2018). Climate 
change will affect the mitigation potential of reforestation due to 
impacts in forest growth and composition, as well as changes in 
disturbances including fire. However, none of the mitigation estimates 
included in the SRCCL account for climate impacts.
SROCC and SRCCL). Since SRCCL, additional studies have been 
published on A/R mitigation potential by Bastin et al. (2019), Lewis 
et al. (2019), Doelman et al. (2019), Favero et al. (2020) and Austin 
et al. (2020). These studies are within the range reported in the 
SRCCL stretching the potentials at the higher range. The rising public

interest in nature-based solutions, along with high profile initiatives 
being launched (UN Decade on Restoration announced in 2019, the 
Bonn challenge on 150 million ha of restored forest in 2020 and the 
one trillion trees campaign launched by the World Economic Forum in 
2020), has prompted intense discussions on the scale, effectiveness, 
and pitfalls of A/R and tree planting for climate mitigation (Luyssaert 
et al. 2018; Bond et al. 2019; Anderegg et al. 2020; Heilmayr et al. 
2020; Holl and Brancalion 2020). The sometimes sole attention on 
afforestation and reforestation – suggesting it may solve the climate 
problem to large extent, in combination with the very high estimates 
of potentials – have led to polarisation in the debate, resulting in 
criticism to these measures or an emphasis on nature restoration only 
(Lewis et al. 2019). Our assessment based on most recent literature 
produced regional economic mitigation potential at USD100 tCO 2–1

estimate of 100–400 MtCO 2 yr–1 in Africa, 210–266 MtCO 2 yr–1 in Asia 
and Pacific, 291 MtCO 2-eq yr–1 in Developed Countries (87% in North 
America), 30 MtCO 2-eq yr–1 in Eastern Europe and West-Central Asia, 
and 345–898 MtCO 2-eq yr–1 in Latin America and Caribbean (Roe 
et al. 2021), which totals to about 1200 MtCO 2 yr–1, leaning to the 
lower range of the potentials in earlier IPCC reports. A recent global 
assessment of the aggregate costs for afforestation and reforestation 
suggests that at USD100 tCO 2–1, 1.6 GtCO 2 yr–1 could be sequestered 
globally for an annual cost of USD130 billion (Austin et al. 2020). 
Sectoral studies that are able to deal with local circumstances and 
limits estimate A/R potentials at 20 MtCO 2 yr–1 in Russia (Eastern 
Europe and West-Central Asia) (Romanovskaya et al. 2020) and 
64 MtCO 2 yr–1 in Europe (Nabuurs et al. 2017). (Domke et al. 2020) 
estimated for the USA an additional 20% sequestration rate from

tree planting to achieve full stocking capacity of all understocked 
productive forestland, in total reaching 187 MtCO 2 yr–1 sequestration. 
A new study on costs in the USA estimates 72–91 MtCO 2 yr–1 could be 
sequestered between now and 2050 for USD100 tCO 2–1 (Wade et al. 
2019). The tropical and subtropical latitudes are the most effective 
for forest restoration in terms of carbon sequestration because of the 
rapid growth and lower albedo of the land surface compared with 
high latitudes (Lewis et al. 2019). Costs may be higher if albedo is 
considered in North America, Russia, and Africa (Favero et al. 2017). 
In addition, a wide variety of sequestration rates have been collected 
and published in the IPCC Good Practice Guidance for the AFOLU 
sector (IPCC 2006).
Critical assessment and conclusion. There is medium 
confidence that the global technical mitigation potential of afforestation and reforestation activities by 2050 is 3.9

(0.5–10.1) GtCO 2 yr–1, and the economic mitigation potential 
(<USD100 tCO 2–1) is 1.6   (0.5–3.0)  GtCO 2  yr–1 (requiring about 
200 Mha). Per hectare a long (about 100 year) sustained effect of 
5–10 t CO 2 ha–1 yr–1 is realistic with ranges between 1–20 t(CO 2) ha–1 
yr–1. Not all sectoral studies rely on economic models that account for 
leakage (Murray et al. 2004; Sohngen and Brown 2004), suggesting 
that technical potential may be overestimated.
7.4.2.3 Improved Forest Management
and barriers. Improved sustainable forest management of already 
managed forests can lead to higher forest carbon stocks, better 
quality of produced wood, continuously produced wood, while 
maintaining and enhancing the forest carbon stock, and can also 
partially prevent and counteract the impacts of disturbances (Kurz 
et al. 2008; Marlon et al. 2012; Abatzoglou and Williams 2016; Seidl

et al. 2017; Nabuurs et al. 2017; Tian et al. 2018; Ekholm 2020). 
Furthermore, it can provide benefits for climate change adaptation, 
biodiversity conservation, microclimatic regulation, soil erosion 
protection and water and flood regulation with reduced lateral 
carbon fluxes (Ashton et al. 2012; Martínez-Mena et al. 2019; Verkerk 
et al. 2020). Often, in existing (managed) forests with existing 
carbon stocks, large changes per hectare cannot be expected, 
although many forest owners may respond to carbon price incentives 
(Favero et al. 2020; Ekholm 2020). The full mitigation effects can be 
assessed in conjunction with the overall forest and wood use system 
i.e., carbon stock changes in standing trees, soil, harvested wood 
products (HWPs) and its bioenergy component with the avoided 
emissions through substitution. Forest management strategies aimed 
at increasing the biomass stock may have adverse side effects, such

as decreasing the stand-level structural complexity, large emphasis 
on pure fast-growing stands, risks for biodiversity and resilience to 
natural disasters.
Generally, measures can consist of one or combination of longer 
rotations, less intensive harvests, continuous-cover forestry, mixed 
stands, more adapted species, selected provenances, high quality 
wood assortments, and so on. Further, there is a trade-off between 
management in various parts of the forest product value chain, 
resulting in a wide range of results on the role of managed forests in 
mitigation (Agostini et al. 2013; Braun et al. 2016; Soimakallio et al. 
2016; Gustavsson et al. 2017; Erb et al. 2017; Favero et al. 2020; 
Hurmekoski et al. 2020). Some studies conclude that reduction in 
forest carbon stocks due to harvest exceeds for decades the joint 
sequestration of carbon in harvested wood product stocks and 
emissions avoided through wood use (Soimakallio et al. 2016; Seppälä

et al. 2019), whereas others emphasise country level examples where 
investments in forest management have led to higher growing stocks 
while producing more wood (Schulze et al. 2020; Ouden et al. 2020; 
Cowie et al. 2021).
and SRCCL); mitigation potential, costs, and pathways. In the 
SRCCL, forest management activities have the potential to mitigate 
0.4–2.1 GtCO 2-eq yr–1 by 2050 (medium confidence) (SRCCL: 
Griscom et al. 2017; Roe et al. 2019). The higher estimate stems from

782
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7assumptions of applications on roughly 1.9 billion ha of already 
managed forest  which can be seen as very optimistic. It combines 
both natural forest management as well as improved plantations, on 
average with a small net additional effect per hectare, not including 
substitution effects in the energy sector nor the buildings sector.
SROCC and SRCCL). The area of forest under management plans 
has increased in all regions since 2000 by 233 Mha (FAO 2020e). The 
roughly 1 billion ha of secondary and degraded forests would be ideal 
to invest in and develop a sustainable sector that pays attention to 
biodiversity, wood provision and climate mitigation at the same time. 
This all depends on the effort made, the development of expertise, 
know-how in the field, nurseries with adapted provenances, etc as

was also found for Russian climate-smart forestry options (Leskinen 
et al. 2020). Regionally, recently updated economic mitigation 
potential at USD100 tCO 2–1 have 179–186 MtCO 2-eq yr–1 in Africa, 
193–313 MtCO 2-eq yr–1 in Asia and Pacific, 215–220 MtCO 2-eq yr–1 
in Developed Countries , 82–152 MtCO 2-eq yr–1 in Eastern Europe 
and West-Central Asia, and 62–204 MtCO 2-eq yr–1 in Latin America 
and Caribbean (Roe et al. 2021).
Regional studies can take into account the local situation better: 
Russia Romanovskaya et al. (2020) estimate the potential of forest 
fires management at 220–420 MtCO 2 yr–1, gentle logging technology 
at 15–59, reduction of wood losses at 61–76 MtCO 2 yr–1. In North 
America, (Austin et al. 2020) estimate that in the next 30 years, 
forest management could contribute 154 MtCO 2 yr–1 in the USA and 
Canada with 81 MtCO 2 yr–1 available at less than USD100 tCO 2–1. In 
one production region (British Columbia) a cost-effective portfolio of

scenarios was simulated that directed more of the harvested wood 
to longer-lived wood products, stopped burning of harvest residues 
and instead produced bioenergy to displace fossil fuel burning, and 
reduced harvest levels in regions with low disturbance rates. Net 
GHG emissions were reduced by an average of –9 MtCO 2-eq yr–1 (Smyth et al. 2020). In Europe, climate-smart forestry could mitigate 
an additional 0.19 GtCO 2 yr–1 by 2050 (Nabuurs et al. 2017), in line 
with the regional estimates in (Roe et al. 2021).
In the tropics, estimates of the pantropical climate mitigation potential 
of natural forest management (a light intensity management in 
secondary forests), across three tropical regions (Latin America, 
Africa, Asia), is around 0.66 GtCO 2-eq yr–1 with Asia responding for 
the largest share followed by Africa and Latin America (Roe et al. 
2021). Selective logging occurs in at least 20% of the world’s tropical

forests and causes at least half of the emissions from tropical forest 
degradation (Asner et al. 2005; Blaser and Küchli 2011; Pearson 
et al. 2017). Reduced-impact logging for climate (RIL-C; promotion 
of reduced wood waste, narrower haul roads, and lower impact 
skidding equipment) has the potential to reduce logging emissions 
by 44% (Ellis et al. 2019), while also providing timber production.
Critical assessment and conclusion. There is medium confidence 
that the global technical mitigation potential for improved forest 
management by 2050 is 1.7 (1–2.1) GtCO 2 yr–1, and the economic 
mitigation potential (<USD100 tCO 2–1) is 1.1 (0.6–1.9) GtCO 2 yr–1. 
Efforts to change forest management do not only require, for example, 
a carbon price incentive, but especially require knowledge, institutions, 
skilled labour, good access and so on. These requirements outline 
that although the potential is of medium size, we estimate a feasible

potential towards the lower end. The net effect is also difficult to 
assess, as management changes impact not only the forest biomass, 
but also the wood chain and substitution effects. Further, leakage can 
arise from efforts to change management for carbon sequestration. 
Efforts, for example to set aside large areas of forest, may be partly 
counteracted by higher harvesting pressures elsewhere (Kallio 
et al. 2018). Studies such as (Austin et al. 2020) implicitly account 
for leakage and thus suggest higher costs than other studies. We 
therefore judge the mitigation potential at medium potential with 
medium agreement.
Box 7.2 | Climate-smart Forestry in Europe
Summary
European forests have been regarded as prospering and increasing for the last five decades. However, these views also changed 
recently. Climate change is putting a large pressure on mono species and high stocked areas of Norway spruce in Central Europe

(Hlásny et al. 2021; Senf and Seidl 2021) with estimates of mortality reaching 200 million m3, biodiversity under pressure, the 
Mediterranean area showing a weak sector and harvesting pressure in the Baltics and north reaching maxima achievable. A European 
strategy for unlocking the EU’s forests and forest sector potential was needed at the time of developing the LULUCF regulation and 
was based on the concept of ‘climate-smart forestry’ (CSF) (Nabuurs et al. 2017; Verkerk et al. 2020).
The idea behind CSF is that it considers the whole value chain from forest to wood products and energy, illustrating that a wide range 
of measures can be applied to provide positive incentives for more firmly integrating climate objectives into the forest and forest 
sector framework. CSF is more than just storing carbon in forest ecosystems; it builds upon three main objectives; (i) reducing and/

or removing GHG emissions; (ii) adapting and building diverse forests for forest resilience to climate change; and (iii) sustainably 
increasing forest productivity and incomes. These three CSF objectives can be achieved by tailoring policy measures and actions to 
regional circumstances in member states’ forest sectors.

783
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.4.2.4 Fire Management (Forest and Grassland/
Savanna Fires)
and barriers. Fire management objectives include safeguarding life, 
property, and resources through the prevention, detection, control, 
restriction, and management of fire for diverse purposes in natural 
ecosystems (SRCCL, Chapter  6). Controlled burning is an effective 
economic method of reducing fire danger and stimulating natural 
regeneration. Co-benefits of fire management include reduced air 
pollution compared to much larger, uncontrolled fires, prevention 
of soil erosion and land degradation, biodiversity conservation 
in rangelands, and improvement of forage quality (Hurteau and 
Brooks 2011; Falk 2017; Hurteau et al. 2019). Fire management is 
still challenging because it is not only fire suppression at times of 
fire, but especially proper natural resource management in between

fire events. Furthermore, it is challenging because of legal and 
policy issues, equity and rights concerns, governance, capacity, and 
research needs (Wiedinmyer and Hurteau 2010; Goldammer 2016; 
Russell-Smith et al. 2017). It will increasingly be needed under future 
enhanced climate change.
SRCCL, fire management is among the nine options that can deliver 
medium-to-large benefits across multiple land challenges (climate 
change mitigation, adaptation, desertification, land degradation, and 
food security) (high confidence). Total emissions from fires have been 
on the order of 8.1 GtCO 2-eq yr–1 in terms of gross biomass loss for 
the period 1997–2016 (SRCCL, Chapter 2, and Cross-Chapter Box 3 
in Chapter  2). Reduction in fire CO 2 emissions was calculated to 
enhance land carbon sink by 0.48 GtCO 2-eq yr–1 for the 1960–2009

period (Arora and Melton 2018) (SRCCL, Table 6.16).Developments since AR5 and IPCC Special Reports  
(SR1.5, SROCC and SRCCL)
Savannas. Savannas constitute one of the most fire-prone vegetation 
types on Earth and are a significant source of GHG emissions. Savanna 
fires contributed 62% (4.92 PgCO 2-eq yr–1)) of gross global mean 
fire emissions between 1997 and 2016. Regrowth from vegetation 
postfire sequesters the CO 2 released into the atmosphere, but not 
the CH 4 and N 2O emissions which contributed an approximate 
net of 2.1 PgCO 2-eq yr−1 (Lipsett-Moore et al. 2018). Therefore, 
implementing prescribed burning with low intensity fires, principally 
in the early dry season, to effectively manage the risk of wildfires 
occurring in the late dry season is associated with reducing emissions 
(Whitehead et al. 2014). Considering this fire management practice, 
estimates of global opportunities for emissions reductions were

estimated at 69.1 MtCO 2-eq yr−1 in Africa (29 countries, with 20 least 
developed African countries accounting for 74% of the mitigation 
potential), 13.3 MtCO 2-eq yr−1 in South America (six countries), and 
6.9 MtCO 2-eq yr–1 in Australia and Papua New Guinea (Lipsett-Moore 
et al. 2018). In Australia, savanna burning emissions abatement 
methodologies have been available since 2012, and abatement has 
exceeded 9.3 MtCO 2-eq mainly through the management of low 
intensity early dry season fire. Until September 2021, 78 projects were 
registered (Australian Government, Clean Energy Regulator, 2021).
Forests. Fire is also a prevalent forest disturbance (Falk et al. 2011; 
Scott et al. 2014; Andela et al. 2019). About 98 Mha of forest were 
affected by fire in 2015, affecting about 4% of the tropical (dry) 
forests, 2% of the subtropical forests, and 1% of temperate and 
boreal forests (FAO 2020a). Between 2001–2018, remote sensing

data showed that tree-covered areas correspond to about 29% of 
the total area burned by wildfires, most in Africa. Prescribed fires are 
also applied routinely in forests worldwide for fuel reduction and Box 7.2 (continued)
The 2015 annual mitigation effect of EU-28 forests via contributions to the forest sink, material substitution and energy substitution is 
estimated at 569 MtCO 2 yr–1, or 13% of total current EU emissions. With the right set of incentives in place at EU and member states 
levels, it was found that the EU-28 has the potential to achieve an additional combined mitigation impact through the implementation 
of CSF of 441 MtCO 2 yr–1 by 2050. Also, with the Green Deal and its biodiversity and forest strategy, more emphasis will be placed on 
forests, forest management and the provision of renewables. It is the diversity of measures (from strict reserves to more intensively

managed systems while adapting the resource) that will determine the success. Only with co-benefits in, for example, nature 
conservation, soil protection, and provision of renewables, wood for buildings and income, the mitigation and adaptation measures 
will be successful.
Interactions, limitations and lessons
Climate-smart forestry is now taking shape across Europe with various research and implementation projects (Climate Smart Forest 
and Nature Management, 2021). Pilots and projects are being implemented by a variety of forest owners, some with more attention 
on biodiversity and adaptation, some with more attention on production functions. They establish examples and in longer term the 
outreach to the 16 million private owners in Europe. However, the right triggers and incentives are often still lacking. For example, 
adapting the spruce forest areas in Central Europe to climate change requires knowledge about different species, biodiversity and

different management options and eventually use in industry. It requires alternative species to be available from the nurseries, as well 
as improved monitoring to assess the success and steer activities.

784
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7ecological reasons (Kalies and Yocom Kent 2016). Fire resilience is 
increasingly managed in Southwestern USA forest landscapes, which 
have experienced droughts and widespread, high-severity wildfires 
(Keeley et al. 2019). In these forests, fire exclusion management, 
coupled with a  warming climate, has led to increasingly severe 
wildfires (Hurteau et al. 2014). However, the impacts of prescribed 
fires in forests in reducing carbon emissions are still inconclusive. 
Some positive impacts of prescribed fires are associated with other 
fuel reduction techniques (Loudermilk et al. 2017; Flanagan et al. 
2019; Stephens et al. 2020), leading to maintaining carbon stocks and 
reducing carbon emissions in the future where extreme fire weather 
events are more frequent (Krofcheck et al. 2018, 2019; Hurteau 
et al. 2019; Bowman et al. 2020a,b; Goodwin et al. 2020). Land

management approaches will certainly need to consider the new 
climatic conditions (e.g., the proportion of days in fire seasons with 
the potential for unmanageable fires more than doubling in some 
regions in northern and eastern boreal forest) (Wotton et al. 2017).
Critical assessment and conclusion. There is low confidence 
that the global technical mitigation potential for grassland and 
savanna fire management by 2050 is 0.1 (0.09–0.1) GtCO 2 yr–1, 
and the economic mitigation potential (<USD100 tCO 2–1) is 0.05 
(0.03–0.07) GtCO 2 yr–1. Savanna fires produce significant emissions 
globally, but prescribed fires in the early dry season could mitigate 
emissions in different regions, particularly Africa. Evidence is less 
clear for fire management of forests, with the contribution of 
GHG mitigation depending on many factors that affect the carbon 
balance (e.g.,  Simmonds et al. 2021). Although prescribed burning

is promoted to reduce uncontrolled wildfires in forests, the benefits 
for the management of carbon stocks are unclear, with different 
studies reporting varying results especially concerning its long-term 
effectiveness (Wotton et al. 2017; Bowman et al. 2020b). Under 
increasing climate change however, an increased attention on fire 
management will be necessary.
7.4.2.5 Reduce Degradation and Conversion 
of Grasslands and Savannas
and barriers. Grasslands cover approximately 40.5% of the 
terrestrial area (i.e., 52.5 million km2) divided as 13.8% woody 
savanna and savanna; 12.7% open and closed shrub; 8.3% non-
woody grassland; and 5.7% is tundra (White et al. 2000). Sub-
Saharan Africa and Asia have the most extensive total area, 14.5 
and 8.9 million km2, respectively. A review by Conant et al. (2017) 
reported based on data on grassland area (FAO 2013) and grassland

soil carbon stocks (Sombroek et al. 1993) a global estimate of about 
343 PgC (in the top 1 m), nearly 50% more than is stored in forests 
worldwide (FAO 2007). Reducing the conversion of grasslands and 
savannas to croplands prevents soil carbon losses by oxidation, and 
to a smaller extent, biomass carbon loss due to vegetation clearing 
(SRCCL, Chapter  6). Restoration of grasslands through enhanced 
soil carbon sequestration, including (i) management of vegetation, 
(ii) animal management, and (iii) fire management, was also 
included in the SRCCL and is covered in Section 7.4.3.1. Similar to 
other measures that reduce conversion, conserving carbon stocks in 
grasslands and savannas can be achieved by controlling conversion drivers (e.g., commercial and subsistence agriculture, see Section 7.3) 
and improving policies and management. In addition to mitigation, 
conserving grasslands provide various socio-economic, biodiversity,

water cycle and other environmental benefits (Claassen et al. 2010; 
Ryals et al. 2015; Bengtsson et al. 2019). Annual operating costs, and 
opportunity costs of income foregone by undertaking the activities 
needed for avoiding conversion of grasslands making costs one of 
the key barriers for implementation (Lipper et al. 2010).
and SRCCL); mitigation potential, costs, and pathways. The 
SRCCL reported a  mitigation potential for reduced conversion of 
grasslands and savannas of 0.03–0.12 GtCO 2-eq yr–1 (Griscom et al. 
2017; IPCC 2019) considering the higher loss of soil organic carbon in 
croplands (Sanderman et al. 2017). Assuming an average starting soil 
organic carbon stock of temperate grasslands (Poeplau et al. 2011), 
and the mean annual global cropland conversion rates (1961–2003) 
(Krause et al. 2017), the equivalent loss of soil organic carbon 
over 20 years would be 14 GtCO 2-eq, for example, 0.7 GtCO 2 yr–1

(SRCCL, Chapter 6). IPCC AR5 and AR4 did not explicitly consider the 
mitigation potential of avoided conversion of grasslands-savannas 
but the management of grazing land is accounted for considering 
plant, animal, and fire management with a mean mitigation potential 
of 0.11–0.80 tCO 2-eq ha–1 yr–1 depending on the climate region. This 
resulted in 0.25 GtCO 2-eq yr–1 at USD20 tCO 2–1 to 1.25 GtCO 2-eq yr–1 
at USD100 tCO 2–1 by 2030.
SROCC and SRCCL). Unlike most of the measures covered in 
Section 7.4, there are currently no global, spatially explicit mitigation 
potential estimates for reduced grassland conversion to generate 
technical and economic potentials by region. Literature developments 
since AR5 and SRCCL are studies that provide mitigation estimates 
in one or a  few countries or regions. Modelling experiments 
comparing Californian forests and grasslands found that grasslands

resulted in a more resilient carbon sink than forests to future climate 
change (Dass et al. 2018). However, previous studies indicated 
that precipitation is a key controller of the carbon storage in these 
grasslands, with the grassland became a carbon sink in 2005, when 
the region received relatively high spring precipitation (Ma et al. 
2007). In North America, grassland conversion was the source for 
77% of all new croplands from 2008–2012 (Lark et al. 2015). Avoided 
conversion of North American grasslands to croplands presents an 
economic mitigation potential of 0.024 GtCO 2-eq yr–1 and technical 
potential of 0.107 GtCO 2-eq yr–1 (Fargione et al. 2018). This potential 
is related mainly to root biomass and soils (81% of emissions from 
soils). Estimates of GHG emissions from any future deforestation in 
Australian savannas also point to the potential mitigation of around 
0.024 GtCO 2-eq yr–1 (Bristow et al. 2016). The expansion of the

Soy Moratorium (SoyM) from the Brazilian Amazon to the Cerrado 
(Brazilian savannas) would prevent the direct conversion of 3.6 Mha 
of native vegetation to soybeans by 2050 and avoid the emission of 
0.02 GtCO 2-eq yr–1 (Soterroni et al. 2019).
Critical assessment and conclusion. There is low confidence that 
the global technical mitigation potential for reduced grassland and 
savanna conversion by 2050 is 0.2 (0.1–0.4) GtCO 2 yr–1, and the

785
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77economic mitigation potential (<USD100 tCO 2–1) is 0.04 GtCO 2 yr–1. 
Most of the carbon sequestration potential is in below-ground 
biomass and soil organic matter. However, estimates of potential 
are still based on few studies and vary according to the levels of 
soil carbon, and ecosystem productivity (e.g., in response to rainfall 
distribution). Conservation of grasslands presents significant benefits 
for desertification control, especially in arid areas (SRCCL, Chapter 3). 
Policies supporting avoided conversion can help protect at-risk 
grasslands, reduce GHG emissions, and produce positive outcomes 
for biodiversity and landowners (Ahlering et al. 2016). In comparison 
to tropical rainforest regions that have been the primary target for 
mitigation policies associated to natural ecosystems (e.g., REDD+), 
conversion grasslands and savannas has received less national and

international attention, despite growing evidence of concentrated 
cropland expansion into these areas with impacts of carbon losses.
7.4.2.6 Reduce Degradation and Conversion 
of Peatlands Activities, Co-benefits, Risks 
and Implementation Barriers
Peatlands are carbon-rich wetland ecosystems with organic soil 
horizons in which soil organic matter concentration exceeds 30% 
(dry weight) and soil carbon concentrations can exceed 50% 
(Page and Baird 2016, Boone Kauffman et al. 2017). Reducing the 
conversion of peatlands avoids emissions of above- and below-
ground biomass and soil carbon due to vegetation clearing, fires, 
and peat decomposition from drainage. Similar to deforestation, 
peatland carbon stocks can be conserved by controlling the drivers 
of conversion and degradation (e.g.,  commercial and subsistence 
agriculture, mining, urban expansion) and improving governance 
and management. Reducing conversion is urgent because peatland

carbon stocks accumulate slowly and persist over millennia; loss of 
existing stocks cannot be easily reversed over the decadal time scales 
needed to meet the Paris Agreement (Goldstein et al. 2020). The main 
co-benefits of reducing conversion of peatlands include conservation 
of a unique biodiversity including many critically endangered species, 
provision of water quality and regulation, and improved public health 
through decreased fire-caused pollutants (Griscom et al. 2017). 
Although reducing peatland conversion will reduce land availability 
for alternative uses including agriculture or other land-based 
mitigation, drained peatlands constitute a small share of agricultural 
land globally while contributing significant emissions (Joosten 2009). 
Mitigation through reduced conversion of peatlands therefore has 
a high potential of avoided emissions per hectare (Roe et al. 2019).
Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC

and SRCCL); mitigation potential, costs, and pathways. In 
the SRCCL (Chapters 2 and 6), it was estimated that avoided peat 
impacts could deliver 0.45–1.22 GtCO 2-eq yr–1 technical potential 
by 2030–2050 (medium confidence) (Hooijer et al. 2010; Griscom 
et al. 2017; Hawken 2017). The mitigation potential estimates cover 
tropical peatlands and include CO 2, N2O and CH 4 emissions. The 
mitigation potential is derived from quantification of losses of carbon 
stocks due to land conversion, shifts in GHG fluxes, alterations in 
net ecosystem productivity, input factors such as fertilisation needs, 
and biophysical climate impacts (e.g., shifts in albedo, water cycles, 
etc.). Tropical peatlands account for only about 10% of peatland area and about 20% of peatland carbon stock but about 80% of peatland 
carbon emissions, primarily from peatland conversion in Indonesia 
(about 60%) and Malaysia (about 10%) (Hooijer et al. 2010; Page

et al. 2011; Leifeld and Menichetti 2018). While the total mitigation 
potential of peatland conservation is considered moderate, the 
per hectare mitigation potential is the highest among land-based 
mitigation measures (Roe et al. 2019).
SROCC and SRCCL). Recent studies continue to report high 
carbon stocks in peatlands and emphasise the vulnerability 
of peatland carbon after conversion. The carbon stocks of 
tropical peatlands are among the highest of any forest, 
1,211–4,257 tCO 2-eq ha–1 in the Peruvian Amazon (Bhomia 
et al. 2019) and 1,956–14,757 tCO 2-eq ha–1 in Indonesia (Novita 
et al. 2021). Ninety percent of tropical peatland carbon stocks 
are vulnerable to emission during conversion and may not be 
recoverable through restoration; in contrast, boreal and temperate 
peatlands hold similar carbon stocks  (1,439–5,619 tCO 2-eq ha–1) 
but only 30% of northern carbon stocks are vulnerable to emission

during conversion and irrecoverable through restoration (Goldstein 
et al. 2020). A  recent study shows global mitigation potential of 
about 0.2 GtCO 2-eq yr–1 at costs up to USD100 tCO 2–1 (Roe et al. 
2021). Another study estimated that 72% of mitigation is achieved 
through avoided soil carbon impacts, with the remainder through 
avoided impacts to vegetation (Bossio et al. 2020). Recent model 
projections show that both peatland protection and peatland 
restoration (Section 7.4.2.7) are needed to achieve a 2°C mitigation 
pathway and that peatland protection and restoration policies 
will have minimal impacts on regional food security (Leifeld et al. 
2019, Humpenöder et al. 2020). Global studies have not accounted 
for extensive peatlands recently reported in the Congo Basin, 
estimated to cover 145,500 km2 and contain 30.6 PgC, as much as 
29% of total tropical peat carbon stock (Dargie et al. 2017). These 
Congo peatlands are relatively intact; continued preservation is

needed to prevent major emissions (Dargie et al. 2019). In northern 
peatlands that are underlain by permafrost roughly 50% of the 
total peatlands north of 23° latitude, (Hugelius et al. 2020), climate 
change (i.e., warming) is the major driver of peatland degradation 
(e.g., through permafrost thaw) (Schuur et al. 2015, Goldstein et al. 
2020). However, in non-permafrost boreal and temperate peatlands, 
reduction of peatland conversion is also a cost-effective mitigation 
strategy. Peatlands are sensitive to climate change and there is 
low confidence about the future peatland sink globally (SRCCL, 
Chapter  2). Permafrost thaw may shift northern peatlands from 
a net carbon sink to net source (Hugelius et al. 2020). Uncertainties 
in peatland extent and the magnitude of existing carbon stocks, in 
both northern (Loisel et al. 2014) and tropical (Dargie et al. 2017) 
latitudes limit understanding of current and future peatland carbon 
dynamics (Minasny et al. 2019).

Critical assessment and conclusion. Based on studies to date, 
there is medium confidence that peatland conservation has 
a  technical potential of 0.86 (0.43–2.02) GtCO 2-eq yr–1 of which 
0.48  (0.2–0.68) GtCO 2-eq yr–1 is available at USD100 tCO 2–1 
(Figure 7.11). High per hectare mitigation potential and high rate of 
co-benefits particularly in tropical countries, support the effectiveness

786
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7of this mitigation strategy (Roe et al. 2019). Feasibility of reducing 
peatland conversion may depend on countries’ governance, financial 
capacity and political will.
7.4.2.7 Peatland Restoration
Activities, co-benefits, risks and implementation barriers. 
Peatland restoration involves restoring degraded and damaged 
peatlands, for example through rewetting and revegetation, which 
both increases carbon accumulation in vegetation and soils and 
avoids ongoing CO 2 emissions. Peatlands only account for about 
3% of the terrestrial surface, predominantly occurring in boreal 
ecosystems (78%), with a  smaller proportion in tropical regions 
(13%), but may store about 600 GtC or 21% of the global total soil 
organic carbon stock of about 3000 Gt (Page et al. 2011; Leifeld 
and Menichetti 2018). Peatland restoration delivers co-benefits 
for biodiversity, as well as regulating water flow and preventing

downstream flooding, while still allowing for extensive management 
such as paludiculture (Tan et al. 2021). Rewetting of peatlands also 
reduces the risk of fire, but may also mobilise salts and contaminants 
in soils (van Diggelen et al. 2020) and in severely degraded peatlands, 
restoration of peatland hydrology and vegetation may not be feasible 
(Andersen et al. 2017). At a  local level, restoration of peatlands 
drained for agriculture could displace food production and damage 
local food supply, although impacts to regional and global food 
security would be minimal (Humpenöder et al. 2020). Collaborative 
and transparent planning processes are needed to reduce conflict 
between competing land uses (Tanneberger et al. 2020b). Adequate 
resources for implementing restoration policies are key to engage 
local communities and maintain livelihoods (Resosudarmo et al. 
2019; Ward et al. 2021).

and SRCCL); mitigation potential, costs, and pathways. Large 
areas (0.51 Mkm2) of global peatlands are degraded of which 
0.2 Mkm2 are tropical peatlands (Griscom et al. 2017; Leifeld and 
Menichetti 2018). According the SRCCL, peatland restoration could 
deliver technical mitigation potentials of 0.15 – 0.81GtCO 2-eq yr–1 
by 2030–2050 (low confidence) (Couwenberg et al. 2010; Griscom 
et al. 2017) (Chapters 2 and 6 of the SRCCL), though there could be 
an increase in methane emissions after restoration (Jauhiainen et al. 
2008). The mitigation potential estimates cover global peatlands and 
include CO 2, N2O and CH 4 emissions. Peatlands are highly sensitive 
to climate change (high confidence), however there are currently no 
studies that estimate future climate effects on mitigation potential 
from peatland restoration.
SROCC and SRCCL). The most recent literature and reviews indicate

with high confidence that restoration would decrease CO 2 emissions 
and with medium confidence that restoration would decrease net 
GHG emissions from degraded peatlands (Wilson et al. 2016; Ojanen 
and Minkkinen 2020; van Diggelen et al. 2020). Although rewetting 
of drained peatlands increases CH 4 emissions, this effect is often 
outweighed by decreases in CO 2 and N 2O emissions but depends 
very much on local circumstances (Günther et al. 2020). Restoration 
and rewetting of almost all drained peatlands is needed by 2050 to meet 1.5°C–2°C pathways which is unlikely to happen (Leifeld et al. 
2019); immediate rewetting and restoration minimises the warming 
from cumulative CO 2 emissions (Nugent et al. 2019).
According to recent data, the technical mitigation potential for global 
peatland restoration is estimated at 0.5–1.3 GtCO 2-eq yr–1 (Leifeld 
and Menichetti 2018; Griscom et al. 2020; Bossio et al. 2020; Roe et al.

2021) (Figure  7.11), with 80% of the mitigation potential derived 
from improvements to soil carbon (Bossio et al. 2020). The regional 
mitigation potentials of all peatlands outlined in Roe et al. (2021) 
reflect the country-level estimates from (Humpenöder et al. 2020).
Climate mitigation effects of peatland rewetting depend on the 
climate zone and land use. Recent analysis shows the strongest 
mitigation gains from rewetting drained temperate and boreal 
peatlands used for agriculture and drained tropical peatlands (Ojanen 
and Minkkinen 2020). However, estimates of emission factors from 
rewetting drained tropical peatlands remain uncertain (Wilson et al. 
2016; Murdiyarso et al. 2019). Topsoil removal, in combination with 
rewetting, may improve restoration success and limit CH 4 emissions 
during restoration of highly degraded temperate peatlands (Zak et al. 
2018). In temperate and boreal regions, co-benefits mentioned above

are major motivations for peatland restoration (Chimner et al. 2017; 
Tanneberger et al. 2020a).
there is medium confidence that peatland restoration has a technical 
potential of 0.79 (0.49–1.3) GtCO 2-eq yr–1 (median) of which 
0.4 (0.2–0.6) GtCO 2-eq yr–1 is available up to USD100 tCO 2–1. The large 
land area of degraded peatlands suggests that significant emissions 
reductions could occur through large-scale restoration especially 
in tropical peatlands. There is medium confidence in the large 
carbon stocks of tropical peat forests (1956–14,757 tCO 2-eq ha–1) 
and large rates of carbon loss associated with land cover change 
(640–1650 tCO 2-eq ha–1) (Goldstein et al. 2020; Novita et al. 
2021). However, large-scale implementation of tropical peatland 
restoration will likely be limited by costs and other demands for these 
tropical lands.
7.4.2.8 Reduce Conversion of Coastal Wetlands

Activities, co-benefits, risks and implementation barriers.  
Reducing conversion of coastal wetlands, including mangroves, 
marshes and seagrass ecosystems, avoids emissions from above and 
below ground biomass and soil carbon through avoided degradation 
and/or loss. Coastal wetlands occur mainly in estuaries and deltas, 
areas that are often densely settled, with livelihoods closely linked 
to coastal ecosystems and resources (Moser et al. 2012). The 
carbon stocks of these highly productive ecosystems are sometimes 
referred to as ‘blue carbon’. Loss of existing stocks cannot be easily 
reversed over decadal time scales (Goldstein et al. 2020). The main 
drivers of conversion include intensive aquaculture, agriculture, salt 
ponds, urbanisation and infrastructure development, the extensive 
use of fertilisers, and extraction of water resources (Lovelock 
et al. 2018). Reduced conversion of coastal wetlands has many co-

benefits, including biodiversity conservation, fisheries production, 
soil stabilisation, water flow and water quality regulation, flooding

787
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77and storm surge prevention, and increased resilience to cyclones 
(Windham-Myers et al. 2018a; UNEP 2020). Risks associated with 
the mitigation potential of coastal wetland conservation include 
uncertain permanence under future climate scenarios, including 
the effects of coastal squeeze, where coastal wetland area may be 
lost if upland area is not available for migration as sea levels rise 
(Lovelock and Reef 2020) (AR6 WGII, Section 3.4.2.5). Preservation 
of coastal wetlands also conflicts with other land use in the coastal 
zone, including aquaculture, agriculture, and human development; 
economic incentives are needed to prioritise wetland preservation 
over more profitable short-term land use. Integration of policies 
and efforts aimed at coastal climate mitigation, adaptation, 
biodiversity conservation, and fisheries, for example through 
integrated coastal zone management and marine spatial planning,

will bundle climate mitigation with co-benefits and optimise 
outcomes (Herr et al. 2017).
Conclusions from AR5 and IPCC Special Reports (SR1.5, 
SROCC and SRCCL); mitigation potential, costs, and pathways. 
Coastal wetlands contain high, yet variable, organic carbon stocks, 
leading to a range of estimates of the global mitigation potential of 
reduced conversion. The SRCCL (Chapter 2) and SROCC (Chapter 5), 
report a technical mitigation potential of 0.15–5.35 GtCO 2-eq yr–1 
by 2050 (Pendleton et al. 2012; Lovelock et al. 2017; Howard et al. 
2017; Griscom et al. 2017). The mitigation potential is derived from 
quantification of losses of carbon stocks in vegetation and soil 
due to land conversion, shifts in GHG fluxes associated with land 
use, and alterations in net ecosystem productivity. The wide range 
in estimates mostly relate to the scope (all coastal ecosystems vs 
mangroves only) and different assumptions on decomposition rates.

Loss rates of coastal wetlands have been estimated at 0.2–3% yr–1, 
depending on the vegetation type and location (Atwood et al. 2017; 
Howard et al. 2017).
SROCC and SRCCL). Global technical mitigation potential for 
conservation of coastal wetlands from recent literature have 
focused on protection of mangroves; estimates range from 
0.06–2.25 GtCO 2-eq yr–1 (Griscom et al. 2020; Bossio et al. 2020) 
with 80% of the mitigation potential derived from improvements to 
soil carbon (Bossio et al. 2020). Regional potentials (Roe et al. 2021) 
reflect mangrove protection; marsh and seagrass protection were not 
included due to lack of country-level data on marsh and seagrass 
distribution and conversion.
Global estimates show mangroves have the largest per hectare carbon 
stocks (see IPCC AR6 WGII Box 3.4 for estimates of carbon stocks, 
burial rates and ecosystem extent for coastal wetland ecosystems).

Mean ecosystem carbon stock in mangroves is 3131 tCO 2-eq ha–1 
among the largest carbon stocks on Earth. Recent studies emphasise 
the variability in total ecosystem carbon stocks for each wetland type, 
based on species and climatic and edaphic conditions (Kauffman 
et al. 2020; Bedulli et al. 2020; Ricart et al. 2020; Alongi et al. 2020; 
F . Wang et al. 2021), and highlight the vulnerability of soil carbon 
below 1 m depth (Arifanti et al. 2019). Sea level strongly influences 
coastal wetland distribution, productivity, and sediment accretion; 
therefore, sea level rise will impact carbon accumulation and persistence of existing carbon stocks (Macreadie et al. 2019) (IPCC 
AR6 WGII Box 3.4).
Recent loss rates of mangroves are 0.16–0.39% yr–1 and are highest 
in South-East Asia (Hamilton and Casey 2016; Friess et al. 2019; 
Hamilton and Casey 2016). Assuming loss of soil carbon to 1 m depth 
after deforestation, avoiding mangrove conversion has the technical

potential to mitigate approximately 23.5–38.7 MtCO 2-eq yr–1 
(Ouyang and Lee 2020); note, this potential is additional to reduced 
conversion of forests (Griscom et al. 2020) (Section  7.4.2.1). 
Regional estimates show that about 85% of mitigation potential 
for avoided mangrove conversion is in South-East Asia and Pacific 
(32 MtCO 2-eq yr–1 at USD100 tCO 2–1), 10% is in Latin American and 
the Caribbean (4 MtCO 2-eq yr–1), and approximately 5% in other 
regions (Griscom et al. 2020; Roe et al. 2021).
Key uncertainties remain in mapping extent and conversion rates 
for salt marshes and seagrasses (McKenzie et al. 2020). Seagrass 
loss rates were estimated at 1–2% yr–1 (Dunic et al. 2021) with 
stabilisation in some regions (de los Santos et al. 2019) (AR6 WGII, 
Section 3.4.2.5); however, loss occurs non-linearly and depends on 
site-specific context. Tidal marsh extent and conversion rates remains 
poorly estimated, outside of the USA, Europe, South Africa, and

Australia (Mcowen et al. 2017; Macreadie et al. 2019).
that coastal wetland protection has a  technical potential of 
0.8 (0.06–5.4) GtCO 2-eq yr–1 of which 0.17 (0.06–0.27) GtCO 2-eq yr–1 
is available up to USD100 tCO 2–1. There is a  high certainty (robust 
evidence, high agreement) that coastal ecosystems have among the 
largest carbon stocks of any ecosystem. As these ecosystems provide 
many important services, reduced conversion of coastal wetlands is 
a valuable mitigation strategy with numerous co-benefits. However,  
the vulnerability of coastal wetlands to climatic and other anthropogenic 
stressors may limit the permanence of climate mitigation.
7.4.2.9 Coastal Wetland Restoration
Coastal wetland restoration involves restoring degraded or damaged 
coastal  wetlands including mangroves, salt marshes, and seagrass

ecosystems, leading to sequestration of ‘blue carbon’ in wetland 
vegetation and soil (SRCCL, Chapter 6; SROCC, Chapter 5). Successful 
approaches to wetland restoration include: (i) passive restoration, 
the removal of anthropogenic activities that are causing degradation 
or preventing recovery; and (ii) active restoration, purposeful 
manipulations to the environment in order to achieve recovery to 
a  naturally functioning system (Elliott et al. 2016) (IPCC AR6 WGII 
Chapter  3). Restoration of coastal wetlands delivers many valuable 
co-benefits, including enhanced water quality, biodiversity, aesthetic 
values, fisheries production (food security), and protection from rising 
sea levels and storm impacts (Barbier et al. 2011; Hochard et al. 2019; 
Sun and Carson 2020; Duarte et al. 2020). Of the 0.3 Mkm2 coastal 
wetlands globally, 0.11 Mkm2 of mangroves are considered feasible 
for restoration (Griscom et al. 2017). Risks associated with coastal

wetland restoration include uncertain permanence under future 
climate scenarios (IPCC AR6 WGII, Box 3.4), partial offsets of mitigation

788
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7through enhanced methane and nitrous oxide release and carbonate 
formation, and competition with other land uses, including aquaculture 
and human settlement and development in the coastal zone (SROCC, 
Chapter 5). To date, many coastal wetland restoration efforts do not 
succeed due to failure to address the drivers of degradation (van 
Katwijk et al. 2016). However, improved frameworks for implementing 
and assessing coastal wetland restoration are emerging that emphasise 
the recovery of ecosystem functions (Zhao et al. 2016; Cadier et al. 
2020). Restoration projects that involve local communities at all stages 
and consider both biophysical and socio-political context are more 
likely to succeed (Brown et al. 2014; Wylie et al. 2016).
The SRCCL reported that mangrove restoration has the technical

potential to mitigate 0.07 GtCO 2 yr–1 through rewetting (Crooks et al. 
2011) and take up 0.02–0.84 GtCO 2 yr–1 from vegetation biomass 
and soil enhancement through 2030 (medium confidence) (Griscom 
et al. 2017). The SROCC concluded that cost-effective coastal blue 
carbon restoration had a potential of about 0.15–0.18 GtCO 2-eq yr–1, 
a  low global potential compared to other ocean-based solutions 
but with extensive co-benefits and limited adverse side effects 
(Gattuso et al. 2018).
SROCC and SRCCL). Recent studies emphasise the time frame needed 
to achieve the full mitigation potential (Duarte et al. 2020; Taillardat 
et al. 2020). The first project-derived estimate of the net GHG benefit 
from seagrass restoration found 1.54 tCO 2-eq (0.42 MgC) ha–1 yr–1 
10 years after restoration began (Oreska et al. 2020); comparable 
to the default emission factor in the Wetlands Supplement (Kennedy

et al. 2014). Recent studies of rehabilitated mangroves also indicate 
that annual carbon sequestration rates in biomass and soils can 
return to natural levels within decades of restoration (Cameron et al. 
2019; Sidik et al. 2019). A  meta-analysis shows increasing carbon 
sequestration rates over the first 15 years of mangrove restoration 
with rates stabilising at 25.7 ± 7.7 tCO 2-eq (7.0 ± 2.1 MgC) ha–1 yr–1 
through forty years, although success depends on climate, sediment 
type, and restoration methods (Sasmito et al. 2019). Overall, 30% of 
mangrove soil carbon stocks and 50–70% of marsh and seagrass 
carbon stocks are unlikely to recover within 30 years of restoration, 
underscoring the importance of preventing conversion of coastal 
wetlands (Goldstein et al. 2020) (Section 7.4.2.8).
coastal wetland restoration is 0.04–0.84 GtCO 2-eq yr–1 (Griscom et al.

2020; Bossio et al. 2020; Roe et al. 2021) with 60% of the mitigation 
potential derived from improvements to soil carbon (Bossio et al. 
2020). Regional potentials based on country-level estimates from 
Griscom et al. (2020) show the technical and economic (up to 
USD100 tCO 2–1) potential of mangrove restoration; seagrass and 
marsh restoration was not included due to lack of country-level data 
on distribution and conversion (but see McKenzie et al. 2020 for 
updates on global seagrass distribution). Although global potential is 
relatively moderate, mitigation can be quite significant for countries 
with extensive coastlines (e.g., Indonesia, Brazil) and for small island 
states where coastal wetlands have been shown to comprise 24–34% of their total national carbon stock (Donato et al. 2012). Furthermore, 
non-climatic co-benefits can strongly motivate coastal wetland 
restoration worldwide (UNEP 2021a). Major successes in both active

and passive restoration of seagrasses have been documented in 
North America and Europe (Lefcheck et al. 2018; de los Santos et al. 
2019; Orth et al. 2020); passive restoration may also be feasible for 
mangroves (Cameron et al. 2019).
There is high site-specific variation in carbon sequestration rates 
and uncertainties regarding the response to future climate change 
(Jennerjahn et al. 2017; Nowicki et al. 2017) (IPCC AR6 WGII Box 3.4). 
Changes in distributions (Kelleway et al. 2017; Wilson and Lotze 2019), 
methane release (Al-Haj and Fulweiler 2020), carbonate formation 
(Saderne et al. 2019), and ecosystem responses to interactive climate 
stressors are not well-understood (Short et al. 2016; Fitzgerald and 
Hughes 2019; Lovelock and Reef 2020).
that coastal wetland restoration has a  technical potential of 
0.3 (0.04–0.84) GtCO 2-eq yr–1 of which 0.1 (0.05–0.2) GtCO 2-eq yr–1

is available up to USD100 tCO 2–1. There is high confidence that 
coastal wetlands, especially mangroves, contain large carbon stocks 
relative to other ecosystems and medium confidence that restoration 
will reinstate pre-disturbance carbon sequestration rates. There is low 
confidence on the response of coastal wetlands to climate change; 
however, there is high confidence that coastal wetland restoration 
will provide a suite of valuable co-benefits.
7.4.3 Agriculture
7.4.3.1 Soil Carbon Management in Croplands and Grasslands
and barriers. Increasing soil organic matter in croplands are 
agricultural management practices that include (i) crop management: 
for example, high input carbon practices such as improved crop 
varieties, crop rotation, use of cover crops, perennial cropping systems 
(including agroforestry; see Section  7.4.3.3), integrated production

systems, crop diversification, agricultural biotechnology; (ii) nutrient 
management including fertilisation with organic amendments/green 
manures (Section 7.4.3.6); (iii) reduced tillage intensity and residue 
retention, (iv) improved water management: including drainage of 
waterlogged mineral soils and irrigation of crops in arid/semi-arid 
conditions, (v) improved rice management (Section  7.4.3.5) and 
(vi) biochar application (P .  Smith et al. 2019a) (Section  7.4.3.2). 
For increased soil organic matter in grasslands, practices include 
(i) management of vegetation: including improved grass varieties/
sward composition, deep rooting grasses, increased productivity, 
and nutrient management, (ii) livestock management: including 
appropriate stocking densities fit to carrying capacity, fodder banks, 
and fodder diversification, and (iii) fire management: improved use of 
fire for sustainable grassland management, including fire prevention

and improved prescribed burning (Smith et al. 2014, 2019b). All these 
measures are recognised as Sustainable Soil Management Practices 
by FAO (Baritz et al. 2018). While there are co-benefits for livelihoods, 
biodiversity, water provision and food security (P . Smith et al. 2019a),

789
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77and impacts on leakage, indirect land-use change and foregone 
sequestration do not apply (since production in not displaced), the 
climate benefits of soil carbon sequestration in croplands can be 
negated if achieved through additional fertiliser inputs (potentially 
causing increased N 2O emissions; (Guenet et al. 2021), and both 
saturation and permanence are relevant concerns. When considering 
implementation barriers, soil carbon management in croplands and 
grasslands is a low-cost option at a high level of technology readiness 
(it is already widely deployed globally) with low socio-cultural and 
institutional barriers, but with difficulty in monitoring and verification 
proving a barrier to implementation (Smith et al. 2020a).
and SRCCL); mitigation potential, costs, and pathways. Building

on AR5, the SRCCL reported the global mitigation potential for soil 
carbon management in croplands to be 1.4–2.3 GtCO 2-eq yr–1 (Smith 
et al. 2014), though the full literature range was 0.3–6.8 GtCO 2-eq yr–1 
(Sommer and Bossio 2014; Powlson et al. 2014; Dickie et al. 2014b; 
Henderson et al. 2015; Herrero et al. 2016; Paustian et al. 2016; 
Zomer et al. 2016; Frank et al. 2017; Conant et al. 2017; Griscom et al. 
2017; Hawken 2017; Sanderman et al. 2017; Fuss et al. 2018; Roe 
et al. 2019). The global mitigation potential for soil organic carbon 
management in grasslands was assessed to be 1.4–1.8 GtCO 2-eq yr–1, 
with the full literature range being 0.1–2.6 GtCO 2-eq yr–1 (Herrero 
et al. 2013; 2016; Conant et al. 2017; Roe et al. 2019). Lower values 
in the range represented economic potentials, while higher values 
represented technical potentials – and uncertainty was expressed by 
reporting the whole range of estimates. The SR1.5 outlined associated

costs reported in literature to range from USD –45 to 100 tCO 2–1, 
describing enhanced soil carbon sequestration as a  cost-effective 
measure (IPCC 2018). Despite significant mitigation potential, there 
is limited inclusion of soil carbon sequestration as a response option 
within IAM mitigation pathways (Rogelj et al. 2018a).
SROCC and SRCCL). No recent literature has been published which 
conflict with the mitigation potentials reported in the SRCCL. Relevant 
papers include Lal et al. (2018) which estimated soil carbon 
sequestration potential to be 0.7–4.1 GtCO 2-eq yr–1 for croplands 
and 1.1–2.9 GtCO 2-eq yr–1 for grasslands. Bossio et al. (2020) 
assessed the contribution of soil carbon sequestration to natural 
climate solutions and found the potential to be 5.5 GtCO 2 yr–1 across 
all ecosystems, with only small portions of this (0.41 GtCO 2-eq yr–1

for cover cropping in croplands; 0.23, 0.15, 0.15 GtCO 2-eq yr–1 for 
avoided grassland conversion, optimal grazing intensity and legumes 
in pastures, respectively) arising from croplands and grasslands. 
Regionally, soil carbon management in croplands is feasible 
anywhere, but effectiveness can be limited in very dry regions 
(Sanderman et al. 2017). For soil carbon management in grasslands 
the feasibility is greatest in areas where grasslands have been 
degraded (e.g., by overgrazing) and soil organic carbon is depleted. 
For well managed grasslands, soil carbon stocks are already high and 
the potential for additional carbon storage is low. Roe et al. (2021) 
estimate the greatest economic (up to USD100 tCO 2–1) potential 
between 2020 and 2050 for croplands to be in Asia and the Pacific 
(339.7 MtCO 2 yr–1) and for grasslands, in Developed Countries 
(253.6 MtCO 2 yr–1).Critical assessment and conclusion. In conclusion, there is medium

confidence that enhanced soil carbon management in croplands has 
a global technical mitigation potential of 1.9 (0.4–6.8) GtCO 2 yr–1, 
and in grasslands of 1.0 (0.2–2.6) GtCO 2 yr–1, of which, 0.6 (04–0.9) 
and 0.9 (0.3–1.6) GtCO 2 yr–1 is estimated to be available at up to 
USD100 tCO 2–1 respectively. Regionally, soil carbon management 
in croplands and grasslands is feasible anywhere, but effectiveness 
can be limited in very dry regions, and for grasslands it is greatest 
in areas where degradation has occurred (e.g.,  by overgrazing) 
and soil organic carbon is depleted. Barriers to implementation 
include regional capacity for monitoring and verification (especially 
in developing countries), and more widely through concerns over 
saturation and permanence.
7.4.3.2 Biochar
and barriers. Biochar is produced by heating organic matter in 
oxygen-limited environments (pyrolysis and gasification) (Lehmann

and Joseph 2012). Feedstocks include forestry and sawmill residues, 
straw, manure and biosolids. When applied to soils, biochar is 
estimated to persist from decades to thousands of years, depending 
on feedstock and production conditions (J. Wang et al. 2016; Singh 
et al. 2015). Biochar systems producing biochar for soil application 
plus bioenergy, generally give greater mitigation than bioenergy 
alone and other uses of biochar, and are recognised as a  CDR 
strategy. Biochar persistence is increased through interaction with 
clay minerals and soil organic matter (Fang et al. 2015). Additional 
CDR benefits arise through ‘negative priming’ whereby biochar 
stabilises soil carbon and rhizodeposits (Weng et al. 2015; J. Wang 
et al. 2016; Archanjo et al. 2017; Hagemann et al. 2017; Han Weng 
et al. 2017; Weng et al. 2018). Besides CDR, additional mitigation 
can arise from displacing fossil fuels with pyrolysis gases, lower

soil N 2O emissions (Cayuela et al. 2014, 2015; Song et al. 2016; He 
et al. 2017; Verhoeven et al. 2017; Borchard et al. 2019), reduced 
nitrogen fertiliser requirements due to reduced nitrogen leaching 
and volatilisation from soils (Liu et al. 2019; Borchard et al. 2019), 
and reduced GHG emissions from compost when biochar is added 
(Agyarko-Mintah et al. 2017; Wu et al. 2017). Biochar application 
to paddy rice has resulted in substantial reductions (20–40% on 
average) in N 2O (Song et al. 2016; Awad et al. 2018; Liu et al. 2018) 
(Section 7.4.3.5) and smaller reduction in CH 4 emissions (Song et al. 
2016; Kammann et al. 2017; Kim et al. 2017a; He et al. 2017; Awad 
et al. 2018). Potential co-benefits include yield increases particularly 
in sandy and acidic soils with low cation exchange capacity (Woolf 
et al. 2016; Jeffery et al. 2017); increased soil water-holding capacity 
(Omondi et al. 2016), nitrogen use efficiency (Liu et al. 2019; Borchard

et al. 2019), biological nitrogen fixation (Van Zwieten et al. 2015); 
adsorption of organic pollutants and heavy metals (e.g., Silvani et al. 
2019); odour reduction from manure handling (e.g.,  Hwang et al. 
2018) and managing forest fuel loads (Puettmann et al. 2020). Due to 
its dark colour, biochar could decrease soil albedo (Meyer et al. 2012), 
though this is insignificant under recommended rates and application 
methods. Biochar could reduce enteric CH 4 emissions when fed to 
ruminants (Section 7.4.3.4). Barriers to upscaling include insufficient 
investment, limited large-scale production facilities, high production 
costs at small scale, lack of agreed approach to monitoring, reporting

790
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7and verification, and limited knowledge, standardisation and quality 
control, restricting user confidence (Gwenzi et al. 2015).
and SRCCL); mitigation potential, costs, and pathways. Biochar is 
discussed as a mitigation option in AR5 and CDR strategy in the SR1.5. 
Consideration of potential was limited as biochar is not included in IAMs. 
The SRCCL estimated mitigation potential of 0.03–6.6 GtCO 2-eq yr–1 
by 2050 based on studies with widely varying assumptions, definitions 
of potential, and scope of mitigation processes included (SRCCL, 
Chapters 2 and 4: (Roberts et al. 2010; Pratt and Moran 2010; Hristov 
et al. 2013; Lee and Day 2013; Dickie et al. 2014a; Hawken 2017; Fuss 
et al. 2018; Powell and Lenton 2012; Woolf et al. 2010).
Developments since AR5 and IPCC Special Reports (SR1.5, SROCC 
and SRCCL). Developments include mechanistic understanding

of ‘negative priming’ and biochar-soil-microbes-plant interactions 
(DeCiucies et al. 2018; Fang et al. 2019). Indirect climate benefits are 
associated with persistent yield response to biochar (Kätterer et al. 
2019; Ye et al. 2020), improved crop water use efficiency (Du et al. 
2018; Gao et al. 2020) and reduced GHG and ammonia emissions 
from compost and manure (Sanchez-Monedero et al. 2018; Bora 
et al. 2020a,b; Zhao et al. 2020). A quantification method based on 
biochar properties is included in the IPCC guidelines for NGHGIs 
(Domke et al. 2019). Studies report a range of biochar responses, from 
positive to occasionally adverse impacts, including on GHG emissions, 
and identify risks (Tisserant and Cherubini 2019). This illustrates the 
expected variability (Lehmann and Rillig 2014) of responses, which 
depend on the biochar type and climatic and edaphic characteristics 
of the site (Zygourakis 2017). Biochar properties vary with feedstock,

production conditions and post-production treatments, so mitigation 
and agronomic benefits are maximised when biochars are chosen to 
suit the application context (Mašek et al. 2018). A recent assessment 
finds greatest economic potential (up to USD100 tCO 2–1) between 
2020 and 2050 to be in Asia and the Pacific (793 MtCO 2 yr–1) 
followed by Developed Countries (447 MtCO 2 yr–1) (Roe et al. 2021). 
Mitigation through biochar will be greatest where biochar is applied 
to responsive soils (acidic, low fertility), where soil N 2O emissions 
are high (intensive horticulture, irrigated crops), and where the 
syngas co-product displaces fossil fuels. Due to the early stage of 
commercialisation, mitigation estimates are based pilot-scale facilities, 
leading to uncertainty. However, the long-term persistence of biochar 
carbon in soils has been widely studied (Singh et al. 2012; Fang et al. 
2019; Zimmerman and Ouyang 2019). The greatest uncertainty is the

availability of sustainably-sourced biomass for biochar production.
Critical assessment and conclusion. Biochar has significant 
mitigation potential through CDR and emissions reduction, and can 
also improve soil properties, enhancing productivity and resilience 
to climate change (medium agreement, robust evidence). There 
is medium evidence that biochar has a  technical potential of 
2.6 (0.2–6.6) GtCO 2-eq yr–1, of which 1.1 (0.3–1.8) GtCO 2-eq yr–1 is 
available up to USD100 tCO 2–1. However, mitigation and agronomic 
co-benefits depend strongly on biochar properties and the soil to 
which biochar is applied (strong agreement, robust evidence). While 
biochar could provide moderate to large mitigation potential, it is not 
yet included in IAMs, which has restricted comparison and integration 
with other CDR strategies.7.4.3.3 Agroforestry
and barriers. Agroforestry is a set of diverse land management systems

that integrate trees and shrubs with crops and/or livestock in space 
and/or time. Agroforestry accumulates carbon in woody vegetation and 
soil (Ramachandran Nair et al. 2010) and offers multiple co-benefits 
such as increased land productivity, diversified livelihoods, reduced 
soil erosion, improved water quality, and more hospitable regional 
climates (Ellison et al. 2017; Kuyah et al. 2019; Mbow et al. 2020; Zhu 
et al. 2020). Incorporation of trees and shrubs in agricultural systems, 
however, can affect food production, biodiversity, local hydrology and 
contribute to social inequality (Amadu et al. 2020; Fleischman et al. 
2020; Holl and Brancalion 2020). To minimise risks and maximise co-
benefits, agroforestry should be implemented as part of support 
systems that deliver tools, and information to increase farmers’ agency. 
This may include reforming policies, strengthening extension systems 
and creating market opportunities that enable adoption (Jamnadass

et al. 2020; Sendzimir et al. 2011; P . Smith et al. 2019a). Consideration 
of carbon sequestration in the context of food and fuel production, as 
well as environmental co-benefits at the farm, local, and regional scales 
can further help support decisions to plant, regenerate and maintain 
agroforestry systems (Kumar and Nair 2011; Miller et al. 2020). In spite 
of the advantages, biophysical and socio-economic factors can limit the 
adoption (Pattanayak et al. 2003). Contextual factors may include, but 
are not limited to; water availability, soil fertility, seed and germplasm 
access, land policies and tenure systems affecting farmer agency, 
access to credit, and to information regarding the optimum species for 
a given location.
The SRCCL estimated the global technical mitigation potential of 
agroforestry, with medium confidence, to be between 0.08 and

5.6 GtCO 2-eq yr–1 by 2050 (Griscom et al. 2017; Dickie et al. 2014a; 
Zomer et al. 2016; Hawken 2017). Estimates are derived from 
syntheses of potential area available for various agroforestry systems, 
for example, windbreaks, farmer managed natural regeneration, and 
alley cropping and average annual rates of carbon accumulation. The 
cost-effective economic potential, also with medium confidence, is 
more limited at 0.3–2.4 GtCO 2-eq yr–1 (Zomer et al. 2016; Griscom 
et al. 2017; Roe et al. 2019). Despite this potential, agroforestry is 
currently not considered in integrated assessment models used for 
mitigation pathways (Section 7.5).
and SRCCL). Updated estimates of agroforestry’s technical mitigation 
potential and synthesised estimates of carbon sequestration across 
agroforestry systems have since been published. The most recent global 
analysis estimates technical potential of 9.4 GtCO 2-eq yr–1 (Chapman

et al. 2020) of agroforestry on 1.87 and 1.89 billion ha of crop and 
pasture lands below median carbon content, respectively. This estimate 
is at least 68% greater than the largest estimate reported in the SRCCL 
(Hawken 2017) and represents a  new conservative upper bound as 
Chapman et al. (2020) only accounted for above-ground carbon. 
Considering both above- and below-ground carbon of windbreaks, 
alley cropping and silvopastoral systems at a  more limited areal 
extent (Griscom et al. 2020), the economic potential of agroforestry

791
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77was estimated to be only about 0.8 GtCO 2-eq yr–1. Variation in 
estimates primarily result from assumptions on the agroforestry 
systems including, extent of implementation and estimated carbon 
sequestration potential when converting to agroforestry.
Regional estimates of mitigation potential are scant with agroforestry 
options differing significantly by geography (Feliciano et al. 2018). For 
example, multi-strata shaded coffee and cacao are successful in the 
humid tropics (Somarriba et al. 2013; Blaser et al. 2018), silvopastoral 
systems are prevalent in Latin American (Peters et al. 2013; Landholm 
et al. 2019) while agrosilvopastoral systems, shelterbelts, hedgerows, 
and windbreaks are common in Europe (Joffre et al. 1988; Rigueiro-
Rodriguez 2009). At the field scale, agroforestry accumulates between 
0.59 and 6.24 t ha–1 yr–1 of carbon above-ground. Below-ground

carbon often constitutes 25% or more of the potential carbon gains 
in agroforestry systems (De Stefano and Jacobson 2018; Cardinael 
et al. 2018). Roe et al. (2021) estimate greatest regional economic 
(up to USD100 tCO 2–1) mitigation potential for the period 2020–2050 
to be in Asia and the Pacific (368.4 MtCO 2-eq yr–1) and Developed 
Countries (264.7 MtCO 2-eq yr–1).
Recent research has also highlighted co-benefits and more precisely 
identified implementation barriers. In addition to aforementioned 
co-benefits, evidence now shows that agroforestry can improve soil health, regarding infiltration and structural stability (Muchane et al. 
2020); reduces ambient temperatures and crop heat stress (Arenas-
Corraliza et al. 2018; Sida et al. 2018); increases groundwater 
recharge in drylands when managed at moderate density (Ilstedt 
et al. 2016; Bargués-Tobella et al. 2020); positively influences human 
health (Rosenstock et al. 2019); and can improve dietary diversity

(McMullin et al. 2019). Along with previously mentioned barriers, low 
social capital, assets, and labour availability have been identified as 
pertinent to adoption. Practically all barriers are interdependent and 
subject to the context of implementation.
confidence that agroforestry has a  technical potential of 
4.1  (0.3–9.4) GtCO 2-eq yr–1 for the period 2020–2050, of which 
0.8 (0.4–1.1) GtCO 2-eq yr–1 is available at USD100 tCO 2–1. Despite 
uncertainty around global estimates due to regional preferences 
for management systems, suitable land availability, and growing 
conditions, there is high confidence in agroforestry’s mitigation 
potential at the field scale. With countless options for farmers and land 
managers to implement agroforestry, there is medium confidence in 
the feasibility of achieving estimated regional mitigation potential. 
Appropriately matching agroforestry options, to local biophysical

and social contexts is important in maximising mitigation and co-
benefits, while avoiding risks (Sinclair and Coe 2019).
Box 7.3 | Case Study: Agroforestry in Brazil – CANOPIES
Summary
Brazilian farmers are integrating trees into their croplands in various ways, ranging from simple to highly complex agroforestry 
systems. While complex systems are more effective in the mitigation of climate change, trade-offs with scalability need to be resolved 
for agroforestry systems to deliver on their potential. The Brazilian-Dutch CANOPIES project (Janssen 2020) is exploring transition 
pathways to agroforestry systems optimised for local ecological and socio-economic conditions.
The climate change mitigation potential of agroforestry systems is widely recognised (Zomer et al. 2016; FAO 2017b) and Brazilian 
farmers and researchers are pioneering diverse ways of integrating trees into croplands, from planting rows of eucalyptus trees in

pastures up to highly complex agroforests consisting of >30 crop and tree species. The degree of complexity influences the multiple 
functions that farmers and societies can attain from agroforestry: the more complex it is, the more it resembles a natural forest with 
associated benefits for its carbon storage capacity and its habitat quality for biodiversity (Santos et al. 2019). However, trade-offs 
exist between the complexity and scalability of agroforestry as complex systems rely on intensive manual labour to achieve high 
productivity (Tscharntke et al. 2011). To date, mechanisation of structurally diverse agroforests is scarce and hence, efficiencies of 
scale are difficult to achieve.
These synergies and trade-offs between complexity, multifunctionality and scalability are studied in the CANOPIES (Co-existence of 
Agriculture and Nature: Optimisation and Planning of Integrated Ecosystem Services) project, a collaboration between Wageningen

University (NL), the University of São Paulo and EMBRAPA (both Brazil). Soil and management data are collected on farms of varying 
complexity to evaluate carbon sequestration and other ecosystem services, economic performance and labour demands.
The trade-off between complexity and labour demand is less pronounced in EMBRAPA’s integrated crop-livestock-forestry (ICLF) 
systems, where grains and pasture are planted between widely spaced tree rows. Here, barriers for implementation relate mostly to 
livestock and grain farmers’ lack of knowledge on forestry management and financing mechanisms5 (Gil et al. 2015). Additionally, linking 
these financing mechanisms to carbon sequestration remains a Monitoring, Reporting and Verification challenge (Smith et al. 2020b).

792
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.4.3.4 Enteric Fermentation
and barriers. Mitigating CH 4 emissions from enteric fermentation 
can be direct (i.e., targeting ruminal methanogenesis and emissions 
per animal or unit of feed consumed) or indirect, by increasing 
production efficiency (i.e., reducing emission intensity per unit of 
product). Measures can be classified as those relating to (i) feeding, 
(ii) supplements, additives and vaccines, and (iii) livestock breeding 
and wider husbandry (Jia et al. 2019). Co-benefits include enhanced 
climate change adaptation and increased food security associated 
with improved livestock breeding (Smith et al. 2014). Risks include 
mitigation persistence, ecological impacts associated with improving 
feed quality and supply, or potential toxicity and animal welfare 
issues concerning feed additives. Implementation barriers include

feeding/administration constraints, the stage of development of 
measures, legal restrictions on emerging technologies and negative 
impacts, such as the previously described risks (Smith et al. 2014; Jia 
et al. 2019; P . Smith et al. 2019a).
and SRCCL); mitigation potential, costs, and pathways. The AR5 
indicated medium (5–15%) technical mitigation potential from both 
feeding and breeding related measures (Smith et al. 2014). More 
recently, the SRCCL estimated with medium confidence, a  global 
potential of 0.12–1.18 GtCO 2-eq yr–1 between 2020 and 2050, with 
the range reflecting technical, economic and sustainability constraints 
(SRCCL, Chapter 2: Hristov et al. 2013; Dickie et al. 2014a; Herrero 
et al. 2016; Griscom et al. 2017). The underlying literature used 
a  mixture of IPCC GWP100 values for CH 4, preventing conversion 
of CO 2-eq to CH 4. Improved livestock feeding and breeding were

included in IAM emission pathway scenarios within the SRCCL and 
SR1.5, although it was suggested that the full mitigation potential of 
enteric CH 4 measures is not captured in current models (Rogelj et al. 
2018b; IPCC 2018).
SROCC and SRCCL). Recent reviews generally identify the same 
measures as those outlined in the SRCCL, with the addition of early 
life manipulation of the ruminal biome (Grossi et al. 2019; Eckard 
and Clark 2020; Thompson and Rowntree 2020; Beauchemin 
et al. 2020; Ku-Vera et al. 2020; Honan et al. 2021). There is 
robust evidence and high agreement that chemically synthesised 
inhibitors are promising emerging near-term measures (Patra 2016; 
Jayanegara et al. 2018; Van Wesemael et al. 2019; Beauchemin et al. 2020) with high (e.g.,  16–70% depending on study) mitigation 
potential reported (e.g.,  Hristov et al. 2015; McGinn et al. 2019; 
Melgar et al. 2020) and commercial availability expected within

two years in some countries (Reisinger et al. 2021). However, their 
mitigation persistence (McGinn et al. 2019), cost (Carroll and 
Daigneault 2019; Alvarez-Hess et al. 2019) and public acceptance 
(Jayasundara et al. 2016) or regulatory approval is currently 
unclear while administration in pasture-based systems is likely 
to be challenging (Patra et al. 2017; Leahy et al. 2019). Research 
into other inhibitors/feeds containing inhibitory compounds, such 
as macroalga or seaweed (Chagas et al. 2019; Kinley et al. 2020; 
Roque et al. 2019), shows promise, although concerns have been 
raised regarding palatability, toxicity, environmental impacts and 
the development of industrial-scale supply chains (Abbott et al. 
2020; Vijn et al. 2020). In the absence of CH 4 vaccines, which are still 
under development (Reisinger et al. 2021) pasture-based and non-
intensive systems remain reliant on increasing production efficiency 
(Beauchemin et al. 2020). Breeding of low emitting animals may

play an important role and is a  subject under ongoing research 
(Pickering et al. 2015; Jonker et al. 2018; López-Paredes et al. 2020).
Approaches differ regionally, with more focus on direct, technical 
options in Developed Countries, and improved efficiency in 
developing countries (Caro Torres et al. 2016; Mottet et al. 2017b; 
MacLeod et al. 2018; Frank et al. 2018). A recent assessment finds 
greatest economic (up to USD100 tCO 2-eq–1) potential (using 
the IPCC AR4 GWP100 value for CH 4) for 2020–2050 in Asia and 
the Pacific (32.9 MtCO 2-eq yr–1) followed by Developed Countries 
(25.5 MtCO 2-eq yr–1) (Roe et al. 2021). Despite numerous country 
and sub-sector specific studies, most of which include cost analysis 
(Hasegawa and Matsuoka 2012; Hoa et al. 2014; Jilani et al. 2015; 
Eory et al. 2015; Pradhan et al. 2017; Pellerin et al. 2017; Ericksen and 
Crane 2018; Habib and Khan 2018; Kashangaki and Ericksen 2018;

Salmon et al. 2018; Brandt et al. 2019b; Kiggundu et al. 2019; 
Kavanagh et al. 2019; Mosnier et al. 2019; Pradhan et al. 2019; Sapkota 
et al. 2019; Carroll and Daigneault 2019; Leahy et al. 2019; Dioha and 
Kumar 2020), sectoral assessment of regional technical and notably 
economic (Beach et al. 2015; USEPA 2019) potential is restricted by 
lack comprehensive and comparable data. Therefore, verification of 
regional estimates indicated by global assessments is challenging. 
Feed quality improvement, which may have considerable potential 
in developing countries (Caro et al. 2016; Mottet et al. 2017a), may 
have negative wider impacts. For example, potential land-use change 
and greater emissions associated with production of concentrates 
(Brandt et al. 2019b).Box 7.3 (continued)
Lessons
Successful examples of how more complex agroforestry can be upscaled do exist in Brazil. For example, on farm trials and consistent

investments over several years have enabled Rizoma Agro to develop a citrus production system that integrates commercial and 
native trees in a large-scale multi-layered agroforestry system. The success of their transition resulted in part from their corporate 
structure that allowed them to tap into the certified Green Bonds market (CBI 2020). However, different transition strategies need to 
be developed for family farmers and their distinct socio-economic conditions.

793
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Critical review and conclusion. Based on studies to date, 
using a  range of IPCC GWP100 values for CH 4, there is medium 
confidence that activities to reduce enteric CH 4 emissions have 
a global technical potential of 0.8 (0.2–1.2) GtCO 2-eq yr–1, of which 
0.2 (0.1–0.3) GtCO 2-eq yr–1 is available up to USD100 tCO 2-eq–1 
(Figure 7.11). The CO 2-eq value may also slightly differ if the GWP100 
IPCC AR6 CH 4 value was uniformly applied within calculations. Lack 
of comparable country and sub-sector studies to assess the context 
applicability of measures, associated costs and realistic adoption 
likelihood, prevents verification of estimates.
7.4.3.5 Improve Rice Management
and barriers. Emissions from rice cultivation mainly concern CH 4 
associated with anaerobic conditions, although N 2O emission also

occur via nitrification and denitrification processes. Measures to 
reduce CH 4 and N 2O emissions include (i) improved water management 
(e.g., single drainage and multiple drainage practices), (ii) improved 
residue management, (iii) improved fertiliser application (e.g., using 
slow release fertiliser and nutrient specific application), and (iv) soil 
amendments (including biochar and organic amendments) (Pandey 
et al. 2014; Kim et al. 2017b; Yagi et al. 2020; Sriphirom et al. 
2020). These measures not only have mitigation potential but can 
improve water use efficiency, reduce overall water use, enhance 
drought adaptation and overall system resilience, improve yield, 
reduce production costs from seed, pesticide, pumping and labour, 
increase farm income, and promote sustainable development 
(Quynh and Sander 2015; Yamaguchi et al. 2017; Tran et al. 2018; 
Sriphirom et al. 2019). However, in terms of mitigation of CH 4 and

N2O, antagonistic effects can occur, whereby water management 
can enhance N 2O emissions due to creation of alternate wet and dry 
conditions (Sriphirom et al. 2019), with trade-offs between CH 4 and 
N2O during the drying period potentially offsetting some mitigation 
benefits. Barriers to adoption may include site-specific limitations 
regarding soil type, percolation and seepage rates or fluctuations 
in precipitation, water canal or irrigation infrastructure, paddy 
surface level and rice field size, and social factors including farmer 
perceptions, pump ownership, and challenges in synchronising water 
management between neighbours and pumping stations (Quynh and 
Sander 2015; Yamaguchi et al. 2017; Yamaguchi et al. 2019).
outlined emissions from rice cultivation of 0.49–0.723 GtCO 2-eq yr–1 
in 2010 with an average annual growth of 0.4% yr–1. The SRCCL

estimated a global mitigation potential from improved rice cultivation 
of 0.08–0.87 GtCO 2-eq yr–1 between 2020 and 2050, with the 
range representing the difference between technical and economic 
constraints, types of activities included (e.g.,  improved water 
management and straw residue management) and GHGs considered 
(Dickie et al. 2014a; Beach et al. 2015; Paustian et al. 2016; Griscom 
et al. 2017; Hawken 2017) (SRCCL, Chapter 2).Developments since AR5 and IPCC Special Reports (SR1.5, 
SROCC and SRCCL). Since AR5 and the SRCCL, studies on mitigation 
have principally focused on water and nutrient management 
practices with the aim of improving overall sustainability as well as 
measurements of site-specific emissions to help improve the resolution 
of regional estimates. Intensity of emissions show considerable 
spatial and temporal variation, dependent on site specific factors 
including degradation of soil organic matter, management of water

levels in the field, the types and amount of fertilisers applied, rice 
variety and local cultivation practices. Variation in CH 4 emissions have 
been found to range from 0.5–41.8 mg m2 hr–1 in South-East Asia 
(Sander et al. 2014; Chidthaisong et al. 2018; Setyanto et al. 2018; 
Sibayan et al. 2018; J. Wang et al. 2018; Maneepitak et al. 2019), 
0.5–37.0 mg m2 hr–1 in Southern and Eastern Asia (Zhang et al. 2010; 
Wang et al. 2012; Oo et al. 2018; J. Wang et al. 2018; Takakai et al. 
2020), and 0.5–10.4 mg m2 hr–1 in North America (J.  Wang et al. 2018). 
Current studies on emissions of N 2O also showed high variation in 
the range of 0.13–654 ug/m2/hr (Akiyama et al. 2005; Islam et al. 
2018; Kritee et al. 2018; Zschornack et al. 2018; Oo et al. 2018).
Recent studies on water management have highlighted the potential 
to mitigate GHG emissions, while also enhancing water use efficiency 
(Tran et al. 2018). A  meta-analysis on multiple drainage systems

found that Alternative Wetting and Drying (AWD) with irrigation 
management, can reduce CH 4 emissions by 20–30% and water use by 
25.7%, though this resulted in a slight yield reduction (5.4%) (Carrijo 
et al. 2017). Other studies have described improved yields associated 
with AWD (Tran et al. 2018). Water management for both single and 
multiple drainage can (most likely) reduce methane emissions by 
about 35% but increase N 2O emissions by about 20% (Yagi et al. 
2020). However, N 2O emissions occur only under dry conditions, 
therefore total reduction in terms of net GWP is approximately 
30%. Emissions of N 2O are higher during dry seasons (Yagi et al. 
2020) and depend on site specific factors as well as the quantity 
of fertiliser and organic matter inputs into the paddy rice system. 
Variability of N 2O emissions from single and multiple drainage can 
range from 0.06–33 kg/ha (Hussain et al. 2015; Kritee et al. 2018).

AWD in Vietnam was found to reduce both CH 4 and N 2O emissions 
by 29–30 and 26–27% respectively with the combination of net 
GWP about 30% as compared to continuous flooding (Tran et al. 
2018). Overall, greatest average economic mitigation potential (up 
to USD100 tCO 2-eq–1) between 2020 and 2050 is estimated to be in 
Asia and the Pacific (147.2 MtCO 2-eq yr–1) followed by Latin America 
and the Caribbean (8.9 MtCO 2-eq yr–1) using the IPCC AR4 GWP100 
value for CH 4 (Roe et al. 2021).
that improved rice management has a  technical potential of 
0.3  (0.1–0.8) GtCO 2-eq yr–1 between 2020 and 2050, of which 
0.2 (0.05–0.3) GtCO 2-eq yr–1 is available up to USD100 tCO 2-eq–1 
(Figure 7.11). Improving rice cultivation practices will not only reduce 
GHG emissions, but also improve production sustainability in terms 
of resource utilisation including water consumption and fertiliser

application. However, emission reductions show high variability and 
are dependent on site specific conditions and cultivation practices.

794
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.4.3.6 Crop Nutrient Management
and barriers. Improved crop nutrient management can reduce N 2O 
emissions from cropland soils. Practices include optimising fertiliser 
application delivery, rates and timing, utilising different fertiliser 
types (i.e., organic manures, composts and synthetic forms), and 
using slow or controlled-released fertilisers or nitrification inhibitors 
(Smith et al. 2014; Griscom et al. 2017; P .  Smith et al. 2019a). In 
addition to individual practices, integrated nutrient management that 
combines crop rotations including intercropping, nitrogen biological 
fixation, reduced tillage, use of cover crops, manure and bio-fertiliser 
application, soil testing and comprehensive nitrogen management 
plans, is suggested as central for optimising fertiliser use, enhancing

nutrient uptake and potentially reducing N 2O emissions (Bationo 
et al. 2012; Lal et al. 2018; Bolinder et al. 2020; Jensen et al. 2020; 
Namatsheve et al. 2020). Such practices may generate additional 
mitigation by indirectly reducing synthetic fertiliser manufacturing 
requirements and associated emissions, though such mitigation 
is accounted for in the Industry Sector and not considered in this 
chapter. Tailored nutrient management approaches, such as 4R 
nutrient stewardship, are implemented in contrasting farming 
systems and contexts and supported by best management practices 
to balance and match nutrient supply with crop requirements, 
provide greater stability in fertiliser performance and to minimise 
N2O emissions and nutrient losses from fields and farms (Fixen 2020; 
Maaz et al. 2021). Co-benefits of improved nutrient management 
can include enhanced soil quality (notably when manure, crop 
residues or compost is utilised), carbon sequestration in soils and

biomass, soil water holding capacity, adaptation capacity, crop 
yields, farm incomes, water quality (from reduced nitrate leaching 
and eutrophication), air quality (from reduced ammonia emissions) 
and in certain cases, it may facilitate land sparing (Sapkota et al. 
2014; Johnston and Bruulsema 2014; Zhang et al. 2017; P . Smith et al. 
2019a; Mbow et al. 2019).
A potential risk under certain circumstances, is yield reduction, while 
implementation of practices should consider current soil nutrient 
status. There are significant regional imbalances, with some regions 
experiencing nutrient surpluses from over fertilisation and others, 
nutrient shortages and chronic deficiencies (FAO 2021e). Additionally, 
depending on context, practices may be inaccessible, expensive or 
require expertise to implement (Hedley 2015; Benson and Mogues 
2018) while impacts of climate change may influence nutrient use 
efficiency (Amouzou et al. 2019) and therefore, mitigation potential.

SRCCL broadly identified the same practices as outlined in AR5 
and estimated that improved cropland nutrient management could 
mitigate between 0.03 and 0.71 GtCO 2-eq yr–1 between 2020 and 
2050 (SRCCL Chapter  2) (Dickie et al. 2014a; Beach et al. 2015; 
Paustian et al. 2016; Griscom et al. 2017; Hawken 2017).Developments since AR5 and IPCC Special Reports (SR1.5, 
SROCC and SRCCL). Research since the SRCCL highlights the 
mitigation potential and co-benefits of adopting improved nutrient 
management strategies, notably precision fertiliser application 
methods and nutrient expert systems, and applicability in both 
large-scale mechanised and small-scale systems (USEPA 2019; 
Hijbeek et al. 2019; Griscom et al. 2020; Tian et al. 2020; Aryal et al. 
2020; Sapkota et al. 2021). Improved crop nutrient management is

feasible in all regions, but effectiveness is context dependent. Sub-
Saharan Africa has one of the lowest global fertiliser consumption 
rates, with increased fertiliser use suggested as necessary to meet 
projected future food requirements (Mueller et al. 2012; ten Berge 
et al. 2019; Adam et al. 2020; Falconnier et al. 2020). Fertiliser use 
in Developed Countries is already high (Figure 7.10) with increased 
nutrient use efficiency among the most promising mitigation 
measures (Roe et al. 2019; Hijbeek et al. 2019). Considering that 
Asia and Pacific, and Developed Countries accounted for the greatest 
share of global nitrogen fertiliser use, it is not surprising that these 
regions are estimated to have greatest economic mitigation potential 
(up to USD100 tCO 2-eq–1) between 2020 and 2050, at 161.8 and 
37.1 MtCO 2-eq yr–1 respectively (using the IPCC AR4 GWP100  value 
for N 2O) (Roe et al. 2021).
Critical assessment and conclusion. There is medium confidence

that crop nutrient management has a  technical potential of 
0.3 (0.06–0.7) GtCO 2-eq yr–1 of which 0.2 (0.05–0.6) GtCO 2-eq yr–1 
is available up to USD100 tCO 2-eq–1. This value is based on GWP100 
using a  mixture of IPCC values for N 2O and may slightly differ if 
calculated using AR6 values. The development of national roadmaps 
for sustainable fertiliser (nutrient) management can help in scaling-
up related practices and in realising this potential. Crop nutrient 
management measures can contribute not only to mitigation, but food 
and nutrition security and wider environmental sustainability goals.

795
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Box 7.4 | Case Study: The Climate-smart Village Approach
Summary
The climate-smart villages (CSV) approach aims to generate local knowledge, with the involvement of farmers, researchers, practitioners, 
and governments, on climate change adaptation and mitigation while improving productivity, food security, and farmers’ livelihoods 
(Aggarwal et al. 2018). This knowledge feeds a global network that includes 36 climate-smart villages in South and South-East Asia, 
West and East Africa, and Latin America.
It is expected that agricultural production systems across the world will change in response to climate change, posing significant 
challenges to the livelihoods and food security of millions of people (Kennedy et al. 2014). Maintaining agricultural growth while 
minimising climate shocks is crucial to building a resilient food production system and meeting sustainable development goals in 
vulnerable countries.

The CSV approach seeks an integrated vision so that sustainable rural development is the final goal for rural communities. At the 
same time, it fosters the understanding of climate change with the implementation of adaptation and mitigation actions, as much as 
possible. Rural communities and local stakeholders are the leaders of this process, where scientists facilitate their knowledge to be 
useful for the communities and learn at the same time about challenges but also the capacity those communities have built through 
time. The portfolio includes weather-smart activities, water-smart practices, seed/breed smart, carbon-/nutrient-smart practices, and 
institutional-/market-smart activities.
The integration of technologies and services that are suitable for the local conditions resulted in many gains for food security and

adaptation and for mitigation where appropriate. It was also shown that, in all regions, there is considerable yield advantage when 
a portfolio of technologies is used, rather than the isolated use of technologies (Govaerts et al. 2005; Zougmoré et al. 2014). Moreover, 
farmers are using research results to promote their products as climate-smart leading to increases in their income (Acosta-Alba 
et al. 2019). However, climatic risk sites and socio-economic conditions together with a lack of resource availability are key issues 
constraining agriculture across all five regions.
Lessons
i. Understanding the priorities, context, challenges, capacity, and characteristics of the territory and the communities regarding 
climate, as well as the environmental and socio-economic dimensions, is the first step. Then, understanding climate vulnerability 
in their agricultural systems based on scientific data but also listening to their experience will set the pathway to identify climate-

smart agriculture (CSA) options (practices and technologies) to reduce such vulnerability.
ii. Building capacity is also a critical element of the CSV approach, rural families learn about the practices and technologies in 
a neighbour’s house, and as part of the process, families commit to sharing their knowledge with other families, to start a scaling-
out process within the communities. Understanding the relationship between climate and their crop is key, as well as the use of 
weather forecasts to plan their agricultural activities.
The assessment of the implementation of the CSA options should be done together with community leaders to understand changes 
in livelihoods and climate vulnerability. Also, knowledge appropriation by community leaders has led to farmer-to-farmer knowledge 
exchange within and outside the community (Ortega Fernandez and Martínez-Barón 2018).
7.4.3.7 Manure Management
Activities, co-benefits, risks and implementation opportunities

and barriers. Manure management measures aim to mitigate CH 4 
and N 2O emissions from manure storage and deposition. Mitigation 
of N 2O considers both direct and indirect (i.e., conversion of ammonia 
and nitrate to N 2O) sources. According to the SRCCL, measures may 
include (i) anaerobic digestion, (ii) applying nitrification or urease 
inhibitors to stored manure or urine patches, (iii) composting, (iv) improved storage and application practices, (v) grazing practices 
and (vi) alteration of livestock diets to reduce nitrogen excretion 
(Mbow et al. 2019; Jia et al. 2019). Implementation of manure 
management with other livestock and soil management measures 
can enhance system resilience, sustainability, food security and help 
prevent land degradation (Smith et al. 2014; Mbow et al. 2019; 
P .  Smith et al. 2019a), while potentially benefiting the localised 
environment, for example, regarding water quality (Di and Cameron

2016). Risks include increased N 2O emission from the application

796
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7of manure to poorly drained or wet soils, trade-offs between N 2O 
and ammonia emissions and potential eco-toxicity associated with 
some measures.
and SRCCL); mitigation potential, costs, and pathways. The  
AR5 reported manure measures to have high (>10%) mitigation 
potential. The SRCCL estimated a  technical global mitigation 
potential between 2020 and 2050 of 0.01–0.26 GtCO 2-eq yr–1, with 
the range depending on economic and sustainable capacity (Dickie 
et al. 2014a; Herrero et al. 2016) (SRCCL, Chapter 2). Conversion of 
estimates to native units is restricted as a mixture of GWP100 values 
was used in underlying studies. Measures considered were typically 
more suited to confined production systems (Jia et al. 2019; Mbow 
et al. 2019), while improved manure management is included within 
IAM emission pathways (Rogelj et al. 2018b).

SROCC and SRCCL). Research published since SRCCL broadly 
focuses on measures relevant to intensive or confined systems 
(e.g., (Hunt et al. 2019; Kavanagh et al. 2019; Sokolov et al. 2020; 
Im et al. 2020; Adghim et al. 2020; Mostafa et al. 2020), highlighting 
co-benefits and risks. For example, measures may enhance nutrient 
recovery, fertiliser value (Sefeedpari et al. 2019; Ba et al. 2020; Yao 
et al. 2020) and secondary processes such as biogas production 
(Shin et al. 2019). However, the potential antagonistic relationship 
between GHG and ammonia mitigation and need for appropriate 
management is emphasised (Aguirre-Villegas et al. 2019; Grossi et 
al. 2019; Kupper et al. 2020; Ba et al. 2020). In some circumstances, 
fugitive emissions may reduce the potential mitigation benefits of 
biogas production (Scheutz and Fredenslund 2019; Bakkaloglu et al. 
2021), while high implementation cost is identified as an adoption

barrier, notably of anaerobic digestion (Liu and Liu 2018; Niles and 
Wiltshire 2019; Ndambi et al. 2019; Ackrill and Abdo 2020; Adghim 
et al. 2020). Nitrification inhibitors have been found to be effective 
at reducing N 2O emissions from pasture deposited urine (López-
Aizpún et al. 2020), although the use of nitrification inhibitors is restricted in some jurisdictions due to concerns regarding residues 
in food products (Di  and Cameron 2016; Eckard and Clark 2020) 
while limited evidence suggests eco-toxicity risk under certain 
circumstances (Kösler et al. 2019). Some forage crops may naturally 
contain inhibitory substances (Simon et al. 2019, 2020; de Klein et al. 
2020), though this warrants further research (Podolyan et al. 2020; 
Gardiner et al. 2020).
Country specific studies provide insight into regionally applicable 
measures, with emphasis on small-scale anaerobic digestion 
(e.g.,  dome digesters), solid manure coverage and daily manure

spreading in Asia and the Pacific, and Africa (Hasegawa and Matsuoka 
2012; Hoa et al. 2014; Jilani et al. 2015; Hasegawa et al. 2016; Pradhan 
et al. 2017; Ericksen and Crane 2018; Pradhan et al. 2019; Kiggundu 
et al. 2019; Dioha and Kumar 2020). Tank/lagoon covers, large-scale 
anaerobic digestion, improved application timing, nitrogen inhibitor 
application to urine patches, soil-liquid separation, reduced livestock 
nitrogen intake, trailing shoe, band or injection slurry spreading and 
acidification are emphasised in Developed Countries (Kaparaju and 
Rintala 2011; Eory et al. 2015; Pape et al. 2016; Jayasundara et al. 
2016; Pellerin et al. 2017; Liu and Liu 2018; Lanigan et al. 2018; 
Carroll and Daigneault 2019; Eckard and Clark 2020). Using IPCC 
AR4 GWP100 values for CH 4 and N 2O, a recent assessment finds 69% 
(63.4 MtCO 2-eq yr–1) of economic potential (up to USD100 tCO 2-eq–1) 
between 2020–2050, to be in Developed Countries (Roe et al. 2021).

that manure management measures have a global technical potential 
of 0.3 (0.1–0.5) GtCO 2-eq yr–1, (using a range of IPCC GWP100 values 
for CH 4 and N 2O), of which 0.1 (0.09–0.1) GtCO 2-eq yr–1 is available 
at up to USD100 tCO 2-eq–1 (Figure  7.11). As with other non-CO 2 
GHG mitigation estimates, values may slightly differ depending upon 
which IPCC GWP100 values were used. There is robust evidence and 
high agreement that there are measures that can be applied in all 
regions, but greatest mitigation potential is estimated in Developed 
Countries in more intensive and confined production systems.
Box 7.5 | Farming System Approaches and Mitigation
There is robust evidence and high agreement that agriculture needs to change to facilitate environment conservation while 
maintaining and where appropriate, increase overall production. The SRCCL identified several farming system approaches, deemed

alternative to conventional systems (Olsson et al. 2019; Mbow et al. 2019; L.G. Smith et al. 2019). These may incorporate several of the 
mitigation measures described in Section 7.4.3, while potentially also delivering environmental co-benefits. This Box assesses evidence 
specifically on the mitigation capacity of some such system approaches. The approaches are not mutually exclusive, may share similar 
principles or practices and can be complimentary. In all cases, mitigation may result from either (i) emission reductions or (ii) enhanced 
carbon sequestration, via combinations of management practices as outlined in Figure 1 within this Box. The approaches will have 
pros and cons concerning multiple factors, including mitigation, yield and co-benefits, with trade-offs subject to the diverse contexts 
and ways in which they are implemented.

797
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Box 7.5 (continued)
1,2
3,4,5,6,7
Integrated 
Production Systems
8,9,10,11
Organic Farming
12,13,14Reduced 
emissionsbCarbon 
Farming System 
ApproachesaManagement 
practices
• Limited synthetic fertiliser inputs
• Improved N use efﬁciency (from improved soil 
quality – promoting nutrient cycling)   • Diverse crop rotations
• Cover crops
• Crop residue/livestock manure/green 
manure/mulch/compost inputs to soil
• Inclusion of agroforestry
quality – promoting nutrient cycling and use 
of cover crops – preventing N leaching)
• Reduced machinery operations
• Reduced SOM oxidation (from reduced tillage)• Minimum/zero tillage
•/uni00A0Diverse crop rotations
•/uni00A0Cover crops
•/uni00A0Crop residue/mulch inputs to soil
• Reduced fertiliser inputs (from e.g., use of 
livestock manure in cropping systems or alley

cropping with N-ﬁxing trees)
• Improved N use efﬁciency (from rotation 
design and improved soil quality)
• Reduced emissions intensity per unit of milk/meat 
(from improved livestock dietsb)
• Reduced deforestation (from increased 
agricultural production per unit of area, 
facilitating reduced LUC)• Grass leys in arable systems
• Inclusion of agroforestry/alley cropping
• Livestock manure/mulch/compost inputs to soil
• Land sparing for afforestation (from increased 
agricultural production per unit of area)
• No synthetic N fertiliser inputs
• Reduced N loading and improved N use efﬁciency 
(from lower livestock stocking rates, reliance on 
biological N ﬁxation and use of cover/catch crops)• Diverse crop rotations
• Cover/catch crops
   manure/compost inputs to soil
Box 7.5, Figure 1 | Potential mitigation mechanisms and associated management practices.  N = nitrogen, SOM = soil organic matter, LUC = land-use

change. a The farming system approaches outlined are not necessarily mutually exclusive. b Only agricultural emissions are considered. Mitigation may also result from 
reduced production of fertilisers and agrochemicals. c Reduced emissions intensity per unit of milk/meat will only result in a reduction in absolute emissions where 
increased productivity facilitates a reduction in animal numbers. 1 = Altieri et al. 2015; 2 = Altieri and Nicholls 2017; 3 = Powlson et al. 2016; 4 = Corbeels et al. 
2019; 5 = Lal 2015; 6 = Gonzalez-Sanchez et al. 2019; 7 = Thierfelder et al. 2017; 8 = Hendrickson et al. 2008; 9 = Weindl et al. 2015; 10 = Thornton and Herrero 
2015; 11 = Lal al. 2020; 12 = Scialabba and Müller–Lindenlauf 2010; 13 = Goh 2011; 14 = IFOAM 2016.

798
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Box 7.5 (continued)
Is there evidence that these approaches deliver mitigation?
 
Agroecology (AE) including Regenerative Agriculture (RA)
There is limited discussion on the mitigation potential of AE (Gliessman 2013; Altieri and Nicholls 2017), but robust evidence that 
AE can improve system resilience and bring multiple co-benefits (Altieri et al. 2015; Mbow et al. 2019; Aguilera et al. 2020; Tittonell 
2020; Wanger et al. 2020) (AR6 WGII Box 5.10). Limited evidence concerning the mitigation capacity of AE at a system level (Saj 
et al. 2017; Snapp et al. 2021) makes conclusions difficult, yet studies into specific practices that may be incorporated, suggest AE 
may have mitigation potential (medium confidence) (Section 7.4.3). However, AE, that incorporates management practices used in 
organic farming (see below), may result in reduced yields, driving compensatory agricultural production elsewhere. Research into

GHG mitigation by AE as a system and impacts of wide-scale implementation is required. Despite absence of a universally accepted 
definition (see Annex I), RA is gaining increasing attention and shares principles of AE. Some descriptions include carbon sequestration 
as a specific aim (Elevitch et al. 2018). Few studies have assessed mitigation potential of RA at a system level (e.g., Colley et al. 2020). 
Like AE, it is likely that RA can contribute to mitigation, the extent to which is currently unclear and by its case-specific design, will 
vary (medium confidence).
Conservation agriculture (CA)
The SRCCL noted both positive and inconclusive results regarding CA and soil carbon, with sustained sequestration dependent on 
productivity and residue returns (Jia et al. 2019; Mirzabaev et al. 2019; Mbow et al. 2019). Recent research is in broad agreement

(Ogle et al. 2019; Corbeels et al. 2020, 2019; Gonzalez-Sanchez et al. 2019; Munkholm et al. 2020) with greatest mitigation potential 
suggested in dry regions (Sun et al. 2020). Theoretically, CA may facilitate improved nitrogen use efficiency (limited evidence) (Lal 
2015; Powlson et al. 2016), though CA appears to have mixed effects on soil N 2O emission (Six et al. 2004; Mei et al. 2018). CA is 
noted for its adaptation benefits, with wide agreement that CA can enhance system resilience to climate related stress, notably in dry 
regions. There is evidence that CA can contribute to mitigation, but its contribution is depended on multiple factors including climate 
and residue returns (high confidence).
Integrated production systems (IPS)
The integration of different enterprises in space and time (e.g., diversified cropping, crop and livestock production, agroforestry),

therefore facilitating interaction and transfer of recourses between systems, is suggested to enhance sustainability and adaptive 
capacity (Hendrickson et al. 2008; Franzluebbers et al. 2014; Lemaire et al. 2014; Weindl et al. 2015; Gil et al. 2017; Olsson et al. 2019; 
Peterson et al. 2020; Walkup et al. 2020; Garrett et al. 2020). Research indicates some mitigation potential, including by facilitating 
sustainable intensification (Box 7.11), though benefits are likely to be highly context specific (Herrero et al. 2013; Carvalho et al. 2014; 
Piva et al. 2014; de Figueiredo et al. 2017; Rosenstock et al. 2014; Weindl et al. 2015; Thornton and Herrero 2015; Descheemaeker et al. 
2016; Lal 2020; Guenet et al. 2021). The other systems outlined within this Box may form or facilitate IPS.
Organic farming (OF)
OF can be considered a form of AE (Lampkin et al. 2017) though it is discussed separately here as it is guided by specific principles

and associated regulations (Annex I). OF is perhaps noted more for potential co-benefits, such as enhanced system resilience and 
biodiversity promotion, than mitigation. Several studies have reviewed the emissions footprint of organic compared to conventional 
systems (Mondelaers et al. 2009; Tuomisto et al. 2012; Skinner et al. 2014; Meier et al. 2015; Seufert and Ramankutty 2017; Clark and 
Tilman 2017; Meemken and Qaim 2018; Bellassen et al. 2021). Acknowledging potential assessment limitations (Meier et al. 2015; 
van der Werf et al. 2020), evidence suggests organic production to typically generate lower emissions per unit of area, while emissions 
per unit of product vary and depend on the product (high agreement, medium evidence). OF has been suggested to increase soil 
carbon sequestration (Gattinger et al. 2012), though definitive conclusions are challenging (Leifeld et al. 2013). Fewer studies consider

impacts of large-scale conversion from conventional to organic production globally. Though context specific (Seufert and Ramankutty 
2017), OF is reported to typically generate lower yields (Seufert et al. 2012; De Ponti et al. 2012; Kirchmann 2019; Biernat et al. 2020). 
Large-scale conversion, without fundamental changes in food systems and diets (Muller et al. 2017; Theurl et al. 2020), may lead to 
increases in absolute emissions from land-use change, driven by greater land requirements to maintain production (L.G. Smith et al. 
2019; Leifeld 2016; Meemken and Qaim 2018).

799
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Box 7.6 | Case Study: Mitigation Options and Costs in the Indian Agricultural Sector
Objective
To assess the technical mitigation potentials of Indian agriculture and costs under a business as usual scenario (BAU) and Mitigation 
scenario up to 2030 (Sapkota et al. 2019).
Results
The study shows that by 2030 under BAU scenario GHG emissions from the agricultural sector in India would be 515 MtCO 2-eq yr–1 
(using GWP100 and IPCC AR4 values) with a technical mitigation potential of 85.5 MtCO 2-eq yr–1 through the adoption of various 
mitigation practices. About 80% of the technical mitigation potential could be achieved by adopting cost-saving measures. 
Three mitigation options, for example, efficient use of fertiliser, zero-tillage, and rice-water management, could deliver more than 50% 
of the total technical abatement potential. Under the BAU scenario the projected GHG emissions from major crop and livestock species

is estimated at 489 MtCO 2-eq in 2030, whereas under mitigation scenario GHG emissions are estimated at 410 MtCO 2-eq implying 
a technical mitigation option of about 78.67 MtCO 2-eq yr–1 (Box 7.6, Figure 1). Major sources of projected emissions under the BAU 
scenario, in order of importance, were cattle, rice, buffalo, and small ruminants. Although livestock production and rice cultivation 
account for a major share of agricultural emissions, the highest mitigation potential was observed in rice (about 36 MtCO 2-eq yr–1) 
followed by buffalo (about 14 MtCO 2-eq yr–1), wheat (about 11 MtCO 2-eq yr–1) and cattle (about 7 MtCO 2-eq yr–1). Crops such as 
cotton and sugarcane each offered mitigation potential of about 5 MtCO 2-eq yr–1 while the mitigation potential from small ruminants 
(goat/sheep) was about 2 MtCO 2-eq yr–1.
Sapkota et al. (2019) also estimated the magnitude of GHG savings 
per year through adoption of various mitigation measures, together

with the total cost and net cost per unit of CO 2-eq abated. When 
the additional benefits of increased yield due to adoption of the 
mitigation measures were considered, about 80% of the technical 
mitigation potential (67.5 out of 85.5 MtCO 2-eq) could be achieved 
by cost-saving measures. When yield benefits were considered, 
green fodder supplements to ruminant diets were the most cost-
effective mitigation measure, followed by vermicomposting 
and improved diet management of small ruminants. Mitigation 
measures such as fertigation and micro-irrigation, various methods 
of restoring degraded land and feed additives in livestock appear 
to be cost-prohibitive, even when considering yield benefits, if 
any. The study accounted for GHG emissions at the farm level 
and excluded emissions arising due to processing, marketing or 
consumption post farm-gate. It also did not include emissions 
from feed production, since livestock in India mostly rely on crop

by-products and concentrates. Further the potential of laser land 
levelling seems exaggerated which may also be redundant with 
already accounted potential from ‘improved water management 
in rice’. The mitigation potential of agroecological approaches/
technologies such as natural farming which is picking up in India in 
recent years has also been overlooked.Box  7.6, Figure  1 | Contribution of various crops and livestock 
species to total agricultural emission in 2012 (baseline) and by 2030 
under business as usual (BAU) and mitigation scenarios for Indian 
agricultural sector. Source: Sapkota et al. (2019). Reprinted from Science 
of The Total Environment, 655, Sapkota T.B.  et al., Cost-effective opportunities 
for climate change mitigation in Indian agriculture., 2019, with permission 
from Elsevier.
7.4.4 Bioenergy and BECCS
Activities, co-benefits, risks and implementation 
opportunities and barriers.  Bioenergy refers to energy products

(solid, liquid and gaseous fuels, electricity, heat) derived from 
multiple biomass sources including organic waste, harvest residues 
and by-flows in the agriculture and forestry sectors, and biomass 
from tree plantations, agroforestry systems, lignocellulosic crops, 
and conventional food/feed crops. It may reduce net GHG emissions 
by displacing the use of coal, oil and natural gas with renewable 
biomass in the production of heat, electricity, and fuels. When combined with carbon capture and storage (BECCS) and biochar 
production, bioenergy systems may provide CDR by durably storing 
biogenic carbon in geological, terrestrial, or ocean reservoirs, or 
in products, further contributing to mitigation (Chum et al. 2011; 
Cabral et al. 2019; Hammar and Levihn 2020; Emenike et al. 2020; 
Moreira et al. 2020b; Y . Wang et al. 2020: Johnsson et al. 2020) 
(Section 7.4.3.2, Chapters 3, 4, 6 and 12).
This section addresses especially aspects related to land use and

biomass supply for bioenergy and BECCS. The mitigation potential 
presented here and in Table 7.3, includes only the CDR component of

800
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7BECCS. The additional mitigation achieved from displacing fossil fuels 
is covered elsewhere (Chapters 6, 8, 9, 10, 11 and 12).
Modern bioenergy systems (as opposed to traditional use of fuelwood 
and other low-quality cooking and heating fuels) currently provide 
approximately 30 EJ yr–1 of primary energy, making up 53% of total 
renewable primary energy supply (IEA 2019). Bioenergy systems 
are commonly integrated within forest and agriculture systems that 
produce food, feed, lumber, paper and other bio-based products. 
They can also be combined with other AFOLU mitigation options: 
deployment of energy crops, agroforestry and A/R can provide 
biomass while increasing land carbon stocks (Sections 7.4.2.2 and 
7.4.3.3) and anaerobic digestion of manure and wastewater, to 
reduce methane emissions, can produce biogas and CO 2 for storage 
(Section 7.4.3.7). But ill-deployment of energy crops can also cause

land carbon losses (Hanssen et al. 2020) and increased biomass 
demand for energy could hamper other mitigation measures such as 
reduced deforestation and degradation (Sections 7.4.2.1).
Bioenergy and BECCS can be associated with a range of co-benefits 
and adverse side effects (Smith et al. 2016; Jia et al. 2019; Calvin 
et al. 2021) (Section  12.5). It is difficult to disentangle bioenergy 
development from the overall development in the AFOLU sector 
given its multiple interactions with food, land, and energy systems. It 
is therefore not possible to precisely determine the scale of bioenergy 
and BECCS deployment at which negative impacts outweigh 
benefits. Important uncertainties include governance systems, 
future food and biomaterials demand, land-use practices, energy 
systems development, climate impacts, and time scale considered 
when weighing negative impacts against benefits (Robledo-Abad 
et al. 2017; Turner et al. 2018b; Daioglou et al. 2019; Wu et al. 2019;

Kalt et al. 2020; Hanssen et al. 2020; Calvin et al. 2021; Cowie et al. 
2021) (SRCCL, Cross-Chapter Box 7; Box 7.7). The use of municipal 
organic waste, harvest residues, and biomass processing by-products 
as feedstock is commonly considered to have relatively lower risk, 
provided that associated land-use practices are sustainable (Cowie 
et al. 2021). Deployment of dedicated biomass production systems 
can have positive and negative implications on mitigation and 
other sustainability criteria, depending on location and previous 
land use, feedstock, management practice, deployment strategy 
and scale (Rulli et al. 2016; Popp et al. 2017; Daioglou et al. 2017; 
Staples et al. 2017; Carvalho et al. 2017; Humpenöder et al. 2018; 
Fujimori et al. 2019; Hasegawa et al. 2020; Drews et al. 2020; Schulze 
et al. 2020; Stenzel et al. 2020; Mouratiadou et al. 2020; Buchspies et al. 
2020; Hanssen et al. 2020, IPBES 2019b) (Sections 12.5 and 17.3.3.1).

and SRCCL); mitigation potential, costs, and pathways. Many 
more stringent mitigation scenarios in AR5 relied heavily on bioenergy 
and BECCS. The SR1.5 reported a  range for the CDR potential of 
BECCS (2100) at 0.5 to 5 GtCO 2-eq yr–1 when applying constraints 
reflecting sustainability concerns, at a cost of 100–200 USD tCO 2–1 
(Fuss et al. 2018). The SRCCL reported a technical CDR potential for 
BECCS at 0.4–11.3 GtCO 2 yr–1 (medium confidence), noting that 
most estimates do not include socio-economic barriers, the impacts 
of future climate change, or non-GHG climate forcing (IPCC. 2019). 
The SR1.5 and SRCCL highlighted that bioenergy and BECCS can be associated with multiple co-benefits and adverse side effects that are 
context specific.
SROCC and SRCCL). The role of bioenergy and BECCS in mitigation

pathways has been reduced as IAM-based studies have incorporated 
broader mitigation portfolios and have explored non-CO 2 emissions 
reduction and a  wider variation of underlying assumptions about 
socio-economic drivers and associated energy and food demand, 
as well as deployment limits such as land availability for A/R and 
for cultivation of crops used for bioenergy and BECCS (Grubler et al. 
2018; Van Vuuren et al. 2018).
Increased availability of spatially explicit data and advances in 
the modelling of crop productivity and land use, land carbon 
stocks, hydrology, and ecosystem properties, have enabled more 
comprehensive analyses of factors that influence the contribution of 
bioenergy and BECCS in IAM-based mitigation scenarios, and also 
associated co-benefits and adverse side effects (Turner et al.2018a; 
Wu et al. 2019, Li et al. 2020, Hanssen et al. 2020; Drews et al. 2020; 
Ai et al. 2021; Hasegawa et al. 2021). Yet, IAMs are still coarse in

local land-use practices. (Daioglou et al. 2019; Wu et al. 2019; 
Moreira et al. 2020b). Literature complementary to IAM studies 
indicate opportunities for integration of biomass production systems 
into agricultural landscapes (e.g.,  agroforestry, double cropping) 
to produce biomass while achieving co-benefits (Section  12.5). 
Similarly, climate-smart forestry puts forward measures (Box  7.3) 
adapted to regional circumstances in forest sectors, enabling co-
benefits in nature conservation, soil protection, employment and 
income generation, and provision of wood for buildings, bioenergy 
and other bio-based products (Nabuurs et al. 2017).
Studies have also investigated the extent and possible use of 
marginal, abandoned, and degraded lands, and approaches to help 
restore the productive value of these lands (Awasthi et al. 2017; 
Fritsche et al. 2017; Chiaramonti and Panoutsou, 2018; Fernando 
et al. 2018; Elbersen et al. 2019; Rahman et al. 2019; Næss et al.

2021). In the SRCCL, the presented range for degraded or abandoned 
land was 32–1400 Mha (Jia et al. 2019). Recent regional assessments 
not included in the SRCCL found up to 69 Mha in EU-28, 185 Mha 
in China, 9.5 Mha in Canada, and 127 Mha in the USA (Emery et al. 
2017; Liu et al. 2017; Elbersen et al. 2019; Zhang et al.2020; Vera 
et al. 2021). The definitions of marginal/abandoned/degraded land, 
and the methods used to assess such lands remain inconsistent 
across studies (Jiang et al. 2019), causing large variation amongst 
them (Jiang et al. 2021). Furthermore, the availability of such lands 
has been contested since they may serve other functions, such as: 
subsistence, biodiversity protection, and so on (Baka 2014).

801
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Box 7.7 | Climate Change Mitigation Value of Bioenergy and BECCS
Besides emissions, and possible avoided emissions, related to the supply chain, the GHG effects of using bioenergy depend on: 
(i) change in GHG emissions when bioenergy substitutes another energy source; and (ii) how the associated land use and possible land-
use change influence the amount of carbon that is stored in vegetation and (Calvin et al. 2021) soils over time. Studies arrive at varying 
mitigation potentials for bioenergy and BECCS due to the large diversity of bioenergy systems, and varying conditions concerning 
where and how they are deployed (Elshout 2015; Harper et al.2018; Muri 2018; Kalt et al.2019; Brandão et al. 2019; Buchspies et al. 
2020; Cowie et al. 2021; Calvin et al. 2021). Important factors include feedstock type, land management practice, energy conversion

efficiency, type of bioenergy product (and possible co-products), emissions intensity of the products being displaced, and the land use/
cover prior to bioenergy deployment (Zhu et al. 2017; Staples et al. 2017; Daioglou et al. 2017; Carvalho et al. 2017; Hanssen et al. 
2020; Mouratiadou et al. 2020). Studies arrive at contrasting conclusions also when similar bioenergy systems and conditions are 
analysed, due to different methodologies, assumptions, and parametrization (Harper et al.2018; Kalt et al.2019; Brandão et al. 2019; 
Albers et al. 2019; Buchspies et al. 2020; Bessou et al. 2020; Rolls and Forster 2020; Cowie et al. 2021).
Box 7.7, Figure 1 shows emissions associated with biomass supply (residues and crops grown on cropland not needed for food) in 
2050, here designated emission-supply curves. The curves are constructed assuming that additional biomass supply consistently comes

from the available land/biomass resource that has the lowest GHG emissions, for example, the marginal GHG emissions increase with 
increasing biomass use for bioenergy. Net negative emissions indicate cases where biomass production increases land carbon stocks.
kgCO2/GJPrim
EJPrim biomass
−25
0 50 100 150 2000255075100Model type
IAM (EMF-33)
 Sectoral models (natural regrowth) 
 Sectoral models (constant land cover)
Box 7.7, Figure 1 | Emissions associated with primary biomass supply in 2050 (residues and crops grown on cropland not needed for food), 
as determined from sectoral models (Daioglou et al. 2017; Kalt et al. 2020), and stylised scenarios from the EMF-33 project using Integrated 
Assessment Models (Rose et al. 2020). All methods include LUC (direct and indirect) emissions. Emissions associated with Natural Regrowth include counterfactual

carbon fluxes (see text). The sectoral models include a more detailed representation of the emissions, including lifecycle emissions from fertiliser production. IAM 
models may include economic feedbacks such as intensification as a result of increasing prices. As an indication: for natural gas the emission factor is around 56, for 
coal around 95 kgCO 2 GJ–1.

802
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7Box 7.7 (continued)
One curve (EMF-33) is determined from stylised scenarios using IAMs (Rose et al. 2020). One of the two curves determined from 
sectoral models, Constant Land Cover, reflects supply chain emissions and changes in land carbon storage caused by the biomass 
supply system itself. These two curves are obtained with modelling approaches compatible with the modelling protocol used for the 
scenarios in the AR6 database, which accounts for the land-use change and all other GHG emissions along a given transformation 
trajectory, enabling assessments of the warming level incurred.
The Natural Regrowth curve attribute additional ‘counterfactual emissions’ to the bioenergy system, corresponding to estimated 
uptake of CO 2 in a counterfactual scenario where land is not used for bioenergy but instead subject to natural vegetation regrowth.

This curve does not show actual emissions from the bioenergy system, but it provides insights in the mitigation value of the bioenergy 
option compared to alternative land-use strategies. To illustrate, if biomass is used instead of a primary energy source with emission 
factor 75 kgCO 2 GJ–1, and the median values in the Natural Regrowth curve are adopted, then the curve indicates that up to about 
150 EJ of biomass can be produced and used for energy while achieving higher net GHG savings than the alternative to set aside the 
same land for natural vegetation regrowth (assuming same conversion factor).
The large ranges in the bars signify the importance of uncertainties and how the biomass is deployed. Variation in energy conversion 
efficiencies and uncertainty about magnitude, timing, and permanence of land carbon storage further complicate the comparison.

Finally, not shown in Box 7.7, Figure 1, the emission-supply curves would be adjusted downwards if displacement of emission intensive 
energy was included or if the bioenergy is combined with CCS to provide CDR.
Critical assessment and conclusion. Recent estimates of technical 
biomass potentials constrained by food security and environmental 
considerations fall within previous ranges corresponding to medium 
agreement, (e.g.,  Turner et al. 2018b; Daioglou et al. 2019; Wu 
et al. 2019, Hansen et al.2020; Kalt et al. 2020) arriving at 4–57 
and 46–245 EJ yr–1 by 2050 for residues and dedicated biomass 
crops, respectively. Based on studies to date, the technical net 
CDR potential of BECCS (including LUC and other supply chain 
emissions, but excluding energy carrier substitution) by 2050 is 
5.9 (0.5–11.3) GtCO 2 yr–1 globally, of which 1.6 (0.5–3.5) GtCO 2 yr–1 
is available at below USD100 tCO 2–1 (medium confidence) (Lenton

2010; Koornneef et al. 2012; McLaren 2012; Powell and Lenton 2012; 
Fuss et al. 2018; Turner et al. 2018a; Hanssen et al. 2020; Roe et al. 
2021) (Figure  7.11). The equivalent economic potential as derived 
from IAMs is 1.8 (0.2–9.9) GtCO 2 yr–1 (Table 7.3).
Technical land availability does not imply that dedicated biomass 
production for bioenergy and BECCS is the most effective use of this 
land for mitigation. Further, implications of deployment for climate 
change mitigation and other sustainability criteria are context 
dependent and influenced by many factors, including rate and total 
scale. While governance has a critical influence on outcome, larger 
scale and higher expansion rate generally translates into higher risk 
for negative outcomes for GHG emissions, biodiversity, food security 
and a  range of other sustainability criteria (Searchinger 2017; 
Vaughan et al. 2018; Rochedo et al. 2018; de Oliveira Garcia et al.

2018; Daioglou et al. 2019; Junginger et al. 2019; Galik et al. 2020; 
Stenzel et al. 2020).
However, literature has also highlighted how the agriculture and 
forestry sectors may respond to increasing demand by devising 
management approaches that enable biomass production for energy 
in conjunction with supply of food, construction timber, and other bio-based products, providing climate change mitigation while enabling 
multiple co-benefits including for nature conservation (Nabuurs 
et al. 2017; Parodi et al. 2018; Springmann et al. 2018; Rosenzweig 
et al. 2020; Clark et al. 2020; Favero et al. 2020; Hanssen et al. 2020) 
(Section 7.4 and Cross-Working Group Box 3 in Chapter 12).
Strategies to enhance the benefits of bioenergy and BECCS 
include (i)  management practices that protect carbon stocks and 
the productive and adaptive capacity of lands, as well as their 
environmental and social functions (van Ittersum et al. 2013, Gerssen-

Gondelach et al. 2015; Moreira et al. 2020b) (ii) supply chains from 
primary production to final consumption that are well managed and 
deployed at appropriate levels (Fajardy et al. 2018; Donnison et al. 
2020); and (iii) development of a cross-sectoral agenda for bio-based 
production within a circular economy, and international cooperation 
and governance of global trade in products to maximise synergies 
while limiting trade-offs concerning environmental, economic 
and social outcomes (very high confidence). Finally, the technical 
feasibility of BECCS depends on investments in and the roll-out of 
advanced bioenergy technologies currently not widely available 
(Baker et al. 2015; Daioglou et al. 2020b).
7.4.5 Demand-side Measures
7.4.5.1 Shift to Sustainable Healthy Diets
and barriers. The term ‘sustainable healthy diets’ refers to dietary 
patterns that ‘promote all dimensions of individuals’ health and well-

being; have low environmental pressure and impact; are accessible, 
affordable, safe and equitable; and are culturally acceptable’ 
(FAO and WHO 2019). In addition to climate mitigation gains,

803
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77a  transition towards more plant-based consumption and reduced 
consumption of animal-based foods, particularly from ruminant 
animals, could reduce pressure on forests and land used for feed, 
support the preservation of biodiversity and planetary health (FAO 
2018c; Theurl et al. 2020), and contribute to preventing forms of 
malnutrition (i.e.,  undernutrition, micronutrient deficiency, and 
obesity) in developing countries (Section  12.4). Other co-benefits 
include lowering the risk of cardiovascular disease, type 2 diabetes, 
and reducing mortality from diet-related non-communicable diseases 
(Toumpanakis et al. 2018; Satija and Hu 2018; Faber et al. 2020; 
Magkos et al. 2020). However, transition towards sustainable healthy 
diets could have adverse impacts on the economic stability of the 
agricultural sector (MacDiarmid 2013; Aschemann-Witzel 2015; 
Van Loo et al. 2017). Therefore, shifting toward sustainable and

healthy diets requires effective food-system oriented reform policies 
that integrate agriculture, health, and environment policies  to 
comprehensively address synergies and conflicts in co-lateral sectors 
(agriculture, trade, health, environment protection etc.) and capture 
spill-over effects, for example, climate change, biodiversity loss, food 
poverty (FAO and WHO 2019; Galli et al. 2020).
and SRCCL); mitigation potential, costs, and pathways. According 
to the AR5, changes in human diets and consumption patterns can 
reduce emissions 5.3 to 20.2 GtCO 2-eq yr–1 by 2050 from diverted 
agricultural production and avoided land-use change (Smith et al. 
2014). In the SRCCL, a ‘contract and converge’ model of transition 
to sustainable healthy diets was suggested as an effective approach, 
reducing food consumption in over-consuming populations and 
increasing consumption of some food groups in populations where

minimum nutritional needs are not met (P . Smith et al. 2019a). The 
total technical mitigation potential of changes in human diets was 
estimated as 0.7–8 GtCO 2-eq yr–1 by 2050 (Tilman and Clark 2014; 
Springmann et al. 2016; Hawken 2017) (SRCCL, Chapter 2 and 6), 
ranging from a 50% adoption of healthy diets (<60g of animal-based 
protein) and only accounting for diverted agricultural production, to 
the global adoption of a vegetarian diet.
SROCC and SRCCL). Since the SRCCL, global studies continue to 
find high mitigation potential from reducing animal-source foods 
and increasing proportions of plant-rich foods in diets. Springmann 
et al. (2018) estimated that diet changes in line with global dietary 
guidelines for total energy intake and consumption of red meat, 
sugar, fruits, and vegetables, could reduce GHG emissions by 29% 
and other environmental impacts by 5–9% compared with the

baseline in 2050. Poore and Nemecek (2018) revealed that shifting 
towards diets that exclude animal-source food could reduce 
land use by 3.1 billion ha, decrease food-related GHG emissions 
by 6.5 GtCO 2-eq yr–1, acidification by 50%, eutrophication by 
49%, and freshwater withdrawals by 19% for a  2010 reference 
year. Frank et al. (2019) estimated non-CO 2 emissions reductions 
of 0.4 GtCO 2-eq yr–1 at a  carbon price of USD100 tCO 2–1 and 
0.6 GtCO 2-eq yr–1 at USD20 tCO 2–1 in 2050 from shifting to lower 
animal-source diets (430 kcal of livestock calorie intake) in developed 
and emerging countries. From a systematic literature review, Ivanova 
et al. (2020) found mitigation potentials of 0.4–2.1 tCO 2-eq capita–1 for a vegan diet, of 0.01–1.5 for a vegetarian diet, and of 0.1–2.0 for 
Mediterranean or similar healthy diet.
Regionally, mitigation potentials for shifting towards sustainable 
healthy diets (50% convergence to <60g of meat-based protein,

only accounting for diverted production) vary across regions. 
A recent assessment finds greatest economic (up to USD100 tCO 2–1) 
potential for 2020–2050 in Asia and the Pacific (609 MtCO 2-eq yr–1) 
followed by Developed Countries (322 MtCO 2-eq yr–1) based on 
IPCC AR4 GWP100  values for CH 4 and N 2O) (Roe et al. 2021). In the 
EU, (Latka et al. 2021) found that moving to healthy diets through 
price incentives could bring about annual reductions of non-CO 2 
emissions from agriculture of 12–111 MtCO 2-eq yr–1. At the country 
level, recent studies show that following National Dietary Guidelines 
(NDG) would reduce food system GHG emissions by 4–42%, confer 
large health gains (1.0–1.5 million quality-adjusted life-years) and 
lower health care system costs (NZD 14–20 billion) in New Zealand 
Drew et al. (2020); reduce 28% of GHG emissions in Argentina 
Arrieta and González (2018); about 25% in Portugal Esteve-Llorens

et al. (2020) and reduce GHG emissions, land use and blue water 
footprint by 15–60% in Spain (Batlle-Bayer et al. 2020). In contrast, 
Aleksandrowicz et al. (2019) found that meeting healthy dietary 
guidelines in India required increased dietary energy intake overall, 
which slightly increased environmental footprints by about 3–5% 
across GHG emissions, blue and green water footprints and land use.
Critical assessment and conclusion. Shifting to sustainable healthy 
diets has large potential to achieve global GHG mitigation targets as 
well as public health and environmental benefits (high confidence). 
Based on studies to date, there is medium confidence that shifting 
toward sustainable healthy diets has a technical potential including 
savings in the full value chain of 3.6 (0.3–8.0) GtCO 2-eq yr–1 of 
which 2.5 (1.5–3.9) GtCO 2-eq yr–1 is considered plausible based 
on a range of GWP100  values for CH 4 and N 2O. When accounting

for diverted agricultural production only, the feasible potential is 
1.7 (1–2.7) GtCO 2-eq yr–1. A shift to more sustainable and healthy 
diets is generally feasible in many regions (medium confidence). 
However, potential varies across regions as diets are location- and 
community- specific, and thus may be influenced by local production 
practices, technical and financial barriers and associated livelihoods, 
everyday life and behavioural and cultural norms around food 
consumption (Meybeck and Gitz 2017; Creutzig et al. 2018; FAO 
2018b). Therefore, a  transition towards low-GHG emission diets 
and achieving their mitigation potential requires a  combination 
of appropriate policies, financial and non-financial incentives and 
awareness-raising campaigns to induce changes in consumer 
behaviour with potential synergies between climate objectives, 
health and equity (Rust et al. 2020).
7.4.5.2 Reduce Food Loss and Waste

and barriers. Food loss and waste (FLW) refer to the edible parts 
of plants and animals produced for human consumption that are 
not ultimately consumed (UNEP 2021b). Food loss occurs through 
spoilage, spilling or other unintended consequences due to limitations 
in agricultural infrastructure, storage and packaging (Parfitt et al.

804
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)72010). Food waste typically takes place at the distribution (retail 
and food service) and consumption stages in the food supply chain 
and refers to food appropriate for human consumption that is 
discarded or left to spoil (HLPE 2014). Options that could reduce FLW 
include: investing in harvesting and post-harvesting technologies in 
developing countries, taxing and other incentives to reduce business 
and consumer-level waste in developed countries, mandatory FLW 
reporting and reduction targets for large food businesses, regulation 
of unfair trading practices, and active marketing of cosmetically 
imperfect products (van Giesen and de Hooge 2019; Sinclair Taylor 
et al. 2019). Other studies suggested providing options of longer-
lasting products and behavioural changes (e.g., through information 
provision) that cause dietary and consumption changes and motivate

consumers to actively make decisions that reduce FLW. Reductions 
of FLW along the food chain bring a range of benefits beyond GHG 
mitigation, including reducing environmental stress (e.g., water and 
land competition, land degradation, desertification), safeguarding 
food security, and reducing poverty (Galford et al. 2020; 
Venkatramanan et al. 2020). Additionally, FLW reduction is crucial for 
achieving SDG 12 which calls for ensuring ‘sustainable consumption 
and production patterns’ through lowering per capita global food 
waste by 50% at the retail and consumer level and reducing food 
losses along food supply chains by 2030. In line with these SDG 
targets, it is estimated that reducing FLW can free up several million 
km2 of land (high confidence). The interlinkages between reducing 
FLW and food system sustainability are discussed in Chapter  12. 
Recent literature identifies a  range of barriers to climate change

mitigation through FLW reduction, which are linked to technological, 
biophysical, socio-economic, financial and cultural contexts at 
regional and local levels (Vogel and Meyer 2018; Gromko and 
Abdurasalova 2019; Rogissart et al. 2019; Blok et al. 2020). Examples 
of these barriers include infrastructural and capacity limitations, 
institutional regulations, financial resources, constraining resources 
(e.g., energy), information gaps (e.g., with retailers), and consumers’ 
behaviour (Gromko and Abdurasalova 2019; Blok et al. 2020).
and SRCCL); mitigation potential, costs, and pathways. In AR5, 
reduced FLW was considered as a  mitigation measure that could 
substantially lower emissions, with estimated mitigation potential of 
0.6–6.0 GtCO 2-eq yr–1 in the food supply chain (Smith et al. 2014). 
In the SRCCL, the technical mitigation potential of reducing food

and agricultural waste was estimated at 0.76–4.5 GtCO 2-eq yr–1 
(Bajželj et al. 2014; Dickie et al. 2014b; Hawken 2017) (SRCCL, 
Chapter 2 and 6).
SROCC and SRCCL). Since the SRCCL, there have been very few 
quantitative estimates of the mitigation potential of FLW reductions. 
Evidence suggests that reducing FLW together with overall food 
intake could have substantial mitigation potential, equating to an 
average of 0.3 tCO 2-eq capita–1 (Ivanova et al. 2020). Some regional 
sectoral studies indicate that reducing FLW in the EU can reduce 
emissions by 186 MtCO 2-eq yr–1, the equivalent of around 15% of the 
environmental impacts (climate, acidification, and eutrophication) 
of the entire food value chain (Scherhaufer et al. 2018). In the UK, 
disruptive low-carbon innovations relating to FLW reduction were found to be associated with potential emissions reductions ranging

between 2.6 and 3.6 MtCO 2-eq (Wilson et al. 2019). Other studies 
investigated the effect of tax mechanisms, such as ‘pay as you 
throw’ for household waste, on the mitigation potential of reducing 
FLW.  Generally, these mechanisms are recognised as particularly 
effective in reducing the amount of waste and increasing the 
recycling rate of households (Carattini et al. 2018; Rogissart et al. 
2019). Technological FWL mitigation opportunities exist throughout 
the food supply chain; post-harvest opportunities for FLW reductions 
are discussed in Chapter  12. Based on IPCC AR4 GWP100  values 
for CH 4 and N 2O, greatest economic mitigation potential (up to 
USD100 tCO 2–1) for the period 2020–2050 from FLW reduction is 
estimated to be in Asia and Pacific (192.3 GtCO 2-eq yr–1) followed by 
Developed Countries (101.6 GtCO 2-eq yr–1) (Roe et al. 2021). These 
estimates reflect diverted agricultural production and do not capture 
potential from avoided land-use changes.

that reduced FLW has large global technical mitigation potential 
of 2.1 (0.1–5.8) GtCO 2-eq yr–1 including savings in the full value 
chain and using GWP100  and a range of IPCC values for CH 4 and 
N2O.  Potentials at 3.7 (2.2–5.1) GtCO 2-eq yr–1 are considered 
plausible. When accounting for diverted agricultural production only, 
the feasible potential is 0.5 (0.0–0.9) GtCO 2-eq yr–1. See the section 
above for the joint land-use effects of food related demand-side 
measures which increases three-fold when accounting for the land-
use effects as well. But this would overlap with other measures and is 
therefore not additive. Regionally, FLW reduction is feasible anywhere 
but its potential needs to be understood in a wider and changing 
socio-cultural context that determines nutrition (high confidence).
7.4.5.3 Improved and Enhanced Use of Wood Products

and barriers. The use of wood products refers to the fate of 
harvested wood for material uses and includes two distinctly different 
components affecting the carbon cycle, including carbon storage in 
wood products and material substitution. When harvested wood is 
used for the manufacture of wood products, carbon remains stored 
in these products depending on their end use and lifetime. Carbon 
storage in wood products can be increased through enhancing 
the inflow of products in use, or effectively reducing the outflow 
of the products after use. This can be achieved through additional 
harvest under sustainable management (Pilli et al. 2015; Johnston 
and Radeloff 2019), changing the allocation of harvested wood to 
long-lived wood products or by increasing products’ lifetime and 
increasing recycling (Brunet-Navarro et al. 2017; Jasinevičius et al. 
2017; Xu et al. 2018; Xie et al. 2021). Material substitution involves

the use of wood for building, textiles, or other applications instead 
of other materials (e.g.,  concrete or steel, which consume more 
energy to produce) to avoid or reduce emissions associated with the 
production, use and disposal of those products it replaces.
The benefits and risks of improved and enhanced improved use of 
wood products are closely linked to forest management. First of all, 
the enhanced use of wood products could potentially activate or 
lead to improved sustainable forest management that can mitigate

805
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77and adapt (Verkerk et al. 2020). Secondly, carbon storage in wood 
products and the potential for substitution effects can be increased 
by additional harvest, but with the risk of decreasing carbon storage 
in forest biomass when not done sustainably (P . Smith et al. 2019a). 
Conversely, reduced harvest may lead to gains in carbon storage 
in forest ecosystems locally, but these gains may be offset through 
international trade of forest products causing increased harvesting 
pressure or even degradation elsewhere (Kastner et al. 2011; Kallio 
et al. 2018; Pendrill et al. 2019b). There are also environmental 
impacts associated with the processing, manufacturing, use and 
disposal of wood products (Adhikari and Ozarska 2018; Baumgartner 
2019). See Section 9.6.4 of this report for additional discussion on 
benefits and risks.

and SRCCL); mitigation potential, costs, and pathways. There 
is strong evidence at the product level that wood products from 
sustainably managed forests are associated with less greenhouse 
emissions in their production, use and disposal over their life-time 
compared to products made from emission-intensive and non-
renewable materials. However, there is still limited understanding of 
the substitution effects at the level of markets, countries (Leskinen 
et al. 2018). The AR5 did not report on the mitigation potential of 
wood products. The SRCCL (Chapters 2 and 6) finds that some studies 
indicate significant mitigation potentials for material substitution, 
but concludes that the global, technical mitigation potential for 
material substitution for construction applications ranges from 
0.25–1 GtCO 2-eq yr–1 (medium confidence) (Miner 2010; McLaren 
2012; Roe et al. 2019).
Developments since AR5 and IPCC Special Reports (SR1.5,

SROCC and SRCCL). Since the SRCCL, several studies have 
examined the mitigation potential of the enhanced and improved use 
of wood products. A global forest sector modelling study (Johnston 
and Radeloff 2019) estimated that carbon storage in wood products 
represented a  net carbon stock increase of 0.34 GtCO 2-eq yr–1 
globally in 2015 and which could provide an average mitigation 
potential (by increasing the HWP pool) of 0.33–0.41 GtCO 2-eq yr–1 
for the period 2020–2050, based on the future socio-economic 
development (SSP scenarios) and its effect on the production and 
consumption of wood products. Traded feedstock provided another 
0.071 GtCO 2 yr–1 of carbon storage in 2015 and 0.12 GtCO 2 yr–1 
by 2065. These potentials exclude the effect of material substitution. 
Another recent study estimated the global mitigation potential of 
mid-rise urban buildings designed with engineered wood products 
at 0.04–3.7 GtCO 2 yr–1 (Churkina et al. 2020). Another study (Oliver

et al. 2014) estimated that using wood to substitute for concrete 
and steel as building materials could provide a technical mitigation 
potential of 0.78–1.73 GtCO 2 yr–1 achieved through carbon storage 
in wood products and through material and energy substitution.
The limited availability or absence of estimates of the future mitigation 
potential of improved use of wood products for many world regions 
represents an important knowledge gap, especially with regards to 
material substitution effects. At the product level, wood products are 
often associated with lower fossil-based emissions from production, 
use and disposal, compared to products made from emission-intensive and non-renewable materials (Sathre and O’Connor 2010; 
Geng et al. 2017; Leskinen et al. 2018).
Critical assessment and conclusion. Based on studies to 
date, there is strong evidence and medium agreement that 
the improved use of wood products has a  technical potential

of 1.0 (0.04–3.7) GtCO 2-eq yr–1 and economic potential of 
0.4  (0.3–0.5) GtCO 2-eq yr–1. There is strong evidence and high 
agreement at the product level that material substitution provides 
on average benefits for climate change mitigation as wood products 
are associated with less fossil-based GHG emissions over their 
lifetime compared to products made from emission-intensive and 
non-renewable materials. However, the evidence at the level of 
markets or countries is uncertain and fairly limited for many parts 
of the world. There is medium confidence that material substitution 
and carbon storage in wood products contribute to climate change 
mitigation when also the carbon balances of forest ecosystems are 
considered of sustainably managed large areas of forests in medium 
term. The total future mitigation potential will depend on the forest 
system considered, the type of wood products that are produced and 
substituted and the assumed production technologies and conversion

efficiencies of these products.
7.5 AFOLU Integrated Models and Scenarios
This section assesses the literature and data available on potential 
future GHG dynamics in the AFOLU sector, the cost-effectiveness of 
different mitigation measures, and consequences of climate change 
mitigation pathways on land-use dynamics as well as relevant 
sustainable development indicators at the regional and global level 
based on global integrated models.
Land-based mitigation options interact and create various trade-
offs, and thus need to be assessed together as well as with 
mitigation options in other sectors, and in combination with other 
sustainability goals (Popp et al. 2014; Obersteiner et al. 2016; 
Roe et al. 2019; Van Vuuren et al. 2019; Prudhomme et al. 2020; 
Strefler et al. 2021). The assessments of individual mitigation 
measures or sectoral estimates used to estimate mitigation 
potential in Section 7.4, when aggregated together, do not account

for interactions and trade-offs. Integrative land-use models (ILMs) 
combine different land-based mitigation options and are partially 
included in Integrated Assessment Models (IAMs) which combine 
insights from various disciplines in a single framework and cover 
the largest sources of anthropogenic GHG emissions from different 
sectors. Over time, ILMs and IAMs have extended their system 
coverage (Johnson et al. 2019). However, the explicit modelling 
and analysis of integrated land-use systems is relatively new 
compared to other sectoral assessments such as the energy system 
(Jia et al. 2019). Consequently, ILMs as well as IAMs differ in their 
portfolio and representation of land-based mitigation options, the 
representation of sustainability goals other than climate action as 
well as the interplay with mitigation in other sectors (van Soest 
et al. 2019; Johnson et al. 2019). These structural differences have 
implications for the regional and global deployment of different

806
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7As a consequence of the relative novelty of land-based mitigation 
assessment in ILMs and IAMs, the portfolio of land-based mitigation 
options does not cover the full option space as outlined in Section 7.4. 
The inclusion and detail of a  specific mitigation measure differs 
across models. Land-based mitigation options are only partially 
included in ILM and IAM analyses, which mostly rely on afforestation/
reforestation and bioenergy with CCS (BECCS). Most ILM and IAM 
scenarios are based on the Shared Socio-economic Pathways (SSPs) 
(Riahi et al. 2017), which is a  set of contrasting future scenarios 
widely used in the research community such as in the CMIP6 exercise, 
the SRCCL and the IPBES global assessment. However, the coverage 
of land-based mitigation options in these scenarios is mostly limited 
to dietary changes, higher efficiency in food processing (especially

in livestock production systems), reduction of food waste, increasing 
agricultural productivity, methane reductions in rice paddies, 
livestock and grazing management for reduced methane emissions 
from enteric fermentation, manure management, improvement of 
N-efficiency, international trade, first generation of biofuels, avoided 
deforestation, afforestation, bioenergy and BECCS (Popp et al. 2017; 
Van Meijl et al. 2018; Frank et al. 2019). Hence, there are mitigation 
options not being broadly included in integrated pathway modelling 
as soil carbon, forest management, agroforestry or wetland management (Humpenöder et al. 2020) which have the potential to 
alter the contribution of land-based mitigation in terms of timing, 
potential and sustainability consequences (Frank et al. 2017).
7.5.1 Regional GHG Emissions and Land Dynamics
In most of the assessed mitigation pathways, the AFOLU sector is 
of great importance for climate change mitigation as it (i) turns

from a  source into a  sink of atmospheric CO 2 due to large-scale 
afforestation and reforestation, (ii) provides high amounts of biomass 
for bioenergy with or without CCS and (iii), even under improved 
agricultural management, still causes residual non-CO 2 emissions 
from agricultural production and (iv) interplays with sustainability 
dimensions other than climate action (Popp et al. 2017; Rogelj 
et al. 2017; Van Vuuren et al. 2018; Frank et al. 2018; Hasegawa 
et al. 2018; van Soest et al. 2019). Regional AFOLU GHG emissions 
in scenarios with <4°C warming in 2100 (scenario category C7), 
as shown in Figure  7.13, are shaped by considerable CH 4 and 
N2O emissions throughout 2050 and 2100, mainly from ASIA and 
AFRICA. CH 4 emissions from enteric fermentation are largely caused 
by ASIA, followed by AFRICA, while CH 4 emissions from paddy rice 
232323232323
707070707070
969696969696
737373737373
737373737373232323232323

737373737373444444444444
909090909090
151151151151151150
999999999998
444444444444
999999999998393939393939
898989898989
146146146146146146
989898989898
393939393939
989898989898CH4|EntF CH4|Rice N2O|AWM N2O|Soil CO2 Land BECCS2050 2100
0 1 2 3 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.0 0.5 1.0 1.5 −2 0 2 4 −4 −2 0REFOECD90+EUMID_EASTLAMASIAAFRICA
REFOECD90+EUMID_EASTLAMASIAAFRICA
GtCO2-eq yr–1<1.5°C <2°C <3°C <4°C
Figure 7.13 | Land-based regional GHG emissions and removals in 2050 (top) and 2100 (bottom) for scenarios from the AR6 Database with <1.5°C (C1,

C2), <2°C (C3, C4), <3°C (C5, C6) and <4°C (C7) global warming in 2100 (scenario type is indicated by colour). The categories shown include CH 4 emissions 
from enteric fermentation (EntF) and rice production (Rice), N 2O emissions from animal waste management (AWM) and fertilisation (Soil). The category CO 2 Land includes CO 2 
emissions from land-use change as well as removals due to afforestation/reforestation. BECCS reflects the CO 2 emissions captured from bioenergy use and stored in geological 
deposits. The annual GHG emission data from various models and scenarios is converted to CO 2 equivalents using GWP factors of 27 for CH 4 and 273 for N 2O. The data is 
summarised in boxplots (Tukey style), which show the median (vertical line), the interquartile range (IQR box) and the range of values within 1.5 × interquartile range at either

end of the box (horizontal lines) across all models and scenarios. The number of data points available for each emission category, scenario type, region and year is shown at the 
edge of each panel. Regional definitions: AFRICA = sub-Saharan Africa, ASIA = Asia, LAM = Latin America and Caribbean, MID_EAST = Middle East, OECD90+EU = OECD 90 
and EU, REF = Reforming Economies of Eastern Europe and the Former Soviet Union.

807
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77production are almost exclusively caused by ASIA.  N 2O emissions 
from animal waste management and soils are more equally 
distributed across region.
In most regions, CH 4 and N 2O emission are both lower in mitigation 
pathways that limit warming to 3°C (>50%) or lower (C1–C6) 
compared to scenarios with <4°C (Popp et al. 2017; Rogelj et al. 
2018a). In particular, the reduction of CH 4 emissions from enteric 
fermentation in ASIA and AFRICA is profound. Land-related CO 2 
emissions, which include emissions from deforestation as well 
as removals from afforestation, are slightly negative (i.e., AFOLU 
systems turn into a  sink) in <1.5°C, <2°C and <3°C mitigation 
pathways compared to <4°C scenarios. Carbon sequestration via 
BECCS is most prominent in ASIA, LAM, AFRICA and OECD90+EU, 
which are also the regions with the highest bioenergy area.
Figure 7.14 indicates that regional land-use dynamics in scenarios with

<4°C warming in 2100 are characterised by rather static agricultural 
land (i.e., cropland and pasture) in ASIA, LAM, OECD90+EU and 
REF , and increasing agricultural land in AFRICA.  Bioenergy area is 
relatively small in all regions. Agricultural land in AFRICA expands at 
the cost of forests and other natural land.The overall land dynamics in <1.5°C, <2°C and <3°C mitigation 
pathways are shaped by land-demanding mitigation options such 
as bioenergy and afforestation, in addition to the demand for other 
agricultural and forest commodities. Bioenergy production and 
afforestation take place largely in the (partly) tropical regions ASIA, 
LAM and AFRICA, but also in OECD90+EU. Land for dedicated second 
generation bioenergy crops and afforestation displace agricultural 
land for food production (cropland and pasture) and other natural 
land. For instance, in the <1.5°C mitigation pathway in ASIA, 
bioenergy and forest area together increased by about 2.1 million km2

between 2020 and 2100, mostly at the cost of cropland and pasture 
(median values). Such large-scale transformations of land use have 
repercussions on biogeochemical cycles (e.g.,  fertiliser and water) 
but also on the economy (e.g.,  food prices) and potential socio-
political conditions.
7.5.2 Marginal Abatement Costs According 
to Integrated Assessments
In this section, Integrated Assessment Model (IAM) results from the 
AR6 database are used to derive marginal abatement costs which 
indicate the economic mitigation potential for the different gases 
(N2O, CH 4, CO 2) related to the AFOLU sector, at the global level and at <1.5°C <2°C <3°C <4°C
101101101101101101
161716171717
767676767676
757777777777
757777777777101101101101101101
757575757575

757777777777Cropland Bioenergy Pasture Forest Other Land2050 2100
−4 −2 0 2 4−4 −2 0 2 4−4 −2 0 2 4−4 −2 0 2 4−4 −2 0 2 4REFOECD90+EUMID_EASTLAMASIAAFRICA
Land-use change wrt to 2020 (106 km2)
Figure 7.14 | Regional change of major land cover types by 2050 (top) and 2100 (bottom) relative to 2020 for scenarios from the AR6 Database with 
<1.5°C (C1, C2), <2°C (C3, C4), <3°C (C5, C6) and <4°C (C7) global warming in 2100 (scenario type is indicated by colour). The data is summarised in boxplots 
(Tukey style), which show the median (vertical line), the interquartile range (IQR box) and the range of values within 1.5 × IQR at either end of the box (horizontal lines) across all

models and scenarios. The number of data points available for each land cover type, scenario type, region and year is shown at the right edge of each panel. Regional definitions: 
AFRICA = sub-Saharan Africa, ASIA = Asia, LAM = Latin America and Caribbean, MID_EAST = Middle East, OECD90+EU = OECD 90 and EU, REF = Reforming Economies of 
Eastern Europe and the Former Soviet Union.

808
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7the level of five world regions. This review provides a complementary 
view on the economic mitigation potentials estimated in Section 7.4 
by implicitly taking into account the interlinkages between the land-
based mitigation options themselves as well as the interlinkages with 
mitigation options in the other sectors such as BECCS. The review 
systematically evaluates a  range of possible economic potential 
estimates across gases, time, and carbon prices.
For different models and scenarios from the AR6 database, 
the amount of mitigated emissions is presented together with the 
respective carbon price (Figure  7.15). To determine mitigation 
3 Scenarios are considered consistent between global and regional results (based on R5 regions), if the sum of regional emissions (or sequestration efforts) does not deviate

more than 10% from the reported global total. To take into account that small absolute values have a higher sensitivity, a deviation of 90% is allowed for absolute 
values below 100.potentials, scenarios are compared to a benchmark scenario which 
usually assumes business-as-usual trends and no explicit additional 
mitigation efforts. Scenarios have been excluded, if they do not have 
an associated benchmark scenario or fail the vetting according to the 
AR6 scenario database, or if they do not report carbon prices and CO 2 
emissions from AFOLU. Scenarios with contradicting assumptions (for 
example, fixing some of the emissions to baseline levels) are excluded. 
Furthermore, only scenarios with consistent3 regional and global 
level results are considered. Mitigation potentials are computed by 
subtracting scenario specific emissions and sequestration amounts 
from their respective benchmark scenario values. This difference −2000
0
2000
4000
6000050100150200250

050100150200250Carbon price USD2010 tCO2–1
GHG Mitigation (MtCO2-eq yr–1) GHG Mitigation (MtCO2-eq yr–1)169
18016883
928335
393529
292912
1512
114
11611480
858061
6761123
9312326
35262030 2050−2000
0
2000
4000
6000(0,50)(50,100)(100,150)(150,200)(200,250)
(0,50)(50,100)(100,150)(150,200)(200,250)CO2 (in CO2-eq yr–1) CH4 (in CO2-eq yr–1) N2O (in CO2-eq yr–1)
Figure 7.15 | Mitigation of CO 2, CH 4 and N 2O emissions (in CO 2-eq yr–1 using IPCC AR6 GWP100 values) from the AFOLU sector for increasing carbon 
price levels for 2030 and 2050.  In the left-hand panels, single data points are generated by comparing emissions between a policy scenario and a related benchmark 
scenario, and mapping these differences with the respective carbon price difference. Plots only show the price range of up to 250 USD2010 tCO 2-eq–1 and the mitigation range

between –2000 and 6000 MtCO 2-eq yr–1 for better visibility. At the right-hand side, based on the same data as left-hand side panels, boxplots show medians (vertical line 
within the boxes), means (dots), 33%–66% intervals (box) and 10%–90% intervals (horizontal lines). Numbers on the very right indicate the number of observations falling 
into the respective price range per variable. A wide range of carbon price induced mitigation options (such as technical, structural and behavioural options in the agricultural 
sector, afforestation, reforestation, natural re-growth or avoided deforestation in the LULUCF sector, excluding carbon capture and sequestration from BECCS) are reflected in 
different scenarios.

809
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77accounts for the mitigation that can be credited to the carbon price 
which is applied in a scenario. A few benchmark scenarios, however, 
apply already low carbon prices. For consistency reasons, a carbon 
price that is applied in a benchmark scenario is subtracted from the 
respective scenario specific carbon price. This may generate a bias 
because low carbon prices tend to have a stronger marginal impact 
on mitigation than high carbon prices. Scenarios with carbon prices 
which become negative due to the correction are not considered. The 
analysis considers all scenarios from the AR6 database which pass 
the criteria and should be considered as an ensemble of opportunity 
This approach is close to integrated assessment marginal abatement 
cost curves (MACCs) as described in the literature (Fujimori et al. 
2016; Frank et al. 2018, 2019; Harmsen et al. 2019) in the sense

that it incorporates besides the technical mitigation options also 
structural options, as well as behavioural changes and market 
feedbacks. Furthermore, indirect emission changes and interactions 
with other sectors can be highly relevant (Daioglou et al. 2019; Kalt 
et al. 2020) and are also accounted for in the presented potentials. 
Hereby, some sequestration efforts can occur in other sectors, while 
leading to less mitigation in the AFOLU sector. For instance, as an 
integral part of many scenarios, BECCS deployment will lead to 
overall emissions reductions, and even provision of CDR as a result of 
the interplay between three direct components (i) LULUCF emissions/
sinks, (ii) reduction of fossil fuel use/emissions, (iii) carbon capture 
and sequestration. Since the latter two effects can compensate for 
the LULUCF effect, BECCS deployment in ambitious stabilisation 
scenarios may lead to reduced sink/increased emissions in LULUCF

(Kalt et al. 2020). The same holds for trade-offs between carbon 
sequestration in forests versus harvested wood products both for 
enhancing the HWP pool and for material substitution. The strengths 
of the competition between biomass use and carbon sequestration 
will depend on the biomass feedstocks considered (Lauri et al. 2019).
In the individual cases, the accounting of all these effects is 
dependent on the respective underlying model and its coverage of 
inter-relations of different sectors and sub-sectors. The presented 
potentials cover a  wide range of models, and additionally, a  wide 
range of background assumptions on macro-economic, technical, 
and behavioural developments as well as policies, which the models 
have been fed with. Subsequently, the range of the resulting marginal 
abatement costs is relatively wide, showing the full range of expected 
contributions from land-use sector mitigation and sequestration in 
applied mitigation pathways.

At the global level, the analysis of the economic mitigation potentials 
from N 2O and CH 4 emissions from AFOLU (which mainly can be related 
to agricultural activities) and CO 2 emissions (which mainly can be 
related to LULUCF emissions) reveals a relatively good agreement of 
models and scenarios in terms of ranking between the gases. On the 
right-hand side panels of Figure 7.15, only small overlaps between 
the ranges (showing the 10–90% intervals of observations) and 
mainly for lower price levels, can be observed, despite all differences 
in underlying model structure and scenario assumptions.N2O emissions show the smallest economic potential of the 
three different gases in 2030 as well as in 2050. The mitigation 
potential increases until a  price range of USD150–200 and to 
a median value of around 0.6 GtCO 2-eq yr–1 mitigation in 2030 and 
0.9 GtCO 2-eq yr–1 in 2050, respectively, while afterwards with higher

prices the expansion is very limited. Mitigation of CH 4 emissions has 
a  higher potential, also with increasing mitigation potentials until 
a price range of USD150–200 in both years, with median mitigation 
of around 1.3 GtCO 2-eq yr–1 in 2030 and around 2.4 GtCO 2-eq yr–1 
in 2050, respectively. The highest mitigation potentials are observed 
for CO 2, but also the highest ranges of observations among the three 
gases. In 2030, a median of 4 GtCO 2-eq yr–1 mitigation potential is 
reported for the price range of USD200–250. In 2050, for the carbon 
price range of between USD100 and USD200, a median of around 
4.8 GtCO 2-eq yr–1 can be observed.
When compared with the sectoral estimates from Harmsen et al. 
(2019), the integrated assessment median potentials are broadly 
comparable for the N 2O mitigation potential; Harmsen et al. 
2050 mitigation potential at USD125 is 0.6 GtCO 2-eq yr–1 while 
the integrated assessment estimate for the same price range is

0.7 GtCO 2-eq yr–1. The difference is substantially larger for the CH 4 
mitigation potential; 0.9 GtCO 2-eq yr–1 in Harmsen et al. while 
2 GtCO 2-eq yr–1 the median integrated assessment estimate. While 
the Harmsen et al. MACCs consider only technological mitigation 
options, integrated assessments typically include also demand side 
response to the carbon price and GHG efficiency improvements 
through structural change and international trade. These additional 
mitigation options can represent more than 60% of the total non-CO 2 
mitigation potential in the agricultural sector, where they are more 
important in the livestock sector, and thus the difference between 
sectoral and integrated assessments is more pronounced for the CH 4 
emissions (Frank et al. 2019).
Economic CO 2 mitigation potentials from land-use change and forestry 
are larger compared to potentials from non-CO 2 gases, and at the 
same time reveal high levels of variation in absolute terms. The 66th

percentile in 2050 goes up to 5.2 GtCO 2-eq yr–1 mitigation, while 
the lowest observations are even negative, indicating higher CO 2 
emissions from land use in scenarios with carbon price compared to 
scenarios without (counterintuitive dynamics explained below).
Land use is at the centre of the interdependencies with other sectors, 
including energy. Some models see a  strong competition between 
BECCS deployment with its respective demand for biomass, and 
CO2 mitigation/sequestration potentials in the land sector. Biomass 
demand may lead to an increase in CO 2 emissions from land use 
despite the application of a carbon price when land-use expansion 
for dedicated biomass production, such as energy plantations, comes 
from carbon rich land use/land cover alternatives, or when increased 
extraction of biomass from existing land uses, such as forest 
management, leads to reduction of the carbon sink (Daioglou 2019;

Luderer et al. 2018) and can explain the high variety of observations 
in some cases. Overall, the large variety of observations shows a large 
variety of plausible results, which can go back to different model 
structures and assumptions, showing a  robust range of plausible 
outcomes (Kriegler et al. 2015).

810
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.5.3 Interaction Between Mitigation in the AFOLU 
Sector and Other SDGs in the Context of 
Integrated Assessments
Besides the level of biomass supply for bioenergy, the adoption of 
SDGs may also significantly impact AFOLU emissions and the land-
use sector’s ability for GHG abatement (Frank et al. 2021). Selected 
SDGs are found to have positive synergies for AFOLU GHG abatement 
and to consistently decrease GHG emissions for both agriculture and 
forestry, thereby allowing for even more rapid and deeper emissions 
cuts. In particular, the decreased consumption of animal products 
and less food waste (SDG 12), and the protection of high biodiversity 
ecosystems such as primary forests (SDG 15) deliver high synergies 
with GHG abatement. On the other hand, protection of highly 
biodiverse ecosystems from conversion (SDG 15) limits the global

biomass potentials for bioenergy (Frank et al. 2021), and while several forestry measures enhancing woody biomass supply for bioenergy may 
have synergies with improving ecosystems conditions, many represent 
a threat to biodiversity (Camia et al. 2020) (Sections 7.6.5 and 17.3.3.7, 
Figure 17.1 and Supplementary Material Table 17.SM.1).
7.5.4 Regional AFOLU Abatement 
for Different Carbon Prices
At the regional level (Figure 7.16), the highest potential from non-CO 2 
emissions abatement, and mostly from CH 4, is reported for ASIA with 
the median of mitigation potential observations from CH 4 increasing 
up to a  price of USD200 in the year 2050, reaching a  median of 
1.2 GtCO 2-eq yr–1. In terms of economic potential, ASIA is followed 
by LAM, AFRICA, and OECD+EU, where emission reduction mainly is 
achieved in the livestock sector.
2030 2050LAM ASIA OECD90+EU REF AFRICA MIDDLE_EAST−500
500
1500
2500
−500
500
1500
2500(0,50)(50,100)(100,150)(150,200)(200,250)

(0,50)(50,100)(100,150)(150,200)(200,250)
(0,50)(50,100)(100,150)(150,200)(200,250)Carbon price USD2010 tCO2–1CO2 (in CO2-eq yr–1) CH4 (in CO2-eq yr–1) N2O (in CO2-eq yr–1)
GHG Mitigation (MtCO2-eq yr–1)
Figure 7.16 | Regional mitigation efforts for CO 2, CH 4 and N 2O emissions (in CO 2-eq yr–1 using IPCC AR6 GWP100 values) from the AFOLU sector for 
increasing carbon price levels for 2030 and 2050.  Underlying datapoints are generated by comparing emissions between a policy scenario and a related benchmark 
scenario, mapping these differences with the respective carbon price differences. Boxplots show Medians (vertical line within the boxes), Means (dots), 33%–66% intervals 
(box) and 10%–90% intervals (horizontal lines) for respective scenarios of carbon prices implemented in intervals of USD50 from a price of USD0 to USD250. Regions: Asia

(ASIA), Latin America and Caribbean (LAM), Middle East (MIDDLE_EAST), Africa (AFRICA), Developed Countries (OECD 90 and EU) (OECD+EU) and Reforming Economies of 
Eastern Europe and the Former Soviet Union (REF).

811
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77The highest potentials from land-related CO 2 emissions, including 
avoided deforestation as well as afforestation, can be observed in 
LAM and AFRICA with strong responses of mitigation (indicated 
by the median value) to carbon prices mainly in the lower range 
of displayed carbon prices. In general, CO 2 mitigation potentials 
show a wide range of results in comparison to non-CO 2 mitigation 
potentials, but mostly also a higher median value. The most extreme 
ranges are reported for the regions LAM and AFRICA. A medium 
potential is reported for ASIA and OECD+EU, while REF has the 
smallest potential according to model submissions. These estimates 
reflect techno-economic potentials and do not necessarily include 
feasibility constraints which are discussed in Chapter 7.6.7.5.5 Illustrative Mitigation Pathways
Different mitigation strategies can achieve the net emission reductions

that would be required to follow a pathway limiting global warming, 
with very different consequences for the land system. Figure  7.17 
shows Illustrative Mitigation Pathways (IMPs) for achieving different 
climate targets highlighting AFOLU mitigation strategies, resulting 
GHG and land-use dynamics as well as the interaction with other 
sectors. For consistency this chapter discusses IMPs as described in 
detail in chapters 1 and 3 of this report but focusing on the land-use 
sector. All pathways are assessed by different IAM realisations and do 
not only reduce GHG emissions but also use CDR options, whereas GtCO2-eq yr–1 106 km2CO2|AFOLU N2O|AFOLU BECCS CH4|AFOLUEmission typea. Global land-based GHG emissions and removals
Cropland Pasture Forest Other land Built−up area BioenergyLand typeb. Global land-use change compared to 2020ModAct
2025 2050 2075 2100−10010Neg-2.0
2025 2050 2075 2100Ren
2025 2050 2075 2100SP
ModAct Neg-2.0 Ren SP2025 2050 2075 2100
−10

2025 2050 2075 2100−2001020
2025 2050 2075 2100 2025 2050 2075 2100 2025 2050 2075 2100
Figure 7.17 | Evolution and breakdown of (a) global land-based GHG emissions and removals and (b) global land-use dynamics under four Illustrative 
Mitigation Pathways, which illustrate the differences in timing and magnitude of land-based mitigation approaches including afforestation and 
BECCS.  All pathways are based on different IAM realisations: ModAct scenario (below 3.0°C, C6) from IMAGE 3.0; IMP Neg-2.0 (limit warming to 2°C (>67%), C3) from AIM/
CGE 2.2; IMP Ren (1.5°C with no or low overshoot, C1) from REMIND-MAgPIE 2.1–4.3; IMP SP (1.5°C with no or low overshoot, C1) from REMIND-MAgPIE 2.1–4.2. In panel 
A the categories CO 2 AFOLU, CH 4 AFOLU and N 2O AFOLU include GHG emissions from land-use change and agricultural land use (including emissions related to bioenergy

production). In addition, the category CO 2 Land includes removals due to afforestation/reforestation. BECCS reflects the CO 2 emissions captured from bioenergy use and stored 
in geological deposits. CH 4 and N 2O emissions are converted to CO 2-eq using GWP100 factors of 27 and 273 respectively.

812
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7the amount and timing varies across pathways, as do the relative 
contributions of different land-based CDR options.
The scenario ModAct (limit warming to 3°C (>50%), C6) is based on 
the prolongation of current trends (SSP2) but contains measures to 
strengthen policies for the implementation of National Determined 
Contributions (NDCs) in all sectors including AFOLU (Grassi et al. 
2018). This pathway shows a strong decrease of CO 2 emissions from 
land-use change in 2030, mainly due to reduced deforestation, as well 
as moderately decreasing N 2O and CH 4 emissions from agricultural 
production due to improved agricultural management and dietary 
shifts away from emissions-intensive livestock products. However, 
in contrast to CO 2 emissions, which turn net-negative around 2050 
due to afforestation/reforestation, CH 4 and N 2O emissions persist 
throughout the century due to difficulties of eliminating these

residual emissions based on existing agricultural management 
methods (Frank et al. 2017; Stevanović et al. 2017). Comparably small 
amounts of BECCS are applied by the end of the century. Forest area 
increases at the cost of other natural vegetation.
IMP-Neg is similar to ModAct scenario in terms of socio-economic 
setting (SSP2) but differs strongly in terms of the mitigation target 
(limit warming to 2°C (>67%), C3) and its strong focus on the supply 
side of mitigation measures with strong reliance on net-negative 
emissions. Consequently, all GHG emission reductions as well as 
afforestation/reforestation and BECCS-based CDR start earlier in 
time at a  higher rate of deployment. However, in contrast to CO 2 
emissions, which turn net-negative around 2030 due to afforestation/
reforestation, CH 4 and N 2O emissions persist throughout the century, 
similar to ModAct, due to ongoing increasing demand for total 
calories and animal-based commodities (Bodirsky et al. 2020) and

difficulties of eliminating these residual emissions based on existing 
agricultural management methods (Stevanović et al. 2017; Frank 
et al. 2017). In addition to abating land-related GHG emissions 
as well as increasing the terrestrial sink, this example also shows 
the potential importance of the land sector in providing biomass 
for BECCS and hence CDR in the energy sector. Cumulative CDR 
(2020–2100) amounts to 502 GtCO 2 for BECCS and 121 GtCO 2 for 
land-use change (including afforestation and reduced deforestation). 
In consequence, compared to ModAct scenario, competition for 
land is increasing and much more other natural land as well as 
agricultural land (cropland and pasture land) is converted to forest or 
bioenergy cropland with potentially severe consequences for various 
sustainability dimensions such as biodiversity (Hof et al. 2018) and 
food security (Fujimori et al. 2019).
IMP-Ren is similar to IMP Neg-2.0 in terms of socio-economic

setting (SSP2) but differs substantially in terms of mitigation target 
and mitigation efforts in the energy sector. Even under the more 
ambitious climate change mitigation target (1.5°C with no or low 
overshoot (OS), C1), the high share of renewable energy in IMP Ren 
strongly reduces the need for large-scale land-based CDR, which is 
reflected in smaller bioenergy and afforestation areas compared to 
IMP Neg-2.0. However, CH 4 and N 2O emissions from AFOLU persist 
throughout the century, similar to ModAct scenario and IMP Neg-2.0.In contrast to IMPs Neg-2.0 and Ren, IMP SP (Soergel et al. 2021; 1.5°C 
with no or low OS, C1) displays a future of generally low resource and 
energy consumption (including healthy diets with low animal-calorie 
shares and low food waste) as well as significant but sustainable 
agricultural intensification in combination with high levels of 
nature protection. This pathway shows a strong near-term decrease

of CO 2 emissions from land-use change, mainly due to reduced 
deforestation, and in difference to all other IMPs described in this 
chapter strongly decreasing N 2O and CH 4 emissions from agricultural 
production due to improved agricultural management but also based 
on dietary shifts away from emissions-intensive livestock products 
as well as lower shares of food waste. In consequence, comparably 
small amounts of land are needed for land demanding mitigation 
activities such as BECCS and afforestation. In particular, the amount 
of agricultural land converted to bioenergy cropland is smaller 
compared to other mitigation pathways. Forest area increases either 
by regrowth of secondary vegetation following the abandonment 
of agricultural land or by afforestation/reforestation at the cost of 
agricultural land.
7.6 Assessment of Economic, Social 
and Policy Responses
7.6.1 Retrospective in Policy Efforts and Achieved 
Mitigation Within AFOLU

Since the establishment of the UNFCCC, international agencies, 
countries, sub-national units and NGO’s have developed policies to 
facilitate and encourage GHG mitigation within AFOLU (Figure 7.18). 
Early guidance and policies focused on developing GHG inventory 
methodology with some emphasis on afforestation and reforestation 
projects, but the Clean Development Mechanism (CDM) in the Kyoto 
Protocol focused attention on emission reduction projects, mostly 
outside of AFOLU. As successive IPCC AR6 WGIII reports illustrated 
large potential for AFOLU mitigation, methods to quantify and 
verify carbon emission reductions emerged within several projects 
in the early 2000s, through both voluntary (e.g.,  the Chicago 
Climate Exchange (CCX)) and regulated (e.g., New South Wales and 
California) markets. The CDM dedicated large attention to LULUCF , 
including dedicated methodologies and bodies. The reasons for 
limited uptake of CDM afforestation/reforestation projects were

multiple and not limited to the regulatory constraints, but also due to 
the low abatement potential (poor cost/performance ratio) compared 
to other mitigation opportunities.
Following COP 13 in Bali, effort shifted to advancing policies to 
reduce deforestation and forest degradation (REDD+) in developing 
countries. According to Simonet et al. (2019), nearly 65 Mha have 
been enrolled in REDD+ type programmes or projects funded 
through a variety of sources, including United Nations Programme 
on Reducing Emissions from Deforestation and Forest Degradation 
(UN-REDD), the World Bank Forest Carbon Partnership Facility, and 
bi-lateral agreements between countries with Norway being the 
largest donor. While there has been considerable focus on forest and

813
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77agricultural project-based mitigation actions, national governments 
were encouraged to incorporate project-based approaches with other 
sectoral strategies in their Nationally Appropriate Mitigation Actions 
(NAMAs) after 2012. NAMAs reflect the country’s proposed strategy 
to reduce net emissions across various sectors within their economy 
(e.g.,  forests or agriculture). More recently, Nationally Determined 
Contributions (NDCs) indicate whether individual countries plan to 
use forestry and agricultural policies or related projects amongst a set 
of measures in other sectors, to reduce their net emissions as part of 
the Paris Agreement (e.g., Forsell et al. 2016; Fyson and Jeffery 2019).
The many protocols now available can be used to quantify the 
potential mitigation to date resulting from various projects or 
programs. For instance, carbon registries issue credits using protocols

that typically account for additionality, permanence and leakage, thus 
providing evidence that the projects are a net carbon benefit to the 
atmosphere. Protocol development engages the scientific community, 
project developers, and the public over a  multi-year period. Some 
protocols have been revised multiple times, such as the USA State of 
California’s forest carbon protocol, which is in its fifth revision, with 
the latest in 2019 (see http://www.climateactionreserve.org/how/
protocols/forest/). Credits from carbon registries feed into regulatory 
programs, such as the cap and trade programme in California, or voluntary offset markets (Hamrick and Gallant 2017a). Although 
AFOLU measures have been deployed across a range of projects and 
programmes globally to reduce net carbon emissions, debate about 
the net carbon benefits of some projects continues (e.g., Krug 2018).
A new assessment of projects over the last two decades finds

emission reductions or offsets of at least 7.9 GtCO 2-eq (using 
GWP100  and a mix of IPCC values for CH 4 and N 2O) over the last 12 
years due to agricultural and forestry activities (Table 7.4). More than 
80% of these emission reductions or offsets have been generated 
by forest-based activities. The total amounts to 0.66 GtCO 2 yr–1 for 
the period 2010–2019, which is 1.2% of total global, and 5.5% of 
AFOLU emissions reported in Table 7.1, over the same time period 
The array of activities in Table 7.4 includes the Clean Development 
Mechanism, REDD+ activities reported in technical annexes of 
country biennial update reports to the UNFCCC, voluntary market 
transactions, and carbon stored as a  result of carbon markets in 
Australia, New Zealand and California in the USA. Although other 
countries and sub-national units have developed programmes 
and policies, these three regions are presented due to their focus

on forest and agricultural carbon mitigation, their use of generally 1992 1997 2007 2012 2015
UNFCCC Kyoto Protocol COP 13 Bali/REDD+ Paris Agreement
DevelopingCOP 18
Compliance 
& voluntary 
market/
ﬁnancing 
timelineInternational 
Agreement
Mechanism 
developedGHG Inventory/
Comprehensive 
Coverage of 
LULUCF & non-CO 2 
emissions in agri.GHG Inventory/
emissions in agri.Nationally 
Appropriate 
Mitigation Actions 
(NAMA)Nationally 
Determined 
Contributions 
(NDC)
Nationally 
(NDC)Nationally 
(NAMA)Clean Development 
Mechanism (CDM)Avoided deforestation
Reference level
Results-based 
payments
1990: Face 
Foundation 
(Netherlands)1996: Noel 
Kempf Mercado 
Project (Bolivia)
1996: American 
Carbon Registry
2004: California early 
action forest/ag.
2005: Veriﬁed 
Carbon Standard2003: Chicago 
Climate Exchange & 
New South Wales 
GHG Scheme
2003: Gold Standard2008–2010: World

Bank FCPF Norway/
NORAD/ 
Amazon Fund/
2012: Cumulative 
forest/ag voluntary 
Transactions exceed 
1 billion USD
Figure 7.18 | Milestones in policy development for AFOLU measures. ‘ag.’ and ‘agri.’ = agriculture.

814
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7accepted protocols or measures and the availability of data to 
quantify outcomes.
The largest share of emission reductions or carbon offsets in Table 7.4 
has been from slowing deforestation and REDD+, specifically 
from efforts in Brazil (86% of total), which substantially reduced 
deforestation rates between 2004 and 2012 (Nepstad et al. 2014), 
as well as other countries in Latin America. With the exception of 
Roopsind et al. (2019), estimated reductions in carbon emissions from 
REDD+ in Table 7.4 are measured relative to a historical baseline. As 
noted in Brazil’s Third Biennial Update Report (Ministry of Foreign 
Affairs 2019), estimates are made in accordance with established 
methodologies to determine the benefits of results-based REDD+ 
payments to Brazil. REDD+ estimates from other countries also have 
been derived from biennial update reports.

Regulatory markets provide the next largest share of carbon removal 
to date. Data from the Australia Emissions Reduction Fund are 
carbon credits issued in for agricultural, and vegetation and savanna 
burning projects. In the case of California, offset credits from forest 
and agricultural activities, using methods approved by a third-party 
certification authority (Climate Action Reserve), have been allowed as 
part of their state-wide cap and trade system. Transaction prices for 
forest and agricultural credits in California were around USD13 tCO 2–1 
in 2018, and represented 7.4% of total market compliance. By the 
end of 2018, 80 MtCO 2 had been used for compliance purposes.For New Zealand, the carbon reduction in Table 7.4 represents forest 
removals that were surrendered from post-1989 forests between 2008 
and the 2020. Unlike offsets in voluntary markets or in California, 
where permanence involves long-term contracts or insurance

pools, forests in the New Zealand market liable for emissions when 
harvested or following land-use change. This means sellers account 
for future emission risks related to harvesting when they enter forests 
into carbon contracts. Offset prices were around USD13 tCO 2–1 in 
2016 but have risen to more than USD20 tCO 2–1 in 2020.
The voluntary market data in Table  7.4 are offsets developed 
under the major standard-setting organisations, and issued from 
2008–2018 (e.g., Hamrick and Gallant 2018). Note that there is some 
potential for double counting of voluntary offsets that may have 
been transacted in the California compliance market, however this 
would only have applied to transactions of US-issued offsets, and 
the largest share of annual transactions of voluntary AFOLU credits 
occurs with credits generated in Latin America, followed by Africa, 
Asia and North America. Europe and Oceania have few voluntary 
carbon market transactions. Within forestry and agriculture, most

of the voluntary offsets were generated by forestry projects. Using 
historical transaction data from various Forest Trends reports, the 
offsets generated were valued at USD46.9 million yr–1. Prices for 
voluntary offset transactions in the period 2014–2016 ranged from 
USD4.90 to USD5.40 tCO 2–1 (Hamrick and Gallant 2017a).
Table 7.4 | Estimates of achieved emission offsets or reductions in AFOLU through 2018.  Data include CDM, voluntary carbon standards, compliance markets, and 
reduced deforestation from official UNFCCC reports. Carbon sequestration due to other government policies not included.
Fund/mechanismTotal emission reductions 
or offsets (MtCO 2-eq)Time frame MtCO 2-eq yr–1 Financing
(million USD yr–1)
CDM-forest a11.3 2007–2015 1.3 –
CDM-agriculture a21.8 2007–2015 2.4 –
REDD+ (Guyana) b12.8 2010–2015 2.1 33.0
Reduced 
Deforestation/REDD+ Brazil c6894.5 2006–2017 574.5 49.2
REDD+ Indonesia c244.9 2013–2017 49.0 13.4
REDD+ Argentina c165.2 2014–2015 55.1 1.4

REDD+ Others c211.8 2010–2017 26.5 46.0
Voluntary Market d95.3 2009–2018 9.5 46.9
Australia ERF e42.7 2012—2019h6.1 53.6
California f122.2 2013–2018 20.4 227.1
New Zealand carbon trading g83.9 2010–2019 8.4 101.7
Total 7,897.4 2007–2018 658.1 h 569.1
a Clean Development Mechanism Registry: https://cdm.unfccc.int/Registry/index.html (accessed 22 June 2021).
b Roopsind et al. 2019.
c UNFCCC REDD+ Web Platform (https://redd.unfccc.int/submissions.html) and UNFCCC Biennial Update Report database (https://unfccc.int/BURs).
d (Hamrick and Gallant 2017a). State of Forest Carbon Finance. Forest Trends Ecosystem Marketplace. Washington, DC, USA.
e Data for Australia carbon credit units (ACCUs) from Australia Emissions Reduction Fund Registry for agricultural and vegetation and savanna burning projects through 
FY2018/19 (downloaded on 24/10/2019): (http://www.cleanenergyregulator.gov.au/ERF/project-and-contracts-registers/project-register) and from Emissions Reduction Fund

auction results to December 2018: (http://www.cleanenergyregulator.gov.au/ERF/auctions-results/december-2018).
f Data from the California Air Resources Board Offset Issuance registry (https://ww2.arb.ca.gov/our-work/programs/compliance-offset-program) for forestry and agricultural 
early action and compliance credits.
g Surrendered forest carbon credits from post-1989 forests in New Zealand. Obtained from New Zealand Environmental Protection Authority. ETS Unit Movement interactive 
report (Excel based). https://www.epa.govt.nz/industry-areas/emissions-trading-scheme/ets-reports/unit-movement/.
h Obtained 13/08/2020. All non-CO 2 gases are converted to CO 2-eq using IPCC GWP100 values recommended at the time the project achieved approval by the relevant 
organisation or agency.

815
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Voluntary finance has amounted to USD0.5 billion over a  10-year 
period for development of forest and agricultural credits. The three 
regulatory markets quantified amount to USD2.7 billion in funding 
from 2010 to 2019. For the most part, this funding has focused on 
forest projects and programs, with agricultural projects accounting 
for 5–10% of the total. In total, reported funding for AFOLU 
projects and programmes has been USD4.4 billion over the past 
decade, or about USD569 million yr–1 (low confidence). The largest 
share of the total carbon includes efforts in the Amazon by Brazil. 
Government expenditures on regulatory programmes and business 
expenditures on voluntary programmes in Brazil (e.g.,  the soy or 
cattle moratoriums) were not included in financing estimates due to 
difficulties obtaining that data. If Brazil and CDM (for which we have

no cost estimates) are left out of the calculation, average cost per ton 
has been USD3.20 tCO 2–1.
The large number of policy approaches described in Table  7.4 
combined with efforts by other international actors, such as the 
Global Environmental Facility (GEF), as well as non-state actors 
(e.g.,  eco-labelling programmes and corporate social responsibility 
initiatives) illustrate significant policy experimentation over the last 
several decades. Despite widespread effort, AFOLU measures have 
thus far failed to achieve the large potential for climate mitigation 
described in earlier IPCC WG III reports (high confidence). The limited 
gains from AFOLU to date appear largely to result from lack of 
investment and other institutional and social barriers, rather than 
methodological concerns (high confidence).
7.6.2 Review of Observed Policies 
and Policy Instruments
7.6.2.1 Economic Incentives
Emissions Trading/Carbon Taxes. While emissions trading

programmes have been developed across the globe, forest and 
agriculture have not been included as part of the cap in any of the 
existing systems. However, offsets from forestry and agriculture 
have been included in several of the trading programs. New Zealand 
has a  hybrid programme where carbon storage in forests can be 
voluntarily entered into the carbon trading program, but once 
entered, forests are counted both as a sink for carbon if net gains 
are positive, and a  source when harvesting occurs. New Zealand 
is considering rules to include agricultural GHG emissions under 
a future cap (Henderson et al. 2020; see: https://www.agmatters.nz/
topics/he-waka-eke-noa/).
The state of California has developed a  formal cap and trade 
programme that allows a limited number of forest and agricultural 
offset credits to be used under the cap. All offsets must meet protocols 
to account for additionality, permanence and leakage. Forest projects

used as offsets in California currently are located in the USA, but 
the California Air Resources Board adopted a tropical forest carbon 
standard, allowing for avoided deforestation projects from outside 
the USA to enter the California market (CARB 2019).Canadian provinces have developed a range of policy options that can 
include carbon offsets. Quebec has an emissions trading programme 
that plans to allow forest and agricultural offsets generated within 
the province to be utilised. Alberta also allows offsets to be utilised by 
regulated sectors while British Columbia allows offsets to be utilised 
by the government for its carbon neutrality goals (Government of 
Alberta, 2021). Over 20 countries and regions have adopted explicit 
carbon taxes on carbon emission sources and fossil fuels, however, 
the charges have not been applied to non-CO 2 agricultural emissions 
(OECD 2021a). California may implement regulations on methane

emissions from cattle, however, regulations if approved, will not 
go into effect until 2024. Institutional and trade-related barriers 
(e.g., leakage) likely will limit widespread implementation of taxes 
on emissions in the food sector globally. Many countries exempt 
purchases of fuels used in agricultural or fishery production from 
fuel or carbon taxes, thus lowering the effective tax rate imposed on 
those sectors (OECD 2021a). Furthermore, bioenergy, produced from 
agricultural products, agricultural waste, and wood is often exempted 
from explicit carbon taxes. Colombia recently implemented a carbon 
tax on liquid fuels but allowed domestically produced forestry credits 
to offset the tax. Colombia also is in the process of developing an 
emissions trading scheme (OECD 2021a).
REDD+/Payment for Ecosystem Services (PES). PES programmes 
for a  variety of ecosystem services have long been utilised for 
conservation (e.g.,  Wunder 2007) and may now be as large as

USD42  billion yr–1 (Salzman et al. 2018). REDD+ emerged in the 
early 2000s and is a widely recognised example of PES programme 
focused on conservation of tropical forests (Table  7.4). However, 
our summation of actually paid funds in Table 7.4 is much smaller 
than what is portrayed by Salzman et al. (2018). REDD+ may 
operate at the country level, or for specific programmes or forests 
within a country. As with other PES programs, REDD+ has evolved 
towards a  results-based programme that involves payments that 
are conditioned on meeting certain successes or milestones, such as 
rates of deforestation (Angelsen 2017).
A large literature has investigated whether PES programmes have 
successfully protected habitats. Studies in the USA found limited 
additionality for programmes that encouraged conservation tillage 
practices, but stronger additionality for programmes that encouraged 
set-asides for grasslands or forests (Woodward et al. 2016; Claassen

et al. 2018), although the set-asides led to estimated leakage of 
20 up to 100% (Pfaff and Robalino 2017; Kallio et al. 2018; Wu 2000). 
Evidence from the EU similarly suggests that payments for some 
agroenvironmental practices may be additional, while others are not 
(Chabé-Ferret and Subervie 2013). Other studies, in particular in Latin 
America where many PES programmes have been implemented, have 
found a wide range of estimates of effectiveness (e.g., Honey-Rosés 
et al. 2011; Robalino and Pfaff 2013; Alix-Garcia et al. 2015; Robalino 
et al. 2015; Mohebalian and Aguilar 2016; Jayachandran et al. 2017; 
Börner et al. 2017; Burivalova et al. 2019). Despite concerns, the 
many lessons learned from PES programme implementation provide 
critical information that will help policymakers refine future PES 
programmes to increase their effectiveness (medium confidence).

816
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7While expectations that carbon-centred REDD+ would be a simple 
and efficient mechanism for climate mitigation have not been met 
(Turnhout et al. 2017; Arts et al. 2019), progress has nonetheless 
occurred. Measuring, monitoring and verification systems have been 
developed and deployed, REDD readiness programmes have improved 
capacity to implement REDD+ on the ground in over 50 countries, and 
a number of countries now have received results-based payments.
Empirical evidence that REDD+ funding has slowed deforestation 
is starting to emerge. Simonet et al. (2019) showed that a REDD+ 
project in Brazil reduced deforestation certainly until 2018, while 
Roopsind et al. (2019) showed that country-level REDD+ payments 
to Guyana encouraged reduced deforestation and increased carbon 
storage. Although more impact evaluation (IE) analysis needs to be 
conducted on REDD+ payments, these studies support the country-

level estimates of carbon benefits from REDD+ shown in Table 7.4. 
Nearly all of the analysis of PES and REDD+ to date has focused 
on the presence or absence of forest cover, with little to no analysis 
having been conducted on forest degradation, conservation, or 
enhancement of forest stocks.
Agroenvironmental Subsidy Programs/PES.  Climate policy 
for agriculture has developed more slowly than in other sectors 
due to concerns with food security and livelihoods, political 
interests,  and difficulties in coordinating diffuse and diverse 
activities and stakeholders (e.g., nutritional health, rural development, 
and biodiversity conservation) (Leahy et al. 2020). However, a review 
of the National Adaptation Programme of Action (NAPAs), National 
Adaptation Plans (NAPs), NAMAs, and NDCs in the Paris Agreement 
suggest an increasing focus of policy makers on agriculture and 
food security. The vast majority of parties to the Paris Agreement

recognise the significant role of agriculture in supporting a secure 
sustainable development pathway (Richards and VanWey 2015) with 
the inclusion of agriculture mitigation in 103 NDCs from a total of 
160 NDC submissions. Livestock is the most frequently cited specific 
agricultural sub-sector, with mitigation activities generally focusing 
on increasing efficiency and productivity.
Agriculture is one of the most subsidised industries globally, especially 
in the European Union and the USA. While subsidy payments over 
the last 20 years have shifted modestly to programmes designed 
to reduce the environmental impact of the agricultural sector, 
only 15–20% of the more than USD700 billion spent globally on 
subsidies are green payments (OECD 2021b). Under the Common 
Agricultural Policy in the EU, up to 30% of the direct payments to 
farmers (Pillar 1) have been green payments (Henderson et al. 2020), 
including some actions that could increase carbon storage or reduce

emissions. Similarly, at least 30% of the rural development payments 
(Pillar 2) are used for measures that reduce environmental impact, 
including reduction of GHG emissions and carbon storage. There is 
limited evidence that these policies contributed to the 20% reduction 
in GHG emissions from the agricultural sector in the EU between 
1990 and 2018 (Baudrier et al. 2015; Eurostat 2020).
The USA spends USD4 billion yr–1 on conservation programs, or 12% of 
net farm income (Department of Agriculture 2020). In real terms, this 
expenditure has remained constant for 15 years, supporting 12 Mha of permanent grass or woodland cover in the Conservation Reserve 
Program (CRP), which has increased soil carbon sequestration by 
3 tCO 2 ha–1 yr–1 (Conant et al. 2017; Paustian et al. 2019), as well as 
other practices that could lower net emissions. Gross GHG emissions 
from the agricultural sector in the US, however, have increased since

1990 (USEPA 2020) due to reductions in the area of land in the 
US CRP programme and changes in crop rotations, both of which 
caused soil carbon stocks to decline (USEPA 2020). When combined 
with increased non-CO 2 gas emissions, the emission intensity of US 
agriculture increased from 1.5 to 1.7 tCO 2 ha–1 between 2005 and 
2018 (high confidence).
China has implemented large conservation programmes that have 
influenced carbon stocks. For example, the Sloping Land Conversion 
Program, combined with other programs, has increased forest cover 
and carbon stocks, reduced erosion and increased other ecosystem 
services in China in recent years (Ouyang et al. 2016). As part of Brazil’s 
national strategy, numerous practices to reduce GHG emissions from 
agriculture, and in particular from the animal agriculture industry, 
have been subsidised. Estimates by Manzatto et al. (2020) suggest 
that the programme may have reduced agricultural emissions by

169 MtCO 2 between 2010 and 2020. Given the large technical and 
economic potential for agroforestry to be deployed in Africa, subsidy 
approaches could be deployed along with other polices to enhance 
carbon through innovative practices such as regreening (Box 7.10).
7.6.2.2 Regulatory Approaches
Regulations on land use include direct controls on how land is 
used, zoning, or legally set limits on converting land from one use to 
another. Since the early 2000s, Brazil has deployed various regulatory 
measures to slow deforestation, including enforcement of regulations 
on land-use change in the legal Amazon area. Enforcement of these 
regulations, among other approaches is credited with encouraging 
the large-scale reduction in deforestation and associated carbon 
emissions after 2004 (Nepstad et al. 2014). Empirical evidence has 
found that regulations reduced deforestation in Brazil (Arima et al. 
2014) but over time, reversals occurred when enforcement was not

consistent (Azevedo et al. 2017) (Box 7.9).
Many OECD countries have strong legal frameworks that influence 
agricultural and forest management on both public and private land. 
These include for example, legal requirements to protect endangered 
species, implement conservation tillage, protect riparian areas, replant 
forests after harvest, maintain historical species composition, forest 
certification, and other approaches. Increasingly, laws support more 
widespread implementation of nature-based solutions for a  range 
of environmental issues (e.g., see European Commission-EU 2021). 
The extent to which the combined influence of these regulations has 
enhanced carbon storage in ecosystems is not quantified although 
they are likely to explain some of the persistent carbon sink that has 
emerged in temperate forests of OECD countries (high confidence). In 
the least developed and developing countries, regulatory approaches

face challenges in part because environmental issues are a  lower 
priority than many other socio-economic issues (e.g.,  poverty, 
opportunity, essential services), and weak governance (Mayer 
Pelicice 2019; Walker et al. 2020) (Box 7.2).

817
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Set asides and protected areas have been a widely utilised approach 
for conservation, and according to (FAO 2020d), 726 Mha (18%) of 
forests are in protected areas globally. A review of land sparing and 
land sharing policies in developing countries indicated that most of 
them follow land sparing models, sometimes in combination with 
land sharing approaches. However, there is still no clear evidence of 
which policy provides the best results for ecosystem services provision, 
conservation, and livelihoods (Mertz and Mertens 2017). The literature 
contains a  wide range of results on the effectiveness of protected 
areas to reduce deforestation (Burivalova et al. 2019), with studies 
suggesting that protected areas provide significant protection of 
forests (e.g., Blackman 2015), modest protection (Andam et al. 2008), 
as well as increases in deforestation (Blackman 2015) and possible

leakage of harvesting to elsewhere (Kallio et al. 2018). An estimate 
of the contributions of protected areas to mitigation between 2000 
and 2012, showed that in the tropics, PAs reduced carbon emissions 
from deforestation by 4.88 PgC, or around 29%, when compared to the 
expected rates of deforestation (Bebber and Butt 2017). In that study, the tropical Americas (368.8 TgC yr−1) had the largest contribution, 
followed by Asia (25.0 TgC yr−1) and Africa (12.7 TgC yr−1). The 
authors concluded that local factors had an important influence on the 
effectiveness of protected areas. For example, in the Brazilian Amazon, 
protected area effectiveness is affected by the government agency 
controlling the land (federal indigenous lands, federal PAs, and state 
PAs) (Herrera et al. 2019). Because protected areas limit not just land-
use change, but also logging or harvesting non-timber forest products, 
they may be relatively costly approaches for forest conservation 

Community forest management (CFM) allows less intensive use 
of forest resources, while at the same time providing carbon benefits 
by protecting forest cover. Community forest management provides 
property rights to communities to manage resources in exchange 
for their efforts to protect those resources. In many cases, the local 
communities are indigenous people who otherwise would have 
insecure tenure due to an advancing agricultural frontier or mining 
Box 7.8 | Management of Native Forests by the Menominee people in North America 
and Lessons From Forest Owner Associations
Summary of the case.Indigenous peoples include more than 5000 different peoples, with over 370 million people, in 70 countries 
on five continents (UNIPP 2012). For example, in Latin America and Caribbean, forests cover more than 80% of the area occupied by 
indigenous peoples (330 million hectares) (FAO and FILAC, 2021) which points to their critical role for forest governance (Garnett et al.

2018; Fa et al. 2020). The Menominee people (Wisconsin, USA) practice sustainable forestry on their reservation according to a land 
ethic integral to the tribal identity. The Tribe calls themselves ‘The Forest Keepers’, recognising that the connection of their future to 
the sustainable management of the forest that allowed the forest volume standing today to be higher than when timber harvesting 
began more than 160 years ago. Management practices are based on continuous forest inventories (Mausel et al. 2017).
Introduction to the case. Forest management and timber harvesting operations began shortly after the Menominee Indian Reservation 
was created by treaty in 1854. The Menominee reservation sits on about 95,000 ha of land in Wisconsin that spans multiple forest types 
and is more diverse than adjacent forests. The collectively maintained reservation has 87% of its land under sustained yield forestry.

Case description. The Tribe, in the 19th century, had already mastered vegetation manipulation with fire, sustainable forestry, 
multiple-use, ecosystem, and adaptive management. The centrepiece of the Tribe’s economy has been its forest product industry, 
Menominee Tribal Enterprises (MTE) (Pecore 1992). A balance between growth and removals and continuous forest inventories (CFI) 
are central for forest management for the past 160 years, aiming not at very large volumes, but at very high-quality trees. During this 
same period, more than 2.3 billion board feet have been harvested from the same area, equivalent to 0.3 m3 ha–1 yr–1.
Interactions and limitations. In 2013, the Menominee Tribe started a collaboration with the US Forest Service to implement climate 
adaptation measures. The Tribe actively works to reduce the risk of forest damage and decided to further promote diversity by

planting tree seedlings adapted to a warming climate (https://toolkit.climate.gov/case-studies/and-trees-will-last-forever). However, 
new challenges are related to increasing pressures on forest ecosystems such as non-native insects, pathogens, weed invasions, and 
the costs for continuous forest inventories to support long-term forest management. 
Identified lessons. The elements of sustainability are intertwined with Menominee history, culture, spirituality, and ethics. The 
balance between the environment, community, and economy for the short term as well as future generations is an example of 
protecting the entire environment as the Menominee land is a non-fragmented remnant of the prehistoric Lake States forest which 
has been dramatically reduced all around the reserve (Schabel and Pecore 1997). These and other types of community forest owner 
associations exist all over the world. Examples are Södra in Sweden (with 52,000 forest owners) (Södra, 2021) or Waldbauernverband

in North-Rhine Westphalia (with 150,000 forest owners and covering 585,000 ha) (AGDW-The Forest Owners, 2021). These are ways 
for small forest owners to educate, jointly put wood on the market, employ better forest management, use machinery together, and 
apply certification jointly. In this manner and with all their diversity of goals, they manage to maintain carbon sinks and stocks, while 
preserving biodiversity and producing wood.

818
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7activity. Other examples are forest owner associations like those 
discussed in Box 7.8. According to the Rights and Resources Initiative 
(2018), the area of forests under community management increased 
globally by 152 Mha from 2002 to 2017, with over 500 Mha under 
community management in 2017. Studies have now shown that 
improved property rights with community forest management can 
reduce deforestation and increase carbon storage (Deininger and 
Minten 2002; Alix-Garcia et al. 2005; Alix-Garcia 2007; Bowler 
et al. 2012; Blackman 2015; Fortmann et al. 2017; Burivalova et al. 
2019). Efforts to expand property rights, especially community forest 
management, have reduced carbon emissions from deforestation in 
tropical forests in the last two decades (high confidence), although 
the extent of carbon savings has not been quantified globally.
Bioenergy targets. Multiple policies have been enacted at national

and supra-national levels to promote the use of bioenergy in the 
transport sector, and for bioelectricity production. Existing policies 
mandate or subsidise the production and use of bioenergy. In 
the past few years, policies have been proposed, put in place or 
updated in Australia (Renewable Energy Target), Brazil (RenovaBio, 
Nationally Determined Contribution), Canada (Clean Fuel Standard), 
China (Biodiesel Industrial Development Policy, Biodiesel Fuel Blend 
Standard), the European Union (Renewable Energy Directive II), the 
USA (Renewable Fuel Standards), Japan (FY2030), Russia (Energy 
Strategy Bill 2035), India (Revised National Policy on Biofuels), and 
South Africa (Biofuels Regulatory Framework).
While current policies focus on bioenergy to decarbonise the energy 
system, some also contain provisions to minimise the potential 
environmental and social trade-offs from bioenergy production. 
For instance, the EU Renewable Energy Directive (EU-RED II) and

US Renewable Energy Standard (US-RFS) assign caps on the use 
of biofuels, which are associated with indirect land-use change 
and food-security concerns. The Netherlands has a stringent set of 
36  sustainability criteria to which the certified biomass needs to 
comply. The EU-RED II also sets a timeline for the complete phase-
out of high-risk biofuels (Section 7.4.4).
7.6.2.3 Voluntary Actions and Agreements
Forest certification programs, such as Forest Sustainability Council 
(FSC) or Programme for the Endorsement of Forest Certification 
(PEFC), are consumer driven, voluntary programmes that influence timber harvesting practices, and may reduce emissions from forest 
degradation with reduced impact logging and other approaches 
(medium confidence). Forest certification has expanded globally  to 
over 440 Mha (Kraxner et al. 2017). As the area of land devoted 
to  certification has increased, the amount of timber produced from

certified land has increased. In 2018, FSC accounted for harvests of 
427 million m3 and jointly FSC and PEFC accounted for 689 million m3 
in 2016 or around 40% of total industrial wood production (FAO 
2018c). There is evidence that reduced impact logging can reduce 
carbon losses in tropical regions (Pearson et al. 2014; Ellis et al. 2019). 
However, there is conflicting evidence about whether forest certification 
reduces deforestation (e.g., Blackman et al. 2018; Tritsch et al. 2020).
Supply chain management in the food sector encourages 
more widespread use of conservation measures in agriculture 
(high confidence). The number of private commitments to reduce 
deforestation from supply chains has greatly increased in recent 
years, with at least 865 public commitments by 447 producers, 
processors, traders, manufacturers and retailers as of December, 2020 
(New York Declaration on Forests 2021). Industry partnerships with

NGOs, such as the Roundtable on Sustainable Palm Oil (RSPO), have 
become more widespread and visible in agricultural production. For 
example, RSPO certifies members all along the supply chain for palm 
oil and claims around 19% of total production. Similar sustainability 
efforts exist for many of the world’s major agricultural products, 
including soybeans, rice, sugar cane, and cattle.
There is evidence that the Amazon Soy Moratorium (ASM), an industry-
NGO effort whereby large industry consumers agreed voluntarily not 
to purchase soybeans grown on land deforested after 2006, reduced 
deforestation in the legal Amazon (Nepstad et al. 2014; Gibbs et al. 
2015). However, recent studies have shown that some deforestation 
from the Amazon was displaced to the Cerrado (Brazilian savannas) 
region (Moffette and Gibbs 2021), which is a  global hotspot for 
biodiversity, and has significant carbon stocks. These results illustrate

the importance of broadening the scope of supply chain management 
to minimise or eliminate displacement (Lima et al. 2019). In addition, 
while voluntary efforts may improve environmental outcomes for a time, 
it is not clear that they are sufficient to deliver long-term reductions in 
deforestation, given the increases in deforestation that have occurred 
in the Amazon in recent years (Box 7.9). Voluntary efforts would be 
more effective at slowing deforestation if they present stronger 
linkages to regulatory or other approaches (Lambin et al. 2018).
Box 7.9 | Case Study: Deforestation Control in the Brazilian Amazon
Summary
Between 2000 and 2004, deforestation rates in the Brazilian Legal Amazon (is a socio-geographic division containing all nine Brazilian 
states in the Amazon basin) increased from 18,226 to 27,772 km2 yr–1 2008 (INPE, 2021). A  set of public policies designed in

participatory process involving federal government, states, municipalities, and civil society successfully reduced deforestation rates 
until 2012. However, deforestation rates increased after 2013, and particularly between 2019 and 2020. Successful deforestation 
control policies are being negatively affected by changes in environmental governance, weak law enforcement, and polarisation of 
the national politics.

819
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Box 7.9 (Continued)
In 2004, the Brazilian federal government started the Action Plan for Prevention and Control of Deforestation in the Legal Amazon 
(PPCDAm) (Ministry of Environment, Government of Brazil, 2018).
The PPCDAm was a  benchmark for the articulation of forest conservation policies that included central and state governments, 
prosecutor offices, and the civil society. The decline in deforestation after 2008 is mostly attributed to these policy options. In 2012, 
deforestation rates decreased to 4,571 km2 yr–1.
Combating deforestation was a  theme in several programs, government plans, and projects not being more restricted to the 
environmental agenda. This broader inclusion resulted from a long process of insertion and articulation in the government dating

back to 2003 while elaborating on the Sustainable Amazon Plan. In May 2003, a historic meeting took place in an Amazonian city, 
with the president of the Republic, state governors, ministers, and various business leaders, civil institutions, and social movements. It 
was presented and approved the document entitled ‘Sustainable Amazonia – Guidelines and Priorities of the Ministry of Environment 
for the Sustainable Development of the Amazon Brazilian’, containing several guidelines for conservation and sustainable use in the 
region. At the meeting, the Union and some states signed a Cooperation Agreement aiming to elaborate a plan for the Amazon, to be 
widely discussed with the various sectors of the regional and national society (Ministerio do Meio Ambiente, MMA, 2013).
The PPCDAm had three main lines of action: (i) territorial management and land use; (ii) command and control; and (iii) promotion

of sustainable practices. During the execution of the 1st and 2nd phases of the PPCDAm (2004–2011), important results in the 
territorial management and land-use component included, for example, the creation of 25 Mha of federal Protected Areas (PAs) 
located mainly in front of the expansion of deforestation, as well as the homologation of 10 Mha of Indigenous Lands. Also, states 
and municipalities created approximately 25 Mha, so that all spheres of government contributed to the expansion of PAs in the 
Brazilian Amazon. In the ‘command and control’ component, agencies performed hundreds of inspection operations against illegal 
activities (e.g., illegal logging) under strategic planning based on technical and territorial priorities. Besides, there was a significant 
improvement of the environmental monitoring systems, involving the analysis of satellite images to guide actions on the ground.

Another policy was the restriction of public credit to enterprises linked to illegal deforestation following a resolution of the Brazilian 
Central Bank (2008) (Ministerio do Meio Ambiente, MMA, 2013). Also, in 2008, Brazil created the Amazon Fund, a REDD+ mechanism 
(Government of Brazil, n.d.).
However, the country’s political polarisation has gradually eroded environmental governance, especially after the Brazilian Forest 
Code changes in 2012 (major environmental law in Brazil), the presidential impeachment in 2016, presidential elections in 2018, and 
the start of the new federal administration in 2019. Successful deforestation control policies are being negatively affected by critical 
changes in the political context, and weakening the environmental rule of law, forest conservation, and sustainable development 
programmes (for example, changes in the Amazon Fund governance in disagreement with the main donors). In 2019, the annual

deforestation rate reached 10,129 km2 being the first time it surpassed 10,000 km2 since 2008 (INPE, 2021). Besides, there has been 
no effective transition from the historical economic model to a sustainable one. The lack of clarity in the ownership of land is still 
a major unresolved issue in the Amazon.
Lessons
The reduction of deforestation in the Brazilian Amazon was possible due to effective political and institutional support for environmental 
conservation. The initiatives of the Action Plan included the expansion of the protected areas network (conservation unities and 
indigenous lands), improvement of deforestation monitoring to the enforcement of environmental laws, and the use of economic 
instruments, for example, by cutting off public credit for municipalities with higher deforestation rates (Ricketts et al. 2010; Souza et al. 
2013; Nepstad et al. 2014; Arima et al. 2014; Blackman and Veit 2018).

The array of public policies and social engagement was a historical and legal breakthrough in global protection. However, the broader 
political and institutional context and actions to reduce the representation and independent control of civil society movements in 
decision-making bodies weaken this structure with significant increases in deforestation rates, burnings, and forest fires.

820
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.6.2.4 Mitigation Effectiveness: Additionality, 
Permanence and Leakage
Additionality, permanence and leakage have been widely discussed 
in the forestry and agricultural mitigation literature (Murray et al. 
2007), including in AR5 (Section 11.3.2 of the AR5WGIII report) and 
earlier assessment reports. Since the earlier assessment reports, 
new studies have emerged to provide new insights on the effect of 
these issues on the credibility of forest and agricultural mitigation. 
This assessment also provides additional context not considered in 
earlier assessments.
Typically, carbon registries will require that project developers 
show additionality by illustrating that the project is not undertaken 
as a  result of a  legal requirement, and that the project achieves 
carbon reductions above and beyond a  business as usual. The 
protocols developed by the California Air Resources Board to ensure

permanence and additionality are strong standards and may even 
limit participation (e.g., Ruseva et al. 2017). The business as usual 
is defined as past management actions by the same entity that 
can be verified. Additionality can thus be observed in the future as 
a  difference from historical actions. This approach has been used 
by several countries in their UNFCCC Biennial Update reports to 
establish reductions in carbon emissions from avoided deforestation 
(e.g., Brazil and Indonesia).However, alternative statistical approaches have been deployed in the 
literature to assess additionality with a quasi-experimental method that 
rely on developing a counterfactual (e.g., Andam et al. 2008; Blackman 
2015; Sills et al. 2015; Fortmann et al. 2017; Roopsind et al. 2019). In 
several studies, additionality in avoided deforestation was established 
after the project had been developed by comparing land-use change in

treated plots where the policy or programme was in effect with land-
use change in similar untreated plot. Alternatively, synthetic matching 
statistically compares trends in a  treated region (i.e., a  region with 
a policy) to trends in a region without the policy, and has been applied 
in a region in Brazil (e.g., Sills et al. 2015), and at the country level 
in Guyana (Roopsind et al. 2019). While these analyses establish that 
many projects to reduce deforestation have overcome hurdles related 
to additionality (high confidence), there has not been a  systematic 
assessment of the elements of project or programme design that lead 
to high levels of additionality. Such assessment could help developers 
design projects to better meet additionality criteria.
The same experimental methods have been applied to analyse 
additionality of the adoption of soil conservation and nutrient 
management practices in agriculture. Claassen et al. (2018) find that

programmes to promote soil conservation are around 50% additional 
across the USA (i.e., 50% of the land enrolled in soil conservation 
programmes would not have been enrolled if not for the programme), 
while Woodward et al. (2016) find that adoption of conservation Box 7.10 | Regreening the Sahel, Northern Africa
More than 200 million trees have regenerated on more than 5 Mha in the Sahel (Sendzimir et al. 2011). The Maradi/Zinder region of 
Niger is the epicentre of experimentation and scale up. This vast geographic extent generates significant mitigation potential despite 
the relatively modest per unit area increase in carbon of about 0.4 MgC ha–1 a–1 (Luedeling and Neufeldt 2012). In addition to the 
carbon benefits, these agroforestry systems decrease erosion, provide animal fodder, recharge groundwater, generate nutrition and 
income benefits and act as safety nets for vulnerable rural households during climate and other shocks (Bayala et al. 2014, 2015;

Binam et al. 2015; Sinare and Gordon 2015; Ilstedt et al. 2016).
Lessons
A mélange of factors contributed to regreening in the Sahel. Increased precipitation, migration, community development, economic 
volatility and local policy reform have all likely played a role (Haglund et al. 2011; Sendzimir et al. 2011; Brandt et al. 2019a; Garrity and 
Bayala 2019); the easing of forestry regulations has been particularly critical in giving farmers greater control over the management 
and use of trees on their land (Garrity et al. 2010). This policy shift was catalysed by greater regional autonomy resulting from 
economic decline and coincided with successful pilots and NGO-led experimentation, cash-for-work, and training efforts to support 
changes in land management (Sendzimir et al. 2011). Participation of farmers in planning and implementation helped align actions 
with local knowledge and goals as well as market opportunities.

Regreening takes place when dormant seed or tree stumps sprout and are cultivated through the technique, called Farmer Managed 
Natural Regeneration (FMNR). Without planting new trees, FMNR is presumed to be cheaper than other approaches to restoration, 
though comparative economic analysis has yet to be conducted (Chomba et al. 2020). Relatively lower investment costs are believed 
to have contributed to the replication across the landscape. These factors worked together to contribute to a groundswell of action 
that affected rights, access, and use of local resources (Tougiani et al. 2009).
Regreening in the Sahel and the consequent transformation of the landscape has resulted from the actions of hundreds of thousands 
of individuals responding to social and biophysical signals (Hanan 2018). This is an example for climate change mitigation, where 
eliminating regulations – versus increasing them – has led to carbon dioxide removal.

821
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77tillage is rarely additional. Claassen et al. (2018) find that payments 
for nutrient management plans are nearly 100% additional, although 
there is little evidence that farmers reduce nutrient inputs when they 
adopt plans. It is not clear if the same policy approaches would lead 
to additionality in other regions.
Permanence focuses on the potential for carbon sequestered in 
offsets to be released in the future due to natural or anthropogenic 
disturbances. Most offset registries have strong permanence 
requirements, although they vary in their specific requirements. The 
Verified Carbon Standard (VCS) from the Verra programme requires 
a  pool of additional carbon credits that provides a  buffer against 
inadvertent losses. The Climate Action Reserve (CAR) protocol for 
forests requires carbon to remain on the site for 100 years. The carbon 
on the site will be verified at pre-determined intervals over the life

of the project. If carbon is diminished on a given site, the credits for 
the site have to be relinquished and the project developer has to use 
credits from their reserve fund (either other projects or purchased 
credits) to make up for the loss. Estimates of leakage in forestry 
projects in AR5 suggest that it can range from 10% to over 90% in 
the USA (Murray et al. 2004), and 20–50% in the tropics (Sohngen 
and Brown 2004) for forest set-asides and reduced harvesting. 
Carbon offset protocols have made a  variety of assumptions. The 
Climate Action Reserve (CAR) assumes it is 20% in the USA. One of 
the voluntary protocols (Verra) uses specific information about the 
location of the project to calculate a location specific leakage factor.
More recent literature has developed explicit estimates of leakage 
based on statistical analysis of carbon projects or programs. The 
literature suggests that there are two economic pathways for leakage

(e.g.,  (Roopsind et al. 2019), either through a  shift in output price 
that occurs when outputs are affected by the policy or programme 
implementation, as described in (Wear and Murray 2004; Murray et al. 
2004; Sohngen and Brown 2004; Gan and McCarl 2007), or through 
a shift in input prices and markets, such as for labour or capital, as 
analysed in (Andam et al. 2008; Alix-Garcia et al. 2012; Honey-Rosés 
et al. 2011; Fortmann et al. 2017). Estimates of leakage through product 
markets (e.g.,  timber prices) have suggested leakage of up to 90% 
(Sohngen and Brown 2004; Murray et al. 2004; Gan and McCarl 2007; 
Kallio et al. 2018), while studies that consider shifts in input markets are 
considerably smaller. The analysis of leakage for the Guyana programme 
by Roopsind et al. (2019) revealed no statistically significant leakage in 
Suriname. A key design feature for any programme to reduce leakage 
is to increase incentives for complementary mitigation policies to be

implemented in areas where leakage may occur. Efforts to continue 
to draw more forests into carbon policy initiatives will reduce leakage 
over time Roopsind et al. (2019), suggesting that if NDCs continue to 
encompass a broader selection of policies, measures and forests over 
time, leakage will decline.
7.6.3  Assessment of Current Policies and Potential 
Future Approaches
The Paris Agreement encourages a wide range of policy approaches, 
including REDD+, sustainable management of forests, joint 
mitigation and adaptation, and emphasises the importance of non-carbon benefits and equity for sustainable development (Martius 
et al. 2016). Around USD0.7 billion yr–1 has been invested in land-
based carbon offsets (Table 7.4), but as noted in Streck (2012), there 
is a large funding gap between these efforts and the scale of efforts 
necessary to meet 1.5 or 2.0°C targets outlined in SR1.5. As Box 7.12 
discusses, forestry actions could achieve up to 5.8 GtCO 2 yr–1 with

costs rising from USD178 billion yr–1 to USD400 billion yr–1 by 2050. 
Over half of this investment is expected to occur in Latin America, 
with 13% in SE Asia and 17% in sub-Saharan Africa (Austin et al. 
2020). Other studies have suggested that similar sized programmes 
are possible, although they do not quantify total costs (e.g., Griscom 
et al. 2017; Busch et al. 2019). The currently quantified efforts 
to reduce net emissions with forests and agricultural actions are 
helpful, but society will need to quickly ramp up investments to 
achieve carbon sequestration levels consistent with high levels of 
mitigation. Only 2.5% of climate mitigation funding goes to land-
based mitigation options, an order of magnitude below the potential 
proportional contribution (Buchner et al. 2015).
To date, there has been significantly less investment in agricultural 
projects than forestry projects to reduce net carbon emissions 
(Table  7.4). For example, the economic potential (available up

to USD100 tCO 2–1) for soil carbon sequestration in croplands is 
1.9  (0.4–6.8) GtCO 2 yr–1 (Section  7.4.3.1), however, less than 2% 
of the carbon in Table 7.4 is derived from soil carbon sequestration 
projects. While reductions in CH 4 emissions due to enteric fermentation 
constitute a large share of potential agricultural mitigation reported 
in Section 7.4, agricultural CH 4 emission reductions so far have been 
relatively modest compared to forestry sequestration. The protocols 
to quantify emission reductions in the agricultural sector are available 
and have been tested, and the main limitation appears to be the 
lack of available financing or the unwillingness to re-direct current 
subsidies (medium confidence).
Although quantified emission reductions in agricultural projects 
are limited to date, a  number of OECD and economy in transition 
parties have reduced their net emissions through carbon storage 
in cropland soils since 2000. These reductions in emissions have

typically resulted from policy innovations outside of the climate 
space, or market trends. For example, in the USA, there has been 
widespread adoption of conservation tillage in the last 30 years as 
a labour-saving crop management technique. In Europe, agricultural 
N2O and CH 4 emissions have declined due to reductions in nutrient 
inputs and cattle numbers (Henderson et al. 2020). These reductions 
may be attributed to mechanism within the Common Agricultural 
Policy (Section 7.6.2.1), but could also be linked to higher nutrient 
prices in the 2000–2014 period. Other environmental policies 
could play a  role, for example, efforts to reduce water pollution 
from phosphorus in The Netherlands, may ultimately reduce cattle 
numbers, also lowering CH 4 emissions.
Numerous developing countries have established policy efforts 
to abate agricultural emissions or increase carbon storage. Brazil, 
for instance, developed a  subsidy programme in 2010 to promote

sustainable development in agriculture, and practices that would 
reduce GHG emissions. Henderson et al. (2020) report that this 
programme reduced GHG emission in agricultural by up to

822
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7170  MtCO 2 between 2010 and 2018. However, the investments in 
low-carbon agriculture in Brazil amounted only 2% of the total funds 
for conventional agriculture in 2019. Programmes on deforestation 
in Brazil had successes and failures, as described in Box  7.9. 
Indonesia has engaged in a wide range of programmes in the REDD+ 
space, including a  moratorium implemented in 2011 to prevent 
the conversion of primary forests and peatlands to oil palm and 
logging concessions (Wijaya et al. 2017; Tacconi and Muttaqin 2019; 
Henderson et al. 2020). Efforts to restore peatlands and forests have 
also been undertaken. Indonesia reports that results-based REDD+ 
programmes have been successful and have led to lower rates of 
deforestation (Table 7.4).
Existing policies focused on GHG management in agriculture 
and forestry is less advanced in Africa than in Latin American and

Asia, however, Henderson et al. (2020) report on 10 countries in 
sub-Saharan Africa that have included explicit policy proposals 
for reducing AFOLU GHG emissions through their NDCs. These 
include efforts to reduce N 2O emission, increase implementation 
of conservation agriculture, improve livestock management, and 
implement forestry and grassland practices, including agroforestry (Box  7.10). Within several of the NDCs, countries have explicitly 
suggested intensification as an approach to reduce emission in the 
livestock sector. However, it is important to note caveats associated 
with pursuing mitigation via intensification (Box 7.11).
The agricultural sector throughout the world is influenced by many 
policies that affect production practices, crop choices and land use. 
It is difficult to quantify the effect of these policies on reference 
level GHG emissions from the sector, as well as the cost estimates

presented in Sections 7.4 and 7.5. The presence of significant subsidy 
programmes intended to improve farmer welfare and rural livelihoods 
makes it more difficult to implement regulatory programmes aimed 
at reducing net emissions in agriculture, however, it may increase 
the potential to implement new subsidy programmes that encourage 
practices aimed at reducing net emissions (medium confidence). 
For instance, in the USA, crop insurance can influence both crop 
choices and land use (Miao et al. 2016; Claassen et al. 2017), both 
of which will affect emission trends. Regulations to limit nutrient 
applications have not been widely considered, however, federal 
subsidy programmes have been implemented to encourage farmers 
to conduct nutrient management planning.
Box 7.11 | Sustainable Intensification Within Agriculture: Evidence and Caveats

Sustainable intensification (SI) has received considerable attention as a suggested means of pursuing increased overall production, 
reducing associated environmental externalities, and potentially releasing agricultural land for alternative uses, such as forestry 
or rewilding (Godfray and Garnett 2014; Pretty 2018). The concept was explored within the SRCCL (SRCCL (Mbow et al. 2019), 
Section 5.6.4.4 and Cross-Chapter Box 6 in Chapter 5). SI is context specific and dynamic, with no universally prescribed methodology 
(HLPE 2019). Equal importance is given to enhancing sustainability as to achieving agricultural intensification. The former aspect is 
often challenging to realise, measure and maintain.
The extent of sustainable intensification
Total global agricultural land area has remained relatively stable while overall production has increased in recent decades (Section 7.3),

indicating that agricultural intensification, as judged by production per unit of land (Petersen and Snapp 2015; OECD and FAO 2019) has 
taken place. However, changes in agricultural land use and degradation of natural resources (UNEP 2019; IPBES 2019b) suggests that 
not all of this intensification is sustainable. Although agricultural intensification has led to less GHG emissions compared to a scenario 
where that intensification had not taken place (Burney et al. 2010), absolute agriculture related emissions have continued to increase 
(Section 7.2). Active pursuit of SI was found to be expanding, with implementation on an increasing area, notably in developing 
countries (Pretty et al. 2018), yet regional agricultural area expansion at the expense of native habitat also continues in such regions 
(Section 7.3). Although there are specific examples of SI (Box 7.13) global progress in achieving SI is acknowledged as slow (Cassman

and Grassini 2020) with potentially multiple, context specific geophysical and socio-economic barriers to implementation (Firbank 
et al. 2018; da Silva et al. 2021).
Preconditions to ensure sustainable intensification
Increasing the total amount of product produced by improving production efficiency (output per unit of input) does not 
guarantee SI.  It will only be successful if increased production efficiency translates into reduced environmental and social impacts 
as well as increased production. For example, AR5 highlighted a growing emphasis on reducing GHG emissions per unit of product 
via increasing production efficiency (Smith et al. 2014), but reductions in absolute GHG emissions will only occur when production 
efficiency increases at a greater rate than the rate at which production increases (Clark et al. 2005).
Defined indicators are required. Measurement of SI requires multiple indicators and metrics. It can be assessed at farm, regional or

global scales and temporal aspect must be considered. SI may warrant whole system redesign or land reallocation (Garnett et al. 2013; 
Pretty et al. 2018). Accordingly, there is high agreement concerning the need to consider multiple environmental and social outcomes

823
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77A factor that will influence future carbon storage in so-called 
land-based reservoirs involves considering short- and long-
term climate benefits, as well as interactions among various natural 
climate solution options. The benefits of various natural climate 
solutions depend on a variety of spatially dependent issues as well as 
institutional factors, including their management status (managed or 
unmanaged systems), their productivity, opportunity costs, technical 
difficulty of implementation, local willingness to consider, property 
rights and institutions, among other factors. Biomass energy, as 
described elsewhere in this chapter and in (Cross-Working Group 
Box  3 in Chapter  12), is a  potential example of an option with 
trade-offs that emerge when policies favour one type of mitigation 
strategy over another. Bioenergy production needs safeguards

to limit negative impacts on carbon stocks on the land base as is 
already in place in the EU Renewable Energy Directive and several 
national schemes in Netherlands, UK and Denmark (Buchholz et al. 
2016; Khanna et al. 2017; DeCicco and Schlesinger 2018; Favero 
et al. 2020). It is argued that a carbon tax on only fossil fuel derived 
emissions, may lead to massive deployment of bioenergy, although 
the effects of such a  policy can be mitigated when combined 
with policies that encourage sustainable forest management and 
protection of forest carbon stocks as well as forest management 
certification (high confidence) (Nabuurs et al. 2017, Baker et al. 2019 
and Favero et al. 2020).
If biomass energy production expands and shifts to carbon 
capture and storage (e.g.,  BECCS) during the century, there could 
be a  significant increase in the area of crop and forestland used 
for biomass energy production (Sections  7.4 and 7.5). BECCS is

not projected to be widely implemented for several decades, but 
in the meantime, policy efforts to advance land-based measures 
including reforestation and restoration activities (Strassburg et al. 2020) combined with sustainable management and provision of 
agricultural and wood products are widely expected to increase the 
terrestrial pool of carbon (Cross-Working Group Box 3 in Chapter 12). 
Carbon sequestration policies, sustainable land management (forest 
and agriculture), and biomass energy policies can be complementary 
(Favero et al. 2017; Baker et al. 2019). However, if private markets 
emerge for biomass and BECCS on the scale suggested in the SR1.5, 
policy efforts must ramp up to substantially value, encourage, and 
protect terrestrial carbon stocks and ecosystems to avoid outcomes 
inconsistent with many SDGs (high confidence).
7.6.4 Barriers and Opportunities for AFOLU Mitigation
The AR5 and other assessments have acknowledged many barriers

and opportunities to effective implementation of AFOLU measures. 
Many of these barriers and opportunities focus on the context in 
developing countries, where a  significant portion of the world’s 
cost-effective mitigation exists, but where domestic financing for 
implementation is likely to be limited. The SSPs capture some of this 
context, and as a  result, IAMs (Section  7.5) exhibit a  wide range 
of land-use outcomes, as well as mitigation potential. Potential 
mitigation, however, will be influenced by barriers and opportunities 
that are not considered by IAMs or by bottom-up studies reviewed 
here. For example, more efficient food production systems, or 
sustainable intensification within agriculture, and globalised trade 
could enhance the extent of natural ecosystems leading to lower 
GHG emissions from the land system and lower food prices (Popp 
et al. 2017), but this (or any) pathway will create new barriers 
to implementation and encourage new opportunities, negating

potential benefits (Box 7.11). It is critically important to consider the 
current context in any country.Box 7.11 (Continued) 
at wider spatial scales, such as catchments or regions (Weltin et al. 2018; Bengochea Paz et al. 2020; Cassman and Grassini 2020). 
Impacts may be considered in relative terms (per area or product unit), with relationships potentially antagonistic. Both area- and 
product unit-based metrics are valid, relevant under different contexts and useful in approaching SI, but do not capture overall impacts 
and trade-offs (Garnett 2014). To reduce the risk of unsustainable intensification, quantitative data and selection of appropriate 
metrics to identify and guide strategies are paramount (Garnett et al. 2013; Gunton et al. 2016; Cassman and Grassini 2020).
Avoiding unsustainable intensification
It is critical that intensification does not drive expansion of unsustainable practices. Increased productivity with associated economic

reward could incentivise and reward agricultural land expansion, or environmentally and socially damaging practices on existing 
and former agricultural land (Ceddia et al. 2013; Phalan 2018). Accordingly, coordinated policies are crucial to ensuring desired 
outcomes (Godfray and Garnett 2014; Reddy et al. 2020; Kassam and Kassam 2020). Barretto et al. (2013) found that in agriculturally 
consolidated areas, land-use intensification coincided with either a  contraction of both cropland and pasture areas, or cropland 
expansion at the expense of pastures, both resulting in a stable farmed area. In contrast, in agricultural frontier areas, land-use 
intensification coincided with expansion of agricultural lands.
In conclusion, SI within agriculture is needed given the rising global population and the need to address multiple environmental 
and social externalities associated with agricultural activities. However, implementation requires strong stakeholder engagement,

appropriate regulations, rigorous monitoring and verification and comprehensive outreach and knowledge exchange programmes.

824
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.6.4.1 Socio-economic Barriers and Opportunities
Design and coverage of financing mechanisms. The lack of 
resources thus far committed to implementing AFOLU mitigation, 
income and access to alternative sources of income in rural households 
that rely on agriculture or forests for their livelihoods remains 
a  considerable barrier to adoption of AFOLU (high confidence). 
Section  7.6.1 illustrates that to date only USD0.7  billion  yr–1 has 
been spent on AFOLU mitigation, well short of the more than 
USD400 billion yr–1 that would be needed to achieve the economic 
potential described in Section  7.4. Despite long-term recognition 
that AFOLU can play an important role in mitigation, the economic 
incentives necessary to achieve AFOLU aspirations as part of the 
Paris Agreement or to maintain temperatures below 2.0°C have not 
emerged. Without quickly ramping up spending, the lack of funding

to implement projects remains a substantial barrier (high confidence). 
Investments are critically important in the livestock sector, which has 
the highest emissions reduction potential among options because 
actions in the sector influence agriculture specific activities, such 
as enteric fermentation, as well as deforestation (Mayberry et al. 
2019). In many countries with export-oriented livestock industries, 
livestock farmers control large swaths of forests or re-forestable 
areas. Incentive mechanisms and funding can encourage adoption 
of mitigation strategies, but funding is currently too low to make 
consistent progress.
Scale and accessibility of financing. The largest share of funding 
to date has been for REDD+, and many of the commitments to 
date suggest that there will be significant funding in this area 
for the foreseeable future. Funding for conservation programmes

in OECD countries and China affects carbon, but has been driven by other objectives such as water quality and species protection. 
Considerably less funding has been available for agricultural 
projects aimed at reducing carbon emissions, and outside of 
voluntary markets, there do not appear to be large sources 
of  funding emerging either through international organisations, 
or national programs. In the agricultural sector, funding for carbon 
must be obtained by redirecting existing resources from non-GHG 
conservation to GHG measures, or by developing new funding 
streams (Henderson et al. 2020).
Risk and uncertainty. Most approaches to reduce emissions, 
especially in agriculture, require new or different technologies that 
involve significant time or financial investments by the implementing 
landholders. Adoption rates are often slow due to risk aversion among 
agricultural operators. Many AFOLU measures require carbon to be

compensated to generate positive returns, reducing the likelihood of 
implementation without clear financial incentives. Research to show 
costs and benefits is lacking in most parts of the world.
Poverty. Mitigation and adaptation can have important implications 
for vulnerable people and communities, for example, mitigation 
activities consistent with scenarios examined in the SR1.5 could raise 
food and fiber prices globally (Section. 7.5). In the NDCs, 82 Parties 
included references to social issues (e.g., poverty, inequality, human 
well-being, marginalisation), with poverty the most cited factor 
(70 Parties). The number of hungry and food insecure people in the 
world is growing, reaching 821 million in 2017, or one in every nine 
people (FAO 2018b), and two-thirds live in rural areas (Laborde 
Debucquet et al. 2020). Consideration of rural poverty and food 
insecurity is central in AFOLU mitigation because there are a large

number of farms in the world (about 570 million), and most are 
Box 7.12 | Financing AFOLU Mitigation; What Are the Costs and Who Pays?
Achieving the large contribution to mitigation that the AFOLU sector can make requires public and private investment. Austin et al. 
(2020) estimate that in forestry, USD178 billion yr–1 is needed over the next decade to achieve 5 GtCO 2 yr–1, and investments need 
to ramp up to USD400 billion yr–1 by 2050 to expand effort to 6 GtCO 2 yr–1. Other land-based options, such as mangrove protection, 
peatland restoration, and agricultural options would increase this total cost estimate, but have smaller to negligible opportunity costs.
Financing needs in AFOLU, and in particular in forestry, include both the direct effects of any changes in activities – costs of planting 
or managing trees, net revenues from harvesting, costs of thinning, costs of fire management, and so on – as well as the opportunity

costs associated with land-use change. Opportunity costs are a critical component of AFOLU finance, and must be included in any 
estimate of the funds necessary to carry out projects. They are largest, as share of total costs, in forestry because they play a prominent 
role in achieving high levels of afforestation, avoided deforestation, and improved forest management. In case of increasing soil 
carbon in croplands through reduced tillage, there are often cost savings associated with increased residues because there is less effort 
tilling, but the carbon effects per hectare are also modest. There could, however, be small opportunity costs in cases where residues 
may otherwise be marketed to a biorefinery. The effect of reduced tillage on yields varies considerably across sites and crop types, but 
tends to enhance yields modestly in the longer-run.

Opportunity costs are a direct financing costs for activities that require land uses to change. For instance a government can encourage 
planting forests on agricultural land by (a) requiring it, (b) setting up a market or market-based incentives, or (c) buying the land and 
doing it themselves. In each case, the required investment is the same – the planting cost plus the net foregone returns of agricultural 
rents – even though a different entity pays the cost. Private entities that pay for carbon credits will also bear the direct costs of 
planting plus the opportunity costs. In the case of avoided deforestation, opportunity costs similarly must be paid to individual actors 
to avoid the deforestation.

825
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77smaller than 2 hectares. It is important to better understand how 
different mitigation policies affect the poor.
Cultural values and social acceptance. Barriers to adoption 
of AFOLU mitigation will be strongest where historical practices 
represent long-standing traditions (high confidence). Adoption 
of new mitigation practices, however, may proceed quickly if the 
technologies can be shown to improve crop yields, reduce costs, 
or otherwise improve livelihoods (Ranjan 2019). AR6 presents new 
estimates of the mitigation potential for shifts in diets and reductions 
in food waste, but given long-standing dietary traditions within most 
cultures, some of the strongest barriers exist for efforts to change 
diets (medium confidence). Furthermore, the large number of 
undernourished who may benefit from increased calories and meat 
will complicate efforts to change diets. Regulatory or tax approaches

will face strong resistance, while efforts to use educational 
approaches and voluntary measures have limited potential to slow 
changes in consumption patterns due to free-riders, rebound effects, 
and other limitations. Food loss and waste occurs across the supply 
chain, creating significant challenges to reduce it (FAO 2019c). Where 
food loss occurs in the production stage, in other words, in fields 
at harvest, there may be opportunities to align reductions in food 
waste with improved production efficiency, however, adoption of 
new production methods often requires new investments or changes 
in labour practices, both of which are barriers.
7.6.4.2 Institutional Barriers and Opportunities
Transparent and accountable governance. Good governance and 
accountability are crucial for implementation of forest and agriculture 
mitigation. Effective nature-based mitigation will require large-scale 
estimation, modelling, monitoring, reporting and verification of

GHG inventories, mitigation actions, as well as their implications for 
sustainable development goals and their interactions with climate 
change impacts and adaptation. Efforts must be made to integrate 
the accounting from projects to the country level. While global 
datasets have emerged to measure forest loss, at least temporarily 
(e.g.,  Hansen et al. 2013), similar datasets do not yet exist for 
forest degradation and agricultural carbon stocks or fluxes. Most 
developing countries have insufficient capacity to address research 
needs, modelling, monitoring, reporting and data requirements 
(Ravindranath et al. 2017), compromising transparency, accuracy, 
completeness, consistency and comparability.
Opportunity for political participation of local stakeholders is barrier 
in most places where forest ownership rights are not sufficiently 
documented (Essl et al. 2018). Since incentives for self-enforcement 
can have an important influence on deforestation rates (Fortmann

et al. 2017), weak governance and insecure property rights are 
significant barriers to introduction of forest carbon offset projects 
in developing countries, where many of the low-cost options for 
such projects exist (Gren and Zeleke 2016). Governance challenges 
exist at all levels of government, with poor coordination, insufficient 
information sharing, and concerns over accountability playing 
a prominent role within REDD+ projects and programmes (Ravikumar 
et al. 2015). In some cases, governments are increasingly centralising 
REDD+ governance and limiting the distribution of governance functions between state and non-state actors (Zelli et al. 2017; Phelps 
et al. 2010). Overlap and duplication in Forest Law Enforcement, 
Governance and Trade (FLEGT) and REDD+ also limits governance 
effectiveness (Gupta et al. 2016).
Clear land tenure and land-use rights. Unclear property rights 
and tenure insecurity undermine the incentives to improve forest and

agricultural productivity, lead to food insecurity, undermine REDD+ 
objectives, discourage adoption of farm conservation practices, 
discourage tree planting and forest management, and exacerbate 
conflict between different land users (Antwi-Agyei et al. 2015; Felker 
et al. 2017; Sunderlin et al. 2018; Borras and Franco 2018; Riggs 
et al. 2018; Kansanga and Luginaah 2019). Some positive signs 
exist as over 500 million hectares of forests have been converted 
to community management with clear property rights in the past 
two decades (Rights and Resources Initiative 2018), but adoption of 
forest and agricultural mitigation practices will be limited in large 
remaining areas with unclear property rights (Gupta et al. 2016).
Lack of institutional capacity. Institutional complexity, or lack 
thereof, represents a major challenge when implementing large and 
complex mitigation programmes (e.g., REDD+) in agriculture, forest

and other land uses (Bäckstrand et al. 2017). Without sufficient 
capacity, many synergies between agricultural and forest programs, 
or mitigation and adaptation opportunities, may be missed (Duguma 
et al. 2014). Another aspect of institutional complexity is the 
different biophysical and socio-economic circumstances as well as 
the public and private financial means involved in the architecture 
and implementation of REDD+ and other initiatives (Zelli et al. 2017).
7.6.4.3 Ecological Barriers and Opportunities
Availability of land and water. Climate mitigation scenarios 
in the two recent special reports (SR1.5 and SRCCL) that aim to 
limit global temperature increase to 2°C or less involve carbon 
dioxide (CO 2) removal from the atmosphere. To support large-scale 
CDR, these scenarios involve significant land-use change, due to 
afforestation/reforestation, avoided deforestation, and deployment 
of biomass energy with carbon capture and storage (BECCS). While

a considerable amount of land is certainly available for new forests 
or new bioenergy crops, that land has current uses that will affect not 
only the costs, but also the willingness of current users or owners, 
to shift uses. Regions with private property rights and a history of 
market-based transactions may be the most feasible for land-use 
change or land management change to occur. Areas with less secure 
tenure or a land market with fewer transactions in general will likely 
face important hurdles that limit the feasibility of implementing 
novel nature-based solutions.
Implementation of nature-based solution may have local or regionally 
important consequences for other ecosystem services, some of which 
may be negative (high confidence). Land-use change has important 
implications for the hydrological cycle, and the large land-use shifts 
suggested for BECCS when not carried out in a  carefully planned 
manner, are expected to increase water demands substantially across

the globe (Stenzel et al. 2019; Rosa et al. 2020). Afforestation can 
have minor to severe consequences for surface water acidification,

826
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)7depending on site-specific factors and exposure to air pollution 
and sea-salts (Futter et al. 2019). The potential effects of coastal 
afforestation on sea-salt related acidification could lead to re-
acidification and damage on aquatic biota.
Specific soil conditions, water availability, GHG emission-
reduction potential as well as natural variability and resilience. 
Recent analysis by (Cook-Patton et al. 2020) illustrates large 
variability in potential rates of carbon accumulation for afforestation 
and reforestation options, both within biomes/ecozones and across 
them. Their results suggest that while there is large potential for 
afforestation and reforestation, the carbon uptake potential in 
land-based climate change mitigation efforts is highly dependent 
on the assumptions related to climate drivers, land use and land 
management, and soil carbon responses to land-use change. Less

analysis has been conducted on bioenergy crop yields, however, 
bioenergy crop yields are also likely to be highly variable, suggesting 
that bioenergy supply could exceed or fall short of expectations in 
a given region, depending on site conditions.
The effects of climate change on ecosystems, including changes in 
crop yields, shifts in terrestrial ecosystem productivity, vegetation 
migration, wildfires, and other disturbances also will affect the 
potential for AFOLU mitigation. Climate is expected to reduce crop 
yields, increase crop and livestock prices, and increase pressure on 
undisturbed forest land for food production creating new barriers and 
increasing costs for implementation of many nature-based mitigation 
techniques (medium confidence) (IPCC AR6 WGII Chapter 5).
The observed increase in the terrestrial sink over the past half century 
can be linked to changes in the global environment, such as increased

atmospheric CO 2 concentrations, N deposition, or changes in climate 
(Ballantyne et al. 2012), though not always proven from ground-based 
information (Vandersleen et al. 2015). While the terrestrial sink relies 
on regrowth in secondary forests (Houghton and Nassikas 2017), 
there is emerging evidence that the sink will slow in the Northern 
Hemisphere as forests age (Nabuurs et al. 2013), although saturation 
may take decades (Zhu et al. 2018). Forest management through 
replanting, variety selection, fertilisation, and other management 
techniques, has increased the terrestrial carbon sink over the last 
century (Mendelsohn and Sohngen 2019). Saturation of the sink in 
situ may not occur when, for example, substitution effects of timber 
usage are also considered.
Increasing concentrations of CO 2 are expected to increase carbon 
stocks globally, with the strongest effects in the tropics (Schimel et al. 
2015; Kim et al. 2017a) (IPCC AR6 WGII Chapter 5) and economic

models suggest that future sink potential may be robust to the 
impacts of climate change (Tian et al. 2018). However, it is uncertain 
if this large terrestrial carbon sink will continue in the future (Aragão 
et al. 2018), as it is increasingly recognised that gains due to CO 2 
fertilisation are constrained by climate and increasing disturbances 
(Schurgers et al. 2018; Duffy et al. 2021) (IPCC AR6 WGII Chapter 5). 
Further, negative synergies between local impacts like deforestation 
and forest fires may interact with global drivers like climate change 
and lead to tipping points (Lovejoy and Nobre 2018). Factors that 
reduce permanence or slow forest growth will drive up costs of forest mitigation measures, suggesting that climate change presents 
a formidable challenge to implementation of nature-based solutions 
beyond 2030 (high confidence).
In addition to climate change, Dooley and Kartha (2018) also note 
that technological and social factors could ultimately limit the

feasibility of agricultural and forestry mitigation options, especially 
when deployed at large scale. Concern is greatest with widespread 
use of bioenergy crops, which could lead to forest losses (Harper 
et al. 2018). Deployment of BECCS and forest-based mitigation 
can be complementary (Favero et al. 2017; Baker et al. 2019), but 
inefficient policy approaches could lead to net carbon emissions if 
BECCS replaces high-carbon content ecosystems with crops.
Adaptation benefits and biodiversity conservation. Biodiversity 
may improve resilience to climate change impacts as more-diverse 
systems could be more resilient to climate change impacts, thereby 
maintaining ecosystem function and preserving biodiversity (Hisano 
et al. 2018). However, losses in ecosystem functions due species 
shifts or reductions in diversity may impair the positive effects of 
biodiversity on ecosystems. Forest management strategies based on 
biodiversity and ecosystems functioning interactions can augment

the effectiveness of forests in reducing climate change impacts 
on ecosystem functioning (high confidence). In spite of the many 
synergies between climate policy instruments and biodiversity 
conservation, however, current policies often fall short of realising 
this potential (Essl et al. 2018).
7.6.4.4 Technological Barriers and Opportunities
Monitoring, reporting, and verification. Development of satellite 
technologies to assess potential deforestation has grown in recent 
years with the release of 30 m data by Hansen et al. (2013), however, 
this data only captures tree cover loss, and increasing accuracy over 
time may limit its use for trend analysis (Ceccherini et al. 2020; Palahí 
et al. 2021). Datasets on forest losses are less well developed for 
reforestation and afforestation. As Mitchell et al. (2017) point out, 
there has been significant improvement in the ability to measure 
changes in tree and carbon density on sites using satellite data, but

these techniques are still evolving and improving and they are not yet 
available for widespread use.
Ground-based forest inventory measurements have been developed 
in many countries, most prominently in the Northern Hemisphere, 
but more and more countries are starting to develop and collect 
national forest inventories. Training and capacity building is going on 
in many developing countries under UNREDD and FAO programmes. 
Additional efforts to harmonise data collection methods and to make 
forest inventory data available to the scientific community would 
improve confidence in forest statistics, and changes in forest statistics 
over time. To some extent the Global Forest Biodiversity Initiative fills 
in this data gap (https://gfbi.udl.cat/).

827
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.6.5 Linkages to Ecosystem Services, Human Well-
being and Adaptation (including SDGs)
The linkage between biodiversity, ecosystem services, human 
well-being and sustainable development is widely acknowledged 
(Millenium Ecosystem Assesment 2005; UNEP 2019). Loss of 
biodiversity and ecosystem services will have an adverse impact on 
quality of life, human well-being and sustainable development (IPBES 
2019a). Such losses will not only affect current economic growth but 
also impede the capacity for future economic growth.
Population growth, economic development, urbanisation, technology, 
climate change, global trade and consumption, policy and governance 
are key drivers of global environmental change over recent decades 
(Kram et al. 2014; UNEP 2019; WWF 2020). Changes in biodiversity and 
ecosystem services are mainly driven by habitat loss, climate change,

invasive species, over-exploitation of natural resources, and pollution 
(Millenium Ecosystem Assesment 2005). The relative importance of 
these drivers varies across biomes, regions, and countries. Climate 
change is expected to be a major driver of biodiversity loss in the 
coming decades, followed by commercial forestry and bioenergy 
production (OECD 2012; UNEP 2019). Population growth along with 
rising incomes and changes in consumption and dietary patterns, will 
exert immense pressure on land and other natural resources (IPCC 
2019). Current estimates suggest that 75% of the land surface has 
been significantly anthropogenically altered, with 66% of the ocean 
area experiencing increasing cumulative impacts and over 85% 
of wetland area lost (IPBES 2019a). As discussed, in Section  7.3, 
land-use change is driven amongst others by agriculture, forestry 
(logging and fuelwood harvesting), infrastructural development and

urbanisation, all of which may also generate localised air, water, 
and soil pollution (IPBES 2019a). Over a  third of the world’s land 
surface and nearly three-quarters of available freshwater resources 
are devoted to crop or livestock production (IPBES 2019a). Despite 
a  slight reduction in global agricultural area since 2000, regional 
agricultural area expansion has occurred in Latin America and the 
Caribbean, Africa and the Middle East (FAO 2019c; OECD and FAO 
2019). The degradation of tropical forests and biodiversity hotspots, 
endangers habitat for many threatened and endemic species, 
and reduces valuable ecosystem services. However, trends vary 
considerably by region. As noted in Section 7.3, global forest area 
declined by roughly 178 Mha between 1990 and 2020 (FAO 2020a), 
though the rate of net forest loss has decreased over the period, 
due to reduced deforestation in some countries and forest gains 
in others. Between 1990 to 2015, forest cover fell by almost 13%

in South-East Asia, largely due to an increase in timber extraction, 
large-scale biofuel plantations and expansion of intensive agriculture 
and shrimp farms, whereas in North-East Asia and South Asia it 
increased by 23% and 6% respectively, through policy instruments 
such as joint forest management, payment for ecosystem services, 
and restoration of degraded forests (IPBES 2018b). It is lamenting 
that the area under natural forests which are rich in biodiversity 
and provide diverse ecosystem services decreased by 301 Mha 
between 1990 and 2020, decreasing in most regions except Europe 
and Oceania with largest losses reported in sub-Saharan Africa 
(FAO 2020a). The increasing trend of mining in forest and coastal 
areas, and in river basins for extracting has had significant negative impacts on biodiversity, air and water quality, water distribution, 
and on human health (Section 7.3). Freshwater ecosystems equally

face a series of combined threats including from land-use change, 
water extraction, exploitation, pollution, climate change and invasive 
species (IPBES 2019a).
7.6.5.1 Ecosystem Services
An evaluation of eighteen ecosystem services over the past five 
decades (1970–2019) found only four (agricultural production, 
fish harvest, bioenergy production and harvest of materials) to 
demonstrate increased performance, while the remaining fourteen, 
mostly concerning regulating and non-material contributions, were 
found to be in decline (IPBES 2019a). The value of global agricultural 
output (over USD3.54 trillion in 2018) had increased approximately 
threefold since 1970, and roundwood production (industrial 
roundwood and fuelwood) by 27%, between 1980 to 2018, reaching 
some 4 billion m3 in 2018. However, the positive trends in these 
four ecosystem services does not indicate long-term sustainability. 
If increases in agricultural production are realised through forest

clearance or through increasing energy-intensive inputs, gains are 
likely to be unsustainable in the long run. Similarly, an increase in 
fish production may involve overfishing, leading to local species 
declines which also impacts fish prices, fishing revenues, and the 
well-being of coastal and fishing communities (Sumaila and Lam 
2020). Climate change and other drivers are likely to affect future fish 
catch potential, although impacts will differ across regions (Sumaila 
et al. 2017; Domke et al. 2019).
The increasing trend in aquaculture production especially in South 
and South-East Asia through intensive methods affects existing food 
production and ecosystems by diverting rice fields or mangroves 
(Bhattacharya and Ninan 2011). Although extensive traditional 
fish farming of carp in central Europe can contribute to landscape 
management, enhance biodiversity and provide ecosystem services, 
there are several barriers to scale up production due to strict

EU environmental regulations, vulnerability to extreme weather 
events, and to avian predators that are protected by EU laws, and 
disadvantages faced by small-scale enterprises that dominate the 
sector (European-Commission 2021). Bioenergy production may have 
high opportunity costs in land-scarce areas and compete with land 
used for food production which threatens food security and affects 
the poor and vulnerable. But these impacts will differ across scale, 
contexts and other factors.
Currently, land degradation is estimated to have reduced productivity 
in 23% of the global terrestrial area, and between USD235 billion 
and USD577 billion in annual global crop output is at risk because 
of pollinator loss (IPBES 2019a). The global trends reviewed above 
are based on data from 2000 studies. It is not clear whether the 
assessment included a quality control check of the studies evaluated 
and suffer from aggregation bias. For instance, a  recent meta-

analysis of global forest valuation studies noted that many studies 
reviewed had shortcomings such as failing to clearly mention the 
methodology and prices used to value the forest ecosystem services, 
double counting, data errors, and so on (Ninan and Inoue 2013). 
Furthermore, the criticisms against the paper by (Costanza et al.

828
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)71997), such as ignoring ecological feedbacks and non-linearities that 
are central to the processes that link all species to each other and their 
habitats, ignoring substitution effects may also apply to the global 
assessment (Smith 1997; Bockstael et al. 2000; Loomis et al. 2000). 
Land degradation has had a pronounced impact on ecosystem functions 
worldwide (IPBES 2018e). Net primary productivity of ecosystem 
biomass and of agriculture is presently lower than it would have been 
under a natural state on 23% of the global terrestrial area, amounting 
to a  5% reduction in total global net primary productivity (IPBES 
2018e). Over the past two centuries, soil organic carbon, an indicator 
of soil health, has seen an estimated 8% loss globally (176 GtC) from 
land conversion and unsustainable land management practices (IPBES 
2018e). Projections to 2050 predict further losses of 36 GtC from soils,

particularly in sub-Saharan Africa. These losses are projected to come 
from the expansion of agricultural land into natural areas (16 GtC), 
degradation due to inappropriate land management (11 GtC) and the 
draining and burning of peatlands (9 GtC) and melting of permafrost  
(IPBES 2018e). Trends in biodiversity measured by the global living 
planet index between 1970 to 2016 indicate a  68% decline in 
monitored population of mammals, birds, amphibians, reptiles, and 
fish (WWF 2020). FAO’s recent report on the state of the world’s 
biodiversity for food and agriculture points to an alarming decline in 
biodiversity for food and agriculture including associated biodiversity 
such as pollination services, microorganisms which are essential for 
production systems (FAO 2019d). These suggest that overall ecosystem 
health is consistently declining with adverse consequences for good 
quality of life, human well-being, and sustainable development.

Although numerous studies have estimated the value of ecosystem 
services for different sites, ecosystems, and regions, these studies 
mostly evaluate ecosystem services at a  single point in time 
(Costanza et al. 1997; Xue and Tisdell 2001; Nahuelhual et al. 2007; de Groot et al. 2012; Ninan and Kontoleon 2016). The few studies 
that have assessed the trends in the value of ecosystem services 
provided by different ecosystems across regions and countries 
indicate a  declining trend (Costanza et al. 2014; Kubiszewski 
et al. 2017). Land-use change is a  major driver behind loss of 
biodiversity and ecosystem services in most regions (IPBES 2018b; 
IPBES 2018c, IPBES 2018d, Rice et al. 2018). Projected impacts of 
land-use change and climate change on biodiversity and ecosystem 
services (material and regulating services) between 2015 to 2050 
were assessed to have relatively less negative impacts under global 
sustainability scenarios as compared to regional competition and

economic optimism scenarios (IPBES 2019a). The projected impacts 
were based on a  subset of Shared Socio-economic Pathway (SSP) 
scenarios and GHG emissions trajectories (RCP) developed in 
support of IPCC assessments. There are synergies, trade-offs and co-
benefits between ecosystem services and mitigation options with 
impacts on ecosystem services differing by scale and contexts (high 
confidence). Measures such as conservation agriculture, agroforestry, 
soil and water conservation, afforestation, adoption of silvopastoral 
systems, can help minimise trade-offs between mitigations options 
and ecosystem services (Duguma et al. 2014). Climate-smart 
agriculture (CSA) is being promoted to enable farmers to make 
agriculture more sustainable and adapt to climate change (Box 7.4). 
However, experience with CSA in Africa has not been encouraging. 
For instance, a  study of climate-smart cocoa production in Ghana 
shows that due to lack of tenure (tree) rights, bureaucratic and legal

hurdles in registering trees in cocoa farms, and other barriers small 
cocoa producers could not realise the project benefits (Box  7.13). 
Experience of CSA in some other sub-Saharan African countries and 
other countries such as Belize too has been constrained by weak 
extension systems and policy implementation, and other barriers 
(Arakelyan et al. 2017; Kongsager 2017).
Box 7.13 | Case Study: Climate-smart Cocoa Production in Ghana
Policy objectives
i. To promote sustainable intensification of cocoa production and enhance the adaptive capacity of small cocoa producers.
ii. To reduce cocoa-induced deforestation and GHG emissions.
iii. To improve productivity, incomes, and livelihoods of smallholder cocoa producers.
Policy mix
The climate-smart cocoa (CSC) production programme in Ghana involved distributing shade tree seedlings that can protect cocoa

plants from heat and water stress, enhance soil organic matter and water holding capacity of soils, and provide other assistance with 
agroforestry, giving access to extension services such as agronomic information and agrochemical inputs. The shade tree seedlings 
were distributed by NGOs, government extension agencies, and the private sector free of charge or at subsidised prices and was 
expected to reduce pressure on forests for growing cocoa plants. The CSC programme was mainly targeted at small farmers who 
constitute about 80% of total farm holdings in Ghana. Although the government extension agency (Cocobod) undertook mass spraying 
or pruning of cocoa farms they found it difficult to access the 800,000 cocoa smallholders spread across the tropical south of the 
country. The project brought all stakeholders together, in other words, the government, private sector, local farmers and civil society or

NGOs to facilitate the sustainable intensification of cocoa production in Ghana. Creation of a community-based governance structure 
was expected to promote benefit sharing, forest conservation, adaptation to climate change, and enhanced livelihood opportunities.

829
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 777.6.5.2 Human Well-being and Sustainable 
Conservation of biodiversity and ecosystem services is part of the 
larger objective of building climate resilience and promoting good 
quality of life, human well-being and sustainable development. 
While two of the 17 SDGs directly relate to nature (SDGs 14 and 15 
covering marine and terrestrial ecosystems and biodiversity), most 
other SDGs relating to poverty, hunger, inequality, health and well-
being, clean sanitation and water, energy, and so on, are directly or 
indirectly linked to nature (Blicharska et al. 2019). A survey among 
experts to assess how 16 ecosystem services could help in achieving 
the SDGs relating to good environment and human well-being 
suggested that ecosystem services could contribute to achieving 
about 41 targets across 12 SDGs (Wood et al. 2018). They also 
indicated cross-target interactions and synergetic outcomes across

many SDGs. Conservation of biodiversity and ecosystem services 
is critical to sustaining the well-being and livelihoods of poor and 
marginalised people, and indigenous communities who depend on 
natural resources (high confidence). Nature provides a broad array of 
goods and services that are critical to good quality of life and human 
well-being. Healthy and diverse ecosystems can play an important 
role in reducing vulnerability and building resilience to disasters 
and extreme weather events (SCBD 2009; The Royal Society Science 
Policy Centre 2014; Ninan and Inoue 2017).Current negative trends in biodiversity and ecosystem services will 
undermine progress towards achieving 80% (35 out of 44) of the 
assessed targets of SDGs related to poverty, hunger, health, water, 
cities, climate, oceans and land (IPBES 2019a). However, Reyers and 
Selig (2020) note that the assessment by (IPBES 2019a) could only 
assess the consequences of trends in biodiversity and ecosystem

services for 35 out of the 169 SDG targets due to data and knowledge 
gaps, and lack of clarity about the relationship between biodiversity, 
ecosystem services and SDGs.
Progress in achieving the 20 Aichi Biodiversity targets which are 
critical for realising the SDGs has been poor with most of the targets 
not being achieved or only partially realised (SCBD 2020). There 
could be synergies and trade-offs between ecosystem services and 
human well-being. For instance, a study notes that although policy 
interventions and incentives to enhance supply of provisioning 
services (e.g.,  agricultural production) have led to higher GDP , it 
may have an adverse effect on the regulatory services of ecosystems 
(Kirchner et al. 2015). However, we are aware of the inadequacies 
of traditional GDP as an indicator of well-being. In this context the 
Dasgupta Biodiversity Review argues for using the inclusive wealth 
approach to accurately measure social well-being by tracking the

changes in produced, human and natural capital (Dasgupta 2021). 
Targets for nature (biodiversity and ecosystem services) should be 
refined so as to fit in with the metrics tracked by the SDGs (IPBES 
2016; Rosa et al. 2017).Box 7.13 (continued)
Governance context
 
Critical enablers
The role assigned to local government mechanisms such as Ghana’s Community Resource Management Area Mechanisms (CREMAs) 
was expected to give a  voice to smallholders who are an important stakeholder in Ghana’s cocoa sector. CREMAs are inclusive 
because authority and ownership of natural resources are devolved to local communities who can thus have a voice in influencing CSC 
policy thereby ensuring equity and adapting CSC to local contexts. However, ensuring the long-term sustainability of CREMAs will help 
to make them a reliable mechanism for farmers to voice their concerns and aspirations, and ensure their independence as a legitimate

governance structure in the long run. The private sector was assigned an important role to popularise climate-smart cocoa production 
in Ghana. However, whether this will work to the advantage of smallholder cocoa producers needs to be seen.
Critical barriers
The policy intervention overlooks the institutional constraints characteristic of the cocoa sector in Ghana where small farmers are 
dominant and have skewed access to resources and markets. Lack of secure tenure (tree rights) where the ownership of shade trees 
and timber vests with the state, bureaucratic and legal hurdles to register trees in their cocoa farms are major constraints that impede 
realisation of the expected benefits of the CSC programme. This is a great disincentive for small cocoa producers to implement CSC 
initiatives and nurture the shade tree seedlings and undertake land improvement measures. The state marketing board has the

monopoly in buying and marketing of cocoa beans including exports which impeded CREMAs or farming communities from directly 
selling their produce to MNCs and traders. However, many MNCs have been involved in setting up of CREMA or similar structures, 
extending premium prices and non-monetary benefits (access to credit, shade tree seedlings, agrochemicals) thus indirectly securing 
their cocoa supply chains. A biased ecological discourse about the benefits of climate-smart agriculture and sustainable intensive 
narrative, complexities regarding the optimal shade levels for growing cocoa, and dependence on agrochemicals are issues that affect 
the success and sustainability of the project intervention. Dominance of private sector players especially MNCs in the sector may be 
detrimental to the interests of smallholder cocoa producers (Nasser et al. 2020).

830
Chapter 7 Agriculture, Forestry and Other Land Uses (AFOLU)77.6.5.3 Land-based Mitigation and Adaptation
Combined mitigation and adaptation approaches have been 
highlighted throughout Section  7.4 regarding specific measures. 
Land-based mitigation and adaptation to the risks posed by climate 
change and extreme weather events can have several co-benefits 
as well as help promote development and conservation goals. 
Land-based mitigation and adaptation will not only help reduce 
GHG emissions in the AFOLU sector, but measures are required to 
closely link up with adaptation. In the central 2°C scenario, improved 
management of land and more efficient forest practices, a reduction 
in deforestation and an increase in afforestation, would account 
for 10% of the total mitigation effort over 2015–2050 (Keramidas 
et al. 2018). If managed and regulated appropriately, the Land 
sector could become carbon-neutral as early as 2030–2035, being

a key sector for emissions reductions beyond 2025 (Keramidas et al. 
2018). Nature-based solutions (NBS) with safeguards has immense 
potential for cost-effective adaptation to climate change; but their 
impacts will vary by scale and contexts (high confidence). Griscom 
et al. 2017 estimate this potential to provide 37% of cost-effective 
CO2 mitigation until 2030 needed to meet 2°C goals with likely co-
benefits for biodiversity. However, due to the time lag for technology 
deployment and natural carbon gain this mitigation potential of NBS 
by 2030 or 2050 can be delayed or much lower than the estimated 
potential (Qin et al. 2021).
7.7 Knowledge Gaps
Closing knowledge gaps and narrowing uncertainties are crucial to 
advance AFOLU mitigation. Knowledge gaps exist across a  range 
of areas, from emissions accounting and mitigation measure 
development to integration of scientific and traditional knowledge 
and development and sustainable implementation strategies. The

following are identified as priorities:
• Uncertainty in contemporary emissions and sinks within AFOLU 
is still high. There is ongoing need to develop and refine emission 
factors, improve activity data and facilitate knowledge exchange, 
concerning inventories and accounting. For example, insufficient 
knowledge on CO 2 emissions relating to forest management and 
burning or draining of organic soils (wetlands and peatlands), 
limits certainty on CO 2 and CH 4 fluxes.
• Improved monitoring of the land CO 2 balance is urgently 
needed, including impacts of land degradation and restoration 
efforts (e.g.,  in tropical and boreal regions), making use of 
combined remote sensing, artificial intelligence, ground-based 
and modelling tools (Grassi et al. 2021). Improved estimates 
would provide more reliable projections of nationally determined 
contributions to emissions reduction and enhancement of sinks, 
and reconciliation of national accounting and modelling results

(Nabuurs et al. 2019).
• The future impacts of climate change on land systems are highly 
uncertain, for example, the role of permafrost thaw, tipping 
points, increased disturbances and enhanced CO 2 fertilisation 
(Friedlingstein et al. 2020). Further research into these mechanisms is critical to better understand the permanence of 
mitigation measures in land sector.
• There is need to understand the role of forest management, 
carbon and nitrogen fertilisation and associated interactions in 
the current forest carbon sink that has emerged in the last 50 to 
70 years. These aspects are likely to explain much of the difference 
between bookkeeping models and other methodologies.
• Continued research into novel and emerging mitigation measures 
and associated cost efficiency (e.g.,  CH 4 inhibitors or vaccines 
for ruminants) is required. In addition to developing specific 
measures, research is also needed into best practice regarding

implementation and optimal agricultural land and livestock 
management at regional and country level. Further research into 
the feasible mitigation potential of sustainable intensification 
in terms of absolute GHG emissions and appropriate policy 
mechanisms, is required to implement and advance this strategy.
• Research into accounting systems and policy options that will 
enable agricultural soil and forest carbon to be utilised as offsets 
(voluntary or regulatory) is needed to increase financing for land-
based CDR. Design of incentives that consider local institutions 
and novel frameworks for cooperation between private finance 
and public governance can encourage investment. Equally, 
research to adjust or remove regulations and subsidy schemes that 
may hamper land-based mitigation efforts, is urgently required.
• Improving mitigation potential estimates, whether derived from 
sectoral studies or IAMs to account for biophysical climate

effects, and impacts of future climate change (e.g.,  mitigation 
permanence), biodiversity loss and corresponding feedbacks is 
needed. IAM ‘usability’ can be enhanced by integrating a wider 
set of measures and incorporating sustainability considerations.
• Research into the feasibility of improving and enhancing 
sustainable agricultural and forestry value chains, provision of 
renewable products (building with wood) and the sustainability 
of bioenergy is critically important. Modelled scenarios do not 
examine many poverty, employment and development trade-offs, 
which are highly context specific and vary enormously by region. 
Trade-off analysis and cost-benefit analysis can assist decision-
making and policy.
• In-depth understanding of mitigation-SDG interactions is critical 
for identifying mitigation options that maximise synergies 
and minimise trade-offs. Mitigation measures have important 
synergies, trade-offs and co-benefits, impacting biodiversity

and resource-use, human well-being, ecosystem services, 
adaptation capacity and many SDGs. In addition to exploring 
localised economic implementation costs, studies are needed to 
understand how measures will impact and interact with wider 
environmental and social factors across localities and contexts.

831
Agriculture, Forestry and Other Land Uses (AFOLU) Chapter 77Frequently Asked Questions (FAQs)
FAQ 7.1 |   Why is the Agriculture, Forestry and Other Land Uses (AFOLU) sector unique when 
considering GHG mitigation?
There are three principal reasons that make the AFOLU sector unique in terms of mitigation:
In contrast to other sectors, AFOLU can facilitate mitigation in several different ways. Specifically, AFOLU can (i) reduce emissions as 
a sector in its own right, (ii) remove meaningful quantities of carbon from the atmosphere and relatively cheaply, and (iii) provide 
raw materials to enable mitigation within other sectors, such as energy, industry or the built environment.
The emissions profile of AFOLU differs from other sectors, with a greater proportion of non-CO 2 gases (N 2O and CH 4). The impacts of 
mitigation efforts within AFOLU can vary according to which gases are targeted, as a result of the differing atmospheric lifetime of

the gases and differing global temperature responses to the accumulation of the specific gases in the atmosphere.
In addition to tackling climate change, AFOLU mitigation measures have capacity, where appropriately implemented, to help address 
some critical, wider challenges, as well as contributing to climate change adaptation. AFOLU is inextricably linked with some of 
the most serious challenges that are suggested to have ever faced humanity, such as large-scale biodiversity loss, environmental 
degradation and the associated consequences. As AFOLU concerns land management and utilises a considerable portion of the 
Earth’s terrestrial area, the sector greatly influences soil, water and air quality, biological and social diversity, the provision of natural 
habitats, and ecosystem functioning, consequently impacting many SDGs.
FAQ 7.2 |  What AFOLU measures have the greatest economic mitigation potential?

Economic mitigation potential refers to the mitigation estimated to be possible at an annual cost of up to USD100 tCO 2–1 mitigated. 
This cost is deemed the price at which society is willing to pay for mitigation and is used as a proxy to estimate the proportion of 
technical mitigation potential that could realistically be implemented. Between 2020 and 2050, measures concerning forests and 
other ecosystem are estimated to have an average annual mitigation potential of 7.3 (3.9–13.1) GtCO 2-eq yr–1 at USD100 tCO 2–1. 
At the same cost, agricultural measures are estimated to have a potential of 4.1 (1.7–6.7) GtCO 2-eq yr–1. Emerging technologies, 
such as CH 4 vaccines and inhibitors, could sustainably increase agricultural mitigation potential in future. The diverted production 
effects of changes in demand (reduced food losses, diet changes and improved and enhanced wood products use), is estimated to

have an economic potential of 2.2 (1.1–3.6) GtCO 2-eq yr–1. However, cost forms only one constraint to mitigation, with realisation 
of economic potential dependent on multiple context-specific environmental and socio-cultural factors.
FAQ 7.3 |   What are potential impacts of large-scale establishment of dedicated bioenergy 
plantations and crops and why is it so controversial?
The potential of bioenergy with carbon capture and storage (BECCS) remains a focus of debate with several studies evaluating the 
level at which BECCS could be sustainably implemented, published since AR5. BECCS involves sequestering carbon through plant 
growth (i.e., in trees or crops) and capturing the carbon generated when this biomass is processed for power or fuel. The captured 
carbon then requires long-term storage in for example, geological, terrestrial or ocean reservoirs, or in products. While appearing to

create a net removal of carbon from the atmosphere, BECCS requires land, water and energy which can create adverse side effects 
at scale. Controversy has arisen because some of the models calculating the energy mix required to keep the temperature to 1.5°C 
have included BECCS at very large scales as a means of both providing energy and removing carbon from the atmosphere to offset 
emissions from industry, power, transport or heat. For example, studies have calculated that for BECCS to achieve 11.5 GtCO 2-eq 
per year of carbon removal in 2100, as envisaged in one scenario, 380–700 Mha or 25–46% of all the world’s arable and cropland 
would be needed. In such a  situation, competition for agricultural land seriously threatens food production and food security, 
while also impacting biodiversity, water and soil quality, and landscape aesthetic value. More recently however, the scenarios for

BECCS have become much more realistic, though concerns regarding impacts on food security and the environment remain, while 
the reliability of models is uncertain due to methodological flaws. Improvements to models are required to better capture wider 
environmental and social impacts of BECCS in order to ascertain its sustainable contribution in emissions pathways. Additionally, 
the opportunity for other options that could negate very large-scale deployment of BECCS, such as other carbon dioxide removal 
measures or more stringent emission reductions in other sectors, could be explored within models.

863
863
Urban Systems and Other Settlements Chapter 88
Although urbanisation is a global trend often associated with 
increased incomes and higher consumption, the growing 
concentration of people and activities is an opportunity to 
increase resource efficiency and decarbonise at scale (very 
high confidence). The same urbanisation level can have large 
variations in per capita urban carbon emissions. For most regions, per 
capita urban emissions are lower than per capita national emissions. 
{8.1.4, 8.3.3, 8.4, Box 8.1}
Most future urban population growth will occur in developing 
countries, where per capita emissions are currently low but 
expected to increase with the construction and use of new 
infrastructure and the built environment, and changes in 
incomes and lifestyles (very high confidence).  The drivers of 
urban greenhouse gas (GHG) emissions are complex and include 
an interplay of population size, income, state of urbanisation, and

how cities are laid out (i.e. urban form). How new cities and towns 
are designed, constructed, managed, and powered will lock-in 
behaviour, lifestyles, and future urban GHG emissions. Low-emission 
urbanisation can improve well-being while minimising impact 
on GHG emissions, but there is risk that urbanisation can lead to 
increased global GHG emissions through increased emissions outside 
the city’s boundaries. {8.1.4, 8.3, Box 8.1, 8.4, 8.6}
The urban share of global GHG emissions (including carbon 
dioxide (CO 2) and methane (CH 4)) is substantive and continues 
to increase (high confidence). In 2015, urban emissions were 
estimated to be 25 GtCO 2-eq (about 62% of the global share) and 
in 2020, 29 GtCO 2-eq (67–72% of the global share).1 About 100 of 
the highest emitting urban areas account for approximately 18% of 
the global carbon footprint. {8.1.6, 8.3.3}
The urban share of regional GHG emissions increased between 
2000 and 2015, with much inter-region variation in the

magnitude of the increase (high confidence). Globally, the urban 
share of national emissions increased 6 percentage points, from 56% 
in 2000 to 62% in 2015. For 2000 to 2015, the urban emissions share 
across AR6 WGIII regions increased from 28% to 38% in Africa, from 
46% to 54% in Asia and Pacific, from 62% to 72% in Developed 
Countries, from 57% to 62% in Eastern Europe and West-Central 
Asia, from 55% to 66% in Latin America and Caribbean, and from 
68% to 69% in the Middle East. {8.1.6, 8.3.3}
Per capita urban GHG emissions increased between 2000 
and 2015, with cities in the Developed Countries region 
producing nearly seven times more per capita than the lowest 
emitting region (medium confidence). From 2000 to 2015, global 
urban GHG emissions per capita increased from 5.5 to 6.2 tCO 2-eq 
per person (an  increase of 11.8%); Africa increased from 1.3 to 
1.5 tCO 2-eq per person (22.6%); Asia and Pacific increased from 3.0 to

1 These estimates are based on consumption-based accounting, including both direct emissions from within urban areas, and indirect emissions from outside urban areas 
related to the production of electricity, goods, and services consumed in cities. Estimates include all CO 2 and CH 4 emission categories except for aviation and marine bunker 
fuels, land-use change, forestry, and agriculture. {8.1, Annex I: Glossary}
2 These scenarios have been assessed by WGI to correspond to intermediate, high, and very low GHG emissions.5.1 tCO 2-eq per person (71.7%); Eastern Europe and West-Central Asia 
increased from 6.9 to 9.8 tCO 2-eq per person (40.9%); Latin America 
and Caribbean increased from 2.7 to 3.7 tCO 2-eq per person (40.4%); 
and Middle East increased from 7.4 to 9.6 tCO 2-eq per person (30.1%). 
Albeit starting from the highest level, Developed Countries had 
a decline of 11.4 to 10.7 tCO 2-eq per person (–6.5%). {8.3.3}

The global share of future urban GHG emissions is expected to 
increase through 2050, with moderate to low mitigation efforts, 
due to growth trends in population, urban land expansion, 
and infrastructure and service demands, but the extent of the 
increase depends on the scenario and the scale and timing 
of urban mitigation action (medium confidence). In modelled 
scenarios, global consumption-based urban CO 2 and CH 4 emissions 
are projected to rise from 29 GtCO 2-eq in 2020 to 34 GtCO 2-eq in 
2050 with moderate mitigation efforts (intermediate GHG emissions, 
SSP2–4.5), and up to 40 GtCO 2-eq in 2050 with low mitigation efforts 
(high GHG emissions, SSP3–7.0). With aggressive and immediate 
mitigation policies to limit global warming to 1.5°C (>50%) with no 
or limited overshoot by the end of the century (very low emissions, 
SSP1–1.9), including high levels of electrification, energy and material 
efficiency, renewable energy preferences, and socio-behavioural

responses, urban GHG emissions could approach net-zero and reach 
a maximum of 3 GtCO 2-eq in 2050. Under a scenario with aggressive 
but not immediate urban mitigation policies to limit global warming 
to 2°C (>67%) (low emissions, SSP1–2.6), urban emissions could 
reach 17 GtCO 2-eq in 2050.2 (Figure TS.13) {8.3.4}
Urban land areas could triple between 2015 and 2050, with 
significant implications for future carbon lock-in. There is 
a  large range in the forecasts of urban land expansion across 
scenarios and models, which highlights an opportunity to shape 
future urban development towards low- or net-zero GHG emissions 
and minimise the loss of carbon stocks and sequestration in the 
agriculture, forestry and other land use (AFOLU) sector due to 
urban land conversion (medium confidence). By 2050, urban areas 
could increase up to 211% over the 2015 global urban extent, with 
the median projected increase ranging from 43% to 106%. While the

largest absolute amount of new urban land is forecasted to occur 
in Asia and Pacific, and in Developed Countries, the highest rate of 
urban land growth is projected to occur in Africa, Eastern Europe and 
West-Central Asia, and in the Middle East. The infrastructure that will 
be constructed concomitant with urban land expansion will lock-in 
patterns of energy consumption that will persist for decades if not 
generations. Furthermore, given past trends, the expansion of urban 
areas is likely to take place on agricultural lands and forests, with 
implications for the loss of carbon stocks and sequestration. {8.3.1, 
8.3.4, 8.4.1, 8.6}
The construction of new, and upgrading of, existing urban 
infrastructure through 2030 will result in significant emissions 
(very high confidence). The construction of new and upgrading

864
864
Chapter 8 Urban Systems and Other Settlements8of existing urban infrastructure using conventional practices and 
technologies can result in significant committed CO 2 emissions, 
ranging from 8.5 GtCO 2 to 14 GtCO 2 annually up to 2030 and more 
than double annual resource requirements for raw materials to about 
90 billion tonnes per year by 2050, up from 40 billion tonnes in 2010 
(medium evidence, high agreement). {8.4.1, 8.6}
Given the dual challenges of rising urban GHG emissions and 
future projections of more frequent extreme climate events, 
there is an urgent need to integrate urban mitigation and 
adaptation strategies for cities to address climate change 
and withstand its effects (very high confidence). Mitigation 
strategies can enhance resilience against climate change impacts 
while contributing to social equity, public health, and human well-
being. Urban mitigation actions that facilitate economic decoupling

can have positive impacts on employment and local economic 
competitiveness. {8.2, Cross-Working Group Box 2, 8.4}
Cities can only achieve net-zero GHG emissions through deep 
decarbonisation and systemic transformation (very high 
confidence). Three broad mitigation strategies have been found to 
be effective in reducing emissions when implemented concurrently: 
(i) reducing or changing urban energy and material use towards 
more sustainable production and consumption across all sectors, 
including through compact and efficient urban forms and supporting 
infrastructure; (ii) electrification and switching to net-zero-emissions 
resources; and (iii) enhancing carbon uptake and storage in the urban 
environment (high evidence, high agreement). Given the regional 
and global reach of urban supply chains, cities can achieve net-zero 
emissions only if emissions are reduced within and outside of their 
administrative boundaries. {8.1.6, 8.3.4, 8.4, 8.6}

Packages of mitigation policies that implement multiple urban-
scale interventions can have cascading effects across sectors, 
reduce GHG emissions outside of a  city’s administrative 
boundaries, and reduce more emissions than the net sum 
of individual interventions, particularly if multiple scales of 
governance are included (high confidence). Cities have the 
ability to implement policy packages across sectors using an urban 
systems approach, especially those that affect key infrastructure 
based on spatial planning, electrification of the urban energy system, 
and urban green and blue infrastructure. The institutional capacity 
of cities to develop, coordinate, and integrate sectoral mitigation 
strategies within their jurisdiction varies by context, particularly 
those related to governance, the regulatory system, and budgetary 
control. {8.4, 8.5, 8.6}
Integrated spatial planning to achieve compact and resource-
efficient urban growth through co-location of higher residential

and job densities, mixed land use, and transit-oriented 
development (TOD) could reduce GHG emissions between 23% 
and 26% by 2050 compared to the business-as-usual scenario 
(robust evidence, high agreement, very high confidence). 
Compact cities with shortened distances between housing and jobs, 
and interventions that support a modal shift away from private motor 
3 These examples are considered to be a subset of nature-based solutions or ecosystem-based approaches.vehicles towards walking, cycling, and low-emissions shared and 
public transportation, passive energy comfort in buildings, and urban 
green infrastructure can deliver significant public health benefits and 
have lower GHG emissions. {8.2, 8.3.4, 8.4, 8.6}
Urban green and blue infrastructure can mitigate climate 
change through carbon sequestration, avoided emissions, and 
reduced energy use while offering multiple co-benefits (robust 
evidence, high agreement). Urban green and blue infrastructure,

including urban forests and street trees, permeable surfaces, and 
green roofs3 offer potential to mitigate climate change directly 
through sequestering and storing carbon, and indirectly by inducing 
a cooling effect that reduces energy demand and reducing energy 
use for water treatment. Global urban trees store approximately 
7.4 billion tonnes of carbon, and sequester approximately 217 million 
tonnes of carbon annually, although urban tree carbon storage and 
sequestration are highly dependent on biome. Among the multiple 
co-benefits of green and blue infrastructure are reducing the urban 
heat island (UHI) effect and heat stress, reducing stormwater runoff, 
improving air quality, and improving mental and physical health of 
urban dwellers. {8.2, 8.4.4}
The potential and sequencing of mitigation strategies to 
reduce GHG emissions will vary depending on a  city’s land 
use, spatial form, development level, and state of urbanisation

(i.e.,  whether it is an established city with existing infrastructure, 
a rapidly growing city with new infrastructure, or an emerging 
city with infrastructure buildup (high confidence). New and 
emerging cities will have significant infrastructure development needs 
to achieve high quality of life, which can be met through energy-
efficient infrastructures and services, and people-centred urban 
design (high confidence). The long lifespan of urban infrastructures 
locks in behaviour and committed emissions. Urban infrastructures 
and urban form can enable socio-cultural and lifestyle changes that 
can significantly reduce carbon footprints. Rapidly growing cities can 
avoid higher future emissions through urban planning to co-locate 
jobs and housing to achieve compact urban form, and by leapfrogging 
to low-carbon technologies. Established cities will achieve the largest 
GHG emissions savings by replacing, repurposing, or retrofitting the

building stock, targeted infilling and densifying, as well as through 
modal shift and the electrification of the urban energy system. New 
and emerging cities have unparalleled potential to become low 
or net-zero GHG emissions while achieving high quality of life by 
creating compact, co-located, and walkable urban areas with mixed 
land use and transit-oriented design, that also preserve existing 
green and blue assets. {8.2, 8.4, 8.6}
With over 880 million people living in informal settlements, 
there are opportunities to harness and enable informal 
practices and institutions in cities related to housing, waste, 
energy, water, and sanitation to reduce resource use and 
mitigate climate change (low evidence, medium agreement). 
The upgrading of informal settlements and inadequate housing to 
improve resilience and well-being offers a chance to create a low-
carbon transition. However, there is limited quantifiable data on

865
865
these practices and their cumulative impacts on GHG emissions. 
{8.1.4, 8.2.2, Cross-Working Group Box 2, 8.3.2, 8.4, 8.6, 8.7}
Achieving transformational changes in cities for climate 
change mitigation and adaptation will require engaging 
multiple scales of governance, including governments and 
non-state actors, and in connection with substantive financing 
beyond sectoral approaches (very high confidence). Large 
and complex infrastructure projects for urban mitigation are often 
beyond the capacity of local municipality budgets, jurisdictions, and 
institutions. Partnerships between cities and international institutions, 
national and regional governments, transnational networks, and 
local stakeholders play a  pivotal role in mobilising global climate 
finance resources for a  range of infrastructure projects with low-
carbon emissions and related spatial planning programmes across 
key sectors. {8.4, 8.5}

866
866
Chapter 8 Urban Systems and Other Settlements88.1 Introduction
8.1.1 What Is New Since AR5?
The Fifth Assessment Report (AR5) of the Intergovernmental Panel on 
Climate Change (IPCC) was the first IPCC report that had a standalone 
chapter on urban mitigation of climate change. The starting point for 
that chapter was how the spatial organisation of urban settlements 
affects greenhouse gas (GHG) emissions and how urban form and 
infrastructure could facilitate mitigation of climate change. A main 
finding in AR5 was that urban form shapes urban energy consumption 
and GHG emissions.
Since AR5, there has been growing scientific literature and policy foci 
on urban strategies for climate change mitigation. There are three 
possible reasons for this. First, according to AR5 Working Group III 
(WGIII) Chapter 12 on Human Settlements, Infrastructure, and Spatial 
Planning, urban areas generate between 71% and 76% of carbon

dioxide (CO 2) emissions from global final energy use and between 
67% and 76% of global energy (Seto et al. 2014). Thus, focusing on 
‘urban systems’ (see Annex I: Glossary and Figure 8.15) addresses 
one of the key drivers of emissions. Second, more than half of the 
world population lives in urban areas, and by mid-century 7  out 
of 10 people on the planet will live in a town or a city (UN DESA 
2019). Thus, coming up with mitigation strategies that are relevant 
to urban settlements is critical for successful mitigation of climate 
change. Third, beyond climate change, there is growing attention on 
cities as major catalysts of change and to help achieve the objectives 
outlined in multiple international frameworks and assessments.
Cities are also gaining traction within the work of the IPCC. The 
IPCC Special Report on Global Warming of 1.5°C (SR1.5 Chapter 4) 
identified four systems that urgently need to change in fundamental

and transformative ways: urban infrastructure, land use and 
ecosystems, industry, and energy. Urban infrastructure was singled 
out but urban systems form a pivotal part of the other three systems 
requiring change (IPCC 2018a) (see ‘infrastructure’ in Glossary). The 
IPCC Special Report on Climate Change and Land (SRCCL) identified 
cities not only as spatial units for land-based mitigation options but 
also places for managing demand for natural resources including 
food, fibre, and water (IPCC 2019).
Other international frameworks are highlighting the importance of 
cities. For example, the Intergovernmental Science-Policy Platform 
on Biodiversity and Ecosystem Services (IPBES) report on nature’s 
contribution to people is clear: cities straddle the biodiversity sphere in 
the sense that they present spatial units of ecosystem fragmentation 
and degradation while at the same time contain spatial units where 
the concentration of biodiversity compares favourably with some

landscapes (IPBES 2019a). Cities are also featured as a key element in 
the transformational governance to tackle both climate change and 
biodiversity and ecosystem challenges in the first-ever IPCC-IPBES 
co-sponsored workshop report (Pörtner et al. 2021) (Section 8.5 and 
see ‘governance’ in Glossary).
The UN Sustainable Development Goals (SDGs) further underscore 
the importance of cities in the international arena with the inclusion of SDG 11 on Sustainable Cities and Communities for ‘inclusive, 
safe, resilient and sustainable’ cities and human settlements 
(United  Nations 2015; Queiroz et al. 2017; United Nations 2019). 
Additionally, UN-Habitat’s New Urban Agenda (NUA) calls for 
various measures, including integrated spatial planning at the 
city-regional scale, to address the systemic challenges included in 
greening cities, among which is emissions reduction and avoidance 
(United Nations 2017).
Since AR5, there has also been an increase in scientific literature

on urban mitigation of climate change, including more diversity of 
mitigation strategies than were covered during AR5 (Lamb et al. 
2018), as well as a growing focus on how strategies at the urban 
scale can have compounding or additive effects beyond urban areas 
(e.g., in rural areas, land-use planning, and the energy sector).
There is more literature on using a systems approach to understand 
the interlinkages between mitigation and adaptation, and situating 
GHG emissions reduction targets within broader social, economic, 
and human well-being contexts and goals (Bai  et al. 2018; Ürge-
Vorsatz et al. 2018; Lin et al. 2021). In particular, the nexus approach, 
such as the water and energy nexus and the water-energy-food 
nexus, is increasingly being used to understand potential emissions 
and energy savings from cross-sectoral linkages that occur in cities 
(Wang and Chen 2016; Engström et al. 2017; Valek et al. 2017). There

is also a growing literature that aims to quantify transboundary urban 
GHG emissions and carbon footprint beyond urban and national 
administrative boundaries (Chen et al. 2016; Hu et al. 2016). Such 
a scope provides a more complete understanding of how local urban 
emissions or local mitigation strategies can have effects on regions’ 
carbon footprint or GHG emissions.
8.1.1.1 City Climate Action
Moreover, cities around the world are putting increasing focus on 
tackling climate change. Since AR5:
• Climate leadership at the local scale is growing with commitment 
from city decision-makers and policymakers to implement local-
scale mitigation strategies (GCoM 2018, 2019; ICLEI 2019a; 
C40 Cities 2020a).
• More than 360 cities announced at the Paris Climate Conference 
that the collective impact of their commitments will lead to 
a  reduction of up to 3.7 GtCO 2-eq (CO 2-equivalent) of urban 
emissions annually by 2030 (Cities for Climate 2015).

• The Global Covenant of Mayors (GCoM), a transnational network 
of more than 10,000 cities, has made commitments to reduce 
urban GHG emissions by up to 1.4–2.3 GtCO 2-eq annually by 
2030 and 2.8–4.2 GtCO 2-eq annually by 2050, compared to 
business-as-usual (GCoM 2018, 2019).
• More than 800 cities have made commitments to achieve 
net-zero GHG emissions, either economy-wide or in a particular 
sector (NewClimate Institute and Data-Driven EnviroLab 2020).
Although most cities and other subnational actors are yet to meet 
their net-zero GHG or CO 2 emissions commitments, the growing 
numbers of those commitments, alongside organisations enabled to

867
867
facilitate reaching those targets, underscore the growing support for 
climate action by city and other subnational leaders.
8.1.1.2 Historical and Future Urban Emissions
One major innovation in this Assessment Report is the inclusion of 
historical and future urban GHG emissions. Urban emissions based 
on consumption-based accounting by regions has been put forth for 
the time frame 1990–2100 using multiple datasets with projections 
given in the framework of the Shared Socio-economic Pathway 
(SSP)–Representative Concentration Pathway (RCP) scenarios. 
This advance has provided a  time dimension to urban footprints 
considering different climate scenarios with implications for urban 
mitigation, allowing a comparison of the way urban emissions and 
their reduction can evolve given different scenario contexts (see 
Glossary for definitions of various ‘pathways’ and ‘scenarios’ in the

context of climate change mitigation, including ‘SSPs’ and ‘RCPs’).
8.1.1.3 Sustainable Development Linkages 
and Feasibility Assessment
Special emphasis is placed on the co-benefits of urban mitigation 
options, including an evaluation of linkages with the SDGs based 
on synergies and/or trade-offs. Urban mitigation options are further 
evaluated based on multiple dimensions according to the feasibility 
assessment (see Section  8.5.5 and Figure  8.19, and Section  8.
SM.2) indicating the enablers and barriers of implementation. These 
advances provide additional guidance for urban mitigation.  
8.1.2 Preparing for the Special Report on Cities 
and Climate Change in AR7
At the 43rd Session of the IPCC in 2016, the IPCC approved a Special 
Report on Climate Change and Cities during the Seventh Assessment 
Cycle of the IPCC (AR7). To stimulate scientific research knowledge 
exchange, the IPCC and nine global partners co-sponsored the IPCC

Cities and Climate Change Science Conference, which brought 
together over 700 researchers, policymakers, and practitioners 
from 80 countries.
The conference identified key research priorities including the 
need for an overarching systems approach to understanding how 
sectors interact in cities as drivers for GHG emissions and the 
relationship between climate and other urban processes, as well as 
achieving transformation towards low-carbon and resilient futures 
(Bai  et al. 2018). The subsequent report on the global research 
and action agenda identifies scale, informality, green and blue 
infrastructure, governance and transformation, as well as financing 
climate action, as areas for scientific research during the AR6 cycle 
and beyond (WCRP 2019).8.1.3 The Scope of the Chapter: A Focus 
on Urban Systems
This chapter takes an urban systems approach and covers the full 
range of urban settlements, including towns, cities, and metropolitan

areas. By ‘urban system’ (Figure  8.15), this chapter refers to two 
related concepts. First, an urban systems approach recognises 
that cities do not function in isolation. Rather, cities exhibit strong 
interdependencies across scales, whether it is within a  region, 
a country, a continent, or worldwide. Cities are embedded in broader 
ecological, economic, technical, institutional, legal, and governance 
structures that often constrain their systemic function, which cannot 
be separated from wider power relations (Bai et al. 2016).
The notion of a system of cities has been around for nearly 100 years 
and recognises that cities are interdependent, in that significant 
changes in one city, such as economic activities, income, or population, 
will affect other cities in the system (Christaller 1933; Berry 1964; 
Marshall 1989). This perspective of an urban system emphasises the 
connections between a city and other cities, as well as between a city

and its hinterlands (Hall and Hay 1980; Ramaswami et al. 2017b; 
Xu et al. 2018c). An important point is that growth in one city affects 
growth in other cities in the global, national or regional system of 
cities (Gabaix 1999; Scholvin et al. 2019; Knoll 2021).
Moreover, there is a hierarchy of cities (Taylor 1997; Liu et al. 2014), 
with very large cities at the top of the hierarchy concentrating political 
power and financial resources, but of which there are very few. Rather, 
the urban system is dominated by small and medium-sized cities 
and towns. With globalisation and increased interconnectedness of 
financial flows, labour, and supply chains, cities across the world 
today have long-distance relationships on multiple dimensions but 
are also connected to their hinterlands for resources.
The second key component of the urban systems lens identifies the 
activities and sectors within a city as being inter-connected – that

cities are ecosystems (Rees 1997; Grimm et al. 2000; Newman and 
Jennings 2008; Acuto et al. 2019; Abdullah and Garcia-Chueca 2020; 
Acuto and Leffel 2021). This urban systems perspective emphasises 
linkages and interrelations within cities. The most evident example of 
this is urban form and infrastructure, which refer to the patterns and 
spatial arrangements of land use, transportation systems, and urban 
design. Changes in urban form and infrastructure can simultaneously 
affect multiple sectors, such as buildings, energy, and transport.
This chapter assesses urban systems beyond simply jurisdictional 
boundaries. Using an urban systems lens has the potential to accelerate 
mitigation beyond a single sector or purely jurisdictional approach 
(Section 8.4). An urban systems perspective presents both challenges 
and opportunities for urban mitigation strategies. It  shows that any 
mitigation option potentially has positive or negative consequences

in other sectors, other settlements, cities, or other parts of the world, 
and requires more careful and comprehensive considerations on the 
broader impacts, including equity and social justice (see Glossary for 
a comprehensive definition of ‘equity’ in the context of mitigation and 
adaptation). This chapter focuses on cities, city regions, metropolitan 
regions, megalopolitans, mega-urban regions, towns, and other types

868
868
Chapter 8 Urban Systems and Other Settlements8of urban configurations because they are the primary sources of 
urban GHG emissions and tend to be where mitigation action can 
be most impactful.
There is no internationally agreed upon definition of ‘urban’, ‘urban 
population’, or ‘urban area’. Countries develop their own definitions 
of urban, often based on a combination of population size or density, 
and other criteria including the percentage of population not 
employed in agriculture, the availability of electricity, piped water, 
or other infrastructures, and characteristics of the built environment, 
such as dwellings and built structures. This chapter assesses 
urban systems, which includes cities and towns. It uses a  similar 
framework to Chapter 6 of AR6 WGII, referring to cities and urban 
settlements as ‘concentrated human habitation centres that exist 
along a continuum’ (Dodman et al. 2022) (for further definitions of

‘urban’, ‘cities’, ‘settlements’ and related terms, see Glossary, and 
WGII Chapter 6).
4 The countries and areas classification in the underlying report for this figure deviates from the standard classification scheme adopted by WGIII as set out in Annex II, Section 1.8.1.4 The Urban Century
The 21st century will be the urban century, defined by a  massive 
increase in global urban populations and a significant building up of 
new urban infrastructure stock to accommodate the growing urban 
population. Six trends in urbanisation are especially important in the 
context of climate change mitigation.
First, the size and relative proportion of the urban population is 
unprecedented and continues to increase. As of 2018, approximately 
55% of the global population lives in urban areas (about 4.3 billion 
people) (UN DESA 2019). It is predicted that 68% of the world 
population will live in urban areas by 2050. This will mean adding

2.5 billion people to urban areas between 2018 and 2050, with 90% 
of this increase taking place in Africa and Asia. There is a  strong 
correlation between the level of urbanisation and the level of 
national income, with considerable variation and complexity in the 
relationship between the two (UN DESA 2019). In general, countries 
with levels of urbanisation of 75% or greater all have high national 
incomes, whereas countries with low levels of urbanisation under 
35% have low national incomes (UN DESA 2019). In general, there 
is a clear positive correlation between the level of urbanisation and 
income levels (Figure 8.1 and Box 8.1).
Figure 8.14: Relationship between urbanisation level and gross national income (GNI).  There is a positive and strong correlation between the urbanisation level 
and gross national income. High-income countries have high levels of urbanisation, on average 80%. Low-income countries have low levels of urbanisation, on average 30%.

869
869
Second, the geographic concentration of the world’s current urban 
population is in emerging economies, and the majority of future 
urban population growth will take place in developing countries and 
least-developed countries (LDCs). About half of the world’s urban 
population in 2018 lived in just seven countries, and about half of 
the increase in urban population through 2050 is projected to be 
concentrated in eight countries (UN DESA 2019) (Figure 8.2). Of these 
eight, seven are emerging economies where there will be a need for 
significant financing to construct housing, roads, and other urban 
infrastructure to accommodate the growth of the urban population. 
How these new cities of tomorrow will be designed and constructed 
will lock-in patterns of urban energy behaviour for decades if not

generations (Sections 8.3.4 and 8.4). Thus, it is essential that urban climate change mitigation strategies include solutions appropriate 
for cities of varying sizes and typologies (Section 8.6 and Figure 8.21).
Third, small and medium-sized cities and towns are a dominant type 
of urban settlement. In 2018, more than half (58%) of the urban 
population lived in cities and towns with fewer than 1  million 
inhabitants and almost half of the world’s urban population (48%) 
lived in settlements with fewer than 500,000 inhabitants (Figure 8.3). 
Although megacities receive a lot of attention, only about 13% of the 
urban population worldwide lived in a megacity – an urban area with 
at least 10 million inhabitants (UN DESA 2019). Thus, there is a need 
for a wide range of strategies for urban mitigation of climate change 
that are appropriate for cities of varying levels of development 
0 0   500  1000  1500  2000  2500  3000  3500  4000  4500Urban population (millions)

All other countries
Republic of Korea
Italy
Egypt
France
Iran (Islamic Republic of)
Turkey
Germany
Pakistan
Nigeria
Mexico
Japan
Indonesia
Brazil
United States of America
India
China   500  1000  1500  2000  2500Urban population increment (millions)
Sudan
Kenya
Vietnam
Brazil
Uganda
Iraq
Mexico
Angola
Egypt
Ethiopia
United Republic of Tanzania
Indonesia
Pakistan
Democratic Republic of the 
Congo
Nigeria
China
India
Figure 8.2: Urban population size in 2018 and increase in the projected urban population.  In 2018, about half of the world’s urban population lived in seven 
countries, and about half of the increase in urban population through 2050 is forecasted to concentrate in eight countries. Source: UN DESA 2019, p. 44.

870
870
Chapter 8 Urban Systems and Other Settlements8
and size, especially smaller cities which often have lower levels of 
financial capacities than large cities.
Fourth, another trend is the rise of megacities and extended 
metropolitan regions. The largest cities around the world are 
becoming even larger, and there is a growing divergence in economic 
power between megacities and other large cities (Kourtit et al. 2015; 
Hoornweg and Pope 2017; Zhao et al. 2017b). Moreover, there is 
evidence that the largest city in each country has an increasing share 
of the national population and economy.
Fifth, population declines have been observed for cities and towns 
across the world, including in Poland, Republic of Korea, Japan, 
United States, Germany, and Ukraine. The majority of cities that 
have experienced population declines are concentrated in Europe. 
Multiple factors contribute to the decline in cities, including declining

industries and the economy, declining fertility, and outmigration 
to larger cities. Shrinking urban populations could offer retrofitting 
opportunities (UNEP 2019) and increasing greenspaces (Jarzebski et al. 2021), but the challenges for these cities differ in scope and 
magnitude from rapidly expanding cities.
Sixth, urbanisation in many emerging economies is characterised 
by informality and an informal economy (Brown and McGranahan 
2016). The urban informal economy includes a  wide array of 
activities, including but not limited to street vending, home-based 
enterprises, unreported income from self-employment, informal 
commerce, domestic service, waste-picking, and urban agriculture. 
The urban informal economy is large and growing. Globally, about 
44% of the urban economy is informal, although there is much 
variation between countries and regions (ILO 2018). Emerging and 
developing economies have the highest percentage of the urban

informal economy, with Africa (76%) and the Arab States (64%) with 
the largest proportion (ILO 2018). Urban informality also extends 
to planning, governance and institutions (Roy 2009; EU 2016; 
Lamson-Hall et al. 2019). Given its prevalence, it is important for 
urban climate change mitigation strategies to account for informality, 
especially in emerging and developing countries (Section 8.3.2).Population, millionMegacities of 10 million 
or more (12.5%)Large cities of 5 to 
10 million (7.7%)Medium-sized cities of 
1 to 5 million (21.9%)Cities of 500,000 to 
1 million (9.8%)
Urban settlements 
with <300,000 (41.5%)All settlements with more than 
1 million inhabitants (42.18%)Cities of 300,000 
to 500,000 (6.5%)
1750275415926325529
42.18%9.8%
6.5%
41.5%
050010001500200025003000350040004500
Urban population (55%)3410
Rural population (45%)
World population

Figure 8.3: Population of the world, by area of residence and size class of urban settlement for 2018. As of 2018, 4.2 billion people or 55% of the world 
population reside in urban settlements while 45% reside in rural areas. The coloured stacked column for the urban population represents the total number of inhabitants 
for a given size class of urban settlements. Megacities of 10 million or more inhabitants have a total of only 529 million inhabitants, corresponding to 12.5% of the urban 
population. In contrast, about 1.8 billion inhabitants reside in urban settlements with fewer than 300,000 inhabitants, corresponding to 41.5% of the urban population. 
The pie chart represents the respective shares for 2018, with 42% of the urban population residing in settlements with more than 1 million inhabitants, and 58% of the

urban population residing in settlements with fewer than 1  million inhabitants. Almost half of the world’s urban population (48%) live in settlements with fewer than 
500,000 inhabitants. Source: adapted from UN DESA 2019, p. 56.

871
871
8.1.5 Urbanisation in Developing Countries
Urbanisation in the 21st century will be dominated by population 
and infrastructure growth in developing countries, and as such it is 
important to highlight three aspects that are unique and especially 
relevant for climate change mitigation. First, urbanisation will 
increase in speed and magnitude. Given their significant impact on 
emissions, mitigation action in Asian cities, especially the large and 
rapidly growing cities, will have significant implications on global 
ambitions (Section 8.3.4).
Second, a number of cities in developing countries lack institutional, 
financial and technical capacities to enable local climate change 
action (Sharifi et al. 2017; Fuhr et al. 2018). While these capacities 
differ across contexts (Hickmann et al. 2017), several governance 
challenges are similar across cities (Gouldson et al. 2015). These

factors also influence the ability of cities to innovate and effectively 
implement mitigation action (Nagendra et al. 2018) (Chapter 17).
Third, there are sizable economic benefits in developing country cities 
that can provide an opportunity to enhance political momentum and 
institutions (Colenbrander et al. 2016). The co-benefits approach 
(Section  8.2), which frames climate objectives alongside other 
development benefits, is increasingly seen as an important concept 
justifying and driving climate change action in developing countries 
(Sethi and Puppim de Oliveira 2018).
Large-scale system transformations are also deeply influenced by 
factors outside governance and institutions, such as private interests 
and power dynamics (Jaglin 2014; Tyfield 2014). In some cases, these 
private interests are tied up with international flows of capital. In 
India, adaptation plans involving networks of private actors and

related mitigation actions have resulted in the dominance of private 
interests. This has led to trade-offs and adverse impacts on the poor 
(Chu 2016; Mehta et al. 2019).
When planning and implementing low-carbon transitions, it is 
important to consider the socio-economic context. An inclusive 
approach emphasises the need to engage non-state actors, including 
businesses, research organisations, non-profit organisations and 
citizens (Lee and Painter 2015; Hale et al. 2020). For example, 
engaging people in defining locally relevant mitigation targets and 
actions has enabled successful transformations in China (Engels 
2018), Africa (Göpfert et al. 2019) and Malaysia (Ho et al. 2015). 
An active research and government collaboration through multiple 
stakeholder interactions in a large economic corridor in Malaysia led 
to the development and implementation of a low-carbon blueprint 
for the region (Ho et al. 2013). Many cities in LDCs and developing

countries lack adequate urban infrastructure and housing. An 
equitable transformation in these cities entails prioritising energy 
access and basic services, including safe drinking water and 
sanitation, to meet basic needs of their populations.8.1.6 Urban Carbon Footprint
Urban areas concentrate GHG fluxes because of the size of the urban 
population, the size and nature of the urban economy, the energy 
and GHGs embodied in the infrastructure (see ‘embodied emissions’ 
in Glossary), and the goods and services imported and exported to 
and from cities (USGCRP 2018).
8.1.6.1 Urban Carbon Cycle
In cities, carbon cycles through natural (e.g.,  vegetation and soils) 
and managed (e.g.,  reservoirs and anthropogenic  – buildings, 
transportation) pools. The accumulation of carbon in urban pools, 
such as buildings or landfills, results from the local or global transfer 
of carbon-containing energy and raw materials used in the city

(Churkina 2008; Pichler et al. 2017; Chen et al. 2020b). Quantitative 
understanding of these transfers and the resulting emissions and 
uptake within an urban area is essential for accurate urban carbon 
accounting (USGCRP 2018). Currently, urban areas are a net source of 
carbon because they emit more carbon than they uptake. Thus, urban 
mitigation strategies require a  twofold strategy: reducing urban 
emissions of carbon into the atmosphere, and enhancing uptake 
of carbon in urban pools (Churkina 2012) (for a broader definition of 
‘carbon cycle’ and related terms such as ‘carbon sink,’ ‘carbon stock,’ 
‘carbon neutrality,’ ‘GHG neutrality,’ and others, see Glossary).
Burning fossil fuels to generate energy for buildings, transportation, 
industry, and other sectors is a  major source of urban GHG 
emissions (Gurney et al. 2015). At the same time, most cities do 
not generate within their boundaries all of the resources they use,

such as electricity, gasoline, cement, water, and food needed for 
local homes and businesses to function (Jacobs 1969), requiring 
consideration of GHG emissions embodied in supply chains serving 
cities. Furthermore, urban vegetation, soils, and aquatic systems can 
both emit or remove carbon from the urban atmosphere and are 
often heavily managed. For example, urban parks, forests, and street 
trees actively remove carbon from the atmosphere through growing 
season photosynthesis. They can become a  net source of carbon 
most often during the dormant season or heat waves. Some of the 
sequestered carbon can be stored in the biomass of urban trees, soils, 
and aquatic systems. Urban infrastructures containing cement also 
uptake carbon through the process of carbonation. The uptake of 
carbon by urban trees is at least two orders of magnitude faster than 
by cement-containing infrastructures (Churkina 2012) (Section 8.4.4 
and Figures 8.17 and 8.18).

8.1.6.2 Urban Emissions Accounting
Urban GHG emissions accounting can determine critical conceptual 
and quantitative aspects of urban GHG emissions. The accounting 
framework chosen can therefore predetermine the emissions 
responsibility, the mitigation options available, and the level of effort 
required to correctly account for emissions (Afionis et al. 2017).
Two main urban carbon accounting advances have occurred since 
AR5. The first includes efforts to better understand and clarify how the 
different urban GHG accounting frameworks that have emerged over

872
872
Chapter 8 Urban Systems and Other Settlements8the past 15 years are interrelated, require different methodological 
tools, and reflect differing perspectives on emissions responsibility 
and quantification effort. The second main advance lies in a series 
of methodological innovations facilitating practical implementation, 
emissions verification, and scaling-up of the different GHG 
accounting approaches. This section provides an overview of the 
most used GHG urban accounting frameworks followed by a review 
of the advances since AR5.
Numerous studies have reviewed urban GHG accounting frameworks 
and methods with somewhat different nomenclatures and categorical 
divisions (Lin et al. 2015; Lombardi et al. 2017; Chen et al. 2019b; 
Arioli et al. 2020; Heinonen et al. 2020; Hachaichi and Baouni 2021; 
Ramaswami et al. 2021). Furthermore, accounting frameworks are 
reflected in multiple protocols used by urban practitioners (BSI 2013;

Fong et al. 2014; ICLEI 2019b). Synthesis of these reviews and 
protocols, as well as the many individual methodological studies 
available, point to four general frameworks of urban GHG accounting: 
(i) territorial accounting (TA); (ii) community-wide infrastructure 
supply chain footprinting (CIF); and (iii and iv) consumption-based 
carbon footprint accounting (CBCF; Wiedmann and Minx 2008). The 
last, CBCF , can be further divided into accounting with a focus on 
household or personal consumption (iii: the personal carbon footprint, 
or PCF); and an approach in which one includes final consumption 
in an area by all consumers (iv: the areal carbon footprint, or ACF) 
(Heinonen et al. 2020). A number of small variations to these general 
categories are found in the literature (Lin et al. 2015; Chen et al. 
2020a), but these four general frameworks capture the important 
distinctive (i.e., policy-relevant) features of urban GHG accounting.

All these approaches are foundationally rooted in the concept of 
urban metabolism, that is, the tracking of material and energy flows 
into, within, and out of cities (Wolman 1965). These frameworks 
all aim to quantify urban GHG emissions but reflect different 
perspectives on where the emission responsibility is allocated in 
addition to how much and which components of the GHG emissions 
associated with the import and export of goods and services to and 
from a  city (‘transboundary embedded/embodied GHG emissions’) 
are included in a given urban emissions account. The four frameworks 
share some common, overlapping GHG emission quantities and their 
interrelationships have been defined mathematically (Chavez and 
Ramaswami 2013).
A key advance since AR5 lies in understanding the different GHG 
accounting frameworks in terms of what they imply for responsibility – 
shared or otherwise – and what they imply for the depth and breadth

of GHG emission reductions. TA focuses on in-city direct emission 
of GHGs to the atmosphere (e.g.,  combustion, net ecosystem 
exchange, methane (CH 4) leakage) within a  chosen geographic 
area (Sovacool and Brown 2010; Gurney et al. 2019). CIF connects 
essential infrastructure use and demand activities in cities with their 
production, by combining TA emissions with the transboundary 
supply chain emissions associated with imported electricity, fuels, 
food, water, building materials, and waste management services 
used in cities (Ramaswami et al. 2008; Kennedy et al. 2009; Chavez 
and Ramaswami 2013).CBCF considers not only the supply-chain-related GHG emissions of 
key infrastructure, but also emissions associated with all goods and 
services across a city, often removing emissions associated with goods 
and services exported from a city (Wiedmann et al. 2016, 2021). The 
distinction between the PCF and ACF variants of the CBCF is primarily

associated with whether the agents responsible for the final demand 
are confined to only city residents (PCF) or all consumers in a city 
(ACF), which can include government consumers, capital formation, 
and other final demand categories (Heinonen et al. 2020).
A recent synthesis of these frameworks in the context of a net-zero 
GHG emissions target suggests that the four frameworks contribute 
to different aspects of decarbonisation policy and can work together 
to inform the overall process of decarbonisation (Ramaswami 
et al. 2021). Furthermore, the relative magnitude of GHG emissions 
for a  given city resulting from the different frameworks is often 
a  reflection of the city’s economic structure as a  ‘consumer’ or 
‘producer’ city (Chavez and Ramaswami 2013; Sudmant et al. 2018).
The TA framework is unique in that it can be independently verified 
through direct measurement of GHGs in the atmosphere, offering

a check on the integrity of emission estimates (Lauvaux et al. 2020; 
Mueller et al. 2021). It is traditionally simpler to estimate by urban 
practitioners given the lower data requirements, and it can be 
relevant to policies aimed specifically at energy consumption and 
mobility activities within city boundaries. However, it will not reflect 
electricity imported for use in cities or lifecycle emissions associated 
with in-city consumption of goods and services.
The CIF framework adds to the TA framework by including GHG 
emissions associated with electricity imports and the lifecycle 
GHG  emissions associated with key infrastructure provisioning 
activities in cities, serving all homes, businesses, and industries. This 
widens both the number of emitting categories and the responsibility 
for those emissions by including infrastructure-related supply chain 
emissions. The CIF framework enables individual cities to connect

community-wide demand for infrastructure and food with their 
transboundary production, strategically aligning their net-zero 
emissions plans with larger-scale net-zero efforts (Ramaswami and 
Chavez 2013; Ramaswami et al. 2021; Seto et al. 2021).
The PCF version of the CBCF shifts the focus of the consumption and 
associated supply chain emissions to only household consumption of 
goods and services (Jones and Kammen 2014). This both reduces the 
TA emissions considered and the supply chain emissions, excluding 
all emissions associated with government, capital formation, and 
exports. The ACF , by contrast, widens the perspective considerably, 
including the TA and supply chain emissions of all consumers in 
a city, but often removing emissions associated with exports.
An additional distinction is the ability to sum up accounts from 
individual cities in a  region or country, for example, directly to 
arrive at a regional or national total. This can only be done for the

TA and PCF frameworks. The ACF and CIF frameworks would require 
adjustment to avoid double-counting emissions (Chen et al. 2020a).

873
873
A second major area of advance since AR5 has been in methods 
to implement, verify and scale up the different GHG footprinting 
approaches. Advances have been made in six key areas: (i) advancing 
urban metabolism accounts integrating stocks and flows, and 
considering biogenic and fossil-fuel-based emissions (Chen et al. 
2020b); (ii) improving fine-scale and near-real-time urban use-activity 
data through new urban data science (Gately et al. 2017; Gurney et al. 
2019; Turner et al. 2020; Yadav et al. 2021); (iii) using atmospheric 
monitoring from the ground, aircraft, and satellites combined with 
inverse modelling to independently quantify TA emissions (Lamb 
et al. 2016; Lauvaux et al. 2016, 2020; Davis et al. 2017; Mitchell 
et al. 2018; Sargent et al. 2018; Turnbull et al. 2019; Wu et al. 2020a); 
(iv)  improving supply chain and input-output modelling, including

the use of physically based input-output models (Wachs and Singh 
2018); (v) establishing the global multi-region input-output models 
(Lenzen et al. 2017; Wiedmann et al. 2021); and (vi) generating multi-
sector use and supply activity data across all cities in a nation, in 
a manner where data aggregate consistently across city, province, 
and national scales (Tong et al. 2021) (Section 8.3).
8.2 Co-benefits and Trade-offs of Urban 
Mitigation Strategies
Co-benefits are ‘the positive effects that a policy or measure aimed 
at one objective might have on other objectives, thereby increasing 
the total benefits to the society or environment’ (IPCC 2018b). AR5 
WGIII Chapter  12 reported a  range of co-benefits associated with 
urban climate change mitigation strategies, including public savings, 
air quality and associated health benefits, and productivity increases 
in urban centres (Seto et al. 2014). Since AR5, evidence continues to

mount on the co-benefits of urban mitigation. Highlighting co-benefits 
could make a strong case for driving impactful mitigation action (Bain 
et al. 2016), especially in developing countries, where development 
benefits can be the argument for faster implementation (Sethi and 
Puppim de Oliveira 2018). Through co-benefits, urban areas can 
couple mitigation, adaptation, and sustainable development while 
closing infrastructure gaps (Thacker et al. 2019; Kamiya et al. 2020).
The urgency of coupling mitigation and adaptation is emphasised 
through a special Cross-Working Group Box on ‘Cities and Climate 
Change’ (Section  8.2.3 and Cross-Working Group Box  2  in this 
chapter). This section further addresses synergies and trade-offs for 
sustainable development with a focus on linkages with the SDGs and 
perspectives for economic development, competitiveness, and equity.  
8.2.1 Sustainable Development
Sustainable development is a  broad concept, encompassing socio-

economic and environmental dimensions, envisaging long-term 
permanence and improvement. While long-term effects are more 
related to resilience  – and hence carry co-benefits and synergies 
with the mitigation of GHG emissions – some short-term milestones 
were defined by the post-2015 UN Sustainable Development Agenda 
SDGs, including a specific goal on climate change (SDG 13) and one 
on making cities inclusive, safe, resilient and sustainable (SDG 11) (United Nations 2015). The SDGs and related indicators can be an 
opportunity to improve cities by using science-based decision-making 
and engaging a diverse set of stakeholders (Simon et al. 2016; Klopp 
and Petretta 2017; Kutty et al. 2020).
There are multiple ways that development pathways can be shifted 
towards sustainability (Section  4.3.3, Cross-Chapter Box  5  in 
Chapter  4, Chapter  17 and Figure  17.1). Urban areas can work 
to redirect development pathways towards sustainability while

increasing co-benefits for urban inhabitants. Figure 8.4 indicates that 
mitigation options for urban systems can provide synergistic linkages 
across a wide range of SDGs, and some cases where linkages can 
produce both synergies and trade-offs. While linkages are based 
on context and the scale of implementation, synergies can be most 
significant when urban areas pursue integrated approaches where 
one mitigation option supports the other (Sections 8.4 and 8.6).
Figure 8.4 summarises an evaluation of the synergies and/or trade-
offs with the SDGs for the mitigation options for urban systems based 
on Supplementary Material 8.SM.1. The evaluations depend on the 
specific urban context, with synergies and/or trade-offs being more 
significant in certain contexts than others. Urban mitigation with 
a view of the SDGs can support shifting pathways of urbanisation 
towards greater sustainability. The feasibility of urban mitigation

options is also malleable and can increase with more ‘enabling 
conditions’ (see Glossary), provided, perhaps, through institutional 
(i.e., financial or governmental) support (Section 8.5). Strengthened 
institutional capacity that supports the coordination of mitigation 
options can increase linkages with the SDGs and their synergies. 
For example, urban land use and spatial planning for walkable and 
co-located densities, together with electrification of the urban energy 
system, can hold more benefits for the SDGs than any one of the 
mitigation options alone (Sections 8.4.2.3, 8.4.3.1 and 8.6).
Evidence on the co-benefits of urban mitigation measures for human 
health has increased significantly since AR5, especially through the 
use of health impact assessments, where energy savings and cleaner 
energy supply structures based on measures for urban planning, 
heating, and transport have reduced CO 2, nitrogen oxides (NO x),

and coarse particulate matter (PM 10) emissions (Diallo et al. 2016). 
Some measures, especially those related to land-use planning and 
transportation, have also increased opportunities for physical activity 
for improved health (Diallo et al. 2016). In developing countries, 
the co-benefits approach has been effective in justifying climate 
change mitigation actions at the local level (Puppim de Oliveira and 
Doll 2016). Mixed-use compact development with sufficient land-
use diversity can have a  positive influence on urban productivity 
(Section  8.4.2). Conversely, urban spatial structures that increase 
walking distances and produce car dependency have negative 
impacts on urban productivity considering congestion as well as 
energy costs (Salat et al. 2017).
There is increasing evidence that climate mitigation measures can 
lower health risks that are related to energy poverty, especially among 
vulnerable groups such as the elderly and in informal settlements

(Monforti-Ferrario et al. 2018). Measures such as renewable energy-
based electrification of the energy system not only reduce outdoor air

874
874
Electriﬁcation of the urban energy system
District heating and cooling networks
Urban green and blue infrastructure
Waste prevention, minimisation and management
Integrating sectors, strategies and innovationsSynergy Both synergy and trade-offs
SDG 1: No Poverty
SDG 2: Zero Hunger
SDG 3: Good Health and Well-being
SDG 4: Quality Education
SDG 5: Gender Equality
SDG 6: Clean Water and SanitationSDG 7: Affordable and Clean Energy
SDG 8: Decent Work and Economic Growth
SDG 9: Industry, Innovation and Infrastructure
SDG 10: Reduced Inequalities
SDG 11: Sustainable Cities and Communities
SDG 12: Responsible Consumption and ProductionSDG 13: Climate Action
SDG 14: Life Below Water
SDG 15: Life on Land
SDG 16: Peace, Justice and Strong Institutions
SDG 17: Partnerships for the GoalsList of SDGs Conﬁdence levels
Low conﬁdence
Medium conﬁdence
High conﬁdence•
••

•••••• ••• ••• ••• •• ••• ••• ••• ••• ••• ••• ••• ••• •• ••• •• •••••• +
••–••+
••–••• +
••–••• +
••–••• +
••• –
••• +
••–••• +
••–••+
••–
••–
••+
••–
••• +
••–••+
••–••• +
••–••
••
••
••
•• •••••••••
••••••••
••••••••••••
•••••
•••••••••••••••
•••
••••••••
••
•••••••••••
••••
•••••••
••••
•••••••••••••Mitigation options
Figure 8.4: Co-benefits of urban mitigation actions.  The first column lists urban mitigation options. The second column indicates synergies with the SDGs. The third column indicates both synergies and/or trade-offs. The dots represent 
confidence levels with the number of dots representing levels from low to high. In the last column, confidence levels for synergies and/or trade-offs are provided separately. A plus sign (+) represents synergy and a minus sign (–) represents

a trade-off. Supplementary Material 8.SM.1 provides 64 references and extends the SDG mappings that are provided in Thacker et al. (2019) and Fuso Nerini et al. (2018). Please see Table 17.SM.1 for details and Annex II for the methodology 
of the SDG assessment.

875
875
pollution, but also enhance indoor air quality by promoting smoke-
free heating and cooking in buildings (Kjellstrom and McMichael 
2013). The environmental and ecological benefits of electrification 
of the urban energy system include improved air quality based 
on a  shift to non-polluting energy sources (Jacobson et al. 2018; 
Ajanovic and Haas 2019; Bagheri et al. 2019; Gai et al. 2020). Across 
74 metropolitan areas around the world, an estimated 408,270 lives 
per year are saved due to air quality improvements that stem from 
a  move to 100% renewable energy (Jacobson et al. 2020). Other 
studies indicate that there is potential to reduce premature mortality 
by up to 7000 people in 53 towns and cities, to create 93,000 new 
jobs, and to lower global climate costs and personal energy costs, 
through renewable energy transformations (Jacobson et al. 2018).
Across 146 signatories of a city climate network, local energy-saving

measures led to 6596 avoided premature deaths and 68,476 years of 
life saved due to improved air quality (Monforti-Ferrario et al. 2018). 
Better air quality further reinforces the health co-benefits of climate 
mitigation measures based on walking and bicycling since evidence 
suggests that increased physical activity in urban outdoor settings 
with low levels of black carbon improves lung function (Laeremans 
et al. 2018). Physical activity can also be fostered through urban 
design measures and policies that promote the development of ample 
and well-connected parks and open spaces, and can lead to physical 
and mental health benefits (Kabisch et al. 2016) (Section  8.4.4 
and Figure 8.18).
Cities in India, Indonesia, Vietnam, and Thailand show that reducing 
emissions from major sources (e.g.,  transport, residential burning, 
biomass open burning, and industry) could bring substantial  
co-benefits of avoided deaths from reduced PM 2.5 (fine inhalable

particulates) emissions and radiative forcing from black carbon 
(Pathak and Shukla 2016; Dhar et al. 2017; Permadi et al. 2017; Karlsson 
et al. 2020), reduced noise, and reduced traffic injuries (Kwan and 
Hashim 2016). Compact city policies and interventions that support 
a  modal shift away from private motor vehicles towards walking, 
cycling, and low-emission public transport delivers significant public 
health benefits (Creutzig 2016; Ürge-Vorsatz et al. 2018). Trade-offs 
associated with compact development include the marginal health 
costs of transport air pollution (Lohrey and Creutzig 2016) and stress 
from traffic noise (Gruebner et al. 2017) (Section 8.4.2.3).
Urban green and blue infrastructure  – a subset of nature-based 
solutions (NBS)  – acts as both climate mitigation and adaptation 
measures by reducing heat stress (Kim and Coseo 2018; Privitera and 
La Rosa 2018; Herath et al. 2021), improving air quality, reducing

noise (Scholz et al. 2018; De la Sota et al. 2019), improving urban 
biodiversity (Hall et al. 2017b), and enhancing well-being, including 
contributions to local development (Lwasa et al. 2015). Health 
benefits from urban forestry and green infrastructure include 
reduced cardiovascular morbidity, improved mental health (van den 
Bosch and Ode Sang 2017; Vujcic et al. 2017; Al-Kindi et al. 2020; 
Sharifi et al. 2021), raised birth weight (Dzhambov et al. 2014), and 
increased life expectancy (Jonker et al. 2014). Urban agriculture, 
including urban orchards, rooftop gardens, and vertical farming 
contribute to enhancing food security and fostering healthier diets (Cole et al. 2018; Petit-Boix and Apul 2018; De la Sota et al. 2019) 
(Section 8.4.4, Figure 8.18 and Box 8.2).
8.2.2 Economic Development, 
Competitiveness, and Equity
Sustainable management of urban ecosystems entails addressing 
economic growth, equity, and good governance. In total, 102 SDG

targets (99 synergies and 51 trade-offs) are identified with published 
evidence of relationships with urban ecosystems – out of the 169 
in the 2030 Agenda (Maes et al. 2019). The targets require action 
in relation to urban ecosystem management, environmental 
improvements, equality related to basic services, long-term economic 
growth, economic savings, stronger governance, and policy 
development at multiple scales.
Mitigation measures related to different sectors can provide  
co-benefits and reduce social inequities. Transport-related measures, 
such as transportation demand management, transit-oriented 
development (TOD), and promotion of active transport modes provide 
economic co-benefits through, for example, reducing health care 
costs linked with pollution and cardiovascular diseases, improving 
labour productivity, and decreasing congestion costs (including 
waste of time and money) (Sharifi et al. 2021). As a case-in-point,

data from cities such as Bangkok, Kuala Lumpur, Jakarta, Manila, 
Beijing, Mexico City, Dakar, and Buenos Aires indicate that economic 
costs of congestion account for a considerable share of their gross 
domestic product (GDP), ranging from 0.7% to 15.0% (Dulal 2017) 
(Section 8.4.2).
Since policy interventions can result in negative impacts or trade-offs 
with other objectives, fostering accessibility, equity, and inclusivity 
for disadvantaged groups is essential (Viguié and Hallegatte 2012; 
Sharifi 2020; Pörtner et al. 2021). Anti-sprawl policies that aim to 
increase density, or the introduction of large green areas in cities 
could increase property prices, resulting in trade-offs with affordable 
housing and pushing urban poor further away from cities (Reckien 
et al. 2017; Alves et al. 2019). Deliberate strategies can improve 
access of low-income populations to jobs, and gender-responsive 
transport systems that can enhance women’s mobility and financial

independence (Viguié and Hallegatte 2012; Lecompte and Juan Pablo 
2017; Reckien et al. 2017; Priya Uteng and Turner 2019).
Low-carbon urban development that triggers economic decoupling 
and involves capacity-building measures could have a  positive 
impact on employment and local competitiveness (Dodman 2009; 
Kalmykova et al. 2015; Chen et al. 2018b; García-Gusano et al. 
2018; Hu et al. 2018; Shen et al. 2018). Sustainable and low-carbon 
urban development that integrates issues of equity, inclusivity, and 
affordability while safeguarding urban livelihoods, providing access 
to basic services, lowering energy bills, addressing energy poverty, 
and improving public health, can also improve the distributional 
effects of existing and future urbanisation (Friend et al. 2016; 
Claude et al.  2017; Colenbrander et al. 2017; Ma et al. 2018; 
Mrówczyńska et al. 2018; Pukšec et al. 2018; Wiktorowicz et al. 2018; 
Ramaswami 2020).

876
876
Chapter 8 Urban Systems and Other Settlements8Depending on the context, green and blue infrastructure can also 
offer considerable economic co-benefits. For example, green roofs and 
facades and other urban greening efforts such as urban agriculture 
and greening streets can improve microclimatic conditions and 
enhance thermal comfort, thereby reducing utility and health care 
costs. The presence of green and blue infrastructure may also increase 
the economic values of nearby properties (Votsis 2017; Alves et al. 
2019) (Section 8.4.4 and Figure 8.18).
Studies in the UK show that beneficiaries are willing to pay (WTP) 
an additional fee (up to 2% more in monthly rent) for proximity to 
green and blue infrastructure, with the WTP varying depending on 
the size and nature of the green space (Mell et al. 2013, 2016). Urban 
agriculture can not only reduce household food expenditure, but 
also provide additional sources of revenue for the city (Ayerakwa

2017; Alves et al. 2019). Based on the assessed literature, there is 
high agreement on the economic co-benefits of green and blue 
infrastructure, but supporting evidence is still limited (Section 8.7).
Implementing waste management and wastewater recycling 
measures can provide additional sources of income for citizens 
and local authorities. Wastewater recycling can minimise the costs 
associated with the renewal of centralised wastewater treatment 
plants (Bernstad Saraiva Schott and Cánovas 2015; Gharfalkar 
et al. 2015; Gonzalez-Valencia et al. 2016; Herrero and Vilella 
2018; Matsuda et al. 2018; Nisbet et al. 2019). Waste management 
and wastewater recycling is also a  pathway for inclusion of the 
informal sector into the urban economy with high agreement and 
medium evidence (Sharifi 2021). Additionally, authorities can sell 
energy generated from wastewater recycling to compensate for 
the wastewater management costs (Colenbrander et al. 2017;

Gondhalekar and Ramsauer 2017). Another measure that contributes 
to reducing household costs is the promotion of behavioural 
measures such as dietary changes that can decrease the demand for 
costly food sources and reduce health care costs through promoting 
healthy diets (Hoppe et al. 2016) (Sections 8.4.5 and 8.4.6).
In addition to cost savings, various measures such as stormwater 
management and urban greening can enhance social equity and 
environmental justice. For example, the thermal comfort benefits 
provided by green and blue infrastructure and passive design measures 
can address issues related to energy poverty and unaffordability of 
expensive air conditioning systems for some social groups (Sharma 
et al. 2018; He et al. 2019). To achieve such benefits, however, the 
costs of integrating green and blue infrastructure and passive design 
measures into building design would need to be minimised. Another 
example is the flood mitigation benefits of stormwater management

measures that can reduce impacts on urban poor who often reside 
in flood-prone and low-lying areas of cities (Adegun 2017; He et al. 
2019). Generally, the urban poor are expected to be disproportionately 
affected by climate change impacts. Carefully designed measures 
that reduce such disproportionate impacts by involving experts, 
authorities and citizens would enhance social equity (Pandey et al. 
2018; He et al. 2019; Mulligan et al. 2020).8.2.3 Coupling Mitigation and Adaptation
There are numerous synergies that come from coupling urban 
adaptation and mitigation. A  number of studies have developed 
methods to assess the synergies between mitigation and adaptation 
strategies, as well as their co-benefits (Solecki et al. 2015; Buonocore 
et al. 2016; Chang et al. 2017; Helgenberger and Jänicke 2017). 
Co-benefits occur when implementing mitigation (or adaptation) 
measures that have positive effects on adaptation (or mitigation)

(Sharifi 2021). In contrast, the trade-offs emerge when measures 
aimed at improving mitigation (adaptation) undermine the ability to 
pursue adaptation (mitigation) targets (Sharifi 2020). The magnitude 
of such co-benefits and trade-offs may vary depending on various 
factors. A  systematic review of over 50 climate change articles 
provides evidence that mitigation can contribute to resilience  – 
especially to temperature changes and flooding  – with varying 
magnitudes, depending on factors such as the type of mitigation 
measure and the scale of implementation (Sharifi 2019).
Measures from different sectors that can provide both mitigation and 
adaptation benefits involve urban planning (Section 8.4.2), buildings 
(Sections 8.4.3.2 and 8.4.4), energy (Section 8.4.3), green and blue 
infrastructure (Section  8.4.4), transportation (Section  8.4.2), socio-
behavioural aspects (Section 8.4.5), urban governance (Section 8.5),

waste (Section  8.4.5.2), and water (Section  8.4.6). In addition 
to their energy-saving and carbon-sequestration benefits, many 
measures can also enhance adaptation to climate threats, such as 
extreme heat, energy shocks, floods, and droughts (Sharifi 2021). 
Existing evidence is mainly related to urban green infrastructure, 
urban planning, transportation, and buildings. There has been 
more emphasis on the potential co-benefits of measures, such 
as proper levels of density, building energy efficiency, distributed 
and decentralised energy infrastructure, green roofs and facades, 
and public/active transport modes. Renewable-based distributed and 
decentralised energy systems improve resilience to energy shocks 
and can enhance adaptation to water stress considering the water-
energy nexus. By further investment on these measures, planners and 
decision makers can ensure enhancing achievement of mitigation/
adaptation co-benefits at the urban level (Sharifi 2021).

As for trade-offs, some mitigation efforts may increase exposure to 
stressors such as flooding and the urban heat island (UHI) effect 
(see  Glossary), thereby reducing the adaptive capacity of citizens. 
For instance, in some contexts, high-density areas that lack adequate 
provision of green and open spaces may intensify the UHI effect 
(Pierer and Creutzig 2019; Xu et al. 2019). There are also concerns 
that some mitigation efforts may diminish adaptive capacity of urban  
poor and marginalised groups through increasing costs of 
urban  services and/or eroding livelihood options. Environmental 
policies designed to meet mitigation targets through phasing out old 
vehicles may erode livelihood options of poor households, thereby 
decreasing their adaptive capacity (Colenbrander et al. 2017). 
Ambitious mitigation and adaptation plans could benefit private 
corporate interests resulting in adverse effects on the urban poor 
(Chu et al. 2018; Mehta et al. 2019).

877
877
Urban green and blue infrastructure such as urban trees, greenspaces, 
and urban waterways can sequester carbon and reduce energy 
demand, and provide adaptation co-benefits by mitigating the UHI 
effect (Berry et al. 2015; Wamsler and Pauleit 2016; WCRP 2019) 
Cross-Working Group Box 2: Cities and Climate Change
Authors: Xuemei Bai (Australia), Vanesa Castán Broto (Spain/United Kingdom), Winston Chow (Singapore), Felix Creutzig (Germany), 
David Dodman (Jamaica/United Kingdom), Rafiq Hamdi (Belgium), Bronwyn Hayward (New Zealand), Şiir Kılkış (Turkey), Shuaib Lwasa 
(Uganda), Timon McPhearson (the United States of America), Minal Pathak (India), Mark Pelling (United Kingdom), Diana Reckien 
(Germany), Karen C. Seto (the United States of America), Ayyoob Sharifi (Iran/Japan), Diana Ürge-Vorsatz (Hungary)

This Cross-Working Group Box on Cities and Climate Change responds to the critical role of urbanisation as a megatrend impacting 
Working Groups (including WGI Box TS.14, WGII Chapter 6 ‘Cities, Settlements and Key Infrastructure’, WGII regional chapters, WGII 
Cross-Chapter Paper ‘Cities and Settlements by the Sea’, and WGIII Chapter 8 ‘Urban Systems and Other Settlements’). This Box 
highlights key findings from WGII and III and substantial gaps in literature where more research is urgently needed relating to 
policy action in cities. It describes methods of addressing mitigation and adaptation in an integrated way across sectors and cities 
to advance sustainable development and equity outcomes and assesses the governance and finance solutions required to support 
climate-resilient responses.

Urbanisation: A megatrend driving global climate risk and potential for low-carbon and resilient futures
Severe weather events, exacerbated by anthropogenic emissions, are already having devastating impacts on people who live in 
urban areas, on the infrastructure that supports these communities, as well as people living in distant places (high confidence) (Cai 
et al. 2019; Folke et al. 2021). Between 2000 and 2015, the global population in locations that were affected by floods grew by 
58–86 million (Tellman et al. 2021). The direct economic costs of all extreme events reached USD210–268 billion in 2020 (Aon 2021; 
Munich RE 2021; WMO 2021) or about USD0.7 billion per day; this figure does not include knock-on costs in supply chains (Kii 2020) 
or lost days of work, implying that the actual economic costs could be far higher. Depending on RCP , between half (RCP2.6) and three-

quarters (RCP8.5) of the global population could be exposed to periods of life-threatening climatic conditions arising from coupled 
impacts of extreme heat and humidity by 2100 (Mora et al. 2017; Huang et al. 2019) (see WGII Section 6.2.2.1, WGII Figure 6.3, and 
WGIII Sections 8.2 and 8.3.4).
Urban systems are now global, as evidenced by the interdependencies between infrastructure, services, and networks driven by 
urban production and consumption; remittance flows and investments reach into rural places, shaping natural resource use far 
from the city and bring risk to the city when these places are impacted by climate change (WGIII Section 8.4 and Figure 8.15). This 
megatrend (Kourtit et al. 2015) amplifies as well as shapes the potential impacts of climate events and integrates the aims and 
approaches for delivering mitigation, adaptation, and sustainable development (medium evidence, high agreement) (Dawson et al.

2018; Tsavdaroglou et al. 2018; Zscheischler et al. 2018). For cities facing flood damage, wide-ranging impacts have been recorded on 
other urban areas near and far (Carter et al. 2021; Simpson et al. 2021) as production and trade is disrupted (Shughrue et al. 2020). 
In the absence of integrated mitigation and adaptation across and between infrastructure systems and local places, impacts that bring 
urban economies to a standstill can extend into supply chains and across energy networks causing power outages.
Urban settlements contribute to climate change, generating about 70% of global CO 2-eq emissions (high confidence) (see 
WGI Box TS.14, WGII Sections 6.1 and 6.2, and WGIII Section 8.3). This global impact feeds back to cities through the exposure of 
infrastructure, people, and business to the impacts of climate-related hazards. Particularly in larger cities, this climate feedback is

exacerbated by local choices in urban design, land use, building design, and human behaviour (Viguié et al. 2020) that shape local 
environmental conditions. Both the local and global combine to increase hazardousness. Certain configurations of urban form and 
their elements can add up to 2°C to warming; concretisation of open space can increase run-off, and building height and orientation 
influences wind direction and strength (see WGII Section 6.3 and WGIII Section 8.4.2).

878
878
Chapter 8 Urban Systems and Other Settlements8Cross-Working Group Box 2 (continued)
Designing for resilient and low-carbon cities today is far easier than retrofitting for risk reduction tomorrow. As urbanisation unfolds, 
its legacy continues to be the locking-in of emissions and vulnerabilities (high confidence) (Seto et al. 2016; Ürge-Vorsatz et al. 
2018) (see WGIII Section 8.4 and Figure 8.15). Retrofitting, disaster reconstruction, and urban regeneration programmes offer scope 
for strategic direction changes to low-carbon and high-resilience urban form and function, so long as they are inclusive in design 
and implementation. Rapid urban growth means new investment, new buildings and infrastructure, new demands for energy and 
transport and new questions about what a healthy and fulfilling urban life can be. The USD90 trillion expected to be invested in new

urban development by 2030 (NCE 2018) is a global opportunity to place adaptation and mitigation directly into urban infrastructure 
and planning, as well as to consider social policy including education, health care, and environmental management (Ürge-Vorsatz 
et al. 2018). If this opportunity is missed, and business-as-usual urbanisation persists, social and physical vulnerability will become 
much more challenging to address.
The benefits of actions taken to reduce GHG emissions and climate stressors diminish with delayed action, indicating the necessity 
for rapid responses. Delaying the same actions for increasing the resilience of infrastructure from 2020 to 2030 is estimated to have 
a median cost of at least USD1 trillion (Hallegatte et al. 2019) while also missing the carbon emissions reductions required in the 
narrowing window of opportunity to limit global warming to 1.5°C (WGI). In contrast, taking integrated actions towards mitigation,

adaptation, and sustainable development will provide multiple benefits for the health and well-being of urban inhabitants and avoid 
stranded assets (see WGII Section 6.3, WGII Chapter 17, Cross-Chapter Box on ‘Feasibility’ in WGII Chapter 18, WGIII Chapter 5, and 
WGIII Section 8.2).
The policy-action gap: urban low-carbon and climate-resilient development
Cities are critical places to realise both adaptation and mitigation actions simultaneously with potential co-benefits that extend 
far beyond cities (medium evidence, high agreement) (Göpfert et al. 2019; Grafakos et al. 2020). Given rapid changes in the built 
environment, transforming the use of materials and the land intensiveness of urban development, including in many parts of the Global 
South, will be critical in the next decades, as well as mainstreaming low-carbon development principles in new urban development in

all regions. Much of this development will be self-built and ‘informal’ – and new modes of governance and planning will be required 
to engage with this. Integrating mitigation and adaptation now rather than later, through reshaping patterns of urban development 
and associated decision-making processes, is a prerequisite for attaining resilient and zero-carbon cities (see WGIII Sections 8.4 and 
8.6, and WGIII Figure 8.21).
evidence, high agreement) (Araos et al. 2017; Aguiar et al. 2018; Olazabal and Ruiz De Gopegui 2021). A review of local climate 
mitigation and adaptation plans across 885 urban areas of the European Union suggests mitigation plans are more common than 
adaptation plans – and that city size, national legislation, and international networks can influence the development of local climate

and adaptation plans with an estimated 80% of those cities with above 500,000 inhabitants having a mitigation and/or an adaptation 
plan (Reckien et al. 2018).
mitigation and adaptation, and help manage possible trade-offs with sustainable development (limited evidence, medium agreement) 
(Grafakos et al. 2019; Landauer et al. 2019; Pierer and Creutzig 2019). An analysis of 315 local authority emission-reduction plans 
reveals that the most common policies cover municipal assets and structures (Palermo et al. 2020a). Estimates of emission reductions 
by non-state and sub-state actors in 10 high-emitting economies projected GHG emissions in 2030 would be 1.2–2.0 GtCO 2-eq yr–1 
or 3.8–5.5% lower compared to scenario projections for current national policies (31.6–36.8 GtCO 2-eq yr–1) if the policies are

fully implemented and do not change the pace of action elsewhere (Kuramochi et al. 2020). The value of integrating mitigation 
and adaptation is underscored in the opportunities for decarbonising existing urban areas, and investing in social, ecological, and 
technological infrastructure resilience (WGII Section 6.4). Integrating mitigation and adaption is challenging (Landauer et al. 2019) but 
can provide multiple benefits for the health and well-being of urban inhabitants (Sharifi 2021) (See WGIII Section 8.2.3).
Effective climate strategies combine mitigation and adaptation responses, including through linking adaptive urban land use with 
GHG emission reductions (medium evidence, high agreement) (Xu et al. 2019; Patterson 2021). For example, urban green and blue 
infrastructure can provide co-benefits for mitigation and adaptation (Ürge-Vorsatz et al. 2018) and is an important entry point for

integrating adaptation and mitigation at the urban level (Frantzeskaki et al. 2019) (see WGIII Section 8.4.4 and WGIII Figure 8.18). 
Grey and physical infrastructure, such as sea defences, can immediately reduce risk, but also transfer risk and limit future options. 
Social policy interventions including social safety nets provide financial security for the most at-risk and can manage vulnerability 
determined by specific hazards or independently.

879
879
Cross-Working Group Box 2 (continued)
Hazard-independent mechanisms for vulnerability reduction – such as population-wide social security – provide resilience in the face 
of unanticipated cascading impacts or surprise and novel climate-related hazard exposure. Social interventions can also support or 
be led by ambitions to reach the SDGs (Archer 2016). Climate-resilient development invites planners to develop interventions and 
monitor the effectiveness of outcomes beyond individual projects and across wider remits that consider sustainable development. 
Curbing the emission impacts of urban activities to reach net-zero emissions in the next decades, while improving the resilience of 
urban areas, necessitates an integrated response now.
Key gaps in knowledge include: urban-enabling environments; the role of smaller settlements, low-income communities, and informal

settlements, as well as those in rental housing spread across the city; and the ways in which actions to reduce supply chain risk can 
be supported to accelerate equitable and sustainable adaptation in the face of financial and governance constraints (Birkmann et al. 
2016; Shi et al. 2016; Rosenzweig et al. 2018; Dulal 2019).
Enabling action
Innovative governance and finance solutions are required to manage complex and interconnected risks across essential key 
infrastructures, networks, and services, as well as to meet basic human needs in urban areas (medium confidence) (Colenbrander 
et al. 2018a; Moser et al. 2019). There are many examples of ‘ready-to-use’ policy tools, technologies, and practical interventions 
for policymakers seeking to act on adaptation and mitigation (Bisaro and Hinkel 2018; Keenan et al. 2019; Chirambo 2021) (see 
WGIII Section 8.5.4). Tax and fiscal incentives for businesses and individuals can help support city-wide behaviour change towards

low-carbon and risk-reducing choices. Change can start where governments have most control – often in public sector institutions 
and investment – but the challenge ahead requires partnership with private sector and community actors acting at scale and with 
accountability. Urban climate governance and finance needs to address urban inequalities at the forefront if the urban opportunity is 
to realise the ambition of the SDGs.
Increasing the pace of investments will put pressure on governance capability, transparency, and accountability of decision-making 
(medium confidence) (see WGII Section 6.4.5). Urban climate action that actively includes local actors is more likely to avoid unintended, 
negative maladaptive impacts and mobilise a wide range of local capacities. In the long run, this is also more likely to carry public 
support, even if some experiments and investments do not deliver the intended social benefits. Legislation, technical capacity, and

governance capability are required to be able to absorb additional finance.
In recent years, about USD384 billion of climate finance has been invested in urban areas per year. This remains at about 10% of the 
annual climate finance that would be necessary for low-carbon and resilient urban development at a global scale (Negreiros et al. 
2021). Rapid deployment of funds to stimulate economies in the recovery from COVID-19 has highlighted the pitfalls of funding 
expansion ahead of policy innovation and capacity building. The result can be an intensification of existing carbon-intensive urban 
forms – exactly the kinds of ‘carbon lock-in’ (see WGIII Glossary and WGIII Section 8.4.1) that have contributed to risk creation and its 
concentration amongst those with little public voice or economic power.
Iterative and experimental approaches to climate adaptation and mitigation decision-making grounded in data and co-generated in

partnership with communities can advance low-carbon climate resilience (medium evidence, high confidence) (Culwick et al. 2019; 
Caldarice et al. 2021; van der Heijden and Hong 2021). Conditions of complexity, uncertainty, and constrained resources require 
innovative solutions that are both adaptive and anticipatory. Complex interactions among multiple agents in times of uncertainty 
makes decision-making about social, economic, governance, and infrastructure choices challenging and can lead decision-makers 
to postpone action. This is the case for those balancing household budgets, residential investment portfolios, and city-wide policy 
responsibilities. Living with climate change requires changes to business-as-usual design-making. Co-design and collaboration with 
communities through iterative policy experimentation can point the way towards climate-resilient development pathways (Ataöv

and Peker 2021). Key to successful learning is transparency in policymaking, inclusive policy processes, and robust local modelling, 
monitoring, and evaluation, which are not yet widely undertaken (Sanchez Rodriguez et al. 2018; Ford et al. 2019).
The diversity of cities’ experiences of climate mitigation and adaptation strategies brings an advantage for those city governments and 
other actors willing to ‘learn together’ (limited evidence, high confidence) (Bellinson and Chu 2019; Haupt and Coppola 2019). While 
contexts are varied, policy options are often similar enough for the sharing of experiments and policy champions. Sharing expertise 
can build on existing regional and global networks, many of which have already placed knowledge, learning, and capacity building 
at the centre of their agendas. Learning from innovative forms of governance and financial investment, as well as strengthening

co-production of policy through inclusive access to knowledge and resources, can help address mismatches in local capacities and 
strengthen wider SDGs and COVID-19 recovery agendas (limited evidence, medium agreement). Perceptions of risk can greatly

880
880
influence the reallocation of capital and shift financial resources (Battiston et al. 2021). Coupling mitigation and adaptation in an 
integrated approach offers opportunities to enhance efficiency, increases the coherence of urban climate action, generates cost 
savings, and provides opportunities to reinvest the savings into new climate action projects to make all urban areas and regions 
more resilient.
mobilisers, and catalysts of action, but few cities are undertaking transformative climate adaptation or mitigation actions (limited 
evidence, medium confidence) (Heikkinen et al. 2019). Local actors are providers of infrastructure and services, regulators of zoning,

and can be conveners and champions of an integrated approach for mitigation and adaptation at multiple levels (limited evidence, high 
confidence). New opportunities in governance and finance can enable cities to pool resources together and aggregate interventions 
to innovate ways of mobilising urban climate finance at scale (Colenbrander et al. 2019; Simpson et al. 2019; White and Wahba 2019). 
However, research increasingly points towards the difficulties faced during the implementation of climate financing in situ, such as 
the fragmentation of structures of governance capable of managing large investments effectively (Mohammed et al. 2019) (see WGIII 
Section 8.5 and WGIII Chapter 13).
Scaling up transformative place-based action for both adaptation and mitigation requires enabling conditions, including land-based 
financing, intermediaries, and local partnerships (medium evidence, high agreement) (Chu et al. 2019; Chaudhuri, 2020) supported

by a new generation of big data approaches. Governance structures that combine actors working at different levels with a different 
mix of tools are effective in addressing challenges related to implementation of integrated action while cross-sectoral coordination 
is necessary (Singh et al. 2020). Joint institutionalisation of mitigation and adaptation in local governance structures can also enable 
integrated action (Göpfert et al. 2020; Hurlimann et al. 2021). However, the proportion of international finance that reaches local 
recipients remains low, despite the repeated focus of climate policy on place-based adaptation and mitigation (Manuamorn et al. 
2020). Green financing instruments that enable local climate action without exacerbating current forms of inequality can jointly 
address mitigation, adaptation, and sustainable development. Climate finance that also reaches beyond larger non-state enterprises

(e.g.,  small and medium-sized enterprises, local communities, or non-governmental organisations (NGOs)), and is inclusive in 
responding to the needs of all urban inhabitants (e.g., disabled individuals, or citizens of different races or ethnicities) is essential 
for inclusive and resilient urban development (Colenbrander et al. 2019; Gabaldón-Estevan et al. 2019; Frenova 2021). Developing 
networks that can exert climate action at scale is another priority for climate finance.
The urban megatrend is an opportunity to transition global society. Enabling urban governance to avert cascading risk and achieve 
low-carbon, resilient development will involve the co-production of policy and planning, rapid implementation and greater cross-
sector coordination, and monitoring and evaluation (limited evidence, medium agreement) (Di Giulio et al. 2018; Grafakos et al. 2019).

New constellations of responsible actors are required to manage hybrid local-city or cross-city risk management and decarbonisation 
initiatives (limited evidence, medium agreement). These may increasingly benefit from linkages across more urban and more rural 
space as recognition of cascading and systemic risk brings recognition of supply chains, remittance flows, and migration trends as 
vectors of risk and resilience. Urban governance will be better prepared in planning, prioritising, and financing the kind of measures 
that can reduce GHG emissions and improve resilience at scale when they consider a view of cascading risks and carbon lock-ins 
globally, while also acting locally to address local limitations and capacities, including the needs and priorities of urban citizens 
(Colenbrander et al. 2018a; Rodrigues 2019).
8.3 Urban Systems and 
Greenhouse Gas Emissions
This section assesses trends in urban land use, the built environment,

and urban GHG emissions, as well as forecasts for urban land 
use and emissions under certain scenarios to 2050 or 2100. These 
trends and scenarios hold implications for optimising the approaches 
to urban climate change mitigation discussed in Sections 8.4 and 8.6.8.3.1 Trends in Urban Land Use and the 
Built Environment
Urban land use is one of the most intensive human impacts on 
the planet (Pouyat et al. 2007; Grimm et al. 2008). Urban land 
expansion to accommodate a growing urban population has resulted 
in the conversion of agricultural land (Pandey et al. 2018; Liu et al. 
2019), deforestation (van Vliet 2019), habitat fragmentation (Liu 
et al. 2016b), biodiversity loss (McDonald et al. 2018, 2020), and 
the modification of urban temperatures and regional precipitation 
patterns (Li et al. 2017; Krayenhoff et al. 2018; Liu and Niyogi 2019; 
Zhang et al. 2019).

881
881
Urban land use and the associated built environment and 
infrastructure shape urban GHG emissions through the demand 
for materials and the ensuing energy-consuming behaviours. In 
particular, the structure of the built environment (i.e.,  its density, 
form, and extent) have long-lasting influence on urban GHG 
emissions, especially those from transport and building energy use, 
as well as the embodied emissions of the urban infrastructure (Butler et al. 2014; Salat et al. 2014; Ramaswami et al. 2016; Seto et al. 2016; 
d’Amour et al. 2017). Thus, understanding trends in urban land use is 
essential for assessing energy behaviour in cities as well as long-term 
mitigation potential (Sections 8.4 and 8.6, and Figure 8.21).
This section draws on the literature to discuss three key trends in 
urban land expansion, and how those relate to GHG emissions.103 people per km2
103 people per km2103 people per km2

103 people per km2
A. All case study locations with a population > 300,000 people
B. Case study locations with a population > 2 million people (large urban centers)
C. Case study locations with a population > 300,000 but< 2 million people (small and medium urban centers)197060
50
40
30
20
1060
50
40
30
20
10
60
50
40
30
20
1060
50
40
30
20
10
60
50
40
30
20
1060
50
40
30
20
10
1 11980
1990
2000
North
AmericaLatin
America
and
CaribbeanEurope Africa Eastern
AsiaAsia-Paciﬁc
DevelopedEurasia Middle
EastSouthern
AsiaSouth-East
Asia and
Developing
Paciﬁc2010
Figure 8.5: Urban population density by decade (1970–2010) grouped by the AR6 WGIII 10-region aggregation.  Panel (a) displays the results from all case 
study locations with a population >300,000. Panels (b) and (c) show results grouped by city size: (b) cities with a population >2 million (large urban centres), and (c) those

with a population >300,000 but <2 million (small and medium urban centres). Box plots show the median, first and third quartiles, and lower and upper mild outlier thresholds 
of bootstrapped average urban population densities at the turn of each decade. The estimates are shown on a logarithmic scale. The data shows an overall trend of declining 
urban population densities among all but one region in the last four decades, at varying rates – although the Latin America and Caribbean region indicates relatively constant 
urban population density over time. The Middle East region is the only region to present with an increase in urban population density across all city sizes. Source: adapted from 
Güneralp et al. (2020).

882
882
Chapter 8 Urban Systems and Other Settlements8a)
b)
Figure  8.6:  (a) Distribution of growth typologies across 10 cities, and (b) sample of 64 cities by region with different patterns of urban growth.  
The empirical data is based on the Global Human Settlement Layer and backscatter power ratio for different patterns of urban growth across the sample of cities. In (b), the 
blue arrows indicate outward urban growth. Other urban patterns indicate stabilised (orange), mature upward (light blue), budding outward (green), and upward and outward 
(red). Note that with few exceptions, each city is comprised of multiple typologies of urban growth. Source: Mahtta et al. (2019).

883
883
First, urban land areas are growing rapidly all around the world. From 
1975 to 2015, urban settlements expanded in size approximately 
2.5 times, accounting for 7.6% of the global land area (Pesaresi 
et al. 2016). Nearly 70% of the total urban expansion between 1992 
and 2015 occurred in Asia and North America (Liu et al. 2020a). By 
2015, the extent of urban and built-up lands was between 0.5% and 
0.6% of the total 130 Mkm2 global ice-free land use, taking up other 
uses such as fertile cropland and natural ecosystems.
Second, as Figure  8.5 shows, urban population densities are 
declining, with significant implications for GHG emissions. From 
1970 to 2010, while the global urban settlement extent doubled 
in size (Pesaresi et al. 2016), most regions (grouped by the AR6 
WGIII 10-region aggregation) exhibited a trend of decreasing urban 
population densities, suggesting expansive urban growth patterns.

Urban population densities have consistently declined in Australia, 
Japan and New Zealand, and Europe, North America, and Southern 
Asia regions, across all city sizes. North America consistently had 
the lowest urban population densities. Notably, the Middle East 
region appears to be the only region exhibiting an overall increasing 
trend across all city-size groups, while Latin America and Caribbean appears to be relatively stable for all city sizes. While the larger cities 
in Africa and South-East Asia and Pacific exhibit slightly stable urban 
population densities, the small and medium-sized cities in those 
regions trend toward lower urban population densities. In large 
urban centres of Eastern Asia and North America, rapid decreases 
in earlier decades seem to have tapered. Compared to larger cities, 
small-medium urban areas with populations of less than 2 million 
have more declines in urban population densities and higher rates of

urban land expansion (Güneralp et al. 2020).
This decline in urban densities is paralleled by an increase in ‘sprawl’, 
or ‘outward’ urban development. Urban expansion occurs in either 
one of three dimensions: (i) outward in a  horizontal manner; (ii) 
upward, by way of vertical growth; or (iii) infill development, 
where unused, abandoned, or underutilised lands within existing 
urban areas are developed or rehabilitated (Figure  8.20). Outward 
expansion results in more urban land area and occurs at the expense 
of other land uses (i.e., the conversion and loss of cropland or forests). 
Vertical expansion results in more multi-storey buildings and taller 
buildings, more floor space per area, and an increase in urban built-
up density. Every city has some combination of outward and upward 
Figure 8.7: Percent of total urban land expansion from other land covers, sorted by the AR6 WGIII 10-region aggregation (1970–2010).  As urban land

has expanded outward, other forms of land cover, including agriculture, ‘nature’ (e.g., forest, grassland, shrubland, water, and bare soil, all of which are disaggregated to the 
bottom half of the plot), and other land covers, have been displaced. Globally, agriculture comprises the majority (about 60%) of the land displaced by urban expansion since 
1970. Forests and shrubland vegetation – important carbon stocks – also make up a significant proportion of displacement. The loss of carbon-sequestering land like forests 
and shrubland independently impacts climate change by reducing global carbon stocks. Eurasia is omitted because there are no case studies from that region that report land 
conversion data. Source: adapted from Güneralp et al. (2020).

884
884
Chapter 8 Urban Systems and Other Settlements8growth in varying degrees (Mahtta et al. 2019) (Figure 8.6). That each 
city is comprised of different and multiple urban growth typologies 
suggests the need for differentiated mitigation strategies for different 
parts of a  single city (Section  8.6 and Figure  8.21). Recent research 
shows that the relative combination of outward versus upward growth 
is a reflection of its economic and urban development (Lall et al. 2021). 
That is, how a city grows – whether upward or outward – is a function 
of its economic development level. Upward growth, or more tall 
buildings, is a reflection of higher land prices (Ahlfeldt and McMillen 
2018; Ahlfeldt and Barr 2020).
An analysis of 478 cities with populations of more than 1  million 
people found that the predominant urban growth pattern worldwide 
is outward expansion, suggesting that cities are becoming more 
expansive than dense (Mahtta et al. 2019) (Figure 8.6). The study also

found that cities within a geographic region exhibit remarkably similar 
patterns of urban growth. Some studies have found a mix of urban 
forms emerging around the world; an analysis of 194 cities identified 
an overall trend (from 1990 to 2015) toward urban forms that are 
a mixture of fragmented and compact (Lemoine-Rodriguez et al. 2020). 
The exception to this trend is a group of large cities in Australia, New 
Zealand, and the United States that are still predominantly fragmented. 
The same study also identified small to medium-sized cities as the most 
dynamic in terms of their expansion and change in their forms.
A third trend in is urban land growth taking place on agricultural land, 
carbon stocks, and other land uses (see ‘carbon stock’ and ‘AFOLU’ – 
agriculture, forestry, and other land uses – in Glossary). As Figure 8.7 
shows, over 60% of the reported urban expansion (nearly 40,000 km2) 
from 1970 to 2010 was formerly agricultural land (Güneralp et al. 2020).

This percentage increased to about 70% for global urban expansion 
that occurred between 1992 and 2015, followed by grasslands (about 
12%) and forests (about 9%) (Liu et al. 2020a). In terms of percent 
of total urban land expansion, the largest conversion of agricultural 
lands to urban land uses from 1970 to 2010 took place in the Eastern 
Asia, and South-East Asia and Pacific regions; the largest proportional 
losses of natural land cover were reported for the North America and 
Australia, Japan and New Zealand regions (Güneralp et al. 2020). At 
a sub-regional level, agricultural land constituted the largest proportion 
of land converted to urban areas in China, India, Europe, Southeast 
Asian countries and the central United States between 1995 and 2015; 
in the eastern United States, most new urban land was converted 
from forests (Liu et al. 2020a). Urban expansion through 2040 may 
lead to the loss of almost 65 Mt of crop production – a scenario that

underscores the ongoing relationship between urbanisation and 
AFOLU (van Vliet et al. 2017) (Chapter 7).
8.3.2 Informal Urban Settlements
About 880 million people currently live in informal settlements  – 
defined as unplanned areas operating outside of legal and regulatory 
systems, where residents have no legal claim over their property and 
have inadequate basic services and infrastructure (United Nations 
2018). Furthermore, upgrading informal settlements and inadequate 
housing is essential for improving resilience to climate change and 
well-being. Given the ubiquity of informal settlements in developing 
countries and LDCs, there is potential to harness informality to accelerate transitions to low-carbon urban development. There are 
several key reasons for their potential to mitigate GHG emissions. First, 
informal urban areas may not require large investments in retrofitting 
as they have developed with minimal investment in large-scale

infrastructure. Second, these areas exhibit flexibility of development 
and can potentially be transformed into an urban form that supports 
low- or carbon-neutral infrastructure for transportation, energy use 
in residential buildings, and other sectors (Baurzhan and Jenkins 
2016; Henneman et al. 2016; Byrne et al. 2017; Oyewo et al. 2019).
Informal urban areas can avoid the conventional trajectory of urban 
development by utilising large-scale strategies, such as micro-scale 
technologies, modal shifts towards compact, walkable urban form, 
as well as decentralised or meso-scale utilities of water, sanitation, 
and service centres – thereby mitigating emissions associated with 
transport and treating wastes (Tongwane et al. 2015; Yang et al. 
2018). Some specific mitigation options include spatial adjustments 
for walkability of neighbourhoods, low-energy-intensive mobility, 
low-energy-intensive residential areas, low-carbon energy sources

at city scale, off-grid utilities, and electrification and enhancement 
of the urban ecology – all of which have multiple potential benefits 
(Colenbrander et al. 2017; Fang et al. 2017; Laramee et al. 2018; van der 
Zwaan et al. 2018; Wu et al. 2018; Silveti and Andersson 2019). Some 
of the co-benefits of the various mitigation options include more job 
opportunities and business start-ups, increased incomes, air quality 
improvement, and enhanced health and well-being (Gebreegziabher 
et al. 2014; Dagnachew et al. 2018; Keramidas et al. 2018; Adams 
et al. 2019; Ambole et al. 2019; Boltz et al. 2019; Moncada et al. 2019; 
Weimann and Oni 2019; Manga et al. 2020) (Section 8.2).
Non-networked and non-centralised urban services and infrastructure in 
informal settlements, including sanitation, waste, water, and electricity, 
serve over 60% of the urban population in developing country cities 
(Lawhon et al. 2018). The alternatives of disruptive, hybrid, largely

non-networked multiplicity of technologies applicable at micro to 
meso scales have potential for low-emissions development in urban 
areas of developing countries (Narayana 2009; Dávila and Daste 2012; 
Radomes Jr and Arango 2015; Potdar et al. 2016; Grové et al. 2018). 
These technologies can be applied in the short term as responses with 
long-term influence on emissions reduction. The cumulative impact of 
the disruptive technologies can reduce emissions by 15– 25% through 
enhanced emissions sinks in small and medium-sized cities (Tongwane 
et al. 2015; du Toit et al. 2018; Nero et al. 2018, 2019; Frantzeskaki et al. 
2019; Mantey and Sakyi 2019; Singh and G. 2019).
8.3.3 Trends in Urban Greenhouse Gas Emissions
One major innovation presented in AR6 – particularly in this chapter – 
is the inclusion of trend data on urban GHG emissions. Using 
multiple datasets in conjunction with the SSP and RCP scenarios, 
this chapter provides an estimate of urban GHG emissions from

1990 through 2100, based on a consumption-based approach. This 
innovation provides, for the first time, a temporal dimension to urban 
footprints considering different climate scenarios with implications 
for urban mitigation. The new analysis presents a  comparison of 
ways urban emissions can evolve given different scenario contexts 
(Section 8.3.4.2). Additionally, new research has quantified trends in

885
885
urban CO 2 emissions and their key drivers across 91 global cities from 
2000 to 2018 (Luqman et al. 2021).
Figures 8.8 and 8.9 present key urban emission metrics and trends for 
six regions (based on the AR6 WGIII regional breakdown) – the first 
for the year 2015, and the latter for both 2000 and 2015.
The key trends are as follows. First, the urban share of global GHG 
emissions (including CO 2 and CH 4) is substantive and continues 
to increase (Figure  8.9). Total urban CO 2-eq emissions based on 
consumption-based accounting were estimated to be 25 GtCO 2-eq, 
or 62% of the global total in 2015, and increased to an estimated 
29 GtCO 2-eq in 2020, representing about 67–72% of global emissions. 
This estimate includes all CO 2 and CH 4 emissions except aviation, 
shipping, and biogenic sources (i.e., land-use change, forestry, and 
agriculture). About 100 of the highest-emitting urban areas account

for approximately 18% of the global carbon footprint (Moran 
et al. 2018). Globally, the urban share of national CO 2-eq emissions 
increased 6 percentage points, from 56% in 2000 to 62% in 2015.
Second, while urban CO 2 emissions were increasing in all urban 
areas, the dominant drivers were dependent upon development level. 
Emissions growth in urban areas other than in Developed Countries 
was driven by increases in area and per capita emissions. Across 
all cities, higher population densities are correlated with lower per 
capita GHG emissions (Luqman et al. 2021).
Third, the urban share of regional GHG emissions increased between 
2000 and 2015, with much inter-region variation in the magnitude 
of the increase (high confidence) (Figure  8.9). Between 2000 and 
2015, the urban emissions share across AR6 WGIII regions (6-region 
aggregation) increased from 28% to 38% in Africa, from 46% to 54% 
in Asia and Pacific, from 62% to 72% in Developed Countries, from

57% to 62% in Eastern Europe and West-Central Asia, from 55% 
to 66% in Latin America and Caribbean, and from 68% to 69% in 
the Middle East.Between 2000 and 2015, urban population, urban CO 2-eq emissions, 
and national CO 2-eq emissions increased as a share of the global total 
in the Asia and Pacific region while the share declined for Developed 
Countries. The urban share of total regional CO 2-eq emissions decreased 
in Developed Countries from 58.2% (2000) to 40.0% (2015). Urban 
per capita CO 2-eq and national per capita CO 2-eq also increased in all 
regions except for the urban per capita CO 2-eq value in the Developed 
Countries region, which declined slightly.
Fourth, the global average per capita urban GHG emissions increased 
between 2000 and 2015, with cities in the Developed Countries 
region producing nearly seven times more per capita than the 
lowest emitting region (medium confidence). From 2000 to 2015,

the global urban GHG emissions per capita increased from 5.5 to 
6.2 tCO 2-eq per person (an increase of 11.8%), with increases across 
five of the six regions: Africa increased from 1.3 to 1.5 tCO 2-eq per 
person (22.6%); Asia and Pacific increased from 3.0 to 5.1 tCO 2-eq 
per person (71.7%); Eastern Europe and West-Central Asia increased 
from 6.9 to 9.8 tCO 2-eq per person (40.9%); Latin America and 
Caribbean increased from 2.7 to 3.7 tCO 2-eq per person (40.4%); 
and the Middle East increased from 7.4 to 9.6 tCO 2-eq per person 
(30.1%). Albeit starting from the highest level, Developed Countries 
had a decline of 11.4 to 10.7 tCO 2-eq per person (–6.5%).
In 2015, regional urban per capita consumption-based CO 2-eq 
emissions were lower than regional consumption-based national per 
capita CO 2-eq emissions in five of the six regions. These regions in 
order of the difference are: Developed Countries (lower by 1.0 tCO 2-eq

per capita); Latin America and Caribbean (lower by 0.8 tCO 2-eq 
per  capita); Eastern Europe and West-Central Asia (lower by 
0.7 tCO 2-eq per capita); Middle East (lower by 0.4 tCO 2-eq per capita); 
and Africa (lower by 0.2 tCO 2-eq per capita); while higher only in the 
Asia and Pacific region (higher by 0.9 tCO 2-eq per capita). All regions 
show convergence of the urban and national per capita CO 2-eq, as 
the urban share of national emissions increases and dominates the 
regional total.
0 300 600 900 1200 1500 1800 2100 2400 2700 3000 3300 3600 3900
Cumulative urban population, millions024681012Urban per capita emissions, tCO2-eq per capitaDeveloped Countries, 23.1%Eastern Europe and 
West-Central Asia, 4.0%
Middle East, 4.3%
Asia and Paciﬁc, 43.5% Latin America 
and Caribbean, 
12.7%
Africa, 12.3%
Figure 8.8: 2015 average urban greenhouse gas emissions per capita, considering carbon dioxide (CO 2) and methane (CH 4) emissions from a consumption-

based perspective, alongside urban population, for regions represented in the AR6 WGIII 6-region aggregation.  The average urban per capita emissions are 
given by the height of the bars while the width represents the urban population for a given region, based on 2015 values for both axes. Provided within the bars are the 
percentage shares of the urban population by region as a share of the total urban population. Source: synthesised based on data from UN DESA (2019) and Gurney et al. (2022).

886
886
a)
b)70%15.0
13.0
11.0
9.0
7.0
5.0
3.0
1.0
–1.060%Percent share (%)
Urban per capita CO2-eq (tCO2-eq per capita)
15.0
13.0
11.0
9.0
7.0
5.0
3.0
1.0
–1.0
Urban per capita CO2-eq (tCO2-eq per capita)50%
40%
30%
20%
10%
0%
70%Africa
Regional urban population share (%) Regional urban CO2-eq share (%)
Urban per capita CO2-eq (t per capita)Regional total CO2-eq share(%)
National per capita CO2-eq (t per capita) Urban share of national CO2-eq emissions (%)Asia and Paciﬁc Developed Countries Eastern Europe and
West-Central Asia
RegionLatin America and
CaribbeanMiddle East
Africa
CaribbeanMiddle East60%Percent share (%)50%
40%

30%
20%
10%
0%Global urban per capita CO2-eq
Figure 8.9:  Changes in six metrics associated with urban and national-scale carbon dioxide (CO 2) and methane (CH 4) emissions represented in the AR6 
WGIII 6-region aggregation, with (a) 2000 and (b) 2015.  The trends in Luqman et al. (2021) were combined with the work of Moran et al. (2018) to estimate the regional 
urban CO 2-eq share of global urban emissions, the urban share of national CO 2-eq emissions, and the urban per capita CO 2-eq emissions by region. This estimate is derived 
from consumption-based accounting that includes both direct emissions from within urban areas and indirect emissions from outside urban areas related to the production 
of electricity, goods, and services consumed in cities. It incorporates all CO 2 and CH 4 emissions except aviation, shipping and biogenic sources (i.e., land-use change, forestry,

and agriculture). The dashed grey line represents the global average urban per capita CO 2-eq emissions. The regional urban population share, regional CO 2-eq share in total 
emissions, and national per capita CO 2-eq emissions by region are given for comparison. Source: adapted from Gurney et al. (2022).5
5 Figure adapted from Global Environmental Change, Vol 73, Gurney et al., Greenhouse gas emissions from global cities under SSP/RCP scenarios, 1990 to 2100, ©2022 
with permission from Elsevier.

887
887
8.3.4 Scenarios of Future Urbanisation 
and Greenhouse Gas Emissions
This section assesses scenarios of future urban land expansion and 
urban GHG emissions. These scenarios have implications for the 
urban climate change mitigation strategies discussed in Sections 8.4 
and 8.6 – in particular, in the context of the potential mitigation and 
development pathways for urban areas under certain scenarios.
8.3.4.1 Urban Land Expansion and Greenhouse Gas Emissions
The uncertainties across urban land expansion forecasts, and 
associated SSPs, highlight an opportunity to pursue compact, 
low or net-zero GHG emissions development that minimises 
land-use competition, avoids carbon lock-in, and preserves carbon-
sequestering areas like forests and grasslands (Sections  8.4. and 
8.6, and Figure 8.21). Among the forecasts available are six global-
scale spatially explicit studies of urban land expansion that have

been published since AR5; four of the six, which present forecasts 
for each of the five SSPs, are considered in Table 8.1 and Figure 8.10 
(Huang et al. 2019; Li et al. 2019b; Chen et al. 2020a; Gao and O’Neill 
2020). All four have forecasts to 2050 but only three to 2100. One of the two not included here (van Vliet et al. 2017) also forecasts land 
displacement due to urban land expansion.
Four overarching findings can be gleaned from these studies.
First, urban land areas will expand significantly by 2050 – by as much 
as 211% (see SSP5 forecast in Huang et al. 2019), but likely within 
a large potential range of about 43–106% over the 2015 extent by 
2050 – to accommodate the growing urban population (Table 8.1). 
Globally, there are large uncertainties and variations among the 
studies  – and between the SSPs  – about the rates and extent of 
future urban expansion, owing to uncertainties about economic 
development and population growth (ranges of estimates are

provided in Table 8.1). Overall, the largest urban extents are forecasted 
under SSP5 (fossil fuel-intensive development) for both 2050 and 
2100, whereas the smallest forecasted urban extents are under 
SSP3 (‘regional rivalry’). Forecasted global urban extents could reach 
between 1 and 2.2 million km2 (median of 1.4 million km2, a 106% 
increase) in 2050 under SSP5, and between 0.85 and 1.5 million km2 
(median of 1 million km2, a 43% increase) in 2050 under SSP3. Under 
SSP1, which is characterised by a focus on sustainability with more 
compact, low-emissions development, urban extents could reach Box 8.1: Does Urbanisation Drive Emissions?
Urbanisation can drive emissions if the process is accompanied by an income increase and higher levels of consumption (Sudmant et al. 
2018). This is typically observed in countries with a large urban-rural disparity in income and basic services, and where urbanisation is

accompanied by economic growth that is coupled to emissions. In addition, the outward expansion of urban land areas often results 
in the conversion and loss of agricultural land (Pandey et al. 2018; Liu et al. 2019), forests (Austin et al. 2019), and other vegetated 
areas, thereby reducing carbon uptake and storage (Quesada et al. 2018) (Section 8.3.1). Furthermore, the buildup and use of urban 
infrastructure (e.g., buildings, power, sanitation) requires large amounts of embodied energy and carbon (Figures 8.17 and 8.22). 
Building new and upgrading existing urban infrastructure could produce cumulative emissions of 226 GtCO 2 by 2050 (Bai et al. 2018).
However, for the same level of consumption and basic services, an average urban dweller often requires less energy than their 
rural counterparts, due to higher population densities that enable sharing of infrastructure and services, and economies of scale.

Whether and to what extent such emission reduction potentials can be realised depends on how cities are designed and laid out 
(i.e., urban form – see Section 8.4.2) as well as how urban infrastructure is built and powered, such as the energy intensity of the 
city’s transportation system, type and level of urban services, the share of renewable energy, as well as the broader national and 
international economic and energy structure that supports the function of the cities (Sections 8.4.3 and 8.6).
Although population-dense cities can be more efficient than rural areas in terms of per capita energy use, and cities contribute less 
GHG emissions per person than low-density suburbs (Jones and Kammen 2014), there is some, albeit limited, evidence that larger 
cities are not more efficient than smaller ones (Fragkias et al. 2013; Ribeiro et al. 2019). A number of studies comparing urban and rural

residents in the same country have shown that urban residents have higher per capita energy consumption and CO 2 emissions (Chen 
et al. 2019a; Hachaichi and Baouni 2021). There is some evidence that the benefits of higher urban densities on reducing per capita 
urban GHG emissions may be offset by higher incomes, smaller household sizes, and, most importantly, higher consumption levels, 
thus creating a counter-effect that could increase GHG emissions with urbanisation (Gill and Moeller 2018).
Many studies have shown that the relationship between urbanisation and GHG emissions is dependent on the level and stage of 
urban development, and follows an inverted U-shaped relationship of the environmental Kuznets curve (Wang et al. 2016, 2022; Zhang 
et al. 2017; Xu et al. 2018a; Zhou et al. 2019) (Sections 8.3.1 and 8.6, and Figure 8.20). Considering existing trends, earlier phases of

urbanisation accompanied by rapid industrialisation, development of secondary industries, and high levels of economic growth, are 
correlated with higher levels of energy consumption and GHG emissions. However, more mature phases of urbanisation, with higher 
levels of economic development and establishment of the service sector, are correlated with lower levels of energy consumption and 
GHG emissions (Khan and Su 2021).

888
888
Chapter 8 Urban Systems and Other Settlements81  million km2 (range of 0.9 to 2  million km2, a  49% increase) in 
2050. By 2100, the forecasted urban extents reach between 1.4 and 
3.6 million km2 (median 2.5 million km2) under SSP5 and between 
1 and 1.5 million km2 (median 1.3 million km2) under SSP3. Across 
the studies, substantially larger amounts of urban land expansion are 
expected after 2050 under SSP5 compared to other SSPs.
Second, there is a wide variation in estimates of urban land expansion 
across regions (using the AR6 WGIII 6-region aggregation). Across all 
four sets of forecasts, current urban land (circa 2015) is the largest 
in Developed Countries and in the Asia and Pacific region, with 
approximately two-thirds of the current urban extent occurring in 
those two regions (Table 8.1 and Figure 8.10). The largest increases 
in urban land by 2050 are expected in the Asia and Pacific and 
Developed Countries regions, across all the SSPs. However, the rate

of increase in urban land in Eastern Europe and West-Central Asia, 
Latin America and Caribbean, and the Middle East is significant and 
urban land could more than double by 2050. One-third of the studies 
conclude that the United States, China, and India will experience 
continued urban land expansion at least until 2050 (Huang et al. 
2019; Li et al. 2019b). However, Li et al. (2019) report that, after 
2050, China could experience a decrease in the rate of urban land 
expansion, while growth will continue for India. This is not surprising 
since India’s urban demographic transition will only get underway 
after the middle of the century, when the urban population is 
expected to exceed the rural population. In contrast, China’s urban 
demographic transition could be nearly complete by 2050.
Third, in spite of these general trends, there are differences in 
forecasted urban expansion in each region across the SSPs and 
studies, with Huang et al. (2019) forecasting the most future

urban land expansion between 2015 and 2050. The range across studies is significant. Under SSP1, urban land areas could increase 
by between 69,000 and 459,000 km2 in Developed Countries, 
77,000–417,000 km2 in Asia and Pacific, and 28,000–216,000 km2 
in Africa. Under SSP3, where urban land expansion is forecasted to 
be the lowest, urban land areas could increase by between 23,000 
and 291,000 km2 in Developed Countries, 57,000–168,000 km2 in 
Asia and Pacific, and 16,000–149,000 km2 in Africa. Under SSP5, 
where urban land expansion is forecasted to be the highest, urban 
land area could increase by between 129,000 and 573,000 km2 in 
Developed Countries, 83,000–472,000 km2 in Asia and Pacific, and 
40,000–222,000 km2 in Africa (Huang et al. 2019; Li et al. 2019b; 
Chen et al. 2020a; Gao and O’Neill 2020). By 2100, however, the 
Developed Countries region is expected to have the most urban 
expansion only in SSP5. In SSP2 and SSP4, the Developed Countries

and Asia and Pacific regions have about equal amounts of new urban 
land; in SSP3, Asia and Pacific has more new urban land forecasted.
Fourth, both the range of estimates and their implications on land-
use competition and urban life point to an opportunity for urban 
areas to consider their urban form when developing. Under the current 
urbanisation trajectory, 50–63% of newly expanded urban areas are 
expected to occur on current croplands (Chen et al. 2020a). However, 
there is significant regional variation; between 2000 and 2040, 12.5% 
of cropland in China and 7.5% of cropland in the Middle East and 
North Africa could potentially be displaced due to urban expansion, 
compared to the world average of 3.7% (van Vliet et al. 2017). As 
urban clusters increase in size and greenspace is converted, future 
urban land expansion is expected to intensify UHIs and exacerbate 
night-time extreme temperatures. An urban footprint increase of 78–

171% by 2050 over the urban footprint in 2015 is expected to result in 
average summer daytime and night-time warming in air temperature 
of 0.5°C–0.7°C, even up to about 3°C in certain locations (Huang 
Table 8.1: Forecasts of total urban land per AR6 WGIII region (6-region aggregation) in 2050 for each SSP, with the median and range of estimates from 
four studies: Huang et al.  (2019), Li et al. (2019), Chen et al. (2020), and Gao and O’Neill (2020). Median estimates for the 2015 urban extent are based on the 
mean/median of estimates in Huang et al. (2019) and Chen et al. (2020). Median and range of estimates for each SSP in 2050 are based on values derived from the four studies: 
Huang et al. (2019), Li et al. (2019), Chen et al. (2020), and Gao and O’Neill (2020). While each study and SSP forecast increases in urban land in each region, the range and 
magnitude vary. Source: data compiled from Huang et al. (2019), Li et al. (2019), Chen et al. (2020), and Gao and O’Neill (2020).

2015
median
(km2; range)SSP1
median
(km2; range)SSP2
median
(km2; range)SSP3
median
(km2; range)SSP4
median
(km2; range)SSP5
median
(km2; range)
Africa64,423 97,718 116,486 96,571 119,971 138,604
(41,472–87,373) (67,488–303,457) (59,638–274,683) (56,071–235,922) (54,633–344,645) (79,612–309,532)
Asia and Pacific241,430 293,647 355,445 296,431 329,485 419,781
(167,548–315,312) (244,575–732,303) (236,677–624,659) (224,520–483,335) (240,639–632,678) (250,670–787,257)
Developed 
Countries260,167 459,624 506,301 414,661 496,526 616,847
(188,660–331,674) (407,483–648,023) (431,592–614,592) (362,063–479,584) (411,320–586,058) (510,468–761,275)
Eastern Europe 
and West-
Central Asia35,970 63,625 65,251 59,779 64,434 76,994
(27,121–44,819) (42,990–91,612) (52,397–91,108) (44,129–90,794) (50,806–86,546) (54,039–93,008)
and Caribbean62,613 86,236 88,793 93,804 85,369 102,343
(60,511–64,716) (63,507–163,329) (86,411–162,526) (65,286–162,669) (82,148–144,940) (82,961–167,102)

Middle East21,192 51,351 51,221 48,032 49,331 55,032
(19,017–23,366) (187,68–69,266) (25,486–69,716) (19,412–63,236) (25,415–71,720) (33,033–75,757)
World685,795 1,023,220 1,174,742 980,719 1,123,900 1,412,390
(669,246–702,343)  (919,185– 1,991,579) (927,820–1,819,174) (850,681–1,493,454) (922,539–1,851,438) (1,018,321–2,180,816)

889
889
Figure 8.10: Forecasts of urban land expansion in 2050 and 2100 according to each SSP and AR6 WGIII 6-region aggregation, by study, where A: Gao 
and O’Neill (2020), B: Chen et al.  (2020a), C: Li et al. (2019), D: Huang et al. (2019), E: mean across studies, and F: median across all studies.  Three studies 
(Li et al. 2019b; Chen et al. 2020a; Gao and O’Neill 2020) report forecasts of urban land expansion to both 2050 and 2100. One study (Huang et al. 2019) reports the forecast 
only to 2050. Global current urban extents and the respective initial years vary slightly among the four studies. Years for values of current urban extent range from 2010 to 
2020. See Table 8.1 for the range of data across the four studies and across SSPs. Source: data compiled form Huang et al. (2019), Li et al. (2019), Chen et al. (2020), and Gao 
and O’Neill (2020).

890
890
Chapter 8 Urban Systems and Other Settlements8et al. 2019). Furthermore, this urban expansion-induced warming is 
on average about half  – and in certain locations nearly twice  – as 
strong as warming that will be caused by GHG emissions based on 
the multi-model ensemble average forecasts in RCP4.5. In short, future 
urban expansion will amplify the background warming caused by GHG 
emissions, with extreme warming most pronounced during night-time 
(very high confidence) (Huang et al. 2019). These findings corroborate 
those in the Technical Summary of AR6 WGI (Arias et al. 2021).
The forecasted amounts and patterns of urban expansion presented 
here bear significant uncertainty due to underlying factors beyond mere 
methodological differences between the studies. These factors include 
potential changes in the social, economic, and institutional dynamics 
that drive urban land development across the world (Güneralp and

Seto 2013). Some of these changes may come in the form of sudden 
shocks such as another global economic crisis or pandemic. The 
forecasts presented here do not take such factors into account.
8.3.4.2 Scenarios of Future Urban Greenhouse Gas Emissions
There remains little globally comprehensive literature on projections 
of future baseline GHG emissions from urban areas or scenarios 
deploying urban mitigation actions on the part of city or regional 
governments. This dearth of research rests on limited urban emissions 
data that are consistent and comparable across the globe, making 
review and synthesis challenging (Creutzig et al. 2016b). Some research 
has presented urban emissions forecasts and related projections, 
including estimated urban energy use in 2050 (Creutzig et al. 2015), 
energy savings for low-carbon development (Creutzig et al. 2016b), 
emission savings from existing and new infrastructure (Creutzig et al.

2016a) (Figure 8.12), and urban emissions from buildings, transport, 
industry, and agriculture (IEA 2016a).
In its study of about 700 urban areas with a  population of at least 
750,000, the Coalition for Urban Transitions (2019), attempts to quantify the urban portion of global GHG emissions, including the residential and 
commercial building, transport, waste, and material production (focusing 
on cement, aluminium, and steel) sectors, along with mitigation 
wedges aimed at staying below a 2°C level of atmospheric warming 
(Figure 8.11). Starting in 2015 with a global urban emissions total of 
almost 14 GtCO 2-eq, the study projects an increase to 17.3 GtCO 2-eq 
by 2050 – but this reduces to 1.8 GtCO 2-eq by 2050 with the inclusion 
of mitigation wedges: 58% from buildings, 21% from transport, 15% 
materials efficiency, and 5% waste, with decarbonisation of electricity 
supply as a cross-cutting strategy across the wedges.

Figure 8.11: Reference scenario and mitigation potential for global urban 
areas in the residential and commercial building, transport, waste, and 
material production sectors.  The top red line indicates the reference scenario 
where no further emissions reduction efforts are taken, while the bottom dark line 
indicates the combined potential of reducing emissions across the sectors displayed. 
Wedges are provided for potential emissions savings associated with decarbonising 
residential buildings, commercial buildings, transport, waste, and materials as 
indicated in the legend. The shaded areas that take place among the wedges with 
lines indicate contributions from decarbonisation of electricity supply. Source: Re-used 
with permission from Coalition for Urban Transitions (2019).
Figure 8.12: Urban infrastructure-based CO 2-eq emission mitigation wedges.  Urban infrastructure-based CO 2-eq emission mitigation wedges across categories of

existing (yellow/green), new (blue), and construction (grey) of urban infrastructure. The wedges include low-carbon energy systems and infrastructure, modal shift, tolls/tax, or 
behavioural change, and reductions from construction materials. Source: re-used with permission from Creutzig et al. (2016a).

891
891
6 These scenarios have been assessed by WGI to correspond to intermediate, high, and very low GHG emissions.
7 The SSP1-RCP1.9 scenario is aligned with the C1 category of the Illustrative Mitigation Pathways (IMPs) that include IMP-LD, IMP-Ren and IMP-SP . Implications are provided in Table 8.3.
8 Figure adapted from Global Environmental Change, Vol 73, Gurney et al., Greenhouse gas emissions from global cities under SSP/RCP scenarios, 1990 to 2100, ©2022 with permission from Elsevier.
Africa
Asia and Paciﬁc
Eastern Europe and West-Central Asia
Latin America and Caribbean
Global Total (GtCO2-eq)
Figure 8.13: Carbon dioxide equivalent (CO 2-eq) emissions from global urban areas in seven SSP-RCP variations spanning the 1990 to 2100 time period.  Urban areas are aggregated to six regional domains based on the

AR6 WGIII 6-region aggregation. Global total CO 2-eq emissions (CO 2 and CH 4 (methane)) are also shown as marked by the dashed line. Future urban emissions in the context of SSP-RCP-Shared Policy Assumption (SPA) variations correspond 
to (a) SSP1-RCP1.9-SPA1, (b) SSP1-RCP2.6-SPA1, (c) SSP4-RCP3.4-SPA4, (d) SSP2-RCP4.5-SPA2, (e) SSP4-RCP6.0-SPA4, (f) SSP3-RCP7.0-SPA0 and (g) SSP5-RCP8.5 based on the marker scenario implementations.6 The first three scenarios 
(a–c) with more stringent reduction pathways represent contexts where urban per capita emissions decline rapidly against various increases in urban population and are oriented to reach net-zero emissions within this century at different 
radiative forcing levels. SSP1 scenarios (a, b) represent contexts where urbanisation takes place rapidly while providing resource efficiency based on compact urban form (Jiang and O’Neill 2017), with high levels of electrification (van Vuuren

et al. 2017b; Rogelj et al. 2018). The scenario context of SSP1-RCP1.9 represents a pathway in which there can be a transformative shift towards sustainability. Note that the scale of panels (f) and (g) is different from the other panels.7 See 
Table 8.2 detailing the SSP-RCPs. Source: adapted from Gurney et al. (2022).8

892
892
a) Resource efﬁcient and compact b) Moderate progressEstimated urban emissions changes in two different scenarios (2020–2030)Urban emissions (GtCO2-eq yr–1)
PaciﬁcEastern
Europe
and West-
Central AsiaEastern
Europe
and West-
Central AsiaLatin
American
and
CaribbeanMiddle
EastTotal
Reduction
in 20309.8
3.4
Developed
CountriesAfrica Asia and
American
and
EastTotal
Increase
in 2030Developed
Countries14
12
10
8
6
4
2
0
2020 Emissions levels Reductions in 2030 Remaining Emissions Any increase in 2030
Figure 8.14: Comparison of urban emissions under different urbanisation scenarios (GtCO 2-eq yr–1) for the AR6 WGIII 6-region aggregation.  The panels 
represent the estimated urban emissions change in two different scenarios for the time period 2020–2030. Panel (a) represents resource efficient and compact urbanisation

while panel (b) represents urbanisation with moderate progress. The two scenarios are consistent with estimated urban emissions under the SSP1-RCP1.9-SPA1 and 
SSP2-RCP4.5-SPA2 scenarios, respectively (Figure 8.13). In both panels, urban emissions estimates for the year 2020 are marked by the lines for each region. In the resource 
efficient and compact scenario, various reductions in urban emissions that take place by 2030 are represented by the dashed areas within the bars. The remaining solid shaded 
areas represent the remaining urban emissions in 2030 for each region on the path towards net-zero emissions. The total reductions in urban emissions worldwide that are given 
by the last dashed grey bar in panel (a) is estimated to be 9.8 GtCO 2-eq yr–1 between 2020 and 2030 in this scenario. In the scenario with moderate progress, there are no

regions with reductions in urban emissions. Above the white lines that represent urban emissions in 2020, the grey shaded areas are the estimated increases for each region so 
that the total urban emissions would increase by 3.4 GtCO 2-eq yr–1 from 2020 levels in 2030 under this scenario. The values are based on urban scenario analyses as given in 
Gurney et al. (2021, 2022). Source: synthesised based on data from Gurney et al. (2022).9
Table 8.2: Synthesis of the urbanisation and scenario contexts of the urban emissions scenarios.  Descriptions for urbanisation are adapted based on Jiang and 
O’Neill (2017) while high, medium, low, or mixed levels in the scenario context are drawn from the marker model implementations of SSP1-SSP5 for IMAGE (van Vuuren et al. 
2017b; Rogelj et al. 2018), MESSAGE-GLOBIOM (Fricko et al. 2017), AIM/CGE (Fujimori et al. 2017), GCAM (Calvin et al. 2017), and REMIND-MAgPIE (Kriegler et al. 2017).

The letters in parentheses refer to the panels in Figure 8.13. Energy and material efficiency relate to energy efficiency improvement and decrease in the intermediate input of 
materials, including steel and cement. Dietary responses include less meat-intensive diets. Implications for urban areas relate to the mitigation options in Section 8.4. Source: 
adapted from Gurney et al. (2022).
SSP/RCP 
frameworkUrbanisation contextScenario context
ElectrificationEnergy and 
material 
efficiencyTechnology 
development/ 
innovationRenewable 
energy 
preferencesBehavioural, 
lifestyle 
and dietary 
responsesAfforestation 
and  
re-forestation
SSP1 
RCP1.9 (a)  
RCP2.6 (b)Resource efficient,  
walkable and sustainable 
rapid urbanisationHigh High High High High High
Implications for urban climate mitigation include:
 –Electrification across the urban energy system while supporting flexibility in end-use
 –Resource efficiency from a consumption-based perspective with cross-sector integration

–Knowledge and financial resources to promote urban experimentation and innovation
 –Empowerment of urban inhabitants for reinforcing positive lock-in for decarbonisation
 –Integration of sectors, strategies and innovations across different typologies and regions
SSP2 
RCP4.5 (d)Moderate progress Medium Medium Medium Medium Medium Medium
SSP3
RCP7.0 (f)Slow urbanisation, inadequate 
urban planningMedium Low Low Medium Low Low
SSP4 
RCP3.4 (c)
RCP6.0 (e)Pace of urbanisation differs 
with inequalitiesMixed Mixed Mixed Mixed Mixed Mixed
SSP5 
RCP8.5 (g)Rapid urbanisation 
with carbon lock-inHigh Low High Low Low –
9 Figure adapted from Global Environmental Change, Vol 73, Gurney et al., Greenhouse gas emissions from global cities under SSP/RCP scenarios, 1990 to 2100, ©2022 

893
893
Similar analysis by the urban networks C40 and GCoM examine 
current and future GHG emissions on smaller subsets of global cities, 
offering further insight on the potential emissions impacts of urban 
mitigation options. However, this analysis is limited to just a sample 
of the global urban landscape and primarily focused on cities in 
the Global North (GCoM 2018, 2019; C40 Cities et al. 2019) with 
methods to project avoided emissions in development (Kovac et al. 
2020). Different scopes of analysis between sectors, as well as limited 
knowledge of the impact of existing and new urban infrastructure, 
limit the possibility of direct comparisons in emissions. Still, the 
shares of urban mitigation potential ranges between 77.7% and 
78.9% for combined strategies that involve decarbonised buildings 
and transport in urban infrastructure, and the wedges approach 
the remaining emissions reductions also considering construction

materials and waste. This data supports urban areas pursuing 
a package of multiple, integrated mitigation strategies in planning 
for decarbonisation (Sections 8.4 and 8.6, and Figure 8.21).
The most comprehensive approach to-date for quantifying urban 
emissions within the global context (Gurney et al. 2021, 2022) 
combines the per capita carbon footprint estimates for 13,000 cities 
from Moran et al. (2018) with projections of the share of urban 
population (Jiang and O’Neill 2017) within the IPCC’s SSP-RCP 
framework (van Vuuren et al. 2014, 2017a; Riahi et al. 2017). Urban 
emissions in seven SSP-RCP scenarios are shown in Figure 8.13 along 
with an estimate of the global total CO 2-eq for context.
In 2020, total urban emissions (including CO 2 and CH 4) derived from 
consumption-based accounting were estimated to be 29 GtCO 2-eq, representing between 67% and 72% of global CO 2 and CH 4 
emissions, excluding aviation, shipping, and biogenic sources of

emissions. By 2050, with moderate to low urban mitigation efforts, 
urban emissions are projected to rise to 34.0 GtCO 2-eq (SSP2-
RCP4.5) or 40.2 GtCO 2-eq (SSP3-RCP7.0) – driven by growing urban 
population, infrastructure, and service demands. However, scenarios 
that involve rapid urbanisation can have different outcomes as seen 
in SSP1-RCP1.9 based on green growth, versus SSP5-RCP8.5 with 
the strongest carbon lock-in lacking any decarbonisation. Other 
scenarios involve mixed and/or low urbanisation, along with other 
differences, including the implementation of electrification, energy, 
and material efficiency, technology development and innovation, 
renewable energy preferences, and behavioural, lifestyle, and dietary 
responses (Table  8.2). With aggressive and immediate mitigation 
efforts to limit global warming to 1.5°C (>50%) with no or limited 
overshoot, urban GHG emissions could approach net-zero and 
reach a  maximum of 3.3 GtCO 2-eq in 2050 (SSP1-RCP1.9). Under

aggressive but not immediate urban mitigation efforts to limit global 
warming to 2°C (>67%), urban emissions could reach 17.2 GtCO 2-eq 
in 2050 (SSP1-RCP2.6).
When 2020 levels are compared to the values for the year 2030, urban 
areas that utilise multiple opportunities towards resource-efficient and 
walkable urbanisation are estimated to represent a savings potential 
of 9.8 GtCO 2-eq of urban emissions, under SSP1-RCP1.9 scenario 
conditions, on the path towards net-zero CO 2 and CH 4 emissions. In 
contrast, urban emissions would increase by 3.4 GtCO 2-eq from 2020 
levels in 2030 under SSP2-RCP4.5 scenario conditions with moderate 
changes lacking ambitious mitigation action (Figure 8.14).
Table 8.3: Cross-cutting implications of the reference scenarios and Illustrative Mitigation Pathways (IMPs) for urban areas.  The IMPs illustrate key themes of

mitigation strategies throughout the WGIII report (Section 3.2.5). The implications of the key themes of the six IMPs (in addition to two pathways illustrative of higher emissions) 
for mitigation in urban areas are represented based on the main storyline elements that involve energy, land use, food biodiversity and lifestyle, as well as policy and innovation. 
The cross-cutting implications of these elements for urban areas, where multiple elements interact, are summarised for each reference scenario and the IMPs. IMP-Ren, IMP-LD 
and IMP-SP represent pathways in the C1 category that also includes SSP1–1.9. Source: adapted from the key themes of the IMPs for urban areas.
Reference 
scenarios and IMPsCross-cutting implications for urban areas
Current Policies  
(CurPol scenario) –Urban mitigation is challenged by overcoming lock-in to fossil fuel consumption; also with car-based and low-density urban growth prevailing

–Consumption patterns have land impacts, supply chains remain the same, urban inhabitants have limited participation in mitigation options
 –Progress in low-carbon urban development takes place at a relatively slower pace and there is limited policy learning within climate networks
Moderate Action  
(ModAct scenarios) –Renewable energy continues to increase its share that is supported by urban areas to a more limited extent with ongoing lock-in effects
 –Changes in land use, consumption patterns, and lifestyles mostly continue as before with negligible changes taking place – if any
 –The fragmented policy landscape also prevails at the urban level with different levels of ambitions and without integration across the urban system
Gradual 
Strengthening  
(IMP-GS) –Urban areas depend upon energy supply from distant power plants or those in rural areas without rapid progress in urban electrification

–Afforestation/reforestation is supported with some delay while lower incentives for limiting growth in urban extent provide inconsistencies
 –The mobilisation of urban actors for GHG emission reductions is strengthened more gradually with stronger coordination taking place after 2030
Net Negative 
Emissions (IMP-Neg) –Urban areas depend upon energy supply from distant power plants or those in rural areas with more limited electrification in urban energy systems
 –Afforestation/reforestation is supported to a certain extent while lower incentives for limiting growth in urban extent provide inconsistencies
 –Urban areas are less prominent in policy and innovation given emphasis on carbon capture and storage (CCS) options. Rural areas are more prominent 
considering BECCS
Renewable Energy 
(IMP-Ren) –Urban areas support renewable energy penetration with electrification of urban infrastructure and sector coupling for increasing system flexibility

–Consumption patterns and urban planning are able to reduce pressures on land use, demand response is increased to support renewables
 –Urban climate governance is enabling rapid deployment of renewable energy while fostering innovation for sustainable urban planning
Low Demand  
(IMP-LD) –Walkable urban form is increased, active and public transport modes are encouraged, low-energy buildings and green-blue infrastructure is integrated
 –Changes in consumption patterns and urban planning reduce pressures on land use to lower levels while service provisioning is improved
 –Urban policymaking is used to accelerate solutions that foster innovation and increased efficiencies across all sectors, including material use
Shifting Pathways  
(IMP-SP) –Urban areas are transformed to be resource efficient, low demand, and renewable energy supportive with an integrated approach in urban planning

–Reinforcing measures enable GHG emission reductions from consumption patterns while also avoiding resource impacts across systems
 –Urban climate mitigation is best aligned with the SDGs to accelerate GHG emission reductions, increasing both scalability and acceptance

894
894
Chapter 8 Urban Systems and Other Settlements8Among the 500 urban areas with the highest consumption-based 
urban emissions footprint in 2015 (Moran et al. 2018), urban-level 
emission scenarios under SSP1 conditions are constructed for 420 
urban areas located across all regions of the world (Kılkış 2021a). 
These scenarios are based on urban-level population projections by 
SSP (Kii 2021), trends in relevant CMIP6 scenarios (Gidden 
et al.  2019), and a  100% renewable energy scenario (Bogdanov 
et al. 2021). In the year 2020, the 420 urban areas are responsible 
for about 10.7 ± 0.32 GtCO 2-eq, or 27% of the global total CO 2 and 
CH4 emissions of about 40 GtCO 2-eq, excluding aviation, shipping, 
and biogenic sources. Under three SSP1-based scenarios, the urban 
emissions of the 420 urban areas in 2030 is projected to be about 
7.0 GtCO 2-eq in SSP1-RCP1.9, 10.5 GtCO 2-eq in SSP1-RCP2.6, and 
5.2 GtCO 2-eq in the SSP1 renewable energy scenario.

The Illustrative Mitigation Pathways (IMPs) represent different 
strategies for maintaining temperature goals that are compliant 
with the Paris Agreement, as well as their comparison with the 
continuation of current policies (Sections  1.5 and 3.2.5, and 
Table  8.3). The key characteristics that define the IMPs involve 
aspects of energy, land use, lifestyle, policy, and innovation. Urban 
areas provide cross-cutting contexts where each of these key 
characteristics can be enabled and have a  particularly important 
role in the transformation pathways for renewable energy (IMP-
Ren), low demand (IMP-LD), and shifting to sustainability (IMP-SP). 
Pathways that are compliant with the Paris Agreement include such 
urban implications as a reversal of decreasing land-use efficiency in 
urban areas to lower energy demand based on spatial planning for 
compact urban form (Section 8.4.2), changes in urban infrastructure 
for supporting demand flexibility to handle variable energy supply

(Section 8.4.3), as well as policies and governance that are conducive 
to innovation in urban areas (Section  8.5). Spatial planning for 
compact urban form can enable reduced energy demand and changes 
in service provisioning, including through walkable neighbourhoods 
and mixed land use, providing venues for socio-behavioural change 
towards active transport (Section  8.4.5). Electrification and sector 
coupling in urban infrastructure can, for instance, be an important 
enabler of supporting higher penetrations of renewable energy in 
the energy system.
8.4 Urban Mitigation Options
Urban mitigation options can be categorised into three broad 
strategies: (i) reducing or changing urban energy and material use 
towards more sustainable production and consumption across 
all sectors, including through spatial planning and infrastructure; 
(ii) electrification and switching to net-zero-emissions resources; and 
(iii) enhancing carbon storage in the urban environment through

urban green and blue infrastructure, which can also offer multiple 
co-benefits. A  fourth, socio-behavioural aspects, can shift energy 
demand and emerge as the result of implementing the strategies. 
Urban mitigation options covered in this section are organised 
around these three strategies and can facilitate deep decarbonisation 
through systemic transformation (see Section  8.6 and Figure  8.21 
for prioritising mitigation options based on urban form and urban 
growth typologies).Urban areas are systems where multiple mitigation options  – 
especially when integrated  – have cascading effects across 
transport, energy, buildings, land use, and behaviour. These 
cascading effects take place both within and across urban systems 
(Figure  8.15). Mitigation actions also occur at multiple urban 
scales, from households and blocks to districts and city regions, 
and can be implemented as standalone sectoral strategies, such as

increasing energy efficiency for appliances, and also as system-wide 
actions. In reducing emissions locally, urban areas can help lower 
emissions outside of their administrative boundaries through their 
use of materials and resources, and by increasing the efficiency of 
infrastructure and energy use beyond what is possible with individual 
sectoral strategies. Urban mitigation policies that implement multiple 
integrated interventions will provide more emissions savings than 
the sum of individual interventions (Sethi et al. 2020).
Integrated action also has a key role in providing benefits for human 
well-being. Urban mitigation options and strategies that are effective, 
efficient, and fair can also support broader sustainability goals 
(Güneralp et al. 2017; Kona et al. 2018; Pasimeni et al. 2019). Due 
to the complex and intensive interactions in urban systems and the 
interlinked nature of the SDGs, cities can be important intervention

points to harness synergies and co-benefits for achieving emissions 
reductions along with other SDGs (Nilsson et al. 2016; Corbett and 
Mellouli 2017) (Section 8.2 and Figure 8.4).
8.4.1 Avoiding Carbon Lock-in
Carbon lock-in occurs as the result of interactions between 
different geographic and administrative scales (institutional lock-
in) and across sectors (infrastructural and technological lock-in), 
which create the conditions for behavioural lock-in covering both 
individual and social structural behaviours (Seto et al. 2016) (see 
Glossary for a broader definition of ‘lock-in’). The way that urban 
areas are designed, laid out, and built (i.e., urban form) affects and 
is affected by the interactions across the different forms of carbon 
lock-in (Figures 8.15 and 8.16). Cities are especially prone to carbon 
lock-in because of the multiple interactions of technological, 
institutional, and behavioural systems, which create inertia and

path dependency that are difficult to break. For example, the 
lock-in of gasoline cars is reinforced by highway and energy 
infrastructures that are further locked-in by social and cultural 
preferences for individual mobility options. The dominance of cars 
and their supporting infrastructures in auto-centric urban forms is 
further reinforced by zoning and urban development patterns, such 
as dispersed and low-density housing distantly located from jobs, 
that create obstacles to creating alternative mobility options (Seto 
et al. 2016; Linton et al. 2021).
Urban infrastructures and the built environment are long-lived assets, 
embodying triple carbon lock-ins in terms of their construction, 
operations, and demolition (Creutzig et al. 2016b; Seto et al. 2016; 
Ürge-Vorsatz et al. 2018). There is much focus in the climate change 
literature on the operational lifetimes of the energy sector, especially 
power plants and the electricity grid, which are between 30 and

60 years (Rode et al. 2017). Yet, in reality, the lifespans of urban

895
895
(a)
Figure 8.15: Urban systems, lock-in, and cascading effects of mitigation strategies.

896
896
infrastructures, especially the basic layout of roadways, are often much 
longer (Reyna and Chester 2015). A number of detailed case studies 
on the evolution of urban road networks for cities around the world 
reveal that the current layout of streets grew out of street networks 
that were established hundreds of years ago (Strano et al. 2012; 
Masucci et al. 2013; Mohajeri and Gudmundsson 2014). Furthermore, 
there is evidence that urban street layout, population growth, urban 
development, and automobile ownership co-evolve (Li et al. 2019a).
For cities to break out of mutually reinforcing carbon lock-in, it will 
require systematic transformation and systems-based planning that 
integrates mitigation strategies across sectors and geopolitical scales. 
Urban energy demand patterns are locked-in whenever incremental 
urban design and planning decisions, coupled with investments in

long-lasting infrastructure, such as roads and buildings, take place 
(Seto et al. 2016). The fundamental building blocks of cities are based 
on the layout of the street network, the size of city blocks, and the 
density of street intersections. If not significantly altered, these three 
factors will continue to shape and lock-in energy demand for decades 
after their initial construction, influencing the mitigation potential of 
urban areas (Section 8.4.2 and Figure 8.22).Avoiding carbon lock-in inherently involves decisions that extend 
beyond the administrative boundaries of cities. This includes pricing 
of low-emissions technology or materials, such as electric battery 
or hydrogen vehicles and buses, although cities can support their 
development and deployment (Cross-Chapter Box 12 in Chapter 16 
on Transition Dynamics). In contrast, urban governments in most 
parts of the world do have powers to set building codes that regulate

materials and construction standards for buildings, including heating 
and cooling technologies, and major appliances. Other examples 
include zoning that determines the location of buildings, land uses, 
standards for densities, and the inclusion of energy planning in their 
building standards and public works, including streets, parks, and 
open spaces (Blanco et al. 2011; Raven et al. 2018).
8.4.2 Spatial Planning, Urban Form, and Infrastructure
Urban form is the resultant pattern and spatial layout of land use, 
transportation networks, and urban design elements, including 
the physical urban extent, configuration of streets and building 
orientation, and the spatial figuration within and throughout cities 
(b)
Figure 8.15 (continued): Urban systems, lock-in, and cascading effects of mitigation strategies.  Cities are systems of interconnected sectors, activities, and

governance structures. Urban-scale mitigation action can have cascading effects across multiple sectors, as shown in panel (a), as well as regional, national, and global impacts 
through supply chains, resource flows, and institutions, as shown in panel (b). Mitigation efforts implemented at larger scales of governance or in sectors that transcend 
urban boundaries, like energy and transportation, can also facilitate and amplify mitigation at the urban scale, as shown by the arrows extending in both directions across 
layers (a). Because urban areas are connected locally and globally, urban mitigation efforts can also impact other cities and surrounding areas (agriculture, forestry and other 
land use (AFOLU)). Cities are prone to carbon lock-in due to the numerous reinforcing interactions among urban infrastructures and technologies, institutions, and individual

and collective behaviours; see the side arrows extending across the layers in panel (a): the yellow arrow represents the infrastructure and technological lock-in involving user 
technologies and supporting infrastructure, the blue arrow indicates lock-in of local to international institutions, and the pink arrow represents behavioural lock-in for individuals 
and society. Urban carbon lock-in is strongly determined by urban form, in particular the layout of streets and land-use mix. The different coloured spatial patterns represent 
varying levels of co-location of housing and jobs, and mobility options (Figure 8.16). Efforts to break urban carbon lock-in require meta-transformations to break inertia in and 
among infrastructures, institutions, and behaviours. Source: adapted in part from Seto et al. (2016).

897
897
and towns (Lynch 1981; Handy 1996). Infrastructure describes the 
physical structures, social and ecological systems, and corresponding 
institutional arrangements that provide services and enable urban 
activity (Dawson et al. 2018; Chester 2019) and comprises services 
and built-up structures that support urban functioning, including 
transportation infrastructure, water and wastewater systems, solid 
waste systems, telecommunications, and power generation and 
distribution (Seto et al. 2014).
8.4.2.1 Urban Form
The AR5 concluded that infrastructure and four dimensions of 
urban form are especially important for driving urban energy use: 
density, land-use mix, connectivity, and accessibility. Specifically, 
low-carbon cities have the following characteristics: (i) co-located 
medium to high densities of housing, jobs, and commerce; (ii) high 
mix of land uses; (iii) high connectivity of streets; and (iv) high levels

of accessibility, distinguished by relatively low travel distances and 
travel times that are enabled by multiple modes of transportation. 
Urban areas with these features tend to have smaller dwelling 
units, smaller parcel sizes, walking opportunities, high density of 
intersections, and are highly accessible to shopping. For brevity, 
we will refer to these characteristics collectively as ‘compact 
and walkable urban form’ (Figure  8.16). Compact and walkable 
urban form has many co-benefits, including mental and physical 
health, lower resource demand, and saving land for AFOLU.  In 
contrast, dispersed and auto-centric urban form is correlated 
with higher GHG emissions, and characterised by separated land 
uses, low population and job densities, large block size, and low 
intersection density.
Since AR5, a  range of studies have been published on the 
relationships between urban spatial structures, urban form, and GHG

emissions. Multiple lines of evidence reaffirm the key findings from 
AR5, especially regarding the mitigation benefits associated with 
reducing vehicle miles or kilometres travelled (VMT/VKT) through spatial planning. There are important cascading effects not only for 
transport but also other key sectors and consumption patterns, such 
as in buildings, households, and energy. However, these benefits 
can be attained only when the existing spatial structure of an urban 
area does not limit locational and mobility options, thereby avoiding 
carbon lock-in through the interaction of infrastructure and the 
resulting socio-behavioural aspects.
Modifying the layout of emerging urbanisation to be more compact, 
walkable, and co-located can reduce future urban energy use by 
20–25% in 2050 while providing a corresponding mitigation potential 
of 23–26% (Creutzig et al. 2015, 2016b; Sethi et al. 2020), forming 
the basis for other urban mitigation options. Cross-Chapter Box 7 in

Chapter 10 provides perspectives on simultaneously reducing urban 
transport emissions, avoiding infrastructure lock-in, and providing 
accessible services. The systemic nature of compact urban form and 
integrated spatial planning influences ‘Avoid-Shift-Improve’ (ASI, see 
Glossary) options across several sectors simultaneously, including for 
mobility and shelter (for an in-depth discussion on the integration of 
service provision solutions within the ASI framework, see Section 5.3).
8.4.2.2 Co-located Housing and Jobs, Mixed Land Use,  
and High Street Connectivity
Integrated spatial planning, co-location of higher residential and 
job densities, and systemic approaches are widely identified with 
development that is characterised by the 5Ds of transit-oriented 
development (TOD) based on density, diversity (mixed land uses), 
design (street connectivity), destination accessibility, and distance to 
transit. Spatial strategies that integrate the 5Ds are shown to reduce

VMT/VKT, and thereby transport-related GHG emissions through 
energy savings. The effect of urban form and built environment 
strategies on VMT per capita varies by a number of factors (Ewing 
and Cervero 2010; Stevens 2017; Blanco and Wikstrom 2018). 
Density and destination accessibility have the highest elasticities, 
followed by design (Stevens 2017). Population-weighted densities for 
Figure 8.16: Urban form and implications for GHG emissions.  Compact and walkable urban form is strongly correlated with low GHG emissions and characterised by 
co-located medium to high densities of housing and jobs, high street density, small block size, and mixed land use (Seto et al. 2014). Higher population densities at places of 
origin (e.g., home) and destination (e.g., employment, shopping) concentrate demand and are necessary for achieving the Avoid-Shift-Improve (ASI) approach for sustainable

mobility (Chapters 5 and 10). Dispersed and auto-centric urban form is strongly correlated with high GHG emissions, and characterised by separated land uses, especially of 
housing and jobs, low street density, large block sizes, and low urban densities. Separated and low densities of employment, retail, and housing increase average travel distances 
for both work and leisure, and make active transport and modal shift a challenge. Since cities are systems, urban form has interacting implications across energy, buildings, 
transport, land use, and individual behaviour. Compact and walkable urban form enables effective mitigation while dispersed and auto-centric urban form locks-in higher levels 
of energy use. The colours represent different land uses and indicate varying levels of co-location and mobility options.

898
898
Chapter 8 Urban Systems and Other Settlements8121 metropolitan areas have further found that the concentration of 
population and jobs along mass transit corridors decreases VMT/VKT 
significantly when compared to more dispersed metropolitan areas. In 
this sample, elasticity rates were twice as high for dense metropolitan 
areas located along mass transit lines (Lee and Lee 2020).
Meta-analyses of the reduction in VMT and the resulting GHG emissions 
consider the existing and still dominant use of emitting transportation 
technology, transportation fleets, and urban form characteristics. Varied 
historical legacies of transportation and the built environment, which 
can be utilised to develop more sustainable cities (Newman et al. 2016, 
2017), are often not taken into account directly. Metropolitan policies 
and spatial planning, as evident in Copenhagen’s Finger Plan, as well 
as strategic spatial planning in Stockholm and Seoul, have been major

tools to restructure urban regions and energy patterns (Sung and Choi 
2017). Road prices and congestion charges can provide the conditions 
for urban inhabitants to shift mobility demands and reduce vehicle 
use (Section  5.6.2). Surprisingly, even cities with higher population 
densities and a greater range of land uses can show declines in these 
important attributes, which can lead to emissions increases, such as 
found in a study of 323 East and South East Asian cities (Chen et al. 
2020c). Conversely, the annual CO 2 emissions reduction of passenger 
cars in compact versus dispersed urban form scenarios can include at 
least a 10% reduction by 2030 (Matsuhashi and Ariga 2016). When 
combined with advances in transport technology, this share increases 
to 64–70% in 2050 based on compact urban form scenarios for 1727 
municipalities (Kii 2020).
As a  reaffirmation of AR5, population density reduces emissions 
per capita in the transport, building, and energy sectors (Baur et al.

2015; Gudipudi et al. 2016; Wang et al. 2017; Yi et al. 2017) (see 
also Sections 8.3.1 and 8.3.4 on past trends and forecasts of urban 
population density and land expansion). Urban compactness tends 
to reduce emissions per capita in the transport sector, especially for 
commuting (Matsuhashi and Ariga 2016; Lee and Lim 2018; Lee and 
Lee 2020). The relative accessibility of neighbourhoods to the rest of 
the region, in addition to the density of individual neighbourhoods, 
is important (Ewing et al. 2018). Creating higher residential and 
employment densities, developing smaller block sizes, and increasing 
housing opportunities in an employment area can significantly 
reduce household car ownership and car driving, and increase the 
share of transit, walk, and bicycle commuting (Ding et al. 2018). In 
addition to population density, land-use mix, rail transit accessibility, 
and street design reduce emissions from transport (Dou et al. 2016;

Cao and Yang 2017; Choi 2018). The impact of population density 
and urban compactness on emissions per capita in the household or 
energy sector is also associated with socioeconomic characteristics 
or lifestyle preferences (Baiocchi et al. 2015; Miao 2017). Changes 
in the attributes of urban form and spatial structure have influences 
on overall energy demand across spatial scales, particularly street, 
block, neighbourhood, and city scales, as well as across the building 
(housing) and transport (mobility) sectors (Silva et al. 2017). 
Understanding the existing trade-offs (or synergetic links) between 
urban form variables across major emissions source sectors, and how 
they impact the size of energy flows within the urban system, is key 
to prioritising action for energy-efficient spatial planning strategies, 
which are likely to vary across urban areas.8.4.2.3 Urban Form, Growth, and Sustainable Development
Spatial planning for compact urban form is a system-wide intervention

(Sethi et al. 2020) and has potential to be combined with sustainable 
development objectives while pursuing climate mitigation for urban 
systems (Große et al. 2016; Cheshmehzangi and Butters 2017; Facchini 
et al. 2017; Lwasa 2017; Stokes and Seto 2019). Compact urban 
form can enable positive impacts on employment and green growth 
given that the local economy is decoupled from GHG emissions and 
related parameters while the concentration of people and activity 
can increase productivity based on both proximity and efficiency (Lee 
and Erickson 2017; Salat et al. 2017; Gao and Newman 2018; Han 
et al. 2018; Li and Liu 2018; Lall et al. 2021).
Public acceptance can have a positive impact on integrated spatial 
planning especially when there is a process of co-design (Grandin 
et al. 2018; Webb et al. 2018). The quality of spatial planning can 
also increase co-benefits for health and well-being, including 
decisions to balance urban green areas with density (Li et al.

2016; Sorkin 2018; Pierer and Creutzig 2019). The distributional 
effects of spatial planning can depend on the policy tools that 
shape the influence of urban densification on affordable housing 
while evidence for transit-induced gentrification is found to be 
partial and inconclusive (Chava and Newman 2016; Jagarnath and 
Thambiran 2018; Padeiro et al. 2019; Debrunner and Hartmann 
2020) (Sections 8.2 and 8.4.4).
Reducing GHG emissions across different urban growth typologies 
(Figure 8.20) depends in part on the ability to integrate opportunities 
for climate mitigation with co-benefits for health and well-being 
(Grandin et al. 2018). At the same time, requirements for institutional 
capacity and governance for cross-sector coordination for integrated 
urban planning is high given the complex relations between urban 
mobility, buildings, energy systems, water systems, ecosystem 
services, other urban sectors, and climate adaptation (Große et al.

2016; Castán Broto 2017a; Endo et al. 2017; Geneletti et al. 2017). 
The capacity for implementing land-use zoning and regulations in 
a way that is consistent with supporting spatial planning for compact 
urban form is not equal across urban areas and depends on different 
contexts as well as institutional capacities (Bakır et al. 2018; Deng 
et al. 2018; Shen et al. 2019).
Currently, integrating spatial planning, urban form, and infrastructure 
in urban mitigation strategies remains limited in mainstream 
practices, including in urban areas targeting an emissions reduction 
of 36–80% in the next decades (Asarpota and Nadin 2020). Capacity 
building for integrated spatial planning for urban mitigation includes 
increasing collaboration among city departments and with civil 
society to develop robust mitigation strategies, bringing together 
civil engineers, architects, urban designers, public policy and spatial 
planners, and enhancing the education of urban professionals

(Asarpota and Nadin 2020) (Section 8.5).
Spatial planning for compact urban form is a  prerequisite for 
efficient urban infrastructure, including district heating and/or 
cooling networks (Swilling et al. 2018; Möller et al. 2019; Persson 
et al. 2019; UNEP IRP 2020). District heating and cooling networks

899
899
benefit from urban design parameters, including density, block 
area, and  elongation that represent the influence of urban density 
on energy density (Fonseca and Schlueter 2015; Shi et al. 2020). Heat-  
demand density is a  function of both population density and 
heat  demand per capita and can be equally present in urban areas 
with high population density or high heat demand per capita (Möller 
et al. 2019; Persson et al. 2019). Low-temperature networks that 
utilise waste heat or renewable energy can provide an option to avoid 
carbon lock-in to fossil fuels while layout and eco-design principles 
can further optimise such networks (Gang et al. 2016; Buffa et al. 
2019; Dominković and Krajačić 2019). Replacing gas-based heating 
and cooling with electrified district heating and cooling networks, for 
instance, provides 65% emissions reductions also involving carbon-

aware scheduling for grid power (De Chalendar et al. 2019). The 
environmental and ecological benefits increase through the interaction 
of urban energy and spatial planning (Tuomisto et al. 2015; Bartolozzi 
et al. 2017; Dénarié et al. 2018; Zhai et al. 2020). These interactions 
include support for demand-side flexibility, spatial planning using 
geographic information systems, and access to renewable and urban 
waste heat sources (Möller et al. 2018; REN21 2020; Sorknæs et al. 
2020; Dorotić et al. 2019) (see Table 8.SM.2 for other references).
8.4.3 Electrification and Switching  
to Net-Zero-Emissions Resources
Pursuing the electrification of mobility, heating, and cooling systems, 
while decarbonising electricity and energy carriers, and switching 
to net-zero materials and supply chains, represent important 
strategies for urban mitigation. Electrification of energy end uses 
in cities and efficient energy demand for heating, transport, and

cooking through multiple options and urban infrastructure, has an 
estimated mitigation potential of at least 6.9 GtCO 2-eq by 2030 
and 15.3 GtCO 2-eq by 2050 (Coalition for Urban Transitions 2019). 
Energy efficiency measures in urban areas can be enabled by urban 
form, building codes, retrofitting and renovation, modal shifts, and 
other options. Decarbonising electricity supply raises the mitigation 
potential of efficient buildings and transport in urban areas to about 
75% of the total estimate (Coalition for Urban Transitions 2019). 
In addition, relatively higher-density urban areas enable more cost-
effective infrastructure investments, including electric public transport 
and large-scale heat pumps in districts that support electrification. 
Urban policymakers can play a key role in supporting carbon-neutral 
energy systems by acting as target setters and planners, demand 
aggregators, regulators, operators, conveners, and facilitators for

coordinated planning and implementation across sectors, urban 
form, and demand (IEA 2021a; IRENA 2021).
8.4.3.1 Electrification and Decarbonisation  
of the Urban Energy System
Urban energy infrastructures often operate as part of larger energy 
systems that can be electrified, decarbonised, and become enablers of 
urban system flexibility through demand-side options. With multiple 
end-use sectors (e.g., transport, buildings) and their interactions with 
land use drawing on the same urban energy system(s), increasing 
electrification is essential for rapid decarbonisation, renewable energy penetration, and demand flexibility (Kammen and Sunter 
2016) (see IMPs in Sections  3.2.5 and 8.3.4). The mitigation 
potential of electrification is ultimately dependent on the carbon 
intensity of the electricity grid (Kennedy 2015; Hofmann et al. 2016; 
Peng et al. 2018; Zhang and Fujimori 2020) and starts providing 
lifecycle emission savings for carbon intensities below a  threshold

of 600 tCO 2-eq GWh–1 (Kennedy et al. 2019). Integrated systems 
of roof-top photovoltaics (PVs) and all-electric vehicles (EVs) alone 
could supply affordable carbon-free electricity to cities and reduce 
CO2 emissions by 54–95% (Brenna et al. 2014; Kobashi et al. 2021). 
Furthermore, electrification and decarbonisation of the urban energy 
system holds widespread importance for climate change mitigation 
across different urban growth typologies and urban form (Section 8.6 
and Figure 8.21) and leads to a multitude of public health co-benefits 
(see Section 8.2).
Strategies that can bring together electrification with reduced energy 
demand based on walkable and compact urban form can accelerate 
and amplify decarbonisation. Taking these considerations  – across 
the energy system, sectors, and land use – contributes to avoiding, 
or breaking out of, carbon lock-in and allows continued emission 
savings as the energy supply is decarbonised (Kennedy et al. 2018;

Teske et al. 2018; Seto et al. 2021). Indeed, electrification is already 
transforming urban areas and settlements and has the potential to 
continue transforming urban areas into net-negative electric cities 
that may sequester more carbon than emitted (Kennedy et al. 2018; 
Seto et al. 2021).
In its simplest form, electrification involves the process of replacing 
fossil fuel-based technologies with electrified innovations such 
as electric vehicles, buses, streetcars, and trains (Sections  10.3 
and 10.4), heat pumps, PVs (Section  6.4.2.1), electric cook-stoves 
(Section  9.8.2.1), and other technologies (Stewart et al. 2018). 
Cost-effective decarbonisation of energy use can be supported by 
electrification in urban areas if there is also demand-side flexibility 
for power, heat, mobility, and water with sector coupling (Guelpa 
et al. 2019; Pfeifer et al. 2021). Overall, demand-side flexibility 
across sectors in urban areas is supported by smart charging, electric

mobility, electrified urban rail, power-to-heat, demand side response, 
and water desalination (Lund et al. 2015; Calvillo et al. 2016; Salpakari 
et al. 2016; Newman 2017; Meschede 2019).
As an enabler, electrification supports integrating net-zero energy 
sources in urban infrastructure across sectors, especially when there 
is more flexible energy demand in mobility, heating, and cooling to 
absorb greater shares of variable renewable energy. In the transport 
sector, smart charging can reduce electric vehicle impacts on peak 
demand by 60% (IEA 2021a). Urban areas that connect efficient 
building clusters with the operation of smart thermal grids in 
district heating and cooling networks with large-scale heat pumps 
can support higher penetrations of variable renewable energy 
in smart energy systems (Lund et al. 2014, 2017). Higher urban 
densities provide the advantage of increasing the penetration of 
renewable power for deep decarbonisation, including mixed-use

neighbourhoods for grid balancing and electric public transport 
(Hsieh et al. 2017; Tong et al. 2017; Fichera et al. 2018; Kobashi et al. 
2020). Based on these opportunities, urban areas that provide low-

900
900
Chapter 8 Urban Systems and Other Settlements8cost options to energy storage for integrating the power sector with 
multiple demands reduce investment needs in grid electricity storage 
capacities (Mathiesen et al. 2015; Lund et al. 2018).
Electrification at the urban scale encompasses strategies to 
aggregate energy loads for demand response in the urban built 
environment to reduce the curtailment of variable renewable 
energy and shifting time-of-use based on smart charging for 
redistributing energy demands (O’Dwyer et al. 2019). Peak shaving 
or shifting takes place among frequent interventions at the urban 
level (Sethi et al. 2020). Business models and utility participation, 
including municipal level demonstrations, can allow for upscaling 
(Gjorgievski et al. 2020; Meha et al. 2020). The urban system can 
support increasing demand-side flexibility in energy systems, 
including in contexts of 100% renewable energy systems (Drysdale 
et al. 2019; Thellufsen et al. 2020).

Smart grids in the urban system
Smart electricity grids enable peak demand reductions, energy 
conservation, and renewable energy penetration, and are a subset 
of smart energy systems. GHG emission reductions from smart grids 
range from 10 to 180 gCO 2 kWh–1 (grams of CO 2 per kilowatt-hour) 
with a median value of 89 gCO 2 kWh–1, depending on the electricity 
mix, penetration of renewable energy, and the system boundary 
(Moretti et al. 2017). Smart electricity grids are characterised by  
bi-directional flows of electricity and information between generators 
and consumers, although some actors can be both as ‘prosumer’ (see 
Glossary). Two-way power flows can be used to establish peer-to-
peer trading (P2P) (Hansen et al. 2020). Business models based on 
local citizen utilities (Green and Newman 2017; Green et al. 2020; 
Syed et al. 2020) and community batteries (Mey and Hicks 2019; 
Green et al. 2020) can support the realisation of distributed energy

and solar energy cities (Galloway and Newman 2014; Byrne and 
Taminiau 2016; Stewart et al. 2018; Allan 2020).
Currently, despite power outages that are costly to local economies, 
the adoption of smart electricity grids or smart energy systems has 
been slow in many developing regions, including in Sub-Saharan Africa 
(Westphal et al. 2017; Kennedy et al. 2019). This is due to a number of 
different factors, such as unreliable existing infrastructure, fractured 
fiscal authority, lack of electricity access in urban areas, upfront cost, 
financial barriers, inefficient pricing of electricity, and low consumer 
education and engagement (Venkatachary et al. 2018; Acakpovi et al. 
2019; Cirolia 2020).
Pathways and trade-offs of electrification in urban systems
Urbanisation and population density are one of the key drivers for  
enabling access to electricity across the world, with benefits 
for sustainable development (Aklin et al. 2018). Grid-connected PV

systems for urban locations that currently lack electricity access can 
allow urban areas to leapfrog based on green electrification (Abid 
et al. 2021). In the Global South, the conversion of public transport 
to electric transport, especially municipal buses (e.g.,  Bengaluru, 
India; Jakarta, Indonesia; Medellín, Colombia; Rio de Janeiro, 
Brazil; Quito, Ecuador) and micro-mobility (e.g.,  e-trikes in Manila, Philippines) have been quantified based on reductions in GHG and 
PM2.5 emissions, avoided premature deaths, and increases in life 
expectancies (IEA 2014; C40 Cities 2018, 2020b,c,d,e). In 22 Latin 
American cities, converting 100% of buses and taxis in 2030 to 
electric was estimated to result in a  reduction of 300 MtCO 2-eq 
compared to 2017 (ONU Medio Ambiente 2017). Yet the scaling 
up of electric vehicles in cities can be examined within a larger set 
of possible social objectives, such as reducing congestion and the 
prioritisation of other forms of mobility.

Electrification requires a  layering of policies at the national, state, 
and local levels. Cities have roles as policy architects, including transit 
planning (e.g., EV targets and low-emission zones, restrictions on the 
types of energy use in new buildings), implementers (e.g., building 
codes and compliance checking, financial incentives to encourage 
consumer uptake of EVs and heat pumps), and complementary 
partners to national and state policymaking (e.g.,  permitting or 
installation of charging infrastructure) (Broekhoff et al. 2015). The 
number of cities that have instituted e-mobility targets that aim 
for a  certain percentage of EVs sold, in circulation or registered, 
is increasing (REN21 2021). Realising the mitigation potential 
of electrification will require fiscal and regulatory policies and 
public investment (Hall et al. 2017a; Deason and Borgeson 2019; 
Wappelhorst et al. 2020) (Section 8.5).
EVs are most rapidly deployed when there has been a  suite of

policies, including deployment targets, regulations and use incentives 
(e.g., zero-emission zone mandates, fuel economy standards, building 
codes), financial incentives (e.g.,  vehicles, chargers), industrial 
policies (e.g.,  subsidies), and fleet procurement (IEA 2016b, 2017, 
2018, 2020a; Cazzola et al. 2019). The policy mix has included 
mandates for bus deployment, purchase subsidies, or split ownership 
of buses and chargers (IEA 2021b) (Chapter 10). Subsidies are often 
critical to address the often-higher upfront costs of electric devices. In 
other instances, the uptake of electric induction stoves was increased 
through government credit and allotment of free electricity (Martínez 
et al. 2017; Gould et al. 2018).
Bringing multiple stakeholders together in local decision-making 
for smart energy systems requires effort beyond usual levels 
while multi-actor settings can be increased to enable institutional 
conditions (Lammers and Hoppe 2019). Public participation and

community involvement in the planning, design and operation 
of urban energy projects can be an enabler of decarbonising 
local energy demands (Corsini et al. 2019). Cooperation across 
institutions is important for municipalities that are engaged in 
strategic energy planning and implementation for smart energy 
systems (Krog 2019) (Section 8.5).
Electrification technologies can present potential trade-offs that can 
be minimised through governance strategies, smart grid technologies, 
circular economy practices, and international cooperation. One 
consideration is the increase in electricity demand (Section 5.3.1.1). 
Across 23 megacities in the world (population greater than 10 million 
people), electrification of the entire gasoline vehicle fleet could increase 
electricity demand on average by 18% (Kennedy et al. 2018). How grid 
capacity will be impacted is dependent on the match between daily

901
901
Material Comparison
Relative Material
Volume by WeightConcrete
(1 tonne)Steel
(1 tonne)Timber
(1 tonne)
Note
Cement ratios of 10%, 15%,
and 20% in concrete are
assumed to estimate
minimum, mean, and
maximum carbon
storage in concrete
Emission Values
tonne C per
1 tonne material
Storage Capacity Values
tonne C per
1 tonne materialKEY
Maximum
MinimumMean
Maximum
Figure 8.17: Relative volume of a given weight, its carbon emissions, and carbon storage capacity of primary structural materials comparing one tonne 
of concrete, steel, and timber.  Concrete and steel have substantial embodied carbon emissions with minimal carbon storage capacities, while timber stores a considerable 
quantity of carbon with a relatively small ratio of carbon emissions-to-material volume. The displayed carbon storage of concrete is the theoretical maximum value, which may

be achieved after hundreds of years. Cement ratios of 10%, 15%, and 20% are assumed to estimate minimum, mean, and maximum carbon storage in concrete. Carbon 
storage of steel is not displayed as it is negligible (0.004 tonne C per tonne of steel). The middle-stacked bars represent the mean carbon emission or mean carbon storage 
values displayed in bold font and underlined. The darker and lighter coloured stacked bars depict the minimum and maximum values. Grey tones represent carbon emissions 
and green tones are given for storage capacity values. Construction materials have radically different volume-to-weight ratios, as well as material intensity (see representations 
of structural columns in the upper panel. These differences should be accounted for in the estimations of their carbon storage and emissions (see also Figure 8.22). Source: 
adapted with permission from Churkina et al. (2020).electricity loads and supply (Tarroja et al. 2018). Materials recycling

of electrification technologies is also key to minimising potential 
environmental and social costs (Church and Crawford 2018; Gaustad 
et al. 2018; Sovacool et al. 2020) and can ensure electrification reaches 
its complete mitigation potential. Circular economy strategies are 
particularly valuable to this goal by creating closed-loop supply chains 
through recycling, material recovery, repair, and reuse. For instance, the 
PV CYCLE programme in Europe prevented more than 30,000 metric 
tonnes of renewable technology from reaching the waste stream 
(Sovacool et al. 2020) (Box 10.6 and ‘circular economy’ in Glossary).8.4.3.2 Switching to Net-zero-emissions Materials 
and Supply Chains
For the carbon embodied in supply chains to become net-zero, all key 
infrastructure and provisioning systems will need to be decarbonised, 
including electricity, mobility, food, water supply, and construction (Seto 
et al. 2021). The growth of global urban populations that is anticipated

over the next several decades will create significant demand for 
buildings and infrastructure. As cities expand in size and density, there 
is an increase in the production of mineral-based structural materials

902
902
Chapter 8 Urban Systems and Other Settlements8and enclosure systems that are conventionally associated with mid- 
and high-rise urban construction morphologies, including concrete, 
steel, aluminium, and glass. This will create a significant spike in GHG 
emissions and discharge of CO 2 at the beginning of each building 
lifecycle, necessitating alternatives (Churkina et al. 2020).
The initial carbon debt incurred in the production stage, even in 
sustainable buildings, can take decades to offset through operational 
stage energy efficiencies alone. Increased reduction in the energy 
demands and GHG emissions associated with the manufacture of 
mineral-based construction materials will be challenging, as these 
industries have already optimised their production processes. Among 
the category of primary structural materials, it is estimated that 
final energy demand for steel production can be reduced by nearly 
30% compared to 2010 levels, with 12% efficiency improvement

for cement (Lechtenböhmer et al. 2016). Even when industries are 
decarbonised, residual CO 2 emissions will remain from associated 
chemical reactions that take place in calcination and use of coke 
from coking coal to reduce iron oxide (Davis et al. 2018). Additionally, 
carbon sequestration by cement occurs over the course of the 
building lifecycle in quantities that would offset only a fraction of 
their production stage carbon spike (Xi et al. 2016; Davis et al. 2018). 
Moreover, there are collateral effects on the carbon cycle related 
to modern construction and associated resource extraction. The 
production of cement, asphalt, and glass requires large amounts of 
sand extracted from beaches, rivers, and seafloors, disturbing aquatic 
ecosystems and reducing their capacity to absorb atmospheric 
carbon. The mining of ore can lead to extensive local deforestation 
and soil degradation (Sonter et al. 2017). Deforestation significantly

weakens the converted land as a carbon sink and in severe cases may 
even create a net emissions source.
A broad-based substitution of monolithic engineered timber systems 
for steel and concrete in mid-rise urban buildings offers the opportunity 
to transform cityscapes from their current status as net sources of 
GHG emissions into large-scale, human-made carbon sinks. The 
storage of photosynthetic forest carbon through the substitution of 
biomass-based structural materials for emissions-intensive steel and 
concrete is an opportunity for urban infrastructure. The construction 
of timber buildings for 2.3 billion new urban dwellers from 2020 to 
2050 could store between 0.01 and 0.68 GtCO 2 per year depending 
on the scenario and the average floor area per capita. Over 30 years, 
wood-based construction can accumulate between 0.25 and 
20 GtCO 2 and reduce cumulative emissions from 4 GtCO 2 (range of 
7–20 GtCO 2) to 2 GtCO 2 (range of 0.3–10 GtCO 2) (high confidence)

(Churkina et al. 2020).
Figure 8.17 indicates that new and emerging structural assemblies in 
engineered timber rival the structural capacity of steel and reinforced 
concrete while offering the benefit of storing significant quantities 
of atmospheric carbon (see also Figure 8.22). ‘Mass timber’ refers to 
engineered wood products that are laminated from smaller boards 
or lamella into larger structural components such as glue-laminated 
(glulam) beams or cross-laminated timber (CLT) panels. Methods 
of mass-timber production that include finger-jointing, longitudinal 
and transverse lamination with both liquid adhesive and mechanical 
fasteners, have allowed for the reformulation of large structural timbers. The parallel-to-grain strength of mass (engineered) timber is 
similar to that of reinforced concrete (Ramage et al. 2017). As much 
as half the weight of a given volume of wood is carbon, sequestered 
during forest growth as a  by-product of photosynthesis (Martin

et al. 2018). Mass timber is inflammable, but in large sections forms 
a self-protective charring layer when exposed to fire that will protect 
the remaining ‘cold wood’ core. This property, formed as massive 
structural sections, is recognised in the fire safety regulations of 
building codes in several countries, which allow mid- and high-rise 
buildings in timber. Ongoing studies have addressed associated 
concerns about the vulnerability of wood to decay and the capacity 
of structural timber systems to withstand seismic and storm-
related stresses.
Transitioning to biomass-based building materials, implemented 
through the adoption of engineered structural timber products and 
assemblies, will succeed as a  mitigation strategy only if working 
forests are managed and harvested sustainably (Churkina et al. 2020). 
Since future urban growth and the construction of timber cities may 
lead to increased timber demand in regions with low forest cover, it

is necessary to systematically analyse timber demand, supply, trade, 
and potential competition for agricultural land in different regions 
(Pomponi et al. 2020). The widespread adoption of biomass-based 
urban construction materials and techniques will demand more 
robust forest and urban land governance and management policies, 
as well as internationally standardised carbon accounting methods 
to properly value and incentivise forest restoration, afforestation, and 
sustainable silviculture.
Expansion of agroforestry practices may help to reduce land-use 
conflicts between forestry and agriculture. Harvesting pressures on 
forests can be reduced through the reuse and recycling of wooden 
components from dismantled timber buildings. Potential synergies 
between the carbon sequestration capacity of forests and the 
associated carbon storage capacity of dense mid-rise cities built from 
engineered timber offer the opportunity to construct carbon sinks

deployed at the scale of landscapes, sinks that are at least as durable 
as other buildings (Churkina et al. 2020). Policies and practices 
promoting design for disassembly and material reuse will increase 
their durability.
8.4.4 Urban Green and Blue Infrastructure
The findings of AR6 WGI and WGII have underscored the importance 
of urban green and blue infrastructure for reducing the total warming 
in urban areas due to its local cooling effect on temperature and 
its benefits for climate adaptation (IPCC 2021; Cross-Working Group 
Box  2  in this chapter). Urban green and blue infrastructure in the 
context of nature-based solutions (NBS) involves the protection, 
sustainable management, and restoration of natural or modified 
ecosystems while simultaneously providing benefits for human 
well-being and biodiversity (IUCN 2021) (see Glossary for additional 
definitions). As an umbrella concept, urban NBS integrates established

ecosystem-based approaches that provide multiple ecosystem 
services and are important in the context of societal challenges 
related to urbanisation, climate change, and reducing GHG emissions

903
903
through the conservation and expansion of carbon sinks (Naumann 
et al. 2014; Raymond et al. 2017) (Section 8.1.6.1).
Urban green and blue infrastructure includes a  wide variety of

options, from street trees, parks, and sustainable urban drainage systems (Davis and Naumann 2017), to building-related green roofs or green facades, including green walls and vertical forests (Enzi et al. 2017). Figure 8.18 synthesises urban green and blue infrastructure based on urban forests, street trees, green roofs, green walls, blue spaces, greenways, and urban agriculture. Key mitigation benefits, adaptation co-benefits, and SDG linkages are represented by types of green and blue infrastructure. Local implementations of urban green and blue infrastructure can pursue these linkages while progressing toward inclusive sustainable urban planning (SDG 11.3) and the provision of safe, inclusive and accessible green and public spaces for all (SDG 11.7) (Butcher-Gollach 2018; Pathak and Mahadevia 2018; Rigolon et al. 2018; Anguelovski et al. 2019; Buyana et al. 2019; Azunre et al. 2021) (Section 8.2).

Figure 8.18: Key mitigation benefits, adaptation co-benefits, and SDG linkages of urban green and blue infrastructure.  Panel (a) illustrates the potential 
integration of various green and blue infrastructure strategies within an urban system.
(a)

904
904
Figure 8.18: Key mitigation benefits, adaptation co-benefits, and SDG linkages of urban green and blue infrastructure. Panel (b) evaluates those strategies in 
the context of their mitigation benefits, adaptation co-benefits, and linkages to the SDGs. Urban forests and street trees provide the greatest mitigation benefit because of their 
ability to sequester and store carbon while simultaneously reducing building energy demand. Moreover, they provide multiple adaptation co-benefits and synergies based on 
the linkages to the SDGs (Figure 8.4). The assessments of mitigation benefits are dependent on context, scale, and spatial arrangement of each green and blue infrastructure 
type and their proximity to buildings. Mitigation benefits due to reducing municipal water use are based on reducing wastewater loads that reduce energy use in wastewater

treatment plants. The sizes of the bars are illustrative and their relative size is based on the authors’ best understanding and assessment of the literature.
(b)

905
905
8.4.4.1 The Mitigation Potential of Urban Trees and 
Associated Co-benefits
Due to their potential to store relatively high amounts of carbon 
compared to other types of urban vegetation, as well as their ability to 
provide many climate mitigation co-benefits (high agreement, robust 
evidence), natural area protection and natural forest management 
in urban areas is an important priority for cities looking to mitigate 
climate change. Globally, urban tree cover averages 26.5%, but 
varies from an average of 12% in deserts to 30.4% in forested 
regions (Nowak and Greenfield 2020).
Global urban tree carbon storage is approximately 7.4 billion tonnes 
(GtC) given 363 million hectares of urban land, 26.5% tree cover, and 
an average carbon storage density of urban tree cover of 7.69 kgC m–2 
(kilograms carbon per square metre) (Nowak et al. 2013; World 
Bank et al. 2013). Estimated global annual carbon sequestration

by urban trees is approximately 217 million tonnes (MtC) given 
an average carbon sequestration density per unit urban tree cover 
of 0.226 kgC m–2 (Nowak et al. 2013). With an average plantable 
(non-tree and non-impervious) space of 48% globally (Nowak and 
Greenfield 2020), the carbon storage value could nearly triple if all 
this space is converted to tree cover. In Europe alone, if 35% of the 
urban surfaces (26,450 km2) were transformed into green surfaces, 
the mitigation potential based on carbon sequestration would be an 
estimated 25.9 MtCO 2 yr−1 with the total mitigation benefit being 
55.8 MtCO 2 yr−1, including an energy saving of about 92 TWh yr−1 
(Quaranta et al. 2021). Other co-benefits include reducing urban 
runoff by about 17.5% and reducing summer temperatures by 
2.5°C–6°C (Quaranta et al. 2021).
Urban tree carbon storage is highly dependent on biome. For example,

carbon sequestered by vegetation in Amazonian forests is two to five times higher compared to boreal and temperate forests (Blais et al. 
2005). At the regional level, the estimated carbon storage density 
rates of tree cover include a  range of 3.14–14.1 kgC m–2 in the 
United States, 3.85–5.58 kgC m–2 in South Korea, 1.53–9.67 kgC m–2 
in Barcelona, Spain, 28.1–28.9 kgC m–2 in Leicester, England, and 
an estimated 6.82 kgC m–2 in Leipzig, Germany and 4.28 kgC m–2 
in Hangzhou, China (Nowak et al. 2013). At the local scale, above- 
and below-ground tree carbon densities can vary substantially, as 
with carbon in soils and dead woody materials. The conservation 
of natural mangroves has been shown to provide urban mitigation 
benefits through carbon sequestration, as demonstrated in the 
Philippines (Abino et al. 2014). Research on urban carbon densities 
from the Southern Hemisphere will contribute to better estimates.

On a per-tree basis, urban trees offer the most potential to mitigate 
climate change through both carbon sequestration and GHG 
emissions reduction from reduced energy use in buildings (Nowak 
et al. 2017). Maximum possible street tree planting among 245 world 
cities could reduce residential electricity use by about 0.9–4.8% 
annually (McDonald et al. 2016). Urban forests in the United States 
reduce building energy use by 7.2%, equating to an emissions 
reduction of 43.8 MtCO 2 annually (Nowak et al. 2017).
Urban trees can also mitigate some of the impacts of climate change 
by reducing the UHI effect and heat stress, reducing stormwater 
runoff, improving air quality, and supporting health and well-being 
in areas where the majority of the world’s population resides 
(Nowak and Dwyer 2007). Urban forest planning and management 
can maximise these benefits for present and future generations by 
sustaining optimal tree cover and health (also see SDG linkages in

Figure 8.4). Urban and peri-urban agriculture can also have economic 
benefits from fruit, ornamental, and medicinal trees (Gopal and 
Nagendra 2014; Lwasa 2017; Lwasa et al. 2018).
Box 8.2: Urban Carbon Storage: An Example from New York City
The structure, composition, extent, and growing conditions of vegetation in cities has an influence on their potential for mitigating 
climate change (Pregitzer et al. 2021). Urban natural areas, particularly forested natural areas, grow in patches and contain many of 
the same components as non-urban forests, such as high tree density, down woody material, and regenerating trees (Box 8.2, Figure 1).
Urban forested natural areas have unique benefits as they can provide habitat for native plants and animals, protecting local 
biodiversity in a  fragmented landscape (Di Giulio et al. 2009). Forests can have a  greater cooling effect on cities than designed

greenspaces, and the bigger the forest the greater the effect (Jaganmohan et al. 2016). In New York City, urban forested natural 
areas have been found to account for the majority of trees estimated in the city (69%), but are a minority of the total tree canopy 
(25%, or 5.5% of the total city land area) (Pregitzer et al. 2019a). In New York City, natural areas are estimated to store a mean of 
263.5 MgC ha–1 (megagram carbon per hectare), adding up to 1.86 TgC (teragram carbon) across the city, with the majority of carbon 
(86%) being stored in the trees and soils (Pregitzer et al. 2021). These estimates are similar to per-hectare estimates of carbon storage 
across different pools in non-urban forest types (Table 1), and 1.5 times greater than estimates for carbon stored in just trees across 
the entire city (Pregitzer et al. 2021).
Within urban natural areas, the amount of carbon stored varies widely based on vegetation type, tree density, and the species

composition (Box 8.2, Figure 1). The oak-hardwood forest type is one of the most abundant in New York City’s natural areas and is 
characterised by large and long-lived native hardwood tree species, with relatively dense wood. These forests store an estimated 
311.5 MgC ha–1. However, non-native exotic invasive species can be prevalent in the understory vegetation layer (<1m height), and 
account for about 50% of cover in New York City (Pregitzer et al. 2019b).

906
906
Chapter 8 Urban Systems and Other Settlements8Box 8.2 (continued)
This could lead to a trajectory where exotic understory species, 
which are often herbaceous, out-compete regenerating trees in 
the understory layer, alter the soil (Ward et al. 2020), and alter the 
forest canopy (Matthews et al. 2016). A change in New York City’s 
vegetation structure and composition to a more open vegetation 
type could reduce the carbon storage by over half (open grassland 
120.1 MgC ha–1).
When compared to estimates of carbon storage presented 
in other studies, the components (pools) of the natural area 
forests in New York City store carbon in similar proportions to 
other non-urban forests (see Table 1). This might suggest that in 
other geographies, similar adjacent non-urban forest types may 
store similar carbon stocks per unit area (medium confidence). 
However, despite similarities to non-urban forests, the urban 
context can lead to altered forest function and carbon cycling

that should be considered. For example, trees growing in urban 
areas have been observed to grow at much higher rates due to 
higher access to light, nutrients, and increased temperatures 
(Gregg et al. 2003; Reinmann et al. 2020).
Higher growth rates coupled with the UHI effect have also 
been suggested to yield greater evaporative cooling by urban 
canopies relative to rural forests (Winbourne et al. 2020). Based 
on estimates in New York City, it is likely that the majority of 
tree biomass, and carbon in trees in cities, could be found in 
urban natural area forest patches (medium agreement, limited 
evidence). More research is needed to map urban natural areas, 
assess vegetation, and differentiate tree canopy types (natural 
versus non-natural) at fine scales within many cities and 
geographies. Accurate maps, as well as greater understanding of 
definitions of urban canopies and vegetation, could lead to better 
accounts for carbon stocks and the many other unique benefits

they provide (Raciti et al. 2012; Pregitzer et al. 2019a).
Despite this potential, natural areas are inherently a minority land-
use type in cities and should be viewed along with other types 
of urban tree canopy that occur in more designed environments 
that might out-perform natural areas in other ecosystem services. 
The mosaic of vegetation characteristics and growing conditions 
will yield different ecosystem services across cities (Pataki et al. 
2011) and should be an important consideration in planning, 
management, and policy in the future.
Box  8.2, Figure  1: Estimates for carbon storage in natural area 
forests in New York City.  (a) Mean estimated carbon stock per hectare in 
natural area forests (Pregitzer et al. 2019a, 2021); (b) estimates for carbon 
stocks vary based on vegetation types; and (c) estimates of the amount of 
carbon stock in different forest pools per hectare. The proportion of the total

estimated carbon stock per pool is out of the total estimated for the entire city 
(1.86 TgC). Source: adapted from Pregitzer et al. (2021).

907
907
Box 8.2 (continued)
Box 8.2, Table 1: A selection of benchmark reference estimates of different carbon pools sampled and the related urban considerations to 
contextualise the results from New York City (NYC), United States (USA) natural area carbon stocks.  The benchmark estimates are intended to provide 
a point of reference to help contextualise the calculations for carbon pools in NYC’s forests. Forest carbon is highly variable and dependent on microclimatic conditions 
such as moisture, microbial communities, and nutrient availability, all of which can be impacted by human activity in urban or altered environments. Standard errors 
and 95% confidence intervals can be found in Pregitzer et al. (2021). DBH: diameter at breast height; DWM: down woody material; CWM: coarse woody material and 
FWM: fine woody material. Source: Pregitzer et al. (2021).
Pool considered in 
NYC natural areaPublished estimates of carbon stock

(MgC ha–1)NYC estimated 
(MgC ha–1)Urban considerations
Live trees: all trees 
(>2 cm DBH) 
including above 
and below ground87.1: northeastern USA (Smith et al. 2013)
73.3: NYC assuming 100% cover  
(Nowak et al. 2013)135.4Lower ozone levels, higher CO 2, warmer temperatures, and higher 
nutrient deposition could lead to increased growth rates and annual 
carbon sequestration. However, pollutants in soil (e.g., heavy metals), 
increased pests, and GHGs in the atmosphere (e.g., NO X and SO 2) 
could decrease annual tree growth and carbon sequestration 
(Gregg et al. 2003)
Groundcover: all 
vegetation growing 
<1 m height1.8: northeastern USA (Smith et al. 2013) 5.5Anthropogenic disturbance creates canopy gaps that accelerate 
herbaceous growth; invasive vines are prevalent in urban forests that 
can alter tree survival and growth and soils (Matthews et al. 2016; 
Ward et al. 2020)
Standing dead trees5.1: northeastern USA (Smith et al. 2013)

2.59: Massachusetts (Liu et al. 2006)5.8 Removal may occur due to safety considerations
CWM: coarse (>10 cm) 
and FWM (>0.1 cm)9.18: CWM – New York state
2.52: CWM – Massachusetts (Liu et al. 2006)
6.37: FWM – New York (Woodall et al. 2013)
3.67: FWM northern hardwood; 0 to 227.94: 
Northern USA (Domke et al. 2016)15.25 (added 
together DWM 
and FWM)Removal may occur due to safety considerations
Litter and duff:  
depth measured12: NYC (Pouyat et al. 2002)
9.36: northern hardwood;
0.04: northern USA (Domke et al. 2016)10.95Decomposition increases with temperature (Hanson et al. 2003); 
decreased ozone levels facilitate litter decay (Carreiro et al. 2009)
Mineral soil 
(organic 30 cm)104: to 30 cm depth, NYC (Cambou et al. 2018)
50: to 10 cm depth, NYC (Pouyat et al. 2002)105.11(30 cm) 
and 77.78 (10 cm)UHI and pollution alter the litter chemistry, decomposer organisms, 
conditions, and resources, which all influence respiration rates

(Carreiro et al. 2009); earthworms, prevalent in urban areas, 
accelerate decay, but some carbon is sequestered in passive pools 
(Pouyat et al. 2002). Soil could be compacted.
8.4.4.2 Benefits of Green Roofs, Green Walls, and Greenways
Green roofs and green walls have potential to mitigate air and 
surface temperature, improve thermal comfort, and mitigate UHI 
effects (Jamei et al. 2021; Wong et al. 2021), while lowering the 
energy demand of buildings (Susca 2019) (Figure 8.18). Green roofs 
have the highest median cooling effect in dry climates (3°C) and 
the lowest cooling effect in hot, humid climates (1°C) (Jamei et al. 
2021). These mitigation potentials depend on numerous factors and 
the scale of implementation. The temperature reduction potential 
for green roofs when compared to conventional roofs can be about 
4°C in winter and about 12°C during summer conditions (Bevilacqua 
et al. 2016). Green roofs can reduce building heating demands by

about 10–30% compared to conventional roofs (Besir and Cuce 
2018), 60–70% compared to black roofs, and 45–60% compared to 
white roofs (Silva et al. 2016). Green walls or facades can provide a  temperature difference between air temperature outside and 
behind a green wall of up to 10°C, with an average difference of 5°C 
in Mediterranean contexts in Europe (Perini et al. 2017). The potential 
of saving energy for air conditioning by green facades can be around 
26% in summer months. Considerations of the spatial context are 
essential given their dependence on climatic conditions (Susca 2019). 
Cities are diverse and emissions savings potentials depend on several 
factors, while the implementation of green roofs or facades may be 
prevented in heritage structures.
Green roofs have been shown to have beneficial effects in stormwater 
reduction (Andrés-Doménech et al. 2018). A global meta-analysis of 
75 international studies on the potential of green roofs to mitigate

runoff indicate that the runoff retention rate was on average 
62% but with a wide range (0–100%) depending on a number of 
interdependent factors (Zheng et al. 2021). These factors relate to the

908
908
Chapter 8 Urban Systems and Other Settlements8characteristics of the rainfall event (e.g., intensity) and characteristics 
of the green roof (e.g., substrate, vegetation type, and size), and of the 
climate and season type. A hydrologic modelling approach applied 
to an Italian case demonstrated that implementing green roofs may 
reduce peak runoff rates and water volumes by up to 35% in a 100% 
green roof conversion scenario (Masseroni and Cislaghi 2016).
Greenways support stormwater management to mitigate water 
runoff and urban floods by reducing the water volume (e.g., through 
infiltration) and by an attenuation or temporal shift of water discharge 
(Fiori and Volpi 2020; Pour et al. 2020). Using green infrastructure 
delays the time to runoff and reduces water volume but depends 
on the magnitude of floods (Qin et al. 2013). Measures are most 
effective for flood mitigation at a local scale; however, as the size of

the catchment increases, the effectiveness of reducing peak discharge 
decreases (Fiori and Volpi 2020). Reduction of water volume through 
infiltration can be more effective with rainfall events on a lower return 
rate. Overall, the required capacity for piped engineered systems 
for water runoff attenuation and mitigation can be reduced while 
lowering flow rates, controlling pollution transport, and increasing 
the capacity to store stormwater (Srishantha and Rathnayake 2017). 
Benefits for flood mitigation require a careful consideration of the 
spatial context of the urban area, the heterogeneity of the rainfall 
events, and characteristics of implementation (Qiu et al. 2021). 
Maintenance costs and stakeholder coordination are other aspects 
requiring attention (Mguni et al. 2016).
Providing a  connected system of greenspace throughout the 
urban area may promote active transportation (Nieuwenhuijsen 
and Khreis 2016), thereby reducing GHG emissions. Soft solutions

for improving green infrastructure connectivity for cycling is an 
urban NBS mitigation measure, although there is low evidence for 
emissions reductions. In the city of Lisbon, Portugal, improvements in 
cycling infrastructure and bike-sharing system resulted in 3.5 times 
more cyclists within two years (Félix et al. 2020). In Copenhagen, the 
cost of cycling (0.08 EUR km-1) is declining and is about six times 
lower than car driving (Euro 0.50/km) (Vedel et al. 2017). In addition, 
participants were willing to cycle 1.84 km longer if the route has 
a  designated cycle track and 0.8 km more if there are also green 
surroundings. Changes in urban landscapes, including through 
the integration of green infrastructure in sustainable urban and 
transport planning, can support the transition from private motorised 
transportation to public and physically active transportation in 
carbon-neutral, more liveable and healthier cities (Nieuwenhuijsen

and Khreis 2016; Nieuwenhuijsen 2020). Car infrastructure can be 
also transferred into public open and green space, such as in the 
Superblock model in Barcelona’s neighbourhoods (Rueda 2019). 
Health impact assessment models estimated that 681 premature 
deaths may be prevented annually with this implementation (Mueller 
et al. 2020) and the creation of greenways in Maanshan, China has 
stimulated interest in walking or cycling (Zhang et al. 2020).8.4.5 Socio-behavioural Aspects
Urban systems shape the behaviour and social structures of their 
residents through urban form, energy systems, and infrastructure – 
all of which provide a  range of options for consumers to make 
choices about residential location, mobility, energy sources, and the 
consumption of materials, food, and other resources. The relative 
availability of options across these sectors has implications on urban 
emissions through individual behaviour. In turn, urban GHG emissions,

as well as emissions from the supply chains of cities, are driven by the 
behaviour and consumption patterns of residents, with households 
accounting for over 60% of carbon emissions globally (Ivanova et al. 
2016). The exclusion of consumption-based emissions and emissions 
that occur outside of city boundaries as a result of urban activities, 
however, will lead to significant undercounting. For example, a study 
of 79 major cities found that about 41% of consumption-based 
carbon footprints (1.8 GtCO 2-eq of 4.4 GtCO 2-eq) occurred outside 
of city boundaries.
Changes in behaviour across all areas (e.g., transport, buildings, food) 
could reduce an individual’s emissions by 5.6–16.2% relative to the 
accumulated GHG emissions from 2011 to 2050 in a baseline scenario 
modelled with the Global Change Assessment Model (van  de Ven 
et al. 2018). In other models, behaviour change in transport and 
residential energy use could reduce emissions by 2 GtCO 2-eq in 2030

compared to 2019 (IEA 2020b) (Chapter  5). Voluntary behaviour 
change can support emissions reduction, but behaviours that are 
not convenient to change are unlikely to shift without changes to 
policy (Sköld et al. 2018). Cities can increase the capability of citizens 
to make sustainable choices by making these choices less onerous, 
through avenues such as changing urban form to increase locational 
and mobility options and providing feedback mechanisms to support 
socio-behavioural change.
Transport emissions can be reduced by options including 
telecommuting (0.3%), taking closer holidays (0.5%), avoiding 
short flights (0.5%), using public transit (0.7%), cycling (0.6%), 
car sharing (1.1%), and carpool commuting (1.2%); all reduction 
estimates reflect cumulative per capita emission savings relative to 
baseline emissions for the period 2011–2050, and assume immediate 
adoption of behavioural changes (van de Ven et al. 2018). Cities can

support voluntary shift to walking, cycling, and transit instead of car 
use through changes to urban form, such as TOD (Kamruzzaman et al. 
2015), increased density of form with co-location of activities (Ma et al. 
2015; Ding et al. 2017; Duranton and Turner 2018; Masoumi 2019), 
and greater intersection density and street integration (Koohsari 
et al. 2016). Mechanisms such as providing financial incentives or 
disincentives for car use can also be effective in reducing emissions 
(Wynes et al. 2018) (Section 8.4.2).
Adopting energy efficient practices in buildings could decrease global 
building energy demand in 2050 by 33–44% compared to a business-
as-usual scenario (Levesque et al. 2019). Reductions in home energy 
use can be achieved by reducing floor area (0.5–3.0%), utilising more 
efficient appliances and lighting (2.7–5.0%), optimising thermostat 
settings (8.3–11%), using efficient heating and cooling technologies

909
909
clothes washing (5.0–5.7%), and optimising dishwashing (1–1.1%) 
(Levesque et al. 2019). Building standards and mandates could work 
towards making these options required or more readily available and 
accessible. Residential appliance use, water heating, and thermostat 
settings can be influenced by feedback on energy use, particularly 
when paired with real-time feedback and/or instructions on how to 
reduce energy use (Kastner and Stern 2015; Stern et al. 2016; Wynes 
et al. 2018; Tiefenbeck et al. 2019). The energy-saving potentials of 
changing occupant behaviour can range between 10% and 25% 
for residential buildings, and between 5% and 30% for commercial 
buildings (Zhang et al. 2018). Households are more likely to invest 
in energy-related home technologies if they believe it financially 
benefits (rather than disadvantages) them, increases comfort, or will 
benefit the natural environment (Kastner and Stern 2015). Social

influences and availability of funding for household energy measures 
also support behaviour change (Kastner and Stern 2015).
8.4.5.1 Increasing Locational and Mobility Options
Spatial planning, urban form, and infrastructure can be utilised to 
deliberately increase both locational and mobility options for socio-
behavioural change in support of urban mitigation. The mitigation 
impacts of active travel can include a reduction of mobility-related 
lifecycle CO 2 emissions by about 0.5 tonnes over a  year when an 
average person cycles one trip per day more, and drives one trip per 
day less, for 200 days a year (Brand et al. 2021). Urban areas that 
develop and implement effective 15/20-minute city programmes 
are very likely to reduce urban energy use and multiply emission 
reductions, representing an important cascading effect.
Accessibility as a criterion widens the focus beyond work trips and 
VKT/VMT, paying attention to a broader set of destinations beyond

workplaces, as well as walking and biking trips or active travel. It 
holds promise for targeting and obtaining greater reductions in GHG 
emissions in household travel by providing access through walking, 
biking, and public transit. Accessibility as a criterion for urban form 
has been embedded in neighbourhood form models since at least 
the last century and in more recent decades in the ‘urban village’ 
concept of the New Urbanism (Duany and Plater-Zyberck 1991) and 
TODs (Calthorpe 1993). However, accessibility did not gain much 
traction in urban planning and transportation until the last decade. 
The experience of cities and metropolitan areas with the COVID-19 
pandemic has led to a further resurgence in interest and importance 
(Handy 2020; Hu et al. 2020), and it is becoming a criterion at the 
core of the concept of the 15/20-minute city (Moreno et al. 2021; 
Pozoukidou and Chatziyiannaki 2021). Initially, neighbourhoods

have been designed to provide quality, reliable services within 15 or 
20 minutes of active transport (i.e., walking or cycling), as well as 
a  variety of housing options and open space (Portland Bureau of 
Planning and Sustainability 2012; Pozoukidou and Chatziyiannaki 
2021; State Government of Victoria 2021). Community life circles 
strategy for urban areas has also emphasised walking access and 
health (Weng et al. 2019; Wu et al. 2021). The growing popularity 
of the 15/20-minute city movement has significant potential for 
reducing VMT/VKT and associated GHG emissions.8.4.5.2 Avoiding, Minimising, and Recycling Waste
The waste sector is a significant source of GHG emissions, particularly 
CH4 (Gonzalez-Valencia et al. 2016; Nisbet et al. 2019). Currently, the 
sector remains the largest contributor to urban emissions after the 
energy sector, even in low-carbon cities (Lu and Li 2019). Since waste 
management systems are usually under the control of municipal

authorities, they are a prime target for city-level mitigation efforts 
with co-benefits (EC 2015, 2020; Gharfalkar et al. 2015; Herrero and 
Vilella 2018; Zaman and Ahsan 2019). Despite general agreement 
on mitigation impacts, quantification remains challenging due to 
differing assumptions for system boundaries and challenges related 
to measuring avoided waste (Zaman and Lehmann 2013; Bernstad 
Saraiva Schott and Cánovas 2015; Matsuda et al. 2018).
The implementation of the waste hierarchy from waste prevention 
onward, as well as the effectiveness of waste separation at 
source, involves socio-behavioural options in the context of urban 
infrastructure (Sun et al. 2018a; Hunter et al. 2019). Managing 
and treating waste as close to the point of generation as possible, 
including distributed waste treatment facilities, can minimise 
transport-related emissions, congestion, and air pollution. Home 
composting and compact urban form can also reduce waste transport

emissions (Oliveira et al. 2017). Decentralised waste management 
can reinforce source-separation behaviour since the resulting 
benefits can be more visible (Eisted et al. 2009; Hoornweg and Bhada-
Tata 2012; Linzner and Lange 2013). Public acceptance for waste 
management is greatest when system costs for citizens are reduced, 
there is greater awareness of primary waste separation at source, 
and there are positive behavioural spill-overs across environmental 
policies (Milutinović et al. 2016; Boyer and Ramaswami 2017; Díaz-
Villavicencio et al. 2017; Slorach et al. 2020). In addition to the choice 
of technology, the costs of waste management options depend on 
the awareness of system users that can represent time-dependent 
costs (Khan et al. 2016; Chifari et al. 2017; Ranieri et al. 2018; Tomić 
and Schneider 2020). Waste management systems and the inclusion 
of materials from multiple urban sectors for alternative by-products

can increase scalability (Eriksson et al. 2015; Boyer and Ramaswami 
2017; D’Adamo et al. 2021). As a broader concept, circular economy 
approaches can contribute to managing waste (Box  12.8) with 
varying emissions impacts (Section 5.3.4).
The generation and composition of waste varies considerably from 
region to region and city to city. So do the levels of institutional 
management, infrastructure, and (informal) work in waste disposal 
activities. Depending on context, policy priorities are directed towards 
reducing waste generation and transforming waste to energy or 
other products in a circular economy (Diaz 2017; Ezeudu and Ezeudu 
2019; Joshi et al. 2019; Calderón Márquez and Rutkowski 2020; 
Fatimah et al. 2020). Similarly, waste generation, waste collection 
coverage, recycling, and composting rates, as well as the means of 
waste disposal and treatment, differ widely, including the logistics of 
urban waste management systems. Multiple factors influence waste

generation, and regions with similar urbanisation rates can generate 
different levels of waste per capita (Kaza et al. 2018).

910
910
Chapter 8 Urban Systems and Other Settlements8Under conventional practices, municipal solid waste is projected to 
increase by about 1.4 Gt between 2016 and 2050, reaching 3.4 Gt 
in 2050 (Kaza et al. 2018). Integrated policymaking can increase the 
energy, material, and emissions benefits in the waste management 
sector (Hjalmarsson 2015; Fang et al. 2017; Jiang et al. 2017). 
Organisational structure and programme administration poses 
demands for institutional capacity, governance, and cross-sectoral 
coordination for obtaining the maximum benefit (Hjalmarsson 
2015; Kalmykova et al. 2016; Conke 2018; Marino et al. 2018; 
Yang et al. 2018).
The informal sector plays a  critical role in waste management, 
particularly but not exclusively in developing countries (Linzner 
and Lange 2013; Dias 2016). Sharing of costs and benefits, 
and  transforming informality of waste recycling activities into 
programmes, can support distributional effects (Conke 2018;

Grové et al. 2018). Balancing centralised and decentralised waste 
management options along low-carbon objectives can address 
potential challenges in transforming informality (de Bercegol and 
Gowda 2019). Overall, the positive impacts of waste management 
on employment and economic growth can be increased when 
informality is transformed to stimulate employment opportunities for 
value-added products with an estimated 45 million jobs in the waste 
management sector by 2030 (Alzate-Arias et al. 2018; Coalition for 
Urban Transitions 2020; Soukiazis and Proença 2020).
8.4.6 Urban-Rural Linkages
Urban-rural linkages, especially through waste, food, and water, are 
prominent elements of the urban system, given that cities are open 
systems that depend on their hinterlands for imports and exports 
(Pichler et al. 2017), and include resources, products for industrial 
production or final use (Section 8.1.6). As supply chains are becoming

increasingly global in nature, so are the resource flows with the 
hinterlands of cities. In addition to measures within the jurisdictional 
boundaries of cities, cities can influence large upstream emissions 
through their supply chains, as well as through activities that rely 
on resources outside city limits. The dual strategy of implementing 
local actions and taking responsibility for the entire supply chains 
of imported and exported goods can reduce GHG emissions outside of  
a city’s administrative boundaries (Figure 8.15).
Waste prevention, minimisation, and management provides the 
potential of alleviating resource usage and upstream emissions from 
urban settlements (Swilling et al. 2018; Chen et al. 2020a; Harris 
et al. 2020). Integrated waste management and zero-waste targets 
can allow urban areas to maximise the mitigation potential while 
reducing pressures on land use and the environment. This mitigation

option reduces emissions due to (i) avoided emissions upstream in 
the supply chain of materials based on measures for recycling and the 
reuse of materials; (ii) avoided emissions due to land-use changes as 
well as emissions that are released into the atmosphere from waste 
disposal; and (ii) avoided primary energy (see Glossary) spending and 
emissions. Socio-behavioural change that reduces waste generation, 
combined with technology and infrastructure according to the 
waste hierarchy, can be especially effective. The mitigation potential of waste-to-energy depends on the technological choices that are 
undertaken (e.g.,  anaerobic digestion of the organic fraction), the 
emissions factor of the energy mix that it replaces, and its broader 
role within integrated municipal solid management practices 
(Eriksson et al. 2015; Potdar et al. 2016; Yu and Zhang 2016; Soares 
and Martins 2017; Alzate-Arias et al. 2018; Islam 2018). The climate

mitigation potential of anaerobic digestion plants can increase when 
power, heat and/or cold is co-produced (Thanopoulos et al. 2020).
Urban food systems, as well as city-regional production and 
distribution of food, factors into supply chains. Reducing food 
demand from urban hinterlands can have a  positive impact on 
energy and water demand for food production (Eigenbrod and 
Gruda 2015) (see ‘food system’ in Glossary). Managing food waste 
in urban areas through recycling or reduction of food waste at 
source of consumption would require behavioural change (Gu et al. 
2019). Urban governments could also support shifts towards more 
climate-friendly diets, including through procurement policies. These 
strategies have created economic opportunities or have enhanced 
food security while reducing the emissions that are associated with 
waste and the transportation of food. Strategies for managing 
food demand in urban areas would depend on the integration of food

systems in urban planning.
Urban and peri-urban agriculture and forestry is pursued by both 
developing and some developed country cities. There is increasing 
evidence for economically feasible, socially acceptable, and 
environmentally supportive urban and peri-urban agricultural 
enterprises although these differ between cities (Brown 2015; 
Eigenbrod and Gruda 2015; Blay-Palmer et al. 2019; De la Sota 
et al. 2019). The pathways include integrated crop-livestock systems, 
urban agroforestry systems, aquaculture-livestock-crop systems, and 
crop systems (Lwasa et al. 2015), while the mitigation potential of 
urban and peri-urban agriculture has medium agreement and low 
evidence. Strategies for urban food production in cities have also 
relied on recycling nutrients from urban waste and utilisation of 
harvested rainwater or wastewater.
Systems for water reallocation between rural areas and urban areas 
will require change by leveraging technological innovations for water

capture, water purification, and reducing water wastage either by 
plugging leakages or changing behaviour in regard to water use 
(Eigenbrod and Gruda 2015; Prior et al. 2018). Reducing energy 
use for urban water systems involves reducing energy requirements 
for water supply, purification, distribution, and drainage (Ahmad 
et al. 2020). Various levels of rainwater harvesting in urban settings 
for supplying end-use water demands or supporting urban food 
production can reduce municipal water demands, including by up to 
20% or more in Cape Town (Fisher-Jeffes et al. 2017).
8.4.7 Cross-sectoral Integration
There are two broad categories of urban mitigation strategies. 
One is from the perspective of key sectors, including clean energy, 
sustainable transport, and construction (Rocha et al. 2017; Álvarez 
Fernández 2018; Magueta et al. 2018; Seo et al. 2018; Waheed

911
911
et al. 2018); the coupling of these sectors can be enabled through 
electrification (Section  8.4.3.1). The other looks at the needs for 
emissions through a more systematic or fundamental understanding 
of urban design, urban form, and urban spatial planning (Wang et al. 
2017; Privitera et al. 2018), and proposes synergistic scenarios for 
their integration for carbon neutrality (Ravetz et al. 2020).
Single-sector analysis in low-carbon urban planning examines 
solutions in supply, demand, operations, and assets management 
either from technological efficiency or from a  system approach. 
For example, the deployment of renewable energy technologies 
for urban mitigation can be evaluated in detail and the transition 
to zero-carbon energy in energy systems and EVs in the transport 
sector can bring about a broad picture for harvesting substantial low-
carbon potentials through urban planning (high agreement, robust

evidence) (Álvarez Fernández 2018; Tarigan and Sagala 2018).
The effects of urban carbon lock-in on land use, energy demand, 
and emissions vary depending on national circumstances (Wang 
et al. 2017; Pan 2020). Systematic consideration of urban spatial 
planning and urban forms, such as polycentric urban regions and 
rational urban population density, is essential not only for liveability 
but also for achieving net-zero GHG emissions as it aims to shorten 
commuting distances and is able to make use of NBS for energy and 
resilience (high agreement, medium evidence). However, crucial 
knowledge gaps remain in this field. There is a shortage of consistent 
and comparable GHG emissions data at the city level and a  lack 
of in-depth understanding of how urban renewal and design can 
contribute to carbon neutrality (Mi et al. 2019).
An assessment of opportunities suggests that strategies for material 
efficiency that cross-cut sectors will have greater impact than those

that focus one-dimensionally on a  single sector (UNEP IRP 2020). 
In the urban context, this implies using less material by the design 
of physical infrastructure based on light-weighting and down-sizing, 
material substitution, prolonged use, as well as enhanced recycling, 
recovery, remanufacturing, and reuse of materials and related 
components. For example, light-weight design in residential buildings 
and passenger vehicles can enable about 20% reductions in lifecycle 
material-related GHG emissions (UNEP IRP 2020).
The context of urban areas as the nexus of both sectors (i.e., energy, 
and urban form and planning) underlines the role of urban planning 
and policies in contributing to reductions in material-related GHG 
emissions while enabling housing and mobility services for the benefit 
of inhabitants. In addition, combining resource efficiency measures 
with strategic densification can increase the GHG reduction potential

and lower resource impacts. While resource efficiency measures are 
estimated to reduce GHG emissions, land use, water consumption, 
and metal use impacts from a  lifecycle assessment perspective 
by 24–47% over a  baseline, combining resource efficiency with 
strategic densification can increase this range to about 36–54% 
over the baseline for a sample of 84 urban settlements worldwide 
(Swilling et al. 2018).
Evidence from a  systematic scoping of urban solutions further 
indicates that the GHG abatement potential of integrating measures across urban sectors is greater than the net sum of individual 
interventions due to the potential of realising synergies when realised 
in tandem, such as urban energy infrastructure and renewable 
energy (Sethi et al. 2020). Similarly, system-wide interventions, such 
as sustainable urban form, are important for increasing the GHG 
abatement potential of interventions based on individual sectoral

projects (Sethi et al. 2020). Overall, the pursuit of inter-linkages 
among urban  interventions is important for accelerating GHG 
reductions in urban areas (Sethi et al. 2020); this is also important for 
reducing reliance on carbon capture and storage technologies (CCS) 
at the global scale (Figures 8.15 and 8.21).
Currently, cross-sectoral integration is one of the main thematic 
areas of climate policy strategies among the actions that are adopted 
by signatories to an urban climate and energy network (Hsu et al. 
2020c). Although not as prevalent as those for efficiency, municipal 
administration, and urban planning measures (Hsu et al. 2020c), 
strategies that are cross-cutting in nature across sectors can provide 
important emission-saving opportunities for accelerating the pace 
of climate mitigation in urban areas. Cross-sectoral integration 
also involves mobilising urban actors to increase innovation in 
energy services and markets beyond individual energy efficiency

actions (Hsu et al. 2020c). Indeed, single-sector versus cross-sector 
strategies for 637 cities from a  developing country can enable an 
additional 15–36% contribution to the national climate mitigation 
reduction potential (Ramaswami et al. 2017a). The strategies at 
the urban level involved those for energy cascading and exchange 
of materials that connected waste, heat, and electricity strategies 
(Section 8.5 and Box 8.4).
The feasibility of upscaling multiple response options depends on 
the urban context as well as the stage of urban development, with 
certain stages providing additional opportunities over others (Dienst 
et al. 2015; Maier 2016; Affolderbach and Schulz 2017; Roldán-
Fontana et al. 2017; Zhao et al. 2017a; Beygo and Yüzer 2017; Lwasa 
2017; Pacheco-Torres et al. 2017; Alhamwi et al. 2018; Kang and Cho 
2018; Lin et al. 2018; Collaço et al. 2019) (Figures  8.19 and 8.21, 
and Section 8.SM.2).
8.5 Governance, Institutions, and Finance

Governance and other institutions act as core components to urban 
systems by facilitating and managing linkages between different 
sectors, geographic regions, and stakeholders. This position renders 
subnational governments and institutions key enablers of climate 
change mitigation (Seto et al. 2016, 2021; Hsu et al. 2018, 2020c; 
Vedeld et al. 2021) (Section 8.4.1). Indeed, since AR5 more research 
has emerged identifying these actors as vehicles through which 
to accelerate local-to-global efforts to decarbonise (IPCC 2018a; 
Hsu et al. 2020b; Salvia et al. 2021; Seto et al. 2021) (Chapter 13, 
Sections  4.2.3, 14.5.5, 15.6.5 and 16.4.7, and ‘subnational actors’ 
in Glossary). The current extent (Section  8.3.3) and projected rise 
(Section 8.3.4.2) in the urban share of global emissions underscores 
the transformative global impact of supporting urban climate 
governance and institutions (Section 8.5.2). Further, the multisector

approach to mitigation emphasised in this chapter (Sections  8.4

912
912
Chapter 8 Urban Systems and Other Settlements8and 8.6, and Figure 8.21) highlights the need for facilitation across 
sectors (Figure 8.19).
8.5.1 Multi-level Governance
IPCC SR1.5 identified multi-level governance (see Glossary for 
full definition) as an enabling condition that facilitates systemic 
transformation consistent with keeping global temperatures below 
1.5°C (IPCC 2018a, pp. 18–19). The involvement of governance at 
multiple levels is necessary to enable cities to plan and implement 
emissions reductions targets (high confidence) (Seto et al. 2021) 
(Boxes  8.3 and 8.4). Further, regional, national, and international 
climate goals are most impactful when local governments are 
involved alongside higher levels, rendering urban areas key foci of 
climate governance more broadly (high confidence) (Fuhr et al. 2018; 
Kern 2019; Hsu et al. 2020b).
Since AR5, multi-level governance has grown in influence within the

literature and has been defined as a framework for understanding the 
complex interaction of the many players involved in GHG generation 
and mitigation across geographic scales  – the ‘vertical’ levels of 
governance from neighbourhoods to the national and international 
levels, and those ‘horizontal’ networks of non-state and subnational 
actors at various scales (Corfee-Morlot et al. 2009; Seto et al. 2014; 
Castán Broto 2017b; Fuhr et al. 2018; Peng and Bai 2018; Kern 2019), 
as well as the complex linkages between them (Vedeld et al. 2021). 
This more inclusive understanding of climate governance provides 
multiple pathways through which urban actors can engage in climate 
policy to reduce emissions.
8.5.1.1 Multi-level, Multi-player Climate Governance 
in Practice
A multi-level, multi-player framework highlights both the 
opportunities and constraints on local autonomy to engage in urban 
mitigation efforts (Castán Broto 2017b; Fuhr et al. 2018; Vedeld

et al. 2021). When multiple actors  – national, regional, and urban 
policymakers, as well as non-state actors and civil society  – work 
together to exploit the opportunities, it leads to the most impactful 
mitigation gains (Melica et al. 2018). This framework also highlights 
the multiple paths and potential synergies available to actors who 
wish to pursue mitigation policies despite not having a full slate of 
enabling conditions (Castán Broto 2017b; Keller 2017; Fuhr et al. 
2018; Hsu et al. 2020b,a; Seto et al. 2021).
For example, Sections 8.4.3. and 8.4.5 highlight how instigating the 
electrification of urban energy systems requires a ‘layered’ approach 
to policy implementation across different levels of governance (see 
Section  8.4.3.1 for specific policy mechanisms associated with 
electrification), with cities playing a  key role in setting standards, 
particularly through mechanisms like building codes (Hsu et al.

2020c; Salvia et al. 2021), as well as through facilitation between 
stakeholders (e.g., consumers, government, utilities) to advocate for 
zero-emissions targets (Linton et al. 2021; Seto et al. 2021). Local 
governments can minimise trade-offs associated with electrification 
technologies by enabling circular economy practices and opportunities (Pan et al. 2015; Gaustad et al. 2018; Sovacool et al. 
2020). These include public-private partnerships between consumers 
and producers, financial and institutional support, and networking 
for stakeholders like entrepreneurs, so as to increase accessibility and 
efficiency of recycling for consumers by providing a clear path from 
consumer waste back to the producers (Pan et al. 2015; Prendeville 
et al. 2018; Fratini et al. 2019). Box  8.3 discusses the mitigation 
benefits of coordination between local and central government in 
the context of Shanghai’s GHG emissions reduction goals.

Still, there are constraints on urban autonomy that might limit urban 
mitigation influence. The capacity of subnational governments to 
autonomously pursue emissions reductions on their own depends 
on different political systems and other aspects of multi-level 
governance, such as innovation, legitimacy, and institutional fit, 
as well as the resources, capacity, and knowledge available to 
subnational technicians and other officials (Widerberg and Pattberg 
2015; Valente de Macedo et al. 2016; Green 2017; Roger et al. 2017). 
Financing is considered one of the most crucial facets of urban 
climate change mitigation. It is also considered one of the biggest 
barriers, given the limited financial capacities of local and regional 
governments (Sections 8.5.4 and 8.5.5).
When sufficient local autonomy is present, local policies have the 
ability to upscale to higher levels of authority, imparting influence 
at higher geographic scales. Established urban climate leaders with

large institutional capacity can influence small and mid-sized cities, 
or other urban areas with less institutional capacity, to enact effective 
climate policies, by engaging with those cities through transnational 
networks and by adopting a public presence of climate leadership 
(Chan et al. 2015; Kern 2019; Seto et al. 2021) (Section  8.5.3). 
Increasingly, subnational actors are also influencing their national 
and international governments through lobbying efforts that call 
on them to adopt more ambitious climate goals and provide more 
support for subnational GHG mitigation efforts (Linton et al. 2021; 
Seto et al. 2021). These dynamics underscore the importance of 
relative local autonomy in urban GHG mitigation policy. They also 
highlight the growing recognition of subnational authorities’ role in 
climate change mitigation by national and international authorities.
The confluence of political will and policy action at the local level,

and growing resources offered through municipal and regional 
networks and agreements, have provided a  platform for urban 
actors to engage in international climate policy (Section  8.5.3). 
This phenomenon is recognised in the Paris Agreement, which, for 
the first time in a multilateral climate treaty, referenced the crucial 
role subnational and non-state actors like local communities have in 
meeting the goals set forth in the agreement (UNFCCC 2015). The 
Durban Platform for Enhanced Action (Widerberg and Pattberg 2015), 
as well as UN-Habitat’s NUA and the 2030 Development Agenda, are 
other examples of the international sphere elevating the local level 
to global influence (Fuhr et al. 2018). Another facet of local-to-global 
action is the emergence of International Cooperative Initiatives 
(ICIs) (Widerberg and Pattberg 2015). One such ICI, the City Hall 
Declaration, was signed alongside the Paris Agreement during the

first Climate Summit for Local Leaders. Signatories included hundreds 
of local government leaders, in partnership with private sector

913
913
representatives and NGOs, who pledged to enact the goals of the 
Paris Agreement through their own spheres of influence (Cities for 
Climate 2015). Similar Summits have been held at each subsequent 
UNFCCC COP (Hsu et al. 2018). Like transnational climate networks, these platforms provide key opportunities to local governments to 
further their own mitigation goals, engage in knowledge transfer 
with other cities and regions, and shape policies at higher levels of 
authority (Cities for Climate 2015; Castán Broto 2017b).
Box 8.3: Coordination of Fragmented Policymaking for Low-carbon Urban Development: 
Example from Shanghai, China
As a growing megacity in the Global South, Shanghai represents the challenge of becoming low carbon despite its economic growth 
and population size (Chen et al. 2017). Shanghai was designated as one of the pilot low-carbon cities by the central government.

The city utilised a coordination mechanism for joining fragmented policymaking across the city’s economy, energy, and environment. 
The coordination mechanism was supported by a direct fund that enabled implementation of cross-sector policies beyond a single-
sector focus across multiple institutions while increasing capacity for enabling a  low-carbon transition for urban sustainability 
(Peng and Bai 2020).
Implementation and governance process
In Shanghai, coordination between the central and local governments had an instrumental role for encouraging low-carbon policy 
experimentation. Using a nested governance framework, the central government provided target setting and performance evaluation 
while the local government initiated pilot projects for low-carbon development. The policy practices in Shanghai surpassed the top-
down targets and annual reporting of GHG emissions, including carbon labelling standards at the local level, pilot programme for

transitioning sub-urban areas, and the engagement of public utilities (Peng and Bai 2018).
Towards low-carbon urban development
New policy measures in Shanghai were built upon a  series of related policies from earlier, ranging from general energy saving 
measures to air pollution reduction. This provided a  continuum of policy learning for implementing low-carbon policy measures. 
An earlier policy was a green electricity scheme based on the Jade Electricity Program while the need for greater public awareness was 
one aspect requiring further attention in policy design (Baeumler et al. 2012), supporting policy-learning for policies later on. The key 
point here is that low-carbon policies were built on and learned from earlier policies with similar goals.
Outcomes and impacts of the policy mix
Trends during 1998 and 2015 indicate that energy intensity decreased from about 130 tonnes per million RMB to about 45 tonnes

per million RMB and carbon intensity decreased from about 0.35 Mt per billion RMB to 0.10 Mt per billion RMB (Peng and Bai 2018). 
These impacts on energy and carbon intensities represent progress, while challenges remain. Among the challenges are the need 
for investment in low-carbon technology and increases in urban carbon sinks (Yang and Li 2018) while cross-sector interaction and 
complexity are increasing.
8.5.2 Mitigation Potential of Urban Subnational Actors
A significant research question that has been paid more attention in 
both the scientific and policy communities is related to subnational 
actors’ role in and contribution to global climate mitigation. The 2018 
UN Environment Programme’s (UNEP) annual Emissions Gap report 
in 2018 included for the first time a  special chapter on subnational 
and non-state (i.e.,  businesses and private) actors and assessed the 
landscape of studies aiming to quantify their contributions to global

climate mitigation. Non-state action on net-zero GHG or CO 2 emissions 
continues to be emphasised (UNEP 2021) (Box 8.4). There has been 
an increase in the number of studies aiming to quantify the overall 
aggregate mitigation impact of subnational climate action globally. 
Estimates for the significance of their impact vary widely, from up to 30 
MtCO 2-eq from 25 cities in the United States in 2030 (Roelfsema 2017), 
to a  2.3 GtCO 2-eq reduction in 2030 compared to a  current policy 
scenario from over 10,239 cities participating in GCoM (Hsu et al. 2018; 
GCoM 2019). For regional governments, the Under 2 Coalition, which includes 260 governments pledging goals to keep global temperature 
rise below 2°C, is estimated to reduce emissions by 4.2 GtCO 2-eq in 
2030, compared to a current policy scenario (Kuramochi et al. 2020).
Some studies suggest that subnational mitigation actions (Roelfsema 
2017; Kuramochi et al. 2020) are in addition to national government

mitigation efforts and can therefore reduce emissions even beyond 
current national policies, helping to ‘bridge the gap’ between emissions 
trajectories consistent with least-cost scenarios for limiting temperature 
rise below 1.5°C or 2°C (Blok et al. 2012). In some countries, such as 
the United States, where national climate policies have been curtailed, 
the potential for cities’ and regions’ emissions reduction pledges to 
make up the country’s Nationally Determined Contribution under the 
Paris Agreement is assessed to be significant (Kuramochi et al. 2020).
These estimates are also often contingent on assumptions that 
subnational actors fulfil their pledges and that these actions do 
not result in rollbacks in climate action (i.e., weakening of national

914
914
Chapter 8 Urban Systems and Other Settlements8climate legislation) from other actors or rebound in emissions growth 
elsewhere, but data tracking or quantifying the likelihood of their 
implementation remains rare (Chan et al. 2018; Hsu et al. 2019; Hale 
et al. 2020; Kuramochi et al. 2020). Reporting networks may attract 
high-performing cities, suggesting an artificially high level of cities 
interested in taking climate action or piloting solutions that may not 
be effective elsewhere (van der Heijden 2018). These studies could 
also present a  conservative view of potential mitigation impact 
because they draw upon publicly reported mitigation actions and 
inventory data, excluding subnational actors that may be taking 
actions but not reporting them (Kuramochi et al. 2020). The nuances 
of likelihood, and the drivers and obstacles of climate action across 
different contexts is a key source of uncertainty around subnational 
actors’ mitigation impacts.

8.5.3 Urban Climate Networks and 
Transnational Governance
As of 2019, more than 10,000 cities and regions (Hsu et al. 2020a) have 
recorded participation in a transnational or cooperative climate action 
network, which are voluntary membership networks of a  range of 
subnational governments such as cities, as well as regional governments 
like states and provinces (Hsu et al. 2020a). These organisations, often 
operating across and between national boundaries, entail some type 
of action on climate change. Among the most prominent climate 
networks are GCoM, ICLEI, and C40, all of which ask their members to 
adopt emission reduction commitments, develop climate action plans, 
and regularly report on emissions inventories.
Municipal and regional networks and agreements have provided 
a platform for urban actors to engage in international climate policy 
(Fraundorfer 2017; Keller 2017; Fuhr et al. 2018; Hsu et al. 2018,

2020b; Westman and Broto 2018; Kern 2019; Seto et al. 2021). Their 
impact comes through (i) providing resources for cities and regions to reduce their GHG emissions and improve environmental quality 
more generally, independent of national policy; (ii) encouraging 
knowledge transfer between member cities and regions; and 
(iii) acting as platforms of national and international policy influence 
(Castán Broto 2017b; Fuhr et al. 2018).
Subnational governments that participate in transnational 
climate networks, however, are primarily located in developed 
countries, particularly Europe and North America, with far less 
representation in developing countries. In one of the largest studies 
of subnational climate mitigation action, more than 93% of just over 
6000 quantifiable subnational climate commitments come from cities 
and regions based in the European Union (NewClimate Institute et al. 
2019). Such gaps in geographic coverage have been attributed to

factors such as the dominating role of Global North actors in the 
convening and diffusion of ‘best practices’ related to climate action 
(Bouteligier 2013), or the more limited autonomy or ability of 
subnational or non-state actors in Global South countries to define 
boundaries and interests separately from national governments, 
particularly those that exercise top-down decision-making or have 
vertically integrated governance structures (Bulkeley et al. 2012). 
Many of the participating subnational actors from under-represented 
regions are large megacities (of 10 million people or more) that will 
play a pivotal role in shaping emissions trajectories (Data Driven Yale 
et al. 2018; NewClimate Institute et al. 2019).
While these networks have proven to be an important resource 
in local-level mitigation, their long-term effects and impact at 
larger scales is less certain (Valente de Macedo et al. 2016; Fuhr 
et al. 2018). Their influence is most effective when multiple levels

of governance are aligned in mitigation policy. Nevertheless, these 
groups have become essential resources to cities and regions with 
limited institutional capacity and support (for more on transnational 
climate networks and transnational governance more broadly, see 
Sections 13.5 and 14.5).
Box 8.4: Net-zero Targets and Urban Settlements
Around the world, net-zero-emissions targets, whether economy-wide or targeting a specific sector (e.g., transport, buildings) or 
emissions scope (e.g., direct scope 1, or both scope 1 and 2), have been adopted by at least 826 cities and 103 regions that represent 
11% of the global population with 846 million people across six continents (NewClimate Institute and Data-Driven EnviroLab 2020). 
In some countries, the share of such cities and regions has reached a critical mass by representing more than 70% of their total 
populations with or without net-zero-emissions targets at the national level.

In some cases, the scope of these targets extends beyond net-zero emissions from any given sector based on direct emissions (see 
Glossary) and encompass downstream emissions from a consumption-based perspective with 195 targets that are found to represent 
economy-wide targets. These commitments range from ‘carbon neutrality’ (see Glossary) or net-zero GHG emissions targets, which 
entail near elimination of cities’ own direct or electricity-based emissions but could involve some type of carbon offsetting, to more 
stringent net-zero-emissions goals (Data-Driven EnviroLab and NewClimate Institute 2020) (for related definitions, such as ‘carbon 
neutrality’, ‘net-zero CO 2 emissions’, ‘net-zero GHG emissions’, and ‘offset’, see Glossary).
Currently, 43% of the urban areas with net-zero-emissions targets have also put into place related action plans while about 24% have

integrated net-zero-emissions targets into formal policies and legislation (Data-Driven EnviroLab and NewClimate Institute 2020). 
Moreover, thousands of urban areas have adopted renewable energy-specific targets for power, heating/cooling and transport and 
about 600 cities are pursuing 100% renewable energy targets (REN21 2019, 2021) with some cities already achieving it.

915
915
8.5.4 Financing Urban Mitigation
Meeting the goals of the Paris Agreement will require fundamental 
changes that will be most successful when cities work together 
with provincial and national leadership and legislation, third-sector 
leadership, transformative action, and supportive financing. Urban 
governments often obtain their powers from provincial, state and/
or national governments, and are subjected to laws and regulations 
to regulate development and implement infrastructure. In addition, 
the sources of revenue are often set at these levels so that many 
urban governments rely on state/provincial and national government 
funds for improving infrastructure, especially transit infrastructure. 
The increasing financialisation of urban infrastructures is another 
factor that can make it more difficult for local governments to 
determine infrastructure choices (O’Brien et al. 2019). Urban transit

system operations, in particular, are heavily subsidised in many 
countries, both locally and by higher levels of government. As a result 
of this interplay of policy and legal powers among various levels of 
government, the lock-in nature of urban infrastructures and built 
environments will require multi-level governance responses to ensure 
meeting decarbonisation targets. The reliance on state and national 
policy and/or funding can accelerate or impede the decarbonisation 
of urban environments (McCarney et al. 2011; McCarney 2019).
The world’s infrastructure spending is expected to more than double 
from 2015 to 2030 under a low-carbon and climate-resilient scenario. 
More than 70% of the infrastructure will concentrate in urban areas 
by requiring USD4.5–5.4 trillion per year (CCFLA 2015).  However, 
today’s climate finance flows for cities or ‘urban climate finance’, 
estimated at USD384 billion annually on average in 2017/18, are

insufficient to meet the USD4.5–5.4 trillion annual investment needs 
for urban mitigation actions across key sectors (CCFLA 2015; CPI and 
World Bank 2021; Negreiros et al. 2021). Low-carbon urban form 
(e.g., compact, high-density, and mixed-use characteristics) is likely 
to economise spending in infrastructure along with the application 
of new technologies and renewable energies that would be able 
to recover the increasing upfront cost of low-carbon infrastructure 
from more efficient operating and energy savings (medium evidence, 
high agreement) (Global Commission on the Economy and Climate 
2014; Foxon et al. 2015; Bhattacharya et al. 2016; Floater et al. 2017; 
Colenbrander et al. 2018b).
Governments have traditionally financed a  large proportion of 
infrastructure investment. When budget powers remain largely 
centralised, intergovernmental transfers will be needed to fund

low-carbon infrastructure in cities. During the COVID-19 pandemic, cities tend to rely more on intergovernmental transfers in the form 
of stimulus packages for economic recovery. Nonetheless, the risk 
of high carbon lock-ins is likely to increase in rapidly growing cities 
if long-term urban mitigation strategies are not incorporated into 
short-term economic recovery actions (Granoff et al. 2016; Floater 
et al. 2017; Colenbrander et al. 2018b; CPI and World Bank 2021; 
Negreiros et al. 2021). Indeed, large and complex infrastructure 
projects for urban mitigation are often beyond the capacity of both 
national government and local municipality budgets. Additionally, the 
COVID-19 pandemic necessitates large government expenditures for 
public health programme and decimates municipal revenue sources 
for urban infrastructure projects in cities.
To meet the multi-trillion-dollar annual investment needs in urban 
areas, cities in partnership with international institutions, national

governments, and local stakeholders increasingly play a  pivotal 
role in mobilising global climate finance resources for a  range of 
low-carbon infrastructure projects and related urban land use and 
spatial planning programmes across key sectors (high confidence). 
In particular, national governments are expected to set up enabling 
conditions for the mobilisation of urban climate finance resource by 
articulating various goals and strategies, improving pricing, regulation 
and standards, and developing investment vehicles and risk sharing 
instruments (Qureshi 2015; Bielenberg et al. 2016; Granoff et al. 2016; 
Floater et al. 2017; Sudmant et al. 2017; Colenbrander et al. 2018b; 
Zhan and de Jong 2018; Hadfield and Cook 2019; CPI and World 
Bank 2021; Negreiros et al. 2021).
Indeed, 75% of the global climate finance for both mitigation and 
adaptation in 2017 and 2018 took the form of commercial financing 
(e.g.,  balance sheets, commercial-rate loans, equity), while 25%

came in the form of concessionary financing (e.g.,  grants, below-
market-rate loans). However, cities in developing countries are 
facing difficulty making use of commercial financing and gaining 
access to international credit markets. Cities without international 
creditworthiness currently rely on local sources, including 
domestic commercial banks (medium evidence, high agreement) 
(Global Commission on the Economy and Climate 2014; CCFLA 2015; 
Floater et al. 2017; Buchner et al. 2019).
Cities with creditworthiness have rapidly become issuers of ‘green 
bonds’ eligible for renewable energy, energy efficiency, low-carbon 
transport, sustainable water, waste, and pollution, and other various 
climate mitigation projects across the global regions since 2013. 
The world’s green bond market reached USD1 trillion in cumulative 
issuance, with issuance of USD280 billion in 2020, during the Box 8.4 (continued)

The extent of realising and implementing these targets with the collective contribution of urban areas to net-zero-emissions scenarios 
with sufficient timing and pace of emission reductions will require a coordinated integration of sectors, strategies, and innovations 
(Swilling et al. 2018; Hsu et al. 2020c; Sethi et al. 2020; UNEP IRP 2020). In turn, the transformation of urban systems can significantly 
impact net-zero-emissions trajectories within mitigation pathways. Institutional capacity, governance, financing, and cross-sector 
coordination is crucial for enabling and accelerating urban actions for rapid decarbonisation.

916
916
Chapter 8 Urban Systems and Other Settlements8COVID-19 pandemic. While green municipal bonds still account 
for a small share of the whole green bond market in 2020, scale is 
predicted to grow further in emerging markets over the coming years. 
Green municipal bonds have great potential for cities to expand and 
diversify their investor base. In addition, the process of issuing green 
municipal bonds is expected to promote cross-sector cooperation 
within a city by bringing together various agencies responsible for 
finance, climate change, infrastructure, planning and design, and 
operation. Indeed, the demand for green bonds presently outstrips 
supply as being constantly over-subscripted (robust evidence, high 
agreement) (Global Commission on the Economy and Climate 2014; 
Saha and D’Almeida 2017; Amundi and IFC 2021).
On the other hand, cities without creditworthiness face difficulty 
making use of commercial financing and getting access to

international credit markets (Global Commission on the Economy 
and Climate 2014; CCFLA 2015; Floater et al. 2017). The lack of 
creditworthiness is one of the main problems preventing cities 
from issuing green municipal bonds in developing countries. As 
a prerequisite for the application of municipal debt-financing, it is an 
essential condition for cities to ensure sufficient own revenues from 
low-carbon urbanisation, or the default risk becomes too high for 
potential investors. Indeed, many cities in developed countries and 
emerging economies have already accumulated substantial amounts 
of debts through bond insurances, and ongoing debt payments 
prevent new investments in low-carbon infrastructure projects.
National governments and multilateral development banks might 
be able to provide support for debt financing by developing 
municipal creditworthiness programme and issuing sovereign 
bonds or providing national guarantees for investors (Floater et al.

2017). Another problem with green municipal bonds is the lack of 
aggregation mechanisms to support various small-scale projects in 
cities. Asset-backed securities are likely to reduce the default risk for 
investors through portfolio diversification and create robust pipelines 
for a bundle of small-scale projects (Granoff et al. 2016; Floater et al. 
2017; Saha and D’Almeida 2017).
In principle, the upfront capital costs of various low-carbon 
infrastructure projects, including the costs of urban climate finance 
(dividend and interest payments), are eventually transferred to users 
and other stakeholders in the forms of taxes, charges, fees, and other 
revenue sources. Nevertheless, small cities in developing countries 
are likely to have a small revenue base, most of which is committed 
to recurring operating costs, associated with weak revenue collection 
and management systems. In recent years, there has been scope to

apply not only user-based but also land-based funding instruments 
for the recovery of upfront capital costs (Braun and Hazelroth 2015; 
Kościelniak and Górka 2016; Floater et al. 2017; Colenbrander et al. 
2018b; Zhan and de Jong 2018; Zhan et al. 2018a).
In practice, however, the application of land-based or ‘land value 
capture’ funding requires cities to arrange various instruments, 
including property (both land and building taxes), betterment levies/
special assessments, impact fees (exactions), tax increment financing, 
land readjustment/land pooling, sales of public land/development 
rights, recurring lease payments, and transfer taxes/stamp duties, across sectors in different urban contexts (Suzuki et al. 2015; 
Chapman 2017; Walters and Gaunter 2017; Berrisford et al. 2018). 
Land value capture is expected not only for cities to generate 
additional revenue streams but also to prevent low-density urban 
expansion around city-fringe locations. Inversely, land value capture

is supposed to perform well when accompanied by low-carbon urban 
form and private real estate investments along with the application 
of green building technologies (robust evidence, high agreement) 
(Suzuki et al. 2015; Floater et al. 2017; Colenbrander et al. 2018b).
For the implementation of land-based funding, property rights are 
essential. However, weak urban-rural governance leads to corruption 
in land occupancy and administration, especially in developing 
countries with no land information system or less reliable paper-
based land records under a  centralised registration system. The 
lack of adequate property rights seriously discourages low-carbon 
infrastructure and real estate investments in growing cities.
The emerging application of blockchain technology for land registry 
and real estate investment is expected to change the governance 
framework, administrative feasibility, allocative efficiency, public

accountability, and political acceptability of land-based funding 
in cities across developed countries, emerging economies, and 
developing countries (Graglia and Mellon 2018; Kshetri and Voas 
2018). Particularly, the concept of a transparent, decentralised public 
ledger is adapted to facilitate value-added property transactions on 
a  P2P basis without centralised intermediate parties and produce 
land-based funding opportunities for low-carbon infrastructure and 
real estate development district-wide and city-wide in unconventional 
ways (Veuger 2017; Nasarre-Aznar 2018).
The consolidation of local transaction records into national or 
supranational registries is likely to support large-scale land 
formalisation, but most pilot programmes are not yet at the scale 
(Graglia and Mellon 2018). Moreover, the potential application 
of blockchain for land-based funding instruments is possibly 
associated with urban form attributes, such as density, compactness,

and land-use mixture, to disincentivise urban expansion and 
emissions growth around city-fringe locations (medium confidence) 
(Allam and Jones 2019).
8.5.5 Barriers and Enablers for Implementation
Irrespective of geography or development level, many cities face 
similar climate governance challenges such as lacking institutional, 
financial, and technical capacities (Gouldson et al. 2015; Hickmann 
and Stehle 2017; Sharifi et al. 2017; Fuhr et al. 2018). Large-scale 
system transformations are also deeply influenced by factors outside 
governance and institutions, such as private interests and power 
dynamics (Jaglin 2014; Tyfield 2014). In some cases, these private 
interests are tied up with international flows of capital. At the local 
level, a  lack of empowerment, high upfront costs, inadequate and 
uncertain funding for mitigation, diverse and conflicting policy 
objectives, multiple agencies and actors with diverse interests, high

levels of informality, and a  siloed approach to climate action are 
constraining factors to mainstreaming climate action (Beermann

917
917
et al. 2016; Gouldson et al. 2016; Pathak and Mahadevia 2018; 
Khosla and Bhardwaj 2019).
Yet urban mitigation options that can be implemented to transform 
urban systems involve the interplay of multiple enablers and 
barriers. Based on a  framework for assessing feasibility from 
a multi-dimensional perspective, feasibility is malleable and various 
enablers can be brought into play to increase the implementation of 
mitigation options. The scope of this assessment enables an approach 
for considering multiple aspects that have an impact on feasibility 
as a tool for policy support (Singh et al. 2020). In Figure 8.19, the 
assessment framework that is based on geophysical, environmental-
ecological, technological, economic, socio-cultural, and institutional 
dimensions is applied to identify the enablers and/or barriers in 
implementing mitigation options in urban systems. The feasibility of

options may differ across context, time, and scale (Section 8.SM.2). 
The line of sight upon which the assessment is based includes urban 
case studies (Lamb et al. 2019) and assessments of land use and 
spatial planning in IPCC SR1.5 (IPCC 2018a).
Across the enablers and barriers of different mitigation options, 
urban land use and spatial planning for increasing co-located 
densities in urban areas has positive impacts in multiple indicators, 
particularly reducing land use and preserving carbon sinks when the 
growth in urban extent is reduced and avoided, which if brought 
into interplay in decision-making, can support the enablers for its 
implementation. Improvements in air quality are possible when 
higher urban densities are combined with modes of active transport, 
electrified mobility as well as urban green and blue infrastructure 
(Sections 8.3.4, 8.4 and 8.6). The demands on geophysical resources, 
including materials for urban development, will depend on whether

additional strategies are in place with largely negative impacts under 
conventional practices. The technological scalability of multiple 
urban mitigation options is favourable while varying according to 
the level of existing urban development and scale of implementation 
(Tables 8.SM.3 and 8.SM.4).
Similarly, multiple mitigation options have positive impacts on 
employment and economic growth, especially when urban densities 
enable productivity. Possible distributional effects, including 
availability of affordable accommodation and access to greenspace, 
are best addressed when urban policy packages combine more 
than one policy objective. Such an approach can provide greater 
support to urban mitigation efforts with progress towards shifting 
urban development to sustainability. The electrification of the urban 
energy system involves multiple enablers that support the feasibility 
of this mitigation option, including positive impacts on health and

well-being. In addition, increases in urban densities can support 
the planning of district heating and cooling networks that can 
decarbonize the built environment at scale with technology readiness 
levels increasing for lower temperature supply options. Preventing, 
minimising, and managing waste as an urban mitigation option can 
be enabled when informality in the sector is transformed to secure 
employment effects and value-addition based on the more circular 
use of resources (Sections 8.4.3 and 8.4.5, and Tables 8.SM.3 and 
8.SM.4 in Supplementary Material 8.2).As a combined evaluation, integrating multiple mitigation options in 
urban systems involves the greatest requirement for strengthening 
institutional capacity and governance through cross-sectoral 
coordination. Notably, integrated action requires significant effort 
to coordinate sectors and strategies across urban growth typologies 
(Sections 8.4 and 8.6, and Figure 8.21). Institutional capacity, if not

strengthened to a suitable level to handle this process – especially 
to break out of carbon lock-in  – can fall short of the efforts this 
entails. These conditions can pose barriers for realising cross-sectoral 
coordination while the formation of partnerships and stakeholder 
engagement take place as important enablers. Overcoming 
institutional challenges for cross-sectoral coordination can support 
realising synergies among the benefits that each mitigation option 
can offer within and across urban systems, including for the SDGs. 
These include those that can be involved in co-located and walkable 
urban form together with decarbonising and electrifying the urban 
energy system as well as urban green and blue infrastructure, 
providing the basis for more liveable, resource efficient and compact 
urban development with benefits for urban inhabitants (Section 8.2).

918
918
Figure 8.19: Feasibility assessment based on the enablers and barriers of implementing mitigation options for urban systems across multiple dimensions.  The figure summarises the extent to which different factors 
would enable or inhibit the deployment of mitigation options in urban systems. These factors are assessed systematically based on 18 indicators in 6 dimensions (geophysical, environmental-ecological, technological, economic, socio-cultural, 
and institutional dimensions). Blue bars indicate the extent to which the indicator enables the implementation of the option (E) and orange bars indicate the extent to which an indicator is a barrier (B) to the deployment of the option, relative 
to the maximum possible barriers and enablers assessed. The shading indicates the level of confidence, with darker shading signifying higher levels of confidence. Supplementary Material 8.SM.2 provides an overview of the extent to which

the feasibility of options may differ across context, time and scale of implementation (Table 8.SM.3) and includes line of sight upon which the assessment is based (Table 8.SM.4). The line of sight builds upon urban case studies in (Lamb et al. 
2019) and assessments for land use and urban planning (IPCC 2018a) involving 414 references. The assessment method is further explained in Annex II, Section 11.
Land use
readiness

Electrification of the urban energy system
Integrating sectors, strategies and innovations
0 50 100

919
919
8.6 A Roadmap for Integrating 
Mitigation Strategies for Different 
Urbanisation Typologies
The most effective and appropriate packages of mitigation 
strategies will vary depending on several dimensions of a city. This 
section brings together the urban mitigation options described 
in Section  8.4 and assesses the range of mitigation potentials for 
different types of cities. There is consensus in the literature that 
mitigation strategies are most effective when multiple interventions 
are coupled together. Urban-scale interventions that implement 
multiple strategies concurrently through policy packages are more 
effective and have greater emissions savings than when single 
interventions are implemented separately. This is because a  city-
wide strategy can have cascading effects across sectors, that have 
multiplicative effects on GHG emissions reduction within and outside

a city’s administrative boundaries. Therefore, city-scale strategies can 
reduce more emissions than the net sum of individual interventions, 
particularly if multiple scales of governance are included (Sections 8.4 
and 8.5). Furthermore, cities have the ability to implement policy 
packages across sectors using an urban systems approach, such as 
through planning, particularly those that affect key infrastructures 
(Figures 8.15, 8.17 and 8.22).
The way that cities are laid out and built will shape the entry 
points for realising systemic transformation across urban form and 
infrastructure, energy systems, and supply chains. Section  8.3.1 
discusses the ongoing trend of rapid urbanisation  – and how it varies through different forms of urban development or ‘typologies’ 
(Figure 8.6). Below, Figure 8.20 distils the typologies of urban growth 
across three categories: emerging, rapidly growing, and established. 
Urban growth is relatively stabilised in established urban areas with

mature urban form while newly taking shape in emerging urban areas. 
In contrast, rapidly growing urban areas experience pronounced 
changes in outward and/or upward growth. These typologies are not 
mutually exclusive, and can co-exist within an urban system; cities 
typically encompass a spectrum of development, with multiple types 
of urban form and various typologies (Mahtta et al. 2019).
Taken together, urban form (Figure  8.16) and growth typology 
(Figure 8.20) can act as a roadmap for cities or sub-city communities 
looking to identify their urban context and, by extension, the 
mitigation opportunities with the greatest potential to reduce GHG 
emissions. Specifically, this considers whether a  city is established 
with existing and managed infrastructure; rapidly growing with 
new and actively developing infrastructure; or emerging with large 
amounts of infrastructure build-up. The long lifespan of urban

infrastructure locks in behaviour and committed emissions. Therefore, 
the sequencing of mitigation strategies is important for determining 
emissions savings in the short and long term. Hence, different types 
of cities will have different mitigation pathways, depending upon 
a city’s urban form and state of that city’s urban development and 
infrastructure; the policy packages and implementation plan that 
provide the highest mitigation potential for rapidly growing cities with 
new infrastructures will differ from those for established cities  
with existing infrastructure.
Figure 8.20: Urban growth typologies define the main patterns of urban development.  Emerging urban areas are undergoing the buildup of new infrastructure. 
These are new urban areas that are budding out. Rapidly growing urban areas are undergoing significant changes in either outward and/or upward growth, accompanied by

large-scale development of new urban infrastructure. Established urban areas are relatively stable with mature urban form and existing urban infrastructures. Each of these 
typologies represents different levels of economic development and state of urbanisation. Rapidly growing urban areas that are building up through vertical development are 
often those with higher levels of economic development. Rapidly growing urban areas that are building outward through horizontal expansion are found at lower levels of 
economic development and are land intensive. Like with urban form, different areas of a single city can undergo different growth typologies. Therefore a city will be comprised 
of multiple urban growth typologies. Source: synthesized from Mahtta et al. (2019) and Lall et al. (2021).

920
920
Mitigation Potential
Spatial Planning, Urban Form,
and InfrastructureElectrification and Net-Zero-
Emissions ResourcesUrban Green and Blue
InfrastructureSocio-Behavioural
Aspects
Figure 8.21: Priorities and potentials for packages of urban mitigation strategies across typologies of urban growth (Figure 8.20) and urban form 
(Figure 8.16).  The horizontal axis represents urban growth typologies based on emerging, rapidly growing, and established urban areas. The vertical axis shows the continuum 
of urban form, from compact and walkable, to dispersed and auto-centric. Urban areas can first locate their relative positioning in this space according to their predominant style 
of urban growth and urban form. The urban mitigation options are bundled across four broad sectors of mitigation strategies: (i) spatial planning, urban form, and infrastructure

(blue); (ii) electrification and net-zero-emissions resources (yellow); (iii) urban green and blue infrastructure (green); and (iv) socio-behavioural aspects (purple). The concentric 
circles indicate lower, medium, and higher mitigation potential considering the context of the urban area. For each city type (circular graphic) the illustrative urban mitigation 
strategy that is considered to provide the greatest cascading effects across mitigation opportunities is represented by a section that is larger relative to others; those strategy 
sections outlined in black are ‘entry points’ for sequencing of strategies. Within each of the larger strategy sections (i.e., spatial planning, urban green and blue infrastructure, 
etc.), the size of the sub-strategy sections are equal and do not suggest any priority or sequencing. The relative sizes of the strategies and extent of mitigation potential are 
illustrative and based on the authors’ best understanding and assessment of the literature.

921
921
Mitigation options that involve spatial planning, urban form, and 
infrastructure – particularly co-located and mixed land use, as well as 
TOD – provide the greatest opportunities when urban areas are rapidly 
growing or emerging (Section  8.4.2). Established urban areas that 
are already compact and walkable have captured mitigation benefits 
from these illustrative strategies to various extents. Conversely, 
established urban areas that are dispersed and auto-centric have 
foregone these opportunities, with the exception of urban infill and 
densification that can be used to transform or continue to transform 
the existing urban form. Figure  8.21 underscores that urban 
mitigation options and illustrative strategies differ by urban growth 
typologies and urban form. Cities can identify their entry points for 
sequencing mitigation strategies.
The emissions reduction potential of urban mitigation options

further varies based on governance contexts, institutional capacity, 
and economic structure, as well as human and physical geography. 
According to the development level, for instance, urban form can 
remain mostly planned or unplanned, taking place spontaneously, 
with persistent urban infrastructure gaps remaining (Lwasa 
et al. 2018; Kareem et al. 2020). Measures for closing the urban 
infrastructure gap while addressing ‘leapfrogging’ opportunities 
(see Glossary) for mitigation and providing co-benefits represent 
possibilities for shifting development paths for sustainability 
(Cross-Chapter Box 5 in Chapter 4).
8.6.1 Mitigation Opportunities for Established Cities
Established cities will achieve the largest GHG emissions 
savings by replacing, repurposing, or retrofitting the building 
stock, encouraging modal shift, electrifying the urban energy 
system, as well as infilling and densifying urban areas.
Shifting pathways to low-carbon development for established cities

with existing infrastructures and locked-in behaviours and lifestyles 
is admittedly challenging. Urban infrastructures such as buildings, 
roads, and pipelines often have long lifetimes that lock-in emissions, 
as well as institutional and individual behaviour. Although the 
expected lifetime of buildings varies considerably by geography, 
design, and materials, typical lifespans are at minimum 30 years to 
more than 100 years.
Cities where urban infrastructure has already been built have 
opportunities to increase energy efficiency measures, prioritise 
compact and mixed-use neighbourhoods through urban regeneration, 
advance the urban energy system through electrification, undertake 
cross-sector synergies, integrate urban green and blue infrastructure, 
encourage behavioural and lifestyle change to reinforce climate 
mitigation, and put into place a wide range of enabling conditions as 
necessary to guide and coordinate actions in the urban system and

its impacts in the global system. Retrofitting buildings with state of 
the art deep-energy retrofit measures could reduce emissions of the 
existing stock by about 30–60% (Creutzig et al. 2016a) and in some 
cases up to 80% (Ürge-Vorsatz et al. 2020) (Section 8.4.3).Established cities that are compact and walkable are likely to have 
low per capita emissions, and thus can keep emissions low by focusing 
on electrification of all urban energy services and using urban green 
and blue infrastructure to sequester and store carbon while reducing 
urban heat stress. Illustrative mitigation strategies with the highest 
mitigation potential are decarbonising electricity and energy carriers 
while electrifying mobility, heating, and cooling (Table  8.3 and 
Figure 8.19). Within integrated strategies, the importance of urban 
forests, street trees, and green space as well as green roofs, walls, 
and retrofits, also have high mitigation potential (Section  8.4.4 

Established cities that are dispersed and auto-centric are likely to 
have higher per capita emissions and thus can reduce emissions 
by focusing on creating modal shift and improving public transit 
systems in order to reduce urban transport emissions, as well 
as focusing on infilling and densifying. Only then can the urban 
form constraints on locational and mobility options be effective 
at reducing transport-based emissions. Among mitigation options 
based on spatial planning, urban form, and infrastructure, urban infill 
and  densification has priority. For these cities, the use of urban 
green and blue infrastructure will be essential to offset residual 
emissions that cannot be reduced because their urban form is already 
established and difficult to change.
System-wide energy savings and emissions reductions for low-
carbon urban development are widely recognised to require both 
behavioural and structural changes (Zhang and Li 2017). Synergies

between social and ecological innovation can reinforce the 
sustainability of urban systems while decoupling energy usage and 
economic growth (Hu et al. 2018; Ma et al. 2018). In addition, an 
integrated sustainable development approach that enables cross-
sector energy efficiency, sustainable transport, renewable energy, 
and local development in urban neighbourhoods can address 
issues of energy poverty (Pukšec et al. 2018). In this context, cross-
sectoral, multi-scale, and public-private collaborative action is crucial 
to steer societies and cities closer to low-carbon futures (Hölscher 
et al. 2019). Such actions include guiding residential living area per 
capita, limiting private vehicle growth, expanding public transport, 
improving the efficiency of urban infrastructure, enhancing urban 
carbon pools, and minimising waste through sustainable, ideally 
circular, waste management (Lin et al. 2018). Through a coordinated

approach, urban areas can be transformed into hubs for renewable 
and distributed energy, sustainable mobility, as well as inclusivity 
and health (Newman et al. 2017; Newman 2020).
Urban design for existing urban areas includes strategies for urban 
energy transitions for carbon neutrality based on renewable energy, 
district heating for the city centre and suburbs, as well as green and 
blue interfaces (Pulselli et al. 2021). Integrated modelling approaches 
for urban energy system planning, including land use and transport 
and flexible demand-side options, is increased when municipal actors 
are also recognised as energy planners (Yazdanie and Orehounig 
2021) (Section 8.4.3). Enablers for action can include the co-design of 
infill residential development through an inclusive and participatory 
process with citizen utilities and disruptive innovation that can 
support net-zero-carbon power while contributing to 1.5°C pathways,

922
922
Chapter 8 Urban Systems and Other Settlements8the SDGs, and affordable housing simultaneously (Wiktorowicz et al. 
2018). Cross-sectoral strategies for established cities, including those 
taking place among 120 urban areas, also involve opportunities for 
sustainable development (Kılkış 2019, 2021b).
A shared understanding for urban transformation through 
a  participatory approach can largely avoid maladaptation and 
contribute to equity (Moglia et al. 2018). Transformative urban 
futures that are radically different from the existing trajectories of 
urbanisation, including in developing countries, can remain within 
planetary boundaries while being inclusive of the urban poor (Friend 
et al. 2016). At the urban policy level, an analysis of 12,000 measures 
in urban-level monitoring emissions inventories based on the mode 
of governance further suggests that local authorities with lower 
population have primarily relied on municipal self-governing while

local authorities with higher population more frequently adopted 
regulatory measures as well as financing and provision (Palermo et al. 
2020b). Policies that relate to education and enabling were uniformly 
adopted regardless of population size (Palermo et al. 2020b). Multi-
disciplinary teams, including urban planners, engineers, architects, 
and environmental institutions, can support local decision-making 
capacities, including for increasing energy efficiency and renewable 
energy considering building intensity and energy use (Mrówczyńska 
et al. 2021) (Section 8.5).
8.6.2 Mitigation Opportunities 
for Rapidly Growing Cities
Rapidly growing cities with new and actively developing 
infrastructures can avoid higher future emissions through 
using urban planning to co-locate jobs and housing, and 
achieve compact urban form; leapfrogging to low-carbon 
technologies; electrifying all urban services, including 
transportation, cooling, heating, cooking, recycling, water

extraction, wastewater recycling, and so on; and preserving 
and managing existing green and blue assets.
Rapidly growing cities have significant opportunities for integrating 
climate mitigation response options in earlier stages of urban 
development, which can provide even greater opportunities for 
avoiding carbon lock-in and shifting pathways towards net-zero 
GHG emissions. In growing cities that are expected to experience 
large increases in population, a  significant share of urban 
development remains to be planned and built. The ability to shift 
these investments towards low-carbon development earlier in the 
process represents an important opportunity for contributing to 
net-zero GHG emissions at the global scale. In particular, evidence 
suggests that investment in low-carbon development measures and 
reinvestment based on the returns of the measures, even without 
considering substantial co-benefits, can provide tipping points for

climate mitigation action and reaching peak emissions at lower 
levels while decoupling emissions from economic growth, even in 
fast-growing megacity contexts with well-established infrastructure 
(Colenbrander et al. 2017).At the same time, some of the rapidly growing cities in developing 
countries can have existing walkable urban design that can be 
maintained and supported with electrified urban rail plus renewable-
energy-based solutions to avoid a shift to private vehicles (Sharma 
2018). In addition, community-based distributed renewable electricity 
can be applicable for the regeneration of informal settlements rather 
than more expensive informal settlement clearance (Teferi and 
Newman 2018). Scalable options for decentralised energy, water, 
and wastewater systems, as well as spatial planning and urban 
agriculture and forestry, are applicable to urban settlements across 
multiple regions simultaneously (Lwasa 2017).

Rapidly urbanising areas can experience pressure for rapid growth 
in urban infrastructure to address growth in population. This 
challenge can be addressed with coordinated urban planning and 
support from enabling conditions for pursuing effective climate 
mitigation (Section  8.5 and Box  8.3). The ability to mobilise low-
carbon development will also increase opportunities for capturing 
co-benefits for urban inhabitants while reducing embodied and 
operational emissions. Transforming urban growth, including its 
impacts on energy and materials, can be carefully addressed with the 
integration of cross-sectoral strategies and policies.
Rapidly growing cities have entry points into an integrated 
strategy based on spatial planning, urban form and infrastructure 
(Figure 8.21). For rapidly growing cities that may be co-located and 
walkable at present, remaining compact is better ensured when 
co-location and mixed land use, as well as TOD, continues to be

prioritised (Section  8.4.2). Concurrently, ensuring that electricity 
and energy carriers are decarbonised while electrifying mobility, 
heating and cooling will support the mitigation potential of these 
cities. Along with an integrated approach across other illustrative 
strategies, switching to net-zero materials and supply chains holds 
importance (Section 8.4.3). Cities that remain compact and walkable 
can provide a  greater array of locational and mobility options to 
the inhabitants that can be adopted for mitigation benefits. Rapidly 
growing cities that may currently be dispersed and auto-centric 
can capture high mitigation potential through urban infill and 
densification. Conserving existing green and blue assets, thereby 
protecting sources of carbon storage and sequestration, as well as 
biodiversity, have high potential for both kinds of existing urban 
form, especially when the rapid growth can be controlled.  
8.6.3 Mitigation Opportunities

for New and Emerging Cities
New and emerging cities have unparalleled potential to 
become low- or net-zero-emissions urban areas while 
achieving high quality of life by creating compact, co-located, 
and walkable urban areas with mixed land use and TOD, that 
also preserve existing green and blue assets.
The fundamental building blocks that make up the physical attributes 
of cities, such as the layout of streets, the size of the city blocks, 
the location of where people live versus where they work, can affect 
and lock in energy demand for long time periods (Seto et al. 2016)

923
923
(Section  8.4.1). A  large share of urban infrastructures that will be 
in place by 2050 has yet to be constructed and their design and 
implementation will determine both future GHG emissions as well as 
the ability to meet mitigation goals (Creutzig et al. 2016a) (Figure 8.10 
and Table 8.1). Thus, there are tremendous opportunities for new and 
emerging cities to be designed and constructed to be low-emissions 
while providing high quality of life for their populations.
The UN International Resource Panel (IRP) estimates that building 
future cities under conventional practices will require a more than 
doubling of material consumption, from 40 billion tonnes annually 
in 2010 to about 90 billion tonnes annually by 2050 (Swilling et al. 
2018). Thus, the demand that new and emerging cities will place 
on natural resource use, materials, and emissions can be minimised

and avoided only if urban settlements are planned and built much 
differently than today, including minimised impacts on land use 
based on compact urban form, lowered use of materials, and related 
cross-sector integration, including energy-driven urban design for 
sustainable urbanisation.
Minimising and avoiding raw material demands depends on 
alternative options while accommodating the urban population. In 
addition, operational emissions that can be committed by new urban 
infrastructure can range between 8.5 GtCO 2 and 14 GtCO 2 annually 
up to 2030 (Erickson and Tempest 2015). Buildings and road networks 
are strongly influenced by urban layouts, densities, and specific uses. 
Cities that are planned and built much differently than today through 
light-weighting, material substitution, resource efficiency, renewable 
energy, and compact urban form, have the potential to support more 
sustainable urbanisation and provide co-benefits for inhabitants

(Figures 8.17 and 8.22).In this context, illustrative mitigation strategies that can serve as 
a  roadmap for emerging cities includes priorities for co-located 
and mixed land use, as well as TOD, within an integrated approach 
(Table  8.3 and Figure  8.19). This has cascading effects, including 
conserving existing green and blue assets (e.g., forests, grasslands, 
wetlands), many of which sequester and store carbon. Priorities 
for decarbonising electricity and energy carriers while electrifying 
mobility, heating, and cooling take place within the integrated 
approach (Section  8.4.3). Increasing greenways and permeable 
surfaces, especially from the design of emerging urban areas onward, 
can be pursued, also for adaptation co-benefits and linkages with the 
SDGs (Section 8.4.4 and Figure 8.18).
In low-energy-driven urban design, parameters are evaluated based 
on the energy performance of the urban area in the early design phase

of future urban development (Shi et al. 2017b). Energy-driven urban 
design generates and optimises urban form according to the energy 
performance outcome (Shi et al. 2017b). Beyond the impact of urban 
form on building energy performance, the approach focuses on the 
interdependencies between urban form and energy infrastructure in 
urban energy systems. The process can provide opportunities for both 
passive options for energy-driven urban design, such as the use of 
solar gain for space heating, or of thermal mass to moderate indoor 
temperatures, as well as active options that involve the use of energy 
infrastructure and technologies while recognising interrelations of 
the system. Future urban settlements can also be planned and built 
with net-zero CO 2 or net-zero GHG emissions, as well as renewable 
energy targets, in mind. Energy master planning of urban areas that 
initially target net-zero operational GHG emissions can be supported

with energy master planning from conceptual design to operation, 
including district-scale energy strategies (Charani Shandiz et al. 2021).
9040
0 20 40 60 80 10020502010
Raw material demands (Gt yr–1)8.514
05101520Committed emissions (GtCO 2  yr–1)Alternative strategies Raw m aterial demands and commi tted emissions from building u rban areas
2.25 times
increase 
(BAU)Possible
range in
2030
Raw 
Material
Demands
Committed 
EmissionsUrban 
with Co-
Beneﬁts
Figure  8.22:  Raw material demands and committed emissions from building urban areas.  The horizontal bars represent the projected increase in raw 
material demands in the year 2050. The vertical bars represent the possible range of committed CO 2 emissions in 2030. The importance of alternative solutions to reduce 
raw material demands and committed emissions while increasing co-benefits is represented by the circular process on the right-hand side. Ranges for committed emissions

from new urban infrastructure are based on Erickson and Tempest (2015) SEI WP 11. Source: drawn using data from Erickson and Tempest (2015) and Swilling et al. (2018).

924
924
Chapter 8 Urban Systems and Other Settlements8Integrated scenarios across sectors at the local level can decouple 
resource usage from economic growth (Hu et al. 2018) and enable 
100% renewable energy scenarios (Zhao et al. 2017a; Bačeković 
and Østergaard 2018). Relative decoupling is obtained (Kalmykova 
et al. 2015) with increasing evidence for turning points in per capita 
emissions, total emissions, or urban metabolism (Chen et al. 2018b; 
Shen et al. 2018). The importance of integrating energy and resource 
efficiency in sustainable and low-carbon city planning (Dienst 
et al. 2015), structural changes, as well as forms of disruptive social 
innovation, such as the ‘sharing economy’ (see Glossary), is also 
evident based on analyses for multiple cities, including those that 
can be used to lower the carbon footprints of urban areas relative to 
sub-urban areas (Chen et al. 2018a).
To minimise carbon footprints, new cities can utilise new intelligence

functions as well as changes in energy sources and material 
processes. Core design strategies of a compact city can be facilitated 
by data-driven decision-making so that new urban intelligence 
functions are holistic and proactive rather than reactive (Bibri 2020). 
In mainstream practices, for example, many cities use environmental 
impact reviews to identify potentially negative consequences of 
individual development projects on environmental conditions on 
a piecemeal project basis.
New cities can utilise: system-wide analyses of construction 
materials, or renewable power sources, that minimise ecosystem 
disruption and energy use, through the use of lifecycle assessments 
for building types permitted in the new city (Ingrao et al. 2019); 
urban-scale metabolic impact assessments for neighbourhoods 
in the city (Pinho and Fernandes 2019); strategic environmental 
assessments (SEAs) that go beyond the individual project and

assess plans for neighbourhoods (Noble and Nwanekezie 2017); or 
modelling of the type and location of building masses, tree canopies 
and parks, and temperature (surface conditions) and prevailing winds 
profiles to reduce the combined effects of climate change and UHI 
phenomena, thus minimising the need for air conditioning (Matsuo 
and Tanaka 2019).
Resource-efficient, compact, sustainable, and liveable urban 
areas can be enabled with an integrated approach across sectors, 
strategies, and innovations. From a geophysical perspective, the use 
of materials with lower lifecycle GHG impacts, including the use of 
timber in urban infrastructure and the selection of urban development 
plans with lower material and land demand can lower the emission 
impacts of existing and future cities (Müller et al. 2013; Carpio et al. 
2016; Liu et al. 2016a; Ramage et al. 2017; Shi et al. 2017a; Stocchero 
et al. 2017; Bai et al. 2018; Zhan et al. 2018b; Swilling et al. 2018;

Xu et al. 2018b; UNEP IRP 2020) (Figure  8.17). The capacity to 
implement relevant policy instruments in an integrated and 
coordinated manner within a  policy mix while leveraging multi-
level support as relevant can increase the enabling conditions for 
urban system transformation (Agyepong and Nhamo 2017; Roppongi 
et al. 2017).  
The integration of urban land use and spatial planning, electrification 
of urban energy systems, renewable energy district heating and 
cooling networks, urban green and blue infrastructure, and circular economy can also have positive impacts on improving air and 
environmental quality with related co-benefits for health and well-
being (Diallo et al. 2016; Nieuwenhuijsen and Khreis 2016; Shakya 
2016; Liu et al. 2017; Ramaswami et al. 2017a; Sun et al. 2018b; 
Tayarani et al. 2018; Park and Sener 2019; González-García et al. 
2021). Low-carbon development options can be implemented in ways

that reduce impacts on water use, including water use efficiency, 
demand management, and water recycling, while increasing water 
quality (Koop and van Leeuwen 2015; Topi et al. 2016; Drangert and 
Sharatchandra 2017; Lam et al. 2017, 2018; Vanham et al. 2017; Kim 
and Chen 2018). The ability to enhance biodiversity while addressing 
climate change depends on improving urban metabolism and 
biophilic urbanism towards urban areas that are able to regenerate 
natural capital (Thomson and Newman 2018; IPBES 2019b).
There are readily available solutions for low-carbon urban 
development that can be further supported by new and emerging 
ones, such as tools for optimising the impact of urban form on energy 
infrastructure (Hu et al. 2015; Shi et al. 2017b; Xue et al. 2017; Dobler 
et al. 2018; Egusquiza et al. 2018; Pedro et al. 2018; Soilán et al. 
2018). The costs of low-carbon urban development are manageable, 
and enhanced with a  portfolio approach for cost-effective, cost-

neutral, and reinvestment options with evidence across different 
urban typologies (Colenbrander et al. 2015, 2017; Gouldson et al. 
2015; Nieuwenhuijsen and Khreis 2016; Saujot and Lefèvre 2016; 
Sudmant et al. 2016; Brozynski and Leibowicz 2018).
can have a positive impact on employment and local competitiveness 
(Kalmykova et al. 2015; Chen et al. 2018b; García-Gusano et al. 2018; 
Hu et al. 2018; Shen et al. 2018). In addition, sustainable urban 
transformation can be supported with participatory approaches 
that provide a  shared understanding of future opportunities 
and challenges where public acceptance increases with citizen 
engagement and citizen empowerment as well as an awareness of 
co-benefits (Blanchet 2015; Bjørkelund et al. 2016; Flacke and de 
Boer 2017; Gao et al. 2017; Neuvonen and Ache 2017; Sharp and 
Salter 2017; Wiktorowicz et al. 2018; Fastenrath and Braun 2018;

Gorissen et al. 2018; Herrmann et al. 2018; Moglia et al. 2018). 
Sustainable and low-carbon urban development that integrates 
issues of equity, inclusivity, and affordability, while safeguarding 
urban livelihoods, providing access to basic services, lowering energy 
bills, addressing energy poverty, and improving public health can also 
improve the distributional effects of existing and future urbanisation 
(Friend et al. 2016; Claude et al. 2017; Colenbrander et al. 2017; Ma 
et al. 2018; Mrówczyńska et al. 2018; Pukšec et al. 2018; Wiktorowicz 
et al. 2018) (Section 8.2).
Information and communications technologies can play an important 
role for integrating mitigation options at the urban systems level 
for achieving zero-carbon cities. Planning for decarbonisation at 
the urban systems level involves integrated considerations of the 
interaction among sectors, including synergies and trade-offs among 
households, businesses, transport, land use, and lifestyles. The

utilisation of big data, artificial intelligence and internet of things 
(IoT) technologies can be used to plan, evaluate and integrate 
rapidly progressing transport and building technologies, such as

925
925
autonomous EVs, zero-energy buildings, and districts as an urban 
system, including energy-driven urban design (Creutzig et al. 2020; 
Yamagata et al. 2020). Community-level energy sharing systems 
will contribute to realising the decarbonisation potential of urban 
systems at community scale, including in smart cities (Section 4.2.5.9, 
Box 10.1, and Cross-Chapter Box 11 in Chapter 16).
8.7 Knowledge Gaps
While there is growing literature on urban NBS, which encompasses 
urban green and blue infrastructure in cities, there is still a knowledge 
gap regarding how these climate mitigation actions can be 
integrated in urban planning and design, as well as their mitigation 
potential, especially for cities that have yet to be built. In moving 
forward with the research agenda on cities and climate change 
science, transformation of urban systems will be critical; however,

understanding this transformation and how best to assess mitigation 
action remain key knowledge gaps (Butcher-Gollach 2018; Pathak 
and Mahadevia 2018; Rigolon et al. 2018; Anguelovski et al. 2019; 
Buyana et al. 2019; Trundle 2020; Azunre et al. 2021).
There is a key knowledge gap in respect to the potential of the informal 
sector in developing country cities. Informality extends beyond 
illegality of economic activities to include housing, locally developed 
off-grid infrastructure, and alternative waste management strategies. 
Limited literature and understanding of the mitigation potential of 
enhanced informal sector is highlighted in the key research agenda 
on cities from the Cities and Climate Change Science Conference 
(Prieur-Richard et al. 2018).
City-level models and data for understanding of urban systems is 
another knowledge gap. With increased availability of open data 
systems, big data and computing capacities, there is an opportunity

for analysis of urban systems (Frantzeskaki et al. 2019).
While there is much literature on urban climate governance, there 
is still limited understanding of the governance models and regimes 
that support multi-level decision-making for mitigation and climate 
action in general. Transformative climate action will require changing 
relationships between actors to utilise the knowledge from data and 
models and deepen understanding of the urban system to support 
8.7.1 COVID-19 and Cities
The COVID-19 pandemic has disrupted many aspects of urban 
life while raising questions about urban densities, transportation, 
public space, and other urban issues. The impact of COVID-19 on 
urban activity and urban GHG emissions may offer insights into urban 
emissions and their behavioural drivers and may include structural 
shifts in emissions that last into the future. The science is unclear as to 
the links between urban characteristics and COVID-19, and involves

multiple aspects. For example, some research shows higher COVID-19 
infection rates with city size (e.g., Dalziel et al. 2018; Stier et al. 2021), 
as well as challenges to epidemic preparedness due to high population density and high volume of public transportation (Layne et al. 2020; 
Lee et al. 2020). Other research from 913 metropolitan areas shows 
that density is unrelated to COVID-19 infection rates and, in fact, has 
been inversely related to COVID-19 mortality rates when controlled 
for metropolitan population.
Densely populated counties are found to have significantly lower 
mortality rates, possibly due to such advantages as better health care 
systems, as well as greater adherence to social-distancing measures 
(Hamidi et al. 2020). Sustainable urbanisation and urban infrastructure 
that address the SDGs can also improve preparedness and resilience 
against future pandemics. For example, long-term exposure to air

pollution has been found to exacerbate the impacts of COVID-19 
infections (Wu et al. 2020b), while urban areas with cleaner air from 
clean energy and greenspace, can provide advantages.
Some studies indicate that socio-economic factors, such as poverty, 
racial and ethnic disparities, and crowding are more significant than 
density in COVID-19 spread and associated mortality rate (Borjas 
2020; Maroko et al. 2020; Lamb et al. 2021). The evidence for the 
connection between household crowding and the risk of contagion 
from infectious diseases is also strong. A  2018 World Health 
Organization (WHO) systematic review of the effect of household 
crowding on health concluded that a majority of studies of the risk of 
non-tuberculosis infectious diseases, including flu-related illnesses, 
were associated with household crowding (Shannon et al. 2018).
Though preliminary, some studies suggest that urban areas saw 
larger overall declines in emissions because of lower commuter

activity and associated emissions. For example, researchers have 
explored the COVID-19 impact in the cities of Los Angeles, Baltimore, 
Washington, DC, and San Francisco Bay Area in the United States. 
In the San Francisco region, a  decline of 30% in anthropogenic 
CO2 was observed, which was primarily due to changes in on-road 
traffic (Turner et al. 2020). Declines in the Washington, DC/Baltimore 
region and in the Los Angeles urban area were 33% and 34%, 
respectively, in the month of April 2020 compared to previous years 
(Yadav et al. 2021).
At the global scale COVID-related lockdown and travel restrictions 
reduced daily CO 2 emissions by –17% in early April 2020 compared 
to 2019 values (Le Quéré et al. 2020; Liu et al. 2020b), though 
subsequent studies have questioned the accuracy of the indirect 
proxy data used (Oda et al. 2021). Research at the national scale 
in the United States found that daily CO 2 emissions declined –15%

during the late March to early June time period (Gillingham et al. 
2020). Research in China estimated that the first quarter of 2020 saw 
an 11.5% decline in CO 2 emissions relative to 2019 (Zheng et al. 2020; 
Han et al. 2021). In Europe, estimates indicated a –12.5% decline in 
the first half of 2020 compared to 2019 (Andreoni 2021). Rebound 
to pre-COVID trajectories has been evidenced following the ease of 
travel restrictions (Le Quéré et al. 2021). It remains unclear to what 
extent COVID resulted in any structural change in the underlying 
drivers of urban emissions.
Changes in local air pollution emissions, particularly due to 
altered transportation patterns, have caused temporary air quality

926
926
Chapter 8 Urban Systems and Other Settlements8improvements in many cities around the world (see critical 
review by Adam et al. 2021). Many outdoor air pollutants, such 
as particulates, nitrogen dioxide, carbon monoxide, and volatile 
organic compounds declined during national lockdowns. Levels 
of tropospheric ozone, however, remained constant or increased. 
A promising transformation that has been observed in many cities 
is an increase in the share of active travel modes such as cycling 
and walking (Sharifi and Khavarian-Garmsir 2020). While this may be 
temporary, other trends, such as increased rates of teleworking and/
or increased reliance on smart solutions that allow remote provision 
of services provide an unprecedented opportunity to transform urban 
travel patterns (Belzunegui-Eraso and Erro-Garcés 2020; Sharifi and 
Khavarian-Garmsir 2020).
Related to the transport sector, the pandemic has resulted in concerns

regarding the safety of public transport modes, which has resulted 
in significant reductions in public transport ridership in some cities 
(Bucsky 2020; de Haas et al. 2020) while providing opportunities 
for urban transitions in others (Newman AO 2020). Considering 
the significance of public transportation for achieving low-carbon 
and inclusive urban development, appropriate response measures 
could enhance health safety of public transport modes and regain 
public trust (Sharifi and Khavarian-Garmsir 2020). Similarly, there is 
a perceived correlation between the higher densities of urban living 
and the risk of increased virus transmission (Hamidi et al. 2020; 
Khavarian-Garmsir et al. 2021).
While city size could be a risk factor with higher transmission in larger 
cities (Hamidi et al. 2020; Stier et al. 2021), there is also evidence 
showing that density is not a major risk factor and indeed cities that 
are more compact have more capacity to respond to and control

the pandemic (Hamidi et al. 2020). Considering the spatial pattern 
of density, even distribution of density can reduce the possibility 
of crowding that is found to contribute to the scale and length of 
virus outbreak in cities. Overall, more research is needed to better 
understand the impacts of density on outbreak dynamics and address 
public health concerns for resilient cities.
Cities could seize this opportunity to provide better infrastructure to 
further foster active transportation. This could, for example, involve 
measures, such as expanding cycling networks and restricting 
existing streets to make them more pedestrian- and cycling-friendly 
contributing to health and adaptation co-benefits, as discussed in 
Section 8.2 (Sharifi 2021). Strengthening the science–policy interface 
is another consideration that could support urban transformation 
(Cross-Chapter Box 1 in Chapter 1).8.7.2 Future Urban Emissions Scenarios

The urban share of global emissions is significant and is expected 
to increase in the coming decades. This places emphasis on the 
need to expand development of urban emissions scenarios within 
climate mitigation scenarios (Gurney et al. 2021, 2022). The literature 
on globally comprehensive analysis of urban emissions within the 
existing IPCC scenario framework remains very limited, curtailing 
understanding of urban emissions tipping points, mitigation 
opportunities and overall climate policy complexity. A review of the 
applications of the SSP-RCP scenario framework also recommended 
downscaling global SSPs to improve the applicability of this  
framework to regional and local scales (O’Neill et al. 2020). 
This remains an urgent need and will require multidisciplinary research 
efforts, particularly as net-zero-emissions targets are emphasised.
8.7.3 Urban Emissions Data
Though there has been a  rapid rise in quantification and analysis

of urban emissions, gaps remain in comprehensive global coverage, 
particularly in the Global South, and reliance on standardised 
frameworks and systematic data are lacking (Gurney and Shepson 
2021; Mueller et al. 2021). The development of protocols by (BSI 
2013; Fong et al. 2014; ICLEI 2019b) that urban areas can use to 
organise emissions accounts has been an important step forward, but 
no single agreed-upon reporting framework exists (Lombardi et al. 
2017; Chen et al. 2019b; Ramaswami et al. 2021). Additionally, there 
is no standardisation of emissions data and limited independent 
validation procedures (Gurney and Shepson 2021). This is partly 
driven by the recognition that urban emissions can be conceptualised 
using different frameworks, each of which has a different meaning 
for different urban communities (Section 8.1.6.2). Equally important 
is the recognition that acquisition and analysis of complex data used

to populate urban GHG inventory protocols remains a  barrier for 
local practitioners (Creutzig et al. 2019). The limited standardisation 
has also led to incomparability of the many individual or city 
cluster analyses that have been accomplished since AR5. Finally, 
comprehensive, global quantification of urban emissions remains 
incomplete in spite of recent efforts (Moran et al. 2018; Zheng 
et al. 2018; Harris et al. 2020; Jiang et al. 2020; Wei et al. 2021; 
Wiedmann et al. 2021).
Similarly, independent verification or evaluation of urban GHG 
emissions has seen a  large number of research studies (e.g.,  Wu 
et al. 2016; Sargent et al. 2018; Whetstone 2018; Lauvaux et al. 
2020). This has been driven by the recognition that self-reported 
approaches may not provide adequate accuracy to track emissions 
changes and provide confidence for mitigation investment (Gurney 
and Shepson 2021).
The most promising approach to independent verification of urban

emissions has been the use of urban atmospheric monitoring (direct 
flux and/or concentration) as a means to assess and track urban GHG 
emissions (Davis et al. 2017). However, like the basic accounting 
approach itself, standardisation and practical deployment and scaling 
is an essential near-term need.

927
927
FAQ 8.1 |  Why are urban areas important to global climate change mitigation?
Over half of the world’s population currently resides in urban areas – a number forecasted to increase to nearly 70% by 2050. 
Urban areas also account for a growing proportion of national and global emissions, depending on emissions scope and geographic 
boundary. These trends are projected to grow in the coming decades; in 2100, some scenarios show the urban share of global 
emissions above 80%, with 63% being the minimum for any scenario (with the shares being in different contexts of emissions 
reduction or increase) (Sections 8.3.3 and 8.3.4). As such, urban climate change mitigation considers the majority of the world’s 
population, as well as some of the key drivers of global emissions. In general, emissions scenarios with limited outward urban land

expansion are also associated with a smaller rise in global temperature (Section 8.3.4).
The urban share of global emissions and its projected growth stem in part from urban carbon lock-in – that is, the path dependency 
and inertia of committed emissions through the long lifespan of urban layout, infrastructures, and behaviour. As such, urban 
mitigation efforts that address lock-in can significantly reduce emissions (Section 8.4.1). Electrification of urban energy systems, 
in tandem with implementing multiple urban-scale mitigation strategies, could reduce urban emissions by 90% by 2050 – thereby 
significantly reducing global emissions (Section 8.3.4). Urban areas can also act as points of intervention to amplify synergies and 
co-benefits for accomplishing the Sustainable Development Goals (Section 8.2).
FAQ 8.2 |  What are the most impactful options cities can take to mitigate urban emissions, and 
how can these be best implemented?

The most impactful urban mitigation plans reduce urban GHG emissions by considering the long lifespan of urban layout and urban 
infrastructures (Sections  8.4.1 and 8.6). Chapter  8  identifies three overarching mitigation strategies with the largest potential 
to decrease current, and avoid future, urban emissions: (i) reducing or changing urban energy and material use towards more 
sustainable production and consumption across all sectors including through spatial planning and infrastructure that supports 
compact, walkable urban form (Section  8.4.2); (ii) decarbonise through electrification of the urban energy system, and switch 
to net-zero-emissions resources (i.e., low-carbon infrastructure) (Section 8.4.3); and (iii) enhance carbon sequestration through 
urban green and blue infrastructure (e.g., green roofs, urban forests and street trees), which can also offer multiple co-benefits like

reducing ground temperatures and supporting public health and well-being (Section 8.4.4). Integrating these mitigation strategies 
across sectors, geographic scales, and levels of governance will yield the greatest emissions savings (Sections 8.4 and 8.5).
A city’s layout, patterns, and spatial arrangements of land use, transportation systems, and built environment (urban form), as well 
as its state and form(s) of development (urban growth typology), can inform the most impactful emissions savings ‘entry points’ 
and priorities for urban mitigation strategies (Sections 8.4.2 and 8.6). For rapidly growing and emerging urban areas, there is the 
opportunity to avoid carbon lock-in by focusing on urban form that promotes low-carbon infrastructure and enables low-impact 
behaviour facilitated by co-located medium to high densities of jobs and housing, walkability, and transit-oriented development

(Sections 8.6.2 and 8.6.3). For established cities, strategies include electrification of the grid and transport, and implementing energy 
efficiency across sectors (Section 8.6.1).
FAQ 8.3 |    How do we estimate global emissions from cities, and how reliable are the estimates?
There are two different emissions estimation techniques applied, individually or in combination, to the four frameworks outlined 
in Section 8.1.6.2 to estimate urban GHG emissions: ‘top-down’ and ‘bottom-up’. The top-down technique uses atmospheric GHG 
concentrations and atmospheric modelling to estimate direct (scope 1) emissions (see Glossary). The bottom-up technique estimates 
emissions using local activity data or direct measurements such as in smokestacks, traffic data, energy consumption information, and 
building use. Bottom-up techniques will often include indirect emissions (see Glossary) from purchased electricity (scope 2) and the

urban supply chain (scope 3). Inclusion of supply-chain emissions often requires additional data such as consumer purchasing data 
and supply chain emission factors. Some researchers also take a hybrid approach combining top-down and bottom-up estimation 
techniques to quantify territorial emissions. Individual self-reported urban inventories from cities have shown chronic underestimation 
when compared to estimates using combined top-down/bottom-up atmospherically calibrated estimation techniques.
No approach has been systematically applied to all cities worldwide. Rather, they have been applied individually or in combination 
to subsets of global cities. Considerable uncertainty remains in estimating urban emissions. However, top-down approaches have 
somewhat more objective techniques for uncertainty estimation in comparison to bottom-up approaches. Furthermore, supply chain 
estimation typically has more uncertainty than direct or territorial emission frameworks.

955
955
Buildings Chapter 99
Global greenhouse gas (GHG) emissions from buildings were 
in 2019 at 12 GtCO 2-eq, equivalent to 21% of global GHG 
emissions that year, out of which 57% were indirect emissions from offsite generation of electricity and heat, 24%  direct emissions produced onsite and 18% were embodied emissions from the use of cement and steel (high evidence, high agreement). More than 95% of emissions from buildings were CO
2 emissions, CH 4 and N 2O represented 0.08%, and emissions 
from halocarbon contributed by 3% to global GHG emissions from buildings. If only CO
2 emissions would be considered, the share of 
CO2 emissions from buildings out of global CO 2 emissions increases

to 31%. Global final energy demand from buildings reached 128.8 EJ in 2019, and global electricity demand was slightly above 43 EJ. The former accounted for 31% of global final energy demand and the latter for 18% of global electricity demand. Residential buildings consumed 70% of global final energy demand from buildings. Over the period 1990–2019, global CO
2 emissions from buildings increased by 50%, 
global final energy demand grew by 38% and global final electricity demand increased by 161% (high evidence,  high agreement) {9.3}.

Drivers of GHG emissions in the building sector were assessed using the SER (Sufficiency, Efficiency, Renewables) framework. Sufficiency measures tackle the causes of GHG emissions by avoiding the demand for energy and materials over the lifecycle of buildings and appliances. Sufficiency differs from efficiency in that the latter is about the continuous short-term marginal technological improvements, which allows doing less with more in relative terms without considering the planetary boundaries, while the former is about long-term actions driven by non-technological solutions (i.e., land-use management and planning), which consume less in absolute term and are determined by biophysical processes. Sufficiency addresses the issue of a  fair consumption of space and resources. The remaining carbon budget, and its normative target for distributional equity, is the upper limit of sufficiency, while requirements for a decent living standard define the minimum level of sufficiency. The SER

framework introduces a hierarchical layering which reduces the cost of constructing and using buildings without reducing the level of comfort of the occupant. Sufficiency interventions in buildings include the optimisation of the use of building, repurposing unused existing buildings, prioritising multi-family homes over single-family buildings, and adjusting the size of buildings to the evolving needs of households by downsizing dwellings. Sufficiency measures do not consume energy during the use phase of buildings.

In most regions, historical improvements in efficiency have 
been approximately matched by growth in floor area per capita. Implementing sufficiency measures that limit growth in floor area per capita, particularly in developed regions, reduces the dependence of climate mitigation on technological solutions (medium evidence,  medium agreement). At a global level, up to 
17% of the mitigation potential could be captured by 2050 through sufficiency interventions (medium evidence,  medium agreement).

Sufficiency is an opportunity to avoid locking buildings in carbon-intensive solutions. Density, compacity, building typologies, bioclimatic design, multi-functionality of space, circular use of materials, use of the thermal mass of buildings (to store heat for the cold season and to protect occupants from high temperatures (i.e., heatwaves), when designing energy services, moving from ownership to usership of appliances and towards more shared space, are among the sufficiency measures already implemented in the leading municipalities. At the global level, the main drivers of emissions include (i) population growth, especially in developing countries, (ii) increase in floor area per capita, driven by the increase of the size of dwellings while the size of households kept decreasing, especially in developed countries, (iii) the inefficiency of the newly constructed buildings, especially indeveloping countries, and the low renovation rates and ambitionlevel in developed countries when

existing buildings are renovated,(iv) the increase in use, number and size of appliances and equipment, especially ICT and cooling, driven by the growing welfare (income),and (v) the continued reliance on fossil fuel-based electricity and heatslow decarbonisation of energy supply. These factors taken togetherare projected to continue driving GHG emissions in the building sector in the future (high evidence,  high agreement) {9.2, 9.3, 9.4, 9.5, 9.6,

and 9.9}.
Bottom-up studies show a mitigation potential up to 85% in 
Europe and North America and up to 45% in Australia, Japan and New Zealand, compared to the baselines by 2050, even though they sometimes decline (robust evidence,  high agreement). In 
developing countries, bottom-up studies estimate the potential of up to 40–80% in 2050, as compared to their sharply growing baselines (medium evidence,  high agreement). The aggregation 
of results from all these bottom-up studies translates into a  global mitigation potential by 2050 of at least 8.2 GtCO
2, 
which is equivalent to 61% of their baseline scenario. The largest mitigation potential (5.4 GtCO
2) is available in developing countries 
while Developed Countries will be able to mitigate 2.7 GtCO 2. These

potentials represent the low estimates, and the real potential is likely to be higher. These estimated potentials would be higher if embodied emissions in buildings and those from halocarbons would be included (low evidence,  high agreement) {9.3, 9.6}.

The development, since the IPCC Fifth Assessment Report (AR5), of integrated approaches to construction and retrofit of buildings has led to the widespread adoption of zero energy/carbon buildings in all climate zones. The complementarity and the interdependency of measures lead to cost reduction while optimising the mitigation potential grasped and avoiding the lock-in-effect. The growing consideration of integrated approach to construction of new buildings as well as to the renovation of existing buildings results in a lower relevance of the step-by-step approach to renovate buildings and to breaking down the potential into cost categories, as to deliver deep mitigation and cost savings technologies and approaches shall be applied together in an integrated and interdependent manner (medium evidence, high agreement). The potential associated with the sufficiency measures as well as the  exchange of appliances, equipment, and lights with efficient ones is at cost below USD0  tCO

2–1 (high evidence, high agreement). The construction of 
high-performance buildings will become by 2050 a business-as-usual technology with costs below USD20 tCO
2–1 in developed countries 
and below USD100 tCO 2–1 in developing countries (medium 
evidence, high agreement). For existing buildings, there have been

956
956
Chapter 9 Buildings9many examples of deep retrofits where additional costs per  CO 2 
abated are not significantly higher than those of shallow retrofits. 
However, for the whole stock they tend to be in cost intervals of 
USD0–200 tCO 2–1 and >USD200 tCO 2–1 (medium evidence, medium 
agreement). Literature emphasizes the critical role of the decade 
between in 2020 and 2030 in accelerating the learning of know-how 
and skills to reduce the costs and remove feasibility constrains for 
achieving high efficiency buildings at scale and set the sector at the 
pathway to realise its full potential (high evidence, high agreement) 
{9.6, 9.9}.
The decarbonisation of buildings is constrained by multiple 
barriers and obstacles as well as limited flow of finance 
(robust evidence, high agreement). The lack of institutional 
capacity, especially in developing countries, and appropriate 
governance structures slow down the decarbonisation of the

global building stock (medium evidence, high agreement). 
The building sector stands out for its high heterogeneity, with many 
different building types, sizes, and operational uses. Its segment 
representing rented property faces principal/agent problems where 
the tenant benefits from the decarbonisation investment made by the 
landlord. The organisational context and the governance structure 
could trigger or hinder the decarbonisation of buildings (high 
evidence, high agreement). Global investment in the decarbonisation 
of buildings was estimated at USD164 billion in 2020, not enough to 
close the investment gap (robust evidence, high agreement) {9.9}.
Policy packages based on the SER (Sufficiency, Efficiency, 
Renewables) framework could grasp the full mitigation 
potential of the global building stock (medium evidence, 
high agreement). Low ambitious policies will lock buildings in 
carbon for decades as buildings last for decades if not centuries

(high evidence, high agreement). Building energy codes is the 
main regulatory instrument to reduce emissions from both 
new and existing buildings (high evidence, high agreement). 
Most advanced building energy codes include bioclimatic design 
requirements to capture the sufficiency potential of buildings, 
efficiency requirements by using the most efficient technologies and 
requirements to increase the integration of renewable energy solutions 
to the building shape. Some announced building energy  codes 
extend these requirements from the use phase to the whole building 
lifecycle. Building energy codes are proven to be especially effective 
if compulsory and combined with other regulatory instruments 
such as minimum energy performance standard for appliances and 
equipment, especially if the performance level is set at the level of 
the best available technologies in the market (robust evidence, high 
agreement). Market-based instruments such as carbon  taxes with

recycling of the revenues and personal or building carbon allowances 
also contribute to foster the decarbonisation of the building sector 
(robust evidence, high agreement). Requirements to limit the use 
of land and property taxes are also considered effective policies to 
limit urban sprawl and to prioritise multi-family buildings over single-
family homes (medium evidence, high agreement) {9.9}. Actions are needed to adapt buildings to future climate while 
ensuring well-being for all. The expected heatwaves will 
inevitably increase cooling needs to limit the health impacts 
of climate change (medium evidence, high agreement). 
Global warming will impact cooling and heating needs but also the 
performance, durability and safety of buildings, especially historical 
and coastal ones, through changes in temperature, humidity, 
concentrations of CO 2 and chloride, and sea level rise. Adaptation 
measures to cope with climate change may increase the demand

for energy and materials leading to an increase in GHG emissions 
if not mitigated. Sufficiency measures such as bioclimatic design 
of buildings, which consider the expected future climate, and 
includes natural ventilation, white walls and nature-based solutions 
(e.g.,  green roofs) will decrease the demand for cooling. Shared 
cooled spaces with highly efficient cooling solutions are among 
the mitigation strategies which can limit the effect of the expected 
heatwaves on people health. Sufficiency, efficiency, and renewable 
energy can be designed to reduce buildings’ vulnerability to climate 
change impacts (medium evidence, high agreement) {9.7, 9.8}. 
Well-designed and effectively implemented mitigation actions 
in the buildings sector have significant potential for achieving 
the United Nations Sustainable Development Goals. The impacts 
of mitigation actions in the building sector go far beyond the goal 
of climate action (SDG 13) and contribute to further meeting fifteen

other SDGs. Mitigation actions in the building sector bring health 
gains through improved indoor air quality and thermal comfort as well 
as reduced financial stresses in all world regions. Overall decarbonised 
building stock contribute to well-being and has significant macro- and 
micro-economic effects, such as increased productivity of labour, job 
creation, reduced poverty, especially energy poverty, and improved 
energy security that ultimately reduces net costs of mitigation 
measures in buildings (high evidence,  high agreement) {9.8}.
COVID-19 emphasised the importance of buildings for human 
well-being. However, the lockdown measures implemented to 
avoid the spread of the virus have also stressed the inequalities 
in the access for all to suitable and healthy buildings, which 
provide natural daylight and clean air to their occupants  
(low evidence,  high agreement). Meeting the new WHO health 
requirements, has also put an emphasis on indoor air quality,

preventive maintenance of centralised mechanical heating, ventilation, 
and cooling systems. Moreover, the lockdown measures have led to 
spreading the South Korean concept of officetel (office-hotel) to many 
countries and to extending it to officetelschool. The projected growth, 
prior to the COVID-19, of 58% of the global residential floor area 
by 2050 compared to the 290 billion m² yr –1 in 2019 might well be 
insufficient. Addressing the new needs for more residential buildings 
may not, necessarily mean constructing new buildings, especially in 
the global North. Repurposing existing non-residential buildings, no 
longer in use due to the expected spread of teleworking triggered by 
the health crisis and enabled by digitalisation, could be the way to 
overcome the new needs for officetelschool buildings triggered by the 
health crisis (low evidence,  high confidence) {9.1, 9.2}.

957
957
Buildings Chapter 999.1 Introduction
Total GHG emissions in the building sector reached 12 GtCO 2-eq 
in 2019, equivalent to 21% of global GHG emissions that year, of 
which 57% were indirect CO 2 emissions from offsite generation 
of electricity and heat, followed by 24% of direct CO 2 emissions 
produced on-site and 18% from the production of cement and steel 
used for construction and/or refurbishment of buildings. If only 
CO2 emissions would be considered, the share of buildings CO 2 
emissions increases to 31% out of global CO 2 emissions. Energy 
use in residential and non-residential buildings contributed 50% 
and 32% respectively, while embodied emissions contributed 18% 
to global building CO 2 emissions. Global final energy demand from 
buildings reached 128.8 EJ in 2019, equivalent to 31% of global 
final energy demand. Residential buildings consumed 70% out of 
global final energy demand from buildings. Electricity demand from

buildings was slightly above 43 EJ in 2019, equivalent to more than 
18% of global electricity demand. Over the period 1990–2019, global 
CO2 emissions from buildings increased by 50%, global final energy 
demand grew by 38%, with 54% increase in non-residential buildings 
and 32% increase in residential ones. Among energy carriers, the 
growth in global final energy demand was strongest for electricity, 
which increased by 161%.
There is growing scientific evidence about the mitigation potential of 
the building sector and its contribution to the decarbonisation of global 
and regional energy systems, and to meeting Paris Agreement goals and Sustainable Development Goals (SDGs) (IPCC, 2018; IEA, 2019c; 
IEA 2019e). Mitigation interventions in buildings are heterogeneous 
in many different aspects, from building components (envelope, 
structure, materials, etc.) to services (shelter, heating, etc.), to 
building types (residential and non-residential, sometimes also called

commercial and public), to building size, function, and climate zone. 
There are also variations between developed and developing countries 
in mitigation interventions to implement, as the former is challenged 
by the renovation of existing buildings while the latter is challenged by 
the need to accelerate the construction of new buildings. 
This chapter aims at updating the knowledge on the building 
sector since the Intergovernmental Panel on Climate Change Fifth 
Assessment Report (IPCC AR5) (Lucon et al. 2014). Changes since AR5 
are reviewed, including: the latest development of building service 
and components (Section  9.2), findings of new building related 
GHG emission trends (Section 9.3), latest technological (Section 9.4) 
and non-technological (Section  9.5) options to mitigate building 
GHG emissions, potential emission reduction from these measures 
at global and regional level (Section  9.6), links to adaptation

(Section 9.7) and sustainable development (Section 9.8), and sectoral 
barriers and policies (Section 9.9). 
The chapter introduces the concept of sufficiency, identified in 
the literature as a  mitigation strategy with high potential, and is 
organised around the Sufficiency, Efficiency, Renewables (SER) 
framework (Box 9.1).
Box 9.1 | SER (Sufficiency, Efficiency, Renewables) Framework
The SER framework was introduced in the late 1990s by a French NGO (Negawatt 2017) advocating for a decarbonised energy transition. 
In 2015, the SER framework was considered in the design of the French energy transition law, and the French energy transition agency 
(ADEME) is developing its 2050 scenario based on the SER framework. 
The three pillars of the SER framework include (i) sufficiency, which tackles the causes of the environmental impacts of human 
activities by avoiding the demand for energy and materials over the lifecycle of buildings and goods, (ii) efficiency, which tackles the

symptoms of the environmental impacts of human activities by improving energy and material intensities, and (iii) the renewables 
pillar, which tackles the consequences of the environmental impacts of human activities by reducing the carbon intensity of energy 
supply (Box 9.1, Figure 1). The SER framework introduces a hierarchical layering, sufficiency first followed by efficiency and renewable, 
which reduces the cost of constructing and using buildings without reducing the level of comfort of the occupant. 
Sufficiency is not a  new concept, its root goes back to the Greek word sôphrosunè, which was translated in Latin to sobrietas, 
in a  sense of enough (Cézard and Mourad 2019). The sufficiency concept was introduced to the sustainability policy debate by 
(Sachs 1993) and to academia by (Princen 2003). Since 1997, Thailand considers sufficiency, which was framed already in 1974 as

Sufficiency Economy Philosophy, as a new paradigm for development with the aim of improving human well-being for all by shifting 
development pathways towards sustainability (Mongsawad 2012). The Thai approach is based on three principles (i) moderation, 
(ii) reasonableness, and (iii) self-immunity. Sufficiency goes beyond the dominant framing of energy demand under efficiency and 
behaviour. Sufficiency is defined as avoiding the demand for materials, energy, land, water and other natural resources while delivering 
a decent living standard for all within the planetary boundaries (Saheb 2021b, Princen 2005). Decent living standards are a set of 
essential material preconditions for human well-being which includes shelter, nutrition, basic amenities, health care, transportation, 
information, education, and public space (Rao and Baer 2012; Rao and Min 2018; Rao et al. 2019). Sufficiency addresses the issue

of a fair consumption of space and resources. The remaining carbon budget, and its normative target for distributional equity, is the 
upper limit of sufficiency, while requirements for a decent living standard define the minimum level of sufficiency. Sufficiency differs 
from efficiency in that the latter is about the continuous short-term marginal technological improvements which allow doing more

958
958
Chapter 9 Buildings9Box 9.1 (continued)
with less in relative terms without considering the planetary boundaries, while the former is about long-term actions driven by non-
technological solutions (i.e., land-use management and planning), which consume less in absolute-term and are determined by the 
biophysical processes (Princen 2003). 
Box 9.1, Figure 1 | SER framework applied to the building sector.  Source: Saheb (2021).
Applying sufficiency principles to buildings requires (i) optimising the use of buildings, (ii) repurposing unused existing ones, 
(iii)  prioritising multi-family homes over single-family buildings, and (iv) adjusting the size of buildings to the evolving needs of 
households by downsizing dwellings (Wilson and Boehland 2005; Duffy 2009; Fuller and Crawford 2011; Stephan et al. 2013; Huebner

and Shipworth 2017; Sandberg 2018; McKinlay et al. 2019; Ellsworth-Krebs 2020; Berrill et al. 2021) (Box 9.1, Figure 2). Avoiding the demand for energy and materials over the life-cycle of buildings and goods
Reducing environmental impacts of the demand for energy and materials
Improving energy and materials intensityEfﬁciencyRenewablesSufﬁciency
SER Framework

959
959
Buildings Chapter 99Box 9.1 (continued)
Box 9.1, Figure 2 | Sufficiency interventions and policies in the building sector. Source: Saheb (2021).
Downsizing dwellings through cohousing strategies by repurposing existing buildings and clustering apartments when buildings 
are renovated and by prioritising multi-family buildings over single-family homes in new developments (Wilson and Boehland 2005; 
Duffy 2009; Fuller and Crawford 2011; Stephan et al. 2013; Huebner and Shipworth 2017; Sandberg 2018; McKinlay et al. 2019; 
Ellsworth-Krebs 2020; Ivanova and Büchs 2020; Berrill and Hertwich 2021) are among the sufficiency measures that avoid the 
demand for materials in the construction phase and energy demand for heating, cooling and lighting in the use phase, especially 
if the conditioned volume and window areas are reduced (Duffy 2009; Heinonen and Junnila 2014). Less space also means less

appliances and equipment and changing preferences towards smaller ones (Aro 2020). Cohousing strategies provide users, in both 
new and existing buildings, a shared space (i.e., for laundry, offices, guest rooms and dining rooms) to complement their private space. 
Thus, reducing per capita consumption of resources including energy, water and electricity (Klocker et al. 2012; N. Klocker 2017), 
while offering social benefits such as limiting loneliness of elderly people and single parents (Wankiewicz 2015; Riedy et al. 2019). 
Senior cooperative housing communities and eco-villages are considered among the cohousing examples to scale-up (Kuhnhenn 
et al. 2020). Local authorities have an important role to play in the metamorphosis of housing by proposing communal spaces to be 
shared (Williams 2008; Marckmann et al. 2012) through urban planning and land-use policies (Duffy 2009; Newton et al. 2017). Thus,

encouraging inter-generational cohousing as well as interactions between people with different social backgrounds (Williams 2008; 
Lietaert 2010). Progressive tax policies based on a cap in the per-capita floor area are also needed to adapt the size of dwellings to 
households’ needs (Murphy 2015; Akenji 2021). 
Efficiency, and especially energy efficiency and more recently resource efficiency, and the integration of renewable to buildings 
are widespread concepts since the oil crisis of the seventies, while only most advanced building energy codes consider sufficiency 
measures (IEA 2013). Efficiency and renewable technologies and interventions are described in Sections 9.4 and 9.9. 
A systematic categorisation of policy interventions in the building sector through the SER framework (Box 9.1, Figure 1) enables 
identification of the policy areas and instruments to consider for the decarbonisation of the building stock, their overlaps as well as

their complementarities. It also shows that sufficiency policies go beyond energy and climate policies to include land-use and urban 
planning policies as well as consumer policies suggesting a need for a different governance including local authorities and a bottom-
up approach driven by citizen engagement. Housing and consumer policiesSize of buildings/appliancesAdjusting the size to the needs
Organising the spaceMulti-family buildings/single-family homesLand use/urban and ﬁscal policies
Pooling and repurposingOwnership/usershipFiscal and consumer policiesConsumer policiesLifetime and use of buildings/appliancesOptimising the use of buildings and appliances
Sufﬁciency 
levers

960
960
Chapter 9 Buildings9Compared to AR5, this assessment introduces four novelties (i) the 
scope of CO 2 emissions has been extended from direct and indirect 
emissions considered in AR5 to include embodied emissions, (ii) beyond 
technological efficiency measures to mitigate GHG emissions in 
buildings, the contribution of non-technological, in particular of 
sufficiency measures to climate mitigation is also considered, 
(iii) compared to the IPCC Special Report on Global Warming of 1.5°C 
(SR1.5), the link to sustainable development, well-being and decent 
living standard for all has been further developed and strengthened, 
and finally (iv) the active role of buildings in the energy system by 
making passive consumers prosumers is also assessed.
COVID-19 emphasised the importance of buildings for human well-
being, however, the lockdown measures implemented to avoid the 
spread of the virus has also stressed the inequalities in the access for

all to suitable and healthy buildings, which provide natural daylight 
and clean air to their occupants (see also Cross-Chapter Box  1 in 
Chapter 1). COVID-19 and the new health recommendations (World 
Health Organization 2021) emphasised the importance of ventilation 
and the importance of indoor air quality (Sundell et al. 2011; Nazaroff 
2013; Fisk 2015; Guyot et al. 2018; Wei et al. 2020). The health crisis 
has also put an emphasis on preventive maintenance of centralised 
mechanical heating, ventilation, and cooling systems. Moreover, the 
lockdown measures have led to spreading the South Korean concept 
of officetel (office-hotel) (Gohaud and Baek 2017) to many countries 
and to extending it to officetelschool. Therefore, the projected growth, 
insufficient. However, addressing the new needs for more residential

buildings may not, necessarily mean constructing new buildings. In 
fact, repurposing existing non-residential buildings, no longer in use 
due to the expected spread of teleworking triggered by the health 
crisis and enabled by digitalisation, could be the way to overcome the 
new needs for officetelschool triggered by the health crisis.The four novelties introduced in this assessment link the building 
sector to other sectors and call for more sectoral coupling when 
designing mitigation solutions. Guidelines and methodologies 
developed in Chapters 1, 2, 3, 4 and 5 are adopted in this chapter. 
Detailed analysis in building GHG emissions is discussed based on 
Chapter 2 and scenarios to assess future emissions and mitigation 
potentials were selected based on Chapters, 3 and 4. There are 
tight linkages between this chapter and Chapter 6, 7, 8, 10 and 11, 
which are sectoral sectors. This chapter focusses more on individual

buildings and building clusters, while Chapter  8 discusses macro 
topics in urban areas. Findings of this chapter provides contribution 
to cross-sectoral prospection (Chapter  12), policies (Chapter  13), 
international cooperation (Chapter  14), investment and finance 
(Chapter 15), innovation (Chapter 16), and sustainable development 
(Chapter 17).
9.2 Services and Components
This section mainly details the boundaries of the building sector; 
mitigation potentials are evaluated in the following sections.
9.2.1 Building Types
Building types and their composition affect the energy consumption for 
building operation as well as the GHG emissions (Hachem-Vermette 
and Singh 2019). They also influence the energy cost (MacNaughton 
et al. 2015) therefore, an identification of building type is required to 
understand the heterogeneity of this sector. Buildings are classified 
as residential and non-residential buildings. Residential buildings can

be classified as slums, single-family house and multi-family house or 
apartment/flats building. Single-family house can be divided between 
single-family detached (including cottages, house barns, etc.) and 
Door
Curtain wall
Floor
FoundationsSubstructureSuperstructureWindow
Building servicesCeilingRoof
Lightweight 
walls
Heavyweight 
walls
Figure 9.1 | The main building components.

961
961
Buildings Chapter 99single-family attached (or terrace house, small multi-family, etc.). 
Another classification is per ownership: owner-occupiers, landlords, 
and owners’ association/condominiums. 
Non-residential buildings have a  much broader use. They include 
cultural buildings (which include theatres and performance, 
museums and exhibits, libraries, and cultural centres), educational 
buildings (kindergarten, schools, higher education, research centre, 
and laboratories), sports (recreation and training, and stadiums), 
healthcare buildings (health, well-being, and veterinary), hospitality 
(hotel, casino, lodging, nightlife buildings, and restaurants and bars), 
commercial buildings and offices (institutional buildings, markets, 
office buildings, retail, and shopping centres), public buildings 
(government buildings, security, and military buildings), religious 
buildings (including worship and burial buildings), and industrial

buildings (factories, energy plants, warehouses, data centres, 
transportation buildings, and agricultural buildings).
9.2.2 Building Components and Construction Methods
An understanding of the methods for assembling various materials, 
elements, and components is necessary during both the design and 
the construction phase of a  building. A building can be broadly 
divided into parts: the substructure which is the underlying structure 
forming the foundation of a building, and the superstructure, which is 
the vertical extension of a building above the foundation.
There is not a  global classification for the building components. 
Nevertheless, Figure  9.1 tries to summarise the building 
components found in literature (Mañá Reixach 2000; Asbjørn 2009; 
Ching 2014). The buildings are divided in the substructure and the 
superstructure. The substructure is the foundation of the building, 
where the footing, basement, and plinth are found. The superstructure

integrates the primary elements (heavyweight walls, columns, floors 
and ceilings, roofs, sills and lintels, and stairs), the supplementary 
components (lightweight walls and curtain walls), the completion 
components (doors and windows), the finishing work (plastering and 
painting), and the buildings services (detailed in Section 9.3).
At a  global level, from historical perspective (from the Neolithic 
to the present), building techniques have evolved to be able to 
solve increasingly complex problems. Vernacular architecture has 
evolved over many years to address problems inherent in housing. 
Through a process of trial and error, populations have found ways 
to cope with the extremes of the weather. The industrial revolution 
was the single most important development in human history 
over the past three centuries. Previously, building materials were 
restricted to a few manmade materials (lime mortar and concrete)

along with those available in nature as timber and stone. Metals 
were not available in sufficient quantity or consistent quality to 
be used as anything more than ornamentation. The structure was 
limited by the capabilities of natural materials; this construction 
method is called on-site construction which all the work is done 
sequentially at the buildings site. The Industrial Revolution 
changed this situation dramatically, new building materials 
emerged (cast-iron, glass structures, steel-reinforced concrete, steel). Iron, steel and concrete were the most important materials 
of the nineteenth century (Wright 2000; De Villanueva Domínguez 
2005). In that context, prefabricated buildings (prefabrication also 
known as pre-assembly or modularisation) appeared within the 
so-called off-site construction. Prefabrication has come to mean 
a method of construction whereby building elements and materials, 
ranging in size from a single component to a complete building,

are manufactured at a  distance from the final building location. 
Prefabricated buildings have been developed rapidly since the 
Second World War and are widely used all over the world (Pons 
2014; Moradibistouni et al. 2018).
Recently, advances in technology have produced new expectations 
in terms of design possibilities. In that context, 3D printing seems 
to have arrived. 3D printing may allow in the future to build faster, 
cheaper and more sustainable (Agustí-Juan et  al. 2017; García 
de Soto et  al. 2018). At the same time, it might introduce new 
aesthetics, new materials, and complex shapes that will be printed 
at the click of a mouse on our computers. Although 3D printing will 
not replace architectural construction, it would allow optimisation 
of various production and assembly processes by introducing new 
sustainable construction processes and tools (De Schutter et  al. 
2018). Nevertheless, what is clear is that 3D printing is a technology

still in development, with a lot of potentials and that it is advancing 
quite quickly (Hager et al. 2016; Stute et al. 2018; Wang et al. 2020). 
9.2.3 Building Services
Building services make buildings more comfortable, functional, 
efficient, and safe. In a  generic point of view, building services 
include shelter, nutrition, sanitation, thermal, visual, and acoustic 
comfort, entertainment, communications, elevators, and illumination. 
In a more holistic view building services are classified as shown in 
Figure 9.2. 
A building management system is a  system of devices configured 
to control, monitor, and manage equipment in or around a building 
or building area and is meant to optimise building operations and 
reduce cost (Schuster et al. 2019). Recent developments include the 
integration of the system with the renewable energy systems (Arnone 
et al. 2016), most improved and effective user interface (Rabe et al.

2018), control systems based on artificial intelligence and internet of 
things (IoT) (Farzaneh et al. 2021).
The use of air conditioning systems in buildings will increase with 
the experienced rise in temperature (Davis and Gertler 2015; 
De Falco et al. 2016) (Figure 9.8). This can ultimately lead to high 
energy consumption rates. Therefore, adoption of energy efficient 
air conditioning is pertinent to balance the provision of comfortable 
indoor conditions and energy consumption. Some of the new 
developments that have been done include ice refrigeration (Xu et al. 
2017), the use of solar photovoltaic power in the air conditioning 
process (Burnett et al. 2014), and use of common thermal storage 
technologies (De Falco et al. 2016) all of which are geared towards 
minimising energy consumption and greenhouse gas emissions.

962
962
Chapter 9 Buildings9
Building designs have to consider provision of adequate ventilation. 
Natural ventilation reduces energy consumption in buildings in warm 
climates compared to air conditioning systems (Taleb 2015; Azmi 
et al. 2017). Enhanced ventilation has higher benefits to the public 
health than the economic costs involved (MacNaughton et al. 2015). 
On the refrigeration systems, the recent developments include the 
use of solar thermoelectric cooling technologies as an energy efficient 
measure (Liu et  al. 2015b); use of nanoparticles for energy  saving 
(Azmi et al. 2017) to mention some. Lambertz et al. (2019) stated that when evaluating the environmental 
impact of buildings, building services are only considered in a very 
simplified way. Moreover, it also highlights that the increasing use 
of new technologies such as Building Information Modelling (BIM) 
allows for a much more efficient and easier calculation process for

building services, thus enabling the use of more robust and complete 
models. Furthermore, recent studies on building services related to 
climate change (Vérez and Cabeza 2021) highlight the importance of 
embodied energy (Parkin et al. 2019) (Section 9.4).Building Services
Safety
Shelter
Daylight and artiﬁcial lighting
Escalators and lifts
Ventilation and refrigeration
Security and alarm systems
Fire detection and protection
Efﬁciency
Energy generation, distribution and supply
Building management systems
Facade engineeringComfort
Water, drainage and plumbing
Indoor air quality
Acoustic comfort
Visual comfort
Pollution
Embodied energy
Embodied carbon
Figure 9.2 | Classification of building services.  The coloured small squares to the left of each building service denote to which other classifications that building service 
may relate to a lesser extent. Source: adapted from Vérez and Cabeza (2021).

963
963
Buildings Chapter 999.3 New Developments in Emission 
Trends and Drivers
9.3.1 Past and Future Emission Trends
Total GHG emissions in the building sector reached 12 GtCO 2-eq in 
2019, equivalent to 21% of global GHG emissions that year. 57% 
of GHG emissions from buildings were indirect CO 2 emissions from 
generation of electricity and heat off-site, 24% were direct CO 2 
emissions produced on-site, and 18% were from the production 
of cement and steel used for construction and refurbishment of 
buildings (see Cross-Chapter Box 3 and Cross-Working Group Box 1 
in Chapter 3, and Figure 9.3a). Halocarbon emissions were equivalent 
to 3% of global building GHG emissions in 2019. In the absence of 
the breakdown of halocarbon emissions per  end-use sectors, they 
have been calculated for the purpose of this chapter, by considering 
that 60% of global halocarbon emissions occur in buildings (Hu et al. 
2020). CH 4 and N 2O emissions were negligible, representing 0.08%

each out of the 2019 global building GHG emissions. Therefore, this 
chapter considers only CO 2 emissions from buildings. By limiting the 
scope of the assessment to CO 2 emissions, the share of emissions from 
buildings increases to 31% of global 2019 CO 2 emissions. Energy use 
in residential and non-residential buildings contributed 50% and 
32% respectively, while embodied emissions contributed 18% to 
global building CO 2 emissions.Over the period 1990–2019, global CO 2 emissions from buildings 
increased by 50%. Global indirect CO 2 emissions increased by 
92%, driven by the increase of fossil fuels-based electrification, 
while global direct emissions decreased by 1%. At regional level, 
emissions in residential buildings decreased in Developed Countries, 
except in Australia, Japan and New Zealand, while they increased 
in developing countries. The highest decrease was observed in 
Europe and Eurasia, with 13.6% decrease of direct emissions and

33% decrease of indirect emissions, while the highest increase of 
direct emissions occurred in Middle East, 198%, and the highest 
increase of indirect emissions occurred in Eastern Asia, 2258%. 
Indirect emissions from non-residential buildings increased in all 
regions. The highest increase occurred in Eastern Asia, 1202%, and 
the lowest increase occurred in Europe and Central Asia, 4%, where 
direct emissions from non-residential buildings decreased by 51%. 
Embodied emissions have also increased in all regions. The highest 
increase occurred in Southern Asia, 334%, while the lowest increase 
occurred in North America, 4% (Figure 9.3b). 
Future emissions were assessed using four global scenarios and their 
respective baselines (Box  9.2). The selection of the scenarios was 
based on the features of each scenario, the geographic scope, and 
the data availability to analyse future building emissions based on 
the SER framework (Box 9.1).1990
2000
2010
2019
2020
2030
2040
2050
2020

2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050Historical
2019 Current
policy scenario
2020 Sustainable
Development Scenario
IMAGE Lifestyle and
Renewable scenario
RECC LED and 2°C
Policy scenario
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
Policy scenario0500100015002000250030003500Eastern Asia
02004006008001000Australia, Japan and New Zealand
–50005001000150020002500South East Asia and Paciﬁc
050010001500200025003000
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050Southern Asia0200400600800Latin America and CaribbeanGHG emissions (MtCO 2-eq yr–1) 
0500100015002000250030003500Europe and Eurasia
0500100015002000Middle East
050010001500200025003000North America

Policy scenario050010001500Africa–20000200040006000800010,00012,00014,00016,00018,000
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050GHG emissions (MtCO 2-eq yr–1) 
Historical IEA 2019 Current
policy scenarioIEA 2020 Sustainable
Development ScenarioIMAGE Lifestyle and
Renewable scenarioRECC LED and 2°C
Policy scenarioIEA 2021 Net-zero
Emissions Scenario
Historical scenarios b)a) Historical scenarios
Non-residential indirect emissionsEmbodied emissions Residential direct emissions Non-residential direct emissions
Residential indirect emissions All buildings non-CO 2 emissionsFigure 9.3 | Building GHG emissions: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net 
zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).

964
964
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040

1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
Residential indirect emissions All buildings non-CO 2 emissions
Figure 9.3 (continued): Building GHG emissions: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development,

and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).  
RECC-LED data include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline scenario (IEA current 
policies scenario).
Box 9.2 | Scenarios Used for the Purpose of This Chapter
Three out of the four scenarios selected, and their related baselines, are based on top-down modelling and were submitted to AR6 
scenario database, which includes in total 931 scenarios with a building module (Annex III; see also Boxes 3.1 and 3.2, and Cross-
Chapter Box 3 in Chapter 3). A fourth scenario, not included in AR6 scenario database, and based on a bottom-up modelling approach 
was added. 
The main features of these scenarios are shortly described below while the underlying modelling approaches are described in Annex III.

965
965
The IEA-NZE scenario projects emissions from the global building 
stock to be lowered to 29 MtCO 2 by 2050 against 1.7 GtCO 2 in the 
IEA-SDS and 3.7 GtCO 2 in IMAGE-LiRE Scenario. These projections 
can be compared to IEA-CPS in which global emissions from buildings 
were projected to be at 13.5 GtCO 2 in 2050, which is equivalent to 
the 2018 emissions level (Figure 9.3a). By 2050, direct emissions from 
residential buildings are projected to be lowered to 108 MtCO 2 in the 
IEA-NZE, this is four times less than the projected direct emissions in 
RECC-LED scenario, six times less than those under the IEA-SDS and 
eleven times less than those in the IMAGE-LiRE scenario. 
In the IEA-NZE scenario, indirect emissions are projected to be below 
zero by 2050 for both residential and non-residential buildings, 
while residual indirect emissions from residential buildings are 
projected to be 125 MtCO 2 in RECC-LED, 634 MtCO 2 in IEA-SDS, and

842 GtCO 2 in IMAGE-LiRE. Residual indirect emissions from non-
residential buildings are projected to be at 1.7 GtCO 2 in IEA SDS and 
double of this in IMAGE-LiRE scenario (Figure 9.3a). Compared to 
IEA-SDS, the highest decrease of emissions in IEA-NZE is expected 
to occur after 2030. Direct emissions from residential buildings 
in IEA-NZE are projected to be, by 2030, at 1.37 GtCO 2, against 
1.7 GtCO 2 in the three other scenarios. The highest cut in emissions 
in IEA-NZE and in IMAGE-LiRE occur through the decarbonisation 
of energy supply. At regional level, by 2050, the lowest emissions are projected 
to occur in developed Asia and Pacific, with 6.73 MtCO 2 under 
RECC-LED scenario and 12.4 MtCO 2 under the IEA-SDS, and the 
highest emissions are projected to occur in Europe and Eurasia in all 
three scenarios, with 152 MtCO 2 in IEA-SDS, 199 MtCO 2 in RECC-LED 
scenario and 381 MtCO 2 in IMAGE-LiRE scenario. Emissions in Africa

are projected to decrease to 10 MtCO 2 in RECC-LED, this is nine 
time less than those of 2019, while they are projected to increase by 
25% in IEA-SDS compared to those of 2019. Compared to IEA-SDS 
and IMAGE-LiRE, RECC-LED projects the highest decreases, over 
the period 2020–2030, of direct emissions in residential buildings 
in all regions, up to 45% in Australia, Japan and New Zealand, and 
Eastern Asia and the highest decreases of indirect emissions, ranging 
from 52% in Eastern Asia to 86% in Latin America and Caribbean. 
Over the same period, the IEA-SDS projects the highest decreases 
of indirect emissions to occur in Australia, Japan and New Zealand, 
and North America. IMAGE-LiRE projects the lowest decreases of 
emissions over the same decade in almost all regions (Figure 9.3b). 
Emissions per  capita from residential buildings at a  global level 
reached 0.85 tCO 2 per person in 2019. The four scenarios assessed

project a  decrease of the global per  capita emissions by 2050, 
ranging from 0 tCO 2 in IEA-NZE 0.21 tCO 2 per  person in IMAGE-
LiRE, a 75% lower than those of 2019 (Figure 9.4a). There are great Box 9.2 (continued)
International Energy Agency (IEA) scenarios:
2021 Net Zero Emissions by 2050 Scenario (NZE) is a normative scenario, which sets out a narrow but achievable pathway for 
the global energy sector to achieve net zero CO 2 emissions by 2050 (IEA 2021a).
2020 Sustainable Development Scenario (SDS), which integrates the impact of COVID-19 on health outcomes and economies. It is 
also a normative scenario, working backwards from climate, clean air, and energy access goals. SDS examines what actions would be 
necessary to achieve these goals. The near-term detail is drawn from the IEA Sustainable Recovery Plan, which boosts economies and 
employment while building cleaner and more resilient energy systems (IEA 2020c).

Analysis of the IEA scenarios above was conducted compared to the 2019 Current Policies Scenario, which shows what happens if the 
world continues along its present path (IEA 2020c), and considered as a baseline scenario. 
IMAGE-Lifestyle-Renewable (LiRE) scenario is based on an updated version of the SSP2 baseline, while also meeting the RCP2.6 
radiative forcing target using carbon prices, together with the increased adoption of additional lifestyle changes, by limiting the 
growth in the floor area per capita in Developed Countries as well as the use of appliances. Regarding energy supply, IMAGE-LiRE 
assumes increased electrification and increased share of renewable in the energy mix (Detlef Van Vuuren et al. 2021). 
Resource Efficiency and Climate Change-Low Energy Demand (RECC-LED) scenario is produced by a global bottom-up model, 
which assesses contributions of resource efficiency to climate change mitigation. RECC-LED estimates the energy and material flows

associated with housing stock growth, driven by population and the floor area per  capita (Pauliuk et  al. 2021). This scenario is 
informed by the Low Energy Demand Scenario (LED), which seeks convergence between developed and developing countries in the 
access to decent living standard (Grubler et al. 2018). 
For consistency between the four scenarios, aggregation of regions in this chapter differs from the one of the IPCC. Europe and Eurasia 
have been grouped into one single region.

966
966
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050Eastern Asia Australia, Japan/uni00A0and New Zealand
South East Asia and Paciﬁc Southern AsiaLatin America and Caribbean Europe and Eurasia Middle East
North AmericaAfrica1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
Historical IEA Current 
Policies Scenario 
IEA-WEO 2020IEA Sustainable and 
Development Scenario 
IEA-WEO 2020IMAGE Lifestyle and
Renewable scenarioIEA Net-zero 
Emissions Scenario 
(IEA-NZE 2021)
Historical scenarios b)a) Historical scenarios1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050CO 2 emissions (direct+indirect) per capita (tCO 2 per capita )
▲▼▲▲
▼▼
▼
▼▼
▼▼▲▲▲
▲
▼▼▼
▼▼▼
▼▼▼▼▼▼▼▼
▲

0.00.20.40.60.81.01.21.41.6
▼▼▼▼▼▼▼▼
▲▲▲▲▲
▲▲0.00.20.40.60.81.01.21.41.61.82.0
▼▲▲
▼▼
▼
▼
▼
▼▼
▼
▼▼
▼▼
0.00.10.20.30.40.5
▼▼▼▼▼▼
▼▼▼▲
▲
▲▲▲
▲
0.00.10.20.30.40.50.60.70.80.91.0
▼
▼
▼▼
▼
▼▼
▼▼▲
▲
▲
▲
▲▲0.00.20.40.60.81.01.21.41.6
▼
▼
▼▼
▼▼
▼▼▲▲▲
▲
▲
▲
▲
▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▲▲ ▲
▲
0.00.20.40.60.81.01.2
▼▼▼▼
▼▼▼▲▲▲
▲
▲
▲
▲
▲CO 2 emissions (direct+indirect) 
per capita (tCO 2 per capita )
IMAGE Lifestyle and 
Renewable ScenarioRECC LED and 2°C policy Historical IEA Sustainable and Development 
Scenario IEA-WEO 2020IEA Current Policies Scenario 
IEA-WEO 2020RECC LED and 
2°C policy0.00.10.20.30.40.50.60.70.80.91.0
▲ ▲▲▲
▼▼
▼
▼
▼▼
▼
▼▼
▼
▼▼
▼
▼
Figure 9.4 | Per capita emissions: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero 
emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).  RECC-LED data

include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

967
967
Buildings Chapter 99differences in the projected per capita emissions under each scenario 
different scenarios across the regions (Figure  9.4b). Compared to 
IEA-SDS and IMAGE-LiRE scenarios, RECC-LED projects the lowest 
emissions per capita in all regions by 2050. Emissions per capita in 
Europe and Eurasia are projected to be the highest in all scenarios by 
2050, ranging from 0.26 tCO 2 in RECC-LED and 0.31 tCO 2 in IEA-SDS 
to 0.65 tCO 2 in IMAGE-LiRE.
9.3.2 Drivers of CO 2 Emissions and Their 
Climate Impact
Building specific drivers of GHG emissions in the four scenarios 
described above are assessed using an index decomposition analysis 
with building specific identities and reflecting the three pillars of 
the Sufficiency, Efficiency, Renewables (SER) framework. Broad 
drivers of GHG emissions such as GDP and population are analysed 
using a Kaya decomposition in Chapter 2. Previous decompositions 
analysing drivers of global GHG emissions in the building sector have

either assessed only the impact of GDP and population as drivers of 
GHG emissions (Lamb et al. 2021) or the impact of building specific 
drivers on energy demand and not on CO 2 emissions (Lucon et al. 
2014; Ürge-Vorsatz et al. 2015; IEA 2020c; ODYSSEE 2020). For this 
assessment, the decomposition was conducted for energy-related 
CO2 emissions for residential buildings only, due to lack of data for 
non-residential buildings. 
The attribution of changes in emissions in the use phase to changes 
in the drivers of population, sufficiency, efficiency, and carbon 
intensity of energy supply is calculated using additive log-mean 
divisia index decomposition analysis (Ang and Zhang 2000). The decomposition of emissions into four driving factors is shown in 
Equation 1, where m2 refers to total floor area, EJ  refers to final 
energy demand, and MtCO 2 refers to the sum of direct and indirect 
CO2 emissions in the use phase. The allocation of changes in emissions

between two cases k and k–1 to changes in a single driving factor D 
is shown in Equation  2. To calculate changes in emissions due to 
a single driver such as population growth, D will take on the value of 
population in the two compared cases. The superscript k stands for 
the case, defined by the time period and scenario of the emissions, for 
example, IEA-CPS baseline scenario in 2050. When decomposing 
emissions between two cases k and k–1, either the time-period, or 
the scenario remains constant. The decomposition was done at the 
highest regional resolution available from each model output, and 
then aggregated to regional or global level. For changes in emissions 
within a  scenario over time, the decomposition is done for every 
decade, and the total 2020–2050 decomposition is then produced by 
summing decompositions of changes in emissions each decade.  
Equation 9.1
Equation 9.2
Over the period 1990–2019, population growth accounted for 28%

of the growth in global emissions in residential buildings, the lack 
of sufficiency policies (growth in floor area per  capita) accounted !"2!"!#$%=	&'(×*²
&'(×	,-
*²×	./&'(
,-=&'(×0122×,22×345 
∆"#2,"#,#$%=	"#2−"#2&'&()#$%
()("#2&'&()#)−()("#2&'&()#$%)	×()-.#
.#$%/ 
b)a)GHG emissions (direct and indirect) MtCO 2-eq GHG emissions (direct and indirect) MtCO 2-eq
Shows the increase of GHG emissions because of the lack 
of sufﬁciency measures and the slow penetration of renewableTotal – 1990, 2020
PopulationEfﬁciency
SufﬁciencyRenewables
Total at the end of the period – 2019, 2020 and 20501990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
1990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
IMAGE International Energy Agency RECCHistorical
Policy Scenario
Scenario
Lifestyle and
Renewable
scenario
SSP2
BaselineBaseline
SSP2-RCP2.6
LED and
2°C policy▲Eastern Asia
Southern AsiaLatin America and CaribbeanEurope and Eurasia
28%52%

16%14%54%
12%
0%0% 0%0.1%
10%31%
6%
0%11%38%
0%
49%30%
21%
64%14%
85%23% 5%
6%
55%22%
23%
17%
30%
43%0200040006000800010,00012,000
1990
2019
2020
2020
2050205020502020
2050
2050
20502050
IMAGE International Energy Agency RECCHistorical 2019 Current 
Policy Scenario2020 Sustainable 
ScenarioLifestyle and
Renewable
scenario2021 Net-zero 
Emissions
ScenarioSSP2
SSP2-RCP2.6LED and 
2°C policy
▲▲▲
▲▲▲
▲ ▲
▲
▲▲ ▲▲
▼▼
▼
▼
▼▼
▼ ▼
▼
▼▼
▼
▼
▼
▼
111%185%340%
35%229%153%
0%0%
41%70%202%0%
0%
54%192%75% 0%
218%
128%22%
70%65%0%
75% 16%12%
20%65%
0500100015002000
34%137%192%31%3%0%0%
57%
3%0%
0% 25%
0%
86%3%11%10%
80%11%21%
16%
62%8%
6%
38%19%29%
41%0600120018002400
6%34%
45%
0%0% 3%19%
0%2%27%
0%41%
24% 0%
37%
14%20%
55%15%
20% 9%
7%
31%49%
9%19%
40%20%
50%44%
33%21%86%
0%0%
16%33%
12%0%
0%14%15%
0%
50%46%3%
17%
65%38% 13%
42%9%
28%14%
35%41%
153%83%
42%40%68%
0%0%
40%21%
0%0%
28%42%
0% 23%28%
30%19%
59%12%
21%15%
22%68%17%
15%29%
47%

05001000
31%38%
13%8%
0%0% 17%3%
0%7%17%16%
0% 42%
32% 21%
17%16%78%5%
25%
24%
55%34%
17%
25%
36%31%
91%58%216%39%90%113%0%0%
26%53%
82%0%
0% 30%86%12%
0%
115%
33%21%
64%73%4%
78%13%8%
25%63%
050010001500Africa
112%16%89%99%44%149%
0%0%
56%53%
83%0%
0%1%71%102%29%
0%
31%65%52%
17%94% 70%72%16%
22%
56%02505007501000
▲
▼▲▲▲▲
▼▲▲▲
▼
▼▲▲▲▲▲▲
▼
▼▲▲▲
▼
▼
▼
▼▲Australia, Japan and New Zealand
14%55%
2%38%
0%0%14%
0% 1%9%
0%45%8%
22%
26%19%
71%3%
7%
38%9%
8%53%
29%
28% 14%36%
40%
0200400600
▲▲
▲▲
▲▲▲
▲▲▲
▲▼
▼▼
▼
▼
▼▼▼
▼▼▼
▼▼
▼ ▼
▼
▼
▲▲▲▲▲
▲▲ ▲
▲▲▲ ▲
▲
▼▼▼▼▼
▼
▼▼▼
▼
▼▼▼
▼
▼▲▲
▲
▲▲▲▲
▲▲▲
▲▼
▼▼▼
▼
▼
▼▼
▼▼▼
▼▼
▼
▼
▼
▼
▲▲▲▲▲
▲▲
▲▲
▲▲▲▲▲
▲
▼▼▼
▼
▼▼▼
▼▼
▼
▼
▼
▼▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▼
▼
▼▲▲▲▲▲
▲▲
▲▲
▲▲
▲▲
▲
South East Asia and Paciﬁc North America
▲▲
▲▲
▲▲▲▲
▲
▲▲▲
▲
▲▲▲▲▲▲▲▲
▲▲▲▲▲▲▲▲▲
▼▼▼▼
▼▼
▼▼▼
▼
▼
▼▼▼▼▼
▼▼▼▼
▼▼▼▼▼▼
▲▲▲▲▲▲▲▲
▲▲▲▲▲
▲▲▲ ▲
▼▼▼▼
▼▼
▼
▼▼
▼

▼Figure 9.5 | Decompositions of changes in historical residential energy emissions 1990–2019, changes in emissions projected by baseline scenarios for 
2020–2050, and differences between scenarios in 2050 using scenarios from three models: IEA, IMAGE, and RECC.  RECC-LED data include only space 
heating and cooling and water heating in residential buildings (a) global resolution, and (b) for nine world regions.

968
968
for 52% and increasing carbon intensity of the global energy mix 
accounted for 16%. Efficiency improvement contributed to decreasing 
global emissions from residential buildings by 49% (Figure 9.5a). The 
sufficiency potential was untapped in all regions over the same period 
while the decarbonisation of the supply was untapped in developing 
countries and to some extent in Asia-Pacific Developed. The highest 
untapped sufficiency and supply decarbonisation potentials occurred 
in Southern Asia where the lack of sufficiency measures has led to 
increasing emissions by 185% and the high carbon intensity of the 
energy mix has led to increasing emissions by 340%. In Developed Countries, the highest untapped sufficiency potential occurred in 
Asia-Pacific Developed region. Middle East is the only region where 
efficiency potential remained untapped (Figure 9.5b).
Scenarios assessed show an increase of the untapped sufficiency

potential at the global level over the period 2020–2050. The highest 
untapped sufficiency potential occurs in IEA scenarios as there are 
no changes in the floor area per  capita across different scenarios. 
The lack of sufficiency measures in current policies will contribute to 
increasing emissions by 54%, offsetting the efficiency improvement b)a)GHG emissions (direct and indirect) MtCO 2-eq GHG emissions (direct and indirect) MtCO 2-eq
2019
2020
2050
2050
2020
2050
2020
2050
20502050
1990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
Scenario
Renewable
scenario
SSP2
LED and

28%52%
16%14%54%
12%
10%31%
6%
0%11%38%
0%
49%30%
21%
64%14%
85%23% 5%
6%
55%22%
23%
17%
30%
1990
2019
2020
2020
2050
2050
20502050
Renewable
Emissions
2°C policy
▲▲▲
▲▲▲
▲ ▲
▲
▲▲ ▲▲
▼▼
▼
▼
▼▼
▼ ▼
▼
▼▼
▼
▼
▼
▼
0%0%
0%
218%
128%22%
70%65%0%
75% 16%12%
20%65%
57%
3%0%
0% 25%
0%
80%11%21%
16%
62%8%
6%
38%19%29%
6%34%
45%
0%0% 3%19%
0%2%27%
0%41%
24% 0%
37%
14%20%
55%15%
20% 9%
7%
31%49%
9%19%
40%20%
50%44%
33%21%86%
0%0%
16%33%
12%0%
0%14%15%
0%
50%46%3%
17%
65%38% 13%
42%9%
28%14%
35%41%
153%83%
42%40%68%

0%0%
40%21%
0%0%
28%42%
0% 23%28%
30%19%
59%12%
21%15%
22%68%17%
15%29%
47%
05001000
31%38%
13%8%
0%0% 17%3%
0% 42%
32% 21%
25%
24%
55%34%
17%
25%
36%31%
26%53%
82%0%
0%
115%
33%21%
64%73%4%
78%13%8%
25%63%
0%0%
56%53%
83%0%
0%
31%65%52%
17% 94% 70%72%16%
22%
▲
▼▲▲▲▲
▼▲▲▲
▼
▼▲▲▲▲▲▲
▼
▼▲▲▲
▼
▼
▼
14%55%
2%38%
0%0%14%
0% 1%9%
0%45%8%
22%
26%19%
71%3%
7%
38%9%
8%53%
29%
28% 14%36%
40%
▲▲
▲▲
▲▲▲
▲▲▲
▲▼
▼▼
▼
▼
▼▼▼
▼▼▼
▼▼
▼ ▼
▼
▼
▲▲▲▲▲
▲▲ ▲
▲▲▲ ▲
▲
▼▼▼▼▼
▼
▼▼▼
▼
▼▼▼
▼
▼▲▲
▲
▲▲▲▲
▲▲▲
▲▼
▼▼▼
▼
▼
▼▼
▼▼▼
▼▼
▼
▼
▼
▼
▲▲▲▲▲
▲▲
▲▲
▲▲▲▲▲
▲
▼▼▼
▼
▼▼▼
▼▼
▼
▼
▼
▼▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▼
▼
▼▲▲▲▲▲
▲▲
▲▲
▲▲
▲▲
▲
▲▲
▲▲
▲▲▲▲
▲
▲▲▲
▲
▲▲▲▲▲▲▲▲
▲▲▲▲▲▲▲▲▲
▼▼▼▼
▼▼
▼▼▼
▼
▼
▼▼▼▼▼
▼▼▼▼
▼▼▼▼▼▼
▲▲▲▲▲▲▲▲
▲▲▲▲▲
▲▲▲ ▲
▼▼▼▼
▼▼
▼
▼▼
▼
▼

Figure 9.5 (continued): Decompositions of changes in historical residential energy emissions 1990–2019, changes in emissions projected by baseline 
scenarios for 2020–2050, and differences between scenarios in 2050 using scenarios from three models: IEA, IMAGE, and RECC.  RECC-LED data include 
only space heating and cooling and water heating in residential buildings (a) global resolution, and (b) for nine world regions.  Emissions are decomposed 
based on changes in driver variables of population, sufficiency (floor area per capita), efficiency (final energy per floor area), and renewables (GHG emissions per final energy). 
‘Renewables’ is a summary term describing changes in GHG intensity of energy supply. Emission projections to 2050, and differences between scenarios in 2050, demonstrate 
mitigation potentials from the dimensions of the SER framework realised in each model scenario. In most regions, historical improvements in efficiency have been approximately

matched by growth in floor area per capita. Implementing sufficiency measures that limit growth in floor area per capita, particularly in developed regions, reduces the 
dependence of climate mitigation on technological solutions.

969
969
Buildings Chapter 991990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050Eastern Asia Australia, Japan and New Zealand
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050Floor area per capita (m2 per capita ) Floor area per capita (m2 per capita )
01020304050607080
▲▼▲▲▲▲▲▲▲
▲▲▲▲▲▲

0102030405060708001020304050607080
01020304050607080▲▼▼▼▼▼▲
▲
▲▲
▲
▲
▲
▲
▲
▲
▼
▼
▼▼
▼
▼▲▲▲▲▲▲▲
▲
▲▲▲▲▲▲
▲▲▲
▲▲▲▲▲▲
▲▲▲▲▲▲
▲▲▲
▲▲▲▲▲▲▲▲▲▲▲▲▲
▲
▲▲
▲
▲▲▲
▼▼▼▼
▲▲▲▲
▲
▲▲
▲
▲▲▲▲▲▲▲▲▲▲
▲▲▲
▲▲▲▼▼▼
▼▼▼
▲▲▲▲
▲
▲▲
▲
▲
▲▲
▲
IEA-WEO 2020051015202530354045
RECC LED and 
2°C policy▲
▲▲▲
▲▲▲
▲▲▲▲▲▲▲
▲
▲
▲
▲
Figure 9.6 | Per capita floor area: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero 

970
970
Chapter 9 Buildings9effect. By setting a cap in the growth of the floor area per capita 
in developed countries, 5% of emission reductions in IMAGE-LiRE 
scenario derives from sufficiency. However, compared to 2020, the 
lack of sufficiency measures in the baseline scenario will contribute 
to increasing emissions by 31%. RECC-LED scenario shows the 
highest global sufficiency potential captured compared to its 
baseline scenario in 2050 as this scenario assumes a reduction in the 
floor area per capita in Developed Countries and slower floor area 
growth in emerging economies. The four scenarios show a  higher 
contribution of the decarbonisation of energy supply to reducing 
emissions than the reduction of energy demand through sufficiency 
and efficiency measures (Figure 9.6a). At regional level, the emissions 
reduction potential from sufficiency is estimated at 25% in North 
America under both IMAGE-LiRE and RECC-LED scenarios and at

19% in both Eastern Asia and Europe/Eurasia regions (Figure 9.6b). 
The highest decarbonisation potential due to growth of renewable 
energy is 75% in Southern Asia under IMAGE-LiRE scenario. 
There is a growing literature on the decarbonisation of end-use sectors 
while providing decent living standard for all (Rao and Pachauri 2017; 
Grubler et  al. 2018; Rao and Min 2018; Rao et  al. 2019; Millward-
Hopkins et  al. 2020). The floor area per  capita is among the gaps 
identified in the convergence between developed and developing 
countries in the access to decent living (Kikstra et  al. 2021) while 
meeting energy needs. In the Low Energy Demand (LED) scenario, 
30 m² per capita is the converging figure assumed by 2050 (Grubler 
et al. 2018) while in the Decent Living with minimum Energy (DLE) 
scenario, (Millward-Hopkins et al. 2020) assumes 15 m² per capita.
Overall, the global residential building stock grew by almost 30%

between 2005 and 2019. However, this growth was not distributed equally across regions and three out of the four scenarios assessed 
do not assume a convergence, by 2050, in the floor area per capita, 
between developed and developing countries. Only RECC-LED 
implements some convergence between Developed Countries and 
emerging economies to a range of 20–40 m² per capita. IEA scenarios 
assume a growth in the floor area per capita in all regions with the 
highest growth in Developed Countries, up to 72 m² per capita in 
North America from 66 m² per capita in 2019. IMAGE-LiRE projects 
a floor area per capita in Africa at 14 m² per person. This is lower than 
the one of 2019, which was at 16 m² per capita (Figure 9.6). Beyond 
capturing the sufficiency potential by limiting the growth in the floor 
area per capita in Developed Countries while ensuring decent living 
standard, the acceptability of the global scenarios by developing

countries is getting attraction in academia (Hickel et al. 2021). 
9.3.3 Energy Demand Trends
Global final energy demand from buildings reached 128.8 EJ in 2019, 
equivalent to 31% of global final energy demand. The same year, 
residential buildings consumed 70% out of global final energy demand 
from buildings. Over the period 1990–2019, global final energy 
demand from buildings grew by 38%, with 54% increase in non-
residential buildings and 32% increase in residential ones. At regional 
level, the highest increase of final energy demand occurred in Middle 
East and Africa in residential buildings and in all South-East Asia and 
Pacific in non-residential ones. By 2050, global final energy demand 
from buildings is projected to be at 86 EJ in IEA-NZE, 111 EJ in IEA-SDS 
and 138 EJ in IMAGE-LiRE. RECC-LED projects the lowest global final 
energy demand, at 15.7 EJ by 2050, but this refers to water heating, 
space heating and cooling in residential buildings only (Figure 9.7a).1990

2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
Renewable scenarioEastern Asia Australia, Japan/uni00A0and New Zealand
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
policy scenarioIEA 2020 Sustainable 
Historical scenarios b)a) Historical scenarios2020 Sustainable and
RECC LED and
2°C policy
Total (EJ yr–1)Final energy demand per fuel (%) 
02468101214
01020304050607080901000510152025
0102030405060708090100
01234
024681990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040

Renewable scenario2020 Sustainable and
2°C policy
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2°C policy024681012140510152025
012345678
Commercial heatSolar thermal Coal Electricity
Oil GeothermalHydrogen
Total energy demand 
(secondary y axis)Bioenergy Gas Traditional use of biomassRECC LED and 
2°C policy020406080100120140
Figure 9.7 | Final energy demand per fuel: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, 
and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).

971
971
Over the period 1990–2019, the use of coal decreased at a global 
level by 59% in residential buildings and 52% in non-residential 
ones. Solar thermal experienced the highest increase, followed by 
geothermal  and electricity. However, by 2019, solar thermal and 
geothermal contributed by only 1% each to global final energy 
demand, while electricity contributed by 51% in non-residential 
buildings and 26% in residential ones. The same year, gas contributed 
by 26% to non-residential final energy demand and 22% to residential 
final energy demand, which makes gas the second energy carrier 
used in buildings after electricity. Over the period 1990–2019, the use of gas grew by 75% in residential buildings and by 46% in non-
residential ones. By 2050, RECC-LED projects electricity to contribute 
by 71% to final energy demand in residential buildings, against 62% 
in IEA-NZE and 59% in IMAGE-LiRE. IEA-NZE is the only scenario to

project less than 1% of gas use by 2050 in residential buildings while 
the contribution of electricity to energy demand of non-residential 
buildings is above 60% in all scenarios. At regional level, the use of 
coal in buildings is projected to disappear while the use of electricity 
is projected to be above 50% in all regions by 2050 (Figure 9.7b). 1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050

2°C policy
01234
024681990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2°C policy
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
012345678

Figure  9.7 (continued): Final energy demand per  fuel: historical based on IEA data and future emissions based on two IEA scenarios (sustainable 
development, and net zero emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario 
(RECC-LED).  RECC-LED data include only space heating and cooling and water heating in residential buildings. The IEA current policies scenario is included as a baseline 
scenario (IEA current policies scenario).

972
972
Chapter 9 Buildings9Hydrogen emerged in the policy debate as an important energy 
carrier for the decarbonisation of the energy system. In the case of the 
building sector, depending on how hydrogen is sourced (Box 12.3), 
converting gas grids to hydrogen might be an appealing option to 
decarbonise heat without putting additional stress on the electricity 
grids. However, according to (Element Energy Ltd 2018; Strbac et al. 
2018; Frazer-Nash Consultancy 2018; Broad et  al. 2020; Gerhardt 
et al. 2020) the delivered cost of heat from hydrogen would be much 
higher than the cost of delivering heat from heat pumps, which could 
also be used for cooling. Repurposing gas grids for pure hydrogen 
networks will also require system modifications such as replacement 
of piping and replacement of gas boilers and cooking appliances, 
a factor cost to be considered when developing hydrogen roadmaps 
for buildings. There are also safety and performance concerns with

domestic hydrogen appliances (Frazer-Nash Consultancy 2018). Over 
the period 1990–2019, hydrogen was not used in the building sector 
and scenarios assessed show a  very modest role for hydrogen in 
buildings by 2050 (Figure 9.7). 
In Developed Countries, biomass is used for generating heat and 
power leading to reduction of indirect emissions from buildings 
(Ortwein 2016) (IEA et al. 2020 c). However, according to (IEA 2019b) 
despite the mitigation potential of biomass, if the wood is available 
locally, its use remains low in Developed Countries. Biomass is 
also used for efficient cook stoves and for heating using modern 
appliances such as pellet-fed central heating boilers. In developing 
countries, traditional use of biomass is characterised by low efficiency 
of combustion (due to low temperatures) leading to high levels of 
pollutants and CO output, as well as low efficiency of heat transfer. 
The traditional use of biomass is associated with public health risks

such as premature deaths related to inhaling fumes from cooking (Dixon et al. 2015; Van de Ven et al. 2019; IEA 2019a; Taylor et al. 
2020). According to (Hanna et al. 2016) policies failed in improving 
the use of biomass. Over the period 1990–2019, the traditional 
use of biomass decreased by 1% and all scenarios assessed do not 
project any traditional use of biomass by 2050. Biomass is also used 
for the construction of buildings, leading to low embodied emissions 
compared to concrete (Heeren et al. 2015; Hart and Pomponi 2020; 
Pauliuk et al. 2021). 
Over the period 1990–2019, space heating was the dominant end-use 
in residential buildings at a global level, followed by water heating, 
cooking, and connected and small appliances (Figure 9.8a). However, 
energy demand from connected and small appliances experienced 
the highest increase, 280%, followed by cooking, 89%, cooling, 75%, 
water heating, 73% and space heating, around 10%. Space heating

energy demand is projected to decline over the period 2020–2050 
in all scenarios assessed. RECC-LED projects the highest decrease, 
77%, of space heating energy demand, against 68% decrease in the 
IEA-NZE. IMAGE-LiRE projects the lowest decrease of heating energy 
demand, 21%. To the contrary, all scenarios confirm cooling as 
a strong emerging trend (Box 9.3) and project an increase of cooling 
energy demand. IMAGE-LiRE projects the highest increase, 143% 
against 45% in the IEA-NZE while RECC-LED projects the lowest 
increase of cooling energy demand, 32%.
There are great differences in the contribution of each end-use to the 
regional energy demand (Figure 9.8b). In 2019, more than 50% of 
residential energy demand in Europe and Eurasia was used for space 
heating while there was no demand for space heating in Middle East, 
reflecting differences in climatic conditions. To the contrary, the share 
of energy demand from cooking out of total represented 53% in the 1990
2000
2010

2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2020 Sustainable and
LED scenarioAfrica1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
Historical 2019 Current
policy scenarioIEA Sustainable and
IEA-WEO 2020
Historical scenarios b)a) Historical scenariosEnergy demand per end-use (EJ yr–1) Energy demand (EJ yr–1)0510152025
012345678
0246810121416

02468101214012345678011223344
02468101214051015202505101520
LED scenario2020 Sustainable and
LED scenario
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
CookingLighting Space heating Space cooling
Water heating Connected and small appliancesCleaning appliances
Refrigeration020406080100120
LED Scenario
Figure 9.8 | Energy per end use: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, and net zero 
emissions), IMAGE Lifestyle-Renewable scenario and Resource Efficiency and Climate Change-Low Energy Demand scenario (RECC-LED).

973
973
Middle East against 5% in Europe and Eurasia reflecting societal 
organisations. The highest contribution of energy demand from 
connected and small appliances to the regional energy demand was 
observed in 2019 in the Asia-Pacific Developed, 24%, followed by the 
region of Southern Asia, South-East Asia and Developing Pacific, with 
17%. Energy demand from cooling was at 9% out of total energy 
demand of Southern Asia, South-East Asia and Developing Pacific 
and at 8% in both Middle East and North America while it was at 
1% in Europe in 2019. 
The increased cooling demand can be partly explained by the 
increased ownership of room air-conditioners per  dwellings in all regions driven by increased wealth and the increased ambient 
temperatures due to global warming (Cayla et al. 2011; Liddle and 
Huntington 2021) (Box 9.3). The highest increase, 32%, in ownership 
of room air-conditioners was observed in Southern Asia and South-

East Asia and Developing Pacific while Europe, Latin America and 
Caribbean countries, Eastern Asia and Africa experienced an increase 
of 21% in households’ ownership of room air-conditioners. The 
lowest increases in room air-conditioners ownership were observed 
in the Middle East and North America with 1% and 8% each as these 
two markets are almost saturated. All scenarios assessed project an 
increase of ownership of cooling appliances in all regions over the 
period 2020–2050. 1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040

2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
012345678
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
1990
2000
2010
2019
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050
2020
2030
2040
2050

Figure 9.8 (continued): Energy per end use: historical based on IEA data and future emissions based on two IEA scenarios (sustainable development, 
RECC-LED data include only space heating and cooling and water heating. The IEA current policies scenario is included as a baseline scenario (IEA current policies scenario).

974
974
Chapter 9 Buildings9Energy demand from connected and small appliances was, at a global 
level, above 7 EJ in 2019 (Figure  9.8a). However, it is likely that 
global energy demand from connected and small appliances is much 
higher as reported data do not include all the connected and small 
appliances used by households and does not capture energy demand 
from data centres (Box 9.3). Over the period 1990–2019, the highest 
increase of energy demand from connected and small appliances, 
4740%, was observed in Eastern Asia, followed by Southern Asia, 
1358% while the lowest increase, 99%, occurred in Asia-Pacific 
Developed countries. The increase of energy demand from connected 
and small appliances is driven by the ownership increase of such appliances all over the world. The highest increase in ownership of 
connected appliances, 403%, was observed in Eastern Asia and the 
lowest increase in ownership of connected appliances was observed

in North America, 94%. Future energy demand is expected to occur 
in the developing world given the projected rate of penetration of 
household appliances and devices (Wolfram et al. 2012). However, 
(Grubler et al. 2018) projects a lower energy demand from connected 
and small appliances by assuming an increase of shared appliances 
and multiple appliances and equipment will be integrated into units 
delivering multiple services.
Box 9.3 | Emerging Energy Demand Trends in Residential Buildings
Literature assessed points to three major energy demand trends:
Cooling energy demand 
In a warming world (IPCC 2021) with a growing population and expanding middle-class, the demand for cooling is likely to increase 
leading to increased emissions if cooling solutions implemented are carbon intensive (Santamouris 2016; Sustainable Energy for All 
2018; Dreyfus et al. 2020b; Kian Jon et al. 2021; UNEP and IEA 2020). Sufficiency measures such as building design and forms, which

allow balancing the size of openings, the volume, the wall and window area, the thermal properties, shading, and orientation are all 
non-cost solutions, which should be considered first to reduce cooling demand. Air conditioning systems using halocarbons are the 
most common solutions used to cool buildings. Up to 4 billion cooling appliances are already installed and this could increase to up 
to 14 billion by 2050 (Peters 2018; Dreyfus et al. 2020b). Energy efficiency of air conditioning systems is of a paramount importance 
to ensuring that the increased demand for cooling will be satisfied without contributing to global warming through halocarbon 
emissions (Campbell 2018; Shah et al. 2015, 2019; UNEP and IEA 2020). The installation of highly efficient technological solutions with 
low global warming potential (GWP), as part of the implementation of the Kigali amendment to the Montreal Protocol, is the second

step towards reducing GHG emissions from cooling. Developing renewable energy solutions integrated to buildings is another track 
to follow to reduce GHG emissions from cooling. 
Electricity energy demand
Building electricity demand was slightly above 43 EJ in 2019, which is equivalent to more than 18% of global electricity demand. Over 
the period 1990–2019, electricity demand increased by 161%. The increase of global electricity demand is driven by the combination 
of rising incomes, income distribution and the S-curve of ownership rates (Wolfram et al. 2012; Gertler et al. 2016). Electricity is used 
in buildings for plug-in appliances, in other words, refrigerators, cleaning appliances, connected and small appliances and lighting. 
An important emerging trend in electricity demand is the use of electricity for thermal energy services (cooking, water and space

heating). The increased penetration of heat pumps is the main driver of the use of electricity for heating. Heat pumps used either 
individually or in conjunction with heat networks can provide heating in cold days and cooling in hot ones. (Lowes et al. 2020) 
suggests electricity is expected to become an important energy vector to decarbonise heating. However, the use of heat pumps will 
increase halocarbon emissions (UNEP and IEA 2020). Connolly (2017), Bloess et al. (2018), and Barnes and Bhagavathy (2020) argue 
for electrification of heat as a cost-effective decarbonisation measure, if electricity is supplied by renewable energy sources (Ruhnau 
et al. 2020). The electrification of the heat supplied to buildings is likely to lead to an additional electricity demand and consequently 
additional investment in new power plants. Thomaßen et al. (2021) identifies flexibility as a key enabler of larger heat electrification

shares. Importantly, heat pumps work at their highest efficiency level in highly efficient buildings and their market uptake is likely to 
require incentives due to their high up-front cost (Hannon 2015; Heinen et al. 2017). 
Digitalisation energy demand
Energy demand from digitalisation occurs in data centres, which are dedicated buildings or part of buildings for accommodating 
large amount of information technologies equipment such as servers, data storage and communication devices, and network devices. 
Data centres are responsible for about 2% of global electricity consumption (Avgerinou et al. 2017; Diguet and Lopez 2019). Energy 
demand from data centres arises from the densely packed configuration of information technologies, which is up to 100 times higher 
than a standard office accommodation (Chu and Wang 2019). Chillers combined with air handling units are usually used to provide

cooling in data centres. Given the high cooling demand of data centres, some additional cooling strategies, such as free cooling, 
liquid cooling, low-grade waste heat recovery, absorption cooling and so on, have been adopted. In addition, heat recovery can

975
975
9.4 Mitigation Technological Options 
and Strategies Towards Zero 
Carbon Buildings
Literature in this topic is extensive, but unfortunately, most studies 
and reviews do not relate themselves to climate change mitigation, 
therefore there is a clear gap in reporting the mitigation potential 
of the different technologies (Cabeza et  al. 2020). It should be 
highlighted that when assessing the literature, it is clear that a lot 
of new research is focused on the improvement of control systems, 
including the use of artificial intelligence or internet of things (IoT).
This section is organised as follow. First, the key points from AR5 
and special reports are summarised, following with a  summary of 
the technological developments since AR5, specially focusing on 
residential buildings.
9.4.1 Key Points From AR5 and Special Reports
The AR5 WG3 Chapter 9 on Buildings (Lucon et al. 2014) presents 
mitigation technology options and practices to achieve large

reductions in building energy use as well as a synthesis of documented 
examples of large reductions in energy use achieved in real, new, and 
retrofitted buildings in a variety of different climates and examples 
of costs at building level. A key point highlighted is the fact that the 
conventional process of designing and constructing buildings and 
its systems is largely linear, losing opportunities for the optimisation of whole buildings. Several technologies are listed as being able to 
achieve significant performance improvements and cost potentials 
(daylighting and electric lighting, household appliances, insulation 
materials, heat pumps, indirect evaporative cooling, advances in 
digital building automation and control systems, and smart meters 
and grids to implement renewable electricity sources).
9.4.2 Embodied Energy and Embodied Carbon
9.4.2.1 Embodied Energy and Embodied Carbon  
in Building Materials
As building energy demand is decreased the importance of

embodied energy and embodied carbon in building materials 
increases (Ürge-Vorsatz et  al. 2020). Buildings are recognised as 
built following five building frames: concrete, wood, masonry, steel, 
and composite frames (International Energy Agency 2019a); but 
other building frames  should be considered to include worldwide 
building construction practice, such as rammed earth and bamboo in 
vernacular design (Cabeza et al. 2021).
The most prominent materials used following these frames 
classifications are the following. Concrete, a  man-made material, 
is the most widely used building material. Wood has been used for 
many centuries for the construction of buildings and other structures 
in the built environment; and it remains as an important construction 
material today. Steel is the strongest building material; it is mainly Box 9.3 (continued)
provide useful heat for industrial and building applications. More recently, data centres are being investigated as a potential resource

for demand response and load balancing (Zheng et al. 2020; Koronen et al. 2020). Supplying data centres with renewable energy 
sources is increasing (Cook et al. 2014) and is expected to continue to increase (Koomey et al. 2011). Estimates of energy demand from 
digitalisation (connected and small appliances, data centres, and data networks) combined vary from 5% to 12% of global electricity 
use (Gelenbe and Caseau 2015; Malmodin and Lundén 2018; Ferreboeuf 2019; Diguet and Lopez 2019). According to (Ferreboeuf 
2019) the annual increase of energy demand from digitalisation could be limited to 1.5% against the current 4% if sufficiency 
measures are adopted along the value chain. 
Digitalisation occurs also at the construction stage. (European Union 2019; Witthoeft and Kosta 2017) identified seven digital 
technologies already in use in the building sector. These technologies include (i) Building Information Modelling/Management (BIM),

(ii) additive manufacturing, also known as 3D printing, (iii) robots, (iv) drones, (v) 3D scanning, (vi) sensors, and (vii) internet of things 
(IoT). BIM supports decision making in the early design stage and allows assessing a variety of design options and their embodied 
emissions (Basbagill et al. 2013; Röck et al. 2018). 3D printing reduces material waste and the duration of the construction phase 
as well as labour accidents (Dixit 2019). Coupling 3D printing and robots allows for increasing productivity through fully automated 
prefabricated buildings. Drones allow for a better monitoring and inspection of construction projects through real-time comparison 
between planned and implemented solutions. Coupling drones with 3D scanning allows predicting building heights and energy 
consumption (Streltsov et  al. 2020). Sensors offer a  continuous data collection and monitoring of end-use services (i.e.,  heating,

cooling, and lighting), thus allowing for preventive maintenance while providing more comfort to end-users. Coupling sensors with 
IoT, which connects to the internet household appliances and devices such as thermostats, enable demand-response, and flexibility to 
reduce peak loads (IEA 2017a; Lyons 2019). Overall, connected appliances offer a variety of opportunities for end-users to optimise 
their energy demand by improving the responsiveness of energy services (IEA 2017a; Nakicenovic et al. 2019) through the use of 
digital goods and services (Wilson et al., 2020) including peer-to-peer electricity trading (Morstyn et al. 2018).

976
976
Chapter 9 Buildings9General timber
Timber softwood air dried
Timber softwood kiln dried
Timber softwood: glulam
Timber hardwood
Cross laminated timber
Bamboo
Adobe
Rammed earth
Compressed earth block
Reinforced concrete
Precast concrete
Concrete 25–30 MPa
Concrete block
Stone
Brick
General steel
Recycled steel
Iron
Cradle to gate
(Product stage)
(Product stage)0 10 20 30 40 50
Embodied energy (MJ kg–1)0 2.5 52.40–10 
0.30–7.4 
2.50–9.70 
4.60–13.20 
0.50–2 
0.55–7.11 
2.580.03–0.49 
0.68–0.83 
0.45–0.60 
1.92–3.10 
0.95–1.90 
0.78–1.40 
0.83–1.25 
0.29–1.26 
1.75–5 
8.90–28.65 24.84–59 
11.70
General timber
Bamboo
Adobe
Stone
Brick
Iron
–2 –1 0 1 2 3
Embodied carbon (kgCO2 kg–1)0 0.10 0.200.13
0.023

0.026–1.14–0.39 0.003–0.41
0.0730.23–1.66
–1.64 – –1.34  
0.076–0.24
0.13–0.21
0.10–0.20
0.14–2.57 0.10–1.26
1.912.21–3.19
0.44CO2
Figure 9.9 | Building materials (a) embodied energy and (b) embodied carbon.  Source: Cabeza et al. (2021).

977
977
Buildings Chapter 99used in industrial facilities and in buildings with big glass envelopes. 
Masonry is a  heterogeneous material using bricks, blocks, and 
others, including the traditional stone. Composite structures are 
those involving multiple dissimilar materials. Bamboo is a traditional 
building material throughout the world tropical and sub-tropical 
regions. Rammed earth can be considered to be included in masonry 
construction, but it is a  structure very much used in developing 
countries and it is finding new interest in developed ones (Cabeza 
The literature evaluating the embodied energy in building materials 
is extensive, but that considering embodied carbon is much more 
scarce (Cabeza et al. 2021). Recently this evaluation is done using the 
methodology lifecycle assessment (LCA), but since the boundaries 
used in those studies are different, varying, for example, in the 
consideration of cradle to grave, cradle to gate, or cradle to cradle,

the comparison is very difficult (Moncaster et al. 2019). A summary of 
the embodied energy and embodied carbon cradle to gate coefficients 
reported in the literature are found in Figure 9.9 (Alcorn and Wood 
1998; Crawford and Treolar 2010; Vukotic et al. 2010; Symons 2011; 
Moncaster and Song 2012; Cabeza et al. 2013; De Wolf et al. 2016; 
Birgisdottir et al. 2017; Pomponi and Moncaster 2016, 2018; Omrany 
et al. 2020; Cabeza et al. 2021). Steel represents the materials with 
higher embodied energy, 32–35 MJ kg–1; embodied energy in masonry 
is higher than in concrete and earth materials, but surprisingly, some 
types of wood have more embodied energy than expected; there are 
dispersion values in the literature depending on the material. On the 
other hand, earth materials and wood have the lowest embodied 
carbon, with less than 0.01 kgCO 2 per kg of material (Cabeza et al. 
2021). The concept of buildings as carbon sinks arise from the idea

that wood stores considerable quantities of carbon with a relatively 
small ratio of carbon emissions to material volume and concrete has 
substantial embodied carbon emissions with minimal carbon storage 
capacity (Sanjuán et al. 2019; Churkina et al. 2020).
9.4.2.2 Embodied Emissions
Embodied emissions from production of materials are an important 
component of building sector emissions, and their share is 
likely to increase as emissions from building energy demand 
decrease (Röck et al. 2020). Embodied emissions trajectories can 
be lowered by limiting the amount of new floor area required 
(Berrill and Hertwich 2021; Fishman et  al. 2021), and reducing 
the quantity and  GHG intensity of materials through material 
efficiency measures such as light-weighting and improved building 
design, material substitution to lower-carbon alternatives, higher 
fabrication yields and scrap recovery during material production, 
and re-use or lifetime extension of building components (Allwood

et al. 2011; Heeren et al. 2015; Hertwich et al. 2019; Churkina et al. 
2020; Pamenter and Myers 2021; Pauliuk et al. 2021). Reducing the 
GHG intensity of energy supply to material production activities 
also has a large influence on reducing overall embodied emissions. 
Figure  9.10 shows projections of embodied emissions to 2050 
from residential buildings in a  baseline scenario (SSP2 baseline) 
and a scenario incorporating multiple material efficiency measures 
and a much faster decarbonisation of energy supply (LED and 2°C 
policy) (Pauliuk et al. 2021). Embodied emissions are projected to be 32% lower in 2050 than 2020 in a baseline scenario, primarily 
due to a lower growth rate of building floor area per population. 
This is because the global population growth rate slows over the 
coming decades, leading to less demand for new floor area relative 
to total population. Further baseline reductions in embodied 
emissions between 2020 and 2050 derive from improvements

in material production and a gradual decline in GHG intensity of 
energy supply. In a  LED + 2°C policy scenario, 2050 embodied 
emissions are 86% lower than the baseline. This reduction of 2050 
emissions comes from contributions of comparable magnitude 
from three sources; slower floor area growth leading to less floor 
area of new construction per  capita (sufficiency), reductions in 
the mass of materials required for each unit of newly built floor 
area (material efficiency), and reduction in the GHG intensity of 
material production, from material substitution to lower carbon 
materials, and faster transition of energy supply. 
The attribution of changes in embodied emissions to changes in the 
drivers of population, sufficiency, material efficiency, and GHG 
intensity of material production is calculated using additive log-
mean divisia index decomposition analysis (Ang and Zhang 2000). 
The decomposition of emissions into four driving factors is shown in

Equation 9.3, where m2NC refers to floor area of new construction, 
kgMat refers to mass of materials used for new construction, and 
kgCO2e refers to embodied GHG emissions in CO 2e. The allocation of 
changes in emissions between two cases k and k–1 to changes in 
a single driving factor D  is shown in Equation 9.4. For instance, to 
calculate changes in emissions due to population growth, D  will take 
on the value of population in the two cases being compared. The 
superscript k stands for the time period and scenario of the 
emissions, for example, SSP2 baseline scenario in 2050. When 
decomposing emissions between two cases k and k–1, either the 
time period or the scenario stays constant. The decomposition is 
done for every region at the highest regional resolution available, 
and aggregation (e.g., to global level) is then done by summing over 
regions. For changes in emissions within a  scenario over time 
(e.g., SSP baseline emissions in 2020 and 2050), the decomposition

is made for every decade, and the total 2020–2050 decomposition is 
then produced by summing decompositions of changes in emissions 
each decade.
Equation 9.3
Equation 9.4!"!!"#$=	%&'×)%&'
%&'×	*+()*
)%&'×	*+&+'!
*+()*=%&'×,-..×/..×012 
∆"#"!"#,%&,&'(=	"#"!"#&−"#"!"#&'(
'(("#"!"#&)−'(("#"!"#&'()	×'(,-&
-&'(.

978
978
b)a)GHG embodied emissions MtCO 2-eqWorldGHG embodied emissions MtCO 2-eq
Shows changes in embodied emissions 
due to changes in driver variables 
between time periods and scenariosTotal
EfﬁciencySufﬁciencyRenewablesRECCEastern Asia
Middle EastRECCSSP2
Baseline
2020SSP2
Baseline
2050LED and 
2°C policy
▲Africa Australia, Japan and New Zealand
16%
0%
32%
16% 0%
27%
30%
28%
2020
20502050
2050▲
▲
▼
▼ ▼
▼
▼
▼
54%
28% 3%13% 0%
28%
35%19%
0250500750
2020
2050
2050▲
▼ ▼
▼ ▼
▼
▼
▼
3%4%
45%
19% 0%18%28%42%
▲▼
▼
▼ ▼
▼
▼
▼2020
2050
2050
14% 8%
11%21% 1%
20%
32%33%
–100
16%
27% 5%8% 0%94%
0% 5%0100200300
22%
24% 1%16% 0%
21%
34%28%
1% 1%
3%
14% 2%
23%
20%48%
0204060
3%
30% 0%
14% 1%
24%
30%35%
31%
2%
25%17% 1%
20%
34%29%
050100150
17%
33% 2%15% 0%15%
35%32%
SSP2

Baseline
2020SSP2
Baseline
2050LED
and 2°C
policy2020
2050
20502020
2050
20502020
2050
2050
2020
2050
20502020
2050
20502020
2050
20502020
2050
2050▼
▼ ▼
▼
▼
▼▲▲
▲
▼▼▼ ▼
▼ ▼▼
▲
▼ ▼
▼ ▼
▼
▼
▼▲▲
▼
▼▼
▼
▼
▼
▲
▼ ▼
▼ ▼
▼
▼
▼
▲
▲
▼
▼ ▼
▼
▼
▼
▲
▼ ▼
▼ ▼
▼
▼
▼
Figure 9.10 | Decompositions of changes in residential embodied emissions projected by baseline scenarios for 2020–2050, and differences between 
scenarios in 2050 using two scenarios from the RECC model.

979
979
Buildings Chapter 999.4.3 Technological Developments Since AR5
9.4.3.1 Overview of Technological Developments
There are many technologies that can reduce energy use in buildings 
(Finnegan et  al. 2018; Kockat et  al. 2018a), and those have been 
extensively investigated. Other technologies that can contribute to 
achieving carbon zero buildings are less present in the literature. 
Common technologies available to achieve zero energy buildings 
were summarised in (Cabeza and Chàfer 2020) and are presented in 
Tables 9.SM.1 to 9.SM.3 in detail, where Figure 9.11 shows a summary.
Other opportunities exist, such as building light-weighting or more 
efficient material production, use and disposal (Hertwich et  al. 
2020), fast-growing biomass sources such as hemp, straw or flax as 
insulation in renovation processes (Pittau et al. 2019), bamboo-based 
construction systems as an alternative to conventional high-impact

systems in tropical and subtropical climates (Zea Escamilla et al. 2018). Earth architecture is still limited to a niche (Morel and Charef 2019). 
See also Cross-Chapter Box 9 in Chapter 13 for carbon dioxide removal 
and its role in mitigation strategies.
9.4.3.2 Appliances and Lighting
Electrical appliances have a  significant contribution to household 
electricity consumption (Pothitou et  al. 2017). Ownership of 
appliances, the use of appliances, and the power demand of the 
appliances are key contributors to domestic electricity consumption 
(Jones et al. 2015). The drivers in energy use of appliances are the 
appliance type (e.g.,  refrigerators), number of households, number 
of appliances per  household, and energy used by each appliance 
(Chu and Bowman 2006; Cabeza et al. 2014; Spiliotopoulos 2019). 
At the same time, household energy-related behaviours are also 
a driver of energy use of appliances (Khosla et al. 2019) (Section 9.5).

Although new technologies such as IoT linked to the appliances 
increase flexibility to reduce peak loads and reduce energy demand Figure 9.10 (continued): Decompositions of changes in residential embodied emissions projected by baseline scenarios for 2020–2050, and differences 
between scenarios in 2050 using two scenarios from the RECC model.  (a) Global resolution, and (b) for nine world regions. Emissions are decomposed based on 
changes in driver variables of population, sufficiency (floor area of new construction per capita), material efficiency (material production per floor area), and renewables (GHG 
emissions per unit material production). ‘Renewables’ is a summary term describing changes in GHG intensity of energy supply. Emission projections to 2050, and differences 
between scenarios in 2050, demonstrate mitigation potentials from the dimensions of the SER framework realised in each model scenario.
Renewables (Table SM 9.3)020406080100Energy savings potential [%]

Sufﬁciency includes those measures that 
do not require energy to be implemented. Efﬁciency includes those measures
that improve the energy intensity.Renewables includes those measures that 
increase building resilience to climate
change impacts.Insulation materials
Trombe wall
Vertical greenery systems
PCM wall systems
AAC Walls 
Double skin walls
Cool roofs
Roof ponds
Green roofs
Efﬁciency (Table SM 9.2) Sufﬁciency (Table SM 9.1)
Geothermal energy or ground
source heat pumps
Solar energy PV
Solar thermal
Biomass energyThermally activated building
systems
Heat pumps
Organic Rankine Cycles
Adiabatic/Evaporative condensers
Smart ventilation
Heat recovery system
Fuel cells
Thermal energy storage
Liquid pressure ampliﬁcation
Chilled-ceiling
Desiccant cooling
Ejector cooling
Variable refrigerant ﬂow
Figure 9.11 | Energy savings potential of technology strategies for climate change mitigation in buildings. Sources: adapted from Imanari et al. (1999); Cabeza

et al. (2010); Fallahi et al. (2010); Prívara et al. (2011); Radhi (2011); Asdrubali et al. (2012); Capozzoli et al. (2013); Chen et al. (2013); de Gracia et al. (2013); Seong and Lim 
(2013); Sourbron et al. (2013); Bojić et al. (2014); Haggag et al. (2014); Sarbu and Sebarchievici (2014); Spanaki et al. (2014); Vakiloroaya et al. (2014); Djedjig et al. (2015); 
Mujahid Rafique et al. (2015); Yang et al. (2015); Andjelković et al. (2016); Costanzo et al. (2016); Coma et al. (2016); Harby et al. (2016); Navarro et al. (2016); Pomponi 
et al. (2016); Coma et al. (2017); Khoshbakht et al. (2017); Saffari et al. (2017); Luo et al. (2017); Jedidi and Benjeddou (2018); Romdhane and Louahlia-Gualous (2018); 
Lee et al. (2018); Alam et al. (2019); Bevilacqua et al. (2019); Gong et al. (2019); Hohne et al. (2019); Irshad et al. (2019); Langevin et al. (2019); Liu et al. (2019); Omara and

Abuelnour (2019); Rosado and Levinson (2019); Soltani et al. (2019); Varela Luján et al. (2019); Zhang et al. (2019); Annibaldi et al. (2020); Cabeza and Chàfer (2020); Dong 
et al. (2020); Nematchoua et al. (2020); Ling et al. (2020); Mahmoud et al. (2020); Peng et al. (2020); Zhang et al. (2020c); Yu et al. (2020).

980
980
(Kramer  et  al. 2020), trends show that appliances account for an 
increasing amount of building energy consumption (Figure  9.8). 
Appliances used in Developed Countries consume electricity and 
not fuels (fossil or renewable), which often have a  relatively high 
carbon footprint. The rapid increase in appliance ownership (Cabeza 
et al. 2018b) can affect the electricity grid. Moreover, energy intensity 
improvement in appliances such as refrigerators, washing machines, 
TVs, and computers has counteracted the substantial increase 
in ownership and use since the year 2000 (International Energy 
Agency 2019b).But appliances are also a significant opportunity for energy efficiency 
improvement. Research on energy efficiency of different appliances 
worldwide showed that this research focused in different time frames 
in different countries (Figure 9.12). This figure presents the number 
of occurrences of a term (the name of a studied appliance) appearing

per year and per country, according to the references obtained from 
a  Scopus search. The figure shows that most research carried out 
was after 2010. And again, this figure shows that research is mostly 
carried out for refrigerators and for brown appliances such as smart 
phones. Moreover, the research carried out worldwide is not only 
devoted to technological aspects, but also to behavioural aspects 
and quality of service (such as digital television or smart phones).
Refrigerator
Freezer
Washing machine
Oven
Cooker
Television
Video recorder
Desktop PC
Mobile phone
Media centre
3D printer/3D printing
Microwave oven
Digital television
Smart phone
Telephone systems
Smart meters
5 1500Number of occurrences
2000Years
2005201020152020
European Union
USA
China
India
UK
Germany
Japan
Spain
Australia
Brazil
Figure 9.12 | Energy efficiency in appliances research.  Year and number of occurrences of different appliances in each studied country/territory.

Table 9.1 | Types of domestic lighting devices and their characteristics.  Source: adapted from Attia et al. (2017).
Type of lighting device Code in plan Lumens per watt [lm W–1]Colour temperature [K] Lifespan [h] Energy use [W]
Incandescent InC 13.9 2700 1000 60
Candle incandescent CnL 14.0 2700 1000 25
Halogen Hal 20.0 3000 5000 60
Fluorescent TL8 FluT8 80.0 3000–6500 20,000 30–40
Compact fluorescent CfL 66.0 2700–6500 10,000 20
LED GLS LeD 100.0 2700–5000 45,000 10
LED spotlight LeD Pin 83.8 2700–6500 45,000 8
Fluorescent T5 FluT5 81.8 2700–6500 50,000 22
LED DT8 LeDT8 111.0 2700–6500 50,000 15

981
981
Buildings Chapter 99Lighting energy accounts for around 19% of global electricity 
consumption (Attia et al. 2017; Enongene et al. 2017; Baloch et al. 
2018). Many studies have reported the correlation between the 
decrease in energy consumption and the improvement of the energy 
efficiency of lighting appliances (Table 9.1). Today, the new standards 
recommend the phase out of incandescent light bulbs, linear 
fluorescent lamps, and halogen lamps and their substitution by more 
efficient technologies such as compact fluorescent lighting (CFL) and 
light-emitting diodes (LEDs) (Figure 9.8). Due to the complexity of 
these systems, simulation tools are used for the design and study 
of such systems, which can be summarised in Baloch et al. (2018).
Single-phase induction motors are extensively used in residential 
appliances and other building low-power applications. Conventional 
motors work with fixed speed regime directly fed from the grid, giving

unsatisfactory performance (low efficiency, poor power factor, and 
poor torque pulsation). Variable speed control techniques improve 
the performance of such motors (Jannati et al. 2017).
Within the control strategies to improve energy efficiency in appliances, 
energy monitoring for energy management has been extensively 
researched. Abubakar et al. (2017) present a review of those methods. 
The paper distinguishes between intrusive load monitoring (ILM), with 
distributed sensing, and non-intrusive load monitoring (NILM), based 
on a single point sensing.
9.4.4 Case Studies
9.4.4.1 Warehouses
Warehouses are major contributors to the rise of greenhouse gas 
emissions in supply chains (Bartolini et  al. 2019). The expanding 
e-commerce sector and the growing demand for mass customisation 
have even led to an increasing need for warehouse space and buildings, 
particularly for serving the uninterrupted customer demand in the

business-to-consumer market. Although warehouses are not specifically 
designed to provide their inhabitants with comfort because they are 
mainly unoccupied, the impact of their activities in the global GHG 
emissions is remarkable. Warehousing activities contribute roughly 
11% of the total GHG emissions generated by the logistics sector across 
the world. Following this global trend, increasing attention to green 
and sustainable warehousing processes has led to many new research 
results regarding management concepts, technologies, and equipment 
to reduce warehouses carbon footprint, that is, the total emissions of 
GHG in carbon equivalents directly caused by warehouses activities.
9.4.4.2 Historical and Heritage Buildings
Historical buildings, defined as those built before 1945, are usually 
low-performance buildings by definition from the space heating point 
of view and represent almost 30–40% of the whole building stock

in European countries (Cabeza et  al. 2018a). Historical buildings 
often contribute to townscape character, they create the urban 
spaces that are enjoyed by residents and attract tourist visitors. They 
may be protected by law from alteration not only limited to their 
visual appearance preservation, but also concerning materials and 
construction techniques to be integrated into original architectures. On the other hand, a heritage building is a historical building which, for 
their immense value, is subject to legal preservation. The integration of 
renewable energy systems in such buildings is more challenging than 
in other buildings. In the review carried out by Cabeza et al. (2018a) 
different case studies are presented and discussed, where heat pumps, 
solar energy and geothermal energy systems are integrated in such 
buildings, after energy efficiency is considered.
9.4.4.3 Positive Energy or Energy Plus Buildings

The integration of energy generation on-site means further contribution 
of buildings towards decarbonisation (Ürge-Vorsatz  et  al.  2020). 
Integration of renewables in buildings should always come after 
maximising the reduction in the demand for energy services through 
sufficiency measures and maximising efficiency improvement to 
reduce energy consumption, but the inclusion of energy generation 
would mean a step forward to distributed energy systems with high 
contribution from buildings, becoming prosumers (Sánchez Ramos et al. 
2019). Decrease price of technologies such as photovoltaic (PV) and the 
integration of energy storage (de Gracia and Cabeza 2015) are essential 
to achieve this objective. Other technologies that could be used are 
photovoltaic/thermal (Sultan and Ervina Efzan 2018), solar/biomass 
hybrid systems (Zhang et al. 2020b), solar thermoelectric (Sarbu and 
Dorca 2018), solar powered sorption systems for cooling (Shirazi et al.

2018), and on-site renewables with battery storage (Liu et al. 2021).
9.4.4.4 District Energy Networks
District heating networks have evolved from systems where heat was 
produced by coal or waste and storage was in the form of steam, to much 
higher energy efficiency networks with water or glycol as the energy 
carrier and fuelled by a wide range of renewable and low carbon fuels. 
Common low carbon fuels for district energy systems include biomass, 
other renewables (i.e., geothermal, PV, and large solar thermal), industry 
surplus heat or power-to-heat concepts, and heat storage including 
seasonal heat storage (Lund et al. 2018). District energy infrastructure 
opens opportunities for integration of several heat and power sources 
and is ‘future proof’ in the sense that the energy source can easily be 
converted or upgraded in the future, with heat distributed through 
the existing district energy network. Latest developments include

the inclusion of smart control and AI (Revesz et  al. 2020), and low 
temperature thermal energy districts. Authors show carbon emissions 
reduction up to 80% compared to the use of gas boilers.
9.4.5 Low- and Net Zero-energy Buildings – 
Exemplary Buildings
Nearly zero energy (NZE) buildings or low-energy buildings are possible 
in all world relevant climate zones (Mata et al. 2020b; Ürge-Vorsatz 
et al. 2020) (Figure 9.13). Moreover, they are possible both for new 
and retrofitted buildings. Different envelope design and technologies 
are needed, depending on the climate and the building shape and 
orientation. For example, using the Passive House standard an annual 
heating and cooling energy demand decrease between 75% and 
95% compared to conventional values can be achieved. Table 9.2 lists 
several exemplary low- and NZE-buildings with some of their feature.

982
982
Table 9.2 | Selected exemplary low- and net zero- energy buildings worldwide.  Sources: adapted from Mørck (2017); Schnieders et al. (2020); Ürge-Vorsatz et al. (2020).
Building name 
and organisationLocationBuilding 
typeEnergy efficiency and renewable energy featuresMeasured energy 
SDB-10 at the 
software development 
company, InfosysIndiaSoftware 
block –Hydronic cooling and a district cooling system with a chilled beam installation
 –Energy-efficient air conditioning and leveraged load diversity across categorised spaces: comfort air 
conditioning (workstations, rooms), critical load conditioning (server, hub, UPS, battery rooms), ventilated 
areas (restrooms, electrical, transformer rooms), and pressurised areas (staircases, lift wells, lobbies)
 –BMS to control and monitor the HVAC system, reduced face velocity across DOAS filters, and coils 
that allow for low pressure dropEPI of 74 kWh m–2, with 
an HVAC peak load of

5.2 W m–2 for a total office 
area of 47,340 m2 and 
total conditioned area 
of 29,115 m2
YS Sun Green Building 
by an electronics 
manufacturing 
company Delta 
Electronics Inc.Taiwan, 
Province of 
ChinaUniversity 
research 
green 
building –Low cost and high efficiency are achieved via passive designs, such as large roofs and protruded 
eaves which are typical shading designs in hot-humid climates and could block around 68% 
of incoming solar radiation annually
 –Porous and wind-channelling designs, such as multiple balconies, windowsills, railings, corridors, 
and make use of stack effect natural ventilation to remove warm indoor air
 –Passive cooling techniques that help reduce the annual air conditioning load by 30%EUI of the whole building is 
29.53 kWh m–2 (82% more 
energy-saving compared to 
the similar type of buildings)
BCA Academy 
BuildingSingaporeAcademy 
Building –Passive design features such a green roof, green walls, daylighting, and stack effect ventilation

–Active designs such as energy-efficient lighting, air conditioning systems, building management 
system with sensors and solar panels
 –Well-insulated, thermal bridge free building envelopeFirst net zero energy 
retrofitted building 
in Southeast Asia
Energy-Plus 
Primary SchoolGermany School –Highly insulated Passive House standard
 –Hybrid (combination of natural and controlled ventilation) ventilation for thermal comfort, air quality, 
user acceptance and energy efficiency
 –Integrated photovoltaic plant and wood pellet driven combined heat and power generation
 –Classrooms are oriented to the south to enable efficient solar shading, natural lighting and passive 
solar heating
 –New and innovative building components including different types of innovative glazing, 
electrochromic glazing, LED lights, filters and control for the ventilation systemOff grid building with an 
EPI of 23 kWh m–2 yr –1
NREL Research 
Support FacilityUSAOffice and 
research

facility –The design maximises passive architectural strategies such as building orientation, north and south 
glazing, daylighting which penetrates deep into the building, natural ventilation, and a structure 
which stores thermal energy 
 –Radiant heating and cooling with radiant piping through all floors, using water as the cooling 
and heating medium in the majority of workspaces instead of forced air
 –Roof-mounted photovoltaic system and adjacent parking structures covered with PV panels EPI of 110 kWh m–2 yr –1 
with a project area of 
20,624.5 m2 to become 
the then largest commercial 
net zero energy building 
in the country
Mohammed Bin 
Rashid Space 
Centre (Schnieders 
et al. 2020)United 
Arab 
Emirates, 
DubaiNon-
residential, 
offices –Exterior walls U-value = 0.08 W m–2 K–1
 –Roof U-value = 0.08 W m–2 K–1
 –Floor slab U-value = 0.108 W m–2 K–1
 –Windows UW = 0.89 W m–2 K–1
 –PVC and aluminium frames, triple solar protective glazing with krypton filling

–Ventilation = MVHR, 89% efficiency
 –Heat pump for cooling with recovery of the rejected heat for DHW and reheating coilCooling and 
dehumidification demand 
= 40 kWh m–2 yr –1 sensible 
cooling +10 kWh m–2 yr –1 
latent cooling
Primary energy demand 
= 143 kWh m–2 yr –1
Sems Have 
(Mørck 2017)Roskilde, 
DenmarkMulti-family 
(retrofit) –Pre-fabricated, lightweight walls 
 –Low-energy glazed windows, basement insulated with expanded clay clinkers under concrete
 –Balanced mechanical ventilation with heat recovery 
 –PVFinal Energy Use:  
24.54 kWh m–2
Primary energy use: 
16.17 kWh m–2
Figure 9.13 | Regional distribution of documented low-energy buildings.  Source: New Building Institute (2019); Ürge-Vorsatz et al. (2020).

983
983
Buildings Chapter 999.5 Non-technological and Behavioural 
Mitigation Options and Strategies
Non-technological (NT) measures are key for low-carbon buildings, 
but still attract less attention than technological measures (Creutzig 
et al. 2016, 2018; Ruparathna et al. 2016; Mundaca et al. 2019; Vence 
and Pereira 2019; Cabeza et al. 2020; Mata et al. 2021b). The section 
is set out to understand, over the building’s lifecycle, NT determinants 
of buildings’ energy demand and emissions (Section 9.5.1); to present 
NT climate mitigation actions (Section  9.5.2); then, to understand 
how to get these actions implemented (Section 9.5.3). The latter is 
a starting point in the design of policies (Section 9.9).
9.5.1 Non-technological Determinants of Energy 
Demand and Carbon Emissions
Buildings climate impact includes CO 2 emissions from operational 
energy use, carbon footprint, PM 2.5 concentrations and embodied 
carbon, and is unequivocally driven by GDP , income, population,

buildings floor area, energy price, climate, behaviour, and social and 
physical environment (Wolske et al. 2020; Mata et al. 2021d).
9.5.1.1 Climate and Physical Environment
Outdoor temperature, heating and cooling degree days, sunshine 
hours, rainfall, humidity and wind are highly determinant of energy 
demand (Tol et al. 2012; Rosenberg 2014; Harold et al. 2015; Risch and 
Salmon 2017; Lindberg et al. 2019). Density, compacity, and spatial 
effects define the surrounding environment and urban microclimate. 
Urban residents usually have a relatively affluent lifestyle, but use 
less energy for heating (Niu et al. 2012; Huang 2015; Rafiee et al. 
2019; Ayoub 2019; Oh and Kim 2019). Urbanisation is discussed 
in Chapter 8.
Climate variability and extreme events may drastically increase peak 
and annual energy consumption (Hong et al. 2013; Cui et al. 2017; 
Mashhoodi et al. 2019). Climate change effects on future demand and

emissions, are discussed in Section 9.7, and effects of temperature on 
health and productivity, in Section 9.8.
9.5.1.2 Characteristics of the Building 
Building typology and floor area (or e.g., number of bedrooms or lot 
size) are correlated to energy demand (Manzano-Agugliaro et al. 2015; 
Moura et al. 2015; Fosas et al. 2018; Morganti et al. 2019; Berrill et al. 
2021). Affluence is embedded in these variables as higher-income 
households have larger homes and lots. Residential consumption 
increases with the number of occupants but consumption per capita 
decreases proportionally to it (Serrano et al. 2017). Construction or 
renovation year has a negative correlation as recently built buildings 
must comply with increasingly strict standards (Brounen et al. 2012; 
Kavousian et  al. 2015; Österbring et  al. 2016). Only for electricity 
consumption no significant correlation is observed to building age 
(Kavousian et  al. 2013). Material choices, bioclimatic and circular

design discussed in Section 9.4.2.9.5.1.3 Socio-demographic Factors
Income is positively correlated to energy demand (Cayla et al. 2011; 
Sreekanth et al. 2011; Couture et al. 2012; Moura et al. 2015; Singh 
et al. 2017; Yu 2017; Bissiri et al. 2019; Mata et al. 2021b). High-income 
households tend to use more efficient appliances and are likely to be 
more educated and environmentally sensitive, but their higher living 
standards require more energy (Harold et al. 2015; Hidalgo et al. 2018). 
Low-income households are in higher risk of fuel poverty (Section 9.8). 
Mixed effects are found for household size, age, gender, ethnicity, 
education levels and tenancy status (Engvall et  al. 2014; Hansen 
2016; Lévy and Belaïd 2018; Arawomo 2019; Rafiee et  al. 2019). 
Single-parent and elderly households consume more gas and 
electricity, and gender has no significant effect (Brounen et al. 2012; 
Harold et al. 2015; Huang 2015). Similarly, larger families use less

electricity per capita (Bedir et al. 2013; Kavousian et al. 2013). Heating 
expenditure tends to be higher for owners than for renters, despite 
the formers tendency to have more efficient appliances (Gillingham 
et al. 2012; Davis, 2012; Kavousian et al. 2015).
9.5.1.4 Behaviour
Occupants presence and movement, interactions with the building, 
comfort-driven adaptations and cultural practices determine energy 
consumption (Hong et al. 2017; Yan et al. 2017; D’Oca et al. 2018; 
Khosla et al. 2019; Li et al. 2019; O’Brien et al. 2020). Households 
consume more on weekends and public holidays, and households with 
employed occupants consume less than self-employed occupants, 
probably because some of the latter jobs are in-house (Harold et al. 
2015; Hidalgo et al. 2018). Understanding and accurate modelling of 
occupant behaviour is crucial to reduce the gap between design and 
energy performance (Gunay et al. 2013; Yan et al. 2017), especially

for more efficient buildings, which rely on passive design features, 
human-centred technologies, and occupant engagement (Grove-
Smith et al. 2018; Pitts 2017). 
9.5.2 Insights From Non-technological  
and Behavioural Interventions
A range of NT actions can substantially reduce buildings energy 
demand and emissions (Figure  9.14; see Supplementary Material 
9.SM.2 for details). The subsections below present insights on the 
variations depending on the solution, subsector, and region.
9.5.2.1 Passive and Active Design, Management, 
and Operation
Bioclimatic design and passive strategies for natural heating, cooling 
and lighting, can greatly reduce buildings’ climate impact, and avoid 
cooling in developing countries (Bienvenido-Huertas et  al. 2021, 
2020; Amirifard et  al. 2019). Design can provide additional small 
savings, for example, by placing refrigerator away from the oven, 
radiators or windows (Christidou et al. 2014). Passive management

refers to adjustments in human behaviour such as adapted clothing, 
allocation of activities in the rooms of the building to minimise the

984
984
energy use (Klein et  al. 2012; Rafsanjani et  al. 2015) or manual 
operation of the building envelope (Rijal et  al. 2012; Volochovic 
et  al. 2012). Quantitative modelling of such measures is most 
common for non-residential buildings, in which adaptive behaviours 
are affected by the office space distribution and interior design, 
amount of occupants, visual comfort, outdoor view, and easy-to-use 
control mechanisms (O’Brien and Gunay 2014; Talele et  al. 2018). 
Socio-demographic factors, personal characteristics and contextual 
factors also influence occupant behaviour and their interactions with 
buildings (D’Oca et al. 2018b; Hong et al. 2020). 
Active management refers to human control of building energy 
systems. Efficient lighting practices can effectively reduce summer 
peak demand (Dixon et  al. 2015; Taniguchi et  al. 2016). On the 
contrary, the application of the daylight-saving time in the US

increases up to 7% lighting consumption (Rakha et  al. 2018). 
Efficient cooking practices for cooking, appliance use (e.g.,  avoid 
stand-by regime, select eco-mode), or for hot water can save up 
to 25% (Peschiera and Taylor 2012; Teng et  al. 2012; Abrahamse 
and Steg 2013; Berezan et al. 2013; Hsiao et al. 2014; Dixon et al. 
2015; Reichert et al. 2016). High behavioural control is so far proven 
difficult to achieve (Ayoub et al. 2014; Sköld et al. 2018). Automated 
controls and technical measures to trigger occupant operations are 
addressed in Section 9.4.9.5.2.2 Limited Demands for Services
Adjustment in the set-point temperature in winter and summer results 
in savings between 5% and 25% (Ayoub et al. 2014; Christidou et al. 
2014; Taniguchi et al. 2016; Sun and Hong 2017). As introduced in 
Section 9.3, a series of recent works study a cap on the living area 
(Mata et  al. 2021a) or an increase in household size (Berrill et  al.

2021). These studies are promising but of limited complexity in 
terms of rebounds, interactions with other measures, and business 
models, thus require further investigation. Professional assistance 
and training on these issues is limited (Maxwell et al. 2018).
Willingness to adopt is found for certain measures (full load to 
laundry appliances, lid on while cooking, turning lights off, defer 
electricity usage and HVAC systems, adjust set-point temperature by 
1°C) but not for others (appliances on standby, using more clothes, 
avoid leaving the TV on while doing other things, defer ovens, ironing 
or heating systems, adjust set-point temperature by 3°C, move to 
a  low energy house or smaller apartment) (Yohanis 2012; Brown 
et  al. 2013; Li et  al. 2017; Sköld et  al. 2018). A positive synergy 
with digitalisation and smart home appliances is identified, driven 
by a  combination of comfort requirements and economic interest,

confirmed by a willingness to defer electricity usage in exchange for 
cost savings (Ferreira et al. 2018; Mata et al. 2020c).Active
and operationCircular 
and sharing
economyLimited/
sufﬁcient
comfort levelsMultiple or 
unspeciﬁedPassive
and operationSocial and
organizational
innovations0%10%20%30%40%50%60%70%80%90%GHG - R
FEC - R
FEC - NRGHG - NR
Figure 9.14 | Energy saving and GHG mitigation potentials for categories of NT interventions for Residential (R) and Non-Residential (NR) buildings, from 
studies with worldwide coverage.  Sources: Roussac and Bright (2012); Van Den Wymelenberg (2012); Rupp et al. (2015); Creutzig et al. (2016); Khosrowpour et al. (2016); 
Ruparathna et al. (2016b); van Sluisveld et al. (2016); Ohueri et al. (2018); Ahl et al. (2019); Bierwirth and Thomas (2019b); Derungs et al. (2019); Grover (2019); Kaminska

(2019); Levesque et al. (2019a); Bavaresco et al. (2020); Cantzler et al. (2020); Ivanova and Büchs (2020b); Wilson et al. (2020b); Harris et al. (2021).

985
985
Buildings Chapter 999.5.2.3 Flexibility of Demand and Comfort Requirements
In a flexible behaviour, the desired level of service is the same, but 
it can be shifted over time, typically allowing automated control, 
for the benefit of the electricity or district heating networks. There 
are substantial economic, technical, and behavioural benefits from 
implementing flexibility measures (Mata et al. 2020c), with unknown 
social impacts.
With demand-side measures (DSM), such as shifting demand 
a  few hours, peak net demand can be reduced by up to 10–20% 
(Stötzer et al. 2015); a similar potential is available for short-term 
load shifting during evening hours (Aryandoust and Lilliestam 2017). 
Although different household types show different consumption 
patterns and thus an individual availability of DSM capacity during 
the day (Fischer et al. 2017), there is limited (Shivakumar et al. 2018) 
or inexistent (Drysdale et al. 2015; Nilsson et al. 2017) information

of consumers’ response to time of use pricing, specifically among 
those living in apartments (Bartusch and Alvehag 2014). Behavioural 
benefits are identified in terms of increased level of energy awareness 
of the users (Rehm et al. 2018), measured deliberate attempts of the 
consumers to reduce and/or shift their electricity usage (Bradley et al. 
2016). Real-time control and behavioural change influence 40% 
of the electricity use during the operational life of non-residential 
buildings (Kamilaris et al. 2014). 
9.5.2.4 Circular and Sharing Economy (CSE)
Non-technological CSE solutions, based on the Regenerate, Share, 
Optimise, Loop, Virtualise, Exchange (ReSOLVE) framework (CE100 
2016; ARUP 2018) include sharing, virtualising and exchanging. 
These are less studied than circular materials, with notably less 
investigation of existing buildings and sharing solutions (Pomponi 
and Moncaster 2017; Høibye and Sand 2018; Kyrö 2020; European 
Commission 2020).

The sharing economy generates an increased utilisation rate of 
products or systems by enabling or offering shared use, access 
or ownership of products and assets that have a  low ownership 
or use rate. Measures include conditioned spaces (accommodation, 
facility rooms, offices) as well as tools and transfer of ownership 
(i.e., second-hand or donation) (Rademaekers et al. 2017; Mercado 
2018; Hertwich et al. 2020; Cantzler et al. 2020; Harris et al. 2021; 
Mata et al. 2021a). The evidence on the link between user behaviour 
and net environmental impacts of sharing options is still limited 
(Laurenti et al. 2019; Mata et al. 2020a; Harris et al. 2021) and even 
begins to be questioned, due to rebounds that partially or fully offset 
the benefits (Agrawal and Bellos 2017; Zink and Geyer 2017). For 
example, the costs savings from reduced ownership can be allocated 
to activities with a  higher carbon intensity, or result in increased

mobility. Both reduced ownership and other circular consumption 
habits show no influence on material footprint, other than mildly 
positive influence in low-income households (Junnila et  al. 2018; 
Ottelin et al. 2020).9.5.2.5 Value-chain, Social and Institutional Innovations
Cooperative efforts are necessary to improve buildings energy 
efficiency (Masuda and Claridge 2014; Kamilaris et  al. 2014; 
Ruparathna et al. 2016). For instance, interdisciplinary understanding 
of organisational culture, occupant behaviour, and technology 
adoption is required to set up occupancy/operation best practises 
(Janda 2014). Similarly, close collaboration of all actors along the 
value chain can reduce by 50% emissions from concrete use (Habert 
et al. 2020); such collaboration can be enhanced in a construction 
project by transforming the project organisation and delivery contract 
to reduce costs and environmental impact (Hall and Bonanomi

2021). Building commissioning helps to reduce energy consumption 
by streamlining the systems, but benefits may not persist. Energy 
communities are discussed later in the chapter. 
NT challenges include training and software costs (tailored learning 
programs, learning-by-doing, human capital mobilisation), client and 
market demand (service specification, design and provision, market 
and financial analysis) and legal issues (volatile energy prices, meeting 
regulation); and partnership, governance and commercialisation. 
These challenges are identified for Building Information Modelling 
(BIM) (Oduyemi et  al. 2017; Rahman and Ayer 2019), PV industry 
(Triana et al. 2018), smart living (Solaimani et al. 2015) or circular 
economy (Vence and Pereira 2019). 
9.5.3 Adoption of Climate Mitigation Solutions – 
Reasons and Willingness
Mixed effects are found for technical issues, attitudes, and values 
(Table 9.3). In spite of proven positive environmental attitudes and

willingness to adopt mitigation solutions, these are outweighed by 
financial aspects all over the world (Mata et  al. 2021b). Adopters 
in Developed Countries are more sensitive towards financial issues 
and comfort disruptions; whereas in other world regions techno-
economic concerns prevail. Private consumers seem ready to support 
stronger governmental action, whereas non-private interventions are 
hindered by constraints in budgets and profits, institutional barriers 
and complexities (Curtis et al. 2017; Zuhaib et al. 2017; Tsoka et al. 
2018; Kim et al. 2019).
A variety of interventions targeted to heterogeneous consumer 
groups and decision makers is needed to fulfil their diverse needs 
(Zhang et al. 2012; Haines and Mitchell 2014; Gram-Hanssen 2014; 
Marshall et  al. 2015; Friege et  al. 2016; Hache et  al. 2017; Liang 
et al. 2017; Ketchman et al. 2018; Soland et al. 2018). Policy reviews 
for specific market segments and empirical studies investigating

investment decisions would benefit from a multidisciplinary approach 
to energy consumption patterns and market maturity (Boyd 2016; 
Heiskanen and Matschoss 2017; Baumhof et al. 2018; Marzano et al. 
2018; Wilson et al. 2018).

986
986
Chapter 9 Buildings9Table 9.3 | Reasons for Adoption of Climate Mitigation Solutions.  The sign represents if the effect is positive (+) or negative (–), and the number of signs represents 
confidence level (++, many references; +, few references) (Mata et al. 2021a).
Climate mitigation solutions for buildingsBuilding envelope
Efficient technical 
systems
On-site renewable 
energy 
Behaviour
Performance 
standards
Low-carbon 
materials
Digitalisation and 
flexibility
Circular and sharing 
economy
Economic
Subsidies/microloans* + ++ ++ + ++ +
Low/high investment costs – +/–– ++/–– +/– +/–– +/– – –
Short payback period + + + + + + +
High potential savings ++ ++ ++ + ++ ++ +
Market-driven demand + + + + +
Higher resale value + + + + +
Operating/maintenance costs + ++/– ++/– + + + +/–
Split incentives – – – – – –
Constrained budgets and profits – –– – –– – –– ––
Price competitive (overall) + + + + + +
Information and support

Governmental support and capacity/lack of +/– +/– ++/– ++/– + +/– –
Institutional barriers and complexities – – – – –– – – –
Information and labelling/lack of +/– ++/– ++/– + ++/– +/– –
Smart metering + + + +
Participative ownership + + + + +
Peer effects + + ++ + +
Professional advice/lack of +/– ++/– ++/– – +/–– – +/– +/–
Social norm + + + + + + +
Previous experience with solution/lack of +/– +/– +/– – – – +/– +/–
Technical
Condition of existing elements + + + + + +
Natural resource availability + + ++ + + +
Performance and maintenance concerns* – – –– –– – – –
Low level of control over appliances – – – – –
Limited alternatives available – – – –
Not compatible with existing equipment – – – – – –
Attitudes and values
Appealing novel technology + + ++ + + + ++ +
Social and egalitarian world views + + + + +
Willingness to pay + ++ + +
Heritage or aesthetic values +/– ++/– +/– +/– +/–
Environmental values + + ++ + ++ + ++ +
Status and comfort/Lack of ++ ++ ++ + ++ +

Discomfort during the retrofitting period – – – – –
Control, privacy, and security/Lack of* +/– +/– – – – +/––
Risk aversion – – – – – –
Social
Size factors (household, building) +/– ++/– + + +
Status (education, income) +/– ++/– +/– +/– +/– + +/–
Socio-demographic (age, gender, and ethnicity) +/– ++/– +/– +/– +/– +/–

987
987
Buildings Chapter 999.5.3.1 Building Envelope
In North America and Europe, personal attitudes, values, and 
existing information and support are the most and equally important 
reasons for improving the building envelope. Consumers have some 
economic concerns and little technical concerns, the latter related to 
the performance and maintenance of the installed solutions (Mata 
et al. 2021a). In other world regions or climate zones the literature 
is limited.
Motivations are often triggered by urgent comfort or replacement 
needs. Maintaining the aesthetic value may as well hinder the 
installation of insulation if no technical solutions are easily available 
(Haines and Mitchell 2014; Bright et al. 2019). Local professionals 
and practitioners can both encourage (Friege 2016; Ozarisoy and 
Altan 2017) and discourage the installation of insulation, according 
to their knowledge and training (Curtis et al. 2017; Zuhaib et al. 2017;

Maxwell et al. 2018; Tsoka et al. 2018). If energy renovations of the 
buildings envelopes are not normative, cooperative ownership may 
be a  barrier in apartment buildings (Miezis et  al. 2016). Similarly, 
product information and labelling may be helpful or overwhelming 
(Ozarisoy and Altan 2017; Lilley et  al. 2017; Bright et  al. 2019). 
Decisions are correlated to governmental support (Swantje et  al. 
2015; Tam et  al. 2016) and peer information (Friege et  al. 2016; 
Friege 2016).
The intervention is required to be cost efficient, although value could 
be placed in the amount of energy saved (Mortensen et  al. 2016; 
Lilley et al. 2017; Howarth and Roberts 2018; Kim et al. 2019) or the 
short payback period (Miezis et al. 2016). Subsidies have a positive 
effect (Swan et al. 2017).
9.5.3.2 Adoption of Efficient HVAC Systems and Appliances
Mixed willingness is found to adopt efficient technologies. While 
Developed Countries are positive towards building envelope

technologies, appliances such as A-rated equipment or condensing 
boilers are negatively perceived (Yohanis 2012). In contrast, adopters 
in Asia are positive towards energy-saving appliances (Liao et  al. 
2020; Spandagos et al. 2020).
Comfort, economic and ecological aspects, as well as information 
influence the purchase of a  heating system (Claudy et  al. 2011; 
Decker and Menrad 2015). Information and support from different 
stakeholders are the most relevant aspects in different geographical 
contexts (Hernandez-Roman et al. 2017; Tumbaz and Moğulkoç 2018; 
Curtis et al. 2018; Bright et al. 2019; Chu and Wang 2019). 
Among high-income countries, economy aspects have positive 
effects, specially reductions in energy bills and financial incentives 
or subsidies (Chun and Jiang 2013; Christidou et al. 2014; Mortensen 
et  al. 2016; Clancy et  al. 2017; Ketchman et  al. 2018). Having 
complementary technologies already in place also has positively

affects adoption (Zografakis et  al. 2012; Clancy et  al. 2017), but 
performance and maintenance concerns appear as barriers (Qiu et al. 
2014). The solutions are positively perceived as high-technology 
innovative, to enhance status, and are supported by peers and own-environmental values (Mortensen et  al. 2016; Heiskanen and 
Matschoss 2017; Ketchman et al. 2018). 
9.5.3.3 Installation of Renewable Energy Sources (RES)
Although consumers are willing to install distributed RES worldwide, 
and information has successfully supported their roll out, economic 
and governmental support is still necessary for their full deployment. 
Technical issues remain for either very novel technologies or for 
the integration of RES in the energy system (Ürge-Vorsatz et  al. 
2020; Mata et al. 2021a). Capacities are to be built by coordinated 
actions by all stakeholders (Musonye et al. 2020). To this aim, energy 
communities and demonstrative interventions at local scale are key

to address technical, financial, regulatory and structural barriers and 
document long-term benefits (von Wirth et al. 2018; Shafique et al. 
2020; Fouladvand et al. 2020).
Regarding solar technologies, heterogeneous decisions are formed 
by socio-demographic, economic and technical predictors interwoven 
with a variety of behavioural traits (Alipour et al. 2020; Khan 2020). 
Studies on PV adoption confirm place-specific (various spatial 
and peer effects), multi-scalar cultural dynamics (Bollinger and 
Gillingham 2012; Schaffer and Brun 2015; Graziano and Gillingham 
2015). Environmental concern and technophilia drive the earliest PV 
adopters, while later adopters value economic gains (Hampton and 
Eckermann 2013; Jager-Waldau et al. 2018; Abreu et al. 2019; Palm 
2020). Previous experience with similar solutions increases adoption 
(Baumhof et al. 2018; Qurashi and Ahmed 2019; Bach et al. 2020; 
Reindl and Palm 2020). 
9.5.3.4 Low-carbon Materials

Studies on low-carbon materials tend to focus on wood-based 
building systems and prefabricated housing construction, mostly 
in high-income countries, as many sustainable managed forestries 
and factories for prefabricated housing concentrated in such regions 
(Mata et al. 2021a). This uneven promotion of wood can lead to its 
overconsumption (Pomponi et al. 2020). 
Although the solutions are not yet implemented at scale, examples 
include the adoption of low carbon cement in Cuba motivated by the 
possibility of supplying the rising demand with low initial investment 
costs (Cancio Díaz et al. 2017) or adoption of bamboo-based social 
houses in The Philippines motivated by local job creation and typhoon 
resistance (Zea Escamilla et al. 2016). More generally, low investment 
costs and high level decision-making, for example, political will and 
environmental values of society, increase the adoption rate of low-
carbon materials (Steinhardt and Manley 2016; Lien and Lolli 2019;

Hertwich et al. 2020). In contrast, observed barriers include lobbying 
by traditional materials industries, short-term political decision 
making (Tozer 2019) and concerns over technical performance, risk 
of damage, and limited alternatives available (Thomas et al. 2014). 
9.5.3.5 Digitalisation and Demand-supply Flexibility
Demand-supply flexibility measures are experimentally being adopted 
in North America, Europe, and Asia-Pacific Developed  regions.

988
988
Chapter 9 Buildings9Changes  in the current regulatory framework would facilitate 
participation based on trust and transparent communication 
(Wolsink 2012; Nyborg and Røpke 2013; Mata et al. 2020b). However, 
consumers expect governments and energy utilities to steer the 
transition (Seidl et al. 2019).
Economic challenges are observed, as unclear business models, 
disadvantageous market models and high costs of advanced smart 
metering. Technical challenges include constraints for HPs and 
seasonality of space heating demands. Social challenges relate to lack 
of awareness of real-time price information and inadequate technical 
understanding. Consumers lack acceptance towards comfort changes 
(noise, overnight heating) and increased automation (Drysdale et al. 
2015; Bradley et  al. 2016; Sweetnam et  al. 2019). Risks identified 
include higher peaks and congestions in low price-hours, difficulties 
in designing electricity tariffs because of conflicts with CO 2 intensity,

and potential instability in the entire electricity system caused by 
tariffs coupling to wholesale electricity pricing.
Emerging market players are changing customer utility relationships, 
as the grid is challenged with intermittent loads and integration 
needs for ICTs, interfering with consumers requirements of autonomy 
and privacy (Wolsink 2012; Parag and Sovacool 2016). Although 
most private PV owners would make their storage system available 
as balancing load for the grid operator, the acquisition of new 
batteries by a majority of consumers requires incentives (Gährs et al. 
2015). For distributed energy hubs, social acceptance depends on the 
amount of local benefits in economic, environmental or social terms 
(Kalkbrenner and Roosen 2015), and increases around demonstration 
projects (von Wirth et al. 2018).
9.5.3.6 Circular and Sharing Economy
The circular and sharing economy begins to be perceived as 
organisational and technologically innovative, with the potential

to provide superior customer value, response to societal trends and 
positive marketing (Mercado 2018; Cantzler et  al. 2020; Nußholz 
et  al. 2020). Although technical and regulatory challenges remain, 
there are key difficulties around the demonstration of a business case 
for both consumers and the supply chain (Pomponi and Moncaster 
2017; Hart et al. 2019).
Government support is needed as an initiator but also to reinforce 
building retrofit targets, promote more stringent energy and material 
standards for new constructions, and protect consumer interests 
(Hongping 2017; Fischer and Pascucci 2017; Patwa et al. 2020). Taxes 
clearly incentivise waste reduction and recycling (Rachel and Travis 
2011; Ajayi et  al. 2015; Volk et  al. 2019). In developing countries, 
broader, international, market boundaries can allow for a  more 
attractive business model (Mohit et al. 2020). Participative and new 
ownership models can favour the adoption of prefabricated buildings

(Steinhardt and Manley 2016). Needs for improvements are observed, 
in terms of design for flexibility and deconstruction, procurement 
and prefabrication and off-site construction, standardisation and 
dimensional coordination, with differences among solutions (Osmani 
2012; Coehlo et al.2013; Lu and Yuan 2013; Cossu and Williams 2015; 
Schiller et al. 2015, 2017; Ajayi et al. 2017; Bakshan et al. 2017).Although training is a basic requirement, attitude, past experience, 
and social pressure can also be highly relevant, as illustrated for 
waste management in a survey to construction site workers (Amal 
et  al. 2017). Traditional community practices of reuse of building 
elements are observed to be replaced by a culture of waste (Ajayi 
et al. 2015; Hongping 2017).
9.6 Global and Regional Mitigation 
Potentials and Costs
9.6.1 Review of Literature Calculating Potentials 
for Different World Countries
Section  9.4 provides an update on technological options and

practices, which allow constructing and retrofitting individual 
buildings to produce very low emissions during their operation 
phase. Since AR5, the world has seen a  growing number of such 
buildings in all populated continents, and a  growing amount of 
literature calculates the mitigation potential for different countries 
if such technologies and practices penetrate at scale. Figure  9.15 
synthesises the results of sixty-seven bottom-up studies, which 
rely on the bottom-up technology-reach approach and assess the 
potential of such technologies and practices, aggregated to stock of 
corresponding products and/or buildings at national level. 
The studies presented in Figure  9.15 rely on all, the combination, 
or either of the following mitigation strategies: the construction 
of new high energy-performance buildings taking the advantage of 
building design, forms, and passive construction methods; the thermal

efficiency improvement of building envelopes of the existing stock; the 
installation of advanced HVAC systems, equipment and appliances; 
the exchange of lights, appliances, and office equipment, including 
ICT, water heating, and cooking with their efficient options; demand-
side management, most often controlling comfort requirements and 
demand-side flexibility and digitalisation; as well as onsite production 
and use of renewable energy. Nearly all studies, which assess 
the technological potential assume such usage of space heating, 
cooling, water heating, and lighting that does not exceed health, 
living, and working standards, thus realising at least a  part of the 
non-technological potential, as presented in Figure 9.14. The results 
presented in Figure  9.15 relate to measures applied within the 
boundaries of the building sector, including the reduction in direct and 
indirect emissions. The results exclude the impact of decarbonisation

measures applied within the boundaries of the energy supply sector, 
that is, the decarbonisation of grid electricity and district heat. 
The analysis of Figure  9.15 illustrates that there is a  large body of 
literature attesting to mitigation potential in the countries of Europe 
and North America of up to 55–85% and in Asia-Pacific Developed of 
up to 45% in 2050, as compared to their sector baseline emissions, 
even though they sometimes decline. For developing countries, 
the literature estimates the potential of up to 40–80% in 2050, as 
compared to their sharply growing baselines. The interpretation 
of these estimates should be cautious because the studies rely 
on assumptions with uncertainties and feasibility constrains (see 
Sections 9.6.4, Figure 9.20 and Supplementary Material Table 9.SM.6).

989
989
The novelty since AR5 is emerging bottom-up literature, which 
attempts to account for potential at national and global level from applying the sufficiency approach (see Box  9.1 in Section  9.1 and decomposition analysis in Section  9.3.2). In spite of the reducing energy use per unit of floor area at an average rate of 1.3% per year, the growth of floor area at an average rate of 3% per year causes rising energy demand and GHG emissions because each new square meter must be served with thermal comfort and/or other amenities
(International Energy Agency 2017; Ellsworth-Krebs 2020). Nearly all 
studies reviewed in F
igure 9.15 assume the further growth of floor 
area per capita until 2050, with many studies of developing countries targeting today per capita floor area as in Europe.
Table  9.4 reviews the bottom-up literature, which quantifies the 
potential from reorganisation of human activities, efficient design,

planning, and use of building space, higher density of building and settlement inhabitancy, redefining and downsizing goods and equipment, limiting their use to health, living, and working standards, and their sharing, recognising the number of square meters and devices as a determinant of GHG emissions that could be impacted via policies and measures. Nearly all national or regional studies originate from Europe and North America recognising challenges, Developed Countries face toward decarbonisation. Thus, Goldstein et  al. (2020) suggested prioritising the reduction in floor space of wealthier population and more efficient space planning because grid decarbonisation is not enough to meet the U.S. target by 2050 whereas affluent suburbs may have 15 times higher emission footprints than nearby neighbourhoods. Cabrera Serrenho et  al. (2019) argue that reducing the UK floor area is a  low cost mitigation option given a low building replacement rate and unreasonably high retrofit costs

of existing buildings. Lorek and Spangenberg (2019) discusses the opportunity of reducing building emissions in Germany fitting better the structure of the dwelling stock to the declined average household size, as most dwellings have 3–4 rooms while most households have only one person.

Whereas these studies suggest sufficiency as an important option for 
Developed Countries, global studies argue that it is also important for the developing world. This is because it provides the means to address inequality, poverty reduction and social inclusion, ensuring 0%10%20%30%40%50%60%70%80%90%100%Mitigation Potential [%]
North
AmericaCO2 mitigation potential
in 2050, as compared 
to the baseline CO2 mitigation potential in 
other year than 2050, as comparedto the baseline or base yearPrimary or ﬁnal energy savings in 2050, as compared to the baseline Primary or ﬁnal energy savings 
in other year than 2050, as compared 
to the baseline or base year
Europe Australia 
and JapanEurasia Africa Middle
AsiaSouth-East Asia
and PaciﬁcLatin
America and
CaribbeanSouthern
Asia
Figure 9.15 | Potential GHG emission reduction in buildings of different world countries grouped by region, as reported by sixty-seven bottom-up

studies.  Sources: North America: Canada (Trottier 2016; Radpour et al. 2017; Subramanyam et al. 2017a,b; Zhang et al. 2020a), the Unites States of America (Gagnon et al. 
2016; Nadel 2016; Yeh et al. 2016; Wilson et al. 2017; Zhang et al. 2020a); Europe: Albania (Novikova et al. 2020, 2018c), Austria (Ploss et al. 2017), Bulgaria, the Czech

Republic, Hungary (Csoknyai et al. 2016), France (Ostermeyer et al. 2018b), the European Union (Duscha et al. 2019; Roscini et al. 2020; Brugger et al. 2021), Germany (Markewitz et al. 2015; Bürger et al. 2019; Ostermeyer et al. 2019b), Greece (Mirasgedis et al. 2017), Italy (Calise et al. 2021; Filippi Oberegger et al. 2020), Lithuania (Toleikyte et al. 2018), Montenegro (Novikova et al. 2018c), Netherlands (Ostermeyer et al. 2018c), Norway (Sandberg et al. 2021), Serbia (Novikova et al. 2018a), Switzerland (Iten et al. 2017; Streicher et al. 2017), Poland (Ostermeyer et al. 2019a), the United Kingdom (Ostermeyer et al. 2018a); Eurasia: Armenia, Georgia (Timilsina et al. 2016); the Russian Federation (Bashmakov 2017; Zhang et al. 2020a); Australia (Energetics 2016; Butler et al. 2020; Zhang et al. 2020a), Japan (Momonoki et al. 2017; Wakiyama and Kuramochi 2017; Minami et al. 2019; Zhang et al. 2020a; Sugyiama et al. 2020); Africa: Egypt (Makumbe et al. 2017; Calise et al. 2021),

Morocco (Merini et al. 2020), Nigeria (Dioha et al. 2019; Kwag et al. 2019; Onyenokporo and Ochedi 2019), Rwanda (Colenbrander et al. 2019), South Africa (Department of Environmental Affairs 2014), Uganda (de la Rue du Can et al. 2018), Algeria, Egypt, Libya, Morocco, Sudan, Tunisia (Krarti -2019); Middle East – Qatar (Krarti et al. 2017; Kamal et al. 2019), Saudi Arabia (Alaidroos and Krarti 2015; Khan et al. 2017), Bahrain, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, State of Palestine, Syrian Arab Republic, United Arab Emirates, Yemen (Krarti 2019); Eastern Asia – China (Tan et al. 2018; Zhou et al. 2018; Xing et al. 2021; Zhang et al. 2020); Southern Asia: India (Yu et al. 2018; de la Rue du Can et al. 2019; Zhang et al. 2020); South-East Asia and Pacific: Indonesia (Kusumadewi and Limmeechokchai 2015, 2017), Thailand (Kusumadewi and Limmeechokchai 2015, 2017; Chaichaloempreecha et al. 2017), Vietnam (ADB 2017), respective countries from the Asia-Pacific Economic

Cooperation (APEC) (Zhang et al. 2020a); Latin America and Caribbean: Brazil (de Melo and de Martino Jannuzzi 2015; González-Mahecha et al. 2019), Colombia (Prada-Hernández et al. 2015), Mexico (Grande-acosta and Islas-samperio 2020; Rosas-Flores and Rosas-Flores 2020).

990
990
the provision of acceptable living standards for the entire global 
population given the planetary boundaries. As Figure 9.6 illustrates, 
the largest share of current construction occurs in developing 
countries, while these countries follow a similar demographic track 
of declining household sizes versus increasing dwelling areas. This 
trajectory translates into the importance of their awareness of the 
likely similar forthcoming challenges, and the need in early efficient 
planning of infrastructure and buildings with a focus on space usage 
and density.
9.6.2 Assessment of the Potentials at Regional 
and Global Level
This section presents an aggregation of bottom-up potential 
estimates for different countries into regional and then global 
figures for 2050, based on literature presented in Section  9.6.1. 
First, national potential estimates reported as a  share of baseline 
emissions in 2050 were aggregated into regional potential estimates.

Second, the latter were multiplied with regional baseline emissions 
to calculate the regional potential in absolute numbers. Third, the 
global potential in absolute numbers was calculated as a  sum of regional absolute potentials. When several bottom-up studies were 
identified for a region, either a rounded average or a rounded median 
figure was taken, giving the preference to the one that was closest 
to the potential estimates of countries with very large contribution to 
regional baseline emissions in 2050 (e.g., to China in Eastern Asia). 
Furthermore, we preferred studies, which assessed the whole or 
a large share of sector emissions and considered a comprehensive 
set of measures. The regional baseline emissions, refer to the World 
Energy Outlook (WEO) Current Policy Scenario (International Energy 
Agency 2019c). The sector mitigation potential reported in Chapter 12 
for the year 2030 was estimated in the same manner.

Figure 9.16 presents the mitigation potential in the building sector 
for the world and each region in 2050, estimated as a  result of 
this aggregation exercise. The potentials presented in the figure 
are different from those reported in Section  9.3.3, where they are 
estimated by IEA and IMAGE hybrid model. The figure provides 
two breakdowns of the potential, into the reduction of direct and 
indirect emissions as well as into the reduction of emissions from 
introducing sufficiency, energy efficiency, and renewable energy 
measures. The potential estimates rely on the incremental stepwise 
approach, assembling the measures according to the SER framework Table 9.4 | Potential GHG emission reduction in the building sector offered by the introduction of sufficiency as a main or additional measure, as 
reported by bottom-up (or hybrid) literature.
Region Reference Scenario and its result Sufficiency for floor space
GlobeGrubler et al.

(2018)The Low Energy Demand Scenario halves the final energy demand of buildings by 2050,
as compared the WEO Current Policy (International Energy Agency 2019c) by modelling
the changes in quantity, types, and energy intensity of services.The scenario assumed a reduction in the
residential and non-residential building floor
area to 29 and 11 m2 cap–1 respectively.
GlobeMillward-Hopkins
et al. (2020)With the changes in structural and technological intensity, the Decent Living 
Energy scenario achieved the decent living standard for all while reducing the final 
energy consumption of buildings by factor three, as compared to the WEO Current 
Policy Scenario (International Energy Agency 2019c).The scenario assumed a reduction in floor area
to 15 m2 cap–1 across the world.
GlobeLevesque et al.
(2019)Realising both the technological and sufficiency potential, the Low Demand Scenario
and the Very Low Demand Scenario calculated a reduction in global building energy

demand by 32% and 45% in 2050, as compared to the business-as-usual baseline.The Low Scenario limited the residential and
non-residential floor area to 70 and 23 m2 cap–1;
the Very Low Scenario – to 45 and 15 m2 cap–1.
EUBierwirth and
Thomas (2019b)For the EU residential sector, the authors calculated potential energy savings of 17%
and 29% from setting the per capita floor area limits.A reduction of the residential floor area to 
30 m2 cap–1 and 35 m2 cap–1, respectively.
EURoscini et al.
(2020)With the help of technological and non-technological measures, the Responsible Policy
Scenario for the EU buildings allows achieving the emission reduction by 60% in 2030,
as compared to 2015.The scenario assumed 6% decrease in the
residential per capita floor area (to max.
44.8 m2 cap–1).
Canada, UK,
France, Italy,
Japan, USA,
GermanyHertwich et al.
(2020)The potential reduction in GHG emissions from the production of building materials

is 56–58% in 2050, as compared to these baseline emissions. The reduction in heating
and cooling energy demand is 9–10% in 2050, as compared to its baseline.Via the efficient use of living space, the scenario
assumed its 20% reduction, as compared to its
baseline development.
UKCabrera Serrenho
et al. (2019)The scenario found that the sufficiency measures allowed mitigating 30% of baseline
emissions of the English building sector in 2050, without other additional measures.The scenario assumed a 10% reduction in the
current floor area per capita by 2050.
USAGoldstein et al.
(2020)The scenario calculated 16% GHG mitigation potential in 2050, as compared 
to the baseline, on the top of two other scenarios assuming building retrofits 
and grid decarbonisation already delivering a 42% emission reduction.The scenario assumed a 10% reduction in
per capita floor area and higher penetration
of onsite renewable energy.
SwitzerlandRoca-Puigròs et al.

(2020)The Green Lifestyle scenario allows achieving 48% energy savings by 2050, as compared
to the baseline, due to sufficiency in the floor area among other measures.The scenario assumed a reduction in residential
floor area. from 47 to 41 m2 cap–1.
France Negawatt (2017)The Negawatt scenario assumes that sufficiency behaviour becomes a mainstream 
across all sectors. In 2050, the final energy savings are 21% and 28% for the residential 
and tertiary sectors respectively, as compared to their baselines.The scenario assumes a limit of the residential
floor at 42 m2 cap–1 due to apartment sharing
and compact urban planning.
FranceVirage-Energie
Nord-Pas-de-
Calais. (2016)The authors assessed sufficiency opportunities across all sectors for the Nord-Pas-de-Calais
region of France. Depending on the level of implementation, sufficiency could reduce the
energy consumption of residential and tertiary buildings by 13–30% in 2050, as compared

to the baseline.The scenario assumed sharing spaces,
downsizing spaces and sharing equipment
from a ‘soft’ to ‘radical’ degree.

991
991
Potential emission reduction from sufﬁciency
Potential emission reduction from demand-side energy efﬁciencyPotential emission reduction from onsite renewable energy technologies
Potential reduction of indirect emissions
Potential emission reduction of direct GHG emissions
Indirect emissionsDirect emissions63089883
4036 403629993479
1168 116817%44%
9%42%10%
025005000750010,00012,50015,000
2020 2050 2050 205061%
12431054
236 236594
589
257 25720%50%
11%34%25%
03006009001200150018002100
2020 2050 2050 2050
188309
162 162114154
58 5821%32%
14%33%5%
2020 2050 2050 205053%1107 1172
566 566921 852
122 12236%30%
13%38%15%
2020 2050 2050 2050MtCO2 North America MtCO2 Europe and Eurasia
66%
53%
235764
396 396118392
153 15321%32%
10%38%5%
0200400600800100012001400
2020 2050 2050 2050516835
241 241218441
172 17221%47%
19%42%8%
2020 2050 2050 2050MtCO2 Africa MtCO2 Middle East
68%

04008001200160020002400
2020 2050 2050 2050MtCO2 Latin America and Caribbean MtCO2 Eastern Asia483406
213 213177
158
91 9112%34%
5%32%10%
2020 2050 2050 2050MtCO2 World MtCO2 Australia, Japan/uni00A0and New Zealand
46%
70%
4861785
742 742152223
117 1175%52%
1%51%5%
2020 2050 2050 205057%
4911600
925 925125188
94945%38%
1%37%5%
2020 2050 2050 2050MtCO2 Southern Asia MtCO2 South-East Asia and Paciﬁc
43%15591960
555 555580483
10510515%58%
9%54%10%
73%
Figure 9.16 | Global and regional estimates of GHG emissions in the building sector in 2020 and 2050, and their potential reduction in 2050 broken 
down by measure (sufficiency/energy efficiency/renewable energy) and by emission source (direct/indirect). Note: the baseline refers to the WEO Current Policy 
Scenario (International Energy Agency 2019c). It may differ from other chapters.

992
992
Chapter 9 Buildings9(Box 9.1) and correcting the amount of the potential at each step for 
the interaction of measures. The sequence of energy efficiency and 
renewable energy measures follow the conclusion of the IPCC Special 
Report on Global Warming of 1.5°C (SR1.5) (Rogelj et al. 2018) that 
lower energy demand allows more choice of low-carbon energy 
supply options, and therefore such sequencing is more beneficial and 
cost-effective.
Figure 9.16 argues that it is possible to mitigate 8.2 GtCO 2 or 61% 
of global building emissions in 2050, as compared to their baseline. 
At least 1.4 GtCO 2 or 10% of baseline emissions could be avoided 
introducing the sufficiency approaches. Further 5.6 GtCO 2 or 42% 
of baseline emissions could be mitigated with the help of energy 
efficiency technologies and practices. Finally, at least 1.1 GtCO 2 or 
9% of baseline emissions could be reduced through the production 
and use of onsite renewable energy. Out of the total potential, the

largest share of 5.4 GtCO 2 will be available in developing countries; 
these countries will be able to reduce 59% of their baseline emissions. 
Developed Countries will be able to mitigate 2.7 GtCO 2 or 65% of 
their baseline emissions. Only few potential studies, often with only 
few mitigation options assessed, were available for the countries of 
South-East Asia and Pacific, Africa, and Latin America and Caribbean; 
therefore, the potential estimates represent low estimates, and the 
real potentials are likely be higher.
9.6.3 Assessment of the Potential Costs
The novelty since AR5 is that a growing number of bottom-up studies 
considers the measures as an integrated package recognising their 
technological complementarity and interdependence, rather than 
the linear process of designing and constructing buildings and 
their systems, or incremental improvements of individual building 
components and energy-using devices during building retrofits,

losing opportunities for the optimisation of whole buildings. 
Therefore, integrated measures rather than the individual measures 
are considered for the estimates of costs and potentials. Figure 9.17 
presents the indicative breakdown of the potential reported in 
Figure 9.16 by measure and cost, to the extent that it was possible 
to disaggregate and align to common characteristics. Whereas the 
breakdown per measure was solely based on the literature reviewed 
in Section  9.6.1, the cost estimates additionally relied on the 
literature presented in this section, Figure 9.20, and Supplementary 
Material Table  9.SM.6. The literature reviewed reports fragmented 
and sometimes contradicting cost-effectiveness information. 
Despite a large number of exemplary buildings achieving very high 
performance in all parts of the world, there is a lack of mainstream 
literature or official studies assessing the costs of these buildings at 
scale (Lovins 2018; Ürge-Vorsatz et al. 2020).

Figure  9.17 indicates that a  very large share of the potential in 
Developed Countries could be realised through the introduction 
of sufficiency measures (at least 18% of their baseline emissions). 
Literature identifies many opportunities, which may help 
operationalise it. These are reorganisation of human activities, 
teleworking, coworking, more efficient space design, planning and 
use, higher density of building and settlement inhabitancy, flexible space, housing swaps, shared homes and facilities, space and room 
renting, and others (Bierwirth and Thomas 2019a; Ivanova and Büchs 
2020; Ellsworth-Krebs 2020). Whereas literature does not provide 
a  robust cost assessment of the sufficiency potential, it indicates 
that these measures are likely to be at no or very little cost (Cabrera 
Serrenho et al. 2019).
The exchange of lights, appliances, and office equipment, including 
ICT, water heating, and cooking technologies could reduce more than

8% and 13% of the total sector baseline emissions in developed 
and developing countries respectively, typically at negative cost 
(Department of Environmental Affairs 2014; de Melo and de Martino 
Jannuzzi 2015; Prada-Hernández et  al. 2015; Subramanyam et  al. 
2017a,b; González-Mahecha et  al. 2019; Grande-Acosta and Islas-
Samperio 2020). This cost-effectiveness is, however, often reduced 
by a larger size of appliances and advanced features, which offset 
a share of positive economic effects (Molenbroek et al. 2015). 
Advanced HVAC technologies backed-up with demand-side 
management, and onsite integrated renewables backed-up with 
demand-side flexibility and digitalisation measures are typically a part 
of the retrofit or construction strategy. Among HVAC technologies, 
heat pumps are very often modelled to become a  central heating 
and cooling technology supplied with renewable electricity. The 
estimates of HVAC cost-effectiveness, including heat pumps, vary in

modelling results from very cost-effective to medium (Department of 
Environmental Affairs 2014; Prada-Hernández et al. 2015; Akander 
et al. 2017; Hirvonen et al. 2020). Among demand-side management, 
demand-side flexibility and digitalisation options, various sensors, 
controls, and energy consumption feedback devices have typically 
negative costs, whereas advanced smart management systems as 
well as thermal and electric storages linked to fluctuating renewables 
are not yet cost-effective (Nguyen et al. 2015; Prada-Hernández et al. 
2015; Huang et al. 2019; Uchman 2021; Duman et al. 2021; Sharda 
et al. 2021; Rashid et al. 2021). Several Developed Countries achieved 
to make onsite renewable energy production and use profitable for at 
least a part of the building stock (Horváth et al. 2016; Akander et al. 
2017; Vimpari and Junnila 2019; Fina et al. 2020), but this is not yet 
the case for developing countries (Kwag et al. 2019; Cruz et al. 2020;

Grande-Acosta and Islas-Samperio 2020). Due to characteristics 
and parameters of different building types, accommodating the 
cost-optimal renewables at large scale is especially difficult in non-
residential buildings and in urban areas, as compared to residential 
buildings and rural areas (Horváth et al. 2016; Fina et al. 2020). 
Literature agrees that new advanced buildings, using design, form, 
and passive building construction equipped with demand-side 
measures, and advanced HVAC technologies can reduce the sector 
total baseline emissions in developed and developing countries by 
at least 10% and 25% in 2050, respectively, and renewable energy 
technologies backed-up with demand-side flexibility and digitalisation 
measures typically installed in new buildings could further reduce 
these emissions by at least 11% and 7% (see also Cross-Chapter 
Box  12 in Chapter  16). The literature, however, provides different

and sometimes conflicting information of their cost-effectiveness. 
Esser et  al. (2019) reported that by 2016, the perceived share of 
buildings similar or close to NZEB in the new construction was just

993
993
above 20% across the EU. In this region, additional investment costs 
were no higher than 15%, as reported for Germany, Italy, Denmark, 
and Slovenia (Erhorn-Kluttig et al. 2019). Still, the European market 
experiences challenges which relate to capacity and readiness, as 
revealed by the Architects’ Council of Europe (ACE) (2019), which 
records a  decline in the share of architects who are designing 
buildings to NZEB standards to more than 50% of their time, from 
14% in 2016 to 11% in 2018. In contrast, the APEC countries reported 
additional investment costs of 67% on average (Xu and Zhang 2017) 
that makes them a key barrier to the NZEB penetration in developing countries as of today (Feng et al. 2019). This calls for additional R&D 
policies and financial incentives to reduce the NZEB costs (Xu and 
Zhang 2017; Kwag et al. 2019).
Thermal efficiency retrofits of existing envelopes followed up by

the exchange of HVAC backed up with demand-side measures 
could reduce the sector total baseline emissions in developed and 
developing countries by at least 18% and 7% respectively in 2050. 
There have been many individual examples of deep building retrofits, 
which incremental costs are not significantly higher than those of 61112252283
668
628
<0 USD per tCO 2
<100 USD per tCO 2
>200 USD per tCO 2Developing countries
13%
7% 7%7%25%
770
339405468
381 381
<20 USD per tCO 2
0–200 USD per tCO 2
>200 USD per tCO 2Avoid the demand for
energy services (sufﬁciency)million tCO 2Developed countries
8% 10% 9%18%
9%11%
* including ICT, water heating, and cooking.
** including the change in construction methods;  management and operation of buildings; and efﬁcient heating, ventilation, and air-conditioning.
*** typically in new high performance buildings.

**** including thermal efﬁciency of building envelopes; management and operation of buildings; and efﬁcient heating, ventilation, and air-conditioning. 
million tCO 2Efﬁcient appliances,
equipment, and lighting*
New buildings with high
energy performance**
Onsite renewable energy
production and use***
The improvement of existing 
building stock****
Avoid the demand for
energy services (sufﬁciency)
Efﬁcient appliances,
The improvement of existing
Figure 9.17 | Indicative breakdown of GHG emission reduction potential of the buildings sector in developed and developing countries into measure 
and costs in 2050, in absolute figures with uncertainty ranges and as a share of their baseline emissions.  Notes: (i) The baseline refers to the WEO Current Policy

Scenario (International Energy Agency 2019c). It may differ from other chapters. (ii) The figure merged the results of Eurasia into those of Developed Countries.

994
994
Chapter 9 Buildings9shallow retrofits. However, the literature tends to agree that cost-
effective or low cost deep retrofits are not universally applicable 
for all cases, especially in historically urban areas, indicating a large 
share of the potential in the high-cost category (Department of 
Environmental Affairs 2014; Akander et al. 2017; Paduos and Corrado 
2017; Semprini et al. 2017; Subramanyam et al. 2017b; Streicher et al. 
2017; Mata et al. 2019). Achieving deep retrofits assumes additional 
measures on the top of business-as-usual retrofits, therefore high 
rate of deep retrofits at acceptable costs are not possible in case of 
low business-as-usual rates (Streicher et al. 2020). 
For a  few studies, which conducted an assessment of the sector 
transformation aiming at emission reduction of 50–80% in 2050 
versus their baseline, the incremental investment need over the 
modelling period is estimated at 0.4–3.3% of the country annual

GDP of the scenario first year (Markewitz et  al. 2015; Bashmakov 
2017; Novikova et  al. 2018c; Kotzur et  al. 2020). These estimates 
represent strictly the incremental share of capital expenditure 
and sometimes installation costs. Therefore, these figures are not 
comparable with investment tracked against the regional or national 
sustainable finance taxonomies, as recently developed in the EU 
(European Parliament and the Council 2020), Russia (Government of 
Russian Federation 2021), South Africa (National Treasury of Republic 
of South Africa 2021), and others, or the growing literature on 
calculating the recent finance flows (Novikova et al. 2019; Valentova 
et al. 2019; Kamenders et al. 2019; Macquarie et al. 2020; Hainaut 
et al. 2021), because they are measured against other methodologies, 
which are not comparable with the methodologies used to derive the 
incremental costs by integrated assessment models and bottom-up

studies. Therefore, the gap between the investment need and recent 
investment flows is likely to be higher, than often reported.
9.6.4 Determinants of the Potentials and Costs
The fact that the largest share of the global flow area is still to be built 
offers a large potential for emission reduction that is, however, only 
feasible if ambitious building energy codes will be applied to this new 
stock (see Section 9.9.3 on building codes). The highest demand for 
additional floor area will occur in developing countries; the building 
replacement is also the highest in developing countries because 
their building lifetime could be as short as 30 years (Lixuan et  al. 
2016; Alaidroos and Krarti 2015). Whereas as of 2018, 73 countries 
had already had building codes or were developing them, only 41 had 
mandatory residential codes and 51 had mandatory non-residential 
codes (Global Alliance for Buildings and Construction et al. 2019).

Therefore, the feasibility of capturing this potential is a  subject to 
greater coverage, adoption, and strength of building codes.
Low rates of building retrofits are the major feasibility constraint 
of building decarbonisation in Developed Countries. Long building 
lifetime and their slow replacement caused a lock-in of low energy 
performance in old buildings of Developed Countries, especially in 
urban areas. A few studies of developing countries, mostly medium 
and high-income, also considered building retrofits (Prada-Hernández 
et al. 2015; Yu et al. 2018b; Zhou et al. 2018; Krarti 2019; Kamal et al. 
2019). The studies in Developed Countries tend to rely on either of the strategies: very ‘deep’ envelope retrofits followed by the exchange 
of HVAC with various advanced alternatives (Csoknyai et al. 2016; 
Novikova et al. 2018c,b; Duscha et al. 2019; Filippi Oberegger et al. 
2020) or more shallow retrofits followed by switching to low-carbon

district heating or by the exchange of current HVAC with heat pumps 
linked to onsite renewables backed up energy storages (Yeh et  al. 
2016; Kotzur et  al. 2020; Hirvonen et  al. 2020). The factors, which 
impact the feasibility of these strategies, therefore, are the building 
retrofit rates and replacement rates of building systems. To achieve 
the building stock decarbonisation by 2050, most studies reviewed 
in Figure 9.16 assume ‘deep’ retrofit rates between 2.5% and 5%, 
and even 10% per  annum. Esser et  al. (2019) reported that the 
annual renovation rate in EU-28 is around 0.2%, with relatively small 
variation across individual EU member states. Sandberg et al. (2016) 
simulated retrofit rates in eleven European countries and concluded 
that only minor future increases in the renovation rates of 0.6–1.6% 
could be expected. Therefore, without strong policies supporting 
these renovations, the feasibility to achieve such high ‘deep’ retrofit 
rates is low.

Among key factors affecting the costs-effectiveness of achieving 
high-performance buildings remain low energy prices in many 
countries worldwide (Alaidroos and Krarti 2015; Akander et al. 2017) 
and high discount rates reflecting low access to capital and high 
barriers. Copiello et al. (2017) found that the discount rate affects the 
economic results of retrofits four times higher than the energy price, 
and therefore the reduction in upfront costs and working out barriers 
are the feasibility enablers.
The good news is that literature expects a significant cost reduction 
for many technologies, which are relevant for the construction of 
high energy-performance buildings and deep retrofits. Applying 
a  technology learning curve to the data available for Europe and 
reviewing dozens of studies available, Köhler et al. (2018) estimated 
the cost reduction potential of biomass boilers, heat pumps, 
ventilation, air conditioning, thermal storages, electricity storages,

solar PVs and solar thermal systems of 14%, 20%, 46–52%, 29%, 
29%, 65%, 57%, and 43% respectively in 2050; no significant cost 
reduction potential was found, however, for established and wide-
spread insulation technologies. More investment into Research, 
Development and Demonstration (RD&D) to reduce the technology 
costs and more financial incentives to encourage uptake of the 
technologies would allow moving along this learning curve. 
Furthermore, some literature argues that the key to cost-effectiveness 
is not necessarily a reduction in costs of technologies, but a know-
how and skills of their choosing, combining, sequencing, and timing 
to take the most benefits of their interdependence, complementarity, 
and synergy as illustrated by many examples (Lovins 2018; Ürge-
Vorsatz et  al. 2020). However, the scenarios reviewed lack such 
approaches in their cost assessments. Few indicative examples of cost 
reduction at scale were provided though not by the scenario literature,

but case studies of the application of One-Stop Shop (OSS) approach 
at scale (Section 9.9.4). In 2013, the Dutch Energiesprong network 
brokered a  deal between Dutch building contractors and housing 
associations to reduce the average retrofit costs from EUR130,000 
down to EUR65,000 for 111,000 homes with building prefabrication

995
995
Buildings Chapter 99systems and project delivery models while targeting energy savings 
of 45–80% (Ürge-Vorsatz et al. 2020); out of which 10,000 retrofits 
have been realised by 2020. The French Observatory of Low Energy 
Buildings reported to achieve the cost-effective deep renovations of 
818 dwellings and 27 detached houses in France setting a cap for 
absolute primary energy consumption to achieve after renovation 
and a  cap for the budget to deliver it. The cost-effectiveness was, 
however, calculated with grants and public subsidies (Saheb 2018). 
The literature emphasises the critical role of the time between in 
2020 and 2030 for the building sector decarbonisation (IEA 2020a; 
Roscini et al. 2020). To set the sector at the pathway to realise its 
whole mitigation potential, it is critical to exponentially accelerate 
the learning of this know-how and skills to reduce the costs and 
remove feasibility constraints to enable the penetration of advanced

technologies at speed that the world has not seen before. The World 
Energy Outlook (IEA 2020c) shown in the Net Zero Emissions by 2050 
Scenario (Box 9.2) the challenges and commitments the sector will 
have to address by 2030. These include bringing new buildings and 
existing buildings to near zero, with a half of existing buildings in 
Developed Countries and a third of existing buildings in developing 
countries being retrofitted by 2030. These also mean banning the 
sale of new fossil fuel-fired boilers, as well as making heat pumps 
and very efficient appliances standard technologies. The Net Zero 
Emissions by 2050 Scenario achieves almost fully to decarbonise 
the sector by 2050, with such commitments reflected neither in 
the planning and modelling efforts (Section  9.9) nor in policies 
and commitments (Section  9.9) of most world countries, with the 
countries of South-East Asia and Pacific, Southern Asia, Africa, and

Latin America and Caribbean having the least research. 
As discussed in Section  9.6.1, the alternative and low-cost 
opportunity to reduce the sector emissions in the countries with high 
floor area per capita and the low stock turnover is offered by the 
introduction of the sufficiency approach. Section  9.9.3.1 discusses 
a range of policy instruments, which could support the realisation of 
the sufficiency potential. As the approach is new, the literature does 
not yet report experiences of these measures. In the framework of 
project OptiWohn, the German cities of Göttingen, Köln und Tübingen 
just started testing the sufficiency approach and policy measures 
for sufficiency (Stadt Göttingen 2020). Therefore, the feasibility 
of realising the sufficiency potential depends on its recognition by 
the energy and climate policy and the introduction of supporting 
measures (Samadi et al. 2017; Ellsworth-Krebs 2020; Goldstein et al.

2020). More research is needed to understand which measures will 
work and which will not. 
Similar to buildings, the energy consumption and associated 
emissions of appliances and equipment is driven by the replacement 
of old appliances and the additional stock due to the increase 
in penetration and saturation of appliances. The feasibility of 
appliance stock replacement with efficient options is higher than 
the feasibility of building stock replacement or retrofit due to their 
smaller size, shorter lifetime, and cheaper costs (Chu and Bowman 
2006; Spiliotopoulos 2019). Some literature argues that once 
appliances achieve a  particular level of efficiency their exchange 
does not bring benefits from the resource efficiency point of view (Hertwich et al. 2019). Even through the data records a permanent 
energy efficiency improvement of individual devices (Figure  9.12), 
their growing offsets energy savings delivered by this improvement.

The emerging literature suggests addressing the growing number of 
energy services and devices as a part of climate and energy policy 
(Bierwirth and Thomas 2019b). Section 9.5.2.2 describes measures 
for limiting demand for these services and Section 9.5.3.6 addresses 
reducing the number of technologies through their ownership and 
use patterns. (Grubler et al. 2018) also suggested redefining energy 
services and aggregating appliances, illustrating the reduction of 
energy demand by a  factor of 30 to substitute over 15 different 
end-use devices with one integrated digital platform. More research 
is needed to understand opportunities to realise this sufficiency 
potential for appliances, and more research is needed to understand 
policies which may support these opportunities (Bierwirth and 
Thomas 2019a). 
The difference between baselines is among the main reason for 
difference between the potential estimates in 2030 reported by

Chapter 6 on buildings of AR4 (Levine et al. 2017) and the current 
section of AR6. For Developed Countries, the sector direct and indirect 
baseline emissions in AR6 are 43% and 28% lower than those in 
AR4 respectively. For developing countries, the sector direct baseline 
emissions in AR6 are 47% lower than those in AR4, and the sector 
indirect baseline emissions are 3% higher than those in AR4. As AR6 
is closer to 2030 than AR4 and thus more precise, the likely reason 
for the difference (besides the fact that some potential was realised) 
is that AR4 overall overestimated the future baseline emissions, and 
it underestimated how quickly the fuel switch to electricity from 
other energy carriers has been happening, especially in developing 
countries. As illustrated, the baseline is one of determinant of 
the potential size and hence, all reported estimates shall only be 
interpreted together with the baseline developments.

The potential is a dynamic value, increasing with the technological 
progress. Most potential studies reviewed in Section 9.6.1 consider 
today mature commercialised or near to commercialisation 
technologies with demonstrated characteristics ‘freezing them’ 
in the potential estimates until the study target year. Until 2050, 
many of these technologies will further improve, and furthermore 
new advanced technologies may emerge. Therefore, the potential 
estimates are likely to be low estimates of the real potential volumes. 
Furthermore, models apply many other assumptions and they cannot 
always capture right emerging societal or innovation trends; these 
trends may also significantly impact the potential size into both 
directions (Brugger et al. 2021).
With the declining amount of emissions during the building 
operation stage, the share of building embodied emissions in their 
lifetime emissions will grow, also due to additional building material

(Peñaloza et al. 2018; Cabeza et al. 2021). Reviewing 650 lifecycle 
assessment case studies, Röck et al. (2020) estimated the contribution 
of embodied emissions to building lifetime emissions up to 45–50% 
for highly efficient buildings, surpassing 90% in extreme cases. 
Recently, a  significant body of research has been dedicated to 
studying the impacts of using bio-based solutions (especially timber)

996
996
Chapter 9 Buildings9for building construction instead of conventional materials, such 
as concrete and steel, because more carbon is stored in bio-based 
construction materials than released during their manufacturing. 
Assuming the aggressive use of timber in mid-rise urban buildings, 
Churkina et al. (2020) estimated the associated mitigation potential 
between 0.04–3.7 GtCO 2 per year depending on how fast countries 
adopt new building practices and floor area per  capita. Based on 
a simplified timber supply-demand model for timber-based new floor 
area globally by 2050, Pomponi et al. (2020) showed that the global 
supply of timber can only be 36% of the global demand for it between 
2020 and 2050; especially much more forest areas will be required 
in Asian countries, such as China and India and American countries, 
such as the USA, Mexico, and Argentina. Goswein et  al. (2021) 
conducted a similar detailed analysis for Europe and concluded that

current European forest areas and wheat plantations are sufficient to 
provide timber and straw for the domestic construction sector. 
The increased use of timber and other bio-based materials in buildings 
brings not only benefits, but also risks. The increased use of timber 
can accelerate degradation through poor management and the 
pressure for deforestation, as already recorded in the Amazon and 
Siberia forests, and the competition for land and resources (Carrasco 
et al. 2017; Brancalion et al. 2018; Hart and Pomponi 2020; Pomponi 
et al. 2020). Churkina et al. (2020) emphasised that promoting the 
use of more timber in buildings requires the parallel strengthening 
of legislation for sustainable forest management, forest certification 
instruments, and care for the people and social organisations that 
live in forests. In tropical and subtropical countries, the use of 
bamboo and other fibres brings more benefits and less risks than

the use of timber (ibid). One of the main barriers associated with the 
use of bio-based materials in buildings is fire safety, although there 
is extensive research on this topic (Östman et  al. 2017; Audebert 
et al. 2019). This is a particularly important criterion for the design of 
medium and high-rise buildings, which tend to be the most adequate 
typologies for denser and more compact cities. Overall, more robust 
models are needed to assess the interlinkages between the enhanced 
use of bio-based materials in the building stock and economic and 
social implications of their larger supply, as well as the associated 
competition between forest and land-use activities (for food), and 
ecological aspects. Furthermore, more research is required on how 
to change forest and building legislation and design a combination 
of policy instruments for the specific political, economic and cultural 
county characteristics (Hildebrandt et al. 2017). Benefits and risks of

enhanced use of wood products in buildings are also discussed in 
Chapter 7, Section 7.4.5.3.
9.7 Links to Adaptation
Buildings are capital-intensive and long-lasting assets designed to 
perform under a  wide range of climate conditions (Hallegatte 2009; 
Pyke et al. 2012). Their long lifespan means that the building stock will 
be exposed to future climate (Hallegatte 2009; de Wilde and Coley 2012; 
Wan et al. 2012) and, as such, adaptation measures will be necessary. 
1 CDD can be generally defined as the monthly or annual sum of the difference between an indoor set point temperature and outdoor air temperature whenever the latter 
is higher than a given threshold temperature (Mistry 2019).The impacts of climate change on buildings can affect building 
structures, building construction, building material properties, indoor 
climate and building energy use (Andrić et al. 2019). Many of those 
impacts and their respective adaptation strategies interact with GHG

mitigation in different ways.
9.7.1 Climate Change Impacts and Adaptation 
in Buildings
A large body of literature on climate impacts on buildings focuses 
on the impacts of climate change on heating and cooling needs 
(de Wilde and Coley 2012; Wan et al. 2012; Andrić et al. 2019). The 
associated impacts on energy consumption are expected to be higher 
in hot summer and warm winter climates, where cooling needs are 
more relevant (Li et al. 2012; Wan et al. 2012; Andrić et al. 2019). If not 
met, this higher demand for thermal comfort can impact health, sleep 
quality and work productivity, having disproportionate effects on 
vulnerable populations and exacerbating energy poverty (Biardeau 
et al. 2020; Sun et al. 2020; Falchetta and Mistry 2021) (Section 9.8). 
Increasing temperatures can lead to higher cooling needs and, 
therefore, energy consumption (Li et  al. 2012; Schaeffer et  al. 
2012; Wan et  al. 2012; Clarke et  al. 2018; International Energy

Agency 2018; Andrić et al. 2019). Higher temperatures increase the 
number of days/hours in which cooling is required and as outdoor 
temperatures increase, the cooling load to maintain the same indoor 
temperature will be higher (Andrić et al. 2019). These two effects are 
often measured by cooling degree-days1 (CDD) and there is a vast 
literature on studies at the global (Isaac and van Vuuren 2009; Atalla 
et  al. 2018; Clarke et  al. 2018; Mistry 2019; Biardeau et  al. 2020) 
and regional level (Zhou et al. 2014; Bezerra et al. 2021; Falchetta 
and Mistry 2021). Other studies use statistical econometric analyses 
to capture the empirical relationship between climate variables and 
energy consumption (Auffhammer and Mansur 2014; van Ruijven 
et al. 2019). A third effect is that higher summer temperatures can 
incentivise the purchase of space cooling equipment (Auffhammer 
2014; De Cian et  al. 2019; Biardeau et  al. 2020), especially in

developing countries (Pavanello et al. 2021). 
The impacts of increased energy demand for cooling can have 
systemic repercussions (Ciscar and Dowling 2014; Ralston Fonseca 
et al. 2019), which in turn can affect the provision of other energy 
services. Space cooling can be an important determinant of peak 
demand, especially in periods of extreme heat (International Energy 
Agency 2018). Warmer climates and higher frequency and intensity 
of heat waves can lead to higher loads (Dirks et al. 2015; Auffhammer 
et al. 2017), increasing the risk of grid failure and supply interruptions.
Although heating demand in cold climate regions can be expected 
to decrease with climate change and, to a  certain extent, outweigh 
the increase in cooling demand, the effects on total primary energy 
requirements are uncertain (Li et  al. 2012; Wan et  al. 2012). Studies 
have found that increases in buildings energy expenditures for cooling

997
997
Buildings Chapter 99more than compensate the savings from lower heating demands in 
most regions (Clarke et al. 2018). In addition, climate change may affect 
the economic feasibility of district heating systems (Andrić et al. 2019). 
In cold climates, a  warming climate can potentially increase the 
risk of overheating in high-performance buildings with increased 
insulation and airtightness to reduce heat losses (Gupta and Gregg 
2012). In such situations, the need for active cooling technologies 
may arise, along with higher energy consumption and GHG emissions 
(Gupta et al. 2015). 
Changes in cloud formation can affect global solar irradiation 
and, therefore, the output of solar photovoltaic panels, possibly 
affecting on-site renewable energy production (Burnett et al. 2014). 
The efficiency of solar photovoltaic panels and their electrical 
components decreases with higher temperatures (Bahaidarah et al.

2013; Simioni and Schaeffer 2019). However, studies have found that 
such effects can be relatively small (Totschnig et al. 2017), making 
solar PV a robust option to adapt to climate change (Shen and Lior 
2016; Santos and Lucena 2021) (see Section 9.4). 
Climate change can also affect the performance, durability and 
safety of buildings and their elements (facades, structure, etc.) 
through changes in temperature, humidity, wind, and chloride and 
CO2 concentrations (Bastidas-Arteaga et al. 2010; Bauer et al. 2018; 
Rodríguez-Rosales et al. 2021; Chen et al. 2021). Historical buildings 
and coastal areas tend to be more vulnerable to these changes 
(Huijbregts et al. 2012; Mosoarca et al. 2019; Cavalagli et al. 2019; 
Rodríguez-Rosales et al. 2021). 
Temperature variations affect the building envelope, for example, 
with cracks and detachment of coatings (Bauer et al. 2016, 2018). 
Higher humidity (caused by wind-driven rain, snow or floods) hastens

deterioration of bio-based materials such as wood and bamboo 
(Brambilla and Gasparri 2020), also deteriorating indoor air quality 
and users health (Huijbregts et al. 2012; Grynning et al. 2017; Lee 
et al. 2020). 
Climate change can accelerate the degradation of reinforced 
concrete structures due to the increase of chloride ingress (Bastidas-
Arteaga et al. 2010) and the concentration of CO 2, which increase 
the corrosion of the embedded steel (Stewart et  al. 2012; Peng 
and Stewart 2016; Chen et al. 2021). Corrosion rates are higher in 
places with higher humidity and humidity fluctuations (Guo et  al. 
2019), and degradation could be faster with combined effects of 
higher temperatures and more frequent and intense precipitations 
(Bastidas-Arteaga et al. 2010; Chen et al. 2021). 
Higher frequency and intensity of hurricanes, storm surges and 
coastal and non-coastal flooding can escalate economic losses to 
civil infrastructure, especially when associated with population

growth and urbanisation in hazardous areas (Bjarnadottir et al. 2011; 
Li et  al. 2016; Lee and Ellingwood 2017). Climate change should 
increase the risk and exposure to damage from flood (de Ruig et al. 
2019), sea level rise (Bosello and De Cian 2014; Zanetti et al. 2016; 
Bove et al. 2020) and more frequent wildfires (Barkhordarian et al. 
2018; Craig et al. 2020).9.7.2 Links Between Mitigation and Adaptation 
Adaptation options interacts with mitigation efforts because 
measures to cope with climate change impacts can increase energy 
and material consumption, which may lead to higher GHG emissions 
(Kalvelage et  al. 2014; Davide et  al. 2019; Sharifi 2020). Energy 
consumption is required to adapt to climate change. Mitigation 
measures, in turn, influence the degree of vulnerability of buildings 
to future climate and, thus, the adaptation required.
Studies have assessed the increases in energy demand to meet indoor

thermal comfort under future climate (de Wilde and Coley 2012; 
Li et al. 2012; Clarke et al. 2018; Andrić et al. 2019). Higher cooling 
needs may induce increases in energy demand (Wan et  al. 2012; 
Li et al. 2012), which could lead to higher emissions, when electricity 
is fossil-based (International Energy Agency 2018; Biardeau et  al. 
2020), and generate higher loads and stress on power systems (Dirks 
et al. 2015; Auffhammer et al. 2017). In this regard, increasing energy 
efficiency of space cooling appliances and adopting dynamic cooling 
setpoint temperatures, can reduce the energy needs for cooling and 
limit additional emissions and pressures on power systems (Davide 
et  al. 2019; Bienvenido-Huertas et  al. 2020; Bezerra et  al. 2021) 
(Section 9.4, Figure 9.11 and Supplementary Material Tables 9.SM.1 
to 9.SM.3). This can also be achieved with on-site renewable energy 
production, especially solar PV for which there can be a  timely

correlation between power supply and cooling demand, improving 
load matching (Salom et al. 2014; Grove-Smith et al. 2018).
Mitigation alternatives through passive approaches may increase 
resilience to climate change impacts on thermal comfort and reduce 
active cooling needs (Wan et al. 2012; van Hooff et al. 2016; Andrić 
et al. 2019; González Mahecha et al. 2020; Rosse Caldas et al. 2020). 
Combining passive measures can help counteracting climate change 
driven increases in energy consumption for achieving thermal 
comfort (Huang and Hwang 2016).
Studies raise the concern that measures aimed at building envelope 
may increase the risk of overheating in a warming climate (Dodoo 
and Gustavsson 2016; Fosas et al. 2018) (Section 9.4). If this is the 
case, there may be a  conflict between mitigation through energy 
efficiency building regulations and climate change adaptation (Fosas 
et al. 2018). However, while overheating may occur as a result of poor

insulation design, better insulation may actually reduce overheating 
when properly projected and the overheating risk can be overcome 
by clever designs (Fosas et al. 2018). 
Strengthening building structures to increase resilience and reduce 
exposure to the risk of extreme events, such as draughts, torrential floods, 
hurricanes and storms, can be partially achieved by improving building 
standards and retrofitting existing buildings (Bjarnadottir et al. 2011). 
However, future climate is not yet considered in parameters of existing 
building energy codes (Steenbergen et  al. 2012). While enhancing 
structural resilience would lead to GHG emissions (Liu and Cui 2018), 
so would disaster recovery and rebuilding. This adaptation-mitigation 
trade-off needs to be further assessed.

998
998
Chapter 9 Buildings9Since adaptation of the existing building stock may be more expensive 
and require building retrofit, climate change must be considered 
in the design of new buildings to ensure performance robustness in 
both current and future climates, which can have implications for 
construction costs (Hallegatte 2009; Pyke et al. 2012; de Wilde and 
Coley 2012; de Rubeis et al. 2020; Picard et al. 2020) and emissions 
(Liu and Cui 2018). Building energy codes and regulations are usually 
based on cost-effectiveness and historical climate data, which can lead 
to the poor design of thermal comfort in future climate (Hallegatte 
2009; Pyke et al. 2012; de Wilde and Coley 2012) and non-efficient 
active adaptive measures based on mechanical air conditioning 
(De Cian et al. 2019) (Section 9.4, Figure 9.11 and Supplementary 
Material Tables 9.SM.1 to 9.SM.3). However, uncertainty about future 
climate change creates difficulties for projecting parameters for the

design of new buildings (Hallegatte 2009; de Wilde and Coley 2012). 
This can be especially relevant for social housing programs (Rubio-
Bellido et al. 2017; Triana et al. 2018; González Mahecha et al. 2020) 
in developing countries. 
The impacts on buildings can lead to higher maintenance needs 
and the consequent embodied environmental impacts related 
to materials production, transportation and end-of-life, which 
account for a relevant share of GHG emissions in buildings lifecycle 
(Rasmussen et  al. 2018). Climate change induced biodegradation 
is especially important for bio-based materials such as wood and 
bamboo (Brambilla and Gasparri 2020) which are important options 
for reducing emissions imbued in buildings’ construction materials 
(Peñaloza et al. 2016; Churkina et al. 2020; Rosse Caldas et al. 2020). 
Although there can potentially be conflicts between climate change 
mitigation and adaptation, these can be dealt with proper planning,

actions, and policies. The challenge is to develop multifunctional 
solutions, technologies and materials that can mitigate GHG 
emissions while improving buildings adaptive capacity. Solutions 
and technologies should reduce not only buildings’ operational 
emissions, but also embodied emissions from manufacturing and 
processing of building materials (Röck et  al. 2020). For instance, 
some building materials, such as bio-concrete, can reduce lifecycle 
emissions of buildings and bring benefits in terms of building thermal 
comfort in tropical and subtropical climates. Also, energy efficiency, 
sufficiency and on-site renewable energy production can help to 
increase building resilience to climate change impacts and reduce 
pressure on the energy system.
9.8 Links to Sustainable Development
9.8.1 Overview of Contribution of Mitigation Options 
to Sustainable Development
A growing body of research acknowledges that mitigation actions

in buildings may have substantial social and economic value 
beyond their direct impact of reducing energy consumption and/
or GHG emissions (IEA 2014; Ürge-Vorsatz et al. 2016; Deng et al. 
2017; Reuter et  al. 2017; US EPA 2018; Kamal et  al. 2019; Bleyl 
et al. 2019) (see also Cross-Chapter Box 6 in Chapter 7). In other 
words, the implementation of these actions in the residential and non-residential sector holds numerous multiple impacts (co-benefits, 
adverse side-effects, trade-offs, risks, etc.) for the economy, society 
and end-users, in both developed and developing economies, which 
can be categorised into the following types (IEA 2014; Ürge-Vorsatz 
et al. 2016; Ferreira et al. 2017; Thema et al. 2017; Reuter et al. 2017; 
US EPA 2018; Nikas et al. 2020): (i) health impacts due to better indoor 
conditions, energy/fuel poverty alleviation, better ambient air quality 
and reduction of the heat island effect; (ii) environmental benefits

such as reduced local air pollution and the associated impact on 
ecosystems (acidification, eutrophication, etc.) and infrastructures, 
reduced sewage production, and so on; (iii) improved resource 
management including water and energy; (iv) impact on social well-
being, including changes in disposable income due to decreased 
energy expenditures and/or distributional costs of new policies, fuel 
poverty alleviation and improved access to energy sources, rebound 
effects, increased productive time for women and children, and 
so on; (v) microeconomic effects (e.g.,  productivity gains in non-
residential buildings, enhanced asset values of green buildings, 
fostering innovation); (vi) macroeconomic effects, including impact 
on GDP driven by energy savings and energy availability, creation of 
new jobs, decreased employment in the fossil energy sector, long-
term reductions in energy prices and possible increases in electricity

prices in the medium run, possible impacts on public budgets, and 
so on; and (vii) energy security implications (e.g., access to modern 
energy resources, reduced import dependency, increase of supplier 
diversity, smaller reserve requirements, increased sovereignty 
and resilience). 
Well-designed and effectively implemented mitigation actions in 
the sector of buildings have significant potential for achieving 
the United Nations (UN) Sustainable Development Goals (SDGs). 
Specifically, the multiple impacts of mitigation policies and measures 
go far beyond the goal of climate action (SDG 13) and contribute 
to further activating a  great variety of other SDGs (Figure  9.18 
presents some indicative examples). Table 9.5 reviews and updates 
the analysis carried out in the context of the IPCC Special Report 
on Global Warming of 1.5°C (SR1.5) (Roy et al. 2018) demonstrating 
that the main categories of GHG emission reduction interventions

in buildings, namely the implementation of energy sufficiency and 
efficiency improvements as well as improved access and fuel switch 
to modern low carbon energy, contribute to achieving 16 out of 
a total of 17 SDGs. 
A review of a relatively limited number of studies made by Ürge-
Vorsatz et al. (2016) and Payne et al. (2015) showed that the size 
of multiple benefits of mitigation actions in the sector of buildings 
may range from 22% up to 7400% of the corresponding energy 
cost savings. In 7 out of 11 case studies reviewed, the value of the 
multiple impacts of mitigation actions was equal or greater than 
the value of energy savings. Even in these studies, several effects 
have not been measured and consequently the size of multiple 
benefits of mitigation actions may be even higher. Quantifying 
and if possible, monetising, these wider impacts of climate action 
would facilitate their inclusion in cost-benefit analysis, strengthen

the adoption of ambitious emissions reduction targets, and improve 
coordination across policy areas reducing costs (Smith et al. 2016; 
Thema et al. 2017).

999
999
Buildings Chapter 99Up to 90% GHG emissions 
reduction in developed countries
Up to 80% of GHG emissions 
reduction in developing countries
Up to 28% higher selling 
prices for decarbonised 
building in developed 
countries
Up to 30 direct and 
indirect jobs per million 
USD invested in building 
retroﬁt or new energy 
efﬁcient buildings
2 million direct jobs from 
transforming fuel-based 
lighting to solar LED lighting 
in developing countries2 hours per day saved for 
women and girls from collecting 
fuel in Africa
24,500 avoided premature 
deaths and 22,300 
disability-adjusted life years 
(DALYs ) of avoided 
asthma in the EU
1.8 million fewer avoided 
premature deaths from HAP 
in developing world in 2030
Up to 2.8 billion people in 
developing countries lifted 
from energy poverty
5 to 8 million households 
in Europe lifted from 
energy poverty

Key point: Achieving SDG targets requires implementation of ambitious climate mitigation policies which include sufﬁciency measures to align 
building design, size and use with SDGs, efﬁciency measures to ensure high penetration of best available technologies and supplying the 
remaining energy needs with renewable energy sources.90% of our time is spent indoorsFigure is same as 9.18
Figure  9.18 | Contribution of mitigation policies of the building sector to meeting sustainable development goals.  Source: based on information from 
IEA(2019d); IEA (2020b); Mills (2016); European Commission (2016); Rafaj et al. (2018); Mzavanadze (2018a); World Health Organization (2016); and literature review 
presented in Section 9.8.5.2.
Table  9.5 | Aspects of mitigation actions in buildings and their contributions to the 2030 Sustainable Development Goals. S: enhancement of energy

sufficiency; E: energy efficiency improvements; R: improved access and fuel switch to lower carbon and renewable energy.
Level of 
impactSDG 
1SDG 
2SDG 
3SDG 
4SDG 
5SDG 
6SDG 
7SDG 
8SDG 
9SDG 
10SDG 
11SDG 
12SDG 
13SDG 
14SDG 
15SDG 
16SDG 
17
SERSERSERSERSERSERSERSERSERSERSERSERSERSERSERSERSER
+3
+2
+1
–1
–2
–3
Dimensions of mitigation actions that impact SDGs
Health impact  
Environmental 
impact    
Resource 
efficienvy   
Impact on social 
well-being  
Microeconomic 
effects  
Macroeconomic 
effects 
Energy security  

1000
1000
9.8.2 Climate Mitigation Actions in Buildings 
and Health Impacts
9.8.2.1 Lack of Access to Clean Energy
In 2018, approximately 2.8 billion people worldwide, most of whom 
live in Asia and Africa, still use polluting fuels, such as fuelwood, 
charcoal, dried crops, cow dung, and so on, in low-efficiency stoves 
for cooking and heating, generating household air pollution (HAP), 
which adversely affects the health of the occupants of the dwellings, 
especially children and women (World Health Organization 2016; 
Rahut et al. 2017; Mehetre et al. 2017; Das et al. 2018; Liu et al. 2018; 
Quinn et al. 2018; Rosenthal et al. 2018; Xin et al. 2018; IEA 2020a). 
Exposure to HAP from burning these fuels is estimated to have caused 
3.8 million deaths from heart diseases, strokes, cancers, acute lower 
respiratory infections in 2016 (World Health Organization 2018). 
It is acknowledged that integrated policies are needed to address

simultaneously universal energy access, limiting climate change 
and reducing air pollution (World Health Organization 2016). Rafaj 
et al. (2018) showed that a scenario achieving these SDGs in 2030 
will imply in 2040 two million fewer premature deaths from HAP compared to current levels, and 1.5 million fewer premature deaths 
in relation to a reference scenario, which assumes the continuation 
of existing and planned policies. The level of incremental investment 
needed in developing countries to achieve universal access to modern 
energy was estimated at around USD0.8 trillion cumulatively to 2040 
in the scenarios examined (Rafaj et al. 2018). 
At the core of these policies is the promotion of improved cook-
stoves and other modern energy-efficient appliances to cook (for the 
health benefits of improved cook-stoves see for example (García-
Frapolli et al. 2010; Malla et al. 2011; Aunan et al. 2013; Jeuland et al.

2018), as well as the use of non-solid fuels by poor households in 
developing countries (Figure 9.19). Most studies agree that the use of 
non-solid energy options such as LPG, ethanol, biogas, piped natural 
gas, and electricity is more effective in reducing the health impacts of 
HAP compared to improved biomass stoves (see for example Larsen 
2016; Rosenthal et  al. 2018; Steenland et  al. 2018; Goldemberg 
et al. 2018). On the other hand, climate change mitigation policies 
(e.g., carbon pricing) may increase the costs of some of these clean 
fuels (e.g.,  LPG, electricity), slowing down their penetration in the 
poor segment of the population and restricting the associated health Notes:  The strength of interaction between mitigation actions and SDGs is described with a seven-point scale (Nilsson et al., 2016). Also, the blue bullet shows the interactions

between co-benefits/risk associated with mitigation actions and the SDGs. SDG 1: Sufficiency and efficiency measures result in reduced energy expenditures and other financial 
savings that further lead to poverty reduction. Access to modern energy forms will largely help alleviate poverty in developing countries as the productive time of women and 
children will increase, new activities can be developed, and so on. The distributional costs of some mitigation policies promoting energy efficiency and lower carbon energy may 
reduce the disposable income of the poor. SDG 2: Energy sufficiency and efficiency measures result in lower energy bills and avoiding the ‘heat or eat’ dilemma. Improved cook-
stoves provide better food security and reduces the danger of fuel shortages in developing countries; under real-world conditions these impacts may be limited as the households

use these stoves irregularly and inappropriately. Green roofs can support food production. Improving energy access enhances agricultural productivity and improves food security; 
on the other hand, increased bioenergy production may restrict the available land for food production. SDG 3: All categories of mitigation action result in health benefits through 
better indoor air quality, energy/fuel poverty alleviation, better ambient air quality, and reduction of the heat island effect. Efficiency measures with inadequate ventilation may 
lead to the “sick building” syndrome symptoms. SDG 4: Energy efficiency measures result in reduced school absenteeism due to better indoor environmental conditions. Also, fuel 
poverty alleviation increases the available space at home for reading. Improved access to electricity and clean fuels enables people living in poor developing countries to read,

while it is also associated with greater school attendance by children. SDG 5: Efficient cook-stoves and improved access to electricity and clean fuels in developing countries will 
result in substantial time savings for women and children, thus increasing the time for rest, communication, education and productive activities. SDG 6: Reduced energy demand 
due to sufficiency and efficiency measures as well as an upscaling of renewable energy sources (RES) can lead to reduced water demand for thermal cooling at energy production 
facilities. Also, water savings result through improved conditions and lower space of dwellings. Improved access to electricity is necessary to treat water at homes. In some 
situations, the switch to bioenergy could increase water use compared to existing conditions. SDG 7: All categories of mitigation action result in energy/fuel poverty alleviation

in both developed and developing countries as well as in improving the security of energy supply. SDG 8: Positive and negative direct and indirect macroeconomic effects (GDP , 
employment, public budgets) associated with lower energy prices due to the reduced energy demand, energy efficiency and RES investments, improved energy access and fostering 
innovation. Also, energy efficient buildings with adequate ventilation, result in productivity gains and improve the competitiveness of the economy. SDG 9: Adoption of distributed 
generation and smart grids helps in infrastructure improvement and expansion. Also, the development of ‘green buildings’ can foster innovation. Reduced energy demand due to 
sufficiency and efficiency measures as well as an upscaling of RES can lead to early retirement of fossil energy infrastructure. SDG 10: Efficient cook-stoves as well as improved

access to electricity and clean fuels in developing countries will result in substantial time savings for women and children, thus enhancing education and the development of 
productive activities. Sufficiency and efficiency measures lead to lower energy expenditures, thus reducing income inequalities. The distributional costs of some mitigation policies 
promoting energy efficiency and lower carbon energy as well as the need for purchasing more expensive equipment and appliances may reduce the disposable income of the poor 
and increase inequalities. SDG 11: Sufficiency and efficiency measures as well as fuel switching to RES and improvements in energy access would eliminate major sources (both 
direct and indirect) of poor air quality (indoor and outdoor). Helpful if in-situ production of RES combined with charging electric two, three and four wheelers at home. Buildings

with high energy efficiency and/or green features are sold/rented at higher prices than conventional, low energy efficient houses. SDG 12: Energy sufficiency and efficiency 
measures as well as deployment of RES result in reduced consumption of natural resources, namely fossil fuels, metal ores, minerals, water, and so on. Negative impacts on natural 
resources could be arisen from increased penetration of new efficient appliances and equipment. SDG 13: See Sections 9.4–9.6. SDG 15: Efficient cookstoves and improved 
access to electricity and clean fuels in developing countries will result in halting deforestation. SDG 16: Building retrofits are associated with lower crime. Improved access to 
electric lighting can improve safety (particularly for women and children). Institutions that are effective, accountable and transparent are needed at all levels of government for

providing energy access and promoting modern renewables as well as boosting sufficiency and efficiency. SDG 17: The development of zero energy buildings requires among 
others capacity building, citizen participation as well as monitoring of the achievements.
Sources: Brounen and Kok (2011); Deng et al. (2012); Zheng et al. (2012); Högberg (2013); Hyland et al. (2013); Kahn and Kok (2014); Koirala et al. (2014); Maidment et al. 
(2014); Mirasgedis et al. (2014); Scott et al. (2014); Bailis et al. (2015); Boermans et al. (2015); Fuerst et al. (2015, 2016); Galán-Marín et al. (2015); Hasegawa et al. (2015); 
Hejazi et al. (2015); Holland et al. (2015); Liddell and Guiney (2015); Liu et al. (2015a); Mattioli and Moulinos (2015); Payne et al. (2015); Torero (2015); Willand et al. (2015a); 
Winter et al. (2015); Baimel et al. (2016); Camarinha-Matos (2016); Cameron et al. (2016); De Ayala et al. (2016); European Commission (2016); Fricko et al. (2016); Hanna et al.

(2016); Jensen et al. (2016); Levy et al. (2016); Markovska et al. (2016); Rao et al. (2016); Smith et al. (2016); Sola et al. (2016); Song et al. (2016); Ürge-Vorsatz et al. (2016); 
Balaban and Puppim de Oliveira (2017); Berrueta et al. (2017); Burney et al. (2017); Mehetre et al. (2017); Mofidi and Akbari (2017); Niemelä et al. (2017); Ortiz et al. (2017); 
Rao and Pachauri (2017); Thema et al. (2017); Thomson et al. (2017a); Zhao et al. (2017); Barnes and Samad (2018); Cedeño-Laurent et al. (2018); Goldemberg et al. (2018); 
Grubler et al. (2018); Jeuland et al. (2018); MacNaughton et al. (2018); McCollum et al. (2018); Mzavanadze (2018a); Rosenthal et al. (2018); Saheb et al. (2018b,a); Steenland 
et al. (2018); Tajani et al. (2018); Venugopal et al. (2018); Walters and Midden (2018); Wierzbicka et al. (2018); Alawneh et al. (2019); Batchelor et al. (2019); Bleyl et al. (2019);

Cajias et al. (2019); Marmolejo-Duarte and Chen (2019); Mastrucci et al. (2019); ESMAP et al. (2020); Teubler et al. (2020); Van de Ven et al. (2020); Nikas et al. (2020); 
Blair et al. (2021).

1001
1001
benefits (Cameron et  al. 2016). In this case, appropriate access 
policies should be designed to efficiently shield poor households 
from the burden of carbon taxation (Cameron et  al. 2016). The 
evaluation of the improved biomass burning cook-stoves under real-
world conditions has shown that they have lower than expected, and 
in many cases limited, long-run health and environmental impacts, 
as the households use these stoves irregularly and inappropriately, 
fail to maintain them, and their usage decline over time (Patange 
et al. 2015; Aung et al. 2016; Hanna et al. 2016; Wathore et al. 2017). 
In this context, the various improved cook-stoves programs should 
consider the mid- and long-term needs of maintenance, repair, or 
replacement to support their sustained use (Shankar et  al. 2014; 
Schilmann et al. 2019).
Electrification of households in rural or remote areas results 
also to significant health benefits. For example, in El Salvador,

rural electrification of households leads to reduced overnight air 
pollutants concentration by 63% due to the substitution of kerosene 
as a lighting source, and 34–44% less acute respiratory infections 
among children under six (Torero 2015). In addition, the connection 
of the health centres to the grid leads to improvements in the quality 
of health care provided (Lenz et al. 2017).
9.8.2.2 Energy/fuel Poverty, Indoor Environmental 
Quality and Health
Living in fuel poverty, and particularly in cold and damp housing is 
related to excess winter mortality and increased morbidity rates due 
to respiratory and cardiovascular diseases, arthritic and rheumatic illnesses, asthma, and so on (Lacroix and Chaton 2015; Payne et al. 
2015; Camprubí et al. 2016; Wilson et al. 2016; Ormandy and Ezratty 
2016; Thema et  al. 2017). In addition, lack of affordable warmth 
can generate stress related to chronic discomfort and high bills,

fear of falling into debt, and a sense of lacking control, which are 
potential drivers of further negative mental health outcomes, such as 
depression (Howden-Chapman et al. 2012; Liddell and Guiney 2015; 
Payne et  al. 2015; Wilson et  al. 2016). Health risks from exposure 
to cold and inadequate indoor environmental quality may be higher 
for low-income, energy-poor households, and in particular for those 
with elderly relatives, young children, and members with existing 
respiratory illness (Payne et al. 2015; Thomson et al. 2017b; Nunes 
2019). High temperatures during summer can also be dangerous 
for people living in buildings with inadequate thermal insulation 
and inappropriate ventilation (Ormandy and Ezratty 2016; Sanchez-
Guevara et al. 2019; Thomson et al. 2019). Summer fuel poverty (or 
summer overheating risk) may increase significantly in the coming 
decades under a warming climate (Section 9.7), with the poorest, who

cannot afford to install air conditioning, and the elderly (Nunes 2020) 
being the most vulnerable. 
Improved energy efficiency in buildings contributes in fuel poverty 
alleviation and brings health gains through improved indoor 
temperatures and comfort as well as reduced fuel consumption and 
associated financial stress (Curl et al. 2015; Lacroix and Chaton 2015; 
Liddell and Guiney 2015; Thomson and Thomas 2015; Willand et al. 
2015; Poortinga et al. 2018). On the other hand, households suffering 
most from fuel poverty experience more barriers for undertaking 
building retrofits (Braubach and Ferrand 2013; Camprubí et al. 2016; Eastern Asia
South Asia, South-East 
Asia and PaciﬁcLatin America and Caribbean
Middle EastAfricaShare of population (%)0.000.100.200.300.400.500.600.700.80
2005 2010 2015 2020 2025 2030
HistoricalSustainable
Scenario0.000.100.200.300.400.500.60

Scenario0.000.020.040.060.080.100.120.140.16
Scenario
0.000.020.040.060.080.100.12
Scenario0.000.100.200.300.400.500.600.700.80
ScenarioShare of the population without
electricity accessShare of the population without 
clean cooking access
Figure 9.19 | Trends on energy access: historical based on IEA statistics data and scenarios based on IEA WEO data.

1002
1002
Chapter 9 Buildings9Charlier et al. 2018), moderating the potential health gains associated 
with implemented energy efficiency programs. This can be avoided if 
implemented policies to tackle fuel poverty target the most socially 
vulnerable households (Lacroix and Chaton 2015; Camprubí et  al. 
2016). Mzavanadze (2018a) estimated that in EU-28 accelerated 
energy efficiency policies, reducing the energy demand in residential 
sector by 333 TWh in 2030 compared to a  reference scenario, 
coupled with strong social policies targeting the most vulnerable 
households, could deliver additional co-benefits in the year of 2030 
of around 24,500 avoided premature deaths due to indoor cold 
and around 22,300 disability adjusted life years (DALYs) of avoided 
asthma due to indoor dampness. The health benefits of these policies 
amount to EUR4.8 billion in 2030. The impacts on inhabitants in 
developing countries would be much greater than those in EU-28

owing to the much higher prevalence of impoverished household.
Apart from thermal comfort, the internal environment of buildings 
impacts public health through a  variety of pathways including 
inadequate ventilation, poor indoor air quality, chemical contaminants 
from indoor or outdoor sources, outdoor noise, or poor lighting. 
The implementation of interventions aiming to improve thermal 
insulation of buildings combined with inadequate ventilation may 
increase the risk of mould and moisture problems due to reduced air 
flow rates, leading to indoor environments that are unhealthy, with 
the occupants suffering from the sick building syndrome symptoms 
(Willand et  al. 2015; Cedeño-Laurent et  al. 2018; Wierzbicka et  al. 
2018). On the other hand, if the implementation of energy efficiency 
interventions or the construction of green buildings is accompanied 
by adequate ventilation, the indoor environmental conditions are

improved through less moisture, mould, pollutant concentrations, 
and allergens, which result in fewer asthma symptoms, respiratory 
risks, chronic obstructive pulmonary diseases, heart disease risks, 
headaches, cancer risks, and so on (Allen et al. 2015; Hamilton et al. 
2015; Thomson and Thomas 2015; Cowell 2016; Doll et  al. 2016; 
Wilson et  al. 2016; Militello-Hourigan and Miller 2018; Underhill 
et  al. 2018; Cedeño-Laurent et  al. 2018). Fisk (2018) showed that 
increased ventilation rates in residential buildings results in health 
benefits ranging from 20% to several-fold improvements; however, 
these benefits do not occur consistently, and ventilation should 
be combined with other exposure control measures. As adequate 
ventilation imposes additional costs, the sick building syndrome 
symptoms are more likely to be seen in low income households 
(Shrubsole et al. 2016). 
The health benefits of residents due to mitigation actions in buildings

are significant (for a review see Maidment et al. 2014; Thomson and 
Thomas 2015; Fisk et al. 2020), and are higher among low income 
households and/or vulnerable groups, including children, the elderly 
and those with pre-existing illnesses (Maidment et  al. 2014; IEA 
2014; Ortiz et al. 2019). Tonn et al. (2018) estimated that the health-
related benefits attributed to the two weatherisation programs 
implemented in the US in 2008 and 2010 exceeds by a factor of 3 
the corresponding energy cost savings yield. IEA (2014) also found 
that the health benefits attributed to energy efficiency retrofit 
programs may outweigh their costs by up to a factor of 3. Ortiz et al. 
(2019) estimated that the energy retrofit of vulnerable households 
in Spain requires an investment of around EUR10.9–12.3 thousands per dwelling and would generate an average saving to the healthcare 
system of EUR372 per  year and dwelling (due to better thermal 
comfort conditions in winter).

9.8.2.3 Outdoor Air Pollution
According to World Health Organization (2018) around 4.2 million 
premature deaths worldwide (in both cities and rural areas) are 
attributed to outdoor air pollution. According to the results of the 
quantitative model (Gu et  al. 2018), the premature mortalities 
attributed to PM 2.5 and O 3 emissions may reach 168000–1796000 
(95% Cl) in 2010. Mitigation actions in residential and non-residential 
sectors decrease the amount of fossil fuels burnt either directly in 
buildings (for heating, cooking, etc.) or indirectly for electricity 
generation and thereby reduce air pollution (e.g., PM, O 3, SO 2, NO x), 
improve ambient air quality and generate significant health benefits 
through avoiding premature deaths, lung cancers, ischemic heart 
diseases, hospital admissions, asthma exacerbations, respiratory 
symptoms, and so on (Levy et  al. 2016; Balaban and Puppim de 
Oliveira 2017; MacNaughton et al. 2018; Karlsson et al. 2020). Several

studies have monetised the health benefits attributed to reduced 
outdoor air pollution due to the implementation of mitigation actions 
in buildings, and their magnitude expressed as a ratio to the value of 
energy savings resulting from the implemented interventions in each 
case, are in the range of 0.08 in EU, 0.18 in Germany, 0.26–0.40 in US, 
0.34 in Brazil, 0.47 in Mexico, 0.74 in Turkey, 8.28 in China and 11.67 
in India (Joyce et al. 2013; Levy et al. 2016; Diaz-Mendez et al. 2018; 
MacNaughton et al. 2018). In developed economies, the estimated 
co-benefits are relatively low due to the fact that the planned 
interventions influence a  quite clean energy source mix (Tuomisto 
et al. 2015; MacNaughton et al. 2018). On the other hand, the health 
co-benefits in question are substantially higher in countries and 
regions with greater dependency on coal for electricity generation 
and higher baseline morbidity and mortality rates (Kheirbek et  al.

2014; MacNaughton et al. 2018). 
9.8.3 Other Environmental Benefits  
of Mitigation Actions
Apart from the health benefits mentioned above, mitigation actions 
in the buildings sector are also associated with environmental 
benefits to ecosystems and crops, by avoiding acidification and 
eutrophication, biodiversity through green roofs and walls, building 
environment through reduced corrosion of materials, and so on 
(Thema et al. 2017; Mzavanadze 2018b; Knapp et al. 2019; Mayrand 
and Clergeau 2018), while some negative effects cannot be excluded 
(Dylewski and Adamczyk 2016). 
Also, very important are the effects of mitigation actions in buildings 
on the reduction of consumption of natural resources, namely fossil 
fuels, metal ores, minerals, and so on. These comprise savings from 
the resulting reduced consumption of fuels, electricity and heat and 
the lifecycle-wide resource demand for their utilities, as well as

potential net savings from the substitution of energy technologies 
used in buildings  – production phase extraction (European 
Commission 2016; Thema et  al. 2017). Teubler et  al. (2020) found

1003
1003
Buildings Chapter 99that the implementation of an energy efficiency scenario in European 
buildings will result in resource savings (considering only those 
associated with the generation of final energy products) of 406 kg 
per MWh lower final energy demand in the residential sector, while 
the corresponding figure for non-residential buildings was estimated 
at 706 kg per MWh of reduced energy demand. On the other hand, 
Smith et al. (2016) claim that a switch to more efficient appliances 
could result in negative impacts from increased resource use, which 
can be mitigated by avoiding premature replacement and maximising 
recycling of old appliances.
Mitigation actions aiming to reduce the embodied energy of buildings 
through using local and sustainable building materials can be used to 
leverage new supply chains (e.g., for forestry products), which in turn 
bring further environmental and social benefits to local communities

(Hashemi et  al. 2015; Cheong and Storey 2019). Furthermore, 
improved insulation and the installation of double- or triple-glazed 
windows result in reduced noise levels. It is worth mentioning that 
for every 1 dB decrease in excess noise, academic performance in 
schools and productivity of employees in office buildings increases by 
0.7% and 0.3% respectively (Kockat et al. 2018b). Smith et al. (2016) 
estimated that in the UK the annual noise benefits associated with 
energy renovations in residential buildings may reach £400 million in 
2030 outweighing the benefits of reduced air pollution. 
9.8.4 Social Wellbeing
9.8.4.1 Energy/Fuel Poverty Alleviation 
In 2018 almost 0.79 billion people in developing countries did not 
have access to electricity, while approximately 2.8 billion people 
relied on polluting fuels and technologies for cooking (IEA 2020a). 
Only in sub-Saharan Africa, about 548 million people (i.e.,  more

than 50% of the population) live without electricity. In developed 
economies, the EU Energy Poverty Observatory estimated that in EU-
28 44.5 million people were unable to keep their homes warm in 2016, 
41.5 million had arrears on their utility bills the same year, 16.3% 
of households faced disproportionately high energy expenditure in 
2010, and 19.2% of households reported being uncomfortably hot 
during summer in 2012 (Thomson and Bouzarovski 2018). Okushima 
(2016), using the ‘expenditure approach’, estimated that fuel poverty 
rates in Japan reached 8.4% in 2013. In the US, in 2015, 17 million 
households (14.4% of the total) received an energy disconnect/
delivery stop notice and 25 million households (21.2% of the total) 
had to forgo food and medicine to pay energy bills (Bednar and 
Reames 2020). 
The implementation of well-designed climate mitigation measures in 
buildings can help to reduce energy/fuel poverty and improve living

conditions with significant benefits for health (Section  9.8.2) and 
well-being (Payne et al. 2015; Smith et al. 2016; Tonn et al. 2018). 
The social implications of energy poverty alleviation for the people 
in low- and middle-income developing countries with no access to 
clean energy fuels are further discussed in Section 9.8.4.2. In other 
developing countries and in developed economies as well, the 
implementation of mitigation measures can improve the ability of households to affordably heat/cool a larger area of the home, thus 
increasing the space available to a family and providing more private 
and comfortable spaces for several activities like homework (Payne 
et  al. 2015). By reducing energy expenditures and making energy 
bills more affordable for households, a ‘heat or eat’ dilemma can be 
avoided resulting in better nutrition and reductions in the number 
of low birthweight babies (Payne et al. 2015; Tonn et al. 2018). Also,

renovated buildings and the resulting better indoor conditions, 
can enable residents to avoid social isolation, improve social 
cohesion, lower crime, and so on (Payne et al. 2015). The European 
Commission (2016) found that under an ambitious recast of Energy 
Performance Buildings Directive (EPBD), the number of households 
that may be lifted from fuel poverty across the EU lies between 
5.17 and 8.26 million. To capture these benefits, mitigation policies 
and  particularly energy renovation programmes should target the 
most vulnerable among the energy-poor households, which very often 
are ignored by the policy makers. In this context, it is recognised that 
fuel poverty should be analysed as a multidimensional social problem 
(Thomson et  al. 2017b; Baker et  al. 2018; Charlier and Legendre 
2019; Mashhoodi et al. 2019), as it is related to energy efficiency, 
household composition, age and health status of its members,

social conditions (single parent families, existence of unemployed 
and retired people, etc.), energy prices, disposable income, and so 
on. In addition, the geographical dimension can have a significant 
impact on the levels of fuel poverty and should be taken into account 
when formulating response policies (Besagni and Borgarello 2019; 
Mashhoodi et al. 2019).
9.8.4.2 Improved Access to Energy Sources, Gender 
Equality and Time Savings
In most low- and middle-income developing countries women and 
children (particularly girls) spend a significant amount of their time for 
gathering fuels for cooking and heating (World Health Organization 
2016; Rosenthal et al. 2018). For example, in Africa more than 70% 
of the children living in households that primarily cook with polluting 
fuels spend at least 15 hours and, in some countries, more than 
30 hours per  week in collecting wood or water, facing significant 
safety risks and constraints on their available time for education and

rest (World Health Organization 2016; Mehetre et  al. 2017). Also, 
in several developing countries (e.g., in most African countries but 
also in India, in rural areas in Latin America and elsewhere) women 
spend several hours to collect fuel wood and cook, thus limiting 
their potential for productive activities for income generation or 
rest (García-Frapolli et  al. 2010; World Health Organization 2016; 
Mehetre et al. 2017). Expanding access to clean household energy 
for cooking, heating and lighting will largely help alleviate these 
burdens (Malla et al. 2011; World Health Organization 2016; Lewis 
et al. 2017; Rosenthal et al. 2018). Jeuland et al. (2018) found that the 
time savings associated with the adoption of cleaner and more fuel-
efficient stoves by low-income households in developing countries 
are amount to USD1.3–1.9 per  household per  month, constituting 
the 23–43% of the total social benefits attributed to the promotion 
of clean stoves.

Electrification of remote rural areas and other regions that do not 
have access to electricity enables people living in poor developing

1004
1004
Chapter 9 Buildings9countries to read, socialise, and be more productive during the 
evening, while it is also associated with greater school attendance 
by children (Torero 2015; Rao et al. 2016; Barnes and Samad 2018). 
Chakravorty et al. (2014) found that a grid connection can increase 
non-agricultural incomes of rural households in India from 9% up 
to 28.6% (assuming a  higher quality of electricity). On the other 
hand, some studies clearly show that electricity consumption for 
connected households is extremely low, with limited penetration of 
electrical appliances (Cameron et al. 2016; Lee et al. 2017) and low 
quality of electricity (Chakravorty et al. 2014). The implementation 
of appropriate policies to overcome bureaucratic red tape, low 
reliability, and credit constraints, is necessary for maximising the 
social benefits of electrification.
9.8.5 Economic Implications of Mitigation Actions
9.8.5.1 Buildings-related Labour Productivity

Low-carbon buildings, and particularly well-designed, operated and 
maintained high-performance buildings with adequate ventilation, 
may result in productivity gains and improve the competitiveness of the 
economy through three different pathways (MacNaughton et al. 2015; 
European Commission 2016; Niemelä et al. 2017; Mofidi and Akbari 
2017; Thema et al. 2017; Bleyl et al. 2019): (i) increasing the amount of 
active time available for productive work by reducing the absenteeism 
from work due to illness, the presenteeism (i.e., working with illness 
or working despite being ill), and the inability to work due to chronic 
diseases caused by the poor indoor environment; (ii) improving the 
indoor air quality and thermal comfort of non-residential buildings, 
which can result in better mental well-being of the employees and 
increased workforce performance; and (iii) reducing the school 
absenteeism due to better indoor environmental conditions, which

may enhance the future earnings ability of the students and restrict the 
parents absenteeism due to care-taking of sick children.
Productivity gains due to increased amount of active time for work 
is directly related to acute and chronic health benefits attributed to 
climate mitigation actions in buildings (Section 9.8.2.2). The bulk of 
studies quantifying the impact of energy efficiency on productivity 
focus on acute health effects. Proper ventilation in buildings is of 
particular importance and can reduce absenteeism due to sick days 
by 0.6–1.9 days per  person per  year (MacNaughton et  al. 2015; 
Ben-David et al. 2017; Thema et al. 2017). In a pan-European study, 
(Chatterjee and Ürge-Vorsatz 2018) showed that deep energy 
retrofits in residential buildings may increase the number of active 
days by 1.78–5.27 (with an average of 3.09) per year and person 
who has actually shifted to a  deep retrofitted building. Similarly,

the interventions in the non-residential buildings result in increased 
active days between 0.79 and 2.43 (with an average of 1.4) per year 
and person shifted to deeply retrofitted non-residential buildings. 
As regards improvements in workforce performance due to improved 
indoor conditions (i.e., air quality, thermal comfort, etc.), (Kozusznik et al. 
2019) conducted a systematic review on whether the implementation 
of energy efficient interventions in office buildings influence well-being 
and job performance of employees. Among the 34 studies included in this review, 31 found neutral to positive effects of green buildings on 
productivity and only 3 studies indicated detrimental outcomes for 
office occupants in terms of job performance. Particularly longitudinal 
studies, which observe and compare the office users’ reactions over 
time in conventional and green buildings, show that green buildings 
have neutral to positive effects on occupants well-being and work

performance (Thatcher and Milner 2016; Candido et al. 2019; Kozusznik 
et al. 2019). Bleyl et al. (2019) estimated that deep energy retrofits in 
office buildings in Belgium would generate a workforce performance 
increase of EUR10.4 to EUR20.8 m–2 renovated. In Europe every 
1°C reduction in overheating during the summer period increases 
students learning performance by 2.3% and workers performance in 
office buildings by 3.6% (Kockat et al. 2018b). Considering the latter 
indicator, it was estimated that by reducing overheating across Europe, 
the overall performance of the workers in office buildings can increase 
by 7–12% (Kockat et al. 2018b).
9.8.5.2 Enhanced Asset Values of Energy Efficient Buildings
A significant number of studies confirm that homes with high energy 
efficiency and/or green features are sold at higher prices than 
conventional, low energy efficient houses. A review of 15 studies 
from 12 different countries showed that energy efficient dwellings

have a  price premium ranging between 1.5% and 28%, with 
a median estimated at 7.8%, for the highest energy efficient category 
examined in each case study compared to reference houses with 
the same characteristics but lower energy efficiency (the detailed 
results of this review are presented in Supplementary Material 
Table 9.SM.5). In a given real estate market, the higher the energy 
efficiency of dwellings compared to conventional housing, the higher 
their selling prices. However, a  number of studies show that this 
premium is largely realised during resale transactions and is smaller 
or even negative in some cases immediately after the completion 
of the construction (Deng and Wu 2014; Yoshida and Sugiura 2015). 
A relatively lower number of studies (also included in Supplementary 
Material Table 9.SM.5) show that energy efficiency and green features 
have also a positive effect on rental prices of dwellings (Hyland et al.

2013; Cajias et al. 2019), but this is weaker compared to sales prices, 
and in a developing country even negative as green buildings, which 
incorporate new technologies such as central air conditioning, are 
associated with higher electricity consumption (Zheng et al. 2012). 
Regarding non-residential buildings, (European Commission 2016) 
reviewed a  number of studies showing that buildings with high 
energy efficiency or certified with green certificates present higher 
sales prices by 5.2–35%, and higher rents by 2.5–11.8%. More 
recent studies in relation to those included in the review confirm 
these results (Mangialardo et  al. 2018; Ott and Hahn 2018) or 
project even higher premiums. Chegut et  al. (2014) found that 
green certification in the London office market results in a premium 
of 19.7% for rents. On the other hand, in Australia, a review study 
showed mixed evidence regarding price differentials emerged as

a  function of energy performance of office buildings (Acil Allen 
Consulting 2015). Other studies have shown that energy efficiency 
and green certifications have been associated with lower default 
rates for commercial mortgages (Wallace et al. 2018; An and Pivo 
2020; Mathew et al. 2021).

1005
1005
Buildings Chapter 99More generally, (Giraudet 2020) based on a  meta-analysis of 
several studies, showed that the capitalisation of energy efficiency is 
observed in building sales and rental (even in the absence of energy 
performance certificates), but the resulting market equilibrium can 
be considered inefficient as rented dwellings are less energy efficient 
than owner-occupied ones.
9.8.5.3 Macroeconomic Effects 
Investments required for the implementation of mitigation actions, 
create, mainly in the short-run, increase in the economic output 
and employment in sectors delivering energy efficiency services and 
products, which are partially counterbalanced by less investments 
and  lower production in other parts of the economy (Yushchenko 
and Patel 2016; European Commission 2016; Thema et al. 2017; US 
EPA 2018) (see also Cross-Working Group Box 1 in Chapter 3). The 
magnitude of these impacts depends on the structure of the economy,

the extent to which energy saving technologies are produced 
domestically or imported from abroad, but also from the growth 
cycle of the economy with the benefits being maximised when the 
related investments are realised in periods of economic recession 
(Mirasgedis et  al. 2014; Yushchenko and Patel 2016; Thema et  al. 
2017). Particularly in developing countries if the mitigation measures 
and other interventions to improve energy access (Figure 9.19) are 
carried out by locals, the impact on economy, employment and social 
well-being will be substantial (Mills 2016; Lehr et al. 2016). As many 
of these programs are carried out with foreign assistance funds, it is 
essential that the funds be spent in-country to the full extent possible, 
while some portion of these funds would need to be devoted to 
institution building and especially training. (Mills 2016) estimated 
that a market transformation from inefficient and polluting fuel-based

lighting to solar-LED systems to fully serve the 112 million households 
that currently lack electricity access will create directly 2 million new 
jobs in these developing countries, while the indirect effects could 
be even greater. IEA (2020a) estimated that 9–30 jobs would be 
generated for every million dollars invested in building retrofits or 
in construction of new energy efficient buildings (gross direct and 
indirect employment), with the highest employment intensity rates 
occurring in developing countries. Correspondingly, 7–16 jobs 
would be created for every million dollars spent in purchasing highly 
efficient and connected appliances, while expanding clean cooking 
through LPG could create 16–75 direct local jobs per million dollars 
invested. Increases in product and employment attributed to energy 
efficiency investments also affect public budgets by increasing 
income and business taxation, reducing unemployment benefits,

and so on. Thema et al. (2017), thus mitigating the impact on public 
deficit of subsidising energy saving measures (Mikulić et al. 2016). 
Furthermore, energy savings due to the implementation of mitigation 
actions will result, mainly in the long-run, in increased disposable 
income for households, which in turn may be spent to buy other 
goods and services, resulting in economic development, creation of 
new permanent employment and positive public budget implications 
(IEA 2014; Thema et al. 2017; US EPA 2018). According to Anderson 
et  al. (2014), the production of these other goods and services is 
usually more labour-intensive compared to energy production, 
resulting in net employment benefits of about 8 jobs per  million dollars of consumer bill savings in the US. These effects may again 
have a  positive impact on public budgets. Furthermore, reduced 
energy consumption on a large scale is likely to have an impact on

lower energy prices and hence on reducing the cost of production 
of various products, improving the productivity of the economy and 
enhancing security of energy supply (IEA 2014; Thema et al. 2017).
9.8.5.4 Energy Security 
GHG emission reduction actions in the sector of buildings affect energy 
systems by: (i) reducing the overall consumption of energy resources, 
especially fossil fuels; (ii) promoting the electrification of thermal 
energy uses; and (iii) enhancing distributed generation through the 
incorporation of RES and other clean and smart technologies in 
buildings. Increasing sufficiency, energy efficiency and penetration of 
RES result in improving the primary energy intensity of the economy 
and reducing dependence on fossil fuels, which for many countries 
are imported energy resources (Boermans et  al. 2015; Markovska 
et al. 2016; Thema et al. 2017). The electrification of thermal energy 
uses is expected to increase the demand for electricity in buildings,

which in most cases can be reversed (at national or regional level) by 
promoting nearly zero energy new buildings and a deep renovation 
of the existing building stock (Boermans et  al. 2015; Couder and 
Verbruggen 2017). In addition, highly efficient buildings can keep 
the desired room temperature stable over a  longer period and 
consequently they have the capability to shift heating and cooling 
operation in time (Boermans et  al. 2015). These result in reduced 
peak demand, lower system losses and avoided generation and 
grid infrastructure investments. As a  significant proportion of the 
global population, particularly in rural and remote locations, still lack 
access to modern energy sources, renewables can be used to power 
distributed generation or micro-grid systems that enable peer-to-
peer energy exchange, constituting a crucial component to improve 
energy security for rural populations (Leibrand et al. 2019; Kirchhoff

and Strunz 2019). For successful development of peer-to-peer micro-
grids, financial incentives to asset owners are critical for ensuring 
their willingness to share their energy resources, while support 
measures should be adopted to ensure that also non-asset holders 
can contribute to investments in energy generation and storage 
equipment and have the ability to sell electricity to others (Kirchhoff 
and Strunz 2019).
9.9 Sectoral Barriers and Policies
9.9.1 Barriers, Feasibility and Acceptance
Understanding the reasons why cost-effective investment in 
building energy efficiency are not taking place as expected by 
rational economic behaviour is critical to design effective policies 
for decarbonise the buildings (Cattano et al. 2013; Cattaneo 2019). 
Barriers depend from the actors (owner, tenant, utility, regulators, 
manufacturers, etc.), their role in energy efficiency project and the 
market, technology, financial economic, social, legal, institutional,

regulatory and policy structures (Reddy 1991; Weber 1997; Sorrell 
et al. 2000; Reddy 2002; Sorrell et al. 2011; Cagno et al. 2012; Bardhan 
et al., 2014; Bagaini et al. 2020; Vogel et al. 2015; Khosla et al. 2017;

1006
1006
Figure 9.20 | Summary of the extent to which different factors would enable or inhibit the deployment of mitigation options in buildings.  Blue bars indicate the extent to which the indicator enables the implementation 
of the option (E) and orange bars indicate the extent to which an indicator is a barrier (B) to the deployment of the option, relative to the maximum possible barriers and enablers assessed. A ‘X’ signifies the indicator is not applicable or does 
not affect the feasibility of the option, while a forward slash/indicates that there is no or limited evidence whether the indicator affects the feasibility of the option. The shading indicates the level of confidence, with darker shading signifying 
higher levels of confidence. Table 9.SM.6 provides an overview of the extent to which the feasibility of options may differ across context (e.g., region), time (e.g., 2030 versus 2050), and scale (e.g., small versus large), and includes a line of

sight on which the assessment is based. The assessment method is explained in Annex II.11.
Geophysical resourses
Land use
readiness
Effects on employment 
and economic growth
and coordination
Building design and performance
Change in construction methods  
and circular economy 
Heating, ventilation and air conditioning (HVAC)
Efficient Appliances
Change in construction materials
Demand Side management

Low Confidence level barriers: Not Applicable B = BarriersE = Enablers
HighLimited or No Evidence Strength of enablers and barriers
0 50 100

1007
1007
Buildings Chapter 99Gupta et al. 2017). Barriers identified for the refurbishment of exiting 
building or construction of new efficient buildings includes: lack 
of high-performance products, construction methods, monitoring 
capacity, investment risks, policies intermittency, information gaps, 
principal agent problems (both tenant and landlord face disincentives 
to invest in energy efficiency), skills of the installers, lack of a trained 
and ready workforce, governance arrangements in collectively owned 
properties and behavioural anomalies (Gillingham and Palmer 2014; 
Buessler et al. 2017; Yang et al. 2019; Do et al. 2020; Dutt 2020; Song 
et al. 2020). A better understanding of behavioural barriers (Frederiks 
et al. 2015) is essential to design effective policies to decarbonise 
the building sector. Energy efficiency in buildings faces one 
additional problem: the sector is highly heterogeneous, with many

different building types, sizes and operational uses. Energy efficiency 
investments do not take place in isolation but in competition with 
other priorities and as part of a  complex, protracted investment 
process (Cooremans 2011). Therefore, a focus on overcoming barriers 
is not enough for effective policy. Organisational context is important 
because the same barrier might have very different organisational 
effects and require very different policy responses (Mallaburn 
2018). Cross-Chapter Box  2 in Chapter  2 presents a  summary of 
methodologies for estimating the macro-level impact of policies on 
indices of GHG mitigation.
Reaching deep decarbonisation levels throughout the lifecycle 
of buildings depends on multidimensional criteria for assessing 
the feasibility of mitigation measures, including criteria related to 
geophysical, environmental-ecological, technological, economic, 
socio-cultural and institutional dimensions. An assessment of

16 feasibility criteria for mitigation measures in the buildings sector 
indicates whether a specific factor, within broader dimensions, acts 
as a barrier or helps enabling such mitigation measures (Figure 9.20, 
Supplementary Material Table  9.SM.6, and Annex II.11). Although 
mitigation measures are aggregated in the assessment of Figure 9.20 
and feasibility results can differ for more specific measures, generally 
speaking, the barriers to mitigation measures in buildings are few, 
sometimes including technological and socio-cultural challenges. 
However, many co-benefits could help enable mitigation in the 
buildings sector. For instance, many measures can have positive 
effects on the environment, health and well-being, and distributional 
potential, all of which can boost their feasibility. The feasibility of 
mitigation measures varies significantly according to socio-economic 
differences across and within countries.
9.9.2 Rebound Effects

In the buildings sector energy efficiency improvements and promotion 
of cleaner fuels can lead to all types of rebound effects,  while 
sufficiency measures lead only to indirect and secondary effects 
(Chitnis et  al. 2013). The consideration of the rebound effects as 
a behavioural economic response of the consumers to cheaper energy 
services can only partially explain the gap between the expected 
and actual energy savings (Galvin and Sunikka-Blank 2017). The 
prebound effect, a term used to describe the situation where there 
is a  significant difference between expected and observed energy 
consumption of non-refurbished buildings, is usually implicated in high rebound effects upon retrofitting (Teli et  al. 2016; Calì et  al. 
2016; Galvin and Sunikka-Blank 2017). The access for all to modern 
energy services such as heating and cooling is one of the well-being 
objectives governments aim for. However, ensuring this access leads

to an increase of energy demand which is considered as a rebound 
effect by (Chitnis et al. 2013; Orea et al. 2015; Poon 2015; Teli et al. 
2016; Seebauer 2018; Sorrell et  al. 2018; Berger and Höltl 2019). 
Aydin et al. (2017) found that in the Netherlands the rebound effect 
for the lowest wealth quantile is double compared to the highest 
wealth quantile. Similar, energy access in developing countries leads 
to an increase consumption compared to very low baselines which 
is considered by some authors as rebound (Copiello 2017). On the 
other hand, in households whose members have a  higher level of 
education and/or strong environmental values, the rebound is lower 
(Seebauer 2018).
Rebound effects in the building sector could be a co-benefit, in cases 
where the mechanisms involved provide faster access to affordable 
energy and/or contribute to improved social well-being, or a trade-off, to 
the extent that the external costs of the increased energy consumption

exceed the welfare benefits of the increased  energy service 
consumption (Chan and Gillingham 2015; Borenstein 2015; Galvin 
and Sunikka-Blank 2017; Sorrell et al. 2018). In cases where rebound 
effects are undesirable, appropriate policies could be implemented for 
their mitigation. 
There is great variation in estimates of the direct and indirect 
rebound effects, which stems from the end-uses included in the 
analysis, differences in definitions and methods used to estimate 
the rebound effects, the quality of the data utilised, the period of 
analysis and the geographical area in consideration (International 
Risk Governance Council 2013; Galvin 2014; Gillingham et al. 2016). 
Several studies examined in the context of this assessment (see 
Supplementary Material Table 9.SM.7) showed that direct rebound 
effects for residential energy consumption, which includes heating, 
are significant and range between –9% and 127%. The direct

rebound effects for energy services other than heating may be lower 
(Chen et al. 2018; Sorrell et al. 2018). The rebound effects may be 
reduced with the time as the occupants learn how to optimally use 
the systems installed in energy renovated buildings (Calì et al. 2016) 
and seem to be lower in the case of major renovations leading to 
NZEB (Corrado et al. 2016). The combined direct and indirect or the 
indirect only rebound effects were found to range between –2% 
and 80%, with a  median at 12% (see Supplementary Material 
Table 9.SM.7). In non-residential buildings the rebound effects may 
be smaller, as the commercial sector is characterised by lower price 
elasticities of energy demand, while the comfort level in commercial 
buildings before renovation is likely to be better compared to 
residential buildings (Qiu 2014).
9.9.3 Policy Packages for the Decarbonisation 
of Buildings
There is no single energy efficiency policy (Wiese et al. 2018) able to

decarbonise the building sector, but a  range of polices are needed, often 
included in a policy package (Kern et al., 2017; Rosenow et al. 2017)

1008
1008
Chapter 9 Buildings9to enhance robustness against risks and uncertainties in both short 
and long-term and addressing the different stakeholder perspectives 
(Forouli et al. 2019; Nikas et al. 2020; Doukas and Nikas 2020). This is 
due to: the many barriers; the different types of buildings (residential, 
non-residential, etc.); the different socio-economic groups of the 
population (social housing, informal settlement, etc.); the country 
development status; the local climate (cooling and/or heating), 
ownership structure (tenant or owner), the age of buildings. Effective 
policy packages include mandatory standards, codes, the provision of 
information, carbon pricing, financing, and technical assistance for 
end-users. Important element related to policy packages is whether 
the policies reinforce each other or diminish the impact of individual 
policies, due to policy ‘overcrowding’. Examples are the EU policy

package for efficiency in buildings (Rosenow and Bayer 2017; BPIE, 
2020; Economidou et al. 2020) and China goal of 10 million m2 NZEB 
during the 13th Five-Year Plan, presented in the Supplementary 
Material (Supplementary Material Section 9.SM.4) (see also Cross-
Chapter Box  10 in Chapter  14 for integrated policymaking for 
sector transitions).
Revisions in tenant and condominium law are necessary for reducing 
disincentives between landlord and tenant or between multiple 
owners, these acts alone cannot incentivise them to uptake an energy 
efficiency upgrade in a property (Economidou and Serrenho, 2019). 
A package addressing split incentives include regulatory measures, 
information measures, labels, individual metering rules and financial 
models designed to distribute costs and benefits to tenants and 
owners in a  transparent and fair way (Bird and Hernández 2012; 
Economidou and Bertoldi 2015; Castellazi et  al. 2017). A more

active engagement of building occupants in energy saving practices, 
the development of agreements benefitting all involved actors, 
acknowledgement of real energy consumption and establishment of 
cost recovery models attached to the property instead of the owner 
are useful measures to address misalignments between actors. 
In Developed Countries policy packages are targeted to increase 
the number and depth of renovations of existing building, while for 
developing countries policies focus on new construction, including 
regulatory measures and incentives, while carbon pricing would be 
more problematic unless there is a strong recycling of the revenues. 
Building energy codes and labels could be based on LCA emissions, 
rather than energy consumption during the use phase of buildings, as 
it is the case in Switzerland and Finland (Kuittinen and Häkkinen 2020).
Policy packages should also combine sufficiency, efficiency, and

renewable energy instruments for buildings, for example some 
national building energy codes already include minimum requirements 
for the use of renewable energy in buildings.
9.9.3.1 Sufficiency and Efficiency Policies
Recently the concept of sufficiency complementary to energy 
efficiency has been introduced in policy making (Brischke et al. 2015; 
Hewitt 2018; Thomas et al. 2019; Bertoldi 2020; Saheb 2021) (Box 9.1). 
2 Sufficiency policies are a set of measures and daily practices that avoid demand for energy, materials, land and water while delivering human well-being for all within 
planetary boundaries.Lorek and Spangenberg (2019b) investigated the limitations of the 
theories of planned behaviour and social practice and proposed an 
approach combining both theories resulting in a heuristic sufficiency 
policy2 tool. Lorek and Spangenberg (2019b) showed that increased 
living area per  person counteracts efficiency gains in buildings

and called for sufficiency policy instruments to efficiency by limit 
building size. This could be achieved via mandatory and prescriptive 
measures, for example, progressive building energy codes (IEA 2013), 
or financial penalties in the form of property taxation (e.g., non-linear 
and progressive taxation), or with mandatory limits on building size 
per  capita. Heindl and Kanschik (2016) suggested that voluntary 
policies promoting sufficiency and proposed that sufficiency should be 
‘integrated in a more comprehensive normative framework related to 
welfare and social justice’. Alcott highlighted that in sufficiency there is 
a loss of utility or welfare (Alcott, 2008). Thomas et al. (2019) described 
some of the possible policies, some based on the sharing economy 
principles, for examples co-sharing space, public authorities facilitating 
the exchange house between young and expanding families with elderly 
people, with reduced need for space. Policies for sufficiency include

land-use and urban planning policies. Berril et  al. (2021) proposed 
removing policies, which support supply of larger home typologies, for 
example, single-family home or local land-use regulations restricting 
construction of multifamily buildings. In non-residential building, 
sufficiency could be implemented through the sharing economy, for 
example with flexible offices space with hot-desking. 
Scholars have identified the ‘energy efficiency gap’ (Hirst and 
Brown 1990; Jaffe and Stavins 1994; Alcott and Greenstone 2012; 
Gillingham and Palmer 2014; Stadelmann 2017) and policies to 
overcome it. Markandya et  al. (2015) and Shen et  al. (2016) have 
classified energy efficiency policies in three broad categories: the 
command and control (e.g.,  mandatory building energy codes; 
mandatory appliances standards, etc.); price instruments (e.g., taxes, 
subsides, tax deductions, credits, permits and tradable obligations,

etc.); and information instruments (e.g., labels, energy audits, smart 
meters and feed-back, etc.). Based on the EU Energy Efficiency 
Directive, the MURE and the IEA energy efficiency policy databases 
(Bertoldi and Mosconi 2020), Bertoldi (2020) proposed six policy 
categories: regulatory, financial and fiscal; information and 
awareness; qualification, training and quality assurance; market-
based instruments: voluntary action. The categorisation of energy 
efficiency policies used in this chapter is aligned with the taxonomy 
used in Chapter 13, sub-section 13.5.1 (economic or market-based 
instruments, regulatory instruments, and other policies). However, 
the classification used here is more granular in order to capture the 
complexity of end-use energy efficiency and buildings. 
1. Regulatory instruments 
Building energy codes. Several scholars highlighted the key 
role of mandatory building energy codes and minimum energy

performance requirements for buildings (Enker and Morrison 2017). 
Wang et al. (2019) finds that, ‘Building energy efficiency standards 
(BEES) are one of the most effective policies to reduce building

1009
1009
Buildings Chapter 99energy consumption, especially in the case of the rapid urbanisation 
content in China’. Ex post policy evaluation shows that stringent 
buildings’ codes reduce energy consumption in buildings and CO 2 
emissions and are cost-effective (Aroonruengsawat 2012; Jacobsen 
and Kotchen 2013; Scott et  al. 2015; Levinson 2016; Kotchen 
2017; Yu et  al. 2017; Yu et  al. 2018; Aydin and Brounen 2019). 
Progressive building energy codes include requirements on efficiency 
improvement but also on sufficiency and share of renewables (Clune 
et  al. 2012; Rosenberg et  al., 2017) and on embodied emissions 
(Schwarz et al. 2020), for example the 2022 ASHRAE Standard 90.1 
includes prescriptive on-site renewable energy requirements for non-
residential building. Evans et  al. (2017; 2018) calls for strengthen 
the compliance checks with efficiency requirements or codes when 
buildings are in operation and highlighted the need for enforcement

of building energy codes to achieve the estimate energy and carbon 
savings recommending actions to improve enforcements, including 
institutional capacity and adequate resources.
Evans et  al. (2017; 2018) identified strengthening the compliance 
checks with codes when buildings are in operation and the need 
for enforcement of building energy codes in order to achieve the 
estimate energy and carbon savings, recommending actions to 
improve enforcements, including institutional capacity and adequate 
resources. Another important issue to be addressed by policies is the 
‘Energy Performance Gap’ (EPG), that is, the gap between design and 
policy intent and actual outcomes. Regulatory and market support 
regimes are based on predictive models (Cohen and Bordass 2015) 
with general assumptions about building types, the way they are 
used and are not covering all energy consumption. In the perspective 
of moving towards net zero carbon, it is important that policy capture

and address the actual in-use performance of buildings (Gupta et al. 
2015; Gupta and Kotopouleas 2018). Outcome-based codes are 
increasingly important because they overcome some limitations of 
prescriptive building energy codes, which typically do not regulate 
all building energy uses or do not regulate measured operational 
energy use in buildings. Regulating all loads, especially plug and 
process loads, is important because they account for an increasingly 
large percentage of total energy use as building envelope and space-
conditioning equipment are becoming more efficient (Denniston 
et al. 2011; Colker 2012; Enker and Morrison 2020).
Building codes could also foster the usage of wood and timber as 
a construction in particular for multi-storey buildings and in the long 
term penalise carbon intensive building materials (Ludwig 2019) with 
policies based on environmental performance assessment of buildings 
and the ‘wood first’ principle (Ludwig 2019; Ramage et al. 2017).

Retro-commissioning is a cost-effective process to periodically check 
the energy performance of existing building and assure energy savings 
are maintained overtime (Kong et al. 2019; Ssembatya et al. 2021).
In countries with low rate of new construction, it is important to 
consider mandatory building energy codes for existing buildings, but 
this may also be relevant for countries with high new construction, 
as they will have soon a large existing building stock. The EU has 
requirements already in place when building undergo a  major 
renovation (Economidou et  al. 2020). Countries considering mandatory regulations for existing buildings include Canada, the 
US (specific cities), China and Singapore. Policies include mandating 
energy retrofits for low performances existing buildings, when sold 
or rented. In countries with increasing building stock, in particular in 
developing countries, policies are more effective when targeting new 
buildings (Kamal et al. 2019).

NZEBs definitions are proposed by (Marszal et al. 2011; Deng and Wu 
2014; Zhang and Zhou 2015; Williams et al. 2016; Wells et al. 2018), 
covering different geographical areas, developing and Developed 
Countries, and both existing buildings and new buildings. In 2019, 
China issued the national standard Technical Standard for Nearly 
Zero Energy Building (MoHURD, 2019). California has also adopted 
a  building energy code mandating for NZEBs for new residential 
buildings in 2020 and 2030 for commercial buildings (Feng et  al. 
2019). Several countries have adopted targets, roadmaps or 
mandatory building energy codes requiring net zero energy buildings 
(NZEBs) for some classes of new buildings (Feng et al. 2019). 
Building labels and Energy Performance Certificates (EPCs). 
Buildings labels are an important instrument, with some limitations. 
Li et  al. (2019b) reviewed the EU mandatory Energy Performance 
Certificates for buildings and proposed several measures to make the

EPC more effective in driving the markets towards low consumption 
buildings. Some authors have indicated that the EPC based on the 
physical properties of the buildings (asset rating) may be misleading 
due to occupancy behaviour (Cohen and Bordass 2015) and 
calculation errors (Crawley et al. 2019). Control authorities can have 
a large impact on the quality of the label (Mallaburn 2018). Labels 
can also include information on the GHG embedded in building 
material or be based on LCA.
US EPA Energy Star and NABERS (Gui and Gou, 2020) are building 
performance labels based on performance, not on modelled energy 
use. Singapore has mandatory building energy labels, as do many 
cities in the US, while India and Brazil have mandatory labels for 
public buildings.
Mandatory energy performance disclosure and benchmarking of 
building energy consumption is a  powerful policy instrument in 
particular for non-residential buildings (Trencher et  al. 2016) and

could be more accurate than energy audits. Gabe (2016) showed that 
mandatory disclosure is more effective than voluntary disclosure. 
Some US cities (e.g., New York) have adopted Emissions Performance 
Standards for buildings, capping CO2 emissions. Accurate statistics 
related to energy use are very important for reducing GHG in building 
sector. In 2015, the Republic of Korea stablished the National 
Building Energy Integrated Management System, where building 
data and energy consumption information are collected for policy 
development and public information.
Energy audits. Energy audits, help to overcome the information 
barriers to efficiency investments, in particular buildings owned 
or occupied by small companies (Kalantzis and Revoltella, 2019). 
In the EU energy audits are mandatory for large companies under 
the Energy Efficiency Directive (Nabitz and Hirzel 2019), with some 
EU Member States having a  long experience with energy audits,

1010
1010
Chapter 9 Buildings9as part of national voluntary agreements with the private sector 
(Rezessy and Bertoldi 2011; Cornelis 2019). Singapore has adopted 
mandatory audit for buildings (Shen et  al. 2016). In the United 
States, several cities have adopted energy informational policies in 
recent years, including mandatory buildings audits (Trencher et  al. 
2016; Kontokosta et al. 2020). The State of New York has in place 
a subsidised energy audit for residential building since 2010 (Boucher 
et al. 2018). It is important to assure the training of auditors and the 
quality of the audit.
Minimum Energy Performance Standards (MEPSs). Mandatory 
minimum efficiency standards for building technical equipment 
and appliances (e.g., HVAC, appliances, ICT, lighting, etc.) is a very 
common, tested and successful policy in most of the OECD countries 
(e.g., EU, US, Canada, Australia, etc.) for improving energy efficiency

(Scott et al. 2015; Wu et al. 2019; Sonnenschein et al. 2019). Brucal 
and Roberts (2019) showed that efficiency standards reduce product 
price. McNeil et  al. (2019) highlighted how efficiency standards 
will help developing countries in reducing the power peak demand 
by a  factor of two, thus reducing large investment costs in new 
generation, transmission, and distribution networks. Mandatory 
standards have been implemented also other large economies, for 
example, Russia, Brazil, India, South Africa, China, Ghana, Kenya and 
Malaysia (Salleh et al. 2019), with an increase in the uptake also in 
developing countries, for example, Ghana, Kenya, Tunisia, and so on. 
In Japan, there is a successful voluntary programme the Top Runner, 
with similar results of mandatory efficiency standards (Inoue and 
Matsumoto 2019).
Appliance energy labelling. Mandatory energy labelling schemes 
for building technical equipment and appliances are very often

implemented together with minimum efficiency standards, with the 
mandatory standard pushing the market towards higher efficiency 
and the label pulling the market (Bertoldi, 2019). OECD countries, 
and many developing countries (for example China, Ghana, Kenya, 
India, South Africa, etc.) (Chunekar 2014; Diawuo et  al. 2018; 
Issock et al. 2018) have adopted mandatory energy labelling. Other 
labelling schemes are of voluntary nature, for example, the Energy 
Star programme in the US (Ohler et  al. 2020), which covers many 
different appliances.
Information campaign. Provision of information (e.g.,  public 
campaigns, targeted technical information, etc.) is a common policy 
instrument to change end-user behaviour. Many authors agree 
that the effect of both targeted and general advertisement and 
campaigns have a short lifetime and the effects tend to decrease 
over time (Reiss and White 2008; Simcock et  al. 2014; Diffney

et al. 2013). The meta-analysis carried out by (Delmas et al. 2013) 
showed that energy audits and personal information were the 
most effective followed by providing individuals with comparisons 
with their peers’ energy use including ‘non-monetary, information-
based’ (Delmas et al. 2013). An effective approach integrates the 
social norm as the basis for information and awareness measures 
on energy behaviour (Schultz et al. 2007; Gifford 2011). Information 
is more successful when it inspires and engages people: how 
people feel about a given situation often has a potent influence 
on their decisions (Slovic and Peters 2006). The message needs to be carefully selected and kept as simple as possible focusing on 
the following: entertain, engage, embed and educate (Dewick and 
Owen 2015). 
Energy consumption feedback with smart meters, smart billing and 
dedicated devices and apps is another instrument recently exploited

to reduce energy consumption (Karlin et  al. 2015; Buchanan et  al. 
2018; Zangheri et  al. 2019) very often coupled with contest-based 
interventions or norm-based interventions (Bergquist et  al. 2019). 
Hargreaves et al. (2018) proposes five core types of action to reduce 
energy use: turn it off, use it less, use it more carefully, improve its 
performance, and replace it/use an alternative. According to Aydin 
et  al. (2018), technology alone will not be enough to achieve the 
desired energy savings due to the rebound effect. The lack of interest 
from household occupants, confusing feedback message and difficulty 
to relate it to practical intervention, overemphasis on financial 
savings and the risks of ‘fallback effects’ where energy use returns 
to previous levels after a  short time or rebound effects has been 
pointed out (Buchanan et al. 2015) as the main reasons for the failing 
of traditional feedback. Labanca and Bertoldi (2018) highlight the

current limitations of policies for energy conservation and suggests 
complementary policy approach based on social practices theories. 
2. Market-based instruments
Carbon allowances. A number of authors (Raux et al. 2015; Fan et al. 
2016; Fawcett and Parag 2017; Li et al. 2015, 2018; Marek et al. 2018; 
Wadud and Chintakayala 2019) have investigated personal carbon 
allowances introduced previously (Ayres 1995; Fleming 1997; Raux 
and Marlot 2005; Bristow et al. 2010; Fawcett 2010; Starkey 2012). 
Although there is not yet any practical implementation of this policy, 
it offers an alternative to carbon taxes, although there are some 
practical issues to be solved before it could be rolled out. Recently the 
city of Lahti in Finland has introduced a personal carbon allowance 
in the transport sector (Kuokkanen et  al. 2020). Under this policy 
instrument governments allocate (free allocation, but allowances 
could also be auctioned) allowances to cover the carbon emission for

one year, associated with energy consumption. Trade of allowances 
between people can be organised. Personal carbon allowances can 
also foster renewable energies (energy consumption without carbon 
emissions) both in the grid and in buildings (e.g.,  solar thermal). 
Personal carbon allowances can make the carbon price more explicit 
to consumers, allowing them to know from the market value of each 
allowance (e.g.,  1 kg of CO 2). This policy instrument will shift the 
responsibility to the individual. Some categories may have limited 
ability to change their carbon budget or to be engaged by this policy 
instruments. In addition, in common with many other environmental 
policies the distributional effects have to be assessed carefully as 
this policy instrument may favour well off people able to purchase 
additional carbon allowances or install technologies that reduce their 
carbon emissions (Burgess 2016; Wang et al. 2017).

The concept of carbon allowances or carbon budget can also be 
applied to buildings, by assigning a  yearly CO 2 emissions budget 
to each building. This policy would be a less complex than personal 
allowances as buildings have metered or billed energy sources 
(e.g.,  gas, electricity, delivered heat, heating oil, etc.). The scheme

1011
1011
Buildings Chapter 99stimulates investments in energy efficiency and on-site renewable 
energies and energy savings resulting from behaviour by buildings 
occupant. For commercial buildings, similar schemes were 
implemented in the UK CRC Energy Efficiency Scheme (closed in 
2019) or the Tokyo Metropolitan Carbon and Trade Scheme (Nishida 
and Hua 2011; Bertoldi et  al. 2013a). Since 2015 the Republic of 
Korea implemented an Emission Trading Scheme, covering buildings 
(Park and Hong 2014; Lee and Yu 2017; Narassimhan et al. 2018). 
More recently under the New York Climate Mobilization Act enacted 
in 2019 New York City Local Law 97 established ‘Carbon Allowances’ 
for large buildings (Spiegel-Feld 2019; Lee 2020). 
Public money can be used to reward and give incentives to energy 
saved, as a result of technology implementation, and/or as a result of 
energy conservation and sufficiency (Eyre 2013; Bertoldi et al. 2013b;

Prasanna et al. 2018). This can be seen as a core feature of the Energy 
Savings Feed-in Tariff (ES-FiT). The ES-FiT is a  performance-based 
subsidy, whereby actions undertaken by end-users  – for example, 
investments in energy efficiency technology measures – are awarded 
based on the real energy savings achieved.
Utilities programmes, energy efficiency resource standard 
and energy efficiency obligations. Ratepayer-funded efficiency 
programmes, energy efficiency obligations, energy efficiency resource 
standards and white certificates have been introduced in some EU 
Member States, in several US States, Australia, South Korea and 
Brazil (Bertoldi et al. 2013a; Palmer et al. 2013; Brennan and Palmer 
2013; Giraudet and Finon 2015; Wirl 2015; Rosenow and Bayer 2017; 
Aldrich and Koerner 2018; Choi et al. 2018a; Fawcett and Darby 2018; 
Fawcett et al. 2019; Nadel, 2019; Sliger and Colburn, 2019; Goldman 
et  al. 2020). This policy instrument helps in improving energy

efficiency in buildings, but there is no evidence that it can foster deep 
renovations of existing buildings. Recently this policy instrument has 
been investigated is some non-OECD countries such as Turkey, where 
white certificates could deliver energy savings with some limitations 
(Duzgun and Komurgoz 2014) and UAE, as a useful instrument to 
foster energy efficiency in buildings (Friedrich and Afshari 2015). 
Another similar market based instrument is the energy saving 
auction mechanism implemented in some US states, Switzerland, 
and in Germany (Langreder et al. 2019; Rosenow et al. 2019; Thomas 
and Rosenow 2020). Energy efficiency projects participate in auctions 
for energy savings based on the cost of the energy saved and receive 
a financial incentive, if successful.
Energy or carbon taxes. Energy and/or carbon taxes are a climate 
policy, which can help in reducing energy consumption (Sen and 
Vollebergh 2018) and manage the rebound effect (Font Vivanco

et al. 2016; Peng et al. 2019; Freire-González 2020; Bertoldi 2020). 
The carbon tax has been adopted mainly in OECD countries and in 
particular in EU Member States (Sen and Vollebergh 2018; Hájek 
et al. 2019; Bertoldi 2020). There is high agreement that carbon taxes 
can be effective in reducing CO 2 emissions (Andersson 2017; IPCC 
2018; Hájek et al. 2019). It is hard to define the optimum level of 
taxation in order to achieve the desired level of energy consumption 
or CO 2 emission reduction (Weisbach et al. 2009). As for other energy 
efficiency policy distributional effect and equity considerations have 
to be carefully considered and mitigated (Borozan 2019). High energy prices tend to reduce the energy consumption particularly in less 
affluent households, and thus attention is needed in order to avoid 
unintended effects such as energy poverty. Bourgeois et al. (2021) 
showed that using carbon tax revenue to finance energy efficiency

investment reduces fuel poverty and increases cost-effectiveness. 
(Giraudet et  al. 2021) assessed the cost-effectiveness of various 
energy efficiency policies in France, concluding that a carbon tax is the 
most effective. In particular, revenues could be invested in frontline 
services that can provide a  range of support  – including advising 
householders on how to improve their homes. Hence, the introduction 
of a carbon tax can be neutral or even positive to the economy, as 
investments in clean technologies generate additional revenues. 
In addition, in the long term, a  carbon/energy tax could gradually 
replace the tax on labour reducing labour cost (e.g., the example of 
the German Eco-tax), thus helping to create additional jobs in the 
economy. In literature, this is known as double dividend (Murtagh 
et al. 2013; Freire-González and Ho 2019). Urban economic researches 
(Creutzig 2014; Borck and Brueckner 2018; Rafaj et al. 2018) have

highlighted that higher carbon price would translate in incentives for 
citizens to live closer to the city centre, which often means less floor 
space, less commuting distance and thus reduced emissions. Xiang 
and Lawley (2019) indicated that the carbon tax in British Columbia 
substantially reduced residential natural gas consumption. Saelim 
(2019) showed that simulated carbon tax on residential consumption 
in Thailand will have a low impact on welfare and it will be slightly 
progressive. Lin and Li (2011) indicate that a carbon tax could reduce 
the energy consumption and boost the uptake of energy efficiency 
and renewable energies, while at the same time may impact social 
welfare and the competitiveness of industry. Solaymani (2017) 
showed that in Malaysia a tax with revenue recycling increases in 
the welfare of rural and urban households. Van Heerden et al. (2016) 
explored economic and environmental effects of the CO 2 tax in South

Africa highlighting the negative impact on GDP . This negative impact 
of the carbon tax on GDP is, however, greatly reduced by the manner 
in which the tax revenue is recycled. National circumstances shall be 
taken into consideration in introducing energy taxes, considering the 
local taxation and energy prices context with regard to sustainable 
development, justice and equity.
A policy, which can have similar impact to a carbon tax and is the 
energy price/subsidy reform, which also involves raising energy 
prices. Energy price/subsidy reform reduces energy consumption and 
greenhouse gas emissions and encourages investment in energy 
efficiency (Coady et al. 2018; Aldubyan and Gasim, 2021). In a similar 
manner, government revenues from subsidies reforms can be used to 
mitigate the distributional impact on vulnerable population groups, 
including direct cash transfer programmes (Rentschler and Brazilian 
2017; Schaffitzel et al. 2020).

Taxes could also be used to penalise inefficient behaviour and favour 
the adoption of efficient behaviour and technologies. Taxes are used 
in some jurisdictions to promote energy efficient appliances with 
lower VAT. Similarly, the annual building/property tax (and also the 
purchase tax) could be based on the CO 2 emissions of the buildings, 
rather than on the value of the building. Tax credits are also an 
important subsidy for the renovation of buildings in France (Giraudet 
2020), Italy (Alberini and Bigano 2015) and other countries.

1012
1012
Chapter 9 Buildings99.9.4 Financing Mechanisms and Business Models 
for Reducing Energy Demand
Grants and subsidies are traditional financing instruments used by 
governments when optimal levels of investments cannot be fully 
supported by the market alone. They can partly help overcoming the 
upfront cost barrier as they directly fill an immediate financial gap 
and thus enable a temporary shift in the market (Newell et al. 2019). 
These forms of support are usually part of policy mixes including 
further fiscal and financial instruments such as feed-in tariffs and 
tax breaks (Polzin et al. 2019). Potential issues with subsidies are the 
limited availability of public financing, the stop and go due to annual 
budget and the competition with commercial financing.
Loans provide liquidity and direct access to capital important in deep 
renovation projects (Rosenow et al. 2014). There is empirical evidence

(Giraudet et al. 2021), that banks make large profits on personal loans 
for renovation purposes. International financing institutions (IFIs) and 
national governments provided subsidies in public-private partnerships 
so that financial institutions can offer customers loans with attractive 
terms (Olmos et al. 2012). Loan guarantees are effective in reducing 
intervention borrowing costs (Soumaré and Lai 2016). Combination 
of grants and subsidised loans financed by IFIs could be an effective 
instrument together with guarantees. An important role in financing 
energy efficiency can be played by green banks, which are publicly 
capitalised entities set up to facilitate private investment in low-
carbon, including energy efficiency (Bahl 2012; Tu and Yen 2015; Linh 
and Anh 2017; Khan 2018). Green banks have been established at the 
national level (e.g., UK, Poland) and in the US at state and city level. 
Wholesaling of EE of loans and utilities programmes, are other important

financing instruments. Another financing mechanism for building 
efficiency upgrades, mainly implemented so far in the US, is efficiency-
as-a-service under an energy services agreement (ESA), where the 
building owners or tenant pay to the efficiency service provider a charge 
based on realised energy savings without any upfront cost (Kim et al. 
2012; Bertoldi, 2020). ESA providers give performance guarantees 
assuming the risk that expected savings would occur (Bertoldi, 2020).
Energy Performance Contracting (EPC) is an agreement between 
a building owner and Energy Services Company (ESCO) for energy 
efficiency improvements. EPC is a  common financing vehicle for 
large buildings and it is well developed in several markets (Carvallo 
et al. 2015; Bertoldi and Boza Kiss, 2017; Stuart et al. 2018; Ruan 
et  al. 2018; Nurcahyanto et  al. 2020; Zheng et  al. 2021). Quality 
standards are a part of the EPC (Augustins et al. 2018). Guarantees

can facilitate the provision of affordable and sufficient financing for 
ESCOs (Bullier and Milin 2013). The ESCO guarantees a certain level 
of energy savings and it shields the client from performance risk. The 
loan goes on the client’s balance sheet and the ESCO assumes full 
project performance risk (Deng et al. 2015). One of the limitations is 
on the depth of the energy renovation in existing buildings. According 
to (Giraudet et  al. 2018), EPC is effective at reducing information 
problems between contractors and investors. 
Energy efficient mortgages are mortgages that credits a home energy 
efficiency by offering preferential mortgage terms to extend existing mortgages to finance efficiency improvements. There are two types of 
energy mortgages: (i) the Energy Efficient Mortgages (EEMs), and (ii) the 
Energy Improvement Mortgages (EIMs), both can help in overcoming 
the main barriers to retrofit policies (Miu et  al. 2018). The success

depends on the improved energy efficiency with a positive impact on 
property value and on the reduction of energy bills and the income 
increase in the household. In the EU, the EeMAP Initiative aims to create 
a standardised energy efficient mortgage template (Bertoldi et al. 2021).
On-bill financing is a mechanism that reduces first-cost barriers by 
linking repayment of energy efficiency investments to the utility 
bill and thereby allowing customers to pay back part or all costs of 
energy efficiency investments over time (Brown 2009). On-bill finance 
programmes can be categorised into: (i) on-bill loans (assignment of 
the obligation to the property) and (ii) on-bill tariffs (payment off in 
case of ownership transfer) (Eadson et  al. 2013). On-bill finance 
programmes can be more effective when set up as a service rather 
than a loan (Mundaca and Klocke 2018).
Property Assessed Clean Energy (PACE) is a  means of financing

energy renovations and renewable energy through the use of specific 
bonds offered by municipal governments to investors (Mills 2016). 
Municipalities use the funds raised to loan money towards energy 
renovations in buildings. The loans are repaid over the assigned long 
term (15–20 years) via an annual assessment on their property tax 
bill (Kirkpatrick and Bennear 2014). This model has been subject 
to consumer protection concerns. Residential PACE programmes 
in California have been shown to increase PV deployment in 
jurisdictions that adopt these programs (Kirkpatrick and Bennear 
2014; Ameli et al. 2017). In US commercial buildings, PACE volumes 
and programs, however, continue to grow (Lee 2020). 
Revolving funds allow reducing investment requirements and 
enhancing energy efficiency investment impacts by recovering 
and reinvesting the savings generated (Setyawan 2014). Revolving 
fund could make retrofit cost-neutral in the long term and could

also dramatically increase low carbon investments, including in 
developing countries (Gouldson et al. 2015).
Carbon finance, started under the Kyoto Protocol with the flexible 
mechanisms and further enhanced under the Paris Agreement 
(Michaelowa et al. 2019), is an activity based on ‘carbon emission rights’ 
and its derivatives (Liu et al. 2015a). Carbon finance can promote low-
cost emission reductions (Zhou and Li 2019). Under Emission Trading 
Schemes or other carbon pricing mechanisms, auctioning carbon 
allowances creates a new revenue stream. Revenues from auctioning 
could be used to finance energy efficiency projects in buildings with 
grants, zero interest loans or guarantees (Wiese et al. 2020).
Crowdfunding is a  new and rapidly growing form of financial 
intermediation that channels funds from investors to borrowers 
(individuals or companies) or users of equity capital (companies) 
without involving traditional financial organisations such as banks

(Miller and Carriveau 2018). Typically, it involves internet-based 
platforms that link savers directly with borrowers (European Union 
2015). It can play a significant role at the start of a renewable and 
sustainable energy projects (Dilger et al. 2017).

1013
1013
Buildings Chapter 99The One-Stop Shop (OSS) service providers for buildings energy 
renovations are organisations, consortia, projects, independent experts 
or advisors that usually cover the whole or large part of the customer 
renovation journey from information, technical assistance, structuring and 
provision of financial support, to the monitoring of savings (Mahapatra 
et al. 2019; Bertoldi 2021b). OSSs are transparent and accessible advisory 
tools from the client perspective and new, innovative business models 
from the supplier perspective (Boza-Kiss and Bertoldi 2018).
9.9.5 Policies Mechanisms for Financing for On-site 
Renewable Energy Generation
On-site renewable energy generation is a  key component for the 
building sector decarbonisation, complementing sufficiency and 
efficiency. Renewable energies (RES) technologies still face barriers 
due to the upfront investment costs, despite the declining price of

some technologies, long pay-back period, unpredictable energy 
production, policy incertitude, architectural (in particular for built-in 
PV) and landscape considerations, technical regulations for access to 
the grid, and future electricity costs (Mah et al. 2018; Agathokleous 
and Kalogirou 2020). 
Several policy instruments for RES have been identified by scholars 
(Fouquet 2013; Azhgaliyeva et  al. 2018; Pitelis et  al. 2020): direct 
investments; feed-in tariffs; grants and subsidies; loans and taxes; 
(tradable) green certificates or renewable/clean energy portfolio 
standards; information and education; strategic planning; codes and 
standards; building codes; priority grid access; research, development 
and deployment and voluntary approaches. There are specific policies 
for renewable heating and cooling (Connor et al. 2013). In 2011, the 
UK introduced the Renewable Heat Incentive (RHI) support scheme 
(Balta-Ozkan et al. 2015; Connor et al. 2015). The RHI guarantee a fixed

payment per unit of heat generated by a renewable heat technology 
for a specific contract duration (Yılmaz Balaman et al. 2019).
The most common implemented policy instruments are the feed-in 
tariffs (FiTs) and the Renewable/Energy Portfolio Standards (RPSs) (Xin-
gang et al. 2017a; Alizada 2018; Bergquist et al. 2020), with FiTs more 
suited for small scale generation. More than 60 countries and regions 
worldwide have implemented one of the two policies (Sun and Nie 2015). 
FiT is a price policy guaranteeing the purchase of energy generation at 
a specific fixed price for a fixed period (Barbosa et al. 2018; Xin-gang 
et al. 2020). RPS is a quantitative policy, which impose mandatory quota 
of RES generation to power generators (Xin-gang et al. 2020). 
A flat rate feed-in tariff (FiT) is a well-tested incentive adopted in many 
jurisdictions to encourage end-users to generate electricity from RES 
using rooftop and on-site PV systems (Pacudan 2018). More recently,

there has been an increasing interest for dynamic FiTs taking into 
account electricity costs, hosting capacity, ambient temperature, and 
time of day (Hayat et al. 2019). Since 2014, EU Member States have 
been obligated to move from FiT to feed-in premium (FiTP) (Hortay 
and Rozner 2019); where a FiTP consist in a premium of top of the 
electricity market price. Lecuyer and Quirion (2019) argued that under 
uncertainty over electricity prices and renewable production costs a flat 
FiT results in higher welfare than a FiTP . One of the main concerns with FiT systems is the increasing cost of policies maintenance (Zhang et al. 
2018; Pereira da Silva et al. 2019; Roberts et al. 2019a). In Germany, 
the financial costs, passed on to consumers in the form a levy on the 
electricity price have increased substantially in recent years (Winter 
and Schlesewsky 2019) resulting in opposition to the FiT in particular 
by non-solar customers. A particular set up of the FiT encourage self-

consumption through net metering and net billing, which has a lower 
financial impact on electricity ratepayers compared with traditional 
FiTs (Pacudan 2018; Roberts et al. 2019b; Vence and Pereira 2019).
In some countries, for example, Australia (Duong et al. 2019), South 
Korea (Choi et al. 2018a), China (Yi et al. 2019), there was a transition 
from subsidies under the FiT to market-based mechanisms, such 
as RPSs and tendering. Compared with FiT, RPS (or Renewable 
Obligations) reduce the subsidy costs (Zhang et al. 2018). A number 
of scholars (Xin-gang et  al. 2017; Liu et  al. 2018a, 2019a) have 
highlighted the RPSs’ effectiveness in promoting the development of 
renewable energy. Other authors (Requate 2015; An et al. 2015) have 
presented possible negative impacts of RPSs. 
Both FiT and RPS can support the development of RES. Scholars 
compared the effectiveness of RPSs and FiTs with mix results and 
different opinions, with some scholars indicating the advantages of

RPS (Ciarreta et al. 2014, 2017; Xin-gang et al. 2017), while Nicolini 
and Tavoni (2017) showed that in Italy FiTs are outperforming RPSs 
and Tradable Green Certificates (TGCs). García-Álvarez et  al. (2018) 
carried out an empirical assessment of FiTs and RPSs for PV systems 
energy in EU over the period 2000–2014 concluding that that FiTs 
have a  significant positive impact on installed PV capacity. This is 
due to the small size of many rooftop installations and the difficulties 
in participating in trading schemes for residential end users. Similar 
conclusions were reached by (Dijkgraaf et al. 2018) assessing 30 OECD 
countries and concluding that there is a ‘positive effect of the presence 
of a FiT on the development of a country’s added yearly capacity of 
PV’. Other scholars (Lewis and Wiser 2007; Lipp 2007; Cory et al. 2009; 
Couture and Gagnon 2010) concluded that FiT can create a  stable 
investment framework and long-term policy certainty and it is better

than RPS for industrial development and job creation. Ouyang and Lin 
(2014) highlighted that RPS has a better implementation effect than 
FiT in China, where FiT required very large subsidy. Ford et al. (2007) 
showed that TGC is a market-based mechanism without the need for 
government subsidies. Marchenko (2008) and W¸ edzik et  al. (2017) 
indicate that the TGCs provide a source of income for investors. Choi 
et al. (2018a) analysed the economic efficiency of FiT and RPS in the 
South Korean, where FiT was implemented from 2002 to 2011 followed 
by an RPS since 2012 (Park and Kim 2018; Choi et al. 2018b). Choi 
concluded that RPS was more efficient for PV from the government’s 
perspective while from an energy producers’ perspective the FiT was 
more efficient. Some scholars proposed a policy combining FiT and RPS 
(Cory et al. 2009). Kwon (2015) and del Río et al. (2017) concluded that 
both FiT and RPS are effective, but policy costs are higher in RPSs than

FiTs. RPS, REC trading and FiT subsidy could also be implemented as 
complementary policies (Zhang et al. 2018). 
Tenders are a fast spreading and effective instrument to attract and 
procure new generation capacity from renewable energy sources 
(Bayer et  al. 2018; Batz and Musgens 2019; Bento et  al.  2020;

1014
1014
Chapter 9 Buildings9Ghazali et  al. 2020; Haelg 2020). A support scheme based on 
tenders allows a  more precise steering of expansion and lower 
risk of excessive support (Gephart et al. 2017). Bento et al. (2020) 
indicated that tendering is more effective in promoting additional 
renewable capacity comparing to other mechanisms such as FiTs. It 
is also important to take into account the rebound effect in energy 
consumption by on-site PV users, which might reduce up to one fifth 
of the carbon benefit of renewable energy (Deng and Newton 2017).
Financing mechanisms for RES are particularly needed in developing 
countries. Most of the common supporting mechanisms (FiT, RPSs, 
PPA, auctions, net metering, etc.) have been implemented in some 
developing countries (Donastorg et  al. 2017). Stable policies and an 
investment-friendly environment are essential to overcome financing 
barriers and attract investors (Donastorg et  al. 2017). Kimura et  al.

(2016) identified the following elements as essential for fostering 
RES in developing countries: innovative business models and financial 
mechanisms/structures; market creation through the implementation of 
market-based mechanisms; stability of policies and renewable energy 
legislation; technical assistance to reduce the uncertainty of renewable 
energy production; electricity market design, which reflects the impact 
on the grid capacity and grid balancing; improved availability of financial 
resources, in particular public, and innovative financial instruments, 
such as carbon financing (Lim et al. 2013; Park et al. 2018; Kim and 
Park 2018); green bonds; public foreign exchange hedging facility for 
renewable energy financing, credit lines; grants and guarantees.
The end-user will be at the centre as a  key participant in the future 
electricity system (Zepter et al. 2019; Lavrijssen and Carrillo Parra, 2017)

providing flexibility, storage, energy productions, peer-to-peer trading, 
electric vehicle charging. Zepter indicates that ‘the current market 
designs and business models lack incentives and  opportunities for electricity consumers to become prosumers and actively participate in 
the market’. Klein et al. (2019) explore the policy options for aligning 
prosumers with the electricity wholesale market, through price and 
scarcity signals. Policies should allow for active markets participation 
of small prosumers (Brown et al. 2019; Zepter et al. 2019), local energy 
communities and new energy market actors such as aggregators (Iria 
and Soares 2019; Brown et  al. 2019). Energy Communities are new 
important players in the energy transition (Sokołowski 2020; Gjorgievski 
et al. 2021). Citizens and local communities can establish local energy 
communities, providing local RES production to serve the community, 
alleviate energy poverty and export energy into the grid (DellaValle and

Sareen, 2020; Hahnel et al. 2020). Energy Communities have as primary 
purpose to provide environmental, economic, or social community 
benefits by engaging in generation, aggregation, energy storage, 
energy efficiency services and charging services for electric vehicles. 
Energy communities help in increasing public acceptance and mobilise 
private funding. Demand response aggregators (Mahmoudi et al. 2017; 
Henriquez et  al. 2018) can aggregate load reductions by a  group of 
consumers, and sell the resulting flexibility to the electricity  market 
(Zancanella et al. 2017). Regulatory frameworks for electricity markets 
should allow demand response to compete on equal footing in energy 
markets and encourage new business models for the provision of 
flexibility to the electricity grid (Shen et al. 2014). Renewable energy and 
sufficiency requirements could be included in building energy codes 
and  implemented in coordination with each other and with climate

policies, for example, carbon pricing (Oikonomou et al. 2014).
9.9.6 Investment in Building Decarbonisation
As Section  9.6.3 points out, the incremental investment cost to 
decarbonise buildings at national level is up to 3.5% GDP per annum 
Other United States Europe China
50100150200
2015 2016 2017 2018 2019 2020 2021EBillion USD (2019)
51015202530
0 0
2015 2016 2017 2018 2019 2020 2021E
Figure 9.21 | Incremental capital expenditure on energy efficiency investment (left) and renewable heat in buildings, 2015–2021. Notes: (i) An energy 
efficiency investment is defined as the incremental spending on new energy-efficient equipment or the full cost of refurbishments that reduce energy use. (ii) Renewable heat 
for end-use include solar thermal applications (for district, space, and water heating), bioenergy and geothermal energy, as well as heat pumps. (iii) The investment in 2021 is 
an estimate. Source: IEA 2021b.

1015
1015
Buildings Chapter 99during the next thirty years (the global GDP in 2019 was USD88 trillion). 
As the following figures illustrate, only a very small share of it is currently 
being invested, leaving a  very large investment gap still to address. 
The incremental capital expenditure on energy efficiency in buildings 
has grown since AR5 to reach the estimated USD193 billion in 2021; 
Europe was the largest investing region, followed by the USA and 
China (Figure 9.21). The incremental capital expenditure on renewable 
energy heat vice versa declined to reach USD24 billion in this year; the 
leading investor was China, followed by Europe (ibid). The total capital 
expenditure on distributed small-scale (less than 1MW) solar systems 
in 2019 was USD52.1 billion, down from the peak of USD71 billion in 
2011; most of this capacity is installed in buildings (Frankfurt School-
UNEP Centre/BNEF 2020). The US was the largest country market

with USD9.6 billion investment; notably USD5 billion was deployed in 
the Middle East and Africa (ibid). IEA (2021b) provided an estimate 
of annual average incremental investment needs in building sector 
decarbonation between 2026 and 2030 of USD711 billion, including 
USD509 billion in building energy efficiency and USD202 billion in 
renewable heat for end-use and electrification in buildings. Such 
investment would allow being on track towards meeting the goals 
of the WEO Net Zero Emissions Scenario, as presented in Box  9.2. 
To  reach these levels, the respective investment must grow from 
their average volumes in 2016–2020 factor 3.6 and 4.5 respectively. 
As the investment  needs estimated by (IEA 2021b) are significantly 
lower the  investment intervals reported by bottom-up literature 
(Section 9.6.3), the actual investment gap is likely to be higher. 
9.9.7 Governance and Institutional Capacity
9.9.7.1 Governance

Multi-level and polycentric governance is essential for implementing 
sufficiency, energy efficiency and renewable energies policies (IPCC 
2018). Policies can be implemented at different levels of government 
and decision making, international, national, regional, and local. 
Policies for building have be adopted at national level (Enker and 
Morrison 2017), at state or regional level (Fournier et al. 2019), or 
at city level (Trencher and van der Heijden 2019). Zhao et al. (2019) 
find that national policies are instrumental in driving low carbon 
developments in buildings.
International agreements (Kyoto, Montreal/Kigali, Paris, etc.) play an 
important role in establishing national energy-efficiency and renewable 
energy policies in several countries (Dhar et al. 2018; Bertoldi 2018). 
Under the Paris Agreement, some NDCs contain emission reduction 
targets for subsectors, for example, buildings, policies for subsectors

and energy efficiency and/or renewable targets (see also Cross-
Chapter Box 5 in Chapter 4). In the EU since 2007 climate and energy 
policies are part of a co-ordinated policy package. EU Member States 
have prepared energy efficiency plans every three years and long-term 
renovation strategies for buildings (Economidou et al. 2020). Under 
the new Energy and Climate Governance Regulation EU Member 
States have submitted at the end of 2020 integrated National Energy 
and Climate Plans, including energy efficiency and renewable plans. 
(Oberthur 2019; Schlacke and Knodt 2019). The integration of energy 
and climate change policies and their governance has been analysed (von Lüpke and Well 2020), highlighting the need of reinforcing the 
institutions, anticipatory governance, the inconsistency of energy 
policies and the emerging multi-level governance.
Some policies are best implemented at international level. Efficiency

requirements for traded goods and the associated test methods could 
be set at global level in order to enlarge the market, avoid technical 
barriers to trade; reduce the manufacturers design and compliance 
costs. International standards could be applied to developing countries 
when specific enabling conditions exist, particularly in regard to 
technology transfer, assistance for capacity buildings and financial 
support. This would also reduce the dumping of inefficient equipment 
in countries with no or lower efficiency requirements. An example is the 
dumping of new or used inefficient cooling equipment in developing 
countries, undermining national and local efforts to manage energy, 
environment, health, and climate goals. Specific regulations can be 
put in place to avoid such environmental dumping, beginning with the 
‘prior informed consent’ as in the Rotterdam Convention and a later 
stage with the adoption of minimum efficiency requirements for

appliances (Andersen et al. 2018; UNEP 2017). Dreyfus et al. (2020a) 
indicates that global policies to promote best technologies currently 
available have the potential to reduce climate emissions from air 
conditionings and refrigeration equipment by 210–460 GtCO 2-eq by 
2060, resulting from the phasing down of HFC and from improved 
energy efficiency. Another example is the commitment by governments 
in promoting improvements in energy efficiency of cooling equipment 
in parallel with the phasedown of HFC refrigerants enshrined in the 
Biarritz Pledge for Fast Action on Efficient Cooling signed in 2019. 
The policy development and implementation costs will be reduced as 
the technical analysis leading to the standard could be shared among 
governments. However, it is important that local small manufacturing 
companies in developing countries have the capacity to invest in 
updating production lines for meeting new stringent international 
efficiency requirements.

Building energy consumption is dependent on local climate and 
building construction traditions, regional and local government 
share an important role in promoting energy efficiency in buildings 
and on-site RES, through local building energy codes, constructions 
permits and urban planning. In South Korea, there is a green building 
certification system operated by the government, based on this, 
Seoul has enacted Seoul’s building standard, which includes more 
stringent requirements. Where it is difficult to retrofit existing 
buildings, for example, historical buildings, cities may impose target 
at district level, where RES could be shared among buildings with 
energy positive buildings compensating for energy consuming 
buildings. Local climate and urban plans could also contribute to 
the integration of the building sector with the local transport, water, 
and energy sectors, requiring, for example, new constructions in

areas served by public transport, close to offices or buildings to be 
ready for e-mobility. Buildings GHG emission reduction shall also 
be considered in greenfield and brownfield developments and urban 
expansion (Loo et al. 2017; Salviati and Ricciardo Lamonica 2020), 
including co-benefits (Zapata-Diomedi et al. 2019).
Energy efficiency, sufficiency, and renewable policies and measures 
will have a  large impact on different stakeholders (citizens,

1016
1016
Chapter 9 Buildings9construction companies; equipment manufacturers; utilities, 
etc.), several studies highlighted the importance of stakeholder 
consultation and active participation in policy making and policy 
implementation (Vasileiadou and Tuinstra 2013; Ingold et  al. 
2020), including voluntary commitments and citizen assemblies. 
In particular, energy user’s role will be transformed from passive role 
to an active role, as outlined in the concept of energy citizenship 
(Campos and Marín-González 2020). The energy citizens need 
and voice should therefore be included in policy processes among 
traditional business players, such as incumbent centralised power 
generation companies and utilities (Van Veelen 2018). Architects and 
engineers play an important role in the decarbonisation of buildings. 
The professional bodies can mandate their members support energy 
efficiency and sufficiency. For example, the US AIA states in their

code of ethics that architects must inform clients of climate risks and 
opportunities for sustainability. The capacity and quality of workforce 
and building construction, retrofit, and service firms are essential to 
execute the fast transition in building systems (Cross-Chapter Box 12 
in Chapter 16).
9.9.7.2 Institutional Capacity 
The concept of institutional capacity is increasingly connected with 
the issue of public governance, emphasising the broad institutional 
context within which individual policies are adopted. Institutions 
are durable and are sources of authority (formal or informal) 
structuring repeated interactions of individuals, companies, civil 
society groups, governments, and other entities. Thus, institutional 
capacity also represents a  broader ‘enabling environment’ which 
forms the basis upon which individuals and organisations interact. 
In general terms, capacity is ‘the ability to perform functions, solve

problems and set and achieve objectives’ (Fukuda-Parr et al. 2002). 
Institutional capacity is an important element for regional sustainable 
development (Farajirad et  al. 2015). The role and importance of 
institutional capacity is fundamental in implementing the building 
decarbonisation. Central and local governments, regulatory 
organisations, financial institutions, standardisation bodies, test 
laboratories, building construction and design companies, qualified 
workforce and stakeholders are key players in supporting the 
implementation of building decarbonisation. 
Governments (from national to local) planning to introduce 
efficiency, RES, and sufficiency policies needs technical capacity 
to set sectoral targets and design policies and introduce effective 
and enforcement with adequate structure and resources for their 
implementation. Policies discussed and agreed with stakeholders 
and based on impartial data and impact assessments, have a higher

possibility of success. Public authorities need technical and economics 
competences to understand complex technical issues and eliminate 
the knowledge gap in comparison to private sector experts, human 
and financial resources to design, implement, revise, and evaluate 
policies. The role of energy efficiency policy evaluation needs to be 
expanded, including the assessment of the rebound effect (Vine 
et  al. 2013). For developing countries international support for 
institutional capacity for policy development, implementation and 
evaluation is of key importance for testing laboratory, standards 
institute, enforcement and compliances technicians and evaluation experts. Thus, in development support, addition to technology 
transfer, also capacity buildings for national and local authorities 
should be provides. The Paris Agreement Article 11 aims at enhancing 
the capacity of decision-making institutions in developing countries 
to support effective implementation.

Enforcement of policies is of key importance. Policies on appliance 
energy standards need to establish criteria for random checks and 
tests of compliance, establish penalties and sanctions for non-
compliance. For building code compliance there is the need to verify 
compliance after construction to verify the consistence with building 
design (Vine et al. 2017). Often local authorities lack resources and 
technical capacity to carry out inspections to check code compliance. 
This issue is even more pressing in countries and cities with large 
informal settlements, where buildings may not be respecting building 
energy codes for safety and health.
9.10 Knowledge Gaps
Insights from regions, sectors, and communities:
• Due to the dominating amount of literature from Developed 
Countries and rapidly developing Asia (China), the evidence 
and therefore conclusions are limited for the developing world. 
In particular, there is limited evidence on the potential and costs

the countries of South-East Asia and Pacific, Africa, and Latin 
America and Caribbean.
• The contribution of indigenous knowledge in the evolvement of 
buildings is not well appreciated. There is a need to understand 
this contribution and provide methodological approaches for 
incorporation of indigenous knowledge. 
• Analysis of emissions and energy demand trends in non-
residential buildings is limited due to the number of building 
types included in this category and the scarcity of data for each 
building type. The use of new data gathering techniques such as 
machine learning, GIS combined with digital technologies to fill in 
this data gap was not identified in the literature. Consideration of 
embodied emissions from building stock growth has only recently 
entered the global scenario literature, and more development is 
expected in this area.
Measures, potentials, and costs:
• There is a lack of scientific reporting of case studies of exemplary

buildings, specially from developing countries. Also, there is 
a lack of identification of researchers on technologies with the 
mitigation potential of such technologies, bringing a  lack in 
quantification of that potential.
• There is limited evidence on sufficiency measures including 
those from behavioural energy saving practices: updated 
categorisations, current adoption rates and willingness to adopt.
• There is limited evidence on circular and shared economy in 
buildings, including taxonomies, potentials, current adoption 
rates and willingness to adopt.

1017
1017
Buildings Chapter 99• Most of the literature on climate change impacts on buildings is 
focused on thermal comfort. There is need for further research 
on climate change impacts on buildings structure, materials and 
construction and the energy and emissions associated with those 
impacts. Also, more studies that assess the role of passive energy 
efficiency measures as adaptation options are needed. Finally, 
regional studies leave out in depth analyses of specific regions.
Feasibility and policies:
• Applications of human centred profiles for targeted policy 
making and considering stages of diffusion of innovation, that 
is: what works (motivation) for whom (different stakeholders, not 
only households) and when (stages of market maturity).
• The multiple co-benefits of mitigation actions are rarely 
integrated into decision-making processes. So, there is a need to 
further develop methodologies to quantify and monetise these

externalities as well as indicators to facilitate their incorporation 
in energy planning. 
• Policies for sufficiency have to be further analysed and tested 
in real situation, including ex ante simulation and ex post 
evaluation. The same is also valid for Personable (tradable) 
Carbon Allowances.
Methods and models:
• There is limited literature on the integration of behavioural 
measures and lifestyle changes in modelling exercises.
• Mitigation potential resulting from the implementation of 
sufficiency measures is not identified in global energy/climate and 
building scenarios despite the growing literature on sufficiency. 
At the best, mitigation potential from behaviour change is 
quantified in energy scenarios; savings from structural changes 
and resource efficiency are not identified in the literature on 
global and building energy models. 
• The actual costs of the potential could be higher to rather 
optimistic assumptions of the modelling literature, for example,

assuming a  2–3% retrofit rate, and even higher, versus the 
current 1%. The uncertainty ranges of potential costs are not 
well understood.
• Despite a  large number of exemplary buildings achieving very 
high performance in all parts of the world and a growing amount 
of modelling literature on the potential, if these will penetrate at 
scale, there is a lack of modelling literature assessing the costs of 
respective actions at national, regional, and global level based on 
comprehensive cost assessments. 
• There is a lack of peer-reviewed literature on investment gaps, 
which compares the investment need in the building sector 
decarbonisation and recent investment flows into it estimated 
with the same costing methodologies.

1018
1018
Chapter 9 Buildings9Frequently Asked Questions (FAQs)
FAQ 9.1 | To which GHG emissions do buildings contribute?
There are three categories of GHG emissions from buildings: 
i. direct emissions which are defined as all on-site fossil fuel or biomass-based combustion activities (i.e.,  use of biomass 
for cooking, or gas for heating and hot water) and F-gas emissions (i.e., use of heating and cooling systems, aerosols, fire 
extinguishers, soundproof);
ii. indirect emissions which occur off-site and are related to heat and electricity production; and 
iii. embodied emissions which are related to extracting, producing, transforming, transporting, and installing the construction 
material and goods used in buildings. 
In 2019, global GHG emissions from buildings were at 12 GtCO 2-eq out of which 24% were direct emissions, 57% were indirect 
emissions, and 18% were embodied emissions. More than 95% of emissions from buildings were CO 2 emissions, CH 4 and N 2O

represented 0.08% each and emissions from halocarbon contributed by 3% to global GHG emissions from buildings.
FAQ 9.2 | What are the co-benefits and trade-offs of mitigation actions in buildings?
Mitigation actions in buildings generate multiple co-benefits (e.g.,  health benefits due to the improved indoor and outdoor 
conditions, productivity gains in non-residential buildings, creation of new jobs particularly at local level, improvements in social 
well-being etc.) beyond their direct impact on reducing energy consumption and GHG emissions. Most studies agree that the value 
of these multiple benefits is greater than the value of energy savings and their inclusion in economic evaluation of mitigation 
actions may improve substantially their cost-effectiveness. It is also worth mentioning that in several cases the buildings sector 
is characterised by strong rebound effects, which could be considered as a co-benefit in cases where the mechanisms involved

provide faster access to affordable energy but also a trade-off in cases where the external costs of increased energy consumption 
exceed the welfare benefits of the increased energy service consumption, thus lowering the economic performance of mitigation 
actions. The magnitude of these co-benefits and trade-offs are characterised by several uncertainties, which may be even higher in 
the future as mitigation actions will be implemented in a changing climate, with changing building operation style and occupant 
behaviour. Mitigation measures influence the degree of vulnerability of buildings to future climate change. For instance, temperature 
rise can increase energy consumption, which may lead to higher GHG emissions. Also, sea level rise, increased storms and rainfall 
under future climate may impact building structure, materials and components, resulting in increased energy consumption and

household expenditure from producing and installing new components and making renovations. Well-planned energy efficiency, 
sufficiency and on-site renewable energy production can help to increase building resilience to climate change impacts and reduce 
adaptation needs.
FAQ 9.3 | Which are the most effective policies and measures to decarbonise the building sector?
Several barriers (information, financing, markets, behavioural, etc.) still prevents the decarbonisation of buildings stock, despite 
the several co-benefits, including large energy savings. Solutions include investments in technological solutions (e.g., insulation, 
efficient equipment, and low-carbon energies and renewable energies) and lifestyle changes. In addition, the concept of sufficiency 
is suggested to be promoted and implemented through policies and information, as technological solutions will be not enough to

decarbonise the building sector. Due to the different types of buildings, occupants, and development stage there is not a single policy, 
which alone will reach the building decarbonisation target. A range of policy instruments ranging from regulatory measures such 
as building energy code for NZEBs and appliance standards, to market-based instruments (carbon tax, personal carbon allowance, 
renewable portfolio standards, etc.) and information. Financing (grants, loans, performance base incentives, pays as you save, etc.) is 
another key enabler for energy efficiency technologies and on-site renewables. Finally, effective governance and strong institutional 
capacity are key to have an effective and successful implementation of policies and financing.

1052
1052
Chapter 10  T ransport10Executive Summary
Meeting climate mitigation goals would require transformative 
changes in the transport sector (high confidence).  In 2019, 
direct greenhouse gas (GHG) emissions from the transport sector 
were 8.7 GtCO 2-eq (up from 5.0 GtCO 2-eq in 1990) and accounted 
for 23% of global energy-related CO 2 emissions. 70% of direct 
transport emissions came from road vehicles, while 1%, 11%, and 
12% came from rail, shipping, and aviation, respectively. Emissions 
from shipping and aviation continue to grow rapidly. Transport-
related emissions in developing regions of the world have increased 
more rapidly than in Europe or North America, a trend that is likely 
to continue in coming decades (high confidence). {10.1, 10.5, 10.6}
Since the IPCC’s Fifth Assessment Report (AR5) there has been 
a growing awareness of the need for demand management 
solutions combined with new technologies, such as the

rapidly growing use of electromobility for land transport and 
the emerging options in advanced biofuels and hydrogen-
based fuels for shipping and aviation.  There is a  growing 
need for systemic infrastructure changes that enable behavioural 
modifications and reductions in demand for transport services that 
can in turn reduce energy demand. The response to the COVID-19 
pandemic has also shown that behavioural interventions can reduce 
transport-related GHG emissions. For example, COVID-19-based 
lockdowns have confirmed the transformative value of telecommuting 
replacing significant numbers of work and personal journeys as well 
as promoting local active transport. There are growing opportunities 
to implement strategies that drive behavioural change and support 
the adoption of new transport technology options. {Chapter 5, 10.2, 
10.3, 10.4, 10.8}
Changes in urban form, behaviour programmes, the circular 
economy, the shared economy, and digitalisation trends

can support systemic changes that lead to reductions in 
demand for transport services or expand the use of more 
efficient transport modes (high confidence).  Cities can reduce 
their transport-related fuel consumption by around 25% through 
combinations of more compact land use and the provision of less 
car-dependent transport infrastructure. Appropriate infrastructure, 
including protected pedestrian and bike pathways, can also support 
much greater localised active travel.1 Transport demand management 
incentives are expected to be necessary to support these systemic 
changes (high confidence). There is mixed evidence of the effect 
of circular economy initiatives, shared economy initiatives, and 
digitalisation on demand for transport services. For example, while 
dematerialisation can reduce the amount of material that needs 
to be transported to manufacturing facilities, an increase in online 
shopping with priority delivery can increase demand for freight

transport. Similarly, while teleworking could reduce travel demand, 
increased ridesharing could increase vehicle-km travelled. {Chapter 1, 
Chapter 5, 10.2, 10.8}
1 Active travel is travel that requires physical effort, for example journeys made by walking or cycling.Battery electric vehicles (BEVs) have lower lifecycle greenhouse 
gas emissions than internal combustion engine vehicles 
(ICEVs) when BEVs are charged with low-carbon electricity 
(high confidence). Electromobility is being rapidly implemented in 
micromobility (e-autorickshaws, e-scooters, e-bikes), in transit systems, 
especially buses, and, to a lesser degree, in the electrification of personal 
vehicles. BEVs could also have the added benefit of supporting grid 
operations. The commercial availability of mature lithium-ion batteries 
(LIBs) has underpinned this growth in electromobility.
As global battery production increases, unit costs are declining. Further

efforts to reduce the GHG footprint of battery production, however, 
are essential for maximising the mitigation potential of BEVs. The 
continued growth of electromobility for land transport would require 
investments in electric charging and related grid infrastructure (high 
confidence). Electromobility powered by low-carbon electricity has 
the potential to rapidly reduce transport GHG and can be applied 
with multiple co-benefits in the developing world’s growing cities 
(high confidence). {10.3, 10.4, 10.8}
Land-based, long-range, heavy-duty trucks can be decarbonised 
through battery electric haulage (including the use of electric 
road systems), complemented by hydrogen- and biofuel-
based fuels in some contexts (medium confidence). These 
same technologies and expanded use of available electric rail 
systems can support rail decarbonisation (medium confidence). 
Initial deployments of battery electric, hydrogen- and bio-based

haulage are underway, and commercial operations of some of these 
technologies are considered feasible by 2030 (medium confidence). 
These technologies nevertheless face challenges regarding driving 
range, capital and operating costs, and infrastructure availability. In 
particular, fuel cell durability, high energy consumption, and costs 
continue to challenge the commercialisation of hydrogen-based fuel 
cell vehicles. Increased capacity for low-carbon hydrogen production 
would also be essential for hydrogen-based fuels to serve as an 
emissions reduction strategy (high confidence). {10.3, 10.4, 10.8}
Decarbonisation options for shipping and aviation still require 
R&D, though advanced biofuels, ammonia, and synthetic fuels 
are emerging as viable options (medium confidence).  Increased 
efficiency has been insufficient to limit the emissions from shipping 
and aviation, and natural gas-based fuels are likely inadequate

to meet stringent decarbonisation goals for these segments (high 
confidence). High energy density, low-carbon fuels are required, but 
they have not yet reached commercial scale. Advanced biofuels could 
provide low-carbon jet fuel (medium confidence). The production of 
synthetic fuels using low-carbon hydrogen with CO 2 captured through 
direct air capture (DAC) or bioenergy with carbon capture and storage 
(BECCS) could provide jet and marine fuels but these options still 
require demonstration at scale (low confidence). Ammonia produced 
with low-carbon hydrogen could also serve as a marine fuel (medium 
confidence). Deployment of these fuels requires reductions in 
production costs. {10.2, 10.3, 10.4, 10.5, 10.6, 10.8}

1053
1053
Transport Chapter 1010
Scenarios from bottom-up and top-down models indicate that 
without intervention, CO 2 emissions from transport could grow 
in the range of 16% and 50% by 2050 (medium confidence). The 
scenarios literature projects continued growth in demand for freight 
and passenger services, particularly in developing countries in Africa 
and Asia (high confidence). This growth is projected to take place 
across all transport modes. Increases in demand notwithstanding, 
scenarios that limit warming to 1.5°C with no or limited overshoot 
suggest that a  59% reduction (42–68% interquartile range) in 
transport-related CO 2 emissions by 2050, compared to modelled 
2020 levels is required. While many global scenarios place greater 
reliance on emissions reduction in sectors other than transport, 
a  quarter of the 1.5°C degree scenarios describe transport-related 
CO2 emissions reductions in excess of 68% (relative to modelled

2020 levels) (medium confidence). Illustrative mitigation pathways 
1.5 renewables (REN) and 1.5 low demand (LD) describe emission 
reductions of 80% and 90% in the transport sector, respectively, 
by 2050. Transport-related emission reductions, however, may not 
happen uniformly across regions. For example, transport emissions 
from the Developed Countries and Eastern European and West-
Central Asian countries decrease from 2020 levels by 2050 across 
all scenarios compatible with a 1.5°C goal (C1–C2 group), but could 
increase in Africa, Asia and Pacific, Latin America and Caribbean, and 
the Middle East in some of these scenarios.2 {10.7}
The scenarios literature indicates that fuel and technology shifts are 
crucial to reducing carbon emissions to meet temperature goals. 
In general terms, electrification tends to play the key role in land-
based transport, but biofuels and hydrogen (and derivatives) could 
play a  role in decarbonisation of freight in some contexts (high

confidence). Biofuels and hydrogen (and derivatives) are likely more 
prominent in shipping and aviation (high confidence). The shifts 
towards these alternative fuels must occur alongside shifts towards 
clean technologies in other sectors (high confidence). {10.7}
There is a  growing awareness of the need to plan for the 
significant expansion of low-carbon energy infrastructure, 
including low-carbon power generation and hydrogen 
production, to support emissions reductions in the transport 
sector (high confidence). Integrated energy planning and 
operations that take into account energy demand and system 
constraints across all sectors (transport, buildings, and industry) offer 
the opportunity to leverage sectoral synergies and avoid inefficient 
allocation of energy resources. Integrated planning of transport 
and power infrastructure would be particularly useful in developing 
countries where ‘greenfield’ development doesn’t suffer from

constraints imposed by legacy systems. {10.3, 10.4, 10.8}
The deployment of low-carbon aviation and shipping fuels 
that support decarbonisation of the transport sector could 
require changes to national and international governance 
structures (medium confidence).  Currently, the Paris Agreement 
does not specifically cover emissions from international shipping 
and aviation. Instead, accounting for emissions from international 
transport in the Nationally Determined Contributions is at the 
2 See Annex II Table 1 for details of regional groupings used in this report.discretion of each country. While the International Civil Aviation 
Organization (ICAO) and International Maritime Organization (IMO) 
have established emissions reductions targets, only strategies to 
improve fuel efficiency and reduce demand have been pursued, and 
there has been minimal commitment to new technologies. Some 
authors in the literature have argued that including international

shipping and aviation under the Paris Agreement could spur stronger 
decarbonisation efforts in these segments. {10.5, 10.6, 10.7}
There are growing concerns about resource availability, labour 
rights, non-climate environmental impacts, and costs of critical 
minerals needed for LIBs (medium confidence).  Emerging 
national strategies on critical minerals and the requirements from 
major vehicle manufacturers are leading to new, more geographically 
diverse mines. The standardisation of battery modules and packaging 
within and across vehicle platforms, as well as increased focus on 
design for recyclability, are important. Given the high degree of 
potential recyclability of LIBs, a  nearly closed-loop system in the 
future could mitigate concerns about critical mineral issues (medium 
confidence). {10.3, 10.8}
Legislated climate strategies are emerging at all levels 
of government and, together with pledges for personal 
choices, could spur the deployment of demand- and supply-

side transport mitigation strategies (medium confidence). 
At the local level, legislation can support local transport plans 
that include commitments or pledges from local institutions to 
encourage behaviour change by adopting an organisational culture 
that motivates sustainable behaviour, with inputs from the creative 
arts. Such institution-led mechanisms could include bike-to-work 
campaigns, free transport passes, parking charges, or eliminating car 
benefits. Community-based solutions like solar sharing, community 
charging, and mobility as a service can generate new opportunities to 
facilitate low-carbon transport futures. At the regional and national 
levels, legislation can include vehicle and fuel efficiency standards, 
R&D support, and large-scale investments in low-carbon transport 
infrastructure. {10.8, Chapter 15}

1054
1054
Chapter 10  T ransport1010.1 Introduction and Overview
This chapter examines the transport sector’s role in climate change 
mitigation. It appraises the transport system’s interactions beyond the 
technology of vehicles and fuels to include the full lifecycle analysis 
of mitigation options, a  review of enabling conditions, and metrics 
that can facilitate advancing transport decarbonisation goals. The 
chapter assesses developments in the systems of land-based transport 
and introduces, as a  new feature since AR5, two separate sections 
focusing on the trends and challenges in aviation and shipping. The  chapter assesses the future trajectories emerging from global, 
energy, and national scenarios and concludes with a discussion on 
enabling conditions for transformative change in the sector.
This section (Section 10.1) discusses how transport relates to virtually 
all the Sustainable Development Goals (SDGs), the trends and drivers

making transport a big contributor to greenhouse gas (GHG) emissions, 
the impacts climate change is having on transport that can be 
addressed as part of mitigation, and the overview of emerging transport 
disruptions with potential to shape a low-carbon transport pathway.
Table 10.1 | Transport and the Sustainable Development Goals: Synergies and trade-offs.
Sustainable Development Goals: Synergies and trade-offs
Basic human needs Earth preconditions Sustainable resource useSocial and economic 
developmentUniversal valuesTransport-related topics (low-carbon transport; active transport; electric vehicles)
Advances in vehicle technology; Improved public transport system –Lower air pollution 
contributes to positive 
health outcomes.
 –Energy access can contribute 
to poverty alleviation.
 –Transport planning is 
a major player in reducing 
poverty in cities.
 –Access to healthcare.
 –Diseases from air pollution.
 –Injuries and deaths from 
traffic accidents.
 –Reduced driving-induced

stress.
 –Links between active 
transport and good health 
with positive effects of 
walking and cycling.
 –Improving road accessibility 
to disabled users.
 –Reduce time spent on 
transport/mobility. –Reduction of GHG emissions 
along the entire value chain, 
e.g., Well-to-Wheel.
 –Further development 
addressing minor GHG 
emissions and pollutants.
 –Transport oriented to 
 –Circular economy principle 
applied to transport. –Share of renewable 
energy use.
 –Energy efficiency of vehicles.
 –Clean and affordable energy 
off-grid.
 –Reduce material consumption 
during production, lifecycle 
analysis of vehicles and their 
operations including entire 
value chains.
 –Closed loop carbon and 
nutrient cycle linked to 
circular economy. –Role of transport for economic 
and human development.
 –Decarbonised public 
transport rather than 
private vehicle use.
 –Sustainable transport 
infrastructure and systems

for cities and rural areas.
 –Affordability of mobility 
services, this can also be 
covered under ‘universal 
access’ to public transport.
 –Accessibility vs mobility: 
mobility to opportunities; 
transport equity; development 
as freedom.
 –Positive economic growth 
(employment) outcomes due 
to resource efficiency and 
lower productive energy cost.
 –Role of transport provision 
in accessing work, 
reconfiguration of social 
norms, as working from home.
 –Transport manufacturers 
as key employers changing 
role of transport-related 
labour due to platform 
economy, and innovations 
in autonomous vehicles. –Gender equality in transport.
 –Reduced inequalities.
 –Enables access to 
quality education.
 –Partnership for the goals.ReferencesGrant et al. 2016; Haines 
et al. 2017; Cheng et al. 2018; 
Nieuwenhuijsen 2018; Smith 
et al. 2018; Sofiev et al. 2018; 
Peden and Puvanachandra 
2019; King and Krizek 2020; 
Macmillan et al. 2020Farzaneh et al. 2019; see

particularly following chapters.SLoCaT 2019; see particularly 
following chapters.Bruun and Givoni 2015; Pojani 
and Stead 2015; Hensher 2017; 
ATAG 2018; Grzelakowski 
2018; Weiss et al. 2018; Brussel 
et al. 2019; Gota et al. 2019; 
Mohammadi et al. 2019; Peden 
and Puvanachandra 2019; 
SLoCaT 2019; Xu et al. 2019Hernandez 2018; Prati 2018; 
Levin and Faith-Ell 2019; 
Vecchio et al. 2020

1055
1055
10.1.1 Transport and the Sustainable 
Development Goals
The adoption of the 2030 Agenda for Sustainable Development by the 
United Nations (UN) has renewed international efforts to pursue and 
accurately measure global actions towards sustainable development 
(United Nations 2015). The 17 SDGs set out the overall goals that 
are further specified by 169 targets and 232 SDG indicators, many of 
which relate to transport (United Nations 2017; Lisowski et al. 2020). 
A sustainable transport system provides safe, inclusive, affordable, 
and clean passenger and freight mobility for current and future 
generations (Williams 2017; Litman 2021) so transport is particularly 
linked to SDGs 3, 7, 8, 9, 11, 12, and 13 (Move Humanity 2018; IRP 2019; WBA 2019; SLoCaT 2019; Yin 2019). Table 10.1 summarises 
transport-related topics for these SDGs and corresponding research. 
Section 17.3.3.7 also provides a cross-sectoral overview of synergies

and trade-offs between climate change mitigation and the SDGs.
10.1.2 Trends, Drivers and the Critical Role 
of Transport in GHG Growth
The transport sector directly emitted around 8.9 Gtonnes (Gt) of carbon 
dioxide equivalent (CO 2-eq) in 2019, up from 5.1 GtCO 2-eq in 1990 
(Figure 10.1). Global transport was the fourth largest source of GHG 
emissions in 2019 following the power, industry, and the agriculture,  (a) Transport global GHG emissions trends
(b) Transport regional GHG emissions trendsInternational aviation
Other (transport)Indirect CO2
Domestic aviation
RailGHG emissions (Gt CO 2-eq yr–1) GHG emissions (Gt CO 2-eq yr–1)057.5105.1 Gt 6.3 Gt 7.6 Gt 8.9 Gt+2.1%/yr +1.7%/yr +1.8%/yr
1990 2000 2010 20192.5
2%
1%7%1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010
2019
1990
2000
2010

2019Africa AU, JP , NZ E. AsiaE. Europe. 
& W.C. Asia Europe Latin Am. Middle E. N. America S. Asia S.E. Asia
00.511.524%6%9%69%2%
Figure 10.1 | Global and regional transport greenhouse gas emissions trends.  Indirect emissions from electricity and heat consumed in transport are shown in 
panel (a) and are primarily linked to the electrification of rail systems. These indirect emissions do not include the full lifecycle emissions of transportation systems (e.g., vehicle 
manufacturing and infrastructure), which are assessed in Section 10.4. International aviation and shipping are included in panel (a) but excluded from panel (b). Indirect 
emissions from fuel production, vehicle manufacturing and infrastructure construction are not included in the sector total. Source: adapted from Lamb et al. (2021) using data 
from Minx et al. (2021).

1056
1056
Chapter 10  T ransport10forestry and land use (AFOLU) sectors. In absolute terms, the transport 
sector accounts for roughly 15% of total GHG emissions and about 23% 
of global energy-related CO 2 emissions (IEA 2020a). Transport-related 
GHG emissions have increased fast over the last two decades, and 
since 2010, the sector’s emissions have increased faster than for any 
other end-use sector, averaging +1.8% annual growth (Section 10.7). 
Addressing emissions from transport is crucial for GHG mitigation 
strategies across many countries, as the sector represents the largest 
energy consuming sector in 40% of countries worldwide. In most 
remaining countries, transport is the second largest energy-consuming 
sector, reflecting different levels of urbanisation and land use patterns, 
speed of demographic changes and socio-economic development (IEA 
2012; Gota et al. 2019; Hasan et al. 2019; Xie et al. 2019).

As of 2019, the largest source of transport emissions is the movement 
of passengers and freight in road transport (6.1 GtCO 2-eq, 69% of the 
sector’s total). International shipping is the second largest emission 
source, contributing 0.8 GtCO 2-eq (9% of the sector’s total), and 
international aviation is third with 0.6 GtCO 2-eq (7% of the sector’s 
total). All other transport emissions sources, including rail, have 
been relatively trivial in comparison, totalling 1.4 GtCO 2-eq in 2019. 
Between 2010 and 2019, international aviation had among the fastest 
growing GHG emissions among all segments (+3.4% per year), while 
road transport remained one of the fastest growing (+1.7% per year) 
among all global energy-using sectors. Note that the COVID-19-
induced economic lockdowns implemented since 2020 have had 
a very substantial impact on transport emissions – higher than any 
other sector (Chapter  2). Preliminary estimates from Crippa et  al.

(2021) suggest that global transport CO 2 emissions declined to 
7.6 GtCO 2 in 2020, a reduction of 11.6% compared to 2019 (Crippa 
et al. 2021; Minx et al. 2021). These lockdowns affected all transport 
segments, and particularly international aviation (estimated 45% 
reduction in 2020 global CO 2 emissions), road transport (–10%), and 
domestic aviation (–9.3%). By comparison, aggregate CO 2 emissions 
across all sectors are estimated to have declined by 5.1% as a result 
of the COVID-19 pandemic (Section 2.2.2).
Growth in transport-related GHG emissions has taken place across 
most world regions (see Figure  10.1b). Between 1990 and 2019, 
growth in emissions was relatively slow in Europe, Australia, Japan and 
New Zealand, Eurasia, and North America while it was unprecedently 
fast in other regions. Driven by economic and population growth, 
the annual growth rates in Eastern Asia, Southern Asia, South-East 
Asia and Pacific, and Africa were 6.1%, 5.2%, 4.7%, and 4.1%,

respectively. Latin America and the Middle East have seen somewhat 
slower growth in transport-related GHG emissions (annual growth 
rates of 2.4% and 3.3%, respectively) (ITF 2019; Minx et al. 2021). 
Section 10.7 provides a more detailed comparison of global transport 
emissions trends with those from regional and sub-sectoral studies.
The rapid growth in global transport emissions is primarily a result of 
the fast growth in global transport activity levels, which grew by 73% 
between 2000 and 2018. Passenger and freight activity growth have 
outpaced energy efficiency and fuel economy improvements in this 
period (ITF 2019). The global increase in passenger travel activities 
has taken place almost entirely in non-OECD countries, often starting 
from low motorisation rates (SLoCaT 2018a). Passenger cars, two- and three-wheelers, and mini buses contribute about 75% of passenger 
transport-related CO 2 emissions, while collective transport services (bus

and railways) generate about 7% of the passenger transport-related 
CO2 emissions despite covering a fifth of passenger transport globally 
(Rodrigue 2017; Halim et al. 2018; Sheng et al. 2018; SLoCaT 2018a; 
Gota et  al. 2019). While alternative lighter powertrains have great 
potential for mitigating GHG emissions from cars, the trend has been 
towards increasing vehicle size and engine power within all vehicle 
size classes, driven by consumer preferences towards larger sport 
utility vehicles (SUVs) (IEA 2020a). On a global scale, SUV sales have 
been constantly growing in the last decade, with 40% of the vehicles 
sold in 2019 being SUVs (IEA 2020a) (Section 10.4, Box 10.3).
Indirect emissions from electricity and heat shown in Figure  10.1 
account for only a  small fraction of current emissions from the 
transport sector (2%) and are associated with electrification of 
certain modes like rail or bus transport (Lamb et al. 2021). Increasing

transport electrification will affect indirect emissions, especially 
where carbon-intense electricity grids operate.
Global freight transport, measured in tonne-kilometres (tkm), 
grew by 68% between 2000 and 2015 and is projected to grow 
3.3 times by 2050 (ITF 2019). If unchecked, this growth will make 
decarbonisation of freight transport very difficult (McKinnon 2018; 
ITF 2019). International trade and global supply chains from industries 
frequently involving large geographical distances are responsible for 
the fast increase of CO 2 emissions from freight transport (Yeh et al. 
2017; McKinnon 2018), which are growing faster than emissions 
from passenger transport (Lamb et  al. 2021). Heavy-duty vehicles 
(HDVs) make a disproportionate contribution to air pollution, relative 
to their global numbers, because of their substantial emissions of 
particulate matter and of black carbon with high short-term warming 
potentials (Anenberg et al. 2019).

On-road passenger and freight vehicles dominate global transport-
related CO 2 emissions and offer the largest mitigation potential (Taptich 
et al. 2016; Halim et al. 2018). This chapter examines a wide range 
of possible transport emission reduction strategies. These strategies 
can be categorised under the ‘Avoid-Shift-Improve’ (ASI) framework 
described in Chapter 5 (Taptich et al. 2016). ‘Avoid’ strategies reduce 
total vehicle travel. They include compact communities and other 
policies that minimise travel distances and promote efficient transport 
through pricing and demand management programmes. ‘Shift’ 
strategies shift travel from higher-emitting to lower-emitting modes. 
These strategies include more multimodal planning that improves 
active and collective transport modes, complete streets roadway 
design, high occupant vehicle priority strategies that favour shared 
modes, Mobility as a Service (MaaS), and multimodal navigation and

payment apps. ‘Improve’ strategies reduce per-kilometre emission 
rates. These strategies include hybrid and electric vehicle incentives, 
lower-carbon and cleaner fuels, high-emitting vehicle scrappage 
programmes, and efficient driving and anti-idling campaigns (Lutsey 
and Sperling 2012; Gota et al. 2015). These topics are assessed within 
the rest of this chapter, including how combinations of ASI with new 
technologies can potentially lead from incremental interventions 
into low-carbon transformative transport improvements that include 
social and equity benefits (Section 10.8).

1057
1057
10.1.3 Climate Adaptation on the Transport Sector
Climate change impacts such as extremely high temperatures, intense 
rainfall leading to flooding, more intense winds and/or storms, and 
sea level rise can seriously impact transport infrastructure, operations, 
and mobility for road, rail, shipping, and aviation. Studies since AR5 
confirm that serious challenges to all transport infrastructures are 
increasing, with consequent delays or derailing (Miao et  al. 2018; 
Moretti and Loprencipe 2018; Pérez-Morales et al. 2019; Palin et al. 
2021). These impacts have been increasingly documented but, 
according to Forzieri et  al. (2018), little is known about the risks 
of multiple climate extremes on critical infrastructures at local to 
continental scales. All roads, bridges, rail systems, and ports are likely 
to be affected to some extent. Flexible pavements are particularly 
vulnerable to extreme high temperatures that can cause permanent

deformation and crumbling of asphalt (Underwood et al. 2017; Qiao 
et al. 2019). Rail systems are also vulnerable, with a variety of hazards, 
both meteorological and non-meteorological, affecting railway asset 
lifetimes. Severe impacts on railway infrastructure and operations 
can arise from the occurrence of temperatures below freezing, excess 
precipitation, storms and wildfires (Thaduri et al. 2020; Palin et al. 
2021) as can impacts on underground transport systems (Forero-
Ortiz et al. 2020).
Most countries are examining opportunities for combined mitigation-
adaptation efforts, using the need to mitigate climate change 
through transport-related GHG emissions reductions and reduction 
of pollutants as the basis for adaptation action (Thornbush et  al. 
2013; Wang et al. 2020). For example, urban sprawl indirectly affects 
climate processes, increasing emissions and vulnerability, which 
worsens the potential to adapt (Congedo and Munafò 2014; Macchi

and Tiepolo 2014). Hence, using a range of forms of rapid transit 
as structuring elements for urban growth can mitigate climate 
change-related risks as well as emissions, reducing impacts on new 
infrastructure, often in more vulnerable areas (Newman et al. 2017). 
Such changes are increasingly seen as having economic benefit 
(Ha et  al. 2017), especially in developing nations (Chang 2016; 
Monioudi et al. 2018).
Since AR5 there has been a growing awareness of the potential and 
actual impacts from global sea level rise due to climate change on 
transport systems (Dawson et al. 2016; Rasmussen et al. 2018; IPCC 
2019; Noland et al. 2019), particularly on port facilities (Stephenson 
et al. 2018; Yang et al. 2018b; Pérez-Morales et al. 2019). Similarly, 
recent studies suggest changes in global jet streams could affect 
the aviation sector (Staples et al. 2018; Becken and Shuker 2019), 
and extreme weather conditions can affect runways (heat buckling)

and aircraft lift. Combined, climate impacts on aviation could result 
in payload restrictions and disruptions (Coffel et al. 2017; Monioudi 
et  al. 2018). According to Williams (2017), studies have indicated 
that the amount of moderate-or-greater clear-air turbulence on 
transatlantic flight routes in winter will increase significantly in 
the future as the climate changes. More research is needed to fully 
understand climate-induced risks to transportation systems.10.1.4 Transport Disruption and Transformation
Available evidence suggests that transport-related CO 2 emissions 
would need to be restricted to about 2 to 3 Gt in 2050 (1.5°C 
scenario-1.5DS, B2DS), or about 70 to 80% below 2015 levels, to meet 
the goals set in the Paris Agreement. It also indicates that a balanced 
and inter-modal application of Avoid, Shift, and Improve measures is 
capable of yielding an estimated reduction in transport emissions of 
2.39 GtCO 2-equivalent by 2030 and 5.74 GtCO 2-equivalent by 2050

(IPCC 2018; Gota et al. 2019). Such a transformative decarbonisation 
of the global transport system requires, in addition to technological 
changes, a  paradigm shift that ensures prioritisation of high-
accessibility transport solutions that minimise the amount of mobility 
required to meet people’s needs, and favours transit and active 
transport modes (Lee and Handy 2018; SLoCaT 2021). These changes 
are sometimes called disruptive as they are frequently surprising in 
how they accelerate through a technological system.
The assessment of transport innovations and their mitigation potentials 
is at the core of how this chapter examines the possibilities for changing 
transport-related GHG trajectories. The transport technology innovation 
literature analysed in this chapter emphasises how a  mixture of 
mitigation technology options and social changes are now converging 
and how, in combination, they may have potential to accelerate trends

toward a low-carbon transport transition. Such changes are considered 
disruptive or transformative (Sprei 2018). Of the current transport 
trends covered in the literature, this chapter focuses on three key 
technology and policy areas: electro-mobility in land-based transport 
vehicles, new fuels for ships and planes, and overall demand reductions 
and efficiency. These strategies are seen as being necessary to integrate 
at all levels of governance and, in combination with the creation of 
fast, extensive, and affordable multimodal public transport networks, 
can help achieve multiple advantages in accordance with SDGs
Electrification of passenger transport in light-duty vehicles (LDVs) is well 
underway as a commercial process with socio-technical transformative 
potential and will be examined in detail in Sections 10.3 and 10.4. 
But the rapid mainstreaming of electric vehicles (EVs) will still need 
enabling conditions for land transport to achieve the shift away from

petroleum fuels, as outlined in Chapter 3 and detailed in Section 10.8. 
The other mitigation options reviewed in this chapter are so far only 
incremental and are less commercial, especially shipping and aviation 
fuels, so stronger enabling conditions are likely, as detailed further in 
Sections 10.5 to 10.8. The enabling conditions that would be needed 
for the development of an emerging technological solution for such 
fuels are likely to be very different from those for electromobility, but 
nevertheless they both will need demand and efficiency changes to 
ensure they are equitable and inclusive.
Section  10.2 sets out the transformation of transport through 
examining systemic changes that affect demand for transport 
services and the efficiency of the system. Section 10.3 looks at the 
most promising technological innovations in vehicles and fuels. 
The  next three sections (10.4, 10.5, and 10.6) examine mitigation

options for land transport, aviation, and shipping. Section  10.7 
describes the space of solutions assessed in a range of integrated 
modelling and sectoral transport scenarios. Finally, Section 10.8 sets

1058
1058
Chapter 10  T ransport10out what would be needed for the most transformative scenario that 
can manage to achieve the broad goals set out in Chapter 3 and the 
transport goals set out in Section 10.7.
10.2 Systemic Changes in the Transport Sector
Systemic change is the emergence of new organisational patterns that 
affect the structure of a system. While much attention has been given 
to engine and fuel technologies to mitigate GHG emissions from the 
transport sector, population dynamics, finance and economic systems, 
urban form, culture, and policy also drive emissions from the sector. 
Thus, systemic change requires innovations in these components. 
These systemic changes offer the opportunity to decouple transport 
emissions from economic growth. In turn, such decoupling allows 
environmental improvements like reduced GHG emissions without 
loss of economic activity (UNEP 2011; UNEP 2013; Newman et  al. 
2017; IPCC 2018).

There is evidence that suggests decoupling of transport emissions and 
economic growth is already happening in developed and developing 
countries. Europe and China have shown the most dramatic changes 
(Huizenga et  al. 2015; Gao and Newman 2018; SLoCaT 2018b) 
and many cities are demonstrating decoupling of transport-related 
emissions through new net zero urban economic activity (Loo and 
Banister 2016; SLoCaT 2018a). A continued and accelerated 
decoupling of the growth of transport-related GHG emissions from 
economic growth is crucial for meeting the SDGs, as outlined in 
Section 10.1. This section focuses on several overlapping components 
of systemic change in the transport sector that affect the drivers of 
GHG emissions: urban form, physical geography, and infrastructure; 
behaviour and mode choice; and new demand concepts. Table 10.3 
at the end of the section provides a high-level summary of the effect 
of these systemic changes on emissions from the transport sector.

10.2.1 Urban Form, Physical Geography, 
and Transport Infrastructure
The physical characteristics that make up built areas define the 
urban form. These physical characteristics include the shape, size, 
density, and configuration of the human settlements. Urban form 
is intrinsically coupled with the infrastructure that allows human 
settlements to operate. In the context of the transport sector, urban form and urban infrastructure influence the time and cost of travel, 
which, in turn, drive travel demand and modal choice (Marchetti and 
Ausubel 2001; Newman and Kenworthy 2015).
Throughout history, three main urban fabrics have developed, each 
with different effects on transport patterns based on a fixed travel 
time budget of around one hour (Newman et  al. 2016). The high-
density urban fabric developed over the past several millennia 
favoured walking and active transport for only a few kilometres (km). 
In the mid-19th  century, urban settlements developed a  medium-

density fabric that favoured trains and trams traveling over 10 to 
30  km corridors. Finally, since the mid-20th  century, urban form 
has favoured automobile travel, enabling mass movement between 
50 and 60 km. Table 10.2 describes the effect of these urban fabrics 
on GHG emissions and other well-being indicators.
Since AR5, urban design has increasingly been seen as a  major 
way to influence the GHG emissions from urban transport systems. 
Indeed, research suggests that implementing urban form changes 
could reduce GHG emissions from urban transport by 25% in 
2050, compared with a business-as-usual scenario (Creutzig et al. 
2015b; Creutzig 2016). Researchers have identified a  variety 
of variables to study the relationship between urban form and 
transport-related GHG emissions. Three notable aspects summarise 
these relationships: urban space utilisation, urban spatial form, 
and urban transportation infrastructure (Tian et  al. 2020). Urban

density (population or employment density) and land-use mix 
define the urban space utilisation. Increases in urban density and 
mixed function can effectively reduce per capita car use by reducing 
the number of trips and shortening travel distances. Similarly, the 
continuity of urban space and the dispersion of centres reduces 
travel distances (Tian et al. 2020), though such changes are rarely 
achieved without shifting transport infrastructure investments 
away from road capacity increases (Newman and Kenworthy 2015; 
McIntosh et al. 2017). For example, increased investment in public 
transport coverage, optimal transfer plans, shorter transit travel 
time, and improved transit travel efficiency make public transit more 
attractive (Heinen et al. 2017; Nugroho et al. 2018a; Nugroho et al. 
2018b) and hence increase density and land values (Sharma and 
Newman 2020). Similarly, forgoing the development of major roads

for the development of pedestrian and bike pathways enhances the 
attractiveness of active transport modes (Zahabi et al. 2016; Keall 
et al. 2018; Tian et al. 2020).
Table 10.2 | The systemic effect of city form and transport emissions.
Annual transport emissions and co-benefits Walking urban fabric Transit urban fabric Automobile urban fabric
Transport GHG 4 tonnes per person 6 tonnes per person 8 tonnes per person
Health benefits from walkability High Medium Low
Equity of locational accessibility High Medium Low
Construction and household waste 0.87 tonnes per person 1.13 tonnes per person 1.59 tonnes per person
Water consumption 35 kilolitre per person 42 kilolitre per person 70 kilolitre per person
Land 133 square metres per person 214 square metres per person 547 square metres per person
Economics of infrastructure and transport operations High Medium Low
Source: Newman et al. (2016); Thomson and Newman (2018); Seto et al. (2021).

1059
1059
10.2.2 Behaviour and Mode Choice
Behaviour continues to be a  major source of interest in the 
decarbonisation of transport as it directly addresses demand. 
Behaviour is about people’s actions based on their preferences. 
Chapter 5 described an ‘Avoid, Shift, Improve’ process for demand-
side changes that affect sectoral emissions. This section discusses 
some of the drivers of behaviour related to the transport sector and 
how they link to this ‘Avoid, Shift, Improve’ process.Avoid: the effect of prices and income on demand. Research 
has shown that household income and price have a strong influence 
on people’s preferences for transport services (Bakhat et  al. 2017; 
Palmer et al. 2018). The relationship between income and demand 
is defined by the income elasticity of demand. For example, research 
suggests that in China, older and wealthier populations continued to 
show a preference for car travel (Yang et al. 2019) while younger and

low-income travellers sought variety in transport modes (Song et al. 
2018). Similarly, Bergantino et  al. (2018b) evaluated the income Ultimately, infrastructure investments influence the structural 
dependence on cars, which in turn influence the lock-in or path 
dependency of transport options with their greenhouse emissions 
(Newman et al. 2015; Grieco and Urry 2016). The 21st century saw 
a  new trend to reach peak car use in some countries as a  result 
of a  revival in walking and transit use (Grieco and Urry 2016; Newman et al. 2017; Gota et al. 2019). While some cities continue 
on a trend towards reaching peak car use on a per-capita basis, for 
example Shanghai and Beijing (Gao and Newman 2020), there is 
a need for increased investments in urban form strategies that can 
continue to reduce car dependency around the world.
Cross-Chapter Box 7 | Urban Form: Simultaneously Reducing Urban Transport Emissions, 
Avoiding Infrastructure Lock-in, and Providing Accessible Services

Authors: Felix Creutzig (Germany), Karen C. Seto (the United States of America), Peter Newman (Australia)
Urban transport is responsible for about 8% of global CO 2 emissions or 3 GtCO 2 per year (Chapters 5 and 8). In contrast to energy 
supply technologies, urban transport directly interacts with mobility lifestyles (Section 5.4). Similarly, non-GHG emission externalities, 
such as congestion, air pollution, noise, and safety, directly affect urban quality of life, and result in considerable welfare losses. 
Low-carbon, highly accessible urban design is not only a major mitigation option, it also provides for more inclusive city services 
related to well-being (Sections 5.1 and 5.2). Urban planning and design of cities for people are central to realise emission reductions 
without relying simply on technologies, though the modes of transport favoured will influence the ability to overcome the lock-in 
around automobile use (Gehl 2010; Creutzig et al. 2015b).

Where lock-in has occurred, other strategies may alleviate the GHG emissions burden. Urban planning still plays a key role in recreating 
local hubs. Available land can be used to build rail-based transit, made financially viable by profiting from land value captured around 
stations (Ratner and Goetz 2013). Shared or pooled mobility can offer flexible on-demand mobility solutions that are efficient also in 
suburbs and for integrating with longer commuting trips (ITF 2017).
Global emissions trajectories of urban transport will be decided in rapidly urbanising Asia and Africa. Urban transport-related GHG 
emissions are driven by incomes and car ownership but there is considerable variation among cities with similar income and car ownership 
levels (Newman and Kenworthy 2015). While electrification is a key strategy to decarbonise urban transport, urban infrastructures can

make a difference of up to a factor of 10 in energy use and induced GHG emissions (Erdogan 2020). Ongoing urbanisation patterns 
risk future lock-in of induced demand on GHG emissions, constraining lifestyles to energy-intensive and high CO 2-related technologies 
(Erickson and Tempest 2015; Seto et al. 2016) (Sections 5.4, 8.2.3 and 10.2.1). Instead, climate solutions can be locked into urban policies 
and infrastructures (Ürge-Vorsatz et al. 2018) especially through the enhancement of the walking and transit urban fabric. Avoiding urban 
sprawl, associated with several externalities (Dieleman and Wegener 2004), is a necessary decarbonisation condition, and can be guided 
macro-economically by increasing fuel prices and marginal costs of motorised transport (Creutzig 2014). Resulting urban forms not only 
reduce GHG emission from transport but also from buildings, as greater compactness results in reduced thermal loss (Borck and Brueckner

2018). Health benefits from reduced car dependence are an increasing element driving this policy agenda (Speck 2018) (Section 10.8).
Low-carbon highly accessible urban design is not only a major mitigation option, it also provides for more inclusive city services related 
to well-being (Sections 5.1 and 5.2). Solutions involve planning cities around walkable sub-centres, where multiple destinations, such 
as shopping, jobs, leisure activities, and others, can be accessed within a 10 minute walk or bicycle ride (Newman and Kenworthy 
2006). Overall, the mitigation potential of urban planning is about 25% in 2050 compared with a business-as-usual scenario (Creutzig 
et al. 2015a; Creutzig et al. 2015b). Much higher levels of decarbonisation can be achieved if cities take on a regenerative development 
approach and act as geo-engineering systems on the atmosphere (Thomson and Newman 2016).

1060
1060
Chapter 10  T ransport10elasticity of transport by mode in the UK. They found that the income 
elasticity for private cars is 0.714, while the income elasticities of rail 
and bus use are 3.253 (the greater elasticity, the more the demand 
will grow or decline, depending on income). Research has also shown 
a positive relationship between income and demand for air travel, 
with income elasticities of air travel demand being positive and 
as large as 2 (Gallet and Doucouliagos 2014; Valdes 2015; Hakim 
and Merkert 2016; Hakim and Merkert 2019; Hanson et al. 2022). 
A survey in 98 Indian cities also showed income as the main factor 
influencing travel demand (Ahmad and de Oliveira 2016). Thus, as 
incomes and wealth across the globe rise, demand for travel is likely 
to increase as well.
The price elasticity of demand measures changes in demand as 
a result of changes in the prices of the services. In a meta-analysis of

the price elasticity of energy demand, Labandeira et al. (2017) report 
the average long-term price elasticity of demand for gasoline and 
diesel to be –0.773 and –0.443, respectively. That is, demand will 
decline with increasing prices. A similar analysis of long-term data in 
the United States (US), the United Kingdom (UK), Sweden, Australia, 
and Germany reports the gasoline price elasticity of demand for car 
travel (as measured through vehicle-kilometre – vkm – per capita) 
ranges between –0.1 and –0.4 (Bastian et al. 2016). For rail travel, 
the price elasticity of demand has been found to range between 
–1.05 and –1.1 (Zeng et al. 2021). Similarly, price elasticities for air 
travel range from –0.53 to –1.91 depending on various factors such 
as purpose of travel (business or leisure), season, and month and day 
of departure (Morlotti et al. 2017). The price elasticities of demand 
suggest that car use is inelastic to prices, while train use is relatively

inelastic to the cost of using rail. Conversely, consumers seem to be 
more responsive to the cost of flying, so that strategies that increase 
the cost of flying are likely to contribute to some avoidance of 
aviation-related GHG emissions.
While the literature continues to show that time, cost, and income 
dominate people’s travel choices (Ahmad and de Oliveira 2016; 
Capurso et al. 2019; He et al. 2020), there is also evidence of a role 
for personal values, and environmental values in particular, shaping 
choices within these structural limitations (Bouman and Steg 2019). 
For example, individuals are more likely to drive less when they care 
about the environment (De Groot et al. 2008; Abrahamse et al. 2009; 
Jakovcevic and Steg 2013; Hiratsuka et al. 2018; Ünal et al. 2019). 
Moreover, emotional and symbolic factors affect the level of car 
use (Steg 2005). Differences in behaviour may also result due 
to differences in gender, age, norms, values, and social status. For

example, women have been shown to be more sensitive to parking 
pricing than men (Simićević et al. 2020).
Finally, structural shocks, such as a financial crisis, a pandemic, or the 
impacts of climate change could affect the price and income elasticities 
of demand for transport services (van Ruijven et al. 2019). COVID-19 
lockdowns reduced travel demand by 19% (aviation by 32%) and 
some of the patterns that have emerged from the lockdowns could 
permanently change the elasticity of demand for transport (Tirachini 
and Cats 2020; Hendrickson and Rilett 2020; Newman 2020a; SLoCaT 
2021; Hanson et  al. 2022). In particular, the COVID-19 lockdowns 
have spurred two major trends: electronic communications replacing many work and personal travel requirements; and revitalised local 
active transport and e-micromobility (Newman 2020a; SLoCaT 
2021). The permanence of these changes post-COVID-19 is 
uncertain but possible (Earley and Newman 2021) (Cross-Chapter

Box 1 in Chapter 1). However, these changes will require growth of 
infrastructure for better ICT bandwidths in developing countries, and 
better provision for micromobility in all cities.
Shift: mode choice for urban and intercity transport. Shifting 
demand patterns (as opposed to avoiding demand) can be particularly 
important in decarbonising the transport sector. As a  result, the 
cross-elasticity of demand across transport modes is of particular 
interest for understanding the opportunities for modal shift. The 
cross-elasticity represents the demand effect on mode i (e.g., bus) 
when an attribute of mode j (e.g., rail) changes marginally. Studies 
on the cross-elasticities of mode choice for urban travel suggest that 
the cross-elasticity for car demand is low, but the cross-elasticities of 
walking, bus, and rail with respect to cars are relatively large (Fearnley 
et al. 2017; Wardman et al. 2018). In practice, these cross-elasticities

suggest that car drivers are not very responsive to increased prices 
for public transit, but transit users are responsive to reductions in the 
cost of driving. When looking at the cross-elasticities of public transit 
options (bus vs metro vs rail), research suggests that consumers are 
particularly sensitive to in-vehicle and waiting time when choosing 
public transit modes (Fearnley et  al. 2018). These general results 
provide additional evidence that increasing the use of active and 
public transport requires interventions that make car use more 
expensive while making public transit more convenient (e.g., smart 
apps that tell the user the exact time for transit arrival (Box 10.1)).
The literature on mode competition for intercity travel reveals that 
while cost of travel is a significant factor (Zhang et al. 2017), sensitivity 
decreases with increasing income as well as when the cost of the 
trip was paid by someone else (Capurso et al. 2019). Some research

suggests little competition between bus and air travel but the cross-
elasticity between air and rail suggest strong interactions (Wardman 
et  al. 2018). Price reduction strategies such as discounted rail fares 
could enhance the switch from air travel to high-speed rail. Both air 
fares and flight frequency impact high-speed rail (HSR) usage (Zhang 
et al. 2019b). Airline companies reduce fares on routes that are directly 
competing with HSR (Bergantino et al. 2018a) and charge high fares 
on non-HSR routes (Xia and Zhang 2016). On the Rome to Milan route, 
better frequency and connections, and low costs of HSR resulting from 
competition between HSR companies have significantly reduced air 
travel and shares of buses and cars (Desmaris and Croccolo 2018).
Finally, and as noted in Chapter  5, recent research shows that 
individual, social, and infrastructure factors also affect people’s mode 
choices. For example, perceptions about common travel behaviour

(what people perceive to be ‘normal’ behaviour) influence their travel 
mode choice. The research suggests that well-informed individuals 
whose personal norms match low-carbon objectives, and who 
believe they have control over their decisions, are most motivated 
to shift mode. Nonetheless, such individual and social norms can 
only marginally influence mode choice unless infrastructure factors 
can enable reasonable time and cost savings (Convery and Williams 
2019; Javaid et al. 2020; Feng et al. 2020; Wang et al. 2021).

1061
1061
Improve: consumer preferences for improved and alternative 
vehicles. While reductions in demand for travel and changes in 
mode choice can contribute to reducing GHG emissions from the 
transport sector, cars are likely to continue to play a prominent role. 
As a  result, improving the performance of cars will be crucial for 
the decarbonisation of the transport sector. Sections 10.3 and 10.4 
describe the technological options available for reduced CO 2 emissions 
from vehicles. The effectiveness in deploying such technologies will 
partly depend on consumer preferences and their effect on adoption 
rates. Given the expanded availability of electric vehicles, there is also 
a growing body of work on the drivers of vehicle choice. A survey in 
Nanjing found women had more diverse travel purposes than men, 
resulting in a  greater acceptance of electric bikes (Lin et  al. 2017). 
Individuals are more likely to adopt an electric vehicle (EV) when they

think this adoption benefits the environment or implies a  positive 
personal attribute (Noppers et al. 2014; Noppers et al. 2015; Haustein 
and Jensen 2018). Other work suggests that people’s preference for EVs 
depends upon vehicle attributes, infrastructure availability, and policies 
that promote EV adoption, specifically, purchasing and operating costs, 
driving range, charging duration, vehicle performance, and brand 
diversity (Liao et  al. 2016). Behaviour change to enable transport 
transformations will need to make the most of these factors while also 
working on the more structural issues of time, space, and cost.
10.2.3 New Demand Concepts
Structural and behavioural choices that drive transport-related GHG 
emissions, such as time and cost based on geography of freight and 
urban fabric, are likely to continue to be major factors. But there is 
also a variation within each structural choice that is based around

personal demand factors related to values that indirectly change 
choices in transport. Chapter  5 identified three megatrends that 
affect demand for services, including circular economy, the shared 
economy, and digitalisation. These three megatrends can have 
specific effect on transport emissions, as described below.
Circular economy. The problem of resources and their environmental 
impacts is driving the move to a  circular economy (Bleischwitz 
et al. 2017). Circular economy principles include increased material 
efficiency, reusing or extending product lifetimes, recycling, and 
green logistics. Dematerialisation, the reduction in the quantity of the 
materials used in the production of one unit of output, is a circular 
economy principle that can affect the operations and emissions of 
the transport sector, as reductions in the quantities of materials used 
reduce transport needs, while reductions in the weight of products

improve the efficiency of transporting them. Dematerialisation can 
occur through more efficient production processes but also when 
a  new product is developed to provide the same functionality as 
multiple products. The best example of this trend is a smart phone, 
which provides the service of at least 22 other former devices (Rifkin 
2019). A move to declutter lifestyles can also drive dematerialisation 
(Whitmarsh et al. 2017). Some potential for dematerialisation has been 
suggested due to 3-D printing, which would also reduce transport 
emissions through localised production of product components 
(d’Aveni 2015; UNCTAD 2018). There is evidence to suggest, however, 
that reductions in material use resulting from more efficient product design or manufacturing are offset by increased consumer demand 
(Kasulaitis et al. 2019). Whether or not dematerialisation can lead 
to reduction of emissions from the transport sector is still an open

question that requires evaluating the entire product ecosystem 
(Van Loon et al. 2014; Coroama et al. 2015; Kasulaitis et al. 2019).
Shared economy. Shared mobility is arguably the most rapidly 
growing and evolving sector of the sharing economy and includes 
bike sharing, e-scooter sharing, car sharing, and on-demand mobility 
(Greenblatt and Shaheen 2015). The values of creating a more shared 
economy are related to both reduced demand and greater efficiency, 
as well as the notion of community well-being associated with the 
act of sharing instead of simply owning for oneself (Maginn et al. 
2018; Sharp 2018). The literature on shared mobility is expanding, 
but there is much uncertainty about the effect shared mobility will 
have on transport demand and associated emissions (Nijland and 
Jordy 2017; ITF 2018a; Tikoudis et al. 2021).
Asia represents the largest car-sharing region with 58% of worldwide 
membership and 43% of global fleets deployed (Dhar et  al. 2020).

Europe accounts for 29% of worldwide members and 37% of shared 
vehicle fleets (Shaheen et  al. 2018). Ride-sourcing and carpooling 
systems are among the many new entrants in the short-term shared 
mobility options. On-demand transport options complemented with 
technology have enhanced the possibility of upscaling (Alonso-
González et al. 2018). Car sharing could provide the same level of service 
as taxis, but taxis could be three times more expensive (Cuevas et al. 
2016). The sharing economy, as an emerging economic-technological 
phenomenon (Kaplan and Haenlein 2010), is likely to be a key driver 
of demand for transport of goods although data shows increasing 
container movement due to online shopping (Suel and Polak 2018).
There is growing evidence that this more structured form of behavioural 
change through shared economy practices, supported by a larger group 
than a single family, has a much greater potential to save transport

emissions, especially when complemented with decarbonised grid 
electricity (Greenblatt and Shaheen 2015; Sharp 2018). Carpooling, 
for example, could result in an 11% reduction in vehicle-kilometres 
and a 12% reduction in emissions, as carpooling requires less empty 
or non-productive passenger-kilometres (pkm) (ITF 2020a; ITF 2020b). 
However, the use of local shared mobility systems such as on-demand 
transport may create more transport emissions if there is an overall 
modal shift out of transit (ITF 2018a; Schaller 2018). Similarly, some 
work suggests that commercial shared vehicle services such as Uber 
and Lyft are leading to increased vehicle km travelled (and associated 
GHG emissions) in part due to deadheading (Schaller 2018; Tirachini 
and Gomez-Lobo 2020; Ward et al. 2021). Successful providers compete 
by optimising personal comfort and convenience rather than enabling 
a sharing culture (Eckhardt and Bardhi 2015), and concerns have been

raised regarding the wider societal impacts of these systems and for 
specific user groups such as older people (Fitt 2018; Marsden 2018). 
Concerns have also been expressed over the financial viability of demand-
responsive transport systems (Ryley et al. 2014; Marsden 2018), how 
the mainstreaming of shared mobility systems can be institutionalised 
equitably, and the operation and governance of existing systems that 
are only mode- and operator-focused (Akyelken et al. 2018; Jittrapirom 
et al. 2018; Pangbourne et al. 2020; Marsden 2018).

1062
1062
Chapter 10  T ransport10Digitalisation. In the context of the transport sector, digitalisation 
has enabled teleworking, which in turn reduces travel demand. On 
the other hand, the prevalence of online shopping, enabled by the 
digital economy, could have mixed effects on transport emissions 
(Le et al. 2021). For example, online shopping could reduce vehicle-
kilometres travelled but the move to expedited or rush delivery could 
mitigate some benefits as it prevents consolidation of freight (Jaller 
and Pahwa 2020).
Digitalisation could also lead to systemic changes by enabling 
smart mobility. The smart mobility paradigm refers to the process 
and practices of assimilation of ICTs and other sophisticated high-
technology innovations into transport (Noy and Givoni 2018). Smart 
mobility can be used to influence transport demand and efficiency 
(Benevolo et  al. 2016). The synergies of emerging technologies

(ICT, internet of things, big data) and shared economy could overcome some of the challenges facing the adoption of emerging technologies 
(Marletto 2014; Chen et al. 2016; Weiss et al. 2018; Taiebat and Xu 
2019) and enable the expected large growth in emerging cities to be 
more sustainable (Docherty et al. 2018). However, ICT, in particular the 
internet of things (IoT), could also cause more global energy demand 
(Hittinger and Jaramillo 2019). Box 10.1 summarises the main smart 
technologies being adopted rapidly by cities across the world and their 
use in transport. There is a growing body of literature about the effect of 
smart technology (including sensors guiding vehicles) on the demand 
for transport services. Smart technologies can improve competitiveness 
of transit and active transport over personal vehicle use by combining 
the introduction of new electro-mobility that improves time and cost 
along with behaviour change factors (Pålsson et  al. 2017; SLoCaT

2018a; SLoCaT 2018b; SLoCaT2021). However, it is unclear what the 
net effect of smart technology on GHG emissions from the transport 
sector will be (Debnath et al. 2014; Lenz and Heinrichs 2017).
Box 10.1 | Smart City Technologies and Transport
Information and communication technology (ICT). ICT is at the core of smart mobility and will provide the avenue for data to be 
collected and shared across the mobility system. The use of ICT can help cities by providing real-time information on mobility options 
that can inform those using private vehicles, along with transit users or those using bikes or walking. ICT can help with ticketing 
and payment for transit or for road user charges (Tafidis et al. 2017; Gössling 2018) when combined with other technologies such as 
Blockchain (Hargroves et al. 2020).
Internet of Things sensors. Sensors can be used to collect data to improve road safety, improve fuel efficiency of vehicles, and reduce

CO2 emissions (Kubba and Jiang 2014; Kavitha et al. 2018). Sensors can also provide data to digitally simulate transport planning 
options, inform the greater utilisation of existing infrastructure and modal interconnections, and significantly improve disaster and 
emergency responses (Hargroves et al. 2017). In particular, IoT sensors can be used to inform the operation of fast-moving trackless 
trams and their associated last-mile connectivity shuttles as part of a transit activated corridor (Newman et al. 2019, 2021).
Mobility as a Service. New, app-based mobility platforms will allow for the integration of different transport modes (such as last-mile 
travel, shared transit, and even micro-transit such as scooters or bikes) into easy-to-use platforms. By integrating these modes, users will 
be able to navigate from A to B to C based on which modes are most efficient, with the necessary bookings and payments being made

through one service. With smart city planning, these platforms can steer users towards shared and rapid transit (which should be the 
centrepiece of these systems), rather than encourage more people to opt for the perceived convenience of booking a single-passenger ride 
(Becker et al. 2020). In low-density car-dependent cities, however, MaaS services such as the use of electric scooters/bikes are less effective 
as the distances are too long and they do not enable the easy sharing that can happen in dense station precincts (Jittrapirom et al. 2017).
Artificial intelligence (AI) and big data analytics. The rapidly growing level of technology enablement of vehicles and urban infrastructure, 
combined with the growing ability to analyse larger and larger data sets, presents a significant opportunity for transport planning, design, 
and operation in the future. These technologies are used together to enable decisions about what kind of transport planning is used down

particular corridors. Options such as predictive congestion management of roads and freeways, simulating planning options, and advanced 
shared transit scheduling can provide value to new and existing transit systems (Toole et al. 2015; Anda et al. 2017; Hargroves et al. 2017).
Blockchain or distributed ledger technology. Blockchain technology provides a non-hackable database that can be programmed to 
enable shared services like a local, solar microgrid where both solar and shared electric vehicles can be managed (Green and Newman 
2017). Blockchain can be used for many transport-related applications including being the basis of MaaS or any local shared mobility 
service as it facilitates shared activity without intermediary controls. Other applications include verified vehicle ownership documentation, 
establishing identification, real-time road user pricing, congestion zone charging, vehicle-generated collision information, collection of

tolls and charges, enhanced freight tracking and authenticity, and automated car parking and payments (Hargroves et al. 2020). This 
type of functionality will be particularly valuable for urban regeneration along a transit activated corridor, where it can be used for 
managing shared solar in and around station precincts as well as managing shared vehicles linked to the whole transport system 
(Newman et al. 2021). This technology can also be used for road user charging along any corridor and by businesses accessing any 
services and in managing freight (Carter and Koh 2018; Nguyen et al. 2019; Hargroves et al. 2020; Sedlmeir et al. 2020).

1063
1063
Autonomous vehicles are the other emerging transport technology 
that have the potential to significantly improve ride quality and 
safety. Planes and high-speed trains are already largely autonomous 
as they are guided in all their movements, especially coming into 
stations and airports, although that does not necessarily mean they 
are driverless. Automation is also being used in new on-road transit 
systems like trackless trams (Ndlovu and Newman 2020)). Private 
vehicles are being fitted with more and more levels of autonomy 
and many are being trialled as ‘driverless’ in cities (Aria et  al. 
2016; Skeete 2018). If autonomous systems can be used to help 
on-road transit become more time- and cost-competitive with cars, 
then the kind of transformative and disruptive changes needed to 
assist decarbonisation of transport become more feasible (Bösch 
et al. 2018; Kassens-Noor et al. 2020; Abe 2021). Similarly, vehicle

automation could improve vehicle efficiency and reduce congestion, 
which would in turn reduce emissions (Vahidi and Sciarretta 2018; 
Massar et al. 2021). On the other hand, if autonomous cars make 
driving more convenient, they could reduce demand for transit (Auld 
et al. 2017; Sonnleitner et al. 2021). Paradoxically, autonomous cars 
could provide access to marginal groups such as the elderly, people 
with disabilities, and those who cannot drive, which could in turn 
increase travel demand (as measured by pkm) (Harper et al. 2016).Heavy haulage trucks in the mining industry are already autonomous 
(Gaber et al. 2021) and automation of long-haul trucks may happen 
sooner than automation of LDVs (Hancock et al. 2019). Autonomous 
trucks may facilitate route and speed optimisation, and reduce fuel 
use, which can in turn reduce emissions (Nasri et al. 2018; Paddeu 
and Denby 2021). There is growing interest in using drones for

package delivery. Drones could have lower impacts than ground-
based delivery and, if deployed carefully, drones could reduce 
energy use and GHG emissions from freight transport (Stolaroff et al. 
2018). Overall, some commentators are optimistic that smart and 
autonomous technologies can transform the GHG emissions from 
the transport sector (Seba 2014; Rifkin 2019; Sedlmeir et al. 2020). 
Others are more sanguine unless policy interventions can enable the 
technologies to be used for purposes that include zero carbon and 
the SDGs (Faisal et al. 2019; Hancock et al. 2019).
10.2.4 Overall Perspectives on Systemic Change
The interactions between systemic factors set out here and technology 
factors discussed in much more detail in the next sections show that 
there is always going to be a  need to integrate both approaches. 
Table 10.3 | Components of systemic change and their impacts on the transport sector.

Systemic change Mechanisms through which it affects emissions in transport sector and is likely to affect emissions
Changes in urban formDenser, more compact polycentric cities with mixed land use patterns can reduce the distance between where people live, work, and pursue leisure 
activities, which can reduce travel demand. Case studies suggest that these changes in urban form could reduce transport-related GHG emissions 
between 4 to 25%, depending on the setting (Creutzig et al. 2015a; Creutzig et al.2015b; Pan et al. 2020).
Investments in transit and active 
transport infrastructureImproving public transit systems and building infrastructure to support active transport modes (walking and biking) could reduce car travel. Case 
studies suggest that active mobility could reduce emissions from urban transport by 2% to 10% depending on the setting (Creutzig et al. 2016;

Zahabi et al. 2016; Keall et al. 2018; Gilby et al. 2019; Neves and Brand 2019; Bagheri et al. 2020; Ivanova et al. 2020; Brand et al. 2021). A shift 
to public transit modes can likely offer significant emissions reductions, but estimates are uncertain.
Changes in economic structuresHigher demand as a result of higher incomes could increase emissions, particularly from aviation and shipping. Higher prices could have the 
opposite effect and reduce emissions. Structural changes associated with financial crises, pandemics, or the impacts of climate change could affect 
the elasticity of demand in uncertain ways. Thus, the effect of changes in economic structures on the GHG emissions from the transport sectors 
is uncertain.
TeleworkingA move towards a digital economy that allows workers to work and access information remotely could reduce travel demand. Case studies

suggest that teleworking could reduce transport emissions by 20% in some instances, but likely by 1%, at most, across the entire transport system 
(Roth et al. 2008; O’Keefe et al. 2016; Shabanpour et al. 2018; O’Brien and Aliabadi 2020).
Dematerialisation of the economyA reduction in goods needed due to combining multiple functions into one device would reduce the need for transport. Reduced weights associated 
with dematerialisation would improve the efficiency of freight transport. However, emissions reductions from these efforts are likely dwarfed by 
increased consumption of goods.
Supply chain managementSupply chains could be optimised to reduce the movement or travel distance of product components. Logistics planning could optimise the use of 
transport infrastructure to increase utilisation rates and decrease travel. The effect of these strategies on the GHG emissions from the transport sector 
is uncertain.

e-commerceThe effect of e-commerce on transport emissions is uncertain. Increased e-commerce would reduce demand for trips to stores but could increase 
demand for freight transport (particularly last-mile delivery) (Jaller and Pahwa 2020; Le et al. 2021).
Smart mobilityICT and smart city technologies can be used to improve the efficiency of operating the transport system. Furthermore, smart technologies can 
improve competitiveness of transit and active transport over personal vehicle use by streamlining mobility options to compete with private cars. 
The effect of smart mobility on the GHG emissions from the transport sector is uncertain (Creutzig 2021).
Shared mobilityShared mobility could increase utilisation rates of LDVs, thus improving the efficiency of the system. However, shared mobility could also divert 
users from transit systems or active transport modes. Studies on ride-sourcing have reported both potential for reductions and increases in transport-

related emissions (Schaller 2018; Ward et al. 2021). Other case studies suggests that carpooling to replace 20% of private car trips could result in 
a 12% reduction in GHG emissions (ITF 2020a; ITF 2020b). Thus, the effect of shared mobility on transport-related GHG emissions is highly uncertain.
Vehicle automationVehicle automation could have positive or negative effects on emissions. Improved transit operations, more efficient traffic management, and 
better routing for light- and heavy-duty transport could reduce emissions (Nasri et al. 2018; Vahidi and Sciarretta 2018; Massar et al. 2021; Paddeu 
and Denby 2021). However, autonomous cars could make car travel more convenient, removing users from transit systems and increasing access 
to marginalised groups, which would in turn increase vehicle-kilometre travelled (Harper et al. 2016; Auld et al. 2017; Sonnleitner et al. 2021). 
Drones could reduce energy use and GHG emissions from freight transport (Stolaroff et al. 2018).

1064
1064
Chapter 10  T ransport10Good technology that has the potential to transform transport will 
not be used unless it fulfils broad mobility and accessibility objectives 
related to time, cost, and well-being. Chapter  5 has set out three 
transport transformations based on demand-side factors with highly 
transformative potential. Table  10.3 provides a  summary of these 
systemic changes and their likely impact on GHG emissions. Note that 
the quantitative estimates provided in the table may not be additive 
and the combined effect of these strategies on GHG emissions from 
the transport sector require additional analysis.
10.3 Transport Technology Innovations 
for Decarbonisation
This section focuses on vehicle technology and low-carbon fuel 
innovations to support decarbonisation of the transport sector. 
Figure 10.2 summarises the major pathways reviewed in this section. The advancements in energy carriers described in Figure  10.2 are

discussed in greater detail in Chapter  6 (Energy) and Chapter  11 
(Industry) but the review presented in this chapter highlights their 
application in the transport sector. This section pays attention to the 
advancements in alternative fuels, electric, and fuel cell technologies 
since AR5.
10.3.1 Alternative Fuels – An Option for Decarbonising 
Internal Combustion Engines
The average fuel consumption of new internal combustion engine 
(ICE) vehicles has improved significantly in recent years due to more 
stringent emissions regulations. However, improvements are now 
slowing down. The average fuel consumption of LDVs decreased by 
only 0.7 % between 2016 and 2017, reaching 7.2 litres of gasoline-
equivalent (Lg-eq) per  100  km in 2017, much slower than the 
Primary
fuelsEnergy
carriersVehicle
technologiesTransportation
segmentHybrid
powertrain
combinations
Advanced
engineLight-duty
road
vehicles
Heavy-duty
road
vehicles
Aviation
Shipping BiofuelsSynthetic

fuelsAmmoniaHydrogenElectricity
Biomass
with/
without
CCSFossil Fuels
with
CCSNuclearRenewable
Fuel cellBattery-
electric
Refuelling infrastructureRefuelling infrastructureCharging infrastructure / Catenary
Catenary Refuelling infrastructure
Refuelling infrastructureRefuelling infrastructure
Refuelling infrastructure
Figure 10.2 | Energy pathways for low-carbon transport technologies.  Primary energy sources are shown in the far left, while the segments of the transport system are in 
the far right. Energy carriers and vehicle technologies are represented in the middle. Primary pathways are shown with solid lines, while dotted lines represent secondary pathways.

1065
1065
1.8 % improvement per year between 2005 and 2016 (GFEI 2020). 
Table  10.4 summarises recent and forthcoming improvements to 
ICE technologies and their effect on emissions from these vehicles. 
However, these improvements are not sufficient to meet deep 
decarbonisation levels in the transport sector. While there is significant 
and growing interest in electric and fuel-cell vehicles, future scenarios 
indicate that a large number of LDV may continue to be operated by 
ICE in conventional, hybrid, and plug-in hybrid configurations over the 
next 30 years (IEA 2019a), unless they are regulated away through 
ICE vehicle sales bans (as some nations have announced) (IEA 2021a). 
Moreover, ICE technologies are likely to remain the prevalent options 
for shipping and aviation. Thus, reducing CO 2 and other emissions from 
ICEs through the use of low-carbon or zero-carbon fuels is essential

to a balanced strategy for limiting atmospheric pollutant levels. Such 
alternative fuels for ICE vehicles include natural gas-based fuels, 
biofuels, ammonia, and other synthetic fuels.
Natural Gas. Natural gas could be used as an alternative fuel to 
replace gasoline and diesel. Natural gas in vehicles can be used as 
compressed natural gas (CNG) and liquefied natural gas (LNG). CNG 
is gaseous at relatively high pressure (10 to 25 megapascal (MPa)) 
and temperature (–40 to 30°C). In contrast, LNG is used in liquid 
form at relatively low pressure (0.1 MPa) and temperature (–160°C). 
Therefore, CNG is particularly suitable for commercial vehicles and 
light- to medium-duty vehicles, whereas LNG is better suited to 
replace diesel in HDVs (Dubov et al. 2020; Dziewiatkowski et al. 2020; 
Yaïci and Ribberink 2021). CNG vehicles have been widely deployed 
in some regions, particularly in Asian-Pacific countries. For example,

there are about 6 million CNG vehicles in China, the most of any 
country (Qin et al. 2020). However, only 20% of vehicles that operate 
using CNG were originally designed as CNG vehicles, with the rest 
being gasoline-fuelled vehicles that have been converted to operate 
with CNG (Chala et al. 2018).Natural gas-based vehicles have certain advantages over 
conventional fuel-powered ICE vehicles, including lower emissions 
of criteria air pollutants, no soot or particulate, low carbon to 
Hydrogen ratio, moderate noise, a wide range of flammability limits, 
and high octane numbers (Kim 2019; Bayat and Ghazikhani 2020). 
Furthermore, the technology readiness level (TRL) of natural gas 
vehicles is very high (TRL 8–9), with direct modification of existing 
gasoline and diesel vehicles possible (Transport and Environment 
2018; Peters et al. 2021; Sahoo and Srivastava 2021). On the other 
hand, methane emissions from the natural gas supply chain and

tailpipe CO 2 emissions remain a  significant concern (Trivedi et  al. 
2020). As a result, natural gas as a transition transportation fuel may 
be limited due to better alternative options being available and due 
to regulatory pressure to decarbonise the transport sector rapidly. 
For example, the International Maritime Office (IMO) has set a target 
of 40% less carbon intensity in shipping by 2030, which cannot be 
obtained by simply switching to natural gas.
Biofuels. Since AR5, the faster than anticipated adoption of 
electromobility, primarily for LDVs, has partially shifted the 
debate around the primary use of biofuels from land transport to 
the shipping and aviation sectors (IEA 2017a; Davis et  al. 2018). 
At the same time, other studies highlight that biofuels may have 
to complement electromobility in road transport, particularly in 
developing countries, offering relevant mitigation opportunities 
in the short- and mid-term (up to 2050) (IEA 2021b). An important

advantage of biofuels is that they can be converted into energy 
carriers compatible with existing technologies, including current 
powertrains and fuel infrastructure. Also, biofuels can diversify 
the supply of transport fuel, raise energy self-sufficiency in many 
countries, and be used as a strategy to diversify and strengthen the 
agro-industrial sector (Puricelli et al. 2021). The use of biofuels as 
a mitigation strategy is driven by a combination of factors, including 
not only the costs and technology readiness levels of the different 
biofuel conversion technologies, but also the availability and costs 
of both biomass feedstocks and alternative mitigation options, 
and the relative speed and scale of the energy transition in energy 
and transport sectors (Box 10.2).
Many studies have addressed the lifecycle emissions of biofuel 
conversion pathways for land transport, aviation, and marine 
applications (Koeble et  al. 2017; Staples et  al. 2018; Tanzer et  al.

2019). Bioenergy technologies generally struggle to compete with 
existing fossil fuel-based ones because of the higher costs involved. 
However, the extent of the cost gap depends critically on the 
availability and costs of biomass feedstock (IEA 2021b). Ethanol from 
corn and sugarcane is commercially available in countries such as 
Brazil and the US. Biodiesel from oil crops and hydro-processed esters 
and fatty acids are available in various countries, notably in Europe 
and parts of Southeast Asia. On the infrastructure side, biomethane 
blending is being implemented in some regions of the US and Europe, 
particularly in Germany, with the help of policy measures (IEA 2021b). 
While many of these biofuel conversion technologies could also be 
implemented using seaweed feedstock options, these value chains 
are not yet mature (Jiang et al. 2016).Table 10.4 | Engine technologies to reduce emissions from light-duty ICE

vehicles and their implementation stage.  Table nomenclature: GDI = Gasoline 
direct injection, VVT = Variable valve technology, CDA = Cylinder deactivation, 
CR  =  compression ratio, GDCI = Gasoline direct injection compression ignition, 
EGR = exhaust gas recirculation, RCCI = Reactivity controlled compression ignition, 
GCI = Gasoline compression ignition. Source: Joshi (2020).
Implementation 
stageEngine technologyCO2 reduction
(%)
Implemented Baseline: GDI, turbo, stoichiometry 0
DevelopmentAtkinson cycle (+ VVT) 3–5
Dynamic CDA + Mild hybrid or Miller 10–15
Lean-burn GDI 10–20
Variable CR 10
Spark assisted GCI 10
GDCI 15–25
Water injection 5–10
Pre-chamber concepts 15–20
Homogeneous lean 15–20
Dedicated EGR 15–20
2-stroke opposed-piston diesel 25–35
RCCI 20–30

1066
1066
Chapter 10  T ransport10
Technologies to produce advanced biofuels from lignocellulosic 
feedstocks have suffered from slow technology development and 
are still struggling to achieve full commercial scale. Their uptake is 
likely to require carbon pricing and/or other regulatory measures, 
such as clean fuel standards in the transport sector or blending 
mandates. Several commercial-scale advanced biofuels projects are 
in development in many parts of the world, encompassing a wide 
selection of technologies and feedstock choices, including carbon 
capture and sequestration (CCS) that supports carbon dioxide 
removal. The success of these projects is vital to moving forward the 
development of advanced biofuels and bringing many of the advanced 
biofuels value chains closer to the market (IEA 2021b). Finally, biofuel 
production and distribution supply chains involve notable transport 
and logistical challenges that need to be overcome (Mawhood et al.

2016; Skeer et al. 2016; IEA 2017a; Puricelli et al. 2021).Table  10.5 summarises performance data for different biofuel 
technologies, while Figure 10.3 shows the technology readiness levels.
Within the aviation sector, jet fuels produced from biomass resources 
(so-called sustainable aviation fuels, or SAF) could offer significant 
climate mitigation opportunities under the right policy circumstances. 
Despite the growing interest in aviation biofuels, demand and 
production volumes remain negligible compared to conventional 
fossil aviation fuels. Nearly all flights powered by biofuels have used 
fuels derived from vegetable oils and fats, and the blending level of 
biofuels into conventional aviation fuels for testing is up to 50% today 
(Mawhood et al. 2016). To date, only one facility in the US is regularly 
producing sustainable aviation fuels based on waste oil feedstocks. 
The potential to scale up bio-based SAF volumes is severely restricted

by the lack of low-cost and sustainable feedstock options (Chapter 7). 
Lignocellulosic feedstocks are considered to have great potential for Box 10.2 | Bridging Land Use and Feedstock Conversion Footprints for Biofuels
Under specific conditions, biofuels may represent an important climate mitigation strategy for the transport sector (Daioglou et al. 2020; 
Muratori et al. 2020). Both the IPCC Special Report on Global Warming of 1.5°C and the IPCC Special Report on Climate Change and  
Land highlighted that biofuels could be associated with climate mitigation co-benefits and adverse side effects to many SDGs. These side 
effects depend on context-specific conditions, including deployment scale, associated land-use changes and agricultural management 
practices (Section 7.4.4 and Box 7.10). There is broad agreement in the literature that the most important factors in determining the

climate footprint of biofuels are the land use and land-use change characteristics associated with biofuel deployment scenarios (Elshout 
et al. 2015; Daioglou et al. 2020). This issue is covered in more detail in Box 7.1. While the mitigation literature primarily focuses on the 
GHG-related climate forcings, note that land is an integral part of the climate system through multiple geophysical and geochemical 
mechanisms (albedo, evaporation, etc.). For example, Sections 2.2.7 and 7.3.4 in the AR6 WGI report indicate that geophysical aspects 
of historical land-use change outweigh the geochemical effects, leading to a net cooling effect. The land-related carbon footprints of 
biofuels presented in Sections 10.4–10.6 are adopted from Chapter 7 (Section 7.4.4, Box 7, and Figure 7.1). The results show how the 
land-related footprint increases due to an increased outtake of biomass, as estimated with different models that rely on global supply

scenarios of biomass for energy and fuel of 100 exajoules (EJ). The integrated assessment models and scenarios used include the EMF 
33 scenarios (IAM-EMF33), from partial models with constant land cover (PM-CLC), and from partial models with natural regrowth 
(PM-NGR). These results are combined with both biomass cultivation emission ranges for advanced biofuels aligned with Koeble et al. 
(2017), El Akkari et al. (2018), Jeswani et al. (2020), and Puricelli et al. (2021) and conversion efficiencies and conversion phase emissions 
as described in Table 10.5. The modelled footprints resulting from land-use changes related to delivering 100 EJ of biomass at global 
level are in the range of 3–77 gCO 2-eq per MJ of advanced biofuel (median 38 gCO 2-eq MJ–1) at an aggregate level for Integrated 
Assessment Models (IAMs) and partial models with constant land cover (Daioglou et al. 2020; Rose et al. 2020). The results for partial

models with natural regrowth are much higher (91–246 CO 2-eq MJ–1 advanced biofuel). The latter ranges may appear in contrast with 
the results from the scenario literature in Section 10.7, where biofuels play a role in many scenarios compatible with low warming 
levels. This contrast is a result of different underlying modelling practices. The general modelling approach used for the scenarios in 
the AR6 database accounts for the land-use change and all other GHG emissions along a given transformation trajectory, enabling 
assessments of the warming level incurred. The results labelled ‘EMF33’ and ‘partial models with constant land cover’ are obtained 
with this modelling approach. The results in the category ‘partial models with natural regrowth’ attribute additional CO 2 emissions to 
the bioenergy system, corresponding to estimated uptake of CO 2 in a counterfactual scenario where land is not used for bioenergy, but

instead subject to natural vegetation regrowth. While the partial analysis provides insights into the implications of alternative land-use 
strategies, such analysis does not identify the actual emissions of bioenergy production. As a result, the partial analysis is not compatible 
with the identification of warming levels incurred by an individual transformation trajectory, and therefore not aligned with the general 
approach applied for the scenarios in the AR6 database.
More details on land-use change impacts and the potential to deliver the projected demands of biofuels at the global level are further 
addressed in Chapter 7. While, in general, the above results cover most of the variety of GHG range intensities of biofuel options 
presented in the literature, the more specific life cycle assessment (LCA) literature should be consulted when considering specific 
combinations of biomass feedstock and conversion technologies in specific regions.

1067
1067
Table 10.5 | Ranges of efficiency, GHG emissions, and relative costs of selected biofuel conversion technologies for road, marine, and aviation biofuels.
Main application Conversion technologyEnergy efficiency 
of conversionaGHG emissions of conversion 
process (gCO 2-eq per MJ of fuel)bRelative cost of 
conversion process
Road Lignocellulosic ethanol 35%c5dMedium
Road/aviation Gasification and Fischer-Tropsch synthesis 57%e<1dHigh
Road Ethanol from sugar and starch 60–70%f1–31dLow
Road Biodiesel from oil crops 95%g12–30dLow
Marine Upgraded pyrolysis oil 30–61%h1–4hMedium
Aviation/marine Hydro-processed esters and fatty acids 80%i3iMedium
Aviation Alcohol to jet 90%j<1kHigh
Road/marine Biomethane from residues 60%ln/a Low
Marine/aviation Hydrothermal liquefaction 35–69%h<1hHigh
Aviation Sugars to hydrocarbons 65%m15mHigh
Road Gasification and syngas fermentation 40%n30–40nHigh

Notes: a Calculated as liquid fuels output divided by energy in feedstock entering the conversion plant; b GHG emissions here refers only to the conversion process. Impacts form 
the different biomass options are not included here as they are addressed in Chapter 7; c Olofsson et al. (2017); d Koeble et al. (2017); e Simell et al. (2014); f de Souza Dias et al. 
(2015); g Castanheira et al. (2015); h Tanzer et al. (2019); i Klein et al. (2018); j Narula et al. (2017); k de Jong et al. (2017); l Salman et al. (2017); m Moreira et al. (2014); Roy 
et al. (2015); Handler et al. (2016); n Salman et al. (2017); Moreira et al. (2014); Roy et al. (2015); Handler et al. (2016).
Conversion technologyTechnology readiness level (TRL)
Lignocellulosic ethanol
Gasiﬁcation and Fischer-Tropsch synthesis
Ethanol from sugar and starch
Biodiesel from oil crops
Upgraded pyrolysis oil
Hydroprocessed esters and fatty acids
Alcohol to jet
Biomethane from residues
Hydrothermal liqueﬁcation

Sugars to hydrocarbons
Gasiﬁcation and syngas fermentation1–3 4 5 6 7 8 9
Research & Development Pilot Demonstration Commercialisation
Figure 10.3 | Commercialisation status of selected biofuels conversion technologies.  The grey boxes represent the current technology readiness level of each 
conversion technology. Source: based on Mawhood et al. (2016), Skeer et al. (2016), IEA (2017a), and Puricelli et al. (2021).

1068
1068
Chapter 10  T ransport10the production of financially competitive bio-based SAF in many 
regions. However, production facilities involve significant capital 
investment and estimated levelised costs are typically more than 
twice the selling price of conventional jet fuel. In some cases (notably 
for vegetable oils), the feedstock price is already higher than that of 
fossil jet fuel (Mawhood et al. 2016). Some promising technological 
routes for producing SAF from lignocellulosic feedstocks are below 
technology readiness level (TRL) 6 (pilot scale), with just a  few 
players involved in the development of these technologies. Although 
it would be physically possible to address the mid-century projections 
for substantial use of biofuels in the aviation sector (according to the 
International Energy Agency (IEA) and other sectoral organisations 
(ICAO 2017)), this fuel deployment scale could only be achieved 
with very large capital investments in bio-based SAF production

infrastructure, and substantial policy support.
In comparison to the aviation sector, the prospects for technology 
deployment are better in the shipping sector. The advantage 
of shipping fuels is that marine engines have a  much higher 
operational flexibility on a  mix of fuels, and shipping fuels do 
not need to undergo as extensive refining processes as road and 
aviation fuels to be considered drop-in. However, biofuels in marine 
engines have only been tested at an experimental or demonstration 
stage, leaving open the question about the scalability of the 
operations, including logistics issues. Similar to the aviation 
sector, securing a  reliable, sustainable biomass feedstock supply 
and mature processing technologies to produce price-competitive 
biofuels at a large scale remains a challenge for the shipping sector 
(Hsieh and Felby 2017). Other drawbacks include industry concerns 
about oxidation, storage, and microbial stability for less purified

or more crude biofuels. Assuming that biofuels are technically 
developed and available for the shipping sector in large quantities, 
a  wider initial introduction of biofuels in the sector is likely to 
depend upon increased environmental regulation of particulate 
and GHG emissions. Biofuels may also offer a significant advantage 
in meeting ambitious sulphur emission reduction targets set by 
the sectoral organisations. More extensive use of marine biofuels 
will most likely be first implemented in inner-city waterways, 
inland river freight routes, and coastal green zones. Given the high 
efficiency of the diesel engine, a large-scale switch to a different 
standard marine propulsion method in the near to medium-term 
future seems unlikely. Thus, much of the effort has been placed on 
developing biofuels compatible with diesel engines. So far, biodiesel 
blends look promising, as it is used in land transport. Hydrotreated

vegetable oil (HVO) is also a  technically good alternative and is 
compatible with current engines and supply chains, while the 
introduction of multifuel engines may open the market for ethanol 
fuels (Hsieh and Felby 2017).
Ammonia. At room temperature and atmospheric pressure, 
ammonia is a colourless gas with a distinct odour. Due to relatively 
mild conditions for liquefaction, ammonia is transferred and stored 
as a liquefied or compressed gas and has been used as an essential 
industrial chemical resource for many products. In addition, since 
ammonia does not contain carbon, it has attracted attention as 
a carbon-neutral fuel that can also improve combustion efficiency (Gill 
et al. 2012). Furthermore, ammonia could also serve as a hydrogen carrier and be used in fuel cells. These characteristics have driven 
increased interest in the low-carbon production of ammonia, which 
would have to be coupled to low-carbon hydrogen production (with

low-carbon electricity providing the needed energy or with CCS).
For conventional internal combustion engines, the use of ammonia 
remains challenging due to the relatively low burning velocity 
and high ignition temperature. Therefore, Frigo and Gentili (2014) 
have suggested a  dual-fuelled spark ignition engine operated by 
liquid ammonia and hydrogen, where hydrogen is generated from 
ammonia using the thermal energy of exhaust gas. On the other 
hand, the high-octane number of ammonia means good knocking 
resistance of spark ignition engines and is promising for improving 
thermal efficiency. For compression ignition engines, the high-ignition 
temperature of ammonia requires a high compression ratio, causing 
an increase in mechanical friction. Since Gray et  al. (1966), many 
studies have shown that the compression ratio can be reduced by 
mixing ammonia with secondary fuels such as diesel and hydrogen 
with low self-ignition temperatures, as summarised by Dimitriou and

Javaid (2020). Using a  secondary fuel with a  high cetane number 
and  the adoption of a  suitable fuel injection timing has enabled 
highly efficient combustion of compression ignition engines in 
the dual fuel mode with ammonia ratios up to 95% (Dimitriou 
and Javaid 2020). One major challenge for realising an ammonia-
fuelled engine is the reduction of unburned ammonia, as described 
in Section 6.4.5 (Reiter and Kong 2011). Processes being examined 
include the use of exhaust gas recirculation (EGR) (Pochet et al. 2017) 
and after treatment systems. However, these processes require space, 
which is a constraint for LDVs and air transport but more practical 
for ships. Shipbuilders are developing an ammonia engine based 
on the existing diesel dual-fuel engine to launch a service in 2025 
(Brown 2019; MAN-ES 2019). Ammonia could therefore contribute 
significantly to decarbonisation in the shipping sector (Section 10.6), 
with potential niche applications elsewhere.

Synthetic fuels. Synthetic fuels can contribute to transport 
decarbonisation through synthesis from electrolytic hydrogen 
produced with low-carbon electricity or hydrogen produced with 
CCS, and captured CO2 using the Fischer-Tropsch process (Liu et al. 
2020a). Due to similar properties of synthetic fuels to those of fossil 
fuels, synthetic fuels can reduce GHG emissions in both existing 
and new vehicles without significant changes to the engine design.  
While the Fischer-Tropsch process is a well-established technology 
(Liu et  al. 2020a), low-carbon synthetic fuel production is still at 
the demonstration stage. Even though their production costs are 
expected to decline in the future due to lower renewable electricity 
prices, increased scale of production, and learning effects, synthetic 
fuels are still up to three times more expensive than conventional 
fossil fuels (Section  6.6.2.4). Furthermore, since the production

of synthetic fuels involves thermodynamic conversion loss, there 
is a concern that the total energy efficiency is lower than that of 
electric vehicles (Yugo and Soler 2019). Given these high costs and 
limited scales, the adoption of synthetic fuels will likely focus on 
the aviation, shipping, and long-distance road transport segments, 
where decarbonisation by electrification is more challenging. In 
particular, synthetic fuels are considered promising as an aviation 
fuel (Section 10.5).

1069
1069
10.3.2 Electric Technologies
Widespread electrification of the transport sector is likely crucial for 
reducing transport emissions and depends on appropriate electrical 
energy storage systems (EES). However, large-scale diffusion of EES 
depends on improvements in energy density (energy stored per unit 
volume), specific energy (energy stored per unit weight), and costs 
(Cano et al. 2018). Recent trends suggest EES-enabled vehicles are 
on a path to becoming the leading technology for LDVs, but their 
contribution to heavy-duty freight is more uncertain.
Electrochemical storage of light and medium-duty vehicles. 
Electrochemical storage, i.e., batteries, are one of the most promising 
forms of energy storage for the transport sector and have dramatically 
improved in their commerciality since AR5. Rechargeable batteries 
are of primary interest for applications within the transport sector,

with a range of mature and emerging chemistries able to support 
the electrification of vehicles. The most significant change since 
AR5 and SPR1.5 is the dramatic rise in lithium-ion batteries (LIB), 
which has enabled electromobility to become a  major feature 
of decarbonisation.
Before the recent growth in market share of LIBs, lead-acid batteries, 
nickel batteries, high-temperature sodium batteries, and redox 
flow batteries were of particular interest for the transport sector 
(Placke et  al. 2017). Due to their low costs, lead-acid batteries 
have been used in smaller automotive vehicles, e.g.,  e-scooters 
and e-rickshaws (Dhar et  al. 2017). However, their application 
in electric vehicles will be limited due to their low specific energy 
(Andwari et  al. 2017). Nickel-metal hydride (NiMH) batteries have 
a better energy density than lead-acid batteries and have been well 
optimised for regenerative braking (Cano et  al. 2018). As a  result,

NiMH batteries were the battery of choice for hybrid electric vehicles 
(HEVs). Ni-Cadmium (NiCd) batteries have energy densities lower 
than NiMH batteries and cost around ten times more than lead-acid 
batteries (Table  6.5). For this reason, NiCd batteries do not have 
major prospects within automotive applications. There are also no 
examples of high-temperature sodium or redox flow batteries being 
used within automotive applications.
Commercial application of LIBs in automotive applications started 
around 2000 when the price of LIBs was more than USD1000 per kWh 
(Schmidt et al. 2017). By 2020, the battery manufacturing capacity 
for automotive applications was around 300 GWh per  year (IEA 
2021a). Furthermore, by 2020, the average battery pack cost had 
come down to USD137 per kWh, a reduction of 89% in real terms 
since 2010 (Henze 2020). Further improvements in specific energy, 
energy density (Nykvist et al. 2015; Placke et al. 2017) and battery

service life (Liu et al. 2017) of LIBs are expected through additional 
design optimisation (Table  6.5). These advances are expected to 
lead to EVs with even longer driving ranges, further supporting the 
uptake of LIBs for transport applications (Cano et al. 2018). However, 
the performance of LIBs under freezing and high temperatures is 
a concern (Liu et al. 2017) for reliability. Auto manufacturers have 
some pre-heating systems for batteries to see that they perform well 
in very cold conditions (Wu et al. 2020).For EVs sold in 2018, the material demand was about 11 kilotonnes (kt) 
of optimised lithium, 15 kt of cobalt, 11 kt of manganese, and 34 kt 
of nickel (IEA 2019a; IEA 2021a). IEA projections for 2030 in the EV 
30@30 scenario show that the demand for these materials would 
increase by 30 times for lithium and around 25 times for cobalt. While 
there are efforts to move away from expensive materials such as

cobalt (IEA 2019a; IEA 2021a), dependence on lithium will remain, 
which may be a  cause of concern (Olivetti et  al. 2017; You and 
Manthiram 2018). A more detailed discussion on resource constraints 
for lithium is provided in Box 10.6.
Externalities from resource extraction are another concern, though 
current volumes of lithium are much smaller than other metals (steel, 
aluminium). As a result, lithium was not even mentioned in UNEP’s 
global resource outlook (IRP 2019). Nonetheless, it is essential to 
manage demand and limit externalities since the demand for lithium 
is going to increase many times in the future. Reuse of LIBs used 
in EVs for stationary energy applications can help in reducing the 
demand for LIBs. However, the main challenges are the difficulty in 
accessing the information on the health of batteries to be recycled 
and technical problems in remanufacturing the batteries for their 
second life (Ahmadi et al. 2017). Recycling lithium from used batteries

could be another possible supply source (Winslow et al. 2018). While 
further R&D is required for commercialisation (Ling et  al., 2018), 
recent efforts at recycling LIBs are very encouraging (Ma et al. 2021). 
The standardisation of battery modules and packaging within and 
across vehicle platforms, increased focus on design for recyclability, 
and supportive regulation are important to enable higher recycling 
rates for LIBs (Harper et al. 2019).
Several next-generation battery chemistries are often referred to 
as post-LIBs (Placke et  al. 2017). These chemistries include metal-
sulphur, metal-air, metal-ion (besides Li), and all-solid-state batteries. 
The long development cycles of the automotive industry (Cano et al. 
2018) and the advantages of LIBs in terms of energy density and cycle 
life (Table 6.5) mean that it is unlikely that post-LIB technologies will 
replace LIBs in the next decade. However, lithium-sulphur, lithium-air,

and zinc-air have emerged as potential alternatives for LIBs. These 
emerging chemistries may also be used to supplement LIBs in dual-
battery configurations, to extend the driving range at lower costs 
or with higher energy density (Cano et  al. 2018). Lithium-sulphur 
(Li-S) batteries have a lithium metal anode with a higher theoretical 
capacity than lithium-ion anodes and much lower-cost sulphur 
cathodes relative to typical Li-ion insertion cathodes (Manthiram 
et  al. 2014). As a  result, Li-S batteries are much cheaper than LIB 
to manufacture and have a  higher energy density (Table  6.5). 
Conversely, these batteries face challenges from sulphur cathodes, 
such as low conductivity of the sulphur and lithium sulphide phases, 
and the relatively high solubility of sulphur species in common 
lithium battery electrolytes, leading to low cycle life (Cano et  al. 
2018). Lithium-air batteries offer a further improvement in specific

energy and energy density above Li–S batteries owing to their use 
of atmospheric oxygen as a cathode in place of sulphur. However, 
their demonstrated cycle life is much lower (Table 6.5). Lithium-air 
batteries also have low specific power. Therefore, lithium-air require 
an extra battery for practical applications (Cano et al. 2018). Finally, 
zinc–air batteries could more likely be used in future EVs because

1070
1070
Chapter 10  T ransport10of their more advanced technology status and higher practically 
achievable energy density (Fu et al. 2017). Like Li-air batteries, their 
poor specific power and energy efficiency will probably prevent zinc-
air batteries from being used as a primary energy source for EVs. Still, 
they could be promising when used in a dual-battery configuration 
(Cano et al. 2018).
The technological readiness of batteries is a crucial parameter in the 
advancement of EVs (Manzetti and Mariasiu 2015). Energy density, 
power density, cycle life, calendar life, and the cost per kWh are the 
pertinent parameters for comparing the technological readiness of 
various battery technologies (Manzetti and Mariasiu 2015; Andwari 
et al. 2017; Lajunen et al. 2018). Table 6.5 provides a summary of 
the values of these parameters for alternative battery technologies. 
LIBs comprehensively dominate the other battery types and are

at a readiness level where they can be applied for land transport 
applications (cars, scooters, electrically-assisted cycles) and at 
battery pack costs below USD150 per  kWh, making EVs cost-
competitive with conventional vehicles (Nykvist et al. 2019). In 2020 
the stock of battery electric LDVs had crossed the 10 million mark 
(IEA 2021a). Schmidt et al. (2017) project that the cost of a battery 
pack for LIBs will reach USD100 per kWh by 2030, but more recent 
trends show this could happen much earlier. For example, according 
to IEA, battery pack costs could be as low as USD80 per kWh by 
2030 (IEA 2019a). In addition, there are clear trends that now 
vehicle manufacturers are offering vehicles with bigger batteries, 
greater driving ranges, higher top speeds, faster acceleration, and 
all size categories (Nykvist et  al. 2019). In 2020 there were over 
600,000 battery electric buses and over 31,000 battery electric 
trucks operating globally (IEA 2021a).

LIBs are not currently envisaged to be suitable for long-haul transport. 
However, several battery technologies are under development 
(Table 6.5), which could further enhance the competitiveness of EVs 
and expand their applicability to very short-haul aviation and ships, 
especially smaller vehicles. Li-S, Li-air, and Zn-air hold the highest 
potential for these segments (Cano et al. 2018). All three of these 
technologies rely on making use of relatively inexpensive elements, 
which can help bring down battery costs (Cano et  al. 2018). The 
main challenge these technologies face is in terms of the cycle 
life. Out of the three, Li-S has already been used for applications in 
unmanned aerial vehicles (Fotouhi et al., 2017) due to relatively high 
specific energy (almost double the state of the art LIBs). However, 
even with low cycle life, Li-air and Zn-air hold good prospects for 
commercialisation as range extender batteries for long-range road

transport and with vehicles that are typically used for city driving 
Alternative electricity storage technologies for heavy-duty 
transport. While LIBs described in the previous section are driving the 
electrification of LDVs, their application to railways, aviation, ships, and 
large vehicles faces challenges due to the higher power requirements 
of these applications. The use of a  capacitor with a  higher power density 
than LIBs could be suitable for the electrification of such vehicles. 
It is one of the solutions for regenerating large and instantaneous 
energy from regenerative brakes. Classical capacitors generally show 
more attractive characteristics in power density (8000–10,000 watts per kilogram (W/kg)) than batteries. However, the energy density is 
poor (1–4 watt-hours per kilogram (Wh/kg)) compared to batteries, 
and there is an issue of self-discharge (González et al. 2016; Poonam

et al. 2019). To improve the energy density, electrochemical double 
layer capacitors (EDLCs; supercapacitor) and hybrid capacitors 
(10–24 Wh/kg, 900–9000 W/kg at the product level) such as Li-ion 
capacitors have been developed. The highest energy density of the 
LIC system (100–140 Wh/kg in the research stage) are approaching 
that of the Li-ion battery systems (80–240 Wh/kg in the product 
stage) (Naoi et  al. 2012; Panja et  al. 2020). Examples of effective 
use of capacitors include a  12-tonne truck with a  capacitor-based 
kinetic energy recovery system that has been reported to save up 
to 32% of the fuel use of a standard truck (Kamdar 2017). Similarly, 
an EDLC bank applied to electric railway systems has been shown to 
result in a 10% reduction in power consumption per day (Takahashi 
et al. 2017). Finally, systems in which capacitors are mounted on an 
electric bus for charging at a stop have been put into practical use,

for example by a trackless tram (Newman et al. 2019). At the bus 
stop, the capacitor is charged at 600 kW for 10 about 40 seconds, 
which provides enough power for about 5 to 10 km (Newman et al. 
2019). In addition, more durable capacitors can achieve a longer life 
than LIB systems (ADB 2018).
Hybrid energy storage (HES) systems, which combine a  capacitor 
and a  battery, achieve both high power and high energy, solving 
problems such as capacity loss of the battery and self-discharge of 
the capacitor. In these systems, the capacitor absorbs the steeper 
power, while the LIB handles the steady power, thereby reducing the 
power loss of the EV to half. Furthermore, since the in-rush current of 
the battery is suppressed, there is an improvement in the reliability 
of the LIB (Noumi et al. 2014). In a hybrid diesel train, 8.2% of the 
regenerative energy is lost due to batteries’ limited charge-discharge 
performance; however, using an EDLC with batteries can save this

energy (Takahashi et al. 2017; Mayrink et al. 2020).
The development of power storage devices and advanced 
integrated system approaches, including power electronics circuits 
such as HES and their control technologies, are important for the 
electrification of mobility. These technologies are solutions that 
could promote the electrification of systems, reduce costs, and 
contribute to the social environment through multiple outcomes in 
the decarbonisation agenda.
10.3.3 Fuel Cell Technologies
In harder-to-electrify transport segments, such as heavy-duty 
vehicles, shipping, and aviation, hydrogen holds significant promise 
for delivering emissions reductions if it is produced using low-carbon 
energy sources. In particular, hydrogen fuel cells are seen as an 
emerging option to power larger vehicles for land-based transport 
(Tokimatsu et al. 2016; IPCC 2018; IEA 2019b). Despite this potential, 
further advancements in technological and economic maturity will

be required in order for hydrogen fuel cells to play a greater role. 
While this section focuses primarily on hydrogen fuel cells, ammonia 
and methanol fuel cells may also emerge as options for low 
power applications.

1071
1071
During the last decade, hydrogen fuel cell vehicles (HFCVs) have 
attracted growing attention, with fuel cell technology improving 
through research and development. Fuel cell systems cost 80% 
to 95% less than they did in the early 2000s, at approximately 
USD50 per kW for light-duty (80 kW) and $100 per kW for medium-
heavy-duty (160 kW). These costs are approaching the US Department 
of Energy’s (US DOE) goal of USD40 per kW in 2025 at a production 
target of 500,000 systems per year (IEA 2019c). In addition to cost 
reductions, the power density of fuel cell stacks has now reached 
around 3.0 kilowatt per  litre (kW/l) and average durability has 
improved to approximately 2000 to 3000 hours (Jouin et al. 2016; 
Kurtz et al. 2019). Despite these improvements, fuel cell systems are 
not yet mature for many commercial applications. For example, the 
US DOE has outlined that for hydrogen fuel cell articulated trucks

(semi-trailers) to compete with diesel vehicles, fuel cell durability 
will need to reach 30,000 hours (US DOE 2019). While some fuel cell 
buses have demonstrated durability close to these targets (Eudy and 
Post 2018a), another review of light fuel cell vehicles found maximum 
durability of 4000 hours (Kurtz et al. 2019). As more fuel cell vehicles 
are trialled, it is expected that further real-world data will become 
available to track ongoing fuel cell durability improvements.
Ammonia and methanol fuel cells are considered to be less mature 
than hydrogen fuel cells. However, they offer the benefit of using 
a  more easily transported fuel that can be directly used without 
converting to hydrogen (Zhao et al. 2019). Conversely, both methanol 
and ammonia are toxic, and in the case of methanol fuel cells, carbon 
dioxide is released as a by-product of generating electricity with the 
fuel cell (Zhao et al. 2019). Due to the lower power output, methanol

and ammonia fuel cells are also not well suited to heavy-duty 
vehicles (Jeerh et al. 2021). They are therefore unlikely to compete 
with hydrogen fuel cells. However, ammonia and methanol could 
be converted to hydrogen at refuelling stations as an alternative to 
being directly used in fuel cells (Zhao et al. 2019).
Several FCV-related technologies are fully ready for demonstration 
and early market deployment, however, further research and 
development will be required to achieve full-scale commercialisation, 
likely from 2030 onwards (Staffell et  al. 2019; Energy Transitions 
Commission 2020; IEA 2021b). Some reports argue that it may be 
possible to achieve serial production of fuel cell heavy-duty trucks 
in the late 2020s, with comparable costs to diesel vehicles achieved 
after 2030 (Jordbakker et  al. 2018). Over the next decade or so, 
hydrogen FCVs could become cost-competitive for various transport 
applications, potentially including long-haul trucks, marine ships,

and aviation (Hydrogen Council 2017; FCHEA 2019; FCHJU 2019; 
BloombergNEF 2020; Hydrogen Council 2020). The speed of fuel 
cell system cost reduction is a key factor for achieving widespread 
uptake. Yet, experts disagree on the relationship between the scale of 
fuel cell demand, cost, and performance improvements (Cano et al. 
2018). Costs of light-, medium-, and heavy-duty fuel cell powertrains 
have decreased by orders of magnitude with further reductions of 
a  factor of two expected with continued technological progress 
(Whiston et al. 2019). For example, the costs of platinum for fuel cell 
stacks have decreased by an order of magnitude (Staffell et al. 2019); 
current generation FCVs use approximately 0.25 g/kW platinum and 
a further reduction of 50–80% is expected by 2030 (Hao et al. 2019).Hydrogen is likely to take diverse roles in the future energy system: 
as a fuel in industry and buildings, as well as transport, and as energy

storage for variable renewable electricity. Further research is required 
to understand better how a hydrogen transport fuel supply system 
fits within the larger hydrogen energy system, especially in terms of 
integration within existing infrastructure, such as the electricity grid 
and the natural gas pipeline system (IEA 2015).
Strong and durable policies would be needed to enable widespread 
use of hydrogen as a  transport fuel and to sustain momentum 
during a  multi-decade transition period for hydrogen FCVs to 
become cost-competitive with electric vehicles (Hydrogen Council 
2017; FCHEA 2019; FCHJU 2019; IEA 2019c; BNEF 2020; Hydrogen 
Council 2020). The analysis suggests that hydrogen is likely to have 
strategic and niche roles in transport, particularly in long-haul 
shipping and aviation. With continuing improvements, hydrogen and 
electrification will likely play a role in decarbonising heavy-duty road 
and rail vehicles.
10.3.4 Refuelling and Charging Infrastructure

The transport sector relies on liquid gasoline, and diesel for land-
based transport, jet fuel for aviation, and heavy fuel oil for shipping. 
Extensive infrastructure for refuelling liquid fossil fuels already 
exists. Ammonia, synthetic fuels, and biofuels have emerged as 
alternative fuels for powering combustion engines and turbines 
used in land, shipping, and aviation (Figure  10.2). Synthetic fuels 
such as e-methanol and Fischer-Tropsch liquids have similar physical 
properties and could be used with existing fossil fuel infrastructure 
(Yugo and Soler, 2019). Similarly, biofuels have been used in several 
countries together with fossil fuels (Panoutsou et al. 2021). Ammonia 
is a  liquid, but only under pressure, and therefore will not be 
compatible with liquid fossil fuel refuelling infrastructure. Ammonia 
is, however, widely used as a fertiliser and chemical raw material and 
10% of annual ammonia production is transported via sea (Gallucci

2021). As such, a number of port facilities include ammonia storage 
and transport infrastructure and the shipping industry has experience 
in handling ammonia (Gallucci 2021). This infrastructure would likely 
need to be extended in order to support the use of ammonia as a fuel 
for shipping and therefore ports are likely to be the primary sites for 
these new refuelling facilities.
EVs and HFCV require separate infrastructure than liquid fuels. The 
successful diffusion of new vehicle technologies is dependent on 
the preceding deployment of infrastructure (Leibowicz 2018), so that 
the deployment of new charging and refuelling infrastructure will be 
critical for supporting the uptake of emerging transport technologies 
like EVs and HFCVs, where it makes sense for each to be deployed. 
As a result, there is likely a need for simultaneous investment in both 
infrastructure and vehicle technologies to accelerate decarbonisation 
of the transport sector.

Charging infrastructure. Charging infrastructure is important 
for a number of key reasons. From a consumer perspective, robust 
and reliable charging infrastructure networks are required to build 
confidence in the technology and overcome the often-cited barrier of

1072
1072
Chapter 10  T ransport10‘range anxiety’ (She et al. 2017). Range anxiety is where consumers 
do not have confidence that an EV will meet their driving range 
requirements. For LDVs, the majority of charging (75–90%) has been 
reported to take place at or near homes (Figenbaum 2017; Webb 
et al. 2019; Wenig et al. 2019). Charging at home is a particularly 
significant factor in the adoption of EVs as consumers are less willing 
to purchase an EV without home charging (Berkeley et al. 2017; Funke 
and Plötz 2017; Nicholas et al. 2017). However, home charging may 
not be an option for all consumers. For example, apartment dwellers 
may face specific challenges in installing charging infrastructure 
(Hall and Lutsey 2020). Thus, the provision of public charging 
infrastructure is another avenue for alleviating range anxiety, 
facilitating longer distance travel in EVs, and in turn, encouraging 
adoption (Hall and Lutsey 2017; Melliger et al. 2018; Narassimhan

and Johnson 2018; Melton et al. 2020). Currently, approximately 10% 
of charging occurs at public locations, roughly split equally between 
alternating current (AC) (slower) and direct current (DC) (fast) 
charging (Figenbaum 2017; Webb et  al. 2019; Wenig et  al. 2019). 
Deploying charging infrastructure at workplaces and commuter car 
parks is also important, particularly as vehicles are parked at these 
locations for many hours. Indeed, around 15–30% of EV charging 
currently occurs at these locations (Figenbaum 2017; Webb et  al. 
2019; Wenig et al. 2019). It has been suggested that automakers and 
utilities could provide support for the installation of home charging 
infrastructure (Hardman et al. 2018), while policymakers can provide 
support for public charging. Such support could come via supportive 
planning policy, building regulations, and financial support. Policy 
support could also incentivise the deployment of charging stations

at workplaces and commuter car parks. Charging at these locations 
would have the added benefit of using excess solar energy generated 
during the day (Hardman et al. 2018; Webb et al. 2019).
While charging infrastructure is of high importance for the 
electrification of light-duty vehicles, arguably it is even more 
important for heavy-duty vehicles, given the costs of high-power 
charging infrastructure. It is estimated that the installed cost of fast-
charging hardware can vary between approximately USD45,000 
to USD200,000 per  charger, depending on the charging rate, the 
number of chargers per  site, and other site conditions (Hall and 
Lutsey 2019; Nelder and Rogers 2019; Nicholas 2019). Deployment 
of shared charging infrastructure at key transport hubs, such as bus 
and truck depots, freight distribution centres, marine shipping ports 
and airports, can encourage a transition to electric vehicles across 
the heavy transport segments. Furthermore, if charging infrastructure

sites are designed to cater for both light- and heavy-duty vehicles, 
infrastructure costs could decrease by increasing utilisation across 
multiple applications and/or fleets (Nelder and Rogers 2019).
There are two types of charging infrastructure for electric vehicles: 
conductive charging involving a  physical connection and wireless/
induction charging. The majority of charging infrastructure deployed 
today for light- and heavy-duty vehicles is conductive. However, 
wireless charging technologies are beginning to emerge – particularly 
for applications like bus rapid transit – with vehicles able to charge 
autonomously while parked and/or in motion (IRENA 2019). For 
road vehicles, electric road systems, or road electrification, is also 
emerging as an alternative form of conductive charging infrastructure that replaces a physical plug (Ainalis et al. 2020; Hill et al. 2020). 
This type of charging infrastructure is particularly relevant for road

freight where load demand is higher. Road electrification can take 
the form of a charging rail built into the road pavement, run along 
the side of the road, through overhead catenary power lines – similar 
to electrical infrastructure used for rail – or at recharging facilities 
at stations along the route. This infrastructure can also be used to 
directly power other electrified powertrains, such as hybrid and HFCV 
(Hardman et al. 2018; Hill et al. 2020).
Charging infrastructure also varies in terms of the level of charging 
power. For light vehicles, charging infrastructure is generally up to 
350 kW, which provides approximately 350 kilometres for every 
10 minutes of charging. For larger vehicles, like buses and trucks, 
charging infrastructure is generally up to 600 kW, providing around 
50–100 km for every 10 minutes of charging (depending on the size 
of the vehicle). Finally, even higher-power charging infrastructure is

currently being developed at rates greater than 1 MW, particularly 
for long-haul trucks and for short-haul marine shipping and 
aviation. For example, one of the largest electric ferries in the world, 
currently operating in Denmark, uses a 4.4 MW charger (Heinemann 
et al. 2020).
Finally, there are several different charging standards, varying across 
transport segments and across geographical locations. Like electrical 
appliances, different EV charging connectors and sockets have 
emerged in different regions, such as CCS2 in Europe (ECA 2021), 
GB/T in China (Hove and Sandalow 2019). Achieving interoperability 
between charging stations is seen as another important issue for 
policymakers to address to provide transparent data to the market 
on where EV chargers are located and a  consistent approach to 
paying for charging sessions (van der Kam and Bekkers 2020). 
Interoperability could also play an important role in enabling smart

charging infrastructure (Neaimeh and Andersen 2020).
Smart charging: electric vehicle-grid integration strategies. 
EVs provide several opportunities for supporting electricity grids 
if appropriately integrated. Conversely, a  lack of integration could 
negatively affect the grid, particularly if several vehicles are charged 
in parallel at higher charging rates during peak demand periods 
(Webb et  al. 2019; Jochem et  al. 2021). There are three primary 
approaches to EV charging. In unmanaged charging, EVs are charged 
ad hoc, whenever connected, regardless of conditions on the broader 
electricity grid (Webb et  al. 2019; Jochem et  al. 2021). Second, in 
managed charging, EVs are charged during periods beneficial to 
the grid, e.g., at periods of high renewable generation and/or low 
demand. Managed charging also allows utilities to regulate the rate 
of charge and can thus provide frequency and regulation services to

the grid (Weis et al. 2014). Finally, in bidirectional charging or vehicle-
to-grid (V2G), EVs are generally subject to managed charging, but an 
extension provides the ability to export electricity from the vehicle’s 
battery back to the building and/or wider electricity grid (Ercan 
et al. 2016; Noel et al. 2019; Jochem et al. 2021). The term ‘smart 
charging’ has become an umbrella term to encompass both managed 
charging  (often referred to as V1G) and V2G. For electric utilities, 
smart charging strategies can provide back-up power, support load 
balancing, reduce peak loads (Zhuk et  al. 2016; Noel  et  al. 2019;

1073
1073
Jochem et al. 2021), reduce the uncertainty in forecasts of daily and 
hourly electrical loads (Peng et al. 2012), and allow greater utilisation 
of generation capacity (Hajimiragha et  al. 2010; Madzharov 
et al. 2014).
Smart charging strategies can also enhance the climate benefits 
of EVs (Yuan et al. 2021). Controlled charging can help avoid high-
carbon electricity sources, decarbonisation of the ancillary service 
markets, or peak shaving of high-carbon electricity sources (Jochem 
et al. 2021). V2G-capable EVs can result in even lower total emissions, 
particularly when compared to other alternatives (Reddy et al. 2016). 
Noel et  al. (2019) analysed V2G pathways in Denmark and noted 
that at a penetration rate of 75% by 2030, USD34 billion in social 
benefits could be accrued (through things like displaced pollution). 
These social benefits translate to USD1,200 per vehicle. V2G-capable

EVs were found to have the potential to reduce carbon emissions 
compared to a conventional gasoline vehicle by up to 59%, assuming 
optimised charging schedules (Hoehne and Chester 2016).
Projections of energy storage suggest smart charging strategies will 
come to play a significant role in future energy systems. Assessment 
of different energy storage technologies for Europe showed that V2G 
offered the most storage potential compared to other options and could 
account for 200 GW of installed capacity by 2060, whereas utility-scale 
batteries and pumped hydro storage could provide 160 GW of storage 
capacity (Després et  al. 2017). Another study found that EVs with 
controlled charging could provide similar services to stationary storage 
but at a far lower cost (Coignard et al. 2018). While most deployments 
of smart charging strategies are still at the pilot stage, the number 
of projects continues to expand, with the V2G Hub documenting at

least 90 V2G projects across 22 countries in 2021 (Vehicle to Grid 
2021). Policymakers have an important role in facilitating collaboration 
between vehicle manufacturers, electricity utilities, infrastructure 
providers, and consumers to enable smart charging strategies and 
ensure EVs can support grid stability and the uptake of renewable 
energy. This is a critical part of decarbonising transport.
Hydrogen infrastructure. HFCVs are reliant on the development 
of widespread and convenient hydrogen refuelling stations 
(FCHEA 2019; IEA 2019c; BNEF 2020). Globally, there are around 
540 hydrogen refuelling stations, with the majority located in North 
America, Europe, Japan, and China (IEA 2021a). Approximately 70% 
of these refuelling stations are open to the public (Coignard et al. 
2018). Typical refuelling stations currently have a refuelling capacity 
of 100 to 350 kg/day (CARB 2019; CARB 2020; H2 Tools 2020; AFDC 
2021). At most, current hydrogen refuelling stations have daily

capacities under 500 kg a day (Liu et al. 2020b).
The design of hydrogen refuelling stations depends on the choice of 
methods for hydrogen supply and delivery, compression and storage, and the dispensing strategy. Hydrogen supply could happen via on-
site production or via transport and delivery of hydrogen produced 
off-site. At the compression stage, hydrogen is compressed to achieve 
the pressure needed for economic stationary and vehicle storage. This 
pressure depends on the storage strategy. Hydrogen can be stored 
as a liquid or a gas. Hydrogen can also be dispensed to vehicles as 
a gas or a liquid, depending on the design of the vehicles (though it 
tests the extremes of temperature range and storage capacity for an 
industrial product). The technological and economic development of 
each of these components continues to be researched.
If hydrogen is produced off site in a large centralised plant, it must 
be stored and delivered to refuelling stations. The cost of hydrogen

delivery depends on the amount of hydrogen delivered, the delivery 
distance, the storage method (compressed gas or cryogenic 
liquid), and the delivery mode (truck or pipeline). Table  10.6 
describes the three primary options for hydrogen delivery. Most 
hydrogen refuelling stations today are supplied by trucks and, very 
occasionally, hydrogen pipelines. Gaseous tube trailers could also be 
used to deliver hydrogen in the near term, or over shorter distances, 
due to the low fixed cost (although the variable cost is high). Both 
liquefied truck trailers and pipelines are recognised as options in 
the medium to long term as they have higher capacities and lower 
costs over longer distances (FCHJU 2019; Li et al. 2020; EU 2021). 
Alternatively, hydrogen can be produced on site using a small-scale 
on-site electrolyser or steam methane reforming unit combined with 
CCS. Hydrogen is generally dispensed to vehicles as a compressed

gas at pressures 350 or 700 bar, or as liquified hydrogen at –253°C 
(Hydrogen Council 2020).
The costs for hydrogen refuelling stations vary widely and remain 
uncertain for the future (IEA 2019c). The IEA reports that the 
investment cost for one hydrogen refuelling station ranges between 
USD0.6 million and USD2 million for hydrogen at a  pressure of 
700 bar and a delivery capacity of 1300 kg per day. The investment 
cost of hydrogen refuelling stations with lower refuelling capacities 
(~50  kg  H2 per  day) delivered at lower pressure (350 bar) range 
between USD0.15–1.6  million. A  separate estimate by the 
International Council for Clean Transport suggests that at a capacity 
of 600 kg of hydrogen per day, the capital cost of a single refuelling 
station would be approximately USD1.8 million (ICCT 2017). Given 
the high investment costs for hydrogen refuelling stations, low 
utilisation can translate into a high price for delivered hydrogen. In

Europe, most pumps operate at less than 10% capacity. For small 
refuelling stations with a capacity of 50 kg H 2 per day, this utilisation 
rate translates to a high price of around USD15–25 per kg H 2 – in 
line with current retail prices (IEA 2019c). The dispensed cost of 
hydrogen is also highly correlated with the cost of electricity, when 
H2 is produced using electrolysis, which is required to produce low-
carbon hydrogen.
Table 10.6 | Overview of three transport technologies for hydrogen delivery in the transport sector showing relative differences.  Source: IEA (2019c).
Capacity Delivery distance Energy loss Fixed costs Variable costs Deployment phase
Gaseous tube trailers Low Low Low Low High Near term
Liquefied truck trailers Medium High High Medium Medium Medium to long term
Hydrogen pipelines High High Low High Low Medium to long term

1074
1074
Chapter 10  T ransport1010.4 Decarbonisation of Land-based Transport
10.4.1 Light-duty Vehicles for Passenger Transport
LDVs represent the main mode of transport for private citizens 
(ITF 2019) and currently represent the largest share of transport 
emissions globally (IEA 2019d). Currently, powertrains depending 
on gasoline and diesel fuels remain the dominant technology in the 
LDV segment (IEA 2019d). HEVs, and fully battery electric vehicles 
(BEVs), however, have become increasingly popular in recent years 
(IEA 2021a). Correspondingly, the number of lifecycle assessment 
(LCA) studies investigating HEVs, BEVs, and fuel cell vehicles have 
increased. While historically the focus has been on the tailpipe 
emissions of LDVs, LCA studies demonstrate the importance of 
including emissions from the entire vehicle value chain, particularly 
for alternative powertrain technologies.
Figure 10.4 presents the cumulative lifecycle emissions for selected

powertrain technologies and fuel chain combinations for compact 
and mid-sized LDVs. This figure summarises the harmonised findings 
from the academic literature reviewed and the data submitted 
through an IPCC data collection effort, as described in Appendix 10.1 
(Hawkins et al. 2013; Messagie et al. 2014; Bauer et al. 2015; Tong 
et al. 2015b; Ellingsen et al. 2016; Gao et al. 2016; Kim and Wallington 
2016; Cai et al. 2017; Evangelisti et al. 2017; Ke et al. 2017; Lombardi 
et al. 2017; Miotti et al. 2017; Valente et al. 2017; Cox et al. 2018; 
de Souza et al. 2018; Elgowainy et al. 2018; Luk et al. 2018; Bekel 
and Pauliuk 2019; Cusenza et al. 2019; Hoque et al. 2019; IEA 2019a; 
Rosenfeld et al. 2019; Shen et al. 2019; Wang et al. 2019; Wu et al. 
2019; Ambrose et  al. 2020; Benajes et  al. 2020; Hill et  al. 2020; 
Knobloch et al. 2020; Prussi et al. 2020; Qiao et al. 2020; Wolfram 
et al. 2020; Zheng et al. 2020; Sacchi 2021; Valente et al. 2021). The

values in the figure (and the remaining figures in this section) depend 
on the 100-year global warming potential (GWP) used in each study, 
which may differ from the recent GWP updates from WGI. However, 
it is unlikely that the qualitative insights gained from the figures in 
this section would change using the update 100-year GWP values.
Furthermore, note that the carbon footprint of biofuels used in 
Figure  10.4 are aggregate numbers not specific to any individual 
value chain or fuel type. They are derived by combining land use-
related carbon emissions from Chapter 7 with conversion efficiencies 
and emissions as described in Section  10.3. Specifically, land-use 
footprints derived from the three modelling approaches employed 
here are: i) Integrated Assessment Models  – Energy Modelling 
Forum 33 (IAM EMF33); ii) Partial models assuming constant land 
cover (CLC), and, iii) Partial models using natural regrowth (NRG). The

emissions factors used here correspond to scenarios where global 
production of biomass for energy purposes are 100 EJ/year, with 
lower emissions factors expected at lower levels of consumption and 
vice versa. Further details are available in Box 10.2 and Chapter 7.
The tailpipe emissions and fuel consumption reported in the literature 
generally do not use empirical emissions data. Rather, they tend to 
report fuel efficiency using driving cycles such as New European 
Driving Cycle or the US Environmental Protection Agency Federal Test 
Procedure. As a result, depending on the driving cycle used, operating emissions reported in literature are possibly underestimated by as 
much as 15–38%, in comparison to real driving emissions (Fontaras 
et  al. 2017; Tsiakmakis et  al. 2017; Triantafyllopoulos et  al. 2019). 
The extent of these underestimations, however, varies between 
powertrain types, engine sizes, driving behaviour and environment.

Current average lifecycle impacts of mid-size ICEVs span from 
approximately 65 gCO 2-eq pkm–1 to 210 gCO 2-eq pkm–1, with 
both values stemming from ICEVs running on biofuels. Between 
this range of values, the current reference technologies are found, 
with diesel-powered ICEVs having total median lifecycle impacts of 
130 gCO 2-eq pkm–1 and gasoline-fuelled vehicle 160 gCO 2-eq pkm–1. 
Fuel consumption dominates the lifecycle emissions of ICEVs, with 
approximately 75% of these emissions arising from the tailpipe and 
fuel chain.
HEVs and plug-in HEVs (PHEVs) vary in terms of degree of powertrain 
electrification. HEVs mainly rely on regenerative braking for charging 
the battery. PHEVs combine regenerative braking with external 
power sources for charging the battery. Operating emissions intensity 
is highly dependent on the degree to which electrified driving is 
performed, which in turn is user- and route-dependent. For PHEVs,

emissions intensity is also dependent on the source of the electricity 
for charging. HEV and PHEV production impacts are comparable 
to the emissions generated for producing ICEVs as the batteries 
are generally small compared to those of BEVs. Current HEVs may 
reduce emissions compared to ICEVs by up to 30%, depending 
on the fuel, yielding median lifecycle intensities varying between 
60 gCO 2-eq pkm–1 (biofuels, EMF33) and 165–170 gCO 2-eq pkm–1 
(biofuels, partial models NRG). Within this wide range, all the 
combinations of electric and fossil-fuelled driving can be found, 
as well as the lifecycle intensity for driving 100% on fossil fuel. 
Because HEVs rely on combustion as the main energy conversion 
process, they offer limited mitigation opportunities. However, 
HEVs represent a suitable temporary solution, yielding a moderate 
mitigation potential, in areas where the electricity mix is currently so 
carbon intensive that the use of PHEVs and BEVs is not an effective

mitigation solution (Wolfram and Wiedmann 2017; Wu et al. 2019).
In contrast to HEVs, PHEVs may provide greater opportunities for 
use-phase emissions reductions for LDVs. These increased potential 
benefits are due to the ability to charge the battery with low-carbon 
electricity and the longer full-electric range in comparison to HEVs 
(Laberteaux et  al. 2019). Consumer behaviour (e.g.,  utility factor 
(UF) and charging patterns), manufacturer settings, and access 
to renewable electricity for charging strongly influence the total 
operational impacts (Wu et al. 2019). The UF is a weighting of the 
percentage of distance covered using the electric charge (charge 
depleting (CD) stage) versus the distance covered using the internal 
combustion engine (charge sustaining (CS) stage) (Paffumi et  al. 
2018). When the PHEV operates in CS mode, the internal combustion 
engine is used for propulsion and to maintain the state of charge

of the battery within a  certain range, together with regenerative 
braking (Plötz et  al. 2018; Raghavan and Tal 2020). When running 
in CS mode, PHEVs have a  reduced mitigation potential and have 
impacts comparable to those of HEVs. On the other hand, when the 
PHEV operates in CD mode, the battery alone provides the required

1075
1075
Gasoline
Diesel
Compressed natural gas
Liqueﬁed petroleum gas
Advanced biofuels, IAM EMF33
Advanced biofuels, PM CLC
Advanced biofuels, PM NRG
Gasoline
Diesel
Low-carbon electricity
Natural gas electricity
Coal electricity
Hydrogen, low-carbon electricity
Hydrogen, natural gas SMR162 [222]
191 [251]
125 [185]
137 [197]
44  [104]
63  [123]
257  [317]
139  [203]
107  [171]
107  [171]
117  [181]
31  [95]
46  [110]
194  [258]
9  [87]
104  [182]
187  [265]
13  [98]
132  [217]
LegendTransport lifecycle GHG intensity,
1.5 occupancy rate (gCO2-eq per passenger-km)Median well-to-wheel emissions intensity
[median lifecycle GHG emissions intensity] (gCO2-eq per vehicle-km)
Lifecycle GHG intensity (gCO2-eq per vehicle-km)0 67 133 200 267
0 100 200 300 400
MedianInterquartiles
Median
95th percentile
Data pointFCV BEV HEV ICEV
Vehicle cycle

emissions intensityLife cycle emissions intensity
(vehicle production + WTW)
lnterquartiles
Figure 10.4 | Life cycle greenhouse gas emissions intensities for mid-sized light-duty vehicle and fuel technologies from the literature.  The primary x-axis 
reports units in gCO 2-eq vkm–1, assuming a vehicle life of 180,000 km. The secondary x-axis uses units of gCO 2-eq pkm–1, assuming a 1.5 occupancy rate. The values in the 
figure rely on the 100-year GWP value embedded in the source data, which may differ slightly from the updated 100-year GWP values from WGI. The shaded area represents 
the interquartile range for combined vehicle manufacturing and end-of-life phases. The length of the box and whiskers represent the interquartile range of the operation phase 
for different fuel chains, while their placement on the x-axis represents the absolute lifecycle climate intensity, that is, includes manufacturing and end-of-life phases. Each

individual marker indicates a data point. ‘Advanced biofuels’ refers to the use of second-generation biofuels and their respective conversion and cultivation emission factors. 
‘IAM EMF33’ refers to emissions factors for advanced biofuels derived from simulation results from the integrated assessment models EMF33 scenarios. ‘PM’ refers to partial 
models, where ‘CLC’ is with constant land cover and ‘NRG’ is with natural regrowth. ‘Hydrogen, low-carbon electricity’ is produced via electrolysis using low-carbon electricity. 
‘Hydrogen, natural gas SMR’ refers to fuels produced via steam methane reforming of natural gas.

1076
1076
Chapter 10  T ransport10propulsion energy (Plötz et al. 2018; Raghavan and Tal 2020). Thus, in 
CD mode, PHEVs hold potential for higher mitigation potential, due 
to the possibility of charging the battery with low-carbon electricity 
sources. Consequently, the UF greatly influences the lifecycle emissions 
of PHEVs. The current peer-reviewed literature presents a wide range 
of UFs mainly due to varying testing protocols applied for estimating 
the fuel efficiency and user behaviour (Pavlovic et al. 2017; Paffumi 
et  al. 2018; Plötz et  al. 2018; Plötz et  al. 2020; Raghavan and Tal 
2020; Hao et al. 2021). These factors make it difficult to harmonise 
and compare impacts across PHEV studies. Due to the low number 
of appropriate PHEV studies relative to the other LDV technologies 
and the complications in harmonising available PHEV results, this 
technology is omitted from Figure  10.4. However, due to the dual

operating nature of PHEV vehicles, one can expect that the lifecycle 
GHG emissions intensities for these vehicles will lie between those 
of their ICEV and BEV counterparts of similar size and performance.
Currently, BEVs have higher manufacturing emissions than 
equivalently-sized ICEVs, with median emissions of 14 tCO 2-eq 
per  vehicle against approximately 10 tCO 2-eq per  vehicle of their 
mid-sized fossil-fuelled counterparts. These higher production 
emissions of BEVs are largely attributed to the battery pack 
manufacturing and to the additional power electronics required. 
As manufacturing technology and capacity utilisation improve and 
globalise to regions with low-carbon electricity, battery manufacturing 
emissions will likely decrease. Due to the higher energy efficiency 
of the electric powertrain, BEVs may compensate for these higher 
production emissions in the driving phase. However, the mitigation

ability of this technology relative to ICEVs is highly dependent on 
the electricity mix used to charge the vehicle. As a consequence of 
the  variety of energy sources available today, current BEVs have 
a wide range of potential average lifecycle impacts, ranging between 60 and 180 gCO 2-eq pkm–1 with electricity generated from wind and 
coal, respectively. The ability to achieve large carbon reductions via 
vehicle electrification is thus highly dependent on the generation of 
low-carbon electricity, with the greatest mitigation effects achieved 
when charging the battery with low-carbon electricity. The literature 
suggests that current BEVs, if manufactured on low-carbon electricity 
as well as operated on low-carbon electricity would have footprints 
as low 22 gCO 2-eq pkm–1 for a  compact-sized car (Ellingsen 
et  al. 2014; Ellingsen et  al. 2016). This value suggests a  reduction 
potential of around 85% compared to similarly-sized fossil fuel

vehicles (median values). Furthermore, BEVs have a  co-benefit of 
reducing local air pollutants that are responsible for human health 
complications, particularly in densely-populated areas (Hawkins et al. 
2013; Ke et al. 2017).
As with BEVs, current HFCVs have higher production emissions than 
similarly-sized ICEVs and BEVs, generating on average approximately 
15 tCO 2-eq per vehicle. As with BEVs, the lifecycle impacts of FCVs 
are highly dependent on the fuel chain. To date, the most common 
method of hydrogen production is steam methane reforming of 
natural gas (Khojasteh Salkuyeh et  al. 2017), which is relatively 
carbon intensive, resulting in lifecycle emissions of approximately 
88 gCO 2-eq pkm–1. Current literature covering lifecycle impacts 
of FCVs shows that vehicles fuelled with hydrogen produced from 
steam methane reforming of natural gas offer little or no mitigation 
potential over ICEVs. Other available hydrogen fuel chains vary widely

in carbon intensity, depending on the synthesis method and the 
energy source used (electrolysis or steam methane reforming; fossil 
fuels or renewables). The least carbon-intensive hydrogen pathways 
rely on electrolysis powered by low-carbon electricity. Compared to 
ICEVs and BEVs, FCVs for LDVs are at a lower technology readiness 
level, as discussed in section 10.3.
Box 10.3 | Vehicle Size Trends and Implications on the Fuel Efficiency of LDVs
Vehicle size trends. On a global scale, SUV sales have been constantly growing in the last decade, with 39% of the vehicles sold in 
2018 being SUVs (IEA 2019d). If the trend towards increasing vehicle size and engine power continues, it may result in higher overall 
emissions from the LDV fleet (relative to smaller vehicles with the same powertrain technology). The magnitude of the influence 
vehicle mass has on fuel efficiency varies with the powertrain, which have different efficiencies. Box 10.3 Figure 1 highlights this

relationship using data from the same literature used to create Figure 10.4. Higher powertrain efficiency results in lower energy losses 
in operation, and thus requires less energy input to move a given mass than a powertrain of lower efficiency. This pattern is illustrated 
by the more gradual slope of BEVs in Box 10.3 Figure 1. The trend towards bigger and heavier vehicles, with consequently higher use 
phase emissions, can be somewhat offset by improvements in powertrain design, fuel efficiency, lightweighting, and aerodynamics 
(Gargoloff et  al. 2018; Wolfram et  al. 2020). The potential improvements provided by these strategies are case specific and not 
thoroughly evaluated in the literature, either individually or as a combination of multiple strategies.
Lightweighting. There is an increasing use of advanced materials such as high-strength steel, aluminium, carbon fibre, and polymer

composites for vehicle lightweighting (Hottle et al. 2017). These materials reduce the mass of the vehicle and thereby also reduce 
the fuel or energy required to drive. Lightweighted components often have higher production emissions than the components they 
replace due to the advanced materials used (Kim and Wallington 2016). Despite these higher production emissions, some studies 
suggest that the reduced fuel consumption over the lifetime of the lightweighted vehicle may provide a net mitigation effect in 
comparison to a non-lightweighted vehicle (Kim and Wallington 2013; Hottle et al. 2017; Milovanoff et al. 2019; Upadhyayula et al. 
2019; Wolfram et al. 2020). However, multiple recent publications have found that in some cases, depending on, for example, vehicle 
size and carbon intensity of the lightweighting materials employed, the GHG emissions avoided due to improved fuel efficiency do not

1077
1077
Two-wheelers, consisting mainly of lower-powered mopeds and 
higher-powered motorcycles, are popular for personal transport 
in densely populated cities, especially in developing countries. 
LCA studies for this class of vehicle are relatively uncommon 
compared to four-wheeled LDVs. In the available results, however, 
two-wheelers exhibit similar trends for the different powertrain 
technologies as the LDVs, with electric powertrains having higher 
production emissions, but usually lower operating emissions. The 
lifecycle emissions intensity for two-wheelers is also generally lower 
than four-wheeled LDVs on a  vehicle-kilometre basis. However, 
two-wheelers generally cannot carry as many passengers as four-
wheeled LDVs. Thus, on a passenger-kilometre basis, a fully occupied 
passenger vehicle may still have lower emissions than a  fully 
occupied two-wheeler. However, today, most passenger vehicles

have relatively low occupancy and thus have a correspondingly high emissions intensity on a pkm basis. This points to the importance 
of utilisation of passenger vehicles at higher occupancies to 
reduce the lifecycle intensity of LDVs on a pkm basis. For example, 
the median emissions intensity of a gasoline passenger vehicle is 
222 gCO 2-eq  vkm–1, and 160 gCO 2-eq vkm–1 for a  gasoline two-
wheeler (Cox and Mutel 2018). At a  maximum occupancy factor 
of four and two passengers, respectively, the transport emissions 
intensity for these vehicles is 55 and 80 gCO 2-eq pkm–1. Under the 
same occupancy rates assumption, BEV two-wheelers recharged on 
the average European electricity mix, achieve lower lifecycle GHG 
intensities than BEV four-wheeled LDVs. On the other hand, FCV 
two-wheelers with hydrogen produced via steam methane reforming 
present higher GHG intensity than their four-wheeled counterparts, 
when compared on a pkm basis at high occupancy rates.Box 10.3 (continued)

offset the higher manufacturing emissions of the vehicle (Luk et al. 2018; Wu et al. 2019). In addition, these advanced materials may 
be challenging to recycle in a way that retains their high technical performance (Meng et al. 2017).
Co-effects on particulate matter. Lightweighting may also alleviate the particulate matter (PM) emissions arising from road and 
brake wear. BEVs are generally heavier than their ICEV counterparts, which may potentially cause higher stress on road surfaces 
and tyres, with consequently higher PM emissions per kilometre driven (Timmers and Achten 2016). Regenerative braking in HEVs, 
BEVs and FCVs, however, reduces the mechanical braking required, and therefore may compensate for the higher brake wear emissions 
from these heavier vehicle types. In addition, BEVs have no tailpipe emissions, which further offsets the increased PM emissions from

road and tyre wear. Therefore, lightweighting strategies may offer a carbon and particulates mitigation effect; however, in some cases, 
other technological options may reduce CO 2 emissions even further.
Box  10.3, Figure  1 | Illustration of energy consumption as a  function of vehicle size (using mass as a  proxy) and powertrain technology.  
FCVs omitted due to lacking data.
Vehicle mass (kg)Energy consumption (MJ km–1)
1200ICEV
HEV
BEVPowertrain type
01
2200 2000 1800 1600 1400
2345

1078
1078
Chapter 10  T ransport10Lifecycle ICEV costs (USD per passenger-km)
Low efﬁciency
0.5
1.0
1.5
2.0
2.5
20
65
110
155
200
1
4
7
10
1313
27
41
55
69
20
65
110
155
200
8
33
58
83
10820,0000.18 0.27 0.35 0.44 0.53
0.22 0.31 0.40 0.48 0.57
0.26 0.35 0.44 0.52 0.61
0.30 0.39 0.48 0.56 0.65
0.34 0.43 0.52 0.60 0.690.16 0.24 0.33 0.42 0.51
0.17 0.26 0.35 0.44 0.53
0.19 0.28 0.37 0.46 0.55
0.21 0.30 0.39 0.48 0.57
0.23 0.32 0.41 0.50 0.59
40,000 60,000
Vehicle purchase price (USD)80,000 100,000 20,000 40,000 60,000
Vehicle purchase price (USD)80,000 100,000
50,0000.29 0.37 0.45 0.53 0.61
0.33 0.41 0.49 0.56 0.64
0.36 0.44 0.52 0.60 0.68
0.40 0.48 0.56 0.63 0.71
0.43 0.51 0.59 0.67 0.750.29 0.37 0.45 0.53 0.61
0.32 0.40 0.48 0.56 0.63
0.35 0.43 0.51 0.58 0.66
0.38 0.46 0.53 0.61 0.69
0.41 0.48 0.56 0.64 0.72
67,500 85,000
Vehicle purchase price (USD)102,500 120,000 50,000 67,500 85,000
Vehicle purchase price (USD)102,500 120,00020,0000.14 0.23 0.32 0.41 0.50
0.15 0.24 0.33 0.42 0.51

0.16 0.25 0.34 0.43 0.52
0.18 0.27 0.36 0.45 0.540.14 0.23 0.32 0.41 0.50
0.14 0.23 0.32 0.41 0.50
0.16 0.25 0.33 0.42 0.51
Vehicle purchase price (USD)80,000 100,000Diesel cost (USD L–1)
Diesel cost (USD GJ–1)Electricity cost (USD/MWh)
Electricity cost (USD GJ–1)Hydrogen cost (USD kg–1)
Hydrogen cost (USD GJ–1)High efﬁciency
Low efﬁciency High efﬁciency
Low efﬁciency High efﬁciencyLifecycle BEV costs (USD per passenger-km)
Lifecycle FCV costs (USD per passenger-km)
Figure 10.5 | LCC for light-duty internal combustion engine vehicles, battery electric vehicles, and hydrogen fuel cell vehicles.  The results for ICEVs represent 
the LCC of a vehicle running on gasoline. However, these values are also representative for ICEVs running on diesel as the costs ranges in the literature for these two solutions

are similar. The secondary y-axis depicts the cost of the different energy carriers normalised in USD per gigajoule for easier cross-comparability.

1079
1079
ICEV, HEV, and PHEV technologies, which are powered using 
combustion engines, have limited potential for deep reduction 
of GHG emissions. Biofuels offer good mitigation potential if low 
land-use change emissions are incurred (e.g., the IAM EMF33 and 
partial models, CLC biofuels pathways shown in Figure  10.4). The 
literature shows large variability, depending on the method of 
calculating associated land-use changes. Resolving these apparent 
methodological differences is important to consolidating the role 
biofuels may play in mitigation, as well as the issues raised in 
Chapter 7 about the conflicts over land use. The mitigation potential 
of battery and fuel cell vehicles is strongly dependent on the carbon 
intensity of their production and the energy carriers used in operation. 
However, these technologies likely offer the highest potential for 
reducing emissions from LDVs. Prior work on the diffusion dynamics

of transport technologies suggests that ‘the diffusion of infrastructure 
precedes the adoption of vehicles, which precedes the expansion of 
travel’ (Leibowicz 2018). These dynamics reinforce the argument 
for strong investments in both the energy infrastructure and the 
vehicle technologies.
To successfully transition towards LDVs utilising low-carbon fuels or 
energy sources, the technologies need to be accessible to as many 
people as possible, which requires competitive costs compared to 
conventional diesel and gasoline vehicles. The lifecycle costs (LCCs) of 
LDVs depend on the purchasing costs of the vehicles, their efficiency, 
the fuel costs, and the discount rate. Figure 10.5 shows the results 
of a  parametric analysis of LCC for diesel LDVs, BEVs, and FCVs. 
The range of vehicle efficiencies captured in Figure 10.5 is the same 
as the range used for Figure  10.4, while the ranges for fuel costs 
and vehicle purchase prices come from the literature. The assumed

discount rate for this parametric analysis is 3%. Appendix  10.2 
includes the details about the method and underlying data used to 
create this figure.
Figure  10.5 shows the range of LCC, in USD per  passenger-
kilometre, for different powertrain technologies, and the influence 
of vehicle efficiency (low or high), vehicle purchase price, and fuel/
electricity cost on the overall LCC. For consistency with Figure 10.4, 
an occupancy rate of 1.5 is assumed. Mid-sized ICEVs have 
a purchase price of USD20,000–40,000, and average fuel costs are 
in the range of USD1–1.5 per litre. With these conditions, the LCC of 
fossil-fuelled LDVs span between USD0.22–0.35 pkm–1 or between 
USD0.17–0.28 pkm–1, for low- and high-efficiency ICEVs respectively 
(Figure 10.5).
BEVs have higher purchase prices than ICEVs, though a sharp decline 
has been observed since AR5. Due to the rapid development of the 
lithium-ion battery technology over the years (Schmidt et al. 2017)

and the introduction of subsidies in several countries, BEVs are quickly 
reaching cost parity with ICEVs. Mid-sized BEVs’ average purchase 
prices are in the range of USD30,000–50,000 but the levelised cost 
of electricity shows a larger spread (USD65–200/MWh) depending on 
the geographical location and the technology (Chapter 6). Therefore, 
assuming purchase price parity between ICEVs and BEVs, BEVs show 
lower LCC (Figure 10.5) due to higher efficiency and the lower cost 
of electricity compared to fossil fuels on a per-gigajoule (GJ) basis 
(secondary y-axis on Figure 10.5).FCVs represent the most expensive solution for LDV, mainly due to 
the currently higher purchase price of the vehicle itself. However, 
given the lower technology readiness level of FCVs and the current 
efforts in the research and development of this technology, FCVs 
could become a viable technology for LDVs in the coming years. The 
issues regarding the extra energy involved in creating the hydrogen

and its delivery to refuelling sites remain, however. The levelised 
cost of hydrogen on a per GJ basis is lower than conventional fossil 
fuels but higher than electricity. In addition, within the levelised 
cost of hydrogen, there are significant cost differences between the 
hydrogen-producing technologies. Conventional technologies such 
as coal gasification and steam methane reforming of natural gas, 
both with and without carbon capture and storage, represent the 
cheapest options (Bekel and Pauliuk 2019; Parkinson et  al. 2019; 
Khzouz et al. 2020; Al-Qahtani et al. 2021). Hydrogen produced via 
electrolysis is currently the most expensive technology, but with 
significant potential cost reductions due to the current technology 
readiness level.
10.4.2 Transit Technologies for Passenger Transport
Buses provide urban and peri-urban transport services to millions of 
people around the world and a growing number of transport agencies

are exploring alternative-fuelled buses. Alternative technologies to 
conventional diesel-powered buses include buses powered with 
CNG, LNG, synthetic fuels, and biofuels (e.g.,  biodiesel, renewable 
diesel, dimethyl ether); diesel hybrid-electric buses; battery electric 
buses; electric catenary buses; and hydrogen fuel cell buses. Rail is 
an alternative mode of transit that could support decarbonisation 
of land-based passenger mobility. Electric rail systems can provide 
urban services (light rail and metro systems), as well as longer-
distance transport. Indeed, many cities of the world already have 
extensive metro systems, and regions like China, Japan and Europe 
have a  robust high-speed intercity railway network. Intercity rail 
transport can be powered with electricity, however, fossil fuels are 
still prevalent for long-distance rail passenger transport in some 
regions. Battery electric long-distance trains may be a future option 
for these areas.

Figure  10.6 shows the lifecycle GHG emissions from different 
powertrain and fuel technologies for buses and passenger rail. The 
data in each panel came from a number of relevant scientific studies 
(Cai et al. 2015; Tong et al. 2015a; Dimoula et al. 2016; de Bortoli et al. 
2017; Valente et al. 2017; Meynerts et al. 2018; IEA 2019e; de Bortoli 
and Christoforou 2020; Hill et al. 2020; Liu et al. 2020a; Valente et al. 
2021). The width of the bar represents the variability in available 
estimates, which is primarily driven by variability in reported vehicle 
efficiency, size, or drive cycle. While some bars overlap, the Figure 
may not fully capture correlations between results. For example, low 
efficiency associated with aggressive drive cycles may drive the upper 
end of the emission ranges for multiple technologies; thus, an overlap 
does not necessarily suggest uncertainty regarding which vehicle 
type would have lower emissions for a comparable trip. Additionally,

reported lifecycle emissions do not include embodied GHG emissions 
associated with infrastructure construction and maintenance. 
These embodied emissions are potentially a  larger fraction of

1080
1080
Chapter 10  T ransport10lifecycle emissions for rail than for other transport modes (Chester 
and Horvath 2012; Chester et al. 2013). One study reported values 
ranging from 10–25 gCO 2 per  passenger-kilometre (International 
Union of Railways 2016), although embodied emissions from rail are 
known to vary widely across case studies (Olugbenga et al. 2019). 
These caveats are also applicable to the other figures in this section.
Figure 10.6 highlights that BEV and FCV buses and passenger rail 
powered with low-carbon electricity or low-carbon hydrogen, could 
offer reductions in GHG emissions compared to diesel-powered buses 
or diesel-powered passenger rail. However, and not surprisingly, 
these technologies would offer only little emissions reductions if 
power generation and hydrogen production rely on fossil fuels. While 
buses powered with CNG and LNG could offer some reductions 
compared to diesel-powered buses, these reductions are unlikely to

be sufficient to contribute to deep decarbonisation of the transport 
sector and they may slow down conversion to low- or zero-carbon 
options already commercially available. Biodiesel and renewable 
diesel fuels (from sources with low upstream emissions and low 
risk of induced land-use change) could offer important near-term 
reductions for buses and passenger rail, as these fuels can often be 
used with existing vehicle infrastructure. They could also be used for 
long haul trucks and trains, shipping and aviation as discussed below 
and in later sections.
There has been growing interest in the production of synthetic fuels 
from CO 2 produced by direct air capture (DAC) processes. Figure 10.6 
includes the lifecycle GHG emissions from buses and passenger 
rail powered with synthetic diesel produced through a DAC system 
paired with a Fischer-Tropsch (FT) process, based on Liu et al. (2020a). 
This process requires the use of hydrogen (as shown in Figure 10.2),

so the emissions factors of the resulting fuel depend on the emissions 
intensity of hydrogen production. An electricity emissions factor less 
than 140 gCO 2-eq kWh–1 would be required for this pathway to 
achieve lower emissions than petroleum diesel (Liu et al. 2020a); for 
example, this would be equivalent to a 75% wind and 25% natural 
gas electricity mix (Appendix 10.1). If the process relied on steam 
methane reforming for hydrogen production or fossil-based power 
generation, synthetic diesel from the DAC-FT process would not 
provide GHG emissions reductions compared to conventional diesel. 
DAC-FT from low-carbon energy sources appears to be promising 
from an emissions standpoint and could warrant the R&D and 
demonstration attention outlined in the rest of the chapter, but it 
cannot be contemplated as a decarbonisation strategy without the 
availability of low-carbon hydrogen.
At high occupancy, both bus and rail transport offer substantial

GHG reduction potential per pkm, even compared with the lowest-
emitting private vehicle options. Even at 20% occupancy, bus and 
rail may still offer emission reductions compared to passenger cars, 
especially notable when comparing BEVs with low-carbon electricity 
(the lowest-emission option for all technologies) across the three 
modes. Only when comparing a  fossil fuel-powered bus at low 
occupancy with a low-carbon powered car at high occupancy is this 
conclusion reversed. Use of public transit systems, especially those 
that rely on buses and passenger rail fuelled with the low-carbon 
fuels previously described, would thus support efforts to decarbonise the transport sector. Use of these public transit systems will depend 
on urban design and consumer preferences (Section 10.2, Chapters 5 
and 8), which in turn depend on time, costs, and behavioural choices.
Figure 10.7 shows the results of a parametric analysis of the LCCs

of transit technologies with the highest potential for GHG emissions 
reductions. As with Figure 10.5, the vehicle efficiency ranges are the 
same as those from the LCA estimates (80% occupancy). Vehicle, 
fuel, and maintenance costs represent ranges in the literature 
(Eudy and Post 2018b; IEA 2019e; Argonne National Laboratory 
2020; BNEF 2020; Eudy and Post 2020; Hydrogen Council 2020; IEA 
2020b; IEA 2020c; IRENA 2020; Johnson et al. 2020; Burnham et al. 
2021; IEA 2021c; IEA 2021d; US Energy Information Administration 
2021), and the discount rate is 3% where applicable. Appendix 10.2 
provides the details behind these estimates. The panels for the ICEV 
can represent buses and passenger trains powered with any form 
of diesel, whether derived from petroleum, synthetic hydrocarbons, 
or biofuels. For reference, global average automotive diesel prices 
from 2015–2020 fluctuated around USD1 per  litre, and the 2019 
world average industrial electricity price was approximately

USD100 per MWh (IEA 2021d). Retail hydrogen prices in excess of 
USD13 per  kilogram have been observed (Eudy and Post 2018a; 
Argonne National Laboratory 2020; Burnham et  al. 2021) though 
current production cost estimates for hydrogen produced from 
electrolysis are far lower (IRENA 2020) (and as reported in Chapter 6), 
at around USD5–7 per kg with future forecasts as low as USD1 per kg 
(BNEF 2020; Hydrogen Council 2020; IRENA 2020) (and as reported 
in Chapter 6).
Under most parameter combinations, rail is the most cost-effective 
option, followed by buses, both of which are an order of magnitude 
cheaper than passenger vehicles. Note that costs per  pkm are 
strongly influenced by occupancy assumptions; at low occupancy 
(e.g.,  <20% for buses and <10% for rail), the cost of transit 
approaches the LCC for passenger cars. For diesel rail and buses, cost 
ranges are driven by fuel costs, whereas vehicles are both important

drivers for electric or hydrogen modes due to high costs (but also 
large projected improvements) associated with batteries and fuel 
cell stacks. Whereas the current state of ICEV technologies is best 
represented by cheap vehicles and low fuel costs for diesel (top left 
of each panel), these costs are likely to rise in future due to stronger 
emission/efficiency regulations and rising crude oil prices. On the 
contrary, the current status of alternative fuels is better represented 
by high capital costs and mid-to-high fuel costs (right side of each 
panel; mid-to-bottom rows), but technology costs are anticipated 
to fall with increasing experience, research, and development. 
Thus, while electric rail is already competitive with diesel rail, and 
electric buses are competitive with diesel buses in the low efficiency 
case, improvements are still required in battery costs to compete 
against modern diesel buses on high efficiency routes, at current

diesel costs. Similarly, improvements to both vehicle cost and fuel 
costs are required for hydrogen vehicles to become cost effective 
compared to their diesel or electric counterparts. At either the 
upper end of the diesel cost range (bottom row of ICEV panels), or 
within the 2030–2050 projections for battery costs, fuel cell costs 
and hydrogen costs (top left of BEV and FCV panels), both battery- 
and hydrogen-powered vehicles become financially attractive.

1081
1081
Diesel
Diesel, high
Diesel
Liqueﬁed natural gas
DAC FT-Diesel, wind electricity
Diesel
Wind electricity
Hydrogen, low-carbon renewable
Hydrogen, natural gas SMR
Diesel
Diesel
Hydrogen, natural gas SMRBuses
Lifecycle GHG intensity, 20% occupancy (gCO2-eq per passenger-km)FCV BEV HEV ICEV FCV BEV HEV ICEVLifecycle GHG intensity, 80% occupancy (g CO2-eq per passenger-km)0 80 160 240 320 400
Rail
Life cycle GHG intensity, 20% occupancy (gCO2-eq per passenger-km)
0 80 160 240 320 4000 20 40 60 80 100

Life cycle GHG intensity, 80% occupancy (gCO2-eq per passenger-km)0 20 40 60 80 100
Figure 10.6 | Lifecycle greenhouse gas intensity of land-based bus and rail technologies.  Each bar represents the range of the lifecycle estimates, bounded by 
minimum and maximum energy use per passenger-kilometre, as reported for each fuel/powertrain combination. The ranges are driven by differences in vehicle characteristics 
and operating efficiency. For energy sources with highly variable upstream emissions, low, medium and/or high representative values are shown as separate rows. The primary 
x-axis shows lifecycle GHG emissions, in gCO 2-eq pkm–1, assuming 80% occupancy; the secondary x-axis assumes 20% occupancy. The values in the figure rely on the 100-year 
GWP value embedded in the source data, which may differ slightly from the updated 100-year GWP values from WGI. For buses, the main bars show full lifecycle, with vertical

bars disaggregating the vehicle cycle. ‘Diesel, high’ references emissions factors for diesel from oil sands. ‘advanced biofuels’, refers to the use of second-generation biofuels and 
their respective conversion and cultivation emissions factors. ‘IAM EMF33’ refers to emissions factors for advanced biofuels derived from simulation results from the integrated 
assessment models EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with constant land cover and ‘NRG’ is with natural regrowth. ‘DAC FT-Diesel, wind electricity’ 
refers to Fischer-Tropsch diesel produced via a CO 2 direct air capture process that uses wind electricity. ‘Hydrogen, low-carbon renewable’ refers to fuels produced via electrolysis 
using low-carbon electricity. ‘Hydrogen, natural gas SMR’ refers to fuels produced via steam methane reforming of natural gas. Results for ICEVs with ‘high emissions DAC FT-

Diesel from natural gas’ are not included here since the lifecycle emissions are estimated to be substantially higher than petroleum diesel ICEVs.

1082
1082
10.4.3 Land-based Freight Transport
As is the case with passenger transport, there is growing interest 
in alternative fuels that could reduce GHG emissions from freight 
transport. Natural gas-based fuels (e.g., CNG, LNG) are an example, 
however these may not lead to drastic reductions in GHG emissions 
compared to diesel. Natural gas-powered vehicles have been 
discussed as a  means to mitigate air quality impacts (Khan et  al. 
2015; Cai et  al. 2017; Pan et  al. 2020), but those impacts are not the focus of this review. Decarbonisation of medium- and heavy-duty 
trucks would likely require the use of low-carbon electricity in battery 
electric trucks, low-carbon hydrogen or ammonia in fuel-cell trucks, 
or bio-based fuels (from sources with low upstream emissions and 
low risk of induced land-use change) used in ICE trucks.
Freight rail is also a major mode for the inland movement of goods.

Trains are more energy efficient (per tkm) than trucks, so expanded 
use of rail systems (particularly in developing countries where Lifecycle costs of buses Lifecycle costs of passenger rail
430Diesel cost (USD L–1)0.5
1.0
1.5
2.0
2.5
Diesel cost (USD L–1)0.5
1.0
1.5
2.0
2.5Diesel cost (USD GJ–1)13
27
41
5530 30 30 31 31
37 37 38 38 39
44 45 45 45 46
51 52 52 53 53
59 59 60 60 6025 25 25 26 26
27 27 28 28 28
29 29 30 30 31
31 32 32 33 33
34 34 34 35 359
15
20
26
318
13
18
23
28
21 25 29 32 36
23 27 30 34 38
25 28 32 36 40
26 30 34 38 41
28 32 36 39 4320 24 28 32 35
21 25 28 32 36
21 25 29 33 36
22 25 29 33 37
22 26 30 33 37
24 27 31 34 38
32 35 39 42 46
39 43 46 50 53
47 51 54 58 61
55 58 62 65 6922 26 29 33 36
25 28 32 35 39
28 31 35 38 42
31 34 37 41 44
33 37 40 44 475
9
14
18
234
5
7
8
9
7
11
16
20
255
6
7
9
10
8
13
17
22
266
7
8
10
11
10
14
19
23
286
8
9
10
12
12
16
20
25
307
9
10
11
1369
Diesel cost (USD GJ–1)13
27
41
55
69Electricity cost (USD GJ–1)
5
18
30
43
55

Electricity cost (USD GJ–1)
5
18
30
43
55Hydrogen cost (USD GJ–1)
8
33
58
83
108
Hydrogen cost (USD GJ–1)
8
33
58
83
108Electricity cost (USD MWh–1)
20
65
110
155
200
Electricity cost (USD MWh–1)
20
65
110
155
200Hydrogen cost (USD kg–1)
1
4
7
10
13
Hydrogen cost (USD kg–1)
1
4
7
10
13447 465 482 500 0.6 0.6
550 712 875 1037 1200 550 712 875 1037 1200 0.6 0.75 0.9 1.05 1.2
0.6 0.9 1.2 1.5 1.8 600 750 900 1050 1200 600 750 900 1050 1200Low efﬁciencyLifecycle ICEV costs (USD per thousand passenger-km)
Vehicle purchase price (thousand USD) Vehicle purchase price (thousand USD) Powertrain and Vehicle O&M (USD km–1)
Vehicle purchase price (thousand USD) Vehicle purchase price (thousand USD) Powertrain and Vehicle O&M (USD/km)
Vehicle purchase price (thousand USD) Vehicle purchase price (thousand USD) Powertrain and Vehicle O&M (USD km–1)Lifecycle ICEV costs (USD per thousand passenger-km)
High efﬁciency High efﬁciency

Low efﬁciency Low efﬁciencyLifecycle BEV costs (USD per thousand passenger-km) Lifecycle BEV costs (USD per thousand passenger-km)
Low efﬁciency Low efﬁciencyLifecycle FCV costs (USD per thousand passenger-km) Lifecycle FCV costs (USD per thousand passenger-km)
High efﬁciency High efﬁciency430 447 465 482 500
Figure 10.7 | Lifecycle costs for internal combustion engine vehicles, battery electric vehicles, and hydrogen fuel cell vehicles for buses and passenger 
rail. The range of efficiencies for each vehicle type are consistent with the range of efficiencies in Figure 10.6 (80% occupancy). The results for the ICEV can be used to evaluate 
the lifecycle costs of ICE buses and passenger rail operated with any form of diesel, whether from petroleum, synthetic hydrocarbons, or biofuel, as the range of efficiencies

of vehicles operating with all these fuels is similar. The secondary y-axis depicts the cost of the different energy carriers normalised in USD/GJ for easier cross-comparability.

1083
1083
Diesel
Ammonia, low carbon renewable
Ammonia, natural gas SMR
Diesel
Diesel
Diesel
DAC FT-Diesel, wind electricityDiesel

Hydrogen, natural gas SMRHydrogen, low-carbon renewable
Ammonia, natural gas SMRFCV BEV HEV ICEV FCV BEV ICEVHeavy-duty trucks
RailLifecycle GHG intensity, 50% payload (gCO2-eq per tonne-km)
0 100 200 300 500 400 600 700 800
FCV BEV HEV ICEVMedium-duty trucks
Lifecycle GHG intensity, 50% payload (gCO2-eq per tonne-km)
Life cycle GHG intensity, 100% payload (gCO2-eq per tonne-km)0 50 100 150 250 200 300 350 400
Lifecycle GHG intensity, 100% payload (gCO2-eq per tonne-km)0 50 100 150 250 200 300 350 400
Life cycle GHG intensity (gCO2-eq per tonne-km)0 50 100 150 250 200 300 350 400
Vehicle cycleVehicle cycle
Figure 10.8 | Lifecycle greenhouse gas intensity of land-based freight technologies and fuel types.

1084
1084
Chapter 10  T ransport10demand for goods could grow exponentially) could provide carbon 
abatement opportunities. While diesel-based locomotives are still 
a  major mode of propulsion used in freight rail, interest in low-
carbon propulsion technologies is growing. Electricity already powers 
freight rail in many European countries using overhead catenaries. 
Other low-carbon technologies for rail may include advanced storage 
technologies, biofuels, synthetic fuels, ammonia, or hydrogen.
Figure 10.8 presents a review of lifecycle GHG emissions from land-
based freight technologies (heavy- and medium-duty trucks, and 
rail). Each panel within the figure represents data in GHG emissions 
per  tonne-kilometre of freight transported by different technology 
and/or fuel types, as indicated by the labels to the left. The data in 
each panel came from a number of relevant scientific studies (Tong 
et al. 2015a; Frattini et al. 2016; Nahlik et al. 2016; Zhao et al. 2016;

CE Delft 2017; Isaac and Fulton 2017; Song et  al. 2017; Valente 
et  al. 2017; Cooper and Balcombe 2019; Lajevardi et  al. 2019; 
Hill et al. 2020; Liu et al. 2020a; Merchan et al. 2020; Prussi et al. 2020; 
Gray et al. 2021; Valente et al. 2021). Similar to the results for buses, 
technologies that offer substantial emissions reductions for freight 
include: ICEV trucks powered with the low-carbon variants for 
biofuels, ammonia or synthetic diesel; BEVs charged with low-carbon 
electricity; and FCVs powered with renewable-based electrolytic 
hydrogen, or ammonia. Since ammonia and Fischer-Tropsch diesel 
are produced from hydrogen, their emissions are higher than the 
source hydrogen, but their logistical advantages over hydrogen are 
also a consideration (Section 10.3).
Trucks exhibit economies of scale in fuel consumption, with 
heavy-duty trucks generally showing lower emissions per tkm than 
medium-duty trucks. Comparing the lifecycle GHG emissions from

trucks and rail, it is clear that rail using internal combustion engines 
is more carbon efficient than using internal combustion trucks. Note 
that the rail emissions are reported for an average representative 
payload, while the trucks are presented at 50% and 100% 
payload, based on available data. The comparison between trucks 
and rail powered with electricity or hydrogen is less clear  – 
especially considering that these values omit embodied GHG 
from infrastructure construction. One study reported embodied 
rail infrastructure emissions of 15 gCO 2 per tonne-kilometre for rail 
(International Union of Railways 2016), although such embodied 
emissions from rail are known to vary widely across case studies 
(Olugbenga et  al. 2019). Regardless, trucks and rail with low-
carbon electricity or low-carbon hydrogen have substantially lower 
emissions than incumbent technologies.For trucks, Figure 10.8 includes two x-axes representing two different

assumptions about their payload, which substantially influence 
emissions per tonne-kilometre. These results highlight the importance 
of truckload planning as an emissions reduction mechanism, for 
example, as also shown in Kaack et  al. (2018). Several studies 
also point to improvements in vehicle efficiency as an important 
mechanism to reduce emissions from freight transport (Taptich et al. 
2016; Kaack et  al. 2018). However, projections for diesel vehicles 
using such efficiencies beyond 2030 are promising, but still far higher 
emitting than vehicles powered with low-carbon sources.
Figure 10.9 shows the results of a parametric analysis of the LCC 
of trucks and freight rail technologies with the highest potential 
for deep GHG reductions. As with Figure 10.8, the vehicle efficiency 
ranges are the same as those from the LCA estimates (80% payload 
for trucks; effective payload as reported by original studies for

rail). Vehicle, fuel and maintenance costs represent ranges in the 
literature (Moultak et  al. 2017; Eudy and Post 2018b; IEA 2019e; 
Argonne National Laboratory 2020; BNEF 2020; IRENA 2020; 
Burnham et al. 2021; IEA 2021c), and the discount rate is 3% where 
applicable (Appendix 10.2). The panels for the ICEV can represent 
trucks and freight trains powered with any form of diesel, whether 
derived from petroleum, synthetic hydrocarbons, or biofuels. See 
discussion preceding Figure 10.7 for additional details about current 
global fuel costs. Under most parameter combinations, rail is the 
more cost-effective option, but the high efficiency case for trucks 
(representing fuel-efficient vehicles, favourable drive cycles and high 
payload) can be more cost-effective than the low efficiency case for 
rail (representing systems with higher fuel consumption and lower 
payload). For BEV trucks, cost ranges are driven by vehicle purchase

price due to the large batteries required and the associated wide 
range between their current high costs and anticipated future cost 
reductions. For all other truck and rail technologies, fuel cost ranges 
play a larger role. Similar to transit technologies, the current state 
of freight ICEV technologies is best represented by cheap vehicles 
and low fuel costs for diesel (top left of each panel), and the current 
status of alternative fuels is better represented by high capital costs 
and mid-to-high fuel costs (right side of each panel; mid-to-bottom 
rows), with expected future increases in ICEV LCC and decreases in 
alternative fuel vehicle LCC. Electric and hydrogen freight rail are 
potentially already competitive with diesel rail (especially electric 
catenary (IEA 2019e)), but low data availability (especially for 
hydrogen efficiency ranges) and wide ranges for reported diesel 
rail efficiency (likely encompassing low capacity utilisation) makes

this comparison challenging. Alternative fuel trucks are currently 
more expensive than diesel trucks, but future increases in diesel 
costs or a respective decrease in hydrogen costs or in BEV capital Figure 10.8 (continued): Lifecycle greenhouse gas intensity of land-based freight technologies and fuel types.  Each bar represents the range of the lifecycle 
estimates, bounded by minimum and maximum energy use per  tkm, as reported for each fuel/powertrain combination. The ranges are driven by differences in vehicle 
characteristics and operating efficiency. For energy sources with highly variable upstream emissions, low, medium and/or high representative values are shown as separate 
rows. For trucks, the primary x-axis shows lifecycle GHG emissions, in gCO 2-eq tkm–1, assuming 100% payload; the secondary x-axis assumes 50% payload. The values in the

figure rely on the 100-year GWP value embedded in the source data, which may differ slightly from the updated 100-year GWP values from WGI. For rail, values represent 
average payloads. For trucks, main bars show full lifecycle, with vertical bars disaggregating the vehicle cycle. ‘Diesel, high’ references emissions factors for diesel from oil sands. 
‘Advanced biofuels’ refers to the use of second-generation biofuels and their respective conversion and cultivation emission factors. ‘IAM EMF33’ refers to emissions factors for 
advanced biofuels derived from simulation results from the EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with constant land cover and ‘NRG’ is with natural 
regrowth. DAC FT-Diesel, wind electricity refers to Fischer-Tropsch diesel produced via a CO 2 direct air capture process that uses wind electricity. ‘Ammonia and Hydrogen, low-

carbon renewable’ refers to fuels produced via electrolysis using low-carbon electricity. ‘Ammonia and Hydrogen, natural gas SMR’ refers to fuels produced via steam methane 
reforming of natural gas.

1085
1085
costs (especially the battery) would enable either alternative fuel 
technology to become financially attractive. These results are largely 
consistent with raw results reported in existing literature, which 
suggest ambiguity over whether BEV trucks are already competitive, 
but more consistency that hydrogen is not yet competitive, but could 
be in future (Zhao et al. 2016; Moultak et al. 2017; Sen et al. 2017; 
White and Sintov 2017; Zhou et al. 2017; Mareev et al. 2018; Yang 
et  al. 2018a; El Hannach et  al. 2019; Lajevardi et  al. 2019; Tanco 
et al. 2019; Burke and Sinha 2020; Jones et al. 2020). There is limited 
data available on the LCC for freight rail, but at least one study 
IEA (2019g) suggests that electric catenary rail is likely to have 
similar costs to diesel rail, while battery electric trains remain more 
expensive and hydrogen rail could become cheaper under forward-
looking cost reduction scenarios.10.4.4 Abatement Costs

Taken together, the results in this section suggest a range of cost-
effective opportunities to reduce GHG emissions from land-based 
transport. Mode shift from cars to passenger transit (bus or rail) 
can reduce GHG emissions while also reducing LCCs, resulting in 
a  negative abatement cost. Likewise, increasing the utilisation of 
vehicles (i.e.,  % occupancy for passenger vehicles or % payload 
for freight vehicles) simultaneously decreases emissions and costs 
per pkm or per tkm, respectively. Within a given mode, alternative fuel 
sources also show strong potential to reduce emissions at minimal 
added costs. For LDVs, BEVs can offer emissions reductions with LCCs 
that are already approaching that for conventional ICEVs. For transit 
and freight, near-term abatement costs for the low-carbon BEV and 
FCV options relative to their diesel counterparts range from near

USD0/tonne CO 2-eq (e.g., BEV buses and BEV passenger rail) into the Lifecycle costs of buses Lifecycle costs of passenger railDiesel cost (USD L–1)0.5 26 28 30 32 34
39 41 43 45 47
52 54 56 58 60
65 67 69 71 73
78 80 82 84 8618 20 22 24
24 26 28 30
29 31 33 35
34 36 38 40
40 42 43 4526
32
37
42
4711
22
33
44
552
3
5
7
8
13 21 29 37 45
17 25 33 41 49
21 29 37 45 53
25 33 41 49 58
29 37 45 53 6212 20 28 36 44
13 21 29 37 46
14 22 31 39 47
16 24 32 40 48
17 25 33 41 505 6 8 10 12
10 12 14 15 17
15 17 19 21 22
21 23 24 26 28
26 28 30 32 332 4 6 8 9
3 5 6 8 10
3 5 7 9 10
4 5 7 9 11
4 6 8 10 11
15 17 19 21 23
26 28 30 32 34
37 39 41 43 45
47 49 51 53 55
58 60 62 64 6614 16 18 20 22
20 22 24 26 28
32 34 36 38 40
39 41 43 45 472 2 3 4 4
4 5 5 6 7
7 7 8 9 9
9 10 10 11 12
12 12 13 14 141.0
1.5
2.0
2.5
1.0
1.5
2.0
27
41
55
69
27
41
55
69
90 130 170 210 250

Vehicle purchase price (thousand USD) Powertrain and Vehicle O&M (USD km–1)90 130 170 210 250 1 1
Vehicle purchase price (thousand USD)Lifecycle ICEV costs (USD per thousand tonne-km) Lifecycle ICEV costs (USD per thousand tonne-km)
Low efﬁciency Low efﬁciency High efﬁciency High efﬁciencyHydrogen cost (USD GJ–1)
8
33
58
83
108
8
33
58
83
108Hydrogen cost (USD kg–1)
1
4
7
10
13
1
4
7
10
13
130 170 210 250 290
Vehicle purchase price (thousand USD)130 170 210 250 290
Vehicle purchase price (thousand USD)2 4 6 8 10
Powertrain and Vehicle O&M (USD km–1)Lifecycle FCV costs (USD per thousand tonne-km) Lifecycle FCV costs (USD per thousand tonne-km)
5
18
30
43
55
5
18
30
43
55Electricity cost (USD MWh–1)
20
65
110
155
200
Electricity cost (USD/MWh)20
65
110
155
200
120 285 450 615 780
Vehicle purchase price (thousand USD)120 285 450 615 780

Vehicle purchase price (thousand USD)6 11 16 21 26
Powertrain and Vehicle O&M (USD km–1)6 11 16 21 26
Powertrain and Vehicle O&M (USD km–1)Lifecycle BEV costs (USD per thousand tonne-km) Lifecycle BEV costs (USD per thousand tonne-km)
Low efﬁciency Low efﬁciency High efﬁciency High efﬁciency
Figure 10.9 | Life cycle costs for internal combustion engine vehicles, battery electric vehicles, and hydrogen fuel cell vehicles for heavy-duty trucks and 
freight rail.  The range of efficiencies for each vehicle type are consistent with the range of efficiencies in Figure 10.8. The results for ICEV can be used to evaluate the lifecycle 
costs of ICE trucks and freight rail operated with any form of diesel, whether from petroleum, synthetic hydrocarbons, or biofuels, as the range of efficiencies of vehicles operating 
with all these fuels is similar. The secondary y-axis depicts the cost of the different energy carriers normalised in USD per GJ for easier cross-comparability.

1086
1086
Chapter 10  T ransport10hundreds or even low thousands of dollars per tonne CO 2-eq (e.g., for 
heavy-duty BEV and FCV trucks at current vehicle and fuel costs). 
With projected future declines in storage, fuel cell, and low-carbon 
hydrogen fuel costs, however, both BEV and FCV technologies can 
likewise offer GHG reductions at negative abatement costs across all 
land-transport modes in 2030 and beyond. Further information about 
costs and potentials is available in Chapter 12.
10.5 Decarbonisation of Aviation
This section addresses the potential for reducing GHG emissions from 
aviation. The overriding constraint on developments in technology 
and energy efficiency for this sector is safety. Governance is 
complex in that international aviation comes under the International 
Civil Aviation Organization (ICAO), a  specialised UN agency. The 
measures to reduce GHG emissions that are considered include both

in-sector (technology, operations, fuels) and out of sector (market-
based measures, high-speed rail modal shift/substitution). Demand 
management is not explicitly considered in this section, as it was 
discussed in 10.2. A limited range of scenarios to 2050 and beyond 
are available and assessed at the end of the section.
10.5.1 Historical and Current Emissions from Aviation
Aviation is widely recognised as a ‘hard-to-decarbonise’ sector (Gota 
et al. 2019) having a strong dependency on liquid fossil fuels and an 
infrastructure that has long ‘lock-in’ timescales, resulting in slow fleet 
turnover times. The principal GHG emitted is CO 2 from the combustion of fossil fuel aviation kerosene (‘Jet-A’), although its non-CO 2 
emissions can also affect climate (Section  10.5.2). International 
emissions of CO 2 are about 65% of the total emissions from aviation 
(Fleming and de Lépinay 2019), which totalled approximately 1 Gt

of CO 2 in 2018. Emissions from this segment of the transport sector 
have been steadily increasing at rates of around 2.5% per year over 
the last two decades (Figure 10.10), although for the period 2010 to 
2018 the rate increased to roughly 4% per year. The latest available 
data (2018) indicate that aviation is responsible for approximately 
2.4% of total anthropogenic emissions of CO 2 (including land-use 
change) on an annual basis (using IEA data, IATA data and global 
emissions data of Le Quéré et al. (2018b)).
10.5.2 Short-lived Climate Forcers and Aviation
Aviation’s net warming effect results from its historical and current 
emissions of CO 2, and non-CO 2 emissions of water vapour, soot, 
sulphur dioxide (from sulphur in the fuel), and nitrogen oxides 
(NO x, = NO + NO 2) (IPCC 1999; Lee et al. 2021; Szopa et al. 2021). 
Although the effective radiative forcing (ERF) of CO 2 from historic 
aviation emissions is not currently the largest forcing term, it is difficult

to address because of the sector’s current dependency on fossil-based 
hydrocarbon fuels and the longevity of CO 2. A residual of emissions of 
CO2 today will still have a warming effect in many thousands of years 
(Archer et al. 2009; Canadell et al. 2021) whereas water vapour, soot, 
and NO x emissions will have long ceased to contribute to warming 
after some decades. As a result, CO 2 mitigation of aviation to net zero 
levels, as required in 1.5°C scenarios, requires fundamental shifts in 
technology, fuel types, or changes of behaviour or demand.
Figure 10.10 | Historical global emissions of CO 2 from aviation, along with capacity and transport work (given in available seat kilometres, ASK; revenue 
passenger-kilometres, RPK).  Source: adapted from Lee et al. (2021) using IEA and other data.

1087
1087
The non-CO 2 effects of aviation on climate fall into the category of 
short-lived climate forcers (SLCFs). Emissions of NO x currently result 
in net positive warming from the formation of short-term ozone 
(warming) and the destruction of ambient methane (cooling). If the 
conditions are suitable, emissions of soot and water vapour can 
trigger the formation of contrails (Kärcher 2018), which can spread 
to form extensive contrail-cirrus cloud coverage. Such cloud coverage 
is estimated to have a combined ERF that is about 57% of the current 
net ERF of global aviation (Lee et al. 2021), although a comparison 
of cirrus cloud observations under pre- and post-COVID-19 pandemic 
conditions suggest that this forcing could be smaller (Digby et  al. 
2021). Additional effects from aviation from aerosol-cloud interactions 
on high-level ice clouds through soot (Chen and Gettelman 2013; 
Zhou and Penner 2014; Penner et al. 2018), and lower-level warm

clouds through sulphur (Righi et al. 2013; Kapadia et al. 2016) are 
highly uncertain, with no best estimates available (Lee et al. 2021). 
In total, the net ERF from aviation’s non-CO 2 SLCFs is estimated to be 
approximately 66% of aviation’s current total forcing. It is important 
to note that the fraction of non-CO 2 forcing to total forcing is not 
a fixed quantity and is dependent on the recent history of growth (or 
otherwise) of CO 2 emissions (Klöwer et al. 2021). The non-CO 2 effects 
from aviation are the subject of discussion for mitigation options 
(Arrowsmith et al. 2020). However, the issues are complex, potentially 
involving technological and operational trade-offs with CO 2.
10.5.3 Mitigation Potential of Fuels, Operations, Energy 
Efficiency, and Market-based Measures
Technology options for engine and airframe. For every kilogram 
of jet fuel combusted, 3.16 kg CO 2 is emitted. Engine and airframe 
manufacturers’ primary objective, after safety issues, is to reduce

direct operating costs, which are highly dependent on fuel burn. 
Large investments have gone into engine technology and aircraft 
aerodynamics to improve fuel burn per  kilometre (Cumpsty et  al. 
2019). There have been major step changes in engine technology 
over time, from early turbojet engines to larger turbofan engines. 
However, the basic configuration of an aircraft has remained more 
or less the same for decades and will likely remain at least to 2037 
(Cumpsty et  al. 2019). Airframes performance has improved over 
the years with better wing design, but large incremental gains have 
become much harder as the technology has matured. For twin-
aisle aircraft, generally used for long ranges, fuel-burn is a pressing 
concern and there have been several all-new aircraft designs with 
improvements in their lift-to-drag ratio (Cumpsty et  al. 2019). The 
principal opportunities for fuel reduction come from improvements

in aerodynamic efficiency, aircraft mass reduction, and propulsion 
system improvements. In the future, Cumpsty et al. (2019) suggest 
that the highest rate of fuel burn reduction achievable for new aircraft 
is likely to be no more than about 1.3% per year, which is well short of 
ICAO’s aspirational goal of 2% global annual average fuel efficiency 
improvement. Radically different aircraft shapes, like the blended 
wing body (where the wings are not distinct from the fuselage), are 
likely to use about 10% less fuel than future advanced aircraft of 
conventional form (Cumpsty et al. 2019). Such improvements would 
be ‘one-off’ gains, do not compensate for growth in emissions of CO 2 
expected to be in excess of 2% per annum, and would take a decade or more to penetrate the fleet completely. Thus, the literature does 
not support the idea that there are large improvements to be made 
in the energy efficiency of aviation that keep pace with the projected 
growth in air transport.

Operational improvements for navigation. From a  global 
perspective, aircraft navigation is relatively efficient, with many long-
haul routes travelling close to great circle trajectories, and avoiding 
headwinds that increase fuel consumption. The ICAO estimates that 
flight inefficiencies on a global basis are currently of the order 2% 
to 6% (ICAO 2019), while Fleming and de Lépinay (2019) project 
operational improvements (air traffic management) of up to 13% on 
a regional basis by 2050. ‘Intermediate stop operations’ have been 
suggested, whereby longer-distance travel is broken into flight legs, 
obviating the need to carry fuel for the whole mission. Linke et al. 
(2017) modelled this operational behaviour on a  global basis and 
calculated a fuel saving of 4.8% over a base case in which normal 
fuel loads were carried. However, this approach increases the number 
of landing/take-off cycles at airports. ‘Formation flying’, which has

the potential to reduce fuel burn on feasible routes, has also been 
proposed (Xu et al. 2014; Marks et al. 2021).
Alternative biofuels, synthetic fuels, and liquid hydrogen. As 
noted above, the scope for reducing CO 2 emissions from aviation 
through improved airplane technology or operations is limited and 
unable to keep up with the projected growth, let al.ne reduce beyond 
the present emission rate at projected levels of demand (assuming 
post-pandemic recovery of traffic). Thus, the literature outlined here 
suggests that the only way for demand for aviation to continue to 
grow without increasing CO 2 emissions is to employ alternative 
lower-carbon bio- or synthetic aviation fuels (Klöwer et al. 2021). For 
shorter ranges, flights of light planes carrying up to 50 passengers 
may be able to use electric power (Sahoo et  al. 2020) but these 
planes are a  small proportion of the global aviation fleet (Epstein 
and O’Flarity 2019; Langford and Hall 2020) and account for less

than 12% of current aviation CO 2 emissions. Alternative lower-
carbon footprint fuels have been certified for use over recent years, 
principally from bio-feedstocks, but are not yet widely available at 
economic prices (Kandaramath Hari et al. 2015; Capaz et al. 2021a). 
In addition, alternative fuels from bio-feedstocks have variable 
carbon footprints because of different lifecycle emissions associated 
with various production methods and associated land-use change 
(de Jong et al. 2017; Staples et al. 2018; Capaz et al. 2021b; Zhao 
The development of ‘sustainable aviation fuels’ (referred to as 
‘SAFs’) that can reduce aviation’s carbon footprint is a  growing 
area of interest and research. Alternative aviation fuels to replace 
fossil-based kerosene have to be certified to an equivalent standard 
as Jet-A for a  variety of parameters associated with safety issues. 
Currently, the organisation responsible for aviation fuel standards,

ASTM International, has certified seven different types of sustainable 
aviation fuels with maximum blends ranging from 10% to 50% 
(Chiaramonti 2019). Effectively, these blend requirements limit the 
amount of non-hydrocarbon fuel (e.g., methanol) that can be added 
at present. While there currently is a  minimum level of aromatic 
hydrocarbon contained in jet fuel to prevent ‘O-ring’ shrinkage in

1088
1088
Chapter 10  T ransport10the fuel seals (Khandelwal et al. 2018), this minimum level can likely 
be lower in the medium to long term, with the added benefits of 
reduced soot formation and reduced contrail cirrus formation (Bier 
et al. 2017; Bier and Burkhardt 2019).
Bio-based fuels can be produced using a  variety of feedstocks 
including cultivated feedstock crops, crop residues, municipal solid 
waste, waste fats, oils and greases, wood products and forestry 
residues (Staples et  al. 2018). Each of these different sources can 
have different associated lifecycle emissions, such that they are 
not net zero CO 2 emissions but have associated emissions of CO 2 
or other GHGs from their production and distribution (Section 10.3, 
Box 10.2). In addition, associated land-use change emissions of CO 2 
represent a  constraint in climate change mitigation potential with 
biofuel (Staples et  al. 2017) and have inherent large uncertainties

(Plevin et al. 2010). Other sustainability issues include food vs fuel 
arguments, water resource use, and impacts on biodiversity. Cost-
effective production, feedstock availability, and certification costs are 
also relevant (Kandaramath Hari et al. 2015). Nonetheless, bio-based 
SAFs have been estimated to achieve lifecycle emissions reductions 
ranging between approximately 2% and 70% under a wide range 
of scenarios (Staples et  al. 2018). For a  set of European aviation 
demand scenarios, Kousoulidou and Lonza (2016) estimated that 
the fuel demand in 2030 would be about 100 million tonnes of oil 
equivalent and biokerosene (HEFA/HVO) penetration would provide 
around 2% of the total fuel demand at that date. Several issues limit 
the expansion of biokerosene for aviation, the primary one being the 
current cost of fossil fuel compared to the costs of SAF production 
(Capaz et  al. 2021a). Other hybrid pathways, for example the

hydrogenation of biofuels (the hydrogen assumed to be generated 
with low-carbon energy), could increase the output and improve 
the economic feasibility of bio-based SAF (Hannula 2016; Albrecht 
et al. 2017).
Costs remain a major barrier for bio-SAF , which cost around three 
times the price of kerosene (Kandaramath Hari et al. 2015). Clearly, 
for SAFs to be economically competitive, large adjustments in prices 
of fossil fuels or the introduction of policies is required. Staples 
et  al. (2018) estimated that in order to introduce bio-SAFs that 
reduce lifecycle GHG emissions by at least 50% by 2050, prices 
and policies were necessary for incentivisation. They estimate the 
need for 268  new biorefineries per  year and capital investments 
of approximately 22 to 88 billion USD2015 per year between 2020 
and 2050. Wise et al. (2017) suggest that carbon prices would help 
leverage production and availability.
Various pathways have been discussed for the production of

non-bio SAFs such as power-to-liquid pathways (Schmidt et  al. 
2018), sometimes termed ‘electro-fuels’ (Goldmann et  al. 2018), 
or more generalised ‘Power-to-X’ pathways (Kober and Bauer 
2019). This process would involve the use of low-carbon electricity, 
CO2, and water to synthesise jet fuel through the Fischer-Tropsch 
process or methanol synthesis. Hydrogen would be produced via 
an electrochemical process, powered by low-carbon energy and 
combined with CO 2 captured directly from the atmosphere or 
through BECCS. The energy requirement from photovoltaics has 
been estimated to be of the order 14 to 20 EJ to phase out aviation fossil fuel by 2050 (Gössling et al. 2021a). These synthetic fuels have 
potential for large lifecycle emissions reductions (Schmidt et  al. 
2016). In comparison to bio-SAF production, the implementation of 
the processes is in its infancy. However, assuming availability of low-
carbon energy electricity, these fuels have much smaller land and

water requirements than bio-SAF . Low carbon-energy supply, scalable 
technology, and therefore costs, represent barriers. Scheelhaase et al. 
(2019) review current estimates of costs, which are estimated to be 
approximately four to six times the price of fossil kerosene.
Liquid hydrogen (LH 2) as a fuel has been discussed for aeronautical 
applications since the 1950s (Brewer 1991) and a few experimental 
aircraft have flown using such a  fuel. Experimental, small aircraft 
have also flown using hydrogen fuel cells. Although the fuel has an 
energy density per unit mass about three times greater than kerosene, 
it has a much lower energy density per unit volume (approximately 
factor 4 (McKinsey 2020)). The increased volume requirement makes 
the fuel less attractive for aviation since it would require the wings 
to be thickened or fuel to take up space in the fuselage. Bicer and 
Dincer (2017) found that LH 2-powered aircraft compared favourably

to conventional kerosene-powered aircraft on a  lifecycle basis, 
providing that the LH 2 was generated from low-carbon energy sources 
(0.014  kgCO 2 per  tkm compared with 1.03 kgCO 2 per  tkm for an 
unspecified passenger aircraft). However, Ramos Pereira et al. (2014) 
also made a lifecycle comparison and found much smaller benefits 
of LH 2-powered aircraft (manufactured from low-carbon energy) 
compared with conventional fossil kerosene. The two studies expose 
the sensitivities of boundaries and assumptions in the analyses. 
Shreyas Harsha (2014) and Rondinelli et  al. (2017) conclude that 
there are many infrastructural barriers but that the environmental 
benefits of low-carbon-based LH 2 could be considerable. Khandelwal 
et  al. (2013) take a  more optimistic view of the prospect of 
LH2-powered aircraft but envisage them within a hydrogen-oriented 
energy economy. A recently commissioned study by the European 
Union (EU)’s Clean Sky undertaking, (McKinsey 2020) addresses

many of the aspects of the opportunities and obstacles in developing 
LH2-powered aircraft. The report provides an optimistic view of the 
feasibility of developing such aircraft for short to medium haul but 
makes clear that new aircraft designs (such as blended-wing body 
aircraft) would be needed for longer distances.
The non-CO 2 impacts of LH 2-powered aircrafts remain poorly 
understood. The emission index of water vapour would be much larger 
(estimated to be 2.6 times greater by Ström and Gierens (2002)) than 
for conventional fuels), and the occurrence of contrails may increase 
but have lower ERF because of the lower optical depth (Marquart 
et al. 2005). Moreover, contrails primarily form on soot particles from 
kerosene-powered aircraft, which would be absent from LH 2 exhaust 
(Kärcher 2018). The overall effect is currently unknown as there are 
no measurements. Potentially, NO x emissions could be lower with 
combustor redesign (Khandelwal et al. 2013).

In conclusion, there are favourable arguments for LH 2-powered 
aircraft, both on an efficiency basis (Verstraete 2013) and an overall 
reduction in GHG emissions, even on an lifecycle basis. However, LH 2 
requires redesign of the aircraft, particularly for long-haul operations. 
Similarly, there would be a need for expanded infrastructure for fuel

1089
1089
manufacture, storage, and distribution at airports, which is likely to 
be more easily overcome if there is a more general move towards 
a hydrogen-based energy economy.
Technological and operational trade-offs between CO 2 and 
non-CO 2 effects. Since aviation has additional non-CO 2 warming 
effects, there has been some discussion as to whether these can be 
addressed by either technological or operational means. For example, 
improved fuel efficiency has resulted from high overall pressure ratio 
engines with large bypass ratios. This improvement has increased 
pressure and temperature at the combustor inlet, with a  resultant 
tendency to increase thermal NO x formation in the combustor. 
Combustor technology aims to reduce this increase, but it represents 
a potential technology trade-off whereby NO x control may be at the 
expense of extra fuel efficiency. Estimating the benefits or disbenefits

of CO 2 (proportional to fuel burned) vs NO x in terms of climate is 
complex (Freeman et al. 2018).
Any global warming potential/temperature change potential type 
emissions equivalency calculation always involves the user selection 
of a  time horizon over which the calculation is made, which is 
a subjective choice (Fuglestvedt et al. 2010). In general, the longer the 
time horizon, the more important CO 2 becomes in comparison with 
a short-lived climate forcing agent. So, for example, a net (overall) 
aviation GWP for a  20-year time horizon is 4.0 times that of CO 2 
alone, but only 1.7 over a 100-year time horizon. Correspondingly, 
a GTP for a 20-year time horizon is 1.3, but it is 1.1 for 100 years 
(Lee et al. 2021).
A widely discussed opportunity for mitigation of non-CO 2 emissions 
from aviation is the avoidance of persistent contrails that can form 
contrail cirrus. Contrails only form in ice-supersaturated air below

a  critical temperature threshold (Kärcher 2018). It is therefore 
feasible to alter flight trajectories to avoid such areas conducive 
to contrail formation, since ice-supersaturated areas tend to be 
tens to  hundreds of kilometres in the horizontal and only a  few 
100 metres in the vertical extent (Gierens et al. 1997). Theoretical 
approaches show that avoidance is possible on a  flight-by-flight 
basis (Matthes et  al. 2017; Teoh et  al. 2020). Case studies have 
shown that flight planning according to trajectories with minimal 
climate impact can substantially (up to 50%) reduce the aircraft’s 
net climate impacts despite small additional CO 2 emissions (Niklaß 
et al. 2019). However, any estimate of the net benefit or disbenefit 
depends firstly on the assumed magnitude of the contrail cirrus 
ERF effect (itself rather uncertain, assessed with a  low confidence 
level) and upon the choice of metric and time horizon applied. While

this is a potentially feasible mitigation option, notwithstanding the 
CO2 per  contrail trade-off question, meteorological models cannot 
currently predict the formation of persistent contrails with sufficient 
accuracy in time and space (Gierens et  al. 2020); this mitigation 
option is speculated to take of the order of up to a decade to mature 
(Arrowsmith et al. 2020).
Market-based offsetting measures. The EU introduced aviation 
into its CO 2 emissions trading scheme (ETS) in 2012. Currently, the 
EU-ETS for aviation includes all flights within the EU as well as to and 
from Eastern European and West-Central Asian states. Globally, ICAO agreed in 2016 to commence, in 2020, the ‘Carbon Offsetting and 
Reduction Scheme for International Aviation’ (CORSIA). The pandemic 
subsequently resulted in the baseline being changed to 2019.
CORSIA has a  phased implementation, with an initial pilot phase 
(2021–2023) and a  first phase (2024–2026) in which states

will participate voluntarily. The second phase will then start in 
2026–2035, and all states will participate unless exempted. States 
may be exempted if they have lower aviation activity levels or based 
on their UN development status. As of September 2021, 109 ICAO 
Member States will voluntarily be participating in CORSIA starting 
in 2022. In terms of routes, only those where both States connecting 
the route are participating are included. There will be a  special 
review of CORSIA by the end of 2032 to determine the termination 
of the scheme, its extension, or any other changes to the scheme 
beyond 2035.
By its nature, CORSIA does not lead to a  reduction in in-sector 
emissions from aviation since the programme deals mostly in 
approved offsets. At its best, CORSIA is a transition arrangement to 
allow aviation to reduce its impact in a more meaningful way later. 
From 2021 onwards, operators can reduce their CORSIA offsetting

requirements by claiming emissions reductions from ‘CORSIA 
Eligible Fuels’ that have demonstrably reduced lifecycle emissions. 
These fuels are currently available at greater costs than the offsets 
(Capaz et al. 2021a). As a result, most currently approved CORSIA 
offsets are avoided emissions, which raises the issue of additionality 
(Warnecke et al. 2019). The nature of avoided emissions is to prevent 
an emission that was otherwise considered to be going to occur, 
for example, prevented deforestation. Avoided emissions are 
‘reductions’ (over a counterfactual) and purchased from other sectors 
that withhold from an intended emission (Becken and Mackey 2017), 
such that if additionality were established, a maximum of 50% of 
the intended emissions are avoided. Some researchers suggest that 
avoided deforestation offsets are not a meaningful reduction, since 
deforestation continues to be a net source of CO 2 emissions (Mackey 
et al. 2013; Friedlingstein et al. 2020).

Modal shift to high-speed rail. Due to the limitations of the current 
suite of aviation mitigation strategies, the potential for high-speed 
rail (HSR) is of increasing interest (Givoni and Banister 2006; Chen 
2017; Bi et al. 2019). The IEA’s Net Zero by 2050 roadmap suggests 
significant behavioural change, with more regional flights shifting to 
HSR in the Net Zero Emissions by 2050 scenario pathway (IEA 2021e). 
For HSR services to be highly competitive with air travel, the optimal 
distance between the departure and arrival points has been found 
to be in the approximate range of 400 to 800 km (Bows et al. 2008; 
Rothengatter 2010), although in the case of China’s HSR operations, 
this range can be extended out to 1000 km, with corresponding air 
services having experienced significant demand reduction upon HSR 
service commencement (Lawrence et  al. 2019). In some instances, 
negative effects on air traffic, air fare, and flight frequency have

occurred at medium-haul distances such as HSR services in China on 
the Wuhan–Guangzhou route (1069 km) and the Beijing–Shanghai 
route (1318 km) (Fu et al. 2015; Zhang and Zhang 2016; Chen 2017; 
Li et  al. 2019; Ma et  al. 2019). This competition at medium-haul 
distances is contrary to that which has been experienced in European

1090
1090
Chapter 10  T ransport10and other markets and may be attributable to China having developed 
a comprehensive network with hub stations, higher average speeds, 
and an integrated domestic market with strong patronage (Zhang 
et al. 2019a).
The LCA literature suggests that the GHG emissions associated with 
HSR vary depending on spatial, temporal, and operational specifics 
(Åkerman 2011; Baron et al. 2011; Chester and Horvath 2012; Yue 
et  al. 2015; Hoyos et  al. 2016; Jones et  al. 2017; Robertson 2016; 
Robertson 2018; Lin et al. 2019). These studies found a wide range of 
approximately 10 to 110 gCO 2 pkm–1 for HSR. This range is principally 
attributable to the sensitivity of operational parameters such as the 
HSR passenger seating capacity, load factor, composition of renewable 
and non-renewable energy sources in electricity production, rolling 
stock energy efficiency and patronage (i.e.,  ridership both actual

and forecast), and line-haul infrastructure specifics (e.g., tunnelling 
and aerial structure requirements for a particular corridor) (Åkerman 
2011; Chester and Horvath 2012; Yue et  al. 2015; Newman et  al. 
2018; Robertson 2018). The prospect for HSR services providing 
freight carriage (especially online purchases) is also growing rapidly 
(Strale 2016; Bi et al. 2019; Liang and Tan 2019) with a demonstrated 
emissions reduction potential from such operations (Hoffrichter 
et al. 2012). However, additional supportive policies will most likely 
be required (Strale 2016; Watson et  al. 2019). Limiting emissions 
avoidance assessments for HSR modal substitution to account only 
for CO 2 emissions ignores aviation’s non-CO 2 effects (Section 10.5.2), 
and likely results in an under-representation of the climate benefits 
of HSR replacing flights.
HSR modal substitution can generate a contra-effect if the air traffic

departure and arrival slots that become available as the result of the 
modal shift are simply reallocated to additional air services (Givoni and 
Banister 2006; Givoni and Dobruszkes 2013; Jiang and Zhang 2016; 
Cornet et al. 2018; Zhang et al. 2019a). Furthermore, HSR services have 
the potential to increase air traffic at a hub airport through improved 
networks but this effect can vary based on the distance of the HSR 
stations from airports (Jiang and Zhang 2014; Xia and Zhang 2016; 
Zhang et  al. 2019b; Liu et  al. 2019). Such rebound effects could be 
managed through policy interventions. For example, in 2021 the French 
government regulated that all airlines operating in France suspend 
domestic airline flights on routes if a direct rail alternative with a travel 
time of less than 2.5 hours is available. Other air travel demand 
reduction measures that have been proposed include regulations to 
ban frequent flyer reward schemes, mandates that all marketing of air

travel declare flight emissions information to the prospective consumer 
(i.e., the carbon footprint of the nominated flight), the introduction of 
a progressive ‘Air Miles Levy’ as well as the inclusion of all taxes and 
duties that are presently exempt from air ticketing (Carmichael 2019). 
Moreover, China has the highest use of HSR in the world in part due to 
its network and competitive speeds and in part due to heavy regulation 
of the airline industry, in particular restrictions imposed on low-cost air 
carrier entry and subsidisation of HSR (Li et al. 2019). These air travel 
demand reduction strategies may induce shifts to other alternative 
modes in addition to stimulating HSR ridership.
Despite the risk of a rebound effect, and due to the probable reality 
of an incremental adoption of sustainable aviation fuel technology in the coming decades, the commencement of appropriate HSR services 
has the potential to provide, particularly in the short- to medium-

term, additional means of aviation emissions mitigation.
10.5.4 Assessment of Aviation-specific Projections 
and Scenarios
The most recent projection from ICAO (prior to the COVID-19 
pandemic) for international traffic (mid-range growth) is shown in 
Figure 10.11. This projection shows the different contributions of 
mitigation measures from two levels of improved technology, as 
well as improvements in air traffic management and infrastructure 
use. The projections indicate an increase in CO 2 emissions by 
a  factor of 2.2 in 2050 over 2020 levels for the most optimistic 
set of mitigation assumptions. The high/low traffic growth 
assumptions would indicate increases by factors of 2.8 and 1.1, 
respectively in 2050, over 2020 levels (again, for the most optimistic 
mitigation assumptions).
The International Energy Agency has published several long-term 
aviation scenarios since AR5 within a  broader scope of energy

projections. Their first set of aviation scenarios include a ‘reference 
technology scenario’, a ‘2°C Scenario’ and a ‘Beyond 2°C Scenario’. 
The scenarios are simplified in assuming a range of growth rates and 
technological/operational improvements (IEA 2017b). Mitigation 
measures brought about by policy and regulation are treated in 
a broad-brush manner, noting possible uses of taxes, carbon pricing, 
price and regulatory signals to promote innovation.
The IEA has more recently presented aviation scenarios to 2070 in 
their ‘Sustainable Development Scenario’ that assume some limited 
reduction in demand post-COVID-19, and potential technology 
improvements in addition to direct reductions in fossil kerosene 
usage from substitution of biofuels and synthetic fuels (IEA 2021b). 
There is much uncertainty in how aviation will recover from the 
COVID-19 pandemic but, in this scenario, air travel returns to 2019 
levels in three years, and then continues to expand, driven by income.

Government policies could dampen demand (12% lower by 2040 
than the IEA ‘Stated Policies Scenario’, which envisages growth at 
3.4% per year, which in turn is lower than ICAO at 4.3%). Mitigation 
takes place largely by fuel substitution with lower-carbon biofuels 
and synthetic fuels, with a  smaller contribution from technology. 
Approximately 85% of the actual cumulative CO 2 emissions (to 2070) 
are attributed to use of fuel at their lowest technology readiness level 
of ‘Prototype’, which is largely made up of biofuels and synthetic 
fuels, as shown in Figure 10.12. Details of the technological scenarios 
and the fuel availability/uptake assumptions are given in IEA (2021b), 
which also makes clear that the relevant policies are not currently in 
place to make any such scenario happen.
Within the Coupled Model Intercomparison Project Phase 6 
emissions database, a  range of aviation emissions scenarios for 
a  range of Shared Socio-economic Pathway (SSP) scenarios are

available (Figure  10.13). This Figure suggests that by 2050, direct 
emissions from aviation could be 1.5 to 6.5 (5–95th percentile) 
times higher than in the 2020 model year under the scenarios that

1091
1091
Additional contribution of technology improvements
Range of 2020 CO2Additional contribution of improved air trafﬁc management 
and infrastructure use
2,000CO2 emissions from international aviation (MtCO2 yr–1)1,750
1,500
1,250
1,000
750
500
250
2005 2010 2015 2020
Year2025 2030 2035 2040 2045 20500
Extrapolation
beyond 2045Extrapolation
beyond 2045CAEP/11 baseline including ﬂeet renewal
Low aircraft technology scenario
Figure 10.11 | Projections of international aviation emissions of CO 2. Data in Mt yr –1, to 2050, showing contributions of improved technology and 
air traffic management and infrastructure use to emissions reductions to 2050.  Data from Fleming and de Lépinay (2019); projections made pre-COVID-19 
global pandemic.
Figure 10.12 | The International Energy Agency’s scenario of future aviation fuel consumption for the States Policies Scenario (‘STEPS’) and composition

of aviation fuel use in the Sustainable Development Scenario.  Source: adapted from IEA (2021b).

1092
1092
exceed warming of 4°C during the 21st century with a likelihood of 
50% or greater (C8). In the C1 (which limit warming to 1.5°C (>50%) 
during the 2st century with no or limited overshoot) and C2 (which 
return warming to 1.5°C (>50%) during the 2st century after a high 
overshoot) scenarios, aviation emissions could still be up to 2.5 times 
higher in 2050 than in the 2020 model year (95th percentile) but may 
need to decrease by 10% by 2050 (5th percentile).
The COVID-19 pandemic of 2020 has changed many activities and, 
consequentially, associated emissions quite dramatically (Le Quéré 
et al. 2018b; Friedlingstein et al. 2020; Liu et al. 2020c; UNEP 2020). 
Aviation was particularly affected, with a  reduction in commercial 
flights in April 2020 of about 74% over 2019 levels, with some 
recovery over the following months, remaining at 42% lower as of 
October 2020 (Petchenik 2021). The industry is considering a range

of potential recovery scenarios, with the International Air Transport 
Association (IATA) speculating that recovery to 2019 levels may take 
up until 2024 (Earley and Newman 2021) (Cross-Chapter Box 1 in 
Chapter 1). Others suggest, however, that the COVID-19 pandemic 
and increased costs as a  result of feed-in quotas or carbon taxes 
could slow down the rate of growth of air travel demand, though 
global demand in 2050 would still grow 57%–187% between 2018 
and 2050 (instead of 250% in a baseline recovery scenario) (Gössling 
et al. 2021a).10.5.5 Accountability and Governance Options
Under Article 2.2 of the Kyoto Protocol, Annex I countries were 
called to ‘…pursue limitation or reduction of emissions of GHGs not 
controlled by the Montreal Protocol from aviation and marine bunker 
fuels, working through the International Civil Aviation Organization 
and the International Maritime Organization, respectively.’ The Paris

Agreement is different, in that ICAO (and the IMO) are not named. 
As a result, the Paris Agreement, through the NDCs, seemingly covers 
CO2 emissions from domestic aviation (currently 35% of the global 
total from aviation) but does not cover emissions from international 
flights. A number of states and regions, including the UK, France, 
Sweden, and Norway, have declared their intentions to include 
international aviation in their net zero commitments, while the EU, 
New Zealand, California, and Denmark are considering doing the 
same (Committee on Climate Change 2019). The Paris Agreement 
describes temperature-based goals, such that it is unclear how 
emissions of GHGs from international aviation would be accounted 
for. Clearly, this is a less than ideal situation for clarity of governance 
of international GHG emissions from both aviation and shipping. At 
its 40th General Assembly (October 2019) the ICAO requested its

Council to ‘…continue to explore the feasibility of a long-term global 
aspirational goal for international aviation, through conducting 
detailed studies assessing the attainability and impacts of any goals 
proposed, including the impact on growth as well as costs in all 
Model/scenarioDirect transport CO2 emissions from shipping [Index, 2020 level = 1.0]
IAM C1: 1.5°C (>50%) low overshoot
IAM C3: limit warming to 2°C (>67%) IAM C4: limit warming to 2°C (>50%)IAM C2: 1.5°C (>50%) high overshoot
IAM C5: limit warming to 2.5°C (>50%)
IAM C7: limit warming to 4°C (>50%) IAM C8: Exceed warming of 4°C (≥50%)IAM C6: limit warming to 3°C (>50%)
0.00.51.01.52.0
Figure 10.13 | CO 2 emissions from AR6 aviation scenarios indexed to 2020 modelled year.  Data from the AR6 scenario database.

1093
1093
countries, especially developing countries, for the progress of the 
work to be presented to the 41st Session of the ICAO Assembly’. 
What form this goal will take is unclear until work is presented to 
the 41st Assembly (Autumn, 2022). It is likely, however, that new 
accountability and governance structures will be needed to support 
decarbonisation of the aviation sector.
10.6 Decarbonisation of Shipping
Maritime transport is considered one of the key cornerstones 
enabling globalisation (Kumar and Hoffmann 2002). But as for 
aviation, shipping has its challenges in decarbonisation, with a strong 
dependency on fossil fuels without major changes since AR5. At 
the same time, the sector has a  range of opportunities that could 
help reduce emissions through not only changing fuels, but also by 
increasing energy efficiency, optimising operations and ship design, 
reducing demand, improving regulations, as well as other options

that will be reviewed in this section.
10.6.1 Historical and Current Emissions from Shipping
Maritime transport volume has increased by 250% over the past 
40 years, reaching an all-time high of 11 billion tonnes of transported 
goods in 2018 (UNCTAD 2019). This growth in transport volumes has 
resulted in continued growth in GHG emissions from the shipping 
sector, despite an improvement in the carbon intensity of ship 
operations, especially since 2014. The estimated total emissions 
from maritime transport can vary depending on data set and 
calculation method, but range over 600–1100 MtCO 2 yr –1 over 
the past decade (Figure  10.14), corresponding to 2–3% of total 
anthropogenic emissions. The legend in Figure  10.14 refers to the 
following data sources: Endresen et al. (2003), Eyring et al. (2005), Dalsøren et  al.  (2009), DNV GL (DNV GL 2019), CAMS-GLOB-SHIP 
(Jalkanen et al. 2014; Granier et al. 2019), EDGAR (Crippa et al. 2019),

Hoesly et al. (2018), Johansson et al. (2017), ICCT (Olmer et al. 2017), 
the IMO GHG Studies; IMO 2nd (Buhaug et al. 2009), IMO 3rd (Smith 
et al. 2014), IMO 4th-vessel and IMO 4th-voyage (Faber et al. 2020), 
and Kramel et al. (2021).
10.6.2 Short-lived Climate Forcers and Shipping
Like aviation, shipping is also a  source of emissions of SLCFs as 
described in Section 10.5, including nitrogen oxides (NO x), sulphur 
oxides (SO x, such as SO 2 and SO 4), carbon monoxide (CO), black 
carbon, and non-methane volatile organic compounds (NMVOCs) 
(Szopa et  al. 2021). Though SLCF have a  shorter lifetime than the 
associated CO 2 emissions, these short-lived forcers can have both 
a cooling effect (e.g., SO x) or a warming effect (e.g., ozone from NO x). 
The cooling from the SLCF from a pulse emission will decay rapidly 
and diminish after a couple of decades, while the warming from the 
long-lived substances lasts for centuries (Szopa et al. 2021).

Emissions of SLCF from shipping not only affect the climate, but 
also the environment, air quality, and human health. Maritime 
transport has been shown to be a major contributor to coastal air 
quality degradation (Zhao et  al. 2013; Jalkanen et  al. 2014; Viana 
et al. 2014; Goldsworthy and Goldsworthy 2015; Goldsworthy 2017). 
Sulphur emissions may contribute towards acidification of the ocean 
(Hassellöv et al. 2013). Furthermore, increases in sulphur deposition 
on the oceans have also been shown to increase the flux of CO 2 
from the oceans to the atmosphere (Hassellöv et al. 2013). To address 
the risks of SO x emissions from shipping, there is now a cap on the on 
the sulphur content permissible in marine fuels (IMO 2013). There is 
also significant uncertainty about the impacts of pollutants emitted 
from ships on the marine environment (Blasco et al. 2014).
Historical CO2 emissions from shippingCO2 emissions [Mt yr–1]
2002Endresen et al. (2003)
Eyring et al. (2005)

Dalsøren et al. (2009)
DNV-GL
CAMS-GLOB-SHIP
EDGAR
Hoesly et al. (2018)
Johansson et al. (2017)
ICCT
IMO 2nd
IMO 3rd
IMO 4th-vessel
IMO 4th-voyage
Kramel et al. (2021)
02004006008001,0001,2001,400
2018 2016 2014 2012 2010 2008 2006 2004
Figure 10.14 | CO 2 emissions (Mt yr –1) from shipping 2000–2018.  Data from various inventories as shown in the label.

1094
1094
Chapter 10  T ransport10Pollution control is implemented to varying degrees in the modelling 
of the SSP scenarios (Rao et  al. 2017); for example, SSPs 1 and 5 
assume that increasing concern for health and the environment result 
in more stringent air pollution policies than today (Szopa et al. 2021). 
There is a downward trend in SO x and NO x emissions from shipping 
in all the SSPs, in compliance with regulations. The SLCF emissions 
reduction efforts, within the maritime sector, are also contributing 
towards achieving the UN SDGs. In essence, while long-lived GHGs 
are important for long-term mitigation targets, accounting for short-
lived climate forcers is important both for current and near-term 
forcing levels as well as broader air pollution and SDG implications.
10.6.3 Shipping in the Arctic
Shipping in the Arctic is a topic of increasing interest. The reduction 
of Arctic summer sea ice increases the access to the northern sea

routes (Smith and Stephenson 2013; Melia et al. 2016; Aksenov et al. 
2017; Fox-Kemper et al. 2021). Literature and public discourse has 
sometimes portrayed this trend as positive (Zhang et al. 2016b), as 
it allows for shorter shipping routes, for example between Asia and 
Europe, with estimated travel time savings of 25–40% (Aksenov 
et al. 2017). However, the acceleration of Arctic cryosphere melt and 
reduced sea ice that enable Arctic shipping reduce surface albedo 
and amplify climate warming (Eyring et al. 2021). Furthermore, local 
air pollutants can play different roles in the Arctic. For example, black 
carbon emissions reduce albedo and absorb heat in air, on snow 
and ice (Browse et al. 2013; Kang et al. 2020; Messner 2020; Eyring 
et  al. 2021). Finally, changing routing from Suez to the northern 
sea routes may reduce total emissions for a voyage, but also shifts 
emissions from low to high latitudes. Changing the location of the

emissions adds complexity to the assessment of the climatic impacts 
of Arctic shipping, as the local conditions are different and the SLCF 
may have a  different impact on clouds, precipitation, albedo and 
local environment (Dalsøren et  al. 2013; Fuglestvedt et  al. 2014; 
Marelle et  al. 2016). Observations have shown that 5–25% of air 
pollution in the Arctic stems from shipping activity within the Arctic 
itself (Aliabadi et al. 2015). Emissions outside the Arctic can affect 
Arctic climate, and changes within the Arctic may have global climate 
impacts. Both modelling and observations have shown that aerosol 
emissions from shipping can have a significant effect on air pollution 
and shortwave radiative forcing (Ødemark et al. 2012; Peters et al. 
2012; Dalsøren et  al. 2013; Roiger et  al. 2014; Righi et  al. 2015; 
Marelle et al. 2016).
Increased Arctic shipping activity may also pose increased risks to 
local marine ecosystems and coastal communities from invasive

species, underwater noise, and pollution (Halliday et al. 2017; IPCC 
2019). Greater levels of Arctic maritime transport and tourism 
have political, as well as socio-economic, implications for trade, 
and nations and economies reliant on the traditional shipping 
corridors. There has been an increase in activity from cargo, tankers, 
supply, and fishing vessels in particular (Winther et al. 2014; Zhao 
et  al. 2015). Projections indicate more navigable Arctic waters in 
the coming decades (Smith and Stephenson 2013; Melia et  al. 
2016) and continued increases in transport volumes through the 
northern sea routes (Corbett et al. 2010; Lasserre and Pelletier 2011; Winther  et  al.  2014). Emission patterns and quantities, however, 
are also likely to change with future regulations from IMO, and 
depend on technology developments, and activity levels which may 
depend upon geopolitics, commodity pricing, trade, natural resource

extraction, insurance costs, taxes, and tourism demand (Johnston 
et al. 2017). The need to include indigenous peoples’ voices when 
shaping policies and governance of shipping activities in the high 
north is increasing (Dawson et al. 2020).
The Arctic climate and environment pose unique hazards and 
challenges with regard to safe and efficient shipping operations: low 
temperature challenges, implications for vessel design, evacuation 
and rescue systems, communications, oil spills, variable sea ice, and 
meteorological conditions (Buixadé Farré et al. 2014). To understand 
the total implications of shipping in the Arctic, including its climate 
impacts, a  holistic view of synergies, trade-offs, and co-benefits is 
needed, with assessments of impacts on not only the physical climate, 
but also the local environment and ecosystems. To further ensure safe 
operations in the Arctic waters, close monitoring of activities may 
be valuable.

10.6.4 Mitigation Potential of Fuels, Operations 
and Energy Efficiency
A range of vessel mitigation options for the international fleet 
exist and are presented in this section. A variety of feedstocks and 
energy carriers can be considered for shipping. As feedstocks, fuels 
from biomass (advanced biofuels), fuels produced from renewable 
electricity and CO 2 capture from flue gas or the air (electro-, e-, or 
power-fuels), and fuels produced via thermochemical processes 
(solar fuels) can be considered. As energy carriers, synthetic fuels 
and the direct use of electricity (stored in batteries) are of relevance. 
The most prominent synthetic fuels discussed in the literature are 
hydrogen, ammonia, methane, methanol, and synthetic hydrocarbon 
diesel. Figure  10.15 shows the emissions reductions potential for 
alternative energy carriers that have been identified as having the 
highest potential to mitigate operational emissions from the sector

(Chatzinikolaou and Ventikos 2014; Brynolf et al. 2014; Teeter and 
Cleary 2014; Traut et al. 2014; Lindstad et al. 2015; Psaraftis 2015; 
Seddiek 2015; Tillig et al. 2015; Winkel et al. 2016; DNV GL 2017; 
Bicer and Dincer 2018a; Biernacki et  al. 2018; Bongartz et  al. 
2018; Gilbert et al. 2018; Hua et al. 2018; ITF 2018b; Singh et al. 2018; 
Balcombe  et  al. 2019; Hansson et  al. 2019; Sharafian et  al. 2019; 
Winebrake et  al. 2019; Czermański et  al. 2020; Faber et  al. 2020; 
Hansson et al. 2020; Kim et al. 2020; Liu et al. 2020a; Nguyen et al. 
2020; Perčić et al. 2020; Sadeghi et al. 2020; Seithe et al. 2020; Xing 
et al. 2020; Valente et al. 2021; Stolz et al. 2021).
Low-carbon hydrogen and ammonia are seen to have positive potential 
as a  decarbonised shipping fuel. Hydrogen and ammonia, when 
produced from renewables or coupled to CCS as opposed to mainly 
by fossil fuels with high lifecycle emissions (Bhandari et  al. 2014),

may contribute to significant CO 2-eq reductions of up to 70–80% 
compared to low-sulphur heavy fuel oil (Bicer and Dincer 2018b; 
Gilbert et al. 2018). These fuels have their own unique transport and 
storage challenges as ammonia requires a pilot fuel due to difficulty

1095
1095
in combustion, and ammonia combustion could lead to elevated 
levels of NO x, N2O, or NH 3 emissions depending on engine technology 
used (DNV GL 2020). There is a need for the further development of 
technology and procedures for safe storage and handling of fuels 
such as hydrogen and ammonia, both onboard and onshore, for faster 
uptake (Hoegh-Guldberg et al. 2019), but they remain an encouraging 
decarbonisation option for shipping in the next decade.
While methanol produced from fossil sources induces an emissions 
increase of +7.5% (+44%), e-methanol (via hydrogen from 
electrolysis based on renewable energy and carbon from direct 
air capture) reduces emissions by 80% (82%). In general, several 
synthetic fuels, such as synthetic diesel, methane, methanol, ethanol, 
and dimethyl ether could in principle be used for shipping (Horvath 
et al. 2018). The mitigation potential of these is fully dependent on

the sourcing of the hydrogen and carbon required for their synthesis.
As noted in Section 10.3, LNG has been found to have a relatively 
limited mitigation potential and may not be viewed as a low-carbon 
alternative, but has a  higher availability than other fuel options 
(Gilbert et al. 2018). Emissions reductions across the full fuel lifecycle 
are found in the order of 10%, with ranges reported from –30% 
(reduction) to +8% (increase), if switching from heavy fuel oil to LNG, 
as indicated in Figure 10.15 (Bengtsson et al. 2011). Regardless of the 
production pathway, the literature points to the risk of methane slip 
(emissions of unburnt methane especially at low engine loads and 
from transport to ports) from LNG-fuelled vessels, with no current 
regulation on emissions caps (Anderson et al. 2015; Ushakov et al. 
2019; Peng et al. 2020). Leakage rates are a critical point for the total 
climate impact of LNG as a fuel, where high pressure engines remedy

this more than low pressure ones. As discussed in Section 10.3, some 
consider LNG as a  transition fuel, while some literature points to 
the risk of stranded assets due to the increasing decarbonisation 
regulation from IMO and the challenge of meeting IMO’s 2030 
emissions reductions targets using this fuel.
In addition to fossil and e-fuels, advanced biofuels might play a role to 
provide the energy demand for future shipping. Biomass is presently 
used to produce alcohol fuels (such as ethanol and methanol), liquid 
biogas, or biodiesel that can be used for shipping and could reduce 
CO2 emissions from this segment. As explained in Box  10.2 and 
Chapter  7, the GHG footprint associated with biofuels is strongly 
dependent on the incurred land use and land-use change emissions. 
Advanced biofuels from processing cellulose rather than sugar are 
likely to be more attractive in terms of the quantities required but 
are not commercially available (Section  10.3). The estimates of

emissions reductions from biofuels shown in Figure  10.15 rely on 
data from the Integrated Assessment Models  – Energy Modelling 
Forum 33 (IAM EMF33), partial models assuming constant land cover 
(CLC), and partial models using natural growth (NRG). Box 10.2 and 
Section 10.4 include a more detailed description of the assumptions 
underlying these models and their estimates. The results based on 
IAM EMF33 and CLC suggests median mitigation potential of around 
73% for advanced biofuels in shipping, while the NRG-based results 
suggest increased emissions from biofuels. The EMF33 and CLC 
results rely on modelling approaches compatible with the scenarios 
in the AR6 database (Chapter 6 and Box 7.7).In addition to fuels, there are other measures that may aid the 
transition to low-carbon shipping. The amounts and speed of uptake 
of alternative low- or zero-carbon fuels in ports depend upon 
investments in infrastructure  – including bunkering infrastructure,

refinery readiness, reliable supply of the fuels, as well as sustainable 
production. The ship lifetime and age also play a  role; retrofitting 
ships to accommodate engines and fuel systems for new fuel types 
may not be an option for older vessels. As such, operational efficiency 
becomes more important (Bullock et  al. 2020). There is some 
potential to continue to improve the energy efficiency of vessels 
through operational changes (Traut et al. 2018), reducing the speed 
or ‘slow steaming’ (Bullock et al. 2020), and improved efficiency in 
port operations (Viktorelius and Lundh 2019; Poulsen and Sampson 
2020). There is also a growing interest in onboard technologies for 
capturing carbon, with prototype ships underway showing 65–90% 
potential reduction in CO 2 emissions (Luo and Wang 2017; Awoyomi 
et al. 2020; Japan Ship Technology Research Association et al. 2020). 
Challenges identified include CO 2 capture efficiency (Zhou and Wang

2014), increased operating costs, and limited onboard power supply 
(Fang et  al. 2019). Furthermore, designing CO 2 storage tanks for 
transport to shore may pose a challenge, as the volume and weight 
of captured CO 2 could be up to four times more than standard oil 
(Decarre et al. 2010).
Changes in design and engineering provide potential for reducing 
emissions from shipping through a range of measures, for example 
by optimising hull design and vessel shape, power and propulsion 
systems that include wind- or solar-assisted propulsion, and through 
improved operations of vessels and ports. Figure 10.15 shows that 
such measures may decrease emissions by 5–40%, though with 
a broad range in potential (Bouman et al. 2017). Nuclear propulsion 
could decrease emissions from individual vessels by 98%. Battery- 
or hybrid-electric ships have been identified as a means to reduce 
emissions in short-sea shipping such as ferries and inland waterways

(Gagatsi et  al. 2016), which may also importantly reduce near-
shore SLCF pollution (Nguyen et al. 2020). Figure 10.15 shows that 
the median emissions from electric ships can be about 40% lower 
than equivalent fossil-based vessels but can vary widely. The wide 
reduction potential of battery electric propulsion is due to different 
assumptions about the CO 2 intensity of the electricity used and the 
levels of CO 2 footprints associated with battery production.
Although projections indicate continued increase in freight demand 
in the future, demand-side reductions could contribute to mitigation. 
The development of autonomous systems may play a role (Colling 
and Hekkenberg 2020; Liu et al. 2021) while 3-D printing can reduce 
all forms of freight as parts and products can be printed instead of 
shipped (UNCTAD 2018). As more than 40% of transported freight is 
fossil fuels, a lessened demand for such products in low-emissions

scenarios should contribute to reducing the overall maritime transport 
needs and hence emissions in the future (Sharmina et  al. 2017). 
An  increase in alternative fuels, on the other hand, may increase 
freight demand (Mander et  al. 2012). Potentials for demand-side 
reduction in shipping emissions may arise from improving processes 
around logistics and packaging, and further taxes and charges could 
serve as leverage for reducing demand and emissions.

1096
1096
Emissions scope of individual data points BoxplotsAdvanced biofuels (EMF33 | Partial models CLC | Partial models NRG) Nuclear (n=6)
Battery electric (n=9)
Shore-side electricity (n=32)
Wind assistance (n=29)
Solar assistance (n=7)Methanol (left: e-fuel; right: fossil; n=22)
Methane (left: e-fuel; right: fossil; n=35)
Hydrogen (left: e-fuel; right: fossil; n=43)
Ammonia (left: e-fuel; right: fossil; n=29)
MedianOutliers95% percentile
InterquartilesTank-to-wake, CO2
Well-to-wake, CO2 Well-to-Wake, CO2-eqTank-To-Wake, CO2-eq
–100 –75 –50 –25 0 25 50 75 100
Emission reduction in % Emission increase in %LCA, CO2-eq–100 –75 –50 –25 0 25 50 75 100 –100 –75 –50 –25 0 25 50 75 100
–100 –75 –50 –25 0 25 50 75 100 –100 –75 –50 –25 0 25 50 75 100

Figure 10.15 | Emissions reductions potential of alternative fuels compared to conventional fuels in the shipping sector.  The x-axis is reported in %. Each 
individual marker represents a data point from the literature, where the brown square indicates a full LCA CO 2-eq value; light blue triangles tank-to-wake CO 2-eq; red triangles 
well-to-wake CO 2-eq; yellow triangles well-to-wake CO 2; and dark blue circles tank-to-wake CO 2 emissions reduction potentials. The values in the Figure rely on the 100-year 
GWP value embedded in the source data, which may differ slightly with the updated 100-year GWP values from WGI. ‘n’ indicates the number of data points per sub-panel. Grey 
shaded boxes represent data where the energy comes from fossil resources, and blue from low-carbon renewable energy sources. ‘Advanced biofuels EMF33’ refers to emissions

factors derived from simulation results from the integrated assessment models EMF33 scenarios (darkest coloured box in top left panel). Biofuels partial models CLC refers to 
partial models with constant land cover. Biofuels partial models NRG refers to partial models with natural regrowth. For ammonia and hydrogen, low-carbon fuel is produced 
via electrolysis using low-carbon electricity, and ‘fossil’ refers to fuels produced via steam methane reforming of natural gas.

1097
1097
The coming decade is projected to be costly for the shipping sector, 
as it is preparing to meet the 2030 and 2050 emissions reduction 
targets set by the IMO (UNCTAD 2018). With enough investments, 
incentives, and regulation, substantial reductions of CO 2 emissions 
from shipping could be achieved through alternative energy carriers. 
The literature suggests that their cost could be manyfold higher 
than for conventional fuels, which in itself could reduce demand 
for shipping, and hence its emissions, but could make the transition 
difficult. R&D may help reduce these costs. The literature points to the 
need for developing technology roadmaps for enabling the maritime 
transport sector to get on to pathways for decarbonisation early 
enough to reach global goals (Kuramochi et  al. 2018). Accounting 
for the full lifecycle emissions of the vessels and the fuels is required 
to meet the overall long-term objectives of cutting GHG and SLCF

emissions. The urgency of implementing measures for reducing 
emissions is considered to be high, considering the lifetime of vessels 
is typically 20 years, if not more.
10.6.5 Accountability and Governance Options
Regulatory frameworks for the shipping sector have been developed 
over time and will continue to be through bodies such as the IMO, 
which was established by the UN to manage international shipping. 
The IMO strategy involves a 50% reduction in GHG emissions from 
international shipping by 2050 compared to 2008 (IMO 2018). The 
strategy includes a  reduction in carbon intensity of international 
shipping by at least 40% by 2030, and 70% by 2050, compared 
to 2008. IMO furthermore aims for the sectoral phase-out of GHG 
emissions as soon as possible this century.
In 2020, the IMO approved the short-term goal-based measure 
to reduce the carbon intensity of existing international vessels. 
This  measure addresses both technical and operational strategies.

The operational element is represented by a  Carbon Intensity 
Indicator (CII), and the technical element is represented by the Energy 
Efficiency Existing Ship Index (EEXI), which will apply to ships from 
2023. The EEXI builds upon the Energy Efficiency Design Index (EEDI), 
which is a legally-binding mitigation regulation for newbuild ships, 
established as a series of baselines for the amount of fuel ships may 
burn for a particular cargo-carrying capacity. The EEDI differs per ship 
segment. For example, ships built in 2022 and beyond should be 
50% more energy efficient than those built in 2013. This legislation 
aims to reduce GHG emissions in particular. Energy efficiency may 
be improved by several of the mitigation options outlined above. 
The Ship Energy Efficiency Management Plan (SEEMP) is seen as the 
international governance instrument to improve energy efficiency and 
hence emissions from ships. SEEMP is a measure to enable changes

to operational measures and retrofits (see Johnson et al. 2013). The 
combination of EEXI, EEDI, and SEEMP may reduce emissions by 
23% by 2030 compared to a ‘no policy’ scenario (Sims et al. 2014). 
With regards to accountability, it is mandatory for ships greater than 
or equal to 5000 gross tonnage to collect fuel consumption data, 
as well as specified data. Such as for transport work. Similarly, 
the EU Monitoring, Reporting and Verification Regulation requires 
mandatory reporting of a vessel’s fuel consumption when operating 
in European waters.Policy choices may enable or hinder changes, and gaps in governance 
structures may, to some degree, hinder the objectives of mechanisms 
like SEEMP to improve energy efficiency and emissions. Policies 
may be developed to incentivise investments in necessary changes 
to the global fleet and related infrastructures. The literature argues 
that regulations and incentives that motivate mitigation through

speed optimisation, ship efficiency improvements, and retrofits with 
lower-carbon technologies at a sub-global scale may contribute to 
immediate reductions in CO 2 emissions from the sector (Bows-Larkin 
2015). The role of the financial sector, through initiatives such as 
the Poseidon Principle, which limit lending to companies that fail 
to uphold environmental standards, could also become increasingly 
important (Sumaila et al. 2021).
It has been proposed to make shipping corporations accountable 
for their emissions by making it mandatory to disclose their vessels’ 
emissions reductions (Rahim et al. 2016). Market-based mechanisms 
may increasingly encourage ship operators to comply with IMO 
GHG regulations. Development of policies such as carbon pricing 
or taxation to enable a business case for adopting low-carbon fuels 
could be a near-term priority for acceleration of transformation of the 
sector (Hoegh-Guldberg et al. 2019). The EU is considering including

shipping in its carbon trading system, with the details still to be 
agreed upon but expected to come into force in 2023, along with the 
CII. The proposition is that shipowners who conduct voyages within 
Europe, or start or end at an EU port, will have to pay for carbon 
permits to cover the CO 2 emitted by their vessel.
Regulations exist also to limit emissions of air pollution from shipping 
with the aim to improve environment and health impacts from 
shipping in ports and coastal communities. In sulphur emission control 
areas (SECAs), the maximum permissible sulphur content in marine 
fuels is 0.10% mass/mass. These are further tightened by the IMO 
legislation on reducing marine fuel sulphur content to a maximum 
of 0.5% in 2020 outside SECAs, compared to 3.5% permissible since 
2012 (MARPOL Convention). The MARPOL Annex VI also limits the 
emissions of ozone-depleting substances and ozone precursors, 
NOx, and volatile organic compounds from tankers (Mertens et  al.

2018). The implementation of the emission control areas have been 
shown to reduce the impacts on health and the environment (Viana 
et al. 2015).
While there are many governance and regulatory initiatives that help 
reduce emissions from the shipping sector, few are transformative 
on their own, unless zero-carbon fuels can become available at 
a reasonable cost as suggested in Sections 10.3 and below.
10.6.6 Transformation Trajectories for 
the Maritime Sector
Figure 10.16 shows CO 2 emissions from shipping in scenarios from 
the AR6 database and the Fourth GHG study by the IMO (Faber et al. 
2020). Panel (a) shows that CO 2 emissions from shipping go down by 
33–70% (5–95th% percentile) by 2050 in the C1 and C2 scenarios, 
which limit warming to 1.5°C (>50%) during the 21st century with 
no or limited overshoot or return warming to 1.5°C (>50%) during

1098
1098
the 21st century after a high overshoot. By 2080, median values for 
the same set of scenarios reach net zero CO 2 emissions. IAMs often 
do not report emissions pathways for shipping transport and the 
sector is underrepresented in most IAMs (Esmeijer et al. 2020). Hence 
pathways established outside IAMs can be different for the sector. 
Indeed, the IMO projections for growth in transport demand (Faber 
et al. 2020) indicate increases of 40–100% by 2050 for the global 
fleet. Faber and et al. (2020), at the same time predict reductions in 
trade for fossil fuels dependent on decarbonisation trajectories. The 
energy efficiency improvements of the vessels in these scenarios are 
typically of 20–30%. This offsets some of the increases from higher 
demand in the future scenarios. Fuels assessed by the Fourth IMO 
GHG study were limited to heavy fuel oil, marine gasoil, LNG, and 
methanol, with a fuels mix ranging from 91–98% conventional fuel

use and a small remainder of alternative fuels (primarily LNG and 
some methanol). Panel (b) shows average fleetwide emissions of CO 2 
based on these aggregate growth and emissions trajectories from 
the IMO scenarios. In these scenarios, CO 2 emissions from shipping 
remain stable or grow compared to 2020 modelled levels. These 
results contrast with the low emissions trajectories in the C1–C2 bin 
in panel (a). It seems evident that the scenarios in the AR6 database 
explore a  broader solutions space for the sector than the Fourth 
GHG study by the IMO. However, the 1.5°C–2°C warming goal has 
led to an IMO 2050 target of 40% reduction in carbon intensity 
by 2030, which would require emissions reduction efforts to begin 
immediately. Results from global models suggest the solutions space 
for deep emissions reductions in shipping is available.
Combinations of measures are likely to be needed for transformative

transitioning of the shipping sector to a low-carbon future, particularly 
if an expected increase in demand for shipping services is realised 
(Smith et al. 2014; Faber et al. 2020). Both GHG and SLCF emissions 
decrease significantly in SSP1-1.9, where mitigation is achieved in the most sustainable way (Rao et al. 2017). Conversely, there are no 
emissions reductions in the scenarios presented by the IMO Fourth 
GHG study, even though these scenarios incorporate some efficiency 
improvements and a slight increase in the use of LNG.
Options outlined in this chapter suggest a combination of policies 
to reduce demand, increase investments by private actors and 
governments, and develop the technology readiness level of 
alternative fuels and related infrastructure (especially synthetic 
fuels). Some literature suggests that battery electric-powered short-
distance sea shipping could yield emissions reductions given access

to low-carbon electricity. For deep sea shipping, advanced biofuels, 
hydrogen, ammonia, and synthetic fuels hold potential for significant 
emissions reductions, depending on GHG characteristics of the fuel 
chain and resource base. Other options, such as optimisation of 
speed and hull design and wind-assisted ships, could also combine 
to make significant contributions by 2050 to further bring emissions 
down. In total a suite of mitigation options exists or is on the horizon 
for the maritime sector.
10.7 Scenarios from Integrated, Sectoral, 
and Regional Models
10.7.1 Transport Scenario Modelling
This section reviews the results of three types of models that 
systemically combine options to assess different approaches to 
generating decarbonisation pathways for the transport system: 
(i) integrated assessment models (IAM); (ii) global transport energy 
models (GTEM); and (iii) national transport-energy models (NTEM)

(Edelenbosch et  al. 2017; Yeh et  al. 2017). Common assumptions 
across the three model types include trajectories of socioeconomic Model/scenario
Direct transport CO2 emissions from shipping 
[Index, 2020 level = 1.0]Direct CO2 emissions from shipping (IMO) 
[Index, 2020 level = 1.0]
IAM C1–2: 1.5°C (>50%)
IAM C6–8: ≥2.5°C (>50%)
IAM C3–5: <2.5°C (>50%)
2.0
1.5
1.0
0.5
0.0
2020 2020 2040 2040 2060 2060 2080 2080 2100 2100
Figure 10.16 | CO 2 emissions from shipping scenarios indexed to 2020 modelled year.  Panel (a) scenarios from the AR6 database. Panel (b) scenarios from the 
Fourth IMO GHG Study (Faber et al., 2020). Figures show median, 5th and 95th percentile (shaded area) for each scenario group.

1099
1099
development, technological development, resource availability, 
policy, and behavioural change. The key differences underlying these models are their depth of technological and behavioural detail versus scope in terms of sectoral and regional coverage. In very general terms, the narrower the scope in terms of sectors and regions, the more depth on spatial, technological, and behavioural detail. A large set of scenarios from these models were collected in a joint effort led by Chapter 3 and supported by Chapter 10 and others. The outcomes from over 100 models have been analysed for this chapter with the methodologies set out in Annex III for the whole report.
GHG emissions from transport are a  function of travel demand,

travel mode, transport technology, GHG intensity of fuels, and energy efficiency. These drivers can be organised around a group of levers that can advance the decarbonisation of the transport system. The levers thus include reducing travel activity, increasing use of lower-carbon modes, and reducing modal energy intensity and fuel carbon content. This section explores each lever’s contributions to the decarbonisation of the transport sector by reviewing the results from the three model types IAM, GTEM, and NTEM.
IAMs integrate factors from other sectors that interact with the

transport system endogenously, such as fuel availability and costs. IAMs minimise mitigation costs to achieve a  temperature goal across all sectors of the economy over a long time horizon (typically to 2100). IAMs typically capture mitigation options for energy and carbon intensity changes with greater technology/fuel details and endogeneity linked to the other sectors. In the scenarios with very large-scale electrification of the transport sector, the coupling with the other sectors in fuel production, storage, and utilisation becomes more important. G-/NTEMs and related regional transport sectoral models have more details on transport demand, technology, behaviours, and policies than IAMs, but treat the interactions with the other sectors exogenously, potentially missing some critical interactions, such as the fuel prices and carbon intensity of electricity. National models have detailed representation of national policies related to transport and energy, sometimes with greater

spatial resolution. Compared with IAMs, G-/NTEMs typically have greater detailed representation to explore mitigation options along the activity and mode dimensions where spatial, cultural, and behavioural details can be more explicitly represented. Section 5 in Annex III provides more details about these types of models. Scenarios for shipping and aviation are handled in more detail in sections 10.5 and 10.6, respectively.

This section applies the following categorisation of scenarios (see 
Table 3.1 for more details):
•
C1 (scenarios that limit warming to 1.5°C (>50%) during the
21st century with no or limited overshoot)
•C2 (scenarios that return warming to 1.5°C (>50%) during the21st century after a high overshoot)
•
C3 (scenarios that limit warming to 2°C (>67%) throughout the21st century)
•
C4 (scenarios that limit warming to 2°C (>50%) throughout the21st century)
•
C5 (scenarios that limit w
arming to 2.5°C (>50%) throughout the 
21st century)•C6 (scenarios that limit warming to 3°C (>50%) throughout the21st century)
•
C7 (scenarios that limit warming to 4°C (>50%) throughout the21st century)
•
C8 (scenarios that exceed warming of 4°C (≥50%) during the21st century)
A large share of the scenarios w
as developed prior to 2020. Results 
from such scenarios are indexed to a  modelled (non-COVID) year 2020, referred to as 2020Mod.
10.7.2  Global Emissions Trajectories

In 2018, transport emitted 8.5 GtCO 2-eq, reaching a near doubling 
from 1990 levels after two decades of 2% per year emissions growth (Section 10.1). Assessing future trajectories, Figure 10.17 provides an overview of direct CO
2 emissions estimates from the transport sector

across IAMs (colour bars) and selected global transport models (grey bars). The results from the IAMs are grouped in bins by temperature goal. Global transport energy models are grouped into reference and policy bins, since the transport sector cannot by itself achieve fixed global temperature goals. The policy scenarios in GTEMs and NTEMs cover a wide range of ‘non-reference’ scenarios, which include, for example, assumptions based on the ‘fair share action’ principles. In these scenarios, transport emissions reach reductions consistent with the overall emissions trajectories aligning with warming levels of 2°C. These scenarios may also consider strengthening existing transport policies, such as increasing fuel economy standards or large-scale deployments of electric vehicles. In most cases, these Policy scenarios are not necessarily in line with the temperature goals explored by the IAMs.
According to the collection of simulations from the IAM and GTEM

models shown in Figure 10.17, global transport emissions could grow up to 2–47% (5–95th percentile) by 2030 and –6–130% by 2050 under the C7 scenarios that limit warming to 4°C (>50%) throughout the 21st century and C8 scenarios that exceed 4°C (≥50%) during the 21st century. Population and GDP growth and the secondary effects, including higher travel service demand per  capita and increased freight activities per  GDP , drive the growth in emissions in these scenarios (Section 10.7.3). Though transport efficiencies (energy use per pkm travelled and per tkm of goods delivered) are expected to continue to improve in line with the historical trends (Section 10.7.4), total transport emissions would grow due to roughly constant carbon intensity (Section 10.7.5) under the C7 and C8 scenarios that limit warming to 4°C (>50%) throughout the 21st  century or exceed 4°C (≥50%) during the 21st century. In these scenarios, Significant increases in emissions (>150% for the medium values by 2050)

would come from Asia and Pacific, the Middle East, and Africa. Compared to estimated 2020 levels, in 2050 Developed Countries would have median 25% decrease in transport emissions in the C7 scenarios that limit warming to 4°C (>50%) throughout the 21st century or median 15% increase in transport emissions in the C8 scenarios that exceed warming of 4°C (≥50%) during the 21st century.

1100
1100
Chapter 10  T ransport102.5
0.0
2030 2050 2100 2030 2050 2100
2030 2050 2100 2030 2050 21000.51.01.52.0
2.5
2.5
0.0 01246810
0.51.01.52.0
2.5
8412529414619682120130
841252941461968212013
84
534611631692042
11
11
11534611631692042
11
11
53
4611631692042
1112529414619682120
13
11
11
53
4611631
5246115316920421152461153169204211
52
46115
31692042
11
69204211
11
5346116
53
11
31692042
11
53461163169204211
53
1111a) World
b) Developed Countries (DEV) c) Eastern Europe and West-Central Asia (EEA)
d) Latin America and Caribbean (LAM) e) Africa (AF)
f) Middle East (ME) g) Asia and Paciﬁc (APC)IAM C1: 1.5°C (>50%) low overshoot
IAM C3: limit warming to 2°C (>67%)
IAM C4: limit warming to 2°C (>50%)
IAM C2: 1.5°C (>50%) high overshoot

IAM C7: limit warming to 4°C (>50%)
IAM C8: Exceed warming of 4°C (≥50%)
Global and National Transport
Energy Models (G-/NTEM) Policy
Energy Models (G-/NTEM) ReferenceIAM C6: limit warming to 3°C (>50%)
AM/sectoral scenarios Illustrative pathway
2.0-Sup
1.5-Sup
1.5-Ren
CurPol
20-Rer
2.0-GS
ModAct
1.5-SF
Figure 10.17 | Direct CO 2 emissions from transport in 2030, 2050, and 2100 indexed to 2020 modelled year across R6 Regions and World.  IAM results are 
grouped by temperature targets. Sectoral studies are grouped by reference and policy categories. Plots show 5–95th percentile, 25–75th percentile, and median. Numbers above 
the bars indicate the number of scenarios. Data from the AR6 scenario database.

1101
1101
To meet temperature goals, by 2050 global transport emissions 
would need to decrease by 17% (+67% to –23% for the 5–95th 
percentile) below 2020Mod levels in the scenarios that limit warming 
to 2°C (>67%), 2°C (>50%) and 2.5 °C (>50%) throughout the 
21st century (C3-C5 scenarios – orange bars), and 47% (14–80% for 
the 5–95th percentile) in the scenarios that limit warming to 1.5°C 
(>50%) during the 21st  century with no or limited overshoot or 
return to 1.5°C (>50%) during the 21st century after high overshoot 
during the 21st  century (C1–C2 scenarios  – green bars). However, 
transport-related emission reductions may not happen uniformly 
across regions. For example, transport emissions from the Developed 
Countries and Eastern Europe and West Central Asia would decrease 
from 2020 levels by 2050 across all C1–C2 scenarios, but could 
increase in Africa, Asia and Pacific, Latin America and Caribbean, and

the Middle East, in some of these scenarios. In particular, the median 
transport emissions in India and Africa could increase by 2050 in C1–
C2 scenarios, while the 95th percentile emissions in Asia and Pacific, 
Latin America and Caribbean, and the Middle East, could be higher 
in 2050 than in 2020.
The Reference scenario emission pathways from GTEMs described in 
Figure 10.17 have similar ranges to C7–C8 scenario groups in 2050. 
The Policy scenarios are roughly in line with C6–C7 scenarios for 
the world region. The results suggest that the majority of the Policy 
scenarios examined by the GTEMS reviewed here are in the range of 
the C3–C6 scenarios examined by the IAMs (Gota et al. 2016; IEA 
2017b; Yeh et al. 2017; Fisch-Romito and Guivarch 2019). The NDCs 
in the transport sector include a mix of measures targeting efficiency 
improvements of vehicles and trucks; improving public transit 
services; decarbonising fuels with alternative fuels and technologies

including biofuels, fossil- or bio-based natural gas, and electrification; 
intelligent transport systems; and vehicle restrictions (Gota et  al. 
2016). Because of the long lag-time for technology turnover, these 
measures are not expected to change 2030 emissions significantly. 
However, they could have greater impacts on 2050 emissions.
Several GTEMs not included in AR6 scenario database have examined 
ambitious CO 2 mitigation scenarios. For example, a meta-analysis of 
scenarios suggests that global transport emissions consistent with 
warming levels of 2°C, would peak in 2020 at around 7–8 GtCO 2 
and decrease to 2.5–9.2 Gt for 2°C, with an average of 5.4 Gt by 
2050 (Gota et  al. 2019). For comparison, the IEA’s Sustainable 
Development Scenario suggests global transport emissions decrease 
to 3.3 Gt (or 55% reduction from 2020 level) by 2050 (IEA 2021f). 
The latest IEA Net Zero by 2050 report proposes transport emissions

to be close to zero by 2050 (IEA 2021e). The latter is lower than 
the interquartile ranges of the C1 group of scenarios from the AR6 
database analysed here.
Low-carbon scenarios are also available from national models (Latin 
America, Brazil, Canada, China, France, Germany, Indonesia, India, 
Italy, Japan, Mexico, South Africa, UK, US) with a good representation 
of the transport sector. The low-carbon scenarios are either defined 
with respect to a global climate stabilisation level of, for example, 
2°C/1.5°C Scenario (Dhar et al. 2018), or a CO 2 target that is more 
stringent than what has been considered in the NDCs, such as the 
net-zero emissions pathways (Bataille et al. 2020; IEA 2021e). These studies have generally used bottom-up models (see Annex III) for the 
analysis, but in some cases, they are run by national teams using 
global models (e.g., the Global Change Assessment Model (GCAM) 
for China and India). National studies show that transport CO 2

emissions could decline significantly in low-carbon scenarios in all 
the developed countries reviewed (Bataille et  al. 2015; Kainuma 
et al. 2015; Hillebrandt et al. 2015; Mathy et al. 2015; Pye et al. 2015; 
Virdis et al. 2015; Williams et al. 2015; Zhang et al. 2016a) in 2050 
from the emissions in 2010 and reductions could vary from 65% to 
95%. However, in developing countries reviewed (Di Sbroiavacca 
et al. 2014; Altieri et al. 2015; Buira and Tovilla 2015; Rovere et al. 
2015; Shukla et al. 2015; Siagian et al. 2015; Teng et al. 2015; Dhar 
et al. 2018), emissions could increase in 2050 in the range of 35% 
to 83% relative to 2010 levels. Transport CO 2 emissions per capita 
in the developing countries were much lower in 2010 (varying 
from 0.15 to 1.39 tCO 2 per capita) relative to developed countries 
(varying from 1.76 to 5.95 tCO 2 per capita). However, results from 
national modelling efforts suggest that, by 2050, the CO 2 emissions

per capita in developed countries (varying from 0.19 to 1.04 tCO 2 
per  capita) could be much lower than in developing countries 
(varying from 0.21 to 1.7 tCO 2 per capita).
The transport scenario literature’s mean outcomes suggest that 
the transport sector may take a  less steep emissions reduction 
trajectory than the cross-sectoral average and still be consistent 
with the 2°C goal. For example, most of the scenarios that limit or 
return warming to 1.5°C (>50%) during the 21st century (C1–C2) 
reach zero emissions by 2060, whereas transport sector emissions 
are estimated in the range of 20% of the 2020Mod level (4–65% 
for the 10th to 90th percentiles) by 2100. This finding is in line with 
perspectives in the literature suggesting that transport is one of the 
most difficult sectors to decarbonise (Davis et  al. 2018). There is, 
however, quite a spread in the results for 2050. Since temperature 
warming levels relate to global emissions from all sectors, modelling

results from IAMs tend to suggest that in the short and medium term, 
there might be lower cost mitigation options outside the transport 
sector. On the other hand, compared with GTEMs/NTEMs, some IAMs 
may have limited mitigation options available, including technology, 
behavioural changes, and policy tools especially for aviation and 
shipping. The models therefore rely on other sectors and/or negative 
emissions elsewhere to achieve the overall desired warming levels. 
This potential shortcoming should be kept in mind when interpreting 
the sectoral results from IAMs.
10.7.3 Transport Activity Trajectories
Growth in passenger and freight travel demand is strongly dependent 
on population growth and GDP . In 2015, transport activities were 
estimated at around 35–50 trillion pkm, or 5,000–7,000 pkm 
per  person per  year, with significant variations among studies 
(IEA 2017b; ITF 2019). The number of passenger cars in use has

grown 45% globally between 2005–2015, with the most significant 
growth occurring in the developing countries of Asia and the Middle 
East (119%), Africa (79%), and South and Central America (80%), 
while growth in Europe and North America is the slowest (21% and 
4% respectively) (IOMVM 2021). On the other hand, car ownership

1102
1102
Chapter 10  T ransport105
0
2050 2100
2050 2100 2030 2050 2100
2050 2100 2030 2050 21002030
2030
2030
2030 2050 2100 2030 2050 210012345
0
2050 2100
2030
2030
2030 2050 2100 2030 2050 21001234
5
01234Freight Passenger
5
01234
5
5
01234
5
5
01234
5
01234Freight Passenger5
01234
5
0123420
0481216
20
04812165
01234
5
01234
G-/NTEM Policy
G-/NTEM Reference
Model type & category
57
5756 99446212575616671
9944621257 1667199446212
33
331859529122911331859529122911 3318595291229111859529122911 33185952912291133 59529122911
33
33185952912291133185952912 1129335952912
11
291229559
18
11
2912295591833112912295591811
291229559
1833
112912295591833112912295591833
331859529
331859529122911 33185952912291133185952912 11
1129
33185952912841133185952912291133
185952912291111
291229559
1833
11
291229559
1833
112912295591833
12
29559
1833

112912295591833112912295591833291229113318595291229113359529122911
1833
112912
295591833
1129122955918331129122955918333311
29
1833185952912291133
185952912 11
29
18595291229111856
166715616671994462125756166719944621257
561667199446212IAM C1: 1.5°C (>50%) low overshoot
IAM C4: limit warming to 2°C (>50%)IAM C2: 1.5°C (>50%) high overshoot
IAM C8: Exceed warming of 4°C (≥50%)IAM C6: limit warming to 3°C (>50%)a) World
b) Developed Countries (DEV) c) Eastern Eur. & West-Central Asia (EEA)
f) Middle East (ME) g) Asia and Paciﬁc (APC)Illustrative pathway
2.0-Sup
1.5-Sup
1.5-Ren
CurPol
20-Rer
2.0-GS
ModAct
1.5-SF
Figure 10.18 | Transport activity trajectories for passenger (bottom panel) and freight (top panel) in 2030, 2050, and 2100 indexed to 2020 modelled

year across R6 Regions and World.  Plots show 5–95th percentile, 25–75th percentile, and median. Numbers above the bars indicate the number of scenarios. Data from 
the AR6 scenario database.

1103
1103
levels in terms of vehicles per  1000 people in 2015 were low in 
developing countries of Asia and the Middle East (141), Africa (42), South and Central America (176), while in Europe and North America they are relatively high (581 and 670 respectively) (IOMVM 2021). The growth rate in commercial vehicles (freight and passenger) was 41% between 2005 and 2015, with a somewhat more even growth across developed and developing countries (IOMVM 2021).
Figure  10.18 shows activity trajectories for both freight and

passenger transport based on the AR6 database for IAMs. According to demand projections from the IAMs, global passenger and freight transport demand could increase relative to a modelled year 2020 across temperature goals. The median transport demand from IAMs for all the scenarios in line with warming levels below 2.5°C (C1–C5) suggests that global passenger transport demand could grow by 1.14–1.3 times in 2030 and by 1.5–1.8 times in 2050 (1.27–2.33 for the 5–95th percentile across C1–C5 scenarios) relative to modelled 2020 level. Developed regions including North America and Europe exhibit lower growth in passenger demand in 2050 compared to developing countries across all the scenarios. In 2030, most of the global passenger demand growth happens in Africa (44% growth relative to 2020), and Asia and Pacific (57% growth in China and 59% growth in India relative to 2020) in the scenarios that limit warming below 2.5°C (>50%) throughout the 21st century (C5). These regions start from

a low level of per capita demand. For example, in India, demand may grow by 84%. However, the per capita demand in 2010 was under 7000  km per  person per  year (Dhar and Shukla 2015). Similarly, in China, demand may grow by 52%, starting from per capita demand of 8000 km per person per year in 2010 (Pan et al. 2018). The per capita passenger demand in these regions was lower than in developed countries in 2010, but it converges towards the per capita passenger transport demand of advanced economies in less stringent climate scenarios (C6–C7). Demand for passenger travel would grow at a  slower rate in the stricter temperature stabilisation scenarios (<2.5°C and 1.5°C scenarios, C1–C5) compared to the scenarios with higher warming levels (C7–C8). The median global passenger demand in the scenarios that limit or return warming to 1.5°C during the 21st century (C1–C2) is 27% lower in 2050 relative to C8.

Due to limited data availability, globally consistent freight data is

difficult to obtain. In 2015, global freight demand was estimated to be 108 trillion tkm, most of which was transported by sea (ITF 2019). The growth rates of freight service demand vary dramatically among different regions: over the 1975–2015 period, road freight activity in India increased more than 9-fold, 30-fold in China, and 2.5-fold in the US (Mulholland et al. 2018). Global freight demand continues to grow but at a slower rate compared to passenger demand across all the scenarios in 2050 compared to modelled 2020 values. Global median freight demand could increase by 1.17–1.28 times in 2030 and 1.18–1.7 times in 2050 in all the scenarios with warming below 2.5°C (C1–C5). Like passenger transport, the models suggest that a  large share of growth occurs in Africa and Asian regions (59% growth in India and 50% growth in China in 2030 relative to a modelled year 2020) in the C5 scenarios that limit warming below 2.5°C (>50%) throughout the 21st  century. Global median freight

demand grows more slowly in the stringent temperature stabilisation scenarios, and is 40% and 22% lower in 2050 in the scenarios that limit or return warming to 1.5°C (>50%) during the 21st  century (C1–C2) and below 2.5°C scenarios (C3–C4), respectively, compared to scenarios with warming levels of above 4°C (C8).

GTEMs show broad ranges for future travel demand, particularly for

the freight sector. These results show more dependency on models than on baseline or policy scenarios. According to ITF Transport Outlook (ITF 2019), global passenger transport and freight demand could more than double by 2050 in a  business-as-usual scenario. Mulholland et  al. (2018) suggest the freight sector could grow 2.4-fold over 2015–2050 in the reference scenario, with the majority of growth attributable to developing countries. The IEA suggests a more modest increase in passenger transport, from 51 trillion pkm in 2014 to 110 trillion pkm in 2060, in a reference scenario without climate policies and a climate scenario that would limit emissions below 2°C. The demand for land-based freight transport in 2060 is, however, slightly lower in the climate scenario (116 trillion tkm) compared to the reference scenario (130 trillion tkm) (IEA 2017b). The ITF , however, suggests that ambitious decarbonisation policies could reduce global demand for passenger transport by 13–20% in

2050, compared to the business-as-usual scenario (ITF 2019; ITF 2021). The reduction in vehicle travel through shared mobility could reduce emissions from urban passenger transport by 30% compared to the business-as-usual scenario. Others suggest that reductions larger than 25%, on average, for both passenger and freight in 2030 and 2050 may be needed to achieve very low carbon emissions pathways (Fisch-Romito and Guivarch 2019). In the absence of large-scale carbon dioxide removal, few global studies highlight the need for significant demand reduction in critical sectors (aviation, shipping and road freight) in well below 2°C scenarios (van Vuuren et al. 2018; Grant et al. 2021; Sharmina et al. 2021).

Many models find small differences in passenger transport demand 
across temperature goals because IAM models rely on historical relationships between population, GDP , and demand for services to estimate future demand. This assumption poses a limitation to the modelling efforts, as mitigation efforts would likely increase travel costs that could result in lower transport demand (Zhang et  al. 2018). In most models, demand is typically an exogenous input. These models often assume mode shifts of activities from the most carbon-intensive modes (driving and flying for passenger travel and trucking for freight) to less carbon-intensive modes (public transit and passenger rail, and freight rail) to reduce emissions.
Traditionally there is a disconnection between IAM models and bottom-

up sectoral or city-based models due to the different scale (both spatial and temporal) and focus (climate mitigation vs urban pollution, safety (Creutzig 2016)). The proliferation of shared and on-demand mobility solutions is leading to rebound effects for travel demand (Chen and Kockelman 2016; Coulombel et al. 2019) and this is a new challenge for modelling. Some IAM studies have recently begun to explore demand-side solutions for reducing transport demand to achieve very low-carbon scenarios through a combination of culture and low-carbon lifestyle (Creutzig et al. 2018; van Vuuren et al. 2018); urban development (Creutzig et  al. 2015a); increased vehicle occupancy (Grubler et al. 2018); improved logistics and streamlined supply chains for the freight sector (Mulholland et  al. 2018); and disruptive low-

1104
1104
Chapter 10  T ransport10carbon innovation, described as technological and  business model 
innovations offering ‘novel value propositions to consumers and which 
can reduce GHG emissions if adopted at scale’ (Wilson et al. 2019). In 
the literature from national models, demand has been differentiated 
between conventional and sustainable development scenarios 
through narratives built around policies, projects, and programmes 
envisaged at the national level (Dhar and Shukla 2015; Shukla et al. 
2015) and price elasticities of travel demand (Dhar et  al. 2018). 
However, a  greater understanding of  the mechanisms underlying 
energy-relevant decisions and behaviours (Brosch et al. 2016), and the 
motivations for sustainable behaviour (Steg et al. 2015), are critically 
needed to realise these solutions.
Overall, passenger and freight activity are likely to continue to 
grow rapidly under the C7 (>3.0°C) scenarios, but most growth

would occur in developing countries. Most models treat travel 
demand exogenously following the growth of population and 
GDP , but they have limited representation of responses to price 
changes, policy incentives, behavioural shifts, nor innovative mobility 
solutions that can be expected to occur in more stringent mitigation 
scenarios. Chapter  5 provides a  more detailed discussion of the 
opportunities for demand changes that may result from social and 
behavioural interventions.
10.7.4 Transport Modes Trajectories
Globally over the last century, shares of faster transport modes have 
generally increased with increasing passenger travel demand (Schäfer 
2017; Schafer and Victor 2000). For short- to medium-distance travel, 
private cars have displaced public transit, particularly in OECD 
countries, due to a variety of factors, including faster travel times in 
many circumstances (Liao et al. 2020); consumers increasingly valuing

time and convenience with GDP growth; and broader transport 
policies, such as provision of road versus public transit infrastructure 
(Mattioli et al. 2020). For long-distance travel, travel via aviation for 
leisure and business has increased (Lee et al. 2021). These trends do 
not hold in all countries and cities, as many now have rail transit 
that is faster than driving (Newman et  al. 2015). For instance, 
public transport demand rose from 1990 through to 2016 in France, 
Denmark, and Finland (eurostat 2019). In general, smaller and denser 
countries and cities with higher or increasing urbanisation rates tend 
to have greater success in increasing public transport share. However, 
other factors, like privatisation of public transit (Bayliss and Mattioli 
2018) and urban form (ITF 2021), also play a role. Different transport 
modes can provide passenger and freight services, affecting the 
emissions trajectories for the sector.

Figure  10.19 shows activity trajectories for freight and passenger 
transport through 2100 relative to a  modelled year 2020 across 
different modes, based on the AR6 database for IAMs and global 
transport models. Globally, climate scenarios from IAMs, and policy 
and reference scenarios from global transport models, indicate 
increasing demand for freight and passenger transport via most modes 
through 2100 (Yeh et al. 2017; Mulholland et al. 2018; Zhang et al. 
2018; Khalili et al. 2019). Road passenger transport exhibits a similar 
increase (roughly tripling) through 2100 across scenarios. For road passenger transport, scenarios that limit or return warming 
to 1.5°C during the 21st  century (C1–C2) have a  smaller increase 
from modelled 2020 levels (median increase of 2.4 times modelled 
2020 levels) than do scenarios with higher warming levels (C3–C8) 
(median increase of 2.7–2.8 times modelled 2020 levels). There are

similar patterns for passenger road transport via light-duty vehicle, 
for which median increases from modelled 2020 levels are smaller 
for C1–C2 (3 times larger) than for C3–C5 (3.1 times larger) or C6–C7 
(3.2 times larger). Passenger transport via aviation exhibits a  2.2 
times median increase relative to modelled 2020 levels under C1–C2 
and C3–C5 scenarios but exhibits a 6.2 times increase under C6–C8. 
The only passenger travel mode that exhibits a decline in its median 
value through 2100 according to IAMs is walking/bicycling, in C3–
C5 and C6–C8 scenarios. However, in C1–C2 scenarios, walking/
bicycling increases by 1.4 times relative to modelled 2020 levels. 
At the 5th percentile of IAM solutions (lower edge of bands in 
Figure 10.19), buses and walking/bicycling for passenger travel both 
exhibit significant declines.
For freight, Figure  10.19 shows that the largest growth occurs in 
transport via road (Mulholland et al. 2018). By 2100, global transport

models suggest a  roughly four-fold increase in median-heavy-duty 
trucking levels relative to modelled 2020 levels, while IAMs suggest 
a  two- to four-fold increase in freight transport by road by 2100. 
Notably, the 95th percentile of IAM solutions see road transport 
by up to 4.7 times through 2100 relative to modelled 2020 levels, 
regardless of warming level. Other freight transport modes – aviation, 
international shipping, navigation, and railways – exhibit less growth 
than road transport. In scenarios that limit or return warming to 
1.5°C (>50%) during the 21st  century (C1–C2), navigation and 
rail transport remain largely unchanged and international shipping 
roughly doubles by 2100. Scenarios with higher warming (i.e., moving 
from C1–C2 to C6–C8) generally lead to more freight by rail and less 
freight by international shipping.
Relative to global trajectories, upper-income regions  – including 
North America, Europe, and the Pacific OECD  – generally see less

growth in passenger road via light-duty vehicle and passenger 
aviation, given more saturated demand for both. Other regions like 
China exhibit similar modal trends as the global average, whereas 
regions such as the African continent and Indian subcontinent exhibit 
significantly larger shifts, proportionally, in modal transport than the 
globe. In particular, the African continent represents the starkest 
departure from global results. Freight and passenger transport modes 
exhibit significantly greater growth across Africa than globally in all 
available scenarios. Across Africa, median freight and passenger 
transport via road from IAMs increases by 5 to 16 times and 4 to 
28 times, respectively, across warming levels by 2100 relative 
to modelled 2020 levels. Even C1 has considerable growth in Africa 
via both modes (3 to 16 times increase for freight and 4 to 29 times 
increase for passenger travel at 5th and 95th percentiles of IAM 
solutions by 2100).

As noted in Section  10.2, commonly explored mitigation options 
related to mode change include a  shift to public transit, shared 
mobility, and demand reductions through various means, including 
improved urban form, teleconferences that replace passenger

1105
1105
2040 2080
 2040 2080
 2040 2080
1.01.52.0
1.0
0.51.52.02.5
1.0
0.51.52.0
1.0
0.51.52.0
1.2
0.81.22.02.53.0
4
3
2
1
4
3
2
145
3
2
1
45
3
2
14
3
2
1
3
2
19
6
3
9
6
3
4
3
2
1
G-/NTEM Policy G-/NTEM ReferenceTransport activity by mode World [Index, 2020 level = 1.0]
Freight all Freight aviation Freight international shipping
Freight navigation Freight rail Freight road
Freight road: HDT Passenger all Passenger aviation
Passenger bicycling and walking Passenger rail Passenger road
Passenger road: 2/3W Passenger road: Bus Passenger road: LDVIAM C1–2: 1.5°C (>50%)
 IAM C6–8: ≥2.5°C (>50%)
 IAM C3–5: <2.5°C (>50%)
Figure 10.19 | Transport activity trajectories for passenger and freight across different modes.  Global passenger (billion pkm per year) and freight (billion tkm 
per year) demand projections relative to a modelled year 2020 index. Results for IAM are for selected stabilisation temperatures by 2100. Also included are global transport

models Reference and Policy scenarios. Data from the AR6 scenario database. Trajectories span the 5th to 95th percentiles across models, with a solid line indicating the median 
value across models.

1106
1106
Chapter 10  T ransport10travel (Creutzig et al. 2018; Grubler et al. 2018; Wilson et al. 2019), 
improved logistics efficiency, green logistics, and streamlined supply 
chains for the freight sector (Mulholland et  al. 2018). NDCs often 
prioritise options like bus improvements and enhanced mobility that 
yield pollution, congestion, and urban development co-benefits, 
especially in medium- and lower-income countries (Fulton et  al. 
2017). Conversely, high-income countries, most of which have 
saturated and entrenched private vehicle ownership, typically focus 
more on technology options, such as electrification and fuel efficiency 
standards (Gota et al. 2016). Available IAM and regional models are 
limited in their ability to represent modal shift strategies. As a result, 
mode shifts alone do not differentiate climate scenarios. While this 
lack of representation is a limitation of the models, it is unlikely that

such interventions would completely negate the increases in demand 
the models suggest. Therefore, transport via light-duty vehicle and 
aviation, freight transport via road, and other modes will likely 
continue to increase through to the end of the century. Consequently, 
fuel and carbon efficiency and fuel energy and technology will 
probably play crucial roles in differentiating climate scenarios, as 
discussed in the following sub-sections.
10.7.5 Energy and Carbon Efficiency Trajectories
This section explores what vehicle energy efficiencies and fuel 
carbon intensity trajectories, from the data available in the AR6 
database from IAMs and GTEMs, could be compatible with different 
temperature goals. Figure 10.20 shows passenger and freight energy 
intensity, and fuel carbon intensity, indexed relative to 2020Mod 
values. The top panel shows passenger energy intensity across all 
modes. LDVs constitute a  major share of this segment. Yeh et  al.

(2017) report 2.5–2.75 MJ vkm–1 in 2020 across models for the LDV 
segment, which is very close to the IEA estimate of 2.5 MJ vkm–1 for 
the global average fuel consumption for LDVs in 2017 (IEA 2020d). 
For reference, these numbers correspond to 1.6–1.7  MJ  pkm–1 for 
an occupancy rate of 1.5. The following results of the AR6 database 
are conditional on the corresponding reductions in fuel carbon 
intensity. Figure  10.20 shows that the scenarios suggest that 
passenger transport’s energy intensity drops to between 10–23% 
(interquartile ranges across C1–C4) in 2030 for scenarios in line with 
warming levels below 2°C. In 2050, the medians across the group 
of scenarios that limit or return warming to 1.5°C (>50%) during 
the 21st century (C1–C2), and scenarios that limit warming to 2°C 
(>67% or >50%) throughout the 2st century (C3–C4) suggest energy 
intensity reductions of 51% and 45–46% respectively. These values

correspond to annual average energy efficiency improvement rates 
of 2.3–2.4% and 2.0–2.1%, respectively, from 2020 to 2050. For 
reference, the IEA reports an annual energy efficiency improvement 
rate of 1.85% per  year in 2005–16 (IEA 2020d). In contrast, the 
results from GTEMs suggest lower energy efficiency improvement, 
with median values for policy scenarios of 39% reduction in 2050, 
corresponding to annual energy efficiency improvement rates close to 
1.6%. The IAM scenarios suggest median energy intensity reductions 
of passenger transport of 57–61% by the end of the century would 
align with warming levels of both 1.5°C and 2°C (C1–C4) given the 
corresponding decarbonisation of the fuels.The scenarios in line with warming levels of 1.5°C or 2°C goals 
(C1 to C4) show different trends for freight’s energy intensity. The 
amount of overshoot and differences in demand for freight services 
and, to some extent, fuel carbon intensities contribute to these

differences. For the two scenarios aligning with the warming levels 
of 1.5°C, the trajectories in 2030 and 2050 are quite different. The 
median C2 scenario that returns warming to 1.5°C (>50%) during 
the 21st century after high overshoot takes a trajectory with lower 
energy intensity improvements in the first half of the century. In 
contrast, the C1 scenario that limits warming to 1.5°C (>50%) during 
the 21st century with no or limited overshoot take on a more steadily 
declining trajectory across the means. The IAMs provide a less clear 
picture of required energy intensity improvements for freight than 
for passenger transport associated with different temperature 
targets. As for the carbon intensity of direct energy used across 
both passenger and freight, the modelling scenarios suggest very 
moderate reductions by 2030. The interquartile ranges for the C1 
scenarios suggest global average reductions in carbon intensity of

5–10%. Across the other scenarios compatible with warming levels 
of 1.5°C or 2°C (C2–C4), the interquartile ranges span from 1–6% 
reductions in carbon intensity of direct energy used for transport. For 
2050, the scenarios suggest that dependence on fuel decarbonisation 
increases with more stringent temperature targets. For the scenarios 
that limits warming to 1.5°C (>50%) during the 21st century with no 
or limited overshoot (C1), global carbon intensity of energy used for 
transport decreases by 37–60% (interquartile range) by 2050 with 
a mean of 50% reduction. The IAM scenarios in the AR6 database 
do not suggest full decarbonisation of transport fuels by 2100. The 
interquartile ranges across the C1–C4 set of scenarios, compatible 
with warming levels of 2°C and less, span from 61–91% reduction 
from 2020Mod levels.
Increasing the occupancy rate of passenger transport (Grubler et  al. 
2018) and reducing empty miles or increasing payload in freight

deliveries (Gucwa and Schäfer 2013; McKinnon 2018) via improved 
logistics efficiency or streamlined supply chains (Mulholland et  al. 
2018), can present significant opportunities to effectively improve 
energy efficiency and decrease GHG emissions in transport. However, 
the recent trends of consumer behaviours have shown a  declining 
occupancy rate of light-duty vehicles in industrialised countries 
(Schäfer and Yeh 2020), and the accelerating growing preference for 
SUVs challenges emissions reductions in the passenger car market (IEA 
2019d). These trends motivate a strong focus on demand-side options.
Based on the scenario literature, a 51% reduction in median energy 
intensity of passenger transport and a  corresponding 38–50% 
reduction in median carbon intensity by 2050 would be aligned 
with transition trajectories yielding warming levels below 1.5°C by 
the end of the century. For comparison, the LCA literature suggests

a switch from current ICEs to current BEVs would yield a reduction 
in energy intensity well beyond 45% and up to 70%, for a  mid-
sized vehicle (Section  10.4). Correspondingly, a  switch from diesel 
or gasoline to low-carbon electricity or low-carbon hydrogen would 
yield carbon intensity reduction beyond the median scenario value. 
Thus, the LCA literature suggests technologies exist today that would 
already match and exceed the median energy and carbon intensities 
values that might be needed by 2050 for low warming levels.

1107
1107
Energy/CO2 intensity of transport World [Index, 2020 level = 1.0]
IAM/sectoral scenarios
IAM C1: 1.5°C (>50%) low overshoot IAM C3: limit warming to 2°C (>67%)
213091 44 5331351
21 309144 5331341
21 3091 44 533134
1
0.00.20.40.60.81.01.22030Passenger energy intensity Freight energy intensity Fuel carbon intensity2050 2100
25 33 133 69 86 39 38 6 25 32 110 55 64 31 36
62532110 5564
3136
6
1312082
196
146 294
1258413 12082 196
146
294
1258413 120 82 196 146 294 12584
Figure 10.20 | Energy efficiency and carbon intensity of transport in 2030, 2050, and 2100 indexed to 2020 modelled year across scenarios.  Plots show

5th/95th percentile, 25th/75th percentile, and median. Numbers above the bars indicate the number of scenarios. Data from the AR6 scenario database.

1108
1108
Chapter 10  T ransport1010.7.6 Fuel Energy and Technology Trajectories
Two mechanisms for reducing carbon emissions from the transport 
sector are fuel switching for current vehicle technologies and 
transitioning to low-carbon vehicle technologies. Figure  10.21 
combines data from IAMs and GTEMs on shares of transport final 
energy by fuel. These shares account for fuel uses across modes – road, aviation, rail, and shipping – and both passenger and freight transport. 
Since the technologies have different conversion efficiencies, these 
shares of final energy by fuel are necessarily different from the shares 
by service (passenger-km or tonne-km) by fuel and shares of vehicle 
stock by fuel. For example, a current battery electric LDV powertrain 
is roughly three times more energy-efficient than a comparable ICE 
powertrain (Section  10.3, Table  10.9 in Appendix 10.1); thus, fuel 
Oil Electricity
Hydrogen Biofuels

OtherFuel shares of transport ﬁnal energy by service World [share]
Energy carrier
Gases
1.0
0.2
0.00.40.60.8
1.0
0.2
C1–2 C3–5 C6–8 C1–2 C3–5 C6–8 C1–2 C3–5 C6–80.00.40.60.82030 2050 2100Freight Passenger2.0-GS 2.0-Sup
CurPol 1.5-SupIllustrative pathway
Figure 10.21 | Global shares of final fuel energy in the transport sector in 2030, 2050, and 2100 for freight and passenger vehicles.  Plots show 10th/90th 
percentile, 25th–75th percentile, and median. Data from the AR6 scenario database.

1109
1109
shares of 0.25 for electricity and 0.75 for oil could correspond to 
vehicle stock shares of 0.5 and 0.5, respectively. In general, while 
models may project that EVs constitute a greater share of road vehicle 
stock, and provide a greater share of road passenger-kilometres, their 
share of transport final energy (Figure 10.21) can still remain lower 
than the final energy share of fuels used in less-efficient (e.g., ICE) 
vehicles. Thus, the shares of transport final energy by fuel presented 
in Figure 10.21 should be interpreted with care.
IAM and GTEM scenarios indicate that fuel and technology shifts 
are crucial to reduce carbon emissions to achieve lower levels of 
warming (Edelenbosch et al. 2017; IEA 2017b). Across the transport 
sector, a  technology shift towards advanced fuel vehicles is the 
dominant driver of decarbonisation in model projections. This trend is 
consistent across climate scenarios, with larger decreases in the final

energy share of oil in scenarios that achieve progressively lower levels 
of warming. Due to efficiency improvements, the higher efficiency 
of advanced fuel vehicles, and slower progress in the freight sector, 
the final energy share of oil decreases more rapidly after 2030. By 
2050, the final energy shares of electricity, biofuels, and alternative 
gaseous fuels increase, with shares from electricity generally about 
twice as high (median values from 10–30% across warming levels) 
as the shares from biofuels and gases (median values from 5–10%). 
While IAMs suggest that the final energy share of hydrogen will 
remain low in 2050, by 2100 the median projections include 5–10% 
hydrogen in transport final energy.
While few IAMs report final energy shares by transport mode or 
passenger/freight, several relevant studies provide insights into 
fuel share trends in passenger LDVs and freight vehicles. The IEA 
suggests that full LDV electrification would be the most promising

low-carbon pathway to meet a  1.75°C goal (IEA 2017b). The MIT 
Economic Projection and Policy Analysis model focuses on the 
future deployment of gasoline versus EV technologies in the global 
LDV stock (Ghandi and Paltsev 2019). These authors estimate that 
the global stock of vehicles could increase from 1.1 billion vehicles 
in 2015 up to 1.8 billion by 2050, with a growth in EVs from about 
1 million vehicles in 2015 up to 500 million in 2050. These changes are 
driven primarily by cost projections (mostly battery cost reductions). 
Similarly, the International Council on Clean Transport (ICCT) indicates 
that EV technology adoption in the light-duty sector can lead to considerable climate benefits. Their scenarios reach nearly 100% 
electrification of LDVs globally, leading to global GHG emissions from 
LDVs ranging from 0% to 50% of 2010 levels in 2050 (Lutsey 2015). 
Khalili et  al.(2019) estimate transport stocks through 2050 under

aggressive climate mitigation scenarios that nearly eliminate road 
transport emissions. They find the demand for passenger transport 
could triple through 2050, but emissions targets could be met 
through widespread adoption of BEVs (80% of LDVs) and, to a lesser 
extent, fuel cell and plug-in hybrid electric vehicles. Contrary to these 
estimates, the US Energy Information Administration finds small 
adoption of electrification for LDVs and instead identifies diffusion of 
natural gas-fuelled LDVs in OECD and, to a greater extent, non-OECD 
countries through 2040. This trend occurs in a reference and a ‘low 
liquids’ case, which lowers LDV ownership growth rates and increases 
preferences for alternative fuel vehicles. A comprehensive overview 
of regional technology adoption models across many methodological 
approaches can be found in Jochem et al. (2018).
In freight transport, studies indicate a  shift toward alternative

fuels would need to be supplemented by efficiency improvements. 
The IEA suggests efficiency improvements would be essential for 
decarbonisation of trucks, aviation, and shipping in the short-to-
medium term. At the same time, the IEA suggests that fuel switching 
to advanced biofuels would be needed to decarbonise freight in 
the long term (IEA 2019d). Mulholland et  al. (2018) investigated 
the impacts of decarbonising road freight in two scenarios: countries 
complying with COP21 pledges and a  second more ambitious 
reduction scenario in line with limiting global temperature rise to 
1.75°C. Despite the deployment of logistics improvements, high-
efficiency technologies, and low-carbon fuels, activity growth leads 
to a 47% increase in energy demand for road freight while overall 
GHG emissions from freight increase by 55% (4.8 GtCO2-eq) in 2050 
(relative to 2015) in the COP21 scenario. In the 1.75°C scenario, 
decarbonisation happens primarily through a  switch to alternative

fuels (hybrid electric and full battery electric trucks), which leads 
to a 60% reduction in GHG emissions from freight in 2050 relative 
to 2015. Khalili et al. (2019) also find substantial shifts to alternative 
fuels in HDVs under aggressive climate mitigation scenarios. Battery 
electric, hydrogen fuel cell, and plug-in hybrid electric vehicles 
constitute 50%, 30%, and 15% of heavy-duty vehicles respectively 
in 2050. They also find 90% of buses would be electrified by 2050.
Box 10.4 | Three Illustrative Mitigation Pathways
Section 10.7 presents the full set of scenarios in the AR6 database and highlights the broader trends of how the transport sector may 
transform in order to be compliant with different warming levels. This box elaborates on three illustrative mitigation pathways (IMPs) 
to exemplify a few different ways the sector may transform. Seven illustrative pathways are introduced in Section 3.2.5. In this box we

focus on three of the IMPs: (i) focus on deep renewable energy penetration and electrification (IMP-Ren), (ii) low demand (IMP-LD), 
and (iii) pathways that align with both Sustainable Development Goals and climate policies (IMP-SP). In particular, the variants of 
these three scenarios limit warming to 1.5°C with no or limited overshoot (C1).
All of the three selected pathways reach global net zero CO 2 emissions across all sectors between 2060 and 2070, but not all reach 
net zero GHG emissions (Figure 3.4). Panel (a) in Box 10.4, Figure 1 below shows the CO 2 trajectories for the transport sector for the 
selected IMPs. Please note that the year 2020 is modelled in these scenarios, therefore, the scenarios do not reflect the effects of

1110
1110
10.7.7 Insights from the Modelling Literature
This section provides an updated, detailed assessment of future 
transport scenarios from IAM, GTEMs, and NTEMs given a wide range 
of assumptions and under a  set of policy targets and conditions. 
The scenario modelling tools are necessary to aggregate individual 
options and understand how they fit into mitigation pathways from 
a systems perspective. The scenarios suggest that 43% (30–63% for the interquartile ranges) reductions in CO 2 emissions from transport 
(below modelled 2020 levels) by 2050 would be compatible with 
warming levels of 1.5°C (C1–C2 group). While the global scenarios 
suggest emissions reductions in energy supply sectors at large precede 
those in the demand sectors (Section 3.4.1), a subset of the scenarios 
also demonstrate that more stringent emission reductions in the 
transport sector are feasible. For example, the illustrative mitigation

pathways IMP-REN and IMP-LD suggest emissions reductions of 80% Box 10.4 (continued)
the COVID-19 pandemic. For the low demand scenarios IMP-LD and renewables pathway IMP-Ren, CO 2 emissions from the transport 
sector decreases to 10% and 20% of modelled 2020 levels by 2050 respectively. In contrast, the IMP-SP has a steady decline of 
transport sector CO 2 emissions over the century. By 2050, this scenario has a 50% reduction in emissions compared to modelled 2020 
levels. Panels (b), (c) and (d) show energy by different fuels for the three selected IMPs. The IMP-SP yields a drop in energy for transport 
of about 40% by the end of the century. CO 2 emissions reductions are obtained through a phase-out of fossil fuels with electricity and 
biofuels, complemented by a minor share of hydrogen, by the end of the century. In IMP-Ren, the fuel energy demand at the end of

the century is on a par with the 2020 levels, but the fuel mix has shifted towards a larger share of electricity complimented by biofuels 
and a minor share of hydrogen. For the IMP-LD scenario, the overall fuel demand decreases by 45% compared to 2020 levels by the 
end of the century. Oil is largely phased out by mid century, with electricity and hydrogen becoming the major fuels in the second half 
of the century. Across the three IMPs, electricity plays a major role, in combination with biofuels, hydrogen, or both.
Box 10.4, Figure 1 | Three Illustrative mitigation pathways for the Transport sector. Panel (a) shows CO 2 emissions from the transport sector indexed to 
simulated non-COVID-2019 2020 levels. Panels (b), (c), and (d) show fuels mix to achieve 1.5°C warming through three illustrative mitigation pathways: IMP-SP , 1.5 
IMP-Ren and IMP-LD, respectively. All data from IPCC AR6 scenario database.1.0 140
120
40
2020 2060 2050 2040 20300.8
0.6
0.4
0.2

0.0 0(a) CO 2 emissions from transport (b) 1.5 SP
(c) 1.5 RenCO 2 emissions indexed to 2020 Modelled EJ fuels per year(d) 1.5 LDEJ fuels per year EJ fuels per year1.5 REN
1.5 SP
1.5 LD
2100 2080GasesFossil
HydrogenBiofuels
20
2020 2060 2050 2040 2030 2100 2080 2090 2070
140
120
40
020
2020 2060 2050 2040 2030 2100 2080 2090 2070100
80
60140
120
40
020
80
60100
80
60

1111
1111
and 90% respectively are feasible by 2050 en route to warming levels 
of 1.5°C with low or no overshoot by the end of the century.
The scenarios from the different models project continued growth in 
demand for freight and passenger services, particularly in developing 
countries. The potential for demand reductions is evident, but 
the specifics of demand-reduction measures remain less explored by the 
scenario literature. This limitation notwithstanding, the IAM and GTEMs 
suggest that interventions that reduce the energy and fuel carbon 
intensity of transport are likely crucial to successful mitigation strategies.
The scenario literature suggests that serious attempts at carbon 
mitigation in the transport sector must examine the uptake of 
alternative fuels. The scenarios described in the IAMs and GTEMs 
literature decarbonise through a  combination of fuels. Across the 
scenarios, electrification plays a key role, complemented by biofuels

and hydrogen. In general terms, electrification tends to play the key 
role in passenger transport while biofuels and hydrogen are more 
prominent in the freight segment. The three illustrative mitigation 
pathways described in Box  10.4 exemplify different ways these 
technologies may be combined and still be compatible with warming 
levels of 1.5°C with low or no overshoot. Shifts towards alternative 
fuels must occur alongside shifts towards clean technologies 
in other sectors, as all alternative fuels have upstream impacts. 
Without considering other sectors, fuel shifts would not yield their 
full mitigation potentials. These collective efforts are particularly 
important for the electrification of transport, as the transformative 
mitigation potential is strongly dependent on the decarbonisation of 
the power sector. In this regard, the scenario literature is well aligned 
with the LCA literature reviewed in Section 10.4.

The models reviewed in this section would all generally be considered 
to have a good representation of fuels, technologies, and costs, but 
they often better represent land transport modes than shipping and 
aviation. While these models have their strengths in some areas, they 
have some limitations in other areas, like behavioural aspects. These 
models are also limited in their ability to account for unexpected 
technological innovation, such as a  breakthrough in heavy vehicle 
fuels, artificial intelligence, autonomy and big data, even the extent 
of digital communications replacing travel (Section 10.2). As a result 
of these limitations, the models cannot yet provide an exhaustive set 
of options for decarbonising the transport sectors. These limitations 
notwithstanding, the models can find solutions encompassing the 
transport sector and its interactions with other sectors that are 
compatible with stringent emissions mitigation efforts. The solutions

space of transportation technology trajectories is therefore wider than 
explored by the models, so there is still a need to better understand 
how all options in combination may support the transformative 
10.8 Enabling Conditions
10.8.1 Conclusions Across the Chapter
This final section draws some conclusions from the chapter and 
provides an overview-based feasibility assessment of the major transport mitigation options, as well as a  description of emerging 
issues. The section ends by outlining an integrated framework for 
enabling the transformative changes that are emerging and required 
to meet the potential transformative scenarios from Section 10.7.
Transport is becoming a major focus for mitigation as its GHG emissions 
are large and growing faster than those of other sectors, especially in 
aviation and shipping. The scenarios literature suggests that without 
mitigation actions, transport emissions could grow by up to 65% by

2050. Alternatively, successful deployment of mitigation strategies 
could reduce sectoral emissions by 68%, which would be consistent 
with the goal of limiting temperature change to 1.5°C above pre-
industrial levels. This chapter has reviewed the literature on all aspects 
of transport and has featured three special points of focus: (i) a survey of 
lifecycle analysis from the academic and industry community that uses 
these tools; (ii) surveying the modelling community for top-down and 
bottom-up approaches to identify decarbonisation pathways for the 
transport sector, and (iii) for the first time in the IPCC, separate sections 
on shipping and aviation. The analysis of the literature suggests three 
crucial components for the decarbonisation of the transport sector: 
demand and efficiency strategies, electromobility, and alternative fuels 
for shipping and aviation.
The challenge of decarbonisation requires a  transition of the

socio-technical system, which depends on the combination of 
technological innovation and societal change (Geels et  al. 2017). 
A socio-technical system includes technology, regulation, user 
practices and markets, cultural meaning, infrastructure, maintenance 
networks, and supply networks (Geels 2005) (Cross Chapter Box 12 
in Chapter 16). The multi-level perspective (MLP) is a framework that 
provides insights to assist policymakers when devising transformative 
transition policies (Rip and Kemp 1998; Geels 2002). Under the MLP 
framework, strategies are grouped into three different categories. 
The Micro level (niche) category includes strategies where innovation 
differs radically to that of the incumbent socio-technical system. 
The niche provides technological innovations a  protected space 
during development and usually requires considerable R&D and 
demonstrations. In the Meso level (regime) state, demonstrations

begin to emerge as options that can be adopted by leading groups 
who begin to overcome lock-in barriers from previous technological 
dependence. Finally, in the Macro level (landscape) stage, 
mainstreaming happens, and the socio-technical system enables 
innovations to break through. Figure 10.22 maps the MLP stages for 
the major mitigation strategies identified in this chapter.
Demand and behaviour. While technology options receive 
substantial attention in this chapter, there are many social and 
equity issues that cannot be neglected in any transformative 
change to mitigate climate change. Transport systems are socio-
economic systems that include systemic factors that are developing 
into potentially transformative drivers of emissions from the sector. 
These systemic drivers include, for example, changes in urban 
form that minimise automobile dependence and reduce stranded 
assets; behaviour change programmes that emphasise shared

values and economies; smart technologies that enable better and 
more equitable options for transit and active transport as well as 
integrated approaches to using autonomous vehicles; new ways of

1112
1112
enabling electric charging systems to fit into electricity grids, creating 
synergistic benefits to grids, improving the value of electric transit, and reducing range anxiety for EV users; and new concepts for the future economy such as circular economy, dematerialisation, and shared economy that have the potential to affect the structure of the transport sector. The efficacy of demand reduction and efficiency opportunities depends on the degree of prioritisation and focus by government policy. Figure  10.22 suggests that innovative demand and efficiency strategies are at the regime scales. While these strategies are moving beyond R&D, they are not mainstreamed yet and have been shown to work much more effectively if combined with technology changes, as has been outlined in the transformative scenarios from Section 10.7 and in Chapter 5.
Electromobility in land-based transport. Since AR5, there has been

a significant breakthrough in the opportunities to reduce transport GHG emissions in an economically efficient way due to electrification of land-based vehicle systems, which are now commercially available. EV technologies are particularly well established for light-duty passenger vehicles, including micromobility. Furthermore, there are positive developments to enable EV technologies for buses, light- and medium-duty trucks, and some rail applications (though advanced biofuels and hydrogen may also contribute to the decarbonisation of these vehicles in some contexts). In developing countries, where micromobility and public transit account for a large share of travel, EVs are ideal to support mitigation of emissions. Finally, demand for critical materials needed for batteries has become a  focus of attention, as described in Box 10.6.
Electromobility options are moving from regime to landscape levels.

This transition is evident in the trend of incumbent automobile manufacturers producing an increasing range of EVs in response to demand, policy, and regulatory signals. EVs for light-duty passenger travel are largely commercial and likely to become competitive with ICE vehicles in the early 2020s (Dia 2019; Bond et al. 2020; Koasidis et al. 2020). As these adopted technologies increase throughout cities and regions, governments and energy suppliers will have to deploy new infrastructure to support them, including reliable low-carbon grids and charging stations (Sierzchula et  al. 2014). In addition, regulatory reviews will be necessary to ensure equitable transition Regime scale
ASI policy interventions
Niche scaleR&D demonstrationsLandscape scaleRemove barriers to mainstream
Time to deliverScale of adoptionLarge
Medium
Small
5 yearsLowHigh• Integrated
strategies fornet zero transport, land use and energy
• Regulatory
reviews onvehicles, landdevelopmentand energy
• Procurement

partnershipson governmentﬂeets, buses, EVs, taxis, tuk-tuks, share bikes
• City and regional
engagementand educationprogrammes• ASI regulations for
each mode
• ASI strategies for each
mode (net zero)
settlement fabric(central, inner, outer, rural, remote)
• ASI engagement and
education programmes• Net zero targets, global
• R&D funds from ports
and companies
• Demonstrations in major
ports
• Local engagement and
education programmes
around the worldMedium
Market 
commerciality
10 years 15 yearsLight vehicle 
electro-
mobility
Personal and 
freight modes 
Travel 
demand 
and vehicle 
efﬁciency
All modes 
Heavy 
vehicle fuels
Shipping and aviation
Figure 10.22 | Mitigation options and enabling conditions for transport.  Niche scale includes strategies that still require innovation.

1113
1113
and achievement of SDGs, addressing the multitude of possible 
barriers that may be present due to the incumbency of traditional 
automotive manufacturers and associated supporting elements of 
the socio-technical system (Newman 2020b) (Chapter 6). Similarly, 
new partnerships between government, industry, and communities 
will be needed to support the transition to electromobility. These 
partnerships could be particularly effective at supporting engagement 
and education programmes (Newman 2020b) (Chapter 8).
Deployment of electromobility is not limited to developed 
countries. The transportation sector in low- and middle-income 
countries includes millions of gas-powered motorcycles within cities 
across Africa, South-East Asia, and South America (Posada et  al. 
2011; Ehebrecht et al. 2018). Many of these motorcycles function as 
taxis. In Kampala, Uganda, estimates place the number of motorcycle

taxis, known locally as boda bodas, at around 40,000 (Ehebrecht et al. 
2018). The popularity of the motorcycle for personal and taxi use is 
due to many factors including lower upfront costs, lack of regulation, 
and mobility in highly congested urban contexts (Posada et al. 2011; 
UNECE 2018). While motorcycles are often seen as a  more fuel-
efficient alternative, emissions can be worse from two-wheelers than 
cars, particularly nitrogen oxides (NOx), carbon monoxide (CO), and 
hydrocarbon emissions (Vasic and Weilenmann 2006; Ehebrecht et al. 
2018). These two-wheeler emissions contribute to dangerous levels of 
air pollution across many cities in low- and middle-income countries. 
In Kampala, for example, air pollution levels frequently exceed levels 
deemed safe for humans by the World Health Organization (Kampala 
Capital City Authority 2018; World Health Organization 2018; Airqo 
2020). To mitigate local and environmental impacts, electric boda

boda providers are emerging in many cities, including Zembo in 
Kampala and Ampersand in Kigali, Rwanda.
Bulawayo, the second-largest city in Zimbabwe, is also looking at 
opportunities for deploying electromobility solutions. The city is now 
growing again after a  difficult recent history, and there is a  new 
emphasis on achieving the Sustainable Development Goals (City 
of Bulawayo 2020a; City of Bulawayo 2020b). With these goals in 
mind, Bulawayo is seeking opportunities for investment that can 
enable leapfrogging in private, fossil fuel vehicle ownership. In 
particular, trackless trams, paired with solar energy, have emerged 
as a potential pathway forward (Kazunga 2019). Trackless trams are 
a new battery-based mid-tier transit system that could enable urban 
development around stations and that use solar energy for powering 
both transit and the surrounding buildings (Newman et  al. 2019). 
The new trams are rail-like in their capacities and speed, providing

a vastly better mobility system that is decarbonised and enables low 
transport costs (Ndlovu and Newman 2020). While this concept is 
only under consideration in Bulawayo, climate funding could enable 
the wider deployment of such projects in developing countries.
Fuels for aviation and shipping. Despite technology improvements 
for land-based transport, equivalent technologies for long distance 
aviation and shipping remain elusive. Alternative fuels for use in 
long-range aviation and shipping are restricted to the niche level. 
The aviation sector is increasingly looking towards synthetic fuels 
using low-carbon combined with CO 2 from direct air capture, while 
shipping is moving towards ammonia produced using low-carbon hydrogen. Biofuels are also of interest for these segments. To move 
out of the niche level, there is a need to set deployment targets to 
support breakthroughs in these fuels. Similarly, there is a need for

regulatory changes to remove barriers in new procurement systems 
that accommodate uncertainty and risks inherent in the early adoption 
of new technologies and infrastructure (Borén 2019; Sclar et  al. 
2019; Marinaro et  al. 2020). R&D programmes and demonstration 
trials are the best focus for achieving fuels for such systems. Finally, 
there is a need for regulatory changes. Such regulatory changes need 
to be coordinated through ICAO and IMO as well as with national 
implementation tools related to the Paris Agreement (see Box 10.5). 
Long-term visions, including creative exercises for cities and regions, 
will be required, providing a  protected space for the purpose of 
trialling new technologies (Borén 2019; Geels 2019).
10.8.2 Feasibility Assessment
Figure 10.23 sets out the feasibility of the core mitigation options using 
the six criteria created for the cross-sectoral analysis. This feasibility 
assessment outlines how the conclusions outlined in Section 10.8.1

fit into the broader criteria created for feasibility in the whole AR6 
report and that emphasise the SDGs. Figure  10.23 highlights that 
there is high confidence that demand reductions and mode shift can 
be feasible as the basis of a GHG emissions mitigation strategy for 
the transport sector. However, demand-side interventions work best 
when integrated with technology changes. The technologies that can 
support such changes have a range of potential limitations as well 
as opportunities. EVs have a reliance on renewable resources (wind, 
solar, and hydro) for power generation, which could pose constraints 
on geophysical resources, land use, and water use. Furthermore, 
expanding the deployment of EVs requires a rapid deployment of new 
power generation capacity and charging infrastructure. The overall 
feasibility of electric vehicles for land transport is likely high and their 
adoption is accelerating. HFCVs for land transport would also have

constraints related to geophysical resource needs, land use, and water 
use. These constraints are likely higher than for EVs, since producing 
hydrogen with electricity reduces the overall efficiency of meeting 
travel demand. Furthermore, the infrastructure needed to produce, 
transport, and deliver hydrogen is under-developed and would require 
significant R&D and a  rapid scale-up. Thus, the feasibility of HFCV 
is likely lower than for EVs. Biofuels could be used in all segments 
of the transport sector, but there may be some concerns about their 
feasibility. Specifically, there are concerns about land use, water use, 
impacts on water quality and eutrophication, and biodiversity impacts. 
Advanced biofuels could mitigate some concerns and the feasibility of 
using these fuels likely varies by world region. The feasibility 
assessment for alternative fuels for shipping and aviation suggests 
that hydrogen-based fuels like ammonia and synthetic fuels have the

lowest technology readiness of all mitigation options considered in 
this chapter. Reliance on electrolytic hydrogen for the production of 
these fuels poses concerns about land and water use. Using ammonia 
for shipping could pose risks for air quality and toxic discharges to the 
environment. The DAC/BECCS infrastructure that would be needed to 
produce synthetic fuel does not yet exist. Thus, the feasibility suggests 
that the technologies for producing and using these hydrogen-based 
fuels for transport are in their infancy.

1114
1114
Figure 10.23 | Summary of the extent to which different factors would enable or inhibit the deployment of mitigation options in transport.  Blue bars indicate the extent to which the indicator enables the implementation 
of the option (E) and orange bars indicate the extent to which an indicator is a barrier (B) to the deployment of the option, relative to the maximum possible barriers and enablers assessed. An ‘X’ signifies the indicator is not applicable or 
does not affect the feasibility of the option, while a forward slash indicates that there is no or limited evidence whether the indicator affects the feasibility of the option. The shading indicates the level of confidence, with darker shading 
signifying higher levels of confidence. Appendix 10.3 provides an overview of the extent to which the feasibility of options may differ across context (e.g., region), time (e.g., 2030 versus 2050), and scale (e.g., small versus large), and

includes a line of sight on which the assessment is based. The assessment method is explained in Annex II.11.
Land use
readiness
Demand reduction and mode shift
Biofuels for land transport, aviation, and shipping
Ammonia for shipping
Synthetic fuels for heavy-duty land transport, 
aviation and shipping
Electric vehicles for land transport
Hydrogen FCV for land transport

0 50 100Limited or no evidence

1115
1115
Box 10.5 | Governance Options for Shipping and Aviation
Whenever borders are crossed, the aviation and shipping sector creates international emissions that are not assigned to states’ 
Nationally Declared Contributions under the Paris Agreement. Emissions from these segments are rapidly growing (apart from 
COVID-19 affecting aviation) and are projected to grow between 60% to 220% by 2050 (IPCC 2018; UNEP 2020). Currently, the 
International Civil Aviation Organization (ICAO) and the International Marine Organization (IMO), specialised UN Agencies, are 
responsible for accounting and suggesting options for managing these emissions.
Transformational goals?
ICAO has two global aspirational goals for the international aviation sector: 2% annual fuel efficiency improvement through 2050; 
and carbon neutral growth from 2020 onwards. To achieve these goals, ICAO has established CORSIA  – Carbon Offsetting and

Reduction Scheme for International Aviation, a market-based programme.
In 2018, IMO adopted an Initial Strategy on the reduction of GHG emissions from ships. This strategy calls for a reduction of the carbon 
intensity of new ships through implementation of further phases of the energy efficiency design index (EEDI). The IMO calls for a 40% 
reduction of the carbon intensity of international shipping by 2030, and is striving for a 70% reduction by 2050. Such reductions in 
carbon intensity would result in an overall decline in emissions of 50% in 2050 (relative to 2008).
These goals are likely insufficiently transformative for the decarbonisation of aviation or shipping, though they are moving towards 
a start of decarbonisation at a period in history where the options are still not clear, as set out in Sections 10.5 and 10.6.
Regulations?
The ICAO is not a regulatory agency, but rather produces standards and recommended practices that are adopted in national and

international legislation. IMO does publish ‘regulations’ but does not have powers of enforcement. Non-compliance can be regulated 
by nation states if they so desire, as a ship’s MARPOL certificate, issued by the flag state of the ship, means there is some responsibility 
for states with global shipping fleets.
Paris?
Some authors in the literature have argued that emissions from international aviation and shipping should be part of the Paris 
Agreement (Gençsü and Hino 2015; Lee 2018; Traut et al. 2018; Rayner 2021), arguing that the shipping and aviation industries 
would prefer emissions to be treated under an international regime rather than a national-oriented regime. If international aviation 
and shipping emissions were a part of the Paris Agreement, it may remove something of the present ambiguity about responsibilities. 
However, inclusion in the Paris Agreement is unlikely to fundamentally change emissions trends unless targets and enforcement

mechanisms are developed, by ICAO and IMO or by nation states through global processes.10.8.3 Emerging Transport Issues
Planning for integration with the power sector: Decarbonising 
the transport sector will require significant growth in low-carbon 
electricity to power EVs, and more so for producing energy-
intensive fuels, such as hydrogen, ammonia and synthetic fuels. 
Higher electricity demand will necessitate greater expansion 
of the power sector and increase land use. The strategic use 
of energy-intensive fuels, focused on harder-to-decarbonise 
transport segments, can minimise the increase in electricity 
demand. Additionally, integrated planning of transport and power 
infrastructure could enable sectoral synergies and reduce the 
environmental, social, and economic impacts of decarbonising 
transport and energy. For example, smart charging of EVs could

support more efficient grid operations. Hydrogen production, which is likely crucial for the decarbonisation of shipping and aviation, 
could also serve as storage for electricity produced during low-
demand periods. Integrated planning of transport and power 
infrastructure would be particularly useful in developing countries 
where ‘greenfield’ development doesn’t suffer from constraints 
imposed by legacy systems.
Shipping and aviation governance: Strategies to deliver 
fuels in sufficient quantity for aviation and shipping to achieve 
transformative targets are growing in intensity and often feature the 
need to review international and national governance. Some authors 
in the literature have argued that the governance of the international 
transport systems could be included in the Paris Agreement process 
(Gençsü and Hino 2015; Lee 2018; Traut et al. 2018). Box 10.6 sets 
out these issues.

1116
1116
Chapter 10  T ransport10Box 10.5 (continued)
Individual nations?
If international regulations are not made, then the transformation of aviation and shipping will be left to individual nations. In 2020, 
Switzerland approved a new CO 2 tax on flights (The Swiss Parliament 2020), with part of its revenues earmarked for the development 
of synthetic aviation fuels, to cover up to 80% of their additional costs compared to fossil jet fuel (Energieradar 2020). Appropriate 
financing frameworks will be a key to the large-scale market adoption of these fuels. Egli et al. (2019) suggest that the successful 
design of investment policies for solar and wind power over the past 20 years could serve as a model for future synthetic aviation 
fuels production projects ‘attracting a broad spectrum of investors in order to create competition that drives down financing cost’, and

with state investment banks building ‘investor confidence in new technologies.’ These national investment policies would provide the 
key enablers for successful deployments.
Managing critical minerals: Critical minerals are required to 
manufacture lithium-ion batteries (LIB) and other renewable power 
technologies. There has been growing awareness that critical minerals 
may face challenges related to resource availability, labour rights, and 
costs. Box 10.6 sets out the issues, showing how emerging national 
strategies on critical minerals, along with requirements from major 
vehicle manufacturers, are addressing the need for rapid development of new mines with a  more balanced geography, less use of cobalt 
through continuing LIB innovations, and a focus on recycling batteries. 
The standardisation of battery modules and packaging within 
and across vehicle platforms, as well as increased focus on design 
for recyclability, are important. Given the high degree of potential

recyclability of LIBs, a near closed-loop system in the future would be 
a feasible opportunity to minimise critical mineral issues.
Box 10.6 | Critical Minerals and The Future of Electromobility and Renewables
The global transition towards renewable energy technologies and battery systems necessarily involves materials, markets, and supply 
chains on a hitherto unknown scale and scope. This has raised concerns regarding mineral requirements central to the feasibility of 
the energy transition. Constituent materials required for the development of these low-carbon technologies are regarded as ‘critical’ 
materials (US Geological Survey 2018; Commonwealth of Australia 2019; Lee et al. 2020; Marinaro et al. 2020; Sovacool et al. 2020). 
‘Critical materials’ are critical because of their economic or national security importance, or high risk of supply disruption. Many of

these materials and rare earth elements (REEs) as ‘technologically critical’, not only due to their strategic or economic importance but 
the risk of short supply or price volatility (Marinaro et al. 2020). In addition to these indicators, production growth and market dynamics 
are also incorporated into screening tools to assess emerging trends in material commodities that are deemed as fundamental to the 
well-being of the nation (NSTC 2018).
The critical materials identified by most nations are: REEs neodymium and dysprosium for permanent magnets in wind turbines and 
electric motors; lithium and cobalt, primarily for batteries though many other metals are involved; and, cadmium, tellurium, selenium, 
gallium and indium for solar PV manufacture (Valero et al. 2018; Giurco et al. 2019). Predictions are that the transition to a clean 
energy world will be significantly energy intensive (World Bank Group 2017; Sovacool et al. 2020), putting pressure on the supply

chain for many of the metals and materials required.
Governance of the sustainability of mining and processing of many of these materials, in areas generally known for their variable 
environmental stewardship, remains inadequate and often a source for conflict. Sovacool et al. (2020) propose four holistic recommendations 
for improvement to make these industries more efficient and resilient: diversification of mining enterprises for local ownership and 
livelihood benefit; improved traceability of material sources and transparency of mining enterprises; exploration of alternative resources; 
and the incorporation of minerals into climate and energy planning by connecting to the NDCs under the Paris Agreement.
Resource constraints?
Valero et al. (2018) highlight that the demand for many of the REEs and other critical minerals will, at the current rate of renewable energy

infrastructure growth, increase by 3000 times or more by 2050. Some believe this growth may reach constraints in supply (Giurco et al. 
2019). Others suggest that the minerals involved are not likely to physically run out (Sovacool et al. 2020) if well managed, especially 
as markets are found in other parts of the world (for example the transition away from lithium from brine lakes to hard rock sources). 
Lithium hydroxide, more suitable for batteries, now competes well, in terms of cost, when extracted from rock sources (Azevedo et al. 
2018) due to the ability to more easily create high quality lithium hydroxide from rock sources, even though brines provide a cheaper

1117
1117
Enabling creative foresight: Human culture has always had 
a  creative instinct that enables the future to be better dealt with 
through imagination (Montgomery 2017). Science and engineering 
have often been preceded by artistic expressions; for example Jules 
Verne first dreamed of the hydrogen future in 1874 in his novel 
The Mysterious Island. Autonomous vehicles have regularly occupied 
the minds of science fiction authors and filmmakers (Braun 2019). 
Such narratives, scenario building, and foresighting are increasingly 
seen as a part of the climate change mitigation process (Lennon et al. 
2015; Muiderman et al. 2020) and can ‘liberate oppressed imaginaries’ 
(Luque-Ayala 2018). Barber (2021) emphasised the important role of 
positive images about the future instead of dystopian visions and the 
impossibility of business-as-usual futures.
Transport visions can be a part of this cultural change as well as the

more frequently presented visions of renewable energy (Wentland 
2016; Breyer et  al. 2017). There are some emerging technologies, 
like Maglev, Hyperloop, and drones that are likely to continue the 
electrification of transport even further (Daim 2021) and which are 
only recently at the imagination stage. Decarbonised visions for 
heavy vehicle systems appear to be a core need from the assessment 
of technologies in this chapter. Such visioning or foresighting requires 
deliberative processes and the literature contains a growing list of 
transport success stories based on such processes (Weymouth and 
Hartz-Karp 2015). Ultimately, reducing GHG emissions from the 
transport sector would benefit from creative visions that integrate a  broad set of ideas about technologies, urban and infrastructure 
planning (including transport, electricity, and telecommunications 
infrastructure), and human behaviour and at the same time can 
create opportunities to achieve the SDGs.

Enabling transport climate emergency plans, local pledges 
and net zero strategies: National, regional and local governments 
are now producing transport plans with a climate emergency focus 
(Jaeger et  al. 2015; Pollard 2019). Such plans are often grounded 
in the goals of the Paris Agreement, based around local low-
carbon transport roadmaps that contain targets for and involve 
commitments or pledges from local stakeholders, such as workplaces, 
local community groups, and civil society organisations. Pledges often 
include phasing out fossil fuel-based cars, buses, and trucks (Plötz 
et  al. 2020), strategies to meet the targets through infrastructure, 
urban regeneration and incentives, and detailed programmes to 
help citizens adopt change. These institution-led mechanisms could 
include bike-to-work campaigns, free transport passes, parking 
charges, or eliminating car benefits. Community-based solutions like 
solar sharing, community charging, and mobility as a  service can

generate new opportunities to facilitate low-carbon transport futures. 
Cities in India and China have established these transport roadmaps, 
which are also supported by the United Nations Centre for Regional 
Development’s Environmentally Sustainable Transport programme 
(Baeumler et al. 2012; Pathak and Shukla 2016; UNCRD 2020). There 
have been concerns raised that these pledges may be used to delay Box 10.6 (continued)
source of lithium (Kavanagh et al. 2018). Australia has proven resources of all the Li-ion battery minerals and has a strategy for their 
ethical and transparent production (Commonwealth of Australia 2019). Changes in the technology have also been used to reduce need 
for certain critical minerals (Månberger and Stenqvist 2018). Recycling of all the minerals is not yet well developed but is likely to be 
increasingly important (Habib and Wenzel 2014; World Bank Group 2017; Giurco et al. 2019; Golroudbary et al. 2019).
International collaboration

There have been many instances since the 1950s when the supply of essential minerals has been restricted by nations in times of 
conflict and world tensions, but international trade has continued under the framework of the World Trade Organization. Keeping 
access open to critical minerals needed for the low-carbon transition will be an essential role of the international community as the 
need for local manufacture of such renewable and electromobility technologies will be necessary for local economies. Nassar et al. 
(2020) report that over the past 30 years the US has become increasingly reliant in imports to meet domestic demand for minerals, 
including REEs. In terms of heavy REEs, essential for permanent magnets for wind turbines, China has a  near-monopoly on REE 
processing, though other mines and manufacturing facilities are now responding to these constrained markets (Stegen 2015; Gulley

et al. 2018; Gulley et al. 2019; Yan et al. 2020). China, on the other hand, is reliant on other nations for the supply of other critical 
metals, particularly cobalt and lithium for batteries.
A number of critical materials strategies have now been developed by nations developing the manufacturing base of new power 
and transport technologies. Some of these strategies pay particular attention to the supply of lithium (Martin et al. 2017; Hache et al. 
2019). For example, Horizon 2020, a substantial EU Research and Innovation programme, couples research and innovation in science, 
industry, and society to foster a circular economy in Europe, thus reducing bottlenecks in the EU nations. Similarly CREEN (Canada 
Rare Earth Elements Network) is supporting the US–EU–Japan resource partnership with Australia (Klossek et al. 2016).
As renewables and electromobility-based development leapfrog into the developing world it will be important to ensure the critical

minerals issues are managed for local security of supply as well as participation in the mining and processing of such minerals to 
enable countries to develop their own employment around renewables and electromobility (Sovacool et al. 2020).

1118
1118
Chapter 10  T ransport10climate action in some cases (Lamb et  al. 2020) but such pledges 
can be calculated at a personal level and applied through every level 
of activity from individual, household, neighbourhood, business, 
city, nation or groups of nations (Meyer and Newman 2020) and 
are increasingly being demonstrated through shared communities 
and local activism (Bloomberg and Pope 2017; Sharp 2018; Figueres 
and  Rivett-Carnac 2020). Finally, the world’s major financing 
institutions are also engaging in decarbonisation efforts by requiring 
their recipients to commit to Net Zero Strategies before they can 
receive their funding (Robins 2018; Newman 2020a) (Chapter  15, 
Cross-Chapter Box 1 in Chapter 1). As a result, transparent methods 
are emerging for calculating what these financing requirements 
mean for transport by companies, cities, regions, and infrastructure projects (Chapters 8 and 15). The continued engagement of financial

institutions may, like in other sectors, become a  major factor in 
enabling transformative futures for transport as long as governance 
and communities continue to express the need for such change.
10.8.4 Tools and Strategies to Enable Decarbonisation 
of the Transport Sector
Using the right tools and strategies is crucial for the successful 
deployment of mitigation options. Table 10.7 summarises the tools and 
strategies required to enable electromobility, new fuels for aviation 
and shipping, and the more social aspects of demand efficiency.
Table 10.7 | Tools and strategies for enabling mitigation options to achieve transformative scenarios.
Tools and 
strategiesTravel demand reduction (TDR) 
and fuel/vehicle efficiencyLight vehicle electromobility systemsAlternative fuel systems 
for Shipping and Aviation
Education and R&DTDR can be assisted with digitalisation, connected 
autonomous vehicle, EVs and mobility as a Service 
(Marsden et al. 2018; Shaheen et al. 2018).

Knowledge gaps on TDR exist for longer distance 
travel (intercity); non-mandatory trips (leisure; 
social trips), and travel by older people. Travel 
demand foresighting tools can be open source 
(Marsden 2018).Behaviour change programmes help EVs 
become more mainstream. R&D will help on 
the socio-economic structures that impede 
adoption of EVs, the urban structures that enable 
reduced car dependence, and how EVs can assist 
grids (Newman 2010; Taiebat and Xu 2019; 
Seto et al. 2021).R&D is critical for new fuels and to test the full 
lifecycle costs of various heavy vehicle options 
(Marinaro et al. 2020).
Access and equityTDR programmes in cities can be inequitable. 
To avoid such inequities, there is a need for 
better links to spatial and economic development 
(Marsden et al.2018), mindful of diverse local 
priorities, personal freedom and personal 
data (Box 10.1).Significant equity issues with EVs in the transition 
period can be overcome with programmes that

enable affordable electric mobility, especially 
public transit (IRENA 2016).Shipping is mostly freight and is less of 
a problem but aviation has big equity issues 
(Bows-Larkin 2015).
Financing economic 
incentives and 
partnershipsCarbon budget implications of different demand 
futures should be published and used to help 
incentivise net zero projects (Marsden 2018). 
Business and community pledges for net zero can 
be set up in partnership agreements (Section 10.8.3).Multiple opportunities for financing, economic 
incentives, and partnerships with clear economic 
benefits can be assured, especially using the role 
of value capture in enabling such benefits. The 
nexus between EVs and the electricity grid needs 
opportunities to demonstrate positive partnership 
projects (Zhang et al. 2014; Mahmud et al. 2018; 
Newman et al. 2018; Sovacool et al. 2018; Sharma 
and Newman 2020).Taking R&D into demonstration projects is 
the main stage for heavy vehicle options and

these are best done as partnerships. Government 
assistance will greatly assist in such projects 
as well as an R&D levy. Abolishing fossil fuel 
subsidies and imposing carbon taxes is likely 
to help in the early stages of heavy vehicle 
transitions (Sclar et al. 2019).
Co-benefits and 
overcoming 
fragmentationProgrammes that focus on people-centred solutions 
for future mobility, with more pluralistic and feasible 
sets of outcomes for all people, can be successful. 
They need to focus on more than simple benefit-
cost ratios and include well-being and livelihoods, 
considering transport as a system rather than loosely 
connected modes, as well as behaviour change 
programmes (Barter and Raad 2000; Newman 2010; 
Martens 2020).The SDG benefits of zero-carbon light vehicle 
transport systems are being demonstrated and 
can now be quantified as nations mainstream this 
transition. Projects with transit and sustainable 
housing are more able to show such benefits.

New benefit-cost ratio methods that focus on 
health benefits in productivity are now favouring 
transit and active transport (Buonocore et al. 
2019; UK DoT 2019; Hamilton et al. 2021).Heavy vehicle systems can also demonstrate 
SDG co-benefits if formulated with these in 
mind. Demonstrations of how innovations can 
also help SDGs will attract more funding. Such 
projects need cross-government consideration 
(Pradhan et al. 2017).
Regulation and 
assessmentImplementing a flexible regulatory framework 
is needed for most TDR (Li and Pye 2018). 
Regulatory assessment can help with potential 
additional (cyber) security risks due to digitalisation, 
autonomous vehicles, the internet of things, and 
big data (Shaheen and Cohen 2019). Assessment 
tools and methods need to take account of greater 
diversity of population, regions, blurring of modes, 
and distinct spatial characteristics (Newman and 
Kenworthy 2015).With zero-carbon light vehicle systems rapidly

growing, the need for a regulated target and 
assessment of regulatory barriers can assist each 
city and region to transition more effectively. 
Regulating EVs for government fleets and 
recharge infrastructure can establish incentives 
(Bocken et al. 2016).Zero-carbon heavy vehicle systems need to 
have regulatory barrier assessments as they 
are being evaluated in R&D demonstrations 
(Sclar et al. 2019).

1119
1119
Transport Chapter 1010Tools and 
institutional 
capacityTDR works better if adaptive decision-making 
approaches focus on more inclusive and whole-
of-system benefit-cost ratios (Marsden 2018; 
Yang et al. 2020).Governance and institutional capacity can 
now provide international exchanges and 
education programmes based on successful cities 
and nations, enabling light vehicle decarbonisation 
to create more efficient and effective 
policy mechanisms towards self-sustaining 
markets (Greene et al. 2014; Skjølsvold and 
Ryghaug 2019).Governance and institutional capacity can help 
make significant progress if targets are backed 
with levies for not complying. Carbon taxes 
would also affect these segments. A review 
of international transport governance is likely 
(Makan and Heyns 2018).
Enabling

infrastructureEnsuring space for active transport and urban 
activities is taken from road space will be necessary 
in some places (Gössling et al. 2021b).
Increasing the proportion of infrastructure that 
supports walking in urban areas will structurally 
enable reductions in car use (Newman and 
Kenworthy 2015) (Section 10.2).
Creating transit activated corridors of transit-oriented 
development-based rail or mid-tier transit using 
value capture for financing will create inherently 
less car dependence (McIntosh et al. 2017; 
Newman et al. 2019).Large-scale electrification of LDVs requires 
expansion of low-carbon power systems, while 
charging or battery swapping infrastructure is 
needed for some segments (Gnann et al. 2018; 
Ahmad et al. 2020).In addition to increasing the capabilities 
to produce low- or zero-carbon fuels for 
shipping and aviation, there is a need to invest 
in supporting infrastructure including low-carbon 
power generation. New hydrogen delivery

and refuelling infrastructure may be needed 
(Maggio et al. 2019). For zero-carbon synthetic 
fuels, infrastructure is needed to support carbon 
capture and CO 2 transport to fuel production 
facilities (Edwards and Celia 2018).

1120
1120
Chapter 10  T ransport10Frequently Asked Questions (FAQs)
FAQ 10.1 |   How important is electromobility in decarbonising transport and are there major 
constraints in battery minerals?
Electromobility is the biggest change in transport since AR5. When powered with low-carbon electricity, electric vehicles (EVs) 
provide a  mechanism for major GHG emissions reductions from the largest sources in the transport sectors, including cars, 
motorbikes, autorickshaws, buses and trucks. The mitigation potential of EVs depends on the decarbonisation of the power system. 
EVs can be charged by home or business renewable power before or in parallel to the transition to grid-based low-carbon power.
Electromobility is happening rapidly in micromobility (e-autorickshaws, e-scooters, e-bikes) and in transit systems, especially buses. 
EV adoption is also accelerating for personal cars. EVs can be used in grid stabilisation through smart charging applications.

The state-of-the-art lithium-lon batteries (LIBs) available in 2020 are superior to alternative cell technologies in terms of battery life, 
energy density, specific energy, and cost. The expected further improvements in LIBs suggest these chemistries will remain superior 
to alternative battery technologies in the medium term, and therefore LIBs will continue to dominate the electric vehicle market.
Dependence on LIB metals will remain, which may be a concern from the perspective of resource availability and costs. However, 
the demand for such metals is much lower than the reserves available, with many new mines starting up in response to the new 
market, particularly in a diversity of places.
Recycling batteries will significantly reduce long-term resource requirements. The standardisation of battery modules and packaging 
within and across vehicle platforms, as well as increased focus on design for recyclability, are important. Many mobility manufacturers

and governments are considering battery recycling issues to ensure the process is mainstreamed.
The most significant enabling condition in electromobility is to provide electric recharging opportunities and an integration strategy 
so that vehicles support the grid.
FAQ 10.2 |   How hard is it to decarbonise heavy vehicles in transport like long-haul trucks, ships 
and planes?
There are few obvious solutions to decarbonising heavy vehicles like international ships and planes. The main focus has been 
increased efficiency, which so far has not prevented these large vehicles from becoming the fastest-growing source of GHG globally. 
These vehicles likely need alternative fuels that can be fitted to the present propulsion systems. Emerging demonstrations suggest 
that ammonia, advanced biofuels, or synthetic fuels could become commercial.
Electric propulsion using hydrogen fuel cells or Li-ion batteries could work with short-haul aviation and shipping, but the large

long-lived vessels and aircraft likely need alternative liquid fuels for most major long-distance functions.
Advanced biofuels, if sourced from resources with low GHG footprints, offer decarbonisation opportunities. As shown in Chapters 2, 
6, and 12, there are multiple issues constraining traditional biofuels. Sustainable land management and feedstocks, as well as R&D 
efforts to improve lignocellulosic conversion routes, are key to maximising the mitigation potential from advanced biofuels.
Synthetic jet and marine fuels can be made using CO 2 captured with DAC/BECCS and low-carbon hydrogen. These fuels may also 
have less contrails-based climate impacts and lower emissions of local air pollutants. However, these fuels still require significant 
R&D and demonstration.
The deployment of low-carbon aviation and shipping fuels that support decarbonisation of the transport sector will likely require 
changes to national and international governance structures.

1121
1121
Transport Chapter 1010Frequently Asked Questions (FAQs)
FAQ 10.3 |   How can governments, communities and individuals reduce demand and be more 
efficient in consuming transport energy?
Cities can reduce their transport-related fuel consumption by around 25% through combinations of more compact land use and less 
car-dependent transport infrastructure.
More traditional programmes for reducing unnecessary high-energy travel through behaviour change programmes (e.g., taxes on 
fuel, parking, and vehicles, or subsidies for alternative low-carbon modes) continue to be evaluated, with mixed results due to the 
dominance of time savings in an individual’s decision-making.
The circular economy, the shared economy, and digitalisation trends can support systemic changes that lead to reductions in 
demand for transport services or expand the use of more efficient transport modes.

COVID-19 lockdowns have confirmed the transformative value of telecommuting, replacing significant numbers of work and 
personal journeys, as well as promoting local active transport. These changes may not last and impacts on productivity and health 
are still to be fully evaluated.
Solutions for individual households and businesses involving pledges and shared communities that set new cultural means of 
reducing fossil fuel consumption, especially in transport, are setting out new approaches for how climate change mitigation can 
be achieved.

1145
1145
Transport Chapter 1010Appendix 10.1: Data and Methods 
for Life Cycle Assessment
IPCC Lifecycle Assessment Data Collection Effort
In mid-2020, the IPCC, in collaboration with the Norwegian 
University of Science and Technology, released a  request for data 
from the lifecycle assessment (LCA) community, to estimate the 
lifecycle greenhouse (GHG) emissions of various passenger and 
freight transport pathways. The data requested included information 
about vehicle and fuel types, vintages, vehicle efficiency, payload, 
emissions from vehicle and battery manufacturing, and fuel cycle 
emissions factors, among others.
Data submissions were received from approximately 20 research 
groups, referencing around 30 unique publications. These submissions 
were supplemented by an additional 20 studies from the literature. 
While much of this literature was focused on LDVs and trucks, 
relatively few studies referenced bus and rail pathways.
Harmonisation method

First, the datapoints were separated into categories based on the 
approximate classification (e.g., heavy-duty vs medium-duty trucks), 
powertrain (i.e., internal combustion engines (ICEV), hybrid electric 
vehicles (HEV), battery electric vehicles (BEV), fuel cell vehicles 
(FCV)), and fuel combination. For each category of vehicle/powertrain/
fuel, a  simplified LCA that harmonises values from across the 
reviewed studies was constructed, using the following basic equation:Where:
• Lifecycle GHG intensity represents the normalised lifecycle GHG 
emissions associated with each transportation mode, measured 
in gCO 2-eq per passenger-kilometre (pkm)or gCO 2-eq per tonne-
kilometre (tkm).
• FC is the fuel consumption of the vehicle in megajoules (MJ) or 
kilowatt hours (kWh) per km.
• P represents the payload (measured in tonnes of cargo) or 
number of passengers, at a  specified utilisation capacity 
(e.g., 50% payload or 80% occupancy).

• EF is an emissions factor representing the lifecycle GHG intensity 
of the fuel used, measured in gCO 2-eq MJ–1 or gCO 2-eq kWh–1. 
A single representative EF value is selected for each fuel type. 
When a given fuel type can be generated in different ways with 
substantially different upstream emissions factors (e.g., hydrogen 
from methane steam reforming vs hydrogen from water 
electrolysis), these are treated as two different fuel categories. 
The fuel emissions factors that were used are presented in 
Table 10.8.
• VC are the vehicle cycle emissions of the vehicle, measured in 
gCO 2-eq per  vehicle. This may include vehicle manufacturing, 
maintenance and end of life, or just manufacturing.
• LVKT is the lifetime vehicle kilometres travelled.
Note: for plug-in hybrid electric vehicles (PHEV), the value of FC/P*EF 
is a weighted sum of this aggregate term for each of battery and 
diesel/gasoline operation.
Fuel emissions factors used are presented in Table 10.8. Note that

the  fuel emissions factors were compiled from several studies 
that used different global warming potential (GWP) values in their 
underlying assumptions, and therefore the numbers reported here !"#$%&%'$ 	)*)	"+,$+-",& =	/0
1∗3/+	50
1∗!567 
Table 10.8 | Fuel emissions factors used to estimate lifecycle greenhouse gas (GHG) emissions of passenger and freight transport pathways.
Fuel Emissions factor Units Source
Gasoline 92 gCO 2-eq MJ–1Submissions to IPCC data call (median)
Diesel 92 gCO 2-eq MJ–1Submissions to IPCC data call (median)
Diesel, high 110 gCO 2-eq MJ–1Diesel from oil sands: average of in-situ pathways (Guo et al. 2020)
Biofuels, IAM EMF33 25 gCO 2-eq MJ–1From Chapter 7
Biofuels, partial models CLC 36 gCO 2-eq MJ–1From Chapter 7
Biofuels, partial models NG 141 gCO 2-eq MJ–1From Chapter 7
Compressed natural gas 71 gCO 2-eq MJ–1Submissions to IPCC data call (median)
Liquefied natural gas 76 gCO 2-eq MJ–1Submissions to IPCC data call (median)

Liquefied petroleum gas 78 gCO 2-eq MJ–1Submissions to IPCC data call (median)
DAC FT-Diesel, wind electricity 12 gCO 2-eq MJ–1From electrolytic hydrogen produced using low-carbon electricity (Liu et al. 2020a)
DAC FT-Diesel, natural gas electricity 370 gCO 2-eq MJ–1From electrolytic hydrogen produced using natural gas electricity; extrapolated from Liu et al. (2020a)
Ammonia, low carbon renewable 3.2 gCO 2-eq MJ–1From electrolytic hydrogen produced using low-carbon electricity via Haber-Bosch (Gray et al. 2021)
Ammonia, natural gas SMR 110 gCO 2-eq MJ–1From H 2 derived from natural gas steam methane reforming; via Haber-Bosch (Frattini et al. 2016)
Hydrogen, low carbon renewable 10 gCO 2-eq MJ–1From electrolysis with low-carbon electricity (Valente et al. 2021)
Hydrogen, natural gas SMR 95 gCO 2-eq MJ–1From steam-methane reforming of fossil fuels
Wind electricity 9.3 gCO 2-eq kWh–1Submissions to IPCC data call (median)

Natural gas electricity 537 gCO 2-eq kWh–1Submissions to IPCC data call (median)
Coal electricity 965 gCO 2-eq kWh–1Submissions to IPCC data call (median)

1146
1146
Chapter 10  T ransport10may be slightly different if the 100-year global warming potential 
(GWP100) from the AR6 had been used. This difference would be 
small given the small contribution from non-CO 2 gases to the total 
lifecycle emissions. For example, methane (CH 4) emissions exist in 
the lifecycle of natural gas supply chains or natural gas-dependent 
supply chains such as hydrogen from steam methane reforming 
(SMR). Recent data from the US suggests emissions of approximately 
0.2–0.3 gCH 4 per MJ natural gas (Littlefield et al. 2017, 2019), which 
would range by no more than 1–2 gCO 2-eq per MJ natural gas (<3% 
of natural gas lifecycle emissions) when converting from a GWP100 
of 25 (AR4) or 36 (AR5) to the current (AR6) GWP100 of 29.8.For LDVs, the entire distribution of estimated lifecycle emissions is 
presented for each vehicle/powertrain/fuel category (as a boxplot) in 
Figure 10.4. For trucks, rail and buses, only the low and high estimates

are presented (as solid bars) in Figures 10.6 and 10.8, since the 
number of datapoints were not sufficient to present as a distribution. 
Table 10.9 presents the low and high estimates of fuel efficiency for 
each category. The references used are reported in the main text.
For transit and freight, the lifecycle harmonisation exercise allows two 
aggregate parameters to vary from the low to high among submitted 
values within each category: FC/P and VC/P . Aggregate parameters 
are used to capture internal correlations (e.g., fuel consumption and 
payload; both depend heavily on vehicle size) and are presented in 
Tables 10.10 to 10.14. The references used are reported in the main text.
Table 10.9 | Range of fuel efficiencies for light-duty vehicles by fuel and powertrain category, per vehicle kilometre.
Fuel PowertrainFuel efficiency
(MJ per vehicle-km)Electric efficiency
(kWh per vehicle-km)
Low High Low High
Compression ignition ICEV 1.34 2.6
Spark ignition ICEV 1.37 2.88

Spark ignition HEV 1.22 2.05
Compression ignition HEV 1.15 1.51
Electricity BEV 0.12 0.242
Hydrogen FCV 1.14 1.39
Table 10.10 | Range of fuel efficiencies for buses by fuel and powertrain category, at 80% occupancy.
(MJ per passenger-km)Electric efficiency
(kWh per passenger-km)
Diesel ICEV 0.16 0.52
CNG ICEV 0.25 0.61
LNG ICEV 0.27 0.37
Biodiesel ICEV 0.16 0.52
DAC FT-Diesel ICEV 0.16 0.52
Diesel HEV 0.11 0.37
Electricity BEV 0.01 0.04
Hydrogen FCV 0.11 0.31
Table 10.11 | Range of fuel efficiencies for passenger rail by fuel and powertrain category, at 80% occupancy.
Diesel ICEV 0.36 0.40
Biofuels ICEV 0.36 0.40
DAC FT-Diesel ICEV 0.36 0.40
Diesel HEV 0.33 0.33
Electricity BEV 0.03 0.03
HydrogenaFCV 0.18 0.18
a Occupancy corresponds to average European occupancy rates (IEA 2019e).

1147
1147
Transport Chapter 1010Table 10.12 | Range of fuel efficiencies for heavy-duty truck by fuel and powertrain category, at 100% payload.
(MJ per tonne-km)Electric efficiency
(kWh per tonne-km)
Diesel ICEV 0.38 0.93
CNG ICEV 0.48 1.45
LNG ICEV 0.43 1.00
Biofuels ICEV 0.38 0.93
AmmoniaaICEV 0.38 0.93
DAC FT-Diesel ICEV 0.38 0.93
Diesel HEV 0.34 0.59
LNG HEV 0.46 0.51
Electricity BEV 0.03 0.09
Hydrogen FCV 0.25 0.43
AmmoniabFCV 0.25 0.43
a Ammonia ICEV trucks are assumed to have the same fuel economy as diesel ICEVs due to lack of data.
b Ammonia FCV trucks are assumed to have the same fuel economy as hydrogen FCVs due to lack of data.
Table 10.13 | Range of fuel efficiencies for medium-duty truck by fuel and powertrain category, at 100% payload.
Diesel ICEV 0.85 2.30
CNG ICEV 1.08 2.54
LNG ICEV 1.05 1.41
Biofuels ICEV 0.85 2.30

AmmoniaaICEV 0.85 2.30
DAC FT-Diesel ICEV 0.85 2.30
Diesel HEV 0.81 1.54
Electricity BEV 0.12 0.22
Hydrogen FCV 0.65 0.99
AmmoniabFCV 0.65 0.99
b Ammonia FCV trucks are assumed to have the same fuel economy as Hydrogen FCVs due to lack of data.
Table 10.14 | Range of fuel efficiencies for freight rail by fuel and powertrain category, at an average payload.
(M per /tonne-km)Electric efficiency
Diesel ICEV 0.11 0.78
Biodiesel ICEV 0.11 0.78
DAC FT-Diesel ICEV 0.11 0.78
Electricity BEV 0.01 0.12
Hydrogen FCV 0.10 0.10

1148
1148
Chapter 10  T ransport10Appendix 10.2: Data and Assumptions 
for Lifecycle Cost Analysis
Fuel cost ranges
For diesel, a range of USD0.5–2.5 per litre is used based on historic 
diesel costs across all OECD countries reported in the IEA Energy Prices 
and Taxes Statistics database (IEA 2021c) since 2010. The lower end 
of this range is consistent with the minimum projected value from the 
2021 US Annual Energy Outlook (low oil price scenario, USD0.55 l–1) 
(US Energy Information Administration 2021). The upper end of 
the range encompasses both the maximum diesel price observed 
in the  2021 US Annual Energy Outlook projections (high oil price 
scenario, USD1.5 l–1) (US Energy Information Administration 2021), 
and the diesel price that would correspond to the 2020 IEA World 
Energy Outlook crude oil price projections (Stated Policies scenario) 
(IEA 2020b), assuming the historical price relationship between

crude oil and diesel is maintained (USD1.5 l–1). For reference, the IEA 
reports current world-average automotive diesel costs to be around 
1 USD l–1 (IEA 2021d). The selected range also captures the current 
range of production costs for values for bio-based and synthetic 
diesels (EUR51–144 MWh–1, corresponding to USD0.6–1.70 l–1), 
which are generally still higher than wholesale petroleum diesel 
costs (EUR30–50 MWh–1, corresponding to USD0.35–0.6 l–1), as 
reported by IEA (IEA 2020c). This range also encompasses costs for 
synthesised electrofuels from electrolytic hydrogen, as reported in 
Chapter 6 (USD1.6 l–1).
The range of electricity costs used here is consistent with the range 
of levelised cost of electricity estimates presented in Chapter  6 
(USD20–200 MWh–1).
For hydrogen, a range of USD1 to USD13 per kilogram is used. The 
upper end of this range corresponds approximately to reported retail 
costs in the US (Eudy and Post 2018b; Argonne National Laboratory

2020; Burnham et al. 2021). Despite the high upper bound, lower costs 
(USD6–7 kg–1) are already consistent with recent cost estimates of 
hydrogen produced via electrolysis (Chapter 6) and current production 
cost estimates from IRENA (IRENA 2020). The lower end of the range 
(USD1 kg–1) corresponds to projected future price decreases for 
electrolytic hydrogen (BNEF 2020; Hydrogen Council 2020; IRENA 
2020), and is consistent with projections from Chapter 6 for the low 
end of long-term future prices for fossil hydrogen with CCS.
Vehicle efficiencies
The vehicle efficiencies used in developing the lifecycle cost estimates 
were derived from the harmonised ranges used to develop lifecycle 
GHG estimates and are presented in Tables 10.9 to 10.14.
Other inputs to bus cost model
For buses, a  40-foot North American transit bus with a  passenger 
capacity of 50, lifetime of 15 years, and an annual distance travelled of

72,400 km based on data in the ANL AFLEET model (Argonne National Laboratory 2020) is assumed. Maintenance costs were assumed to 
be USD0.63 per km for ICEV buses and USD0.38 per km for BEV and 
ICEV buses, also based on data from the AFLEET model (Argonne 
National Laboratory 2020). For ICEV and BEV purchase costs, data 
from the National Renewable Energy Laboratory (Johnson et  al. 
2020) is used for bounding ranges (USD430,000 to 500,000 for 
ICEV and USD579,000 to 1,200,000 for BEV), which encompass the 
default values from AFLEET model (Argonne National Laboratory 
2020). Note that wider ranges are available in the literature (e.g., as 
low as USD120,000 per bus in Burnham et al. (2021) and Harris et al. 
(2020)); but these are not included in the sensitivity analysis to avoid 
conflating disparate vehicles. For FCV buses, the upper bound of the 
purchase price range (USD1,200,000) represents current costs in 
the US (Argonne National Laboratory 2020; Eudy and Post 2020),

and the lower bound represents the target future value from the US 
Department of Energy (Eudy and Post 2020).
Other inputs to rail cost model
For freight and passenger rail, powertrain and vehicle operation and 
maintenance costs in USD per km from the IEA Future of Rail report 
(IEA 2019e) (IEA Figure 2.14 for passenger rail and IEA Figure 2.15 
for freight rail) are used as a proxy for non-fuel costs. The ranges span 
conservative and forward-looking cases. In addition, the range  for 
BEV rail ranges encompass short- and long-distance trains  – 
corresponding to 100–200 km for passenger rail, and 400–750 km 
for freight rail. Note that all values exclude the base vehicle costs, 
but they are expected not to be significant as they are amortised 
over the lifetime distance travelled. For freight rail, a network that 
is representative of North America is assumed, with a  payload of 
2800 tonnes per train (IEA Figure 1.17), assumed to be utilised at

100%, with a lifetime of 10 years, and an average distance travelled 
of 120,000  km yr–1. For BEV freight rail, the range in powertrain 
costs is driven by battery costs of USD250–600 kWh–1, while for 
FCV freight rail, the range in powertrain costs is driven by fuel cell 
stack costs of USD50–1000 kW–1. For passenger rail, a network that 
is representative of Europe is assumed, with an average occupancy of 
180 passengers per train (IEA Figure 1.14), with a lifetime of 10 years, 
and an average distance travelled of 115,000 km per year.
Other inputs to truck cost model
Capital cost ranges vary widely in the literature depending on the 
exact truck model, size and other assumptions. For ICEVs in this 
analysis, the lower bound (USD90,000) corresponds to the 2020 
estimate for China from Moultak et al. (2017), and the upper bound 
(USD250,000) corresponds to the 2030 projection for the US from 
the same study. These values encompass the full range reported

by Argonne (Burnham et  al. 2021). The lower bound BEV cost 
(USD120,000) is taken from 2030 projections for China (Moultak 
et al. 2017) and the upper bound (USD780,000) is taken from 2020 
cost estimates in the US (class 8 sleeper cab tractor) (Burnham et al. 
2021). The lower bound for FCV trucks (USD130,000) corresponds 
to the 2050 estimate for class 8 sleeper cab tractors from Argonne 
National Laboratory and the upper bound (USD290,000) corresponds

1149
1149
Transport Chapter 1010to the 2020 estimate from the same study (Burnham et  al. 2021). 
These values span the full range reported by Moultak et al. (2017) for 
the US, Europe and China from 2020–2030.
The analysis uses a truck lifetime of 10 years and annual distance 
travelled of 140,000 km based on Burnham et al. (2021). An effective 
payload of 17 tonnes (80% of maximum payload of 21 tonnes) is 
assumed based on reported average effective payload submitted 
by Argonne National Laboratory in response to the IPCC LCA data 
collection call. A discount rate of 3% is used, based on Burnham et al. 
(2021) and consistent with the social discount rate from Chapter 3. 
Maintenance costs are assumed to be USD0.15 km–1 for ICEV trucks 
and USD0.09 km–1 for BEV and FCV trucks, as reported in Burnham 

1150
1150
Appendix 10.3: Line of Sight for Feasibility Assessment
Geophysical
Physical potential Geophysical recourses Land use
Demand reduction and mode shift + + +
Role of contextsAdoption of Avoid Shift Improve approach along with improving 
fuel efficiency will have negligible physical constraints; they can 
be implemented across the countries.Reduction in demand, fuel efficiency and demand management measures 
such as Clean Air Zones and parking policies will reduce negative impact 
on land use and resource consumption – without any constraints in terms 
of available resources.Reduction in demand, increase in fuel efficiency and 
demand management measures will have a positive impact 
on land use as compared to ‘without’ them – no likely 
adverse constraints in terms of limited land use (such 
as decline in biofuel).

Line of sightHolguín-Veras, J. and I. Sánchez-Díaz, 2016: Freight Demand Management and the Potential of Receiver-Led Consolidation programs. Transp. Res. Part A Policy Pract., 84, 109–130, doi:10.1016/j.tra.2015.06.013.
Creutzig, F . et al., 2018: Towards demand-side solutions for mitigating climate change. Nat. Clim. Change, 8(4), 260–263, doi:10.1038/s41558-018-0121-1.
Rajé, F ., 2017: Transport, Demand Management and Social Inclusion: The need for ethnic perspectives. Routledge, London, UK, 184 pp.
Dumortier, J., M. Carriquiry, and A. Elobeid, 2021: Where does all the biofuel go? Fuel efficiency gains and its effects on global agricultural production. Energy Policy, 148, 111909, doi:10.1016/j.enpol.2020.111909.
Biofuels for land transport, 
aviation, and shipping+ ± –
Role of contextsClimate conditions are an important factor for bioenergy viability.  
Land availability constraints might be expected for bioenergy

deployment.Land and synthetic fertilisers are examples of limited resources to deploy 
large-scale biofuels, however the extent of these restrictions will depend 
on local and context specific conditions.Implementing biofuels may require additional land 
use. However, it will depend on context and local 
specific conditions.
Line of sightDaioglou, V., J.C. Doelman, B. Wicke, A. Faaij, and D.P . van Vuuren, 2019: Integrated assessment of biomass supply and demand in climate change mitigation scenarios. Glob. Environ. Change, 54, 88–101, 
doi:10.1016/j.gloenvcha.2018.11.012.
Roe, S. et al., 2021: Land-based measures to mitigate climate change: Potential and feasibility by country. Glob. Change Biol., 27(23), 6025–6058, doi:10.1111/gcb.15873.
Ammonia for shipping + + ±
Role of contextsA global ammonia supply chain is already established; the primary 
requirement for delivering greater carbon emissions reductions 
will be through the production of ammonia using green hydrogen

or CCS.The use of ammonia would reduce reliance on fossil fuels for shipping and is 
expected to reduce reliance on natural resources when produced using green 
hydrogen. The primary resource requirements will be the supply of renewable 
electricity and clean water to produce green hydrogen, from which ammonia 
can be produced.No major changes in land use for the vehicle. Increases may 
occur if the hydrogen is produced through electrolysis and 
renewable energy sources or hydrogen production with CCS.
Line of sightBicer, Y . and I. Dincer, 2018: Clean fuel options with hydrogen for sea transportation: A life cycle approach. Int. J. Hydrogen Energy, 43(2), 1179–1193, doi:10.1016/j.ijhydene.2017.10.157.
Gilbert, P . et al., 2018: Assessment of full life-cycle air emissions of alternative shipping fuels. J. Clean. Prod., 172, 855–866, doi:10.1016/j.jclepro.2017.10.165.
Synthetic fuels for heavy-duty land 
transport, aviation, and shipping 
(e.g., DAC-FT)± ± ±

Role of contextsFischer Tropsch chemistry is well established; pilot scale direct air 
capture (DAC) plants are already in operation;
 –Does not qualify as a mitigation option except in regions with 
very low-carbon electricity.+ Gasification can use a wide range of feedstocks; DAC can be applied 
in a wide range of locations 
– Limited information available on potential limits related to large input 
energy requirements, or water use and required sorbents for DAC.No major changes in land use for the vehicle. Potential 
increases in land use for electricity generation (especially 
solar, wind or hydropower) for CO 2 capture and fuel 
production; likely lower land use than crop-based biofuels.

1151
1151
Transport Chapter 1010Geophysical
Line of sightRealmonte, G. et al., 2019: An inter-model assessment of the role 
of direct air capture in deep mitigation pathways. Nat. Commun., 
10(1), 3277, doi:10.1038/s41467-019-10842-5.
Liu, C.M., N.K. Sandhu, S.T. McCoy, and J.A. Bergerson, 2020: 
A life cycle assessment of greenhouse gas emissions from direct 
air capture and Fischer-Tropsch fuel production. Sustain. Energy 
Fuels, 4(6), 3129–3142, doi:10.1039/C9SE00479C.
Ueckerdt, F . et al., 2021: Potential and risks of hydrogen-based 
e-fuels in climate change mitigation. Nat. Clim. Change, 11(5), 
384–393, doi:10.1038/s41558-021-01032-7.Realmonte, G. et al., 2019: An inter-model assessment of the role of direct 
air capture in deep mitigation pathways. Nat. Commun., 10(1), 3277, 
doi:10.1038/s41467-019-10842-5.
Electric vehicles for land transport + ± ±

Role of contextsElectromobility is being adopted across a range of land transport 
options including light-duty vehicles, trains and some heavy-duty 
vehicles, suggesting no physical constraints.Current dominant battery chemistry relies on minerals that may face 
supply constraints, including lithium, cobalt, and nickel. Regional supply/
availability varies. Alternative chemistries exist; recycling may likewise 
alleviate critical material concerns. Similar supply constraints may exist 
for some renewable electricity sources (e.g., solar) required to support EVs. 
May reduce critical materials required for catalytic converters in ICEVs 
(e.g., platinum, palladium, rhodium).No major changes in land use for the vehicle. Potential 
solar, wind or hydropower) and mineral extraction, but may 
be partially offset by a decrease in land use for fossil fuel 
production; likely lower land use than crop-based biofuels,

or technologies with higher electricity use (e.g., those based 
on electrolytic hydrogen).
Line of sightIEA, 2021: Global EV Outlook 2021. International Energy Agency, 
Paris, France, 101 pp.Jones, B., R.J.R. Elliott, and V. Nguyen-Tien, 2020: The EV revolution: The 
road ahead for critical raw materials demand. Appl. Energy, 280, 115072, 
doi:10.1016/J.APENERGY .2020.115072.
Xu, C. et al., 2020: Future material demand for automotive lithium-based 
batteries. Commun. Mater. 2020 11, 1(1), 1–10, doi:10.1038/s43246-020-
00095-x.
IEA, 2021: The Role of Critical Minerals in Clean Energy Transitions. 
International Energy Agency, Paris, France, 287 pp.
Zhang, J. et al., 2016: Assessing Economic Modulation of Future Critical 
Materials Use: The Case of Automotive-Related Platinum Group Metals. 
Environ. Sci. Technol., 50(14), 7687–7695, doi:10.1021/ACS.EST.5B04654.
Milovanoff, A., I.D. Posen, and H.L. MacLean, 2020: Electrification of light-duty

vehicle fleet al.ne will not meet mitigation targets. Nat. Clim. Change, 10(12), 
1102–1107, doi:10.1038/s41558-020-00921-7.Arent, D. et al., 2014: Implications of high renewable 
electricity penetration in the U.S. for water use, greenhouse 
gas emissions, land-use, and materials supply. Appl. Energy, 
123, 368–377, doi:10.1016/j.apenergy.2013.12.022.
Orsi, F ., 2021: On the sustainability of electric vehicles: 
What about their impacts on land use? Sustain. Cities Soc., 
66, 102680, doi:10.1016/J.SCS.2020.102680.
Hydrogen FCV for land transport + ± ±
Role of contextsThe use of fuel cells in the transport sector is growing, and will 
potentially be important in heavy-duty land transport applications.FCVs are reliant on critical minerals for manufacturing fuel cells, electric 
motors and supporting batteries. Platinum is the primary potential resource 
constraint for fuel cells; however, its use may decrease as the technology 
develops, and platinum is highly recyclable.

Line of sight IEA, 2020: Global EV Outlook 2020. Paris, France, 276 pp.Hao, H. et al., 2019: Securing Platinum-Group Metals for Transport 
Low-Carbon Transition. One Earth, 1(1), 117–125, doi:10.1016/ 
j.oneear.2019.08.012.
Rasmussen, K.D., H. Wenzel, C. Bangs, E. Petavratzi, and G. Liu, 2019: Platinum 
Demand and Potential Bottlenecks in the Global Green Transition: A Dynamic 
Material Flow Analysis. Environ. Sci. Technol., 53(19), 11541–11551, 
doi:10.1021/acs.est.9b01912.Orsi, F ., 2021: On the sustainability of electric vehicles: 

1152
1152
Environmental-ecological
Air pollution Toxic waste, ecotoxicity eutrophication Water quantity and quality Biodiversity
Demand reduction 
and mode shift+ 0 0 0
Role of contextsReduction in demand, increase in fuel efficiency and demand management 
measures will improve air quality.Reduction in demand, 
fuel efficiency and demand 
management measures such 
as Clean Air Zones and parking 
Policies will reduce need for 
roads and protect biodiversity.
Line of sightCreutzig, F . et al., 2018: Towards demand-side solutions for mitigating climate change. Nat. Clim. Change, 8(4), 260–263, doi:10.1038/s41558-018-0121-1.

Ambarwati, L., R. Verhaeghe, B. van Arem, and A.J. Pel, 2016: The influence of integrated space–transport development strategies on air pollution in urban areas. Transp. Res. Part D Transp. Environ., 44, 134–146, 
doi:10.1016/j.trd.2016.02.015.
DEFRA and DoT, 2020: Clean Air Zone Framework: Principles for setting up Clean Air Zones in England., Department of Environment Food & Rural Affairs/Department of Transport, Government of UK, London, UK, 35 pp.
aviation, and shipping± ± – –
Role of contextsBiofuels may improve air quality due to reduction in the emission of some 
pollutants, such as SOx and particulate matter, in relation to fossil fuels. 
Evidence is mixed for other pollutants such as NOx. The biofuels supply chain 
(e.g., due to increased fertiliser use) may negatively impact air quality.Increased use of fertilisers and agrochemicals 
due to biofuel production may increase impacts 
in ecotoxicity and eutrophication; some biofuels

may be less toxic than fossil fuel counterparts.Increasing production of biofuels may increase 
pressure on water resources due to the need 
for irrigation. However, some biofuel options 
may also improve these aspects in respect to 
conventional agriculture. These impacts will 
depend on specific local conditions.Additional land use for biofuels 
may increase pressure on 
biodiversity. However, biofuel 
can also increase biodiversity 
depending on the previous land 
use. These impacts will depend 
on specific local conditions 
and previous land uses.
Line of sightRobertson, G.P . et al., 2017: Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Science, 356(6345), doi:10.1126/science.aal2324.
Humpenöder, F . et al., 2018: Large-scale bioenergy production: how to resolve sustainability trade-offs? Environ. Res. Lett., 13(2), 024011, doi:10.1088/1748-9326/aa9e3b.

Ai, Z., N. Hanasaki, V. Heck, T. Hasegawa, and S. Fujimori, 2021: Global bioenergy with carbon capture and storage potential is largely constrained by sustainable irrigation. Nat. Sustain., 4(10), 884–891,  
doi:10.1038/s41893-021-00740-4.
Ammonia for shipping ± – ± Limited Evidence (LE)
Role of contextsIf produced from green hydrogen or coupled with CCS, ammonia could reduce 
short-lived climate forcers and particulate matter precursors including black 
carbon and SO 2. However, the combustion of ammonia could lead to elevated 
levels of nitrogen oxides and ammonia emissions.Ammonia is highly toxic, and therefore requires 
special handling procedures to avoid potentially 
catastrophic leaks into the environment. That 
said, large volumes of ammonia are already safely 
transported internationally due to a high level 
of understanding of safe handling procedures. 
Additionally, the use of ammonia in shipping 
presents a risk of eutrophication and ecotoxicity

from the release of ammonia into the water 
system – either via a fuel leak or via unburnt 
ammonia emissions.May increase or decrease water footprint 
depending on the upstream energy source.Lack of studies assessing 
the potential impacts of the 
technology on biodiversity.
Gilbert, P . et al., 2018: Assessment of full life-cycle air emissions of alternative shipping fuels. J. Clean. Prod., 172(2018), 855–866, doi:10.1016/j.jclepro.2017.10.165.
ABS, 2020: Ammonia as a Marine Fuel. American Bureau of Shipping, Spring, 28 pp.

1153
1153
Transport Chapter 1010Environmental-ecological
Synthetic fuels for heavy-duty 
land transport, aviation, and 
shipping (e.g., DAC-FT)+ NE ± LE
Role of contextsPotential reductions in air pollutants related to reduced presence of sulphur, 
metals, and other contaminants; improvements likely smaller than for electric 
vehicles or hydrogen fuel cell vehicles.DAC requires significant amounts of water, 
which may be a limitation in water stressed 
areas; typically uses less water than crop-
based biofuels.Potential biodiversity issues 
related to electricity generation; 
however fossil fuel supply chains 
also adversely impact biodiversity; 
net effect is unknown.
Line of sightBeyersdorf, A.J. et al., 2014: Reductions in aircraft particulate emissions 
due to the use of Fischer –Tropsch fuels. Atmos. Chem. Phys., 14(1), 11–23, 
doi:10.5194/acp-14-11-2014.

Lobo, P ., D.E. Hagen, and P .D. Whitefield, 2011: Comparison of PM Emissions from 
a Commercial Jet Engine Burning Conventional, Biomass, and Fischer –Tropsch 
Fuels. Environ. Sci. Technol., 45(24), 10744–10749, doi:10.1021/es201902e.
Gill, S.S., A. Tsolakis, K.D. Dearn, and J. Rodríguez-Fernández, 2011: 
Combustion characteristics and emissions of Fischer –Tropsch diesel fuels 
in IC engines. Prog. Energy Combust. Sci., 37(4), 503–523, doi:10.1016/ 
j.pecs.2010.09.001.Realmonte, G. et al., 2019: An inter-model 
assessment of the role of direct air capture 
in deep mitigation pathways. Nat. Commun., 
Byers, E.A., J.W. Hall, J.M. Amezaga, 
G.M. O’Donnell, and A. Leathard, 2016: 
Water and climate risks to power generation 
with carbon capture and storage. Environ. 
Res. Lett., 11(2), 024011, doi:10.1088/1748-
9326/11/2/024011.
Electric vehicles 
for land transport+ – ± LE

Role of contextsElimination of tailpipe emissions. If powered by nuclear or renewables, large 
overall improvements in air pollution. Even if powered partially by fossil fuel 
electricity, tailpipe emissions tend to occur closer to population and thus 
typically have larger impact on human health than powerplant emissions; 
negative air quality impacts may occur, but only in fossil fuel-heavy grids.Some toxic waste associated with mining 
and processing of metals for batteries and 
some renewable electricity supply chains 
(production and disposal).May increase or decrease water footprint 
depending on the upstream electricity source.Potential biodiversity issues 
Line of sightRequia, W.J., M. Mohamed, C.D. Higgins, A. Arain, and M. Ferguson, 2018: 
How clean are electric vehicles? Evidence-based review of the effects of

electric mobility on air pollutants, greenhouse gas emissions and human 
health. Atmos. Environ., 185, 64–77, doi:10.1016/J.ATMOSENV.2018.04.040.
Horton, D.E. et al., 2021: Effect of adoption of electric vehicles on public 
health and air pollution in China: a modelling study. Lancet Planet. Heal., 
doi:10.1016/s2542-5196(21)00092-9.
Gai, Y . et al., 2020: Health and climate benefits of Electric Vehicle Deployment 
in the Greater Toronto and Hamilton Area. Environ. Pollut., 265, 114983, 
doi:10.1016/j.envpol.2020.114983.
Choma, E.F ., J.S. Evans, J.K. Hammitt, J.A. Gómez-Ibáñez, and J.D. Spengler, 2020: 
Assessing the health impacts of electric vehicles through air pollution in the 
United States. Environ. Int., 144, 106015, doi:10.1016/j.envint.2020.106015.
Schnell, J.L. et al., 2019: Air quality impacts from the electrification of light-
duty passenger vehicles in the United States. Atmos. Environ., 208, 95–102, 
doi:10.1016/j.atmosenv.2019.04.003.

Tessum, C.W., J.D. Hill, and J.D. Marshall, 2014: Life cycle air quality impacts 
of conventional and alternative light-duty transportation in the United States. 
Proc. Natl. Acad. Sci., 111(52), 18490–18495, doi:10.1073/pnas.1406853111.Lattanzio, R.K. and C.E. Clark, 2020: Environmental 
Effects of Battery Electric and Internal Combustion 
Engine Vehicles., Congressional Research Service, 
Washington, DC, USA, 41 pp.
Puig-Samper Naranjo, G., D. Bolonio, M.F . Ortega, 
and M.-J. García-Martínez, 2021: Comparative 
life cycle assessment of conventional, electric 
and hybrid passenger vehicles in Spain. 
J. Clean. Prod., 291, 125883, doi:10.1016/ 
j.jclepro.2021.125883.
Bicer, Y . and I. Dincer, 2017: Comparative 
life cycle assessment of hydrogen, methanol 
and electric vehicles from well to wheel. 
Int. J. Hydrogen Energy, 42(6), 3767–3777, 
doi:10.1016/j.ijhydene.2016.07.252.
Hawkins, T.R., B. Singh, G. Majeau-Bettez, 
and A.H. Strømman, 2013: Comparative

Environmental Life Cycle Assessment of 
Conventional and Electric Vehicles. J. Ind. Ecol., 
17(1), doi:10.1111/j.1530-9290.2012.00532.x.Onat, N.C., M. Kucukvar, and O. Tatari, 2018: 
Well-to-wheel water footprints of conventional 
versus electric vehicles in the United 
States: A state-based comparative analysis. 
J. Clean. Prod., 204, 788–802, doi:10.1016/ 
j.jclepro.2018.09.010.
Kim, H.C. et al., 2016: Life Cycle Water Use 
of Ford Focus Gasoline and Ford Focus Electric 
Vehicles. J. Ind. Ecol., 20(5), 1122–1133, 
doi:10.1111/jiec.12329.
Wang, L. et al., 2020: Life cycle water use of 
gasoline and electric light-duty vehicles in 
China. Resour. Conserv. Recycl., 154, 104628, 
doi:10.1016/j.resconrec.2019.104628.

1154
1154
Hydrogen FCV for 
land transport+ ± ± LE
Role of contextsFuel cells’ only tailpipe emission is water vapour. However, blue hydrogen 
production pathways may generate air pollutants near the production sites. 
Overall, FCV would reduce emissions of criteria air pollutants.Mining of platinum group metals may generate 
additional stress on the environment, compared 
to conventional technologies. Furthermore, 
the recycling of fuel cell stacks can generate 
additional impacts.May increase or decrease water footprint 
Line of sightWang, Q., M. Xue, B. Le Lin, Z. Lei, and Z. Zhang, 2020: Well-to-wheel analysis 
of energy consumption, greenhouse gas and air pollutants emissions

of hydrogen fuel cell vehicle in China. J. Clean. Prod., 275, doi:10.1016/ 
j.jclepro.2020.123061.Velandia Vargas, J.E. and J.E.A. Seabra, 2021: Fuel-cell technologies for private vehicles in Brazil: 
Environmental mirage or prospective romance? A comparative life cycle assessment of PEMFC and 
SOFC light-duty vehicles. Sci. Total Environ., 798, 149265, doi:10.1016/j.scitotenv.2021.149265.
Bohnes, F .A., J.S. Gregg, and A. Laurent, 2017: Environmental Impacts of Future Urban Deployment 
of Electric Vehicles: Assessment Framework and Case Study of Copenhagen for 2016–2030. Environ. 
Sci. Technol., 51(23), 13995–14005, doi:10.1021/acs.est.7b01780.
Simplicity Technological scalability Maturity and technology readiness
and mode shift+ + +
Role of contextsApplication of demand reduction and fuel efficiency measures can 
be scaled and developing countries can leapfrog to most advanced 
technology. India skipped Euro V, and implemented Euro VI from IV,

but this shift will require investment in the short term.Technology to deliver demand reduction and fuel efficiency is readily available. Significant economic benefit in short and long term.
Line of sightVashist, D., N. Kumar, and M. Bindra, 2017: Technical Challenges in Shifting from BS IV to BS-VI Automotive Emissions Norms by 2020 in India: A Review. Arch. Curr. Res. Int., 8(1), 1–8, doi:10.9734/ACRI/2017/33781.
aviation, and shipping± ± +
Role of contextsTypically based on internal combustion engines, similar to fossil 
fuels, however, may require engine recalibration.Biofuels are scalable and may benefit from economies of scale; potential for scale up

of sustainable crop production may be limited.There are many biofuels technologies that are already at 
commercial scale, while some technologies for advanced 
biofuels are still under development.
Line of sightMawhood, R., E. Gazis, S. de Jong, R. Hoefnagels, and R. Slade, 2016: Production pathways for renewable jet fuel: a review of commercialization status and future prospects. Biofuels, Bioprod. Biorefining, 10, 462–484, 
doi:10.1002/bbb.1644.
Puricelli, S. et al., 2021: A review on biofuels for light-duty vehicles in Europe. Renew. Sustain. Energy Rev., 137, 110398, doi:10.1016/J.RSER.2020.110398.
Ammonia for shipping – ± ±
Role of contextsRequires either new engines or retrofits for existing engines. It is 
likely some ammonia will need to be mixed with a secondary fuel 
due its relatively low burning velocity and high ignition temperature. 
This would likely require existing powertrains to be modified to 
accept dual fuel mixes, including ammonia. Exhaust treatment

systems are also required to deal with the release of unburnt 
ammonia emissions.Ammonia supply chains are well established; transport and storage more feasible 
than hydrogen; scalability of electrolytic production routes remains a challenge for 
producing low-GHG ammonia.The production, transport and storage of ammonia is mature 
based on existing international supply chains. The use of 
ammonia in ships is still at the early stages of research and 
development. Further research and development will be 
required for ammonia to be widely used in shipping, including 
improving the efficiency of combustion, and treatment of 
exhaust emissions. Ammonia could also potentially be used 
in fuel cell powertrains in the future, but the development 
of this technology is even less mature at present.

1155
1155
Transport Chapter 1010Technological
Line of sightFrigo, S., R. Gentili, and F . De Angelis, 2014: Further Insight into the Possibility to Fuel a SI Engine with Ammonia plus Hydrogen. SAE Technical Paper 2014-32-008, doi:10.4271/2014-32-0082.
Dimitriou, P . and R. Javaid, 2020: A review of ammonia as a compression ignition engine fuel. Int. J. Hydrogen Energy, 45(11), 7098–7118, doi:10.1016/J.IJHYDENE.2019.12.209.
MAN Energy Solutions, 2019: Engineering the future two-stroke green-ammonia engine. MAN Energy Solutions, Copenhagen, Denmark, 20 pp.
Synthetic fuels for 
heavy-duty land transport, 
aviation, and shipping 
(e.g., DAC-FT)+ – –
Role of contexts Can produce drop-in fuels, which use existing engine technologies.Rate at which DAC or other carbon capture can be scaled up is likely a limiting 
factor; large energy inputs (requiring substantial new low-carbon energy resources),

and sorbent requirements likely to be a challenge.Some processes (e.g., Fischer Tropsch) are well established, 
but DAC and BECCS are still at demonstration stage.
Line of sightSutter, D., M. van der Spek, and M. Mazzotti, 2019: 110th 
Anniversary: Evaluation of CO 2-Based and CO 2-Free Synthetic Fuel 
Systems Using a Net-Zero-CO 2-Emission Framework. Ind. Eng. Chem. 
Res., 58(43), 19958–19972, doi:10.1021/acs.iecr.9b00880.
The Royal Society, 2019: Sustainable synthetic carbon based fuels 
for transport: Policy Brief. The Royal Society, London, UK, 46 pp.The Royal Society, 2019: Sustainable synthetic carbon based fuels for transport: 
Policy Brief. The Royal Society, London, UK, 46 pp.
Realmonte, G. et al., 2019: An inter-model assessment of the role of direct air capture 
in deep mitigation pathways. Nat. Commun., 10(1), 3277, doi:10.1038/s41467-019-
10842-5.Liu, C.M., N.K. Sandhu, S.T. McCoy, and J.A. Bergerson, 2020: 
A life cycle assessment of greenhouse gas emissions from

direct air capture and Fischer-Tropsch fuel production. Sustain. 
Energy Fuels, 4(6), 3129–3142, doi:10.1039/C9SE00479C.
Electric vehicles for 
land transport± ± ±
Role of contextsFewer engine components; lower maintenance requirements than 
conventional vehicles; potential concerns surrounding battery size/
weight, charging time, and battery life.Widespread application already feasible; some limits to adoption in remote 
communities or long-haul freight; at large scale, may positively or negatively 
impact electric grid functioning depending on charging behaviour and grid 
integration strategy.+ Technology is mature for light-duty vehicles;  
– Improvements in battery capacity and density as well 
as charging speed required for heavy-duty applications.
Line of sightBurnham, A. et al., 2021: Comprehensive total cost of ownership 
quantification for vehicles with different size classes and 
powertrains. Argonne National Laboratory, US Department

of Energy, Lemont, IL, USA, 227 pp.IEA, 2021: Global EV Outlook 2021. International Energy Agency, Paris, 
France,101 pp.
Milovanoff, A., I.D. Posen, and H.L. MacLean, 2020: Electrification of light-duty 
1102–1107, doi:10.1038/s41558-020-00921-7.
Crozier, C., T. Morstyn, and M. McCulloch, 2020: The opportunity for smart charging 
to mitigate the impact of electric vehicles on transmission and distribution systems. 
Appl. Energy, 268, 114973, doi:10.1016/j.apenergy.2020.114973.
Kapustin, N O. and D.A. Grushevenko, 2020: Long-term electric vehicles outlook 
and their potential impact on electric grid. Energy Policy, 137, 111103, doi:10.1016/ 
j.enpol.2019.111103.
Das, H.S., M.M. Rahman, S. Li, and C.W. Tan, 2020: Electric vehicles standards, 
charging infrastructure, and impact on grid integration: A technological review. 
Renew. Sustain. Energy Rev., 120, 109618, doi:10.1016/j.rser.2019.109618.

Liimatainen, H., O. van Vliet, and D. Aplyn, 2019: The potential of electric 
trucks – An international commodity-level analysis. Appl. Energy, 236, 804–814, 
doi:10.1016/j.apenergy.2018.12.017.
Forrest, K., M. Mac Kinnon, B. Tarroja, and S. Samuelsen, 2020: Estimating the 
technical feasibility of fuel cell and battery electric vehicles for the medium 
and heavy duty sectors in California. Appl. Energy, 276, 115439, doi:10.1016/ 
j.apenergy.2020.115439.IEA, 2021: Global EV Outlook 2021. International Energy 
Agency, Paris, France, 101 pp.
Smith, D. et al., 2020: Medium- and Heavy-Duty Vehicle 
Electrification: An Assessment of Technology and Knowledge 
Gaps. Oak Ridge National Laboratory, Oak Ridge, TN, USA, 
85 pp.
Forrest, K., M. Mac Kinnon, B. Tarroja, and S. Samuelsen, 
2020: Estimating the technical feasibility of fuel cell 
and battery electric vehicles for the medium and heavy 
duty sectors in California. Appl. Energy, 276, 115439, 
doi:10.1016/j.apenergy.2020.115439.

1156
1156
land transport± – –
Role of contextsLower maintenance requirements compared to conventional 
technologies; potential issues with on-vehicle hydrogen storage, 
fuel cell degradation and lifetime; fewer weight and refuelling time 
barriers compared to electric vehicles.Currently the refuelling infrastructure is limited, but it is growing at the pace 
of the technology deployment. Challenges exist with transport and distribution 
of hydrogen. Electrolytic hydrogen not currently produced at scale.The technology is already available to users for light-duty 
vehicle applications and buses, but further improvements 
in fuel cell technology are needed. Use in heavy-duty 
applications is currently constrained. Maturity and technology 
readiness level can vary for different parts of the supply chain, 
and is lower than for EVs.

Line of sightTrencher, G., A. Taeihagh, and M. Yarime, 2020: Overcoming barriers 
to developing and diffusing fuel-cell vehicles: Governance strategies 
and experiences in Japan. Energy Policy, 142, 111533, doi:10.1016/ 
j.enpol.2020.111533.Pollet, B.G., S.S. Kocha, and I. Staffell, 2019: Current status of automotive 
fuel cells for sustainable transport. Curr. Opin. Electrochem., 16 (May), 1–6, 
doi:10.1016/j.coelec.2019.04.021.Wang, J., H. Wang, and Y . Fan, 2018: Techno-Economic 
Challenges of Fuel Cell Commercialization. Engineering, 4(3), 
352–360, doi:10.1016/j.eng.2018.05.007.
Kampker, A. et al., 2020: Challenges towards large-scale 
fuel cell production: Results of an expert assessment study. 
Int. J. Hydrogen Energy, 45(53), 29288–29296, doi:10.1016/ 
J.IJHYDENE.2020.07.180.
4. Economic
Costs in 2030 and long term Employment effects and economic growth
Demand reduction and mode shift + LE
Role of contexts Significant economic benefit in short and long term.

The UK, 2020: The Green Book. HM Treasury, London, UK, https://www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-in-central-governent/the-green-book-2020.
aviation, and shipping± LE
Role of contexts Some biofuels are already cost competitive with fossil fuels. In the future, reduction of costs for advanced biofuels may be a challenge.Biofuels are expected to increase job creation in comparison to fossil fuel 
alternatives. This is still to be further demonstrated.
Line of sightDaioglou, V. et al., 2020: Bioenergy technologies in long-run climate change mitigation: results from the EMF-33 study. Clim. Change, 163(3), 1603–1620, doi:10.1007/s10584-020-02799-y.
Brown, A., et al., 2020. Advanced Biofuels – Potential for Cost Reduction. IEA Bioenergy, Paris, France, 88.

Ammonia for shipping – NE
Role of contexts Green ammonia is likely to be significantly more expensive than conventional fuels for the coming decades.
Line of sightEnergy Transitions Commission, 2021. Making the hydrogen economy possible. Energy Transitions Commission, 92 pp. https://energy-transitions.org/wp-content/uploads/2021/04/ETC-Global-Hydrogen-Report.pdf .
Energy Transitions Commission, 2020. The First Wave: A blueprint for commercial-scale zero-emission shipping pilots. Energy Transitions Commission, 102 pp. https://www.energy-transitions.org/wp-content/
uploads/2020/11/The-first-wave.pdf .
(e.g., DAC-FT)– NE
Role of contexts Large uncertainty on future costs but expected to remain higher than conventional fuels for the coming decades.

1157
1157
Transport Chapter 10104. Economic
Line of sightUeckerdt, F . et al., 2021: Potential and risks of hydrogen-based e-fuels in climate change mitigation. Nat. Clim. Change, 11(5), 384–393, 
doi:10.1038/s41558-021-01032-7.
Zang, G. et al., 2021: Synthetic Methanol/Fischer –Tropsch Fuel Production Capacity, Cost, and Carbon Intensity Utilizing CO 2 from 
Industrial and Power Plants in the United States. Environ. Sci. Technol., 55(11), 7595–7604, doi:10.1021/acs.est.0c08674.
Scheelhaase, J., S. Maertens, and W. Grimme, 2019: Synthetic fuels in aviation – Current barriers and potential political measures. 
Transp. Res. Procedia, 43, 21–30, doi:10.1016/j.trpro.2019.12.015.
Electric vehicles for land transport + LE
Role of contextsLifecycle costs for electric vehicles are anticipated to be lower than for conventional vehicles by 2030; high confidence for light-duty

vehicles; lower confidence for heavy-duty applications.Some grey studies exist on employment effects of electric vehicles; 
however, the peer-reviewed literature is not well developed.
Line of sightIEA, 2021a: Global EV Outlook 2021. International Energy Agency, Paris, France, 101 pp.
Liimatainen, H., O. van Vliet, and D. Aplyn, 2019: The potential of electric trucks – An international commodity-level analysis. 
Appl. Energy, 236, 804–814, doi:10.1016/j.apenergy.2018.12.017.
Kapustin, N.O. and D.A. Grushevenko, 2020: Long-term electric vehicles outlook and their potential impact on electric grid. 
Energy Policy, 137, 111103, doi:10.1016/j.enpol.2019.111103.
Forrest, K., M. Mac Kinnon, B. Tarroja, and S. Samuelsen, 2020: Estimating the technical feasibility of fuel cell and battery electric 
vehicles for the medium and heavy duty sectors in California. Appl. Energy, 276, 115439, doi:10.1016/j.apenergy.2020.115439.
Hydrogen FCV for land transport + LE

Role of contextsLifecycle costs for hydrogen fuel cell vehicles projected to be competitive with conventional vehicles in future, however high 
uncertainty remains.Some studies exist on employment effects of hydrogen economy; however, 
the literature is not well developed and does not apply directly to FCVs.
Line of sightMiotti, M., J. Hofer, and C. Bauer, 2017: Integrated environmental and economic assessment of current and future fuel cell vehicles. 
Int. J. Life Cycle Assess., 22(1), 94–110, doi:10.1007/s11367-015-0986-4.
Ruffini, E. and M. Wei, 2018: Future costs of fuel cell electric vehicles in California using a learning rate approach. Energy, 150, 
329–341, doi:10.1016/j.energy.2018.02.071.
Olabi, A.G., T. Wilberforce, and M.A. Abdelkareem, 2021: Fuel cell application in the automotive industry and future perspective. 
Energy,  214, 118955, doi:10.1016/j.energy.2020.118955.
Public acceptance Effects on health & well-being Distributional effects

Demand reduction and mode shift ± + ±
Role of contextsPublic support for some measures, such as emissions charging 
schemes, can be mixed initially, they are likely to gain acceptance as 
benefits are realised and/or focused. Such as recent COVID-19 road 
network changes in London.Significant economic health and well-being benefits.Some measures, such as travel restrictions, emission charging 
schemes and others, can have mixed distributional effects initially 
(e.g., on accessibility).
Line of sightWinter, A.K. and H. Le, 2020: Mediating an invisible policy problem: Nottingham’s rejection of congestion charging. Local Environ., 25(6), 463–471, doi:10.1080/13549839.2020.1753668.

Adhikari, M., L.P . Ghimire, Y . Kim, P . Aryal, and S.B. Khadka, 2020: Identification and Analysis of Barriers against Electric Vehicle Use. Sustainability, 12(12), 4850, doi:10.3390/su12124850.
TfL (2020) London Streetspace changes. https://www.pgweb.uk/planning-all-subjects/quieter-neighbourhoods/2847-120-doctors-and-nurses-urge-continuation-of-low-traffic-neighbourhoods-and-cycle-lanes-schemes .

1158
1158
aviation, and shipping± LE ±
Role of contextsVaried public acceptance of biofuel options is observed in different 
regions of the world.No known impacts. Food security but agricultural economies.
Line of sightLøkke, S., E. Aramendia, and J. Malskær, 2021: A review of public opinion on liquid biofuels in the EU: Current knowledge and future challenges. Biomass and Bioenergy, 150, 106094, doi:10.1016/ 
j.biombioe.2021.106094.
Taufik, D. and H. Dagevos, 2021: Driving public acceptance (instead of skepticism) of technologies enabling bioenergy production: A corporate social responsibility perspective. J. Clean. Prod., 324, 129273, 
doi:10.1016/j.jclepro.2021.129273.
Ammonia for shipping LE LE LE
Role of contextsSome concerns in industry regarding handling of hazardous fuel; 
limited evidence overall.
Line of sight N/A

(e.g., DAC-FT)LE LE NE
Role of contextsCurrently low public awareness of the technology and little 
evidence regarding associated perceptions.No known impacts.
Electric vehicles for land transport ± ± ±
Role of contextsGrowing public acceptance, especially in some jurisdictions 
(e.g., majority of light-duty vehicle sales in Norway are electric), 
but wide differences across regions; range anxiety remains a barrier 
among some groups.No major impacts; some potential for reduced noise, which 
can improve well-being of city residents but may adversely affect 
pedestrian safety.Higher vehicle purchase price and access to off-road parking limits 
access for some disadvantaged groups; potentially insufficient 
infrastructure for adoption in rural communities (initially); air quality 
improvements may disproportionately benefit disadvantaged 
groups, but may also shift some impacts onto communities

in close proximity to electricity generators.
Line of sightCoffman, M., P . Bernstein, and S. Wee, 2017: Electric vehicles 
revisited: a review of factors that affect adoption. Transp. Rev., 
37(1), 79–93, doi:10.1080/01441647.2016.1217282.
Burkert, A., H. Fechtner, and B. Schmuelling, 2021: Interdisciplinary 
Analysis of Social Acceptance Regarding Electric Vehicles with 
a Focus on Charging Infrastructure and Driving Range in Germany. 
World Electr. Veh. J., 12(1), 25, doi:10.3390/wevj12010025.
Wang, N., L. Tang, and H. Pan, 2018b: Analysis of public 
acceptance of electric vehicles: An empirical study in Shanghai. 
Technol. Forecast. Soc. Change, 126, 284–291, doi:10.1016/ 
j.techfore.2017.09.011.Campello-Vicente, H., R. Peral-Orts, N. Campillo-Davo, and 
E. Velasco-Sanchez, 2017: The effect of electric vehicles on 
urban noise maps. Appl. Acoust., 116, 59–64, doi:10.1016/ 
j.apacoust.2016.09.018.Canepa, K., S. Hardman, and G. Tal, 2019: An early look at

plug-in electric vehicle adoption in disadvantaged communities 
in California. Transp. Policy, 78, 19–30, doi:10.1016/ 
j.tranpol.2019.03.009.
Brown, M.A., A. Soni, M.V Lapsa, K. Southworth, and M. Cox, 
2020: High energy burden and low-income energy affordability: 
conclusions from a literature review. Prog. Energy, 2(4), 42003, 
doi:10.1088/2516-1083/abb954.
Hydrogen FCV for land transport ± ± ±
Role of contextsPublic acceptance is growing in countries where the technology 
is being promoted and subsidised. However, sparse infrastructure, 
high costs and perceived safety concerns are currently barriers 
to a widespread deployment of the technology.No major impacts: some potential for reduced noise, which can 
improve well-being of city residents but may adversely affect 
pedestrian safety.Higher vehicle purchase price limits access for some disadvantaged 
groups; potentially insufficient infrastructure for adoption in 
rural communities (initially); air quality improvements may

1159
1159
Transport Chapter 1010Socio-cultural
Line of sightItaoka, K., A. Saito, and K. Sasaki, 2017: Public perception on hydrogen infrastructure in Japan: Influence of rollout of commercial fuel cell vehicles. Int. J. Hydrogen Energy, 42(11), 7290–7296, doi:10.1016/ 
j.ijhydene.2016.10.123.
Canepa, K., S. Hardman, and G. Tal, 2019: An early look at plug-in electric vehicle adoption in disadvantaged communities in California. Transp. Policy, 78, 19–30, doi:10.1016/j.tranpol.2019.03.009.
Brown, M.A., A. Soni, M. V Lapsa, K. Southworth, and M. Cox, 2020: High energy burden and low-income energy affordability: conclusions from a literature review. Prog. Energy, 2(4), 42003, doi:10.1088/2516-
1083/abb954.
Trencher, G., 2020: Strategies to accelerate the production and diffusion of fuel cell electric vehicles: Experiences from California. Energy Reports, doi:10.1016/j.egyr.2020.09.008.

Political acceptanceInstitutional capacity and governance,  
cross-sectoral coordinationLegal and administrative feasibility
Demand reduction and mode shift ± ± ±
schemes, can be mixed initially, it is likely to gain acceptance as 
benefits are realised and/or focused. Such as recent COVID-19 
road network changes in London.Some local authorities have limited capacity to deliver demand 
management measures as compared to other developed authorities. 
However, this can be mitigated to optioneering processes to select 
the preferred measures in the local context.Legal air quality limits are forcing cities and countries to implement 
travel demand reduction and fuel efficiency measures, such as in 
the UK and Europe. However, there may be legal and administrative 
changes in delivery of measures.

DEFRA and DoT, 2020: Clean Air Zone Framework: Principles for setting up Clean Air Zones in England., Department of Environment Food & Rural Affairs/Department of Transport, Government of UK, London, U35 pp.
aviation, and shipping± ± ±
Role of contextsVaried political support for biofuels deployment in different regions 
of the world.There is varied institutional capacity to coordinate biofuels

deployment in different regions of the world.There are different legal contexts and barriers for biofuels 
implementation on different regions of the world.
Line of sightLynd, L.R., 2017: The grand challenge of cellulosic biofuels. Nat. Biotechnol., 35(10), 912–915, doi:10.1038/nbt.3976.
Markel, E., C. Sims, and B.C. English, 2018: Policy uncertainty and the optimal investment decisions of second-generation biofuel producers. Energy Econ., 76, 89–100, doi:10.1016/j.eneco.2018.09.017.
Ammonia for shipping ± - -
Role of contextsVaried political support for deployment in different regions 
of the world.The major contributor to marine emissions is international shipping, 
which falls under the jurisdiction of the International Maritime 
Organization. Coordination with international governments will 
be required.Potential challenges related to emissions regulations.

Line of sightHoegh-Guldberg, O. et al., 2019: The Ocean as a Solution to Climate Change: Five Opportunities for Action. World Resources Institute, Washington D. C., 116 pp.
Energy Transitions Commission, 2021. Making the hydrogen economy possible. Energy Transitions Commission, https://energy-transitions.org/wp-content/uploads/2021/04/ETC-Global-Hydrogen-Report.pdf .
Energy Transitions Commission, 2020. The First Wave: A blueprint for commercial-scale zero-emission shipping pilots. Energy Transitions Commission, https://www.energy-transitions.org/wp-content/

1160
1160
(e.g., DAC-FT)LE – ±
Role of contextsPlans for adoption of technology remain at early stage; political 
acceptance not known.Synthetic fuel use in aviation and marine shipping requires 
international coordination; challenges exist related to carbon 
accounting frameworks for utilisation of CO 2; likely fewer barriers 
for use of fuel in land transport applications.Legal barriers exist for synthetic fuel use in aviation; need for 
development of CO 2 capture markets; drop-in fuels are compatible 
with existing fuel standards in many jurisdictions.

Line of sight Scheelhaase, J., S. Maertens, and W. Grimme, 2019: Synthetic fuels in aviation – Current barriers and potential political measures. Transp. Res. Procedia, 43, 21–30, doi:10.1016/j.trpro.2019.12.015.
of the world.Coordination needed between transport sector (including vehicle 
manufacturers; charging infrastructure) and power sector (including 
increased generation and transmission; capacity to handle demand 
peaks). Institutional capacity is variable.Compatible with urban low emission zones; grid integration 
may require market and regulatory changes.
Line of sightMilovanoff, A., I.D. Posen, and H.L. MacLean, 2020: Electrification of light-duty vehicle fleet al.ne will not meet mitigation targets. Nat. Clim. Change, 10(12), 1102–1107, doi:10.1038/s41558-020-00921-7.
IEA, 2021: Global EV Outlook 2021. International Energy Agency, Paris, France, 101 pp.

of the world.Coordination needed across sector (including vehicle manufacturers, 
hydrogen producers and refuelling infrastructure). Institutional 
capacity is variable.Compatible with urban low emission zones; fuel distribution 
network may require market and regulatory changes.
Line of sightItaoka, K., A. Saito, and K. Sasaki, 2017: Public perception on hydrogen infrastructure in Japan: Influence of rollout of commercial fuel cell vehicles. Int. J. Hydrogen Energy, 42(11), 7290–7296,  
doi:10.1016/ j.ijhydene.2016.10.123.

1163
1163
Industry Chapter 1111
The Paris Agreement, the Sustainable Development Goals 
(SDGs) and the COVID-19 pandemic provide a  new context for the evolution of industry and the mitigation of industry greenhouse gas (GHG) emissions (high confidence). This chapter 
is focused on what is new since AR5. It emphasises the energy and emissions intensive basic materials industries and key strategies for reaching net zero emissions. {11.1.1}
Net zero CO
2 emissions from the industrial sector are possible 
but challenging (high confidence). Energy efficiency will continue 
to be important. Reduced materials demand, material efficiency,

and circular economy solutions can reduce the need for primary production. Primary production options include switching to new processes that use low to zero GHG energy carriers and feedstocks (e.g., electricity, hydrogen, biofuels, and carbon capture and utilisation (CCU) for carbon feedstock), and carbon capture and storage (CCS) for remaining CO
2. These options require substantial scaling up of 
electricity, hydrogen, recycling, CO 2, and other infrastructure, as 
well as phase-out or conversion of existing industrial plants. While improvements in the GHG intensities of major basic materials have nearly stagnated over the last 30 years, analysis of historical technology shifts and newly available technologies indicate these intensities can be reduced to net zero emissions by mid-century. {11.2, 11.3, 11.4}
Whatever metric is used, industrial emissions have been

growing faster since 2000 than emissions in any other sector, driven by increased basic materials extraction and production (high confidence). GHG emissions attributed to the industrial 
sector originate from fuel combustion, process emissions, product use and waste, which jointly accounted for 14.1 GtCO
2-eq or 24% of 
all direct anthropogenic emissions in 2019, second behind the energy transformation sector. Industry is a  leading GHG emitter  – 20 GtCO
2-eq or 34% of global emissions in 2019  – if indirect 
emissions from power and heat generation are included. The share of emissions originating from direct fuel combustion is decreasing and was 7 GtCO
2-eq, 50% of direct industrial emissions in 2019. {11.2.2}
Global material intensity (in-use stock of manufactured capital, in tonnes per unit of GDP is increasing (high confidence). In-

use stock of manufactured capital per capita has been growing faster than GDP per capita since 2000. Total global in-use stock of manufactured capital grew by 3.4% yr
–1 in 2000–2019. At the 
same time, per capita material stocks in several developed countries have stopped growing, showing a decoupling from GDP per capita. {11.2.1, 11.3.1}
Plastic is the material for which demand has been growing 
the strongest since 1970 (high confidence). The current 
>99%  reliance on fossil feedstock, very low recycling, and highemissions from petrochemical processes is a challenge for reachingnet zero emissions. At the same time, plastics are important forreducing emissions elsewhere, for example, light-weighting vehicles.There are as yet no shared visions for fossil-free plastics, but severalpossibilities. {11.4.1.3}Scenario analyses show that significant cuts in global GHG

emissions and even close to net zero emissions from GHG  intensive industry (e.g.,  steel, plastics, ammonia, and cement) can be achieved by 2050 by deploying multiple available and emerging options (medium confidence). Cutting 
industry emissions significantly requires a  reorientation from the historic focus on important but incremental improvements (e.g., energy efficiency) to transformational changes in energy and feedstock sourcing, materials efficiency, and more circular material flows. {11.3, 11.4}
Key climate mitigation options such as materials efficiency, 
circular material flows and emerging primary processes, are not well represented in climate change scenario modelling and integrated assessment models, albeit with some progress in recent years (high confidence). The character of these interventions

(e.g., appearing in many forms across complex value chains, making cost estimates difficult) combined with the limited data on new fossil-free primary processes help explain why they are less represented in models than, for example, CCS. As a result, overall mitigation costs and the need for CCS may be overestimated. {11.4.2.1}
Electrification is emerging as a  key mitigation option for 
industry (high confidence). Electricity is a versatile energy carrier, 
potentially produced from abundant renewable energy sources or other low carbon options; regional resources and preferences will vary. Using electricity directly, or indirectly via hydrogen from electrolysis for high temperature and chemical feedstock requirements, offers many options to reduce emissions. It also can provide substantial grid balancing services, for example through electrolysis and storage of hydrogen for chemical process use or demand response. {11.3.5}

Carbon is a key building block in organic chemicals, fuels and 
materials, and will remain important (high confidence). In order 
to reach net zero CO
2 emissions for the carbon needed in society 
(e.g.,  plastics, wood, aviation fuels, solvents, etc.), it is important to close the use loops for carbon and carbon dioxide through increased circularity with mechanical and chemical recycling, more efficient use of biomass feedstock with the addition of low GHG hydrogen to increase product yields (e.g.,  for biomethane and methanol), and potentially direct air capture of CO
2 as a new carbon 
source. {11.3, 11.4.1}
Production costs for very low to zero emissions basic materials 
may be high but the cost for final consumers and the general economy will be low (medium confidence). Costs and emissions

reductions potential in industry, and especially heavy industry, are highly contingent on innovation, commercialisation, and market uptake policy. Technologies exist to take all industry sectors to very low or zero emissions but require 5 to 15 years of intensive innovation, commercialisation, and policy to ensure uptake. Mitigation costs are in the rough range of USD50–150 tCO
2-eq–1, with wide variation 
within and outside this band. This affects competitiveness and requires supporting policy. Although production cost increases can be significant, they translate to very small increases in the costs for final products, typically less than a  few percent depending on product, assumptions, and system boundaries. {11.4.1.5}

1164
1164
Chapter 11  Industry11There are several technological options for very low to zero 
emissions steel, but their uptake will require integrated 
material efficiency, recycling, and production decarbonisation 
policies (high confidence). Material efficiency can potentially 
reduce steel demand by up to 40% based on design for less steel use, 
long life, reuse, constructability, and low contamination recycling. 
Secondary production through high quality recycling must be 
maximised. Production decarbonisation will also be required, starting 
with the retrofitting of existing facilities for partial fuel switching 
(e.g., to biomass or hydrogen), CCU and CCS, followed by very low 
and zero emissions production based on high-capture CCS or direct 
hydrogen, or electrolytic iron ore reduction followed by an electric arc 
furnace. {11.3.2, 11.4.1.1}
There are several current and near-horizon options to 
greatly reduce cement and concrete emissions. Producer,

user, and regulator education, as well as innovation and 
commercialisation policy are needed (medium confidence). 
Cement and concrete are currently overused because they are 
inexpensive, durable, and ubiquitous, and consumption decisions 
typically do not give weight to their production emissions. Basic 
material efficiency efforts to use only well-made concrete thoughtfully 
and only where needed (e.g.,  using right-sized, prefabricated 
components) could reduce emissions by 24–50% through lower 
demand for clinker. Cementitious material substitution with various 
materials (e.g.,  ground limestone and calcined clays) can reduce 
process calcination emissions by up to 50% and occasionally much 
more. Until a very low GHG emissions alternative binder to Portland 
cement is commercialised, which does not look promising in the near 
to medium term, CCS will be essential for eliminating the limestone 
calcination process emissions for making clinker, which currently

represent 60% of GHG emissions in best available technology plants. 
{11.3.2, 11.3.6, 11.4.1.2}
While several technological options exist for decarbonising 
the  main industrial feedstock chemicals and their 
derivatives, the costs vary widely (high confidence). Fossil fuel-
based feedstocks are inexpensive and still without carbon pricing, 
and their biomass- and electricity-based replacements will likely 
be more expensive. The chemical industry consumes large amounts 
of hydrogen, ammonia, methanol, carbon monoxide, ethylene, 
propylene, benzene, toluene, and mixed xylenes and aromatics from 
fossil feedstock, and from these basic chemicals produces tens of 
thousands of derivative end-use chemicals. Hydrogen, biogenic or air-
capture carbon, and collected plastic waste for the primary feedstocks 
can greatly reduce total emissions. Biogenic carbon feedstock is likely 
to be limited due to competing land uses. {11.4.1.3}
Light industry and manufacturing can be largely decarbonised

through switching to low GHG fuels (e.g.,  biofuels and 
hydrogen) and electricity (e.g.,  for electrothermal heating 
and heat pumps) (high confidence). Most of these technologies are 
already mature, for example, for low temperature heat, but a major 
challenge is the current low cost of fossil methane and coal relative 
to low and zero GHG electricity, hydrogen, and biofuels. {11.4.1.4}The pulp and paper industry has significant biogenic carbon 
emissions but relatively small fossil carbon emissions. Pulp 
mills have access to biomass residues and by-products and 
in paper mills the use of process heat at low to medium 
temperatures allows for electrification (high confidence). 
Competition for feedstock will increase if wood substitutes for 
building materials and petrochemicals feedstock. The pulp and paper 
industry can also be a source of biogenic carbon dioxide and carbon 
for organic chemicals feedstock and carbon dioxide removal (CDR) 
using CCS. {11.4.1.4}

The geographical distribution of renewable resources has 
implications for industry (medium confidence). The potential 
for zero emission electricity and low-cost hydrogen from electrolysis 
powered by solar and wind, or hydrogen from other very low emission 
sources, may reshape where currently energy and emissions intensive 
basic materials production is located, how value chains are organised, 
trade patterns, and what gets transported in international shipping. 
Regions with bountiful solar and wind resources, or low fugitive 
methane co-located with CCS geology, may become exporters of 
hydrogen or hydrogen carriers such as methanol and ammonia, or 
home to the production of iron and steel, organic platform chemicals, 
and other energy-intensive basic materials. {11.2, 11.4 and Box 11.1}
The level of policy maturity and experience varies widely across 
the mitigation options (high confidence). Energy efficiency 
is a  well-established policy field with decades of experience from

voluntary and negotiated agreements, regulations, energy auditing 
and demand side-management (DSM) programmes (see AR5). In 
contrast, materials demand management and efficiency are not well 
understood and addressed from a  policy perspective. Barriers to 
recycling that policy could address are often specific to the different 
material loops (e.g.,  copper contamination for steel and lack of 
technologies or poor economics for plastics) or waste management 
systems. For electrification and fuel switching the focus has so far 
been mainly on innovation and developing technical supply-side 
solutions rather than creating market demand. {11.5.2, 11.6}
Industry has so far largely been sheltered from the impacts 
of climate policy and carbon pricing due to concerns for 
competitiveness and carbon leakage (high confidence). New 
industrial development policy approaches needed for realising 
a transition to net zero GHG emissions are emerging. The transition

requires a clear direction towards net zero, technology development, 
market demand for low-carbon materials and products, governance 
capacity and learning, socially inclusive phase-out plans, as well as 
international coordination of climate and trade policies. It requires 
comprehensive and sequential industrial policy strategies leading to 
immediate action as well as preparedness for future decarbonisation, 
governance at different levels (from international to local), and 
integration with other policy domains. {11.6}

1165
1165
Industry Chapter 111111.1 Introduction and New Developments
11.1.1 About This Chapter
The AR5 was published in 2014. The Paris Agreement and the 
17  Sustainable Development Goals (SDGs) were adopted in 2015. 
An increasing number of countries have since announced ambitions 
to be carbon neutral by 2045–2060. The COVID-19 pandemic shocked 
the global economy in 2020 and motivated economic stimulus with 
demands for green recovery and concerns for economic security. 
All this has created a new context and a growing recognition that all 
industry, including the energy and emissions intensive industries, 
need to reach net zero GHG emissions. There is an ongoing mind 
shift around the opportunities to do so, with electrification and 
hydrogen emerging among key mitigation options as a  result of 
renewable electricity costs falling rapidly. On the demand side there 
has been renewed attention to end-use demand, material efficiency,

and more and better-quality recycling measures. This chapter takes 
its starting point in this new context and emphasises the need for 
deploying innovative processes and practices in order to limit the 
global warming to 1.5°C or 2°C (IPCC 2018a).
The industrial sector includes ores and minerals mining, manufacturing, 
construction and waste management. It is the largest source of global 
GHG and CO 2 emissions, which include direct and indirect fuel-
combustion-related emissions, emissions from industrial processes 
and products use, as well as from waste. This chapter is focused on 
heavy industry – the high temperature heat and process emissions 
intensive basic materials industries that account for 65% of industrial 
GHG and over 70% of industrial CO 2 emissions (waste excluded), 
where deployment of near -zero emissions technologies can be 
more challenging due to capital intensity and equipment lifetimes 
compared with other manufacturing industries. The transition of

heavy industries to zero emissions requires supplementing the 
traditional toolkit of energy and process efficiency, fuel switching, 
electrification, and decarbonisation of power with material end-use 
demand management and efficiency, circular economy, fossil-free 
feedstocks, carbon capture and utilisation (CCU), and carbon capture 
and storage (CCS). Energy efficiency was extensively treated in AR5 and  
remains a key mitigation option. This chapter is focused mainly on 
new options and developments since AR5, highlighting measures 
along the whole value chains that are required to approach zero 
emissions in primary materials production.
1 Accumulated material stock initially was introduced in the analysis of past trends (Krausmann et al. 2018; Wiedenhofer et al. 2019), but recently it was incorporated 
in different forms in the long-term projections for the whole economy (Krausmann et al. 2020) and for some sectors (buildings and cars in Hertwich et al. (2020)) with

a steadily improving regional resolution (Krausmann et al. 2020).11.1.2 Approach to Understanding Industrial Emissions
The Kaya identity offers a useful tool of decomposing emission sources 
and their drivers, as well as of weighing the mitigation options. 
The one presented below (Equation 11.1) builds on the previous 
assessments (IPCC 2014, 2018b; Hoegh-Guldberg et al. 2018), and 
reflect a  material stock-driven services-oriented vision to better 
highlight the growing importance of industrial processes (dominated 
in emissions  increments in 2010–2019), product use and waste in 
driving emissions. Services delivery (nutrition, shelter, mobility, 
education, etc.; see Chapter 5 for more detail) not only requires energy 
and material flows (fuels, food, feed, fertilisers, packaging, etc.), but 
also material stocks (buildings, roads, vehicles, machinery, etc.), the 
mass of which has already exceeded 1000 Gt (Krausmann et al. 2018).

As material efficiency appears to be an important mitigation option, 
material intensity or productivity (material extraction or consumption 
versus GDP (Oberle et al. 2019; Hertwich et al. 2020)) is reflected in 
the identity with two dimensions: as material stock intensity of GDP 
(tonnes per dollar) and material intensity of building and operating 
accumulated in-use stock.1 For sub-global analysis the ratio of 
domestically used materials to total material production becomes 
important to reflect outsourced materials production and distinguish 
between territorial and consumption-based emissions. The identity 
for industry differs significantly from that for sectors with where 
combustion emissions dominate (Lamb et al. 2021).

1166
1166
Chapter 11  Industry11Recent progress in data availability that allows the integration of 
major emission sources along with socio-economic metabolism, 
material flows and stock analysis enriches the identity for industry 
from a perspective of possible policy interventions (Bashmakov 2021):
Equation 11.1
Equation 11.1 Table 1 | Variables, Factors, Policies and Drivers
Variables FactorsPolicies and 
drivers
𝑷𝑷𝑷𝑷𝑷𝑷  
  PopulationDemographic 
policies
Demand decarbonisation𝑮𝑮𝑮𝑮𝑮𝑮
𝑮𝑮𝑷𝑷𝑮𝑮 
  Services (expressed via 
GDP – final consumption 
and investments needed 
to maintain and expand 
stock) per capitaSufficiency 
and demand 
(reduction)
𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴
𝑮𝑮𝑮𝑮𝑮𝑮 
  Material stock (MStock – 
accumulated in-use stocks 
of materials embodied in 
manufactured fixed capital) 
intensity of GDPMaterial stock 
efficiency 
improvement
𝑴𝑴𝑴𝑴𝑴𝑴+𝑴𝑴𝑴𝑴𝑴𝑴
𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴 
  Material inputs (both 
virgin (primary materials 
extraction, MPR) and 
recycled (secondary materials

use, MSE)) per unit of  
in-use material stockMaterial efficiency, 
substitution and 
circular economy
𝑫𝑫𝑫𝑫 
  Share of allocated 
emissions – consumption 
vs production emissions 
accounting (valid only for 
sub-global levels)*Trade policies 
including carbon 
leakage issues 
(localisation versus 
globalisation)
CBAM
𝑬𝑬
(𝑴𝑴𝑴𝑴𝑴𝑴+𝑴𝑴𝑴𝑴𝑬𝑬) 
  Sum of energy use for 
basic material production 
(Em), processing and other 
operational industrial energy 
use (Eoind) per unit of 
material inputsEnergy efficiency 
of basic materials 
other industrial 
processes
Production decarbonisation(𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮+𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮 )
𝑬𝑬 
  Direct (GHGed) and indirect 
(GHGeind) combustion-
related industrial emissions 
per unit of energyElectrification, 
fuel switching, 
and energy 
decarbonisation 
(hydrogen, CCUS-
fuels)
𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮
𝑴𝑴𝑴𝑴𝑴𝑴+𝑴𝑴𝑴𝑴𝑴𝑴 Emissions from industrial 
processes and product use, 
waste, F-gases, indirect 
nitrogen emissions per unit 
of produced materialsFeedstock

(hydrogen), CCUS-
industrial processes, 
waste and F-gases 
*Dm=1, when territorial emission is considered, and Dm equals the ratio of 
domestically used materials to total material production for the consumption-based 
emission accounting). CBAM – carbon border adjustment mechanism.Factors in Equation 11.1 are interconnected by either positive or 
negative feedbacks: scrap-based production or light-weighing 
improves operational energy efficiency, while growing application of 
carbon capture, use and storage (CCUS) brings it down and increases 
material demands (Hertwich et al. 2019; IEA 2020a, 2021a). There are 
different ways to disaggregate Equation 11.1: by industrial subsectors 
(Bashmakov 2021); by reservoirs of material stock (buildings, 
infrastructure, vehicles, machinery and appliances, packaging, etc.); 
by regions and countries (where carbon leakage becomes relevant); 
by products and production chains (material extraction, production

of basic materials, basic materials processing, production of final 
industrial products); by traditional and low carbon technologies used; 
and by stages of products’ lives including recycling.
An industrial transition to net zero emissions is possible when the 
three last multipliers in Equation 11.1 (in square parentheses) are 
approaching zero. Contributions from different drivers (energy 
efficiency, low carbon electricity and heat, material efficiency, 
switching to low carbon feedstock and CCUS) to this evolution vary 
with time. Energy efficiency dominates in the short- and medium 
term and potentially long term (in the range of 10–40% by 2050) 
(IPCC 2018a; Crijns-Graus et al. 2020; IEA 2020a), but for deep 
decarbonisation trajectories, contributions from the other drivers 
steadily grow, as the share of non-energy sources in industrial 
emissions rises and new technologies to address mitigation from 
these sources mature (Material Economics 2019; CEMBUREAU 2020;

BP 2020; Hertwich et al. 2020, 2019; IEA 2021a, 2020a; Saygin and 
Gielen 2021) (Figure 11.1).

1167
1167
Change relative the base year direct GHG emissions Decarbonising 
demandDecarbonising productionEmissions free electricity 
and heatEnergy efﬁciency Fuel switching and 
electriﬁcation of 
high temperatureFeedstock switching
Circular material ﬂows 
and waste reductionCarbon capture and 
use (CCU) and storage (CCS)Material efﬁciency
Mitigation options
Medium to long term0%50%100%150%
–50%200%
TimeShort to medium term Medium to long term
– Circular material ﬂows and effective industrial 
waste management– Increasing recycling ratesShort to medium term
Base year and contributions from the drivers are only illustrative. Drivers’ contribution varies across industries. Indirect emissions reduction is considered as outcome 
of mitigation activites in the energy sector; see Chapter 6.– Provision of emissions-free elctricity
and high temperature heat– Reduction of indirect emissions via lower-carbon 
electricity and heat supply
– Energy efﬁciency approaching

thermodynamic minimums– Energy efﬁciency improvements to best 
available technologies
– Deep low-carbon electriﬁcation, 
green hydrogen use– Fuel switching, biomass and electricity use for high 
temperature process heat
– Zero emissions feedstock (green hydrogen, biomass) 
for basic materials production– Partial substitution of high-carbon feedstock
– Broad-scale, large-scale concentrated CO2 ﬂow 
and possibly post-combustion CCUS– Small scale and sectorally narrow concentrated 
CO2 ﬂow CCUS
– Material efﬁciency and substitution – Ecodesign, material efﬁciency, demand reduction
Figure 11.1 | Stylised composition and contributions from different drivers to the transition of industry to net zero emissions.

1168
1168
Chapter 11  Industry1111.2 New Trends in Emissions 
and Industrial Development
11.2.1 Major Drivers
The use of materials is deeply coupled with economic development 
and growth. For centuries, humanity has been producing and using 
hundreds of materials (Ashby 2012), the diversity of which skyrocketed 
in the recent half-century to achieve the desired performance and 
functionality of multiple products (density; hardness; compressive 
strength; melting point, resistance to mechanical and thermal shocks 
and to corrosion; transparency; heat- or electricity conductivity; 
chemical neutrality or activity, to name a  few). New functions 
drive the growth of material complexity of products; for example, 
a  modern computer chip embodies over 60 different elements 
(Graedel et al. 2015).
2 In 2020 this factor played on the reduction side as the COVID-19 crisis led to a global decline in demand for basic materials, respective energy use and emissions by

3–5 % (IEA 2020a).Key factors driving up industrial GHG emissions since 1900 include 
population and per capita GDP ,2 while energy efficiency and non-
combustion GHG emissions intensity (from industrial processes 
and waste) has been pushing it down. Material efficiency factors – 
material stock intensity of GDP and ratio of extraction, processing 
and recycling of materials per unit of built capital along with 
combustion-related emissions intensity factors and electrification – 
were cyclically switching their contributions with relatively limited 
overall impact. Growing recycling allowed for replacement of 
some energy-intensive virgin materials and thus contributed to 
mitigation. In 2014–2019, a combination of these drivers allowed 
for a  slowdown in the growth of industrial GHG emissions to 
below 1% (Figure 11.2 and Table 11.1), while to match a net zero 
emissions trajectory it should decline by 2% yr–1 in 2020–2030 and 
by 8.9% yr–1 in 2030–2050 (IEA 2021a).

–4%–3%–2%–1%0%1%2%3%4%5%6%
GDP/POP
Material stock/GDP
Primary material
extraction/material stock
Secondary materials/
 material stock
Energy/materials
GHG emissions (direct
energy)/energy
GHG emissions (indirect
GHG emissions (other)/materials
GHG emissions total (direct,
indirect and other)
GHG emissions direct
GHG emissions (direct energy)
GHG emissions (indirect energy)
GHG emissions (other)AAGR
demand
growth
material
efﬁciency
circularity
energy efﬁciency
fuel switching
feedstock carbon intensity
drivers emissions1900–1950 1950–1970 1970–1990 1990–2000 2000–2010
2010–2019 2014–2019
Figure 11.2 | Average annual growth rates of industrial sector GHG emissions and drivers (1900–2019).  Before 1970, GHG emission (other) is limited to 
that from cement production. Waste emission is excluded. Primary material extraction excludes fuels and biomass. Presented factors correspond directly

to Equation 11.1. Sources: population before 1950 and GDP before 1960: Maddison Project (2018); population from 1950 to 1970: UN (2015); population and GDP for 
1960–2020: World Bank (2021); data on material stock, extraction, and use of secondary materials: Wiedenhofer et al. (2019); data on material extraction: UNEP and IRP 
(2020); industrial energy use for 1900–1970: IIASA (2018), for 1971–2019: IEA (2021b); data on industrial GHG emissions for 1900–1970: CDIAC (2017), for 1970–2019: 
data from Crippa et al. (2021) and Minx et al. (2021).

1169
1169
Industry Chapter 1111There are two major concepts of material efficiency (ME). The 
broader one highlights demand reduction via policies promoting 
more intensive use, assuming sufficient (excluding luxury) living 
space or car ownership providing appropriate service levels  – 
housing days or miles driven and life-time extension (Hertwich et al. 
2019, 2020). This approach focuses on dematerialisation of society 
(Lechtenböhmer and Fischedick 2020), where a  ‘dematerialisation 
multiplier’ (Pauliuk et al. 2021) limits both material stock and GDP 
growth, as progressively fewer materials are required to build and 
operate the physical in-use stock to deliver sufficient services. 
According to the IRP (2020), reducing floor space demand by 20% 
via shared and smaller housing compared to the reference scenario 
would decrease Group of Seven (G7) countries’ GHG emissions from 
the material-cycle of residential construction up to 70% in 2050.

The narrower concept ignores demand and sufficiency aspects and 
focuses on supply chains considering ME as less basic materials 
use to produce a certain final product, for example, a car or a metre 
squared of living space (OECD 2019a; IEA 2020a). No matter if the 
broader or the narrower concept of ME is applied, in 1970–2019 it 
did not contribute much to the decoupling of industrial emissions 
from GDP . This is expected to change in the future (Figure 11.2).
Material efficiency analysis mostly uses material intensity 
or  productivity indicators, which compare material extraction or 
consumption with GDP (Oberle et al. 2019; Hertwich et al. 2020). 
Those indicators are functions of material stock intensity of GDP 
accumulated in-use stock. Coupling services or GDP with the built 
stock allows for a  better evaluation of demand for primary basic

materials (Müller et al. 2011; Liu et al. 2013; Liu and Müller 2013; 
Pauliuk et al. 2013a; Cao et al. 2017; Wiedenhofer et al. 2019; Hertwich 
et al. 2020; Krausmann et al. 2020). Since 1970 material stock growth 
driven by industrialisation and urbanisation slightly exceeded that of 
GDP and there was no decoupling,3 so in Kaya-like identities material 
stock may effectively replace GDP . There are different methods to 
estimate the former (see reviews in Pauliuk et al. (2015, 2019) and 
Wiedenhofer et al. (2019), the results of which are presented for major 
basic materials with some geographical resolution (Liu and Müller 
2013; Pauliuk et al. 2013a) or globally (Graedel et al. 2011; Geyer 
et al. 2017; Krausmann et al. 2018; Pauliuk et al. 2019; Wiedenhofer 
et al. 2019; International Aluminium Institute 2021a).
For a  subset of materials, such as solid wood, paper, plastics, 
iron/steel, aluminium, copper, other metals/minerals, concrete,

asphalt, bricks, aggregate, and glass, total in-use stock escalated 
from 36 Gt back in 1900 to 186 Gt in 1970, 572 Gt in 2000, and 
960 Gt in 2015, and by 2020 it exceeded 1,100 Gt, or 145 tonnes 
per capita (Krausmann et al. 2018, 2020; Wiedenhofer et al. 2019). 
In 1900–2019, the stock grew 31-fold, which is strongly coupled 
with GDP growth (36-fold). As the UK experience shows, material 
stock intensity of GDP may ultimately decline after services 
fully dominate GDP , and this allows for material productivity 
3 This conclusion is also valid separately for developed countries and rest of the world (Krausmann et al. 2020).
4 Cement stock for 2014 was estimated at 75 Gt (Cao et al. 2020).
5 IRP (2020) estimate 2017 material extraction at 94 Gt yr–1.
6 It approaches 60 Gt yr–1 after construction and furniture wood and feedstock fuels are added (Krausmann et al. 2018; Wiedenhofer et al. 2019; UNEP and IRP 2020).improvements to achieve absolute reduction in material use,

as stock expansion slows down (Streeck et al. 2020). While the 
composition of basic materials within the stock of manufactured 
capital was evolving significantly, overall stock use associated 
with a unit of GDP has been evolving over the last half-century in 
a quite narrow range of 7.7–8.6 t per USD1000 (2017 purchasing 
power parity (PPP))  showing neither signs of decoupling from 
GDP , nor saturation as of yet. Mineral building materials (concrete, 
asphalt, bricks, aggregate, and glass) dominate the stock volume by 
mass (94.6% of the whole stock, with the share of concrete alone 
standing at 43.5%), followed by metals (3.5%) and solid wood 
(1.4%). The largest part of in-use stock of our ‘cementing societies’ 
(Cao et al. 2017) is constituted by concrete: about 417 Gt in 2015; 
Krausmann et al. (2018) extrapolated this to 478 Gt (65 tonnes 
per capita) in 2018, which contains about 88 Gt of cement.4 The 
iron and steel stock is assessed at 25–35 Gt (Wiedenhofer et al.

2019; Gielen et al. 2020; Wang et al. 2021), while the plastics stock 
reached 2.5–3.2 Gt (Geyer et al. 2017; Wiedenhofer et al. 2019; 
Saygin and Gielen 2021) and the aluminium stock approached 
1.1 Gt (International Aluminium Institute 2021a), or just 0.1% of 
the total. In sharp contrast to global energy intensity, which has 
more than halved since 1900 (Bashmakov 2019), in 2019 material 
stock intensity (in-use stock of manufactured capital per GDP) was 
only 14% below the 1900 level, but 15% above the 1970 level. 
In-use stock per capita has been growing faster than GDP per 
capita since 2000 (Figure  11.3). The growth rate of total in-use 
stock of manufactured capital was 3.8% in 1971–2000 and 3.5% 
in 2000–2019, or 32–35 Gt yr–1, to which concrete and aggregates 
contributed 88%. Recent demand for stockbuilding materials was 
51–54 Gt yr–1, to which recycled materials recently contributed 
only about 10% of material input. About 46–49 Gt yr–1 was virgin

inputs, which after accounting for processing waste and short-
lived products (over 8 Gt yr–1) scale up to 54–58 Gt yr–1 of primary 
extraction (Krausmann et al. 2017, 2018; UNEP and IRP 2020). The 
above indicates that we have only begun to exploit the potential 
for recycling and circularity more broadly.
Total extraction of all basic materials (including biomass and 
fuels) in 2017 reached 92 Gt yr–1, which is 13 times above the 
1900 level (Figure 11.3).5 When recycled resources are added, total 
material inputs exceed 100 Gt (Circle Economy 2020). In Equation 
11.1 MPR represents only material inputs to the stock, excluding 
dissipative use – biomass (food and feed) and combusted fuels. Total 
extraction of stock building materials (metal ores and non-metallic 
minerals) in 2017 reached 55 Gt yr–1.6 In 1970–2018, it grew 4.3-fold 
and the ratio of MPR to accumulated in-use capital has nearly been 
constant since 1990 along with ratio to GDP (Figure 11.3).

End-of-life waste from accumulated stocks along with  
(re)-manufacturing and construction waste is assessed at 16 Gt yr–1 in 
2014 and can be extrapolated in 2018 to 19 Gt yr–1 (Krausmann et al. 
2018; Wiedenhofer et al. 2019), or 1.8% from stock of manufactured

1170
1170
Chapter 11  Industry11
capital. Less than 6 Gt yr–1 was recycled and used to build the stock 
(about 10% of inputs).7 While the circularity gap is still large, and 
limited circularity was engineered into accumulated stocks,8 material 
recycling mitigated some GHG emissions by replacing energy-
intensive virgin materials.9 When the stock saturates, in closed material 
7 Mayer et al. (2019) found that in 2010–2014 the secondary-to-primary materials ratio for the EU-28 was slightly below 9%.
8 According to Circle Economy (2020) 8.6 Gt yr–1 or 8.6% of total inputs for all resources.
9 Environmental impacts of secondary materials are much (up to an order of magnitude) lower compared to primary materials (OECD 2019a; IEA 2021a; Wang et al. 2021), 
but to enable and mobilise circularity benefits it requires social system and industrial designing transformation (Oberle et al. 2019).loops the end-of-life materials waste has to be equal to material input,

and primary production therefore has to be equal to end-of-life waste 
multiplied by unity minus recycling rate. When the latter grows, as 
the linear metabolism is replaced with the circular one, the share of 
primary materials production in total material input declines.Non-metallic minerals – 
industrial or agricultural 
dominant
Non-metallic minerals – 
construction dominant
Non-ferrous ores
Ferrous ores
Oil shale and tar sands
Petroleum
Coal
Wood
Grazed biomass and 
fodder crops
Wild catch and harvest
Crops
Crop residues
Total excl. biomass and 
combusted fuels
Total excl. fuels and biomass 
per GDP (right axes)0.00.10.20.30.40.50.60.70.80.91.0
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017Gigatonnes (Gt)
kg per USD2010 GDP PPP

Aluminium Cement Plastics Steel GDP Population1990=100
y = 9.4701x0.9597
R2 = 0.9898y = 7.0474x1.0902
R2 = 0.9982
0 2 4 6 8 10 12 14 16In-use marterial stock (tonnes per capita)
GDP per capita, 1000 USD2010 PPP1990
2000
2010
2019
2020
Figure  11.3 |  Raw natural materials extraction since 1970.  In windows: left  – growth of population, GDP and basic materials production (1990 =  100) in 
1990–2020; right – in-use stock per capita vs income level (1900–2018; brown dots are for 2000–2018). The regressions provided show that for more recent years 
elasticity of material stock to GDP was greater than unity, comparing with the lower unity in preceding years. Source: developed based on Maddison Project (2018); 
Wiedenhofer et al. (2019); IEA (2020b); UNEP and IRP (2020); International Aluminium Institute (2021a); Statista (2021a,b); U.S. Geological Survey (2021); World Bank 
(2021); World Steel Association (2021).

1171
1171
Industry Chapter 1111Recycling rates for metals are higher than for other materials: the 
end-of-life scrap input ratio for 13 metals is over 50%, and stays in 
the range of 25–50% for another ten, but even for metals recycling 
flows fail to match the required inputs (Graedel et al. 2011). Globally, 
despite overall recycling rates being at 85%, the all-scrap ratio for 
steel production in recent years stays close to 35–38% (Gielen et al. 
2020; IEA 2021b) ranging from 22% in China (only 10% in 2015) to 
69% in the US and to 83% in Turkey (BIR 2020). For end-of-life scrap 
this ratio declined from 30% in 1995–2010 to 21–25% after 2010 
(Gielen et al. 2020; Wang et al. 2021).
For aluminium, the share of scrap-based production grew from 17% 
in 1962 to 34% in 2010 and stabilised at this level until 2019, while 
the share of end-of-life scrap grew from 1.5% in 1962 to nearly 
20% in 2019 (International Aluminium Institute 2021a). The global

recycling (mostly mechanical) rate for plastics is only 9–10%10 
(Geyer et al. 2017; Saygin and Gielen 2021), and that for paper 
progressed from 34% in 1990 to 44% in 2000 and to over 50% in 
2014–2018 (IEA 2020b).
The limited impacts of material efficiency factors on industrial 
GHG emissions trends reflect the lack of integration of material 
efficiency in energy and climate policies which partly results from the 
inadequacy of monitored indicators to inform policy debates and set 
targets;11 lack of high-level political focus and industrial lobbying; 
uncoordinated policy across institutions and sequential nature of 
decision-making along supply chains; carbon pricing policy lock-in 
with upstream sectors failing to pass carbon costs on to downstream 
sectors (due to compensation mechanisms to reduce carbon leakage) 
and so have no incentives to exploit such options as light-weighting, 
reusing, remanufacturing, recycling, diverting scrap, extending

product lives, using products more intensely, improving process yields, 
and substituting materials (Skelton and Allwood 2017; Gonzalez 
Hernandez et al. 2018b; Hilton et al. 2018). Poor progress with 
material efficiency is part of the reason why industrial GHG emissions 
are perceived as ‘hard to abate’, and many industrial low-carbon 
trajectories to 2050 leave up to 40% of emissions in place (Material 
Economics 2019; IEA 2021a). The importance of this factor activation 
rises as in-use material stock is expected to scale up by a factor of 
2.2–2.7 to reach 2215–2720 Gt by 2050 (Krausmann et al. 2020). 
Material extraction in turn is expected to rise to 140–200 Gt yr–1 by 
2060 (OECD 2019a; Hertwich et al. 2020) providing unsustainable 
pressure on climate and environment and calling for fundamental 
improvements in material productivity.
In 2014–2019, the average annual growth rate (AAGR) of global 
industrial energy use was 0.4% compared to 3.2% in 2000–2014,

following new policies and trends, particularly demonstrated by 
10 IEA (2021a) assesses the global plastics collection rate at 17% for 2020.
11 Significant progress with data and indicators was reached in recent years with the development of several global coverage material flows datasets (Oberle et al. 2019).
12 China contributed three quarters of global industrial energy use increment in 2000–2014. Since 2014 China’s share in global industrial energy use has slowly declined, 
reaching about a third in 2018 (IEA 2020d).
13 This is close to 28.8% average 1900–2018 share of industrial energy use in global primary energy consumption. This share shows a slow decline trend (0.01% yr–1) 
in response to the growing share of services in global GDP , with about 60-year-long cycles.
14 Industry also produces goods traditionally used as feedstock – hydrogen and ammonia – which in the future may be widely used as energy carriers.

15 Mapping global flows of fuel feedstock allows for better tailoring of downstream mitigation options for chemical products (Levi and Cullen 2018).China12 (IEA 2020b,d). Whatever metric is applied, industry (coal 
transformation, mining, quarrying, manufacturing and construction) 
driven mostly by material production, dominates global energy 
consumption. About two fifths of energy produced globally goes to 
industry, directly or indirectly. Direct energy use (including energy 
used in coal transformation) accounts for nearly 30% of total final 
energy consumption. When supplemented by non-energy use, the 
share for the post-AR5 period (2015–2019) stands on average close 
to 40% of final energy consumption, and at 28.5% of primary energy 
use.13 With an account of indirect energy use for the generation 
of power and centralised heat to be consumed in industry, the 
latter scales up to 37%. Industrial energy use may be split by:

material production and extraction (including coal transformation): 
51% on average for 2015–2019; non-energy use (mostly chemical 
feedstock): 22%14; and other energy use (equipment, machinery, 
food and tobacco, textiles, leather, etc.): 27%. Energy use for material 
production and feedstock15 makes about three quarters (73%) of 
industrial energy consumption and is responsible for 77% of its 
increment in 2015–2019 (based on IEA 2021a).
For over a  century, industrial energy efficiency improvements 
have partially offset growth in GHG emissions. Industrial energy 
use per tonne of extracted materials (ores and building materials as 
a proxy for materials going through the whole production chain to 
final products) fell by 20% in 2000–2019 and by 15% in 2010–2019, 
accelerated driven by high energy prices to 2.4% yr–1 in 2014–2019, 
matching the values observed back in 1990–2000 (Figure  11.2). 
Assessed per value added using market exchange rates, industrial

energy intensity globally dropped by 12% in 2010–2018, after its 
4% decline in 2000–2010, resulting in 2000–2018 decline by 15% 
(IEA 2020b,a). The 2020 COVID crisis slowed down energy intensity 
improvements by shifting industrial output towards more energy-
intensive basic materials (IEA 2020e). Specific energy consumption 
per tonne of iron and steel, chemicals and cement production in 2019 
was about 20% below the 2000 level (IEA 2020b,a). This progress 
is driven by moving towards best available technologies (BATs) for 
each product through new and highly efficient production facilities 
in China, India and elsewhere, and by the contribution from recycled 
scrap metals, paper and cardboard.
Physical energy intensity for the production of materials typically 
declines and then stabilises at the BAT level once the market is 
saturated, unless a transformative new technology enters the market 
(Gutowski et al. 2013; Crijns-Graus et al. 2020; IEA 2021a). Thus, the

energy saving effect of switching to secondary used material comes 
to the forefront, as energy consumption per tonne for many basic 
primary materials approach the BATs. This highlights the need to push 
towards circular economy, materials efficiency, reduced demand, and

1172
1172
Chapter 11  Industry11fundamental process changes (e.g., towards electricity and hydrogen-
based steel making). Improved recycling rates allow for a substantial 
reduction in energy use along the whole production chain – material 
extraction, production, and assembling  – which is in great excess 
of energy used for collection, separation, treatment, and scrap 
recycling minus energy used for scrap landfilling. The International 
Energy Agency (IEA 2019b) estimates that by increasing the recycling 
content of fabricated metals, average specific energy consumption 
(SEC) for steel and aluminium may be halved by 2060. Focusing on 
whole systems ‘integrative design’ expands efficiency resource much 
beyond the sum of potentials for individual technologies. Material 
efficiency coupled with energy efficiency can deliver much greater 
savings than energy efficiency alone. Gonzalez Hernandez et al. 
(2018b) stress that presently about half of steel or aluminium are

scrapped in production or oversized for targeted services. They show 
that resource efficiency expressed in exergy as a single metric for both 
material and energy efficiency for the global iron and steel sector is 
only 33%, while secondary steel-making is about twice as efficient 
(66%) as ore-based production (29%). While shifting globally in ore-
based production from the average to the best available level can 
save 6.4 EJ yr–1, the saving potential of shifting to secondary steel-
making is 8 EJ yr–1, and is limited mostly by scrap availability and 
steel quality requirements.
11.2.2 New Trends in Emissions
GHG emissions attributable to the industrial sector (see Chapter 2) 
in 2019 originate from industrial fuel combustion (7.1 GtCO 2-eq 
directly and about 5.9 Gt indirectly from electricity and heat 
generation16); industrial processes (4.5 GtCO 2-eq) and products 
use (0.2 Gt), as well as from waste (2.3 Gt) (Figure  11.4a,b).

Overall industrial direct GHG emissions amount to 14.1 GtCO 2-eq 
(Figure 11.4c and Table 11.1), and scales up to 20 GtCO 2-eq after 
indirect emissions are added,17 putting industry (24%, direct 
emissions) second after the energy sector in total GHG emissions 
and lifting it to the leading position after indirect emissions are 
allocated (34% in 2019).18 The corresponding shares for 1990–2000 
were 21% for direct emissions and 30% for both direct and indirect 
(Crippa et al. 2021; Lamb et al. 2021; Minx et al. 2021). As the 
industrial sector is expected to decarbonise slower than other 
sectors it will keep this leading position for the coming decades 
(IEA 2021a). In 2000–2010, total industrial emissions grew faster 
(3.8% yr–1) than in any other sector (see Chapter 2), mostly due to 
16 Indirect emissions are assessed based on the EDGAR database (Crippa et al. 2021). The IEA database reports 6 Gt of CO 2 for 2019 (IEA 2020f).

17 Based on Crippa et al. (2021) and Minx et al. (2021). In 2019, industrial CO 2-only emissions were 10.4 GtCO 2, which due to wider industrial processes and product use 
(IPPU) coverage exceeds the CO 2 emission assessed by the IEA (2021a) at 8.9 Gt for 2019 and at 8.4–8.5 Gt for 2020.
18 According to the IEA (2020f), industry fuel combustion CO 2-only emissions contributed 24% to total combustion emissions, but combined with indirect emission it 
accounted for 43% in 2018.
19 There are suggestions to incorporate carbon uptake by cement-containing products in IPCC methodology for national GHG inventories (Stripple et al. 2018).
20 Crippa et al. (2021) and the IEA (2020a) assess materials-related scope 1 + 2 (direct and indirect emissions) correspondingly at 10.3 for 2019 and at 10.7 for 2018. 
Hertwich (2021) updated estimates for the global cradle-to-gate material-production-related GHG emissions for 2018 at 11.8 Gt (5.1 Gt for metals, 3.7 Gt for non-metallic

minerals, 1.8 Gt for plastics and rubber, 1 Gt for wood) – which is about 69% of direct and indirect industrial emissions (waste excluded). These assessments are consistent 
as transportation of basic materials contributes around 1 GtCO 2-eq. to GHG emissions.
21 According to Hertwich et al. (2020), of the 11.5 GtCO 2-eq 2015 global materials GHG footprint about 5 Gt were embodied in buildings and infrastructure, and nearly 3 Gt 
in machinery, vehicles, and electronics.the dynamics shown by basic materials extraction and production. 
Industry contributed nearly half (45%) of overall incremental global 
GHG emissions in the 21st century.
Industrial sector GHG emissions accounting is complicated by carbon 
storage in products (Levi and Cullen 2018). About 35% of chemicals’ 
mass is CO 2, which is emitted at use stage  – decomposition of 
fertilisers, or plastic waste incineration (Saygin and Gielen 2021), 
and sinks. Recarbonation and mineralisation  of alkaline industrial

materials and wastes (also known as the ‘sponge effect’) provide 
0.6–1 GtCO 2 yr–1 uptake by cement-containing products19 (Cao et al. 
2020; Guo et al. 2021); see Section 11.3.6 for further discussion in 
decarbonisation context.
In 1970–1990, industrial direct combustion-related emissions were 
growing modestly, and in 1990–2000 even switched to a  slowly 
declining trend, steadily losing their share in overall industrial 
emissions. Electrification was the major driver behind both indirect 
and total industrial emissions in those years. This quiet evolution was 
interrupted in the beginning of the 21st century, when total emissions 
increased by 60–68% depending on the metric applied (the fastest 
growth ever seen). In 2000–2019 iron, steel and cement absolute 
GHGs increased more than any other period in history (Bashmakov 
2021). Emissions froze temporarily in 2014–2016, partly in the wake 
of the financial crisis, but returned to their growth trajectory in

2017–2019 (Figure 11.4a).
The largest incremental contributors to industrial emissions in 
2010–2019 were industrial processes at 40%, then indirect emissions 
(25%), and only then direct combustion (21%), followed by waste 
(14%; Figure 11.4). Therefore, to stop emission growth and to switch 
to a zero-carbon pathway more mitigation efforts should be focused 
on industrial processes, product use and waste decarbonisation, along 
with the transition to low-carbon electrification (Hertwich et al. 2020).
Basic materials production dominates both direct industrial GHG 
emissions (about 62%, waste excluded)20 as well as direct industrial 
CO2 emissions (70%), led by iron and steel, cement, chemicals, and 
non-ferrous metals (Figure  11.4e). Basic materials also contribute 
60% to indirect emissions. In a  zero-carbon power world, with 
industry lagging behind in the decarbonisation of high-temperature 
processes and feedstock, it may replace the energy sector as the

largest generator of indirect emissions embodied in capital stock.21 
According to Circle Economy (2020) and Hertwich et al. (2020), GHG

1173
1173
9.0
10.9
11.317.820.0
6.98.0 7.98.612.414.1
50.0%
33.5%
16.5%
0%10%20%30%40%50%60%70%80%
05101520
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
Share in industrial emissionsGHG Emissions (GtCO 2-eq)Indirect emissions (heat)
Indirect emissions (electricity)
Indirect N 2O emissions from industry
Waste water
Landﬁll and waste incineration
Other industry
Food and tobacco (comb)
Pulp and paper (comb)
Chemicals (IPPU)
Chemicals (comb)
Other non-metallic minerals (IP)
Cement (IPPU)
Non-metallic minerals (comb)
Non-ferrous metals (IPPU)
Non-ferrous metals (comb)
Iron and steel (IPPU)
Iron and steel (comb and fugitive)
Share of basic materials (waste excl.)
Share of fuel combustion
Share of IPPU
Share of waste(a) Industrial emissions by source (left scale) and emissions structure (right scale). Comb – indicates direct emissions from fuel

combustion. IPPU – indicates emissions from industrial processes and product use. Indirect emissions from electricity and heat generation 
are shown on the top. Shares on the right are shown for direct emissions
12.5
0246810121416GtCO 2-eq
GtCO 2-eqF-gases
N2O
CH 4
CO 2
7.054.732.335.91
02468101214161820(b) 2019 direct combustion and process emissions split by GHGs (c) 2019 emissions split by major sources
10.322.25 0.371.17 Indirect
Waste
IPPU
789101112131415Gt CO 2-eq
0123456GtCO 2-eq3.623.704.12
1.552.462.773.01
2.08
1.341.70
1.11
0.161.13EDGARv6
IEA
Wang et al.
EDGARv6
IEA
GCCA
Cao et al.
EDGARv6
IEA
Saygin&Gielen
EDGARv6
IEA
WA - alumSteel
Cement
Chemicals
Non-ferrous8.611.35 0.03 0.090.520.820.19
0.340.03
0.040.05
1.520.15
0.380.0014.112000–20192000
Iron and steel (comb)
Metallurgy (IP)
Cement (IP)
Chemicals (IP)

Landﬁll & waste incineration
Wastewater
Indirect N2O emissions from industry
2019(d) Increments of GHG emissions by sources (direct emissions only) (e) 2019–2020 emissions by major basic materials production
scope 1+2+3
scope 1 processscope 2
scope 1 totalscope 1 
Figure 11.4 | Industrial sector direct global greenhouse gas (GHG) emissions.  Source: calculated based on emissions data from Crippa et al. (2021) and Minx et al. 
(2021). Indirect emissions were assessed using IEA (2021b). For (e): Cao et al. (2020); IEA (2020b, 2021a); GCCA (2021a); International Aluminium Institute (2021a); and Wang 
et al. (2021).

1174
1174
Chapter 11  Industry11Share of basic materials 
(waste excluded)
Share of IPPU 
(waste excluded)CementChemicalsMetalsOther industryLandﬁll and 
waste incinerationWastewater
 Middle E.
 N. America
 S. Asia
 S. E. Asia
A. Paciﬁc
 Africa
 E. Asia
 Euroasia
 EuropeGHG emissions (GtCO2-eq)
0.451990
2000
2010
20191990
2000
2010
2019
1990
2000
2010
20191990
2000
2010
2019
1990
2000
2010
20191990
2000
2010
2019
1990
2000
2010
20191990
2000
2010
2019
1990
2000
2010
20191990
2000
2010
20196 80%
70%
60%
50%
40%
30%
20%
10%
0%
80%
70%
60%
50%
40%
30%
20%
10%
0%5
4
3
2
1
0GHG emissions (GtCO2-eq) GtCO2-eq
GtCO2-eq6
5
4
3
2
1
00.655.80
0.811.34
0.96 0.811.18 1.32
0.79(a) Industrial emissions by sources (right axes) and share of materials and emissions from industrial processes and product use  
in overall industrial emissions
(b) 2010–2019 increments of industrial GHG emissions in 10 world 
regions (direct emissons only)(c) 2019 indirect GHG emissions in 10 world regions

12.44 0.060.420.150.180.240.17 0.03 0.12 0.040.56 14.11
11.011.512.012.513.013.514.014.52010–2019
0.15
0.17
0.20
0.24
0.30
0.35
0.35
0.44
0.51
3.21
0.00.51.01.52.02.53.03.54.02010
and CaribbeanSouthern Asia
Southern AsiaAfrica
AfricaEurasia
EurasiaS-E Asia and Paciﬁc
S-E Asia and PaciﬁcMiddle East
Middle EastAustralia, Japan
and New Zealand
Australia, Japan
and New ZealandEurope
EuropeNorth America
North AmericaEastern Asia
Eastern Asia2019
Figure 11.5 | Industrial sector greenhouse gas (GHG) emissions in 10 world regions (1990–2019).  Source: calculated based on emissions data from Crippa et al. 
(2021). Indirect emissions were assessed using IEA (2021b).
emissions embodied in buildings and infrastructure, machinery and 
transport equipment exceed 50% of their present carbon footprint.
In 1970–2000, direct GHG emissions per unit of energy showed 
a  steady decline interrupted by noticeable growth in 2001–2018

driven by the fast expansion of steel and cement production 
(Figure  11.5; IEA 2021a). Non-energy-related GHG emissions per 
unit of extracted materials decline continuously, as the share of not 
carbon intensive building materials (aggregates and sand) grows.
|Iron and steel carbon intensity stagnated in 1995–2015 due to 
rapid growth in carbon-intensive production in some countries 
(Wang et al. 2021). For aluminium carbon intensity declined in 
2010–2019 by only 2% (International Aluminium Institute 2021a). The carbon intensity of cement-making since 2010 is down by only 
4%. In 1990–2019 it fell by 19.5%, mostly due to energy efficiency 
improvements (by 18.5%) as the carbon intensity of the fuel mix 
declined only by 3% (GCCA 2021b). Historical analysis shows the 
carbon intensity of steel production has declined with ‘stop and 
go’ patterns in 50–60-year cycles, reflective of the major jumps in 
best available technology (BAT). From 1900 to 1935 and from 1960

to 1990 specific scope 1 + 2 + 3 emissions fell by 1.5–2.5 tCO 2 
per tonne, or as much as needed now to achieve net zero. While 
historical declines were mostly due to commissioning large 
capacities with new technologies, with total emissions growing, by 
2050 and beyond the decline will likely materialise via new ultra-
low emission capacity replacements pushing absolute emissions to 
net zero (Bataille et al. 2021b).

1175
1175
11.2.3 Industrial Development Patterns and Supply 
Chains (Regional)
The dramatic increase in industrial emissions after 2000 is clearly 
associated with economic growth in Asia, which dominated both 
absolute and incremental emissions (Figure 11.5a,b).
More recent 2010 to 2019 trends show that regional contributions to 
additional emissions are distributed more evenly, while a large part 
still comes from Asian countries, where both rates of economic growth 
and the share of industrial emissions much exceed the global average. 
All other regions also contributed to total industrial GHG emissions. 
Structural shifts towards emissions from industrial processes and 
products use are common for many regions (Figure 11.5a).
Economic development. Regional differences in emission 
trends are determined by the differences observed in economic

development, trade and supply chain patterns. The major source of industrial emissions is production of energy-intensive materials, 
such as iron and steel, chemicals and petrochemicals, non-ferrous 
metals and non-metallic products. Steel and cement are key inputs 
to urbanisation and infrastructure development (buildings and 
infrastructure are responsible for about three fourths of the steel 
stock). Application of a  ‘services-stock-flow-emissions’ perspective 
(Wiedenhofer et al. 2019; Bashmakov 2021; Haberl et al. 2021) shows 
that relationship patterns between stages of economic development, 
per capita stocks and flows of materials are not trivial with some 
clear transition points. Cao et al. (2017) mapped countries by four 
progressive stages in cement stock per capita S-shape evolution as 
a function of income and urbanisation: initial stage for developing 
countries with a  low level and slow linear growth; take-off stage

with accelerated growth; slowdown stage; and finally a  shrinking 
stage (represented by just a few countries with very high incomes 
exceeding 40,000 USD2010 per capita) and urbanisation levels 
above 80%. Bleischwitz et al. (2018) use a similar approach with five 
stages to study material saturation effects for apparent consumption Table 11.1 | Dynamics and structure of industrial greenhouse gas (GHG) emissions.
Average annual growth rates Share in total industrial sector emissions 2019 
emissions  
MtCO 2-eq1971–
19901991–
20002000–
20102011–
20191970 1990 2000 2010 2019
Direct CO 2 
emissions 
from fuel  
combustionMining (excl. fuels), 
industries and 
construction0.13% –0.18% 4.62% 0.77% 45.8% 37.3% 33.2% 36.6% 34.9% 6981
Iron and steel 0.20% 0.13% 5.62% 2.28% 12.4% 10.2% 9.4% 11.4% 12.4% 2481
Chemical and 
petrochemical3.66% 1.54% 3.16% 1.19% 3.0% 4.9% 5.2% 4.9% 4.9% 977
Non-ferrous metals 2.12% 3.20% 1.12% 1.36% 0.7% 0.8% 1.0% 0.8% 0.8% 163

Non-metallic minerals 2.91% 1.88% 6.24% –0.04% 3.3% 4.6% 5.0% 6.5% 5.7% 1148
Paper, pulp 
and printing0.78% 2.79% 0.09% –2.69% 1.4% 1.3% 1.5% 1.1% 0.7% 150
Food and tobacco 2.55% 1.50% 3.03% –1.04% 1.3% 1.6% 1.7% 1.6% 1.3% 265
Other –1.55% –2.89% 4.61% –0.22% 23.8% 13.8% 9.4% 10.3% 9.0% 1797
Indirect emissions – electricity 2.87% 2.06% 3.00% –0.87% 17.6% 24.6% 27.3% 25.8% 21.2% 4236
Indirect emissions – heat 2.08% –3.09% 2.53% 9.83% 5.6% 6.7% 4.5% 4.0% 8.3% 1663
Industrial  
processes CO 2Total 1.45% 2.16% 5.00% 1.93% 11.0% 11.6% 13.0% 14.9% 15.7% 3144
Non-metallic minerals 2.22% 2.36% 5.66% 1.67% 5.7% 7.0% 8.0% 9.7% 10.0% 2008
petrochemical4.51% 2.52% 3.50% 2.01% 1.5% 2.9% 3.4% 3.4% 3.6% 720
Metallurgy –3.11% 0.37% 5.16% 3.10% 3.6% 1.5% 1.4% 1.7% 2.0% 391
Other 1.55% 2.30% –1.21% 2.89% 0.1% 0.2% 0.2% 0.1% 0.1% 25
Industrial product use GHG –0.22% –0.49% –1.02% 0.41% 2.7% 2.0% 1.7% 1.1% 1.0% 204
Other non-CO 2 GHG –0.60% 5.20% 4.29% 3.20% 5.5% 3.9% 5.8% 6.2% 7.3% 1470

Waste GHG 1.94% 1.35% 1.22% 1.57% 11.9% 13.8% 14.4% 11.4% 11.6% 2327
Total GHG 1.16% 0.98% 3.61% 1.32% 100.0% 100.0% 100.0% 100.0% 100.0% 20,025
Source: calculated based on Crippa et al. (2021); IEA (2021b); and Minx et al. (2021).

1176
1176
Chapter 11  Industry11and stocks per capita for steel, cement, aluminium, and copper. This 
logic may be generalised to other materials from which in-use stock 
is built. While globally cement in-use stock is about 12 tonnes per 
capita, in developed countries it is 15–30 tonnes per capita, but the 
order of magnitude is lower in developing states with high per capita 
escalation rates (Cao et al. 2017). When stocks for some materials 
saturate – per capita stock peaks – the ‘scrap age’ is coming (Pauliuk 
et al. 2013a). Steel in-use stock has already saturated in advanced 
economies at 14 ±  2 tonnes per capita due to largely completed 
urbanisation and infrastructure developments, and a switch towards 
services-dominated economy. This saturation level is three to four 
times that of the present global average, which is below 4 tonnes 
per capita (Pauliuk et al. 2013a; Graedel et al. 2011; Wiedenhofer 
et al. 2019). China is entering the maturing stage of steel and cement

consumption, resulting in a moderate projection of additional demand 
followed by expected industrial emissions peaking in the next 
10 to 15 years (Zhou et al. 2013; Bleischwitz et al. 2018; OECD 2019a; 
Wu et al. 2019; Zhou et al. 2020). But many developing countries 
are still urbanising, and the growing need for infrastructure services 
results in additional demand for steel and cement. Materials intensity 
of the global economy is projected by OECD (2019a) to decline at 
1.3% yr–1 until 2060, driven by improving resource efficiency and 
the switch to circular economy, but with a projected tripling of global 
GDP it means a doubling of projected materials use (OECD 2019a). 
Under the business-as-usual scenario, India’s demand for steel may 
more than quadruple over the next 30 years (de la Rue du Can et al. 
2019; Dhar et al. 2020). In the IEA (2021a) net-zero-energy scenario, 
the saturation effect along with material efficiency counterbalances

activity effects and keeps demand growth for basic materials modest 
while escalate demand for critical materials (copper, lithium, nickel, 
graphite, cobalt and others).
International trade and supply chain. In Equation 11.1 the share 
of allocated emissions (Dm) equals unity when territorial emission is 
considered, and to the ratio of domestically used materials to total 
material production for consumption-based emission accounting. 
Tracking consumption-based emissions provides additional insights 
in the global effectiveness of national climate policies. Carbon 
emissions embodied in international trade are estimated to account 
for 20–30% of global carbon emissions (Meng et al. 2018; OECD.Stat 
2019) and are the reason for different emissions patterns of OECD 
versus non-OECD countries (Chapter 2).
Based on OECD.Stat (2019) datasets, 2015 CO 2 emissions embodied 
in internationally traded industrial products (manufacturing and

mining, excluding fuels) by all countries are assessed at 3  GtCO 2, 
or 30% of direct CO 2 emissions in the industrial sector as reported 
by Crippa et al. (2021). OECD countries collectively have reduced 
territorial emissions (shares of basic materials in direct emissions in 
those regions decline (Figure 11.5b), but demonstrated no progress 
in reducing outsourced emissions embedded in imported industrial 
products (Arto and Dietzenbacher 2014; OECD.Stat 2019). Accounting 
for net carbon emissions embodied in international trade of only 
industrial products (1283 million tCO 2 in 2015) escalates direct OECD 
industrial CO 2 emissions (1333 million tCO 2 of energy-related and 
502 million tCO 2 of industrial processes) 1.7 fold, 2.3-fold for the US, 
1.5-fold for the EU, and more than triples it for the UK, while cutting (Dm) by a third for China and Russia (OECD.Stat 2019; IEA 2020f). In 
most OECD economies, the amount of CO 2 embodied in net import

from non-OECD countries is equal to, or even greater than, the size 
of their Paris 2030 emissions reduction commitments. In the UK, the 
Parliament Committee on Energy and Climate Change requested that 
a consumption-based inventory be complementarily used to assess 
the effectiveness of domestic climate policy in delivering absolute 
global emissions reductions (Barrett et al. 2013; UKCCC 2019a). It 
should be noted that the other side of the coin is that exports from 
countries with lower production carbon intensities can lead to overall 
less emissions than if production took place in countries with high 
carbon intensities, which may become critical in the global evolution 
toward lower emissions. The evolution of Dm to the date was driven 
mostly by factors other than carbon regulation often equipped with 
carbon leakage prevention tools. Empirical tests have failed to date 
to detect meaningful ‘carbon leakage’ and impacts of carbon prices

on net import, direct foreign investments, volumes of production, 
value added, employment, profits, and innovation in industry (Sartor 
2013; Branger et al. 2016; Saussay and Sato 2018; Ellis et al. 2019; 
Naegele and Zaklan 2019; Acworth et al. 2020; Carratù et al. 2020; 
Pyrka et al. 2020; Zachmann and McWilliams 2020). In the coming 
years, availability of large low-cost renewable electricity potential 
and cheap hydrogen may become a  new driver for relocation of 
such carbon intensive industries as steel production (Bataille 2020a; 
Gielen et al. 2020; Bataille et al. 2021a; Saygin and Gielen 2021).
11.3 Technological Developments and Options
The following overview of technical developments and mitigation 
options which relate to the industrial sector is organised in six 
equally important strategies: (i) demand for materials, (ii) materials 
efficiency, (iii) circular economy and industrial waste, (iv) energy 
efficiency, (v) electrification and fuel switching, and (vi) CCUS,

feedstock and biogenic carbon. Each strategy is described in detail, 
followed by a  discussion of possible overlaps and interactions 
between strategies and how conflicts and synergies can be addressed 
through integration of the approaches.
11.3.1 Demand for Materials
Demand for materials is a  key driver of energy consumption and 
CO2 emissions in the industrial sector. Rapid growth in material 
demand over the last quarter century has seen demand for key 
energy-intensive materials increase 2.5- to 3.5-fold (Figure  11.6), 
with growth linked to, and often exceeding, population growth 
and economic development. The International Energy Agency (IEA) 
explains, ‘as economies develop, urbanise, consume more goods and 
build up their infrastructure, material demand per capita tends to 
increase considerably. Once industrialised, an economy’s material 
demand may level off and perhaps even begin to decline’ (IEA 2019b).
The Kaya-like identity presented earlier in the chapter (Equation

11.1) suggests that material demand can be decoupled from 
population and economic development by two means: (i) reducing 
the accumulated material stock (MStock) used to deliver material

1177
1177
Industry Chapter 1111services; and (ii) reducing the material (MPR +  MSE) required to 
maintain material stocks (MStock). Such material demand reduction 
strategies are linked upstream to material efficiency strategies (the 
delivery of goods and services with less material demand, and thus 
energy and emissions) and to demand reduction behaviours, through 
concepts such as sufficiency, sustainable consumption and social 
practice theory (Spangenberg and Lorek 2019). Materials demand 
can also be influenced through urban planning, building codes 
and related socio-cultural norms that shape the overall demand 
for square metres per capita of floor space, mobility and transport 
infrastructures (Chapter 5).
Modelling suggests that per capita material stocks saturate (level 
off) in developed countries and decouple from GDP .  Pauliuk et al. 
(2013b) demonstrated this saturation effect in an analysis of in-use

steel stocks in 200 countries, showing that per capita steel in stocks 
in countries with a long industrial history (e.g., USA, UK, Germany) 
had saturation levels between 11 and 16 tonnes. More recently, 
Bleischwitz et al. (2018) confirmed the occurrence of a  saturation 
effect for four materials (steel, cement, aluminium and copper) in 
four industrialised countries (Germany, Japan, UK and USA) together 
with China. These findings have led to the revision of some material 
demand forecasts, which previously had been based solely on 
population and economic trends.
The saturation effect for material stocks is critical for managing 
material demand in developed countries. Materials are required 
to meet demand for the creation of new stocks and the maintenance 
of existing stocks (Gutowski et al. 2017). Once saturation is attained 
the need for new stocks is minimised, and materials are only required for replacing old stocks and maintenance. Saturation allows material

efficiency strategies (such as light-weight design, longer lifetimes, and 
more intense use) to reduce the required per capita level of material 
stocks, and material circularity strategies (closing material loops 
through remanufacture, reuse and recycling) to lessen the energy and 
carbon impacts required to maintain the material stock. However, 
it should be noted that some materials still show little evidence of 
saturation (i.e., plastics, see Box 11.2). Furthermore, meeting climate 
change targets in developed countries will require the construction 
of new low-carbon infrastructures (i.e., renewable energy generation, 
new energy distribution and storage systems, electric vehicles and 
building heating systems) which may increase demand for emissions 
intensive materials (i.e., steel, concrete and glass).
For developing countries, who are still far from saturation levels, 
strong growth for new products and the creation of new infrastructure

capacity may still drive global material demand. However, there is an 
expectation that economic development can be achieved at lower 
per capita material stock levels, based on the careful deployment of 
material efficiency and circularity by design (Grubler et al. 2018).
11.3.2 Material Efficiency
Material efficiency (ME) – the delivery of goods and services with 
less material  – is increasingly seen as an important strategy for 
reducing GHG emissions in industry (IEA 2017, 2019b). Options to 
improve ME exist at every stage in the lifecycle of materials and 
products, as shown in Figure 11.7. This includes: designing products 
which are lighter, optimising to maintain the end-use service while 1990=100Steel
Cement
Plastics
AluminiumGDP
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

Figure 11.6 | Growth in global demand for selected key materials and global population, 1990–2019.  Notes: based on global values, shown indexed to 1990 
levels (=100). Steel refers to crude steel production. Aluminium refers to primary aluminium production. Plastic refers to the production of a subset of key thermoplastic resins. 
Cement and concrete follow similar demand patterns. Sources: 1990–2018: IEA (2020b). 2019–2020: GCCA (2021a); International Aluminium Institute (2021a); Statista 
(2021b); U.S. Geological Survey (2021); World Bank (2021); World Steel Association (2021).

1178
1178
Chapter 11  Industry11minimising material use, designing for circular principles (i.e., longer 
life, reusability, repairability, and ease of high-quality recycling); 
pushing manufacturing and fabrication process to use materials and 
energy more efficiently and recover material wastes; increasing the 
capacity, intensity of use, and lifetimes of product in use; improving 
the recovery of materials at the end of life, through improved 
remanufacturing, reuse and recycling processes. For more specific 
examples see Allwood et al. (2012); Lovins (2018); Hertwich et al. 
(2019); Scott et al. (2019); and Rissman et al. (2020).
ME provides plentiful options to reduce emissions, yet because 
interventions are dispersed across supply chains and span many 
different stakeholders, this makes assessing mitigation potentials 
and costs more challenging. For this reason, ME interventions have 
traditionally been under-represented in climate change scenario

modelling and integrated assessment models (IAMs) (Grubler et al. 
2018; Allwood 2018). However, two advances in the modelling of 
materials flows have underpinned the recent emergence of ME 
options being included in climate scenario modelling.
Firstly, over many years, the academic community has built up 
detailed global material-flow maps of the processing steps involved 
in making energy-intensive materials. Some prominent recent 
examples include: steel (Gonzalez Hernandez et al. 2018b), pulp 
and paper (Van Ewijk et al. 2018), petrochemicals (Levi and Cullen 
2018). In addition, material-flow maps at the regional and sectoral 
levels have flourished, for example: steel (Serrenho et al. 2016) and 
cement (Shanks et al. 2019) in the UK; automotive sheet-metal 
(Horton et al. 2019); and steel-powder applications (Azevedo et al. 
2018). The detailed and transparent physical mapping of material 
supply chains in this manner enables ME interventions to be traced

back to where emissions are released, and allows these options to be 
compared against decarbonisation and traditional energy efficiency 
measures (Levi and Cullen 2018). For example, a recent analysis by Hertwich et al. (2019) makes the link between ME strategies and 
reducing GHG emissions in buildings, vehicles and electronics, while 
Gonzalez Hernandez et al. (2018a) examines leveraging ME as 
a climate strategy in European Union (EU) policy. Research to explore 
the combined analysis of materials and energy, using exergy analysis 
(for steel: Gonzalez Hernandez et al. 2018b) allows promising 
comparisons across industrial sectors.
Secondly, many ME interventions result in immediate GHG emissions 
savings (short-term), for example, light-weighting products, reusing 
today’s product components, and improving manufacturing yields. 
Yet, for other ME actions emissions savings are delayed temporally 
(long-term). For example, designing a  product for future reuse, or

with a  longer life, only reaps emissions savings at the end of the 
product life, when emissions for a replacement product are avoided. 
Many durable products have long lifetimes (cars >10 years, buildings 
>40 years) which requires dynamic modelling of material stocks, over 
time, to enable these actions to be included in scenario modelling 
activities. Consequently, much effort has been invested recently 
to model material stocks in use, to estimate their lifetimes, and 
anticipate the future waste and replenishment materials to maintain 
existing stocks and grow the material stock base. Dynamic material 
models have been applied to material and product sectors, at the 
country and global level. These include, for example: vehicles stocks 
in the UK (Serrenho et al. 2017; Craglia and Cullen 2020) and in 
China (Liu et al. 2020); buildings stocks in the UK (Cabrera Serrenho 
et al. 2019), China (Hong et al. 2016; Cao et al. 2018, 2019) and

the European Union (Sandberg et al. 2016); electronic equipment 
in Switzerland (Thiébaud et al. 2017); specific material stocks, such 
as cement (Cao et al. 2020, 2017), construction materials (Sverdrup 
et al. 2017; Habert et al. 2020), plastics (Geyer et al. 2017), copper 
(Daehn et al. 2017), and all metals (Elshkaki et al. 2018); all materials 
in China (Jiang et al. 2019), Switzerland (Heeren and Hellweg 2019) 
and the world (Krausmann et al. 2017).DESIGN
Design with less
optimise design,
reduce overdesign,
lightweight
Design for
long life,
reuse,
adaptability,
recoveryUSE
Extend life
repair,
upgrade,
modify
Use more 
intensively
lease,
hire,
share,
borrow,
do withoutMANUFACTURE
Reduce waste
cast near-net shape,
reduce machining,
avoid rework
Recover waste
cascade offcuts,
recycle materialsEND-OF-LIFE
Recover
reuse,
cascade,
remanufacture
Recycle
separate,
clean,
remelt,
reform

Figure 11.7 | Material efficiency (ME) strategies across the value chain.  Source: derived from strategies in Allwood et al. (2012).

1179
1179
Industry Chapter 1111These two advances in the knowledge base have allowed the initial 
inclusion of some ME strategies in energy and climate change 
scenario models. The International Energy Agency (IEA) first created 
a ME scenario (MES) in 2015, with an estimated 17% reduction 
in industrial energy demand in 2040 (IEA 2015). The World Energy 
Outlook report includes a  dedicated sub-chapter with calculations 
explicitly on industrial material efficiency (IEA 2019c). They also 
include ME options in their modelling frameworks and reporting, 
for example for petrochemicals (IEA 2018a), and in the Material 
Efficiency in Clean Energy Transitions report (IEA 2019b). In Grubler 
et al. (2018) 1.5°C Low Energy Demand (LED) scenario, global 
material output decreases by 20% from today, by 2050, with one-
third due to dematerialisation, and two-thirds due to ME, resulting 
in significant emissions savings. Material Economics’ analysis of

Industrial Transformation 2050 (Material Economics 2019), found 
that resource efficiency and circular economy measures (i.e.,  ME) 
could almost halve the 530 MtCO 2 yr–1emitted by the basic materials 
sectors in the EU by 2050. Finally, the Emissions Gap Report, UNEP 
(2019) includes an assessment of potential material efficiency 
savings in residential buildings and cars.
Clearly, more work is required to fully integrate ME strategies into 
mainstream climate change models and future scenarios. Efforts 
are focused on endogenising ME strategies within climate change 
modelling, assessing the synergies and trade-offs which exist 
between energy efficiency and ME interventions, and building up 
data for the assessment of emissions saved and the cost of mitigation 
from real ME actions. This requires analysts to work in cross-
disciplinary teams and to engage with stakeholders from across the 
full breadth of material supply chains. Efforts should be prioritised to

foster engagement between the IAM community and emerging ME 
models based in the Life Cycle Assessment, Resource Efficiency, and 
Industrial Ecology communities (see also Sharmina et al. 2021).
11.3.3 Circular Economy and Industrial Waste
Circular economy (CE) is another effective approach to mitigate 
industrial GHG emissions and has been widely promoted worldwide 
since the fourth IPCC assessment report (AR4). From an industrial 
point of view, CE focuses on closing the loop for materials and energy 
flows by incorporating policies and strategies for more efficient 
energy, materials and water consumption, while emitting minimal 
waste to the environment (Geng et al. 2013). Moving away from 
a linear mode of production (sometimes referred to as an ‘extract-
produce-use-discard’ model), CE promotes the design of durable 
goods that can be easily repaired, with components that can be 
reused, remanufactured, and recycled (Wiebe et al. 2019). In particular,

since CE promotes reduction, reuse and recycling, a large amount of 
energy and GHG-intense virgin material processing can be reduced, 
leading to significant carbon emission reductions. For example, in 
the case of aluminium, the energy efficiency of primary production 
is relatively close to best available technology (Figure 11.8), while 
switching to production using recycled materials requires only about 
5% as much energy (Section 11.4.1.4). However, careful evaluation 
is needed from a lifecycle perspective since some recycling activities may be energy- and emission-intensive, for example, the chemical 
recycling of plastics (Section 11.4.1.3).
As one systemic approach, CE can be seen as conducted at different 
levels, namely, at the micro level (within a single company, such as 
process integration and cleaner production), meso level (between 
three or more companies, such as industrial symbiosis or eco-
industrial parks) and macro level (cross-sectoral cooperation, such

as urban symbiosis or a regional eco-industrial network). Each level 
requires different tools and policies, such as CE-oriented incentive 
and tax policies (macro level), and eco-design regulations (micro 
level). This section is focused on industry and a broader discussion of 
the CE concept is found in Box 12.2 and Section 5.3.4.2.
Micro level: More firms have begun to implement the concept of CE, 
particularly multi-national companies, since they believe that multiple 
benefits can be obtained from CE efforts, and it has become common 
across sectors (D’Amato et al. 2019). Typical CE tools and policies 
at this level include cleaner production, eco-design, environmental 
labelling, process synthesis, and green procurement. For instance, 
leading chemical companies are incorporating CE into their industrial 
practices, for example, through the design of more recyclable 
plastics, a differentiated and market-driven portfolio of resins, films

and adhesives that deliver a total package that is more sustainable, 
cost-efficient and capable of meeting new packaging and plastics 
preferences. Problematically, at the same time the plastics industry 
is improving recyclability, it has, for example, been expanding into 
markets without recycling capacity (Mah 2021). Similarly, automakers 
are pursuing strategies to increase the portion of new vehicles that 
are fully recyclable when they reach the end of life, with increasing 
ambitions for using recycled material, largely motivated by end-of-
life vehicle regulations. This will require networks that are available 
to collect and sort all the materials in vehicles, and policy incentives 
to do it (Wiebe et al. 2019; Soo et al. 2021).
Meso level: Industrial parks first appeared in Manchester, UK, 
at the end of the 19th century and they have been implemented 
in industrialised countries for maximising energy and material

efficiency, which also has merit for CO 2-emissions reduction, as stated 
in AR5. Industrial parks reduce the cost of infrastructure and utilities 
by concentrating industrial activities in planned areas, and  
are typically founded around large, long-term anchor companies. 
Complementary industries and services provided by industrial parks 
can entail diversified effects on the surrounding region and stimulate 
regional development (Huang et al. 2019a). This is crucial for small 
and medium enterprises (SMEs) because they often lack access to 
information and funds for sophisticated technologies.
Typical CE tools and policies at this level include sustainable 
supply chains and industrial symbiosis. A  common platform 
for sharing information and enhancing communication among 
industrial stakeholders through the application of information and 
telecommunication technologies is helpful for facilitating the creation 
of industrial symbiosis. The main benefit of industrial symbiosis is

the overall reduction of both virgin materials and final wastes, as 
well as reduced/avoided transportation costs from by-product

1180
1180
Chapter 11  Industry11exchanges among tenant companies, which can specifically help 
small- and medium-sized enterprises to improve their growth and 
competitiveness. From a climate perspective, this indicates significant 
industrial emission mitigation since the extraction, processing 
of virgin materials and the final disposal of industrial wastes are 
more energy intensive. Also, careful site selection of such parks can 
facilitate the use of renewable energy. Due to these advantages, 
eco-industrial parks have been actively promoted, especially in East 
Asian countries, such as China, Japan and the Republic of Korea 
(South Korea), where national indicators and governance exist (Geng 
et al. 2019). For instance, the successful implementation of industrial 
symbiosis at Dalian Economic and Technological Development 
Zone has achieved significant co-benefits, including GHG-emission 
reduction, economic and social benefits, and improved ecosystem

functions (Liu et al. 2018). Another case at Ulsan industrial park, 
South Korea, estimated that 60,522 tonnes of CO 2 were avoided 
annually through industrial symbiosis between two companies 
(Kim et al. 2018b). The case of China shows the great potential of 
implementing these measures, estimating 111 million tonnes of CO 2 
equivalent will be reduced in 213 national-level industrial parks in 
2030 compared with 2015 (Guo et al. 2018). As such, South Korea’s 
national eco-industrial park project has reduced over 4.7 million 
tonnes of CO 2 equivalent through their industrial symbiosis efforts 
(Park et al. 2019). Meso-level CE solutions have been identified as 
essential for industrial decarbonisation (Section  11.4.3). Moreover, 
waste prevention as the top of the so-called ‘waste hierarchy’ can be 
promoted on the meso level for specific materials or product systems. 
For instance, the European Environment Agency published a report

on plastic waste prevention approaches in all 28 EU-member states 
(Wilts and Bakas 2019). However, challenges exist for industrial 
symbiosis activities, such as inter-firm contractual uncertainties, 
the lack of synergy infrastructure, and the regulations that hamper 
reuse and recycling. Therefore, necessary legal reforms are needed to 
address these implementation barriers.
Macro level: The macro level uses both micro- and meso-level 
tools within a  broader policy strategy, addressing the specific 
challenge of CE as a cross-cutting policy (Wilts et al. 2016). More 
synergy opportunities exist beyond the boundary of one industrial 
park. This indicates the necessity of scaling up industrial symbiosis 
to urban symbiosis. Urban symbiosis is defined as the use of by-
products (waste) from cities as alternative raw materials for energy 
sources for industrial operations (Sun et al. 2017). It is based on 
synergistic opportunity arising from geographic proximity through

the transfer of physical sources (waste materials) for environmental 
and economic benefits. Japan is the first country to promote urban 
symbiosis. For instance, the Kawasaki urban symbiosis efforts can 
save over 114,000 tonnes of CO 2 emissions annually (Ohnishi 
et al. 2017). Another simulation study indicates that Shanghai (the 
largest Chinese city) has the potential to save up to 16.8 MtCO 2 
through recycling all the available wastes (Dong et al. 2018). 
As such, the simulation of urban-energy-symbiosis networks in 
Ulsan, South Korea, indicates that 243,396 tCO 2–1 yr–1 emission 
and USD48 million yr–1 fuel cost can be saved (Kim et al. 2018a). 
Moreover, Wiebe et al. (2019) estimate that the adoption of the 
CE can lead to a  significantly lower global material extraction 
compared to a baseline. Their global results range from a decrease of about 27% in metal extraction to 8% in fossil fuel extraction 
and use, 8% in forestry products, and about 7% in non-metallic

minerals, indicating significant climate change benefits. A macro-
perspective calculation on the circulation of iron in Japan’s future 
society shows that CO 2 emissions from the steel sector can be 
reduced by 56% as per the following assumptions: the amount 
recovered from social stock is the same as the amount of inflow, 
and all scrap was used domestically, and the export of steel 
products is halved (LCS 2018). A  key challenge is to go beyond 
ensuring proper waste management to setting metrics, targets 
and incentives to preserve the incorporated value in specific waste 
streams. Estimations for Germany have shown that despite recycling 
rates of 64% for all solid-waste streams, these activities only lead 
to a  resource-use reduction of only 18% (Steger et al. 2019). In 
general, the identification of the most appropriate CE method 
for different countries requires understanding and information 
exchange on background conditions, local policies and myriad

other factors influencing material flows from the local up to the 
global level (Tapia Carlos et al. 2019). Also, an information platform 
should be created at the national level so that all the stakeholders 
can share their CE technologies and expertise, information (such 
as materials/energy/water consumption data), and identify the 
potential synergy opportunities.
11.3.4 Energy Efficiency
Energy efficiency in industry is an important mitigation option and 
central in keeping 1.5°C within reach (IPCC SR1.5). It has long been 
recognised as the first mitigation option in industry (Yeen Chan and 
Kantamaneni 2016; Nadel and Ungar 2019; IEA 2021a). It allows 
reduction of the necessary scale of deployment for low-carbon 
energy supplies and associated mitigation costs (Energy Transitions 
Commission 2018). The efficiency potentials are greatest in the non-
energy-intensive industries and are often relatively limited in energy-

intensive ones, such as steel (Pardo and Moya 2013; Kuramochi 2016; 
Arens et al. 2017). Deep decarbonisation in these subsectors requires 
fundamental process changes but energy efficiency remains important 
to reduce costs and the need for low-carbon energy supplies.
Below, we focus mainly on the technical progress and on new 
options that are reflected in the literature since AR5 and refer the 
reader there for a broader and deeper treatment of energy efficiency. 
Digitalisation and the development of industrial high-temperature 
heat pumps are two notable technology developments that can 
facilitate energy efficiency improvements.
Industrial energy efficiency can be improved through multiple 
technologies and practices (Tanaka 2011; Fawkes et al. 2016; Lovins 
2018; Crijns-Graus et al. 2020; IEA 2020a). There are two parallel 
processes in improvement of specific energy consumption (SEC): 
progress in energy-efficient BAT and moving the SEC of industrial

plants towards BAT. Both slow down as theoretical thermodynamic 
minimums are approached (Gutowski et al. 2013). For the last 
several decades the focus has been on effective spreading of BAT 
technologies through application of policies for worldwide diffusion 
of energy-saving technologies (Section 11.6). As a result the SEC for

1181
1181
Industry Chapter 1111many basic primary materials is approaching BAT and there are signs 
that energy efficiency improvements have been slowing down over 
recent decades (IEA 2019d, 2020a, 2021a) (Figure 11.8).
11.3.4.1 Heat-use Energy Efficiency Improvement
While about 10% of global GHG emissions originate from combustion 
to produce high-temperature heat for basic material production 
processes (Sandalow et al. 2019), limited efforts have been made 
to decarbonise heat production. There is still a  large potential for 
using various grades of waste heat and the development of high-
temperature heat pumps facilitates its use. NEDO (2019) applies 
a ‘Reduce, Reuse, and Recycle’ concept for improved energy efficiency, 
and we use this frame our discussion of heat efficiency.
Reduce refers to reducing heat needs via improved thermal insulation, 
for example, where porous type insulators have been developed 
with thermal conductivity half of what is traditionally achieved by

heat-resistant bricks under conditions of high compressive strength 
(Fukushima and Yoshizawa 2016). Reuse refers to waste heat 
recovery. A  study for the EU identified a  waste heat potential of 
about 300 TWh yr–1, corresponding to about 10% of total energy 
use in industry. About 50% of this was below 200°C, about 25% at 
temperatures 200°C–500°C, and 25% at temperatures of 500°C and 
above (Papapetrou et al. 2018). A survey conducted in Japan showed 
that 9% of the input energy is lost as waste heat, of which heat below 
199°C accounts for 68% and that below 149°C was 29% (NEDO 
2019). McBrien et al. (2016) identified that in the steel sector process 
heat recovery presently saves 1.8 GJ per tonne of hot rolled steel, 
while integrated across all production processes heat recovery with 
conventional heat exchange could save 2.5 GJ t–1, and it scales up to 3.0 GJ t–1 using an alternative heat exchange that recovers energy

from hot steel. High-temperature industrial heat pumps represent 
a  new and important development for upgrading waste heat and 
at the same time they facilitate electrification. One recent example 
is a high-temperature heat pump that can raise temperatures up to 
165°C at a  coefficient of performance (COP) of 3.5 by recovering 
heat from unused hot water (35°C–65°C) (Arpagaus et al. 2018). 
Commercially available heat pumps can deliver 100°C–150°C but 
at least up to 280°C is feasible (Zühlsdorf et al. 2019). Mechanical 
vapour recompression avoids the loss of latent heat by condensation, 
then it acts as a  highly efficient heat pump with a  5–10 COP 
(Philibert 2017a).
Waste heat to power (WHP), or Recycle in NEDO’s terms, is also an 
under-utilised option. For example, a study for the cement, glass and 
iron industries in China showed that current technology enables only 
7–13% of waste heat to be used for power generation. With improved

technologies, potentially 40–57% of waste heat with temperatures 
above 150°C could be used for power generation via heat recovery. 
Thermal power fluctuations can be a challenge and negatively affect 
the operation and economic feasibility of heat recovery power 
systems such as steam and/or organic Rankine cycle. In such cases, 
latent heat storage technology and intermediate storage units 
may be applied (Jiménez-Arreola et al. 2018). The development of 
thermoelectric conversion materials that produce power from unused 
heat and energy harvested from a higher temperature environment 
is also progressing, with several possible applications in industrial 
processes (Gayner and Kar 2016; Jood et al. 2018; Lv et al. 2018; 
Ohta et al. 2018). A potential early application in industry is to power 
wireless sensors, a  niche that uses microwatts or milliwatts, and 
avoid power cables (Champier 2017).GJ t–1MaxBAT
Average 2000Average 2018
Steel
BF-BOFSteel
DRI-EAFSteel

Scrap-EAFAluminium
primaryAluminium
recycledClinker HVC Ammonia
(excl. feedstock)
Figure 11.8 | Energy efficiency indicators for basic material production.  Energy accounting is based on final energy use. Sectoral boundaries for steel are as defined in 
IEA (2020c).Sources: calculated based on UNIDO (2010); Saygin et al. (2011); Hasanbeigi et al. (2012); Moya and Pardo (2013); Napp et al. (2014); WBCSD (2016); IEA (2017, 
2018b); IEA and WBCSD (2018); IEA (2019b, 2020c); Crijns-Graus et al. (2020); IEA (2020b); International Aluminium Institute (2020).

1182
1182
Chapter 11  Industry1111.3.4.2 Smart Energy Management
Energy management systems to reduce energy costs in an integrated 
and systematic manner were first developed in the 1970s, mainly in 
low-energy-resource countries, for example, by establishing energy 
managers and institutionalising management targets (Tanaka 2011). 
Strategic energy management has since then evolved and been 
promoted through the establishment of dedicated organisational 
infrastructures for energy-use optimisation, such as ISO-50001 
which specifies the requirements for establishing, implementing, 
maintaining, and improving an energy management system (Biel and 
Glock 2016; Tunnessen and Macri 2017). Digitalisation, sometimes 
referred to as Industry 4.0, facilitates further improvements in process 
control and optimisation through technology development involving 
sensors, communications, analytics, digital twins, machine learning, 
virtual reality, and other simulation and computing technologies

(Rogers 2018), all of which can improve energy efficiency. One 
example is combustion control systems, where big data analysis of 
factors affecting boiler efficiency, operation optimisation and load 
forecasting have shown that it can lead to energy savings of 9% 
(Wang et al. 2017).
Smart energy systems with real-time monitoring allow for 
optimisation of innovative technologies, energy demand response, 
balancing of energy supply and demand including that on real-
time pricing, and product quality management, and prediction and 
reduction of idle time for workers and robots (ERIA 2016; Pusnik 
et al. 2016; ISO 2018; Legorburu and Smith 2018; Ferrero et al. 2020; 
Nimbalkar et al. 2020). The IEA estimated that smart manufacturing 
could deliver 15 EJ in energy savings between 2014 and 2030 (IEA 
2019d). Smart manufacturing systems that integrate manufacturing 
intelligence in real time through the entire production operation

have not been yet widely spread in the industry. Examples have 
been demonstrated and integrated in real operation in the electrical 
appliance assembly industry (Yoshimoto 2016). Combining process 
controls and automation allows cost optimisation and improved 
productivity (Edgar and Pistikopoulos 2018).
11.3.5 Electrification and Fuel Switching
The principle of electrification and fuel switching as a GHG mitigation 
strategy is that industries, to the extent possible, switch their end uses 
of energy from a high GHG intensity energy carrier to a lower or zero 
intensity one, including both its direct and indirect production and 
end-use GHG emissions. In general, and non-exclusively, this implies 
a transition from coal (about 0.09 tCO 2 GJ–1 on combustion), refined 
petroleum products (about 0.07 tCO 2 GJ–1), and natural gas (about 
0.05 tCO 2 GJ–1) to biofuels, direct solar heating, electricity, hydrogen, 
ammonia, or net zero synthetic hydrocarbon fuels. Switching to these

energy carriers is not necessarily lower emitting, however; how they 
are made matters.
Fuel switching has already been observed to reduce direct combustion 
CO2 emissions in many jurisdictions. There are significant debates 
about the net effect of upstream fossil fuel production and fugitive 
emissions, but observers have noted that in the case of US power generation it would take a leakage rate of about 2.7% from natural 
gas production to undo the direct fuel switching from coal mitigation 
effect, and the value is likely higher in most cases (Alvarez et al. 2012; 
Hausfather 2015). Coal mine methane emissions are also estimated 
to be substantially higher than previously assessed (Kholod et al. 
2020). Alvarez et al. (2018) estimated US fugitive emissions (not 
including the Permian) at 2.3% of supply, 60% more than previously 
estimated, while recent Canadian papers indicate fugitive emissions 
are at least 50% more than reported (Chan et al. 2020; MacKay et al.

2021). However, given the potential for energy supply infrastructure 
lock-in effects (Tong et al. 2019), purely fossil fuel to fossil fuel 
switching is a limited and potentially dangerous strategy unless it is 
used very carefully and in a limited way.
Biofuels come in many forms, including ones that are nearly identical 
to fossil fuels but sourced from biogenic sources. Solid biomass, either 
direct from wood chips, lignin or processed pellets, is the most  commonly 
used renewable fuel in industry today and  is occasionally used in 
cement kilns and boilers. Biomethane, biomethanol, and bioethanol 
are all commercially made today using fermentation and anaerobic 
digestion techniques and are mostly ‘drop-in’ compatible with 
fossil fuel equivalents. In principle they cycle carbon in and out of 
the atmosphere, but their lifecycle GHG intensities are typically not 
GHG neutral due to land-use changes, soil carbon depletion, fertiliser

use, and other dynamics (Hepburn et al. 2019), and are highly case 
specific. Most commercial biofuel feedstocks come from agricultural 
(e.g.,  corn) and food waste sources, and the feedstock is limited; 
to meet higher levels of biomass use a  transition to using higher 
cellulose feedstocks like straw, switchgrass and wood waste, available 
in much larger quantities, must be fully commercialised and deployed. 
Significant efforts have been made to make ethanol from cellulosic 
biomass, which promises much higher quantities, lower costs, and 
lower intensities, but commercialisation efforts, with a few exceptions, 
have largely not succeeded (Padella et al. 2019). The IEA estimates, 
however, that up to 20% of today’s fossil methane use, including by 
industry, could be met with biomethane (IEA 2020g) by 2040, using 
a mixture of feedstocks and production techniques. Biofuel use may 
also be critical for producing negative emissions when combined with

carbon capture and storage (i.e., bioenergy with carbon capture and 
storage – BECCS). Most production routes for biofuels, biochemicals 
and biogas generate large side streams of concentrated CO 2 which 
is easily captured, and which could become a  source of negative 
emissions (Sanchez et al. 2018) (Section 11.3.6). Finally, it should be 
noted that biofuel combustion can, if inadequately controlled, have 
substantial negative local air quality effects, with implications for 
SDGs 3, 7 and 11.
There is a  large identified potential for direct solar heating in 
industry, especially in regions with strong solar insolation and sectors 
with lower heat needs (<180°C), for example, food and beverage 
processing, textiles, and pulp and paper (Schoeneberger et al. 
2020). The key challenges to adoption are site and use specificity, 
capital intensity, and a lack of standardised mass manufacturing for 
equipment and a supply chain to provide them.

Switching to electricity for end uses, or ‘direct electrification’, is 
a  highly discussed strategy for net zero industrial decarbonisation

1183
1183
Industry Chapter 1111(Lechtenböhmer et al. 2016; Palm et al. 2016; Åhman et al. 2017; 
Axelson et al. 2018; Bataille et al. 2018a; Davis et al. 2018; UKCCC 
2019b; Material Economics 2019). Electricity is a flexible energy carrier 
that can be made from many forms of primary energy, with high 
potential process improvements in terms of end-use efficiency (Eyre 
2021), quality and process controllability, digitisability, and no direct 
local air pollutants (McMillan et al. 2016; Jadun et al. 2017; Deason 
et al. 2018; Mai et al. 2018). The net-GHG effect of electrification 
is contingent on how the electricity is made, and because total 
output increases can be expected, for full effect it should be made 
with a very low GHG intensity primary source (i.e., <50 g  CO2 kWh–1: 
e.g.,  hydroelectricity, nuclear energy, wind, solar photovoltaics, or 
fossil fuels with 95+% carbon capture and storage (IPCC 2014)). This

has strong implications for the electricity sector and its generation 
mix when the goal is a net-zero-emissions electricity system. Despite 
their falling costs, progressively higher mixes of variable wind and 
solar on a given grid will require support from grid flexibility sources, 
including demand response, more transmission, storage on multiple 
time scales, or firm low-to-negative emissions generation sources 
(e.g., nuclear energy, hydrogen fuel cells or turbines, biofuels, fossil or 
biofuels with CCS, and geothermal) to moderate costs (Jenkins et al. 
2018; Sepulveda et al. 2018; Williams et al. 2021). Regions that may 
be slower to reduce the GHG intensity of their electricity production 
will likely need to consider more aggressive use of other measures, 
like energy and material efficiency or bioenergy.
The long-term potential for full-process electrification is a  very 
sector-by-sector and process-by-process phenomenon, with differing

energy and capacity needs, load profiles, stock turnover, capacity for 
demand response, and characteristics of decision-makers. Industrial 
electrification is most viable in the near term in cases with: minimal 
retrofitting and rebuild in processes; with relatively low local 
electricity costs; where the degree of process complexity and process 
integration is more limited and extensive process re-engineering 
would not be required; where combined heat and power is not used; 
where induction heating technologies are viable; and where process 
heating temperatures are lower (Deason et al. 2018).
For these reasons, lighter, manufacturing-orientated industries are 
more readily electrifiable than heavier industry like steel, cement, 
chemicals and other sectors with high heat and feedstock needs. 
Steam boilers, curing, drying and small-scale process heating, with 
typically lower maximum heat temperature needs (<200°C–250°C)

are readily electrifiable with appropriate fossil-fuel-to-electricity 
price ratios (accounting for capital costs and efficiencies), and direct 
induction and infrared heating are available for higher temperature 
needs. These practices are uncommon outside regions with ample 
hydroelectric power due to the currently relatively low cost of 
coal, natural gas and heating oil, and especially when there is no 
carbon combustion cost. Madeddu et al. (2020) argue up to 78% of 
Europe’s industrial energy requirements are electrifiable through 
existing commercial technologies. In contrast, Mai et al. (2018) 
saw only a  moderate industrial heat supply electrification in their 
high-electrification scenario for the US. Electrification has also been 
explored in: raw and recycled steel (Fischedick et al. 2014b; Vogl et al. 
2018); ammonia (Bazzanella and Ausfelder 2017; Philibert 2017a);

and chemicals (Palm et al. 2016; Bazzanella and Ausfelder 2017). While most chemical production of feedstock chemicals (e.g.,  H 2, 
NH3, CO, CH 3OH, C 2H4, C2H6 and C 2H5OH ) is done thermo-catalytically 
today, it is feasible to use direct electrocatalytic production, by itself 
or in combination with utilisation of previously captured carbon 
sources if a fossil fuel feedstock is used, or well-known bio-catalytic 
(e.g., fermentation) and thermo-catalytic processes (Bazzanella and 
Ausfelder 2017; De Luna et al. 2019; Kätelhön et al. 2019). It may even 
be commercially possible to electrify cement sintering and calcination 
through plasma or microwave options (Material Economics 2019).
Increased electrification of industry will result in increased overall 
demand for electricity. For example, 75 TWh of electricity was 
used by steel in the EU in 2015 (out of the 1000 TWh total used 
by industry), Material Economics (2019), varying between their new

process, circularity and CCUS scenarios, projects increased demand 
to 355 (+373%), 214 (+185%) and 238 (+217%) TWh. These values 
are consistent with Vogl et al. (2018), which projects a  tripling of 
electricity demand in the German or Swedish steel industries if 
hydrogen-direct reduced iron and electric arc furnace steel-making 
(DRI EAFs) replaces BF-BOFs. Material Economics (2019) was 
conservative with its use of electricity in chemical production, making 
preferential use of biofeedstocks and some CCUS, and electricity 
demand still rose from 118 TWh to 510, 395 and 413 TWh in their 
three scenarios. Bazzanella and Ausfelder (2017), exploring deeper 
reductions from the chemical sector using more electrochemistry, 
projected scenarios with higher electricity demands of 960–4900 TWh 
(140% of the projected available clean electricity at the time) with 
maximum electricity use. In counterpoint, however, with revised

wind capabilities and costs, the IEA (2019e) Offshore Wind Outlook 
indicates that ten times the current EU electricity use could be 
produced if necessary. Greater use of electro-catalytic versus thermo-
catalytic chemistry, as projected by De Luna et al. (2019), could 
greatly reduce these electricity needs, but the technology readiness 
levels are currently low. Finally, the UKCCC (2019b), which focused 
primarily on CCS for industry in its ‘Further Ambition’ scenario (the 
UK currently consumes about 300 TWh), in its supplementary ‘Further 
Electrification’ scenario projects an additional 300 TWh for general 
electrolysis needs and another 200 TWh for synthetic fuel production.
While it has been demonstrated that almost any heating end use 
can be directly electrified, this would imply very high instantaneous 
thermal loads for blast furnace-basic oxygen furnace (BF-BOF) steel 
production, limestone calcination for cement and lime production, and

other end uses where flame-front (1000°C–1700°C) temperatures are 
currently needed. This indicates a possible need for another energy 
carrier to minimise instantaneous generation and transmission needs. 
These needs can be met at varying current and potential future costs 
using: bioliquids or gases hydrogen, ammonia, or net zero synthetic 
hydrocarbons or alcohols.
Broadly speaking, hydrogen can contribute to a  cleaner energy 
system in two ways: (i) existing applications of hydrogen 
(e.g.,  nitrogen fertiliser production, refinery upgrading) can use 
hydrogen produced using alternative, cleaner production methods; 
(ii) new applications can use low-GHG hydrogen as an alternative 
to current fuels and inputs, or as a complement to the greater use 
of electricity in these applications. In these cases – for example, in

1184
1184
Chapter 11  Industry11transport, heating, industry (e.g., hydrogen-direct reduced iron and 
steel production) and electricity – hydrogen can be used in its pure 
form, or be converted to hydrogen-based fuels, including ammonia, 
or synthetic net zero hydrocarbons and alcohols such as methane 
or methanol (IEA 2019f). The IEA states that hydrogen could be 
used to help integrate more renewables, including by enhancing 
storage options and ‘exporting sunshine and wind’ from places 
with abundant resources; decarbonise steel, chemicals, trucks, ships 
and planes; and boost energy security by diversifying the fuel mix and 
providing flexibility to balance grids (IEA 2019f).
Around 70 Mt yr–1 of pure hydrogen is produced today: 76% from 
natural gas and 23% from coal, resulting in emissions of roughly 
830 MtCO 2 yr–1 in 2016/17 (IEA 2019f), or 4.7% of global industrial 
direct and indirect emissions (waste excluded; Table  11.1). Fuels

refining (about 410 MtCO 2 yr–1) and production of ammonia 
(420 MtCO 2 yr–1) largely dominate its uses. Another 45 Mt hydrogen is 
being produced along with other gases, on purpose or as by-products, 
and used as fuel, to make methanol or as a chemical reactant (IEA 
2019f). Very low and potentially zero GHG (depending on the energy 
source) hydrogen can be made via: electrolysis separation of water 
into hydrogen and oxygen (Glenk and Reichelstein 2019), also known 
as ‘green H 2’; electrothermal separation of water, as done in some 
nuclear plants (Bicer and Dincer 2017); partial oxidation of coal or 
naphtha or steam/auto methane reforming (SMR/ATR) combined 
with CCS (Leeson et al. 2017), or ‘blue H 2’; methane pyrolysis, where 
the hydrogen and carbon are separated thermally and the carbon 
is left as a solid (Abbas and Wan Daud 2010; Ashik et al. 2015), or 
via biomass gasification (Ericsson 2017), which could be negative

emissions if the CO 2 from the gasification process is sequestered. All these processes would in turn need to be run using very low or 
zero GHG energy carriers for the resulting hydrogen to also be low 
in GHG emissions.
Ammonia production, made from hydrogen and nitrogen using 
the Haber-Bosch process, is the most voluminous chemical produced 
from fossil fuels, being used as feedstock for nitrogen fertilisers and 
explosives, as well as a cleanser, a refrigerant, and for other uses. 
Most ammonia is made today using methane as the hydrogen 
feedstock and heat source but has been made using electrolysis-
based hydrogen in the past, and there are several announced 
investments to resume doing so. If ammonia is used as a combustion 
fuel, care must be taken to avoid N 2O as a GHG and NO x in general 
as a local air pollutant.
Hydrogen can also be combined with low-to-zero net GHG carbon 
(Section  11.3.6) and oxygen and made into methane, methanol

and other potential net zero synthetic hydrocarbons and alcohol 
energy carriers using methanation, steam reforming and Fischer-
Tropsch processes, all of which can provide higher degrees of storable 
and shippable high-temperature energy using known industrial 
processes in novel combinations (Bataille et al. 2018a; Davis et al. 
2018). If the hydrogen and oxygen is accessed via electrolysis, the 
terms ‘power-to-fuel’ or ‘e-fuels’ are often used (Ueckerdt et al. 
2021). Given their carbon content, if used as fuels, their carbon will 
eventually be oxidised and emitted as CO 2 to the atmosphere. This 
makes their net-GHG intensity dependent on the carbon source 
(Hepburn et al. 2019), with recycled fossil fuels, biocarbon and direct 
air capture carbon all having very different net-CO 2 impacts – see 
section 11.3.6 on CCS and CCU for elaboration.
Box 11.1 | Hydrogen in Industry

The ‘hydrogen economy’ is a long-touted vision for the energy and transport sectors, and one that has gone through hype-cycles 
since the energy crises in the 1970s (Melton et al. 2016). The widely varying visions of hydrogen futures have mainly been associated 
with fuel cells in vehicles, small-scale decentralised cogeneration of heat and electricity, and to a  certain extent energy storage 
for electricity (Eames et al. 2006; Syniak and Petrov 2008). However, nearly all hydrogen currently produced is used in industry, mainly for  
hydrotreating in oil refineries, to produce ammonia, and in other chemical processes, and it is mostly made using fossil fuels.
In the context of net zero emissions, new visions are emerging in which hydrogen has a central role to play in decarbonising industry. 
Near-term industrial applications for hydrogen include feeding it into ammonia production for fertilisers, while a more novel application

would be as a replacement for coal as the reductant in steel-making, being piloted by the HYBRIT project in Sweden 2020–2021, 
and many companies have initiated hydrogen steel-making projects. As shown in Sections 11.3.5 and 11.3.6, there are many other 
potential applications of hydrogen, some of which are still relatively unexplored. Hydrogen can also be used to produce various lower-
GHG hydrocarbons and alcohols for fuels and chemical feedstocks using carbon from biogenic sources or direct air capture of CO 2 
(Ericsson 2017; Huang et al. 2020).
The geographical distribution of the potential for hydrogen from electrolysis powered by renewables like solar and wind, nuclear 
electrothermally produced hydrogen, and hydrogen from fossil gas with CCS may reshape where heavy industry is located, how 
value chains are organised, and what gets transported in international shipping (Bataille 2020a; Gielen et al. 2020; Bataille et al.

2021a; Saygin and Gielen 2021). Regions with bountiful renewables resources, nuclear, or methane co-located with CCS geology may 
become exporters of hydrogen or hydrogen carriers such as methanol and ammonia, or home to the production of iron and steel, 
organic platform chemicals, and other energy-intensive basic materials. This in turn may generate new trade patterns and needs for 
bulk transport.

1185
1185
Industry Chapter 111111.3.6 CCS, CCU, Carbon Sources, Feedstocks, and Fuels
Carbon is an important and highly flexible building block for a wide 
range of fuels, organic chemicals and materials including methanol, 
ethanol, olefins, plastics, textiles, and wood and paper products. In this 
chapter we define CCS as requiring return of CO 2 from combustion 
or process gases or ambient air to the geosphere for geological time 
periods (i.e., thousands of years) (IPCC 2005; IEA 2009; Bruhn et al. 
2016; IEA 2019g). CCU is defined as being where carbon (as CO or 
CO2) is captured from one process and reused for another, reducing 
emissions from the initial process, but is then potentially but not 
necessarily released to the atmosphere in following processes (Bruhn 
et al. 2016; Detz and van der Zwaan 2019; Tanzer and Ramírez 2019). 
In both cases the net effect on atmospheric emissions depends on the 
initial source of the carbon, be it from a fossil fuel, from biomass, or

from direct air capture (Cuéllar-Franca and Azapagic 2015; Hepburn 
et al. 2019) and the duration of storage or use, which can vary from 
days to millennia.
While CCS and CCU share common capture technologies, what 
happens to the CO 2 and therefore the strategies that will employ 
them can be very different. CCS can help maintain near-CO 2 neutrality 
for fossil CO 2 that passes through the process, with highly varying 
partially negative emissions if the source is biogenic (Hepburn et al. 
2019), and fully negative emissions if the source is air capture, all 
not considering the energy used to drive the above processes. CCS 
has been covered in other IPCC publications at length, for example, 
IPCC (2005), and in most mitigation-oriented assessments since, 
for example, the IEA’s Energy Technology Perspectives (ETP) 2020 
and Net Zero scenario reports (IEA 2021a, 2020a). The potentials and 
costs for CCS in industry vary considerably due to the diversity of

industrial processes (Leeson et al. 2017), as well as the volume and 
purity of different flows of CO 2 (Naims 2016); Kearns et al. (2021) 
provide a recent review. As a general rule it is not possible to capture 
all the CO 2 emissions from an industrial plant. To achieve zero or 
negative emissions, CCS would need to be combined with some use of 
sustainably sourced biofuel or feedstock, or the remaining emissions 
would need to be offset by carbon dioxide removal (CDR) elsewhere.
For concentrated CO 2 sources (e.g., cleaning of wellhead formation 
gas to make it suitable for the pipeline network, hydrogen 
production using steam methane reforming, ethanol fermentation, 
or from combustion of fossil fuels with oxygen in a  nitrogen-free 
environment, i.e.,  ‘oxycombustion’) CCS is already amenable to 
commercial oil and gas reinjection techniques used to eliminate 
hydrogen sulphide gas and brines at prices of USD10–40 tCO 2-eq–1

sequestered (Wilson et al. 2003; Leeson et al. 2017). Most currently 
operating CCS facilities take advantage of concentrated CO 2 flows, 
for example, from formation gas cleaning on the Snoevit and Sleipner 
platforms in Norway, from syngas production for the Al Reyadah 
DRI steel plant in Abu Dhabi, and from SMR hydrogen production 
on the Quest upgrader in Alberta. Since concentrated process CO 2 
emissions are often exempted from existing cap and trade systems, 
these opportunities for CCS have largely gone unexploited. Many 
existing projects partially owe their existence to the utilisation of 
the captured CO 2 for enhanced oil recovery, which in many cases 
counts as both CCS and CCU because of the permanent nature of the CO 2 disposal upon injection if sealed properly (Mac Dowell et al. 
2017). There are several industrial CCS strategies and pilot projects 
working to take advantage of the relative ease of concentrated CO 2

disposal (e.g.,  LEILAC for limestone calcination process emissions 
from cement production, HISARNA direct oxycombustion smelting 
for steel) (Bataille 2020a). An emerging option for storing carbon is 
methane pyrolysis by which methane is split into hydrogen and solid 
carbon that may subsequently be stored (Schneider et al. 2020).
There are several post-combustion CCS projects underway globally 
(IEA 2019g), generally focused on energy production and processing 
rather than industry. Their costs are higher but evolving downward – 
Giannaris et al. (2020) suggest USD47 tCO 2–1 for a  follow-up 
90% capture power generation plant based on learnings from the 
Saskpower Boundary Dam pilot – but crucially these costs are higher 
than implicit and explicit carbon prices almost everywhere, resulting 
in limited investment and learning in these technologies. A  key 
challenge with all CCS strategies, however, is building a gathering

and transport network for CO 2, especially from dispersed existing 
sites; hence most pilot projects are  built near EOR/geological storage 
sites, and the movement towards industrial clustering in the EU and 
UK (UKCCC 2019b), and as suggested in IEA (2019f).
In the case of CCU, CO and CO 2 are captured and subsequently 
converted into valuable products (e.g., building materials, chemicals 
and synthetic fuels) (Styring et al. 2011; Bruhn et al. 2016; Artz et al. 
2018; Brynolf et al. 2018; Daggash et al. 2018; Breyer et al. 2019; 
Kätelhön et al. 2019; Vreys et al. 2019). CCU has been envisioned as part 
of the ‘circular economy’ but conflicting expectations on CCU and its 
association or not with CCS leads to different and contested framings 
(Palm and Nikoleris 2021). The duration of the CO 2 storage in these 
products varies from days to millennia according to the application, 
potentially but not necessarily replacing new fossil, biomass or direct

air capture feedstocks, before meeting one of several possible fates: 
permanent burial, decomposition, recycling or combustion, all with 
differing GHG implications. While the environmental assessment 
of CCS projects is relatively straightforward, however, this is not 
the case for CCU technologies. The net-GHG mitigation impact of 
CCU depends on several factors (e.g., the capture rate, the energy 
requirements, the lifetime of utilisation products, the production 
route that is substituted, and associated room for improvement 
along the traditional route) and has to be determined by lifecycle CO 2 
or GHG analysis (e.g., Nocito and Dibenedetto 2020; and Bruhn et al. 
2016). For example, steel-mill gases containing carbon monoxide and 
carbon dioxide can be used as feedstock together with hydrogen for 
producing chemicals. In this way, the carbon originally contained in 
the coke used in the blast furnace is used again, or cascaded, and

emissions are reduced but not brought to zero. If fossil-sourced CO 2 
is only reused once and then emitted, the maximum reduction is 50% 
(Tanzer and Ramírez 2019). The logic of using steel-mill CO and CO 2 
could equally be applied to gasified biomass, however, with a  far 
lower net-GHG footprint, likely negative, which CCU fed by fossil 
fuels cannot be if end-use combustion is involved.
Partly because of the complexity of the lifecycle analysis accounting, 
the literature on CCU is not always consistent in terms of the net-
GHG impacts of strategies. For example, Artz et al. (2018), focused not

1186
1186
Chapter 11  Industry11just on GHG mitigation but multi-attribute improvements to chemical 
processes from reutilisation of CO 2, suggests the largest reduction 
in the absolute amount of GHGs from CO 2 reutilisation could be 
achieved by the coupling of highly concentrated CO 2 sources with 
carbon-free hydrogen or electrons from low GHG power in so called 
‘power-to-fuel’ scenarios. From the point of view of maximising 
GHG mitigation using surplus ‘curtailed’ renewable power, however, 
Daggash et al. (2018) instead indicates the best use would be for 
direct air capture and CCS. These results depend on what system is 
being measured, and what the objective is.
There are several potential crucial transitional roles for synthetic 
hydrocarbons and alcohols (e.g.,  methane, methanol, ethanol, ethylene, 
diesel and jet fuel) constructed using fossil, biomass or direct carbon 
capture (DAC) and CCU (Breyer et al. 2015; Dimitriou et al.  2015;

Sternberg and Bardow 2015; Fasihi et al. 2017; Bataille et al. 2018a; 
Bataille 2020a). They can allow reductions in the GHG intensity of 
high-value legacy transport, industry and real estate that currently 
runs on fossil fuels but cannot be easily or readily retrofitted. They can 
be used by existing long-lived energy and feedstock infrastructure, 
transport and storage, which can compensate for seasonal supply 
fluctuations and contribute to enhancing energy security (Ampelli 
et al. 2015). Finally, they can reduce the GHG intensity of end uses 
that are very difficult to run on electricity, hydrogen or ammonia 
(e.g.,  long-haul aviation). However, their equivalent mitigation 
cost today would be very high (USD960–1440 tCO 2-eq–1), with 
the potential to fall to USD24–324 tCO 2-eq–1) with commercial 
economies of scale, with very high uncertainty (Hepburn et al. 2019; 
IEA 2020a; Ueckerdt et al. 2021).
A very large and important uncertainty is the long-term demand

for hydrocarbon and alcohol fuels (whether fossil-, biomass- or 
DAC-based), chemical feedstocks (e.g.,  methanol and ethylene) 
and materials, and competition for biomass feedstock with other 
priorities, including agriculture, biodiversity and other proximate 
land-use needs, as well as need for negative emissions through 
BECCS. The current global plastics production of around 350 Mt yr–1 
is almost entirely based on petroleum feedstock and recycling rates 
are very low. If this or future demand were to be 100% biomass-
based it would require tens of exajoules of biomass feedstock (Meys 
et al. 2021). If demand can be lowered and recycling increased 
(mechanical as well as chemical) the demand for biomass feedstock 
can be much lower (Material Economics 2019). Promising routes in 
the short-term would be to utilise CO 2 from anaerobic digestion for 
biogas and fermentation for ethanol in the production of methane or

methanol (Ericsson 2017); methanol can be converted into ethylene 
and propylene in a  methanol-to-olefins process and used in the 
production of plastics (Box 11.2). New process configurations where 
hydrogen is integrated into biomass conversion routes to increase 
yields and utilise all carbon in the feedstock are relatively unexplored 
(Ericsson 2017; De Luna et al. 2019).
There are widely varying estimates of the capacity of CCU to reduce 
GHG emissions and meet the net zero objective. According to Hepburn 
et al. (2019), the estimated potential for the scale of CO 2 utilisation in 
fuels varies widely, from 1 to 4.2 GtCO 2 yr–1, reflecting uncertainties 
in potential market penetration, requiring carbon prices of around USD40 to 80 tCO 2–1, increasing over time. The high end represents 
a future in which synthetic fuels have sizeable market shares, due 
to cost reductions and policy drivers. The low end – which is itself

considerable – represents very modest penetration into the methane 
and fuels markets, but it could also be an overestimate if CO 2-
derived products do not become cost competitive with alternative 
clean energy vectors such as hydrogen or ammonia, or with direct 
sequestration. Brynolf et al. (2018) indicates that a key cost variable 
will be the cost of electrolysers for producing hydrogen. Kätelhön 
et al. (2019) estimate that up to 3.5 GtC yr–1 could be displaced from 
chemical production by 2030 using CCU, but this would require clean 
electricity equivalent to 55% of estimated global power production, 
at the same time other sectors’ demand would also be rising. 
Mac Dowell et al. (2017) suggest that while CCU, and specifically 
CO2-based enhanced oil recovery, may be an important economic 
incentive for early CCS projects (up to 4–8% of required mitigation 
by 2050), it is unlikely the chemical conversion of CO 2 for CCU will 
account for more than 1% of overall mitigation.

Finally, there is another class of CCU activities associated with 
carbonation of alkaline industrial wastes (including iron and steel 
slags, coal fly ash, mining and mineral processing wastes, incinerator 
residues, cement and concrete wastes, and pulp and paper mill 
wastes) using waste or atmospheric CO 2. Given the large volume 
of alkaline wastes produced by industry, capture estimates are as 
high as 4 GtCO 2 yr−1 (Cuéllar-Franca and Azapagic 2015; Ebrahimi 
et al. 2017; Kaliyavaradhan and Ling 2017; Pasquier et al. 2018; 
Huang et al. 2019c; Pan et al. 2020; Zhang et al. 2020) However, as 
some alkaline wastes are already used directly as supplementary 
cementitious materials to reduce clinker-to-cement ratios, and their 
abundant availability in the future is questionable (e.g., steel blast 
furnace slag and coal fly ash), there will be a  strong competition 
between mitigation uses (Section  11.4.2), and the potential for

direct removal by carbonation is estimated at about 1  GtCO 2 yr−1 
(Renforth 2019).
The above CCU literature has identified that there may be a highly 
unpredictable competition between fossil, biogenic and direct air 
capture carbon to provide highly uncertain chemical feedstock, 
material and fuel needs. Fossil waste carbon will likely initially be 
plentiful but will add to net atmospheric CO 2 when released. Biogenic 
carbon is variably, partially net-negative, but the available stock will 
be finite and compete with biodiversity and agriculture needs for 
land. Direct air capture carbon will require significant amounts of 
low-GHG electricity or methane with high-capture rate CCS (Keith 
et al. 2018). There are clearly strong interactive effects between low-
carbon electrification, switching to biomass, hydrogen, ammonia, 
synthetic hydrocarbons via CCU, and CCS. 
11.3.7 Strategy Interactions and Integration
In this section we conceptually address interactions between service

demand, service product intensity, product material efficiency, energy 
efficiency, electrication and fuel switching, CCU and CCS, and what 
conflicts and synergies may exist. Post AR5 a substantial literature 
has emerged, see Rissman et al. (2020), that addresses integrated

1187
1187
Industry Chapter 1111and interactive technical deep decarbonisation pathways for GHG-
intense industrial sectors, and how they interact with the rest of the 
economy (Denis-Ryan et al. 2016; Åhman et al. 2017; Wesseling et al. 
2017; Axelson et al. 2018; Davis et al. 2018; Bataille et al. 2018a; 
Bataille 2020a). It is a  common finding across this literature and 
a  related scenario literature (Energy Transitions Commission 2018; 
Material Economics 2019; UKCCC 2019a,b; IEA 2019b, 2020a; CAT 
2020; IEA 2021a) that deep decarbonisation of industry requires 
integrating all available options. There is no ‘silver bullet’ and so 
all behavioural and technological options have to be mobilised, 
with more emphasis required on the policy mechanisms necessary 
to engage a challenging transition in the coming decades in highly 
competitive, currently GHG-intense, price-sensitive sectors with 
long-lived capital stock (Wesseling et al. 2017; Bataille et al. 2018a;

Bataille 2020a), discussed in the final section of this chapter.
While the strategies are not sequential and interact strongly, we 
discuss them in the order given. Reduced demand through reduced 
service demand and product intensity per service unit (Grubler 
et al. 2018; van Vuuren et al. 2018) reduces the need for the next six 
strategies. Greater material efficiency (see earlier sections) reduces 
the need for the next five, and so on – see Figure 11.9 above.
Circular economy introduces itself throughout, but mainly at the front 
end when designing materials and processes to be more materially 
efficient, efficient in use, and easy to recycle, and at the back end, 
when a material or product’s services life has come to end, and it 
is time for recycling or sustainable disposal (Murray et al. 2017; 
Korhonen et al. 2018). The entire chain’s potential will be maximised 
when these strategies are designed in ahead of time instead of

considered on assembly, or as a retrofit (Allwood et al. 2012; Gonzalez 
Hernandez et al. 2018a; IEA 2019b; Material Economics 2019; Bataille 
2020a). For example, when designing a building: (i) Is the building 
shell, interior mass and ducting orientated for passive heating and 
cooling, and can the shell and roof have building-integrated solar PV 
or added easily, with hard-to-retrofit wiring already incorporated? 
(ii) Are steel and high-quality concrete only used where really needed 
(i.e., for shear, tension and compression strength), can sections be 
prefabricated off-site, can other materials be substituted, such as 
wood? (iii) Can the interior fittings be built with easy-to-recycle 
plastics or other sustainably disposable materials (e.g.,  wood)? 
(iv) Can this building potentially serve multiple purposes through its 
anticipated lifetime, are service conduits oversized and easy to access 
for retrofitting? (v) When it is time to be taken apart, can pieces

be reused, and all componnents recycled at high purity levels, for 
example, can all the copper wiring be easily be found and removed, are the steel beams clearly tagged with their content? The answers to 
these questions will be very regionally and site specific, and require 
revision of educational curricula for the entire supply chain, as well 
as revision of building codes.
Energy efficiency is a  critical strategy for net zero transitions and 
enabling clean electrification (IEA 2021a). Improving the efficiency 
of energy services provision reduces the need for material intensive 
energy supply, energy storage, CCU and CCS infrastructure, and 
limits generation and transmission expansion to reduce an ever-
higherdemand, with associated generation, transmission, and 
distribution losses. Using electricity efficiently can help reduces peak 
demand and the need for peaking plants (currently often powered by 
fossil fuels), and energy storage systems.

Electrification and final energy efficiency are deeply entangled, 
because switching to electricity from fossil fuels in most cases 
improves GJ for GJ end-use energy efficiency: resistance heaters 
are almost 100% efficient, heat pumps can be 300–400% efficient, 
induction melting can improve mixing and temperature control, and 
electric vehicle motors typically translate 90–95% of input electricity 
to motor drive in contrast to 35–45% for a large, modern internal 
combustion engine. Overall, the combined effect could be 40% lower 
global final energy demand assuming renewable electricity is 
used (Eyre 2021).
There are potentially complicated physical and market fuel switching 
relationships between low-GHG electricity, bioliquids and gases, 
hydrogen, ammonia, and synthetic hydrocarbons constructed 
using CCU, with remaining CO2 potentially being disposed of 
using CCS. Whether or not they compete for a  wide range of end

uses and primary demand needs will be regional and whether or 
not infrastructure is available to supply them. Regions with less 
than optimal renewable energy resources, or not sufficient to meet 
growing needs, could potentially indirectly import them as liquid or 
compressed hydrogen, ammonia or synthetic hydrocarbon feedstocks 
made in regions with abundant resources (Armijo and Philibert 2020; 
Bataille 2020a). Large-scale CCU and CCS applications need additional 
basic materials to build corresponding infrastructure and energy to 
operate it, thus reducing overall material and energy efficiencies.
There are different roles for different actors in relation to the different 
mitigation strategies (exemplified in Table 11.2), with institutions and 
supply chains developed to widely varying levels, for example, while 
energy efficiency is a relatively mature strategy with an established 
supply chain, material efficiency is not.Service
Demand
‘Housing’Service
Product
intensity

‘Residential m2 
per person’Product
Material
Efﬁciency
‘kg concrete/
residential m2
& cement/m2
concrete’Circularity
‘Reuse of
cement &
concrete
aggregates’Energy Efﬁciency
‘switch to dry 
long kiln with
 calciner 
preheating from 
kiln off gas’Fuel
Switching
‘coal to gas 
to biomass,
hydrogen or
electricity in
kilns’CCU
‘Utilisation 
of waste gases 
for EOR or 
chemicals
feedstock’CCS
‘Process & Post
(oxy)combustion
and geologic
disposal’
Figure 11.9 | Fully interactive, non-sequential strategies for decarbonising industry.

1188
1188
11.4 Sector Mitigation Pathways  
and Cross-sector Implications
This section continues the discussion of the various mitigation 
options and strategy elements introduced in Section 11.3 and makes 
them explicit for the most relevant industry sectors. For the various 
sectors, Section  11.4.1 concludes with a  tabular overview of key 
technologies and processes, their technology readiness level (TRL), 
potential timing of market penetration, mitigation potential and 
assessment of associated mitigation costs.
An integrated sequencing of mature short-term actions and less 
mature longer-term actions is crucial to avoid lock-in effects. Temporal 
implementation and discussion of the general quantitative role of 
the different options to achieve net zero emissions in the industrial 
sectors is core to the second part of the section (Section 11.4.2), where 
industry-wide mitigation pathways are analysed. This comprises

the collection and discussion of mitigation scenarios available in the 
literature with a high technological resolution for the industry sector 
in addition to a set of illustrative global and national GHG mitigation scenarios selected from chapters  3 and 4, representing different 
GHG mitigation ambitions and different pathways to achieve 
certain mitigation targets. Comparing technology-focused sector-
based scenarios with more top-down-oriented scenario approaches 
allows for a  reciprocal assessment of both perspectives and helps 
to identify robust elements for the transformation of the sector. 
Comparison of real-world conditions within the sector (e.g., industry 
structure and logics, investment cycles, market behaviour, power, and 
institutional capacity) and the transformative pathways described 
in the scenarios helps researchers, analysts, governments, and all 
stakeholders understand the need not only for technological change,

but for structural (e.g.,  new value chains, markets, infrastructures, 
and sectoral couplings) and behavioural (e.g., design practices and 
business models) change at multiple levels.
When undergoing a  transformative process, it is obvious that 
interactions occur within the sector but also on a  cross-sectoral 
basis. Relevant interactions are identified and discussed in the third 
and fourth part of the subsection. Changes are induced along the 
whole value chain, i.e., switching to an alternative (climate-friendly, Table 11.2 | Examples of the potential roles of different actors in relation to different mitigation strategies indicating the importance of engaging a wide 
set of actors across all mitigation strategies.
SectorsDemand control 
measures (DM)Materials 
efficiency (ME)Circular 
efficiencyElectrification, 
hydrogen and 
fuel switchingCCU CCS
Architectural and 
engineering firmsBuild awareness 
on the material 
demand 
implications of

e.g., building codes, 
urban planning and 
infrastructure.Education of 
designers, architects 
and engineers, etc.
Develop design 
tools. Map 
material flows.Design and build 
for e.g., repurpose, 
reuse and 
recycle. Improve 
transparency on 
volumes and flows.Maintain high 
expertise, 
knowledge sharing, 
transparency, and 
benchmarking.Support innovation.
Share best practice. 
Design for dynamic 
demand response 
for grid balancing.Develop allocation 
rules, monitoring 
and transparency.
Coordination and 
collaboration 
across sectors.Transparency, 
labelling.
for transport 
and disposal 
infrastructure.
Industry and  
service sectorDigital solutions to 
reduce office space 
and travel. Service-
oriented business 
models for lower 
product demand.Design for 
durability and 
light weight.
Minimise 
industry scrap.Design for reuse 
and recycling. Use 
recycled feedstock 
and develop 
industrial symbiosis.Maintain energy 

systems.Develop and deploy 
new technologies in 
production, engage 
with lead markets.Develop new 
Engage in new 
value chains and 
collaborations for 
sourcing carbon.Plan for CCS where 
possible and 
phase-out of non-
retrofittable plants 
where necessary.
International bodiesBest practice 
sharing.
Knowledge building 
on demand options.Progressivity in 
standards 
(e.g., ISO).Transparency and 
regulation around 
products, waste 
handling, trade, 
and recycling.Maintain efforts 
for sharing good 
practice and 
knowledge.Coordinate 
innovation efforts, 
technology transfer, 
lead markets, and 
trade policies.Coordinate 
accounting and 
standards. Ensure 
transparency.Align regulation 
export, transport, 
and storage.
Regional 
and national 
government, 
and citiesReconsider spatial 
regulation that 
has demand 
implications.Procurement 
guidelines and 
better indicators. 
Standards and

building codes.Regulation on 
product design 
(e.g., Ecodesign 
Directive).
Collect material-
flow data.Continue energy 
efficiency policies 
such as incentives, 
standards, labels, 
and disclosure 
requirements.R&D and electricity 
infrastructure. 
Policy strategies for 
making investment 
viable (including 
carbon pricing 
instruments).Align regulation 
implementation 
and ensure 
accountability 
for emissions.Develop regulation 
and make 
investment viable.
Resolve long-term 
liabilities.
Civil society 
and consumer 
organisationsInformation and 
advocacy related to 
social norms.Strengthen 
lobby efforts and 
awareness around 
e.g., planned 
obsolescence.Engage in 
standards, 
transparency.Monitor progress.Information 
on embodied 
emissions. Assess 
renewable 
electricity and 
grid expansion.Develop 
standards and 
accounting rules.Ensure transparency 
and accountability.

1189
1189
Industry Chapter 1111e.g., low-GHG hydrogen-based) steel-making process has substantial 
impacts on the value chain, associated sub-suppliers, and electricity 
and coal outputs. In addition, cross-sectoral interactions are 
discussed. This includes feedback loops with other end-use chapters, 
for example, higher material demand through market penetration 
of some GHG mitigation technologies or measures (e.g., insulation 
materials for buildings, steel for windmills) and lower demand 
through others (e.g., less steel for fossil fuel extraction, transport and 
processing), or substantial additional demand of critical materials 
(e.g., the widely varying demands for copper, lithium, nickel, cobalt 
and rare earths for producing windmills, solar panels, and batteries). 
Generally, if consumption- (or behaviour-) driven additional material 
demand creates scarcity it becomes important to increase efforts 
on material efficiency, substitution, recycling/reuse, and sustainable

consumption patterns.
11.4.1 Sector-specific Mitigation Potential and Costs
Based on the general discussion of strategies across industry 
in Section 11.3, this subsection focuses on the sector perspective and 
provides insights into the sector-specific mitigation technologies 
and potentials. As industry is comprised of many different subsectors, 
the discussion here has its focus on the most important sources 
of GHG emissions, that is, steel, cement and concrete, as well as 
chemicals, before other sectors are discussed.
11.4.1.1 Steel
For the period leading up to 2020, in terms of end-use allocation 
globally, approximately 40% of steel is used for structures, 
20%  for industrial equipment, 18% for consumer products, 
13% for infrastructure, and 10% for vehicles (Bataille 2020b). The 
global production of crude steel increased by 41% between 2008 
and 2020 (World Steel Association 2021) and its GHG emissions, 
depending on the scope covered, is 3.7–4.1 GtCO 2-eq. It represented

20% of total global direct industrial emissions in 2019 accounting 
for coke oven and blast furnace gases use (Crippa et al. 2021; Lamb 
et al. 2021; Minx et al. 2021; Olivier and Peters 2018; World Steel 
Association 2021; IEA 2020a) (Figure  11.4 and Table  11.1). 
Steel production can be divided into primary production based on iron 
ore and secondary production based on steel scrap. The blast furnace-
basic oxygen furnace route (BF-BOF) is the main primary steel route 
globally, while the electric arc furnace (EAF) is the preferred process 
for the less energy and emissions-intensive melting and alloying of 
recycled steel scrap. The direct reduced iron (DRI) route is a lesser-
used route that replaces BFs for reducing iron ore, usually followed by 
an EAF . In 2019, 73% of global crude steel production was produced 
in BF-BOFs, while 26% was produced in EAFs, a nominal 5.6% of 
which is DRI (World Steel Association 2021).

An estimated 15% energy efficiency improvement is possible within 
the BF-BOF process (Figure  11.8). Several options exist for deep-
GHG emissions reductions in steel-production processes (Fischedick 
et al. 2014b; Leeson et al. 2017; Axelson et al. 2018; Vogl et al. 
2018; Bataille 2020a; Holappa 2020; Rissman et al. 2020; Fan and 
Friedmann 2021; Wang et al. 2021).Each could reduce specific CO 2 emissions of primary steel production by 80% or more relative to 
today’s dominant BF-BOF route if input streams are based on carbon-
free energy and feedstock sources or if they deploy high-capture CCS:
• Increasing the share of the secondary route can bring 
down emissions quickly and potential emissions savings are 
significant, from a global average 2.3 tCO 2–1 per tonne steel in 
BF-BOFs down to 0.3 (or less) tCO 2–1 per tonne steel in EAFs 
(Pauliuk et al. 2013a; Zhou et al. 2019), the latter depending on 
scrap preheating and electricity GHG intensity. However, realising

this potential is dependent on the availability of regional and 
global scrap supplies and requires careful sorting and scrap 
management, especially to eliminate copper contamination 
(Daehn et al. 2017). There is significant uncertainty about how 
much new scrap will be available and usable (Xylia et al. 2018; 
IEA 2019b; Wang et al. 2021). Most steel is recycled already; 
the gains are mainly to be made in quality (i.e., separation from 
contaminants like copper). End-of-life scrap availability and its 
contribution to steel production will increase as in use stock 
saturates in many countries (Xylia et al. 2016).
• BF-BOFs with CCU or CCS. Abdul Quader et al. (2016) and Fan 
and Friedmann (2021) indicate that it would be difficult to retrofit 
BF-BOFs beyond 50% capture, which is insufficient for long-term 
emission targets but may be useful in some cases for avoiding 
cumulative emissions where other options are not available.

However, BF-BOFs need their furnaces relined every 15–25 years 
(IEA 2021a; Vogl et al. 2021b), at a cost of 80–100% of a new 
build, and this would be an opportunity to build a new facility 
designed for 90%+ capture (e.g., fewer CO 2 outlets). This would 
depend upon access to transport to geology appropriate for CCS. 
• Methane-based syngas (hydrogen and carbon monoxide) 
direct reduced iron (DRI) with CCS.  Most DRI facilities 
currently use a  methane-based syngas of H 2 and CO as both 
reductant and fuel (some use coal). A  syngas DRI-EAF steel-
making facility has been operating in Abu Dhabi since 2016 that 
captures carbon emitted from the DRI furnace (where it is a co-
reductant with hydrogen) and sends it to a nearby oil field for 
enhanced oil recovery.
• Hydrogen-based direct reduced iron (H-DRI) is based on 
the already commercialised DRI technology but using only 
hydrogen as the reductant; pure hydrogen has already been used

commercially by Circored in Trinidad 1999–2008. The reduction 
process of iron ore is typically followed by an EAF for smelting. 
During a  transitional period, DRI could start with methane or 
a  mixture of methane and hydrogen as some of the methane 
(≤30% hydrogen can be substituted with green or blue hydrogen 
without the need to change the process). If the hydrogen is 
produced based on carbon-free sources, this steel-production 
process can be nearly CO 2 neutral (Vogl et al. 2018).
• In the aqueous electrolysis route (small-scale piloted as 
Siderwin during the EU ULCOS programme), the iron ore is 
bathed in an electrolyte solution and an electric current is used 
to remove the oxygen, followed by an electric arc furnace for 
melting and alloying. 
• In the molten oxide electrolysis route, an electric current is 
used to directly reduce and melt the iron ore using electrolysis 
in one step, followed by alloying. These processes both promise

1190
1190
Chapter 11  Industry11a significant increase in energy efficiency compared with the direct 
reduced iron (DRI) and blast furnace routes (Cavaliere 2019). If 
the electricity used is based on carbon-free sources, this steel-
production process can be nearly CO 2 neutral. Both processes 
would require supplemental carbon, but this is typically only up 
to 0.05% per tonne steel, with a  maximum of 2.1%. Aqueous 
electrolysis is possible with today’s electrode technologies, 
while molten oxide electrolysis would require advances in high-
temperature electrodes.
• The HIsarna® process is a  new type of coal-based smelting 
reduction process, which allows certain agglomeration stages 
(coking plant, sintering/pelletising) to be dispensed with. The 
iron ore, with a certain amount of steel scrap, is directly reduced 
to pig iron in a  single reactor. This process is suitable to be 
combined with CCS technology because of its relatively easy to

capture and pure CO 2 exhaust gas flow. CO 2 emission reductions 
of 80% are believed to be realisable relative to the conventional 
blast furnace route (Abdul Quader et al. 2016). The total GHG 
balance also depends on further processing in a  basic oxygen 
furnace or in an EAF . The HIsarna process was small-scale piloted 
under the EU ULCOS program.
• Hydrogen co-firing in BF-BOFs can potentially reduce emission 
by 30–40%, referring to experimental work by the Course50 
projects and Thyssen Krupp, but coke is required to maintain 
stack integrity beyond that.
Reflecting the different conditions at existing and potential future 
plant sites, when choosing one of the above options a combination 
of different measures and structural changes (including electricity, 
hydrogen and CCU or CCS infrastructure needs) will likely be 
necessary in the future to achieve deep reductions in CO 2 emissions 
of steel production.

In addition, increases in material efficiency (e.g., more targeted steel 
use per vehicle, building or piece of infrastructure) and increases in 
the intensity of product use (e.g., sharing cars instead of owning them) 
can contribute significantly to reduce emissions by reducing the need 
for steel production. The IEA (2019b) suggested that up to 24% of 
cement and 40% of steel demand could be plausibly reduced through 
strong material efficiency efforts by 2060. Potential material efficiency 
contribution for the EU is estimated to be much higher – 48% (Material 
Economics 2019). Recycling would cut the average CO 2 emissions 
per tonne of steel produced by 60% (Material Economics 2019), but 
globally by 2050 secondary steel production is limited to 40–56% in 
various scenarios (IEA 2019b), with 46% in the IEA (2021a) and up 
to 56% in 2050 in Xylia et al. (2016). It may scale up to 68% by 2070 
(Xylia et al. 2016). CCU and more directly CCS are other options to

reduce GHG emissions but depend on the full lifecycle net GHGs that 
can be allocated to the process (Section 11.3.6). Bio-based fuels can 
also substitute for some of the coal input, but due to other demands for 
biomass this strategy is likely to be limited to specific cases.
Abatement costs for these strategies vary considerably from case 
to case and for each a plausible cost range is difficult to establish; 
compare this with Table  11.3 (Fischedick et al. 2014b; Leeson 
et al. 2017; Axelson et al. 2018; Vogl et al. 2018; Fan and Friedmann 
2021; Wang et al. 2021). A key point is that while cost of production increases are significant, the effect on final end uses is typically 
very small (Rootzén and Johnsson 2016), with significant policy 
consequences (see Section 11.6 on public and private lead markets 
for cleaner materials).
11.4.1.2 Cement and Concrete
The cement sector is regarded as a sector where mitigation options

are especially narrow (Energy Transitions Commission 2018; Habert 
et al. 2020). Cement is used as the glue to hold together sand, 
gravel and stone aggregates to make concrete, the most consumed 
manufactured substance globally. The production of cement has been 
increasing faster than the global population since the middle of the 
last century (Scrivener et al. 2018). Despite significant improvements 
in energy efficiency over the last couple of decades (e.g., a systematic 
move from wet to dry kilns with calciner preheaters feeding off the 
kilns), the direct emissions of cement production (the sum of energy 
and process emissions) are estimated to be 2.1–2.5 GtCO 2-eq in 2019 
or 14–17% of total global direct industrial GHG emissions (Lehne 
and Preston 2018; Bataille 2020a; Sanjuán et al. 2020; Crippa et al. 
2021; Hertwich 2021; Lamb et al. 2021) (Figure 11.4). Typically, about 
40% of these direct emissions originate from process heating (e.g., for

calcium carbonate (limestone) decomposition into calcium oxide at 
850°C or higher, directly followed by combination with cementitious 
materials  at about 1450°C to make clinker), while 60% are process 
CO2 emissions from the calcium carbonate decomposition (Kajaste 
and Hurme 2016; IEA and WBCSD 2018; Andrew 2019). Some of the 
CO2 is reabsorbed into concrete products and can be seen as avoided 
during the decades-long life of the products; estimates of this flux 
vary between 15 and 30% of the direct emissions (Stripple et al. 
2018; Andersson et al. 2019; Schneider 2019; Cao et al. 2020; GCCA 
2021a). Some companies are mixing CO 2 into hardening concrete, 
both to dispose of the CO 2 and more importantly reduce the need for 
binder (Lim et al. 2019).
One of the simplest and most effective ways to reduce cement and 
concrete emissions is to make stronger concrete through better 
mixing and aggregate sizing and dispersal; poorly and well-made

concrete can vary in strength by a factor of four for a given volume 
(Fechner and Kray 2012; Habert et al. 2020). This argues for a refocus 
of the market away from ‘one size fits all’, often bagged cements to 
professionally mixed clinker, cementitious material and filler mixtures 
appropriate to the needs of the end use.
Architects, engineers and contractors also tend to overbuild with 
cement because it is cheap as well as corrosion- and water-resistant. 
Buildings and infrastructure can be purposefully designed to 
minimise cement use to its essential uses (e.g., compression strength 
and corrosion-resistance), and replace its use with other materials 
(e.g., wood, stone and other fibres) for non-essential uses. This could 
reduce cement use by 20–30% (Imbabi et al. 2012; Brinkerhoff and 
GLDNV 2015; D’Alessandro et al. 2016; Lehne and Preston 2018; IEA 
2019b; Shanks et al. 2019; Habert et al. 2020).
Because so much of the emissions from concrete come from the

limestone calcination to make clinker, anything that reduces use of 
clinker for a  given amount of concrete reduces its GHG intensity.

1191
1191
Industry Chapter 1111While 95% Portland cement is common in some markets, it is typically 
not necessary for all end-use applications, and many markets will add 
blast furnace slag, coal fly ash, or natural pozzolanic materials to 
replace cement as supplementary cementitious materials; 71% was 
the global average clinker content of cement in 2019 (IEA 2020a). 
All these materials are limited in volume, but a  combination of 
roughly two to three parts ground limestone and one part specially 
selected, calcined clays can also be used to replace clinker (Fechner 
and Kray 2012; Lehne and Preston 2018; Habert et al. 2020). Local 
building codes determine what mixes of cementitious materials are 
allowed for given uses and would need to be modified to allow these 
alternative mixtures where appropriate.
Ordinary Portland cement process CO 2 emissions cannot be 
avoided or reduced through the use of non-fossil energy sources.

For this reason, CCS technology, which could capture just the 
process emissions (e.g., the EU LEILAC project, which concentrates 
the process emissions from the limestone calciner, see following 
paragraph) or both the energy and process-related CO 2 emissions, is 
often mentioned as a potentially important element of an ambitious 
mitigation strategy in the cement sector. Different types of CCS 
processes can be deployed, including post-combustion technologies 
such as amine scrubbing and membrane-assisted CO 2-liquefation, 
oxycombustion in a  low-to-zero nitrogen environment (full or 
partial) to produce a  concentrated CO 2 stream for capture and 
disposal, or calcium-looping (Dean et al. 2011). The IEA puts cement 
CCS technologies at the technology readiness level (TRL) 6–8 (IEA 
2020h). These approaches have different strengths and weaknesses 
concerning emission abatement potential, primary energy 
consumption, costs and retrofittability (Hills et al. 2016; Gardarsdottir

et al. 2019; Voldsund et al. 2019). Use of biomass energy combined 
with CCS has the possibility of generating partial negative emissions, 
with the caveats introduced in Section 11.3.6 (Hepburn et al. 2019).
The energy-related emissions of cement production can also be reduced 
by using bioenergy solids, liquids or gases (TRL 9) (IEA and WBCSD 
2018), hydrogen or electricity (TRL 4  according to IEA (2020h)) for 
generating the high-temperature heat at the calciner – hydrogen and 
bioenergy co-burning could be complementary due to their respective 
fast-vs-slow combustion characteristics. In an approach pursued by the 
LEILAC research project, the calcination process step is carried out in 
a  steel vessel that is heated indirectly using natural gas (Hills et al. 
2017). The LEILAC approach makes it possible to capture the process-
related emissions in a comparatively pure CO 2 stream, which reduces 
the energy required for CO 2 capture and purification. This technology

(LEILAC in combination with CCS) could reduce total furnace emissions 
by up to 85% compared with an unabated, fossil fuelled cement plant, 
depending on the type of energy sources used for heating (Hills et al. 
2017). In principle, the LEILAC approach allows the eventual potential 
electrification of the calciner by electrically heating the steel enclosure 
instead of using fossil burners.
In the long run, if some combination of material efficiency, better 
mixing and aggregate sizing, cementitious material substitution and 
90%+ capture CCS with supplemental bioenergy are not feasible in 
some regions or at all to achieve near-zero emissions, alternatives 
to limestone-based ordinary Portland cement may be needed. There are several highly regional alternative chemistries in use that provide 
partial reductions (Fechner and Kray 2012; Lehne and Preston 2018; 
Habert et al. 2020), for example, carbonatable calcium silicate clinkers,

and there have been pilot projects with magnesium-oxide-based 
cements, which could be negative emissions. Lower carbon cement 
chemistries are not nearly as widely available as limestone deposits 
(Material Economics 2019), and would require new materials testing 
protocols, codes, pilots and demonstrations.
Any substantial changes in cement and concrete material efficiency 
or production decarbonisation, however, will require comprehensive 
education and continuing re-education for cement producers, 
architects, engineers, contractors and small, non-professional users 
of cements. It will also require changes to building codes, standards, 
certification, labeling, procurement, incentives, and a  range of 
polices to help create the market will be needed, as well as those 
for information disclosure, and certification for quality. Even an end-
of-pipe solution like CCS will require infrastructure for transport and 
disposal. Abatement costs for these strategies vary considerably

from case to case and for each a plausible cost range is difficult to 
establish, but they are summarised in Table 11.3 from the following 
literature and other sources (Wilson et al. 2003; Fechner and Kray 
2012; Leeson et al. 2017; Moore 2017; Lehne and Preston 2018; IEA 
2019f; Habert et al. 2020).
11.4.1.3 Chemicals
The chemical industry produces a broad range of products that are 
used in a  wide variety of applications. The products range from 
plastics and rubbers to fertilisers, solvents, and specialty chemicals 
such as food additives and pharmaceuticals. The industry is the 
largest industrial energy user and its direct emissions were about 
1.1–1.7 GtCO 2-eq or about 10% of total global direct industrial 
emissions in 2019 (Olivier and Peters 2018; IEA 2019f; Crippa et al. 
2021; Lamb et al. 2021; Minx et al. 2021) (Figure 11.4 and Table 11.1). 
With regard to energy requirements and CO 2 emissions, ammonia,

methanol, olefins, and chlorine production are of great importance 
(Boulamanti and Moya Rivera 2017). Ammonia is primarily used for 
nitrogen fertilisers, methanol for adhesives, resins, and fuels, whereas 
olefins and chlorine are mainly used for the production of polymers, 
which are the main components of plastics.
Technologies and process changes that enable the decarbonisation 
of chemicals production are specific to individual processes. Although 
energy efficiency in the sector has steadily improved over the past 
decades (Boulamanti and Moya Rivera 2017; IEA 2018a) (Figure 11.8), 
a significant share of the emissions is caused by the need for heat 
and steam in the production of primary chemicals (Bazzanella and 
Ausfelder 2017) (Box 11.2). This energy is currently supplied almost 
exclusively through fossil fuels which could be substituted with 
bioenergy, hydrogen, or low or zero carbon electricity, for example,

using electric boilers or high-temperature heat pumps (Bazzanella 
and Ausfelder 2017; Thunman et al. 2019; Saygin and Gielen 2021). 
The chemical industry has among the largest potentials for industrial 
energy demand to be electrified with existing technologies, indicating 
the possibility for a  rapid reduction of energy-related emissions 
(Madeddu et al. 2020).

1192
1192
Chapter 11  Industry11The production of ammonia causes most CO 2 emissions in the 
chemical industry, about 30% according to the IEA (2018a) and 
nearly one third according to Crippa et al. (2021), Lamb et al. (2021) 
and Minx et al. (2021). Ammonia is produced in a catalytic reaction 
between nitrogen and hydrogen  – the latter most often produced 
through natural gas reforming (Stork et al. 2018; Material Economics 
2019) and in some regions through coal gasification, which has 
several times higher associated CO 2 emissions. Future low-carbon 
options include hydrogen from electrolysis using low- or zero-carbon 
energy sources (Philibert 2017a), natural gas reforming with CCS, or 
methane pyrolysis, a process in which methane is transformed into 
hydrogen and solid carbon (Bazzanella and Ausfelder 2017; Material 
Economics 2019; (Section 11.3.5 and Box 11.1). Electrifying ammonia 
production would lead to a decrease in total primary energy demand

compared to conventional production, but a  significant efficiency 
improvement potential remains in novel synthesis processes (Wang 
et al. 2018; Faria 2021). Combining renewable energy sources 
and flexibility measures in the production process could allow for 
low-carbon ammonia production on all continents (Fasihi et al. 
2021). Steam cracking of naphtha and natural gas liquids for the 
production of olefins (i.e.,  ethylene, propylene and butylene), and 
other high-value chemicals is the second most CO 2-emitting process 
in the chemical industry, accounting for another almost 20% of the emissions from the subsector (IEA 2018a). Future lower-carbon 
options include electrifying the heat supply in the steam cracker as 
described above, although this will not remove the associated process 
emissions from the cracking reaction itself or from the combustion 
of the by-products. Further in the future, electrocatalysis of carbon

monoxide, methanol, ethanol, ethylene and formic acid could allow 
direct electric recombination of waste chemical products into new 
intermediate products (De Luna et al. 2019).
A ranking of key emerging technologies with likely deployment dates 
from the present to 2025 relevant for the chemical industry identified 
different carbon capture processes together with electrolytic hydrogen 
production as being of very high importance to reach net zero 
emissions (IEA 2020a). Methane pyrolysis, electrified steam cracking, 
and the biomass-based routes for ethanol-to-ethylene and lignin-to-
BTX were ranked as being of medium importance. While macro-level 
analyses show that large-scale use of carbon circulation through CCU 
is possible in the chemical industry as primary strategy, it would be 
very energy intensive and the climate impact depends significantly 
on the source of and process for capturing the CO 2 (Artz et al. 2018;

Kätelhön et al. 2019; Müller et al. 2020). Significant synergies can be 
found when combining circular CCU approaches with virgin carbon 
feedstocks from biomass (Bachmann et al. 2021; Meys et al. 2021).
Figure  11.10 Feedstock supply and waste treatment in a  scenario with a  combination of mitigation measures in a  pathway for low-
carbon plastics.  Source: From Meys et al., “Achieving net-zero greenhouse gas emission plastics by a  circular carbon economy”. Science, 374(6563), 71–76,  
DOI: 10.1126/science.abg9853. Reprinted with permission from AAAS.

1193
1193
Industry Chapter 1111In a net zero world carbon will still be needed for many chemical 
products, but the sector must also address the lifecycle emissions of 
its products which arise in the use phase, for example, CO 2 released 
from urea fertilisers, or at the end of life, for example, the incineration 
of waste plastics which was estimated to emit 100 Mt globally in 
2015 (Zheng and Suh 2019). Reducing lifecycle emissions can partly 
be achieved by closing the material cycles starting with material and 
product design planning for reuse, remanufacturing, and recycling of 
products – ending up with chemical recycling which yields recycled 
feedstock that substitutes virgin feedstocks for various chemical 
processes (Rahimi and García 2017; Smet and Linder 2019).However, 
the chemical recycling processes which are most well-studied are 
pyrolytic processes which are energy intensive and have significant 
losses of carbon to off-gases and solid residues (Dogu et al. 2021;

Davidson et al. 2021). They are thus associated with significant CO 2 
emissions, which can even be larger in systems with chemical recycling 
than energy recovery (Meys et al. 2020). Further, the products from 
many pyrolytic chemical recycling processes are primarily fuels, 
which then in their subsequent use will emit all contained carbon 
as CO 2 (Vollmer et al. 2020). Achieving carbon neutrality would thus 
require this CO 2 either to be recirculated through energy-consuming 
synthesis routes or to be captured and stored (Geyer et al. 2017; 
Lopez et al. 2018; Material Economics 2019; Thunman et al. 2019). 
As all chemical products are unlikely to fit into chemical recycling 
systems, CCS can be used to capture and store a large share of their 
end-of-life emissions when combined with waste combustion plants 
or heat-demanding facilities like cement kilns (Leeson et al. 2017; 
Tang and You 2018).
Reducing emissions involves demand-side measures, for example,

efficient end use, materials efficiency and slowing demand growth, 
as well as recycling where possible to reduce the need for primary 
production. The following strategies for primary production of 
organic chemicals which will continue to need a carbon source are 
key in avoiding the GHG emissions of chemical products throughout 
their lifecycles:
Recycled feedstocks:  Chemical recycling of plastics unsuitable 
for mechanical recycling was already mentioned. Through pyrolysis 
of old plastics, both gas and a  naphtha-like pyrolysis oil can 
be generated, a  share of which could replace fossil naphtha as 
a feedstock in the steam cracker (Honus et al. 2018a,b). Alternatively, 
waste plastics could be gasified and combined with low-carbon 
hydrogen to a  syngas, for example, the production and methanol 
and derivatives (Lopez et al. 2018; Stork et al. 2018). Other chemical 
recycling options include polymer selective chemolysis, catalytic

cracking, and hydrocracking (Ragaert et al. 2017). Carbon losses and 
process emissions must be minimised and it may thus be necessary 
to combine chemical recycling with CCS to reach near-zero emissions 
(Thunman et al. 2019; Smet and Linder 2019; Meys et al. 2021).Biomass feedstocks: Substituting fossil carbon at the inception of 
a product lifecycle for carbon from renewable sources processed in 
designated biotechnological processes (Lee et al. 2019; Hatti-Kaul 
et al. 2020) using specific biomass resources (Isikgor and Becer 2015) 
or residual streams already available (Abdelaziz et al. 2016). Routes 
with thermochemical and catalytic processes, such as pyrolysis and 
subsequent catalytic upgrading, are also available (Jing et al. 2019).
Synthetic feedstocks: Carbon captured with direct air capture 
or from point sources (bioenergy, chemical recycling, or during 
a  transition period from industrial-processes-emitting fossil CO 2)

can be combined with low-GHG hydrogen into a syngas for further 
valorisation (Kätelhön et al. 2019). Thus, low-carbon methanol can 
be produced and used in methanol-to-olefins/aromatics (MTO/MTA) 
processes, substituting the steam cracker (Gogate 2019) or Fischer-
Tropsch processes could produce synthetic hydrocarbons.
Reflecting the diversity of the sector, the listed options can only be 
illustrative. The above-listed strategies all rely on low-carbon energy 
to reach near-zero emissions. In considering mitigation strategies for 
the sector it will be key to focus on those for which there is a clear 
path towards (close to) zero emissions, with high (carbon) yields over 
the full product value chain and minimal fossil resource use for both 
energy and feedstocks (Saygin and Gielen 2021), with CCU and CCS 
employed for all remnant carbon flows. The necessity of combining 
mitigation approaches in the chemicals industry with low-carbon

energy was recently highlighted in an analysis (Figure 11.10) which 
showed how the combined use of different recycling options, carbon 
capture, and biomass feedstocks was most effective at reducing 
global lifecycle emissions from plastics (Meys et al. 2021). While most 
of the chemical processes for doing all the above are well known 
and have been used commercially at least partly, they have not been 
used at large scale and in an integrated way. In the past, external 
conditions (e.g., availability and price of fossil feedstocks) have not 
set the necessary incentives to implement alternative routes and to 
avoid emitting combustion- and process-related CO 2 emissions to the 
atmosphere. Most of these processes will very likely be more costly 
than using fossil fuels and full-scale commercialisation would require 
significant policy support and the implementation of dedicated 
lead markets (Wesseling et al. 2017; Bataille et al. 2018a; Material

Economics 2019; Wyns et al. 2019). As in other subsectors, abatement 
costs for the various strategies vary considerably across regions and 
products, making it difficult to establish a plausible cost range for 
each (Bazzanella and Ausfelder 2017; Philibert 2017a; Philibert 
2017b; Axelson et al. 2018; IEA 2018a; De Luna et al. 2019; Saygin 
and Gielen 2021).

1194
1194
Chapter 11  Industry11Box 11.2 | Plastics and Climate Change
The global production of plastics has increased rapidly over the past 70 years, with a compound annual growth rate (CAGR) of 8.4%, 
about 2.5 times the growth rate for global GDP (Geyer et al. 2017) and higher than other materials since 1970 (IEA 2019b). Global 
production of plastics is now more than 400 million tonnes, including synthetic fibres (IEA 2019b) The per capita use of plastics 
is still up to 20 times higher in developed countries than in developing countries with low signs of saturation and the potential 
for an increased use is thus still very large (IEA 2018a). Plastics is the largest output category from the petrochemical industry, 
which as a  whole currently uses about 14% of petroleum and 8% of natural gas (IEA 2018a). Forecasts for plastic production 
assuming continued growth at recent rates of about 3.5% point towards a doubled production by 2035, following record-breaking

investments in new and increased production capacity based on petroleum and gas in recent years (CIEL 2017; Bauer and Fontenit 
2021). IEA forecasts show that even in a world where transport demand for oil falls considerably by 2050 from the current about 
100 mbpd, feedstock demand for chemicals will rise from about 12 mbpd to 15–18 mbpd (IEA 2019b). Projections for increasing 
plastic production as well as petroleum use, together with the lack of investments in breakthrough low-emission technologies, do not 
align with necessary emission reductions.
About half of the petroleum that goes into the chemical industry is used for producing plastics, and a significant share of this is 
combusted or lost in the energy-intensive production processes, primarily the steam cracker. GHG emissions from plastic production 
depend on the feedstock used (ethane-based production is associated with lower emissions than naphtha-based), the type of plastic

produced (production of simple polyolefins is associated with lower emissions than more complex plastics such as polystyrene), 
and the contextual energy system (e.g., the GHG intensity of the electricity used) but weighted averages have been estimated to 
be 1.8 tCO 2-eq t–1 for North American production (Daniel Posen et al. 2017) and 2.3 tCO 2-eq t–1 for European production (Material 
Economics 2019). In regions more dependent on coal electricity production the numbers are likely to be higher, and several times 
higher for chemical production using coal as a feedstock – coal-based MTO has seven times higher emissions than olefins from steam 
cracking (Xiang et al. 2014). Coal-based plastic and chemicals production has over the past decade been developed and deployed 
primarily in China (Yang et al. 2019). The production of plastics was thus conservatively estimated to emit 1085 MtCO 2-eq yr–1 in 2015

(Zheng and Suh 2019). Downstream compounding and conversion of plastics was estimated to emit another 535 MtCO 2-eq yr–1, while 
end-of-life treatment added 161 MtCO 2-eq yr–1. While incineration of plastic waste was the cause of only 5% of global plastic lifecycle 
emissions, in regions with waste-to-energy infrastructures this share is significantly larger, for example, 13% of lifecycle emissions 
in Europe (Ive Vanderreydt et al. 2021). The effective recycling rate of plastics remains low relating to a wide range of issues such as 
insufficient collection systems, sorting capacity, contaminants and quality deficiencies in recycled plastics, design of plastics integrated 
in complex products such as electronics and vehicles, heterogenous plastics used in packaging, and illegal international trade.
11.4.1.4  Other Industry Sectors
The other big sources of direct global industrial combustion and 
process CO 2 emissions are light manufacturing and industry (9.7%

in 2016), non-ferrous metals like aluminium (3.1%), pulp and paper 
(1.1%), and food and tobacco (1.9%) (Bataille 2020a; Crippa et al. 
2021; Lamb et al. 2021).
Light manufacturing and industry
Light manufacturing and industry represent a very diverse sector in 
terms of energy service needs (e.g., motive power, ventilation, drying, 
heating, compressed air, etc.) and it comprises both small and large 
plants in different geographical contexts. Most of the direct fossil 
fuel use is for heating and drying, and it can be replaced with low-
GHG electricity through direct resistance, high-temperature heat 
pumps and mechanical vapour recompression, induction, infrared, 
or other electrothermal processes (Lechtenböhmer et al. 2016; 
Bamigbetan et al. 2017). Madeddu et al. (2020) argue up to 78% 
of Europe’s industrial energy requirements are electrifiable through 
existing commercial technologies and 99% with the addition of new

technologies currently under development. Direct solar heating is possible for low temperature needs (<100°C) and concentrating solar 
for higher temperatures. Commercially available heat pumps can 
deliver 100°C–150°C but at least up to 280°C is feasible (Zühlsdorf 
et al. 2019). Plasma torches using electricity can be used where high 
temperatures (>1000°C) are required, but hydrogen, biogenic or 
synthetic combustible hydrocarbons (methane, methanol, ethanol, 
LPG, etc.) can also be used (Bataille et al. 2018a).
There is also a large potential for energy savings through cascading 
in industrial clusters similar to the one at Kalundborg, Denmark. 
Waste heat can be passed at lower and lower temperatures from 
facility to facility or circulated as low-grade steam or hot water, and 
boosted as necessary using heat pumps and direct heating. Such 
geographic clusters would also enable lower-cost infrastructure for 
hydrogen production and storage as well as CO 2 gathering, transport

and disposal (IEA 2019f).
Aluminium and other non-ferrous metals
Demand for aluminium comes from a  variety of end uses where 
a reasonable cost, light-weight metal is desirable. It has historically 
been used in aircraft, window frames, strollers, and beverage

1195
1195
Industry Chapter 1111containers. As fuel economy has become more desirable and 
design improvements have allowed crush bodies made of 
aluminium instead of steel, aluminium has become progressively 
more attractive for cars. Primary aluminium demand is total 
demand (100 Mt yr–1 in 2020) net of manufacturing waste reuse 
(14% of virgin and recycled input) and end-of-life recycling (about 
20% of what reaches market). Primary aluminium consumption 
rose from under 20 Mt yr–1 in 1995 to over 66 Mt primary ingot 
production in 2020 (International Aluminium Institute change to 
2021c). The International Aluminium Institute (2021a) expects total 
aluminium consumption to reach 150–290 Mt yr–1 by 2050 with 
primary aluminium contributing 69–170 Mt and secondary recycled 
91–120 Mt (as in-use stock triples or quadruples). The OECD 
forecasts increases in demand by 2060 for primary aluminium to 
139 Mt yr–1 and for secondary aluminium to 71 Mt yr–1 (OECD

2019a).  Primary (as opposed to recycled) aluminium is generally 
made in a  two-stage process, often geographically separated. In 
the first stage aluminium oxide is extracted from bauxite ore (often 
with other trace elements) using the Bayer hydrometallurgical 
process, which requires up to 200°C heat when sodium hydroxide 
is used to leach the aluminium oxide, and up to 1000°C for kilning. 
This is followed by electrolytic separation of the oxygen from the 
elemental aluminium using the Hall-Héroult process, by far the most 
energy-intense part of making aluminium. This process has large 
potential emissions from the electricity used (12.5 MWh per tonne 
aluminium BAT, 14–15 MWh per tonne average). From bauxite mine 
to aluminium ingot, reported total global average emissions are 
between 12 and 17.6 tCO 2-eq per tonne of aluminium, depending 
on estimates and assumptions made22 (Saevarsdottir et al. 2020). 
About 10% of this, 1.5 tonnes of direct CO 2 per tonne of aluminium

are currently emitted as the graphite electrodes are depleted and 
combine with oxygen, and if less than optimal conditions are 
maintained, perfluorocarbons can be emitted with widely varying 
GHG intensity, up to the equivalent of 2  tCO 2-eq per tonne of 
aluminium. PFC emissions, however, have been greatly reduced 
globally and almost eliminated in well-run facilities. Aluminium, if 
it is not contaminated, is highly recyclable and requires 1/20 of the 
energy required to produce virgin aluminium; increasing aluminium 
recycling rates from the 20–25% global average is a key emissions 
reduction strategy (Haraldsson and Johansson 2018).
The use of low- and zero-GHG electricity (e.g.,  historically from 
hydropower) can reduce the indirect emissions associated with 
making aluminium. A  public-private partnership with financial 
support from the province of Québec and the Canadian federal 
government has recently announced a  fundamental modification

to the Hall-Héroult process by which the graphite electrode process 
emissions can be eliminated by substitution of inert electrodes. 
This technology is slated to be available in 2024 and is potentially 
retrofittable to existing facilities (Saevarsdottir et al. 2020).
Smelting and otherwise processing of other non-ferrous metals 
like nickel, zinc, copper, magnesium and titanium with less overall 
emissions have relatively similar emissions reduction strategies 
22 According to the International Aluminium Institute (2021b), scope 3  (cradle to gate) emissions from the aluminium industry in 2018 reached 1.127 GtCO 2-eq or 
17.6 tCO 2-eq per tonne of primary aluminium. In the Beyond 2°C Scenario (B2DS) it is expected to be reduced to 2.5 tCO 2-eq per tonne.(Bataille and Stiebert 2018): (i) Increase material efficiency; 
(ii)  Increase recycling of existing stock; (iii) Pursue ore-extraction 
processes (e.g., hydro- and electro-metallurgy) that allow more use

of low-carbon electricity as opposed to pyrometallurgy, which uses 
heat to melt and separate the ore after it has been crushed. These 
processes have been used occasionally in the past but have generally 
not been used due to the relatively inexpensive nature of fossil fuels.
Pulp and paper
The pulp and paper industry (PPI) is a  small net-emitter of CO 2, 
assuming the feedstock is sustainably sourced (Chapter 7), but it has 
large emissions of biogenic CO 2 from feedstock (700–800 Mt yr–1) 
(Tanzer et al. 2021). It includes pulp mills, integrated pulp and paper 
mills, and paper mills using virgin pulpwood and other fibre sources, 
residues and co-products from wood products manufacturing, and 
recycled paper as feedstock. Pulp mills typically have access to 
bioenergy in the chemical pulping processes to cover most or all of 
heat and electricity needs, for example, through chemicals recovery 
boilers and steam turbines in the kraft process. Mechanical pulping

mainly uses electricity for energy; decarbonisation thus depends on 
grid emission factors. With the exception of the lime kiln in kraft 
pulp mills, process temperature needs are typically less than or 
equal to 150°C to 200°C, mainly steam for heating and drying. This 
means that this sector can be relatively easily decarbonised through 
continued energy efficiency, fuel switching and electrification, 
including use of high-temperature heat pumps (Ericsson and Nilsson 
2018). Electrification of pulp mills could, in the longer term, make 
bio-residues currently used internally for energy, available as 
a carbon source for chemicals (Meys et al. 2021). The PPI also has the 
capabilities, resources and knowledge, to implement these changes. 
Inertia is mainly caused by equipment turnover rates, relative fuel 
and electricity prices, and the profitability of investments.
A larger and more challenging issue is how the forestry industry

can contribute to the decarbonisation of other sectors and how 
biogenic carbon will be used in a  fossil-free society, for example, 
through developing the forest-based bioeconomy (Pülzl et al. 2014; 
Bauer 2018). In recent years the concept of biorefineries has gained 
increasing traction. Most examples involve innovations for taking by-
products or diverting small streams to produce fuels, chemicals and 
bio-composites that can replace fossil-based products, but there is 
little common vision on what really constitutes a biorefinery (Bauer 
et al. 2017). Some of these options have limited scalability and the 
cellulose fibre remains the core product even in the relatively large 
shift from paper production to textiles fibre production.
Pulp mills have been identified as promising candidates for post-
combustion capture and CCS (Onarheim et al. 2017), which could allow 
some degree of net-negative emissions. For deep decarbonisation

across all sectors, notably switching to biomass feedstock for fuels, 
organic chemicals and plastics, the availability of biogenic carbon (in 
biomass or as biogenic CO 2; Chapter 7) becomes an issue. A scenario 
where biogenic carbon is CCU as feedstock implies large demands

1196
1196
Chapter 11  Industry11for hydrogen, completely new value chains and more closed carbon 
loops, all areas which are as yet largely unexplored (Ericsson 2017; 
Meys et al. 2021).
11.4.1.5 Overview of Estimates of Specific Mitigation Potential 
and Abatement Costs of Key Technologies and 
Processes for Main Industry Sectors
Climate-policy-related literature focusing on deep industrial emission 
reductions has expanded rapidly since AR5. An increasing body of 
research proposes deep decarbonisation pathways for energy-
intensive industries (Figure 11.13). Bataille et al. (2018a) address the 
question of whether it is possible to reduce GHG emissions to very 
low, zero, or negative levels, and identifies preliminary technological 
and policy elements that may allow the transition, including 
the use of policy to drive technological innovation and uptake. 
Material Economics (2019), the IEA (2019b), the Energy Transitions

Commission (2018) and Climate Action Tracker (CAT; 2020) take 
steps to identify pathways integrating energy efficiency, material 
efficiency, circular economy and innovative technologies options to 
cut GHG emissions across basic materials and value chains. The key 
conclusion is that net zero CO 2 emissions from the largest sources 
(steel, plastics, ammonia, and cement) could be achieved by 2050 by 
deploying already available multiple options packaged in different 
ways (Davis et al. 2018; Material Economics 2019; UKCCC 2019b). 
The studies assume that for those technologies that have a kind of 
breakthrough technology status further technological development 
and significant cost reduction can be expected.
Table 11.3, modified from Bataille (2020a) and built from McMillan 
et al. (2016); Bazzanella and Ausfelder (2017); Philibert (2017a); 
Wesseling et al. (2017); Axelson et al. (2018); Bataille et al. (2018a) 
Davis et al. (2018); Energy Transitions Commission (2018); IEA

(2019f, 2020c); Material Economics (2019); and UKCCC (2019b), 
presents carbon intensities that could be achieved by implementing 
mitigation options in major basic material industries, mitigation 
potential, estimates for mitigation costs, TRL and potential year of 
market introduction (Figure 11.13).
Table  11.3 acknowledges that for many carbon-intensive products 
a large variety of novel processes, inputs and practices capable of 
providing very deep emission reductions are already available and 
emerging. However, their application is subject to different economic 
and structural limitations, therefore in the scenarios assuming deep 
decarbonisation by 2050–2060 different technological mixes can be 
observed (Section 11.4.2).
While deep GHG emissions reduction potential is assessed for 
various regions, assessment of associated costs is limited to only 
a few regions; nevertheless those analyses may be illustrative at the

global scale. UKCCC (2019b) provides costs assessments for different 
industrial subsectors (Table  11.3) for the UK.  They provide three 
ranges: core, more ambitious, and when energy and material efficiency 
are limited. The core options range from 2–85 GBP2019 tCO 2-eq–1 
(e.g., reduction in GHG emissions by about 50% by 2050 applying 
energy efficiency (EE), ME, CCS, biomass and electrification). The 
more ambitious options are estimated  at 32–119 GBP2019 tCO 2-eq–1 (e.g.,  90% emissions reduction via widespread deployment of 
hydrogen, electrification or bioenergy for stationary industrial heat/
combustion). Finally, costs range from 33–299 GBP tCO 2-eq–1 when 
energy and material efficiency are limited.
In Material Economics (2019), costs are provided for separate 
technologies and subsectors, and also by pathways, each including 
new industrial processes, circular economy and CCS components 
in different proportions, allowing for the transition to net zero

industrial emission in the EU by 2050. That means that the study 
provides information about the three main mid- to long-term 
options which could enable a  wide abatement of GHG emissions. 
Given different electricity-price scenarios, average abatement costs 
associated with the circular economy-dominated pathway are:  
12–75  EUR2019 tCO 2-eq–1; for the carbon capture-dominated 
pathway 79 EUR2019 tCO 2-eq–1; and for the new processes-
dominated scenario 91 EUR2019 tCO 2-eq–1. Consequently, net-
zero-emission pathways are about 3–25% costlier compared to 
the baseline (Material Economics 2019). According to the Energy 
Transitions Commission (2018), cement decarbonisation would cost 
on average USD110–130 tCO 2–1 depending on the cost scenario. 
Rootzén and Johnsson (2016) state that CO 2 avoidance costs for 
the cement industry vary from 25 to 110 EUR tCO 2–1, depending on 
the capture option considered and on the assumptions made with

respect to the different cost items involved. According to the Energy 
Transitions Commission (2018), steel can be decarbonised on average 
at USD60 tCO 2–1, with highly varying costs depending on low-carbon 
electricity prices.
For customers of final products, information on the potential 
impact of supply-side decarbonisation on final prices may be more 
useful than that of CO 2 abatement costs. A different approach has 
been developed to assess the costs of mitigation by estimating 
the potential impacts of supply-side decarbonisation on final 
product prices. Material Economics (2019) shows that with deep 
decarbonisation, depending on the pathway, steel costs grow by 
20–30%; plastics by 20–45%; ammonia by 15–60%; and cement 
(not concrete) by 70–115%. While these are large and problematic 
cost increases for material producers working with low margins in 
a competitive market, final end-use product price increases are far

less, for example, a  car becomes 0.5% more expensive, supported 
by both Rootzén and Johnsson (2016) and the Energy Transitions 
Commission (2018). For comparison, Rootzén and Johnsson (2017) 
found that decarbonising cement-making, while doubling the cost 
of cement, would add <1% to the costs of a  residential building; 
the Energy Transitions Commission (2018) found concrete would be 
10–30% more expensive, adding USD15,000 or 3% to the price of 
a house including land value. Finally, the IEA (2020a) estimated the 
impact on end-use prices are rather small, even in a net zero scenario; 
they find price increases of 0.2% for a car and 0.6% for a house, 
based on higher costs for steel and cement respectively.
Thus, the price impact scales down going across the value chain and 
might be acceptable for a significant share of customers. However, 
it has to be reflected that the cumulative price increase could be

more significant if several different zero-carbon materials (e.g., steel, 
plastics and aluminium) in the production process of a certain product

1197
1197
Industry Chapter 1111Table 11.3 | Technological potentials and costs for deep decarbonisation of basic industries.  Percentages of maximum reduction are multiplicative, not additive.
SectorCurrent intensity 
(tCO 2-eq t–1)Potential 
GHG 
reductionNASA 
TRLCost per tonne CO 2-eq
(USD2019 tCO 2-eq–1 for 
percentage of emissions)
? = unknownYear 
available, 
assuming 
policy drivers
Iron and steel
Current intensity – all steel (worldsteel) 1.83
Current intensity – ~BF-BOF/Best BF-BOF and NG-DRI  
(with near-zero GHG electricity)2.3/1.8 and 0.7
Current intensity – EAF (depends on electricity intensity 
& pre-heating fuel)≥0 Up to 99%
Material efficiency (IEA 2019 ‘Material Efficiency…’) Up to 40% 9Subject to supply chain building 
codes and educationToday
More recycling; depends on available stock, recycling network, 
quality of scrap, availability of DRI for dilutionHighly regional, 
growing with time9Subject to logistical, transport, 
sorting and recycling 
equipment costsToday

BF-BOF with top gas recirculation and CCU/Sa60% 6–7 USD70–130 t–12025–2030
Syngas (H 2 & CO) DRI EAF with concentrated flow CCU/S ≥ 90% 9 ≥USD40 t–1Today
Hisarna with concentrated CO 2 captureb80–90% 7 USD40–70 t–12025
Hydrogen DRI EAFc – fossil hydrogen with CCS is in operation, 
electrolysis-based hydrogen scheduled for 2026Up to 99% 7USD39–79 t–1 and 
USD46 MWh–1 d 2025
Aqueous (e.g., SIDERWIN) or Molten Oxide (e.g., Boston Metals) 
Electrolysis (MOE)eUp to 99% 3–5 ? 2035–2040
Cement and concrete
Current intensity, about 60% is limestone calcination 0.55
Building design to minimise concrete (IEA 2019b, 2020a) Up to 24% 9Low, education, design and 
logistics related2025
Alternative lower-GHG fuels, e.g., waste (biofuels 
and hydrogen, see above)40% 9 Cost of alt. fuels Today
CCUS for process heating & CaCO 3 calcination CO 2  
(e.g., LEILAC, possible retrofit)f99% 
calc., ≤90% heat5–7≤USD40t–1 calc.  
≤USD120t–1 heat2025

Clinker substitution (e.g., limestone + calcined clays)g40–50% 9Near zero, education, logistics, 
building code revisionsToday
Use of multi-sized and well-dispersed aggregatesdUp to 75% 9 Near zero Today
Magnesium or ultramafic cementsdNegative? 1–4 ? 2040
Current Al intensity, from hydro- to coal-based electricity 
production. 1.5 tCO 2 are produced by graphite electrode decay1.5  t–1 + electricity 
required (i.e.,  
10 t–1 (NG) 
to 18 t–1 (coal))
Inert electrodes and green electricityh100% 6–7 Relatively low 2024
Hydro/electrolytic smelting (with CO 2 CCUS if necessary) Up to 99% 3–9 Ore-specific <2030
Chemicals (see also cross-cutting feedstocks above)i
Catalysis of ammonia from low-/zero-GHG hydrogen H 21.6 (NG), 2.5 
(naptha), 3.8 (coal)≤99% 9 Cost of H 2 Today
Electrocatalysis: CH 4, CH 3OH, C 2H5OH, CO, olefinsjUp to 99% 3 Cost: elec., H 2, CO x 2030

Catalysis of olefins from: (m)ethanol, H 2 and CO x directly 9% 9, 3 Cost: H 2 and CO x <2030
End-use plastics, mainly CCUS and recycling 1.3–4.2, about 2.4 94% 5–6 USD150–240 t–12030?
Full biomass firing, including lime kilns 60–75% 9 About USD50 t–1Today
Other manufacturing
Electrification using current tech (boilers, 90°C–140°C  
heat pumps99% 9 Cost: elec. vs NG 2025
Using new tech (induction, plasma heating) 99% 3–6 2025

1198
1198
have to be combined, indicating the importance of material efficiency 
being applied along with production decarbonisation.
11.4.2 Transformation Pathways
To discuss the general role and temporal implementation of the 
different options for achieving a net zero GHG emissions industry, 
mitigation pathways will be analysed. This starts with showing the results of IAM-based scenarios followed by specific studies which 
provide much higher technological resolution and allow a  much 
deeper look into the interplay of different mitigation strategies. The 
comparison of more technology-focused sector-based scenarios 
with top-down-oriented scenarios provides the opportunity for 
a reciprocal assessment across different modelling philosophies and 
helps to identify robust elements for the transformation of the sector. 
Only some of the scenarios available in the literature allow for at

least rough estimates of the necessary investments and give direction SectorCurrent intensity 
GHG 
assuming 
Cross-cutting (CCUS, H 2, net zero C oOxHy fuels/feedstocks)
CCUS of post-combustion CO 2 diluted in nitrogeneUp to 90% 6–7 ≤USD120 t–12025
CCUS of concentrated CO 2e99% 9 ≤USD40 t–1Today
H2 production: steam or auto-thermal CH 4 reforming with CCSeSMR ≤90% 
ATR >90%6*, 
9**56% @≤USD40 t–1 chem**, 
≤USD120 heat*,+20%/kg≤2025
H2 production: coal with CCUSe≤90% 6 25–50% per H 2 kg–1≤2025
H2 production: alkaline or PEM electrolysisk99% 9About USD50 t–1 or 
<USD20–30 MWh–1Today
H2 production: reversible solid oxide fuel electrolysisj99% 6–8About 40USD t–1 
or <USD40 MWh–12025
H2 production: CH 4 pyrolysis or catalytic crackingl99% 5 ? 2030?
Hydrogen as CH 4 replacement ≤10% 9 See above Today

Biogas or liquid replacement hydrocarbons 60–90% 9Biomass USD per  GJ–1; 
≥USD50 t–1, uncertainToday
Anaerobic digestion/fermentation: CH 4, CH 3OH and C 2H5OHmUp to –99% 9 Biomass cost Today
Methane or methanol from H 2 and CO x (CCUS for excess). 
Maximum –50% reduction if C source is FF50–99% 6–9 Cost: H 2 and CO x Today
850°C woody biomass gasification with CCS for excess carbon: 
CO,  CO2, H2, H2O, CH 4, C2H4 and C 6H6nCould be negative 7–8 About USD50–75 t–1, uncertain Today
Direct air capture for short- and long-chain C oOxHyoUp to 99% 3Cost: E, H 2, COx 
about USD94–232 t–1≤2030
a Data for CCS costs for steel-making: Birat (2012); Leeson et al. (2017); and Axelson et al. (2018).
b  Data for Hisarna: Axelson et al. (2018).
c  Data for hydrogen DRI electric arc furnaces: Fischedick et al. (2014b) and Vogl et al. (2018).
d  Converted from EUR2018 34–68 t–1 and EUR2018 40 MWh–1.

e  Data for Molten Oxide Electrolysis (also known as SIDERWIN): Fischedick et al. 2014b and Axelson et al. 2018. The TRLs differ by source, the value provided is from Axelson 
et al. (2018), based on UCLOS SIDERWIN.
f  Data for making hydrogen from SMR and ATR with CCUS: Leeson et al. (2017); Moore (2017); and IEA (2019f).  The cost of CCS disposal of concentrated sources of CO 2 at 
USD15–40 tCO 2-eq–1 is well established as commercial for direct or EOR purposes and is based on the long-standing practice of disposing of hydrogen sulphide and oil brines 
underground: Wilson et al. (2003) and Leeson et al. (2017). There is a wide variance, however, in estimated tCO 2-eq–1 break-even prices for industrial post-combustion capture 
of CO 2 from sources highly diluted in nitrogen (e.g., Leeson et al. (2017) at USD60–170 tCO 2-eq–1), but most fall under USD120 tCO 2-eq–1.

g  Data for clinker substitution and use of well-mixed and multi-sized aggregates: Fechner and Kray 2012; Lehne and Preston 2018; and Habert et al. 2020).
h  Rio Tinto, Alcoa and Apple have partnered with the governments of Québec and Canada to form a coalition to commercialise inert as opposed to sacrificial graphite electrodes 
by 2024, thereby making the standard Hall-Héroult process very low emissions if low-carbon electricity is used.
i  Data and other information: Bazzanella and Ausfelder (2017); Axelson et al. (2018); IEA (2018a); De Luna et al. (2019); and Philibert (2017b,a).
j  See De Luna et al. (2019) for a state-of-the-art review of electrocatalysis, or direct recombination of organic molecules using electricity and catalysts.
k  Data for hydrogen production from electrolysis: Bazzanella and Ausfelder (2017); Philibert (2017a); Philibert (2017b); IEA (2019f); and Armijo and Philibert (2020).

l  Data for methane pyrolysis to make hydrogen: Abbas and Wan Daud (2010). Data for hydrogen production from methane catalytic cracking: Amin et al. (2011) and 
Ashik et al. (2015).
m  Data for anaerobic digestion or fermentation for the production of methane, methanol and ethanol: De Luna et al. (2019).
n  Data for woody biomass gasification: Li et al. (2019) and van der Meijden et al. (2011).
o  Data on direct air capture of CO 2: Keith et al. (2018) and Fasihi et al. (2019).

1199
1199
Industry Chapter 1111about relevant investment cycles and potential risks of stranded or 
depreciated assets. In some specific cases cost comparisons can be 
translated into expected difference costs not only for the overall 
sector, but also for relevant materials or even consumer products.
11.4.2.1 Central Results From (Top-down) Scenarios Analysis 
and Illustrative Mitigation Pathways Discussion
Chapter 3 conducted a comprehensive analysis of scenarios based 
on IAMs. The resulting database comprises more than 1000 model-
based scenarios published in the literature. The scenarios span 
a broad range along temperature categories from rather baseline-like 
scenarios to the description of pathways that are compatible with 
the 1.5°C target. Comparative discussion of scenarios allows some 
insights with regard to the relevance of mitigation strategies for the 
industry sector (Figure 11.11).
The main results from the Chapter  3  analysis from an industry 
perspective are:

• While all scenarios show a decline in energy and carbon intensity 
over time, final energy demand and associated industry-related 
CO2 emissions increase in many scenarios. Only ambitious 
scenarios (category C1) show significant reduction in final 
energy demand in 2030, more or less constant demand in 2050, but increasing demand in 2100, driven by growing material use 
throughout the 21st century. While carbon intensity shrinks over 
time, energy related CO 2-emissions decline after 2030 even in 
less ambitious scenarios, but particularly in those pursuing 
a temperature increase below 2°C. Reduction of CO 2 emissions 
in the sector are achieved through a combination of technologies 
which includes nearly all options that have been discussed in 
this chapter (Sections 11.3 and 11.4.1). However, there are big 
differences with regard to the intensity by which the various 
options are implemented in the scenarios.  This is particularly

true for CCS for industrial applications and material efficiency 
and material demand management (i.e., service demand, service 
product intensity). The latter options are still under-represented 
in many global IAMs.
• There are only a  few scenarios which allow net-negative CO 2 
emissions for the industry for the second half of the century, 
while most scenarios assessed (including the majority of 1.5°C 
scenarios) end up with still significant positive CO 2 emissions. In 
comparison to the whole system most scenarios expect a slower 
decrease of industry-related emissions.
• There is a great – up to a factor of two – difference in assumptions 
about the GHG mitigation potential associated with different 
carbon cost levels between IAMs and sector-specific industry 
models. Consequently, IAMs pick up mitigation options slower or 
later (or not at all) than models which are more technologically 
0
1
2
3
2030
2050
2100Index ( 2019 = 1)Final energy
–1
0
1
2
3
2030
2050
2100

Index ( 2019 = 1)CO2 emissions
0
1
2030
2050
2100
Index ( 2019 = 1)Carbon intensity Category
C1: Limit warming to 1.5°C 
overshoot
overshoot
C3: Limit warming to 2°C 
(>67%)
C4: Limit warming to 2°C 
(>50%)
C5: Limit warming to 2.5°C 
(>50%)
C6: Limit warming to 3°C 
(>50%)
C7: Limit warming to 4°C 
(>50%)
C8: Exceed warming of 4°C 
(≥50%)
Reference
0.00
0.25
0.50
0.75
1.00
2030
2050
2100
YearIndex ( 2019 = 1)Energy intensity
25
0
50
75
2030
2050
2100
Year% of ﬁnal energyShare of electricity
10
0
20
30
40
2030
2050
2100
Year% of ﬁnal energyShare of gases
Figure 11.11 Industrial final energy (top left), CO 2 emissions (top middle), energy intensity (bottom left), carbon intensity (top right), share of electricity 
(bottom middle), and share of gases (bottom right). Energy intensity is final energy per unit of GDP . Carbon intensity is CO 2 emissions per EJ of final energy. The first

four indicators are indexed to 2019, where values less than 1 indicate a reduction. Industrial-sector CO 2 emissions include fuel-combustion emissions only. Boxes indicate the 
interquartile range, the median is shown with a horizontal black line, while vertical lines show the 5 to 95% interval. Source: data are from the AR6 database; only scenarios 
that pass the vetting criteria are included (Section 3.2).

1200
1200
Chapter 11  Industry11detailed. Due to their top-down perspective IAMs to date have not 
been able to represent the high complexity of industries in terms 
of the broad variety of technologies and processes (particularly 
circularity aspects) and to fully reflect the dynamics of the sector. 
In addition, as energy and carbon price elasticities are still not 
completely understood, primarily cost-driven models have their 
limitations. However, there are several ongoing activities to bring 
more engineering knowledge and technological details into the 
IAM models (Kermeli et al. 2021).
In addition to the more aggregated discussion, the IAMs illustrative 
mitigation pathways (IMPs) allow a deeper look into the transformation 
pathways related to the scenarios. For the illustrative mitigation 
pathways (IMPs) approach, sets of scenarios have been selected which 
represent different levels of GHG mitigation ambitions, scenarios

which rely on different key strategies or even exclude some mitigation 
options, represent delayed actions or SDG-oriented pathways. For more 
detailed information about the selection see Section 3.3.2. Figure 11.12 
compares for a selected number of key variables the results of IMPs 
and puts them in the context of the whole sample of IAMs scenario 
results for three temperature categories.With growing mitigation ambition final energy demand is significantly 
lower in comparison of a current policy pathway (CurPol) and a scenario 
that explores the impact of further moderate actions (ModAct). Based 
on the underlying assumptions, scenarios IMP-SP and IMP-LD are 
characterised by the lowest final energy demand, triggered by high 
energy efficiency improvement rates as well as additional demand side 
measures, while a scenario with extensive use of CDR in the industry 
and the energy sectors to achieve net-negative emissions (IMP-Neg)

leads to a significant increase in final energy demand. Scenario IMP-GS 
represents a pathway where mitigation action is gradually strengthened 
by 2030 compared to pre-COP 26 Nationally Determined Contributions 
(NDCs) shows the lowest final energy demand. All ambitious IMPs show 
substantially increasing contributions from electricity, with electricity’s 
end-use share more than doubling for some of them by 2050 and more 
than tripling by 2100. The share of hydrogen shows a flatter curve for 
many scenarios, reaching 5% (IMP-Ren) in 2050 and up to 20% in 2100 
for some scenarios (Ren, LD). Those scenarios that have a strong focus 
on renewable energy electrification show high shares of hydrogen in 
the sector. In comparison to sector-specific and national studies which 
show typically a range between 5 and 15% by 2050, many IAM IMPs 
expect hydrogen to play a less important role. Results for industrial CCS 
–1000001000020000

YearMtCO2 per year MtCO2 per yearIndustrial CO2 emissions
(Combustion and process)
YearEJ per yearIndustrial ﬁnal energy
0255075
YearIndustrial combustion and process CCS
05101520
Year% of ﬁnal energyShare of hydrogen
C1: Below 1.5°C (>50%) with 
no or limited overshoot
C3: Limit warming to 2°C (>67%)
Reference
CurPol
ModActSP
LD
Ren
GSNegCategory
Illustrative Mitigation Pathway
Figure 11.12 | Comparison of industry-sector-related CO 2 emissions (including process emissions), final energy demand, share of electricity and hydrogen 
in the final energy mix, and industrial carbon capture and storage (CCS) for different mitigation scenarios representing illustrative mitigation pathways

and the full sample of integrated assessment models (IAM) scenario results for three temperature categories (figure based on scenario database). 
Indicators in the Illustrative Mitigation Pathways (lines) and the 5–95% range of reference, 1.5°C and 2°C scenarios (shaded areas). The selected IMPs reflect the following 
characteristics: opportunities for reducing demand (IMP-LD; low demand), the role of deep renewable energy penetration and electrification (IMP-Ren; renewables), extensive 
use of carbon dioxide removal (CDR) in the industry and the energy sectors to achieve net-negative emissions (IMP-Neg), insights into how shifting development can lead 
to deep emission reductions and achieve sustainable development goals (IMP-SP; shifting pathways), and insights into how slower short-term emissions reductions can be

compensated by very fast emission reductions later on (IMP-GS; gradual strengthening). Furthermore, two scenarios were selected to illustrate the consequences of current 
policies and pledges; these are CurPol (Current Policies) and ModAct (Moderate Action), and are referred to as Pathways Illustrative of Higher Emissions. Source: data are from 
the AR6 database; only scenarios that pass the vetting criteria are included (Section 3.2).

1201
1201
Industry Chapter 1111show a  broad variety of contributions, with the GS scenario (where 
hydrogen is not relevant as a mitigation option) representing the upper 
bound to 2050, with almost 2 GtCO 2 yr–1 captured and stored by 2050. 
Beyond 2050 the upper bound is associated with scenario IMP-Neg 
associated with extensive use of CDR in the industry and energy 
sectors to achieve net-negative emissions in the second half of the 
century – more than 6 GtCO 2 yr–1 is captured and stored in 2100 (this 
represents roughly 60% of 2018 direct CO 2 emissions of the sector).
11.4.2.2 In-depth Discussion and ‘Reality’ Check of Pathways 
From Specific Sector Scenarios
Since AR5 a number of studies providing a high technological level 
of detail for the industry sector have been released which describe 
how the industry sector can significantly reduce its GHG emissions 
until the middle of the century. Many of these studies try to

specifically reflect the particular industry sector characteristics and 
barriers that hinder industry to follow an optimal transformation 
pathway. They vary in respect to different characteristics. In respect 
to their geographical scope, some studies analyse the prospects 
for industry sector decarbonisation on a global level (IEA 2017a; 
Energy Transitions Commission 2018; Grubler et al. 2018; IEA 2020a, 
2019b, 2020c; Tchung-Ming et al. 2018); regional level, for example, 
European Commission (2018) and Material Economics (2019); or 
country level – studies for China, from where most industry-related 
emissions come (e.g.,  Zhou et al. 2019).23 In regard to sectoral 
scope, some studies include the entire industry sector, while others 
focus on selected GHG emission intensive sectors, such as steel, 
chemicals and/or concrete. Most of the scenarios focus solely on 
CO2 emissions, that is non-CO 2 emissions of the industrial sector 
are neglected.24

Industry sector mitigation studies also differ in regard to whether 
they develop coherent scenarios or whether they focus on discussing 
and analysing selected key mitigation strategies, without deriving full 
energy and emission scenarios. Coherent scenarios are developed 
in IEA (2017); Energy Transitions Commission (2018); Grubler et al. 
(2018); Tchung-Ming et al. (2018); IEA (2019b, 2020a,c); IEA (2021a); 
and IRENA (2021) on the global level, and in Climact (2018); 
European Commission (2018); and Material Economics (2019) on the 
European level. Recent literature analysing selected key mitigation 
strategies, for example IEA (2019b) and Material Economics (2019) 
has focused either exclusively or to a large extent on analysing the 
potential of materials efficiency and circular economy measures to 
reduce the need for primary raw materials relative to a  business-
as-usual development. The IEA (2021a, 2020a) also provides deep

insights in  to single mitigation strategies for the industry sector, 
particularly the role of CCS.  The following discussion mainly 
23 In addition, there are many other studies available which have developed country-specific, technologically detailed scenarios for industry decarbonisation (e.g., Gerbert 
et al. 2018) and a  few which have investigated the decarbonisation prospects of individual industrial clusters (Schneider 2019), but these types of studies are not 
discussed here.
24 Most of the global mitigation scenarios solely focus on CO 2 emissions. Non-CO 2 emissions make up only a small share of the industry sector’s current CO 2-eq. emissions 
and include N 2O emissions (e.g., from nitric and adipic acid production), CH 4 emissions (e.g., from chemical production and iron and steel production) and various F-gases

(such as perfluorocarbons from primary aluminium production and semiconductor manufacturing) (USEPA and ICF 2012; Gambhir et al. 2017). Mitigation options for these 
non-CO 2 emissions are discussed in Gambhir et al. (2017).
25 Following the description of IEA SDS 2020 would limit the global temperature rise to below 1.8°C with a 66% probability if CO 2 emissions remain at net zero after 2070. 
If CO 2 emissions were to fall below net zero after 2070, then this would increase the possibility of reaching 1.5°C by the end of the century (IEA 2020c).concentrates on scenarios from the IEA. It has to be acknowledged 
that they only represent a small segment of the huge scenario family 
(see the scenario database in Chapter 3), but this approach enables 
to show the chronological evolution of scenarios coming from the 
same institution, using the same modelling approach (which allows 
a  technology-rich analytical backcasting approach), but reflect

additional requests that emerge over time (Table 11.5). In the 2DS 
scenario from the ‘Energy Technology Perspectives (ETP)’ study (IEA 
2017), which intends to describe in great technological detail how 
the global energy system could transform by 2060 so as to be in line 
with limiting global warming to below 2°C, total CO 2 emissions are 
74% lower in 2060 than in 2014, while only 39% lower in the industry 
sector. The Beyond 2°C Scenario (B2DS) of the same study intends 
to show how far known clean energy technologies (including those 
that lead to negative emissions) could go if pushed to their practical 
limits, allowing the future temperature increase to be limited to ‘well 
below’ 2°C and lowering total CO 2 emissions by 100% by 2060 and 
by 75% relative to 2014 in the industry sector.
Technologies penetration assumed in the CTS scenario by 2060 allows 
for an industrial emission cut of 45% from 2017 levels and a 50%

cut against projected 2060 emissions in the Reference Technology 
Scenario (RTS) from the same study (IEA 2019b), similar to IEA’s 
2DS scenario. Energy efficiency improvements and deployment 
of BATs contribute 46% to cumulative emission reduction in 
2018–2060, while fuel switching  (15%), material efficiency (19%) 
and deployment of innovative processes (20%) provide the rest. 
IEA (2020a,c) which continues the Energy Technology Perspectives 
series include the new Sustainable Development Scenario (SDS) 
to describe a  trajectory for emissions consistent with reaching 
global ‘net zero’ CO 2 emissions by around 2070.25 In 2070 the net 
zero balance is reached through a compensation of the remaining 
CO2 emissions (fossil fuel combustion and industrial processes 
still lead to around 3 GtCO 2) by a combination of BECCS and to 
a lesser degree direct air capture and storage. In IEA (2020c) the 
Faster Innovation Case (FIC) shows a possibility to reach a net zero

emissions level globally already in 2050, assuming that technology 
development and market penetration can be significantly 
accelerated. Innovation plays a major role in this scenario as almost 
half of all the additional emissions reductions in 2050 relative to 
the reference case would be from technologies that are in an early 
stage of development and have not yet reached the market today 
(IEA 2020c). The most ambitious IEA scenario NZE2050 (IEA 2021a) 
describes a pathway reaching net zero emissions at system level by 
2050. With 0.52 GtCO 2 industry-related CO 2 emissions (including 
process emissions) it ends up 94% below 2018 levels in 2050. 
Remaining emissions in the industry sector have to be compensated 
by negative emissions (e.g., via DAC).

1202
1202
Two studies complement the discussion of the IEA scenarios and 
are related to the IEA database.26 The ETC Supply Side scenario 
builds on the ETP 2017 study, investigating additional emission 
reduction potentials in the emissions-intensive sectors such as 
heavy industry and heavy-duty transport so as to be able to reach 
net zero emissions by the middle of the century. The LED scenario 
(Grubler et al. 2018) also builds on the ETP 2017 study, but focuses 
on the possible potential of very far-reaching efforts to reduce 
future material demand.
26 Other global mitigation scenarios (e.g., from Tchung-Ming et al. (2018) and Shell Sky Scenario from Shell (2018)) are not included in the following scenario comparison as 
these studies’ energy and emission base year data on the industry sector deviates considerably from the other three studies included in the comparison, which all use IEA

data. Furthermore, unlike the other studies, Tchung-Ming et al. (2018) do not provide detailed information on the steel, chemicals and concrete subsectors. Not included 
here but worth mentioning are many other sector-specific studies, for example Napp et al. (2019, 2014), which consider more technologically advanced decarbonisation 
routes for the sector.A comparison of the different mitigation scenarios shows that they 
depend on how individual mitigation strategies in the industry sector 
(Figure 11.13) are assessed. The use of CCS, for example, is in many 
scenarios assessed as very important, while other scenarios indicate 
that ambitious mitigation levels can be achieved without CCS in the 
industry sector. CCS plays a major role in the B2DS scenario (3.2 GtCO 2 
in 2050), the ETC Supply Side scenario (5.4 GtCO 2 in 2050) and the 
IEA (2020a, 2021a) scenarios (e.g., 2.8 Gt CO 2 in NZE2050 in 2050,

roughly one half of the captured CO 2 is related to cement production), Table 11.4 | Perspectives on industrial sector mitigation potential (comparison of different IEA scenarios).
Reduction of 
direct CO 2 
emissionsScenario assumptionsaIEA (2017, 2020c,i, 2021a) IEA (2019b) IEA (2020a,c)
2030 2050 2060 2050 2070
Baseline direct emissions from industrial sector
Reference 
Technology  
Scenario (RTS)Industry sector improvements in energy 
consumption and CO 2 emissions are 
incremental, in line with currently 
implemented and announced policies 
and targets.9.8 GtCO 2 10.4 GtCO 2 9.7 GtCO 2
Emissions reduction potential
2°C Scenario (2DS)Assumes the decoupling of production 
in industry from CO 2-emissions growth 
across the sector that would be compatible 
with limiting the rise in global mean 
temperature to 2°C by 2100.–7% vs 2014a
–20% vs RTSb–39% vs 2014b
–50% vs RTSb
Beyond 2°C  
Scenario (B2DS)Pushes the available CO 2 abatement 
options in industry to their feasible limits in

order to aim for the ‘well below 2°C’ target.–28% vs 2014
–38% vs RTS–75% vs 2014
–80% vs RTS
Clean Technology 
Scenario (CTS)Strong focus on clean technologies. 
Energy efficiency and deployment of BATs 
contribute 46% to cumulative emission 
reduction in 2018–2060; fuel switch –15%; 
material efficiency – 19%; deployment of 
innovative processes – 20%.5 Gt CO 2 or –45% 
vs 2017 level and 
–50% from 2060 
RTS level
Scenario 2020
(SDS 2020)Leads to net zero emissions globally 
by 2070. Remaining emissions in some 
sectors (including industry) in 2070 will 
be compensated by negative emissions in 
other areas (e.g., through BECCS and DAC).~ 4.0 GtCO 2 ~ 0.6 GtCO 2
Net zero emissions 
(NZE, 2021)Net zero emissions across all sectors 
are reached already by 2050.–23% 
(i.e., 2.1 GtCO 2) 
vs 2018.–94% 
(i.e., 8.4 GtCO 2) 
vs 2018
Faster Innovation 
Case (FIC)Achieves net-zero emissions status already 
by 2050 based on accelerated development

and market penetration of technologies 
which have currently not yet reached 
the market.0.8 Gt CO 2
(mainly steel and 
chemical industry)
a Based on bottom-up technology modelling of five energy-intensive industry subsectors (cement, iron and steel, chemicals and petrochemicals, aluminium, and pulp and paper).
b  Industrial direct CO 2 emissions reached 8.3 GtCO 2 in 2014, 24% of global CO 2 emissions.
Source: IEA (2017, 2019b, 2020a, 2020c,i, 2021a).

1203
1203
Industry Chapter 1111while it is explicitly excluded in the LED scenario. In the latter scenario, 
on the other hand, considerable emission reductions are assumed to 
be achieved by far-reaching reductions in material demand relative 
to a baseline development. In other words, the analysed scenarios 
also suggest that to reach very strong emission reductions from the 
industry sector either CCS needs to be deployed to a great extent or 
considerable material demand reductions will need to be realised. 
Such demand reductions only play a minor role in the 2DS scenario 
and no role in the ETC Supply Side scenario. The SDS described in IEA 
(2020a) provides a pathway where both CCS and material efficiency 
contribute significantly. In SDS material efficiency is a relevant factor 
in several parts of industry, explicitly steel, cement, and chemicals. 
Combining the different material efficiency options including

a substantial part lifetime extension (particularly of buildings) leads 
to 29% less steel production by 2070, 26% less cement production, 
and 25% less chemicals production respectively in comparison to 
the reference line used in the study (Stated Policy Scenario: STEPS). 
Sector- or subsector-specific analysis supports the growing role of 
material efficiency. For the global chemical and petrochemical sector, 
Saygin and Gielen (2021) point out that circular economy (including 
recycling) has to cover 16% of the necessary reduction that is needed 
for the implementation of a 1.5°C scenario.
In all scenarios, the relevance of biomass and electricity in industrial 
final energy demand increases, especially in the more ambitious 
scenarios NZE2050, SDS, ETC Supply Side and LED.  While in all 
scenarios, electrification becomes more and more important, hydrogen 
or hydrogen-derived fuels, on the other hand, do not contribute to

industrial final energy demand by the middle of the century in 2DS 
and B2DS, while LED (1% final energy share in 2050) and particularly 
ETC Supply Side (25% final energy share in 2050) consider hydrogen 
or hydrogen-derived fuels as a significant option. In the updated IEA 
scenarios hydrogen and hydrogen-based fuels already play a  more 
important role. In the SDS share in industry, final energy is around 
10% (IEA 2020a) and in the Faster Innovation Case around 12% (IEA 
2020c) in 2050. In the latter case this is based on the assumption 
that by 2050 on average each year 22 hydrogen-based steel plants 
come into operation (IEA 2020c). In SDS around 60% of the hydrogen 
is produced on-site via water electrolysis while the remaining 40% is 
generated in fossil fuel plants (methane reforming) coupled with CCS 
facilities. In the NZE2050 scenario biomass/biomethane (13%/3%), 
hydrogen (3%), natural gas with CCUS (4%), and coal with CCUS

(4%) are responsible for 27% of the final energy demand of the 
sector. This is much more than in 2018, starting here from roughly 6% 
(only biomass). Direct use of electricity still plays a bigger role in the 
analysis, as share of electricity increases in NZE2050 from 22% in 2018 
to 28% in 2030 and 46% in 2050 (with 15% a part of the electricity 
is used to produce hydrogen). This is reflecting the effect that since 
the publication of older IEA reports more direct electric applications 
for the sector become available. In NZE2050 approximately 25% of 
total heat used in the sector is electrified directly with heat pumps or 
indirectly with synthetic fuels already by 2030.
For B2DS it is assumed that most of the available abatement options 
in the industry sector are pushed to their feasible limits. That leads to 
cumulative direct CO 2 emissions reductions compared to 2DS which

come from: energy efficiency improvements and BAT deployment (42%), innovative processes and CCS (37%), switching to lower 
carbon fuels and feedstocks (13%), and material efficiency strategies 
in manufacturing processes (8%). Energy efficiency improvements 
are particularly important in the first time period.
The IEA World Energy Outlook indicates energy efficiency improvement 
in the 2020 to 2030 period as a major basis to switch from STEPS 
(stated policies) to the SDS (net zero emissions by 2070) pathway 
(IEA 2020i, 2021c). For many energy-intensive industries annual 
efficiency gains have to be almost doubled (e.g., from 0.6% yr–1 to 
1.0% yr–1 for cement production) to contribute sufficiently to the 
overall goal. If net zero CO 2 emissions should be achieved already 
by 2050 as pursued in the NZE2050 scenario (IEA 2020i, 2021c) 
further accelerating energy efficiency improvements are necessary 
(e.g., for cement, annual efficiency gains of 1.75%), leading to the

effect that in 2030 many processes are implemented closely to their 
technological limits. In total, sector final energy demand can be 
held nearly constant at 2018 levels until 2050 and decoupled from 
product demand growth.
The comparative analysis leads to the point that the relevance of 
individual mitigation strategies in different scenarios depends not 
only on a  scenario’s level of ambition. Instead, implicit or explicit 
assumptions about: (i) the costs associated with each strategy, 
(ii) future technological progress and availability of individual 
technologies, and (iii) the future public or political acceptance of 
individual strategies are likely to be main reasons for the observed 
differences between the analysed scenarios. For many energy-
intensive products, technologies capable of deep emission cuts are 
already available. Their application is subject to different economic 
and resources constraints (incremental investment needs, product

prices escalation, requirements for escalation of new low-carbon 
power generation). To fully exploit potential availability of carbon-free 
energy sources (e.g., electricity or hydrogen and related derivates) is 
a fundamental prerequisite and marks the strong interdependencies 
between the industry and the energy sector.
Assessment of the scenario literature allows to conclude that under 
specific conditions strong CO 2-emission reductions in the industry 
sector by 2050–2070 and even net-zero-emission pathways are 
possible. However, there is no consensus on the most plausible or 
most desirable mix of key mitigation strategies to be pursued. In 
addition it has to be stressed that suitable pathways are very country-
specific and depend on the economic structure, resource potentials, 
technological competences, and political preferences and processes 
of the country or region in question (Bataille 2020a).
There is a  consensus among the scenarios that a  significant shift

is needed from a  transition process in the past mainly based on 
marginal (incremental) changes (with a  strong focus on energy 
efficiency efforts) to one based on transformational change. To limit 
the barriers that are associated with transformational change, besides 
overcoming the valley of death for technologies or processes with 
breakthrough character, it is required to carefully identify structural 
change processes which are connected with substantial changes of 
the existing system (including the whole process chain). This has to 
be done at an early stage and has to be linked with considerations

1204
1204
Chapter 11  Industry11about preparatory measures which are able to flank the changes 
and to foster the establishment of new structures (Section  11.6). 
The right sequencing of the various mitigation options and building 
appropriate bridges between the different strategies are important. 
Rissman et al. (2020) proposes three phases of technologies 
deployment for the industry sector: (i) energy/material efficiency 
improvement (mainly incremental) and electrification in combination 
with demonstration projects for new technologies potentially 
important in subsequent phases (2020–2035), (ii) structural shifts 
based on technologies which reach maturity in phase (i) such as CCS 
and alternative materials (2035–2050), (iii) widespread deployment 
for technologies that are nascent today like molten oxide electrolysis-
based steel-making. There are no strong boundaries between the 
different phases and all phases have to be accompanied by effective

policies like R&D programmes and market pull incentives.
Taking the steel sector as an illustrative example, sector-specific 
scenarios examining the possibility to reach GHG reduction beyond 
80% (CAT 2020; Bataille et al. 2021b; IEA 2021a; Vogl et al. 2021b) 
indicate that robust measures comprise direct reduction of iron (DRI) 
with hydrogen in combination with efforts to further close the loops 
and increase availability of scrap metal (reducing the demand for 
primary steel). As hydrogen-based DRI might not be a fully mature 
technology before 2030 (depending on further developments of 
the policy framework and technological progress), risk of path 
dependencies has to be taken into consideration when reinvestments 
in existing production capacities will be required in the coming years. 
For existing plants, implementation of energy efficiency measures 
(e.g.,  utilisation of waste heat, improvement of high-temperature

pumps) could build a  bridge for further mitigation measures but 
have only limited unexhausted potential. As many GHG mitigation 
measures are associated with high investment costs and missing 
operating experience, a  step-by-step implementing process might 
be an appropriate strategy to avoid investment leakage (given the 
27 Note: In the described scenarios CCS was not taken into consideration as a mitigation option by the authors.mostly long operation times, investment cycles have to be used so 
as not to miss opportunities) and to gain experience. In the case 
of steel, companies can start with the integration of a natural gas-
based direct reduced iron furnace feeding the reduced iron to an 
existing blast furnace, blending and later replacing the natural gas by 
hydrogen in a second stage, and later transitioning to a full hydrogen 
DRI EAF or molten oxide electrolysis EAF , all without disturbing the 
local upstream and downstream supply chains.

It is worth mentioning the flexibility of implementing transformational 
changes not the least depends on the age profile and projected 
longevity of existing capital stock, especially the willingness to accept 
the intentional or market-based stranding of high GHG intensity 
investments. This is a relevant aspect in all producing countries, but 
particularly in those countries with a rather young industry structure 
(i.e., comparative low age of existing facilities on average). Tong et al. 
(2019) suggest that in China, using the survival rate as a proxy, less 
than 10% of existing cement or steel production facilities will reach 
their end of operation time by 2050. Vogl et al. (2021b) argue that 
the mean blast furnace campaign is considerably shorter than used 
in Tong et al.(2019), at only 17 years between furnace relining, which 
suggests there is more room for retrofitting with clean steel major 
process technologies than generally assumed. Bataille et al. (2021b)

found if very low carbon intensity processes were mandatory starting 
in 2025, given the lifetimes of existing facilities, major steel process 
lifetimes of up to 27 years would still make a full retrofit cycle with 
low-carbon processes possible.
In general, early adoption of new technologies plays a major role. 
Considering the long operation time (lifetime) of industrial facilities 
(e.g., steel mills and cement kilns) early adoption of new technologies 
is needed to avoid lock-in. For the SDS 2020 scenario, the IEA (2020h) 
calculated the potential cumulative reduction of CO 2 emissions from 
the steel, cement and chemicals sector to be around 57 GtCO 2 if 
Table 11.5 | Contribution to emission reduction of different mitigation strategies for net zero emissions pathways (range represents three different 
pathways for the industry sector in Europe; each related scenario focuses on different key strategies).27
Steel Plastics Ammonia Cement

Contribution to emission reduction (%) (range represents the three different pathways of the study)
Circularity 5–27 15-28 13–22 10–44
Energy efficiency 5–23 2–9
25–841–5
Fossil fuels and waste fuels 9–41 0–27 0–51
Decarbonised electricity 36–59 16–22 29–71
Biomass for fuel or feedstock 5–9 18–22 0–9
End-of-life plastic 16–35
CCS 5–34 0–31 0–57 29–79
Required electrification level
Growth of electricity demand (times 
compared with 2015)3–5 3–4 2–5
Investments and production costs escalation
Investment needs growth (% versus BAU) 25–65 122–199 6–26 22–49
Cost of production (% versus BAU) +2–20 +20–43 +15–111 +70–115
Source: Material Economics (2019).

1205
1205
Industry Chapter 11110.00.10.20.30.40.50.6
2020 2030 2040 2050 2050 2060 2070tCO2-eq/t cementa) Cement Costs rise:  cement – 35–115%; house < 1%
0.11
0.110.080.050.01
0.15
0.120.1720502070
20
0406080100120140
EE ME
CirclularityFeedCIFSW+ElCCUSUS$/tCO2-eq<30$/tCO 2
20–50$/tCO 2
40–100$/tCO 2
60–130$/tCO 2Early adoption MaturePrototype Demonstration EE
FeedCIME CCUS
Circularity FSW+ElCIEl
EE ME
CirclularityFeedCIFSW+ElCCUS2020 2030 2040 2050 2050 2060 2070tCO2-eq/t steelb) Steel Costs rise:  steel – 10–50%; home or car <1% 
20502070
0.14
0.27 0.270.14
0.10
0.17
0.180.16
US$/tCO2-eq
0.00.40.81.21.62.0Direct and indirectDirect and indirect
Direct Direct <20$/t CO 2
<20$/t CO 2
20–50$/t CO 2
20–80$/t CO 2
40–100$/t CO 2
60–100$/t CO 2
EE ME
CirclularityFeedCIFSW+ElCCUS2020 2030 2040 2050 2050 2060 2070tCO2-eq/t chemicalsc) Primary chemicalsCosts rise:
 primary chemicals – 15–115%;
plastic bottle <1% 
0.00.40.81.21.62.0

20502070
0.08
0.21
0.710.260.10
0.32
1.240.20
050100150200250300350400450US$/tCO2-eq<30$/tCO 2
-160–130$/tCO 2
20–150$/tCO 2
40–100/tCO 2
25–150$/tCO 2
EE ME
CirclularityFeedCIFSW+ElCCUS2020 2030 2040 2050 2050 2060 2070tCO2-eqd) Industry (waste excluded)
048121620
20502070
13%
10%
15%40%
35%17%
55%15%
Indirect
Chemicals
Metals
Other industriesDirect and indirectDirect and indirect
Direct Direct<20$/tCO 2
<20$/tCO 2
20–80$/tCO 2
25–150$/tCO 2Cement Cement
Figure 11.13 | Potentials and costs for zero-carbon mitigation options for industry and basic materials: CIEL  – carbon intensity of electricity for indir ect emissions; 
EE – energy efficiency; ME – material efficiency; Circularity – material flows (clinker substituted by coal fly ash, blast furnace slag or other by-products and

waste, steel scrap, plastic recycling, etc.); FeedCI – feedstock carbon intensity (hydrogen, biomass, novel cement, natural clinker substitutes); FSW+El – fuel 
switch and processes electrification with low-carbon electricity.  Ranges for mitigation options are shown based on bottom-up studies for grouped technologies packages,

not for single technologies. In circles, contribution to mitigation from technologies based on their readiness are shown for 2050 (2040) and 2070. Direct emissions include fuel combustion and process emissions. Indirect emissions include emissions attributed to consumed electricity and purchased heat. For basic chemicals only methanol, ammonia and high-value chemicals are considered. The total for industry doesn’t include emissions from waste. Base values for 2020 for direct and indirect emissions were calculated using 2019 GHG emission data (Crippa et al. 2021) and data for materials production from World Steel Association (2020a) and IEA (2021d). Negative mitigation costs for some options like Circularity are not reflected. Data from sources: Pauliuk et al. (2013a); Fawkes et al. (2016); WBCSD (2016); Bazzanella and Ausfelder (2017); IEA (2018a, 2019b,g,h, 2020a,c, 2021a); Lehne and Preston (2018); Scrivener et al. (2018); EUROFER (2019); Friedmann et al. (2019); Material Economics

(2019); Sandalow et al. (2019); CAT (2020); CEMBUREAU (2020); Gielen et al. (2020); Habert et al. (2020); World Steel Association (2020b); Bataille (2020a); GCCA (2021a); and Saygin and Gielen (2021).

1206
1206
Chapter 11  Industry11production technology is changed at its first mandatory retrofit, 
typically 25 years, rather than at 40 years (typical retrofitted lifetime) 
(Figure 11.14). Net zero pathways require that the new facilities 
are based on zero- or near-zero emissions technologies from 2030 
onwards (IEA 2021c).
Another important finding is that material efficiency and demand 
management are still not well represented in the scenario literature. 
Besides IEA (2020a) two of the few exceptions are Material 
Economics (2019) for the EU and Zhou et al. (2019) for China. Zhou 
et al. (2019) describe a consistent mitigation pathway (Reinventing 
Fire scenario) for China where in 2050 CO 2 emissions are at a level 
42% below 2010 emissions. Around 13% of the reduction is related 
to less material demand, mainly based on extension of building and 
infrastructure lifetime, as well as reduction of material losses in

the production process and application of higher quality materials 
particularly high-quality cement (Zhou et al. 2019). For buildings 
and cars, Pauliuk et al. (2021) analysed the potential role of material 
efficiency and demand management strategies on material demand 
to be covered by the industry sector.
For the four subsectors in industry with high emissions, Table 11.5 
shows results from Material Economics (2019) for the EU.  The 
combination of circularity, material and energy efficiency, fossil and 
waste fuels mix, electrification, hydrogen, CCS and biomass use 
varies from scenario to scenario with none of these options ignored, 
but trade-offs are required.
The analysis of net zero emission pathways requires significantly 
higher investments compared to business as usual (BAU): 25–65% for 
steel, 6–26% for ammonia, 22–49% for cement, and with 122–199% 
the highest number for plastics (Material Economics 2019).

While sector-specific cost analyses  are rare in general, there are 
scenarios indicating that pathways to net zero CO 2 emissions in the 
emissions-intensive sectors can be realised with limited additional 
costs. According to the Energy Transitions Commission (2018), deep 
decarbonisation from four major industry subsectors (plastics, steel, 
aluminium and cement) is achievable on a global level with cumulative 
incremental capital investments (2015–2050) limited to about 0.1% 
of aggregate GDP over that period. UKCCC (2019a) assesses that 
total incremental costs (compared to a theoretical scenario with no 
climate change policy action at all) for cutting industrial emissions 
by 90% by 2050 is 0.2% of expected 2050  UK  GDP (UKCCC 
2019a). The additional investment is 0.2% of gross fixed capital 
formation (Material Economics 2019). The IEA (2020a) indicates the 
required annual incremental global investment in heavy industry is

approximately 40 billion 2019USD yr–1 moving from STEPS to the 
SDS scenario (2020–2040), rising to USD55 billion yr–1 (2040–2070), 
effectively 0.05–0.07% of global annual GDP today.
Finally, a  new literature is emerging, based on the new sectoral 
electrification, hydrogen- and CCS- based technologies listed in 
previous sections, considering the possibility of rearranging standard 
supply and process chains using regional and international trade 
in intermediate materials like primary iron, clinker and chemical 
feedstocks, to reduce global emissions by moving production of these materials to regions with large and inexpensive renewable energy 
potential or CCS geology (Bataille 2020a; Gielen et al. 2020; Bataille 
et al. 2021a; Saygin and Gielen 2021).
In a  sequence of sectoral- and industry-wide figures above 
(Figure 11.13), it is shown – starting in the present on the left and 
moving through 2050 to 2070 on the right, how much separate

mitigation strategies can contribute and how they are integrated 
in the literature to reach near-zero emissions. For cement, steel 
and primary chemicals GHG intensities are presented, and for 
all industry absolute GHG emissions are displayed. Effects of the 
following mitigation strategies are reflected: energy efficiency, 
material efficiency, circularity/recycling, feedstock carbon intensity, 
fuel switching, CCU and CCS. Contributions of technologies split by 
their readiness for 2050 and 2070 are provided along with ranges of 
mitigation costs for achieving near-zero emissions for each strategy, 
accompanied by ranges of associated basic materials cost escalations 
and driven by these final products’ prices increments.
11.4.3 Cross-sectoral Interactions and Societal 
Pressure on Industry
Mitigation involves greater integration and coupling between sectors. 
This is widely recognised, for example, in the case of electrification of

transport (Sections 6.6.2 and 10.3.1), but it has been less explored for 
industrial decarbonisation. Industry is a  complex web of subsectors and 
intersectoral interaction and dependence, with associated mitigation 
opportunities and co-benefits and costs (OECD 2019b; Mendez-Alva 
et al. 2021). Implementation of the mitigation options assessed in 
Section 11.3 will result in new sectoral couplings, value chains, and 
business models but also in the phasing out of old ones. Notably, 
electrification in industry, hydrogen and sourcing of non-fossil carbon 
involves profound changes to how industry interacts with electricity 
systems and how industrial subsectors interact. For example, the 
chemicals and forestry industries will become much more coupled if 
various forms of biogenic carbon become an important feedstock for 
plastics (Figure 11.10). Clinker substitution with blast furnace slag 
in the cement industry is a  well-established way of reducing CO 2

emissions (Fechner and Kray 2012), but this slag will no longer be 
available if blast furnaces are phased out. Furthermore, additional 
material demand resulting from mitigation in other sectors, as well as 
adaptation and the importance of material efficiency improvements, 
are issues that have attracted increasing attention since AR5 (IEA 
2019b; Bleischwitz 2020; Hertwich et al. 2020). How future material 
will be affected under different climate scenarios is underexplored 
and typically not accounted for in modelling (Bataille et al. 2021a).
Using industrial waste heat for space heating, via district heating, 
is an established practice that still has a large potential with large 
quantities of low-grade heat being wasted (Fang et al. 2015). For 
Denmark it is estimated that 5.1% of district heating demand could 
be met with waste heat (Bühler et al. 2017) and for four towns 
studied in Austria 3–35% of total heat demand could be met (Karner

et al. 2016). A  European study shows that temporal heat demand 
flexibility could allow for up to 100% utilisation of excess heat 
from industry (Karner et al. 2018). A study of a Swedish chemicals

1207
1207
Industry Chapter 1111complex estimated that 30–50% of excess heat generated on-
site could be recovered with payback periods below three years 
(Eriksson et al. 2018).
A European study found that most of the industrial symbiosis or 
clustering synergies today are in the chemicals sector with shared 
streams of energy, water, and carbon dioxide (Mendez-Alva et al. 
2021). For future mitigation, the UKCCC (2019b) finds that industrial 
clustering may be essential for achieving the necessary efficiencies 
of scale and to build the infrastructure needed for industrial 
electrification; carbon capture, transport and disposal; hydrogen 
production and storage;  heat cascading between industries and to 
other potential heat users (e.g., residential and commercial buildings).
With increasing shares of renewable electricity production there is 
a growing interest in industrial demand response, storage and hybrid 
solutions with on-site PV and combined heat and power (CHP)  (Shoreh

et al. 2016; Scheubel et al. 2017; Schriever and Halstrup 2018). With 
future industrial electrification, and in particular with hydrogen used 
as reduction agent in iron-making or as feedstock in the chemicals 
industry, the level of interaction between industry and power systems 
becomes very high. Large amounts of coking coal, or oil and gas as 
petrochemical energy and feedstock, are then replaced by electricity. 
For example, Meys et al. (2021) estimates a staggering future electricity 
demand of 10,000 TWh in a scenario for a net zero emissions plastics 
production of 1100 Mt in 2050 (see Section 11.3.5 for other estimates 
of electricity demand). Much of this electricity is used to produce 
hydrogen to allow for CCU and this provides a  very large potential 
flexible demand if electrolysers are combined with hydrogen storage. 
Vogl et al. (2018) describe how hydrogen DRI and EAF steel plants can 
be highly flexible in their electricity demand by storing hydrogen or hot-

briquetted iron and increasing the share of scrap in EAF . The IEA (2019f) 
Future of Hydrogen report suggests that hydrogen production and 
storage networks could be in locations with already existing hydrogen 
production and storage, for example, chemical industries, and that 
these could be ideal for system load balancing and demand response, 
and in the case of district heating systems – for heat cascading.
The climate awareness that investors, shareholders, and customers 
demand from companies has been increasing steadily. It is reflected 
in the growing number of environmental management, carbon 
footprint accounting, benchmarking and reporting schemes (e.g., the 
Carbon Disclosure Project, Task Force on Climate-Related Financial 
Disclosures, Environmental Product Declarations, and others, 
e.g.,  Qian et al. 2018) requiring companies to disclose both direct 
and indirect GHG emissions, and creating explicit (for regulatory

schemes) as well as implicit GHG liabilities. This requires harmonised 
and widely accepted methods for environmental and carbon footprint 
accounting (Bashmakov et al. 2021b). From an investor perspective 
there are both physical risks (e.g., potential damages from climate 
change to business) and transition risks (e.g., premature devaluation 
of assets driven by new policies and technologies deployment and 
changes in public and private consumer preferences (NGFS 2019a)). 
Accompanied by reputational risks this leads to increased attention 
to Sustainable and Responsible Investment (SRI) principles and 
increased demands from investors, consumers and governments on 
climate and sustainability reporting and disclosure (NGFS 2019b). For example, Japan’s Keidanren promotes a  scheme by different 
industries to reduce GHG through the global value chain, including 
material procurement, product-use stages, and disposal, regardless

of geographical origin, with provided quantitative visualisation 
(Keidanren (Japan Business Federation) 2018). The EU adopted 
a non-financial disclosure directive in 2014 (Kinderman 2020) and 
a Taxonomy for Sustainable Finance in 2019 (Section 15.6.1).
11.4.4 Links to Climate Change and Adaptation
Sectors that are particularly vulnerable to climate change include 
agriculture, forestry, fisheries and aquaculture, and their downstream 
processing industries (Bezner et al. 2021). Many of the energy-
intensive industries are located based on access to fresh water 
(e.g., pulp and paper) or sea transport (e.g., petrochemicals). Risks 
of major concern for industry include disrupted supply chains and 
energy supplies due to extreme weather events, as well as risks 
associated with droughts, floods with dirty water, sea level rise and 
storm surges (Dodman et al. 2021). Adaptation measures may in 
turn affect the demand for basic materials (e.g., steel and cement),

for example, increased demand to build sea walls and protect 
infrastructure, but we have not found any estimates of the potential 
demand. Increased heat stress is unsafe for outdoor labourers and 
can reduce worker productivity, for example, in outdoor construction, 
resource extraction and waste handling (Ranasinghe et al. 2021).
11.5 Industrial Infrastructure, Policy, and 
Sustainable Development Goal Contexts
11.5.1 Existing Industry Infrastructures
Countries are at different stages of different economic development 
paths. Some are already industrialised, while developing and 
emerging economies are on earlier take-off stages or accelerated 
growth stages and have yet to build the basic infrastructure needed 
to allow for basic mobility, housing, sanitation, and other services 
(Section 11.2.3). The available in-use stock of material per capita and 
in each country therefore differs significantly, and transition pathways

will require a different mix of strategies, depending on each country’s 
material demand to build, maintain, and operate stock of long-lived 
assets. Industrialised economies have much greater opportunities 
for reusing and recycling materials, while emerging economies have 
greater opportunities to avoid carbon lock-in. The IEA projected that 
more than 90% of the additional 2050 production of key materials 
will originate in non-OECD countries (IEA 2017). As incomes rise in 
emerging economies, the industry sector will grow in tandem to meet 
the increased demand for the manufactured goods and raw materials 
essential for infrastructure development. The energy and feedstocks 
needed to support this growth are likely to constitute a large portion 
of the increase in the emerging economies’ GHG emissions in the 
future unless new low-carbon pathways are identified and promoted.
Emissions are typically categorised by the territory, subsector or

group of technologies from which they emanate. An alternative 
subdivision is that between existing sources that will continue to

1208
1208
Chapter 11  Industry11generate emissions in the future, and those that are yet to be built 
(Erickson et al. 2015). The rate of emissions from existing assets will 
eventually tend to zero, but in a timeframe that is relevant to existing 
climate and energy goals, the cumulative contribution to emissions 
from existing infrastructure and equipment is likely to be substantial. 
Aside from the magnitude of the contribution, the distinction between 
emissions from existing and forthcoming assets is instructive because 
of the difference in approach to mitigation that may be necessary or 
desirable in each instance to avoid getting locked into decades of 
highly carbon-intensive operations (Lecocq and Shalizi 2014).
Details of the methodologies to assess ‘carbon lock-in’ or ‘committed 
emissions’ differ across studies but the core components of the 
approaches adopted are common to each: an account of the existing

level of emissions for the scope being assessed is established; this 
level is projected forward with a  stylised decay function that is 
informed by assessments of the current age and typical lifetimes of 
the underlying assets. From this, a cumulative emissions estimate is  
calculated. The future emissions intensity of the operated assets 
is  usually assumed to remain constant, implying that nothing is 
done to retrofit with mitigating technologies (e.g., carbon capture) 
or alter the way in which the plant is operated (e.g., switching to 
an alternative fuel or feedstock). While the quantities of emissions 
derived are often referred to as ‘committed’ or ‘locked-in’, their 
occurrence is of course dependent on a suite of economic, technology 
and policy developments that are highly uncertain.
Data on the current age profile and typical lifetimes of emissions-
intensive industrial equipment are difficult to procure and verify and

most of the studies conducted in this area contain little detail on 
the global industrial sector. Two recent studies are exceptions, both 
of which cover the global energy system, but contain detailed and 
novel analysis on the industrial sector (Tong et al. 2019; IEA 2020a). 
Tong et al. (2019) use unit-level data from China’s Ministry of Ecology 
and Environment to obtain a more robust estimate of the age profile 
of existing capacity in the cement and iron and steel sectors in the 
country. The IEA (2020a) uses proprietary global capacity datasets for 
the iron and steel, cement and chemicals sectors, and historic energy 
consumption data for the remaining industry sectors as a proxy for 
the rate of historic capacity build-up.Both studies come to similar estimates on the average age of cement 
plants and blast furnaces in China of around 10–12 years old, which 
are the figures for which they have overlapping coverage. Both studies

also use the same assumption of the typical lifetime of assets in these 
sectors of 40 years, whereas the IEA (2020a) study uses 30 years for 
chemical sector assets and 25 years for other industrial sectors. The 
studies come to differing estimates of cumulative emissions by 2050 
from the industry sector; 196 GtCO 2 in the IEA (2020a) study, and 
162 GtCO 2 in the Tong et al. (2019) study. This difference is attributable 
to a differing scope of emissions, with the IEA (2020a) study including 
industrial process emissions (which for the cement sector in particular 
are substantial) in addition to the energy-related emissions quantities 
accounted for in the Tong et al. (2019) study. After correcting for this 
difference in scope, the emissions estimates compare favourably.
The IEA (2020a) study provides supplementary analysis for the industry 
sector, examining the impact of considering investment cycles alongside

the typical lifetimes assumed in its core analysis of emissions from 
existing industrial assets. For three heavy industry sectors – iron and 
steel, cement, and chemicals – the decay function applied to emissions 
from existing assets is re-simulated using a 25-year investment cycle 
assumption (Figure  11.14). This is 15 years shorter than the typical 
lifetimes assumed for assets in the iron and steel and cement sectors, 
and five years shorter than that considered for the chemical sector. 
The shorter timeframe for the investment cycle is a simplified way of 
representing the intermediate investments that are made to extend the 
life of a plant, such as the re-lining of a blast furnace, which can occur 
multiple times during the lifetime of an installation. These investments 
can often be similar in magnitude to that of replacing the installation, 
and they represent key points for intervention to reduce emissions.

The findings of this supplementary analysis are that around 40%, or 
60 GtCO 2, could be avoided by 2050 if near-zero emissions options 
are available to replace this capacity, or units are retired, retrofitted 
or refurbished in a  way that significantly mitigates emissions 
(e.g., retrofitting carbon capture, or fuel or process switching to utilise 
bioenergy or low-carbon hydrogen).
As this review was being finalised several papers were released that 
somewhat contradict the Tong et al. (2010) results (Bataille et al. 2021b; 
Vogl et al. 2021b). Broadly speaking, these papers argue that while 
Figure 11.14 | CO 2 emissions from existing heavy industrial assets in the NZE.  Source: International Energy Agency (2021), Net Zero by 2050, IEA, Paris.

1209
1209
Industry Chapter 1111high-emitting facilities may last for a  long time, be difficult to shut 
down early, and are inherent to local boarder supply chains, individual 
major processes that are currently highly GHG intense, such as blast 
furnaces and basic oxygen smelters, could be retired and replaced 
during major retrofits on much shorter time cycles of 15 to 25 years.
The cost of retrofitting or retiring a plant before the end of its lifetime 
depends on plant-specific conditions as well as a range of economic, 
technology and policy developments. For industrial decarbonisation 
it may be a  greater challenge to accelerate the development and 
deployment of zero-emission technologies and systems than to 
handle the economic costs of retiring existing assets before end of 
life. The ‘lock-in’ also goes beyond the lifetime of key process units, 
such as blast furnaces and crackers, since they are typically part

of large integrated plants or clusters with industrial symbiosis, as 
well as infrastructures with feedstock storage, ports, and pipelines. 
Individual industrial plants are often just a small part of a complex 
network of many facilities in an industrial supply chain. In that sense, 
current assessments of ‘carbon lock-in’ rely on simplifications due to 
the high the complexity of industry.
Conditions are also subsector and context specific in terms of 
mitigation options, industry structures, markets, value chains and 
geographical location. For example, the hydrogen steel-making joint 
venture in Sweden involves three different companies headquartered 
in Sweden (in mining, electricity and steel-making, respectively), two 
of which are state-owned, with a shared vision and access to iron ore, 
fossil-free electricity and high-end steel markets (Kushnir et al. 2020). 
In contrast, chemical clusters may consist of several organisations

that are subsidiaries to large multinational corporations with 
headquarters across the world, that also compete in different 
markets. Even in the presence of a local vision for sustainability this 
makes it difficult to engage in formalised collaboration or get support 
from headquarters (Bauer and Fuenfschilling 2019).
Furthermore, it is relevant to consider also institutional and behavioural 
lock-in (Seto et al. 2016). On one side, existing high-emitting 
practices may be favoured through formal and informal institutions 
(e.g., regulations and social norms or expectations, respectively), for 
example, around building construction and food packaging. On the 
other side, mitigation options may face corresponding institutional 
barriers. Examples include how cars are conventionally scrapped 
(i.e.,  crushed, leading to copper contamination of steel) rather 
than being dismantled, or slow permitting procedures for new

infrastructure and industrial installations for reducing emissions.
11.5.2 Current Industrial and Broader Policy Context
The basic motivation for industrial policy historically has been 
economic development and wealth creation. Industrial policy can be 
progressive and promote new developments or be protective to help 
infant or declining industries. It may also involve the phase-out of 
industries, including efforts to retrain workers and create new jobs. 
Industrial policy is not one policy intervention but rather the combined 
effects of many policy instruments that are coordinated towards an 
industrial goal. Industrial policies can be classified as being either vertical or horizontal depending on whether singular sectors or 
technologies are targeted (e.g., through R&D, tariffs and subsidies) 
or the whole economy (e.g., education, infrastructure, and general 
tax policies). The horizontal policies are not always thought of as

industrial policy, although taking a  broad view, including policy 
coordination and institution building, is important for industrial 
policy to be effective (see e.g., Andreoni and Chang 2019).
In the past ten years there has been increasing interest and attention 
to industrial policy. One driver is the desire to retain industry or 
re-industrialise in regions within Europe and North America where 
industry has a long record of declining shares of GDP . The need for 
economic growth and poverty eradication is a key driver in developing 
countries. An important aspect is the need to meet the ‘dual challenge 
of creating wealth for a  growing population while staying within 
planetary boundaries’ (Altenburg and Assman 2017). The need for 
industrial policy that supports environmental goals and green growth 
has been analysed by Rodrik (2014); Aiginger (2014); Warwick (2013); 
and Busch et al. (2018). Similar ideas are taken up in OECD reports

on green growth (OECD 2011) and system innovation (OECD 2015). 
However, these approaches to green industrial policy and innovation 
tend to focus on opportunities for manufacturing industries to 
develop through new markets for cleaner technologies. They rarely 
include explicit attention to the necessity of zero emissions and the 
profound changes in production, use and recycling of basic materials 
that this entails. This may also involve the phase-out or repurposing 
of industries that currently rely on fossil fuels and feedstock.
The policy implications of zero emissions for heavy industries are 
relatively unexplored, although some analyses in this direction 
are available (e.g., Åhman et al. 2017; Philibert 2017a; Wesseling 
et al. 2017; Bataille et al. 2018a; Wyns et al. 2019; Bataille 2020a; 
Fan and Friedmann 2021). For industry, there has been a long time 
focus on energy efficiency policies through voluntary and negotiated

agreements, energy management and audit schemes, and various 
programmes targeting industry (Fischedick et al. 2014a). Since AR5, 
interest in circular economy policies has increased and they have 
become more prevalent across regions and countries, including 
the EU, China, USA., Japan and Brazil (e.g., McDowall et al. 2017; 
Ranta et al. 2018; Geng et al. 2019). For electrification and CCUS, 
efforts are nascent and mainly focused on technology development 
and demonstrations. Policies for demand reduction and materials 
efficiency are still relatively unexplored (e.g., Pollitt et al. 2020 and 
IEA 2019b). Since zero emissions in industry is a  new governance 
challenge it will be important to build awareness and institutional 
capacity in industrialised as well as developing countries.
In the context of climate change policy, it is fair to say that industry has 
so far been sheltered from the increasing costs that decarbonisation

may entail. This is particularly true for the energy- and emissions-
intensive industries where cost increases and lost competitiveness 
may lead to carbon leakage (i.e., that industry relocates to regions 
with less stringent climate policies). Heavy industries typically pay no 
or very low energy taxes and where carbon pricing exists (e.g., in the 
European Trading Scheme) they are sheltered through free allocation 
of emission permits and potentially compensated for resulting 
electricity price increases. For example, Okereke and McDaniels

1210
1210
Chapter 11  Industry11(2012) show how the European steel industry was successful in 
avoiding cost increases and how information asymmetry in the policy 
process was important for that purpose.
11.5.3 Co-benefits of Mitigation Strategies 
and Sustainable Development Goals
The deployment of climate change mitigation strategies is 
primarily influenced by its costs and potential, but also by other 
broader sustainable development factors such as the Sustainable 
Development Goals (SDGs). Mitigation actions therefore are to 
be considered through the prism of impacts on achieving other 
economic, social  and  environmental goals. Those impacts are 
classified as co-benefits when they are positive or as risk when they 
are negative. Co-benefits can serve as additional drivers, while risks 
can inhibit the deployment of available mitigation options. Actions 
taken to mitigate climate change have direct and indirect interactions

with SDGs, both positive (synergies) or negative (trade-offs) (Fuso 
Nerini et al. 2019).
Given the wide range of stakeholders involved in climate actions 
and their (often contradictory) interests and priorities, the nature of 
co-benefits and risk can affect decision-making processes and the 
behaviour of stakeholders (Labella et al. 2020). Co-benefits form an 
important driver supporting the adoption of mitigation strategies, 
yet are commonly overlooked in policymaking. Karlsson et al. (2020), 
based on a  review of 239 peer-reviewed articles concluded that 
diverse co-benefit categories, including air, soil and water quality, 
diet, physical activity, biodiversity, economic performance, and 
energy security, are prevalent in the literature.
11.5.3.1 Sustainable Development Goals Co-benefits Through 
Material Efficiency and Demand Reduction
Material efficiency, an important mitigation option (SDG 13, climate 
action) for heavy industries, is yet to be fully acknowledged and

leveraged (Gonzalez Hernandez et al. 2018a; Sudmant et al. 2018; 
Dawkins et al. 2019). Material efficiency directly addresses SDG 
12 (responsible production and consumption) but also provides 
opportunities to reduce the pressures and impacts on environmental 
systems (SDG 6, clean water and sanitation) (Olivetti and Cullen 2018). 
Exploiting material efficiency usually requires new business models 
and provides potential co-benefits of increased employment and 
economic opportunities (SDG 8, decent work and economic growth).
Material efficiency also provides co-benefits through infrastructural 
development (SDG 9, industry, innovation and infrastructure) 
(Mathews et al. 2018) to support the wide range of potential 
material efficiency strategies including light-weighting, reusing, 
remanufacturing, recycling, diverting scrap, extending product 
lives, using products more intensely, improving process yields, and 
substituting materials (Allwood et al. 2011). Worrell et al. (2016)

also emphasises how material efficiency improvements, in addition 
to limiting the impacts of climate change help deliver sustainable 
production and consumption co-benefits through environmental 
stewardship. Binder and Blankenberg (2017) and Dhandra (2019) show that sustainable consumption is positively related to life 
satisfaction and subjective well-being (SDG 3), and Guillen-Royo 
(2019) adds positive associations with happiness and life satisfaction.
The reduction in excessive consumption and demand for products 
and services generates a  reduction in post-consumption waste 
and so enhances clear water and sanitation (SDG 6) (Govindan 
2018; Minelgaitė and Liobikienė 2019), and reduces waste along 
product supply chains and lifecycles (SDG 12) (Genovese et al. 
2017; UNSD 2020). At the risk side there are possible reductions of 
employment, incomes, sales taxes from the material extraction 
and processing activities, considered as excessive for sustainable

consumption (Thomas 2003).
11.5.3.2 Sustainable Development Goals Co-benefits 
From Circular Economy and Industrial Waste
While the circular economy concept first emerged in the context of 
waste avoidance, resource depletion, closed-loop recycling, etc., it 
has now evolved as a  tool for a  broader systemic national policy 
due to its potential wider benefits (Geng et al. 2013). It represents 
new circular business models that encourage design for reuse 
and to improve material recovery and recycling, and so represents 
a departure from the traditional linear production and consumption 
systems (with landfilling at the end), with a wide range of potential 
co-benefits to a wide range of SDGs (Guo et al. 2016; Genovese et al. 
2017; Schroeder et al. 2019; UNSD 2020).
Genovese et al. (2017) articulates the advantages from an 
environmental and responsible consumption and production point 
of view (SDG 12). Many studies have outlined new business models

based on the circular economy that foster sustainable economic 
growth and the generation of new jobs (SDG 8) (Antikainen and 
Valkokari 2016), as well as global competitiveness and innovation 
in business and the industrial sector (Pieroni et al. 2019), such as its 
potential synergies with industry 4.0 (Garcia-Muiña et al. 2018).
Following a review of the literature, Schroeder et al. (2019) identified 
linkages between circular economy practices and SDGs based on 
a relationship scoring system, and highlighted that such SDGs as SDG 
6 (clean water and sanitation), SDG 7 (affordable and clean energy), 
SDG 8  (decent work and economic growth), SDG 12 (responsible 
consumption and production), and SDG 15 (life on land) all strongly 
benefit from circular economy practices. With the potential to impact 
on all stages of the value chain (micro, meso and macro level of 
the economy), circular economy has also been identified as a  key

industrial strategy to managing waste across sectors.
Chatziaras et al. (2016) highlights the co-benefit to SDG 7 (affordable 
and clean energy) resulting from waste-derived fuel for the cement 
industry. Through the management of industrial waste using circular 
economy practices, studies such as Geng et al. (2012) and Bonato 
and Orsini (2017) have pointed out co-benefits to SDGs beyond 
clear environmental and economic benefits, highlighting how it 
also benefits SDG 3  and 11 through improved social relations 
between industrial sectors and local societies, and improved public 
environmental awareness and public health levels.

1211
1211
Industry Chapter 111111.5.3.3 Sustainable Development Goals Co-benefits 
From Energy Efficiency
Beyond the very direct links between energy and climate change, 
reliable, clean, and affordable energy (SDG 7) presents a  cross-
cutting issue, central to all SDGs and fundamental to development, 
and energy efficiency enables its provision by reducing the direct 
supply and necessary infrastructure required. Energy efficiency 
improvements can be delivered through multiple technical options 
and tested policies, delivering energy and resource savings 
simultaneously with other socio-economic and environmental co-
benefits. At the macro level, this includes enhancement of energy 
security (SDG 16, peace, justice and strong institutions) delivered 
through clean low-carbon energy systems (Fankhauser and Jotzo 
2018). Much of the literature, including Sari and Akkaya (2016), 
Allan et al. (2017) and Garrett-Peltier (2017), points out that energy

efficiency improvements deliver superior employment opportunities 
(SDG 8 – decent work and economic growth), while a limited number 
of studies have reported that it can negatively impact employment in 
fuel supply sectors (Costantini et al. 2018).
Many studies report that energy efficiency improvements are 
essential for supporting overall economic growth, contributing 
to positive changes in multi-factor productivity (SDGs 8  and 9  – 
decent work and economic growth and industry, innovation, and 
infrastructure)  (Lambert et al. 2014; Bataille and Melton 2017; 
Rajbhandari and Zhang 2018; Bashmakov 2019; Stern 2019) 
through industrial innovation (SDG 9) (Kang and Lee 2016), with 
some dissent (e.g., Mahmood and Ahmad 2018). Improved energy 
efficiency against a  background of growing energy prices helps 
industrial plants stay competitive (Bashmakov and Myshak 2018). 
Energy efficiency allows continued economic growth under strong

environmental regulation. Given that energy efficiency measures 
reduce the combustion of fossil fuels it leads to reduced air pollution 
at industrial sites (Williams et al. 2012) and better indoor comfort at 
working places.
Since less energy supply infrastructure is needed in cities and less 
energy is needed to produce materials such as cement and concrete, 
and metals, energy efficiency indirectly supports ‘sustainable cities 
and communities’ (SDG 11) (Di Foggia 2018). In addition, energy 
efficiency in industry reflects achievements in meeting SDG 12 
(responsible consumption and production).
11.5.3.4 Sustainable Development Goals Co-benefits From 
Electrification and Fuel Switching
A key, generally underappreciated SDG benefit of electrification is 
improved urban and indoor air quality (at working places as well) 
and associated health benefits (SDG 3) from clean electrification 
(SDG 7) of industrial facilities (IEA 2016). With energy being such

an important cross-cutting issue to sustainable development, 
some SDGs, such as SDGs 1, 3, 4  and 5  (Harmelink et al. 2018) 
are co-beneficiaries to using electrification and fuel switching as 
a climate action mitigation option.11.5.3.5 Sustainable Development Goals Co-benefits 
from Carbon Capture and Utilisation, 
and Carbon Capture and Storage
CCU and CCS have been identified as playing key roles in the 
transition of industry to net zero. Advancements in the development 
and deployment of both CCS and CCU foster climate action (SDG 13). 
Other co-benefits for CCS include control of non-CO 2 pollutants 
(SDG 3), direct foreign investment and know-how (SDG 9), enhanced 
oil recovery from existing resources, and diversified employment 
prospects and skills (SDG 8) (Bonner 2017). For CCU, the main 
co-benefit related contributions are expected within the context of 
energy transition processes, and in societal advancements that are

linked to technological progress (Olfe-Kräutlein 2020). Therefore, the 
expectations are that the deployment of CCU technologies would 
have least potential for meeting the SDG targets relating to society/
people, compared with the anticipated contributions to the pillars of 
ecology and economy.
These mitigation options carry a  large number of risks as well. 
The high cost of the capture and storage process not only limit 
the technology penetration, but also make energy and products 
more expensive (risk to SDG 7), potential leaks from undersea or 
underground CO 2 storages carries risks for achieving SDGs 6, 14 and 
15. While there are economic costs involved with the deployment 
of CCS and  CCU (Bataille et al. 2018a), there are also significant 
economic and developmental costs associated with taking no action, 
because of the potential negative impact of climate change. CCS and 
CCU have been argued as providing public good (Bergstrom and Ty

2017) and co-benefits to key SDGs (Schipper et al. 2011). On the 
other hand, Fan et al. (2018) among others have noted the potential 
lock-in of existing energy structures due to CCS. Refer to Table 17.1 
for CCS and CCU co-benefits with respect to other sector chapters.
11.6 Policy Approaches and Strategies
Industrial decarbonisation is technically possible on the mid-
century horizon, but requires scale up of technology development 
and deployment, multi-institutional coordination, and sectoral and 
national industrial policies with detailed subsectoral and regional 
mitigation pathways and transparent monitoring and evaluation 
processes (Åhman et al. 2017; Wesseling et al. 2017; Bataille 
et al. 2018a; Rissman et al. 2020; Nilsson et al. 2021). Transitions of 
industrial systems entail innovations, plant and technology phase-
outs, changes across and within existing value chains, new sectoral 
couplings, and large investments in enabling electricity, hydrogen,

and other infrastructures. Low-carbon transitions are likely to be 
contested, non-linear and require a  multi-level perspective policy 
approach that addresses a  large spectrum of social, political, 
cultural and technical changes as well as accompanying phase-out 
policies, and involve a wide range of actors, including civil society 
groups, local authorities, labour unions and industry associations 
e(Geels et al. 2017; Rogge and Johnstone 2017; Yamada and Tanaka 
2019; Koasidis et al. 2020). See also Cross-Chapter Box 12.

1212
1212
Chapter 11  Industry11Deployment of the mitigation options presented in this chapter 
(Sections  11.3 and 11.4) needs support from a  mix of policy 
instruments including: GHG pricing coupled with border adjustments 
or other economic signals for trade-exposed industries; robust 
government support for research, development, and deployment; 
energy, material and emissions standards; recycling policies; 
sectoral technology roadmaps; market pull policies; and support for 
new infrastructure (Figure 11.15) ( Flanagan et al. 2011; Rogge et al. 
2017; Bataille et al. 2018a; Tvinnereim and Mehling 2018; Creutzig 
2019; Bataille 2020a; Rissman et al. 2020). The combination of the 
above will depend on specific sectoral market barriers, technology 
maturity, and local political and social acceptance (Hoppmann et al. 2013; Rogge and Reichardt 2016). Industrial decarbonisation 
policies need to be innovative and definitive about net zero CO 2

emissions to trigger the level of investment needed for the profound 
changes in production, use and recycling of basic materials needed 
(Nilsson et al. 2021). Inclusive and transparent governance that 
assesses industry decarbonisation progress, monitors innovation 
and accountability, and provides regular recommendations for 
policy adjustments is also important for progressing (Mathy et al. 
2016; Bataille 2020a).
The level of policy experience and institutional capacity needed 
varies widely across the mitigation options. In many countries, Circularity & material efﬁciency
Demand pull policies, e.g., quotas and procurement Manage and create demand
Support new value chains, symbiosis and business models
Carbon pricing and regulationPromote new technologies and practicesR&D and investment support
Energy supply and CCUS infrastructure and policy
Processing
UseManufacturingStockRecyclingPrimary 
productionSecondary
DisposalVirgin

feedstockZero emissions electricityNew technologies, electriﬁcation and CCUS
End-of-life reuse andEnergy efﬁciency across the life cycle
Design for materials efﬁciency and circularity
Demand reductionHigh capacity use and longevity
high-quality recycling
Figure 11.15 | Schematic figure showing the lifecycle of materials (green), mitigation options (light blue) and policy approaches (dark blue).

1213
1213
Industry Chapter 1111energy efficiency is a well-established policy field with decades of 
experience from voluntary and negotiated agreements, regulations, 
standards, energy audits, and demand-side management (DSM) 
programmes (see AR5), but there are also many countries where 
the application of energy efficiency policy is absent or nascent (see 
AR5) (Tanaka 2011; Fischedick et al. 2014a; García-Quevedo and 
Jové-Llopis 2021; Saunders et al. 2021). The application of DSM 
and load flexibility will also need to grow with electrification and 
renewable energy integration.
Materials efficiency and circular economy are not well understood 
from a policy perspective and were for a long time neglected in low-
GHG industry roadmaps although they may represent significant 
potential (Allwood et al. 2011; Gonzalez Hernandez et al. 2018b; IEA 
2019b, 2020a; Calisto Friant et al. 2021; Polverini 2021). Material

efficiency is also neglected in products design, architectural and civil 
engineering education, infrastructure and building codes, and 
urban planning (Section  5.6) (Braun et al. 2018; Orr et al. 2019). 
For example, the overuse of steel and concrete in construction is 
well documented but policies or strategies (e.g., design guidelines 
or regulation) for improving the situation are lacking (Dunant et al. 
2018; Shanks et al. 2019). Various circular economy solutions are 
gaining interest from policymakers with examples such as regulations 
and economic incentives for repair and reuse, initiatives to reduce 
planned obsolescence, and setting targets for recycling. Barriers that 
policies need to address are often specific to the different material 
loops (e.g., copper contamination for steel and lack of technologies 
or poor economics for plastics).
There is also a growing interest from policymakers in electrification 
and fuel switching but the focus has been mainly on innovation

and on developing technical production-side solutions rather than 
on creating markets for enabling demand for low-carbon products, 
although the concept of green public procurement is gaining traction. 
The situation is similar for CCU and CCS. Low-carbon technologies 
adoption represents an additional cost to producers, and this must be 
handled through fiscal incentives like tax benefits, GHG pricing, green 
subsidies, regulation and permit procedures. For example, the 45Q tax 
credit provides some incentives to reduce investor risk for CCS and 
attract private investment in the USA (Ochu and Friedmann 2021).
Since industrial decarbonisation is only recently emerging as a policy 
field there is little international collaboration on facilitation (Oberthür 
et al. 2021). Given that most key materials markets are global and 
competitive, unless there is much greater global governance to 
contribute to the decarbonisation of GHG-intensive industry through

intergovernmental and transnational institutions it is questionable 
that the world will achieve industry decarbonisation by 2050.
As GHG pricing, through GHG taxes or cap and trade schemes, has 
remained a  central avenue for climate policy, this section begins 
with a review of how the industrial sector has been concerned with 
these instruments. The rest of the section is then structured into 
five key topics, following insights on key failures that policy must 
address to enable and support large-scale transformations as well 
as the need for complementary mixes of policies to achieve this goal 
(Weber and Rohracher 2012; Rogge and Reichardt 2016; Grillitsch et al. 2019). The section describes how the need to focus on long-
term transitions rather than incremental changes can be managed 
through the planning and strategising of transition pathways; 
discusses the role of research, development, and innovation policy; 
highlights the need for enabling low-carbon demand and market

creation; reflects on the necessity of establishing and maintaining 
a level of knowledge and capacity in the policy domain about the 
industrial transition challenge; and points to the critical importance 
of coherence across geographical and policy contexts. The section 
concludes with a reflection on how different groups of actors needs 
to take up different parts of the responsibility for mitigating climate 
change in the industrial sector.
11.6.1 GHG Prices and GHG Markets
Internalising the cost of GHG emissions in consumer choices 
and producer investment decisions has been a  major strategy 
promoted by economists and considered by policymakers to mitigate 
emissions cost-effectively and to incentivise low-GHG innovations 
in a purportedly technology neutral way (Stiglitz et al. 2017; Boyce 
2018). In the absence of a coordinated effort, individual countries, 
regions and cities have implemented carbon-pricing schemes. As of

23 August 2021, 64 carbon schemes have been implemented or are 
scheduled by law for implementation, covering 22.5% of global GHG 
emissions (World Bank 2020), 35 of which are carbon taxes, primarily 
implemented on a national level and 29 of which are emissions trading 
schemes, spread across national and sub-national jurisdictions.
Assessments of pricing mechanisms show generally that they lead 
to reduced emissions, even in sectors that receive free allocation 
such as industry (Martin et al. 2016; Haites et al. 2018; Narassimhan 
et al. 2018; Metcalf 2019; Bayer and Aklin 2020). However, questions 
remain as to whether these schemes can bring emissions down fast 
enough to reach the Paris Agreement goals (Boyce 2018; Tvinnereim 
and Mehling 2018; World Bank Group 2019). Most carbon prices 
are well below the levels needed to motivate investments in 
high-cost options that are needed to reach net zero emissions 
(Section 11.4.1.5). Among the 64 carbon-price schemes implemented

worldwide today, only nine have carbon prices above USD40 (World 
Bank 2020). These are all based in Europe and include EU Emissions 
Trading System (ETS) (above USD40 since March 2021), Switzerland 
ETS, and seven countries with carbon taxes. Furthermore, emissions-
intensive and trade-exposed (EITE) industries are typically allowed 
exemptions and receive provisions that shelter them from any 
significant cost increase in virtually all pricing schemes (Haites 2018). 
These provisions have been allocated due to concerns about loss of 
competitiveness and carbon leakage which result from relocation and 
increased imports from jurisdictions with no, or weak, GHG emission 
regulations (Branger and Quirion 2014a; Branger and Quirion 2014b; 
Jakob 2021a). Embodied emissions in international trade accounts 
for one quarter of global CO 2 emissions in 2015 (Moran et al. 
2018) and has increased significantly over the past few decades,

representing a  significant challenge to competitiveness related to 
climate policy. CBAM, or CBA are trade-based mechanisms designed 
to ‘equalise’ the carbon costs for domestic and foreign producers. 
They are increasingly being considered by policymakers to address

1214
1214
Chapter 11  Industry11carbon leakage and create a level playing field for products produced 
in jurisdiction with no, or lower, carbon price (Mehling et al. 2019; 
Markkanen et al. 2021). On 14 July 2021, the European Commission 
adopted a proposal for a CBAM that requires importers of aluminium, 
cement, iron and steel, electricity and fertiliser to buy certificates at 
the ETS price for the emissions embedded in the imported products 
(European Commission 2021; Mörsdorf 2021). CBAMs should be 
crafted very carefully, to meet technical and legal challenges (Jakob 
et al. 2014; Sakai and Barrett 2016; Rocchi et al. 2018; Cosbey et al. 
2019; Joltreau and Sommerfeld 2019; Pyrka et al. 2020). Technical 
challenges arise because estimating the price adjustment requires 
reliable data on the GHG content of products imported as well as 
a  clear understanding of the climate policy  implications from the 
countries of imports. Application of pricing tools in industry requires

standardisation (benchmarking) of carbon-intensity assessments at 
products, installations, enterprises, countries, regions, and the global 
level. The limited number of existing benchmarking systems are not 
yet harmonised and thus not able to fulfill this function effectively. 
This limits the scope of products that can potentially be covered by 
CBAM-type policies (Bashmakov et al. 2021a).
Legal challenges arise because CBAM can be perceived as 
a protectionist measure violating the principle of non-discrimination 
under the regulations of the World Trade Organization (WTO). However 
the absence of GHG prices can also been perceived as a  subsidy 
for fossil fuel-based production (Stiglitz 2006; Al Khourdajie and 
Finus 2020; Kuusi et al. 2020). Another argument supporting CBAM 
implementation is the possibility to induce low-GHG investment in 
non-regulated regions (Cosbey et al. 2019).
Thus far, California is the only jurisdiction that has implemented CBA

tariffs applied on electricity imports from neighbouring states and 
provides insights on how a CBA can work in practice by using ‘default’ 
GHG emissions intensity benchmarks (Fowlie et al. 2021). CBAM is an 
approach likely to be applied first to a few selected energy-intensive 
industries that are at risk of carbon leakage, as the EU is considering. 
The implementation of CBA needs to balance applicability versus 
fairness of treatment. An option recently proposed is an individual 
adjustment mechanism to give companies exporting to the EU the 
option to demonstrate their actual carbon intensity (Mehling and Ritz 
2020). Any CBAMs will have to comply with multilaterally agreed 
rules under the WTO Agreements to be implemented.
The adoption of CBAM by different countries may evolve into 
the formation of a  climate club where countries would align on 
specific elements of climate regulation (e.g., primary iron or clinker

intensity) to facilitate implementation and incentivise countries to 
join (Nordhaus 2015; Hagen and Schneider 2021; Tagliapietra and 
Wolff 2021a,b). However, not all countries have the same abilities 
to report, adapt and transition to low-carbon production. The 
implications of CBAMs on trade relationships should be considered 
to avoid country divide and separation from a  common goal of 
global decarbonisation (Michaelowa et al. 2019; Kuusi et al. 2020; 
Banerjee 2021; Eicke et al. 2021; Bashmakov 2021). The globalisation 
of markets and the fragmentation of supply chains complicates the 
assignment of responsibility for GHG emissions mitigations related 
to trade (Jakob et al. 2021). Production-based carbon-price schemes minimise the incentives for downstream carbon abatement due to 
the imperfect pass through of carbon costs and therefore overlook 
demand-side solutions such as material efficiency (Skelton and 
Allwood 2017; Baker 2018). An alternative approach is to set the

carbon pricing downstream on the consumption of carbon-intensive 
materials, whether they are imported or produced locally (Neuhoff 
et al. 2015, 2019; Munnings et al. 2019). However, implementation 
of consumption-based GHG pricing is also challenged by the need 
of product GHG traceability and enforcement transaction costs 
(Jakob et al. 2014; Munnings et al. 2019). Hybrid approaches are 
also considered (Neuhoff et al. 2015; Bataille et al. 2018a; Jakob 
et al. 2021). The efficacy of GHG prices to achieve major industry 
decarbonisation has been challenged by additional real world 
implementation problems, such as highly regionally fragmented 
GHG markets (Boyce 2018; Tvinnereim and Mehling 2018) and the 
difficult social acceptance of price increases (Bailey et al. 2012; 
Raymond 2019). The higher GHG prices likely needed to incentivise 
industry to adopt low-GHG solutions pose social equity issues 
and resistance (Grainger and Kolstad 2010; Bataille et al. 2018b;

Hourcade et al. 2018; Huang et al. 2019b; Wang et al. 2019). GHG 
pricing is also associated with promoting mainly incremental low-
cost options and not investments in radical technical change or 
the transformation of socio-technical systems (Grubb et al. 2014; 
Vogt-Schilb et al. 2018; Stiglitz 2019; Rosenbloom et al. 2020). 
Transparent and strategic management of cap-and-trade proceeds 
toward inclusive decarbonisation transition that support high 
abatement cost options can contribute toward easing these 
shortcomings (Carl and Fedor 2016; Raymond 2019). In California, 
Senate Bill 535 (De  León, Statutes of 2012) require that at least 
a quarter of the proceeds go to projects that provide a benefit to 
disadvantaged communities (California Climate Investments 2020).
Clear and firm emission reduction caps towards 2050 are essential 
for sending strong signals to businesses. However, many researchers 
recognise that complementary policies must be developed to

set current production and consumption patterns toward a  path 
consistent with achieving the Paris Agreement goals as cap-and-
trade or carbon taxes are not enough (Schmalensee and Stavins 
2017; Vogt-Schilb and Hallegatte 2017; Bataille et al. 2018b; Kirchner 
et al. 2019). In this broader policy context, proceeds from pricing 
schemes can be used to support the deployment of options with 
near-term abatement costs that are too high to be incentivised by the 
prevailing carbon price, but which show substantial cost-reduction 
potential with scale and learning, and to ensure a  just transition 
(Wang and Lo 2021).
11.6.2 Transition Pathways Planning and Strategies
Decarbonising the industry sector requires transitioning how material 
and products are produced and used today to development pathways 
that include the strategies outlined in Sections 11.3 and 11.4 and 
Figure  11.15. Such broad approaches require the development

of transition planning that assesses the impacts of the different 
strategies and considers local conditions and social challenges that 
may result from conflicts with established practices and interests, 
with planning and strategies directly linked to these challenges.

1215
1215
Industry Chapter 1111Governments have traditionally used voluntary agreements 
or mandatory energy or emission reduction targets to achieve 
emission reduction for specific emission-intensive sectors (e.g., UK 
Climate Change Agreements; India Performance, Achieve and Trade 
scheme). Sector visions, roadmaps and pathways combined with 
a  larger context of socio-economic goals, with clear objectives 
and policy direction, are needed for every industrial sector to 
achieve decarbonisation and at the time of writing they are 
emerging for some sectors. Grillitsch et al. (2019b) working from 
the socio-technical transitions literature, focuses on the need 
for maintaining ‘directionality’ for innovation (e.g.,  towards net 
zero transformation), the capacity for iterative technological 
and policy ‘experimentation’ and learning, ‘demand articulation’ 
(e.g., engagement of material efficiency and high value circularity),

and ‘policy coordination’ as four main framing challenges. Wesseling 
et al. (2017b) bridges from the socio-technical transitions literature 
to a  world more recognisable by executives and engineers, 
composed of structural components that include actors (e.g., firms, 
trade associations, government, research organisations, consumers, 
etc.), institutions (e.g., legal structures, norms, values and formal 
policies or regulations), technologies (e.g., facilities, infrastructure) 
and system interactions.
Several studies (Åhman et al. 2017; Bataille et al. 2018a; Material 
Economics 2019; Wyns et al. 2019) offer detailed transition plans using 
roughly the same five overarching strategies: (i) policies to encourage 
material efficiency and high quality circularity; (ii) ‘supply push’ R&D 
and early commercialisation as well as ‘demand pull’ to develop niche

markets and help emerging technologies cross ‘the valley of death’; (iii) GHG pricing or regulations with competitiveness provisions to trigger 
innovation and systemic GHG reduction; (iv) long-run, low-cost finance 
mechanisms to enable investment and reduce risk; (v) infrastructure 
planning and construction (e.g. CO 2 transport and disposal, electricity 
and hydrogen transmission and storage), and institutional support 
(e.g., labour market training and transition support; electricity market 
reform). Wesseling et al. (2017b) and (Bataille et al. 2018a) further 
add a  step to conduct ongoing stakeholder engagements, including 
stakeholders with effective ‘veto’ power (i.e., firms, unions, government, 
communities, indigenous groups), to share and gather information, 
educate, debate, and build consensus for a robust, politically resilient 
policy package. This engagement of stakeholders can also bring on 
new supply chain collaborations and bridge the cost pass-through

challenge (e.g.,  the Swedish HYBRIT steel project, or the ELYSIS 
consortium, with plans to bring fully commercialised inert electrodes 
for bauxite electrolysis to market by 2024).
Detailed sectoral roadmaps that assess the technical, economic, 
social and political opportunities and provide a clear path to low-
GHG development are needed to guide policy designs. For example, 
the German state of North Rhine Westphalia passed a Climate Process 
Law that resulted in the adoption of a Climate Protection Plan that 
set subsector targets through a transparent stakeholder engagement 
process based on scenario development and identification of low-
GHG options (Lechtenböhmer et al. 2015), see Box  11.3. Another 
example is the UK set of Industrial Decarbonisation and Energy 
Efficiency Roadmaps to 2050 as well as the UK Strategic Growth 
Plan, which are accompanied by Action Plans for each energy-
intensive subsector.

Box 11.3 | IN4Climate NRW – Initiative for a Climate-friendly Industry in North  
Rhine-Westphalia (NRW)
IN4Climate NRW (www.in4climate.nrw) was launched in September 2019 by the state government of North Rhine-Westphalia 
(IN4climate.NRW 2019) as a platform for collaboration between representatives from industry, science and politics. IN4climate.NRW 
offers a common space to develop innovative strategies for a carbon-neutral industrial sector, bringing together different perspectives 
and competencies.
North Rhine-Westphalia is Germany’s industrial heartland. Around 19% of North Rhine-Westphalia’s GHGs have their origin in the 
industry sector. Consequently, the sector bears a particular responsibility when it comes to climate protection, but the state is also 
a source of high-quality jobs and export value. The NRW government understands that the state’s current competitive advantage can

only be maintained if the regional industry positions itself as a front runner for becoming GHG-neutral.
In working together across different branches (more than 30 companies representing mainly steel, cement, chemical, aluminium 
industry, refineries and energy utilities) and enabling a direct interaction between industry and government officials, IN4Climate 
provides a benefit to the participating companies. People from the different areas are working together in so-called innovation teams 
and underlying working groups with a self-organised process of setting their milestones and working schedule while reflecting long-
term needs as well as short-term requirements based on political or societal discussions.
The innovation teams aim to identify and set concrete impulses for development and implementation of breakthrough technologies, 
specify necessary infrastructures (e.g., for hydrogen production, storage and transport) and appropriate policy settings (i.e., integrated

state, national and European policy mix). They also include an attempt to create a discourse between the public and the industry 
sectors as a kind of sounding board for the early detection of barriers and obstacles.

1216
1216
Chapter 11  Industry1111.6.3 Technological Research, 
Development, and Innovation
Policies for research, development, and innovation (RDI) for industry 
are present in most countries but it is only recently, and mainly in 
developed countries, that decarbonisation of emissions-intensive 
industries has been prioritised (Åhman et al. 2017; Nilsson et al. 2021). 
Emission-intensive industries are characterised by large dominant 
actors and mature process technologies with high fixed cost, long 
payback times and low profit margins on the primary production 
side of the value chain. Investments in RDI are commonly low and 
aimed at incremental improvements to processes and products 
(Wesseling et al. 2017).
11.6.3.1 Applied Research
Investing in RDI for low-GHG process emissions is risky and 
uncompetitive in the absence of convincing climate policy. Research 
investment should be guided by assessing options, technology

readiness levels, and roadmaps towards technology demonstration 
and commercialisation. The potential GHG and environmental 
implications need to be assessed early on to assess the sustainability 
implications and to direct research needs (Yao and Masanet 2018; 
Zimmerman et al. 2020). Strategic areas for RDI can be focused on 
a set of possible process options for producing basic materials using 
fossil-free energy and feedstock, or CCU and CCS (Sections 11.3.5 
and 11.3.6). Policies to enhance RDI include public funding for 
applied research, technological and business model experimentation, 
pilot and demonstration projects, as well as support for education 
and training – which further have the positive side effect of leading 
to spill-overs and network effects through labour market mobility 
and collaboration (Nemet et al. 2018). Innovative business models 
will not emerge if the transition is not considered along the full

value chain with a  focus on materials efficiency, circularity, and 
new roles for industry in a  transitioning energy system, including 
possibly providing demand response for electricity through designed-
in flexibility, for example, by combining electrolysis hydrogen 
production with substantial storage (Vogl et al. 2018).Fostering collaborative innovation across sectors through the 
support of knowledge sharing and capabilities building is important 
as mitigation options involve new or stronger sectoral couplings 
(Tönjes et al. 2020). One example is linking chemicals to forestry in 
the upscaling of forest bio-refineries, although it has proven to be 
difficult to engage a diverse group of actors in such collaborations 
(Karltorp and Sandén 2012; Bauer et al. 2018). Heterogeneous 
collaboration and knowledge exchange can be encouraged through 
conscious design of RDI programs and by supporting network 
initiatives involving diverse actor groups (Van Rijnsoever et al. 2015;

Söderholm et al. 2019).
11.6.3.2 Policy Support From Demonstration to Market
Applied research is relatively inexpensive compared to piloting, 
demonstrations, and early commercialisation, and arguably a lot of it 
has already been done for the key technologies that need to climb the 
technology readiness ladder (see Table 11.3). This includes electricity 
and hydrogen-based processes, electro-thermal technologies, high-
temperature heat pumps, catalysis, lightweight building construction, 
low embodied carbon construction materials, etc. Demonstration to 
market strategies can be particularly successful when the complete 
supply chain is considered. A  prominent example of such an 
integrated supply chain approach is the UK Offshore Wind Accelerator 
Project. Coordinated by the UK Carbon Trust and working with wind 
turbine manufacturers, the project looked across the potential supply 
chain for floating offshore wind and identified what components

manufacturers could innovate and produce by themselves, and where 
there were gaps beyond the capability of any one firm. This process 
led to several key areas of work where the government and firms 
could work together; once the concepts were piloted and proven, 
the firms went back into a competitive mode. The project illustrates 
the potential importance of third parties, including government, in 
creating platforms and opportunities for cross-industry exchange and 
collaboration (Tönjes et al. 2020).Box 11.3 (continued)
The initiative has been successful so far, for example, having developed a clear vision for a hydrogen strategy and an associated policy 
framework as well as a broader decarbonisation strategy for the whole sector. It is present at the national level as well as at the 
European level. Being successful and unique, IN4Climate is useful as a blueprint for other regions and is often visited by companies 
and administration staff from other German states.

It is particularly the so far missing intensive and dedicated cooperation across industrial subsectors that can be seen as a success 
factor. Facing substantial transformation needs associated with structural changes and infrastructure challenges, very often solutions 
can’t be provided and realised by a single sector but need cooperation and coordination. Even more, chicken-and-egg problems like 
the construction of new infrastructures (e.g., for hydrogen and CO 2 disposal) require cooperation and new modes of collaboration. 
IN4Climate provides the necessary link for this.

1217
1217
Industry Chapter 1111Pilot and demonstration projects funded through public-private 
partnerships contributes to risk mitigation for industries and helps 
inform on the feasibility, performance, costs and environmental 
impacts of decarbonisation technologies. Most countries already 
maintain government research and deployment programs. For 
example, Horizon Europe has a  total budget of 95.5 billion EUR 
(USD117 billion) for 2021–2027, of which 30% will be directed 
to green technology research. The EU has conducted several 
demonstration projects for emission-intensive industries, such as 
the Ultra-Low Carbon Steel (ULCOS) project (Abdul Quader et al. 
2016), which led to several small-scale pilots that are now going 
to larger-scale firm pilots (e.g.,  HISARNA, HYBRIT and SIDERWIN). 
Supported by the EU, several cement firms are working together on 
the cement LEILAC project, where a new form of limestone calciner

is being developed to concentrate the process CO 2 emerging from 
quicklime production (about 60% of cement emissions) for eventual 
utilisation or geological storage (as one of many options for cement, 
see for example, Plaza et al. 2020). If LEILAC works, it is conceivable 
that existing cement plants globally that are located near CCS 
opportunities could have their emissions reduced by 60% with one 
major retrofit of the kiln.
Once a technology has been demonstrated with scale-up potential, 
the next stage is commercialisation. This is a very expensive stage, 
where costs are not yet compensated by revenue (see, e.g., Åhman 
et al. 2018 and Nemet et al. 2018). The H-DRI, SIDERWIN and 
LEILAC examples are all at the stage of scaling up. Given the 
resource requirement, a  diversified portfolio of investors and 
support is required to share the risk. LEILAC includes several firms, 
as did the UK Offshore Wind Accelerator. Government funds are

also required and could be refunded in the future through an 
equity position, royalty or tax. Fast-growing economies, which are 
adding new industrial capacity, can provide opportunities to pilot, 
demonstrate and scale up new technologies, as shown by the rapid 
expansion of electric vehicle and solar panel production in China, 
which contributed to driving down costs (Nemet 2019; Hsieh et al. 
2020; Jackson et al. 2021).
Finally, large capital flows towards deployment of low-GHG solutions 
will not materialise without a  growing demand for low-carbon 
materials and products that allows business opportunities. Policy will 
thus be needed to support the first niche markets which are essential 
for refining new decarbonised technologies, troubleshooting, and for 
building manufacturing economies of scale. Market creation does 
however go beyond the nurturing, shielding, and empowerment of 
early niches (Smith and Raven 2012; Raven et al. 2016) and must

also consider how to significantly reshape existing markets to create 
space for decarbonised solutions and crowd out fossil-based ones 
(Mazzucato 2016).11.6.4 Market Pull
The perception of an increasing durable demand for low-GHG 
products induces manufacturers to invest in decarbonisation 
strategies (Olatunji et al. 2019). Policies can support and accelerate 
this process by creating niche markets,  stimulating demand for 
low-carbon products through procurement and financing and by 
addressing informational and other market barriers.
11.6.4.1 Public Procurement
Governments spend a large portion of their budget on the provision 
of products and material through infrastructure development, general 
equipment, and miscellaneous goods. The OECD estimates that an 
average of 30% of general government expenditure goes to public 
procurements in OECD countries, representing 12.6% of GDP , which 
makes government a  powerful market actor (OECD 2021). Public

procurement can therefore create a significant market pull and be 
used to pursue strategic environmental goals (Ghisetti 2017). Local, 
regional and national authorities can use their purchasing power 
to create niche markets and to guarantee demand for low-GHG 
products and material (Wesseling and Edquist 2018; Muslemani et al. 
2021). In some cases, governments will have to adapt government 
procurement policies that are not well suited for the procurement 
of products and services that focus on the decarbonisation benefits 
and longer-term procurement commitments of emissions-reducing 
technologies and projects (Ghisetti 2017). Implementation can 
be challenged by the complexity of criteria, the lack of credible 
information to check GHG intensities and the added time needed 
for selection (Geng and Doberstein 2008; Testa et al. 2012; Bratt 
et al. 2013; Zhu et al. 2013; Cheng et al. 2018; Liu et al. 2019b). To 
ease these hurdles, the EU commission has developed environmental

criteria that can be directly inserted in tender documents (Igarashi 
et al. 2015; European Commission 2016). These criteria are voluntary, 
and the extent of their application varies across public authorities 
(Michelsen and de Boer 2009; Bratt et al. 2013; Testa et al. 2016). In 
the Netherlands, companies achieving a desirable certification level 
under the national CO 2 Performance Ladder obtain a  competitive 
advantage in public procurement (Rietbergen and Blok 2013; 
Rietbergen et al. 2015). Globally, many countries have implemented 
green product procurement or sustainable procurement following 
Sustainable Development Goal (SDG) 12 – ‘Responsible consumption 
and production’ (UNEP 2017). Public procurement is also developing 
at sub-national levels. For example, the state of California in the 
United States of America passed the Buy Clean California Act (AB 262) 
that establishes maximum acceptable global warming potentials for

eligible steel and glass construction materials for public procurement 
(USGBC-LA 2018) (Box 11.4).

1218
1218
Chapter 11  Industry1111.6.4.2 Private Procurement
The number of companies producing sustainability reports has 
increased rapidly over the last decade (Jackson and Belkhir 2018) and 
so has the number of pledges to carbon neutrality announced. This 
trend has mainly been driven by consumer concerns, investor requests, 
and as a business strategy to gain a competitive advantage (Higgins 
and Coffey 2016; Ibáñez-Forés et al. 2016; Koberg and Longoni 2019). 
For example, Apple and the governments of Québec and Canada are 
the financier and lead market maker in the Elysis consortium to bring 
inert electrodes to market for bauxite smelting to make zero-GHG 
aluminium. Aluminium is a very small fraction of the cost of a laptop 
or smartphone, so even expensive low-emissions aluminium adds to 
Apple’s brand at very little cost per unit sold. Some countries are also 
requiring corporate to report their emissions. For example, the French

government requires companies with 500 or more employees and 
financial institutions to report Corporate Social Responsibility (CSR) 
and disclose publicly Scope 1  (direct emissions), Scope 2  (indirect 
emissions from purchased electricity) and Scope 3 (emissions from 
supply chain impacts and consumer usage and end-of-life recycling 
practices) emissions (Mason et al. 2016).
The most common climate mitigation strategies used by corporates 
are to set emissions reduction targets in line with the Paris Agreement 
goals through science-based targets (SBTs) and to develop internal 
carbon pricing (Kuo and Chang 2021). The SBT initiative records 
that 338 SBT companies reduced their emissions by 302 MtCO 2-eq between 2015 and 2019 (SBTi 2021). As of August  2021, 
858  companies had set SBT and over 2000 companies across the 
world currently use internal carbon pricing with a median internal 
carbon price of USD25 per metric tonne of CO 2-eq (Bartlett et al.

2021). The most determined companies have developed internal GHG 
abatement strategies that incorporate their supply chains’ emissions 
(Martí et al. 2015; Gillingham et al. 2017; Tost et al. 2020) and design 
procurement contracts that encourage or require their suppliers 
to also improve their product GHG footprint (Liu et al. 2019a). For 
many corporations, the emissions impact within their supply chain 
far exceeds their operations direct emissions (CDP 2019). Therefore, 
the opportunities to reduce emissions through purchasing goods 
and services from the supply chain (Scope 3) have much greater 
potentials than from direct emissions.
However, these trends have to be approached with caution as some 
of the emissions reductions are not direct emissions reductions 
from companies’ operations, instead often from offset projects of 
varying quality (Chrobak 2021). There is a lack of consistency and 
comparability in the way firms are reporting emissions, which limits

the possibilities to assess companies’ actual ambition and progress 
(Sullivan and Gouldson 2012; Burritt and Schaltegger 2014; Liu et al. 
2015; Rietbergen et al. 2015; Blanco et al. 2016). More research 
is needed to assess the current impacts of corporate voluntary 
climate actions and if these efforts meet the Paris Agreement’s goals 
(Rietbergen et al. 2015; Wang and Sueyoshi 2018). It will be critically 
important that the international corporate accounting frameworks, Box 11.4 | Buy Clean California Act
In October 2017, California passed Assembly Bill (AB) 262, the Buy Clean California Act, a new law requiring state-funded building 
projects to consider the global warming potential (GWP) of certain construction materials during procurement. The goal of AB 262 is 
to use California’s substantial purchasing power to buy low-carbon products. Such low-carbon public procurement will directly reduce

emissions by using lower-carbon products, and indirectly by sending a market signal to manufacturers to reduce their emissions in 
order to stay competitive in California.
The bill requirements are two-pronged: as of January 2020, manufacturers of eligible materials must submit a  facility-specific 
environmental product declaration (EPD), and the eligible materials must demonstrate (through submitted EPDs) GWP below 
the product-specific compliance limits defined by the state Department of General Services (DGS), which will regulate policy 
implementation. The eligible materials include structural steel, carbon steel rebar, flat glass, and mineral wool insulation. In January 
2021, the DGS published maximum acceptable GWP limits for each product category set at the industry average of facility-specific 
GWP for each material. Beginning 1 July 2021, awarding authorities were required to verify GWP compliance for all eligible materials 
(USGBC-LA 2018; DGS 2020).

Prior to adoption of the Buy Clean California Act, the California Department of Transportation (Caltrans) had been evaluating the use of 
lifecycle assessment and EPDs in evaluating materials. In addition to the materials specified in Buy Clean California Act (noted above), 
the Caltrans project includes materials used extensively in transportation (concrete, asphalt, and aggregate). Also, the California High-
Speed Rail project had begun using EPDs as part of its procurement process. The High-Speed Rail Sustainability Report states that the 
construction projects will: (i) require EPDs for construction materials including steel products and concrete mix designs, and (ii) require 
‘optimized lifecycle scores for major materials’ and include additional strategies to reduce impacts across the life cycle of the project 
(Simonen et al. 2019).
Several other states such as Washington, Minnesota, Oregon, Colorado, New York and New Jersey are developing similar types of Buy

1219
1219
Industry Chapter 1111standards, and related guidance (e.g., GHG Protocol) be maintained 
and improved to reflect evolving needs in the global market and to 
allow for comparison of objectives and progress.
11.6.4.3 GHG Content Certifications
The development of GHG labels corresponds to a growing demand 
from consumers desiring information about the climate impacts of 
their consumption (Darnall et al. 2012; Tan et al. 2014; Feucht and 
Zander 2018). GHG labels fill this information gap by empowering 
consumers’ purchasing decisions and creating higher value for 
low-GHG products and materials (Vanclay et al. 2011; Cohen and 
Vandenbergh 2012). The willingness to pay for lower-GHG products 
has been found to be positive but to depend on socio-economic 
consumer characteristics, cultural preferences and the product 
considered (Shuai et al. 2014; de-Magistris and Gracia 2016; Tait 
et al. 2016; Li et al. 2017; Feucht and Zander 2018). Companies and

governments that favour low-GHG products and who are seeking to 
achieve environmental, social, and governance (ESG) goals also need 
readily available and reliable information about the GHG content of 
products and materials they purchase and produce (Long and Young 
2016; Munasinghe et al. 2016).
Numerous methodologies have been developed by public and 
private organisations to meet the needs for credible and comparable 
environmental metrics at the product and organisation levels. Most 
follow lifecycle assessment standards as described in ISO 14040 and 
ISO 14044, ISO 14067 for climate change footprint only and ISO 14025 
(2006) for environmental product declarations (EPD), but the way 
system boundaries are applied in practice varies (Wu et al. 2014; Liu 
et al. 2016). Adoption has been challenged by the complexity and the 
profusion of applications which contribute to confuse stakeholders 
(Gadema and Oglethorpe 2011; Guenther et al. 2012; Brécard 2014).

The options of applying different system boundaries and allocation 
principles involve value judgements that in turn influence the results 
(Tanaka 2008; Finnveden et al. 2009; McManus et al. 2015; Overland 
2019). A  more systematic and coordinated international approach 
based on transparent and reliable data and methodologies is needed 
to induce global low-GHG market development (Pandey et al. 2011; 
Darnall et al. 2012; Tan et al. 2014).
Within the context of GHG content certifications and EPD 
development, more transparency is needed to increase international 
comparability and to validate claims to meet consumers demand 
for low-GHG material and products (Rangelov et al. 2021). Greater 
automation, publicly available reference databases, benchmarking 
systems and increased stakeholder collaboration can also support the 
important role of conveying credible emissions information between 
producers, traders and consumers.
11.6.4.4 Performance Standards and Codes

Policymakers can set minimum performance standards or maximum 
emission content specifications through legislation to increase the 
use of low-GHG materials and products by mandating the adoption 
of low-GHG production and construction processes while requiring 
material and resource efficiency aspects.Construction of buildings represented 11% of energy and process-
related CO 2 emissions globally in 2018 (IEA and UNEP 2019). The 
share of embodied emissions in construction is increasing as building 
energy efficiency is improving and energy supply is decarbonised 
(Chastas et al. 2016). As a  result, jurisdictions are increasingly 
considering new requirements in building codes to reduce embodied 
emissions. This is the case of France’s new building code which is 
shifting from a  thermal regulation (RT 2012) to an environmental 
regulation (RE 2020) to include embodied GHG LCA metrics for 
encouraging use of low-GHG building materials (Ministère de

la Transition écologique et solidaire 2018; Schwarz et al. 2020). 
The 2018 International Green Construction Code (IGCC) provides 
technical requirements that can be adopted by jurisdictions for 
encouraging low-GHG building construction, which also covers 
minimum longevity and durability of structural, building envelope, 
and hardscape materials (Art. 1001.3.2.3) (Celadyn 2014). Low-GHG 
building rating systems, such as LEEDs, are voluntary standards which 
include specific requirements on material resources in their rating 
scale. Trade-offs between energy performance achievement and 
material used in building construction needs to be further assessed 
and considered as low-GHG building code requirements develop. 
Local governments can also lead the way by adopting standards 
for construction. This is the case of the county of Marin in California 
which specifies maximum embodied carbon in kgCO 2-eq m–3 and 
maximum ordinary Portland cement content in lbs/yd3 for different

levels of concrete compressive strength (Marin County 2021).
Governments are also turning their attention to developing standards 
to increase the durability of products and materials by requiring 
options for maintenance, reparability, reusability, upgradability, 
recyclability and waste handling. For example, the EU Ecodesign 
Directive includes new requirements for manufacturers to make 
available for a minimum of seven to 10 years spare parts to repair 
household equipment (Talens Peiró et al. 2020; Calisto Friant et al. 
2021; Nikolaou and Tsagarakis 2021). The European Commission 
plans to widen the resource efficiency requirements beyond energy-
related products to cover products such as textiles and furniture as 
well as high-impact intermediary products such as steel, cement and 
chemicals in a new sustainable product policy legislative initiative. 
(Domenech and Bahn-Walkowiak 2019; Llorente-González and 
Vence 2019; European Commission 2020; Polverini 2021).

Further research is needed to understand how different international 
and national frameworks, codes, and standards that focus on 
emissions can work in unison to amplify their mutually desired 
outcomes. Building performance and market instrument trading 
frameworks recognised globally do not always incentivise the same 
outcomes due to the differences in market approach. LCA metrics are 
a  useful tool to help assess optimal options for ultimate emission 
reduction objectives (Röck et al. 2020; Shadram et al. 2020).
11.6.4.5 Financial Incentives
Fossil-free basic materials production will often lead to higher 
costs of production, for example, 20–40% more for steel, 70–115% 
more for cement, and potentially 15–60% for chemicals (Material 
Economics 2019). There is a nascent literature on what are effectively

1220
1220
Chapter 11  Industry11material ‘feed-in-tariffs’ to bridge the commercialisation ‘valley of 
death’ (Wilson and Grubler 2011) of early development of low-GHG 
materials (Bataille et al. 2018a; Neuhoff et al. 2018; Sartor and Bataille 
2019; Wyns et al. 2019). Renewable electricity support schemes have 
typically been price-based (e.g.,  production subsidies and feed-
in-tariffs) or volume-based (e.g.,  quota obligations and certificate 
schemes) and both principles can be applied when thinking about 
low-GHG materials. Auction schemes are typically used for larger-
scale projects, for example, offshore wind parks.
Based on how feed-in-tariffs worked, a  contract for difference 
(CfD) could guarantee a  minimum and higher-than-market price 
for a given volume of early low-GHG materials. CfDs could be based 
on a minimum effective GHG price reflecting parity with the costs of 
current higher-emitting technologies, or directly on the higher base

capital and operating costs for a  lower-GHG material (Richstein 
2017; Chiappinelli et al. 2019; Sartor and Bataille 2019; Vogl 
et al. 2021a). CfDs can also be offered through low-GHG material 
procurement where an agreed price offsets the incremental cost 
of buying low-GHG content product or material. Private firms, by 
themselves or collectively, can also guarantee a higher than market 
price for low-GHG materials from their supplier for marketing 
purposes (Bataille et al. 2018a; Bataille 2020a). Reverse auctions 
(by which the lowest bidder gets the production subsidy) for low-
GHG materials is also an option but it remains to be analysed 
and explored. While these financial incentive schemes have been 
implemented for renewable energy, their application to incentivise 
and support low-GHG material production have yet to be developed 
and implemented. The German government is currently developing 
a draft law which will allow companies that commit to cut GHG

emissions by more than half using innovative technologies to bid for 
10-year CfDs with a guaranteed price for low-carbon steel, chemical 
and cement products (Agora Energiewende and Wuppertal Institut 
2019; BMU 2021).
New and innovative financial market contracts for basic materials 
that represent low-carbon varieties of conventional materials are 
emerging. This is the case of aluminium for which quantity of low-GHG production already exist in countries where hydroelectric power 
is a common power source. Market developments will allow for low-
GHG aluminium to trade at a premium rate as demand develops. For 
example, Harbor Aluminium has launched a green aluminium spot 
premium at the end of October 2019 and the London Metal Exchange 
has introduced a ‘green aluminium’ spot exchange contract. (LME 
2020; Das 2021).
11.6.4.6 Extended Producer Responsibility
Extended producer responsibility (EPR) systems are increasingly

used by policymakers to require producers to take responsibility for 
the end life of their outputs and to cover the cost of recycling of 
materials or otherwise responsibly managing problematic wastes 
(Kaza et al. 2018). According to the OECD, there are about 400 EPR 
systems in operation worldwide, three quarters of which have been 
established over the last two decades. One third of EPR systems cover 
small consumer electronic equipment, followed by packaging and 
tyres (each 17%), vehicles, lead-acid batteries and a range of other 
products (OECD 2016).
While the economic value of some discarded materials such as steel, 
paper and aluminium is generally high enough to justify the cost 
and efforts of recycling, at current rates of 85%, above 60%, and 
43%, respectively (Graedel et al. 2011; Cullen and Allwood 2013), 
others like plastic or concrete have a much lower re-circularity value 
(Graedel et al. 2011). Most plastic waste ends up in landfills or

dumped in the environment, with 9% recycled and 12% incinerated 
globally (Geyer et al. 2017; UNEP 2018). Collected waste plastics 
from OECD countries were largely exported to China until a ban in 
2018 required OECD countries to review their practices (Qu et al. 
2019). EPR schemes may thus need to be strengthened to actually 
achieve a  reduced use of virgin GHG-intensive materials. The 
potential for re-circularity of unreacted cement and aggregates in 
concrete is increasing as new standards and requirement develops. 
For example, concrete fines are now standardised as a new cement 
constituent in the European standardisation CEN/TC 51 – ‘cements 
and construction limes’.
Box 11.5 | Circular Economy Policy
The implementation of a circular economy relies on the operationalisation of the R-imperatives or strategies which extend from 
the original 3Rs: Reduce, Reuse and Recycle, with the addition of Refuse, Reduce, Resell/Reuse, Repair, Refurbish, Remanufacture,

Repurpose, Recycle, Recover (energy), Re-mine and more (Reike et al. 2018). The R  implementation strategies are diverse across 
countries (Ghisellini et al. 2016; Kalmykova et al. 2018) but, in practice, the lower forms of retention of materials, such as recycling 
and recover (energy), often dominate. The lack of policies for higher retention of material use such as Reduce, Reuse, Repair and 
Remanufacture is due to institutional failures, lack of coordination and lack of strong advocates (Gonzalez Hernandez et al. 2018a).
Policies addressing market barriers to circular business development need to demonstrate that circular products meet quality 
performance standards, ensure that the full environmental costs are reflected in market prices and foster market opportunities for 
circular products exchange, notably through industrial symbiosis clusters and trading platforms (Kirchherr et al. 2018; OECD 2019a;

Hartley et al. 2020; Hertwich 2020). Policy levels span from micro (such as consumer or company) to meso (eco-industrial parks) and 
macro (provinces, regions and cities) (Geng et al. 2019). The creation of eco-industry parks (‘industrial clusters’) has been encouraged 
by governments to facilitate waste exchanges between facilities, where by-products from one industry are used as a feedstock to

1221
1221
Industry Chapter 111111.6.5 Knowledge and Capacity
It is important that government bodies, academia and other 
actors strengthen their knowledge and capacities for the broad 
transformational changes envisioned for industry. In Japan, industry 
has been voluntarily working on GHG reduction, under the Framework 
of Keidanren’s Commitment to a  Low-carbon Society since 2009. 
Government and scientific experts regularly review their commitments 
and discuss results, monitoring methods, and reconsidering goals. 
Industry federations/associations can obtain advice in the follow-
up meetings from other industries and academics. The energy and 
transport sectors have decades of building institutions and expertise, 
whereas industrial decarbonisation is largely a new policy domain. 
Most countries have experience in energy efficiency policies, some 
areas of research and innovation, waste management, regulations 
for operational permits and pollution control, worker safety and

perhaps fuel switching. There is less experience with market demand 
pull policies although low-GHG public procurement is increasingly 
being tested. Circular economy policies are evolving but potential 
policies for managing material demand growth are less understood. 
Material efficiency policies through, for example, product standards 
or regulation against planned obsolescence are nascent but relatively 
unexplored (Gonzalez Hernandez et al. 2018a).
All this argues for active co-oversight, management and assessment 
by government, firms, sector associations and other actors, in 
effect the formation of an active industrial policy that includes 
decarbonisation in its broader mandate of economic and social 
development (OECD 2019b; Bataille 2020a). This could draw from 
the quadruple helix innovation model, which considers the role of 
government, universities, the private sector, the natural environment 
and social systems to foster collaboration in innovation (Carayannis

and Campbell 2019; Durán-Romero et al. 2020). Important aspects of 
governance include mechanisms for monitoring, transparency, and 
accountability. It may involve the development of new evaluation 
approaches, including a  greater focus on ex ante evaluations and 
assessment of, for example, readiness and capacities, rather than ex post evaluations of outcomes. Such organisational routines for 
learning have been identified as a key aspect of policy capacity to 
govern evolutionary processes (Karo and Kattel 2018; Kattel and 
Mazzucato 2018). Although many governments have adopted ideas 
of focusing resources on the mission or challenge of climate change 
mitigation, comparisons between Western and East Asian contexts 
show significant differences in the implementation of governance 
structures (Karo 2018; Mazzucato et al. 2020; Wanzenböck 
et al. 2020). Overall, improved knowledge and stronger expertise is 
important also to handle information asymmetries and the risk of

regulatory capture.
11.6.6 Policy Coherence and integration
Industrial net zero transitions, while technically feasible, involve not 
just a  shift in production technology but major shifts in demand, 
material efficiency, circularity, supply chain structure and geographic 
location, labour training and adaptation, finance, and industrial policy. 
This transition must also link decarbonisation to larger environmental 
and social goals (e.g,. air and water quality, low-GHG growth, poverty 
alleviation, sustainable development goals) (OECD 2019b).
Although there is little evidence of carbon leakage so far it will 
be ever more important to strive for coherence in climate and 
trade policies as some countries take the lead in decarbonising 
internationally traded basic materials (Jakob 2021b). At the time 
of writing the previously academic debate on this issue is shifting 
to real policymaking through debates and negotiations around

carbon border adjustment (Section 11.6.1) and sectoral agreements 
or climate clubs (Nordhaus 2015; Åhman et al. 2017; Jakob 2021a; 
Nilsson et al. 2021). The climate and trade policy integration should 
also consider what is sometimes called positive leakage, that is that 
heavy industry production moves to where it is easier to reach zero 
emissions. As a  result, policy should go beyond border measures 
to include, for example, international technology cooperation and 
transfer and development of shared lead markets.Box 11.5 (continued)
another (Ding and Hua 2012; Jiao and Boons 2014; Shi and Yu 2014; Tian et al. 2014; Winans et al. 2017). Systematic assessment 
of wastes and resources is carried out to assess possible exchange between different supply chains and identify synergies of waste 
streams that include metal scraps, waste plastics, water heat, bagasse, paper, wood scraps, ash, sludge and others (Ding and Hua 
2012; Shi and Yu 2014).

The development of data collection and indicators is nascent and need to ramp up to quantify the impacts and provide evidence to 
improve circular economy and materials efficiency policies. Policymakers need to leverage the potential socio-economic opportunities 
of transitioning to circular economies (Llorente-González and Vence 2020), which shows positive GDP growth and job creation by 
shifting to more labour-intensive recycling plants and repair services than resource-extraction activities (WRAP and Alliance Green 
2015; Cambridge Econometrics et al. 2018). The International Labour Organization estimates that worldwide employment would grow 
by 0.1% by 2030 under a circular economy scenario (ILO 2018). However questions remain if the type of jobs created are concentrated 
in low-wage labour-intensive circular activities which may need targeted policy instruments to improve working conditions (Llorente-
González and Vence 2020).

1222
1222
Chapter 11  Industry11Energy-intensive production steps may move where clean resources 
are most abundant and relatively inexpensive (Gielen et al. 2020; 
Bataille et al. 2021a). For example, steel-making has historically 
located itself near iron ore and coal resources whereas in the future 
it may be located near iron ore and zero-GHG electricity or close 
to carbon storage sites (Fischedick et al. 2014b; Vogl et al. 2018; 
Bataille 2020a). This indicates large changes in industrial and supply 
chain structure, with directly associated needs for employment and 
skills. Some sectors will grow, and some will shrink, with differing 
skill needs. Each new workforce cohort needs the general specific 
skill to provide the employment that is needed at each stage in 
the transition, implicating a need for coordination with policies for 
education and retraining.
Depending on what mixes of deep decarbonisation strategies are

followed in a given region (e.g., material efficiency, electrification, 
hydrogen, biomass, CCU and CCS), infrastructure will need to be 
planned, financed and constructed. The UKCCC Net Zero Technical 
Report describes the infrastructure needs for achieving net zero 
GHG in the UK by 2050 for every sector of the economy (UKCCC 
2019b). Transportation would be facilitated with pipelines or ships 
to allow transfer of captured CO 2 for utilisation and disposal, and 
associated institutional frameworks (IEAGHG 2021). Electrification 
will require market design and transmission to support increased 
generation, transmission, and flexible demand. Hydrogen, CCU, and 
CCS will require significant new or adapted infrastructure. Hydrogen 
and CO 2 pipelines, and expanded electricity transmission, have 
natural monopoly characteristics which are normally governed and 
planned by national and regional grid operators and their regulators.

Industrial clustering (also known as eco-parks), such as those 
planned in Rotterdam (Netherlands) and Teeside (UK), would allow 
more physical and cost-effective sharing of electricity, CCU, CCS, 
and hydrogen infrastructure but is dependent on physical planning, 
permitting, and infrastructure policies.
Costing analysis (Chapter  15) indicates an increased upfront 
need for financial capital which requires policies to encourage 
long-term, patient capital that reflects society’s preferences for investment in industrial decarbonisation and the minimum 10 or 
more years horizon before there are significant new commercially 
available processes.
All the above indicate the need for general industrial policy as part of 
a coherent general economic, taxation, investment, employment and 
social policy for climate change mitigation (Wesseling et al. 2017; 
Bataille et al. 2018a; Wyns et al. 2019; Nilsson et al. 2021).
11.6.7 Roles and Responsibilities

While all climate policy requires topic-specific adaptive governance 
for long-term effectiveness (Mathy et al. 2016), deep decarbonisation 
of heavy industry has special governance challenges, different from 
those for the electricity, transport or buildings sectors (Åhman et al. 
2017; Wesseling et al. 2017; Bataille et al. 2018a). Competition is 
strong, investments are rare, capital intensive and very ‘lumpy’. In an 
atmosphere where transformative innovation is required the process 
is very capital-focused with non-diversifiable risks unless several 
companies are involved. There are significant infrastructure needs for 
electricity, hydrogen, and CCS and CCU. Given there is no ‘natural’ 
market for low-emissions materials, there is a  need to manage 
both the supply and demand sides of the market, especially in early 
phase through lead supplier and markets. Finally, there is a very high 
probability of surprises and substantial learning, which could affect

policy choice, direction, and stringency.
Different types of actors thus have to play different but coordinated 
roles and responsibilities in developing, supporting, and implementing 
policies for an industrial transition. Table  11.6 below shows how 
the different core parts of integrated policymaking for an industrial 
transition may depend on efforts from different actors groups and 
highlights the responsibility of these actor groups in developing 
a  progressive and enabling policy context for the transition. 
This  includes policymakers at local, national, and international 
arenas as well as civil society organisations, industry firms, and 
interest organisations.
Table 11.6 | Examples of the potential roles of different actors in key policy and governance areas for a low-GHG transition to indicate the importance 
of agency and wide stakeholder engagement in the governance of industrial decarbonisation.
ActorsDirection:
strategising pathways

to net zeroInnovation:
RD&D for new 
other solutionsMarket creation:
create and shape 
demand-pull for 
various solutionsKnowledge and 
capacity:
build institutional 
capacity across 
various actorsCoherence:
establish 
and national 
policy coherence
International bodies and 
multilateral collaborationMore attention to industry 
in NDCs. Monitor progress 
and identify gaps. Develop 
international roadmaps.Include heavy industry 
decarbonisation in 
technology cooperation 
(e.g., Mission Innovation).International standards, 
benchmarking systems, 
and GHG labels. Allow for 
creation and protection 
of lead markets.Support knowledge 
building and sharing on 
industrial decarbonisation.Align other conventions 
and arenas (e.g., WTO) with 
climate targets and include 
heavy industry transitions 
in negotiations.
Regional and national 
government and citiesRequire net zero strategies 
in permitting. Set targets 
and facilitate roadmaps

at various levels. Sunset 
clauses and phase-
out agreements for 
polluting plants.Experimentation for 
recycling, materials 
efficiency, and demand 
management. Hydrogen, 
electrification, and other 
infrastructure.Public procurement 
for innovation and 
lead markets. Green 
infrastructure investments.Develop policy expertise for 
industrial transformation. 
Support and facilitate 
material efficiency 
and circular solutions 
through design standards, 
building codes, recycling, 
and waste policy.Support vertical 
policy coherence 
(i.e., international, 
national, city level).

1223
1223
Industry Chapter 111111.7 Knowledge Gaps
An increasing body of research proposes deep decarbonisation 
pathways for energy-intensive industries including mitigation 
options such as materials efficiency, circular economy and new 
primary processes. These options are under-represented in climate 
change scenario modelling and integrated assessment models, some 
of which do not even reflect evolution of demand for basic materials, 
which is a key driver behind energy consumption and GHG emissions 
in the industrial sector. As a result, no agreement is reached so far 
between bottom-up and top-down studies on the effectiveness and 
costs for many promising mitigation options, their respective roles, 
sequencing and packaging within various mitigation pathways.
A significant shift is needed from the transition process of the past 
mainly based on marginal and incremental changes, with a strong 
focus on energy efficiency efforts, to one grounded in transformational

change where there is limited knowledge of how to implement such 
change effectively.
There is a  knowledge gap on comparable, comprehensive, and 
detailed quantitative information on costs and potentials associated 
with the mitigation options for deep decarbonisation in industry, 
as cost estimates are not often comparable due to the regional or 
country focus, differences in costs metrics, currencies, discount rates, 
and energy prices across studies and regions.
A very large and important uncertainty is the availability of biomass 
for deep decarbonisation pathways due to competition for biomass 
feedstock with other priorities and the extent to which electrification 
can reduce the demand for bioenergy in the industry, transport and 
energy sectors.
CCS and CCU are important mitigation options in industry, for which 
the potentials and costs vary considerably depending on the diversity of industrial processes, the volume and purity of carbon dioxide

flows, the energy requirements, the lifetime of utilisation products 
and the production route.
The effectiveness of mitigation policies in industry is poorly known, 
as so far the sector has largely been sheltered from the impacts of 
climate policy due to the concerns of competitiveness and carbon 
leakage. There is a  lack of integration of material efficiency and 
circularity with energy and climate policies which partly results from 
the inadequacy of monitored indicators to inform policy debates 
and set targets, a  lack of high-level political focus, a  history of 
strong industrial lobbying, uncoordinated policy across subsectors 
and institutions, and the sequential nature of decision-making 
along supply chains.
Industry as a whole is a very complex web of sectors, subsectors and 
inter-sectoral interactions and dependence, with diverse associated 
mitigation opportunities and co-benefits and costs. Additional 
knowledge is needed to understand sectoral interactions in the

transformation processes.
Industrial climate mitigation policy is supplemental to many other 
policy instruments developed to reach multiple industrial goals, for 
the range of stakeholders with their interest and priorities reflecting 
the assessment of co-benefits and risk and affecting decision-making 
processes and behaviour of stakeholders. Better knowledge is 
needed to identify the co-benefits for the adoption of climate change 
mitigation strategies.ActorsDirection:
strategising pathways 
capacity:
establish 
Civil societyMonitor and evaluate 
leaders and laggards. 
Support transparency.Engage in responsible 
innovation programs, 
experimentation, and 
social innovation.Progressive labelling,

standards and criteria for 
low emissions materials 
and products (e.g., LCA-
based), including updating.Engage in policy processes 
and build capacity on 
industrial decarbonisation. 
Support consumer 
information and knowledge.Monitor and support policy 
coherence and coordination 
across policy domains 
(trade, climate, waste, etc.).
Industrial sectors 
and associationsAdopt net zero emissions 
targets, roadmaps, and 
policy strategies for 
reaching them. Assess 
whole value chains, 
scope 3 emissions and 
new business models.Share best practice. 
collaboration. Efficient 
markets for new technology 
(e.g., licensing).Work across (new) value 
chains to establish lead 
markets for low emissions 
materials as well as 
for materials efficiency 
and circularity.Education and 
retraining for designers, 
engineers, architects, etc. 
Information sharing and 
transparency to reduce 
information asymmetry.Coordination across policy 
domains (trade, climate,

waste, etc.). Explore 
sectoral couplings, new 
value chains and location 
of heavy industry.
Corporations 
and companiesSet zero emissions targets 
and develop corporate- and 
plant-level roadmaps for 
reaching targets.Lead and participate 
in R&D, pilots, and 
demonstrations. Increase 
and direct R&D efforts at 
reaching net zero.Marketing and procurement 
of low-emissions materials 
and products. Include 
Scope 3 emissions 
to assess impact and 
mitigation strategies.Engage in value chains 
for increased recycling 
and materials efficiency. 
Build knowledge and 
capacity for reorientation 
and transformation.MNCs avoid race 
to the bottom, and 
strategically account for 
high carbon price as part 
of transition strategy.

1224
1224
Chapter 11  Industry11Frequently Asked Questions (FAQs)
FAQ 11.1 |  W hat are the key options to reduce industrial emissions?
Industry has a  diverse set of greenhouse gas (GHG) emission sources across subsectors. To decarbonise industry requires that

we pursue several options simultaneously. These include energy efficiency, materials demand management, improving materials efficiency, more circular material flows, electrification, as well as carbon capture and utilisation (CCU) and carbon capture and storage (CCS). Improved materials efficiency and recycling reduces the need for primary resource extraction and the energy-intensive primary processing steps. Future recycling may include chemical recycling of plastics if quality requirements make mechanical recycling difficult. One approach, albeit energy intensive, is to break down waste plastics to produce new monomer building blocks, potentially based on biogenic carbon and hydrogen instead of fossil feedstock. Hydrogen can also be used as a reduction agent instead of coke and coal in ironmaking. Process emissions from cement production can be captured and stored or used as feedstock for chemicals and materials. Electricity and hydrogen needs can be very large but the potential for

renewable electricity, possibly in combination with other low carbon options, is not a limiting factor.

FAQ 11.2 |  How costly is industrial decarbonisation and will ther e be synergies 
or conflicts with sustainable development?

In most cases and in early stages of deployment, decarbonisation through electrification or CCS will make the primary production of basic materials such as cement, steel, or polyethylene more expensive. However, demand management, energy and materials efficiency, and more circular material flows can dampen the effect of such cost increases. In addition, the cost of energy-intensive materials is typically a very small part of the total price of products, such as an appliance, a bottle of soda or a building, so the effect on consumers is very small. Getting actors to pay more for zero-emission materials is a challenge in supply chains with a strong focus on competitiveness and cutting costs, but it is not a significant problem for the broader economy. Reduced demand for services such as square metres of living space or kilometres of car travel is an option where material living standards are already high. If material living standards are very low, increased material use is often needed

for more sustainable development. The options of materials and energy efficiency, and more circular material flows, generally have synergies with sustainable development. Increased use of electricity, hydrogen, CCU and CCS may have both positive and negative implications for sustainable development and thus require careful assessment and implementation for different contexts.

FAQ 11.3 |  W hat needs to happen for a low-carbon industry transition?

Broad and sequential policy strategies for industrial development and decarbonisation that pursue several mitigation options at the same time are more likely to result in resource-efficient and cost-effective emission reductions. Industrial decarbonisation is a relatively new field and thus building capacity for industrial transition governance is motivated. For example, policy to support materials efficiency or fundamental technology shifts in primary processes is less developed than energy efficiency policy and carbon pricing. Based on shared visions or pathways for a zero-emission industry, industrial policy needs to support development of new technologies and solutions as well as market creation for low- and zero-emission materials and products. This implies coordination across several policy domains including research and innovation, waste and recycling, product standards, digitalisation, taxes, regional development, infrastructure, public procurement, permit procedures and more

to make the transition to a carbon neutral industry. International competition means that trade rules must be evolved to not conflict with industrial decarbonisation. Some local and regional economies may be disadvantaged from the transition which can motivate re-education and other support.

1247
1247
Cross-sectoral Perspectives Chapter 1212
The total emission mitigation potential achievable by the year 
2030, calculated based on sectoral assessments, is sufficient 
to reduce global greenhouse gas emissions to half of the 
current (2019) level or less (robust evidence, high agreement). 
This potential (32–44 GtCO 2-eq) requires implementation of a wide 
range of mitigation options. Options with mitigation costs lower 
than USD20 tCO 2–1 make up more than half of this potential and are 
available for all sectors. {12.2, Table 12.3}
Carbon dioxide removal (CDR) is a  necessary element to 
achieve net zero CO 2 and greenhouse gas (GHG) emissions both 
globally and nationally, counterbalancing residual emissions 
from hard-to-transition sectors. It is a key element in scenarios 
that limit warming to 2°C (>67%) or lower by 2100 (robust 
evidence,  high agreement). Implementation strategies need to

reflect that CDR methods differ in terms of removal process, timescale 
of carbon storage, technological maturity, mitigation potential, cost, 
co-benefits, adverse side effects, and governance requirements. 
All Illustrative Mitigation Pathways (IMPs) use land-based biological 
CDR (primarily afforestation/reforestation (A/R)) and/or bioenergy with 
carbon capture and storage (BECCS) and some include direct air carbon 
capture and storage (DACCS). As a median value (5–95% range) across 
the scenarios that limit warming to 2°C (>67%) or lower, cumulative 
volumes of BECCS, CO 2 removal from AFOLU (mainly A/R), and 
DACCS reach 328 (168–763) gigatonnes of CO 2 equivalent (GtCO 2), 
252  (20–418)  GtCO 2, and 29  (0–339)  GtCO 2 for the 2020–2100 
period, with annual volumes at 2.75  (0.52–9.45)  GtCO 2 yr–1 for 
BECCS, 2.98 (0.23–6.38) GtCO 2 yr–1 for the CO 2 removal from AFOLU 
(mainly A/R), and 0.02 (0–1.74) GtCO 2 yr–1 for DACCS, in 2050. {12.3,

Cross-Chapter Box 8 in this chapter} 
Despite limited current deployment, moderate to large future 
mitigation potentials are estimated for direct air carbon 
capture and sequestration (DACCS), enhanced weathering 
(EW) and ocean-based CDR methods (including ocean alkalinity 
enhancement and ocean  fertilisation) (medium evidence, 
medium agreement). The potential for DACCS (5–40 GtCO 2 yr–1) 
is limited mainly by requirements for low-carbon energy and by cost 
(USD100–300 (full range: USD84–386) tCO 2–1). DACCS is currently 
at a  medium technology readiness level. EW has the potential to 
remove 2–4 (full range: <1 to about 100) GtCO 2 yr–1, at costs ranging 
from USD50 to 200 (full range: USD24–578) tCO 2–1.  Ocean-based 
methods have a combined potential to remove 1–100 GtCO 2 yr–1 at 
costs of USD40–500 tCO 2–1, but their feasibility is uncertain due to 
possible side effects on the marine environment. EW and ocean-based

methods are currently at a low technology readiness level. {12.3}
Realising the full mitigation potential from the food system 
requires change at all stages from producer to consumer 
and waste management, which can be facilitated through 
integrated policy packages (robust evidence, high agreement). 
Some 23–42% of global GHG emissions are associated with 
food systems, while there is still widespread food insecurity and 
malnutrition. Absolute GHG emissions from food systems increased 
from 14 to 17 GtCO 2-eq yr–1 in the period 1990–2018. Both supply and demand-side measures are important to reduce the GHG 
intensity of food systems. Integrated food policy packages based 
on a combination of market-based, administrative, informative, and 
behavioural policies  can reduce cost compared to uncoordinated 
interventions, address multiple sustainability goals, and increase 
acceptance across stakeholders and civil society (limited evidence, 
medium agreement). {7.2, 7.4, 12.4}

Diets high in plant protein and low in meat and dairy are 
associated with lower GHG emissions (robust evidence, high 
agreement). Ruminant meat shows the highest GHG intensity. Beef 
from dairy systems has lower emissions intensity than beef from beef 
herds (8–23 and 17–94  kgCO 2-eq per 100 g  protein, respectively) 
when a share of emissions is allocated to dairy products. The wide 
variation in emissions reflects differences in production systems, 
which range from intensive feedlots with stock raised largely on 
grains through to rangeland and transhumance production systems. 
Where appropriate, a  shift to diets with a  higher share of plant 
protein, moderate intake of animal-source foods and reduced intake 
of added sugars, salt and saturated fats could lead to substantial 
decreases in GHG emissions. Benefits would also include reduced 
land occupation and nutrient losses to the surrounding environment, 
while at the same time providing health benefits and reducing

mortality from diet-related non-communicable diseases. {7.4.5, 12.4}
Emerging food technologies such as cellular fermentation, 
cultured meat, plant-based alternatives to animal-based 
food products, and controlled-environment agriculture, can 
bring substantial reductions in direct GHG emissions from 
food production (limited evidence, high agreement). These 
technologies have lower land, water, and nutrient footprints, and 
address concerns over animal welfare. Access to low-carbon energy 
is needed to realise the full mitigation potential, as some emerging 
technologies are relatively more energy intensive. This also holds for 
deployment of cold chain and packaging technologies, which can help 
reduce food loss and waste, but increase energy and materials use in 
the food system. (limited evidence, high agreement). {11.4.1.3, 12.4}
Scenarios that limit warming to 2°C (>67%) or lower by 2100 
commonly involve extensive mitigation in the agriculture,

forestry and other land use (AFOLU) sector that at the 
same time provides biomass for mitigation in other sectors. 
Bioenergy is the most land intensive renewable energy option, 
but the total land occupation of other renewable energy 
options can become significant in high deployment scenarios 
(robust evidence, high agreement). Growing demands for food, 
feed, biomaterials, and non-fossil fuels increase the competition for 
land and biomass while climate change creates additional stresses on 
land, exacerbating existing risks to livelihoods, biodiversity, human 
and ecosystem health, infrastructure, and food systems. Appropriate 
integration of bioenergy and other bio-based systems, and of other 
mitigation options, with existing land and biomass uses can improve 
resource use efficiency, mitigate pressures on natural ecosystems and 
support adaptation through measures to combat land degradation, 
enhance food security, and improve resilience through maintenance

of the productivity of the land resource base (medium evidence, high 
agreement). {3.2.5, 3.4.6, 12.5}

1248
1248
Chapter 12 Cross-sectoral Perspectives12Bio-based products as part of a  circular bioeconomy have 
potential to support adaptation and mitigation. Key to 
maximising benefits and managing trade-offs are sectoral 
integration, transparent governance, and stakeholder 
involvement (high confidence). A sustainable bioeconomy relying 
on biomass resources will need to be supported by technology 
innovation and international cooperation and governance of global 
trade to disincentivise environmental and social externalities (medium 
confidence). {12.5, Cross-Working Group Box 3 in this chapter}
Coordinated, cross-sectoral approaches to climate change 
mitigation should be adopted to target synergies and minimise 
trade-offs between sectors and with respect to sustainable 
development (robust evidence, high agreement). This requires 
integrated planning using multiple-objective-multiple-impact policy 
frameworks. Strong interdependencies and cross-sectoral linkages

create both opportunities for synergies and the need to address 
trade-offs related to mitigation options and technologies. This can 
only be done if coordinated sectoral approaches to climate change 
mitigation policies that mainstream these interactions are adopted. 
Integrated planning and cross-sectoral alignment of climate change 
policies are particularly evident in developing countries’ Nationally 
Determined Contributions (NDCs) pledged under the Paris Agreement, 
where key priority sectors such as agriculture and energy are closely 
aligned between the proposed mitigation and adaptation actions 
in the context of sustainable development and the Sustainable 
Development Goals (SDGs). {12.6.2}
Carbon leakage is a critical cross-sectoral and cross-country 
consequence of differentiated climate policy (robust 
evidence, medium agreement). Carbon leakage occurs when 
mitigation measures implemented in one country/sector lead to

increased emissions in other countries/sectors. Global commodity 
value chains and associated international transport are important 
mechanisms of carbon leakage. Reducing emissions from the value 
chain and transportation can offer opportunities to mitigate three 
elements of cross-sectoral spillovers and related leakage: (i) domestic 
cross-sectoral spillovers within the same country; (ii) international 
spillovers within a  single sector resulting from substitution of 
domestic production of carbon-intensive goods with their imports 
from abroad; and (iii) international cross-sectoral spillovers among 
sectors in different countries. {12.6.3}
Cross-sectoral considerations in mitigation finance are critical 
for the effectiveness of mitigation action as well as for balancing 
the often conflicting social, developmental, and environmental 
policy goals at the sectoral level (medium evidence, medium 
agreement). True resource mobilisation plans that properly address

mitigation costs and benefits at sectoral level cannot be developed 
in isolation from their cross-sectoral implications. There is an urgent 
need for multilateral financing institutions to align their frameworks 
and delivery mechanisms including the use of blended financing to 
facilitate cross-sectoral solutions as opposed to causing competition 
for resources among sectors. {12.6.4}Understanding the co-benefits and trade-offs associated with 
mitigation is key to supporting societies to prioritise among 
the various sectoral policy options (medium evidence, medium 
agreement). For example, CDR options can have positive impacts 
on ecosystem services and the SDGs, but also potential adverse 
side effects; transforming food systems has potential co-benefits for 
several SDGs, but also trade-offs; and land-based mitigation measures 
may have multiple co-benefits but may also be associated with trade-
offs among environmental, social, and economic objectives. Therefore,

the possible implementation of the different sectoral mitigation 
options would depend on how societies prioritise mitigation versus 
other products and services, including food, material well-being, 
nature conservation and biodiversity protection, as well as on other 
considerations such as society’s future dependence on CDR and on 
carbon-based energy and materials. {12.3, 12.4, 12.5, 12.6.1}
Governance of CDR, food systems and land-based mitigation 
can support effective and equitable policy implementation 
(medium evidence, high agreement). Effectively responding 
to climate change while advancing sustainable development will 
require coordinated efforts among a diverse set of state- and non-
state-actors on global, multinational, national, and sub-national 
levels. Governance arrangements in public policy domains that cut 
through traditional sectors are confronted with specific challenges, 
such as establishing reliable systems for monitoring, reporting and

verification (MRV) that allow evaluation of mitigation outcomes 
and co-benefits. Effectively integrating CDR into mitigation 
portfolios can build on already existing rules, procedures and 
instruments for emissions abatement. Additionally, to accelerate 
research, development, and demonstration, and to incentivise CDR 
deployment, a  political commitment to formal integration into 
existing climate policy frameworks is required, including reliable 
MRV of carbon flows. Food systems governance may be pioneered 
through local food policy initiatives complemented by national 
and international initiatives, but governance on the national level 
tends to be fragmented, and thus have limited capacity to address 
structural issues like inequities in access. The governance of land-
based mitigation, including land-based CDR, can draw on lessons 
from previous experience with regulating biofuels and forest carbon; 
however, integrating these insights requires governance that goes

beyond project-level approaches and emphasises integrated land 
use-planning and management within the frame of the SDGs. {7.4, 
Box 7.2, 7.6, 12.3.3, 12.4, 12.5}

1249
1249
Cross-sectoral Perspectives Chapter 121212.1 Introduction
12.1.1 Chapter Overview
The scope of this chapter was motivated by the need for a succinct 
bottom-up cross-sectoral view of greenhouse gas (GHG) emissions 
mitigation coupled with the desire to provide systemic perspectives 
on critical mitigation potentials and options that go beyond individual 
sectors and cover cross-sectoral topics such as food systems, land 
systems, and carbon dioxide removal (CDR) methods. Driven by this 
motivation, Chapter 12 provides a focused thematic assessment of 
CDR methods and food systems, followed by consideration of land-
related impacts of mitigation options (land-based CDR and other 
mitigation options that occupy land) and other cross-sectoral impacts 
of mitigation, with emphasis on synergies and trade-offs between 
mitigation options, and between mitigation and other environmental 
and socio-economic objectives. The systems focus is unique to

the Sixth Assessment Report (AR6) of the IPCC and is of critical 
policy relevance as it informs coordinated approaches to planning 
interventions that deliver multiple benefits and minimise trade-
offs, and coordinated policy approaches to support such planning, 
to tap relatively under-explored areas for the strengthening and  
acceleration of mitigation efforts in the short to medium term, 
and for dealing with residual emissions in hard-to-transition sectors 
in the medium to long term.
Table 12.1 presents an overview of the cross-sectoral perspectives 
addressed in Chapter 12, mapping the chapter’s main themes to the 
sectoral and global chapters in this report. These mappings reflect 
the cross-sectoral aspects of mitigation options in the context of 
sustainable development, sectoral policy interactions, governance, 
implications in terms of international trade, spillover effects, and 
competitiveness, and cross-sectoral financing options for mitigation.

While some cross-sector technologies are covered in more detail 
in sectoral chapters,  this chapter covers  important cross-sectoral 
linkages and provides synthesis concerning  costs and potentials 
of mitigation  options, and co-benefits and trade-offs that can be 
associated with deployment of mitigation options. Additionally, 
Chapter 12 covers CDR methods and specific considerations related 
to land use and food systems, complementing Chapter  7.  The 
literature assessed in the chapter includes both peer-reviewed and 
grey literature since the Fifth Assessment Report (AR5) of the IPCC, 
including the IPCC Special Report on Global Warming of 1.5°C 
(SR1.5), the IPCC Special Report on Climate Change and Land 
(SRCCL) and the IPCC Special Report on the Ocean and Cryosphere 
in a Changing Climate (SROCC). Knowledge gaps are identified and 
reflected where encountered, as well as in a separate section. Finally, 
a strong link is maintained with sectoral chapters and the relevant

global chapters of this report to ensure consistency.
12.1.2 Chapter Content
Chapters  5  to 11 assess outcomes from mitigation measures that 
are applicable in individual sectors, and potential co-benefits and 
adverse side effects of these individual measures. Chapter 12 brings 
together the cross-sectoral aspects of these assessments including synergies and trade-offs as well as the implications of measures that 
have application in more than one sector and measures 
whose  implementation in one sector impacts implementation in 
Taking stock of the sectoral mitigation assessments, Chapter  12 
provides a  summary synthesis of sectoral mitigation costs and 
potentials in the short and long term along with comparison to the 
top-down integrated assessment model (IAM) assessment literature 
of Chapter  3  and the national/regional assessment  literature of 
Chapter 4.
In the context of cross-sectoral synergies and trade-offs, the chapter

identifies a number of mitigation measures that have application in 
more than one sector. Examples include measures involving product 
and material circularity, which contribute to mitigation of  GHG 
emissions in a number of ways, such as treatment of organic waste 
to reduce methane emissions, avoid emissions through generation 
of renewable energy, and reduce emissions through substitution of 
synthetic fertilisers. Low-carbon energy technologies such as solar 
and wind may be used for grid electricity supply, as embedded 
generation in the buildings sector (e.g., rooftop solar) and for energy 
supply in the agriculture sector. Nuclear and bio-based thermal 
electric generation can provide multiple synergies including base 
load to augment solar and wind, district heating, and seawater 
desalination. Grid-integrated hydrogen systems can buffer variability 
of solar and wind power and are being explored as a  mitigation

option in the transport and industry sectors. Carbon capture and 
storage (CCS) has potential application in a  number of industrial 
processes (cement, iron and steel, petroleum refining and pulp and 
paper) and the fossil fuel electricity sector. When coupled with energy 
recovery from biomass (BECCS), CCS can help to provide CO 2 removal 
from the atmosphere. On the demand side, electric vehicles are also 
considered an option for balancing variable power, energy efficiency 
options find application across the sectors, as does reducing demand 
for goods and services, and improving material use efficiency. Focused 
inquiry into these areas of cross-sectoral perspectives is provided for 
CDR, food systems, and land-based mitigation options.
A range of examples of where mitigation measures result in cross-
sectoral interactions and integration is identified. The mitigation 
potential of electric vehicles, including plug-in hybrids, is linked to

the extent of decarbonisation of the electricity grid, as well as to the 
liquid fuel supply emissions profile. Making buildings energy positive, 
where excess energy is used to charge vehicles, can increase the 
potential of electric and hybrid vehicles. Advanced process control 
and process optimisation in industry can reduce energy demand 
and material inputs, which in turn can reduce emissions linked to 
resource extraction and manufacturing. Trees and green roofs 
planted to counter urban heat islands reduce the demand for energy 
for air conditioning and simultaneously sequester carbon. Material 
and product circularity contributes to mitigation, such as treatment 
of organic waste to reduce methane emissions, generate renewable 
energy, and to substitute for synthetic fertilisers.
The chapter also discusses cross-sectoral mitigation potential related to 
diffusion of general-purpose technologies (GPT), such as electrification,

1250
1250
Chapter 12 Cross-sectoral Perspectives12digitalisation, and hydrogen. Examples include the use of hydrogen as 
an energy carrier, which, when coupled with low-carbon energy, has 
potential for driving mitigation in energy, industry, transport, and 
buildings (Box 12.5), and digitalisation has the potential for reducing 
GHG emissions through energy savings across multiple sectors.
The efficient realisation of the above examples of cross-sectoral 
mitigation would require careful design of government interventions 
across planning, policy, finance, governance, and capacity 
building  fronts. In this respect, Chapter  12 assesses literature on 
cross-sectoral integrated policies, cross-sectoral financing solutions, 
cross-sectoral spillovers and competitiveness effects, and on cross-
sectoral governance for climate change mitigation.
Finally, in the context of cross-sectoral synergies and trade-offs, the 
chapter assesses the non-climate mitigation co-benefits and adverse

effects in relation to SDGs, building on the fast-growing literature on 
the non-climate impacts of mitigation.
12.1.3 Chapter Layout
The chapter is mapped into seven sections. Cost and potentials 
of mitigation technologies are discussed in Section  12.2, where 
a  comparative assessment and a  summary of sectoral mitigation 
cost and potentials is provided in coordination with the sectoral 
Chapters 5 to 11, along with a comparison to aggregate cost and 
potentials based on IAM outputs presented in Chapter 3.
Section  12.3 provides a  synthesis of the state and potential 
contribution of CDR methods for addressing climate change. CDR 
options associated with the agriculture, forestry and other land use (AFOLU) and energy sectors are dealt with in Chapters 6 and 7 and 
synthesised in Section 12.3. Other methods, not dealt with elsewhere, 
are covered in more detail. A comparative assessment is provided for 
the different CDR options in terms of costs, potentials, governance,

impacts and risks, and synergies and trade-offs.
Section  12.4 assesses the literature on food systems and GHG 
emissions. The term ‘food system’ refers to a composite of elements 
(environment, people, inputs, processes, infrastructures, institutions, 
etc.) and activities that relate to the production, processing, 
distribution, preparation and consumption of food, and the outputs 
of these activities, including socio-economic and environmental 
outcomes. Climate change mitigation opportunities and related 
implications for sustainable development and adaptation are 
assessed, including those arising from food production, landscape 
impacts, supply chain and distribution, and diet shifts.
Section 12.5 provides a cross-sectoral perspective on land occupation 
and related impacts, risks and opportunities associated with land-
based mitigation options as well as mitigation options that are not 
designated land based, yet occupy land. It builds on SRCCL and

Chapter 7 in this report, which covers mitigation in AFOLU, including 
biomass production for mitigation in other sectors. In addition to an 
assessment of biophysical and socio-economic risks, impacts and 
opportunities, this section includes a Cross-Working Group Box (WGII 
and WGIII) on Mitigation and Adaptation via the Bioeconomy, and 
a Box on Land Degradation Neutrality as a framework to manage 
trade-offs in land-based mitigation.
Section  12.6 provides a  cross-sectoral perspective on mitigation, 
co-benefits, and trade-offs, including those related to sustainable 
development and adaptation. The synthesised sectoral mitigation 
Table 12.1 | An overview of cross-sector perspectives addressed in Chapter 12.
Sectoral chapters Global chapters
Chapter 12 
themesChapter 5  Chapter 6  Chapter 7  Chapter 8  Chapter 9  Chapter 10 Chapter 11 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Chapter 17
Costs 
& potentialsChange 
in demandRenewables
CCU
CCS
NuclearLand-use 
changeUrban

planning
Cities
DemographicsStandards
ElectrificationHybridisation
Electric 
vehicles
Fuel economy
DecouplingTechnology
Biomass
CCU
CCSEnabling of 
mitigationFinance of 
mitigationSynergies and 
trade-offs 
with SDGs
CDR  BECCSLand-based 
CDRCarbon 
storage in 
buildingsInternational 
Food 
systemsFood demand
Well-beingEnergy 
demand 
of some 
emerging 
optionsAgricultural 
Demand-side 
measuresUrban food 
systems; 
controlled-
environment 
agricultureFood  
transportFood 
processing 
and 
packagingFood system 
transformationGovernanceFood system 
and SDGs
& land useLand use/ 
occupation: 
bioenergy 
hydro solar 
windnuclearA/R Biomass 
Bioenergy
BiocharLand use 
and biomass 
supplyLand use 
supplyGovernanceCo-benefits 
and adverse 
side effects
Cross-
sectoral 
perspectivesElectrification, Hydrogen, Digitalisation, Circularity, Synergies, Trade-offs, SpilloversPolicy 

Policy 
packages
Case studies 
Value chain 
and carbon 
leakageGovernance
LeakageBlended 
financingGeneral-
urpose 
Electrification 
HydrogenSDGs  
co-benefits
Trade-offs

1251
1251
Cross-sectoral Perspectives Chapter 1212synergies and trade-offs are mapped into options/technologies, 
policies, international trade, and finance domains. Cross-sectoral 
mitigation technologies fall into three categories in which the 
implementation of the technology: (i) occurs in parallel in more than 
one sector; (ii) could involve interaction between sectors, and/or 
(iii) could create resource competition among sectors. Policies that 
have direct sectoral effects include specific policies for reducing 
GHG emissions and non-climate policies that yield GHG emissions 
reductions as co-benefits. Policies may also have indirect cross-
sectoral effects, including synergies and trade-offs that may, in 
addition, spill over to other countries.
Section 12.7 provides an overview of knowledge gaps, which could 
be used to inform further research.
12.2 Aggregation of Sectoral 
Costs and Potentials
The aim of this section is to provide a consolidated overview of the

net emissions reduction potentials and costs for mitigation options 
available in the various sectors dealt with in the sectoral Chapters 6, 
7, 9, 10 and 11 of this assessment report. This overview provides 
policymakers with an understanding of which options are more or 
less important in terms of mitigating emissions in the short term 
(here interpreted as 2030), and which ones are more or less costly. 
The intention is not to provide a  high level of accuracy for each 
technology cost or potential, but rather to indicate relative importance 
on a global scale and whether costs are low, intermediate or high. 
The section starts with an introduction (Section  12.2.1), providing 
definitions and the background. Next, ranges of net emission 
reduction potentials and the associated costs for the year 2030 are 
presented (Section  12.2.2) and compared to earlier estimates and 
with the outputs of IAMs (Section 12.2.3). Finally, an outlook to the 
year 2050 is provided (Section 12.2.4).

12.2.1 Introduction
The term ‘mitigation potential’ is used here to report the quantity of 
net greenhouse gas emissions reductions that can be achieved by 
a given mitigation option relative to a specified reference scenario. 
The net greenhouse gas emission reduction is the sum of reduced 
emissions and enhanced sinks. Several types of potential can be 
distinguished. The technical potential is the mitigation potential 
constrained by theoretical limits in addition to the availability of 
technology and practices. Quantification of technical potentials 
primarily takes into account technical considerations, but social, 
economic and/or environmental considerations are sometimes also 
considered, if these represent strong barriers to the deployment of 
an option. The economic potential, being the potential reported in 
this section, is the proportion of the technical potential for which the 
social benefits exceed the social costs, taking into account a social

discount rate and the value of externalities (see Annex I: Glossary). 
In this section, only externalities related to greenhouse gas emissions 
are taken into account. They are represented by using different cost 
cut-off levels of options in terms of USD per tonne of avoided CO 2-eq emissions. Other potentials, such as market potentials, could also be 
considered, but they are not included in this section.
The analysis presented here is based, as far as possible, on 
information contained in Chapters 6, 7, 9, 10 and 11, where costs 
and potentials, referred to here as ‘sectoral mitigation potentials’ 
have been discussed for each individual sector. In the past, these 
were designated as bottom-up potentials, in contrast to the top-
down potentials that are obtained from integrated energy-economic 
models and IAMs. However, IAMs increasingly include ‘bottom-up’ 
elements, which makes the distinction less clear. Still, sectoral studies

often have more technical and economic detail than IAMs. They may 
also provide more up-to-date information on technology options 
and associated costs. However, aggregation of results from sectoral 
studies is more complex, and although interactions and overlap are 
corrected for as far as possible in this analysis, it is recognised that 
such systemic effects are much more rigorously taken into account 
in IAMs. A comparison is made between the sectoral results and the 
outcomes of the IAMs in Section 12.2.3.
Costs of mitigation options will change over time. For many 
technologies, costs will reduce as a result of technological learning. 
An attempt has been made to take into account the average, 
implementation-weighted costs until 2030. However, the underlying 
literature did not always allow such costs to be presented. For the year 
2030, the results are presented similarly to AR4, with a breakdown 
of the potential in ‘cost bins’. For the year 2050, a more qualitative

approach is provided. The origins of the cost data in this section are 
mostly based on studies carried out in the period 2015–2020. Given 
the wide range of the cost bins that are used in this section it is 
not meaningful (and often not possible) to convert to USD values for 
one specific year. This may lead to some extra uncertainty, but this is 
expected to be relatively small.
As indicated previously, net emission reduction potentials are 
presented based on comparison with a  reference scenario. 
Unfortunately, not all costs and potentials found in the literature are 
determined against the same reference scenarios. In this assessment, 
reference scenarios are based on what were assumed current-policy 
scenarios in the period 2015–2019. Typical reference scenarios are the 
Shared Socio-economic Pathway (SSP2) scenarios (Fricko et al. 2017) 
and the Current Policies scenario from the World Energy Outlook 
(WEO) 2019 (IEA 2019). They can both be considered scenarios with

middle-of-the-road expectations on population growth and economic 
development, but there are still some differences between the two 
(Table  12.2). The net emissions reduction potentials reported here 
were generally based on analyses carried out before 2020, so the 
impact of the COVID-19 pandemic was not taken into account. For 
comparison, the Stated Policies scenario of the World Energy Outlook 
2020 (IEA 2020a) is also shown, one of the scenarios in which the 
impact of COVID-19 was considered. Variations of up to 10% between 
the different reference scenarios exist with respect to macro-variables 
such as total primary energy use and total GHG emissions. The 
potential estimates presented below should be interpreted against 
this background. The total emissions under the reference scenarios in 
2030 are expected to be in the range of 54 to 68 GtCO 2-eq yr–1 with 
a median of 60 GtCO 2-eq yr–1 (Table 4.1).

1252
1252
Chapter 12 Cross-sectoral Perspectives12For the energy sector the potentials are determined using the World 
Energy Outlook 2019 Current Policies Scenario as a reference (IEA 
2019). However, for the economic assessment, more recent Levelised 
Costs of Electricity (LCOEs) for different electricity generating 
technologies were used (IEA 2020a). For the AFOLU sector, the 
potentials were derived from a variety of studies. It may be expected 
that the best estimates, as averages, match with the reference in 
a middle-of-the-road scenario. For the buildings sector, the Current 
Policies scenario of World Energy Outlook 2019 (IEA 2019) was 
used as a reference. For the transport sector, the references of the 
underlying sources were used. For the industry sector, the scenarios 
used have emissions that are slightly higher than in the Current 
Policies scenario from the World Energy Outlook 2019 (IEA 2019).
12.2.2 Costs and Potentials of Options for 2030

In this section, we present an overview of mitigation options per 
sector. An overview of net emissions reduction potentials for different 
mitigation options is presented in Table 12.3.
Firstly, a brief overview of the process of data collection is presented, 
with a  more detailed overview being found in Supplementary 
Material 12.SM.1.2. For the energy sector, the starting point for 
the determination of the emissions reduction potentials was the 
Emissions Gap Report (UNEP 2017), but new literature was also 
assessed, and a  few studies that provide updated estimates of 
the mitigation potentials were included. It was found that higher 
mitigation potentials than in the UNEP report are now reported for 
solar and wind energy, but at the same time electricity production by solar and wind energy in the reference scenario has increased, 
compared to earlier versions of the World Energy Outlook. The net 
effect is a  modest increase in the average value of the potential,

and a  wider uncertainty range. Costs of electricity-generating 
technologies are discussed in Section 6.4.7, with a summary of LCOEs 
from the literature being presented in Section 6.4.7. Mitigation costs 
of electricity production technology depend on local conditions 
and on the baseline technology being displaced, and it is difficult 
to determine the distribution over the cost ranges used in this 
assessment. However, it is possible to indicate a broad cost range for 
these technologies. These cost ranges are presented in Table 12.3. For 
onshore wind and utility-scale solar energy, there is strong evidence 
that despite regional differences in resource potential and cost, 
a large part of the mitigation potential can be found in the negative 
cost category or at cost parity with fossil fuel-based options. This is 
also the case for nuclear energy in some regions. Other technologies 
show mostly positive mitigation costs, the highest mitigation costs

are for CCS and bioelectricity with CCS, for details see Supplementary 
Material 12.SM.1.2.
For the AFOLU sector, assessments of global net emissions reduction 
studies were provided in Table 7.3. The number of studies depends 
on the type of mitigation action, but ranges from five to nine. Each 
of these studies relies on a much larger number of underlying data 
sources. From these studies, emissions reduction ranges and best 
estimates were derived. The studies presented refer to different years 
in the period 2020 to 2050, and the mitigation potential presented 
for AFOLU primarily refers to the average over the period 2020 to 
2050. However, because most of the activities involve storage of 
carbon in stocks that accumulate carbon, or conversely decay over Table 12.2 | Key characteristics of the scenarios used as a reference for determining costs and potentials.  The values are for the year 2030.
SSP2 reference
(MESSAGE-GLOBIOM)
(Fricko et al. 2017)All reference scenarios

median (25th–75th 
percentiles in 
parenthesis)
(AR6 scenarios 
database, IIASA, 2021)WEO-2019
(Current Policies)
(IEA 2019)WEO-2020
(Stated Policies)
(IEA 2020a)AR6 WG III Chapter 4
 (Chapter 4, Table 4.1)
Real GDP (purchasing 
power parity, PPP)  
(1012 USD)158
(USD2010)159
(154–171)3.6% p.a.↑
(2018 to 2030)2.9% p.a.↑
(2019 to 2030)
Population (billion) 8.308.30
(8.20–8.34)8.60
Total primary energy 
use (EJ)627670  
(635–718)710 660
Total final energy use (EJ) 499480
(457–508)502 472
Energy-related CO 2 
emissions (Gt)33.037.9
(34.7–41.4)37.4 33.2a37
(35–45)
CO2 emissions energy 
and industry (Gt)37.942.3
(39.0–45.8)36.0
Total CO 2 emissions (Gt) 40.645.7
(41.8–49.4)43
(38–51)
Total greenhouse gas 
emissions (GtCO 2-eq)52.759.7
(55.0–65.8)60
(54–68)
a The difference between WEO-2020 and WEO-2019 is partly explained by the fact that WEO-2019 had two different reference scenarios: Current Policies and Stated Policies.

WEO-2020 has only one reference: the Stated Policies Scenario, which ‘is based on today’s policy settings’. The Stated Policies Scenario in WEO-2019 had energy-related 
emissions of 34.9 GtCO 2. EJ, exajoules (1 x 1018 joules); p.a., per annum.

1253
1253
Cross-sectoral Perspectives Chapter 1212time (e.g., forests, mangroves, peatland soils, agricultural soils, wood 
products), the 2020 to 2050 average provides a good approximation 
of the amount of permanent atmospheric CO 2 mitigation that 
could be available at a given price in 2030. The exception is BECCS, 
which is in an early upscaling phase, so the potential estimated by 
Chapter 7 as an average for the 2020 to 2050 period is not included 
in Table 12.3. Note that for the energy sector a mitigation potential 
for BECCS is provided in Table 12.3.
The emissions reduction potentials for the buildings sector were 
based on the analysis by Chapter  9  authors of a  large number of 
sectoral studies for individual countries or regions. In total, the 
chapter analysed the results of 67 studies that assess the potential 
of technological energy efficiency and onsite renewable energy 
production and use, and the results of 11 studies that assess the

potential of sufficiency measures helping avoid demand for energy 
and materials. The sufficiency measures were included in models by 
reorganisation of human activities; efficient design, planning, and 
use of building space; higher density of building and settlement 
inhabitancy; redefining and downsizing goods and equipment, 
limiting their use to health, living, and working standards, and their 
sharing. Most of these studies targeted 2050 for the decarbonisation 
of buildings; the potentials in 2030 reported here rely on the 
estimates for 2030 provided by these studies or on the interpolated 
estimates targeting these 2050 figures. Based on these individual 
country studies, regional aggregate emissions reduction percentages 
were found. The potential estimates were assembled in the order 
sufficiency, efficiency, renewable options, correcting the amount 
of the potential at each step for the interaction with preceding

measures. Note that the option ‘Enhanced use of wood products’ 
was analysed by Chapter 7, but is listed under the buildings sector in 
Table 12.3, as such enhanced use of wood takes place predominantly 
in the construction sector.
For the transport sector, Chapter 10 provided data on the emissions 
reduction potential for shipping. For the other transportation modes, 
additional sources were used to achieve a  complete overview of 
emissions reduction potentials (for further details, see Supplementary 
Material 12.SM.1.2). A  limited number of estimates for global 
emissions reduction potential is available: the total number of 
sources is about 10, and some estimates rely on just one source. The 
data have been coordinated with Chapter 10 authors.
For the industrial sector, global emissions reduction potentials per 
technology class per sector were derived by Chapter  11 authors, 
using primarily sectoral or technology-oriented literature. The

analysis is based on about 75 studies, including sectoral assessments 
(Sections 11.4.1 and 11.4.2 and Figure 11.13).
For methane emissions reduction from oil and gas operations, coal 
mining, waste treatment and wastewater, an analysis was done, 
based on three major data sources in this area (Harmsen et al. 
2019; US EPA 2019; Höglund-Isaksson et al. 2020); for oil and gas 
operations this was complemented by IEA (2021a). A similar analysis 
for reductions of emissions of fluorinated gases was carried out based 
on analysis by the same institutes (Purohit and Höglund-Isaksson 
2017; Harmsen et al. 2019; US EPA 2019). Data for CDR options not discussed previously (such as DACCS and enhanced weathering) 
were taken from Section 12.3. For more details about data sources 
and data processing, see Supplementary Material 12.SM.1.2.
In Table  12.4 mitigation potentials for all gases are presented 
in  GtCO 2-eq. For most sectors the mitigation potentials (notably

for methane emissions reductions from coal, oil and gas, waste and 
wastewater) have been converted to CO 2-eq using global warming 
potential (GWP) values as presented in AR6 WGIII (Cross-Chapter 
Box 2 in Chapter 2). However, the underlying literature did not always 
accommodate this, in which cases older GWP values apply. Given 
the uncertainty ranges in the mitigation potentials in Table 12.3, the 
impact on the results of using different GWP values is considered 
to be very small.
For all options, uncertainty ranges of the mitigation potentials are 
given in Table  12.3. As far as possible, the ranges represent the 
variation in assessments found in the literature. This is the case 
for wind and solar energy, for the AFOLU options, for the methane 
mitigation options (coal, oil and gas, waste and wastewater) and for 
fluorinated gas mitigation. For the latter options, some variability 
exists for each cost bin, but aggregated over cost ranges the variation

is much smaller, typically ±50%. For the buildings sector and the 
industrial sector options, the uncertainty in the mitigation potential 
is estimated by the lead authors of those chapters. For options for 
which only limited sources were available, an uncertainty range of 
±50% was used. Overall, the uncertainty range per option is typically 
in the range of ±20% to ±60%.
Despite these uncertainties, clearly a number of options with high 
potentials can be identified, including solar energy, wind energy, 
reducing conversion of forests and other natural ecosystems, and 
restoration of forests and other natural ecosystems. As mid-range 
values, they each represent 4 to 7% of total reference emissions for 
2030. Soil carbon sequestration in agriculture and fuel switching 
in industry can also be considered as options with high potential, 
although it should be noted that these options consist of a number 
of discernible sub-options, see Table  12.3. It can be observed that

for each sector, a variety of options is available. Many of the smaller 
options each make up 1 to 2% of the reference emissions for 2030. 
Within this group of smaller options there are some categories that, 
summed together, stand out as substantial: the energy efficiency 
options and the methane mitigations options.
Costs are highly variable across the options. All sectors have several 
options for which at least part of the potential has mitigation costs 
below USD20 tCO 2–1. The only exception is the industrial sector, in 
which only energy efficiency is available below this cost level. At the 
same time, a  substantial part of the emissions reduction potential 
comes at higher cost, much being in the USD20 to 100 tCO 2–1 cost 
ranges. All sectors have substantial additional potential in these 
cost ranges; only for transportation is this limited. Aggregation 
of the potentials per cost bin shows that the potential in these

cost bins is marginally smaller than in the two cheapest cost 
bins. For  some options, potential was identified in the 100 to 
200 tCO 2–1 cost bin. The mitigation potentials identified in this cost 
range make up only a small part of the total mitigation potential.

1254
1254
Chapter 12 Cross-sectoral Perspectives12Table 12.3 | Detailed overview of global net GHG emissions reduction potentials (GtCO 2-eq) in the various cost categories for the year 2030.  Note that 
potentials within and across sectors cannot be summed, as the adoption of some options may affect the mitigation potentials of other options. Only monetary costs and 
benefits of options are taken into account. Negative costs occur when the benefits are higher than the costs. For wind energy, for example, this is the case if production costs 
are lower than those of the fossil alternatives. Ranges are indicated for each option separately, or indicated for the sector as a whole (see Notes column); they reflect full ranges. 
Cost ranges are not cumulative, e.g., to obtain the full potential below USD50 tCO 2-eq–1, the potentials in the cost bins <USD0, USD0–20 and USD20–50 tCO 2-eq–1 need to 
be summed together.
Emissions reduction 
options (including carbon

sequestration options)Cost categories (USD tCO 2-eq–1)
Notes
<0 0–20 20–50 50–100 100–200
Energy sectorCost ranges are derived as ranges of LCOEs for different 
electricity generating technologies and the potentials 
are updated from UNEP (2017).
Wind energy2.1–5.6
(majority in <0 range)Costs for system integration of intermittent renewables are 
not included, but these are expected to have limited impact 
until 2030 and will depend on market design and cross-
sectoral integration.Solar energy2.0–7.0
(majority in <0 range)
Nuclear energy 0.88 ± 50%
Bioelectricity 0.86 ± 50%Biomass use for indoor heating and industrial heat is not 
included here. Currently, about 90% of renewable industrial 
heat consumption is bio-based, mainly in industries that can 
use their own biomass waste and residues (IEA, 2020).
Hydropower 0.32 ± 50%Mitigation costs show large variation and may end up beyond 
these ranges.

Geothermal energy 0.74 ± 50%Mitigation costs show large variation and may end up beyond 
Carbon capture and storage (CCS) 0.54 ± 50%
Bioelectricity with CCS 0.30 ± 50%
CH4 emissions reduction 
from coal mining0.04
(0.01–0.06)0.41
(0.15–0.64)0.03
(0.02–0.05)0.02
(0.01–0.03)
from oil and gas operations0.31
(0.12–0.56)0.61
(0.23–1.30)0.07
(0.03–0.20)0.06
(0.00–0.29)0.10
(0–0.29)
Land-based mitigation options (including agriculture and forestry)Potentials for AFOLU are averages for the period  
2020–2050 and represent a proxy for mitigation 
in 2030.
Technical potentials listed below include the potentials 
already listed in the previous columns.
Note that in Table 7.3 the same potentials are listed,  
but they are cumulative over the cost bins.
Carbon sequestration in agriculture 
(soil carbon sequestration, 
agroforestry and biochar application)0.50
(0.38–0.60)0.73
(0.5–1.0)2.21
(0.6–3.9)Technical potential: 9.5 (range 1.1–25.3).

CH4 and N 2O emissions reduction 
in agriculture (reduced enteric 
fermentation, improved manure 
management, nutrient management, 
rice cultivation)0.35
(0.11–0.84)–0.28
(0.19–0.46)Technical potential: 1.7 (range 0.5–3.2).
GWPs used from AR4 and AR5.
Protection of natural ecosystems 
(avoid deforestation, loss and 
degradation of peatlands, coastal 
wetlands and grasslands)2.28
(1.7–2.9)0.12
(0.06–0.18)1.63
(1.3–4.2)0.22
(0.09–0.45)Technical potential 6.2 (range 2.8–14.4).
Restoration (afforestation, 
reforestation, peatland restoration, 
coastal wetland restoration)0.150.57
(0.2–1.5)1.46
(0.6–2.3)0.66
(0.4–1.1)Technical potential 5.0 (range 1.1–12.3).
Improved forest management,  
fire management0.38
(0.32–0.44)–0.78
(0.32–1.44)Technical potential 1.8 (range 1.1–2.8).
Reduction of food loss  
and food wasteFeasible potential 0.5 (0.1–0.9).
Technical potential 0.7 (0.1–1.6).
Estimates reflect direct mitigation from diverted agricultural

1255
1255
Cross-sectoral Perspectives Chapter 1212Emissions reduction 
options (including carbon 
Notes
Shift to sustainable healthy dietsFeasible potential 1.7 (1.0–2.7).
Technical potential 3.5 (2.1–5.5).
Estimates reflect direct mitigation from diverted agricultural 
production only, not including land-use effects.
BuildingsTo avoid double-counting, the numbers were corrected 
for the potential overlap between options in the order 
sufficiency, efficiency, renewable measures and they 
could be therefore added up. In 2050, much larger and 
cheaper potential is available (see Section 9.6); the 
potential in 2030 is lower and more expensive, mostly 
due to various feasibility constraints.
Sufficiency to avoid demand for 
energy services (e.g., efficient 
building use and increased 
inhabitancy and density)0.56
(0.28–0.84)
Efficient lighting, appliances 
and equipment, including

information and communications 
technologies, water heating 
and cooking technologies0.73
(0.54–0.91)
New buildings with very high energy 
performance (change in construction 
methods, management and operation 
of buildings, efficient heating, 
ventilation and air conditioning)0.35
(0.26–0.53)0.83
(0.62–1.24)
Onsite renewable production and use 
(often backed-up with demand-side 
flexibility and digitalisation measures, 
typically installed in very new high 
energy performance buildings)0.20
(0.15–0.30)0.27
(0.20–0.40)
Improvement of existing building 
stock (thermal efficiency of 
building envelopes, management 
and operation of buildings, 
and efficient heating, ventilation 
and air conditioning leading 
to ‘deep’ energy savings)0.27
(0.20–0.34)Additionally, there is 0.50 (range 0.37–0.62) GtCO 2-eq  
of potential above a price of USD200 tCO 2-eq–1.
Enhanced use of wood productsTechnical potential 1.0 (range 0.04–3.7).
Economic potential 0.38 (range 0.3–0.5) (varying carbon

prices). Potential is mainly in the construction sector.
TransportOptions for the transportation sector have an 
uncertainty of ±50%.
Light duty vehicles – fuel efficiency 0.6
Light duty vehicles – electric vehiclesEstimated potential is 0.5-0.7 GtCO 2-eq, depending on the 
carbon intensity of the electricity supplied to the vehicles. 
Mitigation costs are variable.  
Light duty vehicles – shift to public 
transport0.5
Light duty vehicles – shift to bikes 
and e-bikes0.2
Heavy duty vehicles – fuel efficiency 0.4
Heavy duty vehicles – electric 
vehiclesEstimated potential is 0.2 GtCO 2-eq.  
Mitigation costs are variable.
Heavy duty vehicles – shift to rail No data available.
Shipping – efficiency,  
optimisation, biofuels0.5
(0.4–0.7)
Aviation – energy efficiency 0.12–0.32 Limited evidence.
Biofuels 0.6–0.8

1256
1256
Chapter 12 Cross-sectoral Perspectives12It could be that there is limited potential in this range; however, 
a  more plausible explanation, supported by several authors of 
sectoral chapters, is that this cost range is relatively unexplored.
In this assessment, the emphasis is on the specific mitigation costs of 
the various options, and these are often considered as an indicator 
to prioritise options. However, in such a prioritisation, other elements 
will also play a  role, like the development of technology for the 
longer term (Section 12.2.4) and the need to optimise investments 
over longer time periods, see for example Vogt-Schilb et al. (2018) 
who argue that sometimes it makes sense to start with implementing 
the most expensive option.
In this section, an overview of emissions mitigation options for the 
year 2030 was presented. The overview of the mitigation potential 
is based on a variety of approaches, relying on a large number of

sources, and the number of sources varied strongly from sector to 
sector. The main conclusions from this section are: (i) there is a variety 
of options per sector, (ii) per sector the options combined show 
significant mitigation potential, (iii) there are a  few major options 
and a lot of smaller ones, and (iv) more than half of the potential 
comes at costs below USD20 tCO 2–1 (between sectors: medium to 
robust evidence, high agreement).12.2.3 Aggregation of Sectoral Results and 
Comparison with Earlier Analyses 
and Integrated Assessment Models
In this section, the mitigation potentials are aggregated per sector, 
and then to the global economy. These potentials, which are based 
on sectoral analysis, are then compared to the results from earlier 
assessments and the results from IAMs. Given the incompleteness 
of data on the mitigation potential at mitigation costs larger than 
USD100 tCO 2–1, the focus will be on options with mitigation costs 
below USD100 tCO 2–1.

As suggested previously, the overview presented in Table 12.3 should 
be interpreted with care, as the implementation of one option may 
affect the mitigation potential of another option. Most sectoral chapters 
have supplied mitigation potentials that were already adjusted for 
overlap and mutual influences (industry, buildings, AFOLU). For the 
energy sector, interactions between the options will occur, but parallel 
implementation of all the options seems to be possible; if all options 
at costs levels below USD100 tCO 2–1 were implemented, this would 
lead to an additional power generation with no direct CO 2 emissions 
of 41% of the total projected generation in 2030. This seems to be 
possible, but as higher penetrations are relatively unexplored, we Emissions reduction 
Notes
IndustryThe numbers for the industry sector typically have

an uncertainty of ±25%, unless indicated differently.
The numbers are corrected for overlap between 
the options, except for the 0.15 GtCO 2 potential 
in the highest cost bin. For the rest they can 
be aggregated to provide full potentials.
Energy efficiency 1.14This only applies to more efficient use of fuels.  
More efficient use of electricity is not included.
Material efficiency 0.93
Circularity (enhanced recycling) 0.48
Fuel switching 1.28 0.67 0.15
Feedstock decarbonisation, 
process change0.38
Carbon capture, utilisation 
and storage (CCU and CCS)0.15
(0.08–0.36)
Cementitious material substitution 0.28
Reduction of non-CO 2 emissions 0.2
Cross-sectorial
Emission reduction  
of fluorinated gases0.26
(0.01–0.50)0.68
(0.55–0.90)0.18
(0.01–0.42)0.09
(0–0.20)0.03
(0–0.05)GWPs not updated.
Reduction of CH 4 emissions  
from solid waste0.33
(0.24–0.43)0.11
(0.03–0.15)0.06
(0.03–0.08)0.04
(0.01–0.10)0.08
(0.02–0.12)
from wastewater0.02
(0–0.05)0.03

(0.01–0.05)0.04
(0.01–0.07)0.03
(0.02–0.04)0.07
(0.01–0.16)
Direct air carbon capture  
and storage (DACCS)very smallThere is potential in these categories, but given the current 
technology readiness levels, for 2030 the potential is limited. 
Also, it is not certain whether the costs will have dropped 
below 200 USD tCO 2–1 before 2030. In the longer term, 
much larger potentials are projected, see Section 12.3.1.Enhanced weathering very small

1257
1257
Cross-sectoral Perspectives Chapter 1212apply a smaller uncertainty range at the high end. For the calculation 
of the aggregate potentials in the energy sector, error propagation 
rules were applied. For the transport sector, there will be interaction 
between the technical measures on the one hand and the modal shift 
measures on the other hand. Given the small mitigation contribution 
of the modal shift options, these interactions will be negligible. The 
resulting aggregate mitigation potentials and their uncertainty ranges 
per (sub)sector are given in Table 12.4 (columns indicated ‘AR6’). This 
overview confirms the large potentials per sector, even when taking 
the uncertainty ranges into account.
Calculating aggregated mitigation potentials for the global economy 
requires that interactions between sectors also need to be taken into 
account (Section  12.6). First of all, there may be overlap between

the electricity supply sector and the electricity demand sectors: if the 
electricity sector is extensively decarbonised, the avoided emissions 
due to electricity efficiency measures and local electricity production 
will be significantly reduced. Therefore, this demand-side mitigation 
potential is only taken into account for 25% (reflecting the degree 
of further decarbonisation of the power sector) in the cross-sectoral 
aggregation. For the other demand sectors, this problem does not 
arise. The industry sector did not provide estimates for electricity 
efficiency improvement and in the transport sector the utilisation of 
electricity to date is very low. Electrification options may occur in all 
sectors, but this enhances the mitigation potential in combination with a  decreased carbon intensity of the power sector. For other 
energy sector options, such as methane emissions reduction from 
coal, oil and natural gas operations, the situation is more complex.

The total emissions reduction potential for fossil fuels in the other 
sectors is high. Should this potential be realised, this would lead to 
a reduction of the potential reported here. However, reducing fossil 
fuel use also leads to a  reduction in the upstream CH 4 (methane) 
emissions, so in the case of reducing fossil fuel use, these upstream 
emissions will also be avoided, so no overestimate of the aggregate 
emissions reduction potential occurs.
The total potential, given these corrections for overlap, leads to 
a mid-range value for the total mitigation potential at costs below 
USD100 tCO 2-eq–1 of 38 GtCO 2-eq. Given the fact that it is not to 
be expected that mitigation potentials of the various sectors are 
mutually correlated, that is, it is not to be expected that mitigation 
potentials are all on the high side or all on the low side, the ranges 
are aggregated using error propagation rules, which leads to a range 
for the mitigation potential of 32 to 44 GtCO 2-eq.

Mitigation costs and potentials for 2030 have been presented 
previously, notably in AR4 Chapter 11 on Mitigation from a Cross-
sectoral Perspective (Barker et al. 2007) and the Emissions Gap 
Report (UNEP 2017). Note that AR5 did not provide emissions 
reduction potentials in this form. The aggregated potentials reported 
Table 12.4 | Overview of aggregate sectoral net GHG emissions reduction potentials (GtCO 2-eq) for the year 2030 at costs below USD100 tCO 2-eq–1. 
Comparisons with earlier assessments are also provided. Note that sectors are not entirely comparable across the three different estimates.
SectorMitigation potentials at costs less than USD100 tCO 2-eq–1
AR6
best estimateAR6
rangeAR4
(Barker et al. 
2007)UNEP2017
best estimate
(UNEP 2017)UNEP 2017
range
(UNEP 2017)
Electricity sector 11.0 7.9–12.5
6.2–9.310.3 9.5–11.0
Other energy sector (methane) 1.6 1.1–2.1 2.2 1.7–2.6
Agriculture 4.1 1.7–6.7 2.3–6.4 4.8 3.6–6.0

Forestry and other land use-related options 7.3 3.9–13.1 1.3–4.2 5.3 4.1–6.5
AFOLU demand-side options (estimates reflect direct 
mitigation from diverted agricultural production 
only, not including land-use effects)2.2 1.1–3.6 1.3–3.4
Buildings (potentials up to 
USD200 tCO 2-eq–1 in parentheses)Dir 0.7
(1.1)
Ind 1.3
(2.1)
Tot 2.0 
(3.2)0.5–1.0
 (0.7–1.5)
0.9–1.8
(1.5–3.1)
1.4–2.9
(2.3–4.6)Dir 2.3–2.9
Ind 3.0–3.8
Tot 5.4–6.7Dir 1.9
Ind 4.0
Tot 5.9Dir 1.6–2.1
Transport 3.8 1.9–5.7 1.6–2.5 4.7 4.1–5.3
Industry Dir 5.4 4.0–6.7Dir 2.3–4.9
Ind 0.83
Tot 3.1–5.7Dir 3.9
Ind 1.9
Tot 5.8Dir 3.0–4.8
Fluorinated gases (all sectors) 1.2 0.7–1.5 NE 1.5 1.2–1.8
Waste and wastewater 0.7 0.6–0.8 0.4–1.0 0.4 0.3–0.5
Enhanced weathering – – – 1.0 0.7–1.2
Total of all sectors 38 32–44 15.8–31.1 38 35–41
Note: Dir = reduction of direct emissions, Ind = reduction of indirect emissions (related to electricity production), Tot = reduction of total emissions, NE = not estimated,

1258
1258
Chapter 12 Cross-sectoral Perspectives12here are higher than those estimated in AR4. Note, however, that AR4 
suggested the potentials were underestimated by 10 to 15%, but 
a higher potential still remains in the current assessment. In a sector-
by-sector comparison, higher potentials than in AR4 can be observed 
especially for the energy sector and the forestry sector, and to a more 
limited extent for the industry sector and the transport sector. For 
the energy sector, the change can largely be explained by the higher 
estimates for wind and solar energy and the improved understanding 
of how to integrate high shares of intermittent renewable energy 
sources into power systems. For industry and transport, the higher 
potentials can be partly explained by the inclusion of more options, 
like recycling and material efficiency (for industry) and electric

transportation and modal shifts for transport. For buildings, a lower potential can be observed compared to AR4, one reason is that the 
2030 reference direct and indirect emissions were estimated as 
45% and 11% higher in AR4 than they were in AR6 (signalling a much 
quicker actual switch to electricity than was thought 15 to 20 years 
ago, among other reasons). The other reason for a difference is that 
the scenarios considered in AR4 had 25 to 30 years between their 
start year until the target year of 2030 and the scenarios reviewed 
in AR6 have only 10 to 15 years before 2030. The current retrofitting 
rates of existing buildings and penetration rates of nearly zero-
energy buildings do not allow for decarbonisation of the sector over 
10 to 15 years, but they do over a longer time period. A much larger 
potential than reported here for 2030 can still be realised in the 
timeframe up to 2050 (Section 9.6.2).
GHG Agriculture

GHG Forests etc. CH4 Energy Supply CH4 Waste CO2 Buildings F-Gases CO2 Energy Supply CO2 Industry CO2 Transport0−20
20−50
50−100
100−200
0−20
20−50
50−100
100−200
0−20
20−50
50−100
100−200051015
051015
051015
Carbon Cost Levels (USD2015 tCO2–1) Emissions reduction (GtCO2 yr–1)type
sectoral (incl. indirect)
sectoral
IAM
IAM(*)Emissions reduction at different cost levels (scenarios that limit warming to 2˚C (>67%) or lower)
Figure 12.1 | Comparison of sectoral estimates for emissions reduction potential with the emissions reductions calculated using IAMs.  Emission reductions 
calculated using IAMs are given as box plots of global emissions reductions for each sector (dark blue and brown) at different global carbon cost levels (horizontal axis) for 
2030, based on all scenarios that limit warming to 2°C (>67%) or lower (see Chapter 3) in the AR6 scenarios database (IIASA 2021). For IAMs, the cost levels correspond to the

levels of the carbon price. Hinges in the dark blue box plots represent the interquartile ranges and whiskers extend to 5th and 95th percentiles while the hinges in the brown 
box plots describe the full range, and the middle point indicates the mean, not the median. In yellow, the estimates from the sectoral analysis are given. In all cases, only direct 
emissions reductions are presented, except for the light-blue boxes (for buildings), which include indirect emissions reductions. The light-blue boxes are only given for reasons of 
completeness. For buildings the dark-blue boxes should be compared with the yellow boxes. Light-blue and yellow boxes represent the full ranges of estimates. For IAMs, global 
carbon prices are applied, which are subject to significant uncertainty.

1259
1259
Cross-sectoral Perspectives Chapter 1212Another global analysis was done by McKinsey (2009), which 
presents a  marginal abatement cost curve for 2030, suggesting 
a  total potential of 38  GtCO 2-eq (note that the reference for that 
study is 70 GtCO 2-eq, which is at the high end of the reference range 
used in this assessment).
The potentials reported here are comparable with UNEP (2017). Note 
that material for the energy sector from the UNEP report was partly 
reused in this analysis. Furthermore, some options for the transport 
sector (aviation and biofuels) were identical to the estimates in the 
UNEP report. The remaining mitigation potentials are all based on 
new – and much more extended – assessment. There are some notable 
changes. The AR6 mitigation potential for forestry is substantially 
larger. For buildings the potential is smaller, mainly related to the 
smaller mitigation potential for electric appliances than in the UNEP

report. But overall, the estimates of the total mitigation potential are 
well aligned, which confirms there is substantial consistency across 
various emissions reduction estimates.
The results of the sectoral mitigation potentials are also compared 
with mitigation impacts as calculated by IAMs. To this end, 
cumulative sectoral potentials over cost ranges were determined, 
based on the information in Table  12.3. For options that are in 
various cost ranges, we assumed that they are evenly distributed 
over these cost ranges. The only exception is wind and solar energy, 
for which it is indicated that the majority of the mitigation potential 
is in the negative cost range. It was assumed that the fraction in the 
negative cost range was 60%; the remainder is evenly distributed 
over the other cost ranges. These cumulative potentials were 
compared with emissions reductions realised in IAMs at certain 
price levels for CO 2. Note that these price levels selected in IAMs

are average price levels – not all IAMs use globally uniform carbon 
prices, so underlying these cost levels, there may be regional 
differentiation. Data were taken from the AR6 scenarios database. 
Note that, strictly speaking, not all models in the database are 
IAMs; in this analysis all models in the database were used, but the term IAMs is used as shorthand in the text that follows. All 
scenarios that limit warming to 2°C (>67%) or lower are included 
for the comparison (i.e., the categories of scenarios C1 to C3 in 
Chapter 3). A comparison per sector is provided in Figure 12.1. It 
is important to note that two different things are compared in this 
figure: on the one hand emissions reduction potentials and on the 
other hand realisations of (part of) the potential within the context 
of a certain scenario. Having said that, a number of lessons can be 
learned from the comparison of both.
For the energy supply sector, the emissions reductions projected by

the IAMs are for the higher cost levels comparable with the potentials 
found in the sectoral analysis. But at lower cost levels, the emissions 
reductions as projected by IAMs are smaller than for the sectoral 
analysis. This is likely due to the fact that high costs for solar energy 
and wind energy are assumed in IAM models (Krey et al. 2019; Shiraki 
and Sugiyama 2020). This is not surprising, as the scenario database 
comprises studies dating back to 2015. A more detailed comparison 
for the power sector is given in Figure 12.2. Both the sectoral analysis 
and the IAMs find that both solar and wind energy in particular show 
strong growth potential, although there is a continuing role for other 
low-carbon technologies, like nuclear energy and hydropower.
For the AFOLU sector, the sectoral studies provide net emissions 
reduction potentials comparable with projections from the IAMs at 
costs levels up to USD50 tCO 2-eq–1. However, beyond that level the

mitigation potential found in the sectoral analysis is larger than in 
the IAMs. For agriculture, it can be explained by the fact that carbon 
sequestration options, like soil carbon, biochar and agroforestry, 
have little to no representation in IAMs. Similarly, for forestry and 
other land use-related options, the protection and restoration of 
other ecosystems than forests (peatland, coastal wetlands and 
savannas) are not represented in IAMs. Also note that some IAM 
baselines already have small carbon prices, which induce land-based 
mitigation, while in others, mitigation, particularly from reduced 
deforestation, is part of the storyline even without an implemented 
type
sectoral
IAM
010203040
Solar Wind Hydro Nuclear Biomass GeothermalAbsolute value (not difference from baseline) [EJ yr–1]2030, World
Power Generation
Scenarios that limit warming to 2°C (>67%) or lower

Figure 12.2 | Electricity production in 2030 as calculated by IAMs (dark blue), compared with electricity production potentials found in the sectoral 
analysis (yellow).  Cost cut-offs at USD100 tCO 2–1 are applied to both electricity production in 2030 as calculated by IAMs and electricity production potentials found in the 
sectoral analyses. Hinges in the dark-blue box plots represent the interquartile ranges and whiskers extend to the 5th and 95th percentiles, while the hinges in the yellow box 
plots describe the full range.

1260
1260
Chapter 12 Cross-sectoral Perspectives12carbon price. Both of these effects dampen the mitigation potential 
available in the USD100 tCO 2-eq–1 carbon price scenario from IAMs. 
Furthermore, estimates of mitigation through forestry and other land 
use-related options from the AR6 IAM scenario database represent 
the net emissions from A/R and deforestation, thus are likely to be 
lower than the sectoral estimates of A/R potential expressed as 
gross removals.
For the buildings and transport sectors, the sectoral mitigation 
potentials are higher than those projected by the IAMs. The 
difference in the transport sector is particularly significant. One 
possible explanation is that options with negative costs are already 
included in the reference. In addition, some options, like avoiding 
demand for energy services in the building sector and model shift in 
transportation, are less well represented in IAMs.
For the industry sector, the sectoral emissions reduction potentials

are somewhat higher than those reported on average by IAMs. The 
difference can well be explained by the fact that most IAMs do not 
include circularity options like material efficiency and recycling; 
these options together account for 1.5 GtCO 2-eq at costs levels from 
USD20 tCO 2-eq–1 onwards.
For mitigation of emissions of methane and fluorinated gases, the 
comparability between the sectoral results and IAMs is good.
Overall, it is concluded that there are differences between the 
sectoral analyses and the IAM outcomes, but most of the differences 
can be explained by the exclusion of specific options in most IAMs. 
This comparability confirms the reliability of the sectoral analysis of 
emissions reduction potential. It also demonstrates the added value 
of sectoral analyses of mitigation potentials: they can more rapidly 
adapt to changes in price levels of technologies and adopt new

options for emissions mitigation.In this section, the information on individual options reported  in 
Section  12.2.2 to sectoral and economy-wide totals has been 
aggregated. It is concluded that, based on the sectoral analysis, the 
global mitigation potential is in the range of 32 to 44  GtCO 2-eq. 
This mitigation potential is substantially higher than that reported 
in AR4, but it is comparable to the more recent estimate by UNEP 
(2017). Differences exist with the results of IAMs, but most of these 
can be well explained. The conclusion that the global potential is in 
this range can be drawn with high agreement and robust evidence.
Given the median projection of the reference emissions of 
60 GtCO 2-eq in 2030, the range of mitigation potentials presented 
here is sufficient to bring down global emissions in the year 2030 
to a level of 16 to 28 GtCO 2-eq. Taking into account that there is 
a range in reference projections for 2030 of 54 to 68 GtCO 2-eq, the

resulting emissions level shows a wider range: 12 to 31 GtCO 2-eq. 
This is about, or below half, the most recent (2019) emissions value 
of 59 ± 6.6 GtCO 2-eq (high confidence).
12.2.4 Sectoral Findings on Emission 
Pathways until 2050
As noted previously, a more qualitative approach is followed and less 
quantitative information is presented for 2050. The sectoral results are 
summarised in Table 12.5. In addition to the many technologies that 
already play a  role by 2030 (Table  12.3) additional technologies 
may be needed for deep decarbonisation, for example for managing 
power systems with high shares of intermittent renewable sources 
and for providing new fuels and associated infrastructure for sectors 
that are hard to decarbonise. New processes also play an important 
role, notably for industrial processes. In general, stronger sector 
coupling is needed, particularly increased integration of energy end 
use and supply sectors.

Table 12.5 | Mitigation options and their characteristics for 2050.
Sector Major options Degree to which net zero-GHG is possible
Energy sector.Range of supply-side options possible (see 2030 overview).
Increased share of electricity in final energy use.
Potentially important role for hydrogen, ammonia, etc.Zero CO 2 energy system is possible.
Agriculture, forestry 
and other land use 
(AFOLU).Options comparable to those in 2030. Permanence is important.Some hard-to-abate activities will still have positive emissions, but for 
the sector as a whole, net negative emissions are possible through carbon 
sequestration in agriculture and forestry.
Buildings.Sufficiency, high performance new and existing buildings with efficient 
heating, ventilation, and air conditioning, especially heat pumps, building 
management and operation, efficient appliances, and onsite renewables

backed up with demand flexibility and digitalisation measures.At least 8.2 GtCO 2 or 61% reduction, as compared to the baseline is 
possible with options on the demand side. This is a low estimate, because in 
some developing regions literature is not sufficient to derive a comprehensive 
estimate. Nearly net zero CO 2 emissions is possible if grid electricity 
will also be decarbonised. Carbon storage in buildings provides CDR.
Transport.Electrification can become a major option for many transport modes. 
For long-haul trucking, ships and aviation, in addition biofuels, hydrogen 
and potentially synthetic fuels can be applied.To a large extent if the electricity sector is fully decarbonised and the 
deployment of alternative fuels for long-haul trucking, aviation and shipping 
is successful.
Industry.Stronger role for material efficiency and recycling.
Full decarbonisation through new processes; CCS, CCU and hydrogen

can become dominant.Approx. 85% reduction is possible. Net zero CO 2 emissions is possible 
with retrofitting and early retirement.
Cross-sectoral.Direct air carbon capture and storage.
Enhanced weathering.
Ocean-based methods.Contributes CDR to support net zero GHG by counterbalancing 
sectoral emissions.

1261
1261
Cross-sectoral Perspectives Chapter 121212.3 Carbon Dioxide Removal
Carbon dioxide removal (CDR) refers to a  cluster of technologies, 
practices, and approaches that remove and sequester carbon dioxide 
from the atmosphere and durably store the carbon in geological, 
terrestrial, or ocean reservoirs, or in products. Despite the common 
feature of removing carbon dioxide, CDR methods can be very 
different (Smith et al. 2017). There are proposed methods for removal 
of non-CO 2 greenhouse gases such as methane (Jackson et al. 2019; 
Jackson et al. 2021) but scarcity of literature on these methods 
prevents assessment here.
A number of CDR methods (e.g.,  afforestation/reforestation 
(A/R), bioenergy with carbon capture and storage (BECCS), soil carbon sequestration (SCS), biochar, wetland/peatland 
restoration and coastal restoration) are dealt with elsewhere in 
this report (Chapters  6  and 7). These methods are synthesised

in Section  12.3.2. Others, not dealt with elsewhere,  – direct air 
carbon capture and storage (DACCS), enhanced weathering (EW) of 
minerals and ocean-based approaches including ocean fertilisation 
(OF) and ocean alkalinity enhancement (OAE) – are discussed in 
Sections  12.3.1.1 to 12.3.1.3 below (see also IPCC 2019b and 
AR6 WGI, Section 5.6). Some methods, such as BECCS and DACCS, 
involve carbon storage in geological formations, which is discussed 
in Chapter 6. The climate system and the carbon cycle responses to 
CDR deployment and each method’s physical and biogeochemical 
characteristics such as storage form and duration are assessed in 
Chapters 4 and 5 of the AR6 WGI report.
Cross-Chapter Box 8 | Carbon Dioxide Removal: Key Characteristics and Multiple Roles 
in Mitigation Strategies
Authors: Oliver Geden (Germany), Alaa Al Khourdajie (United Kingdom/Syria), Christopher Bataille (Canada), Göran Berndes

(Sweden), Holly Jean Buck (the United States of America), Katherine Calvin (the United States of America), Annette Cowie (Australia), 
Kiane  de  Kleijne (the Netherlands), Jan Christoph Minx (Germany), Gert-Jan Nabuurs (the Netherlands), Glen P .  Peters (Norway/
Australia), Andy Reisinger (New Zealand), Pete Smith (United Kingdom), Masahiro Sugiyama (Japan)
Carbon dioxide removal (CDR) is a necessary element of mitigation portfolios to achieve net zero CO 2 and GHG emissions both globally 
and nationally, counterbalancing residual emissions from hard-to-transition sectors such as industry, transport and agriculture. CDR 
is a key element in scenarios that limit warming to 2°C (>67%) or lower, regardless of whether global emissions reach near-zero, net 
zero or net-negative levels (Sections 3.3, 3.4, 3.5 and 12.3). While national mitigation portfolios aiming at net zero or net-negative

emissions will need to include some level of CDR, the choice of methods and the scale and timing of their deployment will depend on 
the ambition for gross emissions reductions, how sustainability and feasibility constraints are managed, and how political preferences 
and social acceptability evolve (Section 12.3.3). This box gives an overview of CDR methods, presents a categorisation based on 
the key characteristics of removal processes and storage timescales, and clarifies the multiple roles of CDR in mitigation strategies. 
The term ‘negative emissions’ is used in this report only when referring to the net emissions outcome at a systems level (e.g., ‘net 
negative emissions’ at global, national, sectoral or supply chain levels).
Categorisation of the main CDR methods
CDR refers to anthropogenic activities that remove CO 2 from the atmosphere and store it durably in geological, terrestrial, or ocean

reservoirs, or in products. It includes anthropogenic enhancement of biological, geochemical or chemical CO 2 sinks, but excludes 
natural CO 2 uptake not directly caused by human activities. Increases in land carbon sink strength due to CO 2 fertilisation or other 
indirect effects of human activities are not considered CDR (see Glossary). Carbon capture and storage (CCS) and carbon capture and 
utilisation (CCU) applied to CO 2 from fossil fuel use are not CDR methods as they do not remove CO 2 from the atmosphere. CCS and 
CCU can, however, be part of CDR methods if the CO 2 has been captured from the atmosphere, either indirectly in the form of biomass 
or directly from ambient air, and stored durably in geological reservoirs or products (Sections 11.3.6 and 12.3).
There are many different CDR methods and associated implementation options (Cross-Chapter Box  8, Figure  1). Some of these

methods (including afforestation and improved forest management, wetland restoration and soil carbon sequestration (SCS)) have 
been practised for decades to millennia, although not necessarily with the intention of removing carbon from the atmosphere. 
Conversely, methods such as direct air carbon capture and storage (DACCS), bioenergy with carbon capture and storage (BECCS) 
and enhanced weathering are novel, and while experience is growing, their demonstration and deployment are limited in scale. CDR 
methods have been categorised in different ways in the literature, highlighting different characteristics. In this report, as in AR6 WGI, 
the categorisation is based on the role of CDR methods in the carbon cycle, that is, on the removal process (land-based biological; 
ocean-based biological; geochemical; chemical) and on the timescale of storage (decades to centuries; centuries to millennia;

ten thousand years or longer). The time scale of storage is closely linked to the storage medium: carbon stored in ocean reservoirs 
(through enhanced weathering, ocean alkalinity enhancement or ocean fertilisation) and in geological formations (through BECCS or 
DACCS) generally has longer storage times and is less vulnerable to reversal through human actions or disturbances such as drought 
and wildfire than carbon stored in terrestrial reservoirs (vegetation, soil). Furthermore, carbon stored in vegetation or through SCS has

1262
1262
Chapter 12 Cross-sectoral Perspectives12shorter storage times and is more vulnerable than carbon stored in buildings as wood products; as biochar in soils, cement and other 
materials; or in chemical products made from biomass or potentially through direct air (Fuss et al. 2018; Minx et al. 2018; NASEM 
2019) capture (Section 11.3.6; AR6 WGI, Figure 5.36). Within the same category (e.g., land-based biological CDR) options often differ 
with respect to other dynamic or context-specific dimensions, such as mitigation potential, cost, potential for co-benefits and adverse 
side effects, and technology readiness level (Table 12.6).
Roles of CDR in mitigation strategies
Within ambitious mitigation strategies at global or national levels, CDR cannot serve as a substitute for deep emissions reductions but 
can fulfil multiple complementary roles: it can (i) further reduce net CO 2 or GHG emission levels in the near-term; (ii) counterbalance

residual emissions from hard-to-transition sectors, such as CO 2 from industrial activities and long-distance transport (e.g., aviation, 
shipping), or methane and nitrous oxide from agriculture, in order to help reach net zero CO 2 or GHG emissions in the mid-term; 
(iii) achieve and sustain net-negative CO 2 or GHG emissions in the long-term, by deploying CDR at levels exceeding annual residual 
gross CO 2 or GHG emissions (Sections 2.7.3 and 3.5).
In general, these roles of CDR are not mutually exclusive and can exist in parallel. For example, achieving net zero CO 2 or GHG 
emissions globally might involve some countries already reaching net-negative levels at the time of global net zero, allowing 
other countries more time to achieve this. Equally, achieving net-negative CO 2 emissions globally, which could address a potential 
temperature overshoot by lowering atmospheric CO 2 concentrations, does not necessarily involve all countries reaching net-negative

levels (Rajamani et al. 2021; Rogelj et al. 2021) (Cross-Chapter Box 3 in Chapter 3).
Cross-Chapter Box 8, Figure 2 shows these multiple roles of CDR in a stylised ambitious mitigation pathway that can be applied to 
global and national levels. While such mitigation pathways will differ in their shape and exact composition, they include the same 
basic components: CO 2 emissions from fossil sources, CO 2 emissions from managed land, non-CO 2 emissions, and various forms of 
CDR. Cross-Chapter Box 8, Figure 2 also illustrates the importance of distinguishing between gross CO 2 removals from the atmosphere 
through deployment of CDR methods and the net emissions outcome (i.e., gross emissions minus gross removals).
CDR methods currently deployed on managed land, such as afforestation or reforestation and improved forest management, lead to CO 2 
removals already today, even when net emissions from land use are still positive, for example, when gross emissions from deforestation

and draining peatlands exceed gross removals from afforestation or reforestation and ecosystem conservation (Sections 2.2 and 7.2; Cross-Chapter Box 8 (continued)
Cross-Chapter Box 8, Figure 1 | Carbon dioxide removal taxonomy. Methods are categorised based on removal process (grey shades) and 
storage medium (for which timescales of storage are given, yellow/brown shades). Main implementation options are included for each CDR method. 
Note that specific land-based implementation options can be associated with several CDR methods, for example, agroforestry can support soil carbon sequestration 
and provide biomass for biochar or BECCS. Source: adapted from Minx et al. (2018).

1263
1263
Cross-sectoral Perspectives Chapter 1212Cross-Chapter Box  6  in Chapter  7). As there are currently no removal methods for non-CO 2 gases that have progressed beyond 
conceptual discussions (Jackson et al. 2021), achieving net zero GHG implies gross CO 2 removals to counterbalance residual emissions 
of both CO 2 and non-CO 2 gases, applying 100-year global warming potential (GWP100) as the metric for reporting CO 2-equivalent 
emissions, as required for emissions reporting under the Rulebook of the Paris Agreement (Cross-Chapter Box 2 in Chapter 2).
Net zero CO 2 emissions will be achieved earlier than net zero GHG emissions. As volumes of residual non-CO 2 emissions are expected 
to be significant, this time-lag could reach one to several decades, depending on the respective size and composition of residual GHG 
emissions at the time of net zero CO 2 emissions. Furthermore, counterbalancing residual non-CO 2 emissions by CO 2 removals will lead

to net-negative CO 2 emissions at the time of net zero GHG emissions (Cross-Chapter Box 3 in Chapter 3).Cross-Chapter Box 8 (continued)
2010 2100Gross emissions
Gross CO2 removalsnet zero net zero
(1) Before net zero (2) Net zero CO2 or GHG (3) Net negativeGreenhouse gas emissions (stylised pathway)
Emissions: Non-CO2 GHGs
Emissions: Fossil CO2
Emissions: Managed land
CDR: Removals on managed land
CDR: Other removals
Net GHG emissions
Net CO2 emissions
Cross-Chapter Box 8, Figure 2 | Roles of CDR in global or national mitigation strategies.  Stylised pathway showing multiple functions of CDR in different 
phases of ambitious mitigation: (1) further reducing net CO 2 or GHG emissions levels in near-term; (2) counterbalancing residual emissions to help reach net zero CO 2 
or GHG emissions in the mid-term; (3) achieving and sustaining net-negative CO 2 or GHG emissions in the long-term.
While many governments have included A/R and other forestry

measures in their NDCs under the Paris Agreement (Moe and 
Røttereng 2018; Fyson and Jeffery 2019; Mace et al. 2021), and a few 
countries also mention BECCS, DACCS and enhanced weathering 
in their mid-century low emission development strategies (Buylova 
et al. 2021), very few are pursuing the integration of a broad range 
of CDR methods into national mitigation portfolios so far (Schenuit 
et al. 2021) (Box 12.1). There are concerns that the prospect of large-
scale CDR could, depending on the design of mitigation strategies, 
obstruct near-term emissions reduction efforts (Lenzi et al. 2018; 
Markusson et al. 2018), mask insufficient policy interventions (Geden 
2016; Carton 2019), might lead to an overreliance on technologies 
that are still in their infancy (Anderson and Peters 2016; Larkin et al. 
2018; Grant et al. 2021), could overburden future generations (Lenzi 
2018; Shue 2018; Bednar et al. 2019) might evoke new conflicts over

equitable burden-sharing (Pozo et al. 2020; Lee et al. 2021; Mohan et al. 2021), could impact food security, biodiversity or land rights 
(Buck 2016; Boysen et al. 2017; Dooley and Kartha 2018; Hurlbert 
et al. 2019; Dooley et al. 2021), or might be perceived negatively 
by stakeholders and broader public audiences (Royal Society and 
Royal Academy of Engineering 2018; Colvin et al. 2020). Conversely, 
without considering different timescales of carbon storage (Fuss 
et al. 2018; Hepburn et al. 2019) and implementation of reliable 
measurement, reporting and verification of carbon flows (Mace et al. 
2021), CDR deployment might not deliver the intended benefit of 
removing CO 2 durably from the atmosphere. Furthermore, without 
appropriate incentive schemes and market designs (Honegger et al. 
2021b), CDR implementation options could see under-investment. 
The many challenges in research, development and demonstration 
of novel approaches, to advance innovation according to broader

societal objectives and to bring down costs, could delay their scaling 
up and deployment (Nemet et al. 2018). Depending on the scale

1264
1264
Chapter 12 Cross-sectoral Perspectives12
and deployment scenario, CDR methods could bring about various 
co-benefits and adverse side effects (see below). All this highlights the 
need for appropriate CDR governance and policies (Section 12.3.3).
The volumes of future global CDR deployment assumed in IAM-
based mitigation scenarios are large compared to current volumes of deployment, which presents a challenge since rapid and sustained 
upscaling from a small base is particularly difficult (de Coninck et al. 
2018; Nemet et al. 2018; Hanna et al. 2021). All Illustrative Mitigation 
Pathways (IMPs) that limit warming to 2°C (>67%) or lower use 
some form of CDR.  Across the full range of similarly ambitious 
IAM scenarios (scenario categories C1 to C3; see Section 3.3), the GtCO2 yr–1GtCO2 yr–1BECCS Removal from AFOLU DACCS2000
2025
2050
2075
2100
2000
2025
2050
2075
2100
2000
2025
2050
2075
210001020IMP marker
IMP-GS
IMP-LD
IMP-Neg
IMP-Ren
IMP-SP
BECCS

(n=516)Removal from AFOLU
(n=344)DACCS
(n=197)2020−2030
2020−2050
2020−2100
2020−2030
2020−2050
2020−2100
2020−2030
2020−2050
2020−21000200400600800IMP marker
IMP-GS
IMP-LD
IMP-Neg
IMP-Ren
IMP-SPCumulative sequestrationAnnual CO2 sequestration
Figure 12.3 | Sequestration through three predominant CDR methods: BECCS, CO 2 removal from AFOLU (mainly A/R), and DACCS (upper panels) annual 
sequestration and (lower panels) cumulative sequestration.  The IAM scenarios described in the figure correspond to those that limit warming to 2°C (>67%) or lower. 
The black line in each of the upper panels indicates the median of all the scenarios in categories C1 to C3. Hinges in the lower panels represent the interquartile ranges while 
whiskers extend to 5th and 95th percentiles. The IMPs are highlighted with colours, as shown in the key. The number of scenarios is indicated in the header of each panel. 
The number of scenarios with a non-zero DACCS value is 146.

1265
1265
Cross-sectoral Perspectives Chapter 1212reported annual CO 2 removal from AFOLU (mainly A/R) reaches 
0.86 [0.01–4.11] GtCO 2 yr–1 by 2030, 2.98 [0.23–6.38] GtCO 2 yr–1 
by 2050, and 4.19 [0.1–6.91]  GtCO 2 yr–1 by 2100 (values are the 
medians and bracketed values denote the 5–95th percentile range1). 
The annual BECCS deployment is 0.08 [0–1.09]  GtCO 2 yr–1, 
2.75 [0.52–9.45] GtCO 2 yr–1, and 8.96 [2.63–16.15] GtCO 2 yr–1 for 
these years, respectively. The annual DACCS deployment eaches 
0 [0–0.02] GtCO 2 yr–1 by 2030, 0.02 [0–1.74] GtCO 2 yr–1 by 2050, and 
1.02 [0–12.6] GtCO 2 yr–1 by 2100 (Figure 12.3).2 Reported cumulative 
volumes of BECCS, CO 2 removal from AFOLU, and DACCS reach 
328 [168–763] GtCO 2, 252 [20–418] GtCO 2, and 29 [0–339] GtCO 2 
for the 2020–2100 period, respectively. Reaching the higher end 
of CDR volumes is subject to issues regarding their feasibility (see 
below), especially if achieved with only a  limited number of CDR

methods. Recent studies have identified some drivers for large-
scale CDR deployment in IAM scenarios, including insufficient 
representation of variable renewables, a high discount rate that tends 
to increase initial carbon budget overshoot and therefore inflates 
usage  of  CDR to achieve net-negative emissions at later times, 
omission of CDR  methods aside from BECCS and A/R (Emmerling 
et al. 2019; Hilaire et al. 2019; Köberle 2019), and limited deployment 
of demand-side options (Grubler et al. 2018; van Vuuren et al. 
2018; Daioglou et al. 2019). The levels of CDR in IAMs in modelled 
pathways would change depending on the allowable overshoot of 
policy targets such as temperature or radiative forcing and the costs 
of non-CDR mitigation options (Johansson et al. 2020; van der Wijst 
et al. 2021) (Section 3.2.2).
While many CDR methods are gradually being explored, IAM scenarios 
have focused mostly on BECCS and A/R (Tavoni and Socolow 2013;

Fuhrman et al. 2019; Rickels et al. 2019; Calvin et al. 2021; Diniz 
Oliveira et al. 2021). Although some IAM studies have also included 
other methods such as DACCS (Chen and Tavoni 2013; Marcucci et al. 
2017; Realmonte et al. 2019; Fuhrman et al. 2020; Akimoto et al. 2021; 
Fuhrman et al. 2021a), enhanced weathering (Strefler et al. 2021), SCS 
and biochar (Holz et al. 2018) there is much less literature compared 
to studies on BECCS (Hilaire et al. 2019). A  large-scale coordinated 
IAM study on BECCS (‘EMF-33’) has been conducted (Muratori et al. 
2020; Rose et al. 2020) but none exists for other CDR methods. A recent 
review proposes a combination of various CDR methods (Fuss et al. 
2018) but more in-depth literature on such a  portfolio approach is 
limited (Strefler et al. 2021). A  multi-criteria analysis has identified 
pathways with CDR portfolios different from least-cost pathways often 
dominated by BECCS and A/R (Rueda et al. 2021).

At the national and regional levels, the role of land-based biological 
CDR methods has long been analysed, but there is little detailed 
techno-economic assessment of the role of other CDR methods. There 
is a  small but emerging literature providing such assessments for 
developed countries (Kraxner et al. 2014; Baik et al. 2018; Daggash 
et al. 2018; Patrizio et al. 2018; Sanchez et al. 2018; Breyer et al. 2019; 
Kato and Kurosawa 2019; Larsen et al. 2019; McQueen et al. 2020; 
1 Cumulative levels of CDR from AFOLU cannot be quantified precisely given that: (i) some pathways assess CDR deployment relative to a baseline; and (ii) different models 
use different reporting methodologies that in some cases combine gross emissions and removals in AFOLU. Total CDR from AFOLU equals or exceeds the net negative 
emissions mentioned.

2 We use representative options for labels of each variable reported in the AR6 scenarios database.Bistline and Blanford 2021; García-Freites et al. 2021; Jackson et al. 
2021; Kato and Kurosawa 2021; Negri et al. 2021) while the literature 
outside developed countries is limited (Alatiq et al. 2021; Fuhrman 
et al. 2021b; Weng et al. 2021).
In IAMs, CDR is contributed mainly by the energy sector (through 
BECCS) and AFOLU (through A/R) (Figure 12.3). IAMs are starting to 
include other CDR methods, such as DACCS and enhanced weathering 
(Section 12.3.1), which are yet to be attributed to specific sectors in 
IAMs. Following IPCC guidance for UNFCCC inventories, A/R and SCS 
are reported in land use, land-use change and forestry (LULUCF), while 
BECCS would be reported in the sector where the carbon capture occurs, 
that is, the energy sector in the case of electricity and heat production, 
and the industry sector for BECCS linked to manufacturing (e.g., steel

or hydrogen) (Tanzer et al. 2020; Bui et al. 2021; Tanzer et al. 2021).
12.3.1 CDR Methods Not Assessed Elsewhere 
in This Report: DACCS, Enhanced Weathering 
and Ocean-based Approaches
This section assesses the CDR methods that are not carried out solely 
within conventional sectors and so are not covered in other parts of the 
report: direct air carbon capture and storage, enhanced weathering, 
and ocean-based approaches. It provides an overview of each CDR 
method: their costs, potentials, risks and impacts, co-benefits, and 
their role in mitigation pathways. Since these processes, approaches 
and technologies have medium to low technology readiness levels, 
they are subject to significant uncertainty.
12.3.1.1 Direct Air Carbon Capture and Storage (DACCS)
Direct air capture (DAC) is a  chemical process to capture ambient 
CO2 from the atmosphere. Captured CO 2 can be stored underground 
(direct air carbon capture and storage, DACCS) or utilised in

products (direct air carbon capture and utilisation, DACCU). DACCS 
shares with conventional CCS the transport and storage components 
but is distinct in its capture part. Because CO 2 is a well-mixed GHG, 
DACCS can be sited relatively flexibly, though its locational flexibility 
is constrained by the availability of low-carbon energy and storage 
sites. Capturing the CO 2 involves three basic steps: (i) contacting the 
air, (ii) capturing on a liquid or solid sorbent or a liquid solvent, and 
(iii) regeneration of the solvent or the sorbent (with heat, moisture 
and/or pressure). After capture, the CO 2 stream can be stored 
underground or utilised. The duration of storage is an important 
consideration; geological reservoirs or mineralisation result in removal 
for more than 1000 years. The duration of the removal through DACCU 
(Breyer et al. 2019) varies with the lifetime of respective products 
(Wilcox et al. 2017; Bui et al. 2018; Fuss et al. 2018; Gunnarsson

et al. 2018; Royal Society and Royal Academy of Engineering 2018; 
Creutzig et al. 2019), ranging from weeks to months for synthetic 
fuels to centuries or more for building materials (e.g., concrete cured

1266
1266
Chapter 12 Cross-sectoral Perspectives12using mineral carbonation) (Hepburn et al. 2019). The efficiency and 
environmental impacts of DACCS and DACCU options depend on 
the carbon intensity of the energy input (electricity and heat) and 
other lifecycle assessment (LCA) considerations (Zimmerman 2018; 
Jacobson 2019). See Chapters 6 and 11 for further details regarding 
carbon capture and utilisation. Another key consideration is the net 
carbon CO 2 removal of DACCS over its lifecycle (Madhu et al. 2021). 
Deutz and Bardow (2021) and Terlouw et al. (2021) demonstrated 
that the life-cycle net emissions of DACCS systems can be negative, 
even for existing supply chains and some current energy mixes. They 
found that the GHG intensity of energy sources is a key factor.
DAC options can be differentiated by the specific chemical processes 
used to capture ambient CO 2 from the air and recover it from the

sorbent (Fasihi et al. 2019). The main categories are (i) liquid solvents 
with high-temperature regeneration, (ii) solid sorbents with low-
temperature regeneration and (iii) regenerating by moisturising of 
solid sorbents. Other approaches such as electro-swing (Voskian and 
Hatton 2019) have been proposed but are less developed. Compared 
to other CDR methods, the primary barrier to upscaling DAC is its high 
cost and large energy requirement (high confidence) (Nemet et al. 
2018), which can be reduced through innovation. It has therefore 
attracted entrepreneurs and private investments (IEA 2020b).
Status: There are some demonstration projects by start-up companies 
and academic researchers, who are developing various types of DAC, 
including aqueous potassium solvent with calcium carbonation and 
solid sorbents with heat regeneration (NASEM 2019). These projects 
are supported mostly by private investments and grants or sometimes

serve utilisation niche markets (e.g., CO 2 for beverages, greenhouses, 
enhanced oil recovery). As of 2021, there are more than ten plants 
worldwide, with a scale of ktCO 2 yr–1 or smaller (Larsen et al. 2019; 
NASEM 2019; IEA 2020b). Because of the fundamental difference in 
the CO 2 concentration at the capture stage, DACCS does not benefit 
directly from research, development and demonstration (RD&D) of 
conventional CCS. Public RD&D programmes dedicated to DAC have 
therefore been proposed (Larsen et al. 2019; NASEM 2019). Possible 
research topics include development of new liquid solvents, novel 
solid sorbents, and novel equipment or system designs, and the need 
for third-party evaluation of techno-economic aspects has also been 
emphasised (NASEM 2019). However, since basic research does 
not appear to be a primary barrier, both NASEM (2019) and Larsen 
et al. (2019) argue for a stronger focus on demonstration in the US

context. Though the US and UK governments have begun funding 
DACCS research (IEA 2020b), the scale of R&D activities is limited.
Costs: As the process captures dilute CO 2 (~0.04%) from the 
ambient air, it is less efficient and more costly than conventional 
carbon capture applied to power plants and industrial installations 
(with a  CO 2 concentration of ~10%) (high confidence). The cost 
of a liquid solvent system is dominated by the energy cost (because 
of the much higher energy demand for CO 2 regeneration, which 
reduces the efficiency) while capital costs account for a significant 
share of the cost of solid sorbent systems (Fasihi et al. 2019). The 
range of the DAC cost estimates found in the literature is wide 
(USD60–1000 tCO 2–1) (Fuss et al. 2018) partly because different 
studies assume different use cases, differing phases (first plant vs nth plant) (Lackner et al. 2012), different configurations, and

disparate system boundaries. Estimates of industrial origin are often 
on the lower side (Ishimoto et al. 2017). Fuss et al. (2018) suggest 
a cost range of USD600–1000 tCO 2–1 for first-of-a-kind plants, and 
USD100–300 tCO 2–1 as experience accumulates. An expert elicitation 
study found a similar cost level for 2050 with a median of around 
USD200 tCO 2–1 (Shayegh et al. 2021) (medium evidence, medium 
agreement). NASEM (2019) systematically evaluated the costs of 
different designs and found a range of 84–386 USD2015 tCO 2–1 for 
the designs currently considered by active technology developers. This 
cost range excludes the site-specific costs of transportation or storage.
Potentials: There is no specific study on the potential of DACCS but 
the literature has assumed that the technical potential is virtually 
unlimited provided that high energy requirements could be met 
(medium evidence, high agreement) (Marcucci et al. 2017; Fuss

et al. 2018; Lawrence et al. 2018) since DACCS encounters fewer 
non-cost constraints than any other CDR method. Focusing only 
on the Maghreb region, Breyer et al. (2020) reported an optimistic 
potential 150 GtCO 2 at less than USD61 tCO 2–1 for 2050. Fuss et al. 
(2018) suggest a potential of 0.5–5 GtCO 2 yr–1 by 2050 because of 
environmental side effects and limits to underground storage. In 
addition to the ultimate potentials, Realmonte et al. (2019) noted 
the rate of scale-up as a strong constraint on deployment. Meckling 
and Biber (2021) discuss a policy roadmap to address the political 
economy for upscaling. More systematic analysis on potentials is 
necessary; first and foremost on national and regional levels, including 
the requirements for low-carbon heat and power, water and material 
demand, availability of geological storage and the need for land in 
case of low-density energy sources such as solar or wind power.

Risks and impacts: DACCS requires a considerable amount of energy 
(high confidence), depending on the type of technology, water, and 
make-up sorbents, while its land footprint is small compared to other 
CDR methods (Smith et al. 2016). Yet, depending on the source of 
energy for DACCS (e.g., renewables vs nuclear), DACCS could require 
a significant land footprint (NASEM 2019; Sekera and Lichtenberger 
2020). The theoretical minimum energy requirement for separating 
CO2 gas from the air is about 0.5 GJ tCO 2–1 (Socolow et al. 2011). 
Fasihi et al. (2019) reviewed the published estimates of energy 
requirements and found that for the current technologies, the total 
energy requirement is about 4–10 GJ tCO 2–1, with heat accounting 
for about 80% and electricity about 20% (McQueen et al. 2021). 
At a  10  GtCO 2 yr–1 sequestration scale, this would translate into 
40–100 exajoules (EJ) yr–1 of energy consumption (32–80 EJ yr–1 for

heat and 8–20 EJ yr–1 electricity), which can be contrasted with the 
current primary energy supply of about 600  EJ yr–1 and electricity 
generation of about 100 EJ yr–1. For the solid sorbent technology, 
low-temperature heat could be sourced from heat pumps powered 
by low-carbon sources such as renewables (Breyer et al. 2020), waste 
heat (Beuttler et al. 2019), and nuclear energy (Sandalow et al. 2018). 
Unless sourced from a clean source, this amount of energy could cause 
environmental damage (Jacobson 2019). Because DACCS is an open 
system, water lost from evaporation must be replenished. Water loss 
varies, depending on technology (including adjustable factors such 
as the concentration of the liquid solvent) as well as environmental 
conditions (e.g., temperate vs tropical climates). For a liquid solvent

1267
1267
Cross-sectoral Perspectives Chapter 1212system, it can be 0–50 tH 2O tCO 2–1 (Fasihi et al. 2019). A water loss 
rate of about 1–10 tH 2O tCO 2–1 (Socolow et al. 2011) would translate 
into about 10–100 GtH 2O (10–100 km3) to capture 10 GtCO 2 from 
the atmosphere. Some solid sorbent technologies actually produce 
water as a by-product, for example 0.8–2 tH 2O tCO 2–1 for a solid-
sorbent technology with heat regeneration (Beuttler et al. 2019; 
Fasihi et al. 2019). Large-scale deployment of DACCS would also 
require a  significant quantity of materials, and energy to produce 
them (Chatterjee and Huang 2020). Hydroxide solutions are currently 
being produced as a  by-product of chlorine but replacement 
(make-up) requirement of such materials at scale exceeds the current 
market supply (Realmonte et al. 2019). The land requirements for 
DAC units are not large enough to be of concern (Madhu et al. 2021). 
Furthermore, these can be placed on unproductive lands, in contrast

to biological CDR. Nevertheless, to ensure that CO 2-depleted air does 
not enter the air contactor of an adjacent DAC system, there must be 
enough space between DAC units, similar to wind power turbines. 
Considering this, Socolow et al. (2011) estimated a land footprint of 
1.5 km2 MtCO 2–1. In contrast, large energy requirements can lead to 
significant footprints if low-density energy sources (e.g., solar PV) are 
used (Smith et al. 2016). For the issues associated with CO 2 utilisation 
and storage, see Chapter 6.
Co-benefits: While Wohland et al. (2018) proposed solid sorbent-
based DAC plants as a Power-to-X technology that could use excess 
renewable power (at times of low or even negative prices), such 
operation would add additional costs. Installations would need to 
be designed for intermittent operations (i.e., at low load factors) 
which would negatively affect capital and operation costs (Daggash 
et al. 2018; Sandalow et al. 2018) as a high time-resolution model

suggests a  high utilisation rate (Breyer et al. 2020). Solid sorbent 
DAC designs can potentially remove more water from the ambient 
air than needed for regeneration, thereby delivering surplus water 
that would contribute to SDG 6 (clean water and sanitation) in arid 
regions (Sandalow et al. 2018; Fasihi et al. 2019).
Trade-offs and spillover effects: Liquid solvent DACCS systems 
need substantial amounts of water (Fasihi et al. 2019), although 
much less than BECCS systems (Smith et al. 2016), which could 
negatively affect SDG 6 (clean water and sanitation). Although the 
high energy demand of DACCS could affect SDG 7 (affordable and 
clean energy) negatively through potential competition or positively 
through learning effects (Beuttler et al. 2019), its impact has not been 
thoroughly assessed yet.
Role in mitigation pathways: There are a few IAM studies that 
have explicitly incorporated DACCS. Stringent emissions constraints

in these studies lead to high carbon prices, allowing DACCS to 
play an important role in mitigation. Chen and Tavoni (2013) 
examined the role of DACCS in an IAM (WITCH) and found that 
incorporating DACCS reduces the overall cost of mitigation and tends 
to postpone the timing of mitigation. The scale of capture goes up 
to 37 GtCO 2 yr–1 in 2100. Akimoto et al. (2021) introduced DACCS 
in the IAM DNE21+, and also found the long-term marginal cost of 
abatement is significantly reduced by DACCS. Marcucci et al. (2017) 
ran MERGE-ETL, an integrated model with endogenous learning, and 
showed that DACCS allows for a model solution for the 1.5°C target, and that DACCS substitutes for BECCS under stringent targets. In their 
analysis, DACCS captures up to 38.3 GtCO 2 yr–1 in 2100. Realmonte 
et al. (2019) modelled two types of DACCS (based on liquid and solid 
sorbents) with two IAMs (TIAM-Grantham and WITCH), and showed

that in deep mitigation scenarios, DACCS complements, rather than 
substitutes, other CDR methods such as BECCS, and that DACCS is 
effective at containing mitigation costs. At the national scale, Larsen 
et al. (2019) utilised the Regional Investment and Operations (RIO) 
Platform coupled with the Energy PATHWAYS model, and explicitly 
represented DAC in US energy systems scenarios. They found that in 
a scenario that reaches net zero emissions by 2045, about 0.6 GtCO 2 
or 1.8  GtCO 2 of DACCS would be deployed, depending on the 
availability of biological carbon sinks and bioenergy. The modelling 
supporting the European Commission’s initial proposal for net zero 
GHG emissions by 2050 incorporated DAC, with the captured CO 2 
used for both synthetic fuel production (DACCU) and storage (DACCS) 
(Capros et al. 2019). Fuhrman et al. (2021a) evaluated the role of 
DACCS across five shared socio-economic pathways with the GCAM

modelling framework and identified a substantial role for DACCS in 
mitigation and a  decreased pressure on land and water resources 
from BECCS, even under the assumption of limited energy efficiency 
improvement and conservative cost declines of DACCS technologies. 
The newest iteration of the World Economic Outlook by IEA (2021b) 
deploys CDR on a limited scale, and DACCS removes 0.6 GtCO 2 in 
2050 for its Net Zero CO 2 Emissions scenario.
Status, costs, potentials, risk and impacts, co-benefits, trade-offs and 
spillover effects and the role in mitigation pathways of DACCS are 
summarised in Table 12.6.
12.3.1.2 Enhanced Weathering
Enhanced weathering involves (i) the mining of rocks containing 
minerals that naturally absorb CO 2 from the atmosphere over 
geological timescales (as they become exposed to the atmosphere 
through geological weathering), (ii) the comminution of these rocks 
to increase the surface area, and (iii) the spreading of these crushed

rocks on soils (or in the ocean/coastal environments; Section 12.3.1.3) 
so that they react with atmospheric CO 2 (Schuiling and Krijgsman 
2006; Hartmann et al. 2013; Beerling et al. 2018; Goll et al. 2021). 
Construction waste and waste materials from mining can also be used 
as a  source material for enhanced weathering. Silicate rocks such 
as basalt, containing minerals rich in calcium and magnesium and 
lacking metal ions such as nickel and chromium, are most suitable for 
enhanced weathering (Beerling et al. 2018); they reduce soil solution 
acidity during dissolution, and promote the chemical transformation 
of CO 2 to bicarbonate ions. The bicarbonate ions can precipitate in 
soils and drainage waters as a  solid carbonate mineral (Manning 
2008), or remain dissolved and increase alkalinity levels in the ocean 
when the water reaches the sea (Renforth and Henderson 2017). The 
modelling study by Cipolla et al. (2021) found that rate of weathering

is greater in high rainfall environments, and was increased by organic 
matter amendment.
Status: Enhanced weathering has been demonstrated in the 
laboratory and in small-scale field trials (TRL 3–4) but has yet to 
be demonstrated at scale (Beerling et al. 2018; Amann et al. 2020).

1268
1268
Chapter 12 Cross-sectoral Perspectives12The chemical reactions are well understood (Manning 2008; Gillman 
1980; Gillman et al. 2001), but the behaviour of the crushed rocks 
in the field and potential co-benefits and adverse side effects of 
enhanced weathering require further research (Beerling et al. 2018). 
Small-scale laboratory experiments have calculated weathering 
rates that are orders of magnitude slower than the theoretical limit 
for mass transfer-controlled forsterite (Renforth et al. 2015; Amann 
et al. 2020) and basalt dissolution (Kelland et al. 2020). Uncertainty 
surrounding silicate mineral dissolution rates in soils, the fate of the 
released products, the extent of legacy reserves of mining by-products 
that might be exploited, location and availability of rock extraction 
sites, and the impact on ecosystems remain poorly quantified and 
require further research to better understand feasibility (Renforth

2012; Moosdorf et al. 2014; Beerling et al. 2018). Closely monitored, 
large-scale demonstration projects would allow these aspects to be 
studied (Smith et al. 2019a; Beerling et al. 2020).
Costs: Fuss et al. (2018), in a  systematic review of the costs and 
potentials of CDR methods including enhanced weathering, note 
that costs are closely related to the source of the rock and the 
technology used for rock grinding and material transport (Renforth 
2012; Hartmann et al. 2013; Strefler et al. 2018). Due to differences 
in the methods and assumptions between studies, literature 
ranges are highly uncertain and range from USD15–40 tCO 2–1 to 
USD3460 tCO 2–1 (Köhler et al. 2010; Taylor et al. 2016). Renforth 
(2012) reported operational costs in the UK of applying mafic 
rocks (rocks with high magnesium and iron silicate mineral 
concentrations) of USD70–578 tCO 2–1, and for ultramafic rocks 
(rocks rich in magnesium and iron silicate minerals but with very low

silica content  – the low silica content enhances weathering rates) 
of USD24–123 tCO 2–1. Beerling et al. (2020) combined a  spatially 
resolved weathering model with a  techno-economic assessment 
to suggest costs of between USD54–220 tCO 2–1 (with a  weighted 
mean of USD118–128 tCO 2–1). Fuss et al. (2018) suggested an 
author judgement cost range of USD50–200 tCO 2–1 for a potential of 
2–4 GtCO 2 yr−1 from 2050, excluding biological storage.
Potentials: In a  systematic review of the costs and potentials 
of enhanced weathering, Fuss et al. (2018) report a wide range of 
potentials (limited evidence, low agreement). The highest reported 
regional sequestration potential, 88.1  GtCO 2 yr−1, is reported for 
the spreading of pulverised rock over a very large land area in the 
tropics, a region considered promising given the higher temperatures 
and greater rainfall (Taylor et al. 2016). Considering cropland areas

only, the potential carbon removal was estimated by Strefler et al. 
(2018) to be 95 GtCO 2 yr−1 for dunite and 4.9 GtCO 2 yr−1 for basalt. 
Slightly lower potentials were estimated by Lenton (2014) where 
the potential of carbon removal by enhanced weathering (including 
adding carbonate and olivine to both oceans and soils) was estimated 
to be 3.7  GtCO 2 yr–1 by 2100, but with mean annual removal an 
order of magnitude less at 0.2 GtC-eq yr–1 (Lenton 2014). The 
estimates reported in Smith et al. (2016) are based on the potential 
estimates of Lenton (2014). Beerling et al. (2020) estimate that up 
to 2 GtCO 2 yr–1 could be removed by 2050 by spreading basalt onto 
35–59% (weighted mean 53%) of agricultural land of 12 countries. 
Fuss et al. (2018) provide an author judgement range for potential of 
2–4 GtCO 2 yr−1 for 2050.Risks and impacts: Mining of rocks for enhanced weathering will 
have local impacts and carries risks similar to those associated with

the mining of mineral construction aggregates, with the possible 
additional risk of greater dust generation from fine comminution 
and land application. In addition to direct habitat destruction and 
increased traffic to access mining sites, there could be adverse 
impacts on local water quality (Younger and Wolkersdorfer 2004).
Co-benefits: Enhanced weathering can improve plant growth by 
pH modification and increased mineral supply (Kantola et al. 2017; 
Beerling et al. 2018), can enhance SCS in some soils (Beerling et al. 
2018) thereby protecting against soil erosion (Wright and Upadhyaya 
1998), and increasing the cation exchange capacity, resulting 
in increased nutrient retention and availability (Gillman 1980; 
Baldock and Skjemstad 2000; Gillman et al. 2001; Manning 2010; 
Guntzer et al. 2012; Tubana et al. 2016; Yu et al. 2017; Haque et al. 
2019; Smith et al. 2019a). Through these actions, it can contribute

to SDG  2  (zero hunger), SDG  15 (life on land) (by reducing land 
demand for croplands), SDG  13 (climate action) (through CDR), 
SDG 14 (life below water) (by ameliorating ocean acidification) and 
SDG  6  (clean water and sanitation) (Smith et al. 2019a). To more 
directly ameliorate ocean acidification while increasing CDR and 
reducing impacts on land ecosystems, alkaline minerals could instead 
be directly added to the ocean (Section 12.3.1.3). There are potential 
benefits in poverty reduction through employment of local workers 
in mining (Pegg 2006).
Trade-offs and spillover effects: Air quality could be adversely 
affected by the spreading of rock dust (Edwards et al. 2017), though this 
can partly be ameliorated by water-spraying (Grundnig et al. 2006). As 
noted above, any significant expansion of the mining industry would 
require careful assessment to avoid possible detrimental effects on 
biodiversity (Amundson et al. 2015). The processing of an additional

10 billion tonnes of rock would require up to 3000 Terawatt-hours 
of energy, which could represent approximately 0.1–6 % of global 
electricity use in 2100. The emissions associated with this additional 
energy generation may reduce the net carbon dioxide removal by up 
to 30% with present-day grid average emissions, but this efficiency 
loss would decrease with low-carbon power (Beerling et al. 2020).
Role in mitigation pathways: Only one study to date has included 
enhanced weathering in an integrated assessment model to explore 
mitigation pathways (Strefler et al. 2021).
spillover effects and the role in mitigation pathways of enhanced 
weathering are summarised in Table 12.6.
12.3.1.3  Ocean-based Methods
The ocean, which covers over 70% of the Earth’s surface, contains 
about 38,000 gigatonnes of carbon, some 45 times more than the 
present atmosphere, and oceanic uptake has already consumed

close to 30–40% of anthropogenic carbon emissions (Sabine 
et al. 2004; Gruber et al. 2019). The ocean is characterised by diverse 
biogeochemical cycles involving carbon, and ocean circulation has 
much longer timescales than the atmosphere, meaning that additional

1269
1269
Cross-sectoral Perspectives Chapter 1212anthropogenic carbon could potentially be stored in the ocean for 
centuries to millennia for methods that increase deep ocean-dissolved 
carbon concentrations or temporarily bury the carbon; or essentially 
permanently (over ten thousand years) for methods that store the 
carbon in mineral forms or as ions by increasing alkalinity (Siegel 
et al., 2021) (Cross-Chapter Box 8, Figure 1). A wide range of methods 
and implementation options for marine CDR have been proposed 
(Gattuso et al. 2018; Hoegh-Guldberg et al. 2018; GESAMP 2019). 
The most studied ocean-based CDR methods are ocean fertilisation, 
alkalinity enhancement (including electrochemical methods) and 
intensification of biologically-driven carbon fluxes and storage in 
marine ecosystems, referred to as ‘blue carbon’. The mitigation 
potentials, costs, co-benefits and trade-offs of these three options are 
discussed below. Less well studied are methods including artificial

upwelling, terrestrial biomass dumping into oceans, direct CO 2 
removal from seawater (with CCS), and sinking marine biomass into 
the deep ocean or harvesting it for bioenergy (with CCS) or biochar 
(GESAMP 2019). These methods are summarised briefly below. 
Potential climate response and influence on the carbon budget of 
ocean-based CDR methods are discussed in WGI AR6, Chapter 5.
Ocean fertilisation (OF)
One natural mechanism of carbon transfer from the atmosphere to 
the deep ocean is the ocean biological pump, which is driven by the 
sinking of organic particles from the upper ocean. These particles 
derive ultimately from primary production by phytoplankton and 
most of them are remineralised within the upper ocean with only 
a small fraction reaching the deep ocean where the carbon can be 
sequestered on centennial and longer timescales. Increasing nutrient 
availability would stimulate uptake of CO 2 through phytoplankton

photosynthesis producing organic matter, some of which would be 
exported into the deep ocean, sequestering carbon. In areas of the 
ocean where macronutrients (nitrogen, phosphorus) are available 
in sufficient quantities (about 25% of the total area), the growth 
of phytoplankton is limited by the lack of trace elements such as 
iron. Thus, OF CDR can be based on two implementation options 
to increase the productivity of phytoplankton (Minx et al. 2018): 
macronutrient enrichment and micronutrient enrichment. A  third 
option, highlighted in GESAMP (2019), is based on fertilisation for 
fish stock enhancement, for instance, as naturally occurs in eastern 
boundary current systems. Iron fertilisation is the best-studied OF 
option to date, but knowledge so far is still inadequate to predict 
global ecological and biogeochemical consequences.
Status: OF has a  natural analogue: periods of glaciation in the 
geological past are associated with changes in deposition of dust

containing iron into the ocean. Increased formation of phytoplankton 
has also been observed during seasonal deposition of dust from the 
Arabian Peninsula and ash deposition on the ocean surface after 
volcanic eruptions (Achterberg et al. 2013; Jaccard et al., 2013; Olgun 
et al. 2013; Martínez-García et al. 2014). OF options may appear 
technologically feasible, and enhancement of photosynthesis and 
CO2 uptake from surface waters is confirmed by a number of field 
experiments conducted in different areas of the ocean, but there is 
scientific uncertainty about the proportion of newly-formed organic 
carbon that is transferred to deep ocean, and the longevity of storage (Blain et al. 2008; Williamson et al. 2012; Trull et al. 2015). 
The efficiency of OF also depends on the region and experimental 
conditions, especially in relation to the availability of other nutrients, 
light and temperature (Aumont and Bopp 2006). In the case of

macronutrients, very large quantities are needed and the proposed 
scaling of this technique has been viewed as unrealistic (Williamson 
and Bodle 2016).
Costs: Ocean fertilisation costs depend on nutrient production 
and its delivery to the application area (Jones 2014). The costs 
range from USD2 tCO 2–1 for fertilisation with iron (Boyd 2008) to 
USD457 tCO 2–1 for nitrate (Harrison 2013). Reported costs for 
macronutrient application at USD20 tCO 2–1 (Jones 2014) contrast 
with higher estimates by (Harrison 2013) reporting that low costs are 
due to overestimation of sequestration capacity and underestimation 
of logistical costs. The median of OF cost estimates, USD230 tCO 2–1 
(Gattuso et al., 2021) indicates low cost-effectiveness, albeit 
uncertainties are large.
Potentials: Theoretical calculations indicate that organic carbon 
export increases 2–20 kg per gram of iron added, but experiments 
indicate much lower efficiency: a  significant part of the CO 2 can

be emitted back the atmosphere because much of the organic 
carbon produced is remineralised in the upper ocean. Efficiency also 
varies with location (Bopp et al. 2013). Between studies, there are 
substantial differences in the ratio of iron added to carbon  fixed 
photosynthetically, and in the ratio of iron added to carbon 
eventually sequestered (Trull et al. 2015), which has implications 
both for the success of this strategy and its cost. Estimates indicate 
potentially achievable net sequestration rates of 1–3 GtCO 2 yr–1 for 
iron fertilisation, translating into cumulative CDR of 100–300 GtCO 2 
by 2100 (Ryaboshapko and Revokatova 2015; Minx et al. 2018), 
whereas OF with macronutrients has a  higher theoretical 
potential of 5.5  GtCO 2 yr–1 (Harrison 2017; Gattuso et al. 2021). 
Modelling studies show a maximum effect on atmospheric CO 2 of 
15–45  parts  per  million volume in 2100 (Zeebe and Archer 2005; 
Aumont and Bopp 2006; Keller et al. 2014; Gattuso et al. 2021).

Risks and impacts: Several of the mesoscale iron enrichment 
experiments have seen the emergence of potentially toxic species 
of diatoms (Silver et al. 2010; Trick et al. 2010). There is also (limited) 
evidence of increased concentrations of other GHGs such as methane 
and nitrous oxide during the subsurface decomposition of the sinking 
particles from iron-stimulated blooms (Law 2008). Impacts on 
marine biology and food web structure are not well known, however 
OF at large scale could cause changes in nutrient distributions or 
anoxia in subsurface water (Fuhrman and Capone 1991; DFO 
2010). Other potential risks are perturbation to marine ecosystems 
via reorganisation of community structure, enhanced deep ocean 
acidification (Oschlies et al. 2010) and effects on human food supply.
Co-benefits: Co-benefits of OF include a potential increase in fish 
biomass through enhanced biological production (Minx et al. 2018) 
and reduced ocean acidification in the short term in the upper

ocean (by CO 2 removal), though it could be enhanced in the long 
term in the ocean interior (by CO 2 release) (Oschlies et al., 2010; 
Gattuso et al. 2018).

1270
1270
Chapter 12 Cross-sectoral Perspectives12Trade-offs and spillover effects: Potential drawbacks include 
subsurface ocean acidification and deoxygenation (Cao and Caldeira 
2010; Oschlies et al., 2010; Williamson et al. 2012); altered regional 
meridional nutrient supply and fundamental alteration of food webs 
(GESAMP 2019); and increased production of N 2O and CH 4 (Jin and 
Gruber 2003; Lampitt et al. 2008). Ocean fertilisation is considered 
to have negative consequences for eight SDGs, and a combination 
of both positive and negative consequences for seven SDGs 
(Honegger et al. 2020).
Ocean Aakalinity enhancement (OAE)
CDR through ‘ocean alkalinity enhancement’ or ‘artificial ocean 
alkalinisation’ (Renforth and Henderson 2017) can be based on: 
(i) the dissolution of natural alkaline minerals that are added directly 
to the ocean or coastal environments; (ii) the dissolution of such 
minerals upstream from the ocean (e.g.,  enhanced weathering,

Section  12.3.1.2); (iii) the addition of synthetic alkaline materials 
directly to the ocean or upstream; and (iv) electrochemical processing 
of seawater. In the case of (ii), minerals are dissolved on land and the 
dissolution products are conveyed to the ocean through runoff and 
river flow. These processes result in chemical transformation of CO 2 
and sequestration as bicarbonate and carbonate ions (HCO 3–, CO 32–) 
in the ocean. Imbalances between the input and removal fluxes 
of alkalinity can result in changes in global oceanic alkalinity and 
therefore the capacity of the ocean to store carbon. Such alkalinity-
induced changes in partitioning of carbon between atmosphere and 
ocean are thought to play an important role in controlling climate 
change on timescales of 1000 years and longer (e.g., Zeebe 2012). 
The residence time of dissolved inorganic carbon in the deep ocean 
is around 100,000 years. However, residence time may decrease

if alkalinity is reduced by a  net increase in carbonate minerals by 
either increased formation (precipitation) or reduced dissolution of 
carbonate (Renforth and Henderson 2017). The alkalinity of seawater 
could potentially also be increased by electrochemical methods, 
either directly by reactions at the cathode that increase the alkalinity 
of the surrounding solution that can be discharged into the ocean, or 
by forcing the precipitation of solid alkaline materials (e.g., hydroxide 
minerals) that can then be added to the ocean (e.g., Rau et al. 2013; 
La Plante et al. 2021).
Status: OAE has been demonstrated by a small number of laboratory 
experiments (in addition to enhanced weathering, Section 12.3.1.2). 
The use of enhanced ocean alkalinity for carbon storage was first 
proposed by Kheshgi (1995) who considered the creation of highly 
reactive lime that would readily dissolve in the surface ocean and 
sequester CO 2. An alternative method proposed the dissolution of

carbonate minerals (e.g., calcium carbonate) in the presence of waste 
flue gas CO 2 and seawater as a means capturing CO 2 and converting 
it to bicarbonate ions (Rau and Caldeira 1999; Rau 2011). House 
et al. (2007) proposed the creation of alkalinity in the ocean through 
electrolysis. The fate of the stored carbon is the same for these 
proposals (i.e., HCO 3– and CO 32– ions), but the reaction pathway is 
different. Enhanced weathering of silicate minerals such as olivine 
could add alkalinity to the ocean, for example, by placing olivine sand 
in coastal areas (Meysman and Montserrat 2017; Montserrat et al. 2017). Some authors suggest use of maritime transport to discharge 
calcium hydroxide (slaked lime) (Caserini et al. 2021).
Costs: Techno-economic assessments of OAE largely focus on 
quantifying overall energy and carbon balances. Cost ranges are 
USD40–260 tCO 2–1 (Fuss et al. 2018). Considering life-cycle carbon

and energy balances for various OAE options, adding lime (or other 
reactive calcium or magnesium oxide/hydroxides) to the ocean would 
cost USD64–260 tCO 2–1 (Renforth et al. 2013; Renforth &  Kruger 
2013; Caserini et al. 2019). Rau (2008) and Rau et al. (2018) estimate 
that electrochemical processes for increasing ocean alkalinity may 
have a net cost of USD3–160 tCO 2–1, largely depending on energy 
cost and co-product (H 2) market value. In the case of direct addition 
of alkaline minerals to the ocean (i.e., without calcination), the cost 
is estimated to be USD20–50 tCO 2–1 (Harvey 2008; Köhler et al. 2013; 
Renforth and Henderson 2017).
Potentials: For OAE, the ocean theoretically has the capacity to 
store thousands of  GtCO 2 (cumulatively) without exceeding pre-
industrial levels of carbonate saturation (Renforth and Henderson 
2017) if the impacts were distributed evenly across the surface ocean. 
The potential of increasing ocean alkalinity may be constrained by the

capability to extract, process, and react minerals (Section  12.3.1.2); 
the demand for co-benefits (see below), or to minimise impacts 
around points of addition. Important challenges with respect to the 
detailed quantification of the CO 2 sequestration efficiency include 
nonstoichiometric dissolution, reversed weathering and potential pore 
water saturation in the case of adding minerals to shallow coastal 
environments (Meysman and Montserrat 2017). Fuss et al. (2018) 
suggest storage potentials of 1–100 GtCO 2 yr–1. (González and Ilyina 
2016) suggested that addition of 114 picomoles of alkalinity to the 
surface ocean could remove 3400 GtCO 2 from the atmosphere.
Risks and impacts: For OAE, the local impact of increasing alkalinity 
on ocean chemistry can depend on the speed at which the impacted 
seawater is diluted/circulated and the exchange of CO 2 from the 
atmosphere (Bach et al. 2019). Also, more extreme carbonate

chemistry perturbations due to non-equilibrated alkalinity could 
affect local marine biota (Bach et al. 2019), although biological 
impacts are largely unknown. Air-equilibrated seawater has a much 
lower potential to perturb seawater carbonate chemistry. However, 
seawater with slow air-sea gas exchange, in which alkalinity increases, 
consumes CO 2 from the surrounding water without immediate 
replenishment from the atmosphere, which would increase seawater 
pH and saturation states and may impact marine biota (Meysman 
and Montserrat 2017; Montserrat et al. 2017). It may be possible 
to use this effect to ameliorate ocean acidification. Like enhanced 
weathering, some proposals may result in the dissolution products of 
silicate minerals (e.g., silicon, iron, potassium, nickel) being supplied 
to ocean ecosystems (Meysman and Montserrat 2017; Montserrat 
et al. 2017). Ecological and biogeochemical consequences of OAE

largely depend on the minerals used. When natural minerals such 
as olivine are used, the release of additional Si and Fe could have 
fertilising effects (Bach et al. 2019). In addition to perturbations 
to marine ecosystems via reorganisation of community structure, 
potentially adverse effects of OAE that should be studied include

1271
1271
Cross-sectoral Perspectives Chapter 1212the release of toxic trace metals from some deposited minerals 
(Hartmann et al. 2013).
Co-benefits: Intentional addition of alkalinity to the oceans 
through OAE would decrease the risk to ocean ecosystems caused 
by the  CO 2-induced impact of ocean acidification on marine biota 
and the global carbon cycle (Doney et al. 2009; Köhler et al. 2010; Rau 
et al. 2012; Williamson and Turley 2012; Albright et al. 2016; Bach et al. 
2019). OAE could be jointly implemented with enhanced weathering 
(Section  12.3.1.2), spreading the finely crushed rock in the ocean 
rather than on land. Regional alkalinisation could be effective in 
protecting coral reefs against acidification (Feng et al. 2016; Mongin 
et al., 2021) and coastal OAE could be part of a broader strategy for 
geochemical management of the coastal zone, safeguarding specific 
coastal ecosystems, such as important shellfisheries, from the adverse

impact of ocean acidification (Meysman and Montserrat 2017).
Trade-offs and spillover effects: There is a  paucity of research 
on biological effects of alkalinity addition. The very few studies that 
have explored the impact of elevated alkalinity on ocean ecosystems 
have largely been limited to single species experiments (Cripps et al. 
2013; Gore et al. 2019) and a  constrained field study quantifying 
the net calcification response of a  coral reef flat to alkalinity 
enhancement (Albright et al. 2016). The addition rate would have 
to be great enough to overcome mixing of the local seawater 
with the ambient environment, but not sufficient to detrimentally 
impact ecosystems. More research is required to assess locations in 
which this may be feasible, and how such a  scheme may operate 
(Renforth and Henderson 2017). The environmental impact of 
large-scale release of natural dissolution products into the coastal

environment will strongly depend on the scale of olivine application, 
the characteristics of the coastal water body (e.g., residence time) 
and the particular biota present (e.g., coral reefs will react differently 
compared with seagrasses) (Meysman and Montserrat 2017). Model 
simulations (González et al. 2018) suggest that termination of OAE 
implemented on a massive scale under a high CO 2 emission scenario 
(Representative Concentration Pathway 8.5) might pose high risks to 
biological systems sensitive to rapid environmental changes because 
it would cause a sharp increase in ocean acidification. For example, 
OAE termination would lead to a  decrease in surface pH in warm 
shallow regions where vulnerable coral reefs are located, and a drop 
in the carbonate saturation state. However, other studies with lower 
levels of OAE have shown no termination effect (Keller et al., 2014).
Blue carbon management
The term ‘blue carbon’ was used originally to refer to biological carbon

sequestration in all marine ecosystems, but it is increasingly applied 
to CDR associated with rooted vegetation in the coastal zone, such 
as tidal marshes, mangroves and seagrasses. Potential for carbon 
sequestration in other coastal and non-coastal ecosystems, such as 
macroalgae (e.g., kelp), is debated (Krause-Jensen and Duarte, 2016; 
Krause-Jensen et al., 2018). In this report, blue carbon refers to CDR 
through coastal blue carbon management.
Status: In recent years, there has been increasing research on the 
potential, effectiveness, risks, and possibility of enhancing CO 2 sequestration in shallow coastal ecosystems (Duarte, 2017). About 
20% of the countries that are signatories to the Paris Agreement 
refer to blue carbon approaches for climate change mitigation 
in their NDCs and are moving toward measuring blue carbon in 
inventories. About 40% of those same countries have pledged to 
manage shallow coastal ecosystems for climate change adaptation

(Kuwae and Hori 2019).
Costs: There are large differences in the cost of CDR applying blue 
carbon management methods between different ecosystems (and at 
the local level). Median values are estimated as USD240, 30,000, and 
7800 tCO 2–1, respectively for mangroves, salt marsh and seagrass 
habitats (Gattuso et al. 2021). Currently estimated cost effectiveness 
(for climate change mitigation) is very low (Siikamäki et al. 2012; 
Bayraktarov et al. 2016; Narayan et al. 2016).
Potentials: Globally, the total potential carbon sequestration 
rate through blue carbon CDR is estimated in the range 
0.02–0.08  GtCO 2 yr–1 (Wilcox et al. 2017; National Academies 
of Sciences 2019). Gattuso et al. (2021) estimate the theoretical 
cumulative potential of coastal blue carbon management by 2100 
to be 95 GtCO 2, taking into account the maximum area that can be 
occupied by these habitats and historic losses of mangroves, seagrass 
and salt marsh ecosystems.

Risks and impacts: For blue carbon management, potential risks 
relate to the high sensitivity of coastal ecosystems to external impacts 
associated with both degradation and attempts to increase carbon 
sequestration. Under expected future warming, sea level rise and 
changes in coastal management, blue carbon ecosystems are at risk, 
and their stored carbon is at risk of being lost (Bindoff et al. 2019).
Co-benefits: Blue carbon management provides many non-climatic 
benefits and can contribute to ecosystem-based adaptation, also 
reducing emissions associated with habitat degradation and loss 
(Howard et al. 2017; Hamilton and Friess 2018). Shallow coastal 
ecosystems have been severely affected by human activity; significant 
areas have already been deforested or degraded and continue to be 
denuded. These processes are accompanied by carbon emissions. The 
conservation and restoration of coastal ecosystems, which will lead to

increased carbon sequestration, is also essential for the preservation 
of basic ecosystem services, and healthy ecosystems tend to be more 
resilient to the effects of climate change.
Trade-offs and spillover effects: Blue carbon management 
schemes should consist of a  mix of restoration, conservation and 
areal increase, including complex engineering interventions that 
enhance natural capital, safeguard their resilience and the ecosystem 
services they provide, and decrease the sensitivity of such ecosystems 
to further disturbances.
Overview of other ocean-based CDR approaches
Artificial upwelling: This concept uses pipes or other methods to 
pump nutrient-rich deep ocean water to the surface where it has 
a fertilising effect (see OF section). To achieve CO 2 removal at a Gt 
magnitude, modelling studies have shown that artificial upwelling

1272
1272
Figure 12.4 | Summary of the extent to which different factors would enable or inhibit the deployment of the carbon dioxide removal methods DACCS, EW, ocean fertilisation and blue carbon management.  
Blue bars indicate the extent to which the indicator enables the implementation of the CDR method (E) and orange bars indicate the extent to which an indicator is a barrier (B) to the deployment of the method, relative to the maximum possible 
barriers and enablers assessed. An ‘X’ signifies the indicator is not applicable or does not affect the feasibility of the method, while a forward slash indicates that there is no or limited evidence whether the indicator affects the feasibility of 
the method. The shading indicates the level of confidence, with darker shading signifying higher levels of confidence. Supplementary Material 12.SM.B provides an overview of the factors affecting the feasibility of CDR methods and how they

differ across contexts (e.g., region), time (e.g., 2030 versus 2050), and scale (e.g., small versus large), and includes a line of sight on which the assessment is based. The assessment methodology is explained in Annex II, Part IV , Section 11.
Land use
readiness
Effects on employment  and 
Direct air carbon capture and storage
Enhanced weathering
Ocean fertilisation

1273
1273
Cross-sectoral Perspectives Chapter 1212would have to be implemented on a  massive scale (over 50% of 
the ocean to deliver maximum rate of 10GtCO 2 yr–1 under RCP8.5) 
(Oschlies et al., 2010, Keller et al. 2014). Because the deep water 
is much colder than surface water, at massive scale this could cool 
the Earth’s surface by several degrees, but the cooling effect would 
cease as the deeper ocean warms, and would reverse, leading 
to rapid warming, if the pumping ceased (Oschlies et al., 2010, 
Keller et al. 2014).
Furthermore, the cooling would also severely alter atmospheric 
circulation and precipitation patterns (Kwiatkowski et al. 2015). 
Several upwelling approaches have been developed and tested (Pan 
et al., 2016) and more R&D is underway.
Terrestrial biomass dumping: There are proposals to sink terrestrial 
biomass (crop residues or logs) into the deep ocean as a means of 
sequestering carbon (Strand and Benford 2009). Sinking biochar

has also been proposed (Miller and Orton, 2021). Decomposition 
would be inhibited by the cold and sometimes hypoxic/anoxic 
environment on the ocean floor, and absence of bacteria that 
decompose terrestrial lignocellulosic biomass, so storage timescale 
is estimated at hundreds to thousands of years (Strand and Benford 
2009) (Burdige 2005). Potential side effects on marine ecosystems, 
chemistry, or circulation have not been thoroughly assessed. Neither 
have these concepts been evaluated with respect to the impacts on 
land from enhanced transfer of nutrients and organic matter to the 
ocean, nor the relative merits of alternative applications of residues 
and biochar as an energy source or soil amendment (Chapter 7).
Marine biomass CDR options: Proposals have been made to grow 
macroalgae (Duarte et al., 2017) for BECCS (N’Yeurt et al. 2012; Duarte 
et al. 2013; Chen et al., 2015), to sink cultured macroalgae into the

deep sea, or to use marine algae for biochar (Roberts et al., 2015). 
Naturally-growing sargassum has also been considered for these 
purposes (Bach et al., 2021). Froehlich et al. (2019) found a substantial 
area of the ocean (about 48 million km2) suitable for farming seaweed. 
N’Yeurt et al. (2012) suggested that converting 9% of the oceans to 
macroalgal aquaculture could take up 19 GtCO 2 in biomass, generate 
12 Gt per annum of biogas, and the CO 2 produced by burning the 
biogas could be captured and sequestered. Productivity of farmed 
macroalgae in the open ocean could potentially be enhanced through 
fertilising via artificial upwelling (Fan et al., 2020) or through cultivation 
platforms that dive at night to access nutrient-rich waters below the, 
often nutrient-limited, surface ocean. If the biomass were sunk, it is 
unknown how long the carbon would remain in the deep ocean and 
what the additional impacts would be. Research and development on

macroalgae cultivation and use is currently underway in multiple parts 
of the world, though not necessarily directly focused on CDR. 
Extraction of CO 2 from seawater (with storage): CO 2 can be 
extracted by applying a  vacuum, or by purging with a  gas low in 
CO2 (Koweek et al., 2016). CO 2 stripping can also be accomplished 
by acidifying seawater with a mineral acid, or through electrodialysis 
and electrolysis, to convert bicarbonate ions (HCO 3–) to CO 2 (Willauer 
et al., 2017; Eisaman et al., 2018; Digdaya et al., 2020; Eisaman 2020; 
Sharifian et al., 2021). The removal of CO 2 from the ocean surface 
leads to undersaturation in the water, thus forcing CO 2 to move from the atmosphere into the ocean to restore equilibrium. Electrochemical 
seawater CO 2 extraction has been modelled, prototyped, and 
analysed from a techno-economic perspective (Eisaman et al., 2012; 
Willauer et al., 2017; de Lannoy et al., 2018; Eisaman et al., 2018a; 
Eisaman et al., 2018b).

spillover effects and the role in mitigation pathways of ocean-based 
approaches are summarised in Table 12.6.
12.3.1.4 Feasibility Assessment
Following the framework presented in Section  6.4 and Annex II, 
Part  IV, Section  11, a  multi-dimensional feasibility assessment of 
the CDR methods covered here is provided in Figure  12.4, taking 
into account the assessment presented in this section. Both DACCS 
and EW perform positively on the geophysical and technological 
dimensions while for ocean-based approaches performance is mixed. 
There is limited evidence to assess social-cultural, environmental/
ecological, and institutional dimensions as the literature is still 
nascent for DACCS and EW, while these aspects are positive for blue 
carbon and mixed or negative for ocean fertilisation. On the economic 
dimension, the cost is assessed negatively for all CDR methods.

12.3.2 Consideration of Methods Assessed in 
Sectoral Chapters: A/R, Biochar, BECCS, 
Soil Carbon Sequestration
Status: BECCS, afforestation/reforestation (A/R), soil carbon 
sequestration (SCS) and biochar are land-based biological CDR 
methods (Smith et al. 2016). BECCS combines biomass use for energy 
with CCS to capture and store the biogenic carbon geologically 
(Section  6.4.2.6); A/R and SCS involve fixing atmospheric carbon 
in biomass and soils, and biochar involves converting biomass to 
biochar and using it as a soil amendment. These CDR methods can 
be associated with both co-benefits and adverse side effects (Smith 
et al. 2016; Hurlbert et al. 2019; Mbow et al. 2019; Olsson et al. 2019; 
Schleicher et al. 2019; Smith et al. 2019b; Babin et al. 2021; Dooley 
et al. 2021) (Sections 7.4 and 12.5).
Among CDR methods, BECCS and A/R are most commonly selected by 
IAMs to meet the requirements of scenarios that limit warming to 2°C

(>67%) or lower. This is partially because of the long lead time required 
to refine IAMs to include additional methods and update techno-
economic parameters. Currently, few IAMs represent SCS or biochar 
(Frank et al. 2017). Given the removal potential of SCS and biochar and 
some potential co-benefits, more efforts should be made to include 
these methods within IAMs, so that their mitigation potential can be 
compared to other CDR methods, along with possible co-benefits and 
adverse side effects (Smith et al. 2016; Rogelj et al. 2018) (Section 12.5).
Potential: The technical potential for BECCS by 2050 is estimated at 
0.5–11.3 GtCO 2-eq yr–1 (Table 7.3). These potentials do not include 
avoided emissions resulting from the use of heat, electricity and/or 
fuels provided by the BECCS system, which depend on substitution 
patterns, conversion efficiencies, and supply chain emissions for the

1274
1274
Chapter 12 Cross-sectoral Perspectives12BECCS and substituted energy systems (Box  7.7). The mitigation 
effect of BECCS also depends on how deployment affects land carbon 
stocks and sink strength (Section 7.4.4).
As detailed in Chapter 7, the technical potential for gross removals 
realised through A/R in 2050 is 0.5–10.1  GtCO 2-eq yr–1, and for 
improved forest management the potential is 1–2.1 GtCO 2-eq yr–1 
(including both CDR and emissions reduction). Technical potential for 
SCS in 2050 is estimated to be 0.6–9.4 GtCO 2-eq yr–1, for agroforestry 
it is 0.3–9.4 GtCO 2-eq yr–1, and for biochar it is 0.2–6.6 GtCO 2-eq yr–1. 
Peatland and coastal wetland restoration have a technical potential 
of 0.5–2.1  GtCO 2-eq yr–1 in 2050, with an estimated 80% of the 
potential being CDR.  Note that these potentials reflect only 
biophysical and technological conditions and become reduced when 
factoring in economic, environmental, socio-cultural and institutional

constraints (Table 12.6).
Costs: Costs across technologies vary substantially (Smith 
et al. 2016) and were estimated to be USD15–400 tCO 2–1 for 
BECSS, USD0–240 tCO 2–1 for A/R, –USD45 to +USD100 tCO 2–1 
for SCS and USD10–345 tCO 2–1 for biochar. Fuss et al. (2018) 
estimated abatement cost ranges for BECCS, A/R, SCS and biochar 
to be 100–200, 5–50, 0–100, and 30–120 tCO 2-eq−1 respectively, 
corresponding to 2100  potentials. Ranges for economic potential 
(<USD100 tCO 2–1) reported in Chapter 7 are 0.5–3.0 GtCO 2 yr–1 (A/R); 
0.6–1.9  GtCO 2 yr–1 (improved forest management); 0.7–2.5  GtCO 2 yr–1 
(SCS); 0.4–1.1 GtCO 2 yr–1 (agroforestry); 0.3–1.8 GtCO 2 yr–1 (biochar); 
and 0.2–0.8 GtCO 2 yr–1 (peatland and coastal wetland restoration).
Risks, impacts, and co-benefits: a brief summary of risks, impacts 
and co-benefits is provided here and more detail is provided 
in Chapter  7  and Section  12.5. A/R and biomass production for

BECCS and biochar potentially compete for land, water and other 
resources, implying possible adverse outcomes for ecosystem health, 
biodiversity, livelihoods and food security (medium evidence, high 
agreement) (Smith et al. 2016; Heck et al. 2018; Hurlbert et al. 2019; 
Mbow et al. 2019) (Chapter 7). SCS requires the addition of nitrogen 
and phosphorus to maintain stoichiometry of soil organic matter, 
leading to a potential risk of eutrophication (Fuss et al. 2018). Apart 
from possible negative effects associated with biomass supply, 
adverse side effects from biochar are relatively low if the biomass is 
uncontaminated (Tisserant and Cherubini 2019).
Possible climate risks relate to direct and/or indirect land carbon 
losses (A/R, BECCS, biochar), increased N 2O emissions (BECCS, SCS), 
saturation and non-permanence of carbon storage (A/R, SCS) (Jia et al. 
2019; Smith et al. 2019b) (Chapter 7), and potential CO 2 leakage from

deep geological reservoirs (BECCS) (Chapter 6). Land cover change 
associated with A/R and biomass supply for BECCS and biochar may 
cause albedo changes that reduce mitigation effectiveness (Fuss 
et al. 2018; Jia et al. 2019). Potentially unfavourable albedo change 
resulting from biochar use can be minimised by incorporating biochar 
into the soil (Fuss et al. 2018) (Chapter 7).
Concerning co-benefits, A/R and biomass production for BECCS or 
biochar could improve soil carbon, nutrient and water cycling (robust 
evidence, high agreement), and contribute to market opportunities, employment and local livelihoods, economic diversification, energy 
security, and technology development and transfer (medium evidence, 
high agreement) (Fuss et al. 2018) (Chapter  7). It may contribute 
to reduction of other air pollutants, health benefits, and reduced 
dependency on imported fossil fuels. A/R can improve biodiversity

if native and diverse species are used (Fuss et al. 2018). For biochar, 
additional co-benefits include increased crop yields, reduced drought 
impacts, and reduced CH 4 and N 2O emissions from soils (Joseph et al., 
2021) (Section 7.4.5.2). SCS can improve soil quality and resilience 
and improve agricultural productivity and food security (Frank et al. 
2017; Smith et al. 2019b).
Role in mitigation pathways: Biomass use for BECCS in 2050 is 
61  EJ yr–1 (13–208  EJ yr–1, 5–95th percentile range) in scenarios 
limiting warming to 1.5°C (>50%) with no or limited overshoot (C1, 
excluding traditional energy). This corresponds to 5.3  GtCO 2 yr–1 
(1.1–18 GtCO 2 yr–1) CDR, if assuming 28 kg C GJ–1 biomass carbon 
content and 85% capture rate in BECCS systems. In scenarios that 
limit warming to 2°C (>67%) (C3), biomass use for BECCS in 2050 
is 28 EJ yr–1 (0–96 EJ yr–1, 5–95th percentile range), corresponding 
to 2.4 GtCO 2 yr–1 (0–8.3 GtCO 2 yr–1) CDR. Cumulative CO 2 removal

from AFOLU (mainly through A/R), as reported from models, 
in the period 2020 to 2100 is 262  GtCO 2 (17–397  GtCO 2) and 
209  GtCO 2 (20–415  GtCO 2) in C1 and C3 scenarios, respectively 
(5–95th percentile range).
Uncertainties remain in two main areas: the availability of land and 
biomass, which is affected by many factors (Anandarajah et al. 2018) 
(Chapter 7), and the role of other mitigation measures including CDR 
methods other than A/R and BECCS. Strong near-term climate change 
mitigation to limit overshoot, and deployment of CDR methods other 
than A/R and BECCS, may significantly reduce the contribution of 
these CDR methods in scenarios limiting warming to 1.5°C or 2°C 
(Köberle 2019; Hasegawa et al. 2021).
Trade-offs and spillovers: Some land-based biological CDR 
methods, such as BECCS and A/R, demand land. Combining 
mitigation strategies has the potential to increase overall carbon 
sequestration rates (Humpenöder et al. 2014). However, these

CDR methods may also compete for resources (Frank et al. 2017). 
Land-based mitigation approaches currently propose the use of 
forests (i) as a source of woody biomass for bioenergy and various 
biomaterials and (ii)  for  carbon sequestration in vegetation, soils, 
and forest products. Forests are therefore required to provide 
both provisioning (biomass feedstock) and regulating (carbon 
sequestration) ecosystem services. This multifaceted strategy has 
the potential to result in trade-offs (Makkonen et al. 2015). Some 
land-based mitigation options could conflict with biodiversity goals, 
e.g., A/R using monoculture plantations can reduce species richness 
when introduced into (semi-)natural grasslands (Smith et al. 2019a; 
Dooley et al. 2021). When trade-offs exist between biodiversity 
protection and mitigation objectives, biodiversity is typically given 
a lower priority, especially if the mitigation option is considered risk-

free and economically feasible (Pörtner et al. 2021). Approaches that 
promote synergies, such as sustainable forest management, reducing 
deforestation rates, cultivation of perennial crops for bioenergy in 
sustainable farming practices, and mixed-species forests in A/R, can

1275
1275
Cross-sectoral Perspectives Chapter 1212Table 12.6 | Summary of status, costs, potentials, risk and impacts, co-benefits, trade-offs and spillover effects and the role in mitigation pathways for CDR methods.  Technology readiness level (TRL) is a measure of 
maturity of the CDR method. Scores range from 1 (basic principles defined) to 9 (proven in operational environment). Author judgement ranges (assessed by authors in the literature) are shown, with full literature ranges shown in brackets.
CDR methodStatus 
(TRL) Cost 
(USD tCO 2–1) Mitigation 
Potential 
(GtCO 2 yr–1) Risk and impacts  Co-benefits  Trade-offs and spillover effects Role in modelled 
mitigation pathways Section 
DACCS  6 100–300 
(84–386)  5–40  Increased energy and water useWater produced (solid sorbent  
DAC designs only)Potentially increased emissions from water 
supply and energy generationIn a few IAMs; DACCS 
complements other 
CDR methods12.3.1.1 
Enhanced 
weathering 3–4 50–200

(24–578) 2–4 (<1–95) Mining impacts; air quality impacts of rock 
dust when spreading on soilEnhanced plant growth, reduced erosion, 
enhanced soil carbon, reduced soil acidity,  
enhanced soil water retentionPotentially increased emissions from water 
supply and energy generationIn a few IAMs; 
EW complements other 
CDR methods12.3.1.2 
Ocean alkalinity 
enhancement 1–2  40–260   1–100  Increased seawater pH and saturation states 
may impact marine biota. Possible release of 
nutritive or toxic elements and compounds. 
Mining impactsLimiting ocean acidificationPotentially increased emissions of 
CO2 and dust from mining, transport 
and deployment operationsNo data 12.3.1.3
Ocean 
fertilisation 1–2  50–500  1–3 Nutrient redistribution, restructuring of the 
ecosystem, enhanced oxygen consumption 
and acidification in deeper waters, potential 
for decadal-to-millennial-scale return to the 
atmosphere of nearly all the extra carbon

removed, risks of unintended side effectsIncreased productivity and fisheries,  
reduced upper ocean acidificationSubsurface ocean acidification, 
deoxygenation; altered meridional supply 
of macro-nutrients as they are utilised 
in the iron-fertilised region and become 
unavailable for transport to, and utilisation 
in, other regions, fundamental alteration 
of food webs, biodiversityNo data 12.3.1.3 
in coastal 
ecosystems2–3 Insufficient 
data, estimates 
range from 
~100 to 
~10,000 <1 If degraded or lost, coastal blue carbon 
ecosystems are likely to release most of their 
carbon back to the atmosphere; potential 
for sediment contaminants, toxicity, 
bioaccumulation and biomagnification 
in organisms; issues related to altering 
degradability of coastal plants; use of 
subtidal areas for tidal wetland carbon 
removal; effect of shoreline modifications 
on sediment redeposition and natural marsh 
accretion; abusive use of coastal blue carbon

as means to reclaim land for purposes that 
degrade capacity for carbon removalPotential for many non-climatic benefits 
and can contribute to ecosystem-based 
adaptation, coastal protection, increased 
biodiversity, reduced upper ocean 
acidification; could potentially benefit 
human nutrition or produce fertiliser for 
terrestrial agriculture, anti-methanogenic 
feed additive, or as an industrial 
or materials feedstockIf degraded or lost, coastal blue carbon 
ecosystems are likely to release most of 
their carbon back to the atmosphere. The full 
delivery of the benefits at their maximum 
global capacity will require years to decades 
to be achievedNot incorporated in IAMs, 
but in some bottom-up 
studies: small contribution12.3.1.3, 7.4
BECCS         5–6  15–400  0.5–11 Competition for land and water resources, 
to grow biomass feedstock. Biodiversity 
and carbon stock loss if from unsustainable 
biomass harvestReduction of air pollutants; fuel security,

optimal use of residues, additional income, 
health benefits and if implemented well 
can enhance biodiversity, soil health 
and land carbonCompetition for land with biodiversity 
conservation and food production  Substantial contribution 
in IAMs and bottom-up 
sectoral studies 7.4

1276
1276
(TRL) Cost 
Potential 
Afforestation/
reforestation  8–9 0–240  0.5–10 Reversal of carbon removal through wildfire, 
disease, pests may occur. 
Reduced catchment water yield and lower 
groundwater level if species and biome 
are inappropriateEnhanced employment and local livelihoods, 
improved biodiversity, improved renewable 
wood products provision, soil carbon 
and nutrient cycling. Possibly less pressure 
on primary forestInappropriate deployment at large 
scale can lead to competition for 
land with biodiversity conservation  
and food productionSubstantial contribution in 
IAMs and also in bottom-up 
sectoral studies7.4  
Biochar    6–7 10–345  0.3–6.6 Particulate and GHG emissions from 
production; biodiversity and carbon stock

loss from unsustainable biomass harvestIncreased crop yields and reduced  
non-CO 2 emissions from soil;  
resilience to droughtEnvironmental impacts associated 
with particulate matter; competition 
for biomass resourceIn development – not yet in 
global mitigation pathways 
simulated by IAMs7.4  
sequestration  
in croplands 
and grasslands8–9  -45–100  0.6–9.3 Risk of increased nitrous oxide 
emissions due to higher levels of organic 
nitrogen in the soil; risk of reversal 
of carbon sequestrationImproved soil quality, resilience 
and agricultural productivityAttempts to increase carbon sequestration 
potential at the expense of production. 
Net addition per hectare is very small; 
hard to monitorIn development – not yet in 
simulated by IAMs; in 
bottom-up studies: with 
medium contribution7.4  
Peatland and 
coastal wetland 
restoration 8–9Insufficient 
data 0.5–2.1 Reversal of carbon removal in drought

or future disturbance. Risk of increased 
methane emissionsEnhanced employment and local livelihoods, 
increased productivity of fisheries, improved 
biodiversity, soil carbon and nutrient cyclingCompetition for land for food production on 
some peatlands used for food productionNot in IAMs but some 
bottom-up studies with 
Agroforestry  8–9Insufficient 
data   0.3–9.4Risk that some land area lost from food 
production; requires high skillsEnhanced employment and local livelihoods, 
variety of products, improved soil quality, 
more resilient systemsSome trade-off with agricultural crop 
production, but enhanced biodiversity,  
and resilience of systemNo data from IAMs, but in 
bottom-up sectoral studies. 
with medium contribution7.4  
Improved forest 
management 8–9 Insufficient 
data   0.1–2.1 If improved management is understood 
as merely intensification involving 
increased fertiliser use and introduced 
species, then it could reduce biodiversity

and increase eutrophicationIn case of sustainable forest management, 
it leads to enhanced employment and 
local livelihoods, enhanced biodiversity, 
improved productivityIf it involves increased fertiliser use 
and introduced species, it could reduce 
biodiversity and increase eutrophication  
and upstream GHG emissionsNo data from IAMs, but in 
bottom-up sectoral studies 
with medium contribution7.4

1277
1277
Cross-sectoral Perspectives Chapter 1212mitigate biodiversity impacts and even improve ecosystem capacity 
to support biodiversity while mitigating climate change (Pörtner 
et al. 2021) (Section 12.5). Systematic land-use planning could help 
to deliver land-based mitigation options that also limit trade-offs 
with biodiversity (Longva et al. 2017) (Cross-Working Group Box 3: 
Mitigation and Adaptation via the Bioeconomy, in this chapter).
Status, costs, potentials, risk and impacts, co-benefits, trade-
offs and spillover effects and the role in mitigation pathways of 
A/R, biochar, SCS, peatland and coastal wetland restoration, 
agroforestry and forest management are summarised in Table 12.6. 
See also Section 12.5.
12.3.3 CDR Governance and Policies
As shown in Cross-Chapter Box  8  in this chapter, CDR fulfils 
multiple functions in different phases of ambitious mitigation: 
(i) further reducing net CO 2 or GHG emission levels in the near term;

(ii)  counterbalancing residual emissions (from hard-to-transition 
sectors like transport, industry, or agriculture) to help reach net zero 
CO2 or GHG emissions in the mid term; (iii) achieving and sustaining 
net-negative CO 2 or GHG emissions in the long term. While inclusion 
of emissions and removals on managed land (LULUCF) is mandatory 
for developed countries under UNFCCC inventory rules (Grassi et al. 
2021), not all Annex I countries have included land-based biological 
removals when setting domestic mitigation targets in the past, 
but updated NDCs for 2030 indicate a  shift, most notably in the 
European Union (Gheuens and Oberthür 2021; Schenuit et al. 2021). 
The early literature on CDR governance and policy has been mainly 
conceptual rather than empirical, focusing on high-level principles 
(see the concerns listed in the introduction to Section 12.3) and the 
representation of CDR in global mitigation scenarios (Section 3.2.2).

However, with the widespread adoption of net zero targets and the 
recognition that CDR is a necessary element of mitigation portfolios to 
achieve net zero CO 2 or GHG emissions, countries with national net-
zero emissions targets have begun to integrate CDR into modelled 
national mitigation pathways, increase research, development and 
demonstration (RD&D) efforts on CDR methods, and consider CDR-
specific incentives and policies (Honegger et al. 2021b; Schenuit et al. 
2021) (Box 12.1). Nevertheless, this increasing consideration of CDR 
has not yet extended to net-negative targets and policies to achieve 
these. While the use of CDR at levels that would lead to net negative CO2 or GHG emissions in the long term has been assumed in most 
global mitigation scenarios that limit warming to 1.5°C, net-negative 
emissions trajectories and BECCS as the main CDR method modelled 
to achieve these have not been mirrored by corresponding UNFCCC

decisions so far (Fridahl 2017; Mohan et al. 2021). Likewise, only 
a few national long-term mitigation plans or legal acts entail a vision 
for net-negative GHG emissions (Buylova et al. 2021), for example 
Finland, Sweden, Germany and Fiji.
For countries with emissions targets aiming for net zero or lower, the 
core governance question is not whether CDR should be mobilised 
or not, but which CDR methods governments want to see deployed 
by whom, by when, at which volumes and in which ways (Minx et al. 
2018; Bellamy and Geden 2019). The choice of CDR methods and the 
scale and timing of their deployment will depend on the respective 
ambitions for gross emissions reductions, how sustainability and 
feasibility constraints are managed, and how political preferences 
and social acceptability evolve (Bellamy 2018; Forster et al. 2020; 
Fuss et al. 2020; Waller et al. 2020; Clery et al. 2021; Iyer et al. 2021; 
Rogelj et al. 2021). As examples of emerging CDR policymaking

at (sub-)national levels show, policymakers are beginning to 
incorporate CDR methods beyond those currently dominating global 
mitigation scenarios, that is, BECCS and afforestation/reforestation 
(Bellamy and Geden 2019; Buylova et al. 2021; Schenuit et al. 2021; 
Uden et al. 2021) (Box  12.1). CDR policymaking is faced with the 
need to consider method-specific timescales of CO 2 storage, as 
well as challenges in MRV and accounting, potential co-benefits, 
adverse side effects, interactions with adaptation and trade-offs 
with SDGs (Dooley and Kartha 2018; McLaren et al. 2019; Buck 
et al. 2020; Honegger et al. 2020; Brander et al. 2021; Dooley et al. 
2021; Mace et al. 2021) (Table  12.6). Therefore, CDR governance 
and policymaking are expected to focus on responsibly incentivising 
RD&D and targeted deployment, building on both technical and 
governance experience with already widely practised CDR methods 
like afforestation/reforestation (Lomax et al. 2015; Field and Mach

2017; Bellamy 2018; Carton et al. 2020; VonHedemann et al. 2020), as 
well as learning from two decades of slow-moving CCS deployment 
(Buck 2021; Martin-Roberts et al. 2021; Wang et al. 2021). For some 
less well-understood methods and implementation options, such as 
ocean alkalinisation or enhanced weathering, investment in RD&D 
can help in understanding the risks, rewards, and uncertainties of 
deployment (Nemet et al. 2018; Fajardy et al. 2019; Burns and Corbett 
2020; Goll et al. 2021).
Box 12.1 | Case Study: Emerging CDR Policy, Research and Development in the United Kingdom
Climate change mitigation policies in the UK have been motivated since 2008 by a  domestic, legally-binding framework. This 
framework includes a 2050 target for net zero greenhouse gas emissions, interim targets and an independent advisory body called the 
Climate Change Committee (Muinzer 2019). It has led successive UK governments to publish mitigation plans to 2050, causing policy

to be more forward looking (Averchenkova et al. 2021).
The UK’s targets include emissions and removals from LULUCF . In 2008 the target for 2050 was an economy-wide net emissions 
reduction of at least 80% below 1990 levels. Even the first government plans to achieve this target proposed deployment of removal 
methods, specifically afforestation and wood in construction, increased soil carbon and BECCS (HM Government 2011).

1278
1278
Chapter 12 Cross-sectoral Perspectives12Since the enhancement of carbon sinks is a form of climate change 
mitigation (Honegger et al. 2021a), CDR governance challenges will 
in many respects be similar to those around emissions reduction 
measures, as will policy instruments like RD&D funding, carbon pricing, 
tax or investment credits, certification schemes, and public procurement 
(Sections 13.4, 13.6, 14.4 and 14.5). Effectively integrating CDR into 
mitigation portfolios can build on already existing rules, procedures 
and instruments for emissions abatement (Torvanger 2019; Fridahl 
et al. 2020; Zakkour et al. 2020; Honegger et al. 2021b; Mace et al. 2021; 
Rickels et al. 2021). Additionally, to accelerate RD&D and to incentivise 
CDR deployment, a  political commitment to formal integration into 
existing climate policy frameworks is required (robust evidence, 
high agreement) (Lomax et al. 2015; Geden et al. 2018; Honegger

and Reiner 2018; VonHedemann et al. 2020; Schenuit et al. 2021). 
To avoid CDR being misperceived as a substitute for deep emissions 
reductions, the prioritisation of emissions cuts can be signalled and 
achieved with differentiated target setting for reductions and removals 
(Geden et al. 2019; McLaren et al. 2019). Similarly, sub-targets are 
conceivable for different types of CDR, to prioritise preferred methods 
according to characteristics such as removal processes or timescales of 
storage (Smith 2021).
IPCC guidance on quantifying removals is available for land-based 
biological CDR methods (IPCC 2006, 2019), but has yet to be 
developed for other CDR methods (Royal Society and Royal Academy 
of Engineering 2018). Challenges with development of estimation 
algorithms, data collection, and attribution between sectors and 
countries will need to be overcome (Luisetti et al. 2020; Wedding 
et al. 2021). Trusted methodologies for MRV, required to enable

private sector participation. will need to address the permanence, 
leakage, and saturation challenges with land- and ocean-based 
biological methods (Mace et al. 2021). Protocols that also capture 
social and ecological co-benefits could encourage the adoption of biological CDR methods such as SCS, biochar, A/R and blue carbon 
management (robust evidence, high agreement) (VonHedemann 
et al. 2020; Macreadie et al. 2021).
Private capital and companies, impact investors, and philanthropy 
will play a  role in technical demonstrations and bringing down 
costs, as well as creating demand for carbon removal products on 
voluntary markets, which companies may purchase to fulfil corporate 
social responsibility-driven targets (Friedmann 2019; Fuss et al. 2020; 
Joppa et al. 2021). Niche markets can provide entry points for limited 
deployment of novel CDR methods (Cox and Edwards 2019), but 
targeting currently existing revenue streams by using CO 2 captured

from the atmosphere in Enhanced Oil Recovery and other utilisation 
routes (Mackler et al. 2021; Meckling and Biber 2021) is contested, 
and highlights the importance of choosing appropriate system 
boundaries when assessing supply chains (Tanzer and Ramírez 2019; 
Brander et al. 2021). While the private sector will play a distinct role 
in scaling CDR, governments will need to commit to developing 
infrastructure for the transport and storage of CO 2, including 
financing, permitting, and regulating liabilities (Sanchez et al. 2018; 
Mace et al. 2021; Mackler et al. 2021).
International governance considerations include global technology 
transfer around CDR implementation options (Batres et al. 2021); 
land use change that could affect food production and land condition 
and cause conflict around land tenure and access (Dooley and Kartha 
2018; Hurlbert et al. 2019; Milne et al. 2019); and efforts to create 
sustainable and just supply chains for CDR (Fajardy and Mac Dowell

2020; Tan et al. 2021), such as resources used for BECCS, enhanced 
weathering, or ocean alkalinisation. International governance would 
be particularly important for methods posing transboundary risks, 
especially for ocean-based methods. Specific regulations have so 
far only been developed in the context of the London Protocol, an Box 12.1 (continued)
Adoption of the Paris Agreement in 2015 caused the government to change the legislated 2050 target to a reduction of at least 100% 
(i.e., net zero). Since then, removal of CO 2 and other greenhouse gases has received greater prominence as a distinct topic. The most 
recent national plan (published October 2021) proposes deployment not only of the methods mentioned above, but also DACCS, 
biochar and enhanced weathering. The government has committed to amend accounting of UK targets to include a wider range of 
removal methods beyond LULUCF , and set a target of 5 MtCO 2 yr–1 from methods such as BECCS, DACCS and enhanced weathering

by 2030. It is consulting on markets and incentives for deployment, and exploring new requirements for MRV (HM Government 2021).
In parallel to these policy developments, the UK funds research into technical, environmental and social aspects of removal (Lezaun 
et al. 2021). Research on some elements (e.g., forestry, CCS, soils, bioenergy) have been funded for well over a decade, but the first 
programme dedicated to greenhouse gas removal ran during 2017–2021. This has been followed by two new programmes with 
greater focus on demonstration, totalling GBP100 million over four years (HM Government 2021). A  wide variety of methods is 
supported in these programmes, covering approaches such as CO 2 capture from seawater and capture of methane from cattle, in 
addition to those included already in national mitigation scenarios.
Deployment of removal methods has lagged behind expectations, as national targets for tree planting are not being met and

infrastructure for CO 2 transport and storage is not yet in place (Climate Change Committee 2021). While public awareness around 
carbon removal is low, studies indicate support in general, provided it is perceived as enhancing rather than impeding action to reduce 
emissions (Cox et al. 2020a).

1279
1279
Cross-sectoral Perspectives Chapter 1212international treaty that explicitly regulates ocean fertilisation and 
allows parties to govern other marine CDR methods like ocean 
alkalinity enhancement (GESAMP 2019; Burns and Corbett 2020; 
Boettcher et al. 2021) (Section 14.4.5).
Engagement of civil society organisations and publics will be important 
for shaping CDR policy and deployment (medium evidence,  high 
agreement). Public awareness of CDR and its role in national net 
zero emissions strategies is generally very low (Cox et al. 2020a), and 
perceptions differ across countries and between methods (Bertram 
and Merk 2020; Spence et al. 2021; Sweet et al. 2021; Wenger et al. 
2021). When awareness increases, social processes will shape political 
attitudes on CDR (Shrum et al. 2020), as will efforts to frame particular 
CDR methods as ‘natural’ or ‘technological’ (Osaka et al. 2021), and the 
policy instruments chosen to support CDR (Bellamy et al. 2019). Lack

of confidence in CDR implementation options from both publics and 
investors, and lack of trust in project developers (Cox et al. 2020b) have 
hampered support for CCS (Thomas et al. 2018) and are expected to 
affect deployment of CDR methods with geological storage (Gough and 
Mander 2019). On local and regional scales, CDR projects will need to 
consider air and water quality, impacts to human health, energy needs, 
land use and ecological integrity, and local community engagement 
and procedural justice. Bottom-up and community-driven strategies 
are important for deploying equitable carbon removal projects (Batres 
et al. 2021; Hansson et al. 2021).
12.4 Food systems
12.4.1 Introduction
This section complements Chapter 7 by reviewing recent estimates of 
food system emissions and assessing options beyond the agriculture, 
forestry and land use sectors to mitigate food systems GHG 
emissions. A food system approach enables identification of cross-

sectoral mitigation opportunities including both technological and 
behavioural options. Further, a system approach permits evaluation 
of policies that do not necessarily directly target primary producers 
or consumers, but other food system actors, with possibly higher 
mitigation efficiency. A food system approach was introduced in the 
IPCC Special Report on Climate Change and Land (SRCCL) (Mbow et al. 
2019). Besides major knowledge gaps in the quantification of food 
system GHG emissions (Section 12.4.2), the SRCCL authors identified 
as major knowledge gaps the understanding of the dynamics 
of  dietary change (including behavioural patterns, the adoption of 
plant-based dietary patterns, and interaction with human health and 
nutrition of sustainable healthy diets and associated feedbacks); 
and instruments and mechanisms to accelerate transitions towards 
sustainable and healthy food systems.
Sufficient food and adequate nutrition are fundamental human needs

(HLPE 2020; Ingram 2020). Food needs to be grown and processed, 
transported and distributed, and finally prepared and consumed. Food 
systems range from traditional, involving only few people and short 
supply chains, to modern food systems, comprising complex webs 
involving large numbers of stakeholders and processes that grow and 
transform food commodities into food products and distribute them globally (Gómez and Ricketts 2013; HLPE 2017). A  ‘food system’ 
includes all food chain activities (production, processing, distribution, 
preparation, consumption of food) and the management of food 
loss and wastes. It also includes institutions and infrastructures 
influencing any of these activities, as well as people and systems 
impacted (HLPE 2017; FAO 2018a). Food choices are determined 
by the food environment, consisting of the ‘physical, economic, 
political and socio-cultural context in which consumers engage with

the food system to acquire, prepare and consume food’ (HLPE 2017). 
Food system outcomes encompass food and nutrition, productivity, 
profit and livelihood of food producers and other actors in food value 
chains, but also social outcomes and the impact on the environment 
(Zurek et al. 2018). ‘Sustainable healthy diets’ have been defined 
by FAO and WHO (FAO and WHO 2019) as ‘dietary patterns that 
promote all dimensions of individuals’ health and wellbeing; have 
low environmental pressure and impact; are accessible, affordable, 
safe and equitable; and are culturally acceptable’.
The SRCCL estimated overall global anthropogenic emissions from 
food systems to range between 10.8 and 19.1  GtCO 2-eq yr–1, 
equivalent to 21–37% of total anthropogenic emissions (Mbow 
et al. 2019; Rosenzweig et al. 2020a). The authors identified major 
knowledge gaps for the GHG emissions inventories of food systems, 
particularly in providing disaggregated emissions from the food

industry and transportation. The food system approach taken in 
the SRCCL (Mbow et al. 2019) evaluates the synergies and trade-
offs of food system response options and their implications for food 
security, climate change adaptation and mitigation. This integrated 
framework allows the identification of fundamental attributes of 
responses to maximise co-benefits, while avoiding maladaptation 
measures and adverse side effects. A  food system approach 
supports the design of interconnected climate policy responses to 
tackle climate change, incorporating perspectives of producers 
and consumers. The SRCCL (Mbow et al. 2019) found that the 
technical mitigation potential by 2050 of demand-side responses 
at 0.7–8.0  GtCO 2-eq yr–1 is  comparable to supply-side options at 
2.3–9.6 GtCO 2-eq yr–1. This shows that mitigation actions need to go 
beyond food producers and suppliers to incorporate dietary changes 
and consumers’ behavioural patterns and reveals that producers and

consumers need to work together to reduce GHG emissions.
Though total production of calories is sufficient for the world 
population (Wood et al. 2018; Benton et al. 2019), availability and 
access to food is unequally distributed, and there is a lack of nutrient-
dense foods, fruit and vegetables (Berners-Lee et al. 2018; KC et al. 
2018). In 2019, close to 750 million people were food insecure. An 
estimated 2  billion people lacked adequate access to safe and 
nutritious food in both quality and quantity (FAO et al. 2020). Two 
billion adults are overweight or obese through inadequate nutrition, 
with an upward trend globally (FAO et al. 2019). Low intake of fruit 
and vegetables is further aggravated by high intake rates of refined 
grains, sugar and sodium, together leading to a  high risk of non-
communicable diseases such as cardiovascular disease and type 
2  diabetes (Springmann et al. 2016; Clark et al. 2018; Clark et al.

2019; GBD 2017 Diet Collaborators et al. 2019; Willett et al. 2019) 
(robust evidence, high agreement). At least 340 million children 
under five years of age experience lack of vitamins or other essential

1280
1280
Chapter 12 Cross-sectoral Perspectives12bio-available nutrients, including almost 200 million suffering from 
stunting, wasting or overweight (UNICEF 2019).
Bodirsky et al. (2020) find that the global prevalence of overweight 
will increase to 39–52% of world population in 2050 (from 29% in 
2010; range across the Shared Socio-economic Pathways studied), 
and the prevalence of obesity to 13–20% (9% in 2010). The 
prevalence of underweight people was predicted to approximately 
halve, with absolute numbers stagnating at 0.4–0.7 billion. Although 
many studies represent future pathways of diets and food systems, 
there are few holistic and consistent narratives and quantification 
of the future pathways of diets and food systems (Mitter et al. 2020; 
Mora et al. 2020). Alternative pathways for improved diets and food 
systems have been developed, emphasising climate, environmental 
and  health co-benefits (Bajželj et al. 2014; Hedenus et al. 2014;

Damerau et al. 2016; Weindl et al. 2017a; Weindl et al. 2017b; 
Springmann et al. 2018a; Bodirsky et al. 2020; Prudhomme et al. 
2020; Hamilton et al. 2021), reduced food waste and closing yield 
gaps (Bajželj et al. 2014; Pradhan et al. 2014), nitrogen management 
(Bodirsky et al. 2014), urban and peri-urban agriculture (Kriewald 
et al. 2019) and different sustainability targets (Henry et al. 2018b). 
The UN Food and Agriculture Organization (FAO) has examined 
three alternative food system scenarios: ‘business as usual’, ‘towards 
sustainability’, and ‘stratified societies’ (FAO 2018b). Others have 
identified research priorities or changes in legislation needed to 
support adoption of improved food systems (Mylona et al. 2018).
Malnutrition aggravates susceptibility of children to various 
infectious diseases (França et al. 2009; Farhadi and Ovchinnikov 
2018), and infectious diseases can also decrease nutrient uptake,

thereby promoting malnutrition (Farhadi and Ovchinnikov 2018). 
Contamination of food with bacteria, viruses, parasites and microbial 
toxins can cause foodborne illnesses (Ricci et al. 2017; Abebe 
et al. 2020; Gallo et al. 2020), foodborne substances such as food 
additives and specific proteins can cause adverse reactions, and 
contamination with toxic chemical substances used in agriculture 
and food processing can lead to poisoning or chronic diseases (Gallo 
et al. 2020). Further, health risks from food systems may originate 
from the use of antibiotics in livestock production and the occurrence 
of anti-microbial resistance in pathogens (ECDC et al. 2015; Bennani 
et al. 2020), or zoonotic diseases such as COVID-19 (Gan et al. 2020; 
Patterson et al. 2020; Vågsholm et al. 2020).
Modern food systems are highly consolidated, through vertical and 
horizontal integration (Swinnen and Maertens 2007; Folke et al. 2019).

This consolidation has led to uneven distribution of power across the 
food value chain, with influence concentrated among a few actors in 
the post-farmgate food supply chain (e.g., large food processors and 
retailers), and has contributed to a loss of indigenous agriculture and 
food systems, for example on Pacific Islands (Vogliano et al. 2020). 
While agricultural producers contribute a higher proportion of GHG 
emissions compared with other actors in the supply chain, they have 
relatively little power to change the system (Clapp 2019; Group of 
Chief Scientific Advisors 2020; Leip et al. 2021).
In 2016, the agriculture, fisheries, and forestry sectors employed 
29% of working people; employment within these sectors was 4% in developed countries, down from 9% in 1995, and 57% in least 
developed countries, down from 71% in 1995 (World Bank 2021). 
Employment in other (non-agriculture) food system sectors, such as

the food processing industry and service sectors, differs between food 
systems. The share of total non-farm food system employment ranges 
from 10% in traditional food systems (e.g., sub-Saharan Africa), to over 
50% in food systems in transition (e.g., Brazil), to high shares (80%) 
in modern food systems (e.g., US) (Townsend et al. 2017). The share of 
the food expenditures that farmers receive is decreasing; at the global 
level, this share has been estimated at 27% in 2015 (Yi et al. 2021).
12.4.2 GHG Emissions from Food Systems
12.4.2.1 Sectoral Contribution of GHG Emissions 
from Food Systems
New calculations using the EDGAR v6.0 (Crippa et al. 2021a) and 
FAOSTAT (FAO 2021) databases provide territorial-based food 
system GHG emissions by country globally for the period 1990 
to 2018 (Crippa et al. 2021b). The data are calculated based on 
a combination of country-specific data and aggregated information 
as described by Crippa et al. (2021b) and Tubiello et al. (2021). The

data show that, in 2018, 17 GtCO 2-eq yr–1 (95% confidence range 
13–23  GtCO 2-eq yr–1, calculated according to Solazzo et al. (2020)) 
were associated with the production, processing, distribution, 
consumption of food and management of food system residues. 
This corresponded to 31% (range 23–42%) of total anthropogenic 
GHG emissions of 54  GtCO 2-eq yr–1. Based on the IPCC sectoral 
classification (Table 12.7 and Figure 12.5), the largest contribution 
of food systems GHG emissions in 2018 was from agriculture, that 
is, livestock and crop production systems (6.3 GtCO 2-eq yr–1, range 
2.6–11.9) and land use, land use change and forestry (LULUCF) 
(4.0  GtCO 2-eq yr–1, range 2.1–5.9) (Figure  12.5). Emissions from 
energy use were 3.9  GtCO 2-eq yr–1 (3.6–4.4), waste management 
1.7  GtCO 2-eq yr–1 (0.9–2.6), and industrial processes and product 
use 0.9 GtCO 2-eq yr–1 (0.6–1.1). The share of GHG emissions from 
food systems generated outside the AFOLU (agriculture and LULUCF)

sectors has increased over recent decades, from 28% in 1990 
to 39% in 2018.
Energy: Emissions from energy use occur throughout the food 
supply chain. In 2018, the main contributions came from energy 
industries supplying electricity and heat (970 MtCO 2-eq yr–1), 
manufacturing and construction (920 MtCO 2-eq yr–1, of which 29% 
was attributable to the food, beverage, and tobacco industry), and 
transport (760 MtCO 2-eq yr–1). These emissions were almost entirely 
as CO 2. Energy emissions from forestry and fisheries amounted 
to 480 MtCO 2-eq yr–1, with 91% of emissions as CO 2. Emissions 
from residential and commercial fuel combustion contributed 
250 MtCO 2-eq yr–1 (79% of emissions as CO 2, and with emissions of 
1.7 MtCH 4 yr–1) and 130 MtCO 2-eq yr–1 (with 98% of emissions as 
CO2), respectively.
Refrigeration uses an estimated 43% of energy in the retail sector 
(Behfar et al. 2018) and significantly increases fuel consumption

during distribution. Besides being energy intensive, supermarket

1281
1281
Cross-sectoral Perspectives Chapter 1212refrigeration also contributes to GHG emissions through leakage of 
refrigerants (fluorinated gases, or F-gases), although their contribution 
to food system GHG emissions is estimated to be minor (Crippa et al. 
2021b). The cold chain accounts for approximately 1% of global GHG 
emissions, but as the volume of refrigerators per capita in developing 
countries is reported to be one order of magnitude lower than in 
developed countries (19 m3 versus 200 m3 refrigerated storage 
capacity per 1000 inhabitants), the importance of refrigeration 
to total GHG emissions is expected to increase (James and James 
2010). Although refrigeration gives rise to GHG emissions, both 
household refrigeration and effective cold chains could contribute 
to a substantial reduction in losses of perishable food and thus in 
emissions associated with food provision (University of Birmingham 
2018; James and James 2010). A trade-off exists between reducing

food waste and increased refrigeration emissions, with the benefits 
depending on type of produce, location and technologies used 
(Sustainable Cooling for All 2018; Wu et al. 2019).
Transport has overall a  minor importance for food system GHG 
emissions, with a share of 5% to 6% (Poore and Nemecek 2018; Crippa 
et al. 2021b). The largest contributor to food system transport GHG 
emissions was road transport (92%), followed by marine shipping 
(4%), rail (3%), and aviation (1%). Only looking at energy needs, air 
or road transport consumes one order of magnitude higher energy 
(road: 70–80 MJ t–1 km–1; aviation: 100–200 MJ t–1 km–1) than 
marine shipping (10–20 MJ t–1 km–1) or rail (8–10 MJ t–1 km–1) 
(FAO  2011). For specific food products with high water content, 
relatively low agricultural emissions and high average transport distances, the share of transport in total GHG emissions can be 
over 40% (e.g., bananas, with total global average GHG emissions

of 0.7 kgCO 2-eq kg–1) (Poore and Nemecek 2018), but transport is 
a  minor source of GHG emissions for most food products (Poore 
and Nemecek 2018).
Industry: Direct industrial emissions associated with food systems are 
generated by the refrigerants industry (580 MtCO 2-eq yr–1 as F-gases) 
and the fertiliser industry for ammonia production (280 MtCO 2-eq yr–1 
as CO 2) and nitric acid (60 MtCO 2-eq yr–1 as N 2O). The industry 
sector data account for CO 2 stored in urea (–50 MtCO 2-eq yr–1). 
Packaging contributed about 6% of total food system emissions 
(0.98 GtCO 2-eq yr–1, 91% as CO 2, with CH 4 emissions of 2.8 Mt CH 4 
yr–1). Major emissions sources are pulp and paper (60 MtCO 2-eq yr–1) 
and aluminium (30 MtCO 2-eq yr–1), with ferrous metals, glass, and 
plastics making a smaller contribution. High shares of emissions from 
packaging are found for beverages and some fruit and vegetables 
(Poore and Nemecek 2018).
Waste: Management of waste generated in the food system

(including  food waste, wastewater, packaging waste, etc.) leads 
to biogenic GHG  emissions, and contributed 1.7  GtCO 2-eq yr–1 to 
food systems’ GHG emissions in 2018. Of these emissions, 55% 
were from domestic and commercial wastewater (30 MtCH 4 yr–1 
and  310 ktN 2O yr–1), 36%  from solid waste management (20 MtCH 4 yr–1 
and 310 ktN 2O yr–1), and 8% from industrial wastewater (4 MtCH 4 yr–1 
and 80 ktN 2O yr–1). Emissions from waste incineration and other 
waste management systems contributed 1%.
Table 12.7 | GHG emissions from food systems by sector according to IPCC classification in Mt gas yr–1 and food systems’ share of total anthropogenic 
GHG emissions in 1990 and 2015.
Sector CO2 CH4 N2O F-gases GHG CO2 CH4 N2O F-gases GHG
Emissions (Mt gas yr–1) Share of total sectoral emissions (%)
  1990
1 Energy 2212 10 0  –  2583 10.5 10.2 26.7  –  10.7
2 Industrial processes 190 0 0 0 263 14.5 0 38 4.8 16.2
3 Solvent and Other Product Use 0  –   –   –  0 0.2  –   –   –  0.2

4 Agriculture 102 142 5  –  5370 100 100 99.2  –  99.8
5 LULUCF 4946  –  0  –  5080 181  –  194  –  182
6 Waste 3 40 0  –  1155 29 72.4 99.1  –  73.2
Total 7453 192 6 0 14452 29.3 65.2 84.5 4.8 40.3
Total (MtCO 2-eq yr–1) 7453 5243 1755 0 14452 29.3 63.9 84.5 0.3 40.3
  2015
1 Energy 3449 13 0  –  3927 10.1 9.5 24.1  –  10.2
2 Industrial processes 242 0 0 0 881 7.9 0 28.6 58 20.1
3 Solvent and Other Product Use 7  –   –   –  7 4.1  –   –   –  3.6
4 Agriculture 140 161 7  –  6326 100 100 99.1  –  99.7
5 LULUCF 3823  –  1  –  3982 190  –  229  –  191
6 Waste 5 58 0  –  1699 30.6 71.8 99.1  –  72.9
Total 7666 231 8 0 16821 19.3 61.6 83.7 58 31.1
Total (MtCO 2-eq yr –1) 7666 6317 2256 581 16821 19.3 60.2 83.7 53.6 31.1
Notes: Agricultural emissions include the emissions from the whole sector; biomass production for non-food use currently not differentiated. Non-food system AFOLU emissions are

negative (that is, a net carbon sink), therefore the share of AFOLU food system emissions is >100.  Source: EDGARv6 (Crippa et al. 2019; Crippa et al. 2021b), and FAOSTAT (FAO 2021).  
LULUCF: land use, land-use change and forestry.

1282
1282
Chapter 12 Cross-sectoral Perspectives1212.4.2.2 GHG Intensities of Food Commodities
There is high variability in the GHG emissions of different food 
products and production systems (Figure  12.6). GHG emissions 
intensities  – measured using attributional lifecycle assessment, 
considering the full supply chain, expressed as CO 2-eq per kg of 
product or per kg of protein  – are generally highest for ruminant 
meat, cheese, and certain crustacean species (e.g.,  farmed shrimp 
and prawns, trawled lobster) (Nijdam et al. 2012; Clark and Tilman 
2017; Clune et al. 2017; Hilborn et al. 2018; Poore and Nemecek 
2018) (robust evidence, high agreement). Generally, beef from dairy 
systems has a lower footprint (8–23 kgCO 2-eq per 100 g protein than 
beef from beef herds (17–94 kgCO 2-eq per 100 g protein (Figure 12.6, 
re-calculated from Poore and Nemecek (2018) using AR6 GWPs 
based on a  100year horizon) (medium evidence, high agreement).

The wide variation in emissions from beef reflects differences in 
production systems, which range from intensive feedlots with stock 
raised largely on grains through to rangeland and transhumance 
production systems. Dairy systems are generally more intensive 
production systems, with higher digestibility feed than beef systems. 
Further, emissions from dairy systems are shared between milk and meat, which brings GHG footprints of beef from dairy herds closer to 
those of meat from monogastric animals, with emissions intensities 
of pork (4.4–13  kgCO 2-eq per 100 g  protein) and poultry meat 
(2.3–11 kgCO 2-eq per 100 g protein) (Poore and Nemecek 2018).
Emissions intensities for farmed fish ranged from 2.4–11 kgCO 2-eq 
per 100 g protein (Poore and Nemecek 2018). For Norwegian seafood, 
large differences have been found ranging from 1.1 kgCO 2-eq kg–1 
edible product for herring to more than 8  kgCO 2-eq kg–1 edible 
product for salmon shipped by road and ferry from Oslo to Paris

(Winther et al. 2020). For capture fish, large differences in emissions 
have been found, ranging from 0.2–7.9 kgCO 2-eq kg–1 landed fish 
(Parker et al. 2018), although an environmental comparison of 
capture fish to farmed foods should include other indicators such as 
overfishing. Plant-based foods generally have lower GHG emissions 
(–2.2 to +4.5 kgCO 2-eq per 100 g protein) than farmed animal-based 
foods (Nijdam et al. 2012; Clark and Tilman 2017; Clune et al. 2017; 
Hilborn et al. 2018; Poore and Nemecek 2018) (robust evidence, high 
agreement). Several plant-based foods are associated with emissions 
from land use change, for example, palm oil, soy and coffee (Poore 
and Nemecek 2018), although emissions intensities are context Waste
Energy + IndustryLULUCFCH4
F-gases
N2OCO2199005101520
05101520
1995 2018 2015 2010 2005 2000 199035% 35% 34% 30% 28% 27%24%14.5 15.0 15.015.716.417.1 16.8
6.46.8 6.87.47.88.2 8.336%35% 35%36%36%36%38%
52%52% 52%50% 49% 48%46%

0% 0% 0% 1%2% 3% 3%
12% 12% 12% 13% 13% 13% 13%14.5 15.0 15.015.7 16.417.1 16.8
192 194 193204215225231
7.5 7.9 7.87.9 8.0 8.2
7.78% 8% 9% 9%9%9% 10%
37% 36% 36% 36%37%36% 38%GHG [GtCO2eq yr–1] GHG [GtCO2eq yr–1]
1995 2018 2015 2010 2005 2000
300
200
100
0CH4 [MtCH4 yr–1] N2O [MtN2O yr–1]
1990 1995 2018 2015 2010 2005 2000 1990 1995 2018 2015 2010 2005 2000
10.0
0.02.55.07.5
CO2 [Mt CO2 yr–1]
1990 1995 2018 2015 2010 2005 200020%21% 22%25%26%27%29%
21% 22% 23%24%24%25% 25%
99%9%9%7%7% 7%
8%8%7%8%8%8% 7%
4%4%4%4%5%5% 5%
80%79%80%79%80%80% 81%
74% 72% 71%71% 71%70% 69%
32%34% 35%41% 43% 44%
48%
66%65%63%57%55%54%
50%5% 5% 5%6%5%5%5%
Figure 12.5 | Food system GHG emissions from the agriculture, LULUCF, waste, and energy & industry sectors.  Source: Crippa et al. (2021b).

1283
1283
Cross-sectoral Perspectives Chapter 1212specific (Meijaard et al. 2020) and for plant-based proteins, GHG 
footprints per serving remain lower than those of animal source 
proteins (Kim et al. 2019).
In traditional production systems, especially in developing countries, 
livestock serve multiple functions, providing draught power, fertiliser, 
investment and social status, besides constituting an important source 
of nutrients (Weiler et al. 2014). In landscapes dominated by forests 
or cropland, semi-natural pastures grazed by ruminants provide 
heterogeneity that supports biodiversity (Röös et al. 2016). Grazing 
on marginal land and the use of crop residues and food waste can 
provide human-edible food with lower demands for cropland (Röös 
et al. 2016; Van Zanten et al. 2018; Van Hal et al. 2019). Animal protein 
requires more land than vegetable protein, so switching consumption 
from animal to vegetable proteins could reduce the pressure on

land resources and potentially enable additional mitigation through 
expansion of natural ecosystems, storing carbon  while supporting 
biodiversity, or reforestation to sequester carbon and enhance wood 
supply capacity for the production of bio-based products substituting 
fossil fuels, plastics, cement, etc. (Schmidinger and Stehfest 2012; 
Searchinger et al. 2018b; Hayek et al. 2021). At the same time, 
alternatives to animal-based meat and other livestock products are 
being developed (Figure 12.6). Their increasing visibility in supermarkets 
and catering services, as well as falling production prices, could make 
meat substitutes competitive in one to two decades (Gerhardt et al. 
2019). However, uncertainty around their uptake creates uncertainty 
around their effect on future GHG emissions.12.4.2.3 Territorial National Per Capita GHG Emissions 
Food systems are connected to other societal systems, such as the

energy system, financial system, and transport system (Leip et al. 
2021). Also, food systems are dynamic and continuously changing 
and adapting to existing and anticipated future conditions. Food 
production systems are very diverse and vary by farm size, intensity 
level, farm specialisation, technological level, production methods 
(e.g., organic, conventional, etc.), with differing environmental and 
social consequences (Václavík et al. 2013; Fanzo 2017; Herrero et al. 
2017; Herrero et al. 2021).
Various frameworks have been proposed to assess sustainability of 
food systems, including metrics and indicators on environmental, 
health, economic and equity issues, pointing to the importance of 
recognising the multi-dimensionality of food system outcomes 
(Gustafson et al. 2016; Chaudhary et al. 2018; Hallström et al. 2018; 
Zurek et al. 2018; Eme et al. 2019; Béné et al. 2020; Hebinck et al. 2021). 
Data platforms are being developed, but so far comprehensive data

for evidence-based food system policy are lacking (Fanzo et al. 2020).
To visualise several food systems dimensions in a  GHG context, 
Figure 12.7 shows GHG emissions per capita and year for regional 
country aggregates (Crippa et al. 2021a; Crippa et al. 2021b), 
indicated by the size of the bubbles. The GHG emissions presented 
here are based on territorial accounting similar to the UNFCCC GHG –5 0 5 10 15 20 25 30 35 40 45 50
NutsbPeasPulses excl. peasGroundnutsTofuFish (capture)GrainsaSoymilkEggsFish (farmed)Poultry MeatPig MeatMilkCrustaceans (capture)CheeseBeef (dairy cattle)Crustaceans (farmed)Lamb and MuttonBeef (beef cattle)GHG emissions intensity (kgCO2-eq per 100g of protein)
10th percentile Mean 90th percentile94 kgCO2-eq
(90th percentile)
272528313848380 50 100CH4 share (%)
15
Figure  12.6 |  Ranges of GHG intensities [kgCO 2-eq per 100 g  protein, 10–90th percentile] in protein-rich foods, quantified via a  meta-analysis of

attributional lifecycle assessment studies using economic allocation.  Aggregation of CO 2, CH 4, and N 2O emissions in Poore and Nemecek (2018) updated to use AR6 
100-year GWP . Data for capture fish, crustaceans, and cephalopods from Parker et al. (2018), with post-farm data from Poore and Nemecek (2018), where the ranges represent 
differences across species groups. CH 4 emissions include emissions from manure management, enteric fermentation, and flooded rice only. a Grains are not generally classed as 
protein-rich, but they provide about 41% of global protein intake. Here grains are a weighted average of wheat, maize, oats, and rice by global protein intake. b Conversion of 
annual to perennial crops can lead to carbon sequestration in woody biomass and soil, shown as negative emissions intensity. Source: data from Poore and Nemecek (2018); 
Parker et al. (2018).

1284
1284
inventories: emissions are assigned to the country where they occur, 
not where food is consumed (Crippa et al. 2021a; Crippa et al. 2021b) 
(Section 12.4.2.1). The colours of the bubbles indicate the relative 
contribution of the following risk factors to deaths, according to 
the classification used in the Global Burden of Disease Study: child 
and maternal malnutrition (red, deficiencies of iron, zinc or Vitamin 
A, or low birth weight or child growth failure), dietary risks (yellow, 
for example diets low in vegetables, legumes, whole grains or diets 
high in red and processed meat and sugar-sweetened beverages) 
or high body mass index (blue). The combined contribution 
of these three risk factors to total deaths varies strongly and is 
between 28% and 88% of total deaths. Figure  12.7 shows that 
dietary risk factors are prevalent throughout all regions. Though 
not a complete measure of the health impact of food, these were

selected as a proxy for nutritional adequacy and balance of diets, 
avoidance of food insecurity, over- or mal-nutrition and associated non-communicable diseases (GBD 2017 Diet Collaborators 2018; 
GBD 2017 Diet Collaborators et al. 2019).
The share of GHG emissions from energy use is taken as a proxy for 
the structure of food supply in a region (Section 12.4.1), and the cost 
for food as a  proxy for the structure of the demand side and the 
access to (healthy) food (Chen et al. 2016; Finaret and Masters 2019; 
Hirvonen et al. 2019; HLPE 2020; Springmann et al. 2021), though 
acknowledging the limitations of such a simplification.
While total food system emissions in 2018 range between 0.9 and 
8.5 tCO 2-eq per capita per year between regions, the share of energy 
emissions relative to energy and land-based (agriculture and food 
system land-use change) emissions ranges between 3% and 78%. 
Regional expenditures for food range from USD3.0–8.8 per capita

per day (Figure 12.7), though there is high variability within countries 020%
10%40%
30%60%90%
80%
70%
50%Share of food system GHG emissions from energy
Wholesale cost for food ($ cap–1 day–1)3 4 8 7 6 5 9Relative contribution of risk factors to their combined risk for deaths 
(area of doughnuts relative to total bubble area)
Child and maternal 
malnutrition Dietary risksHigh body-mass 
index
Countries with high share 
of food imports have 
lower territorial GHG 
emissions (seen speciﬁcally 
in the Asia-Paciﬁc 
Developed region).Countries with high share 
of food exports have 
increased territorial GHG 
emissions, and a low to 
moderate share of energy 
food system emissions. Some countries with low/medium industrialisation (low share 
of energy food system emissions) have high per capita 
territorial GHG emissions because of land-use change 
emissions that are partly ‘exported’ to industrialised 
importing consumers. Often this is associated with emerging

dietary risks and challenges of high BMI.
With Increasing industrialisation (high share of energy food 
system emissions) the wholesale cost for food tends to 
increase and health problems due to overweight dominate.Low per capita territorial 
GHG emissions are found 
in countries with low 
consumption rates 
of meat.North and 
West EuropeNorth and 
West EuropeSouth and 
East EuropeSouth and 
East Europe
EurasiaEurasia
North AfricaNorth Africa
South AmericaSouth AmericaMeso 
AmericaMeso 
AmericaCaribbeanCaribbean
South and 
West and 
Middle AfricaSouth and 
West and 
Middle AfricaEast AfricaEast AfricaRest of 
South AsiaRest of 
South AsiaIndia and 
Sri LankaIndia and 
Sri Lanka
South-East 
AsiaSouth-East 
AsiaDeveloping PaciﬁcDeveloping Paciﬁc
Australia and 
New ZealandAustralia and 
New ZealandEast 
AsiaEast 
AsiaUSA and 
CanadaUSA and 
CanadaMiddle 
EastMiddle 
East
Asia-Paciﬁc 
Developed (others)Asia-Paciﬁc

Developed (others)Territorial per capita food system GHG emission intensity 
(tCO2-eq cap–1 year–1)
1 5 10
Figure 12.7 | Regional differences in health outcomes, territorial per capita GHG emissions from national food systems, and share of food system 
GHG emissions from energy use.  GHG emissions are calculated according to the IPCC Tier 1 approach and are assigned to the country where they occur, not necessarily 
where the food is consumed. Health outcome is expressed as relative contribution of each of the following risk factors to their combined risk for deaths: child and maternal 
malnutrition (red), dietary risks (yellow) or high body mass index (blue).  Sources: wholesale cost of food per capita: Springmann et al. 2021); territorial food system GHG 
emissions: EDGAR v.6, Crippa et al. (2021a), recalculated according to Crippa et al. (2021b) using AR6 GWPs; deaths attributed to dietary factors: IHME (2018); GBD 2017 Diet 
Collaborators et al. (2019).

1285
1285
Cross-sectoral Perspectives Chapter 1212and the costs of nutrient-adequate diets often exceeds those of diets 
delivering adequate energy (Hirvonen et al. 2019; Bai et al. 2020; FAO 
et al. 2020). Thus, low-income households in industrialised countries 
can also be affected by food insecurity (Penne and Goedemé 2020).
12.4.3 Mitigation Opportunities
GHG emissions from food systems can be reduced by targeting direct 
or indirect GHG emissions in the supply chain including enhanced 
carbon sequestration, by introducing sustainable production methods 
such as agroecological approaches which can reduce system-level 
GHG emissions of conventional food production and also enhance 
resilience (HLPE 2019), by substituting food products with high GHG 
intensities with others of lower GHG intensities, by reducing food 
over-consumption, and/or by reducing food loss and waste. The 
substitution of food products with others that are more sustainable

and/or healthier is often called ‘dietary shift’.
Clark et al. (2020) showed that even if fossil fuel emissions were 
eliminated immediately, food system emissions alone would 
jeopardise the achievement of the 1.5ºC target and threaten the 
2ºC target. They concluded that both demand-side and supply-side 
strategies are needed, including a  shift to a  diet with lower GHG 
intensity and rich in plant-based ‘conventional’ foods (e.g., pulses, 
nuts), or new food products that could support dietary shift. 
Such dietary shift needs to overcome socio-cultural, knowledge, 
and economic barriers to significantly achieve GHG mitigation 
(Section 12.4.5).
Food losses occur at the farm, post-harvest and during the food 
processing/wholesale stages of a food supply chain, while in the final 
retail and consumption stages the term food waste is used (HLPE 
2014). Typically, food losses are linked to technical issues such as 
lack of infrastructure and storage, while food waste is often caused

by socio-economic and behavioural factors. Mitigation opportunities 
through reducing food waste and loss exist in all food supply chain 
stages and are described in the sub-sections below.
Food system mitigation opportunities are divided into five categories 
as given in Table 12.8:
• Food production from agriculture, aquaculture, and fisheries 
(Chapter 7.4 and Section 12.4.3.1)
• Controlled-environment agriculture (Section 12.4.3.2)
• Emerging food production technologies (Section 12.4.3.3)
• Food processing industries (Section 12.4.3.4)
• Storage and distribution (Section 12.4.3.5)
Food system mitigation opportunities can be either incremental 
or transformative (Kugelberg et al. 2021). Incremental options are 
based on mature technologies, for which processes and causalities 
are understood, and their implementation is generally accepted by 
society. They do not require a substantial change in the way food 
is produced, processed, or consumed and might lead to a  (slight)

shift in production systems or preferences. Transformative mitigation 
opportunities have wider food system implications and usually coincide with a significant change in food choices. They are based 
on technologies that are not yet mature and are expected to 
require further innovation (Klerkx and Rose 2020), and/or mature 
technologies that might already be part of some food systems but are 
not yet widely accepted and have transformative potential if applied 
at large scale, for example consumption of insects (Raheem et al. 
2019a). Many emerging technologies might be seen as a further step 
in agronomic development where land-intensive production methods 
relying on the availability of naturally-available nutrients and water 
are successively replaced with crop variants and cultivation practices 
reducing these dependencies at the cost of larger energy input 
(Winiwarter et al. 2014). Others suggest a  shift to agroecological

approaches combining new scientific insights with local knowledge 
and cultural values (HLPE 2019). Food system transformation can 
lead to regime shifts or (fast) disruptions (Pereira et al. 2020) if driven 
by events that are out of control of private or public measures and 
have a ‘crisis’ character (e.g., BSE) (Skuce et al. 2013).
Table  12.8 summarises the main characteristics of food system 
mitigation opportunities, their effect on GHG emissions, and 
associated co-benefits and adverse effects.
Agricultural food production systems range from smallholder 
subsistence farms to large animal production factories, in open 
spaces, greenhouses, rural areas or urban settings.
Dietary shift: Studies demonstrate that a  shift to diets rich in 
plant-based foods, particularly pulses, nuts, fruits and vegetables, 
such as vegetarian, pescatarian or vegan diets, could lead to 
substantial reduction of greenhouse gas emissions as compared to

current dietary patterns in most industrialised countries, while also 
providing health benefits and reducing mortality from diet-related 
non-communicable diseases (Springmann et al. 2018a; Chen et al. 
2019; Willett et al. 2019; Bodirsky et al. 2020; Costa Leite et al. 2020; 
Ernstoff et al. 2020; Jarmul et al. 2020; Semba et al. 2020; Theurl et al. 
2020; Hamilton et al. 2021).
Pulses such as beans, chickpeas, or lentils, have a protein composition 
complementary to cereals, providing together all essential amino 
acids (Foyer et al. 2016; McDermott and Wyatt 2017). Bio-availability 
of proteins in foods is influenced by several factors, including 
amino acid composition, presence of anti-nutritional factors, and 
preparation method (Hertzler et al. 2020; Weindl et al. 2020; Semba 
et al. 2021). Soy beans, in particular, have a  well-balanced amino 
acid profile with high bio-availability (Leinonen et al. 2019). Pulses

are part of most traditional diets (Semba et al. 2021) and supply up 
to 10–35% of protein in low-income countries, but consumption 
decreases with increasing income and they are globally only a minor 
share of the diet (McDermott and Wyatt 2017). Pulses play a key role 
in crop rotations, fixing nitrogen and breaking disease cycles, but 
yields of pulses are relatively low and have seen small yield increases 
relative to those of cereals (Foyer et al. 2016; McDermott and Wyatt 
2017; Barbieri et al. 2021; Semba et al. 2021).
Technological innovations: have made food production more 
efficient since the onset of agriculture (Winiwarter et al. 2014; 
Herrero et al. 2020). Emerging technologies include digital agriculture

1286
1286
Table 12.8 | Food system mitigation opportunities.
Food system mitigation options
I: incremental; T: transformativeDirect and indirect effect on GHG mitigation
D: direct emissions except emissions from energy use; E: energy 
demand; M: material demand; FL: food losses; FW: food waste
Direction of effect on GHG mitigation: + increased mitigation; 
0 neutral; – decreased mitigationCo-benefits/adverse effects
H: health aspects; A: animal welfare; R: resource use; L: land demand;  
E: ecosystem services; 0: neutral
+ co-benefits; – adverse effectsSource
Food from 
agriculture, 
and fisheries(I)   Dietary shift, in particular 
increased share of plant-based 
protein sourcesD+  ↓ GHG footprintA+  Animal welfare
L+  Land sparing
H+   Good nutritional properties, potentially ↓ risk from zoonotic diseases, pesticides and antibiotics1–5
(I/T)  Digital agriculture D+  ↑ LogisticsL+  Land sparing
R+  ↑ Resource use efficiencies6–7

(T)  Gene technology D+  ↑ Productivity or efficiencyH+  ↑ Nutritional quality
E0  ↓ Use of agrochemicals; ↑ probability of off-target impacts7–11
(I)   Sustainable intensification,  
Land-use optimisationD+  ↓ GHG footprint
E0  Mixed effectsL+  Land sparing
R– Might ↑ pollution/biodiversity loss7, 12
(I)  AgroecologyD+  ↓ GHG/area, positive micro-climatic effects 
E+  ↓ Energy, possibly ↓ transport 
FL+   Circular approachesE+  Focus on co-benefits/ecosystem services
R+  Circular, ↑ nutrient and water use efficiencies13–17
Controlled-
agriculture(T)  Soilless agricultureD+  ↑ productivity, weather independent
FL+  harvest on demand
E-   Currently ↑ energy demand, but ↓ transport,  
building spaces can be used for renewable energyR+  Controlled loops ↑ nutrient and water use efficiency
H+  Crop breeding can be optimised for taste and/or nutritional quality18–24
Emerging food 
production 
technologies(T)  InsectsD0  Good feed conversion efficiency

FW+  Can be fed on food wasteH0  Good nutritional qualities but attention to allergies and food safety issues required 25–28
(I/T)  Algae and bivalves D+ ↓ GHG footprintsA+  Animal welfare
H+  Good nutritional qualities; risk of heavy metal and pathogen contamination
R+  Biofiltration of nutrient-polluted waters29–32
(I/T)    Plant-based alternatives to 
animal-based food productsD+  No emissions from animals, ↓ inputs for feedA+  Animal welfare
H+   Potentially ↓ risk from zoonotic diseases, pesticides and antibiotics;  
but ↑ processing demand31–33
(T)   Cellular agriculture 
(including cultured meat, 
microbial protein)D+  No emissions from animals, high protein conversion efficiency
E–  ↑ Energy need
FLW+ ↓ Food loss and wasteA+  Animal welfare
R+  ↓ Emissions of reactive nitrogen or other pollutants
H0   Potentially ↓ risk from zoonotic diseases, pesticides and antibiotics;  
↑ research on safety aspects needed3, 24
34–42

1287
1287
Cross-sectoral Perspectives Chapter 1212Food system mitigation options
Food 
processing and 
packaging(I)   Valorisation of by-products, 
food loss and waste logistics 
and managementM+  Substitution of bio-based materials
FL+  ↓ of food losses  43–44
(I)  Food conservationFW+  ↓ Food waste
E0   ↑ energy demand but also energy savings possible 
(e.g., refrigeration, transport)  45–46
(I)   Smart packaging  
and other technologiesFW+  ↓ Food waste
M0  ↑ Material demand and ↑ material-efficiency

E0 ↑ Energy demand; energy savings possibleH+  Possibly ↑ freshness/reduced food safety risks 46–49
(I)  Energy efficiency E+  ↓ Energy   50
Storage and 
distribution(I)  Improved logisticsD+  ↓ Transport emissions
FL+  ↓ Losses in transport
FW–  Easier access to food could ↑ food waste 46–47
51–53
(I)   Specific measures to 
reduce food waste in retail 
and food cateringFW+ ↓ Food waste
E+  ↓ Downstream energy demand
M+  ↓ Downstream material demand54–56
(I)   Alternative fuels/ 
transport modesD+  ↓ Emissions from transport
(I)  Energy efficiency E+  ↓ Energy in refrigeration, lightening, climatisation 57–58
(I)  Replacing refrigerants D+  ↓ Emissions from the cold chain50
59–60
Sources: [1] McDermott and Wyatt (2017); [2] Foyer et al. (2016); [3] Semba et al. (2021); [4] Weindl et al. (2020); [5] Hertzler et al. (2020); [6] Finger et al. (2019); [7] Herrero et al. (2020); [8] Steinwand and Ronald (2020); [9] Zhang et al. (2020a);

[10] Ansari et al. (2020); [11] Eckerstorfer et al. (2021); [12] Folberth et al. (2020); [13] HLPE (2019); [14] Wezel et al. (2009); [15] Van Zanten et al. (2018); [16] Van Zanten et al. (2019); [17] van Hal et al. (2019); [18] Beacham et al. (2019); 
[19] Benke and Tomkins (2017); [20] Gómez and Gennaro Izzo (2018); [21] Maucieri et al. (2018); [22] Rufí-Salís et al. (2020); [23] Shamshiri et al. (2018); [24] Graamans et al. (2018); [25] Fasolin et al. (2019); [26] Garofalo et al. (2019); 
[27] Parodi et al. (2018); [28] Varelas (2019); [29] Gentry et al. (2020); [30] Peñalver et al. (2020); [31] Torres-Tiji et al. (2020); [32] Willer and Aldridge (2020); [33] Fresán et al. (2019); [34] Mejia et al. (2019); [35] Tuomisto (2019); [36] Thorrez 
and Vandenburgh (2019); [37] Tuomisto and Teixeira de Mattos (2011); [38] Mattick et al. (2015); [39] Mattick (2018); [40] Souza Filho et al. (2019); [41] Chriki and Hocquette (2020); [42] Hadi and Brightwell (2021); [43] Göbel et al. (2015);

[44] Caldeira et al. (2020); [45] Silva and Sanjuán (2019); [46] FAO (2019a); [47] Molina-Besch et al. (2019); [48] Poyatos-Racionero et al. (2018); [49] Müller and Schmid (2019); [50] Niles et al. (2018); [51] Lindh et al. (2016); [52] Wohner 
et al. (2019); [53] Bajželj et al. (2020); [54] Buisman et al. (2019); [55] Albizzati et al. (2019); [56] Liu et al. (2016); [57] Chaomuang et al. (2017); [58] Lemma et al. (2014); [59] McLinden et al. (2017); [60] Gullo et al. (2017). Food from 
Agriculture, Aquaculture, and Fisheries.

1288
1288
Chapter 12 Cross-sectoral Perspectives12(using advanced sensors, big data), gene technology (crop bio-
fortification, genome editing, crop innovations), sustainable 
intensification (automation of processes, improved inputs, precision 
agriculture) (Herrero et al. 2020), or multi-trophic aquaculture 
approaches (Knowler et al. 2020; Sanz-Lazaro and Sanchez-Jerez 
2020), though literature on aquaculture and fisheries in the context of 
GHG mitigation is limited.
Such technologies may contribute to a reduction of GHG emissions 
at the food system level, enhanced provision of food, better 
consideration of ecosystem services, and/or contribute to nutrition-
sensitive agriculture, for example, by increasing the nutritional quality 
of staple crops, increasing the palatability of leguminous crops such 
as lupines, or increasing the agronomic efficiency or resilience of 
crops with good nutritional characteristics.

For details on agricultural mitigation opportunities refer to Section 7.4.
12.4.3.1 Controlled-environment Agriculture
Controlled-environment agriculture is mainly based on hydroponic or 
aquaponic cultivation systems that do not require soil. Aquaponics 
combine hydroponics with a re-circulating aquaculture compartment 
for integrated production of plants and fish (Junge et al. 2017; 
Maucieri et al. 2018), while aeroponics is a further development of 
hydroponics that replaces water as a growing medium with a mist of 
nutrient solution (Al-Kodmany 2018). Aquaponics could potentially 
produce proteins in urban farms, but the technology is not yet mature 
and its economic and environmental performance is unclear (Love 
et al. 2015; O’Sullivan et al. 2019).
Controlled-environment agriculture is often undertaken in urban 
environments to take advantage of short supply chains (O’Sullivan 
et al. 2019), and might use abandoned buildings or be integrated in

supermarkets, producing for example herbs ‘on demand’.
Optimising growing conditions, hydroponic systems achieve higher 
yields than un-conditioned agriculture (O’Sullivan et al. 2019); 
and yields can be further enhanced in CO 2-enriched atmospheres 
(Shamshiri et al. 2018; Armanda et al. 2019). By using existing 
spaces or modular systems that can be vertically stacked, this 
technology minimises land demand, however it is energy intensive 
and requires large financial investments. So far, only a few crops 
are commercially produced in vertical farms, including lettuce and 
other leafy greens, herbs and some vegetables, due to their short 
growth period and high value (Benke and Tomkins 2017; Armanda 
et al. 2019; Beacham et al. 2019; O’Sullivan et al. 2019). Through 
breeding, other crops could reach commercial feasibility, or crops 
with improved taste or nutritional characteristics can be grown 
(O’Sullivan et al. 2019).

In controlled-environment agriculture, photosynthesis is fuelled by 
artificial light through LEDs or a combination of natural light with 
LEDs. Control of the wave band and light cycle of the LEDs and micro-
climate can be used to optimise photosynthetic activity, yield and 
crop quality (Gómez and Gennaro Izzo 2018; Shamshiri et al. 2018).Co-benefits of controlled-environment agriculture include minimising 
water and nutrient losses as well as agro-chemical use (Al-Kodmany 
2018; Shamshiri et al. 2018; Armanda et al. 2019; Farfan et al. 2019; 
O’Sullivan et al. 2019; Rufí-Salís et al. 2020) (robust evidence, high 
agreement). Water is recycled in a  closed system and additionally 
some plants generate fresh water by evaporation from grey or black 
water, and high nutrient use efficiencies are possible. Food production 
from controlled-environment agriculture is independent of weather 
conditions and able to satisfy some consumer demand for locally-

produced fresh and diverse produce throughout the year (Benke and 
Tomkins 2017; Al-Kodmany 2018; O’Sullivan et al. 2019).
Controlled-environment agriculture is a  very energy intensive technology 
(mainly for cooling) and its GHG intensity depends therefore crucially 
on the source of the energy. Options for reducing GHG intensity include 
reducing energy use through improved lighting and cooling efficiency 
or by employing low-carbon energy sources, potentially integrated into 
the building structure (Benke and Tomkins 2017).
Comprehensive studies assessing the GHG balance of controlled-
environment agriculture are lacking. The overall GHG emissions 
from controlled-environment agriculture is therefore uncertain and 
depends on the balance of reduced GHG emissions from production 
and distribution and reduced land requirements, versus increased 
external energy needs.
12.4.3.2 Emerging Foods and Production Technologies
A diverse range of novel food products and production systems

are emerging, that are proposed to reduce GHG emissions from 
food production, mainly by replacing conventional animal-source food  
with alternative protein sources. Assessments of the potential of 
dietary changes are given in Sections  5.3 and 7.4. Here, we assess 
the GHG intensities of emerging food production technologies. This 
includes products such as insects, algae, mussels and products from 
bio-refineries, some of which have been consumed in certain societies 
and/or in smaller quantities (Pikaar et al. 2018; Jönsson et al. 2019; 
Govorushko 2019; Raheem et al. 2019a; Souza Filho et al. 2019). The 
novel aspect considered here is the scale at which they are proposed to 
replace conventional food with the aim to reduce both negative health 
and environmental impacts. To fully realise the health benefits, dietary 
shifts should also encompass a  reduction in consumption of added 
sugars, salt, saturated fats, and potentially harmful additives (Curtain

and Grafenauer 2019; Fardet and Rock 2019; Petersen et al. 2021).
Meat analogues have attracted substantial venture capital, and 
production costs have dropped considerably in the last decade, with 
some reaching market maturity (Mouat and Prince 2018; Santo et al. 
2020), but there is uncertainty whether they will ‘disrupt’ the food 
market or remain niche products. According to Kumar et al. (2017), the 
demand for plant-based meat analogues is expected to increase as 
their production is relatively cheap and they satisfy consumer demands 
with regard to health and environmental concerns as well as ethical 
and religious requirements. Consumer acceptance is still low for some 
options, especially insects (Aiking and de Boer 2019) and cultured 
meat (Chriki and Hocquette 2020; Siegrist and Hartmann 2020).

1289
1289
Cross-sectoral Perspectives Chapter 1212Insects: Farmed edible insects have a higher feed conversion ratio 
than other animals farmed for food, and have short reproduction 
periods with high biomass production rates (Halloran et al. 2016). 
Insects have good nutritional qualities (Parodi et al. 2018). They 
are suited as a protein source for both humans and livestock, with 
high protein content and favourable fatty acid composition (Fasolin 
et al. 2019; Raheem et al. 2019b). If used as feed, they can grow on 
food waste and manure; if used as food, food safety concerns and 
regulations can restrict the use of manure (Raheem et al. 2019b) or 
food waste (Varelas 2019) as growing substrates, and the dangers 
of pathogenic or toxigenic microorganisms and incidences of anti-
microbial resistance need to be managed (Garofalo et al. 2019).
Algae and bivalves have a high protein content and a favourable 
nutrient profile and can play a  role in providing sustainable food.

Bivalves are high in omega-3 fatty acids and vitamin B12 and 
therefore well suited as replacement of conventional meats, and have 
a lower GHG footprint (Parodi et al. 2018; Willer and Aldridge 2020). 
Micro- and macro algae are rich in omega-3 and omega-6 fatty acids, 
anti-oxidants and vitamins (Parodi et al. 2018; Peñalver et al. 2020; 
Torres-Tiji et al. 2020). Kim et al. (2019) show that diets with modest 
amounts of animals low on the food chain such as forage fish, 
bivalves, or insects have similar GHG intensities to vegan diets. Algae 
and bi-valves can be used to filter nutrients from waters, though care 
is required to avoid accumulation of hazardous substances (Gentry 
et al. 2020; Willer and Aldridge 2020).
Plant-based meat, milk and egg analogues: Demand for plant-
based proteins is increasing and incentivising the development of 
protein crop varieties with improved agronomic performance and/
or nutritional quality (Santo et al. 2020). There is also an emerging

market for meat replacements based on plant proteins, such as 
pulses, cereals, soya, algae and other ingredients mainly used 
to imitate the taste, texture and nutritional profiles of animal-
source food (Kumar et al. 2017; Boukid 2021). Currently, the 
majority of plant-based meat analogues is based on soy (Semba 
et al. 2021). While other products still serve a niche market, their 
share is growing rapidly and some studies project a  sizeable 
share within a decade (Kumar et al. 2017; Jönsson et al. 2019). In 
particular, plant-based milk alternatives have seen large increases 
in market share (Jönsson et al. 2019). A  LCA of 56 plant-based 
meat analogues showed mean GHG intensities (farm to factory) 
of 0.21–0.23 kgCO 2-eq per 100 g of product or 20 g of protein for 
all assessed protein sources (Fresán et al. 2019). Higher footprints 
were found in the meta-review by Santo et al. (2020). Including 
preparation, Meija et al. (2019) found higher emissions for burgers

and sausages as compared to minced products.
Cellular agriculture: The use of fungi, algae and bacteria is an old 
process (beer, bread, yoghurt) and serves, among others, for the 
preservation of products. The concept of cellular agriculture (Mattick 
2018) covers bio-technological processes that use micro-organisms 
to produce acellular (fermentation-based cellular agriculture) or 
cellular products. Yeasts, fungi or bacteria can synthesise acellular 
products such as haem, milk and egg proteins, or protein-rich animal 
feed, other food ingredients, and pharmaceutical and material 
products (Rischer et al. 2020; Mendly-Zambo et al. 2021). Cellular products include cell tissues such as muscle cells to grow cultured 
meat, fish or other cells (Post 2012; Rischer et al. 2020) and products 
where the micro-organisms will be eaten themselves (Pikaar et al. 
2018; Sillman et al. 2019; Schade et al. 2020). Single cell proteins,

combined with photovoltaic electricity generation and direct air 
capture of carbon dioxide, are proposed as highly land- and energy-
efficient alternatives to plant-based protein (Leger et al. 2021). 
Some microbial proteins are produced in a  ‘bioreactor’ and use 
Haber-Bosch nitrogen and vegetable sugars or atmospheric CO 2 as 
source of nitrogen and carbon (Pikaar et al. 2018; Simsa et al. 2019). 
Cultured meat is currently at the research stage and some challenges 
remain, such as the need for animal-based ingredients to ensure fast 
and effective growth of muscle cells; tissue engineering to create 
different meat products; production at scale and at competitive 
costs; and regulatory barriers (Post 2012; Stephens et al. 2018; Rubio 
et al. 2019; Tuomisto 2019; Post et al. 2020). Only a few studies to 
date have quantified the GHG emissions of microbial proteins or 
cultured meat, suggesting GHG emissions at the level of poultry meat

(Tuomisto and Teixeira de Mattos 2011; Mattick et al. 2015; Souza 
Filho et al. 2019; Tuomisto 2019).
A review of LCA studies on different plant-based, animal source and 
nine ‘future food’ protein sources (Parodi et al. 2018) concluded that 
insects, macro-algae, mussels, mycoproteins and cultured meat show 
similar GHG intensities per unit of protein (mean values ranging 
0.3–3.1 kgCO 2-eq per 100 g protein), comparable to milk, eggs, and 
tuna (mean values ranging 1.2–5.4  kgCO 2-eq per 100 g  protein); 
while chlorella and spirulina consume more energy per unit of 
protein and were associated with higher GHG emissions (mean 
values ranging 11–13  kgCO 2-eq per 100 g  protein). As the main 
source of GHG emissions from insects and cellular agriculture foods 
is energy consumption, their GHG intensity improves with increased 
use of low-carbon energy (Smetana et al. 2015; Parodi et al. 2018; 
Pikaar et al. 2018).
Future foods offer other benefits such as lower land requirements,

controlled systems with reduced losses of water and nutrients, 
increased resilience, and possibly reduced hazards from pesticide 
and antibiotics use and zoonotic diseases, although more research 
is needed including on allergenic and other safety aspects, and 
possibly reduced protein bioavailability (Alexander et al. 2017; 
Parodi et al. 2018; Stephens et al. 2018; Fasolin et al. 2019; Chriki 
and Hocquette 2020; Santo et al. 2020; Hadi and Brightwell 2021; 
Tzachor et al. 2021) (medium evidence, high agreement). Research 
is needed also on the effect of processing (Wickramasinghe et al. 
2021), though a randomised crossover trial comparing appetising 
plant foods with meat alternatives found several beneficial and no 
adverse effects from the consumption of the plant-based meats 
(Crimarco et al. 2020).
12.4.3.3 Food Processing and Packaging
Food processing includes preparation and preservation of fresh 
commodities (fruit and vegetables, meat, seafood and dairy

products), grain milling, production of baked goods, and manufacture 
of pre-prepared foods and meals. Food processors range from small 
local operations to large multinational food producers, producing

1290
1290
Chapter 12 Cross-sectoral Perspectives12food for local to global markets. The importance of food processing 
and preservation is particularly evident in developing countries 
which lack cold chains for the preservation and distribution of fresh 
perishable products such as fresh fish (Adeyeye and Oyewole 2016; 
Adeyeye 2017).
Mitigation in food processing largely focuses on reducing food waste 
and fossil energy usage during the processing itself, as well as in the 
transport, packaging and storage of food products for distribution 
and sale (Silva and Sanjuán 2019). Reducing food waste provides 
emissions savings by reducing wastage of primary inputs required 
for food production. Another mitigation route, contributing to the 
circular bioeconomy (Section  12.6.1.2 and Cross-Working Group 
Box  3  in this chapter), is by valorisation of food processing by-
products through recovery of nutrients and/or energy. No global

analyses of the emissions savings potential from the processing step 
in the value chain could be found.
Reduced food waste during food processing can be achieved by 
seeking alternative processing routes (Atuonwu et al. 2018), improved 
communication along the food value chain (Göbel et al. 2015), 
optimisation of food processing facilities, reducing contamination, 
and limiting damages and spillage (HLPE 2014). Optimisation of food 
packaging also plays an important role in reducing food waste, in 
that it can extend product shelf life; protect against damage during 
transport and handling; prevent spoilage; facilitate easy opening and 
emptying; and communicate storage and preparation information to 
consumers (Molina-Besch et al. 2019).
Developments in smart packaging are increasingly contributing 
to reducing food waste along the food value chain. Strategies for 
reducing the environmental impact of packaging include using less,

and more sustainable, materials and a shift to reusable packaging 
(Coelho et al. 2020). Active packaging increases shelf life through 
regulating the environment inside the packaging, including levels of 
oxygen, moisture and chemicals released as the food ages (Emanuel 
and Sandhu 2019). Intelligent packaging communicates information 
on the freshness of the food through indicator labels (Poyatos-
Racionero et al. 2018), and data carriers can store information on 
conditions such as temperature along the entire food chain (Müller 
and Schmid 2019).
LCA can be used to evaluate the benefits and trade-offs associated 
with different processing or packaging types (Silva and Sanjuán 
2019). Some options, such as aluminium, steel and glass, require 
high energy investment in manufacture when produced from primary 
materials, with significant savings in energy through recycling 
being possible (Camaratta et al. 2020). However, these materials

are inert in landfill. Other packaging options, such as paper and 
biodegradable packaging, may require a  lower energy investment 
during manufacture, but may require larger land area and can release 
methane when consigned to anaerobic landfill where there is no 
methane recovery. Nevertheless, packaging accounts for only 1–12% 
(typically around 5%) of the GHG emissions in the lifecycle of a food 
system (Wohner et al. 2019; Crippa et al. 2021b), suggesting that 
its benefits can often outweigh the emissions associated with the 
packaging itself.The second component of mitigation in food processing relates to 
reduction in fossil energy use. Opportunities include energy efficiency 
in processes (also discussed in Section  11.3), the use of heat and 
electricity from low-carbon energy sources in processing (Chapter 6), 
through off-grid thermal processing (sun drying, food smoking) 
and improving logistics efficiencies. Energy-intensive processes

with energy-saving potential include milling and refining (oil seeds, 
corn, sugar), drying, and food safety practices such as sterilisation 
and pasteurisation (Niles et al. 2018). Packaging also plays a role: 
reduced transport energy can be achieved through reducing the mass 
of goods transported and improving packing densities in transport 
vehicles (Lindh et al. 2016; Molina-Besch et al. 2019; Wohner 
et al. 2019). Choice of packaging also influences refrigeration energy 
requirements during transport and storage.
12.4.3.4 Storage and Distribution
Transport mitigation options along the supply chain include 
improved logistics, the use of alternative fuels and transport modes, 
and reduced transport distances. Logistics and alternative fuels and 
transport modes are discussed in Chapter  10. Transport emissions 
might increase with increasing demand for a diversity of foods as 
developing countries become more affluent. New technologies that

enable food on demand or online food shopping systems might further 
increase emissions from food transport; however, the consequences 
are uncertain and might also entail a shift from individual traffic to 
bulk transport. The impact on food waste is also uncertain as more 
targeted delivery options could reduce food waste, but easier access 
to a wider range of food could also foster over-supply and increase 
food waste. Mitigation opportunities in food transport are inherently 
linked to decarbonisation of the transport sector (Chapter 10).
Retail and the food service industry are the main factors shaping the 
external food environment or ‘food entry points’; they are the ‘physical 
spaces where food is obtained; the built environment that allows 
consumers to access these spaces’ (HLPE 2017). These industries 
have significant influence on consumers’ choices and can play a role 
in reducing GHG emissions from food systems. Opportunities are

available for optimisation of inventories in response to consumer 
demands through advanced IT systems (Niles et al. 2018), and for 
discounting foods close to sell-by dates, which can serve to reduce 
both food spoilage and wastage (Buisman et al. 2019).
As one of the highest contributors to energy demand at this stage 
in the food value chain, refrigeration has received a  strong focus 
in mitigation. Efficient refrigeration options include advanced 
refrigeration temperature control systems, and installation of 
more efficient refrigerators, air curtains and closed display fridges 
(Chaomuang et al. 2017). Also related to reducing emissions from 
cooling and refrigeration is the replacement of hydrofluorocarbons 
which have very high GWPs with lower GWP alternatives (Niles et al. 
2018). The use of propane, isobutane, ammonia, hydrofluoroolefins 
and CO 2 (refrigerant R744) are among those that are being explored,

with varying success (McLinden et al. 2017). In recent years, due to 
restrictions on high GWP-refrigerants, a considerable growth in the 
market availability of appliances and systems with non-fluorinated 
refrigerants has been seen (Eckert et al. 2021).

1291
1291
Cross-sectoral Perspectives Chapter 1212Energy efficiency alternatives generic to buildings more broadly are 
also relevant here, including efficient lighting, heating, ventilation, and air conditioning systems and building management, with ventilation being a  particularly high energy user in retail, that warrants attention (Kolokotroni et al. 2015).
In developing countries particularly, better infrastructure for 
transportation and expansion of processing and manufacturing industries can significantly reduce food losses, particularly of highly perishable food (Niles et al. 2018; FAO 2019a).12.4.4  Enabling F ood System Transformation

Food system mitigation potentials in AFOLU are assessed in Section  7.4, and food system mitigation potentials linked to demand-side measures are assessed in Chapter  5. Studies suggest that implementing supply- and demand-side policies in combination makes ambitious mitigation targets easier to achieve (Clark et al.  2020; Global Panel on Agriculture and Food Systems for Nutrition 2020; Temme et al. 2020; Latka et al. 2021a) (high agreement, limited evidence).
Table 12.9 | Assessment of food system policies targeting (post-farm gate) food chain actors and consumers.
Level 
 
G: global/multinational;  
N: national; L: local
Transformative potential
Environmental effectiveness
Cost
Co-benefitsa and adverse 
side effect
Implications for 
coordination, coherence and consistency in policy package
b

Integrated food policy packagesNLcan be controlled cost efficient+ balanced, addresses multiple sustainability goalsReduces cost of uncoordinated interventions; increases acceptance across stakeholders and civil society (robust evidence, high agreement)
Taxes on food productsGN regressive low
# 1– unintended substitution effectsHigh enforcing effect on other food policies; higher acceptance if compensation or hypothecated taxes (medium evidence, high agreement)
GHG taxes on foodGN regressive low
# 2– unintended substitution effectsSupportive, enabling effect on other food policies, agricultural/fishery policies; requires changes in power distribution and trade agreements (medium evidence, medium agreement) + high spillover effect
Trade policies Gimpacts global distributioncomplex effects+ counters leakage effects
Requires changes in existing trade agreements (medium evidence, high agreement) +/– effects on market structure and jobs

Investment into research and innovationGN  none  medium+ high spillover effect + converging with digital societyCan fill targeted gaps for coordinated policy packages (e.g., monitoring methods) (robust evidence, high agreement)
Food and marketing regulationsN  lowCan be supportive; might be supportive to realise innovation; voluntary standards might be less effective (medium evidence, medium agreement)
Organisational-level procurement policiesNL  low+ can address multiple sustainability goalsEnabling effect on other food policies; reaches large share of population (medium evidence, high agreement)
Sustainable food-based dietary guidelinesGNL  none  low+ can address multiple sustainability goalsLittle attention so far on environmental aspects; can serve as benchmark for other policies (labels, food formulation standards, etc.) (medium evidence, medium agreement)

Food labels/ informationGNLeducation level relevantlow + empowers citizens+ increases awareness+ multiple objectivesEffective mainly as part of a policy package; incorporation of other objectives (e.g., animal welfare, fair trade); higher effect if mandatory (medium evidence, medium agreement)
Nudges NL  none  low+ possibly counteracting information deficits in population subgroupsHigh enabling effect on other food policies (medium evidence, high agreement)
Effect of measures:   negative   none/unclear   slightly positive   positive  
Notes:
 #1 Minimum level to be effective 20% price increase; #2 Minimum level to be effective USD50–80 tCO 2-eq. a In addition, all interventions are assumed to address health 
and climate change mitigation. b Requires coordination between policy areas, participation of stakeholders, transparent methods and indicators to manage trade-offs and

prioritisation between possibly conflicting objectives; and suitable indicators for monitoring and evaluation against objectives.

1292
1292
Chapter 12 Cross-sectoral Perspectives12The trends in the global and national food systems towards 
a globalisation of food supply chains and increasing dominance of 
supermarkets and large corporate food processors (Dries et al. 2004; 
Neven and Reardon 2004; Baker and Friel 2016; Andam et al. 2018; 
Popkin and Reardon 2018; Reardon et al. 2019; Pereira et al. 2020) 
have led to environmental, food insecurity and malnutrition problems. 
Studies therefore call for a  transformation of current global and 
national food systems to solve these problems (Schösler and Boer 
2018; McBey et al. 2019; Kugelberg et al. 2021). This has not yet 
been successful, including due to insufficient coordination between 
relevant food system policies (Weber et al. 2020) (medium evidence, 
Different elements of food systems are currently governed by separate 
policy areas that in most countries scarcely interact or cooperate

(Termeer et al. 2018; iPES Food 2019). This compartmentalisation 
makes the identification of synergetic and antagonistic effects 
difficult and faces the possibility of failure due to unintended 
and unanticipated negative impacts on other policy areas and 
consequently lack of agreement and social acceptance (Mylona 
et al. 2018; Brouwer et al. 2020; Mausch et al. 2020; Hebinck et al. 
2021) (Section 12.4.5). This could be overcome through cooperation 
across several policy areas (Sections 12.6.2 and 13.7), in particular 
agriculture, nutrition, health, trade, climate and environment, and an 
inclusive and transparent governance structure (Termeer et al. 2018; 
Bhunnoo 2019; Diercks et al. 2019; Herrero et al. 2021; iPES Food 
2019; Mausch et al. 2020; Kugelberg et al. 2021), making use of 
potential spillover effects (Kanter et al. 2020; OECD 2021).
Transformation of food systems may come from technological, social

or institutional innovations that start as niches but can potentially 
lead to rapid changes, including changes in social conventions 
(Centola et al. 2018; Benton et al. 2019).
Where calories and ruminant animal-source food are consumed in 
excess of health guidelines, reduction of excess meat (and dairy) 
consumption is among the most effective measures to mitigate 
GHG emissions, with a high potential for environment, health, food 
security, biodiversity, and animal welfare co-benefits (Hedenus 
et al. 2014; Springmann et al. 2018a; Chai et al. 2019; Chen et al. 
2019; Kim et al. 2019; Willett et al. 2019; Semba et al. 2020; Theurl 
et al. 2020; Hamilton et al. 2021; Stylianou et al. 2021) (robust 
evidence, high agreement). Dietary changes are relevant for several 
SDGs, in addition to SDG 13 (climate action), including SDG 2 (zero 
hunger), SDG 3 (good health and well-being), SDG 6 (clean water 
and sanitation), SDG 12 (responsible consumption and production),

SDG 14 (life below water) and SDG 15 (life on land) (Bruce M et al. 
2018; Mbow et al. 2019; Vanham et al. 2019; Herrero et al. 2021) 
(Section 12.6.1). However, behavioural change towards diets of lower 
environmental impact and higher nutritional qualities faces barriers 
both from agricultural producers and consumers (Apostolidis and 
McLeay 2016; Aiking and de Boer 2018; de Boer et al. 2018; Milford 
et al. 2019), and requires policy packages that combine informative 
instruments with behavioural, administrative and/or market-based 
instruments, and are attentive to the needs of, and engage, all food 
system stakeholders including civil society networks, and change 
the food environment (Cornelsen et al. 2015; Kraak et al. 2017;  Stoll-Kleemann and Schmidt 2017; El Bilali 2019; iPES Food 2019; 
Milford et al. 2019; Temme et al. 2020) (Section  12.4.1) (robust 
Table  12.9 summarises the implications of a  range of policy instruments

discussed in more detail in the following sub-sections and highlights 
the benefits of integrated policy packages. Furthermore, Table 12.9 
assesses transformative potential, environmental effectiveness, 
feasibility, distributional effect, cost, and cost-benefits and trade-
offs of individual policy instruments, as well as their potential role as 
part of coherent policy packages. Table 12.9 shows that information 
and behavioural policy instruments can have significant but small 
effects in changing diets (robust evidence, medium agreement), but 
are mutually enforcing and might be essential to lower barriers and 
increase acceptance of market-based and administrative instruments 
The policy instruments are assessed in relation to shifting food 
consumption and production towards increased sustainability and 
health. This includes lowering GHG emissions, although not in all 
cases is this the primary focus of the instrument, and in some cases

lowering GHG emissions may not even be explicitly mentioned.
12.4.4.1 Market-based Instruments
Taxes and subsidies: Food-based taxes have largely been 
implemented to reduce non-communicable diseases and sugar intake, 
particularly those targeting sugar-sweetened beverages (WHO 2019). 
Many health-related organisations recommend the introduction of 
such taxes to improve the nutritional quality of marketed products 
and consumers’ diets (Wright et al. 2017; Park and Yu 2019; WHO 
2019), even though the impacts of food taxes are complex due to 
cross-price and substitution effects and supplier reactions (Cornelsen 
et al. 2015; Gren et al. 2019; Blakely et al. 2020) and can have 
a  regressive effect (WHO 2019). Subsidies and taxes are found to 
be effective in changing dietary behaviour at levels above 20% price 
increase (Cornelsen et al. 2015; Niebylski et al. 2015; Nakhimovsky 
et al. 2016; Hagenaars et al. 2017; Mozaffarian et al. 2018), even

though longer-term effects are scarcely studied (Cornelsen et al. 
2015) and effects of sugar tax with tax rates lower than 20% have 
been observed for low-income groups (Temme et al. 2020).
Modelling results show only small consumption shifts with moderate 
meat price increases; and high price increases are required to reach 
mitigation targets, even though model predictions become highly 
uncertain due to lack of observational data (Mazzocchi 2017; 
Bonnet et al. 2018; Fellmann et al. 2018; Zech and Schneider 2019; 
Latka et al. 2021b). Taxes applied at the consumer level are found 
to be more effective than levying the taxes on the production side 
(Springmann et al. 2017).
Unilateral taxes on food with high GHG intensities have been 
shown to induce increases in net export flows, which could reduce 
global prices and increase global demand. Indirect effects on GHG 
mitigation therefore could be reduced by up to 70–90% of national

results (Fellmann et al. 2018; Zech and Schneider 2019) (limited 
evidence, high agreement). The global mitigation potential for GHG

1293
1293
Cross-sectoral Perspectives Chapter 1212taxation of food products at USD52 kgCO 2-eq–1 has been estimated 
at 1 GtCO 2-eq yr–1 (Springmann et al. 2017).
Studies have shown that taxes can improve the nutritional quality of 
diets and reduce GHG emissions from the food system, particularly if 
accompanied by other policies that increase acceptance and elasticity, 
and reduce regressive and distributional problems (Niebylski et al. 2015; 
Hagenaars et al. 2017; Mazzocchi 2017; Springmann et al. 2017; Wright 
et al. 2017; Henderson et al. 2018; Säll 2018; FAO et al. 2020; Penne 
and Goedemé 2020) (robust evidence, high agreement) .
Trade: Since the middle of the last century, global trade in agricultural 
products has contributed to boosting productivity and reducing 
commodity prices, while also incentivising national subsidies for 
farmers to remain competitive in the global market (Benton et al. 
2019). Trade liberalisation has been coined as an essential element

of sustainable food systems, and as one element required to achieve 
sustainable development, that can shift pressure to regions where 
the resources are less scarce (Wood et al. 2018; Traverso and Schiavo 
2020). However, Clapp (2017) argues that the main economic benefit 
of trade liberalisation flows to large transnational firms. Benton and 
Bailey (2019) argue that low food prices in the second half of last 
century contributed to both yield and food waste increases, and to 
a focus on staple crops to the disadvantage of nutrient-dense foods. 
However, global trade can also contribute to economic benefits such 
as jobs and income, reduce food insecurity and facilitate access to 
nutrients (Wood et al. 2018; Hoff et al. 2019; Traverso and Schiavo 
2020; Geyik et al. 2021) and has contributed to increased food 
supply diversity (Kummu et al. 2020). The relevance of trade for food 
security, and adaptation and mitigation of agricultural production,

has also been discussed in Mbow et al. (2019).
Trade policies can be used to protect national food system measures, 
by requiring front-of-package labels, or to impose border taxes on 
unhealthy products (Thow and Nisbett 2019). For example, in the 
frame of the Pacific Obesity Prevention in Communities project, 
the Fijian government implemented three measures (out of seven 
proposed) that eliminated import duties on fruits and vegetables, 
and imposed 15% import duties on unhealthy oils (Latu et al. 
2018). Trade agreements, however, have the potential to undermine 
national efforts to improve public health (Unar-Munguía et al. 2019). 
GHG  mitigation efforts in food supply chains can be counteracted 
by GHG leakage, with a  general increase of environmental and 
social impact in developing countries exporting food products, and 
a  decrease in the developed countries importing food products 
(Fellmann et al. 2018; Sandström et al. 2018; Wiedmann and Lenzen

2018). The demand for agricultural commodities has also been 
associated with tropical deforestation, though a robust estimate on 
the extent of embodied deforestation in food commodities is not 
available (Pendrill et al. 2019).
Investment into research and innovation: El Bilali (2019) assessed 
research gaps in the food system transition literature and found 
a need to develop comparative studies that enable the assessment 
of spatial variability and scalability of food system transitions. The 
author found also that the role of private industry and corporate business is scarcely researched, although they could play a major role 
in food system transitions.
The InterAcademy Partnership assessed how research can contribute 
to providing the required evidence and opportunities for food system 
transitions, with a focus on climate change impacts and mitigation 
(IAP 2018). The project builds on four regional assessments of

opportunities and challenges on food and nutrition security in 
Africa (NASAC 2018), the Americas (IANAS 2018), Asia (AASSA 
2018), and Europe (EASAC 2017). The Partnership concludes with 
a  set of research questions around food systems, that need to be 
better understood: (i) how are sustainable food systems constituted 
in different contexts and at different scales? (ii) how can transition 
towards sustainable food systems be achieved? and (iii) how can 
success and failure be measured along sustainability dimensions 
including climate mitigation?
12.4.4.2 Regulatory and Administrative Instruments
Marketing regulations: Currently, 16 countries regulate marketing 
of unhealthy food to children, mainly on television and in schools 
(Taillie et al. 2019), and many other efforts are ongoing across the 
globe (European Commission 2019). The aim to counter the increase 
in obesity in children and target products high in saturated fats,

trans-fatty acids, free sugars and/or salt (WHO 2010) was endorsed 
by 192 countries (Kovic et al. 2018). Nutrition and health claims for 
products are used by industry to increase sales, for example in the 
sport sector or for breakfast cereals. They can be informative, but can 
also be misleading if misused for promoting unhealthy food (Whalen 
et al. 2018; Ghosh and Sen 2019; Sussman et al. 2019).
Strong statutory marketing regulations can significantly reduce the 
exposure of children to, and sales of, unhealthy food compared 
with voluntary restrictions (Kovic et al. 2018; Temme et al. 2020). 
Data on effectiveness of marketing regulations with a broader food 
sustainability scope are not available. On the other hand, regulations 
that mobilise private investment into emerging food production 
technologies can be instrumental in curbing the cost and making 
them competitive (Bianchi et al. 2018a).
Voluntary sustainability standards: Voluntary sustainability

standards are developed either by a  public entity or by private 
organisations to respond to consumers’ demands for social and 
environmental standards (Fiorini et al. 2019). For example, the Dutch 
Green Protein Alliance, an alliance of government, industry, NGOs and 
academia, formulated a goal to shift the ratio of protein consumption 
from 60% animal source proteins currently to 40% by 2050 (Aiking 
and de Boer 2020), and Cool Food Pledge signatories (organisations 
that serve food, such as restaurants, hospitals and universities) 
committed to a 25% reduction in GHG emissions by 2030, compared 
with 2015 (Cool Food 2020). For firms, obtaining certification under 
such schemes can be costly, and costs are generally borne by the 
producers and/or supply chain stakeholders (Fiorini et al. 2019). The 
effectiveness of private voluntary sustainability standards is uncertain. 
Cazzolla Gatti et al. (2019) have investigated the effectiveness of

the Roundtable on Sustainable Palm Oil on halting forest loss and

1294
1294
Chapter 12 Cross-sectoral Perspectives12habitat degradation in Southeast Asia and concluded that production 
of certified palm oil continued to lead to deforestation.
Organisational procurement: Green public procurement is a policy 
that aims to create additional demand for sustainable products 
(Bergmann Madsen 2018; Mazzocchi and Marino 2019) or decrease 
demand for less sustainable products (e.g.,  the introduction of 
‘Meatless Monday’ by the Norwegian Armed Forces) (Cheng et al. 
2018; Gava et al. 2018; Milford and Kildal 2019; Wilts et al. 2019). 
To improve dietary choices, organisations can increase the price of 
unsustainable options while decreasing the price of sustainable ones, 
or employ information or choice architecture measures (Goggins and 
Rau 2016; Goggins 2018). Procurement guidelines exist at global, 
national, organisational or local levels (Noonan et al. 2013; Neto and 
Gama Caldas 2018). Procurement rules in schools or public canteens

increase the accessibility of healthy food and can improve dietary 
behaviour and decrease purchases of unhealthy food (Cheng et al. 
2018; Temme et al. 2020).
Food regulations: Novel foods based on insects, microbial proteins 
or cellular agriculture must go through authorisation processes to 
ensure compliance with food safety standards before they can be 
sold to consumers. Several countries have ‘novel food’ regulations 
governing the approval of foods for human consumption. For 
example, the European Commission, in its update of the Novel Food 
Regulation in 2015, expanded its definition of novel food to include 
food from cell cultures, or that produced from animals by non-
traditional breeding techniques (EU 2015).
For animal product analogues, regulatory pathways and procedures 
(Stephens et al. 2018) and terminology issues (defining equivalence 
questions) (Carrenõ and Dolle 2018; Pisanello and Ferraris 2018)

need clarification, as does their relation to religious rules (Chriki and 
Hocquette 2020).
Examples of legislation targeting food waste include the French 
ban on wasting food approaching best-before dates, requiring 
its donation to charity organisations (Global Alliance for the 
Future of Food 2020). In Japan, the Food Waste Recycling Law set 
targets for food waste recycling for industries in the food sector 
for 2020, ranging between 50% for restaurants and 95% for food 
manufacturers (Liu et al. 2016).
12.4.4.3 Informative Instruments.
Sustainable food-based dietary guidelines: National food-based 
dietary guidelines (FBDGs) provide science-based recommendations 
on food group consumption quantities. They are available for 94, mostly 
upper- and middle-income, countries globally (Wijesinha-Bettoni et al. 
2021), are adapted to national cultural and socio-economic context, 
and can be used as a benchmark for food formulation standards for

public and private food procurement, or to inform citizens (Bechthold 
et al. 2018; Temme et al. 2020). Most FBDGs are based on health 
considerations and only a few mention environmental sustainability 
aspects (Bechthold et al. 2018; Ritchie et al. 2018; Ahmed et al. 2019; 
Springmann et al. 2020). Implementation of FBDGs so far focuses 
largely in the education and health sectors, with few countries also using their potential for guiding food system policies in other sectors 
(Wijesinha-Bettoni et al. 2021).
Despite the fact that 1.5 billion people follow a vegetarian diet from 
choice or necessity, and that the position statements of various 
nutrition societies point out that vegetarian diets are adequate if 
well planned, few FBDGs give recommendations for vegetarian diets 
(Costa Leite et al. 2020). An increase in consumption of plant-based 
food is a  recurring recommendation in FBDGs, though an explicit 
reduction or limit of animal-source proteins is not often included,

with the exception of red or processed meat (Temme et al. 2020). 
To account for changing dietary trends, however, FBDGs need to 
incorporate sustainability aspects (Herforth et al. 2019). A  healthy 
diet respecting planetary boundaries has been proposed by Willett 
et al. (2019), though some authors have questioned the validity of the 
nutritional (Zagmutt et al. 2019) or environmental implications, such 
as water use (Vanham et al. 2020). In October 2019, 14 global cities 
pledged to adhere to this ‘planetary health diet’ (C40 Cities 2019).
Education on food/nutrition and environment: Some consumers 
are reluctant to adopt sustainable healthy dietary patterns because of  
a lack of awareness of the environmental and health consequences 
of what they eat, but also out of suspicion towards alternatives that 
are perceived as not ‘natural’ and that seem to be difficult to integrate 
into their daily dietary habits (Hartmann and Siegrist 2017; Stephens

et al. 2018; McBey et al. 2019; Siegrist and Hartmann 2020) or simply 
lack of knowledge on how to prepare or eat unfamiliar foods (El Bilali 
2019; Aiking and de Boer 2020; Temme et al. 2020). Misconceptions 
may contribute, for example, to the belief that packaging or ‘food 
miles’ dominate the climate impact of food (Macdiarmid et al. 2016). 
However, spillover effects can induce sustainable behaviour from 
‘entry points’ such as concerns about food waste (El Bilali 2019). 
Early-life experiences are crucial determinants for adopting healthy 
and sustainable lifestyles (Bascopé et al. 2019; McBey et al. 2019), so 
improved understanding of sustainability aspects in the education of 
public health practitioners and in university education is proposed 
(Wegener et al. 2018). Investment in education, particularly of 
women (Vermeulen et al. 2020), might lower the barrier for stronger 
policies to be accepted and effective (McBey et al. 2019; Temme et al.

2020) (medium evidence, high agreement).
Food labels: Instruments to improve transparency and information 
on food sustainability aspects are based on the assumption of the 
‘rational’ consumer. Information gives the necessary freedom of 
choice, but also the responsibility to make the ‘right choice’ (Kersh 
2015; Bucher et al. 2016). Studies find a lack of consumer awareness 
about the link between own food choices and environmental effect 
(Grebitus et al. 2016; Leach et al. 2016; Hartmann and Siegrist 2017; 
de Boer et al. 2018) and so effective messaging is required to raise 
awareness and acceptance of potentially stricter food system policies.
Back-of-package labels usually provide detailed nutritional 
information (Temple 2019). Front-of-package labels simplify and 
interpret the information: for example, the traffic light system or 
the Nutri-Score label used in France (Kanter et al. 2018b) and the 
health star rating used in Australia and New Zealand (Shahid et al.

2020) provide an aggregate rating based on product attributes such

1295
1295
Cross-sectoral Perspectives Chapter 1212as energy, sugar, saturated fat and fibre content; other labels warn 
against frequent consumption (e.g., in the 1990s Finland introduced 
a  mandatory warning for products high in salt; the keyhole label 
was introduced in Sweden in 1989 (Storcksdieck genannt Bonsmann 
et al. 2020); and ‘high in’ (energy/saturated fat/sugar) labels were 
introduced in Chile in 2016 to reduce obesity (Corvalán et al. 2019)). 
Front-of-package labels serve also as an incentive to industry to 
produce healthier or more sustainable products, or can serve as 
a marketing strategy (Van Loo et al. 2014; Apostolidis and McLeay 
2016; Kanter et al. 2018b). Carbon footprint labels can be difficult 
for consumers to understand (Hyland et al. 2017), and simple, 
interpretative summary indicators used on front-of-package labels 
(e.g.,  traffic lights) are more effective than more complex ones 
(Bauer and Reisch 2019; Ikonen et al. 2019; Temple 2019; Tørris and

Mobekk 2019) (robust evidence, high agreement). Reviews find 
mixed results but overall a positive effect of food labels in improving 
direct purchasing decisions (Hieke and Harris 2016; Sarink et al. 2016; 
Anastasiou et al. 2019; Shangguan et al. 2019; Temple 2019), and 
in raising levels of awareness, thus possibly increasing success of 
other policy instruments (Apostolidis and McLeay 2016; Samant and 
Seo 2016; Al-Khudairy et al. 2019; Miller et al. 2019; Temple 2019) 
12.4.4.4 Behavioural Instruments
Choice architecture: Information is more effective if accompanied 
by reinforcement through structural changes or by changing the food 
environment, such as through product placement in supermarkets, 
to overcome the intention–behaviour gap (Bucher et al. 2016; 
Broers et al. 2017; Tørris and Mobekk 2019). Behavioural change 
strategies have also been shown to improve efficiencies of school 
food programmes (Marcano-Olivier et al. 2020).

Environmental considerations rank behind financial, health, or sensory 
factors for determining citizens’ food choices (Leach et al. 2016; 
Hartmann and Siegrist 2017; Neff et al. 2018; Rose 2018; Gustafson 
et al. 2019). There is evidence that choice architecture (‘nudging’) can 
be effective in influencing purchase decisions, but regulators do not 
normally explore this option (Broers et al. 2017). Examples of green 
nudging include making the sustainable option the default option, 
enhancing visibility, accessibility of, or exposure to, sustainable 
products and reducing visibility and accessibility of unsustainable 
products, or increasing the salience of healthy sustainable choices 
through social norms or food labels (Bucher et al. 2016; Wilson et al. 
2016; Broers et al. 2017; Al-Khudairy et al. 2019; Bauer and Reisch 
2019; Ferrari et al. 2019; Weinrich and Elshiewy 2019; Cialdini and 
Jacobson 2021). Available evidence suggests that choice architecture

measures are relatively inexpensive and easy to implement (Ferrari 
et al. 2019; Tørris and Mobekk 2019), they are a preferred solution 
if a restriction of choices is to be avoided (Wilson et al. 2016; Kraak 
et al. 2017; Vecchio and Cavallo 2019), and can be effective (Arno 
and Thomas 2016; Bucher et al. 2016; Bianchi et al. 2018b; Cadario 
and Chandon 2018) if embedded in policy packages (Wilson et al. 
2016; Tørris and Mobekk 2019) (medium evidence, high agreement).
Choice architecture measures are also facilitated by growing market 
shares of animal-free protein sources taken up by discount chains and fast food companies, that enhance visibility of new products 
and ease integration into daily life for consumers, particularly if 
sustainable products are similar to the products they substitute 
(Slade 2018). This effect can be further increased by media and role 
models (Elgaaied-Gambier et al. 2018).
12.4.5 Food Systems Governance

To support the policies outlined in Section  12.4.4, food system 
governance depends on the cooperation of actors across traditional 
sectors in several policy areas, in particular agriculture, nutrition, 
health, trade, climate, and environment (Termeer et al. 2018; Bhunnoo 
2019; Diercks et al. 2019; iPES Food 2019; Rosenzweig et al. 2020b). 
Top-down integration, mandatory mainstreaming, or boundary-
spanning structures like public-private partnerships may be introduced 
to promote coordination (Termeer et al. 2018). ‘Flow-centric’ rather 
than territory-centric governance combined with private governance 
mechanisms has enabled codes of conduct and certification schemes 
(Eakin et al. 2017), for example the Roundtable on Sustainable Palm 
Oil (RSPO), as well as commodity chain transparency initiatives and 
platforms like Trase (Meijaard et al. 2020; Pirard et al. 2020). Trade 
agreements are an emerging arena of governance in which improving

GHG performance may be an objective, and trade agreements can 
involve sustainability assessments.
Research on food system governance is mostly non-empirical or case 
study based, which means that there is limited understanding of 
which governance arrangements work in specific social and ecological 
contexts to produce particular food system outcomes (Delaney et al. 
2018). Research has identified a  number of desirable attributes in 
food systems governance, including adaptive governance (Termeer 
et al. 2018), a systems perspective (Whitfield et al. 2018), governance 
that considers food system resilience (Ericksen 2008; Moragues-
Faus et al. 2017; Meyer 2020), transparency, participation of civil 
society (Candel 2014; Duncan 2015;), and cross-scale governance 
(Moragues-Faus et al. 2017).
Food systems governance has multiple targets and objectives, not 
least contributing to the achievement of the SDGs. GHG emissions

from food systems can be impacted by both interventions targeted at 
different parts of the food system and interventions in other systems, 
such as reducing deforestation or promoting reforestation (Lee 
et al. 2019). For example, policies targeting health can contribute 
to diet shifts away from red meat, while also influencing GHG 
emissions (Springmann et al. 2018b; Semba et al. 2020); national 
and local food self-sufficiency policies may also have GHG impacts 
(Kriewald et al. 2019; Loon et al. 2019). Cross-sectoral governance 
could enhance synergies between reduced GHG emissions from food 
systems and other goals; however, integrative paradigms for cross-
sectoral governance between food and other sectors have faced 
implementation challenges (Delaney et al. 2018). For example, in the 
late 2000s, the water-energy-food nexus emerged as a framework 
for cross-sectoral governance, but has not been well integrated into

policy (Urbinatti et al. 2020), perhaps because of perceptions that it 
is an academic concept, or that it takes a technical-administrative 
view of governance; simply adopting the paradigm is not sufficient

1296
1296
Chapter 12 Cross-sectoral Perspectives12to develop effective nexus governance (Cairns and Krzywoszynska 
2016; Weitz et al. 2017; Pahl-Wostl et al. 2018). Other policy 
paradigms and theoretical frameworks that aim to integrate 
food systems governance include system transition, agroecology, 
multifunctionality in agriculture (Andrée et al. 2018), climate-smart 
agriculture (Taylor 2018) and the circular economy (Box 12.4). Cross-
sectoral coordination on food systems and climate governance 
could be aided by internal recognition and ownership by agencies, 
dedicated budgets for cross-sectoral projects, and consistency in 
budgets (Pardoe et al. 2018) (Boxes 12.1 and 12.2).
Food systems governance is still fragmented at national levels, 
which means that there may be a proliferation of efforts that cannot 
be scaled and are ineffective (Candel 2014). National policies can be 
complemented or possibly pioneered by initiatives at the local level

(de Boer et al. 2018; Rose 2018). The city-region has been proposed 
as a useful focus for food system governance (Vermeulen et al. 2020); 
for example, the Milan Urban Food Policy Pact involves 180 global 
cities committed to integrative food system strategies (Candel 2019; 
Moragues-Faus 2021). Local food policy groups and councils that 
assemble stakeholders from government, civil society, and the private 
sector have formed trans-local networks of place-based local food 
policy groups, with over two hundred food policy councils worldwide 
(Andrée et al. 2018). However, the fluidity and lack of clear agendas and membership structures may hinder their ability to confront 
fundamental structural issues like unsustainable diets or inequities in 
food access (Santo and Moragues-Faus 2019).
Early characterisations of food systems governance featured a binary 
distinction between global and local scales, but this has been

replaced by a relational approach where the local governance is seen 
as a process that relies on the interconnections between scales (Lever 
et al. 2019). Cross-scalar governance is not simply an aggregation 
of local groups, but involves the telecoupling of distant systems; for 
example, transnational NGO networks have been able to link coffee 
retailers in the global North with producers in the global South via 
international NGOs concerned about deforestation and social justice 
(Eakin et al. 2017). Global governance institutions like the Committee 
on World Food Security can promote policy coherence globally and 
reinforce accountability at all levels (McKeon 2015), as can norm-
setting efforts like the Voluntary Guidelines for the Responsible 
Governance of Tenure of Land, Fisheries and Forests (FAO 2012). 
Global multi-stakeholder processes like the UN Food Systems Summit 
can foster the development of principles for guiding further actions

based on sound scientific evidence. The European Commission’s Farm 
to Fork strategy aims to promote policy coherence in food policy at 
EU and national levels, and could be the exemplar of a  genuinely 
integrated food policy (Schebesta and Candel 2020).
Box 12.2 | Case Study: The Finnish Food2030 Strategy
Until 2016, the strategic goals of Finnish food policy were split between different programmes and ministries, resulting in fragmented 
national oversight of the Finnish food system. To enable policy coordination, a national food strategy was adopted in 2017 called 
Food2030 (Government of Finland 2017). Food2030 embodies a holistic food system approach and addresses multiple outcomes of 
the food system, including the competitiveness of the food supply chain and the development of local, organic and climate-friendly 
food production, as well as responsible and sustainable consumption.

The specific policy mix covers a  range of policy instruments to enable changes in agro-food supply, processing and societal norms 
(Kugelberg et al. 2021). The government provides targeted funding and knowledge support to drive technological innovations on climate 
solutions to reduce emissions from food and in the agriculture, forestry and land use sectors. In addition, the Finnish government applies 
administrative means, such as legislation, advice, guidance on public procurement and support schemes to diversify and increase organic 
food production to 20% of arable land, which in turn improve the opportunities for small-scale food production and steer public bodies 
to purchase local and organic food. The Finnish government applies educational and informative instruments to enable a shift to healthy 
and sustainable dietary behaviours. The policy objective is to reduce consumption of meat and replace it with other sources of protein,

aligned with nutrition recommendations and avoiding food waste. The Ministry of Agriculture and Forestry, in collaboration with the 
Finnish Farmer’s unions and the Union of Swedish-speaking Farmers and Forest Owners in Finland, ran a two-year multi-media campaign 
in 2018 with key messages on the sustainability, traceability and safety of locally-produced food (Ministry of Agriculture and Forestry 
2021). A ’Food Facts’ website project (Luke 2021), funded by the Ministry of Agriculture and Forestry in collaboration with the Natural 
Resources Institute Finland and the Finnish Food Safety Authority, helps to raise knowledge about food, which could shape responsible 
individual food behaviour, for example choosing local and sustainable foods and reducing food waste.
A critical enabler for developing a shared food system strategy across sectors and political party boundaries was the implementation

of a one-year inclusive, deliberative and consensual stakeholder engagement process. A wide range of stakeholders could exert real 
influence during the vision-building process, resulting in strong agreement on key policy objectives, and subsequently an important 
leverage point to policy change (Kugelberg et al. 2021). Moreover, cross-sectoral coordination of Food2030 and the government’s 
wider climate action programmes are enabled by a number of institutional mechanisms and collaborative structures, for example the 
advisory board for the food chain, formally established during the agenda-setting stage of Food2030, inter-ministerial committees 
to guide and assess policy implementation, and Our Common Dining Table, a multi-stakeholder partnership that assembles 18 food 
system actors to engage in reflexive discussions about the Finnish food system.

1297
1297
Cross-sectoral Perspectives Chapter 121212.5 Land-related Impacts, Risks 
and Opportunities Associated 
with Mitigation Options
12.5.1 Introduction
This section provides a cross-sectoral perspective on land occupation 
based mitigation options, as well as mitigation options that are not 
designated land-based, yet occupy land. It builds on Chapter 7, which 
covers mitigation in agriculture, forestry and other land use (AFOLU, 
including future availability of biomass resources for mitigation in 
other sectors. It complements Section 12.4, which covers mitigation 
inherent in the food system, as well as Chapters  6, 9, 10 and 11, 
which cover mitigation in the energy, transport, building and industry 
sectors, and Chapters 3 and 4 which cover land and biomass use, 
primarily in energy applications, in mitigation and development 
pathways in the near- to mid-term (Chapter  4) and in pathways

compatible with long-term goals (Chapter 3).
The deployment of climate change mitigation options often affects 
land and water conditions, and ecosystem capacity to support 
biodiversity and a range of ecosystem services (IPCC 2019a; IPBES 
2019) (robust evidence, high agreement). It can increase or decrease 
terrestrial carbon stocks and sink strength, hence impacting the 
mitigation effect positively or negatively. As for any other land uses, 
impacts, risks and opportunities associated with mitigation options 
that occupy land depend on deployment strategy and on contextual 
factors that vary geographically and over time (Doelman et al. 2018; 
Hurlbert et al. 2019; Smith et al. 2019a; Wu et al. 2020) (robust 
The IPCC Special Report on Global Warming of 1.5°C (SR1.5) found 
that large areas may be utilised for A/R and energy crops in modelled 
pathways limiting warming to 1.5°C (Rogelj et al. 2018). The SRCCL

investigated the implications of land-based mitigation measures for 
land degradation, food security and climate change adaptation. It 
focused on identification of synergies and trade-offs associated with 
individual land-based mitigation measures (Smith et al. 2019b). 
In this section we expand beyond the scope of the Special Report 
on Climate Change and Land (SRCCL) assessment to include also mitigation measures that occupy land while not being considered 
land-based measures, we discuss ways to minimise potential 
adverse effects, and we consider the potential for synergies through 
integrating mitigation measures with other land uses, by applying 
a systems perspective that seeks to meet multiple objectives from 
multi-functional landscapes. Mitigation measures with zero land 
occupation, e.g., offshore wind and kelp farming, are not considered.
12.5.2 Land Occupation Associated with Different 
As reported in Chapter  3, in scenarios limiting warming to 1.5°C

(>50%) with no or limited overshoot, median area dedicated for 
energy crops in 2050 is 1.99 (0.56 to 4.82) million square kilometres 
(Mkm2) and median forest area increased 3.22 (–0.67 to 8.90) Mkm2 
in the period 2019 to 2050 (5–95th percentile range, scenario category 
C1). For comparison, the total global areas of forests, cropland and 
pasture (in 2015) are in the SRCCL estimated at about 40 Mkm2, 
15.6 Mkm2, and 27.3 Mkm2, respectively (additionally, 21 Mkm2 of 
savannahs and shrublands are also used for grazing) (IPCC 2019a). 
The SRCCL concluded that conversion of land for A/R and bioenergy 
crops at the scale commonly found in pathways limiting warming to 
1.5°C or 2°C is associated with multiple feasibility and sustainability 
constraints, including land carbon losses (high confidence). Pathways 
in which warming exceeds 1.5°C require less land-based mitigation, 
but the impacts of higher temperatures on regional climate and

land, including land degradation, desertification, and food insecurity, 
become more severe (Smith et al. 2019b).
Depending on emissions-reduction targets, the portfolio of mitigation 
options chosen, and the policies developed to support their 
implementation, different land-use pathways can arise with large 
differences in resulting agricultural and forest area. Some response 
options can be more effective when applied together (Smith et al. 
2019b); for example, dietary change, efficiency increases, and reduced 
wastage can reduce emissions as well as the pressure on land resources, 
potentially enabling additional land-based mitigation such as A/R and 
cultivation of biomass crops for biochar, bioenergy and other bio-based 
products. The SRCCL (Smith et al. 2019b) report that dietary change 
combined with reduction in food loss and waste can reduce the land Box 12.2 (continued)

Critical barriers to strategy and policy formulation include a lack of attention to integrated impact assessments (Kugelberg et al. 2021), 
which blurs a transparent overview of potential trade-offs and hidden conflicts. There were few policy evaluations from independent 
organisations to inform policymaking, reducing the opportunities for more progressive policy approaches. Monitoring and food policy 
evaluation is very close to the ministry in charge, which hampers critical thinking about policy measures (Hildén et al. 2014). In 
addition, there is a lack of standardised indicators covering the whole food system, which hinders comprehensive oversight of progress 
towards a sustainable food system (Kanter et al. 2018a). Some of the problems related to monitoring, reporting and verification (MRV) 
are typical for countries in the EU. To improve, MRV will probably require structural changes, such as efforts to build up institutional

capacity and application of new technology, development of standardised indicators covering the whole food system, regulations 
on transparency and verification, and mechanisms to enable reflexive discussions between business, farmers, public, NGOs and the 
government (Meadowcroft and Steurer 2018; Kanter et al. 2020).

1298
1298
Chapter 12 Cross-sectoral Perspectives12requirement for food production by up to 5.8 Mkm2 (0.8–2.4 Mkm2 
for dietary change; about 2 Mkm2 for reduced post-harvest losses, and 
1.4 Mkm2 for reduced food waste) (Parodi et al. 2018; Springmann et al. 
2018; Clark et al. 2020; Rosenzweig et al. 2020b) (Sections 7.4 and 
12.4). Stronger mitigation action in the near term targeting non-CO 2 
emissions reduction and deployment of other CDR options (DACCS, 
enhanced weathering, ocean-based approaches; see Section 12.3) can 
reduce the land requirement for land-based mitigation (Obersteiner 
et al. 2018; van Vuuren et al. 2018).
Global integrated assessment models (IAMs) provide insights into 
the roles of land-based mitigation in pathways limiting warming to 
1.5°C or 2°C; interaction between land-based and other mitigation 
options such as wind and solar power; influence of land-based 
mitigation on food markets, land use and land carbon; and the role

of BECCS vis-à-vis other CDR options (Chapter 3). However, IAMs do 
not capture more subtle changes in land management and in the 
associated industrial/energy systems due to relatively coarse temporal 
and spatial resolution, and limited representation of land quality and 
feedstocks/management practices, interactions between biomass 
production and conversion systems, and local context, for example, 
governance of land use (Daioglou et al. 2019; Rose et al. 2020; Welfle 
et al. 2020; Calvin et al. 2021). A/R have generally been modelled as 
forests managed for carbon sequestration alone, rather than forestry 
providing both carbon sequestration and biomass supply (Calvin et al. 
2021). Because IAMs do not include options to integrate new biomass 
production with existing agricultural and forestry systems (Paré et al. 
2016; Mansuy et al. 2018; Cossel et al. 2019; Braghiroli and Passarini 
2020; Djomo et al. 2020; Moreira et al. 2020; Strapasson et al. 2020;

Rinke Dias de Souza et al. 2021), they may over-estimate the total 
additional land area required for biomass production. On the other 
hand, some integrated biomass production systems may prove less 
attractive to landholders than growing biomass crops in large blocks, 
from logistic, economic, or other points of view (Ssegane et al. 2016; 
Busch 2017; Ferrarini et al. 2017).
Land occupation associated with mitigation options other than A/R 
and bioenergy is rarely quantified in global scenarios. Stressing 
large uncertainties (e.g.,  type of biomass used and share of solar 
PV integrated in buildings), Luderer et al. (2019) modelled land 
occupation and land transformation associated with a  range of 
alternative power system decarbonisation pathways in the context 
of a  global 2°C climate stabilisation effort. On a  per-megawatt 
hour (MWh) basis, bioelectricity with CCS was most land intensive, 
followed by hydropower, coal with CCS, and concentrated solar

power (CSP), which in turn were around five times as land-intensive 
as wind and solar photovoltaics (PV). A review of studies of power 
densities (electricity generation per unit land area) confirmed the 
relatively larger land occupation associated with biopower, although 
hydropower overlaps with biopower (van Zalk and Behrens 2018). 
This study also quantifies the low land occupation of nuclear energy, 
similar to fossil energy sources.
The land occupation of PV depends on the share of ground-mounted 
versus buildings-integrated PV, the latter assumed to reach 75% 
share by 2050 (Luderer et al. 2019). van de Ven et al. (2021) assumed 
a  3% share of urbanised land in 2050 available for rooftop PV; Capellán-Pérez et al. (2017) and Dupont et al. (2020) report 2–3% 
availability of urbanised surface area, when considering factors 
such as roof slopes and shadows between buildings, and threshold 
relating to energy return on investment. Land occupation of solar

technologies is considered to be underestimated in studies assuming 
ideal conditions, with real occupation being five to ten times higher 
(De Castro et al. 2013; MacKay 2013; Ong et al. 2013; Smil 2015; 
Capellán-Pérez et al. 2017).
Production of hydrogen and synthetic hydrocarbon fuels via 
electrolysis and hydrocarbon synthesis is subject to conversion losses 
that vary depending on technology, system integration and source 
of carbon (Wulf et al. 2020; Ince et al. 2021) (Sections 6.4.4.1 and 
6.4.5.1). Indicative electricity-to-hydrocarbon fuel efficiency loss is 
estimated at about 60% (Ueckerdt et al. 2021). The advantage of 
smaller land occupation for solar, wind, hydro and nuclear, compared 
with biomass-based options, is therefore smaller for hydrocarbon 
fuels than for electricity. Furthermore, biofuels are often co-produced 
with other bio-based products, which further reduces their land 
occupation, although comparisons are complicated by inconsistent

approaches to allocating land occupation between co-products 
(Ahlgren et al. 2015; Czyrnek-Delêtre et al. 2017).
Note that comparisons on a  per-MWh basis do not reflect 
the GHG emissions associated with the power options, or that the 
different options serve different functions in power systems. 
Reservoir hydropower and biomass-based dispatchable power can 
complement other balancing options (e.g.,  battery storage, grid 
extensions and demand-side management (Göransson and Johnsson 
2018) (Chapter 6) to provide power stability and quality needed in 
power systems with large amounts of variable electricity generation 
from wind and solar power plants. Furthermore, the requirements of 
transport in grids, pipelines and so on differ. For example, electricity 
from buildings-integrated PV can be used in the same location as 
it is generated.
The character of land occupation, and, consequently, the associated 
impacts (Section 12.5.3), vary considerably among mitigation options

and also for the same option depending on geographic location, 
scale, system design and deployment strategy (Olsson et al. 2019; 
Ioannidis and Koutsoyiannis 2020; van de Ven et al. 2021). Land 
occupation associated with different mitigation options can be large 
uniform areas (e.g., large solar farms, reservoir hydropower dams, or 
tree plantations), or more distributed, such as wind turbines, solar PV, 
and patches of biomass cultivation integrated with other land uses in 
heterogeneous landscapes (Cacho et al. 2018; Jager and Kreig 2018; 
Correa et al. 2019; Englund et al. 2020a). Studies with broader scope, 
covering total land use requirement induced by plant infrastructure, 
provide a  more complete picture of land footprints. For example, 
Wu et al. (2021) quantified a land footprint for the infrastructure of 
a pilot solar plant being three times the onsite land area. Sonter et al. 
(2020b) found significant overlap of mining areas (82% targeting

materials needed for renewable energy production) and biodiversity 
conservation sites and priorities, suggesting that strategic planning 
is critical to address mining threats to biodiversity (Section 12.5.4) 
along with recycling and exploration of alternative technologies that 
use that use abundant minerals (Box 10.6).

1299
1299
Cross-sectoral Perspectives Chapter 1212There are also situations where expanding mitigation is more 
or less decoupled from additional land use. The use of organic 
consumer waste, harvest residues and processing side-streams in 
the agriculture and forestry sectors can support significant volumes 
of bio-based products with relatively lower land-use change risks 
than dedicated biomass production systems (Hanssen et al. 2019; 
Spinelli et al. 2019; Mouratiadou et al. 2020). Such uses can provide 
waste management solutions while increasing the mitigation 
achieved from the land that is already used for agricultural and 
forest production. Bioenergy accounts for about 90% of renewable 
heat used in industrial applications, mainly in industries that can use 
their own biomass waste and residues, such as the pulp and paper 
industry, food industry, and ethanol production plants (IEA 2020c) 
(Chapters  6  and 11). Heat and electricity produced on-site from

side-streams but not needed for the industrial processes can be sold 
to other users, such as district heating systems. Surplus waste and 
residues can also be used to produce solid and liquid biofuels, or 
be used as feedstock in other industries such as the petrochemical 
industry (IRENA 2018; Lock and Whittle 2018; Thunman et al. 2018; 
IRENA 2019; Haus et al. 2020) (Chapters 6 and 11). Electrification 
and improved process efficiencies can reduce GHG emissions and 
increase the share of harvested biomass that is used for production 
of bio-based products (Johnsson et al. 2019; Madeddu et al. 2020; 
Lipiäinen and Vakkilainen 2021; Rahnama Mobarakeh et al. 2021; 
Silva et al. 2021) (Chapter  11). Besides integrating solar thermal 
panels and solar PV into buildings and other infrastructure, floating 
solar PV panels in, for example, hydropower dams (Ranjbaran et al. 
2019; Cagle et al. 2020; Haas et al. 2020; Lee et al. 2020; Gonzalez

Sanchez et al. 2021), and over canals (Lee et al. 2020; McKuin et al. 
2021) could decouple renewable energy generation from land use 
while simultaneously reducing evaporation losses and potentially 
mitigating aquatic weed growth and climate change impacts on 
water body temperature and stratification (Cagle et al. 2020; Exley 
et al. 2021; Gadzanku et al. 2021; Solomin et al. 2021).
12.5.3 Consequences of Land Occupation: 
Biophysical and Socio-economic Risks, 
Impacts and Opportunities
Land occupation associated with mitigation options can present 
challenges related to impacts and trade-offs, but can also provide 
opportunities and in different ways support the achievement of 
additional societal objectives, including adaptation to climate 
change. This section focuses on mitigation options that have 
significant risks, impacts and/or co-benefits with respect to land 
resources, food security and the environment. Bioenergy (with or

without CCS), biochar and bio-based products require biomass 
feedstocks that can be obtained from purpose-grown crops, 
residues from conventional agriculture and forestry systems, or 
from biomass wastes, each with different implications for the land. 
Here we consider separately (i) ‘biomass-based systems’, including 
dedicated biomass crops (e.g.,  perennial grasses, short rotation 
woody crops) and biomass produced as a co-product of conventional 
agricultural production (e.g.,  maize stover), and (ii) ‘afforestation/
reforestation’, including forests established for ecological restoration 
and plantations grown for forest products and agroforestry, where biomass may also be a  co-product. We then discuss impacts and 
opportunities common to both systems, before considering impacts 
and opportunities associated with non-land-based mitigation options 
that nevertheless occupy land.
Biomass-based systems
Mitigation options that are based on the use of biomass, that is,

bioenergy/BECCS, biochar, wood buildings, and other bio-based 
products, can have different positive and negative effects depending 
on the character of the mitigation option, the land use, the biomass 
conversion process, how the bio-based products are used and what 
other product they substitute (Leskinen et al. 2018; Howard et al. 2021; 
Myllyviita et al. 2021). The impacts of the same mitigation option can 
therefore vary significantly and the outcome in addition depends on 
previous land/biomass use (Cowie et al. 2021). As biomass-based 
systems commonly produce multiple food, material and energy 
products, it is difficult to disentangle impacts associated with individual 
bio-based products (Ahlgren et al. 2015; Djomo et al. 2017; Obydenkova 
et al. 2021). As for other mitigation options, governance has a critical 
influence on outcome, but larger scale and higher expansion rate 
generally translates into higher risk for negative outcomes such as

competition for scarce land, freshwater and phosphorous resources, 
displacement of natural ecosystems, and diminishing capacity of 
agroecosystems to support biodiversity and essential ecosystem 
services, especially if produced without sustainable land management 
and in inappropriate contexts (Popp et al. 2017; Dooley and Kartha 
2018; Hasegawa et al. 2018; Heck et al. 2018; Humpenöder et al. 2018; 
Fujimori et al. 2019; Hurlbert et al. 2019; IPBES 2019; Smith et al. 
2019b; Drews et al. 2020; Hasegawa et al. 2020; Schulze et al. 2020; 
Stenzel et al. 2021) (medium evidence,  high agreement).
Removal of crop and forestry residues can cause land degradation 
through soil erosion and decline in nutrients and soil organic matter 
(Cherubin et al. 2018) (robust evidence, high agreement). These risks 
can be reduced by retaining a proportion of the residues to protect the 
soil surface from erosion and moisture loss and maintain or increase

soil organic matter (Section  7.4.3.6); incorporating a  perennial 
groundcover into annual cropping systems (Moore et al. 2019); 
and by replacing nutrients removed, such as by applying ash from 
bioenergy combustion plants (Kludze et al. 2013; Harris et al. 2015; 
Warren Raffa et al. 2015; de Jong et al. 2017) while safeguarding 
against contamination risks (Pettersson et al. 2020) (medium 
evidence, high agreement). Besides topography, soil, and climate 
conditions, sustainable residue removal rates also depend on the fate 
of extracted biomass. For example, to maintain the same level of soil 
organic carbon, the harvest of straw, if used for combustion (which 
would return no carbon to fields), was estimated to be only 26% of the 
rate that could be extracted if used for anaerobic digestion involving 
return of recalcitrant carbon to fields (Hansen et al. 2020). Similarly, 
biomass pyrolysis produces biochar which can be returned to soils to

counteract carbon losses associated with biomass extraction (Joseph 
et al. 2021; Lehmann et al. 2021).
Expansion of biomass crops, especially monocultures of exotic 
species, can pose risks to natural ecosystems and biodiversity through 
introduction of invasive species and land use change, also impacting

1300
1300
Chapter 12 Cross-sectoral Perspectives12the mitigation value (robust evidence,  high agreement) (Liu et al. 2014; 
El Akkari et al. 2018). Cultivation of conventional oil, sugar, and starch 
crops tends to have larger negative impact than lignocellulosic crops 
(Núñez-Regueiro et al. 2020). Social and environmental outcomes can 
be enhanced through integration of suitable plants (such as perennial 
grasses and short rotation woody crops) into agricultural landscapes 
(within crop rotations or through strategic localisation, for example as 
contour belts, along fencelines and riparian buffers). Such integrated 
systems can provide shelter for livestock, retention of nutrients and 
sediment, erosion control, pollination, pest and disease control, and 
flood regulation (robust evidence,  high agreement) (Berndes et al. 
2008; Christen and Dalgaard 2013; Asbjornsen et al. 2014; Holland 
et al. 2015; Ssegane et al. 2015; Dauber and Miyake 2016; Milner et al.

2016; Ssegane and Negri 2016; Styles et al. 2016; Zheng et al. 2016; 
Ferrarini et al. 2017; Crews et al. 2018; Henry et al. 2018a; Zalesny 
et al. 2019; Osorio et al. 2019; Englund et al. 2020b; Englund et al. 
2021) (Figure 12.8, Box 12.3, and Cross-Working Group Box 3 in this 
chapter). Many of the land use practices described above align with 
agroecology principles (AR6 WGII Section 5.14, AR6 WGII Box 5.11 
and AR6 WGII Cross-Chapter Box NATURAL) and can simultaneously 
contribute to climate change mitigation, climate change adaptation 
and reduced risk of land degradation (IPCC 2019a) (robust evidence, 
Afforestation/reforestation (A/R)
When A/R activities comprise the establishment of natural forests, 
the risk to land is primarily associated with potential displacement 
of previous land use to new locations, which could indirectly 
cause land-use change including deforestation (Sections 7.4.2 and

7.6.2.4). A/R (including agroforestry) aimed at providing timber, fibre, 
biomass, non-timber resources and other ecosystem services can 
provide renewable resources to society and long-term livelihoods 
for communities. Forest management and harvesting regimes 
around the world will adjust in different ways as society seeks to 
meet climate goals. The outcome depends on forest type, climate, forest ownership and the character and product portfolio of the 
associated forest industry (Lauri et al. 2019; Favero et al. 2020). 
How forest carbon stocks, biodiversity, hydrology, and so on are 
affected by changes in forest management and harvesting in turn 
depends on both management practices and the characteristics of 
the forest ecosystems (Eales et al. 2018; Griscom et al. 2018; Kondo 
et al. 2018; Nieminen et al. 2018; Thom et al. 2018; Runting et al. 
2019; Tharammal et al. 2019) (robust evidence, medium agreement). 
As described above, the GHG savings achieved from producing and

using bio-based products will in addition depend on the character 
of existing societal systems, including technical infrastructure and 
markets, as this determines the product substitution patterns.
Environmental and socio-economic co-benefits are enhanced when 
ecological restoration principles are applied (Gann et al. 2019) along 
with effective planning at landscape level and strong governance 
(Morgan et al., 2020). For example, restoration of natural vegetation 
and establishing plantations on degraded land enable organic 
matter to accumulate in the soil and have potential to deliver 
significant co-benefits for biodiversity, land resource condition 
and livelihoods (Box  12.3 and Cross-Working Group Box  3  in this 
chapter). Tree planting and agroforestry on cleared land can deliver 
biodiversity benefits (Seddon et al. 2009; Kavanagh and Stanton 
2012; Law et al. 2014), with biodiversity outcomes influenced by

block size, configuration and species mix (Cunningham et al. 2015; 
Paul et al. 2016) (robust evidence, high agreement).
Risks and opportunities common to biomass production and A/R 
mitigation options
Biomass-based systems and A/R can contribute to addressing 
land degradation through land rehabilitation or restoration 
(Box  12.3). Land-based mitigation options that produce biomass 
for bioenergy/BECCS or biochar through land rehabilitation rather 
than land restoration imply a trade-off between production / carbon 
sequestration and biodiversity outcomes (Hua et al. 2016; Cowie 
Figure 12.8 | Overview of opportunities related to selected land-based climate change mitigation options.

1301
1301
Cross-sectoral Perspectives Chapter 1212et al. 2018). Restoration, seeking to establish native vegetation with 
the aim to maximise ecosystem integrity, landscape connectivity, 
and conservation of on-ground carbon stock, will have higher 
biodiversity benefits than rehabilitation measures (Lin et al. 2013). 
However, sequestration rate declines as forests mature, and the 
sequestered carbon is vulnerable to loss through disturbance such as 
wildfire, so there is a higher risk of reversal of the mitigation benefit 
compared with use of biomass for substitution of fossil fuels and 
GHG-intensive building materials (Russell and Kumar 2017; Dugan 
et al. 2018; Anderegg et al. 2020). Trade-offs between different 
ecosystem services, and between societal objectives including 
climate change mitigation and adaptation, can be managed through 
integrated landscape approaches that aim to create a mosaic of land 
uses, including conservation, agriculture, forestry and settlements

(Freeman et al. 2015; Nielsen 2016; Reed et al. 2016; Sayer et al. 
2017) where each is sited with consideration of land potential and 
socio-economic objectives and context (Cowie et al. 2018) (limited 
Impacts of biomass production and A/R on the hydrological cycle 
and water availability and quality depend on scale, location, 
previous land use/cover and type of biomass production system. 
For example, extraction of logging residues in forests managed for 
timber production has little effect on hydrological flows, while land-
use change to establish dedicated biomass production can have 
a significant effect (Teter et al. 2018; Drews et al. 2020). Deployment 
of A/R can affect temperature, albedo and precipitation locally and 
regionally, and can mitigate or enhance the effects of climate change 
in the affected areas (Stenzel et al. 2021b) (Section  7.2.4). A/R 
activities can increase evapotranspiration, impacting groundwater

and downstream water availability, but can also result in increased 
infiltration to groundwater and improved water quality (Farley 
et al. 2005; Zhang et al. 2016; Zhang et al. 2017; Lu et al. 2018) 
and can be beneficial where historical clearing has caused soil 
salinisation and stream salinity (Farrington and Salama 1996; Marcar 
2016). There is limited evidence that very large-scale land-use or 
vegetation cover changes can alter regional climate and precipitation 
patterns, for example downwind precipitation depends on upwind 
evapotranspiration from forests and other vegetation (Keys et al. 
2016; Ellison et al. 2017; van der Ent and Tuinenburg 2017).
Another example of beneficial effects includes perennial grasses 
and woody crops planted to intercept runoff and subsurface lateral 
flow, reducing nitrate entering groundwater and surface waterbodies 
(Femeena et al. 2018; Woodbury et al. 2018; Griffiths et al. 2019). In

India, Garg et al. (2011) found desirable effects as a result of planting 
Jatropha on wastelands previously used for grazing (which could 
continue in the Jatropha plantations): soil evaporation was reduced, 
as a larger share of the rainfall was channelled to plant transpiration 
and groundwater recharge, and less runoff resulted in reduced soil 
erosion and improved downstream water conditions. Thus, adverse 
effects can be reduced and synergies achieved when plantings are 
sited carefully, with consideration of potential hydrological impacts 
(Davis et al. 2013).
Several biomass conversion technologies can generate co-benefits 
for land and water. Anaerobic digestion of organic wastes (e.g., food waste, manure) produces a nutrient-rich digestate and biogas that can 
be utilised for heating and cooking or upgraded for use in electricity 
generation, industrial processes, or as transportation fuel (Chapter 6)

(Parsaee et al. 2019; Hamelin et al. 2021). The digestate is a rich source 
of nitrogen, phosphorus and other plant nutrients, and its application 
to farmland returns exported nutrients as well as carbon (Cowie 
2020b). Studies have identified potential risks, including manganese 
toxicity, copper and zinc contamination, and ammonia emissions, 
compared with application of undigested animal manure  (Nkoa 
2014). Although the anaerobic digestion process reduces pathogen 
risk compared with undigested manure feedstocks, it does not destroy 
all pathogens (Nag et al. 2019). Leakage of methane is a significant 
risk that needs to be managed, to ensure mitigation potential is 
achieved  (Bruun et al. 2014). Anaerobic digestion of wastewater, 
such as sugarcane vinasse, reduces methane emissions and pollution 
loading as well as producing biogas (Parsaee et al. 2019).
Biorefineries can convert biomass  to food, feed and biomaterials

along with bioenergy  (Aristizábal-Marulanda and Cardona 
Alzate 2019; Schmidt et al. 2019).  Biorefinery plants are 
commonly  characterised  by high process integration to achieve 
high resource use efficiency, minimise waste production and energy 
requirements, and maintain flexibility  towards  changing markets 
for raw materials and products  (Schmidt et al. 2019). Emerging 
technologies can convert biomass that is indigestible for monogastric 
animals or humans (e.g., algae, grass, clover or alfalfa) into food and 
feed products. For example, lactic acid bacteria can facilitate the 
use of green plant biomass such as grasses and clover to produce 
a  protein concentrate suitable for animal feed and other products 
for material or energy use (Lübeck and Lübeck 2019). Selection of 
crops suitable for co-production of protein feed along with biofuels 
and other bio-based products can significantly reduce the land 
conversion pressure by reducing the need to cultivate other crops

(e.g., soybean) for animal feed (Bentsen and Møller 2017; Solati et al. 
2018). Thus, such solutions, using alternatives to high-input, high-
emissions grain-based feed, can enable sustainable intensification of 
agricultural systems with reduced environmental impacts (Jørgensen 
and Lærke 2016). The use of seaweed and algae as biorefinery 
feedstock can facilitate recirculation of nutrients from waters to 
agricultural land, thus reducing eutrophication while substituting 
purpose-grown feed (Thomas et al. 2021).
Pyrolysis can convert organic wastes, including agricultural and 
forestry residues, food waste, manure, poultry litter and sewage 
sludge, into combustible gas and biochar, which can be used 
as a  soil amendment (Joseph et al. 2021; Schmidt et al. 2021) 
(Chapter  7). Pyrolysis facilitates nutrient recovery from biomass 
residues, enabling return to farmland as biochar, noting, however, 
that a large fraction of nitrogen is lost during pyrolysis (Joseph et al.

2021). Conversion to biochar aids the logistics of transport and land 
application of materials such as sewage sludge, by reducing mass 
and volume, improving flow properties, stability and uniformity, 
and decreasing odour.  Pyrolysis is well suited for materials that 
may be contaminated with pathogens, microplastics, and per- and 
polyfluoroalkyl substances, such as abattoir and sewage wastes, 
removing these risks, and reduces availability of heavy metals in 
feedstock (Joseph et al. 2021). Applying biochar to soil sequesters

1302
1302
Chapter 12 Cross-sectoral Perspectives12biochar-carbon for hundreds to thousands of years and can further 
increase soil carbon by reducing mineralisation of soil organic matter 
and newly added plant carbon (Singh et al. 2012; Wang et al. 2016a; 
Weng et al. 2017; Lehmann et al. 2021). Biochars can improve 
a  range of soil properties, but effects vary depending on biochar 
properties, which are determined by feedstock and production 
conditions (Singh et al. 2012; Wang et al. 2016a), and on the soil 
properties where biochar is applied (Razzaghi et al. 2020). Biochars 
can increase nutrient availability, reduce leaching losses (Singh et al. 
2010; Haider et al. 2017) and enhance crop yields, particularly in 
infertile acidic soils (Jeffery et al. 2017), thus supporting food security 
under changing climate. Biochars can enhance infiltration and soil 
water-holding capacity, reducing runoff and leaching, increasing

water retention in the landscape and improving drought tolerance 
and resilience to climate change (Quin et al. 2014; Omondi et al. 
2016). (See Chapter 7 for a review of biochar’s potential contribution 
to climate change mitigation.)
Both A/R and dedicated biomass production could have adverse 
impacts on food security and cause indirect land-use change if 
deployed in locations used for food production (IPCC 2019a). But 
the degree of impact associated with a  certain mitigation option 
also depends on how deployment takes place and the rate and total 
scale of deployment. The highest increases in food insecurity due to 
deployment of land-based mitigation are expected to occur in sub-
Saharan Africa and Asia (Hasegawa et al. 2018). The land area that 
could be used for bioenergy or other land-based mitigation options 
with low to moderate risks to food security depends on patterns 
of socio-economic development, reaching limits between 1  and

4 million km2 (Hurlbert et al. 2019; IPCC 2019a; Smith et al. 2019c).
The use of less productive, degraded/marginal lands has received 
attention as an option for biomass production and other land-based 
mitigation that can improve the productive and adaptive capacity 
of the lands (Liu et al. 2017; Qin et al. 2018; Dias et al. 2021; Kreig 
et al. 2021) (Section 7.4.4 and Cross-Working Group Box 3 in this 
chapter). The potential is however uncertain as biomass growth rates 
may be low, a  variety of assessment approaches have been used, 
and the identification of degraded/marginal land as ‘available’ has 
been contested, as much low productivity land is used informally 
by impoverished communities, particularly for grazing, or may 
be economically infeasible or environmentally undesirable for 
development of energy crops (medium evidence, low agreement) 
(Baka 2013; Fritz et al. 2013; Haberl et al. 2013; Baka 2014).

As many of the SDGs are closely linked to land use, the identification 
and promotion of mitigation options that rely on land uses described 
above can support a  growing use of bio-based products while 
advancing several SDGs, such as SDG 2 (zero hunger), SDG 6 (clean 
water and sanitation), SDG  7  (affordable and clean energy) and 
SDG 15 (life on land) (Fritsche et al. 2017; IRP 2019; Blair et al. 2021). 
Policies supporting the target of Land Degradation Neutrality (LDN) 
(SDG  15.3) encourage planning of measures to counteract loss of 
productive land due to unsustainable agricultural practices and land 
conversion, through sustainable land management and strategic 
restoration and rehabilitation of degraded land (Cowie et al. 2018). 
LDN can thus be an incentive for land-based mitigation measures that build carbon in vegetation and soil, and can provide impetus 
for land-use planning to achieve multifunctional landscapes that

integrate land-based mitigation with other land uses (Box  12.3). 
The application of sustainable land management practices that build 
soil carbon will enhance the productivity and resilience of crop and 
forestry systems, thereby enhancing biomass production (Henry 
et al. 2018a). Non-bio-based mitigation options can enhance land-
based mitigation: (i) enhanced weathering, that is, adding ground 
silicate rock to soil to take up atmospheric CO 2 through chemical 
weathering (Section 12.3), could supply nutrients and alleviate soil 
acidity, thereby boosting productivity of biomass crops and A/R, 
particularly when combined with biochar application (Haque et al. 
2019; De Oliveira Garcia et al. 2020; Buss et al. 2021); and (ii) land 
rehabilitation and enhanced landscape diversity through production 
of biomass crops could simultaneously contribute to climate change 
mitigation, climate change adaptation, addressing land degradation,

increasing biodiversity and improving food security in the longer 
term (Mackey et al. 2020) (Chapter 7).
Wind power
The land requirement and impacts (including visual and noise impacts) 
of onshore wind turbines depend on the size and type of installation, 
and location (Ioannidis and Koutsoyiannis 2020). Wind power and 
agriculture can coexist in beneficial ways and wind power production 
on agriculture land is well established (Fritsche et al. 2017; Miller and 
Keith 2018a). Spatial planning and local stakeholder engagement can 
reduce opposition due to visual landscape impacts and noise (Frolova 
et al. 2019; Hevia-Koch and Ladenburg 2019). Repowering, that is, 
replacing with higher capacity wind turbines, can mitigate additional 
land requirement associated with deployment towards higher share 
of wind in power systems (Pryor et al. 2020).
Mortality and disturbance risks to birds, bats and insects are major 
ecological concerns associated with wind farms (Thaxter et al.

2017; Cook et al. 2018; Heuck et al. 2019; Coppes et al. 2020; Choi 
et al. 2020; Fernández-Bellon 2020; Marques et al. 2020; Voigt 2021). 
Careful siting is critical (May et al. 2021), while painting blades to 
increase the visibility can also reduce mortality due to collision (May 
et al. 2020). Theoretical studies have suggested that wind turbines 
could lead to warmer night temperatures due to atmospheric mixing 
(Keith et al. 2004), later confirmed through observation (Zhou et al. 
2013), although Vautard et al. (2014) found limited impact at scales 
consistent with climate policies. More recent studies report mixed 
results: indications that the warming effect could be substantial with 
widespread deployment (Miller and Keith 2018b) and conversely 
limited impacts on regional climate at 20% of US electricity from 
wind. (Pryor et al. 2020).
As for wind power, land impacts of solar power depend on the

location, size and type of installation (Ioannidis and Koutsoyiannis 
2020). Establishment of large-scale solar farms could have positive 
or negative environmental effects at the site of deployment, 
depending on the location. Solar PV and CSP power installations 
can lock away land areas, displacing other uses (Mohan 2017). Solar

1303
1303
Cross-sectoral Perspectives Chapter 1212PV can be deployed in ways that enhance agriculture: for example, 
Hassanpour Adeh et al. (2018) found that biomass production and 
water use efficiency of pasture increased under elevated solar 
panels. PV systems under development may achieve significant 
power generation without diminishing agricultural output (Miskin 
et al. 2019). Global mapping of solar panel efficiency showed that 
croplands, grasslands and wetlands are located in regions with the 
greatest solar PV potential (Adeh et al. 2019). Dual-use agrivoltaic 
systems are being developed that overcome previously recognised 
negative impact on crop growth, mainly due to shadows (Marrou et al. 
2013a; Marrou et al. 2013b; Armstrong et al. 2016), thus facilitating 
synergistic co-location of solar photovoltaic power and cropping 
(Adeh et al. 2019; Miskin et al. 2019). Assessment of the potential for 
optimising deployment of solar PV and energy crops on abandoned

cropland areas produced an estimate of the technical potential for 
optimal combination at 125 EJ per year (Leirpoll et al. 2021).
Deserts can be well suited for solar PV and CSP farms, especially at 
low latitudes where global horizontal irradiance is high, as there is 
lower competition for land and land carbon loss is minimal, although 
remote locations may pose challenges for power distribution (Xu 
et al. 2016). Solar arrays can reduce the albedo, particularly in 
desert landscapes, which can lead to local temperature increases 
and regional impacts on wind patterns (Millstein and Menon 2011). 
Modelling studies suggest that large-scale wind and solar farms, for 
example in the Sahara (Li et al. 2018), could increase rainfall through 
reduced albedo and increased surface roughness, stimulating 
vegetation growth and further increasing regional rainfall (Li et al. 
2018) (limited evidence). Besides impacts at the site of deployment,

wind and solar power affect land through mining of critical minerals 
required by these technologies (Viebahn et al. 2015; McLellan et al. 
2016; Carrara et al. 2020).
Nuclear power
Nuclear power has land impacts and risks associated with mining 
operations (Falck 2015; Winde et al. 2017; Srivastava et al. 2020) and 
disposal of spent fuel (IAEA 2006a; Ewing et al. 2016; Bruno et al. 
2020), but the land occupation is small compared to many other 
mitigation options. Substantial volumes of water are required for 
cooling (Liao et al. 2016), as for all thermal power plants, but most 
of this water is returned to rivers and other water bodies after use 
(Sesma Martín and Rubio-Varas 2017). Negative impacts on aquatic 
systems can occur due to chemical and thermal pollution loading 
(Fricko et al. 2016; Raptis et al. 2016; Bonansea et al. 2020). The major 
risk to land from nuclear power is that a nuclear accident leads to

radioactive contamination. An extreme example, the 1986 Chernobyl 
accident in Ukraine, resulted in radioactive contamination across 
Europe. Most of the fallout concentrated in Belarus, Ukraine and 
Russia, where some 125,000 km2 of land (more than a third of which 
was in agricultural use) was contaminated. About 350,000 people 
were relocated away from these areas (IAEA 2006b; Sovacool 2008). 
About 116,000 people were permanently evacuated from the 4200 
km Chernobyl exclusion zone (IAEA 2006a). New reactor designs 
accidents significantly (Section 6.4.2.4). An example of alternatives 
to land reclamation for productive purposes, a  national biosphere reserve has been established around Chernobyl to conserve, enhance 
and manage carbon stocks and biodiversity (Deryabina et al. 2015; 
Ewing et al. 2016), although invertebrate and plant populations are 
affected (Mousseau and Møller 2014; Mousseau and Møller 2020).

Reservoir hydropower projects submerge areas as dams are 
established for water storage. Hydropower can be associated with 
significant and highly varying land occupation and carbon footprint 
(Poff and Schmidt 2016; Scherer and Pfister 2016a; dos Santos 
et al. 2017; Ocko and Hamburg 2019). The flooding of land causes 
CH4 emissions due to the anaerobic decomposition of submerged 
vegetation and there is also a  loss of carbon sequestration due 
to mortality of submerged vegetation. The size of GHG emissions 
depends on the amount of vegetation submerged. The carbon 
in accumulated sediments in reservoirs may be released to the 
atmosphere as CO 2 and CH 4 upon decommissioning of dams, and 
while uncertain, estimates indicate that these emissions can make up 
a significant part of the cumulative GHG emissions of hydroelectric 
power plants (Moran et al. 2018; Almeida et al. 2019; Ocko and 
Hamburg 2019). Positive radiative forcing due to lower albedo of

hydropower reservoirs compared to surrounding landscapes can 
reduce mitigation contribution significantly (Wohlfahrt et al. 2021).
Hydropower can have high water usage due to evaporation from 
dams (Scherer and Pfister 2016b). Hydropower projects may impact 
aquatic ecology and biodiversity, necessitate the relocation of local 
communities living within or near the reservoir or construction 
sites, and affect downstream communities (in positive or negative 
ways) (Moran et al. 2018; Barbarossa et al. 2020). Displacement 
as well as resettlement schemes can have both socio-economic 
and environmental consequences including those associated with 
establishment of new agricultural land (Ahsan and Ahmad 2016; 
Nguyen et al. 2017). Dam construction may also stimulate migration 
into the affected region, which can lead to deforestation and other 
negative impacts (Chen et al. 2015). Impacts can be mitigated 
through basin-scale dam planning that considers GHG emissions

along with social and ecological effects (Almeida et al. 2019). Land 
occupation is minimal for run-of-river hydropower installations, but 
without storage they have no resilience to drought and installations 
inhibit dispersal and migration of organisms (Lange et al. 2018). 
Reservoir hydropower schemes can regulate water flows and reduce 
flood damage to agricultural production (Amjath-Babu et al. 2019). 
On the other hand, severe flooding due to failure of hydropower 
dams has caused fatalities, damage to infrastructure and loss of 
productive land (Farrington and Salama 1996; Farley et al. 2005; 
Zhang et al. 2016; Marcar 2016; Zhang et al. 2017; Kalinina et al. 
2018; Lu et al. 2018).
12.5.4 Governance of Land-related Impacts 
of Mitigation Options
The land sector (Chapter 7) contributes to mitigation via emissions 
reduction and enhancement of land carbon sinks, and by providing 
biomass for mitigation in other sectors. Key challenges for governance

1304
1304
Chapter 12 Cross-sectoral Perspectives12of land-based mitigation include social and environmental safeguards 
(Duchelle et al. 2017; Sills et al. 2017; Larson et al. 2018); insufficient 
financing (Turnhout et al. 2017); capturing co-benefits; ensuring 
additionality; addressing non-permanence of carbon sequestration; 
monitoring, reporting, and verification (MRV) of emissions reduction 
and carbon dioxide removals; and avoiding leakage or spillover 
effects. Governance approaches to addressing these challenges are 
discussed in Section  7.6, and include MRV systems and integrity 
criteria for project-level emissions trading; payments for ecosystem 
services; land-use planning and land zoning; certification schemes, 
standards and codes of practice.
With respect to renewable energy options that occupy land, the 
focus of governance has been directed to technological adoption 
and public acceptance (Sequeira and Santos 2018), rather than

land use. Recent work has found that spatial processes shape the 
emerging energy transition, creating zones of friction between 
global investors, national and local governments, and civil society 
(Jepson and Caldas 2017; McEwan 2017). For example, Yenneti et 
al. (2016) have argued that hydropower and ground-based solar 
parks in India, which have involved enclosure of lands designated as 
degraded, displacing pastoral use by vulnerable communities, have 
constituted forms of spatial injustice. Hydropower leads to dam-
induced displacement, and though this can be addressed through 
compensation mechanisms, governance is complicated by a lack of 
transparency in resettlement data (Kirchherr et al. 2016; Kirchherr 
et al. 2019). Renewable energy production is resulting in new land 
conflict frontiers where degraded land is framed as having mitigation 
value such as for palm oil production and wind power in Mexico 
(Backhouse and Lehmann 2020); land use conflict as well as impacts

on wildlife from large-scale solar installations have also emerged 
in the southwestern United States (Mulvaney 2017). The renewable 
energy transition also involves the extraction of critical minerals 
used in renewable energy technologies, such as lithium and cobalt. Governance challenges include the lack of transparent greenhouse 
gas accounting for mining activities (Lee et al. 2020a), and threats to 
biodiversity from land disturbance, which require strategic planning 
to address (Sonter et al. 2020a). Strategic spatial planning is 
needed more generally to address trade-offs between using land for 
renewable energy and food: for example, agriculture can be co-located 
with solar photovoltaics (Barron-Gafford et al. 2019) or wind power 
(Miller and Keith 2018a). Integrative spatial planning can integrate 
renewable energy with not just agriculture, but mobility and housing 
(Hurlbert et al. 2019). Integrated planning is needed to avoid scalar

pitfalls, and local and regional contextualised governance solutions 
need to be sited within a  planetary frame of reference (Biermann 
et al. 2016). Greater planning and coordination are also needed to 
ensure co-benefits from land-based mitigation (Box 12.3) as well as 
from CDR and efforts to reduce food systems emissions.
In emerging domains for governance such as land-based mitigation, 
global institutions, private sector networks and civil society 
organisations are playing key roles in terms of norm-setting. The 
shared languages and theoretical frameworks, or cognitive linkages 
(Pattberg et al. 2018), that arise with polycentric governance can not 
only be helpful in creating expectations and establishing benchmarks 
for (in)appropriate practices where enforceable ‘hard law’ is missing 
(Karlsson-Vinkhuyzen et al. 2018; Gajevic Sayegh 2020), they 
can also form the basis of voluntary guidelines or niche markets

(Box 12.3) However, the ability to apply participatory processes for 
developing voluntary guidelines and other participatory norm-setting 
endeavours varies from place to place. Social and cultural norms 
shape the ability of women, youth, and different ethnic groups to 
participate in governance fora, such as those around agroecological 
transformation (Anderson et al. 2019). Furthermore, establishing 
new norms alone does not solve structural challenges such as lack 
of access to food, nor does it confront power imbalances, or provide 
mechanisms to deal with uncooperative actors (Morrison et al. 2019).
Box 12.3 | Land Degradation Neutrality as a Framework to Manage Trade-offs  
in Land-based Mitigation
The United Nations Convention to Combat Desertification (UNCCD) introduced the concept of Land Degradation Neutrality (LDN), 
defined as ‘a state whereby the amount and quality of land resources necessary to support ecosystem functions and services and

enhance food security remain stable or increase within specified temporal and spatial scales and ecosystems’ (UNCCD 2015), and it 
has been adopted as a target of SDG 15 (life on land). At December 2020, 124 (mostly developing) countries had committed to pursue 
voluntary LDN targets.
The goal of LDN is to maintain or enhance land-based natural capital, and its associated ecosystem services, such as provision 
of food and regulation of water and climate, while enhancing the resilience of the communities that depend on the land. LDN 
encourages a dual-pronged approach promoting sustainable land management (SLM) to avoid or reduce land degradation, combined 
with strategic effort in land restoration and rehabilitation to reverse degradation on degraded lands and thereby deliver the target of 
‘no net loss’ of productive land (Orr et al. 2017).
In the context of LDN, land restoration refers to actions undertaken with the aim of reinstating ecosystem functionality, whereas land

rehabilitation refers to actions undertaken with a goal of provision of goods and services (Cowie et al. 2018). Restoration interventions 
can include destocking to encourage regeneration of native vegetation; shelter belts of local species established from seed or 
seedlings, strategically located to provide wildlife corridors and link habitat; and rewetting drained peatland. ‘Farmer-managed natural 
regeneration’ is a low-cost restoration approach in which regeneration of tree stumps and roots is encouraged, stabilising soil and

1305
1305
Cross-sectoral Perspectives Chapter 1212Box 12.3 (continued)
Box 12.3, Figure 1 | Schematic illustrating the elements of the Land Degradation Neutrality conceptual framework.  Source: Cowie et al. (2018). 
Used with permission.

1306
1306
Chapter 12 Cross-sectoral Perspectives12Box 12.3 (continued)
enhancing soil nutrients and organic matter levels (Chomba et al. 2020; Lohbeck et al. 2020). Rehabilitation actions include 
establishment of energy crops, or afforestation with fast-growing exotic trees to sequester carbon or produce timber. Application of 
biochar can facilitate rehabilitation by enhancing nutrient retention and water-holding capacity, and stimulating microbial activity 
(Cowie 2020a).
SLM, rehabilitation and restoration activities undertaken towards national LDN targets have potential to deliver substantial CDR 
through carbon sequestration in vegetation and soil. In addition, biomass production, for bioenergy or biochar, could be an economically 
viable land use option for reversing degradation, through rehabilitation. Alternatively, a focus on ecological restoration (Gann et al. 
2019) as the strategy for reversing degradation will deliver greater biodiversity benefits.

Achieving neutrality requires estimating the likely impacts of land-use and land-management decisions, to determine the area of land, 
of each land type, that is likely to be degraded (Orr et al. 2017). This information is used to plan interventions to reverse degradation on 
an equal area of the same land type. Therefore, pursuit of LDN requires concerted and coordinated efforts to integrate LDN objectives 
into land-use planning and land management, underpinned by sound understanding of the human–environment system and effective 
governance mechanisms.
Countries are advised to apply a landscape-scale approach for planning LDN interventions, in which land uses are matched to land 
potential, and resilience of current and proposed land uses is considered, to ensure that improvement in land condition is likely to 
be maintained (Cowie 2020a). A  participatory approach that enables effective representation of all stakeholders is encouraged,

to facilitate equitable outcomes from planning decisions, recognising that decisions on LDN interventions are likely to involve trade-
offs between various environmental and socio-economic objectives (Schulze et al. 2021).
Planning and implementation of LDN programmes provides a framework in which locally-adapted land-based mitigation options 
can be integrated with use of land for production, conservation and settlements, in multifunctional landscapes where trade-offs 
are recognised and managed, and synergistic opportunities are sought. LDN is thus a vehicle to focus collaboration in pursuit of the 
multiple land-based objectives of the multilateral environmental agreements and the SDGs.
Table 12.10 collates risks, impacts and opportunities associated with 
different mitigation options that occupy land.
Table 12.10 | Summary of impacts, risks and co-benefits associated with land occupation by mitigation options considered in Section 12.5.

Mitigation option Impacts and risks Opportunities for co-benefits
Non-bio-based options that may displace food production
Solar farmsLand use competition; loss of soil carbon; heat island effect (scale dependent) 
(Sections 12.5.3 and 12.5.4)Target areas unsuitable for agriculture such as deserts (Section 12.5.3)
Hydropower (dams)Land use competition; displacement of natural ecosystems; CO 2 and CH 4 
emissions (Sections 12.5.3 and 12.5.4)Water storage (including for irrigation) and regulation of water flows; 
pumped storage can store excess energy from other renewable generation 
sources (Section 12.5.3)
Non-bio-based options that can (to a varying degree) be integrated with food production
Wind turbinesMay affect local/regional weather and climate (scale dependent); impacts on 
wildlife; visual impacts (Section 12.5.3)Design and siting informed by visual landscape impacts, relevant habitats, 
and flight trajectories of migratory birds (Section 12.5.3)

Solar panels Land use competition (Section 12.5.3)Integration with buildings and other infrastructure; integration 
with food production is being explored (Section 12.5.2)
Enhanced  
weathering (EW)Disturbance at sites of extraction; ineffective in low rainfall regions 
(Section 12.3.1.2)Increased crop yields and biomass production through nutrient supply 
and increasing pH of acid soils; synergies with biochar (Section 12.5.3)
Bio-based options that may displace existing food production
reforestation (A/R)Land use competition, potentially leading to indirect land use change; 
reduced water availability; loss of biodiversity (Section 12.5.3)Strategic siting to minimise adverse impacts on hydrology, land use, 
biodiversity (Section 12.5.3)
Biomass cropsLand use competition, potentially leading to indirect land-use change; 
reduced water availability; reduced soil fertility; loss of biodiversity

(Section 12.5.3)Strategic siting to minimise adverse impacts/enhance beneficial effects 
on land use, landscape variability, biodiversity, soil organic matter, hydrology 
and water quality (Section 12.5.3)

1307
1307
Cross-sectoral Perspectives Chapter 1212Cross-Working Group Box 3 | Mitigation and Adaptation via the Bioeconomy
Authors: Henry Neufeldt (Denmark/Germany), Göran Berndes (Sweden), Almut Arneth (Germany), Rachel Bezner Kerr (the United 
States of America/Canada), Luisa F Cabeza (Spain), Donovan Campbell (Jamaica), Jofre Carnicer Cols (Spain), Annette Cowie (Australia), 
Vassilis Daioglou (Greece), Joanna House (United Kingdom), Adrian Leip (Italy/Germany), Francisco Meza (Chile), Michael Morecroft 
(United Kingdom), Gert-Jan Nabuurs (Netherlands), Camille Parmesan (United Kingdom/the United States of America), Julio C. Postigo 
(the United States of America/Peru), Marta G. Rivera-Ferre (Spain), Raphael Slade (United Kingdom), Maria Cristina Tirado von der 
Pahlen (the United States of America/Spain), Pramod K. Singh (India), Pete Smith (United Kingdom)
Summary statement

The growing demand for biomass offers both opportunities and challenges to mitigate and adapt to climate change and natural 
resource constraints (high confidence). Increased technology innovation, stakeholder integration and transparent governance 
structures and procedures at local to global scales are key to successful bioeconomy deployment maximising benefits and managing 
trade-offs (high confidence).
Limited global land and biomass resources accompanied by growing demands for food, feed, fibre, and fuels, together with prospects 
for a paradigm shift towards phasing out fossil fuels, set the frame for potentially fierce competition for land3 and biomass to meet 
burgeoning demands, even as climate change increasingly limits natural resource potentials (high confidence).
3 For lack of space, the focus is on land only, although the bioeconomy also includes sea-related bioresources.Mitigation option Impacts and risks Opportunities for co-benefits

Bio-based options that can (to a varying degree) be combined with food production
AgroforestryCompetition with adjacent crops and pastures reduces yields  
(Section 7.4.3.3)Shelter for stock and crops, diversification, biomass production, increases 
soil organic matter and soil fertility; increased biodiversity and perennial 
vegetation enhance beneficial organisms; can reduce need for pesticides 
(Sections 7.4.3.3 and 12.5.3)
management in 
croplands and 
grasslandsIncrease in nitrous oxide emissions if fertiliser used to enhance crop 
production; reduced cereal production through increased crop legumes and 
pasture phases could lead to indirect land use change  
(Sections 7.4.3.1 and 7.4.3.6)Increasing soil organic matter improves soil health, increases crop and 
pasture yields and resilience to drought, can reduce fertiliser requirement, 
nutrient leaching and need for land use change (Section 7.4.3.1)
Biochar addition

to soilLand use competition if biochar is produced from purpose-grown biomass. 
Loss of forest carbon stock and impacts on biodiversity if biomass is 
harvested unsustainably. (Section 12.5.3)Facilitate beneficial use of organic residues, to return nutrients to farmland. 
Increased land productivity; increased carbon sequestration in vegetation and 
soil; increased nutrient-use efficiency, and reduced requirement for chemical 
fertiliser (Sections 7.4.3.2 and 12.5.3)
Harvest residue 
extraction and 
use for bioenergy, 
biochar and other  
bio-productsDecline in soil organic matter and soil fertility (Section 12.5.3)Nutrients returned to soil e.g., as ash; reduced fuel load and wildfire risk 
(Sections 7.4.3.2 and 12.5.3)
Manure management 
(i.e., for biogas)Risk of fugitive emissions
Can contain pathogens 
(Sections 7.4.3.7 and 12.5.3)Biogas as renewable energy source, digestate as soil amendment 
(Section 12.5.3)
Options that do not occupy land used for food production
Management of

organic waste (food 
waste, biosolids, 
organic component of 
municipal solid waste)Can contain contaminants (heavy metals, persistent organic pollutants, 
pathogens) (Section 12.5.3)Processing using anaerobic digestion or pyrolysis produces renewable gas 
and soil amendment, enabling return of nutrients to farmland. (Note that 
some feedstock nitrogen is lost in pyrolysis) (Section 12.5.3)
A/R and biomass 
production on 
degraded non-
forested land 
(e.g., abandoned 
agricultural land)High labour and material inputs can be needed; abandoned land can support 
informal grazing and have significant biodiversity value. Reduced water 
availability (Section 12.5.3)Application of biochar can re-establish nutrient cycling; bioenergy crops can 
add organic matter, restoring soil fertility, and can remove heavy metals, 
enabling food production (Sections 7.4.3.2 and 12.5.3)

1308
1308
Chapter 12 Cross-sectoral Perspectives12Cross-Working Group Box 3 (continued)
Sustainable agriculture and forestry, technology innovation in bio-based production within a circular economy, and international 
cooperation and governance of global trade in products to reflect and disincentivise their environmental and social externalities, can 
provide mitigation and adaptation via bioeconomy development that responds to the needs and perspectives of multiple stakeholders 
to achieve outcomes that maximise synergies while limiting trade-offs (high confidence).
There is high confidence that climate change, population growth and changes in per capita consumption will increase pressures 
on managed as well as natural and semi-natural ecosystems, exacerbating existing risks to livelihoods, biodiversity, human and 
ecosystem health, infrastructure, and food systems (Conijn et al. 2018; IPCC 2018; IPCC 2019a; Lade et al. 2020). At the same time,

many global mitigation scenarios presented in IPCC assessment reports rely on large GHG emissions reduction in the AFOLU sector 
and concurrent deployment of reforestation/afforestation and biomass use in a multitude of applications (Rogelj et al. 2018; Hanssen 
et al. 2020) (AR6 WGI Chapters 4 and 5, AR6 WGIII Chapters 3 and 7).
productivity can be enhanced without undermining ecosystem services (Obersteiner et al. 2016; Campbell et al. 2017; Caron et al. 
2018; Conijn et al. 2018; Heck et al. 2018; Searchinger 2018a; Smith et al. 2019). Management intensities can often be adapted to local 
conditions with consideration of other functions and ecosystem services, but at a global scale the challenge remains to avoid further 
deforestation and degradation of intact ecosystems, in particular biodiversity-rich systems (AR6 WGII Cross-Chapter Box NATURAL),

while meeting the growing demands. Further, increased land-use competition can affect food prices and impact food security and 
livelihoods (To and Grafton 2015; Chakravorty et al. 2017), with possible knock-on effects related to civil unrest (Abbott et al. 2017; 
D’Odorico et al. 2018).
Developing new bio-based solutions while mitigating overall biomass demand growth
from cement and steel production while providing carbon storage (Churkina et al. 2020). Substitution of fossil fuels with biomass in 
manufacture of cement and steel can reduce GHG emissions where these materials are difficult to replace. Dispatchable power based 
on biomass can provide power stability and quality as the contribution from solar and wind power increases (AR6 WGIII Chapter 6),

and biofuels can contribute to reducing fossil fuel emissions in the transport and industry sectors (AR6 WGIII Chapters 10 and 11). 
The use of bio-based plastics, chemicals and packaging could be increased, and biorefineries can achieve high resource-use efficiency 
in converting biomass into food, feed, fuels and other bio-based products (Aristizábal-Marulanda and Cardona Alzate 2019; Schmidt 
et al. 2019). There is also scope for substituting existing bio-based products with more benign products. For example, cellulose-based 
textiles can replace cotton, which requires large amounts of water, chemical fertilisers and pesticides to ensure high yields.
GHG balances may limit the mitigation value. Growth in biomass use may in the longer term also be constrained by the need to protect

biodiversity and ecosystems’ capacity to support essential ecosystem services. Biomass use may also be constrained by water scarcity 
and other resource scarcities, and/or challenges related to public perception and acceptance due to impacts caused by biomass 
production and use. Energy conservation and efficiency measures and deployment of technologies and systems that do not rely on 
carbon, such as carbon-free electricity supporting, inter alia, electrification of transport as well as industry processes and residential 
heating (IPCC 2018; UNEP 2019), can constrain the growth in biomass demand when countries seek to phase out fossil fuels and 
other GHG-intensive products while providing an acceptable standard of living. Nevertheless, demand for bio-based products may 
become high where full decoupling from carbon is difficult to achieve (e.g., aviation, bio-based plastics and chemicals) or where

carbon storage is an associated benefit (e.g., wood buildings, BECCS, biochar for soil amendments), leading to challenging trade-offs 
(e.g., food security, biodiversity) that need to be managed in environmentally sustainable and socially just ways.
can also reduce pressures on the remaining land resources. For example, dietary changes toward more plant-based food (where 
appropriate) and reduced food waste can provide climate change mitigation along with health benefits (Willett et al. 2019) (AR6 WGIII 
Sections 7.4 and 12.4) and other co-benefits with regard to food security, adaptation and land use (Mbow et al. 2019; Smith et al. 
2019a) (AR6 WGII Chapter 5). Advancements in the provision of novel food and feed sources (e.g., cultured meat, insects, grass-based

protein feed and cellular agriculture) can also limit the pressures on finite natural resources (Parodi et al. 2018; Zabaniotou 2018) 
(AR6 WGIII Section 12.4).

1309
1309
Cross-sectoral Perspectives Chapter 1212Cross-Working Group Box 3 (continued)
Circular bioeconomy
Circular economy approaches (AR6 WGIII Section 12.6) are commonly depicted by two cycles, where the biological cycle focuses on 
regeneration in the biosphere and the technical cycle focuses on reuse, refurbishment and recycling to maintain value and maximise 
material recovery (Mayer et al. 2019a). Biogenic carbon flows and resources are part of the biological carbon cycle, but carbon-based 
products can be included in, and affect, both the biological and the technical carbon cycles (Kirchherr et al. 2017; Winans et al. 2017; 
Velenturf et al. 2019). The integration of circular economy and bioeconomy principles has been discussed in relation to organic waste 
management (Teigiserova et al. 2020), societal transition and policy development (European Commission 2018; Bugge et al. 2019)

as well as COVID-19 recovery strategies (Palahí et al. 2020). To maintain the natural resource base, circular bioeconomy emphasises 
sustainable land use and the return of biomass and nutrients to the biosphere when it leaves the technical cycle.
Scarcity is an argument for adopting circular economy principles for the management of biomass, as for non-renewable resources. 
Waste avoidance, product reuse and material recycling keep down resource use while maintaining product and material value. However, 
reuse and recycling are not always feasible, for example when biofuels are used for transport and bio-based biodegradable chemicals 
are used to reduce ecological impacts, where losses to the environment are unavoidable. A balanced approach to management of 
biomass resources could start from the perspective of value preservation within the carbon cycle, with possible routes for biomass use

based on the carbon budget defined by the Paris Agreement, principles for sustainable land use and natural ecosystem protection.
Land-use opportunities and challenges in the bioeconomy
depend on context, design and implementation, so actions have to be tailored to the specific conditions to minimise adverse effects 
(Kongsager 2018). This is supported in literature analysing the nexus between land, water, energy and food in the context of climate 
change, which consistently concludes that addressing these different domains together rather than in isolation would enhance 
synergies and reduce trade-offs (Obersteiner et al. 2016; D’Odorico et al. 2018; Soto Golcher and Visseren-Hamakers 2018; Froese 
et al. 2019; Momblanch et al. 2019).

services, such as flood risk management through floodplain restoration, saltmarshes, mangroves or peat renaturation (UNEP 2021; 
AR6 WGII Cross-Chapter Box NATURAL). Climate-smart agriculture can increase productivity while enhancing resilience and reducing 
GHG emissions inherent to production (Lipper et al. 2014; Bell et al. 2018; FAO 2019b; Singh and Chudasama 2021). Similarly, climate-
smart forestry considers the whole value chain and integrates climate objectives into forest sector management through multiple 
measures (from strict reserves to more intensively managed forests) providing mitigation and adaptation benefits (Nabuurs et al. 
2018; Verkerk et al. 2020) (AR6 WGIII Section 7.3).

Cross-Working Group Box 3, Figure 1 | Left:  High-input intensive agriculture, aiming for high yields of a few crop species, with large fields and no semi-natural 
habitats. Right: Agroecological agriculture, supplying a range of ecosystem services, relying on biodiversity and crop and animal diversity instead of external inputs, 
and integrating plant and animal production, with smaller fields and presence of semi-natural habitats. Source: Reprinted by permission from Springer Nature 
Customer Service Centre GmbH: Springer Nature, Nature Sustainability, Towards better representation of organic agriculture in life cycle assessment, Hayo M. G. van 
der Werf et al. © 2020.

1310
1310
Agroecological approaches can be integrated into a wide range of land management practices to support a sustainable bioeconomy 
and address equity considerations (HLPE 2019). Relevant land-use practices, such as agroforestry, intercropping, organic amendments, 
cover crops and rotational grazing, can provide mitigation and support adaption to climate change via food security, livelihoods, 
biodiversity and health co-benefits (Ponisio et al. 2015; Garibaldi et al. 2016; D’Annolfo et al. 2017; Bezner Kerr et al. 2019; Clark 
et al. 2019b; Córdova et al. 2019; HLPE 2019; Mbow et al. 2019; Renard and Tilman 2019; Sinclair et al. 2019; Bharucha et al. 2020; 
Bezner Kerr et al. 2021) (AR6 WGII Cross-Chapter Box NATURAL). Strategic integration of appropriate biomass production systems

into agricultural landscapes can provide biomass for bioenergy and other bio-based products while providing co-benefits such as 
enhanced landscape diversity, habitat quality, retention of nutrients and sediment, erosion control, climate regulation, flood regulation, 
pollination and biological pest and disease control (Christen and Dalgaard 2013; Asbjornsen et al. 2014; Holland et al. 2015; Ssegane 
et al. 2015; Dauber and Miyake 2016; Milner et al. 2016; Ssegane and Negri 2016; Styles et al. 2016; Zumpf et al. 2017; Cacho et al. 
2018; Alam and Dwivedi 2019; Cubins et al. 2019; HLPE 2019; Olsson et al. 2019; Zalesny et al. 2019; Englund et al. 2020) (AR6 WGIII 
Box 12.3). Such approaches can help limit environmental impacts from intensive agriculture while maintaining or increasing land 
productivity and biomass output.
Transitions from conventional to new biomass production and conversion systems include challenges related to cross-sector

integration and limited experience with new crops and land use practices, including needs for specialised equipment (Thornton and 
Herrero 2015; HLPE 2019) (AR6 WGII Section 5.10). Introduction of agroecological approaches and integrated biomass/food crop 
production can result in lower food crop yields per hectare, particularly during transition phases, potentially causing indirect landuse 
change, but can also support higher and more stable yields, reduce costs, and increase profitability under climate change (Muller et al. 
2017; Seufert and Ramakutty 2017; Barbieri et al. 2019; HLPE 2019; Sinclair et al. 2019; Smith et al. 2019a; Smith et al. 2020). Crop 
diversification, organic amendments, and biological pest control (HLPE 2019) can reduce input costs and risks of occupational pesticide 
exposure and food and water contamination (González-Alzaga et al. 2014; EFSA 2017; Mie et al. 2017), reduce farmers’ vulnerability

to climate change (e.g., droughts and spread of pests and diseases affecting plant and animal health) (Delcour et al. 2015; FAO 2020) 
and enhance provisioning and sustaining ecosystem services, such as pollination (D’Annolfo et al. 2017; Sinclair et al. 2019).
Barriers toward wider implementation include absence of policies that compensate land owners for providing enhanced ecosystem 
before longer-term benefits can accrue. Other barriers include limited access to markets, knowledge gaps, financial, technological or 
labour constraints, lack of extension support and insecure land tenure (Jacobi et al. 2017; Kongsager 2017; Hernández-Morcillo et al. 
2018; Iiyama et al. 2018; HLPE 2019). Regional-level agroecology transitions may be facilitated by co-learning platforms, farmer

networks, private sector, civil society groups, regional and local administration and other incentive structures (e.g., price premiums, 
access to credit, regulation) (Coe et al. 2014; Pérez-Marin et al. 2017; Mier y Terán Giménez Cacho et al. 2018; HLPE 2019; Valencia 
et al. 2019; SAEPEA 2020). With the right incentives, improvements can be made with regard to profitability, making alternatives more 
attractive to land owners.
Governing the solution space
Literature analysing the synergies and trade-offs between competing demands for land suggest that solutions are highly contextualised 
in terms of their environmental, socio-economic and governance-related characteristics, making it difficult to devise generic solutions 
(Haasnoot et al. 2020). Aspects of spatial and temporal scale can further enhance the complexity, for instance where transboundary

effects across jurisdictions or upstream-downstream characteristics need to be considered, or where climate change trajectories might 
alter relevant biogeophysical dynamics (Postigo and Young 2021). Nonetheless, there is broad agreement that taking the needs and 
perspectives of multiple stakeholders into account in a transparent process during negotiations improves the chances of achieving 
outcomes that maximise synergies while limiting trade-offs (Ariti et al. 2018; Metternicht 2018; Favretto et al. 2020; Kopáček 2021; 
Muscat et al. 2021). Yet differences in agency and power between stakeholders or anticipated changes in access to or control of 
resources can undermine negotiation results even if there is a common understanding of the overarching benefits of more integrated 
environmental agreements and the need for greater coordination and cooperation to avoid longer-term losses to all (Aarts and

Leeuwis 2010; Weitz et al. 2017). There is also the risk that strong local participatory processes can become disconnected from broader 
national plans, and thus fail to support the achievement of national targets. Thus, connection between levels is needed to ensure that 
ambition for transformative change is not derailed at local level (Aarts and Leeuwis 2010; Postigo and Young 2021).
Decisions on land uses between biomass production for food, feed, fibre or fuel, as well as nature conservation or restoration and 
other uses (e.g., mining, urban infrastructure), depend on differences in perspectives and values. Because the availability of land for 
diverse biomass uses is invariably limited, setting priorities for land-use allocations therefore first depends on making the perspectives 
underlying what is considered as ‘high-value’ explicit (Fischer et al. 2007; Garnett et al. 2015; De Boer and Van Ittersum 2018;

1311
1311
Muscat et al. 2020). Decisions can then be made transparently based on societal norms, needs and the available resource base. 
Prioritisation of land use for the common good therefore requires societal consensus building embedded in the socio-economic and 
cultural fabric of regions, societies and communities. Integration of local decision-making with national planning ensures local actions 
complement national development objectives.
buffering price volatilities and potentially offering producer countries access to global markets, which can be seen as an effective 
adaptation measure (Baldos and Hertel 2015; Costinot et al. 2016; Hertel and Baldos 2016; Gouel and Laborde 2021) (AR6 WGII

Section 5.11). But there is also clear evidence that international trade and the global economy can enhance price volatility, lead 
to food price spikes and affect food security due to climate and other shocks, as seen recently due to the COVID-19 pandemic 
(Cottrell et al. 2019; WFP-FSIN 2020; Verschuur et al. 2021) (AR6 WGII Section 5.12). The continued strong demand for food and 
other bio-based products, mainly from high- and middle-income countries, therefore requires better cooperation between nations 
and global governance of trade to more accurately reflect and disincentivise their environmental and social externalities. Trade in 
agricultural and extractive products driving land-use change in tropical forest and savanna biomes is of major concern because of 
the biodiversity impacts and GHG emissions incurred in their provision (Hosonuma et al. 2012; Forest Trends 2014; Smith et al. 2014;

Henders et al. 2015; Curtis et al. 2018; Pendrill et al. 2019; Seymour and Harris 2019; Kissinger et al. 2021) (AR6 WGII Tropical Forests 
Cross-Chapter Paper).
In summary, there is significant scope for optimising use of land resources to produce more biomass while reducing adverse effects 
(high confidence). Context-specific prioritisation, technology innovation in bio-based production, integrative policies, coordinated 
institutions and improved governance mechanisms to enhance synergies and minimise trade-offs can mitigate the pressure on 
managed as well as natural and semi-natural ecosystems (medium confidence). Yet, energy conservation and efficiency measures, and 
deployment of technologies and systems that do not rely on carbon-based energy and materials, are essential for mitigating biomass 
demand growth as countries pursue ambitious climate goals (high confidence).
12.6 Other Cross-sectoral 
Implications of Mitigation

This section presents further cross-sectoral considerations related 
to GHG mitigation. Firstly, various cross-sectoral perspectives on 
mitigation actions are presented. Then, sectoral policy interactions 
are presented. Finally, implications in terms of international trade 
spillover effects and competitiveness, and finance flows and related 
spillover effects at the sectoral level, are addressed.
12.6.1 Cross-sectoral Perspectives on Mitigation Action
Chapters 5 to 11 present mitigation measures applicable in individual 
sectors, and potential co-benefits and adverse side effects4 of these 
individual measures. This section builds on the sectoral analysis 
of mitigation action from a  cross-sectoral perspective. Firstly, 
Section 12.6.1.1 brings together some of the observations presented 
in the sectoral chapters to show how different mitigation actions in 
different sectors can contribute to the same co-benefits and result

in the same adverse side effects, thereby demonstrating the potential 
synergistic effects. The links between these co-benefits and adverse 
side effects and the SDGs is also demonstrated. In Section 12.6.1.2, 
4 Here, the term co-benefits is used to refer to the additional benefits to society and the environment that are realised in parallel with emissions reductions, while an 
understanding of adverse side effects highlights where policy- and decision makers are required to make trade-offs between mitigation benefits and other impacts. The 
choice of language differs to some degree in other chapters.the focus turns from sector-specific mitigation measures to mitigation 
measures which have cross-sectoral implications, including measures 
that have application in more than one sector and measures where 
implementation in one sector impacts on implementation in another. 
Finally, Section  12.6.1.3 notes the cross-sectoral relevance of

a selection of general-purpose technologies, a topic that is covered 
further in Chapter 16.
12.6.1.1 A Cross-sectoral Perspective on Co-benefits and 
Adverse Side Effects of Mitigation Measures, and 
Links with the SDGs
A body of literature has been developed which addresses the 
co-benefits of climate mitigation action (Karlsson et al. 2020). Adverse 
side effects of mitigation are also well documented. Co-benefits 
and adverse side effects in individual sectors and associated with 
individual mitigation measures are discussed in the individual sector 
chapters (Sections 5.2, 6.7.7, 7.4, 7.6, 8.2, 8.4, 9.8, 10.1.1 and 11.5.3), 
as well as in previous IPCC General and Special Assessment reports. 
The term ‘co-impacts’ has been proposed to capture both the co-
benefits and adverse side effects of mitigation. An alternative framing 
is one of multiple objectives, where climate change mitigation is 
placed alongside other objectives when assessing policy decisions

1312
1312
Chapter 12 Cross-sectoral Perspectives12(Ürge-Vorsatz et al. 2014; Mayrhofer and Gupta 2016; Cohen et al. 
2017; Bhardwaj et al. 2019).
The identification and assessment of co-benefits has been argued to 
serve a  number of functions (Section  1.4) including using them as 
leverage for securing financial support for implementation, providing 
justification of actions which provide a balance of both short- and long-
term benefits and obtaining stakeholder buy-in (robust evidence,  low 
agreement) (Karlsson et al. 2020). Assessment of adverse side effects 
has been suggested to be useful in avoiding unforeseen negative 
impacts of mitigation and providing policy- and decision-makers with 
the information required to make informed trade-offs between climate 
and other benefits of actions (Ürge-Vorsatz et al. 2014; Bhardwaj et al. 
2019; Cohen et al. 2019) (high evidence,  low agreement).
Various approaches to identifying and organising co-impacts in

specific contexts and across sectors have been proposed towards 
providing more comparable and standardised analyses. However, 
consistent quantification of co-impacts, including cost-benefit 
analysis, and the utilisation of the resulting information, remain 
a challenge (Ürge-Vorsatz et al. 2014; Floater et al. 2016; Mayrhofer 
and Gupta 2016; Cohen et al. 2019; Karlsson et al. 2020). This 
challenge is further exacerbated when considering that co-impacts of 
a mitigation measure in one sector can either enhance or reduce the 
co-impacts associated with mitigation in another, or the achievement 
of co-benefits in one geographic location can lead to adverse side 
effects in another. For example, the production of lithium for batteries for energy storage has the potential to contribute to protecting water 
resources and reducing wastes associated with coal-fired power in 
many parts of the world, but mining of lithium has the potential

for creating water and waste challenges if not managed properly 
(Agusdinata et al. 2018; Kaunda 2020).
While earlier literature has suggested that co-impacts assessments 
can support adoption of climate mitigation action, a  more recent 
body of literature has suggested limitations in such framing (Ryan 
2015; Bernauer and McGrath 2016; Walker et al. 2018). Presenting 
general information on co-impacts as a component of a mitigation 
analysis does not always lead to increased support for climate 
mitigation action. Rather, the most effective framing is determined 
by factors relating to local context, type of mitigation action under 
consideration and target stakeholder group. More work has been 
identified to be required to bring context into planning co-impacts 
assessments and communication thereof (Ryan 2015; Bernauer and 
McGrath 2016; Walker et al. 2018) (low evidence, low agreement).
An area where the strong link between the cross-sectoral co-impacts

of mitigation action and global government policies is being clearly 
considered is in the achievement of the SDGs (Obergassel et al. 2017; 
Doukas et al. 2018; Markkanen and Anger-Kraavi 2019; Smith et al. 
2019; van Soest et al. 2019) (Chapters 1 and 17, individual sectoral 
chapters). Figure 12.9 demonstrates these relationships from a cross-
sectoral perspective. It shows the links between sectors which give 
rise to emissions, the mitigation measures that can find application 
A, B, C, etc. – Letters in white:
Positive ChangeA, B, C, etc. – Letters in red:
Negative ChangeI
I, III, VIII
I, III, VIII, IX
 I, III, VIII, IXIIIIII II, VII V, VI, X I, V, VI 
VI XI  XI  I, IX, X  I, IV, IX XIII
XIIIEconomic Co-impacts
Climate ResiliencePolitical and Institutional
Social Co-impactsEnvironmental Co-impactsI.
Climate
A, C, D, EII.
Energy Security
A, EIII.
Investment, 
Growth
A, B, B,  E, F, GIV.
Employment
A, A, B, D, 
D, E, E, G
V.
Land and 
Marine 
Resources
B, D, D,  E
VI.
Water

Quality and 
Volume
B, D, E, F, 
F, G, G
VII.
Air 
Quality
B, E, F, F, 
G, G
VIII.
Energy 
Access
A, B, D, 
D, EIX.
Poverty
A, D, D,  EX.
Food and 
D, EXI.
Health
A, B, C, EXII.
Noise, 
Congestion, 
etc.
B, CXIII.
Political 
Stability, 
Democracy
CG. Process Change C. Planning B. Fuel Changes
A. Energy Efﬁciency  
F. Feedstock Change
E. Low Carbon Energy    D. A/R, SLM, SFMUrban Systems
 Buildings IndustryTransportEnergy
AFOLU
Figure 12.9 | Co-benefits and adverse side effects of mitigation actions with links to the SDGs.  The inner circle represents the sectors in which mitigation occurs. 
The second circle shows different generic types of mitigation actions (A to G), with the symbols showing which sectors they are applicable to. The third circle indicates different 
types of climate related co-benefits (green letters) and adverse side effects (red letters) that may be observed as a result of implementing each of the mitigation actions. Here

I relates to climate resilience, II-IV economic co-impacts, V-VII environmental, VIII-XII social, and XIII political and institutional. The final circle maps co-benefits and adverse side 
effects relevant to the SDGs. Source: re-used with permission from Cohen et al. (2021).

1313
1313
Cross-sectoral Perspectives Chapter 1212in the sector, and co-benefits and adverse side effects of mitigation 
measures and the SDGs (noting that the figure is not intended to be 
comprehensive). Such a framing of co-impacts from a cross-sectoral 
perspective in the context of the SDGs could help to further support 
climate mitigation action, particularly within the context of the Paris 
Agreement (Gomez-Echeverri 2018) (medium evidence, medium 
agreement). Literature sources utilised in the compilation of this 
diagram are presented in Supplementary Material 12.SM.3.
12.6.1.2 Mitigation Measures from a Cross-sectoral Perspective
Three aspects of mitigation from a  cross-sectoral perspective are 
considered, following Barker et al. (2007):
• mitigation measures used in more than one sector;
• implications of mitigation measures for interaction and 
integration between sectors; and
• competition among sectors for scarce resources.

A number of mitigation measures find application in more than 
one sector. Renewable energy technologies such as solar and wind 
may be used for grid electricity supply, as embedded generation in 
the buildings sector and for energy supply in the agriculture sector 
(Shahsavari and Akbari 2018) (Chapters 6, 7 and 8). Hydrogen and 
fuel cells, coupled with low-carbon energy technologies for producing 
the hydrogen, are being explored in transport, urban heat, industry 
and for balancing electricity supply (Dodds et al. 2015; Staffell et al. 
2019) (Chapters  6, 8  and 11). Electric vehicles are considered an 
option for balancing variable power (Kempton and Tomić 2005; Liu 
and Zhong 2019). Carbon capture and storage (CCS) and carbon 
capture and utilisation (CCU) have potential application in a number 
of industrial processes (cement, iron and steel, petroleum refining 
and pulp and paper) (Leeson et al. 2017; Garcia and Berghout 2019)

(Chapters 6 and 11) and the fossil fuel electricity sector (Chapter 6). 
When coupled with energy recovery from biomass, CCS can provide 
a carbon sink (BECCS) (Section 12.5). On the demand side, energy 
efficiency options find application across the sectors (Chapters  6, 
8, 9, 10, and 11), as do reducing demand for goods and services 
(Chapter 5) and improving material efficiency (Section 11.3.2).
A range of examples where mitigation measures result in cross-
potential of electric vehicles, including plug-in hybrids, is linked to 
liquid fuel supply emissions profile (Lutsey 2015). Making buildings 
energy positive, where excess energy is used to charge vehicles, can 
increase the potential of electric and hybrid vehicles (Zhou et al. 
2019). Advanced process control and process optimisation in industry

can reduce energy demand and material inputs (Section 11.3), which 
in turn can reduce emissions linked to resource extraction and 
manufacturing. Reductions in coal-fired power generation through 
replacement with renewables or nuclear power result in a reduction 
in coal mining and its associated emissions. Increased recycling 
results in a reduction in emissions from primary resource extraction. 
CCU can contribute to the transition to more renewable energy 
systems via power-to-X technologies, which enables the production 
of CO 2-based fuels/e-fuels and chemicals using carbon dioxide and hydrogen (Breyer et al. 2015; Anwar et al. 2020). Certain emissions 
reductions in the AFOLU sector are contingent on energy sector 
decarbonisation. Trees and green roofs planted to counter urban 
heat islands reduce the demand for energy for air conditioning and 
simultaneously sequester carbon (Kim and Coseo 2018; Kuronuma 
et al. 2018). Recycling of organic waste avoids methane generation if

the waste would have been disposed of in landfill sites, can generate 
renewable energy if treated through anaerobic digestion, and can 
reduce requirements for synthetic fertiliser production if the nutrient 
value is recovered (Creutzig et al. 2015). Liquid transport biofuels link 
to the land, energy and transport sectors (Section 12.5.2.2).
Demand-side mitigation measures, discussed in Chapter 5, also have 
cross-sectoral implications which need to be taken into account when 
calculating mitigation potentials. Residential electrification has the 
potential to reduce emissions associated with lighting and heating, 
particularly in developing countries where these are currently met 
by fossil fuels and using inefficient technologies, but will increase 
demand for electricity (Chapters 5 and 8 and Sections 6.6.2.3 and 
8.4.3.1). Many industrial processes can also be electrified in the move 
away from fossil reductants and direct energy carriers (Chapter 11).

The impact of electrification on electricity sector emissions will 
depend on whether electricity generation is based on fossil fuels in 
the absence of CCS or low-carbon energy sources (Chapter 5).
At the same time, saving electricity in all sectors reduces the demand 
for electricity, thereby reducing mitigation potential of renewables and 
CCS.  Demand-side flexibility measures and electrification of vehicle 
fleets are supportive of more intermittent renewable energy supply 
options (Sections 6.3.7, 6.4.3.1 and 10.3.4). Production of maize, wheat, 
rice and fresh produce requires lower energy inputs on a lifecycle basis 
than poultry, pork and ruminant-based meats (Clark and Tilman 2017) 
(Section 12.4). It also requires less land area per kilocalorie or protein 
output (Clark and Tilman 2017; Poore and Nemecek 2018), so replacing 
meat with these products makes land available for sequestration, 
biodiversity or other societal needs. However, production of co-products

of the meat industry, such as leather and wool, is reduced, resulting 
in a  need for substitutes. Further discussion and examples of cross-
sectoral implications of mitigation, with respect to cost and potentials, 
are presented in Section 12.2. One final example on this topic included 
here is that of circular economy (Box 12.4).
Finally, in terms of competition among sectors for scarce resources, 
this issue is often considered in the assessments of mitigation 
potentials linked to bioenergy and diets (vegetable vs animal food 
products), land use and water (robust evidence, high agreement) 
(Section 12.5 and Cross-Working Group Box 3 in this Chapter). It is, 
however, also relevant elsewhere. Constraints have been identified 
in the supply of indium, tellurium, silver, lithium, nickel and platinum 
that are required for implementation of some specific renewable 
energy technologies (Watari et al. 2018; Moreau et al. 2019). Other

studies have shown constraints in supply of cobalt, one of the key 
elements used in production of lithium-ion batteries, which has been 
assessed for mitigation potential in energy, transport and buildings 
sectors (medium evidence, high agreement) (Jaffe 2017; Olivetti et al. 
2017), although alternatives to cobalt are being developed (Olivetti 
et al. 2017; Watari et al. 2018).

1314
1314
Chapter 12 Cross-sectoral Perspectives1212.6.1.3 Cross-sectoral Considerations Relating to Emerging 
General-purpose Technologies
General-purpose technologies (GPTs) include, but are not limited 
to, additive manufacturing, artificial intelligence, biotechnology, 
hydrogen, digitalisation, electrification, nanotechnology and robots 
(de Coninck et al. 2018). Many of the individual sectoral chapters have 
identified the roles that such technologies can have in supporting 
mitigation of GHG emissions. Section 16.2.2.3 presents an overview 
of the individual technologies and specific applications thereof.
In this chapter, which focuses on cross-sectoral implications of 
mitigation, it is highlighted that certain of these GPTs will find 
application across the sectors, and there will be synergies and trade-
offs when utilising these technologies in more than sector. One 
example here is the use of hydrogen as an energy carrier, which,

when coupled with low-carbon energy, has potential for driving 
mitigation in energy, industry, transport, and buildings. The increased 
uptake of hydrogen across the economy requires establishment of 
hydrogen production, transport and storage infrastructure which 
could simultaneously support multiple sectors, although there is the 
potential to utilise existing infrastructure in some parts of the world 
(Alanne and Cao 2017).Box 12.5 provides further details on hydrogen in the context of cross-
sectoral mitigation specifically, while further details on the role of 
hydrogen in individual sectors are provided in Chapters 6, 8. 9, 10 and 
11. In contrast, the benefits of digitalisation, which could potentially 
give rise to substantial energy savings across multiple sectors, need 
to be traded off against demand for electricity to operate consumer 
devices, data centres, and data networks. Measures are required 
to increase energy efficiency of these technologies (IEA 2017).

Section 5.3.4.1 of this report provides further information on energy 
and emissions benefits and costs of digitalisation.
With respect to co-impacts of GPTs, the other focus of this chapter, 
it is highlighted that assessment of the environmental, social 
and economic implications of such technologies is challenging and 
context specific, with multiple potential cross-sectoral linkages 
(de  Coninck et al. 2018). Each GPT would need to be explored in 
context of what it is being used for, and potentially in the geographical 
context, in order to understand the co-impacts of its use.Box 12.4 | Circular Economy from a Cross-Sectoral Perspective
Circular economy approaches consider the entire lifecycle of goods and services, and seek to design out waste and pollution, keep 
products and materials in use, and regenerate natural systems (The Ellen MacArthur Foundation 2013; CIRAIG 2015). The use of

circular economy for rethinking how society’s needs for goods and services is delivered in such a way as to minimise resource use 
and environmental impact and maximise societal benefit has been discussed elsewhere in this assessment report (Chapter 5 and 
Section 5.3.4). A wide range of potential application areas is identified, from food systems to bio-based products to plastics to metals 
and minerals to manufactured goods. Circular economy approaches are implicitly cross-sectoral, impacting the energy, industrial, 
AFOLU, waste and other sectors. They will have climate and non-climate co-benefits and trade-offs. The scientific literature mainly 
investigates incremental measures claiming but not demonstrating mitigation; highest mitigation potential is found in the industry, 
energy, and transport sectors; mid-range potential in the waste and building sectors; and lowest mitigation gains in agriculture

(Cantzler et al. 2020). Circular economy thinking has been identified to support increased resilience to the physical effects of climate 
change and contribute to meeting other SDGs, notably SDG  12 (responsible consumption and production) (The Ellen MacArthur 
Foundation 2019).
Circular economy approaches to deployment of low-carbon infrastructure have been suggested to be important to optimise resource 
use and mitigate environmental and societal impacts caused by extraction and manufacturing of composite and critical materials 
as well as infrastructure decommissioning (Jensen and Skelton 2018; Sica et al. 2018; Salim et al. 2019; Watari et al. 2019; Jensen 
et al. 2020; Mignacca et al. 2020). The circular carbon economy is an approach inspired by the circular economy principles that rely 
on a  combination of technologies, including CCU, CCS and CDR, to enable transition pathways especially relevant in economies

dependent on fossil fuel exports (Lee et al. 2017; Alshammari 2020; Morrow and Thompson 2020; Zakkour et al. 2020). The integration 
of circular economy and bioeconomy principles (Cross-Working Group Box 3 in this chapter) is conceptualised in relation to policy 
development (European Commission 2018) as well as COVID-19 recovery strategies (Palahí et al. 2020), emphasising the use of 
renewable energy sources and sustainable management of ecosystems with transformation of biological resources into food, feed, 
energy and biomaterials.
At this stage, however, there is no single global agreement on how circular economy principles are best implemented, and differential 
government support for circular economy interventions is observed in different jurisdictions.

1315
1315
Cross-sectoral Perspectives Chapter 1212Box 12.5 | Hydrogen in the Context of Cross-sectoral Mitigation Options
Interest in hydrogen as an intermediary energy carrier has grown rapidly in the years since the 5th Assessment Report of WGIII (AR5) 
was published. This is reflected in this WGIII assessment report, where the term ‘hydrogen’ is used more than five times more often than 
in AR5. In Chapter 6 of this report, it is shown that hydrogen can be produced with low carbon impact from fossil fuels (Section 6.4.2.6), 
renewable electricity and nuclear energy (Section 6.4.5.1), or biomass (Section 6.4.2.5). In the energy sector, hydrogen is one of the 
options for storage of energy in low-carbon electricity systems (Sections 6.4.4.1 and 6.6.2.2). But, also importantly, hydrogen can be 
produced to be used as a fuel for sectors that are hard to decarbonise; this is possible directly in the form of hydrogen, but also in

the form of ammonia or other energy carriers (Section 6.4.5.1). In the transport sector, fuel cell engines (Section 10.3.3) running on 
hydrogen can become important, especially for heavy duty vehicles (Section 10.4.3). In the industry sector hydrogen already plays an 
important role in the chemical sector (for ammonia and methanol production) (Box 11.1 in Chapter 11) and in the fuel sector (in oil 
refinery processes and for biofuel production) (IEA 2019b). Beyond the production of ammonia and methanol for both established 
and novel applications, the largest potential industrial application for low-carbon hydrogen is seen in steel-making (Section 11.4.1.1). 
Hydrogen and hydrogen derivatives can play a further role as substitute energy carriers (Section 11.3.5) and for the production of 
intermediate chemical products such as methanol, ethanol and ethylene when combined with CCU (Section 11.3.6). For the building

sector, the exploration of the usefulness of hydrogen is at an early stage (Box 9.4).
An overview report (IEA 2019b) already sees opportunities in 2030 for buildings, road freight and passenger vehicles. This report also 
suggests a high potential application in iron and steel production, aviation and maritime transport, and for electricity storage. Several 
industry roadmaps have been published that map out a possible role for hydrogen until 2050. The most well known and ambitious 
is the roadmap by the Hydrogen Council (2017), which sketches a global scenario leading to 78 EJ hydrogen use in 2050, mainly for 
transport, industrial feedstock, industrial energy and to a lesser extent for buildings and power generation. Hydrogen makes up 18% of 
total final energy use in this vision. An analysis by IRENA on hydrogen from renewable sources comes to a substantially lower number:

8 EJ (excluding hydrogen use in power production and feedstock uses). On a regional level, most roadmaps and scenarios have been 
published for the European Union, for example by the Fuel Cell and Hydrogen Joint Undertaking (Blanco et al. 2018; EC 2018; FCH 
2019; Navigant 2019). All these reports have scenario variants with hydrogen share in final energy use of 10% to over 20% by 2050.
When it comes to the production of low-carbon hydrogen, the focus of the attention is on production using electricity from renewable 
sources via electrolysis, so-called ‘green hydrogen’. However, ‘blue hydrogen’, produced out of natural gas with CCS, is also often 
considered. Since a significantly increasing role for hydrogen would require considerable infrastructure investments and would affect 
existing trade flows in raw materials, governments have started to set up national hydrogen strategies, both potential exporting

(e.g., Australia) and importing (e.g., Japan) countries (METI 2017; COAG Energy Council 2019).
As already reported in Chapter 6 (Section 6.2.4.1), production costs of green hydrogen are expected to come down from the current 
levels of above USD100 MWh–1. Price expectations are: EUR40–60 MWh–1 for both green and blue hydrogen production in the EU 
by 2050 (Navigant 2019) with production costs already being lower in North Africa; 42–87 USD MWh–1 for green hydrogen in 2030 
and 20–41 USD MWh–1 in 2050 (BNEF 2020); EUR75 MWh–1 in 2030 (Glenk and Reichelstein 2019). For fossil-based technologies 
combined with CCS, prices may range from USD33–80 MWh–1 (Table 6.8). Such prices can make hydrogen competitive for industrial 
feedstock applications, and probably for several transportation modes in combination with fuel cells, but without further incentives, not 
necessarily for stationary applications in the coming decades: wholesale natural gas prices are expected to range from USD7–31 MWh–1

across regions and scenarios, according to the World Energy Outlook (IEA 2020a); coal prices mostly are even lower than natural gas 
prices (all fossil fuel prices refer to unabated technology and untaxed fuels). The evaluation of macro-economic impacts is relatively 
rare. A study by Mayer et al. (2019b) indicated that a shift to hydrogen in iron and steel production would lead to regional GDP losses 
in the range of 0.4–2.7% in 2050 across EU+3, with some regions making gains under a low-cost electricity scenario.
The IAM scenarios imply a modest role played by hydrogen, with some scenarios featuring higher levels of penetration. The consumption 
of hydrogen is projected to increase by 2050 and onwards in scenarios likely limiting global warming to 2°C or below, and the median 
share of hydrogen in total final energy consumption is 2.1% in 2050 and 5.1% in 2100 (Box 12.4, Figure 1) (Numbers are based on

the AR6 scenarios database). There is large variety in hydrogen shares, but the values of 10% and more of final energy use that occur 
in many roadmaps are only rarely reached in the scenarios. Hydrogen is predominantly used in the industry and transportation sectors. 
In the scenarios, hydrogen is produced mostly by electrolysis and by biomass energy conversion with CCS (Box 12.5, Figure 1). Natural 
gas with CCS is expected to play only a modest role; here a distinct difference between the roadmaps quoted before and the IAM 
results is observed.

1316
1316
Chapter 12 Cross-sectoral Perspectives1212.6.2 Sectoral Policy Interactions (Synergies 
and Trade-offs)
A taxonomy of policy types and attributes is provided by Section 13.6. 
In addition, the sectoral chapters provide an in-depth discussion of 
important mitigation policy issues such as policy overlaps, policy 
mixes, and policy interaction as well as policy design considerations 
and governance. The point of departure for the assessment in this 
chapter is a focus on cross-sectoral perspectives aiming at maximising 
policy synergies and minimising policy trade-offs.
Synergies and trade-offs resulting from mitigation policies are 
not clearly discernible from either sector-level studies or global 
and regional top-down studies. Rather, they would require 
a cross-sectoral integrated policy framework  (von Stechow et al. 
2015; Monier et al. 2018; Pardoe et al. 2018; Singh et al. 2019) or 
multiple-objective-multiple-impact policy assessment framework

identifying key co-impacts and avoiding trade-offs (robust evidence, 
high agreement) (Ürge-Vorsatz et al. 2014).
Sectoral studies typically cover differentiated response measures 
while the IAM literature mostly uses uniform efficient market-based measures. This has important implications for understanding the 
differences in magnitude and distribution of mitigation costs and 
potentials of Section 12.2 (Karplus et al. 2013; Rausch and Karplus 
2014). There is a comprehensive literature on the efficiency of uniform 
carbon pricing compared to sector-specific mitigation approaches, 
but relatively less literature on the distributional impacts of carbon 
taxes and measures to mitigate potential adverse distributional 
impacts (Rausch and Karplus 2014; Rausch and Reilly 2015; Wang 
et al. 2016b; Åhman et al. 2017; Mu et al. 2018). For example, in terms 
of cross-sectoral distributional implications, studies  find negative

competitiveness impacts for the energy-intensive industries (robust 
evidence, medium agreement) (Rausch and Karplus 2014; Wang et al. 
2016b; Åhman et al. 2017). 
Strong interdependencies and cross-sectoral linkages create both 
opportunities for synergies and the need to address trade-offs. This 
calls for coordinated sectoral approaches to climate change mitigation 
policies that mainstream these interactions  (Pardoe et al. 2018). 
Such an approach is also called for in the context of cross-sectoral 
interactions of adaptation and mitigation measures, examples are in 
the agriculture, biodiversity, forests, urban, and water sectors (Arent 
et al. 2014; Berry et al. 2015; Di Gregorio et al. 2017).  Integrated Box 12.5 (continued)
010203040
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Year[%]Share of hydrogen in ﬁnal energy consumption
(scenarios that limit warming to 2°C (>67%) or lower)
sector
Total ﬁnal energy
Industry
Transport
Buildings

Box 12.5, Figure 1 | Fraction of hydrogen (light blue) in total final energy consumption, and for each sector.  Hinges represent the interquartile ranges 
and whiskers extend to 5th and 95th percentiles. Source: AR6 scenarios database.
It is concluded that there is increasing confidence that hydrogen can play a significant role, especially in the transport sector and the 
industrial sector. However, there is much less agreement on timing and volumes, and there is also a range of perspectives on the role 
of the various production methods of hydrogen.

1317
1317
Cross-sectoral Perspectives Chapter 1212planning and cross-sectoral alignment of climate change policies are 
particularly evident in developing countries’ NDCs pledged under 
the Paris Agreement, where key priority sectors such as agriculture 
and energy are closely aligned between the proposed mitigation 
and adaptation actions in the context of sustainable development 
and the SDGs. An example is the integration between climate-smart 
agriculture and low-carbon energy (robust evidence, high agreement) 
(Antwi-Agyei et al. 2018; England et al. 2018). Yet, there appear to be 
significant challenges relating to institutional capacity and resources 
to coordinate and implement such cross-sectoral policy alignment, 
particularly in developing country contexts (Antwi-Agyei et al. 2018).
Another dimension of climate change policy  interactions  in the 
literature is related to trade-offs and synergies between climate

change mitigation and other societal objectives. For example, in 
mitigation policies related to energy, trade-offs and synergies 
between universal electricity access and climate change mitigation 
would call for complementary policies such as pro-poor tariffs, fuel 
subsidies, and broadly integrated policy packages  (Dagnachew 
et al. 2018). In agriculture and forestry, research suggests that 
integrated policy programmes enhance mitigation potentials across 
the land-use-agriculture-forestry nexus and lead to synergies and 
positive  spillovers  (Galik et al. 2019). To maximise synergies and 
deal with trade-offs in such a  cross-sectoral context, evidence-
based/informed and holistic policy analysis approaches like nexus approaches and multi-target back-casting approaches that take into 
account unanticipated outcomes and indirect consequences would 
be needed (robust evidence, high agreement) (Klausbruckner et al. 
2016; Hoff et al. 2019; van der Voorn et al. 2020) (Box 12.6).

The consequences  of large-scale land-based mitigation  for food 
security, biodiversity (Dasgupta 2021), the state of soil, water 
resources, and so on can be significant, depending on many factors, 
such as economic development (including distributional aspects), 
international trade patterns, agronomic development, diets, land-
use governance and policy design, and not least climate change 
itself  (Winchester and Reilly 2015; Fujimori et al. 2018; Hasegawa 
et al. 2018; Van Meijl et al. 2018). Policies and regulations that address 
other aspects apart from climate change can indirectly influence the 
attractiveness of land-based mitigation options. For example, farmers 
may find it attractive to shift from annual food/feed crops to perennial 
grasses and short rotation woody crops (suitable for bioenergy) if 
the previous land uses become increasingly restricted due to impacts 
on groundwater quality and eutrophication of water bodies (robust

evidence, medium agreement) (Sections 12.4 and 12.5). 
Finally, there are knowledge gaps in the literature particularly in 
relation to policy scalability and the extent and magnitude of policy 
interactions when scaling the policy to a  level consistent with 
low GHG emissions pathways such as 2°C and 1.5°C. 
Box 12.6 | Case Study: Sahara Forest Project in Aqaba, Jordan
Nexus framing
Shifting to renewable (in particular solar) energy reduces dependency on fossil fuel imports and greenhouse gas emissions, which is 
crucial for mitigating climate change. Employing renewable energy for desalination of seawater and for cooling of greenhouses in 
integrated production systems can enhance water availability, increase crop productivity and generate co-products and co-benefits 
(e.g., algae, fish, dryland restoration, greening of the desert).
Nexus opportunities
The Sahara Forest project integrated production system uses amply available natural resources, namely solar energy and seawater,

for improving water availability and agricultural/biomass production, while simultaneously providing new employment opportunities. 
Using hydroponic systems and humidity in the air, water needs for food production are 50% lower compared to other greenhouses.
Technical and economic nexus solutions
Several major technologies are combined in the Sahara Forest Project, namely electricity production through the use of solar power 
(PV or CSP), freshwater production through seawater desalination using renewable energy, seawater-cooled greenhouses for food 
production, and outdoor revegetation using run-off from the greenhouses.
Stakeholders involved
The key stakeholders which benefit from such an integrated production system are from the water sector, which urgently requires an 
augmentation of irrigation (and other) water, and the agricultural sector, which relies on the additional desalinated water to maintain

and increase agricultural production. The project also involves public and private sector partners from Jordan and abroad, with little 
engagement of civil society so far.

1318
1318
Chapter 12 Cross-sectoral Perspectives1212.6.3 International Trade Spillover Effects 
and Competitiveness
International spillovers of mitigation policies are effects that 
carbon-abatement measures implemented in one country have on 
sectors in other countries. These effects include (i) carbon leakage 
in manufacture; (ii) the effects on energy trade flows and incomes 
related to fossil fuel exports from major exporters; (iii) technology 
and knowledge spillovers; and (iv) transfer of norms and preferences 
via various approaches to establish sustainability requirements on 
traded goods, such as EU-RED and environmental labelling systems 
to guide consumer choices (robust evidence, medium agreement). 
This section focuses on cross-sectoral aspects of international 
spillovers related to the first two effects.
12.6.3.1 Cross-sectoral Aspects of Carbon Leakage
Carbon leakage occurs when mitigation measures implemented in

one country or sector lead to a  rise in emissions in other countries 
or sectors. Three types of spillovers are possible: (i) domestic cross-
sectoral spillovers when mitigation policy in one sector leads to the 
re-allocation of labour and capital towards the other sectors of the 
same country; (ii) international spillovers within a single sector when 
mitigation policy leads to substitution of domestic production of carbon-
intensive goods with their imports from abroad; and (iii) international 
cross-sectoral spillovers when mitigation policy in one sector in one 
country leads to the rise in emissions in other sectors in other countries. 
While the first two are described in Section 13.6, this section focuses 
on the third. Though some papers address this type of leakage, there is 
still a significant lack of knowledge on this topic.
One possible channel of cross-sectoral international carbon leakage 
is through global value chains. Mitigation policy in one country not

only leads to shifts in competitiveness across industries producing final goods but also across those producing raw materials and 
intermediary goods all over the world.
This type of leakage is especially important because the countries 
that provide basic materials are usually emerging or developing 
economies, many of which have no or limited regulation of GHG 
emissions. For this reason, foreign direct investment in developing 
economies usually leads to an increase in emissions (Kivyiro and 
Arminen 2014; Shahbaz et al. 2015; Bakhsh et al. 2017): in the case 
of basic materials the effect of expansion of economic activity on 
emissions exceeds the effect of technological spillovers, while for 
developed countries the effect is opposite (Shahbaz et al. 2015; 
Pazienza 2019). Meng et al. (2018) calculated that environmental 
cost for generating one unit of GDP through international trade was 
1.4 times higher than that through domestic production in 1995. By

2009, this difference increased to 1.8 times. Carbon leakage due 
to the differences in environmental regulation was the main driver 
of this increase.
In order to address emissions leakage through global value chains, 
Liu and Fan (2017) propose the value-added-based emissions 
accounting principle, which makes it possible to account for GHG 
emissions within the context of the economic benefit principle. 
Davis et al. (2011) notice that the analysis of value chains gives 
an opportunity to find the point where regulation would be the 
most efficient and the least vulnerable to leakage. For instance, 
transaction costs of global climate policy and the risks of leakage 
may be reduced if emissions are regulated at the extraction stage as 
there are far fewer agents involved in this process than in burning of 
fossil fuels or consumption of energy-intensive goods. Li et al. (2020) 
calls for coordinated efforts to reduce emissions embodied in trade

flows in pairs of the economies with the highest leakage, such as 
China and the United States, China and Germany, China and Japan, 
Russia and Germany.Box 12.6 (continued)
Framework conditions
The Sahara Forest Project has been implemented at pilot scale so far, including the first pilot with one hectare and one greenhouse 
pilot in Qatar and a larger ‘launch station’ with three hectares and two greenhouses in Jordan. These pilots have been funded by 
international organisations such as the Norwegian Ministry of Climate and Environment, Norwegian Ministry of Foreign Affairs and the 
European Union. Alignment with national policies, institutions and funding, as well as upscaling of the project, is underway or planned.
Monitoring and evaluation and next steps
The multi-sectoral planning and investments that are needed to upscale the project require cooperation among the water, agriculture,

and energy sectors and an active involvement of local actors, private companies, and investors. These cooperation and involvement 
mechanisms are currently being established in Jordan. Given the emphasis on the economic value of the project, public-private 
partnerships are considered as the appropriate business and governance model, when the project is upscaled. Scenarios for upscaling 
(seawater use primarily in low-lying areas close to the sea, to avoid energy-intensive pumping) include 50 MW of CSP , 50 hectares of 
greenhouses, which would produce 34,000 tonnes of vegetables annually, provide employment for over 800 people, and sequester 
more than 8000 tonnes of CO 2-eq annually.
Source: SFP Foundation; Hoff et al. (2019).

1319
1319
Cross-sectoral Perspectives Chapter 1212Unfortunately, these proposals either face difficulties in collection 
and verification of data on emissions along value chains or require 
a high level of international cooperation, which is hardly achievable 
at the moment. Neuhoff et al. (2016) and Pollitt et al. (2020) focus 
on the regulation of emissions embodied in global value chains 
through national policy instruments. They propose implementation 
of a  charge on consumption of imported basic materials into the 
European emissions trading system. Such a  charge, equivalent to 
around EUR80 tCO 2–1, could reduce the EU’s total CO 2 emissions by 
up to 10% by 2050 (Pollitt et al. 2020) without significant effects 
on competitiveness. This proposal is very close to the carbon border 
adjustment introduced in the EU and described in more detail in 
Sections 13.2 and 13.6.
Cross-sectoral effects of carbon leakage also occur through the

multiplier effect, when the mitigation policy in any sector in country 
A leads to the increase of relative competitiveness and therefore 
production of the same sector in country B, which automatically 
leads to the expansion of economic activity in other sectors of 
country B. This expansion may in turn lead to the rise of production 
and emissions in country A as a result of feedback effects. These 
spillovers should be taken into consideration while designing 
climate policy, along with potential synergies that may appear due 
to joint efforts. However, the scale of these effects with regards 
to leakage should not be overestimated. Even for intrasectoral 
leakage, many ex ante modelling studies generally suggest limited 
carbon leakage rates (Chapter 13). Intersectoral leakage should be 
even less significant. Interregional spillover and feedback effects 
are well studied in China (Zhang 2017; Ning et al. 2019). Even

within a  single country, interregional spillover effects are much 
lower than intraregional effects, and feedback effects are even less 
intense. Cross-sectoral spillovers across national borders as a result 
of mitigation policy should be even smaller, although these are less 
well studied. In future, if the differences in carbon price between 
regions increase, leakage through cross-sectoral multipliers may 
play a more important role.
Another important cross-sectoral aspect of carbon leakage concerns 
the transport sector. If mitigation policy leads to the substitution of 
domestic carbon-intensive production with imports, one of the side 
effects of this substitution is the rise of emissions from transportation 
of imported goods. International transport is responsible for about 
a  third of worldwide trade-related emissions, and over 75%  of 
emissions for major manufacturing categories (Cristea et al. 
2013). Carbon leakage would potentially increase the emissions

from transportation significantly as the trade of major consuming 
economies of the EU and US would shift towards distant trading 
partners in East and South Asia. Meng et al. (2018) consider more 
distant transportation as one of the major contributors to the rise in 
emissions embodied in international trade from 1995 to 2009.
Emissions leakage due to international trade, investment and value 
chains is a  significant obstacle to more ambitious climate policies 
in many regions. However, it does not mean that disruption of 
trade would reduce global emissions. Zhang et al. (2020) show that 
deglobalisation and the drop in international trade may result in 
emissions reductions in the short term, but in the longer term it will make each country build more complete industrial systems to satisfy 
their final demand, although they have comparative disadvantages 
in some production stages. As a  result, emissions would increase.

According to Zhang et al. (2020), for China, the decrease of the 
degree of global value chain participation (which ranges from 0 to 1) 
by 0.1 would lead to an increase in gross carbon intensity of China’s 
exports of 11.7%. On distributional implications, Parrado and De Cian 
(2014) report that trade-driven spillover effects transmitted through 
imports of materials and equipment result in significant inter-sectoral 
distributional effects, with some sectors witnessing substantial 
expansion in activity and emissions and others witnessing a decline 
in activities and emissions.
It should also be mentioned that international trade leads to important 
knowledge and technology spillovers (Sections 16.3 and 16.5) and 
is critically important for achieving other Sustainable Development 
Goals (Section  12.6.1). Any policies imposing additional barriers 
to international trade should therefore be implemented with great 
caution and require comprehensive evaluation of various economic,

social and environmental effects.
12.6.3.2 The Spillover Effects on the Energy Sector
Cross-sectoral trade-related spillovers of mitigation policies include 
their effect on energy prices. Other things being equal, regulation 
of emissions of industrial producers decreases the demand for fossil 
fuels that would reduce prices and encourage the rise of fossil fuel 
consumption in regions with no or weaker climate policies (robust 
Arroyo-Currás et al. (2015) studied the energy channel of carbon 
leakage with the REMIND IAM of the global economy. They came to 
the conclusion that the leakage rate through the energy channel is 
less than 16% of the emissions reductions of regions who introduce 
climate policies first. This result did not differ much for different sizes 
and compositions of the early mover coalition.
Bauer et al. (2015) built a  multi-model scenario ensemble for the 
analysis of energy-related spillovers of mitigation policies and

revealed huge uncertainty: energy-related carbon leakage rates 
varied from negative values to 50%, primarily depending on the 
trends in inter-fuel substitution.
Another kind of spillover in the energy sector concerns the ‘green 
paradox’: announcement of future climate policies causes an 
increase in production and trade in fossil fuels in the short term 
(Jensen et al. 2015; Kotlikoff et al. 2016). The delayed carbon tax 
should therefore be higher than an immediately implemented carbon 
tax in order to achieve the same temperature target (van der Ploeg 
2016). Studies also make a distinction between a ‘weak’ and ‘strong’ 
green paradox (Gerlagh 2011). The former refers to a short-term rise 
in emissions in response to climate policy, while the latter refers to 
rising cumulative damage.
The green paradox may work in different ways for different kinds 
of fossil fuels. For instance, Coulomb and Henriet (2018) show that

1320
1320
Chapter 12 Cross-sectoral Perspectives12increase the discounted profits of the owners of conventional oil and 
gas, compared to the no-regulation baseline, but will decrease these 
profits for coal and unconventional oil and gas producers.
Many studies also distinguish different policy measures by the scale 
of green paradox they provide. The immediate carbon tax is the first-
best instrument from the perspective of global welfare. Delayed 
carbon tax leads to some green paradox but less than in the case of 
support for renewables (Michielsen 2014; van der Ploeg and Rezai 
2019). With respect to the latter, support for renewable electricity 
has a  lower green paradox than support for biofuels (Michielsen 
2014; Gronwald et al. 2017). The existence of the green paradox is 
an additional argument in favour of more decisive climate policy 
now: any postponements will lead to additional consumption of 
fossil fuels and consequently the need for more ambitious and costly

efforts in future.
The effect of fossil fuel production expansion as a result of anticipated 
climate policy may be compensated by the effect of  divestment. 
Delayed climate policy creates incentives for investors to divest 
from fossil fuels. Bauer et al. (2018) show that this divestment effect 
is stronger and thus announcing of climate policies leads to the 
reduction of energy-related emissions.
The implication of the effects of mitigation policies through the 
energy-related spillovers channel is of particular significance to 
oil-exporting countries (medium evidence, medium agreement). 
Emissions-reduction measures lead to decreasing demand for fossil 
fuels and consequently to the decrease in exports from major oil- 
and gas-exporting countries. The case of Russia is one of the most 
illustrative. Makarov et al. (2020) show that the fulfilment by Paris 
Agreement Parties of their NDCs would lead to 25% reduction of

Russia’s energy exports by 2030 with significant reduction of its 
economic growth rates. At the same time, the domestic consumption 
of fossil fuels is anticipated to increase in response to the drop in 
external demand that would provoke carbon leakage (Orlov and 
Aaheim 2017). Such spillovers demonstrate the need for dialogue 
between exporters and importers of fossil fuels while implementing 
the mitigation policies.
12.6.4 Implications of Finance for Cross-sectoral 
Mitigation Synergies and Trade-offs
Finance is a  principal enabler of GHG mitigation and an essential 
component of countries’ NDC packages submitted under the 
Paris Agreement (UNFCCC 2016). The assessment of investment 
requirements for mitigation along with their financing at sectoral 
levels are addressed in detail by sectoral chapters while the 
assessment of financial sources, instruments, and the overall 
mitigation financing gap is addressed by Chapter 15 (Sections 15.3,

15.4, and 15.5). The focus in this chapter with respect to finance is on 
the scope and potential for financing integrated solutions that create 
synergies between and among sectors. 
Cross-sectoral considerations in mitigation finance are critical for the 
effectiveness of mitigation action as well as for balancing the often conflicting social, developmental and environmental policy goals at 
the sectoral level. True measures of mitigation policy impacts  and 
hence plans for resource mobilisation that properly address costs 
and benefits  cannot be developed  in isolation from their  cross-
sectoral  implications.  Unaddressed cross-sectoral coordination and 
interdependency issues are identified as major constraints in raising 
the necessary financial resources for mitigation in a  number of 
countries (Bazilian et al. 2011; Welsch et al. 2014; Hoff et al. 2019a).
Integrated financial solutions to leverage synergies between sectors,

as opposed to purely sector-based financing, at international, 
national,  and local levels are needed to scale  up  GHG mitigation 
potentials.  At the international level,  finance from multilateral 
development banks (MDBs) is a  major source of  GHG  mitigation 
finance in developing countries (medium evidence, medium 
agreement) (World Bank Group 2015; Ha et al. 2016; Bhattacharya 
et al. 2016; Bhattacharya et al. 2018).  In 2018, MDBs reported a total 
of USD30.165 billion in financial commitments to climate change 
mitigation, with 71%  of total mitigation finance being committed 
through investment loans and the rest in the form of equity, guarantees, 
and other instruments.  GHG  reduction activities eligible for MDB 
finance are limited to those compatible with low-emission pathways 
recognising the importance of long-term structural changes, such as 
the shift in energy production to low-carbon energy technologies

and the modal shift to low-carbon modes of transport leveraging 
both greenfield and energy efficiency projects. Sector-wise, the 
MDBs’ mitigation finance for 2018 is allocated to renewable energy 
(29%), transport (18%), energy efficiency (18%), lower-carbon and 
efficient energy generation (7%), agriculture, forestry and land use 
(8%), waste and wastewater (8%), and other sectors (12%) (MDB 
2019). Unfortunately, due to institutional and incentives issues, MDB 
finance has mostly focused on sectoral solutions and has not been able 
to properly leverage cross-sectoral synergies. At the national level, 
applied research has shown that integrated modelling of land, energy 
and water resources not only has the potential to identify superior 
solutions, but also reveals important differences in terms of investment 
requirements and  required financing arrangements  compared 
to  the  traditional sectoral  financing  toolkits  (Welsch et al.

2014). Agriculture, forestry, nature-based solutions and other forms 
of land use are promising sectors for leveraging financing solutions 
to scale up  GHG  mitigation efforts (Section  15.4).  Moving to 
more productive and resilient forms of land use is a  complex 
task, given the cross-cutting nature of land use, which necessarily 
results in apparent trade-offs between mitigation, adaptation, and 
development objectives. Finance is one area to manage these trade-
offs where there may be opportunities to redirect the hundreds 
of billions spent annually on land use around the world towards 
green activities, without sacrificing either productivity or economic 
development  (Falconer et al. 2015).  Nonetheless,  that  would 
require active public support in  design of land-use mitigation and 
adaptation strategies, coordination between public and private 
instruments across land use sectors, and leveraging of  policy  and

financial instruments to redirect finance toward greener land-use 
practices  (limited evidence, medium agreement).  For example,  the 
Welsch et al. (2014) study on Mauritius shows that the promotion 
of a  local biofuel industry from sugar cane could be economically 
favourable in the absence of water constraints, leading to a reduction

1321
1321
Cross-sectoral Perspectives Chapter 1212in petroleum  imports  and GHG emissions while enhancing energy 
security. Yet,  under a  water-constrained scenario as a  result of 
climate change, the need for additional energy to expand irrigation 
to previously rain-fed sugar plantations and to power desalination 
plants yields the opposite result in terms of GHG emissions and 
energy costs, making biofuels a sub-optimal option, and negatively 
affects their economics and the prospects for financing. 
At the local level, integrated planning and financing are needed to 
achieve more sustainable outcomes.  For example, at a  city level, 
integration is needed across sectors such as transport, energy systems, 
buildings, sewage and solid waste to optimise emissions footprints. 
How a  city is designed will  affect transportation demands, which 
makes it either more or less difficult to implement efficient public 
transportation, leading in turn to more or fewer emissions.  Under

such cases, solutions in terms of public and private investment paths 
and financing policies based on purely internal sector considerations 
are bound to cause adverse impacts on other sectors and poor overall 
outcomes (Gouldson et al. 2016).
Availability and access to finance are among the major barriers to 
GHG emissions mitigation across various sectors and technology 
options  (robust evidence, high agreement).  Resource maturity 
mismatches and risk exposure are two main factors limiting ability 
of commercial banks and other private lenders to contribute to 
green finance  (Mazzucato and Semieniuk 2018).  At all levels, 
mobilising the necessary resources  to leverage cross-sectoral 
mitigation synergies  would  require the combination of public and 
private financial sources (Jensen and Dowlatabadi 2018). Traditional 
public financing would be  required to synergise mitigation across 
sectors  where the risk-return and time profiles of investment are

not sufficiently attractive for the business sector.  Over the years, 
private development financing through public-private partnerships 
and other related variants has been  a growing source of finance 
to leverage cross-sectoral  synergies and manage trade-offs 
(Anbumozhi and Timilsina 2018; Attridge and Engen 2019; Ishiwatari 
et al. 2019). Promoting such blended approaches to finance along 
with result-based financing architectures to strengthen delivery 
institutions are advocated as effective means to mainstream cross-
sectoral mitigation finance (limited evidence, high agreement) 
(Attridge and Engen 2019; Ishiwatari et al. 2019). The World Bank 
group and the International Financial Corporation have  used 
the blended  finance results-based approach  to climate financing 
that addresses institutional, infrastructure, and service needs 
across sectors targeting  developing  countries and marginalised 
communities (GPRBA 2019; IDA 2019).12.7 Knowledge Gaps

Finally, the literature review and analysis in Chapter 12 has taken 
account of the post-AR5 literature available and accessible to the 
chapter authors. Nonetheless, the assessment of the chapter is 
incomplete without mentioning knowledge gaps encountered during 
the assessment. These knowledge gaps include:
1. Interactions (synergies and trade-offs) between different CDR 
methods when deployed together are under-researched:
 –co-benefits and trade-offs with biodiversity and ecosystem 
services associated with the implementation of CDR methods.
 –constraining technical costs and potentials for CDR methods 
to define realistically achievable costs and potentials. 
Such research is useful for improving the representation 
of CDR methods in IAMs and country-level mitigation 
pathway modelling.
2. More work is required on how framing and communication 
of mitigation actions in terms of mitigation versus co-benefits 
potential affects public support in different contexts.

3. Additional research work is required to determine the 
cross-sectoral mitigation potential of emerging general-
purpose technologies.
4. There is a  lack of literature on mitigation finance frameworks 
promoting cross-sectoral mitigation linkages.
5. Additional research is needed to better quantify the net GHG 
emissions and co-benefits and adverse effects of emerging 
food technologies.
 –Research in social and behavioural sciences should invest 
in assessing effectiveness of instruments aiming at shifting 
food choices in different national contexts.
 –A better evidence basis is required to understand synergistic 
effects of policies in food system policy packages.
6. There is a  lack of literature on regional and global mitigation 
potential of biomass production systems that are strategically 
deployed in agriculture and forestry landscapes, to achieve 
specific co-benefits.
7. There is a lack of knowledge on land occupation and associated

co-benefits and adverse side effects from large-scale deployment 
of non-AFOLU mitigation options, and how such options can be 
integrated with agriculture and forestry to maximise synergies 
and minimise trade-offs.

1322
1322
Chapter 12 Cross-sectoral Perspectives12Frequently Asked Questions (FAQs)
FAQ 12.1 |   How could new technologies to remove carbon dioxide from the atmosphere 
contribute to climate change mitigation?
Limiting the increase in warming to well below 2°C, and achieving net zero CO 2 or GHG emissions, will require anthropogenic CO 2 
removal from  the atmosphere.
The carbon dioxide removal (CDR) methods studied so far have different removal potentials, costs, co-benefits and side effects. 
Some biological methods for achieving CDR, like afforestation/reforestation or wetland restoration, have long been practised. 
If implemented well, these practices can provide a  range of co-benefits, but they can also have adverse side effects such as 
biodiversity loss or food price increases. Other chemical and geochemical approaches to CDR include direct air carbon capture

and storage (DACCS), enhanced weathering or ocean alkalinity enhancement. They are generally less vulnerable to reversal than 
biological methods.
DACCS uses chemicals that bind to CO 2 directly from the air; the CO 2 is then removed from the sorbent and stored underground or 
mineralised. Enhanced weathering involves the mining of rocks containing minerals that naturally absorb CO 2 from the atmosphere 
over geological timescales, which are crushed to increase the surface area and spread on soils (or elsewhere) where they absorb 
atmospheric CO 2. Ocean alkalinity enhancement involves the extraction, processing, and dissolution of minerals and addition to the 
ocean where they enhance sequestration of CO 2 as bicarbonate and carbonate ions in the ocean.
FAQ 12.2 |   Why is it important to assess mitigation measures from a systemic perspective, rather 
than only looking at their potential to reduce greenhouse gas (GHG) emissions?

Mitigation measures do not only reduce GHGs, but have wider impacts. They can result in decreases or increases in GHG emissions 
in another sector or part of the value chain from where they are applied. They can have wider environmental (e.g., air and water 
pollution, biodiversity), social (e.g., employment creation, health) and economic (e.g., growth, investment) co-benefits or adverse 
side effects. Mitigation and adaptation can also be linked. Taking these considerations into account can help to enhance the benefits 
of mitigation action, and avoid unintended consequences, as well as provide a stronger case for achieving political and societal 
support and raising the finances required for implementation.
FAQ 12.3 |   Why do we need a food systems approach for assessing GHG emissions and mitigation 
opportunities from food systems?
Activities associated with the food system caused about one-third of total anthropogenic GHG emissions in 2015, distributed across

all sectors. Agriculture and fisheries produce crops and animal-source food, which are partly processed in the food industry, packed, 
distributed, retailed, cooked, and finally eaten. Each step is associated with resource use, waste generation, and GHG emissions.
A food systems approach helps identify critical areas as well as novel and alternative approaches to mitigation on both the supply 
side and the demand side of the food system. But complex co-impacts need to be considered and mitigation measures tailored to 
the specific context. International cooperation and governance of global food trade can support both mitigation and adaptation.
There is large scope for emissions reduction in both cropland and grazing production, and also in food processing, storage and 
distribution. Emerging options such as plant-based alternatives to animal food products and food from cellular agriculture are

receiving increasing attention, but their mitigation potential is still uncertain and depends on the GHG intensity of associated energy 
systems due to relatively high energy needs. Diet changes can reduce GHG emissions and also improve health in groups with excess 
consumption of calories and animal food products, which is mainly prevalent in developed countries. Reductions in food loss and 
waste can help reduce GHG emissions further.
Recommendations to buy local food and avoid packaging can contribute to reducing GHG emissions but should not be generalised, 
as trade-offs exist with food waste, GHG footprint at farm gate, and accessibility to diverse healthy diets.

1358
1358
Chapter 13 National and Sub-national Policies and Institutions13Executive Summary
Long-term deep emission reductions, including the reduction 
of emissions to net zero, is best achieved through institutions 
and governance that nurture new mitigation policies, 
while at the same time reconsidering existing policies that 
support continued Greenhouse Gas (GHG) emissions (robust 
evidence, high agreement). To do so effectively, the scope of 
climate governance should include both direct efforts to target GHG 
emissions and indirect opportunities to tackle GHG emissions that 
result from efforts directed towards other policy objectives. {13.2, 
13.5, 13.6, 13.7, 13.9} 
Institutions and governance underpin mitigation by providing 
the legal basis for action. This includes setting up implementing 
organisations and the frameworks through which diverse 
actors interact (medium evidence, high agreement). Institutions

can create mitigation and sectoral policy instruments; policy packages 
for low-carbon system transition; and economy-wide measures for 
systemic restructuring. {13.2, 13.7, 13.9}
Policies have had a  discernible impact on mitigation for 
specific countries, sectors, and technologies (robust evidence, 
high agreement), avoiding emissions of several GtCO2-eq yr–1 
(medium evidence, medium agreement). Both market-based and 
regulatory policies have distinct, but complementary roles. The share 
of global GHG emissions subject to mitigation policy has increased 
rapidly in recent years, but big gaps remain in policy coverage, and the 
stringency of many policies falls short of what is needed to achieve 
strong mitigation outcomes (robust evidence, high agreement). {13.6, 
Cross-Chapter Box 10 in Chapter 14}
Climate laws enable mitigation action by signalling the 
direction of travel, setting targets, mainstreaming mitigation 
into sector policies, enhancing regulatory certainty, creating

law-backed agencies, creating focal points for social 
mobilisation, and attracting international finance (medium 
evidence, high agreement). By 2020, ‘direct’ climate laws primarily 
focused on GHG reductions were present in 56 countries covering 
53% of global emissions, while more than 690 laws, including 
‘indirect’ laws, may also have an effect on mitigation. Among direct 
laws, ‘framework’ laws set an overarching legal basis for mitigation 
either by pursuing a  target and implementation approach, or by 
seeking to mainstream climate objectives through sectoral plans and 
integrative institutions. {13.2}
Institutions can enable improved governance by coordinating 
across sectors, scales and actors, building consensus for 
action, and setting strategies (medium evidence, high 
agreement). Institutions are more stable and effective when they 
are congruous with national context, leading to mitigation-focused 
institutions in some countries and the pursuit of multiple objectives

in others. Sub-national institutions play a  complementary role to 
national institutions by developing locally-relevant visions and plans, 
addressing policy gaps or limits in national institutions, building 
local administrative structures and convening actors for place-based 
decarbonisation. {13.2}Sub-national actors are important for mitigation because 
municipalities and regional governments have jurisdiction 
over climate-relevant sectors such as land-use, waste and 
urban policy; are able to experiment with climate solutions; 
and can forge partnerships with the private sector and 
internationally to leverage enhanced climate action (robust 
evidence, high agreement). More than 10,500 cities and nearly 
250 regions representing more than 2 billion people have pledged 
largely voluntary action to reduce emissions. Indirect gains include 
innovation, establishing norms and developing capacity. However, 
sub-national actors often lack national support, funding, and

capacity to mobilise finance and human resources, and create new 
institutional competences. {13.5}
Climate governance is constrained and enabled by domestic 
structural factors, but it is still possible for actors to make 
substantial changes (medium evidence, high agreement). 
Key structural factors are domestic material endowments (such as 
fossil fuels and land-based resources); domestic political systems; 
and prevalent ideas, values and belief systems. Developing countries 
face additional material constraints in climate governance due to 
development challenges and scarce economic or natural resources. 
a broad group of actors influence how climate governance develop 
over time, including a  range of civic organisations, encompassing 
both pro-and anti-climate action groups. {13.3, 13.4}
Mitigation strategies, instruments and policies that fit with 
dominant ideas, values and belief systems within a  country 
or within a sector are more easily adopted and implemented

(medium evidence, medium agreement). Ideas, values and beliefs 
may change over time. Policies that bring perceived direct benefits, 
such as subsidies, usually receive greater support. The awareness of 
co-benefits for the public increases support of climate policies (robust 
evidence, high agreement). {13.2, 13.3, 13.4}
Climate litigation is growing and can affect the outcome 
and ambition of climate governance (medium evidence, high 
agreement). Since 2015, at least 37 systemic cases have been 
initiated against states that challenge the overall effort of a  state 
to mitigate or adapt to climate change. If successful, such cases 
can lead to an increase in a  country’s overall ambition to tackle 
climate change. Climate litigation has also successfully challenged 
governments’ authorisations of high-emitting projects setting 
precedents in favour of climate action. Climate litigation against 
private sector and financial institutions is also on the rise. {13.4}

The media shapes the public discourse about climate 
mitigation.  This can usefully build public support to 
accelerate mitigation action, but may also be used to impede 
decarbonisation (medium evidence, high agreement). Global 
media coverage (across a study of 59 countries) has been growing, 
from about 47,000  stories in 2016–2017 to about 87,000 in 
2020–2021. Generally, the media representation of climate science 
has increased and become more accurate over time. On occasion, 
the propagation of scientifically misleading information by organised 
counter-movements has fuelled polarisation, with negative 
implications for climate policy. {13.4}

1359
1359
National and Sub-national Policies and Institutions Chapter 1313
Explicit attention to equity and justice is salient to both social 
acceptance and fair and effective policymaking for mitigation 
(robust evidence, high agreement). Distributional implications of 
alternative climate policy choices can be usefully evaluated at city, 
local and national scales as an input to policymaking. Institutions 
and governance frameworks that enable consideration of justice 
and just transitions are likely to build broader support for climate 
policymaking. {13.2, 13.6, 13.8, 13.9}
Carbon pricing is effective in promoting implementation 
of low-cost emissions reductions (robust evidence, high 
agreement). While the coverage of emissions trading and carbon 
taxes has risen to over 20% of global CO 2 emissions, both coverage 
and price are lower than is needed for deep reductions. The design of 
market mechanisms should be effective as well as efficient, balance

distributional goals and find social acceptance. Practical experience 
has driven progress in market mechanism design, especially of 
emissions trading schemes (robust evidence, high agreement). 
Carbon pricing is limited in its effect on adoption of higher-cost 
mitigation options, and where decisions are often not sensitive to 
price incentives such as in energy efficiency, urban planning, and 
infrastructure (robust evidence, medium agreement). Subsidies have 
been used to improve energy efficiency, encourage the uptake of 
renewable energy and other sector-specific emissions saving options 
(robust evidence, high agreement). {13.6}
Regulatory instruments play an important role in achieving 
specific mitigation outcomes in sectoral applications (robust 
evidence,  high agreement) . Regulation is effective in particular 
applications and often enjoys greater political support, but tends to be 
more economically costly, than pricing instruments (robust evidence,

medium agreement) . Flexible forms of regulation (for example, 
performance standards) have achieved aggregate goals for renewable 
energy generation, vehicle efficiency and fuel standards, and energy 
efficiency in buildings and industry (robust evidence,  high agreement). 
Infrastructure investment decisions are significant for mitigation 
because they lock-in high- or low- emissions trajectories over long 
periods. Information and voluntary programmes can contribute to 
overall mitigation outcomes (medium evidence,  high agreement) . 
Designing for overlap and interactions among mitigation policies 
enhances their effectiveness (robust evidence,  high agreement). {13.6}
Removing fossil fuel subsidies would reduce emissions, improve 
public revenue and macroeconomic performance, and yield 
other environmental and sustainable development benefits; 
subsidy removal may have adverse distributional impacts especially on 
the most economically vulnerable groups which, in some cases can

be mitigated by measures such as redistributing revenue saved, all 
of which depend on national circumstances (high confidence); fossil 
fuel subsidy removal is projected by various studies (using alternative 
methodologies) to reduce global CO 2 emissions by 1–4%, and GHG 
emissions by up to 10% by 2030, varying across regions (medium 
confidence). {6.3, 13.6}
National mitigation policies interact internationally with 
effects that both support and hinder mitigation action 
(medium evidence, high agreement). Reductions in demand for 
fossil fuels tend to negatively affect fossil fuel exporting countries (medium evidence, high agreement). Creation of markets for emission 
reduction credits tends to benefit countries able to supply credits. 
Policies to support technology development and diffusion tend to 
have positive spillover effects (medium evidence, high agreement). 
There is no consistent evidence of significant emissions leakage or

competitiveness effects between countries, including for emissions-
intensive trade-exposed industries covered by emission trading 
systems (medium evidence, medium agreement). {13.6}
Policy packages are better able to support socio-technical 
transitions and shifts in development pathways toward low-
carbon futures than are individual policies (robust evidence, 
high agreement). For best effect, they need to be harnessed to 
a  clear vision for change and designed with attention to local 
governance context. Comprehensiveness in coverage, coherence 
to ensure complementarity, and consistency of policies with the 
overarching vision and its objectives are important design criteria. 
Integration across objectives occurs when a  policy package is 
informed by a  clear problem framing and identification of the full 
range relevant policy sub-systems. {13.7}
The co-benefits and trade-offs of integrating adaptation and 
mitigation are most usefully identified and assessed prior

to policy making rather than being accidentally discovered 
(robust evidence, high agreement). This requires strengthening 
relevant national institutions to reduce silos and overlaps, increasing 
knowledge exchange at the country and regional levels, and 
supporting engagement with bilateral and multilateral funding 
partners. Local governments are well placed to develop policies that 
generate social and environmental co-benefits but to do so require 
legal backing and adequate capacity and resources. {13.8}
Climate change mitigation is accelerated when attention 
is  given to integrated policy and economy-wide approaches, and 
when enabling conditions (governance, institutions, behaviour, 
innovation, policy, and finance), are present (robust evidence, 
medium agreement). Accelerating climate mitigation includes 
simultaneously weakening high carbon systems and encouraging 
low-carbon systems; ensuring interaction between adjacent systems

(e.g. energy and agriculture); overcoming resistance to policies 
(e.g., from incumbents in high carbon emitting industries), including 
by providing transitional support to the vulnerable and negatively 
affected by distributional impacts; inducing changes in consumer 
practices and routines; providing transition support; and addressing 
coordination challenges in policy and governance. {13.7, 13.9}
Economy-wide packages, including economic stimulus 
packages, can contribute to shifting sustainable development 
pathways and achieving net zero outcomes while meeting 
short term economic goals (medium evidence, high 
agreement). The 2008–2009 Global Recession showed that 
policies for sustained economic recovery go beyond short-term fiscal 
stimulus to include long-term commitments of public spending on 
the low-carbon economy; pricing reform; addressing affordability; 
and minimising distributional impacts. COVID-19 spurred stimulus

packages and multi-objective recovery policies that may have the 
potential to meet short-term economic goals while enabling longer-
term sustainability goals. {13.9}

1360
1360
Chapter 13 National and Sub-national Policies and Institutions1313.1 Introduction 
This chapter assesses national and sub-national policies and 
institutions. Given the scale and scope of the climate challenge, 
an immediate challenge for this assessment is defining its scope. 
Because a  very wide range of institutions and policies at multiple 
scales carry implications for climate change, the approach followed 
here is to embrace a broad approach. Consequently, institutions and 
policies discussed include dedicated climate laws and organisations 
(Section  13.2) and direct mitigation policies such as carbon taxes 
(Section 13.6), but also those, such as sectoral ministries and their 
policies (Sections  13.6 and 13.7) and sub-national entities such 
as regional bodies, cities, and their policies (Section  13.5), the 
implications of which are salient to mitigation outcomes. This approach 
recognises that there are important linkages with international

climate governance (Chapter 14), notably the role of internationally 
mandated Nationally Determined Contributions’ in stimulating 
domestic policy development (Section 13.2), transnational networks 
in spurring sub-national action (Section  13.5), and international 
effects of domestic policies (Section 13.6).
This encompassing approach to climate governance is also built on 
a  recognition that climate policymaking is routinely formulated in 
the context of multiple policy objectives such as energy security, 
energy access, urban development, and mitigation-adaptation 
linkages. This informs policymaking based on an understanding that 
to fully maximise direct and indirect climate mitigation potential, 
maximising co-benefits and minimising trade-offs should be explicitly 
sought rather than accidentally discovered and policies designed 
accordingly. This understanding also informs the design of institutions 
(Section 13.2) and policies (Sections 13.6 and 13.7) as well as the

linkage between mitigation and adaptation (Section 13.8).
The chapter also engages with several new developments and an 
expansion of the literature since AR5. 
A growing literature assesses how national policymaking on climate 
mitigation is dependent on national politics around, and building 
consensus on, climate action. This, in turn, is shaped by both 
nationally specific structural features (Section 13.3) and the role of 
different actors in the policymaking process (Section 13.4). Important 
new avenues through which climate policy making is shaped, such 
as climate litigation (Section 13.4.2), and channels for public opinion 
formation, such as the media (Section 13.4.3) are also assessed. The 
chapter weaves discussions of the role of justice, understood through 
a discussion of procedural justice (Section 13.2), distributional justice 
(Section 13.6) and vulnerability (Section 13.8), and its role in creating 
public support for climate action (Section 13.9).

A significant new theme is the focus on the dynamic elements of policy 
making, that is, how policy can be designed to accelerate mitigation. 
This includes through technological transitions, socio-technical 
transitions, shifts in development pathways and economy-wide 
measures. This literature emphasises the importance of examining 
not just individual policies, but packages of policies (Section 13.7) 
and how these are enabled by the alignment of policy, institutions, 
finance, behaviour and innovation (Section  13.9). Also new is attention to the opportunities for economy-wide system change 
presented by consideration of post-COVID recovery packages, and 
wider efforts at sustainable economic restructuring (Section  13.9). 
Consistent with the discussion in Chapter 4, these larger approaches 
offer opportunities to undertake systemic restructuring and shift 
development pathways.
Finally, the chapter addresses core themes from earlier assessment

reports, but seeks to do so in an enhanced manner. The discussion of 
climate institutions assesses a growing literature on climate law, as 
well as both purpose-built climate organisations and the layering of 
climate responsibilities on existing organisations at national and sub-
national scales (Section 13.2). The discussion of policies focuses on 
an ex post assessment of policies, as well as the interaction among 
them, and learnings on how they can be combined in packages 
(Sections  13.6 and 13.7). It also lays out a  framework for their 
assessment that encompasses environmental effectiveness, economic 
effectiveness, distributional outcomes, co-benefits, institutional 
requirements, as well as a new criterion of transformational potential 
(Section 13.6). 
The aim of this chapter is to assess the full range of the multi-stranded 
and diverse literature on climate institutions and policy, reflecting the 
richness of real-world climate governance.

13.2 National and Sub-national 
Institutions and Governance
Institutions and governance arrangements can help address ‘policy 
gaps’ and ‘implementation gaps’ (Cross-Chapter Box 4 in Chapter 4) 
that hinder climate mitigation. While the need for institutions and 
governance is universal, individual country approaches vary, based on 
national approaches and circumstances, as discussed in this section. 
Since AR5, the understanding of climate governance has become 
more encompassing and complex, involving multiple actors, decision-
making arenas, levels of decision-making and a variety of political 
goals. Climate governance sometime directly targets GHG emissions; 
at other times mitigation results from measures that primarily aim 
to solve other issues, for instance relating to food production, forest 
management, energy markets, air pollution, transport systems or 
technology development, but with mitigation or adaptation effects 
(Karlsson et al. 2020).

Consistent with usage in this assessment, institutions are rules, norms 
and conventions that guide, constrain or enable behaviours and 
practices, including the organisations through which they operate, 
while governance is the structure, processes and actions that public 
and private actors use to address societal goals (See Glossary for 
complete definitions). Multiple terms are used in the literature to 
discuss climate governance, often varying across countries. Climate 
laws, or legislation, is passed by legislatures, and often sets the 
overarching governance context, but the term is also used to refer 
to legislation that is salient to climate outcomes even if not centrally 
focused on climate change. National strategies, often referred to as 
plans, most often operate through executive action by government,

1361
1361
set guidance for action and often are not legally binding, although 
strategies may also be enshrined in law. Both laws and strategies 
may elaborate targets, or goals, for emissions outcomes, although 
these are not necessary components of laws and strategies. While 
laws typically operate at the national level (states may also make 
laws in federal nations), strategies, plans and targets may also 
operate at the sub-national level.
This section begins with a  discussion of national laws for climate 
action (Section  13.2.1), followed by a  discussion of national 
strategies (Section  13.2.2). The third section examines institutions 
(Section13.2.3), including organisations that are established to 
govern climate actions, and the final section explores sub-national 
institutions and their challenges in influencing climate mitigation 
(Section 13.2.4). 
13.2.1 Climate Laws

National laws that govern climate action often set the legal basis for 
climate action (Averchenkova et al. 2021). This legal basis can serve 
several functions: establish a platform for transparent target setting 
and implementation (Bennett 2018); provide a  signal to actors by 
indicating intent to harness state authority behind climate action 
(Scotford and Minas 2019); promise enhanced regulatory certainty 
(Scotford et al. 2017); create law-backed agencies for coordination, 
compliance and accountability (Scotford and Minas 2019); provide 
a  basis for mainstreaming mitigation into sector action, and 
create focal points for social mobilisation (medium evidence, high 
agreement) (Dubash et al. 2013). For lower/middle income countries, 
in particular, the existence of a  law may also attract international 
finance by serving as a signal of credibility (Fisher et al. 2017). The 
realisation of these potential governance gains depends on local

context, legal design, successful implementation, and complementary 
action at different scales.
There are both narrow and broad definitions of what counts as 
‘climate laws’. The literature distinguishes direct climate laws that 
explicitly considers climate change causes or impacts – for example 
through mention of greenhouse gas reductions in its objectives 
or title (Dubash et al. 2013)  – from indirect laws that have ‘the 
capacity to affect mitigation or adaptation’ through the subjects they 
regulate, for example, through promotion of co-benefits, or creation 
of reporting protocols (Scotford and Minas 2019). Closely related is 
a ‘sectoral approach’ based on the layering of climate considerations 
into existing laws in the absence of an overarching framework 
law (Rumble 2019). Many countries also adopt executive climate 
strategies (discussed in Section  13.2), which may either coexist 
with or substitute for climate laws, and that may also be related to

a country’s NDC process under the Paris Agreement. 
The prevalence of both direct and indirect climate laws has increased 
considerably since 2007, although definitional differences across 
studies complicate a  clear assessment of their relative importance 
(medium evidence, high agreement) (Iacobuta et al. 2018; Nachmany 
1 Data from climate-laws.org, search for mitigation focused legislation for different time frames. Accessed Oct. 31, 2021.and Setzer 2018). Direct climate laws  – with greenhouse gas 
limitation as a direct objective – had been passed in 56 countries 
(of 194 studied) covering 53% of emissions in 2020, with most of 
that rise happening between 2010 and 2015 (Figure  13.1). Both 
direct and indirect laws – those that have an effect on mitigation 
even if this is not the primary outcome – is most closely captured by 
the ‘Climate Change Laws of the World’ database, which illustrates 
the same trend of growing prevalence, documenting 694 mitigation-

related laws by 2020 versus 558 in 2015 and 342 in 2010 (Nachmany 
and Setzer 2018; LSE Grantham Research Institute on Climate 
Change and the Environment 2021).1 Among these, the majority 
are accounted for by sectoral indirect laws. For example, a study of 
Commonwealth countries finds that a  majority of these countries 
have not taken the route of a single overarching law, but rather have 
an array of laws across different areas, for example, Indian laws on 
energy efficiency and Ghana’s laws on renewable energy promotion 
(Scotford et al. 2017).
Some direct climate laws may serve as ‘framework’ laws 
(Averchenkova et al. 2017; Rumble 2019) that set an overarching 
legal context within which other legislation and policies operate. 
Framework laws are intended to provide a  coherent legal basis 
for action, to integrate past legislation in related areas, set clear 
directions for future policy, and create necessary processes and

institutions (medium evidence, medium agreement) (Townshend 
et al. 2013; Averchenkova et al. 2017; Fankhauser et al. 2018; Rumble 
2019; Averchenkova et al. 2021). There are a variety of approaches to 
framework laws. Reviews of climate legislation, many of which draw 
particularly from the long-standing UK Climate Change Act, suggest 
the need for statutory targets with a  long-term direction, shorter 
term instruments such as carbon budgets to induce action toward 
targets, a clear assignment of duties and responsibilities including 
identification of policies and responsibility for their implementation, 
annual reporting to Parliament; an independent body to support 
evidence-based decision-making and rules to govern information 
collection and provision (Barton and Campion 2018; Fankhauser 
et al. 2018; Abraham-Dukuma et al. 2020; Averchenkova et al. 2021). 
However, country examples also suggest other, different approaches

to framework laws. Korea’s Framework Act on Low Carbon, 
Green Growth seeks to shift business and society toward green 
growth through a  process of strategy setting and action plans 
(Jang et al. 2010). Kenya’s framework Climate Change Act creates 
an institutional structure to mainstream climate considerations into 
sectoral decisions, one of several examples across Africa of efforts 
to create framework legislation to promote mainstreaming (Rumble 
2019). Mexico’s General Law on Climate Change includes sectoral 
emission targets, along with the creation of coordinating institutions 
across ministries and sub-national authorities (Averchenkova and 
Guzman Luna 2018). Consequently, different countries have placed 
emphasis on different aspects of framework laws, although the most 
widely prevalent approach is that exemplified by the UK. 
Climate laws spread through multiple mechanisms, including the 
impetus provided by international negotiation events, diffusion by

1362
1362
Chapter 13 National and Sub-national Policies and Institutions130510152025
2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020
DEV APC EEA AFR LAM MDEGHG emissions coverage (GtCO 2-eq)
20%49%52%80%51%48%
05101520253035404550
2010 2015 2020
GlobalGHG emissions coverage (GtCO 2-eq)(a)
Climate legislation in force No climate legislation0
DEV APC EEA AFR LAM MDENumber of countries
0%10%20%30%40%50%60%70%80%90%100%
Global% countries(b)
No climate legislation102030405060
Figure 13.1 | Prevalence of legislation by emissions and number of countries across regions.  Top: Shares of global GHG emissions under national climate change 
legislations – in 2010, 2015 and 2020. Emissions data used are for 2019, since emissions shares across regions deviated from past patterns in 2020 due to COVID.  Bottom:

Number of countries with national climate legislation – in 2010, 2015, and 2020. Climate legislation is defined as an act passed by a parliament that includes in its title or 
objectives reductions in GHGs. AR6 regions: DEV = Developed countries; APC = Asia and Pacific; EEA = Eastern Europe and West-Central Asia; AFR = Africa; LAM = Latin 
America and the Caribbean; MDE = Middle East. Source: updated and adapted with permission from Iacobuta et al. (2018) to reflect AR6 regional aggregation and recent data.

1363
1363
example across countries, and domestic factors such as business 
cycles (medium evidence, medium agreement). Major landmark 
events under the UNFCCC have been associated with increases 
in  national legislation (Iacobuta et al. 2018), with a  stronger 
effect in countries where international commitments are binding 
(Fankhauser et al. 2016). Diffusion through example of legislation 
from other countries has been documented (Fankhauser et al. 2016; 
Fleig et al. 2017; Torney 2017; Inderberg 2019; Torney 2019). For 
example, the UK Climate Change Act was an important influence 
in pursuing similar acts in Finland and Ireland (Torney 2019) and 
was also considered in the formulation of Mexico’s General Law 
on Climate Change (Averchenkova and Guzman Luna 2018). The 
presence of a framework law is positively associated with creation of 
additional supportive legislation (Fankhauser et al. 2015). Domestic

contextual factors can also affect the likelihood of legislation such 
as a weak business cycle that can impact the political willingness to 
pass legislation (Fankhauser et al. 2015). In some cases, civil society 
groups play a  role as advocates for legislation, as occurred in the 
UK (Lockwood 2013; Lorenzoni and Benson 2014; Carter and Childs 
2018; Devaney et al. 2020) and in Germany in the build up to passage 
of their respective Climate Change Act (Flachsland and Levi 2021).
The performance of framework laws suggests a mixed picture. While 
the structure of the UK Act successfully sets a  direction of travel 
and has resulted in a  credible independent body, it performs less 
well in fostering integration across sectoral areas and providing an 
enforcement mechanism (Averchenkova et al. 2021). a  review of 
seven European climate change acts concludes that overall targets 
may not be entirely aligned with planning, reporting and evaluation

mechanisms, and that sanction mechanisms are lacking across the 
board (Nash and Steurer 2019), which limit the scope for legislation 
to perform its integrative task. These observations suggest the need 
for careful attention to the design of framework laws.
There is extremely limited evidence on the aggregate effects of 
climate laws on climate outcomes, although there is a  broader 
literature assessing climate policies (Section  13.6 in this chapter 
and Cross-Chapter Box  10 in Chapter  14). a  single assessment of 
direct and indirect climate laws as well as relevant executive action 
across a  global database finds a  measurable and positive effect: 
global annual emissions have reduced by about 5.9 CO 2 compared 
to an estimation of what they otherwise would have been (Eskander 
and Fankhauser 2020). Climate laws require further research, 
including on the quantification of impact, framework versus sectoral 
approaches, and the various mechanisms through which laws act –

target setting, creating institutional structures, mainstreaming and 
ensuring compliance. 
13.2.2 National Strategies and Nationally 
National climate strategies, which are often formulated through 
executive action, contribute to climate governance in several ways. 
Strategies enable discussion of low-emissions pathways while 
accounting for uncertainty, national circumstances and socio-
economic objectives (Falduto and Rocha 2020). They frequently set out long term emission goals and possible 
trajectories over time, with analysis of technological and economic 
factors (Levin et al. 2018; WRI 2020). This can include quantitative 
modelling of low-emissions transitions and their economic effects 
to inform policymakers and stakeholders of potential outcomes 
(Waisman et al. 2019; Weitzel et al. 2019). Scenario analysis can be 
used to explore how to make strategies more robust in the face of

uncertainty (Sato and Altamirano 2019). Strategies and their regular 
revision can support long-term structural change by stimulating 
deliberation and learning (Voß et al. 2009), and to make the link 
between mitigation and adaptation objectives and actions (Watkiss 
and Klein 2019; Hans et al. 2020). As part of the Paris Agreement 
process, several countries have prepared and submitted long-term 
low-emissions development strategies (Levin et al. 2018), while 
others have different forms of national climate change strategies 
independently of the UNFCCC process. Strategies set over time by 
the European Union are discussed in Box 13.1.
Nationally Determined Contributions (NDCs) prepared under the 
Paris Agreement may be informed by national strategies (Rocha 
and Falduto 2019). But the process of preparing NDCs can itself 
raise political awareness, encourage institutional innovation 
and coordination, and engage stakeholders (Röser et al. 2020).

Nationally Determined Contributions (NDCs) illustrate a diversity of 
approaches: direct mitigation targets, strategies, plans and actions 
for low-GHG emission development, or the pursuit of mitigation 
co-benefits resulting from economic diversification plans and/
or adaptation actions (UNFCCC Secretariat 2021). Figure  13.2 
shows that the prevalence of emission targets increased across all 
regions between 2010 and 2020, the period during which the Paris 
Agreement was reached. 
The NDCs vary in their scope, content and time frame, reflecting 
different national circumstances, and are widely heterogeneous 
in both stringency and coverage of mitigation efforts (UNFCCC 
Secretariat 2016, 2021; Pauw et al. 2018; Campagnolo and Davide 
2019; Pauw et al. 2019). The mitigation targets in the new or updated 
NDCs range from economy-wide absolute emission reduction targets 
to strategies, plans and actions for low-emission development, with

specific time frames or implementation periods specified. Less than 
10% of parties’ NDCs specify when their emissions are expected 
to peak and some of these parties express their target as a carbon 
budget (UNFCCC Secretariat 2021). Many long-term strategies 
submitted by Parties to the UNFCCC refer to net zero emissions or 
climate neutrality, carbon neutrality, or GHG neutrality with reference 
to 2050, 2060 or mid-century targets (UNFCCC Secretariat 2021). The 
growing prevalence and coverage of emission targets is documented 
in Figure 13.2.
Almost all Parties outlined domestic mitigation measures as key 
instruments for achieving mitigation targets in specific priority areas 
such as energy supply (89%), transport (80%), buildings (72%), 
industry (39%), agriculture (67%), LULUCF (75%) and waste (68%). 
Renewable energy generation was the most frequently indicated 
mitigation option (84%), followed by improving energy efficiency

of buildings (63%) and multi-sector energy efficiency improvement 
(48%); afforestation, reforestation and revegetation  (48%);

1364
1364
Chapter 13 National and Sub-national Policies and Institutions130102030405060
2010 2015 2020% countries
Global
Executive GHG emissions target No GHG emissions target Legislative GHG emissions target(b)0510152025
2010 2015 2020GHG emissions coverage (GtCO 2-eq)
Global(a)
25%42%44%24%46%46% 51%12%10%
Figure 13.2 | Prevalence of targets by emissions and number of countries across region.  Top: Shares of global GHG emissions under national climate emission 
targets – in 2010, 2015 and 2020. Emissions data used are for 2019, since emissions shares across regions deviated from past patterns in 2020 due to COVID.  Bottom: Number

of countries with national climate emission targets – in 2010, 2015, and 2020. Emissions reductions targets were taken into account as a legislative target when they were 
defined in a law or as part of a country’s submission under the Kyoto Protocol, or as an executive target when they were included in a national policy or official submissions 
under the UNFCCC. Targets were included if they were economy wide or included at least the energy sector. The proportion of national emissions covered are scaled to reflect 
coverage and whether targets are in GHG or CO 2 terms. AR6 regions: DEV = Developed countries; APC = Asia and Pacific; EEA = Eastern Europe and West-Central Asia; AFR 
= Africa; LAM = Latin America and the Caribbean; MDE = Middle East. Source: updated and adapted with permission from Iacobuta et al. (2018) to reflect AR6 regional 
aggregation and recent data.

1365
1365
Box 13.1 | EU Climate Policy Portfolio and the European Green Deal
The European Union (EU)1 has developed an encompassing climate governance framework (Kulovesi and Oberthür 2020), having 
ratified the Kyoto Protocol in 2002. In 2003 the EU adopted an Emissions Trading System for sectors with large GHG emitters, which 
started in 2005. From 2007 to 2009, the EU revised its climate policies, including for vehicle emissions, renewable energy and energy 
efficiency, and adopted targets for 2020 for GHG emissions reductions, renewable energy shares and energy efficiency improvements. 
It also adopted in 2009 an Effort Sharing Decision for Member States’ emissions reductions for the period 2013–2020 in sectors not 
covered by the ETS (Boasson and Wettestad 2013; Bertoldi 2018). The ETS has been improved multiple times, including through a 2015

Market Stability Reserve to reduce the surplus of emission allowances (Chaton et al. 2018; Wettestad and Jevnaker 2019). In 2010, the 
European Commission created a directorate-general (equal to a ministry at the domestic level) for Climate Action. Between 2014 and 
2018, the EU agreed on emission reduction targets for 2030 of 30% GHG emission reductions compared to 1990, and again revised 
its climate policy portfolio including new targets for renewable energies and energy efficiency and a new Effort Sharing Regulation 
(Fitch-Roy et al. 2019a; Oberthür 2019). 
From 2018, climate planning and reporting has been regulated by the EU Governance Regulation (Regulation (EU) 2018/1999), 
requiring member states to develop detailed and strategic National Energy and Climate Plans (Knodt et al. 2020). In 2019, the 
European Commission, backed by the European Council (heads of states and government in the EU) and the European Parliament,

launched a new broad climate and environment initiative; the ‘European Green Deal’, implying the revision of many EU polices and 
introducing the Climate Pact (European Commission 2019a). This roadmap develops a ‘new growth strategy for the EU’ aimed at 
reaching climate neutrality by 2050 and spans multiple sectors. In 2020, the European Commission introduced a new climate law 
establishing the framework for achieving the climate neutrality by 2050 principle, and upgraded its 2030 GHG emission reduction 
target to at least net 55% reduction, which was adopted in June 2021 (European Commission 2020a). In June 2021, the new policy 
package ‘Fit for 55’ was adopted by the Commission; the packages included a proposal for the revision of the ETS, including its 
extension to shipping and a  separate emission trading system for road transport and buildings, a  revision of the effort sharing

regulation, an amendment of the regulation setting CO 2 emission standards for cars and vans, a revision of the energy tax directive, 
a new carbon border adjustment mechanism, a revision of renewable energy and energy efficiency targets and directives, and a new 
social fund to make the transition to climate neutrality fair.
1 The European Union is an international organisation that is discussed here because it plays a large role in shaping climate obligations and policies of its Member States.and improving energy efficiency of transport (45%) (UNFCCC 
Secretariat 2021). Parties often communicated mitigation options 
related to the circular economy, including reducing waste (29%) 
and recycling waste (30%) and promoting circular economy (25%). 
Many Parties highlighted policy coherence and synergies between 
their mitigation measures and development priorities, which 
included long-term low-emission development strategy(LT-LEDS),

the sustainable development goals (SDGs) and, for some, green 
recovery from the COVID-19 pandemic. 
Some countries approach NDCs as an opportunity to integrate 
mitigation objectives and broader economic shifts or sectoral 
transformations (medium evidence, medium agreement). For 
example, Brazil’s 2016 NDC focussed on emissions from land-use 
change, including agricultural intensification, to align mitigation 
with a  national development strategy of halting deforestation 
in the Amazon, and increasing livestock production (De Oliveira 
Silva et al. 2018). While the forest sector accounts for the bulk of 
Madagascar’s mitigation potential, its NDC promotes GHG mitigation 
in both AFOLU and energy sectors to maximise co-benefits, and 
achieve a higher number of sustainable development goals (SDGs) 
(Nogueira et al. 2020). 13.2.3 Approaches to National Institutions 
and Governance 
13.2.3.1 The Forms of Climate Institutions

Universal ‘best-practice’ formulations of organisations may not be 
applicable across country contexts, but institutions that are suited 
to national context can be ratcheted up over time in their scope 
and effectiveness (medium evidence,  medium agreement). National 
climate institutions take diverse forms because they emerge out of 
country-specific interactions between national climate politics and 
existing institutional structures. Certain institutional forms tend to be 
common across countries, such as expert climate change commissions; 
a  review finds eleven such institutions in existence as of mid-2020. 
Although this institutional form may be common, these commissions 
vary in terms of expertise, independence and focus (Abraham-Dukuma 
et al. 2020), reinforcing the important shaping role of national context. 
A review of institutions in eight countries suggests three broad 
processes through which institutions emerge: ‘purpose-built’

dedicated institutions focused explicitly on mitigation; ‘layering’ of 
mitigation objectives on existing institutions; and ‘latent’ institutions

1366
1366
Chapter 13 National and Sub-national Policies and Institutions13created for other purposes that nonetheless have implications for 
mitigation outcomes (Dubash 2021). In relatively few countries do 
new, purpose-built, legally-mandated bodies created specifically for 
climate mitigation exist although this number is growing; examples 
include the UK (Averchenkova et al. 2018), China (Teng and Wang 
2021), Australia (Keenan et al. 2012) and New Zealand (Timperley 
2020). These cases indicate that dedicated and lasting institutions 
with a  strategic long-term focus on mitigation emerge only under 
conditions of broad national political agreement around climate 
mitigation as a national priority (Dubash 2021). However, the specific 
forms of those institutions differ, as illustrated by the case of the UK’s 
Climate Change Committee established as an independent agency 
(Box  13.2) and China, which is built around a  top-down planning 
structure (Box 13.3).

Where economy-wide institutions do not exist, new institutions may 
still address sub-sets of the challenge. In Australia, while political 
conditions resulted in the repeal of an overarching Clean Energy 
Act in 2014, although a Climate Change Authority continued, other 
institutions primarily focused on the energy sector such as the Clean 
Energy Regulator, the Clean Energy Finance Corporation, and the 
Australia Renewable Agency continued to shape energy outcomes 
(MacNeil 2021). Where new dedicated organisations have not emerged, countries 
may layer climate responsibilities on existing institutions; the 
addition of mitigation to the responsibilities of the US Environmental 
Protection Agency is an example (Mildenberger 2021). Layering 
is also a  common approach when climate change is embedded 
within consideration of multiple objectives of policy. In these cases, 
climate institutions tend to be layered on sectoral institutions for the

pursuit of co-benefits or broader development concerns. Examples 
include India, where energy security was an important objective of 
renewable energy promotion policy (Pillai and Dubash 2021), Brazil’s 
mitigation approach focused on sectoral forest policy (Hochstetler 
2021) and South Africa’s emphasis on job creation as a necessary 
factor in mitigation policy (Chandrashekeran et al. 2017; Rennkamp 
2019). Prior to this process of layering, sectoral institutions, such as 
in forest and energy sectors, may play an important latent role in 
shaping climate outcomes, before climate considerations are part of 
their formal mandate.
New rules and organisations are not only created, they are also 
dismantled or allowed to wither away. Cases of institutional 
dismantling or neglect include the Australian Clean Energy Act 
(Crowley 2017; MacNeil 2021), the Indian Prime Minister’s Council

on Climate Change, which, while formally functional, effectively does Box 13.2 | Climate Change Institutions in the UK
The central institutional arrangements of climate governance in the UK were established by the 2008 Climate Change Act (CCA): 
statutory five-year carbon budgets; an independent advisory body, the Committee on Climate Change (CCC); mandatory progress 
monitoring and reporting to Parliament; and continuous adaptive planning following a five-yearly cycle. The CCC is noteworthy as an 
innovative institution that has also been emulated by other countries.
The design of the CCC was influenced by the concept of independent central banking (Helm et al. 2003). It has established a reputation 
for independent high quality analysis and information dissemination, is frequently referred to in Parliament and widely used by other 
actors in policy debates, all of which suggest a high degree of legitimacy (Averchenkova et al. 2018). However, since the CCC only

recommends rather than sets budgets (McGregor et al. 2012), accountability for meeting the carbon budgets works primarily through 
reputational and political effects rather than legal enforcement. 
Box 13.3 | China’s Climate Change Institutions
Climate governance in China features a combination of top-down planning and vertical accountability (Sims Gallagher and Xuan 2019; 
Teng and Wang 2021). An overarching coordination role is performed by the Leading Group on Carbon Peaking and Carbon Neutrality, 
appointed by and reporting to the Central Committee of the Chinese Communist Party, and the National Leading Group on Climate 
Change Response, Energy Conservation, and Emissions Reduction (NLGCCR), headed by the Premier and consisting of more than 
30 ministers (Wang et al. 2018a). The Department of Climate Change (DCC) under the Ministry of Ecology and Environment (MEE) is

the primary agency in charge of climate issues, with a corresponding local Bureau of Ecology and Environment in each province or 
city. While MEE is the leading agency for climate policy, the National Development and Reform Commission (NDRC) is the leading 
agency for setting overall and industry-specific targets in five-year plans, and thus has a key role in coordinating carbon emissions 
targets with energy and industrial development targets (Wang et al. 2019; Yu 2021). Involvements of ministries related to foreign 
affairs, public finance, science and technology, as well as sector ministries such as transportation, construction, and manufacturing 
industries are also needed to push forward sector-specific climate initiatives. At subsidiary levels of government carbon intensity targets 
are enforced through a ‘targets and responsibilities’ system that is directly linked to the evaluation of governments’ performances (Lin 
2012a; Li et al. 2016).

1367
1367
not meet (Pillai and Dubash 2021), and the weakening of climate 
units inside sectoral ministries in Brazil (Hochstetler 2021). While 
there is limited literature on the robustness of climate institutions, 
case studies suggest institutions are more likely to emerge, persist 
and be effective when institutions map to a  framing of climate 
change that has broad political support (medium evidence, medium 
agreement). Thus while mitigation focused framings and institutions 
may win political support in some countries, in other cases sectorally 
focused or multiple objectives oriented institutions may be most 
useful and resilient (Dubash 2021). 
13.2.3.2 Addressing Climate Governance Challenges
Climate governance challenges include ensuring coordination, 
building consensus by mediating conflict, and setting strategy 
(medium evidence, high agreement). Coordination is important

because climate change is an all-of-economy and society problem 
that requires cross-sectoral and cross-scale action; building 
consensus is needed because large-scale transformations can 
unsettle established interests; and strategy setting is required due 
to the transformative and time-bound nature of climate mitigation 
(Dubash et al. 2021). Yet, climate institutions have a mixed record in 
addressing these challenges. 
Institutions that provide coordination, integration across policy areas 
and mainstreaming are particularly important given the scope and 
scale of climate change (Candel and Biesbroek 2016; Tosun and Lang 
2017) (Section 13.7). Ministries of environment are often appointed 
as de facto agents of coordination, but have been hampered by 
their limited regulative authority and ability to engage in intra-
governmental bargaining with ministries with larger budgets and 
political heft (Aamodt 2018). 
Creation of a  high-level coordinating body to coordinate across

departments and mainstream climate into sectoral actions is 
another common approach (Oulu 2015). For example, Kenya 
has created a  National Climate Change Council, which operates 
through a  climate change directorate in the environment ministry 
to mainstream climate change at the county level (Guey and Bilich 
2019). Zhou and Mori (2011) suggest that well-functioning inter-
agency coordination mechanisms require support from heads of 
government, involvement by industry and environment agencies; and 
engagement by multiple sectoral agencies. However, coordination 
mechanisms without a clear authority and basis for setting directions 
run the risk of ‘negative coordination’, a  process through which 
ministries comment on each other’s proposals, removing any ideas 
that run counter to the interests of their own ministry, leading to 
even weaker decisions (Flachsland and Levi 2021). Countries with 
dedicated, new climate institutions tend to have a more explicit and

authorised body for climate coordination, such as China’s National 
Leading Group (Box 13.3).
Without explicit coordination with finance ministries, there is a risk 
of parallel and non-complementary approaches. For example, the 
South African Treasury pursued a carbon tax without clear indication 
of how it interfaced with a  quantitative sectoral budget approach 
espoused by the environment ministry (Tyler and Hochstetler 2021). Skovgaard (2012) suggests that there is an important distinction 
between finance ministries that bring a  limiting ‘budget frame’ 
to climate action, versus a ‘market failure frame’ that encourages 
broader engagement by relevant ministries.
Coordination within federal systems poses additional complexities, 
such as overlapping authority across jurisdictions, multiple norms in 
place, and approaches to coordination across scales (Brown 2012). 
Multi-level governance systems such as the EU can influence the

design and functioning of climate policies and institutions in member 
states, such as Germany (Skjærseth 2017; Jänicke and Wurzel 2019; 
Flachsland and Levi 2021) and the UK (Lockwood 2021a). In some 
cases, this can result in distinct European modes of governance as has 
been suggested occurred in the case of wind energy (Fitch-Roy 2016).
Within countries, institutional platforms allow federal and sub-
national governments to negotiate and agree on policy trajectories 
(Gordon 2015). In Germany, cooperation is channelled through 
periodic meetings of environment ministers and centre-state working 
groups (Weidner and Mez 2008; Brown 2012), and in Canada 
through bilateral negotiations and side-payments between scales of 
government (Rabe 2007; Gordon 2015). Federal systems might allow 
for sub-national climate action despite constraints at the federal 
level, as has occurred in Australia (Gordon 2015; MacNeil 2021) and

the United States (Rabe 2011; Jordaan et al. 2019; Bromley-Trujillo 
and Holman 2020; Thompson et al. 2020). Where agenda-setting 
rests with the central government, coordination may operate through 
targets, as with China (Qi and Wu 2013), or frameworks for policy 
action, as in India (Vihma 2011; Jogesh and Dubash 2015).
Because transition to a low-carbon future is likely to create winners 
and losers over different time scales; institutions are needed to 
mediate these interests and build consensus on future pathways 
(Kuzemko et al. 2016; Lockwood et al. 2017; Finnegan 2019; 
Mildenberger 2020). Institutions that provide credible knowledge 
can help support ambition. For example, analysis by the UK Climate 
Change committee has been harnessed, including by non-state 
actors, to prevent backsliding on decisions (Lockwood 2021a). 
Institutions can also help create positive feedback by providing 
spaces in decision-making for low-carbon interests (Aklin and

Urpelainen 2013; Roberts et al. 2018; Lockwood et al. 2017; Finnegan 
2019). For example, a renewable energy policy community emerged 
in China through key agenda setting meetings (Shen 2017), and in 
India, a National Solar Mission provided a platform for the renewable 
energy industry (Pillai and Dubash 2021). Conversely, institutions can 
also exert a drag on change through ‘regulatory inertia’, as in the 
case of the UK energy regulator Ofgem, which has exercised veto 
powers in ways that may limit a low-carbon transition (Lockwood 
et al. 2017).
Institutions can also create spaces to accommodate concerns of 
other actors (Upadhyaya et al. 2021). Deliberative bodies, such as 
Germany’s Enquete Commission (Weidner and Mez 2008; Flachsland 
and Levi 2021) or the Brazilian Forum on Climate Change (Tyler and 
Hochstetler 2021) provide a space for reconciling competing visions 
and approaches to climate change. Many countries are creating

deliberative bodies to forge ‘Just Transition’ strategies (Section 13.9).

1368
1368
Chapter 13 National and Sub-national Policies and Institutions13a recent innovation is the creation of Citizens’ Assemblies that bring 
together representative samples of citizens to deliberate on policy 
questions with the intent of informing them (Devaney et al. 2020; 
Sandover et al. 2021). The ability of institutions to forge agreement 
also rests on attention to procedural justice (Box 13.4).
Since addressing climate change requires transformative intent and 
shifting development pathways (Sections 1.6, 3.6, 4.3, 4.4, 13.9, 17.3.2, 
and Cross-Chapter Box  5 in Chapter  4), institutions that can devise 
strategies and set trajectories are useful enablers of transformation. 
Strategy setting often requires an overarching framework such as 
through framework laws that set targets (Averchenkova et al. 2017), 
or identify key sectors and opportunities for low-carbon transition 
(Hochstetler and Kostka 2015) and innovation (UNEP 2018). Few

countries have built deliberate and lasting institutions that provide 
strategic intent, and those that have, have pursued different 
approaches. The UK’s approach rests on five-yearly targets (Box 13.2); 
Germany requires sectoral budgets enforced through the Bundestag 
(Flachsland and Levi 2021); and China uses an apex decision-body to 
set targets (Teng and Wang 2021) (Box 13.3). 
Addressing all of these governance concerns  – coordination, 
mediating interests, and strategy setting  – require attention to 
institutional capacity. These include the capacity to address ‘upstream’ 
policy issues of agenda setting, framing, analysis and policy design; 
pursue goals even while mediating interests (Upadhyaya et al. 2021); identify and manage synergies and trade-offs across climate and 
development objectives (Ürge-Vorsatz et al. 2014; von Stechow et al. 
2015; McCollum et al. 2018); identify and choose amongst possible 
policy options (Howlett and Oliphant 2010); identify areas for

transformation and the means to induce innovation (Patt 2017; UNEP 
2018); and developing the ability to monitor and evaluate outcomes 
(Upadhyaya et al. 2021) (Box 13.5). Domorenok et al. (2021) highlight 
different aspects of the capacity challenge particularly necessary 
for integrated policy making including: the capacity for horizontal 
and vertical coordination; implementation capacity including the 
independence of the state from interests; and administrative capacity 
required to address compound problems. At a basic level, questions 
of governmental capacity – the numbers and training of personnel – 
can shape the choices available for climate institutions and their 
ability to be strategic (Richerzhagen and Scholz 2008; Harrison and 
Kostka 2014; Kim 2016). Box 13.5 describes South Africa’s approach 
to building monitoring and evaluation capacity.
The perceived need for attention to institutional capacity is 
highlighted by the fact that the NDCs of 113 developing countries

out of 169 countries studied list capacity building as a condition of 
NDC implementation (Pauw et al. 2020). While international support 
for capacity is widely articulated as essential for many countries 
(Khan et al. 2020), ensuring the form of capacity is appropriate, 
effective and led domestically remains a  challenge (Nago and Krott 
2020; Sokona 2021). Box 13.4 | Procedural Justice
Decision-making consistent with energy and climate justice requires attention to procedural justice (McCauley and Heffron 2018), which 
includes how decisions are made, and who is involved and has influence on decisions (Sovacool and Dworkin 2015). Procedural justice 
emphasises the importance of equitable access to decision-making processes and non-discriminatory engagement with all stakeholders 
(Jenkins et al. 2016), attention to the capability, particularly of marginalised groups, to shape decisions (Holland 2017) and recognition

of their specific vulnerabilities in collective political processes (Schlosberg 2012). Consensus-building institutions should avoid reducing 
normative questions to technical ones, recognising that values, interests and behaviours are all shaped by ongoing climate governance 
(Ryder 2018; Schwanen 2021). Additionally, communities affected by low-carbon transition may face challenges in articulating their 
understandings and experiences, which needs to be addressed in the design of climate institutions (Ryder 2018; Schwanen 2021). 
Spatially localised alternative discourses of justice are often more recognised socially than national and universal framings of climate 
justice (Bailey 2017). Participatory forms of governance such as climate assemblies and citizen juries (Ney and Verweij 2015) can help 
enhance the legitimacy of institutional decisions, even while empirical assessments suggest that these approaches continue to face

practical challenges (Devaney et al. 2020; Sandover et al. 2021; Creasy et al. 2021). 
Box 13.5 | South Africa’s Monitoring and Evaluation System
South Africa’s national monitoring and evaluation system provides high-level guidance on information requirements and assessment 
methodologies (DEA 2015). The country is developing a comprehensive, integrated National Climate Change Information System, 
to enable tracking, analysis and enhancement of South Africa’s progress towards the country’s transition to a low-carbon economy 
and climate-resilient society (DFFE Republic of South Africa 2021). It includes information on GHG emission reductions achieved, 
observed and projected climate change, impacts and vulnerabilities, the impact of adaptation and mitigation actions, financial flows 
and technology transfer activities. South Africa’s approach is premised upon continuous learning and improvement through a phased 
implementation approach (DEA 2019).

1369
1369
13.2.4 Institution Building at the Sub-national Level
Jurisdiction over significant mitigation-related arenas like planning, 
housing and community development reside at the sub-national 
level. To address linkages between mitigation and local concerns, 
sub-national actors engage in institution building within a broader 
socio-economic and political context, with actors and institutions at 
a multitude of scales shaping the effectiveness of sub-national-scale 
interventions (Romero-Lankao et al. 2018a). Mitigation policies may 
demand coordination between sectoral and jurisdictional units that 
historically have not collaborated; they may require sub-national 
actors to confront politically sensitive issues such as carbon taxes 
or increases in utility rates; and they may demand a redistribution 
of resources to protect endangered ecosystems or vulnerable 
populations (Hughes and Romero-Lankao 2014).

Sub-national actors have built climate institutions by creating new 
visions and narratives, by setting new entities or committing existing 
offices, providing them with funds, staff and legal authority, or by 
experimenting with innovative solutions that could be transferred 
to other local governments or scaled nationally (Hoffmann 2011; 
Hoornweg et al. 2011; Aylett 2015; Hughes and Romero-Lankao 2014; 
Romero-Lankao et al. 2015; Hughes 2019b). These actors have also 
created task forces, referendums, coordination of financial and human 
resources, technical assistance, awareness campaigns and funding 
(Castán Broto 2017; Romero-Lankao et al. 2018a; Hughes 2019b). 
National governments can play a key role supporting planning for 
climate change at the regional and national level, for example, 
through the articulation of climate change action in national urban 
politics (Van Den Berg et al. 2018; Cobbinah et al. 2019). 
13.2.4.1 Significance of Sub-national Networks

Multi-jurisdictional and multi-sectoral sub-national networks in 
dozens of countries globally have helped build climate institutions. 
They have also facilitated social and institutional learning, and 
addressed gaps in national policy (Holden and Larsen 2015; Jordan 
et al. 2015; Setzer 2015; Haarstad 2016; Hermwille 2018; Kammerer 
and Namhata 2018; Rashidi and Patt 2018; Westman and Castan 
Broto 2018; Lee and Jung 2018; Lee 2019; Schwartz 2019). 
Transnational networks have opened opportunities for sub-national 
actors to play a crucial mitigation role in political stalemates (Jones 
2014; Schwartz 2019). The C40, the Global Covenant of Mayors for 
Climate and Energy, and ICLEI have disseminated information on best 
practices and promoted knowledge sharing between sub-national 
governments (Lee 2013; Hakelberg 2014; Heidrich et al. 2016; Kona 
et al. 2016; Di Gregorio et al. 2020) (Section 14.5.5). Organisations 
such as the US Carbon Cycle Working Group of the United States

Global Change Research Program, the Australian Climate Action 
Network, and the Mexican Metropolitan Environmental Commission 
have helped facilitate coordination and learning across multiple 
jurisdictions and sectors, and connected ambiguous spaces between 
public, private and civil society actors (Romero-Lankao et al. 2015; 
Horne and Moloney 2019; Hughes 2019b). Transnational networks have limited influence on climate policies 
where national governments exert top-down control (e.g., in the city 
of Rizhao, China) (Westman et al. 2019); where sub-national actors 
face political fragmentation, lack regulations, and financial and 
human resources; or where vertically-integrated governance exists, 
as in State of São Paulo, Santiago de Chile, and Mexico City (Romero-
Lankao et al. 2015; Setzer 2017).
Public support for sub-national climate institutions increases when 
climate policies are linked to local issues such as travel congestion

alleviation or air pollution control (Puppim de Oliveira 2013; Romero-
Lankao et al. 2013; Simon Rosenthal et al. 2015; Romero-Lankao 
et al. 2015; Ryan 2015), or when embedded in development priorities 
that receive support from the national government or citizens 
(Jörgensen et al. 2015b; Floater et al. 2016; Dubash et al. 2018). 
For example, Indian cities have engaged in international climate 
cooperation seeking innovative solutions to address energy, water 
and infrastructure problems (Beermann et al. 2016). 
13.2.4.2 Factors Influencing Institution Building 
at the Sub-national Level
Availability of federal funding is a fundamental pillar of city actors’ 
capacity to develop mitigation policies. Administrative structures, 
such as the presence of a  professional city manager and staff 
assigned specifically to climate efforts (Simon Rosenthal et al. 2015). 
Cooperation between administrative departments, and the creation

of knowledge and data on energy use and emissions are also 
essential for mitigation planning (Hughes and Romero-Lankao 2014; 
Ryan 2015). For example, the high technical competency of Tokyo’s 
bureaucracy combined with availability of historical and current data 
enabled the city’s unique cap-and-trade system on large building 
facilities (Roppongi et al. 2017).
Visions and narratives about the future benefits or risks of climate 
change are often effectively advanced at the sub-national level, 
drawing on local governmental abilities to bring together actors 
involved in place-based decarbonisation across sectors (Hodson 
and Marvin 2009; Bush et al. 2016; Huang et al. 2018; Prendeville 
et al. 2018; Levenda et al. 2019). For example, in the plans of 
43 C40 Cities, climate action is framed as part of a vision for vibrant, 
economically prosperous, and socially just cities, that are habitable, 
secure, resource-efficient, socially and economically inclusive, and

competitive internationally (Romero-Lankao and Gnatz 2019). 
However, institution building is often constrained by a lack of national 
support, funding, human resources, coalitions, coordination across 
old and new organisations, and the ability to create new institutional 
competences (Valenzuela 2014; Jörgensen et al. 2015a; Ryan 2015; 
Dubash et al. 2018; Romero-Lankao et al. 2018a; Anderton and Setzer 
2018; Cointe 2019; Di Gregorio et al. 2019; Jaccard et al. 2019; Hughes 
2019b). Climate mitigation can also be limited by cultural norms and 
values of policy actors with varying levels of power, and shifting 
alliances (Lachapelle et al. 2012; Damsø et al. 2016; Giampieri et al. 
2019; Romero-Lankao et al. 2018a).

1370
1370
Chapter 13 National and Sub-national Policies and Institutions13Institution building is constrained by inequities; resources, legal remit, 
knowledge, and political clout vary widely within and among sub-
national governments globally (Jörgensen et al. 2015b; Genus and 
Theobald 2016; Joffe and Smith 2016; Klinsky 2018; Reckien et al. 
2018; Markkanen and Anger-Kraavi 2019). Dominant discourses tend 
to prioritise scientific and technical expertise and, thus, they focus on 
infrastructural and economic concerns over the concerns and needs 
of disadvantaged populations (Heikkinen et al. 2019; Romero-Lankao 
and Gnatz 2019).
In addition, expert driven, technical solutions such as infrastructural 
interventions can undermine the knowledge of lower income 
countries, communities or indigenous knowledge holders, yet are 
often used by sub-national governments (Ford et al. 2016; Brattland 
and Mustonen 2018; Nagorny-Koring 2019; Whyte 2017, 2020).

Technical solutions, such as electric vehicles or smart grids rarely 
address the needs and capabilities of disadvantaged communities 
that may not be able to afford these technologies (Mistry 2014; 
Romero-Lankao and Nobler 2021). However, mitigation strategies 
in sectors such as transport and buildings have often focused on 
technical and market outcomes, the benefits of which are limited 
to some, while others experience negative externalities or face 
health risks (Markard 2018; Williams and Doyon 2019; Carley and 
Konisky 2020). Delivering climate justice requires community-
driven approaches to understanding the problem addressing 
structural inequities and fostering justice, while reducing carbon 
emissions (Romero-Lankao et al. 2018b; Carley and Konisky 2020; 
Lewis et al. 2020). 
To address this situation requires procedural justice that involves 
all communities, particularly disadvantaged, in climate mitigation

decisions and policies (Box 13.4). Also essential is recognition justice, 
that addresses past inequities through tools such as subsidies, 
tariffs, rebates, and other policies (Agyeman 2013; Rydin 2013; UN 
Habitat 2016). Both tenets are key to ensure the fair distribution of 
benefits or negative impacts from mitigation policies (distributional 
justice) (McCauley and Heffron 2018; Lewis et al. 2020). However, 
the benefits of inclusive approaches are often overlooked in favour 
of growth oriented mitigation and planning (Rydin 2013; Altenburg 
2011; Smith 2019; Lennon 2020). Box  13.6 discusses how the city of Durban has internalised climate change with attention to 
considerations of justice.
Moreover, deep mitigation requires moving beyond existing 
technological responses (Mulugetta and Castán Broto 2018) to 
policies that correspond to the realities of developing countries 
(Bouteligier 2013). However, best practice approaches tend to

be fragmented due to the requirements of different contexts, and 
often executed as pilot projects that rarely lead to structural change 
(Nagorny-Koring 2019). Instead, context-specific approaches that 
include consideration of values, cultures and governance better 
enable successful translation of best practices (Affolderbach and 
Schulz 2016; Urpelainen 2018).
13.3 Structural Factors that Shape 
Climate Governance
A growing literature suggests that ambitious climate policy emerges 
out of strong domestic political support (medium evidence, medium 
agreement) (Aklin and Mildenberger 2020; Lamb and Minx 2020; 
Colgan et al. 2021). Such support is the outcome of political interest 
constellations and struggles that vary from country to country. 
Structural factors (such as economic wealth and natural resources, 
the character of the national political system, and the dominant ideas, 
values and beliefs) shape how climate change is governed (medium

evidence, high agreement) (Boasson 2015; Hochstetler 2020). This 
section assesses the ways these structural factors affect political 
dynamics and decision-making, and ultimately constrain, sustain or 
enable development of domestic climate governance.
While these structural factors are crucial, they do not determine the 
outlook of given countries’ climate governance, as civic, corporate 
and/or political groups or individuals can be mobilised and seek 
to counteract these structural effects, as indicated in the following 
Section 13.4 that examines the role of various actors and agencies 
in shaping governance processes. Taken together, Sections 13.3 and 
13.4 show that domestic climate governance is not fully constrained 
by structural factors, but rather that diverse actors can and do achieve 
substantial changes. Box 13.6 | Institutionalising Climate Change Within Durban’s Local Government

Durban has effectively linked climate change agendas with ongoing sustainability actions and goals. To do so, adaptation has 
been broadened to include a just transition to a low-carbon future to address development, energy security and GHG reduction 
(Roberts et al. 2016). 
Durban has mainstreamed climate and justice concerns within local government through strong local leadership by key individuals and 
departments; included climate concerns within various municipal short-term and long-term planning processes; mobilised civil society; 
enhanced local and international networking; explored funding opportunities; and restructured institutions (Roberts et al. 2016). 
Durban shows that embedding responses to climate change within local government activities requires that climate change is made 
relevant locally and framed within a broader environmental justice framework (Roberts 2010). Civil society has been key in balancing

the influence of the private sector on Durban’s dynamic political process (Aylett 2013).

1371
1371
13.3.1 Material Endowments 
Material endowments are natural and economic resources, such as 
fossil fuels and renewable energy, forests and land, and economic or 
financial resources, which tend to shape developments of domestic 
climate governance (medium evidence, high agreement) (Friedrichs 
and Inderwildi 2013; Lachapelle and Paterson 2013; Bang et al. 2015; 
Lamb and Minx 2020). Most countries’ social and economic systems 
are largely developed on the basis of their material endowment, and 
thus they contribute to shape the distribution of political power in 
that country (Hall and Soskice 2001). Material endowments are by 
no means the only influencing factor, and actors may succeed to 
either circumvent or exploit material endowments to impact climate 
governance (limited evidence, medium agreement) (Boasson 2015; 
Green and Hale 2017; Aklin and Mildenberger 2020).

Since countries are not bound by their material endowment, countries 
with similar material endowments may differ in climate governance, 
whereas those with notable differences in material endowments 
may have similar policies. For instance, countries with rich fossil fuel 
endowments are found either adopting rather ambitious emission 
reduction targets and measures, or remaining weak in developing 
domestic climate policies (Eckersley 2013; Farstad 2019). Further, 
countries with radically different electricity systems and energy 
resource potentials are found developing rather similar renewables 
support schemes such as feed-in-tariff subsidies and competitive 
tendering programmes (Dobrotkova et al. 2018; Vanegas Cantarero 
2020; Boasson et al. 2021). Some policy instruments are widely 
applied in both developed and developing countries with similar 
or different material endowment. For example, renewable energy 
auctions have been experimented by over 100 countries by the end

of 2018 (IRENA 2019).
Rich carbon-intensive resources and well developed infrastructure 
can make low-carbon activities relatively less economically profitable, 
and negatively influence some perceptions of climate mitigation 
potential (Bertram et al. 2015a; Erickson et al. 2015). If effective 
climate policies are introduced despite this, they can alter the 
importance of country’s material endowments in a way that underpin 
more forceful climate governance over time. For instance, policy 
interventions to limit fossil fuel exploitation or support renewable 
energy deployment may change the value of these energy resources 
over time (Schmitz et al. 2015; Ürge-Vorsatz et al. 2018; Chailleux 
2020; Colgan et al. 2021). 
Developing countries face additional material constraints in climate 
governance due to challenges associated with underdevelopment 
and scarce economic or natural resources (medium evidence,  high 
agreement). Hence, many developing countries design domestic

climate mitigation policies in combination with policy goals that 
address various developmental challenges (von Stechow et al. 
2016; Deng et al. 2017; Thornton and Comberti 2017; Campagnolo 
and Davide 2019), such as air quality, urban transportation, energy 
access, and poverty alleviation (Klausbruckner et al. 2016; Li 
et al. 2016; Melamed et al. 2016; Slovic et al. 2016; Khreis et al. 
2017; Geall et al. 2018; Xie et al. 2018). Combining climate and 
developmental policies for beneficial synergies should not overlook potential trade-offs and challenges (Dagnachew et al. 2018; Ellis 
and Tschakert 2019; Peñasco et al. 2021) (Section  13.7.2 for 
wider discussion).
13.3.2 Political Systems
The effectiveness of domestic climate governance will significantly 
rely on how well it fits with the features of the countries’ specific 
political systems (limited evidence, high agreement) (Schmitz 
2017; Lamb and Minx 2020). Political systems have developed

over generations and constitute a set of formal institutions, such as 
laws and regulations, bureaucratic structures, political executives, 
legislative assemblies and political parties (Egeberg 1999; Pierson 
2004). Different political systems create differing conditions for 
climate governance to emerge and evolve, but because political 
systems are so politically and historically entrenched they are not 
likely to change quickly even though this could facilitate domestic 
climate mitigation efforts (medium evidence, high agreement) 
(Duit and Galaz 2008; Boasson et al. 2021). In addition, variations 
in governance capacities also affect climate policy making and 
implementation (Meckling and Nahm 2018). 
Broader public participation and more open contestation spaces tend 
to nurture more encompassing climate policies, facilitate stronger 
commitments to international agreements (Bättig and Bernauer 2009; 
Böhmelt et al. 2016), achieve more success in decoupling economic

growth from CO 2 emissions (Lægreid and Povitkina 2018), reduce 
more CO 2 emissions (Clulow 2019; von Stein 2020), and maintain 
lower deforestation rates (medium evidence, medium agreement) 
(Buitenzorgy and Mol 2011). States with less public participation 
and contestation space can also develop ambitious climate emission 
reduction targets and institutions (Zimmer et al. 2015; Eckersley 
2016; Han 2017; Engels 2018), but the drivers and effects of climate 
policies within less open and liberal political contexts has not yet 
been sufficiently investigated. 
Election systems based on proportional representation tend to have 
lower emissions, higher energy efficiency, higher renewable energy 
deployment, and more climate friendly investment than systems 
where leadership candidates have to secure a majority of the votes 
to be elected (medium evidence, high agreement) (Fredriksson and 
Millimet 2004; Lachapelle and Paterson 2013; Finnegan 2019). Such

systems better enable voters supporting ambitious climate positions 
to influence policymaking (Harrison and Sundstrom 2010; Willis 2018), 
place less political risks on legislators from additional costs incurred 
from climate actions on voters (Finnegan 2018, 2019), and strengthen 
credible commitments to climate policy (Lockwood 2021b). Similarly, 
rules that govern the relationship between governments and civic 
societies in decision-making have also been shown to matter in 
climate governance. Corporatist societies, where economic groups 
are formally involved in public policy making, have better climate-
related outcomes (lower CO 2 emissions and higher low-carbon 
investments) than liberal-pluralist countries, where a  larger array 
of non-governmental organisations compete for informal influence, 
often through lobbying (medium evidence, medium agreement) 
(Liefferink et al. 2009; Jahn 2016; Finnegan 2018).

1372
1372
Chapter 13 National and Sub-national Policies and Institutions13Political parties with similar ideological roots in different countries 
(for instance social democratic or conservative parties) may 
have different positions on climate governance across countries 
(Boasson et al. 2021). Nevertheless, on average, a higher share of 
green parties in a parliament is associated with lower greenhouse 
gas emissions (Neumayer 2003; Jensen and Spoon 2011; Mourao 
2019), and left-wing parties tend to adopt more pro-climate 
policy positions (medium evidence,  high agreement) (Carter 2013; 
Tobin 2017; Farstad 2018; Ladrech and Little 2019). There is also 
evidence, however, that conservative parties in some countries 
support climate measures (Båtstrand 2015) and consensus can be 
achieved on climate actions across the political spectrum (Thonig 
et al. 2021). At the same time, it seems harder to get support for 
new climate governance initiatives in systems where many political

groups can block decision due to many veto points, for instance 
in systems with bicameralism (the legislature is divided into two 
separate assemblies) and/or in federalist governments (where 
regions have national political representation, e.g. USA and Brazil) 
(medium evidence,  high agreement) (Madden 2014; von Stein 
2020) although federal systems hold out the possibility of sub-
national action when federal agreement is limited (Section 13.2). 
There remains a  limited literature on the role of green parties 
and veto points in developing countries (Haynes 1999; Kernecker 
and Wagner 2019). 
In any political system, climate policy adoption and implementation 
may be obstructed by corrupt practices (Rafaty 2018; Fredriksson 
and Neumayer 2016) that entail an abuse of entrusted power for 
private gain (medium evidence,  high agreement) (Treisman 2000). 
Evidence shows that CO 2 emissions levels can be affected by

corruption, either through the direct negative effect of corruption 
on law enforcement, including in the forestry sector (Sundström 
2016), or through the negative effect of corruption on countries’ 
income (Welsch 2004).These early findings are reinforced by 
studies of a global sample of countries (Cole 2007) and from across 
the developing world (Sahli and Rejeb 2015; Bae et al. 2017; Wang 
et al. 2018b; Ridzuan et al. 2019; Habib et al. 2020). Corruption 
also disrupts public support of climate policies by affecting the 
levels of trust (medium evidence,  high agreement) (Harring 2013; 
Fairbrother et al. 2019; Davidovic and Harring 2020), which then 
impact on the compliance of climate policies. More research is 
required to further understand the causal mechanisms between 
corrupt practices and emissions.
13.3.3 Ideas, Values and Belief Systems
Ideas, values and beliefs affect climate governance by shaping 
people’s perceptions, attitude, and preferences on specific policy and

governance issues (medium evidence, high agreement) (Boasson 
2015; McCright et al. 2016b; Schifeling and Hoffman 2019; Leipold 
et al. 2019; Boasson et al. 2021). While these are often entrenched, 
they can also change, for instance when facing growing exposures 
to climate risks, stronger scientific evidence, and dominant public or 
political discourse (Mayer et al. 2017; Diehl et al. 2021). While change 
tend to be incremental, the pace of change may vary substantially 
across countries and specific climate issue areas.However, new norms sometimes only influence political discussion 
and not actual governance. For instance, more ambitious climate 
emission reduction targets may not lead to more effective mitigation 
actions or policy instruments. Put another way, words do not replace 
actions (Geden 2016). 
Different sets of beliefs can shape climate-related policies, targets, 
and instruments (Boasson and Wettestad 2013; Boasson 2015;

Boasson et al. 2021). First, beliefs link climate governance with social 
justice concerns; policies, targets and instruments may therefore 
reflect justice issues (Fuller and McCauley 2016; Reckien et al. 2017; 
McCauley and Heffron 2018; Routledge et al. 2018; Bäckstrand and 
Lövbrand 2006, 2019). Second, climate mitigation may be seen as 
primarily a market correction issue and mitigation compatible with 
economic growth, as exemplified by ecological modernisation (Mol 
et al. 2009; Bäckstrand and Lövbrand 2006, 2019), climate capitalism 
(Newell and Paterson 2010), market logics (Boasson 2015; Boasson 
et al. 2021) or a global commons approach (Bernstein and Hoffmann 
2019). Third, climate governance may be understood relative to policies 
on technological innovation and progress, often conceptualised as 
social-technical transformations (Geels et al. 2017a). 
Significant variation in ideas, values and beliefs related to climate

governance are detected across and within regions, countries, 
societies, organisations, and individuals (medium evidence, medium 
agreement) (Shwom et al. 2015; Boasson et al. 2021; Knox-Hayes 
2016; Wettestad and Gulbrandsen 2018). These factors provide the 
context for climate policymaking and include differences in countries’ 
histories (Aamodt 2018; Aamodt and Boasson 2020); the political 
culture and regulatory traditions in governing environmental and 
energy issues (Tosun 2018; Aamodt 2018; Boasson et al. 2021); 
and even bureaucrats’ educational background (Rickards et al. 
2014). Structural factors in a  country, such as deeply held value 
systems, are not changed rapidly, just as political systems or natural 
endowments, are not changed rapidly. Consequently, climate policy 
and governance is more effective if it takes into account these deep-
rooted values and beliefs.
Differences in dominant individual preferences may also be important.

The factors that shape individual ideas, values and beliefs about 
climate governance include trust in politicians, the state and other 
people in general (Drews and van den Bergh 2016; Harring et al. 2019; 
Huber et al. 2020), fairness beliefs, variation in political orientation 
(left leaning more concerned), and class (medium evidence, medium 
agreement) (Schmitz et al. 2018; Inglehart and Norris 2017). 
Levels of climate change concern on the individual level have 
increased in most countries (Shwom et al. 2015), and vary with 
gender (females are more concerned), and place of residence (urban 
residents are more concerned) (Shwom et al. 2015; McCright et al. 
2016a; Ziegler 2017). The higher educated in developing countries 
tend to be more concerned (Lee et al. 2015) while individuals working 
in polluting industries tend to oppose forceful climate governance 
(Bechtel et al. 2019; Mildenberger 2020). 
Shifts in mainstream ideas, values and beliefs can underpin changes

in climate policy choices and policy outcomes (limited evidence,

1373
1373
medium agreement) (Schleich et al. 2018; Mildenberger and Tingley 
2019). For example, emission trading schemes are welcomed as a new 
regulatory instrument in China in the context of its market-oriented 
reforms and ideological shift in the past decades (Lo 2013). Based 
on the study of 167 nation-states and 95 sub-national jurisdictions 
with carbon pricing, researchers find that that high public belief in 
climate science underpin adoption of systems that produce a rather 
high carbon price (Levi et al. 2020). These public opinions need to 
be identified and leveraged in supporting specific policy choices 
or changes (Mildenberger and Tingley 2019). Policy support tends 
to be greater if people believe effective measures are being taken 
by other actors, including other households (Bostrom et al. 2018; 
Marlon et al. 2019), and other countries and at the international level 
(Schleich et al. 2018).

On the other hand, anti-climate ideas or beliefs may arise due to 
the introduction of more constraining or ambitious climate policies, 
for example protests in reaction to toll roads in Norway, which 
increase the cost of driving, or protests in France against increasing 
carbon taxes (Grossman 2019; Wanvik and Haarstad 2021). The 
policy implication is that vulnerable or effected groups should be 
considered when introducing policy change, and that participation, 
transparency, and good communication all helps to reduce climate-
related discontent. 
Survey-based studies of public perceptions on hypothetical policy 
instruments or activities, such as carbon taxes or energy infrastructure, 
suggest that linking climate policy to other economic and social 
reforms can increase public support for climate governance (Carattini 
et al. 2019; Bergquist et al. 2020). People and politicians tend to 
underestimate other peoples’ and politicians’ willingness to support

mitigation policies (Hurlstone et al. 2014; Mildenberger and Tingley 
2019), but if actors are informed about other actors actual perceptions 
and behaviours this may reduce the tendency to underestimate 
climate governance support (Mildenberger and Tingley 2019).
13.4 Actors Shaping Climate Governance 
While Section 13.3 shows that structural factors condition climate 
governance, their ultimate importance also depends on whether and 
how various actors are mobilised (Hochstetler 2020; Boasson 2015). 
a  wide range of regional and local governments as well as non-
governmental actors have become increasingly engaged in climate 
governance, for instance through public-private partnerships and 
transnational networks (Jordan et al. 2015; Dorsch and Flachsland 
2017; Jordan et al. 2018) and through the media and litigation, as 
discussed here. 
Climate governance processes result from both slow-moving 
incremental changes to policy and more rapid bursts of change

due to, for example, responses to dramatic weather events, general 
elections or global climate summits (medium evidence, high 
agreement) (Aamodt and Stensdal 2017; Jordan and Moore 2020; 
Boasson et al. 2021). While Section 13.3 assessed how entrenched 
structural factors conditions climate governance developments, this 
section examines how actors are able to alter climate governance by engaging the climate policy process, undertaking litigation and 
interacting with media.
13.4.1 Actors and Agency in the Public Process
A broad array of actors are engaged in shaping mitigation policy 
processes, including politicians and political parties, corporate actors, 
citizen groups, indigenous peoples organisations, labour unions and 
international organisations. Actors aiming to influence the climate-
related policymaking process are studied together to understand 
climate policy dynamics and outcomes (Bulkeley 2000; Fisher 2004;

Jost and Jacob 2004; Jasny et al. 2015; Fisher and Leifeld 2019; Jasny 
and Fisher 2019) and collaboration and influence within climate 
policy networks (Ingold and Fischer 2014; McAllister et al. 2014; 
Wagner and Ylä-Anttila 2018; Kammerer et al. 2021). Most research, 
however, focuses on one particular type of actor. 
Political actors are decision-makers, and also influence whether 
climate governance is perceived as urgent and appropriate (Okereke 
et al. 2019; Ferrante and Fearnside 2019; Boasson et al. 2021). They 
include political parties, legislative assemblies and committees, 
governmental executives and the political leaders of governmental 
ministries (Boasson 2015). They are more likely to pay attention to 
climate issues when polling indicates high political salience with the 
public (Carter 2006, 2014), or when it becomes a  contested issue 
among differing political parties (Boasson et al. 2021). Fluctuations

in the public’s interest and attention may underpin a  disjointed 
approach in politicians’ engagement (Willis 2017, 2018). Policy 
implementation can be hampered if political actors propose frequent 
policy changes (Boasson et al. 2021). 
Corporate actors often influence policies and their adoption (Pulver 
and Benney 2013; Mildenberger 2020; Goldberg et al. 2020). 
Corporate actors acting individually or through industry associations, 
have worked to sway climate policy in different countries (Falkner 
2008; Bernhagen 2008; Newell and Paterson 2010; Meckling 2011; 
Mildenberger 2020). Their ability varies by country and issue (medium 
evidence, medium agreement) (Skjærseth and Skodvin 2010; 
Boasson and Wettestad 2013; Boasson 2015; Boasson et al. 2021) 
and depends on material endowments (Moe Singh 2012), access 
to the political system (Dillon et al. 2018; Mildenberger 2020), and 
the ability to shape ideas, values and belief systems (Boasson 2015).

Corporate actors tend to change their climate policy preferences over 
time, as indicated by longitudinal studies of some European countries 
(Boasson and Wettestad 2013; Boasson 2015; Boasson et al. 2021).
Corporate actors are crucial to policy implementation because they 
are prominent emitters of the greenhouse gases and owners of 
carbon-intensive technologies and potential providers of solutions 
as developers, owners and adopters of low emission practices and 
technologies (Falkner 2008; Perrow and Pulver 2015). Many climate 
policies and measures rely on businesses’ willingness to exploit 
newly created economic opportunities, such as support schemes 
for renewable energy and energy efficiency sector or carbon pricing 
(Olsen 2007; Newell and Paterson 2010; Shen 2015; World Bank 
2019). Some corporate actors provide climate solutions, such as

1374
1374
Chapter 13 National and Sub-national Policies and Institutions13renewable energy deployment, and have successfully influenced 
climate policy development related to feed-in tariffs, taxations, 
quotas, or emission trading schemes, in the EU (Boasson 2019), 
Germany (Leiren and Reimer 2018), the USA (Stokes and Breetz 
2018), the Nordic countries (Kooij et al. 2018), China (Shen 2017) 
and Japan (Li et al. 2019).
Fossil fuel industries have been important agenda-setters in many 
countries, including the USA (Dunlap and McCright 2015; Supran and 
Oreskes 2017; Downie 2018), the EU (Skjærseth and Skodvin 2010; 
Boasson and Wettestad 2013), Australia (Ayling 2017), China (Shen 
and Xie 2018; Tan et al. 2021), India (Schmitz 2017; Blondeel and Van 
de Graaf 2018), and Mexico (Pulver 2007), with differing positions 
and impacts across countries (Kim et al. 2016; Nasiritousi 2017). 
In the US, the oil industry has underpinned emergence of climate

scepticism (Dunlap and McCright 2015; Farrell 2016a; Supran and 
Oreskes 2017), and its spread abroad (Dunlap and Jacques 2013; 
Engels et al. 2013; Painter and Gavin 2016). Corporate opposition 
to climate policies is often facilitated by a broad coalition of firms 
(Cory et al. 2021). 
Conservative foundations, sometimes financed by business revenues, 
have funded a  diversity of types of groups, including think-tanks, 
philanthropic foundations, or activist networks to oppose climate 
policy (Brulle 2014, 2019). However, there is limited knowledge 
about the conditions under which actors opposed to climate action 
succeed in shaping climate governance (Kinniburgh 2019; Martin 
and Islar 2021).
Some labour unions have developed positions and programmes on 
climate change (Snell and Fairbrother 2010; Stevins 2013; Räthzel 
et al. 2018), formed alliances with other actors in the field of climate 
policy (Stevis 2018) and participated in domestic policy networks on

climate change (Jost and Jacob 2004), but we know little about their 
relative importance or success. In countries with significant fossil fuel 
resources such as Australia, Norway, and the United States, labour 
unions, particularly industrial unions, tend to contribute to reducing 
the ambition of domestic climate policies mainly due to the concern 
of job losses (Mildenberger 2020). Other studies find that the role of 
labour unions varies across countries (Glynn et al. 2017).
Civil society actors can involve citizens working collectively to 
change individual behaviours that have climate implications. For 
example, environmental movements that involve various forms of 
collective efforts encourage their members to make personal lifestyle 
changes that reduce their individual carbon footprints (Ergas 2010; 
Middlemiss 2011; Haenfler et al. 2012; Cronin et al. 2014; Saunders 
et al. 2014; Büchs et al. 2015; Wynes et al. 2018). These efforts seek

to change individual members’ consumer behaviours by reducing 
car-use and flying, shifting to non-fossil fuel sources for individual 
sources of electricity, and eating less dairy or meat (Cherry 2006; 
Ergas 2010; Middlemiss 2011; Haenfler et al. 2012; Stuart et al. 2013; 
Cronin et al. 2014; Saunders et al. 2014; Büchs et al. 2015; Wynes and 
Nicholas 2017; Wynes et al. 2018; Thøgersen et al. 2021). Consumer/
citizen engagement is sometimes encouraged through governmental 
directives, such as the ‘renewable energy communities’ granted by the 
EU renewable energy directive 2018/2001 (The European Parliament and the Council of the European Union 2018). To date, there are only 
a limited number of case studies that measure the direct effect of 
participation in these types of movements as it relates to climate 
outcomes (Saunders et al. 2014; Vestergren et al. 2018, 2019). 
Citizens with less access to resources and power also participate

by challenging nodes of power  – policymakers, regulators, and 
businesses  – to change their behaviours and/or accelerate their 
efforts. Tactics include lobbying, legal challenges, shareholder 
activism, coop board stewardship, and voting (Gillan and Starks 
2007; Schlozman et al. 2012; Viardot 2013; Bratton and McCahery 
2015; Yildiz et al. 2015; Olzak et al. 2016). Citizens provide the labour 
and political will needed to pressure political and economic actors 
to enact emission-reducing policies, as well as providing resistance 
to them (Fox and Brown 1998; Boli and Thomas 1999; Oreskes and 
Conway 2012; McAdam 2017). 
Other citizen engagement involves a range of more confrontational 
tactics, such as boycotting, striking, protesting, and direct action 
targeting politicians, policymakers, and businesses (Fisher et al. 2005; 
Tarrow 2005; Fisher 2010; Saunders et al. 2012; Walgrave et al. 2012; 
Wahlström et al. 2013; Eilstrup-Sangiovanni and Bondaroff 2014;

Hadden 2014, 2015; O’Brien et al. 2018; Chamorel 2019; Cock 2019; 
2019b; Hadden and Jasny 2019; Swim et al. 2019). Climate strikes and 
other more confrontational forms of climate activism have become 
increasingly common (O’Brien et al. 2018; Evensen 2019; D.A. Fisher 
2019; Boulianne et al. 2020; Martiskainen et al. 2020; de Moor et al. 
2021; Fisher and Nasrin 2021a). Very few studies look specifically at 
the effect of these tactics on actual climate-related outcomes and 
more research is needed to understand the climate effects of citizen 
engagement and activism (Fisher and Nasrin 2021b).
Citizen engagement has also become common among indigenous 
groups who tend to have limited structural power but often aim to 
shape the formation and effects of projects that have implications 
to  climate change. These include opposing extraction and 
transportation of fossil fuels on their traditional lands (especially 
in the Americas) (Bebbington and Bury 2013; Hindery 2013; Coryat

2015; Claeys and Delgado Pugley 2017; Wood and Rossiter 2017); 
large-scale climate mitigation projects that may affect traditional 
rights (Brannstrom et al. 2017; Moreira et al. 2019; Zárate-Toledo 
et al. 2019); supporting deployment of small-scale renewable energy 
initiatives (Thornton and Comberti 2017); seeking to influence the 
development of REDD+ policies through opposition (Reed 2011); 
and participation in consultation processes and multi-stakeholder 
bodies (Bushley 2014; Gebara et al. 2014; Astuti and McGregor 2015; 
Kashwan 2015; Jodoin 2017). Indigenous groups have been reported 
to have had some influence on some climate discussions, particularly 
forest management and siting of renewable energy (Claeys and 
Delgado Pugley 2017; Jodoin 2017; Thornton and Comberti 2017). 
Further, more scientific assessments are required on the role of 
indigenous groups in climate activism and policy (Jodoin 2017; 
Claeys and Delgado Pugley 2017; Thornton and Comberti 2017).

Activism, including litigation, as well as the tactics of protest and 
strikes, have played a  substantial role in pressuring governments 
to create environmental laws and environmental agencies tasked

1375
1375
with enforcing environmental laws that aimed to maintain clean air 
and water in countries around the world (medium evidence, high 
agreement) (McCloskey 1991; Schreurs 1997; Rucht 1999; Brulle 
2000; Steinhardt and Wu 2016; Longhofer et al. 2016; Wong 2018). 
Several studies find environmental NGOs have a positive effect on 
reductions in carbon emissions, whether through effects that operate 
across countries or (Frank et al. 2000; Schofer and Hironaka 2005; 
Jorgenson et al. 2011; Baxter et al. 2013; Longhofer and Jorgenson 
2017; Grant et al. 2018) through impact of NGOs within nations 
(Shwom 2011; Dietz et al. 2015; Grant and Vasi 2017). 
At the same time, other research has documented various forms of 
backlash against climate policies, both in terms of voting behaviour, 
as well as other collective efforts (Hill et al. 2010; Williamson et al. 
2011; McAdam and Boudet 2012; Wright and Boudet 2012; Walker

et al. 2014; Boudet et al. 2016; Fast et al. 2016; Krause et al. 2016; 
Lyon 2016; Mayer 2016; Stokes 2016; Stokes and Warshaw 2017; 
Muradian and Pascual 2020; Stokes 2020). In a systematic analysis 
that includes movements against fossil fuel investments along 
with those against low-carbon emitting projects around the world, 
research finds that a quarter of all projects (no matter their targets) 
were cancelled after facing resistance (Temper et al. 2020).
A range of international organisations can be important, particularly 
in developing countries, for instance by assisting in framing of national 
climate governance and supporting the design of climate policies 
through technical assistance projects (Talaei et al. 2014; Ortega Díaz 
and Gutiérrez 2018; Kukkonen et al. 2018; Bhamidipati et al. 2019; 
Charlery and Trærup 2019). Yet for these climate aid initiatives to 
work effectively requires improved institutional architecture, better

appreciation of local contexts, and more inclusive and transparent 
governance, based on evidence from many multilateral mechanisms 
like REDD+, CDM, GEF and GCF (Gomez 2013; Arndt and Tarp 2017), 
and bilateral programmes on energy, agriculture and land-use sectors (Arndt and Tarp 2017; Rogner and Leung 2018; Moss and 
Bazilian 2018). 
13.4.2 Shaping Climate Governance Through Litigation
Outside the formal climate policy processes, climate litigation is 
another important arena for various actors to confront and interact 
over how climate change should be governed (robust evidence, 
high agreement) (Wilensky 2015; Peel and Osofsky 2015, 2018; 
Bouwer 2018; Setzer and Byrnes 2019; Calzadilla 2019; Setzer and 
Vanhala 2019; Paiement 2020; Wegener 2020). Climate litigation is 
an attempt to control, order or influence the behaviour of others in 
relation to climate governance, and it has been used by a wide variety

of litigants (governments, private actors, civil society and individuals) 
at multiple scales (local, regional, national and international) 
(Osofsky 2007; Lin 2012b; Keele 2017; McCormick et al. 2018; Peel 
and Osofsky 2018; Setzer and Vanhala 2019). Climate litigation 
has become increasingly common (UNEP2020b), but its prevalence 
varies across countries (medium evidence, high agreement) (Peel 
and Osofsky 2015; Wilensky 2015; Bouwer 2018; Lin and Kysar 2020; 
Setzer and Higham 2021). This is not surprising, given that courts 
play differing roles across varying political systems and law traditions 
(La Porta et al. 1998). 
This sub-section focuses on relevant climate litigation for policies and 
institutions. Climate litigation is further discussed in Sections 14.5.1.2 
(linkages between mitigation and human rights) and Section 14.5.3 
(cross-country implications and international courts/tribunals). 
The vast majority of climate cases have emerged in United States,

Australia and Europe, and more recently in developing countries 
(Humby 2018; Kotze and du Plessis 2019; Peel and Lin 2019; Setzer 
and Benjamin 2019; Zhao et al. 2019; Rodríguez-Garavito 2020). Box 13.7 | Civic Engagement: The School Strike Movement
On Friday 20 August 2018, Greta Thunberg participated in the first climate school strike. Since then, Fridays for Future – the name of 
the group coordinating this tactic of skipping school on Fridays to protest inaction on climate change – has spread around the world.
In March 2019, the first global climate strike took place, turning out more than one million people around the world (Carrington 2019). 
Six months later in September 2019, young people and adults responded to a call to participate in climate strikes as part of the ‘Global 
Week for Future’ surrounding the UN Climate Action Summit (Thunberg 2019), and the number of participants globally jumped

to an estimated six million people (Taylor et al. 2019). Although a handful of studies have reported on who was involved in these 
strikes, how they were connected, and their messaging (Marris 2019; Wahlström et al. 2019; Evensen 2019; D. Fisher 2019; Boulianne  
et al. 2020; Bevan et al. 2020; Han and Ahn 2020; Holmberg and Alvinius 2020; Jung et al. 2020; Martiskainen et al. 2020; Thackeray et al.  
2020; Trihartono et al. 2020; de Moor et al. 2021; Fisher and Nasrin 2021b), its consequences in terms of political outcomes and 
emissions reductions have yet to be fully understood (Fisher and Nasrin 2021b). 
Although digital activism makes it easier to connect globally, it is unclear how digital technology will affect the youth climate 
movement, and its effects on carbon emissions. Research suggests that online activism is likely to involve a more limited range of

participants and perspectives (Bennett 2013; Elliott and Earl 2018). Digital tactics could also mean that groups are less embedded 
in communities and less successful at creating durable social ties, factors that have been found to lead to longer term engagement 
(Tufekci 2017; Rohlinger and Bunnage 2018; Shirky 2010).

1376
1376
Chapter 13 National and Sub-national Policies and Institutions13As  of 31 May 2021, 1841 cases of climate change litigation from 
around the world had been identified. Of these, 1387 were filed 
before courts in the United States, while the remaining 454 were 
filed in 39 other countries and 13 international or regional courts 
and tribunals (including the courts of the European Union). Outside 
the US, Australia (115), the UK (73) and the EU (58) remain the 
jurisdictions with the highest volume of cases. The majority of cases, 
1006, have been filed since 2015 (Setzer and Higham 2021). The 
number of climate litigation cases in developing countries is also 
growing. There are at least 58 cases in 18 Global South jurisdictions 
(robust evidence, high agreement) (Humby 2018; Kotze and du Plessis 
2019; Peel and Lin 2019; Setzer and Benjamin 2019; Zhao et al. 2019; 
Rodríguez-Garavito 2020; Setzer and Higham 2021).

Overall, courts have also played a  more active role for climate 
governance in democratic political systems (Peel and Osofsky 2015; 
Eskander et al. 2021). Whether and to what extent differing law 
traditions and political systems influence the role and importance 
of climate litigation has, however, not been examined enough 
scientifically (Setzer and Vanhala 2019; Peel and Osofsky 2020). 
The majority of climate change litigation cases are brought against 
governments, by civic and non-governmental organisations and 
corporations (Eisenstat 2011; Markell and Ruhl 2012; Wilensky 2015; 
Fisher et al. 2017; Setzer and Higham 2021). Many, although not all 
of these cases, seek to ensure that governmental action on climate 
change is more ambitious, and better aligned with the need to avert or 
respond to climate impacts identified and predicted by the scientific 
community (Markell and Ruhl 2012; Setzer and Higham 2021).

Climate aligned cases against governments can be divided into two 
distinct categories: claims challenging the overall effort of a  State 
or its organs to mitigate or adapt to climate change (sometimes 
referred to as ‘systemic climate litigation’) (Jackson 2020) and claims 
regarding authorisation of third-party activity (Bouwer 2018; Gerrard 
2021; Ghaleigh 2021). 
Systemic climate litigation that seeks an increase in a  country’s 
ambition to tackle climate change has been a growing trend since 
the first court victories in the Urgenda case in the Netherlands (see 
Box  13.8 below) and the Leghari case in Pakistan in 2015. These 
cases motivated a wave of similar climate change litigation across 
the world (Roy and Woerdman 2016; Ferreira 2016; Peeters 2016; Mayer 2019; Paiement 2020; Barritt 2020; Sindico et al. 2021). 
Between 2015 and 2021, individuals and communities initiated at 
least 37 cases (including Urgenda and Leghari) against states (Setzer

and Higham 2021), challenging the effectiveness of legislation and 
policy goals (Jackson 2020; Setzer and Higham 2021). Some cases 
also seek to shape new legal concepts such as ‘rights of nature’ 
recognised in the Future Generations case in Colombia (Savaresi and 
Auz 2019; Rodríguez-Garavito 2020) and ‘ecological damage’ in the 
case of Notre Affaire à Tous and others vs France (Torre-Schaub 2021).
Moreover, there are a  number of regulatory challenges to state 
authorisation of high-emitting projects, which differs from systemic 
cases against states (Bouwer 2018; Hughes 2019a). For instance, the 
High Court in Pretoria, South Africa, concluded that climate change 
is a  relevant consideration for approving coal-fired power plants 
(Humby 2018). Similarly, the Federal Court of Australia concluded 
that the Minister for the Environment owed a  duty of care to 
Australian children in respect to climate impacts when exercising

a statutory power to decide whether to authorise a major extension 
to an existing coal mine (Peel and Markey-Towler 2021).
Climate change litigation has also been brought against corporations 
by regional or local governments and non-governmental 
organisations (Wilensky 2015; Ganguly et al. 2018; Foerster 2019). 
One type of private climate change litigation alleges climate change-
related damage and seeks compensation from major carbon polluters 
(Ganguly et al. 2018; Wewerinke-Singh and Salili 2020). The litigators 
claim that major oil producers are historically responsible for 
a significant portion of global greenhouse gas emissions (Heede 2014; 
Frumhoff et al. 2015; Ekwurzel et al. 2017; Stuart-Smith et al. 2021). 
These cases rely on advancements in climate science, specifically 
climate attribution (Marjanac et al. 2017; Marjanac and Patton 2018; 
McCormick et al. 2018; Minnerop and Otto 2020; Burger et al. 2020b;

Stuart-Smith et al. 2021). It is alleged that major carbon emitters had 
knowledge and awareness of climate change and yet took actions 
to confound or mislead the public about climate science (Supran 
and Oreskes 2017). Strategic climate change litigation has also been 
used to hold corporations to specific human rights responsibilities 
(Savaresi and Auz 2019; Savaresi and Setzer 2021) (Box 13.8). 
In addition to direct cases targeting high emitters, litigation is also 
now being used to argue against financial investments in the fossil Box 13.8 | An Example of Systemic Climate Litigation: Urgenda vs State of the Netherlands
The judgement in Urgenda vs State of the Netherlands established the linkage between a state’s international duty, domestic actions, 
and human rights commitments as to the recommendations of IPCC’s AR5 (Burgers and Staal 2019; Antonopoulos 2020). It was the

first to impose a specific emissions reduction target on a state (de Graaf and Jans 2015; Cox 2016; Loth 2016). The District Court of The 
Hague ordered the Dutch Government to reduce emissions by at least 25% by the end of 2020. Following the decision of the district 
court of The Hague in 2015 the Dutch government announced that it would adopt additional measures to achieve the 25% emissions 
reduction target by 2020 (Mayer 2019). The decision was upheld by the Court of Appeal in 2018 and the Supreme Court in 2019. 
Since the first judgment in 2015 significant changes in the climate policy environment have been reported, the results of which have 
included the introduction of a Climate Act and the decision to close all remaining coal fired power plants by 2030 (Verschuuren 2019; 
Wonneberger and Vliegenthart 2021).

1377
1377
fuel industry (Franta 2017; Colombo 2021). In May 2021, the Hague 
District Court of the Netherlands issued a ground-breaking judgment holding energy company Royal Dutch Shell (RDS) legally responsible for greenhouse gas emissions from its entire value chain (Macchi and Zeben 2021). Claims have also been brought against banks, pension funds and investment funds for failing to incorporate climate risk into their decision-making, and to disclose climate risk to their beneficiaries (Wasim 2019; Solana 2020; Bowman and Wiseman 2020). These litigation cases also impact on the financial market without directly involving specific financial institutions into the case (Solana 2020) but somehow aim to change their risk perceptions and attitude on high carbon activities (Griffin 2020). 
The outcomes of climate litigation can affect the stringency and

ambitiousness of climate governance (McCormick et al. 2018; Eskander et al. 2021). In the United States, pro-regulation litigants more commonly win in relation to renewable energy and energy efficiency cases, and more frequently lose in relations to coal-fired power plant cases (McCormick et al. 2018). Outside the US, more than half (58%) of litigation have outcomes that are aligned with climate action (Setzer and Higham 2021). But these cases can also have impacts outside of the legal proceedings before, during and after the case has been brought and decided (Setzer and Vanhala 2019). These impacts include changes in the behaviour of the parties (Peel and Osofsky 2015; Pals 2021), public opinion (Hilson 2019; Burgers 2020), financial and reputational consequences for involved actors (Solana 2020), and impact on further litigation (Barritt 2020). Individual cases have also attracted considerable media attention, which in turn can influence how climate policy is perceived (Nosek 2018;

Barritt and Sediti 2019; Hilson 2019; Paiement 2020). While there is evidence to show the influence of some key cases on climate agenda-setting (Wonneberger and Vliegenthart 2021), it is still unclear the extent to which climate litigation actually results in new climate rules and policies (Peel and Osofsky 2018; Setzer and Vanhala 2019; Peel and Osofsky 2020) and to what degree this holds true for all cases (Jodoin et al. 2020). However, there is now increasing academic agreement that climate litigation has become a powerful force in climate governance UNEP 2020b; Burgers 2020). In general, litigations can be applied to constrain both public and private entities, and to shape structural factors mentioned in Section 13.3, such as the beliefs and institutions around climate governance.

13.4.3  Media as Communicative Platforms for Shaping 
Climate Governance 
Media is another platform for various actors to present, interpret and 
shape debates around climate change and its governance (Tindall et al. 2018). The media coverage of climate change has grown steadily since 1980s (O’Neill et al. 2015; Boykoff et al. 2019), but the level and type of coverage differs over time and from country to country (robust evidence, high agreement) (Boykoff 2011; Schmidt et al. 2013; Schäfer and Schlichting 2014). Media can be a useful conduit to build public support to accelerate mitigation action, but may also be utilised to impede decarbonisation endeavours (Boykoff 2011; O’Neill et al. 2015; Farrell 2016b; Carmichael et al. 2017; Carmichael and Brulle 2018). Different media systems in different regions and countries and with unique cultural and political traditions also affect how climate change is communicated (Eskjær 2013).

A broad variety of media platforms cover climate change issues, 
including traditional news media, such as newspapers and broadcasting, digital social media (Walter et al. 2018), creative narratives such as climate fiction and films (Svoboda 2016); humour and entertainment media (Brewer and McKnight 2015; Skurka et al. 2018; Boykoff and Osnes 2019); and strategic communications campaigns (Hansen and Machin 2008; Hoewe and Ahern 2017). Media coverage can have far-reaching consequences on policy processes, but we know less about its relative importance compared to other policy shaping factors (medium evidence, medium agreement) (Liu et al. 2011; Boykoff 2011; Hmielowski et al. 2014). 
Popular culture images, science fictions and films of ecological

catastrophe can dramatically and emotively convey the dangers of climate change (Bulfin 2017). The overall accuracy of the media coverage on climate change has improved from 2005 to 2019 in the United Kingdom (UK), Australia, New Zealand, Canada, and the USA (McAllister et al. 2021). Moreover, coverage of climate science is increasing. One study (MeCCO) has tracked media coverage of climate change from over 127 sources from 59 countries in North and Latin America, Europe, Middle East, Africa, Asia and Oceania (Boykoff et al. 2021). It shows the number of media science stories in those sources grew steadily from 47,376 per annum to 86,587 per annum between 2017 and 2021 across print, broadcast, digital media and entertainment (Boykoff et al. 2021). 
However, increasing media coverage does not always lead to more

accurate coverage of climate change mitigation, as it can also spur diffusion of misinformation (Boykoff and Yulsman 2013; van der Linden et al. 2015; Whitmarsh and Corner 2017; Fahy 2018; Painter 2019). In addition, media professionals have at times drawn on the norm of representing both sides of a controversy, bearing the risk of the disproportionate representation of scepticism of anthropogenic climate change despite the convergent agreement in climate science that humans contribute to climate change, (robust evidence, high agreement) (Freudenburg and Muselli 2010; Boykoff 2013; Painter and Gavin 2016; Tindall et al. 2018; McAllister et al. 2021). This occurs despite increasing consensus among journalists regarding the basic scientific understanding of climate change (Brüggemann and Engesser 2017). 
Accurate transference of the climate science has been undermined

significantly by climate change counter-movements, particularly in the USA (McCright and Dunlap 2000, 2003; Jacques et al. 2008; Brulle et al. 2012; Boussalis and Coan 2016; Farrell 2016a; Carmichael et al. 2017; Carmichael and Brulle 2018; Boykoff and Farrell 2019; Almiron and Xifra 2019) in both legacy and new/social media environments through misinformation (robust evidence,  high agreement) (van der Linden et al. 
2017), including about the causes and consequences of climate change (Brulle 2014; Farrell 2016a; Farrell 2016b; Supran and Oreskes 2017). Misinformation can rapidly spread through social media (Walter et al. 2018). Together with the proliferation of suspicions of ‘fake news’ and ‘post-truth’, some traditional and social media contents have fuelled polarisation and partisan divides on climate change in many countries

1378
1378
Chapter 13 National and Sub-national Policies and Institutions13(Feldman et al. 2017; Hornsey et al. 2018), which can further deter 
development of new and ambitious climate policy (Tindall et al. 2018). 
Further, the ideological stance of media also influences the intensity 
and content of media coverage, in developed and developing countries 
alike (Dotson et al. 2012; Stoddart and Tindall 2015). 
Who dominates the debate on media, and how open the debate can be 
varies significantly across countries (Takahashi 2011; Poberezhskaya 
2015) based on participants’ material and technological power. Fossil 
fuel industries have unique access to mainstream media (Geels 2014) 
via advertisements, shaping narratives of media reports, and exerting 
political influence in countries like Australia and the USA (Holmes 
and Star 2018; Karceski et al. 2020). For social media, novel technical 
tools, such as automated bots, are emerging to shape climate change

discussion on major online platforms such as Twitter (Marlow et al. 
2021). Open debates can underpin the adoption of more ambitions 
climate policy (Lyytimäki 2011). Media coverage on energy saving, 
patriotism, and social justice in the countries like USA and the UK have 
helped connect mitigation of climate change with other concerns, 
thereby raising support to climate action (Leiserowitz 2006; Trope 
et al. 2007; Doyle 2016; Corner and Clarke 2017; Whitmarsh and 
Corner 2017; Markowitz and Guckian 2018). Further, media coverage 
of climate change mitigation has influenced public opinions through 
discussions on political, economic, scientific and cultural themes 
about climate change (medium evidence, high agreement) (Irwin and 
Wynne 1996; Smith 2000; Boykoff 2011; O’Neill et al. 2015). 
Common challenges in reporting climate change exist around the 
world (Schmidt et al. 2013; Schäfer and Painter 2021), but particularly

so in the developing countries, due to lower capacities, lack of 
journalists’ training in complex climate subjects, and lack of access 
to clear, timely and understandable climate-related resources and 
images in newsrooms (robust evidence, high agreement) (Harbinson 
2006; Shanahan 2009; Broadbent et al. 2016; Lück et al. 2018). 
Ugandan journalist Patrick Luganda has said, ‘Those most at risk from 
the impacts of climate change typically have had access to the least 
information about it through mass media.’ (Boykoff, 2011), indicating 
that information availability and capacity is a manifestation of global 
climate (in)justice.
13.5 Sub-national Actors, Networks, 
and Partnerships
In many countries, sub-national actors and networks are a  crucial 
component of climate mitigation as they have remit over land-
use planning, waste management, infrastructure, housing and 
community development, and their jurisdictions are often where the

impacts of climate change are felt (robust evidence, high agreement). 
Depending on the legal framework and other institutional constraints, 
sub-national actors play crucial roles in developing, delivering 
and contesting decarbonisation visions and pathways (Schroeder 
et al. 2013; Ryan 2015; Abbott et al. 2016; Bäckstrand et al. 2017; 
Amundsen et al. 2018; Fuhr et al. 2018) (Section 13.3.3). 
Sub-national actors include organisations, jurisdictions, and 
networks (e.g.,  a coalition of cities or state authorities). These are either formal or informal, profit or non-profit and 
public  or  private  (Avelino and  Wittmayer 2016). For example, 
corporations are formal, private, and for-profit, the state and labour 
organisations are  formal, public, and non-profit, and communities 
are private, informal, and non-profit. An intermediary sector, crossing 
the boundaries between private and public, for profit and non-profit,

includes energy cooperatives, not-for-profit energy enterprises, and 
the scientific community (Avelino and Wittmayer 2016). 
To address the challenge of climate mitigation, a  range of actors 
across sectors and jurisdictions have created coalitions for climate 
governance, operating as actor-networks. For example, mitigation 
policies are particularly effective when they are integrated with 
co-benefits such as health, biodiversity, and poverty reduction 
(Romero-Lankao et al. 2018a). Transnational business and public-
private partnerships and initiatives, as well as international cooperation 
at the sub-national and city levels are discussed in Chapter 14.
13.5.1 Actor-networks and Policies 
The decision adopting the Paris Agreement welcomed contributions 
of sub-national actors to mobilising and scaling up ambitious 
climate action (see also Chapter 14). They engage in climate relevant 
mechanisms, such as the Sustainable Development Goals and the

New Urban Agenda. Sub-national actors fill a gap in national policies, 
participate in transnational and sub-national climate governance 
networks and facilitate learning and exchange among governmental, 
community, and private organisations at multiple levels, gathering 
knowledge and best practices such as emission inventories and risk 
management tools that can be applied in multiple contexts (Kona 
et al. 2016; Sharifi and Yamagata 2016; Michaelowa and Michaelowa 
2017; Warbroek and Hoppe 2017; Bai et al. 2018; Busch et al. 2018; 
Hsu et al. 2018; Lee and Jung 2018; Marvin et al. 2018; Romero-
Lankao et al. 2018b; Ürge-Vorsatz and Seto 2018; Amundsen et al. 
2018; Heikkinen et al. 2019; Hultman et al. 2020). 
Sub-national climate change policies exist in more than 142 countries 
and exemplify the increasing significance of mitigation policy 
at  the  sub-national level (Hsu et al. 2018). However, estimations 
of the number of sub-national actors pledging voluntary climate

action are challenging and underreporting is a concern (Hsu et al. 
2018; Chan and Morrow 2019). As can be seen in Figure 13.3 more 
than 10,500 cities and nearly 250 regions representing more than 
2 billion people, factoring for overlaps in population between these 
jurisdictions, have pledged climate action as of December 2020 (Hsu 
et al. 2020a). More jurisdictions in Europe and North America have 
pledged action, but in terms of population almost all regions are 
substantially engaged in sub-national action.
Many of these efforts are organised around transnational or regional 
networks. For example, a  coalition of 130 sub-national (in other 
words, state, and regional) governments, representing 21% of the 
global economy and 672 million people, has pledged about 9% 
emissions reduction compared to a  base year (CDP 2020). More 
than 10,000 cities, representing more than 10% of the global 
population, participate in the Global Covenant of Mayors, C40 Cities

1379
1379
(Global Covenant of Mayors for Climate and Energy 2018), and 
ICLEI’s – Local Governments for Sustainability carbon registry (Hsu 
et al. 2018). In Europe alone, more than 6000 cities have adopted 
their own climate action plans (Palermo et al. 2020a) and nearly 
300 US sub-national actors  – cities and states  – were committed 
to maintaining momentum for climate action as part of the ‘We Are 
Still In’ coalition (We Are Still In coalition 2020) in the absence of 
national US climate legislation. Further, as of October 2020, more 
than 826  cities and 103 regional governments had made specific 
pledges to decarbonise, whether in a specific sector (e.g., buildings, 
electricity, or transport) or through their entire economies, pledging 
to reduce their overall emissions by at least 80% (NewClimate 
Institute and Data Driven EnviroLab 2020). Cities such as Barcelona,

Spain and Seattle, Washington have adopted net zero goals for 2050 
in policy legislation, while many more cities throughout the world, 
including the Global South such as Addis Ababa in Ethiopia, have net 
zero targets under consideration (ECIU 2019, 2021).
Sub-national mitigation policies are highlighted below, based on the 
taxonomy of policies in Section 13.6.1: 
a) Economic instruments: as of 2020, there were carbon pricing 
initiatives (ETS, carbon tax or both) in 24 sub-national jurisdictions 
(World Bank 2021a). Examples include emission trading systems 
within North America, such as the Regional Greenhouse Gas 
Initiative (RGGI) and Western Climate Initiative (which also 
includes two Canadian provinces); tax rebates for the purchase of EVs; a  carbon tax in British Columbia; and a  cap-and-trade 
scheme in Metropolitan Tokyo (Houle et al. 2015; Murray and 
Rivers 2015; Hibbard et al. 2018; Bernard and Kichian 2019;

Raymond 2019; Xiang and Lawley 2019; Chan and Morrow 2019). 
b) Regulatory instruments: policies such as land use and 
transportation planning, performance standards for buildings, 
utilities, transport electrification, and energy use by public 
utilities, buildings and fleets are widely prevalent (Bulkeley 
2013; Jones 2013; C40 and ARUP 2015; Martinez et al. 2015; 
Hewitt and Coakley 2019; Palermo et al. 2020b). Policies such 
as regulatory restrictions, low emission zones, parking controls, 
delivery planning and freight routes, focus on traffic management 
and reduction of local air pollution but also have a mitigation 
impact (Slovic et al. 2016; Khreis et al. 2017; Letnik et al. 2018). 
For instance, in coordination with national governments, sub-
national actors in China, Europe and USA have introduced access 
to priority lanes, free parking and other strategies fostering the 
roll-out of EVs (Creutzig 2016; Zhang and Bai 2017; Teske et al.

2018; Zhang and Qin 2018; Romero-Lankao et al. 2021). 
c) Land-use planning addresses building form, density, energy, 
and transport, which are relevant for decarbonisation (Creutzig 
et al. 2015; Torabi Moghadam et al. 2017; Teske et al. 2018). Its 
effectiveness is limited by absent or fragmented jurisdiction, 
financial resources and powers, competition between authorities 
and policy domains, and national policies that restrict local 
governments’ ability to enact more ambitious policies (Fudge 
et al. 2016; Gouldson et al. 2016; Petersen 2016). Most rapidly 
Figure 13.3 | Sub-national GHG mitigation commitments: Total population by IPCC region.  Population of sub-national actors (cities and regions) recording climate 
action commitments as captured in the ClimActor dataset. Population calculation considers overlap between City and Regions by only accounting for population once for Cities

and Regions that are nested jurisdictions. Source: adapted with permission from Hsu et al. (2020a) to reflect IPCC AR6 aggregation. Compiled in 2020 from multiple sources 
based on most recent year of data available.

1380
1380
Chapter 13 National and Sub-national Policies and Institutions13growing smaller cities in Latin America, Asia and Africa lack 
capacity for urban planning and enforcement (Romero-Lankao 
et al. 2015; Creutzig 2016).
d) Other policies: these include information and capacity building, 
such as carbon labelling aimed at providing carbon footprint 
information to consumers (Liu et al. 2016); disclosure and 
benchmarking policies in buildings to increase awareness of 
energy issues and track mitigation progress (Hsu et al. 2017; 
Papadopoulos et al. 2018); and procurement guidelines developed 
by associations (Sustainable Purchasing Leadership Council 2021). 
For instance, a building retrofit programme was initiated in New 
York and Melbourne to foster energy efficiency improvements 
through knowledge provision, training, and consultation 
(Trencher et al. 2016; Trencher and van der Heijden 2019).  
Also significant is government provision of public good, services,

and infrastructure (Romero Lankao et al. 2019), which includes 
provision of electric buses or buses on renewable fuels for public 
transportation (Kamiya and Teter 2019) and zero emission urban 
freight transport (Quak et al. 2019), sustainable food procurement 
for public organisations in cities (Smith et al. 2016), decentralised 
energy resources (Marquardt 2014; Hirt et al. 2021; Kahsar 2021), 
and green electricity purchase via community choice aggregation 
programmes and franchise agreements (Armstrong 2019).
13.5.2 Partnerships and Experiments
Partnerships, such as those among private and public, or 
transnational and sub-national entities, have been found to enable 
better mitigation results in areas outside direct government control 
such as residential energy use, emissions from local businesses, or 
private vehicles (Fenwick et al. 2012; Castán Broto and Bulkeley 
2013; Aylett 2014; Hamilton et al. 2014; Bulkeley et al. 2016;

Wakabayashi and Arimura 2016; Grandin et al. 2018). Partnerships 
take advantage of investments that match available grants or enable 
a local energy project, or enhance the scope or impact of mitigation 
(Burch et al. 2013). 
Sub-national actors have also been associated with experiments and 
laboratories, which promise to achieve the deep change required to 
address the climate mitigation gap (Smeds and Acuto 2018; Marvin 
et al. 2018). Experiments span smart technologies, for example, in 
Malmö, Sweden (Parks 2019), Eco-Art, Transformation-Labs and 
other approaches that question the cultural basis of current energy 
regimes and seek reimagined or reinvented futures (Castán Broto and 
Bulkeley 2013; Guy et al. 2015; Voytenko et al. 2016; Hodson et al. 
2018; Peng and Bai 2018; Smeds and Acuto 2018; Culwick et al. 2019; 
Pereira et al. 2019; Sengers et al. 2019). They may include governance 
experiments, from formally defined policy experiments to informal

initiatives that mobilise new governance concepts (Kivimaa et al. 
2017a; Turnheim et al. 2018), and co-design initiatives and grassroots 
innovations (Martiskainen 2017; Sheikh and Bhaduri 2021). These 
initiatives often expand the scope for citizen participation. For 
example, Urban Living Labs foster innovation, coproducing responses 
to existing problems of energy use, energy poverty and mobility that 
integrate scientific and expert knowledge with local knowledge 
and common values (Voytenko et al. 2016; Marvin et al. 2018). The European Network of Living Labs  – with a  global outreach  – has 
established a model of open and citizen-centric innovation for policy 
making. The proliferation of Climate Assemblies at the national and 
sub-national level further emphasises the increasing role that citizens 
can play in both innovating and planning for carbon mitigation 
(Sandover et al. 2021).
State and local authorities are often central to initiating and

implementing experiments and use an incremental, ‘learning by 
doing’ governing approach (Bai et al. 2010; Nevens et al. 2013; 
Castán Broto and Bulkeley 2013; Mcguirk et al. 2015; Nagorny-
Koring and Nochta 2018; Hodson et al. 2018; Peng and Bai 2018; 
Smeds and Acuto 2018; Culwick et al. 2019; Sengers et al. 2019). 
Experiments relate to technological learning and changes in policies, 
practices, services, user behaviour, business models, institutions, and 
governance (Castán Broto and Bulkeley 2013; Wieczorek et al. 2015; 
Kivimaa et al. 2017a; Laurent and Pontille 2018; Torrens et al. 2019). 
Experimentation has contributed to learning, changes in outcomes 
when implemented, and shifts in the political landscape (Turnheim 
et al. 2018). Experiments, however, are often isolated and do 
not always result in longer-term, more widespread changes. The 
transformative potential (understood as changes in the fundamental 
attributes of natural and human systems, see Annex I: Glossary) of

experiments is constrained by uncertainty about locally relevant 
climate change solutions and effects; a lack of comprehensive, and 
sectorally inclusive national policy frameworks for decarbonisation; 
budgetary and staffing limitations; and a  lack of institutional and 
political capacity to deliver integrated and planned approaches 
(Evans and Karvonen 2014; Mcguirk et al. 2015; Bulkeley et al. 
2016; Voytenko et al. 2016; Wittmayer et al. 2016; Webb et al. 2017; 
Grandin et al. 2018; Hölscher et al. 2018; Nagorny-Koring 2019; 
Sengers et al. 2019).
13.5.3 Performance and Global Mitigation Impact 
The performance of sub-national actors’ mitigation policies have 
been measured using criteria such as existence of mitigation targets, 
incentives for mitigation, definition of a  baseline, and existence 
of a  monitoring, reporting, and verification procedure (Hsu et al. 
2019). Existing evaluations range from small-scale studies assessing

the mitigation potential of commitments by sub-national regions, 
cities and companies in the USA or in 10 high-emitting economies 
(Roelfsema 2017; Hsu et al. 2019), to larger studies finding that over 
9149 cities worldwide could mitigate 1400 MtCO 2-eq in 2030 (Global 
Covenant of Mayors for Climate and Energy 2018; Hsu et al. 2018, 
2019). These sub-national mitigation potential estimates vary since 
a  range of approaches exists for accounting for overlaps between 
sub-national governments and their nested jurisdictions (e.g., states, 
provinces, and national governments) (Roelfsema et al. 2018; Hsu et al. 
2019). One analysis found that the cities of New York, Berlin, London, 
Greater Toronto, Boston, and Seattle have achieved on average 
a 0.27 tCO 2-eq per capita per year reduction (Kennedy et al. 2012). 
Hsu et al. (2020c) found that 60% of more than 1000 European cities, 
representing 6% of the EU’s total emissions, are on track to achieving

their targets, reducing more than 51 MtCO 2-eq. While evidence is

1381
1381
limited, there are concerns that implementation challenges persist 
with city level plans, particularly tied to management of initiatives 
and engagement of the population (Messori et al. 2020).
Whether participation in transnational climate initiatives impacts 
sub-national governments’ achievement on climate mitigation goals 
is uncertain. Some find that higher ambition in climate mitigation 
commitments did not translate into greater mitigation (Kona 
et al. 2016; Hsu et al. 2019). Other studies associate participation in 
networks with increased solar photovoltaic systems (PV) investment 
(Khan and Sovacool 2016; Steffen et al. 2019), and with potential to 
achieve carbon emissions reductions per capita in line with a global 
2°C scenario (Kona et al. 2016).
Reporting networks may attract high-performing actors, suggesting 
an artificially high level of cities interested in taking climate

action or piloting solutions (self-selection bias) that may not be 
effective elsewhere (van der Heijden 2018). Many studies present 
a  conservative view of potential mitigation impact because they 
draw upon publicly reported mitigation actions and exclude sub-
national actions that are not reported (Kuramochi et al. 2020).
In addition to direct mitigation contributions, climate action 
partnerships may deliver indirect effects that, while difficult 
to quantify, ensure long-term change (Chan et al. 2015). 
Experimentation and policy innovation helps to establish best 
practices (Hoffmann 2011); set new norms for ambitious climate 
action that help build coalitions (Chan et al. 2015; Bernstein and 
Hoffmann 2018); and translate into knowledge sharing or capacity 
building (Lee  and Koski 2012; Hakelberg 2014; Purdon 2015; 
Acuto and Rayner 2016). Emergent research explores whether, in 
addition to realising outcomes, mitigation initiatives also provide

the resources, skills and networks that governments and other 
stakeholders currently use to target other development goals (Shaw 
et al. 2014; Wolfram 2016; Wiedenhofer et al. 2018; Amundsen et al. 
2018; Heikkinen et al. 2019).13.6 Policy Instruments and Evaluation 
Institutions and governance processes described in previous section 
result in specific policies, that governments then implement and 
that shape actions of many stakeholders. This section assesses 
the empirical experience with the range of policy instruments 
available to governments with which to shape mitigation outcomes. 
Section  13.7 that follows deals with how these instruments are 
combined into packages, and Section 13.9 addresses economy-wide 
measures and issues.
 Many different policy instruments for GHG reduction are in use. 
They fall into a few major categories that share key characteristics. 
This section provides one possible taxonomy of these major types of

policy instruments, presents a set of criteria for policy evaluation, and 
synthesises the literature on the most common mitigation policies. 
The emphasis is on recent empirical evidence on the performance 
of different policy instruments and lessons that can be drawn from 
these experiences. This builds on and enhances the AR5 Chapter 15, 
which provided a more theoretical treatment of policy instruments 
for mitigation.
13.6.1 Taxonomy and Overview of Mitigation Policies
13.6.1.1 Taxonomy of Mitigation Policies
A large number of policies and policy instruments can affect GHG 
emissions and/or sequestration, whether their primary purpose is 
climate change mitigation or not. Consequently, consistent with the 
approach in this chapter, this section adopts a broad interpretation to 
what is considered mitigation policy. Also, the section recognises the 
multiplicity of policies that overlap and interact. 
Environmental policy instruments, including for climate change

mitigation, have long been grouped into three main categories  – 
(i) economic instruments, (ii) regulatory instruments, and (iii) other 
instruments  – although the specific terms differ across disciplines 
and additional categories are common (Kneese and Schultze 1975; 
Jaffe and Stavins 1995; Nordhaus 2013; Wurzel et al. 2013). Examples 
of common policies in each category are shown in Table 13.1, but this 
is not a comprehensive list. Principles of and empirical experience 
with the various instruments are synthesised in Sections 13.6.3 to 
13.6.5, international interactions are covered in Section 13.6.6. 
Table 13.1 | Classification of mitigation policies.
Category Examples of common types of mitigation policy instruments
Economic instrumentsCarbon taxes, GHG emissions trading, fossil fuel taxes, tax credits, grants, renewable energy subsidies, fossil fuel subsidy reductions, 
offsets, R&D subsidies, loan guarantees

Regulatory instrumentsEnergy efficiency standards, renewable portfolio standards, vehicle emission standards, ban on SF 6 uses, biofuel content mandates, 
emission performance standards, methane regulations, land-use controls
Other instruments Information programmes, voluntary agreements, infrastructure, government technology procurement policies, corporate carbon reporting

1382
1382
Chapter 13 National and Sub-national Policies and Institutions1313.6.1.2 Coverage of Mitigation Policies
An increasing share of global emissions sources is subject to 
mitigation policies, though coverage is still incomplete (Eskander and 
Fankhauser 2020; Nascimento et al. 2021). 
While consistent information on global prevalence of policies is not 
available, in G20 countries the use of various policy instruments 
has increased steadily over the past two decades (Nascimento et al. 
2021). The share of countries that had mitigation policy instruments 
in place rose across all sectoral categories, albeit to different extents 
in different sectors and for different policy instruments (Figure 13.4). 
Among G20 countries the electricity and heat generation has the 
greatest number of policies in place, and the agriculture and forestry 
sector the fewest (Nascimento et al. 2021). 
The mix of policies has shifted towards more regulatory instruments

and carbon pricing relative to information policies and voluntary 
action (Schmidt and Fleig 2018; Eskander and Fankhauser 2020). 
The IEA database, which tracks renewable energy and energy 
efficiency policies at the national and sub-national levels for about 
160 countries, indicates an average of about 225 new renewable 
energy and energy efficiency policies annually from 2010 through 
2019 with a peak in the number of new renewable energy policies in 
2011 (IEA 2021). 
While an increasing share of CO 2 emissions from fossil fuel combustion 
is subject to mitigation policies, there remain many countries 
and sectors where no dedicated mitigation policies apply to fuel combustion. Fossil fuel use is subject to energy taxes in the majority 
but not all jurisdictions, and in some instances, it is subsidised. 
The main gaps in current mitigation policy coverage are non-CO 2 
emissions and CO 2 emissions associated with production of industrial

materials and chemical feedstocks, which are connected to broader 
questions of shifting to cleaner production systems (Bataille et al. 
2018a; Davis et al. 2018). Sequestration policies focus mainly on 
forestry and carbon capture and storage (CCS) with limited support 
for other carbon dioxide removal and use options (Geden et al. 2019; 
Vonhedemann et al. 2020). 
13.6.1.3 Stringency and Overall Effectiveness 
of Mitigation Policies
The stringency of mitigation policies varies greatly by country, sector 
and policy (Box 13.9). Stringency can be increased through sequential 
changes to policies (Pahle et al. 2018). 
Estimates of the effective carbon price (as an estimate of overall 
stringency across policy instruments) differ greatly between countries 
and sectors (World Bank 2021a). Countries with higher overall 
effective carbon prices tend to have lower carbon intensity of energy 
supply and lower emissions intensity of the economy, as shown in

an analysis of 42 G20 and OECD countries (OECD 2018). The carbon 
price that prevails under a carbon tax or ETS is not directly a measure 
of policy stringency across an economy, as the carbon prices typically 
only cover a share of total emissions, and rebates or free allowance 
allocations can limit effectiveness (OECD 2018). At low emissions 
prices, mitigation incentives are small; as of April 2021, seventeen 
Figure 13.4 | Share of countries that adopted different policy instruments in different sectors, 2000–2020 (three year moving average).  Source: reproduced 
with permission from Nascimento et al. (2021).

1383
1383
jurisdictions with a carbon pricing policy had a tax rate or allowance 
price less than USD5 per tCO 2 (World Bank 2021a). 
Other policies, such as fossil fuel subsidies, may provide incentives to 
increase emissions thus limiting the effectiveness of the mitigation 
policy (Section 13.6.3.6). Those effects may be complex and difficult 
to identify. In most countries trade policy provides an implicit 
subsidy to CO 2 emissions (Shapiro 2020). The analysis of emissions 
from energy use in buildings in Chapter 9 illustrates the factors that 
support and counteract mitigation policies.
Furthermore, emissions pricing policies encourage reduction of 
emissions whose marginal abatement cost is lower than the tax/
allowance price, so they have limited impact on emissions with 
higher abatement costs such as industrial process emissions (Bataille 
et al. 2018a; Davis et al. 2018). EU ETS emission reductions have

been achieved mainly through implementation of low cost measures 
such as energy efficiency and fuel switching rather than more costly 
industrial process emissions. 
Estimating the overall effectiveness of mitigation policies is 
difficult because of the need to identify which observed changes 
in emissions and their drivers are attributable to policy effort and 
which to other factors. Cross-Chapter Box 10 in Chapter 14 brings together several lines of evidence to indicate that mitigation policies 
have had a discernible impact on mitigation for specific countries, 
sectors and technologies and led to avoided global emissions to 
date by several billion tonnes CO 2-eq annually (medium evidence, 
medium agreement). 
13.6.2 Evaluation Criteria
Policy evaluation is a  ‘careful, retrospective assessment of merit, 
worth and value of the administration, output and outcomes of 
government interventions’ (Vedung 2005). The inherent complexity

of climate mitigation policies calls for the application of multiple 
criteria, and reflexiveness of analysis with regard to governments’ 
and societies’ objectives for policies (Huitema et al. 2011).
Evaluation of climate mitigation policy tends to focus on the 
environmental effectiveness and economic efficiency or cost-
effectiveness of GHG mitigation policies, with distributional 
equity sometimes as an additional criterion. In policy design and 
implementation there is rising interest in co-benefits and side-
effects of climate policies, as well as institutional requirements for 
implementation and the potential of policies to have transformative 
effect on systems. Table 13.2 elaborates. Box 13.9 | Comparing the Stringency of Mitigation Policies
Comparing the stringency of policies over time or across jurisdictions is very challenging and there is no single widely accepted metric

or methodology (Compston and Bailey 2016; Burck et al. 2019; Tosun and Schnepf 2020; Fekete et al. 2021). Policies are also assessed 
for their estimated effect on emissions, however, this requires estimation of a counterfactual baseline and isolation of other effects 
(Cross-Chapter Box 10 in Chapter 14). Economic instruments can be compared on the basis of their price or cost per tCO 2-eq. Even 
that is fraught with complexity in the context of different definitions and estimations for fossil fuel taxes and subsidies. For non-price 
policies an implicit or equivalent carbon price can be estimated. Factors such as the tax treatment of compliance costs can increase 
complexity. Accounting for the combined effect of overlapping policies presents additional challenges and such estimates are subject 
to numerous limitations.
Table 13.2 | Criteria for evaluation and assessment of policy instruments and packages.
Criterion Description

effectiveness Reducing GHG emissions is the primary goal of mitigation policies and therefore a fundamental criterion in evaluation. Environmental effectiveness has 
temporal and spatial dimensions. 
Economic effectiveness Climate change mitigation policies usually carry economic costs, and/or bring economic benefits other than through avoided future climate change. 
Economic effectiveness requires minimising costs and maximising benefits. 
Distributional effects The costs and benefits of policies are usually distributed unequally among different groups within a society (Zachmann et al. 2018), for example between 
industry, consumers, taxpayers; poor and rich households; different industries; different regions and countries. Policy design affects distributional effects, 
and equity can be taken into account in policy design in order to achieve political support for climate policies (Baranzini et al. 2017). 
Co-benefits, negative

side-effects Climate change mitigation policies can have effects on other objectives, either positive co-benefits (Mayrhofer and Gupta 2016; Karlsson et al. 2020) or 
negative side-effects. Conversely, impacts on emissions can arise as side-effects of other policies. There can be various interactions between climate change 
mitigation and the Sustainable Development Goals (Liu et al. 2019).
requirements Effective implementation of policies requires that specific institutional prerequisites are met. These include effective monitoring of activities or emissions 
and enforcement, and institutional structures for the design, oversight and revision and updating of policies. Requirements differ between policy 
instruments. a separate consideration is the overall feasibility of a policy within a jurisdiction, including political feasibility (Jewell and Cherp 2020).

Transformative potential Transformational change is a process that involves profound change resulting in fundamentally different structures (Nalau and Handmer 2015), or 
a substantial shift in a system’s underlying structure (Hermwille et al. 2015). Climate change mitigation policies can be seen has having transformative 
potential if they can fundamentally change emissions trajectories, or facilitate technologies, practices or products with far lower emissions.

1384
1384
Chapter 13 National and Sub-national Policies and Institutions13Not all criteria are applicable to all instruments or in all circumstances 
and the relative importance of different criteria depend on the 
objectives in the specific the context. a  given policy instrument 
may score highly on only some assessment criteria. In practice, 
the empirical evidence seldom exists for assessment of a  policy 
instrument across all criteria. 
13.6.3 Economic Instruments
Economic instruments, including carbon taxes, emissions trading 
systems (ETS), purchases of emission reduction credits, subsidies for 
energy efficiency, renewables and research and development and 
fossil fuel subsidy removal, provide a  financial incentive to reduce 
emissions. Pricing instruments, especially ETS and carbon taxes, 
have become more prevalent in recent years (Section 13.6.1). They 
have proven effective in promoting implementation of the low-cost

emissions reductions, and practical experience has driven progress in 
market mechanism design (robust evidence, high agreement). 
13.6.3.1 Carbon Taxes
A carbon tax is a charge on carbon dioxide or other greenhouse gases 
imposed on specified emitters or products. In practice features such 
as exemptions and multiple rates can lead to debate as to whether 
a specific tax is a carbon tax (Haites 2018). While other taxes can also 
reduce emissions by increasing the price of GHG emitting products, 
the result may be inefficient unless the tax rate is proportional to the 
emissions intensity. a tax on value of fossil fuels, for example, could 
raise the price on natural gas more than the price of coal, and hence 
increase emissions if the resulting substitution towards coal were to 
outweigh reductions in energy use. 
As of April 2021, 27 carbon taxes had been implemented by national 
governments, mostly in Europe (World Bank 2021a). Most of the

taxes apply to fossil fuels used for transportation and heating and 
cover between 3% and 79% of the jurisdiction’s emissions. Several 
countries also tax F-gases. Tax rates vary widely from less than USD1 
to over USD137 per tCO 2-eq. a  few jurisdictions lowered existing 
fuel taxes when they implemented the carbon tax, thus reducing the 
effective tax rate (OECD 2021a). How the tax revenue is used varies 
widely by jurisdiction. 
Carbon taxes tend to garner the least public support among possible 
mitigation policy options (Rhodes et al. 2017; Rabe 2018; Maestre-
Andrés et al. 2019; Criqui et al. 2019) although some regulations also 
meet with opposition (Attari et al. 2009). Policymakers sometimes 
use the revenue to build support for the tax, allocating some to 
address regressivity, to address competitiveness claims by industry, 
to reduce the economic cost by lowering existing taxes, and to fund 
environmental projects (Gavard et al. 2018; Klenert et al. 2018;

Levi et al. 2020). 
Carbon tax rates can be adjusted for inflation, increases in income, 
the effects of technological change, changing policy ambition, or 
2 The UK was a member of the EU ETS until December 31, 2020. A UK Emissions Trading Scheme (UK ETS) came into effect on 1 January 2021.the addition or subtraction of other policies. In practice, numerous 
jurisdictions have not increased their tax rates annually and some 
scheduled tax increases have not been implemented (Haites 
et al. 2018). Predictability of future tax rates helps improve economic 
performance (Bosetti and Victor 2011; Brunner et al. 2012). 
Uncertainty about the future existence of a carbon price can hinder 
investment (Jotzo et al. 2012) and uncertainty about future price 
levels can increase the resource costs of carbon pricing (Aldy and 
Armitage 2020).
13.6.3.2 Emission Trading Systems
The most common ETS design  – cap-and-trade  – sets a  limit on

aggregate GHG emissions by specified sources, distributes tradable 
allowances approximately equal to the limit, and requires regulated 
emitters to submit allowances equal to their verified emissions. The 
price of allowances is determined by the market, except in cases 
where government determined price floors or ceilings apply. 
ETSs for GHGs were in place in 38 countries as of April 2021 
(World Bank 2021a). The EU ETS, which covers 30 countries, was 
recently displaced by China’s national ETS as the largest. ETSs tend 
to cover emissions by large industrial and electricity generating 
facilities.2 Allowance prices as of April 1, 2021 ranged from just 
over USD1 to USD50, and coverage between 9% and 80% of the 
jurisdiction’s emissions. 
Multiple regional pilot ETSs with different designs have been 
implemented in China since 2013 to provide input to the design of 
a national system that is to become the world’s largest ETS (Jotzo

et al. 2018; Qian et al. 2018; Stoerk et al. 2019). Assessments have 
identified potential improvements to emissions reporting procedures 
(Zhang et al. 2019) and the pilot ETS designs (Deng et al. 2018). China’s 
national ETS covering over 2200 heat and power plants with annual 
emissions of about 4 GtCO 2took effect in 2021 (World Bank 2021a). 
All of the ETSs for which data are available have accumulated surplus 
allowances which reduces their effectiveness (Haites 2018). Surplus 
allowances indicate that the caps set earlier were not stringent 
relative to emissions trends. Most of those ETSs have implemented 
measures to reduce the surplus including removal/cancellation of 
allowances and more rapid reduction of the cap. Several ETSs have 
adopted mechanisms to remove excess allowances from the market 
when supply is abundant and release additional allowances into the 
market when the supply is limited, such as the EU ‘market stability

reserve’ (Hepburn et al. 2016; Bruninx et al. 2020). Initial indications 
are that this mechanism is at least partially successful in stabilising 
prices in response to short term disruptions such as the COVID-19 
economic shock (Gerlagh et al. 2020; Bocklet et al. 2019). 
Some ETS also include provisions to limit the range of market prices, 
making them ‘hybrids’ (Pizer 2002). a price floor assures a minimum 
level of policy effect if demand for allowances is low relative to the 
ETS emissions cap. It is usually implemented through a minimum price 
at auction, as for example in California’s ETS (Borenstein et al. 2019).

1385
1385
a price ceiling allows the government to issue unlimited additional 
allowances at a pre-determined price to limit the maximum cost of 
mitigation. Price ceilings have not been activated to date. 
13.6.3.3 Evaluation of Carbon Pricing Experience
A carbon tax or GHG ETS increases the prices of emissions intensive 
goods thus creating incentives to reduce emissions (Stavins 2019) for 
a comparison of a tax and ETS). The principal advantage of a pricing 
policy is that it promotes implementation of low-cost reductions; for 
a carbon tax, reductions whose cost per tCO 2-eq reduced is lower 
than the tax and for an ETS the lowest cost (per tCO 2-eq) reductions 
sufficient to meet the cap. Both a tax and an ETS can be designed 
to limit adverse economic impacts on regulated sources and 
emissions leakage.
The corresponding limitations of pricing policies are that they have

limited impact on adoption of mitigation measures when decisions 
are not sensitive to prices and do not encourage adoption of higher 
cost mitigation measures. Their effectiveness in influencing long-term 
investments depends on the expectation that the policy will continue 
and expectations related to future tax rates or allowance prices 
(Brunner et al. 2012). Other policies can be used in combination with 
carbon pricing to address these limitations.
The number of pricing policies has increased steadily and covered 
21.5% of global GHG emissions in 2020 (World Bank 2021a). Effective 
coverage is lower because virtually all jurisdictions with a  pricing 
policy have other policies that affect some of the same emissions. For 
example, a few jurisdictions reduced existing fuel taxes when they 
introduced their carbon tax thus reducing the effective tax rate, and 
many jurisdictions have two or more pricing policies 
Environmental effectiveness and co-benefits

There is abundant evidence that carbon pricing policies reduce 
emissions. Statistical studies of emissions trends in jurisdictions with 
and without carbon pricing find a significant impact after controlling 
for other policies and structural factors (Best et al. 2020; Rafaty et al. 
2020). Numerous assessments of specific policies, especially the EU 
ETS and the British Columbia carbon tax, conclude that most have 
reduced emissions (robust evidence, high agreement) (Narassimhan 
et al. 2018; Haites et al. 2018; Aydin and Esen 2018; Pretis 2019; 
Andersson 2019; FSR Climate 2019; Metcalf and Stock 2020; Rafaty 
et al. 2020; Bayer and Aklin 2020; Diaz et al. 2020; Green 2021; 
Arimura and Abe 2021). 
Estimating the emission reductions due to a specific policy is difficult 
due to the effects of overlapping policies and exogenous factors such 
as fossil fuel price changes and economic conditions. Studies that 
attempt to attribute a  share of the reductions achieved to the EU

ETS place its contribution at 3–25% (FSR Climate 2019; Bayer and 
Aklin 2020; Chèze et al. 2020). The relationship between a carbon tax 
and the resulting emission reductions is complex and is influenced 
by changes in fossil fuel prices, changes in fossil fuel taxes, and other 
mitigation policies (Aydin and Esen 2018). But the effectiveness of a  carbon tax generally is higher in countries where it constitutes 
a large part of the fossil fuel price (Andersson 2019). 
Few of the world’s carbon prices are at a level consistent with various 
estimates of the carbon price needed to meet the Paris Agreement 
goals. In modelling of mitigation pathways that limit warming to 
2°C (>50%)(Section  3.6.1) marginal abatement costs of carbon in 
2030 are about 60 to 120 USD2015 per tCO 2, and about 170 to 
290  USD2015 per tCO 2 in pathways that limit warming to 1.5°C 
(>50%) with no or limited overshoot (Section  3.6). One synthesis

study estimates necessary prices at USD40–80 per tCO 2 by 2020 
(High-Level Commission on Carbon Prices 2017). Only a  small 
minority of carbon pricing schemes in 2021 had prices above USD40 
per tCO 2, and all of these were in European jurisdictions (World Bank 
2021a). Most carbon pricing systems apply only to some share of 
the total emissions in a jurisdiction, so the headline carbon price is 
higher than the average carbon price that applies across an economy 
(World Bank 2021a). 
Where ETS or carbon taxes exist, they apply to different proportions 
of the jurisdiction’s greenhouse gas emissions. The share of emissions 
covered by ETSs in 2020 varied widely, ranged from 9% (Canada) 
to 80% (California) while the share of emissions covered by carbon 
taxes ranged from 3% (Latvia and Spain) to 80% (South Africa) 
(World Bank 2021a).Where carbon pricing policies are effective in 
reducing GHG emissions, they usually also generate co-benefits

including better air quality. For example, a Chinese study of air quality 
benefits from lower fossil fuel use under carbon pricing suggests that 
prospective health co-benefits would partially or fully offset the cost 
of the carbon policy (Li et al. 2018). Depending upon the jurisdiction 
(for example, if there are fossil fuel subsidies) carbon pricing could 
also reduce the economic distortions of fossil fuel subsidies, improve 
energy security through greater reliance on local energy sources and 
reduce exposure to fossil fuel market volatility. Substantial carbon 
prices would be in the domestic self-interest of many countries if 
co-benefits were fully factored in (Parry et al. 2015).
Economic effectiveness
Economic theory suggests that carbon pricing policies are on the 
whole more cost effective than regulations or subsidies at reducing 
emissions (Gugler et al. 2021). Any mitigation policy imposes costs on

the regulated entities. In some cases entities may be able to recover 
some or all of the costs through higher prices (Neuhoff and Ritz 2019; 
Cludius et al. 2020). International competition from less stringently 
regulated firms limits the ability of emissions-intensive, trade-
exposed (EITE) firms to raise their prices. Thus, a unilateral mitigation 
policy creates a risk of adverse economic impacts, including loss of 
sales, employment, profits, for such firms and associated emissions 
leakage (Section 13.6.6.1).
Pricing policies can be designed to minimise these risks; free 
allowances can be issued to EITE participants in an ETS and taxes 
can provide exemptions or rebates. An extensive ex post literature 
finds no statistically significant adverse impacts on competitiveness 
or leakage (13.6.6.1).

1386
1386
Chapter 13 National and Sub-national Policies and Institutions13An ex post analysis of European carbon taxes finds no robust 
evidence of a negative effect on employment or GDP growth (Metcalf 
and Stock 2020). The British Columbia carbon tax led to a  small 
net increase in employment (Yamazaki 2017) with no significant 
negative impacts on GDP possibly due to full recycling of the tax 
revenue (Bernard and Kichian 2021). Few carbon taxes apply to EITE 
sources (Timilsina 2018), so competitiveness impacts usually are not 
a particular concern. 
Government revenue generated by carbon pricing policies globally 
was approximately 53 billion USD in 2020 split almost evenly 
between carbon taxes and ETS allowance sales (World Bank 2021). 
Revenue raised though carbon pricing is generally considered 
a relatively efficient form of taxation and a large share of revenue 
enters general government budgets (Postic and Fetet 2020). Some of

the revenue is returned to emitters or earmarked for environmental 
purposes. Allowance allocation and revenue spending measures have 
been used to create public support for many carbon pricing policies 
including at every major reform stage of the EU ETS (Klenert et al. 
2018; Dorsch et al. 2020) (Box 5.11).
Distributional effects 
The most commonly studied distributional impact is the direct impact 
of a carbon tax on household income. Typically it is regressive; the tax 
induced increase in energy expenditures represents a larger share of 
household income for lower income households (Grainger and Kolstad 
2010; Timilsina 2018; Dorband et al. 2019; Ohlendorf et al. 2021). 
Governments can rebate part or all of the revenue to low-income 
households, or implement other changes to taxation and transfer 
systems to achieve desired distributional outcomes (Jacobs and van 
der Ploeg 2019; Saelim 2019; Sallee 2019) (Box 5.11). The full impact

of the tax – after any distribution of tax revenue to households and 
typically adverse effects on investors – generally is less regressive or 
progressive (Williams III et al. 2015; Goulder et al. 2019). Where the 
tax revenue is treated as general revenue the government relies on 
existing income redistribution policies (such as income taxes) and 
social safety net programmes to address the distributional impacts.
Carbon taxes on fossil fuels have effects similar to the removal of 
fossil fuel subsidies (Ohlendorf et al. 2021) (Section 13.6.3.6). Even 
if a carbon tax is progressive it increases prices for fuels, electricity, 
transport, food and other goods and services that adversely affect 
the most economically vulnerable. Redistribution of tax revenue 
is critical to address the adverse impacts on low-income groups 
(Dorband et al. 2019) (Box 5.11). In countries with a limited capacity 
to collect taxes and distribute revenues to low-income households,

such as some developing countries, carbon taxes may have greater 
distributional consequences.
Distributional effects have generally not been a significant issue for 
ETSs. Equity for industrial participants typically is addressed through 
free allocation of allowances. Impacts on household incomes, 
with the exception of electricity prices, are too small or indirect to 
be a  concern. Some systems are designed to limit electricity price 
increases (Petek 2020) or use some revenue for bill assistance to low-
income households (RGGI 2019). Technological change
Carbon pricing, especially an ETS that covers industrial sources, 
stimulates technological change by participants and others (Calel 
and Dechezleprêtre 2016; FSR Climate 2019; van den Bergh and 
Savin 2021) (Section 13.6.6.3 and Chapter 16). The purpose of pricing 
policies is to encourage implementation of the lowest cost mitigation 
measures. Pricing policies therefore are more likely to stimulate

quick, low cost innovation such as fuel switching and energy 
efficiency, rather than long term, costly technology development 
such as renewable energy or industrial process technologies (Calel 
2020; Lilliestam et al. 2021). To encourage long-term technology 
development carbon pricing policies need to be complemented by 
other mitigation and research and development (R&D) policies. 
13.6.3.4 Offset Credits
Offset credits are voluntary GHG emission reductions for which 
tradable credits are issued by a  supervisory body (Michaelowa 
et al. 2019b). a buyer can use purchased credits to offset an equal 
quantity of its emissions. In a voluntary market governments, firms 
and individuals purchase credits to offset emissions generated by 
their actions, such as air travel. a compliance market al.ows specified 
offset credits to be used for compliance with mitigation policies, 
especially ETSs, carbon taxes and low-carbon fuel standards. (Newell

et al. 2013; Bento et al. 2016; Michaelowa et al. 2019a).
When used for compliance, governments typically specify 
a maximum quantity of offset credits that can be used, as well as 
the types of emission reduction actions, the project start dates and 
the geographic regions eligible credits. Initially, the EU ETS, Swiss 
ETS and New Zealand ETS accepted credits issued under the Kyoto 
Protocol (Chapter 14), but they terminated or severely constrained 
the quantity of international credits allowed for compliance use after 
2014 (Shishlov et al. 2016) (Section 13.6.6). 
A key question for any offset credit is whether the emission 
reductions are ‘additional’: reductions that only happen because of 
the offset credit payment (Greiner and Michaelowa 2003; Millard-
Ball and Ortolano 2010; van Benthem and Kerr 2013; Burke 2016; 
Bento et al. 2016). To assess additionality and to determine the 
quantity of credits to be issued, regulators develop methodologies

to estimate baseline (business-as-usual) emissions in the absence of 
offset payments (Newell et al. 2013; Bento et al. 2016). Credits are 
issued for the difference between the baseline and actual emissions 
with adjustments for possible emissions increases outside the project 
boundary (Rosendahl and Strand 2011). Some research suggests that 
procedural and measurement advances can significantly reduce the 
risk of severe non-additionality (Mason and Plantinga 2013; Bento 
et al. 2016; Michaelowa et al. 2019a). 
13.6.3.5 Subsidies for Mitigation
Subsidies for mitigation encourage individuals and firms to invest 
in assets that reduce emissions, changes in processes or innovation. 
Subsidies have been used to improve energy efficiency, encourage 
the uptake of renewable energy and other sector-specific emissions

1387
1387
saving options (Chapters  6 to 11), and to promote innovation. 
Targeted subsidies can achieve specific mitigation goals yet have 
intrinsically narrower coverage than more broad-based pricing 
instruments. Subsidies are often used not only to achieve emissions 
reductions but to address market imperfections or to achieve 
distributional or strategic objectives. Subsidies are often used 
alongside or in combination with other policy instruments, and are 
provided at widely differing cost per unit of emissions reduced. 
Governments routinely provide direct funding for basic research, 
subsidies for R&D to private companies, and co-funding of research and 
deployment with industry (Dzonzi-Undi and Li 2016). Research 
subsidies have been found to be positively correlated with green 
product innovation in a  study in Germany, Switzerland and Austria

(Stucki et al. 2018). Government subsidies for R&D have been found to 
greatly increase the green innovation performance of energy intensive 
firms in China (Bai et al. 2019). For more detail see Chapter 16. 
Subsidies of different forms are often provided for emissions savings 
investments to businesses and for the retrofit of buildings for energy 
efficiency. Emissions reductions from energy efficiencies can often 
be achieved at low cost, but evidence for some schemes suggests 
lower effectiveness in emissions reductions than expected ex ante 
(Fowlie et al. 2018; Valentová et al. 2019). Tax credits can be used to 
encourage firms to produce or invest in low-carbon emission energy 
and low-emission equipment. Investment subsidies have been found 
to be more effective in reducing costs and uncertainties in solar 
energy technologies than production subsidies (Flowers et al. 2016). 
Subsidies have been provided extensively and in many countries for

the deployment of household rooftop solar systems, and increasingly 
also for commercial scale renewable energy projects, typically using 
‘feed-in tariffs’ that provide a payment for electricity generated above 
the market price (Pyrgou et al. 2016). Such schemes have  proven 
effective in deploying renewable energy, but lock-in subsidies for long 
periods of time. In some cases they provide subsidies at higher levels 
than would be required to motivate deployment (del Río and Linares 
2014). High levels of net subsidies have been shown to diminish 
incentives for optimal siting of renewable energy installations 
(Penasco et al. 2019).
A variant of subsidies for deployment of renewable energy are 
auctioned feed-in tariffs or auctioned contracts-for-difference, where 
commercial providers bid in a competitive process. Auctions typically 
lead to lower price premiums (Eberhard and Kåberger 2016; Roberts 
2020) but efficient outcomes depend on auction design and market

structure (Grashof et al. 2020), although an emergent literature also 
questions whether spread of auctions is due to performance or the 
dynamics of the policy formulation process (Fitch-Roy et al. 2019b; 
Grashof et al. 2020; Grashof 2021). The prequalification requirements 
or the assessment criteria in the auctions sometimes also include 
local co-benefits such as local economic diversification (Buckman 
et al. 2019; White et al. 2021).
Support for rollout clean technologies at high prices can be 
economically beneficial in the long run if costs are reduced greatly 
as a function of deployment (Newbery 2018). Deployment support, much of it in the form of feed-in tariffs in Germany, enabled the 
scaling up of the global solar photovoltaic industry and attendant 
large reductions in production costs that by 2020 made solar power 
cost competitive with fossil fuels (Buchholz et al. 2019). There is also 
evidence for increased innovation activity as a result of solar feed-in

tariffs (Böhringer et al. 2017b). 
Many governments have also provided subsidies for the purchase 
of electric vehicles, including with strong effect in China (Ma et al. 
2017), Norway (Baldursson et al. 2021) and other countries, and 
sometimes at relatively high rates (Kong and Hardman 2019).
13.6.3.6 Removal of Fossil Fuel Subsidies
Many governments subsidise fossil fuel consumption and/or 
production through a variety of mechanisms (Burniaux and Chateau 
2014) (Figure  13.5). Different approaches exist to defining the 
scope and estimating the magnitude of fossil fuel subsidies (Koplow 
2018), and all involve estimates, so the magnitudes are uncertain. 
Rationalising inefficient fossil fuel subsidies is one of the indicators to 
measure progress toward Sustainable Development Goal 12: Ensure 
sustainable consumption and production patterns (UNEP 2019a). 
Consumption subsidies represent approximately 70% of the total.

Most of the subsidies go to petroleum, which accounts for roughly 
50% of the consumption subsidies and 75% of the production 
subsidies (IEA 2020; OECD 2020). Much of the variation in the 
consumption subsidies is due to fluctuations in the world price of oil 
which is used as the reference price. 
Reducing fossil fuel subsidies would lower CO 2 emissions, 
increase government revenues (Jakob et al. 2015; Dennis 2016; 
Gass and Echeverria 2017; Rentschler and Bazilian 2017; Monasterolo 
and  Raberto 2019), improve macroeconomic performance 
(Monasterolo  and Raberto 2019), and yield other environmental 
and sustainable development benefits (robust evidence,  medium 
agreement) (Jakob et al. 2015; Rentschler and Bazilian 2017; Solarin 
2020). The benefits of gasoline subsidies in developing countries 
accrue mainly to higher income groups, so subsidy reduction usually 
will reduce inequality (Coady et al. 2015; Dennis 2016; Monasterolo

and Raberto 2019; Labeaga et al. 2021). Some subsidies, like tiered 
electricity rates, benefit low-income groups. Reductions of broad 
subsidies lead to price increases for fuels, electricity, transport, food and 
other goods and services that adversely affect the most economically 
vulnerable (Coady et al. 2015; Zeng and Chen 2016; Rentschler and 
Bazilian 2017). Distributing some of the revenue saved can mitigate 
the adverse economic impacts on low-income groups (Dennis 2016; 
Zeng and Chen 2016; Labeaga et al. 2021; Schaffitzel et al. 2020). 
The emissions reduction that could be achieved from fossil fuel subsidy 
removal depends on the specific context such as magnitude and nature 
of subsidies, energy prices and demand elasticities, and how the fiscal 
savings from reduced subsidies are used. Modelling studies of global 
fossil fuel subsidy removal result in projected emission reductions 
of between 1% and 10% by 2030 (Delpiazzo et al. 2015; IEA 2015;

Jewell et al. 2018; IISD 2019) and between 6.4% and 8.2% by 2050 
(Schwanitz et al. 2014; Burniaux and Chateau 2014).

1388
1388
Chapter 13 National and Sub-national Policies and Institutions13An extensive literature documents the difficulties of phasing out 
fossil fuel subsidies (Schmidt et al. 2017; Gass and Echeverria 2017; 
Skovgaard and van Asselt 2018; Kyle 2018; Perry 2020; Gençsü et al. 
2020). Fossil fuel industries lobby to maintain producer subsidies and 
consumers protest if they are adversely affected by subsidy reductions 
(Fouquet 2016; Coxhead and Grainger 2018). Yemen (2005 and 2014), 
Cameroon (2008), Bolivia (2010), Nigeria (2012), Ecuador (2019) 
all abandoned subsidy reform attempts following public protests 
(Rentschler and Bazilian 2017; Mahdavi et al. 2020). Indonesia is an 
example where fossil fuel subsidy removal was successful, helped by 
social assistance programmes and a communication effort about the 
benefits of reform (Chelminski 2018; Burke and Kurniawati 2018). 
To-date instances of fossil fuel subsidy reform or removal have

been driven largely by national fiscal and economic considerations 
(Skovgaard and van Asselt 2019).
13.6.4 Regulatory Instruments
Regulatory instruments are applied by governments to cause the 
adoption of desired processes, technologies, products (including 
energy products) or outcomes (including emission levels). Failure to 
comply incurs financial penalties and/or legal sanctions. Regulatory 
instruments range from performance standards, which prescribe 
compliance outcomes – and in some cases allow flexibility to achieve 
compliance, including the trading of credits  – to more prescriptive 
technology-specific standards, also known as command-and-control 
regulation. Regulatory instruments play an important role to achieve 
specific mitigation outcomes in sectoral applications (robust evidence, 
high agreement). Mitigation by regulation often enjoys greater political 
support but tends to be more economically costly than mitigation by

pricing instruments (robust evidence,  medium agreement).13.6.4.1 Performance Standards, Including Tradable Credits
Performance standards grant regulated entities freedom to choose 
the technologies and methods to reach a general objective, such as 
a minimum market share of zero-emission vehicles or of renewable 
electricity, or a maximum emissions intensity of electricity generated. 
Tradable performance standards allow regulated entities to trade 
compliance achievement credits; under-performers can buy surplus 
credits from over-performers thereby reducing the aggregate cost of 
compliance (Fischer 2008).
Tradable performance standards have been applied to numerous 
sectors including electricity generation, personal vehicles, building 
energy efficiency, appliances, and large industry. An important 
application is Renewable Portfolio Standards (RPS) for electricity 
supply, which require that a  minimum percentage of electricity is

generated from specified renewable sources sometimes including 
nuclear and fossil fuels with CCS when referred to as a clean electricity 
standard (Young and Bistline 2018) (Chapter 6). This creates a price 
incentive to invest in renewable generation capacity. Such incentives 
can equivalently be created through feed-in tariffs, a form of subsidy 
(Section 13.6.3) and some jurisdictions have had both instruments 
(Matsumoto et al. 2017). RPS can differ in features and stringency, 
and are in operation in many countries and sub-national jurisdictions, 
including a majority of US states (Carley et al. 2018). 
Vehicle emissions standards are a  common form of performance 
standard with flexibility (Chapter  9). a  corporate fuel efficiency 
standard specifies an average energy use and/or GHG emissions 
per kilometre travelled for vehicles sold by a manufacturer. Another 
version of this policy, the zero-emission vehicle (ZEV) standard,

requires vehicle sellers to achieve minimum requirements for sales IMF IEA OECD IEA+OECD0100200300400500600700800900
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020Total fossil fuel subsidies, billion US$
Figure 13.5 | Total fossil fuel subsidies, 2010–2019, in USD billion (USD2021 for IMF, USD2019 for others).  Source: data from OECD (2020) (43 countries, mainly 
production subsidies), IEA (2020) (40 countries, mainly consumption subsidies), IMF (Parry et al. 2021; explicit subsidies for all countries).

1389
1389
of zero-emission vehicles (Bhardwaj et al. 2020). Both instruments 
allow manufacturers to use tradable credits to achieve compliance. 
Low-carbon fuel standards (LCFS), which set an average life-cycle 
carbon intensity for energy that declines over time, are another 
example. LCFS are in place in many different jurisdictions (Chapter 9) 
and have been applied to petroleum products, natural gas, hydrogen 
and electricity (Yeh et al. 2016). An LCFS allows regulated entities 
to trade credits creating the potential for high carbon intensity fuel 
suppliers to cross-subsidise low-carbon intensity transport energy 
providers including low-carbon biofuels, hydrogen and electricity 
(Axsen et al. 2020). 
Trading and other flexibility mechanisms improve the economic 
efficiency of standards by harmonising the marginal abatement 
costs among companies or installations subject to the standard.

Nevertheless tradable performance standards are less economically 
efficient in achieving emissions reductions than carbon pricing, 
sometimes by a significant amount (Giraudet and Quirion 2008; Chen 
et al. 2014; Holland et al. 2015; Fox et al. 2017; Zhang et al. 2018).
13.6.4.2 Technology Standards
Technology standards take a more prescriptive approach by requiring 
a  specific technology, process or product. They typically take one 
of three forms: requirements for specific pollution abatement 
technologies; requirements for specific production methods; or 
requirements for specific goods such as energy efficient appliances. 
They can also take the form of phase-out mandates, as applied for 
example to planned bans of internal combustion engines for road 
transport (Bhagavathy and McCulloch 2020), coal use; for example, 
Germany’s decisions to phase out coal (Oei et al. 2020), and some 
industry processes and products, for example, hydrofluorocarbons

(HFCs) and use of sulphur hexafluoride (SF 6) in some products (see 
Box 13.10 on non-CO 2 gases). Technology standards are also referred 
to as command-and-control standards, prescriptive standards, or 
design standards. 
Technology standards are a common climate policy particularly at the 
sector level (Chapters 6–11). Technology standards tend to score lower 
in terms of economic efficiency than carbon pricing and performance 
standards (Besanko 1987). But they may be the best instrument for 
situations where decisions are not very responsive to price signals 
such as consumer choices related to energy efficiency and recycling 
and decisions relating to urban land use and infrastructure choices. 
By mandating specific compliance pathways, technology standards 
risk locking-in a  high-cost pathway when lower cost options are 
available or may emerge through market incentives and innovation (Raff and Walter 2020). Furthermore, standards may require high-

cost GHG reductions in one sector while missing low-cost options 
in another sector. Technology standards can also stifle innovation by 
blocking alternative technologies from entering the market (Sachs 
2012). Benefits of technology standards include their potential to 
achieve emission reductions in a  relatively short time frame and 
that their effectiveness can be estimated with some confidence 
(Montgomery et al. 2019). 
13.6.4.3 Performance of Regulatory Instruments
Regulatory policy instruments tend to be more economically costly 
than pricing instruments, as explained above. However, regulatory 
policies may be preferred for other reasons.
In some cases, regulatory policy can elicit greater political support 
than pricing policy (Tobler et al. 2012; Lam 2015; Drews and van 
den Bergh 2016). For example, USA citizens have expressed more 
support for flexible regulation like the RPS than for carbon taxes 
(Rabe 2018). And a survey in British Columbia a few years after the

simultaneous implementation of a carbon tax and two regulations – 
the LCFS and a clean electricity standard – found much less strong 
opposition to the regulations, even after being informed that they 
were costlier to consumers (Rhodes et al. 2017). The degree of public 
support for regulations depends, however, on the type of regulation, 
as outright technology prohibitions can be unpopular (Attari et al. 
2009; Cherry et al. 2012).
In comparison to economic instruments, regulatory policies tend to 
cause greater cost of living increases in percentage terms for lower 
income consumers – called policy regressivity (Levinson 2019; Davis 
and Knittel 2019). And unlike carbon taxes, regulations do not generate 
revenues that can be used to compensate lower income groups.
A renewable energy procurement obligation in South Africa 
successfully required local hiring with perceived positive results 
(Walwyn and Brent 2015; Pahle et al. 2016), a  clean energy

regulation in Korea was perceived to provide greater employment 
opportunities (Lee 2017), and a UK obligation on energy companies 
to provide energy retrofits to low-income households improved 
energy affordability according to participants (Elsharkawy and 
Rutherford 2018). 
From an energy system transformation perspective, technology 
standards, including phase-out mandates, have particular promise 
to achieve profound change in specific sectors and technologies 
(Tvinnereim and Mehling 2018). As such policies change the 
technologies available in the market, then economic instruments can 
also have a greater effect (Pahle et al. 2018).

1390
1390
Chapter 13 National and Sub-national Policies and Institutions13Box 13.10 | Policies to Limit Emissions of Non-CO 2 Gases
Non-CO 2 gases weighted by their 100-year GWPs represent approximately 25% of global GHG emissions, of which methane (CH 4) 
accounts for 18%, nitrous oxide (N 2O) 4%, and fluorinated gases (HFCs, PFCs, SF 6 and NF 3) 2% (Minx et al. 2021). Only a small share 
of these emissions are subject to mitigation policies. 
Methane (CH 4). Anthropogenic sources include agriculture, mainly livestock and rice paddies, fossil fuel extraction and processing, 
fuel combustion, some industrial processes, landfills, and wastewater treatment (EPA 2019). Atmospheric measurements indicate that 
methane emissions from fossil fuel production are larger than shown in emissions inventories (Schwietzke et al. 2016). Only a small 
fraction of global CH 4 emissions is regulated. Mitigation policies focus on landfills, coal mines, and oil and gas operations.

Regulations and incentives to capture and utilise methane from coal seams came into effect in China in 2010 (Tan 2018; Tao et al. 
2019). Inventory data suggest that emissions peaked and began a slow decline after 2010 (Gao et al. 2020) though satellite data 
indicate that China’s methane emissions, largely attributable to coal mining, continued to rise in line with pre-2010 trends (Miller et al. 
2019). Methane emissions from sources including agriculture, waste and industry are included in some offset credit schemes, including 
the CDM and at national level in Australia’s Emissions Reductions Fund (Australian Climate Change Authority 2017) and the Chinese 
Certified Emission Reduction (CCER) scheme (Lo and Cong 2017). 
Nitrous oxide (N 2O). N2O emissions are produced by agricultural soil management, livestock waste management, fossil 
fuel combustion, and adipic acid and nitric acid production (EPA 2019). Most N 2O emissions are not regulated and global emissions

have been increasing. N 2O emissions by adipic and nitric acid plants in the EU are covered by the ETS (Winiwarter et al. 2018). 
N2O  emissions are included in some offset schemes. China, the United States, Singapore, Egypt, and Russia produce 86% of 
industrial N 2O emissions offering the potential for targeted mitigation action (EPA 2019).
Hydrofluorocarbons (HFCs). Most HFCs are used as substitutes for ozone depleting substances. The Kigali Amendment (KA) to 
the Montreal Protocol will reduce HFC use by 85% by 2047 (UN Environment 2018). To help meet their KA commitments developed 
country parties have been implementing regulations to limit imports, production and exports of HFCs and to limit specific uses of HFCs. 
The EU, for example, issues tradable quota for imports, production and exports of HFCs. Prices of HFCs have increased as expected 
(Kleinschmidt 2020) which has led to smuggling of HFCs into the EU (European Commission 2019b). HFC use has been slightly (1–6%)

below the limit each year from 2015 through 2018 (EEA 2019). China and India released national cooling action plans in 2019, laying 
out detailed, cross-sectoral plans to provide sustainable, climate friendly, safe and affordable cooling (Dean et al. 2020). 
Perfluorocarbons (PFCs), sulphur hexafluoride (SF 6) and nitrogen trifluoride (NF 3). With the exception of SF 6, these gases are 
emitted by industrial activities located in the European Economic Area (EEA) and a limited number (fewer than 30) of other countries. 
Regulations in Europe, Japan and the USA focus on leak reduction as well as collection and reuse of SF 6 from electrical equipment. 
Other uses of SF 6 are banned in Europe (European Union 2014). 
PFCs are generated during the aluminium smelting process if the alumina level in the electrolytic bath falls below critical levels (EPA 
2019). In Europe these emissions are covered by the EU ETS. The industry is eliminating the emissions through improved process

control and a shift to different production processes. 
The semiconductor industry uses HFCs, PFCs, SF 6 and NF 3 for etching and deposition chamber cleaning (EPA 2019) and has a voluntary 
target of reducing GHG emissions 30% from 2010 by 2020 (World Semiconductor Council 2017). Europe regulates production, import, 
export, destruction and feedstock use of PFCs and SF 6, but not NF 3 (EEA 2019). In addition, fluorinated gases are taxed in Denmark, 
Norway, Slovenia and Spain.

1391
1391
In some jurisdictions, the analysis of regulatory instruments is subject 
to an assessment on the basis of a shadow cost of carbon, which 
can influence the choice and design of regulations that affect GHG 
emissions (Box 13.11). 
13.6.5 Other Policy Instruments
A range of other mitigation policy instruments are in use, often 
playing a complementary role to pricing and standards.
13.6.5.1 Transition Support Policies
Effective climate change mitigation can cause economic and social 
disruption where there is transformative change, such as changes 
in energy systems away from fossil fuels (Section 13.9). Transitional 
assistance policies can be aimed to ameliorate effects on consumers, 
workers, communities, corporations or countries (Green and Gambhir 
2020) in order to create broad coalitions of supporters or to limit 
opposition (Vogt-Schilb and Hallegatte 2017). 
13.6.5.2 Information Programmes

Information programmes, including energy efficiency labels, energy 
audits, certification, carbon labelling and information disclosure, are 
in wide use in particular for energy consumption. They can reduce 
GHG emissions by promoting voluntary technology choices and 
behavioural changes by firms and households. 
Energy efficiency labelling is in widespread use, including for 
buildings, and for end users products including cars and appliances. 
Carbon labelling is used for example for food (Camilleri et al. 2019) 
and tourism (Gössling and Buckley 2016). Information measures also 
include specific information systems such as smart electricity meters 
(Zangheri et al. 2019). Chapters 5 and 9 provide detail.
Information programmes can correct for a range of market failures 
related to imperfect information and consumer perceptions (Allcott 
2016). Alongside mandatory standards (13.6.4), information 
programmes can nudge firms and consumers to focus on often

overlooked operating cost reductions (Carroll et al. 2022). For 
example, consumers who are shown energy efficiency labels on 
average buy more energy efficient appliances than those who are not (Stadelmann and Schubert 2018). Information policies can also 
support the changing of social norms about consumption choices, 
which have been shown to raise public support for pricing and 
regulatory policy instruments (Gössling et al. 2020).
Energy audits provide tailored information about potential energy 
savings and benchmarking of best practices through a network of 
peers. Typical examples include the United States Better Buildings 
Challenge that has provided energy audits to support USA commercial 
and industrial building owners, energy savings have been estimated 
at 18% to 30% (Asensio and Delmas 2017); and Germany’s energy 
audit scheme for SMEs achieving reductions in energy consumption 
of 5–70% (Kluczek and Olszewski 2017).

Consumption-oriented policy instruments seek to reduce GHG 
emissions by changing consumer behaviour directly, via retailers 
or via the supply chain. Aspects that hold promise are technology 
lists, supply chain procurement by leading retailers or business 
associations, a  carbon-intensive materials charge and selected 
infrastructure improvements (Grubb et al. 2020).
The information provided to consumers in labelling programmes is 
often not detailed enough to yield best possible results (Davis and 
Metcalf 2016). Providing information about running costs tends to be 
more effective than providing data on energy use (Damigos et al. 2020). 
Sound implementation of labelling programmes requires appropriate 
calculation methodology and tools, training and public awareness 
(Liang Wong and Krüger 2017). In systems where manufacturers self-
report performance of their products, there tends to be misreporting 
and skewed energy efficiency labelling (Goeschl 2019).

A new form of information programmes are financial accounting 
standards as frameworks to encourage or require companies to 
disclose how the transition risks from shifting to a  low-carbon 
economy and physical climate change impacts may affect their 
business or asset values (Chapter  15). The most prominent such 
standard was issued in 2017 by the Financial Stability Board’s Task 
Force on Climate-related Financial Disclosures. It has found rapid 
uptake among regulators and investors (O’Dwyer and Unerman 2020). 
Traditionally, corporate reporting has treated climate risks in a highly 
varied and often minimal way (Foerster et al. 2017). Disclosure of 
climate-related risks creates incentives for companies to improve Box 13.11 | Shadow Cost of Carbon in Regulatory Analysis
In some jurisdictions, public administrations are required to apply a shadow cost of carbon to regulatory analysis.

Traditionally, for example in widespread application in the United States, the shadow cost of carbon is calibrated to an estimate of the 
social cost of carbon as an approximation of expected future cumulative economic damage from a unit of greenhouse gas emissions 
(Metcalf and Stock 2017). Social cost of carbon is usually estimated using integrated assessment models and is subject to fundamental 
uncertainties (Pezzey 2019). An alternative approach, used for example in regulatory analysis in the United Kingdom since 2009, is 
to define a carbon price that is thought to be consistent with a particular targeted emissions outcome. This approach also requires 
a number of assumptions, including about future marginal costs of mitigation (Aldy et al. 2021).

1392
1392
Chapter 13 National and Sub-national Policies and Institutions13their carbon and climate change exposure, and ultimately regulatory 
standards for climate risk (Eccles and Krzus 2018). Disclosure can 
also reinforce calls for divestment in fossil fuel assets predominantly 
promoted by civil society organisations (Ayling and Gunningham 
2017), raising moral principles and arguments about the financial risks 
inherent in fossil fuel investments (Green 2018; Blondeel et al. 2019).
13.6.5.3 Public Procurement and Investment
National, sub-national and local governments determine many 
aspects of infrastructure planning, fund investment in areas such 
as energy, transport and the built environment, and purchase 
goods and services, including for government administration and 
military provisioning. 
Public procurement rules usually mandate cost effectiveness but only 
in some cases allow or mandate climate change consideration in public

purchasing, for example in EU public purchasing guidelines (Martinez 
Romera and Caranta 2017). Green procurement for buildings has 
been undertaken in Malaysia (Bohari et al. 2017). a  paper cites 
Taiwan (province of China) green public procurement law, which has 
contributed to reduced emissions intensity (Tsai 2017). In practice, 
awareness and knowledge of ‘green’ public procurement techniques 
and procedures is decisive for climate-friendly procurement (Testa 
et al. 2016). Experiences in low-carbon infrastructure procurement 
point to procedures being tailored to concerns about competition, 
transaction costs and innovation (Kadefors et al. 2020).
Infrastructure investment decisions lock-in high or low emissions 
trajectories over long periods. Low-emissions infrastructure can 
enable or increase productivity of private low-carbon investments 
(Jaumotte et al. 2021) and is typically only a little more expensive

over its lifetime, but faces additional barriers including higher upfront 
costs, lack of pricing of externalities, or lack of information or aversion 
to novel products (Granoff et al. 2016). In low-income developing 
countries, where infrastructure has historically lagged developed 
countries, some of these hurdles can be exacerbated by overall more 
difficult conditions for public investment (Gurara et al. 2018). 
Governments can also promote low-emissions investments 
through public-private partnerships and government owned ‘green 
banks’ that provide loans on commercial or concessional basis for 
environmentally friendly private sector investments (David and 
Venkatachalam 2019; Ziolo et al. 2019). Public funding or financial 
guarantees such as contracts-for-difference can alleviate financial 
risk in the early stages of technology deployment, creating pathways 
to commercial viability (Bataille 2020). Government provision can also play an important role in economic

stimulus programs, including as implemented in response to the 
pandemic of 2020–2021. Such programmes can support low-
emissions infrastructure and equipment, and industrial or business 
development (Elkerbout et al. 2020; Hainsch et al. 2020; Barbier 
2020; Hepburn et al. 2020). 
13.6.5.4 Voluntary Agreements
Voluntary Agreements result from negotiations between 
governments and industrial sectors that commit to achieve agreed 
goals (Mundaca and Markandya 2016). When used as part of 
a broader policy framework, they can enhance the cost effectiveness 
of individual firms in attaining emission reductions while pricing 
or regulations drive participation in the agreement (Dawson and 
Segerson 2008). 
Public voluntary programmes, where a  government regulator 
develops programs to which industries and firms may choose to 
participate on a voluntary basis, have been implemented in numerous 
countries. For example, the United States Environmental Protection

Agency introduced numerous voluntary programmes with industry to 
offer technical support in promoting energy efficiency and emissions 
reductions, among other initiatives (EPA 2017). a European example 
is the EU Ecolabel Award programme (European Commission 2020b). 
Agreements for industrial energy efficiency in Europe (Cornelis 2019) 
and Japan (Wakabayashi and Arimura 2016) have been particularly 
effective in addressing information barriers and for smaller 
companies. The International Civil Aviation Organization’s CORSIA 
scheme (Prussi et al. 2021) is an example of an international industry-
based public voluntary programme.
Voluntary agreements are often implemented in conjunction with 
economic or regulatory instruments, and sometimes are used to 
gain insights ahead of implementation of regulatory standards, as 
in the case of energy efficiency PVPs in South Korea (Seok et al. 
2021). In some cases, industries use voluntary agreements as partial

fulfilment of a  regulation (Rezessy and Bertoldi 2011; Langpap 
2015). For example, the Netherlands have permitted participating 
industries to be exempt from certain energy taxes and emissions 
regulations (Veum 2018). 
Box 13.12 | Technology and Research and Development Policy
Private businesses tend to under-invest in research and development because of market failures (Geroski 1995), hence there is 
a case for governments to support research and technology development. a range of different policy instruments are used, including 
government funding, preferential tax treatment, intellectual property rules, and policies to support the deployment and diffusion of 
new technologies. Chapter 16 treats innovation policy in-depth.

1393
1393
13.6.6 International Interactions of National 
Mitigation Policies
One country’s mitigation policy can impact other countries in various 
ways including changes in their GHG emissions (leakage), creation 
of markets for emission reduction credits, technology development 
and diffusion (spillovers), and reduction in the value of their fossil 
fuel resources. 
13.6.6.1 Leakage Effects
Compliance with a  mitigation policy can affect the emissions of 
foreign sources via several channels over different time scales (Zhang 
and Zhang 2017) (Box 13.13 ). The effects may interact and yield a net 
increase or decrease in emissions. The leakage channel that is of most 
concern to policymakers is adverse international competitiveness 
impacts from domestic climate policies.
In principle, implementation of a  mitigation policy in one country 
creates an incentive to shift production of tradable goods whose

costs are increased by the policy to other countries with less costly 
emissions limitation policies (Section  12.6.3). Such ‘leakage’ could 
to some extent negate emissions reductions in the first country, 
depending on the relative emissions intensity of production in 
both countries. 
Ex ante modelling studies typically estimate significant leakage for 
unilateral policies to reduce emissions due to production of emissions 
intensive products such as steel, aluminium, and cement (Carbone and Rivers 2017). However, the results are highly dependent on 
assumptions and typically do not reflect policy designs specifically 
aimed at minimising or preventing leakage (Fowlie and Reguant 2018).
Numerous ex post analyses, mainly for the EU ETS, find no evidence 
of any or significant adverse competitiveness impacts and conclude 
that there was consequently no or insignificant leakage (medium 
evidence, medium agreement) (Branger et al. 2016; Haites et al. 2018;

Koch and Basse Mama 2019; FSR Climate 2019; aus dem Moore et al. 
2019; Venmans et al. 2020; Kuusi et al. 2020; Verde 2020; Borghesi 
et al. 2020). This is attributed to large allocations of free allowances 
to emissions-intensive, trade-exposed sources, relatively low 
allowance prices, the ability of firms in some sectors to pass costs on 
to consumers, energy’s relatively low share of production costs, and 
small but statistically significant effects on innovation (Joltreau and 
Sommerfeld 2019). Few carbon taxes apply to emissions-intensive, 
trade-exposed sources (Timilsina 2018), so competitiveness impacts 
usually are not a particular concern. 
Policies intended to address leakage include a  border 
carbon  adjustment (Ward et al. 2019; Ismer et al. 2020). 
a border carbon adjustment (BCA) imposes costs – a tax or allowance 
purchase obligation  – on imports of carbon-intensive goods 
equivalent to those borne by domestic products possibly mirrored

by rebates for exports (Böhringer et al. 2012; Fischer and Fox 2012; 
Zhang 2012; Böhringer et al. 2017c) (Chapter 14). A BCA faces the 
practical challenge of determining the carbon content of imports 
(Böhringer et al. 2017a) and the design needs to be consistent with 
WTO rules and other international agreements (Cosbey et al. 2019; 
Box 13.13 | Possible Sources of Leakage
Competitiveness: Mitigation policy raises the costs and product prices of regulated sources which causes production to shift to 
unregulated sources, increasing their emissions.
Fossil fuel channel: Regulated sources reduce their fossil fuel use, which lowers fossil fuel prices and increases consumption and 
associated emissions by unregulated sources.
Land-use channel: Mitigation policies that change land use lead to land use and emissions changes in other jurisdictions (Bastos 
Lima et al. 2019).

Terms of trade effect: Price increases for the products of regulated sources shift consumption to other goods, which raises emissions 
due to the higher output of those goods.
Technology channel: Mitigation policy induces low-carbon innovation, which reduces emissions by sources that adopt the innovations 
that may include unregulated sources (Gerlagh and Kuik 2007).
Abatement resource effect: Regulated sources increase use of clean inputs, which reduces inputs available to unregulated sources 
and so limits their output and emissions (Baylis et al. 2014).
Scale channel: Changes to the output of regulated and unregulated sources affect their emissions intensities so emissions changes 
are not proportional to output changes (Antweiler et al. 2001).
Intertemporal channel: Capital stocks of all sources are fixed initially but change over time affecting the costs, prices, output and 
emissions of regulated and unregulated products.

1394
1394
Chapter 13 National and Sub-national Policies and Institutions13Mehling et al. 2019). Model estimates indicate that a BCA reduces 
but does not eliminate leakage (Branger and Quirion 2014). No BCA 
has yet been implemented for international trade although such 
a measure is currently under consideration by some governments.
13.6.6.2 Market for Emission Reduction Credits
A mitigation policy may allow the use of credits issued for emission 
reductions in other countries for compliance purposes (see also 
Section 13.6.3.4 on offset credits and Chapter 14 on international credit 
mechanisms). Creation of international markets for emission reduction 
credits tends to benefit other countries through financial flows in 
return for emissions credit sales (medium evidence,  high agreement). 
The EU, New Zealand and Switzerland allowed participants in their 
emissions trading systems to use credits issued under the Kyoto 
Protocol mechanisms, including the Clean Development Mechanism

(CDM), for compliance. From 2008 through 2014, participants used 
3.76 million imported credits for compliance of which 80% were 
CDM credits (Haites 2016).3 Use of imported credits has fallen to 
very low levels since 2014 (World Bank 2014; Shishlov et al. 2016).4
The Clean Development Mechanism (CDM) is the world’s largest 
offset programme (Chapter  14). From 2001 to 2019 over 7500 
projects with projected emission reductions in excess of 8000 MtCO 2-
eq were implemented in 114 developing countries using some 
140  different emissions reduction methodologies (UNFCCC 2012; 
UNEP DTU Partnership 2020). Credits reflecting over 2000 MtCO 2-eq 
of emission reductions by 3260 projects have been issued. To address 
additionality and other concerns the CDM Executive Board frequently 
updated its approved project methodologies.
13.6.6.3 Technology Spillovers
Mitigation policies stimulate low-carbon R&D by entities subject 
to those policies and by other domestic and foreign entities (FSR

Climate 2019). Policies to support technology development and 
diffusion tend to have positive spillover effects between countries 
(medium evidence, high agreement) (Section 16.3).
Innovation activity in response to a  mitigation policy varies by 
policy type (Jaffe et al. 2002) and stringency (Johnstone et al. 2012). 
In addition, many governments have policies to stimulate R&D, 
further increasing low-carbon R&D activity by domestic researchers. 
Emitters in other countries may adopt some of the new low-carbon 
technologies thus reducing emissions elsewhere. Technology 
development and diffusion is reviewed in Chapter 16.
13.6.6.4 Value of Fossil Fuel Resources
Fossil fuel resources are a significant source of exports, employment 
and government revenues for many countries. The value of these 
resources depends on demand for the fuel and competing supplies in 
the relevant international markets. Discoveries and new production 
3 2010 through 2014 for the New Zealand ETS.

4 All three ETSs were modified after 2012 including provisions that affected compliance use of imported credits.technologies reduce the value of established resources. Mitigation 
policies that reduce the use of fossil fuels also reduce the value of 
these resources. A  single policy in one country is unlikely to have 
a noticeable effect on the international price, but similar policies in 
multiple countries could adversely affect the value of the resources. 
For fossil fuel exporting countries, mitigation policies consistent with 
the Paris Agreement goals could result in greater costs from changes 
in fossil fuel prices due to lower international demand than domestic 
policy costs (medium evidence, high agreement) (Liu et al. 2020).
The impact on the value of established resources will be mitigated, to 
some extent, by the reduced incentive to explore for and develop new 
fossil fuel supplies. Nevertheless, efforts to lower global emissions will

mean substantially less demand for fossil fuels, with the majority of 
current coal reserves and large shares of known gas and oil reserves 
needing to remain unused, with great diversity in impacts between 
different countries (McGlade and Ekins 2015) (Chapters 3, 6, 15). 
Estimates of the potential future loss in value differ greatly. There 
is uncertainty about remaining future fossil fuel use under different 
mitigation scenarios, as well as future fossil fuel prices depending 
on extraction costs, market structures and policies. Estimates of total 
cumulative fossil fuel revenue lost range between 5–67 trillion USD 
(Bauer et al. 2015) with an estimate of the net present value of lost 
profit of around 10 trillion USD (Bauer et al. 2016). Policies that 
constrain supply of fossil fuels in the context of mitigation objectives 
could limit financial losses to fossil fuel producers (Chapter 14). 
13.7 Integrated Policy Packages for 
Mitigation and Multiple Objectives

Since AR5, the literature on climate policies and policymaking 
has expanded in two significant directions. First, there is growing 
recognition that mitigation policy occurs in the context of multiple 
climate and development objectives (Chapter  4). Different aspects 
of these linkages are discussed across the AR6 WGIII report, 
including concepts and framings (Section 1.6.2), shifting sustainable 
development pathways (Section  4.3 and Cross-Chapter Box  5 in 
Chapter 4), cross-sectoral interactions (Sections 12.6.1 and 12.6.2), 
evidence of co-impacts (Section  17.3), links with adaptation 
(Section 4.4.2) and accelerating the transition (Sections 13.9, 17.1.1, 
17.4.5 and 17.4.6). While the concept of development pathways is 
salient in all countries, it may particularly resonate with policymakers 
in developing countries focused on providing basic needs and 
addressing poverty and inequality, including energy poverty (Ahmad

2009; Fuso Nerini et al. 2019; Bel and Teixidó 2020; Caetano et al. 
2020; Röser et al. 2020). Consequently, some countries may frame 
policies predominantly in terms of accelerating mitigation, while 
in others a  multiple objectives approach linked to development 
pathways may dominate, depending on their specific socio-economic 
contexts and priorities, governance capacities (McMeekin et al. 2019) 
and perceptions of historical responsibility (Winkler and Rajamani 
2014; Friman and Hjerpe 2015; Winkler et al. 2015; Pan et al. 2017).

1395
1395
Second, since AR5 there is growing attention to enabling transitions 
over time. Literature on socio-technical transitions, rooted in innovation 
studies, highlights the need for different policy focus at different stages 
of a transition (Geels et al. 2017b,a; Köhler et al. 2019) (Section 1.7.3). 
Other literature examines how broad patterns of development drive 
both social and mitigation outcomes through shifts in policies and 
a  re-alignment of enabling conditions (Chapter  4). Explicit efforts 
to shift development pathways, for example by shifting patterns of 
energy demand and urbanisation, therefore offer broader mitigation 
opportunities (Cross-Chapter Box  5 in Chapter  4). Common to both 
approaches is an emphasis beyond the short term, and attention to 
enabling longer-term structural shifts in economies and societies.
Taking these trends into account, Figure  13.6 outlines the climate

policy landscape, and how it maps to different parts of this Working 
Group III report. One axis of variation captures alternative framings of 
desired outcomes in national policymaking – mitigation versus multiple 
objectives, while the second captures the shift in policymaking from 
an initial focus on shifting incentives through largely individual policy 
instruments, to explicit consideration of how policies and economy-
wide measures, including those that shift incentives, can combine to 
enable transitions. As a result, Figure 13.6 represents interconnected 
policy ideas, but backed by distinct strands of literature. Notably, each 
of these categories is salient to climate policymaking, although the 
balance may differ depending on country context. 
This section particularly focuses on climate policymaking for 
transition – both socio-technical transitions and shifts in development 
pathways, while direct climate policies and co-benefits are addressed

in other parts of the report, as indicated in Figure 13.6. This section 
focuses in particular on lessons for designing policy packages for transitions, and is complemented by discussion in Section 13.8 on 
integration between adaptation and mitigation, and Section  13.9 
on economy-wide measures and the broader enabling conditions 
necessary to accelerate mitigation.
13.7.1 Policy Packages for Low-carbon 
Sustainable Transitions
Since AR5 an emergent multidisciplinary literature on policy 
packages, or policy mixes, has emerged that examine how policies 
may be combined for sustainable low-carbon transitions (Rogge 
and Reichardt 2016; Kern et al. 2019). This literature covers various 
sectors including: energy (Rogge et al. 2017); transport (Givoni et al. 
2013); industry (Scordato et al. 2018); agri-food (Kalfagianni and Kuik 
2017); and forestry (Scullion et al. 2016). 
A central theme in the literature is that transitions require policy

interventions to address system level changes, thereby going beyond 
addressing market failures in two ways. First, structural system 
changes are needed for low-carbon transitions, including building 
low-carbon infrastructure (or example aligning electricity grids and 
storage with the requirements of new low-carbon technology), 
and  adjusting existing institutions to low-carbon solutions (for 
example by reforming electricity market design) (Bak et al. 2017; 
Patt and Lilliestam 2018). Second, explicit transformational system 
changes are necessary, including efforts at directing transformations, 
such as clear direction setting through the elaboration of shared 
visions, and coordination across diverse actors across different policy 
fields, such as climate and industrial policy, and across governance 
levels (Uyarra et al. 2016; Nemet et al. 2017). Framing of outcome
Enhancing mitigationAddressing multiple objectives of mitigation 
and development
Approach to

policymakingShifting 
incentives ‘Direct mitigation focus’ 
(Section 13.6; 2.8)
Objective: reduce GHG emissions now
Literature: how to design and implement policy instruments, 
with attention to distributional and other concerns
Examples: carbon tax, cap and trade, border carbon adjustment, 
disclosure policies‘Co-benefits’ 
(Sections 17.3; 5.6.2; 12.4.4)
Objective: synergies between mitigation and development 
Literature: scope for and policies to realise synergies and avoid trade-
offs across climate and development objectives
Examples: appliance standards, fuel taxes, community forest 
management, sustainable dietary guidelines, green building codes, 
packages for air pollution, packages for public transport
Enabling 
transition ‘Socio-technical transitions’ 
(Sections 1.7.3; 5.5; 10.8; 6.7; Cross-Chapter Box 12 in Chapter 16)
Objective: accelerate low-carbon shifts in socio-technical systems
Literature: understand socio-technical transition processes, integrated

policies for different stages of a technology ‘S-curve’ and explore 
structural, social and political elements of transitions
Examples: packages for renewable energy transition and coal phase-out; 
diffusion of electric vehicles, process and fuel switching in key industries‘System transitions to shift development pathways’ 
(Sections 11.6.6; 7.4.5; 13.9; 17.3.3; Cross-Chapter Box 5 in Chapter 4;  
Cross-Chapter Box 9 in Chapter 13) 
Objective: accelerate system transitions and shift development pathways 
to expand mitigation options and meet other development goals 
Literature: examines how structural development patterns and broad 
cross-sector and economy-wide measures drive ability to mitigate while 
achieving development goals through integrated policies and aligning 
enabling conditions 
Examples: packages for sustainable urbanisation, land-energy-water 
nexus approaches, green industrial policy, regional just transition plans

1396
1396
Chapter 13 National and Sub-national Policies and Institutions13There are some specific suggestions for policy packages: Van den 
Bergh et al. (2021) suggest that innovation support and information 
provision combined with a carbon tax or market, or adoption subsidy 
leads to both effective and efficient outcomes. Others question 
the viability of universally applicable policy packages, and suggest 
packages need to be tailored to local objectives (del Río 2014) 
Consequently, much of the literature focuses on broad principles for 
design of policy packages and mixes, as discussed below.
Comprehensiveness, balance and consistency are important criteria 
for policy packages or mixes (robust evidence, high agreement) 
(Rogge and Reichardt 2016; Scobie 2016; Carter et al. 2018; Santos-
lacueva and González 2018). Comprehensiveness assesses the 
extensiveness of policy packages, including the breadth of system 
and market failures it addresses (Rogge and Reichardt 2016). For

example, instrument mixes that include only moderate carbon 
pricing, but are complemented by policies supporting new low-carbon technologies and a  moratorium on coal-fired power plants 
may not only be politically more feasible than stringent carbon pricing 
alone, but may also limit efficiency losses and lower distributional 
impacts  (Bertram et al. 2015b). Balance captures whether policy 
instruments are deployed in complementary ways given their different 
purposes, combining for example technology-push approaches such 
as public R&D with demand-pull approaches such as an energy tax. 
A combination of technology-push and demand-pull approaches has 
been shown to support innovation in energy efficient technologies 
in OECD countries (Costantini et al. 2017). Consistency addresses 
the alignment of policy instruments among each other and with the 
policy strategy, which may have multiple and not always consistent 
objectives (Rogge 2019). Consistency of policy mixes has been

identified as an important driver of low-carbon transformation, 
particularly for renewable energy (Lieu et al. 2018; Rogge and 
Schleich 2018). Box 13.14 summarises the economics literature on 
how policies interact, to inform design of packages. 
Box 13.14 | Policy Interactions of Carbon Pricing and Other Instruments
The economics literature provides insights on policy interactions among the multiple overlapping policies that directly or indirectly 
affect GHG emissions, including when different levels of government are involved. Multiple mitigation policies can be theoretically 
justified if there are multiple objectives or market failures or to achieve distributional objectives and increase policy effectiveness 
(Stiglitz 2019). Examples include the coexistence of the EU ETS with vehicle emission standards and energy efficiency standards (Rey 
et al. 2013), and the fact that 85% of the emissions covered by California’s ETS are also subject to other policies (Bang et al. 2017;

Mazmanian et al. 2020). Policy interactions are also widespread among energy efficiency policies (Wiese et al. 2018). 
Interactive effects can influence the costs of policy outcomes. With multiple overlapping and possibly non-optimal policies, the effect 
on total cost is not clear. A modelling study of USA mitigation policy finds the costs of using heterogeneous sub-national policies 
to achieve decarbonisation targets is 10% higher than national uniform policies (Peng et al. 2021). When multiple policy goals are 
sought, such as mitigation and R&D, a portfolio of optimal policies achieves the goals at significantly lower cost (Fischer and Newell 
2008). In some cases, overlapping mitigation policies can raise the cost of mitigation (Böhringer et al. 2016) while lowering the cost 
of achieving other goals, such as energy efficiency improvements and expansion of renewable energy (Rosenow et al. 2016; Lecuyer

and Quirion 2019). It is possible that one or more of the policies is made redundant (Aune and Golombek 2021).
While overlapping policies may raise the cost of mitigation, they increase the likelihood of achieving an emission reduction goal. Policy 
overlap will lead to different optimal carbon prices across jurisdictions (Bataille et al. 2018b). The existence of overlapping policies will 
usually increase administrative and compliance costs. However, ex post analysis shows that transaction costs of mitigation policies are 
low and are not a decisive factor in policy choice (Joas and Flachsland 2016). 
The effectiveness, as well as economic and distributional effects, of a given mitigation policy will depend on the interactions among all 
the policies that affect the targeted emissions. Because a market instrument interacts with every other policy that affects the targeted

emissions, interactions tend to be more complex for market instruments than for regulations that mandate specific emission reduction 
actions by targeted sources independent of other policies.
An ETS scheme implemented with existing mitigation policies may be subject to the ‘waterbed effect’ – emission reductions undertaken 
by some emitters may be offset by higher emissions by other ETS participants due to overlapping mitigation policies (Schatzki and 
Stavins 2012). This reduces the impact of the ETS and lowers carbon trading prices (Perino 2018). However ex post assessments 
find net emissions reductions. ETS design features such as a price floor and ‘market stability reserve’ can limit the waterbed effect 
(Edenhofer et al. 2017; Kollenberg and Taschini 2019; Narassimhan et al. 2018; FSR Climate 2019). 
A carbon tax, unlike the allowance price, does not change in response to the effect of overlapping policies but those policies may

reduce emissions by sources subject to the tax and so lower the emission reductions achieved by the tax (Goulder and Stavins 2011).

1397
1397
Policy packages aimed at low-carbon transitions are more effective 
when they include elements to enhance the phase out of carbon-
intensive technologies and practices – often called exnovation – in 
addition to supporting low-carbon niches (Kivimaa and Kern 2016; 
David 2017). Such policies include stringent carbon pricing; changes 
in regime rules such as design of electricity markets; reduced support 
for dominant regime technologies such as removing tax deductions 
for private motor transport based on internal combustion engines; 
and changes in the balance of representation of incumbents versus 
new entrants in deliberation and advisory bodies. For example, CGE 
modelling for China’s fossil fuel subsidy reform found that integrating 
both creation and destabilisation policies is able to reduce rebound 
effects and make the policy mix more effective (Li et al. 2017).

Sweden’s pulp and paper industry shows that destabilisation policies 
including deregulation of the electricity market and a  carbon tax 
were an important complement to support policies (Scordato et al. 
2018), and other studies show complementary results for Finland’s 
building sector (Kivimaa et al. 2017b) and Norway’s transport and 
energy sector (Ćetković and Skjærseth 2019).
Policy packages for low-carbon transitions are more successful 
if they take into account the potential for political contestation 
and resistance from incumbents who benefit from high-carbon 
systems (medium evidence,  high agreement) (Geels 2014; Roberts 
et al. 2018; Kern and Rogge 2018; Rosenbloom 2018). To do so, 
policies can be sequenced so as to address political obstacles, for 
example, by initially starting with policies to facilitate the entry 
of new firms engaged in low-carbon technologies (Pahle et al. 
2018). Such policies can generate positive feedbacks by creating

constituencies for continuation of those policies, but need to be 
designed to do so from the outset (Edmondson et al. 2019, 2020). 
For example, supporting renewable energies through feed-in tariffs 
can buttress coalitions for more ambitious climate policy, such as 
through carbon pricing (Meckling et al. 2015). However, negative 
policy feedback may also arise from ineffective policy instruments 
that lose public support, or create concentrated losses that arouse 
oppositional coalitions (Edmondson et al. 2019). Feedback loops 
can operate through changes in resources available to actors; 
changes in expectations; and changes in government capacities 
(Edmondson et al. 2019). 
Another promising strategy is to design short-term policies which 
might help to provide later entry points for more ambitious climate 
policy (Kriegler et al. 2018) and supportive institutions. The sequencing of policies can build coalitions for climate policy, starting with green

industrial policy (e.g. supporting renewable energies through feed-
in tariffs) and introducing or making carbon pricing more stringent 
when supportive coalitions of stringent climate policy have been 
formed (Meckling et al. 2015). Similarly, investing in supportive 
institutions, with competencies compatible with low-carbon futures, 
are a necessary supportive element of transitions (Pahle et al. 2018; 
Rosenbloom et al. 2019; Domorenok et al. 2021).
13.7.2 Policy Integration for Multiple Objectives 
and Shifting Development Pathways 
This sub-section assesses policy integration and packages required 
to enable shifts in development pathways, with a  particular focus 
on sectoral scale transitions. However, because shifting development 
pathways requires broad transformative change, it complements 
discussion on broader shifts in policymaking such as fiscal, educational, 
and infrastructure policies (Cross-Chapter Box 5 in Chapter 4) and to

the alignment of a wide range of enabling conditions required for 
system transitions (Section 13.9). 
In many countries, and particularly when climate policy occurs in the 
context of sustainable development, policymakers seek to address 
climate mitigation in the context of multiple economic and social 
policy objectives (medium evidence, robust agreement) (Halsnæs 
et al. 2014; Campagnolo and Davide 2019; Cohen et al. 2019). Studies 
suggest that co-benefits of climate policies are substantial, especially 
in relation to air quality, and can yield better mitigation and overall 
welfare, yet these are commonly overlooked in policymaking (robust 
evidence, robust agreement) (Nemet et al. 2010; Ürge-Vorsatz et al. 
2014; von Stechow et al. 2015; Mayrhofer and Gupta 2016; Roy 
et al. 2018; Bhardwaj et al. 2019; Karlsson et al. 2020). Other studies 
have shown the existence of strong complementarities between the 
SDGs and realisation of NDC pledges by countries (McCollum et al.

2018). An explicit attention to development pathways can enhance 
the scope for mitigation, by paying explicit attention to development 
choices that lock-in or lock-out opportunities for mitigation, such as 
around land use and infrastructure choices (Cross-Chapter Box 5 in 
Chapter 4). While the pay-offs are considerable to an approach to 
mitigation that takes into account linkages to multiple objectives 
and the opportunity to shift development pathways, there are 
also associated challenges with implementing this approach 
to policymaking.Box 13.14 (continued)
Policy interactions often occur with the introduction of new mitigation policy instruments. For example, in China several sub-national 
ETSs exist alongside policies to reduce emission intensity, increase energy efficiency and expand renewable energy supplies (Zhang 
2015). These quantity-based ETSs interact with many other policies (Duan et al. 2017), for example price-based provincial carbon

intensity targets (Qian et al. 2017). They also interact with the level of market regulation; for example, full effectiveness of emissions 
pricing would require electricity market reform in China (Teng et al. 2017).

1398
1398
Chapter 13 National and Sub-national Policies and Institutions13First, spanning policy arenas and addressing multiple objectives 
places considerable requirements of coordination on the policymaking 
process (Howlett and del Rio 2015; Obersteiner et al. 2016). Climate 
policy integration suggests several steps should precede actual policy 
formulation, beginning with a clear articulation of the policy frame or 
problem statement (Adelle and Russel 2013; Candel and Biesbroek 
2016). For example, a greenhouse gas limitation framework versus 
a co-benefits framing would likely yield different policy approaches. It 
is then useful to identify the range of actors and institutions involved 
in climate governance – the policy subsystem, the goals articulated, 
the level at which goals are articulated and the links with other 
related policy goals such as energy security or energy access (Candel 
and Biesbroek 2016). The adoption of specific packages of policy

instruments should ideally follow these prior steps that define the 
scope of the problem, actors and goals. 
In practice, integration has to occur in the context of an already 
existing policy structure, which suggests the need for finding 
windows of opportunity to bring about integration, which can 
be created by international events, alignments with domestic 
institutional procedures, and openings created by policy 
entrepreneurs (Garcia Hernandez and Bolwig 2020). Integration 
also has to occur in the context of existing organisational routines 
and cultures, which can pose a barrier to integration (Uittenbroek 
2016). Experience from the EU suggests that disagreements 
at the level of policy instruments are amenable to resolution 
by deliberation, while normative disagreements at the level of 
objectives require a  hierarchical decision structure (Skovgaard 
2018). As this discussion suggests, the challenge of integration

operates in two dimensions: horizontal  – between sectoral 
authorities such as ministries or policy domains such as forestry – 
or vertical – either between constitutional levels of power or within 
the internal mandates and interactions of a  sector (Howlett and 
del Rio 2015; Di Gregorio et al. 2017). There are also important 
temporal dimensions to policy goals, as policy and benchmarks 
have to address not just immediate success but also indications of 
future transformation (Dupont and Oberthür 2012; Dupont 2015). 
Second policymaking for shifting development pathways has to 
account for inherent uncertainties in future development paths 
(Moallemi and Malekpour 2018; Castrejon-Campos et al. 2020). 
These uncertainties may be greater in developing countries that 
are growing rapidly and where structural features of the economy 
including infrastructure and urbanisation patterns are fluid. For 
example, reviews of modelling studies of Chinese (Grubb et al. 2015)

and Indian emissions futures (Spencer and Dubash 2021) find that 
differences in projections can substantially be accounted for by 
alternative assumptions about future economic structural shifts. 
Consequently, an important design consideration is that policy 
packages should be robust, that is, perform satisfactorily for all 
key objectives under a  broad range of plausible futures (Kwakkel 
et al. 2016; Maier et al. 2016; Castrejon-Campos et al. 2020). Such 
an approach to decision-making can be contrasted with one that tries to design an optimal policy package for the ‘best guess’ future 
scenario (Maier et al. 2016). Moreover, policy packages can usefully 
be adapted dynamically to changing circumstances as part of the 
policy process (Haasnoot et al. 2013; Hamarat et al. 2014; Maier 
et al. 2016) including by using exploratory modelling techniques that 
allow comparison of trade-offs across alternative future scenarios

(Hamarat et al. 2014). Another approach is to link quantitative 
models with a  participatory process that enables decision-makers 
to test the implications of alternative interventions (Moallemi and 
Malekpour 2018). Rosenbloom et al. (2019) suggest that because 
policy mixes should adapt to changing circumstances, instead of 
stability of a particular mix, transitions require embedding policies 
within a  long-term orientation toward a  low-carbon economy, 
including a transition agenda, social legitimacy for this agenda, and 
an appropriate ecosystem of institutions.
Third, achieving changes in development pathways requires engaging 
with place-specific context. It requires attention to existing policies, 
political interests that may gain or lose from a transition, and locally 
specific governance enablers and disablers. As a result, while there 
may be approaches that carry over from one context to another, 
implementation requires careful tailoring of transition approaches to

specific policy and governance contexts. Cross-Chapter Box 9 in this 
chapter summarises case studies of sectoral transitions from other 
chapters in this report (Chapters 5 to 12) to illustrate this complexity. 
Broader macroeconomic transformative shifts are discussed in more 
detail in Section 13.9.
Common to all the sectoral cases in Cross-Chapter Box  9 is 
a  future-oriented vision of sectoral transition often focused on 
multiple objectives, such as designing tram-based public transport 
systems in Bulawayo, Zimbabwe to simultaneously stimulate 
urban centers, create jobs and enable low-carbon transportation. 
Sectoral transitions are enabled by policy mixes that bring together 
different combinations of instruments  – including regulations, 
financial incentives, convening, education and outreach, voluntary 
agreements, procurement and creation of new institutions  – to 
work together in a  complementary manner. The effectiveness of

a  policy mix depends on conditions beyond design considerations 
and also rests on the larger governance context within which sector 
transitions occur, which can include enabling and disabling elements. 
Enabling factors illustrated in Cross-Chapter Box  9 include strong 
high level political support, for example to address deforestation in 
Brazil despite powerful logging and farmer interests, or policy design 
to win over existing private interests, for example, by harnessing 
distribution networks of kerosene providers to new LPG technology 
in Indonesia. Disabling conditions include local institutional contexts, 
such as the lack of tree and land tenure in Ghana, which, along 
with the monopoly of the state marketing board, posed obstacles 
to Ghana’s low-carbon cocoa transition. These examples emphasise 
the  importance of attention to local context if policy integration 
and the design of policy mixes are to effectively lead to transitions

1399
1399
Cross-Chapter Box 9 | Case Studies of Integrated Policymaking for Sector Transitions
Authors: Parth Bhatia (India), Navroz K. Dubash (India), Igor Bashmakov (the Russian Federation), Paolo Bertoldi (Italy), Mercedes 
Bustamante (Brazil), Michael Craig (the United States of America), Stephane de la Rue du Can (the United States of America), Manfred 
Fischedick (Germany) Amit Garg (India), Oliver Geden (Germany), Robert Germeshausen (Germany), Siir Kilkis (Turkey), Susanna 
Kugelberg (Denmark), Andreas Loeschel (Germany), Cheikh Mbow (Senegal), Yacob Mulugetta (Ethiopia), Gert-Jan Nabuurs (the 
Netherlands), Vinnet Ndlovu (Zimbabwe/Australia), Peter Newman (Australia), Lars Nilsson (Sweden), Karachepone Ninan (India) 
Real world sectoral transitions reinforce critical lessons on policy integration: a high-level strategic goal (Column a in Cross-Chapter

Box  9, Table  1), the need for a  clear sector outcome framing (column B), a  carefully coordinated mix of policy instruments and 
governance actions (column C), and the importance of context-specific governance factors (column D). Illustrative examples, drawn 
from sectors, help elucidate the complexity of policymaking in driving sectoral transitions. 
Cross-Chapter Box 9, Table 1 | Case studies of integrated policymaking for sector transitions.
A. Illustrative 
caseB. Objective C. Policy mix D. Governance context
      Enablers Barriers
Shift in mobility 
service provision 
in Kolkata, India 
[Box 5.8] –Improve system 
efficiency, 
sustainability 
and comfort
 –Shift public 
perceptions of  
public transport –Strengthen coordination between modes
 –Formalise and green auto-rickshaws
 –Procure fuel efficient, comfortable,  
low-floor AC buses 
 –Ban cycling on busy roads
 –Deploy policy actors as change-agents, 
mediating between interest groups –Cultural norms around informal

transport sharing, linked to high 
levels of social trust
 –Historically crucial role of buses 
in transit 
 –App-cab companies shifting norms 
and formalising mobility sharing
 –Digitalisation and safety on board –Complexity: multiple modes with 
separate networks and meanings
 –Accommodating and addressing 
legitimate concerns from 
social movements about the 
exclusionary effects of ‘premium’ 
fares, cycling bans on busy roads
LPG Subsidy 
(‘Zero Kero’) 
Program, 
Indonesia 
[Box 6.3]Decrease fiscal 
expenditures on 
kerosene subsidies 
for cooking –Subsidise provision of Liquefied Petroleum 
Gas (LPG) cylinders and initial equipment
 –Convert existing kerosene suppliers 
to LPG suppliers –Provincial government and industry 
support in targeting beneficiaries 
and implementation
 –Synergies in kerosene and LPG 
distribution infrastructures –Continued user preference for 
traditional solid fuels
 –Reduced GHG benefits as subsidy 
shifted between fossil fuels
Action Plan

for Prevention 
and Control of 
Deforestation 
in the Legal 
Amazon, Brazil 
[Box 7.9]Control deforestation 
and promote 
development –Expand protected areas; homologation of 
indigenous lands
 –Improve inspections, satellite-based 
monitoring 
 –Restrict public credit for enterprises 
and municipalities with high 
deforestation rates
 –Set up a REDD+ mechanism 
(Amazon Fund) –Participatory agenda-setting process
 –Cross-sectoral consultations on 
conservation guidelines
 –Mainstreaming of deforestation 
in government programmes 
and projects –Political polarisation leading 
to erosion of environmental 
 –Reduced representation and 
independence of civil society 
in decision-making bodies
 –Lack of clarity around 
land ownership
Climate Smart 
Cocoa (CSC) 
production, 
Ghana [Box 7.12] –Promote sustainable 
intensification of 
cocoa production
 –Reduce deforestation
 –Enhance 
incomes and 
adaptive capacities –Distribute shade tree seedlings

–Provide access to agronomic information 
and agrochemical inputs
 –Design a multi-stakeholder programme 
including MNCs, farmers and NGOs –Local resource governance 
mechanisms ensuring voice 
for smallholders
 –Community governance allowed 
adapting to local context
 –Private sector role in  
popularising CSC –Lack of secure tenure (tree rights)
 –Bureaucratic and legal hurdles to 
register trees
 –State monopoly on cocoa 
marketing, export
Coordination 
mechanism 
for joining 
fragmented 
urban 
policymaking 
in Shanghai, 
China [Box 8.3]Integrate policymaking 
across objectives, 
towards low-carbon 
urban development –Combine central targets and evaluation 
with local flexibility for initiating varied 
policy experiments
 –Establish a local leadership team for 
coordinating cross-sectoral policies 
involving multiple institutions
 –Create a direct programme fund for 
implementation and capacity-building –Strong vertical linkages between 
Central and local levels

–Mandate for policy learning 
to inform national policy 
 –Experience with mainstreaming 
mitigation in related areas  
(e.g. air pollution) –Challenging starting point –  
low share of RE, high dependency 
on fossil fuels
 –Continued need for 
high investments 
in a developing context

1400
1400
Chapter 13 National and Sub-national Policies and Institutions13Cross-Chapter Box 9 (continued)
13.8 Integrating Adaptation, Mitigation 
and Sustainable Development 
There is growing consensus that integration of adaptation and 
mitigation will advance progress towards sustainable development, 
and that ambitious mitigation efforts will reduce the need for 
adaptation in the long term (robust evidence, high agreement) (IPCC 
2014a). There is no level of mitigation, however, that will completely 
erase the need for adaptation to climate change (robust evidence, 
high agreement) (Mauritsen and Pincus 2017). It is therefore urgent 
to design and implement a  multi-objective policy framework for 
mitigation, adaptation, and sustainable development that considers 
issues of equity and long-term developmental pathways across 
regions (robust evidence, high agreement) (Jordan et al. 2018; 
Mills-Novoa and Liverman 2019; Wang and Chen 2019). This section

explores the logic behind the integration of adaptation and mitigation 
in practice (Section  13.8.1), the approaches to this integration 
including climate-resilient pathways, ecosystem-based solutions, 
and a nexus approach (Section 13.8.2); examples of the adaption and mitigation relationships and linkages (Section 13.8.3); and enabling 
and disabling factors for governance of mitigation and adaption.
13.8.1 Synergies Between Adaptation and Mitigation
Integrated climate-development actions require a  context-specific 
understanding of synergies and trade-offs with other policy 
priorities (Figure  13.6) with the aim of implementing mitigation/
adaptation policies that reduce GHG emissions while simultaneously 
strengthening resilience and reducing vulnerability (robust evidence, 
high agreement) (Klein et al. 2005; IPCC 2007; Zhao et al. 2018; Mills-
Novoa and Liverman 2019; Solecki et al. 2019). Efficient, equitable 
and inclusive policies which also acknowledge and contribute

directly to other pressing priorities such reducing poverty, improving 
health, providing access to clean water, and fostering sustainable 
consumption and production practices are helpful for mitigation/
adaptation goals (robust evidence, high agreement) (Landauer et al. 
2019; Grafakos et al. 2020). A. Illustrative 
Policy package 
for building 
energy efficiency, 
EU [Box 9.SM.1]Reduce energy 
consumption, 
integrating RE 
and mitigating 
GHG emissions 
from buildings –Energy performance standards, set at 
nearly zero energy for new buildings
 –Energy performance standards 
for appliances
 –Energy performance certificates shown 
during sale
 –Long-term renovation strategies –Binding EU-level targets, 
directives and sectoral effort 
sharing regulations
 –Supportive urban policies, 
coordinated through 
city partnerships
 –Funds raised from allowances 
auctioned under ETS –Inadequate local technical

capacity to implement 
multiple instruments
 –Complex governance structure 
leading to uneven stringency
African 
electromobility – 
trackless trams 
with solar in 
Bulawayo and 
e-motorbikes 
in Kampala 
[Box 10.4] –Leapfrog into 
a decarbonised 
transport future
 –Achieve 
multiple social 
benefits beyond 
mobility provision –Develop urban centres with solar at 
station precincts 
 –Public-private partnerships for financing
 –Sanction demonstration projects for new 
electric transit and new electric motorbikes 
(for freight) –‘Achieving SDGs’ was an enabling 
policy framing
 –Multi-objective policy process 
for mobility, mitigation and 
manufacturing
 –Potential for funding through 
climate finance
 –Co-benefits such as local 
employment generation –Economic decline in the first 
decade of the 21st century
 –Limited fiscal capacity for public 
funding of infrastructure
 –Inadequate charging 
infrastructure for e-motorbikes
Initiative for 
a climate-friendly 
industry in North

Rhine Westphalia 
(NRW), Germany 
[Box 11.3]Collaboratively 
develop innovative 
strategies towards 
a net zero industrial 
sector, while securing 
competitiveness –Build platform to bring together industry, 
scientists and government in self-
organised innovation teams
 –Intensive cross-branch cooperation to 
articulate policy/infrastructure needs –NRW is Germany’s industrial 
heartland, with an export-oriented 
industrial base
 –Established government– 
industry ties
 –Active discourse between industry 
and publicCompliance rules preventing  
in-depth cooperation
Food2030 
Strategy, Finland 
[Box 12.2] –Local, organic and 
climate friendly 
food production
 –Responsible and 
healthy food 
 –A competitive food 
supply chain –Target funding and knowledge support 
for innovations
 –Apply administrative means (legislation, 
guidance) to increase organic food 
production and procurement
 –Use education and information 
instruments to shift behaviour

(media campaigns, websites) –Year-long deliberative stakeholder 
engagement process across sectors 
 –Institutional structures for 
agenda-setting, guiding 
policy implementation and 
reflexive discussions –Weak role of integrated 
impact assessments to inform 
agenda-setting
 –Monitoring and evaluation close 
to ministry in charge
 –Lack of standardised indicators  
of food system sustainability

1401
1401
Adaptation and mitigation are deeply linked in practice  – at the 
local level, for instance, asset managers address integrated low-
carbon resilience to climate change impacts and urban planners 
do the same (Ürge-Vorsatz et al. 2018; Grafakos et al. 2020) 
(Table 13.3). Similarly, ecosystem-based (or nature-based) solutions, 
may generate co-benefits by simultaneously sinking carbon, cooling 
urban areas through shading, purifying water, improving biodiversity, 
and offering recreational opportunities that improve public health 
(Raymond et al. 2017). Accurately identifying and qualitatively or 
quantitatively assessing these co-benefits (Stadelmann et al. 2014; 
Leiter and Pringle 2018; Leiter et al. 2019) is central to an integrated 
adaptation and mitigation policy evaluation. 
Some studies press the need to consider the complex ways that power

and interests influence how collective decisions are made, and who 
benefits from and pays for these decisions, of climate policy and to be 
aware of unintended consequences, especially for vulnerable people 
living under poor conditions (Mayrhofer and Gupta 2016; De Oliveira 
Silva et al. 2018). The specific adaptation and mitigation linkages will 
differ by country and region, as illustrated by Box 13.15.
13.8.2 Frameworks That Enable the Integration 
of Adaption and Mitigation
The IPCC’s Fifth Assessment Report (AR5) emphasised the 
importance of climate-resilient pathways – development trajectories 
that combine adaptation and mitigation through specific actions 
to achieve the sustainable development goals (Prasad et al. 2009; 
Lewison et al. 2015; Fankhauser and McDermott 2016; Romero-
Lankao et al. 2016; Solecki et al. 2019) – from the household to the state level, since risks and opportunities vary by location and the

specific local development context (robust evidence, high agreement) 
(IPCC 2014b; Denton et al. 2015). 
Synergies between adaptation and mitigation are included in 
many of the NDCs submitted to the UNFCCC, as part of overall 
low-emissions climate-resilient development strategies (UNFCCC 
Secretariat 2016). a majority of developing countries have agreed to 
develop National Adaptation Plans (NAPs) in which many initiatives 
contribute simultaneously to the SDGs (Schipper et al. 2020) as well 
to mitigation efforts (Hönle et al. 2019; Atteridge et al. 2020). For 
example, developing countries recognise that adaptation actions 
in sectors such as agriculture, forestry and land-use management 
can reduce GHGs. Nevertheless, other more complex trade-offs 
also exist between bioenergy production or reforestation and 
the land needed for agricultural adaptation and food security 
(African Development Bank 2019; Hönle et al. 2019; Nyiwul 2019)

(Chapter 7). For some of the Small Islands Development States (SIDS), 
forestry and coastal management, including mangrove planting, 
saltmarsh and seagrass are sectors that intertwine both mitigation 
and adaptation (Duarte et al. 2013; Atteridge et al. 2020). Integrated 
efforts also occur at the city level, such as the Climate Change Action 
Plan of Wellington City, which includes enhancing forest sinks to 
increase carbon sequestration while at the same time protecting 
biodiversity and reducing groundwater runoff as rainfall increases 
(Grafakos et al. 2019).
To fully maximise their potential co-benefits and trade-offs of 
integrating adaptation and mitigation, these should be explicitly 
sought, rather than accidentally discovered (Spencer et al. 2017; 
Berry et al. 2015), and policies designed to account for both (robust Box 13.15 | Adaptation and Mitigation Synergies in Africa

Synergies between mitigation and adaptation actions and sustainable development that can enhance the quality and pace of 
development in Africa exist at both sectoral and national levels. Available data on NDCs show the top mitigation priorities in African 
countries include energy, forestry, transport and agriculture and waste, and adaptation priorities focus on agriculture, water, energy 
and forestry. The energy sector dominates in mitigation actions and the agricultural sector is the main focus of adaptation measures, 
with the latter sector being a slightly larger source of greenhouse gases than the former (Mbeva et al. 2015; African Development 
Bank 2019; Nyiwul 2019).
Renewable energy development can support synergies between mitigation and adaptation by stimulating local and national 
economies through microenterprise development; providing off-grid affordable and accessible solutions; and contributing to poverty

reduction through increased locally available resource use and employment and increased technical skills (Nyiwul 2019; Dal Maso 
et al. 2020). The Paris Agreement’s technology transfer and funding mechanisms could reduce renewable energy costs and providing 
scale economics to local economies. 
Barriers to achieving these synergies include the absence of suitable macro-and micro- level policy environments for adaptation 
and mitigation actions; coherent climate change policy frameworks and governance structures to support adaptation; institutional 
and capacity deficiencies in climate and policy research such as on data integration and technical analysis; and the high financial 
needs associated with the cost of mitigation and adaptation (African Development Bank 2019; Nyiwul 2019). Strengthening of 
national institutions and policies can support maximising synergies and co-benefits between adaptation and mitigation to reduce silos

and redundant overlaps, increase knowledge exchange at the country and regional levels, and support engagement with bilateral and 
multilateral partners and mobilising finance through the mechanisms available (African Development Bank 2019).

1402
1402
Chapter 13 National and Sub-national Policies and Institutions13evidence, high agreement) (Caetano et al. 2020). For example, the 
REDD+ initiative focus on mitigation by carbon sequestration was 
set up to provide co-benefits such as: nature protection, political 
inclusion, monetary income, economic opportunities. However, 
some unintended trade-offs may have occurred such as physical 
displacement, loss of livelihoods, increased human–wildlife conflicts, 
property claims, food security concerns, and an unequal distribution 
of benefits to local population groups (Bushley 2014; Duguma 
et al. 2014a; Gebara et al. 2014; Kongsager and Corbera 2015; 
Anderson et al. 2016; Di Gregorio et al. 2016, 2017). Ultimately, 
ecosystem (or nature-based) strategies, such as the use of wetlands to create accessible recreational areas that improve public health 
while improving biodiversity, sinking carbon and protecting 
neighbourhoods from extreme flooding events, may lead to more

efficient and cost-effective policies (Klein et al. 2005; Locatelli et al. 
2011; Kongsager et al. 2016; Mills-Novoa and Liverman 2019). 
The ‘nexus’ approach is another widely used framework that describes 
the linkages between water, energy, food, health and other socio-
economic factors in some integrated assessment approaches (Rasul 
and Sharma 2016). The Food-Energy-Water (FEW)nexus, for example, 
considers how water is required for energy production and supply Box 13.16 | Latin America Region Adaptation Linking Mitigation: REDD+ Lessons
Thirty-three countries in the Latin American region have submitted their NDCs, and 70% of their initiatives have included mitigation 
and adaptation options focusing on sustainable development (Bárcena et al. 2018; Kissinger et al. 2019). However, most of these 
policies are disconnected across sectors (Loaiza et al. 2017; Locatelli et al. 2017). National governments have identified their relevant

sectors as: energy, agriculture, forestry, land-use change, biodiversity, and water resources (see Figure 1 below). The region houses 
57% of the primary forest of the planet. REDD+ aims to reduce GHG while provide ecosystems services to vulnerable communities 
(Bárcena et al. 2018). Lessons from successful REDD+ programmes include the benefits of a multilevel structure from international to 
national down to strong community organisation, as well as secure resources funding, with most of the projects relying on external 
sources of funding (medium evidence, high agreement) (Loaiza et al. 2017; Kissinger et al. 2019). However, there is limited evidence 
of effective adaptation co-benefits, which may be related to the lack of provision of forest standards; a disproportionate focus on 
mitigation and lack of attention to the well-being of the population in rural and agricultural areas (Kongsager and Corbera 2015).

Conflicts have emerged over political views, government priorities of resources (oil, bioenergy, hydropower), and weak governance 
among national and local authorities, indigenous groups and other stakeholders such as NGOs which play a  critical role in the 
technological and financial support for the REDD+ initiative (Reed 2011; Kashwan 2015; Gebara et al. 2014; Locatelli et al. 2011, 
2017). a more holistic approach which recognises these social, environmental and political drivers would appear to have benefits but 
assessment is needed to allow evidence-based actionable policy statements. 
Mitigation Adaptation02468101214161820
Energy Agriculture
and cattleOther Industry Water
resourcesWaste Tourism Transport Biodiversity Land use
changeForest Coastal
ZoneHealth
Box 13.16, Figure 1 | Latin America and Caribbean: high priority sectors for mitigation and adaptation.  Number of countries that name the following

sector in their national climate change plans and/or communications. The purple and green bars represent adaptation and mitigation respectively. Source: reproduced 
with permission from Bárcena et al. (2018).

1403
1403
(and thus tied to mitigation), how energy is needed to treat and 
transport water, and how both are critical to adaptable and resilient 
food production systems (Mohtar and Daher 2014; Biggs et al. 2015). 
Climate change impacts all these dimensions in the form of multi-
hazard risk (Froese and Schilling 2019). Although integrative, the FEW 
nexus faces many challenges including: limited knowledge integration; 
coordination between different institutions and levels of government; 
politics and power; cultural values; and ways of managing climate 
risk (Leck and Roberts 2015; Romero-Lankao et al. 2017; Mercure 
et al. 2019). More empirical assessment is needed to identify potential 
overlaps between sectoral portfolios, as this could help to delineate 
resources allocation for synergies and to avoid trade-offs. 13.8.3 Relationships Between Mitigation and 
Adaptation Measures

There are multiple ways that mitigation and adaptation may be 
integrated. Table 13.3 sets out those relationships broken down into 
four areas: adaptation that contributes to mitigation; mitigation that 
contributes to adaptation; holistic, sustainability first strategies; and 
trade-offs. The table shows that more holistic and sustainability-
oriented policies can open up the possibility for accelerated transitions 
across multiple priority domains (robust evidence, high agreement). 
Table 13.3 | Relationships between adaptation and mitigation measures.  
Policy/action Interrelation explained Reference
Adaptation that contributes to mitigation
Coastal adaptation and blue carbon; 
developing strategies for conservation 
and restoration of blue carbon 
ecosystems generating resilient 
communities and landscapes. 
 –Contributes to carbon storage 
and sequestration.Conservation of habitats and ecosystems, protect communities from extreme events, increase

food security, and provide ecosystem services. At the same time, restoration of mangroves, 
tidal marshes, and seagrasses have high rates of carbon sequestration, act as long-term carbon 
sinks, and are contained within clear national jurisdictions. Example: conservation programmes 
on Brazilian mangroves, Spanish seagrass meadows, the Great Barriers Reef in Australia, 
and Coastal Management Strategy in New Zealand. Andresen et al. (2012); 
Herr and Landis (2016); Duarte 
(2017); Doll and Oliveira 
(2017); Howard et al. (2017); 
Gattuso et al. (2018); Cooley et al. 
(2019); Karani and Failler (2020); 
Lovelock and Reef (2020)
Nature-Based Solutions (Nbs); Nature-
based solutions are interventions that 
use the natural functions of healthy 
ecosystems to protect the environment 
but also provide numerous economic 
and social benefits.
 –Contributes to carbon 
storage and sequestration using

individual and clustered trees.NbS complement and shares common elements with a wide variety of other approaches to 
building the resilience of social-ecological systems. Policies at national and sub-national level 
include community-based adaptation, ecosystem-based disaster risk reduction, climate-smart 
agriculture, and green infrastructure, and often place emphasis on using participatory and 
inclusive processes and community/stakeholder engagement. Examples: Mexico and the 
United Kingdom provide support for NbS in their national biodiversity strategies and action 
plans some related to water management. UK launched the Green Recovery Challenge Fund 
to create jobs with a focus on tree planting and the rehabilitation of peatlands.Doswald and Osti (2011); 
Secretariat of the 
Convention on Biological 
Diversity (2019); Ihobe – 
Environmental Management 
Agency (2017); Zwierzchowska 
et al. (2019); Seddon et al. (2020); 
Choi et al. (2021); OECD (2021b)

Ecosystem-based Adaptation (Eba); 
use biodiversity and ecosystem 
services to help people to adapt to 
the adverse effects of climate change, 
aiming to maintain and increase the 
resilience and reduce the vulnerability 
of ecosystems and people. 
and sequestration.EbA involves the conservation, sustainable management and restoration of ecosystems, 
such as forests, grasslands, wetlands, mangroves or coral reefs to reduce the harmful 
impacts of climate hazards including shifting patterns or levels of rainfall, changes in 
maximum and minimum temperatures, stronger storms, and increasingly variable climatic 
conditions. Examples: some NDCs include EbA and NbS harmonising national policies 
(for example: National Adaptation Plan) with other national climate and development policy 
processes, such as: water resources management plan, disaster risk reduction strategies, 
land planning codes. IPBES (2019); Doswald 
et al. (2014); Secretariat of

the Convention on Biological 
Diversity (2009); McAllister (2007); 
Colls et al. (2009); Rubio (2017); 
Raymond et al. (2017); Duarte 
(2017); Gattuso et al. (2018)
Urban Greening; urban forestry, planting 
in road reserves and tree planting along 
main streets.
and sequestration.
 –Energy use reduction.Urban afforestation and reforestation produce cooling effect and water retention while helping 
to reducing carbon dioxide from the atmosphere. Green walls and rooftops increase energy 
efficiency of buildings and decrease water runoff and provide insulation for the buildings. 
Examples: Wellington City Council and other entities must comply with the New Zealand 
Emission Trading System regulatory framework that provides guidance and requirements 
of climate change planning and implementation for both mitigation and adaptation (M&A).Santamouris (2014); Sharifi 
and Yamagata (2016); Grafakos 
et al. (2018); Pasimeni et al. (2019); 
Anderson et al. (2016)

Climate adaptation plans at city level; 
sub-national policies that would lead 
to carbon reduction to support climate 
mitigation. Contribution to mitigation:
 –Carbon storage and sequestration.
 –Energy use reduction.
 –Renewable energy.Cities with Climate Actions Plans include urban spatial planning and capacity-building initiatives. 
Some cities with adaptation and mitigation combined climate change action plans are: Bangkok, 
Chicago, Montevideo, Wellington, Durban, Paris, Mexico City, and Melaka. And cities with 
A&M actions are: Los Angeles, Vancouver, Barcelona, London, Accra, Santiago de Chile, Bogota, 
Curitiba, and other. 
Co-benefits generated by climate actions at cities: heat stress reduction; water scarcity, 
stormwater and flood management; air quality improvement, human health and well-being, 
aesthetic/amenity, recreation/tourism, environmental justice, real estate value, food production, 
green jobs opportunities.Garcetti (2019); Horne (2020);

Barcelona City Council (2018); 
Greater London Authority (2018); 
Accra Metropolitan Assembly 
(2020); Choi et al. (2021); Grafakos 
et al. (2019); Nakano et al. (2017); 
Peng and Bai (2018); Zen et al. 
(2019); Bai et al. (2018)

1404
1404
Chapter 13 National and Sub-national Policies and Institutions13Policy/action Interrelation explained Reference
Mitigation that contributes to adaptation
Green Infrastructure; policies to support 
the design and implementation of 
a hybrid network of natural, semi-natural, 
and engineered features within, around, 
and beyond urban areas at all scales, 
to provide multiple ecosystem services 
and benefits.
 –Reduced energy consumption. Adaptation benefits: flood management, heat stress reduction individually, or jointly, coastal 
protection, water scarcity management, groundwater resources, ecosystem resilience 
improvement, air quality, water supply, flood control, water quality improvement, groundwater 
recharge. Social co-benefits: aesthetic, recreation, environmental education, improved human 
health/well-being, social cohesion, and poverty reduction. Policy examples: national building

code guidelines, flood safety standards, local land-use plans, local building codes, integrated 
water management for flood control. Atchison (2019); Conger and Chang 
(2019); Schoonees et al. (2019); 
De la Sota et al. (2019); Choi et al. 
(2021); Zwierzchowska et al. (2019)
REDD+ Strategies; an incentive for 
developing countries to increase 
carbon sinks, to protect their forest 
resources and coastal wetlands. Mostly 
are national strategies led by the state 
with contribution of international donors. 
 –Renewable energy.REDD+ strategies aim to generate social benefits such as poverty reduction, and ecological 
services such as water supply, water quality enhancement, conserves soil and water by 
reducing erosion. For example, indigenous communities of Socio Bosque in Ecuador have 
sustained livelihoods and maintaining ties to land, place, space, and cosmovision. While in

Cameroon, upfront contextual inequities with respect to technical capabilities, power, gender, 
level of education, and wealth have been barriers to individuals’ likelihood of participating 
in and benefiting from the projects.McBurney (2021); Tegegne et al. 
(2021); Anderson et al. (2016); 
Busch et al. (2011); Bushley (2014); 
Dickson and Kapos (2012); Froese 
and Schilling (2019); Gebara 
et al. (2014); Pham et al. (2014); 
Jodoin (2017)
Household energy-efficiency 
and renewable energy measures; 
energy policies may improve socio-
economic development.
 –Energy use reduction.Energy Efficiency (EE) emerges as a feasible and sustainable solution in Latin America, to 
minimise energy consumption, increase competitiveness levels and reduce carbon footprint. 
Achieving high levels of EE in the building sector requires  new policies and strengthening their 
legal framework. Microenterprise development contributes to poverty reductions as renewable

energy stimulate local and national economies. Chan et al. (2017); Silvero 
et al. (2019); Zabaloy et al. (2019); 
Alves et al. (2020); Nyiwul (2019); 
Dal Maso et al. (2020)
Sustainability first: holistic approaches
Integrated community 
sustainability plans.Climate change mitigation and adaptation are embedded in a plan to improve affordability, 
biodiversity, public health, and other aspects of communities.Burch et al. (2014); Shaw 
et al. (2014); Stuart et al. (2016); 
Dale et al. (2020)
Inclusive future visioning using social-
ecological systems or socio-technical 
systems thinking.Participatory processes that highlight the cultural and social dimensions of climate change 
responses and synergies/trade-offs between priorities rather than an exclusive focus on technical 
aspects of solutions.Gillard et al. (2016); 
Krzywoszynska et al. (2016)
Climate Resilience Cities; integrating 
New Urban Agenda (NUA), SDGs, 
climate actions for A&M, and Disaster

Risk Reduction (DRR) for local and 
sub-national governments, and DRR 
within a multi-hazard approach based 
on Sendai Framework. Resilient cities are including SDGs, targets, A&M options and DRR to build a resilient plan for 
urban planning, health, life quality and jobs creation.
Climate mitigation and sustainable energy actions adopted at the local level are interconnected. 
For instance, cities with Sustainable Energy and Climate Action Plan, which required the 
establishment of a baseline emission inventory and the adoption of policy measures, are already 
showing a tangible achievement regarding sustainable goals.Barcelona City Council (2018); 
Garcetti (2019); Accra Metropolitan 
Assembly (2020); Blok 2016; 
Giampieri et al. (2019); Gomez 
Echeverri (2018); Long and Rice 
(2019); Pasimeni et al. (2019); 
Romero-Lankao et al. (2016)
Land-use strategies; for mitigation 
or adaptation considered in isolation, 
may cause a conflict in land planning.

–Carbon storage and sequestration.
 –Renewable energy.Increasing density of land use, land-use mix and transit connectivity could increase climate stress 
and reduce green open spaces. It may increase the urban heat island impacting human health, 
and expose population to coastal inundation. Some of the policies and strategies to minimise 
this are: land-use planning, zoning, land-use permits, mobilising private finance in the protection 
of watersheds, integrated coastal zone management, flood safety standards, and other. More 
assessment is needed prior to new land use to reduce or prevent actions which negatively alter 
ecosystem services and environmental justice. O’Donnell (2019); Bush and Doyon 
(2019); Grafakos et al. (2019); 
Landauer et al. (2015); Viguié 
and Hallegatte (2012); Floater 
et al. (2016); Xu et al. (2019); 
Landauer et al. (2019)
Low-carbon, net zero and climate change 
resilient building codes that fail to 
account for affordability.

–Energy reduction.
 –Renewable energy. Low-carbon or net zero emissions have multi-objective strategies, integrated policies, 
regulations, and actions at the national and sub-national levels. Trade-offs may be related to 
policy mechanisms that must be implemented comprehensively, not individually. However, 
different administrative levels and institutions may create a barrier to inter-sectoral coordination. 
For example: ‘Greening’ programmes may produce positive mitigation and adaptation outcomes 
but may also accelerate displacement and gentrification at city level. Chaker et al. (2021); del Río and 
Cerdá (2017); Choi et al. (2021); 
Papadis and Tsatsaronis (2020); 
Wolch et al. (2014); Garcia-Lamarca 
et al. (2021); Haase et al. (2017); 
Sharifi (2020); Viguié and Hallegatte 
(2012); del Río (2014) 
13.8.3.1 Governing the Linkages Between Mitigation and 
Adaptation at the Local, Regional, and Global Scales 
International policy frameworks, such as the 2015 Paris Agreement,

the Sendai Framework for Disaster Disk Reduction, and the New Urban 
Agenda for sustainable urban systems, provide an integrated approach 
for both adaptation and mitigation, while promoting sustainable development and climate resilience across scales (from global, regional, 
to local government actions (robust evidence,  high agreement) (Duguma 
et al. 2014b; Heidrich et al. 2016; Di Gregorio et al. 2017; Locatelli et al. 
2017; Nachmany and Setzer 2018; Mills-Novoa and Liverman 2019). 
Even so, the specific ways that these linkages are governed vary widely 
depending on institutional and jurisdictional scale, competing policy 
priorities, and available capacity (Landauer et al. 2019).

1405
1405
Supranational levels of action such as the EU climate change policy 
have influenced the development and implementation of Climate 
Change Action Plans (CCAPs) at the sub-national level (Heidrich 
et al. 2016; Villarroel Walker et al. 2017; Reckien et al. 2018). While 
adaptation is gaining prominence and is increasingly included in the 
NDCs of EU nations, the implementation of adaptation and mitigation 
by EU states are at different stages (Fleig et al. 2017). Fleig et al. 
(2017) found that all EU states, with the exception of Hungary, have 
adopted a framework of laws tackling mitigation and adaptation to 
climate change. However, an assessment of climate legislation in 
Europe pointed out that there has been little coordination between 
mitigation and adaptation, and that implementation varies according 
to different national conditions (Nachmany et al. 2015). More recently,

however, integrated adaptation/mitigation plans have been prepared 
in Europe under the Covenant of Mayors, in which synergies and 
trade-offs can be better revealed and assessed (Bertoldi et al. 2020).
Local governments and cities are increasingly emerging as important 
climate change actors (Gordon and Acuto 2015) (Section  13.5). 
While cities and local governments are developing Climate Change 
Action Plans (CCAPs), plans that explicitly integrate the design and 
implementation of adaptation and mitigation are a minor percentage, 
with few cities establishing inter-relationships between them (Nordic 
Council of Ministers 2017; Grafakos et al. 2018). Compared to national 
climate governance, local governments are more likely to develop and 
advance climate policies, generating socio-economic or environmental 
co-benefits, and improve communities’ quality of life (Gill et al. 2007; 
Bowen et al. 2014; Duguma et al. 2014b; Mayrhofer and Gupta 2016;

Deng et al. 2017; Hennessey et al. 2017). There may be a disconnect, 
however, between the responsibility that a particular jurisdiction has 
over mitigation and adaptation (city officials, for instance) and the 
scale of resources or capacities that they have available to bring to 
bear on the problem (regional to national provision of energy and 
transport) (Di Gregorio et al. 2019; Dale et al. 2020). 
13.8.4 Integrated Governance Including Equity 
and Sustainable Development
Climate policy integration carries implications for the pursuit of the 
SDGs, given that it is nearly impossible to achieve the desired socio-
economic gains if fundamental environmental issues, such as climate 
change, are not addressed (Gomez-Echeverri 2018). Research on 
climate resilient development pathways (Roy et al. 2018), for instance, 
argues for long-term policy planning that combines the governance 
of national climate and SD goals, builds institutional capacity across

all sectors, jurisdictions, and actors, and enhances participation and 
transparency (robust evidence, high agreement) (Chapter 4 and 17).
In the Global South, climate change policies are often established in 
the context of sustainable development and of other pressing local 
priorities (e.g.,  air pollution, health, and food security). National 
climate policy in these countries tends to give prominence to 
adaptation based on country vulnerability, climatic risk, gender-based differences in exposur to that risk, and the importance of local/
traditional and indigenous knowledge (Beg et al. 2002; Duguma 
et al. 2014b). Despite the evidence that integrated mitigation and 
adaptation policies can be effective and efficient (Klein et al. 2005) 
and can potentially reduce trade-offs, there is still limited evidence 
of how such integrated policies would specifically contribute to 
progress on the SDGs (robust evidence, high agreement) (Kongsager

et al.2016; Di Gregorio et al. 2017; Antwi-Agyei et al. 2018; De Coninck 
et al. 2018; Campagnolo and Davide 2019).
Where mainstreaming of environmental concerns has been 
attempted through national plans, they have had success in some 
cases when backed by strong political commitments that support 
a vertical coordination structure rather than horizontal structures led 
by the focus ministry (Nunan et al. 2012). Such political commitments 
are therefore crucial to success but insufficient in and of themselves 
(Runhaar et al. 2018; Wamsler et al. 2020). Integration of the budget 
process is particularly important, as are aligned time frames across 
different objectives (Saito 2013). Recognition of the functional 
interactions across policy sectors is improved by a  translation of 
long-term policy objectives into a plan that aligns with integration 
goals (Corry 2012; Oels 2012; Dupont 2019).
There are important links between inequality, justice and climate

change (Ikeme 2003; Bailey 2017). Many of these operate through 
the benefits, costs and risks of climate action (distributive justice), 
while others focus on differential participation and recognition of 
sub-national actors and marginalised groups (procedural justice) 
(Bulkeley and Castán Broto 2013; Bulkeley et al. 2013; Hughes 2013; 
Reckien et al. 2018; Romero-Lankao and Gnatz 2019). 
Justice principles are rarely incorporated in climate change framing 
and action (Sovacool and Dworkin 2015; Genus and Theobald 2016; 
Heikkinen et al. 2019; Romero-Lankao and Gnatz 2019). Yet, equity 
is salient to mitigation debates, because climate change mitigation 
policies can have also negative impacts (Brugnach et al. 2017; 
Ramos-Castillo et al. 2017; Klinsky 2018), exacerbated by poverty, 
inequality and corruption (Reckien et al. 2018; Markkanen and 
Anger-Kraavi 2019). The siting of facilities and infrastructure that 
advance decarbonisation (such as public transit infrastructure,

renewable energy facilities and so on) may have implications for 
environmental justice. Integrated attention to justice in climate, 
environment and energy, as well as involvement of host communities 
in siting assessments and decision-making processes, can help to 
avoid such conflict (McCord et al. 2020; Hughes and Hoffmann 2020). 
As a  result, successful policy integration goes beyond optimising 
public management routines, and must resolve key trade-offs 
between actors and objectives (Meadowcroft 2009; Nordbeck and 
Steurer 2016). 
The potential for transformative climate change policy that delivers 
both adaptation and mitigation is also shaped by a  number of 
enabling and disabling factors tied to governance processes (robust 
evidence, high agreement) (Burch et al. 2014) (Section 13.9).

1406
1406
Chapter 13 National and Sub-national Policies and Institutions1313.9 Accelerating Mitigation Through  
Cross-sectoral and Economy-wide  
System Change
13.9.1 Introduction
Section  13.9 assesses literature related to economy wide and 
cross  – sector systemic change as an approach to accelerate 
climate mitigation. 
It focuses specifically on policy and institutions, as two of the six 
enabling conditions for economy-wide system change and thus 
provides a third dimension of the role of policy and institutions to 
climate mitigation. Enabling conditions in general are discussed in 
Chapter 4 of the SR1.5 (IPCC 2018), as well as Chapter 4 of this report. This section follows on from Section 13.6 (single policy instruments) 
and 13.7 (policy packages). Section  13.9 literature follows closely 
on from Section 13.7 literature on policy packages, which discusses 
change within one system, although there remains an overlap.

Section 13.9.2 provides a brief introduction to policy and institutions 
as two of the six dimensions of enabling conditions, and the 
importance of enabling conditions to systemic change and climate 
mitigation. Section 13.9.3 briefly introduces actions for transformative 
justice, which seek to restructure the underlying system framework 
that produces mitigation inequalities. Section  13.9.4 provides 
a  brief overview of net zero policies and targets (often no more 
than aspirational), which imply economy-wide measures and 
system change. Section  13.9.5 assesses the literature arguing for 
a  system restructuring approach to climate mitigation, based on Box 13.17 | Enabling and Disabling Factors for Integrated Governance of Mitigation  
Ensuring participatory governance and social inclusion. Interlinkages in the food-energy-water nexus highlight the importance

of inclusive processes (Shaw et al. 2014; Nakano et al. 2017; Cook and Chu 2018; Romero-Lankao and Gnatz 2019). The cultivation of 
urban grassroots innovations and social innovation may accelerate progress (Wolfram and Frantzeskaki 2016), as may the development 
of carefully-designed climate and energy dialogues that enable learning among multiple stakeholders (Cashore et al. 2019).
Considering synergies and trade-offs with broader sustainable development priorities. The explicit consideration of synergies 
and trade-offs will enable more integrated policy making (Dang et al. 2003; von Stechow et al. 2015). Policy frameworks to do so 
are just emerging, such as analysis of trade-offs between energy and water policies and agriculture (Huggel et al. 2015; Antwi-
Agyei et al. 2018). 
Employing a diverse set of tools to reach targets. Building codes, land-use plans, public education initiatives, and nature-based

solutions such as green ways may impact adaptation and mitigation simultaneously (Burch et al. 2014). Ecological restoration provides 
another suite of tools, for instance the Brazilian target of restoring and reforesting 0.12 million km2 of forests by 2030, which can 
enhance biodiversity and ecosystem services while also sinking carbon (Bustamante et al. 2019). Mandatory retrofits to improve 
indoor air quality can also increase energy efficiency and resilience to climate change impacts (Friel et al. 2011; Houghton 2011).
Monitoring and evaluating key indicators, beyond only greenhouse gas emissions, such as biodiversity, water quality, 
and affordability: An integrated approach requires robust process for collecting data on these indicators. Challenges are related to 
the limited evidence-base on synergies, co-benefits, and trade-offs across sectors and jurisdictions (Di Gregorio et al. 2016; Kongsager

et al. 2016; Locatelli et al. 2017; Zen et al. 2019). Moreover, adaptation policies mostly lack measurable targets or expected outcomes 
increasing the challenge of designing an integrated framework (OECD 2017).
Iterative and adaptive management. Adaptive management helps to address the underlying uncertainty (Kundzewicz et al. 2018) 
that characterises implementation of integrated approaches to adaptation and mitigation. Policy integration needs to be considered 
iteratively along the process of development, implementation, and evaluation of climate policies. 
Strategic partnerships that coordinate efforts. Strategic partnerships among diverse actors, therefore, bring diverse technical 
skills and capacities to the endeavour (Burch et al. 2016; Islam and Khan 2017). However, realising strategic approaches for joint 
adaptation and mitigation require adequate financial, technical and human resources.

Participatory and collaborative planning approaches can help overcome injustices and address power differentials. 
Participatory and collaborative planning approaches can provide multiple spaces of deliberation where marginalised voices can be 
heard (Blue and Medlock 2014; UN Habitat 2016; Castán Broto and Westman 2017; Waisman et al. 2019). These tools organise climate 
and sustainability action by addressing its democratic deficit and facilitating the recognition of multiple perspectives in environmental 
planning alongside material limits of development (Agyeman 2013).

1407
1407
systemic restructuring. Section  13.9.6 assesses the literature on 
stimulus packages and green new deals which aim for systemic 
change, sometimes with value for climate mitigation. And finally, 
Section 13.9.7 assesses emerging literatures which argues that there 
are existing challenges to accelerating climate mitigation that may 
be overcome by systemic change and targeted actions.
13.9.2 Enabling Acceleration
IPCC AR6 WG3, particularly Chapter  4, following on from the IPCC 
WG3 SR1.5 (IPCC 2018), has highlighted the importance of enabling 
conditions for delivering successful climate mitigation actions. The 
AR6 Glossary term for enabling conditions is: ‘enabling conditions 
include finance, technological innovation, strengthening policy 
instruments,  institutional capacity,  multi-level governance, and changes 
in human behaviour and lifestyles (medium evidence,  high agreement)

(see Glossary). The IPCC SR1.5 report adds to these six dimensions 
saying enabling conditions also includes ‘inclusive processes, attention 
to power asymmetries and unequal opportunities for development and 
reconsideration of values’ (medium evidence,  high agreement) (IPCC 
2018). Not only is the presence of enabling conditions necessary for 
delivering the successful implementation of single policy instruments 
and policy packages, but also for delivering systemic change (medium 
evidence,  high agreement) (de Coninck et al. 2018; IPCC 2018; Waisman 
et al. 2019). The feasibility of 1.5°C compatible pathways is contingent 
upon enabling conditions for systemic change (medium evidence,  high 
agreement) (de Coninck et al. 2018; Waisman et al. 2019).
At the same time, again following on from SR1.5 report, Section 1.8.1 
explains that there are six feasibility dimensions of successful delivery 
of climate goals. These feasibility dimensions include geophysical;

environmental and ecological; technological; economic; behaviour 
and lifestyles and institutional dimensions. The presence or absence 
of enabling conditions would affect the feasibility of mitigation 
as well as adaptation pathways and can reduce trade-offs while 
amplifying synergies between options (Waisman et al. 2019). Policies 
and institutions, which are two of the six enabling conditions, are 
therefore central to accelerated mitigation and systemic change. 
Identifying, and ensuring, the presence of all the enabling conditions 
for any given goal, including systemic transformation and acceleration 
of climate mitigation, is an important first step (medium evidence, 
medium agreement) (Roberts et al. 2018; Le Treut et al. 2021; Singh 
and Chudasama 2021). 
13.9.3 Transformative Justice Action 
and Climate Mitigation
Chapter 4 is the lead chapter of this report for justice and climate 
mitigation issues, and includes an overview of institutions which

have been set up to ensure a Just climate transition (Section 4.5). 
Chapter  13 has sought to integrate justice issues in Section  13.2 
in reference to procedural justice and the impact of inequalities 
on sub-national institutions, Section 13.6 in regard to distribution, 
and Section  13.8 in relation to integrating mitigation and 
adaptation policies.This sub-section introduces the concept of transformative justice 
as part of measures intending to accelerate mitigation. Fair and 
effective climate policymaking requires institutional practices to: 
consider the distributional impacts of climate policy in the design 
and implementation of every policy (Agyeman 2013; Castán Broto 
and Westman 2017); align mitigation with other objectives such as 
inclusion and poverty reduction (Hughes and Hoffmann 2020; Rice 
et al. 2020; Hess and McKane 2021); represent a variety of voices, 
especially those of the most vulnerable (Bullard et al. 2008; Temper

et al. 2018); and rely on open processes of participation (robust 
evidence, high agreement) (Anguelovski et al. 2016; Bouzarovski 
et al. 2018; Rice et al. 2020).
Distributive approaches to climate justice address injustices related 
to access to resources and protection from impacts. There is an 
important difference between affirmative and transformative justice 
action (Fraser 1995; Agyeman et al. 2016; Castán Broto and Westman 
2019): Affirmative action includes policies and strategies that seek 
to correct inequitable outcomes without disturbing the underlying 
political framework while transformative action seeks to correct 
inequitable outcomes by restructuring the underlying framework that 
produces inequalities. 
Transformative action that responds to distributive justice concerns 
include economy-wide actions via stimulus packages (such as 
the European Green Deal and the New Green Deal in the USA) 
(Section  13.9.5). Other examples are the increasing number of

climate litigation suits that are transforming the way distributive 
dimensions of climate justice are understood (Section 13.4.2). 
13.9.4 Net Zero Emissions Targets
The last few years have seen a  proliferation of net zero emission 
targets set by national and regional governments, cities as well as 
companies and institutions (NewClimate Institute and Data Driven 
EnviroLab 2020; Black et al. 2021; Rogelj et al. 2021) (see also Cross-
Chapter Box 3 in Chapter 3). Meeting these targets implies economy-
wide systemic change (medium evidence, high agreement).
The Energy & Climate Intelligence Unit (ECIU) Net Zero Tracker divides 
countries into those which have net zero emissions achieved, have it 
in law, have proposed legislation, have it in policy documents or have 
emission reduction targets under discussion in some form. a recent 
study estimated that 131 countries have either adopted, announced 
or are discussing net zero GHG emissions targets, covering 72% of

global emissions (Höhne et al. 2021). Out of those, as of 1 October 
2021, the ECIU Net Zero Tracker states that Germany, Sweden, the 
European Union, Japan, United Kingdom, France, Canada, South 
Korea, Spain, Denmark, New Zealand, Hungary and Luxembourg 
have net zero targets set in law (ECIU 2021).
Some have argued that the expansion of these emission reduction 
targets marks an important increase in climate mitigation momentum 
since the Paris Agreement of 2015 and the 2018 IPCC Special Report 
on Global Warming of 1.5°C (Black et al. 2021; Höhne et al. 2021). On 
the other hand net zero emission targets in their current state vary

1408
1408
Chapter 13 National and Sub-national Policies and Institutions13enormously in scope, quality and transparency – with many countries 
at the discussion stage – and this makes scrutiny and comparison 
difficult (NewClimate Institute and Data Driven EnviroLab 2020; 
Black et al. 2021; Rogelj et al. 2021).
In order to realise the mitigation potential of net zero emission 
targets some areas within the targets might need to be changed. For 
example, this includes clearer definitions; well defined time frames 
and scopes; focusing on direct emission reductions within their own 
territory; minimal reliance on offsets; scrutiny of use and risks of CO 2 
removal; attention to equity, near-term action coupled with long-
term intent setting; and ongoing monitoring and review (medium 
evidence, high agreement) (Levin et al. 2020; NewClimate Institute 
and Data Driven EnviroLab 2020; Black et al. 2021; Höhne et al. 2021; 
Rogelj et al. 2021; World Bank 2021b).

13.9.5 Systemic Responses for Climate Mitigation
There is now a significant body of work which explicitly states, or 
implicitly accepts, that systemic change may be necessary to deliver 
successful climate mitigation, including net zero targets. Newell 
phrases this as the difference between ‘plug and play’ mitigation 
applications where one aspect of a  system is changed while 
everything in the system remains the same compared to systemic 
change, with change affecting all the system (Newell 2021a,b). This 
section highlights an emergent, multidisciplinary literature since 
IPCC AR5, which suggests that acceleration to decarbonised systems 
via a sustainable development pathway may be better achieved by 
moving from a single policy instrument or mix of policies approach to 
a systemic economy-wide approach (Figure 13.6). 
The complexity and multi-facetted challenges of rapidly decarbonising 
our current interconnected systems (such as energy, food, health)

in a  just way has led Michaelowa et al. (2018) to conclude that 
implementation of strong mitigation policy packages that are needed 
requires a systemic change in policymaking.
Multiple modelling assessments of different development and 
mitigation pathways are available. Most of these analyses which lead 
to significant climate mitigation assume significant systemic change 
across social, technological, and economic aspects of a country for 
example, India (Gupta et al. 2020); Japan (Sugiyama et al. 2021) and 
the globe (Rogelj et al. 2015; Dejuán et al. 2020).
UNEP (2020) argued that major, long-term sectoral transformation 
across multiple systems is needed to reach net zero GHG emissions. 
Bernstein and Hoffmann (2019) and Rockström et al. (2017) 
argue that the presence of multi-level, multi-sectoral lock-ins of 
overlapping and interdependent political, economic, technological 
and cultural forces mean that a new approach of coordinated, cross-

economy, systemic climate mitigation is necessary. Creutzig et al. 
(2018) propose a resetting of the approach to consumption and use 
of resources to that of demand side solutions, which would have 
ongoing economy-wide systemic implications.Others focus more on single system reconfigurations, such as the 
energy system (Matthes 2017; Tozer 2020); urban systems (Holtz et al. 
2018); or the political system (Somerville 2020; Newell and Simms 
2020). Becken (2019) argues that only systemic changes at a large 
scale will be sufficient to break or disrupt existing arrangements and 
routines in the tourism industry.
Others argue for thinking about mitigation in even wider ways. O’Brien 
(2018) posits that sector-focused, or a silo approach, to mitigation 
may need to give way to decisions and policies which reach across 
sectoral, geographic and political boundaries and involve a  broad 
set of interrelated processes  – practical, political and personal.

Gillard et al. (Gillard et al. 2016) argue that a response to climate 
change has to move beyond incremental responses, aiming instead 
for a  society-wide transformation which goes beyond a  system 
perspective to include learning from social theory; while Eyre et al. 
(2018) argue that moving beyond incremental emissions reductions 
will require expanding the focus of efforts beyond the technical to 
include people, and their behaviour and attitudes. Stoddard et al. 
(2021) argue that ‘more sustainable and just futures require a radical 
reconfiguration of long-run socio-cultural and political economic 
norms and institutions’. They focus on nine themes: international 
climate governance, the vested interests of the fossil fuel industry, 
geopolitics and militarism, economics and financialisation, mitigation 
modelling, energy supply systems, inequity, high carbon lifestyles and 
social imaginaries. 
13.9.6 Economy-wide Measures

Economy-wide stimulus packages which have occurred post 
COVID-19, and in some cases in response to environmental concerns, 
have the ability to undermine or aid climate mitigation (medium 
evidence, high agreement). Attention in the early efforts of their 
development and design can contribute to shifting sustainable 
development pathways and net zero outcomes, while meeting short-
term economic goals (medium evidence, high agreement) (Hepburn 
et al. 2020; Hanna et al. 2020).
Economy-wide packages, as a  way to stimulate and/or restructure 
domestic economies to deliver particular, desired outcomes is 
a widely accepted tool of government (for example the Roosevelt’s 
New Deal packages in the USA between 1933 and 1939). a number 
of country-level stimulus package were put in place after the 2008 
Global Recession, and there was support for a Global Green New Deal 
from UNEP (Steiner 2009; Barbier 2010). Cross-economy structural

change packages may provide opportunities for another approach to 
accelerate climate mitigation. 
This approach has already been taken up to some degree by 
a  number of countries/blocs. For example, California as well as 
Germany, through the German Energiewende, are early examples of 
a USA state and a country which have tried to link their economies to 
a sustainable future through energy-wide efforts of structural change 
(Morris and Jungjohann 2016; Burger et al. 2020a).

1409
1409
In addition to these economy-wide measures, there have since been 
cross-economy Green New Deals implemented such as the European 
Green Deal (Elkerbout et al. 2020; Hainsch et al. 2020; UNEP 2020a) 
(Box 13.1) with calls for other New Deals, for example a Blue New 
Deal (Dundas et al. 2020), or deals to bring together climate and 
justice goals (Hathaway 2020; MacArthur et al. 2020). 
The COVID-19 Pandemic has resulted in global economic recession, 
which many Governments have responded to with economic 
stimulus programmes. See also Cross-Chapter Box 1 in Chapter 1 on 
COVID-19. It has also led to more analysis of the potential of cross-
economy stimulus packages to benefit climate goals, including what 
lessons can be learned from the stimulus packages put in place as 
a result of the 2008–2009 Global Recession. 
The United Nations Environment Programme (UNEP) reviewed the

green stimulus plans of the G20 following the 2008–2009 recession 
to examine what worked; what did not; and the lessons which could 
be learnt (Barbier 2010). This work was updated (Barbier 2020) 
and concluded that the constituents of successful green stimulus 
frameworks were long-term commitments in public spending; pricing 
reform; ensuring concerns about affordability were overcome; and 
minimising unwanted distributional impacts. Others argue that 
post-2008 recession stimulus package outcomes benefited both 
environmental and industrial objectives and that a long-term policy 
commitment to the transition to a sustainable, low-carbon economy 
makes sense from both an environmental and industrial strategy 
point of view (Fankhauser et al. 2013). 
With the outbreak of the COVID-19 Pandemic in 2020, past stimulus 
packages have been further investigated. One study interviewed 
231 central bank officials and identified five key policies for both

economic multipliers and climate impacts metrics (Hepburn 
et al. 2020). These were expenditure on clean physical infrastructure; 
building energy efficiency retrofits; investment in education and 
training; natural capital investment; and clean R&D. However, the mix 
of effective policies may differ in lower and middle income countries: 
rural support spending was more relevant, while clean R&D was less 
so. The study illuminated that there were different phases to recovery 
packages: the initial ‘rescue’ spending but then a second ‘recovery’ 
phase that can be more fairly rated green or not green. Recovery 
phase policies can deliver both economic and climate goals  – 
co-benefits can be captured (i.e. support for EV infrastructure can 
also reduce local air pollution etc.) – but package design is important 
(Hepburn et al. 2020). 
Others provide a framework which allows a systematic evaluation 
of options, given objectives and indicators, for COVID-19 stimulus

packages (e.g. Dupont et al. 2020; Jotzo et al. 2020; OECD 2021c). 
Jotzo et al. (2020) conclude that the programmes that most closely 
match green stimulus are afforestation and ecosystem restoration 
programmes, energy efficiency upgrades and RE projects. These type 
of policies provide short-term goals of COVID-19 while also making 
progress on longer terms objectives (Jotzo et al. 2020). The IMF 
concluded that a comprehensive mitigation policy package combining 
carbon pricing and government green infrastructure spending (that 
is partly debt financed) can reduce emissions substantially while boosting economic activity, supporting the recovery from the 
COVID-19 pandemic (Jaumotte et al. 2020).
Conversely, other short-term fiscal or recovery measures in stimulus 
packages may perpetuate high carbon and environmental damaging 
systems. These include fossil fuel based infrastructure investment; 
fiscal incentives for high carbon technologies or projects; waivers

or roll-backs of environmental regulation; bailouts of fossil fuel 
intensive companies without conditions for low-carbon transitions 
or environmental sustainability (UNEP 2020a; O’Callaghan and 
Murdock 2021; Vivid Economics 2021).
Of the USD17.2 trillion so far spent on stimulus packages, 
USD4.8  trillion (28% of the total as of July 2021) is linked to 
environmental outcomes (Vivid Economics 2021). This study relates 
to 30 countries: the G20 and 10 others. The packages in EU, Denmark, 
Canada, France, Spain, the UK, Sweden, Finland and Germany 
(German Federal Ministry of Finance 2020; Vivid Economics 2021) 
result in net benefits for the environment. a number of studies provide 
differing conclusions with respect to net benefits or otherwise for the 
environment for a number of countries (Climate Action Tracker 2020; 
UNEP 2020a; Vivid Economics 2021). An OECD database found that, 
as of mid-July 2021, 21% of economic recovery spending in OECD, EU

and Key Partners is allocated to environmentally positive measures 
(OECD 2021c). O’Callaghan and Murdock (2021) reviewed the 
50 countries with the greatest stimulus spend in 2020 and find that 
13% of the spend is directed to long-term recovery type measures, of 
which 18% is spent on green recovery. This is a total of 2.5% of total 
spend or 368 billion USD on green initiatives. 
13.9.7 Steps for Acceleration
The multidisciplinary literature exploring how to accelerate 
climate mitigation and transition to low GHG economies and 
systems has grown rapidly over the last few years. Acceleration 
is also confirmed as an important sub-theme of the more specific 
transition literature (Köhler et al. 2019). While literature focusing 
on how to accelerate the impact of climate mitigation is derived 
from empirical evidence, there is very little ex post evidence of 
directed acceleration approaches.
The overlapping discussions of how to accelerate climate mitigation;

transition to low-carbon economies; and shift development pathways 
depends heavily on country-specific dynamics in political coalitions, 
material endowments, industry strategy, cultural discourses, and civil 
society pressures (Sections 13.2, 13.3, 13.4, 13.7, and 13.8). Ambition 
for acceleration at different scales and stringency (whether for cities, 
country climate policies, country industrial strategies, or national 
economic restructuring) increase governance challenges, including 
coordination across stakeholders, institutions, and scales. ‘There is 
therefore no “one-size-fits-all” blueprint for accelerating low-carbon 
transitions’ (medium evidence, high agreement) (Geels et al. 2017a; 
Roberts et al. 2018).
Markard et al. (2020) describe the key challenges to accelerating 
climate mitigation and sustainability transitions as:

1410
1410
Chapter 13 National and Sub-national Policies and Institutions131. The ability for low-carbon innovations to emerge in whole systems. 
Two critical issues need to occur to overcome this challenge 
(i) complementary interactions between different elements. For 
example, in an electricity system, the integration of renewable 
energy requires complementary storage technologies etc. and 
(ii)  changes in system architecture. Thus, in the accelerating 
phase, policy has to shift from stimulating singular innovations 
towards managing wider system transformation. 
2. The need for greater interactions between adjacent systems: 
interactions between multiple systems increases the complexity 
of the transition. Policies are linked to institutions or government 
departments, and they are often compartmentalised into different 
policy areas (e.g. energy policy and transport policy). Increasing 
and coordinating that interaction adds complexity.

3. The resistance from declining industries; acceleration  
of sustainability transitions will involve the phase out of 
unsustainable technologies. As a  result, acceleration towards 
a  sustainability transition may be resisted  – whether business 
models, or where jobs are involved. Political struggles and conflicts 
are an inherent part of accelerating transitions, one strategy to 
deal with this resistance is to accomplish wide societal support 
for long-term transition targets and to form broad constituencies 
of actors in favour of those transitions. 
4. The need for changes in consumer practices and routines; this 
challenge relates to changes in social practices that may be 
required for mainstreaming of sustainable technologies. For 
example, electric vehicles require changes in trip planning and 
refuelling practices. Reducing levels or types of consumption is 
also desirable. 
5. Coordination challenges in policy and governance. There is an

increasing complexity of governance which can be overcome 
by stronger vertical and horizontal policy coordination 
across systems.
The acceleration literature links two over-arching actions: first, 
a  strategic targeting approach to overcoming the challenges to 
acceleration by a parallel focus on undermining high carbon systems 
while simultaneously encouraging low-carbon systems; and second, 
focusing on a coordinated, cross-economy systemic response, including 
harnessing enabling conditions (robust evidence,  high agreement) 
(Rogelj et al. 2015; Geels et al. 2017b; Hvelplund and Djørup 2017; 
Gomez Echeverri 2018; Markard 2018; Tvinnereim and Mehling 2018; 
O’Brien 2018; Roberts et al. 2018; Hess 2019; Kotilainen et al. 2019; 
Victor et al. 2019; European Environment Agency 2019; Rosenbloom 
and Rinscheid 2020; Newell and Simms 2020; Otto et al. 2020; Strauch 
2020; Burger et al. 2020a; Hsu et al. 2020b; Rosenbloom et al. 2020).

Strategic targeting, or the identifying of specific intervention points 
(Kanger et al. 2020), points of leverage (Abson et al. 2017), or upward 
cascading tipping points (Sharpe and Lenton 2021), broadly means 
choosing particular actions which will lead to a greater acceleration 
of climate mitigation across systems. For example, Dorninger 
et al.(2020) provide a  quantitative systematic review of empirical 
research addressing sustainability interventions. They take ‘leverage 
points’ – places in complex systems where relatively small changes 
can lead to potentially transformative systemic changes – to classify 
different interventions according to their potential for system-wide transformative change. They argue that ‘deep leverage points’ – the 
goals of a system, its intent, and rules – need to be addressed more 
directly, and they provide analysis of the food and energy systems. 
The strategic choosing of policies and points of intervention is

linked to the importance of choosing self-reinforcing actions for 
acceleration (Rosenbloom et al. 2018; Butler-Sloss et al. 2021; Sharpe 
and Lenton 2021; Jordan and Moore 2020; Bang 2021). Butler-Sloss 
et al. (2021) explains the types of self-reinforcing actions (or feedback 
loops) which can encourage or undermine rapid transformation of 
energy systems. 
An example of this first overarching action, the strategic targeting of 
the challenges to acceleration, is the focus on undermining carbon- 
intensive systems, thereby reducing opposition to more generalised 
acceleration policies, including the encouragement of low-carbon 
systems (robust evidence, high agreement) (Hvelplund and Djørup 
2017; Rosenbloom 2018; Roberts and Geels 2019; Victor et al. 
2019; Rosenbloom et al. 2020; Rosenbloom and Rinscheid 2020). 
Undermining high carbon systems includes deliberately phasing out 
unsustainable technologies and systems (Kivimaa and Kern 2016;

David 2017; European Environment Agency 2019; Johnsson et al. 
2019; UNEP 2019b; Carter and McKenzie 2020; Newell and Simms 
2020); confronting the issues of incumbent resistance (Roberts et al. 
2018); and avoiding future emissions and energy excess by reducing 
demand (Rogelj et al. 2015; UNEP 2019b; Victor et al. 2019).
Other strategic goals include tackling the equity and justice issues of 
‘stranded regions’ (Spencer et al. 2018); paying greater attention to 
system architecture to enable increased acceleration to low-carbon 
electricity supply, in this case in the wind industry (McMeekin et al. 
2019); and the importance of maintaining global ecosystem of low-
carbon supply chains (Goldthau and Hughes 2020). 
Other strategic goals combine national and global action. For 
example, global NGO coalitions have formed around strategic policy 
outcomes such as the ‘Keep it in the Ground’ movement (Carter 
and McKenzie 2020), and are supported via coordinated networks,

such as the Powering Past Coal Alliance (Jewell et al. 2019), and 
with knowledge dissemination, for example, the ‘Fossil Fuel Cuts 
Database’ (Gaulin and Le Billon 2020). 
The second overarching point highlighted by the literature is the 
benefits of focusing on a  coordinated, cross-economy systemic 
response. Coordination is central to this. For example, coordination 
of actions and coherent narratives across sectors and cross 
economy, including within and between all governance levels and 
scales of actions, is beneficial for acceleration (robust evidence, 
high agreement) (Zürn and Faude 2013; Hawkey and Webb 2014; 
Huttunen et al. 2014; Magro et al. 2014; Warren et al. 2016; Köhler 
et al. 2019; Kotilainen et al. 2019; McMeekin et al. 2019; Victor et al. 
2019; Hsu et al. 2020b). Victor et al. (2019) provide a framework of 
how to prioritise the most urgent actions for climate mitigation and 
they give practical case studies of how to improve coordination to

accelerate reconfiguration of systems for economy-wide climate 
mitigation in sectors such as power; cars; shipping; aviation; 
buildings; cement; and plastics.

1411
1411
However, coordination is a  necessary but insufficient condition of 
acceleration. All enabling conditions are required to deliver systemic 
transformation (Section 13.9.2).
Other disciplines argue that social transformation is likely to be 
as important as the technical challenges in a  coordinated, cross-
economy approach to acceleration. For example, some argue for 
social tipping interventions (STI) alongside other technical and 
political interventions so that they can ‘activate contagious processes 
of rapidly spreading technologies, behaviours, social norms, and 
structural reorganisation’ (Otto et al. 2020). They argue that these 
STIs are inter alia: removing fossil fuel subsidies and incentivising 
decentralised energy generation; building carbon neutral cities; 
divesting from assets linked to fossil fuels; revealing the moral 
implications of fossil fuels; strengthening climate education and

engagement; and disclosing information of GHG emissions (Otto et al. 
2020). Others illuminate the importance of narratives and framings 
in the take-up (or not) of acceleration actions (Sovacool et al. 2020). 
Others are optimistic about the possibilities of transformation but 
also highlight the importance of political economy for rapid and just 
transitions (Newell and Simms 2020; Newell 2021).
In summary, a  synthesis of the multidisciplinary, acceleration 
literature suggests that climate mitigation is a multifaceted problem 
which spans cross-economy and society issues, and that solutions to 
acceleration may lie in coordinated systemic approaches to change 
and strategic targeting of leverage points. Broadly, this literature 
agrees on a dual approach of non-incremental systemic change and 
a targeting of specific acceleration challenges, with tailored actions 
drawing on enabling conditions. The underlying argument of this is

that there is a strategic logic to focusing on actions which undermine 
high carbon systems at the same time as encouraging low-carbon 
systems. If high carbon systems are weakened then this may 
reduce the opposition to policies and actions aimed at accelerating 
climate mitigation, enabling more support for low-carbon systems. 
In addition, targeting of actions which may create ‘tipping point 
cascades’ which increase the rate of decarbonisation may also be 
beneficial. Finally, new modes of governance may be better suited to 
this approach in the context of transformative change.13.10 Further Research 
Research has expanded in a  number of areas relevant to climate 
mitigation, yet there is considerable scope to add to knowledge. 
Key areas for research exist in climate institutions and governance, 
politics, policies and acceleration of action. In each area there is an 
overarching need for more ex post analysis of impact, more cases

from the developing world, and understanding how institutions and 
policies work in combination with each other.
13.10.1 Climate Institutions, Governance and Actors
• The different approaches to framework legislation, how it can be 
tailored to country context and evolve over time, how it diffuses 
across countries, and ex post analysis of its impact.
• Approaches to mainstreaming climate governance across sectors 
and at different scales, and developing governmental and non-
governmental capacity to bring about long-term low-carbon 
transformations and associated capacity needs.
• The drivers of sub-national climate action, the scope for 
coordination or leakage with other scales of action, and the 
effect, in practice on GHG outcomes.
• Comparative research on how countries develop NDCs, and 
whether and how that shapes national policy processes.
13.10.2 Climate Politics
• The full range of approaches that governments and non-
governmental actors may take to overcome lock-in to

carbon-intensive activities including through addressing 
material endowments, cultural values, institutional settings 
and behaviours.
• The factors that influence emergence of popular movements for 
and against climate actions, and their direct and indirect impacts.
• The role of civic organisations in climate governance, including 
religious organisations, consumer groups, indigenous 
communities, labour unions, and development aid organisations. 
• The relationship between climate governance approaches and 
differing political systems, including the role of corruption on 
climate governance. 
• The impacts of media  – traditional and social  – on climate 
mitigation, including the role of disinformation. 
• The role of corporate actors in climate governance across a broad 
range of industries. 
• Systematic comparative research on the differing role of climate 
litigation across various juridical systems.

1412
1412
Chapter 13 National and Sub-national Policies and Institutions1313.10.3 Climate Policies
• Greater ex post empirical studies of mitigation policy outcomes, 
their design features, the impacts of policy instruments under 
different conditions of implementation, especially in developing 
countries. Such research needs to assess the effectiveness, 
economic and distributional effects, co-benefits and side effects, 
and transformational potential of mitigation policies. 
• Understand how packages of policies are designed and 
implemented, including with attention to local context 
and trade-offs.
• Policy design and institutional needs for the explicit purpose of 
net zero transitions. 
• Greater understanding of the differences between, and benefits 
of, policy packages and economy-wide measures for in-system 
and cross-system structural change.
• Policies and packages for emissions sources that are unregulated 
or under-regulated, including industrial and non-CO 2 emissions.

• The existence and extent of carbon leakage across countries, 
the relative impact of different channels of leakage, and the 
implications of policy instruments designed to address leakage. 
13.10.4 Coordination and Acceleration of Climate Action
• How to ensure a just transition that gains wide popular support 
through research on actual and perceived distributional effects 
across countries and contexts. 
• How to coordinate and integrate for climate mitigation, 
between what actors, sectors, governance scale and goals, and 
how to evaluate.
• Knowledge on the political and policy related links between 
adaptation and mitigation across sectors and countries.
• Further theoretical and empirical research on the necessary 
institutional, cultural, social and political conditions to accelerate 
• How to transform developed and developing economies 
and societies for acceleration, including by shifting 

• The approaches to, and value of, coordinated, cross economy 
structural change, including Green New Deal approaches, as 
a way to accelerate GHG reduction.

1413
1413
FAQ 13.1 |  What roles do national play in climate mitigation, and how can they be effective? 
Institutions and governance underpin mitigation. Climate laws provide the legal basis for action, organisations through which policies 
are developed and implemented, and frameworks through which diverse actors interact. Specific organisations, such as expert 
committees, can inform emission reduction targets, inform the creation of policies and packages, and strengthen accountability. 
Institutions enable strategic thinking, building consensus among stakeholders and enhanced coordination. 
Climate governance is constrained and enabled by countries’ political systems, material endowments and their ideas, values and 
belief systems, which leads to a variety of country-specific approaches to climate mitigation.

Countries follow diverse approaches. Some countries focus on greenhouse gases emissions by adopting comprehensive climate laws 
and creating dedicated ministries and institutions focused on climate change. Others consider climate change among broader scope 
of policy objectives, such as poverty alleviation, energy security, economic development and co-benefits of climate actions, with the 
involvement of existing agencies and ministries. See also FAQ 13.3 on sub-national climate mitigation.
FAQ 13.2 |  What policies and strategies can be applied to combat climate change?
Institutions can enable creation of mitigation and sectoral policy instruments; policy packages for low-carbon system transition, 
and economy-wide measures for systemic restructuring. Policy instruments to reduce greenhouses gas emissions include economic 
instruments, regulatory instruments and other approaches.

Economic policy instruments directly influence prices to achieve emission reductions through taxes, permit trading, offset systems, 
subsidies, and border tax adjustments, and are effective in promoting implementation of low-cost emissions reductions. Regulatory 
instruments help achieve specific mitigation outcomes particularly in sectoral applications, by establishing technology or performance 
requirements. Other instruments include information programmes, government provision of goods, services and infrastructure, 
divestment strategies, and voluntary agreements between governments and private firms.
Climate policy instruments can be sector-specific or economy-wide and could be applied at national, regional, or local levels. 
Policymakers may directly target GHG emission reduction or seek to achieve multiple objectives, such as urbanisation or energy

security, with the effect of reducing emissions. In practice, climate mitigation policy instruments operate in combination with other 
policy tools, and require attention to the interaction effects between instruments. At all levels of governance, coverage, stringency 
and design of climate policies define their efficiency in reducing greenhouse gases emissions.
Policy packages, when designed with attention to interactive effects, local governance context, and harnessed to a clear vision for 
change, are better able to support socio-technical transitions and shifts in development pathways toward low-carbon futures than 
individual policies. See also Chapter 14 on international climate governance. 
FAQ 13.3 |  How can actions at the sub-national level contribute to climate mitigation? 
Sub-national actors (for example individuals, organisations, jurisdictions and networks at regional, local and city levels) often

have a remit over areas salient to climate mitigation, such as land-use planning, waste management, infrastructure, housing, and 
community development. Despite constraints on legal authority and dependence on national policy priorities in many countries, sub-
national climate change policies exist in more than 120 countries. However, they often lack national support, funding, and capacity, 
and adequate coordination with other scales. Sub-national climate action in support of specific goals is more likely to succeed when 
linked to local issues such as travel congestion alleviation, air pollution control.
The main drivers of climate actions at sub-national levels include high levels of citizen concern, jurisdictional authority and funding, 
institutional capacity, national level support and effective linkage to development objectives. Sub-national governments often 
initiate and implement policy experiments that could be scaled to other levels of governance.

1453
International Cooperation Chapter 1414Executive Summary
International cooperation is having positive and measurable 
results (high confidence). The Kyoto Protocol led to measurable 
and substantial avoided emissions, including in 20 countries with 
Kyoto first commitment period targets that have experienced 
a  decade of declining absolute emissions. It also built national 
capacity for greenhouse gas (GHG) accounting, catalysed the 
creation of GHG markets, and increased investments in low-carbon 
technologies (medium confidence). Other international agreements 
and institutions have led to avoided carbon dioxide (CO 2) emissions 
from land use practices, as well as avoided emissions of some non-
CO2 greenhouse gases (medium confidence). {14.3, 14.5, 14.6}
New forms of international cooperation have emerged 
since the Intergovernmental Panel on Climate Change’s 
Fifth Assessment Report (IPCC AR5) in line with an evolving

understanding of effective mitigation policies, processes, and 
institutions. Both new and pre-existing forms of cooperation 
are vital for achieving climate mitigation goals in the context 
of sustainable development (high confidence). While previous 
IPCC assessments have noted important synergies between 
the outcomes of climate mitigation and achieving sustainable 
development objectives, there now appear to be synergies between 
the two processes themselves (medium confidence). Since AR5, 
international cooperation has shifted towards facilitating national-
level mitigation action through numerous channels. These now 
include both processes established under the United Nations 
Framework Convention on Climate Change (UNFCCC) regime and 
regional and sectoral agreements and organisations. {14.2, 14.3, 
14.5, 14.6}
Participation in international agreements and transboundary 
networks is associated with the adoption of climate policies

at the national and sub-national levels, as well as by non-state 
actors (high confidence). International cooperation helps countries 
achieve long-term mitigation targets when it supports development 
and diffusion of low-carbon technologies, often at the level of 
individual sectors, which can simultaneously lead to significant 
benefits in the areas of sustainable development and equity (medium 
confidence). {14.2, 14.3, 14.5, 14.6}
International cooperation under the United Nations (UN) 
climate regime has taken an important new direction with 
the entry into force of the 2015 Paris Agreement, which 
strengthened the objective of the UN climate regime, 
including its long-term temperature goal, while adopting 
a  different architecture from that of the Kyoto Protocol to 
achieve it (high confidence). The core national commitments 
under the Kyoto Protocol have been legally binding quantified 
emission targets for developed countries tied to well-defined

mechanisms for monitoring and enforcement. By contrast, the 
commitments under the Paris Agreement are primarily procedural, 
extend to all Parties, and are designed to trigger domestic policies 
and measures, enhance transparency, stimulate climate investments, 
particularly in developing countries, and to lead iteratively to rising 
levels of ambition across all countries (high confidence). Issues of equity remain of central importance in the UN climate regime, 
notwithstanding shifts in the operationalisation of ‘common but 
differentiated responsibilities and respective capabilities’ from Kyoto 
to Paris (high confidence). {14.3} 
There are conflicting views on whether the Paris Agreement’s 
commitments and mechanisms will lead to the attainment of 
its stated goals. Arguments in support of the Paris Agreement are 
that the processes it initiates and supports will in multiple ways lead, 
and indeed have already led, to rising levels of ambition over time.

The recent proliferation of national mid-century net zero GHG targets 
can be attributed in part to the Paris Agreement (medium confidence). 
Moreover, its processes and commitments will enhance countries’ 
abilities to achieve their stated level of ambition, particularly among 
developing countries (medium confidence). Arguments against 
the Paris Agreement are that it lacks a  mechanism to review the 
adequacy of individual Parties’ Nationally Determined Contributions 
(NDCs), that collectively current NDCs are inconsistent in their level 
of ambition with achieving the Paris Agreement’s temperature goal, 
that its processes will not lead to sufficiently rising levels of ambition 
in the NDCs, and that NDCs will not be achieved because the targets, 
policies and measures they contain are not legally binding at the 
international level (medium confidence). To some extent, arguments 
on both sides are aligned with different analytic frameworks,

including assumptions about the main barriers to mitigation that 
international cooperation can help overcome (medium confidence). 
The extent to which countries increase the ambition of their NDCs 
and ensure they are effectively implemented will depend in part 
on the successful  implementation of the support mechanisms in 
the Paris Agreement, and in turn will determine whether the goals 
of the Paris Agreement are met (high confidence). {14.2, 14.3, 14.4} 
International cooperation outside the UNFCCC processes 
and agreements provides critical support for mitigation 
in particular regions, sectors and industries, for particular 
types of emissions, and at the sub- and transnational levels 
(high confidence). Agreements addressing ozone depletion, 
transboundary air pollution, and release of mercury are all leading 
to reductions in the emissions of specific greenhouse gases (high 
confidence). Cooperation is occurring at multiple governance levels

including cities. Transnational partnerships and alliances involving 
non-state and sub-national actors are also playing a  growing role 
in stimulating low-carbon technology diffusion and emissions 
reductions (medium confidence). Such transnational efforts include 
those focused on climate litigation; the impacts of these are unclear 
but promising. Climate change is being addressed in a  growing 
number of international agreements operating at sectoral levels, 
as well as within the practices of many multilateral organisations 
and institutions (high confidence). Sub-global and regional 
cooperation, often described as climate clubs, can play an important 
role in accelerating mitigation, including the potential for reducing 
mitigation costs through linking national carbon markets, although 
actual examples of these remain limited (high confidence). {14.2, 
14.4, 14.5, 14.6} 
International cooperation will need to be strengthened in

1454
Chapter 14 International Cooperation14consistent with limiting temperature rise to well below 2°C 
in the context of sustainable development and equity (high 
confidence). Many developing countries’ NDCs have components 
or additional actions that are conditional on receiving assistance 
with respect to finance, technology development and transfer, and 
capacity building, greater than what has been provided to date (high 
confidence). Sectoral and sub-global cooperation is providing critical 
support, and yet there is room for further progress. In some cases, 
notably with respect to aviation and shipping, sectoral agreements 
have adopted climate mitigation goals that fall far short of what 
would be required to achieve the temperature goal of the Paris 
Agreement (high confidence). Moreover, there are cases where 
international cooperation may be hindering mitigation efforts, 
namely evidence that trade and investment agreements, as well as

agreements within the energy sector, impede national mitigation 
efforts (medium confidence). International cooperation is emerging 
but so far fails to fully address transboundary issues associated with 
Solar Radiation Modification and CO 2 removal (high confidence). 
{14.2, 14.3, 14.4, 14.5, 14.6}

1455
International Cooperation Chapter 141414.1 Introduction
This chapter assesses the role and effectiveness of international 
cooperation in mitigating climate change. Such cooperation includes 
multilateral global cooperative agreements among nation states such 
as the 1992 United Nations Framework Convention on Climate Change 
(UNFCCC), and its related legal instruments, the 1997 Kyoto Protocol 
and the 2015 Paris Agreement, but also plurilateral agreements 
involving fewer states, as well as those focused on particular economic 
and policy sectors, such as components of the energy system. 
Moreover, this chapter assesses the role of transnational agreements 
and cooperative arrangements between non-state and sub-national 
actors, including municipal governments, private sector firms and 
industry consortia, and civil society organisations. This chapter does 
not assess international cooperation within the European Union, as 
this is covered in Chapter 13 of this report.

Past IPCC assessment reports have discussed the theoretical literature, 
providing insights into the rationale for international cooperation, as 
well as guidance as to its structure and implementation. This chapter 
limits such theoretical discussion primarily to the new developments 
since the Fifth Assessment Report (AR5). Important developments in 
this respect include attention to climate clubs (groups of countries and 
potentially non-state actors that can work together to achieve particular 
objectives), and the effects of framing the global climate change 
mitigation challenge as one of accelerating a socio-technical transition 
or transformation, shifting development pathways accordingly, in 
addition to (or rather than) solving a global commons problem. This 
chapter draws from theory to identify a set of criteria by which to assess 
the effectiveness of existing forms of international cooperation.
The rest of this chapter describes existing cooperative international

agreements, institutions, and initiatives with a view to clarifying how 
they operate, what effects they have, and ultimately, whether they 
work. At the heart of this international institutional architecture lies 
the Paris Agreement, which sets the overall approach for international 
cooperation under the UNFCCC at the global level. In many ways, 
the Paris Agreement reshapes the structure of such cooperation, 
from one oriented primarily towards target setting, monitoring, and 
enforcement, to one that is oriented towards supporting and enabling 
nationally determined actions (including targets), monitoring as well 
as catalysing non-state and sub-national actions at multiple levels 
of governance. In addition to the Paris Agreement, many forms of 
cooperation have taken shape in parallel: those designed to address 
other environmental problems that have a  significant impact on 
climate mitigation; those operating at the sub-global or sectoral

level; and those where the main participants are non-state actors. 
The chapter ends with an overall assessment of the effectiveness of 
current international cooperation and identifies areas that would 
benefit from improved and enhanced action.
14.1.1 Key Findings From the Fifth Assessment Report
The AR5 found that two characteristics of climate change make 
international cooperation essential: that it is a  global commons 
problem that needs to be addressed in a  coordinated fashion at the global scale;  and  that given the global diversity with respect 
to  opportunities for and cost of mitigation, there are economic 
efficiencies associated with cooperative solutions (Section  13.2.1.1). 
Consequently, AR5 found  evidence  to  suggest that climate policies 
that are implemented across geographical regions would be more 
effective in terms of both their environmental consequences and 
their economic costs (Sections 13.6, 13.13 and 14.4). The AR5 also

suggested  that regional cooperation  could  offer opportunities 
beyond what countries may be able to achieve by themselves. These 
opportunities are due to geographic proximity, shared infrastructure 
and policy frameworks, trade, and cross-border investments, and 
examples included renewable energy pools across borders, networks 
of energy infrastructure and coordinated forestry policies (Sections 1.2, 
6.6, 14.2 and 15.2). The AR5 also suggested that policy linkages exist 
across regional, national, and sub-national scales (Sections 13.3.1 and 
13.5.1.3). For these reasons, AR5 suggested that although the UNFCCC 
remains the primary international forum for climate negotiations, 
many other institutions engaged at the global, regional, and local 
levels do and should play an active role (Sections  13.3.1, 13.4.1.4 
and 13.5).  AR5 also noted that the inclusion of climate change 
issues across a variety of forums often creates institutional linkages

between mitigation and adaptation (Sections 13.3–13.5). In addition 
to centralised cooperation and governance, with a primary focus on 
the UNFCCC and its associated institutions, AR5 noted the emergence 
of new transnational climate-related institutions of decentralised 
authority such as public-private sector partnerships, private sector 
governance initiatives, transnational non-governmental organisation 
(NGO) programmes, and city-led initiatives (Sections 13.2, 13.3.1 and 
13.12). It noted that these have resulted in a multiplicity of cooperative 
efforts in the form of multilateral agreements, harmonised national 
policies and decentralised but coordinated  national  and  regional 
policies (Sections 13.3.2, 13.4.1 and 14.4). Finally, it suggested that 
international cooperation may  also  have a  role in promoting active 
engagement of the private sector in technological innovation and 
cooperative efforts leading to technology transfer and the development

of new technologies (Sections 13.3, 13.9 and 13.12). 
14.1.2 Developments Since the Fifth Assessment Report 
14.1.2.1 Negotiation of the Paris Agreement 
The key development since AR5 has been the negotiation and 
adoption of the Paris Agreement, which, building on the UNFCCC, 
introduces a new approach to global climate governance. This new 
approach, as discussed below (Section  14.3.1.1), is driven by the 
need to engage developing countries in emissions reductions beyond 
those they had taken on voluntarily under the Cancun Agreements, 
extend mitigation commitments to those developed countries that 
had rejected or withdrawn from the Kyoto Protocol, and to respond to 
the rapidly changing geopolitical context (Section 14.3.1.2). 
14.1.2.2 2030 Agenda for Sustainable Development and the 
It has long been clear that a failure to mitigate climate change would 
exacerbate existing poverty, accentuate vulnerability and worsen

1456
Chapter 14 International Cooperation14inequality (Denton at al. 2014), but there is an emerging attempt to 
harmonise mitigation actions with those oriented towards social and 
economic development. A key development since AR5 is the adoption 
in 2015 of the 2030 Agenda for Sustainable Development, which 
contains 17 Sustainable Development Goals (SDGs). This Agenda 
offers an aspirational narrative, coherent framework and actionable 
agenda for addressing diverse issues of development through goals 
that balance the economic, social and environmental dimensions 
of sustainable development as well as issues of governance and 
institutions (ICSU ISSC 2015). Scholars have noted that these 
dimensions of sustainable development are inter-dependent (Nilsson 
et al. 2016), and, as such it is difficult if not impossible to achieve 
economic and social gains while neglecting environmental concerns, 
including climate change (Le Blanc 2015). The SDGs are closely

linked to the Paris Agreement, adopted a few weeks later. There is 
a growing body of literature that examines the interlinkages between 
SDGs, including SDG 13 (taking urgent action to combat climate 
change) and others, concluding that without a proper response to 
climate change, success in many of the other SDGs would be difficult 
if not impossible (ICSU ISSC 2015; Le Blanc 2015; Nilsson et al. 
2016; Weitz et al. 2018). Likewise, failure to achieve the SDGs will 
have a detrimental effect on the ability to limit climate change to 
manageable levels. Initiatives such as The World in 2050 (TWI2050 
2018), a large research initiative by a global consortium of research 
and policy institutions, work on the premise that pursuing climate 
action and sustainable development in an integrated and coherent 
way, based on a sound understanding of development pathways and 
dynamics, is the strongest approach to enable countries to achieve 
their objectives in both agreements.

14.1.2.3 IPCC Special Reports
Further key developments since AR5 include the release of three 
IPCC special reports. The first of these assessed the differential 
impacts of limiting climate change to 1.5°C global average warming 
compared to 2°C warming, indicated the emissions reductions and 
enabling conditions necessary to stay within this limit (IPCC 2018a). 
While the events that have unfolded since the report are not yet 
comprehensively documented in literature, arguably the report has 
led to a  renewed perception of the urgency of climate mitigation 
(Wolf et al. 2019). In particular, the report appears to have crystalised 
media coverage in some parts of the world around a need to reduce 
emissions to net zero by 2050 (whether of GHGs or CO 2), rather than 
delaying such reductions until the latter half of the century, as had 
been previously understood and indicated in the Paris Agreement. 
Its release is hence one factor explaining the rise in transnational

climate mobilisation efforts (Boykoff and Pearman 2019). It has also 
played a  role, in addition to the Paris Agreement (Geden 2016a), 
in the numerous announcements, pledges and indications by 
governments, including by all G7 countries, of their adoption of net 
zero GHG targets for 2050. The other two special reports focused on 
ocean and the cryosphere (IPCC 2019a), and the potential of land-
related responses to contribute to adaptation and mitigation (IPCC 
2019b). There has been no literature directly tying the publication 
of these latter two reports to changes in international cooperation. 
However, the 25th UNFCCC Conference of Parties in Madrid in 2019 
convened a  dialogue on ocean and climate change to consider how to strengthen mitigation and adaptation action in this context 
(UNFCCC 2019a, para. 31).
14.2 Evaluating International Cooperation
This section describes recent insights from social science theory that

can shed light on the need for and ideal structure of international 
cooperation. This section starts by describing developments in 
framing the underlying problem, moves towards a  body of theory 
describing the benefits of multilateral sub-global action, and ends 
with a theory-based articulation of criteria to assess the effectiveness 
of international cooperation.
14.2.1 Framing Concepts for Assessment  
of the Paris Agreement
Previous IPCC reports have framed international climate cooperation, 
and indeed climate mitigation more generally, primarily as addressing 
a global commons problem (Stavins et al. 2014). In this report, by 
contrast, multiple framings are considered. Chapter  1  introduces 
four analytic frameworks: aggregated economic approaches such as 
cost-benefit analysis, which maps onto the global commons framing; 
ethical approaches; analysis of transitions and transformations; and 
psychology and politics of changing course. Here, we highlight some

of the findings that are of relevance to international cooperation.
When applied to the international context, the public good (or global 
commons) framing stresses that the incentives for mitigation at 
the global level are greater than they are for any single country, since 
the latter does not enjoy the benefits of its own mitigation efforts 
that accrue outside its own borders (Stavins et al. 2014; Patt 2017). 
This framing does not preclude countries engaging in mitigation, 
even ambitious mitigation, but it suggests that these countries’ level 
of ambition and speed of abatement would be greater if they were 
part of a cooperative agreement. 
Theoretical economists have shown that reaching such a  global 
agreement is difficult, due to countries’ incentives to free-ride, 
namely benefit from other countries’ abatement efforts while 
failing to abate themselves (Barrett 1994; Gollier and Tirole 2015). 
Numerical models that integrate game theoretic concepts, whether

based on optimal control theory or on dynamic programming, 
consistently confirm this insight, at least in the absence of transfers 
(Germain et al. 2003; Lessmann et al. 2015; Chander 2017). Recent 
contributions suggest that regional or sectoral agreements, or 
agreements focused on a  particular subset of GHGs, can be seen 
as building blocks towards a global approach (Asheim et al. 2006; 
Froyn and Hovi 2008; Sabel and Victor 2017; Stewart et al. 2017). In 
a  dynamic context, this gradual approach through building blocks 
can alleviate the free-riding problem and ultimately lead to global 
cooperation (Caparrós and Péreau 2017). Much of this literature is 
subsumed under the concept of ‘climate clubs’ described in the next 
section. Other developments based on dynamic game theory suggest 
that the free-riding problem can be mitigated if the treaties do not 
prescribe countries’ levels of green investment and the duration of

1457
International Cooperation Chapter 1414the agreement, as countries can credibly threaten potential free-
riders with a  short-term agreement where green investments will 
be insufficient due to the hold-up problem (Battaglini and Harstad 
2016). Finally, thresholds and potential climate catastrophes have 
also been shown, theoretically and numerically, to reduce free-riding 
incentives, especially for countries that may become pivotal in failing 
to avoid the threshold (Barrett 2013; Emmerling et al. 2020).
In addition to mitigation in the form of emissions abatement, 
innovation in green technologies also has public good features, 
leading for the same reasons to less innovation than would be 
globally ideal (Jaffe et al. 2005). Here as well, theory suggests that 
there are benefits from cooperation on technology development 
at the regional or sectoral levels, but also that cooperation on 
technology, especially for breakthrough technologies, may prove

to be easier than for abatement (El-Sayed and Rubio 2014; Rubio 
2017). In a dynamic context, the combination of infrastructure lock-
in, network effects with high switching cost, and dynamic market 
failures suggests that deployment and adoption of clean technologies 
is path dependent (Acemoglu et al. 2012; Aghion et al. 2014), with 
a  multiplicity of possible equilibria. This implies that no outcome 
is guaranteed, although the most likely pathway will depend on 
economic expectations and initial conditions of the innovation 
process (Krugman 1991). Therefore, the government has a  role 
to play, either by shifting expectations (e.g., credibly committing to 
climate policy), or by changing initial conditions (e.g.,  investing in 
green infrastructure or subsidising clean energy research) (Acemoglu 
et al. 2012; Aghion et al. 2014). This result is exacerbated by the 
irreversibility of energy investments and the extremely long periods

of operation of the typical energy investment (Caparrós et al. 2015; 
Baldwin et al. 2020).
While the public goods and global commons framing concentrates 
on free-riding incentives as the primary barrier to mitigation taking 
place at a pace that would be globally optimal, other factors arise 
across the four analytic frameworks. For example, within the political 
framework, Beiser-McGrath and Bernauer (2021) highlight that not 
just the incentive to free-ride, but also the knowledge that another 
major emitter is free-riding, could lessen a country’s political incentive 
to mitigate. Aklin and Mildenberger (2020) present evidence to 
suggest that distributive conflict within countries, rather than free-
riding across countries, is the primary barrier to ambitious national-
level action. Another barrier could be a lack of understanding and 
experience with particular policy approaches; there is evidence that 
participation in cooperative agreements could facilitate information

exchange across borders and lead to enhanced mitigation policy 
adoption (Rashidi and Patt 2018).
The analytic approach focusing on transitions and transformation 
focuses on path-dependent processes as an impediment to the shift 
to low-carbon technologies and systems. Cross-Chapter Box 12 on 
Transition Dynamics (Chapter  16) summarises the key points of 
this literature. This chapter describes how the two framings focus 
on different indicators of progress, and potentially different types 
of cooperative action within the international context. This chapter 
highlights in later sections conflicting views on whether the Paris 
Agreement is likely to prove effective (Section  14.3.3.2). To some extent, the dichotomy of views aligns with the two framings: analysis 
implicitly aligned with the global commons framing is negative about 
the Paris architecture, whereas that aligned with the transitions 
framing is more positive (Kern and Rogge 2016; Patt 2017; Roberts 
et al. 2018).

Within the global commons framing, the primary indicator of 
progress is the actual level of GHG emissions, and the effectiveness 
of policies can be measured in terms of whether such emissions rise 
or fall (Patt 2017; Hanna and Victor 2021). The fact that the sum of 
all countries’ emissions has continued to grow (IPCC 2018a), even 
as there has been a  global recognition that they should decline, 
is seen as being consistent with the absence of a  strong global 
agreement. Within this framing, there is traditionally an emphasis on 
treaties’ containing self-enforcing agreements (Olmstead and Stavins 
2012), ideally through binding commitments, as a  way of dealing 
with the overarching problem of free-ridership (Barrett 1994; Finus 
and Caparrós 2015; Tulkens 2019). However, as discussed above, 
the emphasis has now shifted to a gradual cooperation approach, 
either regional or sectoral, as an alternative way of dealing with free-

riding incentives (Caparrós and Péreau 2017; Sabel and Victor 2017; 
Stewart et al. 2017). The gradual linkage of emissions trading systems 
(discussed in Section 14.4.4), goes in the same direction. There is also 
literature suggesting that the diversity of the countries involved may 
in fact be an asset to reduce the free-rider incentive (Pavlova and De 
Zeeuw 2013; Finus and McGinty 2019), which argues in favour of 
a system where all countries, irrespectively of their income levels, are 
fully involved in mitigation, unlike the Kyoto Protocol and in line with 
the Paris Agreement. Finally, recent efforts have discussed potential 
synergies between mitigation and adaptation efforts in a strategic 
context (Bayramoglu et al. 2018) (Section  14.5.1.2) In general, 
current efforts go beyond considering climate policy as a mitigation-
only issue, much in line with the discussion about linkages between 
climate change and sustainable development policies described in

detail in Chapters 1 and 4 of this report. 
In the transitions framing, by contrast, global emissions levels are 
viewed as the end (and often greatly delayed) result of a  large 
number of transformative processes. International cooperation may 
be effective at stimulating such processes, even if a change in global 
emissions is not yet evident, implying that short-term changes in 
emissions levels may be a misleading indicator of progress towards 
long-term goals (Patt 2017). Hanna and Victor (2021) suggest 
a  particular focus on technical advances and deployment patterns 
in niche low-carbon technologies, such as wind and solar power, 
and electric vehicles. However, this is one among many suggestions: 
the literature does not identify a single clear indicator to use, and 
there are many metrics of technological progress and transformation, 
described in Section 16.3.3 of this report. These can include national-
level emissions among countries participating in particular forms of

cooperation, as well as leading indicators of such emissions such as 
changes in low-carbon technology deployment and cost. 
Just as the transition framing highlights indicators of progress other 
than global emissions, it de-emphasises the importance of achieving 
cost-effectiveness with respect to global emissions. Hence, this strand 
of the literature does not generally support the use of international

1458
Chapter 14 International Cooperation14carbon markets, suggesting that these can delay transformative 
processes within countries that are key drivers of technological 
change (Cullenward and Victor 2020). For similar reasons, achieving 
cross-sectoral cost-effectiveness, a goal of many carbon markets, is 
not seen as a high priority. Instead, within the transitions framing, 
the emphasis with respect to treaty design is often on providing 
mechanisms to support Parties’ voluntary actions, such as with 
financial and capacity-building support for new technologies and 
technology regimes (Victor et al. 2019). The transitions literature also 
highlights impediments to transformation as being sector specific, 
and hence the importance of international cooperation addressing 
sector-specific issues (Victor et al. 2019). While such attention 
often starts with promoting innovation and diffusion of low-carbon 
technologies that are critical to a sector’s functioning, it often ends

with policies aimed at phasing out the high-carbon technologies 
once they are no longer needed (Markard 2018). In line with this, 
many scholars have suggested value in supply-side international 
agreements, aimed at phasing out the production and use of fossil 
fuels (Collier and Venables 2014; Piggot et al. 2018; Asheim et al. 
2019; Newell and Simms 2020).
Analytic approaches centred on equity and development figure 
prominently within this report, with many of the key concepts 
addressed in Chapter  4. Primarily the focus is on aligning climate 
policy at the international level with efforts to shift development 
pathways towards improved quality of life and greater sustainability 
(Cross-Chapter Box 5 in Chapter 4). There are also overlaps between 
the equity framework and the others. Within the global commons 
framing, the emphasis is on international carbon markets to 
reduce the costs from climate policies, and as way of generating

financial flows to developing countries (Michaelowa et al. 2019a). 
The transitions framing, while focused empirically primarily on 
industrialised countries, nevertheless aligns with an understanding of 
climate mitigation taking place within a wider development agenda; 
in many cases it is a lack of development that creates a barrier to rapid 
system transformation, which international cooperation can address 
(Delina and Sovacool 2018) (Cross-Chapter Box 12 in Chapter 16).14.2.2 Climate Clubs and Building Blocks
A recent development in the literature on international climate 
governance has been increased attention to the potential for climate 
clubs (Victor 2011). Hovi et al. (2016) define these as ‘any international 
actor group that (1) starts with fewer members than the UNFCCC 
has and (2) aims to cooperate on one or more climate change-
related activities, notably mitigation, adaptation, climate engineering 
or climate compensation’. While providing public goods (such as

mitigation), they also offer member-only benefits (such as preferential 
tariff rates) to entice membership. In practice, climate clubs are sub-
global arrangements, and formal agreement by interstate treaty is not 
a prerequisite. Actors do not have to be states, although in the literature 
on climate clubs states have hitherto dominated. The literature has an 
essentially static dimension that focuses on the incentives for actors 
to join such a club, and a dynamic one which focuses on the ‘building 
blocks’ for global cooperative agreements. 
The literature focusing on the static aspects of clubs highlight that 
they represent ‘coalitions of the willing’ (Falkner 2016a; Gampfer 
2016; Falkner et al. 2021), which offer a package of benefits, part of 
which are pure public goods (available also to non-club members), 
and others are club benefits that are only available to members (Hovi 
et al. 2016). The members-only or excludable part can be a system

of transfers within the club to compensate the countries with higher 
costs. For example, the benefit from participating in the club can be to 
have access to a common emissions trading system, which in general 
is more attractive the larger the diversity of the countries involved, 
although this is not a general result, as discussed in detail in Doda 
and Taschini (2017). However, as costs and effort-sharing agreements 
are unsuccessful in a static context (Barrett 1994), mainly due to free-
rider incentives, several studies have proposed using tariffs on trade 
or other forms of sanctions to reduce incentives for free-riding (Helm 
and Sprinz 2000; Eyland and Zaccour 2012; Anouliès 2015; Nordhaus 
2015; Al Khourdajie and Finus 2020). For example, Nordhaus (2015) 
uses a  coalition formation game model to show that a  uniform 
percentage tariff on the imports from nonparticipants into the club 
region (at a relatively low tariff rate of about 2%) can induce high

participation within a range of carbon price values. More recently, 
Al Khourdajie and Finus (2020) show that border carbon adjustments 
Table 14.1 | Key climate club static modelling results.
Aakre et al. (2018) Nordhaus (2015) Hovi et al. (2017);  
Sprinz et al. (2018) Sælen (2020);  
Sælen et al. (2020) 
ScopeTransboundary black carbon and 
methane in the ArcticGlobal emissions Global emissions Global emissions
Modelling methodTM5-FASST model (‘reduced-form air 
quality and impact evaluation tool’)C-DICE (coalition formation game based on 
a static version of the multiregional DICE-
RICE optimisation model) Agent-based model Agent-based model
Border tax adjustment No Yes No No
Key resultsBlack carbon can be more easily 
controlled than methane, based on 
self-interest; inclusion of non-Arctic 
Council major polluters desirable to 
control pollutantsFor non-participants in mitigation efforts, 
modest tariffs on trade are advised to 
stabilise coalition formation for emissions

reductionsClimate clubs can substantially 
reduce GHG emissions, provided 
club goods are present. The 
(potential) departure of a single 
major actor (e.g., USA) reduces 
emissions coverage, yet is rarely 
fatal to the existence of the club The architecture of the Paris 
Agreement will achieve the 2°C 
goal only under a very fortunate 
constellation of parameters. 
Potential withdrawal (e.g., USA) 
further reduces these chances 
considerably

1459
International Cooperation Chapter 1414and an open membership policy can lead to a large stable climate 
agreement, including full participation. Table 14.1 presents a number 
of key results related to climate clubs from a static context.
In a  dynamic context, the literature on climate clubs highlights the 
co-called ‘building blocks’ approach (Stewart et al. 2013a,b, 2017). 
This is a bottom-up strategy designed to create an array of smaller-
scale, specialised initiatives for transnational cooperation in particular 
sectors and/or geographic areas with a wide range of participants. As 
part of this literature, Potoski and Prakash (2013) provide a conceptual 
overview of voluntary environmental clubs, showing that many climate 
clubs do not require demanding obligations for membership and that 
a  substantial segment thereof are mostly informational (Weischer 
et al. 2012; Andresen 2014). Also crafted onto the building blocks

approach, Potoski (2017) demonstrates the theoretical potential for 
green certification and green technology clubs. Green (2017) further 
highlights the potential of ‘pseudo-clubs’ with fluid membership 
and limited member benefits to promote the diffusion and uptake 
of mitigation standards. Falkner et al. (2021) suggest a  typology of 
normative, bargaining, and transformational clubs. Before the adoption 
of the Paris Agreement, some literature suggested that the emergence 
of climate clubs in parallel to the multilateral climate regime would lead 
to ‘forum shopping’, with states choosing the governance arrangement 
that best suits their interests (McGee and Taplin 2006; van Asselt 
2007; Biermann et al. 2009; Oh and Matsuoka 2017). However, more 
recent literature suggests that climate clubs complement rather than 
challenge the international regime established by the UNFCCC (van 
Asselt and Zelli 2014; Falkner 2016a; Draguljić 2019).

In this dynamic context, one question is whether to negotiate a single 
global agreement or to start with smaller agreements in the hope that 
they will eventually evolve into a larger agreement. It has been debated 
extensively in the context of free trade whether a multilateral (global) 
negotiating approach is preferable to a  regional approach, seen as 
a building block towards global free trade. Aghion et al. (2007) analysed 
this issue formally for trade, showing that a  leader would always 
choose to move directly to a global agreement. In the case of climate 
change, it appears that even the mildest form of club discussed above 
(an efforts and costs sharing agreement, as in the case of the linkage 
of emissions trading systems) can yield global cooperation following 
a building blocks approach, and that the sequential path relying on 
building blocks may be the only way to reach global cooperation over 
time (Caparrós and Péreau 2017). While the existence of a  nearly

universal agreement such as the Paris Agreement may arguably have 
rendered this discussion less relevant, the Paris Agreement co-exists, 
and will likely continue to do so, with a  multitude of sectoral and 
regional agreements, meaning that this discussion is still relevant for 
the evolution of these complementary regimes. 
Results based on an agent-based model suggest that climate clubs 
result in major emissions reductions if there is a  sufficiently high 
provision of the club good and if initial membership by several states 
with sufficient emissions weight materialises. Such configurations 
allow the club to grow over time to enable effective global action 
(Hovi et al. 2017). The departure of a  major emitter (specifically 
the United States) triggered a scientific discussion on the stability of 
the Paris Agreement. Sprinz et al. (2018) explore whether climate clubs are stable against a leader willing to change its status, for example,

from leader to follower, or even completely leaving the climate club, 
finding in most cases such stability to exist. Related studies on the 
macroeconomic incentives for climate clubs by Paroussos et al. 
(2019) show that climate clubs are reasonably stable, both internally 
and externally (i.e., no member willing to leave and no new member 
willing to join), and climate clubs that include obligations in line 
with the 2°C goal combined with financial incentives can facilitate 
technology diffusion. The authors also show that preferential trade 
arrangements for low-carbon goods can reduce the macroeconomic 
effects of mitigation policies. Aakre et al. (2018) show numerically 
that small groups of countries can limit black carbon in the Arctic, 
driven mainly for reasons of self-interest, yet reducing methane 
requires larger coalitions due to its larger geographical dispersal and 
requires stronger cooperation.
14.2.3 Assessment Criteria

This section identifies a set of criteria for assessing the effectiveness 
of international cooperation, which is applied later in the chapter. 
Lessons from the implementation of other multilateral environmental 
agreements (MEAs) can provide some guidance. There is considerable 
literature on this topic, most of which predates AR5, and which will 
therefore not be covered in detail. Issues include ways to enhance 
compliance, and the fact that a  low level of compliance with an 
MEA does not necessarily mean that the MEA has no effect (Downs 
et al. 1996; Victor et al. 1998; Weiss and Jacobson 1998). Recent 
research examines effectiveness from the viewpoint of the extent to 
which an MEA influences domestic action, including the adoption of 
implementing legislation and policies (Brandi et al. 2019). 
Many have pointed to the Montreal Protocol, addressing stratospheric 
ozone loss, as an example of a  successful treaty because of its

ultimate environmental effectiveness, and relevance for solving 
climate change. Scholarship emerging since AR5 emphasises that 
the Paris Agreement has a greater ‘bottom-up’ character than many 
other MEAs, including the Montreal or Kyoto Protocols, allowing for 
more decentralised ‘polycentric’ forms of governance that engage 
diverse actors at the regional, national and sub-national levels 
(Ostrom 2010; Jordan et al. 2015; Falkner 2016b; Victor 2016). 
Given the differences in architecture, lessons drawn from studies 
of MEA regimes need to be supplemented with assessments of the 
effectiveness of cooperative efforts at other governance levels and in 
other forums. Emerging research in this area proposes methodologies 
for this task (Hsu et al. 2019a). Findings highlight the persistence 
of similar imbalances between developed and developing countries 
as at the global level, as well as the need for more effective ways

to incentivise private sector engagement in transnational climate 
governance (Chan et al. 2018).
While environmental outcomes and economic performance have 
been long-standing criteria for assessment of effectiveness, 
the other elements deserve some note. It is the case that the 
achievement of climate objectives, such as limiting global average 
warming to 1.5°C–2°C, will require the transition from high- to 
low-carbon technologies and the transformation of the sectors

1460
Chapter 14 International Cooperation14and social environments within which those technologies operate. 
Such transformations are not linear processes, and hence many of 
the early steps taken –  such as supporting early diffusion of new 
renewable energy technologies – will have little immediate effect on 
GHG emissions (Patt 2015; Geels et al. 2017). Hence, activities that 
contribute to transformative potential include technology transfer 
and financial support for low-carbon infrastructure, especially where 
the latter is not tied to immediate emissions reductions. Assessing 
the transformative potential of international cooperation takes these 
factors into account. Equity and distributive outcomes are of central 
importance to the climate change debate, and hence for evaluating 
the effects of policies. Equity encompasses the notion of distributive 
justice which refers to the distribution of goods, burdens, costs and

benefits, as well as procedural-related issues (Kverndokk 2018). 
Finally, the literature on the performance of other MEAs highlights 
the importance of institutional strength, which can include regulative 
quality, mechanisms to enhance transparency and accountability, 
and administrative capacity. Regulative quality includes guidance 
and signalling (Oberthür et al. 2017), as well as clear rules and 
standards to facilitate collective action (Oberthür and Bodle 2016). 
The literature is clear that legally-binding obligations (which 
require the formal expression of state consent) and non-binding 
recommendations can each be appropriate, depending on the 
particular circumstances (Skjærseth et al. 2006), and indeed it has 
been argued that for climate change non-binding recommendations 
may better fit the capacity of global governance organisations (Victor 
2011). Mechanisms to enhance transparency and accountability are

essential to collect, protect, and analyse relevant data about Parties’ 
implementation of their obligations, and to identify and address 
challenges in implementation (Kramarz and Park 2016; Kinley et al. 
2020). Administrative capacity refers to the strength of the formal 
bodies established to serve the Parties to the regime and help 
ensure compliance and goal attainment (Andler and Behrle 2009; 
Bauer et al. 2017). 
In addition to building on the social science theory just described, 
we recognise that it is also important to strike a  balance 
between applying the same standards developed and applied to 
international cooperation in AR5 and maintaining consistency with 
other chapters of this report (primarily Chapters 1, 4, 13 and 15). 
Table 14.2 presents a set of criteria that do this, and which are then 
applied later in the chapter.14.3 The UNFCCC and the Paris Agreement 
14.3.1 The UN Climate Change Regime
14.3.1.1  Instruments and Milestones

The international climate change regime, in evolution for three 
decades, comprises the 1992 UNFCCC, the 1997 Kyoto Protocol, and 
the 2015 Paris Agreement. The UNFCCC is a ‘framework’ convention, 
capturing broad convergence among states on an objective, a set of 
principles, and general obligations relating to mitigation, adaptation, 
reporting and support. The UNFCCC categorises Parties into Annex 
I and Annex II. Annex I Parties, comprising developed country Parties, 
have a goal to return, individually or jointly, their GHG emissions to 
1990 levels by 2000. Annex II Parties, comprising developed country 
Parties except for those with economies in transition, have additional 
obligations relating to the provision of financial and technology 
support. Parties including developing country Parties, characterised as 
non-Annex-I Parties, have reporting obligations, as well as obligations 
to take policies and measures on mitigation and adaptation. The

UNFCCC also establishes the institutional building blocks for global 
climate governance. Both the 1997 Kyoto Protocol and the 2015 Paris 
Agreement are distinct but ‘related legal instruments’ in that only 
Parties to the UNFCCC can be Parties to these later instruments.
The Kyoto Protocol specifies GHG emissions reduction targets for 
the 2008–2012 commitment period for countries listed in its Annex 
B (which broadly corresponds to Annex I to the UNFCCC) (UNFCCC 
1997, Art. 3  and Annex B). The Kyoto Protocol entered into force 
in 2005. Shortly thereafter, states began negotiating a  second 
commitment period under the Protocol for Annex B Parties, as well 
as initiating a  process under the UNFCCC to consider long-term 
cooperation among all Parties.
At the 13th Conference of the Parties to the UNFCCC (COP13) in 
Bali in 2007, Parties adopted the Bali Action Plan which launched 
negotiations aimed at an agreed outcome enhancing the UNFCCC’s

‘full, effective and sustained implementation’. The agreed outcome 
was to be adopted at COP15 in Copenhagen in 2009, but negotiations 
failed to deliver a consensus document. The result instead was the 
Copenhagen Accord, which was taken note of by the COP . While 
it was a political agreement with no formal legal status under the 
UNFCCC, it reflected significant progress on several fronts and set 
in place the building blocks for the Paris Agreement, namely: setting Table 14.2 | Criteria for assessing effectiveness of international cooperation.
Environmental outcomesTo what extent does international cooperation lead to identifiable environmental benefits, namely the reduction of economy-wide and sectoral 
emissions of greenhouse gases from pre-existing levels or ‘business as usual’ scenarios?
Transformative potentialTo what extent does international cooperation contribute to the enabling conditions for transitioning to a zero-carbon economy and sustainable

development pathways at the global, national, or sectoral levels?
Distributive outcomesTo what extent does international cooperation lead to greater equity with respect to the costs, benefits, and burdens of mitigation actions, taking 
into account current and historical contributions and circumstances? 
Economic performance To what extent does international cooperation promote the achievement of economically efficient and cost-effective mitigation activities?
Institutional strengthTo what extent does international cooperation create the institutional framework needed for the achievement of internationally agreed-upon 
goals, and contribute to national, sub-national, and sectoral institutions needed for decentralised and bottom-up mitigation governance?

1461
International Cooperation Chapter 1414a goal of limiting global temperature increase to below 2°C; calling 
on all countries to put forward mitigation pledges; establishing 
broad new terms for the reporting and verification of countries’ 
actions; setting a goal of mobilising USD100 billion a year by 2020 
from a  wide variety of sources, public and private, bilateral and 
multilateral, including alternative sources of finance; and, calling 
for the establishment of a new Green Climate Fund and Technology 
Mechanism (Rajamani 2010; Rogelj et al. 2010; UNFCCC 2010a). One 
hundred and forty states endorsed the Copenhagen Accord, with 85 
countries entering pledges to reduce their emissions or constrain 
their growth by 2020 (Christensen and Olhoff 2019). At COP16 in Cancun in 2010, Parties adopted a set of decisions termed 
the Cancun Agreements that effectively formalised the core elements 
of the Copenhagen Accord, and the pledges states made, under the

UNFCCC.  The Cancun Agreements were regarded as an interim 
arrangement through to 2020, and Parties left the door open to further 
negotiations, in line with negotiations launched in 2005, toward 
a legally-binding successor to the Kyoto Protocol (Freestone 2010; Liu 
2011a). Collectively the G20 states are on track to meeting the mid 
level of their Cancun pledges, although there is uncertainty about some 
individual pledges. However, there is significant gap between annual 
Table 14.3 | Continuities in and differences between the UNFCCC, Paris Agreement and the Kyoto Protocol.
Feature UNFCCC Kyoto Protocol Paris Agreement 
Objective To stabilise GHGs in the atmosphere at a level that would 
prevent dangerous anthropogenic interference with the climate 
system, in a timeframe to protect food security, enable natural 
ecosystem adaptability and permit economic development in 
a sustainable mannerPrimarily mitigation-focused 
(although in pursuit of the

UNFCCC objective) Mitigation in line with a long-term temperature goal, 
adaptation and finance goals, as well as sustainable 
development and equity (also, in pursuit of the 
UNFCCC objective) 
Architecture ‘Framework’ agreement with agreement on principles such 
as ‘common but differentiated responsibilities and respective 
capabilities’), division of countries into Annexes, with different 
groups of countries with differentiated commitments Differentiated targets, based on 
national offers submitted to the 
multilateral negotiation process, 
and multilaterally negotiated 
common metrics Nationally Determined Contributions subject to 
transparency, multilateral consideration of progress, 
common metrics in inventories and accounting 
mitigation-related 
commitments Annex I Parties with a GHG stabilisation goal, all Parties to 
take policies and measuresUNFCCC Annex I/Kyoto Annex 
B Parties only All Parties

Targets GHG stabilisation goal for Annex I Parties (‘quasi target’)Legally-binding, differentiated 
mitigation targets inscribed 
in treaty Non-binding (in terms of results) contributions 
incorporated in Parties’ NDCs, and provisions 
including those relating to highest possible ambition, 
progression and ‘common but differentiated 
responsibilities and respective capabilities’, in light 
of different national circumstances 
Timetable Aim to return to 1990 levels of GHGs by 2000Two commitment periods 
(2008–2012; 2013–2020) Initial NDCs for timeframes from 2020 running 
through to 2025 or 2030 with new or updated NDCs 
every five years, and encouragement to submit long-
term low-GHG emission development strategies 
Adaptation Parties to cooperate in preparing for adaptation to the impacts 
of climate change Parties to formulate and 
implement national adaptation 
measures, share of proceeds 
from CDM to fund adaptation Qualitative global goal on adaptation to enhance

adaptative capacity and resilience, and reduce 
vulnerability, Parties to undertake national 
adaptation planning and implementation 
Loss and Damage Not covered Not covered Cooperation and facilitation to enhance 
understanding, action and support for loss and 
damage, including through the Warsaw International 
Mechanism on Loss and Damage under the UNFCCC 
Transparency National communications from Parties, with differing 
content and set to differing timeframes for different 
categories of PartiesReporting and review – Annex 
B Parties only Enhanced transparency framework and five-yearly 
global stocktake for a collective assessment of 
progress towards goals – all Parties 
Support Annex II commitments relating to provision of 
finance, development and transfer of technology 
to developing countriesAdvances UNFCCC 
Annex II commitments 
relating to provision 
of finance, development 
and transfer of technology

to developing countries Enhances reporting in relation to support, expands 
the base of donors, and tailors support to the needs 
and capacities of developing countries 
Implementation National implementation, communication on implementationMarket mechanisms 
(International Emissions Trading, 
Joint Implementation, CDM) Voluntary cooperation on mitigation (through 
market-based and non-market approaches); 
encouragement of REDD+ (guidance and rules 
under negotiation) 
Compliance Multilateral consultative process, never adoptedCompliance committee 
with facilitative and 
enforcement branches; 
sanctions for non-compliance Committee to promote compliance and facilitate 
implementation; no sanctions

1462
Chapter 14 International Cooperation14emissions expected under full implementation of pledges and the level 
consistent with the 2°C goal (Christensen and Olhoff 2019).
At the 2011 Durban climate conference, Parties launched negotiations 
for ‘a Protocol, another legal instrument or agreed outcome with legal 
force’ with a scheduled end to the negotiations in 2015 (UNFCCC 
2012, Dec. 1, para. 2). At the 2012 Doha climate conference, Parties 
adopted a  second commitment period for the Kyoto Protocol, 
running from 2013–2020. The Doha Amendment entered into force 
on 31 December 2020. Given the subsequent adoption of the Paris 
Agreement, the Kyoto Protocol is unlikely to continue beyond 2020 
(Bodansky et al. 2017a). At the end of the compliance assessment 
period under the Kyoto Protocol, Annex B  Parties were in full 
compliance with their targets for the first commitment period; in 
some cases through the use of the Protocol’s flexibility mechanisms 
(Shishlov et al. 2016).

Although both the Kyoto Protocol and Paris Agreement are under 
the UNFCCC, they are generally seen as representing fundamentally 
different approaches to international cooperation on climate change 
(Held and Roger 2018; Falkner 2016b). The Paris Agreement has been 
characterised as a ‘decisive break’ from the Kyoto Protocol (Keohane 
and Oppenheimer 2016). Some note that the mitigation efforts under 
the Kyoto Protocol take the form of targets that, albeit based on 
national self-selection, were part of the multilateral negotiation process, 
whereas under the Paris Agreement Parties make Nationally Determined 
Contributions. The different approaches have been characterised by 
some as a distinction between a ‘top down’ and ‘bottom up’ approach 
(Bodansky and Rajamani 2016; Bodansky et al. 2016; Chan et al. 2016; 
Doelle 2016) but others disagree with such a characterisation, pointing 
to continuities within the regime, for example, in terms of rules for

reporting and review, and crossover and use of common institutional 
arrangements (Depledge 2017; Allan 2019). Some note, in any case, 
that the Kyoto Protocol’s core obligations are substantive obligations 
of result, while many of the Paris Agreement’s core obligations are 
procedural obligations, complemented by obligations of conduct 
(Rajamani 2016a; Mayer 2018a). 
The differences between and continuities in the three treaties that 
comprise the UN climate regime are summarised in Table  14.3. 
The Kyoto targets apply only to Annex I Parties, but the procedural 
obligations relating to NDCs in the Paris Agreement apply to all 
Parties, with flexibilities in relation to some obligations for Least 
Developed Countries (LDCs), Small Island Developing States (SIDS), 
and developing countries that need them in light of their capacities. 
The Kyoto targets are housed in its Annex B, therefore requiring 
a formal process of amendment for revision, whereas the Paris NDCs

are located in an online registry that is maintained by the Secretariat, 
but to which Parties can upload their own NDCs. The Kyoto Protocol 
allows Annex B  Parties to use three market-based mechanisms 
– the Clean Development Mechanism (CDM), Joint Implementation 
and International Emissions Trading – to fulfil a part of their GHG 
targets. The Paris Agreement recognises that Parties may choose 
to cooperate voluntarily on markets, in the form of cooperative 
approaches under Article 6.2, and a mechanism with international 
oversight under Article 6.4, subject to guidance and rules that are 
yet to be adopted. These rules relate to integrity and accounting  (La  Hoz  Theuer et al. 2019). Article 5  also provides explicit 
endorsement of reducing emissions from deforestation and forest 
degradation and fostering conservation (REDD+). The Kyoto Protocol 
contains an extensive reporting and review process, backed by 
a compliance mechanism. This mechanism includes an enforcement

branch, to ensure compliance, and sanction non-compliance (through 
the withdrawal of benefits such as participation in market-based 
mechanisms), with its national system requirements, and GHG 
targets. By contrast, the Paris Agreement relies on informational 
requirements and flows to enhance the clarity of NDCs, and to track 
progress in the implementation and achievement of NDCs.
14.3.1.2 Negotiating Context and Dynamics
The 2015 Paris Agreement was negotiated in a  starkly different 
geopolitical context to that of the 1992 UNFCCC and the 1997 Kyoto 
Protocol (Streck and Terhalle 2013; Ciplet et al. 2015). The ‘rupturing 
binary balance of superpowers’ of the 1980s had given way to 
a multipolar world with several distinctive trends: emerging economies 
began challenging US dominance (Ciplet et al. 2015); industrialised 
countries’ emissions peaked in the 2010s and started declining, 
while emissions from emerging economies began to grow (Falkner

2019); the EU stretched eastwards and became increasingly supra-
national (Kinley et al. 2020); disparities within the group of developing 
countries increased (Ciplet et al. 2015); and the role of non-state actors 
in mitigation efforts has grown more salient (Bäckstrand et al. 2017; 
Kuyper et al. 2018b; Falkner 2019). The rise of emerging powers, many 
of whom now have ‘veto power’, however, some noted, did not detract 
from the unequal development and inequality at the heart of global 
environmental politics (Hurrell and Sengupta 2012).
In this altered context, unlike in the 1990s when the main cleavages 
were between the EU and the US (Hurrell and Sengupta 2012), US–
China ‘great power politics’ came to be seen as determinative of 
outcomes in the climate change negotiations (Terhalle and Depledge 
2013). The US–China joint announcement (Whitehouse 2014), 
for instance, before the 2014 Lima climate conference, brokered

the deal on differentiation that came to be embodied in the Paris 
Agreement (Rajamani 2016a; Ciplet and Roberts 2017). Others have 
identified, on the basis of economic standing, political influence, and 
emissions levels, three influential groups – the first comprising the 
USA with Japan, Canada, and Russia, the second comprising the 
EU and the third comprising China, India and Brazil (Brenton 2013). 
The emergence of the Major Economies Fora, among other climate 
clubs (discussed in Section 14.2.2) reflects this development (Brenton 
2013). It also represents a ‘minilateral’ forum, built on a recognition 
of power asymmetries, in which negotiating compromises are 
politically tested and fed into multilateral processes (Falkner 2016a). 
Beyond these countries, in the decade leading up to the Paris climate 
negotiations, increasing differences within the group of developing 
countries divided the 134-strong developing country alliance of the

G77/China into several interest-based coalitions (Vihma et al. 2011; 
Bodansky et al. 2017b). A division emerged between the vulnerable 
least developed and small island states on the one side and rapidly 
developing economies, the BASIC (Brazil, South Africa, India and 
China) on the other, as the latter are ‘decidedly not developed but

1463
International Cooperation Chapter 1414not wholly developing’ (Hochstetler and Milkoreit 2013). This fissure 
in part led to the High Ambition Coalition in Paris between vulnerable 
countries and the more progressive industrialised countries (Ciplet 
and Roberts 2017). A  division also emerged between the BASIC 
countries (Hurrell and Sengupta 2012), that each have distinctive 
identities and positions (Hochstetler and Milkoreit 2013). In the lead 
up to the Paris negotiations, China and India formed the Like-Minded 
Developing Countries with the Organization of the Petroleum 
Exporting Countries (OPEC) and the Bolivarian Alliance for the 
Peoples of our Americas (ALBA) countries, to resist the erosion of 
differentiation in the regime. Yet, the ‘complex and competing’ 
identities of India and China, with differing capacities, challenges 
and self-images, have also influenced the negotiations (Ciplet and 
Roberts 2017; Rajamani 2017). Other developing countries’ coalitions

also played an important role in striking the final deal in Paris. The 
Alliance of Small Island States, despite their lack of structural power, 
played a leading role, in particular in relation to the inclusion of the 
1.5°C long-term temperature goal in the UN climate regime (Agueda 
Corneloup and Mol 2014; Ourbak and Magnan 2018). The Association 
of the Latin American and Caribbean Countries (AILAC) that emerged 
in 2012 also played a decisive role in fostering ambition (Edwards 
et al. 2017; Watts and Depledge 2018).
Leadership is essential to reaching international agreements and 
overcoming collective action problems (Parker et al. 2015). The Paris 
negotiations were faced, as a reflection of the multipolarity that had 
emerged, with a ‘fragmented leadership landscape’ with the USA, EU, and China being perceived as leaders at different points in time 
and to varying degrees (Karlsson et al. 2012; Parker et al. 2014). Small

island states are also credited with demonstrating ‘moral leadership’ 
(Agueda Corneloup and Mol 2014), and non-state and sub-national 
actors are beginning to be recognised as pioneers and leaders (Wurzel 
et al. 2019). There is also a burgeoning literature on the emergence of 
diffused leadership and the salience of followers (Parker et al. 2014; 
Busby and Urpelainen 2020).
It is in the context of this complex, multipolar and highly differentiated 
world – with a heterogeneity of interests, constraints and capacities, 
increased contestations over shares of the carbon and development 
space, as well as diffused leadership – that the Paris Agreement was 
negotiated. This context fundamentally influenced the shape of the 
Paris Agreement, in particular on issues relating to its architecture, 
‘legalisation’ (Karlas 2017) and differentiation (Bodansky et al. 
2017b; Kinley et al. 2020), all of which are discussed below.
14.3.2 Elements of the Paris Agreement Relevant 
to Mitigation

The 2015 Paris Agreement to the UNFCCC, which entered into force 
on 4 November 2016, and has 193 Parties as of March 2022, is at 
the centre of international cooperative efforts for climate change 
mitigation and adaptation in the post-2020 period. Although its legal 
form was heavily disputed, especially in the initial part of its four-year 
Goals: Mitigation (well below 2°C, pursuing 1.5°C), adaptation, and ﬁnance implemented  to 
reﬂect equity and CBDRRC in light of differing national circumstances (Arts 2, 4.1, 7.1)
Loss &
Damage
(Art. 8)Paris Agreement Warsaw
Mechanism
Support Transparency
(Art. 4)Adaptation
(Art. 7)Finance
(Art. 9)Technology
(Art. 10)Capacity-building
(Art. 11)
Implementation and compliance 
(Art. 15)Sinks (Art. 5)
Cooperative mechanisms (Art. 6)NSA
measures
Domestic
measures Informs
review and
updating of
NDCsInforms
understanding
of support
needsUNFCCC
Global Stocktake every 5 years on basis of science and equity

to assess collective progress towards goals (Art. 14)NDCsAmbitious efforts by all parties towards purpose of Agreement, with progression over time 
and support for developing countries (Art. 3) 
5-yearly, reﬂecting highest possible 
ambition and progression, and long-term 
low-GHG emissions strategies
Figure 14.1 | Key features of the Paris Agreement.  Arrows illustrate the interrelationship between the different features of the Paris Agreement, in particular between 
the Agreement’s goals, required actions through NDCs, support (finance, technology and capacity building), transparency framework and global stocktake process. The figure 
also represents points of interconnection with domestic mitigation measures, whether taken by state Parties or by non-state actors (NSAs). This figure is illustrative rather than 
exhaustive of the features and interconnections.

1464
Chapter 14 International Cooperation14negotiating process (Rajamani 2015; Maljean-Dubois and Wemaëre 
2016; Bodansky et al. 2017b; Klein et al. 2017), the Paris Agreement 
is a treaty containing provisions of differing levels of ‘bindingness’ 
(Bodansky 2016; Oberthür and Bodle 2016; Rajamani 2016b). The 
legal character of provisions within a treaty, and the extent to which 
particular provisions lend themselves to assessments of compliance 
or non-compliance, depends on factors such as the normative content 
of the provision, the precision of its terms, the language used, and 
the oversight mechanisms in place (Werksman 2010; Bodansky 
2015; Oberthür and Bodle 2016; Rajamani 2016b). Assessed on these 
criteria, the Paris Agreement contains the full spectrum of provisions, 
from hard to soft law (Rajamani 2016b; Pickering et al. 2019) and even 
‘non-law’, provisions that do not have standard-setting or normative

content but which play a narrative-building and context-setting role 
(Rajamani 2016b). The Paris Agreement, along with the  UNFCCC 
and the Kyoto Protocol, can be interpreted in light of the customary 
international law principle of harm prevention according to which 
states must exercise due diligence in seeking to prevent activities 
within their jurisdiction from causing extraterritorial environmental 
harm (Mayer 2016a; Maljean-Dubois 2019). The key features of the 
Paris Agreement are set out in Box 14.1.
Figure  14.1 illustrates graphically the key features of the Paris 
Agreement. The Paris Agreement is based on a  set of binding 
procedural obligations requiring Parties to ‘prepare, communicate, 
and maintain’ ‘Nationally Determined Contributions’ (NDCs) 
(UNFCCC 2015a, Art. 4.2) every five years (UNFCCC 2015a, Art. 4.9). 
These obligations are complemented by: (1) an ‘ambition cycle’ that 
expects Parties, informed by five-yearly global stocktakes (Art. 14), to

submit successive NDCs representing a progression on their previous 
NDCs (UNFCCC 2015a; Bodansky et al. 2017b), and (2) an ‘enhanced 
transparency framework’ that places extensive informational 
demands on Parties, tailored to capacities, and establishes review 
processes to enable tracking of progress towards achievement of 
NDCs (Oberthür and Bodle 2016). In contrast to the Kyoto Protocol 
with its internationally inscribed targets and timetable for emissions 
reduction for developed countries, the Paris Agreement contains 
Nationally Determined Contributions embedded in an international 
system of transparency and accountability for all countries (Doelle 
2016; Maljean-Dubois and Wemaëre 2016) accompanied by a shared 
global goal, in particular in relation to a temperature limit. 
14.3.2.1  Context and Purpose
The preamble of the Paris Agreement lists several factors that provide 
the interpretative context for the Agreement (Bodansky et al. 2017b;

Carazo 2017), including a  reference to human rights. The human 
rights implications of climate impacts garnered particular attention 
in the lead up to Paris (Duyck 2015; Mayer 2016b). In particular, the 
Human Rights Council, its special procedures mechanisms, and the 
Office of the High Commissioner for Human Rights, through a series 
of resolutions, reports, and activities, advocated a  rights-based 
approach to climate impacts, and sought to integrate this approach in 
the climate change regime. The Paris Agreement’s preambular recital 
on human rights recommends that Parties, ‘when taking action to 
address human rights’, take into account ‘their respective obligations on human rights’ (UNFCCC 2015a, preambular recital 14), a first for 
an environmental treaty (Knox 2016). The ‘respective obligations’ 
referred to in the Paris Agreement could potentially include those 
relating to the right to life (UNGA 1948, Art. 3, 1966, Art. 6), right

to health (UNGA 1966b, Art. 12), right to development, right to 
an adequate standard of living, including the right to food (UNGA 
1966b, Art. 11), which has been read to include the right to water 
and sanitation (CESCR 2002, 2010), the right to housing (CESCR 
1991), and the right to self-determination, including as applied in the 
context of indigenous peoples (UNGA 1966a,b, Art. 1). In addition, 
climate impacts contribute to displacement and migration (Mayer 
and Crépeau 2016; McAdam 2016), and have disproportionate 
effects on women (Pearse 2017). There are differing views on the 
value and operational impact of the human rights recital in the Paris 
Agreement (Adelman 2018; Boyle 2018; Duyck et al. 2018; Rajamani 
2018; Savaresi 2018; Knox 2019). Notwithstanding proposals from 
some Parties and stakeholders to mainstream and operationalise 
human rights in the climate regime post-Paris (Duyck et al. 2018), and

references to human rights in COP decisions, the 2018 Paris Rulebook 
contains limited and guarded references to human rights (Duyck 
2019; Rajamani 2019) (Section 14.5.1.2). In addition to the reference 
to human rights, the preamble also notes the importance of ‘ensuring 
the integrity of all ecosystems, including oceans and the protection 
of biodiversity’ which provides opportunities for integrating and 
mainstreaming other environmental protections.
The overall purpose of international cooperation through the Paris 
Agreement is to enhance the implementation of the UNFCCC, 
including its objective of stabilising atmospheric GHG concentrations 
‘at a level that would prevent dangerous anthropogenic interference 
with the climate system’ (UNFCCC 1992, Art. 2). The Paris Agreement 
aims to strengthen the global response to the threat of climate 
change, in the context of sustainable development and efforts to 
eradicate poverty, by inter alia ‘[h]olding the increase in the global

average temperature to well below 2°C above pre-industrial levels 
and pursuing efforts to limit the temperature increase to 1.5°C 
above pre-industrial levels’ (UNFCCC 2015a, Art. 2(1)(a)). There is an 
ongoing structured expert dialogue under the UNFCCC in the context 
of the second periodic review of the long-term global goal (the first 
was held between 2013–2015) aimed at enhancing understanding 
of the long-term global goal, pathways to achieving it, and assessing 
the aggregate effect of steps taken by Parties to achieve the goal.
Some authors interpret the Paris Agreement’s temperature goal as 
a single goal with two inseparable elements, the well below 2°C goal 
pressing towards 1.5°C (Rajamani and Werksman 2018), but others 
interpret the goal as a unitary one of 1.5°C with minimal overshoot 
(Mace 2016). Yet others interpret 1.5°C as the limit within the long-
term temperature goal, and that it ‘signals an increase in both the

margin and likelihood by which warming is to be kept below 2°C’ 
(Schleussner et al. 2016). Although having a long-term goal has clear 
advantages, the literature highlights the issue of credibility, given 
the lengthy timeframe involved (Urpelainen 2011), and stresses 
that future regulators may have incentives to relax current climate 
plans, which could have a  significant effect on the achieved GHG 
stabilisation level (Gerlagh and Michielsen 2015).

1465
International Cooperation Chapter 1414As the risks of adverse climate impacts, even with a ‘well below’ 2°C 
increase, are substantial, the purpose of the Paris Agreement extends 
to increasing adaptive capacity and fostering climate resilience 
(UNFCCC 2015a, Art. 2(1)(b)), as well as redirecting investment and 
finance flows (UNFCCC 2015a, Art. (2)(1)(c); Thorgeirsson 2017). 
The finance and adaptation goals are not quantified in the Paris 
Agreement itself but the temperature goal and the pathways they 
generate may, some argue, enable a  quantitative assessment of 
the resources necessary to reach these goals, and the nature of the 
impacts requiring adaptation (Rajamani and Werksman 2018). The 
decision accompanying the Paris Agreement resolves to set a new 
collective quantified finance goal prior to 2025 (not explicitly limited 
to developed countries), with USD100 billion yr–1 as a floor (UNFCCC 
2016a, para. 53; Bodansky et al. 2017b). Article 2  also references

sustainable development and poverty eradication, and thus implicitly 
underscores the need to integrate the SDGs in the implementation of 
the Paris Agreement (Sindico 2016). 
The Paris Agreement’s purpose is accompanied by an expectation 
that the Agreement ‘will be’ implemented to ‘reflect equity and 
the principle of common but differentiated responsibilities and 
respective capabilities (CBDRRC), in the light of different national 
circumstances’ (UNFCCC 2015a, Art. 2.2). This provision generates 
an expectation that Parties will implement the agreement to 
reflect CBDRRC, and is not an obligation to do so (Rajamani 
2016a). Further, the inclusion of the term ‘in light of different 
national circumstances’ introduces a  dynamic element into the 
interpretation of the CBDRRC principle. As national circumstances 
evolve, the application of the principle will also evolve (Rajamani 
2016a). This change in the articulation of the CBDRRC principle is

reflected in the shifts in the nature and extent of differentiation 
in the climate change regime (Maljean-Dubois 2016; Rajamani 
2016a; Voigt and Ferreira 2016a), including through a shift towards 
‘procedurally-oriented differentiation’ for developing countries 
(Huggins and Karim 2016).
Although NDCs are developed by individual state Parties, the Paris 
Agreement requires that these are undertaken by Parties ‘with 
a  view’ to achieving the Agreement’s purpose and collectively 
‘represent a progression over time’ (UNFCCC 2015a, Art. 3). The Paris 
Agreement also encourages Parties to align the ambition of their 
NDCs with the temperature goal through the Agreement’s ‘ambition 
cycle’, thus imparting operational relevance to the temperature goal 
(Rajamani and Werksman 2018). 
Article 4.1 contains a  further non-binding requirement that Parties 
‘aim’ to reach global peaking of GHG ‘as soon as possible’ and 
to undertake rapid reductions thereafter to achieve net zero GHG

emissions ‘in the second half of the century’. Some argue this implies 
a need to reach net zero GHG emissions in the third quarter of the 
21st century (Rogelj et al. 2015; IPCC 2018b) (Chapter 2, Table 2.4 and 
Cross-Chapter Box 3 in Chapter 3). To reach net zero CO 2 around 2050, 
in the short-term global net human-caused CO 2 emissions would need 
to fall by about 45% to 60% from 2010 levels by 2030 (IPCC 2018b). 
Achieving the Paris Agreement’s Article 4.1 aim potentially implies that 
global warming will peak and then follow a gradually declining path, 
potentially to below 1.5°C warming (Rogelj et al. 2021). Albeit non-binding, Article 4.1 has acted as a  catalyst for several 
national net-zero GHG targets, as well as net-zero CO 2 and GHG 
targets across local governments, sectors, businesses, and other actors 
(Day et al. 2020). There is a wide variation in the targets that have 
been adopted – in terms of their legal character (policy statement,

executive order or national legislation), scope (GHGs or CO 2) and 
coverage (sectors or economy-wide). National net-zero targets could 
be reflected in the long-term strategies that states are urged to 
submit under Article 4.19, but only a few states have submitted such 
strategies thus far. The Paris Rulebook, agreed at the Agreement’s first 
meeting of the Parties in 2018, further strengthens the operational 
relevance of the temperature goal by requiring Parties to provide 
information when submitting their NDCs on how these contribute 
towards achieving the objective identified in UNFCCC Article 2, and 
Paris Agreement Articles 2.1 (a) and 4.1 (UNFCCC 2019b, Annex I, 
para. 7). Parties could in this context include information on how their 
short-term actions align with their long-term net zero GHG or CO 2 
targets, thereby enhancing the credibility of their long-term goals.
At last count 131 countries had adopted or had net zero targets

(whether of carbon or GHG) in the pipeline, covering 72% of global 
emissions. If these targets are fully implemented some estimate 
that this could bring temperature increase down to 2°C–2.4°C by 
2100 as compared to current policies which are estimated to lead 
to a  temperature increase of 2.9–3.2°C, and NDCs submitted to 
the Paris Agreement which are estimated to lead to a temperature 
increase of 2.4°C–2.9°C (Höhne et al. 2021). 
It is worth noting that Article 4.1 recognises that ‘peaking will take 
longer for developing countries’ and that the balance between 
emissions and removals needs to be on the ‘basis of equity, and 
in the context of sustainable development and efforts to eradicate 
poverty’. This suggests that not all countries are expected to reach 
net zero GHG emissions at the same time, or in the same manner. 
If global cost-effective 1.5°C and 2°C scenarios from integrated 
assessment models are taken, without applying an equity principle,

the results suggest that domestic net zero GHG and CO 2 emissions 
would be reached a decade earlier than the global average in Brazil 
and the USA and later in India and Indonesia (van Soest et al. 2021). 
By contrast, if equity principles are taken into account countries like 
Canada and the EU would be expected to phase out earlier than 
the cost-optimal scenarios indicate, and countries like China and 
Brazil could phase out emissions later, as well as other countries with 
lower per-capita emissions (van Soest et al. 2021). Some suggest that 
the application of such fairness considerations could bring forward 
the net zero GHG date for big emitting countries by up to 15 to 
35 years as compared to the global least-cost scenarios (Lee et al. 
2021b). In any case, reaching net zero GHG emissions requires to 
some extent the use of carbon dioxide removal (CDR) methods as 
there are important sources of non-CO 2 GHGs, such as methane and

nitrous oxide, that cannot be fully eliminated (IPCC 2018b). However, 
there are divergent views on different CDR methods, policy choices 
determine the degree to which and the type of CDR methods that 
are considered and there is a  patchwork of applicable regulatory 
instruments. There are also uncertainties and governance challenges 
associated with CDR methods which render tracking progress against 
net zero GHG emissions challenging (Mace et al. 2021). Researchers

1466
Chapter 14 International Cooperation14have noted that given the key role of CDR in net zero targets and 
1.5°C compatible pathways, and the fact that it presents ‘significant 
costs to current and future generations’, it is important to consider 
what an equitable distribution of CDR might look like (UNFCCC 
2019c; Day et al. 2020; Lee et al. 2021b).
14.3.2.2 NDCs, Progression and Ambition
Each Party to the Paris Agreement has a  procedural obligation to 
‘prepare, communicate and maintain’ successive NDCs ‘that it intends 
to achieve’. Parties have a further procedural obligation to ‘pursue 
domestic mitigation measures’ (UNFCCC 2015a, Art.  4.2). These 
procedural obligations are coupled with an obligation of conduct to 
make best efforts to achieve the objectives of NDCs (Rajamani 2016a; 
Mayer 2018b). Many states have adopted climate policies and laws, 
discussed in Chapter 13, and captured in databases (LSE 2020).

The framing and content of NDCs is thus largely left up to Parties, 
although certain normative expectations apply. These include 
developed country leadership through these Parties undertaking 
economy-wide absolute emissions reduction targets (UNFCCC 2015a, 
Art. 4.4), as well as ‘progression’ and ‘highest possible ambition’ 
reflecting ‘common but differentiated responsibilities and respective 
capabilities in light of different national circumstances’ (Art. 4.3). 
There is ‘a firm expectation’ that for every five-year cycle a  Party 
puts forward a new or updated NDC that is ‘more ambitious than 
their last’ (Rajamani 2016a). While what represents a Party’s highest 
possible ambition and progression is not prescribed by the Agreement 
or elaborated in the Paris Rulebook (Rajamani and Bodansky 2019), 
these obligations could be read to imply a due diligence standard 
(Voigt and Ferreira 2016b).
In communicating their NDCs every five years (UNFCCC 2015a,

Art. 4.9), all Parties have an obligation to ‘provide the information 
necessary for clarity, transparency and understanding’ (UNFCCC 
2015a, Art. 4.8). These requirements are further elaborated in 
the Paris Rulebook (Doelle 2019; UNFCCC 2019b). This includes 
requirements –  for Parties’ second and subsequent NDCs –  to 
provide quantifiable information on the reference point, for example 
base year, reference indicators and target relative to the reference 
indicator (UNFCCC 2019b, Annex I, para. 1). It also requires Parties to 
provide information on how they consider their contribution ‘fair and 
ambitious in light of different national circumstances’, and how they 
address the normative expectations of developed country leadership, 
progression and highest possible ambition (UNFCCC 2019b, Annex I, 
para. 6). However, Parties are required to provide the enumerated 
information only ‘as applicable’ to their NDC (UNFCCC 2019b,

Annex I, para. 7). This allows Parties to determine the informational 
requirements placed on them through their choice of NDC. In respect 
of Parties’ first NDCs or NDCs updated by 2020, such quantifiable 
information ‘may’ be included, ‘as appropriate’, signalling a  softer 
requirement, although Parties are ‘strongly encouraged’ to provide 
this information (UNFCCC 2019b, Annex I, para. 9). 
Parties’ first NDCs submitted to the provisional registry maintained by 
the UNFCCC Secretariat vary in terms of target type, reference year 
or points, timeframes, and scope and coverage of GHGs. A significant number of NDCs include adaptation, and several NDCs have 
conditional components, for instance, being conditional on the use of 
market mechanisms or on the availability of support (UNFCCC 2016b). 
There are wide variations across NDCs. Uncertainties are generated 
through interpretative ambiguities in the assumptions underlying

NDCs (Rogelj et al. 2017). According to the assessment in this report, 
current policies lead to median global GHG emissions of 63 gigatonnes 
of CO 2 equivalent  (GtCO 2-eq), with a full range of 57–70 by 2030 and 
unconditional and conditional NDCs to 59 (55–65) and 56 (52–61) 
GtCO 2-eq, respectively (Table  4.1). Many omit important mitigation 
sectors, provide little detail on financing implementation, and are not 
effective in meeting assessment and review needs (Pauw et al. 2018). 
Although, it is estimated that the land use sector could contribute as 
much as 20% of the full mitigation potential of all the intended NDC 
targets (Forsell et al. 2016), there are variations in how the land use 
component is included, and the related information provided, leading 
to large uncertainties on whether and how these will contribute to 
the achievement of the NDCs (Forsell et al. 2016; Grassi et al. 2017; 
Obergassel et al. 2017a; Benveniste et al. 2018; Fyson and Jeffery

2019). All these variations make it challenging to aggregate the efforts 
of countries and compare them to each other (Carraro 2016). Although 
Parties attempted to discipline the variation in NDCs, including whether 
they could be conditional, through elaborating the ‘features’ of NDCs 
in the Rulebook, no agreement was possible on this. Thus, Parties 
continue to enjoy considerable discretion in the formulation of NDCs 
(Rajamani and Bodansky 2019; Weikmans et al. 2020). 
There are several approaches to evaluating NDCs, incorporating 
indicators such as CO 2 emissions, GDP , energy intensity of GDP , 
CO2 per energy unit, CO 2 intensity of fossil fuels, and share of fossil 
fuels in total energy use (Peters et al. 2017). However, some favour 
approaches that use metrics beyond emissions such as infrastructure 
investment, energy demand, or installed power capacity (Iyer et al. 
2017; Jeffery et al. 2018). One approach is to combine the comparison

of aggregate NDC emissions using Integrated Assessment Model 
scenarios with modelling of NDC scenarios directly, and carbon 
budget analyses (Jeffery et al. 2018). Another approach is to engage 
in a  comprehensive assessment of multiple indicators that reflect 
the different viewpoints of the Parties to the UNFCCC (Aldy et al. 
2017; Höhne et al. 2018). These different approaches are described in 
greater depth in Section 4.2.2.
It is clear, however, that the NDCs communicated by Parties for the 
2020–2030 period are insufficient to achieve the temperature goal 
(den Elzen et al. 2016; Rogelj et al. 2016; Schleussner et al. 2016; 
Robiou du Pont and Meinshausen 2018; UNEP 2018a; Alcaraz et al. 
2019; UNEP 2019, 2020), and the emissions gap is larger than ever 
(Christensen and Olhoff 2019) (Chapter 4). The IPCC Special Report 
on Global Warming of 1.5°C (SR1.5) notes that pathways that limit 
global warming to 1.5°C with no or limited overshoot show up to

40–50% reduction of total GHG emissions from 2010 levels by 2030, 
and that current pathways reflected in the NDCs are consistent with 
cost-effective pathways that result in a  global warming of about 
3°C by 2100 (IPCC 2018b Summary for Policymakers D.1.1). Analysis 
by the UNFCCC Secretariat of the second round of those NDCs 
submitted by October 2021 suggests that ‘total global GHG emission 
level, taking into account full implementation of all the latest NDCs

1467
International Cooperation Chapter 1414(including their conditional elements), implies possibility of global 
emissions peaking before 2030’. However, such total global GHG 
emission level in 2030 is still expected to be 15.9% above the 2010 
level. This ‘implies an urgent need for either a significant increase in 
the level of ambition of NDCs between now and 2030 or a significant 
overachievement of the latest NDCs, or a  combination of both.’ 
(UNFCCC 2021a). 
Many NDCs with conditional elements may not be feasible as 
the conditions are not clearly defined and existing promises of 
support are insufficient (Pauw et al. 2020). Moreover, ‘leadership 
by conditional commitments’ (when some states promise to take 
stronger commitments if others do so as well), and the system of 
pledge-and-review, may lead to decreasing rather than deeper 
contributions over time (Helland et al. 2017). Some note, however, 
that many of the NDCs are conservative and may be overachieved,

that NDCs may be strengthened over time as expected under the 
Paris Agreement, and that there are significant non-state actions 
that have not been adequately captured in the NDCs (Höhne et al. 
2017). Further, if all NDCs with and without conditional elements are 
implemented, net land use, land use change and forestry emissions 
will decrease in 2030 compared to 2010 levels, but large uncertainties 
remain on how Parties estimate, project and account for emissions 
and removals from this sector (Forsell et al. 2016; Fyson and Jeffery 2019). According to the estimates in Table  4.3, communicated 
unconditional commitments imply about a  7% reduction of world 
emissions by 2030, in terms of Kyoto GHGs, compared to a scenario 
where only current policies are in place. If conditional commitments 
are also included, the reduction in world emissions by 2030 would 
be about 12%. 
In this context, it should be noted that many NDCs have been

formulated with conditional elements, and such NDCs require 
international cooperation on finance, technology and capacity 
building (Kissinger et al. 2019), potentially including through Article 
6  in the form of bilateral agreements and market mechanisms 
(UNFCCC 2016b). More broadly, some argue that there is a ‘policy 
inconsistency’ between the facilitative, ‘bottom up’ architecture 
of the Paris Agreement, and both the setting of the long-term 
temperature goal and expectations that it will be delivered (Geden 
2016b). As Figure  14.2 shows, there is a  large share of additional 
effort needed to reach a 1.5°C compatible path by 2030 (and even 
a 2°C compatible path). International coordination and cooperation 
are crucial in enhancing the ambition of current pledges, as countries 
will be more willing to increase their ambition if matched by other 
countries (coordination) and if cost-minimising agreements between

developed and developing countries, through Article 6  and other 
means, are fully developed (cooperation) (Sælen 2020).0102030
1.5°C from 2020 onwards <2°C from 2020 onwardsGap in emission reductions (domestic action, international coordination and cooperation) (iii)
NDCs with conditional elements (contingent on international cooperation) (ii)
NDCs without conditional elements, or ‘unconditional’ (mainly domestic action) (i)GtCO 2-eq yr–1 by 2030°°
Figure 14.2 | The role of international cooperation in the reductions in annual emissions by 2030 needed to follow a 1.5°C (respectively <2°C) cost-effective 
path from 2020 onwards.  The figure represents the additional contribution of pledges included in the NDCs over current policies at the global level, and the remaining gap 
in emissions reductions needed to move from current policies to pathways that limit warning to 1.5°C (>50%) with no or limited overshoot, and those to limit warming to

2°C (>67%). Median values are used, showing the confidence interval for the total effort. See Figure 1 in Cross-Chapter Box 4 in Chapter 4, and Tables 4.2 and 4.3 for details. 
(i) The grey share represents NDCs with abatement efforts pledged without any conditions (called ‘unconditional’ in the literature). They are based mainly on domestic abatement 
actions, although countries can use international cooperation to meet their targets. (ii) The blue share represents NDCs with conditional components. They require international 
cooperation, for example bilateral agreements under Article 6, financing or monetary and/or technological transfers. (iii) The remaining gap in emissions reductions – the yellow 
share – can potentially be achieved through national and international actions. International coordination of more ambitious efforts promotes global ambition and international 
cooperation provides the cost-saving basis for more ambitious NDCs.

1468
Chapter 14 International Cooperation1414.3.2.3 NDCs, Fairness and Equity
The Paris Agreement encourages Parties, while submitting their NDCs, 
to explain how these are ‘fair and ambitious’ (UNFCCC 2015a, Art. 4.8 
read with UNFCCC 2016a, para. 27). The Rulebook obliges Parties to 
provide information on ‘fairness considerations, including reflecting 
on equity’ as applicable to their NDC (Rajamani and Bodansky 2019; 
UNFCCC 2019b paras. 7a and 9, Annex, paras. 6(a) and (b)). Although 
equity within nations and between communities is also important, 
much of the literature on fairness and equity in the context of NDCs 
focuses on equity between nations.
In the first round of NDCs, most Parties declared their NDCs as fair 
(Robiou du Pont et al. 2017). Their claims, however, were largely 
unsubstantiated or drawn from analysis by in-country experts 
(Winkler et al. 2018). At least some of the indicators Parties 
have identified in their NDCs as justifying the ‘fairness’ of their

contributions, such as a  ‘small share of global emissions’, ‘cost-
effectiveness’ and assumptions that privilege current emissions 
levels (‘grandfathering’) are not, according to one group of scholars, 
in accordance with principles of international environmental law 
(Rajamani et al. 2021).  Moreover, the NDCs reveal long-standing 
institutional divisions and divergent climate priorities between 
Annex I and non-Annex I Parties, suggesting that equity and fairness 
concerns remain salient (Stephenson et al. 2019). Fairness concerns 
also affect the share of CDR responsibilities for major emitters if they 
delay near-term mitigation action (Fyson et al. 2020).
It is challenging, however, to determine ‘fair shares’, and address 
fairness and equity in a  world of voluntary climate contributions 
(Chan 2016a), in particular, since these contributions are insufficient 
(Section  14.3.2.2.). Self-differentiation in contributions has also

led to fairness and equity being discussed in terms of individual 
Nationally Determined Contributions rather than between categories 
of countries (Chan 2016a). In the climate change regime, one option 
is for Parties to provide more rigorous information under the Paris 
Agreement to assess fair shares (Winkler et al. 2018), and another 
is for Parties to articulate what equity principles they have adopted 
in determining their NDCs and how they have operationalised 
these principles, and to explain their mitigation targets in terms of 
the portion of the appropriated global carbon budget (Hales and 
Mackey 2018). 
Equity is critical to addressing climate change, including through the 
Paris Agreement (Klinsky et al. 2017), however, since the political 
feasibility of developing equity principles within the climate change 
regime is low, the onus is on mechanisms and actors outside the 
regime to develop these (Lawrence and Reder 2019). Equity and

fairness concerns are being raised in national and regional courts 
that are increasingly being asked to determine if the climate actions 
pledged by states are adequate in relation to their fair share 
(The Supreme Court of the Netherlands 2019; European Court of 
Human Rights 2020; German Constitutional Court 2021), as it is 
only in relation to such a ‘fair share’ that the adequacy of a state’s 
contribution can be assessed in the context of a  global collective 
action problem (Section 13.5.5). Some domestic courts have stressed 
that as climate change is a  global problem of cumulative impact, all emissions contribute to the problem regardless of their relative 
size and there is a  clear articulation under the UNFCCC and Paris 
Agreement for developed countries to ‘take the lead’ in addressing 
GHG emissions (Preston 2020). Given the limited avenues for 
multilateral determination of fairness, several researchers have

argued that the onus is on the scientific community to generate 
methods to assess fairness (Herrala and Goel 2016; Lawrence and 
Reder 2019). Peer-to-peer comparisons also potentially create 
pressure for ambitious NDCs (Aldy et al. 2017). 
There are a range of options to assess or introduce fairness. These 
include: adopting differentiation in financing rather than in mitigation 
(Gajevic Sayegh 2017); adopting a carbon budget approach (Hales 
and Mackey 2018; Alcaraz et al. 2019), which may occur through 
the transparency processes (Hales and Mackey 2018); quantifying 
national emissions allocations using different equity approaches, 
including those reconciling finance and emissions rights distributions 
(Robiou du Pont et al. 2017); combining equity concepts in a bottom-
up manner using different sovereign approaches (Robiou du Pont 
and Meinshausen 2018), using data on adopted emissions targets to 
find an ethical framework consistent with the observed distribution

(Sheriff 2019); adopting common metrics for policy assessment 
(Bretschger 2017); and developing a  template for organising 
metrics on mitigation effort – emissions reductions, implicit prices, 
and costs – for both ex-ante and ex-post review (Aldy et al. 2017). 
The burden of agricultural mitigation can also be distributed using 
different approaches from effort sharing (responsibility, capability, 
need, equal cumulative per-capita emissions) (Richards et al. 2018). 
Further, there are temporal (inter-generational) and spatial (inter-
regional) dimensions to the distribution of the mitigation burden, 
with additional emissions reductions in 2030 improving both inter-
generational and inter-regional equity (Liu et al. 2016). Some of the 
equity approaches rely on ‘grandfathering’ as an allocation principle, 
which some argue has led to ‘cascading biases’ against developing 
countries (Kartha et al. 2018), and is morally ‘perverse’ (Caney

2011). While no country’s NDC explicitly supports the grandfathering 
approach, many countries describe as ‘fair and ambitious’ NDCs 
that assume grandfathering as the starting point (Robiou du Pont 
et al. 2017). It is worth noting that the existence of multiple metrics 
associated with a range of equity approaches has implications for how 
the ambition and ‘fair’ share of each state is arrived at; some average 
out multiple approaches and indicators (Hof et al. 2012; Meinshausen 
et al. 2015; Robiou du Pont and Meinshausen 2018), others exclude 
indicators and approaches that do not, in their interpretation, accord 
with principles of international environmental law (Rajamani et al. 
2021). One group of scholars has suggested that utilitarianism offers 
an ‘ethically minimal and conceptually parsimonious’ benchmark that 
promotes equity, climate and development (Budolfson et al. 2021).
14.3.2.4  Transparency and Accountability

Although NDCs reflect a  ‘bottom-up’, self-differentiated approach 
to climate mitigation actions, the Paris Agreement couples this to 
an international transparency framework designed, among other 
things, to track progress in implementing and achieving mitigation 
contributions (UNFCCC 2015a, Art. 13). This transparency framework 
builds on the processes that already exist under the UNFCCC. The

1469
International Cooperation Chapter 1414transparency framework under the Paris Agreement is applicable 
to all Parties, although with flexibilities for developing country 
Parties that need it in light of their capacities (Mayer 2019). Each 
Party is required to submit a  national inventory report, as well as 
‘the information necessary to track progress in implementing and 
achieving’ its NDC (UNFCCC 2015a, Art. 13.7) biennially (UNFCCC 
2016a, para. 90). The Paris Rulebook requires all Parties to submit 
their national inventory reports using the 2006 IPCC Guidelines 
(UNFCCC 2019b, Annex, para. 20). 
In relation to the provision of information necessary to track progress 
towards implementation and achievement of NDCs, the Paris Rulebook 
allows each Party to choose its own qualitative or quantitative 
indicators (UNFCCC 2019k, Annex, para. 65), a significant concession 
to national sovereignty (Rajamani and Bodansky 2019). The Rulebook

phases in common reporting requirements for developed and 
developing countries (except LDCs and SIDS) at the latest by 2024 
(UNFCCC 2019k, para. 3), but offers flexibilities in ‘scope, frequency, 
and level of detail of reporting, and in the scope of the review’ for 
those developing countries that need it in light of their capacities 
(UNFCCC 2019k, Annex, para. 5). Some differentiation also remains 
for information on support provided to developing countries (Winkler 
et al. 2017), with developed country Parties required to report such 
information biennially, while others are only ‘encouraged’ to do so 
(UNFCCC 2015a, Art. 9.7). 
The information provided by Parties in biennial transparency 
reports and GHG inventories will undergo technical expert review, 
which must include assistance in identifying capacity-building 
needs for developing country Parties that need it in light of their 
capacities. Each Party is also required to participate in a ‘facilitative,

multilateral consideration of progress’ of implementation and 
achievement of its NDC. Although the aim of these processes is to 
expose each Party’s actions on mitigation to international review, 
thus establishing a  weak form of accountability for NDCs at the 
international level, the Rulebook circumscribes the reach of these 
processes (Rajamani and Bodansky 2019). The technical expert 
review teams are prohibited in mandatory terms from making 
‘political judgments’ or reviewing the ‘adequacy or appropriateness’ 
of a Party’s NDC, domestic actions, or support provided (UNFCCC 
2019k, Annex, para. 149). This, among other such provisions, has led 
some to argue that the scope and practice of existing transparency 
arrangements reflect rather than mediate ongoing disputes around 
responsibility, differentiation and burden sharing, and thus there 
is limited answerability through transparency (Gupta and van 
Asselt 2019). There are also limits to the extent that the enhanced

transparency framework will reduce ambiguities and associated 
uncertainties, for instance, in how land use, land-use change 
and forestry (LULUCF) are incorporated into the NDCs (Fyson 
and Jeffery 2019), and lead to increased ambition (Weikmans 
et al. 2020). More broadly, there has been ‘weak’ translation of 
transparency norms into accountability (Ciplet et al. 2018). Hence, 
the Paris Agreement’s effectiveness in ensuring NDCs are achieved 
will depend on additional accountability pathways at the domestic 
level involving political processes and civil society engagement 
(Jacquet and Jamieson 2016; van Asselt 2016; Campbell-Duruflé 
2018a; Karlsson-Vinkhuyzen et al. 2018).14.3.2.5  Global Stocktake
The Paris Agreement’s transparency framework is complemented by 
the global stocktake, which will take place every five years (starting 
in 2023) and assess the collective progress towards achieving the 
Agreement’s purpose and long-term goals (UNFCCC 2015a, Art. 14).

The scope of the global stocktake is comprehensive  – covering 
mitigation, adaptation and means of implementation and support – 
and the process is to be facilitative and consultative. The Paris 
Rulebook outlines the scope of the global stocktake to include social 
and economic consequences and impacts of response measures, 
and loss and damage associated with the adverse effects of climate 
change (UNFCCC 2019f, paras. 8–10).
The global stocktake is to occur ‘in the light of equity and the 
best available science’. While the focus of the global stocktake is 
on collective and not individual progress towards the goals of the 
Agreement, the inclusion of equity in the global stocktake enables 
a discussion on equitable burden sharing (Rajamani 2016a; Winkler 
2020), and for equity metrics to be factored in (Robiou du Pont and 
Meinshausen 2018). The Paris Rulebook includes consideration of the 
modalities and sources of inputs for the global stocktake (UNFCCC

2019f, paras. 1, 2, 13, 27, 31, 36h and 37g), which arguably will result 
in equity being factored into the outcome of the stocktake (Winkler 
2020). The Rulebook does not, however, some argue, resolve the 
tension between the collective nature of the assessment that is 
authorised by the stocktake and the individual assessments required 
to determine relative ‘fair share’ (Rajamani and Bodansky 2019; 
Zahar 2019). 
The global stocktake is seen as crucial to encouraging Parties to 
increase the ambition of their NDCs (Huang 2018; Hermwille et al. 
2019; Milkoreit and Haapala 2019) as its outcome ‘shall inform Parties 
in updating and enhancing, in a nationally determined manner, their 
actions and support’ (Art. 14.3) (Rajamani 2016a; Friedrich 2017; 
Zahar 2019). The Rulebook provides for the stocktake to draw on 
a wide variety of inputs sourced from a full range of actors, including 
‘non-Party stakeholders’ (UNFCCC 2019f, para. 37). However, the

Rulebook specifies that the global stocktake will be ‘a Party-driven 
process’ (UNFCCC 2019f, para. 10), will not have an ‘individual Party 
focus’, and will include only ‘non-policy prescriptive consideration of 
collective progress’ (UNFCCC 2019f, para. 14).
14.3.2.6 Conservation of Sinks and Reservoirs, 
Including Forests
Article 5  of the Paris Agreement calls for Parties to take action to 
conserve and enhance sinks and reservoirs of greenhouse gases, 
including biomass in terrestrial, coastal, and marine ecosystems, 
and encourages countries to take action to support the REDD+ 
framework under the Convention. The explicit inclusion of land use 
sector activities, including forest conservation, is potentially, while 
cautiously, a ‘game changer’ as it encourages countries to safeguard 
ecosystems for climate mitigation purposes (Grassi et al. 2017). 
Analyses of Parties’ NDCs shows pledged mitigation from land 
use, and forests in particular, provides a  quarter of the emissions

reductions planned by Parties and, if fully implemented, would result

1470
Chapter 14 International Cooperation14in forests becoming a net sink of carbon by 2030 (Forsell et al. 2016; 
Grassi et al. 2017). 
A key action endorsed by Article 5  is REDD+, which refers to 
initiatives established under the UNFCCC for reducing emissions from 
deforestation and forest degradation and the role of conservation, 
sustainable management of forests and enhancement of forest 
carbon stocks in developing countries. It remains an evolving concept 
and some identified weaknesses are being addressed, including 
the issues of scale (project-based vs sub-national jurisdictional 
approach), problems with leakage, reversal, and benefit sharing, as 
well as safeguards against potential impacts on local and indigenous 
communities. Nevertheless, REDD+ shows several innovations 
under the climate regime with regard to international cooperation. 
The legal system for REDD+ manages to reconcile flexibility 
(creating consensus) and legal security. It shows a high standard of

effectiveness (Dellaux 2017).
Article 5.2 encourages Parties to implement and support the existing 
framework for REDD+, including through ‘results-based payments’, 
that is provision of financial payments for verified avoided or reduced 
forest carbon emissions (Turnhout et al. 2017). The existing REDD+ 
framework set up under decisions of the UNFCCC COP includes 
the Warsaw Framework for REDD+, which specifies modalities for 
measuring, reporting and verifying greenhouse gas emissions and 
removals. This provides an essential tool for linking REDD+ activities 
to results-based finance (Voigt and Ferreira 2015). Appropriate 
finance support for REDD+ is also considered critical to move from 
its inclusion in many countries’ NDCs to implementation on the 
ground (Hein et al. 2018). Since public finance for REDD+ is limited, 
private sector participation is expected by some to leverage REDD+ 
(Streck and Parker 2012; Henderson et al. 2013; Pistorius and Kiff

2015; Seymour and Busch 2016; Ehara et al. 2019). Article 5.2 also 
encourages Parties’ support for ‘alternative policy approaches’ to 
forest conservation and sustainable management such as ‘joint 
mitigation and adaptation approaches’. It reaffirms the importance 
of incentivising, as appropriate, non-carbon benefits associated 
with such approaches (e.g.,  improvements in the livelihoods of 
forest-dependent communities, facilitating poverty reduction and 
sustainable development). This provision, along with the support for 
non-market mechanisms in Article 6 (discussed below), is seen as an 
avenue for cooperative joint mitigation–adaptation and non-market 
REDD+ activities with co-benefits for biodiversity conservation 
(Gupta and Dube 2018).
14.3.2.7  Cooperative Approaches
Article 6 of the Paris Agreement provides for voluntary cooperative 
approaches. Its potential importance in terms of project-based 
cooperation should be viewed against the background of key lessons

from the market-based mechanisms under the Kyoto Protocol, 
particularly the Clean Development Mechanism (CDM). The CDM has 
been used for implementing bilateral strategies and unilateral (non-
market) actions for instance in India (Phillips and Newell 2013), hence 
arguably covering all the mechanisms now included in Article 6 of the 
Paris Agreement. As we describe in Section 14.3.3.1, below, ex post 
evaluation of the Kyoto market mechanisms, in particular the CDM, have been at best mixed. However, Article 6 goes beyond the project-
based approach followed by the CDM, as hinted by the emerging 
landscape of activities based on Article 6 (Greiner et al. 2020), such 
as the bilateral treaty signed under the framework of Article 6  in 
October 2020 by Switzerland and Peru (Section 14.4.4).
This experience from the CDM is relevant to the implementation 
of Article 6  (4) of the Paris Agreement. It addresses a  number of

specific types of cooperative approaches, including those involving 
the use of internationally transferred mitigation outcomes (ITMOs) 
towards NDCs, a  ‘mechanism to contribute to mitigation and 
support sustainable development’, and a framework for non-market 
approaches such as many aspects of REDD+. 
Article 6.1 recognises the role that cooperative approaches can play, 
on a  voluntary basis, in implementing Parties’ NDCs ‘in order to 
allow for higher ambition’ in their mitigation actions and to promote 
sustainable development and environmental integrity. Article 6.2 
indicates that ITMOs can originate from a variety of sources, and that 
Parties using ITMOs to achieve their NDCs shall promote sustainable 
development, ensure environmental integrity, ensure transparency, 
including in governance, and apply ‘robust accounting’ in accordance 
with Conference of the Parties serving as the Meeting of the Parties 
to the Paris Agreement (CMA) guidance to prevent double counting.

While this provision, unlike Article 17 of the Kyoto Protocol, does not 
create an international carbon market, it enables Parties to pursue 
this option should they choose to do so, for example, through the 
linking of domestic or regional carbon markets (Marcu 2016; Müller 
and Michaelowa 2019). Article 6.2 could also be implemented in 
other ways, including direct transfers between governments, linkage 
of mitigation policies across two or more Parties, sectoral or activity 
crediting mechanisms, and other forms of cooperation involving 
public or private entities, or both (Howard 2017). 
Assessments of the potential of Article 6.2 generally find that 
ITMOs are likely to result in cost reductions in achieving mitigation 
outcomes, with the potential for such reductions to enhance 
ambition and accelerate Parties’ progression of mitigation pledges 
across NDC cycles (Fujimori et al. 2016; Gao et al. 2016; Mehling 
2019). However, studies applying insights from the CDM highlight

environmental integrity risks associated with using ITMOs under the 
Paris Agreement given the challenges that the diverse scope, metrics, 
types and timeframes of NDC targets pose for robust accounting 
(Schneider and La Hoz Theuer 2019) and the potential for transfers of 
‘hot air’, as occurred under the Kyoto Protocol (La Hoz Theuer et al. 
2019). These studies collectively affirm that robust governance on 
accounting for ITMOs, and for reporting and review, will be critical 
to ensuring the environmental integrity of NDCs making use of them 
(Mehling 2019; Müller and Michaelowa 2019). 
Article 6.4 concerns the mitigation mechanism, with some similarities 
to the Kyoto Protocol’s CDM. Unlike the CDM, there is no restriction 
on which Parties can host mitigation projects and which Parties can 
use the resulting emissions reductions towards their NDCs (Marcu 
2016). This central mechanism will operate under the authority and

guidance of the CMA, and is to be supervised by a body designated 
by the CMA (Marcu 2016).

1471
International Cooperation Chapter 1414The Article 6.4 central mechanism is intended to promote mitigation 
while fostering sustainable development. The decision adopting the 
Paris Agreement specifies experience with Kyoto market mechanisms 
as a  basis for the new mitigation mechanism (UNFCCC 2016a, 
para. 37(f)). Compared with the CDM under the Kyoto Protocol, the 
central mechanism has a more balanced focus on both climate and 
development objectives, and a stronger political mandate to measure 
sustainable development impact and to verify that the impacts are 
‘real, measurable, and long-term’ (Olsen et al. 2018). There are also 
opportunities to integrate human rights into the central mechanism 
(Obergassel et al. 2017b; Calzadilla 2018). It is further subject to 
the requirement that it must deliver ‘an overall mitigation in global 
emissions’, which is framed by the general objectives of Article 6 for 
cooperation to enhance ambition (Kreibich 2018).

Negotiations over rules to operationalise Article 6  have thus far 
proven intractable, failing to deliver both at COP24 in Katowice in 
2018, where the rest of the Paris Rulebook was agreed, and in COP25 
in Madrid in 2019. Ongoing points of negotiation have included: 
whether to permit the carryover and use of Kyoto CDM credits and 
assigned amount units into the Article 6.4 mechanism, whether to 
impose a  mandatory share of proceeds on Article 6.2 mechanism 
to fund adaptation, like for Article 6.4; and whether and how credits 
generated under Article 6.4 should be subject to accounting rules 
under Article 6.2 (Michaelowa et al. 2020a). 
14.3.2.8  Finance Flows 
Finance is the first of three means of support specified under the 
Paris Agreement to accomplish its objectives relating to mitigation 
(and adaptation) (UNFCCC 2015a, Art. 14.1). This sub-section 
discusses the provision made in the Paris Agreement for international

cooperation on finance. Section  14.4.1 below considers broader 
cooperative efforts on public and private finance flows for climate 
mitigation, including by multilateral development banks and through 
instruments such as green bonds.
As highlighted above, the objective of the Paris Agreement includes 
the goal of ‘[m]aking finance flows consistent with a pathway towards 
low greenhouse gas emissions and climate-resilient development’ 
(UNFCCC 2015a, Art. 2.1(c)). Alignment of financial flows, and in 
some cases provision of finance, will be critical to the achievement of 
many Parties’ NDCs, particularly those that are framed in conditional 
terms (Zhang and Pan 2016; Kissinger et al. 2019) (Chapter 15). 
International cooperation on climate finance represents ‘a complex 
and fragmented landscape’ with a range of different mechanisms 
and forums involved (Pickering et al. 2017; Roberts and Weikmans 
2017). These include entities set up under the international climate

change regime, such as the UNFCCC financial mechanism, with the 
Global Environment Facility (GEF) and Green Climate Fund (GCF) 
as operating entities; special funds, such as the Special Climate 
Change Fund, the Least Developed Countries Fund (both managed 
by the GEF), and the Adaptation Fund established under the Kyoto 
Protocol; the Standing Committee on Finance, a constituted body 
which assists the COP in exercising its functions with respect 
to the UNFCCC financial mechanism; and other bodies outside of the international climate change regime, such as the Climate 
Investment Funds administered through multilateral development 
banks (the role of these banks in climate finance is discussed 
further in Section 14.4.1 below). 
Pursuant to decisions adopted at the Paris and Katowice conferences, 
Parties agreed that the operating entities of the financial 
mechanism – GEF and GCF – as well as the Special Climate Change

Fund, the Least Developed Countries Fund, the Adaptation Fund and the 
Standing Committee on Finance, all serve the Paris Agreement (UNFCCC 
2016a, paras. 58 and 63, 2019e,g). The GCF , which became operational 
in 2015, is the largest dedicated international climate change fund 
and plays a key role in channelling financial resources to developing 
countries (Antimiani et al. 2017; Brechin and Espinoza 2017). 
Much of the current literature on climate finance and the Paris 
Agreement focuses on the obligations of developed countries to 
provide climate finance to assist the implementation of mitigation and 
adaptation actions by developing countries. The principal provision on 
finance in the Paris Agreement is the binding obligation on developed 
country Parties to provide financial resources to assist developing 
country Parties (UNFCCC 2015a, Art. 9.1). This provision applies to 
both mitigation and adaptation and is in continuation of existing

developed country Parties’ obligations under the UNFCCC.  This signals 
that the Paris Agreement finance requirements must be interpreted 
in light of the UNFCCC (Yamineva 2016). The novelty introduced by 
the Paris Agreement is a further expansion in the potential pool of 
donor countries as Article 9.2 encourages ‘other Parties’ to provide 
or continue to provide such support on a voluntary basis. However, 
‘as part of the global effort, developed countries should continue 
to take the lead in mobilising climate finance’, with a ‘significant 
role’ for public funds, and an expectation that such mobilisation of 
finance ‘should represent a  progression beyond previous efforts’. 
Beyond this, there are no new recognised promises (Ciplet et al. 
2018). In the Paris Agreement, Parties formalised the continuation of 
the existing collective mobilisation goal to raise USD100 billion yr–1 
through to 2025 in the context of meaningful mitigation actions and

transparency on implementation. The Paris Agreement decision also 
provided for the CMA by 2025 to set a new collective quantified goal 
from a floor of USD100 billion yr–1, taking into account the needs 
and priorities of developing countries (UNFCCC 2016a, para. 53). This 
new collective goal on finance is not explicitly limited to developed 
countries and could therefore encompass finance flows from 
developing countries’ donors (Bodansky et al. 2017b). Deliberations 
on setting a new collective quantified goal on finance is expected to 
be initiated at COP26 in 2021 (UNFCCC 2019g,e; Zhang 2019).
It is widely recognised that the USD100 billion yr–1 figure is a fraction 
of the broader finance and investment needs of mitigation and 
adaptation embodied in the Paris Agreement (Peake and Ekins 
2017). One estimate, based on a review of 160 Intended Nationally 
Determined Contributions ((I)NDCs), suggests the financial demand

for both mitigation and adaptation needs of developing countries 
could reach USD474 billion yr–1 by 2030 (Zhang and Pan 2016). The 
Organisation for Economic Co-operation and Development (OECD) 
reports that climate finance provided and mobilised by developed 
countries was USD79.6 billion in 2019. This finance included

1472
Chapter 14 International Cooperation14four components: bilateral public, multilateral public (attributed 
to developed countries), officially supported export credits and 
mobilised private finance (OECD 2021) (Section 15.3.2 and Box 15.4). 
More broadly, there is recognition of the need for better accounting, 
transparency and reporting rules to allow evaluation of the fulfilment 
of finance pledges and the effectiveness of how funding is used (Xu 
et al. 2016; Roberts et al. 2017; Jachnik et al. 2019; Gupta and van 
Asselt 2019; Roberts et al. 2021). There is also a concern about climate 
finance being new and additional though the Paris Agreement does not 
make an explicit reference to it, nor is there a clear understanding of 
what constitutes new and additional (UNFCCC 2018; Carty et al. 2020; 
Mitchell et al. 2021). Some authors see the ‘enhanced transparency 
framework’ of the Paris Agreement (Section 14.3.2.4), and the specific

requirements for developed countries to provide, biennially, indicative 
quantitative and qualitative information as well as report on financial 
support and mobilisation efforts (Articles 9.5 and 9.7), as promising 
marked improvements (Weikmans and Roberts 2019), including for 
the fairness of effort-sharing on climate finance provision (Pickering 
et al. 2015). Others offer a more circumspect view of the transformative 
capability of these transparency systems (Ciplet et al. 2018). 
The more limited literature focusing on the specific finance needs of 
developing countries, particularly those expressed in NDCs conditional 
on international climate finance, suggests that once all countries have 
fully costed their NDCs, the demand for (public and private) finance 
to support NDC implementation is likely to be orders of magnitude 
larger than funds available from bilateral and multilateral sources. 
For some sectors, such as forestry and land use, this could leave ‘NDC

ambitions... in a precarious position, unless more diversified options 
are pursued to reach climate goals’ (Kissinger et al. 2019). In addition, 
there is a  need for fiscal policy reform in developing countries to 
ensure international climate finance flows are not undercut by public 
and private finance supporting unsustainable activities (Kissinger 
et al. 2019). During the 2018 Katowice conference, UNFCCC Parties 
requested the Standing Committee on Finance to prepare, every 
four years, a report on the determination of the needs of developing 
country Parties related to implementing the Convention and the Paris 
Agreement, for consideration by Parties at COP26 (UNFCCC 2019c).
14.3.2.9  Technology Development and Transfer
Technology development and transfer is the second of three ‘means 
of implementation and support’ specified under the Paris Agreement 
to accomplish its objectives relating to mitigation (and adaptation)

(UNFCCC 2015a, Art. 14.1). This sub-section discusses the provision 
made in the Paris Agreement for international cooperation on 
technology development and transfer. Section 14.4.2 below considers 
broader cooperative efforts on technology development and transfer 
under the UNFCCC.  Both sections complement the discussion in 
Section  16.6 on the role of international cooperation in fostering 
The importance of technology as a  means of implementation for 
climate mitigation obligations under the Paris Agreement is evident 
from Parties’ NDCs. Of the 168 NDCs submitted as of June 2019, 
109 were expressed as conditional upon support for technology development and transfer, with 70 Parties requesting technological 
support for both mitigation and adaptation, and 37 Parties for 
mitigation only (Pauw et al. 2020). Thirty-eight LDCs (79%) and 
29 SIDS made their NDCs conditional on technology transfer, as did 
50 middle-income countries (Pauw et al. 2020).

While technology is seen as a  key means of implementation and 
support for Paris Agreement commitments, the issue of technology 
development and the transfer of environmentally sound technologies 
for climate mitigation was heavily contested between developed and 
developing countries in the Paris negotiations, and these differences 
are likely to persist as the Paris Agreement is implemented (Oh 2019). 
Contestations continued in negotiations for the Paris Rulebook, 
particularly regarding the meaning of technological innovation, 
which actors should be supported, and how support should be 
provided by the UNFCCC (Oh 2020a). 
Article 10 of the Paris Agreement articulates a shared ‘long-term 
vision on the importance of fully realising technology development 
and transfer in order to improve resilience to climate change 
and to reduce greenhouse gas emissions’ (UNFCCC 2015, Art. 
10.1). All Parties are required ‘to strengthen cooperative action

on technology development and transfer’ (UNFCCC 2015, Art. 
10.2). In addition, support, including financial support, ‘shall be 
provided’ to developing country Parties for the implementation 
of Article 10, ‘including for strengthening cooperative action on 
technology development and transfer at different stages of the 
technology cycle, with a  view to achieving a  balance between 
support for mitigation and adaptation’ (UNFCCC 2015, Art. 10.6). 
Available information on efforts related to support on technology 
development and transfer for developing country Parties is also one 
of the matters to be taken into account in the global stocktake 
(UNFCCC 2015, Art. 10.6) (Section 14.3.2.5).
The Paris Agreement emphasises that efforts to accelerate, encourage 
and enable innovation are ‘critical for an effective long-term global 
response to climate change and promoting economic growth and 
sustainable development’ and urges that they be supported, as

appropriate, by the Technology Mechanism and Financial Mechanism 
of the UNFCCC (UNFCCC 2015, Art. 10.5). This support should be 
directed to developing country Parties ‘for collaborative approaches 
to research and development, and facilitating access to technology, 
in particular for early stages of the technology cycle’ (UNFCCC 
2015, Art. 10.5). Inadequate support for research and development, 
particularly in developing countries, has been identified in previous 
studies of technology interventions by international institutions as 
a  key technology innovation gap that might be addressed by the 
Technology Mechanism (de Coninck and Puig 2015).
To support Parties’ cooperative action, the Technology Mechanism, 
established in 2010 under the UNFCCC (Section 14.4.2), will serve the 
Paris Agreement, subject to guidance of a new ‘technology framework’ 
(UNFCCC 2015, Art. 10.4). The latter was strongly advocated by 
the African group in the negotiations for the Paris Agreement (Oh

2020a), and was adopted in 2018 as part of the Paris Rulebook, with 
implementation entrusted to the component bodies of the Technology 
Mechanism. The guiding principles of the framework are coherence,

1473
International Cooperation Chapter 1414inclusiveness, a  results-oriented approach, a  transformational 
approach and transparency. Its ‘key themes’ include innovation, 
implementation, enabling environment and capacity building, 
collaboration and stakeholder engagement, and support (UNFCCC 
2019e, Annex). A  number of ‘actions and activities’ are elaborated 
for each thematic area. These include: enhancing engagement and 
collaboration with relevant stakeholders, including local communities 
and authorities, national planners, the private sector and civil society 
organisations, in the planning and implementation of Technology 
Mechanism activities; facilitating Parties undertaking, updating and 
implementing technology needs assessments (TNAs) and aligning 
these with NDCs; and enhancing the collaboration of the Technology 
Mechanism with the Financial Mechanism for enhanced support for 
technology development and transfer. As regards TNAs, while some

developing countries have already used the results of their TNA process 
in NDC development, other countries might benefit from following the 
TNA process, including its stakeholder involvement and multi-criteria 
decision analysis methodology, to strengthen their NDCs (Hofman and 
van der Gaast 2019).
14.3.2.10 Capacity Building
Together with finance, and technology development and transfer, 
capacity building is the third of ‘the means of implementation and 
support’ specified under the Paris Agreement (UNFCCC 2015a, 
Art. 14.1). Capacity building has primarily been implemented through 
partnerships, collaboration and different cooperative activities, inside 
and outside the UNFCCC. This sub-section discusses the provision 
made in the Paris Agreement for international cooperation on capacity 
building. Section 14.4.3 below considers broader cooperative efforts 
on capacity building within the UNFCCC. 
In its annual synthesis report for 2018, the UNFCCC secretariat

stressed the importance of capacity building for the implementation 
of the Paris Agreement and NDCs, with a focus on measures already 
in place, regional and cooperative activities, and capacity-building 
needs for strengthening NDCs (UNFCCC 2019h). Of the 168 NDCs 
submitted as of June 2019, capacity building was the most frequently 
requested type of support (113 of 136 conditional NDCs) (Pauw 
et al. 2020). The focus of capacity-building activities is on enabling 
developing countries to take effective climate change action, 
given that many developing countries continue to face significant 
capacity challenges, undermining their ability to effectively or fully 
carry out the climate actions they intend to pursue (Dagnet et al. 
2016). Content analysis of NDCs shows that capacity building for 
adaptation is prioritised over mitigation for developing countries, 
with the element of capacity building most indicated in NDCs being

research and technology (Khan et al. 2020). In addition, developing 
countries’ needs for education, training and awareness raising for 
climate change mitigation and adaptation feature prominently in 
NDCs, particularly those of LDCs (Khan et al. 2020). Differences are 
evident though between capacity-building needs expressed in the 
NDCs of LDCs (noting that Khan et al.s review was limited to NDCs 
in English) compared with those of upper-middle-income developing 
countries as categorised by the World Bank (World Bank 2021); the 
latter have more focus on mitigation with an emphasis on technology 
development and transfer (Khan et al. 2020).The Paris Agreement urges all Parties to cooperate to enhance 
the capacity of developing countries to implement the Agreement 
(UNFCCC 2015a, Art. 11.3), with a particular focus on LDCs and SIDS 
(UNFCCC 2015a, Art. 11.1). Developed country Parties are specifically 
urged to enhance support for capacity-building actions in developing

country Parties (UNFCCC 2015a, Art. 11.3). Article 12 of the Paris 
Agreement addresses cooperative measures to enhance climate 
change education, training, public awareness, public participation 
and public access to information, which can also be seen as elements 
of capacity building (Khan et al. 2020). Under the Paris Rulebook, 
efforts related to the implementation of Article 12 are referred to as 
‘Action for Climate Empowerment’ and Parties are invited to develop 
and implement national strategies on this topic, taking into account 
their national circumstances (UNFCCC 2019i, para. 6). Actions to 
enhance climate change education, training, public awareness, 
public participation, public access to information, and regional and 
international cooperation may also be taken into account by Parties in 
the global stocktake process under Article 14 of the Paris Agreement 
(UNFCCC 2019i, para. 9).
Under the Paris Agreement, capacity-building can take a  range of

forms, including: facilitating technology development, dissemination 
and deployment; access to climate finance; education, training 
and public awareness; and the transparent, timely and accurate 
communication of information (UNFCCC 2015a, Art. 11.1) 
(Section  14.3.2.4). Principles guiding capacity-building support are 
that it should be: country-driven; based on and responsive to national 
needs; fostering country ownership of Parties at multiple levels; 
guided by lessons learned; and an effective, iterative process that is 
participatory, cross-cutting and gender-responsive (UNFCCC 2015a, 
Art. 11.2). Parties undertaking capacity building for developing 
country Parties must ‘regularly communicate on these actions or 
measures’. Developing country Parties have a  soft requirement 
(‘should’) to communicate progress made on implementing capacity-
building plans, policies, actions or measures to implement the Paris 
Agreement (UNFCCC 2015a, Art. 11.4).

Article 11.5 provides that capacity-building activities ‘shall be 
enhanced through appropriate institutional arrangements to support 
the implementation of this Agreement, including the appropriate 
institutional arrangements established under the Convention that 
serve this Agreement’. The COP decision accompanying the Paris 
Agreement established the Paris Committee on Capacity-building, 
with the aim to ‘address gaps and needs, both current and emerging, 
in implementing capacity-building in developing country Parties and 
further enhancing capacity-building efforts, including with regard 
to coherence and coordination in capacity-building activities under 
the Convention’ (UNFCCC 2016a, para. 71). The activities of the 
Committee are discussed further in Section 14.4.3 below. The relevant 
COP decision also established the Capacity-building Initiative for 
Transparency (UNFCCC 2016a, para. 84), which is managed by the

GEF and designed to support developing country Parties in meeting 
the reporting and transparency requirements under Article 13 of the 
Paris Agreement (Robinson 2018). 
Studies on past capacity-building support for climate mitigation 
offer some lessons for ensuring effectiveness of arrangements

1474
Chapter 14 International Cooperation14under the Paris Agreement. For example, Umemiya et al. (2020) 
suggest the need for a  common monitoring system at the global 
level, and evaluation research at the project level, to achieve more 
effective capacity-building support. Khan et al. (2020) articulate 
‘four key pillars’ of a  sustainable capacity-building system for 
implementation of NDCs in developing countries: universities 
in developing countries as institutional hubs; strengthened civil 
society networks and partnerships; long-term programmatic finance 
support; and consideration of a capacity-building mechanism under 
the UNFCCC – paralleling the Technology Mechanism – to marshal, 
coordinate and monitor capacity-building activities and resources.
14.3.2.11  Implementation and Compliance
The Paris Agreement establishes a  mechanism to facilitate 
implementation and promote compliance under Article 15. This mechanism is to operate in a transparent, non-adversarial and

non-punitive manner (Voigt 2016; Campbell-Duruflé 2018b; Oberthür 
and Northrop 2018) that distinguishes it from the more stringent 
compliance procedures of the Kyoto Protocol’s Enforcement branch. 
The Paris Rulebook elaborated the modalities and procedures for 
the implementation and compliance mechanism, specifying the 
nature and composition of the compliance committee, the situations 
triggering its procedures, and the facilitative measures it can apply, 
which include a  ‘finding of fact’ in limited situations, dialogue, 
assistance and recommendations (UNFCCC 2019e). The compliance 
committee is focused on ensuring compliance with a  core set of 
binding procedural obligations (UNFCCC 2019j, Annex, Para. 22). This 
compliance committee, characterised as ‘one of its kind’ and an ‘an 
important cornerstone’ of the Agreement’s legitimacy, effectiveness 
and longevity (Zihua et al. 2019), is designed to facilitate compliance 
rather than penalise non-compliance.

Box 14.1 | Key Features of the Paris Agreement Relevant to Mitigation
The Paris Agreement’s overall aim is to strengthen the global response to the threat of climate change, in the context of sustainable 
development and efforts to eradicate poverty. This aim is explicitly linked to enhancing implementation of the UNFCCC, including its 
objective in Article 2 of stabilising greenhouse gas concentrations at a level that would ‘prevent dangerous anthropogenic interference 
with the climate system’. The Agreement sets three goals:
i. Temperature: holding the global average temperature increase to well below 2°C above pre-industrial levels and pursuing efforts 
to limit the temperature increase to 1.5°C above pre-industrial levels.
ii. Adaptation and climate resilience: increasing the ability to adapt to the adverse impacts of climate change and foster climate 
resilience and low greenhouse gas emissions development, in a manner that does not threaten food production.

iii. Finance: making finance flows consistent with a  pathway towards low greenhouse gas emissions and climate-resilient 
In order to achieve the long-term temperature goal, Parties aim to reach global peaking of emissions as soon as possible, recognising 
that peaking will take longer for developing countries, and then to undertake rapid reductions in accordance with the best available 
science. This is designed to reach global net zero GHG emissions in the second half of the century, with the emissions reductions effort 
to be determined on the basis of equity and in the context of sustainable development and efforts to eradicate poverty. In addition, 
implementation of the Agreement as a whole is expected to reflect equity and Parties’ ‘common but differentiated responsibilities and 
respective capabilities’, in light of different national circumstances.

The core mitigation commitments of Parties under the Paris Agreement centre on preparing, communicating and maintaining 
successive ‘Nationally Determined Contributions’ (NDCs), the contents of which countries determine for themselves. All Parties must 
have NDCs and pursue domestic mitigation measures with the aim of achieving the objectives of their NDCs, but Parties’ NDCs are 
neither subject to a review of adequacy (at an individual level) nor to legally binding obligations of result. The compliance mechanism 
is correspondingly facilitative.
The Paris Agreement establishes a global goal on adaptation, and recognises the importance of averting, minimising and addressing 
loss and damage associated with the adverse effects of climate change. 
The efficacy of the Paris Agreement in achieving its goals is therefore dependent upon at least three additional elements:

i. Ratcheting of NDCs: Parties must submit a new or updated NDC every five years that is in line with the Paris Agreement’s 
expectations of progression over time and the Party’s highest possible ambition, reflecting common but differentiated 
responsibilities and respective capabilities in light of different national circumstances.

1475
International Cooperation Chapter 1414Box 14.1 (continued)
ii. Enhanced transparency framework: Parties’ actions to implement their NDCs are subject to international transparency and 
review requirements, which will generate information that may also be used by domestic constituencies and peers to pressure 
governments to increase the ambition of their NDCs. 
iii. Collective global stocktake: The global stocktake undertaken every five years, starting in 2023, will review the collective 
progress of countries in achieving the Paris Agreement’s goals, in light of equity and best available science. The outcome of the 
global stocktake informs Parties in updating and enhancing their subsequent NDCs. 
These international processes establish an iterative ambition cycle for the preparation, communication, implementation and review 
of NDCs.
For developing countries, the Paris Agreement recognises that increasing mitigation ambition and realising long-term low-emissions

development pathways can be bolstered by the provision of financial resources, capacity building, and technology development and 
transfer. In continuation of existing obligations under the Convention, developed countries are obliged to provide financial assistance 
to developing countries with respect to mitigation and adaptation. The Paris Agreement also recognises that Parties may choose to 
voluntarily cooperate in the implementation of their NDCs to allow for higher ambition in their mitigation and adaptation actions and 
to promote sustainable development and environmental integrity.
14.3.3 Effectiveness of the Kyoto Protocol  
and the Paris Agreement
14.3.3.1 Ex-post Assessment of the Kyoto Protocol’s Effects
Previous assessment reports have assessed the Kyoto Protocol with 
respect to each of the criteria identified in this chapter. However, at 
the time of AR5, it was premature to assess the impact of Kyoto on

emissions, as these data had not been entirely compiled yet. Since 
AR5, a  number of studies have done so. Chapter 2 of this report 
lists at least 18 countries that have sustained absolute emissions 
reductions for at least a decade, nearly all of which are countries 
that had Kyoto targets for the first commitment period. Most studies 
have concluded that Kyoto did cause emissions reductions. Such 
studies find a positive, statistically significant impact on emissions 
reductions in Annex I countries (Kim et al. 2020), Annex B countries 
(Grunewald and Martínez-Zarzoso 2012; Kumazawa and Callaghan 
2012; Grunewald and Martínez-Zarzoso 2016; Maamoun 2019), or all 
countries respectively (Aichele and Felbermayr 2013; Iwata and Okada 
2014). Overall, countries with emissions reduction obligations emit on 
average less CO 2 than similar countries without emissions reduction 
obligations – with estimates ranging from 3–50% (Grunewald and

Martínez-Zarzoso 2012, 2016). Maamoun (2019) estimates that the 
Kyoto Protocol reduced GHG emissions of Annex B countries by 7% 
on average below a  no-Kyoto scenario between 2005 and 2012. 
Aichele and Felbermayr (2013) conclude that Kyoto reduced CO 2 and 
GHG emissions by 10% compared to the counterfactual. By contrast, 
Almer and Winkler (2017) find no evidence for binding emission 
targets under Kyoto inducing significant and lasting emissions 
reductions for any of the Annex B  or non-Annex B  countries. The 
authors identify both negative and positive associations between 
Kyoto and emissions for several countries in several years, but no 
coherent picture emerges. Hartl (2019) calculates a  Kyoto leakage 
share in global CO 2 trade of 4.3% for 2002–2009.In terms of transformative potential, the Kyoto Protocol has been found 
to increase international patent applications for renewable energy 
technologies, especially in the case of solar energy technologies

and especially in countries with more stringent emissions reduction 
targets, and has even led to an increase in patent applications in 
developing countries not obliged to reduce emissions under Kyoto 
(Miyamoto and Takeuchi 2019). Kyoto also had a  positive and 
statistically significant impact on the cost-effectiveness of renewable 
energy projects, as well as renewable energy capacity development, 
as it stimulated the introduction of domestic renewable energy 
policies (Liu et al. 2019).
The issue of institutional strength of Kyoto has been analysed by many 
authors, and much of this has been assessed in previous assessment 
reports. Since AR5, several papers question the environmental efficacy 
of the Kyoto Protocol based on its institutional design (Rosen 2015; 
Kuriyama and Abe 2018). Particular attention has focused on Kyoto’s 
market mechanisms (Erickson et al. 2014; Kollmuss et al. 2015).
As described in previous IPCC reports and above, the 1997 Kyoto

Protocol included three international market-based mechanisms. 
These operated among Annex I Parties (i.e., International Emissions 
Trading and Joint Implementation) and between Annex I Parties and 
non-Annex I countries (i.e., the CDM) (Grubb et al. 2014; World Bank 
2018). Joint Implementation led to limited volumes of emissions 
credit transactions, mostly from economies in transition but also 
some Western European countries; International Emissions Trading 
also led only to limited transaction volumes (Shishlov et al. 2016).
Of the Kyoto Protocol’s mechanisms, the CDM market has led to 
a greater amount of activity, with a ‘gold rush’ period between 2005 
and 2012. The main buyers of CDM credits were private companies 
surrendering them within the European Union (EU) Emissions Trading 
System (ETS). Once the EU tightened its rules and restricted the use

1476
Chapter 14 International Cooperation14of CDM credits in 2011, there was a sharp drop in the price of CDM 
credits in 2012. This price never recovered, as the demand for 
CDM was very weak after 2012, in part because of the difficulties 
encountered in securing the entry into force of the Doha Amendment 
(Michaelowa et al. 2019b). 
Assessing the effectiveness of Kyoto’s market mechanisms is 
challenging, and the results have been mixed (Aichele and Felbermayr 
2013; Iwata and Okada 2014; Kuriyama and Abe 2018). Kuriyama 
and Abe (2018) assessed emissions reduction quantities taking into 
account heightened criteria for additionality. They identified annual 
energy-related emissions reductions of 49 MtCO 2-eq yr–1 flowing 
from the CDM, and non-energy related emissions reductions of 
177 MtCO 2-eq yr–1. Others have pointed to issues associated with 
non-energy related emissions reductions that suggest the latter 
estimate may be of questionable reliability, while also noting that

regulatory tightening led later CDM projects to perform better with 
respect to the additionality criterion (Michaelowa et al. 2019b). 
The  CDM’s contribution to capacity building in some developing 
countries has been identified as possibly its most important 
achievement (Spalding-Fecher et al. 2012; Gandenberger et al. 2015; 
Murata et al. 2016; Xu et al. 2016; Dong and Holm Olsen 2017; 
Lindberg et al. 2018). There is evidence that the CDM lowered 
compliance costs for Annex 1  countries by at least USD3.6 billion 
(Spalding-Fecher et al. 2012). In host countries, the CDM led to the 
establishment of national approval bodies and the development of 
an ecosystem of consultants and auditors (Michaelowa et al. 2019b). 
On the negative side, there are numerous findings that the CDM, 
especially at first, failed to lead to additional emissions cuts in host 
countries, meaning that the overall effect of CDM projects was to

raise global emissions. Cames et al. (2016) concluded that over 70% 
of CDM projects led to emissions reductions that were likely less than 
projected, including the absence of additional reductions, while only 
7% of projects led to actual additional emissions reductions that 
had a high likelihood of meeting or exceeding the ex-ante estimates. 
The primary reason the authors gave was associated with the low 
price for CDM credits; this meant that the contribution of the CDM 
to project finance was negligible, suggesting that most CDM projects 
would have been built anyway. A meta-analysis of ex-post studies of 
global carbon markets, which include the CDM, found net combined 
effects on emissions to be negligible (Green 2021). Across the board, 
CDM projects have been criticised for lack of ‘additionality’, problems 
of baseline determination, uneven geographic coverage (Michaelowa 
and Michaelowa 2011a; Cames et al. 2016; Michaelowa et al. 2019b),

as well as failing to address human rights concerns (Schade and 
Obergassel 2014). 
14.3.3.2 Effectiveness of the Paris Agreement
Given the comparatively recent conclusion of the Paris Agreement, 
evidence is still being gathered to assess its effectiveness in practice, 
in particular, since its long-term effectiveness hinges on states 
communicating more ambitious NDCs in successive cycles over 
time. Assessments of the Paris Agreement on paper are necessarily 
speculative and limited by the lack of credible counterfactuals. 
Despite these limitations, numerous assessments exist of the potential for international cooperation under the Paris Agreement to 
advance climate change mitigation. 
These assessments are mixed and reflect uncertainty over the outcomes 
the Paris Agreement will achieve (Christoff 2016; Clémençon 2016; 
Keohane and Oppenheimer 2016; Young 2016; Dimitrov et al. 2019; 
Raiser et al. 2020). There is a divide between studies that do not expect

a positive outcome from the Paris Agreement and those that do. The 
former base this assessment on factors such as: a lack of clarity in 
the expression of obligations and objectives; a lack of concrete plans 
collectively to achieve the temperature goal; extensive use of soft 
law (i.e., non-legally binding) provisions; limited incentives to avoid 
free-riding; and the Agreement’s weak enforcement provisions (Allan 
2019), as well as US non-cooperation under the Trump administration 
and the resulting gap in mitigation, finance and governance (Bang 
et al. 2016; Spash 2016; Tulkens 2016; Chai et al. 2017; Lawrence 
and Wong 2017; Thompson 2017; Barrett 2018; Kemp 2018). Studies 
expecting a positive outcome emphasise factors such as: the breadth 
of participation enabled by self-differentiated NDCs; the ‘logic’ of 
domestic climate policies driving greater national ambition; the 
multiplicity of actors engaged by the Paris Agreement’s facilitative

architecture; the falling cost of low-carbon technologies; provision 
for financial, technology and capacity-building support to developing 
country Parties; possibilities for voluntary cooperation on mitigation 
under Article 6; and the potential for progressive ratcheting up of 
Parties’ pledges over time fostered by transparency of reporting and 
international scrutiny of national justifications of the ‘fairness’ of 
contributions (Caparrós 2016; Chan 2016a; Falkner 2016b; Victor 
2016; Morgan and Northrop 2017; Urpelainen and Van de Graaf 
2018; Hale 2020; Tørstad 2020). Turning to the assessment criteria 
articulated in this chapter, the following preliminary assessments of 
the Paris Agreement can be made. 
In relation to the criterion of environmental effectiveness, the 
Paris Agreement exceeds the Kyoto Protocol in terms of coverage 
of GHGs and participation of states in mitigation actions. In terms 
of coverage of GHGs, the Kyoto Protocol limits its coverage to

a defined basket of gases identified in its Annex A (carbon dioxide 
(CO 2), methane (CH 4), nitrous oxide (N 2O), hydrofluorocarbons 
(HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF 6), as well 
as nitrogen trifluoride (NF 3)). The Paris Agreement does not specify 
the coverage of gases, thus Parties may cover the full spectrum of 
GHGs in their NDCs as encouraged by the accounting provisions 
in Annex II to Decision  18/CMA.1 (or conversely they may choose 
to exclude important mitigation sectors) and there is also the 
possibility to include other pollutants such as short-lived climate 
forcers like black carbon. Article 4.4 calls on developed countries 
to undertake economy-wide emissions reduction targets with 
the expectation that developing country Parties will also move to 
introduce these over time. Moreover, the Paris Agreement makes 
express reference to Parties taking action to conserve and enhance

‘sinks and reservoirs of greenhouse gases’ (Article 5). As under the 
UNFCCC and Kyoto Protocol, this allows for coverage of land use, 
land-use change and forestry and agriculture, forestry and other land 
use (AFOLU) emissions, both CO 2 and other Kyoto Annex A gases, 
as well as methane (Pekkarinen 2020). A few countries, particularly 
LDCs, include quantified non-CO 2 emissions reductions from the

1477
International Cooperation Chapter 1414agricultural sector in their NDCs, and many others include agriculture 
in their economy-wide targets (Richards et al. 2018). Some studies 
find that agricultural development pathways with mitigation co-
benefits can deliver 21–40% of needed mitigation for the ‘well below 
2°C’ limit, thus necessitating ‘transformative technical and policy 
options’ (Wollenberg et al. 2016). Other studies indicate that broader 
‘natural climate solutions, including forests, can provide 37% of the 
cost-effective CO 2 mitigation needed through 2030 for a more than 
66% chance of holding warming to below 2°C’ (Griscom et al. 2017). 
As Figure 14.2 illustrates graphically, communicated unconditional 
NDCs, if achieved, lead to a reduction of about 7% of world emissions 
by 2030 in relation to the Kyoto GHGs, and NDCs with conditional 
elements increase this reduction to about 12% (den Elzen et al. 2016).

Although there are uncertainties in the extent to which countries will 
meet the conditional elements of their NDCs, the experience with 
the Cancun pledges has been positive, as countries will collectively 
meet their pledges by 2020, and even individual pledges will be met 
in most cases, although arguably helped by the COVID-19 pandemic 
(UNEP 2020). In any case, the main challenge that remains is to close 
the emissions gap, the difference between what has been pledged 
and what needs to be achieved by 2030 to reach a 1.5°C compatible 
path (respectively 2°C) (Roelfsema et al. 2020; UNEP 2020, see 
also Cross-Chapter Box 4 in Chapter 4). In terms of participation of 
states in mitigation actions, the Paris Agreement performs better 
than the Kyoto Protocol. The latter contains mitigation targets 
only for developed countries listed in its Annex B, while the Paris 
Agreement extends binding procedural obligations in relation to

mitigation contributions to all states. It is noted, however, that the 
Paris Agreement represented a weakening of commitments for those 
industrialised countries that were Parties to the Kyoto Protocol, 
although a strengthening for those that were not, and for developing 
countries (Oberthür and Groen 2020). Finally, some analysts have 
suggested that the recent proliferation of national mid-century net-
zero targets – currently 127 countries have considered or adopted 
such targets –  can be attributed, at least in part, to participation 
in the Paris Agreement and having agreed to its Article 4 (Climate 
Action Tracker 2020a; Day et al. 2020).
In relation to the criterion of transformative potential, there is, as 
yet, limited empirical data or theoretical analysis on which to assess 
the Paris Agreement’s transformative potential. The IPCC Special 
Report on Global Warming of 1.5°C concluded that pathways 
limiting global warming to 1.5°C would require systems transitions

that are ‘unprecedented in terms of scale’ (IPCC 2018b). There is 
limited evidence to suggest that this is underway, although there 
are arguments made that Paris has the right structure to achieve this. 
The linking of the UNFCCC financial apparatus, including the GCF , 
to the Paris Agreement, and the provisions on technology support 
and capacity building, provide potential avenues for promoting 
increased investment flows into low-carbon technologies and 
development pathways, as Labordena et al. (2017) show in the case 
of solar energy development in Africa. Similarly, Kern and Rogge 
(2016) argue that the Paris Agreement’s global commitment towards 
complete decarbonisation may play a  critical role in accelerating 
underlying system transitions, by sending a strong signal as to the 
actions needed by national governments and other international support. Victor et al. (2019) argue that international cooperation that

enhances transformative potential needs to operate at the sectoral 
level, as the barriers to transformation are highly specific to each 
sector; the Paris Agreement’s broad consensus around a clear level of 
ambition sends a strong signal on what is needed in each sector, but 
on its own will do little unless bolstered with sector-specific action 
(Geels et al. 2019). On the less optimistic side, it is noted that the 
extent of the ‘investment signal’ sent by the Agreement to business 
is unclear (Kemp 2018), and it is also unclear to what extent the Paris 
Agreement is fostering investment in break-through technologies. 
United States non-cooperation from 2017 to 2020 posed a significant 
threat to adequate investment flows through the GCF (Chai et al. 
2017; Urpelainen and Van de Graaf 2018). 
In relation to the criterion of distributive outcomes, the Paris 
Agreement performs well in some respects but less well in others,

and its performance relative to the Kyoto Protocol is arguably lower in 
respect of some indicators such as industrialised country leadership, 
and differentiation in favour of developing countries. While the 
Kyoto Protocol implemented a multilaterally agreed burden-sharing 
arrangement set out in the UNFCCC and reflected in Annex-based 
differentiation in mitigation obligations, the Paris Agreement relies 
on NDCs, accompanied by self-assessments of the fairness of these 
contributions; some of these do not accord with equity principles of 
international environmental law, although it is worth noting that the 
Kyoto Protocol was also not fully consistent with such principles. At 
present, mechanisms in the Paris Agreement for promoting equitable 
burden sharing and evaluating the fairness of Parties’ contributions 
are undefined, although numerous proposals have been developed 
in the literature Herrala and Goel 2016; (Ritchie and Reay 2017;

Robiou du Pont et al. 2017; Alcaraz et al. 2019; Sheriff 2019) 
(Section  14.3.2.3). Zimm and Nakicenovic (2020) analysed the first 
set of NDCs and concluded that they would result in a decrease in the 
inequality of per capita emissions across countries. In relation to other 
indicators, such as the provision of support, the distributive outcomes 
of the Paris Agreement are dependent on the availability of support 
through mechanisms such as the GCF to meet the mitigation and 
adaptation financing needs of developing countries (Antimiani et al. 
2017; Chan et al. 2018). One study suggests that the implementation 
of the emissions reduction objectives stated in the NDCs implies 
trade-offs with poverty reduction efforts needed to achieve SDGs 
(Campagnolo and Davide 2019), while other studies offer evidence 
that the immediate economic, environmental, and social benefits 
of mitigation in line with developing countries’ NDCs exceed those

NDCs’ costs, and ultimately align with the SDGs (Antwi-Agyei 
et al. 2018; Vandyck et al. 2018; Caetano et al. 2020) (Chapter 17). 
In relation to the promotion of co-benefits, the Paris Agreement has 
enhanced mechanisms for promoting co-benefits (e.g., in some cases 
for biodiversity conservation through the endorsement of REDD+ 
initiatives and activities) and linkages to sustainable development 
(e.g., through the Article 6.4 mechanism). Finally, in its preambular 
text the Paris Agreement endorses both a human rights perspective 
and the concept of just transitions, creating potential hooks for further 
elaboration and expansion of these principles in mitigation actions.
On the criterion of economic performance, the Paris Agreement’s 
performance is potentially enhanced by the capacity for Parties

1478
Chapter 14 International Cooperation14to link mitigation policies, therefore improving aggregate cost-
effectiveness. Voluntary cooperation under Article 6  of the Paris 
Agreement could facilitate such linkage of mitigation policies (Chan 
et al. 2018). A  combination of common accounting rules and the 
absence of restrictive criteria and conditions on the use of ITMOs 
could accelerate linkage and increase the latitude of Parties to scale 
up the ambition of their NDCs. However, significant question marks 
remain over how the environmental integrity of traded emissions 
reductions can be ensured (Mehling 2019). The ability of Article 6 to 
contribute to the goal of the Paris Agreement will depend on the 
extent to which the rules ensure environmental integrity and avoid 
double counting, while utilising the full potential of cooperative 
efforts (Michaelowa et al. 2019a; Schneider et al. 2019). 
In relation to the criterion of institutional strength, the Paris

Agreement’s signalling and guidance function is, however, 
arguably high. The Paris Agreement has the potential to interact 
with complementary approaches to climate governance emerging 
beyond it (Held and Roger 2018). It may also be used by public-
sector organisations –  organised and mobilised in many countries 
and transnationally – as a point of leverage in domestic politics to 
encourage countries to take costly mitigation actions (Keohane and 
Oppenheimer 2016). More broadly, the Paris Agreement’s architecture 
provides flexibility for decentralised forms of governance (Jordan 
et al. 2015; Victor 2016) (Section 14.5). The Agreement has served 
a  catalytic and facilitative role in enabling and facilitating climate 
action from non-state and sub-state actors (Chan et al. 2015; Chan 
et al. 2016; Hale 2016; Bäckstrand et al. 2017; Kuyper et al. 2018b). 
Such action could potentially ‘bridge’ the ambition gap created by

insufficient NDCs from Parties (Hsu et al. 2019b). The 2018 UNEP 
Emissions Gap Report estimates that if ‘cooperative initiatives are 
scaled up to their fullest potential’, the impact of non-state and sub-
national actors could be up to 1–23 GtCO 2-eq yr–1 by 2030 compared 
to current policy, which could bridge the gap (Lui et al. 2021). 
However, at present such a contribution is limited (Michaelowa and 
Michaelowa 2017; UNEP 2018a). Non-state actors are also playing 
a role in enhancing the ambition of individual NDCs by challenging 
their adequacy in national courts (Chapter 13 and Section 14.5.3).
The Paris Agreement’s institutional strength in terms of ‘rules and 
standards to facilitate collective action’ is disputed given the current 
lack of comparable information in NDCs (Peters et al. 2017; Pauw et al. 
2018; Mayer 2019; Zihua et al. 2019), and the extent to which its 
language, as well as that of the Rulebook, strikes a balance in favour

of discretion over prescriptiveness (Rajamani and Bodansky 2019). 
Similarly, in terms of ‘mechanisms to enhance  transparency and 
accountability’, although detailed rules relating to transparency 
have been developed under the Paris Rulebook, these rules permit 
Parties considerable self-determination in the extent and manner 
of application (Rajamani and Bodansky 2019), and may not lead 
to further ambition (Weikmans et al. 2020). Further the Paris 
Agreement’s compliance committee is facilitative and designed to 
ensure compliance with the procedural obligations in the Agreement 
rather than with the NDCs themselves, which are not subject 
to obligations of result. The Paris Agreement does, however, seek to 
support the building of transparency-related capacity of developing countries, potentially triggering institutional capacity-building at the 
national, sub-national and sectoral levels (Section 14.3.2.7).
Ultimately, the overall effectiveness of the Paris Agreement depends

on its ability to lead to ratcheting up of collective climate action 
to meet the long-term global temperature goal (Bang et al. 2016; 
Christoff 2016; Young 2016; Dimitrov et al. 2019; Gupta and van 
Asselt 2019). As noted above, there is some evidence that this is 
already occurring. The design of the Paris Agreement, with ‘nationally 
determined’ contributions at its centre, countenances an initial 
shortfall in collective ambition in relation to the long-term global 
temperature goal on the understanding and expectation that Parties 
will enhance the ambition of their NDCs over time (Article 4). This 
is essential given the current shortfall in ambition. The pathways 
reflecting current NDCs, according to various estimates, imply 
global warming in the range of 3°C by 2100 (UNFCCC 2016b; UNEP 
2018a) (Box  4.3). NDCs will need to be substantially scaled up if 
the temperature goal of the Paris Agreement is to be met (Rogelj

et al. 2016; Rogelj et al. 2018; Höhne et al. 2017, 2018; UNEP 2020). 
The Paris Agreement’s ‘ambition cycle’ is designed to trigger such 
enhanced ambition over time. Some studies find that like-minded 
climate mitigation clubs can deliver substantial emissions reductions 
(Hovi et al. 2017) and are reasonably stable despite the departure 
of a  major emitter such as the United States (Sprinz et al. 2018); 
other studies find that conditional commitments in the context 
of a  pledge and review mechanism are unlikely to substantially 
increase countries’ contributions to emissions reductions (Helland 
et al. 2017), and hence need to be complemented by the adoption of 
instruments designed differently from the Paris Agreement (Barrett 
and Dannenberg 2016). In any case, high (but not perfect) levels of 
mean compliance rates with the Paris Agreement have to be assumed 
for reaching the ‘well below 2°C’ temperature goal (Sælen 2020; 
Sælen et al. 2020). This is by no means assured.

In conclusion, it remains to be seen whether the Paris Agreement will 
deliver the collective ambition necessary to meet the temperature 
goal. While the Paris Agreement does not contain strong and stringent 
obligations of result for major emitters, backed by a  demanding 
compliance system, it establishes binding procedural obligations, lays 
out a range of normative expectations, and creates mechanisms for 
regular review, stock taking, and revision of NDCs. In combination 
with complementary approaches to climate governance, engagement 
of a wide range of non-state and sub-national actors, and domestic 
enforcement mechanisms, these have the potential to deliver the 
necessary collective ambition and implementation. Whether it will do 
so, remains to be seen.

1479
International Cooperation Chapter 1414Cross-Chapter Box 10 | Policy Attribution – Methodologies for Estimating the Macro-level 
Impact of Mitigation Policies on Indices of Greenhouse Gas Mitigation
Authors: Mustafa Babiker (Sudan/Saudi Arabia), Paolo Bertoldi (Italy), Christopher Bataille (Canada), Felix Creutzig (Germany), 
Navroz  K.  Dubash (India), Michael Grubb (United Kingdom), Erik Haites (Canada), Ben Hinder (United Kingdom), Janna Hoppe 
(Switzerland), Yong-Gun Kim (Republic of Korea), Gregory F . Nemet (the United States of America/Canada), Anthony Patt (Switzerland), 
Yamina Saheb (France), Raphael Slade (United Kingdom)
This report notes both a growing prevalence of mitigation policies over the past quarter century (Chapter 13), and ‘signs of progress’ 
including various quantified indices of GHG mitigation (Table 2.4). Even though policies implemented and planned to date are clearly

insufficient for meeting the Paris long-term temperature goals, a natural question is to what extent the observed macro-level changes 
(global, national, sectoral, technological) can be attributed to policy developments. This Assessment Report is the first to address that 
question. This box describes the methods for conducting such ‘attribution analysis’ as well as its key results, focusing on the extent to 
which polices have affected three main types of ‘outcome indices’: 
• GHG emissions: emissions volumes and trends at various levels of governance including sub- and supra-national levels, and 
within and across sectors.
• Proximate emission drivers: trends in the factors that drive emissions, distinguished through decomposition analyses, notably: 
energy/GDP intensity and carbon/energy intensity (for energy-related emissions); indices of land use such as deforestation rates (for

LULUCF/AFOLU); and more sector-specific component drivers such as the floor area per capita, or passenger kilometres per capita. 
• Technologies: developments in key low-carbon technologies that are likely to have a strong influence on future emissions trends, 
notably levels of new investment and capacity expansions, as well as technology costs, with a focus on those highlighted in 
Figure 2.30.
Policy attribution examines the extent to which emission-relevant outcomes on these indices – charted for countries, sectors and 
technologies, particularly in Chapter 2 and the sectoral chapters – may be reasonably attributed to policies implemented prior to the 
observed changes. Such policies include regulatory instruments such as energy efficiency programmes or technical standards and 
codes, carbon pricing, financial support for low-carbon energy technologies and efficiency, voluntary agreements, and regulation

of land-use practices. The sectoral chapters give more detail along with some accounts of policy, while trends in mitigation policy 
adoption are summarised in Chapter 13.
In reviewing hundreds of scientific studies cited in this report, the impacts of adopted policies on observed outcomes were assessed. 
The vast majority of these studies examine particular instruments in particular contexts, as covered in the sectoral chapters and 
Chapter 13; only a few have appraised global impacts of policies, directly or plausibly inferred (the most significant are cited in 
Figure 1 in this Cross-Chapter Box). Typically, studies consider ‘mitigation policies’ to be those adopted with either a primary objective 
of reducing GHG emissions or emissions reductions as one among multiple objectives. 
Policies differ in design, scope, and stringency, may change over time as they require amendments or new laws, and often partially

overlap with other instruments. Overall, the literature indicates that policy mixes are, theoretically and empirically, more effective in 
reducing emissions, stimulating innovation, and inducing behavioural change than stand-alone policy instruments (Sections 5.6 and 
13.7) (Rosenow et al. 2017; Best and Burke 2018; Sethi et al. 2020). Nevertheless, these factors complicate analysis, because they give 
rise to the potential for double counting emissions reductions that have been observed, and which separate studies can attribute to 
different policy instruments. 
Efforts to attribute observed outcomes to a policy or policy mix is also greatly complicated by the influence of many exogenous factors, 
including fossil fuel prices and socio-economic conditions. Likewise, technological progress can result from both exogenous causes, such 
as ‘spillover’ from other sectors, and policy pressure. Further, other policies, such as fossil fuel subsidies as well as trade-related policies,

can partially counteract the effect of mitigation policies by increasing the demand for energy or carbon-intensive goods and services. 
In some cases, policies aimed at development, energy security, or air quality have climate co-benefits, while others increase emissions.   
Studies have applied a number of methods to identify the actual effects of mitigation policies in the presence of such confounding 
factors. These include statistical attribution methodologies, including experimental and quasi-experimental design, instrumental 
variable approaches, and simple correlational methods. Typically, the relevant mitigation metric is the outcome variable, while 
measures of policies and other factors act as explanatory variables. Other methodologies include aggregations and extrapolations

1480
Chapter 14 International Cooperation14Cross-Chapter Box 10 (continued)
from micro-level data evaluation, and inference from combining multiple lines of analysis, including expert opinion. Additionally, the 
literature contains reviews, many of them systematic in nature, that assess and aggregate multiple empirical studies. 
With these considerations in mind, multiple lines of evidence, based upon the literature, support a  set of high-level findings, as 
illustrated in Figure 1 in this Cross-Chapter Box, as follows. 
1. GHG Emissions. There is robust evidence with a high level of agreement that mitigation policies have had a discernible impact on 
emissions. Several lines of evidence indicate that mitigation policies have led to avoided global emissions to date of several billion 
tonnes CO 2-eq annually. The figure in this box shows a selection of results giving rise to this estimate.

As a starting point, one methodologically sophisticated econometric study links global mitigation policies (defined as climate laws 
and executive orders) to emission outcomes; it estimates emission savings of 5.9 GtCO 2 yr–1 in 2016 compared to a no-policy world 
(Eskander and Fankhauser 2020) (Section 13.6.2).
A second line of evidence derives from analyses of the Kyoto Protocol. Countries which took on Kyoto Protocol targets accounted 
for about 24% of global emissions during the first commitment period (2008–12). The most recent robust econometric assessment 
(Maamoun 2019) estimates that these countries cut GHG emissions by about 7% on average over 2005–2012, rising over the period to 
around 12% (1.3 GtCO 2-eq yr–1) relative to a no-Kyoto scenario. This is consistent with estimates of Grunewald and Martinez (2016) of 
about 800 MtCO 2-eq yr–1 averaged to 2009. Developing countries’ emissions reduction projects through the CDM (defined in Article 12

of the Kyoto Protocol) were certified as growing to over 240 MtCO 2-eq yr–1 by 2012 (UNFCC 2021c). With debates about the full 
 
Policies
Increase in number of mitigation policies implemented worldwide
Technologies GHG emissionsProximate
emission drivers
Increased investments 
in and diffusion of 
low-carbon technologies , 
especially for wind and 
solar energy, electric 
vehicles, energy-efﬁcient 
appliances and 
low-carbon heating.
Decline in costs of 
low-carbon 
technologies, e.g., solar 
PV, battery technology. e.g., RD&D funding, technology
support instrumentse.g., regulation, carbon pricing, 
voluntary agreementse.g., policy mixes including regulation, 
technology support, carbon pricing
Reductions in energy 
intensity globally and in 
all but one world region.
Reductions in carbon 
intensity in Europe, 
Eurasia, the Middle East, 
North America; and 
globally.
Reductions in the rate 
of deforestation in 
several countries, 
especially developing

countries.Reductions in average annual GHG emissions growth (2.3% in 2000–10; 
1.3% in 2010–18). Sustained emissions reductions in 24 countries, relative 
decoupling in 58 countries by 2015.
Estimates of avoided CO2-eq emissions attributable to policies, compared to no-policy
5.9 Gt yr–1in 2016; 38 GtCO2-eq cumulatively since 1999Eskander and
Fankhauser (2020)
Contextual analyses and comparative indications
4–5 Gt yr–1Cumulative impact of policies on 2019 emissions, from
emission trends reported in Chapter 2, assuming
incremental policy-related avoided emissions of  
0.1 GtCO2-eq, annually from 2010Chapter 1,
Section 1.4.8
3.81 Gt yr–1Projected by Annex I countries for 2020 in their BR4s, 
from 2811 Policies and Measures, impacts reported 
for 38% of themUNFCCC (2020)1.3 Gt yr–1in Annex B country as a result of the Kyoto Protocol 
(or –7% yr–1 on average over 2005–12)Maamoun (2019)
1.8–3 Gt yr–1as the result of various policy instruments with

demonstrable impact. Includes at least 500 MtCO2-eq yr–1
from energy efﬁ ciency programmes; and 1.3 GtCO2-eq yr–1 
from renewables diffusionAggregation of 
multiple sources
Cross-Chapter Box 10, Figure 1 | Policy impacts on key outcome indices. The figure shows the impacts of policies on three indices: proximate 
emission drivers, technologies and GHG emissions, including several lines of evidence on GHG abatement attributable to policies.

1481
International Cooperation Chapter 1414Cross-Chapter Box 10 (continued)
extent of ‘additionality’, academic assessments of savings from the CDM have been slightly lower, with particular concerns around 
some non-energy projects (Section 14.3.3.1).
A third line of evidence derives from studies that identify policy-related, absolute reductions from historical levels in particular 
countries and sectors through decomposition analyses (Le Quéré et al. 2019; Lamb et al. 2021), or evaluate the impact of particular 
policies, such as carbon pricing systems. From a wide range of estimates in the literature (Sections 2.8.2.2 and 13.6), many evaluations 
of the EU ETS suggest that it has reduced emissions by around 3% to 9% relative to unregulated firms and/or sectors (Schäfer 2019; 
Colmer et al. 2020), while other factors, both policy (energy efficiency and renewable support) and exogenous trends, played a larger 
role in the overall reductions seen (Haites 2018).

These findings derived from the peer-reviewed literature are also consistent with two additional sets of analysis. The first set concerns 
trends in emissions, drawing directly from Chapters 2, 6 and 11, showing that global annual emission growth has slowed, as evidenced 
by annual emission increments of 0.55 GtCO 2-eq yr–1 between 2011 and 2019 compared to 1.014 GtCO 2-eq yr–1 in 2000 and 2008. 
This suggests avoided emissions of 4–5 GtCO 2-eq yr–1 (see also Figure 1.1d). The second set concerns emissions reductions projected 
by Annex I governments for 2020 in their fourth biennial reports to the UNFCCC. It is important to note that these are mostly projected 
annual savings from implemented policies (not ex-post evaluations), and there are considerable differences in countries’ estimation 
methodologies. Nevertheless, combining estimates from 38% of the total of 2,811 reported policies and measures yields an overall 
estimate of 3.81 GtCO 2-eq yr–1 emission savings (UNFCCC 2020d).

2. Proximate emission drivers. With less overt focus on emissions, studies of trends in energy efficiency, carbon intensity, or 
deforestation often point to associated policies. The literature includes an increasing number of studies on demonstrable progress in 
developing countries. For example, South and South-East Asia have seen energy intensity in buildings improving at about 5–6% yr–1 
since 2010 (Figure 2.22). In India alone, innovative programmes in efficient air conditioning, LED lighting, and industrial efficiency are 
reported as saving around 25 Mtoe in 2019–2020, thus leading to avoided emissions of over 150 MtCO 2 yr–1 (Malhotra et al. 2021) 
(Box 16.3). Likewise, reductions in deforestation rates in several South and Central American and Asian countries are at least partly 
attributable to ecosystem payments, land-use regulation, and internal efforts (Section 7.6.2). Finally, the policy-driven displacement of

fossil fuel combustion by renewables in energy has led to reductions in carbon intensity in several world regions (Chapters 2 and 6). 
3. Technologies. The literature indicates unambiguously that the rapid expansion of low-carbon energy technologies is substantially 
attributable to policy (Sections 6.7.5 and 16.5). Technology-specific adoption incentives have led to a greater use of less carbon-
intensive (e.g., renewable electricity) and less energy-intensive (especially in transport and buildings) technologies. As Chapters 2 and 
6 of this report note that modern renewable energy sources currently satisfy over 9% of global electricity demand, and this is largely 
attributable to policy. There are no global-level studies estimating the avoided emissions due to renewable energy support policies, 
but there are methods that have been developed to link renewable energy penetration to avoided emissions, such as that of IRENA

(2021). Using that method, and assuming that 70% of modern renewable energy expansion has been policy induced, yields an 
estimate of avoided emissions of 1.3 GtCO 2-eq yr–1 in 2019. Furthermore, observed cost reductions are the result of policy-driven 
capacity expansion as well as publicly funded resarch and development, in individual countries and globally. These correspond with 
induced effects on number of patents, ‘learning curve’ correlations with deployed capacity, and cost component and related case study 
analyses (Kavlak et al. 2018; Nemet 2019; Popp 2019; Grubb et al. 2021). 
14.4 Supplementary Means and Mechanisms 
of Implementation
As discussed above, the Paris Agreement sets in place a  new 
framework for international climate policy albeit one that is 
embedded in the wider climate regime complex (Coen et al. 2020). 
Whereas international governance had earlier assumed centre stage, 
the Paris Agreement recognises the salience of domestic politics

in the governance of climate change (Kinley et al. 2020). The new 
architecture also provides more flexibility for recognising the 
benefits of working in diverse forms and groups and allows for more 
decentralised ‘polycentric’ forms of governance (Jordan et al. 2015; Victor 2016). The next two sections address this complementarity 
between the Paris Agreement and other agreements and institutions.
The Paris Agreement identifies a  number of pathways, or means 
of implementation, towards accomplishing rapid mitigation and 
the achieving of its temperature goal: finance; capacity building; 
technology and innovation; and cooperative approaches and markets 
(Sections 14.3.2.7–14.3.2.10 above). In this section, we examine 
each of these means and mechanisms of implementation, and the 
agreements and institutions lying outside of the Paris Agreement 
that contribute to each. In the following section, 14.5, we examine 
the agreements and institutions playing other governance roles:

1482
Chapter 14 International Cooperation14TypeInstrument/
organisationMitigation Transparency Sinks Markets Finance TechnologyCapacity 
building
Global treatiesMontreal 
Protocol14.5.1.1 14.5.1.1
CBD 14.5.1.1 14.5.2.1
UNCCD 14.5.2.1 14.5.2.1
Minimata 
Mercury 
Convention14.5.1.1
United Nations 
programmes 
and specialised 
agenciesUN REDD+ 
programme14.5.1.1 14.5.2.1 14.5.2.1 14.4.3
UNEP 14.5.1.1 14.4.3
UNDP 14.4.3
UNIDO 14.4.1.2
UNOSSC 14.4.1.2
FAO 14.5.2.1 14.4.1.2
ICAO 14.5.2.3 14.5.2.3 14.5.2.3
IMO 14.5.2.3 14.5.2.3 14.5.2.3
Other global 
organisationsIEA 14.5.2.2
IRENA 14.5.2.2 14.5.2.2 14.5.2.2
MDBs 14.4.1.2 14.4.1.2 14.5.4 14.4.4 14.4.1.2 14.4.3
Regional, multi- 
and bilateral 
agreementsLRTAP 14.5.1.1
MIGA 14.5.2.2
PPCA 14.5.2.2
Regional trade 
agreements14.5.1.3 14.5.1.3 14.5.1.3
Bilateral 
programmes14.4.4 14.4.1.1 14.4.1.1 14.4.3
International 
science 
programmes14.4.2
South–South 
Cooperation14.5.1.4 14.5.1.4 14.4.3
Non-state trans-

national actorsGlobal city 
networks14.5.5 14.5.5 14.5.5 14.5.5 14.5.5
NGOs14.5.2.2 14.5.4 14.5.3
Social 
movements14.5.3 14.5.3
Business 
partnerships14.5.4 14.5.4 14.5.4 14.5.4 14.5.4
Figure 14.3 | Climate governance beyond the UNFCCC.  The figure shows those relationships, marked in blue, between international governance activities, described in 
the text, that relate to activities of the UNFCCC and Paris Agreement.
regulating activities in particular sectors; linking climate mitigation 
with other activities such as adaptation; and stimulating and 
coordinating the actions of non-state actors at a global scale. 
Figure  14.3 maps out the interlinkages described in the text of 
Sections 14.4 and 14.5. It is an incomplete list, but illustrates 
clearly that across multiple types of governance, there are multiple 
instruments or organisations with activities connected to the 
different governance roles associated with the Paris Agreement and

the UNFCCC more generally.14.4.1 Finance 
International cooperation on climate finance is underpinned by 
various articles of the UNFCCC including Articles 4.3, 4.4, 4.5, 4.7 
and 11.5 (UNFCCC 1992). This was further amplified through the 
commitment by developed countries in the Copenhagen Accord 
and the Cancun Agreements to mobilise jointly through various 
sources USD100 billion yr–1 by 2020 to meet the needs of the 
developing countries (UNFCCC 2010b). This commitment was made 
in the context of meaningful mitigation action and transparency

1483
International Cooperation Chapter 1414of implementation. As mentioned in Section  14.3.2.8, in the Paris 
Agreement the binding obligation on developed country Parties to 
provide financial resources to assist developing country Parties 
applies to both mitigation and adaptation (UNFCCC 2015a, Art. 
9.1). In 2019, climate finance provided and mobilised by developed 
countries was in the order of USD79.6 billion, coming from different 
channels including bilateral and multilateral channels, and also 
through mobilisation of the private sector attributable to these 
channels (OECD 2021). A majority (two-thirds) of these flows targeted 
mitigation action exclusively (Chapter 15). These estimates, however, 
have been criticised on various grounds, including that they are an 
overestimate and do not represent climate-specific net assistance 
only; that in grant equivalence terms the order of magnitude is 
lower; and the questionable extent of transparency of information

on mobilised private finance, as well as the direction of these flows 
(Carty et al. 2020). On balance, such assessments need to be viewed 
in the context of the original commitment, the source of the data 
and the evolving guidance, and modalities and procedures from the 
UNFCCC processes. As mentioned in Chapter 15, the measurement 
of climate finance flows continues to face definitional, coverage and 
reliability issues, despite progress made by various data providers 
and collators (Section 15.3.2).
The multiplicity of actors providing financial support has resulted in 
a fragmented international climate finance architecture as indicated 
in Section 14.3.2.8. It is also seen as a system which allows for speed, 
flexibility and innovation (Pickering et al. 2017). However, the system 
is not yet delivering adequate flows given the needs of developing 
countries (Section 14.3.2.8). An early indication of these self-assessed

needs is provided in the conditional NDCs. Of the 136 conditional 
NDCs submitted by June 2019, 110 have components or additional 
actions conditioned on financing support for mitigation and 79 
have components or additional actions for support for adaptation 
(Pauw et al. 2020). While the Paris Agreement did not explicitly 
countenance conditionality for actions in developing countries, it is 
generally understood that the ambition and effectiveness of climate 
ambition in these countries is dependent on financial support (Voigt 
and Ferreira 2016b). 
14.4.1.1 Bilateral Finance
The Paris Agreement and the imperative for sustainable development 
reinforce the need to forge strong linkages between climate and 
development (Fay et al. 2015). This in turn has highlighted the urgent 
need for greater attention to the relationship between development 
assistance and finance, and climate change (Steele 2015). 
The UNFCCC website cites some 20 bilateral development agencies

providing support to climate change programmes in developing 
countries (UNFCCC 2020a). These agencies provide a  mix of 
development cooperation, policy advice and support and financing 
for climate change projects. Since the year 2000, the OECD 
Development Assistance Committee has been tracking trends in 
climate-related development finance and assistance. The amount of 
bilateral development finance with climate relevance has increased 
substantially since 2000 (OECD 2019a). For 2019, it was reported 
to be USD28.8 billion in direct finance and USD2.6 billion through export credit agencies. Further, another USD34.1 billion of the climate 
finance provided through multilateral channels is attributable to the 
developed countries (OECD 2021). The OECD methodology has been 
critiqued as it uses Rio markers, the limitations of which could lead 
to erroneous reporting and assessment of finance provided as well 
as of the mitigation outcome (Michaelowa and Michaelowa 2011b;

Weikmans and Roberts 2019). This issue is to be addressed through 
the modalities, procedures and guidance under the Enhanced 
Transparency Framework of the Paris Agreement (Section 14.3.2.4), 
through the mandate to the Subsidiary Body for Scientific and 
Technological Advice (SBSTA) to develop common tabular formats for 
the reporting of information on, inter alia, financial support provided, 
mobilised and received (UNFCCC 2019k). Until then, the Biennial 
Assessment Report prepared by the Standing Committee on Finance 
provides the best available information on financial support.
14.4.1.2 Multilateral Finance
Multilateral development banks (MDBs) comprise six global 
development banks: the European Investment Bank, International 
Fund for Agricultural Development, International Investment Bank, 
New Development Bank, OPEC Fund for International Development, 
and the World Bank Group; six regional development banks: 
the African Development Bank, Asian Development Bank, Asian

Infrastructure Investment Bank, European Bank for Reconstruction 
and Development, Inter-American Development Bank, and the Islamic 
Development Bank; and 13 sub-regional development banks: the Arab 
Bank for Economic Development in Africa, Arab Fund for Economic 
and Social Development, Black Sea Trade and Development Bank, 
Caribbean Development Bank, Central American Bank for Economic 
Integration, Development Bank of the Central African States, 
Development Bank of Latin America, East African Development 
Bank, Eastern and Southern African Trade and Development Bank, 
Economic Cooperation Organization Trade and Development Bank, 
Economic Community of West African States Bank for Investment 
and Development, Eurasian Development Bank, and the West African 
Development Bank. Together they play a  key role in international 
cooperation at the global, regional and sub-regional levels because 
of their growing mandates and proximity to policymakers (Engen and

Prizzon 2018). For many, climate change is a growing priority and for 
some, because of the needs of the regions or sub-regions in which 
they operate, climate change is embedded in many of their operations. 
In 2015, 20 representative MDBs and members of the International 
Development Finance Club unveiled five voluntary principles to 
mainstream climate action in their investments: commitment 
to  climate strategies, managing climate risks, promoting climate 
smart objectives, improving climate performance and accounting 
for their own actions (World Bank 2015a; Institute for Climate 
Economics 2017). The members subscribing to these principles had 
grown to 44 as of January 2020. Arguably, it is only through closer 
linkages between climate and development that significant inroads 
can be made in addressing climate change. MDBs can play a major 
role through the totality of their portfolios (Larsen et al. 2018). 
The MDBs as a  cohort have been collaborating and coordinating

1484
Chapter 14 International Cooperation14in 2012 at the UN Conference on Sustainable Development in Rio 
de Janeiro (Inter-American Development Bank 2012). This has 
engendered other forms of collaboration among the MDBs, including, 
commitments to: collectively total at least USD65 billion annually by 
2025 in climate finance, with USD50 billion for low- and middle-
income economies; to mobilise a further USD40 billion annually by 
2025 from private sector investors, including through the increased 
provision of technical assistance, use of guarantees, and other de-
risking instruments; to help clients deliver on the goals of the Paris 
Agreement; to build a  transparency framework on the impact of 
MDBs’ activities; and to enable clients to move away from fossil 
fuels (Asian Development Bank 2019). While the share of MDBs in 
direct climate financing is small, their role in influencing national 
development banks and local financial institutions, and leveraging

and crowding in private investments in financing sustainable 
infrastructure, is widely recognised (NCE 2016). However, with this 
recognition there is also an exhortation to do more to align with the 
goals of the Paris Agreement, including a comprehensive examination 
of their portfolios beyond investments that directly support climate 
action to also enabling the long-term net zero GHG emissions 
trajectory (Larsen et al. 2018; Cochran and Pauthier 2019). Further, 
a recent assessment has shown that MDBs perform relatively better 
in mobilising other public finance than private co-financing (Thwaites 
2020). In addition, the banks have launched or are members of 
significant initiatives such as the Climate and Clean Air Coalition to 
reduce emissions of shortlived climate pollutants, the Carbon Pricing 
Leadership Coalition, the Coalition for Climate Resilient Investment 
and the Coalition of Finance Ministers for Climate Action. These help

to spur action at different levels, from economic analysis to carbon 
financing, and convenors of finance and development ministers for 
climate action, with leadership of many of these initiatives led by the 
World Bank.
The multilateral climate funds also have a role in the international 
climate finance architecture. This includes, as mentioned in 
Section  14.3.2.8, those established under the UNFCCC’s financial 
mechanism, its operating entities, the Global Environment Facility 
(GEF), which also manages two special funds, the Special Climate 
Change Fund and the Least Developed Countries Fund; and the 
Green Climate Fund (GCF), also an operating entity of the financial 
mechanism which in 2015, was given a  special role in supporting 
the Paris Agreement. The GCF aims to provide funding at scale, 
balanced between mitigation and adaptation, using various financial 
instruments including grants, loans, equity, guarantees or others

to activities that are aligned with the priorities of the countries 
compatible with the principle of country ownership (GCF 2011). 
The GCF faces many challenges. While some see the GCF as an 
opportunity to transform and rationalise what is now a complex and 
fragmented climate finance architecture with insufficient resources 
and overlapping remits (Nakhooda et al. 2014), others see it as an 
opportunity to address the frequent tensions which arise between 
mitigation-focused transformation and national priorities of countries. 
This tension is at the heart of the principle of country ownership and 
the need for transformational change (Winkler and Dubash 2016). 
Leveraging private funds and investments by the public sector and 
taking risks to unlock climate action are also expressed strategic 
aims of the GCF . The UN system is also supporting climate action through much-
needed technical assistance and capacity building, which is

complementary to the financial flows insofar as it enables countries 
with relevant tools and methodologies to assess their needs, 
develop national climate finance roadmaps, establish relevant 
institutional mechanisms to receive support and track it, enhance 
readiness to access financing, and include climate action across 
relevant national financial planning and budgeting processes (UN 
2017a). The United Nations Development Programme (UNDP) is the 
largest implementer of climate action among the UN Agencies, with 
others, such as the Food and Agriculture Organization (FAO), United 
Nations Environment Programme (UNEP), United Nations Industrial 
Development Organisation (UNIDO), and United Nations Office for 
South-South Cooperation (UNOSSC), providing relevant support.
The current architecture of climate finance is one that is primarily 
based on north-south, developed-developing country dichotomies. 
The Paris Agreement, however, has clearly recognised the role of

climate finance flows across developing countries, thereby enhancing 
the scope of international cooperation (Voigt and Ferreira 2016b). 
Estimates of such flows, though, are not readily available. According 
to one estimate in 2020 the flows among non-OECD countries were 
of the order of USD29 billion (CPI 2021).
14.4.1.3 Private Sector Financing
There is a growing recognition of the importance of mobilising private 
sector financing including for climate action (World Bank 2015b; 
Michaelowa et al. 2020b). An early example of the mobilisation of 
the private sector in a  cooperative mode for mitigation outcomes 
is evidenced from the Clean Development Mechanism of the Kyoto 
Protocol and the linking with the European Union’s Emissions 
Trading System, both triggered by relevant provisions in the Kyoto 
Protocol (Section 14.4.4) and lessons learned from this are relevant 
for development of market mechanisms in the post Paris Agreement

period (Michaelowa et al. 2019b). In 2019 and 2020, on average for 
the two years, public and private climate financing was on the order 
of USD632 billion, of which USD310 billion originated from the private 
sector. However, as much as 76% of the (overall) finance stayed in 
the country of origin. This trends holds true also for private finance 
(CPI 2021). Figure 14.4 depicts the international climate finance flows 
totalling USD161 billion reported in 2020, about 19% of which were 
private flows. For (international) mitigation financing flows of USD116 
billion, the share provided by private sources was 24%. 
Foreign direct investments and their greening are seen as a channel 
for increasing cooperation. An assessment of the greenfield foreign 
direct investment in different sectors shows the growing share of 
renewable energy at USD92.2 billion (12% of the volume and 38% 
of the number of projects) (FDI Intelligence 2020). Coal, oil and gas

sectors maintain the top spot for capital investments globally. Over 
the last decade there is growing issuance of green bonds with non-
financial private sector issuance gaining ground (Almeida 2020). 
While it is questionable if green bonds have a significant impact on 
shifting capital from non-sustainable to sustainable investments, 
they do incentivise the issuing organisations to enhance their green 
ambition and have led to an appreciation within capital markets of

1485
International Cooperation Chapter 1414green frameworks and guidelines and signalled new expectations 
(Maltais and Nykvist 2020). In parallel, institutional investors 
including pension funds are seeking investments that align with the 
Paris Agreement (IIGCC 2020). However, the readiness of institutional 
investors to make this transition is arguable (OECD 2019b; Ameli et al. 
2020). This evidence suggests that international private financing 
could play an important role but this potential is yet to be realised 
(Chapter 15).
14.4.2 Science, Technology and Innovation
Science, technology and innovation are essential for the design of 
effective measures to address climate change and, more generally, for 
economic and social development (de Coninck and Sagar 2015a). The 
OECD finds that single countries alone often cannot provide effective 
solutions to today’s global challenges, as these cross national borders 
and affect different actors (OECD 2012). Madani (2020) shows how

conflict, including international sanctions, can reduce science and 
innovation capacity, which is not evenly distributed, particularly 
across the developed and the developing world. For this reason, 
many countries have introduced strategies and policies to enhance 
international cooperation in science and technology (Chen et al. 
2019). Partnerships and international cooperation can play a role in 
establishing domestic innovation systems, which enable more effective 
science and technology innovation (de Coninck and Sagar 2015b,a). International cooperation in science and technology occurs across 
different levels, with a growing number of international cooperation 
initiatives aimed at research and collaborative action in technology 
development. Weart (2012) finds that such global efforts are effective 
in advancing climate change science due to the international nature of 
the challenge. Global research programmes and institutions have also

provided the scientific basis for major international environmental 
treaties. For example, the Long-Range Transboundary Air Pollution 
Convention and the Montreal Protocol were both informed by scientific 
assessments based on collaboration and cooperation of scientists 
across several geographies (Andresen et al. 2000). Furthermore, 
the Global Energy Assessment (GEA) provided the scientific basis 
and evidence for the 2030 Agenda for Sustainable Development, 
in particular SDG 7  to ensure access to affordable, reliable and 
sustainable modern energy for all (GEA 2012). The GEA drew on 
the expertise of scientists from over 60 countries and institutions. 
Several other platforms exist to provide scientists and policymakers 
an opportunity for joint research and knowledge sharing, such as The 
World in 2050, an initiative that brings together scientists from some 
40 institutions from around the world to provide the science for SDG

and Paris Agreement implementation (TWI2050 2018). 
Non-state actors are also increasingly collaborating internationally. 
Such collaborations, referred to as international cooperative 
initiatives (ICIs), bring together multi-stakeholder groups across 
industry, communities, and regions, and operate both within Adaptation and multiple
objectives: USD45 billionMitigation:
USD116 billion
23% Bilateral development
ﬁnancial institutions
25% Government & agencies
40% Multilateral development
ﬁnancial institutions 3% Multilateral funds
2% Public funds
2% State-owned entitites
4% Corporations
2% Institutional investors10% Bilateral development
42% Multilateral development
ﬁnancial institutions 1% Export credit agencies
3% Government & agencies
2% Multilateral funds
1% State-owned
12% Commercial
ﬁnancial institutions7% National development
10% Corporations
2% Funds
1% Institutional investorsPublic: 81%

USD130 billion
Mitigation: 73%
USD116 billionAdaptation
and multiple
objectives: 27%
USD45 billionPrivate: 19%
USD31 billion
Mitigation:
67%
Adaptation and
multiple objectives: 33%
Public: 76%
Private: 24%Public: 94%
Private: 6%Mitigation:
92%
multiple objectives: 8%USD161
billion
USD161
billion(a) (c) 
(b) 
Figure 14.4 | International finance flows.  Total international climate financial flows for 2020 were USD161 billion. By comparison, public sector bilateral and multilateral 
finance in 2017 for fossil fuel development, including gas pipelines, was roughly USD4 billion. Part (a) disaggregates total financial flows according to public and private sources, 
and indicates the breakdown between mitigation on the one hand, and adaptation and multiple objectives on the other, within each source. Part (b) disaggregates total financial

flows according to intended purpose, namely mitigation or adaptation and multiple objectives, and disaggregates each type according to source. Part (c) provides additional 
detail on the relative contributions of different public and private sources. Sources: data from CPI 2021; OECD 2021.

1486
Chapter 14 International Cooperation14and outside the UNFCCC process. Lui et al. (2021) find that such 
initiatives could make a  major contribution to global emissions 
reduction, Bakhtiari (2018) finds that the impact on greenhouse gas 
reduction of these initiatives is hindered due to a lack of coordination 
between ICIs, overlap with other activities conducted by the UNFCCC 
and governments, and a  lack of monitoring systems to measure 
impact. Increasing the exchange of information between ICIs, 
enhancing monitoring systems, and increasing collaborative research 
in science and technology would help address these issues (Boekholt 
et al. 2009; Bakhtiari 2018).
At the level of research institutes, there has been a major shift to 
a more structured and global type of cooperation in research; Wagner 
et al. (2017) found significant increases in both the proportion of 
papers written by author teams from multiple countries and in

the number of countries participating in such collaboration, over the 
time period 1990–2013. Although only a portion of these scientific 
papers address the issue of climate change specifically, this growth 
of scientific collaboration across borders provides a comprehensive 
view of the conducive environment in which climate science 
collaboration has grown. 
However, there are areas in which international cooperation can 
be strengthened. Both the Paris Agreement and the 2030 Agenda 
for Sustainable Development call for more creative forms of 
international cooperation in science that help bridge the science 
and policy interface, and provide learning processes and places to 
deliberate on possible policy pathways across disciplines on a more 
sustainable and long-lasting basis. Scientific assessments, such as the 
IPCC and Intergovernmental Science-Policy Platform on Biodiversity 
and Ecosystem Services (IPBES) offer this possibility, but processes

need to be enriched for this to happen more effectively (Kowarsch 
et al. 2016). 
A particular locus for international cooperation on technology 
development and innovation is found within institutions and 
mechanisms of the UN climate regime. The UNFCCC, in Article 4.1(c), 
calls on ‘all Parties’ to ‘promote and cooperate in the development, 
application and diffusion, including transfer, of technologies, 
practices and processes that control, reduce or prevent anthropogenic 
emissions of greenhouse gases’ and places responsibility on 
developed country Parties to ‘take all practicable steps to promote, 
facilitate and finance, as appropriate, the transfer of, or access to 
environmentally sound technologies and know-how to other Parties, 
particularly developing country Parties, to enable them to implement 
the provisions of the Convention’ (UNFCCC 1992, Art. 4.5). The issue 
of technology development and transfer has continued to receive

much attention in the international climate policy domain since its 
initial inclusion in the UNFCCC in 1992 – albeit often overshadowed 
by dominant discourses around market-based mechanisms – and its 
role in reducing GHG emissions and adapting to the consequences of 
climate change ‘is seen as becoming ever more critical’ (de Coninck 
and Sagar 2015a). Milestones in the development of international 
cooperation on climate technologies under the UNFCCC have 
included: (i) the development of a  technology transfer framework 
and establishment of the Expert Group on Technology Transfer (EGTT) 
under the SBSTA in 2001; (ii) recommendations for enhancing the technology transfer framework put forward at the Bali COP in 2007 
and creation of the Poznan strategic programme on technology 
transfer under the GEF; and (iii) the establishment of the Technology 
Mechanism by the COP in 2010 as part of the Cancun Agreements 
(UNFCCC 2010b). The Technology Mechanism is presently the

principal avenue within the UNFCCC for facilitating cooperation on 
the development and transfer of climate technologies to developing 
countries (UNFCCC 2015b). As discussed in Section 14.3.2.9 above, 
the Paris Agreement tasks the Technology Mechanism also to serve 
the Paris Agreement (UNFCCC 2015b, Art. 10.3). 
The Technology Mechanism consists of the Technology Executive 
Committee (TEC) (replacing the EGTT), as its policy arm, and the Climate 
Technology Centre and Network (CTCN), as its implementation arm 
(UNFCCC 2015b). The TEC focuses on identifying and recommending 
policies that can support countries in enhancing and accelerating 
the development and transfer of climate technologies (UNFCCC 
2020b). The CTCN facilitates the transfer of technologies through 
three core services: (i) providing technical assistance at the request 
of developing countries; (ii) creating access to information and 
knowledge on climate technologies; and (iii) fostering collaboration

and capacity building (CTCN 2020a). The CTCN ‘network’ consists 
of a diverse set of climate technology stakeholders from academic, 
finance, non-government, private sector, public sector, and research 
entities, together with more than 150 National Designated Entities, 
which serve as CTCN national focal points. Through its network, the 
CTCN seeks to mobilise policy and technical expertise to deliver 
technology solutions, capacity-building and implementation advice 
to developing countries (CTCN 2020b). At the Katowice UNFCCC 
Conference of the Parties in 2018, the TEC and CTCN were requested 
to incorporate the technology framework developed pursuant to 
Article 10 of the Paris Agreement into their respective workplans and 
programmes of work (UNFCCC 2019f). 
The Joint Annual Report of the TEC and CTCN for 2019 indicated that, 
as of July 2019, the CTCN had engaged with 93 developing country 
Parties regarding a  total of 273 requests for technical assistance,

including 11 multi-country requests. Nearly three-quarters (72.9%) of 
requests received by the CTCN had a mitigation component, with two-
thirds of those mitigation requests related to either renewable energy 
or energy efficiency. Requests for decision-making or information tools 
are received most frequently (28% of requests), followed by requests 
for technology feasibility studies (20%) and technology identification 
and prioritisation (18%) (TEC and CTCN 2019). 
The CTCN is presently funded from ‘various sources, ranging from the 
[UNFCCC] Financial Mechanism to philanthropic and private sector 
sources, as well as by financial and in-kind contributions from the 
co-hosts of the CTCN and from participants in the Network’ (TEC and 
CTCN 2019, para. 97). Oh (2020b) describes the institution as ‘mainly 
financially dependent on bilateral donations from developed countries 
and multilateral support’. Nevertheless, inadequate funding of the

CTCN poses a problem for its effectiveness and capacity to contribute 
to implementation of the Paris Agreement. A  2017 independent 
review of the CTCN identified ‘limited availability of funding’ as a key 
constraint on its ability to deliver services at the expected level and 
recommended that ‘[b]etter predictability and security over financial

1487
International Cooperation Chapter 1414resources will ensure that the CTCN can continue to successfully 
respond to its COP mandate and the needs and expectations of 
developing countries’ (Ernst & Young 2017, para. 84). The 2019 Joint 
Report of the TEC and CTCN indicates that resource mobilisation for 
the Network remains a challenge (TEC and CTCN 2019, pp. 23–24).
The importance of ‘financial support’ for strengthening cooperative 
action on technology development and transfer was recognised in 
Article 10.6 of the Paris Agreement. The technology framework 
established by the Paris Rulebook specifies actions and activities 
relating to the thematic area of ‘support’ as including: (i) enhancing 
the collaboration of the Technology Mechanism with the Financial 
Mechanism; (ii) identifying and promoting innovative finance and 
investment at different stages of the technology cycle; (iii) providing 
enhanced technical support to developing country Parties, in

a  country-driven manner, and facilitating their access to financing 
for innovation, enabling environments and capacity building, 
developing and implementing the results of TNAs, and engagement 
and collaboration with stakeholders, including organisational and 
institutional support; and (d) enhancing the mobilisation of various 
types of support, including pro bono and in-kind support, from various 
sources for the implementation of actions and activities under each 
key theme of the technology framework. 
Notwithstanding the technology framework’s directive for enhanced 
collaboration of the Technology and Financial Mechanisms of 
the UNFCCC, linkages between them, and particularly to the GCF , 
continue to engender political contestation between developing and 
developed countries (Oh 2020b). Developing countries sought to 
address concerns over the unsustainable funding status of the CTCN 
by advocating linkage through a funding arrangement or financial

linkage, whereas developed countries favour the design of an 
institutional linkage maintaining the different and separate mandates 
of the CTCN and the GCF (Oh 2020a,b). With no resolution reached, 
the UNFCCC COP requested the Subsidiary Body for Implementation, 
at its fifty-third session, to take stock of progress in strengthening 
the linkages between the Technology Mechanism and the Financial 
Mechanism with a  view to recommending a  draft decision for 
consideration and adoption by the Glasgow COP , scheduled for 2021 
(UNFCCC 2019l).
14.4.3 Capacity Building
International climate cooperation has long focused on supporting 
developing countries in building capacity to implement climate 
mitigation actions. While there is no universally agreed definition of 
capacity building and the UNFCCC does not define the term (Khan 
et al. 2020), elements of capacity building can be discerned from 
the Convention’s provisions on education and training programmes

(UNFCCC 1992, Art. 6), as well as the reference in Article 9(2)(d) to 
the SBSTA providing support for ‘endogenous capacity-building in 
developing countries’. 
Capacity building is generally conceived as taking place at three 
levels: individual (focused on knowledge, skills and training), 
organisational/institutional (focusing on organisational performance and institutional cooperation) and systemic (creating enabling 
environments through regulatory and economic policies (Khan et al. 
2020; UNFCCC 2021b). In its annual synthesis report for 2018, the 
UNFCCC secretariat compiled information submitted by Parties on 
the implementation of capacity building in developing countries, 
highlighting cooperative and regional activities on NDCs, including 
projects to build capacity for implementation, workshops related to 
transparency under the Paris Agreement and collaboration to provide 
coaching and training (UNFCCC 2019h). A  number of developing

country Parties also highlighted their contributions to South–South 
cooperation (discussed further in Section  14.5.1.4), and identified 
capacity-building projects undertaken with others (e.g.,  capacity-
building for risk management in Latin America and the Caribbean, 
improving capacity for measurement, reporting and verification 
through the Alliance of the Pacific and a  climate action package 
launched by Singapore). 
Beyond the UNFCCC, other climate cooperation and partnership 
activities on capacity building include climate-related bilateral 
cooperation and those organised by the OECD, IFDD (Francophonie 
Institute for Sustainable Development), UNDP National 
Communications Support Programme, UNEP and the World Bank. 
Climate-related bilateral cooperation provides important human 
and institutional capacity building support for climate change 
actions and activities in developing countries, particularly through

developed countries’ bilateral cooperation structures, such as the 
French Development Agency (AFD), the German Development Agency 
(The Deutsche Gesellschaft für Internationale Zusammenarbeit – GIZ), 
the Japanese International Cooperation Agency (JICA) and others.
There are also a  number of regional cooperative structures with 
capacity-building components, including ClimaSouth, Euroclima+, 
the UN-REDD Programme, the Caribbean Regional Strategic 
Programme for Resilience, the Caribbean Climate Online Risk and 
Adaptation Tool, a project on accelerating low carbon and resilient 
society realisation in the Southeast Asian region, the World Health 
Organisation’s Global Salm-Surv network, the Red Iberoamericana 
de Oficinas de Cambio Climático network and the Africa Adaptation 
Initiative. Many climate-related capacity-building initiatives, 
including those coordinated or funded by international or regional 
institutions, are implemented at the national and sub-national levels,

often with the involvement of universities, consultancy groups and 
civil society actors. 
It is also noted that comprehensive support is provided by the GCF 
to developing countries (GCF , 2020). This support is made available 
and accessible for all developing countries through three different 
GCF tools: the Readiness Programme, the Project Preparation 
Facility, and the funding of transformative projects and programmes. 
The goal of the Readiness Programme is to strengthen institutional 
capacities, governance mechanisms, and planning and programming 
competencies in support of developing countries’ transformational 
long-term climate policies (GCF , 2020). Despite a  decades-long 
process of capacity-building efforts under many development and 
environmental regimes, including the UNFCCC, progress has been 
uneven and largely unsuccessful in establishing institution-based

1488
Chapter 14 International Cooperation14capacity in developing countries (Robinson 2018). In an effort to 
improve capacity-building efforts within the UNFCCC, in 2015, 
the Paris Committee on Capacity-building (PCCB) was established 
by the COP decision accompanying the Paris Agreement as the 
primary body for enhancing capacity-building efforts, including by 
improving coherence and coordination in capacity-building activities 
(UNFCCC 2016a, para. 71). The activities of the Committee include 
the provision of guidance and technical support on climate change 
training and capacity building, raising awareness and sharing climate 
information and knowledge. During 2020, the PCCB was able, despite 
the COVID-19 situation, to hold its fourth meeting, implement and 
assess its 2017–2020 work plan, and develop and agree on its future 
roadmap (2021–2024) (UNFCCC Subsidiary Body for Implementation 
2020). Non-governmental organisations such as the Coalition on

Paris Agreement Capacity-building provide expert input to the PCCB. 
Quantifying the contribution of capacity-building efforts to climate 
mitigation is acknowledged to be ‘difficult, if not impossible’ (Hsu 
et al. 2019a). Nonetheless, such activities ‘may play a valuable role in 
building a foundation for future reductions’ by providing ‘necessary 
catalytic linkages between actors’ (Hsu et al. 2019a).
14.4.4 Cooperative Mechanisms and Markets
In theory, trading carbon assets can reduce the costs of global climate 
mitigation, by helping facilitate abatement of greenhouse gases 
at least-cost locations. This could help countries ratchet up their 
ambitions more than in a situation without such mechanisms (Mehling 
et al. 2018), particularly if mechanisms are scaled up from projects and 
programmes (Michaelowa et al. 2019b). Progress as to developing 
such mechanisms has however so far been moderate and uneven. 
Of the three international market-based mechanisms under the 1997

Kyoto Protocol discussed in Section 14.3.2.7, and in previous IPCC 
reports, only the CDM or a similar mechanism may have a role to 
play under the Paris Agreement, although the precise terms are yet 
to be decided. 
Article 6, also discussed in Section 14.3.2.7, is the main framework 
to foster enhanced cooperation within the Paris Agreement. 
Although there is an emerging global landscape of activities based 
on Article 6 (Greiner et al. 2020), such as the bilateral treaty signed 
under the framework of Article 6 in October 2020 by Switzerland 
and Peru, the possibilities of bilateral cooperation are yet to be fully 
exploited. As discussed above, adequate accounting rules are key to 
the success of Article 6. Sectoral agreements are also a promising 
cooperative mechanism, as discussed in Section  14.5.2. In fact, 
both bilateral and sectoral agreements have the potential to 
enhance the ambition of the Parties involved and can eventually

serve as building blocks towards more comprehensive agreements 
(Section 14.2.2). 
A relevant and promising new development is the international 
linkage of existing regional or national emissions trading systems 
(ETS). Several ETS are now operational in different jurisdictions, including the EU, Switzerland, China, South Korea, New Zealand, 
Kazakhstan and several US states and Canadian provinces (Wettestad 
and Gulbrandsen 2018). More systems are in the pipeline, including 
Mexico and Thailand (ICAP 2019). The link between the EU and 
Switzerland entered into force in January 2020 and other linkages 
are being negotiated. Scholars analyse the potential benefits of these 
multilateral linkages and demonstrate that these can be significant 
(Doda et al. 2019; Doda and Taschini 2017). Over time, the linkages 
of national emissions trading systems can be seen as building blocks 
to a  strategic enlargement of international cooperation (Caparrós

and Péreau 2017; Mehling 2019). The World Bank has emerged as an 
important lynchpin and facilitator of knowledge-building and sharing 
of lessons about the design and linking of carbon markets, through 
initiatives such as the Partnership for Market Readiness, Networked 
Carbon Markets and the Carbon Pricing Leadership Coalition 
(Wettestad et al. 2021).
However, it is important to distinguish between theory and practice. 
The practice of ETS linking so far demonstrates a few attempts that 
did not result in linkages due to shifts of governments and political 
preferences (for instance the process between the EU and Australia, 
and Ontario withdrawing from the Western Climate Initiative) (Bailey 
and Inderberg 2018). It is worth noting that the linking of carbon 
markets raises problems of distribution of costs and loss of political 
control and hence does not offer a politically easy alternative route 
to a  truly international carbon market. Careful, piecemeal and

incremental linking may be the most feasible approach forward 
(Green et al. 2014; Gulbrandsen et al. 2019). It is premature for any 
serious assessment of the practice of ETS linking to be conducted. 
Environmental effectiveness, transformative potential, economic 
performance, institutional strength and even distributional outcomes 
can potentially be significant and positive if linking is done carefully 
(Doda and Taschini 2017; Mehling et al. 2018; Doda et al. 2019), but 
are all marginal if one focuses on existing experiences (Spalding-
Fecher et al. 2012; Haites 2016; Schneider et al. 2017; La Hoz Theuer 
et al. 2019; Schneider et al. 2019).
14.4.5 International Governance of SRM and CDR 
While Solar Radiation Modification (SRM) and carbon dioxide 
removal (CDR) were often referred to as ‘geoengineering’ in earlier 
IPCC reports and in the literature, IPCC SR1.5 started to explore 
SRM and CDR more thoroughly and to highlight the differences

between –  but also within –  both approaches more clearly. This 
section assesses international governance of both SRM and CDR, 
recognising that CDR, as a mitigation option, is covered elsewhere in 
this report, whereas SRM is not. Chapter 12 of this report covers the 
emerging national, sub-national and non-state governance of CDR, 
while Chapters 6, 7 and 12 also assess the mitigation potential, risks 
and co-benefits of some CDR options. Chapters 4 and 5 of AR6 WGI 
assess the physical climate system and biogeochemical responses 
to different SRM and CDR methods. Cross-Working Group Box 4 on 
SRM (AR6 WGII, Chapter  16; and Cross-Working Group Box  4  in 
this chapter) gives a brief overview of Solar Radiation Modification 
methods, risks, benefits, ethics and governance.

1489
International Cooperation Chapter 1414Cross-Working Group Box 4 | Solar Radiation Modification 
Authors: Govindasamy Bala (India), Heleen de Coninck (the Netherlands), Oliver Geden (Germany), Veronika Ginzburg (the Russian 
Federation), Katharine J. Mach (the United States of America), Anthony Patt (Switzerland), Sonia I. Seneviratne (Switzerland), Masahiro 
Sugiyama (Japan), Christopher H. Trisos (South Africa), Maarten van Aalst (the Netherlands)
Proposed Solar Radiation Modification schemes 
This cross-working group box assesses Solar Radiation Modification (SRM) proposals, their potential contribution to reducing or 
increasing climate risk, as well as other risks they may pose (categorised as risks from responses to climate change in the IPCC AR6 
risk definition in 1.2.1.1), and related perception, ethics and governance questions.
SRM refers to proposals to increase the reflection of shortwave radiation (sunlight) back to space to counteract anthropogenic warming

and some of its harmful impacts (de Coninck et al. 2018) (AR6 WGI Chapters 4 and 5). A number of SRM options have been proposed, 
including: stratospheric aerosol interventions (SAI), marine cloud brightening (MCB), ground-based albedo modifications (GBAM), and 
ocean albedo change (OAC). Although not strictly a form of SRM, cirrus cloud thinning (CCT) has been proposed to cool the planet 
by increasing the escape of longwave thermal radiation to space and is included here for consistency with previous assessments 
(de Coninck et al. 2018). SAI is the most-researched proposal. Modelling studies show SRM could reduce surface temperatures and 
potentially ameliorate some climate change risks (with more confidence for SAI than other options), but SRM could also introduce 
a range of new risks.
There is high agreement in the literature that for addressing climate change risks, SRM cannot be the main policy response to climate

change and is, at best, a supplement to achieving sustained net zero or net negative CO 2 emission levels globally (de Coninck et al. 
2018; MacMartin et al. 2018; Buck et al. 2020; National Academies of Sciences Engineering and Medecine 2021). SRM contrasts with 
climate change mitigation activities, such as emissions reductions and CDR, as it introduces a ‘mask’ to the climate change problem 
by altering the Earth’s radiation budget, rather than attempting to address the root cause of the problem, which is the increase in 
GHGs in the atmosphere. In addition, the effects of proposed SRM options would only last as long as a deployment is maintained – for 
example, requiring a yearly injection of aerosols in the case of SAI as the lifetime of aerosols in the stratosphere is one to three years 
(Niemeier et al. 2011) or continuous spraying of sea salt in the case of MCB as the lifetime of sea salt aerosols in the atmosphere is

only about 10 days – which contrasts with the long lifetime of CO 2 and its climate effects, with global warming resulting from CO 2 
emissions likely remaining at a similar level for a hundred years or more (MacDougall et al. 2020) and long-term climate effects of 
emitted CO 2 remaining for several hundreds to thousands of years (Solomon et al. 2009).
The choice of SRM deployment scenarios and reference scenarios is crucial in assessment of SRM risks and its effectiveness in 
attenuating climate change risks (Keith and MacMartin 2015; Honegger et al. 2021a). Most climate model simulations have used 
scenarios with highly stylised large SRM forcing to fully counteract large amounts of warming in order to enhance the signal-to-noise 
ratio of climate responses to SRM (Kravitz et al. 2015; Sugiyama et al. 2018a; Krishnamohan et al. 2019).
The effects of SRM fundamentally depend on a variety of choices about deployment (Sugiyama et al. 2018b), including: its position

in the portfolio of human responses to climate change (e.g., the magnitude of SRM used against the background radiative forcing), 
governance of research and potential deployment strategies, and technical details (latitude, materials, and season, among others, 
see AR6 WGI Chapter 4.6.3.3). The plausibility of many SRM scenarios is highly contested and not all scenarios are equally plausible 
because of socio-political considerations (Talberg et al. 2018), as with, for example, CDR (Fuss et al. 2014, 2018). Development 
of scenarios and their selection in assessments should reflect a diverse set of societal values with public and stakeholder inputs 
(Sugiyama et al. 2018a; Low and Honegger 2020), as depending on the focus of a limited climate model simulation, SRM could look 
grossly risky or highly beneficial (Pereira et al. 2021).
In the context of reaching the long-term global temperature goal of the Paris Agreement, there are different hypothetical scenarios

emissions with total reliance on SRM, and regionally heterogeneous SRM. Each scenario presents different levels and distributions 
of SRM benefits, side effects, and risks. The more intense the SRM deployment, the larger is the likelihood for the risks of side effects 
and environmental risks (e.g., Heutel et al., 2018). Regional disparities in climate hazards may result from both regionally-deployed 
SRM options such as GBAM, and more globally uniform SRM such as SAI (Jones et al. 2018; Seneviratne et al. 2018). There is an 
emerging literature on smaller forcings of SAI to reduce global average warming, for instance, to hold global warming to 1.5°C or 2°C 
alongside ambitious conventional mitigation (Jones et al. 2018; MacMartin et al. 2018), or bring down temperature after an overshoot

1490
Chapter 14 International Cooperation14Cross-Working Group Box 4 (continued)
(Tilmes et al. 2020). If emissions reductions and CDR are deemed insufficient, SRM may be seen by some as the only option left to 
ensure the achievement of the Paris Agreement’s temperature goal by 2100.
Since AR5, hundreds of climate modelling studies have simulated effects of SRM on climate hazards (Kravitz et al. 2015; Tilmes et al. 
2018). Modelling studies have shown SRM has the potential to offset some effects of increasing GHGs on the global and regional 
climate, including the increase in frequency and intensity of extremes of temperature and precipitation, melting of Arctic sea ice and 
mountain glaciers, weakening of Atlantic meridional overturning circulation, changes in frequency and intensity of tropical cyclones,

and decrease in soil moisture (AR6 WGI, Chapter  4). However, while SRM may be effective in alleviating anthropogenic climate 
Cross-Working Group Box 4, Table 1 | SRM options and their potential climate and non-climate impacts. Description, potential climate impacts, 
potential impacts on human and natural systems, and termination effects of a number of SRM options: stratospheric aerosol interventions 
(SAI), marine cloud brightening (MCB), ocean albedo change (OAC), ground-based albedo modifications (GBAM), and cirrus cloud thinning (CCT).
into the stratosphere or 
a gas which then
clouds, making them 
more reflectiveIncrease surface albedo of 
the ocean (e.g., by creating 
microbubbles or placing 
reflective foam on 
the surface)Whitening roofs, 
changes in land use 
management (e.g., no-till

farming, bioengineering 
to make crop leaves 
more reflective), desert 
albedo enhancement, 
covering glaciers with 
reflective sheetingSeeding to promote 
radiation to escape 
to space
Potential 
other than 
reduced warmingChange precipitation 
and runoff pattern; 
reduced temperature and 
recovery; changes 
in surface ozone 
and UV radiationChange in land–sea 
regional precipitation 
and runoff changes Change in land–sea 
and runoff changes.Changes in regional 
regional extremes and

and precipitation pattern, 
Potential impacts 
on human and 
natural systemsChanges in crop yields, 
over land UnresearchedAltered photosynthesis and 
effects on biodiversity Altered photosynthesis  
and carbon uptake 
Termination 
effectsSudden and sustained 
warming offset. Sudden and sustained
depends on the degree of

result in rapid warming. 
depends on the degree 
of warming offset. GBAM can be maintained 
of warming offset. Sudden and sustained
result in rapid warming. 
of warming offset. 
this box)Visioni et al. (2017)
Tilmes et al. (2018)
Simpson et al. (2019)Latham et al. (2012)
Ahlm et al. (2017)
Stjern et al. (2018)Evans et al. (2010)
Crook et al. (2015)Davin et al. (2014)
Crook et al. (2015)
Zhang et al. (2016)
Field et al. (2018)
Seneviratne et al. (2018)Storelvmo and Herger 
(2014)
Jackson et al. (2016)
Duan et al. (2020)
Gasparini et al. (2020)

1491
International Cooperation Chapter 1414Cross-Working Group Box 4 (continued)
warming either locally or globally, it would not maintain the climate in a present-day state nor return the climate to a pre-industrial 
state (climate averaged over 1850–1900) (AR6 WGI, Box 1.2) in all regions and in all seasons even when used to fully offset the global 
mean warming (high confidence) (AR6 WGI Chapter 4). This is because the climate forcing and response to SRM options are different 
from the forcing and response to GHG increase. Because of these differences in climate forcing and response patterns, the regional 
and seasonal climates of a world with a global mean warming of 1.5°C or 2°C achieved via SRM would be different from a world with 
similar global mean warming but achieved through mitigation (MacMartin et al. 2018). At the regional scale and seasonal timescale

there could be considerable residual climate change and/or overcompensating change (e.g., more cooling, wetting or drying than just 
what’s needed to offset warming, drying or wetting due to anthropogenic greenhouse gas emissions), and there is low confidence in 
understanding of the climate response to SRM at the regional scale (AR6 WGI, Chapter 4).
SAI implemented to partially offset warming (e.g., offsetting half of global warming) may have potential to ameliorate hazards in 
multiple regions and reduce negative residual change, such as drying compared to present-day climate, that are associated with fully 
offsetting global mean warming (Irvine and Keith 2020), but may also increase flood and drought risk in Europe compared to unmitigated 
warming (Jones et al. 2021). Recent modelling studies suggest it is conceptually possible to meet multiple climate objectives through

optimally designed SRM strategies (WGI, Chapter 4). Nevertheless, large uncertainties still exist for climate processes associated with 
SRM options (e.g., aerosol-cloud-radiation interaction) (AR6 WGI, Chapter 4) (Kravitz and MacMartin 2020).
Compared with climate hazards, many fewer studies have examined SRM risks – the potential adverse consequences to people and 
ecosystems from the combination of climate hazards, exposure and vulnerability – or the potential for SRM to reduce risk (Curry et al. 
2014; Irvine et al. 2017). Risk analyses have often used inputs from climate models forced with stylised representations of SRM, such 
atmosphere, which include more complex cloud-radiative feedbacks. Most studies have used scenarios where SAI is deployed to hold 

There is low confidence and large uncertainty in projected impacts of SRM on crop yields due in part to a limited number of studies. 
Because SRM would result in only a slight reduction in CO 2 concentrations relative to the emissions scenario without SRM (AR6 
WGI, Chapter  5), the CO 2 fertilisation effect on plant productivity is nearly the same in emissions scenarios with and without 
SRM. Nevertheless, changes in climate due to SRM are likely to have some impacts on crop yields. A single study indicates MCB may 
reduce crop failure rates compared to climate change from a doubling of CO 2 pre-industrial concentrations (Parkes et al. 2015). Models 
suggest SAI cooling would reduce crop productivity at higher latitudes compared to a scenario without SRM by reducing the growing 
season length, but benefit crop productivity in lower latitudes by reducing heat stress (Pongratz et al. 2012; Xia et al. 2014; Zhan et al.

2019). Crop productivity is also projected to be reduced where SAI reduces rainfall relative to the scenario without SRM, including 
a case where reduced Asian summer monsoon rainfall causes a reduction in groundnut yields (Xia et al. 2014; Yang et al. 2016). 
SAI will increase the fraction of diffuse sunlight, which is projected to increase photosynthesis in forested canopy, but will reduce 
the direct and total available sunlight, which tends to reduce photosynthesis. As total sunlight is reduced, there is a net reduction 
in crop photosynthesis with the result that any benefits to crops from avoided heat stress may be offset by reduced photosynthesis, 
as indicated by a single statistical modelling study (Proctor et al. 2018). SAI would reduce average surface ozone concentration (Xia 
et al. 2017) mainly as a result of aerosol-induced reduction in stratospheric ozone in polar regions, resulting in reduced downward

transport of ozone to the troposphere (Pitari et al. 2014; Tilmes et al. 2018). The reduction in stratospheric ozone also allows more UV 
radiation to reach the surface. The reduction in surface ozone, together with an increase in surface UV radiation, would have important 
implications for crop yields but there is low confidence in our understanding of the net impact.
Few studies have assessed potential SRM impacts on human health and well-being. SAI using sulfate aerosols is projected to deplete 
would reduce mortality from air pollution, with net changes in mortality uncertain and depending on aerosol type and deployment

scenario (Effiong and Neitzel 2016; Eastham et al. 2018; Dai et al. 2020). However, these effects may be small compared to changes 
in risk from infectious disease (e.g., mosquito-borne illnesses) or food security due to SRM influences on climate (Carlson et al. 2022). 
Using volcanic eruptions as a natural analogue, a sudden implementation of SAI that forced the El Niño–Southern Oscillation (ENSO) 
system may increase risk of severe cholera outbreaks in Bengal (Trisos et al. 2018; Pinke et al. 2019). Considering only mean annual 
between countries compared to the highest warming pathway (RCP8.5) (Harding et al. 2020). Some integrated assessment model

1492
scenarios have included SAI (Arino et al. 2016; Emmerling and Tavoni 2018; Heutel et al. 2018; Helwegen et al. 2019; Rickels et al. 
2020) showing the indirect costs and benefits to welfare dominate, since the direct economic cost of SAI itself is expected to be 
relatively low (Moriyama et al. 2017; Smith and Wagner 2018). There is a general lack of research on the wide scope of potential risk 
or risk reduction to human health, well-being and sustainable development from SRM and on their distribution across countries and 
vulnerable groups (Honegger et al. 2021a; Carlson et al. 2022).
may lead to counter-geoengineering measures such as deliberate release of warming agents or destruction of deployment equipment

(Parker et al. 2018). Game-theoretic models and laboratory experiments indicate a powerful actor or group with a higher preference 
for SRM may use SAI to cool the planet beyond what is socially optimal, imposing welfare losses on others although this cooling does 
not necessarily imply excluded countries would be worse off relative to a world of unmitigated warming (Ricke et al. 2013; Weitzman 
2015; Abatayo et al. 2020). In this context, counter-geoengineering may promote international cooperation or lead to large welfare 
losses (Helwegen et al. 2019; Abatayo et al. 2020).
increasing in the atmosphere or affect the resulting ocean acidification under continued anthropogenic emissions (high confidence) 
(AR6 WGI, Chapter 5). 
Few studies have assessed potential SRM impacts on ecosystems. SAI and MCB may reduce risk of coral reef bleaching compared

to global warming with no SAI (Latham et al. 2013; Kwiatkowski et al. 2015), but risks to marine life from ocean acidification would 
remain, because SRM proposals do not reduce elevated anthropogenic atmospheric CO 2 concentrations. MCB could cause changes in 
marine net primary productivity by reducing light availability in deployment regions, with important fishing regions off the west coast 
of South America showing both large increases and decreases in productivity (Partanen et al. 2016; Keller 2018).
There is large uncertainty in terrestrial ecosystem responses to SRM.  By decoupling increases in atmospheric greenhouse gas 
concentrations and temperature, SAI could generate substantial impacts on large-scale biogeochemical cycles, with feedbacks to 
regional and global climate variability and change (Zarnetske et al. 2021). Compared to a high CO 2 world without SRM, global-scale

SRM simulations indicate reducing heat stress in low latitudes would increase plant productivity, but cooling would also slow down 
the process of nitrogen mineralisation, which could decrease plant productivity (Glienke et al. 2015; Duan et al. 2020). In high latitude 
and polar regions SRM may limit vegetation growth compared to a high CO 2 world without SRM, but net primary productivity may still 
be higher than pre-industrial climate (Glienke et al. 2015). Tropical forests cycle more carbon and water than other terrestrial biomes 
but large areas of the tropics may tip between savanna and tropical forest depending on rainfall and fire (Beer et al. 2010; Staver et 
al. 2011). Thus, SAI-induced reductions in precipitation in Amazonia and central Africa are expected to change the biogeography of 
tropical ecosystems in ways different both from present-day climate and global warming without SAI (Simpson et al. 2019; Zarnetske

et al. 2021). This would have potentially large consequences for ecosystem services (AR6 WGII, Chapters 2 and 9). When designing 
and evaluating SAI scenarios, biome-specific responses need to be considered if SAI approaches are to benefit rather than harm 
ecosystems. Regional precipitation change and sea salt deposition over land from MCB may increase or decrease primary productivity 
in tropical rainforests (Muri et al. 2015). SRM that fully offsets warming could reduce the dispersal velocity required for species to 
track shifting temperature niches whereas partially offsetting warming with SAI would not reduce this risk unless rates of warming 
were also reduced (Trisos et al. 2018; Dagon and Schrag 2019). SAI may reduce high fire-risk weather in Australia, Europe and parts 
of the Americas, compared to global warming without SAI (Burton et al. 2018). Yet SAI using sulphur injection could shift the spatial

distribution of acid-induced aluminium soil toxicity into relatively undisturbed ecosystems in Europe and North America (Visioni et 
al. 2020). For the same amount of global mean cooling, SAI, MCB, and CCT would have different effects on gross and net primary 
productivity because of different spatial patterns of temperature, available sunlight, and hydrological cycle changes (Duan et al. 
2020). Large-scale modification of land surfaces for GBAM may have strong trade-offs with biodiversity and other ecosystem services, 
including food security (Seneviratne et al. 2018). Although existing studies indicate SRM will have widespread impacts on ecosystems, 
risks and potential for risk reduction for marine and terrestrial ecosystems and biodiversity remain largely unknown.

1493
A sudden and sustained termination of SRM in a high CO 2 emissions scenario would cause rapid climate change (high confidence) 
(AR6 WGI, Chapter 4). More scenario analysis is needed on the potential likelihood of sudden termination (Kosugi 2013; Irvine and 
Keith 2020). A gradual phase-out of SRM combined with emissions reduction and CDR could avoid these termination effects (medium 
confidence) (MacMartin et al. 2014; Keith and MacMartin 2015; Tilmes et al. 2016). Several studies find that large and extremely rapid 
warming and abrupt changes to the water cycle would occur within a decade if a sudden termination of SAI occurred (McCusker et 
al. 2014; Crook et al. 2015). The size of this ‘termination shock’ is proportional to the amount of radiative forcing being masked by 
SAI. A sudden termination of SAI could place many thousands of species at risk of extinction, because the resulting rapid warming

would be too fast for species to track the changing climate (Trisos et al. 2018).
(Burns et al. 2016; Cummings et al. 2017). Most studies have been limited to Western societies with some exceptions. Studies have 
repeatedly found that respondents are largely unaware of SRM (Merk et al. 2015). In the context of this general lack of familiarity, 
the publics prefer carbon dioxide removal (CDR) to SRM (Pidgeon et al. 2012), are very cautious about SRM deployment because of 
potential environmental side effects and governance concerns, and mostly reject deployment for the foreseeable future. Studies also 
suggest conditional and reluctant support for research, including proposed field experiments, with conditions of proper governance

(Sugiyama et al. 2020). Recent studies show that the perception varies with the intensity of deliberation (Merk et al. 2019), and that 
the public distinguishes different funding sources (Nelson et al. 2021). Limited studies for developing countries show a tendency for 
respondents to be more open to SRM (Visschers et al. 2017; Sugiyama et al. 2020), perhaps because they experience climate change 
more directly (Carr and Yung 2018). In some Anglophone countries, a small portion of the public believes in chemtrail conspiracy 
theories, which are easily found in social media (Tingley and Wagner 2017; Allgaier 2019). Since researchers rarely distinguish different 
SRM options in engagement studies, there remains uncertainty in public perception.
Ethics 
There is broad literature on ethical considerations around SRM, mainly stemming from philosophy or political theory, and mainly

focused on SAI (Flegal et al. 2019). There is concern that publicly debating, researching and potentially deploying SAI could involve 
a ‘moral hazard’, with potential to obstruct ongoing and future mitigation efforts (Morrow 2014; Baatz 2016; McLaren 2016), while 
empirical evidence is limited and mostly at the individual, not societal, level (Burns et al. 2016; Merk et al. 2016; Merk et al. 2019). 
There is low agreement whether research and outdoors experimentation will create a ‘slippery slope’ toward eventual deployment, 
leading to a lock-in to long-term SRM, or whether it can be effectively regulated at a later stage to avoid undesirable outcomes (Hulme 
2014; Parker 2014; Callies 2019; McKinnon 2019). Regarding potential deployment of SRM, procedural, distributive and recognitional 
conceptions of justice are being explored (Svoboda and Irvine 2014; Svoboda 2017; Preston and Carr 2018; Hourdequin 2019). With

the SRM research community’s increasing focus on distributional impacts of SAI, researchers have started more explicitly considering 
inequality in participation and inclusion of vulnerable countries and marginalised social groups (Flegal and Gupta 2018; Whyte 
2018; Táíwò and Talati 2021), including considering stopping research (Stephens and Surprise 2020; National Academies of Sciences 
Engineering and Medecine 2021). There is recognition that SRM research has been conducted predominantly by a relatively small 
number of experts in the Global North, and that more can be done to enable participation from diverse peoples and geographies in 
setting research agendas and research governance priorities, and undertaking research, with initial efforts to this effect (Rahman et al. 
2018), noting that unequal power relations in participation could influence SRM research governance and have potential implications

for policy (Winickoff et al. 2015; Frumhoff and Stephens 2018; Whyte 2018; Biermann and Möller 2019; McLaren and Corry 2021; 
National Academies of Sciences Engineering and Medecine 2021; Táíwò and Talati 2021).
Currently, there is no dedicated, formal international SRM governance for research, development, demonstration, or deployment (AR6 
WGIII, Chapter 14). Some multilateral agreements – such as the UN Convention on Biological Diversity or the Vienna Convention on 
the Protection of the Ozone Layer – indirectly and partially cover SRM, but none is comprehensive and the lack of robust and formal 
SRM governance poses risks (Ricke et al. 2013; Talberg et al. 2018; Reynolds 2019a). While governance objectives range broadly, from 
prohibition to enabling research and potentially deployment (Sugiyama et al. 2018b; Gupta et al. 2020), there is agreement that SRM

governance should cover all interacting stages of research through to any potential, eventual deployment with rules, institutions, 
and norms (Reynolds 2019b). Accordingly, governance arrangements are co-evolving with respective SRM technologies across the 
interacting stages of research, development, demonstration, and – potentially – deployment (Rayner et al. 2013; Parker 2014; Parson 
2014). Stakeholders are developing governance already in outdoors research; for example, for MCB and OAC experiments on the

1494
Great Barrier Reef (McDonald et al. 2019). Co-evolution of governance and SRM research provides a chance for responsibly developing 
SRM technologies with broader public participation and political legitimacy, guarding against potential risks and harms relevant 
across a full range of scenarios, and ensuring that SRM is considered only as a part of a broader portfolio of responses to climate 
change (Stilgoe 2015; Nicholson et al. 2018). For SAI, large-scale outdoor experiments even with low radiative forcing could be 
transboundary and those with deployment-scale radiative forcing may not be distinguished from deployment, such that MacMartin 
and Kravitz (2019) argue for continued reliance on modelling until a decision on whether and how to deploy is made, with modelling 
helping governance development. 
14.4.5.1 Global Governance of Solar Radiation Modification 
and Associated Risks

Solar radiation modification, in the literature also referred to as ‘solar 
geoengineering’, refers to the intentional modification of the Earth’s 
shortwave radiative budget, such as by increasing the reflection of 
sunlight back to space, with the aim of reducing warming. Several 
SRM options have been proposed, including stratospheric aerosol 
injection (SAI), marine cloud brightening (MCB), ground-based 
albedo modifications (GBAM), and ocean albedo change (OAC). SRM 
has been discussed as a potential response option within a broader 
climate risk management strategy, as a  supplement to emissions 
reduction, carbon dioxide removal and adaptation (Crutzen 2006; 
Shepherd 2009; Caldeira and Bala 2017; Buck et al. 2020), for 
example as a temporary measure to slow the rate of warming (Keith 
and MacMartin 2015) or address temperature overshoot (MacMartin 
et al. 2018; Tilmes et al. 2020). SRM assessments of potential benefits

and risks still primarily rely on modelling efforts and their underlying 
scenario assumptions (Sugiyama et al. 2018a), for example in the 
context of the Geoengineering Model Intercomparison Project 
GeoMIP6 (Kravitz et al. 2015). Recently, small-scale MCB and 
OAC experiments started to take place on the Great Barrier Reef 
(McDonald et al. 2019).
SAI –  the most researched SRM method –  poses significant 
international governance challenges since it could potentially 
be deployed uni- or minilaterally and alter the global mean 
temperature much faster than any other climate policy measure, 
at comparatively low direct costs (Parson 2014; Nicholson et al. 
2018; Smith and Wagner 2018; Sugiyama et al. 2018b; Reynolds 
2019a). While being dependent on the design of deployment 
systems, both geophysical benefits and adverse effects would 
potentially be unevenly distributed (AR6 WGI, Chapter  4). 
Perceived local harm could exacerbate geopolitical conflicts, not

least depending on which countries are part of a  deployment 
coalition (Maas and Scheffran 2012; Zürn and Schäfer 2013), but 
also because immediate attribution of climatic impacts to detected 
SAI deployment would not be possible. Uncoordinated or poorly 
researched deployment by a limited number of states, triggered by 
perceived climate emergencies, could create international tensions 
(Corry 2017; Lederer and Kreuter 2018). An additional risk is that 
of rapid temperature rise following an abrupt end of SAI activities 
(Parker and Irvine 2018; Rabitz 2019).While there is room for national and even sub-national governance of 
SAI – for example on research (differentiating indoor from open-air) 
(Jinnah et al. 2018; Hubert 2020) and public engagement (Bellamy 
and Lezaun 2017; Flegal et al. 2019) – international governance of 
SAI faces the challenge that comprehensive institutional architectures 
designed too far in advance could prove either too restrictive or too

permissive in light of subsequent political, institutional, geophysical 
and technological developments (Sugiyama et al. 2018a; Reynolds 
2019a). Views on governance encompass a broad range, from aiming 
to restrict to wanting to enable research and potentially deployment; 
in between these poles, other authors stress the operationalisation 
of the precautionary approach: preventing deployment until specific 
criteria regarding scientific consensus, impact assessments and 
governance issues are met (Tedsen and Homann 2013; Wieding 
et al. 2020). Many scholars suggest that governance arrangements 
ought to co-evolve with respective SRM technologies (Parker 2014), 
including that it stay at least one step ahead of research, development, 
demonstration, and – potentially – deployment (Rayner et al. 2013; 
Parson 2014). With the modelling community’s increasing focus on 
showing that, and in what ways, SAI could help to minimise climate

change impacts in the Global South, the SRM governance literature 
has come to include considerations of how SAI could contribute 
to global equity (Horton and Keith 2016; Flegal and Gupta 2018; 
Hourdequin 2018).
Given that risks and potential benefits of SRM proposals differ 
substantially and their large-scale deployment is highly speculative, 
there is a wide array of concrete proposals for near-term anticipatory 
or adaptive governance. Numerous authors suggest a  wide range 
of governance principles Nicholson et al. (2018) encapsulate most 
of these in suggesting a  list of four: (i) Guard against potential 
risks and harm; (ii) Enable appropriate research and development 
of scientific knowledge; (iii) Legitimise any future research or 
policymaking through active and informed public and expert 
community engagement; (iv) Ensure that SRM is considered only 
as a  part of a  broader, mitigation-centred portfolio of responses

to climate change. Regarding international institutionalisation, 
options range from formal integration into existing UN bodies like 
the UNFCCC (Nicholson et al. 2018) or the Convention on Biological 
Diversity (CBD) (Bodle et al. 2014) to the creation of specific, but 
less formalised global fora (Parson and Ernst 2013) to forms of club 
governance (Bodansky 2013; Lloyd and Oppenheimer 2014). Recent 
years have also seen the emergence of transnational non-state actors

1495
International Cooperation Chapter 1414focusing on SRM governance, primarily expert networks and NGOs 
(Horton and Koremenos 2020).
Currently, there is no targeted international law relating to SRM, 
although some multilateral agreements –  such as the Convention 
on Biological Diversity, the UN Convention on the Law of the 
Sea, the Environmental Modification Convention, and the Vienna 
Convention on the Protection of the Ozone Layer and its Montreal 
Protocol  –  contain provisions applicable to SRM (Bodansky 2013; 
Jinnah and Nicholson 2019; Reynolds 2019a).
14.4.5.2 Carbon Dioxide Removal 
from the ocean and atmosphere and durably store it in geological, 
terrestrial, or ocean reservoirs, or in products (Table 12.6). In contrast 
to SRM, CDR does not necessarily impose transboundary risks, except

insofar as misleading accounting of its use and deployment could give 
a false picture of countries’ overall mitigation efforts. CDR is clearly 
a form of climate change mitigation, and as described in Chapter 12 
is needed to counterbalance residual GHG emissions that may prove 
hard to abate (e.g., from industry, aviation or agriculture) in the context 
of reaching net zero emissions both globally – in the context of Article 
4 of the Paris Agreement – and nationally. CDR could also later be used 
for reducing atmospheric CO 2 concentrations by providing net negative 
emissions at the global level (Fuglestvedt et al. 2018; Bellamy and 
Geden 2019). Despite the common feature of removing carbon dioxide, 
technologies like afforestation/reforestation, soil carbon sequestration, 
bioenergy with carbon capture and storage, direct air capture with 
carbon storage, enhanced weathering, ocean alkalinity enhancement 
or ocean fertilisation are very different, as are the governance

challenges. Chapter  12 highlights the sustainable development risks 
associated with land and water use that are connected to the biological 
approaches to CDR. As a public good which largely lacks incentives 
to be pursued as a business case, most types of CDR require a suite 
of dedicated policy instruments that address both near-term needs as 
well as long-term continuity at scale (Honegger et al. 2021b).
CDR methods other than afforestation/reforestation and soil carbon 
sequestration have only played a minor role in UNFCCC negotiations 
so far (Fridahl 2017; Rumpel et al. 2020). To accelerate, and indeed 
better manage CDR globally, stringent rules and practices regarding 
emissions accounting, measuring, reporting and verifying and 
project-based market mechanisms have been proposed (Honegger 
and Reiner 2018; Mace et al. 2018). Given their historic responsibility, 
it can be expected that developed countries would carry the main

burden of researching, developing, demonstrating and deploying 
CDR, or finance such projects in other countries (Fyson et al. 2020; 
Pozo et al. 2020). McLaren et al. (2019) suggest that there is 
a  rationale for separating the international commitments for net 
negative emissions from those for emissions reductions.
Specific regulations on CDR options have been limited to those 
posing transboundary risks, namely the use of ocean fertilisation. 
In a series of separate decisions from 2008 to 2013, Parties to the 
London Convention and Protocol limited ocean fertilisation activities to only those of a  research character, and in 2012 the CBD made 
a  non-legally-binding decision to do the same, further requiring 
such research activities to be limited scale, and carried out under 
controlled conditions, until more knowledge is gained to be able to 
assess the risks (GESAMP 2019; Burns and Corbett 2020). In doing 
so they have taken a  precautionary approach (Sands and  Peel,

2018). The London Convention and Protocol has also developed 
an Assessment Framework for Scientific Research Involving Ocean 
Fertilisation (London Convention/Protocol 2010) and in 2013 adopted 
amendments (which are not yet in force) to regulate marine carbon 
dioxide removal activities, including ocean fertilisation. 
14.5 Multi-level, Multi-actor Governance
The Paris Agreement sets in place a new framework for international 
climate policy (Paroussos et al. 2019), which some cite as evidence 
of ‘hybrid multilateralism’ (Christoff 2016; Savaresi 2016; Bäckstrand 
et al. 2017). While a  trend of widening involvement of non-state 
actors was evident prior to conclusion of the Paris Agreement, 
particularly at UNFCCC COPs, the ‘new landscape of international 
climate cooperation’ features an ‘intensified interplay between 
state and non-state actors’, including civil society and social 
movements, business actors, and sub-national or substate actors,

such as local governments and cities (Bäckstrand et al. 2017, p. 562). 
This involvement of other actors beyond states in international climate  
cooperation is facilitated by the Paris Agreement’s ‘hybrid 
climate policy architecture’ (Bodansky et al. 2016) (Section 14.3.1.1), 
which acknowledges the primacy of domestic politics in climate change 
and invites the mobilisation of international and domestic pressure 
to make the Agreement effective (Falkner 2016b). In this landscape, 
there is greater flexibility for more decentralised ‘polycentric’ forms 
of climate governance and recognition of the benefits of working in 
diverse forms and groups to realise global climate mitigation goals 
(Jordan et al. 2015; Oberthür 2016) (Section 1.9). 
Increasing attention has focused on the role of multi-level, multi-
actor cooperation among actors, groupings and agreements beyond 
the UNFCCC climate regime as potential ‘building blocks’ towards

enhanced international action on climate mitigation (Falkner 
2016a; Caparrós and Péreau 2017; Potoski 2017; Stewart et al. 
2017). This can include agreements on emissions and technologies 
at the regional or sub-global level, what scholars often refer to as 
‘climate clubs’ (Nordhaus 2015; Hovi et al. 2016; Green 2017; Sprinz 
et al. 2018). One forum through which such agreements are often 
discussed, in support of UNFCCC objectives, is high-level meetings 
of political leaders, such as the G7 and G20 states (Livingston 2016). 
It also includes cooperation on narrower sets of issues than are 
found within the Paris Agreement, for instance, other international 
environmental agreements dealing with a  particular subset of 
GHGs; linkages with, or leveraging of, efforts or agreements in other 
spheres such as adaptation, human rights or trade; agreements 
within particular economic sectors; or transnational initiatives

involving global cooperative efforts by different types of non-state 
actors. Cooperative efforts in each of these forums are reviewed 
in the following sections of the chapter. Section  14.5.1 discusses 
international cooperation at multiple governance levels (global,

1496
Chapter 14 International Cooperation14sub-global and regional); Section 14.5.2 discusses cooperation with 
international sectoral agreements and institutions such as in the 
forestry, energy and transportation sectors; and Sections 14.5.3–
14.5.5 discuss transnational cooperation across civil society and 
social movements, business partnerships and investor coalitions, and 
between sub-national entities and cities, respectively. 
A key idea underpinning this analysis is that decomposition of 
the larger challenge of climate mitigation into ‘smaller units’ may 
facilitate more effective cooperation (Sabel and Victor 2017) and 
complement cooperation in the UN climate regime (Stewart et al. 
2017). However, it is recognised that significant uncertainty remains 
over the feasibility and costs of these efforts (Sabel and Victor 2017), 
as well as whether they ultimately strengthen progress on climate 
mitigation in the multilateral climate arena (Falkner 2016a).

14.5.1 International Cooperation at Multiple 
Governance Levels
14.5.1.1 Role of Other Environmental Agreements
International cooperation on climate change mitigation takes place 
at multiple governance levels, including under a range of multilateral 
environmental agreements (MEAs) beyond those of the international 
climate regime. 
The 1987 Montreal Protocol on Substances that Deplete the Ozone 
Layer (the Montreal Protocol) is the leading example of a  non-
climate MEA with significant implications for mitigating climate 
change (Barrett 2008). The Montreal Protocol regulates a  number 
of substances that are both ozone-depleting substances (ODS) and 
GHGs with a significant global warming potential (GWP), including 
chlorofluorocarbons, halons and hydrochlorofluorocarbons (HCFCs). 
As a  result, implementation of phase-out requirements for these 
substances under the Montreal Protocol has made a  significant 
contribution to mitigating climate change (Molina et al. 2009)

(Section 9.9.7.1). Velders et al. (2007) found that over the period from 
1990 to 2010, the reduction in GWP100-weighted ODS emissions 
expected with compliance to the provisions of the Montreal Protocol 
was 8 GtCO 2-eq yr–1, an amount substantially greater than the first 
commitment period Kyoto reduction target. Young et al. (2021) 
suggest that the Montreal Protocol may also be helping to mitigate 
climate change through avoided decreases in the land carbon sink.
The 2016 Kigali Amendment to the Montreal Protocol applies to the 
production and consumption of hydrofluorocarbons (HFCs). HFCs, 
which are widely used as refrigerants (Abas et al. 2018), have a high 
GWP100 of 14,600 for HFC-23, and are not ODS (Section 9.9.7.1). 
The Kigali Amendment addresses the risk that the phase-out of 
HCFCs under the Montreal Protocol and their replacement with 
HFCs could exacerbate global warming (Akanle 2010; Hurwitz 
et al. 2016), especially with the predicted growth in HFC usage for

applications like air conditioners (Velders et al. 2015). In this way it 
creates a cooperative rather than a conflictual relationship between 
addressing ozone depletion and the climate protection goals of the 
UNFCCC regime (Hoch et al. 2019). The Kigali Amendment requires developed country Parties to phase down HFCs by 85% from 2011 
to 2013 levels by 2036. Developing country Parties are permitted 
longer phase-down periods (out to 2045 and 2047), but must freeze 
production and consumption between 2024 and 2028 (Ripley and 
Verkuijl 2016; UN 2016). A ban on trade in HFCs with non-Parties 
will come into effect from 1  January 2033. For HFC-23, which is 
a  by-product of HCFC production rather than an ODS, Parties are 
required to report production and consumption data, and to destroy 
all emissions of HFC-23 occurring as part of HCFCs or HFCs to the 
extent practicable from 2020 onwards using approved technologies 
(Ripley and Verkuijl 2016).

Full compliance with the Kigali Amendment is predicted to reduce 
HFC emissions by 61% of the global baseline by 2050 (Höglund-
Isaksson et al. 2017), with avoided global warming in 2100 due to 
HFCs from a baseline of 0.3°C–0.5°C to less than 0.1°C (WMO 2018). 
Examining the interplay of the Kigali Amendment with the Paris 
Agreement, Hoch et al. (2019) show how the Article 6 mechanisms 
under the Paris Agreement could generate financial incentives for 
HFC mitigation and related energy efficiency improvements. Early 
action under Article 6  of the Paris Agreement could drive down 
baseline levels of HFCs for developing countries (calculated in 
light of future production and consumption in the early- and mid-
2020s) thus generating long-term mitigation benefits under the 
Kigali Amendment (Hoch et al. 2019). However, achievement of the 
objectives of the Kigali Amendment is dependent on its ratification 
by key developed countries, such as the United States, and the

provision of funds by developed countries through the Protocol’s 
Multilateral Fund to meet developing countries’ agreed incremental 
costs of implementation (Roberts 2017). The Kigali Amendment came 
into force on 1 January 2019 and has been ratified by 118 of the 198 
Parties to the Montreal Protocol. 
MEAs dealing with transboundary air pollution, such as the 
Convention on Long-Range Transboundary Air Pollution (CLRTAP) 
and its implementing protocols, which regulate non-GHGs like 
particulates, nitrogen oxides and ground-level ozone, can also have 
potential benefits for climate change mitigation (Erickson 2017). 
Studies have indicated that rigorous air quality controls targeting 
short-lived climate forcers, like methane, ozone and black carbon, 
could slow global mean temperature rise by about 0.5°C by mid-
century (Schmale et al. 2014). Steps in this direction were taken with 
2012 amendments to the CLRTAP Gothenburg Protocol (initially

adopted in 1999) to include black carbon, which is an important 
driver of climate change in the Arctic region (Yamineva and Kulovesi 
2018). The amended Protocol, which has 28 Parties including the US 
and EU, entered into force in October 2019. However, its limits on 
black carbon have been criticised as insufficiently ambitious in light of 
scientific assessments (Khan and Kulovesi 2018). There is still a non-
negligible uncertainty in the assessment of radiative forcing of each 
short-lived climate forcer (SLCF), and the results of AR6 WGI have 
been updated since AR5. For example, the assessment of Emission-
based Radiative Forcing from Black Carbon emissions was revised 
downward in AR6 (AR6 WGI Section  6.4.2). When discussing co-
benefits with MEAs related to transboundary air pollution, attention 
should be paid to the uncertainty in radiative forcing of SLCFs and 
the update of relevant scientific knowledge.

1497
International Cooperation Chapter 1414Another MEA that may play a  role in aiding climate change 
mitigation is the 2013 Minamata Convention on Mercury, which 
came into force on 16 August 2017. Coal burning for electricity 
generation represents the second largest source (behind artisanal 
and small-scale gold mining) of anthropogenic mercury emissions to 
air (UNEP 2013). Efforts to control and reduce atmospheric emissions 
of mercury from coal-fired power generation under the Minamata 
Convention may reduce GHG emissions from this source (Eriksen 
and Perrez 2014; Selin 2014). For instance, Giang et al. (2015) have 
modelled the implications of the Minamata Convention for mercury 
emissions from coal-fired power generation in India and China, 
concluding that reducing mercury emissions from present-day levels 
in these countries is likely to require ‘avoiding coal consumption and 
transitioning toward less carbon-intensive energy sources’ (Giang

et al. 2015). Parties to the Minamata Convention include five of the 
six top global CO 2 emitters – China, the United States, the EU, India 
and Japan (Russia has not ratified the Convention). The Minamata 
Convention also establishes an Implementation and Compliance 
Committee to review compliance with its provisions on a ‘facilitative’ 
basis (Eriksen and Perrez 2014). 
MEAs that require state Parties to conserve habitat (such as the 
Convention on Biological Diversity) or to protect certain ecosystems like 
wetlands (such as the Ramsar Convention on Wetlands of International 
Importance Especially as Waterfowl Habitat) may also have co-benefits 
for climate change mitigation through the adoption of well-planned 
conservation policies (Phelps et al. 2012; Gilroy et al. 2014). At 
a theoretical level, REDD+ activities have been identified as a particular 
opportunity for achieving climate mitigation objectives while also

conserving tropical forest biodiversity and ecosystem services. Elements 
of REDD+ that promise greatest effectiveness for climate change 
mitigation (e.g., greater finance combined with reference levels which 
reduce leakage by promoting broad participation across countries with 
both high and low historical deforestation rates) also offer the greatest 
benefits for biodiversity conservation (Busch et al. 2011). However, 
actual biodiversity and ecosystem service co-benefits are dependent 
on the design and  implementation of REDD+ programmes (Ehara 
et al. 2014; Panfil and Harvey 2016), with limited empirical evidence 
to date of emissions reductions from these programmes (Newton et al. 
2016; Johnson et al. 2019), and concerns about whether they meet 
equity and justice considerations (Schroeder and McDermott 2014) 
(Section 7.6.1). 
14.5.1.2 Linkages with Sustainable Development, Adaptation, 
Loss and Damage, and Human Rights

As discussed in Chapter  1, the emerging framing for the issue of 
climate mitigation is that it is no longer to be considered in isolation 
but rather in the context of its linkages with other areas. Adaptation, 
loss and damage, human rights and sustainable development are 
all areas where there are clear or potential overlaps, synergies, and 
conflicts with the cooperation underway in relation to mitigation.
The IPCC defines adaptation as: ‘in human systems, the process of 
adjustment to actual or expected climate and its effects, in order to 
moderate harm or exploit beneficial opportunities. In natural systems, 
the process of adjustment to actual climate and its effect; human intervention may facilitate adjustment to expected climate and its 
effects’ (Annex I: Glossary).
Adaptation involves actions to lessen the harm associated with 
climate change, or take advantage of potential gains (Smit and 
Wandel 2006). It can seek to reduce present and future exposure

to specific climate risks (Adger et al. 2003), mainstream climate 
information into existing planning efforts (Gupta et al. 2010; van der 
Voorn et al. 2012; van der Voorn et al. 2017), and reduce vulnerability 
(or increase resilience) of people or communities to the effects of 
climate change (Kasperson and Kasperson 2001). There is a  body 
of literature highlighting potential synergies and conflicts between 
adaptation actions – in any of the three areas above – and mitigation 
actions –  and potential strategies for resolving them (Locatelli 
et al. 2011; Casado-Asensio and Steurer 2014; Duguma et al. 2014; 
Suckall et al. 2015; Watkiss et al. 2015; van der Voorn et al. 2020). 
In a strategic context, this issue has been analysed in Bayramoglu 
et al. (2018), Eisenack and Kähler (2016) and Ingham et al. (2013), 
among others. Bayramoglu et al. (2018) analyse the strategic 
interaction between mitigation, as a  public good, and adaptation,

essentially a  private good, showing that the fear that adaptation 
will reduce the incentives to mitigate carbon emissions may not be 
justified. On the contrary, adaptation can reduce free-rider incentives 
(lead to larger self-enforcing agreements), yielding higher global 
mitigation levels and welfare, if adaptation efforts cause mitigation 
levels between different countries to be complements instead of 
strategic substitutes (Ingham et al. 2013). 
Distinct from project or programmatic level activities, however, 
international cooperation for adaptation operates to provide finance 
and technical assistance (Bouwer and Aerts 2006). In most cases it 
involves transboundary actions, such as in the case of transboundary 
watershed management (Wilder et al. 2010; Milman et al. 2013; 
van der Voorn et al. 2017). In others it involves the mainstreaming 
of climate change projections into existing treaties, such as for the 
protection of migratory species (Trouwborst et al. 2012).

International cooperation in mitigation and adaptation share many 
of the same challenges, including the need for effective institutions. 
The UNFCCC, for example, addresses international financial support 
for adaptation and for mitigation in the same general category, and 
subjects them to the same sets of institutional constraints (Peterson 
and Skovgaard 2019). Sovacool and Linnér (2016) argue that the history 
of the UNFCCC and its sub-agreements has been shaped by an implicit 
bargain that developing countries participate in global mitigation policy 
in return for receiving financial and technical assistance for adaptation 
and development from industrialised countries and international 
green funds. Khan and Roberts (2013) contend that this played out 
poorly under the Kyoto framework: the Protocol’s basic architecture, 
oriented around legally binding commitments, was not amenable to 
merging the issues of adaptation and mitigation. Kuyper et al. Kuyper

et al. (2018a) argue that the movement from the Kyoto Protocol to the 
Paris Agreement represents a shift in this regard; the Paris Agreement 
was designed not primarily as a  mitigation policy instrument, but 
rather one encompassing mitigation, adaptation, and development 
concerns. While this argument suggests that the Paris architecture, 
involving voluntary mitigation actions and a greater attention to issues

1498
Chapter 14 International Cooperation14of financial support and transparency, functions better to leverage 
adaptation support into meaningful mitigation actions, there are 
only few papers that examine this issue. Stua (2017a,b) explores the 
relevance of the so-called ‘share of proceeds’ included in Article 6 of 
the Paris Agreement as a key tool for leveraging adaptation though 
There are recognised limits to adaptation (Dow et al. 2013), and 
exceeding these limits results in loss and damage, a  topic that 
is gathering salience in the policy discourse. Roberts et al. (2014) 
focused on ‘loss and damage’, essentially those climate change 
impacts which cannot be avoided through adaptation. The Paris 
Agreement contains a  free-standing article on loss and damage 
(UNFCCC 2015a), focused on cooperation and facilitation, under 
which Parties have established a clearing house on risk transfer, and 
a Task Force on Displacement (UNFCCC 2016a). The COP decision

accompanying the Paris Agreement specifies that ‘Article 8  does 
not involve or provide a  basis for any liability or compensation’ 
(UNFCCC 2016a). There is range of views on the treatment of loss 
and damage in the Paris Agreement, how responsibility for loss and 
damage should be allocated (Lees 2017; McNamara and Jackson 
2019), and how it could be financed (Roberts et al. 2017; Gewirtzman 
et al. 2018). Some scholars argue that there are continuing options 
to pursue compensation and liability in the climate change regime 
(Mace and Verheyen 2016; Gsottbauer et al. 2018). There have also 
been efforts to establish accountability of companies – particularly 
‘carbon majors’ – for climate damage in domestic courts (Ganguly 
et al. 2018; Benjamin 2021). For states that have suffered loss and 
damage there is also the option to pursue ‘state responsibility’ claims 
under customary international law and international human rights

law (Wewerinke-Singh 2018; Wewerinke-Singh and Salili 2020).
One scholar argues that climate impacts are ‘incremental violence 
structurally over-determined by international relations of power and 
control’ that affect most those who have contributed the least to 
GHG emissions (Dehm 2020). Calls for compensation or reparation 
for loss and damage are therefore a  demand for climate justice 
(Dehm 2020). Many small island states entered declarations on 
acceptance of the UNFCCC and Paris Agreement that they continue 
to have rights under international law regarding state responsibility 
for the adverse effects of climate change, and that no provision in 
these treaties can be interpreted as derogating from any claims or 
rights concerning compensation and liability due to the adverse 
The adoption in 2013 of the Warsaw International Mechanism on 
Loss and Damage as part of the UNFCCC occurred despite the historic

opposition of the United States to this policy. Vanhala and Hestbaek 
(2016) examine the roles of ‘frame contestation’ (contestations 
over different framings of loss and damage, whether as ‘liability 
and compensation’ or ‘risk management and insurance’ or other) and 
ambiguity in accounting for the evolution and institutionalisation 
of the loss and damage norm within the UNFCCC. However, there 
is little international agreement on the scope of loss and damage 
programmes, and especially how they would be funded and by 
whom (Gewirtzman et al. 2018). Moreover, non-economic loss and 
damage (NELD) forms a  distinct theme that refers to the climate-related losses of items both material and non-material that are not 
commonly traded in the market, but whose loss is still experienced 
as such by those affected. Examples of NELD include loss of cultural 
identity, sacred places, human health and lives (Serdeczny 2019). The

Santiago Network is part of the Warsaw International Mechanism, to 
catalyse the technical assistance of relevant organisations, bodies, 
networks and experts, for the implementation of relevant approaches 
to avert, minimise and address loss and damage at the local, national 
and regional levels, in developing countries that are particularly 
vulnerable to the adverse effects of climate change (UNFCCC 2020c). 
There are direct links between climate mitigation efforts, adaptation 
and loss and damage –  the higher the collective mitigation 
ambition and the likelihood of achieving it, the lower the scale of 
adaptation ultimately needed and the lower the scale of loss and 
damage anticipated. The liability of states, either individually or 
collectively, for loss and damage is contested, and no litigation has 
yet been successfully launched to pursue such claims. The science 
of attribution, however, is developing (Otto et al. 2017; Skeie et al.

2017; Marjanac and Patton 2018; Patton 2021) and while it has 
the potential to address the thorny issue of causation, and thus 
compensation (Stuart-Smith et al. 2021), it could also be used to 
develop strategies for climate resilience (James et al. 2014). 
There are also direct links between mitigation and sustainable 
development. The international agendas for mitigation and 
sustainable development have shaped each other, around concepts 
such as ‘common but differentiated responsibilities and respective 
capabilities’, as well as the distinction – in the UNFCCC and later the 
Kyoto Protocol – between Annex I and non-Annex I countries (Victor 
2011; Patt 2015). The same implicit bargain that developing countries 
would support mitigation efforts in return for assistance with respect 
to adaptation also applies to support for development (Sovacool 
and Linnér 2016). That linkage between mitigation and sustainable 
development has become even more specific with the Paris Agreement

and the 2030 Agenda for Sustainable Development, each of which 
explicitly pursues a set of goals that encompass both mitigation and 
development (Schmieg et al. 2017), reflecting the recognition that 
achieving sustainable development and climate mitigation goals are 
mutually dependent (Gomez-Echeverri 2018). It is well accepted that 
the long-term effects of climate mitigation will benefit sustainable 
development. A  more contested finding is whether the mitigation 
actions themselves promote or hinder short-term poverty alleviation. 
One study, analysing the economic effects of developing countries’ 
NDCs, finds that mitigation actions slow down poverty reduction 
efforts (Campagnolo and Davide 2019). Other studies suggest 
possible synergies between low-carbon development and economic 
development (Hanger et al. 2016; Labordena et al. 2017; Dzebo 
et al. 2019). These studies typically converge on the fact that financial

assistance flowing from developed to developing countries enhances 
any possible synergies or lessens the conflicts. However, mitigation 
measures can also have negative impacts on gender equality, and 
peace and justice (Dzebo et al. 2019). The International Monetary 
Fund (IMF) has also taken on board the climate challenge and is 
examining the role of fiscal and macroeconomic policies to address 
the climate challenge for supporting its members with appropriate 
policy responses.

1499
International Cooperation Chapter 1414The literature also identifies institutional synergies at the international 
level, related to the importance of addressing climate change and 
development in an integrated, coordinated and comprehensive 
manner across constituencies, sectors and administrative and 
geographical boundaries (Le Blanc 2015). The literature also stresses 
the important role that robust institutions have in making this 
happen, including in international cooperation in key sectors for 
climate action as well for development (Waage et al. 2015). Since 
the publication of AR5, which emphasised the need for a  type of 
development that combines both mitigation and adaptation as a way 
to strengthen resilience, much of the literature has focused on ways 
to address these linkages and the role institutions play in key sectors 
that are often the subject of international cooperation – for example, 
environmental and soil degradation, climate, energy, water resources,

and forestry (Hogl et al. 2016). An assessment of thematic policy 
coherence between the voluntary domestic contributions regarding 
the Paris Agreement and the 2030 Agenda should be integrated in 
national policy cycles for sustainable and climate policymaking to 
identify overlaps, gaps, mutual benefits and trade-offs in national 
policies (Janetschek et al. 2020). 
It is only since 2008 that the relationship between climate change 
and human rights has become a  focus of international law and 
policymaking. It is not just climate impacts that threaten the 
enjoyment of human rights but also the mitigation responses to 
climate change that affect human rights (Shi et al. 2017). The issue of 
human rights–climate change linkages was first taken up by the UN 
Human Rights Council in 2008, but has since rapidly gained ground 
with UN human rights treaty bodies issuing comments (e.g., Human 
Rights Committee 2018), recommendations (e.g.,  Committee on the

Elimination of Discrimination against Women 2018) and even a joint 
statement (Office of the High Commissioner for Human Rights 2019) 
on the impacts of climate change on the enjoyment of human rights. 
Climate change effects and related disasters have the potential to 
affect human rights broadly, for instance, by giving rise to deaths, 
disease or malnutrition (right to life, right to health), threatening food 
security or livelihoods (right to food), impacting upon water supplies 
and compromising access to safe drinking water (right to  water), 
destroying coastal settlements through storm surge (right to adequate 
housing), and in some cases forcing relocation as traditional territories 
become uninhabitable (UNGA 2019). In addition, the right to a healthy 
environment, recognised in 2021 as an autonomous right at the 
international level by the Human Rights Council (UN Human Rights 
Council 2021), arguably extends to a right to a ‘safe climate’ shaped in

part by the Paris Agreement (UNGA 2019).
As the intersections between climate impacts and human rights have 
become increasingly clear, litigants have begun to use human rights 
arguments, with a growing receptivity among courts towards such 
arguments in climate change cases (Peel and Osofsky 2018; Savaresi 
and Auz 2019; Macchi and van Zeben 2021). In the landmark 
Urgenda climate case in 2019, the Dutch Supreme Court interpreted 
the European Convention on Human Rights in light of customary 
international law and the UN climate change regime and ordered 
the state to reduce greenhouse gas emissions by 25% by 2020 
compared to 1990 (The Supreme Court of the Netherlands 2019). In  the Neubauer case in 2021, the German Federal Constitutional 
Court ordered the German legislature, in light of its obligations, 
including on rights protections, to set clear provisions for reduction 
targets from 2031 onward by the end of 2022 (German Constitutional

Court 2021). There are cases in the Global South as well (Peel and Lin 
2019; Setzer and Benjamin 2020), with the Supreme Court in Nepal 
in its 2018 decision in Shrestha ordering the government to amend 
its existing laws and introduce a new consolidated law to address 
climate mitigation and adaptation as this would protect the rights to 
life, food, and a clean environment, and give effect to the 2015 Paris 
Agreement (The Supreme Court of Nepal 2018). There are dozens of 
further cases in national and regional courts, increasingly based on 
human rights claims, and this trend is only likely to grow (Shi et al. 
2017; Peel and Osofsky 2018; Beauregard et al. 2021). These cases 
face procedural hurdles, such as standing, as well as substantive 
difficulties, for instance, with regard to the primarily territorial 
scope of state obligations to protect human rights (Boyle 2018; 
Mayer 2021), however, there are increasing instances of successful 
outcomes across the world.

14.5.1.3 Trade Agreements
As discussed in AR5, policies to open up trade can have a range of 
effects on GHG emissions, just as mitigation policies can influence 
trade flows among countries. Trade rules may impede mitigation 
action by limiting countries’ discretion in adopting trade-related 
climate policies, but they also have the potential to stimulate the 
international adoption and diffusion of mitigation technologies and 
policies (Droege et al. 2017).
The mitigation impacts of trade agreements are difficult to ascertain, 
and the limited evidence is mixed. Examining the effects of three 
free trade agreements (FTAs) – Mercosur, the North American Free 
Trade Agreement (NAFTA) and the Australia–United States Free Trade 
Agreement – on GHG emissions, Nemati et al. (2019) find that these 
effects depend on the relative income levels of the countries involved, 
and that FTAs between developed and developing countries may

increase emissions in the long run. However, studies also suggest 
that FTAs incorporating specific environmental or climate-related 
provisions can help reduce GHG emissions (Baghdadi et al. 2013; 
Sorgho and Tharakan 2020).
Investment agreements, which are often integrated in FTAs, seek 
to encourage the flow of foreign investment through investment 
protection. While international investment agreements hold potential 
to increase low-carbon investment in host countries (PAGE 2018), 
these agreements have tended to protect investor rights, constraining 
the latitude of host countries in adopting environmental policies 
(Miles 2019). Moreover, international investment agreements may 
lead to ‘regulatory chill’, which may lead to countries refraining 
from or delaying the adoption of mitigation policies, such as phasing 
out fossil fuels (Tienhaara 2018). More contemporary investment 
agreements seek to better balance the rights and obligations of

investors and host countries, and in theory offer greater regulatory 
space to host countries (UNCTAD 2019), although it is unclear to 
what extent this will hold true in practice.

1500
Chapter 14 International Cooperation14In their NDCs, Parties mention various trade-related mitigation 
measures, including import bans, standards and labelling schemes, 
border carbon adjustments (BCAs; see also Chapter 13), renewable 
energy support measures, fossil fuel subsidy reform, and the use 
of international market mechanisms (Brandi 2017). Some of these 
‘response measures’ (Chan 2016b) may raise questions concerning 
their consistency with trade agreements of the World Trade 
Organization (WTO). Non-discrimination is one of the foundational 
rules of the WTO. This means, among others, that ‘like’ imported and 
domestic products are not treated differently (‘national treatment’) 
and that a  WTO member should not discriminate between other 
members (‘most-favoured-nation treatment’). These principles are 
elaborated in a set of agreements on the trade in goods and services, 
including the General Agreement on Tariffs and Trade (GATT), the

General Agreement on Trade in Services(GATS), the Agreement on 
Technical Barriers to Trade (TBT), and the Agreement on Subsidies 
and Countervailing Measures (ASCM).Several measures that can be adopted as part of carbon pricing 
instruments to address carbon leakage concerns have been examined 
in the light of WTO rules. For instance, depending on the specific 
design, the free allocation of emissions allowances under an ETS could 
be considered a subsidy inconsistent with the ASCM (Rubini and Jegou 
2012; Ismer et al. 2021). The WTO compatibility of another measure 
to counter carbon leakage, BCAs, has also been widely discussed 
(Box  14.2). Alternatives to BCAs, such as consumption charges on 
carbon-intensive materials (Pollitt et al. 2020), can be consistent with 
WTO law, as they do not involve discrimination between domestic 
and  foreign products based on their carbon intensity (Ismer and 
Neuhoff 2007; Tamiotti 2011; Pauwelyn 2013; Holzer 2014; Ismer

and Haussner 2016; Cosbey et al. 2019; European Commission 2019; 
Mehling et al. 2019; Porterfield 2019; Ismer et al. 2020).
Box 14.2 | Border Carbon Adjustments and International Climate and Trade Cooperation
Analyses of the WTO compatibility of BCAs (Ismer and Neuhoff 2007; Tamiotti 2011; Hillman 2013; Pauwelyn 2013; Holzer 2014; 
Trachtman 2017; Cosbey et al. 2019; Mehling et al. 2019; Porterfield 2019) gained new currency following the legislative proposal to 
introduce a ‘carbon border adjustment mechanism’ in the EU (European Commission 2021). BCAs can in principle be designed and 
implemented in accordance with international trade law, but the details matter (Tamiotti et al. 2009). To increase the likelihood that 
a BCA will be compatible with international trade law, studies suggest that it would need to: have a clear environmental rationale 
(i.e., reduce carbon leakage); apply to imports and exclude exports; consider the actual carbon intensity of foreign producers; account

for the mitigation efforts by other countries; and provide for fairness and due process in its design and implementation (Pauwelyn 
2013; Trachtman 2017; Cosbey et al. 2019; Mehling et al. 2019).
BCAs may also raise concerns regarding their consistency with international climate change agreements (Hertel 2011; Davidson Ladly 
2012; Ravikumar 2020). To mitigate these concerns, BCAs could include special provisions (e.g., exemptions) for LDCs, or channel 
revenues from the BCA to developing countries to support low-carbon and climate-resilient development (Grubb 2011; Springmann 
2013; Mehling et al. 2019). Moreover, international dialogue on principles and best practices guiding BCAs could help to ensure that 
such measures do not hinder international cooperation on climate change and trade (Bernasconi-Osterwalder and Cosbey 2021). 
Other regulatory measures may also target the GHG emissions 
associated with the production of goods (Dobson 2018). These

measures include bans on carbon-intensive materials, emissions 
standards for the production process of imported goods, and 
carbon footprint labels (Kloeckner 2012; Holzer and Lim 2020; 
Gerres et al. 2021). The compatibility of such measures with trade 
agreements remains subject to debate. While non-discriminatory 
measures targeting the emissions from a  product itself (e.g.,  fuel 
efficiency standards for cars) are more likely to be allowed than 
measures targeting the production process of a good (Green 2005), 
some studies suggest that differentiation between products based 
on their production process may be compatible with WTO rules 
(Benoit 2011; McAusland and Najjar 2015). (Mayr et al. 2020) find 
that sustainability standards targeting the emissions from indirect 
land use change associated with the production of biofuels may be 
inconsistent with the TBT Agreement. Importantly, trade rules express 
a strong preference for the international harmonisation of standards

over unilateral measures (Delimatsis 2016).Renewable energy support measures may be at odds with the ASCM, 
the GATT, and the WTO Agreement on Trade-Related Investment 
Measures. In WTO disputes, measures adopted in Canada, India, and 
the United States to support clean energy generation were found to 
be inconsistent with WTO law due to the use of discriminatory local 
content requirements, such as the requirement to use domestically 
produced goods in the production of renewable energy (Cosbey and 
Mavroidis 2014; Kulovesi 2014; Lewis 2014; Wu and Salzman 2014; 
Charnovitz and Fischer 2015; Shadikhodjaev 2015; Espa and Marín 
Durán 2018).
Some measures may both lower trade barriers and potentially bring 
about GHG emissions reductions. An example is the liberalisation of 
trade in environmental goods (Hu et al. 2020). In 2012, the Asia-
Pacific Economic Cooperation economies agreed to reduce tariffs for 
a list of 54 environmental goods (including, for example, solar cells;

but excluding, for example, biofuels or batteries for electric vehicles). 
However, negotiations on an Environmental Goods Agreement under

1501
International Cooperation Chapter 1414the WTO stalled in 2016 due in part to disagreement over which 
goods to include (de Melo and Solleder 2020). Another example is 
fossil fuel subsidy reform, which may reduce GHG emissions (Jewell 
et al. 2018; Chepeliev and van der Mensbrugghe 2020; Erickson et al. 
2020) and lower trade distortions (Burniaux et al. 2011; Moerenhout 
and Irschlinger 2020). However, fossil fuel subsidies have largely 
remained unchallenged before the WTO due to legal and political 
hurdles (Asmelash 2015; De Bièvre et al. 2017; Meyer 2017; Steenblik 
et al. 2018; Verkuijl et al. 2019).
With limited progress in the multilateral trading system, some studies 
suggest that regional FTAs hold potential for strengthening climate 
governance. In some cases, climate-related provisions in such FTAs 
can go beyond provisions in the Kyoto Protocol and Paris Agreement, 
addressing for instance cooperation on carbon markets or electric

vehicles (Gehring et al. 2013; van Asselt 2017; Morin and Jinnah 2018; 
Gehring and Morison 2020). However, Morin and Jinnah (2018) find 
that these provisions are at times vaguely formulated, not subject 
to third-party dispute settlement, and without sanctions or remedy 
in case of violations. Moreover, such provisions are not widely used 
in FTAs, and they are not adopted by the largest GHG emitters. 
For instance, the 2019 United States–Mexico–Canada Agreement, 
NAFTA’s successor, does not include any specific provisions on climate 
change, although it could implement cooperative mitigation actions 
through its Commission for Environmental Cooperation (Laurens 
A trend in international economic governance has been the adoption 
of ‘mega-regional’ trade agreements involving nations responsible 
for a substantial share of world trade, such as the Comprehensive 
and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the

EU-Canada Comprehensive Economic and Trade Agreement (CETA), 
and the Regional Comprehensive Economic Partnership (RCEP) in 
East Asia. Given the size of the markets covered by these agreements, 
they hold potential to diffuse climate mitigation standards (Meltzer 
2013; Holzer and Cottier 2015). While CETA includes climate-
related provisions and Parties have made a  broad commitment to 
implement the Paris Agreement (Laurens et al. 2019), and the CPTPP 
includes provisions promoting cooperation on clean energy and low-
emissions technologies, the RCEP does not include specific provisions 
on climate change.
Studies have discussed various options to minimise conflicts, and 
strengthen the role of trade agreements in climate action, although 
the mitigation benefits and distributional effects of these options 
have yet to be assessed. Some options require multilateral action, 
including: (i) the amendment of WTO agreements to accommodate

climate action; (ii) the adoption of a ‘climate waiver’ that temporarily 
relieves WTO members from their obligations; (iii) a ‘peace clause’ 
through which members commit to refraining from challenging 
each other’s measures; (iv) an ‘authoritative interpretation’ by WTO 
members of ambiguous WTO provisions; (v) improved transparency 
of the climate impacts of trade measures; (vi) the inclusion of 
climate expertise in WTO disputes; and (vii) intensified institutional 
coordination between the WTO and UNFCCC (Hufbauer et al. 2009; 
Epps and Green 2010; Bacchus 2016; Droege et al. 2017; Das et al. 
2019). In addition, issue-specific suggestions have been put forward, such as reinstating an exception for environmentally-motivated 
subsidies under the ASCM (Horlick and Clarke 2017).
Options can also be pursued at the plurilateral and regional levels. 
Several studies suggest that climate clubs (Section  14.2.2) could 
employ trade measures, such as lower tariffs for climate-related

goods and services, or BCAs, to attract club members (Nordhaus 
2015; Brewer et al. 2016; Keohane et al. 2017; Stua 2017a; Banks 
and Fitzgerald 2020). Another option is to negotiate a  new 
agreement addressing both climate change and trade. Negotiations 
between six countries (Costa Rica, Fiji, Iceland, New Zealand, 
Norway, Switzerland) were launched in 2019 on a new Agreement 
on Climate Change, Trade and Sustainability (ACCTS), which, if 
successfully concluded, would liberalise trade in environmental 
goods and services, create new rules to remove fossil fuel subsidies, 
and develop guidelines for voluntary eco-labels (Steenblik and 
Droege 2019). At the regional level, countries could further opt for 
the inclusion of climate provisions in the (re)negotiation of FTAs 
(Morin and Jinnah 2018; Yamaguchi 2020). Moreover, the conduct 
of climate impact assessments of FTAs could help identify options 
to achieve both climate and trade objectives (Porterfield et al. 2017).

In their assessment of the feasibility of various options for reform, 
Das et al. (2019) find that the near-term feasibility of options that 
require consensus at the multilateral level (notably amendments of 
WTO agreements) is low. By contrast, options involving a  smaller 
number of Parties, as well as options that can be implemented by 
WTO members on a voluntary basis, face fewer constraints.
For international investment agreements, various other suggestions 
have been put forward to accommodate climate change concerns. 
These include incorporating climate change through ongoing reform 
processes, such as reform of investor-state dispute settlement under 
the UN Commission on International Trade Law; modernisation 
of the Energy Charter Treaty; the (re)negotiation of international 
investment agreements; and the adoption of a  specific treaty to 
promote investment in climate action (Brauch et al. 2019; Tienhaara 
and Cotula 2020; Yamaguchi 2020; Cima 2021).

14.5.1.4 South-South cooperation
South-South cooperation (SSC) and triangular cooperation (TrC) are 
bold, innovative, and rapidly developing means of strengthening 
cooperation for the achievement of the SDGs (FAO 2018). SSC is 
gaining momentum in achieving sustainable development and 
climate actions in developing countries (UN 2017b). Through SSC, 
countries are able to map their capacity needs and knowledge gaps 
and find sustainable, cost-effective, long-lasting and economically 
viable solutions (FAO 2019). In the UN Climate Change Engagement 
Strategy 2017 (UNOSSC 2017), South-South Cooperation Action Plan 
is identified as a substantive pillar to support.
In 2019, the role of South-South and triangular cooperation was 
further highlighted with the BAPA+40 Outcome document (UN 
2019), noting outstanding contributions to alleviating global 
inequality, promoting sustainable development and climate actions, 
promoting gender equality and enriching multilateral mechanisms.

1502
Chapter 14 International Cooperation14recognised in the document reflecting its increasingly relevant role in 
the implementation of the SDGs (UN 2019).
There has been a recent resurgence of South-South cooperation (Gray 
and Gills 2016). The South-South Cooperation Action Plan was adopted 
by the UN as a  substantive pillar to support the implementation of 
the UN Climate Change Engagement Strategy 2017 (UNOSSC 2017). 
Liu et al. (2017a) explored prospects for South–South cooperation for 
large-scale ecological restoration, which is an important solution to 
mitigate climate change. Emphasis is given to experience and expertise 
sharing, co-financing, and co-development of new knowledge and 
know-how for more effective policy and practice worldwide, especially 
in developing and newly industrialised countries.
Janus et al. (2014) explore evolving development cooperation and 
its future governance architecture based on The Global Partnership

for Effective Development Cooperation and The United Nations 
Development Cooperation Forum. Drawing on evidence from the 
hydropower, solar and wind energy industry in China, Urban (2018) 
introduces the concept of ‘geographies of technology transfer and 
cooperation’ and challenges the North–South technology transfer 
and cooperation paradigm for low-carbon innovation and climate 
change mitigation. While North–South technology transfer and 
cooperation (NSTT) for low-carbon energy technology has been 
implemented for decades, South–South technology transfer and 
cooperation (SSTT) and South–North technology transfer and 
cooperation (SNTT) have only recently emerged. Kirchherr and Urban 
(2018) provide a meta-synthesis of the scholarly writings on NSTT, 
SSTT and SNTT from the past 30 years. The discussion focuses on 
core drivers and inhibitors of technology transfer and cooperation, 
outcomes as well as outcome determinants. A case study of transfer

of low-carbon energy innovation and its opportunities and barriers, 
based on the first large Chinese-funded and Chinese-built dam in 
Cambodia is presented by Hensengerth (2017). 
Hensengerth (2017) explores the role that technology transfer/
cooperation from Europe played in shaping firm-level wind energy 
technologies in China and India and discusses the recent technology 
cooperation between the Chinese, Indian, and European wind firms. 
The research finds that firm-level technology transfer/cooperation 
shaped the leading wind energy technologies in China and to a lesser 
extent in India. Thus, the technology cooperation between China, India, 
and Europe has become multi-faceted and increasingly Southern-led.
Rampa et al. (2012) focus on the manner in which African states 
understand and approach new opportunities for cooperation with 
emerging powers, especially China, India and Brazil, including the 
crucial issue of whether they seek joint development initiatives with

both traditional partners and emerging powers. UN (2018) presents 
and analyses case studies of SSTT in Asia and Pacific and Latin 
America and Caribbean regions. Illustrative case studies on TrC can 
be consulted in Shimoda and Nakazawa (2012), and specific cases on 
biofuel SSC and TrC in UNCTAD (2012).
The central argument in the majority of these case studies is that 
South–South cooperation, which is value-neutral, is contributing 
to sustainable development and capacity building (Rampa et al. 2012; Shimoda and Nakazawa 2012; UN 2018). An important new 
development in SSC is that in relation to some technologies the 
cooperation is increasingly led by Southern countries (for instance, 
wind energy between Europe, India and China), challenging the 
classical North–South technology cooperation paradigm. More 
broadly, Parties should ensure the sustainability of cooperation, 
rather than focusing on short-term goals (Eyben 2013). The Belt and

Road Initiative (BRI) is a classic example of a recent SSC initiative 
led by China. According to a joint study by Tsinghua University and 
Vivid Economics, the 126 countries in the BRI region, excluding 
China, currently account for about 28% of global GHG emissions, but 
this proportion may increase to around 66% by 2050 if the carbon 
intensity of these economies only decreases slowly (according to 
historical patterns shown by developing countries). In this context 
it is important to highlight that China has already outlined a vision 
for a  green BRI, and recently increased its commitment through 
the Green Investment Principles initiative, announcing a  new 
international coalition to improve sustainability and promote green 
infrastructure (Jun and Zadek 2019).
Information on triangular cooperation is more readily available than 
on South–South cooperation though some UN organisations such 
as UNDP and FAO have established platforms for the latter which

also include climate projects. Further, although there are many 
South–South cooperation initiatives involving the development 
and transfer of climate technologies, the understanding of the 
motivations, approaches and designs is limited and not easily 
accessible. There is no dedicated platform for South–South and 
triangular cooperation on climate technologies. Hence, it is still too 
early to fully assess the achievements in the field of climate action 
(UNFCCC and UNOSSC 2018). In order to maximise its unique 
contribution to Agenda 2030, Southern providers recognise the 
benefits of measuring and monitoring South–South cooperation, 
and there is a  clear demand for better information from partner 
countries. Di Ciommo (2017) argues that ‘better data could support 
monitoring and evaluation, improve effectiveness, explore synergies 
with other resources, and ensure accountability’ to a  diverse set 
of stakeholders. Besharati et al. (2017) present a  framework of

20 indicators, organised in five dimensions, that researchers and 
policymakers can use to access the quality and effectiveness of SSC 
and its contribution to sustainable development.
The global landscape of development cooperation has changed 
dramatically in recent years, with countries of the South engaging 
in collaborative learning models to share innovative, adaptable and 
cost-efficient solutions to their development and socio-economic-
environmental challenges, ranging from poverty and education 
to climate change. The proliferation of new actors and cross-
regional modalities has enriched the understanding and practice of 
development cooperation and generated important changes in the 
global development architecture towards a more inclusive, effective, 
and horizontal development agenda. South–South cooperation 
will grow in the future, while it is complimentary to North–South 
cooperation. However, there are knowledge gaps in relation to the

precise volume, impact, effectiveness and quality of development 
cooperation from emerging development partners. This gap needs to 
be plugged, and evidence on such cooperation strengthened.

1503
International Cooperation Chapter 141414.5.2 International Sectoral Agreements 
and Institutions
Sectors refer to distinct areas of economic activity, often subject to 
their own governance regimes; examples include energy production, 
mobility, and manufacturing. A  sectoral agreement could include 
virtually any type of commitment with implications for mitigation. 
It could establish sectoral emission targets, on either an absolute or 
an indexed basis. It could also require states (or particular groups 
of states, if commitments are differentiated) to adopt uniform or 
harmonised policies and measures for a sector, such as technology-
based standards, taxes, or best-practice standards, as well as 
providing for cooperation on technology research or deployment.
14.5.2.1 Forestry, Land Use and REDD+
Since 2008, several, often overlapping, voluntary and non-binding 
international efforts and agreements have been adopted to reduce

net emissions from the forestry sector. These initiatives have varying 
levels of private sector involvement and different objectives, 
targets, and timelines. Some efforts focus on reducing emissions 
from deforestation and degradation, while other focus on the 
enhancement of sinks through restoration of cleared or degraded 
landscapes. These initiatives do not elaborate specific policies, 
procedures, or implementation mechanisms. They set targets, 
frameworks, and milestones, aiming to catalyse further action, 
investment, and transparency in conservation and consolidate 
individual country efforts.
After the UN-sponsored Tropical Forestry Action Plan (Winterbottom 
1990; Seymour and Busch 2016), among the longest standing 
programmes in the forestry sector are the World Bank-sponsored 
Forest Carbon Partnership Facility in 2007, which helps facilitate 
funding for REDD+ readiness and specific projects, in addition to

preparing countries for results-based payments and future carbon 
markets while securing local communities’ benefits managed sub-
nationally, and the UN REDD+ Programme initiated in 2008, which 
aims to reduce forest emissions and enhance carbon stocks in 
forests while contributing to national sustainable development in 
developing countries, after the 2007 COP13 in Bali formally adopted 
REDD+ in the UNFCCC decisions and incorporated it in the Bali 
Plan of Action. As discussed above, Article 5 of the Paris Agreement 
encourages Parties to take action to implement and support REDD+. 
These efforts tend to focus on reducing emissions through the 
creation of protected areas, payments for ecosystem services, and/
or land tenure reform (Pirard et al. 2019). The UNREDD+ programme 
supports national REDD+ efforts, inclusion of stakeholders in 
relevant dialogues, and capacity building toward REDD+ readiness 
in partner countries. To date the conservation and emissions

impacts of REDD+ remain misunderstood (Pirard et al. 2019), but 
while existing evidence suggests that reductions in deforestation 
from sub-national REDD+ initiatives have been limited (Bos et al. 
2017) it shows an increasing prominence (Maguire et al. 2021). 
Additionally, the Green Climate Fund has carried out results-based 
payments within REDD+. Eight countries have so far received 
significant funding (GCF 2021). The shift in the REDD+ focus from 
ecosystem service payment to domestic policy realignments and incentive structure has changed the way REDD+ was developed and 
implemented (Brockhaus et al. 2017). Large-scale market resources 
have not fully materialised as a global carbon market system that 
explicitly integrates REDD+ remains under development (Angelsen 
2017). Public funding for REDD+ is also limited (Climate Focus 2017). 
Leading up to the adoption of the Paris Agreement, the governments 
of Germany, Norway, and the United Kingdom formed a partnership

in 2014 called ‘GNU’ to support results-based financing for REDD+, 
with Norway emerging as one of, if not the single largest, major 
donor for REDD+ through its pledge in 2007 of approximately USD3 
billion annually. Norway pledged USD1 billion for Brazil in 2008 and 
the same for Indonesia in 2010 (Schroeder et al. 2020). Meanwhile, 
REDD+ Early Movers was established with support from Germany, 
and the Central African Forest Initiative, a collaborative partnership 
between the European Union, Germany, Norway, France, and the 
United Kingdom was also set up. It supports six central African 
countries in fighting deforestation.
More recently, the Lowering Emissions by Accelerating Forest Finance 
(LEAF) Coalition was established, consisting of the governments of 
Norway, the UK, and the USA and initially nine companies, to accelerate 
REDD+ with a jurisdictional approach. LEAF uses the Architecture for 
REDD+ Transactions (ART)’s The REDD+ Environmental Excellence

Standard (TREES), coordinated by Emergent, a non-profit intermediary 
between tropical countries and the private sector. Three jurisdictions 
in Brazil and two countries have already submitted concept notes 
to ART to receive results-based payments. REDD+ initiatives with 
a jurisdictional approach have also been adopted in various markets, 
such as the Carbon Offsetting and Reduction Scheme for International 
Aviation (CORSIA) (Maguire 2021). In addition to Brazil, Indonesia has 
attracted significant interest as a host country for REDD+. Indonesia 
ranks second, after Brazil, as the largest producer of deforestation-
related GHG emissions (Zarin et al. 2016), but it has committed 
to a  large reduction of deforestation in its NDC (Government of 
Indonesia 2016). Australia has collaborated on scientific research 
and emissions reduction monitoring (Tacconi 2017). It took a while, 
however, before emissions reductions were witnessed (Meehan et al.

2019). The expansion of commodity plantations, however, conflict 
with reduction ambitions (Anderson et al. 2016; Irawan et al. 2019) In 
addition to implementation at the site and jurisdictional levels, legal 
enforcement (Tacconi et al. 2019) as well as policy and regulatory 
reforms (Ekawati et al. 2019) appear to be needed.
Another relevant initiative is one under the 2015 United Nations 
Convention to Combat Desertification (UNCCD), which targets land 
degradation neutrality, that is, ‘a state whereby the amount and 
quality of land resources, necessary to support ecosystem functions 
and services and enhance food security, remains stable or increases 
within specified temporal and spatial scales and ecosystems’ (Orr 
et al. 2017). This overarching goal was recognised as also being 
critical to reaching the more specific avoided deforestation and 
degradation and restoration goals of the UNFCCC and UNCBD. The 
Land Degradation Neutrality (LDN) initiative from UNCCD includes

target-setting programmes that assist countries by providing 
practical tools and guidance for the establishment of the voluntary 
targets and to formulate associated measures to achieve LDN 
and accelerate implementation of projects (Chasek et al. 2019).

1504
Chapter 14 International Cooperation14Today, 124 countries have committed to their LDN national targets 
(UNCCD 2015). The LDN Fund is an investment vehicle launched in 
UNCCD COP 13 in 2017, which exists to provide long-term financing 
for private projects and programmes for countries to achieve their 
LDN targets. According to the UNCCD, most of the funds will be 
invested in developing countries. 
Recent efforts towards the enhancement of sinks from the forestry 
sector have the overarching goal of reaching zero gross deforestation 
globally, that is, eliminating the clearing of all natural forests. The 
New York Declaration on Forests (NYDF) was the first international 
pledge to call for a halving of natural forest loss by 2020 and the 
complete elimination of natural forest loss by 2030 (Climate Focus 
2016). It was endorsed at the United Nations Climate Summit in 
September 2014.  By September 2019  the list of NYDF supporters

included over 200  actors: national  governments, sub-national 
governments, multi-national companies, groups representing 
indigenous communities, and non-government organisations. These 
endorsers committed to doing their part to achieve the NYDF’s ten 
goals, which included ending deforestation for agricultural expansion 
by 2020, reducing deforestation from other sectors, restoring forests, 
and providing financing for forest action (Forest Declaration 2019). 
These goals are assessed and tracked through the NYDF Progress 
Assessment, which includes NYDF Assessment Partners that collect 
data, generate analysis, and release the finding based on the NYDF 
framework and goals. 
The effectiveness of these agreements, which lack binding rules, can 
only be judged by the supplementary actions they have catalysed. 
The NYDF contributed to the development of several other zero-
deforestation pledges, including the Amsterdam Declarations by seven

European nations to achieve fully sustainable and deforestation-free 
agro-commodity supply chains in Europe by 2020 and over 150 
individual company commitments to not source products associated 
with deforestation (Donofrio et al. 2017; Lambin et al. 2018). Recent 
studies indicate that these efforts currently lack the potential to 
achieve wide-scale reductions in clearing and associated emissions 
due to weak implementation (Garrett et al. 2019), although in some 
cases in Indonesia and elsewhere the commodity supply chain 
sustainability drive appears to contribute to lowering deforestation 
(Wijaya et al. 2019; Chain Reaction Research 2020; Schulte et al. 
2020). The NYDF may have triggered small additional reductions 
in deforestation in some areas, particularly for soy, and to a lesser 
extent cattle, in the Brazilian Amazon (Lambin et al. 2018), but these 
effects were temporary, as efforts are being actively reversed and

deforestation has increased again significantly. Deforestation rates 
have escalated in Brazil, with the rate in June 2019 (the first dry-
season month in the new administration) up 88% over the 2018 rate 
in the same month (INPE 2019). Curtis et al. (2018) find global targets 
are clearly not being met. More recent increase in the deforestation 
rate remains to be assessed. NYDF confirms that the initiative did not 
reach its zero-deforestation goal (NYDF Assessment Partners 2020).
In 2010, the Parties to the CBD adopted the Strategic Plan for 
Biodiversity 2011–2020 which included 20 targets known as the 
Aichi Biodiversity targets (Marques et al. 2014). Of relevance to the 
forestry sector, Aichi Target 15 sets the goal of enhancing ecosystem resilience and the contribution of biodiversity to carbon stocks 
though conservation and restoration, including ‘restoration of at least 
15% of degraded ecosystems’ (UNCBD 2010). The plan elaborates

milestones, including the development of national plans for potential 
restoration levels and contributions to biodiversity protection, carbon 
sequestration, and climate adaptation to be integrated into other 
national strategies, including REDD+. In 2020, however, the CBD 
found that while progress was evident for the majority of the Aichi 
Biodiversity Targets, it was not sufficient for the achievement of the 
targets by 2020 (CBD 2020).
Recent efforts toward negative emissions through restoration 
include the Bonn Challenge, the African Forest Landscape 
Restoration Initiative (AFR100) and Initiative 20x20. The Bonn 
Challenge, initiated in 2011 by the Government of Germany and 
the International Union for Conservation of Nature, is intended to 
catalyse the existing international AFOLU commitments. It aimed 
to bring 150 million hectares (Mha) of the world’s deforested and 
degraded land into restoration by 2020, and 350 Mha by 2030. AFR

has the goal of restoring 100 Mha specifically in Africa (AUDA-NEPAD 
2019), while 20x20 aims to restore 20 Mha in Latin America and the 
Caribbean (Anderson and Peimbert 2019). Increasing commitments 
for restoration have created momentum for restoration interventions 
(Chazdon et al. 2017; Mansourian et al. 2017; Djenontin et al. 2018). 
To date 97 Mha has been pledged in NDCs. Yet only a small part of this 
goal has been achieved. The Bonn Challenge Barometer – a progress-
tracking framework and tool to support pledgers –  indicates that 
27 Mha (InfoFLR 2018) are currently being restored, equivalent to 
1.379 GtCO2-eq sequestered (Dave et al. 2019). A key challenge in 
scaling up restoration has been to mobilise sufficient financing (Liagre 
et al. 2015; Djenontin et al. 2018). This underscores the importance 
of building international financing for restoration (equivalent to the 
Forest Carbon Partnership Facility focused on avoided deforestation 
and degradation).

In sum, existing international agreements have had a small impact 
on reducing emissions from the AFOLU sector and some success in 
achieving the enhancement of sinks through restoration. However, 
these outcomes are nowhere near levels required to meet the Paris 
Agreement temperature goal –  which would require turning land 
use and forests globally from a  net anthropogenic source during 
1990–2010 to a net sink of carbon by 2030, and providing a quarter 
of emissions reductions planned by countries (Grassi et al. 2017). The 
AFOLU sector has so far contributed only modestly to net mitigation 
(Chapter 7).
14.5.2.2 Energy Sector
International cooperation on issues of energy supply and security has 
a long and complicated history. There exists a plethora of institutions, 
organisations, and agreements concerned with managing the sector. 
There have been efforts to map the relevant actors, with authors 
in one case identifying six primary organisations (Kérébel and

Keppler 2009), in another 16 (Lesage et al. 2010), and in a  third 
50 (Sovacool and Florini 2012). At the same time, very little of that 
history has had climate mitigation as its core focus. Global energy 
governance has encompassed five broad goals – security of energy

1505
International Cooperation Chapter 1414supply and demand, economic development, international security, 
environmental sustainability, and domestic good governance – and 
as only one of these provides an entry point for climate mitigation, 
effort in this direction has often been lost (van de Graaf and 
Colgan 2016). To take one example, during the 1980s and 1990s 
a  combination of bilateral development support and lending 
practices from multilateral development banks pushed developing 
countries to adopt power market reforms consistent with the 
Washington Consensus: towards liberalised power markets and away 
from state-owned monopolies. The goals of these reforms did not 
include an environmental component, and among the results was 
new investment in fossil-fired thermal power generation (Foster and 
Rana 2020).
As Goldthau and Witte (2010) document, the majority of governance 
efforts, outside of oil and gas producing states, was oriented towards

ensuring reliable and affordable access for oil and gas imports. For 
example, the original rationale for the creation of the International 
Energy Agency (IEA), during the oil crisis of 1973–74, was to manage 
a  mechanism to ensure importing countries’ access to oil (van de 
Graaf and Lesage 2009). On the other side of the aisle, oil exporting 
countries created the international institution OPEC to enable them 
to influence oil output, thereby stabilising prices and revenues for 
exporting countries (Fattouh and Mahadeva 2013). For years, energy 
governance was seen as a  zero-sum game between these poles 
(Goldthau and Witte 2010). The only international governance agency 
focusing on low-carbon energy sources was the International Atomic 
Energy Agency, with a dual mission of promoting nuclear energy and 
nuclear weapons non-proliferation (Scheinman 1987).
More recently, however, new institutions have emerged, and existing

institutions have realigned their missions, in order to promote capacity 
building and global investment in low-carbon energy technologies. 
Collectively, these developments may support the emergence of 
a  nascent field of global sustainable energy governance, in which 
a  broad range of global, regional, national, sub-national and non-
state actors, in aggregate, shape, direct and implement the low 
carbon transition through climate change mitigation activities, which 
produce concomitant societal benefits (Bruce 2018). Beginning in the 
1990s, for example, the IEA began to broaden its mission from one 
concerned primarily with security of oil supplies, which encompassed 
conservation of energy resources, to one also concerned with the 
sustainability of energy use, including work programmes on energy 
efficiency and clean energy technologies and scenarios (van de Graaf 
and Lesage 2009). Scholars have suggested that it was the widespread

perception that the IEA was primarily interested in promoting the 
continued use of fossil fuels, and underplaying the potential role of 
renewable technologies, that led a  number of IEA member states 
to successfully push for the creation of a parallel organisation, the 
International Renewable Energy Agency (IRENA), which was then 
established in 2009 (van de Graaf 2013). An assessment of IRENA’s 
activities in 2015 suggested that the agency has a  positive effect 
related to three core activities: offering advisory services to member 
states regarding renewable energy technologies and systems; serving 
as a  focal point for data and analysis for renewable energy; and, 
mobilising other international institutions, such as multilateral 
development banks, promoting renewable energy (Urpelainen and Van de Graaf 2015). The United Nations, including its various agencies 
such as the Committee on Sustainable Energy within the United

Nations Economic Commission for Europe, has also played a  role 
in the realignment of global energy governance towards mitigation 
efforts. As a precursor to SDG 7, the United Nations initiated in 2011 
the Sustainable Energy for All initiative, which in addition to aiming 
for universal access to modern energy services, included the goals of 
doubling the rate of improvement in energy efficiency, and doubling 
by 2030 the share of renewable energy in the global energy mix 
(Bruce 2018).
Sub-global agreements have also started to emerge, examples of 
issue-specific climate clubs. In 2015, 70 solar-rich countries signed 
a framework agreement dedicated towards promoting solar energy 
development (ISA 2015). In 2017 the Powering Past Coal Alliance was 
formed, uniting a  set of states, businesses, and non-governmental 
organisations around the goal of eliminating coal-fired power 
generation by 2050 (Jewell et al. 2019; Blondeel et al. 2020). Scholars

have argued that greater attention to supply-side agreements such 
as this – focusing on reducing and ultimately eliminating the supply 
of carbon-intensive energy sources – would strengthen the UNFCCC 
and Paris Agreement (Collier and Venables 2014; Piggot et al. 2018; 
Asheim et al. 2019; Newell and Simms 2020). Chapter 6 of this report, 
on energy systems, notes the importance of regional cooperation on 
electric grid development, seen as necessary to enable higher shares 
of solar and wind power penetration (RGI 2011). Finally, a number 
of transnational organisations and activities have emerged, such as 
REN21, a  global community of renewable energy experts (REN21 
2019), and RE100, an NGO-led initiative to enlist multilateral 
companies to shift towards 100% renewable energy in their value 
chains (RE100 2019).
Whether a  result of the above activities or not, multilateral 
development banks’ lending practices have shifted in the direction

of renewable energy (Delina 2017), a point also raised in Chapter 15 
of this report. Activities include new sources of project finance, 
concessional loans, as well as loan guarantees, the latter through the 
Multilateral Investment Guarantee Agency (Multilateral Investment 
Guarantee Agency 2019). This appears to matter. For example, Frisari 
and Stadelmann (2015) find concessional lending by multilateral 
development banks to solar energy projects in Morocco and India to 
have reduced overall project costs, due to more attractive financing 
conditions from additional lenders, as well as reducing the costs 
to local governments. Labordena et al. (2017) projected these 
results into the future, and found that with the drop in financing 
costs, renewable energy projects serving all major demand centres 
in sub-Saharan Africa could reach cost parity with fossil fuels by 
2025, whereas without the drop in financing costs associated with

concessional lending, this would not be the case. Similarly, Creutzig 
et al. (2017) suggest that greater international attention to finance 
could be instrumental in the full development of solar energy.
Despite improvements in the international governance of energy, it 
still appears that a great deal of this is still concerned with promoting 
further development of fossil fuels. One aspect of this is the 
development of international legal norms. A large number of bilateral 
and multilateral agreements, including the 1994 Energy Charter

1506
Chapter 14 International Cooperation14Treaty, include provisions for using a system of investor–state dispute 
settlement (ISDS) designed to protect the interests of investors in 
energy projects from national policies that could lead their assets to 
be stranded. Numerous scholars have pointed to ISDS being able to 
be used by fossil-fuel companies to block national legislation aimed 
at phasing out the use of their assets (Tienhaara 2018; Bos and Gupta 
2019). Another aspect is finance; Gallagher et al. (2018) examine the 
role of national development finance systems. While there has been 
a great deal of finance devoted to renewable energy, they find the 
majority of finance devoted to projects associated either with fossil 
fuel extraction or with fossil fuel-fired power generation. 
Given the complexity of global energy governance, it is impossible 
to make a  definitive statement about its overall contribution to

mitigation efforts. Three statements, do however, appear to be 
robust. First, prior to the emergence of climate change on the global 
political agenda, international cooperation in the area of energy 
was primarily aimed at expanding and protecting the use of fossil 
energy, and these goals were entrenched in a number of multilateral 
organisations. Second, since the 1990s, international cooperation 
has gradually taken climate mitigation on board as one of its goals, 
seeing a realignment of many pre-existing organisations priorities, 
and the formation of a number of new international arrangements 
oriented towards the development of renewable energy resources. 
Third, the realignment is far from complete, and there are still 
examples of international cooperation having a  chilling effect on 
climate mitigation, particularly through financing and investment 
practices, including legal norms designed to protect the interests of 
owners of fossil assets. 
14.5.2.3 Transportation

The transportation sector has been a particular focus of cooperative 
efforts on climate mitigation that extend beyond the sphere of the 
UNFCCC climate regime. A  number of these cooperative efforts 
involve transnational public-private partnerships, such as the 
European-based Transport Decarbonisation Alliance, which brings 
together countries, regions, cities and companies working towards 
the goal of a ‘net-zero emission mobility system before 2050’ (TDA 
2019). Other efforts are centred in specialised UN agencies, such 
as the International Civil Aviation Organization (ICAO) and the 
International Maritime Organization (IMO). 
Measures introduced by the ICAO and IMO have addressed CO 2 
emissions from international shipping and aviation. Emissions from 
these parts of the transportation sector are generally excluded 
from national emissions reduction policies and NDCs because the 
‘international’ location of emissions release makes allocation to

individual nations difficult (Bows-Larkin 2015; Lyle 2018; Hoch et al. 
2019). The measures adopted by ICAO take the form of standards and 
recommended practices that are adopted in national legislation. IMO 
publishes ‘regulations’ but does not have a power of enforcement, 
with non-compliance a responsibility of flag states that issue a ship’s 
‘MARPOL’ certificate.
As discussed in Chapter 2 and Figure SPM.4, international aviation 
currently accounts for approximately 1% of global GHG  emissions, with international shipping contributing 1.2% of global GHG  
emissions. These international transport emissions are projected 
to be between approximately 60% and 220% of global emissions 
of CO 2 in 2050, as represented by the four main illustrative model 
pathways in SR1.5 (Rogelj et al. 2018; UNEP 2020) Notably, however, 
the climate impact of aviation emissions is estimated to be two to 
four times higher due to non-CO 2 effects (Terrenoire et al. 2019; Lee

et al. 2021a). Increases in trans-Arctic shipping and tourism activities 
with sea ice loss are also forecast to have strong regional effects due 
to ships’ gas and particulate emissions (Stephenson et al. 2018). 
The Kyoto Protocol required Annex I  Parties to pursue emissions 
reductions from aviation and marine bunker fuels by working through 
IMO and ICAO (UNFCCC 1997, Art. 2.2). Limited progress was made 
by these organisations on emissions controls in the ensuing decades 
(Liu 2011b), but greater action was prompted by conclusion of the 
SDGs and Paris Agreement (Martinez Romera 2016), together with 
unilateral action, such as the EU’s inclusion of aviation emissions in 
its Emissions Trading System (Dobson 2020). 
The Paris Agreement neither explicitly addresses emissions from 
international aviation and shipping, nor repeats the Kyoto Protocol’s 
provision requiring Parties to work through ICAO/IMO to address

these emissions (Hoch et al. 2019). This leaves unclear the status of 
the Kyoto Protocol’s Article 2.2 directive after 2020 (Martinez Romera 
2016; Dobson 2020), potentially opening up scope for more attention 
to aviation and shipping emissions under the Paris Agreement 
(Doelle and Chircop 2019). Some commentators have suggested that 
emissions from international aviation and shipping should be part of 
the Paris Agreement (Gençsü and Hino 2015; Traut et al. 2018), and 
shipping and aviation industries themselves may prefer emissions to 
be treated under an international regime rather than a nationally-
oriented one (Gilbert and Bows 2012). In the case of shipping 
emissions, there is nothing in the Paris Agreement to prevent a Party 
from including international shipping in some form in its NDC (Doelle 
and Chircop 2019) Under the Paris Rulebook, Parties ‘should report 
international aviation and marine bunker fuel emissions as two

separate entries and should not include such emissions in national 
totals but report them distinctly, if disaggregated data are available’ 
(UNFCCC 2019d). 
ICAO has an overarching climate goal to ‘limit or reduce the impact 
of aviation greenhouse gas emissions on the global climate’ with 
respect to international aviation. In order to achieve this, ICAO has 
two global aspirational goals for the international aviation sector, of 
2% annual fuel efficiency improvement through 2050 and carbon 
neutral growth from 2020 onwards (ICAO 2016). In order to achieve 
these global aspirational goals, ICAO is pursuing a  ‘basket’ of 
mitigation measures for the aviation sector consisting of technical 
and operational measures, such as a  CO 2 emissions standard for 
new aircraft adopted in 2016, measures on sustainable alternative 
fuels and a market-based measure, known as the Carbon Offset and 
Reduction Scheme for International Aviation (CORSIA), which the

triennial ICAO Assembly of 193 Member States resolved to establish 
in 2016 (ICAO 2016). In line with the 2016 ICAO Assembly Resolution 
that established CORSIA, in mid-2018, the ICAO’s 36-member state 
governing Council adopted a series of Standards and Recommended

1507
International Cooperation Chapter 1414Practices (SARPs), now contained in Annex 16, Volume IV of the 
Chicago Convention (1944), as a  common basis for CORSIA’s 
implementation and enforcement by each state and its aeroplane 
operators. From 1  January 2019, the CORSIA SARPs require states 
and their operators to undertake an annual process of monitoring, 
verification, and reporting of emissions from all international flights, 
including to establish CORSIA’s emissions baseline (ICAO 2019). 
Based on this emissions data, CORSIA’s carbon offsetting obligations 
commenced in 2021, with three-year compliance cycles, including 
a pilot phase in 2021–2023. States have the option to participate 
in the pilot phase and the subsequent voluntary three-year cycle in 
2024–2026. CORSIA becomes mandatory from 2027 onwards for 
states whose share in the total international revenue tonnes per 
kilometre is above a  certain threshold (Hoch et al. 2019). Under

CORSIA, aviation CO 2 emissions are not capped, but rather emissions 
that exceed the CORSIA baseline are compensated through use of 
‘offset units’ from emissions reduction projects in other industries 
(Erling 2018). However, it is unclear whether the goal of carbon 
neutral growth and further CO 2 emissions reduction in the sector 
will be sufficiently incentivised solely through the use of such offsets 
in combination with ICAO’s manufacturing standards, programmes, 
and state action plans, without additional measures being taken, 
for example, constraints on demand (Lyle 2018). If countries such 
as China, Brazil, India and Russia do not participate in CORSIA’s 
voluntary offsetting requirements this could significantly undermine 
its capacity to deliver fully on the sectoral goal by limiting coverage 
of the scheme to less than 50% of international aviation CO 2 
emissions in the period 2021–2026 (Hoch et al. 2019; Climate Action

Tracker 2020b). In addition, a  wide range of offsets are approved 
as ‘eligible emissions units’ in CORSIA, including several certified 
under voluntary carbon offset schemes, which may go beyond those 
eventually agreed under the Paris Agreement Article 6  mechanism 
(Hoch et al. 2019). It is noted, however, that ICAO applies a set of 
‘Emissions Unit Eligibility Criteria’, agreed in March 2019, which 
specify required design elements for eligible programmes. In June 
2020, the ICAO Council decided to define 2019 emissions levels, 
rather than an average of 2019 and 2020 emissions, as the baseline 
year for at least the first three years of CORSIA, although there were 
significant reductions (45–60%) in aviation CO 2 emissions in 2020 
compared with 2019 as a result of reductions in air travel associated 
with the COVID-19 pandemic (Climate Action Tracker 2020b).
Other measures adopted by ICAO include an aircraft CO 2 emissions

standard that applies to new aircraft type designs from 2020, and 
to aircraft type designs already in production as of 2023 (Smith 
and Ahmad 2018). Overall, CORSIA and regional measures, such 
as the EU ETS, are estimated to reduce aviation carbon emissions 
by only 0.8% per year from 2017–2030 (noting, however, that ‘if 
non-CO 2 emissions are included in the analysis, then emissions will 
increase’) (Larsson et al. 2019). Accordingly, pathways consistent 
with the temperature goal of the Paris Agreement are likely to 
require more stringent international measures for the aviation sector 
(Larsson et al. 2019). 
Similar to ICAO, the IMO has a stated vision of remaining committed 
to reducing greenhouse gas emissions from international shipping and, as a  matter of urgency, aims to phase them out as soon as 
possible in this century. IMO has considered a range of measures to 
monitor and reduce shipping emissions. In 2016, the IMO’s Marine

Environment Protection Committee (MEPC) approved an amendment 
to the MARPOL Convention Annex VI for the introduction of 
a mandatory global data collection scheme for fuel oil consumption 
of ships (Dobson 2020). Other IMO measures have focused on 
energy efficiency (Martinez Romera 2016). The IMO’s Energy 
Efficiency Design Index (EEDI), which is mandatory for new ships, 
is intended, over a ten-year period, to improve energy efficiency by 
up to 30% in several categories of ships propelled by diesel engines 
(Smith and Ahmad 2018). In May 2019, the MEPC approved draft 
amendments to the MARPOL Convention Annex VI, which if adopted, 
will bring forward the entry into force of the third phase of the EEDI 
requirements to 2022 instead of 2025 (IMO 2019; Joung et al. 2020). 
However, it is unlikely that the EEDI and other IMO technical and 
operational measures will be sufficient to produce ‘the necessary 
emissions reduction because of the future growth in international

seaborne trade and world population’ (Shi and Gullett 2018). 
Consequently, in 2018, the IMO adopted an initial strategy on 
reduction of GHG emissions from ships (IMO 2018). This includes 
a goal for declining carbon intensity of the sector by reducing CO 2 
emissions per transport work, as an average across international 
shipping, by at least 40% by 2030, and pursuing efforts towards 
70% by 2050, compared to 2008 levels (IMO 2018, Para. 3.1). The 
strategy also aims for peaking of total annual GHG emissions from 
international shipping as soon as possible and a  reduction by at 
least 50% by 2050 compared to 2008 levels, while pursuing efforts 
towards phasing them out ‘as soon as possible in this century’ as 
a point ‘on a pathway of CO 2 emissions reduction consistent with 
the Paris Agreement temperature goals’ (IMO 2018, Para. 2, 3.1). The 
shipping industry is on track to overachieve the 2030 carbon intensity

target but not its 2050 target (Climate Action Tracker 2020c). The 
initial IMO strategy is to be kept under review by the MEPC with 
a view to adoption of a revised strategy in 2023. 
The IMO’s initial strategy identifies a series of candidate short-term 
(2018–2023), medium-term (2023–2030) and long-term (beyond 
2030) measures for achieving its emissions reduction goals, including 
possible market-based measures in the medium-to-long term (IMO 
2018, paras. 4.7–4.9). Further progress on market-based measures 
faces difficulty in light of conflicts between the CBDRRC principle of 
the climate regime and the traditional non-discrimination approach 
and principle of no more favourable treatment enshrined in MARPOL 
and other IMO conventions (Zhang 2016). Both the CBDRRC and non-
discrimination principles are designated as ‘principles guiding the 
initial strategy’ (IMO 2018, Para. 3.2). The challenges encountered 
in introducing global market-based measures for shipping emissions

under the IMO have prompted regional initiatives such as the 
proposed extension of the EU ETS to emissions from maritime 
activities (Christodoulou et al. 2021), which was announced on 
14 July 2021 by the EU Commission as part of its ‘Fit for 55’ legislative 
package (European Commission 2021).
While the IMO strategy is viewed as a reasonable first step that is 
ambitious for the shipping industry, achieving the ‘vision’ of alignment

1508
Chapter 14 International Cooperation14with the temperature goals of the Paris Agreement requires concrete 
implementation measures and strengthened targets in the next 
iteration in 2023 (Doelle and Chircop 2019; Climate Action Tracker 
2020c). As a step towards this, in 2020, the IMO’s MEPC put forward 
draft amendments to the MARPOL Convention that would require 
ships to combine a technical and an operational approach to reduce 
their carbon intensity. These amendments were formally adopted by 
the Committee at its session in June 2021.
14.5.3 Civil Society and Social Movements
Transnationally organised civil society actors have had long-standing 
involvement in international climate policy, with a particular focus 
on consulting or knowledge-sharing where they are present in 
transnational climate governance initiatives (Michaelowa and 
Michaelowa 2017). The term ‘civil society’ generally denotes ‘the 
voluntary association of individuals in the public sphere beyond the

realms of the state, the market and the family’ (de Bakker et al. 2013, 
p. 575). Whereas civil society organisations are usually involved in 
lobbying or advocacy activities in a public arena, social movements 
focus on mobilisation and action for social change (Daniel and 
Neubert 2019). Examples of civil society groups involved in 
international climate policy include non-governmental organisations 
(NGOs) such as Greenpeace International, the World Wide Fund 
for Nature, the Environmental Defense Fund, the World Resources 
Institute, Friends of the Earth and Earthjustice among many others, 
as well as NGO networks such as the Climate Action Network, which 
has over 1300 NGO members in more than 130 countries, working 
to promote government and individual action to limit human-
induced climate change to ecologically sustainable levels (Climate 
Action Network International 2020). The influence of civil society 
engagement in global climate governance is well acknowledged,

with these organisations’ globally dispersed constituencies and non-
state status offering perspectives that differ in significant ways from 
those of many negotiating states (Derman 2014). 
Historically, the issue of climate change did not give rise to intense, 
organised transnational protest characteristic of social movements 
(McAdam 2017). During the 1990s and early 2000s, the activities 
of the global climate movement were concentrated in developed 
countries and largely sought to exercise influence through 
participation in UNFCCC COPs and side events (Almeida 2019). The 
mid-2000s onwards, however, saw the beginnings of use of more 
non-institutionalised tactics, such as simultaneous demonstrations 
across several countries, focusing on a  grassroots call for climate 
justice that grew out of previous environmental justice movements 
(Almeida 2019). Groups representing indigenous peoples, youth, 
women, and labour rights brought to the fore new tools of contention

and new issues in the UNFCCC, such as questions of a just transition 
and gender equity (Allan 2020). 
Climate justice has been variously defined, but centres on addressing 
the disproportionate impacts of climate change on the most vulnerable 
populations and calls for community sovereignty and functioning 
(Schlosberg and Collins 2014; Tramel 2016). Contemporary climate 
justice groups mobilise multiple strands of environmental justice movements from the Global North and South, as well as from distinct 
indigenous rights and peasant rights movements, and are organised 
as a  decentralised network of semiautonomous, coordinated units 
(Claeys and Delgado Pugley 2017; Tormos-Aponte and García-López 
2018). The climate justice movement held global days of protest 
in most of the world’s countries in 2014 and 2015, and mobilised 
another large campaign in 2018 (Almeida 2019). The polycentric 
arrangement of the global climate movement allows simultaneous

influence on multiple sites of climate governance, from the local to 
the global levels (Tormos-Aponte and García-López 2018). 
Prominent examples of new climate social movements that operate 
transnationally are Extinction Rebellion and Fridays for Future, 
which collectively held hundreds of coordinated protests across the 
globe in 2019–2021, marking out ‘the transnational climate justice 
movement as one of the most extensive social movements on the 
planet’ (Almeida 2019). Fridays for Future is a children’s and youth 
movement that began in August 2018, inspired by the actions of 
then 15-year old Greta Thunberg who pledged to strike in front of the 
Swedish parliament every Friday to protest against a lack of action 
on climate change in line with the Paris Agreement targets (Fridays 
for Future 2019). Fridays for Future events worldwide encompass 
more than 200 countries and millions of strikers. The movement is

unusual for its focus on children and the rights of future generations, 
with children’s resistance having received little previous attention 
in the literature. Fridays for Future is regarded as a  progressive 
resistance movement that has quickly achieved global prominence 
(for example, Thunberg was invited to address governments at the 
UN Climate Summit in New York in September 2019) and is credited 
with helping to support the discourse about the responsibility of 
humanity as a  whole for climate change (Holmberg and Alvinius 
2019). Whereas Fridays for Future has focused on periodic protest 
action, Extinction Rebellion has pursued a  campaign based on 
sustained non-violent direct citizen action that is focused on three 
key demands: declaration of a ‘climate emergency’, acting now to halt 
biodiversity loss and reduce greenhouse gas emissions to net zero by 
2025, and creation of a citizen’s assembly on climate and ecological

justice (Booth 2019; Extinction Rebellion 2019). The movement first 
arose in the United Kingdom– where it claimed credit for adoption 
of a climate emergency declaration by the UK government – but now 
has a presence in 45 countries with some 650 groups having formed 
globally (Gunningham 2019).
The Paris Agreement’s preamble explicitly recognises the importance 
of engaging ‘various actors’ in addressing climate change, and 
the decision adopting the Agreement created the Non-state Actor 
Zone for Climate Action platform to aid in scaling up these efforts. 
Specific initiatives have also been taken to facilitate participation 
of particular groups, such as the UNFCCC’s Local Communities and 
Indigenous Peoples Platform, which commenced work in Katowice 
in 2019. Climate movements based in the Global South, as well 
as in indigenous territories, are playing an increasingly important 
role in transnational negotiations through networks such as the

Indigenous Peoples Platform. These groups highlight the voices 
and perspectives of communities and peoples particularly affected 
by climate change. For instance, the Pacific Climate Warriors is 
a grassroots network of young people from various countries in the

1509
International Cooperation Chapter 1414Pacific Islands region whose activities focus on resisting narratives 
of future inevitability of their Pacific homelands disappearing, and 
re-envisioning islanders as warriors defending rights to homeland 
and culture (McNamara and Farbotko 2017). Youth global climate 
activism, particularly involving young indigenous climate activists, is 
another notable recent development. Although there remains little 
published literature on indigenous youth climate activism (MacKay 
et al. 2020), analysis of online sources indicates the emergence of 
several such groups, including the Pacific Climate Warriors and Te Ara 
Whatu from Aotearoa New Zealand (Ritchie 2021), as well as Seed 
Mob in Australia. 
Transnational civil society organisations advocating for climate 
justice in global governance have articulated policy positions around 
rights protections, responsibility-based approaches to climate

finance, and the need for transparency and accountability (Derman 
2014). Another recent area of activity, which overlaps with that of 
emerging investor alliances (Section 14.5.4), is the sustainability of 
capital investment in fossil fuel assets. Efforts to shift away from 
fossil fuels led by civil society include the Beyond Coal Campaign 
(in the USA and Europe) and the organisation for a Fossil Fuel Non-
proliferation Treaty. 350.org has supported mobilisation of youth 
and university students around a campaign of divestment that has 
grown into a  global movement (Gunningham 2019). As Mormann 
(2020) notes, as of November 2020 ‘more than 1,200 institutional 
investors managing over USD14 trillion of assets around the world 
have committed to divest some or all of their fossil fuel holdings’. 
Studies suggest that the direct impacts of the divestment movement 
have so far been small, given a  failure to differentiate between

different types of fossil fuel companies, a lack of engagement with 
retail investors, and a lack of guidance for investors on clean energy 
re-investment (Osofsky et al. 2019; Mormann 2020). The movement 
has had a more significant impact on public discourse by raising the 
profile of climate change as a financial risk for investors (Bergman 
2018). Blondeel et al. (2019) also find that broader appeal of the 
divestment norm was achieved when moral arguments were linked 
to financial ones, through the advocacy of economic actors, such as 
the Bank of England’s governor.
Climate justice campaigns by transnational civil society organisations 
increasingly embrace action through the courts. Chapter 13 discusses 
the growth and policy impact of such ‘climate litigation’ brought by 
civil society actors in domestic courts, which is attracting increasing 
attention in the literature (Setzer and Vanhala 2019; Peel and Osofsky 
2020). Transnational and international court actions focused on

climate change, by contrast, have been relatively few in number (Peel 
and Lin 2019). This reflects – at least in part – the procedural hurdles 
to bringing such claims, as in many international courts and tribunals 
(outside of the area of human rights or investor–state arbitration) 
litigation can only be brought by states (Bruce 2017). However, there 
have been active discussions about seeking an advisory opinion 
from the International Court of Justice (ICJ) on states’ international 
obligations regarding the reduction of greenhouse gas emissions 
(Sands 2016; Wewerinke-Singh and Salili 2020), or bringing a case to 
the International Tribunal for the Law of the Sea on marine pollution 
harms caused by climate change (Boyle 2019). In September 2021 the 
Government of Vanuatu announced a campaign to seek an advisory opinion from the ICJ. The aim of climate litigation more generally is 
to supplement other regulatory efforts by filling gaps and ensuring

that interpretations of laws and policies are aligned with climate 
mitigation goals (Osofsky 2010).
The overall impact of transnationally-organised civil society action 
and social movements for international cooperation on climate 
change mitigation has not been comprehensively evaluated in 
the literature. This may reflect the polycentric organisation of the 
movement, which poses challenges for coordinating between groups 
operating in different contexts, acting with different strategies and 
around multiple issues, and lobbying multiple decision-making 
bodies at various levels of government in a sustainable way (Tormos-
Aponte and García-López 2018). There is some literature emerging 
on environmental defenders and their need for protection against 
violence and repression, particularly in the case of indigenous 
environmental defenders who face significantly higher rates of violence 
(Scheidel et al. 2020). Scheidel et al. (2020) also find that combining

strategies of preventive mobilisation, protest diversification and 
litigation can enhance rates of success for environmental defenders 
in halting environmentally destructive projects. In the area of climate 
litigation, commentators have noted the potential for activists and 
even researchers to suffer retaliation through the courts as a result of 
‘strategic lawsuits against public participation’ and lawsuits against 
researchers brought by fossil fuel interests (Setzer and Byrnes 2019; 
Setzer and Benjamin 2020). Influence of social movements may be 
enhanced through taking advantage of ‘movement spillover’ (the 
involvement of activists in more than one movement) (Hadden 2014) 
and coordination of activities with a range of ‘non-state governors’, 
including cities, sub-national governments, and investor groups 
(Gunningham 2019). Studies of general societal change suggest that 
once 3.5% of the population are mobilised on an issue, far-reaching

change becomes possible (Gladwell 2002; Chenoweth and Belgioioso 
2019) – a tipping point that may be approaching in the case of climate 
change (Gunningham 2019). As noted in Chapter 5, in the particular 
case of low-carbon technologies, ‘if 10–30% of the population were 
to demonstrate commitment to low-carbon technologies, behaviours, 
and lifestyles, new social norms would be established’. 
14.5.4 Transnational Business and Public-Private 
Partnerships and Initiatives
Combined national climate commitments fall far short of the Paris 
Agreement’s long-term temperature goals. Similar political ambition 
gaps persist across various areas of sustainable development. Many 
therefore argue that actions by non-state actors, such as businesses 
and investors, cities and regions, and NGOs, are crucial. However, non-
state climate and sustainability actions may not be self-reinforcing 
but may heavily depend on supporting mechanisms. Governance

risk-reduction strategies can be combined to maximize non-state 
potential in sustainable and climate-resilient transformations (Chan 
et al. 2019). 
An important feature of the evolving international climate policy 
landscape of recent years is the entrepreneurship of UN agencies 
such as UNEP and UNDP , as well as international organisations such

1510
Chapter 14 International Cooperation14as the World Bank, in initiating public-private partnerships (PPPs). 
Andonova (2017) calls this ‘governance entrepreneurship’. Such 
partnerships can be defined as ‘voluntary agreements between 
public actors (international organisations, states, or sub-state public 
authorities) and non-state actors (non-governmental organisations 
(NGOs), companies, foundations, etc.) on a  set of governance 
objectives and norms, rules, practices, and/or implementation 
procedures and their attainment across multiple jurisdictions and 
levels of governance’ (Andonova 2017). Partnerships may carry 
out different main functions: first, policy development, establishing 
new agreements on norms, rules, or standards among a broader set 
of governmental and non-governmental actors; second, enabling 
implementation and delivery of services, by combining resources from 
governmental and non-governmental actors; and, third, knowledge

production and dissemination, to for example, the evolution of 
relevant public policies. 
An example of a  prominent PPP in the area of climate mitigation 
is the Renewable Energy Network (REN21 2019), which is a global 
multi-stakeholder network focused on promoting renewable energy 
policies in support of the transition to renewable energy through 
knowledge, established in 2004. It includes members from industry, 
NGOs, intergovernmental organisations, and science and academia. 
Another example is the Green Economy Coalition founded in 2009 
to bring to bear the perspectives of workers, business, poor people, 
the environment community, and academics in the transition to 
a greener and more sustainable economy. Another example is that in 
2015, Peru, in collaboration with France and the UNFCCC Secretariat, 
launched the Non-state Actor Zone for Climate Action, an online 
platform to showcase commitments to climate action by companies,

cities, regions and investors (Chan et al. 2016; Bertoldi et al. 2018). 
More recently, the UNFCCC Race to Zero initiative led by High-level 
Climate Champions Nigel Topping and Gonzalo Muñoz seeks to 
mobilise actors beyond national governments to join the Climate 
Ambition Alliance and pursue net zero CO 2 targets. Its membership 
includes 454 cities, 23 regions, 1391 businesses, 74 of the biggest 
investors, and 569 universities.
PPPs may also be developed to assist with implementation and 
support of states’ climate mitigation commitments. For instance, 
UNEP has initiated a  number of PPPs for climate change finance. 
These are designed to increase financing for the purposes of 
disseminating low-carbon technologies to tackle climate change and 
promote clean energy in many parts of developing countries (UNEP 
2018b; Charlery and Traerup 2019). 
In the same vein, in 2010 FAO delivered the Framework for Assessing 
and Monitoring Forest Governance. The Framework draws on several

approaches currently in use or under development in major forest 
governance-related processes and initiatives, including the World 
Bank’s Framework for Forest Governance Reform. The Framework 
builds on the understanding that governance is both the context 
and the product of the interaction of a  range of actors and 
stakeholders with diverse interests (FAO 2010). For example, UNFCCC 
and the UN-REDD programme focus on REDD+ and UNEP focuses on 
The Economics of Ecosystems and Biodiversity (TEEB), institutional 
mechanisms that have been conceptualised as a ‘win-win-win’ for mitigating climate, protecting biodiversity and conserving indigenous 
culture by institutionalising payments on carbon sequestration and 
biodiversity conservation values of ecosystems services from global 
to local communities. These mechanisms include public-private 
partnership, and NGO participation. REDD+ and TEEB allocation 
policies will be interventions in a  highly complex system, and will

inevitably involve trade-offs; therefore, it is important to question 
the ‘win-win-win’ discourse (Zia and Kauffman 2018; Goulder et al. 
2019). The initial investment and the longer periods of recovery of 
investment are sometimes barriers to private investment. In this 
sense, it is important to have government incentives and encourage 
public-private investment (Ivanova and Lopez 2013).
The World Bank has also established several partnerships since 2010, 
mainly in the field of carbon pricing. Prominent examples are the 
Networked Carbon Markets initiative (established 2013’ spanning 
both governmental actors and experts’ now entering a  phase II) 
and the Carbon Pricing Leadership Coalition, established in 2015 
and spanning a wide range of governmental and non-governmental 
actors, not least within business (World Bank 2018; World Bank 2019; 
Wettestad et al. 2021). These partnerships deal with knowledge 
production and dissemination and seek to enable implementation

of carbon pricing policies. The leadership role of the international 
‘heavyweight’ World Bank gives these partnerships additional 
comparative political weight, meaning also a  potentially greater 
involvement of powerful finance ministries/ministers generally 
involved in Bank matters and meetings. 
PPPs for cooperation on climate mitigation goals have emerged 
at multiple levels of governance beyond the realm of international 
organisations. For example, PPP funding for cities expanded rapidly 
in the 1990s and outpaced official external assistance almost 
tenfold. Most of the PPP infrastructure investment has been aimed 
at telecommunications, followed by energy. However, with the 
exception of the telecommunications sector, PPP investments have 
generally bypassed low-income countries (Ivanova 2017). It is 
therefore not surprising that PPPs have added relatively little to the 
financing of urban capital in developing countries over the past two

decades (Bahl and Linn 2014). Liu and Waibel (2010) argue that the 
inherent risk of urban investment is the main obstacle to increasing 
the flow of private capital. Nevertheless, there have been cases where 
PPP investments have exceeded official external aid flows even for 
water and sanitation, and highly visible projects have been funded 
with PPPs in selected metropolitan areas of developing countries, 
including urban rail projects in Bangkok, Kuala Lumpur, and Manila 
(Liu and Waibel 2010). 
Local governments are also creating cross-sector social partnerships 
(CSSPs) at the sub-national level, entities created for addressing social, 
economic, and/or environmental issues with partner organisations 
from the public, private and civil society sectors (Crane and Seitanidi 
2014). In particular, with support from international networks such 
as ICLEI Local Governments for Sustainability, C40, Global Covenant 
of Mayors, and Global 100% Renewable Energy, local governments

around the world are committing to aggressive carbon reduction 
targets for their cities (Ivanova et al. 2015; Clarke and Ordonez-Ponce 
2017; Kona et al. 2018). Research on CSSPs implementing community

1511
International Cooperation Chapter 1414sustainability plans shows that climate change is one of the four 
most common issues, after waste, energy and water (which are also 
highly relevant to climate mitigation) (MacDonald et al. 2017). 
Community climate action plans consider all GHGs emitted within 
the local geographic boundaries, including from industry, home 
heating, burning fuel in vehicles, and so on. It is these community 
plans that require large multi-stakeholder partnerships to be 
successful. Partners in these partnerships generally include the local 
government departments, other government departments, utilities, 
large businesses, Chambers of Commerce, some small and medium-
sized enterprises, universities, schools, and local civil society groups 
(Clarke and MacDonald 2016). Research shows that the partnership’s 
structural features enable the achievement of plan outcomes, such as 
reducing GHG emissions, while also generating value for the partners

(Austin and Seitanidi 2012; Clarke and MacDonald 2016; Clarke 
and Ordonez-Ponce 2017). Stua (2017b) explores the Mitigation 
Alliances (MAs) on the national level. The internal governance model 
of MAs consists of overarching authorities mandated to harmonise 
the overall organisational structure. These authorities guarantee an 
effective, equitable and transparent functioning of the MA’s pillars 
(the demand, supply, and exchange of mitigation outcomes), in line 
with the principles and criteria of the Paris Agreement. This hybrid 
governance model relies upon its unique links with international 
climate institutions (Stua 2017a).
Transnational business partnerships are a  growing feature of the 
landscape of multi-level, multi-actor governance of climate change. 
Many business leaders embraced the ethos of ‘business cannot 
succeed in societies that fail’. Examples of this line of reasoning 
are: poverty limits consumer spending, political instability disrupts

business activity, and climate change threatens the production 
and distribution of goods and services. Such situations endanger 
multinational enterprise investments, global asset management 
funds, and the core business of international insurance companies 
and pension funds (van Tulder et al. 2021).
A leading example is the World Business Council on Sustainable 
Development (WBCSD), a global, CEO-led organisation of over 200 
leading businesses working together to accelerate the transition 
to a sustainable world. Member companies come from all business 
sectors and all major economies, representing a combined revenue of 
more than USD8.5 trillion and with 19 million employees. The WBCSD 
aims to enhance ‘the business case for sustainability through tools, 
services, models and experiences’. It includes a Global Network of 
almost 70 national business councils across the globe. The overall 
vision is to create a world where more than 9 billion people are all

living well and within the boundaries of our planet, by 2050. Vision 
2050,  released in 2010, explored what a  sustainable world would 
look like in 2050, how such a world could be realised, and the role 
that business can play in making that vision a reality. A few years 
later, Action2020 took that Vision and translated it into a roadmap 
of necessary business actions and solutions (WBCSD 2019). WBCSD 
focuses on those areas where business operates and can make an 
impact. They identify six transformation systems that are critical 
in this regard: Circular Economy, Climate and Energy, Cities and 
Mobility, Food and Nature, People and Redefining Value. All have an impact on climate. An important initiative launched in September 
2008 –Natural Climate Solutions – has the objective of leveraging 
business investment to capture carbon out of the atmosphere. This 
initiative has built strong cross-sectoral partnerships and is intended

to tap into this immense emissions reduction solution potential 
through natural methods with the help of private investment.
The Global Methane Initiative (GMI) is a  multilateral partnership 
launched in 2004 by the United States Environmental Protection 
Agency along with 36 other countries to generate a voluntary, non-
binding agenda for global collaboration to decrease anthropogenic 
methane releases. The GMI builds on the Methane to Markets 
(M2M) Partnership, an international partnership launched in 2004. In 
addition to the GMI’s own financial assistance, the initiative receives 
financial backing from the Global Methane Fund (GMF) for methane 
reduction projects. The GMF is a fund created by governments and 
private donors (Leonard 2014).
Another potentially influential type of transnational business 
partnership is investor coalitions or alliances formed for the purpose 
of pushing investee companies to adopt stronger measures for

stranded asset management and climate change mitigation. MacLeod 
& Park (2011, p. 55) argue that these transnational groups ‘attempt 
to re-orient and “regulate” the behaviour of business by holding 
corporations accountable via mechanisms of information sharing, 
monitoring of environmental impacts, and disclosure of activities 
related to the corporate climate footprint’. This favours a theory of 
active ownership (investor engagement with corporate boards) over 
capital divestment as the optimal pathway to shape the behaviour 
of corporate actors on climate risk (Kruitwagen et al. 2017; Krueger 
et al. 2020).
Transnational cooperative action by investors on climate mitigation 
has been facilitated by international standard-setting on issues 
of climate risk and disclosure. For example, in 2017 the Financial 
Stability Board’s Taskforce on Climate-related Financial Disclosures 
(TCFD) adopted international recommendations for climate risk

disclosure (TCFD 2017). These recommendations, which apply to all 
financial-sector organisations, including banks, insurance companies, 
asset managers, and asset owners, have received strong support 
from investor coalitions globally, including Climate Action 100+ 
(with 300 investors with more than USD33 trillion in assets under 
management), the Global Investor Coalition on Climate Change 
(a coalition of regional investor groups across Asia, Australia, Europe 
and North America) and the Institutional Investors Group on Climate 
Change (IIGCC). One of the key recommendations of the TCFD calls 
for stress-testing of investment portfolios taking into consideration 
different climate-related scenarios, including a 2°C or lower scenario. 
Broad adoption of the TCFD recommendations could provide a basis 
for decisions by investors to shift assets away from climate-risk 
exposed assets such as fossil fuel extraction projects (Osofsky et al.

2019). There is strong evidence showing the urgent need for scaling-
up climate finance to mitigate greenhouse gases in line with pursuit 
of limiting the temperature increase to 1.5°C above pre-industrial 
levels, and to support adaptation to safeguard the international 
community from the consequences of a  changing climate. While 
public actors have a responsibility to deploy climate finance, it is clear

1512
Chapter 14 International Cooperation14that the contribution from the private sector needs to be significant 
(Gardiner et al. 2016). 
As most of these partnerships are of recent vintage an assessment of 
their effectiveness is premature. Instead, partnerships can be assessed 
on the basis of the three main functions introduced earlier. Starting 
with policy development, that is, establishing new agreements on 
norms, rules, or standards among a  broader set of governmental 
and non-governmental actors, this is not the most prominent aspect 
of partnerships so far, although both the cities’ networks and risk 
disclosure recommendations include some elements of this. The 
second element, enabling implementation and delivery of services, 
by combining resources from governmental and non-governmental 
actors, seems to be a  more prominent part of the partnerships 
(Ivanova et al. 2020). Both UNEP financing, the WBCSD, the REDD+

and TEEB mechanisms, and PPP funding for cities are examples here. 
Finally, the third element, knowledge production and dissemination, 
for example, contributing to the evolution of relevant public policies, 
is the most prominent part of these partnerships, with the majority 
including such activities. 
There is a relatively large volume of literature that assesses PPPs in 
general. Much of this applies to partnerships which, either by design 
or not, advance climate goals. This literature provides a good starting 
point for assessing these partnerships as they become operational. 
These can help assess whether such partnerships are worth the effort 
in terms of their performance and effectiveness (Liu et al. 2017b), their 
economic and social value added (Quélin et al. 2017), their efficiency 
(Estache and Saussier 2014) and the possible risks associated with 
them (Grimsey and Mervyn 2002). 
What is less common, but gradually growing, is an important and

more relevant literature on criteria to assess sustainability and impact 
on climate and development goals. Michaelowa and Michaelowa 
(2017) assess 109 transnational partnerships and alliances based 
on four design criteria: existence of mitigation targets; incentives for 
mitigation; definition of a baseline; and existence of a monitoring, 
reporting, and verification procedure . About half of the initiatives 
do not meet any of these criteria, and not even 15% satisfy three 
or more. A recent study using a systematic review of business and 
public administration literature on PPPs concludes that research in 
the past rarely incorporated sustainability concepts. The authors 
propose a research agenda and a series of success factors that, if 
appropriately managed, can contribute to sustainable development, 
and in so doing contribute to a more solid scientific evaluation of 
PPPs (Pinz et al. 2018). There is evidence that with the adoption

of the Sustainable Development Goals, many of which are directly 
linked to climate goals, PPPs will become even more prominent as 
they will be called upon to provide resources, knowledge, expertise, 
and implementation support in a  very ambitious agenda. PPPs in 
the developing world need to take into account different cultural 
and social decision-making processes, language differences, 
and unfamiliar bureaucracy (Gardiner et al. 2016). Having more 
evidence on what norms and standards in relation to sustainability 
are used and their governance is essential (Axel 2019). The issue 
of double counting should be revised. GHGs are accounted both 
at the national and sub-national level or company level (Schneider et al. 2014). Some recent studies aim to provide systems to assess 
the impact of PPPs beyond the much-used notion of value for 
money. One of these recent studies proposes a conceptual model 
that addresses six dimensions relevant to economic, social and

environmental progress. These include resilience and environment, 
access of services to the population, scalability and replicability, 
economic impact, inclusiveness, and finally, degree of engagement 
of stakeholders (Berrone et al. 2019). These systems will most likely 
continue to evolve.
14.5.5 International Cooperation at the Sub-national 
and City Levels
Local and regional governments have an important role to play in 
global climate action, something recognised by the Paris Agreement, 
and also assessed in Sections 13.3.2 and 13.3.4 of this report. There 
are several ways they can be useful. First, sub-national governments 
can contribute insights and experience that provide valuable lessons 
to national governments, as well as offering needed implementation 
capacity (GIZ 2017; Leffel 2018). A great deal of policymaking has 
occurred at the level of city governments in particular. Cities have 
been responsible for more than 70% of global greenhouse gas

emissions and generate over 80% of global income (World Bank 
2010), and many of them have started to take their own initiative 
in enacting and developing mitigation policies (CDP 2015). Most of 
these activities aim at the reduction of GHG emissions in the sectors 
of energy, transportation, urban land use and waste (Bulkeley 2010; 
Xuemei 2007), and are motivated by concerns not only over climate, 
but also a  consideration of local co-benefits (Rashidi et al. 2017, 
2019). Second, sub-national governments can fill the void in policy 
leadership in cases where national governments are ineffectual, even 
to the point of claiming leadership and authority with respect to 
foreign affairs (Leffel 2018). International cooperation plays a role in 
such action. Several international networks, such as C40, ICLEI, Mayors 
for Climate Protection, and the Covenant of Mayors have played an 
important role in defining and developing climate-policy initiatives

at the city level (Fünfgeld 2015). While the networks differ from each 
other, they generally are voluntary and non-hierarchical, intended to 
support the horizontal diffusion of innovative climate policies through 
information-sharing platforms linked to specific goals that member 
cities make (Kern and Bulkeley 2009). The literature has addressed 
the questions of why cities join the networks (Betsill and Bulkeley 
2004; Pitt 2010), what recognition benefits cities can expect (Buis 
2009; Kern and Bulkeley 2009), and how memberships can provide 
visibility to leverage international funding (Betsill and Bulkeley 2004; 
Heinrichs et al. 2013). Membership in the networks has been found 
to be a significant predictor of cities’ adoption of mitigation policies, 
even when controlling for national-level policies that may be in place 
(Rashidi and Patt 2018). Kona et al. (2018) find that cities belonging 
to the Covenant of Mayors are engaging in emissions reductions at

a  rate consistent with achieving a  2°C global temperature target. 
Kona et al. (2021) document this trend continuing.
With respect to their role in formal international cooperation, however, 
it is unclear what authority, as a non-state actor, they actually have. 
Cities, for example, are members of transnational initiatives aimed at

1513
International Cooperation Chapter 1414non-state actors, such as Global Climate Action, originally the Non-
state Actor Zone for Climate Action, under the UNFCCC. While there is 
reason to believe that such membership can add value to mitigation 
efforts, one study suggests that the environmental effects have yet 
to be reliably quantified (Hsu et al. 2019a). By contrast, Kuramochi 
et al. (2020) provide evidence that non-state actors are leading 
to significant emissions reductions beyond what countries would 
otherwise be achieving. In terms of institutional strength, Michaelowa 
and Michaelowa (2017) suggest that few such networks fulfil 
governance criteria, and hence challenge their effectiveness. Several 
researchers suggest that their role is important in informal ways, given 
issues about the legitimacy of non-state actors (Chan et al. 2016; 
Nasiritousi et al. 2016). Bäckstrand et al. (2017) advance the concept

of ‘hybrid multilateralism’ as a heuristic to capture this intensified 
interplay between state and non-state actors in the new landscape of 
international climate cooperation. The effectiveness of such non-state 
government actors should be measured not only by their contribution 
to mitigation, but also by their success to enhance the accountability, 
transparency and deliberative quality of the UNFCCC and the Paris 
Agreement (Chan et al. 2015; Busby 2016; Hale et al. 2016). In the 
post-Paris era, effectiveness also revolves around how to align non-
state and intergovernmental action in a comprehensive framework 
that can help achieve low carbon futures (Chan et al. 2016). Stua 
(2017b) suggests that networks involving non-state actors can play 
an important role in enhancing transparency. Such effectiveness has 
to be complemented also by normative questions, applying a  set 
of democratic values: participation, deliberation, accountability,

and transparency (Bäckstrand and Kuyper 2017). Such concepts of 
polycentric governance offer new opportunities for climate action, 
but it has been argued that it is too early to judge their importance 
and effects (Jordan et al. 2015).
14.6 Synthesis
14.6.1 Changing Nature of International Cooperation
The main development since AR5 in terms of international climate 
cooperation has been the shift from the Kyoto Protocol to the Paris 
Agreement as the primary multilateral driver of climate mitigation 
policy worldwide (Section  14.3). Most ex-post assessments of the 
Kyoto Protocol suggest that it did lead to emissions reductions in 
countries with binding targets, in addition to changing investment 
patterns in low-carbon technologies. As noted earlier, the Paris 
Agreement is tailored to the evolving understanding of the climate 
mitigation challenge as well as shifting political imperatives and 
constraints. Whether the Paris Agreement will in fact be effective

in supporting global action sufficient to achieve its objectives is 
contested, with competing arguments in the scientific literature 
supporting different views. To some extent these views align with the 
different analytic frameworks (Section 14.2.1): the Paris Agreement 
does not address the free-riding issue seen as important within the 
global commons framing, but may provide the necessary incentives 
and support mechanisms viewed as important under the political and 
transitions framings, respectively. The strongest critique of the Paris 
Agreement is that current NDCs themselves fail by a wide margin to add up to the level of aggregate emissions reductions necessary to 
achieve the objectives of holding global average warming well below 
2°C, much less 1.5°C (Section  14.3.3 Figure  14.2), and that there 
is no legally binding obligation to achieve the NDCs. Arguments in 
support of Paris are that it puts in place the processes, and generates

normative expectations, that nudge NDCs to become progressively 
more ambitious over time, including in developing countries. The 
growing number of countries with mid-century net-zero GHG or CO 2 
targets, consistent with Article 4 of the Paris Agreement, lends support 
to this proposition, although there is as yet no empirical literature 
drawing an unambiguous connection. The collective quantified goal 
from a  floor of USD100 billion a  year in transfers to developing 
countries, the Green Climate Fund and other provisions on finance in 
the Paris Agreement have also been recognised as key to cooperation 
(Sections 14.3.2.8 and 14.4.1). But then these arguments are met 
with counter arguments, that even with Paris processes in place, 
given the logic of iterative, rising levels of ambition over time, this 
is unlikely to happen within the narrow window of opportunity that 
exists to avert dangerous levels of global warming (Section 14.3.3).

The degree to which countries are willing to increase the ambition 
and secure the achievement of their NDCs over time will be an 
important indicator of the success of the Paris Agreement; evidence 
of this was expected by the end of 2020, but the COVID-19 pandemic 
has delayed the process of updating NDCs. 
An increasing role is also played by other cooperative agreements, in 
particular (potentially) under Article 6 (Sections 14.3.2.10 and 14.4.4), 
transnational partnerships, and the institutions that support them. This 
fits both a transitions narrative that cooperation at the sub-global and 
sectoral levels is necessary to enable specific system transformations, 
and a recent emphasis in the public goods literature on club goods and 
a gradual approach to cooperation, also referred to as building blocks 
or incremental approach (Sections 14.2 and 14.5.1.4). There has been 
little analysis of whether these other agreements are of sufficient scale

and scope to ensure that transformations happen quickly enough. This 
chapter, appraising them together, concludes that they are not. First, 
many agreements, such as those related to trade, may stand in the 
way of bottom-up mitigation efforts (Section 14.5.1.3). Second, many 
sectoral agreements aimed at decarbonisation –  such as within the 
air travel sector – have not yet adopted targets comparable in scale, 
scope or legal character to those adopted under the Paris Agreement 
(Section 14.5.2.3). Third, there are many sectors for which there are 
no agreements in place. At the same time, there are some important 
bright spots, many in the area of transnational partnerships. A growing 
number of cities have committed themselves to adopting urban policies 
that will place them on a path to rapid decarbonisation, while learning 
from each other how to implement successful policies to realise climate 
goals (Section 14.5.5). An increasing number of large corporations have

committed to decarbonising their industrial processes and supply chains 
(Section  14.5.4). And an ever-increasing number of non-state actors 
are adopting goals and initiating mitigation actions (Section 14.5.3). 
These goals and actions, some argue, could bridge the mitigation gap 
created by inadequate NDCs, although the empirical literature to date 
challenges this, suggesting that there is less transparency and limited 
accountability for such actions, and mitigation targets and incentives 
are also not clear (Sections 14.3.3 and 14.5).

1514
Chapter 14 International Cooperation1414.6.2 Overall Assessment of International Cooperation
This section provides an overall assessment of international 
cooperation, taking into account the combined effects of cooperation 
within the UNFCCC process, other global agreements, as well as 
regional, sectoral, and transnational processes. Recent literature 
consistent with the transitions framing highlights that cooperation 
can be particularly effective when it addresses issues on a  sector-
by-sector basis (Geels et al. 2019). Table 14.4 below summarises the 
effects of international cooperation on mitigation efforts in each of 
the sectoral areas covered in Chapters 5 to 12 of this report. As it 
indicates, there are some strong areas of sector-specific cooperation, 
but also some important weaknesses. Formal agreements and 
programmes, both multilateral and bilateral, are advancing mitigation 
efforts in energy, AFOLU, and transportation, while transnational

networks and partnerships are addressing issues in urban systems, 
industry, and buildings. Although many of the concerns relevant for 
buildings may be embedded in the energy sector with respect to their 
operation, and the industrial sector with respect to their materials, 
reinforcing the networks with more formal agreements could be 
vital to putting these sectors on a pathway to net zero GHG or CO 2 
emissions. Several of the sectors have very little formal cooperation 
at the international level, and a common theme across many of them 
is a need for increased financial flows to achieve particular objectives.
Table  14.5 provides examples of mechanisms addressing each 
of the assessment criteria identified in Section 14.2.3. The effects of  
different forms of international cooperation are separated out, 
including not only UNFCCC and other multilateral processes, but also 
sub-global and sectoral agreements. Several points stand out. First,

the Paris Agreement has the potential to significantly advance the UN climate regime’s transformative potential. Second, the international 
market mechanisms under Article 6  – should an agreement on 
implementation deals be reached – allow a shift from projects and 
programmes to policy-based and sectoral generation of emissions 
credits. Moreover, the sectoral agreement CORSIA also makes use 
of such credits. Third, there is a lack of attention to both distributive 
outcomes and institutional support within sectoral agreements, 
representing a serious gap in efforts to harmonise mitigation with 
equity and sustainable development. Fourth, there are transnational 
partnerships and initiatives, representing the actions of non-state 
actors, addressing each of the assessment criteria, with the exception 
of economic effectiveness.
Table 14.4 | Effects of international cooperation on sectoral mitigation efforts.
Sector Key strengths Key gaps and weaknesses
Demand, services,

social aspectsAdoption of SDGs addressing social inequities and 
sustainable development in the context of mitigationLittle international attention to demand-side mitigation issues
EnergyGreater incorporation of climate goals into sectoral 
agreements and institutions; formation of new specialised 
agencies (e.g., IRENA, SE4All) devoted to climate-
compatible energyNeed for enhanced financial support to place low-carbon energy sources on an equal footing 
with carbon-emitting energy in developing countries; investor–state dispute settlement 
mechanisms designed to protect the interests of companies engaged in high-carbon energy 
supply from national policies; ensuring just transition; and, addressing stranded assets
AFOLUBilateral support for REDD+ activities; transnational 
partnerships disincentivising use of products from 
degraded landsNeed for increased global finance for forest restoration projects and REDD+ activities;

failure of national governments to meet internationally agreed upon targets with 
respect to deforestation and restoration; no cooperative mechanisms in place to address 
agricultural emissions
Urban systemsTransnational partnerships enhancing the capacity 
of municipal governments to design and implement 
effective policiesNeed for increased financial support for climate-compatible urban infrastructure development
BuildingsTransnational initiative aimed at developing 
regional roadmapsNeed for formal international cooperation to enhance mitigation activities in buildings
TransportSectoral agreements in aviation and shipping begin 
to address climate concernsNeed to raise the level of ambition in sectoral agreements consistent with the Paris 
Agreement and complete decarbonisation, especially as emissions from international aviation 
and shipping continue to grow, unaccounted for in NDCs
IndustryTransnational partnerships and networks encouraging

the adoption of zero-emission supply chain targetsNo formal multilateral or bilateral cooperation to address issues of decarbonisation 
in industry
Cross-sectoral, including 
CDR and SRMInternational agreements addressing risks  
of ocean-based CDRLack of cooperative mechanisms addressing risks and benefits of SRM; lack of 
cooperative mechanisms addressing financial and governance aspects of land- 
and technology-based CDR

1515
International Cooperation Chapter 1414Table 14.5 | Illustrative examples of multi-level governance addressing criteria of effectiveness.
effectivenessTransformative 
potentialDistributive 
outcomesEconomic  
effectivenessInstitutional strength
UNFCCCStabilisation goal, 
and quasi-targets for 
industrialised countriesFinancial mechanism; 
technology mechanism, 
provisions for 
capacity buildingFinancial mechanism, 
transfers from 
developed to 
developing countries; 
leadership role 
for industrialised 
countries listed 
in Annex 1Reporting requirements; capacity building 
for national climate change offices
Kyoto ProtocolBinding national 
targets for 
industrialised countriesAdaptation Fund; 
targets restricted 
to industrialised 
countriesMarket-based 
mechanismsEmissions accounting and reporting requirements; 
institutional capacity building
Paris AgreementNDCs and the 
global stocktakeMechanisms for 
capacity building and 
technology development

and transferFurthering financial 
commitments 
under the UNFCCC, 
including enhanced 
transparency 
on financeVoluntary 
cooperationMechanism for enhanced transparency
Other multilateral 
agreements
(Montreal Protocol, 
SDG 7, etc.)Phase out of ozone-
depleting substances 
with high global 
warming potential; 
significant effects on 
GHG mitigation Ozone Fund; 
technology transfer; 
development and 
sharing of knowledge 
and expertiseSDGs embedding 
mitigation in 
developmentProcesses for adjustment and amendment;  
reporting requirements
Multilateral and 
regional economic 
agreements 
and institutionsHarmonised lending 
practices of MDBs; 
mainstreaming climate 
change into IMF 
practices; liberalisation 
of trade in climate-
friendly goods and 
services; negative effect 
from regulatory chillConcessional 
financing agreementsPotentially negative results from dispute  
settlement processes
Sectoral agreements 
and institutionsClimate mitigation 
targets and actions

in AFOLU, energy, 
and transport Institutions devoted 
to developing and 
deploying zero-carbon 
energy technologies 
(e.g., IRENA)Use of carbon 
offsets to reduce 
growth in emissions 
from aviation
Transnational 
partnershipsYouth climate 
movement raising 
mitigation and fossil 
fuel divestment on 
political agendas  
and in financial sectorNon-state actor 
commitments to 
renewable energy-based 
supply chainsClimate justice  
legal initiativesCity networks providing information exchange  
and technical support

1516
Chapter 14 International Cooperation1414.7 Knowledge Gaps
Any assessment of the effectiveness of international cooperation 
is limited by the methodological challenge of observing sufficient 
variance in cooperation in order to support inference on effects. 
There is little in the way of cross-sectional variance, given that 
most of the governance mechanisms assessed here are global in 
their geographical coverage. One exception is with respect to the 
effects of the Kyoto Protocol, which we have reported. Time series 
analysis is also challenging, given that other determinants of climate 
mitigation, including technology costs and the effects of national and 
sub-national level policies, are rapidly evolving. Thus, this chapter 
primarily reviews scholarship that compares observations with 
theory-based counterfactual scenarios.
Many of the international agreements and institutions discussed in 
this chapter, in particular the Paris Agreement, are new. The logic and

architecture of the Paris Agreement, in particular, breaks new ground, 
and there is limited evaluation of prior experience in the form of 
analogous treaties to draw on. Such instruments have evolved in 
response to geopolitical and other drivers that are changing rapidly, 
and will continue to shape the nature of international cooperation 
under it and triggered by it. The Paris Agreement is also, in common 
with other multilateral agreements, a  ‘living instrument’ evolving 
through interpretative and operationalising rules, and forms of 
implementation, that Parties continue to negotiate at conferences 
year on year. It is a constant ‘work in progress’ and thus challenging 
to assess at any given point in time. The Paris Agreement also engages 
a larger set of variables – given its privileging of national autonomy 
and politics, integration with the sustainable development agenda, 
and its engagement with actions and actors at multiple levels – than

earlier international agreements, which further complicates the task 
of tracing causality between observed effects and international 
cooperation through the Paris Agreement.
Understanding of the effectiveness of international agreements and 
institutions is driven entirely by theory-driven prediction of how 
the world will evolve, both with these agreements in place and 
without them. The predictions in particular are problematic, because 
governance regimes are complex adaptive systems, making it 
impossible to predict how they will evolve over time, and hence what 
their effects will be. Time will cure this in part, as it will generate 
observations of the world with the new regime in place, which we can 
compare to the counterfactual situation of the new regime’s being 
absent, which may be a simpler situation to model. But even here our 
modelling capacity is limited: it may simply never be possible to know 
with a high degree of confidence whether international cooperation,

such as that embodied in the Paris Agreement, is having a significant 
effect, no matter how much data are accumulated. 
Given the importance of theory for guiding assessments of the past 
and likely future impacts of policies, it is important to note that 
among the alternative theoretical frameworks for analysis, some have 
been much more extensively developed in the literature than others. 
This chapter has noted in particular the partial dichotomy between 
a  global commons framing of climate change and a  transitions 
framing, which include different indicators to be used to evaluate the effectiveness of policies. The latter framing is particularly under-
developed. Greater development of theories resting in social science 
disciplines such as economic geography, sociology, and psychology 
could potentially provide a more complete picture of the nature and 
effectiveness of international cooperation.

1517
International Cooperation Chapter 1414Frequently Asked Questions (FAQs)
FAQ 14.1 |  Is international cooperation working?
Yes, to an extent. Countries’ emissions were in line with their internationally agreed targets: the collective greenhouse gas (GHG) 
mitigation target for Annex I countries in the UNFCCC to return to their 1990 emissions levels by 2000, and their individual targets 
in the Kyoto Protocol for 2008–12. Numerous studies suggest that participation in the Kyoto Protocol led to substantial reductions in 
national GHG emissions, as well increased levels of innovation and investment in low-carbon technologies. In this latter respect, the 
Kyoto Protocol set in motion some of the transformational changes that will be required to meet the temperature goal of the Paris 
Agreement. It is too soon to tell whether the processes and commitments embodied in the Paris Agreement will be effective in achieving

its stated goals with respect to limiting temperature rise, adaptation, and financial flows. There is, however, evidence that its entry into 
force has been a contributing factor to many countries’ adopting mid-century targets of net-zero GHG or CO 2 emissions. 
FAQ 14.2 |  What is the future role of international cooperation in the context  
of the Paris Agreement?
Continued international cooperation remains critically important both to stimulate countries’ enhanced levels of mitigation ambition, 
and through various means of support to increase the likelihood that they achieve these objectives. The latter is particularly the 
case in developing countries, where mitigation efforts often rely on bilateral and multilateral cooperation on low-carbon finance, 
technology support, capacity building, and enhanced South-South cooperation. The Paris Agreement is structured around Nationally

Determined Contributions that are subject to an international oversight system, and bolstered through international support. 
The international oversight system is designed to generate transparency and accountability for individual emissions reduction 
contributions, and regular moments for stock-taking of these efforts towards global goals. Such enhanced transparency may instil 
confidence and trust, and foster solidarity among nations, with theory-based arguments that this will lead to greater levels of 
ambition. Together with other cooperative agreements at the sub-global and sectoral levels, as well as a  growing number of 
transnational networks and initiatives, the implementation of all of these mechanisms is likely to play an important role in making 
political, economic, and social conditions more favourable to ambitious mitigation efforts in the context of sustainable development 
and efforts to eradicate poverty.

FAQ 14.3 |   Are there any important gaps in international cooperation, which will need to be 
filled in order for countries to achieve the objectives of the Paris Agreement, such 
as holding temperature increase to well below 2°C and pursuing efforts towards 
1.5°C above pre-industrial levels?
While international cooperation is contributing to global mitigation efforts, its effects are far from uniform. Cooperation has 
contributed to setting a global direction of travel, and to falling greenhouse gas emissions in many countries and avoided emissions 
in others. It remains to be seen whether it can achieve the kind of transformational changes needed to achieve the Paris Agreement’s 
long-term global goals. There appears to be a large potential role for international cooperation to better address sector-specific 
technical and infrastructure challenges that are associated with such transformational changes. Finalising the rules to pursue

voluntary cooperation, such as through international carbon market mechanisms and public climate finance in the implementation 
of NDCs, without compromising environmental integrity, may play an important role in accelerating mitigation efforts in developing 
countries. Finally, there is room for international cooperation to more explicitly address transboundary issues associated with carbon 
dioxide removal and solar radiation management.

1549
Investment and Finance Chapter 1515
Finance to reduce net greenhouse gas (GHG) emissions and 
enhance resilience to climate impacts represents a  critical 
enabling factor for the low carbon transition. Fundamental 
inequities in access to finance as well as its terms and 
conditions, and countries’ exposure to physical impacts of 
climate change overall result in a  worsening outlook for 
a  global just transition (high confidence). Decarbonising the 
economy requires global action to address fundamental economic 
inequities and overcome the climate investment trap that exists for 
many developing countries. For these countries the costs and risks 
of financing often represent a significant challenge for stakeholders 
at all levels. This challenge is exacerbated by these countries’ 
general economic vulnerability and indebtedness. The rising public 
fiscal costs of mitigation, and of adapting to climate shocks, are

affecting many countries and worsening public indebtedness and 
country credit ratings at a time when there were already significant 
stresses on public finances. The COVID-19 pandemic has made these 
stresses worse and tightened public finances still further. Other major 
challenges for commercial climate finance include: the mismatch 
between capital and investment needs,1 home bias2 considerations, 
differences in risk perceptions for regions, as well as limited 
institutional capacity to ensure safeguards represent. {15.2, 15.6.3}
Investors, central banks, and financial regulators are driving 
increased awareness of climate risk. This increased awareness 
can support climate policy development and implementation 
(high confidence). Climate-related financial risks arise from physical 
impacts of climate change (already relevant in the short term), and 
from a  disorderly transition to a  low-carbon economy. Awareness

of these risks is increasing leading also to concerns about financial 
stability. Financial regulators and institutions have responded with 
multiple regulatory and voluntary initiatives by to assess and address 
these risks. Yet despite these initiatives, climate-related financial risks 
remain greatly underestimated by financial institutions and markets 
limiting the capital reallocation needed for the low-carbon transition. 
Moreover, risks relating to national and international inequity  – 
which act as a barrier to the transformation – are not yet reflected in 
decisions by the financial community. Stronger steering by regulators 
and policy makers has the potential to close this gap. Despite the 
increasing attention of investors to climate change, there is limited 
evidence that this attention has directly impacted emission reductions. 
This leaves high uncertainty, both near-term (2021–30) and longer-
term (2021–50), on the feasibility of an alignment of financial flows

with the Paris Agreement (high confidence). {15.2, 15.6}
Progress on the alignment of financial flows with low GHG 
emissions pathways remains slow. There is a climate financing 
gap which reflects a persistent misallocation of global capital 
(high confidence). Persistently high levels of both public and private 
1 The term Investment ‘Needs’ used in the chapter means equal to the term Investment Requirement used in SPM.
2 Most of climate finance stays within national borders, especially private climate flows (over 90%). Reasons are national policy support, differences in regulatory standards, 
exchange rate, political and governance risks, to information market failures.
3 In modelled pathways, regional investments are projected to occur when and where they are most cost-effective to limit global warming. The model quantifications help

to identify high-priority areas for cost-effective investments, but do not provide any indication on who would finance the regional investments.fossil-fuel related financing continue to be of major concern despite 
recent commitments. This reflects policy misalignment, the current 
perceived risk-return profile of fossil fuel-related investments, and 
political economy constraints (high confidence). {15.3}
Estimates of climate finance flows – which refers to local, national, 
or transnational financing from public, private, multilateral, bilateral 
and alternative sources, to support mitigation and adaptation actions 
addressing climate change – exhibit highly divergent patterns across 
regions and sectors and a slowing growth. {15.3}
When the perceived risks are too high the misallocation of abundant 
savings persists. Investors refrain from investing in infrastructure 
and industry in search of safer financial assets, even earning low or 
negative real returns. {15.2, 15.3}

Global climate finance is heavily focused on mitigation (more than 
90% on average between 2017–2020). This is despite the significant 
economic effects of climate change’s expected physical impacts, 
and the increasing awareness of these effects on financial stability. 
To meet the needs for rapid deployment of mitigation options, 
global mitigation investments are expected to need to increase by 
the factor of 3 to 6 (medium confidence). The gaps are wide for all 
sectors and represent a major challenge for developing countries,3 
especially Least-Developed Countries (LDCs), where flows have to 
increase by factor 4 to 7, for specific sectors like agriculture, forestry 
and other land use (AFOLU) in relative terms, and for specific groups 
with limited access to, and high costs of, climate finance (high 
confidence). {15.4, 15.5}
The actual size of sectoral and regional climate financing gaps 
is only one component driving the magnitude of the challenge,

with financial and economic viability, access to capital markets, 
investment requirements for adaptation, reduction of losses 
and damages, climate-responsive social protection, appropriate 
regulatory frameworks and institutional capacity to attract and 
facilitate investments and ensure safeguards being decisive to scale-
up financing. Financing needs for the creation and strengthening 
of regulatory environment and institutional capacity, upstream 
financing needs as well as R&D and venture capital for development 
of new technologies and business models are often overlooked 
despite their critical role to facilitate the deployment of scaled-up 
climate finance (high confidence). {15.4.1, 15.5.2}
The relatively slow implementation of commitments by 
countries and stakeholders in the financial system to  scale 
up  climate finance reflects neither the urgent need for 
ambitious  climate action, nor the economic rationale

for ambitious climate action (high confidence). Delayed climate 
investments and financing  – and limited alignment of investment 
activity with the Paris Agreement – will result in significant carbon

1550
Chapter 15 Investment and Finance15lock-ins, stranded assets, and other additional costs. This will 
particularly impact urban infrastructure and the energy and transport 
sectors (high confidence). A  common understanding of debt 
sustainability and debt transparency, including negative implications 
of deferred climate investments on future GDP , and how stranded 
assets and resources may be compensated, has not yet been 
developed (medium confidence). {15.6}
The greater the urgency of action to remain on a  1.5°C pathway 
the greater need for parallel investment decisions in upstream and 
downstream parts of the value chain. Greater urgency also reduces 
the lead times to build trust in regulatory frameworks. Consequently, 
many investment decisions will need to be made based on the long-
term global goals. This highlights the importance of trust in political 
leadership which, in turn, affects risk perception and ultimately 
financing costs (high confidence). {15.6.1, 15.6.2}

There is a  mismatch between capital availability in the developed 
world and the future emissions expected in developing countries. 
This emphasises the need to recognise the explicit and positive social 
value of global cross-border mitigation financing. A significant push 
for international climate finance access for vulnerable and poor 
countries is particularly important given these countries’ high costs 
of financing, debt stress and the impacts of ongoing climate change 
(high confidence). {15.2, 15.3.2.3, 15.5.2, 15.6.1, 15.6.7}
Ambitious global climate policy coordination and stepped-up 
(public) climate financing over the next decade (2021–2030) 
can help address macroeconomic uncertainty and alleviate 
developing countries’ debt burden post-COVID-19. It can 
also help redirect capital markets and overcome challenges 
relating to the need for parallel investments in mitigation and 
the up-front risks that deter economically sound low carbon

projects. (high confidence). Providing strong climate policy signals 
helps guide investment decisions. Credible and clear signalling by 
governments and the international community reduce uncertainty 
for financial decision-makers and help reduce transition risk. In 
addition to indirect and direct subsidies, the public sector’s role in 
addressing market failures, barriers, provision of information, and risk 
sharing (equity, various forms of public guarantees) can encourage 
the efficient mobilisation of private sector finance (high confidence). 
{15.2, 15.6.1, 15.6.2}
The mutual benefits of coordinated support for climate mitigation 
and adaptation in the next decade for both developed and developing 
regions could potentially be very high in the post-COVID-19 era. 
Climate compatible stimulus packages could significantly reduce the 
macro-financial uncertainty generated by the pandemic and increase 
the sustainability of the world economic recovery. {15.2, 15.3.2.3,

15.5.2, 15.6.1, 15.6.7}
Political leadership and intervention remain central to addressing 
uncertainty as a  fundamental barrier for a  redirection of financial 
flows. Existing policy misalignments  – for example in fossil fuel 
subsidies – undermine the credibility of public commitments, reduce 
perceived transition risks and limit financial sector action (high 
confidence). {15.2, 15.3.3, 15.6.1, 15.6.2, 15.6.3}Innovative financing approaches could help reduce the systemic 
underpricing of climate risk in markets and foster demand for 
Paris-aligned investment opportunities. Approaches include 
de-risking investments, robust ‘green’ labelling and disclosure 
schemes, in addition to a  regulatory focus on transparency 
and reforming international monetary system financial sector 
regulations (medium confidence). Markets for green bonds, ESG 
(environmental, social, and governance), and sustainable finance 
products have grown significantly since the Fifth Assessment Report

of the Intergovernmental Panel on Climate Change (IPCC AR5) and 
the landscape continues to evolve. Underpinning this evolution 
is investors’ preference for scalable and identifiable low-carbon 
investment opportunities. These relatively new labelled financial 
products will help by allowing a  smooth integration into existing 
asset allocation models (high confidence). Markets for green bonds, 
ESG (environmental, social, and governance), and sustainable 
finance products have also increased significantly since AR5, but 
challenges nevertheless remain, in particular there are concerns 
about ‘greenwashing’ and the limited application of these markets 
to developing countries. New business models (e.g., pay-as-you-go) 
can facilitate the aggregation of small-scale financing needs and 
provide scalable investment opportunities with more attractive risk-
return profiles. Support and guidance for enhancing transparency

can promote capital markets’ climate financing by providing quality 
information to price climate risks and opportunities. Examples 
include Sustainable Development Goals (SDG) and environmental, 
social and governance (ESG) disclosure, scenario analysis and 
climate risk assessments, including the Task Force on Climate-Related 
Financial Disclosures (TCFD). The outcome of these market-correcting 
approaches on capital flows cannot be taken for granted, however, 
without appropriate fiscal, monetary and financial policies. Mitigation 
policies will be required to enhance the risk-weighted return of low-
emission and climate-resilient options, and – supported by progress in 
transparent and scientifically based projects’ assessment methods – 
to accelerate the emergence and support for financial products 
based on real projects, such as green bonds, and phase out fossil fuel 
subsidies. Greater public-private cooperation can also encourage the

private sector to increase and broaden investments, within a context 
of safeguards and standards, and this can be integrated into national 
climate change policies and plans. {15.1, 15.2.4, 15.3.1, 15.3.2, 
15.3.3, 15.5.2, 15.6.1, 15.6.2, 15.6.6, 15.6.7, 15.6.8}.
The following policy options can have important long-term 
catalytic benefits (high confidence). (i) Stepped-up both the 
quantum and composition of financial, technical support and 
partnership in low-income and vulnerable countries alongside low-
carbon energy access in low-income countries, such as in sub-Saharan 
Africa, which currently receives less than 5% of global climate 
financing flows; (ii) continued strong role of international and national 
financial institutions, including multilateral, especially location-based 
regional, and national development banks; (iii)  de-risking cross-
border investments in low-carbon infrastructure, development of 
local green bond markets, and the alignment of climate and non-

climate policies, including direct and indirect supports on fossil 
fuels, consistent with the climate goals; (iv) lowering financing costs 
including transaction costs and addressing risks through funds and 
risk-sharing mechanisms for under-served groups; (v) accelerated

1551
Investment and Finance Chapter 1515finance for nature-based solutions, including mitigation in the 
forest sector (REDD+), and climate-responsive social protection; 
(vi) improved financing instruments for loss and damage events, 
including risk-pooling-transfer-sharing for climate risk insurance; 
(vii)  economic instruments, such as phasing in carbon pricing and 
phasing out fossil fuel subsidies in a  way that addresses equity 
and access; and (viii) gender-responsive and women-empowered 
programmes. {15.2.3, 15.2.4, 15.3.1, 15.3.2.2, 15.3.3, 15.4.1, 15.4.2, 
15.4.3, 15.5.2, 15.6, 15.6.2, 15.6.4, 15.6.5, 15.6.6, 15.6.7, 15.6.8.2}

1552
Chapter 15 Investment and Finance1515.1 Climate Finance – Key Concepts 
and Scope
Finance for climate action (or climate finance), environmental finance 
(which also covers other environmental priorities such as water, air 
pollution and biodiversity), and sustainable finance (which encompasses 
issues relating to socio-economic impacts, poverty alleviation and 
empowerment) are interrelated rather than mutually exclusive concepts 
(UNEP Inquiry 2016a; ICMA 2020a). Their combination is needed to 
align mitigation investments with multiple SDGs, and at a minimum, 
minimise the conflicts between climate targets and SDGs not being 
targeted. From a climate policy perspective, climate finance refers to 
finance ‘whose expected effect is to reduce net GHG emissions and/or 
enhance resilience to the impacts of climate variability and projected 
climate change’ (UNFCCC 2018a). However, as pinpointed in the AR5, significant room for interpretation and context-specific considerations

remains. Further, such definition needs to be put in perspective with the 
expectations of investors and financiers (see Box 15.2).
Specifying the scope of climate finance requires defining two terms: 
what qualifies as ‘finance’ and as ‘climate’ respectively. In terms 
of what type of finance to consider, options include considering 
investments or total costs (Box 15.1), stocks or flows, gross or net 
(the latter taking into account reflows and/or depreciation), and 
domestic or cross-border, public or private (Box  15.2). In terms of 
what may be considered as ‘climate’, a  key difference relates to 
measuring climate-specific finance (only accounts for the portion 
of finance resulting in climate benefits) or climate-related finance 
(captures total project costs and aims to measure the mainstreaming 
of climate considerations). One should even consider the investments 
Box 15.1 | Core Terms

This box defines some core terms used in this chapter as well as in other chapters addressing finance issues: cost, investment, 
financing, public and private. The chapter makes broad use of the term finance to refer to all types of transactions involving monetary 
amounts. It avoids the use of the terms funds and funding to the extent possible, which should otherwise be understood as synonyms 
for money and money provided.
Cost, investment and financing: different but intertwined concepts. Cost encompasses capital expenditures (CAPEX or upfront 
investment value leveraged over the lifetime of a project) operating and maintenance expenditures (OPEX), as well as financing costs. 
Note that some projects e.g., related to technical assistance may only involve OPEX (e.g., staff costs) but no CAPEX, or may not incur 
direct financing costs (e.g., if fully financed via own funds and grants).

Investment, in an economic sense, is the purchase of (or CAPEX for) a physical asset (notably infrastructure or equipment) or intangible 
asset (e.g., patents, IT solutions) not consumed immediately but used over time. For financial investors, physical and intangible assets 
take the form of financial assets such as bonds or stocks which are expected to provide income or be sold at a higher price later. In 
practice, investment decisions are motivated by a calculation of risk-weighted expected returns that takes into account all expected 
costs, as well as the different types of risks, discussed in Section 15.6.1, that may impact the returns of the investment and even turn 
them into losses.
Incremental cost (or investment) accounts for the difference between the cost (or investment value) of a climate project compared to 
the cost (or investment value) of a counterfactual reference project (or investment). In cases where climate projects and investments

are more cost effective than the counterfactual, the incremental cost will be negative.
Financing refers to the process of securing the money needed to cover an investment or project cost. Financing can rely on debt 
(e.g., through bond issuance or loan subscription), equity issuances (listed or unlisted shares), own funds (typically savings or auto-
financing through retained earnings), as well as on grants and subsidies
Public and private: statistical standard and grey zones. International statistics classify economic actors as pertaining to the 
public or private sectors. Households always qualify as private and governmental bodies and agencies as public. Criteria are needed 
for other types of actors such as enterprises and financial institutions. Most statistics rely on the majority ownership and control 
principle. This is the case for the Balance of Payment, which records transactions between residents of a country and the rest of the 
world (IMF 2009).

Such a strict boundary between public and private sectors may not always be suitable for mapping and assessing investment and 
financing activities. On the one hand, some publicly owned entities may have a mandate to operate on a fully- or semi-commercial 
basis, for example state-owned enterprises, commercial banks, and pension funds, as well as sovereign wealth funds. On the other 
hand, some privately owned or controlled entities can pursue not-for-profit objectives, e.g., philanthropies and charities. The present 
chapter considers these nuances to the extent made possible by available data and information.

1553
Investment and Finance Chapter 1515decided for reasons unrelated with climate objectives but which 
contribute to these objectives (hydroelectricity, rail transportation).
In many cases, the scope of what may be considered as ‘climate 
finance’ will also depend on the context of implementation such as 
priorities and activities listed in countries’ Nationally Determined 
Contributions (NDCs) under the Paris Agreement (UNFCCC 2019a) 
as well as national development plans more broadly targeting the 
achievement of SDGs. Hence, rather than opposing the different 
options listed above, the choice of one or the other depends on the 
desired scope of measurement, which in turn depends on the policy 
objective being pursued. The increasingly diverse initiatives and body 
of grey literature address a range of different information needs. They 
provide analyses at the levels of domestic finance flows (e.g., UNDP 
2015; Hainaut and Cochran 2018), international flows (e.g. OECD

2016; AfDB et al. 2018), global flows (UNFCCC 2018a; Buchner et al. 
2019), the financial system (e.g.,  UNEP Inquiry 2016a) or specific 
financial instruments such as bonds (e.g.,  CBI 2018). Common 
frameworks, reporting transparency are, however, necessary in order 
to identify overlaps, commonalities and differences between these 
different measurements in terms of scope and underlying definitions. 
In that regard, the developments of national and international 
taxonomies, definitions and standards can help, as further discussed 
in Section 15.6, and Chapter 17 in AR6 WGII report.
Beyond the need to scale up levels of climate finance, the Paris 
Agreement provides a  broad policy environment and momentum 
for a more systemic and transformational change in investment and 
financing strategies and patterns. Article 2.1c, which calls for ‘making 
finance flows consistent with a  pathway towards low greenhouse gas emissions and climate-resilient development’, positions finance

as one of the Agreement’s three overarching goals (UNFCCC 2015). 
This formulation is a recognition that the mitigation and resilience 
goals cannot be achieved without finance, both in the real economy 
and in the financial system, being made consistent with these goals 
(Zamarioli et al. 2021). It has in turn contributed to the development 
of the concept of alignment (with the Paris Agreement) used in 
the financial sector (banks, institutional investors), businesses, and 
public institutions (development banks, public budgets). As a result, 
since AR5, in addition to measuring and analysing climate finance, 
an increasing focus has been placed on assessing the consistency or 
alignment, as well as respectively the inconsistency or misalignment, 
of finance with climate policy objectives, as for instance illustrated 
by the multilateral development banks’ joint framework for aligning 
their activities with the goals of the Paris Agreements (MDBs 2018).

Assessing climate consistency or alignment implies looking at all 
investment and financing activities, whether they target, contribute 
to, undermine or have no particular impact on climate objectives. This 
all-encompassing scope notably includes remaining investments and 
financing for high-GHG emission activities that may be incompatible 
with remaining carbon budgets, but also activities that may play 
a  transition role in climate mitigation pathways and scenarios 
(Section 15.3.2.3). As a result, any meaningful assessment of progress 
requires the use of different shades to assess activities based on 
their negative, neutral (‘do no harm’) or positive contributions, 
(e.g.,  CICERO 2015; Cochran and Pauthier 2019; Natixis 2019). 
Doing so in practice requires the development of robust definitions, 
assessment methods and metrics, an area of work and research 
that remained under development at the time of writing. A  range 
Box 15.2 | International Climate Finance Architecture

International climate finance can flow through different bilateral, multilateral, and other channels, involving a range of different types 
of institutions both public (official) and private (commercial) with different mandates and focuses. In practice, the architecture of 
international public climate finance is rapidly evolving, with the creation by traditional donors of new public sources and channels 
over the years (Watson and Schalatek 2019), as well as emergence of new providers of development co-operation, both bilateral 
(Benn and Luijkx 2017) and multilateral (e.g., Asian Infrastructure Investment Bank), as well as of non-governmental actors such as 
philanthropies (OECD 2018a).
The operationalisation of the Green Climate Fund (GCF), which channels the majority of its funds via accredited entities, has notably 
attracted particular attention since AR5. Section 14.3.2 (in Chapter 14) provides a further assessment of progress and challenges of

financial mechanisms under the United Nations Framework Convention on Climate Change (UNFCCC), such as the GCF , the Global 
Environment Facility (GEF) and the Adaptation Fund (AF).
The multiplication of sources and channels of international climate finance can help address growing climate-related needs, and 
partly results from increased decentralisation as well financial innovation, which in turn can increase the effectiveness of finance 
provided. There is, however, also evidence that increased complexity implies transaction costs (Brunner and Enting 2014), in part due 
to bureaucracy and intra-governmental factors (Peterson and Skovgaard 2019), which constitutes a barrier to low-carbon projects 
and are often not accounted for in assessments of international climate finance. On the ground, activities by international providers 
operating in the same countries may overlap, with sub-optimal coordination and hence duplication of efforts, both on the bilateral and

multilateral sides (Ahluwalia et al. 2016; Gallagher et al. 2018; Humphrey and Michaelowa 2019), as well as risks of fragmentation of 
efforts (Watson and Schalatek 2020) which slows down coordination with international providers, national development banks and 
other domestic institutions.

1554
Chapter 15 Investment and Finance15of financial sector coalitions and civil society organisations as well 
as commercial services providers to the financial industry have 
developed frameworks, approaches and metrics, mainly focusing 
on investment portfolios (Institut Louis Bachelier et al.. 2020; IIGCC 
2021; TCFD Portfolio Alignment Team 2021; UN-Convened Net-Zero 
Asset Owner Alliance 2021), and, to a lesser extent for real economy 
investments (Micale et al. 2020; Jachnik and Dobrinevski 2021).
Key findings from AR5 and other IPCC publications. For the 
first time the IPCC in AR5 (Clarke et al. 2014) elaborated on the role 
of finance in a  dedicated chapter. In the following year, the Paris 
Agreement (UNFCCC 2015) recognised the transformative role of 
finance, as a  means to achieving climate outcomes, and the need 
to align financial flows with the long-term global goals even as 
implementation issues were left unresolved (Bodle and Noens 2018).

AR5 noted the absence of a  clear definition and measurement of 
climate finance flows, a  difficulty that continues (Weikmans and 
Roberts 2019) (Sections 15.2 and 15.3). The approach taken in AR5 
was to report ranges of available information on climate finance flows 
from diverse sources, using a broad definition of climate finance, as in 
the Biennial Assessments in 2014 and again in 2018 (UNFCCC 2014a, 
2018a) of the Standing Committee under the UNFCCC: Climate 
finance is taken to refer to local, national or transnational financing – 
drawn from public, private and alternative sources of financing – that 
seeks to support mitigation and adaptation actions that address 
climate change (UNFCCC 2014b). For this chapter, while the focus is 
primarily on mitigation, adaptation, resilience and loss and damage 
4 In the chapter, USD units are used as reported in the original sources in general. Some monetary quantities have been adjusted selectively for achieving comparability by

deflating the values to constant USD 2015. In such cases, the unit is explicitly expressed as USD 2015.financing needs cannot be entirely separated because of structural 
relationships, synergies, trade-offs and policy coherence requirements 
between these sub-categories of climate finance (Box 15.1).
The AR5 concluded that published assessments of financial flows 
whose expected effect was to reduce net greenhouse gas (GHG) 
emissions and/or to enhance resilience to climate change aggregated 
USD343–385 billion4 yr–1 globally between 2010 and 2012 (medium 
confidence). Most (95% of total) went towards mitigation, which 
was nevertheless underfinanced and adaptation even more so. 
Measurement of progress towards the commitment by developed 
countries to provide USD100 billion yr–1 by 2020 to developing 
countries, for both mitigation and adaptation (Bhattacharya et  al. 
2020)  – a narrower goal than overall levels of climate finance  –

continued to be a  challenge, given the lack of clear definition 
of such finance, although there remain divergent perspectives 
(Section  15.2.4). As against these flows, annual need for global 
aggregate mitigation finance between 2020 and 2030 was cited 
briefly in the AR5 to be about USD635 billion (mean annual), both 
public and private, implying that the reported ‘gap’ in mitigation 
financing of estimated flows during 2010 to 2012 was slightly under 
one-half of that required (IPCC 2014).
More recent published data from the Biennial Assessments 
(UNFCCC 2018a) and the Special Report on Global Warming of 1.5°C 
(IPCC 2018) have revised upwards the needs of financing between 
2020 and 2030 to 2035 to contain global temperature rise to below 
Box 15.3 | Mitigation, Adaptation and Other Related Climate Finance Merit Joint Examination
Mitigation finance deals with investments that aim to reduce global carbon emissions, while adaptation finance deals with the

consequences of climate change (Lindenberg and Pauw 2013). Mitigation affects the scale of adaptation needs and adaptation 
may have strong synergies and co-benefits as well as trade-offs with mitigation (Grafakos et al. 2019). If mitigation investments 
are inadequate to reducing global warming (as in the last decade) with asymmetric adverse impacts in lower latitudes and low-
lying geographies, the scale of adaptation investments has to rise and the benefits of stronger adaptation responses may be high 
(Markandya and González-Eguino 2019). If adaptation investments build greater resilience, they might even moderate mitigation 
financing costs. Similar policy coherence considerations apply to disaster risk reduction financing, the scale of which depends on 
success with both adaptation and mitigation (Mysiak et al. 2018). The same financial actors, especially governments and the private

sector, decide at any given time on their relative allocations of available financing for mitigation, adaptation and disaster-risk reduction 
from a constrained common pool of resources. The trade-offs and substitutability between closely-linked alternative uses of funds, 
therefore, make it essential for a simultaneous assessment of needs – as in parts of this chapter. Climate finance versus the financing 
of other Sustainable Development Goals (SDGs) faces a similar issue. A key agreement was that climate financing should be ‘new 
and additional’ and not at the cost of SDGs. Resources prioritising climate at the cost of non-climate development finance increase 
the vulnerability of a population for any given level of climate shocks, and additionality of climate financing is thus essential (Brown 
et al. 2010). Policy coherence is also the reason why mitigation finance cannot be separated from consideration of spending and

subsidies on fossil fuels. Climate change may additionally cause the breaching of physical and social adaptation limits, resulting in 
climate-related residual risks (i.e., potential impacts after all feasible mitigation, adaptation, and disaster risk reduction measures have 
been implemented) (Mechler et al. 2020). Because these residual losses and damages from climate-related risks are related to overall 
mitigation and adaptation efforts, the magnitude of potential impacts is related to the overall quantum of mitigation, adaptation, and 
disaster risk reduction finance available (Frame et al. 2020). All categories of climate finance thus need to be considered together in 
discussions around climate finance.

1555
Investment and Finance Chapter 15152°C and 1.5°C respectively by 2100: USD1.7 trillion yr–1 (mean) 
in the Biennial Assessment 2018 for the former, and for the latter, 
USD2.4 trillion yr–1 (mean) for the energy sector alone (and three 
times higher if transport and other sectors were to be included). 
The resulting estimated gaps in annual mitigation financing during 
2014 to 2017, using reporting of climate financing from published 
sources, was about 67% for 2015, and 76% for the energy sector 
alone in 2017 (medium confidence), and greater if other sectors were 
to be included. While the annual reported flows of climate financing 
showed some moderate progress (Section  15.3), from earlier 
USD364  billion (mean 2010/2011) to about USD600 billion (mean 
2017/2020), with a slowing in the most recent period 2014 to 2017, 
the gap in financing was reported to have widened considerably 
(Sections 15.4 and 15.5). In the context of policy coherence, it is also

important to note that reported annual investments going into the 
fossil fuel sectors, oil and gas upstream and coal mining, during 
the same period were about the same size as global climate finance, 
although the absence of alternative financing and access to low-
carbon energy is a complicating factor.
Adaptation financing needs, meanwhile, were rising rapidly. 
The Adaption Gap Report 2020 (UNEP 2021) reported that the 
current efforts are insufficient to narrow the adaptation finance 
gap, and additional adaptation finance is necessary, particularly 
in developing countries. The gap is expected to be aggravated by 
COVID-19 (high confidence). It reaffirmed earlier assessments that 
by 2030 (2050) the estimated costs of adaptation ranges between 
USD140 and 300 billion yr–1 (USD280 and 500 billion yr–1). Against 
this, the reported actual global public finance flows for adaptation 
in 2019/2020 were estimated at 46 billion (Naran et al. 2021). The

costs of climate disasters meanwhile continued to rise, affecting low-
income developing countries the most. Climate natural disasters – 
not all necessarily attributable to climate change  – caused some 
USD300 billion yr–1 economic losses and well-being losses of about 
USD520 billion yr–1 (Hallegatte et al. 2017).
15.2 Background Considerations
The institutions under climate finance in this chapter refer to the 
set of financial actors, instruments and markets that are recognised 
to play a key role in financing decisions on climate mitigation and 
adaptation. For a definition of climate financial stock and flows see 
further Section 15.3 and the Glossary. The issue of climate finance is 
closely related to the conversation on international cooperation and 
the question of how cross-border investments can support climate 
mitigation and adaptation in developing countries. However, the issue 
is also related to more general questions of how financial institutions,

both public and private, can assess climate risks and opportunities 
from all investments, and what roles states, policymakers, regulators 
and markets can play in making them more sustainable. In particular, 
the question of the respective roles of the public and private 
financial actors has become important in deliberations on climate 
finance in recent years. The broader macroeconomic context is an 
important starting point. Four major events and macro trends mark 
the developments in climate finance in the previous five years and 
likely developments in the near term.• First, the 2015 Paris Agreement, with the engagement of the 
financial sector institutions in the climate agenda, has been 
followed by a  series of related developments in financial 
regulation in relation to climate change and in particular to the 
disclosure of climate-related financial risk (high confidence) 
(Section 15.2.1).
• Second, the last five years have been characterised by a series

of interconnected ‘headwinds’ (Section 15.2.2), including rising 
private and public debt and policy uncertainty which work 
against the objective of filling the climate investment gap 
• Third, the 2020 COVID-19 pandemic crisis has put enormous 
additional strain on the global economy, debt and the availability 
of finance, which will be longer lasting (Section 15.2.3). At the 
same time, while it is still too early to draw positive conclusions, 
this crisis highlights opportunities in terms of political and policy 
feasibility and behavioural change in respect of realigning 
climate finance (medium confidence).
• Fourth, the sharp rise in global inequality and the effects of the 
pandemic have brought into renewed sharp focus the need for 
a Just Transition (Section 15.2.4) and a realignment of climate 
finance and policies that would be beneficial for a  new social 
compact towards a  more sustainable world that addresses

energy equity and environmental justice (high confidence).
15.2.1 Paris Agreement and the Engagement of 
the Financial Sector in the Climate Agenda
This is the first IPCC Assessment Report chapter on investment 
and finance since the 2015 Paris Agreement, which represented 
a landmark event for climate finance because for the first time the 
key role of aligning financial flows to climate goals was spelled out. 
Since then, the financial sector has recognised the opportunity and 
has stepped up to centre-stage in the global policy conversation on 
climate change. While before the Paris Agreement, only few financial 
professionals and regulators were acquainted with climate change, 
today climate change is acknowledged as a strategic priority in most 
financial institutions. This is a major change in the policy landscape 
from AR5. However, this does not mean that finance necessarily plays 
an adequate enabling role for climate investments. On the contrary,

the literature shows that without appropriate conditions, finance can 
represent a barrier to filling the climate investment gap (Hafner et al. 
2020). Indeed, despite the enormous acceleration in policy initiatives 
(e.g., NGFS 2020) and coalitions of the willing in the private sectors, 
the effect in terms of closing the investment gap identified already in 
AR5 has been limited (Section 15.5.2).
Financial investors have started to account for climate risk in some 
contexts but they do so only to a limited extent (Monasterolo and 
de Angelis 2020; Alessi et  al. 2021; Bolton and Kacperczyk 2021) 
and the reasons for these remain unclear. Two aspects are relevant 
here. The first is the endogenous nature of climate financial risk 
and opportunities (with the term ‘risk’ meaning here the potential 
for adverse financial impact, whether or not the distribution of 
losses is known). Academics and practitioners in finance are aware

that financial risk can in certain contexts be endogenous, that is,

1556
Chapter 15 Investment and Finance15the materialisation of losses is affected by the action of financial 
players themselves. However, the standard treatment of risk both in 
financial valuation models and in asset pricing assumes that risk is 
exogenous. In contrast, endogeneity is a  key feature of climate risk 
because today’s perception of climate risk affects climate investment, 
which in turn affects directly the future risk. This endogeneity leads 
to the fact that multiple and rather different mitigation scenarios are 
possible (Chapter 3). Moreover, the likelihood of occurrence of each 
alternative scenario is very hard to estimate. Further, the assessment of 
climate-related financial risk requires to combine information related 
to mitigation scenarios as well as climate impact scenarios, leaving 
open an important knowledge gap for the next years (Section 15.6.1).
The second aspect is that the multiplicity of equilibria results in

a  coordination problem whereby the majority of investors wait to 
move and reallocate their investments until they can follow a clear 
signal. Despite the initial momentum of the Paris Agreement, for 
many investors, both public and private, the policy signal seems not 
strong enough to induce them to align their investment portfolios to 
climate goals.
Analyses of the dynamics of the low-carbon transition suggest that 
it does not occur by itself and that it requires a policy signal credible 
enough in the perception of market players and investors (Battiston 
et al. 2021b). Credibility could require a policy commitment device 
(Brunner et al. 2012). The commitment would also need to be large 
enough (analogous to the ‘whatever it takes’ statement by the 
European Central Bank during the 2011–2012 European sovereign 
crisis (Kalinowski and Chenet 2020)). In principle, public investments 
in low-carbon infrastructures (or private-public partnerships) as well

as regulation could provide credible signals if their magnitude and 
time horizon are appropriate (past experiences with feed-in-tariffs 
(FiTs) models across countries provide useful lessons).
15.2.2 Macroeconomic Context
Entering 2020, the world already faced large macroeconomic 
headwinds to meeting the climate finance gap in the near term – 
barring some globally coordinated action. While an understanding of 
the disaggregated country-by-country, sector-by-sector, project-by-
project, and instrument-by-instrument approach to raising climate 
finances analysed in the later parts of this chapter remains important, 
macroeconomic drivers of finance remain crucial in the near term.
Near-term finance financial flows in aggregate often show strong 
empirically observed cycles over time, especially in terms of 
macroeconomic and financial cycles. By near-term, we mean here the 
likely cycle over the next five to ten years (2020–2025 and 2020–2030),

as influenced by global macroeconomic real business cycles (output, 
investment and consumption), with periodic asymmetric downside 
impacts and crises (Gertler and Kiyotaki 2010; Borio 2014; Jordà 
et al. 2017; Borio et al. 2018). Financial cycles typically have strong 
co-movements (asset prices, credit growth, interest rates, leverage, 
risk factors, market fear, macro-prudential and central bank policies) 
(Coeurdacier and Rey 2013), they have large consequences for all types of financial flows such as equity, bond and banking credit 
markets, which in turn are likely to impact climate finance flows to 
all sub-sectors and geographies (with greater expected volatility in 
more risky and more leveraged regions). This is in contrast to longer-
term trend considerations (2020–2050) that typically focus the 
attention on drivers of disaggregated flows of climate finance and 
policies. The upward trends of the cycles tend to favour speculative

bubbles like real estates at the expense of investment in production 
and infrastructures whereas the asymmetric downsides raise 
uncertainty and risks for longer-term investments on newer climate 
technologies, and favour a flight to near-term safety (e.g., lowest risk 
non-climate short-term treasury investments, highest creditworthy 
countries, and away from cross-border investments (Section 15.5) – 
making the challenge of longer-term low-carbon transition more 
difficult. In this respect, the impact of financial regulation is unclear. 
On the one hand, it could be argued that the tighter bank regulations 
under Basel III, combined with an economic environment with higher 
uncertainty and flatter yield curve, can push banks to retrench 
from climate finance projects (Blended Finance Taskforce, 2018a), 
since banks tend to limit loan maturity to five or eight years, while 
infrastructure projects typically require the amortisation of debt over

15 to 20 years (Arezki et al. 2016). On the other hand, other studies 
report that stricter capital requirements are not a driving factor for 
moving away from sustainability projects (CISL and UNEP FI 2014).
Four key aspects of the global macroeconomy, each slightly different, 
pointed in a cascading fashion towards a deteriorating environment 
for stepped-up climate financing over the next crucial decade 
(2020–2030), even before COVID-19. The argument is often made 
that there is enough climate financing available if the right projects 
and enabling policy actions (‘bankable projects’) present themselves 
(Cuntz et  al. 2017; Meltzer 2018). The attention to ‘bankability’ 
does not however address access and equity issues (Bayliss and 
Van Waeyenberge 2018). Some significant gains in climate financing 
at the sectoral and microeconomic levels were nevertheless 
happening in specific segments, such as solar energy financing and

labelled green bonds (although how much of such labelled financing 
is incremental to unlabelled financing that might have happened 
anyway remains uncertain) (Tolliver et al. 2019). Issues of ‘labelling’ 
(Cornell 2020) apply even more to ESG (environmental, social and 
governance) investments, which started to grow rapidly after 2016 
(Section  15.6.5). Overall, these increments for climate finance 
remained, however, small in aggregate relative to the size of the shifts 
in climate financing required in the coming decade. Annual energy 
investments in developing regions (other than China) which account 
for two-thirds of the world population, with least costs of mitigation 
per tonne of emissions (one-half that in developed regions), and 
for the bulk of future expected global GHG emissions, saw a 20% 
decline since 2016, and only a one-fifth share of global clean energy 
investment, reflecting persistent financing problems and costs of

mobilising finance towards clean energy transition, even prior to the 
pandemic (IEA 2021a). In the words of a macroeconomic institution, 
‘tangible policy responses to reduce greenhouse gas emissions have 
been grossly insufficient to date’ (IMF 2020a). The reason is in part 
global macroeconomic headwinds, which show a relative stagnation 
since 2016 and limited cross-border flows in particular (Yeo 2019).

1557
Investment and Finance Chapter 1515Slowing and more unstable GDP growth. The first headwind 
was more unstable and slowing GDP growth at individual country 
levels and in aggregate because of worsening climate change impact 
events (Donadelli et al. 2019; Kahn et al. 2019). As each warmer year 
keeps producing more negative impacts – arising from greater and 
rising variability and intensity of rainfall, floods, droughts, forest fires 
and storms – the negative consequences have become more macro-
economically significant, and worst for the most climate-vulnerable 
developing countries (high confidence). Paradoxically, while these 
effects should have raised the social returns and incentives to invest 
more in future climate mitigation, a standard public policy argument, 
these macroeconomic shocks may work in the opposite direction 
for private decisions by raising the financing costs now (Cherif and 
Hasanov 2018). With some climate tipping points, potentially in

the near-term reach (see AR6 WGI Chapter 4) the uncertainty with 
regard to the economic viability and growth prospects of selected 
macroeconomically critical sectors increases significantly (AR6 WGII 
Chapters 8 and 17). Taking account of other behavioural failures, this 
was creating a barrier for proactive and accelerated mitigation and 
adaptation action.
Public finances. The second headwind was rising public fiscal costs 
of mitigation and adapting to rising climate shocks affecting many 
countries, which were negatively impacting public indebtedness and 
country credit ratings (Cevik and Jalles 2020; Klusak et al. 2021) at 
a time of growing stresses on public finances and debt (Benali et al. 
2018; Kling et  al. 2018; Kose et  al. 2020) (high confidence). Every 
climate shock and slowing growth puts greater pressures on public 
finances to offset these impacts. Crucially, the negative consequences 
were typically greater at the lower end of income distributions

everywhere (Acevedo et al. 2018; Aggarwal 2019). As a result, the 
standard prescription of raising distributionally adverse carbon taxes 
and reducing fossil fuel subsidies to raise resources faced political 
pushback in several countries (Copland 2020; Green 2021), and low 
rates elsewhere. Reduced taxes on capital, by contrast, was viewed 
as a way to improve growth (Bhattarai et al. 2018; Font et al. 2018), 
and working against broader fiscal action. Progress with carbon 
pricing remained modest across 44 OECD and G20 countries, with 
55–70% of all carbon emissions from energy use entirely unpriced 
as of 2018 (OECD 2021a). Climate-vulnerable countries meanwhile 
faced sharply rising cost of sovereign debt. Buhr et al. (2018) calculate 
the additional financing costs of Climate Vulnerable Forum countries 
of USD40 billion5 on government debt over the past 10 years and 
USD62 billion for the next 10 years. Including private financing cost,

the amount increases to USD146–168 billion over the next decade.
Credit risks. The third headwind is rising financial and insurance 
sector risks and stresses (distinct from real ‘physical’ climate 
risks above) arising from the impacts of climate change, and 
systematically affecting both national and international financial 
institutions and raising their credit risks (high confidence) (Dafermos 
et al. 2018; Rudebusch 2019; Battiston et al. 2021a). Central banks 
are beginning to take notice (Carney 2019; NGFS 2019). It is also 
the case that, even if at greater risk from stranded assets in the 
5 In the chapter, USD units are used as reported in the original sources in general. Some monetary quantities have been adjusted selectively for achieving comparability by 
deflating the values to constant USD 2015. In such cases, the unit is explicitly expressed as USD 2015.future, the large-scale financing of new fossil fuel projects by large

global financial institutions rose significantly since 2016, because 
of perceived lower private risks and higher private returns in these 
investments and other factors than in alternative but perceived more 
risky low-carbon investments.
Global growth. The fourth headwind entering 2020 was the sharply 
slowing global macroeconomic growth, and prospects for near-
term recession (which occurred in the pandemic). During global real 
and financial cycle downturns (Jordà et  al. 2019), the perception 
of general financial risk rises, causing financial institutions and 
savers to reallocate their financing to risk-free global assets (high 
confidence). This ‘flight to safety’ was evident even before the recent 
pandemic, marked by an extraordinary tripling of financial assets to 
about USD16.5 trillion in negative-interest earning ‘safer’ assets in 
2019 in world debt markets – enough to have nearly closed the total 
financing gap in climate finance over a decade.

15.2.3 Impact of COVID-19 Pandemic
The macroeconomic headwinds have worsened dramatically with the 
onset of COVID-19. Almost two years after the pandemic started, it 
is still too uncertain and early to conclude impacts of the pandemic 
until 2025–2030, especially as they affect climate finance. Multiple 
waves of the pandemic, new virus mutations, accumulating human 
toll, and growing vaccine coverage but vastly differing access across 
developed versus developing regions, are evident. They are causing 
divergent impacts across sectors and countries, which combined with 
the divergent ability of countries and regions to mount sufficient 
fiscal and monetary policy actions imply continued high uncertainty 
on the economic recovery paths from the crisis. The situation remains 
more precarious in middle- and low-income developing countries 
(IMF 2021a). While recovery is happening, the job losses have been 
large, poverty rates have climbed, public health systems are suffering

long-term consequences, education gains have been set back, public 
debt levels are higher (5–10% of GDP higher), financial institutions 
have come under longer-term stress, a larger number of developing 
countries are facing debt distress, and many key high-contact sectors, 
such as tourism and trade, will take time to recover (Eichengreen et al. 
2021). The implication is negative headwinds for climate finance with 
public attention focused on pandemic relief and recovery and limited 
(and divergent) fiscal headroom for a  low-carbon transition, with 
considerable uncertainties ahead (Hepburn et al. 2020b; Maffettone 
and Oldani 2020; Steffen et al. 2020).
The larger and still open public policy choice question that COVID-19 
now raises is whether there is room for public policy globally and 
in respect of their individual economies to integrate climate more 
centrally to their growth, jobs and sustainable development strategies

worldwide for ecological and economic survival. The outcomes will 
depend on the robustness of recovery from the pandemic, and the 
still evolving public policy responses to the climate agenda in the 
recovery process. Private equity and asset markets have recovered

1558
Chapter 15 Investment and Finance15surprisingly rapidly during the pandemic (in response to the massive 
fiscal and central bank actions generating large excess savings 
with very low or negative yields boosting stock markets). On public 
spending, some early studies suggest that the immediate economic 
recovery packages were falling well short of being sufficiently 
climate sustainable (Gosens and Jotzo 2020; Kuzemko et al. 2020; 
O’Callaghan 2021) but several governments have also announced 
intentions to spend more on a green recovery, ‘build back better’ and 
Just Transition efforts (Section  15.2.4), although outcomes remain 
highly uncertain (Lehmann et al. 2021; Markandya et al. 2021).
An important immediate finding from the COVID-19 crisis was that the 
slowdown in economic activity is illustrating some of these choices: 
immediately after the onset, more costly and carbon-intensive coal 
use for energy use tumbled in major countries such as China and

the USA, while the forced ‘stay-at-home’ policies adopted around the 
major economies of the world led to a –30–35% decline in individual 
country GDP , and was in turn associated with a  decrease in daily 
global CO 2 emissions by –26% at their peak in individual countries, 
and –17% globally (–11 to –25% for ±1σ) by early April 2020 
compared with the mean 2019 levels, with just under half coming 
from changes in surface transport, city congestion and country 
mobility (Le Quéré et al. 2020). Along with the carbon emissions drop 
was a dramatic improvement in other parameters such as clean air 
quality. Moreover, longer-term behavioural impacts are also possible: 
a dramatic acceleration of digital technologies in communications, 
travel, retail trade and transport. The question however is whether 
the world might revert to the earlier carbon-intensive path of 
recovery, or to a different future, and the choice of policies in shaping

this future. Studies generally suggest that the gains from long-term 
impacts of the pandemic on future global warming will be limited 
and depend more on the nature of public policy actions and long-
term commitments by countries to raise their ambitions, not just 
on climate but on sustainable development broadly (Barbier 2020; 
Barbier and Burgess 2020; Forster et al. 2020; Gillingham et al. 2020; 
Reilly et al. 2021). The positive lesson is clear: opportunities exist for 
accelerating structural change, and for a re-orientation of economic 
activity modes to a low-carbon use strategy in areas such as coal use 
in energy consumption and surface transport, city congestion and 
in-country mobility, for which lower-cost alternatives exist and offer 
potentially dramatic gains (Hepburn et al. 2020b).
A new consensus and compact towards such a structural change and 
economic stimulus instruments may therefore need to be redrawn

worldwide, where an accelerated low-carbon transition is a priority; 
and accelerated climate finance to spur these investments may gain 
by becoming fully and rapidly integrated with near-term economic 
stimulus, growth and macroeconomic strategies for governments, 
central banks, and private financial systems alike. If that were 
to happen, COVID-19 may well be a  turning point for sustainable 
climate policy and financing. Absent that, a return to ‘business-as-
usual’ modes will mean a likely down-cycle in climate financing and 
investments in the near term.
Expectations that the recovery package stimulus will increase economic 
activity rely on the assumption that increased credit investment 
will have a  positive effect on demand, the so-called demand-led policy (Mercure et  al. 2019). The argument for a  green recovery 
also draws on the experience from the post-global financial crisis 
in 2008–2009 recovery, in which large economies such as China,

South Korea, the USA and the EU observed that green investments 
propelled the development of new industrial sectors. Noticeably, 
this had a  positive net effect on job creation when compared to 
the investment in traditional infrastructure (UKERC 2014; Vona 
et al. 2018; Jaeger et al. 2020). For a more in-depth discussion on 
macroeconomic-finance possible response see Section  15.6.3. 
Here, we conclude with the options for reviving a  better globally 
coordinated macroeconomic climate action. The options are some 
combinations of five possible elements:
1. Reaffirmation of a  strong financial agenda in future UNFCCC 
Conference of Parties meetings, and a  new collective finance 
target, which will need to be undertaken by 2025. Given that 
the shortfalls in financing are likely to be acute for developing 
regions and especially the more debt-stressed and vulnerable 
(Dibley et  al. 2021; Elkhishin and Mohieldin 2021; Laskaridis

2021; Umar et al. 2021), developed countries may wish to step 
up their collective support (Resano and Gallego 2021). One 
possibility is to expedite the new Special Drawing Rights (SDR) 
issuance allocation rules for the USD650 billion recently (2021) 
approved, most of which will go to increase the reserves of G7 
and other high-income countries unless voluntarily reallocated 
towards the needs of the most vulnerable low-income countries, 
raising resources potentially ‘larger than the Marshall Plan in 
today’s money’ (IMF 2021b; Jensen 2021; Obstfeld and Truman 
2021), with decisions to be taken. Ameli et al. (2021a) note the 
climate investment trap of the current high cost of finance that 
effectively lowers green electricity production possibilities in 
Africa for a  cost optimal pathway. Other initiatives could also 
include G7 and G20 governments (especially with the lead taken 
by the developed members for cross-border support to avoid

over-burdening public resources in developing countries) running 
coordinated fiscal deficits to accelerate the financing of low 
carbon investments (‘green fiscal stimulus’).
2. Introducing new actions, including regulatory, to take some of the 
risks off the table from institutional financial players investing in 
climate mitigation investment and insurance. This could include 
the provision of larger sovereign guarantees to such private 
finance, primarily from developed countries but jointly with 
developing countries to create a  level playing field (Dafermos 
et al. 2021) backed by explicit and transparent recognition of the 
‘social value of mitigation actions’ or SVMAs, as fiscally superior 
(because of bigger ‘multipliers’ of such fiscal action to catalyse 
private investment than direct public investment) and the bigger 
social value of such investments (Article 108, UNFCCC) (Hourcade 
et al. 2018; Krogstrup and Oman 2019).

3. Facilitating and incentivising much larger flows of cross-border 
climate financing which is especially crucial for such investments 
to happen in developing regions, where as much as two-thirds 
of collective investment may need to happen (IEA 2021a), and 
where the role of multilateral, regional and global institutions 
such as the International Monetary Fund (IMF) (including the 
expansion in availability of climate SDRs referred to earlier) 
could be important.

1559
Investment and Finance Chapter 15154. Global central banks acting in coordination to include climate 
finance as an intrinsic part of their monetary policy and stimulus 
(Carney 2019; Jordà et al. 2019; Hilmi et al. 2021; Schoenmaker 
2021; Svartzman et al. 2021).
5. An acceleration of Just Transition initiatives, outlined further 
below (Section 15.2.4).
15.2.4 Climate Finance and Just Transition
Climate finance in support of a Just Transition is likely to be a key 
to a  successful low-carbon transition globally (high confidence). 
Ambitious global climate agreements are likely to work far better 
by maximising cooperative arrangements (IPCC 2018; Gazzotti et al. 
2021) with greater financing support from developed to developing 
regions in recognition of ‘common but differentiated responsibilities 
and respective capabilities’ and a  greater ethical sense of climate 
justice (Khan et al. 2020; Sardo 2020; Warner 2020; Pearson et al.

2021). While Just Transition issues apply within developed countries 
as well (see later discussion), these are of relatively second-order 
significance to addressing climate justice issues between richer and 
poorer countries – given the scale of financing and existing social 
safety nets in the former and their absence in the latter. For example, 
over the past three decades drought in Africa has caused more 
climate-related mortality than all climate-related events combined 
from the rest of the world (Warner 2020). These issues can however 
serve both as a  bridge and a  barrier to greater cooperation on 
climate change. The key is to build greater mutual trust with clearer 
commitments and well-structured key decisions and instruments 
(Sardo 2020; Pearson et al. 2021).
The Just Transition discussion has picked up steam. It was explicitly 
recognised in the Paris Agreement and the 2018 Just Transition 
Declaration signed by 53 countries at COP24, which ‘recognised the

need to factor in the needs of workers and communities to build 
public support for a rapid shift to a zero-carbon economy.’ Originally 
proposed by global trade unions in the 1980s, the recent discourse 
has become broader. It has coalesced into a more inclusive process 
to reduce inequality across all three areas of energy, environment 
and climate (McCauley and Heffron 2018; Bainton et  al. 2021). It 
seeks accelerated public policy support to ensure environmental 
sustainability, decent work, social inclusion and poverty eradiation 
(Burrow 2017), widely shared benefits, and protection of indigenous 
rights, and livelihoods of communities and workers who stand to 
lose (including workers in fossil fuel sectors such as coal and oil 
and gas) (UNFCCC 2018b; EBRD 2020; Jenkins et al. 2020). Because 
the process involves ‘climate justice’ and equity within and across 
generations, it involves difficult political trade-offs (Newell and

Mulvaney 2013). The implications for a  Just Transition in climate 
finance are clear: expanding equitable and greater access to climate 
finance for vulnerable countries, communities and sectors, not just 
for the most profitable private investment opportunities, and a larger 
role for public finance in fulfilling existing finance commitments 
(Bracking and Leffel 2021; Kuhl 2021; Long 2021; Roberts et al. 2021). 
Large shocks such as pandemics, and slow-growing ones such as 
climate, are typically known to worsen inequality (IMF and World Bank 2020). Evidence from 133 countries between 2001–2018 
suggests that such shocks can cause social unrest, and migration 
pressures, especially when starting inequality is high and social 
transfers are low (Saadi Sedik and Xu 2020). Additionally, climate 
policies are more politically difficult to implement when the setting 
is one of high inequality but much less politically costly where

incomes are more evenly distributed with stronger social safety nets 
(Furceri et al. 2021). A redrawn social compact incorporating climate 
(Beck et al. 2021) that would adopt redistributive taxes and lower 
carbon consumption, and strengthen state capacity to deliver safety 
nets, health and education with accelerated climate and environmental 
sustainability within and across countries, is increasingly recognised 
as important. Countries, regions and coordination bodies of the larger 
countries (G7, G20) have already begun such a shift to financing of 
a Just Transition, but primarily focused on the developed countries, 
although gaps remain (Krawchenko and Gordon 2021).
Such a redrawing of a social compact has happened significantly in 
the past, for example, after the 1860s ‘gilded age of capital’ with 
the enlargement of the franchise in democratisation waves in Europe 
and the Americas (Dasgupta and Ziblatt 2015, 2016). Not only was

social conflict avoided but growth outcomes became more equitable 
and faster. Similarly, comprehensive modern social safety nets and 
progressive taxation, which started in the Great Depression and was 
extended in the post-war period, had both a positive pro-growth and 
lower inequality effects (Brida et al. 2020).
There are three levels at which policy attention on climate financing 
now may need to be focused. The first is the need to address the 
global equity issues in climate finance in a more carefully constructed 
globally cooperative public policy approach. The second is to address 
issues appropriately with enhanced support, at the national level. 
The third is to work it down further, to addressing needs at local 
community levels. Because private investors and financing mostly 
deal with allocation to climate finance at a  global portfolio level, 
then to allocation by countries, and finally to individual projects, the

challenge for them is to refocus attention to Just Transition issues at 
the country level, but also globally as well as locally (in other words, 
at all three levels).
Climate finance will likely face greater challenges in the post-
pandemic context (Hanna et al. 2020; Henry et al. 2020). Evidence 
from the COVID-19 pandemic suggests that those in greatest 
vulnerability often had the least access to human, physical, and 
financial resources (Ruger and Horton 2020). It has also left in in 
its wake divergent prospects for economic recovery, with rising 
constraints on credit ratings and costly debt burden in many 
developing countries contrasted with the exceptionally low interest 
rate settings in developed economies driving the limited fiscal space 
in the former groups (Benmelech and Tzur-Ilan 2020). Similarly, 
monetary policies are likely to be much tighter in developing countries 
in part structurally because of the absence of ‘exceptional privilege’

of global reserve currencies in developed economies.
The result is a divergence in recovery prospects in the aftermath of the 
pandemic, with output losses (compared to potential) set to worsen in 
developing economies (excluding China) as compared to developed

1560
Chapter 15 Investment and Finance15countries (IMF 2020b). In these circumstances, a  coordinated and 
cooperative approach, instead of unilateralism, might work better 
(McKibbin and Vines 2020). In the case of climate, simulations clearly 
suggest the need and advantages of better coordinated climate action 
with stepped-up Paris Agreement envisaged transfers (IMF 2020b). 
Several options in international climate finance arrangements to 
support a Just Transition are both available and urgent.
As a  first priority, measures might need to accelerate a  mix of 
equitable financial grants, low-interest loans, guarantees and 
workable business models access across countries and borders, 
from developed countries to low-income countries. A  big push on 
low-carbon energy access globally, especially in large low-income 
regions such as Africa, with accelerated financial transfers, makes 
sense (Boamah 2020). For about one billion people globally at the

base of the pyramid without access to modern low-carbon energy 
access, such an action, with enormous immediate leap-frogging 
potential, would be a key pathway to achieve the SDGs, ensure that 
high-carbon energy use is avoided, such as the burning of biomass 
and forests for charcoal, and improve air quality and public health, 
especially women’s health (van der Zwaan et al. 2018; Nathwani and 
Kammen 2019; Dalla Longa and van der Zwaan 2021; Michaelowa 
et al. 2021; Osabuohien et al. 2021).
A second priority is to accelerate the implementation of the 
USD100  billion a  year (and likely more, given growing financing 
gaps) in climate finance commitments expressed in the Copenhagen 
Agreement Accord (and reiterated since) from developed to 
developing countries, and to build greater confidence by agreeing 
rapidly on key definitions. Shifting to a grant equivalent net flows 
definition of climate finance, which is now universally accepted for

all other aid flows by all parties since 2014 and which took effect 
since 2019 on every other public international good finance provision 
(under the SDGs), with the sole exception of climate finance, would 
resolve many uncertainties: the disbursement of climate finance flows 
on a  grant equivalent basis that is comparable across institutions, 
instruments and countries, and measurement with greater accuracy 
about the effective transfer of resources. The journey to get to a clear 
and precise definition of net official overseas development assistance 
(ODA) took time. The original proposal was first initiated in the 1960s 
(Pincus 1963) but it was not till multilateral development banks 
(MDBs) and others laid out the compelling reasons why (Chang et al. 
1998) that this was accomplished: especially to resolve decades of 
confusion and inconsistency between different types of financial 
flows and hence the perennial measurement problems and ‘the

compromise between political expediency and statistical reality’ 
(Bulow and Rogoff 2005; Hynes and Scott 2013; Scott 2015, 2017).
A third related and increasingly crucial priority is to expedite the 
operational definition of blended finance and promote the use of 
public guarantee instruments. Private flows to accelerate the low-
carbon transition in developing countries would benefit enormously, 
by gaining clearer access to public international funds and support 
defined on a grant equivalent basis, provided development and climate 
finance operational definitions and procedures were improved on an 
urgent basis (Blended Finance Taskforce 2018a; OECD-DAC 2021). 
When blended and supported by public finance and policy, the grant equivalency measure can easily and more accurately measure the 
value and benefit of blended public and private finance by comparing 
the effective interest cost (and volume) gain with such financing,

against the benchmark costs without such blending. Here again, 
a pressing challenge is to improve the operational definitions of what 
counts as ODA within blended finance. Blended finance remains very 
poorly defined and accounted (Pereira 2017; Andersen et al. 2019; 
Attridge and Engen 2019; Basile and Dutra 2019). Guarantees are 
expressly not included in the definition of ODA (Garbacz et al. 2021). 
As a result, bilateral and multilateral agencies have no incentive or 
limited authority and basis to use such instruments, while multilateral 
development banks continue to approach guarantees with great 
caution because of the limits of their original charters (World Bank 
2009) and require counter-indemnities by recipient countries, internal 
and historic agency inertia, perceived loss of control over the use 
of funds (compared to their preferred direct project-based lending) 
and employ restrictive accounting rules for capital provisioning of

guarantees at 100% of their face value to maintain AAA ratings 
with credit rating agencies (Humphrey 2017; Pereira dos Santos 
and Kearney 2018; Bandura and Ramanujam 2019; Hourcade et al. 
2021a). Largely because of such official uncertainty the actual flows 
of blended finance and guarantees continue to remain a very small 
share (typically, less than 5%) of official and multilateral finance 
flows to lower project risks and costs, and hence the potential for 
large-scale accelerated low-carbon private investments in developing 
countries. Public guarantees can offer a fifteen times multiplier effect 
on the scale of low-carbon investments generated with such support, 
compared to a 1:1 ratio in direct financing (Hourcade et al. 2021a).
It makes sense to expedite these operational procedures (Khan et al. 
2020) which cannot be otherwise explained except in terms of avoiding 
responsibilities, even where the benefits would be high (Klöck et al.

2018). It also causes (unnecessary) fragmentation and complexity 
and often ‘strategic’ ambiguity by many actors (Pickering et al. 2017), 
which worsens the possibilities for international cooperation, a critical 
requirement to achieve the Paris goals (IPCC 2018). The world would 
gain collectively if these issues were to be decided soon. The absence 
of such a collective decision continues to be exceptionally costly for 
the implementation of the Paris Agreement because of the fractious 
and seemingly insoluble negotiating climate and a breakdown of trust 
that this has created (Roberts and Weikmans 2017).
A fourth priority is expanding jobs and dealing with job losses in the 
global low-carbon transition (Carley and Konisky 2020; Crowe and Li 
2020; Pai et al. 2020; Cunningham and Schmillen 2021; Hanto et al. 
2021), especially in coal and other sectors, as well as land and other 
effects for indigenous communities (Zografos and Robbins 2020).

Many countries, especially low-income countries, remain dependent 
on fossil fuels for their energy and exports and jobs, and support 
for their transition to a low-carbon future will be essential. Global 
recovery from the pandemic will take longer than initially envisaged 
(IMF 2021c; OECD 2021b) and an accelerated climate action for 
a Build Back Better global infrastructure plan with better and more 
resilient jobs might play a  key role as part of the Just Transitions. 
Already, there is substantial evidence (Sulich et al. 2020; Dell’Anna 
2021; Dordmond et al. 2021) that a more sustainable climate path 
would generate many more net productive jobs (with much higher

1561
Investment and Finance Chapter 1515employment multipliers and mutual gains from given spending) than 
would any other large-scale alternative. But this would nevertheless 
require a carefully managed transition globally, including access to 
much larger volumes of climate financing in developing economies 
(Muttitt and Kartha 2020). The multilateral finance institutions have 
generally played a  supportive role, expanding their financing to 
developing countries during the pandemic (even as bilateral aid flows 
have fallen sharply), but have been hampered by the constraints on 
their mandates and instruments (as noted earlier). Political leadership 
and direction will be again crucial to enhance their roles. The recent 
expansion of SDR quotas at the IMF similarly might help, but the 
current distributions of quota benefits flow primarily to the developed 
countries and do little to expand investment flows on a longer-term

basis for a global expansion in growth and job opportunities in the 
low-carbon transition.
As a  fifth priority, transformative climate financing options based 
on equity and global sustainability objectives may also need to 
consider a greater mix of public pricing and taxation options on the 
consumption side (Arrow et al. 2004; Folke et al. 2021). Two-thirds of 
global GHG emissions directly or indirectly are linked to household 
consumption, with average per capita carbon footprint of North 
America and Europe of 13.4 and 7.5 tCO 2-eq per capita, respectively, 
compared to 1.7 in Africa and Middle East (Gough 2020) and as 
high as 200 tCO 2-eq per capita among the top 1% in some high-
income geographies versus 0.1 tCO 2-eq at the other end of the 
income distribution in some least-developed countries (Chancel 
and Piketty 2015). Globally, the highest-expenditure households 
account for eleven times the per capita emissions of lowest-

expenditure households, with rising carbon income elasticities that 
suggest ‘redistribution of carbon shares from global elites to global 
poor’ as welfare efficient (Chancel and Piketty 2015; Hubacek et al. 
2017). Within countries and regions, and within sectors, similar 
patterns hold. The top 10% of the population with the highest 
per capita footprints account for 27% of the EU carbon footprint, 
and the top 1% have a carbon footprint of 55 tCO 2-eq per capita, 
with air transport the most elastic, unequal and carbon-intensive 
consumption (Ivanova and Wood 2020). Similarly, within sectors, 
there are large differences in carbon-intensity in the building sector 
in North America (Goldstein et  al. 2020) and across cities where 
consumption-based GHG emissions vary widely across the world 
(ranging from 1.8 to 25.9 tCO 2-eq per capita).
Numerous options exist (Broeks et al. 2020; Nyfors et al. 2020) for such 
carbon consumption reduction measures, while potentially improving

societal well-being, for example: (i) inner-city zoning restrictions 
on private cars and promoting walking/bicycle use and improved 
shared low-carbon transport infrastructure; (ii) advertising regulation 
and carbon taxes and fees on high-carbon luxury status goods and 
services; (iii) subsidies and exemptions for low-carbon options, higher 
value-added taxes on specific high-carbon products and services, 
subsidies for public low-carbon options such as commuter transport, 
and other behavioural nudges (Reisch et al. 2021); and (iv) framing 
options (emphasising total cost of car over lifetimes), mandatory 
smart metering, collective goods and services (leasing, renting, 
sharing options) and others. Finally, reducing subsidies on fossil fuels, 
raising the progressivity of taxes and raising overall wealth taxes on the richest households, which have been sharply falling (Scheuer and 
Slemrod 2021) even as global income and wealth have risen, with

regressive and falling overall taxes (Alvaredo et al. 2020; Saez and 
Zucman 2020), could effectively generate significant revenues (over 
1% of GDP yr–1), about the same size as the proposed global USD50 
pertonne carbon price proposed and estimated by the IMF/OECD 
2021 report to the G20 (IMF and OECD 2021) to cover expected net 
interest costs on overall decarbonisation initiatives and financing of 
green new deals (Schroeder 2021).
These five options identified above on near-term actions and 
priorities will however, require greater collective political leadership. 
A review of past crisis episodes suggests that collective actions to 
avoid large global or multi-country risks work well primarily when 
the problems are well defined, a small number of actors are involved, 
solutions are relatively well established scientifically, and public 
costs to address them are relatively small (Sandler 1998, 2015) (for 
example, dealing with early pandemic outbreaks such as Ebola,

TB, and cholera; extending global vaccination programmes such as 
smallpox, measles and polio; early warning systems and actions for 
natural disasters such as tsunamis, hurricanes/cyclones and volcanic 
disasters; the Montreal Protocol for ozone-depleting refrigerants, 
and renewables wind and solar energy development). They do not 
appear to work as well for more complex global collective action 
problems which concern a  number of economic actors, sectors, 
without inexpensive and mature technological options, and where 
political and institutional governance is fragmented. Greater 
political coordination is needed because the impacts are often not 
near term or imminent, but diffuse, slow moving and long term, 
and where preventive disaster avoidance is costly even when these 
costs are  low compared to the longer-term damages  – till tipping 
points are reached of the need for reduced ‘stressors’ and increasing

‘facilitators’ (Jagers et al. 2020). But by then, it may be too late.
Private institutional investors equally might equally wish to pay 
greater attention to the Just Transition finance issues. It would be 
useful for investors to identify ways to support to such initiatives, 
and more clearly identify the benefits of such transition measures 
envisaged by both countries and investment financing proposals, 
including incorporating Just Transition consideration in their support 
to broader ESG and green financing initiatives.
The second level of attention needed on Just Transitions has to 
do with inequities within a  large country setting, developed or 
developing. The Just Transition issue exists within developed 
countries as well. As the ongoing pandemic illustrates, the first 
climate burden hit is often felt most acutely at the level of states 
and cities, with many smaller ones without enough fiscal capacity 
or ability to mount an adequate discretionary counter policy. Only

national governments have the ability to borrow more in their fiscal 
accounts to address large collective problems, whether pandemics 
or climate change. Therefore, it is important that national policies 
and funds be available for programmes to address the Just Transition 
issues for larger subnational states, cities and regions. This would be 
helped by countries including Just Transition initiatives in their NDCs 
for financing (as South Africa has recently done), and attention by 
external financing agencies and MDBs to large-scale adverse impacts

1562
Chapter 15 Investment and Finance15in their climate policies and investments. For example, the EU Green 
Deal plans (Nae and Panie 2021) include several initiatives (focusing 
on industries, regions and workers adversely affected, with explicit 
programmes to address them).
The third level of argument is for a shift in focus from an exclusive 
attention to financing of mitigation and low-carbon new 
investments projects to also better understanding and addressing 
the local adverse impacts of climate change on communities and 
people, who are vulnerable and increasingly dispossessed due to 
losses and damages from climate change or even those who are 
impacted by decarbonisation measures in the fossil fuel sectors and 
transportation, as well as those who are harmed by polluting sectors: 
indigenous men and women, minorities and generally the poor. It is 
evident that very few resources are available to countries, investors,

civil society, and smaller development institutions seeking to achieve 
a just transition (Robins and Rydge 2020).
Finally, greater support is warranted for smaller towns and cities, local 
networks, small and medium-sized enterprises (SMEs), communities, 
local authorities and universities for projects, research ideas and 
proposals (Lubell and Morrison 2021; Moftakhari et al. 2021; Stehle 
2021; Vedeld et al. 2021).
15.3 Assessment of Current Financial Flows
15.3.1 Financial Flows and Stocks: Orders of Magnitude
Assessments of finance for climate action need to be placed within 
the broader perspective of all investments and financing flows and 
stocks. This section provides aggregate level reference points of 
relevance to the remainder of this chapter, notably when assessing 
current levels of climate and fossil fuel-related investments and

6 In the chapter, USD units are used as reported in the original sources in general. Some monetary quantities have been adjusted selectively for achieving comparability by 
deflating the values to constant USD 2015. In such cases, the unit is explicitly expressed as USD 2015.financing (Sections  15.3.2.3 and 15.3.2.4 respectively), as well 
as estimates of investment and financing needed to meet climate 
objectives (Section 15.4).
Measures of financial flows and stocks provide complementary 
and interrelated insights into trends over time: the accumulation 
of flows, measured per unit of time, results in stocks, observed at 
a given point in time (IMF 2009; UN and ECB 2015). On the flows 
side, GDP , a System of National Accounts (SNA) statistical standard 
that measures the monetary value of final goods and services 
produced in a country in a given period of time. In 2020, global GDP 
represented above USD 2015 70 trillion6 (down from around 80 trillion

USD 2015 in 2019), out of which developed countries represented 
approximately 60% (Figure 15.1); a slowly decreasing share over 
the last years. The GDP metric is useful here as an indicator of the 
level of activity of an economy but gives no indication relating to 
human well-being or SDG achievements (Giannetti et al. 2015) as it 
counts positively activities that negatively impact the environment, 
without making deductions for the depletion and degradation of 
natural resources.
Gross-fixed capital formation (GFCF), another SNA standard that 
covers tangible assets (notably infrastructure and equipment) and 
intangible assets, is a good proxy for investment flows in the real 
economy. In 2019, global GFCF reached around 20 trillion USD 2015 
compared to around 14 trillion USD 2015 in 2010, a more than 40% 
increase (Figure  15.2). Global GFCF represents about a  quarter of 
global GDP , a relatively stable ratio since 2008. This share is, however,

much higher for emerging economies, notably in Asia, which are 
building new infrastructure at scale. As analysed in  Sections  15.4 
and 15.5, infrastructure investment needs and gaps in developing 
countries are significant. How these are met over the next 
decade will critically influence the likelihood of reaching the Paris 
Agreement goals.
80
60
40
20 GDP in USD2015 by type of economy
080
60
40
202010
2011
2012
2013
2014
2015
2016
2017
2018
2019
20200
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
DevelopedDeveloping LDC
Africa Developed Asia and 
PaciﬁcLatin America Eurasia Middle East
Figure 15.1 | Financial flows – GDP (trillion USD 2015) by type of economy (left) and region (right).  Note: Regional breakdown based on official UN country 
classification. GDP in trillion USD 2015. Source: World Bank Data (2020a). Numbers represent aggregated country data. Last updated data on 15 September 2021. CC BY-4.0.

1563
On the stock side, an increasingly significant portion of the growing 
value of financial capital (stocks in particular) may be disconnected 
from the value of underlying productive capital in the real economy 
(Igan et  al. 2020). This trend, however, remains uneven between 
developed countries, most of which have relatively deep capital 
markets, and developing countries at different stages of development 
(Section  15.6.7). Bonds, a  form of debt financing, represent 
a significant share of total financial assets. As of August 2020, the 
overall size of the global bond markets (amount outstanding) was 
estimated at approximately USD128.3 trillion, out of which over two 
thirds was from ‘supranational, sovereign, and agencies’, and just 
under a third from corporations (ICMA 2020b). As discussed later in 
the chapter, since AR5, an increasing number and volume of bonds 
have been earmarked for climate action but these still only represent

less than 1% of the total bond market. As of end-2020, climate-
aligned bonds outstanding were estimated at USD0.9 trillion (Giorgi 
and Michetti 2021), though already raising concerns in terms of both 
underlying definitions (Section 15.6.6) and risks of increased climate-
related indebtedness (Section 15.6.1, 15.6.3).From the perspective of climate change action, these orders of 
magnitude make it possible to highlight the relatively small size 
of current climate finance flows and relatively larger size of remaining 
fossil fuel-related finance flows (discussed in the following two sub-
sections), as well as, more generally, the significant overall scale 
of financial flows and stocks that have to be made consistent with 
climate goals. These orders of magnitude further make it possible to 
put in perspective climate-related investment needs (Section 15.4) 
and gaps (Section 15.5).
15.3.2 Estimates of Climate Finance Flows

The measurement of climate finance flows continues to face similar 
definitional, coverage and reliability issues as at the time of AR5 and 
the Special Report on Global Warming of 1.5°C, despite progress 
made (more sources, greater frequency, and some definitional 
improvements) by a  range of data providers and collators. Based 
on available estimates (Table 15.1 and Figure 15.3), flows of annual 101520
525 GFCF in USD 2015 by Type of Economy GFCF in USD 2015 by R6 
0 0
PaciﬁcLatin America Eurasia Middle East2010
2011
2012
2013
2014
2015
2016
2017
2018
2019101520
5252010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Figure 15.2 | Financial flows – GFCF (trillion USD 2015) by type of economy (left) and region (right).  Note: Regional breakdown based on official UN country

classification. GDP in trillion USD 2015. Gross fixed capital formation (GFCF) includes land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment 
purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Source: 
World Bank Data (2020b). Data for 2020 not available. Last updated data on 15 September 2021. CC BY-4.0.
Table 15.1 | Total climate finance flows between 2013 and 2020.
Source (type) 2013 2014 2015 2016 2017 2018 2019 2020
UNFCCC SCF (total high) 687 584 680 681 Published after lit. cut-off n/a n/a
Deflated to USD 2015 706 590 680 674
UNFCCC SCF (total low/CPI) 339 392 472 456 /608 /540 /623 /640
Deflated to USD 2015 349 396 472 451 /590 /513 /581 /590
Note: CPI: Climate Policy Initiative; SCF: Standing Committee on Finance. Numbers in current billion USD. Deflated to USD 2015 in italic. Given the variations in numbers reported

by different entities, changes in data, definitions and methodologies over time, there is low confidence attached to the aggregate numbers presented here. The higher bound 
reported in the SCF’s Biennial Assessment reports includes estimates from the International Energy Agency on energy efficiency investments, which are excludes from the lower 
bound and CPI’s estimates. Sources: UNFCCC (2018a); Buchner et al. (2019); Naran et al. (2021).

1564
Chapter 15 Investment and Finance15
global climate finance are on an upward trend since AR5, reaching 
a high-bound estimate of USD681 billion in 2016 (UNFCCC 2018a), 
representing USD674 billion 2015. Latest available estimates indicate 
a  drop in 2018 (Buchner et  al. 2019) and a  rebound in 2019 and 
2020 (medium confidence) (Naran et al. 2021). Although not directly 
comparable in terms of scope, current climate finance flows remain 
small (approx. 3%) compared to the GFCF reference point introduced 
in Section 15.3.1, and need to be put in perspective with remaining 
fossil fuel financing (medium confidence) (Section 15.3.2.3).
At an aggregate level, in both developed and developing countries, 
the vast majority of tracked climate finance is sourced from domestic 
or national markets rather than cross-border financing (Buchner et al. 
2019). This reinforces the point that national policies and settings 
remain crucial (Section 15.6.2), along with the development of local

capital markets (Section 15.6.7).
Climate finance in developing countries remains heavily concentrated 
in a few large economies (high confidence), with Brazil, India, China 
and South Africa accounting for around one-quarter to more than 
a third depending on the year, a share similar to that represented 
by developed countries. Least-developed countries (LDCs), on 
the other hand, continue to represent less than 5% year-on-year 
(medium confidence) (BNEF 2019; Buchner et al. 2019). Further, the 
relatively modest growth of climate finance in developed countries is 
a matter of concern given that economic circumstances are, in most 
cases, relatively more amenable to greater financing, savings and 
affordability than in developing countries.
At a global level, the majority of tracked climate finance is assessed 
as coming from private actors (Buchner et al. 2019), although, the 
boundaries between private and public finance include significant

grey zones (Box 15.2), which implies that different definitions could 
lead to different conclusions (Yeo 2019; Weikmans and Roberts 2019). However, private investments in climate projects and activities often 
benefit from public support in the form of co-financing, guarantees 
or fiscal measures. In terms of financial instruments and mechanisms, 
debt as well as balance sheet financing (which can rely on both 
own resources and further debt) and project financing (combining 
a large debt portion and smaller equity portion) represent the lion’s 
share. In this context, the rapid rise of climate-related bond issuances 
since AR5 (Giorgi and Michetti 2021) represents an opportunity 
for scaling up climate finance but also poses underlying issues of 
integrity (Nicol et al. 2018a; Shishlov et al. 2018) and additionality 
(Schneeweiss 2019), as further discussed in Section  15.6.5, and 
needs to be considered in the context of overall indebtedness and

debt sustainability (Sections 15.6.1 and 15.6.3).
Mitigation continues to represent the lion’s share of global climate 
finance (consistently above 90% between 2017 and 2020), and in 
particular renewable energy, followed by energy efficiency and 
transport (high confidence) (UNFCCC 2018a; Buchner et  al. 2019). 
While capacity additions on the ground kept rising, falling technology 
costs in certain sectors (e.g., solar energy) has had a negative impact 
on the year-on-year trend that can be observed in terms of volumes 
of climate finance (BNEF 2019; IRENA 2019a). However, such cost 
reduction could free up investment and financing capacities for 
potential use in other climate-related activities.
Tracking adaptation finance continues to pose significant challenges 
in terms of data and methods. Notably, the mainstreaming of 
resilience into investments and business decisions makes it difficult 
to identify relevant activities within financial datasets (Agrawala

et  al. 2011; Brown et  al. 2015; Averchenkova et  al. 2016). Despite 
these limitations, evidence shows that finance for adaptation 
remains fragmented and significantly below rapidly rising needs 
(Section  15.4 and Cross-Chapter Box FINANCE: Finance for 
Adaptation and Resilience in Chapter  17 of AR6 WGII report).  0 Type of economy Sector
0 0
No sector Electricity Other
and ForestEnergy 
EfﬁciencyTransport Policy Waste water100200300400500600700
2014
2015
2016
2017
2018
2019
2020100200300400500600700
2014
2015
2016
2017
2018
2019
2020
Figure 15.3 | Available estimates of global climate finance between 2014 and 2020.  Note: Numbers in current billion USD. Deflated to USD 2015 see Table 15.1 in italic.  
Type of Economy figure (left): Regional breakdown based on official UN country classification. ‘0’ no regional mapping information available. Sectoral figure (right):  Policy includes

Disaster Risk Management; Policy and national budget support and capacity building. Transport includes Sustainable/Low-carbon Transport. Energy Efficiency includes Industry, 
Extractive Industries, Manufacturing & Trade, Low-carbon Technologies, Information and Communications Technology, Buildings and Infrastructure. Electricity includes Renewable 
Energy Feneration, “Infrastructure, energy and other built environment”, Transmission and Distribution Systems, and Energy Systems. No sector means no sector information 
available, or negligible flows. Other includes Non-energy GHG reductions, Coastal Protection. Source: own calculations, based on Naran et al. (2021).

1565
Investment and Finance Chapter 1515Box 15.4 | Measuring Progress Towards the USD100 Billion yr–1 by 2020 Goal – 
Issues of Method
In 2009, at COP15, Parties to the UNFCCC agreed the following: ‘In the context of meaningful mitigation actions and transparency 
on implementation, developed countries commit to a goal of mobilising jointly USD100 billion a year by 2020 to address the needs 
of developing countries. This funding will come from a wide variety of sources, public and private, bilateral and multilateral, including 
alternative sources of finance’ (UNFCCC 2009).
This goal is further embedded as a target under SDG 13 Climate Action. While the parameters for what and how to count were not 
defined when the goal was set, progress in this area has been achieved under the UNFCCC (UNFCCC 2019b) and via a UN-driven 
independent expert review (Bhattacharya et al. 2020).

There remain well documented interpretations and debates on how to account for progress (Clapp et al. 2012; Stadelmann et al. 
2013; Jachnik et al. 2015; Weikmans and Roberts 2019). Different interpretations relate mainly to the type and proportion of activities 
that may qualify as ‘climate’ on the one hand, and to how to account for different types of finance (and financial instruments) on 
the other hand. As an example, there are different points at which financing can be measured, for example, pledges, commitments, 
disbursements. There can be significant lags between these different points in time, for example disbursements may spread over 
time. Further, the choice of point of measurement can have an impact on both the volumes and on the characteristics (geographical 
origin, labelling as public or private) of the finance tracked. The enhanced transparency framework under the Paris Agreement may

lead to improvements and more consensus in the way climate finance is accounted for and reported under the UNFCCC. Available 
analyses specifically aimed at assessing progress towards the USD100 billion goal remain rare, for example the UNFCCC SCF Biennial 
Assessments do not directly address this point (UNFCCC 2018a). Dedicated OECD reports provide figures based on accounting for 
gross flows of climate finance based on analysing activity-level data recorded by the UNFCCC (bilateral public climate finance) and 
the OECD (multilateral public climate finance, mobilised private climate finance and climate-related export credits) (OECD 2015a; 
OECD 2019a; OECD 2020b). For 2018, the OECD analysis resulted in a total of USD78.9 billion, out of which USD62.2 billion of public 
finance, USD2.1 billion of export credits and USD14.5 billion of private finance was mobilised. Mitigation represented 73% of the total, 
adaptation 19% and cross-cutting activities 8%.

Reports by Oxfam provide a complementary view on public climate finance, building on OECD figures and underlying data sources 
to translate gross flows of bilateral and multilateral public climate finance in grant equivalent terms, while also, for some activities, 
applying discounts to the proportion considered as climate finance (Carty et al. 2016; Carty and Le Comte 2018; Carty et al 2020). 
The resulting annual averages for 2015–2016 and 2017–2018 range between 32% (low bound) and 44% (high bound) of gross 
public climate finance. The difference with OECD figures stems from the high share represented by loans, both concessional and non-
concessional, in public climate finance, that is, 74% in 2018 (OECD 2020b).
A point of method that attracts much attention relates to how to account for private finance mobilised. The OECD, through its 
Development Assistance Committee, established an international standard to measure private finance mobilised by official

development finance, which consists in methods tailored to different financial mechanisms. These methods take into account the role 
of, risk taken, and/or amount provided by all official actors involved in a given project, including recipient country institutions, thereby 
also avoiding risks of double counting (OECD 2019b). MDBs apply a different method (World Bank 2018a) in their joint climate finance 
reporting (AfDB et al. 2020), which neither correspond to the geographical scope of the USD100 billion goal, nor address the issue of 
attribution to the extent required in that context.
Notwithstanding methodological discussions under the UNFCCC, there is still some distance from the USD100 billion a  year 
commitment being achieved, including in terms of further prioritising adaptation. While the scope of the commitment corresponds to 
only a fraction of the larger sums needed (Section 15.4), its fulfilment can both contribute to climate action in developing countries

as well as to trust building in international climate negotiations. Combined with further clarity on geographical and sectoral gaps, 
this can, in turn, facilitate the implementation of better coordinated and cooperative arrangements for mobilising funds (Peake and 
Ekins 2017).

1566
Chapter 15 Investment and Finance15Further, there is increasing awareness about the need to better 
understand and address the interlinkages between climate change 
adaptation and disaster risk reduction (DRR) towards achieving 
resilience (OECD 2020a). Watson et  al. (2015) however, note that 
between 2003 and 2014, of the USD2 billion that flowed through 
dedicated climate change adaptation funds, only USD369 million 
explicitly went to DRR activities (Climate Funds Update 2014; 
Nakhooda et al. 2014a; Nakhooda et al. 2014b; Watson et al. 2015). 
For the private sector, insurance and reinsurance remain the dominant 
way to transfer risk as discussed in Section 15.6.4).
More generally, significant gaps remain to track climate finance 
comprehensively at a global level:
• Available estimates are based on a good coverage of investments 
in renewable energy and, where available, energy efficiency and 
transport, while other sectors remain more difficult to track, such

as industry, agriculture and land use (high confidence) (UNFCCC 
2018a; Buchner et al. 2019).
• In contrast to international public climate finance, domestic 
public finance data remain partial despite initiatives to track 
domestic climate finance (e.g.,  Hainaut and Cochran 2018) 
and public expenditures (high confidence) (for instance based 
on the UNDP’s Climate Public Expenditure and Institutional 
Review approach).
Data on private and commercial finance remain very patchy, 
particularly for corporate financing (including debt financing 
provided by commercial banks), for which it is difficult to establish 
a link with activities and projects on the ground (high confidence). 
Further, as individual sources of aggregate reporting (UNFCCC 
2018a; Buchner et al. 2019; FS-UNEP Centre and BNEF 2020) tend to 
rely on the same main data sources (notably the BNEF commercial 
database for renewable energy investments) as well as to cross-

check numbers against similar other sources, there is a potential for 
‘group-think’ and bias.
Such data gaps as well as varying definitions of what qualifies as 
‘climate’ (or more broadly as ‘green’ and ‘sustainable’) not only pose 
a  measurement challenge. They also result in a  lack of clarity for 
investors and financiers seeking climate-related opportunities. Such 
uncertainty can lead both to reduced climate finance as well as to 
a lack of transparency in climate-related reporting (further discussed 
in Section 15.6.1), which in turn further hinders reliable measurement.
In terms of finance provided and mobilised by developed countries for 
climate action in developing countries, while accounting scope and 
methodologies continue to be debated (Box 15.4), progress has been 
achieved on these matters in the context of the UNFCCC (UNFCCC 
2019b). A consensus, however, exists, on a need to further scale up 
public finance and improve its effectiveness in mobilising private

finance (OECD 2020b), as well as to further prioritise adaptation 
financing, in particular towards the most vulnerable countries (Carty 
et al. 2020). The relatively low share of adaptation in international 
climate finance to date may in part be due to a low level of obligation 
and precision in global adaptation rules and commitments (Hall and 
Persson 2018). Further, providers of international climate finance may have more incentive to support mitigation over adaptation as 
mitigation benefits are global while the benefits of adaptation are 
local or regional (Abadie et al. 2013).
15.3.3 Fossil Fuel-related and Transition Finance
As called for by Article 2.1c of the Paris Agreement and introduced 
in Section  15.3.1, achieving the goal of the Paris Agreement of 
holding the increase in the global average temperature to well below 
temperature increase to 1.5°C above pre-industrial levels requires

making all finance consistent with this goal. Data on investments 
and financing to high GHG activities remain very partial and 
difficult to access, as relevant actors currently have little incentive 
or obligations to disclose such information compared to reporting 
on and communicating about their activities contributing to climate 
action. Further, the development of methodologies to assess 
finance for activities misaligned with climate mitigation goals, for 
hard- and costly-to-abate sectors such as heavy industries, as well 
as for activities that eventually need to be phased out but can play 
a  transition role for a  given period, remain work in progress. This 
results in limited empirical evidence to date.
In modelled pathways that limit warming to 1.5°C (>50%) with no or 
limited overshoot, however, make it clear that the share of fossil fuels 
in energy supply has to decrease (see Chapter 3). For instance, the

International Energy Agency (IEA) Net Zero by 2050 scenario relies 
on halting sales of new internal combustion engine passenger cars 
by 2035, rapid and steady decrease of the production of coal (minus 
90%), oil (minus 75%) and natural gas (minus 55%) by 2050, and 
phasing out all unabated coal and oil power plants by 2040 (IEA 
2021b). To avoid locking GHG emissions incompatible with remaining 
carbon budgets, this implies a rapid scaling down of new fossil fuel-
related investments, combined with a scaling up of financing to allow 
energy and infrastructure systems to transition (high confidence).
The IEA provides comprehensive analyses of global energy 
investments, estimated at about USD1.8 trillion a year over 2017–
2019 (IEA 2019a, 2020a), and expected to reach that level again in 
2021 after a drop to about 1.6 trillion in 2020 (IEA 2021c). Energy 
investments represent about 8% of global GFCF (Section 15.3.2.1).

In the power sector, fossil fuel-related investments reached an 
estimated USD120 billion yr–1 on average over 2019–2020, which 
remains well above the level that underpin the IEA’s own Paris-
compatible Sustainable Development Scenario (SDS) and Net Zero 
Emission (NZE) scenarios. The IEA observes a similar inconsistency 
for supply-side new investments: in 2019–2020 on average yr–1, an 
estimated USD650 billion were invested in oil supply and close to 
USD100 billion in coal supply. These estimates also result in fossil 
fuel investments remaining larger in aggregate than the total 
tracked climate finance worldwide (Section  15.3.2.2). For oil and 
gas companies, which are amongst the world’s largest corporations 
and sometimes government owned or backed, low-carbon solutions 
are estimated to represent less than 1% of capital expenditure 
(IEA 2020b). As discussed in the remainder of this chapter, shifting 
investments towards low-GHG solutions requires a combination of

1567
Investment and Finance Chapter 1515conducive public policies, attractive investment opportunities, as well 
as the availability of financing to finance such a transition.
In terms of financing provided to fossil fuel investments, available 
analyses point out a still significant role played by commercial banks 
and export credit agencies. Commercial banks provide both direct 
lending as well as underwriting services, the latter facilitating capital 
raising from investors in the form of bond or share issuance. Available 
estimates indicate that lending and underwriting extended over 
2016– 2019 by 35 of the world’s largest banks to 2100 companies 
active across the fossil fuel lifecycle reached USD687 billion yr–1 on 
average (Rainforest Action Network et al. 2020). Official export credit 
agencies, which are owned or backed by their government, de-risk 
exports by providing guarantees and insurances or, less often, loans.

In 2016–2018, available estimates indicate the provision of about 
USD31 billion yr–1 worth of fossil fuel-related official export credits, 
out of which close to 80% was for oil and gas, and over 20% for coal 
(DeAngelis and Tucker 2020).
Finance for new fossil fuel-related assets lock in future GHG 
emissions that may be inconsistent with remaining carbon budgets 
and, as discussed above, with emission pathways to reach the Paris 
Agreement goals. This inconsistency exposes investors and asset 
owners to the risk of stranded assets, which results from potential 
sharp strengthening climate public policies, that is, transition risk. As 
a result, a growing number of investors and financiers are assessing 
climate-related risks with the aim to disclose information about their 
current level of exposure (to both transition and physical climate-
related risks), as well as to inform their future decisions (TCFD 2017). 
Reporting to date is, however, inconsistent across geographies and

jurisdictions (CDSB and CDP 2018; Perera et  al. 2019), with also 
a wide variety of metrics, methodologies, and approaches developed 
by commercial providers that contribute to disparate outcomes 
(Kotsantonis and Serafeim 2019; Boffo and Patalano 2020). Further, 
as developed in Section 15.6.1, there is currently not enough evidence 
in order to conclude whether climate-related risk assessments result 
in increased climate action and alignment with the goals of the Paris 
Agreement (The 2° Investing Initiative and Wüest Partner 2020).
As developed in Section  15.6.3, the insufficient level of ambition 
and coherence of public policies at national and international 
levels remains the root cause of the still significant misalignment 
of investment and financing compared to pathways compatible 
with the Paris Agreement temperature goal (UNEP 2018). Such lack 
of coherence includes low pricing of carbon and of environmental

externalities more generally, as well as misaligned policies in non-
climate policy areas such as fiscal, trade, industrial and investment 
policy, and financial regulation (OECD 2015b), as further specified in 
the sectoral Chapters 6 to 12.
The most documented policy misalignment relates to the remaining 
very large scale of public direct and indirect financial support for 
fossil fuel-related production and consumption in many parts of 
the world (Bast et al. 2015; Coady et al. 2017; Climate Transparency 
2020). Fossil fuel subsidies are embedded across economic sectors as 
7 The term Investment ‘Needs’ used in the chapter means equal to the term Investment Requirement used in SPM.well as policy areas, for example, from a trade policy perspective, in 
most countries, import tariffs and non-tariff barriers are substantially 
lower on relatively more CO 2 intensive industries (Shapiro 2020). 
Available inventories of fossil fuel subsidies (in the form of direct

budgetary transfers, revenue forgone, risk transfers, or induced 
transfers), covering 76 economies, indicate a rise to USD340 billion 
in 2017, a  5% increase compared to 2016. Such trend is due to 
slowed down progress in reducing support among OECD and G20 
economies in 2017 (OECD 2018b) and to a rise in fossil fuel subsidies 
for consumption in several developing economies (Matsumura 
and Adam 2019), which, in turn, reduces the efficiency of public 
instruments and incentives aimed at redirecting investments and 
financing towards low-GHG activities.
As a  result, the demand for fossil fuels, especially in the energy 
production, transport and buildings sectors, remain high, and the 
risk-return profile of fossil fuel-related investments is still positive 
in many instance (Hanif et al. 2019). Political economy constraints of 
fossil fuel subsidy reform continue to be a major hurdle for climate 
action (Schwanitz et  al. 2014; Röttgers and Anderson 2018), as

further discussed in Section 15.5.2. and Chapter 13.
15.4 Financing Needs
15.4.1 Definitions of Financing Needs
Financing needs7 are discussed in various contexts, only one being 
international climate politics and finance. Also, financing needs are 
used as an indicator for required system changes (when compared 
to current flows and asset bases) and an indicator for near- to long-
term investment opportunities from the perspective of investors 
and corporates. Investment needs are widely used as an indicator 
focusing on initial investments required to realise new infrastructure. 
It compares relatively well with private sector flows dominated 
by return-generating investments but lacks comparability and 
explanatory power regarding the needs in the context of international 
climate cooperation, where considerations on economic costs play 
a more substantial role. Chapter 12 elaborates on global economic 
cost estimates for various technologies. This indicator includes both

costs and benefits of options, of which investment-related costs 
make up only one component. Both analyses offer complementary 
insights. There are financing needs not directly related to the 
realisation of physical infrastructure and which are not covered 
in both investment and cost estimates. For instance, the needs for 
building institutional capacity to achieve social and economic goals 
and to strengthen knowledge, skills, national and international 
cooperation might not be significant, but an enabling environment 
for future investments would not be established without satisfying it. 
Moreover, comprehending financial needs for addressing economic 
losses due to climate change can hardly be measured in terms of the 
indicators introduced before.
Understanding the magnitude of the challenge to scale up finance in 
sectors and regions requires a more comprehensive (and qualitative)

1568
Chapter 15 Investment and Finance15assessment of the needs. For finance to become an enabler of the 
transition, domestic and international public interventions can be 
needed to ensure enough supply of finance across sectors, regions 
and stakeholders. The location of financing needs and vicinity to 
capital matter given home bias (Fuchs et  al. 2017; OECD 2017a; 
Ito and McCauley 2019) (prioritising own country or regions), 
transaction costs and risk considerations (Section 15.2). Most of the 
finance is mobilised domestically but the depth of capital markets is 
substantially greater in developed countries, increasing the challenges 
to mobilise substantial volumes of additional financing for many 
developing countries. The same applies to various stakeholders with 
limited connections into the financial sector. In addition, governments 
enabling financial market frameworks, guidelines and supportive 
infrastructure is crucial for inclusive finance for the bottom of the

pyramid, especially disadvantaged and economically marginalised 
segments of society.
The attractiveness of a sector and region for capital markets depends 
on several factors. Some essential elements are the duration of loan 
and profile as long-term loans and heavily heterogeneous returns 
represent challenges in financing mitigation technologies and 
policies. After the financial crisis and restricted access to long-term 
debt, capital intensity of technologies and resulting long payback 
periods of investment opportunities for mitigation technologies have 
been a crucial challenge (Bertoldi et al. 2021). Also, implicit discount 
rates applied during the investment decision process vary depending 
on the payback profile, with research mainly covering the difference 
between the financing of assets generating revenues versus costs 
(Jaffe et al. 2004; Schleich et al. 2016). In addition, a low correlation 
between the climate projects and dominating asset classes might

provide an opportunity in climate action by satisfying the appetite 
of institutional investors, which tend to manage portfolios with 
consideration of the Markowitz modern portfolio theory (optimising 
return and risk of a portfolio through diversification) (Marinoni et al. 
2011). Transaction cost is a significant barrier to the diffusion and 
commercialisation of low-carbon technologies and business models 
and adaptation action. High transaction costs, attributed to various 
factors, such as complexity and limited standardisation of investments, 
limited pipelines, complex institutional and administrative procedures, 
create significant opportunity costs of green investments comparing 
with other standard investments (IRENA 2016; Nelson et al. 2016; 
Feldman et al. 2018). For example, transaction costs are commonly 
observed in small-scale, dispersed independent renewable energy 
systems, especially in rural areas, and energy efficiency projects

(Hunecke et al. 2019). A more robust standardisation and alignment of 
Power Purchase Agreement (PPA) terms with best practices globally 
has led to a  substantially increased interest in capital markets in 
developing countries (WBCSD 2016; Schmidt et al. 2019; World Bank 
2021). Notably, PPA significantly increases the probability of more 
balanced investment and development outcomes and ultimately 
more sustainable independent power projects in developing 
countries. Therefore, lowering transaction costs would be essential 
for creating investor appetite. The role of intermediaries bundling 
demand for financing has been demonstrated to reduce transaction 
costs and to reach investors’ critical size. In addition, new innovative 
approaches, such as fintech and blockchain (Section  15.6.8), have 
been discussed for providing new opportunities in the energy sector.Economic viability of investments – ideally not relying on the pricing

of positive externalities  – has been a  critical driver of momentum 
in the past. The falling technology costs and the competitiveness 
of renewable technologies, especially solar PV and wind, have 
accelerated the deployment of renewable technologies over the past 
years. Renewable energy technologies are now often competitive, 
and have even become the cheapest, in many countries, even without 
financial support (FS-UNEP Centre and BNEF 2015, 2016, 2017, 
2018, 2019; IEA 2020c; IRENA 2020a) and without pricing of the 
avoided carbon emissions. In contrast, the dependency on regulatory 
interventions and public financial support to create financial viability 
has provided a source of volatile investor appetite. The annual volume 
of renewable investment by country is often volatile, reflecting ending 
and new regulations and policies (IEA 2019a).
For example, the recent Chinese policy direction towards tougher 
access to and a  substantial cut in feed-in-tariffs in 2018 led to

a significant drop in renewable investment and new capacity addition 
in China (FS-UNEP Centre and BNEF 2019; Hove 2020). However, the 
significant bouncing back of newly installed capacity (72 GW wind 
power and 47 GW solar power in 2020) shows the strong development 
of zero-carbon power generation driven by lower cost and policies to 
support them by energy revolution strategies in China. Investors had 
proven to be willing to work with transparent support mechanisms, 
such as with the Clean Development Mechanism (CDM), which 
stimulated emission reductions and allowed industrialised countries 
to implement emission-reduction projects in developing countries to 
meet their emission targets (Michaelowa et al. 2019). However, the 
collapse of carbon markets and prices, especially of the EU Emissions 
Trading System, led to the continuous decline of Certified Emission 
Reductions issuances from CDM in the past years (World Bank Group

2020). Also, the dependency on regulatory intervention to ensure fair 
market access only has proven to burden investor appetite.
A significant share of investment needs in heavily regulated sectors, 
such as electricity, public transport, and telecom, emphasises the 
importance of regulatory intervention, such as ownership and market 
access (OECD 2017b). For instance, energy-system developments 
require effective and credible commitments and action by 
policymakers to ensure an efficient capital allocation aligned with 
climate targets (Bertram et al. 2021).
There is a  lot of discussion about the regulated ownership of the 
private sector (European Commission 2017) and the restructuring of 
electricity market contributed to low level of investment in baseline 
electricity capacity and in investment research and innovation. These 
changes create uncertainty of investment, and barriers to market 
entry and exit also potentially limit the competition in the market

and restrict the entrance of new investment (Finon 2006; Joskow 
2007; Grubb and Newbery 2018). This is also the case in developing 
countries (Foster and Rana 2020).
The positive development in the energy sector has benefitted from 
the evident stand-alone character of renewable energy generation 
projects. First movers realised these projects with investors and 
developers acting from conviction (Steffen et al. 2018). Such action is 
not possible to this extent in energy efficiency with related investment

1569
Investment and Finance Chapter 1515rather representing an add-on component and consequently 
requiring the support of decision-makers used to business-as-usual 
projects. Despite the benefits that improvement of energy efficiency 
has in contributing to curbing energy consumption, mitigating 
greenhouse gas emissions, and providing multiple co-benefits (IEA 
2014a), investment in energy efficiency is a low priority for firms, and 
the financial environment is not favourable due to lack of awareness 
of energy efficiency by financial institutions, existing administrative 
barriers, lack of expertise to develop projects, asymmetric information, 
and split incentives (UNEP DTU 2017; Cattaneo 2019). While Energy 
Service Companies’ (ESCO) business models are expected to facilitate 
the investment in energy efficiency by sharing a portion of financial 
risk and providing expertise, there has been limited progress made 
with ESCO business models, and only slightly over 20% of projects

used financing through ESCOs (UNEP DTU 2017).
The investment needs and existing challenges differ by sector. Each 
sector has different characteristics along the arguments listed above 
making the supply of finance by commercial investors an enabling 
factor or barrier. In the transport sector, transformation towards green 
mobility would provide significant co-benefits for human health by 
reducing transport-related air pollution, so the transport sector cannot 
achieve such transformation in isolation from other sectors. However, 
a considerable involvement of the public sector in many transportation 
infrastructure projects is given, and the absence of a standard solution 
increases transaction costs (including bidding package, estimating, 
drawing up a contract, administering the contract, corruption, and 
so on). Financial constraints, including access to adequate finance, 
pose a significant challenge in the agriculture sector, especially for

SMEs and smallholder farmers. The distortion created by government 
failure and a lack of effective policies create barriers to financing for 
agriculture. The inability to manage the impact of the agriculture-
related risks, such as seasonality, increases uncertainty in financial 
management. Moreover, inadequate infrastructure, such as electricity 
and telecommunication, makes it difficult for financial institutions to 
reach agricultural SMEs and farmers and increases transaction costs 
(World Bank 2016). Low economies of scale, low bargaining power, 
poor connectivity to markets, and information asymmetry also lead 
to higher transaction costs (Pingali et  al. 2019). In the industrial 
manufacturing and residential sector, gaining energy efficiency 
remains one of the critical challenges. Investment in achieving energy 
efficiency encounters some challenges when it may not necessarily 
generate direct or indirect benefits, such as increase in production

capacity or productivity and improvement in product quality. Also, 
early-stage, high upfront cost and future, stable revenue stream 
structure suggest the needs for a better enabling environment, such 
as a robust financial market, awareness of financial institutions, and 
regulatory frameworks (e.g., stringent building codes, incentives for 
ESCOs) (IEA 2014a; Barnsley et al. 2015).
15.4.2 Quantitative Assessment of Financing Needs
Multiple stakeholders prepare and present quantitative financing 
needs assessments with methodologies applied to vary significantly 
representing a major challenge for aggregation of needs (e.g., Osama 
et  al. (2021) for African countries), most of them with a  focus on scenarios likely to limit warming to 2°C or lower. The differences 
relate to the scope of the assessments regarding sectors, regions and 
periods, top-down versus bottom-up approaches, and methodological 
issues around boundaries of climate-related investment needs,

particularly full vs incremental costs and the exclusion or inclusion 
of consumer-level investments. Information on investment needs 
and financing options in NDCs mirrors this challenge and is heavily 
heterogeneous (Zhang and Pan 2016).
In particular, for global approaches, modelling assumptions are often 
heavily standardised, focusing on technology costs. Only limited 
global analysis is available on incremental costs and investments, 
reflecting the reality of developing countries, also considering the 
interplay with significant infrastructure finance gaps, and can hardly 
serve as a robust basis for negotiations about international public 
climate finance. The focus on investment irrespective of uncertainty 
as well as other qualitative aspects of needs does not allow for 
a straightforward analysis of the need for public finance to leverage 
private sector financing and of the country heterogeneity in terms of 
investment risks and access to capital (Clark et al. 2018).

One source of uncertainty about the investment estimates for the 
power sector is the evolution of the levelised cost of technical 
options in the future, for example the continuation of the observed 
declining costs trends of renewable energy (IRENA 2020b) which has 
been underestimated in many modelling exercises. The learning by 
doing processes and economies of scale might be at least partially 
outweighed, in all countries and more specifically in Small Island 
Developing States (SIDS) and other developing countries because of 
different risk factors, scales of installations, accessibility, and others 
(Lucas et  al. 2015; van der Zwaan et  al. 2018). These parameters, 
together with transaction costs/soft costs (Section  15.5), financing 
costs and the level of technical competences need to be better 
represented in the future to represent the ‘climate investment trap’ 
in many developing countries (García de Fonseca et al. 2019). This

‘climate investment trap’, as flagged by Ameli et al. (2021a), is created 
by existing and expected physical effects of climate change, higher 
financing costs and resulting lower investment levels in developing 
countries. Applying significantly standardised assumptions can 
consequently not provide robust insights for specific country groups. 
This will require progress in the spatiotemporal granularity of the 
models (Collins et al. 2016).
Another source of uncertainty about the financing needs is the 
interplays between (i) the baseline economic growth rates, (ii) the link 
between economic growth and energy demand, including rebound 
effects of energy efficiency gains, (iii) the evolution of microeconomic 
parameters such as fossil fuel prices, interest rates, currency 
exchange rates (iv) the level of integration between climate policies 
and sectoral policies and their efficacy, and (v) the impact of climate 
policies on growth and the capacity of fiscal and financial policies

to offset their adverse effect (IPCC 2014; IPCC 2018). Integrated 
assessment models (IAMs) try to capture some of these interplays 
even though they typically do not capture the financial constraints 
and the structural causes of the infrastructure investment gap. Many 
of them rely on growth models with full exploitation of the means 
of production (labour and capital). They nevertheless provide useful

1570
Chapter 15 Investment and Finance15indications of the orders of magnitude at play over the long run, and 
the determinants of their uncertainty. Global yearly average low-
carbon investment needs until 2030 for electricity, transportation, 
AFOLU and energy efficiency measures including industry and 
buildings are estimated between 3% and 6% of the world’s GDP 
according to the analysis in Section 15.5. The incremental costs of 
low-carbon options are less than that and their funding could be 
achieved without reducing global consumption by reallocating 
1.4% to 3.9% of global savings. 2.4% on average (see Box 4.8 of 
SR1.5 (IPCC 2018)) currently flow towards real estate, land and liquid 
financial vehicles. For the short-term decisions, the major information 
they give is the uncertainty range because this is an indicator of the 
risks decision-makers need.
While the AR6 Scenarios Database provides good transparency with

regard to technology costs for electricity generation, assumptions 
driving in particular investments in energy efficiency are rarely made 
available in both IAM-based assessments and also other studies. 
Taking into account the much broader range of tested and untested 
technologies the confidence levels, in particular for 2050 estimates, 
remain low but can provide an initial indication. Also, the ranges 
allow for a rough indication on possible ‘green’ investment volumes 
and respective asset allocation for financial sector stakeholders.
Using global scenarios assessed in Chapter  3  for assessing 
investment requirements. Tables  15.2 and 15.3 present the 
analysis of investment requirements in global modelled mitigation 
pathways assessed in Chapter 3 for key energy sub-sectors within 
modelled global pathways that limit warming to 2°C (>67%) or 
lower. These pathways explore the energy, land-use, and climate 
system interactions and thus help identify required energy sector

transformations to reach specific long-term climate targets. However, 
reporting of investment needs outside the energy sector was scarce, 
reducing the explanatory power of the shown total investment need 
in the context of overall investment needs (Ekholm et  al. 2013; 
IPCC 2018, Box 4.8; McCollum et al. 2018; Bertram et al. 2021). The 
modelling of these scenarios is done with a  variation of scenario 
assumptions along different dimensions (inter alia policy, socio-
economic development and technology availability), as well as with 
different modelling tools which represent different assumptions about the structural functioning of the energy-economy-land-
use system (see Annex III: ‘Scenarios and modelling methods’ for 
details). Tables 15.2 and 15.3 focus on the near-term (2023–2032) 
investment requirements in the energy sector and how these differ 
depending on temperature category. Figures 3.36 and 3.37 present 
the data for the medium term (2023–2052). The results highlight

both requirements for increased investments and a shift from fossil 
towards renewable technologies and efficiency for more ambitious 
temperature categories. The substantial ranges within each category 
reflect multiple pathways, differentiated by socio-economic 
assumptions, technology, and so on. It is necessary to open up these 
extra dimensions and contrast them with national and sub-regional 
analysis to understand how investment requirements depend 
on particular circumstances and assumptions within a  country 
for a  specific technology. Limiting peak temperature to levels of 
1.5°C–2°C requires rapid decarbonisation of the global energy 
systems, with the fastest relative emission reductions occurring in the 
power generation sector (Hirth and Steckel 2016; Luderer et al. 2018).
This requires fast shifts of investment as infrastructures in the power 
sector generally have long lifetimes of a  few decades. in global

modelled pathways that limit warming to 1.5°C (>50%) with no 
or limited overshoot, investments into non-biomass renewables 
(especially solar and wind, but also including hydro, geothermal, and 
others not shown in Table 15.2) increase to over USD1 trillion yr–1 
in 2030, increasing by more than factor 3 over the values of around 
USD250–300 billion yr–1 that have been relatively stable over the 
last decade (IEA 2019a). Overall, electricity generation investments 
increase considerably, reflecting the higher relevance of capital 
expenditures in decarbonised electricity systems. While decreasing 
technology costs have substantially reduced the challenge of high 
capital intensity, still remaining relative disadvantages in terms of 
capital intensity of low-carbon power technologies can especially 
create obstacles for fast decarbonisation in countries with high 
interest rates, which decrease the competitiveness of those

technologies (Iyer et al. 2015; Hirth and Steckel 2016; Steckel and 
Jakob 2018; Schmidt et  al. 2019). CCS as well as nuclear will not 
drive investment needs until 2030, given considerably longer lead-
times for these technologies, and the lack of a  significant project 
pipeline currently.
Table 15.2 | Global average yearly investments from 2023–2032 for electricity supply in billion USD 2015.
Category Fossil Nuclear StorageTransmission 
and distributionNon-Biomass Renewables
AllThereof
Solar Wind
C1 53 [50] 127 [52] 221 [39] 549 [50] 1190 [52] 498 [52] 390 [52]
(Range) (34;115) (85;165) (88;295) (422;787) (688;1430) (292;603) (273;578)
C2 78 [100] 116 [92] 57 [66] 489 [81] 736 [96] 312 [96] 237 [96]
(Range) (50;129) (61;150) (37;139) (401;620) (482;848) (181;385) (174;328)
C3 75 [221] 96 [190] 28 [129] 389 [157] 639 [207] 220 [207] 266 [207]
(Range) (52;129) (50;122) (8;155) (326;760) (432;820) (167;345) (137;353)

Note: Global average yearly investments from 2023–2032 (in USD 2015). Electricity subcomponents are not exhaustive. Hydro, geothermal, biomass and others are not shown, as these 
are shown to be of smaller magnitude (Chapter 3). Difference between non-biomass renewables and solar/wind represents hydro and in some scenarios geothermal, tidal, and ocean. 
Scenarios are grouped into common AR6 categories (vertical axis, C1–C3). The numbers represent medians across all scenarios within one category, and rounded brackets indicate 
inter-quartile ranges, while the numbers in squared brackets indicate number of scenarios. C6, C7, and C8 are not shown in Table 15.2. Reference C5 category for Transmission and 
Distribution (T&D) is 364bn (294bn to 445bn) [111] used for calculation of incremental needs in Figure 15.4. Data source: AR6 Scenarios Database.

1571
What is apparent is that the bulk of investment requirements 
corresponds to medium- and low-income countries in Asia, Latin 
America, the Middle East and Africa, as these still have growing 
energy demand, and it is still considerably lower than the global 
average. This illustrates a vital opportunity to ensure the build-up of 
sustainable energy infrastructures in these regions and constitutes 
a  risk of additional carbon lock-in if investments into fossil infrastructures, especially coal-fired power plants, and uncontrolled 
urban expansion, continue.
Investment needs in electrification derived from IAMs do not 
include systematically investments in end-use equipment and 
distribution (Box 4.8 in SR1.5 (IPCC 2018)). Model-based estimates 
of investment needs don’t have the regional granularity to single out Table 15.3 | Regional average yearly investments from 2023–2032 for electricity supply in billion USD 2015.
AfricaEast
AsiaEuropeSouth

AsiaLatin 
AmericaMiddle
EastNorth 
AmericaAustralia, 
Japan, 
and New 
ZealandEast. Eur. 
W.C. AsiaSouth 
East Asia
Non-biomass renewables
C1 41 [39] 302 [41] 130 [41] 120 [41] 69 [41] 67 [41] 177 [41] 37 [41] 48 [41] 85 [41]
(Range) (36;66) (188;356) (101;150) (83;164) (55;97) (31;90) (149;222) (28;39) (35;65) (59;141)
C2 32 [77] 179 [87] 95 [87] 69 [87] 55 [87] 28 [87] 106 [87] 19 [87] 17 [87] 63 [87]
(Range) (27;42) (124;255) (64;104) (35;84) (27;73) (19;43) (73;134) (12;29) (10;37) (35;78)
C3 17 [170] 166 [185] 91 [185] 53 [182] 53 [185] 22 [182] 119 [185] 22 [179] 15 [185] 38 [182]
(Range) (12;47) (108;200) (42;118) (35;80) (25;81) (11;32) (71;167) (12;30) (11;30) (22;67)
Thereof solar
C1 16 [39] 134 [41] 43 [41] 53 [41] 22 [41] 33 [41] 81 [41] 11 [41] 20 [41] 33 [41]
(Range) (8;24) (89;147) (38;55) (37;82) (14;34) (16;40) (75;95) (10;16) (10;25) (17;56)
C2 10 [77] 83 [87] 34 [87] 37 [87] 16 [87] 15 [82] 44 [87] 7 [80] 5 [81] 20 [87]

(Range) (6;14) (54;125) (19;47) (17;41) (8;21) (10;23) (18;69) (4;10) (1;12) (9;33)
C3 7 [170] 53 [185] 28 [184] 23 [182] 12 [184] 12 [164] 32 [185] 9 [157] 8 [164] 14 [182]
(Range) (3;14) (42;83) (17;36) (17;39) (5;25) (9;20) (21;74) (4;11) (3;12) (7;27)
Thereof wind
C1 10 [39] 133 [41] 59 [41] 45 [41] 19 [41] 22 [41] 58 [41] 20 [41] 17 [41] 28 [41]
(Range) (4;30) (86;164) (29;86) (23;71) (15;26) (13;39) (44;122) (12;25) (10;23) (17;52)
C2 5 [77] 63 [87] 41 [83] 23 [87] 15 [87] 8 [81] 31 [87] 8 [87] 4 [81] 19 [87]
(Range) (4;14) (44;102) (9;59) (14;30) (7;18) (3;16) (19;75) (5;12) (2;12) (6;23)
C3 3 [170] 64 [185] 59 [169] 21 [182] 12 [184] 10 [160] 52 [184] 10 [179] 4 [164] 10 [182]
(Range) (2;15) (40;93) (12;65) (12;37) (7;22) (5;13) (19;86) (6;13) (2;10) (5;32)
Storage
C1 3 [27] 68 [32] 46 [32] 27 [32] 7 [29] 13 [30] 56 [30] 4 [32] 3 [24] 15 [30]
(Range) (0;8) (30;80) (9;54) (24;45) (2;11) (3;19) (30;62) (2;6) (0;4) (1;30)

C2 2 [36] 19 [60] 18 [52] 10 [57] 3 [42] 3 [31] 13 [44] 1 [43] 0 [20] 3 [41]
(Range) (0;4) (6;36) (7;35) (4;17) (1;8) (0;4) (11;34) (1;2) (0;0) (2;13)
C3 4 [78] 20 [106] 22 [92] 9 [107] 9 [85] 4 [78] 29 [81] 1 [90] 0 [78] 9 [83]
(Range) (0;6) (1;33) (3;41) (1;21) (0;13) (0;9) (2;42) (0;2) (0;1) (0;16)
Transmission and distribution
C1 24 [39] 147 [39] 67 [39] 51 [39] 40 [39] 27 [39] 87 [39] 16 [39] 24 [39] 64 [39]
(Range) (13;39) (96;250) (61;105) (46;97) (29;62) (22;40) (70;120) (13;19) (18;35) (26;94)
C2 24 [77] 132 [77] 60 [77] 49 [77] 36 [77] 33 [77] 70 [77] 14 [77] 26 [77] 36 [77]
(Range) (14;30) (84;175) (48;79) (43;56) (28;45) (27;37) (53;92) (8;19) (17;34) (28;61)
C3 14 [150] 93 [153] 61 [153] 46 [150] 26 [153] 25 [150] 70 [153] 14 [147] 23 [153] 26 [150]
(Range) (10;37) (74;190) (52;86) (38;86) (21;62) (17;40) (52;90) (11;16) (17;27) (17;87)
C5 13 [109] 81 [110] 55 [110] 41 [109] 25 [110] 23 [109] 58 [110] 14 [109] 23 [110] 25 [109]

(Range) (9;13) (67;160) (46;59) (22;46) (19;28) (15;28) (51;67) (12;16) (16;26) (17;29)
Note: Average yearly investments from 2023–2032 for electricity generation capacity, by aggregate regions (in billion USD 2015). Further notes see Table 15.2. Reference C5 
category for Transmission and Distribution shown in Table 15.2 as it is used for calculation of incremental needs for Figure 15.4. Vertical axis, C4–C8 except Transmission and 
Distribution not shown. Data source: AR6 Scenarios Database.

1572
Chapter 15 Investment and Finance15LDCs, as model regions typically are defined based on geographic 
proximity and therefore aggregate LDCs and other countries. With 
the average electricity consumption per capita in Africa increasing 
to 0.68–0.87 (1.43–2.92) MWh in 2030 (2050) yr–1 and remaining 
at the very low end of the global range [0.46 in Africa compared 
to the upper end of 12.02 in North America, MWh per capita and 
year in 2020], the targeted full electrification until 2030 appears 
unrealistic across all scenarios. SEforAll and IEA estimate assumed 
investment needs to decentralised end-user electrification to come 
in around USD40 billion on average until 2030 (SEforALL and CPI 
2020; IEA 2021d).
Quantitative analysis of investment needs in energy 
generation based on IRENA and IEA data and comparison 
to AR6 scenario database output.
According to IRENA, the government plans in place today call 
for investing at least USD95 trillion in energy systems over the

coming three decades (2016–2050) (IRENA 2020c). Redirecting and 
increasing investments to ensure a climate-safe future (Transforming 
Energy Scenario, TES) would require reaching on average around 
1  trillion  USD 2015 yr–1 (average until 2030) for electricity generation 
as well as grids and storage, increasing to above 2 trillion USD 2015 yr–1 
(average until 2030) in the 1.5 scenario (IRENA 2021). IEA’s respective 
SDS and NZE scenarios come in at average annual investments 
between USD1.0  trillion yr–1 and USD1.6 trillion yr–1 (average until 
2030) (IEA 2021b). These additional data points for the C1 and C3 
category underpin the range presented in the AR6 Scenarios Database 
for needs until 2032 despite the slightly varying periods.
In contrast to the IAMs, IRENA and IEA assessments do not allow 
for an analysis of mitigation-driven investment needs in transmission 
and distribution, which likely results in an overestimation of the

mitigation-driven investment needs in their analysis.
It is worth highlighting that driven by technology cost assumptions, 
IRENA forecasts falling average annual investments needs for energy, 
but also energy efficiency, for the period 2030–2050 compared to 
2020–2030. In the 1.5°C scenario (1.5-S) the total annual investment 
needs excluding fossils and nuclear decrease from 5.0 trillion USD 2015 
until 2030 yr–1 to 3.8 trillion USD 2015 yr–1 for 2030–2050 (IRENA 
2021). In IAM scenarios of Category C1, electricity supply investments 
(including generation, transmission and distribution, and storage) 
remain flat at 2.2 trillion USD 2015 yr–1 through the coming three 
decades in absolute terms. Given rising GDP , the complementary 
methods and sources thus consistently point to a peak in electricity 
supply investments as a percentage of GDP in mitigation scenarios 
in the coming decade. This reflects the fact that the coming decade

requires low-carbon power generation investments to both cover 
the demand increase and (partly premature) replacement of fossil 
generation capacities, both concentrated in emerging and developing 
countries. Relative investment numbers for electricity measured 
against GDP then decrease towards 2050, as they only need to 
cover natural replacement and increasing demands (which due 
to electrification will also pick up in developed countries), and due to 
further declining technology costs. Investments for low-carbon fuel supply like hydrogen and synthetic fuels, and for direct electrification 
equipment (heat pumps, electric vehicles (EV), etc.) scale up from 
much lower levels and will likely continue to grow as a share of GDP 
until mid-century, though uncertainties and accounting is still much 
more uncertain. (Bertram et al. 2021).
Quantitative analysis of investment needs in other sectors. 
As described above, investment needs in non-energy sectors tend to

be ignored in many integrated assessment models with studies for 
individual countries or regions providing a more fragmented picture 
only. However, the quality of estimates is likely not to be less robust 
given the drawbacks of integrated assessment models.
Chapter  7  stresses the importance of opportunity costs for AFOLU 
mitigation options, in particular for afforestation and avoided 
deforestation projects, and derives net annual costs of around 
USD278 billion yr–1 in the next several decades, mostly opportunity 
costs. Net costs of delivering 5-6 Gt CO 2 yr–1 of forest related carbon 
sequestration and emission reduction around 2050 as assessed with 
sectoral models are estimated to reach to ~ USD400 billion yr–1 by 
2050, excluding externality costs (Chapter 7.4).
Energy efficiency. Estimates on energy investment needs vary 
significantly with a low level of transparency with regard to underlying 
technology cost assumptions burdening the confidence levels.

IRENA only selectively reports financing needs for energy 
efficiency in buildings and industry as separate categories. For the 
1.5-S average yr–1 needs until 2050 come in at 963 billion USD 2015 
for buildings, 102 billion USD 2015 for heat pumps, and 354 billion 
USD 2015 for industry. Applying the relative share of these categories 
on higher total needs until 2030, around 1.8 trillion USD 2015  yr–1 
in buildings and industry are needed in the 1.5-S.  For the TES 
cumulative energy efficiency investment needs until 2030 are stated 
at 29 trillion USD 2015 translating into an yearly average of around 
1.7  trillion USD 2015 yr–1, excluding transportation. IEA estimates 
come in at a much lower level at 0.6 and 0.8 billion USD 2015 yr–1 on 
average between 2026–2030 for their SDS and NZE scenarios.
Transportation. For the transportation sector, OECD has presented 
the most comprehensive assessment of financing needs in the AR6

database based on IEA data with the annual average coming in at 
USD2.7 trillion between 2015 and 2035 i In modelled global pathways 
that limit warming to 2°C (>67%). The assessment comprises road, 
rail and airports/ports infrastructure, with only rail infrastructure 
being considered in this analysis.
On a regional level, Oxford Economics (2017) shows that annual 
infrastructure investments between 2016 and 2040 vary widely. 
For all available countries (n=50) estimates count close to 
0.4 trillion USD 2015 yr–1, including 0.217 trillion USD 2015 yr–1 for China. 
Based on available data for nine African countries, investments in rail 
infrastructure range from USD0.1 billion in Senegal to USD1.6 billion 
in Nigeria. Osama et al. (2021) highlight a USD4.7 billion financing gap 
for African countries in the transport sector. In Latin America Oxford 
Economics (2017) identifies Brazil as frontrunner of required rail

investments with USD8.3 billion, followed by Peru with USD2.3 billion.

1573
Investment and Finance Chapter 1515In total, developed countries’ financing needs mount up to almost 
USD120 billion yr–1 (n=15, mean=7.97bn USD) for rail infrastructure. 
Financing needs in developing countries (excluding LDCs and excluding 
China) mount up to almost USD50 billion yr–1 (n=27, mean=1.78bn USD, 
excluding China). Oxford Economics (2017) reports rail infrastructure 
financing needs for China of more than USD200 billion yr–1 between 
2016 and 2040.
Fisch-Romito and Guivarch (2019) show, by endogenising the impact 
of urban infrastructure policies on mobility needs and modal choices 
that transportation investment needs globally might be lower in low-
carbon pathways compared with baselines, with lower investments in 
road and air infrastructure. This does mean that higher investments 
are not needed over the following two decades; this is confirmed by 
Rozenberg and Fay (2019) that strong policy integration between urban,

transportation and energy policies reduce the total investment gap.
IRENA as well as IEA have presented estimates for energy efficiency 
investments in the transport sector. For the 1.5-S scenario, IRENA 
indicates average investment needs of USD 2015 0.2 trillion yr–1 for EV 
infrastructure, USD 2015 0.2 trillion yr–1 for transport energy efficiency 
and USD 2015 0.3 trillion yr–1 for EV batteries (average until 2030) (IRENA 
2020d). IEA indicates a total of around 0.6 and 0.7 trillion USD 2015 yr–1 
for transport energy efficiency in the SDS and IEA scenarios for the 
2026–2030 period (IEA 2021c). Many investment categories relating to 
mitigation options, in particular with regard to behavioural change and 
transport mode changes (Chapter 10, Figure SPM.8), are neglected in 
these analyses despite their significant mitigation potential.
AFOLU.  The Food and Land Use Coalition estimates additional 
investment needs for ten critical transitions for the global food and

land use systems to achieve the long-term global goal (LTGG) 
and  SDGs. Additional annual investment needs until 2030 add up 
to USD300–350 billion. Considering the change in global diets 
as well as the land-based nature-based solutions only, annual 
investment needs would come in between USD110–135 billion. Chapter  7  stresses the importance of opportunity costs for AFOLU 
mitigation options, in particular for afforestation projects, and derives 
average yearly investment needs of around 278 billion USD 2015 yr–1 
until 2030 rising to 431 billion USD 2015 yr–1 over the next several 
decades, including opportunity costs. The estimate is based on 
an assumption of emission reductions consistent with pathways 
C1–C4, leading to average abatement of 9.1 GtCO 2 yr–1 (median 
range 6.7–12.3 GtCO 2 yr–1) from 2020–2050 and marginal costs 
of USD100 per tonne CO 2, excluding investments in bioenergy with 
carbon capture and storage and changes in food consumption and

food waste (Section  7.4). The largest investments are projected to 
occur in Latin America, South-East Asia, and Africa, constituting 61% 
of total expenditure. The implied change of land use might trigger 
negative effects on other SDGs which need to be addressed to offer 
robust safeguards and labelling for investors.
However, given the strong interlinkage of the presented transitions 
and accumulated effects, climate change related investments 
can hardly be separated (The Food and Land Use Coalition 2019). 
Shakhovskoy et  al. (2019) present an overview of financing needs 
of small-scale farmers globally, however, without focusing on the 
required climate-related investments. According to their assessment, 
270 million smallholder farmers in South and South-East Asia, sub-
Saharan Africa and Latin America face approximately USD240 billion 
of financing needs, thereof USD100 billion short-term agricultural 
needs, USD88 billion long-term agricultural needs and USD50 billion

non-agricultural needs (Shakhovskoy et  al. 2019). These numbers 
can only provide ‘an indication of the magnitude of the climate 
investments required in small-scale agriculture’ (CPI 2020). 
Table 15.4 summarises the studies used as well as adjustments made 
to determine needs for the gap discussion in Section 15.5.2.
Adaptation financing needs. Financing needs for adaptation 
are even more difficult to define than those of mitigation because 
mobilising specific adaptation investments is only part of the challenge 
since ultimately improving societies’ adaptive capacities depends on 
Table 15.4 | Sector studies to determine average financing needs.
Sector StudiesGlobal ranges trillion USD 
yr–1 – Confidence LevelRegional breakdown Comment
EnergyIAM database, SEforAll (SEforALL and CPI 2020), 
IRENA 1.5-S and TES scenarios (IRENA 2021), 
IEA SDS and NZE scenarios (IEA 2021b)0.8–1.5 High confidenceDetailed breakdown for 
R10 possible for IAM 
database and applied

to the derived rangeMedium 
confidenceWide ranges primarily driven by varying 
assumptions with regard to grid 
investments relating to the increased 
renewable energy penetration.
Energy 
EfficiencyIRENA 1.5-S and TES scenarios, IEA SDS  
and NZE scenarios0.5–1.7Medium 
confidenceAdjustments required to 
regional categorisation 
by IEA and IRENALow-medium 
confidenceMedium confidence levels due to missing 
transparency with regard to underlying 
assumptions on technology costs. Low-
to-medium confidence level on regional 
allocations due to required adjustments.
TransportOECD/IEA (OECD 2017b) and Oxford Economics 
(2017) on rail investment data, IRENA 1.5-S and 
TES scenarios, IEA SDS and NZE scenarios for 
transport (energy efficiency) and electrification1.0–1.1Medium 
confidenceNeeds including battery costs, not 
total costs, of electric vehicles, likely 
underestimation of needs due to missing

data points on rail infrastructure.
AFOLUChapter 7 analysis, Section 7.4; The Food and 
Land Use Coalition (Land use Coalition (2019); 
(Shakhovskoy et al. 2019)0.1–0.3 High confidenceBreakdown for 
R10 possible for 
Chapter 7 analysisMedium 
confidenceUpper end of range includes opportunity 
costs as these likely increase costs of 
investment in land.
Note: Total range USD2.3 trillion to USD4.5 trillion yr–1.

1574
Chapter 15 Investment and Finance15the SDGs’ fulfilment (Hallegatte et al. 2016). Bridging the investment 
gap on irrigation, water supply, health care, energy access, and quality 
buildings is an essential enabling condition for adapting to climate 
change. The scenario analysis conducted by Rozenberg and Fay (2019) 
show that fulfilling the SDGs to improve the adaptive capacity of low- 
and middle-income countries would require investments in water 
supply, sanitation, irrigation and flood protection that would account 
for about 0.5% of developing countries’ GDP in a baseline scenario 
to 1.85% and 1% with a  strong and anticipatory policy integration 
(USD664 billion and 351 billion on average by 2030).
Most studies choose to assess public sector projects, ignoring 
household-level investments as well as private sector adaptation 
(UNEP 2018; Buchner et al. 2019). UNEP’s 2020 Adaptation Gap Report 
estimates adaptation costs amounting to 140–300 billion  USD  yr–1

in 2030 and USD280–500 billion yr–1 in 2050 (UNEP 2021). Over 
100 countries included adaptation components in their intended 
NDCs (INDCs) and approximately 25% of these referenced national 
adaptation plans (NAPs) (GIZ 2017a) but estimates of the financing 
required for NAP processes is not available. These NAPs, as formally 
agreed under the UNFCCC in 2010, are iterative, continuous processes 
that have multiple stages with a developmental phase that requires 
country-specific financing of primarily which comprises grants, bond 
issuance or debt conversion (NDC Partnership 2020, NAP Global 
Network 2017). At the same time, multilateral climate funds such as the 
Green Climate Fund and the GEF/Least Developed Countries Fund offer 
‘readiness and preparatory support’ and implementation for the NAPs 
and adaptation planning process (GCF 2020a; GEF 2021a,b). There has 
been no significant updating of adaptation cost estimates since UNEP’s

(UNEP 2016, 2018). The Global Commission on Adaptation makes the 
case that investing USD1.8 trillion in early warning system, climate-
resilient infrastructure, global mangrove and resilient water resources 
would generate about USD1.7 trillion in benefits due to avoided cost 
and non-monetary and social resources (Verkooijen 2019; UNEP 2021).
There is increasing recognition of rising adaptation challenges and 
associated costs within and across developed countries. Undoubtedly 
many developed countries are spending more on a  wide range of 
adaptation issues, both as preventive measures and building 
resilience (greening infrastructure, climate-proofing major projects 
and managing climate-related risks) against the impacts of climate 
change extreme weather events (US GCRP 2018a). Developed 
countries’ climate change adaptation spending covers areas such as 
federal insurance programmes, federal, state and local property and 
infrastructure, supply chains, and water systems.

15.5 Considerations on Financing Gaps 
and Drivers
15.5.1 Definitions
The analysis of financing gaps in climate action, which is used to 
measure implementation action and mitigation impact (FS-UNEP 
Centre and BNEF 2019) cannot be carried out as a  pure demand-
side challenge, in isolation from the analysis of barriers to deploy 
funds (e.g.,  Ramlee and Berma 2013) and to take investment initiatives. These barriers are ‘friction that prevents socially optimal 
investments from being commercially attractive’ (Druce et al. 2016). 
They are at the root of the ‘microeconomic paradox’ of a deficit of 
infrastructure investments despite a  real return between 4% and 
8% (Bhattacharya et  al. 2016), of the low share of carbon-saving 
potentials tapped by dedicated policies such as energy renovation 
programmes (Ürge-Vorsatz et  al. 2018), and, more generally of 
a demand for climate finance lower than the volume of economically

viable projects (de Gouvello and Zelenko 2010; Timilsina et al. 2010).
A few exercises tried assess the consequences of the perpetuation of 
these drivers on the magnitude of the financing gap. They suggest, 
comparing the evolution of the infrastructure investment trends 
(beyond energy) by comparison with what they should be in an optimal 
scenario, a cumulative deficit between 19% (Oxford Economics 2017) 
and 32% (Arezki et al. 2016). The volume of this gap is of the same order 
of magnitude as the incremental infrastructure investments (energy and 
beyond) for meeting a 1.5°C target (2.4% of the world GDP on average) 
(Box 4.8 of SR1.5 (IPCC 2018)) calculated by exercises assuming no 
pre-existing investment gap. This figure is consistent with the 1.5% 
to 1.8% assessed by the European Commission (2020) for Europe and 
the 2% of the IMF (2021d) for the G20, which do not encompass many 
developing countries for which economic take-off is today fossil fuels

dependent. For low- and middle-income economies, Rozenberg and 
Fay’s (2019) results suggest to increase the infrastructure investments 
by 2.5 to 6 percentage points of GDP to cover both the reduction of the 
structural investment gap and the specific additional costs for bridging 
it with low-carbon and climate-resilient options. These assessments 
indicate the challenge at stake but do not exist at very disaggregated 
sectoral and regional levels for sectors other than energy.
The below quantitative analysis does not differentiate between 
financing gaps driven by barriers within or outside the financial sector 
given that the IAM models as well as most other studies used do 
not incorporate actual risk ranges depending on policy strength and 
coherence and institutional capacity, low-carbon policy risks, lack of 
long-term capital, cross-border currency fluctuation, and pre-investment 
costs and barriers within the financial sector that discourage private

sector financing. They comprise short-termism (UNEP Inquiry 2016b), 
high perceived risks for mitigation-relevant technologies and/or 
regions (information gap through incomplete/asymmetric information, 
(Kempa and Moslener 2017; Clark et  al. 2018)), lack of carbon 
pricing effects (Best and Burke 2018), home bias (results in limited 
balancing for regional mismatches between current capital and needs 
distribution, (Boissinot et  al. 2016)), and perceived high opportunity 
and transaction costs (results from limited visibility of future pipelines 
and policy interventions; SME financing tickets and the missing middle, 
(Grubler et al. 2016)). In addition, barriers outside the financial sector 
will have to be addressed to close future financing gaps. The mix and 
dominance of individual barriers might vary significantly across sectors 
and regions and is analysed below.
The interpretation of the quantitative analysis thus needs to be

performed, taking into account the qualitative needs assessment in 
Section 15.4.1 and the evolution of parameters that determine the risk-
weighted relative attractiveness of low-carbon and climate-resilient 
investments compared to other investment opportunities. With some

1575
Investment and Finance Chapter 1515institutions having announced climate finance commitments and/or 
targets (see also Box 15.4), the actual asset allocation of commercial 
financial sector players including sectoral and regional focus will 
respond to tangible and financially viable investment opportunities 
available in the short term. Robust long-term pathways to create 
such conditions for a  significant private sector involvement rarely 
exist and expectations on private sector involvement in some critical 
sectors/regions might be too high (Clark et al. 2018).15.5.2 Identified Financing Gaps for Sector and Regions
The following section compares recent climate finance flows as 
reported by CPI and IEA to needs derived in Section 15.4, ignoring the 
slight mismatch in time horizons. The analysis ignores interlinked gaps, 
in particular infrastructure investment gaps and other SDG-related 
investment gaps, which need to be addressed in parallel to reach the

LTGG but also at least partially to facilitate green investments.
By sector
By type of economyEnergy efﬁciency
Developing countries
By region
Europe
Eastern Europe and West-Central AsiaLatin America and Caribbean
Africa
Middle EastNorth America
South-East Asia and PaciﬁcSouthern AsiaDeveloped countriesAgriculture, forestry and other land useElectricityTransport
0 1000 1500 2000 2500 3000 500Multiplication factors*
Lower
rangeUpper
range
x10 x31x2 x5
x3
x5
x6x7 x14
x12
x14 x28x12x3x4x2x3 x6
x4
x7 x15x5x4 x7x7
x7 x7
x2 x4x2
x8
x7
Multiplication factors indicate the x-fold 
increase between yearly mitigation ﬂows 
to average yearly mitigation investment needs. 
Globally, current mitigation ﬁnancial ﬂows 
are a factor of three to six below the average 
levels up to 2030.2017
2018
20192020
Annual mitigation investment 
needs (averaged until 2030)IEA data mean 
2017–2020Average ﬂows Yearly mitigation 
investment ﬂows

(USD2015 yr –1) in:*Actual yearly ﬂows compared to average annual needs (billion USD 2015 yr –1)
Figure 15.4 | Breakdown of recent average (downstream) mitigation investments and model-based investment requirements for 2020–2030 (USD billion) 
in scenarios that likely limit warming to 2°C or lower. Mitigation investment flows and model-based investment requirements by sector / segment (energy efficiency in 
buildings and industry, transport including efficiency, electricity generation, transmission and distribution including electrification, and agriculture, forestry and other land use), by 
type of economy, and by region (see Annex II Part I Section 1: By region is based on intermediate level (R10) classification scheme. By type of economy is based on intermediate 
level (R10) classification scheme, which considers ‘North America’, ‘Europe’, and ’Australia, Japan and New Zealand’ as developed countries, and the other seven regions as

developing countries). Breakdown by sector / segment may differ slightly from sectoral analysis in other contexts due to the availability of investment needs data. The granularity 
of the models assessed in Chapter 3, and other studies, do not allow for a robust assessment of the specific investment needs of LDCs or SIDSs. Investment requirements in 
developing countries might be underestimated due to missing data points as well as underestimated technology costs. In modelled pathways, regional investments are projected 
to occur when and where they are cost cost-effective to limit global warming. The model quantifications help to identify high-priority areas for cost-effective investments, but do 
not provide any indication on who would finance the regional investments. Investment requirements and flows covering downstream / mitigation technology deployment only.

Data includes investments with a direct mitigation effect, and in the case of electricity, additional transmission and distribution investments. See section 15.4.2 Quantitative 
assessment of financing needs for detailed data on investment requirements. Data on mitigation investment flows are based on a single series of reports (Climate Policy 
Initiative, CPI) which assembles data from multiple sources. Investment flows for energy efficiency are adjusted based on data from the International Energy Agency (IEA). 
Data on mitigation investments do not include technical assistance (i.e., policy and national budget support or capacity building), other non-technology deployment financing. 
Adaptation only flows are also excluded. Data on mitigation investment requirements for electricity are based on emission pathways C1, C2 and C3 (Table SPM.1). For electricity

investment requirements, the upper end refers to the mean of C1 pathways and the lower end to the mean of C3 pathways. Data points for energy efficiency, transport and 
AFOLU cannot always be linked to C1–C3 scenarios. Data do not include needs for adaptation or general infrastructure investment or investment related to meeting the SDGs 
other than mitigation, which may be at least partially required to facilitate mitigation. The multiplication factors show the ratio of average annual model-based mitigation 
investment requirements (2020–2030) and most recent annual mitigation investments (averaged for 2017–2020). The lower and upper multiplication factors refer to the lower 
and upper ends of the range of investment needs.
Given the multiple sources and lack of harmonised methodologies, the data can only be indicative of the size and pattern of investment gaps. The gap between most recent

flows and required investments is only a single indicator. A more comprehensive (and qualitative) assessment is required in order to understand the magnitude of the challenge 
of scaling up investment in sectors and regions. The analysis also does not consider the effects of misaligned flows. {15.3, 15.4, 15.5, Table 15.2, Table 15.3, Table 15.4}

1576
Chapter 15 Investment and Finance15Total investments in mitigation need to increase by around three and 
six times with significant gaps existing across sectors and regions8 
(high confidence). The findings on still significant gaps and limited 
progress over the past few years to some extent seem to contradict 
the massive increase in commitments by financial institutions. As 
discussed in Section 15.6, the investment gap is not due to global 
scarcity of funds.
However, these investment gaps have little explanatory power 
in terms of the magnitude of the challenge to mobilise funding. 
In addition to measurement challenges from different definitions 
and data gaps, sectors and regions offer highly divergent financial 
risk-return profiles, in particular due to missing or weak regulatory 
environments consistent with ambitions levels, and economic costs 
as well as limited local capital markets, limited institutional capacity

to ensure safeguard, standardisation, scalability and replicability of 
investment opportunities and financing models, and a pipeline ready 
for commercial investments. Moreover, soft costs and institutional 
capacity for enabling environment that can be prerequisite 
for addressing financing gaps are ignored when focusing on 
investment cost needs.
Sectoral considerations. The renewable energy sector attracted the 
highest level of financing in absolute and relative terms with business 
models in generation being proven and rapidly falling technology 
costs driving the competitiveness of solar photovoltaic and onshore 
wind, even without taking account of the mitigation component 
(FS-UNEP Centre and BNEF 2019; IRENA 2020a). This investment 
activity comes in line with the first generation of NDCs and their 
heavy focus on mitigation opportunities in the renewable energy 
sector (Pauw et al. 2016; Schletz et al. 2017). Still, the investment gap

tends to remain stable with flows over the past years not showing 
an upward trend.
Comparing annual average total investments in global fuel supply 
and the power sector of approximately USD1.5 trillion9 yr–1 in 
2019 (IEA 2020a) to the investment in the Stated Policies Scenario 
(approximately 1.7 trillion USD 2015 yr–1) and the Sustainable 
Development Scenario (approximately 1.8 trillion USD 2015 yr–1) in 
2030 underlines the required shift of existing capital investment 
from fossil to renewables even more than the need to increase sector 
allocations (Granoff et al. 2016; McCollum et al. 2018).
Ensuring access to the heavily regulated electricity markets is a key 
driver for an accelerated private sector engagement (IFC 2016; 
FS-UNEP Centre and BNEF 2018; REN21 2019), with phasing out of 
support schemes and regulatory uncertainty being a major driver for 
reduced investment volumes in various regional markets in the past

years (FS-UNEP Centre and BNEF 2015, 2016, 2017, 2018, 2020). 
Strategic investors and corporate investments by utilities dominate 
the investment activity in developed countries and countries in 
transition (BNEF 2019) based on the competitiveness of renewable 
8 In modelled pathways, regional investments are projected to occur when and where they are most cost-effective to limit global warming. The model quantifications help 
to identify high-priority areas for cost-effective investments, but do not provide any indication on who would finance the regional investments.
9 In the chapter, USD units are used as reported in the original sources in general. Some monetary quantities have been adjusted selectively for achieving comparability by 
deflating the values to constant US Dollar 2015. In such cases, the unit is explicitly expressed as USD 2015.energy sources. Reasonable auction results based on a substantial 
private-sector competition for investments have also been achieved

in selected developing countries driven by rather standardised 
contract structures and the increased availability of risk mitigation 
instruments addressing political and regulatory risks and home 
bias constraints (FS-UNEP Centre and BNEF 2019; IRENA 2020a). 
Development finance institution (DFI) climate portfolios tend to be 
driven by concessional loans for renewable energy generation assets 
with equity often being provided by (semi-) commercial investors 
(Section  15.3) which will have to change to accelerate renewable 
energy investment activity.
Given the wide range of estimates on current investment flows into 
energy efficiency, substantial uncertainty exists with regard to the 
magnitude of the investment gaps. While CPI publishes investment 
levels of 41 billion USD 2015 in 2019 and 24 billion USD 2015 in 2020 for 
energy efficiency, counting majorly international flows, IEA results 
come in at a much higher level of around 250 billion USD 2015 annually

between 2017 and 2020 (IEA 2021c) and IRENA (2020c) estimates 
energy efficiency investments in buildings between 2017–2019 at an 
average of USD139 billion yr–1.
Public sector investments in the transport sector have increased 
significantly in the past years reflecting the increased interest 
of capital markets in renewable energy and the efficient and 
corresponding reallocation of public funding. Provision of funding 
by capital markets for public transport infrastructure among others 
heavily depends on suitable financing vehicles and increased funding 
for development of projects with a  low level of standardisation 
(OECD 2015a).
Both IRENA and IEA include only incremental costs of EVs in their 
estimates on needs while CPI, when measuring actual flows, includes 
those at full costs. Total private flows for EVs included in CPI numbers 
amount to USD41 billion in 2018 (Buchner et al. 2019), representing 
more than 80% of private sector finance into the transport sector,

around one third of total public and private funding to the transport 
sector in 2018. This likely results in an underestimation of the 
financing gap – in addition to the fact that estimates for investment 
needs for rail infrastructure are only available for selected countries.
Current financing of land-based mitigation options is less than 
USD1 billion yr–1 representing only 2.5% of climate mitigation funding, 
significantly below the potential proportional contribution (Buchner 
et  al. 2019). A  stronger focus on deforestation-free value chain, 
including a  stronger reflection in taxonomies and financial sector 
investment decision processes are necessary to ensure an alignment 
of financial flows with the LTGG. Taking into account the specifics of 
land-based mitigation (in particular long investment horizons, strong 
dependency on the monetisation of mitigation effects, strong public 
sector involvement) a significant scale-up of commercial financing to

the sector can hardly be expected in the absence of strong climate

1577
Investment and Finance Chapter 1515policies (Clark et  al. 2018). Agriculture is likely to develop more 
potential to mobilise private finance than the forest sector given its 
strong linkage to food security and hunger and shorter payback periods. 
The significant gap in land-based mitigation finance also indicates the 
crucial lack of finance to the bottom of the pyramid.
Agricultural support is an important source of distortions to 
agricultural incentives in both rich and poor countries (Mamun et al. 
2019) ranging from the largest component of the support, market 
price supports, increased gross revenue to farmers as a  result of 
higher prices due to market barriers created by government policies, 
to production payments and other support including input subsidy 
(e.g., fertiliser subsidy)  (Searchinger et al. 2020). USD600 billion of 
annual governmental support for agriculture in the OECD database 
contributes only modestly to the related objectives of boosting crop

yields and just transition (Searchinger et al. 2020). A review of NDCs 
of 40 developing countries which submitted a NDC to the UNFCCC 
Interim NDC Registry by April 2017, and include within their NDC 
efforts to REDD+ via support from the UN-REDD Programme and/or 
World Bank Forest Carbon Partnership Facility, indicates that none 
of the countries reviewed mention fiscal policy reform of existing 
finance flows to agricultural commodity production or other publicly 
supported programmes that affect the direct and underlying drivers 
of land use conversion (Kissinger et al. 2019).
Analysis by region and type of economy. The analysis of 
gaps by type of economy illustrates the challenge for developing 
countries. Estimated mitigation financing needs as a  percentage 
of mean 2017–2020 GDP in USD 2015 comes in at around 2–4% for 
developed countries, and around 4-9% for developing countries (high 
confidence) (Figure 15.4). Climate finance flows have to increase by

a factor of four to seven in developing countries and three to five 
in developed countries. This disparity is further exacerbated when 
considering adaptation, infrastructure and SDG-related investment 
needs (high confidence) (Hourcade et al. 2021a). However, differences 
across developing countries are significant. Flows to Eastern Asia, 
with its annual average flows (2017–2020) of 252 billion USD 2015 
being dominated by China (more than 95% of total mitigation 
flows to Eastern Asia), would have to increase by a  factor of two 
to four, a  comparable level to developed countries. Section  15.6.2 
elaborates on outlooks with regard to fiscal space and ability to tap 
capital markets, in particular for developing countries. In particular, 
attention must accelerate on low-income Africa. This large continent 
currently contributes very little to global emissions, but its rapidly 
rising energy demands and renewable energy potential versus its

growing reliance on fossil fuels and ‘cheap’ biomass (especially 
fuelwood for cooking and charcoal, with impacts on deforestation) 
amid fast-rising urbanisation makes it imperative that institutional 
investors and policymakers recognise the very large ‘leap-frog’ 
potential for the renewable energy transition as well as risks of lock-
in effects in infrastructure more generally in Africa that is critical to 
hold the global temperatures rise to well below 2°C in the longer 
term (2020–2050). Overlooking this transition opportunity, rivalling 
China, India, USA and Europe, would be costly. Policies centred 
around the accelerated development of local capital markets for 
energy transitions  – with support from external grants, supra-
national guarantees and recognition of carbon remediation assets – are crucial options here, as in other low-income countries and 
regional settings. Notably, climate finance flows to African countries

might have even decreased for mitigation technology deployment 
(stagnated for adaptation between 2017 and 2020), widening the 
finance gap in African countries in the recent years (high confidence).
Over 80% of climate finance is reported to originate and stay within 
borders, and even higher for private climate flows (over 90%) 
(Boissinot et  al. 2016). There are multiple reasons for such ‘home 
bias’ in finance – national policy support, differences in regulatory 
standards, exchange rate, political and governance risks, as well 
as information market failures. The extensive home bias means 
that even if national actions are announced and intended to be 
implemented unilaterally and voluntarily, the ability to implement 
them requires access to climate finance which is constrained by the 
relative ability of financial and capital markets at home to provide 
such financing, and access to global capital markets that requires

supporting institutional policies in source countries. ‘Enabling’ public 
policies and actions locally (cities, states, countries and regions), to 
reduce investment risks and boost domestic climate capital markets 
financing, and to enlarge the pool of external climate financing 
sources with policy support from source capital countries thus matters 
at a general level. The biggest challenge in climate finance is likely to 
be in developing countries, even in the presence of enabling policies 
and quite apart from any other considerations such as equity and 
climate justice (Klinsky et al. 2017) or questions about the equitable 
allocations of future ‘climate budgets’ (Gignac and Matthews 2015). 
The differentiation between developed and developing countries 
matters most on financing. Most developed countries have already 
achieved very high levels of incomes, have the largest pool of capital 
stock and financial capital (which can be more easily redeployed

within these countries given the home bias of financial markets), the 
most well-developed financial markets and the highest sovereign 
credit ratings, in addition to starting with very high levels of per 
capita carbon consumption – factors that should allow the fastest 
adjustment to low-carbon investments and transition in these 
countries from domestic policies alone. The financial and economic 
circumstances are more challenging in many developing countries, 
even within a heterogeneity of circumstances across countries. The 
dilemma, however, is that the fastest rates of the expected increase 
in future carbon emissions are in developing countries. The biggest 
challenge of climate finance globally is thus likely to be  the 
constraints to climate financing because of the opportunity costs 
and relative under-development of capital markets and financing 
constraints (and costs) at home in developing countries, and the

relative availability or absence of adequate financing policy support 
internationally from developed countries. The Paris Agreement and 
commitment by developed countries to support the climate financing 
needs of developing countries thus continue to matter a great deal.
Soft costs/institutional capacity (Osama et  al. 2021). Most 
funding needs assessments focus on technology costs and ignore 
the cascade of financing needs as outlined above. International 
grant funding or national budget allocations for soft costs like 
the creation of a  regulatory environment can be a  prerequisite 
for the supply of commercial financing for the deployment of 
technologies. Such critical funding needs might represent a small

1578
Chapter 15 Investment and Finance15share of overall investment needs but current (relatively small) 
gaps in funding of policy reforms can hinder or delay deployment 
of large volumes of funding in later years. The role, as well as the 
approximate volumes of such required timely international grant 
funding or national budget allocations, appear underestimated in 
research. The numbers available for the creation of an enabling 
environment for medium-sized renewable energy (RE) projects in 
Uganda (GET FiT Uganda 2018) are illustrative only and cannot be 
transferred as assumptions to other countries without taking into 
account potentially varying starting points in terms of institutional 
readiness, pipelines, as well as the general business environment. 
GET FiT Uganda supported 170 MWp of medium-scale RE capacity 
triggering investments of USD453 million (GET FiT Uganda 2018), 
international results-based incremental cost support amounted to

USD92 million and project preparation, technical assistance, and 
implementation support, required USD8 million, excluding support 
from national agencies.
There is strong evidence of the correlation between institutional 
capacity of countries and international climate finance flows towards 
those economies (Adenle et al. 2017; Stender et al. 2019) and a strong 
need for robust institutional capacity to manage the transformation 
in a sustainable and human rights based way (Duyck et al. 2018). 
One example to consider unaddressed social concerns is the ongoing 
call for feedback by the European Commission and its platform on 
sustainable finance. It argues for a social taxonomy, that can support 
the identification of financing opportunities for economic activities 
contributing to social objectives (European Commission 2021b). 
SEforAll has highlighted the issue of investments not going to the 
countries with the greatest need, also partly driven by institutional

capacity levels (SEforALL and CPI 2020). Also, most of the developing 
countries’ NDCs are conditional upon international support for 
capacity building (Pauw et al. 2020). The Climate Technology Centre 
and Network (CTCN) was created as an operational arm of the UNFCCC 
Technology Mechanism with the mandate to respond to requests from 
developing countries. Initial evaluations of the mechanism underpin 
its importance and value for developing countries but stress long 
lead times and predictability of future international public finance 
to maintain operations as key challenges (UNFCCC 2017; DANIDA 
2018). While limited pipelines, limited absorptive capacities as well 
as restricted institutional capacity of countries are often stated as 
challenges for an accelerated deployment of finance (Adenle et al. 
2017), the question remains on the role of international public climate 
finance to address this gap and whether a concrete current financing

gap exists for patient institutional capacity building. While current 
short-term, mostly project-related, capacity building often fails to 
meet needs but alternative, well-structured patient interventions and 
finance could play an important role (Saldanha 2006; Hope 2011) 
accepting other barriers than financing playing a role as well. One 
reason why international public climate finance is not sufficiently 
directed to such needs might be the complexity in measuring 
intangible, direct outcomes like improved institutional capacity (Clark 
10 Those under the UNFCCC, such as the GCF through its USD3 million per country readiness and preparatory support programme, the Least Developed Countries Fund (LDCF) 
and the Special Climate Change Fund (SCCF), the Pilot Program for Climate Resilience (PPCR) and the Adaptation for Smallholder Agriculture Programme (ASAP) are focused

on supporting the preparatory process of the NAPs. But the Adaptation Fund will support the implementation of concrete projects up to USD10 million per country.Early stage/venture capital financing/pilot project financing. 
Early-stage companies in impact investment sectors with business 
solutions can contribute positively to climate impact. Figure SPM.8 
highlights the need for new business models facilitating parts of the 
behavioural change. Also, SE4All has underpinned the need for an 
expansion of available business models to achieve universal access 
(SEforALL and CPI 2020). Further research and development needs range 
from resource efficiency of proven technologies and next generation 
technologies but also new technologies (Chapter 16). Access to early 
stage financing remains critical with performance in recent years being 
weak (Gaddy et al. 2016). This historically weak performance of clean 
tech start-ups burdens the interest of investors in the sector on the

one hand and discourages experienced executive talent (Wang and 
Yee 2020). Besides that, the concentration of venture capital markets 
in the USA, Europe and India represents a major challenge (FS-UNEP 
Centre and BNEF 2019; Statistica 2021). With regard to commercial-
scale demonstration projects, IEA estimates a need of USD90 billion of 
public sector finance before 2030 having around USD25 billion already 
planned by governments to 2030 (IEA 2021c).
Need for parallel rather than sequential investment decisions. 
The needs and gaps assessment does not include upstream 
investment needs required to facilitate the technology deployment 
as foreseen in the scenarios presented above. For example, for their 
transforming energy scenario IRENA estimates the number of EVs 
to increase from around 8 million units in 2019 to 269 million units 
in 2030 (IRENA 2020c). This would require investments in battery 
factories amounting to approximately USD207 billion with further

investment requirements in the value chain (IRENA 2020d). This 
illustrates the extent of parallel investments based on goals rather 
than concrete regulatory interventions and/or demand and poses 
a problem of upfront investment risks for each industry in the chain 
in the absence of certainty of the presence of parallel decisions in the 
upstream and downstream links in the chain. This is a typical element 
of the ‘valley of the death’ of innovation (Scherer et al. 2000; Åhman 
et al. 2017). It discourages risk-taking and slows down the learning-
by-doing processes, economies of scale and increasing returns to 
adoption needed for lowering the costs of systemic technical change 
(Kahouli-Brahmi 2009; Weiss et  al. 2010). Implications for risk 
perception, financing costs as well as investment decision-making 
processes and ultimately for feasibility are rarely considered.
Finance for adaptation and resilience. As explained early, the

reduction of the infrastructure gap to increase societies’ resilience 
and the implementation of the NAPs will require more and higher 
levels of sustained financing. Activities mobilised for adaptation and 
resilience are often not marketable and their financing will continue 
coming from the public sector (Murphy and Parry 2020) and, at the 
international level, from grants-based technical assistance or through 
budgetary support or basket finance for large projects/programmes 
or sector-wide approaches or multilateral finance under (Non-)
UNFCCC10 that also anticipate supporting NAP implementation  – 
particularly those involving incremental costs and co-benefits,

1579
Investment and Finance Chapter 1515which will include sectoral approaches such as water, energy, 
infrastructures, and food production. According to the UNFCCC, ‘in 
2015–2016, 3% of international public adaptation finance flows 
was supplied by multilateral climate funds, while 84% came from 
development finance institutions and 13% from other government 
sources’ (UNFCCC 2019c). Comprehensive reporting on adaptation 
finance by Murphy and Parry (2020) and Buchner et al. (2019) argues 
that flows of finance for adaptation action in developing countries 
in 2017 and 2018 were estimated to be approximately USD30 
billion; this plus an additional estimated flow of USD12 billion for 
dual adaptation and mitigation actions totalled USD42 billion, 
accounting for 7.25% of the total estimated international public and 
private flows of climate finance (Buchner et al. 2019). They are far 
below the financing needs given in Section 15.4. To date, the private

sector has limited involvement in NAPs and adaptation projects and 
planning but can be involved through public-private partnership 
(Section  15.6.2.1) and other incentives provided by governments 
(Schmidt-Traub and Sachs 2015; Druce et al. 2016; Koh et al. 2016; 
UNEP 2016; NAP Global Network 2017; Murphy and Parry 2020) 
and innovative private financing mechanisms such as green and 
blue bonds. However, adaptation financing is only about 2% of the 
share of green bond financing raised up to June 2019 (UNFCCC 
2019c),11 whereas it is about 10% of sovereign green bonds raised 
(UNFCCC 2019d). (Tuhkanen 2020), in a  detailed review of green 
bond issuance in the Environmental Finance Data base 2019, found 
that between March 2010 to April 2019, ‘5% of all green bonds 
issued were categorised as adaptation and that ‘the private sector 
accounts for a  significant proportion of adaptation-related green 
bond issuances’ (Tuhkanen 2020). However, GIZ (2017b), Nicol et al.

(2017, 2018a), and Tuhkanen (2020) highlight that there is scepticism 
about this stream of finance for adaptation due to the factors that 
have thus far limited the private sector’s involvement in adaptation: 
lack of resilience-related revenue streams, the small scale of some 
adaptation projects and the overall ‘intangibility’ of financing 
adaptation projects (Larsen et al. 2019).
Financing for resilience is limited, unpredictable, fragmented and 
focused on few projects or sectors and short term as opposed to 
programmatic and long term (10–15 years) finacing to build resilience 
(ISDR 2009, 2011; Kellett and Peters 2014; Watson et al. 2015). Market-
based mechanisms are available but not equally accessible to all 
developing countries, particularly SIDS and LDCs, and such mechanisms 
can undermine debt sustainability (OECD and World Bank 2016). While 
resilience financing is mainly grant funding, concessional loans are

increasing substantially and are key sources of financing for disaster 
and resilience, particularly for upper-middle-income countries (OECD 
and World Bank 2016). The combination of these trends can contribute 
to greater levels of indebtedness among many developing countries, 
many of which are already at or approaching debt distress.
Social protection systems can be linked with a  number of the 
instruments already considered: reserve funds, insurance and 
catastrophe bonds, regional risk-sharing facilities, contingent credit, 
in addition to traditional international aid and disaster response. 
Hallegatte et  al. (2017) recommend combining adaptive social 
11 According to the climate bonds initiative, total green bond finance raised in 2018 was USD168.5 billion across 44 countries (UNFCCC 2019c).protection with financial instruments in a consistent policy package, 
which includes financial instruments to deliver adequate liquidity

and contingency plans for the disbursement of funds post disaster. 
Challenges related to financing residual climate-related losses and 
damages are particularly high for developing countries. Financing 
losses and damages from extreme events requires rapid pay-outs; the 
cost of financing for many developing countries is already quite high; 
and the expense of risk financing is expected to increase as disasters 
become more frequent, intense and more costly, not only due to 
climate change but also due to higher levels of exposure. Addressing 
both extreme and slow onset climate impacts requires designing 
adequate financial protection systems for reaching the most 
vulnerable. Moreover, some fraction of losses and damages, both 
material and non-material, are not commonly valued in monetary 
terms (non-economic loss) and hence financing requirements are 
hard to estimate. These non-market-based residual impacts include

loss of cultural identity, sacred places, human health and lives (Ameli 
et al. 2021a; Paul 2019; Serdeczny 2019).
15.6 Approaches to Accelerate Alignment 
of Financial Flows with Long-term 
Global Goals
Near-term actions to shift the financial system over the next decade 
are critically important and possible with globally coordinated 
efforts. Taking into account the inertia of the financial system as 
well as the magnitude of the challenge to align financial flows with 
the long-term global goals, fast action is required to ensure the 
readiness of the financial sector as an enabler of the transition (high 
confidence). The following subsections elaborate on key areas which 
can have a catalytic effect in terms of addressing existing barriers – 
besides political leadership and interventions discussed in other 
Chapters of AR6.
Addressing knowledge gaps with regard to climate risk analysis and 
transparency will be one key driver for more appropriate climate

risk assessment and efficient capital allocation (Section  15.6.1), 
efficient enabling environments to support the reduction of financing 
costs and reduce dependency on public financing (Section 15.6.2), 
a  revised common understanding of debt sustainability, including 
that negative implications of deferred climate investments on future 
GDP , particularly stranded assets and resources to be compensated, 
can facilitate the stronger access to public climate finance, 
domestically and internationally (Section 15.6.3), climate risk pooling 
and insurance approaches are a key element of financing of a just 
transition (Section 15.6.4), the supply of finance to a widened focus 
on relevant actors can ensure transformational climate action at all 
levels (Section 15.6.5), new green asset classes and financial products 
can attract the attention of capital markets and support the scale 
up of financing by providing standardised investment opportunities

which can be well integrated in existing investment processes 
(Section 15.6.6), a stronger focus on the development of local capital 
markets can help mobilise new investor groups and to some extent 
mitigate home bias effects (Section  15.6.7), new business models

1580
and financing approaches can help to overcome barriers related 
to transactions costs by aggregating and/or transferring financing 
needs and establishing a supply of finance for needs of stakeholder 
groups lacking financial inclusion (Section 15.6.8).
15.6.1 Addressing Knowledge Gaps with Regard 
to Climate Risk Analysis and Transparency
Climate change as a source of financial risk.
Achieving climate mitigation and adaptation objectives requires 
ambitious climate finance flows in the near-term, that is, 5–10 years 
ahead. However, knowledge gaps in the assessment of climate-
related financial risk are a  key barrier to such climate finance 
flows. Therefore, this section discusses the main knowledge gaps 
that are currently being addressed in the literature and those that 
remain outstanding.
Climate-related financial risk is meant here as the potential adverse 
impact of climate change on the value of financial assets. A recent

but remarkable development since AR5 is that climate change has 
been explicitly recognised by financial supervisors as a  source of 
financial risk that matters both for financial institutions and citizens’ 
savings (Bolton et al. 2020). Previously, climate change was mostly regarded in the finance community only as an ethical issue. The 
reasons why climate change implies financial risk are not new and 
are discussed more in detail below. What is new is that climate 
enters now as a factor in the assessment of financial institutions’ risk 
(e.g., the European Central Bank or the European Banking Authority) 
and credit rating (Section  15.6.3), and, going forward, into stress-
test exercises. This implies changes in incentives of the supervised 
financial actors, both public and private, and thus changes in the 
landscape of mitigation action by generating a  new potential for 
climate finance flows. However, critical knowledge gaps remain.

In particular, the underestimation of climate-related financial risk 
by public and private financial actors can explain that the current 
allocation of capital among financial institutions is often inconsistent 
with the mitigation objectives (Rempel et al. 2020). Moreover, even 
a  correct assessment of risk, which could provide incentives for 
divesting from carbon-intensive activities, does not necessarily lead 
to investing in the technical options needed for deep decarbonisation. 
Therefore, understanding the dynamics of the low-carbon transition 
require to fill in at the same time gaps about risk and gaps about 
investments in enabling activities in a broader sense.
Physical risk. On the one hand, unmitigated climate change implies 
an increased potential for adverse socio-economic impacts especially 
in more exposed economic activities and areas (high confidence). 
Accordingly, physical risk refers to the component of financial risk F
More key areas
with catalitic
effects to

address
barriersFinance as: Enabler and Follower
Resulting effect
of increased
competitiveness
of green risk-
return proﬁles
Synergies
between
real econ./
ﬁn. sect.
Creating Momentum for  
E
E
F
E
Appropriate
consideration of
physical and
transition risk
Pricing of
carbon
externalities/
regulative
law
15.6.1
15.6.215.6.4 15.6.3
15.6.515.6.7
15.6.8
15.6.6
Intl. Public
climate
ﬁnance15.6.515.6.1 Section:Consistent ﬁnancial ﬂows Inconsistent ﬁnancial ﬂows Net ﬂows 
<2º
>3º
Figure 15.5 | Visual abstract to address financing gaps in Section 15.6.

1581
Investment and Finance Chapter 1515associated with the adverse physical impact of hazards related to 
climate change (e.g., extreme weather events or sea level rise) on the financial value of assets such as industrial plants or real estate. In turn, these losses can translate into losses on the values of financial assets issued by exposed companies (e.g., equity/bonds) and or sovereign entities as well as losses for insurance companies. The assessment of climate financial physical risks poses challenges in terms of data, methods and scenarios. It requires cross-match scenarios of climate-related hazards at granular geographical scale, with the geolocation and financial value of physical assets. The relationship between the value of physical assets (such as plants or real estate) and the financial value of securities issued by the owners of those assets is not straightforward. Further, the repercussion of climate-related hazards on sovereign risk should also be accounted for.

Transition risks and opportunities. On the other hand, the 
mitigation of climate change, by means of a  transition to a  low-carbon economy, requires a  transformation of the energy and production system at a pace and scale that implies adverse impacts on a range of economic activities, but also opportunities for some other activities (high confidence). If these impacts are factored in by financial markets, they are reflected in the value of financial assets. Thus, transition risks and opportunities refers to the component of 
financial risk (opportunities) associated with negative (positive) adjustments in assets’ values resulting directly or indirectly from the low-carbon transition.
The concepts of carbon stranded assets (see e.g., Leaton and Sussams 
2011), and orderly vs disorderly transition (Sussams et al. 2015) which

emerged in the NGO community, have provided powerful metaphors to conceptualise transition risks and have evolved into concepts used also by financial supervisors (NGFS 2019)and academics. The term carbon stranded assets refers to fossil fuel-related assets (fuel 
or equipment) that become unproductive. An orderly transition is defined here as a situation in which market players are able to fully anticipate the price adjustments that could arise from the transition. In this case, there would still be losses associated with stranded assets, but it would be possible for market players to spread losses over time and plan ahead. In contrast, a  disorderly transition

is defined here as a situation in which a transition to a low-carbon economy on a  2°C path is achieved (i.e.,  by about 2040), but the impact of climate policies in terms of reallocation of capital into low-carbon activities and the corresponding adjustment in prices of financial assets (e.g., bonds and equity shares) is large, sudden and not fully anticipated by market players and investors. Note the impact could be unanticipated even if the date of the introduction is known in advance by the market players. There are several reasons why such adjustments could occur. One simple argument is that the political economy of the transition is characterised by forces pulling in different directions, including opposing interests within the industry, and mounting pressure from social awareness of unmitigated climate risks. Politics will have to find a  synthesis and the outcome could

12 In context, while belonging to grey literature, reports from financial supervisors or non- academic stakeholders can be of interest for what they document in terms of 
changes in perception and incentives among the market players and hence of the dynamics of climate finance flows.
13 In the chapter, USD units are used as reported in the original sources in general. Some monetary quantities have been adjusted selectively for achieving comparability by deflating the values to constant USD
2015. In such cases, the unit is explicitly expressed as USD 2015.remain uncertain until it suddenly unravels. Note also that, in order

to be relevant for financial risk, the disorderly transition does not need to be a catastrophic scenario in terms of the fabric of markets. It also does not automatically entail systemic risk, as discussed below. Knowledge gaps in this area are related to emerging questions, including: What are, in detail, the transmission channels of physical and transition risk? How to assess the magnitude of the exposure to these risks for financial institutions and ultimately for people’s savings? How do transition risk and opportunities depend on the future scenarios of climate change and climate policies? How to deal with the intrinsic uncertainty around the scenarios? To what extent could an underestimation of climate-related financial risk feed back on the alignment of climate finance flows and hamper the low-carbon transition? Should climate risk be explicitly accounted for in regulatory frameworks for financial institutions, such as Basel III for banks and national frameworks for insurance?

What lessons from the 2008 financial crisis are relevant here, regarding moral hazard and the trustworthiness of credit risk ratings? The attention of both practitioners and the scientific community to these questions has grown since the Paris Agreement. In the following we review some of the findings from the literature, but the field is relatively young and many of the questions are still open.

12 Damages from climate 
change are expected to escalate dramatically in Europe (Forzieri et al. 2018) and in some EU countries there is already some evidence that banks, anticipating possible losses on the their loan books, lend proportionally less as a consequence.
Assessment of physical risk. There is a literature on estimates of 
economic losses on physical assets (see Cross-Working Group Box ECONOMIC in chapter 16 of AR6 WGII). Here we discuss some figures and mechanisms that are relevant for the financial system. Significant cost increases have been observed related to increases in frequency and magnitude of extreme events (high confidence) (Section 15.4.2). 
At the global level, the expected ‘climate value at risk’ (climate VaR) of financial assets has been estimated to be 1.8% along a business-as-usual emissions path (Dietz et al. 2016), with however, a concentration of risk in the tail (e.g., 99th VaR equals to 16.9%, or USD24.2

13 trillion, in 2016). Climate-related impacts are estimated 
to increase the frequency of banking crises (up over 200% across scenarios) while rescuing insolvent banks could increase the ratio of public debt to gross domestic product by a factor of two (Lamperti et al. 2019). Further assessments of physical risk for financial assets (Mandel 2020), accounting in particular for the propagation of losses through financial networks, estimate global yearly GDP losses at 7.1% (1.13%) in 2080, without adaptation (with adaptation), the former corresponding to a 10-fold increase with respect to the current yearly losses (0.76% of global GDP). Finally, climate physical risk can impact on the value of sovereign bonds (one of the top asset classes by size), in particular for vulnerable countries (Volz et al. 2020).
Insurance pay-outs for catastrophes have increased significantly over 
the last 10 years, with dramatic cost spikes in years with multiple

1582
Chapter 15 Investment and Finance15major catastrophes (such as in 2018 with hurricanes Harvey, Irma, 
and Maria). This trend is expected to continue. The indirect costs of 
a climate-related flooding event can be up to 50% of the total costs, 
the majority of which is not covered by insurance (Alnes et al. 2018) 
(Section15.6.4). The gap between total damage losses and insurance 
pay-outs has increased over the past 10 years (Swiss Re Institute 
2019). Indeed, the probability of ‘extreme but plausible’ scenarios 
will be progressively revised upwards in the ‘value at risk’. As a result 
it becomes more difficult to find financial actors willing to provide 
insurance, as was observed for real estate in relation to flood and 
wildfires in California (Ouazad and Kahn 2019). This progressive 
adjustment would keep the financial system safe (Climate-Related 
Market Risk Subcommittee 2020; Keenan and Bradt 2020), but 
transfer to taxpayers the onus of damage compensation and the

financing of adaptation investments (OECD 2021c) as well as build 
up latent liabilities.
Assessment of transition risk. Carbon stranded assets. Fossil 
fuel reserve and resource estimates exceed in equivalent quantity 
of CO 2 with virtual certainty the carbon budget available to reach 
the 1.5°C and 2°C targets (high confidence)  (Meinshausen et al. 
2009; McGlade and Ekins 2015; Millar et  al. 2017). In relative 
terms, stranded assets of fossil fuel companies amount to 82% 
of global coal reserves, 49%  of global gas reserves and 33% of 
global oil reserves (McGlade and Ekins 2015). This suggests that 
only less than the whole quantity of fossil fuels currently valued 
(either currently extracted, waiting for extraction as reserves or 
assets on company balance sheets) can yield economic return if 
the carbon budget is respected. The devaluation of fossil fuel assets 
implies financial losses for both the public sector (Section 15.6.8)

and the private sector (Coffin and Grant 2019). Global estimates 
of potential stranded fossil fuel assets amount to at least 1 trillion, 
based on ongoing low-carbon technology trends and in the 
absence of climate policies (cumulated to 2035 with 10% discount 
rate applied; USD8  trillion without discounting (Mercure et  al. 
2018a)). With worldwide climate policies to achieve the 2°C target 
with 75% likelihood, this could increase to over USD4 trillion (until 
2035, 10% discount rate; USD12 trillion without discounting). 
Other estimates indicate USD8–15 trillion (until 2050, 5% discount 
rate, (Bauer et al. 2015)) and USD185 trillion (cumulated to year 
2115 using combined social and private discount rate (Linquiti and 
Cogswell 2016)). However the geographical distribution of potential 
stranded fossil fuel assets (also called ‘unburnable carbon’) is 
not even across the world due to differences in production costs

(McGlade and Ekins 2015). In this context, a delayed deployment of 
climate finance and consequently limited alignment of investment 
activity with the Paris Agreement tend to strengthen carbon and 
thus to increase the magnitude of stranded assets.
Assets directly and indirectly exposed to transition risk. 
In terms of types of assets and economic activities, the focus 
of estimates of carbon stranded assets tends to be on physical 
reserves of fossil fuel (e.g.,  oil fields) and sometimes financial 
assets of fossil fuel companies (van der Ploeg and Rezai 2020). 
However, a precondition for a broader analysis of transition risks and 
opportunities is to go beyond the narrative of stranded assets and 
to consider a classification of sectors of all the economic activities that could be affected (Monasterolo 2020). This, in turn depends 
on their direct or indirect role in the GHG value chain, their level of 
substitutability with respect to fossil fuel and their role in the policy

landscape. Moreover, such a classification needs to be replicable and 
comparable across portfolios and jurisdictions. One classification that 
meets these criteria is the Climate Policy Relevant Sectors (CPRS) 
(Battiston et  al. 2017) which has been used in several studies by 
financial supervisors (EIOPA 2018; ECB 2019; EBA 2020; ESMA 2020). 
The CPRS classification builds on the international classification of 
economic activities (ISIC) to map the most granular level (4 digits) 
into a  small set of categories characterised by differing types of 
risk: fossil fuel (i.e., all activities whose revenues depend mostly and 
directly on fossil fuel, including concession of reserves and operating 
industrial plants for extraction and refinement); electricity (affected in 
terms of input but that can in principle diversify their energy sources); 
energy intensive (e.g., steel or cement production plants, automotive 
manufacturing plants), which are affected in terms of energy cost

but not in terms of the main input); and transport and buildings 
(affected in terms of both energy sources and specific policies). All 
financial assets (e.g., bonds, equity shares, loans) having as issuers 
or counterparties firms whose revenues depend significantly on the 
above activities are thus potentially exposed to transition risks and 
opportunities. Further, investors’ portfolios have to be part of the 
analysis since changes in financial assets values affect the stability 
of financial institutions and can thus feed back into the transition 
dynamics itself (e.g., through cost of debt for firms and through costs 
for assisting the financial sector). One outstanding challenge for the 
analysis of investors’ exposure to climate risks is the difficulty of 
gathering granular and standardised information on the breakdown 
of non-financial firms’ revenues and CAPEX in terms of low-/high-
carbon activities (high confidence).

Several financial supervisors have conducted assessments of 
transition risk for the financial system at the regional level. For 
instance, the European Central Bank (ECB) reported preliminary 
estimates of aggregate exposures of financial institutions to CPRS 
relative to their total debt securities holdings as ranging between 
1% for banks to about 9% for investment funds (ECB 2019). The 
European Insurance and Occupational Pensions Authority (EIOPA) 
reported aggregate exposures to CPRS of EU insurance companies 
at about 13% of their total securities holdings (EIOPA 2018). 
Further analyses on the EU securities holdings indicate that among 
financial investments in bonds issued by non-financial corporations, 
EU institutions hold exposures to CPRS ranging between 36.8% for 
investment funds to 47.7% for insurance corporations; analogous 
figures for equity holdings range from 36.4% for banks to 43.1% for 
pension funds (Alessi et al. 2019). Another study indicates that losses

on EU insurance portfolios of sovereign bonds could reach up to 1%, 
in conservative scenarios (Battiston et al. 2019).
Given the magnitude of the assets that are potentially exposed, 
reported in the previously cited studies, a delayed or uncoordinated 
transition risk can have implications for financial stability not only 
at the level of individual financial institutions, but also at the macro 
level. The possible systemic nature of climate financial risk has been 
highlighted on the basis of general equilibrium economic analysis 
(Stern and Stiglitz 2021).

1583
Investment and Finance Chapter 1515Some financial authorities recognise that climate change represents 
a major source of systemic risk, particularly for banks with portfolios 
concentrated in certain economic sectors or geographical areas 
(de Guindos 2021). Specifically, the concern that central banks would 
have to act as ‘climate rescuers of last resort’ in a systemic financial 
crisis stemming from some combination of physical and transition 
risk has been raised in the financial supervisor community (Bolton 
et al. 2020). The systemic nature of climate risk is reinforced by the 
possible presence of moral hazard. Indeed, if a  sufficient number 
of financial actors have an incentive to downplay climate-related 
financial risk, then systemic risk builds up in the financial system, 
eventually materialising for taxpayers (Climate-Related Market Risk 
Subcommittee 2020). While such type of risk may go undetected to

standard market indicators for a while, it can materialise with a time 
delay, similarly to the developments observed in the run up to the 
2008 financial crisis.
These considerations are part of an ongoing discussion on whether 
the current financial frameworks, including Basel III, should 
incorporate explicitly climate risk as a  systemic risk. In particular, 
the challenges in quantifying the extent of climate risk, reviewed in 
this section, especially if risk is systemic, raise the question whether 
a combination of quantitative and qualitative restrictions on banks’ 
portfolios could be put in place to limit the build-up of climate risks 
(Baranović et al. 2021).
Endogeneity of risk and multiplicity of scenarios. One 
fundamental challenge is that climate-related financial risk is 
endogenous (high confidence). This means that the perception of 
the risk changes the risk itself, unlike most contexts of financial 
risk. Indeed, transition risk depends on whether governments and

firms continue on a business-as-usual pathway (i.e., misaligned with 
the Paris Agreement targets) or engage on a  climate mitigation 
pathway. But the realisation of the transition pathway depends 
itself on how, collectively, society, including financial investors and 
supervisors, perceive the risk of taking or not taking the transition 
scenario. The circularity between perception of risk and realisation 
of the scenario implies that multiple scenarios are possible, and that 
which scenario is ultimately realised can depend on policy action. The 
coordination problem associated also with low-carbon investments 
opportunities increases the uncertainty. Further, not all low-carbon 
activities are directly functional to the transition (e.g., investments in 
pharmaceutical, IT companies, or financial intermediaries), thus not 
all reallocations of capital lead to the same path.
In this context, probabilities of occurrence of scenarios are difficult

to assess and this is important because risks vary widely across 
the different scenarios. In this context a major challenge is the fat-
tail nature of physical risk. One the one hand, forecasts of climate 
change and its impact on humans and ecosystems imply tail events 
(Weitzman 2014) and tipping points which cannot be overcome by 
model consensus (Knutti 2010). On the other hand, everything else 
the same, costs and benefits vary substantially with assumptions on 
agents’ utility, productivity, and intertemporal discount rate, which 
ultimately depend on philosophical and ethical considerations 
(Nordhaus 2007; Stern 2008; Pindyck 2013). Thus, more knowledge 
is needed on the interaction of climate physical and transition risks, the possible reinforcing feedbacks and transmission channels to 
the economy and to finance. Moreover, models need to account 
for compound risk, that is, the interaction of climate physical and/

or transition risk with other sources of risk such as pandemics, 
such as COVID-19.
Challenges for climate transition scenarios. The endogeneity of 
risk and its associated deep uncertainty implies that the standard 
approach to financial risk, consisting of computing expected 
values and risk based on historical values of market prices, is not 
adequate for climate risk (high confidence) (Bolton et  al. 2020). 
To address this challenge, a  recent stream of work has developed 
an approach to make use of climate policy scenarios to derive risk 
measures (e.g., expected shortfall) for financial assets and portfolios, 
conditioned to scenarios of disorderly transition (Battiston et al. 2017; 
Monasterolo and Battiston 2020; Roncoroni et al. 2020). In particular, 
climate policy shocks on the output of low-/high-carbon economic 
activities are calculated based on trajectories of energy technologies 
as provided by large-scale Integrated Assessment Models (Kriegler

et al. 2015; McCollum et al. 2018) conditioned to the introduction of 
specific climate policies over time. This approach allows to conduct 
climate stress-tests both at the level of financial institutions and at 
the level of the financial system of a given jurisdiction.
In a  similar spirit, recently, the community of financial supervisors 
in collaboration with the community of climate economics has 
identified a set of climate policy scenarios, based on large-scale IAM, 
as candidate scenarios for assessing transition risk (Monasterolo 
and Battiston 2020). These scenarios have been used, for instance, 
in an assessment of transition risk conducted at a national central 
bank (Allen et al. 2020). This development is key to mainstreaming 
the assessment of transition risk among financial institutions, but the 
following challenges emerge (high confidence). First, a  consensus 
among financial supervisors and actors on scenarios of transition

risk that are too mild could lead to a  systematic underestimation 
of risk. The reason is that the default probability of leveraged 
financial institutions is sensitive to errors in the estimation of the 
loss distribution and hence sensitive on the choice of transition 
scenarios (Battiston and Monasterolo 2020). This in turn could 
lead to an allocation of capital across low-/high-carbon activities 
that is insufficient to cater for the investment needs of the low-
carbon transition.
Second, IAM do not contain a description of the financial system in 
terms of actors and instruments and make assumptions on agents’ 
expectations that could be inconsistent with the nature of a disorderly 
transition (Espagne 2018; Pollitt and Mercure 2018a; Battiston 
et al. 2020b). In particular, IAMs solve for least cost pathways to an 
emissions target in 2100 (AR4 WGIII SPM Box 3), while the financial 
sector’s time horizon is much shorter and risk is an important factor 
in investment decisions.

Third, the current modelling frameworks used to develop climate 
mitigation scenarios, which are based on large-scale IAM, assume that 
the financial system acts always as an enabler and do not account for 
the fact that, under some condition (i.e., if there is underestimation 
of climate transition risk) can also act as a barrier to the transition

1584
Chapter 15 Investment and Finance15(Battiston et al. 2020a) because it invests disproportionately more in 
high-carbon activities.
Macroeconomic implications of the technological transition. 
Global macroeconomic changes that may affect asset prices are 
expected to take place as a result of a possible reduction in growth 
or contraction of fossil fuel demand, in scenarios in which climate 
targets are met according to carbon budgets, but also following 
ongoing energy efficiency changes (high confidence) (Clarke et  al. 
2014; Mercure et al. 2018a). A review of the economic mechanisms 
involved in the accumulation of systemic risk associated with 
declining industries, with focus on fossil fuels, is given by Semieniuk 
et al. (2021). An example is the transport sector, which uses around 
50% of oil extracted (IEA 2018; Thomä 2018). A rapid diffusion of 
EV (and other alternative vehicle types) poses an important risk as

it could lead to oil demand peaking far before mid-century (Mercure 
et al. 2018b; 2021). New technologies and fuel switching in aviation, 
heavy industry and shipping could further displace liquid fossil fuel 
demand (IEA 2017). A  rapid diffusion of solar photovoltaic could 
displace electricity generation based predominantly on coal and 
gas (Sussams and Leaton 2017). A  rapid diffusion of household 
and  commercial indoor heating and cooling based on electricity 
could further reduce the demand for oil, coal and gas (Knobloch et al. 
2019). Parallels can be made with earlier literature on great waves of 
innovation, eras of clustered technological innovation and diffusion 
between which periods of economic, financial and social instability 
have emerged (Freeman and Louca 2001; Perez 2009).
Due to the predominantly international nature of fossil fuel markets, 
assets may be at risk from regulatory and technological changes both

domestically and in foreign countries (medium confidence). Fossil fuel 
exporting nations with lower competitiveness could lose substantial 
amounts of industrial activity and employment in scenarios of peaking 
or declining demand for fossil fuels. In scenarios of peaking oil demand, 
production is likely to concentrate towards the Middle East and OPEC 
countries (IEA 2017). Since state-owned fossil fuel companies tend to 
enjoy lower production costs, privately-owned fossil fuel companies 
are more at risk (Thomä 2018). Losses of employment may be directly 
linked to losses of fossil fuel-related industrial activity or indirectly 
linked through losses of large institutions, notably of government 
income from extraction royalties and export duties. A  multiplier 
effect may take place making losses of employment spill out of fossil 
fuel extraction, transformation and transportation sectors into other 
supplying sectors (Mercure et al. 2018a).

Main regulatory developments and voluntary responses to 
climate risk. Framing climate risk as a  financial risk (not just as 
an ethical issue) is key for it to become an actionable criterion for 
investment decision among mainstream investors (high confidence) 
(TCFD 2019). Since 2015 financial supervisors and central banks 
(e.g.,  the Financial Stability Board, the G20 Green Finance Study 
Group, and the Network for Greening the Financial System (NGFS)) 
have played a  central role in raising awareness and increasing 
transparency of the potential material financial impacts of climate 
change within the financial sector (Bank of England 2015, 2018; 
TCFD 2019). The NGFS initiative has engaged, in particular, in the 
elaboration of climate financial risk scenarios.Although disclosure has increased since the TCFD recommendations 
were published, the information is still insufficient for investors 
and more clarity is needed on potential financial impacts and

how resilient corporate strategies are under different scenarios 
(TCFD 2019). Several efforts to provide guidance and tools for the 
application of the TCFD recommendations have been made (using 
Sustainability Accounting Standards Board (SASB) Standards and the 
Climate Disclosure Standards Board (CDSB) Framework to Enhance 
Climate-Related Financial Disclosures in Mainstream Reporting TCFD 
Implementation Guide (UNEP FI 2018; CDSB and SASB 2019). Results 
of voluntary reporting have been mixed, with one study pointing 
to unreliable and incomparable results reported by the US utilities 
sector to the CDP (Stanny 2018).
There have been also similar initiatives at the national level (DNB 
2017; UK Government 2017; US GCRP 2018b). In particular, France 
was the first country to mandate climate risk disclosure from financial 
institutions (via Article 173 of the law on energy transition). However, 
disclosure responses have been so far mixed in scope and detail, with

the majority of insurance companies not reporting on physical risk 
(Evain et al. 2018). In the UK, mandatory GHG emissions reporting for 
UK-listed companies has not led to substantial emissions reductions 
to date but could be laying the foundation for future mitigation (Tang 
and Demeritt 2018).
A key recent development is the EU Taxonomy for Sustainable Finance 
(TEG 2019), which provides a classification of economic activities that 
(among other dimensions) contribute to climate mitigation or can be 
enabling for the low-carbon transition. Indirectly, such classification 
provides useful information on investors’ exposure to transition risk 
(Alessi et  al. 2019; ESMA 2020). Finally, many consultancies have 
stepped forward offering services related to climate risk. However, 
the methods are typically proprietary, non-transparent, or based 
primarily on carbon footprinting, which is a necessary but insufficient 
measure of climate risk. Further, ESG (environmental, social and

governance) metrics can be useful but are, alone, inadequate to 
assess climate risk.
Illustrative mitigation pathways and financial risk for end-users 
of climate scenarios
Decision-makers in financial risk management make increasing use 
of climate policy scenarios, in line with the TCFD guidelines and 
the recommendations of the NGFS. In order to reduce the number 
of scenarios to consider, Illustrative Mitigation Pathways (IMPs, 
Chapter  3), have been elaborated to illustrate key features that 
characterise the possible climate (policy) futures. The following 
considerations can be useful for scenario end-users who carry out 
risk analyses on the basis of the scenarios described in Chapter 3. It is 
possible to associate climate policy scenarios with levels of physical 
and/or transition risk, but these are not provided with the scenario 
data themselves.
On the one hand, each scenario is associated with a warming path,

which in turn, on the basis of the results from WGII, implies certain 
levels of physical risk (AR6 WGII Chapter 16). However, climate impacts 
are not accounted for in the scenarios. Moreover, levels of risk may vary

1585
Investment and Finance Chapter 1515with the reason for concern and with the speed of the implementation 
of adaptation. On the other hand, while mitigation can come with 
transition risk, in the case of lack of coordination among the actors, 
as discussed earlier in this section, this is not modelled explicitly in the 
trajectories, since the financial sector is not represented in underlying 
models. The scientific state of the art in climate-related financial risk 
offers an analysis that is not yet comprehensive of both the physical 
and transition risk dimensions in the same quantitative framework. 
However, decision-makers can follow a  mixed approach where they 
can combine quantitative risk assessment for transition risk with more 
qualitative risk analysis related to physical risk.
Figure 15.6 represents sequences of events following along a scenario 
both in terms of physical risk (left) and transition risk (right). Four

groups of IMPs (more are considered based on the warming level 
they lead to in 2100. Current Policies (CurPol) considers climate 
policies implemented in 2020 with only a  gradual strengthening 
afterwards, leading to above 4°C warming (with respect to pre-
industrial levels). Moderate Action (ModAct) explores the impact of 
implementing the NDCs (pledged mitigation targets) as formulated 
in 2020 and some further strengthening afterwards, thereby limiting 
warming to less than 4°C (>50%), but above 3°C (>50%). In these 
two scenarios, there is no stabilisation of temperature, meaning 
that further warming occurs after 2100 (and higher risk) even if 
stabilisation could be eventually achieved. They are referred to as pathways with higher emissions. The warming levels reached along 
these two scenarios imply physical risk levels that are ‘Moderate’ until 
2050 and ‘Very High’ in 2050–2100 (with low levels of adaptation).

Noting, that ‘Moderate’ physical risk can mean for some countries 
(i.e.,  SIDS) significant and even hardly absorbable consequences 
(i.e., reaching hard adaptation limits). Transition risk is not relevant 
for these scenarios, since a transition is not pursued.
Illustrative Mitigation Pathways include two groups of scenarios 
consistent with modelled global pathways that limit warming to 2°C 
(>67%) or lower, respectively. The two groups are representative 
for the IMPs defined in Chapter  3. In these scenarios, warming is 
stabilised before 2100. The warming levels along these paths imply 
‘Moderate’ physical risk until 2050 and ‘High’ risk in 2050–2100 
(with low levels of adaptation). Transition risk can arise along these 
trajectories from changes in expectations of economic actors about 
which of the scenarios is about to materialise. These changes imply, in 
turn, possible large variations in the financial valuation of securities

and contracts, with losses on the portfolio of institutional investors 
and households. High policy credibility is key to avoiding transition 
risk, by making expectations consistent early on with the scenario. 
Low credibility can delay the adjustment of expectations by several 
years, leading either to a  late and sudden adjustment. However, 
if the policy never becomes credible, this changes the scenario since 
the initial target is not met.
Physical
riskImplementation
progressCurrent
policy ambitionPolicy
credibilityTransition
risk
2050–2100 2020–2050
Very high
High
ModerateLow
Very high
High
ModerateModerate
Moderate
Moderate
Moderate
Low
High
Low
HighLow
High
Low
HighLow
High
HighLow
LowIP CurPol
(Above 4˚C)
IP ModAct
(Above 3˚C)
IMP
<2˚C
IMP
1.5˚C2020–2050
Figure 15.6 | Schematic representation of climate scenarios in terms of both physical and transition risk.  While the figure does not cover all possible events,

it maps out how the combination of stated targets can lead to different paths in terms of risk, depending on implementation progress and policy credibility. IMP 1.5°C and 
IMP <2°C are representative for IMP-GS (Sens. Neg; Ren), IMP-Neg, IMP-LD; IMP-Ren; IMP-SP . Note that the figure defines ‘High’ progress as higher, but it is important that the 
physical risk varies by region and country. This means, that ‘Moderate’ physical risk can be significant and even hardly absorbable for some countries.

1586
Chapter 15 Investment and Finance1515.6.2 Enabling Environments
The Paris Agreement recognised for the first time the key role of 
aligning financial flows to climate goals. It further emphasises 
the importance of making financial flows consistent with climate 
actions and SDGs (Zamarioli et  al. 2021).This alignment has now 
to be operated in a  specific environment where the scaling-up of 
climate policies is conditional upon their contribution to post-
COVID-19 recovery packages (Sections  15.2.2 and 15.2.3 and 
Box  15.6). The enabling environments that are to be established 
account for the structural parameters of the underinvestment in 
long-term assets. The persistent gap between the ‘propensity to save’ 
and ‘propensity to invest’ (Summers 2016) obstructs the scaling 
up of climate investments, and it results from a short-term bias of 
economic and financial decision-making (Miles 1993; Bushee 2001; 
Black and Fraser 2002) that returns weighted on short-term risk

dominate the investment horizon of financial actors. Overcoming this 
bias is the objective of an enabling environment apt to launch of 
a self-reinforcing circle of trust between project initiators, industry, 
institutional investors, the banking system, and governments.
The role of government is crucial for creating an enabling 
environment for climate (Clark 2018), and governments are critical 
in the launching and maintenance of this circle of trust by lowering 
the political, regulatory, macroeconomic and business risks (high 
confidence). The issue is not just to progressively enlarge the space 
of low-carbon investments but to replace one system (fossil fuels 
energy system) rapidly with another (low-carbon energy system). 
This is a wave of ‘creative destruction’ with the public support for 
developing new markets and new entrepreneurship and finance for 
green products and technologies in a context which requires strong

complementarities between Schumpeterian (technological) and 
Keynesian (demand-related) policies (Dosi et al. 2017). However, it 
is challenging to overcome the constraint of public budget under the 
pressure of competing demands and of creditworthy constraints for 
countries that do not have an easy access to reserve currencies. It is 
needed to maximise, both at the national and international levels, the 
leverage ratio of public funds engaged in blended finance for climate 
change which is currently very low, especially in developing countries 
(Attridge and Engen 2019).
Transparency: Policy de-risking measures, such as robust policy 
design and better transparency, as well as financial de-risking 
measures, such as green bonds and guarantees, at both domestic 
and international levels, enhance the attractiveness of clean 
energy investments (high confidence) (Steckel and Jakob 2018). 
Organisations such as the Task Force on Climate-related Financial

Disclosures (TCFD) can help increase capital markets’ climate 
financing, including private sector, by providing financial markets 
with information to price climate-related risks and opportunities 
(TCFD 2020). However, risk disclosures alone would likely be 
insufficient as long as market failures that inhibit the emergence of 
low-carbon investment initiatives with positive risk-weighted returns 
(high confidence) (Christophers 2017; Ameli et al. 2020).
Central banks and climate change. Central banks in all economies 
will likely have to play a  critical role in supporting the financing of fiscal operations, particularly in a  post-COVID-19 world (high 
confidence). Instruments and institutional arrangements for better 
international monetary policy coordination will likely be necessary in 
the context of growing external debt stress and negative credit rating 
pressures facing both emerging and low-income countries. Central

bankers have started examining the implications of disruptive risks 
of climate change, as part of their core mandate of managing the 
stability of the financial system (Chenet et al. 2021). Climate-related 
risk assessments and disclosure, including central banks’ stress 
testing of climate change risks, can be considered as a  first step 
(Rudebusch 2019), although such risk assessments and disclosure 
may not be enough by themselves to spur increased institutional 
low-carbon climate finance (Ameli et al. 2020).
Green quantitative easing (QE) is now being examined as a tool for 
enabling climate investments (Dafermos et al. 2018) in which central 
banks could explicitly conduct a  programme of purchases of low-
carbon assets (Aglietta et al. 2015). A green QE programme ‘would 
have the benefit of providing large amounts of additional liquidity 
to companies interested’ in green projects (medium confidence) 
(Campiglio et  al. 2018). Green QE would have positive effects

for stimulating a  low-carbon transition, such as accelerating the 
development of green bond markets (Hilmi et al. 2021), encouraging 
investments and banking reserves, and reducing risks of stranded 
assets, while it might increase income inequality and financial 
instability (Monasterolo and Raberto 2017). While the short-term 
effectiveness would not be substantial, the central bank’s purchase 
of green bonds could have a positive effect on green investment in 
the long run (Dafermos et al. 2018). However, the use of green QE 
needs to be cautious on potential issues, such as undermining the 
central bank’s independence, affecting the central bank’s portfolio 
by including green assets with poor financial risk standards, and 
potential regulatory capture and rent-seeking behaviours (Krogstrup 
and Oman 2019).
Additional monetary policies and macroprudential financial 
regulation may facilitate the expected role of carbon pricing on

boosting low-carbon investments (medium confidence) (D’Orazio 
and Popoyan 2019). Commercial banks may not respond to the price 
signal and allocate credits to low-carbon investments due to the 
existence of market failure (Campiglio 2016). This could support the 
productivity of green capital goods and encourage green investments 
in the short term, but might cause financial instability by raising 
non-performing loans ratio of dirty investments and creating green 
bubbles (Dunz et al. 2021). Financial supervisors needs to implement 
stricter guidelines to overcome the greenwashing challenges 
(Caldecott 2020).
Efficient financial markets and financial regulation. An 
influential efficient financial markets hypothesis (Fama 1970, 1991, 
1997) proceeds from the assumption that in well-developed financial 
markets, available information at any point of time is already well 
captured in capital markets with many participants. Despite increasing

challenges to the theory (Sewell 2011), especially by repeated 
episodes of global financial crashes and crises, and other widely 
noted anomalies, a weaker form of the efficient markets hypothesis 
may still apply (medium confidence). It is arguable that accumulating

1587
Investment and Finance Chapter 1515scientific evidence of climate impacts is being accompanied by 
rising levels of climate finance. Banks and institutional investors 
are also progressively rebalancing their investment portfolios away 
from fossil fuels and towards low-carbon investments (IEA 2019b; 
Monasterolo and de Angelis 2020). In the meantime, the world runs 
the risk of sharp adjustments, crises and irreversible ‘tipping points’ 
(Lontzek et al. 2015) sufficiently destabilising climate outcomes. This 
leads to the policy prescription towards financial regulatory agencies 
requiring greater and swifter disclosure of information about rising 
climate risks faced by financial institutions in projects and portfolios 
and central bank attention to systemic climate risk problems as 
one possible route of policy action (Carney 2015; Dietz et al. 2016; 
Zenghelis and Stern 2016; Campiglio et al. 2018). However, disclosure

requirements of risks and information in private settings remain 
mostly voluntary and difficult to implement (Battiston et  al. 2017; 
Monasterolo et al. 2017).
Nevertheless, financial markets are innovating in search of solutions 
(Section  15.6.6). Recognising and dealing with stranded fossil 
fuel assets is also a  key area of growing concern that financial 
institutions are beginning to grapple with. Larger institutions with 
more patient capital (pensions, insurance) are also increasingly 
beginning to enter the financing of projects and green bond markets. 
The case for efficient financial markets in developing countries is 
worse (Abbasi and Riaz 2016; Hong et al. 2019) because of weaker 
financial institutions (Hamid et al. 2017), heightened credit rationing 
behaviour (Bond et al. 2015), and high risk aversion as most markets 
are rated as junk, or below/barely investment grade (Hanusch 
et  al. 2016). Other constraints such as limited long-term financial

instruments and underdeveloped domestic capital markets, absence 
of significant domestic bond markets for investments other than 
sovereign borrowing, and inadequate term and tenor of financing, 
make the efficient markets thesis practically inapplicable for most 
developing countries.
Markets, finance and creative destruction. Branches of macro-
innovation theory could be grouped into two principal classes 
(Mercure et  al. 2016): ‘equilibrium  – optimisation’ theories that 
treat innovators as rational perfectly informed agents and reaching 
equilibrium under market price signals; and ‘non-equilibrium’ theory 
where market choices are shaped by history and institutional forces 
and the role of public policy is to intervene in processes, given 
a historical context, to promote a better outcome or new economic 
trajectory. The latter suggests that new technologies might not 
find their way to the market without price or regulatory policies to

reduce uncertainty on expected economic returns. A key issue is the 
perception of risk by investors and financial institutions. The financial 
system is part of complex policy packages involving multiple 
instruments (cutting subsidies to fossil fuels, supporting clean 
energy innovation and diffusion, levelling the institutional playing 
field and making risks transparent) (Polzin 2017) and the needed big 
systemic push (Kern and Rogge 2016) requires it takes on the role of 
‘institutional innovation intermediaries’ (Polzin et al. 2016).
As far as climate finance is concerned, public R&D support had 
large cross-border knowledge spill-overs indicating that openness to trade was important, capacity expansion had positive effects on 
learning-by-doing on innovation over time, and that feed-in-tariffs 
(FiTs), in particular, had positive impacts on technology diffusion 
(Grafström and Lindman 2017) (Box 16.4). The FiTs programme has

been associated with rapid increase in early renewables capacity 
expansion across the world by reducing market risks in financing and 
stability in project revenues (Menanteau et al. 2003; Jacobsson et al. 
2009) (Section 9.9.5). Competitive auctions where the bidder with 
the lowest price or other criteria is selected for government’s call for 
tender are increasingly being utilised as an alternative to FITs due to 
their strengths of flexibility, potential for real price discovery, ability 
to ensure greater certainty in price and quantity, and capability to 
guarantee commitments and transparency (IRENA and CEM 2015).
Outside of renewable energy, scattered but numerous examples are 
available on the role of innovative public policy to spur and create 
new markets and technologies (Arent et al. 2017): (i) proactive role 
of the state in energy transitions (e.g., the retirement of all coal-fired 
power plants in Ontario, Canada, between 2007 and 2014 (Kern

and Rogge 2016; Sovacool 2016)); (ii) too early exit and design 
problems not considering the market acceptability and financing 
issues (e.g., energy-efficient retrofitting in housing in UK (Rosenow 
and Eyre 2016), low or negative returns in reality versus engineering 
estimates in weatherisation programmes in US (Fowlie et al. 2018)); 
and (iii) energy performance contracting for sharing the business 
risks and profits and improving energy efficiency (energy service 
companies (Bertoldi and Boza-Kiss 2017; Qin et al. 2017) and utility 
energy service contracts in the USA (Clark 2018)).
Crowding out. Literature has discussed the risks of low effectiveness 
of public interventions and of a  crowding out effect of climate-
targeted public support to other innovation sectors (Buchner et al. 
2013). However, much academic literature suggests no strong 
evidence of crowding out. (Deleidi et al. 2020). Examining the effect 
of public investment on private investment into renewables in

17 countries over 2004–2014, showed that the concept of crowding 
out or in does not apply well to sectoral studies and found that public 
investments positively support private investments in general.
Support climate action via carbon pricing, taxes, and emission 
trading systems. Literature and evidence suggest that futures 
markets regarding climate are incomplete because they do not price 
in externalities (Scholtens 2017). As a result, low-carbon investments 
do not take place to socially and economically optimal levels, and 
the correct market signals would involve setting carbon prices 
high enough or equivalent trading in reduced carbon emissions 
by regulatory action to induce sufficient and faster shift towards 
low-carbon investments (high confidence) (Aghion et  al. 2016). 
Nonetheless, durable carbon pricing in economic and political 
systems must be implemented and approached combining related 
elements to both price and quantity (Grubb 2014).

The introduction of fiscal measures, such as carbon taxes, or market-
based pricing, such as emission trading schemes, to reflect carbon 
pricing have benefits and drawbacks that policymakers need to 
consider, taking account of both country-specific conditions and

1588
Chapter 15 Investment and Finance15policy characteristics. Carbon tax can be a simpler and easier way to 
implement carbon pricing, especially in developing countries, because 
countries can utilise the existing fiscal tools and do not need concrete 
enabling conditions as market-based frameworks (high confidence). 
The reallocation of revenues from carbon taxes can be used for 
low-carbon investments, supporting poorer sections of society and 
fostering technological change (High-Level Commission on Carbon 
Prices 2017). In combination with other policies, such as subsidies 
and public R&D on resource-saving technologies, properly designed 
carbon taxes can facilitate the shift towards low-carbon, resource-
efficient investments (Bovari et  al. 2018; Naqvi and Stockhammer 
2018; Dunz et al. 2021) (Section 9.9.3). The effectiveness of carbon 
pricing has been supported by various evidence. EU ETS has 
cut emissions by 42.8% in the main sectors covered (European

Commission 2021a), and China had achieved emissions reductions 
and energy conservation through its pilot ETS between 2013 and 
2015 (Zhang et  al. 2019; Hu et  al. 2020). Institutional learning, 
administrative prudence, appropriate carbon revenue management 
and stakeholder engagement are key ingredients for successful ETS 
regimes (Narassimhan et al. 2018).
The presence of carbon prices can promote low-carbon technologies 
and investments (Best and Burke 2018), and price signals, including 
carbon taxation, provide powerful and efficient incentives for 
households and firms to reduce CO 2 emissions (IMF 2019). The 
expansion of carbon prices is dependent on country-specific fiscal 
and social policies to hedge against regressive impacts on welfare, 
competitiveness, and employment (Michaelowa et  al. 2018). Such 
impacts need to be offset using the proceeds of carbon taxes 
or auctioned emission allowances to reduce distortive taxation

(Bovenberg and de Mooij 1994; Goulder 1995; de Mooij 2000; 
Chiroleu-Assouline and Fodha 2014) and fund compensating 
measures for the population sections that are most adversely 
impacted (Combet et al. 2010; Jaccard 2012; Klenert et al. 2018). This 
is more difficult for developing countries with a large share of energy-
intensive activities, fossil fuel exporting countries and countries 
which have lower potential to mitigate impacts due to lower wages 
or existing taxes (Lefèvre et al. 2018).
Non-carbon price instruments, such as market-oriented regulation 
and public programmes involving low-carbon infrastructure, may 
be preferable in developing countries where market and regulatory 
failure and political economy constraints are more prevalent (Finon 
2019). While carbon pricing was suggested by many economists 
and researchers (Nordhaus 2015; Pahle et al. 2018), overcoming the 
political and regulatory barriers would be necessary for the further

implementation of an effective carbon pricing scheme nationally and 
internationally. Without strong political support, the effectiveness 
of carbon pricing would be limited to least-cost movements 
(Meckling et al. 2015).
Role of domestic financing sources. Efforts to address climate 
change can be scaled up through the mobilisation of domestic 
funds (Fonta et  al. 2018). Publicly organised and supported low-
carbon infrastructures through resurrected national development 
banks may be justified (Mazzucato and Penna 2016). It is important to efficiently allocate the public financing, and State Investment 
Banks (SIBs) can take up key roles (i) to provide capital to assist with 
overcoming financial barriers, (ii) to signal and direct investments 
towards green projects, and (iii) to attract private investors by taking 
up a de-risking role. Also, they can become a first mover by investing 
in new and innovative technologies or business models (Geddes

et al. 2018). State-owned enterprises (SOEs) can also have an overall 
positive effect on renewables investments, outweighing any effect of 
crowding out private competitors (Prag et al. 2018). Green investment 
banks can assist in the green transition by developing valuable 
expertise in implementing effective public interventions to overcome 
investment barriers and mobilise private investment in infrastructure 
(OECD 2015c). De-risking measures may reduce investment risks, 
but lacking research and data availability hinders designing such 
measures (Dietz et al. 2016). Local governments’ efforts to de-risk by 
securitisation might have negative effects by narrowing the scope for 
a green developmental state and encouraging privatisation of public 
services (Gabor 2019).
The potential role of coordinated multilateral initiatives. 
There is a growing awareness of the low leverage ratio of public to 
private capital in climate blended finance (Blended Finance Taskforce

2018b) and of a ‘glass ceiling’, caused by a mix of agencies’ inertia 
and perceived loss of control over the use of funds, on the use of 
public guarantees by MDBs to increase it (high confidence) (Gropp 
et al. 2014; Schiff and Dithrich 2017; Lee et al. 2018). Many proposals 
have emerged for multilateral guarantee funds: Green Infrastructure 
Funds (de Gouvello and Zelenko 2010; Studart and Gallagher 2015), 
Multilateral Investment Guarantee Agency (Enhanced Green MIGA) 
(Déau and Touati 2018), guarantee funds to bridge the infrastructure 
investment gap (Arezki et al. 2016), and multi-sovereign guarantee 
mechanisms (Dasgupta et  al. 2019). The obstacle of limited fiscal 
space for economic recovery and climate actions in low-income and 
some emerging economies can be overcome only in a multilateral 
setting. Several multilateral actions are being envisaged: G20’s 
suspension of official bilateral debt payments, IMF’s adoption of new

SDRs allocation (IMF 2021b). However, any form of unconventional 
debt relief will generate development and climate benefits only if 
they credibly target bridging the countries’ infrastructure gap with 
low-carbon climate-resilient options.
Of interest in multilateral settings is a  credibility-enhancing effect 
provided by reciprocal gains for both the donor and the host 
country. Guarantor countries can compensate the public cost of their 
commitments with the fiscal revenues of induced exports. As to the 
host countries, they would benefit from new capital inflows and the 
grant equivalents of reduced debt service which might potentially 
go far beyond USD100 billion yr–1 (Hourcade et al. 2021a). A second 
interest would be to support a learning process about agreed-upon 
assessment and monitoring methods using clear metrics. Developing 
standardised and science-based assessment methods at low 
transaction costs is essential to strengthen the credibility of green

investments and the emergence of a pipeline of high-quality bankable 
projects which can be capitalised in the form of credible assets and 
supported with transparent and credible domestic spending. Multi-
sovereign guarantees would provide a quality backing to developing

1589
countries and allow for expanding developing countries’ access to 
capital markets at a lower cost and longer maturities, overcome the 
Basel III’s liquidity impediment and the EU’s Solvency II directive 
on liquidity (Blended Finance Taskforce 2018b), and accelerate the 
recognition of climate assets by investors seeking safe investment 
havens (Hourcade et  al. 2021b). They would also strengthen the 
efficacy of climate disclosure through high grades climate assets 
and minimise the risks of ‘greening’ of the portfolios by investing 
in ‘carbon neutral’ activities and not in low-carbon infrastructures. 
Finally, they would free up grant capacities for SDGs and adaptation 
that mostly involve non-marketable activities by crowding in private 
investments for marketable mitigation activities.
15.6.2.1 The Public-Private and Mobilisation Narrative 
and Current Initiatives
Financing by development finance institutions and development

banks aims to address market failures and barriers related to limited 
access to capital as well as provide direct and indirect subsidisation 
by accepting higher risk, longer loan tenors and/or lower pricing. 
Many development and climate projects in developing and emerging 
countries have traditionally been supported with concessional loans by development finance institutions and/or international financial 
institutions (DFIs/IFIs). With an increasing number of sectors 
becoming viable and increasing complaints of private sector players 
with regard to crowding out (Bahal et al. 2018), a stronger separation 
and crowding in of commercial financing at the project/asset level 
is targeted. MDBs and IFIs were crucial for opening and growth in 
the early years of the green bonds, which represent a  substantial 
share of issuances (CBI 2019a). Drivers of an efficient private sector 
involvement are stronger incentives to have projects delivered on

time and in budget as well as market competition (Hodge et  al. 
2018). It remains key that the private sector mobilisation goes hand 
in hand with institutional capacity building as well as strong sectoral 
development in the host country, as a strong, knowledgeable public 
partner with the ability to manage the private sector is a dominating 
success factor for public-private cooperation (WEF 2013; Yescombe 
2017; Hodge et al. 2018).
Limited research is available on the efficiency of mobilisation of 
the private sector at the various levels and/or the theory of change 
attached to the different approaches as applied in classical public-
private partnerships. Also, transparency on current flows and private 
involvement at the various levels is limited with no differentiation Box 15.5 | The Role of Enabling Environments for Decreasing Economic Cost 
of Renewable Energy

A widely used indicator for the relative attractiveness of renewable energy but also development of price levels is the levelised cost of 
energy (LCOE). It is applied by a wide range of public and private stakeholders when tracking progress with regard to cost degression 
(Aldersey-Williams and Rubert 2019). LCOE calculation methodologies vary but in principle consider project-level costs only (NEA 
1989). Besides other weaknesses, the LCOE concept usually does not consider societal costs resulting from de-risking instruments 
and/or other public interventions/support and therefore caution has to be applied when using the LCOE as the sole indicator of the 
success of enabling environments. The yearly IRENA mapping on renewable energy auction results demonstrates the extremely broad 
ranges of LCOEs (equal to the agreed tariffs) for renewable energy which can be observed (IRENA 2019a). For example, in 2018,

solar PV LCOEs for utility-scale projects came in between USD0.04 kWh–1 and USD0.35 kWh–1 with a global weighted average of 
USD0.085 kWh–1. However, comparative analysis taking into account societal costs is hardly available driven by challenges in the 
context of the quantification of public support.
The GET FiT concept argued that the mitigation of political and regulatory risk by sovereign and international guarantees is cost-
efficient in developing countries, illustrating the estimated impact of such risk-mitigation instruments on equity and debt financing 
costs, and consequently required feed-in tariff levels (Deutsche Bank Climate Change Advisors 2011). The impact of financing costs on 
cost of renewable energy generation is well researched with significant differences across countries and technologies being observed, 
with major drivers being the regulatory framework as well as the availability and type of public support instruments (Geddes et al.

2018; Steffen 2019). With a focus on developing countries and based on a case study in Thailand Huenteler et al. (2016) demonstrate 
the significant effect of regulatory environments but also local learning and skilled workforce on cost of renewables. The effect of 
those exceeds the one of global technology learning curves.
Egli et al. (2018) identify macroeconomic conditions (general interest rate) and experience effects within the renewable energy finance 
industry as key drivers in developed countries with a stable regulatory environment, contributing 5% (PV) and 24% (wind) to the 
observed reductions in LCOEs in the German market with a relatively stable regulatory environment. They conclude that ‘extant studies 
may overestimate technological learning and that increases in the general interest rate may increase renewable energies’ LCOEs, 
casting doubt on the efficacy of plans to phase out policy support’ (Egli et al. 2018). A rising general interest rate level could heavily

impact LCOEs – for Germany, a rise of interest rates to pre-financial crisis levels in five years could increase LCOEs of solar and wind 
by 11–25% respectively (Schmidt et al. 2019).

1590
Chapter 15 Investment and Finance15being made in reporting (e.g., GCF co-financing reporting). Limited 
prioritisation and agreement on prioritisation of sectors and/or 
project categories being ready and/or preferred for direct private 
sector involvement might become a challenge in the coming years 
(high confidence) (Sudmant et al. 2017a; Sudmant et al. 2017b).
Public guarantees have been increasingly proposed to expand 
climate finance, especially from the private sector, with scarce public 
finance, by reducing the risk premium of the low-carbon investment 
opportunities (de Gouvello and Zelenko 2010; Emin et  al. 2014; 
Studart and Gallagher 2015; Schiff and Dithrich 2017; Lee et  al. 
2018; Steckel and Jakob 2018). They have the advantage of a broad 
coverage including the ‘macro’ country risks and to tackle the up-
front risks during the preparation, bidding and development phases 
of the project lifecycle that deter project initiators, especially for

capital-intensive and immature options. Insurances are also powerful 
de-risking instruments (Déau and Touati 2018) but they entitle the 
issuer to review claims concerning events and cannot cope with 
up-front costs. Contractual arrangements like power purchase 
agreements are powerful instruments to reduce market risks through 
a guaranteed price but they weigh on public budgets. Risk-sharing 
that brings together public agencies, firms, local authorities, private 
corporates, professional cooperatives, and institutional financiers can 
reduce costs (UNEP 2011), and support the deployment of innovative 
business models (Déau and Touati 2018). Combined with emission 
taxes they can contribute to reducing credit rationing of immature 
and risky low-carbon technologies (Haas and Kempa 2020).
15.6.3 Considerations on Availability and Effectiveness 
of Public Sector Funding
The gap analysis as well as other considerations presented in this

chapter illustrate the critical role of increased volumes and efficient 
allocation of public finance to reach the long-term global goals, both 
nationally and internationally.
Higher public spending levels driven by the impacts of 
COVID-19 and related recovery packages. Higher levels of public 
funding represent a  massive chance but also a  substantial risk. 
A missing alignment of public funding and investment activity with 
the Paris Agreement (and Sustainable Development Goals) would 
result in significant carbon lock-ins, stranded assets and thus increase 
transition risks and ultimately economic costs of the transition (high 
confidence). Using IMF data for stimulus packages, Andrijevic et al. 
(2020) estimated that COVID-19-related fiscal expenditure had 
surpassed USD12 trillion by October 2020 (80% in OECD countries), 
a  third of which being spent in liquidity support and health care. 
Total stimulus pledged to date is ten times higher than low-Paris-

consistent carbon investment needs from 2020–2024 (Andrijevic 
et  al. 2020; Vivid Economics 2020). Overall, stimulus packages 
launched include USD3.5 trillion to sectors directly affecting future 
emissions, with overall fossil fuel investment flows outweighing low-
carbon technology investment (Vivid Economics 2020).
Lessons from the global financial crises show that although 
deep economic crises create a  sharp short-term emission drop, and green stimulus is argued to be the ideal response to tackle 
both the economic and the climate crises at once, disparities 
between regional strategies hinder the low-carbon transition (high 
confidence). Indeed, inconsistent policies within countries can also 
counterbalance emission reductions from green stimulus, as well as 
a lack of transparency and green spending pledged not materialising 
(Jaeger et al. 2020). Also, aggressive monetary policy as a response 
to the global financial crisis, including quantitative easing that did

not target low-carbon sectors, has been heavily criticised (Jaeger 
et al. 2020). The COVID-19 crisis recovery, in contrast, benefits from 
developments which have taken place since, such as an emerging 
climate-risk awareness from the financial sector, reflected in the 
call from the Coalition of Finance Ministers for Climate Action 
(Coalition of Finance Ministers for Climate Action 2020), which unites 
50 countries’ finance ministers, for a climate-resilient recovery.
The steep decrease in renewable electricity costs since 2010 also 
represents a relevant driver for a low-carbon recovery (Jaeger et al. 
2020). Many more sectors are starting to show similar opportunities 
for rapid growth with supportive public spending such as low-carbon 
transport and buildings (IEA 2020d). Expectations that the package 
will increase economic activity rely on the assumption that increased 
credit will have a positive effect on demand, the so-called demand-

led policy (Mercure et al. 2019). Boosting investment should propel 
job creation, increasing household income and therefore demand 
across economic sectors (high confidence). A similar plan has also 
been proposed by the US administration and the European Union 
through the Next Generation EU (European Council 2020).
Nevertheless, three uncertainties remain. First, only those countries 
and regions with highest credit-ratings (AAA or AA) with access to 
deep financial markets and excess savings will be able to mount 
such counter-cyclical climate investment paths, typically high-income 
developed economies (high confidence). In more debt constrained 
developing countries lower access to global savings pools  because 
of higher risk perceptions and lower credit ratings (BBB or less), 
exacerbated by COVID-19, are already leading to credit downgrades 
and defaults (Kose et al. 2020) and have long tended to be fiscally pro-

cyclical (McManus and Ozkan 2015). These include the general class 
of virtually all major emerging and especially low-income developing 
countries, to which such demand-stimulating counter-cyclical climate-
consistent borrowing path is likely. To access such funds, these 
countries would need globally coordinated fiscal policy and explicit 
supporting cross-border instruments, such as sovereign guarantees, 
strengthening local capital markets and boosting the USD100 billion 
annual climate finance commitment (Dasgupta et al. 2019).
Second, a  strong assumption is that voters will be politically supportive 
of extended and increased fiscal deficit spending on climate on 
top of COVID-19-related emergency spending and governments 
will overcome treasury biases towards fiscal conservatism (to 
preserve credit ratings). However, evidence strongly suggests that 
voters (and credit rating agencies) tend to be fiscally conservative

(Peltzman 1992; Lowry et al. 1998; Alesina et al. 2011; Borge and 
Hopland 2020), especially where expenditures involve higher taxes 
in the future and do not identifiably flow back to their local bases 
(the ‘public good’ problem) (high confidence). Such mistrust has

1591
Investment and Finance Chapter 1515been a reason for abortive return to fiscal austerity often in the past 
(most recently during global financial crisis) and may benefit for 
political support by consistently reframing the climate expenditures 
in terms of job creation benefits (Bougrine 2012), effectiveness of 
least-cost fiscal spending on climate for reviving private activity, 
and the avoidance of catastrophic losses (Huebscher et, al. 2020) 
from higher carbon emissions.  A new understanding of debt 
sustainability including negative implications of deferred climate 
investments on future GDP has not yet been mainstreamed (see more on the debt sustainability discussion below (e.g., Buhr et al. 
2018; Fresnillo 2020a). In addition, implications on the availability 
of international public finance flows are not yet clear since current 
additional funding prioritises urgent health care support rather than 
an increase in predictable mid-/long-term financial support. Heavy

investment needs for recovery packages in developed countries on 
the one hand and their international climate finance commitments 
on the other might be perceived to compete for available ‘perceived 
as appropriate’ budgets.
Box 15.6 | Macroeconomics and Finance of a Post-COVID-19 Green Stimulus Economic 
Recovery Path
Financial history suggests that capital markets may be willing to accommodate extended public borrowing for transient spending 
spikes (Barro 1987) when macroeconomic conditions suggest excess savings relative to private investment opportunities (Summers 
2015) and when public spending is seen as timely, effective and productive, with governments able to repay when conditions improve 
as economic crisis conditions abate (high confidence). A surge in global climate mitigation spending in the post-pandemic recovery 
may be an important opportunity, which global capital markets are signalling (Global Investor Statement 2019). The standard ‘neo-

classical’ macroeconomic model is often used in integrated energy-economy-climate assessments (Balint et al. 2016; Nordhaus 2018). 
This class of Computable General Equilibrium (CGE) models, however, has a limited treatment of the financial sector and assumes 
that all resources and factors of production are fully employed, there is no idle capacity and no inter-temporal financial intermediation 
(Pollitt and Mercure 2018b). Investment cannot assume larger values than the sum of previously determined savings, as a fixed 
proportion of income. Such constraint, as stressed by Mercure et al. (2019), implies that investment in low-carbon infrastructure, under 
the equilibrium assumptions, necessarily creates a (neo-Ricardian) crowding-out effect that contracts the remaining sectors. Box 15.6, 
Figure 1 shows the implications (in the red-shaded part of Figure 1).

Post-Keynesian demand-side macroeconomic models, with financial sectors and supply-side effects, in contrast, allow for the reality 
of non-equilibrium situations: persistent short- to medium-term underemployed economy-wide resources and excess savings over 
investment because of unexpected shocks, such as COVID-19. In these settings, economic stimulus packages allow a faster recovery 
Non-equilibrium /
Money creation
Equilibrium /
Crowding-out – no borrowingChange in GDP relative to baselineTimeDebt repayment phase
Return to optimal investment trajectoryEnergy Transition
??Year 2050
Investment /
Borrowing phase
Resource diversion /
Crowding-out
Box  15.6, Figure  1  | Two worlds  – energy transition outcomes under alternative model assumptions (Keynesian vs General Equilibrium).  
Source: Mercure et al. (2019).

1592
Considerations on global debt levels and debt sustainability 
as well as implications for climate finance. The Paris Agreement 
marked the consensus of the international community that 
a temperature increase of well below 2°C needs to be achieved and 
the SR1.5 has demonstrated the economic viability of 1.5°C. However, 
in terms of increase of supply of, in particular, public finance, often the 
debate is still driven by the question on affordability, considerations 
around financial debt sustainability and budgetary constraints 
against the background of macroeconomic headwinds – even more 
in the (post-)COVID-19 world (high confidence). The level of climate 
alignment of debt is hardly considered in debt-related regulation and/
or debt sustainability agreements like the Maastricht Treaty ceilings 
(3% of GDP government deficit and 60% of GDP (gross) government 
debt) not considering economic costs of deferred climate action as

well as economic benefits of the transformation.Robust studies on the economic costs and benefits in the 
short- to long-term of reaching the LTGG exist for only few 
countries and/or regions, primarily in the developed world (high 
confidence) (e.g. BCG 2018; McKinsey 2020a). With many studies 
underpinning the strong economic rationale for high investments 
in the short-term(e.g.,  McKinsey 2020a), regional differences are 
significant highlighting the need for extensive cooperation and 
solidarity initiatives.
For many developing countries, the focus of debt sustainability 
discussions is on the negative effect of climate change on the future 
GDP and the uncertainty with regard to short-term effects of climate 
change and their economic implications (high confidence). With long-
term economic impacts of climate change being in the focus of the

modelling community, the volatility of GDP in the short term driven with demand-led effects: ‘Economic multipliers are near zero when the economy operates near capacity. In contrast, during crises such 
as the GFC, economic multipliers can be high’ (Blanchard and Leigh 2013; Hepburn et al. 2020b). The expected results are opposite 
to the standard supply-led equilibrium models as a response to investment stimulus (the green-shaded part of Box 15.6, Figure 1), as 
intended by ‘green-stimulus’ packages such as proposed by the EU (Balint et al. 2016; Mercure et al. 2019).
Even if demand-led models work better in depressions, the question nevertheless is whether the additional public borrowing for such 
‘green stimulus’ can be undertaken by market borrowings given already high public debt levels and recovered in the future from taxes 
as the economy revives. The results of recent macroeconomic modelling work (Liu et al. 2021) represented by 10 major countries/

regions suggests answers. It uses a non-standard macroeconomic framework, with Keynesian features such as financial and labour 
market rigidities and fiscal and monetary rules (McKibbin and Wilcoxen 2013). First, a global ‘green stimulus’ of about an average of 
0.8% of GDP annually in additional fiscal spending between 2020–30 would be required to accelerate the emissions reduction path 
required for a 1.5°C transition. Second, such a stimulus would also accelerate the global recovery by boosting GDP growth rates by 
about 0.6% annually during the critical post-COVID period. Third, the optimal tax policy would be to backload the carbon taxes to 
later in the macroeconomic cycle, both because this would avoid dampening near-term growth while pre-announced carbon tax plans 
would incentivise long-term private energy transition investment decisions today and provide neutral borrowing. This macroeconomic

modelling path thus replicates the ‘green stimulus’ impacts expected in theory (Box 15.6, Figure 1). There are also some other additional 
features of the modelled proposal: (i) fiscal stimulus – needed in the aftermath of the pandemic – can be an opportunity to boost green 
and resilient public infrastructure; (ii) green research and development ‘subsidies’ are feasible to boost technological innovations; and 
(iii) income transfers to lower income groups are necessary to offset negative impacts of rising carbon taxes.
Substantial effects of the COVID-19 pandemic, which is relatively unique in its public health impacts when combined with the 
consequences of deep economy-wide shocks (economic downturn, public finances, and debt), are expected to last for decades 
even in the absence of no significant future recurrence. A scenario where the pandemic recurs mildly every year for the foreseeable

future further hinders GDP and investment recovery, where growth is unlikely to rebound to previous trajectories, even within OECD 
economies (McKibbin and Vines 2020) and with worse effects in developing regions. History is strongly supportive: studies on the 
longevity of pandemics’ impacts indicate significant macroeconomic effects persisting for decades, with depressed real rates of return, 
increased precautionary savings (Jordà et al. 2020), unemployment (Rodríguez-Caballero and Vera-Valdés 2020) and social unrest 
(Barrett and Chen 2021). The direct effect on emissions is likely to be a small reduction from previous trajectories, but the longer-
lasting impacts are more on the macroeconomic-finance side. Pandemic responses have increased sovereign debt across countries 
in all income bands (IMF 2021e). However, its sharp increase in most developing economies and regions has caused debt distress

(Bulow et al. 2021), widening the gap in developing countries’ access to capital (Hourcade et al. 2021b). While strong coordinated 
international recovery strategies with climate-compatible economic stimulus is justified (Barbier 2020; Barbier and Burgess 2020; 
IMF 2020c; Le Quéré et al. 2021; Pollitt et al. 2021), national recovery packages announced do not show substantial alignment with 
climate goals (D’Orazio 2021; Hourcade et al. 2021b; Rochedo et al. 2021; Shan et al. 2021). Contradictory post-COVID-19 investments 
in fossil fuel-based infrastructure may create new carbon lock-ins, which would either hinder climate targets or create stranded assets 
(Hepburn et al. 2020a; Le Quéré et al. 2021; Shan et al. 2021), whilst deepening global inequalities (Hourcade et al. 2021b).Box 15.6 (continued)

1593
Investment and Finance Chapter 1515by shocks is more difficult to analyse and requires country-specific 
deep-dives. IPCC scenario data is often not sufficient to perform such 
analysis with additional assumptions being needed (Acevedo 2016). 
For debt sustainability analysis, these more short-term impacts are, 
however, a  crucial driver with transparency being limited to the 
significance of climate-related revision of estimates. The latter might 
result in a continued overestimation of future GDP as happened in 
the past, increasing the vulnerability of highly indebted countries 
(Guzman 2016; Mallucci 2020). While climate change considerations 
have already impacted country ratings and debt sustainability 
assessments (and financing costs), it is unclear whether current 
GDP forecasts are realistic. The review of the IMF debt sustainability 
framework leads to a  stronger focus on vulnerability rather than 
only income thresholds when deciding upon eligibility for debt relief

and/or concessional resources (Mitchell 2015), which could become 
a mitigation factor for the challenge described before.
Debt levels globally but particularly in developing and vulnerable 
countries have significantly increased over the past years with 
current and expected climate change impacts further burdening 
debt sustainability (high confidence). For low- and middle-income 
countries, 2018 marked a  new peak of debt levels amounting to 
51% of GDP; between 2010 and 2018, external debt payments as 
a percentage of government budget grew by 83% in low- and middle-
income countries, from an average of 6.71% in 2010 to an average 
of 12.56% in 2018 (Fresnillo 2020b). COVID-19 has further reduced 
the fiscal space of many developing governments and/or increased 
the likelihood of debt stress. With many vulnerable countries 
already being burdened with higher financing costs, this limited 
fiscal space further shrinks their ability to actively steer the required

transformation (Buhr et al. 2018). Limited progress in increasing debt 
transparency remains another burden (Section 15.6.7).
Considering the need for responses to both short-term liquidity 
issues and long-term fiscal space, current G20/IMF/World Bank debt 
service suspension initiatives are focused on the liquidity issue rather 
than underlying problems of more structural nature of many low-
income countries (Fresnillo 2020a). In order to ensure fiscal space 
for climate action in the coming decade, a mix between debt relief, 
deferrals of liabilities, extended debt levels and sustainable lending 
practices including new solidarity structures need to be considered 
in addition to higher levels of bilateral and multilateral lending to 
reduce dependency on capital markets and to bridge the availability 
of sustainably structured loans for highly vulnerable and indebted 
countries. More standardised debt-for-climate swaps, a higher share

of GDP-linked bonds or structures ensuring (partial) debt cancellation 
in case countries are hit by physical climate change impacts/shocks 
appear possible. The ‘hurricane’ clause introduced by Grenada, or 
wider natural disaster clauses provide issuers with an option to 
defer payments of interest and principal in the event of a qualifying 
natural disaster and can reduce short-term debt stress (UN Addis 
Ababa Action Agenda Art. 102) (UN 2015a). A mainstreaming of such 
clauses has been pushed by various international institutions. The 
collective action clause might be a good example of a loan/debt term 
which became market standard. Definition of triggers is likely the 
most complex challenge in this context.The use of debt-for-nature and debt-for-climate-swaps is still very 
limited and not mainstreamed but offers significant potential if used 
correctly (high confidence).
An increasing number of debt-for-climate/nature swaps have been

seen in recent years applied primarily in international climate 
cooperation and in bilateral contexts, however, not (yet) to an extent 
addressing severe and acute debt crises (Essers et al. 2021; Volz et al. 
2021) offering significant potential if used correctly (Warland and 
Michaelowa 2015). Significant lead times, needs-based structuring, 
transparency with regard to the additionality of financed climate 
action, uncertainty with regard to own resource constraints and ODA 
accountability remain as barriers for a massive scale-up needed to 
make transactions relevant (Mitchell 2015; Fuller et al. 2018; Essers 
et al. 2021). At the same time, the limitation of the use of debt-based 
instruments as a response to climate-related disasters and counter-
cyclical loans might be necessary (Griffith-Jones and Tyson 2010).
Ensuring efficient debt restructuring and debt relief in events of 
extreme shocks and imminent over-indebtedness and sovereign

debt default are further crucial elements with a joint responsibility of 
debtors and creditors (UN 2015a). In this context, the Commonwealth 
Secretariat flagged that the diversification of the lender portfolio 
made debt restructuring more difficult with more and more 
heterogeneous stakeholders being involved (Mitchell 2015) and the 
UN AAAA raising concerns about non-cooperative creditors and 
disruption of timely completion of debt restructuring (UN 2015a). 
This is a side effect of a stronger use of capital markets, which needs 
to be carefully considered in the context of sovereign bond issuances 
(Section 15.6.7).
Stranded assets. The debate around stranded assets focuses strongly 
on the loss of value to financial assets for investors (Section 15.6.1), 
however, stranded assets and resources in the context of the 
transition towards a low-emission economy ‘are expected to become 
a major economic burden for states and hence the tax payers’ (high

confidence)  (EEAC 2016). Assets include not only financial assets 
but also infrastructure, equipment, contracts, know-how, jobs as 
well as stranded resources (Bos and Gupta 2019). Besides financial 
investors and fiscal budgets, consumers remain vulnerable to stranded 
investments. Against the background of the frequent simultaneousness 
of losses occurring for financial investors on the one hand and negative 
employment effects as well as regional development and fiscal effects 
on the other hand, negotiations about compensations and public 
support to compensate for negative effects of phasing out of polluting 
technologies often remain interlinked and compensation mechanisms 
and related redistribution effects untransparent.
Recent phase-out deals tend to aim for (partial or full) compensation 
rather than no relief for losses. In contrast to the line of argument in 
the tobacco industry, the backward-looking approach and a resulting

obligation of compensation by investors in polluting assets can be 
observed rarely with the forward-looking approach of compensations 
by future winners for current losers dominating – despite the high 
level of awareness about carbon externalities and resulting climate 
change impacts among polluters for many years (van der Ploeg and

1594
Chapter 15 Investment and Finance15Rezai 2020). In particular, transactions in the energy sector show 
a high level of investor protection also against much needed climate 
action which is also well illustrated by the share of claims settled 
in favour of foreign investors under the Energy Charter Treaty and 
investor-state dispute settlement (Bos and Gupta 2019).
Late government action can delay action and consequently strengthen 
the magnitude of action needed at a  later point in time with 
implications for employment and economic development in impacted 
regions requiring higher level of fiscal burden (high confidence). This 
has also been considered in the context of global climate cooperation 
with prolonged support for polluting infrastructure resulting in heavy 
lock-in effects and higher economic costs in the long run (Bos and 
Gupta 2019). Despite a  significant share of fossil resources which 
need to become stranded in developing countries to reach the

LTGG, REDD+ remains a singular example for international financial 
cooperation in the context of compensation for stranded resources.
15.6.4 Climate Risk Pooling and Insurance Approaches
Since 2000, the world has been experiencing significant increase in 
economic losses and damages from natural disasters and weather 
perils such as tropical cyclones, earthquakes, flooding and drought. 
Total global estimate of damage is about USD4210 billion, 2000–2018 
(Aon Benfield UCL Hazard Research Centre 2019). The largest portion 
of this is attributed to tropical cyclones (USD1253 billion), followed 
by flooding (USD914 billion), earthquakes (USD757 billion) and 
drought (approximately USD372 billion, or about USD20 billion yr–1 
losses) (Aon Benfield UCL Hazard Research Centre 2019). In the 
period 2017–2018, natural catastrophe losses totalled approximately 
USD219 billion (Bevere 2019). According to the National Oceanic and

Atmospheric Administration, 14 weather and climate disasters cost 
USD91 billion in 2018 (NOAA NCEI 2019). The European Environment 
Agency reports that ‘disasters caused by weather and climate-related 
extremes accounted for some 83% of the monetary losses over the 
period 1980–2017’ for EU Member States (EU-28) and that ‘weather 
and climate-related losses amounted to EUR426 billion (at 2017 
values)’. For the EEA member countries (EEA-33), the ‘total reported 
economic losses caused by weather and climate-related extremes’ 
over the same period amounted to approximately EUR453 billion 
(EEA 2019). Asia Pacific and Oceania has been particularly impacted 
by typhoon and flooding (China, India, the Philippines) resulting in 
economic losses of USD58 billion, 2000–2017, and a  combination 
of flooding, typhoon and drought totalling USD89 billion in 2018 
(inclusive of loss by private insurers and government sponsored 
programmes (Aon Benfield UCL Hazard Research Centre 2019). Based

on past historical analysis, a region such as the Caribbean, which has 
experienced climate-related losses equal to 1% of GDP each year 
since 1960, is expected to have significant increases in such losses 
in the future leading to possibly upwards of 8% of projected GDP 
in 2080 (Commonwealth Secretariat 2016). Similarly, Latin American 
countries, such as Argentina, El Salvador and Guatemala, experienced 
severe losses in agriculture totalling about USD6 billion due to 
drought in 2018 (Aon Benfield UCL Hazard Research Centre 2019). 
In the African region, where climate is projected to get significantly 
warmer, continuing severe drought in parts of East Africa, Tropical Cyclone Idai, had devastating economic impacts for Mozambique, 
Zimbabwe and Malawi (WMO 2019). According to Munich Re, loss 
from about 100 significant events in 2018 for Africa are estimated at 
USD1.4 billion (Munich Re 2019).
While there are questions about the sufficiency of insurance

products to address the losses and damages of climate-
related disasters, insurance can help to cover immediate 
needs directly, provide rapid response and transfer financial 
risk in times of extreme crisis (high confidence) (GIZ 2015; 
Lucas 2015; Schoenmaker and Zachmann 2015; Hermann et al. 
2016; Wolfrom and Yokoi-Arai 2016; Kreft and Schäfer 2017; 
UNESCAP 2017; Matias et  al. 2018; UNECA 2018; Broberg 
and Hovani-Bue 2019; EEA 2019; Martinez-Diaz et  al. 2019). 
Commercial insurability is heavily driven by the predictability 
of losses and the resulting ability to calculate insurance 
premium levels properly. Climate change has become a major 
factor of increasing uncertainty. The previously strong reliance 
on historic data in calculation of premium levels may be but 
a starting point given the likely need for upward adjustment 
due to climate change and potential consequential economic 
damage. Different risk perceptions between policyholders and

insurers will create contrary assessments on premium levels 
and consequently underinsurance. McKinsey (2020b) also 
stresses the systemic effect of climate change on insurers’ 
business models and resulting availability of appropriate 
insurance products.
The conventional approach to such protective or hedging position 
has been indemnity and other classical insurance micro-, meso- and 
macro-level schemes (Hermann et  al. 2016). These include micro 
insurance schemes such as index insurance and weather derivative 
approaches that cover individuals’ specific needs such as coverage 
for farm crops. Meso-level insurance schemes, which primarily benefit 
intermediary institutions, such as NGOs, credit unions, financial 
institutions and farmer credit entities, seek to reduce losses caused 
by credit default thereby ‘enhancing investment potential’, whereas 
macro-level insurance schemes ‘allow both insured and uninsured 
individuals to be compensated for damages caused by extreme

weather events’ (Hermann et al. 2016). These macro-level insurance 
schemes include catastrophe bonds and weather derivatives and 
so on, that transfer risk to capital markets (Hermann et  al. 2016). 
Over the last decades, there has been a  trend towards weather-
index insurance and other parametric insurance products based on 
predefined pay-out risk pooling instruments. It has gained favour 
with governments in developing regions such as Africa, the Caribbean 
and the Pacific because it provides certainty and predictability about 
funding – financial preparedness – for emergency actions and initial 
reconstruction and reduces moral hazard. This ‘financial resilience’ is 
also increasingly appealing to the business sector, particularly micro, 
small and medium enterprises (MSMEs), in developing countries 
(MEFIN Network and GI RFPI Asia 2016; Woods 2016; Schaer 
and Kuruppu 2018).
To date, sovereign parametric climate risk pooling as a  way of

managing climate risk does not seem to have much traction in 
developed countries and does not appear to be attractive to

1595
Investment and Finance Chapter 1515actors in the G20 countries. No G20 members are yet party to any 
climate risk pooling initiative (Kreft and Schäfer 2017). However, 
international bilateral donors such as the USAID and the UK 
Foreign, Commonwealth and Development Office (FCDO, formerly 
DFID), and the multilateral development banks are all, to different 
extent, supporters of the various climate risk pooling initiatives now 
operational in developing countries.
As noted also in IPCC AR5, risk sharing and risk transfer strategies 
provide ‘pre-disaster financing arrangements that shift economic 
risk from one party to another’ (IPCC 2012). Risk pooling among 
countries and regions is relatively advantageous when compared 
to conventional insurance because of the effective subsidising of 
‘affected regions’ using revenues from unaffected regions which 
involve pooling among a large subset of countries (high confidence)

(Lucas 2015). In general, the premiums are less costly than what an 
individual country or entity can achieve and disbursement is rapid 
and there are also fewer transaction costs (Lucas 2015; World Bank 
2015). The World Bank argues that the experience with the Pacific 
Catastrophe Risk Insurance Pilot (PCRIP) and Africa Risk Capacity 
risk pooling (ARC) show savings of 50% in obtaining insurance cover 
for pooled risk compared with purchasing comparable coverage 
individually (Lucas 2015; World Bank 2015; ARC 2016). However, 
it requires, as noted by UNESCAP , ‘extensive coordination across 
participating countries, and entities’ (Lucas 2015).
At the same time, this approach has substantial basis risk (actual 
losses do not equal financial compensation) (high confidence) 
(Hermann et al. 2016). With parametric insurance, pay-outs are pre-
defined and based on risk modelling rather than on-the-ground 
damage assessment so may be less than, equal to, or greater than

the actual damage. It does not cover actual losses and damage and  
therefore, may be insufficient to meet the cost of rehabilitation 
and  reconstruction. It may also be ‘non-viable’ or damaging to 
livelihoods in the long run (UNFCCC 2008; Hellmuth et  al. 2009; 
Hermann et al. 2016). Additionally, if the required threshold is not met, 
there may be no pay-out, though a country may have experienced 
substantial damages from a  climatic event. This occurred for the 
Solomon Islands in 2014 which discontinued its insurance with the 
Pacific Catastrophe Risk Insurance Pilot when neither its Santa Cruz 
earthquake nor the 2014 flash floods were eligible to receive a pay-
out under the terms of the insurance (Lucas 2015).
Increasingly, climate risk insurance schemes are being blended 
into disaster risk management as part of a  comprehensive risk 
management approach (high confidence). The best-known example 
is the Caribbean Catastrophe Risk Insurance Facility (CCRIF SPC

2018), which involves cooperation among Caribbean states, Japan, 
Canada, UK and France and international organisations such as 
the World Bank (UNESCAP 2017). But there are growing platforms 
of such an approach mainly under the umbrella of the G7’s 
InsuResilience Initative (Deutsche Klimafinanzierung 2020), including, 
the Pacific Catastrophe Risk Assessment and Financing Initiative for 
14 According to the V20, ‘the term “climate-smart” captures the need for two types of climate-related insurance products for MSMEs in vulnerable economies: (1) Climate 
risk insurance (2) Insurance products which enable low carbon investments, and thereby contribute to increased efficiencies through cost-savings from cheaper low-carbon 
technologies’ (V20 2021).the Pacific Islands (PCRAFI), the African Risk Capacity (ARC Agency 
and its financial affiliate), and the African Risk Capacity Limited (ARC 
Ltd/ the ARC Group) (ARC 2016) and in the Asian region, the South

East Asian Disaster Risk Insurance Facility (SEADRIF) and the ASEAN 
Disaster Risk Financing and Insurance Program (ADRFI), (SEADRIF 
2018; GIZ and World Bank 2019; Martinez-Diaz et  al. 2019; Vyas 
et al. 2019; World Bank 2019a). The group of 20 vulnerable countries 
(V20) has also developed a Sustainable Insurance Facility (SIF), billed 
as a  technical assistance facility for climate-smart14 insurance for 
MSMEs in 48 developing countries as well as potentially to de-risk 
renewable energy in these countries and regions (ACT Alliance 2020; 
V20 2020; V20 2021).
However, as noted above, climate risk pooling is not a  panacea. 
There are very obvious and significant challenges. According to 
Kreft and Schäfer (2017), limitations of insurance schemes include 
coordination challenges, limited scope, destabilisation due to exit of 
one or more members as premiums rise and inadequate attention to 
permanence (Schaeffer et al. 2014). There are also challenges with

risk diversification, replication, and scalability (high confidence). For 
example, CCRIF is extending both its membership and diversifying 
its geographic dimensions into Central America in seeking to lower 
covariate risk (similar shocks among cohorts such as droughts or 
floods). Under the SPC portfolio, CCRIF is able to segregate risk 
across the regions. Risk insurance does not obviate from the need to 
engage in capacity building to scale-up as well as having process for 
addressing systemic risk. Currently, risk pools have limited sectoral 
reach and may cover agriculture but not other important sectors 
such as fisheries and public utilities. Only recently (July 2019) has 
CCRIF initiated coverage of fisheries with the development of its 
Caribbean Oceans and Aquaculture Sustainability Facility (COAST) 
instrument (CCRIF SPC 2019; ACT Alliance 2020). Historically, risk 
pool mechanisms, like CCRIF and ARC, only cover a small subset of

perils, such as tropical cyclones, earthquakes and excess rainfall but 
do not include other perils such as drought. Since 2016, ARC has 
increased its scope to cover drought and in 2019 launched ARC 
Replica, which not only covers drought but offers premiums and 
coverage to NGOs and the World Food Programme through the 
START Network and a pastoral drought product for protecting small 
farmers and ensuring food security. In some regions and countries, 
there may also be limited access to reinsurance (Schaeffer et  al. 
2014; Lucas 2015). An important down-side of climate risk pooling is 
that it does not cover the actual cost of damage and losses. Though 
on the positive side, pay-out may exceed costs, but it may also be 
less than costs. Hence, the parametric approach is not a  panacea 
and does not preclude having recourse to conventional indemnity 
insurance, which will cover full damage costs after a climate change

event as it involves full on-the-ground assessment of factors such as 
the necessity and costs of repair versus, say, replacement value of 
damaged infrastructure. This may be important for governmental and 
publicly provided services such as schools, hospitals, roads, airports, 
communications equipment and water supply facilities. Given 
the growing popularity of parametric insurance and climate risk

1596
Chapter 15 Investment and Finance15pooling, there are very ambitious attempts to expand this approach 
on several fronts (Scherer 2017). Schoenmaker and Zachmann 
(2015) have proposed a  global climate risk pool to help the most 
vulnerable countries. The pathway to this includes capacity building 
in underdeveloped financing sectors of developing countries. 
They argue that as climate extremes become more normalised, they 
will wipe out significant parts of the infrastructure and productive 
capacity of developing countries. This will have knock-on impact 
on fiscal capacity due to lowered tax revenue and high rebuilding 
costs. ‘Developing countries’, Schoenmaker and Zachmann (2015) 
argue, ‘cannot insure against such events on a  market basis, nor 
would it be sensible to divert scarce fiscal resources away from 
infrastructure investment into accumulating a  financial buffer for 
such situations’. In that context, Schoenmaker and Zachmann (2015)

call for international risk pooling as ‘the only sensible strategy’, 
especially if it addresses the major gaps in climate risk insurance for 
poor and vulnerable communities by enhancing demand through 
‘smart support instrument’ for premium support such as full or 
partial premium subsidies and investment in providing risk reduction 
(Schäfer et al. 2016; Le Quesne et al. 2017; MCII 2018; Vyas et al. 
2019). This, it is argued, may help to smoothen out the limited uptake 
of regional institutions such as ARC and CCRIF SPC, which are only 
in three regions of the world (with missing mechanism in South 
America) (Kreft and Schäfer 2017). Existing regional mechanisms, 
while they may perform very well, only cover a portion of climatic 
hazards and tend to have limited subscribers. For example, across the 
key four sovereign risk pools (ARC, CRIFSPC, PCRAFI and SEADRIF), 
though there are 68 countries only one-third or 32% have purchased

coverage in 2019 and 46% ‘did not deploy disaster risk financing 
instruments’ (ACT Alliance 2020).
Other gaps and challenges flagged by Kreft and Schäfer (2017) 
include limited coverage of the full spectrum of contingency risks 
experienced by countries, inadequate role of risk management as 
a standard for all regional pools, though there are some emerging 
best practices in terms of data provision on weather-related risks, 
and incentivisation of risk reduction (high confidence). Here, they 
recognise the work of Africa Risk Capacity for not only providing the 
infrastructure to trigger disbursement but for also promoting national 
risk analysis. Another important gap in the landscape of climate risk 
pooling is lack of attention to financial institutions’ lending portfolios 
that are vulnerable to weather shocks. In this regard subsidies as part 
of innovative financing schemes facilitated by the donor community 
can encourage the uptake of meso-level climate risk insurance

solutions (Kreft and Schäfer 2017).
In the literature, there are two attempts at systematic evaluation 
or comprehensive assessment of regional climate risk pools: 
a  comprehensive study by Scherer (2017) and FCDO’s ten-year 
evaluation (2015–2024). Overall, neither of these studies draw 
adverse conclusions about regional climate risk pooling initiatives/
mechanisms. According to Scherer, ‘it appears that insurances work 
in principle and there is certainly success’ and ‘initial experiences 
demonstrate regional climate risk insurances works’. The author 
cited the 28 pay-outs to 16 countries of USD106 million arguing that 
it provides cash-starved countries with much needed cash (Scherer 
2017, p. 4). The FCDO study (Scott 2017) examines the uptake of ARC and its impact on reducing vulnerability to disasters. It notes 
that there is scarce literature on disaster risk insurance mechanisms 
in terms of impacts. In its current sample of 20 countries as of

November 2017, four are projected to experience food security crisis 
(IPC Level 3) but are not signatories to the ARC, which may signal 
that ARC is not attractive to all food insecure countries and that 
there is no overwhelming appetite for ARC among poorer countries. 
Additionally, Panda and Surminski (2020) research the importance 
of indicators and frameworks for monitoring the performance and 
impact of Coalition for Disaster Resilient Infrastructure (CDRI) but 
make no final assessment of any of the regional climate risk pool. 
However, they propose mechanisms to improve the transparency and 
accountability of the system. Scherer (2017), Forest (2018) and Panda 
and Surminski (2020) seem to indicate that there is ‘enthusiasm to 
support and scale-up regional climate risk insurance’ (Scherer 2017, 
p. 4) Examples of this support include: the Germany Ministry for 
Economic Cooperation and Development (BMZ) has provided USD5.9

million for the World Food Programme (WFP) to protect 1.2 million 
vulnerable African farmers with climate risk insurance, through ARC 
Replica, and the G7 InsuResilience Vision 2025, which has committed 
to ensuring 400–500 million poor persons are covered against disaster 
shock by pre-arranged finance and insurance mechanism by 2025; 
some of this will be through ARC (WFP 2020). Of course, this does 
not mean that risk pools are without challenges or are not failing on 
specific sets of metrics. Forest (2018) flags three failing areas: policy 
holder and hazard coverage, the cost of premium and risk transfer 
parameters, and the use of pay-out, which in most cases are up to 
the government. Here, ARC is flagged among the three regional risk 
pools, as the only one with contingency plan requirements that can 
support effective use of pay-outs. Other research exploring climate 
risk pooling and its impacts flag lack of transparency around pay-out,

premium or risk transfer parameters. Ultimately, climate risk pools 
are not full insurance; they offer only limited coverage. Entities such 
as the U4 Anti-Corruption Help Desk are exploring how to mitigate 
potential corruption with regard to climate risk insurance.
15.6.5 Widen the Focus of Relevant Actors: Role of 
Communities, Cities and Subnational Levels
There is an urgency and demand to meet the financial needs of the 
climate change actions not only at the national level but also at 
the  subnational level, to achieve low-carbon and climate-resilient 
cities and communities (high confidence) (Barnard 2015; Moro 
et al. 2018). Scaling up subnational climate finance and investment 
is a necessary condition to achieve climate change mitigation and 
adaptation action (Ahmad et al. 2019).
The importance of exploring effective subnational climate 
finance. Stronger subnational climate action is indispensable to 
adapt cities to build more sustainable, climate-positive communities

(Kuramochi et  al. 2020). It has transformative potential as a  key 
enabler of inclusive urban economic development through the 
building of resilient communities (high confidence) (Floater et  al. 
2017a; Colenbrander et  al. 2018b; Ahmad et  al. 2019). Yet the 
significant potential of subnational climate finance mechanisms 
remains unfulfilled. Policy frameworks, governance, and choices at

1597
Investment and Finance Chapter 1515higher levels underpin subnational climate investments (Colenbrander 
et al. 2018b; Hadfield and Cook 2019). To scale climate investment, 
a systematicunderstanding of the preconditions to mobilising high-
potential financing instruments at the national and subnational 
levels is necessary.
Subnational climate finance needs and flows. Subnational 
climate finance covers financing mechanisms reaching or utilising 
subnational actors to develop climate positive investment in urban 
areas. The fragility of interconnected national and subnational 
finances affects subnational finance flows, including the impact 
of the social-economic crisis (Canuto and Liu 2010; Ahrend et  al. 
2013). The effect of deficit in investment for global infrastructure 
towards the growing subnational-level debt also creates pressure on 
subnational finances and constrains future access to financing (high 
confidence) (Smoke 2019).

The International Finance Corporation estimates a  cumulative 
climate investment opportunity of USD29.4 trillion across six urban 
sectors (waste, renewable energy, public transportation, water, EVs, 
and green buildings) in emerging market cities, cities in developing 
countries with more than 500,000 population, to 2030 (IFC 2018). 
However, the State of Cities Climate Finance report estimated that 
an average of USD384 billion was invested in urban climate finance 
annually in 2017–2018 (Negreiros et  al. 2021). The International 
Institute for Environment and Development estimates that out of the 
USD17.4 billion total investments in climate finance, less than 10% 
(USD1.5 billion) was approved for locally-focused climate change 
projects between 2003 and 2016 (Soanes et al. 2017).
Subnational climate public and private finance. Urban climate  
finance and investment are prominent in the subnational 
climate  finance landscape (CCFLA 2015; Buchner et  al. 2019).

Finance mechanisms that can support climate investment for the 
urban sector include public-private partnerships (PPPs); international 
finance; national investment vehicles; pricing, regulation, standards; 
land value capture; debt finance; and fiscal decentralisation (Granoff 
et al. 2016; Floater et al. 2017b; Gorelick 2018; White and Wahba 
2019). Among these mechanisms, PPPs, debt finance, and land value 
capture have the potential to mobilise private finance (Ahmad et al. 
2019). Better standardisation in processes is needed, including those 
bearing on contracts and regulatory arrangement, to reflect local 
specificities (Bayliss and Van Waeyenberge 2018) (Section 15.6.1.1).
PPPs are particularly important in cities with mature financial systems 
as the effectiveness of PPPs depends on appropriate investment 
architecture at scale and government capacity (high confidence). Such 
cities can enable infrastructure such as renewable energy production

and distribution, water networks, and building developments to 
generate consumer revenue streams that incentivise private investors 
to purchase equity as a long-term investment (Floater et al. 2017b).
National-level investment vehicles can provide leadership for 
subnational climate financing and crowd in private finance by 
providing early-stage market support to technologies or evidence 
related to asset performance and costs-benefits (high confidence). 
The use of carbon pricing is increasing at the subnational level along with regulation and standards on negative externalities, such 
as pollution, to steer investment towards climate financing (World 
Bank Group 2019).
Debt financing via subnational bonds and borrowing, including 
municipal bonds, is another potential tool for raising upfront capital, 
especially for rich cities (high confidence). The share of subnational, 
sub-sovereign, and sovereign bonds could grow over time, given

efforts to expand the creditworthiness and ensure a  sufficient 
supply of own-source revenue to reduce the default risk. As of now, 
subnational and sub-sovereign bonds are constrained by public 
finance limits and the fiscal capacities of governments. However, 
while green bonds have potential for growth at the subnational level 
and may result in a lower cost of capital in some cases, the market 
faces challenges related to scaling up and has been associated with 
limited measurable environmental impact to date (Section 15.6.8). 
Further, bonds with lower credit ratings drive higher issuance costs 
for climate risk cities, for example, costs related to disclosure and 
reporting (Painter 2020).
Key challenges of subnational climate finance. Across all types 
of cities, five key challenges constrain the flow of subnational climate 
finance (high confidence): (i) difficulties in mobilising and scaling-
up private financing (Granoff et  al. 2016); (ii) deficient existing

architecture in providing investment on the scale and with the 
characteristics needed (Anguelovski and Carmin 2011; Brugmann 
2012); (iii) political-economic uncertainties, primarily related to 
innovation and lock-in barriers that increase investment risks (Unruh 
2002; Cook and Chu 2018; White and Wahba 2019); (iv) the deficit in 
investment for global infrastructure affects the growing subnational-
level debt (Canuto and Liu 2010); and (v) insufficient positive value 
capture (Foxon et al. 2015).
Different finance challenges between rich and poor cities. 
Access to capital markets has been one of the major sources for 
subnational financing and is generally limited to rich cities, and much 
of this occurs through loans (high confidence). Different challenges 
to accessing capital markets associated with wealthy and poorer 
cities are compounded into three main issues: (i) scarcity and access 
of financial resources (Bahl and Linn 2014; Colenbrander et al. 2018b;

Cook and Chu 2018; Gorelick 2018); (ii) the level of implication from 
the existing distributional uncertainties to the current financing of 
infrastructural decarbonisation across carbon markets (Silver 2015); 
and (iii) the policy and jurisdictional ambiguity in urban public 
finance institutions (Padigala and Kraleti 2014; Cook and Chu 2018). 
In poorer cities, these differing features continue to be inhibited by 
contextual characteristics of subnational finance, including gaps in 
domestic and foreign capital (Meltzer 2016), the mismatch between 
investment needs and available finance (Gorelick 2018), weak 
financial autonomy, insufficient financial maturity, investment-grade 
credit ratings in local debt markets (Bahl and Linn 2014), scarce 
diversified funding sources and stakeholders (Gorelick 2018; Zhan 
et al. 2018; Zhan and de Jong 2018) and weak enabling environments 
(Granoff et al. 2016).
The depth and character of the local capital market also affect

cities differently in generating bonds (high confidence). Challenges

1598
Chapter 15 Investment and Finance15facing cities in developing countries include insufficient appropriate 
institutional arrangements, the issues of minimum size, and high 
transaction costs associated with green bonds (Banga 2019). 
Green projects and project pipelines are generally smaller in scale 
feasible for a bond market transaction (Saha and D’Almeida 2017; 
DFID 2020). De-risking in the different phases of long-term project 
financing can be promoted to improve the appetite of capital markets 
(Section in 15.6.7).
Climate investment and finance for communities. There is 
insufficient evidence about which financing schemes contribute 
to climate change mitigation and adaptations at community level 
(high confidence). There is growing interest in the linkages between 
microfinance and adaptation in the agriculture sector (Agrawala and 
Carraro 2010; Fenton et al. 2015; Chirambo 2016; CIF 2018; Dowla 
2018), the finance for community-based adaptation actions (Fenton

et al. 2014; Sharma et al. 2014), and the relations between remittances 
and adaptation (Le De et al. 2013). However, there is less discussion 
on community finance aside from the benefits of community finance 
and village funds in contributing to close investment gaps and 
community-based mitigation in the renewable energy and forest 
sectors (Ebers Broughel and Hampl 2018; Bauwens 2019; Watts et al. 
2019) The full potential and barriers of the community finance model 
are still unknown and research needs to expand understanding of 
favourable policy environments for community finance (Bauwens 
2019; Watts et al. 2019).
Implications for the transformation pathway. Cities often have 
capacity constraints on planning and preparing capital investment 
plans. Integrated urban capital investment planning is an option 
to develop cross-sectoral solutions that reduce investment needs, 
boost coordination capacity, and increase climate-smart impacts

(high confidence) (Negreiros et  al. 2021). In countries with weak 
and poorly functioning intergovernmental systems, alliances and 
networks may influence their organisational ability to translate 
adaptive capacity for transformation into actions (Leck and Roberts 
2015; Colenbrander et  al. 2018a). Deepening understanding of 
country-specific enabling environment for mobilising urban climate 
finance among and within cities and communities, design of policy, 
institutional practices and intergovernmental systems are needed to 
reduce negative implications of transformation (Steele et al. 2015).
15.6.6 Innovative Financial Products
Innovative financial products with increased transparency on 
climate risk have attracted investor demand, and can facilitate 
investor identification of low-carbon investments (high confidence). 
Innovative products may not necessarily increase financial flows 
for climate solutions in the near term, however they can help build

capacity on climate risk and opportunities within institutions and 
companies to pave the way for increased flows over time.
Investor demand is driving developments in innovative 
financial products (high confidence). Since AR5, innovative 
financial products such as sustainability and green-labelled financial 
products have proliferated (Section 15.3). These financial products are not necessarily ‘new’ in terms of financial design but are 
packaged or labelled in an innovative way to attract responsible and 
impact-oriented institutional investors.
The growth and diversity of the green bond market illustrates how 
innovative financial products can attract both public and private 
investors (high confidence). Demand for green financial products 
initially stemmed from public sector pension funds. Pension funds and 
insurance companies in OECD countries have traditionally favoured 
bonds as an asset class with lower risk (OECD and Bloomberg 2015).

Since AR5, labelled green bonds have grown significantly, exceeding 
USD290 billion issued in 2020 with a  total of USD1.1 trillion in 
outstanding bonds (CBI 2021a) (Section 15.6.7). Corporates, financial 
institutions and government-backed entities (for example in real 
estate, retail, manufacturing, energy utilities) issued the largest 
volumes, with use of proceeds focused primarily on GHG mitigation 
in energy, buildings and transport projects (CBI 2021a). Given 
their focus on GHG mitigation, green bonds are also sometimes 
referred to as climate bonds, but the common market terminology 
is ‘green’. Municipal green bond issuance has also been growing 
(Section  15.6.7). Beyond green bonds, additional products such as 
green loans, green commercial paper, green initial public offerings 
(IPOs), green commodities, and sustainability-linked bonds and loans 
have also been introduced in the market (CBI 2019a) (Section 15.6.7).

Investor demand for green bonds is evidenced by over-subscription 
of deals. Recent studies indicate an over-subscription for green-
labelled bonds by an average of between three and five times, as 
compared to non-labelled bonds (Gore and Berrospi 2019; Nauman 
2020). Results of a  survey of global treasurers showed a  higher 
demand for green bonds than non-labelled bonds for 70% of the 
respondents (CBI 2020a).
The financial crisis associated with COVID-19 has put increased 
pressure on debt issuers, and the extent to which the increase in 
indebtedness for sovereigns and corporates has been financed via 
climate-related-labelled debt products is not known. Further, at this 
time there is no identified literature assessing the degree to which 
international versus domestic investors are financing sovereign 
green debt in developing countries (Section 15.6.7) However, since 
the onset of the COVID-19 crisis, continued steady growth in issuance

has been observed broadly across sustainable bonds (including 
green, social and sustainability bonds), with more significant growth 
in social bonds to support the COVID-19 recovery (Maltais and 
Nykvist 2020; CBI 2021a).
Index providers and exchanges can also play a  supporting role in 
transparency for identification of benchmarks and innovative financial 
products for climate action. Low-carbon indices have proliferated in 
recent years, with varying approaches including reduced exposure 
to fossil, best-in-class performers within a sector, and fossil-free (UN 
PRI 2018) (see discussion on ESG index performance that follows 
in this section). Indices can provide transparency on low-carbon 
opportunities, making it simpler for funds and investors to identify 
green investment options. Exchanges can also play a  supporting 
role to the uptake of green financial products through transparent

1599
Investment and Finance Chapter 1515listings and requirements to improve credibility of green labelling. 
The number of green or sustainability bond listing segments tripled 
from five in 2016 to 15 in 2018 (SSE 2018). Green security listings 
can also be used to enhance local capital markets (Section 15.6.7).
Significant potential exists for continued growth in innovative 
financial products, though some challenges remain (high 
confidence). Despite recent growth and diversification, green bonds 
face several challenges in scaling up. Issuance of green-labelled 
bonds constitutes approximately 1% of the global bond market 
issuance (ICMA 2020b; CBI 2021a) Potential exists to increase 
issuance amongst corporates, for instance, and across a  broader 
regional scope (although subject to limitations of local capital 
markets). Yet there remain several challenges to growing the green 
bond market, including inter alia concerns about greenwashing and

limitations in application to developing countries (Shishlov et  al. 
2018; Banga 2019).
There is no globally accepted definition of green bonds, and varied 
definitions of eligible green activities are evolving across regional 
bond markets. Beyond the most commonly used green label, other 
related labels such as blue, sustainable, transition, sustainable 
development goal (SDG), social and environmental, social and 
governance (ESG) have some overlapping applications (Schumacher 
2020). The degree to which these labels represent climate-relevant 
investments depends on underlying criteria and how they are applied 
(Section 15.6.4).
There are several initiatives aimed at protecting the integrity of 
the green label. Guidance on use and management of proceeds 
established by the International Capital Markets Association’s 
Green Bond Principles (GBP) is followed on a voluntary basis, which 
notes eligible use of proceeds as primarily climate mitigation and

adaptation projects. The GBP also recommend independent external 
reviews at the time of issuance, with 89% of green bond issuers in 
2020 having external reviews at the time of issuance (CBI 2021a). 
In addition to best practice based on voluntary principles, a further 
check on greenwashing, although insufficient on its own, is the fear 
of reputation risk on behalf of investors, issuers and intermediaries in 
the age of social media (Hoepner et al. 2017; Deschryver and de Mariz 
2020). A report on post-issuance green bond impact reporting notes 
that despite concerns (Shishlov et al. 2018), greenwashing incidence 
is rare, with 77% of green bond issuers reporting on allocation and 
59% reporting on impact, but with significant variance in quality and 
consistency of impact reporting (CBI 2021b).
Financial disclosure regulatory developments can help further 
align and specify definitions of green in the financial sector but are

not a  substitute for climate policy (high confidence). Developing 
a common basis for understanding a green label could further reduce 
uncertainty or concerns of greenwashing. Regulatory developments in 
some regions seek to further guard against greenwashing with more 
specific definitions. The EU sustainable finance package, including 
the EU Taxonomy and EU Green Bond Standard draft regulations, is 
the broadest reaching, but not the only, regional initiative focused on 
disclosure of climate risk (Section 15.6.3). Taxonomies across regions 
are not always aligned on what can constitute a  green project, for example with respect to transition activities (Pfaff et  al. 2021) 
(Section 15.6.7). While standardisation can help reduce uncertainty 
in markets with imperfect knowledge, the green bond market is 
currently developing and is expected to continue to reflect regional 
differences in economic governance approaches (Nedopil et al. 2021).

Regulations may also have trade-offs in terms of transaction costs for 
green financial product issuers. Classification approaches can also 
face challenges, depending on how they are designed, in their ability 
to capture new technologies and social impacts (Section 15.4).
Green bonds have been primarily targeting climate mitigation projects, 
with far fewer projects identified as adaptation. Green bonds mainly 
finance projects in the energy, buildings and transportation sectors, 
which constituted 85% of the use of proceeds of green bonds in 
2020 (CBI 2020b, 2021a). Agriculture and forestry projects, including 
adaptation projects, have been less suited to be financed in a bond 
structure, which could be in part due to the more dispersed and 
smaller nature of the projects and in part due to project ‘bankability’ 
or ability to contribute steady streams of financing to pay back the 
terms of a bond. However, adaptation projects may not be identified

as such as resiliency becomes more mainstreamed into infrastructure 
planning (Section 15.3.2).
While green bonds have the potential to further support financial 
flows to developing countries, local capital markets can be at varying 
stages of development (Banga 2019) (Sections 15.6.2 and 15.6.7). 
While multilateral and bilateral development finance institutions 
have been active in the green bond market, global issuance in 
2020 in the top 10 countries included only one developing country 
(CBI 2021a). Targeting international investors can be enhanced via  
de-risking activities (15.6.4).
Identifying green financial products can increase uptake and 
may result in a lower cost of capital in certain parts of the 
market (high confidence). Investors face a systematic underpricing 
of climate risk in financial markets (Krogstrup and Oman 2019; 
Kumar et al. 2019). Transparent identification of financial products 
can make it easier for investors to include low-carbon products in

their portfolios. Investors with mandates that include or are focused 
on climate change are showing an interest in green-labelled financial 
products. Investors that identify themselves as green constitute 
approximately 53% of the investor base for green bonds in the first 
half of 2019 (CBI 2019b).
There is some evidence of a  premium, or an acceptance of lower 
yields by the investor, for green bonds (medium confidence). A survey 
of recent literature finds some consensus of the existence of a green 
premium in 56% of the studies on the primary markets (with 
a  wide variance of premium amount), and 70% of the studies on 
the secondary market (with an average premium of –1 to –9 basis 
points), particularly for government issued, investment grade and 
green bonds that follow defined governance and reporting practices 
(MacAskill et al. 2021). In the US municipal bond market, as credit 
quality for green-labelled bonds has increased in the past few years,

some studies show a  positive premium for green bonds is arising 
(Baker et  al. 2018; Karpf and Mandel 2018), or appearing only in 
the secondary market (Partridge and Medda 2020), while others

1600
Chapter 15 Investment and Finance15find no evidence of a premium (Hyun et al. 2019; Larcker and Watts 
2020). Several studies also show a recent emergence of a premium 
and oversubscription for some green-labelled bonds denominated in 
EUR (CBI 2019b), in some cases for both USD or EUR green bonds 
(Ehlers and Packer 2017), with a wide variation in the range of the 
observed difference in basis points focusing on the secondary market 
(Gianfrate and Peri 2019; Nanayakkara and Colombage 2019; Zerbib 
2019), with financial institution and corporate green bonds exhibiting 
a  marginal premium compared with their non-green comparisons 
(Hachenberg and Schiereck 2018; Kempa et al. 2021).
Spillover effects of green bonds may also impact equity markets and 
other financing conditions. Stock prices have been shown to positively 
respond to green bond issuance (Tang and Zhang 2020). One study 
linked enhanced credit quality induced by issuing green-labelled

bonds to a lower cost of capital for corporate issuers (Agliardi and 
Agliardi 2019). Issuers’ reputation and use of third-party verification 
can also improve financing conditions for green bonds (Bachelet 
et al. 2019). Green bonds are strongly dependent on fixed income 
market movements and are impacted by significant price spillover 
from the corporate and treasury bond markets (Reboredo 2018). 
A simulation of future green sovereign bond issuances shows that 
this can promote green finance via firm’s expectations and the credit 
market (Monasterolo and Raberto 2018).
Financial flows via these instruments have limited measurable 
environmental impact to date, however they can support 
capacity building on climate risk and opportunities within 
institutions to realise future impacts (high confidence). There 
is a  lack of evidence to date that green and sustainable financial 
products have significant impacts in terms of climate change

mitigation and adaptation Box 15.7). Further, new products must be 
coupled with tightened climate policy and a reduction in investments 
associated with GHG-emitting activities to make a difference on the 
climate (Section 15.3.3.2).
It is challenging to link specific emission reductions with specific 
instruments that mainly target climate activities such as green bonds. Data challenges point to an inability to link emission reductions, 
including Scope 3 GHG emissions, at the organisation or firm level 
with green bond use-of-proceeds issuance (Ehlers et  al. 2020; 
Tuhkanen and Vulturius 2020). However one study found evidence 
of a  signalling effect of issuing green bonds resulting in emission 
reductions at the corporate level following issuance (Flammer 2020), 
and another study characterised the lifecycle emissions of renewable 
energy financed by green bonds, indicating potentially substantive 
avoided emissions but with variance up to a  factor of 12 across

bonds depending on underlying assumptions (Gibon et  al. 2020). 
There is also a lack of impact reporting requirements and consistency 
in the green bond market. Impact reporting is not typically required 
for green bond listings on specific exchanges, nor are there any 
requirements for independent reviews of impact reporting, however 
this could change in future if investors apply pressure.
Green-labelled products may not necessarily result in increased 
financial flows to climate projects, although there can be benefits 
from capacity building with issuing institutions. Green bonds 
can be used  to finance new climate projects or refinance existing 
climate  projects, and thus do not necessarily result in finance 
for new  climate projects constituting additional GHG reductions 
(a framing used in the Clean Development Mechanism). The labelling 
process itself may not necessarily lead to additional financing (Dupre

et al. 2018; Nicol et al. 2018b). However, the labelling process has 
merit in contributing to building capacity within issuing institutions 
on climate change (Schneeweiss 2019), which could support 
identification of new green projects in the pipeline.
Climate risk disclosure initiatives, some of which are voluntary in nature, 
may have a limited direct climate impact. Transparency on climate risk 
may not change investor decisions nor result in divestment, especially 
in the emerging economies, as support and clear direction from 
regulatory and policy mechanisms are required to drive institutional 
investors at large (Ameli et  al. 2021b). On the other hand, there is 
evidence of reduced fossil fuel investments following mandatory 
climate risk disclosure requirements, indicating a  broader signalling 
effect of transparency (Mésonnier and Nguyen 2021).  
Box 15.7 | Impact of ESG and Sustainable Finance Products and Strategies

While scaling up climate finance remains a challenge (Section 15.3.2), there is consensus that investments that are managed taking 
into account broader sustainability criteria have increased consistently and ESG integration into sustainable investment is increasingly 
being mainstreamed by the financial sector over recent years (Maiti 2021). The United Nations Principles for Responsible Investment 
(PRI) grew to over 3000 signatories in 2020, representing over USD100 trillion in assets under management (UN PRI 2020). And 
according to the 2018 biennial assessment by Global Sustainable Investment Alliance,15 sustainable investments in five major 
developed economies grew by 34% in the two-year period following the 2016 assessment. The primary ESG approaches leveraged 
were exclusion criteria and ESG integration, which together amounted to over USD37 trillion, accounting for two-thirds of the assessed

sustainable investments, with novel strategies such as best-in class screening and sustainability-themed investing showing significant 
growth, although together they accounted for around 6% of these investments (GSIA 2019). Shareholder activism or corporate 
engagement is the other key approach, which has been well established and continued to grow to nearly USD10 trillion (GSIA 2019).
15 GSIA is an international collaboration of membership-based sustainable investment organisations.

1601
Investment and Finance Chapter 1515However, research indicates that ESG strategies by themselves do not yield meaningful social or environmental outcomes (Kölbel et al. 
2020). When it comes to the tangible impact of the financial sector on addressing climate change and sustainable development, there 
remains ambiguity. There is a growing need for more robust assessment of ESG scores, including establishing higher standardisation of 
scoring processes and a common understanding of the different ESG criteria and their tangible impact on addressing climate change. 
The issue was highlighted in an assessment of six of the leading ESG rating agencies’ company ratings under the MIT Aggregate 
Confusion Project, which found the correlation among them to be 0.61, leading them to conclude that available ESG data was ‘noisy 
and unreliable’ (Berg et al. 2020). This need is reaffirmed by Drempetic et al. (2020), who claim that a thorough investigation of ESG

scores remains a relatively neglected topic, with extraneous factors, such as firm size, influencing the score (Drempetic et al. 2020).
There continues to be a research gap in assessing the direct impact of ESG and sustainable investments on climate change indicators, 
with most existing studies assessing the co-relation between either the factors driving the sustainable finance trends and the impact 
on sustainable investments, or sustainable investments and the impact on corporate financial performance. Nevertheless, since 
the post-SDG adoption period, there has been a notable uptake on research linking sustainable business practices and financial 
performance (Muhmad and Muhamad 2020). This research shows that there is a  growing business case for ESG investing, with 
evidence increasingly indicating a non-negative co-relation between ESG, SDG adoption and corporate financial performance (Friede

et al. 2015; Muhmad and Muhamad 2020), and ESG performance having a positive relation with stock returns (Consolandi et al. 
2020). Research focused on developed economies also indicates towards a positive relation between ESG criteria and disclosure, and 
economic sustainability of a firm (Giese et al. 2019; Alsayegh et al. 2020) and allays investor fears by showing that sustainable finance 
initiatives, such as divestment, do not adversely impact investment portfolio performance (Henriques and Sadorsky 2018; Trinks et al. 
2018). It should be reiterated that this research assesses the co-relation between ESG criteria and corporate financial performance, 
with the researchers in some cases, such as Friede et al. (2015), including disclaimers of the results being inconclusive and highlighting 
the need for a deeper assessment for linking ESG criteria with impact on financial performance.

On the other hand, there is growing evidence for a sustainable investment lens having a broader positive impact on creating an 
enabling environment and strengthening the case for such investments. For instance, corporate social responsibility (CSR) activities 
and investments on the environment dimension, specifically in the areas of emission and resource reduction, were found to be 
profitable and a predictor of future abnormal returns in the longer term, from additional cash flow and additional demand (Dorfleitner 
et al. 2018). These factors could be contributing to the increasing trend of sustainable and green investments, and can be said to be 
further reiterated by the spate of investor-led collaborative initiatives and recent announcements by leading finance institutes in the 
developed economies, which is well recorded in a range of recent grey literature, including new climate-aligned investment strategies 
and ambition towards net zero targets.

Yet there is also a risk of companies announcing projected sustainability or net zero targets and claiming the associated positive 
reputational impact, while having no clear action plan in place to achieve these. The lack of mandatory reporting frameworks, which 
results in an over-reliance on self-reported carbon data by companies for ESG assessments, can be a primary contributor (In and 
Schumacher 2021).
While there is a lack of research on the impact of sustainable finance products, divestment impact has been assessed in more detail. 
Although the research here also points towards the ambiguous direct impact of divestment on reducing GHG emissions or on the 
financial performance of fossil fuel companies, its indirect impact on framing the narrative around sustainable finance decisions 
(Bergman 2018), and the inherent potential of the divestment movement for building awareness and mobilising broader public support

for effective climate policies, have been better researched and could be considered to be the more relevant outcomes (Braungardt 
et al. 2019). Arguments against divestment point to its largely symbolic nature, but Braungardt et al. (2019) elaborate on the broader 
positive impacts of divestment, which include its ability to spur climate action as a moral imperative and stigmatise and reduce the 
power of the fossil fuel lobby, and the potential of the approach to mitigate systemic financial risks arising due to climate change and 
address the legal responsibilities of investors merging in this regard.
Challenges remain with regards to overlapping definitions of sustainable and ESG investment opportunities, which also vary 
depending on social norms and pathways. There is also a general need for more extensive ESG disclosure at a corporate level, against 
the background of emerging mandatory impact reporting for asset managers in some regions. A  movement is building towards

sustainable investment strategies and increased sustainable development awareness in the financial sector (Muhmad and Muhamad 
2020; Maiti 2021), which points to the ability of civil society movements, such as divestment campaigns, to have some influence on 
investor behaviour, although there are other influences such as climate risk disclosure initiatives and regulations.Box 15.7 (continued)

1602
Chapter 15 Investment and Finance1515.6.7 Development of Local Capital Markets
International situational context. Developing countries make 
up two-thirds of the world’s population and carry carbon-intensive 
economies where 70% of investments (see Chapter  3) need to be 
conducted to limit warming to 2°C. The focus for climate investments 
has been on China, USA, Europe, India and the G20 (UNEP 2019) 
but studies highlight Paris and SDG attention should be devoted to 
Africa, LDCs and SIDS (African Union Commission 2015; Feindouno et al. 
2020; GCA-AAI 2020; Warner 2020; AOSIS 2021). The ‘special needs, 
circumstances and vulnerability’ of African, LDC and SIDS nations are 
recognised under UNFCCC and UN agreements (UN 2009, 2015a,b,c; 
UNFCCC 2010, 2015; Pauw et  al. 2019). These nations currently 
contribute very little to global emissions. Developing countries with their 
growing economies, including the vast African continent roughly the

size of China, Europe, USA, and India combined (IEA 2014b, p. 20) with 
a 1 billion population expected to double by 2050, growing reliance on 
fossil fuels and ‘cheap’ biomass (charcoal use and deforestation) amid 
rising urbanisation and industrialisation ambitions – collectively these 
nations hold large leap-frog potential for the energy transition as well 
as risks of infrastructure lock-in. Accelerated international cooperation 
is a critical enabler (IPCC 2018) in recognising this potential. This could 
mobilise global savings, scale up development of local capital markets 
for accelerated low-carbon investment and adaptation in low- and 
lower-middle-income countries as well as tackle illicit finance including 
tax avoidance leakages that deprive developing countries of valuable 
resources (US DoJ 2009; Hearson 2014; Hanlon 2017; US DoJ 2019; 
IATFD 2021). Diversifying funding sources is important at a time hard-

currency Eurobond issuances reach records (Panizza and Taddei 2020; 
Moody’s Investors Service 2021). Otherwise, the structure of voluntary, 
nationally oriented, and financially fragmented arrangements under 
the Paris Agreement (Chapter 17) could lead to ‘regional rivalry’ (SSP 3) 
pathways (IPCC 2018; Gazzotti et al. 2021). The benefits are many times 
greater than apparent costs in terms of expected decline in global GHG 
emissions and attaining SDGs. These could even generate large ‘win-
win’ opportunities back in capital source countries which will benefit 
from a flow back in import demand (Hourcade et al. 2021a).
Lessons from literature on policy options in mobilising capital 
for Paris and SDGs in developing countries can be summarised 
as follows: 
1. development of national just transition strategies meet the 
USD100 billion commitment on a  grant-equivalent basis to 
support NDCs that integrate policies on COVID-19 recovery,

climate action, sustainable development and equity; 
2. increase the leverage of public funds on diverse sources of 
private capital through de-risking investments and public-private 
partnerships involving location-based entities with AAA-rated 
players and institutional investors; 
3. coordination of project preparation and development of project 
pipelines by infrastructure coordinator agencies, one-stop 
structuring and financing shops, project risk facilities provided 
by entities such as cities’ development banks, green banks, 
a world climate bank, global guarantee mechanism, and global 
infrastructure investment platform;4. development of local currency bond markets backed by cross-
border guarantees, technical assistance, remediation assets, 
especially by regional and national players whose mandates 
include nurturing local capital markets to support bond yield 
curve development and exchange listing options;
5. adopting advances in science-based assessment methods to

foster accountability; 
(a) for project assessment, measuring, reporting and verifying, 
and certification,
(b) for disclosures in climate, fossil fuels, SDGs, debt transparency 
and debt sustainability, and 
(c) for progress on UN systems of national accounts particularly 
for public sector finance statistics.
Whole-of-society approach to mobilising diverse capital. There’s 
no shortage of money globally: it is simply that it has yet to travel 
to where it’s most needed. One challenge is unlocking unencumbered 
endowments to contribute to Paris and SDGs (high confidence) . 
The aggregate global wealth figures exceed USD200 trillion (Davies 
et al. 2016; UBS 2017; Credit Suisse 2020; Heredia et al. 2020). Some 
developing countries have run pilots for investing in government 
bonds capitalising on fintech growth discussed (The Economist 2017; 
Akwagyiram and Ohuocha 2021) (Section 15.6.6). Others are developing 
green products to encourage uptake by middle class retail investors

(Eurosif 2018; UK DMO 2021). Millennial-aged inheritors expected to 
receive intergenerational transfers mobilised by global citizen activism 
(Chapter 2) invest in green retail and tech products (Morgan Stanley 
2017; UBS 2017; Capgemini 2021). Historic inequity and diaspora-
related private and public resources pledged and debated during 
the COVID-19 pandemic might have potential to contribute towards 
Paris and SDGs (Olusoga 2015; Glueck and Friedman 2020; Hall 2020; 
Piketty 2020; Timsit 2020; Goldman Sachs 2021; Guthrie 2021; Mieu 
2021; Wagner 2021). Philanthropic institutions use grants, debt, equity, 
guarantees and issue investment grade bonds in using unencumbered 
endowments (Manilla 2018; Covington 2020; Moody’s Investors 
Service 2020) but only about 2% of their resources are dedicated to 
climate action (Williams T., 2015; Kramer 2017; Morena 2018; Delanoë 
et al. 2021). The pandemic exemplified the unprecedented collaboration

and mobilisation of multilateral and scientific communities supported 
by the COVAX risk sharing mechanism for COVID-19 vaccines with 
pooling of financial and scientific resources (OECD 2021d). This 
momentum in international cooperation can be harnessed to galvanise 
resources, including for teaching of sciences in developing countries 
important in tackling society challenges, alleviating poverty (TWAS 
2021) and inequity legacies compounded by climate impacts debated 
by many (Henochsberg 2016; Obregon 2018; Fernandez et al. 2021; 
The Economist 2021). Suggestions towards equitable models include 
‘global adaptation funding approaches’ (Chancel and Piketty 2015), 
a ‘world climate bank’ to finance climate investments through long-
term bonds (Foley 2009; Broome 2012; Broome and Foley 2016), 
a ‘cities development bank’ (Alexander et al. 2019), and ‘public debt 
financing models’ (Rendall 2021) for generations to share the burden

1603
Investment and Finance Chapter 1515Local financial institutions with local markets knowledge 
could benefit from technical assistance and partnership to 
scale up their potential with institutional investors better 
mobilised (high confidence). The Global South has some 260 public 
development banks/PDBs representing USD5 trillion in assets with 
a worldwide PDB capacity to provide more than USD400 billion yr–1 
of climate finance (IDFC and GCF 2020). Case studies discuss the 
potential for diaspora bond issuance being deployed for climate 
investments including securitisation of remittances as collateral 
for infrastructure bonds (Ketkar and Ratha 2010; Akkoyunlu and 
Stern 2012; Gelb et al. 2021). Such instruments could help harness 
diaspora remittances, whose flows rose from under USD 100 billion 
to USD530 billion during 1990–2018 (World Bank 2019c). PDBs could 
benefit from technical partnership with multilaterals and other local

banks (Torres and Zeidan 2016). Their knowledge of local markets, can 
help build project pipelines (Figure 15.7) to channel local, domestic 
and international capital (Griffith-Jones et  al. 2020). Institutional 
domestic and international investors have growing assets estimated 
to exceed USD100 trillion (high confidence) (Willis Towers Watson 
2020; UN PRI 2020; Halland et al. 2021; Heredia et al. 2021; Inderst 
2021) and could be better mobilised. Some 36% of total assets under 
management (AUM) by the 100 largest asset owners come from 
pensions and sovereign wealth funds in the Asia Pacific region, with 
the remainder split almost evenly across Europe, the Middle East, 
Africa and North America. The largest pension fund in South Africa 
held about USD130 billion AUM in 2019 and African institutional 
investors held USD1.8 trillion in 2020 (PwC 2015; GEPF 2019; Bagus 
et al. 2020; GCA 2021a). UK NGO War on Want’s (2016) analysis of

101 fossil fuel and mining companies on the London Stock Exchange 
estimates these as holding USD1 trillion assets inside Africa. The 
Latin America and Caribbean region holds just about USD1 trillion 
AUM (Curtis 2016; Serebrisky et al. 2015; Cavallo and Powell 2019).
Investors with accumulated private capital are reported as 
looking for climate investments to ensure Just Transition, 
alignment with Paris and SDGs. However, progress remains 
pilot, slow and piecemeal (high confidence). Global investors 
have published statements on their possible contribution, with 
recommendations to governments on de-risking to accelerate private 
sector investment to support Paris-aligned NDCs in developing 
countries (IIGCC 2015; IIGCC 2017; Global Investor Statement 
2018; IIGCC 2018; Global Investor Statement 2019; IIGCC 2020). 
In March 2020, the UN Principles for Responsible Investment (PRI), 
had 3038 members representing USD103 trillion (UN PRI 2020);

another coalition of investors published COVID-19 recovery plans 
(Investor Agenda 2020) and the Net Zero Asset Managers initiative 
was launched in December 2020 (NZAM 2020). However, it is still 
unclear how these pronouncements will be transformed to adequate 
financial flows and volumes of investment pipelines (IEA 2021d) 
(Chapter  3). Rempel and Gupta (2020) posit that a  proportion of 
institutional holding is in fossil fuels. Clean energy transition minerals 
raise ESG questions around inclusive development for indigenous 
populations and require changes to supply chains exploiting child 
labour (Herrington 2021; IEA 2021a; IEA 2021f).
Options to mobilise institutional investors currently remain small 
pilots, relative to Paris and SDG ambitions (high confidence). In terms of examples: in the women of colour-led arena, a Chicago pension 
fund invested in a developing country using a  private equity fund; 
(Langhorne 2021, USAID 2021). Institutional BlackRock’s blended

finance vehicle with OECD MDB partners focuses on developing 
countries (BlackRock 2021). In regional AAA MDB partnerships, the 
African Development Bank (AfDB) collaborates with African nations 
through a  regional infrastructure fund (Africa50 2019); the Asian 
Development Bank (ADB) collaborates with a  Philippines state-
owned pension fund and Dutch pension fund in using a  private equity 
fund to catalyse private sector investment (ADB 2012). A UN entity 
with several pooled public-private investment platforms includes an 
SDG blended finance vehicle (UN CDF 2020a; 2020b). A multilateral 
International Finance Corporation (IFC) blended finance fund, 
supported by a  sovereign guarantee from Sweden’s SIDA, and 
separately a  USD1 billion green bond fund by IFC and Europe’s 
Amundi asset manager buy green securities issued by developing 
country banks financing local currency climate investments (IFC 2018, 
2021; Amundi and IFC 2019). The key parameter is the investment

multiplier, the ratio of private investment mobilised by a  given 
amount of public funds which varies by product type. IFC’s portfolio 
of blended finance investments point to a self-reported range of 3 to 
15 times for project debt and even higher levels (10 to 30) for debt 
finance provided on concessional terms (IFC 2021a). Although an 
AAA-rated IFC blended finance fund was established in 2013, most 
investors joined in 2017 with insurers AXA and Swiss Re investing 
USD500 million each to bring the fund to USD7 billion raised 
from eight global investors (Attridge and Gouett 2021). Critics of 
blended finance mechanisms point to lack of data transparency 
hampering independent assessment on (i) value for public money 
and costs of blending versus other financial mechanisms, (ii) risks 
and benefits of de-risking private capital to collateralising climate-
vulnerable Global South populations, (iii) lack of partnership with

local players, and (iv) complex structures (Akyüz 2017; Mawdsley 
2018; Convergence 2020; Attridge and Gouett 2021; Gabor 2021). 
Whilst blended finance transactions (BFTF 2018) are quite common 
in mature regulated markets with mandatory reporting requirements 
(Morse 2015; ICAEW 2021), the additional finance mobilised and 
their developmental impact remain unknown due to poor reporting 
that hammpers evidence-based policy making (Attridge and Gouett 
2021). Projects that are aligned with blended finance principles in 
the UN Addis Agenda (UN 2015a), and take account of local contexts 
by partnering with local actors, are much more likely to have 
sustainable impacts.
De-risking tools to lower capital costs and mobilise diverse 
investors. Paris-aligned NDCs that integrate policies on COVID-19 
pandemic recovery, climate action, sustainable development, just 
transition and equity can harness co-benefits including contribution

to Invisible UN SDG 7  energy poverty sectors (high confidence). 
Developing countries require access to affordable finance for projects 
ranging from clean cooking solutions (Accenture 2018; World Bank 
et  al. 2021); decentralised energy systems, intra-country power 
stations and regionally shared power pools with their associated 
energy distribution networks (IEA 2020d; IRENA 2020c). Close 
to 3 billion people in Africa and developing Asia have no access to 
clean cooking. For sub-Saharan Africa, the acute lack of electricity 
access lags behind all regions on SDG 7 indicators, impacting mostly

1604
Chapter 15 Investment and Finance15women and children (IEA 2014b; IRENA 2020b,c; IEA et  al. 2021; 
ESMAP 2020; Zhang 2021) (Box  6.1). These dire statistics remind 
of compounding tensions: historical inequities and the associated 
‘first comer’ exploiting African resources for development elsewhere, 
the local climate change, ‘latecomer’ capacity development and 
technology transfer challenges, illicit mining finance and stranded 
assets (Curtis 2016; Bos and Gupta 2019; UNU-INRA 2019; Arezki 
2021). The COVID-19 pandemic exacerbates this tension with 
more people pushed below the poverty line (Sumner et  al. 2020) 
(section 15.6.4, Box 15.6 on post-COVID). Recent analysis points to 
the 60 largest banks providing USD3.8 trillion to fossil fuel companies 
since 2016, including inside Africa (Rainforest Action Network et al. 
2021). IMF estimated fossil fuel subsidies totalling USD5.2 trillion or 
6.5% of global GDP in 2017 (Coady et al. 2019) to be compared with

the USD2.4 trillion yr–1 energy investments over the next decade to 
limit global warming to 1.5°C (IPCC 2018). Analysts point to models 
in improvements to resources husbandry that include (i) developing 
strong minerals sector governance through sovereign wealth funds 
for domestic development (Wills et al. 2016) and (ii) compensation for 
Africa (Walsh et al. 2021) leaving fossil fuels underground (McGlade 
and Ekins 2015) in the Just Transition (Section  15.2.4) and Right 
to Develop debates as assets continue to be mined (IEA 2019c). In 
many developing regions, some of the world’s best renewable energy 
sources remain out of reach due to high costs which can be up to 
seven times those in developed countries (IEA 2021d). Shifting some 
risks through financial de-risking approaches could be instrumental (Schmidt 2014; Sweerts et al. 2019; Drumheller et al. 2020; Matthäus 
and Mehling 2020).
Combining approaches: (i) developed countries meeting

UNFCCC USD100 billion commitment on a  grant-equivalent 
basis, (ii) stepped up technical assistance, (iii) infrastructure 
coordination, (iv) knowledge sharing by project preparation 
entities, and (iv) harnessing project risk facilities such as 
guarantees could be instrumental for scaling climate finance 
for Paris-SDGs (high confidence). Figure 15.7 illustrates the interplay 
between infrastructure project financing phases, bond refinancing 
and opportunities for developing bond yield curve benchmarks in 
nurturing local capital markets and mobilising diverse investors. 
These project financing phases have varying risk-return profiles 
and different benchmarks to track performance are often required 
by investors for different securities that might be created (Ketterer 
and Powell 2018).
An ODI (2018) survey of private and public project preparation facilities 
internationally showed high failure rates in early project preparation

phases with recommendations on ‘one-stop-shops’ and knowledge 
sharing on effective approaches. During the very high-risk concept 
phase (Figure  15.7)  – grants and technical assistance de-risk with 
design concepts, project proposals and feasibility studies completed 
to ‘kick-start’ the right projects. The early-stage developmental phase 
is characterised by short-term debt in the two to five years phase to 
ConceptEarly-stage
developmentAdvanced
developmentFinancial
close
ConstructionCOD
Commercial
operationFocus of most institutional investors
2–10% of project costs
Cost Progress Risk Gap
Figure 15.7 | Bond refinancing mobilises institutional investors in mature project phase.  De-risk early-stage infrastructure projects.  Source: adapted from 
PIDG (2019).

1605
Investment and Finance Chapter 1515complete construction enabled by concession finance. Bank loans are 
paid back by issuing bonds once the construction phase is completed. 
Such bond refinancing over say, 15–25 years, in the low-risk mature 
project phase can provide a lower cost of capital. Market-making to 
develop a pipeline of investment opportunities uses a complimentary 
mix of high-risk capital options in the form of grants, guarantees, 
equity, and mezzanine financing that can help (Attridge and Gouett 
2021): (i) reduce up-front risks in the early phases, (ii) allow banks to 
recycle loans to new projects, and (iii) galvanise multilateral technical 
assistance for building bond yield curve benchmarks and de-risking 
local currency bond issuance of long tenors such as green bonds/
resilience bonds (Berensmann et al. 2015; CBI 2015; Mercer 2018; 
Dasgupta et al. 2019; PIDG 2019; Braga et al. 2021; CBI et al. 2021;

Hourcade et al. 2021a,b). Convergence (2019) points to investment 
from commercial banks with commercial debt of 11–15 years 
maturity being covered by guarantees. To achieve scale, some have 
issued special purpose vehicle (SPV) green infrastructure project 
bonds combining tenors up to 15 years with credit ratings assigned 
to mobilise investors with community trusts for local participation 
(Kaminker and Stewart 2012; Mathews and Kidney 2012; Mbeng 
Mezui and Hundal 2013; Essers et al. 2016; Moody’s Investors Service 
2016; Ng and Tao 2016; Harber 2017). Bond refinancing could be 
facilitated through standardised national infrastructure style bonds, 
national infrastructure funds (Amonya 2009; Ketterer and Powell 
2018) and country SPV infrastructure funds issuing bonds (Cavallo 
and Powell 2019) embedding MDBs.
Existing project risk facilities including guarantees could 
benefit from coordination, scaling and better reporting

frameworks (high confidence). Individual and clubs of developed 
and developing countries currently provide public guarantees 
(ADB 2015, 2018; IIGCC 2015; Pereira Dos Santos 2018; GGGI 
2019; Garbacz et al. 2021). However MDB business models impose 
limitations on use of guarantees and collaboration with other MDBs 
(Gropp et al. 2014; Schiff and Dithrich 2017; Lee et al. 2018; Pereira 
dos Santos and Kearney 2018). Loans continue to dominate as the 
financial instrument of choice by MDBs and DFIs, with guarantees 
mobilising the most private finance for OECD reported data, even 
if their use remains limited (IATFD 2020; OECD 2020c; Attridge 
and Gouett 2021). Ramping up the use of guarantees to mobilise 
private investment raises questions around understanding efficacy 
in the design as there is no one size that fits all and more research 
is required to better understand this aspect (Convergence 2019). 
Sample guarantee forms in literature: (i) single-country Sweden and

USA DFI forms (SIDA 2016, DCA 2018), (ii) multilateral institution 
offerings (Pereira Dos Santos 2018; IRENA 2020e), (iii) multi-
sovereign guarantees one-stop platforms such as those on the 
PIDG/GuarantCo (PIDG 2019) and Africa Guarantee Fund owned 
by DFIs, including the African Development Bank (AfDB), the French 
Development Agency (AFD), the Nordic Development Fund (NDF), 
and the KfW Development Bank (AGF 2020), (iv) MIGA, established 
to provide political risk guarantees (enhanced green MIGA) (Déau 
and Touati 2018), (v) multilateral partnerships with developing 
nations via infrastructure funds (Section  15.6.7.2) and green 
infrastructure options (de Gouvello and Zelenko 2010; Studart and 
Gallagher 2015), (vi) guarantees embedded in project risk facilities 
such as currency fund TCX established by 22 DFIs (TCX 2020), and (vii) ASEAN and African multi-sovereign regional local currency bond 
guarantee funds and a co-guarantee platform (GGGI 2019; Garbacz

et al. 2021). Fossil fuels currently benefit from de-risking tools from 
export credit agencies (Lawrence and Archer 2021), with questions 
around sustainable development (Wright 2011); Gupta et al. (2020) 
argue that these could be deployed for renewable energy. Sample 
project facilities reflecting the diverse project types across developing 
country regions can include i) UNEP Seed Capital ii) C40 Cities Facility 
iii) Blue Natural Capital Facility (IUCN 2021); iv) Clean Cooking Fund 
(ESMAP 2021) v) opportunities for guarantees in LDCs (Garbacz et al. 
2021) vi) World Bank’s Renewables Risk Mitigation (GCF 2021) and 
World Bank’s Global Infrastructure Facility (GGGI 2019).. Multilaterals 
offer credit enhancement to manage both actual and perceived risks: 
in India’s corporate sector, renewable energy SPV project bonds 
have been guaranteed jointly by ADB and an infrastructure company 
raising the credit rating from sub-investment grade to investment

grade to lower borrowing costs (ADB 2018; Agarwal and Singh 2018; 
Carrasco 2018).
Investment vehicles into green infrastructure come in various forms 
(high confidence) and can include indirect corporate investment such 
as bonds; semi-direct investment funds via pooled vehicles such as 
infrastructure funds and private equity funds and project investment 
(direct) in green projects through equity and debt including loans, 
project bonds and green bonds. For pension funds in Australia and 
Canada, direct investment in infrastructure is about 5% of total AUM 
(Inderst and Della Croce 2013) whilst less than 1% for OECD pension 
funds goes to green infrastructure (Kaminker et  al. 2013). Some 
regional developing country institutional investors use a variety of 
investment vehicles that span SPVs, private equity, domestic and 
regional local currency bond markets with statutory level mandates 
to address historic inequities (GEPF 2019). Cross-border collaboration

in regional power markets such as Europe’s Nordpool; for developing 
countries could be led by repository of technical partnership from 
infrastructure funds and multilaterals (Oseni and Pollitt 2016; Juvonen 
et al. 2019; Chen et al. 2020; Nordpool 2021). Barriers to investments 
include non-standardised investment vehicles of scale and lack 
of national infrastructure road maps to give investor confidence 
in government commitment. Some have set up infrastructure 
coordinating entities embedding local science and engineering R&D 
(IPA 2021; National Infrastructure Commission 2021). Arezki et  al. 
(2016) argue that coordination within existing platforms could create 
a  global infrastructure investment platform for de-risking through 
guarantees and securitisation; Matthäus and (Mehling (2020) point 
to a global guarantee mechanism. Such AAA multilateral approaches 
create credibility-enhancing effects in developing capital markets.

Hourcade et al. (2021a) suggest that the overall economic efficiency 
could be higher with guarantees calibrated per tonne on an agreed 
‘social, economic, and environmental value of mitigation actions 
[and] their co-benefits’ (Article 108, Paris Agreement) basis, which 
would operate as a notional carbon price (High-Level Commission on 
Carbon Prices 2017). The grant equivalent of guarantees and induced 
equity inflows could be far beyond the USD100 billion promise. 
Such cooperative solutions in adopting development of local capital 
markets would end the drawbacks of the current plethora of low-
scale fragmented project-by-project and ‘special-purpose’ pilots 
and programmes.

1606
Chapter 15 Investment and Finance15Harnessing existing bond markets and securities exchanges 
in nascent markets. The G20 has an action plan to support 
strengthening local currency bond markets and development of local 
capital markets is also part of the option for financing UN SDGs in 
developing countries (UN 2015a, 2019, 2020; IATFD 2016, 2021). 
Primers are available on bond market development to support policy 
choices (World Bank and IMF 2001; Silva et  al. 2020; World Bank 
2020; Adrian et al 2021; IMF and World Bank 2021). Developing 
government bond yield curves with different maturities can be an 
important policy objective (high confidence). This can support pricing 
discovery, liquidity (Wooldridge 2001) and can be achieved through 
step by step tranches from shorter to longer maturities to boost 
confidence and encourage municipals and other quasi-sovereigns. 
Money market instruments (such as, green commercial paper) anchor

the short end of the yield curve with bonds of varying maturity issued 
by sovereign/quasi-sovereign entities (national treasuries, SOEs, 
municipalities) to mobilise investors (Goodfriend 2011; LSEG 2018; 
Tolliver et al. 2019). A variety of bonds are being used for developing 
countries including green (Ketterer et  al. 2019), blue-water (Roth 
et  al. 2019), transition, SDG/social, biodiversity bonds (Aglionby 
2019), green/resilience bonds (AAC 2021); gender bonds (Andrade 
and Prado 2020) diaspora (LSEG 2017) and infrastructure project 
bonds (CBK 2021). Local policymakers would gain from technical 
and financial assistance in building green yield curves, for example 
with support from multilaterals (EIB 2012; IATFD 2016; Shi 2017; EIB 
2018; Impact Investing Institute 2021). Green bonds are one of the 
most readily accessible to help fund Paris goals (Tolliver et al. 2019; 
Tuhkanen and Vulturius 2020). Section 15.3.2 refers to the growth in

labelled bond markets (CBI 2021a), low borrowing costs and yield 
curve building in Europe (Bahceli 2020; Serenelli 2021; Stubbington 
2021; UK DMO 2021). For developing countries, labelled bonds have 
mostly been in hard currency (e.g. Smith 2021) despite local currency 
markets making up more than 80% total debt stock (IMF and World 
Bank 2016; Silva et al. 2020; Adrian et al 2021; Inderst 2021). The 
labelled bonds issuance by multilaterals do not currently mobilise 
the trillion levels needed. Research studies show that participating 
in green bond markets in part depends on a country having credible 
NDCs (Tolliver et  al. 2020a; Tolliver et  al. 2020b) and highlights 
diverse approaches working together to support local bond market 
development (Amacker and Donovan 2021; ICMA 2021; IMF and 
World Bank 2021).
Technical assistance options would benefit from coordination. 
Labelled bond costs remain high. Developing countries 
are using fiscal incentives, grants, and guarantees to

support nascent bond markets with most taxonomies under 
development (high confidence). Technical assistance requirements 
to improve the investment climate and bond market development 
will vary across national capacities. These would benefit from 
the USD100 billion UNFCCC grant equivalent basis to develop 
(i) regulatory and policy frameworks; (ii) UN national statistical 
systems (Singh et al. 2016; MacFeely and Barnat 2017; Paris21 2018; 
Bleeker and Abdulkadri 2020); (iii) credible NDC and SDG investment 
plans;  (iv) project assessment certification and taxonomies; (v) bond 
market guidelines; and (vi) public finance management (US DoJ 
2009; US DoJ 2019). Other technical assistance channels include 
diaspora entities, universities and learned societies (ICEAW 2012; UNFCCC 2021). LDCs supported by humanitarian entities are 
least likely to have active capital markets (ICRC 2020; IDFC 2020; 
Cao et al. 2021b). Clubs of LDCs are partnering with AAA MDBs in

aggregation approaches (AfDB 2020; GCF 2020b). Some UN entities 
provide technical assistance on municipal aggregation of projects 
(UN CDF 2021a), with Africa, LDC, SIDS nations and cities accessing 
green technical facilities and listings for labelled bonds (C40 Cities 
Climate Leadership Group 2016; Gorelick 2018; Jackson 2019; FSD 
Africa and CBI 2020; Gorelick and Walmsley 2020; MoE Fiji 2020; IFC 
2021c). Elevated climate risks imperil developing country ability to 
repay debts (Schmidt 2014; Buhr et al. 2018; Volz et al. 2020; Dibley 
et al. 2021). To lower overall costs and achieve more, entities have 
accessed technical assistance, listed local currency labelled bonds, 
and used credit enhancing bond guarantees, regulatory treatments 
and philanthropy schemes (Europe 2020 Project Bond Initiative 2012; 
SBN 2018; Agliardi and Agliardi 2019; Banga 2019). In the regions, 
China issued guidelines for stock exchanges and regulatory support

for green bonds (Cao and Ma 2021), India issued regulations for 
local issuance of green bonds (CBI 2019a), while in the Latin America 
and Caribbean region, both plain vanilla and labelled bonds use the 
same authority (Ketterer et al. 2019). African, LDC and SIDS nations 
are reviewing ways to harness local exchanges (SSE 2018; GCF 
2019; World Bank et al. 2021b; UN CDF 2021b). For taxonomies, the 
differences reflect the multitude of local Just Transition pathways, 
some with a  purely environmental focus and others incorporating 
livelihood improvements (ICMA 2021). The sustainable bond market 
has been expanding as transition bonds become listed in anticipation 
of future developments (Roos 2021).
Progress towards transparency using scientific-based methods 
to build trust and accountability. After 60 years of development 
finance, critics underline limits coming from i) multilaterals model, 
lack of transparency around aid and debt (Mkandawire 2010; Lee

2017; PWYF 2019; Bradlow 2021; Gianfagna et  al. 2021) ii) illicit 
finance (Plank 1993; Sachs and Warner 2001; Hanlon 2016; US DoJ 
2019) ) iii) lack of developed country commitment to pledges (Nhamo 
and Nhamo 2016) iv) unregulated players as financial intermediaries 
in blended finance (Pereira 2017; Donaldson and Hawkes 2018; 
Attridge and Engen 2019; Tan 2019) v) weak accountability reflected 
in soft SDG data and vi) burden of responsibility in mobilising Paris 
and SDG resources to countries with historically soft institutional 
capacity (Hickel 2015; Donald and Way 2016; Scheyvens et al. 2016; 
Liverman 2018). Literature around trust in blended finance pinpoints 
four progress areas in accountability. First, debt transparency through 
public debt registries, centralised UN legacy debt restructuring and 
science-centred UN national statistical systems (Donaldson and 
Hawkes 2018; Jubilee Debt Campaign 2019; Stiglitz and Rashid 2020).

Second, international reporting bell-weathers could be called upon to 
produce harmonised mandatory reporting frameworks that capitalise 
on TCFD to capture climate, debt sustainability (Section 15.6.7.3), SDG 
and fossil fuels (GISD 2020). Third, standardisation of assessment by 
third parties of the quantity and values of carbon saved by green 
projects (Hourcade et al. 2012) and of their contribution to quantified 
performance biodiversity targets (Finance for Biodiversity Initiative 
2021) to facilitate their bundling, securitisation and repackaging in 
standardised liquid products and bonds (Arezki et al. 2016; Blended 
Finance Taskforce 2018a).

1607
Investment and Finance Chapter 151515.6.8 Facilitating the Development of New Business 
Models and Financing Approaches
New and innovative business models and financing approaches have 
emerged to help overcome barriers related to transactions costs by 
aggregating and/or transferring financing needs and establishing 
supply of finance for stakeholder groups lacking financial inclusion 
15.6.8.1 Service-based Business Models in the Energy 
and Transport Sectors
Energy as a service (EaaS) is a business model whereby customers 
pay for an energy service without having to make any upfront capital 
investment (PwC 2014; Hamwi and Lizarralde 2017; Cleary and 
Palmer 2019). EaaS performance-based contracts can also be a form 
of ‘creative financing; for capital improvement that makes it possible 
to fund energy upgrades from cost reductions and deployment 
of decentralised renewable energy (KPMG 2015; Moles-Grueso

et al. 2021). Innovation in EaaS has started at the household level, 
where smart meters using real-time data are used to predict peak 
demand levels and optimise electricity dispatch (Chasin et al. 2020; 
Government of UK 2016; Smart Energy International 2018).
Aggregators. An aggregator is a  grouping of agents in a  power 
system to act as a  single entity when engaging in power system 
markets (MIT 2016). Aggregators can use operation optimisation 
platforms to provide real-time operating reserve capacity and 
a range of balancing services to integrate higher shares of variable 
renewable energy (Zancanella et  al. 2016; Ma et  al. 2017; Enbala 
2018; Research and Markets 2017; IRENA 2019b). This makes 
a  business case for deferred investments in grid infrastructure 
(medium confidence). Aggregating and managing demand-response 
of heat systems (micro CHP and heat pumps) has shown reduction in 
peak demand (TNO 2016).

Peer-to-peer (P2P) electricity trading. Producers and consumers 
can directly trade electricity with other consumers in an online 
marketplace to avoid the relatively high tariffs and the relatively 
low buy-back rates of traditional utilities (Liu et  al. 2019; IRENA 
2020f). P2P models trading with distributed energy resources reduce 
transmission losses and congestion (Mengelkamp et al. 2018; SEDA 
2020; Lumenaza 2020; Sonnen 2020; UNFCCC 2020).
Community ownership models. Community ownership models 
refer to the collective ownership and management of energy-related 
assets with lower levels of investment, usually distributed renewable 
energy resources but also recently in heating systems and energy 
services (e.g., storage and charging) (Gall 2018; IRENA 2018; Kelly 
and Hanna 2019; Singh et al. 2019; Bisello et al. 2021; Maclurcan and 
Hinton 2021). Community ownership projects may need significant 
upfront investments, and the ability of communities to raise the

required financing might prove insufficient, which can be supported 
by microcredits in the initial stages of the projects (Aitken 2013; 
Federici 2014; REN21 2016; Rescoop 2020).Payment method: Pay-as-you-go (PayGo). PayGo business 
models emerged to address the energy access challenge and 
provide chiefly solar energy at affordable prices, using mobile 
telecommunication to facilitate payment through instalments;  Yadav 
et al. 2019). However, PayGo has the technology and product risk, 
requires a  financially viable and large customer base, and the 
system supplier must provide a significant portion of the finance 
and requires substantial equity and working capital (C40 Cities 
Climate Leadership Group 2018).
Transport sector business models. Analog to EaaS, mobility as 
a service (MaaS) offers a business model whereby customers pay for 
a  mobility service without making any upfront capital investment 
(e.g., buying a car). MaaS tends to deliver significant urban benefits

(e.g., cleaner air) and brings in efficiency gains in the use of resources 
(high confidence). However, the switch to MaaS hardly improves the 
carbon footprint and further tempted on-demand mobility is likely 
to nurture carbon emissions (Suatmadi et  al. 2019). Therefore, to 
support climate change mitigation, MaaS must be integrated with 
the deployment of smart charging of electric (autonomous) vehicles 
coupled to renewable energy sources (IRENA 2019d; Jones and 
Leibowicz 2019).
Financial technology applications to climate change. Financial 
technology, abbreviated as ‘fintech’, applies to data-driven 
technological solutions that aim to improve financial services 
(Dorfleitner et al. 2017; Lee and Shin 2018; Schueffel 2018). Fintech 
can enhance climate investment in innovative financial products and 
build trust through data, but also presents some challenges including 
potentially significant emissions from increased energy use with

distributed transactions (Lei et al. 2021). Blockchain is a key fintech 
that secures individual transactions in a  distributed system, which 
can have many applications with high impact potential but is also 
associated with uncertainty (OECD 2019c; World Energy Council 
2019). Fintech applications with climate change mitigation potential 
have been growing recently, including tracking payment or asset 
history for credit scoring in AFOLU activities (Nassiry 2018; Davidovic 
et al. 2019), blockchain supported grid transactions (Livingston et al. 
2018), carbon accounting throughout value chains (World Bank 
2018b), or transparency and verification mechanisms for green 
financial instrument investors (Kyriakou et al. 2017; Stockholm Green 
Digital Finance 2017). Generally, blockchain and digital currency 
applications are not well covered by governance systems (Tapscott 
and Kirkland 2016; Nassiry 2018), which could lead to problems with

security (Davidovic et al. 2019), and some licensing and prudential 
supervision frameworks are in flux.
15.6.8.2 Nature-Based Solutions Including REDD+
Nature-based solutions are ‘actions to protect, sustainably manage 
human well-being and biodiversity benefits’ (Cohen-Shacham et al. 
2016). Nature-based solutions consist of a wide range of measures 
including ecosystem-based mitigation and adaptation.

1608
Chapter 15 Investment and Finance15The studies on investment and finance for nature-based solutions 
is still limited. However, frameworks and schemes to incentivise 
the implementation of nature-based solutions, such as reducing 
emissions from deforestation and forest degradation and the role of 
conservation, sustainable management of forests and enhancement 
of forest carbon stocks in developing countries (REDD+), which 
contributes to climate change mitigation, has been actively 
discussed under the UNFCCC, with lessons from finance for REDD+ 
being available.
If effectively implemented, nature-based solutions can be cost-
effective measures and able to provide multiple benefits, such as 
enhanced climate resilience, enhanced climate change mitigation, 
biodiversity habitat, water filtration, soil health, and amenity values 
(high confidence) (Griscom et al. 2017; Keesstra et al. 2018; OECD 
2019d; Griscom et al. 2020; Dasgupta 2021).

Nature-based solutions have large potential to address climate 
change and other sustainable development issues (high confidence). 
Nature-based solutions are undercapitalised and the limited 
investment and finance, especially limited private capital, is widely 
recognised as one of the main barriers to the implementation and 
monitoring of the nature-based solutions (Seddon et  al. 2020; 
Toxopeus and Polzin 2021; UNEP et al. 2021) Finance and investment 
models that generate their own revenues or consistently save costs 
are necessary to reduce dependency on grants (Schäfer et al. 2019; 
Wamsler et al. 2020).
REDD+. REDD+ can significantly contribute to climate change 
mitigation and also produce other co-benefits like climate 
change adaptation, biodiversity conservation, and poverty reduction, 
if well-implemented (high confidence) (Milbank et al. 2018; Morita 
and Matsumoto 2018). We use the term REDD+ broadly, not limited

to REDD+ implemented under the UNFCCC decisions, including 
Warsaw Framework for REDD+ (Chapter 14), but include voluntary 
REDD+ projects, such as projects which utilise voluntary carbon 
markets. Finance is a core element that incentivises and implements 
REDD+ activities. Various financial sources are financing REDD+ 
activities, including bilateral and multilateral, public and private, 
and international and domestic sources, with linking with several 
finance approaches/mechanisms including results-based finance 
and voluntary carbon markets (FAO 2018). However, there is lack 
of sufficient finance for REDD+ (Lujan and Silva-Chávez 2018; 
Maguire et  al. 2021). REDD+ under the UNFCCC is implemented 
in three phases: readiness, implementation, and results-based 
payment phases. The Ecosystem Marketplace identified that at least 
USD5.4 billion in REDD+ in three phases funding has been committed 
through multiple development finance institutions so far (Maguire

et al. 2021), and public funds are main sources that are supporting 
three phases, and most of the REDD+ finance was spent on the 
readiness phase (Atmadja et al. 2018; Lujan and Silva-Chávez 2018; 
Watson and Schalatek 2021). There is a significant gap between the 
existing finance and finance needs of REDD+ in each phase (Lujan 
and Silva-Chávez 2018). Furthermore, private sector contributions to 
REDD+ are currently limited mostly to the project-scale payments for 
carbon offsets/units through voluntary carbon markets (McFarland 
2015; Lujan and Silva-Chávez 2018).Current main challenges of REDD+ finance include the uncertainty of 
compliance carbon markets (which allow regulated entities to obtain 
and surrender emissions allowances or offsets to meet regulatory 
emissions reduction targets) (Maguire et al. 2021), as well as limited 
engagement of the private sector in REDD+ finance (high confidence). 
With regard to the compliance carbon markets, at the international

level, integrating climate cooperation through carbon markets into 
Article 6 of the Paris Agreement and including REDD+ has potential 
to enable emission reduction in more cost-effective ways, while the 
links between carbon markets and REDD+ under Article 6 is under 
discussion at the UNFCCC (Environmental Defense Fund 2019; 
Maguire et al. 2021) (Chapter 14). At the national and subnational 
levels, although compliance carbon markets such as in New Zealand, 
Australia and Colombia allow forest carbon units, how REDD+ will 
be dealt in the national and subnational government-led compliance 
carbon markets is uncertain (Streck 2020; Maguire et al. 2021). As 
for limited engagement of the private sector in REDD+ finance, there 
are various reasons why mobilising more private finance in REDD+ is 
difficult (Dixon and Challies 2015; Laing et al. 2016; Golub et al. 2018; 
Ehara et al. 2019; Streck 2020). The challenges include the needs of

a  clear understanding of carbon rights and transparent regulation 
on who can benefit from national REDD+ (Streck 2020); a  clear 
regulatory framework and market certainty (Dixon and Challies 2015; 
Laing et al. 2016; Golub et al. 2018; Ehara et al. 2019); strong forest 
governance (Streck 2020), and implementation of REDD+ activities 
at national and subnational levels. Other challenges are associated 
with the nature of forest-based mitigation activities, the costs and 
complexity of monitoring, reporting and verification of REDD+ 
activities, because of the need to consider the risks of permanence, 
carbon leakage, and precisely determine and monitor the forest 
carbon sinks (van der Gaast et al. 2018; Yanai et al. 2020). Although 
REDD+ has many challenges to mobilise more private finance, there 
is discussion on exploring other finance opportunities for the forest 
sector, such as building new blended finance models combining

different funding sources like public and private finance (Streck 2016; 
Rode et al. 2019), and developing enhanced bonds for forest-based 
mitigation activities (World Bank 2017).
Private finance opportunities for nature-based solutions. The 
development of nature-based solutions faces barriers that relate to 
the value proposition, value delivery and value capture of nature-
based solutions business models and sustainable sources of public/
private finance to tap into (high confidence) (Toxopeus and Polzin 
2017; Mok et al. 2021). However, the demand for establishing new 
finance and business models to attract both public and private 
finance to nature-based solutions is increasing in a wide range of 
topics such as urban areas, forestry and agriculture sectors, and blue 
natural capital including mangroves and coral reefs (Toxopeus and 
Polzin 2017; EIB 2019; Cziesielski et al. 2021; Mok et al. 2021; Thiele 
et al. 2021; UNEP et al. 2021). Furthermore, the recognition of the

needs of financial institutions to identify the physical, transition and 
reputational risks resulting from not only climate change but also loss 
of biodiversity is gradually increasing (De Nederlandsche Bank and 
PBL Netherlands Environmental Assessment Agency 2020; Dasgupta 
2021; TNFD 2021). Development of finance and business models for 
nature-based solutions needs to be explored, for example through 
utilising a  wide range of financial instruments (e.g.,  equity, loans,

1609
Investment and Finance Chapter 1515bonds, and insurance), and creating standard metrics, baselines and 
common characteristics for nature-based solutions to promote the 
creation of a new asset class (Thiele et al. 2021; UNEP et al. 2021).
15.6.8.3 Exploring Gender-responsive Climate Finance
Global and national recognition of the lack of finance for women has 
led to increasing emphasis on financial inclusion for women (high 
confidence). Currently, it is estimated that 980 million women are 
excluded from formal financial system (Miles and Wiedmaier-Pfister 
2018); and there is a  9% gender gap in financial access across 
developing countries (Demirguc-Kunt et al. 2018). This gender gap 
is the percentage difference between men and women with bank 
accounts as measured and reported in the Global Financial Inclusion 
(Global Findex) database. Policies and frameworks to expand and 
enhance financial inclusion also extend to the area of climate

finance (high confidence). Since AR5, there remain many questions 
and not enough evidence on the gender, distribution and allocative 
effectiveness of climate finance in the context of gender equality and 
women’s empowerment (Williams M., 2015; Chan et al. 2018; Wong 
et al. 2019). Nonetheless, the existing global policy framework (entry 
points, policy priorities, etc.) of climate funds is gradually improving 
in order to support women’s financial inclusion in both the public 
and the private dimensions of climate finance/investment (Schalatek 
2015; Chan et  al. 2018; Schalatek 2020). At the level of public 
multilateral climate funds, there have been significant improvements 
in integrating gender equality and women’s empowerment 
issues in the governance structures, policies, project approval and 
implementation processes of existing multilateral climate funds such 
as the UNFCCC’s funds managed by the Global Environment Facility,

the Green Climate Fund and the World Bank’s CIFs (high confidence) 
(Schalatek 2015; Williams M., 2015; Sellers 2016; GCF 2017). But 
according to a  recent evaluation report, the integration of gender 
into operational policies and programmes is fragmented and there is 
lack of an ‘adequate, systematic and comprehensive gender equality 
approach for the allocation and distribution of funds for projects 
and programmes on the ground’ (GEF Independent Evaluation 
Office 2017; Schalatek 2018). The review found that ‘almost half 
of the analysed sample of 70 climate projects were judged to be 
largely gender-blind, and only 5% considered to have successfully 
mainstreamed gender, including in two Least Developed Countries 
Fund adaptation projects’ (GEF Independent Evaluation Office 
2017; Schalatek 2018). While the GCF requires funding proposals 
to consider gender impact as part of their investment framework,16 
the fund does not have its own funding stream targeted to women’s

project on the ground, nor is there as yet an evaluation as to how 
entities are actually implementing gender action plan in the projects. 
In the case of the CIFs, as noted by Schalatek (2018), ‘gender is not 
included in the operational principles of the Pilot Program on Climate 
Resilience (PPCR), which funds programmatic adaptation portfolios 
in a  few developing countries, although most pilot countries have 
16 Notably, the GCF provides guidance to Accredited Entities submitting funding proposals on the inclusion of an initial gender and social assessment during the project 
planning, preparation and development stage and a gender and social inclusion action plan at the project preparation stage.
17 The Women’s Livelihood Bond (WLB) series has been on the market since 2017 when WLB1 was launched. WLB2 issuance of USD12 million arrived January 2020. WLB3

was launched December 2020 to support 180,000 underserved women and women entrepreneurs in the Asia Pacific region to respond, to recover from, and to build 
resilience in the aftermath of the COVID-19 pandemic (Rockfeller Foundation and Shujog 2016; IIX 2020).included some gender dimensions’. And, ‘gender is not integrated into 
the operations of the Clean Technology Fund (CTF), which finances 
large-scale mitigation in large economies and accounts for 70% of 
the CIFs’ pledged funding portfolio of 8.2 billion USD’ (Schalatek 
2018). However, both the Forest Investment Program (FIP) and the 
Scaling-Up Renewable Energy in Low-Income Countries Program 
(SREP) have integrated gender equality as either a co-benefit or core 
criteria of these programmes (Schalatek 2018).
Overall, efforts to promote gender responsive/sensitive climate 
finance, at national and local levels, both in the public and private 
dimensions and more specifically in mitigation-oriented sectors such

as clean and renewable energy, remain deficient (high confidence). 
Recent developments in the capital markets in the areas of social 
bond are focused around gender bonds – debt instruments targeted 
to activities and behaviours that are relevant to gender equality and 
women’s empowerment. These bonds are aligned with Sustainability-
linked Bonds as well as Social Bonds Principles of the International 
Capital Market Association. Issuances of gender-labelled bonds 
are increasing in the Asia Pacific region (the most comprehensive 
initiative is the Impact Investment Exchange’s (IIX) multi-country 
USD150 million Women’s Livelihood Bond17) and in Latin America, 
Colombia, Mexico and Panama each have gender bond issuances). 
Additionally, a few developing countries, such as Pakistan (May 2021) 
and Morocco (March 2021) have issued gender bond guidelines for 
financial market participants.
Linkage to sectoral climate change issues and gender and

climate finance. Subsets of actions designed to enhance women’s 
more formal integration into climate policies, programmes and 
actions by the global private sector include: investment in clean 
energy, redirecting funds to support women and vulnerable regions 
as a component of social and green bonds as well as insurance for 
climate risk management. In the latter context, insurance providers 
are arguing that ‘given the fact that women are disproportionately 
affected by climate change, there could be new finance innovations 
to address this gap’.(Miles and Wiedmaier-Pfister 2018). AXA 
and IFC estimate that the global women’s insurance market 
has the opportunity to grow to three times its current size, to 
UDS1.7  trillion by 2030 (AXA Group et  al. 2015; GIZ et  al. 2017). 
However, across the board, and in particular with regard to public 
funds, despite improvements in the substantive gender sensitisation 
and operational gender responsiveness of multilateral and bilateral

climate finance funds operations, current flows of public and climate 
finance do not seem to be going to women and local communities 
in significant amounts (Chan et  al. 2018; Schalatek 2020). At the 
same time, evaluations of the effectiveness of climate finance show 
that equitable flow of climate finance can play an important role 
in levelling the playing field and in enabling women and men to 
successfully respond to climate change and to enable the success and 
sustainability of local response in ensuring effective and sustainable

1610
Chapter 15 Investment and Finance15climate strategies that can contribute to the global goals of the Paris 
Agreement (Minniti and Naudé 2010; Bird et al. 2013; Barrett 2014; 
Eastin 2018). This is particularly, so in the case of female-owned MSMEs, who, the literature increasingly shows, are key to promoting 
resilience at micro and macro scale in many developing countries 
(Omolo et al. 2017; Atela et al. 2018; Crick, F . et al. 2018).
FAQ 15.1 |   What’s the role of climate finance and the finance sector for a transformation towards 
a sustainable future?
The Paris Agreement has widened the scope of all financial flows from climate finance only to the full alignment of finance flows 
with the long-term goals of the Paris Agreement. While climate finance relates historically to the financial support of developed 
countries to developing countries, the Paris Agreement and its Article 2.1(c) have developed a new narrative that goes much beyond

traditional flows and relates to all sectors and actors. Finance flows are consistent when the effects are either neutral with or 
without positive climate co-benefits to climate objectives; or explicitly targeted to climate benefits in adaptation and/or mitigation 
result areas. Climate-related financial risk is still massively underestimated by financial institutions, financial decision-makers more 
generally and also among public sector stakeholders, limiting the sector’s potential of being an enabler of the transition. The private 
sector has started to recognise climate-related risks and consequently redirect investment flows. Dynamics vary across sectors and 
regions with the financial sector being an enabler of transitions in only some selected (sub-)sectors and regions. Consistent, credible, 
timely and forward-looking political leadership remains central to strengthen the financial sector as enabler.

FAQ 15.2 |   What’s the current status of global climate finance and the alignment of global 
financial flows with the Paris Agreement?
There is no agreed definition of climate finance. The term ‘climate finance’ is applied to the financial resources devoted to addressing 
climate change by all public and private actors from global to local scales, including international financial flows to developing 
countries to assist them in addressing climate change. Total climate finance includes all financial flows whose expected effect aims 
to reduce net greenhouse gas (GHG) emissions and/or to enhance resilience to the impacts of current and projected climate change. 
This includes private and public funds, domestic and international flows and expenditures. Tracking of climate finance flows faces 
limitations, in particular for national climate finance flows.
Progress on the alignment of financial flows with low GHG emissions pathways remains slow. Annual global climate finance

flows are on an upward trend since the Fifth Assessment Report, according to the Climate Policy Initiative reaching more than 
USD630 billion in 2019/2020, however, growth has likely slowed down and flows remain significantly below needs. This is driven 
by barriers within and outside the financial sector. More than 90% of financing is allocated to mitigation activities despite the 
strong economic rationale of adaptation action. Adjusting for higher estimates on current flows for energy efficiency based on 
International Energy Agency data, the dominance of mitigation becomes even stronger. Persistently high levels of both public and 
private fossil-fuel related financing as well as other misaligned flows continue to be of major concern despite recent commitments. 
Significant progress has been made in the commercial finance sector with regard to the awareness of climate risks resulting from

inadequate financial flows and climate action. However, a more consequent investment and policy decision-making that enables 
a rapid redirection of financial flows is needed. Regulatory support as a catalyser is an essential driver of such redirections. Dynamics 
across sectors and regions vary, with some being better positioned to close financing gaps and to benefit from an enabling role of 
finance in the short-term.
FAQ 15.3 |  What defines a financing gap, and where are the critically identified gaps?
A financing gap is defined as the difference between current flows and average needs to meet the long-term goals of the Paris 
Agreement. Gaps are driven by various barriers inside (short-termism, information gaps, home bias, limited visibility of future 
pipelines) and outside (e.g., missing pricing of externalities, missing regulatory frameworks) of the financial sector. Current mitigation

financing flows come in significantly below average needs across all regions and sectors despite the availability of sufficient capital 
on a global basis. Globally, yearly climate finance flows have to increase by a factor between three and six to meet average annual 
needs between 2020 and 2030.
Gaps are in particular concerning for many developing countries, with COVID-19 exacerbating the macroeconomic outlook and 
fiscal space for governments. Also, limited institutional capacity represents a key barrier for many developing countries, burdening 
risk perceptions and access to appropriately priced financing as well as limiting their ability to actively manage the transformation. 
Existing fundamental inequities in access to finance, as well as its terms and conditions, and countries’ exposure to physical impacts 
of climate change, overall result in a worsening outlook for a global just transition.

1644
Chapter 16 Innovation, Technology Development and Transfer16
Innovation in climate mitigation technologies has seen 
enormous activity and significant progress in recent years. 
Innovation has also led to, and exacerbated, trade-offs 
in relation to sustainable development (high confidence). 
Innovation can leverage action to mitigate climate change by 
reinforcing other interventions. In conjunction with other enabling 
conditions, innovation can support system transitions to limit 
warming and help shift development pathways. The currently 
widespread implementation of solar photovoltaic (solar PV) and 
light-emitting diodes (LEDs), for instance, could not have happened 
without technological innovation (high confidence). Technological 
innovation can also bring about new and improved ways of delivering 
services that are essential to human well-being. At the same time 
as delivering benefits, innovation can result in trade-offs that

undermine both progress on mitigation and progress towards other 
Sustainable Development Goals (SDGs). Trade-offs include negative 
externalities  – for instance, greater environmental pollution and 
social inequalities – rebound effects leading to lower net emission 
reductions or even increases in emissions, and increased dependency 
on foreign knowledge and providers (high confidence). Effective 
governance and policy has the potential to avoid and minimise such 
misalignments (medium evidence, high agreement). {16.1, 16.2, 
16.3, 16.4, 16.5.1, 16.6}
A systemic view of innovation to direct and organise the 
processes has grown over the last decade. This systemic view 
of innovation takes into account the role of actors, institutions 
and their interactions, and can inform how innovation systems 
that vary across technologies, sectors and countries, can 
be strengthened (high confidence). Where a  systemic view of 
innovation has been taken, it has enabled the development and

implementation of indicators that are better able to provide insights 
into innovation processes. This, in turn, has enabled the analysis 
and strengthening of innovation systems. Traditional quantitative 
innovation indicators mainly include research and development 
(R&D) investments and patents. Systemic indicators of innovation, 
however, go well beyond these approaches. They include structural 
innovation system elements including actors and networks, as well 
as indicators for how innovation systems function, such as access to 
finance, employment in relevant sectors, and lobbying activities. For 
example, in Latin America, monitoring systemic innovation indicators 
for the effectiveness of agroecological mitigation approaches has 
provided insights on the appropriateness and social alignment of 
new technologies and practices. Climate-energy-economy models, 
including integrated assessment models, generally employ a stylised

and necessarily incomplete view of innovation, and have yet to 
incorporate a systemic representation of innovation systems. {16.2, 
16.2.4, 16.3, 16.3.4, 16.5, Table 16.7, Box 16.1, Box 16.3, Box 16.10}
A systemic perspective on technological change can provide 
insights to policymakers supporting their selection of effective 
innovation policy instruments (high confidence). A combination 
of scaled-up innovation investments with demand-pull interventions 
can achieve faster technology unit cost reductions and more rapid 
scale-up than either approach in isolation (high confidence). These innovation policy instruments would nonetheless have to be tailored 
to local development priorities, to the specific context of different 
countries, and to the technology being supported. The timing of 
interventions and any trade-offs with sustainable development 
also need to be addressed. Public R&D funding and support, as 
well as innovation procurement, have proven valuable for fostering

innovation in small to medium cleantech firms. Innovation outcomes 
of policy instruments not necessarily aimed at innovation, such 
as feed-in tariffs, auctions, emissions trading schemes, taxes and 
renewable portfolio standards, vary from negligible to positive for 
climate change mitigation. Some specific designs of environmental 
taxation can also result in negative distributional outcomes. Most 
of the available literature and evidence on innovation systems 
come from industrialised countries and larger developing countries. 
However, there is a  growing body of evidence from developing 
countries and Small Island Developing States (SIDS). {16.4, 16.4.4.3, 
16.4.4.4, 16.5, 16.7}
Experience and analyses show that technological change is 
inhibited if technological innovation system functions are 
not adequately fulfilled. This inhibition occurs more often 
in developing countries (high confidence). Examples of such 
functions are knowledge development, resource mobilisation, and

activities that shape the needs, requirements and expectations 
of actors within the innovation system (guidance of the search). 
Capabilities play a key role in these functions, the build-up of which 
can be enhanced by domestic measures, but also by international 
cooperation (high confidence). For instance, innovation cooperation 
on wind energy has contributed to the accelerated global spread 
of this technology. As another example, the policy guidance by the 
Indian government, which also promoted development of data, 
testing capabilities and knowledge within the private sector, has been 
a key determinant of the success of an energy-efficiency programme 
for air conditioners and refrigerators in India. {16.3, 16.5, 16.6, Cross-
Chapter Box 12 in this chapter, Box 16.2}
Consistent with innovation system approaches, the sharing 
of knowledge and experiences between developed and 
developing countries can contribute to addressing global

climate and SDGs. The effectiveness of such international 
cooperation arrangements, however, depends on the way 
they are developed and implemented (high confidence). The 
effectiveness and sustainable development benefits of technology 
sharing under market conditions appear to be determined primarily 
by the complexity of technologies, local capabilities and the 
policy regime. This suggests that the development of planning 
and innovation capabilities remains necessary, especially in least-
developed countries and SIDS. International diffusion of low-emission 
technologies is also facilitated by knowledge spillovers from regions 
engaged in clean R&D (medium confidence). {16.6}
The evidence on the role of intellectual property rights (IPR) 
in innovation is mixed. Some literature suggests that it is 
a  barrier, while other sources suggest that it is an enabler 
to the diffusion of climate-related technologies (medium

confidence). There is agreement that countries with well-developed 
institutional capacity may benefit from a strengthened IPR regime,

1645
Innovation, Technology Development and Transfer Chapter 1616
but that countries with limited capabilities might face greater barriers 
to innovation as a consequence. This enhances the continued need for 
capacity building. Ideas to improve the alignment of the global IPR 
regime and address climate change include specific arrangements for 
least-developed countries, case-by-case decision-making and patent-
pooling institutions. {16.2.3.3, 16.5, Box 16.9}
Although some initiatives have mobilised investments in 
developing countries, gaps in innovation cooperation remain, 
including in the Paris Agreement instruments. These gaps 
could be filled by enhancing financial support for international 
technology cooperation, by strengthening cooperative 
approaches, and by helping build suitable capacity in developing 
countries across all technological innovation system functions 
(high confidence). The implementation of current arrangements of

international cooperation for technology development and transfer, 
as well as capacity building, are insufficient to meet climate objectives 
and contribute to sustainable development. For example, despite 
building a  large market for mitigation technologies in developing 
countries, the lack of a systemic perspective in the implementation 
of the Clean Development Mechanism, operational since the mid-
2000s, has only led to some technology transfer, especially to larger 
developing countries, but limited capacity building and minimal 
technology development (medium confidence). In the current 
climate regime, a more systemic approach to innovation cooperation 
could be introduced by linking technology institutions, such as the 
Technology Mechanism, and financial actors, such as the financial 
mechanism. {16.5.3}
Countries are exposed to sustainable development challenges 
in parallel with the challenges that relate to climate change.

Addressing both sets of challenges simultaneously presents 
multiple and recurrent obstacles that systemic approaches 
to technological change could help resolve, provided they 
are well managed (high confidence). Obstacles include both 
entrenched power relations dominated by vested interests that 
control and benefit from existing technologies, and governance 
structures that continue to reproduce unsustainable patterns of 
production and consumption (medium confidence). Studies also 
highlight the potential for cultural factors to strongly influence the 
pace and direction of technological change. Sustainable solutions 
require adoption and mainstreaming of locally novel technologies 
that can meet local needs, and simultaneously address the SDGs. 
Acknowledging the systemic nature of technological innovation, 
which involves many levels of actors, stages of innovation and 
scales, can lead to new opportunities to shift development pathways

towards sustainability. {16.4, 16.5, 16.6}
An area where sustainable development, climate change 
mitigation and technological change interact is digitalisation. 
Digital technologies can promote large increases in energy 
efficiency through coordination and an economic shift to 
services, but they can also greatly increase energy demand 
because of the energy used in digital devices. System-level 
rebound effects may also occur (high confidence). Digital 
devices, including servers, increase pressure on the environment due 
to the demand for rare metals and end-of-life disposal. The absence of adequate governance in many countries can lead to harsh 
working conditions and unregulated disposal of electronic waste. 
Digitalisation also affects firms’ competitiveness, the demand for 
skills, and the distribution of, and access to, resources. The existing 
digital divide, especially in developing countries, and the lack of 
appropriate governance of the digital revolution can hamper the

role that digitalisation could play in supporting the achievement of 
stringent mitigation targets. At present, the understanding of both 
the direct and indirect impacts of digitalisation on energy use, carbon 
emissions and potential mitigation, is limited (medium confidence). 
{Cross-Chapter Box 11 in this chapter, 16.2}
Strategies for climate change mitigation can be most effective 
in accelerating transformative change when actions taken to 
strengthen one set of enabling conditions also reinforce and 
strengthen the effectiveness of other enabling conditions 
(medium confidence). Applying transition or system dynamics 
to decisions can help policymakers take advantage of such high-
leverage intervention points, address the specific characteristics of 
technological stages, and respond to societal dynamics. Inspiration 
can be drawn from the global unit cost reductions of solar PV, which 
were accelerated by a combination of factors interacting in a mutually

reinforcing way across a limited group of countries (high confidence). 
{Box 16.4, Cross-Chapter Box 12 in this chapter}
Better and more comprehensive data on innovation indicators 
can provide timely insights for policymakers and policy design 
locally, nationally and internationally, especially for developing 
countries, where such insights are missing more often. Data 
needed include those that can show the strength of technological, 
sectoral and national innovation systems. It is also necessary to 
validate current results and generate insights from theoretical 
frameworks and empirical studies for developing countries contexts. 
Innovation studies on adaptation and mitigation other than energy 
and ex-post assessments of the effectiveness of various innovation-
related policies and interventions, including R&D, would also provide 
benefits. Furthermore, methodological developments to improve the 
ability of integrated assessment models (IAMs) to capture energy

innovation system dynamics, and the relevant institutions and 
policies (including design and implementation), would allow for more 
realistic assessment. {16.2, 16.3, 16.7}

1646
16.1 Introduction
Technological change and innovation are considered key drivers of 
economic growth and social progress (Brandão Santana et al. 2015; 
Heeks and Stanforth 2015). Increased production and consumption 
of goods and services creates economic benefits through higher 
demands for improved technologies (Gossart 2015). Since the 
Industrial Revolution, however, and notwithstanding the benefits, this 
production and consumption trend and the technological changes 
associated with it have also come at the cost of long-term damage 
to the life support systems of our planet (Alarcón and Vos 2015; 
Steffen et al. 2015). The significance of such impacts depends on the 
technology, but also on the intrinsic characteristics of the country or 
region analysed (Brandão Santana et al. 2015).
Other chapters in this volume have discussed technological change in

various ways, including as a framing issue (Chapter 1), in the context 
of specific sectors (Chapters 6–11), for specific purposes (Chapter 12) 
and as a  matter of policy, international cooperation and finance 
(Chapters  13–15). Chapter  2  discusses past trends in technological 
change and chapters 3 and 4 discuss it in the context of future modelling. 
In general, implicitly or explicitly, technological change is assigned an 
important role in climate change mitigation and achieving sustainable 
development (Thacker et al. 2019), as also discussed in past IPCC reports 
(IPCC 2014, 2018a). Chapter  16 describes how a  well-established 
innovation system at a national level, guided by well-designed policies, 
can contribute to achieving mitigation and adaptation targets along 
with broader Sustainable Development Goals (SDGs), while avoiding 
undesired consequences of technological change.
The environmental impacts of social and economic activities,

including emissions of greenhouse gases (GHGs), are greatly 
influenced by the rate and direction of technological changes (Jaffe 
et al. 2000). Technological changes usually designed and used to 
increase productivity and reduce the use of natural resources can 
lead to increased production and consumption of goods and services 
through different rebound effects that diminish the potential benefits 
of reducing the pressure on the environment (Kemp and Soete 1990; 
Grübler 1998; Sorrell 2007; Barker et al. 2009; Gossart 2015).
Those environmental impacts depend not only on which technologies 
are used, but also on how they are used (Grübler et al. 1999a). Technological change is not exogenous to social and economic 
systems; technologies are not conceived, selected, and applied 
autonomously (Grubler et al. 2018). Underlying driving forces of 
the problem, such as more resource-intensive lifestyles and larger 
populations (Hertwich and Peters 2009; UNEP 2014), remain largely

unchallenged. Comprehensive knowledge of the direct and indirect 
effects of technological changes on physical and social systems 
could improve decision-making, including in those cases where 
technological change mitigates environmental impacts.
A sustainable global future for people and nature requires rapid and 
transformative societal change by integrating technical, governance 
(including participation), financial and societal aspects of the solutions 
to be implemented (Sachs et al. 2019; Pörtner et al. 2021). A growing 
body of interdisciplinary research from around the world can inform 
implementation of adaptive solutions that address the benefits and 
drawbacks of linkages in social-ecological complexity, including 
externalities and rebound effects from innovation and technological 
transformation (Balvanera et al. 2017; Pörtner et al. 2021).
Technological change and transitional knowledge can reinforce 
each other. The value of traditional wisdom and its technological

practices provide examples of sustainable and adaptive systems 
that could potentially adapt to and mitigate climate change (Kuoljok 
2019; Singh et al. 2020). Peasants and traditional farmers have been 
able to respond well to climate changes through their wisdom and 
traditional  practices (Nicholls and Alteri 2013). The integration of 
the traditional wisdom with new technologies can offer new and 
effective solutions (Galloway McLean 2010).
Achieving climate change mitigation and other SDGs thus also 
requires rapid diffusion of knowledge and technological innovations. 
However, these are hampered by various barriers, some of which are 
illustrated in Table 16.1 (Markard et al. 2020).
The literature has been growing rapidly over the past decades on 
how, in a systemic way, the barriers to sustainability transition can be 
overcome in various circumstances. A central element is that national 
systems of innovation can help achieve both climate change goals and

SDGs, by integrating new ideas, devices, resources, new and traditional 
knowledge, and technological changes for more effective and adaptive 
solutions (Lundvall 1992). At the organisational level, innovation is seen 
Table 16.1 | Overview of challenges to accelerated diffusion of technological innovations.  Source: based on Markard et al. (2020).
Challenges Description Examples
Innovations in whole systemsSince entire systems are changing, changes in system architecture 
are also needed, which may not keep pace.Decentralisation of electricity supply and integration of variable sources.
Interaction between multiple 
systems and subsystemsSimultaneous, accelerating changes multiple systems or sectors, 
vying for the same resources and showing other interactions.Electrification of transport, heating and industry all using the same 
renewable electricity source.
Industry decline and 
incumbent resistanceDecline of existing industries and businesses can lead to incumbents

slowing down change, and resistance, e.g., from unions or workers.Traditional car industry leading to facture closures, demise of coal mining 
and coal-fired power generation leading to local job loss.
Consumers and 
social practicesConsumers need to change practices and demand patterns.Reduced car ownership in a sharing economy, trip planning for public 
and non-motorised transport, fuelling practices in electric driving.
Coordination in 
governance and policyIncreasing complexity of governance requires coordination between 
multiple levels of government and a multitude of actors relevant to 
the transition, e.g., communities, financial institutions, private sector.Multilevel governance between European Commission and member states 
in Energy Union package.

1647
as a process that can bring value by means of creating more effective 
products, services, processes, technologies, policies and business 
models that are applicable to commercial, business, financial and even 
societal or political organisations (Brooks 1980; Arthur 2009).
The literature refers to the terms ‘technology push‘, ‘market pull‘, 
‘regulatory push-pull‘, and ‘firm specific factors‘ as drivers for 
innovation, mostly to inform policymakers (Zubeltzu-Jaka et al. 2018). 
There has also been growing interest in social drivers, motivated by the 
recognition of social issues, such as unemployment and public health, 
linked to the deployment of innovative low-carbon technologies 
(Altantsetseg et al. 2020). Policy and social factors and the diverse 
trajectories of innovation are influenced by regional and national 
conditions (Tariq et al. 2017), and such local needs and purposes need

to be considered in crafting international policies aimed at fostering 
the global transition towards increased sustainability (Caravella and 
Crespi 2020). From this standpoint, a multidimensional, multi-actor, 
systemic innovation approach would be needed to enhance global 
innovation diffusion (de Jesus and Mendonça 2018), especially if this 
is to lead to overall sustainability improvements rather than result in 
new sustainability challenges.
Policies to mitigate climate change do not always take into account 
the effects of mitigation technologies on other environmental 
and social challenges (Arvesen et al. 2011). Policies also often 
disregard the strong linkages between technological innovation 
and social innovation; the latter is understood to be the use of soft 
technologies that brings about transformation through establishing 
new institutions, new practices, and new models to create a positive 
societal impact, characterised by collaboration that crosses traditional

roles and boundaries, between citizens, civil society, the state, and 
the private sector (Reynolds et al. 2017). Market forces do not provide 
sufficient incentives for investment in development or diffusion of 
technologies, leaving a role for public policy to create the conditions 
to assure a  systemic innovation approach (Popp 2010; Popp and 
Newell 2012). Moreover, public action is more than just addressing 
market failure, it is an unalienable element of an innovation system 
(Mazzucato 2013).
Coupling technological innovation with sustainable development 
and the SDGs would need to address overall social, environmental, and 
economic consequences, given that public policy is intertwined with 
innovation, technological changes and other factors in a  complex 
manner. Chapter 16 is organised in the following manner to provide 
an overview of innovation and technology development and transfer 
for climate change and sustainable development.

Section  16.2 discusses drivers of innovation process, including 
macro factors that can redirect technological change towards low-
carbon options. Representations of these drivers in mathematical 
and statistical models allow for explaining the past and constructing 
projections of future technological change. They also integrate the 
analysis of drivers and consequences of technological change within 
economic-energy-economy (or integrated assessment) models 
(Chapter  3). The section also describes the different phases of 
innovation and metrics, such as the widely used but also criticised 
technology readiness levels (TRLs).Section  16.3 discusses innovation as a  systemic process based on 
recent literature. While the innovation process is often stylised as 
a linear process, innovation is now predominantly seen as a systemic 
process in that it is a result of actions by, and interactions among, 
a  large set of actors, whose activities are shaped by, and shape,

the context in which they operate and the user group with which 
they are engaging.
Section  16.4 presents innovation and technology policy, including 
technology push (e.g.,  publicly funded R&D) and demand-pull 
(e.g.,  governmental procurement programmes) instruments 
that address potential market failures related to innovation and 
technology diffusion. The section also assesses the cost-effectiveness 
of innovation policies as well as other policy assessment criteria 
introduced in Chapter 13.
Section  16.5 assesses the role of international cooperation in 
technology development and transfer, in particular the mechanisms 
established under the UN Framework Convention on Climate Change 
(UNFCCC), but also other international initiatives for technology 
cooperation. The discussion on international cooperation includes 
information exchange, research, development and demonstration 
cooperation, access to financial instruments, intellectual

property rights, as well as promotion of domestic capacities and 
capacity building.
Section  16.6 describes the role of technology in sustainable 
development, including unintended effects of technological changes, 
and synthesises the chapter.
Finally, Section  16.7 discusses gaps in knowledge emerging 
from this chapter.
16.2 Elements, Drivers and Modelling 
of Technology Innovation
Models of the innovation process, its drivers and incentives 
provide a  tool for technology assessment, constructing projections 
of technological change and identifying which macro conditions 
facilitate development of low-carbon technologies. The distinction 
between stages of the innovation process allows for assessment of 
technology readiness (Section 16.2.1). Qualitative and quantitative 
analysis of the main elements underpinning innovation  – research 
and development (R&D), learning by doing, and spillovers – allows 
for an explanation of past and projected future technological changes

(Section 16.2.2). In addition, general purpose technologies can play 
a role in climate change mitigation.
In the context of mitigation pathways, the feasibility of any emission 
reduction targets depends on the ability to promote innovation 
in low- and zero-carbon technologies, as opposed to any other 
technology. For this reason, Section 16.2.3 reviews the literature of 
the levers influencing the direction of technological change in favour 
of low- and zero-carbon technologies. Moreover, representation of 
drivers in mathematical and statistical models from Section 16.2.2 
allows integration of its analysis with economic and climate effects

1648
within integrated assessment models (IAMs), hence permitting more 
precise modelling of decarbonisation pathways (Section 16.2.4).
In addition to technological innovation, other innovation approaches 
are relevant in the context of climate mitigation and more broadly 
sustainable development (Section  16.6). Frugal innovations, that 
is, ‘good enough‘ innovations that fulfil the needs of non-affluent 
consumers mostly in developing countries (Hossain 2018), are 
characterised by low costs, concentration on core functionalities, and 
optimised performance level (Weyrauch and Herstatt 2016) and are 
hence often associated with (ecological and social) sustainability 
(Albert 2019). Grassroots innovations are products, services 
and  processes developed to address specific local challenges and 
opportunities, and which can generate novel, bottom-up solutions

responding to local situations, interests and values. (Pellicer-Sifres 
et al. 2018; Dana et al. 2021).
16.2.1 Stages of the Innovation Process
The innovation cycle is commonly thought of as having three 
distinct innovation phases on the path between basic research 
and commercial application: Research and development (R&D); 
demonstration; and deployment and diffusion (IPCC 2007). Each 
of these phases differs with respect to the kind of activity carried 
out, the type of actors involved and their roles, financing needs, and 
the associated risks and uncertainties. All phases involve a process 
of trial and error, and failure is common; the share of innovation 
that successfully reaches the deployment phase is small. The path 
occurring between basic research and commercialisation is not linear 
(Section 16.3); it often requires a long time and is characterised by 
significant bottlenecks and roadblocks. Furthermore, technologies

may regress in the innovation cycle, rather than move forward (Skea et al. 2019). Successfully passing from each stage to the next 
one in the innovation cycle requires overcoming ‘valleys of deaths’ 
(Auerswald and Branscomb 2003; UNFCCC 2017), most notably the 
demonstration phase (Frank et al. 1996; Weyant 2011; Nemet et al. 
2018). Over time, new and improved technologies are discovered; 
this often makes the dominant technology obsolete, but this is not 
discussed in this report.
Table  16.2 summarises the different innovation stages and main 
funding actors, and maps phases into the technology readiness levels 
(TRLs) discussed in Section 16.2.1.4.
16.2.1.1 Research and Development
This phase of the innovation process focuses on generating 
knowledge or solving particular problems by creating a combination 
of artefacts to perform a particular function, or to achieve a specific 
goal. R&D activities comprise basic research, applied research

and technology development. Basic research is experimental or 
theoretical work undertaken primarily to acquire new knowledge 
of the underlying foundations of phenomena and observable facts, 
without any particular application or use in view. Applied research is 
original investigation undertaken in order to acquire new knowledge, 
primarily directed towards a  specific, practical aim or objective 
(OECD 2015a). Importantly, R&D activities can be incremental – that 
is, focused on addressing a  specific need by marginally improving 
an existing technology  – or radical, representing a  paradigm shift, 
promoted by new opportunities arising with the accumulation of new 
knowledge (Mendonça et al. 2018). Technology development, often 
leading to prototyping, consists of generating a  working model of 
the technology that is usable in the real world, proving the usability 
and customer desirability of the technology, and giving an idea of

its design, features and function (OECD 2015a). These early stages 
Table 16.2 | Stages of the innovation process (Section 16.2.1) mapped onto technology readiness levels (Section 16.2.1.4).  Source: adapted from Auerswald 
and Branscomb (2003), TEC (2017), IEA (2020a).
Stage Main funding actors Phases Related technology readiness levels (TRLs)
Research and 
developmentGovernments
FirmsBasic research  1 –  Initial idea (basic principles defined)
Applied research 
and technology 
development 2 –  Application formulated (technology concept and application of solution formulated)
 3 –  Concept needs validation (solutions need to be prototyped and applied)
 4 –  Early prototype (prototype proven in test conditions)
 5 –  Full prototype at scale (components proven in conditions to be deployed)
DemonstrationGovernments
Firms
Venture Capital
Angel investorsExperimental 
pilot project or 
full-scale testing 6 –  Full prototype at scale (prototype proven at scale in conditions to be deployed)

7 –  Pre-commercial demonstration (solutions working in expected conditions)
 8 –  First-of-a-kind commercial (commercial demonstration, full-scale deployment in final form)
 9 –  Commercial operation in early environment (solution is commercial available, needs evolutionary 
improvement to stay competitive)
10 –  Integration needed at scale (solution is commercial and competitive but needs further integration efforts)
11 – Proof of stability reached (predictable growth)
Deployment 
and diffusionFirms
Private equity
Commercial banks
Mutual fundsCommercialisation 
and scale-up
(business)
International organisations 
and financial institutions
Non-governmental 
organisations (NGOs)Transfer

1649
of technological innovation are referred to as the ‘formative phase’, 
during which the conditions are shaped for a technology to emerge 
and become established in the market (Wilson and Grubler 2013) and 
the constitutive elements of the innovation system emerging around 
a  particular technology are set up (Bento and Wilson 2016; Bento 
et al. 2018) (Section 16.3).
The outcomes of R&D are uncertain: the amount of knowledge that 
will result from any given research project or investment is unknown 
ex ante (Rosenberg 1998). This risk to funders (Goldstein and 
Kearney 2020) translates into underinvestment in R&D due to low 
appropriability (Weyant 2011; Sagar and Majumdar 2014). In the case 
of climate mitigation technologies, low innovation incentives for the 
private sector also result from a negative environmental externality 
(Jaffe et al. 2005). Furthermore, in the absence of stringent climate

policies and targets, incumbent fossil-based energy technologies are 
characterised by lower financing risk, are heavily subsidised (Davis 
2014; Kotchen 2021), and depreciate slowly (Arrow 1962a; Nanda 
et al. 2016; Semieniuk et al. 2021) (Section 16.2.3). In this context, 
public research funding plays a  key role in supporting high-risk 
R&D, both in developed and developing economies: it can provide 
patient and steady funding not tied to short-term investment returns 
(Kammen and Nemet 2007; Anadon et al. 2014; Mazzucato 2015a; 
Chan and Diaz Anadon 2016; Anadón et al. 2017; Howell 2017; 
Zhang et al. 2019) (Section 16.4). Public policies also play a role in 
increasing private incentives in energy research and development 
funding (Nemet 2013). R&D statistics are an important indicator 
of innovation and are collected following the rules of the Frascati 
Manual (OECD 2015a) (Section 16.3.3, Box 16.3 and Table 16.7).
16.2.1.2 Demonstration

Demonstration is carried out through pilot projects or large-scale 
testing in the real world. Successfully demonstrating a  technology 
shows its utility and that it is able to achieve its intended purpose and, 
consequently, that the risk of failure is reduced (i.e., that it has market 
potential) (Hellsmark et al. 2016). Demonstration projects are an 
important step to promote the deployment of low-carbon energy and 
industrial technologies in the context of the transition. Government 
funding often plays a large role in energy technology demonstration 
projects because scaling up hardware energy technologies is 
expensive and risky (Brown and Hendry 2009; Hellsmark et al. 2016). 
Governments’ engagement in low-carbon technology demonstration 
also signals support for businesses willing to take the investment 
risk (Mazzucato 2016). Venture capital, traditionally not tailored 
for energy investment, can also play an increasingly important role,

thanks to the incentives (e.g., through de-risking) provided by public 
funding and policies (Gaddy et al. 2017; IEA 2017a).
16.2.1.3 Deployment and Diffusion
Deployment entails producing a technology at large scale and scaling 
up its adoption and use across individual firms or households in a given 
market, and across different markets (Jaffe 2015). In the context of 
climate change mitigation and adaptation technologies, the purposeful 
diffusion to developing countries, is referred to as ‘technology transfer’. 
Most recently, the term ‘innovation cooperation’ has been proposed to indicate that technologies needs to be co-developed and adapted 
to local contexts (Pandey et al. 2021). Innovation cooperation is an 
important component of stringent mitigation strategies as well as 
international agreements (Section 16.5).
Diffusion is often sluggish due to lock-in of dominant technologies 
(Liebowitz and Margolis 1995; Unruh 2000; Ivanova et al. 2018), as

well as the time needed to diffuse information about the technologies, 
heterogeneity among adopters, the incentive to wait until costs fall 
even further, the presence of behavioural and institutional barriers, 
and the uncertainty surrounding mitigation policies and long-
term commitments to climate targets (Gillingham and Sweeney 
2012; Corey 2014; Jaffe 2015; Haelg et al. 2018). In addition, novel 
technology has been hindered by the actions of powerful incumbents 
who accrue economic and political advantages over time, as in the 
case of renewable energy generation (Unruh 2002; Supran and 
Oreskes 2017; Hoppmann et al. 2019).
Technologies have been shown to penetrate the market with a gradual 
non-linear process in a  characteristic logistic (S-shaped) curve 
(Grübler 1996; Rogers 2003). The time needed to reach widespread 
adoption varies greatly across technologies relevant for adaptation 
and mitigation (Gross et al. 2018); in the case of energy technologies,

the time needed for technologies to get from a  10–90%  market 
share of saturation ranges between 5 to over 70 years (Wilson 2012). 
Investment in commercialisation of low-emission technology is largely 
provided by private financiers; however, governments play a key role 
in ensuring incentives through supportive policies, including R&D 
expenditures providing signals to private investors (Haelg et al. 2018), 
pricing carbon dioxide emissions, public procurement, technology 
standards, information diffusion and the regulation for end-lifecycle 
treatment of products (Cross and Murray 2018) (Section 16.4).
16.2.1.4 Technology Readiness Levels
Technology readiness levels (TRLs) are a categorisation that enables 
consistent, uniform discussions of technical maturity across different 
types of technology. They were developed by the National Aeronautics 
and Space Administration (NASA) in the 1970s (Mankins 1995, 2009) 
and originally used to describe the readiness of components forming

part of a technological system. Over time, more classifications of TRLs 
have been introduced, notably the one used by the European Union 
(EU). Most recently, the International Energy Agency (IEA) extended 
previous classifications to include the later stages of the innovation 
process (IEA 2020b) and applied it to compare the market readiness 
of clean energy technologies and their components (OECD 2015a; 
IEA 2020b). TRLs are currently widely used by engineers, business 
people, research funders and investors, often to assess the readiness 
of whole technologies rather than single components. To determine 
a TRL for a  given technology, a  technology readiness assessment 
(TRA) is carried out to examine programme concepts, technology 
requirements, and demonstrated technology capabilities. In the most 
recent version of the IEA (IEA 2020b), TRLs range from 1 to 11, with 
11 indicating the most mature (Table 16.2).
The purpose of TRLs is to support decision-making. They are applied

to avoid the premature application of technologies, which would lead

1650
to increased costs and project schedule extensions (US Department 
of Energy 2011). They are used for risk management, and can also be 
used to make decisions regarding technology funding, and to support 
the management of the R&D process within a given organisation or 
country (De Rose et al. 2017).
In practice, the usefulness of TRLs is limited by several factors. These 
include limited applicability in complex technologies or systems, 
the fact that they do not define obsolescence, nor account for 
manufacturability, commercialisation or the readiness of organisations 
to implement innovations (European Association of Research 
Technology Organisations 2014) and do not consider any type of 
technology-system mismatch or the relevance of the products’ operation 
environment to the system under consideration (Mankins 2009). Many 
of these limitations can be eased by using TRLs in combination with

other indicators such as system readiness levels and other economic 
indicators on, for example, investments and returns (IEA 2020b).
16.2.2 Sources of Technological Change
The speed of technological change could be explained with the 
key drivers of innovations process: R&D effort; learning by doing; 
and spillover effects. In addition, new innovations are sometimes 
enabled by the development of general purpose technologies, such 
as digitalisation.
16.2.2.1 Learning by Doing and Research and Development
Learning by doing and R&D efforts are two factors commonly 
used by the literature to explain past and projected future speed 
of technological change (Klaassen et al. 2005; Mayer et al. 2012; 
Bettencourt et al. 2013). Learning by doing is the interaction 
of workers with new machines or processes that allows more 
efficient use (Arrow 1962b). R&D effort is dedicated to looking for 
new solutions (e.g.,  blueprints) that could increase the efficiency

of existing production methods or result in entirely new methods, 
products or services (Section 16.2.1.1).
1  For example, see Spence (1981) and Bhattacharya (1984) for a discussion of first-mover advantages.Learning by doing and R&D are interdependent. Young (1993) 
postulates that learning by doing cannot continue forever without 
R&D because it is bounded by an upper physical productivity limit of 
an existing technology. R&D can shift this limit because it allows for 
replacing the existing technology with a new one. On the other hand, 
incentives to invest in R&D depend on the future cost of manufacturing, 
which in turn depends on the scale of learning by doing. The empirical 
evidence for virtuous circle between cost reduction, market growth and 
R&D were found in the case of the photovoltaic (PV) market (Watanabe 
et al. 2000) (Box 16.4), but could also lead to path dependency and 
lock-in (Erickson et al. 2015). Sections 16.4.4 and 13.7.3.1 discuss how

simultaneous use of technology push and pull policies could amplify 
the effects of research and learning.
The benefits of R&D and learning by doing are larger at the economy 
level than at the firm level (Arrow 1962b; Romer 1990;). As a result, 
when left to its own, the market tends to generate less investment 
than socially optimal. For instance, if the cost of a  technology is 
too high before a large amount of learning by doing has occurred, 
there is a risk that it will not be adopted by the market, even if it 
is economically advantageous for the society. Indeed, initially new 
technologies are often expensive and cannot compete with the 
incumbent technologies (Cowan 1990). Large numbers of adopters 
could lower this cost via learning by doing to a  level sufficient to 
beat the incumbent technology (Gruebler et al. 2012). However, firms 
could hesitate to be the first adopter and bear the high cost (Isoard 
and Soria 2001). If this disadvantage overwhelms the advantages of

being a first mover1 and if adopters are not able to coordinate, it will 
lead to situation of a lock-in (Gruebler et al. 2012).
The failure of markets to deliver the size of R&D investment and 
learning by doing that would be socially optimal is one of the 
justifications of government intervention. Policies to address these 
market failures can be categorised as technology-push and demand-
pull policies. The role of these policies is explained in Table 16.3.
Section 16.4 discusses individual policy instruments in greater detail.
Table  16.3 |  Categories of policies and interventions accelerating technological changes, the factors promoting them and slowing them down, 
illustrated with examples.
What it refers toWhat promotes 
technological changeWhat slows down 
technological changeExamples
Technology pushSupport the creation 
of new knowledge to 
make it easier to invest 
in innovationResearch and development 
(R&D), funding and performance 
of early demonstrations

(Brown and Hendry 2009; 
Hellsmark et al. 2016)Inadequate supply of trained 
scientists and engineers 
(Popp and Newell 2012); 
gap with demand pull 
(Grübler et al. 1999b)Japan’s Project Sunshine, the US Project Independence in the 
1970s. Breakthrough Energy Coalition and Mission Innovation, 
respectively private- and public-sector international collaborations 
to respectively focus energy innovation and double energy R&D, 
both initiated concurrently with the Paris Agreement in 2015 
(Sanchez and Sivaram 2017)
Demand pullInstruments creating 
market opportunitiesEnlarging potential markets, 
increasing adoption of new 
fuels and mitigation technology
Digital innovations
Social innovation 
and awarenessWillingness of consumers to 
accept new technology
Policy and political volatility 
can deter investmentSubsidies for wind power California, the German feed-in tariff for 
photovoltaic, quotas for electric vehicles in China (F . Wang et al. 
2017) and Norway (Pereirinha et al. 2018)

Biofuels (Brazil)
Social innovation with wind energy (Denmark, Germany)

1651
The size of the learning-by-doing effect is quantified in literature 
using learning rates, that is estimates of negative correlation between 
costs and size of deployment of technologies. The results from this 
literature include estimates for energy technologies (McDonald 
and Schrattenholzer 2001), electricity generation technologies 
(Rubin et al. 2015; Samadi 2018), for storage (Schmidt 2017), for 
end-of-pipe control (Kang et al. 2020) and for energy demand and 
energy supply technologies (Weiss et al. 2010). Meta-analyses find 
that learning rates vary across technologies, within technologies, 
and over time (Nemet 2009a; Rubin et al. 2015; Wei et al. 2017). 
Moreover, different components of one technology have different 
learning rates (Elshurafa et al. 2018). Central tendencies are around 
20% cost reduction for each doubling of deployment (McDonald and 
Schrattenholzer 2001).

Studies of correlation between cumulative deployment of 
technologies and costs are not sufficiently precise to disentangle 
the causal effect of increase in deployment from the causal effects 
of R&D and other factors (Nemet 2006). Numerous subsequent 
studies attempted to, among others issues, separate the effect 
of learning by doing and R&D (Klaassen et al. 2005; Mayer et al. 
2012; Bettencourt et al. 2013), economies of scale (Arce 2014), and 
knowledge spillovers (Nemet 2012). Once those other factors are 
accounted for, some empirical studies find that the role of learning 
by doing in driving down the costs becomes minor (Nemet 2006; 
Kavlak et al. 2018). In addition, the relation could reflect reverse 
causality: increase in deployment could be an effect (and not 
a  cause) of a  drop in price (Nordhaus 2014; Witajewski-Baltvilks 
et al. 2015). Nevertheless, in some applications, learning curves can 
be a useful proxy and heuristic (Nagy et al. 2013).

The negative relation between costs and experience is a reason to invest 
in a narrow set of technologies; the uncertainty regarding the parameters 
of this relation is the reason to invest in wider ranges of technologies 
(Fleming and Sorenson 2001; Way et al. 2019). Concentrating 
investment in narrow sets of technologies (specialisation) enables 
fast accumulation of experience for these technologies and large cost 
reductions. However, when the potency of technology is uncertain, one 
does not know which technology is truly optimal in the long run. The 
narrower the set, the higher the risk that the optimal technology will 
not be supported, and hence will not benefit from learning by doing. 
Widening the set of supported technologies would reduce this risk 
(Way et al. 2019). Uncertainty is present because noise in historical data 
hides the true value of learning rates, and due to unanticipated future 
shocks to technology costs (Lafond et al. 2018). Ignoring uncertainty

in integrated assessment models implies that these model results are 
biased towards supporting a  narrow set of technologies, neglecting 
the benefits of decreasing risk through diversification (Sawulski and 
Witajewski-Baltvilks 2020).
16.2.2.2 Knowledge Spillovers
Knowledge spillovers drive continuous technological change (Romer 
1990; Rivera-Batiz and Romer 1991) and are for that reason relevant 
to climate technologies as well as incumbent, carbon-intensive 
technologies. Knowledge embedded in innovations by one innovator gives an opportunity for others to create new innovations and 
increase the knowledge stock even further. The constant growth of 
knowledge stock through spillovers translates into constant growth 
of productivity and cost reduction.
By allowing for experimenting with existing knowledge and combining 
different technologies, knowledge spillovers can result in the 
emergence of novel technological solutions, which has been referred

to as ‘recombinant innovation’ (Weitzman 1998; Fleming and Sorenson 
2001; Olsson and Frey 2002; Tsur and Zemel 2007; Arthur 2009). 
Recombinant innovations speed up technological change by combining 
different technological solutions, and make things happen that would 
be impossible with only incremental innovations (van den Bergh 2008; 
Safarzyńska and van den Bergh 2010; Frenken et al. 2012). It has been 
shown that 77% of all patents granted between 1790 and 2010 in 
the USA are coded by a  combination of at least two technology 
codes (Youn et al. 2015). Spillovers related to energy and low-carbon 
technologies have been documented by a number of empirical studies 
(high confidence) (Popp 2002; Verdolini and Galeotti 2011; Aghion et al. 
2016; Witajewski-Baltvilks et al. 2017; Conti et al. 2018). The presence 
of spillovers can have both positive and negative impacts on climate 
change mitigation (high confidence).

The spillover effect associated with innovation in carbon-intensive 
technologies may lead to lock-in of fossil-fuel technologies. 
Continuous technological change of carbon-intensive industry raises 
the bar for clean technologies: a larger drop in clean technologies’ 
cost is necessary to become competitive (Acemoglu et al. 2012; 
Aghion et al. 2016). The implication is that delaying climate policy 
increases the cost of that policy (Aghion 2019).
On the other hand, the spillover effect associated with innovation 
in low-emission technologies increases the potency of climate policy 
(Aghion 2019). For instance, a policy that encourages clean innovation 
leads to accumulation of knowledge in clean industry which, through 
spillover effects, encourages further innovation in clean industries. 
Once the stock of knowledge is sufficiently large, the value of clean 
industries will be so high that technology firms will invest there,

even without policy incentives. Once this point is reached, the policy 
intervention can be discontinued (Acemoglu et al. 2012).
In addition, the presence of spillovers implies that a  unilateral 
effort to reduce emissions in one region could reduce emissions 
in other regions (medium confidence) (Golombek and Hoel 2004; 
Gerlagh and Kuik 2014). For instance, in the presence of spillovers, 
a carbon tax that incentivises clean technological change increases 
the competitiveness of clean technologies not only locally, but also 
abroad. The size of this effect depends on the size of the spillovers. 
If they are sufficiently strong, the reduction of emissions abroad due 
to clean technological change could be larger than the increase of 
emissions due to carbon leakage (Gerlagh and Kuik 2014). Different 
types of carbon leakage are discussed in Chapter 13, Section 13.7.1, 
and other consequences of spillovers for the design of policy are 
discussed in Chapter 13, Section 13.7.3.

1652
16.2.2.3 General-purpose Technologies and Digitalisation
General-purpose technologies (GPTs) provide solutions that could 
be applied across sectors and industries (Goldfarb 2011) by creating 
technological platforms for a  growing number of interrelated 
innovations. Examples of GPTs relevant to climate change mitigation 
are hydrogen and fuel cell technology, which may find applications 
in transport, industry and distributed generation (Hanley et al. 2018), 
and nanotechnology which played a significant role in advancement 
of all the different types of renewable energy options (Hussein 2015). 
Assessing the environmental, social and economic implications of 
such technologies, including increased emissions through energy 
use, is challenging (Section  5.3.4.1 and Cross-Chapter Box  11 in 
this chapter).
Several GPTs relevant for climate mitigation and adaptation emerged

as a  result of digitalisation, namely the adoption or increase in 
the use of information and communication technologies (ICTs) by 
citizens, organisations, industries or countries, and the associated 
restructuring of several domains of social life and of the economy 
around digital technologies and infrastructures (Brennen and 
Kreiss 2016; IEA 2017b). The digital revolution is underpinned by 
innovation in key technologies, for example, ubiquitous connected 
consumer devices such as mobile phones (Grubler et al. 2018), rapid 
expansions of global internet infrastructure and access (World Bank 
2014), and steep cost reductions and performance improvements in 
computing devices, sensors, and digital communication technologies 
(Verma et al. 2020). The increasing pace at which the physical and digital worlds are converging increases the relevance of disruptive 
digitalisation in the context of climate mitigation and sustainability

challenges (European Commission 2020) (Cross-Chapter Box  11 in 
this chapter and Chapter 4, Section 4.4.1).
Digital technologies require energy, but increase efficiency, potentially 
offering technology-specific greenhouse gas (GHG) emission savings; 
they also have larger system-wide impacts (Kaack et  al. 2021). In 
industrial sectors, robotisation, smart manufacturing (SM), internet 
of things (IoT), artificial intelligence (AI), and additive manufacturing 
(AM or 3D printing) have the potential to reduce material demand 
and promote energy management (Section 11.3.4.2). Smart mobility 
is changing transport demand and efficiency (Section 10.2.3). Smart 
devices in buildings, the deployment of smart grids and the provision 
of renewable energy increase the role of demand-side management 
(Serrenho and Bertoldi 2019) (Sections 9.4 and 9.5), and support the 
shift away from asset redundancy (Section 6.4.3). Digital solutions

are equally important on the supply side, for example, by accelerating 
innovation with simulations and deep learning (Rolnick et  al. 
2021) or realising flexible and decentralised opportunities through 
energy-as-a-service concepts and particularly with pay-as-you-go 
(Section 15.6.8).
Yet, increased digitalisation could increase energy demand, thus 
wiping away potential efficiency benefits, unless appropriately 
governed (IPCC 2018a). Moreover, digital technologies could 
negatively impact labour demand and increase inequality (Cross-
Chapter Box 11 in this chapter).
Cross-Chapter Box 11 | Digitalisation: Efficiency Potentials and Governance Considerations
Authors: Felix Creutzig (Germany), Elena Verdolini (Italy), Paolo Bertoldi (Italy), Luisa F . Cabeza (Spain), María Josefina Figueroa 
Meza (Venezuela/Denmark), Kirsten Halsnæs (Denmark), Joni Jupesta (Indonesia/Japan), Şiir Kilkiş (Turkey), Michael König

(Germany), Eric Masanet (the United States of America), Nikola Milojevic-Dupont (France), Joyashree Roy (India/Thailand), Ayyoob 
Sharifi (Iran/Japan)
Digital technologies impact positively and negatively on GHG emissions through: their own carbon footprint; technology 
application for mitigation; and induced larger social change. Digital technologies also raise broader sustainability 
concerns due to their use of rare materials and associated waste, and their potential negative impact on inequalities 
and labour demand.
Direct impacts emerge because digital technologies consume large amounts of energy, but also have the potential to 
steeply increase energy efficiency in all end-use sectors through material input savings and increased coordination 
(medium evidence, medium agreement) (Horner et al. 2016; Huang et al. 2016; IEA 2017b; Jones 2018). Global energy demand

from digital appliances reached 7.14 EJ in 2018 (Chapter 9, Box 9.5), implying higher related carbon emissions. However, a small 
smartphone offers services previously requiring many different devices (Grubler et al. 2018). Demand for data services is increasing 
rapidly; quantitative estimates of the growth of associated energy demand range from slow and marginal to rapid and sizeable, 
depending the efficiency trends of digital technologies (Avgerinou et al. 2017; Vranken 2017; Stoll et al. 2019; Masanet et al. 2020) 
(Section 5.3.4.1). Renewable energy can serve as a low-carbon energy provider for the operation of a data centre, which in turn can 
provide waste heat for other purposes. Digital technologies can markedly increase the energy efficiency of mobility and residential 
and public buildings, especially in the context of systems integration (IEA 2020a). Reduction in energy demand and associated

GHG emissions from buildings and industry, while maintaining service levels is estimated at 5 to 10%, with larger savings possible. 
Approaches include building energy management systems (BEMS), home energy management system (HEMS), demand response

1653
and smart charging (Cross-Chapter Box 11, Table 1). Data centres can also play a role in energy system management, for example, 
by increasing renewable energy generation through predictive control (Dabbagh et al. 2019), and by helping to drive the market for 
battery storage and fuel cells (Riekstin et al. 2014). Temporal and spatial scheduling of electricity demand can provide about 10 GW 
in demand response in the European electricity system in 2030 (Wahlroos et al. 2017, 2018; Koronen et al. 2020; Laine et al. 2020).
However, system-wide effects may endanger energy and GHG emission savings (high evidence, high agreement). Economic 
growth resulting from higher energy and labour productivities can increase energy demand (Lange et al. 2020) and associated GHG 
emissions. Importantly, digitalisation can also benefit carbon-intensive technologies (Victor 2018). Impacts on GHG emissions are

varied in smart and shared mobility systems, as ride hailing increases GHG emissions due to deadheading, whereas shared pooled 
mobility and shared cycling reduce GHG emissions, as occupancy levels and/or weight per person km transported improve (Section 5.3). 
Energy and GHG emission impacts from the ubiquitous deployment of smart sensors and service optimisation applications in smart 
cities are insufficiently assessed in the literature (Milojevic-Dupont and Creutzig 2021). Systemic effects have wider boundaries of 
analysis, including broader environmental impacts (e.g., demand for rare materials, disposal of digital devices). These need to be 
Cross-Chapter Box  11, Table  1  | Selected sector approaches for reducing GHG emissions that are supported by new digital technologies. 
Contributions of digitalisation include a) supporting role (+), b) necessary role in mix of tools (++), c) necessary unique contribution (+++), but digitalisation may

also increase emissions (−). (Chapters 5, 8, 9 and 11).
Sector ApproachQuantitative 
evidenceContribution of 
digitalisationSystems perspective 
and broader societal 
impactsReferences
energy useNudges (feedback, 
information, etc.)2–4% reduction in 
global household 
energy use possible+ In combination with 
monetary incentives,  
non-digital informationNew appliances 
increase consumptionZangheri et al. (2019); Buckley 
(2020); Nawaz et al. (2020); 
Khanna et al. (2021)
Smart mobilityShared mobility 
and digital feedback 
(ecodriving)Reduction for shared 
cycling and shared 
pooled mobility; 
increase for ride 
hailing/ ride sourcing; 
reduction for ecodriving− or ++ Apps together with 
big data and machine learning 
algorithm key precondition for 
new shared mobilityRide hailing increases 
GHG emissions, especially 
due to deadheadingZeng et al. (2017);  
OECD and ITF (2020)
Smart citiesUsing digital 
devices and big 
data to make urban 
transport and building

use more efficientPrecise data about 
roadway use can 
reduce material 
intensity and 
associated GHG 
emissions by 90%++ Big data analysis necessary 
for optimisationEfficiency gains are often 
compensated by more 
driving and other rebound 
effects; privacy concerns 
linked with digital 
devices in homesMilojevic-Dupont and Creutzig 
(2021) (Chapter 10, Box 10.1)
AgriculturePrecision agriculture 
through sensors and 
satellites providing 
information on soil 
moisture, temperature, 
crop growth and 
livestock feed levelsVery high potential for 
variable-rate nitrogen 
application, moderate 
potential for variable-
rate irrigation+ ICTs provide information 
and technologies which enables 
farmers to increase yields, 
optimise crop management, 
reduce fertilisers and 
pesticides, feed and water; 
increases efficiency of 
labour-intensive tasksThe digital divide is 
growing fast, especially 
between modern and 
subsistence farming;
Privacy and data may

erode trust in technologiesDeichmann et al. (2016); 
Chlingaryan et al. (2018); 
Soto Embodas et al. (2019); 
Townsend et al. (2019)
IndustryIndustrial internet 
of things (IIoT)Process, activity and 
functional optimisation 
increases energy and 
carbon efficiency++ Increased efficiency
++ 1.3 GtCO 2-eq estimated 
abatement potential 
in manufacturing
+ Promote sustainable 
business modelsOptimisation in value 
chains can reduce 
wasted resourcesGeSI (2012); Wang et al. (2016); 
Parida et al. (2019);  
Rolnick et al. (2021)
Load 
and battery 
storage 
optimisationBig data analysis for 
optimising demand 
using flexible load 
of appliances 
with batteriesReduces capacity 
intended for peak 
demand, shifts 
demand to align with 
intermittent renewable 
energy availability+ Accelerated experimentation 
in material science with artificial 
intelligence
++ / +++ Forecast and control 
algorithms for storage and 
dispatch managementFacilitate integration of

renewable energy sources
Improve utilisation of 
generation assets
System-wide rebound 
effects possibleAkorede et al. (2010); 
Aghaei and Alizadeh (2013); 
de Sisternes et al. (2016); 
Voyant et al. (2017); Gür 
(2018); Hirsch et al. (2018); 
Sivaram (2018a); Vázquez-
Canteli and Nagy (2019) 
(Chapter 6, Section 6.4)Cross-Chapter Box 11 (continued)

1654
integrated holistically within policy design (Kunkel and Matthess 2020), but they are difficult to quantify and investigate (Bieser and 
Hilty 2018). Policies and adequate infrastructures and choice architectures can help manage and contain the negative repercussions 
of systemic effects (Sections 5.4, 5.6 and 9.9).
Broader societal impacts of digitalisation can also influence climate mitigation because of induced demand for 
consumption goods, impacts on firms’ competitiveness, changes the demand for skills and labour, worsening of 
inequality – including reduced access to services due to the digital divide – and governance aspects (low evidence, 
medium agreement) (Sections 4.4, 5.3 and 5.6). Digital technologies expand production possibilities in sectors other than ICTs 
through robotics, smart manufacturing, and 3D printing, and have major implications on consumption patterns (Matthess and Kunkel

2020). Initial evidence suggests that robots displace routine jobs and certain skills, change the demand for high-skilled and low-skilled 
workers, and suppress wages (Acemoglu and Restrepo 2019). Digitalisation can thus reduce consumers’ liquidity and consumption 
(Mian et al. 2020) and contribute to global inequality, including across the gender dimension, raising fairness concerns (Kerras et al. 
2020; Vassilakopoulou and Hustad 2021). Digital technologies can lead to additional concentration in economic power (e.g., Rikap 
2020) and lower competition; however, open source digital technologies can counter this tendency (e.g., Rotz et al. 2019). Digital 
technologies play a role in mobilising citizens for climate and sustainability actions (Segerberg 2017; Westerhoff et al. 2018).
Whether the digital revolution will be an enabler or a  barrier for decarbonisation will ultimately depend on the

governance of both digital decarbonisation pathways and digitalisation in general (medium evidence, high agreement). 
The understanding of the disruptive potential of the wide range of digital technologies is limited due to their ground-breaking 
nature, which makes it hard to extrapolate from previous history/experience. Municipal and national entities can make use of digital 
technologies to manage and govern energy use and GHG emissions in their jurisdiction (Bibri 2019a,b) and break down solution 
strategies to specific infrastructures, building, and places, relying on remote sensing and mapping data, and contextual machine 
learning about their use (Milojevic-Dupont and Creutzig 2021). Mobility apps can provide mobility-as-a-service access to cities, ensuring 
due preference to active and healthy modes (Section 9.9 for the example of the Finnish city of Lahti). Trusted data governance can

promote the implementation of local climate solutions, supported by available big data on infrastructures and environmental quality 
(Hansen and Porter 2017; Hughes et al. 2020). Governance decisions, such as taxing data, prohibiting surveillance technologies, or 
releasing data that enable accountability, can change digitalisation pathways, and thus underlying GHG emission (Hughes et al. 2020).
Closing the digital gap in developing countries and rural communities enables an opportunity for leapfrogging (medium 
evidence, medium agreement). Communication technologies (such as mobile phones) enable the participation of rural communities, 
especially in developing countries, and promote technological leapfrogging, for example, decentralised renewable energies and smart 
farming (Ugur and Mitra 2017; Foster and Azmeh 2020; Arfanuzzaman 2021). Digital technologies have sector-specific potentials and

barriers, and may benefit certain regions/areas/socio-economic groups more than others. For example, integrated mobility services 
benefit cities more than rural and peripheral areas (OECD 2017).
Appropriate mechanisms also need to be designed to govern digitalisation as a megatrend (medium evidence, high 
agreement). Digitalisation is expected to be a  fast process, but this transformation takes place against entrenched individual 
behaviours, existing infrastructure, the legacy of time frames, vested interest and slow institutional processes, and requires trust from 
consumers, producers and institutions. A core question relates to who controls and manages data created by everyday operations (calls, 
shopping, weather data, service use, and so on). Regulations that limit or ban the expropriation and exploitation of behavioural data, 
sourced via smartphones, represent crucial aspects in digitalisation pathways, alongside the possibility to create climate movements

and political pressure from the civil society. Governance mechanisms need to be developed to ensure that digital technologies such 
as AI take over ethical choices (Craglia et al. 2018; Rahwan et al. 2019). Appropriate governance is necessary for digitalisation to 
effectively work in tandem with established mitigation technologies and choice architectures. Consideration of system-wide effects 
and overall management is essential to avoid runaway effects. Overall governance of digitalisation remains a challenge, and will have 
large-scale repercussions on energy demand and GHG emissions.Cross-Chapter Box 11 (continued)

1655
16.2.2.4 Explaining Past and Projecting Future 
Technology Cost Changes
Researchers and policymakers alike are interested in using observed 
empirical patterns of learning to project future reductions in costs 
of technologies. Studies cutting across a  wide range of industrial 
sectors (not just energy) have tried to relate cost reductions to 
different functional forms, including cost reductions as a function of 
time (Moore’s law) and cost reductions as a  function of production 
or deployment (Wright’s law, also known as Henderson’s law), finding 
that those two forms perform better than alternatives combining 
different factors, with costs as a function of production (Wright’s law) 
performing marginally better (Nagy et al. 2013). A comparison of expert 
elicitation and model-based forecasts of the future cost of technologies 
for the energy transition indicates that model-based forecast medians

were closer to the average realised values in 2019 (Meng et al. 2021).
Recent studies attempt to separate the influence of learning by doing 
(which is a basis of Wright’s law) versus other factors in explaining 
cost reductions, specifically in energy technologies. Some studies 
explain cost reductions with two factors: cumulative deployment 
(as proxy for experience); and R&D investment – see the ‘two factor’ 
learning curve (Klaassen et al. 2005). However, reliable information 
on public energy R&D investments for developing countries is not 
systematically collected. Available data for OECD countries cannot be 
precisely assigned to specific industrial sectors or sub-technologies 
(Verdolini et al. 2018). Some learning-curve studies take into account 
that historical variation in technology costs could be explained by 
variation in key materials and fuel costs – for example, steel costs 
for wind turbines (Qiu and Anadon 2012), silicon costs (Nemet 2006;

Kavlak et al. 2018) as well as coal and coal plant construction costs 
(McNerney et al. 2011). Economies of scale played a significant role in 
the PV cost reductions since the early 2000s (Yu et al. 2011) (Box 16.4), 
which can also become the case in organic PV technologies (Gambhir 
et al. 2016; Kavlak et al. 2018).
16.2.3 Directing Technological Change
Technological change is characterised not only by its speed, but also 
its direction. The early works that considered the role of technology 
in economic and productivity growth (Solow 1957; Nelson and Phelps 
1966) assumed that technology can move forward along only one 
dimension – every improvement led to an increase in efficiency and 
increased demand for all factors of production. This view, however, 
ignores the potency of technological change to alter the otherwise 
fixed relation between economic growth and the use of resources.
Technological change that saves fossil fuels could decouple economic

growth and CO 2 emissions (Acemoglu et al. 2012, 2014; Hémous 
2016; Greaker et al. 2018). Saving of fossils could be obtained with 
increasing efficiency of producing alternatives to fossils (Acemoglu 
et al. 2012, 2014). This is the case of oil consumption by combustion 
engine cars which could be substituted with electric cars (Aghion 
et al. 2016). If there is no close substitute for a ‘dirty resource’, then 
its intensity in production could still be reduced by increasing the efficiency of the dirty resource relative to the efficiency of other 
inputs (Hassler et al. 2012; André and Smulders 2014; Witajewski-
Baltvilks et al. 2017). For instance, energy efficiency improvement 
leads to a drop in relative demand for energy (Hassler et al. 2012; 
Witajewski-Baltvilks et al. 2017).
16.2.3.1 Determinants of Technological Change Direction: 
Prices, Market Size and Government
Firms change their choice of technology upon change in prices: when

one input (e.g.,  energy) becomes relatively expensive, firms pick 
technologies that allow them to economise on that input, according 
to price-induced technological change theory (Reder and Hicks 
1965; Samuelson 1965; Sue Wing 2006). For example, an increase 
in oil price will lead to a  choice of fuel-saving technologies. Such 
a response of technological change was evident during the oil-price 
shocks in the 1970s (Hassler et al. 2012). Technological change that 
is induced by an increase in price of a resource can never lead to 
an increase in use of that resource. In other words, rebound effects 
associated with induced technological change can never offset 
the saving effect of that technological change (Antosiewicz and 
Witajewski-Baltvilks 2021).
The impact of energy prices on the size of low-carbon technological 
change is supported by large number of empirical studies (Popp 
2019; Grubb and Wieners 2020). Studies document that higher

energy prices are associated with a  higher number of low-carbon 
energy or energy efficiency patents (Newell et al. 1999; Popp 2002; 
Verdolini and Galeotti 2011; Noailly and Smeets 2015; Ley et al. 2016; 
Witajewski-Baltvilks et al. 2017; Lin and Chen 2019). Sue Wing (2008) 
finds that innovation induced by energy prices had a minor impact 
on the decline in US energy intensity in the last decades of the 20th 
century, and that autonomous technological change played a more 
important role. Several studies explore the impact of a carbon tax on 
green innovation (Section 16.4). However, disentangling the effect of 
policy tools is complex because the presence of some policies could 
distort the functioning of other policies (Böhringer and Rosendahl 
2010; Fischer et al. 2017) and because the impact of policies could be 
lagged in time (Antosiewicz and Witajewski-Baltvilks 2021).
The direction of technological change depends also on the market size

for dirty technologies relative to the size of other markets (Acemoglu 
et al. 2014). Due to this dependence, climate and trade policy choices 
in a single region can alter the direction of technological change at 
the global level (Section 16.2.3.3).
The value of the market for clean technologies is determined not only 
by current profit, but also by a  firm’s expectation of future profits 
(Alkemade and Suurs 2012; Greaker et al. 2018; Aghion 2019). 
One implication is that bolstering the credibility and durability of 
policies related to low-carbon technology is crucial to accelerating 
technological change and inducing the private sector investment 
required (Helm et al. 2003), especially in the rapidly growing 
economies of Asia and Africa which are on the brink of making major 
decisions about the type of infrastructure they build as they grow, 
develop, and industrialise (Nemet et al. 2017).

1656
If governments commit to climate policies, firms expect that the future 
size of markets for clean technologies will be large and they are eager 
to redirect research effort towards development of these technologies 
today. The commitment would also incentivise acquiring skills that 
could further reduce the costs of those technologies (Aghion 2019). 
However, historical evidence shows that policies related to energy 
and climate over the long term have tended to change (Taylor 2012; 
Nemet et al. 2013; Koch et al. 2016). Still, where enhancing policy 
durability has proven infeasible, multiple uncorrelated potentially 
overlapping policies can provide sufficient incentives (Nemet 2010).
16.2.3.2 Determinants of Direction of Technological Change: 
Financial Markets
The challenges of investing in innovation in energy when compared 
to other important areas, such as ICT and medicine are also reflected

in the trends in venture capital funding. Research found that early-
stage investments in cleantech companies were more likely to fail 
and returned less capital than comparable investments in software 
and medical technology (Gaddy et al. 2017). This led to investors 
retreating from hardware technologies required for renewable energy 
generation and storage, and moving to software-based technologies 
and demand-side solutions (Bumpus and Comello 2017).
The preference for particular types of investments in renewable 
energy technologies depends on investors attitude to risk (Mazzucato 
and Semieniuk 2018). Some investors invest in only one technology, 
others may spread their investments, or invest predominantly in high-
risk technologies. The distribution of different types of investors will 
affect whether finance goes to support deployment of new high-risk 
technologies, or diffusion of more mature, less-risky technologies

characterised by incremental innovations. The role of finance in 
directing investment is further discussed in Chapter 15, Section 15.6.2.
16.2.3.3 Internationalisation of Green Technological Change
A unilateral effort to reduce emissions (via a combination of climate, 
industrial and trade policies) in a  coalition of regions that are 
technology leaders will reduce the cost of clean technologies, and 
induce emissions reduction in the countries outside the coalition 
(Golombek and Hoel 2004; Di Maria and Smulders 2005; Di Maria 
and van der Werf 2008; Hémous 2016; van den Bijgaart 2017). 
The literature suggests various mechanisms leading to this result. 
Di Maria and van der Werf (2008) argue that the effort to reduce 
emissions in one region reduces global demand for ‘dirty goods’. This 
will redirect global innovation towards clean technologies, leading to 
a drop in the cost of clean production in every region.The model in Hemous (2016) predicts that such a coalition could

induce acceleration of clean technological change through a mix 
of carbon taxation, clean R&D subsidies and trade policies in that 
region leading to reduction of cost of clean production inside the 
coalition. Export of goods produced with clean technologies to 
a region outside the coalition reduces demand for dirty goods in that 
region. In the model by van den Bijgaart (2017) local advancements 
of clean technologies by a coalition with strong R&D potential are 
imitated outside the coalition. Furthermore, advancements of clean 
technologies will incentivise future clean R&D outside the coalition 
due to intertemporal knowledge spillovers. In Golombek and 
Hoel (2004) an increase in environmental concern in one region 
increases abatement R&D in that region. Part of this knowledge 
spills over to other regions, increasing their incentive to increase 
abatement too, provided that the latter regions did not invest 
in abatement before.

However, this chain breaks if the regions that are behind the 
technological frontier (i.e., technological followers) are not able 
to absorb the solutions developed by regions at the frontier. New 
technologies might fail due to deficiencies of political, commercial, 
industrial, and financial institutions, which we list in Table  16.4. 
For instance, countries might not benefit fully from international 
knowledge spillovers due to insufficient domestic R&D investment, 
since local knowledge is needed to determine the appropriateness 
of technologies for the local market, adapting them, installing and 
using effectively (Gruebler et al. 2012). From the policy perspective, 
this implies that simple transfer of technologies could be insufficient 
to guarantee adoption of new technologies (Gruebler et al. 2012).
Research relying on patent citations has indicated that Foreign 
Direct Investment (FDI) is a  mechanism for firms to contribute to

the recipient country’s innovation output as well as benefit from the 
recipient country in industrialised countries (Branstetter 2006) and 
in developing countries (Newman et al. 2015). However, insights 
specific for energy or climate change mitigation areas are not 
available, nor is there much information about how other innovation 
metrics may react to FDI.
Finally, technologies could be not efficient in developing countries, 
even if they are efficient in countries at the technological frontier. For 
instance, technologies that are highly capital intensive and labour 
saving will be efficient only in countries where costs of capital are 
low and costs of labour are high. Similarly, technologies which 
require a large number of skilled labour will be more competitive in 
a country where skilled labour is abundant (and hence cheap) than 
where it is scarce (Basu and Weil 1998; Caselli and Coleman 2006).

Table 16.4 | Examples of institutional deficiencies preventing deployment of new technologies in countries behind the technological frontier.
Institutions Examples of deficiencies Literature reference
Industrial Inability to benefit fully from international knowledge spillover due to insufficient domestic R&D investment Mancusi (2008); Unel (2008); Gruebler et al. (2012)
Commercial Insufficient experience with the organisation and management of large-scale enterprise Abramovitz (1986); Aghion et al. (2005)
Political Vested interests and customary relations among firms and between employers and employees Olson (1982); Abramovitz (1986)
Financial Financial markets incapable of mobilising capital for individual firms at large scale Abramovitz (1986); Aghion et al. (2005)

1657
16.2.3.4 Market Failures in Directing Technological Change
Market forces alone cannot deliver Pareto optimal (i.e., social) 
efficiency due to at least two types of externalities: GHG emissions 
that cause climate damage; and knowledge spillovers that benefit 
firms other than the inventor. Nordhaus (2011) argues that these two 
problems would have to be tackled separately: once the favourable 
intellectual property right regimes (i.e., the laws or rules or regulation 
on protection and enforcement) are in place, a price on carbon that 
corrects the emission externality is sufficient to induce optimal level of 
green technological change. Acemoglu et al. (2012) demonstrates that 
subsidising clean technologies (and not dirty ones) is also necessary 
to break the lock-in of dirty technological change. Recommendations 
for technical changes are often based on climate considerations

only and neglect secondary externalities and environmental costs of 
technology choices (such as loss of biodiversity due to inappropriate 
scale-up of bioenergy use). The scale of adverse side effects and 
co-benefits varies considerably between low-carbon technologies in 
the energy sector (Luderer et al. 2019).
16.2.4 Representation of the Innovation Process 
in Modelled Decarbonisation Pathways
A variety of models are used to generate climate mitigation 
pathways, compatible with 2°C and well below 2°C targets. 
These  include integrated assessment models (IAMs), energy 
system models, computable general equilibrium models, and agent 
based models. They range from global (Chapter 3) to national models 
and include both top-down and bottom-up approaches (Chapter 4). 
Innovation in energy technologies, which comprises the development 
and diffusion of low-, zero- and negative-carbon energy options, 
but also investments to increase energy efficiency, is a key driver of

emissions reductions in model-based scenarios.
16.2.4.1 Technology Cost Development
Assumptions on energy technology cost developments is one of the 
factors that determine the speed and magnitude of the deployment 
in climate-energy-economy models. The modelling is informed by the 
empirical literature that estimates the rates of cost reduction for energy 
technologies. A first strand of literature relies on the extrapolation of 
historical data, assuming that costs decrease either as a power law 
of cumulative production, exponentially with time (Nagy et al. 2013) 
or as a function of technical performance metrics (Koh and Magee 
2008). Another approach relies on expert estimates of how future 
costs will evolve, including expert elicitations (Verdolini et al. 2018).
In these models, technology costs may evolve exogenously or 
endogenously (Mercure et al. 2016; Krey et al. 2019). In the first case, 
technology costs are assumed to vary over time at some predefined

rate, generally extrapolated from past observed patterns or based 
on expert estimates. This formulation of cost dynamics generally 
underestimates future costs (Meng et al. 2021) as, among other things, 
it does not capture any policy-induced carbon-saving technological 
change or any spillover arising from the accumulation of national 
and international knowledge (Sections 16.2.2 and 16.2.3) or positive macroeconomic effects of a  transition (Karkatsoulis et al. 2016). 
The influence of cost and diffusion assumptions may be evaluated 
through sensitivity analysis. In the second case, costs are a function 
of a  choice variable within the model. For instance, technology 
costs decrease as a function of either cumulative installed capacity 
(learning by doing) (Seebregts et al. 1998; Kypreos and Bahn 2003) 
or R&D investments or spillovers from other sectors and countries.
One factor in this ‘learning by researching’ is applied to a wide range of

energy technologies but also to model improvements in the efficiency 
of energy use (Goulder and Schneider 1999; Popp 2004). More complex 
formulations include two-factor learning processes (Criqui et al. 2015; 
Emmerling et al. 2016; Paroussos et al. 2020) (Section  16.2.2.1), 
multifactor learning curves (Kahouli 2011; Yu et al. 2011), or other drivers 
of cost reduction such as economies of scale and markets (Elia et al. 
2021). The application of two-factor learning curves to model energy 
technology costs is often constrained by the lack of information on 
public and/or private energy R&D investments in many fast-developing 
and developing countries (Verdolini et al. 2018). The approach used to 
model energy technology cost reductions varies across technologies, 
even within the same model, depending on the availability of data and/
or the level of maturity. Less mature technologies generally depend 
highly on learning by research, whereas learning by doing dominates

in more mature technologies (Jamasb 2007).
In addition to learning, knowledge spillover effects are also 
integrated in climate-energy-economy models to reflect the fact that 
innovation in a given country depends also on knowledge generated 
elsewhere (Emmerling et al. 2016; Fragkiadakis et al. 2020). Models 
with a more detailed representation of sectors (Paroussos et al. 2020) 
can use spillover matrices to include bilateral spillovers and compute 
learning rates that depend on the human capital stock and the 
regional and/or sectoral absorption rates (Fragkiadakis et al. 
2020). Accounting for knowledge spillovers in the EU for PV, wind 
turbines, electric vehicles, biofuels, industry materials, batteries and 
advanced heating and cooking appliances can lead to the following 
results in a  decarbonisation scenario over the period 2020–2050 
as compared to the reference scenario: an increase of 1.0–1.4% in 
GDP , 2.1–2.3% in investment, and 0.2–0.4% in employment by clean

energy technologies (Paroussos et al. 2017). When comparing two 
possible EU transition strategies – being a first-mover with strong 
unilateral emission reduction strategy until 2030 versus postponing 
action for the period after 2030  – endogenous technical progress 
in the green technologies sector can alleviate most of the negative 
effects of pioneering low-carbon transformation associated with loss 
of competitiveness and carbon leakage (Karkatsoulis et al. 2016).
16.2.4.2 Technology Deployment and Diffusion
To simulate possible paths of energy technology diffusion for different 
decarbonisation targets, models rely on assumptions about the cost of 
a given technology relative to the costs of other technologies, and its 
ability to supply the energy demand under the relevant energy system 
and physical constraints. These assumptions include, for example, 
considerations regarding renewable intermittency, inertia on technology

lifetime (for instance, under less stringent temperature scenarios, early 
retirement of fossil plants does not take place), distribution, capacity

1658
and market growth constraints, as well as the presence of policies. 
These factors change the relative price of technologies. Furthermore, 
technological diffusion in one country is also influenced by technology 
advancements in other regions (Kriegler et al. 2015).
Technology diffusion may also be strongly influenced, either positively 
or negatively, by a number of non-cost, non-technological barriers or 
enablers regarding behaviours, society and institutions (Knobloch and 
Mercure 2016). These include network or infrastructure externalities, the 
co-evolution of technology clusters over time (‘path dependence’), the 
risk-aversion of users, personal preferences and perceptions and lack 
of adequate institutional framework which may negatively influence 
the speed of (low-carbon) technological innovation and diffusion, 
heterogeneous agents with different preferences or expectations, multi-

objectives and/or competitiveness advantages and uncertainty around 
the presence and the level of environmental policies and institutional 
and administrative barriers (Marangoni and Tavoni 2014; Baker et al. 
2015; Iyer et al. 2015; Napp et al. 2017; Biresselioglu et al. 2020; van 
Sluisveld et al. 2020). These types of barriers to technology diffusion are 
currently not explicitly detailed in most of the climate-energy-economy 
models. Rather, they are accounted for in models through scenario 
narratives, such as the ones in the Shared Socioeconomic Pathways 
(Riahi et al. 2017), in which assumptions about technology adoption 
are spanned over a plausible range of values. Complementary methods 
are increasingly used to explore their importance in future scenarios 
(Turnheim et al. 2015; Geels et al. 2016; Doukas et al. 2018; Gambhir 
et al. 2019; Trutnevyte et al. 2019). It takes a very complex modelling 
framework to include all aspects affecting technology cost reductions

and technology diffusion, such as heterogeneous agents (Lamperti 
et al. 2020), regional labour costs (Skelton et al. 2020), materials 
cost and trade and perfect foresight multi-objective optimisation 
(Aleluia Reis et al. 2021). So far, no model can account for all these 
interactions simultaneously.Another key aspect of decarbonisation regards issues of acceptability 
and social inclusion in decision-making. Participatory processes 
involving stakeholders can be implemented using several methods to 
incorporate qualitative elements in model-based scenarios on future 
change (van Vliet et al. 2010; Nikas et al. 2017, 2018; Doukas and 
Nikas 2020; van der Voorn et al. 2020).
16.2.4.3 Implications for the Modelling of Technical Change 
in Decarbonisation Pathways
Although the debate is still ongoing, preliminary conclusions indicate 
that integrated assessment models tend to underestimate innovation 
on energy supply but overestimate the contributions by energy

efficiency (IPCC 2018b). Scenarios emerging from cost-optimal 
climate-energy-economy models are too pessimistic, especially in the 
case of rapidly changing technologies such as wind and batteries in 
the past decade. Conversely, they tend to be too optimistic regarding 
the timing of action, or the availability of a  given technology and 
its speed of diffusion (Shiraki and Sugiyama 2020). Furthermore, 
some technological and economic transformations may emerge as 
technically feasible from IAMs, but are not realistic if taking into 
account political economy, international politics, human behaviours, 
and cultural factors (Bosetti 2021).
There is a range of projected energy technology supply costs included 
in the IPCC’s Sixth Assessment Report (AR6) Scenario Database 
(Box  16.1). Variations of costs over time and across scenarios 
are within ranges comparable to those observed in recent years. 
Conversely, model results show that limiting warming to 2°C or

1.5°C will require faster diffusion of installed capacity of low-carbon 
energy options and a  rapid phase-out of fossil-based options. This 
points to the importance of focusing on overcoming real-life barriers 
to technology deployment.
Box 16.1 | Comparing Observed Energy Technology Costs and Deployment Rates with 
Projections from AR6 Global Modelled Pathways
Currently observed costs and deployment for electricity supply technologies from a variety of sources are compared with projections 
from two different sets of scenarios contained in the AR6 Scenario database: (i) scenarios that limit warming to 3°C (>50%) and 
scenarios that limit warming to 4°C (>50%), and (ii) scenarios that limit warming to 2°C (>67%) or lower (AR6 Scenarios Database). 
Global aggregate costs are shown for the following technologies: coal with carbon dioxide capture and storage (CCS), gas with CCS, 
nuclear, solar PV, onshore and offshore wind.

The decrease in forecasted capital costs is not large compared to current capital costs for most technologies, and does not differ much 
between the two sets of scenarios (Box 16.1, Figure 1a). For offshore wind some of the models are more optimistic than the current 
reality (Timilsina 2020). Several sources of current solar PV costs report values that are at the low end of the AR6 Scenario Database. 
By 2050, the median technology cost forecasts decrease by between 5% for nuclear and 45–52% for solar (Box 16.1, Figure 1c).
Median values of renewables installed capacity increase with respect to 2020 capacity in scenarios that limit warming to 3°C (>50%) 
and in scenarios that limit warming to 4°C (>50%) (Box 16.1 Figure 1b), where energy and climate policies are implemented in 
line with NDCs announced prior to COP26. More stringent targets (2°C) are achieved through a higher deployment of renewable

technologies: by 2050 solar (wind) capacity is estimated to increase by a factor of 15 (10) (Box 16.1, Figure 1c). This is accompanied by 
an almost complete phase-out of coal (–87%). The percentage of median changes in installed capacity in scenarios that limit warming 
to 3°C (>50%) and in scenarios that limit warming to 4°C (>50%)is within comparable ranges of that observed in the last decade. In 
the case of scenarios that limit warming to 2°C (>67%) or lower, capacity installed is higher for renewable technologies and nuclear, 
and lower for fossil-based technologies (Box 16.1, Figure 1c).

1659
Box 16.1 (continued)
The higher deployment in scenarios that limit warming to 2°C (>67%) or lower cannot be explained solely as a result of technology 
cost dynamics. In IAMs, technology deployment is also governed by system constraints that characterise both 3°C (>50%) and 
4°C (>50%) scenarios, for example, the flexibility of the energy system, the availability of storage technologies. From a modelling point 
of view, implementing more stringent climate policies to meet the 2°C limit forces models to find solutions, even if costly, to meet 
those intermittency and flexibility constraints and temperature target constraints.
Box 16.1, Figure 1 | Global technology cost and deployment in two groups of AR6 scenarios: (i) scenarios that limit warming to 3°C (>50%) 
and scenarios that limit warming to 4°C (>50%) (“Reference and current policies”), and (ii) scenarios that limit warming to 2°C (>67%) or

lower (“2°C and 1.5°C”).  Panel (a) Current capital costs are sourced from Table 1 (Timilsina 2020); distribution of capital costs in 2030 and 2050 (AR6 Scenarios 
Database). Blue symbols represent the mean. ‘Current’ capital costs for coal and gas plants with CCS are not available; Panel (b) Total installed capacity in 2019 (IEA 
2020c; IRENA 2020a, b); distribution of total installed capacity in 2030 and 2050 (AR6 Scenario Database). Blue symbols represent the mean; Panel (c) Percentage 
of change in capital costs and installed capacity between (2010–2020) and percentage of median change (2020–2030 and 2020–2050) (Median year–Median 2020)/
Median 2020*100. ‘M’ indicates the number of models, ‘S’ the number of scenarios for which this data is available. ‘Reference and current policies’ are scenarios that 
limit warming to 3°C (>50%) and scenarios that limit warming to 4°C (>50%) (C6 and C7 AR6 scenario categories). ‘2C and 1.5C’ are scenarios that limit warming

to 2°C (>67%) or lower (C1, C2 and C3 AR6 scenario categories). Each model may have submitted data for more than one model version.

1660
16.3 A Systemic View of Technological 
Innovation Processes
The innovation process, which consists of a set of sequential phases 
(Section 16.2.1), is often simplified to a linear process. Yet, it is now well 
understood that it is also characterised by numerous kinds of interactions 
and feedbacks between the domains of knowledge  generation, 
knowledge translation and application, and knowledge use (Kline 
and Rosenberg 1986). Furthermore, it is not just invention that leads 
to technological change; the cumulative contribution of incremental 
innovations over time can be very significant (Kline and Rosenberg 
1986). Innovations can come, not just from formal research and 
development (R&D) but also sources such as production engineers 
and the shop floor (Kline and Rosenberg 1986; Freeman 1995).
This section reviews the literature focusing on innovation as a systemic

process. This now predominant view enriches the understanding of 
innovation as presented in Section 16.2; it conceptualises innovation 
as the result of actions by, and interactions among, a large set of 
actors, whose activities are shaped by, and shape, the context in 
which they operate and the user group with which they are engaging. 
This section aligns with the discussion of socio-technical transitions 
(Section 1.7.3, Chapter 5 Supplementary Material, and Cross-Chapter 
Box 12 in this chapter).
16.3.1 Frameworks for Analysing Technological 
The resulting overarching framework that is commonly used in the 
innovation scholarship and in policy analyses is termed an ‘innovation 
system’, where the key constituents of the systems are actors, their 
interactions, and the institutional landscape, including formal rules, 
such as laws, and informal restraints, such as culture and codes of 
conduct, that govern the behaviour of the actors (North 1991).

One application of this framework, national innovation systems 
(NIS), highlight the importance of national and regional relationships 
for determining the technological and industrial capabilities and 
development of a  country (Lundvall 1992; Nelson 1993; Freeman 
1995). Nelson (1993) and Freeman (1995) highlight the role of institutions that determine the innovative performance of national 
firms as a  way to understand differences across countries, while 
Lundvall (1992) focuses on the ‘elements and relationships which 
interact in the production, diffusion and use of new, and economically 
useful, knowledge’ – that is, notions of interactive learning, in which 
user-producer relationships are particularly important (Lundvall 
1988). Building on this, various other applications of the ‘innovation 
system’ framework have emerged in the literature.
Technological innovation systems (TIS), with a technology or a set of

technologies (more narrowly or broadly defined in different cases) as 
the unit of analysis, focus on explaining what accelerates or hinders 
their development and diffusion. Carlsson and Stankiewicz (1991) 
define a  technological system as ‘a dynamic network of agents 
interacting in a specific economic/industrial area under a particular 
institutional infrastructure and involved in the generation, diffusion, 
and utilisation of technology’. More recent work takes a ‘functional 
approach’ to TIS (Hekkert et al. 2007; Bergek et al. 2008), which 
was later expanded with explanations of how some of the sectoral, 
geographical and political dimensions intersect with technology 
innovation systems (Bergek et al. 2015; Quitzow 2015).
Sectoral innovation systems (SIS) are based on the understanding 
that the constellation of relevant actors and institutions will vary 
across industrial sectors, with each sector operating under a different

technological regime and under different competitive or market 
conditions. A sectoral innovation, thus, can be defined as ‘that system 
(group) of firms active in developing and making a sector’s products 
and in generating and utilising a sector’s technologies’ (Breschi and 
Malerba 1997).
Regional innovation systems (RIS) and global innovation systems 
(GIS), recognise that the many innovation processes have a spatial 
dimension, where the development of system resources such as 
knowledge, market access, financial investment, and technology 
legitimacy may well draw on actors, networks, and institutions within 
a region (Cooke et al. 1997). In other cases, the distribution of many 
innovation processes are highly internationalised and therefore 
outside specific territorial boundaries (Binz and Truffer 2017). 
Importantly, Binz and Truffer (2017) note that the GIS framework 
‘differentiates between an industry’s dominant innovation mode...

Table 16.5 | Functions that the literature identified as key for well-performing technological innovation systems.  Source: based on Hekkert et al. (2007) and 
Bergek et al. (2008).
Functions Description
Entrepreneurial activities and experimentation Entrepreneurial activities and experimentation for translating new knowledge and/or market opportunities into real-world application
Knowledge development Knowledge development includes both learning by searching and learning by doing
Knowledge diffusionKnowledge diffusion through networks, both among members of a community (e.g., scientific researchers) and across communities 
(e.g., universities, business, policy, and users)
Guidance of search Guidance of search directs the investments in innovation in consonance with signals from the market, firms or government
Market formationMarket formation through customers or government policy is necessary to allow new technologies to compete with 
incumbent technologies

Resource mobilisation Resource mobilisation pertains to the basic inputs – human and financial capital – to the innovation process
Creation of legitimacy/counteract  
resistance to changeCreation of legitimacy or counteracting resistance to change, through activities that allow a new technology to become accepted 
by users, often despite opposition by incumbent interests
Development of external economies Development of external economies, or the degree to which other interests benefit from the new technology

1661
and the economic system of valuation in which markets for the 
innovation are constructed’.
The relevance of mission-oriented innovation systems (MIS), comes 
into focus with the move towards mission-oriented programmes as 
part of the increasing innovation policy efforts to address societal 
challenges. Accordingly, an MIS is seen as consisting of ‘networks of 
agents and sets of institutions that contribute to the development 
and diffusion of innovative solutions with the aim to define, pursue 
and complete a societal mission’ (Hekkert et al. 2020).
Notably the innovation systems approach has been used in a number of 
climate-relevant areas such as agriculture (Echeverría 1998; Horton and 
Mackay 2003; Brooks and Loevinsohn 2011; Klerkx et al. 2012), energy 
(Sagar and Holdren 2002; OECD 2006; Gallagher et al. 2012; Wieczorek 
et al. 2013; Darmani et al. 2014; Mignon and Bergek 2016), industry

(Koasidis et al. 2020b) and transport (Koasidis et al. 2020a), and 
sustainable development (Anadon et al. 2016b; Clark et al. 2016; 
Bryden and Gezelius 2017; Nikas et al. 2020).
A number of functions can be used to understand and characterise 
the performance of technological innovation systems (Hekkert et al. 
2007; Bergek et al. 2008). The most common functions are listed in 
Table 16.5.
Evidence from empirical case studies indicates that all the above 
functions are important and that they interact with one another 
(Hekkert and Negro 2009). The approach therefore serves as both 
a rationale for and a guide to innovation policy (Bergek et al. 2010).
A much-used, complementary systemic framework is the Multi-Level 
Perspective (MLP) (Geels 2002), which focuses mainly on the diffusion 
of technologies in relation to incumbent technologies in their sector 
and the overall economy. A key point of MLP is that new technologies

need to establish themselves in a stable ‘socio-technical regime’ and 
are therefore generally at a disadvantage, not just because of their 
low technological maturity, but also because of an unwelcoming 
system. The MLP highlights that the uptake of technologies in 
society is an evolutionary process, which can be best understood 
as a  combination of ‘variation, selection and retention’ as well as 
‘unfolding and reconfiguration’ (Geels 2002). Thus, new technologies 
in their early stages need to be selected and supported at the micro-
level by niche markets, possibly through a directed process that has 
been termed ‘strategic niche management’ (Kemp et al. 1998). As, 
at the landscape level, pressures on incumbent regimes mount, and 
those regimes destabilise, the niche technologies get a  chance to 
get established in a  new socio-technical regime. This allows these 
technologies to grow and stabilise, shaping a changed or sometimes

radically renewed socio-technical regime. The MLP takes a systematic 
and comprehensive view about how to nurture and shape 
technological transitions by understanding them as evolutionary, 
multidirectional and cumulative socio-technical processes playing 
out at multiple levels over time, with a concomitant expansion in the 
scale and scope of the transition (Elzen et al. 2004; Geels 2005). There 
have been numerous studies that draw on the MLP to understand 
different aspects of climate technology innovation and diffusion 
(van Bree et al. 2010; Geels 2012; Geels et al. 2017).Systemic analyses of innovation have predominantly focused on 
industrialised countries There have been some efforts to use the 
innovation systems lens for the developing country context (Jacobsson 
and Bergek 2006; Altenburg 2009; Lundvall et al. 2009; Tigabu et al. 
2015; Tigabu 2018; Choi and Zo 2019) and specific suggestions on 
ways for developing countries to strengthening their innovation

systems (e.g.,  by universities taking on a  ‘developmental’ role 
(Arocena et al. 2015), or industry associations acting as intermediaries 
to build institutional capacities (Watkins et al. 2015; Khan et al. 2020), 
including specifically for addressing climate challenges (Sagar et al. 
2009; Ockwell and Byrne 2016). But the conditions in developing 
countries are quite different, leading to suggestions that different 
theoretical conceptualisations of the innovation systems approach 
may be needed for these countries (Arocena and Sutz 2020), although 
a system perspective would still be appropriate (Boodoo et al. 2018).
16.3.2 Identifying Systemic Failures to Innovation 
in Climate-related Technologies
Traditional perspectives on innovation policy were mostly science-
driven, and focused on strengthening invention and its translation 
into application in a narrow sense. Also, a second main traditional 
perspective on innovation policy was focused on correcting for

‘market failures’ (Weber and Truffer 2017) (Section 16.2). The more 
recent understanding of, and shift of focus to, the systemic nature 
on the innovation and diffusion of technologies has implications 
for innovation policy, since innovation outcomes depend not just 
on inputs such as R&D, but much more on the functioning of the 
overall innovation system (see Sections  16.3.1 and 16.4). Policies 
can therefore be directed at innovation systems components and 
processes that need the greatest attention or support. This may 
include, for example, strengthening the capabilities of weak actors 
and improving interactions between actors (Jacobsson et al. 2017; 
Weber and Truffer 2017). At the same time, a systemic perspective 
also brings into sharp relief the notion of ‘system failures’ (Weber 
and Truffer 2017).
Systemic failures include: infrastructural failures; hard (e.g.,  laws, 
regulation) and soft (e.g., culture, social norms) institutional failures;

interaction failures (strong and weak network failures); capability 
failures relating to firms and other actors; lock-in; and directional, 
reflexivity, and coordination failures (Klein Woolthuis et  al. 2005; 
Chaminade and Esquist 2010; Negro et  al. 2012; Weber and 
Rohracher 2012; Wieczorek and Hekkert 2012). Most of the literature 
that unpacks such failures and explores ways to overcome them is on 
energy-related innovation policy. For example, Table 16.6 summarises 
a meta-study (Negro et al. 2012) that examined cases of renewable 
energy technologies trying to disrupt incumbents across a range of 
countries to understand the roles, and relative importance, of the 
‘systemic problems’ highlighted in Section 16.3.1.
Depending on the sector, specific technology characteristics, and 
national and regional context, the relevance of these systemic 
problems varies (Trianni et al. 2013; Bauer et al. 2017; Wesseling and

Van der Vooren 2017; Koasidis et al. 2020a, b), suggesting that the 
innovation policy mix has to be tailor-made to respond to the diversity

1662
of systemic failures (Rogge et al. 2017). An illustration of how such 
systemic failures have been addressed is given in Box  16.2, which 
shows how the Indian government designed its standards and labelling 
programme for energy-efficient air conditioners and refrigerators. The success of this programme resulted from the careful attention to 
bring on board and coordinate the relevant actors and resources, the 
design of the standards, and ensuring effective administration and 
enforcement of the standards (Malhotra et al. 2021).
Table 16.6 | Examination of systemic problems preventing renewable energy technologies from reaching their potential, including number of case 
studies in which the particular ‘systemic problem’ was identified.  Source: Negro et al. (2012).
Systemic 
problemsEmpirical sub-categoriesNo. of 
cases

Hard institutions –‘Stop and go policy’: lack of continuity and long-term regulations; inconsistent policy and existing laws and regulations
 –‘Attention shift’: policymakers only support technologies if they contribute to the solving of a current problem
 –‘Misalignment’ between policies on sector level such as agriculture, waste, and on governmental levels, i.e., EU, national, regional level, etc.
 –‘Valley of Death’: lack of subsidies, feed-in tariffs, tax exemption, laws, emission regulations, venture capital to move technology from 
experimental phase towards commercialisation phase51
Market structures –Large-scale criteria
 –Incremental/near-to-market innovation
 –Incumbent’s dominance30
Soft institutions –Lack of legitimacy
 –Different actors opposing change28
Capabilities/
capacities –Lack of technological knowledge of policymakers and engineers
 –Lack of ability of entrepreneurs to pack together, to formulate clear message, to lobby to the government

–Lack of users to formulate demand
 –Lack of skilled staff19
Knowledge 
infrastructure –Wrong focus or not specific courses at universities knowledge institutes
 –Gap/misalignment between knowledge produced at universities and what is needed in practice16
Too weak interactions –Individualistic entrepreneurs
 –No networks, no platforms
 –Lack of knowledge diffusion between actors
 –Lack of attention for learning by doing13
Too strong 
interactions –Strong dependence on government action or dominant partners (incumbents)
 –Networks allows no access to new entrants8
Physical infrastructure –No access to existing electricity or gas grid for renewable energy technologies
 –No decentralised, small-scale grid
 –No refill infrastructure for biofuels, hydrogen, biogas2
Box 16.2 | Standards and Labelling for Energy Efficient Refrigerators and Air Conditioners 
in India2

Energy efficiency is often characterised as a ‘low-hanging fruit’ for reducing energy use. However, systemic failures such as lack 
of access to capital, hidden costs of implementation, and imperfect information can result in low investments into adoption and 
innovation in energy efficiency measures (Sorrell et al. 2004). To address such barriers, India’s governmental Bureau of Energy 
Efficiency (BEE) introduced the Standards and Labelling (S&L) programme to promote innovation in energy efficient appliances in 
2006 (Sundaramoorthy and Walia 2017). While context-dependent, the programme’s design, policies and scale-up contain lessons for 
addressing systemic failures elsewhere too.
Programme design and addressing of early systemic barriers
To design the S&L programme, BEE drew on the international experiences and technical expertise of the Collaborative Labelling

and Appliance Standards Program (CLASP) – a non-profit organisation that provides technical and policy support to governments in 
implementing S&L programmes. For example, since there was no data on the efficiency of appliances in the Indian market, CLASP 
assisted with early data collection efforts, resulting in a focus on refrigerators and air conditioners (McNeil et al. 2008).
2 This section draws on The role of capacity-building in policies for climate change mitigation and sustainable development: The case of energy efficiency in India, 
(Malhotra et al. 2021).

1663
Besides drawing from international knowledge, the involvement of manufacturers, testing laboratories, and customers was crucial for 
the functioning of the innovation system.
To involve manufacturers, BEE employed three strategies to set the standards at an ambitious yet acceptable level. First, BEE enlisted 
the Indian Institute of Technology (IIT) Delhi (a public technical university) to engage with manufacturers and to demonstrate 
cost-effective designs of energy-efficient appliances. Second, BEE agreed to make the standards voluntary from 2006 to 2010. In 
return, the manufacturers agreed to mandatory and progressively more stringent standards starting in 2010. Third, BEE established 
a  multistakeholder committee with representation from BEE, the Bureau of Indian Standards, appliance manufacturers, test

laboratories, independent experts, and consumer groups (Jairaj et al. 2016) to ensure that adequately stringent standards are 
negotiated every two years.
At this time, India had virtually no capacity for independent testing of appliances. Here, too, BEE used multiple approaches towards 
creating the actors and resources needed for the innovation system to function. First, BEE funded the Central Power Research Institute 
(CPRI) – a national laboratory for applied research, testing and certification of electrical equipment – to set up refrigerator and AC 
testing facilities. Second, they invited bids from private laboratories, thus creating a demand for testing facilities. Third, BEE developed 
testing protocols in partnership with universities. Australian standards for testing frost-free refrigerators were adopted until local 
standards were developed. Thus, once the testing laboratories, protocols and benchmark prices for testing were in place, the appliance

manufacturers could employ their services.
Finally, a customer outreach programme was conducted from 2006 to 2008 to inform customers about energy-efficient appliances, to 
enable them to interpret the labels correctly, and to understand their purchase decisions and information sources (Jain et al. 2018; Joshi 
et al. 2019). BEE initiated a capacity-building programme for retailers to be an information source for customers. A comprehensive 
document with details of different models and labels was provided to retailers, together with a condensed booklet to be shared 
with customers.
Adapting policies to technologies and local context
While many of India’s standards and testing protocols were based on international standards, they needed to be adapted to the 
Indian context. For example, because of higher temperatures in India, the reference outside temperature of 32°C for refrigerators was 
changed to 36°C.

AC testing protocols also had to be adapted because of the emergence of inverter-based ACs. Existing testing done only at a single 
temperature did not value inverter-based ACs’ better average performance as compared to fixed-speed ACs over a range of temperatures. 
Thus, the Indian Seasonal Energy Efficiency Ratio (ISEER) was developed for Indian temperature conditions in 2015 by studying 
International Organization for Standardization (ISO) standards and through consultations with manufacturers (Mukherjee et al. 2020).
These measures had multiple effects on technological change. As a result of stringent standards, India has some of the most efficient 
refrigerators globally. In the case of ACs, the ISEER accelerated technological change by favouring inverter-based ACs over fixed-speed 
ACs, driving down their costs and increasing their market shares (BEE 2020).
Scaling up policies for market transformation

As the S&L programme was expanded, BEE took measures to standardise, codify and automate it. For example, to process a high 
volume of applications for labels efficiently, an online application portal with objective and transparent certification criteria was 
created. This gave certainty to the manufacturers, enabling diversity and faster diffusion of energy-efficient appliances. Thus by 2019, 
the programme expanded to cover thousands of products across 23 appliance types (BEE 2020).
Besides issuing labels, the enforcement of standards also needed to be scaled up efficiently. BEE developed protocols for randomly 
sampling appliances for testing. Manufacturers were given a fixed period to rectify products that did not meet the standards, failing 
which they would be penalised and the test results would be made public.Box 16.2 (continued)

1664
16.3.3 Indicators for Technological Innovation
Assessing the state of technological innovation helps in understanding 
the progress of current efforts and policies in meeting stated 
objectives, and how we might design policies to do better.
Traditionally, input measures such as research, development and 
demonstration (RD&D) investments, and output measures such 
as scientific publication and patents were used to characterise 
innovation activities (Freeman and Soete 2009). This is partly because 
of the successes of specialised R&D efforts (Freeman 1995), the 
predominant linear model of innovation, and because such measures can (relatively) easily be obtained and compared. In the realm of 
energy-related innovation, RD&D investments remain the single 
most-used indicator to measure inputs into the innovation process 
(Box 16.3). Patent counts are a widely used indicator of the outputs

of the innovation process, especially because they are detailed 
enough to provide information on specific adaptation and mitigation 
technologies. Mitigation and adaptation technologies have their own 
classification (Y02) with the European Patent Office (EPO) (Veefkind 
et al. 2012; Angelucci et al. 2018), which can be complemented with 
keyword search and manual inspection (Persoon et al. 2020; Surana 
et al. 2020b). However, using energy-related patents as an indicator of 
innovative activities is complicated by several issues (de Rassenfosse 
Box 16.3 | Investments in Public Energy Research and Development
Public energy R&D investments are a  crucial driver of energy technology innovation (Sections  16.2.1.1 and 16.4.1). Box  16.3, 
Figure 1 shows the time profile of energy-related RD&D budgets in OECD countries as well as some key events which coincided with

developments of spending (IEA 2019). Such data on other countries, in particular developing countries, are not available, although 
recent evidence suggests that expenditures are increasing there (IEA 2020c). The IEA collected partial data from China and India in the 
context of Mission Innovation, but this is only available starting from 2014 and thus not included in Figure 1.
The figure illustrates two points. First, energy-related RD&D has risen slowly in the last 20 years, and is now reaching levels comparable 
with the peak of energy RD&D investments following the two oil crises. Second, over time there has been a reorientation of the 
portfolio of funded energy technologies away from nuclear energy. In 2019, around 80% of all public energy RD&D spending was 
on low-emission technologies – energy efficiency, carbon dioxide capture, use and storage, renewables, nuclear, hydrogen, energy

storage and cross-cutting issues such as smart grids. A more detailed discussion of the time profile of RD&D spending in IEA countries, 
including as a share of GDP , is available in IEA (2020b).
25,000USD (2019 prices and exchange rates)Unallocated Other cross-cutting
technologies/researchOther power and 
storage technologiesHydrogen and 
fuel cells
Renewables Fossil fuels Energy efﬁciency Nuclear
0500010,00015,00020,000
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018Figure is same as TS.26
Box 16.3, Figure 1 | Fraction of public energy RD&D spending by technology over time for IEA (largely OECD) countries between 1974 and 2018.  
Sources: RD&D Database (2019), IEA (2019) (extracted on November 11, 2020).

1665
Table 16.7 | Commonly used quantitative innovation metrics, organised by inputs, outputs and outcomes.  Sources: based on Sagar and Holdren (2002); 
Gallagher et al. (2006, 2011, 2012); Hekkert et al. (2007); Gruebler et al. (2012); Hu et al. (2018); Miremadi et al. (2018); Avelino et al. (2019).
Function Input indicators Output indicatorsOutcome 
indicatorsActors PoliciesStructural 
and systemic 
indicators
Knowledge 
developmentHigher education 
investments
Research and development 
(R&D) investments
Number of researchers
R&D projects over timeScientific publications
Highly-cited publications
Patents
New product 
configurationsNumber of technologies 
developed (proof-of-
concept/prototypes)
Increase in number 
of researchers
Learning ratesGovernments
Private corporations
UniversitiesResearch programmes and 
Intellectual Property Rights 
(IPR) policies
International technical 
norms (e.g., standards)

Higher education policiesWell-defined 
processes to define 
research priorities
Stakeholder 
involvement in 
priority-setting
Knowledge 
diffusionR&D networks
Number of research 
exchange programmes
Number of scientific 
conferencesCitations to literature  
or patents
Public-private  
co-publications
Co-patenting
Number of  
co-developed products
International scientific 
Number of workshops and 
conferencesNumber of licensed 
patents
Number of technologies 
transferred
Knowledge-intensive 
services exports
Number of patent 
applications by 
foreigners
Number of researchers 
working internationallyGovernments
Scientific societies
UniversitiesDevelopment of 
communication centres
Facilitation of the 
development of networks
Open-access publication 
policies
IPR policies
International policy: 
e.g., treaties, clean 
development mechanismAccessibility to 
Strength of 
linkage among key 

to framework 
ICT accesset al. 2013; Haščič and Migotto 2015; Jaffe and de Rassenfosse 2017), 
including the fact that the scope of what are considered climate 
mitigation inventions is not always clear or straightforward.
Conversely, private energy R&D investments and investments by 
financing firms cannot be precisely assessed for a number of reasons, 
including limited reporting and the difficulty of singling out energy-
related investments. This inability to precisely quantify private 
investments in energy R&D leads to a patchy understanding of the 
energy innovation system, and how private energy R&D investments 
responds to public energy R&D investments. Overall, evidence shows 
that some of the industrial sectors that are important for meeting 
climate goals (electricity, agriculture and forestry, mining, oil and gas, 
and other energy-intensive industrial sectors) are investing relatively

small fractions of sales on R&D (medium evidence, high agreement) 
(Jasmab and Pollitt 2005; Jamasb and Pollitt 2008; Sanyal and Cohen 
2009; European Commission 2015; American Energy Innovation 
Council 2017; Gaddy et al. 2017; National Science Board 2018).
Financing firms also play an important role in the energy innovation 
process, but data availability is limited. The venture capital (VC) 
financing model, used to overcome the ‘valley of death’ in the biotech 
and IT space (Frank et al. 1996), has not been as suitable for hardware 
start-ups in the energy space: for example, the percentage of exit 
outcomes in cleantech start-ups was almost half of that in medical 
start-ups, and less than a  third of software investments (Gaddy 
et al. 2017). The current VC model and other private finance do 
not sufficiently cover the need to demonstrate energy technologies 
at scale (Anadón 2012; Mazzucato 2013; Nemet et al. 2018). This

greater difficulty in reaching the market compared to other sectors may have contributed to a reduction in private equity and venture 
capital finance for renewable energy technologies after the boom of 
the late 2000s (Frankfurt School-UNEP Centre/BNEF 2019).
Quantitative indicators such as energy-related RD&D spending are 
insufficient for the assessment of innovation systems (David and 
Foray 1995): they only provide a partial view into innovation activities, 
and one that is potentially misleading (Freeman and Soete 2009). 
Qualitative indicators measuring the more intangible aspects of 
the innovation process and system are crucial to fully understand the 
innovation dynamics in a climate or energy technologies or sectors 
(Gallagher et al. 2006), including in relation to adopting an adaptive 
learning strategy and supporting learning through demonstration 
projects (Chan et al. 2017).
In Table  16.7, both quantitative and qualitative indicators for

systemic innovation are outlined, using clean energy innovation 
as an illustrative example, and drawing on a broad literature base, 
taking into account both the input-output-outcome classification and 
its variations (Freeman and Soete 1997; Sagar and Holdren 2002; Hu 
et al. 2018), combined with the functions of technological innovation 
systems (Miremadi et al. 2018), while also being cognisant of the 
specific role of key actors and institutions (Gallagher et al. 2012). 
A specific assessment of innovation may focus on part of such a list of 
indicators, depending on what aspect of innovation is being studied, 
whether the analysis takes a  more or less systemic perspective, 
and the specific technology and geography considered. Similarly, 
innovation policies may be designed to specifically boost only some 
of these aspects, depending on whether a  given country/region is 
committed to strengthen a given technology or phase.

1666
Chapter 16 Innovation, Technology Development and Transfer16Function Input indicators Output indicatorsOutcome 
Guidance 
of searchPolicy action plans and 
long-term targets
Shared strategies and 
roadmaps
Articulation of interest 
from lead customers
Expectations of markets/
profitsLevel of media coverage
Scenarios and 
foresight projectsBudget allocations
Mission-oriented 
innovation programmesGovernments
Interest groups
MediaTargets set by government 
for industry
Innovation policies
Credible political supportMedia strength
Resource 
mobilisationAccess to finance
Graduate in Science, 
Technology, Engineering, 
and Mathematics (STEM)
Gross expenditure on R&D/
total expenditure
Domestic credit to private 
sector
Number of researchers in 
R&D per capita
Public energy R&D 
expenditure/total 
expenditure
Expenditure on education
complementary assets 
and/or infrastructure 
(e.g., charging

infrastructure for electric 
vehicles, smart grids)
Venture capital on dealsNumber of green projects/
technologies funded
Share of domestic credit 
granted to low-carbon 
technology projects
granted to projects 
developing complementary 
assets/infrastructureEmployment in 
knowledge-intensive 
Employment in relevant 
industries
Scale of innovative 
Rate of growth of 
dedicated investment
Availability of 
and infrastructureGovernments
Private firms
Private investors 
(angel, venture 
capital, private 
equity)
BanksFinancial resources support
Development of innovative 
financing
International agreements 
(e.g., technology 
agreements)
Infrastructure support
Project/programme 
Higher education policies
Entrepreneurial 
activitiesNumber of new entrants
Percentage of clean energy 
start-ups/incumbents
Access to finance for 
cleantech start-upsSmall and medium-
sized enterprises (SMEs)

introducing product or 
process innovation
Market introduction of new 
technological products
Number of new businesses
Experimental application 
projects
Creative goods exportsPrivate firms
Government
Risk-capital 
providers
PhilanthropistsEase of starting a business
Risk-capital policies
Start-up support 
programmes
Incubator programmesStart-up support 
services
Market 
formationPublic market support
High-tech importsMarket penetration of new 
technologies
Increase in 
installed capacity
Number of niche markets
commercialisedEnvironmental 
Level of environmental 
impact on society
Renewable energy jobs
Renewable energy 
Trade of energy 
and equipment
High-tech exportsPrivate firms
institutions 
regulating trade, 
finance, investment, 
environment, 
security, and health 
issuesEnvironmental and energy 
Fiscal and financial 
incentives
Cleantech-friendly policy 
processes
Transparency

Specific tax regimesResource endowments
Attractiveness of 
Coordination across 
relevant actors 
(e.g., renewable 
energy producers, 
grid operators, 
companies)
Creation of 
legitimacyYouth and public 
demonstration
Lobbying activities
Regulatory acceptance and 
Technology supportLevel of discussion/
debate among key 
stakeholders (public, firms, 
policymakers, etc.)
Greater recognition 
of benefitsPublic opinion
Policymaker opinion
Executive opinion 
on regulation
standards and 
certificationGovernments
Stakeholders
Citizens
PhilanthropistsRegulatory quality
Regulatory instruments
Political consistencyParticipatory 
processes

1667
The systemic approach to innovation and transition dynamics (Cross-
Chapter Box 12 in this chapter) has advanced our understanding of 
the complexity of the innovation process, pointing to the importance 
of assessing the efficiency and effectiveness in producing, diffusing 
and exploiting knowledge  (Lundvall 1992), including how the 
existing stock of knowledge may be recombined and used for new 
applications (David and  Foray 1995). There remains a  crucial need 
for more relevant and comprehensive approaches of assessing 
innovation (Freeman and Soete 2009; Dziallas and Blind 2019). 
In the context of climate mitigation, innovation is a means to an end; 
therefore, there is the need to consider the processes by which the 
output of innovation (e.g.,  patents) are translated into real-world 
outcomes (e.g.,  deployment of low-carbon technologies) (Freeman

and Soete 1997; Sagar and Holdren 2002). Currently, there is no 
available set of quantitative metrics that, collectively, can help get 
a  picture of innovation in a  particular energy technology or set 
of energy technologies. Also we are still lacking an understanding of  
how to systematically use qualitative indicators to characterise the 
more intangible aspects of the energy innovation system and to 
improve front-end innovation decisions (Dziallas and Blind 2019).16.3.4 Emerging Policy Perspectives 
on Systemic Transformations
Because of the multiple market, government, system, and other 
failures that are associated with the energy system, a range of policy 
interventions are usually required to enable the development and 
introduction of new technologies in the market (Jaffe et  al. 2005; 
Bürer and Wüstenhagen 2009; Negro et  al. 2012; Twomey 2012; 
Veugelers 2012; Weber and Rohracher 2012) used in what is termed

as ‘policy mixes’ (Rogge and Reichardt 2016; Edmondson et al. 2019, 
2020; Rogge et al. 2020). Empirical research shows that, in the energy 
and environment space, when new technologies were developed and 
introduced in the market, it was usually at least partly as a  result 
of a  range of policies that shaped the socio-technical system 
(robust evidence, high agreement) (Bunn et al. 2014; Bergek et al. 
2015; Rogge and Reichardt 2016; Nemet 2019). An example of this 
systemic and dynamic nature of policies is the 70-year innovation 
journey of solar photovoltaic (PV), covering multiple countries, which 
is reviewed in Box 16.4.
Box 16.4 | Sources of Cost Reductions in Solar Photovoltaics
No single country persisted in developing solar photovoltaic (PV): five countries each made a  distinct contribution, 
with each leader relinquishing its lead. The free flow of ideas, people, machines, finance, and products across countries

explains the success of solar PVs. Barriers to knowledge flow delay innovation.
Solar PV has attracted interest for decades, and until recently was seen as an intriguing novelty, serving a niche, but widely dismissed 
as a serious answer to climate change and other social problems associated with energy use. Since the IPCC’s Fifth Assessment 
Report (AR5), PV has become a substantial global industry – a truly disruptive technology that has generated trade disputes among 
superpowers, threatened the solvency of large energy companies, and prompted reconsideration of electric utility regulation rooted in 
the 1930s. More favourably, its continually falling costs and rapid adoption are improving air quality and facilitating climate change 
mitigation. PV is now so inexpensive that it is important in an expanding set of countries. In 2020, 41 countries, in six continents, had 
each installed at least 1GW of solar (IRENA 2020a).

The cost of generating electricity from solar PV is now lower in sunny locations than running existing fossil fuel power plants (IEA 
2020c) (Chapter 6). Prices in 2020 were below where even the most optimistic experts expected they would be in 2030.
The costs of solar PV modules have fallen by more than a factor of 10,000 since they were first commercialised in 1957. This four 
orders of magnitude cost reduction from the first commercial application in 1958 until 2018 can be summarised as the result of 
distinct contributions by the USA, Japan, Germany, Australia, and China – in that sequence (Green 2019; Nemet 2019). As shown in 
Box 16.4, Figure 1, PV improved as the result of:
i. scientific contributions in the 1800s and early 1900s, in Europe and the USA, that provided a fundamental understanding of the 
ways that light interacts with molecular structures, leading to the development of the p-n junction to separate electrons and holes 
(Einstein 1905; Ohl 1941);

ii. a breakthrough at a corporate laboratory in the USA in 1954 that made a commercially available PV device available and led to 
the first substantial orders, by the US Navy in 1957 (Ohl 1946; Gertner 2013);
iii. a government R&D and public procurement effort in the 1970s in the USA, that enlisted skilled scientists and engineers into the 
effort and stimulated the first commercial production lines (Christensen 1985; Blieden 1999; Laird 2001);
iv. Japanese electronic conglomerates, with experience in semiconductors, serving niche markets in the 1980s and in 1994 launching 
the world’s first major rooftop subsidy programme, with a declining rebate schedule, and demonstrating there was substantial 
consumer demand for PV (Kimura and Suzuki 2006);
v. Germany passing a feed-in tariff in 2000 that quadrupled the market for PV, catalysing development of PV-specific production 
equipment that automated and scaled PV manufacturing (RESA 2001; Lauber and Jacobsson 2016);

1668
vi. Chinese entrepreneurs, almost all trained in Australia and using Australian-invented passivated emitter rear cell technology, 
building supply chains and factories of gigawatt scale in the 2000s. China became the world’s leading installer of PVs from 2013 
onward (Quitzow 2015; Helveston and Nahm 2019); and
vii. a cohort of adopters with high willingness to pay, accessing information from neighbours, and installer firms that learnt from their 
installation experience as well as that of their competitors, to lower soft costs (Ardani and Margolis 2015; Gillingham et al. 2016).
As this evolution makes clear, no individual country persisted in leading the technology, and every world-leading firm lost its lead 
within a few years (Green 2019). Solar followed an overlapping but sequential process of technology creation, market creation and

cost reduction (comparable to emergence, early adoption, diffusion and stabilisation in Cross-Chapter Box 12 in this chapter). In the 
technology creation phase, examples of central processes include flows of knowledge from one person to another, between firms, 
and between countries as well as US and Japanese R&D funding in the 1970s and early 1980s. During market creation, PVs modular 
scale allowed it to serve a variety of niche markets from satellites in the 1950s to toys in the 1980s, when Germany transformed 
the industry from niche to mass market with its subsidy programme that began in 2000 and became important for PV in 2004. The 
dramatic increase in size combined with its 20-year guaranteed contracts reduced risk for investors and created confidence in PV’s 
long-term growth. Supportive policies also emerged outside Germany, in Spain, Italy, California, and China, which spread the risk,

even as national policy support was more volatile. Rapid and deep cost reductions were made possible by: learning by doing in the 
process of operating, optimising, and combining production equipment; investing and improving each manufacturing line to gradually 
scale up to massive sizes; and incremental improvements in the PV devices themselves.
Central to PV development has been its modularity, which provided two distinct advantages: access to niche markets, and iterative 
improvement. Solar has been deployed as a commercial technology across nine orders of magnitude: from a 1W cell in a calculator to 
a 1GW plant in the Egyptian desert, and almost every scale in between. This modular scale enabled PV to serve a sequence of policy-
independent niche markets (such as satellites and telecoms applications), which generally increased in size and decreased in willingness

to pay, in line with the technology cost reductions. This modular scale also enabled a large number of iterations, such that in 2020 
over three billion solar panels had been produced. Compared to, for instance, approximately 1000 nuclear reactors that were ever 
constructed, a million times more opportunities for learning by doing were available to solar PV: to make incremental improvements, 
to introduce new manufacturing equipment, to optimise that equipment, and to learn from failures. More generally, recent work has 
pointed to the benefits of modularity in the speed of adoption (Wilson et al. 2020) and learning rates (Sweerts et al. 2020).
While many technologies do not fit into the solar model, some – including micro nuclear reactors and direct air capture – also have modular 
characteristics that make them suitable for following solar’s path and benefit from solar’s drivers. However, it took solar PV 60 years to

become cheap, which is too slow for addressing climate change if a technology is now still at the lab scale. A challenge in learning from 
the solar model is therefore how to use public policy to speed up innovation over much shorter time frames, for example, 15 or fewer years.Box 16.4 (continued)
$10$100$1000$10,000$100,000
1905 1950 1960 1970 1980 1990 2000 2010 2020USD2016 MWh–1
Box 16.4, Figure 1 | Milestones in the development of low-cost solar photovoltaics.  Source: Nemet (2019).

1669
There are many definitions of policy mixes from various disciplines 
(Rogge et al. 2017), including environmental economics (Lehmann 
2012), policy studies (Kern and Howlett 2009) and innovation 
studies. Generally speaking, a  policy mix can be characterised by 
a combination of building blocks, namely elements, processes and 
characteristics, which can be specified using different dimensions 
(Rogge and Reichardt 2016). Elements include: (i) the policy 
strategy with its objectives and principal plans; (ii) the mix of 
policy instruments; and (iii) instrument design. The content of 
these elements is the result of policy processes. Both elements and 
processes can be described by their characteristics in terms of the 
consistency of the elements, the coherence of the processes, and 
the credibility and comprehensiveness of the policy mix in different 
policy, governance, geography and temporal context (Rogge and

Reichardt 2016). Other aspects in the evaluation of policy mixes 
include framework conditions, the type of policy instrument and the 
lower level of policy granularity, namely design elements or design 
features (del Río 2014; del Río and Cerdá 2017). In addition, many 
have argued for the need to craft policies that affect different actors 
in the transition, some supporting and some ‘destabilising’ (Geels 
2002; Kivimaa and Kern 2016).
Learning from the innovation systems literature, some of the 
recent policy focus is not only directed on innovation policies 
that can optimise the innovation system to improve economic 
competitiveness and growth, but also policies that can induce 
strategic directionality and guide processes of transformative 
changes towards desired societal objectives (Mitcham 2003; Steneck 
2006). Therefore, the aim is to connect innovation policy with societal 
challenges and transformative changes through engagement with

a  variety of actors and ideas and incorporating equity, nowadays 
often referred to as a ‘just transition’ (Newell and Mulvaney 2013; 
Swilling et al. 2016; Heffron and McCauley 2018; Jasanoff 2018) 
(Chapters 1 and 17). This new policy paradigm is opening up a new 
discursive space, shaping policy outcomes, and giving rise to the 
emerging idea of transformative innovation policy (Fagerberg 2018; 
Diercks et al. 2019).
Transformative innovation policy has a broader coverage of the 
innovation process with a  much wider participation of actors, 
activities and modes of innovation. It is often expressed as socio-
technical transitions (Elzen et al. 2004; Turnheim and Sovacool 
2020) or societal transformations (Scoones 2015; Roberts et al. 
2018). Transformative innovation policy encompasses different 
ideas and concepts that aim to address the societal challenges 
involving a  variety of discussions, including social innovation 
(Mulgan 2012), complex adaptive systems (Gunderson and

Holling 2002), eco-innovation (Kemp 2011) and a framework for 
responsible innovation (Stilgoe et al. 2013), value-sensitive design 
(Friedman and Hendry 2019) and social-technical integration 
(Fisher et al. 2006).16.4 Innovation Policies and Institutions
Building on the frameworks for identifying market failures 
(Section 16.2) and systemic failures (Section 16.3) in the innovation 
system for climate-related technologies, Section  16.4 proceeds as 
follows. First, it considers some of the policy instruments introduced 
in Chapter 13 that are particularly relevant for the pace and direction 
of innovation in technologies for climate change mitigation and 
adaptation. Second, it explains why governments put in place 
policies to promote innovation in climate-related technologies. 
Third, it takes stock of the overall empirical and theoretical evidence 
regarding the relationship between policy instruments with a direct

and an indirect impact on innovation outcomes (including intellectual 
property regimes) and also other outcomes (competitiveness and 
distributional outcomes). Fourth, it assesses the evidence on the 
impact of trade-related policies and of sub-national policies aiming 
to develop cleantech industrial clusters.
This section focuses on innovation policies and institutions which are 
implemented at the national level. Whenever relevant, this section 
highlights examples of policies or initiatives that delve more deeply 
into the main high-level sectors: power, transport, industry, buildings, 
and agriculture, forestry and other land-use (AFOLU). Whenever 
possible, this section also discusses issues in policy selection, design, 
and implementation that have been identified as more relevant in 
developing countries and emerging economies.
Overall, this section shows that national and subnational policies 
and institutions are one of the main factors determining the

redirection and acceleration of technological innovation and low-
emission technological change (Anadon et al. 2016b; Rogge and 
Reichardt 2016; Åhman et al. 2017; Anadón et al. 2017; Roberts 
et al. 2018) (robust evidence, high agreement). Both technology 
push (e.g., scientific training, research and development (R&D)) and 
demand pull (e.g., economic and fiscal support and regulatory policy 
instruments), as well as instruments promoting knowledge flows and 
especially research-firm technology transfer, can be part of the mix 
(robust evidence, medium agreement) (Sections 16.2 and 16.3).
Public R&D investments in energy and climate-related technologies 
have a positive impact on innovation outcomes (medium evidence, 
high agreement). The evidence on procurement is generally positive, 
but limited. The economic policy instruments that can be classified 
as market pull instruments when it comes to the competitiveness

outcome (at least in the short term) is more mixed. The review of the 
literature in this section shows that market pull policy instruments 
had positive but also some negative impacts on outcomes in some 
instances on some aspects of competitiveness and distributional 
outcomes (medium evidence, medium agreement) (Peñasco et al. 
2021). For several of them – such as carbon taxes or feed-in tariffs – 
the evidence of a positive impact on innovation is more consistent 
than the others. Evidence suggests that complementary policies 
or improved policy design can mitigate such short-term negative 
distributional impacts.

1670
16.4.1 Overview of Policy Instruments for Climate 
Technology Innovation
Government policies can influence changes in technologies, as well 
as changes to the systems they support (Somanathan et al. 2014) 
(Chapter 13 and Sections 16.2 and 16.3).
Technology-push policy instruments stimulate innovation by increasing 
the supply of new knowledge through funding  and  performing 
research; increasing the supply of trained scientists and engineers 
which contribute to knowledge-generation and provide technological 
opportunities, which private firms can decide to commercialise 
(Mowery and Rosenberg 1979; Anadon and Holdren 2009; Nemet 
2009b; Mazzucato 2013).
Governments can also stimulate technological change through 
demand-pull (or market-pull) instruments which support market 
creation or expansion and technology transfer, and thus promote 
learning by doing, economies of scale, and automation (Section 16.2).

Demand-pull policy instruments include regulation, carbon prices, 
subsidies that reduce the cost of adoption, public procurement, 
and intellectual property regulation. Typically, technology push is 
especially important for early-stage technologies, characterised 
by higher uncertainty and lower appropriability (Section  16.2); 
demand-pull instruments become more relevant in the later stages 
of the innovation process (Mowery and Rosenberg 1979; Anadon and 
Holdren 2009; Nemet 2009b) (Section 16.2).The second column of Table  16.8 summarises the set of policies 
shaping broader climate outcomes over the past few decades in many 
countries outlined in Chapter 13, Section 13.6, which groups them 
into economic and financial, regulatory, and soft instruments. Other 
policies, such as monetary, banking and trade policies, for instance, can 
also shape innovation, but most government action to shape energy 
has not focused on them. As Table 16.8 shows, this section discusses

the set of policy instruments on innovation outcomes, or a  subset 
of the ‘Transformative Potential’ criterion presented in Chapter 13, 
and thus complements the more general discussion presented there. 
Table 16.8 specifically prioritises the impact of the subset of policy 
instruments on innovation outcomes for which evidence is available. 
This focus is complemented by a  discussion of the impact of the 
same policy instruments on competitiveness (a subcomponent of the 
economic effectiveness evaluation criterion) and on distributional 
outcomes. Many of the policy instrument types listed in Table 16.8 
have been implemented or proposed to address the different types of 
market or systemic failures or bottlenecks described in Sections 16.2 
and 16.3 (OECD 2011a).
Section  16.3 characterised technological innovation as a  systemic, 
non-linear and dynamic process. Figure 16.1 below presents a stylised 
(and necessarily incomplete) view connecting the innovation process

stages presented in Section  16.2, some of the key mechanisms in 
technology innovation systems, and some of the decarbonisation 
policy instruments that have been assessed in terms of their impact 
on technological innovation outcomes in Section  16.4.4. As noted 
in the caption and discussed in Section  16.4.4, regulatory policy 
instruments also shape the early stages of technology development.
Table 16.8 | Overview of policy instrument types covered in Chapter 13 and their correspondence to the subset of policy instrument types reviewed in 
Chapter 16 with a focus on innovation outcomes.
 High-level 
categorisationLower-level policy instrument type in Chapter 13Policy instrument types reviewed in Section 16.4 
(for definitions see Peñasco et al. 2021)
Economic or financial 
policy instrument typesResearch and development (R&D) investments R&D investments (including demonstration) (Box 16.3)
Subsidies for mitigationFeed-in tariffs or premia (set administratively)
Energy auctions

Other public financing options (public investment banks, loans, loan guarantees)
Emissions trading schemes Emissions trading scheme
Carbon taxes Taxes/tax relief (including carbon taxes, energy taxes and congestion taxes)
Government provision Government provision (focus on innovation procurement)
Removing fossil fuel subsidies Not covered
Border carbon adjustments Not covered
Offsets Not covered
Regulatory policy 
instrument typesPerformance standards (including with tradeable credits)Renewable obligations with tradeable green certificates
Efficiency obligations with tradeable white certificates
Clean energy or renewable portfolio standards (electricity)
Building codes (building efficiency codes)
Fuel efficiency standards
Appliance efficiency standards
Technology standards Not covered
Soft policy instrumentsDivestment and disclosure Not covered
Voluntary agreements (public voluntary programmes 
and negotiated agreements)Voluntary agreements
Energy labels

1671
16.4.2 The Drivers and Politics of National Policies 
for Climate Change Mitigation and Adaptation
Governments around the world implement innovation policies in the 
energy and climate space with the aim of simultaneously advancing 
environmental, industrial policy (or competitiveness), and security 
goals (Anadón 2012; Surana and Anadon 2015; Meckling et al. 
2017; Matsuo and Schmidt 2019; Peñasco et al. 2021) (medium 
evidence,  medium agreement). Co-benefits of policies shaping 
technological innovation in climate-related technologies, including 
competitiveness, health, and improved distributional impacts can 
be drivers of climate mitigation policy in the innovation sphere 
(Stokes and Warshaw 2017; Deng et al. 2018; Probst et al. 2020). 
For instance, this was the case for climate and air pollution policies 
with local content requirements for different types of renewable

energy projects in places including China (Qiu and Anadon 2012; 
Lewis 2014), India (Behuria 2020), South Africa (Kuntze and 
Moerenhout 2012), and Canada (Genest 2014) (robust evidence, 
The emergence of industries and support groups can lead 
to more sustained support for innovation policies (Meckling 
et al. 2015; Schmidt and Sewerin 2017 Stokes and Breetz 2018; 
Meckling 2019; Meckling and Nahm 2019; Schmid et al. 2020). 
Conversely, policies shaping technology innovation contribute to 
the creation and evolution of different stakeholder groups (robust 
evidence,  high agreement). Most of the literature on the role of 
the politics and interest groups has focused on renewable energy technologies, although there is some work on heating in buildings 
(Wesche et al. 2019).
As novel technologies become cost-competitive, opposition of 
incumbents usually grows, as well as the dangers of lock-in that can

be posed by the new winner. Addressing this involves adapting policy 
Three phases of politics in the development of policies to meet climate 
and industrial objectives can be identified, at the top, the middle 
and the bottom of the experience curve (Breetz et al. 2018) (see 
also Figure 16.1, and Geels 2002). In the first phase of ‘niche market 
diffusion’, the politics of more sustained support for a technology or 
set of technologies become possible after a group of economic winners 
and ‘clean energy constituencies’ are created (Meckling et al. 2015). 
When technologies grow out of the niche (second phase), they pose 
a  more serious competition to incumbents who may become more 
vocal opponents of additional support for innovation in the competing 
technologies (Geels 2014; Stokes 2016). In a  third phase, path-
dependence in policymaking and lock-in in institutions need to change

to accommodate new infrastructure, the integration of technologies, 
the emergence of complementary technologies and of new regulatory 
regimes (Levin et al. 2012; Aklin and Urpelainen 2013).Demonstration
Prototype
Research
(Fundamental science and 
technology exploration)Early adoptionMarket adoption 
and scale-upMaturityPublic policy instruments supporting innovation
Second generation
(learning from early 
ideas)Second generation
ideas)Dead endDead endFirst commercial-
scale exampleFirst commercial-
scale exampleNiche marketsNiche marketsEconomies of scaleEconomies of scaleContinuous incremental 
improvementsContinuous incremental 
improvements
Potential future 
disruptorPotential future 
disruptorKnowledge 
transferKnowledge 
transfer
First generationFirst generation
Third generation
(new ideas arising 
from experience)Third generation
from experience)Market creation 
(e.g., feed-in tariffs, 
tax subsidies, loans, 
procurement, carbon

pricing)
ResearchRenewable 
standards
standards
provision; labelling; 
mandated purchasesRegulatory and 
soft instrumentsEconomic and 
ﬁnancial policy 
instruments
Time  Innovation stage
Figure 16.1 | Technology innovation process and the (illustrative) and role of different public policy instruments (on the right-hand side).  Source: adapted 
from IEA (2020a). Note that, as shown in Section 16.4.4, demand-pull instruments in the regulatory instrument category, for instance, can also shape the early stages of the 
innovation process. Their position on the latter stages is highlighted in this figure because typically these instruments have been introduced in latter stages of the development 
of the technology.

1672
16.4.3 Indicators to Assess the Innovation, 
Competitiveness and Distributional 
Outcomes of Policy Instruments
If policy instruments are created to (at least partly) shape innovation 
for systemic transitions to a zero-carbon future, they also need to 
be evaluated on their impact on the whole socio-technical system 
(Neij and Åstrand 2006) and a  wide range of goals, including 
distributional impacts and competitiveness and jobs (Stern 2007; 
Peñasco et  al. 2021). Given this and the current policy focus on 
green recovery and green industrial policy, we assess impacts 
on competitiveness and equity, although we primarily focus on 
innovation outcomes. Table 16.9 lists the selected set of indicators 
used to assess the impact of the policy instrument types covered 
in the right-hand column in Table 16.8. The table does not include 
technology diffusion or deployment because these are covered in

the technological effectiveness evaluation criterion in Chapter 13. 
As noted in section  16.2, it is very difficult to measure or fully 
understand innovation with one or even several indicators. In 
addition, all indicators have strengths and weaknesses, and may 
be more relevant in some countries and sectors than in others. The 
literature assessing the impact of different policy instruments on 
innovation often covers just one of the various indicators listed in 
the second column of Table 16.9.
16.4.4 Assessment of Innovation and Other Impacts 
of Innovation Policy Instruments
While it is very difficult to attribute a causal relationship between 
a  particular policy instrument implementation and different 
innovation indicators, given the complexity of the innovation system 
(Section 16.3), there is a large volume of quantitative and qualitative 
literature aiming to identify such an impact.
16.4.4.1 Assessment of the Impact on Innovation of Technology

Push Policy Instruments: Public RD&D Investments, 
Other R&D Incentives and Public Procurement
Economic and direct investment policy instrument types are 
typically associated with a direct focus on technological innovation: 
research and development (R&D) grants, R&D tax credits, prizes, 
national laboratories, technology incubators (including support 
for business development, plans), novel direct funding instruments (e.g.,  Advanced Research Projects Agency–Energy (ARPA-E)), and 
innovation procurement.
Public research, development and demonstration (RD&D) 
investments have been found to have a positive impact on different 
innovation in energy- and climate-related technologies (robust 
evidence,  high agreement), but the assessment relies almost entirely 
on evidence from industrialised countries. Out of 17 publications 
focusing on this assessment, only three found no relationship 
between R&D funding and innovation metrics (Doblinger et al. 2019;

Goldstein et al. 2020; Peñasco et al. 2021). Sixteen of them used ex 
post quantitative methods, and one relied on theoretical ex ante 
assessment; only two of them included some non-industrialised 
countries, with one being the theoretical analysis. The evidence 
available does not point to public R&D funding for climate-related 
technologies crowding out private R&D (an important driver of 
innovation) but instead crowding it in. Box  16.6 summarises the 
evidence available of the impact of ARPA-E (a public institution 
created in the USA in 2009 to allocate public R&D funding in energy) 
on innovation and competitiveness outcomes. Another institution 
supporting energy R&D that is the subject of much interest is the 
institutions of the Fraunhofer Society.
No evidence has been found regarding the specific impact of R&D 
tax credits on climate mitigation or adaptation technologies, but it 
is worth noting that, generally speaking, R&D tax credits are found

to incentivise innovation in firms, with a greater impact on small and 
medium firms (OECD 2020). This is consistent with the fact that most 
of the evidence on the positive impact of public R&D support schemes 
covers small and medium firms (Howell 2017; Doblinger et al. 2019; 
Goldstein et al. 2020). Although there is a high level of agreement in 
the literature regarding the impact of R&D investments on innovation 
outcomes in climate-related technologies, it is important to note that 
this evidence comes from industrialised countries. This does not mean 
that public R&D investments in energy have been found to have 
no impact on developing countries innovation or competitiveness 
outcomes, but rather that we were not able to find such studies 
focussing on developing countries.
Overall, public procurement has high potential to incentivise 
innovation in climate technologies, but the evidence is mixed, 
particularly in developing countries (limited evidence,  medium

agreement). Public procurement accounted for 13% of gross 
domestic product (GDP) in OECD in 2013 and much more in some 
Table 16.9 | Outcomes (first row) and indicators (second row) to evaluate the impact of policies shaping innovation to foster carbon neutral economies. 
Sources: innovation outcomes indicators are sourced from Del Rio and Cerdá (2014), Grubb et al. (2021) and Peñasco et al. (2021); the indicators under the competitiveness 
and distributional effects criteria are sourced from Peñasco et al. (2021).
Policy instrument 
OutcomesInnovation
(Part of Chapter 13 ‘Transformative 
potential’ evaluation criterion)Competitiveness
(Part of Chapter 13 ‘Economic 
effectiveness’ evaluation criterion)Distributional impacts
(Defined in the same way as in Chapter13)
Examples of indicators 
used for each outcome 
in the literatureR&D investments, cost improvements, 
learning rates, patents, publications, 
reductions in abatement costs, energy

efficiency improvements, other performance 
characteristics, firms reporting carbon 
saving innovationIndustry creation, net job creation, export 
of renewable energy technology equipment, 
economic growth (GNP , GDP), productivity, 
other investmentsLevel and incidence of support costs, change in 
spending on electricity as a percentage of total 
household spending, participation of different 
stakeholders, international equity (e.g., tCO 2-eq 
per capita), unequal access between large vs. 
small producers or firms

1673
emerging and developing economies (Baron 2016). Its main goal 
is to acquire products or services to improve public services, 
infrastructures and facilities and, in some cases, to also incentivise 
innovation. It is important to implement several steps in the public 
procurement procedure to improve transparency, minimise waste, 
fraud and corruption of public fund. These steps range from the 
assessment of a  need, issuance of a  tender, to the monitoring 
of delivery of the good or service. Box  16.5 outlines a  public 
procurement programme that was implemented in The Netherlands 
in 2005 with a focus on green technologies. In spite of the fact that 
green procurement policies have been implemented, the literature 
assessing the innovation impact of public procurement programmes 
is relatively limited, and suggests either a  positive impact or no 
impact (Alvarez and Rubio 2015; Baron 2016; Fernández-Sastre

and Montalvo-Quizhpi 2019; Peñasco et al. 2021). The majority of 
cases where the impact is positive are analyses of industrialised 
countries, while no impact emerges in the case of a  developing 
country (Ecuador). More empirical research in both developing and 
developed countries is needed to understand the impact of public 
procurement, which has the potential to support the achievement 
of other societal challenges (Edler and Georghiou 2007; Henderson 
and Newell 2011; Baron 2016; ICLEI 2018).16.4.4.2 Assessment of the Impact on Competitiveness 
of Technology Push Policy Instruments: Public 
RD&D Investments, Other R&D Incentives and 
Public Procurement
Public R&D investments in the energy, renewables, and environment 
space are generally associated with positive impacts on industrial 
development or ‘competitiveness outcome’ (robust evidence,  medium 
agreement). In a  number of cases, negligible or negative impacts

emerge (Doblinger et al. 2019; Goldstein et al. 2020; Peñasco et al. 2021).  
The majority of the 15 analyses rely on ex post quantitative methods, 
while only four use ex ante modelling approaches. Also, in this case, 
the vast majority of the evidence is from industrialised countries.
There is limited and mixed evidence regarding the (positive or 
negative) impact of public procurement for low-carbon or climate 
technologies in developed countries (limited evidence,  low 
agreement), and none from developing countries. All of the four 
evaluations identified in the Peñasco et al. (2021) review relied 
on qualitative methods. One found  a  positive impact, another 
a negative impact and two others found no impact. All of the studies 
covered European country experiences.Box 16.5 | Green Public Procurement in The Netherlands
In 2005, the Dutch national government acknowledged a move in the House of Representatives to utilise their annual spending power

to promote the market for sustainable goods and services, as well as to act as a role model. Hence, a policy for environmentally-friendly 
procurement was developed and implemented across the national, local and provincial governments. Subsequently, sustainable public 
procurement has expanded into a multidimensional policy in The Netherlands, accommodating policies on green public procurement, 
bio-based public procurement, international social criteria, social return on investment, innovation-oriented public procurement and 
circular economy.
The Green Public Procurement (GPP) policy is targeted at minimising the negative impacts of production and consumption on the nature 
environment (Melissen and Reinders 2012; Cerutti et al. 2016). It includes a wide range of environmental criteria for different product 
groups that public organisations frequently procure, such as office equipment, uniforms, road works and catering. There are 45 product

groups (Melissen and Reinders, 2012) and six product clusters as part of the government’s purchasing in terms of sustainability 
(PIANOo Expertisecentrum 2020). The six product clusters are: i) automation and telecommunications; ii) energy; iii) ground, road and 
hydraulic engineering; iv) office facilities and services; v) office buildings; and vi) transport (PIANOo Expertisecentrum 2020).The GPP 
2020 Tender Implementation Plan spells out the terms and conditions for green public procurement. Some of these are confidential 
documents and are not shared online. Others are available for download. The tender implementation plan for The Netherlands is 
available on https://gpp2020.eu/low-carbon-tenders/open-tenders/. One of the important scenarios is that the public procurers need 
the details of Life Cycle Analysis (LCA) carried out in a tool called DuboCalc, which calculates the environmental impacts of the

materials and methods of an infrastructural projects. GPP 2020 has reported that three million tonnes of CO 2 would be saved in 
The Netherlands alone if all Dutch public authorities applied the national Sustainable Public Procurement Criteria.
Research has been carried out to determine the prime mover for implementing Green Public Procurement. An online survey was 
administered among public procurement officers who subscribed to the newsletters of two Dutch associations that provide advice and 
training to public procurers. This yielded a sample size of more than 200 (Grandia and Voncken 2019). The first association is called 
Nevi which is the only organisation in The Netherlands that offers certified procurement training programmes. The second association 
is called PIANOo which is a public procurement expertise centre paid by the Dutch national government to bring together relevant

information regarding public procurement and provide public procurers with useful tools through their websites, workshops, meetings 
and annual conferences. The data from the survey was then analysed using structural equations modelling (SEM) and the results 
show that ability, motivation and opportunities affect the implementation of GPP . Particularly, opportunity was found to affect GPP , 
innovation-oriented public procurement and the circular economy, but not the other types of public procurement.

1674
R&D and procurement policies have a positive impact on distributional 
outcomes (limited evidence, high agreement). Peñasco et al. (2021) 
identify three evaluations of the impact of RD&D funding on 
distributional outcomes (two using quantitative methods and one ex 
ante theoretical methods) and one of procurement on distributional 
outcomes (relying on qualitative analysis).
16.4.4.3 Emerging Insights on Different Public R&D 
and Demonstration Funding Schemes
The ability of a given R&D policy instrument to impact innovation and 
competitiveness depends to some extent on policy design features 
(limited evidence, high agreement). As discussed in Section 16.4.4.4, 
this is not unique to R&D funding. Most of these assessments use 
a  limited number of indicators (e.g.,  patents and publications and 
follow-on private financing, firm growth and survival, respectively),

and are focused on the energy sector, and on the USA and other 
industrialised countries. Extrapolating to emerging economies and 
low-income countries is difficult. There is no evidence on the impact 
of different ways of allocating public energy R&D investments in the 
context of developing countries.Block funding, which tends to be more flexible, can lead to research 
that is more productive or novel, but there are other factors that can 
affect the extent to which block funding can lead to more or less 
novel outcomes (limited evidence, medium agreement). Research 
on national research laboratories, which conduct at least 30%  of 
all research in 68 countries around the world (Anadon et al. 2016a), 
are a widespread mechanism to carry out public R&D and allocate 
funds, but assessments of their performance is limited to developed 
countries. R&D priorities are also guided by institutions, and 
research focused on general technology innovation policy finds that

institutions often do not embody the goals of the poor or marginalised 
(Anadon et al. 2016b).
In the case of the US Department of Energy, block funding that can be 
quickly allocated to novel projects (such as that allocated to National 
Labs as part of the Laboratory Directed Research and Development 
funding) has been found to be associated with improved innovation 
indicators (Anadon et al. 2016a). Research in Japan on R&D funding 
in general (not for climate-related technologies) however, indicates 
that R&D funds allocated competitively result in higher novelty for 
‘high status’ (the term used in the paper to refer to senior male 
researchers), while block funding was associated with research 
of higher novelty for ‘lower status’ researchers (e.g., junior female 
researchers) (Wang et al. 2018).
Box 16.6 | ARPA-E – A Novel R&D Funding Allocation Mechanism Focused on an Energy Mission

One approach for allocating public R&D funds in energy involves relying on active programme managers and having clear technology 
development missions that focus on high-risk high-reward areas and projects. This approach can be exemplified by a relatively new 
energy R&D funding agency in the USA, the Advanced Research Projects Agency for Energy (ARPA-E). This agency was created in 
2009 and it was modelled on the experience of Defense Advanced Research Projects Agency (DARPA) – a US government agency 
funding high-risk, high-reward research in defence-related areas (Bonvillian and Van Atta 2011; US National Academies of Sciences 
Engineering and Medicine 2017; Bonvillian 2018). DARPA programme managers had a lot of discretion for making decisions about 
funding projects, but since energy R&D funding is usually more politically vulnerable than defence R&D funding, the ARPA-E model 
involved programme managers requesting external review as an informational input (Azoulay et al. 2019).

As for DARPA, ARPA-E programme managers use an active management approach that involves empowering programme manages to 
make decisions about funding allocation, milestones and goals. ARPA-E managers also differ from other R&D allocation mechanisms 
in that ARPA-E staff retain some control on the funded projects after the allocation of funds. As argued by Azoulay et al. (2019), 
even though this relative control over the project can result in a reduction in the flexibility of funded researchers, some ‘exploration’ 
happens at the programme manager level.
Research on ARPA-E also sheds light on the process of project selection, or how programme managers decide what projects to fund. 
Programme managers do not just follow the rankings of peer reviewers (sometimes projects with very disparate rankings were funded) 
and in many cases programme managers reported using information from review comments instead of the rankings (Goldstein and

Kearney 2020). Azoulay et al. (2019) suggest that, if expert disagreement is a useful proxy for uncertainty in research, then the use of 
individual discretion in ARPA-E would result in a portfolio of projects with a higher level of uncertainty, as defined by disagreement 
among reviewers. Moreover, under the premise that uncertainty is a corollary to novelty, individual discretion is an antidote to novelty 
bias in peer review.
While innovation is notoriously hard to track and, particularly for emerging technologies, it can take a lot time to assess, early analysis 
has shown that this mission-orientation and more ‘actively managed’ R&D funding programme may yield greater innovation patenting 
outcomes than other US energy R&D funding programmes, and a greater or similar rate of academic publications when compared to 
other public funding agencies in energy in the USA, ranging from the Office of Science, the more applied Office of Energy Efficiency

and Renewable Energy, or the small grants office (US National Academies of Sciences Engineering and Medicine 2017; Goldstein and

1675
Public financing for R&D and research collaboration in the energy 
sector is important for small firms, at least in industrialised countries, 
and it does not seem to crowd out private investment in R&D (medium 
evidence, high agreement). Small US and UK firms accrue more 
patents and financing when provided with cash incentives for R&D 
in the form of grants (Howell 2017; Pless 2019). US cleantech start-
ups which partner with government partners for joint technology 
development or licensing partnerships accrue more patents and 
follow-on financing (Doblinger et al. 2019).
Overall, the body of literature on public R&D funding design in 
energy- and climate-related technologies provides some high-level 
guidance on how to make the most of these direct RD&D investments 
in energy technologies in the climate change mitigation space, 
including: giving researchers and technical experts autonomy and

influence over funding decisions; incorporating technology transfer in 
research organisations; focusing demonstration projects on learning; 
incentivising international collaboration in energy research; adopting 
an adaptive learning strategy; and making funding stable and 
predictable (Narayanamurti et al. 2009; Narayanamurti and Odumosu 
2016; Chan et al. 2017) (medium evidence, high agreement).
Without carefully designed public funding for demonstration efforts, 
often in a  cost-shared manner with industry, the experimentation 
at larger scales needed for more novel technologies needed for 
climate change mitigation may not take place. (medium evidence, 
high agreement). Government funding, specifically for technology 
demonstration projects, for RD&D in energy technologies, plays 
a crucial supporting role (Section 16.2.1). Governments can facilitate 
knowledge spillovers between firms, between countries, and 
between technologies (Cohen et al. 2002; Baudry and Bonnet 2019)

(Section 16.2).
16.4.4.4 Assessment of the Impact on Innovation and 
on Competitiveness and Distributional Outcomes 
of Market Pull Policy Instruments
Demand-pull policies such as tradeable green certificates, taxes, 
or auctions, are essential to support scaling-up efforts (Remer and 
Mattos 2003; Wilson 2012; Nahm and Steinfeld 2014). Just as for 
R&D investments, research has indicated that effective demand 
pull needs to be credible, durable, and aligned with other policies 
(Nemet et al. 2017) and that the effectiveness of different demand-
pull instruments depends on policy design (del Río and Kiefer 2021). Historical analyses of the relative importance of demand pull and 
technology push are clear: both are needed to provide robust 
incentives for investment in innovation. Interactions between them 
are central as their combination enables innovators to connect 
a technical opportunity with a market opportunity (Freeman 1995;

Jacobsson et al. 2004; Grubler and Wilson 2013). It is important to 
note that these market pull policies are often put in place primarily 
to  meet security and/or environmental goals, although innovation 
and competitiveness are sometimes also pursued explicitly.
Emissions trading schemes
Overall evidence suggests that the emissions trading schemes, as 
currently designed, have not significantly contributed to innovation 
outcomes (medium evidence, medium/high agreement).
Penasco et al. (2021) review 20 evaluations: eight identified a positive 
impact (although in at least two cases, the paper indicated that the 
impact was small or negligible); 11 no impact; and one was associated 
with a negative impact on innovation indicators. The studies that found 
no impact and the studies that found some impact covered all three 
methods (quantitative ex post, qualitative and theoretical and ex ante 
analysis). Another review focused only on empirical studies (mainly

quantitative but also qualitative), covered a slightly longer period and 
identified 19 studies (15 using quantitative methods) (Lilliestam et al. 
2021). With a narrower set of indicators of innovation, they concluded 
that there was very little empirical evidence linking innovation with 
the emissions trading schemes studied to date (Lilliestam et al. 2021). 
This review focused mainly on papers evaluating the earlier stages 
of the European Emissions Trading Scheme, which featured relatively 
low carbon dioxide prices, and covered a small set of firms, showing 
that carbon pricing policy design is an important determinant of 
innovation outcomes. Combining both reviews, there are a  total 
of 27  individual studies, some of them providing mixed evidence 
of impact, and 23 of them suggest there was no impact or that (in 
a  couple of cases) it was small. It is important to note that some 
researchers note that, for particular subsectors and actors, emissions

trading schemes have had an impact on patenting trends (Calel and 
Dechezleprêtre 2016). Overall the expectation is that higher prices 
and coverage would result in higher impacts and that, over time, the 
impact on innovation would grow.Narayanamurti 2018). In addition, research analysing the first cohort of cleantech start-ups has found that start-ups supported by 
ARPA-E had more innovative outcomes when compared to those that had applied but not received funding, with others that had not 
received any government support, and with others that had received other types of government R&D support (Goldstein et al. 2020). 
Overall, the mission-oriented ARPA-E approach has been successful in the USA when it comes to innovation outcomes. The extent to 
which it can yield the same outcomes in other geographies with different innovation and financing environments remains unknown. 
(limited evidence, high agreement).Box 16.6 (continued)

1676
Carbon and environmental taxes
The impact of carbon taxes on innovation outcomes is more positive 
than that for emissions trading schemes, but the evidence is more 
limited (limited evidence, medium agreement). Assessments of their 
impact on innovation metrics have been very limited, with only four 
studies (three quantitative and one ex ante). Three of the studies 
found a positive impact of carbon taxes on innovation outcomes, and 
one found no impact (Peñasco et al. 2021).
Depending on the design (including the value and coverage of the 
tax), carbon taxes can either have positive, negative or null impact 
on competitiveness and distributional outcomes (medium evidence, 
medium agreement). The evidence on the impact of carbon taxes on 
competitiveness is significant (a total of 27 evaluations) and mixed, 
with six of them reporting some positive impacts, 10  reporting

no impact, and 11 reporting negative impacts (so 59% were 
not associated with negative impacts). Most of the evaluations 
reporting negative impacts were theoretical assessments, and only 
three ex post quantitative analysis (Peñasco et al. 2021). Twenty-
four evaluations covered distributional impacts of carbon taxes and 
other environmental taxes, the majority (15) found the existence of 
some negative distributional impacts, six found positive impacts, 
and three  found no distributional impacts. Differences in the 
assessment results stem from the design of the taxes (Peñasco et al. 
2021). It is important to note that, once again, the evidence comes 
from industrialised countries and emerging economies.
Feed-in-tariffs
Many factors affect the impacts of feed-in tariffs (FITs) on outcomes 
other than innovation (robust evidence,  high agreement). While 
FITs have been generally associated with positive innovation

outcomes, some of the differences found in the literature may arise 
from differences in the evaluation method (Peñasco et al. 2021) or 
differences in policy design (e.g., the level and the rate of decrease 
of the tariff) (Hoppmann et al. 2014), the policy mixes (Rogge et al. 
2017), the technologies targeted and their stage of development 
(Huenteler et al. 2016b), and the geographical and temporal context 
of where the policy was put in place (Section 16.3). Research has 
also found that, particularly for less mature technologies, a higher 
technology specificity in the design of FITs is associated with more 
innovation (Del Río 2012). FITs yield better results if they account 
for the specificities of the country; or else, the technology and the 
policy could result in negative distributional and (to a lesser extent) 
competitiveness impacts. Meckling et al. (2017) indicate that an 
‘enduring challenge’ of technology-specific industrial policy such as

some FITs is to avoid locking in suboptimal clean technologies – 
a challenge which, among other options, could be overcome with 
targeted niche procurement for next-generation technologies. 
Other authors have cautioned that the move from renewable FITs 
to auctions may favour existing PVs (e.g.,  polysilicon) over more 
novel solar power technologies (Sivaram 2018b) such as thin-film 
PV, amorphous PV, and perovskites.
Policy design, policy mixes, and domestic capacity and infrastructure 
are important factors determining the extent to which economic policy instruments in industrialised countries and emerging economies 
can also lead to positive (or at least not negative) competitiveness 
outcomes and distributional outcomes (medium evidence, medium 
agreement) (Section 16.3). Prioritising low-cost energy generation in 
the design of FIT schemes can result in a lower focus of innovation 
efforts on more novel technologies and greater barriers to incumbents

in less mature technologies (Hoppmann et al. 2013). Similarly, case 
study research from Mexico and South Africa indicates that focusing 
on low-cost renewable energy generation can only result in a greater 
reliance on existing foreign value chains and capital, and thus in lower 
or negative impacts on domestic competitiveness. In other words, 
some approaches can hinder the development of the local capabilities 
that could result in greater long-term benefits domestically (Matsuo 
and Schmidt 2019). Evidence for developing countries indicates that 
local and absorptive capacity also play an important role, in particular, 
on the ability of policies to contribute to competitiveness or industrial 
policy goals (Binz and Anadon 2018). Research comparing China’s 
and India’s policies and outcomes on wind energy also suggest 
that policy durability and systemic approaches can affect industrial 
outcomes (Surana and Anadon 2015).

The evidence of the impact of renewable energy auctions on 
innovation outcomes is very small and provides mixed results 
(limited evidence,  low agreement). Out of six evaluations, three 
identify positive impacts, two no impacts, and one negative impacts. 
All of the evaluations but one were qualitative or theoretical, 
and the quantitative assessment indicated no impact (Peñasco 
et al. 2021). There is more evidence covering emerging economies 
analysing the impacts of auctions when compared to other policy 
instrument types. For example, there is work comparing the 
approaches to renewable energy auctions in South Africa and 
Denmark (Toke 2015) finding a positive impact on the latter stages 
of innovation (mainly deployment), and broader work on auctions 
covering OECD countries as well as Brazil, South Africa and China 
not finding a significant impact on innovation (Wigand et al. 2016). 
Work comparing renewable energy auctions in different countries

in South America generally finds a  positive impact on innovation 
outcomes (Mastropietro et al. 2014). The body of evidence 
on the impact of auctions on competitiveness is also limited 
(six  evaluations) and indicates negative outcomes of renewable 
auctions of competitiveness (limited evidence,  low agreement). As 
with other policies, the design of the auctions can affect innovation 
outcomes (del Río and Kiefer 2021). Only two studies investigated 
distributional outcomes, and both were negative.
Other financial instruments
There is no explicit literature on the ability of green public banks, and 
targeted loans, and loan guarantees to lead to upstream innovation 
investments and activities, although there is evidence on their role 
in deployment (Geddes et al. 2018). This notwithstanding, the key 
role of these institutions is in the innovation system (OECD 2015b; 
Geddes et al. 2018) (Sections 16.2.1 and 16.3) and the belief that

they can de-risk scale-up and the testing of business models (Geddes 
et al. 2018; Probst et al. 2021) (Chapter 17).

1677
Renewable obligations with tradeable green certificates
There is mixed evidence of the impact of tradeable green certificates 
(TGCs) on innovation (limited evidence, low agreement) and 
competitiveness (limited evidence, low agreement). Out of the 
11 evaluations in Peñasco et al. (2021), six found no impact, 
two  a  positive impact, and three a  negative impact. All of them 
used a qualitative research approach. Of the six studies focusing on 
competitiveness outcomes, three conclude that TGCs have had no 
impact on competitiveness, while two indicate a negative impact and 
one a  positive impact. Only one of the studies was quantitative, 
and did not identify an impact on competitiveness.
TGCs are associated with the existence of negative distributional 
impacts in most applications (medium evidence, high agreement). 
Ten out of 12 studies identify the existence of some negative impacts.

All but one of these studies (which focused on India) are based on 
analysis of policies implemented in industrialised countries.
Clean energy and renewable portfolio standards
The impact of renewable portfolio standards without tradeable 
credits on innovation outcomes is negligible or very small (medium 
evidence,  medium agreement). Out of the nine studies, seven 
reported no impact on innovation outcomes and two a  positive 
impact (Peñasco et al. 2021). Most of these papers focused on 
patenting and private R&D innovation indicators and not cost 
reductions. Impact on competitiveness is found to be negligible 
or positive (limited evidence,  medium agreement). Out of eight 
evaluations, five  report a  positive impact and three a  negligible 
impact; only two  are quantitative studies (Peñasco et al. 2021). 
Negative distributional impacts from renewable portfolio standards 
can emerge in some cases (limited evidence,  low agreement). Out

of eight evaluations, four identified positive impacts, and four 
negative impacts; all of the studies identifying a  positive impact 
were theoretical. There are efforts focused on clean energy portfolio 
standards which include technologies beyond renewables.
Efficiency obligations with tradeable credits
The impact of tradeable white certificates in innovation is largely 
positive, but the evidence is limited (limited evidence, medium/
high agreement). Out of four evaluations, only one of which was 
quantitative, three report a  positive impact and one reports no 
impact (Peñasco et al. 2021). The impact of white certificates on 
competitiveness is positive (limited evidence, high agreement) 
while the impact on distributional outcomes is very mixed (limited 
evidence, low agreement). Two theoretical studies report positive 
competitiveness impacts. Out of 11 evaluations of distributional 
outcomes, eight rely on theoretical ex ante approaches. Of

the 11 evaluations: seven reported positive impacts (four of 
them  using theoretical methods); three indicated negative impacts 
(using theoretical methods); and one reported no impact.Building codes
There is evidence of the impact of building codes on innovation 
outcomes (Peñasco et al. 2021). Only two studies assessed 
competitiveness impacts (one identified positive impacts and 
one negligible ones) and three studies identified distributional 
impacts, all positive.
Overall, the evidence on the impact of the market pull policy 
instruments covered in Section  16.4.4.4 when it comes to the 
competitiveness outcome (at least in the short term) is more mixed. 
For some of them, the evidence of a positive impact on innovation 
is more consistent than the others (for carbon taxes or FITs, for 
example). Peñasco et al. (2021) found that the disagreements in the 
evidence regarding the positive, negative or no impact of a policy on

competitiveness or distributional outcomes can often be explained by 
differences in policy design, differences in geographical or temporal 
context (since the review included evidence from countries from all 
over the world), or on how policy mixes may have affected the ability 
of the research design of the underlying papers to separate the 
impact of the policy under consideration from the others.
16.4.4.5 Assessment of the Impact on Innovation, 
Competitiveness and Distributional Outcomes 
of Regulatory Policy Instruments Targeting 
Efficiency Improvements
There is medium evidence that the introduction of flexible, 
performance-based environmental regulation on energy efficiency in 
general (e.g., efficiency standards) can stimulate innovative responses 
in firms (Ambec et al. 2013; Popp 2019) (medium evidence, high 
agreement). Evidence comes from both observational studies that 
examine patenting, R&D or technological responses to regulatory

interventions, and from surveys and qualitative case studies in 
which firms report regulatory compliance as a driving force for the 
introduction of environmentally-beneficial innovations (Grubb et al. 
2021). While the literature examining the impact of environmental 
regulation on innovation is large, there have been fewer studies on 
the innovation effects of minimum energy or emissions performance 
regulations specifically relating to climate mitigation. We discuss in 
turn two types of efficiency regulations: on vehicles, and on appliances.
Relationship between automotive efficiency 
regulations and innovation
The announcement, introduction and tightening of vehicle fleet 
efficiency or greenhouse gas (GHG) emission standards either at 
the national or sub-national level positively impacts innovation as 
measured by patents (Barbieri 2015) or vehicle characteristics (Knittel 
2011; Kiso 2019) as summarised in a review by Grubb et al. (2021).

Detailed studies on the innovation effects of national pollutant 
(rather than energy) regulations on automotive innovation also 
indicate that introducing or tightening performance standards has 
driven technological change (Lee et al. 2010). Some studies in the 
USA that examine periods in which little regulatory change took 
place have found that the effects of performance standards on fuel 
economy have been small (Knittel 2011) or not significant relative

1678
to the innovation effects of prices (Crabb and Johnson 2010). This 
is at least in part because ongoing efficiency improvements during 
this period were offset by increases in other product attributes. For 
example, a  study by Knittel (2011) observed that size and power 
increased without a  corresponding increase in fuel consumption. 
It has also been observed that regulatory design may introduce 
distortions that affect automotive innovation choices: in particular, 
fuel economy standards based on weight classes have been observed 
to distort light-weighting strategies for fuel efficiency in both China 
(Hao et al. 2016) and Japan (Ito and Sallee 2018).
A number of studies have focused on the impacts of a sub-national 
technology-forcing policy: the California Zero Emission Vehicle (ZEV) 
mandate. When it was introduced in 1990, this policy required

automotive firms to ensure that 2% of the vehicles they sold in 1998 
would be zero-emission. In the years immediately after introduction 
of the policy, automotive firms reported that it was a  significant 
stimulus to their R&D activity in electric vehicles (Brown et al. 
1995). Quantitative evidence examining patents and prototypes has 
indicated that the stringency of the policy was a significant factor in 
stimulating innovation, though this was, in part, dependent on firm 
strategy (Sierzchula and Nemet 2015). As for the previous instruments, 
most of the evidence comes from industrialised countries, and 
additional research on other countries would be beneficial.
Relationship between appliance efficiency standards and innovation
Regulation-driven deployment of existing technologies can generate 
innovation in those technologies through learning by- doing, induced 
R&D and other mechanisms, although not in all cases (medium

evidence, medium agreement) (Grubb et al. 2021). The introduction or 
tightening of minimum energy performance standards for appliances 
(and for buildings, in Noailly (2012)) have driven innovation 
responses, using direct measures of product attributes (Newell et al. 
1999) and patents (Noailly 2012; Kim and Brown 2019), though not 
all studies have found a significant relationship (Girod et al. 2017). 
There is also evidence of a  correlation between regulation-driven 
deployment of energy-efficient products with accelerated learning in 
those technologies (Van Buskirk et al. 2014; Wei et al. 2017).
In addition to observational studies, evidence on the relationship 
between innovation and regulation comes from surveys in which 
respondents are asked whether they have engaged in innovation 
leading to energy saving or reduced GHG emissions, and what the 
motivations were for such innovation. Survey evidence has found

that expected or current regulation can drive both R&D investment 
and decisions to adopt or introduce innovations that reduce energy 
consumption or CO 2 emissions (Horbach et  al. 2012; Grubb et  al. 
2021). Survey-based studies, however, tend not to specify the type 
of regulation.
Competitiveness and distributional impacts associated with vehicles 
and appliance performance standards
Minimum energy performance standards and appliance standards 
have been known to result in negative distributional impacts 
(limited evidence, medium/high agreement). Several studies focused on the USA have highlighted that minimum energy performance 
standards for vehicles tend to be regressive, with poorer households 
disproportionately affected (Jacobsen 2013; Levinson 2019), 
particularly when second-hand vehicles are taken into account (Davis 
and Knittel 2019). Similar arguments, though with less evidence, have 
been made for appliance standards (Sutherland 2006).

Overall, the extent to which regulations in energy efficiency 
result in positive or negative competitiveness impacts in firms is 
mixed (limited evidence, high disagreement). A  meta-analysis of 
107 studies, of which 13 focused on regulations relating to energy 
consumption or GHG emissions, found that around half showed that 
regulations resulted in competitiveness impacts, while half did not 
(Cohen and Tubb 2018). Cohen and Tubb (2018) also found that 
studies examining performance-based regulations were less likely 
to find positive competitiveness impacts than those that examined 
market-based instruments.
Insights into causal mechanisms and co-evolutionary dynamics 
from case studies on efficiency regulations
While most of the literature addresses the extent to which regulation 
can induce innovation, a  number of case studies highlight that 
innovation can also influence regulation, as the costs of imposing 
regulation are reduced and political interests emerge that seek to

exploit competitive advantages conferred by successfully developing 
energy-efficient or low-carbon technologies (medium evidence, 
high agreement). Case studies map the causal mechanisms relating 
regulations and innovation responses in specific firms or industries 
(Gann et al. 1998; Kemp 2005; Ruby 2015; Wesseling et al. 2015).
16.4.4.6 Assessment of the Impact on Innovation and 
of Soft Instruments
Energy labels and innovation
The literature specifically focusing on the impacts of labels is very 
limited and indicates positive outcomes (limited evidence, high 
agreement). Energy labels may accompany a  minimum energy 
performance standard, and the outcomes of these policies are often 
combined in literature (IEA 2015). But again, given the limited 
evidence, more research is needed. Although there are many studies 
on energy efficiency more broadly and for both standards and labels,

only eight studies specifically focus on labels. Furthermore, seven of 
them report positive outcomes and one negative outcomes. Six of the 
studies used qualitative methods mentioning the impacts of labelling 
on the development of new products (Wiel et al. 2006). Research 
specifically comparing voluntary labels with other mechanisms found 
a significant and positive relationship between labels and the number 
of energy-efficient inventions (Girod et al. 2017). More research is 
needed, especially in developing countries, that have extensive 
labelling programmes in place, and also with quantitative methods, to 
develop evidence on the impacts of labelling on innovation. Box 16.7 
discusses an example of a combination of policy instruments in China 
including labelling, sale bans and financial support.

1679
Voluntary approaches and innovation
Voluntary approaches have a largely positive impact on innovation 
for those that choose to participate (robust evidence, medium 
agreement). Research on voluntary approaches focuses on firms 
adopting voluntary environmental management systems that can 
be certified based on standards of the widely adopted International 
Organization for Standardization (ISO 14001  – standard for 
environmental management) or the European Union’s Eco-
Management and Auditing Scheme (EMAS), which is partly mandatory. 
Out of 16 analyses: 70% report positive innovation outcomes in terms 
of patents, products or processes; 17% report negligible impacts; and 
13% report negative impacts. Positive innovation outcomes have 
been linked to firms’ internal resource management practices and 
were found to be strengthened in firms with mature environmental

management systems and in the presence of other environmental 
regulations (Inoue et al. 2013; He and Shen 2019; Li et al. 2019a). 
Overall, studies are concentrated in a  few countries that do not 
fully capture where environmental management systems have been 
actually adopted (Boiral et al. 2018). There is a need for research in 
analyses of such instruments in emerging economies, including China 
and India, and methodologically in qualitative and longitudinal 
analyses (Boiral et al. 2018).Competitiveness and distributional outcomes of soft instruments
The outcomes for performance or endorsement labels have been 
associated with positive competitiveness outcomes (medium evidence, 
medium agreement) . Out of 19 studies, 89% report positive impact and 
11% negligible impact. Although there are several studies analysing 
competitiveness-related metrics, evidence on most individual metrics 
is sporadic, except for housing premiums. A large number of studies

quantitatively assessing competitiveness find that green labels in 
buildings are associated with housing price premiums in multiple 
countries and regions (Fuerst and McAllister 2011; Kahn and Kok 2014; 
Zhang et al. 2017). Of those studies, 32% were qualitative, associating 
appliance labelling programmes with employment and industry 
development (European Commission 2018). There is a research gap in 
analyses of developing countries, and also in quantitatively assessing 
outcomes beyond housing price premiums.
A few studies on the distributional outcomes of voluntary labelling 
programmes point to positive impacts (limited evidence, high 
agreement). All four studies that focus on benefits for consumers 
and tenants report positive impacts (Devine and Kok 2015). Although 
there are benefits for utility companies and other stakeholders, more 
research is needed to specifically attribute these benefits to voluntary

labels rather than energy efficiency programmes in general.Box 16.7 | China Energy Labelling Policies, Combined with Sale Bans and Financial Subsidies
From 1970 to 2001, China was able to significantly limit energy demand growth through energy-efficiency programmes. Energy use 
per unit of gross domestic product (GDP) declined by approximately 5% yr–1 during this period. However, between 2002 and 2005, 
energy demand per unit of GDP increased on average by 3.8% yr–1. To curb this energy growth, in 2005, the Chinese government 
announced a mandatory goal of 20% reduction of energy intensity between 2006 and 2010 (Zhou et al. 2010; Lo 2014).
An energy labelling system was passed in 2004. It requires manufacturers to provide information about the efficiency of their electrical 
appliances to consumers. From 2004 to 2010, 23 electrical appliances (including refrigerators, air conditioners and flat-screen TVs)

being labelled as energy efficient with five different grades – grade 1 being the most energy efficient and grade 5 the least efficient. 
Any appliances with an efficiency grade higher than 5 cannot be sold in the market.
In addition to providing information to consumers, the National Development and Reform Commission, (which was in charge of 
designing the policies), and the Ministry of Finance launched in 2009 the ‘energy-saving products and civilian-benefiting project’ 
(Zhan et al. 2011). It covered air conditioners, refrigerators, flat panel televisions, washing machines, electrical efficient lighting, energy 
saving and new energy vehicles with the energy grades at 1 or 2. The project also included financial subsidies for the enterprises 
producing these products. The standard design of these financial subsidies involved the government paying for the price difference

of energy-efficient products and general products. The manufacturers that produce the energy-efficient products receive financial 
subsidies directly from the government (Z. Wang et al. 2017).
Before 2008, the market share of grade 1 and grade 2 air conditioners was about 5%, and about 70% of all air conditioners were grade 
5 (the most inefficient). Driven by the financial subsidies, the selling price of the highly efficient air conditioners became competitive 
with that of the general air conditioners. Hence, the sales of energy-efficient air conditioners increased substantially, making the 
market share of grade 1 and 2 air conditioners about 80% in 2010 (Z. Wang et al. 2017). According to the information from China’s 
National Institute of Standardization, the energy label system saved more than 1.5 hundred billion kWh power between 2005 and 
March 2010, equivalent to more than 60 million tonnes of standard coal, 1.4 billion tonnes of carbon dioxide emissions, and 60 tonnes

of sulphur dioxide emissions (Zhan et al. 2011), which significantly contributed to energy saving goals of China’s 11th Five-Year Plan.

1680
Voluntary agreements are associated with positive competitiveness 
outcomes (medium evidence, medium agreement): 14 out of 19 
evaluations identified were associated with positive outcomes, 
while three were associated with negligible outcomes, and 
two with negative outcomes. Research found an increase in 
perceived firm financial performance (de Jong et al. 2014; Moon 
et al. 2014). Studies also show an association with higher exports 
as more environmentally-conscious trade partners increasingly value 
environmental certifications (Bellesi et al. 2005). More research is 
needed to develop evidence on metrics of competitiveness besides 
firms’ financial performance, and especially in developing countries.
Voluntary agreements are associated with a  positive impact on 
distributional outcomes (limited evidence,  high agreement). Five 
studies, mainly using qualitative approaches, report a  positive

association between a firm adopting an environmental management 
system and impacts on its supply chains. There is a need for more 
studies with quantitative assessments and geographical diversity.16.4.4.7 Summary of the Size and Direction of the Evidence of 
All Policy Instrument Types on Innovation Outcomes
Positive impacts have been identified more frequently in some 
policies than in others. There is also a lot of variation in the density 
of the literature. Developing countries are severely underrepresented 
in the decarbonisation policy instrument evaluation literature 
aiming to understand the impact on innovation. (high evidence, 
Figure 16.2 below indicates the extent to which some decarbonisation 
policy instruments have been more or less investigated in terms of 
their impact on innovation outcomes (as described in Table 16.9). For 
example, it indicates the extent to which there has been a greater

focus of evaluations of the impact of R&D investments, emissions 
trading schemes and voluntary approaches on innovation. It also 
shows a  limited amount of evidence on procurement, efficiency 
obligations with tradeable green certificates (TGCs), building 
codes and auctions.Number of evaluationsPositive Negative Negligible
Energy labelsBuilding codes
Appliance
efﬁciency standardsFuel efﬁciency standardsRenewable
portfolio standardsEfﬁciency obligations
 with TWCsRenewable obligations
with TGCsAuctionsFeed-in tariffsCarbon taxesEmissions trading schemesProcurementR&D investments
Voluntary approaches051015202530
Policy instrument type
Figure  16.2 |  Number of evaluations available for each policy instrument type covered regarding their impact on innovation and direction of the 
assessment.  The vertical axis displays the number of evaluations claiming to isolate the impact of each policy instrument type on innovation outcomes as listed in Table 16.9.

The colour indicates whether each evaluation identified a positive impact on the innovation outcome (blue), the existence of a negative impact (in red), and no impact (in grey). 
It builds on Grubb et al. (2021), Lilliestam et al. (2021) and Peñasco et al. (2021), and additional studies identified as part of these reviews. TGC stands for tradeable green 
certificates. TWC stands for tradeable white certificates.

1681
16.4.5 Trade Instruments and their Impact 
on Innovation
There has been long-standing interest on the impact of Foreign Direct 
Investment (FDI) on domestic capacity, innovation and environmental 
outcomes. While this section looks at the impact of trade instruments 
on innovation, it does not cover the much larger body of evidence on 
the relationship between FDI and economic development and growth.
Overall, research indicates that trade can facilitate the entrance 
of new technologies, but the impact on innovation is less clear 
(limited evidence, low agreement). A recent study indicates that, for 
countries with high environmental performance, FDI has a negligible 
impact on environmental performance, while countries with a lower 
environmental performance may benefit from FDI in terms of their 
environmental performance (Li et al. 2019b). One analysis on China

links FDI with improved environmental performance and energy 
efficiency and also innovation outcomes in general (Gao and Zhang 
2013). Other work links FDI with increased productivity across firms 
(not just those engaged in climate-related technologies) through 
spillovers (Newman et al. 2015). In addition, Brandão and Ehrl 
(2019) indicate that productivity of the electric power industry is 
more influenced by the transfer of embodied technology from other 
industries than by investments of the power industry. Also, they 
find that countries with high R&D stocks are the main sources of 
international technology spillovers and the source countries may also 
benefit from the spillover.
Other emerging work investigates the role of local content 
requirements on innovation outcomes and suggests that it can lead to 
increased power costs (negative distributional impacts). The benefits 
to the domestic innovation system, measured by patents or exports,

are unclear if the policies are not part of a holistic and longer-lasting 
policy framework (Probst et al. 2020).
16.4.6 Intellectual Property Rights, Legal Framework 
and the Impact on Innovation
Virtually all countries around the world have instituted systems for 
the protection of creations and inventions, known as intellectual 
property rights (IPR) systems (WIPO 2021). While several types 
of intellectual property exist  – patents, copyright, design rights, 
trademarks, and more – this section will focus on patents, as the most 
relevant property right for technological innovations (WIPO 2008), 
and hence the most relevant for policy instruments in this context.
Patent systems aim to promote innovation and economic growth, 
by stimulating both the creation of new knowledge and diffusion of 
that knowledge (high evidence, high agreement). National patent 
systems, as institutions, play a  central role in theories on national

innovation systems (high evidence, strong agreement). Patent 
systems are usually instituted to promote innovation and economic 
growth (Machlup and Penrose 1950; Nelson and Mazzoleni 1996; 
Encaoua et al. 2006). Some countries explicitly refer to this purpose 
in their law or legislation – for instance, the US Constitution states 
the purpose of the US IP rights system to ‘promote the progress of science and useful arts’. Patent systems aim to reach their goals by 
trying to strike a balance between the creation of new knowledge and 
diffusion of that knowledge (Scotchmer and Green 1990; Devlin 2010; 
Anadon et al. 2016b). They promote the creation of new knowledge 
(e.g., technological inventions) by providing a temporary, exclusive 
right to the holder of the patent, thus providing incentives to develop 
such new knowledge and helping parties to justify investments in 
R&D.  They promote the diffusion of this new knowledge via the

detailed disclosure of the invention in the patent publication, 
and by enabling a ‘market for knowledge’ via trading patents and 
issuing licences (Arora et al. 2004). Although IP protections provide 
incentives to invest in innovation, they can also restrict the use of 
new knowledge by raising prices or blocking follow-on innovation 
(Wallerstein et al. 1993; Stiglitz 2008). As institutions, national patent 
systems feature prominently in models and theories of national 
innovation systems (Edquist 1997; Klein Woolthuis et al. 2005).
The degree to which patent systems actually promote innovation 
is subject to debate. Patent protection has been found to have 
a positive impact on R&D activities in patent-intensive industries, but 
this effect was found to be conditional on access to finance (Maskus 
et al. 2019). Patents are believed to be especially important to 
facilitate innovation in selected areas such as pharmaceuticals, where

investments in developments and clinical trials are high, imitation 
costs are low, and there is often a one-to-one relationship between 
a patent and a product, referred to as a ‘discrete’ product industry 
(Cohen et al. 2000). At the same time, an increasing body of theoretical 
and empirical literature suggests that the proliferation of patents 
also discourages innovation (medium evidence, low agreement). 
Theoretical contributions note that a appropriability regime that is too 
stringent may greatly limit the diffusion of advanced technological 
knowledge and eventually block the development of differentiated 
technological capabilities within an industry, in what is called an 
‘appropriability trap’ (Edquist 1997; Klein Woolthuis et al. 2005). 
There has been a long-standing debate on the impact of patents and 
other IP rights on innovation and economic development (Machlup 
1958; Hall and Helmers 2019). Jaffe and Lerner (2004) and Bessen

and Meurer (2009) highlight how IP rights also hamper innovation 
in a variety of ways. Other contributions in the literature focus on 
more specific factors. For example, Shapiro (2001) discusses ‘patent 
thickets’, where overlapping sets of patent rights mean that those 
seeking to commercialise new technology need to obtain licences 
from multiple patentees. Heller and Eisenberg (1998) argue that 
a ‘tragedy of the anticommons’ is likely to emerge when too many 
parties obtain the right to exclude others from using fragmented 
and overlapping pieces of knowledge  – ultimately leading to no 
one having the privilege of using the results of biomedical research. 
Reitzig et al. (2007) describe the damaging effects of extreme 
business strategies employing patents, such as ‘patent trolling’.
In general, IP protection and enforcement may have different 
impacts on economic growth in different types of countries (limited

evidence, high agreement). There has been a  significant degree of 
harmonisation and cooperation between national IP systems over 
time. The most recent milestone is the World Trade Organization 
(WTO) 1994 Trade-Related Aspects of Intellectual Property Rights 
(TRIPS) Agreement, entered into by all WTO members, which

1682
sets down minimum standards for the regulation by national 
governments of many forms of IP as applied to nationals of other 
WTO member nations (WTO 1994). Developing countries successfully 
managed to include some flexibilities into TRIPS, both in terms of 
timing of legislative reform, and the content of the reforms. In an 
attempt to understand the  effects of the introduction of TRIPS, 
Falvey et al. (2006) find that the effect of IP protection on growth 
is positively and significantly related to growth for low- and high-
income countries, but not for middle-income countries. They argue 
that low-income countries benefit from increased technology flows, 
but middle-income countries may have offsetting losses from the 
reduced scope for imitation. Note that Falvey et al. (2006) do not 
break down their results in different technological areas, and they 
do not focus on innovation, but instead on growth. It has been

argued that the increasingly globalised IP regime through initiatives 
such as the TRIPS agreement will diminish prospects for technology 
transfer and competition in developing countries, particularly for 
several important technology areas related to meeting sustainable 
development needs (Maskus and Reichman 2017).
In principle, patent holders are not required to take their protected 
invention into use, and neither have the obligation to allow 
(i.e., license) others to use the inventions in question (high evidence, 
high agreement). Studies have shown that the way patent holders 
use their patent differs considerably across industrial sectors: 
in pharmaceutics, patents are typically used to enable exclusive 
production of a certain good (and obtain monopoly rents), while in 
industries such as computers, semiconductors, and communications, 
patents are often used to strengthen positions in cross-licensing 
negotiations and to generate licensing income (Cohen et al. 2000;

Foray 2004). There are also companies that predominantly obtain 
patents for defensive reasons: they seek freedom to design and 
manufacture, and by owning a  patent portfolio themselves, they 
hope to prevent becoming the target of litigation by other patent 
holders (Hall and Ziedonis 2001). Patents are often used strategically 
to impede the development and diffusion of competing, alternative 
products, processes or services, by employing strategies known as 
‘blanketing’ and ‘fencing’ (Grandstrand 2000), although the research 
is not specific to the climate space.
There are notable but specific exceptions to the general principle that 
patent holders are not obliged to license their patent to others. These 
exceptions include the compulsory licence, fair, reasonable and non-
discriminatory (FRAND) policies, and statement on licences of right 
(high evidence, high agreement). While patent holders are, in principle,

free to choose not to license their innovation, there are three important 
exceptions to this. First, most national patent laws have provisions for 
compulsory licensing, meaning that a government allows someone 
else to produce a patented product or process without the consent of 
the patent holder, or plans to use the patent-protected invention itself 
(WTO 2020). Compulsory licences may be issued in cases of public 
interest or events of abuse of the patent (WIPO 2008; Biadgleng 
2009). Compulsory licensing is explicitly allowed in the WTO TRIPS 
agreement, and its use in context of medicine (for instance, to control 
diseases of public health importance, including HIV, tuberculosis and 
malaria) is further clarified in the ‘DOHA Declaration’ from 2001 
(Reichman 2009; WHO 2020). Second, standard-setting organisations have policies to include patented inventions in their standards only 
if the patent holder is willing to commit FRAND licensing conditions

for those patents (Contreras 2015). While a patent holder can choose 
not to make such a commitment, by doing so, its patent is no longer 
a candidate for inclusion in the standard. In the (many) fields where 
standards are of key importance, it is very unusual for patent holders 
not to be willing to enter into FRAND commitments (Bekkers 2017). 
Third, when a patent holder files at the patent office and opts for the 
‘licence of right’ regime, in return for reduced patent fees, they enter 
into a contractual agreement that obliges them to license the patent 
to those who request it. While not all national patent systems feature 
this regime, it is a feature present in the new European Community 
patent (EPO 2017), and may therefore increase in importance.
For a discussion on the impact of intellectual property rights (IPR) on 
international technology diffusion, see Box 16.9 in Section 16.5.
16.4.7 Sub-national Innovation Policies 
and Industrial Clusters

Research examining the impacts of sub-national policies on 
innovation and competitiveness is sporadic – regional variations have 
been quantitatively assessed in the USA or China, or with case studies 
in these and other countries. Research on wind energy in the USA, 
distributed PV balance of systems in China, and renewable energy 
technologies in Italy have found that policies that incentivised local 
demand were associated with inducing innovation, measured with 
patents (Corsatea 2016; Fu et al. 2018; Gao and Rai 2019). Different 
policies may have different impacts – for example, in the USA, state-
level tax incentives and subsidies induced innovation within the 
state; but for renewable portfolio standards, policies in other states 
were associated with innovation because of impact on demand, but 
own-state policies were not (Fu et al. 2018). Research has also noted 
that the outcomes of policy and regulation on innovation are spatially

heterogenous, because of differences in local planning authorities 
and capabilities (Corsatea 2016; Song et al. 2019).
Sub-national deployment policies have been associated with different 
impacts on competitiveness metrics (limited evidence, medium 
agreement). Research on green jobs shows positive association 
between sub-national policies and green jobs or green firms at the 
metropolitan level as well as the state of provincial level, in both 
China and the USA (Yi 2013; Yi and Liu 2015; Lee 2017), while others 
find no impact of renewable portfolio standards on green job growth 
in the state (Bowen et al. 2013). Other examples of competitiveness 
are in the impact of regional green industrial policy in Brazil’s Rio 
Grande do Sul region in attracting auctioned contracts for wind 
energy (Adami et al. 2017) or in the changes in net positive state 
revenues associated with removing tax incentives for wind producers 
in Idaho in the USA (Black et al. 2014).

Sub-national policies also directly support innovation and 
competitiveness through green incubators and direct grants or R&D 
funding for local companies working on clean energy, intending to 
promote local economic development (limited evidence, medium 
agreement). The literature on the impacts of such policies on innovation

1683
and competitiveness is sparse. Some case studies and programme 
evaluation reports, primarily in the USA, have identified the impacts 
of sub-national policies on competitiveness —  for example, job 
creation from direct R&D funding in North Carolina (Hall and Link 
2015), perceptions for local industry development and support for 
follow-on financing for companies receiving state-funded grants in 
Colorado (Surana et al. 2020b), and return on investments for the 
state in research and innovation spending from the New York state’s 
energy agency (NYSERDA 2020). There is a general paucity of metrics 
on innovation and competitiveness for systematic assessments of 
such programmes in developed countries, and even more so in India 
and other developing countries where such programmes have been 
increasing (Gonsalves and Rogerson 2019; Surana et al. 2020a).
Although states and local governments increasingly support clean

energy deployment as well as directly support innovation, given its 
link with economic development goals, there is a lack of systematic 
research on the impacts of these policies at the subnational level. 
More research  – qualitative and quantitative, and in developed 
and developing countries  – is needed to systematically develop 
evidence on these impacts and to understand the reasons behind 
regional differences in terms of the type of policy as well as the 
capabilities in the region.
16.4.8 System-oriented Policies and Instruments
Although previous sections summarised the research disentangling 
the role of individual policies in advancing or hindering innovation 
(as well as impacts on other objectives), other research has tried 
to characterise the impact of a policy mix on a particular outcome. 
Although the outcome studied was not innovation, but diffusion 
(technology effectiveness is in the set of criteria outlined in

Chapter 13), it seems relevant to discuss overall findings. Research 
reviewing renewable energy policies in nine OECD countries 
concludes that, over time, a  broad set of policies characterised by 
a ‘balance’ metric has been put in place. This research also identifies 
a  significant negative association between the balance of policies 
in renewable energy and the diffusion of total renewable energy 
capacity, but no significant effect of the overall intensity (coded as 
the 46 weighted average of six indicators) on renewable capacity 
(Schmidt and Sewerin 2019). This indicates that a neutral conception 
of balance across all possible policies may not be desirable, and that 
policy mix intensity by itself does not explain technology diffusion.
A growing body of research aims to understand how different 
policies interact and how to characterise policy mixes (del Río 2010; 
Howlett and del Rio 2015; Rogge and Reichardt 2016; del Río and

Cerdá 2017). The empirical impact on the innovation outcomes 
is not yet discussed. A more detailed discussion of this literature is 
located in Chapter 13.
An emerging stream of research in complex systems suggests that 
relatively small changes in policy near a  possible tipping point in 
climate impacts in areas, including changing strategies related 
to investments in innovation, could trigger large positive societal 
feedbacks in the long term (Farmer et al. 2019; Otto et al. 2020).16.5 International Technology Transfer and 
Cooperation for Transformative Change
This section covers international transfer and cooperation in relation 
to climate-related technologies, ‘the flows of know-how, experience 
and equipment for mitigating and adapting to climate change 
amongst different stakeholders’ (IPCC 2000) as well as innovation 
to support transformative change compared to AR5 (IPCC 2014) 
and the IPCC Special Report on Global Warming of 1.5°C (SR1.5)

(IPCC 2018a). This complements the discussion on international 
cooperation on science and technology in Chapter 14.
This section first outlines the needs and opportunities for 
international transfer and cooperation on low-emission technologies. 
It then describes the main objectives and roles of these activities, 
and then reviews recent institutional approaches within and outside 
the UN Framework Convention on Climate Change (UNFCCC) to 
support international technology transfer and cooperation. Finally, it 
discusses emerging ideas for international technology transfer and 
cooperation, and possible modifications to support the achievement 
of climate change and Sustainable Development Goals (SDGs), 
building up to Section 16.6.
16.5.1 International Cooperation on Technology 
Development and Transfer: Needs 
and Opportunities
With the submission of their Nationally Determined Contributions 
(NDCs) as part of the Paris Agreement, most developing countries are

now engaged in climate mitigation and adaptation. While technology 
is seen as one of the ‘means of implementation’ of climate action, 
developing countries often have relatively limited technology 
innovation capabilities, which requires them to access technologies 
developed in higher-income countries with stronger innovation 
systems (Popp 2011; Binz et al. 2012; Urban 2018). In many cases, 
these technologies require adaptation for the local context and needs 
(Sagar 2009; Anadon et al. 2016b), and innovation capabilities are 
required to suitably adapt these technologies for local use and also 
to create new markets and business models that are required for 
successful deployment (Sagar 2009; Ockwell et al. 2015; Ockwell and 
Byrne 2016). This can lead to dependencies on foreign knowledge 
and providers (Ockwell and Byrne 2016), negative impacts in terms of 
higher costs (Huenteler et al. 2016a), balance of payments constraints,

and vulnerability to external shocks (Ebeling 2020).
The climate technology transition can also yield other development 
benefits, for instance better health, increased energy access, 
poverty alleviation and economic competitiveness (Deng et al. 
2018), including industrial development, job creation and economic 
growth (Porter and Van der Linde 1995; Altenburg and Rodrik 
2017; Lema et al. 2020; Pegels and Altenburg 2020) (Section 16.6). 
The growing complexity of technologies and global competition 
have made technology development a globalised process involving 
the flow of knowledge and products across borders (Lehoux et al. 
2014; Koengkan et al. 2020). For instance, in electronics production, 
Asian economies have captured co-location synergies and dominate

1684
production and assembly of product components, whereas American 
firms have adopted ‘design-only’ strategies (Tassey 2014). In the 
context of renewable energy technologies, ‘green global division of 
labour’ has been observed, with countries specialising in investments 
in research and development (R&D), manufacturing or deployment of 
renewables (Lachapelle et al. 2017). In the case of solar photovoltaic 
(PV), for example, while many technical innovations emerged from 
the USA, Japan and China emphasised the manufacture of physical 
modules (Deutch and Steinfeld 2013) (Box 16.4).
Such globalisation of production and supply chains opens up 
economic development opportunities for developing countries 
(Lema et al. 2020). At the same time, not all countries benefit from 
the globalisation of innovation – barriers remain related to finance, 
environmental performance, human capabilities and cost (Weiss and

Bonvillian 2013; Egli et al. 2018), with developing countries being 
particularly disadvantaged at leveraging these opportunities. The gap 
in low-carbon technology innovation between countries appears to 
have reduced only among OECD countries (Yan et al. 2017; Du and 
Li 2019; Du et al. 2019) and the lower-income countries are not 
able to benefit as much from low-carbon technologies. For instance, 
in the case of agriculture, Fuglie (2018) notes that international 
R&D spillovers seem to have benefitted developed countries more 
than developing countries. Gross et al. (2018) also argue that the 
development timescales for new energy technologies can extend 
up to 70 years, even within one country. They recommend that 
innovation efforts be balanced between early-stage R&D spending, 
and commercialising already low-emission technologies in the 
demonstration phase and diffusing them globally.
Thus international cooperation on technology development and

transfer can enable developing countries to achieve their climate 
goals more effectively, while also addressing other SDGs  – taking 
advantage, where possible, of the globalisation of innovation and 
production (Lema et al. 2020). Earlier assessments in AR5 and SR1.5 
have made it clear that international technology transfer and 
cooperation could play a role in climate policy at both the international 
and the domestic policy level (Somanathan et al. 2014; Stavins et al. 
2014; IPCC 2018b) and for low-carbon development at the regional 
level (Agrawala et al. 2014). The Paris Agreement also reflects this 
view by noting that countries shall strengthen cooperative action on 
technology development and transfer regarding two main aspects: 
(i) promoting collaborative approaches to R&D; and (ii) facilitating 
access to technology to developing country Parties (UNFCCC 2015). 
Furthermore, both in literature and in UNFCCC deliberations, South-

South technology transfer is highlighted (Khosla et al. 2017) as 
a complement to the transfer of technology and know-how from the 
North to the South.
This is consistent with literature that suggests that greenhouse 
gas (GHG) mitigation in developing countries can be enhanced by: 
(i) technology development and transfer collaboration and a ‘needs-
driven’ approach; (ii) development of the specific types of capacity 
required across the entire innovation chain; and (iii) strengthening of 
the coordination and agendas across and between governance levels 
(including domestic and international levels) (Khosla et al. 2017; 
Zhou 2019; Upadhyaya et al. 2020).16.5.2 Objectives and Roles of International Technology 
Transfer and Cooperation Efforts
International efforts involving technology transfer can have different 
objectives and roles. These include access to knowledge and 
financial resources as well as promotion of new industries in both

the developed and recipient country (Huh and Kim 2018). Based 
on an econometric analysis of international technology transfer 
factors and characteristics of Clean Development Mechanism (CDM) 
projects, Gandenberger et al. (2016) find that complexity and novelty 
of technologies explain whether a CDM project includes hardware 
technology transfer, and that factors like project size and absorptive 
capacity of the host country do not seem to be drivers. Halleck Vega 
and Mandel (2018) argue that ‘long-term economic relations’, for 
instance being part of a customs union, affect technological diffusion 
between countries in the case of wind energy, and indicate that this 
has resulted in low-income countries being largely overlooked.
There is some literature studying whether technology cooperation 
could complement or replace international cooperation based on 
emission reductions, such as in the Kyoto Protocol, and whether

that would have positive impacts on climate change mitigation and 
compliance. A handful of papers conducted game-theoretic analysis on 
technology cooperation, sometimes as an alternative for cooperation 
on emission reductions, and found partially positive effects (Bosetti 
et al. 2017; Narita and Wagner 2017; Rubio 2017; Verdolini and 
Bosetti 2017). However, Sarr and Swanson (2017) model that, due to 
the rebound effect, technology development and transfer of resource-
saving technologies may not lead to envisioned emission reductions.
While technology cooperation can be aimed at emission reduction 
through mitigation projects, as indicated above, not all cooperative 
actions directly result in mitigation outcomes. Overall, technology 
transfer broadly has focused on: (i) enhanced climate technology 
absorption and deployment in developing countries; and 
(ii)  enhanced research, development and demonstration (RD&D) 
through cooperation and knowledge spillovers.

16.5.2.1 Enhancing Low-emission Technology Uptake 
in Developing Countries
Real-world outcomes in terms of low-emission technology 
deployment in developing countries may vary significantly, depending 
on the nature of the international engagement and the domestic 
context. While there has been some success in the enhancement 
of technology deployment through technology transfer in some 
developing countries (de la Tour et al. 2011; Zhang and Gallagher 
2016), many others, and particularly least-developed countries, 
are lagging behind (Glachant and Dechezleprêtre 2017). Glachant 
and Dechezleprêtre (2017) indicate that this is due to the lack of 
participation in economic globalisation and that climate negotiations 
could facilitate technology transfer to those countries through the 
creation of global demand for low-emission technologies through 
stronger mitigation targets that will result in lowering of costs and 
therefore enhanced technology diffusion. A  broader perspective

presents a host of other factors that govern technology diffusion and 
commercialisation in developing countries, including: investment;

1685
social, cultural and behavioural, marketing and market building; 
macroeconomics; and support policy (Bakhtiar et al. 2020). Ramos 
Mejía et al. (2018) indicate that the governance of low-emission 
technology transfer and deployment in developing countries is 
frequently negatively affected by a mixture of well- and ill-functioning 
institutions  – for instance, in a  context of market imperfection, 
clientelist and social exclusive communities and patrimonial and/
or marketised states. Furthermore, existing interests, such as fossil 
fuel production, may also impede the deployment of low-emission 
technologies, as highlighted in case studies of Vietnam and Indonesia 
(Dorband et al. 2020; Ordonez et al. 2021). It is for such reasons that 
both domestic efforts and international engagement are seen as 
necessary to facilitate technology transfer as well as deployment in

developing countries (Boyd 2012). The same has been seen as true 
in the case of agriculture, where the very successful international 
research efforts of the CGIAR – with remarkably favourable benefit-
cost ratios (Alston et al. 2021) – were complemented by the national 
agricultural research systems for effective uptake of high-yielding 
varieties of crops (Evenson and Gollin 2003).
One key area for underpinning effective technology uptake in 
developing countries relates to capabilities for managing technological 
change. This includes the capabilities to innovate, implement, and 
undertake integrated planning. There is much research to indicate 
that the ability of a  country’s firms to adopt new technologies is 
determined by its absorptive capacity, which includes its own R&D 
activities, human capacity (e.g.,  technical personnel), government 
involvement (including institutional capacity), the infrastructure in the

country (Kumar et al. 1999), and knowledge and capacity as part of its 
‘intangible assets’ or the ‘software’ (Ockwell et al. 2015; da Silva et al. 
2019; Corsi et al. 2020). For sustainable development, the capacity 
to plan in an integrated way and implement the SDGs (Khalili et al. 
2015; Elder et al. 2016), including using participatory approaches 
(Disterheft et al. 2015), is a  conditional means of implementation. 
It also is argued that, if human capital were the focus of international 
climate negotiations as well as national climate policy, it could 
change the political economy in favour of climate mitigation, which 
is needed for developing such capabilities in advance to keep up with 
the required speed of transformation (Ockwell et al. 2015; Hsu 2017; 
IPCC 2018b; Upadhyaya et al. 2020). In a  global analysis of wind 
energy using econometric analysis, Halleck-Vega et al. (2018) lend 
quantitative credibility to the claim that a  technology skill base is

a  key determinant of technological diffusion. Activities to enhance 
capabilities include informational contacts, research activities, 
consulting, education and training, and activities related to technical 
facilities (Huh and Kim 2018; Khan et al. 2020).
There are multiple studies drawing on empirical work that also 
support this conclusion. For South-South technology transfer between 
India and Kenya, not just technical characteristics, but also mutual 
learning on how to address common problems of electricity access 
and poverty, was suggested as an important condition for success 
(Ulsrud et al. 2018). Olawuyi (2018) discusses the specific capability 
gap in Africa, despite decades of technology transfer efforts under 
various mechanisms and programmes of the UNFCCC.  The study 
suggests that barriers need to be resolved by African countries 
themselves, in particular: inadequate access to information about imported climate technologies; lack of domestic capacities to

deploy and maintain imported technologies; the weak regulatory 
environment to stimulate clean technology entrepreneurship; the 
absence or inadequacy of climate change laws; and weak legal 
protection for imported technologies. Moreover, Ziervogel et al. 
(2021) indicate that, for transformative adaptation, transdisciplinary 
approaches and capacity-building shifting, ‘the co-creation of 
contextual understandings’ instead of top-down transfer of existing 
knowledge would deliver better results. Despite the understanding of 
the importance of the capacity issue, significant gaps still remain on 
this front (TEC 2019) (Section 16.5.4).
16.5.2.2 Enhancing RD&D and Knowledge Spillovers
As mentioned earlier, RD&D can aid the development of new 
technologies as well as their adoption for new use contexts. 
Therefore, it is not surprising that international cooperation on RD&D 
is identified as a  mechanism to promote low-carbon innovation

(Suzuki 2015; Mission Innovation 2019; TEC 2021). This has resulted 
in a  variety of international initiatives to cooperate on technology 
in order to create knowledge spillovers and develop capacity. For 
example, the UNFCCC Technology Mechanism, among other things, 
aims to facilitate finance for RD&D of climate technologies by helping 
with readiness activities for developing country actors. In particular 
preparing early-stage technologies for a  smoother transition to 
deployment and commercialisation has been emphasised in the 
context of the Technology Executive Committee (TEC) (TEC 2017). 
There are numerous multilateral, bilateral and private programmes that 
have facilitated RD&D, biased mostly towards mitigation (as opposed 
to adaptation) activities. Many programmes that seemed to be 
about RD&D were in reality dialogues about research coordination 
(Ockwell et al. 2015). There are also a  variety of possible bilateral

and multilateral models and approaches for engaging in joint R&D 
(Mission Innovation 2019). An update by the TEC (2021) reviewing 
good practices in international cooperation of technology confirmed 
the conclusions of Ockwell et al. (2015), and moreover highlighted 
that most initiatives are led by the public sector, and that the private 
sector tended to get involved only in incubation, commercialisation 
and diffusion phases. It also concluded that, although participation of 
larger, higher-income developing countries seems to have increased, 
participation of least-developed countries is still very low.
16.5.3 International Technology Transfer and 
Cooperation: Recent Institutional Approaches
The sections below discuss the literature on various categories of 
international technology cooperation and transfer.
16.5.3.1 UNFCCC Technology and Capacity-building Institutions
Technology development and transfer have been a part of UNFCCC

discussions and developments in the context of the international 
climate negotiations ever since its agreement in 1992, as assessed in 
AR5 (Stavins et al. 2014). Support on ‘Technology Needs Assessment’ 
to developing countries was the first major action undertaken by 
the UNFCCC, and this has undergone different cycles of learning

1686
(Nygaard and Hansen 2015; Hofman and van der Gaast 2019). 
Since 2009, the UNFCCC discussions on technology development 
and transfer have focused on the Technology Mechanism under the 
Cancun Agreements of 2010, which can be seen as the global climate 
governance answer to redistributive claims by developing countries 
(McGee and Wenta 2014). The Technology Mechanism consists 
of the TEC and the Climate Technology Centre & Network (CTCN). 
An independent review of CTCN, evaluated it on five dimensions – 
relevance, effectiveness, efficiency, impacts and sustainability – and 
indicated that the organisation is achieving its mandate in all these 
dimensions, although there are some possible areas of improvement. 
The review also specifically noted that ‘the lack of predictability 
and security over financial resources significantly affected 
the CTCN’s ability to deliver services at the expected level, as did the

CTCN’s lack of human and organizational resources and the capacity 
of NDEs [National Designated Entities].’ (TEC 2017). The CTCN has 
overcome some of the limitations imposed by resource constraints 
by acting as a  matchmaker from an open-innovation perspective 
(Lee and Mwebaza 2020). The CTCN’s lack of financial sustainability 
has been a  recurring issue, which may potentially be resolved by 
deepening the linkage between the CTCN and Green Climate Fund 
(Oh 2020). In the meanwhile, the Green Climate Fund is planning to establish the Climate Innovation Facility to support and accelerate 
early-stage innovations and climate technologies through the 
establishment of regional innovation hubs and climate accelerators 
as well as a climate growth fund (Green Climate Fund 2020).
The ‘technology’ discussion has been further strengthened by the 
Paris Agreement, in which Article 10 is fully devoted to technology 
development and transfer (UNFCCC 2015). However, the political

discussions around technology continue to be characterised by 
viewing technology mostly as hardware (Haselip et al. 2015), and 
relatively limited in scope (de Coninck and Sagar 2017). The workplans 
of the TEC and the CTCN do, however, indicate a broadening of the 
perspective on technology (CTCN 2019; TEC 2019).
Since the Kyoto Protocol’s CDM has been operational, studies have 
assessed its hypothesised contribution to technology transfer, 
including transfer of knowledge. Though not an explicit objective 
of the CDM, numerous papers have investigated whether CDM 
projects contribute to technology transfer (Michaelowa et al. 2019). 
The literature varies in its assessment. Some find extensive use 
of domestic technology and hence lower levels of international 
technology transfer (Doranova et al. 2010), while others indicate Box 16.8 | Capacity Building and Innovation for Early Warning Systems in Small Island 
Developing States

One of the areas of international cooperation on capacity building is adaptation, which has been highlighted by both the Technology 
Executive Committee (TEC) (Ockwell et al. 2015; TEC 2015) and the Paris Committee on Capacity-building (UNFCCC 2020b) as an area 
where capacity gaps remain, especially in Small Island Developing States (SIDS).
While adaptation was initially conceived primarily in terms of infrastructural adjustments to long-term changes in average conditions 
(e.g., rising sea levels), a key innovation in recent years has been to couple such long-term risk management to existing efforts to 
manage disaster risk, specifically including early warning systems, enabling early action in the face of climate- and weather-risk at 
much shorter timescales (IPCC 2012), with potentially significant rates of return (Rogers and Tsirkunov 2010; Hallegatte 2012; Global 
Commission on Adaptation 2019).

In recent years, deliberate international climate finance investments have focused on ensuring that developing countries (and especially 
SIDS and least-developed countries) have access to improvements in hydrometeorological observations, modelling, and prediction 
capacity, sometimes with a particular focus on the people intended to benefit from the information produced (CREWS 2016). For 
instance, on the Eastern Caribbean SIDS of Dominica, researchers took a community-based approach to identify the mediating factors 
affecting the challenges to coastal fishing communities in the aftermath of two extreme weather events (in particular hurricane Maria 
in 2017) (Turner et al. 2020). Adopting an adaptive capacity framework (Cinner et al. 2018), they identified ‘intangible resources’ that 
people relied on in their post-disaster response as important for starting up fishery, but also went beyond that framework to conclude

that the response ability on the part of governmental organisations as well as other actors (e.g., fish vendors) in the supply chain is also 
a requirement for rebuilding and restarting income-generating activity (Turner et al. 2020). Numerous other studies have highlighted 
capacity-building as adaptation priorities (Basel et al. 2020; Kuhl et al. 2020; Sarker et al. 2020; Vogel et al. 2020; Williams et al. 2020).
One of several helpful innovations in these efforts is impact-based forecasting (Harrowsmith et al. 2020), which provides forecasts 
targeted at the impact of the hazard rather than simply the meteorological variable. This enables a much easier coupling to early 
action in response to the information, and a more appropriate response afterwards. Automatic responses to warnings have also 
been adopted in the humanitarian field for anticipatory action ahead of (rather than simply in response to) disasters triggered by

natural hazards (Coughlan de Perez et al. 2015). This has resulted in a rapid scale-up of such anticipatory financing mechanisms to 
tens of countries over the past few years, and emerging evidence of its effectiveness. Still, the response is lacking in coherence and 
comprehensiveness, resulting in calls for a more systematic evidence agenda for anticipatory action (Weingärtner et al. 2020).

1687
that around 40% of projects feature hardware or other types of 
international transfer of technology (Seres et al. 2009; Murphy et al. 
2015), depending on the nature of technology, the host country and 
region (Cui et al. 2020) and the project type (Karakosta et al. 2012). 
The CDM was generally positively evaluated on its contribution to 
technology transfer. However, it was also regarded critically as the 
market-responsiveness and following of export implies a bias to larger, 
more advanced economies rather than those countries most in need 
of technology transfer (Gandenberger et al. 2016), although some 
countries have managed to correct that by directing the projects, sub-
nationally, to provinces with the greatest need (Bayer et al. 2016). 
Also, the focus on hardware in evaluations of technology transfer 
under the CDM has been criticised (Haselip et al. 2015; Michaelowa

et al. 2019). Indeed, although many studies do go beyond hardware 
in their evaluations (e.g., Murphy et al. 2015), the degree to which 
the project leads to a change in the national system of innovation or 
institutional capacity development is not commonly assessed, or has 
been assessed as limited (de Coninck and Puig 2015).
There is significantly less literature on capacity building under 
the UNFCCC, especially as it relates to managing the technology 
transition. In a  legal analysis, D’Auvergne and Nummelin (2017) 
indicate the nature, scope and principles of Article 11 on capacity 
building of the Paris Agreement as being demand- and country-
driven, following a needs approach, fostering national, subnational 
and local ownership, and being iterative, incorporating the lessons 
learnt, as well as participatory, cross-cutting and gender-response. 
They also highlight that it is novel that least-developed countries

and Small Island Developing States (SIDS) are called out as the most 
vulnerable and most in need of capacity building, and that it raises 
a ‘legal expectation’ that all parties ‘should’ cooperate to enhance the 
capacity in developing countries to implement the Paris Agreement. 
These aspects are reflected in the terms of reference of the Paris 
Committee on Capacity-building (PCCB) that was established in 2015 
at the 21st Conference of the Parties (UNFCCC 2016; D’Auvergne 
and Nummelin 2017), and was extended by five years at the 25th 
Conference of the Parties in 2019 (UNFCCC 2020a, b). In its work 
plan for 2020–2024, its aims include ‘identifying capacity gaps and 
needs, both current and emerging, and recommending ways to 
address them’.An example of how innovative technologies combined with capacity 
development, and how institutional innovation is combined in the 
context of adaptation to extreme weather in SIDS can be found in 
Box 16.8.

From the broader assessment above, despite limitations of available 
information, it is clear that the number of initiatives and activities 
on international cooperation and technology transfer and capacity 
building seem to have been enhanced since the Cancun Agreements 
and the Paris Agreement (TEC 2021). However, much more can be 
done, given the complexity and magnitude of the requirements in 
terms of coverage of activities, the amount of committed funding, 
and its effectiveness. Some assessments of UNFCCC instruments 
specifically for technology transfer to developing countries have 
indicated that functions such as knowledge development, market 
formation and legitimacy in developing countries’ low-emission 
technological innovation systems would need much more support to 
fulfil the Paris Agreement goals (de Coninck and Puig 2015; Ockwell 
et al. 2015); such areas would benefit from continued attention, 
given their role in the overall climate technology transition.

16.5.3.2 International RD&D Cooperation 
and Capacity-building Initiatives
Besides the UNFCCC mechanisms, there are numerous other initiatives 
that promote international cooperation on RD&D as well as capacity 
building. Some of them are based on the notion of ‘mission-oriented 
innovation policy’ (Mazzucato and Semieniuk 2017; Mazzucato 2018), 
which shapes markets rather than merely corrects market failures.
For instance, Mission Innovation is a global initiative consisting of 23 
member countries and the European Commission working together 
to reinvigorate and accelerate global clean energy innovation 
with the objective to make clean energy widely affordable with 
improved reliability and secured supply of energy. The goal is to 
accelerate clean energy innovation in order to limit the rise in 
the global temperature to well below 2°C. The members seek to 
foster international collaboration among its members and increase

public investments in clean energy R&D with the engagement 
of the private sector. A  recent assessment shows that, although 
Box 16.9 | Intellectual Property Rights (IPR) Regimes and Technology Transfer
In the global context of climate mitigation technologies, it has been noted that technologies have been developed primarily in 
industrialised countries but are urgently required in fast-growing emerging economies (Dechezleprêtre et al. 2011). International 
technology transfers can take place via three primary channels: (i) trade in goods, where technology is embedded in products; 
(ii) Foreign Direct Investment (FDI), where enterprises transfer firm-specific technology to foreign affiliates; and (iii) patent licences, 
where third parties obtain the right to use technologies. IPRs are relevant for all these three channels.
Not surprisingly, the role of IPRs in international transfer of climate mitigation technologies has been much discussed but also

described as particularly controversial (Abdel-Latif 2015). The relationships between IPR, innovation, international technology transfer 
and local mitigation and adaptation are complex (Maskus 2010; Abdel-Latif 2015; Li et al. 2020) and there is no clear consensus on 
what kind of an IPR regime will be most beneficial for promoting technology transfer.

1688
expenditures are rising, the aims were not met by 2020 (Myslikova 
and Gallagher 2020). Gross et al. (2018) caution against too much 
focus on R&D efforts for energy technologies to address climate 
change, including for Mission Innovation. They argue that, given the 
timescales of commercialisation, developing new technologies now 
would mean they would be commercially too late for addressing 
climate change. Huh and Kim (2018) discuss two ‘knowledge and 
technology transfer’ projects that were eventually not pursued 
beyond the feasibility study phase due to cooperation and 
commitment problems between national and local governments, 
and they highlight the need for ownership and engagement of local 
residents and recipient governments.
Intellectual property rights (IPR) regimes (Box 16.9) can be an enabler 
or a barrier to energy transition. For more background on IPR and 
impact on innovation, see Section 16.4.6.

16.5.4 Emerging Ideas for International Technology 
Transfer and Cooperation
As with the broader innovation literature (Section 16.3), and drawing 
on such literature, there has been an emergence of a  greater 
understanding of, and emphasis on, the role of innovation systems 
(at national, sectoral, and technological levels) as a  way to help developing countries with the climate technology transition (TEC 
2015; Ockwell and Byrne 2016). This has given rise to several 
proposals, discussed here and summarised in Figure 16.3.
Enhancing deployment and diffusion of climate technologies in 
developing countries would require a variety of actors with sufficient 
capabilities (robust evidence, medium agreement) (Kumar et al. 1999; 
Sagar et al. 2009; Ockwell et al. 2018). This may include strengthening 
existing actors (Malhotra et al. 2021), supporting science, technology, 
and innovation-based start-ups to meet social goals (Surana et al.

2020b), and developing entities and programmes that are intended 
to address specific gaps relating to technology development and 
deployment (Sagar et al. 2009; Ockwell et al. 2018).
There is also an increasing emphasis on the relevance of 
participative social innovation, local grounding and policy learning 
as a replacement of the expert-led technological change (Chaudhary 
et al. 2012; Disterheft et al. 2015; Kowarsch et al. 2016). Others have 
suggested a shift to international innovation cooperation rather than 
technology transfer, which implies a donor-recipient relationship. The 
notion of innovation cooperation also makes more explicit the focus 
on innovation processes and systems (Pandey et al. 2021). A broad 
transformative agenda therefore proposes that contemporary societal 
challenges are complex and multivariegated in scope and will require

the actions of a diverse set of actors to formulate and address the Several studies argue that, particularly in developing nations, the global IPR regime has resulted in delayed access, reduced competition 
and higher prices (Littleton 2008; Zhuang 2017) and that climate-change-related technology transfer is insufficiently stimulated under 
the current IPR regime. Compulsory licensing (as already used in medicine) is one of the routes proposed to repair this (Littleton 2008; 
Abdel-Latif 2015).
There is little systematic evidence that patents and other IPRs restrict access to environmentally-sound technologies, since these 
technologies are mostly in sectors based on mature technologies where numerous substitutes among global competitors are available 
(Maskus 2010). This might, however, change in the future – for instance, with new technologies based on plants, via biotechnologies 
and synthetic fuels (Maskus 2010), for which Correa et al. (2020) already find some evidence.

There is also literature suggesting that weak IPR regimes have a ‘strong and negative impact on the international diffusion of 
patented knowledge’ (Dechezleprêtre et al. 2013; Glachant and Dechezleprêtre 2017). Also, patents may support market transactions 
in technology, including international technology transfer, especially to middle-income countries and larger developing countries 
(Maskus 2010; Hall and Helmers 2019) but least-developed countries may be better served by building capacity to absorb and 
implement technology (Hall and Helmers 2010; Maskus 2010; Sanni et al. 2016; Glachant and Dechezleprêtre 2017). It is also argued 
that it is not even clear that the patent system as it exists today is the most appropriate vehicle for encouraging international access 
(Hall and Helmers 2010; Maskus 2010; Sanni et al. 2016; Glachant and Dechezleprêtre 2017). Given the large variation in perspectives

on the role of IPRs in technology transfer, there is a need for more evidence and analysis to better understand if, and under what 
conditions, IPR may hinder or promote technology transfer (TEC 2012).
In terms of ways forward to meet the challenge of climate change, different suggestions are made in the context of IPR that can help 
to further improve international technology transfer of climate mitigation technologies, including through the Trade-Related Aspects of 
Intellectual Property Rights (TRIPS) Agreement, by making decisions on IPR to developing countries on a case-by-case basis, by developing 
countries experimenting more with policies on IPR protection, or through brokering or patent-pooling institutions (Littleton 2009; Maskus 
and Reichman 2017; Dussaux et al. 2018). Others also suggest that distinctions among country groups be made on the basis of levels

of technological and economic development, with least-developed countries getting particular attention (Zhuang 2017; Abbott 2018).Box 16.9 (continued)

1689
policy, implying that social, institutional and behavioural changes 
next to technological innovations are the possible solutions (Geels 
2004) (see also Cross-Chapter Box 12 in this chapter).
Several authors have proposed new mechanisms for international 
cooperation on technology. Ockwell and Byrne (2016) argue that 
a role for the UNFCCC Technology Mechanism could be to support 
Climate Relevant Innovation-system Builders (CRIBs) in developing 
countries, institutions locally that develop capabilities that ‘form 
the bedrock of transformative, climate-compatible, technological 
change and development’. Khan et al. (2020) propose a  specific 
variant with universities in developing countries serving as ‘central 
hubs’ for capacity building to implement the NDCs as well as other 
climate policy and planning instruments; they also suggest that 
developing countries outline their capacity-building needs more

clearly in their NDCs.Building on an earlier discussion of technology-oriented and 
sectoral agreements (Meckling and Chung 2009) and the potential 
for international cooperation in energy-intensive industry (Åhman 
et al. 2017), where deep emission reduction measures require 
transformative changes (Chapter 11), Oberthür et al. (2021) propose 
that that a  way forward for the global governance for energy-
intensive industry could be through sub-sector ‘clubs’ that include 
governmental, private and societal actors (Oberthür et al. 2021).
Figure 16.3 summarises examples of emerging ideas for international 
cooperation on climate technology, their relation to the objectives 
and existing efforts, and the level of development of the innovation 
system around a technology (Hekkert et al. 2007; Bergek et al. 2008) 
or in nations (Lundvall et al. 2009).International climate technology 
transfer objectivesCurrent mechanisms 
and meansExamples of

emerging ideasLess mature More matureInnovation systemEnhancing RD&D and
knowledge spillovers
Build capacity
for innovation
Build capacity for
implementation and
integrated planning
Enhancing climate
technology
implementation in
developing countriesInternational RD&D cooperation
mechanisms, e.g.
• IEA Technology
Cooperation Programmes
• CGIAR
• Mission Innovation
• Bilateral and regional initiatives
UNFCCC mechanisms
and institutions
• CDM (Kyoto Protocol)
• Technology Mechanism
(Cancun Agreements)
• Technology framework
(Paris Agreement)
• Paris Committee on
Capacity Building
Finance, trade and associated
frameworksPromoting developing country
participation in technology
cooperation programmes
Climate-Related Innovation
System Builders
Developing countries 
universities as central hubs 
of capacity building
Private sector and donor-led 
initiatives (e.g., Climate 
Innovation Centres)Sectoral agreements
• Iron & steel
• Cement
International emission
standards
• Personal vehicles

• Cooling devices
Figure  16.3 |  Examples of recent mechanisms and emerging ideas (right column) in relation to level of maturity of the national or technological 
innovation system, objectives of international climate technology transfer efforts and current mechanisms and means.  Sources: Sagar (2009); Ockwell and 
Byrne (2016); Khan et al. (2020); Oberthür et al. (2021).

1690
16.6 Technological Change and 
This section considers technological innovation in the broader 
context of sustainable development, recognising that technological 
change happens within social and economic systems, and therefore 
technologies are conceived and applied in relation to those systems 
(Grübler 1998). Simplifications of complex interactions between 
physical and social systems and incomplete knowledge of the 
indirect effects of technological innovation may systematically lead 
to underestimation of environmental impacts and overestimation of 
our ability to mitigate climate change (Hertwich and Peters 2009; 
Arvesen et al. 2011).
Previous sections of the chapter discussed how a systemic approach, 
appropriate public policies and international cooperation on 
innovation can enhance technological innovation. This section 
provides more details on how innovation and technological change,

sustainable development and climate change mitigation intertwine.
16.6.1 Linking Sustainable Development 
and Technological Change
Sustainable development and technological change are deeply 
related (UNCTAD 2019). Technology has been critical for increasing 
productivity as the dominant driving force for economic growth. Also, the concentration of technology in few hands has boosted 
consumption of goods and services which are not necessarily aligned 
with the Sustainable Development Goals (SDGs) (Walsh et al. 2020). It 
has been suggested that, in order to address sustainable development 
challenges, science and technology actors would have to change their 
relation to policymakers (Ravetz and Funtowicz 1999) as well as the 
public (Jasanoff 2003). This has been further elaborated for the SDGs. 
The scale and ambition of the SDGs call for a change in development 
patterns that require a  fundamental shift in: current best practices;

guidelines for technological and investment decisions; and the wider 
socio-institutional systems (UNCTAD 2019; Pegels and Altenburg 
2020). This is needed as not all innovation will lead to sustainable 
development patterns (Altenburg and Pegels 2012; Lema et al. 2015).
Current SDG implementation gaps reflect, to some extent, 
inadequate understanding of the complex relationships among the 
goals (Waiswa et al. 2019; Skene 2020), as well as their synergies and 
trade-offs, including how they limit the range of responses available 
to communities and governments, and potential injustices (Thornton 
and Comberti 2017). These relationships have been approached by 
focusing primarily on synergies and trade-offs while lacking the 
holistic perspective necessary to achieve all the goals (Nilsson et al. 
2016; Roy et al. 2018).
A more holistic framework could envisage the SDGs as outcomes of 
stakeholder engagement and learning processes directed at achieving

a balance between human development and environmental protection Social emphasis
Environmental emphasis Low HighLow High
GREEN INNOVATIONS TRADITIONAL INNOVATIONS
• Primary focus is given to environmental dimension
and associated concerns when developing and/or
adopting this type of innovation;
• Social dimension/concerns and economic
dimension/concerns are subservient
(i.e., often compromised to maximise
environmental outcome).• Social, environmental and economic dimensions
and their associated concerns are considered in
a balanced approach when developing and/or
• There is no maximisation opportunities, but
satisfactory solutions that allow all the three
pillars to be considered simultaneously.SUSTAINABLE INNOVATIONS
• Primary focus is given to the economic dimension
• Environmental dimension/concerns and social

(i.e., often compromised to maximise 
economic/ﬁnancial outcome).• Primary focus is given to the social dimension
and associated concerns when developing
and/or adopting this type of innovation;
• Environmental dimension/concerns and
economic dimension/concerns are subservient
social outcome).SOCIAL INNOVATIONS
Figure 16.4 | Considerations and typology of innovations for sustainable development.  Source: Silvestre and Ţîrcă (2019).

1691
(Gibbons 1999; Jasanoff 2003), to the extent that the two can be 
separated. From a science, technology and innovation perspective, Fu 
et al. (2019) distinguish three categories of SDGs. The first category 
comprises those SDGs representing essential human needs for which 
inputs that put pressure on sustainable development would need 
to be minimised. These include Zero hunger (SDG 2), Clear water 
and sanitation (SDG 6) and Affordable and clean energy (SDG 7) 
resources, which continue to rely on production technologies and 
practices that are eroding ecosystem services, potentially hampering 
the realisation of SDGs 15 (Life on land) and 14 (Life below water) 
(Díaz et al. 2019). The second category includes those related to 
governance and which compete with each other for scarce resources, 
such as Industry, innovation and infrastructure (SDG 9) and Climate

action (SDG 13), which require an interdisciplinary perspective. The 
third category are those that require maximum realisation, include 
No poverty (SDG 1), Quality education (SDG 4) and Gender equality 
(SDG 5) (Fu et al. 2019).
Resolving tensions between the SDGs requires adoption and 
mainstreaming of novel technologies that can meet needs while 
reducing resource waste and improving resource-use efficiency, and 
acknowledging the systemic nature of technological innovation, 
which involves many levels of actors, stages of innovation and scales 
(Anadon et al. 2016b). Changes in production technology have been 
found effective to overcome trade-offs between food and water 
goals (Gao and Bryan 2017). Innovative technologies at the food, 
water and energy nexus are transforming production processes 
in industrialised and developing countries, such as developments in 
agrivoltaics, which is co-development of land for agriculture and solar

with water conservation benefits (Barron-Gafford et al. 2019; Lytle 
et al. 2020; Schindele et al. 2020), and other renewably powered 
low- to zero-carbon food, water and energy systems (He et al. 2019). 
Silvestre and Ţîrcă (2019) indicate that maximising both social and environmental aims is not possible, but that sustainable innovations 
include satisfactory solutions for social, environmental and economic 
pillars (Figure 16.4).
There is evidence that technological changes can catalyse 
implementation of the reforms needed to the manner in which goods 
and services are distributed among people (Fu et al. 2019). A recently 
developed theoretical framework based on a  capability approach 
(CA) has been used to evaluate the quality of human life and the 
process of development (Haenssgen and Ariana 2018). Variations of 
the CA have been applied to exploratory studies of the link between 
technological change, human development, and economic growth

(Mayer 2001; Mormina 2019). This suggests that the transformative 
potential of technology as an enabling condition is not intrinsic, 
but is assigned to it by people within a given technological context. 
A failure to recognise and account for this property of technology is 
a  root cause of many failed attempts at techno-fixing sustainable 
development projects (Stilgoe et al. 2013; Fazey et al. 2020).
The basic rationale for governance of technological change is the 
creation and maintenance of an enabling environment for climate 
and SDG-oriented technological change (Avelino et al. 2019). Such 
an environment poses high demands on governance and policy to 
coordinate with actors and provide a  direction for innovation and 
technological change. Cross-Chapter Box  12 illustrates how the 
dynamics of socio-technical transitions and shifting development 
pathways towards sustainable development offer options for 
policymakers and other actors to accelerate the system transitions

needed for both climate change mitigation and sustainable 
development. Governance interventions to implement the SDGs will 
need to be operationalised at sub-national, national and global levels 
and support integration of resource concerns in policy, planning and 
implementation (UNEP 2015; Williams et al. 2020).
Cross-Chapter Box 12 | Transition Dynamics
Authors: Anthony Patt (Switzerland), Heleen de Coninck (the Netherlands), Xuemei Bai (Australia), Paolo Bertoldi (Italy), Sarah Burch 
(Canada), Clara Caiafa (Brazil/the Netherlands), Felix Creutzig (Germany), Renée van Diemen (the Netherlands/United Kingdom), 
Frank Geels (United Kingdom/the Netherlands), Michael Grubb (United Kingdom), María Josefina Figueroa Meza (Venezuela/Denmark), 
Şiir Kilkiş (Turkey), Jonathan Köhler (Germany), Catherine Mitchell (United Kingdom), Lars J. Nilsson (Sweden), Patricia Perkins (Canada), 
Yamina Saheb (France/Algeria), Harald Winkler (South Africa)

Numerous studies suggest that transformational changes would be required in many areas of society if climate change is to be limited 
to 2°C warming or less. Many of these involve shifts to low-carbon technologies, such as renewable energy, which typically involve 
changes in associated regulatory and social systems; others more explicitly concern behavioural shifts, such as towards plant-based 
diets or cleaner cooking fuels, or, at the broadest level, a shift in development pathways. Chapter 1 establishes an analytic framework 
focusing on transitions, which chapters 5, 13, 14, 15 and 16 further develop. In this Cross-Chapter Box, we provide a complementary 
overview of the dynamics of different kinds of transformational changes for climate mitigation and sustainable development. We first 
focus on insights from socio-technical transitions approaches, and then expand to broader system transitions.
Dynamics of socio-technical transitions

A large volume of literature documents the processes associated with transformational changes in technology and the social systems 
associated with their production and use (Geels 2019; Köhler et al. 2019). Transformational technological change typically goes hand 
in hand with shifts in knowledge, behaviour, institutions, and markets (Geels and Schot 2010; Markard et al. 2012); stickiness in these

1692
factors often keeps society ‘locked in’ to those technologies already in widespread use, rather than allowing a shift to new ones – 
even those that offer benefits (David 1985; Arthur 1994). Exceptions often follow consistent patterns (Geels 2002; Unruh 2002); since 
AR5 a growing number of scholars have suggested using these insights to design more effective climate policies and actions (Geels 
et al. 2017). Chapter 1 (Section 1.7 and Figure 1.6) represents technology diffusion and a corresponding shift in policy emphasis 
as a continuous process; it is also useful to identify a sequence of distinct stages that typically occur, associating each stage with 
a distinct set of processes, challenges, and effective policies (Patt and Lilliestam 2018; Victor et al. 2019). Consistent with elsewhere

in this report (Section 5.5.2 and Supplementary Material 5.5.3 in Chapter 5, and Section 16.3 in Chapter 16), Cross-Chapter Box 12 
Figure 1 elaborates on four distinct stages: it portrays these as occurring in a cycle, recognising that even transformative technologies 
will eventually be replaced with newer ones.
The emergence stage is marked by experimentation, innovation in the laboratory, and demonstration in the field, to produce 
technologies and system architectures (Geels 2005). By its very nature, experimentation includes both successes and failures, and 
implies high risks. Because of these risks, especially in the case of fundamentally new technologies, government funding for research, 
development and demonstration (RD&D) projects is crucial to sustaining development (Mazzucato 2015b).
The second stage is early adoption, during which successful technologies jump from the laboratory to limited commercial application

(Pearson and Foxon 2012). Reaching this stage is often described as crossing the ‘Valley of Death’, because the cost/performance ratio Cross-Chapter Box 12 (continued)
Barriers to change
– Cross-sectoral interactions
– Displacement of affected 
workers, asset ownersBarriers to change
– High investment costs
– High uncertainties 
and risks
– Low public acceptance
– Continued mismatch with networks, 
institutions, behaviours
– Political opposition from dominant 
market actorsProcesses
– From mainstream to dominant
– Phase-out of old technologies, systems
Public policies
– Technology standards and prohibitions
– Support for displaced workers and industriesProcesses
– Research, development and demonstration
– Successes and frequent failures
– Support for research, development, and demonstration
1. Emergence
4. Stabilisation
Processes
– Initial growth in protected 
niches
– Cost reductions, 
performance gains
– Reduction of technology 

– Market incentives: 
subsidies, quotas
– Government procurement 
and tenders2. Early adoption
Processes
– Fast diffusion rates
– Restructuring of institutions, 
behaviour, and infrastructure
– Infrastructure development
– Revised regulatory framework
– Enhancement of ﬁnancial ﬂows3. Diffusion– Economic: declining costs relative 
to performance
– Technical: expanding infrastructure 
networks
– Social: increasing public familiarity 
and acceptance
– Political: emerging industry 
support and interest groupsPositive feedbacks 
accelerating change
Cross-Chapter Box 12, Figure 1 | Stages of socio-technical transition processes.

1693
for these new market entrants is too low for them to appear viable to investors (Murphy and Edwards 2003). A key process in the early 
adoption phase is induced innovation, a result of incremental improvements in both design and production processes, and of mass-
production of a growing share of key components (Nemet 2006; Grubb et al. 2021). There is diversity across classes of technologies, 
and learning tends to occur faster for technologies that are modular (Wilson et al. 2020) – such as photovoltaics – and slower for 
those that require site- or context-specific engineering, such as in the shift to low-carbon materials production (Malhotra and Schmidt 
2020). Public policies that create a secure return on investment for project developers can lead to learning associated with industry

expansion (Chapter 16, Figure 16.1); typically these are economically and politically viable when they promote growth within a market 
niche, causing little disruption to the mainstream market (Roberts et al. 2018). Direct support mechanisms are effective, including 
cross-subsidies (such as feed-in tariffs) and market quotas (such as renewable portfolio standards) (Geels et al. 2017b; Patt and 
Lilliestam 2018; and Chapter 9 for assessment of early adoption policies in the building sector). The value of these policies is less in 
their immediate emissions reductions, but more in generating the conditions for self-sustaining transformational change to take place 
as technologies later move from niche to mainstream (Hanna and Victor 2021).
The third stage, diffusion, is where niche technologies become mainstream, with accelerating diffusion rates (Sections  1.7 and

16.4), and is marked by changes to the socio-technical ‘regime’, including infrastructure networks, value chains, user practices, 
and institutions. This stage is often the most visible and turbulent, because more widespread adoption of a new technology gives 
rise to structural changes in institutions and actors’ behaviour (e.g., increased adoption of smartphones to new payment systems 
and social media), and because when incumbent market actors become threatened, they often contest policies promoting the new 
technologies (Köhler et al. 2019). In the diffusion stage, policy emphasis is shifted from financial support during the early adoption 
stage, towards supporting regime-level factors needed to sustain, or cope with, rapid and widespread diffusion (Markard 2018). 
These factors and policies are context specific. For example, Patt et al. (2019) document that the policies needed to expand residential

charging networks for electric vehicles depend on the local structure of the housing market.
The fourth stage is stabilisation, in which the new technologies, systems, and behaviours are both standardised and insulated from 
rebound effects and backsliding (Andersen and Gulbrandsen 2020). Sectoral bans on further investment in high-carbon technologies 
may become politically feasible at this point (Breetz et al. 2018; Economidou et al. 2020). The decline of previously dominant products 
or industries can lead to calls for policymakers to help those negatively affected, enabling a just transition (McCauley and Heffron 
2018; Newell and Simms 2020). Political opposition to the system reconfiguration that comes with integration and stabilisation can 
also be overcome by offering incumbent actors an attractive exit strategy (de Gooyert et al. 2016).
Because different sectors are at different stages of low-carbon transitions, and because the barriers that policies need to address are

stage- and often context-specific, effective policies stimulating socio-technical transitions operate primarily at the sectoral level (Victor 
et al. 2019). This is particularly the case during early adoption, where economic barriers predominate; during diffusion, policies that address 
regime-level factors often need to deal with cross-sectoral linkages and coupling, such as those between power generation, transportation, 
and heating (Patt 2015; Bloess 2019; Fridgen et al. 2020). The entire cycle can take multiple decades. However, later stages can go faster by 
building on the earlier stages that have taken place elsewhere. For example, early RD&D into wind energy took place primarily in Denmark, 
was followed by early adoption in Denmark, Germany, and Spain, before other countries, including the USA, India, and China, leapfrogged 
directly to the diffusion stage (Chaudhary et al. 2015; Dai and Xue 2015; Lacal-Arántegui 2019). A similar pattern played out for solar

power (Nemet 2019). International cooperation, geared towards technology transfer, capacity and institution-building, and finance, can 
help ensure that developing countries leapfrog to low-carbon technologies that have undergone commercialisation elsewhere (Adenle et al. 
2015; Fankhauser and Jotzo 2018) (see also Chapter 5, Box 5.9, Chapter 15, Section 15.5, and Section 16.5 in this chapter).
This report contains numerous examples of the positive feedbacks in the centre of Cross-Chapter Box 12, Figure 1, predominantly 
arising during the early adoption and diffusion stages, and leading to rapid or unexpected acceleration of change. For example, public 
acceptance of meat alternatives leads to firms improving the products, increasing political and economic feedbacks (Section 5.4 
and Box 5.5). Declining costs in solar and wind cause new investment in the power-generation sector being dominated by those

technologies, leading to increased political support and further cost reductions (Chapter 6). In buildings (Chapter 9) and personal mobility 
(Chapter 10), low-carbon heating systems and electric vehicles are gaining public acceptance, leading to improved infrastructure and 
human resources, more employment in those sectors, and behavioural contagion. Some have argued that technologies cross societal 
tipping points on account of these feedbacks (Obama 2017; Sharpe and Lenton 2021).
Dynamics between enabling conditions for system transitions
Abson et al. (2017) argue that it is possible to make use of ‘leverage points’ inherent in system dynamics in order to accelerate 
sustainability transitions. Otto et al. (2020) argue that interventions geared towards the social factors driving change can ‘activate Cross-Chapter Box 12 (continued)

1694
contagious processes’ leading to the transformative changes required for climate mitigation. These self-reinforcing dynamics involve 
the interaction of enabling conditions, including public policy and governance, institutional and technological innovation capacity, 
behaviour change, and finance. For example, Mercure et al. (2018) simulated financial flows into fossil-fuel extraction, and showed 
how investors taking into account transition risk in combination with technological innovation would lead to the enhancement of 
investments in low-carbon assets and further enhanced innovation. As another example, behaviour, lifestyle, and policy can also 
initiate demand-side transitions (Tziva et al. 2020) (Chapter 5), such as with food systems (Rust et al. 2020) (Section 7.4.5), and can 
contribute to both resilience and carbon storage (Sendzimir et al. 2011) (Box 16.5).

In the urban context, the concept of sustainability experiments has been used to examine innovative policies and practices adopted 
by cities that have significant impact on transition towards low-carbon and sustainable futures (Bai et al. 2010; Castán Broto and 
Bulkeley 2013). Individual innovative practices can potentially be upscaled to achieve low-carbon transition in cities (Peng and Bai 
2018), leading to a  process of broadening and scaling innovative practices in other cities (Peng et al. 2019).  Such sustainability 
experiments give rise to new actor networks, which in some cases may accelerate change, and in others may lead to conflict (Bulkeley 
et al. 2014). As in the diffusion phase in Cross-Chapter Box 12, Figure 1, contextual factors play a strong role. Examining historical 
transitions to cycling across European cities, Oldenziel et al. (2016) found that contextual factors, including specific configurations of

actors, can lead to very different outcomes. Kraus and Koch (2021) found a short-term social shock – such as the COVID-19 crisis – to 
lead to differential increases in cycling behaviour, contingent on other enabling conditions.
Linking system dynamics to development pathways and broader societal goals
Transition dynamics insights can be broadened to shifting development pathways. Development paths are characterised by particular 
sets of interlinking regime rules and behaviours, including inertia and cascading effects over time, and are reinforced at multiple levels, 
with varied capacities and constraints on local agency occurring at each level (Burch et al. 2014) (Cross-Chapter Box 5 in Chapter 4). 
This is also observed by Schot and Kanger (2018), who identify a needed change in a ‘meta-regime’, crossing sectoral lines in linking 
value chains or infrastructure and overall development objectives. In the context of the UN climate change regime, international

cooperation can bring together such best practices and lessons learnt (Adenle et al. 2015; Pandey et al. 2021). This is especially 
relevant for developing countries, which often depend on technologies and financial resources from abroad, witnessing their pace 
and direction influenced by transnational actors (Marquardt et al. 2016; Bhamidipati et al. 2019), and benefitting little in terms of 
participating in high value-added activities (Whittaker et al. 2020).
System transitions differ according to context, such as across industrialised and developing countries (Ramos-Mejía et al. 2018), 
and within countries. Lower levels of social capital and trust negatively impact niche commercialisation (Lepoutre and Oguntoye 
2018). In contexts of poverty and inequality, stakeholders’ – including users’ – capabilities for meaningful participation are limited, 
and transition outcomes can end up marginalising or further excluding social groups (Osongo and Schot 2017; Hansen et al. 2018).

Many studies of transitions in developing countries make note of the importance of innovation in the informal sector (Charmes 2016) 
(Box 5.10 in Chapter 5). Facilitating informal sector access to renewable energy sources, safe and sustainable buildings, and finance 
can advance low-carbon transitions (McCauley et al. 2019; Masuku and Nzewi 2021). On the contrary, disregarding its importance can 
result in misleading or ineffective innovation and climate strategies (Maharajh and Kraemer-Mbula 2010; Mazhar and Ummad 2014; 
de Beer et al. 2016; Masuku and Nzewi 2021).
Policies shifting innovation in climate-compatible directions can also reinforce other development benefits, for instance better health, 
increased energy access, poverty alleviation and economic competitiveness (Deng et al. 2018; IPCC 2018a; Karlsson et al. 2020). 
Development benefits, in turn, can create feedback effects that sustain public support for subsequent policies, and hence help to

secure effective long-term climate mitigation (Geels 2014; Meckling et al. 2015; Schmidt and Sewerin 2017; Breetz et al. 2018), 
increasing legitimacy of environmental sustainability actions (Hansen et al. 2018; Herslund et al. 2018; van Welie and Romijn 2018) 
and addressing negative socio-economic impacts (Deng et al. 2018; McCauley and Heffron 2018; Eisenberg 2019; Henry et al. 2020).
Summary and gaps in knowledge
Strategies to accelerate climate mitigation can be most effective at accelerating and achieving transformative change when they are 
synchronised with transition processes in systems. They address technological stage characteristics, take advantage of high-leverage 
intervention points, and respond to societal dynamics (Abson et al. 2017; Geels et al. 2017; Köhler et al. 2019). Gaps in knowledge 
remain on how to tailor policy mixes, the interaction of enabling conditions, the generalisability of socio-technical transition insights

to other types of systems, and how to harness these insights to better shift development pathways.Cross-Chapter Box 12 (continued)

1695
16.6.2 Sustainable Development and Technological 
Innovation: Synergies, Trade-offs and Governance
16.6.2.1 Synergies and Trade-offs
Policies that shift innovation in climate compatible directions 
can promote other development benefits, for instance, better 
health, increased energy access, poverty alleviation and economic 
competitiveness (Deng et al. 2018) (Cross-Chapter Box 12). Economic 
competitiveness co-benefits can emerge as climate mitigation 
policies trigger innovation that can be leveraged for promoting 
industrial development, job creation and economic growth, both 
in terms of localising low-emission energy technologies value 
chains as well as increased energy efficiency and avoided carbon 
lock-ins (Section  16.4). However, without adequate capabilities, 
co-benefits at the local level would be minimal, and they would 
probably materialise far from where activities take place (Ockwell

and Byrne 2016; Vasconcellos and Caiado Couto 2021). Innovation 
and technological change can also empower citizens. Grass-roots 
innovation promotes the participation of grass-roots actors, such 
as social movements and networks of academics, activists and 
practitioners, and facilitate experimenting with alternative forms 
of knowledge creation (Seyfang and Smith 2007; UNCTAD 2019). 
Examples of ordinary people and entrepreneurs adopting and 
adapting technologies to local needs to address locally defined needs 
have been documented in the development literature (van Welie and 
Romijn 2018) (Box 16.10). Digital technologies can empower citizens 
and communities in decentralised energy systems, contributing not 
only to a more sustainable but also to a more democratic and fairer 
energy system (Van Summeren et al. 2021) (Section 5.4 in Chapter 5, 
and Cross-Chapter Box 11 in this chapter).
Therefore, even though science, technology and innovation is an

explicit focus of SDG 9, it is an enabler of most SDGs (UNCTAD 2019). 
Striving for synergies between innovation and technological change 
for climate change mitigation with other SDGs can help to secure 
effective long-term climate mitigation, as development benefits can 
create feedback effects that sustain public and political support for 
subsequent climate mitigation policies (Geels 2014; Meckling et al. 
2015; Cross-Chapter Box 12 in this chapter). However, innovation is 
not always geared to sustainable development – for instance, firms 
tend to know how to innovate when value chains are left intact (Hall 
and Martin 2005), which is usually not the case in systemic transitions.
A comprehensive study of these effects distinguishes among 
‘… anticipated-intended, anticipated-unintended, and unanticipated-
unintended consequences’ (Tonn and Stiefel 2019). Theoretical and 
empirical studies have demonstrated that unintended consequences

are typical of complex adaptive systems, and while a  few are 
predictable, a  much larger number are not (Sadras 2020). Even 
when unintended consequences are unanticipated, they can be 
prevented through actor responses, for instance, rebound effects 
following the introduction of energy-efficient technologies. Other 
examples of unintended consequences include worse-than-expected 
physical damage to infrastructure and resistance from communities 
in the rapidly growing ocean renewable energy sector (Quirapas and Taeihagh 2020), and gaps between expected and actual performance 
of building-integrated photovoltaic (BIPV) technology (Boyd and 
Schweber 2018; Gram-Hanssen and Georg 2018). In the agricultural 
sector, new technologies and associated practices that target the 
fitness of crop pests have been found to favour resistant variants. 
Unintended consequences of digitalisation are reported as well 
(Lynch et al. 2019) (Cross-Chapter Box 11 in this chapter).

Innovation and climate mitigation policies can also have negative 
socio-economic impacts, and not all countries, actors and regions 
around the world benefit equally from rapid technological change 
(Deng et al. 2018; McCauley and Heffron 2018; Eisenberg 2019; 
UNCTAD 2019; Henry et al. 2020). In fact, socio-technical transitions 
often create winners and losers (Roberts et al. 2018). Technological 
change can reinforce existing divides between women and men, rural 
and urban populations, and rich and poor communities: older workers 
displaced by technological change will not qualify for jobs if they 
were unable to acquire new skills; weak educational systems may not 
prepare young people for emerging employment opportunities; and 
disadvantaged social groups, including women in many countries, 
often have fewer opportunities for formal education (McCauley and 
Heffron 2018; UNCTAD 2019). That is a risk regarding technological

change for climate change mitigation, as emerging evidence 
suggests that the energy transition can create jobs and productivity 
opportunities in the renewable energy sector, but will also lead to 
job losses in fossil fuel and exposed sectors (Le Treut et al. 2021). At 
the same time, these new jobs may use more intensively high-level 
cognitive and interpersonal skills compared to regular, traditional 
jobs, requiring higher levels of human capital dimensions such as 
formal education, work experience and on-the-job training (Consoli 
et al. 2016). Despite the empowerment potentials of decentralised 
energy systems, not all societal groups are equally positioned to 
benefit from energy community policies, with issues of energy 
justice taking place within initiatives, between initiatives and related 
actors, as well as beyond initiatives (Calzadilla and Mauger 2018; 
van Bommel and Höffken 2021).
The opportunities and challenges of technological change can

also differ within country regions and between countries (Garcia-
Casals et al. 2019). Within countries, Vasconcellos and Caiado 
Couto (2021) show that, in the absence of policies and capacity-
building activities which promote local recruiting, a significant part 
of total benefits of wind projects, especially high-income jobs and 
high value-added activities, is captured by already higher-income 
regions. Between countries, developing countries usually have lower 
innovation capabilities, which means they need to import low-
emission technology from abroad and are also less able to adapt 
these technologies to local conditions and create new markets and 
business models. This can lead to external dependencies and limit 
opportunities to leverage economic benefits from technology transfer 
(Section 16.5.1).
This means that, in countries below the technological frontier, the 
contribution of technological change to climate change mitigation

can happen primarily through the adoption and less through the 
development of new technologies, which can reduce potential

1696
economic and welfare benefits from rapid technological change 
(UNCTAD 2019). The adoption of consumer information and 
communication technology (ICT) (Baller et al. 2016) or renewable 
energy technology (Lema et al. 2021) cannot bring least-developed 
economies close to the technological frontier without appropriate 
technological capabilities in other sectors, and an enabling innovation 
system (Ockwell and Mallett 2012; Sagar and Majumdar 2014; 
Ockwell et al. 2018; UNCTAD 2019; Malhotra et al. 2021; Vasconcellos 
and Caiado Couto 2021). It has been argued widely that both hard 
and soft infrastructure, as well as appropriate policy frameworks 
and capability building, would facilitate developing countries’ 
engagement in long-term technological innovation and sustainable 
industrial development, and eventually in achieving the SDGs 
(Ockwell and Byrne 2016; Altenburg and Rodrik 2017; UNCTAD 2019).

16.6.2.2 Challenges to Governing Innovation for 
Dominant economic systems and centralised governance structures 
continue to reproduce unsustainable patterns of production and 
consumption, reinforcing many economic and governance structures 
from local through national and global scales (Johnstone and Newell 
2018). Technological change, as an inherently complex process 
(Funtowicz 2020), poses governance challenges (Bukkens et al. 2020) 
requiring social innovation (Repo and Matschoss 2019) (Section 5.6 
and Chapter 13).
Prospects for effectively governing SDG-oriented technological 
transformations require, at a minimum, balanced views and new tools 
for securing the scientific legitimacy and credibility to connect public 
policy and technological change in society (Jasanoff 2018; Sadras 
2020). Many frameworks of governance have been proposed, such 
as reflexive governance (Voss et al. 2006), polycentric governance

(Ostrom 2010), collaborative governance (Bodin 2017), adaptive 
governance (Munene et al. 2018) and transformative governance 
(Rijke et al. 2013; Westley et al. 2013) (Chapters 13 and 14).
A particular class of barriers to the development and adoption of new 
technologies comprises entrenched power relations dominated by 
vested interests that control and benefit from existing technologies 
(Chaffin et al. 2016; Dorband et al. 2020). Such interests can generate 
balancing feedbacks within multilevel social-technological regimes 
that are related to technological lock-in, including allocations of 
investment between fossil and renewable energy technologies 
(Unruh 2002; Sagar et al. 2009; Seto et al. 2016).
Weaker coordination and implementation capacity in some developing 
countries can undermine the ability to avoid trade-offs with other 
development objectives – such as reinforced inequalities or excessive 
indebtedness and increased external dependency  – and can limit

the potential of leveraging economic benefits from technologies 
transferred from abroad (Section  16.5 and Cross-Chapter Box  12 
in this chapter). Van Welie and Romijn (2018) show that, in a low-
income setting, the exclusion of some local stakeholders from the 
decision-making process may undermine sustainability transitions 
efforts. Countries with high levels of inequality can be more prone 
to elite capture, non-transparent political decision-making processes, relations based on clientelism and patronage, and no independent 
judiciary (Jasanoff 2018), although in particular contexts, non-elites 
manage to exert influence (Moldalieva and Heathershaw 2020). 
The dominance of incumbents, however, implies that sustainable 
technological transitions could be achieved without yielding any 
social and democratic benefits (Hansen et al. 2018). In the cultural 
domain, a  recurrent policy challenge that has been observed in

most countries is the limited public support for development and 
deployment of low-carbon technologies (Bernauer and McGrath 
2016). The conventional approach to mobilising such support has 
been to portray technological change as a  means of minimising 
climate change. Empirical studies show that simply reframing climate 
policy is highly unlikely to build and sustain public support (Bernauer 
and McGrath 2016).
Finally, there is a link between social and technological innovation; any 
innovation is grounded in complex socio-economic arrangements, to 
which governance arrangements would need to respond (Sections 5.5 
and 5.6, Chapter 13, and Cross-Chapter Box 12 in this chapter). Social 
innovation can contribute to maximising synergies and minimising 
trade-offs in relation to technological and other innovative practices, 
but for this to materialise, national, regional and local circumstances 
need to be taken into account and, if needed, changed. Even in

circumstances of high capabilities, the extent that social innovation 
might help to promote synergies and avoid trade-offs is not easy to 
evaluate (Grimm et al. 2013).
16.6.3 Actions that Maximise Synergies and 
Minimise Trade-offs Between Innovation 
Technological innovation may bring significant synergy in pursuing 
SDGs, but it may also create challenges to the economy, human 
well-being, and the environment (Schillo and Robinson 2017; 
Thacker et al. 2019; Walsh et al. 2020). The degree of potential 
synergies and trade-offs among SDGs differs from country to country 
and over time (Section  16.6.1.1). These potentials will depend on 
available resources, geographical conditions, development stage 
and policy measures. Even though synergies and trade-offs related 
to technological innovation have received the least attention from 
researchers (Deng et al. 2018), literature show that higher synergy

was found where countries’ policies take into account the linkages 
between sectors (Mainali et al. 2018). For technology innovation to be 
effective in enhancing synergies and reducing trade-offs, its role and 
nature in production and consumption patterns, as well as in value 
chains and in the wider economy, requires clarification. Technology 
ownership and control together with its current orientation and 
focus towards productivity, needs to be revised if a  meaningful 
contribution to the implementation of the SDGs is to be achieved 
in a transformative way (Walsh et al. 2020). Responsible innovation, 
combining anticipation, reflexivity, inclusion and responsiveness, has 
been suggested as a framework for conducting innovation (Stilgoe 
et al. 2013). Also inclusive innovation (Hoffecker 2021) could make 
sure that unheard voices and interests are included in decision-
making, and that methods for this have been implemented in practice 
(Douthwaite and Hoffecker 2017).

1697
There are several examples of how to maximise synergies and avoid 
or minimise trade-offs when bringing technological innovation to the 
ground. When implementing off-grid solar energy in Rwanda, synergies 
were found between 80 of the 169 SDG targets, demonstrating 
how mainstreaming off-grid policies and prioritising investment in 
the off-grid sector can realise human development and well-being, 
build physical and social infrastructures, and achieve sustainable 
management of environmental resources (Bisaga et al. 2021). Another 
example is related to wind power in Northeast of Brazil where the 
creation of direct and indirect jobs has been demonstrated in areas 
where capabilities are high, as well as associated improvements 
in wholesale and retail trade and real estate activities, though this 
also emphasises the need for capacity development along with

international collaboration projects (Vasconcellos and Caiado 
Couto 2021). Other examples include studies raising awareness on 
solar energy and women’s empowerment (Winther et al. 2018) and 
recycling and waste (Cross and Murray 2018).
Other actions with the potential to maximise synergies are those 
related to community or grassroots technological innovation. The importance of the link between technological innovation and 
community action and its contribution to sustainable development 
is usually underestimated. Further research is needed on this and, 
most importantly, its inclusion in the political agenda on sustainable 
development (Seyfang and Smith 2007). On the other hand, when 
technological innovation occurs far from where is implemented and 
participation in the production, and hence training activities of local 
actors is minimal, co-benefits and synergies among SDGs are limited 
and usually far below expectations (Bhamidipati and Hansen 2021;

Vasconcellos and Caiado Couto 2021). Actions by policymakers that 
safeguard environmental and social aspects can boost synergies 
and maximise those co-benefits (Lema et al. 2021). Given that 
technological change impacts countries, regions and social groups 
differently, transition policies can be designed to ensure that all 
regions and communities are able to take advantage of the energy 
and other transitions (McCauley and Heffron 2018; Henry et al. 2020).
Box  16.10 provides insights on how a  systemic approach to 
technological innovation can contribute to reconcile synergies and 
trade-offs to achieve sustainable development and mitigation goals.
Box 16.10 | Agroecological Approaches: The Role of Local and Indigenous Knowledge 
and Innovation
Major improvements in agricultural productivity have been recorded over recent decades (FAO 2018a). However, progress has also

come with social and environmental costs, high levels of greenhouse gas (GHG) emissions, and rising demand for natural resources 
(UNEP 2013; UNEP 2017; FAO 2018a; Bringezu 2019; Díaz et al. 2019).
Trend analysis indicates that a large share of the global demand for land is projected to be supplied by South America, in particular the 
Amazon (Lambin and Meyfroidt 2011; TEEB 2018) and Gran Chaco forests (Grau et al. 2015). In developing countries, land use change 
for satisfying international meat demand is leading to deforestation. In Brazil, the amount of GHGs emitted by the beef cattle sector 
alone represents 65% of the agricultural sector’s emissions and 15% of the country’s overall emissions (May 2019).
Agricultural and food systems are complex and diverse; they include traditional food systems, mixed food systems and modern food 
systems (Pengue et al. 2018). Multiple forms of visible and invisible flows of natural resources exist in global food systems (Pascual

et al. 2017; TEEB 2018; IPBES 2019).
Technological practices, management and changes in the food chain could help adapt to climate change, reduce emissions and absorb 
carbon in soil, thus contributing to carbon dioxide removal (IPCC, 2018, 2019). A range of technologies can be implemented – from 
highly technological options, such as transgenic crops resistant to drought (González et al. 2019), salt or pesticides (OECD 2011b; 
Kim and Kwak 2020) or smart and 4.0 agriculture (Klerkx et al. 2019), to more frugal, low-cost technologies such as agroecological 
approaches adapted to local circumstances (Francis et al. 2003; FAO 2018b). These agroecological approaches are the subject of this box.
For developing countries, agroecological approaches could tackle climate change challenges and food security (WGII-report, Chapter 5, 
Box 5.10). Small Island Developing States (SIDS) support livelihoods to develop local food value chains that can promote sustainable

management of natural resources, preserve biodiversity and help build resilience to climate change impacts and natural disasters 
(FAO 2019). Other advantages of agroecological practices include their adaptation to different social, economic and ecological 
environments (Altieri and Nicholls 2017), the fact that they are physical and financial capital-extensive, and are well-integrated 
with the social and cultural capital of rural territories and local resources (knowledge, natural resources, etc.), without leading to 
technological dependencies (Côte et al. 2019).
Agroecology is a dynamic concept that has gained prominence in scientific, agricultural and political discourses in recent years (Wezel 
et al. 2020; Anderson et al. 2021) (Chapter 7, Chapter 5, WGII Box 5.10). Three of the different agroecological approaches are briefly 
discussed here: agroecological intensification; agroforestry; and biochar use in rice paddy fields.

1698
Agricultural intensification provides ways to use land, water and energy resources to ensure adequate food supply while also 
addressing concerns about climate change and biodiversity (Cassman and Grassini 2020). The term ecological intensification 
(Tittonell 2014) focuses on biological and ecological processes and functions in agroecosystems. In line with the development of the 
concept of agroecology, agroecological intensification integrates social and cultural perspectives (Wezel et al. 2015). Agroecological 
intensification (Mockshell and Villarino 2019) for sub-Saharan Africa aims to address employment and food security challenges (Pretty 
et al. 2011; Altieri et al. 2015).
Another example of an agroecological approach is agroforestry. Agroforestry provides examples of positive agroecological feedbacks,

such as ‘the regreening of the Sahel’ in Niger. The practice is based on the assisted natural regeneration of trees in cultivated fields, an 
old method that was slowly dying out, but which innovative public policies (the transfer of property rights over trees from the state to 
farmers) helped restore (Sendzimir et al. 2011).
Rice paddy fields are a major source of methane. Climate change impacts and adaptation strategies can affect rice production and 
rice farmers’ net income. Biochar use in rice paddy fields has been advocated as a potential strategy to reduce GHG emissions from 
soils, enhance soil carbon stocks and nitrogen retention, and improve soil function and crop productivity (Mohammadi et al. 2020).
The contributions of indigenous people (Díaz et al. 2019), heritage agriculture (Koohafkan and Altieri 2010) and peasants’ agroecological 
knowledge (Holt-Giménez 2002) to technological innovation offer a wide array of options for management of land, soils, biodiversity

and enhanced food security without depending on modern, foreign agricultural technologies (Denevan 1995). In farming agriculture 
and food systems, innovation and technology based on nature could help to reduce climate change impacts (Griscom et al. 2017). 
Evidence suggests that there are benefits to integrating tradition with new technologies in order to design new approaches to 
farming, and that these are greatest when they are tailored to local circumstances (Nicholls and Altieri 2018).Box 16.10 (continued)
16.6.4 Climate Change, Sustainable 
Development and Innovation
This section gives a  synthesis of this chapter on innovation 
and technology development and transfer, connecting it to 
In conjunction with other enabling conditions, technological 
innovation can support system transitions to limit warming, help 
shift development pathways, and bring about new and improved 
ways of delivering goods and services that are essential to human

well-being (high confidence). At the same time, however, innovation 
can result in trade-offs that undermine progress on mitigation 
and towards other SDGs. Trade-offs include negative externalities, 
such as environmental impacts and social inequalities, rebound 
effects leading to lower net emission reductions or even increases 
in emissions, and increased dependency on foreign knowledge and 
providers (high confidence). Digitalisation, for example, holds both 
opportunity for emission reduction and emission-saving behaviour 
change, but at the same time causes significant environmental, social 
and greenhouse gas (GHG) impacts (high confidence).
A systemic view of innovation that takes into account the roles 
of actors, institutions, and their interactions, can contribute to 
enhanced understanding of processes and outcomes of technological 
innovation, and to interventions and arrangements that can help 
innovation. It can also play a  role in clarifying the synergies and

trade-offs between technological innovation and the SDGs. Effective 
governance and policy, implemented in an inclusive, responsible and holistic way, could make innovation policy more effective, and avoid 
and minimise misalignments between climate change mitigation, 
technological innovation, and other societal goals (medium evidence, 
A special feature is the dynamics of transitions. Like other enabling 
conditions, technological innovation plays a  balancing role  – by 
inhibiting change as innovation strengthens incumbent technologies 
and practices – and a reinforcing role, by allowing new technologies 
and practices to disrupt the existing socio-technical regimes (high 
confidence). Appropriate innovation policies can help to better 
organise innovation systems, while other policies (technology push 
and demand pull) can provide suitable resources and incentives to 
support and guide these innovation systems towards societally-

desirable outcomes, ensure the innovations are deployed at scale, 
and direct these dynamics towards system transitions for climate 
change mitigation, and also towards addressing other SDGs. This 
means taking into account the full lifecycle or value chain as well as 
analysis of synergies and trade-offs.
Against this backdrop, international cooperation on technological 
innovation is one of the enablers of climate action in developing 
countries on both mitigation and adaptation (high confidence). 
Experiences with international cooperation on technology 
development and deployment suggest that such activities are most 
effective when they: are approached as ‘innovation cooperation’ that 
engenders a holistic, systemic view of innovation requirements; are 
an equitable partnership between donors and recipients; and develop 
local innovation capabilities (medium evidence, high agreement).

1699
Chapter  17, in particular Section  17.4, connects technological 
innovation with other enabling conditions, such as behaviour, 
institutional capacity and multilevel governance, to clarify the actions 
that could be taken, holistically and in conjunction, to strengthen and 
accelerate the system transitions required to limit warming to be in 
line with the Paris Agreement and to place countries in sustainable 
16.7 Knowledge Gaps
Filling gaps in literature availability, data collection, modelling, 
application of frameworks and further analysis in several sectors 
will improve knowledge on innovation and technology development 
and transfer, including research and development (R&D) to support 
policymaking in climate change mitigation as well as adaptation. 
These policies and related interventions need to benefit from data 
and methodologies for the ex post evaluation of their effectiveness.

This section addresses identified knowledge gaps related to: 
what extent developing countries are represented in studies on 
innovation and technology development and transfer; national 
contexts and local innovation capacity; potential and actual 
contributions of businesses; literature emphasis on mitigation; 
indicators to assess innovation systems; non-technical barriers for 
the feasibility of decarbonisation pathways; the role of domestic 
intellectual property rights (IPR) policy; digitalisation in low-
emissions pathways; and Paris Agreement compliance regarding 
technology and capacity building.
Representation of developing countries
One of knowledge gaps identified when assessing the literature 
is on the representation of developing countries in studies on 
innovation and technology development and transfer. This includes 
the conceptual core disciplines of the economics of innovation, 
innovation systems and sustainability transitions. This is true for

studies on developing countries, and for authors originating from, or 
active in, developing country contexts. The evidence of the impact of 
decarbonisation policy instruments applied to developing countries 
or Small Island Developing States (SIDS) is limited. Expanding the 
knowledge base with studies that focus on developing countries 
would not only allow for testing whether the theories (developed 
by predominantly by developed-country researchers for industrialised 
countries) hold in developing country contexts, but also yield policy 
insights that could help both domestic and international policymakers 
working on climate-related technology cooperation.
National contexts and local innovation capacity
While a  growing body of literature has shown how technology 
characteristics and complexity, national context and innovation 
capacity can influence the capacity of a  country’s innovation 
ecosystem as a  result of incentive and attraction policies, more

research is needed to help prioritise and design policies in different national contexts. Important knowledge gaps need to be filled 
regarding the impact of ‘green’ public procurement, lending, ‘green’ 
public banking, and building code policies on innovation outcomes.
There is also a superficial understanding of the potential and actual 
contributions of businesses, educational institutions and socially 
responsible programmes, particularly in developing countries, as 
sources of innovation and early adopters of new technologies, and 
a notable lack of knowledge about indigenous practices.
Emphasis on mitigation
Current literature has a strong bias to studies originating from and 
based on developed countries. Also, innovation and technology 
literature is skewed to mitigation and, specifically, energy. Literature 
on technology innovation for adaptation is largely missing.
In the area of innovation studies, data are limited on the different

indicators used to assess the strength of the innovation system, (even 
for energy), including global figures on R&D and demonstration 
spending, also for developing countries, and their effectiveness. There 
is also a lack of a comprehensive framework and detailed data to 
assess the strengths of low-emission innovation systems, including 
interactions among actors, innovation policy implementation, and 
strength of institutions.
Indicators to assess innovation systems
Another gap in knowledge remains between the results from energy-
climate-economy models and those emerging from systems and 
sustainability transition approaches, empirical case studies, and the 
innovation system literature. If this gap is filled, understanding 
could be improved of the feasibility of decarbonisation pathways in 
light of the many non-technical barriers to technology deployment 
and diffusion.
Non-technical barriers for the feasibility 
of decarbonisation pathways

In the field of policy instruments, existing evaluations provide 
insufficient evidence to assess the impact of decarbonisation policy 
instruments on innovation, as these evaluations mainly focus on 
environmental or technological effects.
Domestic IPR policy
The potential positive or negative role of domestic IPR policy in 
technology transfer to least-developed countries remains unclear as 
the literature does not show agreement. Moreover, gaps remain in 
impact evaluations of sub-national green industrial policies, which 
are of growing importance. The interaction between subnational and 
national decarbonisation policies to advance innovation would also 
benefit from further research, particularly in developing countries.

1700
Digitalisation in low-emissions pathways 
and digitalisation
The understanding of the role of digitalisation in decarbonisation 
pathways is lacking and needs to be studied from several angles. 
Existing studies do not sufficiently take into account knowledge on 
the energy impact of digital technologies, in particular the increase 
in energy demand by digital devices, and the increase in energy 
efficiency. Studies would benefit from being technology/sector/
country-specific.
Further exploration is needed into the way digitalisation influences 
the framework conditions that cause decarbonisation, the socio-
economic and behavioural barriers influencing the diffusion of 
technologies in the long-term scenarios, and the relationship with 
society and its effects.
Given the implications of the digital revolution for sustainability, 
a  better characterisation of governance aspects would increase

understanding of the implications for policymakers of digitalisation 
and the possibilities for it and other general-purpose technologies.
Research (theoretical and empirical) on the impacts of imitation, or 
adaptation of new technological solutions invented in one region 
and used in other regions, could fill knowledge gaps and accelerate 
diffusion of climate-related technologies, while taking care not to 
reduce the incentive for inventors to search for new solutions.
Paris Agreement compliance
An independent assessment is underway to look at the compliance 
of the Paris Agreement with regard to technology and capacity 
building as means of implementation. The Enhanced Transparency 
Framework for action and support is developing a methodology for 
monitoring, reporting and verification. There is a lack of analysis of 
the full landscape of international cooperation, of the effectiveness 
of the UN Framework Convention on Climate Change (UNFCCC) and

the Paris Agreement, and what is needed to meet their objectives.

1701
Frequently Asked Questions (FAQs) 
FAQ 16.1 |   Will innovation and technological changes be enough to meet the Paris 
Agreement objectives?
The Paris Agreement stressed the importance of development and transfer of technologies to improve resilience to climate change 
and to reduce greenhouse gas emissions. However, innovation and even fast technological change will not be enough to achieve 
Paris Agreement mitigation objectives. Other changes are necessary across the production and consumption system and the society 
in general, including behavioural changes.
Technological changes never happen in a vacuum; they are always accompanied by, for instance, people changing habits, companies 
changing value chains, or banks changing risk profiles. Therefore, technological changes driven by holistic approaches can contribute

to accelerate and spread those changes towards the achievement of climate and sustainable development goals.
In innovation studies, such systemic approaches are said to strengthen the functions of technological or national innovation systems, 
so that climate-friendly technologies can flourish. Innovation policies can help respond to local priorities and prevent unintended 
and undesirable consequences of technological change, such as unequal access to new technologies across countries and between 
income groups, environmental degradation and negative effects on employment.
FAQ 16.2 |   What can be done to promote innovation for climate change and the widespread 
diffusion of low-emission and climate-resilient technology?
The speed and success of innovation processes could be enhanced with the involvement of a  wider range of actors from the 
industry, research and financial communities working in partnerships at national, regional and international levels. Public policies

play a critical role to bring together these different actors and create the necessary enabling conditions, including financial support, 
through different instruments as well as institutional and human capacities.
The increasing complexity of technologies requires cooperation if their widespread diffusion is to be achieved. Cooperation includes 
the necessary knowledge flow within and between countries and regions. This knowledge flow can take the form of exchanging 
experiences, ideas, skills, and practices, among others.
FAQ 16.3 |   What is the role of international technology cooperation in addressing 
climate change?
Technologies that are currently known but not yet widely used need to be spread around the world, and adapted to local preferences 
and conditions. Innovation capabilities are required not only to adapt new technologies for local use, but also to create new markets 
and business models. International technology cooperation can serve that purpose.

In fact, evidence shows that international cooperation on technology development and transfer can help developing countries to 
achieve their climate goals more effectively and, if this is done properly, can also help to addressing other sustainable development 
goals. Many initiatives exist both regionally and globally to help countries in achieving technology development and transfer 
through partnerships and research collaboration that include developed and developing countries, with a key role for technological 
institutions and universities. Enhancing current activities would help an effective, long-term global response to climate change, 
while promoting sustainable development.
Globalisation of production and supply of goods and services, including innovation and new technologies, may open up opportunities 
for developing countries to advance technology diffusion; however, so far not all countries have benefitted from the globalisation

of innovation due to different barriers, such as access to finance and technical capabilities. These asymmetries between countries in 
the globalisation process can also lead to dependencies on foreign knowledge and providers.
Not all technology cooperation directly results in mitigation outcomes. Overall, technology transfer broadly has focused on enhancing 
climate technology absorption and deployment in developing countries as well as research, development and demonstration, and 
knowledge spillovers.
The Paris Agreement also reflects this view by noting that countries shall strengthen cooperative action on technology development 
and transfer regarding two main aspects: (i) promoting collaborative approaches to research and development; and (ii) facilitating 
access to technology to developing country Parties.

1729
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717
Accelerating climate actions and progress towards a  just

transition is essential to reducing climate risks and addressing sustainable development priorities, including water, food and human security (robust evidence, high agreement). Accelerating action in the context of sustainable development involves not only expediting the pace of change (speed) but also addressing  the underlying drivers of vulnerability and high emissions (quality and depth of change) and enabling diverse communities, sectors, stakeholders, regions and cultures (scale and breadth of change) to participate in just, equitable and inclusive processes that improve the health and well-being of people and the planet. Looking at climate change from a justice perspective means placing the emphasis on (i) the protection of vulnerable populations and low-income countries from the impacts of climate change, (ii) mitigating the  effects ofthe transformations, and (iii) ensuring an equitable decarbonisedworld. {17.1.1}

While transition pathways will vary across countries, they are 
likely to be challenging in many contexts (robust evidence, high agreement). Climate change is the result of decades of unsustainable production and consumption patterns (for example, energy production and land use), as well as governance arrangements and political economic institutions that lock in resource-intensive development patterns (robust evidence, high agreement). Reframing development objectives and shifting development pathways towards sustainability can help transform these patterns and practices, allowing space for transitions to transform unsustainable systems (medium evidence, high agreement). {17.1.1.2}
Sustainable development can enhance sectoral integration

and social inclusion (robust evidence, high agreement). Inclusion merits attention because equity within and across countries is critical to transitions that are not simply rapid but also sustainable and just. Resource shortages, social divisions, inequitable distributions of wealth, poor infrastructure and limited access to advanced technologies can constrain the options and capacities for developing countries to achieve sustainable and just transitions (medium evidence, high agreement). {17.1.1.2}
Concrete actions aligning sustainable development and

climate mitigation and partnerships can support transitions. Strengthening different stakeholders’ ‘response capacities’ to mitigate and adapt to a changing climate will be critical for a  sustainable transition (robust evidence, high agreement). Response capacities can be increased by means of alignment across multiple stakeholders at different levels of decision-making. This alignment will also help achieve synergies and manage trade-offs between climate and sectoral policies by breaking down sectoral silos and overcoming the multiple barriers that prevent transitions from gaining traction and gathering momentum (medium evidence, high agreement). {17.1.1.1}
Economics, psychology, governance, and systems research have

pointed to a range of factors that influence the speed, scale and quality of transitions (robust evidence, high agreement). Views nonetheless differ on how much market-correcting policies; shift preferences (economics); shifts in individual and collective mindsets (psychology); and multi-level governance arrangements and inclusive political institutions (governance) contribute to system transitions (medium evidence, high agreement). {17.2}
While economics, psychology, governance and systems

thinking emphasise different enablers of transitions, they often share a view that strengthening synergies and avoiding trade-offs between climate and sustainable development priorities can overcome barriers to transitions (medium evidence, high agreement). A  growing body of research and evidence can show which factors in the views from economics, psychology, governance and systems affect how interrelationships are managed between climate, mitigation policies and sustainable development. Greater integration between studies based on different methodological approaches can show how to construct an enabling environment that increases the feasibility and sustainability of transitions. {17.2, 17.3, 17.4}
Short- and long-term studies of transformations using

macroeconomic models and integrated assessment models (IAMs) have identified synergies and trade-offs of mitigation options in the context of development pathways that align sustainable development and climate change (robust evidence, high agreement). IAMs often look at climate change mitigation 
and Sustainable Development Goals (SDGs) in an aggregate manner: supplementing this aggregate view with detail-rich studies involving SDGs can build support for transitions within and across countries (medium evidence, medium agreement). {17.3.2}
The impacts of climate change mitigation and adaptation

responses, are highly context-specific and scale-dependent. There are synergies and trade-offs between adaptation and mitigation as well as synergies and trade-offs with sustainable development (robust evidence, high agreement). A strong link exists between sustainable development, vulnerability and climate risks, as limited economic, social and institutional resources often result in low adaptive capacities and high vulnerability, especially in developing countries. Resource limitations in these countries can similarly weaken the capacity for climate mitigation and adaptation. The move towards climate-resilient societies requires transformational or deep systemic change. This has important implications for countries’ sustainable development pathways (medium evidence, high agreement). {17.3.3.6}
Sectoral mitigation options present synergies with the SDGs,

but there are also trade-offs, which can become barriers to implementation. Such trade-offs are particularly identified in relation to the use of land for bioenergy crops, water and food access, and competition for land between forest or food production (robust evidence, high agreement). Many industrial mitigation options, such as efficiency improvements, waste management and the circular economy, have synergies with the SDGs relating to access to food, water and energy (robust evidence, high agreement). The promotion of renewable energy in some industrial sectors can imply stranded energy supply investments, which need to

1730
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17be taken into consideration (medium evidence, medium agreement). 
The agriculture, forestry, and other land uses (AFOLU) sector offers 
many low-cost mitigation options, but actions aimed at producing 
bioenergy, extending food access and protecting biodiversity can 
also create trade-offs between different land uses (robust evidence, 
high agreement). Some options can help to minimise these trade-
offs, for example, integrated land management, cross-sectoral 
policies and efficiency improvements. Lifestyle changes, including 
dietary changes and reduced food waste, have several synergies 
with climate mitigation and the SDGs (medium evidence, medium 
agreement). Cross-sectoral policies are important in avoiding trade-
offs, to ensure that synergies between mitigation and SDGs are 
captured, and to ensure local people are involved in the development

of new products, as well as production and consumption practices. 
There can be many synergies in urban areas between mitigation 
policies and the SDGs, but capturing these depends on the overall 
planning of urban structures and on local integrated policies, where, 
for example, affordable housing and spatial planning as a  climate 
mitigation measure are combined with walkable urban areas, green 
electrification and clean renewable energy. Such integrated options 
can also reduce the pressures on agricultural land by reducing urban 
growth, thus improving food security. Access to green electricity 
can also support quality education (medium evidence, medium 
agreement). {17.3.3, 17.3.3.1, 17.3.3.3}
Digitalisation could facilitate a fast transition to sustainable 
development and low-emission pathways by contributing 
to efficiency improvements, cross-sectoral coordination 
and a circular economy with new IT services and decreasing

resource use (low evidence, medium agreement). Several 
synergies with SDGs could emerge in terms of energy, food and water 
access, health and education, as well as trade-offs, for example, in 
relation to reduced employment, increasing energy demand and 
increasing demand for services, all implying increased GHG emissions. 
However, developing countries with limited internet access and poor 
infrastructure could be excluded from the benefits of digitalisation 
(medium evidence, medium agreement). {17.3.3}
Actions aligning sustainable development and climate 
mitigation and partnerships can support transitions. 
Strengthening different stakeholders’ ‘response capacities’ to 
mitigate and adapt to a changing climate will be critical for 
a  sustainable transition (robust evidence, high agreement). 
Response capacities can be increased by means of alignment across 
multiple stakeholders at different levels of decision-making. This

alignment will also help achieve synergies and manage trade-offs 
between climate and sectoral policies by breaking down sectoral 
silos and overcoming the multiple barriers that prevent transitions 
from gaining traction and gathering momentum (medium evidence, 
high agreement). {17.1.1.1}
The landscape of transitions to sustainable development is 
changing rapidly, with multiple transitions already underway. 
This creates the room to manage these transitions in ways 
that prioritise the needs for workers in vulnerable sectors 
(land, energy) to secure their jobs and maintain secure and 
healthy lifestyles, especially as the risks multiply for those exposed to heavy industrial jobs and associated outcomes 
(medium evidence, high agreement). A  just transition 
incorporates key principles, such as respect and dignity for vulnerable 
groups, the creation of decent jobs, social protection, employment 
rights, fairness in energy access and use, and social dialogue and

democratic consultation with the relevant stakeholders, while coping 
with the effects of asset-stranding and the transition to green and 
clean economies (medium evidence, medium agreement). The 
economic implications of the transition will be felt especially strongly 
by developing countries, with high dependence on hydrocarbon 
products for revenue streams, as they will be exposed to reduced 
fiscal incomes given a low demand for oil and consequent fall in oil 
prices (limited evidence, medium agreement). {17.3.2, 17.3.2.3}
Countries with assets that are at risk of becoming stranded 
may lack the relevant resources, knowledge, autonomy 
or agency to reorientate, or to decide on the speed, scale 
and quality of the transition (limited evidence, medium 
agreement). The urgency of mitigation might overshadow some 
of the other priorities related to the transition, like climate change 
adaptation and its  inherent vulnerabilities. Consequently, the

transition imperative could reduce the scope and autonomy for local 
priority-setting and could ignore the additional risks in countries 
with a low capacity to adapt. A just transition will depend on local 
contexts, regional priorities, the starting points of different countries 
in the transition and the speed at which they want to travel. Both 
mitigation and adaptation warrant urgent and prompt action 
given current and continuing greenhouse gas (GHG) emissions and 
associated negative impacts on humanity and ecosystems (limited 
evidence, medium agreement). {17.3.2}
A wide range of factors have been found to enable sustainability 
transitions, ranging from technological innovations to shifts in 
markets, and from policies and governance arrangements to 
shifts in belief systems and market forces (robust evidence, 
high agreement). Many of these factors come together in 
a co-evolutionary process that has unfolded globally, internationally

and locally over several decades (low evidence, high agreement). 
Those same conditions that may serve to impede the transition 
(i.e.,  organisational structure, behaviour, technological lock-in) 
can also ‘flip’ to enable both it and the framing of sustainable 
development policies to create a stronger basis and policy support 
(robust evidence, high agreement). It is important to note that 
strong shocks to these systems, including accelerating climate 
change impacts, economic crises and political changes, may provide 
crucial openings for accelerated transitions to sustainable systems. 
For example, rebuilding more sustainably after an extreme event, 
or renewed public debate about the drivers of social and economic 
vulnerability to multiple stressors (medium evidence, medium 
agreement). {17.4}
Sustainable development and deep decarbonisation will 
involve people and communities being connected through 
various means, including globally via the internet and digital

technologies, in ways that prompt shifts in thinking and 
behaviour consistent with climate change goals (medium 
evidence, medium agreement). Individuals and organisations

1731
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717like institutional entrepreneurs can function to build transformative 
capacity through collective action (robust evidence, high agreement), 
but private-sector entrepreneurs can also play an important 
role in fostering and accelerating the transitions to sustainable 
development (robust evidence, medium agreement). Ultimately, 
the adoption of coordinated, multi-sectoral policies targeting new 
and rapid innovation can help national economies take advantage 
of widespread decarbonisation. Green industrial policies that focus 
on building domestic supply chains and capacities can help states 
prepare for the influx of renewable CDR-methods, or mechanisms 
for carbon capture and storage (CCS) (medium evidence, medium 
agreement). {17.4.2}
Accelerating the transition to sustainability will be enabled 
by explicit consideration being given to the principles of

justice, equality and fairness. Interventions to promote 
sustainability transitions that account for local context 
(including unequal access to resources, capacity and 
technology) in the development process are necessary but 
not sufficient in creating a just transition (low evidence, high 
agreement). {17.4.6}

1732
Chapter 17 Accelerating the Transition in the Context of Sustainable Development1717.1 Introduction
This chapter focuses on the opportunities and challenges for 
‘accelerating the transition in the context of sustainable development’. 
The chapter suggests that accelerating transitions in the context 
of sustainable development requires more than concentrating on 
speed. Rather, it involves expediting the pace of change (speed) 
while also removing the underlying drivers of vulnerability and high 
emissions (quality and depth), and aligning the interests of different 
communities, regions, sectors, stakeholders and cultures (scale 
and breadth). One key to enabling deep and broad transitions is 
integrating the views of different government agencies, businesses 
and non-governmental organizations (NGOs) in transition processes. 
Another critical driver of deep and broad transitions is engaging 
and empowering workers, youth, women, the poor, minorities and

marginalised stakeholders in just, equitable and inclusive processes. 
The result of such processes will be the transformation of large-scale 
socio-economic systems to restore the health and well-being of the 
planet and the people on it.  
Section  17.1 begins by reviewing how climate and sustainability 
issues have been discussed in the Intergovernmental Panel on 
Climate Change (IPCC), as well as international climate change 
and sustainable development processes at different levels. It 
further introduces key themes addressed in the chapter’s remaining 
subsections. Section 17.2 provides an overview of how key theories 
understand transitions and transformation, and notes a  shared 
concern over leveraging synergies and managing trade-offs between 
climate change and sustainable development across different 
disciplines. Section  17.3 provides an assessment of the mitigation 
options that can help achieve these synergies and avoid trade-offs.

Section 17.4 pulls together the theoretical and empirical aspects by 
detailing the essential elements of an enabling environment that 
helps drive forward transitions that are quick, deep, broad and, 
ultimately, sustainable.
17.1.1 Integrating Climate Change and Sustainable 
Development in International Assessments
Climate change not only poses a profound challenge to sustainable 
development, it is inexorably linked to it. From the early stages of 
the IPCC assessment process, this challenge and the inherent link 
between climate change and sustainable development have been 
well recognised. For example, the First Assessment Report (FAR) 
highlighted the relevance of sustainable development for climate 
policy. The Second Assessment Report (SAR) went further to include 
equity issues in its presentation of sustainable development. The 
Third Assessment Report (TAR) (Banuri et al. 2001) made the link 
even stronger, noting that ‘parties have a right to and should promote

sustainable development’ (as stated in the text of the UNFCCC 2015 
(Article 3.4)), and offering an early review of studies integrating 
sustainable development and climate change. The Fourth Assessment 
Report (AR4) (Sathaye et al. 2007) added an additional perspective 
to these interconnections, acknowledging the existence of a two-way 
relationship between sustainable development and climate change.The Fifth Assessment Report (AR5) (Denton et al. 2014; Fleurbaey et al. 
2014) and the Special Report on Global Warming of 1.5°C (SR1.5) 
(IPCC 2018; Roy et al. 2018a) have arguably made the strongest 
links between climate and sustainable development to date. One of 
the key messages of AR5 was that the implementation of climate 
mitigation and adaptation actions could help promote sustainable 
development, and it emphasised the need for transformational 
changes in this regard. The AR5 also concluded that the link between

climate change and sustainable development is cross-cutting and 
complex, and that thus the impacts of climate change are threatening 
the efforts being made to achieve sustainable development. The 
SR1.5 helped systematise these links by mapping the synergies and 
trade-offs between selected SDG indicators and climate mitigation 
(IPCC 2018; Roy et al. 2018b) (Section 17.3).
Despite the clear links between sustainable development and climate  
change being recognised from the early stages of the IPCC, 
climate  change has often been portrayed as an environmental 
problem to be addressed chiefly by environmental ministries (Brown 
et al. 2007; Munasinghe 2007; Swart and Raes 2007). However, this 
perception has evolved over time. It is now increasingly common to 
see governments and other actors understand the wider ramifications 
of a  changing climate for sustainable development. In a  growing 
number of studies, work on climate policies and just transitions

towards sustainable development are framed as going hand in 
hand (Fuso Nerini et al. 2019; Dugarova and Gülasan 2017; Sanchez 
Rodriguez et al. 2018; Schramade 2017; Zhenmin and Espinosa 2019).
17.1.2 Integrating Climate Change and 
Sustainable Development in International 
Policymaking Processes
Among the reasons for the growing realisation of these 
interdependencies are milestones in international climate and 
sustainable development processes. As outlined in Chapter  14, 
the year 2015 was a  turning point due to two agreements: 
(i) the Paris Agreement; and (ii) the 2030 Agenda for Sustainable 
Development and its 17 Sustainable Development Goals (SDGs) 
(Farzaneh et al. 2021).
Following a long history of references to sustainable development in 
the UNFCCC and related agreements, the Paris Agreement helped to 
strengthen the links between climate and sustainable development 
by emphasising that sustainability is related to its objectives

(Sindico 2016; UNFCCC 2016). One of the ways that it helped 
tighten this link is by institutionalising bottom-up pledges and the 
review architecture. Toward this end, the Paris Agreement instituted 
Nationally Determined Contributions (NDCs) as vehicles through 
which countries make pledges and demonstrate their commitment to 
climate action. Although there was no clear guidance on what should 
be included in the NDCs, some of the requirements were elaborated 
in the Paris Rulebook . Some of the submitted NDCs included only 
mitigation efforts, but others set out mitigation and adaptation goals 
aligning NDC commitments to national planning processes, while yet 
others mentioned links with the SDGs.

1733
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717Another way that the Paris Agreement and the NDCs could strengthen 
their links to sustainable development is to update country-specific 
climate pledges. Countries are free to choose their targets and the 
means and instruments with which to implement them. A  core 
feature of the NDCs was that countries submit NDCs every five 
years, giving them an opportunity to assess themselves relative to 
other countries, raise their ambitions and learn from their peers. 
Moreover, it was emphasised that countries should not ‘backslide’ 
in subsequent NDCs, thus ensuring that countries should always be 
forward-looking in respect of increasing their ambitions to deliver the 
Paris Goals. (Höhne et al. 2017) found that, in developing countries 
especially, the NDC preparation process has improved national 
climate policymaking.
Despite some favourable reviews, several assessments of specific

countries’ NDCs (Andries et al. 2017; Rogelj et al. 2016; Vandyck 
et al. 2016) have assessed that those submitted for 2020–2030 are 
insufficient for delivering on the Paris goals. Updated and/or new 
NDCs were therefore submitted by the end of 2020. However, an 
assessment of those NDCs revealed that the level of ambition was 
significantly lower than the goals of the Paris Agreement (UNFCCO 
2020) (see also this chapter). One of the urgent calls in Paris was to 
assess the impacts and efforts that need to be undertaken to keep 
global warming well below 2°C in relation to pre-industrial levels 
and evaluate related global GHG emission pathways (UNFCCC 2015). 
Although the initial NDCs fell short of these goals, the idea was that 
NDCs would be living documents that could ratchet up climate 
action and ambition.
Countries have also started to take actions on the SDGs themselves 
(Antwi-Agyei et al. 2018a; UNDESA 2016, 2017, 2018). The SDGs were

perceived as a novel approach to development and as establishing 
a universal agenda for the transformation of development patterns 
and socio-economic systems. At their core, the SDGs hold that 
building an integrated framework for action necessitates addressing 
the economic, social and environmental dimensions of sustainable 
development in an integrated manner (Biermann et al. 2017; Kanie 
and Biermann 2017). The SDGs take multiple elements of development 
into account in aiming to offer coherent, well-integrated, overarching 
approaches to a  range of sustainability challenges, including 
One way a link is made between climate and the SDGs is through 
Voluntary National Reviews (VNRs). Paralleling the bottom-up 
orientation of the Paris Agreement and the NDCs, every year 
approximately forty countries voluntarily share their VNRs with the 
international community at the High-Level Political Forum (HLPF).

Even more flexible than the NDCs, the VNRs can include content 
such as a summary of key policies and measures that are intended to 
achieve the SDGs, a list of the means of implementation that support 
the SDGs, and related challenges and needs. The VNRs also often 
cover SDG 13 (climate action) as well as many other issues connected 
with climate change. Even with these links, implementation of the 
SDGs should be mentioned as part of national development processes 
reflecting different countries’ different priorities, visions and plans 
(Hanson and Korbla P . Puplampu 2018; Marcotullio et al. 2018; OECD 
2016; P . Puplampu et al. 2017; Srikanth 2018).Yet another way that the 2030 Agenda for Sustainable Development 
underlines the importance of capturing synergies is its calls for 
policy coherence (SDGs 14 and 17). Policy coherence and integration 
between sectors are two of the most critical factors in breaking down

the silo mode of working of different sectors. Working across climate 
and other sustainability agendas is essential to coherence.
A final way that the sustainability and climate agendas have been 
linked is through vertical integration. Following a similar trend that 
appeared with Agenda 21, for which many cities adopted local plans, 
a growing number of cities have introduced Voluntary Local Reviews 
(VLRs). The VLRs resemble the VNRs, but place the emphasis on local 
actions and needs regarding the SDGs (and some links to climate 
change) (Ortíz-Moya et al. 2021). The 2019 SDG Report shows that 
150 countries have developed national urban plans, almost half 
of them also being in the implementation phase (United Nations 
General Assembly 2019).
17.1.3 Integrating Climate Change and Sustainable 
Development in Other Policymaking Processes
Other non-UN-led initiatives involving international organisations or 
clusters of countries have also helped to raise the issue of sustainable

development as a framework for mitigation. The OECD, for instance, 
assesses different types of investments and economic activities 
with reference to their significance for environmental sustainability 
(OECD 2020), while G20 countries have drawn up action agendas 
with sustainable development (UToronto 2016). Meanwhile, the 
Petersberg Climate Dialogue, a  political movement convened by 
major country-group representatives and launched in 2010 by the 
German government, has also called for sustainability to be an 
intrinsic part of the transition (UNFCCO 2020) (BMU 2018).
Due in part to the shifting orientation of these international 
processes, there is growing evidence of action on climate change 
and sustainable development at other levels of decision-making. 
National policies often aim to implement climate change policies 
in the context of sustainable development (Chimhowu et al. 2019; 
Chirambo 2018; ECLAC 2017; Fuseini and Kemp 2015; Galli et al.

2018; Haywood et al. 2019; Ministry of Environment of Jordan 2016; 
McKenzie and Abdulkadri 2018; UNDESA 2016, 2017, 2018; UN 
Women 2017). Some countries are adjusting their existing policies 
to build on themes familiar to sustainable development (Lucas et al. 
2016), including renewable energy and energy efficiency (Fastenrath 
and Braun 2018; Kousksou et al. 2015), urban planning (Gorissen 
et al. 2018; Loorbach et al. 2016; Mendizabal et al. 2018), health 
systems (Pencheon 2018; Roschnik et al. 2017) and agricultural 
systems (Lipper and Zilberman 2018; Shaw and Roberts 2017). Cross-
cutting and integrated approaches, such as the circular economy, 
have also been gaining traction in some European countries (EESC 
2015) and G20 countries (Noura et al. 2020). Many of these efforts 
have also extended up to the regional and down to the local level 
(Gorissen et al. 2018; Hess 2014; Shaw and Roberts 2017).
There has also been a shift to actors outside government aligning

climate with sustainable development. An assessment by (Hoyer 2020)

1734
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17found that collective action against climate change by businesses, 
governments and civil society, reinforced through partnerships and 
coalitions across departments, industries and supply chains, can 
deliver significant development impacts. In order for this diverse 
collection of stakeholders to take action, a  fundamental paradigm 
shift is needed from a  linear model of knowledge-generation to 
an interdisciplinary model that co-produces knowledge (Liu et al. 
2019). In fact, some have argued that accelerating just transitions 
for purposes of sustainable development requires the involvement of 
several actors, institutions and disciplines (Delina and Sovacool 2018). 
Not only do these roles need to be discussed more thoroughly (Kern 
and Rogge 2016); (den Elzen et al. 2019), but it is also important to 
survey different views on transitions and transformations. A variety

of theories that are useful for explaining the causes and constraints 
regarding transitions are examined in Section 17.2.
17.2 Accelerating Transitions in the 
Context of Sustainable Development: 
Definitions and Theories
This section focuses on  how different theoretical frameworks can 
help us understand and explain what is meant by accelerating 
transitions in the context of sustainable development. As suggested 
in Section  17.1, the reference to ‘in the context of sustainable 
development’ suggests that sustainable transitions require more 
than speed, also necessitating removing  the underlying drivers of 
vulnerability and high emissions (quality and depth of transitions), 
while also aligning the interests of different individuals, communities, 
sectors, stakeholders and cultures (scale and breadth of transitions).
The outcome of sustainable transitions is a sustainable transformation. 
While transitions involve ‘processes that shift development pathways

and reorient energy, transport, urban and other subsystems’ (Loorbach 
et al. 2017) (Chapter 16), transformation is the resulting ‘fundamental 
reorganisation of large-scale socio-economic systems’ (Hölscher et al. 
2018). Such a  fundamental reorganisation often requires dynamic 
multi-stage transition processes that change everything from public 
policies and prevailing technologies to individual lifestyles, and social 
norms to governance arrangements and institutions of political 
economy. This set of factors can lock-in development pathways 
and prevent transitions from gathering the momentum needed for 
transformations. Chapter  16 provides an overview of the multi-
stage transition dynamics involved in moving from experimentation 
to commercialisation to integration to stabilisation. That overview 
describes how transitions can break through lock-ins and result in 
a transformation.
While there may be a relatively consistent set of transition dynamics

for all countries, pathways are likely to vary across and even within 
countries. This variation is due to different development levels, 
starting points, capacities, agencies, geographies, power dynamics, 
political economies, ecosystems and other contextual factors. Given 
the diversity of contributing factors, a sustainable transition is likely 
to be a complex and multi-faceted process which cannot be reduced 
to a  single dimension (Köhler et al. 2019). Even with this multi-
dimensionality, transition processes are likely to gain speed and become more sustainable as decision-makers adopt targeted policies 
and other interventions. Many disciplines have reflected on the roles of 
and relative influence on the policies and interventions that can drive 
transitions. The following discussion describes this diversity of views 
with a  survey of how prominent lines of economic, psychological, 
institutional and systems thinking explain transitions. Though these

disciplines differ greatly, they often stress that leveraging synergies 
and managing trade-offs between climate change and sustainable 
development can help advance a transition.
17.2.1 Economics
This section concentrates on economic explanations for transitions. 
At the core of many of these explanations is the assumption that 
economic development can deliver multiple economic, social and 
environmental benefits. Many modern economic systems may 
nonetheless struggle to deliver these benefits due to major disruptions 
and shocks such as climate change (Heal 2020). One way to limit 
disruptions to free markets are targeted interventions in free markets 
such as taxes or regulation. These targeted interventions motivate 
firms and other entities to internalise GHGs and other pollutants, 
potentially paving the way for a sustainable transition (Arrow et al. 
2004; Chichilnisky and Heal 1998).
A related line of thought common to economic explanations

involves the principles of ‘weak sustainability’. These principles 
suggest that the substitution of exhaustible resources is, to some 
extent, feasible (Arrow et al. 2004). One way to capitalise on this 
substitution is to target investments at technological change, green 
growth, and research and development. Targeted investments in the 
form of subsidies can encourage the substitution of exhaustible by 
non-exhaustible resources. To illustrate with a  concrete example, 
investments in renewable energy can not only mitigate climate 
change but also offset the use of exhaustible fossil fuels and 
boost energy security (Heal 2020). It is nonetheless important 
to note that the principle of ‘weak sustainability’ contrasts with 
‘strong sustainability’ or ‘integrated sustainability’ principles. These 
stronger principles suggest that constraints on resources restrict 
such substitutions (Rockström et al. 2009). These constraints

merit attention because some scarce non-substitutable forms of 
natural capital can be exhausted (Bateman and Mace 2020). There 
is hence a  need to capitalise on possible synergies such as those 
with other development priorities and trade-offs, for example, the 
exhaustion of non-substitutable resources. Capturing these synergies 
and managing these trade-offs is consistent with sustainable 
development, a state where the needs of the present generation do 
not compromise the ability of future generations to meet their own 
needs (Bruntland, WCED 1987).
As suggested above, aligning climate investments with other 
sustainable development objectives is critical to a  transition. In 
order to support better investments in sustainable development, 
financing schemes, including environmental, social and governance 
(ESG) disclosure schemes and the Task Force on Climate-related 
Financial Disclosures (TCFD), can play important roles (Executive

Summary in Chapter  15 of this report). After COVID-19, economic

1735
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717recovery packages have increased government-led investments 
(Section 1.3.3), which could potentially be aligned with sustainable 
development. Technological change and innovation are considered 
key drivers of economic growth and of many aspects of social 
progress (Section 16.1), but if technological innovation policies are 
coordinated with the shift to sustainable development pathways, 
then the economic benefits of technological change could come at 
the cost of increasing climate risks (Gossart 2015) Alarcón and Vos 
2015). The environmental impacts of social and economic activities, 
including emissions of GHGs, are greatly influenced by the rate and 
direction of technological changes. Innovation and technological 
transformations present trade-offs that create externalities and 
rebound effects. This suggests that a sustainable future for people

and nature requires rapid, radical and transformative societal change 
by integrating the technical, governance, financial and societal 
aspects (Pörtner et al. 2021) (Section 16.1).
One area that is pertinent to transitions and has received 
considerable attention in economic modelling involving climate 
change is innovation. In particular, some studies have shown how 
low-cost innovations and improvements in end-use technologies 
have significant potential for emissions reductions as well as 
sustainable development (Wilson et al. 2019). Currently information 
technologies are improving rapidly, and the internet of things (IoT), 
AI and Big Data can all contribute to other development needs. This 
is often the case in end-use sectors, as the benefits accrue directly 
to the individuals who use the new innovations. The achievement 
and widespread deployment of fully autonomous cars, for example, 
will bring about broader car- and ride-sharing with negative or low

additional costs compared to more conventional approaches to car 
ownership, with their typically very low load factors. (Grubler et al. 
2018) estimate that the Low Energy Demand (LED) scenario which 
assumes information technology innovations and induced social 
changes, including a sharing economy, have considerable potential 
for harmonising the multiple achievements of SDGs with low marginal 
abatement costs compared with other scenarios (IPCC 2018).
It is nonetheless important to highlight a caveat to the above logic on 
innovation. Whether a technological innovation is wholly sustainable 
or not becomes less clear when considering its effects on the wider 
economy. To illustrate, some models predict that CO 2 marginal 
abatement costs in the power sector will be USD240 and USD565 
tCO 2 for the 2ºC and below 2ºC goals, respectively (IEA 2017).
In theory, if marginal abatement costs meet marginal climate damage, 
mitigation measures are economically optimal in the long run. Yet

marginal damage from climate change is notoriously uncertain, and 
economic theories do not always reflect climate-related damage. 
On the other hand, marginal abatement mitigation costs impose 
additional costs in the short term. These added costs can cause 
productivity in capital to decline through increases in the prices of 
energy and products in which the energies are embodied. These 
increased costs can restrict the ability to invest in and achieve the 
sustainable development priorities. However, precisely the opposite 
can occur when innovation reduces additional costs or achieves 
negative costs. If technological innovation leads to the accumulation 
of capital and productivity increases due to the substitution of energy, material and labour, these are likely to deliver sustainable 
development and climate mitigation benefits.
17.2.2 Institutions, Governance and Political Economy
This subsection focuses on institutions, governance and the political

economy. Institutional and governance arrangements can influence 
which actors possess authority, as well as how motivated they are 
to cooperate in transition processes that are directed at finding 
solutions to climate change and other sustainability challenges. Often 
cooperation is enabled when policy frameworks or institutions align 
climate change with the political and economic interests of national 
governments, cities or businesses, and when institutional and 
governance arguments that support that alignment expand the scale 
of the transitions. However, there may also be political and economic 
interests and structures that can lock-in unsustainable development 
patterns, frustrate this alignment and slow down transitions (Haas 
2021; Mattioli et al. 2020; Newell and Mulvaney 2013; Power 2016).
An extensive literature has examined how the international 
climate agreements and architecture influence collaboration across

countries regarding climate and sustainable development to support 
a  transition (Bradley 2005). For example, international institutions 
offer opportunities for governments and other actors to share new 
perspectives on integrated solutions (Cole 2015). For some observers, 
however, decades of difficulties in crafting a comprehensive climate 
change agreement and the resulting fragmented climate policy 
landscape have been inimical to the collaboration needed for 
a  transition (van Asselt 2014; Nasiritousi and Bäckstrand 2019) 
(Chapters 1 and 13). Yet others see the potential for more incremental 
cooperation across countries, even without a single, integrated form 
of climate governance (Keohane and Victor 2016).
A related argument suggests that fragmentation at the global level 
provides opportunities for cooperation at the national level (Kanie 
and Biermann 2017). For example, in contrast to the relatively top-
down Kyoto Protocol, the bottom-up pledge and review architecture

of the Paris Agreement has prompted national governments to 
integrate climate change with other sustainable development 
priorities (Nachmany and SetzerJoana 2018; Townshend et al. 
2013). Concrete examples included incorporating the SDGs into the 
NDCs as an international response to climate change (The Energy 
and Resources Institute 2017) or bringing climate into sustainable 
development strategies and so-called Voluntary National Reviews 
(VNRs) as part of the SDGs and the 2030 Agenda process (Elder and 
King 2018; Elder and Bartalini 2019).
Another branch of institutional research is concerned with the 
interactions between multiple levels of governance. In this multi-level 
governance perspective, cities and other sub-national governments 
often lead transitions by devising innovative solutions to contribute 
to climate and local energy, transport, the environment, resilience 
and other forms of sustainability (Bellinson and Chu 2019; Doll and

Puppim De Oliveira 2017; Geels 2011; Koehn 2008; Rabe 2007; 
van der Heijden et al. 2019). A complementary perspective suggests 
that national governments can help scale up transitions by allocating

1736
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17resources and can provide the technical support that can spread 
innovative solutions (Bowman et al. 2017; Corfee-Morlot et al. 2009; 
Gordon 2015). Such support has become increasingly important during 
the pandemic, as national governments transfer funds for investments 
in climate-friendly infrastructure, transport systems and energy 
systems. This line of thinking is supported by calls to strengthen vertical 
and horizontal integration within and across government agencies and 
stakeholders in ways that can enhance policy coherence (Amanuma 
et al. 2018; OECD 2018, 2019). The incoherence or misalignment 
between national and local fiscal institutions and policies can restrict 
the ability of local governments to secure resources for climate-friendly 
investments. Such investments are particularly likely to flow, as more 
local governments have adopted net-zero targets, climate emergency

declarations and action plans that can stimulate innovations (Davidson 
et al. 2020). Others have seen greater potential for collaboration and 
innovation, with more multi-centred or polycentric forms of governance 
that lead to the formulation and dissemination of transformative 
solutions to climate and other environmental challenges (Ostrom 
2008). Though much of the above governance research has focused 
on western countries, there are some applications in other regions and 
countries such as China (Gu et al. 2020).
Yet another set of channels facilitating integration between climate 
and other concerns are networks of like-minded actors working 
across administrative borders and physical boundaries. For instance, 
city networks such as the Global Covenant of Mayors for Climate 
& Energy (Covenant of Mayors 2019), the World Mayors Council on 
Climate Change (ICLEI 2019; C40 Cities 2019) and the United Nations 
Office for Disaster Risk Reduction (UNDRR 2019) have agreed to

share decision-making tools and good practices, and to sponsor 
ambition-raising campaigns that help align climate and sustainable 
development concerns within and across cities (Betsill and Bulkeley 
2006) (Chapter  8 and Section  17.3.3.5). This can be particularly 
important for less capable ‘following’ and ‘laggard’ cities needing 
greater financing and other forms of support to move a transition 
forward (Fuhr et al. 2018).
Furthermore, sub-national governments may often work together with 
civil-society groups to create new networked forms of governance 
(Biermann et al. 2012). Other forms of multi-stakeholder partnerships 
focusing on issues with strong climate synergies, such as forms of 
air pollution known as short-lived climate pollutants (Climate and 
Clean Air Coalition (CCAC)) or transport (Sustainable Low Carbon 
Transport Partnership (SLoCaT)), take their cue from global scientific 
communities or civic-minded advocacy groups that transmit

knowledge across boundaries (Keck and Sikkink 1999). There is also 
scope for suggesting that the international climate regime serves 
a Global Framework for Climate Action (GFCA) in helping orchestrate 
the multilateral climate regime and non-state and sub-national 
initiatives (Chan and Pauw 2014), though questions remain about its 
actual impacts on mitigation (Michaelowa and Michaelowa 2017).
Policymaking institutions and networks are themselves policies. 
A significant literature has looked at integrated policy frameworks 
and efforts across sectors, including climate adaptation and 
mitigation, as drivers of transitions (Landauer et al. 2015; Favretto 
et al. 2018; Obersteiner et al. 2016; Steen and Weaver 2017; Thornton  and Comberti 2017). Policy coherence between climate 
and other development objectives is often considered essential to 
sustainable development (Sovacool 2018). A similar discussion about 
synergies and conflicts has been raised on the relationship between

resilience and sustainability (Marchese et al. 2018). To help achieve 
coherence, there have been some efforts to develop suitable tools 
and decision-making frameworks (Scobie 2016).
A related line of reasoning has suggested that sustainable 
development often requires not one but a mix of policy instruments 
to bring about the multiple policy effects needed for social and 
technological change (Edmondson et al. 2019; Rogge and Johnstone 
2017). Following these calls, some governments have aimed to 
address climate change and sustainability jointly with coherent and 
integrated approaches to achieving these agendas (Chimhowu et al. 
2019), although for some countries Small Island Developing States 
(SIDS) this has proven more challenging (Scobie 2016).
Though the above work tends to downplay politics and business, 
others suggest that political economy should feature prominently in 
transitions. Some branches of political-economy research underline

how resource-intensive and fossil-fuel industries leverage their 
resources and positions to undermine transitions (Jones, C.A. and 
Levy 2009; Newell and Paterson 2010; Zhao et al. 2013; Geels 2014; 
Moe 2014) (Chapter 1). These vested interests can lock-in status quo 
policies in countries where political systems offer interest groups 
more opportunities to veto or overturn climate- or eco-friendly 
proposals (Madden 2014). Companies with a  strong interest in 
earning profits and building competitiveness from conventional 
fossil fuel-based energy systems have particularly strong incentives 
to capture politicians and agencies (Meckling and Nahm 2018). 
Such strategies can be particularly powerful when combined with 
concerns over job losses and dislocation, preventing transitions 
from gaining traction (Haas 2021; Mattioli et al. 2020; Newell and 
Mulvaney 2013; Power 2016).
This suggests that politics can be an impediment to change: other

studies argue instead that politics can be harnessed to drive 
transitions forward. For example, some observers contend that 
building coalitions around green industrial policies and sequencing 
reforms to reward industries in such coalitions can align otherwise 
divergent interests and inject momentum into transitions (Meckling 
et al. 2015). Others see the effects of political economy varying 
over time depending upon external market conditions. To illustrate, 
renewable feed-in tariffs in Europe persisted for over two decades 
and were crucial in wind and solar power technologies making the 
breakthrough. But once competition from China led to the demise 
of European technology providers, and once European populations 
started to oppose surcharges on their electricity bills, feed-in 
tariffs were abolished by politicians in the purely national interest 
(Michaelowa and Michaelowa 2017).
17.2.3 Psychology, Individual Beliefs and Social Change

This subsection draws on value- and action-oriented research that 
employs inter- or transdisciplinary methods such as transactional

1737
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717psychology, transformative science and similarly focused disciplines 
(Wamsler et al. 2021). These approaches frequently encourage 
researchers to participate in transitions that induce changes in the 
researcher’s own beliefs while triggering wider shifts in social norms 
(including human stewardship for the natural environment) (Adger 
et al. 2013; Hulme 2009; Ives et al. 2019; O’Brien 2018). This research 
also emphasises how changes in individual beliefs could lead to 
climate actions that contribute to more sustainable, equitable and 
just societies (e.g., ‘the mind- & paradigm shifts’) (Göpel et al. 2016). 
They further suggest the potential for virtuous cycles of individual-
level and wider social changes that ultimately benefit the climate 
(Banks 2007; Day et al. 2014; Lockhart 2011; Montuori and Donnelly 
2018; Power 2016).

The starting point for this virtuous circle are inner transitions. Inner 
transitions occur within individuals, organisations and even larger 
jurisdictions that alter beliefs and actions involving climate change 
(Woiwode et al. 2021). An inner transition within an individual (see 
e.g.,  Parodi and Tamm 2018) typically involves a  person gaining 
a deepening sense of peace and a willingness to help others, as well 
as protecting the climate and the planet (see e.g., Banks 2007; Power 
2016). Inner transition can imply that individuals become sympathetic 
to concerns that include climate issues and values connected to 
nature. For instance, they may include a desire to become a steward 
of nature (Buijs et al. 2018); ‘live according to the principles of 
integrated sustainability’ (Schweizer-Ries 2018); ‘achieve the good 
life’ (Asara et al. 2015; Escobar 2015; Kallis 2017; Latouche 2018) 
(Chapter  5 and Section  1.6.2); or protect the well-being of other

living creatures (Chapter 5 and Section 1.6.3.1).
Examples have also been seen in relation to a similar set of inner 
transitions to individuals, organisations and societies, which involve 
embracing post-development, degrowth, or non-material values 
that challenge carbon-intensive lifestyles and development models 
(D’Alisa 2014; Kothari 2019; Neuteleers and Engelen 2015; Paech 
2017). These shifts in values can occur when humans reconnect 
with nature, deepen their consciousness and take responsibility for 
protecting the planet and its climate (Cross et al. 2019; Martinez-
Juarez et al. 2015; Speldewinde et al. 2015). Changes in both 
values and beliefs may also emerge through consciousness-raising 
processes where people cooperate in ways that would protect the 
climate ((Banks 2007; Hedlund-de Witt et al. 2014; Woiwode and 
Woiwode 2019) (Section 1.6.4).
Many of the above-mentioned beliefs and values that support 
climate actions have spread through expanding interests in

conservationist world views, indigenous cultures (see, for example, 
Lockhart 2011) and branches of neuroscience and psychology that 
suggest different notions of the self (Hüther 2018; Lewis 2016; 
Seligman and Csikszentmihalyi 2014). These beliefs and values 
can also be spread through meditation, yoga or other social 
practices that encourage lower-carbon lifestyles (Woiwode and 
Woiwode 2019). Another channel for spreading climate concerns is 
sustainability culture, which is premised on connecting people and 
communities, and has also benefited from the internet and digital 
technologies that support these connections (see e.g.,  Bradbury 
2015; Scharmer 2018). The spread of this culture, in turn, has led to the creation of social fields that allow changes to happen (see 
e.g., Gillard et al. 2016) or has promoted low-carbon thinking and 
related behavioural changes (O’Brien 2018; Veciana and Ottmar 
2018). Studies of social contagions may also offer insights into the

mechanisms that lead to the adoption of new values and related 
climate actions (see e.g.,  Iacopini et al. 2019). It is nonetheless 
worth highlighting that communication networks and other 
mechanisms promoting the spread of interpersonal communication 
that can spread pro-climate views may also lead to the proliferation 
of climate scepticism and denial (Leombruni 2015). At the same 
time, some studies suggest that such scepticism can be countered 
by the generation of more credible information on climate change 
(Samantray and Pin 2019).
One of the more direct channels through which transitions spread 
are climate change education and action-oriented research (Fazey 
et al. 2018; Ives et al. 2019; Scharmer 2018; Schäpke et al. 2018; 
Schneidewind et al. 2016). For instance, research using ‘social 
experiments’ or ‘real-world labs’ has helped give rise to shifts in 
mindsets on energy, food, transport and other systems that can

benefit the climate (Bernstein and Hoffmann 2018; Berkhout et al. 
2010; Bulkeley et al. 2015; Hoffmann 2010). In much the same way, 
the acquisition of transformational knowledge and transformative 
learning (Lange 2018; O’Neil and Boyce 2018; Pomeroy and Oliver 
2018; Walsh et al. 2020; Williams 2013) contributes to thinking and 
acting that open climate-friendly development pathways (Berkhout 
et al. 2010; Lo and Castán Broto 2019; Roberts et al. 2018; Turnheim 
and Nykvist 2019)) (Section 1.7.2). First-person and action research 
can also facilitate similar changes that bring about climate actions 
(see e.g., Dick 2007; Streck 2007; Hutchison and Walton 2015; 
Bradbury et al. 2019).
17.2.4 System-level Explanations
Systems explanations help explain the dynamics of transitions toward 
sustainable development while explicitly uncovering links between 
the human and natural worlds, the socio-cultural embeddedness 
of technology, and the inertia behind high-carbon development

pathways. This line of thinking often envisages transitions emerging 
from complex systems in which many different elements interact at 
small scales and spontaneously self-organise to produce behaviour 
that is unexpected, unmanaged and fundamentally different from the 
sum of the system’s constituent parts.
Social-ecological systems theory describes the processes of 
exchange and interaction between human and ecological systems, 
investigating in particular non-linear feedback occurring across 
different scales (Folke 2006; Holling 2001). This approach has 
informed subsequent theoretical and empirical developments, 
including the ‘planetary boundaries’ approach (Rockström et al. 
2009), conceptualisations of vulnerability and adaptive capacity 
(Hinkel 2011; Pelling 2010) and more recent explorations of 
urban resilience (Romero-Lankao et al. 2016) and regenerative 
sustainability (Clayton and Radcliffe 2018; Robinson and Cole

2015). Employing a  systems lens to address the ‘root causes’ of 
unsustainable development pathways (such as dysfunctional social

1738
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17or economic arrangements) rather than the ‘symptoms’ (dwelling 
quality, vehicle efficiency, etc.) can trigger the non-linear change 
needed for a  transformation to take place (Pelling et al. 2015). 
Exploring synergies between climate change adaptation, mitigation 
and other sustainability priorities (such as biodiversity and social 
equity, for instance) (Beg 2002; Burch et al. 2014; Shaw et al. 2014) 
may help to yield these transformative outcomes, though data 
regarding the specific nature of these synergies is still emerging.
Socio-technical transition theory, on the other hand, explores 
the ways in which technologies such as low-carbon vehicles or 
regenerative buildings are bound up in a  web of social practices, 
physical infrastructure, market rules, regulations, norms and habits 
(see, e.g, (Loorbach et al. 2017). Radical social and technical

innovations can emerge that ultimately challenge destabilised 
or increasingly ineffective and undesirable incumbents, but path 
dependencies often stymie these transition processes, suggesting an 
important role for governance actors (Burch 2017; Frantzeskaki et al. 
2012; Holscher et al. 2019).
This also reveals the large-scale macroeconomic, political and 
cultural trends (or contexts) that may reinforce or call into question 
the usefulness of current systems of production and consumption. 
One branch of this theory, transition management (Kern and Smith 
2008; Loorbach 2010), explores ways of guiding a socio-technical 
system from one path to another. In particular, it highlights 
interactions between actors, technologies and institutions, and the 
complex governance mechanisms that facilitate them (Smith et al. 
2005). The challenge, in part, becomes linking radical short-term 
innovations with longer-term visions of sustainability (Loorbach

and Rotmans 2010) and creating opportunities for collaborative 
course-correction in light of new information or unexpected 
outcomes (Burch 2017).
17.2.5 Conclusions
This section has surveyed several explanations for interventions that 
can give rise to transitions. The review suggests that there are several 
differences between these various perspectives. Whether individuals, 
organisations, markets or socio-technical systems drive or undermine 
transitions is a key distinction. These differences have implications 
for the evidence these claims draw on in support of their arguments. 
For instance, some of the explanations tend to employ qualitative 
evidence to explain changes in attitudes at the individual or 
community levels as paving the way for broader changes to cultures 
and belief systems. Others assess how institutional arrangements 
can be reformed in order to align climate with the sustainable 
development agenda to enable a transition.

While there are indeed significant differences between explanations, 
there are also important parallels. Such parallels begin with a shared 
emphasis on synergies and trade-offs between climate and sustainable 
development. Most explanations tend to underline the importance of 
synergies in aligning the climate with broader sustainability agendas. 
Most importantly, many of the explanations are complementary with 
the systems-level discussion in that they offer a broad framework, while economic, psychological and governance theories offer more 
specific insights. Moving a  transition forward will often require 
drawing upon insights from multiple schools of thought. Though it 
is unlikely that a one-size-fits-all set of factors will drive a transition, 
there is a  growing body of empirical evidence shedding light on 
the factors that can strengthen synergies between climate and the 
broader sustainable development agenda.
17.3 Assessment of the Results of Studies

Where Decarbonisation Transitions 
are Framed Within the Context of 
17.3.1 Introduction
This section assesses studies based on the links between sustainable 
development and climate change mitigation in order to facilitate 
robust conclusions on synergies and trade-offs between different 
policy objectives across methodologies, scenarios and sectors. 
Conclusions are drawn based on national and sub-national, sectoral 
and cross-sectoral, short- and long-term transition studies presented 
in this and other sections of the report as a basis for establishing an 
overall picture of how sustainable development and climate change 
policies can be linked as a basis for accelerated transitions.
This section focuses initially on issues related to short- and long-term 
transitions to meet climate change and sustainable development 
goals in the context of the UNFCCC and the UN 2030 Agenda for 
Sustainable Development. Global-modelling results and economy-

wide studies are then assessed, followed by a discussion of specific 
challenges in relation to renewable-energy penetration and phasing 
out fossil fuels, stranded assets and just transitions. Key synergies and 
trade-offs between meeting the UN 2030 Sustainable Development 
Goals (SDGs) and mitigation are then illustrated by means of cross-
sectoral examples. Finally, this section presents an overview of the 
assessment of SDG synergies and trade-offs based on all sectoral 
chapters in this report for a range of key mitigation options.
17.3.2 Short-term and Long-term Transitions
It is increasingly being recognised that sustainable development 
policy goals and meeting short- and long-term climate policy goals 
are closely linked (IPCC 2018). It is also being realised that, under 
the Paris Agreement, climate change policies should be integrated 
into sustainable development agendas, while the UN 2030 Agenda 
as well includes SDG 13 on climate actions. In this way, both UN

agreements provide joint opportunities for systematic transitions 
in support of both climate change and sustainable development. 
Achievement of the Paris Agreement’s goals will require a rapid and 
deep worldwide transition in all GHG emissions sectors, including 
land use, energy, industry, buildings, transport and cities, as well as in 
consumption and behaviour (UNEP 2019). Meeting the goals of such 
a transformation requires that the long-term targets and pathways to 
fulfil the stabilisation scenarios play an important role in guiding the 
direction and pathways of short-term transitions. There is therefore

1739
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717a need for long- and short-term policies and investment decisions to 
be closely coordinated.
In the context of the Paris Agreement, countries have submitted 
their initial plans for the decarbonisation of their economies to 
the UNFCCC in the form of their so-called National Determined 
Contributions (NDCs). The ambitions of the NDCs are closely related 
to the ongoing UNFCCC negotiations over the financial measures and 
forms of compensation. Although the Paris Agreement emphasises 
the links between climate policies and sustainable development, the 
UN’s 2030 Agenda for Sustainable Development and the SDGs are 
not very well represented at present in the NDCs, according to Fuso 
Nerini et al. (2019). Very few of the NDCs include any reference to 
the SDGs, which (Fuso Nerini et al. 2019) highlight as a barrier to the 
successful implementation of the Paris Agreement, which induces

them to call for a more holistic policy approach. Campagnolo and 
Davide (2019) have assessed the impacts of the submitted NDCs on 
poverty eradication and inequalities of income based on empirical 
research and a  global Computable General Equilibrium (CGE) 
model. One conclusion is that the NDCs of less developed countries 
would tend to reduce poverty alleviation, but this can be offset if 
international financial support is provided for the mitigation actions.
The alignment of climate-policy targets in the NDCs with sustainable 
development has been assessed by means of integrated assessment 
models (IAMs), macroeconomic and sectoral modelling. (Iyer et al. 
2018) based on IAM-based studies, the implications of framing NDCs 
being placed more narrowly on mitigation targets rather than on 
a  framing in which the impacts on sustainable development were 
explicitly taken into consideration. It was thus concluded that some

SDGs would be directly supported as a side benefit of the climate 
policy targets included in the NDCs, while other SDGs needed 
a special policy design going beyond narrow climate policy objectives. 
(Iyer et al. 2018) also assessed the regional distribution of efforts in 
terms of domestic mitigation costs and SDG impacts and concluded 
that the geographical distribution of mitigation costs and  SDG 
benefits were not similar, so a  special effort would be needed to 
match climate policies and policies to meet the SDGs. Accordingly, 
a national decision-making perspective suggests that SDGs should 
be integrated into national climate policies.
The NDCs submitted to the Paris Agreement have demonstrated 
a lack of progress in meeting the long-term temperature goals. In 
the context of the UN’s 2030 Agenda for Sustainable Development, 
the UN Sustainable Development Report 2019 (Sachs et al. 2019) 
also concluded that there is a particular lack of progress in achieving

SDG 13 (climate action), SDG 14 (life below water) and SDG 15 (life 
on land). Given the close link between the SDGs and climate change 
policies, the current obstacles in meeting the former could also be 
a barrier to realising transitions to low-carbon societies. Conversely, 
opportunities to leverage the SDGs could in many cases involve 
climate actions, since policies enabling climate adaptation and 
mitigation could also support food and energy security and water 
conservation if they were well designed (see the detailed discussion 
in the section on synergies and trade-offs between climate policies 
and meeting the SDGs in Section 17.3.3.7, Chapter 3, and IPCC 2018).
These findings point to a specific need to align economic and social development perspectives, climate change and natural systems. 
priorities differ across countries and over time. These priorities are

strongly linked to local contexts and depend on which dimension 
of the improvement in the well-being of people is considered to be 
the most urgent. Eradicating poverty and reducing inequality are key 
development priorities for many low- and middle-income countries 
(Section 4.3.2.1).
A key barrier to the development of national plans and policies to 
meet the UN 2030 SDGs is the lack of finance. (Sachs et al. 2019) 
conclude that meeting the SDGs to achieve social transformations 
worldwide would require 2–3% of global GDP and that it would 
be a  huge challenge to ensure that finance is targeted to the 
world’s poorest countries and people. The UN Secretary-General 
has called for the allocation of finance to meet the UN’s 2030 
Agenda with a strong emphasis on the private sector, but to date 
no governance frameworks or associated financial modalities have 
been established in the UN or the UNFCCC context for the formal

alignment of sustainable development and transitions to take place 
in accordance with the low global temperature-stabilisation targets 
in the Paris Agreement. Accelerating investments, particularly in 
low-income countries, will be required to meet both the Paris goals 
and the SDGs (Section 15.6.7). The mismatch between capital and 
investment needs, home-bias considerations and differences in risk 
perceptions between rich and poor represent major challenges for 
private finance. Green bond markets and markets for sustainable 
financal products have increased significantly, and the landscape has 
continued to evolve since AR5 (Executive Summary in Chapter 15). 
Special efforts and activities are particularly required for raising 
finance in developing countries.
Based on the Paris Agreement, the UNFCCC has invited countries to 
communicate their mid-century and long-term low-GHG emission-
development strategies by 2020 (UNFCCC 2019). National long-term

low-emission development strategies and their global stocktake in 
the UNFCCC context provide a platform for informing the long-term 
strategic thinking on transitions towards low-carbon societies. One 
specific value of these plans is that they reflect how specific transition 
pathways, policies and measures can work in different parts of the 
world in a  very context-specific way, that is, by taking context-
specific issues and stakeholder perspectives into consideration. Many 
nations have submitted national long-term strategies to the UNFCCC, 
including sustainable development perspectives (see Section  4.2.4 
for a review of the plans and scientific assessments).
17.3.2.1 Model Assessments on the Sustainable Development 
Pathways for Decarbonisation
This section assesses the model evaluations of the sustainable 
development pathways for decarbonisation, including the 
co-benefits and trade-offs involving explorations of alternative future

development pathways as a  basis for clarifying societal objectives 
and understanding the restrictions. Shifting development pathways to 
increased sustainability involves a number of complex issues, which 
are difficult to integrate into models. For a more detailed discussion 
about this, see Section 4.4.1 and Cross-Chapter Box 5 in Chapter 4.

1740
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17Development pathways that focus narrowly on climate mitigation or 
economic growth will not lead to the SDGs and long-term climate-
stabilisation objectives being achieved. The best chances of doing 
this lie in development pathways that can maximise the synergies 
between climate mitigation and sustainable development more 
broadly (Section  1.3.2). Areas of focal modelling include green 
investments, technological change, employment generation and the 
performance of policy instruments, such as green taxes, subsidies, 
emission permits, investments and finance. Short- and long-term 
macroeconomic models have been used to assess the impacts of 
such policy instruments. Jaumotte et al. (2021) analyse the economic 
impacts on net zero emissions by 2050 with a focus on short-term 
economic policies and the integration of climate policies such as

CO2 taxes with green reform policies. This may imply the co-creation 
of benefits between climate policy objectives, and macroeconomic 
policy goals such as employment creation.
There is an emerging modelling literature focusing on the synergies 
and trade-offs between low-carbon development pathways and 
various aspects of sustainable development. The early literature, 
including that on IAMs, and macroeconomic and sectoral models, 
mainly focused on the co-benefits of mitigation policies in terms of 
reduced air pollution, energy security and to some extent employment 
generation security (IPCC 2014, 2018c) (Chapter  6). Some models 
have been developed further with assessments of a broader range 
of the joint benefits of mitigation, health, water, land use and food 
security (Clarke et al. 2014; IPCC 2014, 2018; Kolstad et al. 2014). 
According to Chapter 1, there is a need to incorporate issues and 
enablers further, including a wide range of non-climate risks, varying

forms of innovation, possibilities for behavioural and social change, 
feasible policies and equity issues (Executive Summary in Chapter 1).
IAMs and macroeconomic models typically calculate mitigation costs 
based on the assumption that markets internalise externalities like 
GHG emissions through carbon prices (Barker et al. 2016; IEA 2017, 
2019). Yet, there are legitimate questions to be asked about whether 
carbon pricing will be efficient if markets are inefficient (World 
Bank 2019). However, market inefficiencies are difficult to integrate 
into the models. How GHG emissions taxes would actually work is 
thus quite uncertain based on the modelling studies (Barker et al. 
2016; Fontana and Sawyer 2016; Meyer et al. 2018). Despite these 
limitations, the use of GHG emission taxes as an effective instrument 
based on modelling results in practice has implications for public 
policies and private-sector investments.
Despite the shortcomings of conventional economic thought and

models already pointed out, improved models have demonstrated 
new perspectives on how mitigation costs can be assessed 
in macroeconomic models. For instance, while a  conventional 
perspective might suggest that climate change mitigation costs 
can limit investments in sustainability because they reduce the 
productivity of capital by increasing energy prices and the products in 
which energies are embodied, another perspective is that innovation 
can imply increases in efficiency and that the substitution of energy, 
material and labour can lead to the accumulation of capital and 
productivity gains. This appears to occur with innovations in end-use 
energy applications generating emissions reductions and delivering on other sustainable development benefits (Wilson et al. 2019). 
Similarly, IAM models have been applied to model the potential for 
Low Energy Demand (LED) scenarios associated with demand-side 
innovations in the service sector. (Grubler et al. 2018) have developed

a climate-friendly LED scenario which assumes information technology 
innovations such as the internet of things (IoT) and induced social 
changes such as the sharing economy. Nonetheless there are still 
very important limits on the degree to which highly aggregated IAM 
models and macroeconomic models can integrate ethics, equity and 
several other key policy-relevant aspects of sustainable development 
(Easterlin et al. 2010; Koch 2020). A key limitation in this context is 
that, while all countries share the totality of the SDGs, development 
priorities differ across countries and over time. Moreover, these 
priorities are strongly linked to local contexts, and this can only be 
reflected directly in national models (Section 4.3.2).
An example of a  project that assesses the economy-wide impacts 
of linking sustainable development with deep decarbonisation is 
the Deep Decarbonisation Pathways Project (DDPP) (Bataille et al.

2016), which is undertaking a  comparative assessment of studies 
of 16 countries representing more than 74% of global energy-
related emissions for the pathway to 2ºC stabilisation scenarios. 
The DDPP’s methodology is to combine scenario analysis in different 
national contexts using macroeconomic models and sectoral models 
and to facilitate a  consistent cross-country analysis using a  set of 
common assumptions.
The key conclusions of the DDPP team on the economy-wide impacts 
are that country-based studies such as South Africa’s demonstrate 
that it is possible to improve income distribution, alleviate poverty 
and reduce unemployment while simultaneously transitioning 
to a  low-carbon economy (Altieri et al. 2016). The DDPP in Japan 
explores whether energy security can be enhanced through increases 
in renewable energy (Oshiro et al. 2016). The reduction of uncontrolled 
fossil fuel emissions has significant public-health benefits according

to the  Chinese and Indian DDPPs, as fossil fuel combustion is the 
major source of air pollution.
For example, in the Chinese DDPP , deep decarboniation scenarios 
have resulted in reductions of 42–79% in primary air pollutants 
(e.g.,  SO2, NO x, particulate matter (PM2.5), volatile organic 
compounds (VOCs), and NH 3), thus meeting air-quality standards in 
major cities. The deep decarbonisation scenarios include the large 
and fast energy-efficient improvements required to improve energy 
access and affordability. The DDPP studies are thus an example of an 
approach in which national deep-carbonisation scenarios are linked 
to the development goals of income generation, energy access and 
affordability, employment, health and environmental policy.
Sustainable development scenarios have also been developed by 
the Low-Carbon Society’s (LCS) assessments (Kainuma et al. 2012), 
in which multiple sustainable development and climate change

mitigation goals were assessed jointly. The scenario analysis was 
conducted for Asian countries such as South Korea, Japan, India, 
China and Nepal with a  soft linked IAM using economy-wide and 
sectoral models and linked to very active stakeholder engagement 
in order to reflect national policy perspectives and priorities. Some of

1741
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717the models are economy-wide global IAMs, while others are national 
partial equilibrium models.
The LCS scenarios also include a specific attempt to include ongoing 
dialogues with policymakers and stakeholders in order to reflect 
governance and enabling factors, and to enable the modelling 
processes to reflect political realism as far as possible. Diverse 
stakeholders who acted as validators of the scientific process were 
included, stakeholder preferences were revealed, and recipients and 
users of the LCS outputs were included in ongoing dialogues on 
outputs and in interpreting the results. The aim of the stakeholder 
interactions was thus to fill the gap between typical laboratory-
style IAMs and down-scaled but unaligned practical assessments 
performed at disaggregated geographical and sector-specific scales.
Energy scenarios for sustainable development were included in

The World Energy Outlook of the IEA (IEA 2019, 2020) in terms of 
a Sustainable Development Scenario (SDS), which assessed not only 
SDG 13 (climate action) but also SDG 7 (affordable and clean energy) 
and SDG 3.9 (air pollution). This scenario takes as its starting point 
the policy goal of meeting these SDGs and then assesses the costs 
of meeting an emissions reduction target of 70% of CO 2 from the 
energy system by 2030. The scenario concludes that retrofitting coal-
fired power plants with pollution controls is the cheapest option for 
dealing with local pollution in the short term, but that this is not 
consistent with meeting the long-term emissions goals of the Paris 
Agreement. The SDS scenario combines the goal of reducing the 
amount of CO 2 in the energy system by 70%, with large decreases 
in energy-related emissions of NO X, SO 2 and PM 2.5, leading to a fall 
of 40–60% by 2030, and to 2.5 million fewer premature deaths from

air pollution in 2030 than in the Stated Policies Scenario (STEPS), 
which represent a  continuation of current trends in the energy 
system (IEA 2020).
The costs of energy-system transitions have been assessed by 
several energy-system studies. The economic costs of meeting the 
different goals depend on the stringency of the mitigation target, as 
well as economic (fuel prices, etc.) and technological developments 
(technology availability, capital costs, etc.). In addition, changes in 
infrastructure and behavioural patterns and lifestyles matter. Model-
based assessments vary, depending on these assumptions and 
differences in modelling approaches (Krey et al. 2019) (Section 6.7.7). 
Country characteristics determine the social, economic and technical 
priorities for low-emission pathways. Domestic policy circumstances 
impact on pathways and costs, for example, when affordability and 
energy-security concerns are emphasised (Oshiro et al. 2016).

and within countries, and may affect impact on the poorest through 
their effects on energy and food prices (Hasegawa et al. 2018; 
Fujimori et al. 2019) (Section 3.6.4), while higher levels of warming 
are projected to generate higher inequality between countries as well 
as within them (Chapter 16). Mitigation thus can reduce economic 
inequalities and poverty by avoiding such impacts (Section 3.6.4).
Improved air quality and the associated health effects are the 
co-benefit category dominating model-based assessments of co-benefits, but a few studies have also covered other aspects, such 
as the health effects of dietary change and biodiversity impacts 
(Sections  3.6.3 and 17.3). Mitigation has implications for global 
economic inequalities through different channels and can compound 
or lessen inequalities, avoid impacts and create co-benefits that

reduce inequalities (Section  3.6.4). There are, however, several 
challenges involved in balancing the dilemmas associated with 
meeting the SDGs, such as, for example, energy access, equity and 
sustainability. Fossil fuel-dependent developing countries cannot 
transition to low-carbon economics without considering the wider 
impacts on development by doing so (Section 3.7.3).
Climate change has negative impacts on agricultural productivity in 
general, including unequal geographical distribution (Chapter 3). On 
top of that, there is also a risk that climate change mitigation aimed 
at achieving stringent climate goals could negatively affect food 
access and food security (Akimoto et al. 2012; Fujimori et al. 2019; 
Hasegawa et al. 2018). If not managed properly, the risk of hunger 
due to climate policies such as large-scale bioenergy production 
increases remarkably if the 2°C and 1.5°C targets are implemented

(Section 3.7.1). Taking the highest median values from different IAMs 
for given classes of scenarios, up to 14.9 GtCO 2 yr–1 carbon dioxide 
removal (CDR) from BECCS is required in 2100, and 2.4 GtCO 2 yr–1 for 
afforestation. Across the different scenarios, median changes in global 
forest area throughout the 21st century reach the required 7.2 Mkm2 
increases between 2010 and 2100, and agricultural land used for 
second-generation bioenergy crop production may require up to 
6.6 Mkm2 in 2100, increasing the competition for land and potentially 
affecting sustainable development (AR6 scenarios database).
Reducing climate change can reduce the share of the global 
population exposed to increased stress from reductions in water 
resources (Arnell and Lloyd-Hughes 2014) and therefore to 
water  scarcity as defined by a  cumulative abstraction-to-demand 
ratio (Hanasaki et al. 2013). (Byers et al. 2018), show that 8–14% of 
the population will be exposed to severe reductions in water

supply if average temperatures increase between 1.5°C and 2°C 
(Section 3.7.2). (Hayashi et al. 2018) assess the water availability for 
different emission pathways, including the 2°C and 1.5°C targets, 
in light of the various factors governing availability. There are very 
different impacts among nations. In Afghanistan, Pakistan and South 
Africa, water stress is estimated to increase by 2050 mainly due to 
increases in irrigation water associated with the rising demand for 
food, and climate change will already increase water stress within 
the next decades. Other factors, such as changes in the demand for 
municipal water, water for electricity generation, other industrial 
water, and water for livestock due to climate change mitigation, are 
of limited importance.
(Vandyck et al. 2018) estimate that the 2°C pathway would 
reduce air pollution and avoid 0.7–1.5 million premature deaths 
in 2050 compared to current levels. It is generally agreed that in

both developed and developing countries there are additional 
benefits of climate change mitigation in terms of improved air 
quality (Section 3.7.4). (Markandya et al. 2018) assessed the health 
co-benefits of air pollution reductions and the mitigation costs of 
the Paris Agreement using global scenarios for up to 2050. They

1742
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17concluded that the health co-benefits substantially outweighed the 
policy costs of achieving the NDC targets and either 2°C or 1.5°C 
stabilisation. The ratio of health co-benefits to the mitigation costs 
ranged from 1.4 to 2.45, depending on the scenario. The extra 
effort of trying to pursue the 1.5°C target instead of the 2°C target 
would generate a substantial net benefit in some areas. In India, the 
co-health benefits were valued at USD3.28–8.4 trillion and those 
in China at USD0.27–2.31 trillion. (Gi et al. 2019) also show that 
developing countries such as India have a huge potential to produce 
co-benefits. In addition, this implies that while the cost advantages of 
simultaneously achieving reductions of CO 2 emissions and of PM 2.5 
are clear, the advantages for integrated measures could be limited, 
as the costs greatly depend on the CO 2 emissions reduction target.

(Grubler et al. 2018) models a pathway leading to global temperature 
change of less than 1.5°C without carbon capture and storage 
(CCS), taking end-use changes into account, including innovations in 
information technologies and changes to consumer behaviour apart 
from passive consumption. The pathway estimates global final-energy 
demand of 245 EJ yr–1 in 2050, which is much lower than in existing 
studies (Section  5.3.3). It also shows the possibilities of creating 
synergies between multiple SDGs, including hunger, health, energy 
access and land use. Integrated technological and social innovations 
will increase the opportunity to achieve sustainable development. 
(Millward-Hopkins et al. 2020) estimate global final energy at 149 EJ yr–1 
in 2050 as required to provide decent material living standards, which 
is much lower than the 1.5°C scenario ranges (330–480 EJ yr–1 in 
2050) of IAMs (IPCC 2018) and the 390 EJ yr–1 in the IEA SDS (IEA

2019), and also lower than (Grubler et al. 2018). The conclusion is that, 
although providing material living standards does not guarantee that 
every person will live a good life, there are large potentials in achieving 
low energy demand with sustainable development.
An overview of the co-benefits and trade-offs of several SDGs based 
on modelling results is provided in Figure 3.39 (Section 3.7). Selected 
mitigation co-benefits and trade-offs are provided in relation to meeting 
the 1.5°C temperature goal based on a subset of models and scenarios, 
despite many IAMs so far not having comprehensive coverage of the 
Sustainable Development Goals (Rao et al. 2017; van  Soest et al. 
2019). There are several co-benefits of mitigation policies, including 
increased forest cover (SDG 15) and reduced mortality from ambient 
PM2.5 pollution (SDG 3) compared to reference scenarios. However, 
mitigation policies can also cause higher food prices and thus increase

the share of the global population at risk from hunger (SDG 2), while 
also relying on solid fuels (SDGs 7 and 3) as side effects. It is then 
concluded in Section 3.7 that these trade-offs can be balanced through 
targeted support measures and/or additional SD policies (Bertram et al. 
2018; Cameron et al. 2016; Fujimori et al. 2019).
The World in 2050 Initiative (TWI2050) includes a  comprehensive 
assessment of technologies, economies and societies embodied in 
the SDGs (IIASA 2018). The assessment addresses social dynamics, 
governance and sustainable development pathways within the areas 
of human capacity and demography, consumption and production, 
decarbonisation and energy, food, the biosphere and water, smart 
cities and digitalisation. The report concludes that the 17 SDGs 
are integrated and complementary and need to be addressed in unison. Studies using global IAMs that were presented in the GEO6 
report (United Nations Environment Programme 2019, Chapter 22)

concluded that transitions to low-carbon pathways will require 
a broad portfolio of measures, including a mixture of technological 
improvements, lifestyle changes and localised solutions. The many 
different challenges require dedicated measures to improve access 
to, for example, food, water and energy, while at the same time 
reducing the pressure on environmental resources and ecosystems. 
A  key contribution may be a  redistribution of access to resources, 
where both physical access and affordability play a role. The IAMs 
cover large countries and regions, and localised solutions are not 
properly addressed in the modelling results. This implies that, for 
example, trade-offs between energy access and affordability are not 
fully represented in aggregate modelling results.
There are also several country-level studies for deep emissions 
reductions (see Chapter 4 for an overview of the results). The studies

find significant impacts of mitigation policies at the sectoral level, 
reflecting the fact that the sectoral scope does not allow for as much 
flexibility in mitigation measures despite macroeconomic impacts 
being assessed to be small (Executive Summary in Chapter  4). 
Another key lesson is that the detailed design of mitigation policies 
is critical for the distributional impacts (Executive Summary in 
Chapter 4). The potential mitigation measures, the potential economic 
growth, the political priorities and so forth are different among 
nations, and there may be several emissions-reduction transition 
pathways to long-term goals among nations (Figure 4.2).
17.3.2.2 Renewable Energy Penetration and Fossil Fuel  
Phase-out
As pointed out in Chapter  6, the achievement of long-term 
temperature goals in line with the Paris Agreement requires the 
rapid penetration of renewable energy and a timely phasing out of 
fossil fuels, especially coal, from the global energy system. Limiting

warming to 1.5°C (>50%) with no or limited overshoot means that 
global CO 2 emissions must reach ‘net zero’ in 2050/2060 (IPCC 
2018). Net zero emissions imply that fossil fuel use is minimised 
and replaced by renewables and other low-carbon primary forms of 
energy, or that the residual emissions from fossil fuels are offset by 
carbon dioxide removal (CDR). The 1.5°C scenario requires a 2–3% 
annual improvement rate in carbon intensities till 2050. The historical 
record only shows a slight improvement in the carbon intensity rate 
of global energy supplies, far from what is required to limit global 
warming to 2°C (>67%), or limit warming to 1.5°C (>50%) with no 
The role of coal in the global energy system is changing fast. Given 
the global temperature goals of the Paris Agreement, the global 
coal sector needs a transition to near zero by 2050 – earlier in some 
regions (Bauer et al. 2018; IEA 2017; IPCC 2018). Other global trends,

including air quality, water shortages, the improved cost efficiencies 
of renewables, the technical availability of energy storage and the 
economic rebalancing of emerging countries, are also driving global 
coal consumption to a plateau followed by a reverse (Sator 2018; 
Spencer et al. 2018). The world should be prepared for a managed 
transition away from coal and should identify appropriate transition

1743
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717options for the future of coal, which can include both the penetration 
of renewable energy and improvements in energy efficiency 
(Shah et al. 2015).
Phasing out fossil fuels from energy systems is technically possible 
and is estimated to be relatively low in cost (Chapter 6). The cost of 
low-carbon alternatives, including onshore and offshore wind, solar 
photovoltaic (PV) and electric vehicles, has been reduced substantially 
in recent years and has become competitive with fossil fuels (Shen 
et al. 2020). However, studies show that replacing fossil fuels with 
renewables can have major synergies and trade-offs with a broader 
agenda of sustainable development (Swain and Karimu 2020), 
including land use and food security (McCollum et al. 2018), decent 
jobs and economic growth (Swain and Karimu 2020). Clarke et al. 
(AR5 WG III Table  6.7) provides detailed mapping of the sectoral

co-benefits and adverse side-impacts of and links to transformation 
pathways. In Section 17.3.3.7, this is supplemented with a mapping 
of the synergies and trade-offs between the deployment of renewable 
energy and the SDGs.
The general conclusion is that the potential co-benefits of renewable-
energy end-use measures outweigh the adverse impacts in most 
sectors and in relation to the SDGs, though this is not the case for the 
AFOLU (agriculture, forestry and other land use) sectors. Some locally 
negative economic impacts can result in increased energy costs and 
competition over land areas and water resources. Some sectors may 
also experience increasing unemployment as a consequence of the 
transition process. Although the deployment of renewable energy 
will generate a  new industry and associated jobs and benefits in some areas and economies, these impacts will often not directly 
replace or offset activities in areas that have been heavily dependent

on the fossil fuel industry.
The transition to low-emission pathways will require policy efforts 
that also address the emissions that are locked-in to existing 
infrastructure such as power plants, factories, cargo ships and other 
infrastructure already in use: for example, today coal-fired power 
plants account for 30% of all energy-related emissions (IEA 2019). 
Over the past twenty years, Asia has accounted for 90% of all coal-
fired capacity built worldwide, and these plants have potentially long 
operational lifetimes ahead of them. In developing economies in Asia, 
existing coal-fired plants are just twelve years old on average. There 
are three options for bringing down emissions from the existing stock 
of plants: to retrofit them with carbon capture and storage (CCS) or 
biomass co-firing equipment; to repurpose them to focus on providing 
system adequacy and flexibility while reducing operations; and to 
retire them early. In the IEA Sustainable Development Scenario, most

of the 2080 GW of existing coal-fired capacity would be affected by 
one of these three options.
Even though the transition away from fossil fuels is desirable and 
technically feasible, it is still largely constrained by existing fossil 
fuel-based infrastructure and stranded investments. The ‘committed’ 
emissions from existing fossil fuel infrastructure may consume all 
the remaining carbon budget in the 1.5°C scenario, or two thirds of 
the carbon budget in the 2°C scenario (Tong et al. 2019). (Kefford 
et al. 2018) assess the early retirement of fossil fuel power plants 
in the US, EU, China and India based on the IEA 2°C scenario and 
Box 17.1 | Case Study: Coal Transitions
The coal transition will pose challenges not only to the power sector, but even more importantly to coal mining. A less diversified 
local economy, low labour mobility and heavy dependence on coal revenues will make closing down coal production particularly

challenging from a political economy perspective. Policy is needed to support and invest in impacted areas to smooth the transition, 
absorb the impact and incentivise new opportunities. A supportive policy for the transition could include both short-term support 
and long-term investment. Short-term compensation could be helpful for local workers, communities, companies and governments to 
manage the consequences of coal closures. Earlier involvement with local stakeholders using a structured approach is crucial and will 
make the transition policy more targeted and better administered. The long-term policy should target support to the local economy 
and workers to move beyond coal, including a  strategic plan to transform the impacted area, investment in local infrastructure 
and education, and preference policies to incentivise emerging businesses. Most importantly, ex ante policy implementation is far

better than ex post compensation. Even without the climate imperative, historical evidence shows that coal closures can happen 
surprisingly fast.
Presently, coal-fired power plants play a key role in the German energy system, providing almost 46% of the electricity consumed 
in Germany. These coal power plants play a crucial role in balancing fluctuations in producing electricity form renewables (Parra 
et al. 2019). Political and economic considerations, at least regionally, are also of great importance in the coal sector due to the 
approximately 35,000 people employed within it (including coal mining and the power stations themselves). For a long time, coal-
fired power plants were able to protect their position in Germany, but against the background of decreasing public acceptance, 
economic problems resulting from the growing use of renewables and ambitious GHG reduction targets, the sector cannot resist

the political pressure against it any longer. The governing parties have agreed to establish a commission called ‘Growth, structural 
change and employment’ to develop a strategy for phasing out coal-fired power plants (E3G Annual Review 2018). This Commission 
consists of experts and stakeholders from industry, associations, unions, the scientific community, pressure groups and politicians. Its 
establishment shows that the phasing-out process deserves close attention and that management policies must be implemented to 
ensure a soft landing for the electricity sector.

1744
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17conclude that a massive early retirement of coal-fired power plants 
is needed, and that two to three standard 500 MW generators will 
need to come offline every week for fifteen years. This high rate is 
the result of a  very large deployment of coal-fired power plants 
from 2004 to 2012. The early phasing out of this infrastructure will 
result in a  significant share of stranded assets (Ansari and Holz 
2020) with an impact on workers, local communities, companies and 
governments (van der Ploeg and Rezai 2020). The challenge is thus to 
manage a transition which delivers the rapid phasing out of existing 
fossil fuel-based infrastructure while also developing a new energy 
system based on low-carbon alternatives within a very short window 
of opportunity.
Chapter  6 similarly concludes that the transition towards a  high 
penetration of renewable systems faces various challenges in the

technical, environmental and socio-economic fields. The integration 
of renewables into the grid requires not only sufficient flexibility 
in power grids and intensive coordination with other sources of 
generation, but also a  fundamental change in long-term planning 
and grid operation (see Chapter 6 for more detail on these issues).
Examples from various countries show that, compared with top-
down decision-making, bottom-up policymaking involving local 
stakeholders could enable regions to benefit and reduce their 
resistance to transitions. (Kainuma et al. 2012) conclude that social 
dialogue is a  critical condition for engaging local workers and 
communities in managing the transitions with the necessary support 
from transition assistance. They also point out that macro-level 
policies, training programmes, participatory processes and specific 
programmes to support employment creation for workers in fossil 
fuel-dependent industries are needed.

Examples of challenges in transitions away from using coal are 
given in Box 17.1.
The transition towards a  high-penetration renewable system also 
raises concerns over the availability of rare metals for batteries like 
lithium and cobalt. While metal reserves are unlikely to limit the 
growth rate or total amount of solar and wind energy, used battery 
technologies and the known reserves currently being exploited are 
not compatible with the transition scenario due to insufficient cobalt 
and lithium reserves (Månberger and Stenqvist 2018). Global lithium 
production rose by roughly 13% from 2016 to 2017, to 43,000 Mt 
in 2018 (Golberg 2021). Africa has rich reserves of lithium and is 
expected to produce 15% of the world’s supply soon (Rosenberg 
et al. 2019). Such reserves are found in Zimbabwe, Botswana, 
Mozambique, Namibia, South Africa (Steenkamp 2017) and the 
Democratic Republic of Congo (Roker 2018).
The demand for these resources as ingredients in rechargeable

batteries is growing rapidly, with global demand for cobalt set to 
quadruple to over 190,000 tons by 2026. The DRC is a mineral-rich 
country (Smith et al. 2019a) with rich reserves of fossil fuels (coal 
and oil) (Buzananakova 2015). The extraction of lithium and cobalt 
can be environmentally and socially damaging, though its use as 
a  principal component in most rechargeable batteries for electric 
vehicles and electronic smart grids affords it high sustainability value. Chapter 10 includes a more detailed assessment of the issues with 
mining these rare metals, as well as the associated social problems, 
including exploitative working conditions and child labour, the latter 
a major issue that needs to be taken into consideration in transitions. 
Recycling batteries is also highlighted as a  major supplementary 
policy if negative environmental side impacts are to be avoided 
(Rosendahl and Rubiano 2019). In the future, more attention should

be paid to reducing vulnerability through subsidising R&D in rare-
metals recycling, establishing systems to incentivise the collection 
of rare-metal waste and promoting technological progress using 
abundant metals as a  replacement for rare metals (Rosendahl 
and Rubiano 2019).
17.3.2.3 Stranded Assets, Inequality and Just Transitions
As the momentum towards achieving carbon neutrality grows, the risk 
of certain assets becoming stranded is on the increase. International 
policies and the push for low-carbon technologies in the context 
of climate change are reducing the demand for and value of fossil 
fuel products. Stranded assets become devalued before the end of 
their economic life or can no longer be monetised due to changes in 
policies and regulatory frameworks, technological change, security, 
or environmental disruption. In short, stranded assets are ‘assets 
that have suffered from unanticipated or premature write-down,

devaluations or conversions to liabilities’ (Caldecott et al. 2013).
Stranded assets are likely to ‘lose economic value ahead of their 
anticipated useful life’ (Bos and Gupta 2019). They are often 
described as creative when they become stranded because of 
innovation, competition or economic growth (Gupta et al. 2020). 
Divestment refers to ‘the action or process of selling off subsidiary 
business interests or investments’. This often occurs due to changing 
social norms and perceptions of climate change.
Indeed, pressure is mounting on fossil fuel industries to remove their 
capital from heavy carbon industries. As the former Governor of the 
Bank of England, Mark Carney, remarked, a wholesale reassessment 
of prospects, especially if it were to occur suddenly, could potentially 
destabilise markets, sparking a pro-cyclical crystallisation of losses 
and a persistent tightening of financial conditions. In other words,

an abrupt resolution to the tragedy of horizons itself poses a  risk 
to financial stability (OECD 2015). The divestment narrative is also 
based on the view that a shift away from intensive carbon resources 
will be significant, as the ‘less value will be destroyed, […] the 
more can be re-invested in low carbon infrastructure’ (OECD 2015). 
Social movements are critical to triggering rapid transformational 
change and moving away from dangerous levels of climate change 
(Mckibben 2012). Although divestment is hailed as a  necessary 
action to decouple fossil fuel from growth and force carbon-
intensive industries to go out of business, there is the sense that 
there is no shortage of investors who are willing to buy shares, so 
that such resources are not stranded, but simply relocated. Criticism 
has been levelled at the divestment movement for not having 
a significant impact on funding fossil fuels and not being sufficiently

in tune with other wide-ranging complexities that go beyond the 
moral dimensions (Bergman 2018). Despite being labelled a ‘moral 
entrepreneur’, the divestment movement has the potential to disrupt

1745
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717current practices in the fossil fuel industry, shape a  ‘disruptive 
innovation’ and contribute to a strategy for decarbonising economies 
globally (Bergman 2018). Divestment is contributing to the political 
situation that is ‘weakening the political and economic stronghold of 
the fossil fuel industry’ (Grady-Benson and Sarathy 2016).
The risks attached to the stranding of fossil fuel assets have increased 
with the recent and sustained plunge in oil prices because of the 
global health pandemic (COVID-19) and the concomitant economic 
downturn, forcing demand to plummet to unprecedentedly low levels. 
(Oil prices have recently increased.) Many economies in transition 
and countries dependent on fossil fuels are going through turbulent 
times where asset and transition management will be critical (UNEP/
SEI 2020). However, COVID-19 provides a foretaste of what a low-

carbon transition could look like, especially if assets become stranded 
in an effort to respond to the call for action in ‘building back better’ 
and putting clean energy jobs and the just transition at the heart 
of the post-COVID-19 recovery (IEA 2020; United Nations General 
Assembly 2021). COVID-19 provides a useful proxy for issuing two 
alerts. First, it is a  reminder of the urgency of addressing climate 
change, given that delaying the move away from stranded assets 
will further worsen climate change. Second, failure to recognise 
the threat from stranded assets will result in new assets becoming 
stranded (Rempel and Gupta 2021). Hence, the momentum towards 
a transformational push is resting on a new opportunity ushered in 
by COVID-19 to emphasise the urgency for a new departure towards 
rapid emissions reductions (Cronin et al. 2021).
The stranded assets narrative has focused overwhelmingly on 
consumption by companies: not much emphasis has been placed

on  the commercialisation- and investment-related aspects. In 
addition, other carbon-intensive activities can also run the risk of 
being stranded, such as cement, petrochemicals, steel and aviation 
(Baron and David 2015). This is why stranded assets are often 
referred to as having a cascading impact on several other sectors.
Transitions are broad-based and complex, involving governance 
structures, institutions and climate vulnerabilities, and there is a need 
to include historical responsibility, resource intensity and capacity 
differentials, thus relegating the debate across simplistic binary lines 
of developed versus developing countries (Carney 2016). Hence, 
transition processes will have to respond to several preconditions 
and structural inequalities related to climate finance, energy 
poverty, vulnerabilities and the broader macroeconomic implications 
associated with managing the debt burden, fiscal deficits and

uneven terms of development in developing countries. In addition 
to structural inequalities, the COVID-19 pandemic has severely 
disrupted energy and food systems, and reduced the speed at which 
developing countries can procure new low-carbon technologies and 
decouple economic growth from fossil fuels (Winkler 2020). For 
instance, global supply-chain transition costs might be lower when 
compared to in-country supply chains, as became evident when 
COVID-19 created further disruption to renewable-energy projects 
(Cronin et al. 2021). Moreover, developing countries can experience 
difficulties in phasing out old technologies, especially if the latter has 
a  cost disadvantage, has not benefitted from an established track 
record and its performance is uncertain (Bos and Gupta 2019). There is the risk of lock-in effects related to grandfathering when emitters 
comply with less stringent standards.
Despite their efforts in deploying renewable energies, many

developing countries are still contending with problems related to 
the immaturity of the current technologies and the challenges of 
battery storage. In short, the transition to low-carbon development 
must consider the challenges of renewable-energy penetration and 
existing energy-related vulnerabilities and inequalities. There are 
power asymmetries between first-comers and latecomers, especially 
in cases where mature technologies can be located in countries with 
less stringent laws and standards. Carbon leakage has implications 
for just transitions, as carbon-intensive industries can move their 
dirty industries to developing countries as a  way of outsourcing 
the production of carbon (Bos and Gupta 2019; UNU-INRA 2020). 
When the challenge of climate mitigation is transferred to developing 
countries in the form of carbon leakage, the risks of carbon lock-in for 
developing countries are heightened (Bos and Gupta 2019).

Overcoming the carbon lock-in is not simply a matter of the right 
policies or switching to low-carbon technologies. Indeed, it would 
mean a radical change in the existing power relations between fossil 
fuel industries and their governments and social structural behaviour 
(Seto et al. 2016). Some actions to fix the climate change problem 
can themselves create injustices, thereby challenging sustainable 
development (Cronin et al. 2021). Not paying sufficient attention to 
perceptions of injustice related to the rights to development, energy 
and resource sovereignty can further create resistance to climate 
action (Cronin et al. 2021).
The shrinking carbon budget has raised questions over whether to 
meet our commitment to 2°C if fossil fuel resources were to be mined 
or left stranded, as McGlade and Ekins argue: ‘… [a] large portion 
of the reserve base and an even more significant proportion of the 
resource base should not be produced if the temperature rise is to

remain below 2 degrees C’ (McGlade and Ekins 2015). This logic 
means that developing countries that rely on fossil fuel extraction 
will need to replace their hydrocarbon revenues with other income-
generating activities. Stranded assets remind most oil-producing 
governments that fossil fuel assets do not have a durable value and 
are vulnerable to politico-economic forces and fluctuations. The goal 
of staying within the 1.5°C temperature goal, in line with the Paris 
Agreement, is already part of the policy vision and planning of large 
fossil fuel-consuming economies. For early fossil-fuel producers, 
however, the reality that their resources may not yield the desired 
returns is often perceived as bad news, particularly in the context of 
the increasing depreciation of fossil fuel products.
Stranded assets raise fundamental questions related to issues of 
equity and just transitions:
• Who decides which resources should be stranded?

• Who shoulders the burden of the transition and losses 
incurred from moving away from heavy industries with 
associated compensation?
• How should the advantages of short-term fossil fuel exploitation 
be shared based on the principle of distributive justice?

1746
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17The transition to a  low-carbon development is wired in issues of 
justice and equity: how do you align carbon reductions to meet the 
needs of humanity? Distributive justice calls for a fairer sharing of 
the benefits and burdens of the transition process, while procedural 
justice is essentially about ensuring that the demands of vulnerable 
groups are not ignored in the pull to the transition. The impacts 
of climate change and the mitigation burdens are experienced 
differently by different social actors, with indigenous communities 
facing multiple threats and being subjected to unequal power 
dynamics (Sovacool 2021).
Nonetheless, the production of fossil fuels is central to many 
economies with numerous development implications related to 
rents associated with export revenues, energy security and poverty 
alleviation (Lazarus and van Asselt 2018). The central question is: who

decides which types of carbon should be burnable or non-burnable? 
Hence, social equality is at the heart of the transition process, but it 
falls short of a response on how to chart a new road map towards 
carbon neutrality, especially given that fossil fuel producers and 
investors tend to belong to large, powerful companies and wield 
a great deal of influence and power, especially when their entrenched 
interests are at stake (Lazarus and van Asselt 2018). The question of 
whether developing countries should be compensated for foregoing 
their resources in light of their current development needs has not 
yielded many results and had only limited success in mobilising 
international finance, as demonstrated by the case of Yasuni-ITT in 
Ecuador (Sovacool and Scarpaci 2016). According to (Sovacool et al. 
2021), affected communities and their views may be discounted and 
excluded from planning, which can neglect important matters such

as rights, recognition and representation (Sovacool 2021).
Fossil fuel-dependent countries are doubly exposed to the vulnerability 
related to climate change impacts and are being targeted in the global 
effort to address the problem (Peszko et al. 2020). Countries that are 
heavily reliant on oil, coal and gas are also those most at risk from 
a low-carbon transition that may curtail the activities of their fossil 
fuel industries and render the value chains and economies associated 
with the exploitation of fossil fuels unviable (Peszko et al. 2020).
Developing countries in Latin America and Africa that are reliant 
on revenue streams from fossil fuels may not see these returns 
converted into much-needed infrastructure and other social and 
economic amenities that can reduce poverty. However, given the 
falling prices of renewables, developing countries do not have to face 
the burden of retrofitting their infrastructure to align with new low-

carbon industries, since they can leapfrog technologies and shape 
a sustainable trajectory that is more resilient and fit for the future.
However, the transition towards a carbon-neutral world is complex 
and non-linear, and it will likely result in some disruptions, with 
manifest equality implications, given the scale of the transformation 
envisaged. There are parallel movements that can be observed. On 
the one hand, divestment initiatives are underway to move away 
from carbon-intensive investments. On the other hand, hydrocarbon-
rich countries in some parts of the developing world are identifying 
new opportunities to reduce the fiscal loss associated with the loss 
of fossil fuel revenues. Indeed, with global investment in energy expected to shrink by 20% in 2021, this has created fiscal challenges 
for countries that are heavily reliant on fossil fuel products as their 
main source of revenue.
Other disruptions are linked to redundant contracts and postponed or

cancelled explorations, as many oil companies are diversifying their 
production in the wake of the pandemic and are cutting back on 
planned hydrocarbon investments (Denton et al. 2021). These failed 
concessions and disruptions have implications for the just transition, 
especially in developing countries without the financial ability to 
pull out of fossil fuels and to diversify with the same urgency as the 
industrialised nations (Peszko et al. 2020). For instance, in South Africa, 
which is seeking to divest away from coal and decarbonise its energy 
sector, if the transition is not properly managed, this could lead to 
a loss in revenue of R1.8 trillion (USD125 billion), thus compromising 
the government’s ability to support social spending (Huxham et al. 
2019). Emerging oil producers like Uganda are having to postpone 
the start of production. Eni and Total, two of the largest international 
oil and gas majors in Africa, have already signalled they are making

25% cuts to their investment in exploration and production projects in 
2020, representing a EUR4 billion reduction in foreign direct investment 
for Total and a USD2 billion reduction for Eni (Le Bec 2020).
A poorly managed transition will reproduce inequalities, thus 
contradicting the very essence of a  just, sustainable, inclusive 
transition. Revenues from oil and gas have been ploughed into social 
safety nets and are supporting free senior high-school education 
in countries such as Ghana, thus enabling the realisation of SDG 
4 (quality education) (UNU-INRA 2020). The move from fossil fuels 
towards a low-carbon economy has economic implications for lower-
income countries that are dependent on hydrocarbon resources, are 
endowed with significant untapped oil and gas reserves, and may not 
have the transitional tools to move towards low-carbon technologies 
or economies (Peszko et al. 2020).
The energy transition landscape is changing rapidly, and we are

witnessing multiple transitions. This creates room to manage the 
transition in ways that will prioritise the need for workers in vulnerable 
sectors (land, energy) to secure their jobs and to maintain a secure 
and healthy lifestyle, especially as the risks multiply for those who 
are exposed to heavy industrial jobs and all the associated outcomes. 
The shift to carbon neutrality is being driven by convergent factors 
related to energy security and the benefits of climate mitigation, 
including the health impacts of air pollution and consumer demand 
(Svobodova et al. 2020).
Climate change is high on the global agenda, as is energy’s role in 
decarbonising the economy, giving rise to a number of equality issues. 
(Oswald et al. 2020) have shown that economic inequality translates 
into inequality in energy consumption, as well as emissions. This is 
largely because people with different levels of purchasing power 
make use of different goods and services, which are sustained by

different energy quantities and carriers (Oswald et al. 2020; Poblete-
Cazenave et al. 2021).
A study by (Bai et al. 2020) shows that an increase in income inequality 
in China hinders the carbon abatement effect of innovations in

1747
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717renewable-energy technologies, possibly even leading to an increase 
in carbon emissions, while a  decrease in inequality of incomes is 
conducive to giving play to the role of this carbon abatement effect, 
thereby indicating that there is an important correlation between 
the goals of ‘sustainable social development’ and ‘sustainable 
ecological development’.
India is home to one sixth of world’s population but accounts for only 
6.8% of global energy use and consumes only 5.25% of electricity 
produced globally. During the period 1990–1991 to 2014–2015, 
overall energy intensity in India declined from 0.007 Mtoe per billion 
INR of GDP to 0.004 Mtoe per billion INR of GDP , an annual average 
decline of 2%. The industrial sector is making the highest contribution 
CO2 mitigation by reducing its energy intensity (Roy et al. 2021).
Household carbon emissions are mainly affected by incomes and

other key demographic factors. Understanding the contribution 
of these factors can inform climate responsibilities and potential 
demand-side climate-mitigation strategies. A  study by (Feng et al. 
2021) on inequalities in household carbon the in USA shows that 
the per-capita carbon footprint (CF) of the highest income group 
(>USD200,000 yr–1) with 32.3 tonnes is about 2.6 times the per-
capita CF of the lowest income group (<USD15,000 yr–1) with 12.3 
tonnes. Most contributors of high carbon footprints across income 
groups in the US are heating, cooling and private transport, which 
reflects US settlement structures and lifestyles, heavily reliant as they 
are on cars and living in large houses.
Studies by (Jaccard et al. 2021) on energy in Europe shown a top-to-
bottom decile ratio (90:10) of 7.2 for expenditure, 3.1 for net energy 
and 2.6 for carbon. Given such inequalities, these two targets can 
only be met through the use of carbon capture and storage (CCS),

large efficiency improvements and an extremely low minimum final 
energy use of 28 GJ per adult equivalent. Assuming a more realistic 
minimum energy use of about 55 GJ per adult equivalent and no 
CCS deployment, the 1.5°C target can only be achieved at near full 
equality. The authors conclude that achieving both stated goals is 
an immense and widely underestimated challenge, the successful 
management of which requires far greater room for manoeuvre in 
monetary and fiscal terms than is reflected in the current European 
political discourse.
The ‘Just Transition’ concept has evolved over the years (Sweeney 
and Treat 2018) and is still undergoing further evolution. It 
emphasises the key principles of respect and dignity for vulnerable 
rights, fairness in energy access and use, and social dialogue and 
democratic consultation with relevant stakeholders, whilst coping

with the effects of asset-stranding or the transition to green and 
clean economies. The concept has come under increased scrutiny, 
with its protagonists emphasising the need to focus on the equality 
of the transition, not simply on its speed (Forsyth 2014). The 
emphasis on justice is also gaining in momentum, with a growing 
recognition that the sustainability transition is about justice in the 
transition and not simply about economics (Newell and Mulvaney 
2013; Swilling, M. Annecke 2010; Williams and Doyon 2020). 
Scholars are increasingly of the view that a  transition involving low-carbon development should not replace old forms of injustice 
with new ones (Setyowati 2021).
The economic implications of the transition will be felt by developing 
countries with high degrees of dependence on hydrocarbon products 
as a revenue stream, as they are exposed to reduced fiscal incomes, 
given the low demand for oil and low oil prices, and the associated

economic fallout of the pandemic. This link with stranded assets is 
important, but it may be overlooked, as countries whose assets are 
becoming stranded may not have the relevant resources, knowledge, 
autonomy or agency to design a fresh orientation or decide on the 
transition. In addition, some developing countries are dependent not 
only on fossil fuel revenues, but also on foreign exchange earnings 
from exports. This dependence comes into sharp focus when one 
considers that 30% of the Malaysian government’s revenues are 
linked to petroleum products, and that Mozambique, by exploiting 
its newly discovered natural-gas reserves, can earn seven times 
the country’s current GDP over a  period of 25 years (Cronin et al. 
2021). Thus, any attempt to accelerate the transition to low-carbon 
development must take into account foreign exchange, domestic 
revenue and employment generation, which are precisely what ensure

the attractiveness of fossil fuel industries (Addison and Roe 2018).
Energy use and its deployment are sovereign matters. State 
responsibilities over the control and use of natural resources 
concern both current and future generations (Carney 2016). Climate 
change impacts will disable the food, water and energy systems 
of the most vulnerable. Therefore, the resources required to enable 
a just transition are predicated on good leadership and governance 
institutions that will support quality and justice-based transitions. 
Beyond energy systems, changes to land systems can benefit from 
sustainable land management in ways that will reduce the pressure 
on land for food and at the same time support carbon storage. With 
land coming under increased pressure, land and forest management 
are critical for carbon sequestration, as well as other ecosystem 
benefits. Extractive processes have impacts on land, and often there

are few if any redistributive benefits for communities in regions 
where extraction takes place. In addition, extraction of strategic 
minerals such as cobalt, copper and lithium have been linked to 
violence, human rights abuses and conflict (Cronin et al. 2021).
However, in the race to achieve carbon neutrality by 2050, some of the 
other priorities of the transition, like climate change adaptation and 
its inherent vulnerabilities, might become muted, given the urgency 
to mitigate at all costs. Consequently, the transition imperative 
reduces the scope for local priority-setting and ignores the additional 
risks faced by countries with the least capacity to adapt. Equally, the 
‘just transition’ is often seen through the prism of job losses and 
the attendant retooling and reskilling imperatives necessary to re-
dynamise local businesses, especially those that may fail as a result 
of mine closures. It is equally important to consider current disparities

in knowledge and capacity which could maintain the existing 
inequalities in the global regional distribution of costs and benefits. 
One striking example is the manufacturing of PV in India when 
compared to manufacturing PV in China. In China, manufacturing 
costs are lower than in India, as are import tariffs (Behuria 2020). 
Similarly, a solar industry might have greater development prospects

1748
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17in one region than another given existing regional disparities in 
human capital, infrastructure, finance and technological development 
(Cronin et al. 2021).
Low-carbon transitions and equality implications will depend on 
local contexts, regional priorities, the points of departure of different 
countries in the transition and the speed at which they will want 
to travel. Hence, timing and scope are important elements that are 
associated more with a quality transition than a race to the bottom. 
To date, the debate has had some obvious blind spots, not least 
considerations of power, politics and political economy (Denton et al. 
2021). Certainly, the transition will create winners and losers, as 
well as stakeholders that can frame their economic interests so as to 
determine the orientation, pace, timing and scope of the transition.

The determination of a  just transition is complex and not simply 
dependent on the allocation of perceived risks or solutions, but rather 
on how risks and solutions are defined (Forsyth 2014). Acting urgently 
to achieve environmental solutions or meet transition imperatives has 
certain risks given the need to go beyond commonplace definitions 
of the just transition by emphasising the distributive or procedural 
aspects. The framing of policies to align with fast and low-cost 
mitigation without paying sufficient attention to social and economic 
resilience creates its own potential risks and can enhance social 
vulnerability rather than address it. The need to distribute climate 
change solutions must not delegitimise appropriate economic growth 
strategies, nor indeed create the additional risks of policy imposition. 
Perceptions of justice with regard to environmental problems and 
solutions matter equally. Hence, the types of transition pathway that

are chosen may have equality implications. Mitigation at all costs, if 
done ‘cheaply and crudely’, can create additional problems for social 
justice and inclusive development (Forsyth 2014).
The assumption that the benefits of mitigation are enough to 
offset trade-offs with other policy objectives can be questioned. 
If one accepts the argument that not all adaptation addresses 
vulnerability concerns (Kjellén 2006), and that some adaptation 
strategies can heighten vulnerabilities if there are flaws in their 
design and implementation, then the same logic applies, namely 
that not all mitigation is necessarily beneficial. Hence the emphasis 
on the transition resulting from mitigation should be placed not 
only on speed or cost-effectiveness, but also on the legitimacy of 
the actions, and whether the transition is well designed or not. In 
short, justice is not always a shorthand for acting ethically, but rather

a point of reasoning on what is considered legitimate. Planning for 
the transition often discounts human rights and social inclusivity that 
can occur as the result of a rapid transition. The emphasis should be 
placed on the management of the transition rather than the speed – 
for instance, if in the rush to build new hydropower energy sources 
implies that populations are displaced, then this constitutes a human 
rights violation (Castro et al. 2016; Piggot et al. 2019).
Ambitious climate goals can increase the urgency of mitigation 
and accelerate the speed at which carbon neutrality is achieved. 
However, if the transition is done with speed, then this will leave 
diversification efforts stymied, particularly in developing countries 
that are highly dependent on fossil fuel revenue streams (UNEP/SEI 2020). Transition decisions and policies may also have far-reaching 
gendered implications, as the closure of mines is often linked to

several ancillary business impacts where men are laid off and women 
may have to take on multiple jobs to compensate for the reduction in 
the household‘s income (Piggot et al. 2019; UNU-INRA 2020).
A just transition holds out the prospects for alternative high-quality 
jobs, public-health improvements and an opportunity to focus on 
well-being and prosperity, with spillover benefits to urban areas and 
economic systems. Nonetheless, countries that transition from fossil 
fuels experience different challenges, different levels of dependence 
and have different capacities to transition. There will be countries 
with lower capacity and higher dependence, and vice versa (UNEP/
SEI 2020).
Deciding on matters of justice is essential to the transition, and there 
are several inherent questions to consider when thinking through 
the allocation of costs and benefits, as is the case with distributive 
justice. How matters are defined and who defines matters such as

the timing of phasing out, prioritising which energy sources need to 
be phased out and who might be affected are all political economy 
questions (Piggot et al. 2019).
Similarly, when considering issues of procedural justice, there are 
matters related to interests, participation and power dynamics that 
are essential to the process, but that might also subvert the process, 
depending on whose rights, whose participation and whose power 
are being put in jeopardy (Forsyth 2014; Piggot et al. 2019). Hence, 
both distribution and procedure matter, as do inter-generational 
and intra-generational equity in planning transitions. Six critical 
variables can shape or inhibit the transition process. These 
are dependence, timing, capacity, agency, scope and inclusion 
(Denton et al. 2021).
Dependence, or the extent to which a  country may depend on 
revenue streams from fossil fuels, will determine its ability to 
manage the transition from fossil fuels. Countries who rely on the

proceeds from hydrocarbon resources as economic rents to support 
fiscal income and spending on public service-related needs such as 
education, health and infrastructure, export earnings and foreign 
exchange reserves will have greater difficulties in foregoing their 
fossil fuel resources.
Timing: the transition pathway has to be aligned with a timetable 
which is anchored in national development priorities. For example, 
South Africa’s Integrated Resource Planning indicates that the 
transition away from coal, if not aligned with national development 
priorities, will reproduce new forms of inequality. In addition, if the 
transition is imposed and its timing is not organic, then this might 
also produce social inequalities.
Capacity: transitions need to reflect spaces and planning. If 
knowledge about the transition pathway is not adequately mastered 
or in place, this can disable the process or steer it in the wrong

direction. Capacity also relates to several attributes, including 
technical, governance, institutional, technologies, and economic 
resources to manage the transition. Poorer countries will have

1749
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717difficulties in managing all these resources, as well as absorbing the 
costs associated with the transition (UNEP/SEI 2020).
Agency: transitions are inherently about the sovereign right to 
determine one’s orientation towards low-carbon development. 
However, given the urgency to stick to the Paris Agreement and the 
new conditionalities related to post-COVID stimulus packages, 
the absence of agency to deal with the transition might jeopardise its 
flow, orientation and pace (Newell and Mulvaney 2013).
Scope: the extent to which the transition is rolled out and its potential 
impacts. If transition policies are ambitious in making commensurate 
diversification investments, this may enable job creation, but it may 
also affect employees who are insufficiently prepared to undertake 
new jobs and skills.
Inclusion: who is considered in the transition process and how

their interests and risks are assessed are important aspects of 
transition pathways. Stakeholders with strong vested interests may 
resist the transition, especially as it moves towards diversification 
activities and policies.
17.3.3 Cross-sectoral Transitions
Transitions will involve multiple sectoral- and cross-sectoral policies. 
Section 17.3.3 presents a range of studies and conclusions on the 
relationship between climate change mitigation goals and meeting 
the SDGs in order to identify major synergies and trade-offs. 
The interactions are manifold and complex  (Nilsson et al. 2016; 
Pradhan et al. 2017) (Section 4.3.1.2). Here we draw on conclusions 
from sectoral chapters and add additional studies as a  basis for 
drawing more general conclusions about agriculture, food and land 
use, the water-energy-food nexus, industry, cities, infrastructure 
and transportation, cross-sectoral digitalisation, and mitigation and 
adaptation relations.

17.3.3.1 Agriculture, Forestry and Other Land Uses (AFOLU)
Sustainable development and mitigation policies are closely linked in 
the agriculture, food and land-use sectors. We assess synergies and 
trade-offs between meeting the SDGs and reducing GHG emissions 
within the sectors based on modelling studies and case studies 
illustrating how trade-offs between SDG 2 (zero hunger, biomass for 
energy) and SDG 15 (life on land) can be addressed by cross-sectoral 
mitigation options.
Chapter  7 emphasises the high expectations on land to deliver 
mitigation, yet the pressures on land have grown with population, 
dietary changes, the impacts of climate change and the conversion 
of uncultivated land to agriculture and other land uses. Agriculture, 
forestry and other land uses (AFOLU) are expected to play a vital role 
in the portfolio of mitigation options across all sectors. The AFOLU 
sector is also the only one in which it is currently feasible to achieve

carbon dioxide removal (CDR) from the atmosphere, including 
afforestarion/reforestation (A/R), improved forest management and 
soil carbon sequestration (SCR) (Chapters 7 and 12). The AFOLU sector has a significant mitigation potential, with many scenarios showing 
a shift to net-negative CO 2 emissions during the 21st century. Total 
cumulative AFOLU CO 2 sequestration varies widely across scenarios, 
with as much as 415 GtCO 2 being  sequestered between 2010 and 
2100 in the most stringent mitigation scenarios. The largest share 
of net-GHG emissions reductions from AFOLU in both the 1.5°C 
and 2°C scenarios is from forestry-related measures, such as 
afforestation, reforestation and reduced deforestation. Afforestation, 
reforestation and forest management result in substantial CDR in 
many scenarios. CO 2 and CH 4 show larger and more rapid declines 
than N 2O, an indication of the difficulties of reducing N 2O emissions 
in agriculture (Chapter 3).

The Global Assessment on Biodiversity and Ecosystem Services 
Report (IPBES 2019, Chapter 5) assessed the relationship between 
meeting the goals of the Paris Agreement and SDGs 2 (zero hunger), 
7 (affordable and clean energy) and 15 (life on land). It concluded 
that a  large expansion of the amount of land used for bioenergy 
production would not be compatible with these SDGs. However, 
combining bioenergy options with other mitigation options, like 
more efficient land management and the restoration of nature, could 
contribute to welfare improvements and to accessing food and water. 
Demand-side climate-mitigation measures, like energy-efficiency 
improvements, reduced meat consumption and reduced food waste, 
were considered to be the most economically attractive and efficient 
options in order to support low GHG emissions, food security and 
biodiversity objectives. Implementing such options, however, can 
involve challenges in terms of lifestyle changes (IPBES 2019).

The potential joint contribution of food and land-use systems to 
sustainable development and climate change has also been addressed 
in policy programmes by the UN, local governments and the private 
sector. These programmes address options for pursuing sustainable 
development and climate change jointly, such as agroforestry, 
agricultural intensification, better agriculture practices and avoided 
deforestation. (Griggs and Stafford-Smith 2013) assess production- 
and consumption-based methods of achieving joint sustainability and 
climate-change mitigation in food systems, concluding that efficiency 
improvements in agricultural production systems can provide large 
benefits. Given the expectations of high levels of population growth 
and the strong increase in the demand for meat and dairy products, 
there is also a need for the careful management of dietary changes, 
as well for those areas which could be used most effectively for 
livestock and plant production.

Loss of biodiversity has been highlighted in several studies as 
a  major trade-off of the low stabilisation scenarios (Prudhomme 
et al. 2020). A wide range of mitigation and adaptation responses – 
for example, preserving natural ecosystems such as peatland, 
coastal lands and forests, reducing the competition for land, 
fire management, soil management and most risk-management 
options  – have the potential to make positive contributions to 
sustainable development, ecosystems services and other social 
goals (McElwee et al. 2020). (Smith et al. 2019a) also stressed 
that agricultural practices (e.g.,  improving yields, agroforestry), 
forest conservation  (e.g.,  afforestation, reforestation), soil carbon 
sequestration (e.g.,  biochar addition to soils) and the removal of

1750
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17carbon dioxide (e.g.,  BECCS) could contribute to climate change 
mitigation (Smith et al. 2019a). However, there are also options that 
could improve biodiversity if they were implemented jointly with 
climate change mitigation in AFOLOU. In their study, (Leclère et al. 
2020) show that increasing conservation management, restoring 
degraded land and generalised landscape-level conservation 
planning could be positive for biodiversity. In general, the ambitious 
conservation efforts and transformations of food systems are central 
to an effective post-2020 biodiversity strategy.
The IPCC Special Report on Climate Change and Land (IPCC 2019) 
emphasises the need for governance in order to avoid conflict 
between sustainable development and land-use management. It 
states: ‘Measuring progress towards goals is important in decision-
making and adaptive governance to create common understanding

and advance policy effectiveness’. The report concludes that 
measurable indicators are very useful in linking land-use policies, the 
NDCs and the SDGs.
One example of an area where special governance efforts have been 
called for is the protection of forestry, ecosystem services and local 
livelihoods in a context of the large-scale deployment of high-value 
crops like palm oil, short-term, high income-generating activities and 
sustainable development. Serious challenges are already being seen 
within these areas according to (IPBES 2019).
Palm oil is one example of a product with potentially major trade-
offs between meeting the SDGs and climate change mitigation in 
the agriculture, forest and other land uses (AFOLU) sector. Currently 
the area under oil palms is showing a tremendous increase, mostly 
in forest conversions to oil-palm plantations (Austin et al. 2019; 
Gaveau et al. 2016; Schoneveld et al. 2019). The conversion of peat

swamp forest and mineral forest to oil palms will yield different 
amounts of CO 2. A  study by (Novita et al. 2020) shows that the 
carbon stock of primary peat-swamp forest was 1770 MgC ha–1 
compared to a carbon stock of oil palm of 759 MgC ha–1. The study 
conducted by Guillaume et al. shows that the carbon stock in mineral 
soils was 284 MgC ha–1 compared to that in rainforest, which was 
110.76  Mg C ha–1 (Guillaume et al. 2018).
Restoring peatlands is one of the most promising strategies for 
achieving nature-based CDR (Girardin et al. 2021; Seddon et al. 2021). 
A study by (Novita et al. 2021) shows that significantly different CO 2 
emissions for different land-use categories are influenced more 
by the water-table depth and latitude position for those locations 
relative to other observed parameters, such as bulk density, air 
temperature and rainfall.
Given that the frequent peatland fires in Indonesia were caused

by land clearances in the replanting season, multi-stakeholder 
collaboration between oil-palm plantations, local communities 
and local governments over practices such as zero burning when 
clearing land might be one of the most effective ways to reduce the 
deforestation impact of oil palm (Jupesta et al. 2020). Behavioural 
changes as a  mitigation option have been suggested as a  major 
factor in aligning sustainable development, climate change and land 
management. In the absence of the policy intervention, the expansion of oil-palm plantations has provided limited benefits to indigenous 
and Afro-descended communities. Even when oil-palm expansion 
improves rural livelihoods, the benefits are unevenly distributed 
across the rural population (Andrianto et al. 2019; Castellanos-
Navarrete et al. 2021). In any case, while oil-palm production can 
improve smallholders’ livelihoods in certain circumstances, this sector

offers limited opportunities for agricultural labourers, especially 
women (Castellanos-Navarrete et al. 2019).
Economy-wide mitigation costs can be effectively limited by 
lifestyle, technology and policy choices, as well as benefitting from 
synergies with the SDGs. Synergies come from the consumption side 
by managing demand. For example, reducing food waste leads to 
resources being saved because water, land use, energy consumption 
and greenhouse gas emissions are all reduced (Chapter 3).
Chapter  12 emphasised that diets high in plant protein and low 
in meat, in particular red meat, are associated with lower GHG 
emissions. Emerging food-chain technologies such as microbial, 
plant, or insect-based protein promise substantial reductions in direct 
GHG emissions from food production. The full mitigation potential of 
such technologies can only be realised in low-GHG energy systems.
(Springmann et al. 2018) conclude that reductions in food waste could

be a very important option for reducing agricultural GHG emissions, the 
demand for agricultural land and water, and nitrogen and phosphorous 
applications. In addition to the possibility to reduce food waste, their 
study analysed several other options for reducing the environmental 
effects of the food system, including dietary changes in the direction 
of healthier, more plant-based diets and improvements in technologies 
and management. It was concluded that, relative to a baseline scenario 
for 2050, dietary changes in the direction of healthier diets could reduce 
GHG emissions by 29% and 5–9% respectively in a dietary-guideline 
scenario, and by 56% and 6–22% respectively in a more plant-based 
diet scenario. Demand-side, service-oriented solutions vary between 
and within countries and regions, according to living conditions and 
context. Avoiding food waste reduces GHG emissions substantially. 
Dietary shifts to plant-based nutrition lead to healthier lives and reduce

GHG emissions (Section 5.3).
A similar study also found a positive impact form zero food waste. 
The ‘no food waste’ scenario could decrease global average food 
calorie availability by 120 kcal person−1 d–1 and protein availability 
by 4.6 g protein person−1 d−1 relative to their baseline levels, thus 
reducing required crop and livestock production by 490 and 190 Mt 
respectively. This lower level of production reduces agricultural 
land use by 57 Mha and thus mitigates the associated side effects 
on the environment. The lower levels of production also reduce 
the requirements for fertilisers and water by 10 Mt and 110 km3 
respectively, and GHG emissions are reduced by 410 MtCO 2-eq yr–1 
relative to the 2030 baseline. Reducing food waste can contribute 
to lessening the demand for food, feed and other resources such 
as water and nitrogen, reducing the pressure on land and the 
environment while ending hunger (Hasegawa et al. 2019).

In 2007, Britain launched a nationwide initiative to reduce household 
food waste, which achieved a  21% reduction within five years

1751
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717(FAO 2019). The basis of this initiative was the ‘Love Food, Hate 
Waste’ radio, TV, print and online media campaign run by a  non-
profit organisation, the Waste and Resources Action Programme 
(WRAP). The campaign raised awareness among consumers about 
how much food they waste, how it affects their household budgets 
and what they can do about it. This initiative collaborated with 
food manufacturers and retailers to stimulate innovation, such as 
resealable packaging, shared meal-planning and food-storage tips. 
The total implementation costs during the five-year period were 
estimated at GBP26 million, from which it was households that 
derived the most benefit, estimated to be worth GBP6.5 billion. Local 
authorities also realised a substantial GBP86 million worth of savings 
in food-waste disposal costs. As for the private sector, the benefits

took the form of increased product shelf lives and reduced product 
loss. While households started to consume more efficiently and 
companies may have experienced a decline in food sales, the latter 
also stated that the non-financial benefits, such as strengthened 
consumer relationships, had offset the costs.
The Asia Pacific Economic Cooperation (APEC) group of countries 
has also created several types of public-private partnership to 
tackle food waste and reduce losses. Most of these partnerships are 
focused on food-waste recycling in both developed and developing 
countries (Rogelj et al. 2018). APEC members stated that knowledge-
sharing and improved policy and project management were the most 
important advantages of public-private partnerships.
The inextricably intertwined factors in decision-making are 
influenced by the characteristics of the person, in interaction with 
the characteristics of more sustainable practices and products,

which interact with a particular context that includes the immediate 
environment (e.g.,  household, farm), the indirect environment 
(e.g.,  community) and macro-environmental factors (e.g.,  the 
political, financial and economic contexts) (Hoek et al. 2021). Hence, 
to influence people to make decisions in favour of sustainable 
food production or consumption, a  wider perspective is needed 
on decision-making processes and behavioural change, in which 
individuals are not targeted in isolation, but in interaction with this 
wider systemic environment.
In conclusion, the AFOLU sector offers many low-cost mitigation 
options, which, however, can also create trade-offs between land use 
for food, energy, forest and biodiversity. Some options can help to 
mitigate such trade-offs, like agricultural practices (e.g., improved yields, 
agroforestry), forest conservation (e.g., afforestation, reforestation), soil 
carbon sequestration (e.g., biochar addition to soils) and the removal

of carbon dioxide (e.g.,  BECCS), which could contribute to climate 
change mitigation. Lifestyle changes, including dietary changes and 
reduced food waste, are tightly embedded in modes of behaviour that 
are influenced by the immediate environment (e.g., household, farm), 
the indirect environment (e.g., community) and macro-environmental 
factors (e.g., political, financial and economic contexts). Achieving zero 
food waste could reduce the demands for land (SDG 15), water use 
(SDG 6) and chemical fertilisers (SDG 9), leading to GHG emissions 
reductions (SDG 13) by encouraging sustainable consumption and 
production practices (SDG 12).17.3.3.2 Water-Energy-Food Nexus
This section addresses the links between water, energy and food in the 
context of sustainable development and the associated synergies and 
trade-offs, with links to related chapters. The focus outline includes 
scoping and the relationship with the SDGs, general climate change

impacts on global water resources, energy-system impacts and the 
relationship to renewables, enabling strategies, trade-offs and cross-
sectoral implications (see also Chapter 12), nexus-management tools 
and strategies, and a box with examples from India and South Africa.
The continually increasing pressures on natural resources, such 
as land and water, due to the rising demands from increases in 
population and living standards, which also require more energy, 
emphasises the need to integrate sustainable planning and 
exploitation (Bleischwitz et al. 2018).
The water-energy-food nexus (WEFN) is at the epicentre of these 
challenges, which are of global relevance and are the focus of policies 
and planning at all levels and sectors of global society. The nexus 
between water, energy and food (Zhang et al. 2018) is tight and 
complex, and needs careful attention and deciphering across spatio-
temporal scales, sectors and interests to balance proper management

and trade-offs and to pursue sustainable development (Biggs et al. 
2015; Dai et al. 2018; Hamiche et al. 2016). The WEFN touches upon 
the majority of the UN’s SDGs, such as SDG 2, SDG 6, SDG 7 and SDGs 
11–15 (Bleischwitz et al. 2018), and deals with basic commodities, 
thus guaranteeing the basic livelihoods of the global population.
The task of gaining an improved understanding of WEFN processes 
across disciplines such as the natural sciences, economics, the social 
sciences and politics has been further exacerbated by climate change, 
population growth and resource depletion. In light of the system of 
interlinkages involved, the WEFN concept essentially also covers land 
(Ringler et al. 2013) and climate (Brouwer et al. 2018; Sušnik et al. 
2018), and can be further assessed in light of the relevant economic, 
ecological, social and SDG aspects (Fan et al. 2019a). The nexus 
approach was introduced in the early 2010s, when it was argued

that advantages could be gained by adopting a  nexus approach 
with regard to cross-sectoral and human–nature dependencies and 
by taking externalities into account (Hoffmann 2011). Hence, within 
the nexus, obvious trade-offs exist with competing interests, such as 
water availability versus food production.
Climate change is projected to impact on the distribution, magnitude 
and variability of global water resources. A  yearly increase in 
precipitation of 7% globally is expected by 2100 in a high-emissions 
scenario (RCP8.5), although with significant inter-model, inter-
regional and inter-temporal differences (Giorgi et al. 2019). Similarly, 
extreme events related to the water balance, such as droughts and 
extreme precipitation, are projected to shift in the future (RCP4.5) 
towards 2100: for example, the number of consecutive dry days is 
projected to increase in the Mediterranean region, southern Africa, 
Australia and the Amazon (Chen et al. 2014). In impact terms, an

increase of 20–30% in global water use is expected by 2050 due to 
the industrial and domestic demand for water. Already 4 billion people

1752
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17experience severe water scarcity for at least one month per year 
(WWAP-UNESCO 2019).
Globally, climate change has been shown to cause increases of 4%, 8% 
and 10% in the share of population being exposed to water scarcities 
under the 1.5°C, 2°C and 3°C scenarios for global warming respectively 
(RCP8.5) (Koutroulis et al. 2019). At the same time, climate change is 
projected to cause a general increase in extreme events and climate 
variability, placing a substantial burden on society and the economy 
(Hall et al. 2014). Other than the human influence on the global hydro-
climate, human activities have been shown to surpass even the impact 
of climate change in low to moderate emission scenarios of the water 
balance (Haddeland et al. 2014). Similar conclusions have been found 
by (Destouni et al. 2013; Koutroulis et al. 2019).
An obvious consequence of the impact of climate change on future

hydro-climatic patterns is the fact that the energy system is projected 
to experience vast impacts through climate change (Fricko et al. 
2016; Van Vliet et al. 2016a; van Vliet et al. 2016b) (Chapter 6). In 
the short run, where fossil fuel sources make up a significant share of 
the global energy grid, climate impacts related to water availability 
and water temperatures will affect thermoelectric power generation, 
which relies mainly on water cooling (Larsen and Drews 2019; 
Pan et al. 2018); water is also used for pollution and dust control, 
cleaning, and so on (Larsen et al. 2019). Currently, 98% of electricity 
generation relies on thermoelectric power (81%) and hydropower 
(17%) (van Vliet et al. 2016a).
Of these thermoelectric sources, the vast majority employ substantial 
amounts of water for cooling purposes, although there is a  trend 
currently towards implementing more hybrid or drier forms of cooling 
(Larsen et al. 2019).

The renewable-energy conversion technologies that are currently 
dominant globally and are projected to remain so are less vulnerable 
to water deficiencies than fossil-based technologies, since no cooling is 
used. These renewable-energy conversion sources include, for example, 
wind, solar PV and wave energy. The implementation of such sources 
will, in the longer run, have the potential to reduce water usage by the 
energy sector substantially (Lohrmann et al. 2019). Also, an increasing 
share of renewables within desalination, as well as improved irrigation 
efficiencies, have been shown to potentially improve the inter-sectorial 
WEFN water balance (Lohrmann et al. 2019; Caldera and Breyer 2020). 
Some less dominant renewable-energy technologies do use water for 
cooling, such as geothermal energy and concentrating solar power 
(CSP), if wet cooling is employed. Despite the general detachment from 
water resources, wind and solar PV, for example, are highly dependent

on climate change patterns, including variability depending on future 
energy-storage capacities and on-/off-grid solutions (Schlott et al. 
2018). Furthermore, regardless of whether or not they are based on 
renewables, climate change will affect energy usage across sectors, 
such as heating and cooling in the building stock. The energy systems 
in question need to be able to handle variations and extremes in 
demand (Larsen et al. 2020).
For the 2080s compared to 1971–2000, an increase of 2.4% to 6.3% 
in the global gross hydropower potential, from the hydrological side alone, is seen across all scenarios (van Vliet et al. 2016a) (Chapter 6). 
Alongside the global increase in hydropower potential, the global 
mean water-discharge cooling capacity, which also relates to 
water temperatures, experiences a decrease of 4.5% to 15% across 
the scenarios. In very general and global terms, when combined, 
these changes support the shift towards sources of renewable

energy, including hydropower, in the energy mix. When it comes to 
ensuring stability in the management of the electricity grid, hydro-
climatological extremes have the potential to pose vast difficulties 
in certain regions and/or seasons depending on the nature of the 
energy mix (Van Vliet et al. 2016c). Van Vliet et al. (2016b) showed 
significant reductions in both thermoelectric and hydropower 
electricity capacities, exemplified by the 2003 European drought, 
which resulted in reductions of 4.7% and 6.6%, respectively.
The energy sector is vulnerable to production losses caused mainly 
by heatwaves and droughts, whereas coastal and fluvial floods are 
also responsible for a  large relative share of the energy sector’s 
vulnerability, as assessed by (Forzieri et al. 2018) for Europe in 2100. 
In total, heatwaves and droughts will be responsible for 94% of the 
damage costs to the European energy system compared to 40%

today. Similarly, (Craig et al. 2018) show that, despite potentially 
minor spatio-temporally aggregated differences for various energy-
system components, such as demand, thermoelectric power, wind, 
and so on, the aggregated impact of climate change across these 
components will cause a  significant impact on the energy system, 
as currently exemplified by the USA. In terms of investments and 
management, it is important to unravel these cross-component 
relations in light of the projected nature of the future climate.
In the ongoing transition towards renewable sources of energy (see 
also Chapters 3, 4 and 6), the impact of the hydro-climate on energy 
production continues to be highly relevant (Jones and Warner 2016). 
As the shares of thermoelectric energy production in the energy 
grid go down along with the introduction of thermoelectric cooling 
technologies using smaller amounts of water, new energy sources 
and technologies are being introduced, and existing sources scaled

up. Of these, hydropower, wind and solar energy are the key energy 
sources currently and will be in the near future, making up 2.5% and 
1.8% of the total global primary energy supply in 2017 respectively 
(IEA 2019). Wind and solar energy are directly independent of water 
in themselves, but are dependent on atmospheric conditions related 
to processes that also drive the water balance and circulation. 
Hydropower, on the other hand, is directly influenced by and 
dependent on the supply of water, while at the same time being 
an essential counter-component to seasonality and climatological 
variation, as well as to current and future demand curves and diurnal 
variations, as against wind and solar energy (De Barbosa et al. 2017).
Furthermore, policy instruments in power-system management, 
here exemplified by hydropower in a  climate-change scenario, 
have been shown to enhance energy production during droughts 
(Gjorgiev and Sansavini 2018). The significant influence of variation

in the planning of renewable energy for the 21st century has also 
been highlighted by (Bloomfield et al. 2016). At the same time, the 
integration of renewables must account for lower thermoelectric 
efficiencies and capacities due to increases in temperature

1753
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717(van Vliet et al. 2016a), power-plant closures during extreme weather 
events due to a lack of cooling capacity (Forzieri et al. 2018), and further 
efficiency reductions and penalties following the implementation of 
CCS technologies in the effort to reach the GHG mitigation targets 
(Byers et al. 2015). However, more recent studies find more promising 
amounts of water being used for energy conversion (IEAGHG 2020; 
Magneschi et al. 2017).
The extraction, distribution and wastewater processes of 
anthropogenic water-management systems similarly use vast 
amounts of energy, making the proper management of water 
essential to reduce energy usage and GHG emissions (Nair et al. 
2014)Chapter 11). One study reports that the water sector accounts 
for 5% of total US GHG emissions (Rothausen and Conway 2011). 
Within the WEFN, there is an obvious trade-off between water

availability and food production, competing demands that pose a risk 
to the supply of the basic commodities of food, energy and water in 
line with the SDGs (Bleischwitz et al. 2018; Gao et al. 2019), all of 
which have the potential for inter-sectorial or inter-regional conflicts 
(Froese and Schilling 2019). Currently, 24% of the global population 
live in regions with constant water-scarce food production, and 
19% experience occasional water scarcities (Kummu et al. 2014). 
To counterbalance the demand for food and comestibles in regions 
that experience constant or intermittent supplies, transportation 
is needed, which in itself requires suitable infrastructure, energy 
supplies, a well-functioning trading environment and support policies. 
Of the 2.6  billion people who experience constant or occasional 
water scarcities in food production, 55% rely on international 
trade, 21% on domestic trade and the remainder on water stocks 
(Kummu et al. 2014).

The relations between the influence of hydro-climatic variability, 
socio-economic conditions and patterns of water scarcity have been 
addressed by (Veldkamp et al. 2015). A key finding of this study was 
the ability of the hydro-climate and the socio-economy to interact, 
enforcing or attenuating each other, though with the former acting 
as the key immediate driver, and the influence of the latter emerging 
after six to ten years.
The trade-offs between competing demands have been investigated 
on a  continental scale in the US Great Plains, highlighting the 
influence of irrigation in mitigating reductions in crop yields (Zhang 
et al. 2018). Despite crop-yield reductions of 50% in dry years 
compared to wet years, a  key conclusion was that the irrigation 
should be counterbalanced against general water and energy 
savings within the context of trade-offs. In East Asia, the WEFN has 
been quantified, highlighting obvious trade-offs between economic

growth, environmental issues and food security (White et al. 2018). 
This same study also highlights the concept of a  virtual WEFN 
that includes water embodied within products that are traded and 
shipped. (Liu et al. 2019) find an urgent need for proper assessment 
methods, including of trade within the WEFN, due to the significant 
resource allocations.
Within the WEFN, the implementation of policies to achieve low 
stabilisation targets is strongly linked to sustainable development 
within the water sector with regard to water management and water conservation, indicating that additional coherence in policies 
affecting the water, energy and food sectors (among others) will 
be critical in achieving the SDGs (Chapter 7). Subsidised fertilisers, 
energy and crops can drive unsustainable levels of water usage 
and pollution in agriculture. More than half the world’s population, 
roughly 4.3 billion people in 2016, live in areas where the demand for

water resources outstrips sustainable supplies for at least part of the 
year. Irrigated agriculture is already using around 70% of the available 
freshwater, and the large seasonal variations in water supply and the 
needs of different crops can create conflicts between water needs 
across sectors at different time scales (Wada et al. 2016). However, as 
there is little potential for increasing irrigation or expanding cropland 
(Steffen et al. 2015), gaps in food production gaps must be closed by 
increasing productivity and cropping densities on currently harvested 
land by increasing either rain-fed yields or water-use efficiency 
(Alexandratos and Bruinsma 2012).
It has been argued that applying an integrated approach to water-
energy-climate-food resource management and policymaking is 
highly beneficial in properly addressing the co-benefits and trade-
offs (Brouwer et al. 2018; Howells et al. 2013), accommodating the

SDGs (Rasul 2016) and, in general, assessing enabling strategies 
to improve resource efficiency (Dai et al. 2018). For an integrated 
approach to analysing the WEFN, a number of modelling approaches, 
tools and frameworks have been proposed (Brouwer et al. 2018; 
de Strasser et al. 2016; Gao et al. 2019; Larsen et al. 2019; Smajgl 
et al. 2016), often involving multi-objective calibration. Such tools 
enable decision-makers to evaluate the optimal water-allocation and 
energy-saving solutions for the specific geography in question. As an 
example, (Scott 2011) found the higher transportability of electricity, 
compared to water, pivotal in water-energy adaptation solutions in 
the USA, while arguing for the additional coordination of water and 
energy policies as a key instrument in balancing the trade-offs.
Common to all these integrated efforts is the challenge involved in 
making comparisons across studies due to the combined complexities

of assumptions, model codes, regions, variables, forcings, and so 
on. To accommodate these challenges, (Larsen et al. 2019) suggest 
employing shared criteria and forcing data to enable cross-model 
comparisons and uncertainty estimates, as also highlighted by 
(Brouwer et al. 2018). Other limitations in current WEFN research 
are partial system descriptions, the failure to address uncertainties, 
system boundaries, and evaluation methods and metrics (Zhang 
et al. 2018). The lack of proper access to WEFN data and data 
quality has been highlighted by (D’Odorico et al. 2018; Larsen et al. 
2019). Furthermore, gaps have been identified between theory and 
end-user applications in the lack of any focus on food nutritional 
values as opposed to calories alone, in the understanding of water 
availability in relation to management practices, in integrating 
new energy technologies and in the resulting environmental issues 
(D’Odorico et al. 2018).

Therefore, looking ahead, future fields of WEFN research should 
provide greater insights into all these aspects. Holistic frameworks 
have been put forward to facilitate methods of WEFN management 
by focusing on, for example, the geographical complexities with 
regard to transboundary challenges within hydrological catchments

1754
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17(de Strasser et al. 2016), aligning policy incentives (Rasul 2016) and 
making synergies and trade-offs in relation to WEFN SDG targets (Fader 
et al. 2018), and so on. The roles of all levels of government in optimal 
WEFN management are also highlighted in (Kurian 2017), especially 
with regard to shaping the behaviour of individuals. Furthermore, 
(Kurian 2017) highlights the challenges involved in science and policy 
communicating with one another and in the provision of optimal 
instruments and guidelines. Engaging non-experts and end-users in 
scientific processes is seen as essential to capturing previous failures 
and successes, and to ensure that understanding the challenges is 
updated to help shape the research questions.
Coordination of water use across different sectors and deltas is 
an important factor in sustainable water management. Examples

of instruments and policies that support this from India and Sub-
Saharan Africa in relation to the groundwater crisis are given below. 
India is the world’s largest user of groundwater for irrigation, which 
covers more than half of the country’s total irrigated agricultural area, 
is responsible for 70% of food production and supports more than 
50% of the population (700 million people) (Chapter 7). However, 
excessive extraction of groundwater is depleting aquifers across the 
country, and falls in the water table have become pervasive. Improved 
water-use efficiency in irrigated agriculture is being considered, both 
globally and in India, as a way of meeting future food requirements 
with increasingly scarce water resources (Fishman et al. 2015).
The entirety of Sub-Saharan Africa has an undeveloped potential 
for groundwater exploitation, despite the general perception 
of a  global groundwater crisis, this being due to the absence of

services to support groundwater development (Cobbing 2020). It is 
estimated that most Sub-Saharan countries in Africa utilise less than 
5% of their national sustainable yields (Cobbing and Hiller 2019). 
The initial tool for driving sustainable groundwater exploitation is 
a change in the narrative of a lack of resources in order to stimulate 
increased agricultural production and increased fulfilment of the 
SDGs (Cobbing 2020). Quantitative measures of actual groundwater 
vulnerability based on multiple indicators have been calculated 
by, for example, (van Rooyen et al. 2020), showing that 20.4% of 
South Africa’s current water resources are highly vulnerable and are 
projected to worsen fifty years into the future.
Despite the positive perspectives regarding Sub-Saharan groundwater 
resources, the 2015–2017 water crisis in South Africa, including in 
Cape Town, clearly predicts vulnerability to climate variability (Carvalho

Resende et al. 2019), which is predicted to increase. Serving as 
inspiration for the future mitigation of water depletion, (Olivier and Xu 
2019) suggest certain governance tools to improve the diversification 
of water sources and the management of existing supplies.
17.3.3.3 Industry
Industrial transformation is a core component in achieving sustainable 
development. Across all industrial sectors, the development and 
deployment of innovative technologies, business models and policy 
approaches at scale will be essential in accelerating progress both 
with meeting the economic and social development goals and with 
achieving low emissions. In this section, we assess the synergies and trade-offs between mitigation options and the SDGs, with a specific 
focus on asking whether economic growth and employment creation 
can work jointly with climate actions and other SDGs in least 
developed and developing countries. Examples of synergies and

trade-offs are provided based on the conclusions of Chapter  9 on 
the building sector and Chapter  11 on industry. The potential for 
greening industry is discussed in relation to eco-industrial parks, with 
examples from Ethiopia, China, South Africa and Ghana.
Chapter  11 concludes that achieving net zero emissions from the 
industrial sector are possible. This will require the provision of 
electricity free from greenhouse gas (GHG) emissions, including 
from other energy carriers, increased electrification, low-carbon 
feedstocks, and a combination of energy efficiency, reduced demand 
for materials, a  more circular economy, electrification and carbon 
capture, use and storage (CCUS).
The potential co-benefits of mitigation options in industry has 
been mapped out in Chapter  11 in relation to five categories of 
mitigation options: material efficiency and reductions in the demand 
for materials, the circular economy and industrial waste, carbon

capture and storage, energy efficiency, and electrification and fuel 
switching (Figure  11.15). In particular, the first two categories of 
options are assessed as having several co-benefits for the SDGs, 
including SDGs 3, 5, 7, 8, 9 11, 12, and 15. Some studies also point 
out the potential trade-offs in respect of employment and the costs 
of cleaner production. The other options primarily impact on climate 
actions, decent work and employment, and industry as such.
(Okereke et al. 2019) offer important generic conclusions on green 
industrialisation and the transition based on a  study of socio-
technical transition in Ethiopia. The importance of drivers for 
changes in terms of clear policy goals and government support 
for  green growth and climate policies, as well as support from 
a  strong culture of innovation, is emphasised. The study also 
identifies key barriers in relation to stakeholder interactions, 
the availability of resources and the ongoing tensions between

ambitions for high economic growth and climate change. Green 
innovation in industry critically depends on regulations. (Gramkow 
and Anger-Kraavi 2018) have assessed the role of fiscal policies in 
greening Brazilian industry based on an econometric analysis of 
24 manufacturing sectors. They conclude that instruments like low-
cost finance for innovation and support to sustainable practices 
effectively promote green innovation.
(Luken 2019) have assessed the drivers, barriers and enablers for green 
industry in Sub-Saharan Africa, concluding that major barriers exist 
related to material and input costs, as well as product requirements 
in foreign markets, and that as a result there are trade-offs between 
economic and environmental performance. Studies of ten countries 
are reviewed, and although they suffer from limited information, they 
conclude similarly that further progress is being hindered by poor 
access to finance and weak government regulation. (Greenberg and

Rogerson 2014). They similarly conclude that the greening of industry 
in South Africa is lagging behind due to economic barriers and weak 
governance, despite its high priority in government planning and 
among international partners.

1755
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717Ghana has launched a ‘One District One Factory’ (1D1F) initiative, 
aimed at establishing at least one factory or enterprise in each of 
Ghana’s 216 districts as a means of creating economic growth poles 
to accelerate the development of these areas and create jobs for 
the country’s increasingly youthful population. The policy aims to 
transform the structure of the economy from one dependent on the 
production and export of raw materials to a value-added industrialised 
economy driven primarily by the private sector (Yaw 2018). As has 
been pointed out by (Mensah et al. 2021), in its initial design the 
programme did not take environmental quality into consideration. 
Although it was successful in creating economic growth, exports 
and employment, the environmental impacts have been negative. 
It has therefore been recommended that environmental regulations

be imposed on foreign investments. Similar conclusions have been 
drawn by (Solarin et al. 2017).
Chapter 11 concludes that eco-industrial parks, in which businesses 
cooperate with each other in order to avoid environmental pressure 
and support sustainable development, have delivered several benefits 
in relation to overall reductions in both virgin materials and final 
wastes, implying significant reductions in industrial GHG emissions. 
Due to these advantages, eco-industrial parks have been actively 
promoted, especially in East Asian countries such as China, Japan 
and in the Republic of Korea (South Korea), where national indicators 
and governance exist (Geng et al. 2019; Geng and Hengxin 2009).
(Zeng et al. 2020) have assessed the role of eco-industrial parks in 
China’s green transformation for 33 development zones in relation 
to contributions to GDP , industrial value added, exports, water 
and energy consumption, CO 2 levels and sulphur emissions. They

concluded that industrial parks have played a very important role in 
China’s industrialisation, and that this structure has supported the 
decoupling of economic growth and energy and water consumption 
from the environmental impacts. However, improved environmental 
performance would require better access to finance and a  higher 
priority by management.
Eco-industrial parks have been promoted in Ethiopia by the 
government and UNIDO, based on the expectation that they could 
help to boost the economy (UNIDO 2018). One of the success 
stories is an industrial park in Hawassa, a  nation-level textile and 
garment industrial park with a ‘zero emissions commitment’ based 
on renewable energy and energy-efficient technologies. However, 
the concept of the industrial park, including feasible policies and 
institutional arrangements, is new to Ethiopia’s regulatory processes, 
and this has created problems for management, knowledge and 
governance, hindering their fast implementation.

A number of business associations have developed strategies for 
sustainable development and climate change, including corporate 
social responsibility (CSR). International initiatives have included 
the promotion of CSR initiatives by international investors in low-
income countries to support a broad range of development priorities, 
including social working conditions, eliminating child labour and 
climate change (Lamb et al. 2017). (Leventon et al. 2015) evaluated 
the role of mining industries in Zambia in supporting climate-
compatible development and concluded that, although the industry has played a  positive role in avoiding migration and pressure on 
forest resources, there is a lack of coordination between government 
and industry initiatives.
It can be concluded that most of the mitigation options in industry 
considered in this section could have synergies with the SDGs, but 
also that some of the renewable-energy options could indicate some

trade-offs in relation to land use, with implications for food- and 
water security and costs. Carbon capture and storage (CCS) could 
play an enabling role in the provision of reliable, sustainable and 
modern energy and could support decarbonisation, but it can also be 
costly (IEAGHG 2020; Mikunda et al. 2021). The provision of water for 
CCS can include both synergies and trade-offs with the SDGs due to 
recent progress in water-management technologies (Giannaris et al. 
2020; IEAGHG 2020; Mikunda et al. 2021).
17.3.3.4 Cities, Infrastructure and Transportation
With 80% of the global population expected to be urban by 2050, 
cities will shape development paths for the foreseeable future 
(United Nations 2018). The challenge for many policymakers is to 
construct development paths that make cities clean, prosperous and 
liveable while mitigating climate change and building resilience to 
heatwaves, flooding and other climate risks. The IPCC SR1.5 report

sees achieving these objectives as feasible: cities could potentially 
realise significant climate and sustainable-development benefits 
from shifting development paths (Wiktorowicz et al. 2018). This 
section assesses the synergies and trade-offs between meeting the 
SDGs and climate change mitigation, as well as providing a general 
overview of mitigation options in cities and of enabling factors, 
including city networks and plans for jointly addressing the SDGs 
and climate change mitigation.
Chapter 8 concludes that urban areas potentially offer several joint 
benefits between mitigation and the SDGs, and that since AR5, 
evidence of the co-benefits of urban mitigation continues to grow. 
In developing countries, a co-benefits approach that frames climate 
objectives alongside other development benefits arise increasingly 
being seen as an important concept justifying and driving climate 
change actions in developing countries (Sethi and Puppum De Oliveria

2018; Seto et al. 2016).
Evidence of the co-benefits of urban mitigation measures on human 
health has increased significantly since the IPCC AR5, especially 
through the use of health-impact assessments in cities like Geneva, 
where energy savings and cleaner energy-supply structures based on 
measures for urban planning, heating and transport have reduced 
CO2, NO x and PM 10 emissions and increased the opportunities for 
physical activity for the prevention of cardiovascular diseases 
(Diallo et al. 2016).
There is increasing evidence that climate-mitigation measures can 
lower health risks that are related to energy poverty, especially in 
vulnerable groups, such as the elderly (Monforti-Ferrario et al. 2019). 
Moreover, the use of urban forestry and green infrastructure as 
both a  climate mitigation and an adaptation measure can reduce 
heat stress (Kim and Coseo 2019; Privitera and La Rosa 2017)

1756
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17while removing air pollutants to improve air quality (Scholz et al. 
2018; De la Sota et al. 2019) and enhancing well-being, including 
contributions to local development and possible reductions of 
inequalities (Lwasa et al. 2015). Other studies evidence the potential 
to reduce premature mortality by up to 7000 in 53 towns and cities, 
to create 93,000 net new jobs and lower global climate costs, as well 
as reduce personal energy costs based on road maps for renewable-
energy transformations (Jacobson et al. 2018).
The co-benefits of energy-saving measures described by 
146  signatories to a  city climate network due to improved air 
quality have been quantified as 6596 avoided premature deaths 
(with a 95% confidence interval of 4356 to 8572 avoided premature 
deaths) and 68,476 years of life saved (with a  95% confidence 
interval of 45,403 and 89,358 years of life saved) (Monforti-Ferrario

et al. 2019). Better air quality further reinforces the health co-benefits 
of climate-mitigation measures based on walking and cycling, since 
the evidence suggests that increased physical activity in urban 
outdoor settings with low levels of black carbon improves lung 
function (Laeremans et al. 2018). Chapter 9 shows that mitigation 
actions in buildings have multiple co-benefits resulting in substantial 
social and economic value beyond their direct impacts on reducing 
energy consumption and GHG emissions, thus contributing to the 
achievement of almost all the UN’s SDGs. Most studies agree that 
the value of these multiple benefits is greater than the value of the 
energy savings, while their quantification and inclusion in decision-
making processes will strengthen the adoption of ambitious reduction 
targets and improve coordination across policy areas.
There are several examples of cities that have developed plans for

meeting both the SDGs and mitigation, which demonstrates the 
feasibility of meeting these objectives jointly. Quito, Ecuador, a city 
with large carbon footprints (Global Opportunity Explorer 2019) and 
climate vulnerabilities, has adopted low-carbon plans that aim to 
achieve the climate goals while introducing net-zero energy buildings 
and reducing water stress (Ordoñez et al. 2019; Marcotullio et al. 
2018). Several cities in China, Indonesia and Japan have invested in 
green-city initiatives by means of green infrastructural investments, 
which is claimed to be a  form of smart investment. Through this 
type of investment, economic growth and greenhouse gas (GHG) 
emissions reductions can be achieved in cities (Jupesta et al. 2016). 
Multi-level governance arrangements, public-private cooperation 
and robust urban-data platforms are among the factors enabling 
the pursuit of these objectives within countries (Corfee-Morlot et al.

2009; Gordon 2015; Creutzig et al. 2019; Yarime 2017).
In addition to the mostly domestic enablers listed previously, some 
cities have also benefited from working with international networks. 
The Global Covenant of Mayors for Climate & Energy (Covenant of 
Mayors 2019), the World Mayors Council on Climate Change, ECLEI, 
C40, and UNDRR (C40 Cities 2019; ECLEI 2019; UNDRR 2019) have 
provided targeted support, disseminated information and tools, and 
sponsored campaigns (Race to Zero) to motivate cities to embrace 
climate and sustainability objectives. Despite this support, it should 
be stressed that most cities are in the early stages of climate 
planning (Eisenack and Reckien 2013; Reckien et al. 2018; Climate-
ADAPT 2019). Furthermore, in some cases city policymakers may fail to highlight the synergies and trade-offs between climate and 
sustainable development or rebrand GHG-intensive practices as 
‘sustainable’ in relevant plans (Tozer 2018).

With regard to city networks, Section  8.5 concludes that the 
importance of urban-scale policies for sustainability has increasingly 
been recognised by international organisations and national and 
regional governments. For example, in 2015, more than 150 national 
leaders adopted the UN’s 2030 Sustainable Development Agenda, 
including stand-alone SDG 11 (sustainable cities and communities) 
(UN 2015 p. 14). The following year, 170 countries agreed to the UN 
New Urban Agenda (NUA), a central part of which is recognising the 
importance of national urban policies (NUPs) as a key to achieving 
national economic, social and environmental goals (United Nations 
2015a 2017). Similarly, the Sendai Framework for Disaster Risk 
Reduction identifies the need to focus on unplanned and rapid 
urbanisation to reduce exposure and vulnerability to the risks of 
disasters (United Nations 2015b).
For many cities, a  key to reorienting development paths will be investing

in sustainable, low-carbon infrastructure. Because infrastructure has 
a  long lifetime and influences everything from lifestyle choices to 
consumption patterns, decisions over an estimated USD90 trillion of 
infrastructure investment (from now to 2030) will be critical in order 
to avoid becoming locked-in to unsustainable paths (WRI 2016). This 
is particularly true in developing countries, where demands for new 
buildings, roads, energy and waste-management systems are already 
surging. To some extent, policies that accelerate building renovation 
rates, including voluntary programmes (Van der Heijden 2018), can 
support transitions down more sustainable paths (Kuramochi et al. 
2018). Factoring climate and sustainable development considerations 
into policy tools that facilitate the quantitative emission performance 
standard (EPS) and the inclusion of climate and sustainable 
development benefits and risks in infrastructure assessments or

risk-adjusted returns on investments in development banks could 
also prove useful (Rydge et al. 2015). Strong policy signals from the 
UNFCCC and from national climate policies and strategies (including 
NDCs) could facilitate uptake of the relevant policies and the use 
of these tools.
Infrastructural investments will also have wide-ranging implications 
for sustainable, low-carbon urban development, namely transport 
and mobility. To some extent, decision-making frameworks such as 
Avoid-Shift-Improve (ASI) could help make these patterns low carbon 
and sustainable (Dalkmann and Brannigan 2007; Wittneben et al. 
2009). Mixed land-use planning and compact cities can not only help 
avoid emissions or shift travellers into cleaner modes (Cervero 2009), 
they can also improve air quality, reduce commuting times, enhance 
energy security and improve connectivity (Zusman et al. 2011; Pathak 
and Shukla 2016).
17.3.3.5 Mitigation-adaptation Relations

The section will consider the links between mitigation and adaptation 
options in the context of sustainable development and the associated 
synergies and trade-offs. Cross-cutting conclusions will be drawn 
based on Chapter  3 and the sectoral chapters of AR6 WGIII and

1757
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717Chapter 18 of AR6 WGII. The focus will be on the following sectors: 
agriculture, food and land use; water-energy-food; industry and the 
circular economy; and urban areas.
IPCC AR6 WGII, concludes that coherent and integrated policy 
planning is needed in order to support integrated climate change 
adaptation and mitigation policies, and that this is a key component 
of climate-resilient development pathways. Section  4.5.2 assesses 
development pathways and the specific links between mitigation 
and adaptation, concluding that there can be co-benefits, and trade-
offs, where mitigation implies maladaptation. However, adaptation 
can also be a prerequisite for mitigation. It is therefore concluded 
that making development pathways more sustainable can build the 
capacity for both mitigation and adaptation.
Climate actions, including climate change mitigation and adaptation,

are highly scale-dependent, and solutions are very context-specific. 
Especially in developing countries, a  strong link exists between 
sustainable development, vulnerability and climate risks, as limited 
economic, social and institutional resources often result in low 
adaptive capacities and high vulnerability. Similarly, the limitations 
in resources also constitute key elements weakening the capacity 
for climate change mitigation (Jakob et al. 2014). The change to 
climate-resilient societies requires transformational or systemic 
changes, which also have important implications for the suite of 
available sustainable-development pathways (Kates et al. 2012; 
Lemos et al. 2013). Thornton and Comberti (2017) point to the 
need for social-ecological transformations to take place if synergies 
between mitigation and adaptation are to be captured, based on 
the argument that incremental adaptation will not be sufficient 
when climate change impacts can be extreme or rapid and when

deep decarbonisation simultaneously involves social change 
(Chapter 18 in AR6 WGII).
As discussed in AR6 WGII, Section  18.4, there are synergies and 
trade-offs between adaptation and sustainable development, as 
well as between mitigation and sustainable development, which 
is supported by comprehensive assessments such as that by Dovie 
(2019) and Sharifi (2020). Links between mitigation and adaptation 
options are identified in Chapter 18 in AR6 WGII, such as expected 
changes in energy demand due to climate change interacting with 
energy-system development and mitigation options, changes to 
agricultural production practices to manage the risks of potential 
changes in weather patterns affecting land-based emissions and 
mitigation strategies, or mitigation strategies that place additional 
demands on resources and markets. This increases the pressures on 
and costs of adaptation or ecosystem restoration linked to carbon

sequestration and the benefits in terms of the resilience of natural 
and managed ecosystems, but it also could restrict mitigation options 
and increase costs. Chapter 3 of AR6 WGIII similarly concludes that 
the connectedness and coherence of actions to mitigate climate 
change could support the conservation and adaptation of ecosystems 
and meet the Sustainable Development Goals more widely.
Options to reduce agricultural demand (e.g., dietary change, reducing 
food waste) can have co-benefits for adaptation through reductions 
in the demand for land and water (Smith et al. 2019b). For example, Grubler et al. (2018) show that stringent climate-mitigation pathways 
without reliance on BECCS can be achieved through efficiency 
improvements and reduced energy service and consumption levels 
in high-income countries.
Agriculture, food and land use is the sector where most climate 
policy options can simultaneously generate impacts on mitigation,

adaptation and the SDGs (Locatelli et al. 2015; Kongsager et al. 
2016). Bryan et al. (2013) identified a range of synergies and trade-
offs across adaptation, mitigation and the SDGs in Kenya, given 
the diversity of its climatic and ecological conditions. Improved 
management of soil fertility and improved livestock-feeding 
practices could provide benefits to both climate change mitigation 
and adaptation, as well as increase income generation from farming. 
However, other improvements to agricultural management in Kenya, 
for example, soil water conservation, could only provide benefits 
across all three domains in some specific sub-regions.
Conservation agriculture can yield mitigation co-benefits through 
improved fertiliser use or the efficient use of machinery and fossil 
fuels (Harvey et al. 2014; Pradhan et al. 2018; Cui et al. 2019). 
Climate-smart agriculture (CSA) ties mitigation to adaptation 
through its three pillars of increased productivity, mitigation and

adaptation (Lipper et al. 2014), although managing trade-offs among 
the three pillars requires care (Kongsager et al. 2016; Thornton and 
Comberti 2017; Soussana et al. 2019). Sustainable intensification 
also complements CSA (Campbell et al. 2014). Enhanced sustainable 
adaption can lead to effective emission-reduction benefits, such 
as climate-smart agricultural technologies (Nefzaoui et al. 2012; 
Poudel 2014) and ecosystem-based adaptation. (Berry, P et al. 
2015; Geneletti and Zardo 2016; Warmenbol and Smith 2018) have 
shown how increases in livelihoods can contribute to climate change 
mitigation in Europe.
Agroforestry can sustain or increase food production in some systems 
and increase farmers’ resilience to climate change (Jones et al. 2013). 
Some sustainable agricultural practices have trade-offs, and their 
implementation can have negative effects on adaptation or other 
ecosystem services. Agricultural practices can aid both mitigation

and adaptation on the ground, but yields may be lower, so there may 
be a trade-off between resilience to climate change and efficiency. 
Interconnections within the global agricultural system may also 
lead to deforestation elsewhere (Erb et al. 2016). Implementation of 
sustainable agriculture can increase or decrease yields, depending on 
context (Pretty et al. 2006) (Chapter 4).
greenhouse gas (GHG) emissions in the AFOLU sector, but also help 
augment the sector’s role as a carbon sink by increasing forest and 
tree cover through afforestation and agroforestry activities, and other 
eco-system-based approaches. Some of these options, however, can 
also have negative impacts on GHG emissions in the form of indirect 
impacts on land use (Córdova 2019) (for a more detailed discussion, 
see Chapter 7). If managed and regulated appropriately, the land use,

land-use change and forestry (LULUCF) sector could play a key role 
in mitigation and be a key sector for emissions reductions beyond 
2025 instead of contributing substantially to emissions reductions

1758
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17beyond 2025 (Córdova et al. 2019; Keramidas et al. 2018). However, 
the large-scale deployment of intensive bioenergy plantations, 
including monocultures, replacing natural forests and subsistence 
farmlands are likely to have negative impacts on biodiversity and can 
threaten food and water security, as well as local livelihoods, partly 
by intensifying social conflicts, partly by reducing resilience (Díaz 
et al. 2019). Expansion on to abandoned or unused croplands and 
pastures nonetheless presents significant global potential, and will 
avoid the sustainability risks of expanding agriculture into natural 
vegetation (Næss et al. 2021).
Based on a literature review, (Berry, P et al. 2015) identified water-
saving and irrigation techniques in agriculture as attractive adaptation 
options that have positive synergies with mitigation in increasing soil

carbon, reducing energy consumption and reducing CH 4 emissions 
from intermittent rice-paddy irrigation. These measures could, 
however, reduce water flows in rivers and adversely affect wetlands 
and biodiversity. The study also concluded that afforestation could 
reduce peak water flows and increase carbon sequestration, but 
trade-offs could emerge in relation to the increased demand for water.
Fast-growing tree monocultures or biofuel crops may enhance 
carbon stocks but reduce downstream water availability and the 
availability of agricultural land (Harvey et al. 2014). Similarly, in some 
dry environments, agroforestry can increase competition with crops 
and pastureland, decreasing productivity and reducing the yields of 
catchment water (Schrobback et al. 2011) (Chapter 7).
Hydropower dams are among the low-cost mitigation options, provided 
the cost of constructing the plant is taken into account, but they could

have serious trade-offs in relation to key sustainable-development 
aspects, since in respect of water and land availability dams can have 
negative effects on ecosystems and livelihoods, thereby implying 
increased vulnerabilities. Section  17.3.3.2 on the water-energy-
food nexus includes examples of trade-offs between the benefits of 
producing electricity from hydropower dams and the trade-offs with 
ecosystem services and using land for agriculture and livelihoods.
There are several potentially strong links between climate change 
adaptation in industry and climate change mitigation. Various supply 
chains can be affected by climate change, energy supply and water 
supply, and other resources can be disrupted by climate events. 
Adaptation measures can influence GHG emissions in their turn 
and thus mitigation because of the demand for basic materials, for 
example, as well as by influencing outdoor environments and labour 
productivity (Section 11.17.1.4).

Implementing adaptation options in industry can also imply 
increasing the demand for packaging materials such as plastics and 
for access to refrigeration. These are among the adaptation options 
that are dependent on temperature and storage possibilities, as well 
as being major sources of GHG emissions.
An increasing number of cities are becoming involved in voluntary 
actions and networks aimed at drawing up integrated plans for 
sustainable development and climate change mitigation and 
adaptation, including cities in both high- and low-income countries around the world. (Grafakos et al. 2019; Sanchez Rodriguez et al. 
2018) concluded that cities are an obvious place for the development 
of plans that can capture several synergies between sustainable 
development and climate-resilient pathways. (Kim and Grafakos 
2019; Landauer et al. 2019) similarly concluded that cities are an 
obvious platform for the development of integrated planning efforts

because of the scale of policies and actions, which could potentially 
match the different policy domains. (Kim and Grafakos 2019) 
assessed the level of integration of mitigation and adaptation in 
urban climate change plans across 44 major Latin American cities, 
concluding that the integration of climate change mitigation and 
adaption plans was very weak in about half the cities and that 
limited donor finance was a main barrier. The authors also mention 
barriers in relation to governance and the weakness or lack of legal 
frameworks. The integration of SDGs with adaptation could help 
increase the willingness of politicians to implement climate actions, 
as well as provide stronger arguments for investing the required 
resources (Sanchez Rodriguez et al. 2018).
The local integration of planning and policy implementation practices 
was also examined by (Newell et al. 2018) in a study of 11 Canadian 
communities. It was concluded that, in order to put plans into

practice, a  deeper understanding needs to be established of the 
potential synergies and trade-offs between sustainable development 
and climate change mitigation and adaptation. A model was applied 
to the evaluation of key impacts, including energy innovation, 
transportation, the greening of cities and city life. The impact 
assessment came to the conclusion that multiple benefits, costs 
and conflicting areas could be involved, and that bringing a broad 
range of stakeholders into policy implementation was therefore to 
be recommended.
There are several links between mitigation and adaptation options 
in the building sector, as pointed out in Chapter  9. Adaptation 
can increase energy consumption and associated GHG emissions 
(Kalvelage et al. 2013; Campagnolo and Davide 2019), for example, 
in relation to the demand for energy to meet indoor thermal comfort 
requirements in a future warmer climate (de Wilde and Coley 2012;

Li and Yao 2012; Clarke et al. 2018). Mitigation alternatives using 
passive approaches may increase resilience to the impacts of climate 
change on thermal comfort and could reduce cooling needs (Wan 
et al. 2012; Andrić et al. 2019). However, climate change may reduce 
their effectiveness (Ürge-Vorsatz et al. 2014).
Mitigation and the co-benefits of adaptation in urban areas in 
relation to air quality, health, green jobs and equality issues are 
dealt with in Section 8.2, where it is concluded that most mitigation 
options will have positive impacts on adaptation, with the exception 
of compact cities, with trade-offs between mitigation and adaptation. 
This is because decreasing urban sprawl can increase the risks of 
flooding and heat stress. Detailed mapping between mitigation and 
adaptation in urban areas shows that there are many, very close 
interactions between the two policy domains and that coordinated 
governance across sectors is therefore called for.

Rebuilding and refurbishment after climate hazards can increase 
energy consumption and GHG emissions in the construction and

1759
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717building materials sectors, as it could make the existing building 
stock more climate-resilient (Hallegatte 2009; de Wilde and Coley 
2012; Pyke et al. 2012) and thus also support implementation of 
the Sendai Framework on Disaster Risk Reduction (United Nations 
2015b). Climate change in the form of extremely high temperatures, 
intense rainfall leading to flooding, more intense winds and/or 
storms and sea level rises (SLRs) can seriously impact transport 
infrastructure, including the operations and mobility of road, rail, 
shipping and aviation; Chapter 10 assesses the impacts on subsectors 
within transportation. At the same time, these sectors are major 
targets for GHG mitigation options, and many countries are currently 
examining what to do in terms of combined mitigation-adaptation 
efforts, using the need to mitigate climate change through transport-

related GHG emissions reductions and pollutants as the basis for 
adaptation action (Thornbush et al. 2013; Wang and Chen 2019). For 
example, urban sprawl indirectly affects climate processes, increasing 
emissions and vulnerability, which worsens the ability to adapt 
(Congedo and Munafò 2014). Hence greater use of rail by passengers 
and freight will reduce the pressures on the roads, while having less 
urban sprawl will reduce the impacts on new infrastructure, often in 
more vulnerable areas (IPCC 2019; Newman et al. 2017).
Despite many links between mitigation and adaptation options, 
including synergies and trade-offs, Chapter 13 concludes that there 
are few frameworks for integrated policy implementation. One review 
of climate legislation in Europe found a lack of coordination between 
mitigation and adaptation, their implementation varying according 
to different national circumstances (Nachmany et al. 2015).

In developing and least-developed countries (LDCs), there are many 
examples of climate policies in the NDCs that have been drawn up in 
the context of sustainable development and that cover both mitigation 
and adaptation (Beg 2002; Duguma et al. 2014)) (Chapter  13). 
However, there are many barriers to joint policy implementation. 
Despite the emphasis on both mitigation and adaptation policies, 
there is very limited literature on how to design and implement 
integrated policies (Di Gregorio et al. 2017; Shaw et al. 2014). For 
example, the links within the water-energy-food nexus require 
coordination among sectoral institutions and capacity-building in 
innovative frameworks linking science, practice and policy at multiple 
levels (Cook and Chu 2018; Nakano 2017; Shaw et al. 2014).
Another challenge is the shortage of financial, technical and human 
resources for implementing joint adaptation and mitigation policies 
(Antwi-Agyei et al. 2018b; Chu 2018; David and Venkatachalam

2019; Kedia 2016; Satterthwaite 2017). Several studies have stressed 
that the lack of finance for integrating policy implementation 
between sustainable development and climate change mitigation 
and adaptation may constitute barriers to the implementation of 
adaptation projects to protect least-developed countries (LDCs) with 
many vulnerabilities.
(Locatelli et al. 2016) come to similar conclusions regarding finance 
based on interviews with multilateral development banks, green 
funds and government organisations in respect of the agricultural 
and forestry sectors. International climate finance has been totally 
dominated by mitigation projects. Those who were interviewed were asked about their willingness to change this balance and 
to commit more resources to projects that address both climate 
change mitigation and adaptation. More than two thirds of those 
interviewed, however, raised concerns that integrated projects

could be too complicated and that a greater alignment of financial 
models across different policy domains could entail greater 
financial risks. Another barrier mentioned in respect of finance was 
that mitigation projects were primarily aimed at GHG emissions 
reductions, while adaptation projects had more national benefits 
and were also more suitable for community development and 
promoting equality and fairness. In an assessment of 201 projects 
in the forestry and agricultural sectors in the tropics, (Kongsager 
et al. 2016), found that a majority of the projects contributed to 
both adaptation and mitigation or at least had the potential 
to do so, despite the separation between these two objectives by 
international and national institutions.
17.3.3.6 Cross-sectoral Digitalisation
In this section, the potential role of digitalisation as a  facilitator 
of a  fast transition to sustainable development and low-emission

pathways is assessed based on sectoral examples. The contributions 
of digital technology could contribute to efficiency improvements, 
cross-sectoral coordination, including new IT services, and 
decreasing resource use, implying several synergies with the SDGs, 
as well as trade-offs, for example, in relation to reduced employment, 
increasing energy demand and the increasing demand for services, 
possibly increasing GHG emissions.
The COVID-19 pandemic caused radical temporary breaks with 
past energy-use trends. How post-pandemic recovery will impact 
on the longer-term energy transition is unclear. Recovering from 
the pandemic with energy-efficient practices embedded in new 
patterns of travel, work, consumption and production reduces 
climate mitigation challenges (Kikstra et al. 2021). The potential of 
digital contact tracing to slow the spread of a virus had been quietly 
explored for over a decade before the COVID-19 pandemic thrust the

technology into the spotlight (Cebrian 2021). The COVID-19 crisis is 
among the most disruptive events in recent decades and has had 
consequences for consumer behaviour. During the lockdowns in 
most countries, consumers have turned to online shopping for food 
products, personal hygiene and disinfection (Cruz-Cárdenas et al. 
2021), making society more digitally literate.
The cost of new services provided by digitalisation can be high, and 
this could imply barriers for low-income countries in joining new 
global information-sharing systems and markets. Altogether this 
implies that any assessment of the contribution of digitalisation 
to support the SDGs and low-carbon pathways will only be able 
to provide very context-specific results. Digital technologies could 
potentially disrupt production processes in nearly every sector of 
the economy. However, as an emerging area experiencing the rapid 
penetration of many sectors, there could be a window of opportunity

for integrating sustainable development and low-emission pathways. 
(IIASA 2020) concludes that the digital revolution is characterised by 
many innovative technologies, which can create both synergies and 
trade-offs with the SDGs (IIASA 2020).

1760
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17Digital technologies could potentially disrupt production processes 
in nearly every sector of the economy. However, as an emerging 
area experiencing the rapid penetration of many sectors, there 
could be a  window of opportunity for integrating sustainable 
development and low-emission pathways. TWI2050 (2020) 
concludes that the digital revolution is characterised by many 
innovative technologies, which can create both synergies and 
WBSD (2019) has assessed the potential of communication 
technologies (ICT) to contribute to the transition to a  global low-
carbon economy in the energy, transportation, building, industry, and 
other sectors. The potential is estimated to be around 15% CO 2-eq 
emissions reductions in 2020 compared with a  business-as-usual 
scenario. A range of ICT solutions have been highlighted, including

smart motors and industrial process-management in industry, traffic-
flow management, efficient engines for transport, smart logistics and 
smart-energy systems.
The TWI2050 2019 report (IIASA 2019) assessed both the positive 
and negative impacts of digitalisation in the context of sustainable 
development. It found that efficiency improvements, reduced 
resource consumption and new services can support the SDGs, but 
also that there were challenges, including in relation to equality, 
facing the least-developed and developing countries because of their 
low level of access to technologies. The necessary preconditions for 
successful digital transformation include prosperity, social inclusion, 
environmental sustainability, protection of jobs and good governance 
of sustainability transitions. One negative impact of digitalisation 
could be the rebound effects, where easier access to services could 
increase demand and with it GHG emissions. Digitalisation in the

manufacturing sector could also provide a comparative advantage 
to developed countries due to the falling importance of labour costs, 
while the barriers to emerging economies seeking to enter global 
markets could accordingly be increased.
In respect of governance, (Krishnan et al. 2020) point out that the 
creation of synergies between sustainable development and low-
emission urbanisation based on digitalisation could face barriers in 
the form of inadequate knowledge of structures and value creation 
through ecosystems that would need to be addressed by means 
of smart digitalising, requiring organisational measures to support 
Urban areas are one of the main arenas for new digital solutions due 
to rapid urbanisation rates and high concentrations of settlements, 
businesses and supply systems, which offer great potential for large-
scale digital systems. The emergence of smart cities has supported the

uptake of smart integrated energy, transportation, water and waste-
management systems, while synergies have been created in terms 
of more flexible and efficient systems. In its 2018 Policy and Action 
document, the Japanese Business Federation (Keidanren) launched 
Society 5.0, which includes plans for smart-city development (Carraz 
and Yuko 2019; Narvaez Rojas et al. 2021). To achieve smart cities, 
Society 5.0 aimed to facilitate diverse lifestyles and business success, 
while the quality of life offered by these options will be enhanced. 
It also aims to offer high-standard medical and educational services. Autonomous vehicles will be available and integrated with smart-
grid systems in order to facilitate mobility and flexibility in energy 
supply with a high share of renewable energy.
Chapter 6 of this report on ‘Energy Systems’ points out that there are 
many smart-energy options with the potential to support sustainable

development by facilitating the integration of high shares of 
fluctuating renewable energy in electricity systems, potentially 
storing energy in electric vehicle (EV) batteries or fuel cells, and 
applying load shifting by varying prices over time. It is concluded that 
very large efficiency gains are expected to emerge from digitalisation 
in the energy sector (Figure 6.18).
Section  9.9.2 in Chapter  9 concludes that the improved energy 
efficiency and falling costs in the building sector that could result 
from digitalisation could have rebound effects in increasing both 
energy consumption and comfort levels. Increasing GHG emissions 
could be the result, but if low-income consumers are given faster 
access to affordable energy, this could agree with the SDGs, making 
it desirable to integrate policies targeting mitigation.
Section  10.1.2 in Chapter  10 discusses how the sharing economy, 
which, for example, could be facilitated by ICT platforms, could

influence both mitigation and the SDGs. On the one hand, sharing 
has the potential to save transport emissions, especially if EVs are 
supplied with decarbonised grid electricity. However, an increase in 
transport emissions could result from this if increasing demand and 
higher comfort levels are facilitated, for example, by making access 
to EVs relatively easy compared with mass transit. Another possible 
trade-off is that the supply of public transport services would be 
limited to the elderly and other user groups.
Green innovation in agriculture is another emerging area in which 
digitalisation is making huge progress. From the perspective of water 
provision, weather data can be used to predict rain amounts so 
that farmers can better manage the application of farm chemicals 
to minimise polluting aquifers and surface-water systems used 
for drinking water. Meanwhile, smart meters, on-site and remote 
sensors and satellite data connected to mobile devices allow real-

time monitoring of crop-water and optimal irrigation requirements. 
On the supply side, remote tele-control systems and efficient 
irrigation technologies enable farmers to control and optimise  the 
quantity and timing of water applications, while minimising 
the energy-consumption trade-offs of pressurised irrigation in both 
rural and urban agricultural contexts (Germer et al. 2011; Ruiz-
Garcia et al. 2009).
Technology-driven precision agriculture, which combines 
geomorphology, satellite imagery, global positioning and smart 
sensors, enables enormous increases in efficiency and productivity. 
Taken together, these technologies provide farmers with a decision-
support system in real time for the whole farm. Arguably, the world 
could feed the projected rise in population without radical changes 
to current agricultural practices if food waste can be minimised or 
eliminated. Digital technologies will contribute to minimising these

losses through increased efficiencies in supply chains, better shipping 
and transit systems, and improved refrigeration.

1761
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717In conclusion, in most cases digitalisation options may have both 
positive synergistic impacts on mitigation and the SDGs and some 
negative trade-offs. Energy-sector options are assessed primarily as 
having synergies, while some digitalisation options in transport could 
increase the demand for emission-intensive modes of transport. 
Digital platforms for the sharing economy could have both positive 
and negative impacts depending on the goods and services that are 
actually exchanged (Cross-Chapter Box  6 in Chapter  7). Options related 
to agriculture and the water-energy-food nexus (WEFN) could help 
manage resources more efficiently across sectors, which could create 
synergies. Digitalisation can also raise a number of ethical challenges 
according to (Clark et al. 2019). Wider public discussion of internet-
based activities was accordingly recommended, including topics such

as the negotiation of online consent and the use of data for which 
consent has not been obtained.
17.3.3.7 Cross-sectoral Overview of Synergies and Trade-offs 
Between Climate Change Mitigation and the SDGs
Based on a qualitative assessment in the sectoral Chapters 6, 7, 8, 
9, 10, and 11, Figure 17.1 below provides an overview of the most 
likely links between sectoral mitigation options and SDGs in terms 
of synergies and trade-offs. The general overview provided in the 
figure is supplemented by specific sector-by-sector comments on 
how the synergies and trade-offs mapped depend on the scale of 
implementation and the overall development context of places 
where the mitigation options are implemented. For some mitigation 
options these scaling and context-specific issues imply that there 
can be both synergies and trade-offs in relation to specific SDGs. In 
addition to the information provided in Figure 17.1, Supplementary

Material Table  17.SM.1 includes the detailed background material 
provided by the sectoral chapters in terms of qualitative information 
for each of the synergies and trade-offs mapped.
The assessment of synergies and trade-offs presented in Figure 17.1 
depends on the underlying literature assessed by the sectoral 
chapters. In cases where no information about the links between 
specific mitigation options and SDGs are indicated, this does not 
imply that there are no links, but rather that the links have not been 
assessed by the literature.
Most of the energy-sector options are assessed as having synergies 
with several SDGs, but there could be mixed synergies and trade-
offs between SDG 2 (zero hunger) for wind and solar energy, and for 
hydropower due to land-use conflicts and fishery damage. Offshore 
wind could also have both synergies and trade-offs with SDG 14 (life 
below water) dependent on scale and implementation site, and it is

emphasised that land-use should be coordinated with biodiversity 
concerns. Both wind and solar energy are assessed as having trade-
offs with SDG 12 (responsible production and consumption) due to 
significant material consumption and disposal needs.
Geothermal energy is assessed as having synergies with SDG 1 (no 
poverty) due to energy access, and mixed synergies and trade-offs 
in relation to SDG 3 (good health and well-being) due to reduced 
air pollution, but with some risks in relation to water pollution, 
and in relation to SDG 6 (clean water and sanitation), if it is not well managed. Nuclear power is assessed as having synergies with 
SDG 3 (good health and well-being) due to reduced air pollution, but 
potential trade-offs in relation to SDG 6 (clean water and sanitation) 
due to high water consumption, and water consumption issues are 
also possible in relation to many of the other mitigation options 
in the energy sector. Synergies are identified in relation to SDG 12

(responsible production and consumption) for nuclear power due 
to low material consumption. CCUS has been assessed as having 
trade-offs in relation to SDG 1 (no poverty) due to high costs and 
SDG 6 (clean water and sanitation) due to high water consumption. 
Synergies are related to SDG 3 (good health and well-being), and to 
SDG 9 (industry, innovation and infrastructure) due to the facilitation 
of decarbonisation of industrial processes. Both synergies and trade-
offs could arrive in relation to SDG 12 (responsible production and 
consumption), since some rare chemicals and other inputs could in 
some cases be used with large-scale applications.
Bioenergy use as a  fuel is assessed as one of the energy-sector 
mitigation options with most synergies and trade-offs with the SDGs. 
There could be synergies with SDG 1 (no poverty), with SDG 8 (decent 
work and economic growth) and SDG 9 (industry, innovation and 
infrastructure). This option, however, if combined with CCS, can be

expensive and can compromise SDG 1 (no poverty) due to the high 
costs involved.
Agriculture, forestry and other land use (AFOLU) mitigation options 
are very closely linked to the SDGs and offer both synergies and 
trade-offs, which in many cases are highly dependent on the scale 
of implementation. All the mitigation options included in Figure 17.1 
are assessed as potentially having synergies with SDG 1 (no poverty), 
but trade-offs could also happen if large areas are used for biocrops 
and taken away from other activities, thus causing poverty, as well as 
in relation to food costs if healthier diets are made more expensive. 
In relation to SDG 2 (zero hunger), most of the mitigation options 
are assessed as being associated with both synergies and trade-
offs. Trade-offs are particularly a  risk with large-scale applications 
of afforestation projects, bioenergy crops and other land-hungry 
activities, which can crowd out food production.

SDG 3 (good health and well-being) can be supported by many 
mitigation options in the agriculture, forestry and food sectors, 
primarily due to the reduced environmental impacts, and the same 
is the case with SDG 14 (life below water) due to decreased nutrient 
loads, and SDG 15 (life on land) due to increased biodiversity, 
with the caveat however, that SDGs 14 and 15 could have both 
synergies and trade-offs dependent on land use. It is considered that 
there could be both synergies and trade-offs in relation to SDG 8 
(decent work and economic growth) due to competition over land 
use related to the mitigation options reducing deforestation and 
reforestation and restoration, and the same is the case in relation 
to SDG 7 (affordable and clean energy) depending on the economic 
outcome of the mitigation options. Similarly, the mitigation option 
of reduced CH4 and N 2O emissions from agriculture are assessed as 
having mixed impacts on SDG 8 (decent work and economic growth),

and SDG 9 (industry, innovation and infrastructure) depending on 
innovative food production. The mitigation options of reforestation 
and forest management are assessed as having mixed impacts on

1762
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17
Type of relations:
1 No poverty
3 Good health and wellbeing
4 Quality education
6 Clean water and sanitation
7 Affordable and clean energy
9 Industry, innovation and infrastructure14 Life below water
17 Partnership for the goalsConﬁdence level:
Low conﬁdenceDeforestation, loss and 
degradation of peatlands 
and coastal wetlands2Soil carbon management 
in cropland and grasslands, 
agroforestry, biochar1
Lower of the two conﬁdence 
levels has been reported4Timber, biomass, agri. feedstock3Related Sustainable Development Goals:
Not assessed due 
to limited literature5Sectoral and system mitigation options Chapter sourceRelation with Sustainable Development Goals

17 16 15 14 12 11 109 8 7 6 5 4 3 2 1Urban systemsUrban land use and spatial planning Sections 8.2, 8.4, 8.6
Electriﬁcation of the urban energy system Sections 8.2, 8.4, 8.6
District heating and cooling networks Sections 8.2, 8.4, 8.6
Urban green and blue infrastructure Sections 8.2, 8.4, 8.6
Waste prevention, minimisation and management Sections 8.2, 8.4, 8.6
Integrating sectors, strategies and innovations Sections 8.2, 8.4, 8.6TransportFuel efﬁciency – light-duty vehicle Sections 10.3, 10.4, 10.8
Electric light-duty vehicles Sections 10.3, 10.4, 10.8
Shift to public transport Sections 10.2, 10.8, Table 10.3
Shift to bikes, e-bikes and non motorised transport Sections 10.2, 10.8, Table 10.3
Fuel efﬁciency – heavy-duty vehicle Sections 10.3, 10.4, 10.8
Sections 10.3, 10.4, 10.8 Fuel shift (including electricity) – heavy-duty vehicle
Shipping efﬁciency, logistics optimisation, new fuels Sections 10.6, 10.8
Aviation – energy efﬁciency, new fuels Sections 10.5, 10.8

Biofuels Sections 10.3, 10.4, 10.5, 10.6, 10.8IndustryEnergy efﬁciency Section 11.5.3
Material efﬁciency and demand reduction Section 11.5.3
Circular material ﬂows Section 11.5.3
Electriﬁcation Sections 11.5.3, 6.7.7
CCS and carbon capture and utilisation (CCU) Section 11.5.3Agriculture, forestry and
other land use (AFOLU)Carbon sequestration in agriculture  1Sections 7.3, 7.4, 7.6
Reduce CH4 and N2O emission in agriculture Section 7.4
Reduced conversion of forests and other ecosystems  2 Section 7.4
Improved sustainable forest management Section 7.4
Reduce food loss and food waste Section 7.5
Shift to balanced, sustainable healthy diets Section 7.4
Renewables supply  3Section 7.6Ecosystem restoration, reforestation, afforestation Section 7.4Energy systemsWind energy Sections 6.4.2, 6.7.7
Solar energy Sections 6.4.2, 6.7.7
Hydropower Section 6.4.2
Geothermal energy Section 6.4.2
Carbon capture and storage (CCS) Section 6.4.2, 6.7.7 Bioenergy Sections 6.4.2, 12.5, Box 6.1

Nuclear power Section 6.4.2, Figure 6.18
10 Reduced inequalities
11 Sustainable cities and communities
12 Responsible consumption and production
13 Climate actionBuildingsDemand-side management Section 9.8, Table 9.5
Highly energy efﬁcient building envelope Section 9.8, Table 9.5
Efﬁcient heating, ventilation and air conditioning (HVAC) Section 9.8, Table 9.5
Efﬁcient appliances Section 9.8, Table 9.5 
Building design and performance Section 9.8, Table 9.5
Change in construction methods and circular economy Sections 9.4, 9.5
Change in construction materials Section 9.4On-site and nearby production and use of renewables Section 9.8, Table 9.5 
Synergies
Both synergies and trade-offs  4
Blanks represent no assessment  5
Figure 17.1 | Trade-offs and synergies between sectoral mitigation options and the Sustainable Development Goals (SDGs).

1763
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717SDG 10 (reduced inequalities) depending on the involvement of 
local communities in projects. The assessment emphasises that the 
synergies and trade-offs of the mitigation options with the SDGs 
in this sector are very context- and scale-dependent, depending 
on how measures are carried out, for example, in relation to the 
enhanced production of renewables needed to replace fossil fuel-
based products. If done on a massive scale and not adapted to local 
circumstances, there are adverse implications for food security, 
livelihoods and biodiversity.
All the urban mitigation options that have been assessed are 
considered to have synergies with the SDGs, and in a  few cases 
both synergies and trade-offs are identified. In general, many links 
between mitigation options in the urban area and the SDGs have 
been identified in the literature. Urban land use and spatial planning,

for example, can support SDG 1 (no poverty), and can also reduce 
vulnerability to climate change if integrated planning is undertaken, 
while access to food (SDG 2: zero hunger), and water (SDG 6: clean 
water and sanitation) can also be achieved if supported by integrated 
planning. Electrification, district heating, and green-and-blue 
infrastructure in urban areas are expected to have synergies with all 
the SDGs addressed by the reviewed studies.
Mitigation options like waste-prevention minimisation and 
management are also assessed as having many synergies with 
the SDGs, but trade-offs could depend on the application of air-
pollution control technologies, and on the character of informal 
waste-recycling activities. The impacts of the possible synergies and/
or trade-offs with the SDGs will change according to the specific 
urban context. Synergies and/or trade-offs may be more significant in 
certain contexts than others. Regarding the SDGs, urban mitigation

can support shifting pathways of urbanisation towards sustainability. 
The feasibility of urban mitigation options is also malleable and can 
increase with more enablers. Strengthened institutional capacity 
that also supports the scale and coordination of the mitigation 
options can increase the synergies between urban mitigation 
options and the SDGs.
As for the urban mitigation options, the reviewed building-sector 
studies reveal a lot of links between mitigation and the SDGs. Highly 
efficient building envelopes are expected to have synergies with the 
SDGs in all cases except those with potential trade-offs in relation to 
SDG 10 (reduced inequalities). Many SDG synergies are also identified 
for the building design and performance, heating, ventilation and air 
conditioning, and efficient appliances mitigation options. However, 
some trade-offs could appear in relation to SDG 8 (decent work and 
economic growth) due to macroeconomic impacts of reduced energy

consumption, decreasing prices and stranded investments. Similar 
issues related to the economic impacts of reduced energy demand 
are also highlighted for all the other mitigation options, including 
for the building sector. In relation to construction materials and the 
circular economy, some trade-offs have been identified in relation to 
SDG 6 (clean water and sanitation) and SDG 15 (life on land) related 
to the use of bio-based materials.
Consideration of the building sector highlights important context-
specific issues related to synergies and trade-offs between mitigation options and SDGs such as the economic impacts (synergies and 
trade-offs) associated with reduced energy demand, resulting in 
lower energy prices, energy-efficiency investments, the fostering of 
innovation and improvements in labour productivity. Furthermore, 
the distributional costs of some mitigation policies may hinder the 
implementation of these measures. In this case, appropriate access

policies should be designed to shield poor households efficiently from 
the burden of carbon taxation. Under real-world conditions, improved 
cookstoves have shown smaller, and in many cases limited, long-term 
health and environmental impacts than expected, as the households 
use these stoves irregularly and inappropriately, and fail to maintain 
them, so that their usage declines over time. Specific distributional 
issues are highlighted in relation to various cookstove programmes.
The mitigation options in the transportation sector are assessed as 
having synergies with SDG 1 (no poverty) and SDG 3 (good health and 
well-being) due to reduced environmental pollution, with exceptions 
in relation to pollution from biofuels and the risks of traffic accidents. 
Trade-offs are also mentioned in relation SDG 2 (zero hunger) where 
the production of biofuels takes land away from food production. 
Synergies are assessed in relation to SDG 7 (affordable and clean

energy), SDG 8 (decent work and economic growth) and SDG 9 
(industry, innovation and infrastructure). It is emphasised that some 
mitigation options, like the increased penetration of electric vehicles, 
require innovative business models, and that digitalisation and 
automatic vehicles will support the socio-economic structures that 
impede adoption of EVs and the urban structures that enable reduced 
car dependence. In conclusion, there is a  need for investments in 
infrastructure that can support alternative fuels for light-duty vehicles 
(LDVs). The large-scale electrification of LDVs requires the expansion 
of low-carbon power systems, while charging or battery-swapping 
infrastructure is needed for some segments.
The mitigation options in the industrial sector have been assessed 
primarily as having synergies with meeting the SDGs. Several options, 
including energy efficiency, material recycling and electrification, are

assessed has being able to create increased employment and business 
opportunities related to SDG 8 (decent work and economic growth), 
but material-efficiency improvements could reduce tax revenues. 
Electrification is assessed as having many synergies with SDGs, such 
as supporting SDG 1 (no poverty), SDG 2 (zero hunger), and SDG 3 
(good health and well-being). CCS applied in industry is assessed as 
having synergies in terms of the control of non-CO2 pollutants (such 
as sulphur dioxide), but increases in non-CO 2 pollutants (such as 
particulate matter, nitrogen oxide and ammonia). The conclusion is 
that 15–25% additional energy will be required by CCS technologies 
compared with conventional plants, implying that production costs 
could increase significantly. For the industrial sector in general, it is 
concluded that the balance between synergies and trade-offs between 
mitigation options and SDGs in industry depends on technology and

the scale of the sharing of co-benefits across regions, as well as on the 
sharing of benefits in business models over whole value chains.
Thus, a  number of cross-sectoral conclusions on synergies and 
trade-offs between mitigation options and the SDGs appear from 
the overview provided in Figure 17.1. There are many synergies in 
all sectors between mitigation options and the SDGs, and in a few

1764
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17cases there are also significant trade-offs that it is very important to 
address, since they can compromise major SDGs including SDG 1 (no 
poverty), SDG 2 (zero hunger), and in some cases SDG 14 (life below 
water) and SDG 15 (life on land). In particular, mitigation options 
in relation to land use, such as afforestation and reforestation and 
bioenergy crops, can in some cases imply trade-offs with access to 
food and local sharing of benefits, but synergies can also exist if 
proper land management and cross-sectoral policies take sustainable 
land use into account. The impacts and trade-offs for this sector 
are highly scale- and context-dependent, so the final outcome of 
mitigation policies should be considered in detail.
The urban systems and transportation could potentially achieve many 
synergies between mitigation policies and the SDGs, but integrated

planning and infrastructure management are critical to avoiding 
trade-offs. Similarly, the buildings sector and industry have identified 
many potential synergies between mitigation options and the SDGs, 
but that raises issues related to the costs of new technologies, and 
in relation to households and buildings, important equity issues are 
emerging in relation to the ability of low-income groups to afford 
the introduction of new technologies. Altogether these cross-sectoral 
conclusions call for a need to support policies that aid coordination 
between different sectoral domains and that include context-specific 
assessments of the sharing of benefits and costs related to the 
implementation of mitigation options.
17.4 Key Barriers and Enablers of the 
Transition: Synthesising Results
This section provides a deep and broad synthesis of theory (Section 17.2) 
and evidence (Section  17.3) in order to identify the conditions that

either enable or inhibit transitions to sustainable low-carbon futures. 
Following the literature on sustainability transitions (Cross-Chapter 
Box 12 in Chapter 16), the section finds that there is rarely any one 
single factor promoting or preventing such transitions. Rather, marked 
departures from business as usual typically involve several factors, 
including technological innovations, shifts in markets, concerted 
efforts by scientists and civil-society organisations to raise awareness 
of the costs of continued emissions, social movements, policies and 
governance arrangements, and changes in belief systems and values.
All of this comes together in a  co-evolutionary process that has 
unfolded globally, internationally and locally over several decades 
(Hansen and Nygaard 2014; Rogge et al. 2017; Sorman et al. 2020), 
and that may be guided or facilitated by interventions that target 
leverage points in the underlying development path (Burch and Di

Bella 2021; Leventon et al. 2021). While transitions necessarily follow 
context-specific trajectories, more general lessons can be drawn by 
comparing the empirical details with both system-level and narrower 
explanations of change.
Sections  17.2 and 17.3 show that transitions often face multiple 
barriers, including infrastructure lock-in, behavioural, cultural 
and institutional inertia (Markard et al. 2020), trade-offs between 
transitions and other social or political priorities (Chu 2016), cost and 
a  reliable (and growing) supply of renewable-energy technologies and constituent materials (García-Olivares et al. 2018). Transitions 
away from fossil fuels and toward renewable energy-based systems, 
for instance, will require significant land-use decisions to avoid 
negative trade-offs with biodiversity and food security (Capellán-
Pérez et al. 2017). Previous sections underline a related need to move 
beyond focusing on ‘rational’ assessments of the costs and benefits

of policies and technologies to involve people at all levels in order 
to overcome these multiple barriers. A transition to a lower carbon 
system is unlikely to happen even if models find it technically feasible 
and cost-effective. Rather, achieving a  transition requires breaking 
locked-in high-carbon technological trajectories, path dependencies 
and resistance to change from the industries and actors that are 
benefiting from the current system (Rogge et al. 2017). Lock-in effects 
may be weaker in sectors and policy areas where fewer technologies 
exist, potentially opening the door to innovations that embed the 
climate in broader sustainability objectives (e.g.,  technologies and 
innovations that support the integration of food, water and energy 
goals). Such effects may still happen when there are significant 
information asymmetries and high-cost barriers to action, as can 
occur when working across multiple climate and development-
related sectors (Kemp and Never 2017).

However, the same conditions that may serve to impede a transition 
(i.e.,  organisational structure, behaviour, technological lock-in) can 
also be ‘flipped’ to enable it (Burch 2010; Lee et al. 2017), while the 
framing of policies that are relevant to the sustainable development 
agenda can also create a stronger basis and stronger policy support. 
The technological developments and broader cultural changes that 
may generate new social demands on infrastructure to contribute to 
sustainable development will involve a process of social learning and 
awareness building (Naber et al. 2017; Sengers et al. 2019). However, 
it is also important to note that strong shocks to these systems, 
including accelerated climate change impacts, economic crises and 
political changes, may provide crucial openings for accelerated 
transitions to sustainable systems through fundamental institutional 
changes (Broto et al. 2014). The global COVID-19 pandemic is

one such shock that has sparked widespread conversations about 
recovery that is fundamentally more sustainable, equitable and 
resilient (McNeely and Munasinghe 2021). Key enabling conditions 
appear to be individual and collective actions, including leadership 
and education; financial, material, social and technical drivers that 
foster innovation; robust national and regional innovation systems 
that enhance technological diffusion (Wieczorek 2018); supportive 
policy and governance dynamics at multiple levels that permit both 
agility and coherence (Göpel et al. 2016); measures to recognise and 
address the challenges to equality inherent in the transition; and long-
range, holistic planning that explicitly seeks synergies between 
climate change and sustainable development while avoiding trade-
offs. The sections that follow seek to assess and integrate these key 
categories of the barriers to and enablers of an accelerated transition 
to sustainable development pathways.

17.4.1 Behavioural and Lifestyle Changes
Transitions toward more sustainable development pathways are both 
an individual and a  collective challenge, requiring an examination

1765
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717of the role of values, attitudes, beliefs and structures that shape 
behaviour, and of the dynamics of social movements and education 
at the local community, regional and global levels. Labelling the 
carbon included in products, for example, could help the decision-
making process and increase awareness and knowledge. Individual 
action suggests aggregated but uncoordinated actions taken 
by individuals, whereas collective sustainability actions involve 
coordination, a  process of participation and governance that may 
ensure more efficient, equitable and effective outcomes. There is 
evidence that the behaviour of individuals and households are part 
of a more encompassing collective action (Section 5.4.1).
Indeed, individual actions are necessary but insufficient to deliver 
transformative mitigation, and it is suggested that this be coupled

with collective actions to accelerate the transition to sustainable 
development (Dugast et al. 2019). Actors with conflicting interests 
will compete to frame mitigation technologies that either ‘build or 
erode’ the legitimacy of the technology, contested framing sites 
that can occur between incumbent and emerging actors or between 
actors in new but competing spaces (Rosenbloom et al. 2016). How 
narratives are built around desired development pathways and 
specific emerging technologies, as well as how local values are 
integrated into visions of the future, have relevance for how these 
experiments are managed and enabled to expand (Horcea-Milcu 
et al. 2020; Lam et al. 2020).
17.4.1.1 Social Movements and Education
Sustainable development and deep decarbonisation will involve 
people and communities being connected locally through various 
means – including globally via the internet and digital technologies 
(Bradbury 2015; Scharmer 2018; Scharmer, C, Kaufer 2015) – in ways

that form social fields that allow sustainability to unfold (Gillard 
et al. 2016), and that prompt other shifts in thinking and behaviour 
that are consistent with the 1.5°C goal (O’Brien 2018; Veciana and 
Ottmar 2018). Indeed, social movements serve to develop collective 
identities, foster collective learning and accelerate collective action 
ranging from energy justice (Campos and Marín-González 2020) 
(Section 17.4.5) to restricting fossil fuel extraction and supply (Piggot 
2018). This does not apply only to adults: as seen in the ‘Fridays for 
Future’ marches, the young are also involving themselves politically 
(Peterson et al. 2019). Many initiatives have started with these 
marches, including ‘science for future’ and new forms of sustainability 
science (Shrivastava et al. 2020).
It was Theory-U (Scharmer 2018), building on the work of scholars 
such as Schein, Lewin and Senge) that inspired a so-called ‘massive

open online course’ (MOOC) jointly initiated by the Bhutan Happiness 
Institute and German Technical Assistance (GIZ) in 2015, since when 
it has been developed further and adapted to transform business, 
society and self as one example of how social movements can go 
together with science and education. It brings together people from 
different professions, cultures and continents in shared discussions 
and practices of sustainability. It also included marginalised 
communities and is shifting towards more sustainable lifestyles in all 
sectors (Nikas et al. 2020), including climate action.Moreover, approaches like the ‘Art of Hosting’ (Sandfort and Quick 
2015) and qualitative research methods such as storytelling and 
first-person research, as well as second-person inquiries, for example 
(Scharmer, C, Kaufer 2015; Trullen and Torbert 2004; Varela 1999), 
have been employed to bridge differences in cultures and sciences,

as well as to forge connections between those working on climate 
change and sustainable development. Likewise, experiential tools, 
simulations and role-playing games have been shown to increase 
knowledge of the causes and consequences of climate change, the 
sense of urgency around action and the desire to pursue further 
learning (Ahamer 2013; Eisenack and Reckien 2013; Hallinger et al. 
2020; Rooney-Varga et al. 2020).
The results from these research communities reveal how 
experiential learning takes place and how it encourages bonding 
between people, society and nature. This can be achieved by going 
jointly and consciously into nature (Gioacchino 2019), by creating 
spaces for intensive-dialogue sessions with colleagues (Goldman-
Schuyler et al. 2017) and forming, for example, a very practical u.lab 
hub, which involves following the MIT-u.lab course with a  local 
community and is accompanied scientifically (Pomeroy and Oliver

2018). Others have pointed to social networks such as the ‘transition 
initiative’ (Hopkins 2010), eco-village networks (Barani et al. 2018), 
civil-society movements (Seyfang and Smith 2007) and intentional 
communities (Grinde et al. 2018; Veciana and Ottmar 2018) as ways 
of generating the shared understandings that are central to inner 
and outer transitions, as well as the broader development of social 
movements. In some cases, these networks build on principles 
like permaculture to encourage people to ‘observe and interact’, 
‘produce no waste’ and ‘design from patterns to details’, not only 
in agriculture and gardening, but also in sustainable businesses 
and technologies to reduce CO 2 emissions (Ferguson and Lovell 
2014; Lessem 2018).
A related line of inquiry involves education for sustainable 
development (ESD). This builds on the UNESCO programme, ‘ESD for 
2030’, and involves core values like peace culture, valuing cultural

diversity and living global citizenship. One of the core insights 
from research on ESC is lifelong education continuing outside the 
classroom, a  lifelong learning process that involves sustained 
actions by all ages and social segments (Hume and Barry 2015) and 
achieving collaboration (Munger and Riemer 2012). Some authors 
have pointed to good levels of communication either directly or 
through the internet as the key to facilitating this learning (Sandfort 
and Quick 2015). Others have noted that transformative learning – 
that is, deepening the learning process – is critical because it helps to 
induce both shared awareness and collective actions (Brundiers et al. 
2010; Singleton 2015; Wamsler and Brink 2018).
A final area of work points to the importance of moving toward the 
knowledge production that underpins awareness-raising (Pelling 
et al. 2015). The accumulation of applied knowledge is leading 
increasingly to the co-design of participatory research with local

stakeholders who are investigating and transforming their own 
situations in line with climate action and sustainable development 
(Wiek et al. 2012; Abson et al. 2017; Fazey et al. 2018).

1766
Chapter 17 Accelerating the Transition in the Context of Sustainable Development1717.4.1.2 Habits, Values and Awareness
Many of the cases that explore transitions to sustainable development 
point to ingrained habits, values and awareness levels as the most 
persistent yet least visible barriers to a  transition. For example, in 
the transport sector, individuals can quickly become accustomed 
to personal vehicles, making it difficult for them to transition to 
sustainable, low-carbon modes of public transport. Demand for high-
carbon transportation may also be locked-in, and habits reinforced, 
if low-cost housing (for instance) is not sufficiently served by more 
sustainable (i.e., mass transit, safe cycling and walking infrastructure) 
transportation options (Mattioli et al. 2020).
This is made all the more challenging because car-manufacturing 
‘incumbents’ utilise information campaigns directed at the public,

pursue lobbying and consulting with policymakers, and set technical 
standards that privilege the status quo and prevent the entry of more 
sustainable innovations (Smink et al. 2015; Turnheim and Nykvist 
2019). Tools such as congestion pricing, however, have been shown 
to be effective in motivating the switch from single-occupancy 
vehicle use to public transit, thus improving air quality and reducing 
traffic delays in dense city centres (Baghestani et al. 2020).
Complicating the problem further is that even well-intentioned 
top-down programmes initiated by an external actor may in some 
cases ultimately hinder transformative change (Breukers et al. 2017). 
For instance, in Delhi, India, attempts to introduce ostensibly more 
sustainable bus rapid transit (BRT) systems failed in part due to 
an arguably top-down approach that had limited public support. 
It may nonetheless be difficult to win public support (Bachus and 
Vanswijgenhoven 2018), and even grassroots initiatives may

themselves be contested and dynamic, making it difficult to 
generate the collective push to drive a bottom-up transition forward 
(Hakansson 2018).
However, dominant, top-down approaches and local, grassroots 
‘alternative’ approaches and values do overlap and interact. For 
example, in Manchester, UK, dominant and alternative discourses 
interact with each other to create sustainable transformations 
through re-scaling (decentralising) energy generation, creating local 
engagement with sustainability, supporting green infrastructure 
to reduce costs, reclaiming local land, transforming industrial 
infrastructure and creating examples of sustainable living 
(Hodson et al. 2017).
Embedding local values in higher-level policy frameworks is also 
significant for forest communities in Nepal and Uganda. Even so, 
policy intermediaries are not confident that these values will be 
advanced due largely to an emphasis on carbon accounting and the

distribution of benefits (Reckien et al. 2018). In this case, however, 
norm entrepreneurs were able to promote the importance of local 
values through the formation of grassroots associations, media 
campaigns and international support networks (Reckien et al. 2018).17.4.2 Technological and Social Innovation
Individuals and organisations, like institutional entrepreneurs, can 
function to build transformative capacity through collective action 
(Brodnik and Brown 2018). The transition from a traditional water-
management system to the Water Sensitive Urban Design (WSUD) 
model in Melbourne offers an illustration of how whole systems can 
be changed in an urban system.
Private-sector entrepreneurs also play an important role in fostering 
and accelerating transitions to sustainable development (Burch et al. 
2016; Ehnert et al. 2018a; Dale et al. 2017). Sustainable entrepreneurs 
(SEs), for instance, are described as those who participate in the

development of an innovation while simultaneously being rooted 
in the incumbent energy-intensive system. SE actors who have 
developed longer-term relationships, both formal and informal, with 
the public authorities can have considerable influence on developing 
novel renewable-energy technologies (Gasbarro et al. 2017). 
Institutions and policies that nurture the activities of sustainable 
entrepreneurs, in particular small- and medium-sized enterprises 
(Burch et al. 2016), can facilitate and strengthen transitions toward 
more sustainable development pathways, as can more fundamental 
adjustments to underlying business models, rather than relying only 
on incremental adjustments in the efficiency with which resources 
are used (Burch and Di Bella 2021).
The creation and growth of sustainable energy and clean-tech clusters 
enable economic development and transformation on regional scales. 
Such clusters can put pressure on incumbent technologies and rules

to accelerate energy transitions. Successful clusters are nurtured by 
multi-institutional and multi-stakeholder actors building institutional 
support networks, facilitating collaboration between sectors and 
actors, and promoting learning and social change. Notably, regional 
economic clusters generate a buzz, which can have a strong influence 
on public acceptance, support and enthusiasm for socio-technical 
transitions (McCauley and Stephens 2012).
In Norway, many incumbent energy firms have already expanded 
their operations into the alternative-energy sector as both 
producers and suppliers (who often follow the lead of producers). 
Producers are responding to perceptions of larger-scale changes 
in the energy landscape (e.g.,  the green shift), along with 
uncertainties in their own sectors, and innovation can spill across 
actors in multiple sectors  (Koasidis et al. 2020). While these firms 
are expanding out of self-interest, the expansion provides more

legitimacy to new forms of technology and enables transfers of 
knowledge and resources to be introduced within this developing 
niche (Steen and Weaver 2017). Many large, well-established firms 
are pursuing sustainability agendas and opting for transparency 
with regard to their greenhouse gas emissions (Kolk et al. 2008; 
Guenther et al. 2016), supply-chain management (Formentini and 
Taticchi 2016) and sustainable technology or service development 
(Dangelico et al. 2016).

1767
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717Experiments with the transition open up pathways that can lead to 
energy transitions on broader scales. Experiments can build capacity 
by developing networks and building bridges between diverse actors, 
leveraging capital from government funds, de-risking private- and 
public-sector investment, and acting as hubs for public education 
and engagement (Rosenbloom et al. 2018).
Material barriers and spatial dynamics (Coenen et al. 2012; Hansen 
and Coenen 2015) are other critical obstacles to innovation: often, 
infrastructure and built environments change more slowly than 
policies and institutions due to the inherently long lifespans of 
fixed assets (Turnheim and Nykvist 2019). The example of transport 
infrastructure in Ontario, Canada, illustrates the need to integrate 
climate change into these infrastructural decisions in the very short

term to combat the risk of being left with unsustainable planning 
features long into the future, especially combustion engines, 
significant road networks and suburbanisation (Birch 2016).
17.4.3 Financial Systems and Economic Instruments
Market-oriented policies, such as carbon taxes and green finance, 
can promote low-carbon technology and encourage both private 
and public investment in enabling transitions. Policies that are 
currently being tested include loan guarantees for renewable-energy 
investments in Mali, policy insurance to reduce credit defaults within 
the feed-in tariff regime in Germany, or pledged funding to fully 
finance or partner private firms in order to advance renewable-energy 
projects (Roy et al. 2018a). However, there may be some limitations in 
using carbon pricing alone (rather than in combination with flexible 
regulations and incentives) where market failures hinder low-carbon 
investments (Campiglio 2016; World Bank 2019) and high political

costs are incurred (Van Der Ploeg 2011).
Many forms of transformational change to energy systems are 
not possible when financial systems still privilege investing in 
unsustainable, carbon-intensive sectors. One of the root causes of the 
failure of traditional financial systems is the undervaluation of natural 
capital and unsettled property-right issues that are associated with 
it. The exclusion of proper rents for scarcities or for global and local 
externalities, including climate change, can undermine larger-scale 
changes to energy systems (Clark et al. 2018). But even smaller-
scale low-carbon energy and infrastructure projects can fail to get 
off the ground if uncertainty and investment risk discourage project 
planning and bank-lending programmes (Bolton et al. 2016). The EU’s 
previous actions regarding the ‘shareholder maximisation norm’ and 
non-binding measures have created path dependencies, limiting its

flexibility in creating sustainable financial legislation. However, the 
Sustainable Finance Initiative and the Single Market may prove to be 
‘policy hotspots’ in encouraging sustainable finance (Ahlström 2019). 
Taking advantage of these hotspots may be crucial in overcoming 
path dependencies and setting new ones in motion.
One possible positive turn in this regard is the acceleration in 
investing in the environment (impact and ESG) globally: for instance, 
there is evidence that some institutional investors are divesting from 
coal, potentially auguring well for the future (Richardson 2017). The encouragement of governance and policy reforms that could 
facilitate similar expansions of investment in sustainable firms and 
sectors (Clark et al. 2018; Owen et al. 2018) could contribute to the 
dynamic feedback that gives a transition lift and injects momentum 
into it. Also, the degrowth movement, with its focus on sustainability

over profitability, has the potential to speed up transformations using 
alternative practices such as fostering the exchange of non-monetary 
goods and services if large numbers of stakeholders want to invest in 
these areas (Chiengkul 2017).
17.4.4 Institutional Capacities  
and Multi-level Governance
Capable institutions and multi-level governance often support 
the inter-agency coordination and stakeholder coalitions that 
drive sustainable transitions. Such institutions and governance 
arrangements are frequently required to formulate and implement 
the multi-sectoral policies that spur the adoption and scaling of 
innovative solutions to climate change and other sustainable 
development challenges. For example, such institutional and 
governance conditions have helped support the industrial policies 
that will be needed to spread renewables through the creation of 
domestic supply chains (Zenghelis 2020) or to pilot CDR methods 
(Quarton and Samsatli 2020).

However, government agencies with climate and other remits do not 
always work well together: the absence of coordination and consensus-
building mechanisms can further deepen inter-agency conflicts that 
stall a transition. These challenges appear not only within but also 
between levels of decision-making. Studies of developing megacities, 
for instance, have found the lack of mechanisms promoting vertical 
cross-level integration to be a sizeable constraint on decarbonisation 
(Canitez 2019). Differences in perspectives across non-state actors 
can similarly frustrate transitions in areas such as green buildings 
(Song et al. 2020).
Here coordination complicates matters: coalition-building may 
require mutually reinforcing changes to institutions and policies. 
For example, decentralised renewable energy has made progress 
in Argentina, but consumer electricity subsidies give agencies and 
firms supporting conventional energy an advantage over those

promoting renewable energy. Similarly, the lack of concrete guidance 
in green finance policies can deprive government agencies and other 
stakeholders of the information needed to balance ecological and 
financial goals (Wang and Zhi 2016). Many of these challenges can 
be particularly formidable in developing countries, where agencies 
lack sufficient financial and other capacities. A lack of government 
funds to cover ongoing maintenance costs along with resource 
shortages in rural locations can pose constraints on sustainable 
energy (Schaube et al. 2018).
Building inter-agency or multiple stakeholders is frequently 
challenging because of the mutually reinforcing interactions 
between institutions and ideas. The imperceptible embedding of 
long-standing development paradigms (such as ‘grow now, clean up 
later’) in agency rules and standard operating procedures can make

1768
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17changes to governance arrangements challenging. This is partly 
because these rules and procedures can also shape the interests of 
key decision-makers (e.g., the head of an environmental agency). For 
some, this suggests a need to look not just at changing prevailing 
ideas and interests, but also at broader institutional and governance 
arrangements (Kern 2011).
However, institutional and governance reforms can be more than 
a  technical exercise. Political, economic and other power relations 
can lock-in dominant institutional and economic structures, making 
the integration of climate and sustainable development agendas 
exceedingly difficult. For example, though there have been recent 
reforms, the initial lack of early progress in Australia’s energy 
transition is partly attributable to institutions of political economy 
being oriented to providing steady supplies of affordable fossil fuels

(Warren et al. 2016).
This suggests that it is important to look closely at the pre-existing 
political economic system as well as the institutional context and 
capacities in assessing the prospects for transitions to sustainability. 
Furthermore, this is how existing institutions interact with ideas 
that often strengthen lock-ins. To illustrate, studies have shown that 
the status-quo orientations of leaders (including decision-makers’ 
disciplinary backgrounds, world views and perceptions of risk) 
(Willis 2018), as well as the organisational culture and management 
paradigms within which they operate, affect the speed and ambitions 
of climate policies (Rickards et al. 2014).
Some studies have focused on factors that can break institutional 
and ideational lock-ins (Arranz 2017), while others have found 
that intentional higher-level (or, in the language of socio-technical 
transitions, ‘landscape’) pressures can be the destabilising force

needed to move transitions forward (Falcone and Sica 2015). Often 
the state or national government (as the sovereign that determines 
how resources are used and allocated) can play a  key role in 
destabilising incumbent energy regimes, a role that is significantly 
strengthened by public support (Arranz 2017; Avelino et al. 2016). 
However, this role is not limited to government insiders. In some 
contexts, regime outsiders have also played a  pivotal role in 
destabilising regimes by combining persuasive narratives that gain 
market influence (Arranz 2017). Carbon-intensive luxury goods and 
services for wealthy consumers, for instance, especially if applied 
at the ‘acceleration’ phase of a transition, can help transform long-
term social practices and behaviour and dissolve the ‘structural 
imperative for growth’ (Wiedmann et al. 2020). In a similar fashion, 
environmental taxes can remove ‘locked-in’ technology and place

pressure on dominant regimes to become more sustainable (Bachus 
and Vanswijgenhoven 2018).
In many contexts, it is not multiple institutional and policy variables 
that come together to break unsustainable inertias. In South 
Korea, where the state was an initiator and enabler of change, the 
clean-energy transition took much longer than anticipated due to 
private-sector resistance. However, when policymakers focused on 
incorporating adaptive learning and flexibility into their decision-
making, public- and private-sector interests gradually converged and 
joined with top-down policymaking to drive the transition forward (Lee et al. 2019). Thus, a political strategy can help align the interests 
and institutions needed to break lock-ins.
This becomes clear in studies that show that political coalitions can 
affect the speed of transitions (Hess 2014). These same studies show 
that incumbent industry coalitions are now competing with ‘green’

coalitions in terms of campaign spending over environmentally 
friendly ballot proposals (Hess 2014). Another way of shifting 
political-economic incentives is by offering a  realistic exit strategy 
for incumbents, like interventions that provide long-term incentives 
for renewable-energy firms (de Gooyert et al. 2016; Hamman 2019).
Overall, the previous subsection suggests that complementary 
policies and institutions that simultaneously integrate across multiple 
sectors and scales and also alter political economic structures that 
lock in a  carbon-intensive energy system are more likely to move 
a sustainable transition forward (Burch 2010). Yet, despite a trend in 
climate governance towards greater integration and inclusivity and 
certain other novel governance approaches, traditional approaches 
to governance and a tendency to incrementalism remain dominant 
(Holscher et al. 2019). Building the governance arrangements and

capacities that prioritise climate change across all sectors and scales 
while destabilising entrenched interests and putting pressure on 
existing norms, rules and practices is still needed in many contexts 
(Holscher et al. 2019).
At least three themes require further research in the scholarship on 
the governance of transitions: (i) the role of coalitions in supporting 
and hindering acceleration; (ii) the role of feedback, through which 
policies may shape actor preferences, which in turn create stronger 
policies; and (iii) the role of broader contexts (political economies, 
institutions, cultural norms, and technical systems) in creating 
conditions for acceleration (Roberts et al. 2018). Importantly, 
these themes may serve as both barriers to and opportunities for 
transitions (ibid.).
17.4.5 Equity in a Just Transition
Energy justice, although increasingly being emphasised (Pellegrini-
Masini et al. 2020), has been under-represented in the literature on

sustainability and in debates on energy transitions, and it remains 
a  contested term with multiple meanings (Green and Gambhir 
2020). Energy justice includes affordability, sustainability, equality 
(accessibility for current and future households) and respect (ensuring 
that innovations do not impose further burdens on particular groups) 
(Fuso Nerini et al. 2019). Furthermore, it suggests that a just transition 
is a shared responsibility among countries that are making more rapid 
progress towards net-negative emissions and those economies that 
are focused on pressing development priorities related to improved 
health, well-being and prosperity (van den Berg et al. 2020).
Looking at climate change from a justice perspective means placing 
the emphasis on (i) the protection of vulnerable populations from 
the impacts of climate change; (ii) mitigating the effects of the 
transformations themselves, including easing the transition for

those whose livelihoods currently rely on fossil fuel-based sectors;

1769
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717and (iii) envisaging an equitable decarbonised world. Neglecting 
issues of justice risks a  backlash against climate action generally, 
particularly from those who stand to lose from such actions 
(Patterson et al. 2018), and it will also have implications for the pace, 
scale and quality of the transition. Explicit interventions to promote 
sustainability transitions that integrate local spaces into the whole 
development process are necessary but not sufficient in creating 
a just transition (Breukers et al. 2017; Ehnert et al. 2018b).
Renewable energy transitions in rural, impoverished locations can 
simultaneously reinforce and disrupt local power structures and 
inequalities. Policy interventions to help the most impoverished 
individuals in a  community gain access to the new energy 
infrastructure are critical in ensuring that existing inequalities are not

reinforced. Individuals who are empowered by energy development 
projects can influence the onward extension of sustainable energy 
to other communities (Ahlborg 2017). In Denmark in the 1970s, 
for example, grassroots windmill cooperatives opened a  pathway 
to the creation of one of the world’s largest wind-energy markets. 
The unique dynamics of grassroots-led changes mean that new 
technologies and low-carbon initiatives develop strong foundations 
by being designed, tested and improved in the early stages with 
reference to the socio-political contexts in which they will grow later 
(Ornetzeder and Rohracher 2013).
Intersectional theory can shine a light on the hidden costs of resource 
extraction, as well as renewable-energy development (see, for instance, 
(Chatalova and Balmann 2017), which go beyond environmental or 
health risks to include the socio-cultural impacts on both communities 
adjacent to these sites and those who work in them (Daum 2018).

Indeed, development decisions often do not properly integrate the 
burdens and risks placed on marginalised groups, such as indigenous 
peoples, while risk assessments tend to reinforce existing power 
imbalances by failing to differentiate between how benefits and risks 
might impact on certain groups (Healy et al. 2019; Kojola 2019). In 
some cases, such as the deployment of small-scale solar power in 
Tanzania by a non-profit organisation, an explicit gender lens on the 
impacts of energy poverty revealed the significant socio-economic 
benefits of improving access to renewable energy (Gray et al. 2019).
17.4.6 Holistic Planning and the Nexus Approach
Poor sectoral coordination and institutional fragmentation have 
triggered a  wide range of unsustainable uses of resources and 
threatened the long-term sustainability of food, water and energy 
security (Rasul 2016). Greater policy coherence among the three 
sectors is critical to moving to a  sustainable and efficient use of

resources (United Nations 2019), given that political ambition, 
values, the energy mix, infrastructure and innovation capacities 
collectively shape transition outcomes (Neofytou et al. 2020). 
Capacity- and coalition-building, particularly among sub-national 
and non-state actors (e.g.,  non-governmental organisations) is 
a particularly important enabler of greater coherence (Bernstein and 
Hoffmann 2018). The nexus approach, a systems-based methodology 
that focuses attention on the many ways in which natural 
resources are deeply interwoven and mutually interdependent, can strengthen coordination and help to avoid maladaptive pathways 
(Cremades et al. 2016).
A major shift is required in the decision-making process in the 
direction of taking a  holistic view, developing institutional 
mechanisms to coordinate the actions of diverse actors and 
strengthening complementarities and synergies (Nikas et al. 
2020; Rasul 2016). Currently, nexus approaches have moved from

purely conceptual arguments to application and implementation. 
(Liu et al. 2018) suggest the need for a  systematic procedure and 
provide perspectives on future directions. These include expanding 
nexus frameworks that take into account interaction linkages with 
the SDGs, incorporating overlooked drivers and regions, diversifying 
nexus toolboxes and making these strategies central to policymaking 
and governance in integrating and implementing the SDGs.
In respect of processes, (Seyfang and Haxeltine 2012) found a lack 
of realistic and achievable expectations among both members 
(internally) and the wider public (externally), which hampers the 
acceleration of transitions. This movement could concentrate 
strategically on developing and promoting short-term steps 
towards shared long-term visions, including clearly identifiable 
goals and end-points. Sustainability science must link research on 
problem structures with a  solutions-oriented approach that seeks

to understand, conceptualise and foster experiments in how socio-
technical innovations for sustainability develop, are diffused and are 
scaled up (Miller et al. 2014).
Various strategies and processes have been explored that might 
facilitate the translation of barriers into enablers, thus accelerating 
transitions to sustainable development. Common themes include 
frequent monitoring and system evaluation to reveal the barriers 
in the first place, the collaborative co-creation and envisioning of 
pathways toward sustainable development, ambitious goal-setting, 
the strategic tackling of sources of path dependence or inertia, 
iterative evaluations of progress and risk management, adaptive 
management and building in opportunities for agile course-correction 
at multiple levels of governance (Burch et al. 2014; Halbe et al. 
2015). Given the political infeasibility of stable, long-term climate 
policies, the better choice may be to embrace uncertainty in specific

policies but entrench the low-carbon transition as the overarching 
goal. Framing climate policy too narrowly, rather than taking a more 
holistic, sustainable development-oriented approach, may tie success 
to single policies, rather than allowing for system-wide change.
Decarbonisation may be encouraged by embedding the transition in 
a  broader socio-economic agenda, focusing on constructing social 
legitimacy to justify the transformation, encouraging municipalities 
with a material interest in the transition and reforming institutions to 
support the long-term transition goals (Rosenbloom et al. 2019). In 
jurisdictions where climate and energy policy have been integrated 
and harmonised, such as the UK, progress has been made in 
transitioning to sustainable energy (Warren et al. 2016).
Developing countries that are rich in fossil fuels now have an 
opportunity to reset their development trajectories by focusing on

those opportunities that will offer resilient development in land-use

1770
Chapter 17 Accelerating the Transition in the Context of Sustainable Development17change, low-carbon energy generation and not least more efficient 
resource-planning (UNDRR 2019). Resource-rich developing countries 
can choose an alternative pathway by deciding to monetise carbon 
capital and diversifying away from the high-carbon aspects of risk. 
Countries rich in hydrocarbons can diversify their energy mix and 
maximise their renewable-energy potential. For instance, Namibia, 
a net importer of electricity, is seeking to reduce its current dependence 
on hydrocarbons by promoting solar energy. The government has 
issued permits allowing independent power producers (IPPs) to sell 
directly to consumers, thus ending the monopoly hitherto enjoyed by 
the state utility company NamPower (Kruger et al. 2019).
Cities are important spaces where the momentum to achieve low-
carbon transitions can be built (Burch 2010; Holscher et al. 2019;

Shaw et al. 2014), especially where centralised energy structures and 
national governance and politics are posing deep-rooted challenges 
to change (Dowling et al. 2018; Meadowcroft 2011). Cities can enter 
networks and partnerships with other cities and multi-level actors, 
spaces that are important for capacity-building and accelerating 
change (Dale et al. 2020; Heikkinen et al. 2019; Westman et al. 2021).
Addressing the uncertainties and complexities associated with 
locally, regionally and nationally sustainable development pathways 
requires creative methods and participatory processes. These may 
include powerful visualisations that make the implications of climate 
change (and decarbonisation) clear locally (Shaw et al. 2014; Sheppard 
et al. 2011), other visual aids or ‘progress wheels’ that effectively 
communicate the relevant contexts (Glaas et al. 2019), storytelling and 
mapping, and both analogue and digital games (Mangnus et al. 2019).
17.5 Conclusions

This chapter has been concerned to assess the opportunities and 
challenges for acceleration in the context of sustainable development. 
As such, many of the claims reviewed involve not only increasing 
the speed of the transition but also ensuring that it is just, equitable 
and delivers a  wider range of environmental and social benefits. 
A sustainability transition requires removing the underlying drivers 
of vulnerability and high emissions (quality and depth) while aligning 
the interests of different communities, regions, sectors, stakeholders 
and cultures (scale and breadth).
Interest in a  sustainability transition has grown steadily over the 
history of the IPCC and of climate and related policy processes. That 
interest hit a high point in 2015 with the Paris Agreement and the UN 
2030 Agenda for Sustainable Development and its 17 SDGs. It has 
continued to remain high as countries have issued NDCs on climate 
change, VNRs on the SDGs and, in some instances, integrated

climate  and SDG plans (or similarly themed integrated actions, 
e.g., circular economy plans). Interest has also gained momentum as 
local governments, businesses and other stakeholders have followed 
suit with climate change- or SDG-related plans.
Implementing many of the recent pledges, however, has proved 
challenging. Part of the challenge is a  need to address everything 
from public policies and prevailing technologies to individual lifestyles 
and social norms, to governance arrangements and institutions with associated political economy implications. These factors can lock-in 
development pathways and prevent transitions from gathering the 
momentum needed for large-scale transformations of socio-economic 
systems. Another consideration is that transition pathways are likely 
to vary across and within countries due to different development 
levels, starting points, differential vulnerabilities, capacities, agencies,

geographies, power dynamics, political economies, ecosystems and 
other contextual factors.
Even with this diversity, prominent lines of economic, institutional, 
psychological and systems thinking have reflected on interventions 
that can enable transitions. Because these disciplines often focus on 
different levels of analysis and draw upon diverse analytical methods 
and empirical evidence, the recommended interventions also tend 
to vary. For instance, economic arguments often point to the need 
for targeted regulation or investments, institutional claims centre 
on multi-level governance reforms, and psychology encourages 
participation to change mindsets and social norms. Systems-level 
perspectives offer a useful frame for bringing together these views, but 
may not capture the richness and details of them treated separately. 
Greater inter- and transdisciplinary research is needed to integrate 
the more focused interventions and show how they work together in

a system. Such research will be particularly important for working on 
the concern running through these studies: strengthening synergies 
between climate and the broader sustainable development agenda.
National and sub-national, sectoral and cross-sectoral, short- and 
long-term transition studies have assessed the links between 
sustainable development and mitigation policies and synergies and 
the trade-offs between the different policy domains. Some general 
conclusions can be drawn on synergies and trade-offs, despite the 
actual impacts of policy implementation depending on scale, context 
and the development starting point.
From a cross-sectoral perspective, it can be concluded that the AFOLU 
sector offers many low-cost mitigation options with synergetic SDG 
impacts, which, however, can also create trade-offs between land use 
mitigate such trade-offs, like agricultural practices, forest conservation

and soil carbon sequestration. Lifestyle changes, including dietary 
changes and reduced food waste, could jointly support the SDGs and 
mitigation. Industry also offers several mitigation options with SDG 
synergies, for example, related to energy efficiency and the circular 
economy. Some of the renewable-energy options in industry could 
indicate some trade-offs in relation to land use, with implications 
for food- and water security and costs. Cities provide a  promising 
basis for implementing mitigation with SDG synergies, particularly 
if urban planning, transportation, infrastructure and settlements are 
coordinated jointly. Similarly, studies of the building sector have 
identified many synergies between the SDGs and mitigation, but 
there are issues related to the costs of new technologies. Also, in 
relation to households and buildings, important equity issues emerge 
due to the ability of low-income groups to afford the introduction of

new technologies. Altogether these cross-sectoral conclusions create 
a need for policies to address both synergies and trade-offs, as well as 
for coordination between different sectoral domains. Context-specific 
assessments of synergies and trade-offs are here important, as is 
sharing the benefits and costs associated with mitigation policies.

1771
Accelerating the Transition in the Context of Sustainable Development  Chapter 1717Several opportunities for creating SDG synergies and avoiding trade-
offs have also been identified in relation to integrated adaptation 
and mitigation policies. The AFOLU sector has a  large potential for 
integrating adaptation and mitigation policies related to agriculture, 
bioenergy crops, forestry and water use. As was concluded for 
mitigation options, integrated adaptation and mitigation policies also 
entail the risks of creating trade-offs in relation to food, water, energy 
access and biodiversity. There are several potentially strong links 
between climate change adaptation in industry and climate change 
adaptation more generally. Various supply chains can be affected by 
climate change, and mitigation options related to energy and water 
supply can be disrupted by climate events, implying that great benefits 
may come from integrating adaptation in industrial planning efforts.

Adaptation options in industry can imply increasing the demand for 
packaging materials such as plastics and for access to refrigeration, 
which are also major sources of GHG emissions, which then would 
require further mitigation options. Mitigation and the co-benefits 
of adaptation in urban areas in relation to air quality, health, green 
jobs and equality issues can in most cases be synergetic and can also 
support the SDGs. One exception are compact cities, with their trade-
offs between mitigation and adaptation because decreasing urban 
sprawl can increase the risks of flooding and heat stress. Detailed 
mapping of mitigation and adaptation in urban areas shows that there 
are many, very close interactions between the two policy domains and 
that coordinated governance across sectors is therefore called for.
Meeting the ambitions of the Paris Agreement will require phasing 
out fossil fuels from energy systems, which is technically possible and

is estimated to be relatively low in cost. However, studies also show 
that replacing fossil fuels with renewables can have major synergies 
and trade-offs with a  broader agenda of sustainable development 
if a balance is established in relation to land use, food security and 
job creation (McCollum et al. 2018). Furthermore, the transition to 
low-emission pathways will require policy efforts that also address 
the emissions locked-in to existing infrastructure, like power plants, 
factories, cargo ships and other infrastructure already in use: for 
example, today coal-fired power plants account for 30% of all energy-
related emissions. Thus, even though the transition away from fossil 
fuels is desirable and technically feasible, it is still largely constrained 
by existing fossil fuel-based infrastructure and the existence of 
stranded investments. The ‘committed’ emissions from existing fossil 
fuel infrastructure may consume all the remaining carbon budget in the

1.5°C scenario or two thirds of the carbon budget in the 2°C scenario.
Stranded hydrocarbon assets, including hydrocarbon resources and the 
infrastructure from which they are produced, and investments made in 
exploration and production activities, are likely to become unusable, 
lose value or may end up as liabilities before the end of the anticipated 
economic lifetime. This phenomenon is rapidly becoming a global reality 
as social norms change and the pressure to reduce emissions mounts. 
Energy and other forms of structural inequities are likely to make the 
transition planning more challenging, especially given stranded assets.
Countries dependent on fossil fuel income will need to forego these 
revenues to keep well within the Paris Agreement requirements 
and align with the rapidly growing divestment movement. Climate 
injustice, energy poverty and COVID-19 have reduced the space and manoeuverability for developing countries to innovate and use

surplus funds to procure new and clean technologies. A rising debt 
burden already hamstrings many. Decisions on how to spend the 
remaining carbon budget and who has the right to decide on what 
to do with existing fossil fuels reflect the complexity of the transition 
and its non-linear character. Given the asymmetrical dimension of 
energy production, distribution and use, it is likely that stranded 
assets will have implications for oil-producing countries, especially 
for early producers who perceive that new-found oil and gas will 
open doors to new forms of prosperity.
While the transitional drivers are not in place in some developing 
countries, that is, technology, infrastructure, knowledge, and finance, 
among others, investing in new forms of renewable energy for the 
land, energy, or water sectors will see the emergence of a  more 
diversified economy and one less vulnerable to carbon and other 
exogenous risks. The transition away from fossil fuels will come

with hard choices. Still, these choices can enable a  sustainable 
development world and reduce the many asymmetries and injustices 
inherent in the current system, not least the gaping energy disparities 
that divide the developed and the developing world.
Equality and justice are central dimensions of transitions in the context 
of sustainable development. Viewing climate change through the lens 
of justice requires a focus on the protection of vulnerable populations 
from the impacts of climate change, addressing the unequal distribution 
of the costs and consequences of the transitions themselves, including 
for those whose livelihoods are rooted in fossil fuel-based sectors, and 
developing more creative and participatory processes for envisioning 
an equitable decarbonised world. Neglecting issues of justice will have 
implications for the pace, scale and quality of the transition.
Ultimately, the evidence demonstrates that there is rarely any one

single factor promoting or preventing transitions. A  constellation 
of elements come into play, including technological innovations, 
shifts in markets, social and behavioural dynamics, and governance 
arrangements. Indeed, transitions require an examination of the role 
of values, attitudes, beliefs and the structures that shape behaviour, as 
well as the dynamics of social movements and education at multiple 
levels. Likewise, technological and social innovation both play an 
important role in enabling transitions, highlighting the importance of 
actors, and promoting learning and social change. Financial tools 
and economic instruments are crucial enablers, since many forms 
of transformational change to energy systems are not possible 
when financial systems still privilege investing in unsustainable,

carbon-intensive sectors. These instruments are deployed within 
the context of the multi-level governance of climate change, which 
suggests the importance of complementary policies and institutions 
that simultaneously integrate across multiple sectors and scales to 
address the multiple sources of lock-in that are shaping the current 
carbon-intensive energy system. Systems-oriented approaches, 
which holistically address the intersections among climate, water 
and energy (for instance), have significant potential to reveal and 
help avoid trade-offs, foster experimentation, and deliver a range of 
co-benefits on the path towards sustainable development.

1772
FAQ 17.1 |   Will decarbonisation efforts slow or accelerate sustainable development transitions?
Sustainable development offers a comprehensive pathway to achieving ambitious climate change mitigation goals. Sustainable 
development requires the pursuit of synergies and the avoidance of trade-offs between the economic, social and environmental 
dimensions of development. It can thus provide pathways that accelerate progress towards ambitious climate change mitigation 
goals. Factoring in equality and distributional effects will be particularly important in the pursuit of sustainable policies and 
partnerships, and in accelerating the transition to sustainable development. Using climate change as a key conduit can only work if 
synergies across sectors are exploited and if policy implementation is supported by national and international partnerships.

The speed, quality, depth and scale of the transition will depend on the developmental starting point, that is, on explicit goals as well 
as the enabling environment consisting of individual behaviour, mindsets, beliefs and actions, social cohesion, governance, policies, 
institutions, social and technological innovations, and so on. The integration of both climate change mitigation and adaptation 
policies in sustainable development is also essential in the establishment of fair and robust transformation pathways.
FAQ 17.2 |   What role do considerations of justice and inclusivity play in the transition towards 
sustainable development?
Negative economic and social impacts in some regions could emerge as a consequence of ambitious climate change mitigation 
policies if these are not aligned with key sustainable development aspirations such as those represented by the Sustainable

Development Goals (SDGs) on ‘no poverty, energy-, water- and food access’, and so on, which could in turn slow down the transition 
process. Nonetheless, many climate change mitigation policies could generate incomes, new jobs and other benefits. Capturing 
these benefits could require specific policies and investments to be targeted directly towards including all parts of society in the 
new activities and industries created by the climate change mitigation policies, and that activities that are reduced in the context of 
transitions to a low-carbon future, including industries and geographical areas, are seeing new opportunities. Poor understanding 
of how governance at multiple levels can meet these challenges to the transition may fail to make significant progress in relation 
to national policies and a  global climate agreement. It may therefore either support or weaken the climate architecture, thus 
constituting a limiting factor.

FAQ 17.3 |   How critical are the roles of institutions in accelerating the transition and what can 
governance enable?
Institutions are critical in accelerating the transition towards sustainable development: they can help to shape climate change 
response strategies in terms of both adaptation and mitigation. Local institutions are the custodians of critical adaptation services, 
ranging from the mobilisation of resources, skills development and capacity-building to the dissemination of critical strategies. 
Transitions towards sustainable development are mediated by actors within particular institutions, the governance mechanisms they 
use as implementing tools and the political coalitions they form to enable action. Patterns of production and consumption have 
implications for a low-carbon development, and many of these patterns can act as barriers or opportunities towards sustainable

development. Trade policies, international economic issues and international financial flows can positively support the speed and 
scale of the transition; alternatively, they can have negative impacts on policies that may inhibit the process. Nonetheless, contextual 
factors are a fundamental part of the change process, and institutions and their governance systems provide pathways that can 
influence contextual realities on the ground. For instance, politically vested interests may lead powerful lobby groups or coalition 
networks to influence the direction of the transition, or they could put pressure on a given political elite through the imposition of 
regulatory standards, taxation, incentives and policies that may speed or delay the transition process. Civil-society institutions, such 
as NGOs or research centres, can act as effective governance ‘watchdogs’ in the transition process, particularly when they exercise

a challenge function and question government actions in respect of transitions related to sustainable development.

4SPM
1 The Report covers literature accepted for publication by 11 October 2021.
2 Each finding is grounded in an evaluation of underlying evidence and agreement. A level of confidence is expressed using five qualifiers, typeset in italics: very low, 
low, medium, high and very high. The assessed likelihood of an outcome or a result is described as: virtually certain 99–100% probability; very likely 90–100%; likely 
66–100%; more likely than not 50–100%; about as likely as not 33–66%; unlikely 0–33%; very unlikely 0–10%; exceptionally unlikely 0–1%. Additional terms may 
also be used when appropriate, consistent with the IPCC uncertainty guidance: https://www.ipcc.ch/site/assets/uploads/2018/05/uncertainty-guidance-note.pdf.

3 The three Special Reports are: Global Warming of 1.5°C: an IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (2018); Climate Change and Land: an IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (2019); IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).

4 The term ‘temperature’ is used in reference to 'global surface temperatures' throughout this SPM as defined in footnote 8 of the AR6 WGI SPM (see note 14 of Table SPM.2). Emission pathways and associated temperature changes are calculated using various forms of models, as summarised in Box SPM.1 and Chapter 3, and discussed in Annex III.A. Intr oduction and Framing
The Working Group III (WGIII) contribution to the IPCC’s Sixth Assessment Report (AR6) assesses literature on the scientific, technological, 
environmental, economic and social aspects of mitigation of climate change.1 Levels of confidence2 are given in () brackets. Numerical 
ranges are presented in square [] brackets. References to Chapters, Sections, Figures and Boxes in the underlying report and Technical Summary (TS) are given in {} brackets.
The report reflects new findings in the relevant literature and builds on previous IPCC reports, including the WGIII contribution to the

IPCC’s Fifth Assessment Report (AR5), the WGI and WGII contributions to AR6 and the three Special Reports in the Sixth Assessment cycle,
3 as well as other UN assessments. Some of the main developments relevant for this report include {TS.1, TS.2}:
•An evolving international landscape
. The literature reflects, among other factors: developments in the UN Framework Convention 
on Climate Change (UNFCCC) process, including the outcomes of the Kyoto Protocol and the adoption of the Paris Agreement{13, 14, 15, 16}; the UN 2030 Agenda for Sustainable Development including the Sustainable Development Goals (SDGs) {1, 3, 4, 17}; and the evolving roles of international cooperation {14}, finance {15} and innovation {16}. 
•
Incr
easing diversity of actors and approaches to mitigation. Recent literature highlights the growing role of non-state and

sub-national actors including cities, businesses, Indigenous Peoples, citizens including local communities and youth, transnationalinitiatives, and public-private entities in the global effort to address climate change {5, 13, 14, 15, 16, 17}. Literature documents theglobal spread of climate policies and cost declines of existing and emerging low emission technologies, along with varied types andlevels of mitigation efforts, and sustained reductions in greenhouse gas (GHG) emissions in some countries {2, 5, 6, 8, 12, 13, 16},and the impacts of, and some lessons from, the COVID-19 pandemic. {1, 2, 3, 5, 13, 15, Box TS.1, Cross-Chapter Box 1 in Chapter 1}
•
Close linkages between climate change mitigation,
 adaptation and development pathways. The development pathways

taken by countries at all stages of economic development impact GHG emissions and hence shape mitigation challenges andopportunities, which vary across countries and regions. Literature explores how development choices and the establishment ofenabling conditions for action and support influence the feasibility and the cost of limiting emissions {1, 3, 4, 5, 13, 15, 16}.Literature highlights that climate change mitigation action designed and conducted in the context of sustainable development,equity, and poverty eradication, and rooted in the development aspirations of the societies within which they take place, will bemore acceptable, durable and effective {1, 3, 4, 5}. This report covers mitigation from both targeted measures, and from policies andgovernance with other primary objectives.
•
New appr
oaches in the assessment. In addition to the sectoral and systems chapters {3, 6, 7, 8, 9, 10, 11, 12}, the report includes,

for the first time in a WGIII report, chapters dedicated to demand for services, and social aspects of mitigation {5, Box TS.11},and to innovation, technology development and transfer {16}. The assessment of future pathways in this report covers near term(to 2030), medium term (up to 2050), and long term (to 2100) time scales, combining assessment of existing pledges and actions{4, 5}, with an assessment of emissions reductions, and their implications, associated with long-term temperature outcomes upto the year 2100 {3}.
4 The assessment of modelled global pathways addresses ways of shifting development pathways towards

5SPM
Summary for Policymakerssustainability. Strengthened collaboration between IPCC Working Groups is reflected in Cross-Working Group Boxes that integrate 
physical science, climate risks and adaptation, and the mitigation of climate change.5 
• Increasing diversity of analytic frameworks from multiple disciplines including social sciences. This report identifies 
multiple analytic frameworks to assess the drivers of, barriers to and options for, mitigation action. These include: economic 
efficiency, including the benefits of avoided impacts; ethics and equity; interlinked technological and social transition processes; 
and socio-political frameworks, including institutions and governance {1, 3, 13, Cross-Chapter Box 12 in Chapter 16}. These help 
to identify risks and opportunities for action, including co-benefits and just and equitable transitions at local, national and global 
scales. {1, 3, 4, 5, 13, 14, 16, 17}

Section B of this Summary for Policymakers (SPM) assesses Recent developments and current trends, including data uncertainties and 
gaps. Section C, System transformations to limit global warming, identifies emission pathways and alternative mitigation portfolios 
consistent with limiting global warming to different levels, and assesses specific mitigation options at the sectoral and system level. 
Section D addresses Linkages between mitigation, adaptation, and sustainable development. Section E, Strengthening the response, 
assesses knowledge of how enabling conditions of institutional design, policy, finance, innovation and governance arrangements can 
contribute to climate change mitigation in the context of sustainable development.   
5 Namely: Economic Benefits from Avoided Climate Impacts along Long-Term Mitigation Pathways {Cross-Working Group Box 1 in Chapter 3}; Urban: Cities and

Climate Change {Cross-Working Group Box 2 in Chapter 8}; and Mitigation and Adaptation via the Bioeconomy {Cross-Working Group Box 3 in Chapter 12}.

6SPM
Summary for PolicymakersB. Recent Developments and Current Trends
6 Net GHG emissions in this report refer to releases of greenhouse gases from anthropogenic sources minus removals by anthropogenic sinks, for those species of 
gases that are reported under the common reporting format of the United Nations Framework Convention on Climate Change (UNFCCC): CO 2 from fossil fuel 
combustion and industrial processes (CO 2-FFI); net CO 2 emissions from land use, land-use change and forestry (CO 2-LULUCF); methane (CH 4); nitrous oxide (N 2O); 
and fluorinated gases (F-gases) comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF 6), as well as nitrogen trifluoride (NF 3). 
Different datasets for GHG emissions exist, with varying time horizons and coverage of sectors and gases, including some that go back to 1850. In this report,

GHG emissions are assessed from 1990, and CO 2 sometimes also from 1850. Reasons for this include data availability and robustness, scope of the assessed 
literature, and the differing warming impacts of non-CO 2 gases over time.
7 GHG emission metrics are used to express emissions of different greenhouse gases in a common unit. Aggregated GHG emissions in this report are stated in  
CO 2-equivalent (CO 2-eq) using the Global Warming Potential with a time horizon of 100 years (GWP100) with values based on the contribution of Working Group 
I to the AR6. The choice of metric depends on the purpose of the analysis, and all GHG emission metrics have limitations and uncertainties, given that they simplify 
the complexity of the physical climate system and its response to past and future GHG emissions. {Cross-Chapter Box 2 in Chapter 2, Supplementary Material 
2.SM.3, Box TS.2; AR6 WGI Chapter 7 Supplementary Material}

8 In this SPM, uncertainty in historic GHG emissions is reported using 90% uncertainty intervals unless stated otherwise. GHG emission levels are rounded to two 
significant digits; as a consequence, small differences in sums due to rounding may occur.
9 Global databases make different choices about which emissions and removals occurring on land are considered anthropogenic. Currently, net CO 2 fluxes from land 
reported by global bookkeeping models used here are estimated to be about 5.5 GtCO 2 yr–1 higher than the aggregate global net emissions based on national GHG 
inventories. This difference, which has been considered in the literature, mainly reflects differences in how anthropogenic forest sinks and areas of managed land are 
defined. Other reasons for this difference, which are more difficult to quantify, can arise from the limited representation of land management in global models and

varying levels of accuracy and completeness of estimated LULUCF fluxes in national GHG inventories. Neither method is inherently preferable. Even when the same 
methodological approach is applied, the large uncertainty of CO 2-LULUCF emissions can lead to substantial revisions to estimated emissions. {Cross-Chapter Box 3 
in Chapter 3, 7.2, SRCCL SPM A.3.3}
10 For consistency with WGI, historical cumulative CO 2 emissions from 1850 to 2019 are reported using 68% confidence intervals.B.1  Total net anthropogenic GHG emissions6 have continued to rise during the period 2010–2019, as have 
cumulative net CO2 emissions since 1850. Average annual GHG emissions during 2010–2019 were 
higher than in any previous decade, but the rate of growth between 2010 and 2019 was lower than 
that between 2000 and 2009. (high confidence) (Figure SPM.1) {Figure 2.2, Figure 2.5, Table 2.1, 2.2, 
Figure TS.2}

B.1.1  Global net anthropogenic GHG emissions were 59 ± 6.6 GtCO2-eq7,8 in 2019, about 12% (6.5 GtCO2-eq) higher than in 2010 
and 54% (21 GtCO2-eq) higher than in 1990. The annual average during the decade 2010–2019 was 56 ± 6.0 GtCO2-eq, 
9.1 GtCO2-eq yr–1 higher than in 2000–2009. This is the highest increase in average decadal emissions on record. The average 
annual rate of growth slowed from 2.1% yr–1 between 2000 and 2009 to 1.3% yr–1 between 2010 and 2019. (high confidence) 
(Figure SPM.1) {Figure 2.2, Figure 2.5, Table 2.1, 2.2, Figure TS.2}
B.1.2  Growth in anthropogenic emissions has persisted across all major groups of GHGs since 1990, albeit at different rates. 
By 2019, the largest growth in absolute emissions occurred in CO2 from fossil fuels and industry followed by CH4, whereas the 
highest relative growth occurred in fluorinated gases, starting from low levels in 1990 (high confidence). Net anthropogenic

CO2 emissions from land use, land-use change and forestry (CO2-LULUCF) are subject to large uncertainties and high annual 
variability, with low confidence even in the direction of the long-term trend.9 (Figure SPM.1) {Figure 2.2, Figure 2.5, 2.2, 
Figure TS.2}
B.1.3  Historical cumulative net CO2 emissions from 1850 to 2019 were 2400 ± 240 GtCO2 (high confidence). Of these, more than half 
(58%) occurred between 1850 and 1989 [1400 ± 195 GtCO2], and about 42% between 1990 and 2019 [1000 ± 90 GtCO2]. About 
17% of historical cumulative net CO2 emissions since 1850 occurred between 2010 and 2019 [410 ± 30 GtCO2].10 By comparison, 
the current central estimate of the remaining carbon budget from 2020 onwards for limiting warming to 1.5°C with a probability 
of 50% has been assessed as 500 GtCO2, and as 1150 GtCO2 for a probability of 67% for limiting warming to 2°C. Remaining

carbon budgets depend on the amount of non-CO2 mitigation (±220 GtCO2) and are further subject to geophysical uncertainties. 
Based on central estimates only, cumulative net CO2 emissions between 2010 and 2019 compare to about four-fifths of the 
size of the remaining carbon budget from 2020 onwards for a 50% probability of limiting global warming to 1.5°C, and about 
one-third of the remaining carbon budget for a 67% probability to limit global warming to 2°C. Even when taking uncertainties 
into account, historical emissions between 1850 and 2019 constitute a large share of total carbon budgets for these global

7SPM
Summary for Policymakerswarming levels.11,12 Based on central estimates only, historical cumulative net CO2 emissions between 1850 and 2019 amount to 
about four-fifths12 of the total carbon budget for a 50% probability of limiting global warming to 1.5°C (central estimate about 
2900 GtCO2), and to about two thirds12 of the total carbon budget for a 67% probability to limit global warming to 2°C (central 
estimate about 3550 GtCO2). {Figure 2.7, 2.2, Figure TS.3, WGI Table SPM.2}
B.1.4   Emissions of CO2-FFI dropped temporarily in the first half of 2020 due to responses to the COVID-19 pandemic (high confidence), 
but rebounded by the end of the year (medium confidence). The annual average CO2-FFI emissions reduction in 2020 relative 
to 2019 was about 5.8% [5.1–6.3%], or 2.2 [1.9–2.4] GtCO2 (high confidence). The full GHG emissions impact of the COVID-19 
pandemic could not be assessed due to a lack of data regarding non-CO2 GHG emissions in 2020. {Cross-Chapter Box 1 in

Chapter 1, Figure 2.6, 2.2, Box TS.1, Box TS.1 Figure 1} 
38Gt
+0.7% yr  –1
+2.1% yr  –1
gases (F-gases)a. Global net anthropogenic GHG emissions 1990–2019 (5)
b. Global anthropogenic GHG emissions and uncertainties by gas – relative to 1990Global net anthropogenic emissions have continued to rise across all major groups of greenhouse gases.
GHG emissions (GtCO2-eq yr  –1) GHG emissions (%)
0201959 ± 6.6 Gt
2019 
emissions 
increase
in 2019, 
relative 
to 1990 (%)

0
40021%
13%
59%2%
5%
20%
12%
61%2%
5%
18%
10%
65%2%
4%
18%
11%
64%1%
5%
Figure SPM.1 | Global net anthropogenic GHG emissions (GtCO 2-eq yr–1) 1990–2019.  Global net anthropogenic GHG emissions include CO 2 from fossil fuel 
combustion and industrial processes (CO 2-FFI); net CO 2 from land use, land-use change and forestry (CO 2-LULUCF)9; methane (CH 4); nitrous oxide (N 2O); and fluorinated 
gases (HFCs, PFCs, SF 6, NF 3).6 Panel a shows aggregate annual global net anthropogenic GHG emissions by groups of gases from 1990 to 2019 reported in GtCO 2-eq 
converted based on global warming potentials with a 100-year time horizon (GWP100-AR6) from the IPCC Sixth Assessment Report Working Group I (Chapter 7). 
The fraction of global emissions for each gas is shown for 1990, 2000, 2010 and 2019; as well as the aggregate average annual growth rate between these decades.

At the right side of Panel a, GHG emissions in 2019 are broken down into individual components with the associated uncertainties (90% confidence interval) indicated by 
the error bars: CO 2-FFI ±8%; CO 2-LULUCF ±70%; CH 4 ±30%; N 2O ±60%; F-gases ±30%; GHG ±11%. Uncertainties in GHG emissions are assessed in Supplementary 
Material 2.2. The single-year peak of emissions in 1997 was due to higher CO 2-LULUCF emissions from a forest and peat fire event in South East Asia. Panel b shows 
global anthropogenic CO 2-FFI, net CO 2-LULUCF , CH 4, N 2O and F-gas emissions individually for the period 1990–2019, normalised relative to 100 in 1990. Note the 
different scale for the included F-gas emissions compared to other gases, highlighting its rapid growth from a low base. Shaded areas indicate the uncertainty range. 
Uncertainty ranges as shown here are specific for individual groups of greenhouse gases and cannot be compared. The table shows the central estimate for: absolute

emissions in 2019; the absolute change in emissions between 1990 and 2019; and emissions in 2019 expressed as a percentage of 1990 emissions. {2.2, Figure 2.5, 
Supplementary Material 2.2, Figure TS.2}
11 The carbon budget is the maximum amount of cumulative net global anthropogenic CO 2 emissions that would result in limiting global warming to a given level with 
a given likelihood, taking into account the effect of other anthropogenic climate forcers. This is referred to as the ‘total carbon budget’ when expressed starting from 
the pre-industrial period, and as the ‘remaining carbon budget’ when expressed from a recent specified date. The total carbon budgets reported here are the sum 
of historical emissions from 1850 to 2019 and the remaining carbon budgets from 2020 onwards, which extend until global net zero CO 2 emissions are reached. 
{Annex I: Glossary; WGI SPM}

12 Uncertainties for total carbon budgets have not been assessed and could affect the specific calculated fractions.

8SPM
Summary for PolicymakersB.2  Net anthropogenic GHG emissions have increased since 2010 across all major sectors globally. An 
increasing share of emissions can be attributed to urban areas. Emissions reductions in CO2 from 
fossil fuels and industrial processes (CO2-FFI), due to improvements in energy intensity of GDP and 
carbon intensity of energy, have been less than emissions increases from rising global activity levels 
in industry, energy supply, transport, agriculture and buildings. (high confidence) {2.2, 2.4, 6.3, 7.2, 8.3, 
9.3, 10.1, 11.2}
B.2.1  In 2019, approximately 34% (20 GtCO2-eq) of total net anthropogenic GHG emissions came from the energy supply sector, 
24% (14 GtCO2-eq) from industry, 22% (13 GtCO2-eq) from agriculture, forestry and other land use (AFOLU), 15% (8.7 GtCO2-eq) 
from transport and 6% (3.3 GtCO2-eq) from buildings.13 If emissions from electricity and heat production are attributed to the

sectors that use the final energy, 90% of these indirect emissions are allocated to the industry and buildings sectors, increasing 
their relative GHG emissions shares from 24% to 34%, and from 6% to 16%, respectively. After reallocating emissions from 
electricity and heat production, the energy supply sector accounts for 12% of global net anthropogenic GHG emissions. 
(high confidence) {Figure 2.12, 2.2, 6.3, 7.2, 9.3, 10.1, 11.2, Figure TS.6}
B.2.2  Average annual GHG emissions growth between 2010 and 2019 slowed compared to the previous decade in energy supply 
(from 2.3% to 1.0%) and industry (from 3.4% to 1.4%), but remained roughly constant at about 2% yr–1 in the transport 
sector (high confidence). Emissions growth in AFOLU, comprising emissions from agriculture (mainly CH4 and N2O) and 
forestry and other land use (mainly CO2) is more uncertain than in other sectors due to the high share and uncertainty of

CO2-LULUCF emissions (medium confidence). About half of total net AFOLU emissions are from CO2-LULUCF , predominantly 
from deforestation14 (medium confidence). {Figure 2.13, 2.2, 6.3, 7.2, Figure 7.3, 9.3, 10.1, 11.2, TS.3}
B.2.3  The global share of emissions that can be attributed to urban areas is increasing. In 2015, urban emissions were estimated 
to be 25 GtCO2-eq (about 62% of the global share) and in 2020, 29 GtCO2-eq (67–72% of the global share).15 The drivers of 
urban GHG emission are complex and include population size, income, state of urbanisation and urban form. (high confidence) 
{8.1, 8.3}
B.2.4  Global energy intensity (total primary energy per unit GDP) decreased by 2% yr–1 between 2010 and 2019. Carbon intensity 
(CO2 from fossil fuel combustion and industrial processes (CO2-FFI) per unit primary energy) decreased by 0.3% yr–1, with large 
regional variations, over the same period mainly due to fuel switching from coal to gas, reduced expansion of coal capacity,

and increased use of renewables. This reversed the trend observed for 2000–2009. For comparison, the carbon intensity of 
primary energy is projected to decrease globally by about 3.5% yr–1 between 2020 and 2050 in modelled scenarios that limit 
warming to 2°C (>67%), and by about 7.7% yr–1 globally in scenarios that limit warming to 1.5°C (>50%) with no or limited 
overshoot.16 (high confidence) {Figure 2.16, 2.2, 2.4, Table 3.4, 3.4, 6.3}
13 Sector definitions can be found in Annex II.9.1.
14 Land overall constituted a net sink of –6.6 (±4.6) GtCO 2 yr–1 for the period 2010–2019, comprising a gross sink of –12.5 (±3.2) GtCO 2 yr–1 resulting from responses 
of all land to both anthropogenic environmental change and natural climate variability, and net anthropogenic CO 2-LULUCF emissions +5.7 (±4.0) GtCO 2 yr–1 based 
on bookkeeping models. {Table 2.1, 7.2, Table 7.1}

15 This estimate is based on consumption-based accounting, including both direct emissions from within urban areas, and indirect emissions from outside urban areas 
related to the production of electricity, goods and services consumed in cities. These estimates include all CO 2 and CH 4 emission categories except for aviation and 
marine bunker fuels, land-use change, forestry and agriculture. {8.1, Annex I: Glossary}
16 See Box SPM.1 for the categorisation of modelled long-term emission scenarios based on projected temperature outcomes and associated probabilities adopted in 
this report.

9SPM
Summary for PolicymakersB.3  Regional contributions17 to global GHG emissions continue to differ widely. Variations in regional, 
and national per capita emissions partly reflect different development stages, but they also vary 
widely at similar income levels. The 10% of households with the highest per capita emissions 
contribute a disproportionately large share of global household GHG emissions. At least 18 countries 
have sustained GHG emission reductions for longer than 10 years. (high confidence) (Figure SPM.2) 
{Figure 1.1, Figure 2.9, Figure 2.10, Figure 2.25, 2.2, 2.3, 2.4, 2.5, 2.6, Figure TS.4, Figure TS.5}
B.3.1  GHG emissions trends over 1990–2019 vary widely across regions and over time, and across different stages of development, 
as shown in Figure SPM.2. Average global per capita net anthropogenic GHG emissions increased from 7.7 to 7.8 tCO2-eq,

ranging from 2.6 tCO2-eq to 19 tCO2-eq across regions. Least developed countries (LDCs) and Small Island Developing States 
(SIDS) have much lower per capita emissions (1.7 tCO2-eq and 4.6 tCO2-eq, respectively) than the global average (6.9 tCO2-eq), 
excluding CO2-LULUCF .18 (high confidence) (Figure SPM.2) {Figure1.2, Figure 2.9, Figure 2.10, 2.2, Figure TS.4}
B.3.2  Historical contributions to cumulative net anthropogenic CO2 emissions between 1850 and 2019 vary substantially across 
regions in terms of total magnitude, but also in terms of contributions to CO2-FFI (1650 ± 73 GtCO2-eq) and net CO2-LULUCF 
(760 ± 220 GtCO2-eq) emissions.10 Globally, the major share of cumulative CO2-FFI emissions is concentrated in a few regions, 
while cumulative CO2-LULUCF9 emissions are concentrated in other regions. LDCs contributed less than 0.4% of historical 
cumulative CO2-FFI emissions between 1850 and 2019, while SIDS contributed 0.5%. (high confidence) (Figure SPM.2)

{Figure 2.10, 2.2, TS.3, Figure 2.7}
B.3.3  In 2019, around 48% of the global population lives in countries emitting on average more than 6 tCO2-eq per capita, excluding 
CO2-LULUCF . 35% live in countries emitting more than 9 tCO2-eq per capita. Another 41% live in countries emitting less than 
3 tCO2-eq per capita. A substantial share of the population in these low-emitting countries lack access to modern energy 
services.19 Eradicating extreme poverty, energy poverty, and providing decent living standards20 to all in these regions in 
the context of achieving sustainable development objectives, in the near-term, can be achieved without significant global 
emissions growth. (high confidence) (Figure SPM.2) {Figure 1.2, 2.2, 2.4, 2.6, 3.7, 4.2, 6.7, Figure TS.4, Figure TS.5}
B.3.4  Globally, the 10% of households with the highest per capita emissions contribute 34–45% of global consumption-based

household GHG emissions,21 while the middle 40% contribute 40–53%, and the bottom 50% contribute 13–15%. (high 
confidence) {2.6, Figure 2.25}
B.3.5  At least 18 countries have sustained production-based GHG and consumption-based CO2 emission reductions for longer than 
10 years. Reductions were linked to energy supply decarbonisation, energy efficiency gains, and energy demand reduction, 
which resulted from both policies and changes in economic structure. Some countries have reduced production-based GHG 
emissions by a third or more since peaking, and some have achieved several years of consecutive reduction rates of around 
4% yr–1, comparable to global reductions in scenarios limiting warming to 2°C (>67%) or lower. These reductions have only 
partly offset global emissions growth. (high confidence) (Figure SPM.2) {Figure TS.4, 2.2, 1.3.2}
17 See Annex II, Part 1 for regional groupings adopted in this report.

18 In 2019, LDCs are estimated to have emitted 3.3% of global GHG emissions, and SIDS are estimated to have emitted 0.6% of global GHG emissions, excluding 
CO 2-LULUCF . These country groupings cut across geographic regions and are not depicted separately in Figure SPM.2. {Figure 2.10}
19 In this report, access to modern energy services is defined as access to clean, reliable and affordable energy services for cooking and heating, lighting, communications, 
and productive uses. {Annex I: Glossary}
20 In this report, decent living standards are defined as a set of minimum material requirements essential for achieving basic human well-being, including nutrition, 
shelter, basic living conditions, clothing, health care, education, and mobility. {5.1}
21 Consumption-based emissions refer to emissions released to the atmosphere to generate the goods and services consumed by a certain entity (e.g., a person, firm,

country, or region). The bottom 50% of emitters spend less than USD3 PPP (purchasing power parity) per capita per day. The top 10% of emitters (an open-ended 
category) spend more than USD23 PPP per capita per day. The wide range of estimates for the contribution of the top 10% results from the wide range of spending 
in this category and differing methods in the assessed literature. {2.6, Annex I: Glossary}

10SPM
Summary for PolicymakersFossil fuel and industry 
(CO2-FFI)All GHG emissions
Net CO2 from land 
(CO2-LULUCF)
Other GHG emissions
a. Global net anthropogenic GHG emissions by region (1990–2019)Emissions have grown in most regions but are distributed unevenly, both in the present day and 
cumulatively since 1850.
0405060GHG emissions (GtCO2-eq yr  –1)
c. Net anthropogenic GHG emissions per capita 
and for total population, per region (2019)
d. Regional indicators (2019) and regional production vs consumption accounting (2018)
GHG emissions (tCO2-eq per capita)
Population (millions)Eastern AsiaNorth AmericaLatin America and CaribbeanSouth-East Asia and PaciﬁcAfricaSouthern AsiaEuropeEastern Europe and West-Central AsiaMiddle EastAustralia, Japan and New ZealandInternational shipping and aviation
27%
7%7%7%9%
7%8%8%8%
7%8%8%
Africa

Production-based emissions (tCO 2-FFI per person, based on 2018 data) 1.2 10 8.4 9.2 6.5 2.8 8.7 16 2.6 1.6
Consumption-based emissions (tCO 2-FFI per person, based on 2018 data) 0.84 11 6.7 6.2 7.8 2.8 7.6 17 2.5 1.5% GHG contributions 9% 3% 27% 6% 8% 10% 5% 12% 9% 8%Population (million persons, 2019) 1292 157 1471 291 620 646 252 366 674 1836
GHG per capita (tCO 2-eq per person) 3.9 13 11 13 7.8 9.2 13 19 7.9 2.6GDP per capita (USD1000 ppp2017 per person)  1 5.0 43 17 20 43 15 20 61 12 6.2
Net GHG 2019  2 (production basis)
CO 2-FFI, 2018, per personGHG emissions intensity (tCO 2-eq / USD1000 ppp 2017) 0.78 0.30 0.62 0.64 0.18 0.61 0.64 0.31 0.65 0.42Australia, 
Japan, 
New 
ZealandEastern 
AsiaEastern 
Europe, 
West-
Central 
AsiaEurope Latin 
America 
and 
CaribbeanMiddle 
EastNorth 
AmericaSouth-East 
Asia and 
PaciﬁcSouthern 
Asia16%14%3%5%2%2%2%2%
5%
6%
13%10%8%
0 2000 4000 6000 8000
CO2 emissions (GtCO2)Africa
Australia, Japan and New ZealandEastern Asia

Eastern Europe and West-Central AsiaEurope
International shipping and aviationLatin America and Caribbean
South-East Asia and Paciﬁc
0 200 400 600b. Historical cumulative net anthropogenic CO2 emissions 
per region (1850–2019)
4%
1 GDP per capita in 2019 in USD2017 currency purchasing power basis.
2 Includes CO 2-FFI, CO 2-LULUCF and Other GHGs, excluding international aviation and shipping.0510152038Gt 42Gt 53Gt 59Gt
Middle East 
Africa Eastern Asia
South-East Asia and Paciﬁc Latin America and Caribbean
Europe
Southern 
AsiaNorth America 
Australia, Japan and New Zealand 
The regional groupings used in this ﬁgure are for statistical purposes only and are described in Annex II, Part I.16%
4%
2%8%12%
11%
10%
7%
2%23%
Figure SPM.2 | Regional GHG emissions, and the regional proportion of total cumulative production-based CO 2 emissions from 1850 to 2019.

11SPM
Summary for PolicymakersFigure SPM.2 (continued): Regional GHG emissions, and the regional proportion of total cumulative production-based CO 2 emissions from 1850 
to 2019.  Panel a shows global net anthropogenic GHG emissions by region (in GtCO 2-eq yr–1 (GWP100-AR6)) for the time period 1990–2019.6 Percentage values 
refer to the contribution of each region to total GHG emissions in each respective time period. The single-year peak of emissions in 1997 was due to higher CO 2-LULUCF 
emissions from a forest and peat fire event in South East Asia. Regions are as grouped in Annex II. Panel b shows the share of historical cumulative net anthropogenic 
CO 2 emissions per region from 1850 to 2019 in GtCO 2. This includes CO 2 from fossil fuel combustion and industrial processes (CO 2-FFI) and net CO 2 emissions from 
land use, land-use change, forestry (CO 2-LULUCF). Other GHG emissions are not included.6 CO 2-LULUCF emissions are subject to high uncertainties, reflected by

a global uncertainty estimate of ±70% (90% confidence interval). Panel c shows the distribution of regional GHG emissions in tonnes CO 2-eq per capita by region in 
2019. GHG emissions are categorised into: CO 2-FFI; net CO 2-LULUCF; and other GHG emissions (methane, nitrous oxide, fluorinated gases, expressed in CO 2-eq using 
GWP100-AR6). The height of each rectangle shows per capita emissions, the width shows the population of the region, so that the area of the rectangles refers to the 
total emissions for each region. Emissions from international aviation and shipping are not included. In the case of two regions, the area for CO 2-LULUCF is below the 
axis, indicating net CO 2 removals rather than emissions. CO 2-LULUCF emissions are subject to high uncertainties, reflected by a global uncertainty estimate of ±70% 
(90% confidence interval). Panel d shows population, GDP per person, emission indicators by region in 2019 for percentage GHG contributions, total GHG per person,

and total GHG emissions intensity, together with production-based and consumption-based CO 2-FFI data, which is assessed in this report up to 2018. Consumption-based 
emissions are emissions released to the atmosphere in order to generate the goods and services consumed by a certain entity (e.g., region). Emissions from international 
aviation and shipping are not included. {1.3, Figure 1.2, 2.2, Figure 2.9, Figure 2.10, Figure 2.11, Annex II}  
B.4  The unit costs of several low-emission technologies have fallen continuously since 2010. Innovation 
policy packages have enabled these cost reductions and supported global adoption. Both tailored 
policies and comprehensive policies addressing innovation systems have helped overcome the 
distributional, environmental and social impacts potentially associated with global diffusion of 
low-emission technologies. Innovation has lagged in developing countries due to weaker enabling

conditions. Digitalisation can enable emission reductions, but can have adverse side effects unless 
appropriately governed. (high confidence) (Figure SPM.3) {2.2, 6.3, 6.4, 7.2, 12.2, 16.2, 16.4, 16.5, 
Cross-Chapter Box 11 in Chapter 16}
B.4.1  From 2010 to 2019, there have been sustained decreases in the unit costs of solar energy (85%), wind energy (55%), and 
lithium-ion batteries (85%), and large increases in their deployment, e.g., >10× for solar and >100× for electric vehicles (EVs), 
varying widely across regions (Figure SPM.3). The mix of policy instruments which reduced costs and stimulated adoption 
includes public R&D, funding for demonstration and pilot projects, and demand pull instruments such as deployment subsidies 
to attain scale. In comparison to modular small-unit size technologies, the empirical record shows that multiple large-scale 
mitigation technologies, with fewer opportunities for learning, have seen minimal cost reductions and their adoption has

grown slowly. (high confidence) {1.3, 1.5, Figure 2.5, 2.5, 6.3, 6.4, 7.2, 11.3, 12.2, 12.3, 12.6, 13.6, 16.3, 16.4, 16.6}
B.4.2  Policy packages tailored to national contexts and technological characteristics have been effective in supporting low-emission 
innovation and technology diffusion. Appropriately designed policies and governance have helped address distributional 
impacts and rebound effects. Innovation has provided opportunities to lower emissions and reduce emission growth 
and created social and environmental co-benefits (high confidence). Adoption of low-emission technologies lags in most 
developing countries, particularly least developed ones, due in part to weaker enabling conditions, including limited finance, 
technology development and transfer, and capacity. In many countries, especially those with limited institutional capacities, 
several adverse side effects have been observed as a result of diffusion of low-emission technology, for example, low-value

employment, and dependency on foreign knowledge and suppliers. Low-emission innovation along with strengthened 
enabling conditions can reinforce development benefits, which can, in turn, create feedbacks towards greater public support 
for policy. (medium confidence) {9.9, 13.6, 13.7, 16.3, 16.4, 16.5, 16.6, Cross-Chapter Box 12 in Chapter 16, TS.3}
B.4.3  Digital technologies can contribute to mitigation of climate change and the achievement of several SDGs (high confidence). 
For example, sensors, internet of things, robotics, and artificial intelligence can improve energy management in all sectors, 
increase energy efficiency, and promote the adoption of many low-emission technologies, including decentralised renewable 
energy, while creating economic opportunities (high confidence). However, some of these climate change mitigation gains can 
be reduced or counterbalanced by growth in demand for goods and services due to the use of digital devices (high confidence).

Digitalisation can involve trade-offs across several SDGs, for example, increasing electronic waste, negative impacts on labour 
markets, and exacerbating the existing digital divide. Digital technology supports decarbonisation only if appropriately 
governed (high confidence). {5.3, 10, 12.6, 16.2, Cross-Chapter Box 11 in Chapter 16, TS.5, Box TS.14}

12SPM
Adoption (millions of EVs)Cost (USD 2020/MWh)
Li-on battery packs (USD2020/kWh)
12001600
800
400

Figure SPM.3 | Unit cost reductions and use in some rapidly changing mitigation technologies.  The top panel shows global costs per unit of energy 
(USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range 
between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding 
to USD55–148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. 
For batteries, costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs 
per MWh of electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made.

However, it does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that 
may modify the overall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each 
technology, in GW of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate 
the change since AR5. Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of 
total electricity production (for PV , onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different

capacity factors; for example, for the same amount of installed capacity, wind produces about twice as much electricity as solar PV . {2.5, 6.4} Renewable energy and 
battery technologies were selected as illustrative examples because they have recently shown rapid changes in costs and adoption, and because consistent data are 
available. Other mitigation options assessed in the report are not included as they do not meet these criteria.

13SPM
Summary for PolicymakersB.5  There has been a  consistent expansion of policies and laws addressing mitigation since AR5. This 
has led to the avoidance of emissions that would otherwise have occurred and increased investment 
in low-GHG technologies and infrastructure. Policy coverage of emissions is uneven across sectors. 
Progress on the alignment of financial flows towards the goals of the Paris Agreement remains slow 
and tracked climate finance flows are distributed unevenly across regions and sectors. (high confidence) 
{5.6, 13.2, 13.4, 13.5, 13.6, 13.9, 14.3, 14.4, 14.5, Cross-Chapter Box 10 in Chapter 14, 15.3, 15.5}
B.5.1  The Kyoto Protocol led to reduced emissions in some countries and was instrumental in building national and international 
capacity for GHG reporting, accounting and emissions markets (high confidence). At least 18 countries that had Kyoto targets

for the first commitment period have had sustained absolute emission reductions for at least a decade from 2005, of which 
two were countries with economies in transition (very high confidence). The Paris Agreement, with near universal participation, 
has led to policy development and target-setting at national and sub-national levels, in particular in relation to mitigation, as 
well as enhanced transparency of climate action and support (medium confidence). {14.3, 14.6}
B.5.2  The application of diverse policy instruments for mitigation at the national and sub-national levels has grown consistently 
across a range of sectors (high confidence). By 2020, over 20% of global GHG emissions were covered by carbon taxes 
or emissions trading systems, although coverage and prices have been insufficient to achieve deep reductions (medium 
confidence). By 2020, there were ‘direct’ climate laws focused primarily on GHG reductions in 56 countries covering 53% of

global emissions (medium confidence). Policy coverage remains limited for emissions from agriculture and the production 
of industrial materials and feedstocks (high confidence). {5.6, 7.6, 11.5, 11.6, 13.2, 13.6}
B.5.3  In many countries, policies have enhanced energy efficiency, reduced rates of deforestation and accelerated technology 
deployment, leading to avoided and in some cases reduced or removed emissions (high confidence). Multiple lines of 
evidence suggest that mitigation policies have led to avoided global emissions of several GtCO2-eq yr–1 (medium confidence). 
At least 1.8 GtCO2-eq yr–1 can be accounted for by aggregating separate estimates for the effects of economic and regulatory 
instruments. Growing numbers of laws and executive orders have impacted global emissions and were estimated to result in 
5.9 GtCO2-eq yr–1 less emissions in 2016 than they otherwise would have been. (medium confidence) (Figure SPM.3) {2.2, 2.8,

6.7, 7.6, 9.9, 10.8, 13.6, Cross-chapter Box 10 in Chapter 14} 
B.5.4  Annual tracked total financial flows for climate mitigation and adaptation increased by up to 60% between 2013/14 and 
2019/20 (in USD2015), but average growth has slowed since 201822 (medium confidence). These financial flows remained 
heavily focused on mitigation, are uneven, and have developed heterogeneously across regions and sectors (high confidence). 
In 2018, public and publicly mobilised private climate finance flows from developed to developing countries were below 
the collective goal under the UNFCCC and Paris Agreement to mobilise USD100 billion per year by 2020 in the context 
of meaningful mitigation action and transparency on implementation (medium confidence). Public and private finance 
flows for fossil fuels are still greater than those for climate adaptation and mitigation (high confidence). Markets for green

bonds, ESG (environmental, social and governance) and sustainable finance products have expanded significantly since AR5. 
Challenges remain, in particular around integrity and additionality, as well as the limited applicability of these markets to 
many developing countries. (high confidence) {Box 15.4, 15.3, 15.5, 15.6, Box 15.7} 
22 Estimates of financial flows (comprising both private and public, domestic and international flows) are based on a single report which assembles data from multiple 
sources and which has applied various changes to their methodology over the past years. Such data can suggest broad trends but is subject to uncertainties.

14SPM
Summary for PolicymakersB.6  Global GHG emissions in 2030 associated with the implementation of Nationally Determined 
Contributions (NDCs) announced prior to COP2623 would make it likely that warming will exceed 1.5°C 
during the 21st century.24 Likely limiting warming to below 2°C would then rely on a rapid acceleration 
of mitigation efforts after 2030. Policies implemented by the end of 202025 are projected to result in 
higher global GHG emissions than those implied by NDCs. (high confidence) (Figure SPM.4) {3.3, 3.5, 
4.2, Cross-Chapter Box 4 in Chapter 4}
B.6.1  Policies implemented by the end of 2020 are projected to result in higher global GHG emissions than those implied by NDCs, 
indicating an implementation gap. A gap remains between global GHG emissions in 2030 associated with the implementation 
of NDCs announced prior to COP26 and those associated with modelled mitigation pathways assuming immediate action

(for quantification see Table SPM.1).26 The magnitude of the emissions gap depends on the global warming level considered and 
whether only unconditional or also conditional elements of NDCs27 are considered.28 (high confidence) {3.5, 4.2, Cross-Chapter 
Box 4 in Chapter 4}
B.6.2  Global emissions in 2030 associated with the implementation of NDCs announced prior to COP26 are lower than the emissions 
implied by the original NDCs29 (high confidence). The original emissions gap has fallen by about 20% to one-third relative to 
pathways that limit warming to 2°C (>67%) with immediate action (category C3a in Table SPM.2), and by about 15–20% 
relative to pathways limiting warming to 1.5°C (>50%) with no or limited overshoot (category C1 in Table SPM.2) (medium 
confidence). (Figure SPM.4) {3.5, 4.2, Cross-Chapter Box 4 in Chapter 4}

23 NDCs announced prior to COP26 refer to the most recent Nationally Determined Contributions submitted to the UNFCCC up to the literature cut-off date of this 
report, 11 October 2021, and revised NDCs announced by China, Japan and the Republic of Korea prior to October 2021 but only submitted thereafter. 25 NDC 
updates were submitted between 12 October 2021 and the start of COP26.
24 This implies that mitigation after 2030 can no longer establish a pathway with less than 67% probability to exceed 1.5°C during the 21st century, a defining feature 
of the class of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot assessed in this report (category C1 in Table SPM.2). These pathways limit 
warming to 1.6°C or lower throughout the 21st century with a 50% likelihood.
25 The policy cut-off date in studies used to project GHG emissions of ‘policies implemented by the end of 2020’ varies between July 2019 and November 2020. {Table 4.2}

26 Immediate action in modelled global pathways refers to the adoption between 2020 and at latest before 2025 of climate policies intended to limit global warming 
to a given level. Modelled pathways that limit warming to 2°C (>67%) based on immediate action are summarised in category C3a in Table SPM.2. All assessed 
modelled global pathways that limit warming to 1.5°C (>50%) with no or limited overshoot assume immediate action as defined here (Category C1 in Table SPM.2).
27 In this report, ‘unconditional’ elements of NDCs refer to mitigation efforts put forward without any conditions. ‘Conditional’ elements refer to mitigation efforts that 
are contingent on international cooperation, for example bilateral and multilateral agreements, financing or monetary and/or technological transfers. This terminology 
is used in the literature and the UNFCCC’s NDC Synthesis Reports, not by the Paris Agreement. {4.2.1, 14.3.2}

28 Two types of gaps are assessed: the implementation gap is calculated as the difference between the median of global emissions in 2030 implied by policies 
implemented by the end of 2020 and those implied by NDCs announced prior to COP26. The emissions gap is calculated as the difference between GHG emissions 
implied by the NDCs (minimum/maximum emissions in 2030) and the median of global GHG emissions in modelled pathways limiting warming to specific levels 
based on immediate action and with stated likelihoods as indicated (Table SPM.2).
29 Original NDCs refer to those submitted to the UNFCCC in 2015 and 2016. Unconditional elements of NDCs announced prior to COP26 imply global GHG emissions 
in 2030 that are 3.8 [3.0–5.3] GtCO 2-eq yr–1 lower than those from the original NDCs, and 4.5 [2.7–6.3] GtCO 2-eq yr–1 lower when conditional elements of NDCs

are included. NDC updates at or after COP26 could further change the implied emissions.Table SPM.1 | Projected global emissions in 2030 associated with policies implemented by the end of 2020 and NDCs announced prior to COP26, 
and associated emissions gaps. *Emissions projections for 2030 and absolute differences in emissions are based on emissions of 52–56 GtCO 2-eq yr–1 in 2019 as 
assumed in underlying model studies. (medium confidence) {4.2, Table 4.3, Cross-Chapter Box 4 in Chapter 4}
Implied by policies 
implemented by 
the end of 2020
(GtCO 2-eq yr–1)Implied by NDCs announced prior to COP26
Unconditional elements
(GtCO 2-eq yr–1) Including conditional 
elements
(GtCO 2-eq yr–1)
Median projected global emissions (min–max)* 57 [52–60] 53 [50–57] 50 [47–55]
Implementation gap between implemented policies 
and NDCs (median)  4 7
Emissions gap between NDCs and pathways that limit 
warming to 2°C (>67%) with immediate action   10–16 6–14

warming to 1.5°C (>50%) with no or limited overshoot 
with immediate action 19–26 16–23

15SPM
Summary for PolicymakersB.6.3  Modelled global emission pathways consistent with NDCs announced prior to COP26 that limit warming to 2°C (>67%) 
(category C3b in Table SPM.2) imply annual average global GHG emissions reduction rates of 0–0.7 GtCO2-eq yr–1 during the 
decade 2020–2030, with an unprecedented acceleration to 1.4–2.0 GtCO2-eq yr–1 during 2030–2050 (medium confidence). 
Continued investments in unabated high-emitting infrastructure and limited development and deployment of low-emitting 
alternatives prior to 2030 would act as barriers to this acceleration and increase feasibility risks (high confidence). {3.3, 3.5, 
3.8, Cross-Chapter Box 5 in Chapter 4}
B.6.4  Modelled global emission pathways consistent with NDCs announced prior to COP26 will likely exceed 1.5°C during the 21st 
century. Those pathways that then return warming to 1.5°C by 2100 with a likelihood of 50% or greater imply a temperature

overshoot of 0.15°C–0.3°C (42 pathways in category C2 in Table SPM.2). In such pathways, global cumulative net-negative 
CO2 emissions are –380 [–860 to –200] GtCO230 in the second half of the century, and there is a rapid acceleration of other 
mitigation efforts across all sectors after 2030. Such overshoot pathways imply increased climate-related risk, and are subject to 
increased feasibility concerns,31 and greater social and environmental risks, compared to pathways that limit warming to 1.5°C 
(>50%) with no or limited overshoot. (high confidence) (Figure SPM.4, Table SPM.2) {3.3, 3.5, 3.8, 12.3; AR6 WGII SPM B.6}
a. Global GHG emissions b. 2030 c. 2050 d. 2100Projected global GHG emissions from NDCs announced prior to COP26 would make it likely that 
warming will exceed 1.5°C and also make it harder after 2030 to limit warming to below 2°C.
102030
0
102030
04050607080 80GHG emissions (GtCO2-eq yr  –1)

95th
Median
5th75th
25th
Policy 
for 2030
88
Figure SPM.4 | Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) and projected emission 
outcomes from near-term policy assessments for 2030 (Panel b).  
30 Median and very likely range [5th to 95th percentile].
31 Returning to below 1.5°C in 2100 from GHG emissions levels in 2030 associated with the implementation of NDCs is infeasible for some models due to model-specific

constraints on the deployment of mitigation technologies and the availability of net negative CO 2 emissions.

16SPM
Summary for PolicymakersFigure SPM.4 (continued): Global GHG emissions of modelled pathways (funnels in Panel a, and associated bars in Panels b, c, d) and projected 
emission outcomes from near-term policy assessments for 2030 (Panel b).  Panel a  shows global GHG emissions over 2015–2050 for four types of assessed 
modelled global pathways: 
 –Trend from implemented policies: Pathways with projected near-term GHG emissions in line with policies implemented until the end of 2020 and extended with 
comparable ambition levels beyond 2030 (29 scenarios across categories C5–C7, Table SPM.2).
 –Limit to 2°C (>67%) or return warming to 1.5°C (>50%) after a high overshoot, NDCs until 2030: Pathways with GHG emissions until 2030 associated with the 
implementation of NDCs announced prior to COP26, followed by accelerated emissions reductions likely to limit warming to 2°C (C3b, Table SPM.2) or to return

warming to 1.5°C with a probability of 50% or greater after high overshoot (subset of 42 scenarios from C2, Table SPM.2).
 –Limit to 2°C (>67%) with immediate action:  Pathways that limit warming to 2°C (>67%) with immediate action after 202026 (C3a, Table SPM.2). 
 –Limit to 1.5°C (>50%) with no or limited overshoot: Pathways limiting warming to 1.5°C with no or limited overshoot (C1, Table SPM.2 C1). All these pathways 
assume immediate action after 2020. 
Past GHG emissions for 2010–2015 used to project global warming outcomes of the modelled pathways are shown by a black line32 and past global GHG emissions in 
2015 and 2019 as assessed in Chapter 2 are shown by whiskers. Panels b, c and d show snapshots of the GHG emission ranges of the modelled pathways in 2030, 
2050, and 2100, respectively. Panel b also shows projected emissions outcomes from near-term policy assessments in 2030 from Chapter 4.2 (Tables 4.2 and 4.3; median

and full range). GHG emissions are in CO 2-equivalent using GWP100 from AR6 WGI. {3.5, 4.2, Table 4.2, Table 4.3, Cross-Chapter Box 4 in Chapter 4}
B.7  Projected cumulative future CO2 emissions over the lifetime of existing and currently planned fossil 
fuel infrastructure without additional abatement exceed the total cumulative net CO2 emissions in 
pathways that limit warming to 1.5°C (>50%) with no or limited overshoot. They are approximately 
equal to total cumulative net CO2 emissions in pathways that limit warming to 2°C (>67%). (high 
confidence) {2.7, 3.3}
B.7.1  If historical operating patterns are maintained,33 and without additional abatement,34 estimated cumulative future CO2 
emissions from existing fossil fuel infrastructure, the majority of which is in the power sector, would, from 2018 until the end 
of its lifetime, amount to 660 [460–890] GtCO2. They would amount to 850 [600–1100] GtCO2 when unabated emissions from

currently planned infrastructure in the power sector is included. These estimates compare with cumulative global net CO2 
emissions from all sectors of 510 [330–710] GtCO2 until the time of reaching net zero CO2 emissions35 in pathways that limit 
warming to 1.5°C (>50%) with no or limited overshoot, and 890 [640–1160] GtCO2 in pathways that limit warming to 2°C 
(>67%). (high confidence) (Table SPM.2) {2.7, Figure 2.26, Figure TS.8}
B.7.2  In modelled global pathways that limit warming to 2°C (>67%) or lower, most remaining fossil fuel CO2 emissions until 
the time of global net zero CO2 emissions are projected to occur outside the power sector, mainly in industry and transport. 
Decommissioning and reduced utilisation of existing fossil fuel-based power sector infrastructure, retrofitting existing 
installations with CCS,36 switches to low-carbon fuels, and cancellation of new coal installations without CCS are major

options that can contribute to aligning future CO2 emissions from the power sector with emissions in the assessed global 
modelled least-cost pathways. The most appropriate strategies will depend on national and regional circumstances, including 
enabling conditions and technology availability. (high confidence) (Box SPM.1) {Table 2.7, 2.7, 3.4, 6.3, 6.5, 6.7} 
32 See Box SPM.1 for a description of the approach to project global warming outcomes of modelled pathways and its consistency with the climate assessment in AR6 WGI.
33 Historical operating patterns are described by load factors and lifetimes of fossil fuel installations as observed in the past (average and range).
34 Abatement here refers to human interventions that reduce the amount of greenhouse gases that are released from fossil fuel infrastructure to the atmosphere.

35 Total cumulative CO 2 emissions up to the time of global net zero CO 2 emissions are similar but not identical to the remaining carbon budget for a given temperature 
limit assessed by Working Group I. This is because the modelled emission scenarios assessed by Working Group III cover a range of temperature levels up to a specific 
limit, and exhibit a variety of reductions in non-CO 2 emissions that also contribute to overall warming. {Box 3.4}
36 In this context, capture rates of new installations with CCS are assumed to be 90–95%+ {11.3.5}. Capture rates for retrofit installations can be comparable, if plants 
are specifically designed for CCS retrofits {11.3.6}.

17SPM
Summary for PolicymakersC. System Transformations to Limit Global Warming
37 All reported warming levels are relative to the period 1850–1900. If not otherwise specified, ‘pathways’ always refer to pathways computed with a  model. 
Immediate action in the pathways refers to the adoption of climate policies between 2020 and at latest 2025 intended to limit global warming at a given level.
38 Long-term warming is calculated from all modelled pathways assuming mitigation efforts consistent with national policies that were implemented by the end of 
2020 (scenarios that fall into policy category P1b of Chapter 3) and that pass through the 2030 GHG emissions ranges of such pathways assessed in Chapter 4 
(see footnote 25). {3.2, Table 4.2}
39 Warming estimates refer to the 50th and [5th–95th] percentile across the modelled pathways and the median temperature change estimate of the probabilistic WGI 
climate model emulators (see Table SPM.2 footnote a).

40 In this report, emissions reductions are reported relative to 2019 modelled emission levels, while in SR1.5 emissions reductions were calculated relative to 2010. 
Between 2010 and 2019 global GHG and global CO 2 emissions have grown by 12% (6.5 GtCO 2-eq) and 13% (5.0 GtCO 2) respectively. In global modelled 
pathways assessed in this report that limit warming to 1.5°C (>50%) with no or limited overshoot, GHG emissions are projected to be reduced by 37% [28–57%] 
in 2030 relative to 2010. In the same type of pathways assessed in SR1.5, reported GHG emissions reductions in 2030 were 39–51% (interquartile range) relative 
to 2010. In absolute terms, the 2030 GHG emissions levels of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot are higher in AR6 
(31 [21–36] GtCO 2-eq) than in SR1.5 (28 (26–31 interquartile range) GtCO 2-eq). (Figure SPM.1, Table SPM.2) {3.3, SR1.5}

41 Scenarios in this category limit peak warming to 2°C throughout the 21st century with close to, or more than, 90% likelihood.
42 This category contains 91 scenarios with immediate action and 42 scenarios that are consistent with the NDCs until 2030.
43 These numbers for CH 4, N2O, and F-gases are rounded to the nearest 5% except numbers below 5%.C.1  Global GHG emissions are projected to peak between 2020 and at the latest before 2025 in global 
modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and in those that 
limit warming to 2°C (>67%) and assume immediate action (see Table SPM.2 footnote i).  37  In both 
types of modelled pathways, rapid and deep GHG emissions reductions follow throughout 2030, 2040 
and 2050 (high confidence). Without a strengthening of policies beyond those that are implemented by 
the end of 2020, GHG emissions are projected to rise beyond 2025, leading to a median global warming

of 3.2 [2.2 to 3.5] °C by 210038, 39 (medium confidence). (Table SPM.2, Figure SPM.4, Figure SPM.5) 
{3.3, 3.4} 
C.1.1  Net global GHG emissions are projected to fall from 2019 levels by 27% [13–45%] by 2030 and 63% [52–76%]40 by 2050 in 
global modelled pathways that limit warming to 2°C (>67%) and assuming immediate action (category C3a, Table SPM.2). 
This compares with reductions of 43% [34–60%] by 2030 and 84% [73–98%] by 2050 in pathways that limit warming to 
1.5°C (>50%) with no or limited overshoot (C1, Table SPM.2) (high confidence).41 In modelled pathways that return warming 
to 1.5°C (>50%) after a high overshoot,42 GHG emissions are reduced by 23% [0–44%] in 2030 and by 75% [62–91%] in 
2050 (C2, Table SPM.2) (high confidence). Modelled pathways that are consistent with NDCs announced prior to COP26 
until 2030 and assume no increase in ambition thereafter have higher emissions, leading to a median global warming of

2.8 [2.1–3.4] °C by 2100 (medium confidence).23 (Figure SPM.4) {3.3}
C.1.2  In modelled pathways that limit warming to 2°C (>67%) assuming immediate action, global net CO2 emissions are reduced 
compared to modelled 2019 emissions by 27% [11–46%] in 2030 and by 52% [36–70%] in 2040; and global CH4 emissions are 
reduced by 24% [9–53%] in 2030 and by 37% [20–60%] in 2040. In pathways that limit warming to 1.5°C (>50%) with no or 
limited overshoot global net CO2 emissions are reduced compared to modelled 2019 emissions by 48% [36–69%] in 2030 and 
by 80% [61–109%] in 2040; and global CH4 emissions are reduced by 34% [21–57%] in 2030 and 44% [31–63%] in 2040. 
There are similar reductions of non-CO2 emissions by 2050 in both types of pathways: CH4 is reduced by 45% [25–70%];  
N2O is reduced by 20% [–5 to +55%]; and F-gases are reduced by 85% [20–90%].43 Across most modelled pathways, this is the

maximum technical potential for anthropogenic CH4 reductions in the underlying models (high confidence). Further emissions 
reductions, as illustrated by the IMP-SP pathway, may be achieved through changes in activity levels and/or technological 
innovations beyond those represented in the majority of the pathways (medium confidence). Higher emissions reductions of 
CH4 could further reduce peak warming. (high confidence) (Figure SPM.5) {3.3}
C.1.3  In modelled pathways consistent with the continuation of policies implemented by the end of 2020, GHG emissions continue to 
rise, leading to global warming of 3.2 [2.2–3.5] °C by 2100 (within C5–C7, Table SPM.2) (medium confidence). Pathways that 
exceed warming of >4°C (≥50%) (C8, SSP5-8.5, Table SPM.2) would imply a reversal of current technology and/or mitigation 
policy trends (medium confidence). Such warming could occur in emission pathways consistent with policies implemented

by the end of 2020 if climate sensitivity is higher than central estimates (high confidence). (Table SPM.2, Figure  SPM.4) 
{3.3, Box 3.3}

18SPM
Table SPM.2 | Key characteristics of the modelled global emissions pathways.  Summary of projected CO 2 and GHG emissions, projected net zero timings and the resulting global warming outcomes. Pathways are categorised 
(rows), according to their likelihood of limiting warming to different peak warming levels (if peak temperature occurs before 2100) and 2100 warming levels. Values shown are for the median [p50] and 5th–95th percentiles [p5–p95], noting 
that not all pathways achieve net zero CO 2 or GHGs.
p50  
[p5–p95] aGHG emissions  
(GtCO 2-eq yr–1) gGHG emissions reductions  
from 2019  
(%) hEmissions milestones i, jCumulative CO 2 
emissions (GtCO 2) mCumulative 
CO 2 
emissions 
(GtCO 2)Global mean 
temperature changes 
50% probability 
(°C) nLikelihood of peak  global 
warming staying below (%) o
Categoryb, c, d 
[# pathways]Category/subset labelWGI SSP 
& WGIII 
IPs/IMPs 
alignmente, f2030 2040 2050 2030 2040 2050Peak CO 2

emissions  
(% peak 
emissions  
(% peak 
before 2100)Net zero CO 2 
(% net zero 
pathways)Net zero 
GHGs  
(% net zero 
pathways) k, l2020 to 
net zero 
CO 22020–2100Year of 
net zero CO 2 
to 2100at peak 
warming2100 <1.5°C <2.0°C <3.0°C
Modelled global emissions pathways categorised 
by projected global warming levels (GWL). Detailed 
likelihood definitions are provided in SPM Box1.  
The five illustrative scenarios (SSPx-yy) considered by 
AR6 WGI and the Illustrative (Mitigation) Pathways 
assessed in WGIII are aligned with the temperature 
categories and are indicated in a separate column. 
Global emission pathways contain regionally 
differentiated information. This assessment 
focuses on their global characteristics.Projected median annual GHG 
emissions in the year across the 
scenarios, with the 5th–95th 
Modelled GHG emissions in 2019:  
55 [53–58] GtCO 2-eq.Projected median GHG emissions 
reductions of pathways in the year

across the scenarios compared to 
modelled 2019, with the 5th–95th 
percentile in brackets. Negative 
numbers indicate increase in 
emissions compared to 2019.Median 5-year intervals at 
which projected CO 2 & GHG 
emissions peak, with the 
5th–95th percentile interval in 
square brackets. Percentage of 
peaking pathways is denoted 
in round brackets. 
emissions peak in 2100 or 
beyond for that percentile.Median 5-year intervals at 
emissions  of pathways in 
this category reach net zero, 
with the 5th–95th percentile 
Percentage of net zero 
pathways is denoted in round 
brackets. 
CO 2 emissions across 
reaching net zero or until 
percentile interval 
in square brackets.Median 
CO 2 emissions 
between 
the year of

and 2100. More 
results in 
greater 
declines 
after peak.Projected temperature 
change of pathways 
in this category (50% 
uncertainties), relative 
to 1850–1900, at peak 
warming and in 2100, 
for the median value 
projected pathways in this category 
stay below a given global warming 
level, with the 5th–95th percentile 
interval in square brackets.
C1 [97] limit warming to 
or limited overshoot 31
[21–36]17
[6–23]9
[1–15]43
[34–60]69
[58–90]84
[73–98]
(100%)
(52%)
[2050–...]510
[–210 to 
570]–220
[–660 to –20]1.6
[33–58]90
[99–100]
C1a [50]… with net zero  
GHGsSSP1–1.9, 
SP
LD33
[22–37]18
[6–24]8
[0–15]41
[31–59]66
[58–89]85
(100%)

[–220 to 
620]–360
[–680 to 
–140]1.6
[34–60]90
[99–100]
C1b [47]…  without net zero 
GHGsRen29
[21–36]16
[7–21]9
[4–13]48
[35–61]70
[62–87]84
[76–93]…–… [0%] 460
[–440 to 0]1.6
[33–56]89
[99–100] […–…]
C2 [133]return warming to 
a high overshootNeg42
[31–55]25
[17–34]14
[5–21]23
[0–44]55
[40–71]75
(100%)
(87%)
[2055–...]720
[–90 to 
620]–360
[–680 to –60]1.7
[15–42]82
[99–100] [2020–2030] [2020–2025]
C3 [311]limit warming  
[32–55]29
[20–36]20
[13–26]21
[1–42]46
[34–63]64
(93%)
[2055–...]...–... (30%)
[2075–...]890
[–290 to 0]1.7
[13–41]76
[68–91]99
[98–100][2020–2030] [2020–2025]
starting in 2020SSP1–2.640

[30–49]29
[21–36]20
[14–27]27
[13–45]47
[35–63]63
[2020–2025]2070–2075 
(91%)
[2055–...]...–... (24%)
[2080–...]860
[–280 to 0]1.7
[14–42]78
[98–100]

19SPM
Summary for Policymakersp50  
from 2019  
CO 2 
emissions 
& WGIII 
IPs/IMPs 
alignmente, f2030 2040 2050 2030 2040 2050Peak CO 2 
emissions  
(% peak 
emissions  
(% peak 
(% net zero 
GHGs  
(% net zero 
net zero 
The five illustrative scenarios (SSPx-yy) considered by

reductions of pathways in the year 
beyond for that percentile.Median 5-year intervals at

brackets. 
between 
the year of 
results in 
greater 
declines 
projected pathways in this category

C3b [97] … NDCs until 2030 GS52
[47–56]29
[20–36]18
[10–25]5
[0–14]46
[34–63]68
[56–82]
(97%)
[2055–2090]...–... (41%)
[2075–...]910
[–300 to 0]1.8
[12–35]73
[67–87]99
[98–99]
C4 [159]limit warming  
to 2°C (>50%)50
[41–56]38
[28–44]28
[19–35]10
[0–27]31
[20–50]49
(86%)
[2065–...]...–... (31%)
[2075–...]1210
[–390 to 0]1.9
[7–22]59
[50–77]98
[95–99]
C5 [212]limit warming  
to 2.5°C (>50%)52
[46–56]45
[37–53]39
[30–49]6
[–1 to 18]18
[4–33]29
[11–48]...–... (41%)
[2080–...]...–... (12%)
[2090–...]1780
[1400–
2360]1780
[1260–
2360]0
[–160 to 0]2.2
[0–10]37
[18–59]91
[83–98]
C6 [97]limit warming  
to 3°C (>50%)SSP2–4.5
ModAct54
[50–62]53
[48–61]52
[45–57]2
[–10 to 
11]3
[–14 to 
14]5
[–2 to 18]2030–2035

(96%)2020–2025 
(97%)
no net zero no net zero2790
[2440–
3520]
no net zerotemperature 
does not 
peak by 
21002.7
[0–0]8
[2–18]71
[53–88]
C7 [164]limit warming  
to 4°C (>50%)SSP3–7.0
CurPol62
[53–69]67
[56–76]70
[–18 to 3]–19
[–31 to 1]–24
[–41 to 
–2]2085–2090 
(57%)2090–2095 
(56%)4220
[3160–
5000]3.5
[0–0]0
[0–2]22
[7–60][2040–...]
C8 [29]exceed warming  
of 4°C (≥50%)SSP5–8.571
[69–81]80
[78–96]88
[–34 to 
–17]–35
[–65 to 
–29]–46
[–92 to 
[2070–...]5600
4.2
[0–0]0
[0–0]4
[0–11][4910–
7450]Table SPM.2 (continued):

20SPM
Summary for PolicymakersTable SPM.2 (continued): 
columns these values relate to the distribution of all the pathways in that category. Harmonised emissions values are given for consistency with projected global 
warming outcomes using climate emulators. Based on the assessment of climate emulators in AR6 WGI (WG1 Chapter 7, Box 7.1), two climate emulators are used for 
the probabilistic assessment of the resulting warming of the pathways. For the ‘Temperature change’ and ‘Likelihood’ columns, the single upper-row values represent 
the 50th percentile across the pathways in that category and the median [50th percentile] across the warming estimates of the probabilistic MAGICC climate model

emulator. For the bracketed ranges, the median warming for every pathway in that category is calculated for each of the two climate model emulators (MAGICC and 
FaIR). Subsequently, the 5th and 95th percentile values across all pathways for each emulator are calculated. The coolest and warmest outcomes (i.e., the lowest p5 of 
two emulators, and the highest p95, respectively) are shown in square brackets. These ranges therefore cover both the uncertainty of the emissions pathways as well as 
the climate emulators’ uncertainty.
b For a description of pathways categories see Box SPM.1.

analysis have been added to C3a, whilst 10 pathways with specifically designed near-term action until 2030, whose emissions fall below those implied by NDCs 
announced prior to COP26, are not included in either of the two subsets.
common features such as deep and rapid emissions reductions, but also different combinations of sectoral mitigation strategies. See Box SPM.1 for an introduction of 
the IPs and IMPs, and Chapter 3 for full descriptions. {3.2, 3.3, Annex III.II.4}

(CO 2 and GHGs) are assessed for five-year reporting intervals starting in 2020. The interval 2020–2025 signifies that projected emissions peak as soon as possible 
between 2020 and at latest before 2025. The upper five-year interval refers to the median interval within which the emissions peak or reach net zero. Ranges in 
square brackets underneath refer to the range across the pathways, comprising the lower bound of the 5th percentile five-year interval and the upper bound of the 
95th percentile five-year interval. Numbers in round brackets signify the fraction of pathways that reach specific milestones.

l For cases where models do not report all GHGs, missing GHG species are infilled and aggregated into a Kyoto basket of GHG emissions in CO 2-eq defined by the 
100-year global warming potential. For each pathway, reporting of CO 2, CH 4, and N 2O emissions was the minimum required for the assessment of the climate response

and the assignment to a climate category. Emissions pathways without climate assessment are not included in the ranges presented here. {See Annex III.II.5}

21SPM
Summary for PolicymakersC.1.4   Global modelled pathways falling into the lowest temperature category of the assessed literature (C1, Table SPM.2) are on 
average associated with a higher median peak warming in AR6 compared to pathways in the same category in SR1.5. In the 
modelled pathways in AR6, the likelihood of limiting warming to 1.5°C has on average declined compared to SR1.5. This is 
because GHG emissions have risen since 2017, and many recent pathways have higher projected emissions by 2030, higher 
cumulative net CO2 emissions and slightly later dates for reaching net zero CO2 or net zero GHG emissions. High mitigation 
challenges, for example, due to assumptions of slow technological change, high levels of global population growth, and high 
fragmentation as in the Shared Socio-economic Pathway SSP3, may render modelled pathways that limit warming to 2°C

(>67%) or lower infeasible. (medium confidence) (Table SPM.2, Box SPM.1) {3.3, 3.8, Annex III Figure II.1, Annex III Figure II.3}
Box SPM.1 | Assessment of Modelled Global Emission Scenarios
A wide range of modelled global emission pathways and scenarios from the literature is assessed in this report, including 
pathways and scenarios with and without mitigation.44 Emissions pathways and scenarios project the evolution of GHG 
emissions based on a set of internally consistent assumptions about future socio-economic conditions and related mitigation 
measures.45 These are quantitative projections and are neither predictions nor forecasts. Around half of all modelled global 
emission scenarios assume cost-effective approaches that rely on least-cost emission abatement options globally. The other 
half look at existing policies and regionally and sectorally differentiated actions. Most do not make explicit assumptions about

global equity, environmental justice or intra-regional income distribution. Global emission pathways, including those based 
on cost-effective approaches, contain regionally differentiated assumptions and outcomes, and have to be assessed with the 
careful recognition of these assumptions. This assessment focuses on their global characteristics. The majority of the assessed 
scenarios (about 80%) have become available since the SR1.5, but some were assessed in that report. Scenarios with and 
without mitigation were categorised based on their projected global warming over the 21st century, following the same scheme 
as in the SR1.5 for warming up to and including 2°C. {1.5, 3.2, 3.3, Annex III.II.2, Annex III.II.3}
Scenario categories are defined by their likelihood of exceeding global warming levels (at peak and in 2100) and referred 
to in this report as follows:46,47

• Category C1 comprises modelled scenarios that limit warming to 1.5°C in 2100 with a likelihood of greater than 50%, 
and reach or exceed warming of 1.5°C during the 21st century with a likelihood of 67% or less. In this report, these scenarios 
are referred to as scenarios that limit warming to 1.5°C (>50%) with no or limited overshoot. Limited overshoot refers to 
exceeding 1.5°C global warming by up to about 0.1°C and for up to several decades.48 
• Category C2 comprises modelled scenarios that limit warming to 1.5°C in 2100 with a likelihood of greater than 50%, 
and exceed warming of 1.5°C during the 21st century with a likelihood of greater than 67%. In this report, these scenarios 
are also referred to as scenarios that return warming to 1.5°C (>50%) after a high overshoot. High overshoot refers to 
temporarily exceeding 1.5°C global warming by 0.1°C–0.3°C for up to several decades.

• Category C3 comprises modelled scenarios that limit peak warming to 2°C throughout the 21st century with a likelihood of 
greater than 67%. In this report, these scenarios are also referred to as scenarios that limit warming to 2°C (>67%).
• Categories C4, C5, C6 and C7 comprise modelled scenarios that limit warming to 2°C, 2.5°C, 3°C, 4°C, respectively, 
throughout the 21st century with a likelihood of greater than 50%. In some scenarios in C4 and many scenarios in C5–C7, 
warming continues beyond the 21st century.
44 In the literature, the terms ‘pathways’ and ‘scenarios’ are used interchangeably, with the former more frequently used in relation to climate goals. For this reason, 
this SPM uses mostly the term (emissions and mitigation) pathways. {Annex III.II.1.1}
45 Key assumptions relate to technology development in agriculture and energy systems and socio-economic development, including demographic and economic

projections. IPCC is neutral with regard to the assumptions underlying the scenarios in the literature assessed in this report, which do not cover all possible 
futures. Additional scenarios may be developed. The underlying population assumptions range from 8.5 to 9.7 billion in 2050 and 7.4 to 10.9 billion in 2100 
(5–95th percentile) starting from 7.6 billion in 2019. The underlying assumptions on global GDP growth (ppp) range from 2.5 to 3.5% per year in the 2019–2050 
period and 1.3 to 2.1% per year in the 2050–2100 (5–95th percentile). Many underlying assumptions are regionally differentiated. {1.5; 3.2; 3.3; Figure 3.9; 
Annex III.II.1.4; Annex III.II.3}
46 The future scenario projections presented here are consistent with the total observed increase in global surface temperature between 1850–1900 and 1995–2014 
as well as to 2011–2020 (with best estimates of 0.85°C and 1.09°C, respectively) assessed in WGI. The largest contributor to historical human-induced warming is CO 2,

with historical cumulative CO 2 emissions from 1850 to 2019 being 2400 ± 240 GtCO 2. {WGI SPM A.1.2, WGI Table SPM.2, WGI Table 5.1, WGIII SPM Section B}.
47 In case no explicit likelihood is provided, the reported warming levels are associated with a likelihood of >50%.
48 Scenarios in this category are found to have simultaneous likelihood to limit peak global warming to 2°C throughout the 21st century of close to and more than 90%.

22SPM
Summary for Policymakers 
Box SPM.1 (continued)
• Category C8 comprises modelled scenarios that exceed warming of 4°C during the 21st century with a likelihood of 50% or 
greater. In these scenarios warming continues to rise beyond the 21st century.
Categories of modelled scenarios are distinct and do not overlap; they do not contain categories consistent with lower levels of 
global warming, for example, the category of C3 scenarios that limit warming to 2°C (>67%) does not include the C1 and C2 
scenarios that limit or return warming to 1.5°C (>50%). Where relevant, scenarios belonging to the group of categories C1–C3 
are referred to in this report as scenarios that limit warming to 2°C (>67%) or lower.
Methods to project global warming associated with the scenarios were updated to ensure consistency with the AR6 WGI 
assessment of physical climate science.49 {3.2, Annex III.II.2.5; AR6 WGI Cross-Chapter Box 7.1}

49 This involved improved methodologies to use climate emulators (MAGICC7 and FAIR v1.6), which were evaluated and calibrated to closely match the global 
warming response to emissions as assessed in AR6 WGI. It included harmonisation of global GHG emissions in 2015 in modelled scenarios (51–56 GtCO 2-eq; 
5th to 95th percentiles) with the corresponding emission value underlying the CMIP6 projected climate response assessed by WGI (54 GtCO 2-eq), based on similar 
data sources of historical emissions that are updated over time. The assessment of past GHG emissions in Chapter 2 of the report is based on a more recent 
dataset providing emissions of 57 [±6.3] GtCO 2-eq in 2015 (B.1). Differences are well within the assessed uncertainty range, and arise mainly from differences 
in estimated CO 2-LULUCF emissions, which are subject to large uncertainties, high annual variability and revisions over time. Projected rates of global emission

reduction in mitigation scenarios are reported relative to modelled global emissions in 2019 rather than the global emissions reported in Chapter 2; this ensures 
internal consistency in assumptions about emission sources and activities, as well as consistency with temperature projections based on the physical climate science 
assessment by WG I. {Annex III.II.2.5}
The range of assessed scenarios results in a range of 21st century projected global warming. 
a. Median global warming across scenarios in categories C1 to C8 b. Peak and 2100 global warming across 
scenario categories, IMPs and SSPx-y 
scenarios considered by AR6 WGIGlobal warming relative to 1850–1900 (°C)
0123456
IMP
SSPx-y
ﬁlled: Peak warming (over the 21st century)

medians of scenariosC8
C7
C6
C5
C4
C3
C2
C1
C7
Box SPM.1, Figure 1 | Projected global mean warming of the ensemble of modelled scenarios included in the climate categories C1–C8 
and IMPs (based on emulators calibrated to the WGI assessment), as well as five illustrative scenarios (SSPx-y) as considered by AR6 WGI. 
Panel a  shows the p5–p95 range of projected median warming across global modelled pathways within a  category, with the category medians (line). 
Panel b  shows the peak and 2100 emulated temperature outcomes for the categories C1 to C8 and for IMPs, and the five illustrative scenarios (SSPx-y) 
as considered by AR6 WGI. The boxes show the p5–p95 range within each scenario category, as in panel a. The combined p5–p95 range across scenarios and

the climate uncertainty for each category C1–C8 is also shown for 2100 warming (thin vertical lines). (Table SPM.2) {Figure 3.11; AR6 WGI Figure SPM.8}

23SPM
These updated methods affect the categorisation of some scenarios. On average across scenarios, peak global warming is 
projected to be lower by up to about 0.05 [±0.1] °C than if the same scenarios were evaluated using the SR1.5 methodology, 
and global warming in 2100 is projected to be lower by about 0.1 [±0.1] °C. {Annex III.II.2.5.1, Annex III Figure II.3}
Resulting changes to the emission characteristics of scenario categories described in Table SPM.2 interact with changes in 
the characteristics of the wider range of emission scenarios published since the SR1.5. Proportionally more scenarios assessed 
in AR6 are designed to limit temperature overshoot and more scenarios limit large-scale net negative CO2 emissions than in 
SR1.5. As a result, AR6 scenarios in the lowest temperature category (C1) generally reach net zero GHG emissions later in the

21st century than scenarios in the same category assessed in SR1.5, and about half do not reach net zero GHG by 2100. The rate 
of decline of GHG emissions in the near term by 2030 in category C1 scenarios is very similar to the assessed rate in SR1.5, but 
absolute GHG emissions of category C1 scenarios in AR6 are slightly higher in 2030 than in SR1.5, since the reductions start from 
a higher emissions level in 2020. (Table SPM.2) {Annex III, 2.5, 3.2, 3.3}
The large number of global emissions scenarios assessed, including 1202 scenarios with projected global warming outcomes 
using climate emulators, come from a wide range of modelling approaches. They include the five illustrative scenarios (Shared 
Socio-economic Pathways; SSPs) assessed by WGI for their climate outcomes but cover a wider and more varied set in terms 
of assumptions and modelled outcomes. For this assessment, Illustrative Mitigation Pathways (IMPs) were selected from this

larger set to illustrate a range of different mitigation strategies that would be consistent with different warming levels. The IMPs 
illustrate pathways that achieve deep and rapid emissions reductions through different combinations of mitigation strategies. 
The IMPs are not intended to be comprehensive and do not address all possible themes in the underlying report. They differ in 
terms of their focus, for example, placing greater emphasis on renewables (IMP-Ren), deployment of carbon dioxide removal 
that results in net negative global GHG emissions (IMP-Neg), and efficient resource use as well as shifts in consumption patterns 
globally, leading to low demand for resources, while ensuring a high level of services and satisfying basic needs (IMP-LD) (Figure 
SPM.5). Other IMPs illustrate the implications  of a less rapid introduction of mitigation measures followed by a subsequent

gradual strengthening (IMP-GS), and how shifting global pathways towards sustainable development, including by reducing 
inequality, can lead to mitigation (IMP-SP). The IMPs reach different climate goals as indicated in Table SPM.2 and Box SPM.1, 
Figure 1. {1.5, 3.1, 3.2, 3.3, 3.6, Figure 3.7, Figure 3.8, Box 3.4, Annex III.II.2.4}
C.2  Global net zero CO2 emissions are reached in the early 2050s in modelled pathways that limit warming 
to 1.5°C (>50%) with no or limited overshoot, and around the early 2070s in modelled pathways 
that limit warming to 2°C (>67%). Many of these pathways continue to net negative CO2 emissions 
after the point of net zero. These pathways also include deep reductions in other GHG emissions. The 
level of peak warming depends on cumulative CO2 emissions until the time of net zero CO2 and the 
change in non-CO2 climate forcers by the time of peaking. Deep GHG emissions reductions by 2030

and 2040, particularly reductions of methane emissions, lower peak warming, reduce the likelihood 
of overshooting warming limits and lead to less reliance on net negative CO2 emissions that reverse 
warming in the latter half of the century. Reaching and sustaining global net zero GHG emissions results 
in a gradual decline in warming. (high confidence) (Table SPM.2) {3.3, 3.5, Box 3.4, Cross-Chapter Box 3 
in Chapter 3, AR6 WGI SPM D1.8}
C.2.1  Modelled global pathways limiting warming to 1.5°C (>50%) with no or limited overshoot are associated with projected 
cumulative net CO2 emissions50 until the time of net zero CO2 of 510 [330–710] GtCO2. Pathways limiting warming to 2°C 
(>67%) are associated with 890 [640–1160] GtCO2 (Table SPM.2). (high confidence) {3.3, Box 3.4}
C.2.2  Modelled global pathways that limit warming to 1.5°C (>50%) with no or limited overshoot involve more rapid and deeper

near-term GHG emissions reductions through to 2030, and are projected to have less net negative CO2 emissions and less 
carbon dioxide removal (CDR) in the longer term, than pathways that return warming to 1.5°C (>50%) after a high overshoot 
(C2 category). Modelled pathways that limit warming to 2°C (>67%) have on average lower net negative CO2 emissions 
compared to pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and pathways that return warming 
50 Cumulative net CO 2 emissions from the beginning of the year 2020 until the time of net zero CO 2 in assessed pathways are consistent with the remaining carbon 
budgets assessed by WGI, taking account of the ranges in the WGIII temperature categories and warming from non-CO 2 gases. {Box 3.4}

24SPM
Summary for Policymakersto 1.5°C (>50%) after a high overshoot (C1 and C2 categories respectively). Modelled pathways that return warming to 
1.5°C (>50%) after a  high overshoot (C2 category) show near-term GHG emissions reductions similar to pathways that 
limit warming to 2°C (>67%) (C3 category). For a  given peak global warming level, greater and more rapid  near-term 
GHG emissions reductions are associated with later net zero CO2 dates. (high confidence) (Table SPM.2) {3.3, Table  3.5, 
Cross-Chapter Box 3 in Chapter 3, Annex I: Glossary}
C.2.3  Future non-CO2 warming depends on reductions in non-CO2 GHGs,  aerosols and their precursors, and ozone precursor 
emissions. In modelled global low-emission pathways, the projected reduction of cooling and warming aerosol emissions 
over time leads to net warming in the near- to mid-term. In these mitigation pathways, the projected reductions of cooling

aerosols are mostly due to reduced fossil fuel combustion that was not equipped with effective air pollution controls. Non-CO2 
GHG emissions at the time of net zero CO2 are projected to be of similar magnitude in modelled pathways that limit warming 
to 2°C (>67%) or lower. These non-CO2 GHG emissions are about 8 [5–11] GtCO2-eq yr–1, with the largest fraction from CH4 
(60% [55–80%]), followed by N2O (30% [20–35%]) and F-gases (3% [2–20%]).51 Due to the short lifetime of CH4 in the 
atmosphere, projected deep reduction of CH4 emissions up until the time of net zero CO2 in modelled mitigation pathways 
effectively reduces peak global warming. (high confidence) {3.3; AR6 WGI SPM D1.7}
C.2.4  At the time of global net zero GHG emissions, net negative CO2 emissions counterbalance metric-weighted non-CO2 GHG 
emissions. Typical emissions pathways that reach and sustain global net zero GHG emissions based on the 100-year global

warming potential (GWP-100)7 are projected to result in a gradual decline of global warming. About half of the assessed 
pathways that limit warming to 1.5°C (>50%) with no or limited overshoot (C1 category) reach net zero GHG emissions 
during the second half of the 21st century. These pathways show greater reduction in global warming after the peak to 
1.2 [1.1–1.4] °C by 2100 than modelled pathways in the same category that do not reach net zero GHG emissions before 2100 
and that result in warming of 1.4 [1.3–1.5] °C by 2100. In modelled pathways that limit warming to 2°C (>67%) (C3 category), 
there is no significant difference in warming by 2100 between those pathways that reach net zero GHGs (around 30%) and 
those that do not (high confidence). In pathways that limit warming to 2°C (>67%) or lower and that do reach net zero GHG, 
net zero GHG occurs around 10–40 years later than net zero CO2 emissions (medium confidence). {Cross-Chapter Box 2 in

Chapter 2, 3.3, Cross-Chapter Box 3 in Chapter 3; AR6 WGI SPM D1.8}
C.3    All global modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot, and 
those that limit warming to 2°C (>67%), involve rapid and deep and in most cases immediate GHG 
emission reductions in all sectors. Modelled mitigation strategies to achieve these reductions include 
transitioning from fossil fuels without CCS to very low- or zero-carbon energy sources, such as renewables 
or fossil fuels with CCS, demand side measures and improving efficiency, reducing non-CO2 emissions, and 
deploying carbon dioxide removal (CDR) methods to counterbalance residual GHG emissions. Illustrative 
Mitigation Pathways (IMPs) show different combinations of sectoral mitigation strategies consistent with 
a given warming level. (high confidence) (Figure SPM.5) {3.2, 3.3, 3.4, 6.4, 6.6}

C.3.1  There is a variation in the contributions of different sectors in modelled mitigation pathways, as illustrated by the Illustrative 
Mitigation Pathways (IMPs). However, modelled pathways that limit warming to 2°C (>67%) or lower share common 
characteristics, including rapid and deep GHG emission reductions. Doing less in one sector needs to be compensated by 
further reductions in other sectors if warming is to be limited. (high confidence) (Figure SPM.5) {3.2, 3.3, 3.4}
C.3.2  In modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot, the global use of coal, oil 
and gas in 2050 is projected to decline with median values of about 95%, 60% and 45% respectively, compared to 2019. 
The interquartile ranges are (80 to 100%), (40 to 75%) and (20 to 60%) and the p5–p95 ranges are [60 to 100%], [25 to 90%] 
and [–30 to +85%], respectively. In modelled pathways that limit warming to 2°C (>67%), these projected declines have

a median value and interquartile range of 85% (65 to 95%), 30% (15 to 50%) and 15% (–10 to +40%) respectively by 
2050. The use of coal, oil and gas without CCS in modelled pathways that limit warming to 1.5°C (>50%) with no or limited 
overshoot is projected to be reduced to a greater degree, with median values of about 100%, 60% and 70% in 2050 compared 
to 2019. The interquartile ranges are (95 to 100%), (45 to 75%) and (60 to 80%) and the p5–p95 ranges about [85 to 100%], 
[25  to  90%] and [35 to  90%] for coal, oil and gas respectively. In these global modelled pathways, in 2050 almost all 
electricity is supplied from zero- or low-carbon sources, such as renewables or fossil fuels with CCS, combined with increased 
51 All numbers here rounded to the closest 5%, except values below 5% (for F-gases).

25SPM
Summary for Policymakerselectrification of energy demand. As indicated by the ranges, choices in one sector can be compensated for by choices in 
another while being consistent with assessed warming levels.52 (high confidence) {3.4, 3.5, Table 3.6, Figure 3.22, Figure 6.35}  
C.3.3  In modelled pathways that reach global net zero CO2 emissions: at the point they reach net zero, 5–16 GtCO2 of emissions from 
some sectors are compensated for by net negative CO2 emissions in other sectors. In most global modelled pathways that limit 
warming to 2°C (>67%) or lower, the AFOLU sector, via reforestation and reduced deforestation, and the energy supply sector 
reach net zero CO2 emissions earlier than the buildings, industry and transport sectors. (high confidence) (Figure SPM.5e,f) {3.4}
C.3.4  In modelled pathways that reach global net zero GHG emissions, at the point they reach net zero GHG, around 74% [54 to 90%]

of global emissions reductions are achieved by CO2 reductions in energy supply and demand, 13% [4 to 20%] by CO2 mitigation 
options in the AFOLU sector, and 13% [10 to 18%] through the reduction of non-CO2 emissions from land-use, energy and 
industry (medium confidence). (Figure SPM.5f) {3.3, 3.4}
C.3.5  Methods and levels of CDR deployment in global modelled mitigation pathways vary depending on assumptions about costs, 
availability and constraints.53 In modelled pathways that report CDR and that limit warming to 1.5°C (>50%) with no or 
limited overshoot, global cumulative CDR during 2020–2100 from bioenergy with carbon dioxide capture and storage (BECCS) 
and direct air carbon dioxide capture and storage (DACCS) is 30–780 GtCO2 and 0–310 GtCO2, respectively. In these modelled 
pathways, the AFOLU sector contributes 20–400 GtCO2 net negative emissions. Total cumulative net negative CO2 emissions

including CDR deployment across all options represented in these modelled pathways are 20–660 GtCO2. In modelled pathways 
that limit warming to 2°C (>67%), global cumulative CDR during 2020–2100 from BECCS and DACCS is 170–650 GtCO2 and 
0–250 GtCO2 respectively, the AFOLU sector contributes 10–250 GtCO2 net negative emissions, and total cumulative net 
negative CO2 emissions are around 40 [0–290] GtCO2. (Table SPM.2) (high confidence) {Table 3.2, 3.3, 3.4}
C.3.6  All mitigation strategies face implementation challenges, including technology risks, scaling, and costs. Many challenges, such 
as dependence on CDR, pressure on land and biodiversity (e.g., bioenergy) and reliance on technologies with high upfront 
investments (e.g., nuclear), are significantly reduced in modelled pathways that assume using resources more efficiently 
(e.g., IMP-LD) or that shift global development towards sustainability (e.g., IMP-SP). (high confidence) (Figure SPM.5) {3.2, 3.4, 
3.7, 3.8, 4.3, 5.1}

52 Most but not all models include the use of fossil fuels for feedstock with varying underlying standards.
53 Aggregate levels of CDR deployment are higher than total net negative CO 2 emissions given that some of the deployed CDR is used to counterbalance remaining 
gross emissions. Total net negative CO 2 emissions in modelled pathways might not match the aggregated net negative CO 2 emissions attributed to individual CDR 
methods. Ranges refer to the 5–95th percentile across modelled pathways that include the specific CDR method. Cumulative levels of CDR from AFOLU cannot be 
quantified precisely given that: (i) some pathways assess CDR deployment relative to a baseline; and (ii) different models use different reporting methodologies that 
in some cases combine gross emissions and removals in AFOLU. Total CDR from AFOLU equals or exceeds the net negative emissions mentioned.

26SPM
MtCH4 yr  –1
MtN2O yr  –1
0 5 /uni00A010 /uni00A015 /uni00A020 /uni00A0
c. Net global CH4 emissions d. Net global N2O emissions C3
C1C3
C1
95th
Median
5th75th
25th

2100 2080 2060 2040 2020 2000 2100 2080 2060 2040 2020 20002100 2080 2060 2040 2020 2000
CO2 
comparisonGtCO 2 yr  –1GtCO2-eq yr  –1GtCO2-eq yr  –1
051015
GtCO2-eq yr  –1
023
145–100%–50%Modelled
NDC range
Figure SPM.5 | Illustrative Mitigation Pathways (IMPs) and net zero CO 2 and GHG emissions strategies.

27SPM
Summary for PolicymakersGtCO2-eq yr  –1
–100102030405060
0%  20%  40%  60%  80%  100%  Net zero CO 2 and net zero GHG emissionsare possible through different modelled mitigation pathways. 
% of modelled 2019 emissionse. Sector al GHG emissions at the time of net-zer o 
CO2 emissions   (compar ed to modelled 2019 emissions)f. Contributions to r eaching net zer o GHG emissions 
(for all scenarios r eaching net-zer o GHGs)IMP-GS
IMP-Neg
IMP-LD
IMP-SP
IMP-Ren
Sources
Sinks
At time of net-zero CO2 2019Direct
Indirect
Direct:
emissions)Indirect:
Figure SPM.5 (continued): Illustrative Mitigation Pathways (IMPs) and net zero CO 2 and GHG emissions strategies.  Panels a  and b  show the

development of global GHG and CO 2 emissions in modelled global pathways (upper sub-panels) and the associated timing of when GHG and CO 2 emissions reach net 
zero (lower sub-panels). Panels c and d show the development of global CH 4 and N 2O emissions, respectively. Coloured ranges denote the 5th to 95th percentile across 
pathways. The red ranges depict emissions pathways assuming policies that were implemented by the end of 2020 and pathways assuming implementation of NDCs 
(announced prior to COP26). Ranges of modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot are shown in light blue (category C1) and 
pathways that limit warming to 2°C (>67%) are shown in light purple (category C3). The grey range comprises all assessed pathways (C1–C8) from the 5th percentile 
of the lowest warming category (C1) to the 95th percentile of the highest warming category (C8). The modelled pathway ranges are compared to the emissions from

two pathways illustrative of high emissions (CurPol and ModAct) and five IMPs: IMP-LD, IMP-Ren, IMP-SP , IMP-Neg and IMP-GS. Emissions are harmonised to the same 
2015 base year. The vertical error bars in 2015 show the 5–95th percentile uncertainty range of the non-harmonised emissions across the pathways, and the uncertainty 
range, and median value, in emission estimates for 2015 and 2019. The vertical error bars in 2030 (panel a) depict the assessed range of the NDCs, as announced prior 
to COP26 (Figure SPM.4).23 Panel e shows the sectoral contributions of CO 2 and non-CO 2 emissions sources and sinks at the time when net zero CO 2 emissions are 
reached in the IMPs. Positive and negative emissions for different IMPs are compared to the GHG emissions from the year 2019. Energy supply (neg.) includes BECCS 
and DACCS. DACCS features in only two of the five IMPs (IMP-REN and IMP-GS) and contributes <1% and 64%, respectively, to the net negative emissions in Energy

Supply (neg.). Panel f shows the contribution of different sectors and sources to the emissions reductions from a 2019 baseline for reaching net zero GHG emissions. 
Bars denote the median emissions reductions for all pathways that reach net zero GHG emissions. The whiskers indicate the p5–p95 range. The contributions of the 
service sectors (transport, buildings, industry) are split into direct (demand-side) as well as indirect (supply-side) CO 2 emissions reductions. Direct emissions represent 
demand-side emissions due to the fuel use in the respective demand sector. Indirect emissions represent upstream emissions due to industrial processes and energy 
conversion, transmission and distribution. In addition, the contributions from the LULUCF sector and reductions from non-CO 2 emissions sources (green and grey bars) 
are displayed. {3.3, 3.4}

28SPM
Summary for PolicymakersC.4  Reducing GHG emissions across the full energy sector requires major transitions, including a substantial 
reduction in overall fossil fuel use, the deployment of low-emission energy sources, switching to 
alternative energy carriers, and energy efficiency and conservation. The continued installation of 
unabated fossil fuel54 infrastructure will ‘lock-in’ GHG emissions. (high confidence) {2.7, 6.6, 6.7, 16.4}
C.4.1  Net-zero CO2 energy systems entail: a substantial reduction in overall fossil fuel use, minimal use of unabated fossil fuels, 
and use of CCS in the remaining fossil fuel system;54 electricity systems that emit no net CO2; widespread electrification of 
the energy system including end uses; energy carriers such as sustainable biofuels, low-emissions hydrogen, and derivatives 
in applications less amenable to electrification; energy conservation and efficiency; and greater physical, institutional, and

operational integration across the energy system. CDR will be needed to counterbalance residual emissions in the energy 
sector. The most appropriate strategies depend on national and regional circumstances, including enabling conditions and 
technology availability. (high confidence) {3.4, 6.6, 11.3, 16.4}
C.4.2  Unit cost reductions in key technologies, notably wind power, solar power, and storage, have increased the economic 
attractiveness of low-emission energy sector transitions through 2030. Maintaining emission-intensive systems may, in some 
regions and sectors, be more expensive than transitioning to low emission systems. Low-emission energy sector transitions 
will have multiple co-benefits, including improvements in air quality and health.  The long-term economic attractiveness of 
deploying energy system mitigation options depends, inter alia, on policy design and implementation, technology availability

and performance, institutional capacity, equity, access to finance, and public and political support. (high confidence) 
(Figure SPM.3) {3.4, 6.4, 6.6, 6.7, 13.7}
C.4.3  Electricity systems powered predominantly by renewables are becoming increasingly viable. Electricity systems in some 
countries and regions are already predominantly powered by renewables. It will be more challenging to supply the entire 
energy system with renewable energy. Even though operational, technological, economic, regulatory, and social challenges 
remain, a variety of systemic solutions to accommodate large shares of renewables in the energy system have emerged. A broad 
portfolio of options, such as integrating systems, coupling sectors, energy storage, smart grids, demand-side management, 
sustainable biofuels, electrolytic hydrogen and derivatives, and others will ultimately be needed to accommodate large shares 
of renewables in energy systems. (high confidence) {Box 6.8, 6.4, 6.6}

C.4.4  Limiting global warming to 2°C or below will leave a substantial amount of fossil fuels unburned and could strand considerable 
fossil fuel infrastructure (high confidence). Depending on its availability, CCS could allow fossil fuels to be used longer, reducing 
stranded assets (high confidence). The combined global discounted value of the unburned fossil fuels and stranded fossil fuel 
infrastructure has been projected to be around USD1–4 trillion from 2015 to 2050 to limit global warming to approximately 
2°C, and it will be higher if global warming is limited to approximately 1.5°C (medium confidence). In this context, coal assets 
are projected to be at risk of being stranded before 2030, while oil and gas assets are projected to be more at risk of being 
stranded towards mid-century. A low-emission energy sector transition is projected to reduce international trade in fossil fuels. 
(high confidence) {6.7, Figure 6.35}

C.4.5  Global methane emissions from energy supply, primarily fugitive emissions from production and transport of fossil fuels, 
accounted for about 18% [13–23%] of global GHG emissions from energy supply, 32% [22–42%] of global CH4 emissions, 
and 6% [4–8%] of global GHG emissions in 2019 (high confidence). About 50–80% of CH4 emissions from these fossil fuels 
could be avoided with currently available technologies at less than USD50 tCO2-eq–1 (medium confidence). {6.3, 6.4.2, Box 6.5, 
11.3, 2.2.2, Table 2.1, Figure 2.5, Annex1: Glossary}
C.4.6   CCS is an option to reduce emissions from large-scale fossil-based energy and industry sources, provided geological storage 
is available. When CO2 is captured directly from the atmosphere (DACCS), or from biomass (BECCS), CCS provides the 
storage component of these CDR methods. CO2 capture and subsurface injection is a mature technology for gas processing

and enhanced oil recovery. In contrast to the oil and gas sector, CCS is less mature in the power sector, as well as in cement and 
chemicals production, where it is a critical mitigation option. The technical geological CO2 storage capacity is estimated to be 
on the order of 1000 GtCO2, which is more than the CO2 storage requirements through 2100 to limit global warming to 1.5°C, 
although the regional availability of geological storage could be a limiting factor. If the geological storage site is appropriately 
selected and managed, it is estimated that the CO2 can be permanently isolated from the atmosphere. Implementation of 
CCS currently faces technological, economic, institutional, ecological-environmental and socio-cultural barriers. Currently, 
global rates of CCS deployment are far below those in modelled pathways limiting global warming to 1.5°C or 2°C. Enabling

54 In this context, ‘unabated fossil fuels’ refers to fossil fuels produced and used without interventions that substantially reduce the amount of GHG emitted throughout 
the life cycle; for example, capturing 90% or more CO 2 from power plants, or 50–80% of fugitive methane emissions from energy supply. {Box 6.5, 11.3}

29SPM
Summary for Policymakersconditions such as policy instruments, greater public support and technological innovation could reduce these barriers. (high 
confidence) {2.5, 6.3, 6.4, 6.7, 11.3, 11.4, Cross-Chapter Box 8 in Chapter 12, Figure TS.31; SRCCL Chapter 5}
C.5  Net zero CO2 emissions from the industrial sector are challenging but possible. Reducing industry 
emissions will entail coordinated action throughout value chains to promote all mitigation options, 
including demand management, energy and materials efficiency, circular material flows, as well as 
abatement technologies and transformational changes in production processes. Progressing towards 
net zero GHG emissions from industry will be enabled by the adoption of new production processes 
using low- and zero-GHG electricity, hydrogen, fuels, and carbon management. (high confidence) {11.2, 
11.3, 11.4, Box TS.4}

C.5.1  The use of steel, cement, plastics, and other materials is increasing globally, and in most regions. There are many 
sustainable options for demand management, materials efficiency, and circular material flows that can contribute 
to reduced emissions, but how these can be applied will vary across regions and different materials. These options 
have a potential for being more used in industrial practice and would need more attention from industrial policy. 
These options, as well as new production technologies, are generally not considered in recent global scenarios 
nor in national economy-wide scenarios due to relative newness. As a consequence, the mitigation potential in 
some scenarios is underestimated compared to bottom-up industry-specific models. (high confidence) {3.4, 5.3, 
Figure 5.7, 11.2, Box 11.2, 11.3, 11.4, 11.5.2, 11.6} 
C.5.2  For almost all basic materials –  primary metals,55 building materials and chemicals – many low- to zero-GHG intensity

production processes are at the pilot to near-commercial and in some cases commercial stage but they are not yet established 
industrial practice. Introducing new sustainable production processes for basic materials could increase production costs but, 
given that only a small fraction of consumer costs are based on materials, such new processes are expected to translate into 
minimal cost increases for final consumers. Hydrogen direct reduction for primary steelmaking is near-commercial in some 
regions. Until new chemistries are mastered, deep reduction of cement process emissions will rely on already commercialised 
cementitious material substitution and the availability of CCS. Reducing emissions from the production and use of chemicals 
would need to rely on a life cycle approach, including increased plastics recycling, fuel and feedstock switching, and carbon

sourced through biogenic sources, and, depending on availability, carbon capture and use (CCU), direct air CO2 capture, as 
well as CCS. Light industry, mining and manufacturing have the potential to be decarbonised through available abatement 
technologies (e.g., material efficiency, circularity), electrification (e.g., electrothermal heating, heat pumps) and low- or 
zero-GHG emitting fuels (e.g., hydrogen, ammonia, and bio-based and other synthetic fuels). (high confidence) {Table 11.4, 
Box 11.2, 11.3, 11.4}
C.5.3  Action to reduce industry sector emissions may change the location of GHG-intensive industries and the organisation of value 
chains. Regions with abundant low-GHG energy and feedstocks have the potential to become exporters of hydrogen-based 
chemicals and materials processed using low-carbon electricity and hydrogen. Such reallocation will have global distributional 
effects on employment and economic structure. (medium confidence) {Box 11.1}

C.5.4  Emissions-intensive and highly traded basic materials industries are exposed to international competition, and international 
cooperation and coordination may be particularly important in enabling change. For sustainable industrial transitions, broad 
and sequential national and sub-national policy strategies reflecting regional contexts will be required. These may combine 
policy packages including: transparent GHG accounting and standards; demand management; materials and energy efficiency 
policies; R&D and niche markets for commercialisation of low-emission materials and products; economic and regulatory 
instruments to drive market uptake; high quality recycling, low-emissions energy and other abatement infrastructure (e.g., for 
CCS); and socially inclusive phase-out plans of emissions-intensive facilities within the context of just transitions. The coverage

of mitigation policies could be expanded nationally and sub-nationally to include all industrial emission sources, and both 
available and emerging mitigation options. (high confidence) {11.6}
55 Primary metals refers to virgin metals produced from ore.

30SPM
Summary for PolicymakersC.6  Urban areas can create opportunities to increase resource efficiency and significantly reduce GHG 
emissions through the systemic transition of infrastructure and urban form through low-emission 
development pathways towards net-zero emissions. Ambitious mitigation efforts for established, 
rapidly growing and emerging cities will encompass (i) reducing or changing energy and material 
consumption, (ii) electrification, and (iii) enhancing carbon uptake and storage in the urban environment. 
Cities can achieve net-zero emissions, but only if emissions are reduced within and outside of their 
administrative boundaries through supply chains, which will have beneficial cascading effects across 
other sectors. (very high confidence) {8.2, 8.3, 8.4, 8.5, 8.6, Figure 8.21, 13.2}
C.6.1  In modelled scenarios, global consumption-based urban CO2 and CH4 emissions15 are projected to rise from 29 GtCO2-eq in 2020

to 34 GtCO2-eq in 2050 with moderate mitigation efforts (intermediate GHG emissions, SSP2-4.5), and up to 40 GtCO2-eq in 
2050 with low mitigation efforts (high GHG emissions, SSP3-7.0). With ambitious and immediate mitigation efforts, including 
high levels of electrification and improved energy and material efficiency, global consumption-based urban CO2 and CH4 
emissions could be reduced to 3  GtCO2-eq in 2050 in the modelled scenario with very low GHG emissions (SSP1-1.9).56 
(medium confidence) {8.3}
C.6.2  The potential and sequencing of mitigation strategies to reduce GHG emissions will vary depending on a city’s land use, 
spatial form, development level, and state of urbanisation (high confidence). Strategies for established cities to achieve large 
GHG emissions savings include efficiently improving, repurposing or retrofitting the building stock, targeted infilling, and

supporting non-motorised (e.g., walking, bicycling) and public transport. Rapidly growing cities can avoid future emissions 
by co-locating jobs and housing to achieve compact urban form, and by leapfrogging or transitioning to low-emissions 
technologies. New and emerging cities will have significant infrastructure development needs to achieve high quality of life, 
which can be met through energy efficient infrastructures and services, and people-centred urban design (high confidence). 
For cities, three broad mitigation strategies have been found to be effective when implemented concurrently: (i) reducing or 
changing energy and material use towards more sustainable production and consumption; (ii) electrification in combination 
with switching to low-emission energy sources; and (iii) enhancing carbon uptake and storage in the urban environment, for 
example through bio-based building materials, permeable surfaces, green roofs, trees, green spaces, rivers, ponds and lakes.57

(very high confidence) {5.3, Figure 5.7, Supplementary Material Table 5.SM.2, 8.2, 8.4, 8.6, Figure 8.21, 9.4, 9.6, 10.2} 
C.6.3  The implementation of packages of multiple city-scale mitigation strategies can have cascading effects across sectors 
and reduce GHG emissions both within and outside a city’s administrative boundaries. The capacity of cities to develop and 
implement mitigation strategies varies with the broader regulatory and institutional settings, as well as enabling conditions, 
including access to financial and technological resources, local governance capacity, engagement of civil society, and municipal 
budgetary powers. (very high confidence) {Figure 5.7, Supplementary Material Table 5.SM.2, 8.4, 8.5, 8.6, 13.2, 13.3, 13.5, 
13.7, Cross-Chapter Box 9 in Chapter 13}
C.6.4  A growing number of cities are setting climate targets, including net-zero GHG targets. Given the regional and global reach

of urban consumption patterns and supply chains, the full potential for reducing consumption-based urban emissions to net 
zero GHG can be met only when emissions beyond cities’ administrative boundaries are also addressed. The effectiveness of 
these strategies depends on cooperation and coordination with national and sub-national governments, industry, and civil 
society, and whether cities have adequate capacity to plan and implement mitigation strategies. Cities can play a positive role 
in reducing emissions across supply chains that extend beyond cities’ administrative boundaries, for example through building 
codes and the choice of construction materials. (very high confidence) {8.4, Box 8.4, 8.5, 9.6, 9.9, 13.5, 13.9}
56 These scenarios have been assessed by WGI to correspond to intermediate, high and very low GHG emissions.
57 These examples are considered to be a subset of nature-based solutions or ecosystem-based approaches.

31SPM
Summary for PolicymakersC.7.  In modelled global scenarios, existing buildings, if retrofitted, and buildings yet to be built, are 
projected to approach net zero GHG emissions in 2050 if policy packages, which combine ambitious 
sufficiency, efficiency, and renewable energy measures, are effectively implemented and barriers to 
decarbonisation are removed. Low ambition policies increase the risk of locking-in buildings’ carbon 
for decades, while well-designed and effectively implemented mitigation interventions (in both new 
buildings and existing ones if retrofitted), have significant potential to contribute to achieving SDGs in 
all regions while adapting buildings to future climate. (high confidence) {9.1, 9.3, 9.4, 9.5, 9.6, 9.9}
C.7.1  In 2019, global direct and indirect GHG emissions from buildings and emissions from cement and steel use for building

construction and renovation were 12 GtCO2-eq. These emissions include indirect emissions from offsite generation of electricity 
and heat, direct emissions produced onsite and emissions from cement and steel used for building construction and renovation. 
In 2019, global direct and indirect emissions from non-residential buildings increased by about 55% and those from residential 
buildings increased by about 50% compared to 1990. The latter increase, according to the decomposition analysis, was mainly 
driven by the increase of the floor area per capita, population growth and the increased use of emission-intensive electricity 
and heat while efficiency improvements have partly decreased emissions. There are great differences in the contribution of 
each of these drivers to regional emissions. (high confidence) {9.3}
C.7.2  Integrated design approaches to the construction and retrofit of buildings have led to increasing examples of zero energy

or zero carbon buildings in several regions. However, the low renovation rates and low ambition of retrofitted buildings 
have hindered the decrease of emissions. Mitigation interventions at the design stage include buildings typology, form, 
and multi-functionality to allow for adjusting the size of buildings to the evolving needs of their users and repurposing 
unused existing buildings to avoid using GHG-intensive materials and additional land. Mitigation interventions include: at the 
construction phase, low-emission construction materials, highly efficient building envelope and the integration of renewable 
energy solutions;58 at the use phase, highly efficient appliances/equipment, the optimisation of the use of buildings and their 
supply with low-emission energy sources; and at the disposal phase, recycling and re-using construction materials. (high 
confidence) {9.4, 9.5, 9.6, 9.7}

C.7.3  By 2050, bottom-up studies show that up to 61% (8.2 GtCO2) of global building emissions could be mitigated. Sufficiency 
policies59 that avoid the demand for energy and materials contribute 10% to this potential, energy efficiency policies contribute 
42%, and renewable energy policies 9%. The largest share of the mitigation potential of new buildings is available in 
developing countries while in developed countries the highest mitigation potential is within the retrofit of existing buildings. 
The 2020–2030 decade is critical for accelerating the learning of know-how, building the technical and institutional capacity, 
setting the appropriate governance structures, ensuring the flow of finance, and in developing the skills needed to fully 
capture the mitigation potential of buildings. (high confidence) {9.3, 9.4, 9.5, 9.6, 9.7, 9.9}

58 Integration of renewable energy solutions refers to the integration of solutions such as solar photovoltaics, small wind turbines, solar thermal collectors,  
and biomass boilers.
59 Sufficiency policies are a set of measures and daily practices that avoid demand for energy, materials, land and water while delivering human well-being for all within 
planetary boundaries.

32SPM
Summary for PolicymakersC.8  Demand-side options and low-GHG emissions technologies can reduce transport sector emissions 
in developed countries and limit emissions growth in developing countries (high confidence). 
Demand-focused interventions can reduce demand for all transport services and support the shift to 
more energy efficient transport modes (medium confidence). Electric vehicles powered by low-emissions 
electricity offer the largest decarbonisation potential for land-based transport, on a life cycle basis (high 
confidence). Sustainable biofuels can offer additional mitigation benefits in land-based transport in 
the short and medium term (medium confidence). Sustainable biofuels, low-emissions hydrogen, and 
derivatives (including synthetic fuels) can support mitigation of CO2 emissions from shipping, aviation, 
and heavy-duty land transport but require production process improvements and cost reductions

(medium confidence). Many mitigation strategies in the transport sector would have various co-benefits, 
including air quality improvements, health benefits, equitable access to transportation services, reduced 
congestion, and reduced material demand (high confidence). {10.2, 10.4, 10.5, 10.6, 10.7}
C.8.1  In scenarios that limit warming to 1.5°C (>50%) with no or limited overshoot, global transport-related CO2 emissions fall by 
59% (42–68% interquartile range) by 2050 relative to modelled 2020 emissions, but with regionally differentiated trends (high 
confidence). In global modelled scenarios that limit warming to 2°C (>67%), transport-related CO2 emissions are projected 
to decrease by 29% [14–44% interquartile range] by 2050 compared to modelled 2020 emissions. In both categories of 
scenarios, the transport sector likely does not reach zero CO2 emissions by 2100 so negative emissions are likely needed to

counterbalance residual CO2 emissions from the sector (high confidence). {3.4, 10.7}
C.8.2  Changes in urban form (e.g., density, land-use mix, connectivity, and accessibility) in combination with programmes that 
encourage changes in consumer behaviour (e.g., transport pricing) could reduce transport-related greenhouse gas emissions in 
developed countries and slow growth in emissions in developing countries (high confidence). Investments in public inter- and 
intra-city transport and active transport infrastructure (e.g., bicycle and pedestrian pathways) can further support the shift to 
less GHG-intensive transport modes (high confidence). Combinations of systemic changes, including teleworking, digitalisation, 
dematerialisation, supply chain management, and smart and shared mobility may reduce demand for passenger and freight 
services across land, air, and sea (high confidence). Some of these changes could lead to induced demand for transport and

energy services, which may decrease their GHG emissions reduction potential (medium confidence). {5.3, 10.2, 10.8} 
C.8.3  Electric vehicles powered by low-GHG emissions electricity have large potential to reduce land-based transport GHG emissions, 
on a  life cycle basis (high confidence). Costs of electrified vehicles, including automobiles, two- and three-wheelers, and 
buses, are decreasing and their adoption is accelerating, but they require continued investments in supporting infrastructure 
to increase scale of deployment (high confidence). Advances in battery technologies could facilitate the electrification of 
heavy-duty trucks and complement conventional electric rail systems (medium confidence). There are growing concerns 
about critical minerals needed for batteries. Material and supply diversification strategies, energy and material efficiency 
improvements, and circular material flows can reduce the environmental footprint and material supply risks for battery

production (medium confidence). Sourced sustainably and with low-GHG emissions feedstocks, bio-based fuels, blended or 
unblended with fossil fuels, can provide mitigation benefits, particularly in the short and medium term (medium confidence). 
Low-GHG emissions hydrogen and hydrogen derivatives, including synthetic fuels, can offer mitigation potential in some 
contexts and land-based transport segments (medium confidence). {3.4, 6.3, 10.3, 10.4, 10.7, 10.8, Box 10.6}
C.8.4  While efficiency improvements (e.g., optimised aircraft and vessel designs, mass reduction, and propulsion system 
improvements) can provide some mitigation potential, additional CO2 emissions mitigation technologies for aviation and 
shipping will be required (high confidence). For aviation, such technologies include high energy density biofuels (high 
confidence), and low-emission hydrogen and synthetic fuels (medium confidence). Alternative fuels for shipping include

low-emission hydrogen, ammonia, biofuels, and other synthetic fuels (medium confidence). Electrification could play a niche 
role for aviation and shipping for short trips (medium confidence) and can reduce emissions from port and airport operations 
(high confidence). Improvements to national and international governance structures would further enable the decarbonisation 
of shipping and aviation (medium confidence). Such improvements could include, for example, the implementation of stricter 
efficiency and carbon intensity standards for the sectors (medium confidence). {10.3. 10.5, 10.6, 10.7, 10.8, Box 10.5}
C.8.5  The substantial potential for GHG emissions reductions, both direct and indirect, in the transport sector largely depends on 
power sector decarbonisation, and low-emissions feedstocks and production chains (high confidence). Integrated transport 
and energy infrastructure planning and operations can enable sectoral synergies and reduce the environmental, social, and

economic impacts of decarbonising the transport and energy sectors (high confidence). Technology transfer and financing can 
support developing countries leapfrogging or transitioning to low-emissions transport systems thereby providing multiple 
co-benefits (high confidence). {10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8}

33SPM
Summary for PolicymakersC.9  AFOLU mitigation options, when sustainably implemented, can deliver large-scale GHG emission 
reductions and enhanced removals, but cannot fully compensate for delayed action in other sectors. 
In addition, sustainably sourced agricultural and forest products can be used instead of more 
GHG-intensive products in other sectors. Barriers to implementation and trade-offs may result from the 
impacts of climate change, competing demands on land, conflicts with food security and livelihoods, 
the complexity of land ownership and management systems, and cultural aspects. There are many 
country-specific opportunities to provide co-benefits (such as biodiversity conservation, ecosystem 
services, and livelihoods) and avoid risks (for example, through adaptation to climate change). (high 
confidence) {7.4, 7.6, 7.7, 12.5, 12.6}
C.9.1  The projected economic mitigation potential of AFOLU options between 2020 and 2050, at costs below USD100 tCO2-eq–1,

is 8–14 GtCO2-eq yr–1  60 (high confidence). 30–50% of this potential is available at less than USD20 tCO2-eq and could be upscaled 
in the near term across most regions (high confidence). The largest share of this economic potential [4.2–7.4 GtCO2-eq yr–1] 
comes from the conservation, improved management, and restoration of forests and other ecosystems (coastal wetlands, 
peatlands, savannas and grasslands), with reduced deforestation in tropical regions having the highest total mitigation. 
Improved and sustainable crop and livestock management, and carbon sequestration in agriculture (the latter including soil 
carbon management in croplands and grasslands, agroforestry and biochar), can contribute 1.8–4.1 GtCO2-eq yr–1 reduction. 
Demand-side and material substitution measures, such as shifting to balanced, sustainable healthy diets,61 reducing food loss 
and waste, and using bio-materials, can contribute 2.1 [1.1–3.6] GtCO2-eq yr–1 reduction. In addition, demand-side measures

together with the sustainable intensification of agriculture can reduce ecosystem conversion and CH4 and N2O emissions, 
and free up land for reforestation and restoration, and the production of renewable energy. The improved and expanded 
use of wood products sourced from sustainably managed forests also has potential through the allocation of harvested 
wood to longer-lived products, increasing recycling or material substitution. AFOLU mitigation measures cannot compensate 
for delayed emission reductions in other sectors. Persistent and region-specific barriers continue to hamper the economic 
and political feasibility of deploying AFOLU mitigation options. Assisting countries to overcome barriers will help to achieve 
significant mitigation (medium confidence). (Figure SPM.6) {7.1, 7.4, 7.5, 7.6}
C.9.2  AFOLU carbon sequestration and GHG emission reduction options have both co-benefits and risks in terms of biodiversity and

ecosystem conservation, food and water security, wood supply, livelihoods and land tenure and land-use rights of Indigenous 
Peoples, local communities and small land owners. Many options have co-benefits but those that compete for land and 
land-based resources can pose risks. The scale of benefit or risk largely depends on the type of activity undertaken, deployment 
strategy (e.g., scale, method), and context (e.g., soil, biome, climate, food system, land ownership) that vary geographically 
and over time. Risks can be avoided when AFOLU mitigation is pursued in response to the needs and perspectives of multiple 
stakeholders to achieve outcomes that maximize co-benefits while limiting trade-offs. (high confidence) {7.4, 7.6, 12.3}
C.9.3  Realising the AFOLU mitigation potential entails overcoming institutional, economic and policy constraints and managing 
potential trade-offs (high confidence). Land-use decisions are often spread across a wide range of land owners; demand-side

measures depend on billions of consumers in diverse contexts. Barriers to the implementation of AFOLU mitigation include 
insufficient institutional and financial support, uncertainty over long-term additionality and trade-offs, weak governance, 
insecure land ownership, low incomes and the lack of access to alternative sources of income, and the risk of reversal. Limited 
access to technology, data, and know-how is a barrier to implementation. Research and development are key for all measures. 
For example, measures for the mitigation of agricultural CH4 and N2O emissions with emerging technologies show promising 
results. However, the mitigation of agricultural CH4 and N2O emissions is still constrained by cost, the diversity and complexity 
of agricultural systems, and by increasing demands to raise agricultural yields, and increasing demand for livestock products. 
(high confidence) {7.4, 7.6}

C.9.4  Net costs of delivering 5–6 GtCO2 yr–1 of forest-related carbon sequestration and emission reduction as assessed with sectoral 
models are estimated to reach to about USD400 billion yr–1 by 2050. The costs of other AFOLU mitigation measures are highly 
context specific. Financing needs in AFOLU, and in particular in forestry, include both the direct effects of any changes in 
60 The global top-down estimates and sectoral bottom-up estimates described here do not include the substitution of emissions from fossil fuels and GHG-intensive 
materials. 8–14 GtCO 2-eq yr–1 represents the mean of the AFOLU economic mitigation potential estimates from top-down estimates (lower bound of range) and 
global sectoral bottom-up estimates (upper bound of range). The full range from top-down estimates is 4.1–17.3 GtCO 2-eq yr–1 using a ‘no policy’ baseline. The full 
range from global sectoral studies is 6.7–23.4 GtCO 2-eq yr–1 using a variety of baselines. (high confidence)

61 ‘Sustainable healthy diets’ promote all dimensions of individuals’ health and well-being; have low environmental pressure and impact; are accessible, affordable, 
safe and equitable; and are culturally acceptable, as described in FAO and WHO. The related concept of ‘balanced diets’ refers to diets that feature plant-based foods, 
such as those based on coarse grains, legumes, fruits and vegetables, nuts and seeds, and animal-sourced food produced in resilient, sustainable and low-GHG 
emission systems, as described in SRCCL.

34SPM
Summary for Policymakersactivities as well as the opportunity costs associated with land-use change. Enhanced monitoring, reporting and verification 
capacity, and the rule of law, are crucial for land-based mitigation in combination with policies also recognising interactions 
with wider ecosystem services, could enable engagement by a wider array of actors, including private businesses, NGOs, and 
Indigenous Peoples and local communities. (medium confidence) {7.6, 7.7}
C.9.5  Context specific policies and measures have been effective in demonstrating the delivery of AFOLU carbon sequestration and 
GHG emission reduction options but the above-mentioned constraints hinder large scale implementation (medium confidence). 
Deploying land-based mitigation can draw on lessons from experience with regulations, policies, economic incentives, 
payments (e.g., for biofuels, control of nutrient pollution, water regulations, conservation and forest carbon, ecosystem

services, and rural livelihoods), and from diverse forms of knowledge such as Indigenous knowledge, local knowledge and 
scientific knowledge. Indigenous Peoples, private forest owners, local farmers and communities manage a significant share of 
global forests and agricultural land and play a central role in land-based mitigation options. Scaling successful policies and 
measures relies on governance that emphasises integrated land-use planning and management framed by SDGs, with support 
for implementation. (high confidence) {7.4, Box 7.2, 7.6}
C.10  Demand-side mitigation encompasses changes in infrastructure use, end-use technology adoption, 
and socio-cultural and behavioural change. Demand-side measures and new ways of end-use service 
provision can reduce global GHG emissions in end-use sectors by 40–70% by 2050 compared to baseline 
scenarios, while some regions and socioeconomic groups require additional energy and resources.

Demand-side mitigation response options are consistent with improving basic well-being for all. (high 
confidence) (Figure SPM.6) {5.3, 5.4, Figure 5.6, Figure 5.14, 8.2, 9.4, 10.2, 11.3, 11.4, 12.4, Figure TS.22}
C.10.1  Infrastructure design and access, and technology access and adoption, including information and communication 
technologies, influence patterns of demand and ways of providing services, such as mobility, shelter, water, sanitation, and 
nutrition. Illustrative global low-demand scenarios, accounting for regional differences, indicate that more efficient end-use 
energy conversion can improve services while reducing the need for upstream energy by 45% by 2050 compared to 2020. 
Demand-side mitigation potential differs between and within regions, and some regions and populations require additional 
energy, capacity, and resources for human well-being. The lowest population quartile by income worldwide faces shortfalls in

shelter, mobility, and nutrition. (high confidence) {5.2, 5.3, 5.4, 5.5, Figure 5.6, Figure 5.10, Table 5.2, Figure TS.20, Figure TS.22}
C.10.2  By 2050, comprehensive demand-side strategies across all sectors could reduce direct and indirect CO2 and non-CO2 GHG 
emissions in three end-use sectors (buildings, land transport, and food) globally by 40%–70% compared to the 2050 emissions 
projection of two scenarios consistent with policies announced by national governments until 2020. With policy support, 
socio-cultural options and behavioural change can reduce global GHG emissions of end-use sectors by at least 5% rapidly, 
with most of the potential in developed countries, and more until 2050, if combined with improved infrastructure design and 
access. Individuals with high socio-economic status contribute disproportionately to emissions and have the highest potential for

emissions reductions, e.g., as citizens, investors, consumers, role models, and professionals. (high confidence) (Figure SPM.6) {5.2, 
5.3, 5.4, 5.5, 5.6, Supplementary Material Table 5.SM.2, 8.4, 9.9, 13.2, 13.5, 13.8, Figure TS.20} 
C.10.3  A range of 5–30% of global annual GHG emissions from end-use sectors are avoidable by 2050, compared to 2050 emissions 
projection of two scenarios consistent with policies announced by national governments until 2020, through changes in the 
built environment, new and repurposed infrastructures and service provision through compact cities, co-location of jobs and 
housing, more efficient use of floor space and energy in buildings, and reallocation of street space for active mobility (high 
confidence). (Figure SPM.6) {5.3.1, 5.3.3, 5.4, Figure 5.7, Figure 5.13, Table 5.1, Table 5.5, Supplementary Material Table 5.
SM.2, 8.4, 9.5, 10.2, 11.3, 11.4, Table 11.6, Box TS.12}

C.10.4  Choice architecture62 can help end-users adopt, as relevant to consumers, culture and country contexts, low-GHG-intensive 
options such as balanced, sustainable healthy diets61 acknowledging nutritional needs; food waste reduction; adaptive heating 
and cooling choices for thermal comfort; building-integrated renewable energy; and electric light-duty vehicles, and shifts to 
walking, cycling, shared pooled and public transit; and sustainable consumption by intensive use of longer-lived repairable 
products (high confidence). Addressing inequality and many forms of status consumption63 and focusing on wellbeing 
supports climate change mitigation efforts (high confidence). (Figure SPM.6) {2.4.3, 2.6.2, 4.2.5, 5.1, 5.2, 5.3, 5.4, Figure 5.4, 
Figure 5.10, Table 5.2, Supplementary Material Table 5.SM.2, 7.4.5, 8.2, 8.4, 9.4, 10.2, 12.4, Figure TS.20}

62 ‘Choice architecture’ describes the presentation of choices to consumers, and the impact that presentation has on consumer decision-making.
63 ‘Status consumption’ refers to the consumption of goods and services which publicly demonstrates social prestige.

35SPM
Summary for PolicymakersDemand-side mitigation can be achieved through changes in socio-cultural factors, infrastructure 
15
10
5
End-use
sectors
assumed to be addressed 

Load managementGtCO2 yr  –115
10
5
0
GtCO2 yr  –115
10
5
0c. Electricity: indicative impacts
Industry
Buildings
measures 

Manufactured products Mobility ShelterLand transport Industry Buildings
telecommuting; active 
mobility through 
walking and cyclingSocial practices resulting 
in energy saving; lifestyle 
and behavioural changesSocio-cultural factors
and glass; labelling low- 
emissions materials 
and productsPublic transport; shared 
mobility; compact cities; 
rationalisation of living

efﬁcient vehiclesEnergy efﬁcient 
Total emissions 2050: Mean IEA-STEPS IP_ModAct
Figure SPM.6 | Indicative potential of demand-side mitigation options by 2050.  Figure SPM.6 covers the indicative potential of demand-side options for the 
year 2050. Figure SPM.7 covers cost and potentials for the year 2030. Demand-side mitigation response options are categorised into three broad domains: ‘socio-cultural 
factors’, associated with individual choices, behaviour, lifestyle changes, social norms, and culture; ‘infrastructure use’, related to the design and use of supporting hard

and soft infrastructure that enables changes in individual choices and behaviour; and ‘end-use technology adoption’, referring to the uptake of technologies by end-users. 
Demand-side mitigation is a central element of the IMP-LD and IMP-SP scenarios (Figure SPM.5). Panel a (Nutrition) demand-side potentials in 2050 assessment is 
based on bottom-up studies and is estimated following the 2050 baseline for the food sector presented in peer-reviewed literature (more information in Supplementary 
Material 5.II, and Section 7.4.5). Panel b (Manufactured products, mobility, shelter) the assessment of potentials for total emissions in 2050 are estimated based on 
approximately 500 bottom-up studies representing all global regions (detailed list is in Supplementary Material Table 5.SM.2). Baseline is provided by the sectoral mean

GHG emissions in 2050 of the two scenarios consistent with policies announced by national governments until 2020. The heights of the coloured columns represent the 
potentials represented by the median value. These are based on a range of values available in the case studies from literature shown in Supplementary Material 5.SM.II. 
The range is shown by the dots connected by dotted lines representing the highest and the lowest potentials reported in the literature. Panel a shows the demand-side 
potential of socio-cultural factors and infrastructure use. The median value of direct emissions (mostly non-CO 2) reduction through socio-cultural factors is 1.9 GtCO 2-eq 
without considering land-use change through reforestation of freed up land. If changes in land-use pattern enabled by this change in food demand are considered,

the indicative potential could reach 7 GtCO 2-eq. Panel b illustrates mitigation potential in industry, land transport and buildings end-use sectors through demand-side 
options. Key options are presented in the summary table below the figure and the details are in Supplementary Material Table 5.SM.2. Panel c visualises how sectoral 
demand-side mitigation options (presented in panel b) change demand on the electricity distribution system. Electricity accounts for an increasing proportion of final 
energy demand in 2050 (additional electricity bar) in line with multiple bottom-up studies (detailed list is in Supplementary Material Table 5.SM.3), and Chapter 6 
(Section 6.6). These studies are used to compute the impact of end-use electrification which increases overall electricity demand. Some of the projected increase in 
electricity demand can be avoided through demand-side mitigation options in the domains of socio-cultural factors and infrastructure use in end-use electricity use

in buildings, industry, and land transport found in literature based on bottom-up assessments. Dark grey columns show the emissions that cannot be avoided through 
demand-side mitigation options. {5.3, Figure 5.7, Supplementary Material 5.SM.II}

36SPM
Summary for PolicymakersC.11  The deployment of carbon dioxide removal (CDR) to counterbalance hard-to-abate residual emissions is 
unavoidable if net zero CO2 or GHG emissions are to be achieved. The scale and timing of deployment will 
depend on the trajectories of gross emission reductions in different sectors. Upscaling the deployment 
of CDR depends on developing effective approaches to address feasibility and sustainability constraints 
especially at large scales. (high confidence) {3.4, 7.4, 12.3, Cross-Chapter Box 8 in Chapter 12}
C.11.1  CDR refers to anthropogenic activities that remove CO2 from the atmosphere and store it durably in geological, terrestrial, or 
ocean reservoirs, or in products. CDR methods vary in terms of their maturity, removal process, time scale of carbon storage, 
storage medium, mitigation potential, cost, co-benefits, impacts and risks, and governance requirements (high confidence).

Specifically, maturity ranges from lower maturity (e.g., ocean alkalinisation) to higher maturity (e.g., reforestation); removal 
and storage potential ranges from lower potential (<1  GtCO2 yr–1, e.g., blue carbon management) to higher potential 
(>3 GtCO2 yr–1, e.g., agroforestry); costs range from lower cost (e.g., USD-45–100 per tCO2 for soil carbon sequestration) to 
higher cost (e.g., USD100–300 per tCO2 for DACCS) (medium confidence). Estimated storage time scales vary from decades 
to centuries for methods that store carbon in vegetation and through soil carbon management, to 10,000 years or more 
for methods that store carbon in geological formations (high confidence). The processes by which CO2 is removed from the 
atmosphere are categorised as biological, geochemical or chemical. Afforestation, reforestation, improved forest management, 
agroforestry and soil carbon sequestration are currently the only widely practiced CDR methods (high confidence). {7.4, 7.6,

12.3, Table 12.6, Cross-Chapter Box 8 in Chapter 12, Table TS.7; AR6 WGI 5.6}
C.11.2  The impacts, risks and co-benefits of CDR deployment for ecosystems, biodiversity and people will be highly variable 
depending on the method, site-specific context, implementation and scale (high confidence). Reforestation, improved forest 
management, soil carbon sequestration, peatland restoration and blue carbon management are examples of methods that 
can enhance biodiversity and ecosystem functions, employment and local livelihoods, depending on context (high confidence). 
In contrast, afforestation or production of biomass crops for BECCS or biochar, when poorly implemented, can have adverse 
socio-economic and environmental impacts, including on biodiversity, food and water security, local livelihoods and on the 
rights of Indigenous Peoples, especially if implemented at large scales and where land tenure is insecure (high confidence).

Ocean fertilisation, if implemented, could lead to nutrient redistribution, restructuring of ecosystems, enhanced oxygen 
consumption and acidification in deeper waters (medium confidence). {7.4, 7.6, 12.3, 12.5}
C.11.3  The removal and storage of CO2 through vegetation and soil management can be reversed by human or natural disturbances; 
it is also prone to climate change impacts. In comparison, CO2 stored in geological and ocean reservoirs (via BECCS, DACCS, 
ocean alkalinisation) and as carbon in biochar is less prone to reversal. (high confidence) {6.4, 7.4, 12.3}
C.11.4  In addition to deep, rapid, and sustained emission reductions CDR can fulfil three different complementary roles globally or at 
country level: lowering net CO2 or net GHG emissions in the near term; counterbalancing ‘hard-to-abate’ residual emissions 
(e.g., emissions from agriculture, aviation, shipping, industrial processes) in order to help reach net zero CO2 or net zero GHG

emissions in the mid-term; and achieving net negative CO2 or GHG emissions in the long term if deployed at levels exceeding 
annual residual emissions. (high confidence) {3.3, 7.4, 11.3, 12.3, Cross-Chapter Box 8 in Chapter 12}
C.11.5  Rapid emission reductions in all sectors interact with future scale of deployment of CDR methods, and their associated risks, 
impacts and co-benefits. Upscaling the deployment of CDR methods depends on developing effective approaches to address 
sustainability and feasibility constraints, potential impacts, co-benefits and risks. Enablers of CDR include accelerated research, 
development and demonstration, improved tools for risk assessment and management, targeted incentives and development 
of agreed methods for measurement, reporting and verification of carbon flows. (high confidence) {3.4, 7.6, 12.3}

37SPM
Summary for PolicymakersC.12  Mitigation options costing USD100 tCO2-eq–1 or less could reduce global GHG emissions by at least half 
the 2019 level by 2030 (high confidence). Global GDP continues to grow in modelled pathways64 but, 
without accounting for the economic benefits of mitigation action from avoided damages from climate 
change nor from reduced adaptation costs, it is a few percent lower in 2050 compared to pathways 
without mitigation beyond current policies. The global economic benefit of limiting warming to 2°C 
is reported to exceed the cost of mitigation in most of the assessed literature (medium confidence). 
(Figure SPM.7) {3.6, 3.8, Cross-Working Group Box 1 in Chapter 3, 12.2, Box TS.7}
C.12.1  Based on a detailed sectoral assessment of mitigation options, it is estimated that mitigation options costing USD100 tCO2-eq–1 
or less could reduce global GHG emissions by at least half of the 2019 level by 2030 (options costing less than USD20 tCO2-eq–1

are estimated to make up more than half of this potential).65 For a smaller part of the potential, deployment leads to net 
cost savings. Large contributions with costs less than USD20 tCO2-eq–1 come from solar and wind energy, energy efficiency 
improvements, reduced conversion of natural ecosystems, and CH4 emissions reductions (coal mining, oil and gas, waste). 
The mitigation potentials and mitigation costs of individual technologies in a specific context or region may differ greatly 
from the provided estimates. The assessment of the underlying literature suggests that the relative contribution of the various 
options could change beyond 2030. (medium confidence) (Figure SPM.7) {12.2}
C.12.2  The aggregate effects of climate change mitigation on global GDP are small compared to global projected GDP growth 
in assessed modelled global scenarios that quantify the macroeconomic implications of climate change mitigation, but

that do not account for damages from climate change nor adaptation costs (high confidence). For example, compared to 
pathways that assume the continuation of policies implemented by the end of 2020, assessed global GDP reached in 2050 
is reduced by 1.3–2.7% in modelled pathways assuming coordinated global action starting between now and 2025 at the 
latest to limit warming to 2°C (>67%). The corresponding average reduction in annual global GDP growth over 2020–2050 
is 0.04–0.09 percentage points. In assessed modelled pathways, regardless of the level of mitigation action, global GDP is 
projected to at least double (increase by at least 100%) over 2020–2050. For modelled global pathways in other temperature 
categories, the reductions in global GDP in 2050 compared to pathways that assume the continuation of policies implemented 
by the end of 2020 are as follows: 2.6–4.2% (C1), 1.6–2.8% (C2), 0.8–2.1% (C4), 0.5–1.2% (C5). The corresponding reductions

in average annual global GDP growth over 2020–2050, in percentage points, are as follows: 0.09–0.14 (C1), 0.05–0.09 (C2), 
0.03–0.07 (C4), 0.02–0.04 (C5).66 There are large variations in the modelled effects of mitigation on GDP across regions, 
depending notably on economic structure, regional emissions reductions, policy design and level of international cooperation67 
(high confidence). Country-level studies also show large variations in the effect of mitigation on GDP depending notably on 
the level of mitigation and on the way it is achieved (high confidence). Macroeconomic implications of mitigation co-benefits 
and trade-offs are not quantified comprehensively across the above scenarios and depend strongly on mitigation strategies 
(high confidence). {3.6, 4.2, Box TS.7, Annex III.I.2, Annex III.I.9, Annex III.I.10 and Annex III.II.3}
C.12.3  Estimates of aggregate economic benefits from avoiding damages from climate change, and from reduced adaptation costs,

increase with the stringency of mitigation (high confidence). Models that incorporate the economic damages from climate 
change find that the global cost of limiting warming to 2°C over the 21st century is lower than the global economic benefits 
of reducing warming, unless: (i) climate damages are towards the low end of the range; or, (ii) future damages are discounted 
at high rates (medium confidence).68 Modelled pathways with a peak in global emissions between now and 2025 at the latest, 
compared to modelled pathways with a later peak in global emissions, entail more rapid near-term transitions and higher 
up-front investments, but bring long-term gains for the economy, as well as earlier benefits of avoided climate change impacts 
(high confidence). The precise magnitude of these gains and benefits is difficult to quantify. {1.7, 3.6, Cross-Working Group 
Box 1 in Chapter 3, Box TS.7; AR6 WGII SPM B.4} 
64 In modelled pathways that limit warming to 2°C (>67%) or lower.

65 The methodology underlying the assessment is described in the caption to Figure SPM.7.
66 These estimates are based on 311 pathways that report effects of mitigation on GDP and that could be classified in temperature categories, but that do not account 
for damages from climate change nor adaptation costs and that mostly do not reflect the economic impacts of mitigation co-benefits and trade-offs. The ranges 
given are interquartile ranges. The macroeconomic implications quantified vary largely depending on technology assumptions, climate/emissions target formulation, 
model structure and assumptions, and the extent to which pre-existing inefficiencies are considered. Models that produced the pathways classified in temperature 
categories do not represent the full diversity of existing modelling paradigms, and there are in the literature models that find higher mitigation costs, or conversely

lower mitigation costs and even gains. {1.7, 3.2, 3.6, Annex III.I.2, Annex III.I.9, Annex III.I.10 and Annex III.II.3}
67 In modelled cost-effective pathways with a globally uniform carbon price, without international financial transfers or complementary policies, carbon intensive 
and energy exporting countries are projected to bear relatively higher mitigation costs because of a deeper transformation of their economies and changes in 
international energy markets. {3.6}
68 The evidence is too limited to make a similar robust conclusion for limiting warming to 1.5°C.

38SPM
Summary for Policymakers0 2 4 6Potential contribution to net emission reduction, 2030 (GtCO2-eq yr–1)
0 2 4 6GtCO2-eq yr–1Mitigation optionsMany options available now in all sectors are estimated to offer substantial potential to reduce 
net emissions by 2030. Relative potentials and costs will vary across countries and in the longer
term compared to 2030.
EnergyWind energy
Nuclear energyBioelectricity
Geothermal energy
Carbon capture and storage (CCS)
Bioelectricity with CCS
Reduce CH4 emission from coal mining
Reduce CH4 emission from oil and gasAFOLUImproved sustainable forest managementCarbon sequestration in agriculture
Reduce CH4 and N2O emission in agriculture
Reduced conversion of forests and other ecosystems
Ecosystem restoration, afforestation, reforestation
Reduce food loss and food waste 
Shift to balanced, sustainable healthy dietsBuildingsAvoid demand for energy services
Efﬁcient lighting, appliances and equipment

New buildings with high energy performance
Onsite renewable production and use
Improvement of existing building stock
Enhanced use of wood productsTransportFuel-efﬁcient light-duty vehicles
Electric light-duty vehicles
Shift to public transportation
Shift to bikes and e-bikes
Fuel-efﬁcient heavy-duty vehicles
Electric heavy-duty vehicles, incl. buses
Shipping – efﬁciency and optimisation
Aviation – energy efﬁciency
BiofuelsIndustry
Reduction of non-CO2 emissionsEnergy efﬁciency
Material efﬁciency
Enhanced recycling
Fuel switching (electr, nat. gas, bio-energy, H2)
Feedstock decarbonisation, process change
Carbon capture with utilisation (CCU) and CCS
Cementitious material substitutionOtherReduce emission of ﬂuorinated gas
Reduce CH4 emissions from solid waste
Reduce CH4 emissions from wastewaterCosts are lower than the reference
0–20 (USD tCO 2-eq–1)
20–50 (USD tCO 2-eq–1)
50–100 (USD tCO 2-eq–1)
100–200 (USD tCO 2-eq–1)
Cost not allocated due to high 
variability or lack of data

Uncertainty range applies to 
the total potential contribution
to emission reduction. The 
individual cost ranges are also
associated with uncertaintyNet lifetime cost of options:
Figure SPM.7 | Overview of mitigation options and their estimated ranges of costs and potentials in 2030.

39SPM
Summary for PolicymakersFigure SPM.7 (continued): Overview of mitigation options and their estimated ranges of costs and potentials in 2030.  Costs shown are net lifetime 
costs of avoided greenhouse gas emissions. Costs are calculated relative to a reference technology. The assessments per sector were carried out using a common 
methodology, including definition of potentials, target year, reference scenarios, and cost definitions. The mitigation potential (shown in the horizontal axis) is the 
quantity of net GHG emission reductions that can be achieved by a given mitigation option relative to a specified emission baseline. Net GHG emission reductions are 
the sum of reduced emissions and/or enhanced sinks. The baseline used consists of current policy (around 2019) reference scenarios from the AR6 scenarios database

(25/75 percentile values). The assessment relies on approximately 175 underlying sources, that together give a fair representation of emission reduction potentials across 
all regions. The mitigation potentials are assessed independently for each option and are not necessarily additive. {12.2.1, 12.2.2} The length of the solid bars represents 
the mitigation potential of an option. The error bars display the full ranges of the estimates for the total mitigation potentials. Sources of uncertainty for the cost estimates 
include assumptions on the rate of technological advancement, regional differences, and economies of scale, among others. Those uncertainties are not displayed in 
the figure. Potentials are broken down into cost categories, indicated by different colours (see legend). Only discounted lifetime monetary costs are considered. Where

a gradual colour transition is shown, the breakdown of the potential into cost categories is not well known or depends heavily on factors such as geographical location, 
resource availability, and regional circumstances, and the colours indicate the range of estimates. Costs were taken directly from the underlying studies (mostly in the 
period 2015–2020) or recent datasets. No correction for inflation was applied, given the wide cost ranges used. The cost of the reference technologies were also taken 
from the underlying studies and recent datasets. Cost reductions through technological learning are taken into account.69
 –When interpreting this figure, the following should be taken into account:
 –The mitigation potential is uncertain, as it will depend on the reference technology (and emissions) being displaced, the rate of new technology adoption,  
and several other factors.

–Cost and mitigation potential estimates were extrapolated from available sectoral studies. Actual costs and potentials would vary by place, context and time.
 –Beyond 2030, the relative importance of the assessed mitigation options is expected to change, in particular while pursuing long-term mitigation goals, recognising 
also that the emphasis for particular options will vary across regions (for specific mitigation options see SPM Sections C4.1, C5.2, C7.3, C8.3 and C9.1).
 –Different options have different feasibilities beyond the cost aspects, which are not reflected in the figure (compare with SPM Section E.1).
 –The potentials in the cost range USD100–200 tCO 2-eq–1 may be underestimated for some options. 
 –Costs for accommodating the integration of variable renewable energy sources in electricity systems are expected to be modest until 2030, and are not included 
because of complexities in attributing such costs to individual technology options.

–Cost range categories are ordered from low to high. This order does not imply any sequence of implementation.
 –Externalities are not taken into account. {12.2, Table 12.3, 6.4, Table 7.3, Supplementary Material Table 9.SM.2, Supplementary Material Table 9.SM.3, 10.6, 11.4, 
Figure 11.13, Supplementary Material 12.SM.1.2.3}
69 For nuclear energy, modelled costs for long-term storage of radioactive waste are included.

40SPM
Summary for PolicymakersD. Linkages between Mitigation, Adaptation,  
70 Potential risks, knowledge gaps due to the relative immaturity of use of biochar as a  soil amendment and unknown impacts of widespread application,  
and co-benefits of biochar are reviewed in Section 7.4.3.2.D.1  Accelerated and equitable climate action in mitigating, and adapting to, climate change impacts is 
critical to sustainable development. Climate change actions can also result in some trade-offs. The 
trade-offs of individual options could be managed through policy design. The Sustainable Development 
Goals (SDGs) adopted under the UN 2030 Agenda for Sustainable Development can be used as a basis 
for evaluating climate action in the context of sustainable development. (high confidence) (Figure 
SPM.8) {1.6, 3.7, 17.3, Figure TS.29}
D.1.1  Human-induced climate change is a consequence of more than a century of net GHG emissions from unsustainable energy

use, land-use and land use change, lifestyle and patterns of consumption and production. Without urgent, effective and 
equitable mitigation actions, climate change increasingly threatens the health and livelihoods of people around the globe, 
ecosystem health and biodiversity. There are both synergies and trade-offs between climate action and the pursuit of other 
SDGs. Accelerated and equitable climate action in mitigating, and adapting to, climate change impacts is critical to sustainable 
development. (high confidence) {1.6, Cross-Chapter Box  5 in Chapter  4, 7.2, 7.3, 17.3; AR6 WGI SPM.A, Figure SPM.2; 
AR6 WGII SPM.B2, Figure SPM.3, Figure SPM.4b, Figure SPM.5}
D.1.2  Synergies and trade-offs depend on the development context including inequalities, with consideration of climate justice. 
They also depend on means of implementation, intra- and inter-sectoral interactions, cooperation between countries and

regions, the sequencing, timing and stringency of mitigation actions, governance, and policy design. Maximising synergies 
and avoiding trade-offs pose particular challenges for developing countries, vulnerable populations, and Indigenous Peoples 
with limited institutional, technological and financial capacity, and with constrained social, human, and economic capital. 
Trade-offs can be evaluated and minimised by giving emphasis to capacity building, finance, governance, technology transfer, 
investments, and development and social equity considerations with meaningful participation of Indigenous Peoples and 
vulnerable populations. (high confidence) {1.6, 1.7, 3.7, 5.2, 5.6, 7.4, 7.6, 17.4}
D.1.3  There are potential synergies between sustainable development and energy efficiency, renewable energy, urban planning 
with more green spaces, reduced air pollution, and demand-side mitigation including shifts to balanced, sustainable healthy

diets (high confidence). Electrification combined with low-GHG energy, and shifts to public transport can enhance health, 
employment, and can elicit energy security and deliver equity (high confidence). In industry, electrification and circular 
material flows contribute to reduced environmental pressures and increased economic activity and employment. However, 
some industrial options could impose high costs (medium confidence). (Figure SPM.8) {5.2, 8.2, 11.3, 11.5, 17.3, Figure TS.29}  
D.1.4  Land-based options such as reforestation and forest conservation, avoided deforestation, restoration and conservation of 
natural ecosystems and biodiversity, improved sustainable forest management, agroforestry, soil carbon management and 
options that reduce CH4 and N2O emissions in agriculture from livestock and soil, can have multiple synergies with the SDGs. 
These include enhancing sustainable agricultural productivity and resilience, food security, providing additional biomass for

human use, and addressing land degradation. Maximising synergies and managing trade-offs depend on specific practices, 
scale of implementation, governance, capacity building, integration with existing land use, and the involvement of local 
communities and Indigenous Peoples and through benefit-sharing, supported by frameworks such as Land Degradation 
Neutrality within the UNCCD. (high confidence) {3.7, 7.4, 12.5, 17.3}
D.1.5   Trade-offs in terms of employment, water use, land-use competition and biodiversity, as well as access to, and the affordability 
of, energy, food, and water can be avoided by well-implemented land-based mitigation options, especially those that do 
not threaten existing sustainable land uses and land rights, though more frameworks for integrated policy implementation 
are required. The sustainability of bioenergy and other bio-based products is influenced by feedstock, land management

practice, climatic region, the context of existing land management, and the timing, scale and speed of deployment. (medium 
confidence) {3.5, 3.7, 7.4, 12.4, 12.5, 17.1}
D.1.6  CDR methods such as soil carbon sequestration and biochar70 can improve soil quality and food production capacity. Ecosystem 
restoration and reforestation sequester carbon in plants and soil, and can enhance biodiversity and provide additional

41SPM
Summary for Policymakersbiomass, but can displace food production and livelihoods, which calls for integrated approaches to land-use planning, to 
meet multiple objectives including food security. However, due to limited application of some of the options today, there are 
some uncertainties about potential benefits. (high confidence) {3.7, 7.4, 7.6, 12.5, 17.3, Table TS.7}
Lower of the two conﬁdence

levels has been reported4Timber, biomass, agri. feedstock3 
Related Sustainable Development Goals:

Synergies

Blanks represent no assessment  5Mitigation options have synergies with many Sustainable Development Goals, but some options 
can also have trade-offs. The synergies and trade-offs vary dependent on context and scale.
Figure SPM.8 | Synergies and trade-offs between sectoral and system mitigation options and the SDGs.

42SPM
Summary for PolicymakersFigure SPM.8 (continued): Synergies and trade-offs between sectoral and system mitigation options and the SDGs.  The sectoral chapters (Chapters 6–11) 
include qualitative assessments of synergies and trade-offs between sectoral mitigation options and the SDGs. Figure SPM.8 presents a summary of the chapter-level 
assessment for selected mitigation options (see Supplementary Material Table 17.SM.1 for the underlying assessment). The last column provides a line of sight to the 
sectoral chapters, which provide details on context specificity and dependence of interactions on the scale of implementation. Blank cells indicate that interactions have not 
been assessed due to limited literature. They do not indicate the absence of interactions between mitigation options and the SDGs. Confidence levels depend on the quality

of evidence and level of agreement in the underlying literature assessed by the sectoral chapters. Where both synergies and trade-offs exist, the lower of the confidence 
levels for these interactions is used. Some mitigation options may have applications in more than one sector or system. The interactions between mitigation options and the 
SDGs might differ depending on the sector or system, and also on the context and the scale of implementation. Scale of implementation particularly matters when there 
is competition for scarce resources. {6.3, 6.4, 6.7, 7.3, 7.4, 7.5, 7.6, 8.2, 8.4, 8.6, Figure 8.4, Supplementary Material Table 8.SM.1, Supplementary Material Table 8.SM.2, 
9.4, 9.5, 9.8, Table 9.5, 10.3, 10.4, 10.5, 10.6, 10.8, Table 10.3, 11.5, 12.5, 17.3, Figure 17.1, Supplementary Material Table 17.SM.1, Annex II.IV .12}
D.2  There is a  strong link between sustainable development, vulnerability and climate risks. Limited

economic, social and institutional resources often result in high vulnerability and low adaptive 
capacity, especially in developing countries (medium confidence). Several response options deliver 
both mitigation and adaptation outcomes, especially in human settlements, land management, and 
in relation to ecosystems. However, land and aquatic ecosystems can be adversely affected by some 
mitigation actions, depending on their implementation (medium confidence). Coordinated cross-sectoral 
policies and planning can maximise synergies and avoid or reduce trade-offs between mitigation and 
adaptation (high confidence). {3.7, 4.4, 13.8, 17.3; AR6 WGII}
D.2.1  Sustainable urban planning and infrastructure design including green roofs and facades, networks of parks and open spaces, 
management of urban forests and wetlands, urban agriculture, and water-sensitive design can deliver both mitigation and

adaptation benefits in settlements (medium confidence). These options can also reduce flood risks, pressure on urban sewer 
systems, urban heat island effects, and can deliver health benefits from reduced air pollution (high confidence). There could 
also be trade-offs. For example, increasing urban density to reduce travel demand, could imply high vulnerability to heat waves 
and flooding (high confidence). (Figure SPM.8) {3.7, 8.2, 8.4, 12.5, 13.8, 17.3} 
D.2.2  Land-related mitigation options with potential co-benefits for adaptation include agroforestry, cover crops, intercropping, 
perennial plants, restoring natural vegetation and rehabilitating degraded land. These can enhance resilience by maintaining 
land productivity and protecting and diversifying livelihoods. Restoration of mangroves and coastal wetlands sequesters 
carbon, while also reducing coastal erosion and protecting against storm surges, thus, reducing the risks from sea level rise

and extreme weather. (high confidence) {4.4, 7.4, 7.6, 12.5, 13.8}
D.2.3  Some mitigation options can increase competition for scarce resources including land, water and biomass. Consequently, 
these can also reduce adaptive capacity, especially if deployed at larger scale and with high expansion rates thus exacerbating 
existing risks, in particular where land and water resources are very limited. Examples include the large-scale or poorly 
planned deployment of bioenergy, biochar, and afforestation of naturally unforested land. (high confidence) {12.5, 17.3}
D.2.4  Coordinated policies, equitable partnerships and integration of adaptation and mitigation within and across sectors can 
maximise synergies and minimise trade-offs and thereby enhance the support for climate action (medium confidence). Even if 
extensive global mitigation efforts are implemented, there will be a large need for financial, technical, and human resources

for adaptation. Absence or limited resources in social and institutional systems can lead to poorly coordinated responses, thus 
reducing the potential for maximising mitigation and adaptation benefits, and increasing risk (high confidence). {12.6, 13.8, 
17.1, 17.3}

43SPM
Summary for PolicymakersD.3  Enhanced mitigation and broader action to shift development pathways towards sustainability will 
have distributional consequences within and between countries. Attention to equity and broad and 
meaningful participation of all relevant actors in decision-making at all scales can build social trust, 
and deepen and widen support for transformative changes. (high confidence) {3.6, 4.2, 4.5, 5.2, 13.2, 
17.3, 17.4}
D.3.1  Countries at all stages of economic development seek to improve the well-being of people, and their development priorities 
reflect different starting points and contexts. Different contexts include social, economic, environmental, cultural, or political 
conditions, resource endowment, capabilities, international environment, and history. The enabling conditions for shifting 
development pathways towards increased sustainability will therefore also differ, giving rise to different needs. (high

confidence) (Figure SPM.2) {1.6, 1.7, 2.4, 2.6, Cross-Chapter Box 5 in Chapter 4, 4.3.2, 17.4}
D.3.2  Ambitious mitigation pathways imply large and sometimes disruptive changes in economic structure, with significant 
distributional consequences, within and between countries. Equity remains a  central element in the UN climate regime, 
notwithstanding shifts in differentiation between states over time and challenges in assessing fair shares. Distributional 
consequences within and between countries include shifting of income and employment during the transition from 
high- to low-emissions activities. While some jobs may be lost, low-emissions development can also open more opportunities 
to enhance skills and create more jobs that last, with differences across countries and sectors. Integrated policy packages can 
improve the ability to integrate considerations of equity, gender equality and justice. (high confidence) {1.4, 1.6, 3.6, 4.2, 5.2, 
Box 11.1, 14.3, 15.2, 15.5, 15.6}

D.3.3  Inequalities in the distribution of emissions and in the impacts of mitigation policies within countries affect social cohesion 
and the acceptability of mitigation and other environmental policies. Equity and just transitions can enable deeper ambitions 
for accelerated mitigation. Applying just transition principles and implementing them through collective and participatory 
decision-making processes is an effective way of integrating equity principles into policies at all scales, in different ways 
depending on national circumstances (medium confidence). This is already taking place in many countries and regions, as 
national just transition commissions or task forces, and related national policies, have been established in several countries. 
A multitude of actors, networks, and movements are engaged (high confidence). {1.6, 1.7, 2.4, 2.6, 4.5, 13.2, 13.9, 14.3, 14.5}

D.3.4  Broadening equitable access to domestic and international finance, technologies that facilitate mitigation, and capacity, while 
explicitly addressing needs can further integrate equity and justice into national and international policies and act as a catalyst 
for accelerating mitigation and shifting development pathways (medium confidence). The consideration of ethics and equity 
can help address the uneven distribution of adverse impacts associated with 1.5°C and higher levels of global warming, 
in all societies (high confidence). Consideration of climate justice can help to facilitate shifting development pathways 
towards sustainability, including through equitable sharing of benefits and burdens of mitigation, increasing resilience to the 
impacts of climate change, especially for vulnerable countries and communities, and equitably supporting those in need (high

confidence). {1.4, 1.6, 1.7, 3.6, 4.2, 4.5, Box 5.10, 13.4, 13.8, 13.9, 14.3, 14.5, 15.2, 15.5, 15.6, 16.5, 17.3, 17.4; SR1.5 SPM, 
AR6 WGII Chapter 18}

44SPM
Summary for PolicymakersE. Strengthening the Response
71 In this report, the term ‘feasibility’ refers to the potential for a mitigation or adaptation option to be implemented. Factors influencing feasibility are context-dependent 
and may change over time. Feasibility depends on geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors that enable 
or constrain the implementation of an option. The feasibility of options may change when different options are combined and increase when enabling conditions 
are strengthened.
72 In this report, the term ‘enabling conditions’ refers to conditions that enhance the feasibility of adaptation and mitigation options. Enabling conditions include 
finance, technological innovation, strengthening policy instruments, institutional capacity, multi-level governance, and changes in human behaviour and lifestyles.

73 The future feasibility challenges described in the modelled pathways may differ from the real-world feasibility experiences of the past.E.1  There are mitigation options which are feasible71 to deploy at scale in the near term. Feasibility 
differs across sectors and regions, and according to capacities and the speed and scale of 
implementation. Barriers to feasibility would need to be reduced or removed, and enabling conditions72 
strengthened to deploy mitigation options at scale. These barriers and enablers include geophysical, 
environmental-ecological, technological, and economic factors, and especially institutional and 
socio-cultural factors. Strengthened near-term action beyond the NDCs (announced prior to UNFCCC 
COP26) can reduce and/or avoid long-term feasibility challenges of global modelled pathways that 
limit warming to 1.5°C (>50%) with no or limited overshoot. (high confidence) {3.8, 6.4, 8.5, 9.9, 10.8, 
12.3, Figure TS.31, Annex II.IV.11}

E.1.1  Several mitigation options, notably solar energy, wind energy, electrification of urban systems, urban green infrastructure, 
energy efficiency, demand-side management, improved forest- and crop/grassland management, and reduced food waste and 
loss, are technically viable, are becoming increasingly cost effective, and are generally supported by the public. This enables 
deployment in many regions (high confidence). While many mitigation options have environmental co-benefits, including 
improved air quality and reducing toxic waste, many also have adverse environmental impacts, such as reduced biodiversity, 
when applied at very large scale, for example very large scale bioenergy or large scale use of battery storage, that would 
have to be managed (medium confidence). Almost all mitigation options face institutional barriers that need to be addressed

to enable their application at scale (medium confidence). {6.4, Figure  6.19, 7.4, 8.5, Figure  8.19, 9.9, Figure  9.20, 10.8, 
Figure 10.23, 12.3, Figure 12.4, Figure TS.31} 
E.1.2   The feasibility of mitigation options varies according to context and time. For example, the institutional capacity to support 
deployment varies across countries; the feasibility of options that involve large-scale land-use changes varies across regions; 
spatial planning has a higher potential at early stages of urban development; the potential of geothermal is site specific; 
and capacities, cultural and local conditions can either inhibit or enable demand-side responses. The deployment of solar 
and wind energy has been assessed to become increasingly feasible over time. The feasibility of some options can increase 
when combined or integrated, such as using land for both agriculture and centralised solar production. (high confidence)

{6.4, 6.6, Supplementary Material Table 6.SM, 7.4, 8.5, Supplementary Material Table 8.SM.2, 9.9, Supplementary Material 
Table 9.SM.1, 10.8, Appendix 10.3, 12.3, Tables 12.SM.2.1 to 12.SM.2.6}
E.1.3   Feasibility depends on the scale and speed of implementation. Most options face barriers when they are implemented rapidly 
at a large scale, but the scale at which barriers manifest themselves varies. Strengthened and coordinated near-term actions in 
cost-effective modelled global pathways  that limit warming to 2°C (>67%) or lower, reduce the overall risks to the feasibility 
of the system transitions, compared to modelled pathways with relatively delayed or uncoordinated action.73 (high confidence) 
{3.8, 6.4, 10.8, 12.3}

45SPM
Summary for PolicymakersE.2  In all countries, mitigation efforts embedded within the wider development context can increase the 
pace, depth and breadth of emissions reductions (medium confidence). Policies that shift development 
pathways towards sustainability can broaden the portfolio of available mitigation responses, and 
enable the pursuit of synergies with development objectives (medium confidence). Actions can be 
taken now to shift development pathways and accelerate mitigation and transitions across systems 
(high confidence). {4.3, 4.4, Cross-Chapter Box 5 in Chapter 4, 5.2, 5.4, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5} 
E.2.1  Current development pathways may create behavioural, spatial, economic and social barriers to accelerated mitigation at all 
scales (high confidence). Choices made by policymakers, citizens, the private sector and other stakeholders influence societies’

development pathways (high confidence). Actions that steer, for example, energy and land systems transitions, economy-wide 
structural change, and behaviour change, can shift development pathways towards sustainability74 (medium confidence). 
{4.3, Cross-Chapter Box 5 in Chapter 4, 5.4, 13.9}
E.2.2   Combining mitigation with policies to shift development pathways, such as broader sectoral policies, policies that induce 
lifestyle or behaviour changes, financial regulation, or macroeconomic policies can overcome barriers and open up a broader 
range of mitigation options (high confidence). It can also facilitate the combination of mitigation and other development goals 
(high confidence). For example, measures promoting walkable urban areas combined with electrification and renewable energy 
can create health co-benefits from cleaner air and benefits from enhanced mobility (high confidence). Coordinated housing

policies that broaden relocation options can make mitigation measures in transport more effective (medium confidence). 
{3.2, 4.3, 4.4, Cross-Chapter Box 5 in Chapter 4, 5.3, 8.2, 8.4}
E.2.3   Institutional and regulatory capacity, innovation, finance, improved governance and collaboration across scales, and 
multi-objective policies enable enhanced mitigation and shifts in development pathways. Such interventions can be mutually 
reinforcing and establish positive feedback mechanisms, resulting in accelerated mitigation. (high confidence) {4.4, 5.4, 
Figure 5.14, 5.6, 9.9, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5, Cross-Chapter Box 12 in Chapter 16}
E.2.4  Enhanced action on all the above enabling conditions can be taken now (high confidence). In some situations, such as with 
innovation in technology at an early stage of development and some changes in behaviour towards low emissions, because

the enabling conditions may take time to be established, action in the near term can yield accelerated mitigation in the 
mid-term (medium confidence). In other situations, the enabling conditions can be put in place and yield results in a relatively 
short time frame, for example the provision of energy related information, advice and feedback to promote energy saving 
behaviour (high confidence). {4.4, 5.4, Figure 5.14, 5.6, 6.7, 9.9, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5, Cross-Chapter Box 12 
in Chapter 16}
E.3  Climate governance, acting through laws, strategies and institutions, based on national circumstances, 
supports mitigation by providing frameworks through which diverse actors interact, and a basis for 
policy development and implementation (medium confidence). Climate governance is most effective 
when it integrates across multiple policy domains, helps realise synergies and minimise trade-offs,

and connects national and sub-national policymaking levels (high confidence). Effective and equitable 
climate governance builds on engagement with civil society actors, political actors, businesses, youth, 
labour, media, Indigenous Peoples and local communities (medium confidence). {5.4, 5.6, 8.5, 9.9, 13.2, 
13.7, 13.9}
E.3.1   Climate governance enables mitigation by providing an overall direction, setting targets, mainstreaming climate action 
across policy domains, enhancing regulatory certainty, creating specialised organisations and creating the context to mobilise 
finance (medium confidence). These functions can be promoted by climate-relevant laws, which are growing in number, or 
climate strategies, among others, based on national and sub-national context (medium confidence). Framework laws set 
an overarching legal basis, either operating through a target and implementation approach, or a sectoral mainstreaming

approach, or both, depending on national circumstance (medium confidence). Direct national and sub-national laws that 
explicitly target mitigation and indirect laws that impact emissions through mitigation-related policy domains have both been 
shown to be relevant to mitigation outcomes (medium confidence). {13.2}
74 Sustainability may be interpreted differently in various contexts as societies pursue a variety of sustainable development objectives.

46SPM
Summary for PolicymakersE.3.2  Effective national climate institutions address coordination across sectors, scales and actors, build consensus for action 
among diverse interests, and inform strategy setting (medium confidence). These functions are often accomplished through 
independent national expert bodies, and high-level coordinating bodies that transcend departmental mandates. Complementary 
sub-national institutions tailor mitigation actions to local context and enable experimentation but can be limited by inequities 
and resource and capacity constraints (high confidence). Effective governance requires adequate institutional capacity at all 
levels (high confidence). {4.4, 8.5, 9.9, 11.3, 11.5, 11.6, 13.2, 13.5, 13.7, 13.9}
E.3.3   The extent to which civil society actors, political actors, businesses, youth, labour, media, Indigenous Peoples, and local

communities are engaged influences political support for climate change mitigation and eventual policy outcomes. Structural 
factors of national circumstances and capabilities (e.g., economic and natural endowments, political systems and cultural 
factors and gender considerations) affect the breadth and depth of climate governance. Mitigation options that align with 
prevalent ideas, values and beliefs are more easily adopted and implemented. Climate-related litigation, for example by 
governments, private sector, civil society and individuals, is growing -  with a  large number of cases in some developed 
countries, and with a much smaller number in some developing countries - and in some cases, has influenced the outcome 
and ambition of climate governance. (medium confidence) {5.2, 5.4, 5.5, 5.6, 9.9, 13.3, 13.4}
E.4  Many regulatory and economic instruments have already been deployed successfully. Instrument

design can help address equity and other objectives. These instruments could support deep emissions 
reductions and stimulate innovation if scaled up and applied more widely (high confidence). Policy 
packages that enable innovation and build capacity are better able to support a  shift towards 
equitable low-emission futures than are individual policies (high confidence). Economy-wide packages, 
consistent with national circumstances, can meet short-term economic goals while reducing emissions 
and shifting development pathways towards sustainability (medium confidence). {Cross-Chapter Box 5 
in Chapter 4, 13.6, 13.7, 13.9, 16.3, 16.4, 16.6}
E.4.1   A wide range of regulatory instruments at the sectoral level have proven effective in reducing emissions. These instruments, 
and broad-based approaches including relevant economic instruments,75 are complementary (high confidence). Regulatory

instruments that are designed to be implemented with flexibility mechanisms can reduce costs (medium confidence). Scaling 
up and enhancing the use of regulatory instruments, consistent with national circumstances, could improve mitigation 
outcomes in sectoral applications, including but not limited to renewable energy, land use and zoning, building codes, vehicle 
and energy efficiency, fuel standards, and low-emissions industrial processes and materials (high confidence). {6.7, 7.6, 8.4, 
9.9, 10.4, 11.5, 11.6, 13.6}
E.4.2   Economic instruments have been effective in reducing emissions, complemented by regulatory instruments mainly at the 
national and also sub-national and regional level (high confidence). Where implemented, carbon pricing instruments have 
incentivised low-cost emissions reduction measures, but have been less effective, on their own and at prevailing prices during

the assessment period, in promoting the higher-cost measures necessary for further reductions (medium confidence). Equity and 
distributional impacts of such carbon pricing instruments can be addressed by using revenue from carbon taxes or emissions 
trading to support low-income households, among other approaches (high confidence). Practical experience has informed 
instrument design and helped to improve predictability, environmental effectiveness, economic efficiency, distributional goals 
and social acceptance (high confidence). Removing fossil fuel subsidies would reduce emissions, improve public revenue and 
macroeconomic performance, and yield other environmental and sustainable development benefits; subsidy removal may 
have adverse distributional impacts especially on the most economically vulnerable groups which, in some cases can be 
mitigated by measures such as redistributing revenue saved, all of which depend on national circumstances (high confidence);

fossil fuel subsidy removal is projected by various studies to reduce global CO2 emissions by 1–4%, and GHG emissions by up 
to 10% by 2030, varying across regions (medium confidence). {6.3, 13.6}
E.4.3  Low-emission technological innovation is strengthened through the combination of dedicated technology-push policies and 
investments (e.g., for scientific training, R&D, demonstration), with tailored demand-pull policies (e.g., standards, feed-in 
tariffs, taxes), which create incentives and market opportunities. Developing countries’ abilities to deploy low-emission 
technologies, seize socio-economic benefits and manage trade-offs would be enhanced with increased financial resources 
and capacity for innovation which are currently concentrated in developed countries, alongside technology transfer. (high 
confidence) {16.2, 16.3, 16.4, 16.5}

75 Economic instruments are structured to provide a financial incentive to reduce emissions and include, among others, market- and price-based instruments.

47SPM
Summary for PolicymakersE.4.4   Effective policy packages would be comprehensive in coverage, harnessed to a clear vision for change, balanced across objectives, 
aligned with specific technology and system needs, consistent in terms of design and tailored to national circumstances. 
They are better able to realise synergies and avoid trade-offs across climate and development objectives. Examples include: 
emissions reductions from buildings through a mix of efficiency targets, building codes, appliance performance standards, 
information provision, carbon pricing, finance and technical assistance; and industrial GHG emissions reductions through 
innovation support, market creation and capacity building. (high confidence) {4.4, 6.7, 9.9, 11.6, 13.7, 13.9, 16.3, 16.4}
E.4.5   Economy-wide packages that support mitigation and avoid negative environmental outcomes include: long-term public

spending commitments; pricing reform; and investment in education and training, natural capital, R&D and infrastructure (high 
confidence). They can meet short-term economic goals while reducing emissions and shifting development pathways towards 
sustainability (medium confidence). Infrastructure investments can be designed to promote low-emissions futures that meet 
development needs (medium confidence). {Cross-Chapter Box 5 in Chapter 4, 5.4, 5.6, 8.5, 13.6, 13.9, 16.3, 16.5, 16.6}
E.4.6   National policies to support technology development and diffusion, and participation in international markets for emission 
reduction, can bring positive spillover effects for other countries (medium confidence), although reduced demand for fossil 
fuels could result in costs to exporting countries (high confidence). There is no consistent evidence that current emission 
trading systems have led to significant emissions leakage, which can be attributed to design features aimed at minimising

competitiveness effects, among other reasons (medium confidence). {13.6, 13.7, 13.8, 16.2, 16.3, 16.4} 
E.5  Tracked financial flows fall short of the levels needed to achieve mitigation goals across all sectors 
and regions. The challenge of closing gaps is largest in developing countries as a whole. Scaling up 
mitigation financial flows can be supported by clear policy choices and signals from governments 
and the international community (high confidence). Accelerated international financial cooperation is 
a critical enabler of low-GHG and just transitions, and can address inequities in access to finance and 
the costs of, and vulnerability to, the impacts of climate change (high confidence). {15.2, 15.3, 15.4, 
15.5, 15.6} 
E.5.1  Average annual modelled investment requirements for 2020 to 2030 in scenarios that limit warming to 2°C or 1.5°C are a factor

of three to six greater than current levels, and total mitigation investments (public, private, domestic and international) would 
need to increase across all sectors and regions (medium confidence). Mitigation investment gaps are wide for all sectors, 
and widest for the AFOLU sector in relative terms and for developing countries76 (high confidence). Financing and investment 
requirements for adaptation, reduction of losses and damages, general infrastructure, regulatory environment and capacity 
building, and climate-responsive social protection further exacerbate the magnitude of the challenges for developing countries 
to attract financing (high confidence). {3.2, 14.4, 15.1, 15.2, 15.3, 15.4, 15.5}
E.5.2  There is sufficient global capital and liquidity to close global investment gaps, given the size of the global financial system, 
but there are barriers to redirect capital to climate action both within and outside the global financial sector, and in

the macroeconomic headwinds facing developing regions. Barriers to the deployment of commercial finance from within the  
financial sector as well as macroeconomic considerations include: inadequate assessment of climate-related risks and 
investment opportunities; regional mismatch between available capital and investment needs; home bias factors; country 
indebtedness levels; economic vulnerability; and limited institutional capacities (high confidence). Challenges from outside 
the financial sector include: limited local capital markets; unattractive risk-return profiles, in particular due to missing or weak 
regulatory environments consistent with ambition levels; limited institutional capacity to ensure safeguards; standardisation, 
aggregation, scalability and replicability of investment opportunities and financing models; and, a pipeline ready for commercial 
investments. (high confidence) {15.2, 15.3, 15.5, 15.6}

E.5.3  Accelerated financial support for developing countries from developed countries and other sources is a critical enabler to 
enhance mitigation action and address inequities in access to finance, including its costs, terms and conditions, and economic 
vulnerability to climate change for developing countries (high confidence). Scaled-up public grants for mitigation and 
adaptation funding for vulnerable regions, especially in Sub-Saharan Africa, would be cost-effective and have high social 
returns in terms of access to basic energy (high confidence). Options for scaling up mitigation in developing regions include: 
increased levels of public finance and publicly mobilised private finance flows from developed to developing countries in the 
context of the USD100 billion-a-year goal; increase the use of public guarantees to reduce risks and leverage private flows

76 In modelled pathways, regional investments are projected to occur when and where they are most cost-effective to limit global warming. The model quantifications 
help to identify high-priority areas for cost-effective investments, but do not provide any indication on who would finance the regional investments.

48SPM
Summary for Policymakersat lower cost; local capital markets development; and building greater trust in international cooperation processes (high 
confidence). A coordinated effort to make the post-pandemic recovery sustainable and increased flows of financing over the 
next decade can accelerate climate action, including in developing regions and countries facing high debt costs, debt distress 
and macroeconomic uncertainty (high confidence). {15.2, 15.3, 15.4, 15.5, 15.6, Box 15.6}
E.5.4  Clear signalling by governments and the international community, including a stronger alignment of public sector finance and 
policy, and higher levels of public sector climate finance, reduces uncertainty and transition risks for the private sector.  Depending 
on national contexts, investors and financial intermediaries, central banks, and financial regulators can support climate action

and can shift the systemic underpricing of climate-related risk by increasing awareness, transparency and consideration of 
climate-related risk, and investment opportunities. Financial flows can also be aligned with funding needs through: greater 
support for technology development; a  continued role for multilateral and national climate funds and development banks; 
lowering financing costs for underserved groups through entities such as green banks existing in some countries, funds and 
risk-sharing mechanisms; economic instruments which consider economic and social equity and distributional impacts; 
gender-responsive and women-empowerment programmes as well as enhanced access to finance for local communities and 
Indigenous Peoples and small land owners; and greater public-private cooperation. (high confidence) {15.2, 15.5, 15.6}
E.6  International cooperation is a critical enabler for achieving ambitious climate change mitigation goals.

The UNFCCC, Kyoto Protocol, and Paris Agreement are supporting rising levels of national ambition and 
encouraging development and implementation of climate policies, although gaps remain. Partnerships, 
agreements, institutions and initiatives operating at the sub-global and sectoral levels and engaging 
multiple actors are emerging, with mixed levels of effectiveness. (high confidence) {8.5, 14.2, 14.3, 
14.5, 14.6, 15.6, 16.5} 
E.6.1  Internationally agreed processes and goals, such as those in the UNFCCC, Kyoto Protocol, and Paris Agreement – including 
transparency requirements for national reporting on emissions, actions and support, and tracking progress towards the 
achievement of Nationally Determined Contributions – are enhancing international cooperation, national ambition and policy 
development. International financial, technology and capacity building support to developing countries will enable greater

implementation and encourage ambitious Nationally Determined Contributions over time. (medium confidence) {14.3}  
E.6.2   International cooperation on technology development and transfer accompanied by capacity building, knowledge sharing, 
and technical and financial support can accelerate the global diffusion of mitigation technologies, practices and policies at 
national and sub-national levels, and align these with other development objectives (high confidence). Challenges in and 
opportunities to enhance innovation cooperation exist, including in the implementation of elements of the UNFCCC and the 
Paris Agreement as per the literature assessed, such as in relation to technology development and transfer, and finance (high 
confidence). International cooperation on innovation works best when tailored to specific institutional and capability contexts, 
when it benefits local value chains, when partners collaborate equitably and on voluntary and mutually agreed terms, when

all relevant voices are heard, and when capacity building is an integral part of the effort (medium confidence). Support to 
strengthen technological innovation systems and innovation capabilities, including through financial support in developing 
countries would enhance engagement in and improve international cooperation on innovation (high confidence). {4.4, 14.2, 
14.4, 16.3, 16.5, 16.6} 
E.6.3  Transnational partnerships can stimulate policy development, low-emissions technology diffusion and emission reductions by 
linking sub-national and other actors, including cities, regions, non-governmental organisations and private sector entities, and 
by enhancing interactions between state and non-state actors. While this potential of transnational partnerships is evident, 
uncertainties remain over their costs, feasibility, and effectiveness. Transnational networks of city governments are leading to

enhanced ambition and policy development and a growing exchange of experience and best practices (medium confidence). 
{8.5, 11.6, 14.5, 16.5, Cross-Chapter Box 12 in Chapter 16}
E.6.4   International environmental and sectoral agreements, institutions, and initiatives are helping, and in some cases may help, to 
stimulate low-GHG emissions investment and reduce emissions. Agreements addressing ozone depletion and transboundary 
air pollution are contributing to mitigation, and in other areas, such as atmospheric emissions of mercury, may contribute to 
mitigation (high confidence). Trade rules have the potential to stimulate international adoption of mitigation technologies 
and policies, but may also limit countries’ ability to adopt trade-related climate policies (medium confidence). Current sectoral 
levels of ambition vary, with emission reduction aspirations in international aviation and shipping lower than in many other 
sectors (medium confidence). {14.5, 14.6}

v
ForewordForeword
Foreword
Climate Change 2022: Mitigation of Climate Change is the third part 
of the Sixth Assessment Report (AR6) of the Intergovernmental Panel 
on Climate Change (IPCC) and was prepared by its Working Group III. 
The volume provides an updated global assessment of current and 
projected emissions from all sources and sectors, mitigation options that 
reduce emissions or remove greenhouse gases from the atmosphere, 
and progress towards meeting climate ambitions. It assesses what is 
required to achieve net zero emissions as pledged by many countries.
This report shows that greenhouse gas emissions over the last decade 
are at the highest levels in human history. It shows that urgent action 
is needed. Unless there are immediate and deep emissions reductions 
across all sectors, limiting global warming to 1.5°C will be beyond 
reach. Global greenhouse gas emissions implied by Nationally 
Determined Contributions announced prior to COP26 make it likely

that warming will exceed 1.5°C and will also make it harder to limit 
warming to below 2°C.
But there are positive signs and increased evidence of climate action.  
Options are available now in every sector that can at least halve 
emissions by 2030. Some countries have already achieved a steady 
decrease in emissions consistent with limiting warming to 2°C. Costs 
for some forms of renewable energy have fallen, use of renewables 
continues to rise and, in some countries and regions, electricity 
systems are already predominantly powered by renewables.
This IPCC report highlights for the first time the social and 
demand-side aspects of climate mitigation. As long as the necessary 
policies, infrastructure and technologies are in place, changes to 
lifestyles and behaviour have the potential for large reductions in 
global greenhouse gas emissions and, at the same time, lead to 
improved wellbeing.
The report calls attention to the deep links between climate mitigation

and sustainable development. It draws attention to the way that 
climate action is intimately connected to addressing the nature crisis. 
Attention to equity and just transitions can support deeper ambition 
for accelerated climate action.
The findings in this report have considerably enhanced our 
understanding of available mitigation pathways. The timing of this 
report is critical. It provides crucial information that informs the first 
Global Stocktake under the Paris Agreement. It demands the urgent 
attention of policymakers and the general public.As an intergovernmental body jointly established in 1988 by the 
World Meteorological Organization (WMO) and the United Nations 
Environment Programme (UNEP), the IPCC has successfully provided 
policymakers with the most authoritative and objective scientific 
and technical assessments, which are policy relevant without being 
policy prescriptive. Beginning in 1990, this series of IPCC Assessment

Reports, Special Reports, Technical Papers, Methodology Reports and 
other products have become standard works of reference. 
This Working Group III assessment was made possible thanks to 
the commitment and dedication of many hundreds of experts, 
representing a wide range of regions and scientific disciplines. WMO 
and UNEP are proud that so many of the experts belong to their 
communities and networks.
We express our deep gratitude to all authors, review editors and 
expert reviewers for devoting their knowledge, expertise and time. 
We note the particular challenges presented by the COVID-19 
pandemic and the burdens placed on experts. We would like to thank 
the staff of the Working Group III Technical Support Unit and the IPCC 
Secretariat for their dedication.
We are also thankful to the governments that supported their scientists’ 
participation in developing this report and that contributed to the IPCC 
Trust Fund to provide for the essential participation of experts from

developing countries and countries with economies in transition.
We would like to express our appreciation to the government of Ethiopia 
for hosting the scoping meeting for the IPCC’s Sixth Assessment 
Report, to the governments of the United Kingdom of Great Britain and 
Northern Ireland, India, Ecuador and Italy for hosting drafting sessions 
for the Working Group III contribution. The latter two meetings were 
held in a virtual format due to the COVID-19 pandemic.  We also express 
our thanks to the government of the United Kingdom for hosting the 
Fourteenth Session of Working Group III for approval of the Working 
Group III Report. The generous financial support by the government 
of the United Kingdom, and the hosting of the Working Group III 
Technical Support Unit by Imperial College London (United Kingdom) 
and Ahmedabad University (India), is gratefully acknowledged.
We would particularly like to thank Dr. Hoesung Lee, Chairman of the

IPCC, for his direction and guidance of the IPCC and we express our 
deep gratitude to Professor Priyadarshi R. Shukla and Professor Jim 
Skea, the Co-Chairs of Working Group III, for their tireless leadership 
throughout the development and production of this report.
Petteri Taalas  
Secretary-General  
World Meteorological OrganizationInger Andersen  
Executive Director  
United Nations Environment Programme

vi
PrefacePreface
Preface
The Working Group III (WG III) contribution to the Sixth Assessment 
Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) provides a comprehensive and transparent assessment of the scientific literature on climate change mitigation. It builds upon the WG III contribution to the IPCC’s Fifth Assessment Report (AR5) in 2014, the WG I and WG II contributions to the AR6, and the three AR6 Special Reports: Global Warming of 1.5°C; Climate Change and Land; and The 
Ocean and Cryosphere in a Changing Climate.
The report assesses progress in climate change mitigation and options 
for reducing emissions and enhancing sinks. It evaluates the societal implications of mitigation actions, without recommending any specific options.
Scope of the Report

The scoping of the WG III contribution to AR6 was driven by three guiding principles: to achieve a better synthesis between higher-level whole system and grounded bottom-up insights into technologies and other approaches for reducing emissions; to make wider use of social science disciplines, especially for gaining insight into issues related to lifestyle, behaviour, consumption and socio-technical transitions; and to link climate change mitigation better to other agreed policy goals both nationally and internationally.
The core of the report remains, as in AR5, a set of chapters devoted

to different sectors, broadly aligned with the categorisation used in the IPCC Guidelines for National Greenhouse Gas Inventories. These chapters cover emission trends and drivers, mitigation costs and potentials, regional specificities, and sector specific barriers, policies, financing and enabling conditions. A systems level perspective was followed where appropriate. A cross-sectoral perspectives chapter integrates findings from the sectoral chapters and assesses approaches falling outside the scope of individual sectors. 
As in the AR5, there is a chapter on recent trends and drivers, with

the scope expanded to cover historic emissions and recent policy developments. Following the pattern established in the WG III AR5 report, and the Special Report on Global Warming of 1.5°C, this report assesses published emission scenarios with a 21st century perspective. Modelled emission scenarios are categorised according to climate outcomes, allowing a handshake with the WG I assessment. To meet the goal of linking top-down and bottom-up insights, the report includes an additional pathways chapter that provides a mid-century perspective, focussing on national and regional scales and the alignment between development pathways and mitigation actions.
As in the AR5, this report addresses mitigation enablers such as

international cooperation, finance and investment, and policies and institutions, with a greater emphasis placed on the role of institutions than in the AR5. A new chapter is dedicated to the assessment of innovation systems, technology development and technology transfer. A further novelty is a chapter that assesses the literature on human behaviour, lifestyle and culture, and its implications for mitigation action. This chapter touches on patterns of development and human well-being, and circular and sharing economy concepts. It brings a wide range of disciplines, notably from the social sciences, within the scope of the WG III assessment.
Linkages with development and specifically the Sustainable 
Development Goals (SDGs) permeate the WG III report. This framing is set up in Chapter 1, and the threads are drawn together in the final chapter where linkages between mitigation and the SDGs are systematically assessed.
The AR6 has benefited from close and unprecedented collaboration

between the three IPCC WGs: with WG I on scenarios and with WG II on urban systems, land use and development pathways. This collaboration is manifested in a number of Cross-Working Group boxes covering topics such as the economic benefits from avoided impacts along mitigation pathways, climate change and urban areas, mitigation and adaptation through the bioeconomy, and solar radiation modification.
Structure of the Report
This report consists of a Summary for Policymakers, a Technical Summary, 17 Chapters, six Annexes, and Index, as well as online Supplementary Material to chapters. 
Chapters 1 (Introduction and framing) and 17 (Accelerating the

transition in the context of sustainable development), the first and final chapters of the report, set climate change mitigation in the context of sustainable development. Chapter 1 sets out the evolving policy landscape for climate mitigation, provides the reader with the framing of, and context for, the report, and highlights key concepts. Chapter 17 adopts an integrative perspective on sustainable development and climate change responses, identifying synergies and trade-offs, and explores joint responses to climate change and sustainable development challenges.
Chapters 2–4 take a high-level view of trends and future pathways

using three different time frames. Chapter 2 (Emissions trends and drivers) covers historic and current emission trends and socio-economic and demographic drivers of emissions. It also maps developments in technologies and policies since the AR5. Chapter 3 (Mitigation pathways compatible with long-term goals) assesses modelled emission pathways compatible with the Paris Agreement and higher warming levels. It addresses socio-cultural-techno-economic assumptions, technological and behavioural aspects of mitigation pathways, and links to adaptation and sustainable development. Chapter 4 (Mitigation and development pathways in the near- to mid-term) takes a mid-century perspective, considering national, regional and international scales and the implications

vii
PrefacePrefaceof mitigation for national development objectives including 
employment, competitiveness, poverty eradication and the SDGs. Annex III (Scenarios and modelling methods) provides methodological background to Chapters 3 and 4.
Chapter 5 (Demand, services and social aspects of mitigation), 
a new chapter in AR6, explores how mitigation interacts with meeting human needs and access to services. It explores, inter alia: sustainable production and consumption; patterns of development and indicators of wellbeing; the role of culture, social norms, practices and behaviour changes; the sharing economy and circular economy; and policies facilitating behavioural and lifestyle change.
Chapters 6–12 (Energy systems; Agriculture, Forestry, and Other

Land Uses (AFOLU); Urban systems and other settlements; Buildings; Transport; Industry; Cross-sectoral perspectives) assess the potential for emissions reductions in specific systems and sectors, taking into account trends in emissions and their key drivers, global and regional costs and potentials, links to climate adaptation and associated risks and co-benefits, and sector specific barriers, policies, financing and enabling conditions. Specificities include fugitive emissions and carbon capture and storage (Energy), provision of food, feed, fibre, wood, biomass for energy and other ecosystem services (AFOLU), demographic changes and urban form (Urban systems and other settlements), mitigation strategies including efficiency, sufficiency and renewables (Buildings), access to mobility (Transport), and resource efficiency (Industry). Chapter 12 (Cross-sectoral perspectives) synthesises costs and potentials, and co-benefits and trade-offs, across sectors; it also addresses cross-cutting

approaches such as carbon dioxide removal and mitigation opportunities in the food system.

Chapters 13–16 address enabling conditions for mitigation action.

Chapter 13 (National and sub-national policies and institutions) provides insights from national and subnational plans and strategies, including trends in legislation and institutions. Chapter 14 (International cooperation) assesses international cooperation and institutions, including linkages with non-climate organisations and processes, international sectoral agreements, and institutions for finance and investment and capacity building. Chapter 15 (Investment and finance) assesses scenarios of, and needs for, mitigation investment and financial flows, and the means of mobilising climate finance at the national and sub-national levels. Chapter 16 (Innovation, technology development and transfer) examines the role of innovation, technology development, diffusion and transfer in contributing to sustainable development and the aims of the Paris Agreement. It addresses specific challenges in emerging economies and least developed countries.The Assessment Process

This WG III contribution to the AR6 has been prepared in accordance with IPCC rules and procedures. A scoping meeting was held in May 2017 and the outlines for the contributions of the three WGs were approved at the 46th Session of the Panel in September 2017. 
Governments and IPCC observer organisations nominated experts for 
the author teams. The team of 199 Coordinating Lead Authors and Lead Authors, plus 38 Review Editors, selected by the WG III Bureau, was accepted at the 55th Session of the IPCC Bureau in January 2018. More than 350 Contributing Authors provided text for the author teams. 
Drafts were subject to two rounds of formal review and revision 
followed by a final round of government comments on the Summary for Policymakers. More than 59,000 written comments were submitted by more than 1,600 expert reviewers and 42 governments. For each chapter, the review process was monitored by Review Editors to ensure that all comments received appropriate consideration.

During the review periods and in the run-up to the approval session, 
webinars were held with governments and two of the UNFCCC non-governmental organisation (NGO) constituencies, the Business and Industry NGOs (BINGOs), and the Environmental NGOs (ENGOs). These informal webinars offered an opportunity for authors to present draft material to IPCC audiences and to receive additional feedback.
The Report was accepted by the Panel at its 56th Session. The Summary 
for Policymakers was approved line-by-line and the  underlying chapters were accepted at the 14th Session of IPCC WG III from 21 March – 4 April 2022, hosted virtually by the United Kingdom of Great Britain and Northern Ireland (UK).

The report was made possible thanks to the expertise, hard work and commitment to excellence shown by Coordinating Lead Authors and Lead Authors, with inputs from many Contributing Authors. Their efforts and stamina are particularly commendable given the additional demands and stresses imposed by virtual working as a consequence of the COVID pandemic. 
We gratefully acknowledge the support of the Chapter Scientists, 
who worked tirelessly alongside the authors to deliver their chapters to the highest possible standards. Their time, dedication and hard work is greatly appreciated. 
We would like to express our appreciation to the Government and 
Expert Reviewers for the time and energy they invested to provide constructive and useful comments on the draft reports. Our Review Editors were also critical in the AR6 process, helping author teams to process comments, and assuring an objective discussion of relevant issues.

viii
PrefacePrefaceWe wish to thank the governments and other institutions for generous 
support which enabled the authors, Review Editors and Government and Expert Reviewers to participate. 
We would like to thank the Vice-Chairs of the WG III Bureau, who 
provided invaluable scientific input and thoughtful advice throughout the AR6 process: Amjad Abdulla, Carlo Carraro, Diriba Korecha Dadi, Ramón Pichs-Madruga, Nagmeldin G.E. Mahmoud, Andy Reisinger and Diana Ürge-Vorsatz. Specific thanks are due to Andy Reisinger, who together with the Co-Chairs acted as an editor of the Summary for Policymakers, and to Ramón Pichs-Madruga and Diana Ürge-Vorsatz, who took on roles of editors of the Technical Summary.
We thank the Integrated Assessment Modelling Consortium (IAMC)

and the International Institute for Applied Systems Analysis (IIASA) for facilitating the development, and hosting, of the AR6 Scenario Explorer and Database. Particular thanks are due to Edward Byers and Keywan Riahi of IIASA who led this initiative. We thank the International Energy Agency (IEA), in particular Roberta Quadrelli and her team, for providing us with access to their data products. We also thank Monica Crippa and her team at the Joint Research Centre of the European Commission for access to the EDGAR (Emissions Database for Global Atmospheric Research), and William Lamb and Jan Minx who led the assessment of emissions data across the report. 
We acknowledge support from countries hosting WG III Lead Author

Meetings (LAMs): the UK for hosting the first LAM in Edinburgh (March 2019); India for hosting the second LAM in New Delhi (September 2019); Ecuador for hosting the third LAM virtually (April 2020); and Italy for hosting the fourth LAM, also held virtually (April 2021). We thank the government of Ethiopia for hosting the Scoping Meeting for the report in Addis Ababa (May 2017).
We are especially grateful for the support of the UK government, in

particular the Department of Business, Energy and Industrial Strategy (BEIS) and the Engineering and Physical Sciences Research Council (EPSRC), for funding the WG III Technical Support Unit (TSU). Jolene Cook, Eleanor Webster, Rhian Rees-Owen, Sarah Honour, Cathy Johnson, Julie Maclean, Alice Montgomery, Caroline Prescott, and Andrew Russell at BEIS, and Jim Fleming, Kathryn Magnay, Strachan McCormick, Kate Bowman and Jasmine Cain at EPSRC were always ready to dedicate time and energy to the needs of the team. BEIS also organised the venue hosting the core team for the 14th Session of IPCC WG III. 
We are grateful for the close collaboration with authors, Bureau

members and members of the Technical Support Units from WGs I and II, and Task Force on National Greenhouse Gas Inventories (TFI).. We especially thank WG I Co-Chairs Valérie Masson-Delmotte and Panmao Zhai, WG II Co-Chairs Hans-Otto Portner and Debra Roberts, and the Co-Chairs of the Task Force on Greenhouse Gas Inventories, Eduardo Calvo Buendía and Kiyoto Tanabe, for their collegial spirit and mutual support during the assessment. We extend our gratitude to the IPCC leadership. The Executive Committee, notably Vice-Chairs Ko Barrett, Thelma Krug, Youba Sokona, strengthened the work of all three WGs. We thank IPCC Chair, Hoesung Lee, for his leadership.
We would like to thank the Secretary of the IPCC, Abdalah Mokssit,

and Deputy Secretaries, Ermira Fida and Kerstin Stendahl, and their colleagues Mudathir Abdallah, Jesbin Baidya, Laura Biagioni, Annie Courtin, Oksana Ekzarkho, Judith Ewa, Joelle Fernandez, Emelie Larrode, Jennifer Lew Schneider, Jonathan Lynn, Andrej Mahecic, Nina Peeva, Sophie Schlingemann, Mxolisi Shongwe, Melissa Walsh, and Werani Zabula, for their guidance in implementing IPCC processes, their logistical support, their close collaboration on communications, and for enabling the participation of experts from developing countries through the IPCC Trust Fund. 
We wish to thank Sue Escott of Escott-Hunt and staff at the UN

Foundation for their insight and hard work to ensure the effective communication of the messages in the report. Thanks are expressed to John Schwartz and his team at Soapbox Communications Ltd for graphics design support and for typesetting the final report, in particular Ian Blenkinsop, Margherita Cardoso, Autumn Forecast, Žiga Kropivšek, Sharon Mah, Jenny McCarten, Polly O’Hara, Francesca Romano and Alice Woodward, to Georgie Bowden for her careful compilation of the report index, and to Matt Lloyd, Jenny van der Meijden, and the team at Cambridge University Press for final publication of the report.
We are grateful to the members of the core team at the 14th Session

of WG III, especially the IPCC Vice Chairs, WG I and II Vice-Chairs Greg Flato, Jan Fuglestvedt and Mark Howden, WG I, II and SYR TSU members, in particular Nada Caud, Sarah Connors, Melissa Gomis, Tijama Kerscher, Katherine Leitzell, Noëmie Leprince-Ringuet, Sina Löschke, Tom Maycock, Katja Mintenbeck, Komila Nabiyeva, Clotilde Péan, Anna Pirani, Elvira Poloczanska, Jussi Savolainen, and Melinda Tignor, as well as Sophie Berger, Kiane de Kleijne, and Chloe Ludden. 
It is a pleasure to acknowledge the tireless work, resilience and good

humour of the staff of the WG III TSU throughout the many challenges faced during the assessment cycle. Our thanks go to Raphael Slade, Roger Fradera, Minal Pathak, Alaa Al Khourdajie, Malek Belkacemi, Renée van Diemen, Apoorva Hasija, Géninha Lisboa, Sigourney Luz, Juliette Malley, David McCollum, Shreya Some, and Purvi Vyas, and also to past team members: Elizabeth Huntley, Katie Kissick, Suvadip Neogi and Joanna Portugal-Pereira. Your professionalism, creativity and indomitable spirit has been exemplary and continually inspiring. Finally, on behalf of all participants, we thank colleagues, family and friends for their understanding and support throughout the production of this report, which has taken place during unprecedented times.
Sincerely,
Jim Skea  Co-Chair Working Group III 
Priyadarshi R. Shukla Co-Chair Working Group III

ix
In memoriamPreface
In memoriam
Cristóbal Díaz Morejón
(1949–2021)
Lead Author of Chapter 17 on Accelerating the transition in the context of sustainable development
Cristóbal Díaz Morejón was an internationally renowned expert across a range of environmental disciplines. Over the course of a busy 
and successful career he led projects on the salinity of soils, water resource management, environmental strategy, desertification and droughts, and energy efficiency, amongst others. He represented Cuba in many international meetings on water resources and sustainable development. In 1994 he was awarded a Medal by the Academy of Sciences of Cuba on its 30th Anniversary, and in 2004, the “Juan Tomas Roig” Medal for 25 years dedicated to research. A contributor to IPCC reports since 2004, he was an intelligent, knowledgeable, dedicated and kind colleague, and will be sorely missed.

SPM3Summary for
Policymakers
Drafting Authors:
Jim Skea (United Kingdom), Priyadarshi R. Shukla (India), Andy Reisinger (New Zealand),
RaphaelSlade (United Kingdom), Minal Pathak (India),Alaa Al Khourdajie (United Kingdom/Syria),
Renée van Diemen (the Netherlands/United Kingdom), Amjad Abdulla (Maldives), KeigoAkimoto
(Japan), Mustafa Babiker (Sudan/Saudi Arabia), Quan Bai (China), Igor A. Bashmakov (the Russian
Federation), Christopher Bataille (Canada), Göran Berndes (Sweden), GabrielBlanco (Argentina),
Kornelis Blok (the Netherlands), Mercedes Bustamante (Brazil), Edward Byers (Austria/Ireland),
LuisaF.Cabeza (Spain), Katherine Calvin (the United States of America), CarloCarraro (Italy),
LeonClarke (the United States of America), Annette Cowie (Australia), Felix Creutzig (Germany),
Diriba Korecha Dadi (Ethiopia), Dipak Dasgupta (India), Heleen de Coninck (the Netherlands), Fatima
Denton (the Gambia), Shobhakar Dhakal (Nepal/Thailand), Navroz K. Dubash (India), Oliver Geden

(Germany), Michael Grubb (United Kingdom), Céline Guivarch (France), Shreekant Gupta (India),
Andrea N. Hahmann (Chile/Denmark), Kirsten Halsnaes (Denmark), Paulina Jaramillo (the United
States of America), Kejun Jiang (China), Frank Jotzo (Australia), Tae Yong Jung (Republic of Korea),
Suzana Kahn Ribeiro (Brazil), Smail Khennas (Algeria), Şiir Kılkış (Turkey), Silvia Kreibiehl (Germany),
Volker Krey (Germany/Austria), Elmar Kriegler (Germany), William F. Lamb (Germany/United
Kingdom), Franck Lecocq (France), Shuaib Lwasa (Uganda), Nagmeldin Mahmoud (Sudan), Cheikh
Mbow (Senegal), David McCollum (the United States of America), Jan Christoph Minx (Germany),
Catherine Mitchell (United Kingdom), Rachid Mrabet (Morocco), Yacob Mulugetta (Ethiopia/ United
Kingdom), Gert-Jan Nabuurs (the Netherlands), Gregory F. Nemet (the United States of America/
Canada), Peter Newman (Australia), Leila Niamir (Iran/Germany), Lars J. Nilsson (Sweden),

Sudarmanto Budi Nugroho (Indonesia), Chukwumerije Okereke (Nigeria/ United Kingdom), Shonali
Pachauri (India), Anthony Patt (Switzerland), Ramón Pichs-Madruga (Cuba), Joana Portugal-Pereira
(Brazil), Lavanya Rajamani (India), Keywan Riahi (Austria), Joyashree Roy (India/Thailand), Yamina
Saheb (France/Algeria), Roberto Schaeffer (Brazil), Karen C. Seto (the United States of America),
Shreya Some (India), Linda Steg (the Netherlands), Ferenc L. Toth (Austria/Hungary), Diana Ürge-
Vorsatz (Hungary), Detlef P. van Vuuren (the Netherlands), Elena Verdolini (Italy), Purvi Vyas (India),
Yi-Ming Wei (China), Mariama Williams (Jamaica/theUnited States of America), HaraldWinkler
(South Africa).
Contributing Authors:
Parth Bhatia (India), Sarah Burch (Canada), Jeremy Emmet-Booth (New Zealand), Jan S.
Fuglestvedt (Norway), Meredith Kelller (the United States of America), Jarmo Kikstra (Austria/
the Netherlands), Michael König (Germany), Malte Meinshausen (Australia/Germany), Zebedee

Nicholls (Australia), Kaj-Ivar van der Wijst (the Netherlands).
This Summary for Policymakers should be cited as:
IPCC, 2022: Summary for Policymakers [P.R. Shukla, J. Skea, A. Reisinger, R. Slade, R. Fradera, M. Pathak, A. Al
Khourdajie, M. Belkacemi, R. van Diemen, A. Hasija, G. Lisboa, S. Luz, J. Malley, D. McCollum, S. Some, P. Vyas,
(eds.)]. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie,
R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J.
Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA. doi:
10.1017/9781009157926.001

4SPMSummary for Policymakers
A. Intr oduction and Framing

4 The term ‘temperature’ is used in reference to 'global surface temperatures' throughout this SPM as defined in footnote 8 of the AR6 WGI SPM (see note 14 of Table SPM.2). Emission pathways and associated temperature changes are calculated using various forms of models, as summarised in Box SPM.1 and Chapter 3, and discussed in Annex III.

5 Namely: Economic Benefits from Avoided Climate Impacts along Long-Term Mitigation Pathways {Cross-Working Group Box 1 in Chapter 3}; Urban: Cities and Climate Change {Cross-Working Group Box 2 in Chapter 8}; and Mitigation and Adaptation via the Bioeconomy {Cross-Working Group Box 3 in Chapter 12}. The Working Group III (WGIII) contribution to the IPCC’s Sixth Assessment Report (AR6) assesses literature on the scientific, technological, 

•An evolving international landscape . The literature reflects, among other factors: developments in the UN Framework Convention 
•
Increasing diversity of actors and approaches to mitigation. Recent literature highlights the growing role of non-state and

•
Close linkages between climate change mitigation,  adaptation and development pathways. The development pathways

•
New appr oaches in the assessment. In addition to the sectoral and systems chapters {3, 6, 7, 8, 9, 10, 11, 12}, the report includes,

sustainability. Strengthened collaboration between IPCC Working Groups is reflected in Cross-Working Group Boxes that integratephysical science, climate risks and adaptation, and the mitigation of climate change.
5

5SPM
Summary for Policymakers•Incr easing diversity of analytic frameworks from multiple disciplines including social sciences. This report identifies
multiple analytic frameworks to assess the drivers of, barriers to and options for, mitigation action. These include: economic
efficiency, including the benefits of avoided impacts; ethics and equity; interlinked technological and social transition processes;and socio-political frameworks, including institutions and governance {1, 3, 13, Cross-Chapter Box 12 in Chapter 16}. These helpto identify risks and opportunities for action, including co-benefits and just and equitable transitions at local, national and globalscales. {1, 3, 4, 5, 13, 14, 16, 17}

Section B of this Summary for Policymakers (SPM) assesses Recent developments and current trends, including data uncertainties and gaps. Section C, System transformations to limit global warming, identifies emission pathways and alternative mitigation portfolios consistent with limiting global warming to different levels, and assesses specific mitigation options at the sectoral and system level. Section D addresses Linkages between mitigation, adaptation, and sustainable development. Section E, Strengthening the response, assesses knowledge of how enabling conditions of institutional design, policy, finance, innovation and governance arrangements can contribute to climate change mitigation in the context of sustainable development.

6SPM
Summary for PolicymakersB. Recent Developments and Curr ent Trends
Different datasets for GHG emissions exist, with varying time horizons and coverage of sectors and gases, including some that go back to 1850. In this report, GHG emissions are assessed from 1990, and CO

2 sometimes also from 1850. Reasons for this include data availability and robustness, scope of the assessed 
I to the AR6. The choice of metric depends on the purpose of the analysis, and all GHG emission metrics have limitations and uncertainties, given that they simplify the complexity of the physical climate system and its response to past and future GHG emissions. {Cross-Chapter Box 2 in Chapter 2, Supplementary Material 2.SM.3, Box TS.2; AR6 WGI Chapter 7 Supplementary Material}

8 In this SPM, uncertainty in historic GHG emissions is reported using 90% uncertainty intervals unless stated otherwise. GHG emission levels are rounded to two significant digits; as a consequence, small differences in sums due to rounding may occur.
reported by global bookkeeping models used here are estimated to be about 5.5 GtCO 2 yr–1 higher than the aggregate global net emissions based on national GHG

inventories. This difference, which has been considered in the literature, mainly reflects differences in how anthropogenic forest sinks and areas of managed land are defined. Other reasons for this difference, which are more difficult to quantify, can arise from the limited representation of land management in global models and varying levels of accuracy and completeness of estimated LULUCF fluxes in national GHG inventories. Neither method is inherently preferable. Even when the same methodological approach is applied, the large uncertainty of CO
2-LULUCF emissions can lead to substantial revisions to estimated emissions. {Cross-Chapter Box 3 
10 For consistency with WGI, historical cumulative CO 2 emissions from 1850 to 2019 are reported using 68% confidence intervals.B.1  T otal net anthropogenic GHG emissions6 have continued to rise during the period 2010–2019, as have

cumulative net CO2 emissions since 1850. Average annual GHG emissions during 2010–2019 were
higher than in any previous decade, but the rate of growth between 2010 and 2019 was lower than
that between 2000 and 2009. (high confidence) (Figure SPM.1) {Figure 2.2, Figure 2.5, Table 2.1, 2.2,Figure TS.2}
B.1.1  Global net anthropogenic GHG emissions were 59 ±  6.6 GtCO2-eq7,8 in 2019, about 12% (6.5 GtCO2-eq) higher than in 2010
and 54% (21  GtCO2-eq) higher than in 1990. The annual average during the decade 2010–2019 was 56 ± 6.0 GtCO2-eq, 
9.1 GtCO2-eq yr–1 higher than in 2000–2009. This is the highest increase in average decadal emissions on record. The average
annual rate of growth slowed from 2.1% yr–1 between 2000 and 2009 to 1.3% yr–1 between 2010 and 2019. (high confidence)
B.1.2  Growth in anthropogenic emissions has persisted across all major groups of GHGs since 1990, albeit at different rates.

By 2019, the largest growth in absolute emissions occurred in CO2 from fossil fuels and industry followed by CH4, whereas the
CO2 emissions from land use, land-use change and forestry (CO2-LULUCF) are subject to large uncertainties and high annual
variability, with low confidence even in the direction of the long-term trend.9 (Figure SPM.1) {Figure 2.2, Figure 2.5, 2.2,
B.1.3  Historical cumulative net CO2 emissions from 1850 to 2019 were 2400 ± 240 GtCO2 (high confidence). Of these, more than half
17% of historical cumulative net CO2 emissions since 1850 occurred between 2010 and 2019 [410 ± 30 GtCO2].10 By comparison,

the current central estimate of the remaining carbon budget from 2020 onwards for limiting warming to 1.5°C with a probabilityof 50% has been assessed as 500 GtCO
2, and as 1150 GtCO2 for a probability of 67% for limiting warming to 2°C. Remaining
Based on central estimates only, cumulative net CO2 emissions between 2010 and 2019 compare to about four-fifths of the
size of the remaining carbon budget from 2020 onwards for a 50% probability of limiting global warming to 1.5°C, and aboutone-third of the remaining carbon budget for a 67% probability to limit global warming to 2°C. Even when taking uncertaintiesinto account, historical emissions between 1850 and 2019 constitute a large share of total carbon budgets for these global

7SPM
B.1.4  Emissions of CO2-FFI dropped temporarily in the first half of 2020 due to responses to the COVID-19 pandemic (high confidence), 

38Gt
+0.7% yr  –1
+2.1% yr  –1
change, forestry (CO
2-LULUCF)Methane (CH4)Nitrous 
GHG emissions (GtCO2-eq yr–1) GHG emissions (%)
2019 
emissions (GtCO
2-eq)1990–2019 increase(GtCO
2-eq)Emissions 
in 2019, relative to 1990 (%)
CO
2-FFI 38 ± 3 15 167

0
40021%
13%
59%2%
5%
20%
12%
61%2%
5%
18%
10%
65%2%
4%
18%
11%
64%1%
5%
converted based on global warming potentials with a 100-year time horizon (GWP100-AR6) from the IPCC Sixth Assessment Report Working Group I (Chapter 7).

The fraction of global emissions for each gas is shown for 1990, 2000, 2010 and 2019; as well as the aggregate average annual growth rate between these decades. At the right side of Panel a, GHG emissions in 2019 are broken down into individual components with the associated uncertainties (90% confidence interval) indicated by the error bars: CO
2-FFI ±8%; CO 2-LULUCF ±70%; CH 4 ±30%; N 2O ±60%; F-gases ±30%; GHG ±11%. Uncertainties in GHG emissions are assessed in Supplementary 
global anthropogenic CO 2-FFI, net CO 2-LULUCF , CH 4, N 2O and F-gas emissions individually for the period 1990–2019, normalised relative to 100 in 1990. Note the

different scale for the included F-gas emissions compared to other gases, highlighting its rapid growth from a low base. Shaded areas indicate the uncertainty range. Uncertainty ranges as shown here are specific for individual groups of greenhouse gases and cannot be compared. The table shows the central estimate for: absolute emissions in 2019; the absolute change in emissions between 1990 and 2019; and emissions in 2019 expressed as a percentage of 1990 emissions. {2.2, Figure 2.5, Supplementary Material 2.2, Figure TS.2}
a given likelihood, taking into account the effect of other anthropogenic climate forcers. This is referred to as the ‘total carbon budget’ when expressed starting from

the pre-industrial period, and as the ‘remaining carbon budget’ when expressed from a recent specified date. The total carbon budgets reported here are the sum of historical emissions from 1850 to 2019 and the remaining carbon budgets from 2020 onwards, which extend until global net zero CO
2 emissions are reached. 

8SPM
carbon intensity of energy, have been less than emissions increases from rising global activity levels in industry, energy supply, transport, agriculture and buildings. (high confidence) {2.2, 2.4, 6.3, 7.2, 8.3, 9.3, 10.1, 11.2}

their relative GHG emissions shares from 24% to 34%, and from 6% to 16%, respectively. After reallocating emissions from electricity and heat production, the energy supply sector accounts for 12% of global net anthropogenic GHG emissions. (high confidence) {Figure 2.12, 2.2, 6.3, 7.2, 9.3, 10.1, 11.2, Figure TS.6}
B.2.2  Average annual GHG emissions growth between 2010 and 2019 slowed compared to the previous decade in energy supply (from 2.3% to 1.0%) and industry (from 3.4% to 1.4%), but remained roughly constant at about 2% yr
–1 in the transport 

B.2.3  The global share of emissions that can be attributed to urban areas is increasing. In 2015, urban emissions were estimated to be 25 GtCO
2-eq (about 62% of the global share) and in 2020, 29 GtCO2-eq (67–72% of the global share).15 The drivers of 
urban GHG emission are complex and include population size, income, state of urbanisation and urban form. (high confidence) {8.1, 8.3}
B.2.4  Global energy intensity (total primary energy per unit GDP) decreased by 2% yr
–1 between 2010 and 2019. Carbon intensity 
(CO2 from fossil fuel combustion and industrial processes (CO2-FFI) per unit primary energy) decreased by 0.3% yr–1, with large

regional variations, over the same period mainly due to fuel switching from coal to gas, reduced expansion of coal capacity, and increased use of renewables. This reversed the trend observed for 2000–2009. For comparison, the carbon intensity of primary energy is projected to decrease globally by about 3.5% yr
–1 between 2020 and 2050 in modelled scenarios that limit 
of all land to both anthropogenic environmental change and natural climate variability, and net anthropogenic CO 2-LULUCF emissions +5.7 (±4.0) GtCO 2 yr–1 based

16 See Box SPM.1 for the categorisation of modelled long-term emission scenarios based on projected temperature outcomes and associated probabilities adopted in this report.

9SPM
widely at similar income levels. The 10% of households with the highest per capita emissions contribute a disproportionately large share of global household GHG emissions. At least 18 countries have sustained GHG emission reductions for longer than 10 years. (high confidence) (Figure SPM.2) {Figure 1.1, Figure 2.9, Figure 2.10, Figure 2.25, 2.2, 2.3, 2.4, 2.5, 2.6, Figure TS.4, Figure TS.5}

the context of achieving sustainable development objectives, in the near-term, can be achieved without significant global emissions growth. (high confidence) (Figure SPM.2) {Figure 1.2, 2.2, 2.4, 2.6, 3.7, 4.2, 6.7, Figure TS.4, Figure TS.5}
B.3.4  Globally, the 10% of households with the highest per capita emissions contribute 34–45% of global consumption-based household GHG emissions,

21 while the middle 40% contribute 40–53%, and the bottom 50% contribute 13–15%. (high 
10 years. Reductions were linked to energy supply decarbonisation, energy efficiency gains, and energy demand reduction, which resulted from both policies and changes in economic structure. Some countries have reduced production-based GHG emissions by a third or more since peaking, and some have achieved several years of consecutive reduction rates of around 4% yr
–1, comparable to global reductions in scenarios limiting warming to 2°C (>67%) or lower. These reductions have only 

19 In this report, access to modern energy services is defined as access to clean, reliable and affordable energy services for cooking and heating, lighting, communications, and productive uses. {Annex I: Glossary}
20 In this report, decent living standards are defined as a set of minimum material requirements essential for achieving basic human well-being, including nutrition, shelter, basic living conditions, clothing, health care, education, and mobility. {5.1}

21 Consumption-based emissions refer to emissions released to the atmosphere to generate the goods and services consumed by a certain entity (e.g., a person, firm, country, or region). The bottom 50% of emitters spend less than USD3 PPP (purchasing power parity) per capita per day. The top 10% of emitters (an open-ended category) spend more than USD23 PPP per capita per day. The wide range of estimates for the contribution of the top 10% results from the wide range of spending in this category and differing methods in the assessed literature. {2.6, Annex I: Glossary}

10SPM
2-LULUCF)
c. Net anthr opogenic GHG emissions per capita
and for total population,  per region (2019)
27%
7%7%7%9%
7%8%8%8%
7%8%8%
Africa

Japan, New ZealandEastern AsiaEastern Europe, West-Central AsiaEurope Latin 
America and CaribbeanMiddle EastNorth AmericaSouth-East Asia and PaciﬁcSouthern Asia16%14%3%5%2%2%2%2%
5%
6%
13%10%8%

GWP100-AR6). The height of each rectangle shows per capita emissions, the width shows the population of the region, so that the area of the rectangles refers to the total emissions for each region. Emissions from international aviation and shipping are not included. In the case of two regions, the area for CO
2-LULUCF is below the 
axis, indicating net CO 2 removals rather than emissions. CO 2-LULUCF emissions are subject to high uncertainties, reflected by a global uncertainty estimate of ±70%

(90% confidence interval). Panel d shows population, GDP per person, emission indicators by region in 2019 for percentage GHG contributions, total GHG per person, and total GHG emissions intensity, together with production-based and consumption-based CO
2-FFI data, which is assessed in this report up to 2018. Consumption-based 
emissions are emissions released to the atmosphere in order to generate the goods and services consumed by a certain entity (e.g., region). Emissions from international aviation and shipping are not included. {1.3, Figure 1.2, 2.2, Figure 2.9, Figure 2.10, Figure 2.11, Annex II}  
B.4  The unit costs of several low-emission technologies have fallen continuously since 2010. Innovation

policy packages have enabled these cost reductions and supported global adoption. Both tailored policies and comprehensive policies addressing innovation systems have helped overcome the distributional, environmental and social impacts potentially associated with global diffusion of low-emission technologies. Innovation has lagged in developing countries due to weaker enabling conditions. Digitalisation can enable emission reductions, but can have adverse side effects unless appropriately governed. (high confidence) (Figure SPM.3) {2.2, 6.3, 6.4, 7.2, 12.2, 16.2, 16.4, 16.5, Cross-Chapter Box 11 in Chapter 16}
B.4.1  From 2010 to 2019, there have been sustained decreases in the unit costs of solar energy (85%), wind energy (55%), and

lithium-ion batteries (85%), and large increases in their deployment, e.g., >10× for solar and >100× for electric vehicles (EVs), varying widely across regions (Figure SPM.3). The mix of policy instruments which reduced costs and stimulated adoption includes public R&D, funding for demonstration and pilot projects, and demand pull instruments such as deployment subsidies to attain scale. In comparison to modular small-unit size technologies, the empirical record shows that multiple large-scale mitigation technologies, with fewer opportunities for learning, have seen minimal cost reductions and their adoption has grown slowly. (high confidence) {1.3, 1.5, Figure 2.5, 2.5, 6.3, 6.4, 7.2, 11.3, 12.2, 12.3, 12.6, 13.6, 16.3, 16.4, 16.6}

B.4.2  Policy packages tailored to national contexts and technological characteristics have been effective in supporting low-emission innovation and technology diffusion. Appropriately designed policies and governance have helped address distributional impacts and rebound effects. Innovation has provided opportunities to lower emissions and reduce emission growth and created social and environmental co-benefits (high confidence). Adoption of low-emission technologies lags in most developing countries, particularly least developed ones, due in part to weaker enabling conditions, including limited finance, technology development and transfer, and capacity. In many countries, especially those with limited institutional capacities, several adverse side effects have been observed as a result of diffusion of low-emission technology, for example, low-value employment, and dependency on foreign knowledge and suppliers. Low-emission innovation along with strengthened enabling conditions can

reinforce development benefits, which can, in turn, create feedbacks towards greater public support for policy. (medium confidence) {9.9, 13.6, 13.7, 16.3, 16.4, 16.5, 16.6, Cross-Chapter Box 12 in Chapter 16, TS.3}

B.4.3  Digital technologies can contribute to mitigation of climate change and the achievement of several SDGs (high confidence). For example, sensors, internet of things, robotics, and artificial intelligence can improve energy management in all sectors, increase energy efficiency, and promote the adoption of many low-emission technologies, including decentralised renewable energy, while creating economic opportunities (high confidence). However, some of these climate change mitigation gains can be reduced or counterbalanced by growth in demand for goods and services due to the use of digital devices (high confidence). Digitalisation can involve trade-offs across several SDGs, for example, increasing electronic waste, negative impacts on labour markets, and exacerbating the existing digital divide. Digital technology supports decarbonisation only if appropriately governed (high confidence). {5.3, 10, 12.6, 16.2, Cross-Chapter Box 11 in Chapter 16, TS.5, Box TS.14}

12SPM
electric vehicles (EVs) Photovoltaics (PV)Concentrating solar power (CSP) Onshore wind Offshore windThe unit costs of some forms of renewable energy and of batteries for passenger EVs have fallen, 
12001600
800
400

(USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range

between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding to USD55–148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. For batteries, costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs per MWh of electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made. However, it does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that may modify the overall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each

technology, in GW of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5. Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production (for PV , onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, for the same amount of installed capacity, wind produces about twice as much electricity as solar PV . {2.5, 6.4} Renewable energy and battery technologies were selected as illustrative examples because they have recently shown rapid changes in costs and adoption, and because consistent data are available. Other mitigation options assessed in the report are not included as they do not meet these criteria.

13SPM
has led to the avoidance of emissions that would otherwise have occurred and increased investment in low-GHG technologies and infrastructure. Policy coverage of emissions is uneven across sectors. Progress on the alignment of financial flows towards the goals of the Paris Agreement remains slow and tracked climate finance flows are distributed unevenly across regions and sectors. (high confidence) {5.6, 13.2, 13.4, 13.5, 13.6, 13.9, 14.3, 14.4, 14.5, Cross-Chapter Box 10 in Chapter 14, 15.3, 15.5}
B.5.1  The Kyoto Protocol led to reduced emissions in some countries and was instrumental in building national and international

capacity for GHG reporting, accounting and emissions markets (high confidence). At least 18 countries that had Kyoto targets for the first commitment period have had sustained absolute emission reductions for at least a decade from 2005, of which two were countries with economies in transition (very high confidence). The Paris Agreement, with near universal participation, has led to policy development and target-setting at national and sub-national levels, in particular in relation to mitigation, as well as enhanced transparency of climate action and support (medium confidence). {14.3, 14.6}

B.5.2  The application of diverse policy instruments for mitigation at the national and sub-national levels has grown consistently across a range of sectors (high confidence). By 2020, over 20% of global GHG emissions were covered by carbon taxes or emissions trading systems, although coverage and prices have been insufficient to achieve deep reductions (medium confidence). By 2020, there were ‘direct’ climate laws focused primarily on GHG reductions in 56 countries covering 53% of global emissions (medium confidence). Policy coverage remains limited for emissions from agriculture and the production of industrial materials and feedstocks (high confidence). {5.6, 7.6, 11.5, 11.6, 13.2, 13.6}

B.5.3  In many countries, policies have enhanced energy efficiency, reduced rates of deforestation and accelerated technology deployment, leading to avoided and in some cases reduced or removed emissions (high confidence). Multiple lines of evidence suggest that mitigation policies have led to avoided global emissions of several GtCO
2-eq yr–1 (medium confidence). 
instruments. Growing numbers of laws and executive orders have impacted global emissions and were estimated to result in 5.9 GtCO
2-eq yr–1 less emissions in 2016 than they otherwise would have been. (medium confidence) (Figure SPM.3) {2.2, 2.8, 
B.5.4  Annual tracked total financial flows for climate mitigation and adaptation increased by up to 60% between 2013/14 and 2019/20 (in USD2015), but average growth has slowed since 2018

22 (medium confidence). These financial flows remained 
heavily focused on mitigation, are uneven, and have developed heterogeneously across regions and sectors (high confidence). In 2018, public and publicly mobilised private climate finance flows from developed to developing countries were below the collective goal under the UNFCCC and Paris Agreement to mobilise USD100 billion per year by 2020 in the context of meaningful mitigation action and transparency on implementation (medium confidence). Public and private finance 
flows for fossil fuels are still greater than those for climate adaptation and mitigation (high confidence). Markets for green bonds, ESG (environmental, social and governance) and sustainable finance products have expanded significantly since AR5. Challenges remain, in particular around integrity and additionality, as well as the limited applicability of these markets to many developing countries. (high confidence) {Box 15.4, 15.3, 15.5, 15.6, Box 15.7}

14SPM
higher global GHG emissions than those implied by NDCs. (high confidence) (Figure SPM.4) {3.3, 3.5, 4.2, Cross-Chapter Box 4 in Chapter 4}
indicating an implementation gap. A gap remains between global GHG emissions in 2030 associated with the implementation of NDCs announced prior to COP26 and those associated with modelled mitigation pathways assuming immediate action (for quantification see Table SPM.1).

26 The magnitude of the emissions gap depends on the global warming level considered and 
B.6.2  Global emissions in 2030 associated with the implementation of NDCs announced prior to COP26 are lower than the emissions implied by the original NDCs
29 (high confidence). The original emissions gap has fallen by about 20% to one-third relative to 
pathways that limit warming to 2°C (>67%) with immediate action (category C3a in Table SPM.2), and by about 15–20% relative to pathways limiting warming to 1.5°C (>50%) with no or limited overshoot (category C1 in Table SPM.2) (medium confidence). (Figure SPM.4) {3.5, 4.2, Cross-Chapter Box 4 in Chapter 4}
23 NDCs announced prior to COP26 refer to the most recent Nationally Determined Contributions submitted to the UNFCCC up to the literature cut-off date of this

report, 11 October 2021, and revised NDCs announced by China, Japan and the Republic of Korea prior to October 2021 but only submitted thereafter. 25 NDC updates were submitted between 12 October 2021 and the start of COP26.
24 This implies that mitigation after 2030 can no longer establish a pathway with less than 67% probability to exceed 1.5°C during the 21st century, a defining feature of the class of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot assessed in this report (category C1 in Table SPM.2). These pathways limit warming to 1.6°C or lower throughout the 21st century with a 50% likelihood.

26 Immediate action in modelled global pathways refers to the adoption between 2020 and at latest before 2025 of climate policies intended to limit global warming to a given level. Modelled pathways that limit warming to 2°C (>67%) based on immediate action are summarised in category C3a in Table SPM.2. All assessed modelled global pathways that limit warming to 1.5°C (>50%) with no or limited overshoot assume immediate action as defined here (Category C1 in Table SPM.2).
27 In this report, ‘unconditional’ elements of NDCs refer to mitigation efforts put forward without any conditions. ‘Conditional’ elements refer to mitigation efforts that are contingent on international cooperation, for example bilateral and multilateral agreements, financing or monetary and/or technological transfers. This terminology is used in the literature and the UNFCCC’s NDC Synthesis Reports, not by the Paris Agreement. {4.2.1, 14.3.2}

28 Two types of gaps are assessed: the implementation gap is calculated as the difference between the median of global emissions in 2030 implied by policies implemented by the end of 2020 and those implied by NDCs announced prior to COP26. The emissions gap is calculated as the difference between GHG emissions implied by the NDCs (minimum/maximum emissions in 2030) and the median of global GHG emissions in modelled pathways limiting warming to specific levels based on immediate action and with stated likelihoods as indicated (Table SPM.2).
29 Original NDCs refer to those submitted to the UNFCCC in 2015 and 2016. Unconditional elements of NDCs announced prior to COP26 imply global GHG emissions in 2030 that are 3.8 [3.0–5.3] GtCO
2-eq yr–1 lower than those from the original NDCs, and 4.5 [2.7–6.3] GtCO 2-eq yr–1 lower when conditional elements of NDCs

are included. NDC updates at or after COP26 could further change the implied emissions.Table SPM.1 | Projected global emissions in 2030 associated with policies implemented by the end of 2020 and NDCs announced prior to COP26, and associated emissions gaps. *Emissions projections for 2030 and absolute differences in emissions are based on emissions of 52–56 GtCO
2-eq yr–1 in 2019 as 
elements
Emissions gap between NDCs and pathways that limit warming to 2°C (>67%) with immediate action   10–16 6–14

Emissions gap between NDCs and pathways that limit warming to 1.5°C (>50%) with no or limited overshoot with immediate action 19–26 16–23

15SPM
Continued investments in unabated high-emitting infrastructure and limited development and deployment of low-emitting alternatives prior to 2030 would act as barriers to this acceleration and increase feasibility risks (high confidence). {3.3, 3.5, 3.8, Cross-Chapter Box 5 in Chapter 4}

B.6.4  Modelled global emission pathways consistent with NDCs announced prior to COP26 will likely exceed 1.5°C during the 21st century. Those pathways that then return warming to 1.5°C by 2100 with a likelihood of 50% or greater imply a temperature overshoot of 0.15°C–0.3°C (42 pathways in category C2 in Table SPM.2). In such pathways, global cumulative net-negative CO
2 emissions are –380 [–860 to –200] GtCO230 in the second half of the century, and there is a rapid acceleration of other 
mitigation efforts across all sectors after 2030. Such overshoot pathways imply increased climate-related risk, and are subject to increased feasibility concerns,
31 and greater social and environmental risks, compared to pathways that limit warming to 1.5°C 

102030
0
102030
95th
Median
5th75th
25th
Policy 
assessments for 2030
88

31 Returning to below 1.5°C in 2100 from GHG emissions levels in 2030 associated with the implementation of NDCs is infeasible for some models due to model-specific constraints on the deployment of mitigation technologies and the availability of net negative CO
2 emissions.

16SPM
 –Limit to 2°C (>67%) or return warming to 1.5°C (>50%) after a high overshoot, NDCs until 2030: Pathways with GHG emissions until 2030 associated with the implementation of NDCs announced prior to COP26, followed by accelerated emissions reductions likely to limit warming to 2°C (C3b, Table SPM.2) or to return

 –Limit to 2°C (>67%) with immediate action:  Pathways that limit warming to 2°C (>67%) with immediate action after 2020
26 (C3a, Table SPM.2). 
 –Limit to 1.5°C (>50%) with no or limited overshoot: Pathways limiting warming to 1.5°C with no or limited overshoot (C1, Table SPM.2 C1). All these pathways assume immediate action after 2020. 
Past GHG emissions for 2010–2015 used to project global warming outcomes of the modelled pathways are shown by a black line
32 and past global GHG emissions in 
2015 and 2019 as assessed in Chapter 2 are shown by whiskers. Panels b, c and d show snapshots of the GHG emission ranges of the modelled pathways in 2030, 2050, and 2100, respectively. Panel b also shows projected emissions outcomes from near-term policy assessments in 2030 from Chapter 4.2 (Tables 4.2 and 4.3; median and full range). GHG emissions are in CO

2-equivalent using GWP100 from AR6 WGI. {3.5, 4.2, Table 4.2, Table 4.3, Cross-Chapter Box 4 in Chapter 4}

Decommissioning and reduced utilisation of existing fossil fuel-based power sector infrastructure, retrofitting existing installations with CCS,
36 switches to low-carbon fuels, and cancellation of new coal installations without CCS are major

modelled least-cost pathways. The most appropriate strategies will depend on national and regional circumstances, including enabling conditions and technology availability. (high confidence) (Box SPM.1) {Table 2.7, 2.7, 3.4, 6.3, 6.5, 6.7} 

36 In this context, capture rates of new installations with CCS are assumed to be 90–95%+ {11.3.5}. Capture rates for retrofit installations can be comparable, if plants are specifically designed for CCS retrofits {11.3.6}.

17SPM
38 Long-term warming is calculated from all modelled pathways assuming mitigation efforts consistent with national policies that were implemented by the end of 2020 (scenarios that fall into policy category P1b of Chapter 3) and that pass through the 2030 GHG emissions ranges of such pathways assessed in Chapter 4
 
39 Warming estimates refer to the 50th and [5th–95th] percentile across the modelled pathways and the median temperature change estimate of the probabilistic WGI climate model emulators (see Table SPM.2 footnote a).

40 In this report, emissions reductions are reported relative to 2019 modelled emission levels, while in SR1.5 emissions reductions were calculated relative to 2010. Between 2010 and 2019 global GHG and global CO
2 emissions have grown by 12% (6.5 GtCO 2-eq) and 13% (5.0 GtCO 2) respectively. In global modelled 
pathways assessed in this report that limit warming to 1.5°C (>50%) with no or limited overshoot, GHG emissions are projected to be reduced by 37% [28–57%] in 2030 relative to 2010. In the same type of pathways assessed in SR1.5, reported GHG emissions reductions in 2030 were 39–51% (interquartile range) relative to 2010. In absolute terms, the 2030 GHG emissions levels of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot are higher in AR6 (31 [21–36] GtCO
2-eq) than in SR1.5 (28 (26–31 interquartile range) GtCO 2-eq). (Figure SPM.1, Table SPM.2) {3.3, SR1.5 Figure SPM.3b}

modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and in those that limit warming to 2°C (>67%) and assume immediate action (see Table SPM.2 footnote i).
 37  In both 
types of modelled pathways, rapid and deep GHG emissions reductions follow throughout 2030, 2040 and 2050 (high confidence). Without a strengthening of policies beyond those that are implemented by the end of 2020, GHG emissions are projected to rise beyond 2025, leading to a median global warming of 3.2 [2.2 to 3.5] °C by 2100

38, 39 (medium confidence). (Table SPM.2, Figure SPM.4, Figure SPM.5) 
{3.3, 3.4} 
This compares with reductions of 43% [34–60%] by 2030 and 84% [73–98%] by 2050 in pathways that limit warming to 1.5°C (>50%) with no or limited overshoot (C1, Table SPM.2) (high confidence).
41 In modelled pathways that return warming 
2050 (C2, Table SPM.2) (high confidence). Modelled pathways that are consistent with NDCs announced prior to COP26 until 2030 and assume no increase in ambition thereafter have higher emissions, leading to a median global warming of 2.8 [2.1–3.4] °C by 2100 (medium confidence).
23 (Figure SPM.4) {3.3}

reduced by 24% [9–53%] in 2030 and by 37% [20–60%] in 2040. In pathways that limit warming to 1.5°C (>50%) with no or limited overshoot
 global net CO2 emissions are reduced compared to modelled 2019 emissions by 48% [36–69%] in 2030 and 
N2O is reduced by 20% [–5 to +55%]; and F-gases are reduced by 85% [20–90%].43 Across most modelled pathways, this is the 
maximum technical potential for anthropogenic CH4 reductions in the underlying models (high confidence). Further emissions

reductions, as illustrated by the IMP-SP pathway, may be achieved through changes in activity levels and/or technological innovations beyond those represented in the majority of the pathways (medium confidence). Higher emissions reductions of CH
4 could further reduce peak warming. (high confidence) (Figure SPM.5) {3.3}
C.1.3  In modelled pathways consistent with the continuation of policies implemented by the end of 2020, GHG emissions continue to rise, leading to global warming of 3.2 [2.2–3.5] °C by 2100 (within C5–C7, Table SPM.2) (medium confidence). Pathways that exceed warming of >4°C (≥50%) (C8, SSP5-8.5, Table SPM.2) would imply a reversal of current technology and/or mitigation policy trends (medium confidence). Such warming could occur in emission pathways consistent with policies implemented by the end of 2020 if climate sensitivity is higher than central estimates (high confidence). (Table SPM.2, Figure  SPM.4) {3.3, Box 3.3}

emissions  
(% peak 
emissions  
(% peak 
(% net zero 
GHGs  
(% net zero 
net zero 
by projected global warming levels (GWL). Detailed likelihood definitions are provided in SPM Box 1.
AR6 WGI and the Illustrative (Mitigation) Pathways assessed in WGIII are aligned with the temperature categories and are indicated in a separate column. Global emission pathways contain regionally differentiated information. This assessment focuses on their global characteristics.Projected median annual GHG emissions in the year across the scenarios, with the 5th–95th percentile in brackets.
55 [53–58] GtCO
2-eq.Projected median GHG emissions

reductions of pathways in the year across the scenarios compared to modelled 2019, with the 5th–95th percentile in brackets. Negative numbers indicate increase in emissions compared to 2019.Median 5-year intervals at which projected CO
2 & GHG 
emissions peak, with the 5th–95th percentile interval in square brackets. Percentage of peaking pathways is denoted in round brackets. 
emissions peak in 2100 or beyond for that percentile.Median 5-year intervals at which projected CO
2 & GHG 
emissions  of pathways in this category reach net zero, with the 5th–95th percentile interval in square brackets. Percentage of net zero pathways is denoted in round brackets. 
net zero  not reached for that percentile.Median cumulative net CO
2 emissions across 
the projected scenarios in this category until reaching net zero or until 2100, with the 5th–95th percentile interval in square brackets.Median cumulative net-negative CO
2 emissions

between the year of net zero CO
2 
and 2100. More net-negative results in greater temperature declines after peak.Projected temperature change of pathways in this category (50% probability across the range of climate uncertainties), relative to 1850–1900, at peak warming and in 2100, for the median value across the scenarios and the 5th–95th percentile interval in square brackets.Median likelihood that the projected pathways in this category stay below a given global warming level, with the 5th–95th percentile interval in square brackets.
C1 [97] limit warming to 1.5°C (>50%) with no or limited overshoot 31
[21–36]17
[6–23]9
[1–15]43
[34–60]69
[58–90]84
[73–98]
(100%)
(52%)
[–210 to 
570]–220
[33–58]90
[99–100]
C1a [50]… with net zero  GHGsSSP1–1.9, 
SP
LD33
[22–37]18
[6–24]8
[0–15]41
[31–59]66
[58–89]85
(100%)

[–220 to 
620]–360
[–680 to 
–140]1.6
[34–60]90
[99–100]
C1b [47]…  without net zero GHGsRen29
[21–36]16
[7–21]9
[4–13]48
[35–61]70
[62–87]84
[33–56]89
C2 [133]return warming to 1.5°C (>50%) after a high overshootNeg42
[31–55]25
[17–34]14
[5–21]23
[0–44]55
[40–71]75
(100%)
(87%)
[–90 to 
620]–360
[15–42]82
C3 [311]limit warming  to 2°C (>67%)44
[32–55]29
[20–36]20
[13–26]21
[1–42]46
[34–63]64
(93%)
[13–41]76
[68–91]99
C3a [204]… with action starting in 2020SSP1–2.6
40
[30–49]29
[21–36]20

[14–27]27
[13–45]47
[35–63]63
(91%)
[14–42]78
[98–100]

19SPM
from 2019  
CO 2 
emissions 
& WGIII 
IPs/IMPs 
emissions  
(% peak 
emissions  
(% peak 
(% net zero 
GHGs  
(% net zero 
net zero 

2-eq.Projected median GHG emissions 
2 & GHG 
2 & GHG

2

[47–56]29
[20–36]18
[10–25]5
[0–14]46
[34–63]68
[56–82]
(97%)
[12–35]73
[67–87]99
[98–99]
C4 [159]limit warming  to 2°C (>50%)50
[41–56]38
[28–44]28
[19–35]10
[0–27]31
[20–50]49
(86%)

[7–22]59
[50–77]98
[95–99]
C5 [212]limit warming  to 2.5°C (>50%)52
[46–56]45
[37–53]39
[30–49]6
[4–33]29
[1400–
2360]1780
[1260–
2360]0
[0–10]37
[18–59]91
[83–98]
C6 [97]limit warming  to 3°C (>50%)SSP2–4.5
ModAct54
[50–62]53
[48–61]52
[45–57]2
[–10 to 
11]3
[–14 to 
14]5
[–2 to 18]2030–2035 
(97%)
[2440–
3520]
does not 
peak by 
21002.7
[0–0]8
[2–18]71
[53–88]
C7 [164]limit warming  to 4°C (>50%)SSP3–7.0
CurPol62
[53–69]67
[56–76]70
[–41 to 
(56%)4220
[3160–
5000]3.5
[0–0]0
[0–2]22
C8 [29]exceed warming  of 4°C (≥50%)SSP5–8.571
[69–81]80
[78–96]88
[–34 to 
–17]–35
[–65 to 
–29]–46
[–92 to 
4.2
[0–0]0
[0–0]4

20SPM
columns these values relate to the distribution of all the pathways in that category. Harmonised emissions values are given for consistency with projected global

warming outcomes using climate emulators. Based on the assessment of climate emulators in AR6 WGI (WG1 Chapter 7, Box 7.1), two climate emulators are used for the probabilistic assessment of the resulting warming of the pathways. For the ‘Temperature change’ and ‘Likelihood’ columns, the single upper-row values represent the 50th percentile across the pathways in that category and the median [50th percentile] across the warming estimates of the probabilistic MAGICC climate model emulator. For the bracketed ranges, the median warming for every pathway in that category is calculated for each of the two climate model emulators (MAGICC and FaIR). Subsequently, the 5th and 95th percentile values across all pathways for each emulator are calculated. The coolest and warmest outcomes (i.e., the lowest p5 of two emulators, and the highest p95, respectively) are shown in square brackets. These ranges therefore cover both the uncertainty of the emissions pathways as well as the climate

d C3 pathways are sub-categorised according to the timing of policy action to match the emissions pathways in Figure SPM.4. Two pathways derived from a cost-benefit 
analysis have been added to C3a, whilst 10 pathways with specifically designed near-term action until 2030, whose emissions fall below those implied by NDCs announced prior to COP26, are not included in either of the two subsets.
common features such as deep and rapid emissions reductions, but also different combinations of sectoral mitigation strategies. See Box SPM.1 for an introduction of the IPs and IMPs, and Chapter 3 for full descriptions. {3.2, 3.3, Annex III.II.4}

emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.
h Rates of global emission reduction in mitigation pathways are reported on a pathway-by-pathway basis relative to harmonised modelled global emissions in 2019

rather than the global emissions reported in SPM Section B and Chapter 2; this ensures internal consistency in assumptions about emission sources and activities, as well as consistency with temperature projections based on the physical climate science assessment by WGI.
49 {Annex III.II.2.5}. Negative values (e.g., in C7, C8) represent an 
(CO 2 and GHGs) are assessed for five-year reporting intervals starting in 2020. The interval 2020–2025 signifies that projected emissions peak as soon as possible

between 2020 and at latest before 2025. The upper five-year interval refers to the median interval within which the emissions peak or reach net zero. Ranges in square brackets underneath refer to the range across the pathways, comprising the lower bound of the 5th percentile five-year interval and the upper bound of the 95th percentile five-year interval. Numbers in round brackets signify the fraction of pathways that reach specific milestones.

brackets). If the fraction of pathways that reach net zero before 2100 is lower than the fraction of pathways covered by a percentile (e.g., 0.95 for the 95th percentile), the percentile is not defined and denoted with ‘…’. The fraction of pathways reaching net zero includes all with reported non-harmonised, and/or harmonised emissions profiles that reach net zero. Pathways were counted when at least one of the two profiles fell below 100 MtCO
2 yr–1 until 2100.

emulators consistent with the AR6 WGI assessment. The probabilities refer to the probability at peak temperature. Note that in the case of temperature overshoot (e.g., category C2 and some pathways in C1), the probabilities of staying below at the end of the century are higher than the probabilities at peak temperature.

21SPM
average associated with a higher median peak warming in AR6 compared to pathways in the same category in SR1.5. In the modelled pathways in AR6, the likelihood of limiting warming to 1.5°C has on average declined compared to SR1.5. This is because GHG emissions have risen since 2017, and many recent pathways have higher projected emissions by 2030, higher cumulative net CO
2 emissions and slightly later dates for reaching net zero CO2 or net zero GHG emissions. High mitigation 
challenges, for example, due to assumptions of slow technological change, high levels of global population growth, and high fragmentation as in the Shared Socio-economic Pathway SSP3, may render modelled pathways that limit warming to 2°C

A wide range of modelled global emission pathways and scenarios from the literature is assessed in this report, including pathways and scenarios with and without mitigation.
44 Emissions pathways and scenarios project the evolution of GHG 
emissions based on a set of internally consistent assumptions about future socio-economic conditions and related mitigation measures.
45 These are quantitative projections and are neither predictions nor forecasts. Around half of all modelled global

emission scenarios assume cost-effective approaches that rely on least-cost emission abatement options globally. The other half look at existing policies and regionally and sectorally differentiated actions. Most do not make explicit assumptions about global equity, environmental justice or intra-regional income distribution. Global emission pathways, including those based on cost-effective approaches, contain regionally differentiated assumptions and outcomes, and have to be assessed with the careful recognition of these assumptions. This assessment focuses on their global characteristics. The majority of the assessed scenarios (about 80%) have become available since the SR1.5, but some were assessed in that report. Scenarios with and without mitigation were categorised based on their projected global warming over the 21st century, following the same scheme as in the SR1.5 for warming up to and including 2°C. {1.5, 3.2, 3.3, Annex III.II.2, Annex III.II.3}

to in this report as follows:
46,47
• Category C1 comprises modelled scenarios that limit warming to 1.5°C in 2100 with a likelihood of greater than 50%, and reach or exceed warming of 1.5°C during the 21st century with a likelihood of 67% or less. In this report, these scenarios are referred to as scenarios that limit warming to 1.5°C (>50%) with no or limited overshoot. Limited overshoot refers to exceeding 1.5°C global warming by up to about 0.1°C and for up to several decades.
48

• Category C2 comprises modelled scenarios that limit warming to 1.5°C in 2100 with a likelihood of greater than 50%, and exceed warming of 1.5°C during the 21st century with a likelihood of greater than 67%. In this report, these scenarios are also referred to as scenarios that return warming to 1.5°C (>50%) after a high overshoot. High overshoot refers to temporarily exceeding 1.5°C global warming by 0.1°C–0.3°C for up to several decades.
• Category C3 comprises modelled scenarios that limit peak warming to 2°C throughout the 21st century with a likelihood of greater than 67%. In this report, these scenarios are also referred to as scenarios that limit warming to 2°C (>67%).
• Categories C4, C5, C6 and C7 comprise modelled scenarios that limit warming to 2°C, 2.5°C, 3°C, 4°C, respectively, throughout the 21st century with a likelihood of greater than 50%. In some scenarios in C4 and many scenarios in C5–C7, warming continues beyond the 21st century.

45 Key assumptions relate to technology development in agriculture and energy systems and socio-economic development, including demographic and economic projections. IPCC is neutral with regard to the assumptions underlying the scenarios in the literature assessed in this report, which do not cover all possible futures. Additional scenarios may be developed. The underlying population assumptions range from 8.5 to 9.7 billion in 2050 and 7.4 to 10.9 billion in 2100 (5–95th percentile) starting from 7.6 billion in 2019. The underlying assumptions on global GDP growth (ppp) range from 2.5 to 3.5% per year in the 2019–2050 period and 1.3 to 2.1% per year in the 2050–2100 (5–95th percentile). Many underlying assumptions are regionally differentiated. {1.5; 3.2; 3.3; Figure 3.9; Annex III.II.1.4; Annex III.II.3}

46 The future scenario projections presented here are consistent with the total observed increase in global surface temperature between 1850–1900 and 1995–2014 as well as to 2011–2020 (with best estimates of 0.85°C and 1.09°C, respectively) assessed in WGI. The largest contributor to historical human-induced warming is CO
2, 

22SPM
Categories of modelled scenarios are distinct and do not overlap; they do not contain categories consistent with lower levels of global warming, for example, the category of C3 scenarios that limit warming to 2°C (>67%) does not include the C1 and C2 scenarios that limit or return warming to 1.5°C (>50%). Where relevant, scenarios belonging to the group of categories C1–C3 are referred to in this report as scenarios that limit warming to 2°C (>67%) or lower.
assessment of physical climate science.
49 {3.2, Annex III.II.2.5; AR6 WGI Cross-Chapter Box 7.1}

data sources of historical emissions that are updated over time. The assessment of past GHG emissions in Chapter 2 of the report is based on a more recent dataset providing emissions of 57 [±6.3] GtCO
2-eq in 2015 (B.1). Differences are well within the assessed uncertainty range, and arise mainly from differences 

reduction in mitigation scenarios are reported relative to modelled global emissions in 2019 rather than the global emissions reported in Chapter 2; this ensures internal consistency in assumptions about emission sources and activities, as well as consistency with temperature projections based on the physical climate science assessment by WG I. {Annex III.II.2.5}
scenario categories, IMPs and SSPx-y scenarios considered by AR6 WGIGlobal warming relative to 1850–1900 (°C)
0123456
IMP
SSPx-y

open: 2100 warmingClimate & scenario uncertainty: 5–95% across scenariosof 5–95% 2100 warmingScenario range withincategory: 5–95% acrossmedians of scenarios
C8
C7
C6
C5
C4
C3
C2
C1
C7
Box SPM.1, Figure 1 | Projected global mean warming of the ensemble of modelled scenarios included in the climate categories C1–C8 and IMPs (based on emulators calibrated to the WGI assessment), as well as five illustrative scenarios (SSPx-y) as considered by AR6 WGI. Panel a  shows the p5–p95 range of projected median warming across global modelled pathways within a  category, with the category medians (line). Panel b  shows the peak and 2100 emulated temperature outcomes for the categories C1 to C8 and for IMPs, and the five illustrative scenarios (SSPx-y)

as considered by AR6 WGI. The boxes show the p5–p95 range within each scenario category, as in panel a. The combined p5–p95 range across scenarios and the climate uncertainty for each category C1–C8 is also shown for 2100 warming (thin vertical lines). (Table SPM.2) {Figure 3.11; AR6 WGI Figure SPM.8}

23SPM
projected to be lower by up to about 0.05 [±0.1] °C than if the same scenarios were evaluated using the SR1.5 methodology, and global warming in 2100 is projected to be lower by about 0.1 [±0.1] °C. {Annex III.II.2.5.1, Annex III Figure II.3}
the characteristics of the wider range of emission scenarios published since the SR1.5. Proportionally more scenarios assessed in AR6 are designed to limit temperature overshoot and more scenarios limit large-scale net negative CO
2 emissions than in

SR1.5. As a result, AR6 scenarios in the lowest temperature category (C1) generally reach net zero GHG emissions later in the 21st century than scenarios in the same category assessed in SR1.5, and about half do not reach net zero GHG by 2100. The rate of decline of GHG emissions in the near term by 2030 in category C1 scenarios is very similar to the assessed rate in SR1.5, but absolute GHG emissions of category C1 scenarios in AR6 are slightly higher in 2030 than in SR1.5, since the reductions start from a higher emissions level in 2020. (Table SPM.2) {Annex III, 2.5, 3.2, 3.3}
The large number of global emissions scenarios assessed, including 1202 scenarios with projected global warming outcomes

using climate emulators, come from a wide range of modelling approaches. They include the five illustrative scenarios (Shared Socio-economic Pathways; SSPs) assessed by WGI for their climate outcomes but cover a wider and more varied set in terms of assumptions and modelled outcomes. For this assessment, Illustrative Mitigation Pathways (IMPs) were selected from this larger set to illustrate a range of different mitigation strategies that would be consistent with different warming levels. The IMPs illustrate pathways that achieve deep and rapid emissions reductions through different combinations of mitigation strategies. The IMPs are not intended to be comprehensive and do not address all possible themes in the underlying report. They differ in terms of their focus, for example, placing greater emphasis on renewables (IMP-Ren), deployment of carbon dioxide removal that results in net negative global GHG emissions (IMP-Neg), and efficient resource use as well as shifts in consumption

patterns globally, leading to low demand for resources, while ensuring a high level of services and satisfying basic needs (IMP-LD) (Figure SPM.5). Other IMPs illustrate the implications  of a less rapid introduction of mitigation measures followed by a subsequent gradual strengthening (IMP-GS), and how shifting global pathways towards sustainable development, including by reducing inequality, can lead to mitigation (IMP-SP). The IMPs reach different climate goals as indicated in Table SPM.2 and Box SPM.1, Figure 1. {1.5, 3.1, 3.2, 3.3, 3.6, Figure 3.7, Figure 3.8, Box 3.4, Annex III.II.2.4}

after the point of net zero. These pathways also include deep reductions in other GHG emissions. The level of peak warming depends on cumulative CO
2 emissions until the time of net zero CO2 and the 
change in non-CO2 climate forcers by the time of peaking. Deep GHG emissions reductions by 2030 
and 2040, particularly reductions of methane emissions, lower peak warming, reduce the likelihood of overshooting warming limits and lead to less reliance on net negative CO
2 emissions that reverse

warming in the latter half of the century. Reaching and sustaining global net zero GHG emissions results in a gradual decline in warming. (high confidence) (Table SPM.2) {3.3, 3.5, Box 3.4, Cross-Chapter Box 3 in Chapter 3, AR6 WGI SPM D1.8}
C.2.2  Modelled global pathways that limit warming to 1.5°C (>50%) with no or limited overshoot involve more rapid and deeper near-term GHG emissions reductions through to 2030, and are projected to have less net negative CO
2 emissions and less

carbon dioxide removal (CDR) in the longer term, than pathways that return warming to 1.5°C (>50%) after a high overshoot (C2 category). Modelled pathways that limit warming to 2°C (>67%) have on average lower net negative CO

24SPM
1.5°C (>50%) after a  high overshoot (C2 category) show near-term GHG emissions reductions similar to pathways that limit warming to 2°C (>67%) (C3 category). For a  given peak global warming level, greater and more rapid  near-term GHG emissions reductions are associated with later net zero CO
2 dates. (high confidence) (Table SPM.2) {3.3, Table  3.5, 
C.2.3  Future non-CO2 warming depends on reductions in non-CO2 GHGs,  aerosols and their precursors, and ozone precursor

emissions. In modelled global low-emission pathways, the projected reduction of cooling and warming aerosol emissions over time leads to net warming in the near- to mid-term. In these mitigation pathways, the projected reductions of cooling aerosols are mostly due to reduced fossil fuel combustion that was not equipped with effective air pollution controls. Non-CO
2 

emissions. Typical emissions pathways that reach and sustain global net zero GHG emissions based on the 100-year global warming potential (GWP-100)
7 are projected to result in a gradual decline of global warming. About half of the assessed

pathways that limit warming to 1.5°C (>50%) with no or limited overshoot (C1 category) reach net zero GHG emissions during the second half of the 21st century. These pathways show greater reduction in global warming after the peak to 1.2 [1.1–1.4] °C by 2100 than modelled pathways in the same category that do not reach net zero GHG emissions before 2100 and that result in warming of 1.4 [1.3–1.5] °C by 2100. In modelled pathways that limit warming to 2°C (>67%) (C3 category), there is no significant difference in warming by 2100 between those pathways that reach net zero GHGs (around 30%) and those that do not (high confidence). In pathways that limit warming to 2°C (>67%) or lower and that do reach net zero GHG, net zero GHG occurs around 10–40 years later than net zero CO
2 emissions (medium confidence). {Cross-Chapter Box 2 in 

those that limit warming to 2°C (>67%), involve rapid and deep and in most cases immediate GHG emission reductions in all sectors. Modelled mitigation strategies to achieve these reductions include transitioning from fossil fuels without CCS to very low- or zero-carbon energy sources, such as renewables or fossil fuels with CCS, demand side measures and improving efficiency, reducing non-CO
2 emissions, and 
deploying carbon dioxide removal (CDR) methods to counterbalance residual GHG emissions. Illustrative Mitigation Pathways (IMPs) show different combinations of sectoral mitigation strategies consistent with a given warming level. (high confidence) (Figure SPM.5) {3.2, 3.3, 3.4, 6.4, 6.6}
C.3.1  There is a variation in the contributions of different sectors in modelled mitigation pathways, as illustrated by the Illustrative

Mitigation Pathways (IMPs). However, modelled pathways that limit warming to 2°C (>67%) or lower share common characteristics, including rapid and deep GHG emission reductions. Doing less in one sector needs to be compensated by further reductions in other sectors if warming is to be limited. (high confidence) (Figure SPM.5) {3.2, 3.3, 3.4}

C.3.2  In modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot, the global use of coal, oil and gas in 2050 is projected to decline with median values of about 95%, 60% and 45% respectively, compared to 2019. The interquartile ranges are (80 to 100%), (40 to 75%) and (20 to 60%) and the p5–p95 ranges are [60 to 100%], [25 to 90%] and [–30 to +85%], respectively. In modelled pathways that limit warming to 2°C (>67%), these projected declines have a median value and interquartile range of 85% (65 to 95%), 30% (15 to 50%) and 15% (–10 to +40%) respectively by 2050. The use of coal, oil and gas without CCS in modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot is projected to be reduced to a greater degree, with median values of about 100%, 60% and 70% in 2050 compared to 2019. The interquartile ranges are (95 to 100%), (45 to 75%) and (60 to 80%) and the p5–p95 ranges about [85 to 100%], [25  to  90%] and [35 to  90%] for coal,

oil and gas respectively. In these global modelled pathways, in 2050 almost all electricity is supplied from zero- or low-carbon sources, such as renewables or fossil fuels with CCS, combined with increased

25SPM
warming to 2°C (>67%) or lower, the AFOLU sector, via reforestation and reduced deforestation, and the energy supply sector reach net zero CO
2 emissions earlier than the buildings, industry and transport sectors. (high confidence) (Figure SPM.5e,f) {3.4}
C.3.4  In modelled pathways that reach global net zero GHG emissions, at the point they reach net zero GHG, around 74% [54 to 90%] of global emissions reductions are achieved by CO

2 reductions in energy supply and demand, 13% [4 to 20%] by CO2 mitigation 
C.3.5  Methods and levels of CDR deployment in global modelled mitigation pathways vary depending on assumptions about costs, availability and constraints.
53 In modelled pathways that report CDR and that limit warming to 1.5°C (>50%) with no or 
limited overshoot, global cumulative CDR during 2020–2100 from bioenergy with carbon dioxide capture and storage (BECCS) and direct air carbon dioxide capture and storage (DACCS) is 30–780 GtCO
2 and 0–310 GtCO2, respectively. In these modelled 
pathways, the AFOLU sector contributes 20–400 GtCO2 net negative emissions. Total cumulative net negative CO2 emissions 
including CDR deployment across all options represented in these modelled pathways are 20–660 GtCO2. In modelled pathways

C.3.6  All mitigation strategies face implementation challenges, including technology risks, scaling, and costs. Many challenges, such as dependence on CDR, pressure on land and biodiversity (e.g., bioenergy) and reliance on technologies with high upfront investments (e.g., nuclear), are significantly reduced in modelled pathways that assume using resources more efficiently (e.g., IMP-LD) or that shift global development towards sustainability (e.g., IMP-SP). (high confidence) (Figure SPM.5) {3.2, 3.4, 3.7, 3.8, 4.3, 5.1}

quantified precisely given that: (i) some pathways assess CDR deployment relative to a baseline; and (ii) different models use different reporting methodologies that in some cases combine gross emissions and removals in AFOLU. Total CDR from AFOLU equals or exceeds the net negative emissions mentioned.

26SPM
MtCH4 yr  –1
MtN2O yr  –1
C1C3C1
with no or limited overshoot (C1)(very likely range)IMP-LD (C1)
95th
Median
5th75th
25th

27SPM
IMP-Neg
IMP-LD
IMP-SP
IMP-Ren
Sources
Sinks
Indirect
Direct:
energy emissionsTotal indirect energy emissions (equals sum of energy supply emissions)Indirect:

pathways. The red ranges depict emissions pathways assuming policies that were implemented by the end of 2020 and pathways assuming implementation of NDCs

(announced prior to COP26). Ranges of modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot are shown in light blue (category C1) and pathways that limit warming to 2°C (>67%) are shown in light purple (category C3). The grey range comprises all assessed pathways (C1–C8) from the 5th percentile of the lowest warming category (C1) to the 95th percentile of the highest warming category (C8). The modelled pathway ranges are compared to the emissions from two pathways illustrative of high emissions (CurPol and ModAct) and five IMPs: IMP-LD, IMP-Ren, IMP-SP , IMP-Neg and IMP-GS. Emissions are harmonised to the same 2015 base year. The vertical error bars in 2015 show the 5–95th percentile uncertainty range of the non-harmonised emissions across the pathways, and the uncertainty range, and median value, in emission estimates for 2015 and 2019. The vertical error bars in 2030 (panel a) depict the assessed range of the NDCs, as announced prior to COP26 (Figure

23 Panel e shows the sectoral contributions of CO 2 and non-CO 2 emissions sources and sinks at the time when net zero CO 2 emissions are 
reached in the IMPs. Positive and negative emissions for different IMPs are compared to the GHG emissions from the year 2019. Energy supply (neg.) includes BECCS and DACCS. DACCS features in only two of the five IMPs (IMP-REN and IMP-GS) and contributes <1% and 64%, respectively, to the net negative emissions in Energy Supply (neg.). Panel f shows the contribution of different sectors and sources to the emissions reductions from a 2019 baseline for reaching net zero GHG emissions. Bars denote the median emissions reductions for all pathways that reach net zero GHG emissions. The whiskers indicate the p5–p95 range. The contributions of the service sectors (transport, buildings, industry) are split into direct (demand-side) as well as indirect (supply-side) CO
2 emissions reductions. Direct emissions represent

demand-side emissions due to the fuel use in the respective demand sector. Indirect emissions represent upstream emissions due to industrial processes and energy conversion, transmission and distribution. In addition, the contributions from the LULUCF sector and reductions from non-CO
2 emissions sources (green and grey bars) 

28SPM
reduction in overall fossil fuel use, the deployment of low-emission energy sources, switching to alternative energy carriers, and energy efficiency and conservation. The continued installation of unabated fossil fuel
54 infrastructure will ‘lock-in’ GHG emissions. (high confidence) {2.7, 6.6, 6.7, 16.4}
the energy system including end uses; energy carriers such as sustainable biofuels, low-emissions hydrogen, and derivatives

in applications less amenable to electrification; energy conservation and efficiency; and greater physical, institutional, and operational integration across the energy system. CDR will be needed to counterbalance residual emissions in the energy sector. The most appropriate strategies depend on national and regional circumstances, including enabling conditions and technology availability. (high confidence) {3.4, 6.6, 11.3, 16.4}

C.4.2  Unit cost reductions in key technologies, notably wind power, solar power, and storage, have increased the economic attractiveness of low-emission energy sector transitions through 2030. Maintaining emission-intensive systems may, in some regions and sectors, be more expensive than transitioning to low emission systems. Low-emission energy sector transitions will have multiple co-benefits, including improvements in air quality and health.  The long-term economic attractiveness of deploying energy system mitigation options depends, inter alia, on policy design and implementation, technology availability and performance, institutional capacity, equity, access to finance, and public and political support. (high confidence) (Figure SPM.3) {3.4, 6.4, 6.6, 6.7, 13.7}

C.4.3  Electricity systems powered predominantly by renewables are becoming increasingly viable. Electricity systems in some countries and regions are already predominantly powered by renewables. It will be more challenging to supply the entire energy system with renewable energy. Even though operational, technological, economic, regulatory, and social challenges remain, a variety of systemic solutions to accommodate large shares of renewables in the energy system have emerged. A broad portfolio of options, such as integrating systems, coupling sectors, energy storage, smart grids, demand-side management, sustainable biofuels, electrolytic hydrogen and derivatives, and others will ultimately be needed to accommodate large shares of renewables in energy systems. (high confidence) {Box 6.8, 6.4, 6.6}

C.4.4  Limiting global warming to 2°C or below will leave a substantial amount of fossil fuels unburned and could strand considerable fossil fuel infrastructure (high confidence). Depending on its availability, CCS could allow fossil fuels to be used longer, reducing stranded assets (high confidence). The combined global discounted value of the unburned fossil fuels and stranded fossil fuel infrastructure has been projected to be around USD1–4 trillion from 2015 to 2050 to limit global warming to approximately 2°C, and it will be higher if global warming is limited to approximately 1.5°C (medium confidence). In this context, coal assets are projected to be at risk of being stranded before 2030, while oil and gas assets are projected to be more at risk of being stranded towards mid-century. A low-emission energy sector transition is projected to reduce international trade in fossil fuels. (high confidence) {6.7, Figure 6.35}

C.4.5  Global methane emissions from energy supply, primarily fugitive emissions from production and transport of fossil fuels, accounted for about 18% [13–23%] of global GHG emissions from energy supply, 32% [22–42%] of global CH
4 emissions, 
C.4.6   CCS is an option to reduce emissions from large-scale fossil-based energy and industry sources, provided geological storage is available. When CO
2 is captured directly from the atmosphere (DACCS), or from biomass (BECCS), CCS provides the 

and enhanced oil recovery. In contrast to the oil and gas sector, CCS is less mature in the power sector, as well as in cement and chemicals production, where it is a critical mitigation option. The technical geological CO
2 storage capacity is estimated to be 
although the regional availability of geological storage could be a limiting factor. If the geological storage site is appropriately selected and managed, it is estimated that the CO
2 can be permanently isolated from the atmosphere. Implementation of 
CCS currently faces technological, economic, institutional, ecological-environmental and socio-cultural barriers. Currently, global rates of CCS deployment are far below those in modelled pathways limiting global warming to 1.5°C or 2°C. Enabling

29SPM
including demand management, energy and materials efficiency, circular material flows, as well as abatement technologies and transformational changes in production processes. Progressing towards net zero GHG emissions from industry will be enabled by the adoption of new production processes using low- and zero-GHG electricity, hydrogen, fuels, and carbon management. (high confidence) {11.2, 11.3, 11.4, Box TS.4}

C.5.1  The use of steel, cement, plastics, and other materials is increasing globally, and in most regions. There are many sustainable options for demand management, materials efficiency, and circular material flows that can contribute to reduced emissions, but how these can be applied will vary across regions and different materials. These options have a potential for being more used in industrial practice and would need more attention from industrial policy. These options, as well as new production technologies, are generally not considered in recent global scenarios nor in national economy-wide scenarios due to relative newness. As a consequence, the mitigation potential in some scenarios is underestimated compared to bottom-up industry-specific models. (high confidence) {3.4, 5.3, Figure 5.7, 11.2, Box 11.2, 11.3, 11.4, 11.5.2, 11.6} 

given that only a small fraction of consumer costs are based on materials, such new processes are expected to translate into minimal cost increases for final consumers. Hydrogen direct reduction for primary steelmaking is near-commercial in some 
regions. Until new chemistries are mastered, deep reduction of cement process emissions will rely on already commercialised cementitious material substitution and the availability of CCS. Reducing emissions from the production and use of chemicals would need to rely on a life cycle approach, including increased plastics recycling, fuel and feedstock switching, and carbon sourced through biogenic sources, and, depending on availability, carbon capture and use (CCU), direct air CO
2 capture, as

well as CCS. Light industry, mining and manufacturing have the potential to be decarbonised through available abatement technologies (e.g., material efficiency, circularity), electrification (e.g., electrothermal heating, heat pumps) and low- or zero-GHG emitting fuels (e.g., hydrogen, ammonia, and bio-based and other synthetic fuels). (high confidence) {Table 11.4, Box 11.2, 11.3, 11.4}
C.5.3  Action to reduce industry sector emissions may change the location of GHG-intensive industries and the organisation of value chains. Regions with abundant low-GHG energy and feedstocks have the potential to become exporters of hydrogen-based 
chemicals and materials processed using low-carbon electricity and hydrogen. Such reallocation will have global distributional effects on employment and economic structure. (medium confidence) {Box 11.1}

C.5.4  Emissions-intensive and highly traded basic materials industries are exposed to international competition, and international cooperation and coordination may be particularly important in enabling change. For sustainable industrial transitions, broad and sequential national and sub-national policy strategies reflecting regional contexts will be required. These may combine policy packages including: transparent GHG accounting and standards; demand management; materials and energy efficiency policies; R&D and niche markets for commercialisation of low-emission materials and products; economic and regulatory instruments to drive market uptake; high quality recycling, low-emissions energy and other abatement infrastructure (e.g., for CCS); and socially inclusive phase-out plans of emissions-intensive facilities within the context of just transitions. The coverage of mitigation policies could be expanded nationally and sub-nationally to include all industrial emission sources, and

both available and emerging mitigation options. (high confidence) {11.6}

30SPM
emissions through the systemic transition of infrastructure and urban form through low-emission development pathways towards net-zero emissions. Ambitious mitigation efforts for established, rapidly growing and emerging cities will encompass (i) reducing or changing energy and material consumption, (ii) electrification, and (iii) enhancing carbon uptake and storage in the urban environment. Cities can achieve net-zero emissions, but only if emissions are reduced within and outside of their administrative boundaries through supply chains, which will have beneficial cascading effects across other sectors. (very high confidence) {8.2, 8.3, 8.4, 8.5, 8.6, Figure 8.21, 13.2}

C.6.2  The potential and sequencing of mitigation strategies to reduce GHG emissions will vary depending on a city’s land use, spatial form, development level, and state of urbanisation (high confidence). Strategies for established cities to achieve large GHG emissions savings include efficiently improving, repurposing or retrofitting the building stock, targeted infilling, and supporting non-motorised (e.g., walking, bicycling) and public transport. Rapidly growing cities can avoid future emissions by co-locating jobs and housing to achieve compact urban form, and by leapfrogging or transitioning to low-emissions technologies. New and emerging cities will have significant infrastructure development needs to achieve high quality of life, which can be met through energy efficient infrastructures and services, and people-centred urban design (high confidence). For cities, three broad mitigation strategies have been found to be effective when implemented concurrently: (i) reducing or

changing energy and material use towards more sustainable production and consumption; (ii) electrification in combination with switching to low-emission energy sources; and (iii) enhancing carbon uptake and storage in the urban environment, for example through bio-based building materials, permeable surfaces, green roofs, trees, green spaces, rivers, ponds and lakes.

57 
C.6.3  The implementation of packages of multiple city-scale mitigation strategies can have cascading effects across sectors and reduce GHG emissions both within and outside a city’s administrative boundaries. The capacity of cities to develop and implement mitigation strategies varies with the broader regulatory and institutional settings, as well as enabling conditions, including access to financial and technological resources, local governance capacity, engagement of civil society, and municipal budgetary powers. (very high confidence) {Figure 5.7, Supplementary Material Table 5.SM.2, 8.4, 8.5, 8.6, 13.2, 13.3, 13.5, 

C.6.4  A growing number of cities are setting climate targets, including net-zero GHG targets. Given the regional and global reach of urban consumption patterns and supply chains, the full potential for reducing consumption-based urban emissions to net zero GHG can be met only when emissions beyond cities’ administrative boundaries are also addressed. The effectiveness of these strategies depends on cooperation and coordination with national and sub-national governments, industry, and civil society, and whether cities have adequate capacity to plan and implement mitigation strategies. Cities can play a positive role in reducing emissions across supply chains that extend beyond cities’ administrative boundaries, for example through building codes and the choice of construction materials. (very high confidence) {8.4, Box 8.4, 8.5, 9.6, 9.9, 13.5, 13.9}

31SPM
projected to approach net zero GHG emissions in 2050 if policy packages, which combine ambitious sufficiency, efficiency, and renewable energy measures, are effectively implemented and barriers to decarbonisation are removed. Low ambition policies increase the risk of locking-in buildings’ carbon for decades, while well-designed and effectively implemented mitigation interventions (in both new buildings and existing ones if retrofitted), have significant potential to contribute to achieving SDGs in all regions while adapting buildings to future climate. (high confidence) {9.1, 9.3, 9.4, 9.5, 9.6, 9.9}

and heat, direct emissions produced onsite and emissions from cement and steel used for building construction and renovation. In 2019, global direct and indirect emissions from non-residential buildings increased by about 55% and those from residential buildings increased by about 50% compared to 1990. The latter increase, according to the decomposition analysis, was mainly driven by the increase of the floor area per capita, population growth and the increased use of emission-intensive electricity and heat while efficiency improvements have partly decreased emissions. There are great differences in the contribution of each of these drivers to regional emissions. (high confidence) {9.3}

C.7.2  Integrated design approaches to the construction and retrofit of buildings have led to increasing examples of zero energy or zero carbon buildings in several regions. However, the low renovation rates and low ambition of retrofitted buildings have hindered the decrease of emissions. Mitigation interventions at the design stage include buildings typology, form, and multi-functionality to allow for adjusting the size of buildings to the evolving needs of their users and repurposing unused existing buildings to avoid using GHG-intensive materials and additional land. Mitigation interventions include: at the construction phase, low-emission construction materials, highly efficient building envelope and the integration of renewable energy solutions;
58 at the use phase, highly efficient appliances/equipment, the optimisation of the use of buildings and their

supply with low-emission energy sources; and at the disposal phase, recycling and re-using construction materials. (high confidence) {9.4, 9.5, 9.6, 9.7}
C.7.3  By 2050, bottom-up studies show that up to 61% (8.2 GtCO
2) of global building emissions could be mitigated. Sufficiency 
policies59 that avoid the demand for energy and materials contribute 10% to this potential, energy efficiency policies contribute

42%, and renewable energy policies 9%. The largest share of the mitigation potential of new buildings is available in developing countries while in developed countries the highest mitigation potential is within the retrofit of existing buildings. The 2020–2030 decade is critical for accelerating the learning of know-how, building the technical and institutional capacity, setting the appropriate governance structures, ensuring the flow of finance, and in developing the skills needed to fully capture the mitigation potential of buildings. (high confidence) {9.3, 9.4, 9.5, 9.6, 9.7, 9.9}

32SPM
in developed countries and limit emissions growth in developing countries (high confidence). Demand-focused interventions can reduce demand for all transport services and support the shift to more energy efficient transport modes (medium confidence). Electric vehicles powered by low-emissions electricity offer the largest decarbonisation potential for land-based transport, on a life cycle basis (high confidence). Sustainable biofuels can offer additional mitigation benefits in land-based transport in the short and medium term (medium confidence). Sustainable biofuels, low-emissions hydrogen, and derivatives (including synthetic fuels) can support mitigation of CO
2 emissions from shipping, aviation,

and heavy-duty land transport but require production process improvements and cost reductions (medium confidence). Many mitigation strategies in the transport sector would have various co-benefits, 
including air quality improvements, health benefits, equitable access to transportation services, reduced congestion, and reduced material demand (high confidence). {10.2, 10.4, 10.5, 10.6, 10.7}
to decrease by 29% [14–44% interquartile range] by 2050 compared to modelled 2020 emissions. In both categories of scenarios, the transport sector likely does not reach zero CO

2 emissions by 2100 so negative emissions are likely needed to 

C.8.2  Changes in urban form (e.g., density, land-use mix, connectivity, and accessibility) in combination with programmes that encourage changes in consumer behaviour (e.g., transport pricing) could reduce transport-related greenhouse gas emissions in developed countries and slow growth in emissions in developing countries (high confidence). Investments in public inter- and intra-city transport and active transport infrastructure (e.g., bicycle and pedestrian pathways) can further support the shift to less GHG-intensive transport modes (high confidence). Combinations of systemic changes, including teleworking, digitalisation, dematerialisation, supply chain management, and smart and shared mobility may reduce demand for passenger and freight services across land, air, and sea (high confidence). Some of these changes could lead to induced demand for transport and energy services, which may decrease their GHG emissions reduction potential (medium confidence). {5.3, 10.2, 10.8}

C.8.3  Electric vehicles powered by low-GHG emissions electricity have large potential to reduce land-based transport GHG emissions, on a  life cycle basis (high confidence). Costs of electrified vehicles, including automobiles, two- and three-wheelers, and buses, are decreasing and their adoption is accelerating, but they require continued investments in supporting infrastructure to increase scale of deployment (high confidence). Advances in battery technologies could facilitate the electrification of heavy-duty trucks and complement conventional electric rail systems (medium confidence). There are growing concerns about critical minerals needed for batteries. Material and supply diversification strategies, energy and material efficiency improvements, and circular material flows can reduce the environmental footprint and material supply risks for battery production (medium confidence). Sourced sustainably and with low-GHG emissions feedstocks, bio-based fuels, blended or unblended

with fossil fuels, can provide mitigation benefits, particularly in the short and medium term (medium confidence). Low-GHG emissions hydrogen and hydrogen derivatives, including synthetic fuels, can offer mitigation potential in some contexts and land-based transport segments (medium confidence). {3.4, 6.3, 10.3, 10.4, 10.7, 10.8, Box 10.6}

C.8.4  While efficiency improvements (e.g., optimised aircraft and vessel designs, mass reduction, and propulsion system improvements) can provide some mitigation potential, additional CO
2 emissions mitigation technologies for aviation and

shipping will be required (high confidence). For aviation, such technologies include high energy density biofuels (high confidence), and low-emission hydrogen and synthetic fuels (medium confidence). Alternative fuels for shipping include low-emission hydrogen, ammonia, biofuels, and other synthetic fuels (medium confidence). Electrification could play a niche role for aviation and shipping for short trips (medium confidence) and can reduce emissions from port and airport operations (high confidence). Improvements to national and international governance structures would further enable the decarbonisation of shipping and aviation (medium confidence). Such improvements could include, for example, the implementation of stricter efficiency and carbon intensity standards for the sectors (medium confidence). {10.3. 10.5, 10.6, 10.7, 10.8, Box 10.5}

C.8.5  The substantial potential for GHG emissions reductions, both direct and indirect, in the transport sector largely depends on power sector decarbonisation, and low-emissions feedstocks and production chains (high confidence). Integrated transport and energy infrastructure planning and operations can enable sectoral synergies and reduce the environmental, social, and economic impacts of decarbonising the transport and energy sectors (high confidence). Technology transfer and financing can support developing countries leapfrogging or transitioning to low-emissions transport systems thereby providing multiple co-benefits (high confidence). {10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8}

33SPM
reductions and enhanced removals, but cannot fully compensate for delayed action in other sectors. In addition, sustainably sourced agricultural and forest products can be used instead of more GHG-intensive products in other sectors. Barriers to implementation and trade-offs may result from the impacts of climate change, competing demands on land, conflicts with food security and livelihoods, the complexity of land ownership and management systems, and cultural aspects. There are many country-specific opportunities to provide co-benefits (such as biodiversity conservation, ecosystem services, and livelihoods) and avoid risks (for example, through adaptation to climate change). (high confidence) {7.4, 7.6, 7.7, 12.5, 12.6}

peatlands, savannas and grasslands), with reduced deforestation in tropical regions having the highest total mitigation. Improved and sustainable crop and livestock management, and carbon sequestration in agriculture (the latter including soil carbon management in croplands and grasslands, agroforestry and biochar), can contribute 1.8–4.1 GtCO
2-eq yr–1 reduction. 

and free up land for reforestation and restoration, and the production of renewable energy. The improved and expanded use of wood products sourced from sustainably managed forests also has potential through the allocation of harvested wood to longer-lived products, increasing recycling or material substitution. AFOLU mitigation measures cannot compensate for delayed emission reductions in other sectors. Persistent and region-specific barriers continue to hamper the economic and political feasibility of deploying AFOLU mitigation options. Assisting countries to overcome barriers will help to achieve significant mitigation (medium confidence). (Figure SPM.6) {7.1, 7.4, 7.5, 7.6}

C.9.2  AFOLU carbon sequestration and GHG emission reduction options have both co-benefits and risks in terms of biodiversity and ecosystem conservation, food and water security, wood supply, livelihoods and land tenure and land-use rights of Indigenous Peoples, local communities and small land owners. Many options have co-benefits but those that compete for land and land-based resources can pose risks. The scale of benefit or risk largely depends on the type of activity undertaken, deployment strategy (e.g., scale, method), and context (e.g., soil, biome, climate, food system, land ownership) that vary geographically and over time. Risks can be avoided when AFOLU mitigation is pursued in response to the needs and perspectives of multiple stakeholders to achieve outcomes that maximize co-benefits while limiting trade-offs. (high confidence) {7.4, 7.6, 12.3}

C.9.3  Realising the AFOLU mitigation potential entails overcoming institutional, economic and policy constraints and managing potential trade-offs (high confidence). Land-use decisions are often spread across a wide range of land owners; demand-side measures depend on billions of consumers in diverse contexts. Barriers to the implementation of AFOLU mitigation include insufficient institutional and financial support, uncertainty over long-term additionality and trade-offs, weak governance, insecure land ownership, low incomes and the lack of access to alternative sources of income, and the risk of reversal. Limited access to technology, data, and know-how is a barrier to implementation. Research and development are key for all measures. For example, measures for the mitigation of agricultural CH
4 and N2O emissions with emerging technologies show promising

of agricultural systems, and by increasing demands to raise agricultural yields, and increasing demand for livestock products. (high confidence) {7.4, 7.6}
C.9.4  Net costs of delivering 5–6 GtCO
2 yr–1 of forest-related carbon sequestration and emission reduction as assessed with sectoral 
materials. 8–14 GtCO 2-eq yr–1 represents the mean of the AFOLU economic mitigation potential estimates from top-down estimates (lower bound of range) and

61 ‘Sustainable healthy diets’ promote all dimensions of individuals’ health and well-being; have low environmental pressure and impact; are accessible, affordable, safe and equitable; and are culturally acceptable, as described in FAO and WHO. The related concept of ‘balanced diets’ refers to diets that feature plant-based foods, such as those based on coarse grains, legumes, fruits and vegetables, nuts and seeds, and animal-sourced food produced in resilient, sustainable and low-GHG emission systems, as described in SRCCL.

34SPM
capacity, and the rule of law, are crucial for land-based mitigation in combination with policies also recognising interactions with wider ecosystem services, could enable engagement by a wider array of actors, including private businesses, NGOs, and Indigenous Peoples and local communities. (medium confidence) {7.6, 7.7}

C.9.5  Context specific policies and measures have been effective in demonstrating the delivery of AFOLU carbon sequestration and GHG emission reduction options but the above-mentioned constraints hinder large scale implementation (medium confidence). Deploying land-based mitigation can draw on lessons from experience with regulations, policies, economic incentives, payments (e.g., for biofuels, control of nutrient pollution, water regulations, conservation and forest carbon, ecosystem services, and rural livelihoods), and from diverse forms of knowledge such as Indigenous knowledge, local knowledge and scientific knowledge. Indigenous Peoples, private forest owners, local farmers and communities manage a significant share of global forests and agricultural land and play a central role in land-based mitigation options. Scaling successful policies and measures relies on governance that emphasises integrated land-use planning and management framed by SDGs, with support for

and socio-cultural and behavioural change. Demand-side measures and new ways of end-use service provision can reduce global GHG emissions in end-use sectors by 40–70% by 2050 compared to baseline scenarios, while some regions and socioeconomic groups require additional energy and resources. Demand-side mitigation response options are consistent with improving basic well-being for all. (high confidence) (Figure SPM.6) {5.3, 5.4, Figure 5.6, Figure 5.14, 8.2, 9.4, 10.2, 11.3, 11.4, 12.4, Figure TS.22}
C.10.1  Infrastructure design and access, and technology access and adoption, including information and communication

technologies, influence patterns of demand and ways of providing services, such as mobility, shelter, water, sanitation, and nutrition. Illustrative global low-demand scenarios, accounting for regional differences, indicate that more efficient end-use energy conversion can improve services while reducing the need for upstream energy by 45% by 2050 compared to 2020. Demand-side mitigation potential differs between and within regions, and some regions and populations require additional energy, capacity, and resources for human well-being. The lowest population quartile by income worldwide faces shortfalls in shelter, mobility, and nutrition. (high confidence) {5.2, 5.3, 5.4, 5.5, Figure 5.6, Figure 5.10, Table 5.2, Figure TS.20, Figure TS.22}
C.10.2  By 2050, comprehensive demand-side strategies could reduce direct and indirect CO
2 and non-CO2 GHG emissions in three

end-use sectors (buildings, land transport, and food) globally by 40%–70% compared to the 2050 emissions projection of two scenarios consistent with policies announced by national governments until 2020. With policy support, socio-cultural options and behavioural change can reduce global GHG emissions of end-use sectors by at least 5% rapidly, with most of the potential in developed countries, and more until 2050, if combined with improved infrastructure design and access. Individuals with high socio-economic status contribute disproportionately to emissions and have the highest potential for emissions reductions, e.g., as citizens, investors, consumers, role models, and professionals. (high confidence) (Figure SPM.6) {5.2, 5.3, 5.4, 5.5, 5.6, Supplementary Material Table 5.SM.2, 8.4, 9.9, 13.2, 13.5, 13.8, Figure TS.20}

C.10.3  A range of 5–30% of global annual GHG emissions from end-use sectors are avoidable by 2050, compared to 2050 emissions projection of two scenarios consistent with policies announced by national governments until 2020, through changes in the built environment, new and repurposed infrastructures and service provision through compact cities, co-location of jobs and housing, more efficient use of floor space and energy in buildings, and reallocation of street space for active mobility (high confidence). (Figure SPM.6) {5.3.1, 5.3.3, 5.4, Figure 5.7, Figure 5.13, Table 5.1, Table 5.5, Supplementary Material Table 5.SM.2, 8.4, 9.5, 10.2, 11.3, 11.4, Table 11.6, Box TS.12}
C.10.4  Choice architecture
62 can help end-users adopt, as relevant to consumers, culture and country contexts, low-GHG-intensive 
options such as balanced, sustainable healthy diets61 acknowledging nutritional needs; food waste reduction; adaptive heating

and cooling choices for thermal comfort; building-integrated renewable energy; and electric light-duty vehicles, and shifts to walking, cycling, shared pooled and public transit; and sustainable consumption by intensive use of longer-lived repairable products (high confidence). Addressing inequality and many forms of status consumption
63 and focusing on wellbeing 
supports climate change mitigation efforts (high confidence). (Figure SPM.6) {2.4.3, 2.6.2, 4.2.5, 5.1, 5.2, 5.3, 5.4, Figure 5.4, Figure 5.10, Table 5.2, Supplementary Material Table 5.SM.2, 7.4.5, 8.2, 8.4, 9.4, 10.2, 12.4, Figure TS.20}

35SPM
15
10
50GtCO2-eq yr  –1
related emissions, excludingreforestation of freed up landAFOLU
End-use
sectors
avoided or reduced through demand-side options are assumed to be addressed by supply-side optionsTotal emissions 2050

10
50
GtCO2 yr  –11510
50c. Electricity: indicative impacts
electricity generation to enable the end-use sectors’ substitution of electricity for fossil fuels, e.g. via heat pumps and electric cars {Table SM5.3; 6.6}Additional electriﬁcation (+60%)
Industry
Buildings
mitigation options (in end-use sectors: buildings, industry and land transport) which has potential to reduce electricity demand
3Demand-side 
measures –73%a. Nutrition 
sustainable healthy diets), avoidance of food wasteand over-consumption

choices; ﬁnancial incentives; waste management; recycling infrastructure
available (for lab-based meat and similar options – no quantitative literature available, overall potential considered in socio-cultural factors)b. Manufactured products, mobility, shelter 
sustainable consumption, such as intensive use of longer-lived repairable productsTeleworking or telecommuting; active mobility through walking and cyclingSocial practices resulting in energy saving; lifestyle and behavioural changesSocio-cultural factors
Networks established

for recycling, repurposing, remanufacturing and reuse of metals, plastics and glass; labelling low- emissions materials and productsPublic transport; shared mobility; compact cities; spatial planningCompact cities; rationalisation of living ﬂoor space; architectural design; urban planning (e.g., green roof, cool roof, urban green spaces etc.)Infrastructure use
access material-efﬁcient products and services; access to energy-efﬁcient and CO
2 neutral materialsElectric vehicles; shift to more efﬁcient vehiclesEnergy efﬁcient building envelopes and appliances; shift to renewablesEnd-use technology adoption
Figure SPM.6 | Indicative potential of demand-side mitigation options by 2050.  Figure SPM.6 covers the indicative potential of demand-side options for the

year 2050. Figure SPM.7 covers cost and potentials for the year 2030. Demand-side mitigation response options are categorised into three broad domains: ‘socio-cultural

factors’, associated with individual choices, behaviour, lifestyle changes, social norms, and culture; ‘infrastructure use’, related to the design and use of supporting hard and soft infrastructure that enables changes in individual choices and behaviour; and ‘end-use technology adoption’, referring to the uptake of technologies by end-users. Demand-side mitigation is a central element of the IMP-LD and IMP-SP scenarios (Figure SPM.5). Panel a (Nutrition) demand-side potentials in 2050 assessment is based on bottom-up studies and is estimated following the 2050 baseline for the food sector presented in peer-reviewed literature (more information in Supplementary Material 5.II, and Section 7.4.5). Panel b (Manufactured products, mobility, shelter) the assessment of potentials for total emissions in 2050 are estimated based on approximately 500 bottom-up studies representing all global regions (detailed list is in Supplementary Material Table 5.SM.2). Baseline is provided by the sectoral

mean GHG emissions in 2050 of the two scenarios consistent with policies announced by national governments until 2020. The heights of the coloured columns represent the potentials represented by the median value. These are based on a range of values available in the case studies from literature shown in Supplementary Material 5.SM.II. The range is shown by the dots connected by dotted lines representing the highest and the lowest potentials reported in the literature. Panel a shows the demand-side potential of socio-cultural factors and infrastructure use. The median value of direct emissions (mostly non-CO

2) reduction through socio-cultural factors is 1.9 GtCO 2-eq 
without considering land-use change through reforestation of freed up land. If changes in land-use pattern enabled by this change in food demand are considered, the indicative potential could reach 7 GtCO
2-eq. Panel b illustrates mitigation potential in industry, land transport and buildings end-use sectors through demand-side

options. Key options are presented in the summary table below the figure and the details are in Supplementary Material Table 5.SM.2. Panel c visualises how sectoral demand-side mitigation options (presented in panel b) change demand on the electricity distribution system. Electricity accounts for an increasing proportion of final energy demand in 2050 (additional electricity bar) in line with multiple bottom-up studies (detailed list is in Supplementary Material Table 5.SM.3), and Chapter 6 (Section 6.6). These studies are used to compute the impact of end-use electrification which increases overall electricity demand. Some of the projected increase in electricity demand can be avoided through demand-side mitigation options in the domains of socio-cultural factors and infrastructure use in end-use electricity use in buildings, industry, and land transport found in literature based on bottom-up assessments. Dark grey columns show the emissions that cannot be avoided through demand-side

mitigation options. {5.3, Figure 5.7, Supplementary Material 5.SM.II}

36SPM
depend on the trajectories of gross emission reductions in different sectors. Upscaling the deployment of CDR depends on developing effective approaches to address feasibility and sustainability constraints especially at large scales. (high confidence) {3.4, 7.4, 12.3, Cross-Chapter Box 8 in Chapter 12}
ocean reservoirs, or in products. CDR methods vary in terms of their maturity, removal process, time scale of carbon storage,

storage medium, mitigation potential, cost, co-benefits, impacts and risks, and governance requirements (high confidence). Specifically, maturity ranges from lower maturity (e.g., ocean alkalinisation) to higher maturity (e.g., reforestation); removal and storage potential ranges from lower potential (<1  GtCO
2 yr–1, e.g., blue carbon management) to higher potential 
to centuries for methods that store carbon in vegetation and through soil carbon management, to 10,000 years or more for methods that store carbon in geological formations (high confidence). The processes by which CO
2 is removed from the

atmosphere are categorised as biological, geochemical or chemical. Afforestation, reforestation, improved forest management, agroforestry and soil carbon sequestration are currently the only widely practiced CDR methods (high confidence). {7.4, 7.6, 12.3, Table 12.6, Cross-Chapter Box 8 in Chapter 12, Table TS.7; AR6 WGI 5.6}

C.11.2  The impacts, risks and co-benefits of CDR deployment for ecosystems, biodiversity and people will be highly variable depending on the method, site-specific context, implementation and scale (high confidence). Reforestation, improved forest management, soil carbon sequestration, peatland restoration and blue carbon management are examples of methods that can enhance biodiversity and ecosystem functions, employment and local livelihoods, depending on context (high confidence). In contrast, afforestation or production of biomass crops for BECCS or biochar, when poorly implemented, can have adverse socio-economic and environmental impacts, including on biodiversity, food and water security, local livelihoods and on the rights of Indigenous Peoples, especially if implemented at large scales and where land tenure is insecure (high confidence). Ocean fertilisation, if implemented, could lead to nutrient redistribution, restructuring of ecosystems, enhanced oxygen consumption and

acidification in deeper waters (medium confidence). {7.4, 7.6, 12.3, 12.5}

C.11.3  The removal and storage of CO
2 through vegetation and soil management can be reversed by human or natural disturbances; 
C.11.4  In addition to deep, rapid, and sustained emission reductions CDR can fulfil three different complementary roles globally or at country level: lowering net CO
2 or net GHG emissions in the near term; counterbalancing ‘hard-to-abate’ residual emissions 
(e.g., emissions from agriculture, aviation, shipping, industrial processes) in order to help reach net zero CO2 or net zero GHG 

C.11.5  Rapid emission reductions in all sectors interact with future scale of deployment of CDR methods, and their associated risks, impacts and co-benefits. Upscaling the deployment of CDR methods depends on developing effective approaches to address sustainability and feasibility constraints, potential impacts, co-benefits and risks. Enablers of CDR include accelerated research, development and demonstration, improved tools for risk assessment and management, targeted incentives and development of agreed methods for measurement, reporting and verification of carbon flows. (high confidence) {3.4, 7.6, 12.3}

37SPM
change nor from reduced adaptation costs, it is a few percent lower in 2050 compared to pathways without mitigation beyond current policies. The global economic benefit of limiting warming to 2°C is reported to exceed the cost of mitigation in most of the assessed literature (medium confidence). (Figure SPM.7) {3.6, 3.8, Cross-Working Group Box 1 in Chapter 3, 12.2, Box TS.7}

from the provided estimates. The assessment of the underlying literature suggests that the relative contribution of the various options could change beyond 2030. (medium confidence) (Figure SPM.7) {12.2}

C.12.2  The aggregate effects of climate change mitigation on global GDP are small compared to global projected GDP growth in assessed modelled global scenarios that quantify the macroeconomic implications of climate change mitigation, but that do not account for damages from climate change nor adaptation costs (high confidence). For example, compared to pathways that assume the continuation of policies implemented by the end of 2020, assessed global GDP reached in 2050 is reduced by 1.3–2.7% in modelled pathways assuming coordinated global action starting between now and 2025 at the latest to limit warming to 2°C (>67%). The corresponding average reduction in annual global GDP growth over 2020–2050 is 0.04–0.09 percentage points. In assessed modelled pathways, regardless of the level of mitigation action, global GDP is projected to at least double (increase by at least 100%) over 2020–2050. For modelled global pathways in other temperature categories, the reductions in global GDP in

2050 compared to pathways that assume the continuation of policies implemented by the end of 2020 are as follows: 2.6–4.2% (C1), 1.6–2.8% (C2), 0.8–2.1% (C4), 0.5–1.2% (C5). The corresponding reductions

in average annual global GDP growth over 2020–2050, in percentage points, are as follows: 0.09–0.14 (C1), 0.05–0.09 (C2), 0.03–0.07 (C4), 0.02–0.04 (C5).
66 There are large variations in the modelled effects of mitigation on GDP across regions, 
(high confidence). Country-level studies also show large variations in the effect of mitigation on GDP depending notably on the level of mitigation and on the way it is achieved (high confidence). Macroeconomic implications of mitigation co-benefits and trade-offs are not quantified comprehensively across the above scenarios and depend strongly on mitigation strategies (high confidence). {3.6, 4.2, Box TS.7, Annex III.I.2, Annex III.I.9, Annex III.I.10 and Annex III.II.3}

C.12.3  Estimates of aggregate economic benefits from avoiding damages from climate change, and from reduced adaptation costs, increase with the stringency of mitigation (high confidence). Models that incorporate the economic damages from climate change find that the global cost of limiting warming to 2°C over the 21st century is lower than the global economic benefits of reducing warming, unless: (i) climate damages are towards the low end of the range; or, (ii) future damages are discounted at high rates (medium confidence).
68 Modelled pathways with a peak in global emissions between now and 2025 at the latest,

compared to modelled pathways with a later peak in global emissions, entail more rapid near-term transitions and higher up-front investments, but bring long-term gains for the economy, as well as earlier benefits of avoided climate change impacts (high confidence). The precise magnitude of these gains and benefits is difficult to quantify. {1.7, 3.6, Cross-Working Group Box 1 in Chapter 3, Box TS.7; AR6 WGII SPM B.4} 
66 These estimates are based on 311 pathways that report effects of mitigation on GDP and that could be classified in temperature categories, but that do not account

for damages from climate change nor adaptation costs and that mostly do not reflect the economic impacts of mitigation co-benefits and trade-offs. The ranges given are interquartile ranges. The macroeconomic implications quantified vary largely depending on technology assumptions, climate/emissions target formulation, model structure and assumptions, and the extent to which pre-existing inefficiencies are considered. Models that produced the pathways classified in temperature categories do not represent the full diversity of existing modelling paradigms, and there are in the literature models that find higher mitigation costs, or conversely lower mitigation costs and even gains. {1.7, 3.2, 3.6, Annex III.I.2, Annex III.I.9, Annex III.I.10 and Annex III.II.3}

67 In modelled cost-effective pathways with a globally uniform carbon price, without international financial transfers or complementary policies, carbon intensive and energy exporting countries are projected to bear relatively higher mitigation costs because of a deeper transformation of their economies and changes in international energy markets. {3.6}

38SPM
net emissions by 2030. Relative potentials and costs will vary across countries and in the longerterm compared to 2030.

the total potential contributionto emission reduction. The individual cost ranges are alsoassociated with uncertaintyNet lifetime cost of options:

39SPM
methodology, including definition of potentials, target year, reference scenarios, and cost definitions. The mitigation potential (shown in the horizontal axis) is the

quantity of net GHG emission reductions that can be achieved by a given mitigation option relative to a specified emission baseline. Net GHG emission reductions are the sum of reduced emissions and/or enhanced sinks. The baseline used consists of current policy (around 2019) reference scenarios from the AR6 scenarios database (25/75 percentile values). The assessment relies on approximately 175 underlying sources, that together give a fair representation of emission reduction potentials across all regions. The mitigation potentials are assessed independently for each option and are not necessarily additive. {12.2.1, 12.2.2} The length of the solid bars represents the mitigation potential of an option. The error bars display the full ranges of the estimates for the total mitigation potentials. Sources of uncertainty for the cost estimates include assumptions on the rate of technological advancement, regional differences, and economies of scale, among others. Those uncertainties are not

displayed in the figure. Potentials are broken down into cost categories, indicated by different colours (see legend). Only discounted lifetime monetary costs are considered. Where a gradual colour transition is shown, the breakdown of the potential into cost categories is not well known or depends heavily on factors such as geographical location, resource availability, and regional circumstances, and the colours indicate the range of estimates. Costs were taken directly from the underlying studies (mostly in the period 2015–2020) or recent datasets. No correction for inflation was applied, given the wide cost ranges used. The cost of the reference technologies were also taken from the underlying studies and recent datasets. Cost reductions through technological learning are taken into account.

69
and several other factors. 
 –Cost and mitigation potential estimates were extrapolated from available sectoral studies. Actual costs and potentials would vary by place, context and time.
 –Beyond 2030, the relative importance of the assessed mitigation options is expected to change, in particular while pursuing long-term mitigation goals, recognising also that the emphasis for particular options will vary across regions (for specific mitigation options see SPM Sections C4.1, C5.2, C7.3, C8.3 and C9.1).
 –The potentials in the cost range USD100–200 tCO
2-eq–1 may be underestimated for some options.

–Costs for accommodating the integration of variable renewable energy sources in electricity systems are expected to be modest until 2030, and are not included because of complexities in attributing such costs to individual technology options.
 –Cost range categories are ordered from low to high. This order does not imply any sequence of implementation.
 –Externalities are not taken into account. {12.2, Table 12.3, 6.4, Table 7.3, Supplementary Material Table 9.SM.2, Supplementary Material Table 9.SM.3, 10.6, 11.4, Figure 11.13, Supplementary Material 12.SM.1.2.3}

40SPM
critical to sustainable development. Climate change actions can also result in some trade-offs. The trade-offs of individual options could be managed through policy design. The Sustainable Development Goals (SDGs) adopted under the UN 2030 Agenda for Sustainable Development can be used as a basis for evaluating climate action in the context of sustainable development. (high confidence) (Figure SPM.8) {1.6, 3.7, 17.3, Figure TS.29}

use, land-use and land use change, lifestyle and patterns of consumption and production. Without urgent, effective and equitable mitigation actions, climate change increasingly threatens the health and livelihoods of people around the globe, ecosystem health and biodiversity. There are both synergies and trade-offs between climate action and the pursuit of other SDGs. Accelerated and equitable climate action in mitigating, and adapting to, climate change impacts is critical to sustainable development. (high confidence) {1.6, Cross-Chapter Box  5 in Chapter  4, 7.2, 7.3, 17.3; AR6 WGI SPM.A, Figure SPM.2; AR6 WGII SPM.B2, Figure SPM.3, Figure SPM.4b, Figure SPM.5}

D.1.2  Synergies and trade-offs depend on the development context including inequalities, with consideration of climate justice. They also depend on means of implementation, intra- and inter-sectoral interactions, cooperation between countries and regions, the sequencing, timing and stringency of mitigation actions, governance, and policy design. Maximising synergies and avoiding trade-offs pose particular challenges for developing countries, vulnerable populations, and Indigenous Peoples with limited institutional, technological and financial capacity, and with constrained social, human, and economic capital. Trade-offs can be evaluated and minimised by giving emphasis to capacity building, finance, governance, technology transfer, investments, and development and social equity considerations with meaningful participation of Indigenous Peoples and vulnerable populations. (high confidence) {1.6, 1.7, 3.7, 5.2, 5.6, 7.4, 7.6, 17.4}

D.1.3  There are potential synergies between sustainable development and energy efficiency, renewable energy, urban planning with more green spaces, reduced air pollution, and demand-side mitigation including shifts to balanced, sustainable healthy diets (high confidence). Electrification combined with low-GHG energy, and shifts to public transport can enhance health, employment, and can elicit energy security and deliver equity (high confidence). In industry, electrification and circular material flows contribute to reduced environmental pressures and increased economic activity and employment. However, some industrial options could impose high costs (medium confidence). (Figure SPM.8) {5.2, 8.2, 11.3, 11.5, 17.3, Figure TS.29}

D.1.4  Land-based options such as reforestation and forest conservation, avoided deforestation, restoration and conservation of natural ecosystems and biodiversity, improved sustainable forest management, agroforestry, soil carbon management and options that reduce CH
4 and N2O emissions in agriculture from livestock and soil, can have multiple synergies with the SDGs. 
These include enhancing sustainable agricultural productivity and resilience, food security, providing additional biomass for human use, and addressing land degradation. Maximising synergies and managing trade-offs depend on specific practices, scale of implementation, governance, capacity building, integration with existing land use, and the involvement of local communities and Indigenous Peoples and through benefit-sharing, supported by frameworks such as Land Degradation Neutrality within the UNCCD. (high confidence) {3.7, 7.4, 12.5, 17.3}

D.1.5   Trade-offs in terms of employment, water use, land-use competition and biodiversity, as well as access to, and the affordability of, energy, food, and water can be avoided by well-implemented land-based mitigation options, especially those that do not threaten existing sustainable land uses and land rights, though more frameworks for integrated policy implementation are required. The sustainability of bioenergy and other bio-based products is influenced by feedstock, land management practice, climatic region, the context of existing land management, and the timing, scale and speed of deployment. (medium confidence) {3.5, 3.7, 7.4, 12.4, 12.5, 17.1}
D.1.6  CDR methods such as soil carbon sequestration and biochar
70 can improve soil quality and food production capacity. Ecosystem 

41SPM
meet multiple objectives including food security. However, due to limited application of some of the options today, there are some uncertainties about potential benefits. (high confidence) {3.7, 7.4, 7.6, 12.5, 17.3, Table TS.7}
4 Quality education5 Gender equality6 Clean water and sanitation
Medium conﬁdenceLow conﬁdenceDeforestation, loss and 
degradation of peatlands and coastal wetlands2Soil carbon management in cropland and grasslands, agroforestry, biochar1

Lower of the two conﬁdence levels has been reported4Timber, biomass, agri. feedstock3 

42SPM
include qualitative assessments of synergies and trade-offs between sectoral mitigation options and the SDGs. Figure SPM.8 presents a summary of the chapter-level

assessment for selected mitigation options (see Supplementary Material Table 17.SM.1 for the underlying assessment). The last column provides a line of sight to the sectoral chapters, which provide details on context specificity and dependence of interactions on the scale of implementation. Blank cells indicate that interactions have not been assessed due to limited literature. They do not indicate the absence of interactions between mitigation options and the SDGs. Confidence levels depend on the quality of evidence and level of agreement in the underlying literature assessed by the sectoral chapters. Where both synergies and trade-offs exist, the lower of the confidence levels for these interactions is used. Some mitigation options may have applications in more than one sector or system. The interactions between mitigation options and the SDGs might differ depending on the sector or system, and also on the context and the scale of implementation. Scale of implementation particularly

matters when there is competition for scarce resources. {6.3, 6.4, 6.7, 7.3, 7.4, 7.5, 7.6, 8.2, 8.4, 8.6, Figure 8.4, Supplementary Material Table 8.SM.1, Supplementary Material Table 8.SM.2, 9.4, 9.5, 9.8, Table 9.5, 10.3, 10.4, 10.5, 10.6, 10.8, Table 10.3, 11.5, 12.5, 17.3, Figure 17.1, Supplementary Material Table 17.SM.1, Annex II.IV .12}

D.2  There is a  strong link between sustainable development, vulnerability and climate risks. Limited 
economic, social and institutional resources often result in high vulnerability and low adaptive capacity, especially in developing countries (medium confidence). Several response options deliver both mitigation and adaptation outcomes, especially in human settlements, land management, and in relation to ecosystems. However, land and aquatic ecosystems can be adversely affected by some mitigation actions, depending on their implementation (medium confidence). Coordinated cross-sectoral policies and planning can maximise synergies and avoid or reduce trade-offs between mitigation and adaptation (high confidence). {3.7, 4.4, 13.8, 17.3; AR6 WGII}
D.2.1  Sustainable urban planning and infrastructure design including green roofs and facades, networks of parks and open spaces,

management of urban forests and wetlands, urban agriculture, and water-sensitive design can deliver both mitigation and adaptation benefits in settlements (medium confidence). These options can also reduce flood risks, pressure on urban sewer systems, urban heat island effects, and can deliver health benefits from reduced air pollution (high confidence). There could also be trade-offs. For example, increasing urban density to reduce travel demand, could imply high vulnerability to heat waves and flooding (high confidence). (Figure SPM.8) {3.7, 8.2, 8.4, 12.5, 13.8, 17.3}

D.2.2  Land-related mitigation options with potential co-benefits for adaptation include agroforestry, cover crops, intercropping, perennial plants, restoring natural vegetation and rehabilitating degraded land. These can enhance resilience by maintaining land productivity and protecting and diversifying livelihoods. Restoration of mangroves and coastal wetlands sequesters carbon, while also reducing coastal erosion and protecting against storm surges, thus, reducing the risks from sea level rise and extreme weather. (high confidence) {4.4, 7.4, 7.6, 12.5, 13.8}

D.2.3  Some mitigation options can increase competition for scarce resources including land, water and biomass. Consequently, these can also reduce adaptive capacity, especially if deployed at larger scale and with high expansion rates thus exacerbating existing risks, in particular where land and water resources are very limited. Examples include the large-scale or poorly planned deployment of bioenergy, biochar, and afforestation of naturally unforested land. (high confidence) {12.5, 17.3}

D.2.4  Coordinated policies, equitable partnerships and integration of adaptation and mitigation within and across sectors can maximise synergies and minimise trade-offs and thereby enhance the support for climate action (medium confidence). Even if extensive global mitigation efforts are implemented, there will be a large need for financial, technical, and human resources for adaptation. Absence or limited resources in social and institutional systems can lead to poorly coordinated responses, thus reducing the potential for maximising mitigation and adaptation benefits, and increasing risk (high confidence). {12.6, 13.8, 17.1, 17.3}

43SPM
have distributional consequences within and between countries. Attention to equity and broad and meaningful participation of all relevant actors in decision-making at all scales can build social trust, and deepen and widen support for transformative changes. (high confidence) {3.6, 4.2, 4.5, 5.2, 13.2, 17.3, 17.4}
D.3.1  Countries at all stages of economic development seek to improve the well-being of people, and their development priorities

reflect different starting points and contexts. Different contexts include social, economic, environmental, cultural, or political conditions, resource endowment, capabilities, international environment, and history. The enabling conditions for shifting development pathways towards increased sustainability will therefore also differ, giving rise to different needs. (high confidence) (Figure SPM.2) {1.6, 1.7, 2.4, 2.6, Cross-Chapter Box 5 in Chapter 4, 4.3.2, 17.4}

D.3.2  Ambitious mitigation pathways imply large and sometimes disruptive changes in economic structure, with significant distributional consequences, within and between countries. Equity remains a  central element in the UN climate regime, notwithstanding shifts in differentiation between states over time and challenges in assessing fair shares. Distributional consequences within and between countries include shifting of income and employment during the transition from high- to low-emissions activities. While some jobs may be lost, low-emissions development can also open more opportunities to enhance skills and create more jobs that last, with differences across countries and sectors. Integrated policy packages can improve the ability to integrate considerations of equity, gender equality and justice. (high confidence) {1.4, 1.6, 3.6, 4.2, 5.2, Box 11.1, 14.3, 15.2, 15.5, 15.6}

D.3.3  Inequalities in the distribution of emissions and in the impacts of mitigation policies within countries affect social cohesion and the acceptability of mitigation and other environmental policies. Equity and just transitions can enable deeper ambitions for accelerated mitigation. Applying just transition principles and implementing them through collective and participatory decision-making processes is an effective way of integrating equity principles into policies at all scales, in different ways depending on national circumstances (medium confidence). This is already taking place in many countries and regions, as national just transition commissions or task forces, and related national policies, have been established in several countries. A multitude of actors, networks, and movements are engaged (high confidence). {1.6, 1.7, 2.4, 2.6, 4.5, 13.2, 13.9, 14.3, 14.5}

D.3.4  Broadening equitable access to domestic and international finance, technologies that facilitate mitigation, and capacity, while explicitly addressing needs can further integrate equity and justice into national and international policies and act as a catalyst for accelerating mitigation and shifting development pathways (medium confidence). The consideration of ethics and equity can help address the uneven distribution of adverse impacts associated with 1.5°C and higher levels of global warming, in all societies (high confidence). Consideration of climate justice can help to facilitate shifting development pathways towards sustainability, including through equitable sharing of benefits and burdens of mitigation, increasing resilience to the impacts of climate change, especially for vulnerable countries and communities, and equitably supporting those in need (high confidence). {1.4, 1.6, 1.7, 3.6, 4.2, 4.5, Box 5.10, 13.4, 13.8, 13.9, 14.3, 14.5, 15.2, 15.5, 15.6, 16.5, 17.3,

44SPM
and may change over time. Feasibility depends on geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors that enable or constrain the implementation of an option. The feasibility of options may change when different options are combined and increase when enabling conditions are strengthened.
72 In this report, the term ‘enabling conditions’ refers to conditions that enhance the feasibility of adaptation and mitigation options. Enabling conditions include finance, technological innovation, strengthening policy instruments, institutional capacity, multi-level governance, and changes in human behaviour and lifestyles.

strengthened to deploy mitigation options at scale. These barriers and enablers include geophysical, environmental-ecological, technological, and economic factors, and especially institutional and socio-cultural factors. Strengthened near-term action beyond the NDCs (announced prior to UNFCCC COP26) can reduce and/or avoid long-term feasibility challenges of global modelled pathways that limit warming to 1.5°C (>50%) with no or limited overshoot. (high confidence) {3.8, 6.4, 8.5, 9.9, 10.8, 12.3, Figure TS.31, Annex II.IV.11}

E.1.1  Several mitigation options, notably solar energy, wind energy, electrification of urban systems, urban green infrastructure,

energy efficiency, demand-side management, improved forest- and crop/grassland management, and reduced food waste and loss, are technically viable, are becoming increasingly cost effective, and are generally supported by the public. This enables deployment in many regions (high confidence). While many mitigation options have environmental co-benefits, including improved air quality and reducing toxic waste, many also have adverse environmental impacts, such as reduced biodiversity, when applied at very large scale, for example very large scale bioenergy or large scale use of battery storage, that would have to be managed (medium confidence). Almost all mitigation options face institutional barriers that need to be addressed to enable their application at scale (medium confidence). {6.4, Figure  6.19, 7.4, 8.5, Figure  8.19, 9.9, Figure  9.20, 10.8, Figure 10.23, 12.3, Figure 12.4, Figure TS.31}

E.1.2   The feasibility of mitigation options varies according to context and time. For example, the institutional capacity to support deployment varies across countries; the feasibility of options that involve large-scale land-use changes varies across regions; spatial planning has a higher potential at early stages of urban development; the potential of geothermal is site specific; and capacities, cultural and local conditions can either inhibit or enable demand-side responses. The deployment of solar and wind energy has been assessed to become increasingly feasible over time. The feasibility of some options can increase when combined or integrated, such as using land for both agriculture and centralised solar production. (high confidence) {6.4, 6.6, Supplementary Material Table 6.SM, 7.4, 8.5, Supplementary Material Table 8.SM.2, 9.9, Supplementary Material Table 9.SM.1, 10.8, Appendix 10.3, 12.3, Tables 12.SM.2.1 to 12.SM.2.6}

E.1.3   Feasibility depends on the scale and speed of implementation. Most options face barriers when they are implemented rapidly at a large scale, but the scale at which barriers manifest themselves varies. Strengthened and coordinated near-term actions in cost-effective modelled global pathways  that limit warming to 2°C (>67%) or lower, reduce the overall risks to the feasibility of the system transitions, compared to modelled pathways with relatively delayed or uncoordinated action.
73 (high confidence) 

45SPM
pace, depth and breadth of emissions reductions (medium confidence). Policies that shift development pathways towards sustainability can broaden the portfolio of available mitigation responses, and enable the pursuit of synergies with development objectives (medium confidence). Actions can be taken now to shift development pathways and accelerate mitigation and transitions across systems (high confidence). {4.3, 4.4, Cross-Chapter Box 5 in Chapter 4, 5.2, 5.4, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5} 
E.2.1  Current development pathways may create behavioural, spatial, economic and social barriers to accelerated mitigation at all

scales (high confidence). Choices made by policymakers, citizens, the private sector and other stakeholders influence societies’ development pathways (high confidence). Actions that steer, for example, energy and land systems transitions, economy-wide structural change, and behaviour change, can shift development pathways towards sustainability
74 (medium confidence). 

E.2.2   Combining mitigation with policies to shift development pathways, such as broader sectoral policies, policies that induce lifestyle or behaviour changes, financial regulation, or macroeconomic policies can overcome barriers and open up a broader range of mitigation options (high confidence). It can also facilitate the combination of mitigation and other development goals (high confidence). For example, measures promoting walkable urban areas combined with electrification and renewable energy can create health co-benefits from cleaner air and benefits from enhanced mobility (high confidence). Coordinated housing policies that broaden relocation options can make mitigation measures in transport more effective (medium confidence). {3.2, 4.3, 4.4, Cross-Chapter Box 5 in Chapter 4, 5.3, 8.2, 8.4}

E.2.3   Institutional and regulatory capacity, innovation, finance, improved governance and collaboration across scales, and multi-objective policies enable enhanced mitigation and shifts in development pathways. Such interventions can be mutually reinforcing and establish positive feedback mechanisms, resulting in accelerated mitigation. (high confidence) {4.4, 5.4, Figure 5.14, 5.6, 9.9, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5, Cross-Chapter Box 12 in Chapter 16}

E.2.4  Enhanced action on all the above enabling conditions can be taken now (high confidence). In some situations, such as with innovation in technology at an early stage of development and some changes in behaviour towards low emissions, because the enabling conditions may take time to be established, action in the near term can yield accelerated mitigation in the mid-term (medium confidence). In other situations, the enabling conditions can be put in place and yield results in a relatively short time frame, for example the provision of energy related information, advice and feedback to promote energy saving behaviour (high confidence). {4.4, 5.4, Figure 5.14, 5.6, 6.7, 9.9, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5, Cross-Chapter Box 12 in Chapter 16}
E.3  Climate governance, acting through laws, strategies and institutions, based on national circumstances,

supports mitigation by providing frameworks through which diverse actors interact, and a basis for policy development and implementation (medium confidence). Climate governance is most effective when it integrates across multiple policy domains, helps realise synergies and minimise trade-offs, and connects national and sub-national policymaking levels (high confidence). Effective and equitable climate governance builds on engagement with civil society actors, political actors, businesses, youth, labour, media, Indigenous Peoples and local communities (medium confidence). {5.4, 5.6, 8.5, 9.9, 13.2, 
E.3.1   Climate governance enables mitigation by providing an overall direction, setting targets, mainstreaming climate action

across policy domains, enhancing regulatory certainty, creating specialised organisations and creating the context to mobilise finance (medium confidence). These functions can be promoted by climate-relevant laws, which are growing in number, or climate strategies, among others, based on national and sub-national context (medium confidence). Framework laws set an overarching legal basis, either operating through a target and implementation approach, or a sectoral mainstreaming approach, or both, depending on national circumstance (medium confidence). Direct national and sub-national laws that explicitly target mitigation and indirect laws that impact emissions through mitigation-related policy domains have both been shown to be relevant to mitigation outcomes (medium confidence). {13.2}

46SPM
among diverse interests, and inform strategy setting (medium confidence). These functions are often accomplished through independent national expert bodies, and high-level coordinating bodies that transcend departmental mandates. Complementary sub-national institutions tailor mitigation actions to local context and enable experimentation but can be limited by inequities and resource and capacity constraints (high confidence). Effective governance requires adequate institutional capacity at all levels (high confidence). {4.4, 8.5, 9.9, 11.3, 11.5, 11.6, 13.2, 13.5, 13.7, 13.9}

E.3.3   The extent to which civil society actors, political actors, businesses, youth, labour, media, Indigenous Peoples, and local communities are engaged influences political support for climate change mitigation and eventual policy outcomes. Structural factors of national circumstances and capabilities (e.g., economic and natural endowments, political systems and cultural factors and gender considerations) affect the breadth and depth of climate governance. Mitigation options that align with prevalent ideas, values and beliefs are more easily adopted and implemented. Climate-related litigation, for example by governments, private sector, civil society and individuals, is growing -  with a  large number of cases in some developed countries, and with a much smaller number in some developing countries - and in some cases, has influenced the outcome and ambition of climate governance. (medium confidence) {5.2, 5.4, 5.5, 5.6, 9.9, 13.3, 13.4}

E.4  Many regulatory and economic instruments have already been deployed successfully. Instrument 
design can help address equity and other objectives. These instruments could support deep emissions reductions and stimulate innovation if scaled up and applied more widely (high confidence). Policy packages that enable innovation and build capacity are better able to support a  shift towards equitable low-emission futures than are individual policies (high confidence). Economy-wide packages, consistent with national circumstances, can meet short-term economic goals while reducing emissions and shifting development pathways towards sustainability (medium confidence). {Cross-Chapter Box 5 in Chapter 4, 13.6, 13.7, 13.9, 16.3, 16.4, 16.6}

up and enhancing the use of regulatory instruments, consistent with national circumstances, could improve mitigation outcomes in sectoral applications, including but not limited to renewable energy, land use and zoning, building codes, vehicle and energy efficiency, fuel standards, and low-emissions industrial processes and materials (high confidence). {6.7, 7.6, 8.4, 9.9, 10.4, 11.5, 11.6, 13.6}

E.4.2   Economic instruments have been effective in reducing emissions, complemented by regulatory instruments mainly at the national and also sub-national and regional level (high confidence). Where implemented, carbon pricing instruments have incentivised low-cost emissions reduction measures, but have been less effective, on their own and at prevailing prices during the assessment period, in promoting the higher-cost measures necessary for further reductions (medium confidence). Equity and distributional impacts of such carbon pricing instruments can be addressed by using revenue from carbon taxes or emissions trading to support low-income households, among other approaches (high confidence). Practical experience has informed instrument design and helped to improve predictability, environmental effectiveness, economic efficiency, distributional goals and social acceptance (high confidence). Removing fossil fuel subsidies would reduce emissions, improve public revenue and

macroeconomic performance, and yield other environmental and sustainable development benefits; subsidy removal may have adverse distributional impacts especially on the most economically vulnerable groups which, in some cases can be mitigated by measures such as redistributing revenue saved, all of which depend on national circumstances (high confidence); fossil fuel subsidy removal is projected by various studies to reduce global CO

2 emissions by 1–4%, and GHG emissions by up 
E.4.3  Low-emission technological innovation is strengthened through the combination of dedicated technology-push policies and investments (e.g., for scientific training, R&D, demonstration), with tailored demand-pull policies (e.g., standards, feed-in tariffs, taxes), which create incentives and market opportunities. Developing countries’ abilities to deploy low-emission technologies, seize socio-economic benefits and manage trade-offs would be enhanced with increased financial resources and capacity for innovation which are currently concentrated in developed countries, alongside technology transfer. (high confidence) {16.2, 16.3, 16.4, 16.5}

47SPM
aligned with specific technology and system needs, consistent in terms of design and tailored to national circumstances. They are better able to realise synergies and avoid trade-offs across climate and development objectives. Examples include: emissions reductions from buildings through a mix of efficiency targets, building codes, appliance performance standards, information provision, carbon pricing, finance and technical assistance; and industrial GHG emissions reductions through innovation support, market creation and capacity building. (high confidence) {4.4, 6.7, 9.9, 11.6, 13.7, 13.9, 16.3, 16.4}

E.4.5   Economy-wide packages that support mitigation and avoid negative environmental outcomes include: long-term public spending commitments; pricing reform; and investment in education and training, natural capital, R&D and infrastructure (high confidence). They can meet short-term economic goals while reducing emissions and shifting development pathways towards sustainability (medium confidence). Infrastructure investments can be designed to promote low-emissions futures that meet development needs (medium confidence). {Cross-Chapter Box 5 in Chapter 4, 5.4, 5.6, 8.5, 13.6, 13.9, 16.3, 16.5, 16.6}

E.4.6   National policies to support technology development and diffusion, and participation in international markets for emission reduction, can bring positive spillover effects for other countries (medium confidence), although reduced demand for fossil fuels could result in costs to exporting countries (high confidence). There is no consistent evidence that current emission trading systems have led to significant emissions leakage, which can be attributed to design features aimed at minimising competitiveness effects, among other reasons (medium confidence). {13.6, 13.7, 13.8, 16.2, 16.3, 16.4} 
E.5  Tracked financial flows fall short of the levels needed to achieve mitigation goals across all sectors

and regions. The challenge of closing gaps is largest in developing countries as a whole. Scaling up mitigation financial flows can be supported by clear policy choices and signals from governments and the international community (high confidence). Accelerated international financial cooperation is a critical enabler of low-GHG and just transitions, and can address inequities in access to finance and the costs of, and vulnerability to, the impacts of climate change (high confidence). {15.2, 15.3, 15.4, 15.5, 15.6} 
E.5.1  Average annual modelled investment requirements for 2020 to 2030 in scenarios that limit warming to 2°C or 1.5°C are a factor 
of three to six greater than current levels, and total mitigation investments (public, private, domestic and international) would need to increase across all sectors and regions (medium confidence). Mitigation investment gaps are wide for all sectors, and widest for the AFOLU sector in relative terms and for developing countries

76 (high confidence). Financing and investment 
requirements for adaptation, reduction of losses and damages, general infrastructure, regulatory environment and capacity building, and climate-responsive social protection further exacerbate the magnitude of the challenges for developing countries to attract financing (high confidence). {3.2, 14.4, 15.1, 15.2, 15.3, 15.4, 15.5}
E.5.2  There is sufficient global capital and liquidity to close global investment gaps, given the size of the global financial system, but there are barriers to redirect capital to climate action both within and outside the global financial sector, and in the macroeconomic headwinds facing developing regions. Barriers to the deployment of commercial finance from within the

financial sector as well as macroeconomic considerations include: inadequate assessment of climate-related risks and investment opportunities; regional mismatch between available capital and investment needs; home bias factors; country indebtedness levels; economic vulnerability; and limited institutional capacities (high confidence). Challenges from outside the financial sector include: limited local capital markets; unattractive risk-return profiles, in particular due to missing or weak regulatory environments consistent with ambition levels; limited institutional capacity to ensure safeguards; standardisation, aggregation, scalability and replicability of investment opportunities and financing models; and, a pipeline ready for commercial investments. (high confidence) {15.2, 15.3, 15.5, 15.6}

E.5.3  Accelerated financial support for developing countries from developed countries and other sources is a critical enabler to enhance mitigation action and address inequities in access to finance, including its costs, terms and conditions, and economic vulnerability to climate change for developing countries (high confidence). Scaled-up public grants for mitigation and adaptation funding for vulnerable regions, especially in Sub-Saharan Africa, would be cost-effective and have high social returns in terms of access to basic energy (high confidence). Options for scaling up mitigation in developing regions include: increased levels of public finance and publicly mobilised private finance flows from developed to developing countries in the context of the USD100 billion-a-year goal; increase the use of public guarantees to reduce risks and leverage private flows

48SPM
next decade can accelerate climate action, including in developing regions and countries facing high debt costs, debt distress and macroeconomic uncertainty (high confidence). {15.2, 15.3, 15.4, 15.5, 15.6, Box 15.6}

E.5.4  Clear signalling by governments and the international community, including a stronger alignment of public sector finance and policy, and higher levels of public sector climate finance, reduces uncertainty and transition risks for the private sector.  Depending on national contexts, investors and financial intermediaries, central banks, and financial regulators can support climate action and can shift the systemic underpricing of climate-related risk by increasing awareness, transparency and consideration of climate-related risk, and investment opportunities. Financial flows can also be aligned with funding needs through: greater support for technology development; a  continued role for multilateral and national climate funds and development banks; lowering financing costs for underserved groups through entities such as green banks existing in some countries, funds and risk-sharing mechanisms; economic instruments which consider economic and social equity and distributional

impacts; gender-responsive and women-empowerment programmes as well as enhanced access to finance for local communities and Indigenous Peoples and small land owners; and greater public-private cooperation. (high confidence) {15.2, 15.5, 15.6}

E.6  International cooperation is a critical enabler for achieving ambitious climate change mitigation goals. 
The UNFCCC, Kyoto Protocol, and Paris Agreement are supporting rising levels of national ambition and encouraging development and implementation of climate policies, although gaps remain. Partnerships, agreements, institutions and initiatives operating at the sub-global and sectoral levels and engaging multiple actors are emerging, with mixed levels of effectiveness. (high confidence) {8.5, 14.2, 14.3, 14.5, 14.6, 15.6, 16.5} 
E.6.1  Internationally agreed processes and goals, such as those in the UNFCCC, Kyoto Protocol, and Paris Agreement – including

transparency requirements for national reporting on emissions, actions and support, and tracking progress towards the achievement of Nationally Determined Contributions – are enhancing international cooperation, national ambition and policy development. International financial, technology and capacity building support to developing countries will enable greater implementation and encourage ambitious Nationally Determined Contributions over time. (medium confidence) {14.3}

E.6.2   International cooperation on technology development and transfer accompanied by capacity building, knowledge sharing, and technical and financial support can accelerate the global diffusion of mitigation technologies, practices and policies at national and sub-national levels, and align these with other development objectives (high confidence). Challenges in and opportunities to enhance innovation cooperation exist, including in the implementation of elements of the UNFCCC and the Paris Agreement as per the literature assessed, such as in relation to technology development and transfer, and finance (high confidence). International cooperation on innovation works best when tailored to specific institutional and capability contexts, when it benefits local value chains, when partners collaborate equitably and on voluntary and mutually agreed terms, when all relevant voices are heard, and when capacity building is an integral part of the effort (medium confidence). Support to

strengthen technological innovation systems and innovation capabilities, including through financial support in developing countries would enhance engagement in and improve international cooperation on innovation (high confidence). {4.4, 14.2, 14.4, 16.3, 16.5, 16.6}

E.6.3  Transnational partnerships can stimulate policy development, low-emissions technology diffusion and emission reductions by linking sub-national and other actors, including cities, regions, non-governmental organisations and private sector entities, and by enhancing interactions between state and non-state actors. While this potential of transnational partnerships is evident, uncertainties remain over their costs, feasibility, and effectiveness. Transnational networks of city governments are leading to enhanced ambition and policy development and a growing exchange of experience and best practices (medium confidence). {8.5, 11.6, 14.5, 16.5, Cross-Chapter Box 12 in Chapter 16}

E.6.4   International environmental and sectoral agreements, institutions, and initiatives are helping, and in some cases may help, to stimulate low-GHG emissions investment and reduce emissions. Agreements addressing ozone depletion and transboundary air pollution are contributing to mitigation, and in other areas, such as atmospheric emissions of mercury, may contribute to mitigation (high confidence). Trade rules have the potential to stimulate international adoption of mitigation technologies and policies, but may also limit countries’ ability to adopt trade-related climate policies (medium confidence). Current sectoral levels of ambition vary, with emission reduction aspirations in international aviation and shipping lower than in many other sectors (medium confidence). {14.5, 14.6}

TS Technical Summary51
Coordinating Lead Authors:
Minal Pathak (India), Raphael Slade (United Kingdom), Ramón Pichs-Madruga (Cuba), 
Diana Ürge-Vorsatz (Hungary), Priyadarshi R. Shukla (India), Jim Skea (United Kingdom)
Lead Authors:

Amjad Abdulla (Maldives), Alaa Al Khourdajie (United Kingdom/Syria), Mustafa Babiker (Sudan/Saudi Arabia), Quan Bai (China), Igor A. Bashmakov (the Russian Federation), Christopher Bataille (Canada), Göran Berndes (Sweden), Gabriel Blanco (Argentina), Luisa  F .  Cabeza (Spain), Carlo  Carraro (Italy), Leon Clarke (the United States of America), Heleen de Coninck (the  Netherlands), Felix  Creutzig (Germany), Diriba Korecha Dadi (Ethiopia), Fatima  Denton (the Gambia), Shobhakar Dhakal (Nepal/Thailand), Renée van Diemen (the Netherlands/United Kingdom), Navroz K. Dubash (India), Amit Garg (India), Oliver Geden (Germany), Michael Grubb (United Kingdom), Céline Guivarch (France), Kirsten Halsnaes (Denmark), Paulina Jaramillo (the United States of America), Tae Yong Jung (Republic of Korea), Suzana Kahn Ribeiro (Brazil), Şiir Kılkış (Turkey), Alexandre Koberle (Brazil/United Kingdom), Silvia Kreibiehl (Germany), Elmar Kriegler (Germany), William F . Lamb (Germany/United Kingdom), Franck

Lecocq (France), Shuaib Lwasa (Uganda), Nagmeldin  Mahmoud (Sudan), Eric Masanet (the United States of America), David McCollum (the United States of America), Jan Christoph  Minx (Germany), Catherine Mitchell (United Kingdom), Kanako  Morita (Japan), Rachid Mrabet (Morocco), Gert-Jan Nabuurs (the Netherlands), Peter Newman (Australia), Leila Niamir (Iran/Germany), Lars J. Nilsson (Sweden), Chukwumerije Okereke (Nigeria/United Kingdom), Anthony Patt (Switzerland), Joana Portugal-Pereira (Brazil), Lavanya  Rajamani (India), Andy  Reisinger (New Zealand), Keywan Riahi (Austria), Joyashree Roy (India/Thailand), Ambuj Sagar (India), Yamina Saheb (France/Algeria), Roberto Schaeffer (Brazil), Karen  C.  Seto (the United States of America), Pete Smith (United Kingdom), Shreya Some (India), Benjamin K. Sovacool (Denmark/United Kingdom), Linda Steg (the Netherlands), Massimo Tavoni (Italy), Ferenc L. Toth (Austria/Hungary), Purvi Vyas (India), Yi-Ming Wei (China), Jake  Whitehead (Australia),

Thomas Wiedmann (Australia/Germany), Harald Winkler (South Africa)

Review Editors:
Arnulf Jäger-Waldau (Italy/Germany), Tek Sapkota (Nepal/Canada)
This Technical Summary should be cited as:
M. Pathak, R. Slade, P .R. Shukla, J. Skea, R. Pichs-Madruga, D. Ürge-Vorsatz,2022: Technical Summary. In: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P .R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P . Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY , USA. doi: 10.1017/9781009157926.002.

53
TS
Technical SummaryTS.1 Introduction
The Working Group III (WGIII) contribution to the IPCC’s Sixth 
Assessment Report (AR6) assesses the current state of knowledge on the scientific, technological, environmental, economic and social aspects of climate change mitigation. It builds on previous IPCC reports, including the WGIII contribution to the IPCC’s Fifth Assessment Report  (AR5) and the three Special Reports of the Sixth Assessment cycle on: Global Warming of 1.5°C (SR1.5); Climate Change and Land (SRCCL); and the 
Ocean and Cryosphere in a Changing Climate (SROCC).
1 
The report assesses new literature, methodological and recent developments, and changes in approaches towards climate change mitigation since the IPCC AR5 report was published in 2014.
The global science and policy landscape on climate change mitigation

has evolved since AR5. The development of the literature reflects, among other factors, the UN Framework Convention on Climate Change (UNFCCC), the outcomes of its Kyoto Protocol and the goals of the Paris Agreement {13, 14, 15}, and the UN 2030 Agenda for Sustainable Development {1, 4, 17}. Literature further highlights the growing role of non-state and sub-national actors in the global effort to address climate change, including cities, businesses, citizens, transnational initiatives and public-private entities {5, 8, 13}. It draws attention to the decreasing cost of some low-emission technologies {2, 6, 12} and the evolving role of international cooperation {14}, finance {15} and innovation {16}. Emerging literature examines the global spread of climate policies, strengthened mitigation actions in developing countries, sustained reductions in greenhouse gas (GHG) emissions in some developed countries and the continuing challenges for mitigation. {2, 13}

There are ever closer linkages between climate change mitigation, 
development pathways and the pursuit of Sustainable Development Goals (SDGs). Development pathways largely drive GHG emissions and hence shape the mitigation challenge and the portfolio of available responses {4}. The co-benefits and risks of mitigation responses also differ according to stages of development and national capabilities {1, 2, 3, 4, 13}. Climate change mitigation framed in the context of sustainable development, equity, and poverty eradication, and rooted in the development aspirations of the society within which they take place, will be more acceptable, durable and effective. {1, 4, 17}
This report includes new assessment approaches that go beyond

those evaluated in the previous IPCC WGIII reports. In addition to sectoral and systems chapters {6, 7, 8, 9, 10, 11}, this report includes, for the first time, chapters dedicated to cross-sectoral perspectives {12}, demand, services and social aspects of mitigation (Box  TS.11) {5}, and innovation, technology development and transfer {16}. The assessment of future pathways combines a forward-looking assessment of  near- to medium-term perspectives up to 2050, including ways of shifting development pathways towards sustainability {4}, with an assessment of long-term outcome-oriented 
1 The three Special Reports are: Global Warming of 1.5°C: an IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission

pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (2018); Climate Change and Land: an IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (2019); IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).pathways up to 2100 {3}. Collaboration between the IPCC Working

Groups is reflected in Cross-Working Group boxes which address topics such as the economic benefits from avoided impacts along mitigation pathways {Cross-Working Group Box  1 in Chapter  3}, climate change and urban areas {Cross-Working Group Box  2 in Chapter  8}, mitigation and adaptation through the bioeconomy {Cross-Working Group Box  3 in Chapter  12} and Solar Radiation Modification (SRM) {Cross-Working Group Box  4 in Chapter  14}. This assessment also gives greater attention than AR5 to social, economic and environmental dimensions of mitigation actions, and institutional, legal and financial aspects. {5, 13, 14, 15} 
The report draws from literature on broad and diverse analytic

frameworks across multiple disciplines. These include, inter alia: economic and environmental efficiency {1}; ethics and equity {4, 5, 17}; innovation and the dynamics of socio-technical transitions {16}; and socio-political-institutional frameworks {1, 5, 13, 14, 17}. These help to identify synergies and trade-offs with Sustainable Development Goals (SDGs), challenges and windows of opportunity for action including co-benefits, and equitable transitions at local, national and global scales. {1, 5, 13, 14, 16} 
This Technical Summary (TS) of the WGIII contribution to the IPCC’s 
Sixth Assessment Report (AR6) broadly follows the report chapter order and is structured as follows. 
• TS Section 2 (TS.2) sets out how the global context for mitigation 
has changed and summarises signs of progress and continuing challenges. 
• TS Section 3 (TS.3) evaluates emission trends and drivers including recent sectoral, financial, technological and policy developments.

• TS Section  4 (TS.4) identifies mitigation and development pathways in the near and mid-term to 2050, and in the longer term to 2100. This section includes an assessment of how mitigation pathways deploying different portfolios of mitigation responses are consistent with limiting global warming to different levels. 
• TS Section  5 (TS.5) summarises recent advances in knowledge across sectors and systems including energy, urban and other settlements, transport, buildings, industry, and agriculture, forestry and other land-use (AFOLU).
• TS Section 6 (TS.6) examines how enabling conditions including behaviour and lifestyle, policy, governance and institutional capacity, international cooperation, finance, and innovation and technology can accelerate mitigation in the context of 
• TS Section  7 (TS.7) evaluates how mitigation can be achieved in the context of sustainable development, while maximising co-benefits and minimising risks.

54
TSTechnical SummaryThroughout this Technical Summary the validity of findings, 
confidence in findings, and cross-references to Technical Summary sections, figures and tables are shown in ( ) brackets.
2 References to 
the underlying report are shown in { } brackets.
2 Each finding is grounded in an evaluation of the underlying evidence, typeset in italics. The validity of a finding is evaluated in terms of the evidence quality – ‘limited’, ‘ medium’, ‘ robust’ – and 
the degree of agreement between sources – ‘low’, ‘ medium’, ‘ high’. A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high. Generally, the level of 
confidence is highest where there is robust evidence from multiple sources and high agreement. For findings with, for example, ‘robust evidence, medium agreement’, a confidence statement

may not always be appropriate. The assessed likelihood of an outcome or a result is described as: virtually certain (99–100% probability); very likely (90–100%); likely (66–100%); about as likely 
as not (33–66%); unlikely (0–33%); very unlikely (0–10%); exceptionally unlikely (0–1%). Additional terms may also be used when appropriate, consistent with the IPCC uncertainty guidance: https://www.ipcc.ch/site/assets/uploads/2018/05/uncertainty-guidance-note.pdf .

55TS
Technical SummaryTS.2 The Changed Global Context, Signs 
of Progress and Continuing Challenges
Since the IPCC’s Fifth Assessment Report (AR5), important 
changes that have emerged include the specific objectives established in the Paris Agreement of 2015 (for temperature, adaptation and finance), rising climate impacts, and higher levels of societal awareness and support for climate action (high confidence). Meeting the long-term temperature goal in the 
Paris Agreement, however, implies a rapid inflection in GHG emission trends and accelerating decline towards ‘net zero’. This is implausible without urgent and ambitious action at all scales. {1.2, 1.3, 1.5, 1.6, Chapters 3 and 4}
Effective and equitable climate policies are largely compatible

with the broader goal of sustainable development and efforts to eradicate poverty as enshrined in the UN 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), notwithstanding trade-offs in some cases (high  confidence). Taking urgent action to combat climate change and its impacts is one of the 17 SDGs (SDG 13). However, climate change mitigation also has synergies and/or trade-offs with many other SDGs. There has been a  strong relationship between development and GHG emissions, as historically both per-capita and absolute emissions have risen with industrialisation. However, recent evidence shows countries can grow their economies while reducing emissions. Countries have different priorities in achieving the SDGs and reducing emissions as informed by their respective national conditions and capabilities. Given the differences in GHG emissions contributions, degree of vulnerability and impacts, as well as capacities within and between nations,

equity and justice are important considerations for effective climate policy and for securing national and international support for deep decarbonisation. Achieving sustainable development and eradicating poverty would involve effective and equitable climate policies at all levels from local to global scale. Failure to address questions of equity and justice over time can undermine social cohesion and stability. International cooperation can enhance efforts to achieve ambitious global climate mitigation in the context of sustainable development. Pathways that illustrate movement towards fulfilling the SDGs are shown in Figure TS.1. {1.4, 1.6, Chapters 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13 and 17}

The transition to a  low-carbon economy depends on 
a  wide range of closely intertwined drivers and constraints, including policies and technologies where notable advances over the past decade have opened up new and large-scale opportunities for deep decarbonisation, and for alternative development pathways which could deliver multiple social and developmental goals (high confidence). Drivers for, and constraints on, low-carbon societal transitions comprise economic and technological factors (the means by which services such as

food, heating and shelter are provided and for whom, the emissions intensity of traded products, finance and investment), socio-political issues (political economy, equity and fairness, social innovation and behaviour change), and institutional factors (legal framework and institutions, and the quality of international cooperation). In addition to being deeply intertwined, all the factors matter to varying degrees, depending on the prevailing social, economic, cultural and political context. They often both drive and inhibit transitions at the same time, within and across different scales. The development and deployment 
of innovative technologies and systems at scale are important for achieving deep decarbonisation, and in recent years, the cost of several low-carbon technologies has declined sharply as deployment has risen rapidly. (Figure TS.7) {1.3, 1.4, Chapters 2, 4, 5, 13,14} 
Accelerating mitigation to prevent dangerous anthropogenic

interference with the climate system will require the integration of broadened assessment frameworks and tools that combine multiple perspectives, applied in a  context of multi-level governance (high confidence).  Analysing a challenge on the scale

of fully decarbonising our economies entails integration of multiple analytic frameworks. Approaches to risk assessment and resilience, established across IPCC Working Groups, are complemented by frameworks for probing the challenges in implementing mitigation. Aggregate frameworks include cost-effectiveness analysis towards given objectives, and cost-benefit analysis, both of which have been developing to take fuller account of advances in understanding risks and innovation, the dynamics of sectors and systems and of climate impacts, and welfare economic theory including growing consensus on long-term discounting. Ethical frameworks consider the fairness of processes and outcomes which can help ameliorate distributional impacts across income groups, countries and generations. Transition and transformation frameworks explain and evaluate the dynamics of

transitions to low-carbon systems arising from interactions amongst levels. Psychological, behavioural and political frameworks outline the 
constraints (and opportunities) arising from human psychology and the power of incumbent interests. A comprehensive understanding of climate mitigation must combine these multiple frameworks. Together with established risk frameworks, these collectively help to explain potential synergies and trade-offs in mitigation, implying a need for a wide portfolio of policies attuned to different actors and levels of decision-making, and underpin ‘just transition’ strategies in diverse contexts. {1.2.2, 1.7, 1.8, Figure 1.7}
The speed, direction, and depth of any transition will be 
determined by choices in the environmental, technological, economic, socio-cultural and institutional realms (high confidence).  Transitions in specific systems can be gradual or can

be rapid and disruptive. The pace of a  transition can be impeded by ‘lock-in’ generated by existing physical capital, institutions, and social norms. The interaction between politics, economics and power relationships is central to explaining why broad commitments do not always translate to urgent action. At the same time, attention to, and support for, climate policies and low-carbon societal transitions has generally increased, as the impacts have become more salient. Both public and private financing and financial structures strongly affect the scale and balance of high- and low-carbon investments. Societal and behavioural norms, regulations and institutions are essential conditions to accelerate low-carbon transitions in multiple sectors, whilst addressing distributional concerns endemic to any major transition. The COVID-19 pandemic has also had far-reaching impacts on the global economic and social system, and recovery will present both challenges and opportunities for climate

mitigation. (Box TS.1) {1.3, Box 1.1, 1.4, 1.8, Chapters 2, 3, 4, 5, 15, 17}

56
TSTechnical SummaryDeveloped Countries
USA and 
Canada
Eurasia
Middle 
East
Pathways

Figure TS.1 | Sustainable development pathways towards fulfilling the Sustainable Development Goals. The graph shows global average per-capita GHG 
emissions (vertical axis) and relative ‘Historic Index of Human Development’ (HIHD) levels (horizonal) have increased globally since the industrial revolution (grey line). 
The bubbles on the graph show regional per-capita GHG emissions and human development levels in the year 2015, illustrating large disparities. Pathways towards fulfilling the Paris Agreement (and SDG 13) involve global average per-capita GHG emissions below about 5 tCO
2-eq by 2030. Likewise, to fulfil SDGs 3, 4 and 8, HIHD levels (see footnote 7

in Chapter 1) need to be at least 0.5 or greater. This suggests a ‘sustainable development zone’ for year 2030 (in pale brown); the in-figure text also suggests a ‘sustainable development corridor’, where countries limit per-capita GHG emissions while improving levels of human development over time. The emphasis of pathways into the sustainable 
development zone differ (dashed brown arrows), but in each case transformations are needed in how human development is attained while limiting GHG emissions.

57TS
Technical SummaryAchieving the global transition to a  low-carbon, climate-

resilient and sustainable world requires purposeful and increasingly coordinated planning and decisions at many scales of governance including local, sub-national, national and global levels (high confidence). Accelerating mitigation globally would imply strengthening policies adopted to date, expanding the effort across options, sectors, and countries, and broadening responses to include more diverse actors and societal processes at multiple – including international – levels. The effective governance of climate change entails strong action across multiple jurisdictions and decision-making levels, including regular evaluation and learning. Choices that cause climate change as well as the processes for making and implementing relevant decisions involve a range of non-nation state actors such as cities, businesses, and civil society organisations. At global, national and sub-national levels, climate change actions are interwoven with, and embedded in, the context of much broader

social, economic and political goals. Therefore, the governance required to address climate change has to navigate power, political, economic, and social dynamics at all levels of decision-making. Effective climate-governing institutions, and openness to experimentation on a variety of institutional arrangements, policies and programmes can play a  vital role in engaging stakeholders and building momentum for effective climate action. {1.4, 1.9, Chapters 8, 13, 15, 17}

Table TS.1 | Signs of progress and continuing challenges.
Signs of progress Continuing challenges
Emissions trends
The rate of global GHG emissions growth has slowed in recent years, from 
2.1% yr –1 between 2000 and 2009, to 1.3% yr –1 in between 2010 and 2019. (TS.3) {2.2}GHG emissions have continued to grow at high absolute rates. Emissions increased by 8.9 GtCO
2-eq from 2000 to 2009 and by 6.5 GtCO 2-eq from 2010 to 2019, reaching 
59 GtCO 2-eq in 2019. (TS.3) {2.2}
A growing number of countries have reduced both territorial carbon dioxide (CO
2) and GHG emissions and consumption-based CO 2 emissions in absolute 
terms for at least 10 years. These include mainly European countries, some of which have reduced production-based GHG emissions by a third or more since peaking. Some countries have achieved several years of rapid sustained CO
2 reduction rates of 4% yr –1.

(TS.3) {2.2}The combined emissions reductions achieved by some countries have been outweighed by rapid emissions growth elsewhere, particularly among developing countries that have grown from a much lower base of per-capita emissions. Uncertainties in emissions levels and changes over time prevents a precise assessment of reductions in some cases. The per-capita emissions of developed countries remain high, particularly in Australia, Canada, and the United States of America. {2.2}
Lockdown policies in response to COVID-19 led to an estimated global drop of 5.8% in CO
2 emissions in 2020 relative to 2019. Energy demand reduction occurred 
across sectors, except in residential buildings due to teleworking and homeschooling. The transport sector was particularly impacted and international aviation emissions declined by 45%. (Box TS.1) {2.2}Atmospheric CO
2 concentrations continued to rise in 2020 and emissions have

already rebounded as lockdown policies are eased. Economic recovery packages currently include support for fossil fuel industries. (Boxes TS.1 and TS.8)
Sectors
Multiple low-carbon electricity generation and storage technologies have made

rapid progress: costs have reduced, deployment has scaled up, and performance has improved. These include solar photovoltaics (PV), onshore and offshore wind, and batteries. In many contexts solar PV and onshore wind power are now competitive with fossil-based generation. (TS.3) {2.5, 6.3}Although deployment is increasing rapidly, low-carbon electricity generation deployment levels and rates are currently insufficient to meet stringent climate goals. The combined market share of solar PV and wind generation technologies are still below 10%. Global low-carbon electricity generation will have to reach 100% by 2050, which is challenged by the continuous global increase in electricity demand. The contribution of biomass has absolute limits. (TS.5) {2.5}

The rate of emissions growth from coal slowed since 2010 as coal power plants were retired in the US and Europe, fewer new plants were added in China, and a large number of planned global plants were scrapped or converted to co-firing with biomass. (TS.3) {2.7, 6.3}Global coal emissions may not have peaked yet, and a few countries and international development banks continue to fund and develop new coal capacity, especially abroad. The lifetime emissions of current fossil-based energy infrastructures may already exceed the remaining carbon budget for keeping warming below 1.5°C. (TS.3) {2.2, 2.7, 6.7}

Deforestation has declined since 2010 and net forest cover increased. Government initiatives and international moratoria were successful in reducing deforestation in the Amazon between 2004 and 2015, while regrowth and regeneration occurred in Europe, Eurasia and North America. (TS.5.6.1) {7.3.1}The long-term maintenance of low deforestation rates is challenging. Deforestation in the Amazon has risen again over the past four years. Other parts of the world also face steady, or rapidly increasing, deforestation. {7.3.1}

Electrification of public transport services is demonstrated as a feasible, scalable and affordable mitigation option to decarbonise mass transportation. Electric vehicles (e-vehicles) are the fastest growing segment of the automobile industry, having achieved double-digit market share by 2020 in many countries. When charged with low-carbon electricity, these vehicles can significantly reduce emissions. {10.4}Transport emissions have remained roughly constant, growing at an average of 2% yr 
–1 between 2010 and 2019 due to the persistence of high travel demand, heavier 
vehicles, low efficiencies, and car-centric development. The full decarbonisation of e-vehicles requires that they are charged with zero-carbon electricity, and that car production, shipping, aviation and supply chains are decarbonised. (TS.3) {2.4} 
There has been a significant global transition from coal and biomass use in buildings towards modern energy carriers and efficient conversion technologies.

This led to efficiency improvements and some emissions reductions in developed countries, as well as significant gains in health and well-being outcomes in developing regions. Nearly zero energy buildings (nZEB) or low-energy buildings are achievable in all regions and climate zones for both new and existing buildings. {9.3, 9.8}There is a significant lock-in risk in all regions given the long lifespans of buildings and the low ambition of building policies. This is the case for both existing buildings in developed countries, and also for new buildings in developing countries that are also challenged by the lack of technical capacity and effective governance. Emissions reductions in developed countries have been outweighed by the increase in population growth, floor area per capita and the demand for electricity and heat. {9.3, 9.9}

The decarbonisation of most industrial processes has been demonstrated using technologies that include electricity and hydrogen for energy and feedstocks, carbon capture and utilisation technologies, and innovation in circular material flows. (TS.5.5) {11.2}Industry emissions continue to increase, driven by a strong global demand for basic materials. Without reductions in material demand growth and a very rapid scale-up of low-carbon innovations, the long lifetimes of industrial capital stock risks locking-in emissions for decades to come. (TS.5.5) {11.2}

58
TSTechnical SummarySigns of progress Continuing challenges
Policies and investment
The Paris Agreement established a new global policy architecture to meet 
stringent climate goals, while avoiding many areas of deadlock that had arisen in trying to extend the Kyoto Protocol. (TS.6.3)Current national pledges under the Paris Agreement
3 are insufficient to 
limit warming to 1.5°C (>50%) with no or limited overshoot, and would require an abrupt acceleration of mitigation efforts after 2030 to limit warming to 2°C (>67%). (TS.6.3)
Most wealthy countries, and a growing list of developing countries, have signalled an intention to achieve net zero GHG (or net zero CO
2) emissions by

mid-century. National economy-wide GHG emissions targets covered 90% of global emissions in 2020 compared to 49% in 2010. Direct and indirect climate legislation has also steadily increased and this is supported by a growing list of financial investors. (TS.6.2)Many net-zero targets are ambiguously defined, and the policies needed to achieve them are not yet in place. Opposition from status quo interests, as well as insufficient low-carbon financial flows, act as barriers to establishing and implementing stringent climate policies covering all sectors. (Box TS.6) {13.4}
The global coverage of mandatory policies – pricing and regulation – has increased, and sectoral coverage of mitigation policies has expanded. Emission trading and carbon taxes now cover over 20% of global CO
2 emissions. Allowance prices

as of 1 April 2021 ranged from just over USD1 to USD50, covering between 9% and 80% of a jurisdiction’s emissions {13.6.3}. Many countries have introduced sectoral regulations that block new investment in fossil fuel technologies. (TS.6)There is incomplete global policy coverage of non-CO
2 gases, CO 2 from industrial 
processes, and emissions outside the energy sector. Few of the world’s carbon prices are at a level consistent with various estimates of the carbon price needed to limit warming to 2°C or 1.5°C. {13.6}

There has been a marked increase in civic and private engagement with climate governance. This includes business measures to limit emissions, invest in reforestation and develop carbon-neutral value chains such as using wood for construction. There is an upsurge in climate activism, and growing engagement of groups such as labour unions {1.3.3, 5.2.3}. The media coverage of climate change has also grown steadily across platforms and has generally become more accurate over time. (TS.6.2)There is no conclusive evidence that an increase in engagement results in overall pro-mitigation outcomes. A broad group of actors influence how climate governance develops over time, including a range of civic organisations, encompassing both pro-and anti-climate action groups. Accurate transference of the climate science has been undermined significantly by climate change counter-movements, in both legacy and new/social media environments through misinformation. (TS.6.2)

3 Current NDCs refer to Nationally Determined Contributions submitted to the UNFCCC, as well as publicly announced but not yet submitted mitigation pledges with sufficient detail on targets, 
reflected in studies published up to 11 October 2021. Revised NDCs submitted or announced after 11 October 2021 are not included. Intended Nationally Determined Contributions (INDCs) were converted to NDCs as countries ratified the Paris Agreement. Original INDCs and NDCs refer to those submitted to the UNFCCC in 2015 and 2016.GHG emissions continued to rise to 2019, although the growth

of global GHG emissions has slowed over the past decade (high confidence). Delivering the updated Nationally Determined Contributions (NDCs) to 2030 would turn this into decline, but the implied global emissions by 2030, still exceed pathways consistent with 1.5°C by a large margin and are near the upper end of the range of modelled pathways that limit warming to 2°C (>67%) or below. In all chapters of this report there is evidence of progress towards deeper mitigation, but there remain many obstacles to be overcome. Table TS.1 summarises some of the key signs of progress in emission trends, sectors, policies and investment, as well as the challenges that persist. Table TS.1 (continued):

59TS
Technical SummaryTS.3 Emission Trends and Drivers
Global net anthropogenic GHG emissions during the decade 
2010–2019 were higher than any previous time in human history (high confidence).  Since 2010, GHG emissions have 
continued to grow reaching 59 ± 6.6 GtCO
2-eq in 2019,4 but the 
average annual growth in the last decade (1.3%, 2010–2019) was lower than in the previous decade (2.1%, 2000–2009) (high confidence). Average annual GHG emissions were 56 GtCO
2-eq yr –1 
for 2010–2019 (the highest decadal average on record) growing by about 9.1 GtCO
2-eq yr –1 from the previous decade (2000–2009) (high 
confidence). (Figure TS.2) {2.2.2, Table 2.1, Figure 2.5}
4 Emissions of GHGs are weighed by global warming potentials (GWPs) with a 100-year time horizon (GWP100) from the Sixth Assessment Report. GWP100 is commonly used in wide parts of the

literature on climate change mitigation and is required for reporting emissions under the United Nations Framework Convention on Climate Change (UNFCCC). All metrics have limitations and uncertainties. {Cross-Chapter Box 2, Annex II.II.8}
5 In 2019, CO
2 from fossil fuel and industry (FFI) was 38 ± 3.0 Gt; CO 2 from net land use, land-use change and forestry (LULUCF) was 6.6 ± 4.6 Gt.
6 Fluorinated gases, also known as ‘F-gases’, include: hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluouride (SF 6) and nitrogen trifluouride (NF 3).Emissions growth has varied, but has persisted, across all 
groups of greenhouse gases (high confidence). The average annual emission levels of the last decade (2010–2019) were higher than in any previous decade for each group of greenhouse gases (high confidence). In 2019, CO
2 emissions were 45 ± 5.5 GtCO 2,5 methane 
(CH 4) 11 ± 3.2 GtCO 2-eq, nitrous oxide (N 2O) 2.7 ± 1.6 GtCO 2-eq and

fluorinated gases (F-gases6) 1.4 ± 0.41 GtCO 2-eq. Compared to 1990, 
the magnitude and speed of these increases differed across gases: CO 2 
from fossil fuel and industry (FFI) grew by 15 GtCO 2-eq yr –1 (67%), 
CH 4 by 2.4 GtCO 2-eq yr –1 (29%), F-gases by 0.97 GtCO 2-eq yr –1 
(250%), N 2O by 0.65 GtCO 2-eq yr –1 (33%). CO 2 emissions from 
net land use, land-use change and forestry (LULUCF) have shown Global net anthropogenic emissions have continued to rise across all major groups of greenhouse gases.
38Gt
+0.7% yr  –1
+2.1% yr  –1
use, land-use change, forestry (CO
2-LULUCF)Methane (CH4)Nitrous oxide (N
2O)Fluorinated gases (F-gases)a. Global net anthropogenic GHG emissions 1990–2019 (5)
b. Global anthropogenic GHG emissions and uncertainties by gas – relative to 1990GHG emissions (GtCO2-eq yr  –1) GHG emissions (%)
2019 

CO
0
40021%
13%
59%2%
5%
20%
12%
61%2%
5%
18%
10%
65%2%
4%
18%
11%
64%1%
5%
Figure TS.2 | Global net anthropogenic GHG emissions (GtCO 2-eq yr–1) 1990–2019. Global net anthropogenic GHG emissions include CO 2 from fossil fuel combustion 
and industrial processes (CO 2-FFI); net CO 2 from land use, land-use change and forestry (CO 2-LULUCF)5; methane (CH 4); nitrous oxide (N 2O); and fluorinated gases (HFCs, PFCs,

SF6, NF 3).6 Panel a shows aggregate annual global net anthropogenic GHG emissions by groups of gases from 1990 to 2019 reported in GtCO 2-eq converted based on global 
warming potentials with a 100-year time horizon (GWP100-AR6) from the IPCC Sixth Assessment Report Working Group I (Chapter 7). The fraction of global emissions for each 
gas is shown for 1990, 2000, 2010 and 2019; as well as the aggregate average annual growth rate between these decades. At the right side of Panel a, GHG emissions in 2019 are broken down into individual components with the associated uncertainties (90% confidence interval) indicated by the error bars: CO
2-FFI ±8%; CO 2-LULUCF ±70%; 
CH 4 ±30%; N 2O ±60%; F-gases ±30%; GHG ±11%. Uncertainties in GHG emissions are assessed in Supplementary Material 2.2. The single-year peak of emissions in 1997

was due to higher CO 2-LULUCF emissions from a forest and peat fire event in South East Asia. Panel b shows global anthropogenic CO 2-FFI, net CO 2-LULUCF , CH 4, N 2O and 
F-gas emissions individually for the period 1990–2019, normalised relative to 100 in 1990. Note the different scale for the included F-gas emissions compared to other gases, highlighting its rapid growth from a low base. Shaded areas indicate the uncertainty range. Uncertainty ranges as shown here are specific for individual groups of greenhouse gases and cannot be compared. The table shows the central estimate for: absolute emissions in 2019; the absolute change in emissions between 1990 and 2019; and emissions in 2019 expressed as a percentage of 1990 emissions. {2.2, Figure 2.5, Supplementary Material 2.2, Figure TS.2}

60
TSTechnical Summarylittle long-term change, with large uncertainties preventing the 
detection of statistically significant trends. F-gases excluded from GHG emissions inventories such as chlorofluorocarbons and hydrochlorofluorocarbons are about the same size as those included (high confidence). (Figure TS.2) {2.2.1, 2.2.2, Table 2.1, Figures 2.2, 2.3 and 2.5}
7 IEA: International Energy Agency
8 EDGAR: Emissions Database for Global Atmospheric ResearchGlobally, gross domestic product (GDP) per capita and population growth remained the strongest drivers of CO
2 
emissions from fossil fuel combustion in the last decade (high confidence). Trends since 1990 continued in the years 2010 to 2019 with GDP per capita and population growth increasing emissions by 2.3% yr
 –1 and 1.2% yr –1, respectively. This growth outpaced the 
reduction in the use of energy per unit of GDP (–2% yr –1, globally) as 
well as improvements in the carbon intensity of energy (–0.3% yr –1).

{2.4.1, Figure 2.19}
Box TS.1 | The COVID-19 Pandemic: Impact on Emissions and Opportunities for Mitigation
The COVID-19 pandemic triggered the deepest global economic contraction as well as CO 2 emission reductions since the Second World 
War {2.2.2}. While emissions and most economies rebounded in 2020, some impacts of the pandemic could last well beyond this. Owing to the very recent nature of this event, it remains unclear what the exact short- and long-term impacts on global emissions drivers, trends, macroeconomics and finance will be. 
Starting in the spring of 2020 a major break in global emissions trends was observed due to lockdown policies implemented in 
response to the pandemic. Overall, global CO
2-FFI emissions are estimated to have declined by 5.8% (5.1–6.3%) in 2020, or about 
2.2 (1.9–2.4%) GtCO 2 in total. This exceeds any previous global emissions decline since 1970 both in relative and absolute terms

(Box TS.1, Figure 1). During periods of economic lockdown, daily emissions, estimated based on activity and power-generation data, declined substantially compared to 2019, particularly in April 2020 – as shown in Box TS.1, Figure 1 – but rebounded by the end of 2020. Impacts were differentiated by sector, with road transport and aviation particularly affected. Different databases estimate the total power-sector CO
2 reduction from 2019 to 2020 at 3% (IEA7) and 4.5% (EDGAR8). Approaches that predict near real-time 
estimates of the power-sector reduction are more uncertain and estimates range more widely between 1.8%, 4.1% and 6.8%, the latter taking into account the over-proportional reduction of coal generation due to low gas prices and merit order effects.
The lockdowns implemented in many countries accelerated some specific trends, such as the uptake in urban cycling. The acceptability

of collective social change over a longer term towards less resource-intensive lifestyles, however, depends on the social mandate for change. This mandate can be built through public participation, discussion and debate, to produce recommendations that inform policymaking. {Box 5.2} 
Most countries were forced to undertake unprecedented levels of short-term public expenditures in 2021. This is expected to slow 
economic growth and may squeeze financial resources for mitigation and relevant investments in the near future. Pandemic responses have increased sovereign debt across countries in all income bands and the sharp increase in most developing economies and regions has caused debt distress, widening the gap in developing countries’ access to capital. {15.6.3}
The wider overall reduction in energy investment has prompted a relative shift towards low-carbon investment particularly for major

future investment decisions by the private sector {15.2.1, 15.3.1, 15.6.1}. Some countries and regions have prioritised green stimulus expenditures, for example, as part of a ‘Green New Deal’ {Box 13.1}. This is motivated by assessments that investing in new growth industries can boost the macroeconomic effectiveness (‘multipliers’) of public spending, crowd-in and revive private investment, whilst also delivering on mitigation commitments. {15.2.3}
The impacts of COVID-19 may have temporarily set back development and the delivery of many SDGs. It also distracts political 
and financial capacity away from efforts to accelerate climate change mitigation and shift development pathways to increased sustainability. Yet, studies of previous post-shock periods suggest that waves of innovation that are ready to emerge can be accelerated by crises, which may prompt new behaviours, weaken incumbent systems, and initiate rapid reform. {1.6.5}

Institutional change can be slow but major economic dislocation can create significant opportunities for new ways of financing and 
enabling ‘leapfrogging’ investment {10.8}. Given the unambiguous risks of climate change, and consequent stranded asset risks from new fossil fuel investments {Box 6.11}, the most robust recoveries may well be those which align with lower carbon and resilient development pathways.

61TS
Technical SummaryBox TS.1 (continued)
−0.3%1991 Soviet Union dissolusion−0.3%2008 Global ﬁnancial crisis−1%2020 COVID-19 pandemic
−5.1% (EDGAR)−5.6% (GCB)−6.3% (BP)−5.8% (IEA)−6.0% (CM)
10203040
Jan Apr JulOct Jan Apr JulOct Jan Apr JulOct Jan Apr JulOct Jan Apr JulOct−10.0−7.5−5.0−2.50.02.5 
Total
Box TS.1, Figure 1 | Global carbon emissions in 2020 and the impact of COVID-19. Panel (a) depicts carbon emissions from fossil fuel and industry over the

past five decades. The single-year declines in emissions following major economic and geopolitical events are shown, as well as the decline recorded in five different datasets for emissions in 2020 compared to 2019. Panel (b) depicts the perturbation of daily carbon emissions in 2020 compared to 2019, showing the impact of COVID-19 lockdown policies. {Figure 2.6}
Cumulative net CO 2 emissions over the last decade (2010–2019) 
are about the same size as the remaining carbon budget to 
limit warming to 1.5°C (>67%) (medium confidence). 62% of total cumulative CO
2 emissions from 1850 to 2019 occurred since 
1970 (1500 ± 140 GtCO 2), about 43% since 1990 (1000 ± 90 GtCO 2), 
and about 17% since 2010 (410 ± 30 GtCO 2). For comparison, the 
remaining carbon budget for keeping warming to 1.5°C with a 67% (50%) probability is about 400 (500) ± 220 GtCO
2 (Figure TS.3). {2.2.2, 
Figure 2.7, AR6 WGI Chapter 5.5, AR6 WGI Chapter 5, Table 5.8}

reductions over periods longer than 10 years – a few at rates that are broadly consistent with the global rates described in climate change mitigation scenarios that limit warming to 2°C (>67%) (high confidence). At least 18 countries have reduced CO
2 
and GHG emissions for longer than 10 years. Reduction rates in a few countries have reached 4% in some years, in line with global rates observed in pathways that limit warming to 2°C (>67%). However, the total reduction in annual GHG emissions of these countries is small (about 3.2 GtCO
2-eq yr –1) compared to global emissions growth observed over the last decades. Complementary evidence suggests that countries have decoupled territorial CO
2 emissions from GDP , but 
fewer have decoupled consumption-based emissions from GDP . Decoupling has mostly occurred in countries with high per-capita GDP and high per-capita CO
2 emissions. (Figure TS.4, Box TS.2) {2.2.3,

62
TSTechnical Summary93%31%
68%34%
13%
49%32%
14%
33%
18%4%
33%
1%
18%
29%
Cement
LULUCFCO2 emissions (GtCO2/yr  )(a) Long term trend of anthropogenic CO 2 emissions sources
40
30
20
10
0
300020001000
0
1850–
19891990–
20092010–
20191.5°C
budget2°C
1400
± 195620
± 60410
± 30500
± 2201200
± 220
Figure TS.3 | Historic anthropogenic CO 2 emission and cumulative CO 2 emissions (1850–2019) as well as remaining carbon budgets for limiting warming 
to 1.5°C (>67%) and 2°C (>67%). Panel (a) shows historic annual anthropogenic CO 2 emissions (GtCO 2 yr –1) by fuel type and process. Panel (b) shows historic cumulative 
anthropogenic CO 2 emissions for the periods 1850–1989, 1990–2009, and 2010–2019 as well as remaining future carbon budgets as of 1 January 2020 to limit warming to

1.5°C and 2°C at the 67th percentile of the transient climate response to cumulative CO 2 emissions. The whiskers indicate a budget uncertainty of ±220 GtCO 2-eq for each 
budget and the aggregate uncertainty range at one standard deviation for historical cumulative CO 2 emissions, consistent with WGI. {Figure 2.7}
Figure TS.4 | Emissions have grown in most regions, although some countries have achieved sustained emission reductions in line with 2°C scenarios. (a) Global net anthropogenic GHG emissions by region (1990–2019)
102030
0405060
Eastern AsiaNorth AmericaLatin Am. & CaribbeanS.E. Asia and PaciﬁcAfricaS. AsiaEuropeE. Europe & W.C. AsiaMiddle EastAus., Japan and NZShipping and aviation
27%
7%7%7%9%
7%8%8%8%
7%8%8%
5%
6%
13%10%8%38Gt 42Gt 53Gt 59Gt
2000 1990 2010 2019GHG emissions (GtCO2-eq yr  –1)
Average annual emissions change (2010−2019)Scenario reduction 
rates (2020−2040)
Japan, New

Europe and 
West Central
Asia and
AsiaC3a: Likely
below 2°C 
with action
2020C1: 1.5°C
with no
or limited
emissions growth rates (%)(b)

63TS
Technical SummaryBox TS.2 | Greenhouse Gas (GHG) Emission Metrics Provide Simplified Information 
About the Effects of Different Greenhouse Gases
lifetimes and impacts on the climate system. GHG emission metrics provide simplified information about the effect that emissions of different gases have on global temperature or other aspects of climate, usually expressed relative to the effect of emitting CO
2.9 This 
information can support choices about priorities, trade-offs and synergies in mitigation policies and emission targets for non-CO 2 gases 
relative to CO 2 as well as baskets of gases expressed in CO 2-eq. 
The choice of metric can affect the timing and emphasis placed on reducing emissions of short-lived climate forcers (SLCFs) relative to CO

2 within multi-gas abatement strategies as well as the costs of such strategies. Different metric choices can also alter the time at 
which net zero GHG emissions are calculated to be reached for any given emissions scenario. A wide range of GHG emission metrics has been published in the scientific literature, which differ in terms of: (i) the key measure of climate change they consider, (ii) whether they consider climate outcomes for a specified point in time or integrated over a specified time horizon, (iii) the time horizon over which the metric is applied, (iv) whether they apply to a single emission pulse, to emissions sustained over a period of time, or to a combination of both, and (v) whether they consider the climate effect from an emission compared to the absence of that emission, or compared to a reference emissions level or climate state. {Annex II}
Parties to the Paris Agreement decided to report aggregated emissions and removals (expressed as CO

2-eq) based on the Global Warming 
Potential (GWP) with a time horizon of 100 years (GWP100) using values from IPCC AR5 or from a subsequent IPCC report as agreed upon by 
the CMA,10 and to account for future Nationally Determined Contributions (NDCs) in accordance with this approach. Parties may also report 
supplemental information on aggregate emissions and removals, expressed as CO 2-eq, using other GHG emission metrics assessed by the IPCC. 
The WGIII contribution to AR6 uses updated GWP100 values from AR6 WGI to report aggregate emissions and removals unless stated otherwise. These reflect updated scientific understanding of the response of the climate system to emissions of different gases and include a  methodological update to incorporate climate-carbon cycle feedbacks associated with the emission of non-CO
2 gases

(see Annex II.II.8 for a list of GWP100 metric values). The choice of GWP100 was made inter alia for consistency with decisions under the Rulebook for the Paris Agreement and because it is the dominant metric used in the literature assessed by WGIII. Furthermore, for mitigation pathways that limit global warming to 2°C (>67%) or lower, using GWP100 to inform cost-effective abatement choices between gases would achieve such long-term temperature goals at close to least global cost within a few percent (high confidence).
However, GWP100 is not well-suited to estimate the cumulative effect on climate from sustained SLCF emissions and the resulting 
warming at specific points in time. This is because the warming caused by an individual SLCF emission pulse is not permanent, and hence, unlike CO
2, the warming from successive SLCF emission pulses over multiple decades or centuries depends mostly on their

ongoing rate of emissions rather than cumulative emissions. Recently developed step/pulse metrics such as the CGTP (combined global temperature change potential) and GWP* (referred to as GWP-star and indicated by an asterisk) recognise that a sustained increase/decrease in the rate of SLCF emissions has indeed a  similar effect on global surface temperature as one-off emission/removal of CO
2. These metrics use this relationship to calculate the CO 2 emissions or removals that would result in roughly the same 
temperature change as a sustained change in the rate of SLCF emissions (CGTP) over a given time period, or as a varying time series of CH
4 emissions (GWP*). IFrom a mitigation perspective, this makes these metrics well-suited in principle to estimate the effect on the

remaining carbon budget from more, or less, ambitious SLCF mitigation over multiple decades compared to a given reference scenario (high confidence). However, potential application in wider climate policy (e.g., to inform equitable and ambitious emission targets or to support sector-specific mitigation policies) is contested and relevant literature still limited.
9 Emission metrics also exist for aerosols, but these are not commonly used in climate policy. This assessment focuses on GHG emission metrics only.
10 The CMA is the Conference of the Parties serving as the Meeting of the Parties to the Paris Agreement. See 18/CMA.1 (Annex, para. 37) and 4/CMA.1 (Annex II, para. 1) regarding the use of GHG 
emission metrics in reporting of emissions and removals and accounting for Parties’ NDCs.Figure TS.4 (continued): Emissions have grown in most regions, although some countries have achieved sustained emission reductions in line with

2°C scenarios. Change in regional GHG emissions and rates of change compatible with warming targets. Panel (a): Regional GHG emission trends (in GtCO 2-eq yr –1 
(GWP100; AR6) for the time period 1990–2019. Panel (b):  Historical GHG emissions change by region (2010–2019). Circles depict countries, scaled by total emissions in 2019, 
short horizontal lines depict the average change by region. Also shown are global rates of reduction over the period 2020–2040 in scenarios assessed in AR6 that limit global warming to 1.5°C and 2°C with different probabilities. The 5–95th percentile range of emissions changes for scenarios below 1.5°C with no or limited overshoot (scenario category C1) and scenarios below 2°C (>67%) with immediate action (scenario category C3a) are shown as a shaded area with a horizontal line at the mean value. Panel b excludes CO

2 LULUCF due to a lack of consistent historical national data, and International Shipping and Aviation, which cannot be allocated to regions. Global rates of reduction 
in scenarios are shown for illustrative purposes only and do not suggest rates of reduction at the regional or national level. {Figures 2.9 and 2.11}

64
TSTechnical SummaryBox TS.2 (continued)
to past and future GHG emissions. For this reason, the WGIII contribution to the AR6 reports emissions and mitigation options for individual gases where possible; CO
2-equivalent emissions are reported in addition to individual gas emissions where this is judged to 
be policy-relevant. This approach aims to reduce the ambiguity regarding actual climate outcomes over time arising from the use of any specific GHG emission metric. {Cross-Chapter Box 2 in Chapter 2, SM.2.3, Annex II.II.8; AR6 WGI Chapter 7.6}
(b) Net anthropogenic GHG emissions per capita 
(c) Regional indicators (2019) and regional production vs consumption accounting (2018)
Population (millions)
Africa

GHG per capita (tCO 2-eq per person) 3.9 13 11 13 7.8 9.2 13 19 7.9 2.6GDP per capita (USD1000 ppp 2017 per person)  1 5.0 43 17 20 43 15 20 61 12 6.2
CO 2-FFI, 2018, per personGHG emissions intensity (tCO 2-eq / USD1000 ppp 2017) 0.78 0.30 0.62 0.64 0.18 0.61 0.64 0.31 0.65 0.42Australia, Japan, New ZealandEastern AsiaEastern Europe, WestCentral AsiaEurope Latin America and CaribbeanMiddle EastNorth AmericaSouth-East Asia and PaciﬁcSouthern Asia

0 200 400 600(a) Historical cumulative net anthropogenic CO2 emissions 
4%
2 Includes CO 2-FFI, CO 2-LULUCF and Other GHGs, excluding international aviation and shipping.05101520
Europe
Southern 
4%
2%8%12%
11%
10%
7%
2%23%Fossil fuel and industry 
(CO2-FFI)Net CO2 from land use, land-use 
change, forestry (CO2-LULUCF)Other GHG emissions

Figure TS.5 | Global emissions are distributed unevenly, both in the present day and cumulatively since 1850. Panel (a) shows the distribution of regional 
GHG emissions in tonnes CO 2-eq per capita by region in 2019. GHG emissions are categorised into: CO 2 fossil fuel and industry (CO 2-FFI); CO 2 land use, land-use change and 
forestry (CO 2-LULUCF); and other GHG emissions (CH 4, nitrous oxide, F-gas, expressed in CO 2-eq using GWP100). The height of each rectangle shows per-capita emissions, the 
width shows the population of the region, so that the area of the rectangles refers to the total emissions for each regional. Percentages refer to overall GHG contributions to total global emissions in 2019. Emissions from international aviation and shipping are not included. Panel (b)  shows the share of historical net CO
2 emissions per region

from 1850 to 2019. This includes CO 2-FFI and CO 2-LULUCF (GtCO 2). Other GHG emissions are not included. Emissions from international aviation and shipping are included. 
Panel (c) shows population, GDP per person, emission indicators by region in 2019 for percentage GHG contributions, total GHG per person, and total GHG emissions intensity, together with production-based and consumption-based CO
2-FFI data, which is assessed in this report up to 2018. Consumption-based emissions are emissions released to 
the atmosphere in order to generate the goods and services consumed by a certain entity (e.g., region). Emissions from international aviation and shipping are not included. {1.3, Figure 1.2a, 2.2, Figure 2.10}

65TS
Technical SummaryConsumption-based CO 2  emissions in Developed Countries 
regions (high confidence). In Developed Countries, consumption-based CO
2 emissions peaked at 15 GtCO 2 in 2007, declining to about 
13 GtCO 2 in 2018. The Asia and Developing Pacific region, with 52% 
of the current global population, has become a  major contributor to consumption-based CO
2 emission growth since 2000 (5.5% yr –1 
for 2000–2018); in 2015 it exceeded the Developed Countries region, with 16% of global population, as the largest emitter of consumption-based CO
2. {2.3.2, Figure 2.14}
Carbon-intensity improvements in the production of traded products has led to a net reduction in CO
2 emissions embodied

in international trade (high confidence). A decrease in the carbon intensity of traded products has offset increased trade volumes between 2006 and 2016. Emissions embodied in internationally traded products depend on the composition of the global supply chain across sectors and countries and the respective carbon intensity of production processes (emissions per unit of economic output). {2.3, 2.4} 
Developed Countries tend to be net CO
2 emission importers, 
whereas developing countries tend to be net emission 
exporters (high confidence). Net CO 2  emission transfers from 
developing to Developed Countries via global supply chains have decreased between 2006 and 2016. Between 2004 and 2011, 
CO
2 emissions embodied in trade between developing countries have 
more than doubled (from 0.47 to 1.1 Gt) with the centre of trade activities shifting from Europe to Asia. {2.3.4, Figure 2.15}
Territorial emissions from developing country regions

continue to grow, mostly driven by increased consumption and investment, albeit starting from a  low base of per-capita emissions and with a  lower historic contribution  to cumulative emissions than developed countries (high confidence). Average 2019 per-capita CO
2-FFI emissions in three 
developing regions, Africa (1.2 tCO 2), Asia and Pacific (4.4 tCO 2), and 
Latin America and Caribbean (2.7 tCO 2), remained less than half of 
Developed Countries’ 2019 CO 2-FFI emissions (9.5 tCO 2). In these 
three developing regions together, CO 2-FFI emissions grew by 26% 
between 2010 and 2019 (compared to 260% between 1990 and 2010). In contrast, in Developed Countries emissions contracted by 9.9% between 2010 and 2019 and by 9.6% between 1990 and 2010. Historically, these three developing regions together contributed 28% to cumulative CO
2-FFI emissions between 1850 and 2019, whereas

Developed Countries contributed 57%, and least developed countries contributed 0.4%. (Figure TS.5) {2.2, Figures 2.9 and 2.10}
Globally, households with income in the top 10% contribute 
about 36–45% of global GHG emissions  (robust evidence, medium agreement). About two thirds of the top 10% live in Developed Countries and one third in other economies. The lifestyle consumption emissions of the middle income and poorest citizens in emerging economies are between five and 50 times below their counterparts in high-income countries  (medium confidence). Increasing inequality within a country can exacerbate dilemmas of 
11 Bookkeeping models and dynamic global vegetation models.redistribution and social cohesion, and affect the willingness of the rich and poor to accept policies to protect the environment, and to accept and afford lifestyle changes that favour mitigation (medium confidence). {2.6.1, 2.6.2, Figure 2.29}
Globally, GHG emissions continued to rise across all sectors and

subsectors, and most rapidly in transport and industry (high confidence). In 2019, 34% (20 GtCO
2-eq) of global GHG emissions 
(13 GtCO 2-eq) from agriculture, forestry and other land use (AFOLU), 
15% (8.7 GtCO 2-eq) from transport, and 5.6% (3.3  GtCO 2-eq) from 
buildings. Once indirect emissions from energy use are considered, the relative shares of industry and buildings emissions rise to 34% and 16%, respectively. Average annual GHG emissions growth during 2010–2019 slowed compared to the previous decade in energy supply (from 2.3% to 1.0%) and industry (from 3.4% to 1.4%, direct emissions only), but remained roughly constant at about 2% yr
 –1 in the transport 
sector (high confidence). Emission growth in AFOLU is more uncertain due to the high share of CO
2-LULUCF emissions (medium confidence). 
(Figure TS.8) {2.2.4, Figure 2.13 and Figures 2.16–2.21}

There is a discrepancy, equating to 5.5 GtCO 2 yr –1, between 
CO 2 fluxes. Accounting for this discrepancy would assist in 
assessing collective progress in a  global stocktake (high confidence).  The principal accounting approaches are national GHG 
inventories (NGHGI) and global modelling
11 approaches. NGHGI, 
based on IPCC guidelines, consider a much larger area of forest to be under human management than global models. NGHGI consider the fluxes due to human-induced environmental change on this area to be anthropogenic and are thus reported. Global models, in contrast, consider these fluxes to be natural and are excluded from the total reported anthropogenic land CO
2 flux. The accounting method used

will affect the assessment of collective progress in a global stocktake (medium confidence) {Cross-Chapter Box  6 in Chapter  7}. In the absence of these adjustments, allowing a like-with-like comparison, collective progress would appear better than it is. {7.2}
This accounting discrepancy also applies to Integrated 
Assessment Models (IAMs), with the consequence that anthropogenic land CO
2 fluxes reported in IAM pathways 
cannot be compared directly with those reported in national GHG inventories (high confidence). Methodologies enabling a  more like-for-like comparison between models’ and countries’ approaches would support  more accurate assessment of the collective progress achieved under the Paris Agreement. {3.4, 7.2.2} 
Average annual growth in GHG emissions from energy supply

decreased from 2.3% for 2000–2009 to 1.0% for 2010–2019 (high confidence). This slowing of growth is attributable to further improvements in energy efficiency and reductions in the carbon intensity of energy supply driven by fuel switching from coal to gas, reduced expansion of coal capacity, particularly in Eastern Asia, and the increased use of renewables (medium confidence). (Figure TS.6) {2.2.4, 2.4.2.1, Figure 2.17}

66
TSTechnical SummaryThe industry, buildings and transport sectors make up 44% 
of global GHG emissions, or 66% when the emissions from electricity and heat production are reallocated as indirect emissions (high confidence). This reallocation makes a substantial 
difference to overall industry and buildings emissions as shown in Figure TS.6. Industry, buildings, and transport emissions are driven, respectively, by the large rise in demand for basic materials and manufactured products, a  global trend of increasing floor space per capita, building energy service use, travel distances, and vehicle size and weight. Between 2010 and 2019, aviation grew particularly fast on average at about 3.3% per annum. Globally, energy efficiency has improved in all three demand sectors, but carbon intensities have not. (Figure TS.6) {2.2.4, Figures 2.18, 2.19 and 2.20}
Providing access to modern energy services universally would

increase global GHG emissions by a few percent at most (high confidence).  The additional energy demand needed to support decent 
living standards
12 for all is estimated to be well below current average 
energy consumption (medium evidence, high agreement). More 
equitable income distribution could also reduce carbon emissions, but the nature of this relationship can vary by level of income and development (limited evidence,  medium agreement). {2.4.3}
12 Decent Living Standards (DLS) – a benchmark of material conditions for human well-being – overlaps with many Sustainable Development Goals (SDGs). Minimum requirements of energy use 
consistent with enabling well-being for all is between 20 and 50 GJ per capita yr –1 depending on the context. (Figure TS.22) {5.2.2, 5.2.2, Box 5.3}Evidence of rapid energy transitions exists in some case 
studies (medium confidence). Emerging evidence since AR5 on

past energy transitions identifies a  growing number of cases of accelerated technology diffusion at sub-global scales and describes mechanisms by which future energy transitions may occur more quickly than those in the past. Important drivers include technology transfer and cooperation, international policy and financial support, and harnessing synergies among technologies within a sustainable energy system perspective (medium confidence). A fast global low-carbon energy transition enabled by finance to facilitate low-carbon technology adoption in developing and particularly in least developed countries can facilitate achieving climate stabilisation targets (high confidence). {2.5.2, Table 2.5}Direct emissions by sector (59 GtCO2-eq) 
other land use (AFOLU) 22%Industry24%Electricity+heat23%Transport15%Buildings5.6%Other energy10%

Agriculture, forestry and other land use (AFOLU) 22%Industry 34%Transport 15%Other energy12%Buildings 16%
– Rail 0.4%– Domestic aviation 0.7%– Other (transport) 0.9%– International aviation 
1.1%
1.3%
(CO
2, CH 4) 0.1%
– LULUCF CO 2 11%– Cement (process only) 2.6%– Waste 3.9%
– Chemicals 6.3%– Metals 7.8%– Other (industry) 13%– Petroleum 
4.7%– Non-CO
2 (all buildings) 
0.1%
– Non-residential 5.9%– Residential 11%– Energy systems 8.5%– Industry 43.0%– AFOLU 0.0%

Figure TS.6 | Total anthropogenic direct and indirect GHG emissions for the year 2019 (in GtCO 2-eq) by sector and subsector. Direct emissions estimates 
assign emissions to the sector in which they arise (scope 1 reporting). Indirect emissions – as used here – refer to the reallocation of emissions from electricity and heat to the sector of final use (scope 2 reporting). Note that cement refers to process emissions only, as a lack of data prevents the full reallocation of indirect emissions to this sector. More comprehensive conceptualisations of indirect emissions including all products and services (scope 3 reporting) are discussed in Section 2.3. Emissions are converted into CO
2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Percentages may not add up to 100 across 
categories due to rounding at the second significant digit. {Figure 2.12, 2.3}

67TS
since AR5 – in cost, performance, and adoption – enhancing the feasibility of rapid energy transitions (high confidence). The rapid deployment and unit cost decrease of modular technologies like solar, wind, and batteries have occurred much faster than anticipated by experts and modelled in previous mitigation scenarios, as shown in Figure TS.7 (high confidence). The political, economic, social, and technical feasibility of solar energy, wind energy and electricity storage technologies has improved dramatically over the past few years. In contrast, the adoption of nuclear energy and CO
2 capture

and storage (CCS) in the electricity sector has been slower than the growth rates anticipated in stabilisation scenarios. Emerging evidence since AR5 indicates that small-scale technologies (e.g., solar, batteries) tend to improve faster and be adopted more quickly than large-scale technologies (nuclear, CCS) (medium confidence). (Figure TS.7, Box TS.15) {2.5.3, 2.5.4, Figures 2.22 and 2.23}Robust incentives for investment in innovation, especially incentives reinforced by national policy and international agreements, are central to accelerating low-carbon technological change (robust evidence, medium agreement). Policies have driven

innovation, including instruments for technology push (e.g., scientific training, research and development (R&D)) and demand pull (e.g., carbon pricing, adoption subsidies), as well as those promoting knowledge flows and especially technology transfer. The magnitude of the scale-up challenge elevates the importance of rapid technology development and adoption. This includes ensuring participation of developing countries in an enhanced global flow of knowledge, skills, experience, equipment, and technology; which in turn requires strong financial, institutional, and capacity-building support. {16.4, 16.5}
electric vehicles (EVs) Photovoltaics (PV)Concentrating solar power (CSP) Onshore wind Offshore windThe unit costs of some forms of renewable energy and of batteries for passenger EVs have fallen,

12001600
800
400
Figure TS.7 | The unit costs of batteries and some forms of renewable energy have fallen significantly, and their adoption continues to increase. The

top panel shows global costs per unit of energy (USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light blue shaded areas show the range between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 2020 (corresponding to USD55–148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many places. For batteries, costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance costs per MWh of electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made. However, it does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account

other environmental and social externalities that may modify the overall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each technology, in GW of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5. Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production (for PV , onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, for the same amount of installed capacity, wind produces about twice as much electricity as solar PV . {2.5, 6.4} Renewable energy and battery technologies were selected as illustrative examples because they have recently shown rapid

changes in costs and adoption, and because consistent data are available. Other mitigation options assessed in the report are not included as they do not meet these criteria.

68
TSTechnical SummaryEstimates of future CO 2 emissions from existing fossil fuel 
emissions in pathways limiting warming to 1.5°C (>50%) with 
no or limited overshoot (high confidence).  Assuming variations 
in historic patterns of use and decommissioning, estimated future CO
2 emissions from existing fossil fuel infrastructure alone are 
cumulative net CO 2 emissions until reaching net zero CO 2 of 
510 (330–710) GtCO 2 in pathways that limit warming to 1.5°C 
(>50%) with no or limited overshoot, and 890 (640–1160) GtCO 2 
in pathways that limit warming to 2°C (>67%) (high confidence). While most future CO
2 emissions from existing and currently 
planned fossil fuel infrastructure are situated in the power sector, most remaining fossil fuel CO
2 emissions in pathways that limit

warming to 2°C (>67%) and below are from non-electric energy – most importantly from the industry and transportation sectors (high confidence). Decommissioning and reduced utilisation of existing fossil fuel installations in the power sector as well as cancellation of new installations are required to align future CO
2 emissions from the 
power sector with projections in these pathways (high confidence). (Figure TS.8) {2.7.2, 2.7.3, Figure 2.26, Tables 2.6 and 2.7}92
800
600
400
200
0
gas and oil
5101200
LAMEIT
Asia
97
Figure TS.8 | Future CO 2 emissions from existing and currently planned fossil fuel infrastructure in the context of the Paris Agreement carbon budgets in

GtCO 2 based on historic patterns of infrastructure lifetimes and Future CO 2 emissions estimates of existing infrastructure for the electricity sector as well as all other 
sectors (industry, transport, buildings, other fossil fuel infrastructures) and of proposed infrastructures for coal power as well as gas and oil power. Grey bars on the right depict 
the range (5–95th percentile) in overall cumulative net CO 2 emissions until reaching net zero CO 2 in pathways that limit warming to 1.5°C (>50%) with no or limited overshoot 
(1.5°C scenarios), and in pathways that limit warming to 2°C (>67%) (2°C scenarios). {Figure 2.26}

69TS
Technical SummaryTS.4 Mitigation and Development Pathways
While previous WGIII assessments have explored mitigation 
pathways, since AR5 there has been an increasing emphasis in the literature on development pathways, and in particular at the national scale. Chapter  4 assesses near-term (2019–2030) to mid-term (2030–2050) pathways, complementing Chapter  3 which focuses on long-term pathways (up to 2100). While there is considerable literature on country-level mitigation pathways, including but not limited to NDCs, the country distribution of this literature is very unequal (high confidence). {4.2.1, Cross-Chapter Box 4 in Chapter 4}TS.4.1 Mitigation and Development Pathways 
in the Near- to Mid-term
gap, despite mitigation efforts including those in Nationally Determined Contributions (NDCs). In this report the emissions gap is understood as the difference between projected global

emissions with Nationally Determined Contributions (NDCs) in 2030, and emissions in 2030 if mitigation pathways consistent with the Paris temperature goals were achieved. The term implementation gap refers to the gap between NDC mitigation pledges and the expected outcome of existing policies. 
102030
0
102030
95th
Median5th75th25th
Policy 

Figure TS.9 | Aggregate greenhouse gas (GHG) emissions of global mitigation pathways (coloured funnels and bars) and projected emission outcomes from current policies and emissions implied by unconditional and conditional elements of NDCs, based on updates available by 11 October 2021 (grey bars). Shaded areas show GHG emission medians and 25–75th percentiles over 2020–2050 for four types of pathways in the AR6 scenario database: (i) pathways with near-term emissions developments in line with current policies and extended with comparable ambition levels beyond 2030; (ii) pathways likely to limit warming to 2°C with near-term emissions developments reflecting 2030 emissions implied by current NDCs followed by accelerated emissions reductions; (iii) pathways likely to limit warming to 2°C based on immediate actions from 2020 onwards; (iv) pathways that limit warming to 1.5°C with no or limited overshoot. Right-hand panels show two snapshots of the 2030 and 2050 emission ranges of the

percentiles). The 2030 snapshot includes the projected emissions 
from the implementation of the NDCs as assessed in Section 4.2 (Table 4.1; median and full range). Historic GHG emissions trends as used in model studies are shown for 2010–2015. GHG emissions are in CO
2-equivalent using GWP100 values from AR6. {3.5, Table 4.1, Cross-Chapter Box 4 in Chapter 4}

70
TSTechnical SummaryPathways consistent with the implementation and extrapolation 
of countries’ current13 policies see GHG emissions reaching 57 
(52–60) GtCO 2-eq yr –1 by 2030 and to 46–67 GtCO 2-eq yr –1 by 
2050, leading to a median global warming of 2.4°C to 3.5°C by 2100 (medium confidence). NDCs with unconditional and conditional elements
14 lead to 53 (50–57) and 50 (47–55) GtCO 2-eq, 
respectively (medium confidence) {Table  4.1}. This leaves median estimated emissions gaps of 14–23 GtCO
2-eq to limit warming to 2°C 
and 25–34 GtCO 2-eq to limit warming to 1.5°C relative to mitigation 
pathways. (Figure TS.9) {Cross-Chapter Box 4, Figure 1 in Chapter 4}
Projected global emissions from aggregated NDCs place

limiting global warming to 1.5°C beyond reach and make it harder after 2030 to limit warming to 2°C (high confidence). Pathways following NDCs until 2030 show a  smaller reduction in fossil fuel use, slower deployment of low-carbon alternatives, and a smaller reduction in CO
2, CH 4 and overall GHG emissions in 
2030 compared to immediate action scenarios. This is followed by a  much faster reduction of emissions and fossil fuels after 2030, and a larger increase in the deployment of low-carbon alternatives during the medium term in order to get close to the levels of the immediate action pathways in 2050. Those pathways also deploy a larger amount of carbon dioxide removal (CDR) to compensate for higher emissions before 2030. The faster transition during 2030 to 2050 entails greater investment in fossil fuel infrastructure and lower deployment of low-carbon alternatives in 2030, which adds to the socio-economic challenges in realising the higher transition rates. (TS.4.2) {3.5}

Studies evaluating up to 105 updated NDCs
15 indicate that 
emissions in NDCs with conditional elements have been 
reduced by 4.5 (2.7–6.3) GtCO 2-eq. This closes the emission 
gaps by about one third to 2°C and about 20% to 1.5°C compared to the original NDCs submitted in 2015/16 (medium confidence) {4.2.2, Cross-Chapter Box 4 in Chapter 4}. An implementation gap also exists between the projected emissions with ‘current policies’ and the projected emissions resulting from the implementation of the unconditional and conditional elements of NDCs; this is estimated to be around 4 and 7 GtCO
2-eq in 2030, respectively

(medium confidence) {4.2.2}. Many countries would therefore require additional policies and associated action on climate change to meet their autonomously determined mitigation targets as specified under the first NDCs (limited evidence). The disruptions triggered by the COVID-19 pandemic increase uncertainty over the range of projections relative to pre-COVID-19 literature. As indicated by a growing number of studies at the national and global level, how large near- to mid-term emissions implications of the COVID-19 pandemic are, to a large degree depends on how stimulus or recovery packages are designed. {4.2}
13 Current NDCs refers to the most recent Nationally Determined Contributions submitted to the UNFCCC as well as those publicly announced (with sufficient detail on targets, but not yet submitted) 
up to 11 October 2021, and reflected in literature published up to 11 October 2021. Original INDCs and NDCs refer to those submitted to the UNFCCC in 2015 and 2016.

14 See {4.2.1} for descriptions of ‘unconditional’ and ‘conditional’ elements of NDCs.
15 Submitted by 11 October 2021.There is a need to explore how accelerated mitigation – relative 
to NDCs and current policies – could close both emission gaps and implementation gaps.  There is increasing understanding of the

technical content of accelerated mitigation pathways, differentiated by national circumstances, with considerable, though uneven, literature at country-level (medium evidence, high agreement). Transformative technological and institutional changes for the near term include demand reductions through efficiency and  reduced activity, rapid decarbonisation of the electricity sector and low-carbon electrification of buildings, industry and transport (robust evidence, medium agreement). A focus on energy use and supply is essential, but not sufficient on its own – the land sector and food systems deserve attention. The literature does not adequately include demand-side options and systems analysis, and captures the impact from non-CO
2 GHGs (medium confidence). {4.2.5}

If obstacles to accelerated mitigation are rooted in underlying structural features of society, then transforming such structures can support emission reductions {4.2.6}. Countries and regions will have different starting points for transition pathways. Some critical differences between countries include climate conditions resulting in different heating and cooling needs, endowments with different energy resources, patterns of spatial development, and political and economic conditions {4.2.5}. The way countries develop determines their capacity to accelerate mitigation and achieve other sustainable development objectives simultaneously (medium confidence) {4.3.1,  4.3.2}. Yet meeting ambitious mitigation and development goals cannot be achieved through incremental change (robust evidence, medium agreement). Though development pathways result from the actions of a  wide range of actors, it is possible to shift development pathways through policies and enhancing enabling conditions

Shifting development pathways towards sustainability offers 
ways to broaden the range of levers and enablers that a society can use to accelerate mitigation and increases the likelihood of making progress simultaneously on climate action and other development goals (Box TS.3) {Cross-Chapter Box 5 in Chapter  4, Figure  4.7, 4.3}.  There are practical options to shift

development pathways in ways that advance mitigation and other sustainable development objectives, support political feasibility, increase resources to meet multiple goals, and reduce emissions (limited evidence, high agreement). Concrete examples, assessed in Chapter 4 of this report, include high-employment and low-emissions structural change; fiscal reforms for mitigation and social contract, combining housing policies to deliver both housing and transport mitigation; and changed economic, social and spatial patterns of agriculture sector development, providing the basis for sustained reductions in emissions from deforestation. {4.4.1, 4.4, 1.10}

71TS
Technical SummaryTable TS.2 | Comparison of key characteristics of mitigation pathways with immediate action towards limiting warming to 1.5-2°C vs. pathways following 
NDCs announced prior to COP26 until 2030.  Key characteristics are reported for five groups of mitigation pathways: (i) immediate action to limit warming to 1.5°C 
(>50%) with no or limited overshoot (C1 in Table TS.3; 97 scenarios), (ii) near term action following the NDCs until 2030 and returning warming to 1.5°C (> 50%) by 2100

after a high overshoot (subset of 42 scenarios following the NDCs until 2030 in C2), (iii) immediate action to limit warming to 2°C (>67%), (C3a in Table TS.3; 204 scenarios), (iv) near term action following the NDCs until 2030 followed by post-2030 action to limit warming to 2°C (>67%) (C3b in Table TS.3; 97 scenarios). Also shown are the characteristics for (v) the combined class of all scenarios that limit warming to 2°C (>67%). The groups (i), (iii), and the combination of (ii) and (iv) are depicted in Figure TS.9. Reported are median and interquartile ranges (in brackets) for selected global indicators. Numbers are rounded to the nearest five, with the exception of cumulative net negative CO
2 emissions rounded to the nearest 10. Changes from 2019 are relative to modelled 2019 values. Emissions reductions are based on harmonised model emissions used for 
the climate assessment. {Section 3.5} {Table 3.6}
Global indicators1.5°C (>50%)1.5°C (>50%) 
by 21002°C (>67%)

Immediate action, with 
no or limited overshoot NDCs until 2030, with 
overshoot before 2100Immediate action NDCs until 2030 All 
Cumulative net negative CO 2 emissions 
until 2100 (GtCO 2)220 (70,430) 380 (300,470) 30 (0,130) 60 (20,210) 40 (10,180)
 in 2050 (% rel to 2019) –100 (–105,–95) –85 (–95,–80) –70 (–80,–65) –75 (–80,–65) –75 (–80,–65)
Change in net land use CO 2 emissions in 2030 
(% rel to 2019)–100 (–105,–95) –30 (–60,–20) –90 (–105,–75) –20 (–80,–20) –80 (–100,–30)

Change in primary energy from coal in 2030 
(% rel to 2019)–75 (–80,–65) –10 (–20,–5) –50 (–65,–35) –15 (–20,–10) –35 (–55,–20)
Change in primary energy from oil in 2030 (% rel to 2019)–10 (–25,0) 5 (5,10) 0 (–10,10) 10 (5,10) 5 (0,10)
Change in primary energy from gas in 2030 (% rel to 2019)–10 (–30,0) 15 (10,25) 10 (0,15) 15 (10,15) 10 (0,15)
 in 2050 (% rel to 2019) –45 (–60,–20) –45 (–55,–30) –10 (–35,15) –30 (–45,–5) –15 (–40,10)
Change in primary energy from nuclear in 2030 (% rel to 2019)40 (10,70) 10 (0,25) 35 (5,50) 10 (0,30) 25 (0,45)

in 2050 (% rel to 2019) 90 (15,295) 100 (45,130) 85 (30,200) 75 (30,120) 80 (30,140)
Change in primary energy from modern biomass in 2030 (% rel to 2019)75 (55,130) 45 (20,75) 60 (35,105) 45 (20,80) 55 (35,105)
Change in primary energy from non-biomass renewables in 2030 (% rel to 2019)225 (155,270) 100 (85,145) 150 (115,190) 115 (85,130) 130 (90,170)
Change in carbon intensity of electricity in 2030 (% rel to 2019)–75 (–80,–70) –30 (–40,–30) –60 (–70,–50) –35 (–40,–30) –50 (–65,–35)

72
TSTechnical SummaryBox TS.3 | Shifting Development Pathways to Increase Sustainability  
and Broaden Mitigation Options
In this report, development pathways refer to the patterns of development resulting from multiple decisions and choices made by many 
actors in the national and global contexts. Each society whether in developing or developed regions follows its own pattern of growth (Figure TS.13). Development pathways can also be described at smaller scales (e.g., for regions or cities) and for sectoral systems.
Development pathways are major drivers of GHG emissions {1, 2}. There is compelling evidence to show that continuing along 
existing development pathways will not achieve rapid and deep emission reductions. In the absence of shifts in development pathways, conventional mitigation policy instruments may not be able to limit global emissions to a degree sufficient to meet ambitious mitigation goals or they may only be able to do so at very high economic and social costs.

Policies to shift development pathways, on the other hand, make mitigation policies more effective. Shifting development pathways 
broadens the scope for synergies between sustainable development objectives and mitigation. Development pathways also determine the enablers and levers available for adaptation {AR6 WGII TS.E.1.2} and for achieving other SDGs.
can go hand in hand {Chapter 3, Figure 3.33, Chapters 6–12, and 17}. Integrated policies can support the creation of synergies between action to combat climate change and its impacts (SDG 13 – climate action) and other SDGs. For example, when measures

promoting walkable urban areas are combined with electrification and clean renewable energy, there are several co-benefits to be attained. These include reduced pressures on agricultural land from reduced urban growth, health co-benefits from cleaner air, and benefits from enhanced mobility {8.2, 8.4, 4.4.1}. Energy efficiency in buildings and energy poverty alleviation through improved access to clean fuels also deliver significant health benefits. {9.8.1 and 9.8.2}
However, decisions about mitigation actions, and their timing and scale, may entail trade-offs with the achievement of other national

development objectives in the near, mid- and long term {Chapter 12}. In the near term, for example, regulations may ban vehicles from city centres to reduce congestion and local air pollution but reduce mobility and choice. Increasing green spaces within cities without caps on housing prices may involve trade-offs with affordable housing and push low-income residents outside the city {8.2.2}. In the mid- and long term, large-scale deployment of biomass energy raises concerns about food security and biodiversity conservation {3.7.1, 3.7.5, 7.4.4, 9.8.1, 12.5.2, 12.5.3}. Prioritising is one way to manage these trade-offs, addressing some national development objectives earlier than others. Another way is to adopt policy packages aimed at shifting development pathways towards increased sustainability (SDPS) as they expand the range of tools available to simultaneously achieve multiple development objectives and accelerate mitigation. (Box TS.3, Figure 1)

What does shifting development pathways towards increased sustainability entail?
Shifting development pathways towards increased sustainability implies making transformative changes that disrupt existing developmental trends. Such choices would not be marginal, but include technological, systemic and socio-behavioural changes {4.4}. Decision points also arise with new infrastructure, sustainable supply chains, institutional capacities for evidence-based and integrated decision-making, financial alignment towards low-carbon socially responsible investments, just transitions and shifts in behaviour and norms to support shifts away from fossil fuel consumption. Adopting multi-level governance modes, tackling corruption where it inhibits shifts to sustainability, and improving social and political trust are also key for aligning and supporting long-term environmentally just policies and processes. {4.4, Cross-Chapter Box 5 in Chapter 4} 
How can development pathways be ‘shifted’?

Shifting development paths is complex. Changes that involve ‘dissimilar, unfamiliar and more complex science-based components’ take more time, acceptance and legitimation and involve complex social learning, even when they promise large gains. Despite the complexities of the interactions that result in patterns of development, history also shows that societies can influence the direction of development pathways based on choices made by decision-makers, citizens, the private sector, and social stakeholders. Shifts in development pathways result from both sustained political interventions and bottom-up changes in public opinion. Collective action by individuals as part of social movements or lifestyle changes underpins system change. {5.2.3, 5.4.1, 5.4.5}
Sectoral transitions that aim to shift development pathways often have multiple objectives and deploy a diverse mix of policies and

institutional measures. Context-specific governance conditions can significantly enable or disable sectoral transitions. {Cross-Chapter Box 12 in Chapter 16}

73TS
Technical SummaryBox TS.3 (continued)
The necessary transformational changes are anticipated to be more acceptable if rooted in the development aspirations of the economy 
and society within which they take place and may enable a new social contract to address a complex set of interlinkages across sectors, classes, and the whole economy. Taking advantage of windows of opportunity and disruptions to mindsets and socio-technical systems could advance deeper transformations. 
How can shifts in development pathways be implemented by actors in different contexts?

Shifting development pathways to increased sustainability is a shared aspiration. Yet since countries differ in starting points (e.g., social, economic, cultural, political) and historical backgrounds, they have different urgent needs in terms of facilitating the economic, social, and environmental dimensions of sustainable development and, therefore, give different priorities {4.3.2, 17.1}. The appropriate set of policies to shift development pathways thus depends on national circumstances and capacities. 
Shifting development pathways towards sustainability needs to be supported by multilateral partnerships to strengthen suitable

capacity, technological innovation (TS.6.5), and financial flows (TS.6.4). The international community can play a particularly key role by helping ensure the necessary broad participation in climate-mitigation efforts, including by countries at different development levels, through sustained support for policies and partnerships that support shifting development pathways towards sustainability while promoting equity and being mindful of different transition capacities. {4.3, 16.5, 16.6}
Box TS.3, Figure  1 | Shifting development pathways to increased sustainability: choices by a  wide range of actors at key decision points on 
development pathways can reduce barriers and provide more tools to accelerate mitigation and achieve other Sustainable Development Goals. {4.7}Very low emissions
Most SDGs
Many SDGs
Few SDGs
Policymakers, organisations and individuals

Decision pointsDecisions on a broad range of development choices (not only climate policy choices)ToolsSome development pathways will provide more policy tools to accelerate mitigation and achieve other SDGs
Time

74
TSTechnical SummaryPolicies can shift development pathways. There are examples 
of policies implemented in the pursuit of overall societal development objectives, such as job creation, macroeconomic stability, economic growth, and public health and welfare.  
In  some countries, such policies are framed as part of a  Just 
Transition (Box TS.3), however, they can have major influence on mitigative capacity, and hence can be seen as tools to broaden mitigation options (medium confidence) {4.3.3}. Coordinated policy

mixes would need to orchestrate multiple actors – individuals, groups and collectives, corporate actors, institutions and infrastructure actors  – to deepen decarbonisation and shift pathways towards sustainability. Shifts in one country may spill over to other countries. Shifting development pathways can jointly support mitigation and adaptation {4.4.2}. Some studies explore the risks of high complexity and potential delay attached to shifting development pathways. (Box TS.4, Figure TS.11) {4.4.3}
An increasing number of mitigation strategies up to 2050 
(mid-term) have been developed by various actors. A growing number of such strategies aim at net zero GHG or CO
2 
emissions, but it is not yet possible to draw global implications due to the limited size of sample (medium evidence, low agreement) {4.2.4}.  Non-state actors are also engaging in a wide

range of mitigation initiatives. When adding up emission reduction potentials, sub-national and non-state international cooperative initiatives could reduce emissions by up to about 20 GtCO
2-eq in 
2030 (limited evidence, medium agreement) {4.2.3}. Yet perceived or real conflicts between mitigation and other SDGs can impede such action. If undertaken without precaution, accelerated mitigation is found to have significant implications for development objectives and macroeconomic costs at country level. The literature shows that the employment effect of mitigation policies tends to be limited on aggregate but can be significant at sectoral level (limited evidence, medium agreement). Detailed design of mitigation policies is critical for distributional impacts and avoiding lock-in (high confidence), though further research is needed in that direction. {4.2.6}
The literature identifies a  broad set of enabling conditions

that can both foster shifting development pathways and accelerated mitigation (medium evidence, high agreement). Policy integration is a necessary component of shifting development pathways, addressing multiple objectives. To this aim, mobilising a range of policies is preferable to single policy instruments (high confidence). {4.4.1}. Governance for climate mitigation and shifting development pathways is enhanced when tailored to national and local contexts. Improved institutions and effective governance enable ambitious action on climate and can help bridge implementation gaps (medium evidence, high agreement). Given that strengthening institutions may be a long-term endeavour, it needs attention in the near term {4.4.1}. Accelerated mitigation and shifting development pathways necessitates both redirecting existing financial flows from high- to low-emissions technologies and systems, and providing additional resources to overcome current financial barriers (high confidence)

{4.4.1}. Opportunities exist in the near term to close the finance gap {15.2.2}. At the national level, public finance for actions promoting sustainable development helps broaden the scope of mitigation (medium confidence). Changes in behaviour and lifestyles are important to move beyond mitigation as incremental change, and when supporting shifts to more sustainable development pathways will broaden the scope of mitigation (medium confidence). {4.4.1, Figure 4.8}

Some enabling conditions can be put in place relatively quickly 
while some others may take time to establish underscoring the importance of early action (high confidence). Depending on context, some enabling conditions such as promoting innovation may take time to establish. Other enabling conditions, such as improved access to financing, can be put in place in a  relatively short time frame, and can yield rapid results {4.4, Figure 5.14, 13.9, 14.5, 15.6, 16.3, 16.4, 16.5, Cross-Chapter Box 12 in Chapter 16}. Focusing on development pathways and considering how to shift them may also yield rapid results by providing tools to accelerate mitigation and achieve other sustainable development goals {4.4.1}. Charting just transitions to net zero may provide a vision, which policy measures can help achieve (Boxes TS.4 and TS.8). 
Equity can be an important enabler, increasing the level of

ambition for accelerated mitigation (high confidence) {4.5}. Equity deals with the distribution of costs and benefits and how these are shared, as per social contracts, national policy and international agreements. Transition pathways have distributional consequences such as large changes in employment and economic structure (high confidence). The Just Transition concept has become an international 
focal point tying together social movements, trade unions, and other key stakeholders to ensure equity is better accounted for in low-carbon transitions (Box TS.4). The effectiveness of cooperative action and the perception of fairness of such arrangements are closely related in that pathways that prioritise equity and allow broad stakeholder participation can enable broader consensus for the transformational change implicit in the need for deeper mitigation (robust evidence, medium agreement). (Box TS.4) {4.5, Figure 4.9}

75TS
Technical SummaryBox TS.4 | Just Transition
The Just Transition framework refers to a set of principles, processes and practices aimed at ensuring that no people, workers, places, 
sectors, countries or regions are left behind in the move from a high-carbon to a low-carbon economy. It includes respect and dignity for vulnerable groups; creation of decent jobs; social protection; employment rights; fairness in energy access and use and social dialogue and democratic consultation with relevant stakeholders. 
The concept has evolved, becoming prominent in the United States of America in 1980, related to environmental regulations that resulted 
in job losses from highly polluting industries. Traced from a purely labour movement, trade union space, the Just Transition framework emphasises that decent work and environmental protection are not incompatible. During COP 24, with the Just Transition Silesia Declaration, the concept gained in recognition and was signed by 56 heads of state.

Implicit in a Just Transition is the notion of well-being, equity and justice – the realisation that transitions are inherently disruptive 
and deliberate effort may be required to ensure communities dependent on fossil-fuel based economies and industries do not suffer disproportionately {Chapter 4}. ‘Just Transitions’ are integral to the European Union as mentioned in the EU Green Deal, the Scottish Government’s development plans and other national low-carbon transition strategies. The US Green New Deal Resolution puts structural inequality, poverty mitigation, and ‘Just Transitions’ at its centre. There is a growing awareness of the need for shifting finance towards Just Transition in the context of COVID-19, in particular, public finance and governance have a major role in allowing a Just Transition more broadly {Chapter 15}.
In the immediate aftermath of the COVID-19 pandemic, low oil prices created additional financial problems for fossil fuel producer

countries faced with loss of revenue and reduced fiscal latitude and space. Public spending and social safety nets associated with the proceeds from producer economies can be affected as assets become stranded and spending on strategic sustainable development goals such as free education and health-care services are neglected. Fiscal challenges are intricately linked to ‘Just Transitions’ and the management associated with sustainable energy transition. There is no certainty on how energy systems will recover post-COVID-19. However, ‘Just Transitions’ will have equity implications if stimulus packages are implemented without due regard for the differentiated scales and speeds and national and regional contexts, especially in the context of developing countries.
A Just Transition entails targeted and proactive measures from governments, agencies, and other non-state authorities to ensure that

any negative social, environmental, or economic impacts of economy-wide transitions are minimised, whilst benefits are maximised for those disproportionally affected. These proactive measures include eradication of poverty, regulating prosperity and creating jobs in ‘green’ sectors. In addition, governments, polluting industries, corporations, and those more able to pay higher associated taxes, can pay for transition costs by providing a welfare safety net and adequate compensation to people, communities, and regions that have been impacted by pollution, or are marginalised, or are negatively impacted by a transition from a high- to low-carbon economy and society. There is, nonetheless, increased recognition that resources that can enable the transition, international development institutions, as well as other transitional drivers such as tools, strategies and finance, are scarce. A sample of global efforts is summarised in Box TS.4, Figure 1.

76
TSTechnical SummaryBox TS.4 (continued)
Box TS.4 Figure 1 | Just Transitions around the world, 2020. Panel (a) shows commissions, task forces, and dialogues behind a Just Transition in many 
countries. Panel (b) shows the funds related to the Just Transition within the European Union Green Deal. Panel (c) shows the European Union’s Platform for Coal 
Regions in Transition. {Figure 4.9}Canada: Task Force 
on Just Transition for Canadian Coal Power Workers
Spain: Framework Agreement for a Just Transition on Coal Mining and Sustainable Development UK: Scottish Just Transition CommissionFrance: 2018 Ecological Transition Contracts programme
Greece: National Just Transition Fund for Lignite areasFinland: Working group to ensure a fair and just transition and acceptability of climate measures Czech Republic: Czech Coal Commission
Slovakia: Transformation Action Plan of coal region Upper NitraChina: Mine closure provisions in the 13th Five Year Plan for Coal Industry Development, 2016–2020

Mining Social Package and Special Privileges for the mining communesGermany: German Commission on Growth, Structural Change and Employment (German Coal Commission)
United States: Partnership for Opportunity and Workforce and Economic Revitalisation Plan (POWER+)Costa Rica: National Decarbonisation Plan 2018–2050
Ghana: The National Dialogue on Decent Work and ‘Just Transition’ to a Sustainable Economy and Society
South Africa: National Planning Just Transition Dialogue + Presidential Climate CommissionIreland: Just Transition Fund IrelandItaly: Enel’s Just Transition Framework and Futur-e projectNew Zealand: ‘Just Transitions Unit’ within the ministry of Business, Innovation and Employment (MBIE)(a) Just Transition commissions, task forces and dialogues
CA
US
CR
SACH
AUS
PO
GRIT
GHUKIE
Authority
Silesia,  Lower Silesia,  Greater

77TS
Technical SummaryTS.4.2 Long-term Mitigation Pathways
The characteristics of a  wide range of long-term mitigation 
pathways, their common elements and differences are assessed in Chapter 3. Differences between pathways typically represent choices that can steer the system in alternative directions through the selection of different combinations of response options (high confidence). More than 2000 quantitative emissions pathways were submitted to the AR6 scenarios database, of which more than 1200 pathways included sufficient information for the associated warming to be assessed (consistent with AR6 WGI methods). (Box TS.5) {3.2, 3.3}
Many pathways in the literature show how to limit global

warming to 2°C (>67%) with no overshoot or to limit warming to 1.5°C (>50%) with limited overshoot compared to 1850–1900. The likelihood of limiting warming to 1.5°C with no or limited overshoot has dropped in AR6 WGIII compared to AR6 SR1.5 because global GHG emissions have risen since 2017, leading to higher near-term emissions (2030) and higher cumulative CO
2 emissions until the time of net zero (medium 
confidence). Only a  small number of published pathways limit global warming to 1.5°C without overshoot over the course of the 21st century. {3.3, Annex III.II.3}
Mitigation pathways limiting warming to 1.5°C with no or 
limited overshoot reach 50% CO
2 reductions in the 2030s, 
relative to 2019, then reduce emissions further to reach net zero CO
2 emissions in the 2050s. Pathways limiting warming 
to 2°C (>67%) reach 50% reductions in the 2040s and net zero CO
2 by the 2070s (medium confidence). (Figure TS.10, 
Box TS.6) {3.3}

Cost-effective mitigation pathways assuming immediate 
action to limit warming to 2°C (>67%) are associated with net global GHG emissions of 30–49 GtCO
2-eq yr –1 by 2030 
and 14–27 GtCO 2-eq yr –1 by 2050 (medium confidence). This 
corresponds to reductions, relative to 2019 levels, of 13–45% by 2030 and 52–76% by 2050. Pathways that limit global warming to below 1.5°C with no or limited overshoot require a further acceleration in the pace of transformation, with net GHG emissions typically around 21–36 GtCO
2-eq yr –1 by 2030 and 1–15 GtCO 2-eq yr –1 by 2050; this 
corresponds to reductions of 34–60% by 2030 and 73–98% by 2050 relative to 2019 levels. {3.3}
Box TS.5 | Illustrative Mitigation Pathways (IMPs), and Shared Socio-economic Pathways (SSPs)
The Illustrative Mitigation Pathways (IMPs)

The over 2500 model-based pathways submitted to the AR6 scenarios database pathways explore different possible evolutions of future energy and land use (with and without climate policy) and the consequences for greenhouse gas emissions. 
From the full range of pathways, five archetype scenarios – referred to in this report as Illustrative Mitigation Pathways (IMPs) –

were selected to illustrate key mitigation-strategy themes that flow through several chapters in this report. A further two pathways illustrative of high emissions assuming continuation of current policies or moderately increased action were selected to show the consequences of current policies and pledges. Together these pathways provide illustrations of potential future developments that can be shaped by human choices, including: Where are current policies and pledges leading us? What is needed to reach specific temperature goals? What are the consequences of using different strategies to meet these goals? What are the consequences of delay? How can we shift development from current practices to give higher priority to sustainability and the SDGs? 
Each of the IMPs comprises: a  storyline and a  quantitative illustration. The storyline describes the key characteristics of the pathway

qualitatively; the quantitative illustration is selected from the literature on long-term scenarios to effectively represent the IMP 
numerically. The five Illustrative Mitigation Pathways (IMPs) each emphasise a different scenario element as its defining feature, and are named accordingly: heavy reliance on renewables (IMP-Ren), strong emphasis on low demand for energy (IMP-LD), extensive use 
of carbon dioxide removal (CDR) in the energy and the industry sectors to achieve net negative emissions (IMP-Neg), mitigation in the context of broader sustainable development and shifting development pathways (IMP-SP), and the implications of a less rapid and gradual strengthening of near-term mitigation actions (IMP-GS). In some cases, sectoral chapters may use different quantifications

that follow the same storyline narrative but contain data that better exemplify the chapter’s assessment. Some IMP variants are also used to explore the sensitivity around alternative temperature goals. {3.2, 3.3}
The two additional pathways illustrative of higher emissions are current policies (CurPol) and moderate action (ModAct). This framework is summarised in Box TS.5, Table.1 below, which also shows where the IMPs are situated with respect to the classification 
of emissions scenarios into warming levels (C1–C8) introduced in Chapter 3, and the CMIP6 (Coupled Model Intercomparison Project 6) 
scenarios used in the AR6 WGI report.

78
TSTechnical SummaryBox TS.5 (continued)
Box TS.5, Table.1 | Illustrative Mitigation Pathways (IMPs) and pathways illustrative of higher emissions in relation to scenarios’ categories, 
and CMIP6 scenarios.
Classification of emissions scenarios 
into warming levels: C1–C8Pathways illustrative of 
higher emissionsIllustrative mitigation 
pathways (IMPs)CMIP6 scenarios
C8 exceeding warming of 4°C (≥50%) SSP5-8.5
C7 limit warming to 4°C (>50%) CurPol SSP3-7.0
C6 limit warming to 3°C (>50%) ModAct SSP2-4.5
C5 limit warming to 2.5°C (>50%) SSP4-3.7
C4 limit warming to 2°C (>50%)
C3 limit warming to 2°C (>67%)IMP-GS
(Sensitivities: Neg; Ren)SSP2-2.6
C2 return warming to 1.5°C (>50%) after a high overshoot IMP-Neg
C1 limit warming to 1.5°C (>50%) with no or limited overshootIMP-LD
IMP-Ren
IMP-SPSSP1-1.9
The Shared Socio-economic Pathways (SSPs)
First published in 2017, the Shared Socio-economic Pathways (SSPs) are alternative projections of socio-economic developments that

may influence future GHG emissions. 
The initial set of SSP narratives described worlds with different challenges to mitigation and adaptation: SSP1 (sustainability), SSP2 
(middle of the road), SSP3 (regional rivalry), SSP4 (inequality) and SSP5 (rapid growth). The SSPs were subsequently quantified in terms of energy, land-use change, and emission pathways for both (i) no-climate-policy reference scenarios and (ii) mitigation scenarios that follow similar radiative forcing pathways as the representative concentration pathways (RCPs) assessed in AR5 WGI. {3.2.3}
Most of the scenarios in the AR6 database are SSP-based. The majority of the assessed scenarios are consistent with SSP2. Using

the SSPs permits a more systematic assessment of future GHG emissions and their uncertainties than was possible in AR5. The main emissions drivers across the SSPs include growth in population reaching 8.5–9.7 billion by 2050, and an increase in global GDP of 2.7–4.1% per year between 2015 and 2050. Final energy demand in the absence of any new climate policies is projected to grow to around 480 to 750 EJ yr
 –1 in 2050 (compared to around 390 EJ yr –1 in 2015) (medium confidence). The highest emissions scenarios in 
the literature result in global warming of >5°C by 2100, based on assumptions of rapid economic growth and pervasive climate policy failures (high confidence). {3.3}

79TS
Technical SummaryTable TS.3 | GHG, CO 2 emissions and warming characteristics of different mitigation pathways submitted to the AR6 scenarios database, and as categorised in the climate assessment. {Table 3.2}
p50  
from 2019  
CO 2 
emissions 
& WGIII 
IPs/IMPs 
emissions  
(% peak 
emissions  
(% peak 
(% net zero 
GHGs  
(% net zero 
net zero 
Modelled global emissions pathways categorised

by projected global warming levels (GWL). Detailed likelihood definitions are provided in SPM Box 1.  
2 & GHG

2 & GHG 
2

[21–36]17
[6–23]9
[1–15]43
[34–60]69
[58–90]84
[73–98]
(100%)
(52%)
[–210 to 
570]–220
[33–58]90
[99–100]
SP
LD33
[22–37]18
[6–24]8
[0–15]41
[31–59]66
[58–89]85
(100%)
[–220 to 
620]–360

[–680 to 
–140]1.6
[34–60]90
[99–100]
[21–36]16
[7–21]9
[4–13]48
[35–61]70
[62–87]84
[33–56]89
[31–55]25
[17–34]14
[5–21]23
[0–44]55
[40–71]75
(100%)
(87%)
[–90 to 
620]–360
[15–42]82
[32–55]29
[20–36]20
[13–26]21
[1–42]46
[34–63]64
(93%)
[13–41]76
[68–91]99
40
[30–49]29
[21–36]20
[14–27]27
[13–45]47
[35–63]63

(91%)
[14–42]78
[98–100]

80
TSTechnical SummaryTable TS.3 (continued):
p50  
from 2019  
CO 2 
emissions 
& WGIII 
IPs/IMPs 
emissions  
(% peak 
emissions  
(% peak 
(% net zero 
GHGs  
(% net zero 
net zero 

[7–22]59
[50–77]98
[95–99]
[46–56]45
[37–53]39
[30–49]6
[4–33]29
[1400–
2360]1780
[1260–
2360]0
[0–10]37
[18–59]91
[83–98]
ModAct54
[50–62]53
[48–61]52
[45–57]2
[–10 to 
11]3
[–14 to 
14]5
(97%)
[2440–
3520]
does not 
peak by 
21002.7
[0–0]8
[2–18]71
[53–88]
CurPol62
[53–69]67
[56–76]70
[–41 to 
(56%)4220
[3160–
5000]3.5
[0–0]0
[0–2]22
[69–81]80
[78–96]88
[–34 to 
–17]–35
[–65 to 
–29]–46
[–92 to 
4.2
[0–0]0
[0–0]4
7450]

81TS
Technical SummaryTable TS.3 (continued): 
columns these values relate to the distribution of all the pathways in that category. Harmonised emissions values are given for consistency with projected global warming

outcomes using climate emulators. Based on the assessment of climate emulators in AR6 WGI (WG1 Chapter 7, Box 7.1), two climate emulators are used for the probabilistic assessment of the resulting warming of the pathways. For the ‘Temperature change’ and ‘Likelihood’ columns, the single upper-row values represent the 50th percentile across the pathways in that category and the median [50th percentile] across the warming estimates of the probabilistic MAGICC climate model emulator. For the bracketed ranges, the median warming for every pathway in that category is calculated for each of the two climate model emulators (MAGICC and FaIR). Subsequently, the 5th and 95th percentile values across all pathways for each emulator are calculated. The coolest and warmest outcomes (i.e., the lowest p5 of two emulators, and the highest p95, respectively) are shown in square brackets. These ranges therefore cover both the uncertainty of the emissions pathways as well as the climate emulators’

c All global warming levels are relative to 1850–1900. (See footnote n below and Box SPM.1 for more details.)

[53–66 GtCO 2-eq]. (Figure SPM.1, Figure SPM.2, Box SPM.1) 
h Rates of global emission reduction in mitigation pathways are reported on a pathway-by-pathway basis relative to harmonised modelled global emissions in 2019 rather

than the global emissions reported in SPM Section B and Chapter 2; this ensures internal consistency in assumptions about emission sources and activities, as well as consistency with temperature projections based on the physical climate science assessment by WGI. {Annex III.II.2.5}. Negative values (e.g., in C7, C8) represent an increase in emissions.
(CO 2 and GHGs) are assessed for five-year reporting intervals starting in 2020. The interval 2020–2025 signifies that projected emissions peak as soon as possible between

2020 and at latest before 2025. The upper five-year interval refers to the median interval within which the emissions peak or reach net zero. Ranges in square brackets underneath refer to the range across the pathways, comprising the lower bound of the 5th percentile five-year interval and the upper bound of the 95th percentile five-year interval. Numbers in round brackets signify the fraction of pathways that reach specific milestones.

assignment to a climate category. Emissions pathways without climate assessment are not included in the ranges presented here. {See Annex III.II.5}
consistency with the WGI assessment of the remaining carbon budget. {Box 3.4}
warming for each pathway assessed using the probabilistic climate model emulators calibrated to the AR6 WGI assessment. (See also Box SPM.1) {Annex III.II.2.5; WGI Cross-Chapter Box 7.1} 

82
TSTechnical SummaryPathways following current NDCs until 2030 reach annual 
emissions of 47–57 GtCO 2-eq yr –1 by 2030, thereby making it 
impossible to limit warming to 1.5°C (>50%) with no or limited overshoot and strongly increasing the challenge of limiting warming to 2°C (>67%) (high confidence). A high overshoot of 1.5°C increases the risks from climate impacts and increases dependence on large-scale carbon dioxide removal (CDR) from the atmosphere. A future consistent with current NDCs implies higher fossil fuel deployment and lower reliance on low-carbon alternatives until 2030, compared to mitigation pathways describing immediate action that limits warming to 1.5°C (>50%) with no or limited overshoot, or limits warming to 2°C (>67%) and below. After following the NDCs to 2030, to limit warming to 2°C (>67%) the pace of global GHG emission reductions would need to abruptly increase from 2030 onward to an average of 1.3–2.1 GtCO
2-eq per year between 2030 and 2050. This

is similar to the global CO 2 emission reductions in 2020 that occurred 
due to the COVID-19 pandemic lockdowns, and around 70% faster than in pathways where immediate action is taken to limit warming to 2°C (>67%). Accelerating emission reductions after following an NDC pathway to 2030 would also be particularly challenging because of the continued buildup of fossil fuel infrastructure that would take place between now and 2030. (TS4.1, Table TS.3) {3.5, 4.2}
Pathways accelerating action compared to current NDCs  – 
that reduce annual GHG emissions to 47 (38–51) GtCO
2-eq by 
2030 (which is 3–9 GtCO 2-eq below projected emissions from

fully implementing current NDCs) – make it less challenging to limit warming to 2°C (>67%) after 2030 (medium confidence). The accelerated action pathways are characterised by a global, but regionally differentiated, roll-out of regulatory and pricing policies. Compared to current NDCs, they describe less fossil fuel use and more low-carbon fuel use until 2030; they narrow, but do not close the gap to pathways that assume immediate global action using all available least-cost abatement options. All delayed or accelerated action pathways limiting warming to below 2°C (>67%) converge to a global mitigation regime at some point after 2030 by putting a significant value on reducing carbon and other GHG emissions in all sectors and regions. {3.5}
In mitigation pathways, peak warming is determined by the 
cumulative net CO
2 emissions until the time of net zero CO 2 
together with the warming contribution of other GHGs and climate forcers at that time (high confidence).  Cumulative 
net CO

2 emissions from 2020 to the time of net zero CO 2 are 510 
(330–710) GtCO 2 in pathways that limit warming to 1.5°C (>50%) 
with no or limited overshoot and 890 (640–1160) GtCO 2 in pathways 
limiting warming to 2°C (>67%). These estimates are consistent with the AR6 WGI assessment of remaining carbon budgets adjusting for methodological differences and non-CO
2 warming. {3.3, Box 3.4}
Rapid reductions in non-CO 2 GHGs, particularly CH 4, would 
lower the level of peak warming (high confidence). Non-CO 2 
emissions  – at the time of reaching net zero CO 2  – range between 
4–11 GtCO 2-eq yr –1 in pathways limiting warming to 2°C (>67%) or 
below. CH 4 is reduced by around 20% (1–46%) in 2030 and almost 
16 Reductions greater than 100% in energy supply and AFOLU indicate that these sectors would become carbon sinks.50% (26–64%) in 2050, relative to 2019. CH 4 emission reductions in

pathways limiting warming to 1.5°C with no or limited overshoot are substantially higher by 2030, 33% (19–57%), but only moderately so by 2050, 50% (33–69%). CH
4 emissions reductions are thus attainable 
at comparatively low costs, but, at the same time, reductions are limited in scope in most 1.5°C–2°C pathways. Deeper CH
4 emissions reductions 
by 2050 could further constrain the peak warming. N 2O emissions are 
also reduced, but similar to CH 4, N 2O emission reductions saturate 
for more stringent climate goals. The emissions of cooling aerosols in mitigation pathways decrease as fossil fuels use is reduced. The overall impact on non-CO
2-related warming combines all these factors. {3.3}
Net zero GHG emissions imply net negative CO 2 emissions at 
a  level that compensates for residual non-CO 2 emissions.

Only 30% of the pathways limiting warming to 2°C (>67%) or below reach net zero GHG emissions in the 21st  century (high confidence). In those pathways reaching net zero GHGs, net zero GHGs is achieved around 10–20 years later than net zero CO
2 
is achieved (medium confidence). The reported quantity of residual non-CO
2 emissions depends on accounting choices, and in particular 
the choice of GHG metric (Box TS.2). Reaching and sustaining global net zero GHG emissions – when emissions are measured and reported in terms of GWP100 – results in a gradual decline in temperature (high confidence). (Box TS.6) {3.3}
Pathways that limit warming to 2°C (>67%) or lower exhibit 
substantial reductions in emissions from all sectors (high confidence). Pathways that limit warming to 1.5°C (>50%) with no or limited overshoot entail CO
2 emissions reductions between 2019 
and 2050 of around 77% (31–96%) for energy demand, around 115% (90–167%) for energy supply, and around 148% (94–387%) for AFOLU.

16 In pathways that limit warming to 2°C (>67%), projected 
CO 2 emissions are reduced between 2019 and 2050 by around 49% 
for energy demand, 97% for energy supply, and 136% for AFOLU (medium confidence). {3.4}
If warming is to be limited, delaying or failing to achieve 
emissions reductions in one sector or region necessitates compensating reductions in other sectors or regions (high confidence). Mitigation pathways show differences in the timing 
of decarbonisation and when net zero CO
2 emissions are achieved 
across sectors and regions. At the time of global net zero CO 2 
emissions, emissions in some sectors and regions are positive while others are negative; whether specific sectors and regions are positive or negative depends on the availability and cost of mitigation options in those regions, and the policies implemented. In cost-effective mitigation pathways, the energy supply sector typically reaches net zero CO
2 before the economy as a whole, while the demand sectors

reach net zero CO 2 later, if ever (high confidence). (Figure TS.10) {3.4}
Pathways limiting warming to 2°C (>67%) or 1.5°C involve substantial reductions in fossil fuel consumption and a  near elimination of coal use without CCS (high confidence). These pathways show an increase in low-carbon energy, with 88% (69–97%) of primary energy coming from low-carbon sources by 2100. {3.4}

83TS
Technical SummaryMtCH4 yr  –1
MtN2O yr  –1
(a) Net global GHG emissions (b) Net global CO 2 emissions
(c) Net global CH4 emissions (d) Net global N2O emissions C3
C1C3C1
Implemented policies and 2030 pledges(very likely range) CurPol (C7)
Limit warming to 1.5°C (>50%)with no or limited overshoot (C1)(very likely range)IMP-LD (C1)
95th

CO2 
051015
023
NDC range
Figure TS.10 | Mitigation pathways that limit warming to 1.5°C, or 2°C, involve deep, rapid and sustained emissions reductions. Net zero CO 2 and net 
zero GHG emissions are possible through different mitigation portfolios.

84
TSTechnical SummaryFigure TS.10 (continued): Mitigation pathways that limit warming to 1.5°C, or 2°C, involve deep, rapid and sustained emissions reductions. Net zero CO 2 
and net zero GHG emissions are possible through different mitigation portfolios. Panels (a) and (b) show the development of global GHG and CO 2 emissions 
in modelled global pathways (upper sub-panels) and the associated timing of when GHG and CO 2 emissions reach net zero (lower sub-panels). Panels (c) and (d) show the 
development of global CH 4 and N 2O emissions, respectively. Coloured ranges denote the 5th to 95th percentile across pathways. The red ranges depict emissions pathways 
assuming policies that were implemented by the end of 2020 and pathways assuming implementation of NDCs (announced prior to COP26). Ranges of modelled pathways that

limit warming to 1.5°C (>50%) with no or limited overshoot are shown in light blue (category C1) and pathways that limit warming to 2°C (>67%) are shown in light purple (category C3). The grey range comprises all assessed pathways (C1–C8) from the 5th percentile of the lowest warming category (C1) to the 95th percentile of the highest warming category (C8). The modelled pathway ranges are compared to the emissions from two pathways illustrative of high emissions (CurPol and ModAct) and five IMPs: IMP-LD, IMP-Ren, IMP-SP , IMP-Neg and IMP-GS. Emissions are harmonised to the same 2015 base year. The vertical error bars in 2015 show the 5–95th percentile uncertainty range of the non-harmonised emissions across the pathways, and the uncertainty range, and median value, in emission estimates for 2015 and 2019. The vertical error bars in 2030 (panel a) depict the assessed range of the NDCs, as announced prior to COP26.

17 Panel (e) shows the sectoral contributions of CO 2 and non-CO 2 emissions sources 
and sinks at the time when net zero CO 2 emissions are reached in the IMPs. Positive and negative emissions for different IMPs are compared to the GHG emissions from the 
year 2019. Energy supply (neg.) includes BECCS and DACCS. DACCS features in only two of the five IMPs (IMP-REN and IMP-GS) and contributes <1% and 64%, respectively, to the net negative emissions in Energy Supply (neg.). Panel (f) shows the contribution of different sectors and sources to the emissions reductions from a 2019 baseline for reaching net zero GHG emissions. Bars denote the median emissions reductions for all pathways that reach net zero GHG emissions. The whiskers indicate the p5–p95 range. The contributions of the service sectors (transport, buildings, industry) are split into direct (demand-side) as well as indirect (supply-side) CO
2 emissions reductions. Direct

emissions represent demand-side emissions due to the fuel use in the respective demand sector. Indirect emissions represent upstream emissions due to industrial processes and energy conversion, transmission and distribution. In addition, the contributions from the LULUCF sector and reductions from non-CO
2 emissions sources (green and grey 
bars) are displayed. {3.3, 3.4}
17 NDCs announced prior to COP26 refer to the most recent Nationally Determined Contributions submitted to the UNFCCC up to the literature cut-off date of this report, 11 October 2021, and 
revised NDCs announced by China, Japan and the Republic of Korea prior to October 2021 but only submitted thereafter. 25 NDC updates were submitted between 12 October 2021 and the start of COP26.Stringent emissions reductions at the level required for 2°C

or 1.5°C are achieved through the increased electrification of buildings, transport, and industry, consequently all pathways entail increased electricity generation (high confidence).  
Nearly all electricity in pathways limiting warming to 2°C (>67%) or 1.5°C (>50%) is also from low- or no-carbon technologies, with 
different shares across pathways of: nuclear, biomass, non-biomass renewables, and fossil fuels in combination with CCS. {3.4}Measures required to limit warming to 2°C (>67%) or below can result in large-scale transformation of the land surface (high confidence).  These pathways are projected to reach net zero 
CO
2 emissions in the AFOLU sector between the 2020s and 2070. GtCO2-eq yr  –1
0%  20%  40%  60%  80%  100%  Net zero CO 2 and net zero GHG emissions are possible through different modelled mitigation pathways. 
% of modelled 2019 emissions(e) Sector al GHG emissions at the time of net-zer o

CO2 emissions   (compar ed to modelled 2019 emissions)(f) Contributions to r eaching net zer o GHG emissions 
IMP-Neg
IMP-LD
IMP-SP
IMP-Ren
Sources
Sinks
Indirect
Direct:

85TS
Technical SummaryPathways limiting warming to 1.5°C with no or limited 
overshoot show an increase in forest cover of about 322 (–67  to 890) million ha in 2050 (high confidence).  In these 
pathways the cropland area to supply biomass for bioenergy (including bioenergy with carbon capture and storage (BECCS)) is around 199 (56–482) million ha in 2050. The use of bioenergy can lead to either increased or reduced emissions, depending on the scale of deployment, conversion technology, fuel displaced, and how, and where, the biomass is produced (high confidence). {3.4}
Pathways limiting warming to 2°C (>67%) or 1.5°C (>50%) 
require some amount of CDR to compensate for residual GHG emissions, even alongside substantial direct emissions reductions are achieved in all sectors and regions (high confidence). CDR deployment in pathways serves multiple purposes: accelerating the pace of emissions reductions, offsetting residual emissions, and creating the option for net negative CO

in case temperature reductions need to be achieved in the long term (high confidence). CDR options in pathways are mostly limited to BECCS, afforestation and direct air CO
2 capture and storage (DACCS). 
CDR through some measures in AFOLU can be maintained for decades but not over the very long term because these sinks will ultimately saturate (high confidence). {3.4} 
Mitigation pathways show reductions in energy demand, 
relative to reference scenarios that assume continuation of current policies, through a  diverse set of demand-side interventions (high confidence). Bottom-up and non-IAM studies show significant potential for demand-side mitigation. A stronger emphasis on demand-side mitigation implies less dependence on CDR and, consequently, reduced pressure on land and biodiversity. {3.4, 3.7}
Limiting warming requires shifting energy investments away

from fossil fuels and towards low-carbon technologies (high confidence).  The bulk of investments are needed in medium- and 
low-income regions. Investment needs in the electricity sector are on average 2.3 trillion USD2015 yr
 –1 over 2023–2052 for pathways 
limiting temperature to 1.5°C (>50%) with no or limited overshoot, and 1.7 trillion USD2015 yr
 –1 for pathways limiting warming to 2°C 
Pathways that avoid overshoot of 2°C (>67%) warming require 
more rapid near-term transformations and are associated with 
higher upfront transition costs, but at the same time bring long-term gains for the economy as well as earlier benefits in avoided climate change impacts (high confidence).  This 
conclusion is independent of the discount rate applied, though the modelled cost-optimal balance of mitigation action over time does depend on the discount rate. Lower discount rates favour earlier mitigation, reducing reliance on CDR and temperature overshoot. {3.6.1, 3.8}

18 Numbers in parentheses represent he interquartile range of the scenario samples.Mitigation pathways that limit warming to 2°C (>67%) entail losses in global GDP with respect to reference scenarios of between 1.3% and 2.7% in 2050. In pathways limiting warming to 1.5°C (>50%) with no or limited overshoot, losses are between 2.6% and 4.2%. These estimates do not account for the economic benefits of avoided climate change impacts (medium confidence).  In mitigation pathways limiting 
warming to 2°C (>67%), marginal abatement costs of carbon are about 90 (60–120) USD2015 tCO
2 in 2030 and about 210 (140–340) 
USD2015/tCO 2 in 2050. This compares with about 220 (170–290) 
USD2015  tCO 2 in 2030 and about 630 (430–990) USD2015 tCO 2 
in 205018 in pathways that limit warming to 1.5°C (>50%) with

no or limited overshoot. Reference scenarios, in the AR6 scenarios database, describe possible emission trajectories in the absence of new stringent climate policies. Reference scenarios have a  broad range depending on socio-economic assumptions and model characteristics. {3.2.1, 3.6.1}
The global benefits of pathways limiting warming to 2°C 
(>67%) outweigh global mitigation costs over the 21st century, if aggregated economic impacts of climate change are at the moderate to high end of the assessed range, and a  weight consistent with economic theory is given to economic impacts over the long term. This holds true even without accounting for benefits in other sustainable development dimensions or non-market damages from climate change (medium confidence).

The aggregate global economic repercussions of mitigation pathways include: the macroeconomic impacts of investments in low-carbon solutions and structural changes away from emitting activities; co-benefits and adverse side effects of mitigation; avoided climate change impacts; and reduced adaptation costs. Existing quantifications of the global aggregate economic impacts show a strong dependence on socio-economic development conditions, as these shape exposure and vulnerability and adaptation opportunities and responses. Avoided impacts for poorer households and poorer countries represent a  smaller share in aggregate economic quantifications expressed in GDP or monetary terms, whereas their well-being and welfare effects are comparatively larger. When aggregate economic benefits from avoided climate change impacts are accounted for, mitigation is a welfare-enhancing strategy (high confidence) . {3.6.2}

The economic benefits on human health from air quality improvement arising from mitigation action can be of the same order of magnitude as mitigation costs, and potentially even larger (medium confidence).  {3.6.3}
Differences in aggregate employment between mitigation pathways and reference scenarios are relatively small, although there may be substantial reallocations across sectors, with job creation in some sectors and job losses in others (medium confidence).  The net employment effect (and whether employment 
increases or decreases) depends on the scenario assumptions, modelling framework, and modelled policy design. Mitigation has implications for employment through multiple channels, each of which impacts geographies, sectors and skill categories differently. {3.6.4}

86
TSTechnical SummaryThe economic repercussions of mitigation vary widely across 
regions and households, depending on policy design and the level of international cooperation (high confidence). Delayed global cooperation increases policy costs across regions, especially in those that are relatively carbon intensive at present (high confidence). Pathways with uniform carbon values show higher mitigation costs in more carbon-intensive regions, in fossil fuel-exporting regions, and in poorer regions (high confidence). Aggregate quantifications 
19 In this assessment the terms net zero CO 2 emissions and carbon neutrality have different meanings and are only equivalent at the global scale. At the scale of regions, or sectors, each term applies

different system boundaries. This is also the case for the related terms net zero GHG and GHG neutrality. {Cross-Chapter Box 3 in Chapter 3}expressed in GDP or monetary terms undervalue the economic effects on households in poorer countries; the actual effects on welfare and well-being are comparatively larger (high confidence). Mitigation at the speed and scale required to limit warming to 2°C (>67%) or below implies deep economic and structural changes, thereby raising multiple types of distributional concerns across regions, income classes, and sectors (high confidence). (Box TS.7) {3.6.1, 3.6.4}
Box TS.6 | Understanding Net Zero CO 2 and Net Zero GHG Emissions
Reaching net zero CO 2 emissions19 globally along with reductions in other GHG emissions is necessary to halt global warming at any 
level. At the point of net zero, the amount of CO 2 human activity is putting into the atmosphere equals the amount of CO 2 human

activity is removing from the atmosphere. Reaching and sustaining net zero CO 2 emissions globally would stabilise CO 2-induced 
warming. Moving to net negative CO 2 emissions globally would reduce peak cumulative net CO 2 emissions – which occurs at the time 
of reaching net zero CO 2 emissions – and lead to a peak and decline in CO 2-induced warming. {Cross-Chapter Box 3 in Chapter 3}
Reaching net zero CO 2 emissions sooner can reduce cumulative CO 2 emissions and result in less human-induced global warming. 
Overall human-induced warming depends not only on CO 2 emissions but also on the contribution from other anthropogenic climate 
forcers, including aerosols and other GHGs (e.g., CH 4 and F-gases). To halt total human-induced warming, emissions of other GHGs, in 
particular CH 4, need to be strongly reduced.
In the AR6 scenario database, global emissions pathways limi warming to 1.5°C (>50%) with no or limited overshoot reach net zero CO

2 emissions between 2050–2055 (2035–2070) (median and 5–95th percentile ranges; 100% of pathways); pathways limiting 
warming to 2°C (>67%) reach net zero CO 2 emissions between 2070–2075 (2055–…) (median and 5–95th percentile ranges; 90% of 
pathways). This is later than assessed in the AR6 SR1.5 primarily due to more pathways in the literature that approach net zero CO 2 
emissions more gradually after a rapid decline of emissions until 2040. (Box TS.6, Figure 1) 
It does not mean that the world has more time for emissions reductions while still limiting warming to 1.5°C than reported in the 
SR1.5. It only means that the exact timing of reaching net zero CO 2 after a steep decline of CO 2 emissions until 2040 can show some 
variation. The SR1.5 median value of 2050 is still close to the middle of the current range. If emissions are reduced less rapidly in the period up to 2030, an earlier net zero year is needed.
Reaching net zero GHG emissions requires net negative CO

2 emissions to balance residual CH 4, N 2O and F-gas emissions. If achieved 
globally, net zero GHG emissions would reduce global warming from an earlier peak. Around half global emission pathways limiting 
warming to 1.5°C (>50%), and a  third of pathways limiting warming to 2°C (>67%), reach net zero GHG emissions (based on GWP100) in the second half of the century, around 10 to 40 years later than net zero CO
2 emissions. They show warming being halted 
at some peak value followed by a gradual decline towards the end of the century. The remainder of the pathways do not reach net zero GHG emissions during the 21st century and show little decline of warming after it stabilised. 
Global net zero CO
2 or GHG emissions can be achieved even while some sectors and regions continue to be net emitters, provided that 
others achieve net GHG removal. Sectors and regions have different potentials and costs to achieve net zero or even net GHG removal.

The adoption and implementation of net zero emission targets by countries and regions depends on multiple factors, including equity and capacity criteria and international and cross-sectoral mechanisms to balance emissions and removals. The formulation of net zero pathways by countries will benefit from clarity on scope, plans of action, and fairness. Achieving net zero emission targets relies on policies, institutions and milestones against which to track progress.

87TS
Technical SummaryBox TS.6 (continued)
Box TS.6, Figure 1 | CO 2 Emissions (panel (a)) and temperature change (panel (b)) of three alternative pathways limiting warming to 2°C (>67%) 
and reaching net zero CO 2 emissions at different points in time.  Limiting warming to a specific level can be consistent with a range of dates when net zero 
CO 2 emissions need to be achieved. This difference in the date of net zero CO 2 emissions reflects the different emissions profiles that are possible while staying within 
a specific carbon budget and the associated warming limit. Shifting the year of net zero to a later point in time (>2050), however, requires more rapid and deeper 
near-term emissions reductions (in 2030 and 2040) if warming is to be limited to the same level. Funnels show pathways limiting warming to 1.5°C (>50%) with no or limited overshoot (light blue) and limiting warming to 2°C (>67%) (beige).Net global CO 2 emissions Median global warming from 1850–1990

2000 2020 2060 2100 2040 2080[°C][GtCO2 yr  –1]
near-term emission reductions allow reaching net zero CO
2 at a later point in time
Box TS.7 | The Long-term Economic Benefits of Mitigation from Avoided Climate Change Impacts
Integrated studies use either a cost-effectiveness analysis (CEA) approach (minimising the total mitigation costs of achieving a given

policy goal) or a cost-benefit analysis (CBA) approach (balancing the cost and benefits of climate action). In the majority of studies that have produced the body of work on the cost of mitigation assessed in this report, a CEA approach is adopted, and the feedbacks of climate change impacts on the economic development pathways are not accounted for. This omission of climate impacts leads to overly optimistic economic projections in the reference scenarios, in particular in reference scenarios with no or limited mitigation action where the extent of global warming is the greatest. Mitigation cost estimates computed against no or limited policy reference scenarios therefore omit economic benefits brought by avoided climate change impact along mitigation pathways. {1.7, 3.6.1}
The difference in aggregate economic impacts from climate change between two given temperature levels represents the aggregate

economic benefits arising from avoided climate change impacts due to mitigation action. Estimates of these benefits vary widely, depending on the methodology used and impacts included, as well as on assumed socio-economic development conditions, which shape exposure and vulnerability. The aggregate economic benefits of avoiding climate impacts increase with the stringency of the mitigation. Global economic impact studies with regional estimates find large differences across regions, with developing and 
transitional economies typically more vulnerable. Furthermore, avoided impacts for poorer households and poorer countries represent a smaller share in aggregate quantifications expressed in GDP terms or monetary terms, compared to their influence on well-being and welfare (high confidence). {3.6.2, Cross-Working Group Box 1 in Chapter 3}

88
TSTechnical SummaryBox TS.7 (continued)
CBA analysis and CBA integrated assessment models (IAMs) remain limited in their ability to represent all damages from climate

change, including non-monetary damages, and capture the uncertain and heterogeneous nature of damages and the risk of catastrophic damages, such that other lines of evidence should be considered in decision-making. However, emerging evidence suggests that, even without accounting for co-benefits of mitigation on other sustainable development dimensions, the global benefits of pathways limiting warming to 2°C (>67%) outweigh global mitigation costs over the 21st century (medium confidence). Depending on the study, the reason for this result lies in assumptions of economic damages from climate change in the higher end of available estimates, in the consideration of risks of tipping points or damages to natural capital and non-market goods, or in the combination of updated representations of carbon cycle and climate modules, updated damage estimates and updated representations of economic and mitigation dynamics. In the studies that perform a sensitivity analysis, this result is found to

be robust to a wide range of assumptions on social preferences (in particular on inequality aversion and pure rate of time preference), and holds except if assumptions of economic damages from climate change are in the lower end of available estimates and the pure rate of time preference is in the higher range of values usually considered (typically above 1.5%). However, although such pathways bring overall net benefits over time

(in terms of aggregate discounted present value), they involve distributional consequences between and within generations. {3.6.2}

89TS
Technical SummaryTS.5 Mitigation Responses 
in Sectors and Systems
Chapters 5 to 12 assess recent advances in knowledge in individual 
sectors and systems. These chapters  – Energy (Chapter  6), Urban 
and Other Settlements (Chapter 8), Transport (Chapter 10), Buildings 
Chapter 9), Industry (Chapter 11), and Agriculture, Forestry and Other Land Use (AFOLU) (Chapter  7)  – correspond broadly to the IPCC 
National Greenhouse Gas Inventory reporting categories and build on similar chapters in previous WGIII reports. Chapters 5 and 12 tie together the cross-sectoral aspects of this group of chapters including the assessment of costs and potentials, demand-side aspects of mitigation, and carbon dioxide removal (CDR). 
TS.5.1 Energy
A broad-based approach to deploying energy-sector mitigation options can reduce emissions over the next ten years and set the stage for still deeper reductions beyond 2030 (high confidence).

There are substantial, cost-effective opportunities to reduce emissions rapidly, including in electricity generation, but near-term reductions will not be sufficient to limit warming to 2°C (>67%) or limit warming to 1.5°C (>50%) with no or limited overshoot. {6.4, 6.6, 6.7}
Warming cannot be limited to 2°C or 1.5°C without rapid and 
deep reductions in energy system CO
2 and GHG emissions 
(high confidence).  In scenarios limiting warming to 1.5°C (>50%) 
with no or limited overshoot (likely below 2°C), net energy system CO
2 emissions fall by 87–97% (interquartile range 60–79%) in 
2050. In 2030, in scenarios limiting warming to 1.5°C with no or limited overshoot, net CO
2 and GHG emissions fall by 35–51% and 
38–52% respectively. In scenarios limiting warming to 1.5°C with no or limited overshoot (likely below 2°C), net electricity sector CO
2 
emissions reach zero globally between 2045 and 2055 (2050 and 2080) (high confidence). {6.7}

Limiting warming to 2°C or 1.5°C will require substantial 
energy system changes over the next 30 years. This includes reduced fossil fuel consumption, increased production from low- and zero-carbon energy sources, and increased use of electricity and alternative energy carriers (high confidence).  Coal 
consumption without CCS falls by 67–82% (interquartile range) in 2030 in scenarios limiting warming to 1.5°C with no or limited overshoot. Oil and gas consumption fall more slowly. Low-carbon sources produce 93–97% of global electricity by 2050 in scenarios that limit warming to 2°C (>67%) or below. In scenarios limiting warming to 1.5°C with no or limited overshoot (likely below 2°C), electricity supplies 48–58% (36–47%) of final energy in 2050, up from 20% in 2019. {6.7}
Net zero energy systems will share common characteristics,

but the approach in every country will depend on national circumstances (high confidence). Common characteristics of net-zero energy systems will include: (i) electricity systems that produce no net CO
2 or remove CO 2 from the atmosphere; (ii)  widespread 
electrification of end uses, including light-duty transport, space heating, and cooking; (iii) substantially lower use of fossil fuels than today; (iv) use of alternative energy carriers such as hydrogen, bioenergy, and ammonia to substitute for fossil fuels in sectors less amenable to electrification; (v) more efficient use of energy than today; (vi) greater energy system integration across regions and across components of the energy system; and (vii) use of CO
2 removal 
including DACCS and BECCS to offset residual emissions. {6.6}
Energy demands and energy sector emissions have continued 
to rise (high confidence). From 2015 to 2019, global final energy consumption grew by 6.6%, CO
2 emissions from the global

energy system grew by 4.6%, and total GHG emissions from energy supply rose by 2.7%. Fugitive CH
4 emissions from oil, gas, and coal, 
accounted for 18% of GHG emissions in 2019. Coal electricity capacity grew by 7.6% between 2015 and 2019, as new builds in some countries offset declines in others. Total consumption of oil and oil products increased by 5%, and natural gas consumption grew by 15%. Declining energy intensity in almost all regions has been balanced by increased energy consumption. {6.3}
The unit costs for several key energy system mitigation 
options have dropped rapidly over the last five years, notably solar PV, wind power, and batteries (high confidence).  From

2015 to 2020, the costs of electricity from PV and wind dropped 56% and 45%, respectively, and battery prices dropped by 64%. Electricity from PV and wind is now cheaper than electricity from fossil sources in many regions, electric vehicles are increasingly competitive with internal combustion engines, and large-scale battery storage on electricity grids is increasingly viable. (Figure TS.7) {6.3, 6.4}
increased rapidly driven by policy, societal pressure to limit fossil generation, low interest rates, and cost reductions (high confidence).  Solar PV grew by 170% (to 680 TWh); wind grew by

70% (to 1420 TWh) from 2015 to 2019. Solar PV and wind together accounted for 21% of total low-carbon electricity generation and 8% of total electricity generation in 2019. Nuclear generation grew 9% between 2015 and 2019 and accounted for 10% of total generation in 2019 (2790 TWh); hydro-electric power grew by 10% and accounted for 16% (4290 TWh) of total generation. In total, low- and zero-carbon electricity generation technologies produced 37% of global electricity in 2019. {6.3, 6.4}
If investments in coal and other fossil infrastructure continue, 
energy systems will be locked-in to higher emissions, making it harder to limit warming to 2°C or 1.5°C (high confidence). Many 
aspects of the energy system – physical infrastructure; institutions, laws, and regulations; and behaviour – are resistant to change or take many years to change. New investments in coal-fired electricity without CCS are inconsistent with limiting warming to well below 2°C. {6.3, 6.7}

Limiting warming to 2°C or 1.5°C will strand fossil-related 
assets, including fossil infrastructure and unburned fossil fuel resources (high confidence).  The economic impacts of stranded 
assets could amount to trillions of dollars. Coal assets are most vulnerable over the coming decade; oil and gas assets are more vulnerable toward mid-century. CCS can allow fossil fuels to be used longer, reducing potential stranded assets. (Box TS.8) {6.7}

90
TSTechnical SummaryBox TS.8 | Stranded Assets
Limiting warming to 2°C or 1.5°C is expected to result in the ‘stranding’ of carbon-intensive assets. Stranded assets can be broadly 
defined as assets which ‘suffer from unanticipated or premature write-offs, downward revaluations or conversion to liabilities’. Climate policies, other policies and regulations, innovation in competing technologies, and shifts in fuel prices could all lead to stranded assets. The loss of wealth from stranded assets would create risks for financial market stabilityand reduce fiscal revenue for hydrocarbon-dependent economies, which in turn could affect macroeconomic stability and the prospects for a Just Transition. (Box TS.4) {6.7, 15.6, Chapter 17} 
Two types of assets are at risk of being stranded: (i) in-ground fossil resources and (ii) human-made capital assets (e.g., power plants

and cars). About 30% of oil, 50% of gas, and 80% of coal reserves will remain unburnable if warming is limited to 2°C. {6.7, Box 6.11} 
Practically all long-lived technologies and investments that cannot be adapted to low-carbon and zero-emission modes could face 
stranding under climate policy – depending on their current age and expected lifetimes. Scenario evidence suggests that without carbon capture, the worldwide fleet of coal- and gas power plants would need to retire about 23 and 17 years earlier than expected lifetimes, respectively, in order to limit global warming to 1.5°C and 2°C {2.7}. Blast furnaces and cement factories without CCS {11.4}, new fleets of airplanes and internal combustion engine vehicles {10.4, 10.5}, and new urban infrastructures adapted to sprawl and motorisation may also be stranded. {Chapter 8; Box 10.1}
Many countries, businesses, and individuals stand to lose wealth from stranded assets. Countries, businesses, and individuals may

therefore desire to keep assets in operation even if financial, social, or environmental concerns call for retirement. This creates political economic risks, including actions by asset owners to hinder climate policy reform {6.7; Box 6.11}. It will be easier to retire these assets if the risks are communicated, if sustainability reporting is mandated and enforced, and if corporations are protected with arrangements that shield them from short-term shareholder value maximisation. 
Without early retirements, or reductions in utilisation, the current fossil infrastructure will emit more GHGs than is compatible with

limiting warming to 1.5°C {2.7}. Including the pipeline of planned investments would push these future emissions into the uncertainty range of 2°C carbon budgets {2.7}. Continuing to build new coal-fired power plants and other fossil infrastructure will increase future transition costs and may jeopardise efforts to limit warming to 2°C (>67%) or 1.5°C with no or limited overshoot. One study has estimated that USD11.8 trillion in current assets will need to be stranded by 2050 for a 2°C world; further delaying action for another 10 years would result in an additional USD7.7 trillion in stranded assets by 2050. {15.5.2}  
Experience from past stranding indicates that compensation for the devaluation costs of private-sector stakeholders by the public 
sector is common. Limiting new investments in fossil technologies hence also reduces public finance risks in the long term. {15.6.3}

A low-carbon energy transition will shift investment patterns and create new economic opportunities (high confidence). Total 
energy investment needs will rise, relative to today, over the next decades, if warming is limited to 2°C or lower (>67%), or if warming is limited to 1.5°C (>50%) with no or limited overshoot. These increases will be far less pronounced, however, than the reallocations of investment flows that are anticipated across subsectors, namely from fossil fuels (extraction, conversion, and electricity generation) without CCS and toward renewables, nuclear power, CCS, electricity networks and storage, and end-use energy efficiency. A significant and growing share of investments between now and 2050 will be made in emerging economies, particularly in Asia. {6.7} 
Climate change will affect many future local and national low-

carbon energy systems. The impacts, however, are uncertain, particularly at the regional scale (high confidence). Climate change will alter hydropower production, bioenergy and agricultural yields, thermal power plant efficiencies, and demands for heating and cooling, and it will directly impact power system infrastructure. Climate change will not affect wind and solar resources to the extent that it would compromise their ability to reduce emissions. {6.5}Electricity systems powered predominantly by renewables will be increasingly viable over the coming decades, but it will be challenging to supply the entire energy system with renewable energy (high confidence). Large shares of variable solar PV and wind power can be incorporated in electricity grids through batteries, hydrogen, and other forms of storage; transmission; flexible non-renewable generation; advanced controls; and greater demand-side responses. Because some applications (e.g.,  aviation) are not currently amenable to

electrification, it is anticipated that 100% renewable energy systems will need to include alternative fuels such as hydrogen or biofuels. Economic, regulatory, social, and operational challenges increase with higher shares of renewable electricity and energy. The ability to overcome these challenges in practice is not fully understood. (Box TS.9) {6.6}

91TS
Technical SummaryBox TS.9 | The Transformation in Energy Carriers: Electrification and Hydrogen
To use energy, it must be ‘carried’ from where it was produced – at a power plant, for example, or a refinery, or a coal mine – to where 
it is used. As countries reduce CO 2 emissions, they will need to switch from gasoline and other petroleum-based fuels, natural gas, coal, 
and electricity produced from these fossil fuels to energy carriers with little or no carbon footprint. An important question is which new energy carriers will emerge to support low-carbon transitions. 
Low-carbon energy systems are expected to rely heavily on end-use electrification, where electricity produced with low GHG emissions

is used for building and industrial heating, transport and other applications that rely heavily on fossil fuels at present. But not all end-uses are expected to be commercially electrifiable in the short to medium term {11.3.5}, and many will require low GHG liquid and gaseous fuels, that is, hydrogen, ammonia, and biogenic and synthetic low GHG hydrocarbons made from low GHG hydrogen, oxygen and carbon sources (the latter from CCU,
20 biomass, or direct air capture {11.3.6}). The future role of hydrogen and hydrogen derivatives

will depend on how quickly and how far production technology improves, that is, from electrolysis (‘green’), biogasification, and fossil fuel reforming with CCS (‘blue’) sources. As a general rule, and across all sectors, it is more efficient to use electricity directly and avoid the progressively larger conversion losses from producing hydrogen, ammonia, or constructed low GHG hydrocarbons. What hydrogen does do, however, is add time and space option value to electricity produced using variable clean sources, for use as hydrogen, as stored future electricity via a fuel cell or turbine, or as an industrial feedstock. Furthermore, electrification and hydrogen involve a symbiotic range of general-purpose technologies, such as electric motors, power electronics, heat pumps, batteries, electrolysis, fuel cells, and so on, that have different applications across sectors but cumulative economies of innovation and production scale benefits. Finally, neither electrification nor hydrogen

For almost 140 years we have primarily produced electricity by burning coal, oil, and gas to drive steam turbines connected to 
electricity generators. When switching to low-carbon energy sources – renewable sources, nuclear power, and fossil or bioenergy with CCS – electricity is expected to become a more pervasive energy carrier. Electricity is a versatile energy carrier, with much higher end-use efficiencies than fuels, and it can be used directly to avoid conversion losses. 
An increasing reliance on electricity from variable renewable sources, notably wind and solar power, disrupts old concepts and makes

many existing guidelines obsolete for power system planning, for example, that specific generation types are needed for baseload, intermediate load, and peak load to follow and meet demand. In future power systems with high shares of variable electricity from renewable sources, system planning and markets will focus more on demand flexibility, grid infrastructure and interconnections, storage on various timelines (on the minute, hourly, overnight and seasonal scale), and increased coupling between the energy sector and the building, transport and industrial sectors. This shifts the focus to energy systems that can handle variable supply rather than always follow demand. Hydrogen may prove valuable to improve the resilience of electricity systems with high penetration of variable renewable electricity. Flexible hydrogen electrolysis, hydrogen power plants and long-duration hydrogen storage may all improve resilience. Electricity-to-hydrogen-to-electricity round-trip efficiencies are

Electrification is expected to be the dominant strategy in buildings as electricity is increasingly used for heating and for cooking.

Electricity will help to integrate renewable energy into buildings and will also lead to more flexible demand for heating, cooling, and electricity. District heating and cooling offers potential for demand flexibility through energy storage and supply flexibility through cogeneration. Heat pumps are increasingly used in buildings and industry for heating and cooling {9.3.3, Box  9.3}. The ease of switching to electricity means that hydrogen is not expected to be a dominant pathway for buildings {Box 9.6}. Using electricity directly for heating, cooling and other building energy demand is more efficient than using hydrogen as a fuel, for example, in boilers or fuel cells. In addition, electricity distribution is already well developed in many regions compared to essentially non-existent hydrogen infrastructure, except for a few chemicals industry pipelines. At the same time, hydrogen could potentially be used for on-site storage should technology advance sufficiently.

20 Carbon dioxide capture and utilisation (CCU) refers to a process in which CO 2 is captured and the carbon is then used in a product. The climate effect of CCU depends on the product lifetime, 
the product it displaces, and the CO 2 source (fossil, biomass or atmosphere). CCU is sometimes referred to as carbon dioxide capture and use, or carbon capture and utilisation.

92
TSTechnical SummaryBox TS.9 (continued)
Electrification is already occurring in several modes of personal and light-freight transport, and vehicle-to-grid solutions for flexibility 
have been extensively explored in the literature and small-scale pilots. The role of hydrogen in transport depends on how far technology develops. Batteries are currently a more attractive option than hydrogen and fuel cells for light-duty vehicles. Hydrogen and hydrogen-derived synthetic fuels, such as ammonia and methanol, may have a more important role in heavy vehicles, shipping, and aviation {10.3}. Current transport of fossil fuels may be replaced by future transport of hydrogen and hydrogen carriers such as ammonia and methanol, or energy-intensive basic materials processed with hydrogen (e.g., reduced iron) in regions with bountiful renewable resources. {Box 11.1}

Both light and heavy industry are potentially large and flexible users of electricity for both final energy use (e.g., directly and using heat 
pumps in light industry) and for feedstocks (e.g., hydrogen for steel-making and chemicals). For example, industrial process heat demand, ranging from below 100°C to above 1000°C, can be met through a wide range of electrically powered technologies instead of using fuels. Future demand for hydrogen (e.g., for nitrogen fertiliser or as a reduction agent in steel production) also offers electricity-demand flexibility for electrolysis through hydrogen storage and flexible production cycles {11.3.5}. The main use of hydrogen and hydrogen carriers in industry is expected to be as feedstock (e.g., for ammonia and organic chemicals) rather than for energy as industrial electrification increases.
Multiple energy supply options are available to reduce emissions over the next decade (high confidence).  Nuclear

power and hydropower are already established technologies. Solar PV and wind are now cheaper than fossil-generated electricity in many locations. Bioenergy accounts for about a tenth of global primary energy. Carbon capture is widely used in the oil and gas industry, with early applications in electricity production and biofuels. It will not be possible to widely deploy all of these and other options without efforts to address the geophysical, environmental-ecological, economic, technological, socio-cultural, and institutional factors that can facilitate or hinder their implementation (high confidence). (Figures TS.11 and TS.31) {6.4}
Enhanced integration across energy system sectors and across

scales will lower costs and facilitate low-carbon energy system transitions (high confidence). Greater integration between the electricity sector and end-use sectors can facilitate integration of variable renewable energy options. Energy systems can be integrated across district, regional, national, and international scales (high confidence). {6.4, 6.6}
Coal
162
Transport121Oil191
Natural gas140
Residential and commercial120
Electricity 39 EJ
Heat 6 EJ
Hydro
15
Biomass57Solar4Wind5
Geothermal– other 1Nuclear10
Non-energy use and non-speciﬁed45Global energy use, 2019
102
4943
46
244422532
19 4182
29
4112
2912
1116
329
8
5
Figure TS.11 | Global energy flows within the 2019 global energy system (top panel) and within two illustrative future, net-zero CO 2 emissions global 
energy system (bottom panels).

93TS
Technical SummaryCoal
2
Transport99Oil28
Residential and commercial154
Electricity 141 EJ
Heat 76 EJ
20
Hydro24
Biomass101Solar261
Wind104Geothermal– other 10
Nuclear2Power
plants 
and heat
Reﬁnery100
3939
395
16 6 727
7
218 2
6
Coal82
Transport139Oil124
Electricity 85 EJ
Heat 24 EJ
100
Hydro30
Biomass156
Solar91Wind93Geothermal– other 1Nuclear28
Power
plants 
and heat
Reﬁnery
15418
64
9
7175
26
3
41
14
5
479714
1
13
Non-energy use and non-speciﬁed11
Figure TS.11 continued: Global energy flows within the 2019 global energy system (top panel) and within two illustrative future, net-zero CO 2 emissions

global energy system (bottom panels). Source: IEA, AR6 Scenarios Database. Flows below 1 EJ are not represented. The illustrative net-zero scenarios correspond to 
the years in which net energy system CO 2 emissions reach zero – 2045 in IMP-Ren and 2060 in IMP-Neg-2.0. Source: data from IMP-Ren: Luderer et al.(2022); IMP-Neg-2.0: 
Riahi, K. et al. 2021.

94
TSTechnical SummaryThe viable speed and scope of a  low-carbon energy system 
transition will depend on how well it can support SDGs and other societal objectives (high confidence). Energy systems are linked to a range of societal objectives, including energy access, air and water pollution, health, energy security, water security, food security, economic prosperity, international competitiveness, and employment. These linkages and their importance vary among regions. Energy-sector mitigation and efforts to achieve SDGs generally support one another, though there are important region-specific exceptions (high confidence). (Figure TS.29) {6.1, 6.7}
The economic outcomes of low-carbon transitions in some

sectors and regions may be on par with, or superior to those of an emissions-intensive future (high confidence). Cost reductions in key technologies, particularly in electricity and light-duty transport, have increased the economic attractiveness of near-term low-carbon transitions. Long-term mitigation costs are not well understood and depend on policy design and implementation, and the future costs and availability of technologies. Advances in low-carbon energy resources and carriers such as next-generation biofuels, hydrogen produced from electrolysis, synthetic fuels, and carbon-neutral ammonia would substantially improve the economics of net zero energy systems (medium confidence). {6.4, 6.7}
TS.5.2 Urban Systems and Other Settlements

Although urbanisation is a global trend often associated with increased incomes and higher consumption, the growing concentration of people and activities is an opportunity to increase resource efficiency and decarbonise at scale (very high confidence). The same urbanisation level can have large variations in per-capita urban carbon emissions. For most regions, per-capita urban emissions are lower than per-capita national emissions (excluding aviation, shipping and biogenic sources) (very high confidence). {8.1.4, 8.3.3, 8.4, Box 8.1}
countries, where per-capita emissions are currently low, but are expected to increase with the construction and use of new infrastructure, and the built environment, and changes in incomes and lifestyles (very high confidence).  The drivers

of urban GHG emissions are complex and include an interplay of population size, income, state of urbanisation, and how cities are laid out (i.e., urban form). How new cities and towns are designed, constructed, managed, and powered will lock-in behaviour, lifestyles, and future urban GHG emissions. Urban strategies can improve well-being while minimising impact on GHG emissions. However, urbanisation can result in increased global GHG emissions through emissions outside the city’s boundaries (very high confidence). {8.1.4, 8.3, Box 8.1, 8.4, 8.6}
21 These estimates are based on consumption-based accounting, including both direct emissions from within urban areas, and indirect emissions from outside urban areas related to the production 
of electricity, goods, and services consumed in cities. Estimates include all CO 2 and CH 4 emission categories except for aviation and marine bunker fuels, land-use change, forestry, and agriculture.

{8.1, Annex I: Glossary}The urban share of combined global CO 2 and CH 4) emissions 
is substantial and continues to increase (high confidence). 
In 2015, urban emissions were estimated to be 25GtCO 2-eq (about 
62% of the global share) and in 2020 were 29 GtCO 2-eq (67–72% of 
the global share).21 Around 100 of the highest-emitting urban areas 
account for approximately 18% of the global carbon footprint (high confidence). {8.1, 8.3}
The urban share of regional GHG emissions increased between

2000 and 2015, with much inter-regional variation in the magnitude of the increase (high confidence). Globally, the urban share of national emissions increased six percentage points, from 56% in 2000 to 62% in 2015. For 2000 to 2015, the urban emissions share increased from 28% to 38% in Africa, from 46% to 54% in Asia and Pacific, from 62% to 72% in Developed Countries, from 57% to 62% in Eastern Europe and West Central Asia, from 55% to 66% in Latin America and Caribbean, and from 68% to 69% in the Middle East (high confidence). {8.1.6, 8.3.3}
Per-capita urban GHG emissions increased between 2000 and 
2015, with cities in developed countries accounting for nearly seven times more per capita than the lowest emitting region (medium confidence). From 2000 to 2015, global urban GHG 
emissions per capita increased from 5.5 to 6.2 tCO
2-eq per person 
(an increase of 11.8%). Emissions in Africa increased from 1.3 to 1.5 tCO
2-eq per person (22.6%); in Asia and Pacific from 3.0 to 5.1

tCO 2-eq per person (71.7%); in Eastern Europe and West Central 
Asia from 6.9 to 9.8 tCO 2-eq per person (40.9%); in Latin America 
and the Caribbean from 2.7 to 3.7 tCO 2-eq per  person (40.4%); 
and in the Middle East from 7.4 to 9.6 tCO 2-eq per person (30.1%). 
Albeit starting from the highest level, developed countries showed a  modest decline of 11.4 to 10.7 tCO
2-eq per  person (–6.5%). 
(Figure TS.12) {8.3.3}

95TS
a)
b)70%15.0
13.011.09.07.05.03.01.0–1.060%Percent share (%)
15.013.011.09.07.05.03.01.0–1.0
40%
30%20%10%
0%
70%Africa
Regional urban population share (%) Regional urban CO
2-eq share (%)
Africa
40%
30%20%10%
0%Global urban per capita CO
2-eq

Figure TS.12 | Changes in six metrics associated with urban and national-scale combined CO 2 and CH 4 emissions represented in the AR6 WGIII six-region 
aggregation, with (a) 2000 and (b) 2015. The trends in Luqman et al. (2021) were combined with the work of Moran et al. (2018) to estimate the regional urban CO 2-eq 
share of global urban emissions, the urban share of national CO 2-eq emissions, and the urban per capita CO 2-eq emissions by region. This estimate is derived from consumption-
based accounting that includes both direct emissions from within urban areas and indirect emissions from outside urban areas related to the production of electricity, goods, 
and services consumed in cities. It incorporates all CO 2 and CH 4 emissions except aviation, shipping and biogenic sources (i.e., land-use change, forestry, and agriculture). The

dashed grey line represents the global average urban per capita CO 2-eq emissions. The regional urban population share, regional CO 2-eq share in total emissions, and national 
per capita CO 2-eq emissions by region are given for comparison. Source: adapted from Gurney et al. (2022).

96
TSTechnical SummaryThe global share of future urban GHG emissions is expected to 
increase through 2050 with moderate to low mitigation efforts due to growth trends in population, urban land expansion, and infrastructure and service demands, but the extent of the increase depends on the scenario and the scale and timing of urban mitigation action (medium confidence). In modelled 
scenarios, global consumption-based urban CO
2 and CH 4 emissions 
2050 with moderate mitigation efforts (intermediate GHG emissions, SSP2-4.5), and up to 40 GtCO
2-eq in 2050 with low mitigation efforts 
(high GHG emissions, SSP 3-7.0). With aggressive and immediate mitigation efforts to limit global warming to 1.5°C (>50%) with no or limited overshoot by the end of the century (very low emissions, 
22 These scenarios have been assessed by WGI to correspond to intermediate, high, and very low GHG emissions.

23 These scenarios have been assessed by WGI to correspond to intermediate, high, and very low GHG emissions.SSP1-1.9), including high levels of electrification, energy and material efficiency, renewable energy preferences, and socio-behavioural responses, urban GHG emissions could approach net-zero and reach a maximum of 3 GtCO
2-eq in 2050. Under a scenario with aggressive 
but not immediate urban mitigation policies to limit global warming to 2°C (>67%) (low emissions, SSP1-2.6), urban emissions could reach 17 GtCO
2-eq in 2050.23 (Figure TS.13) {8.3.4}
Urban land areas could triple between 2015 and 2050, with significant implications for future carbon lock-in (medium confidence). There is a  large range in the forecasts of urban land expansion across scenarios and models, which highlights an opportunity to shape future urban development towards low- or net zero GHG

Resource efﬁcient and compact Moderate progress(b) Estimated urban emissions changes in two different scenarios (2020–2030)(a) Urban emissions (GtCO2-eq yr–1)
Europe
and WestLatin
American
and
EastTotal
Reduction
in 20309.8
3.4
Developed
Europe
American
and
EastTotal
Increase
1210
86420
2020 Emissions Levels
Reductions in 2030Remaining EmissionsAfrica
Latin America and Caribbean Middle East Global total (GtCO2-eq)Asia and Paciﬁc Developed Countries Eastern Europe and West-Central Asia
Figure TS.13 | Panel (a): carbon dioxide-equivalent emissions from global urban areas from 1990 to 2100. Urban areas are aggregated to six regional 
domains;  Panel (b): comparison of urban emissions under different urbanisation scenarios (GtCO 2-eq yr –1) for different regions.21 {Figures 8.13 and 8.14}

97TS
Technical Summaryemissions. By 2050, urban areas could increase up to 211% over the 2015 
global urban extent, with the median projected increase ranging from 43% to 106%. While the largest absolute amount of new urban land is forecasted to occur in Asia and Pacific, and in Developed Countries, the highest rate of urban land growth is projected to occur in Africa, Eastern Europe and West Central Asia, and in the Middle East. Given past trends, the expansion of urban areas is expected to take place on agricultural lands and forests, with implications for the loss of carbon stocks. The infrastructure that will be constructed concomitant with urban land expansion will lock-in patterns of energy consumption that will persist for decades. {8.3.1, 8.3.4, 8.4.1, 8.6}
The construction of new, and upgrading of existing, urban 
infrastructure through 2030 will add to emissions (medium evidence, high agreement). The construction of new and upgrading

of existing urban infrastructure using conventional practices and technologies can result in a  significant increase in CO
2 emissions, 
than double annual resource requirements for raw materials to about 90 billion tonnes per year by 2050, up from 40 billion tonnes in 2010. {8.4.1, 8.6}
future projections of more frequent extreme climate events, there is an urgent need to integrate urban mitigation and adaptation strategies for cities to address climate change (very high confidence). Mitigation strategies can enhance resilience against climate change impacts while contributing to social equity, public health, and human well-being. Urban mitigation actions that facilitate economic decoupling can have positive impacts on employment and local economic competitiveness. {8.2, Cross-Working Group Box 2 in Chapter 8, 8.4}

Cities can achieve net-zero GHG emissions only through deep 
decarbonisation and systemic transformation (very high confidence). Three broad mitigation strategies have been found to be effective in reducing emissions when implemented concurrently: (i) reducing or changing urban energy and material use towards more sustainable production and consumption across all sectors, including through compact and efficient urban forms and supporting infrastructure; (ii) electrification and switching to low-carbon energy sources; and (iii) enhancing carbon uptake and storage in the urban environment (high confidence). Given the regional and global reach of urban supply chains, cities can achieve net-zero emissions only if emissions are reduced both within and outside of their administrative boundaries through supply chains. {8.1.6, 8.3.4, 8.4, 8.6}
Packages of mitigation policies that implement multiple

urban-scale interventions can have cascading effects across sectors, reduce GHG emissions outside a city’s administrative boundaries, and reduce emissions more than the net sum of individual interventions, particularly if multiple scales of governance are included (high confidence). Cities have the 
ability to implement policy packages across sectors using an urban systems approach, especially those that affect key infrastructure 
24 These examples are considered to be a subset of ‘nature-based solutions’ or ‘ecosystem-based approaches’.based on spatial planning, electrification of the urban energy system, and urban green and blue infrastructure. The institutional capacity of cities to develop, coordinate, and integrate sectoral mitigation strategies within their jurisdiction varies by context, particularly those related to governance, the regulatory system, and budgetary control. {8.4, 8.5, 8.6}

efficient urban growth through co-location of higher residential and job densities, mixed land use, and transit-oriented development could reduce urban energy use between 23% and 26% by 2050 compared to the business-as-usual scenario (high confidence). Compact cities with shortened distances between 
housing and jobs, and interventions that support a modal shift away from private motor vehicles towards walking, cycling, and low-emissions shared, or public, transportation, passive energy comfort in buildings, and urban green infrastructure can deliver significant public health benefits and lower GHG emissions. {8.2, 8.3.4, 8.4, 8.6}
change through carbon sinks, avoided emissions, and reduced energy use while offering multiple co-benefits (high confidence). Urban green and blue infrastructure, including urban forests and street trees, permeable surfaces, and green roofs
24 offer

potentials to mitigate climate change directly through storing carbon, and indirectly by inducing a cooling effect that both reduces energy demand and reduces energy use for water treatment. Globally, urban trees store approximately 7.4 billion tonnes of carbon, and sequester approximately 217 million tonnes of carbon annually, although carbon storage is highly dependent on biome. Among the multiple co-benefits of green and blue infrastructure are reducing the urban heat island (UHI) effect and heat stress, reducing stormwater runoff, improving air quality, and improving the mental and physical health of urban dwellers. Many of these options also provide benefits to climate adaptation. (high agreement, robust evidence) {8.2, 8.4.4}
The potential and sequencing of mitigation strategies to reduce

GHG emissions will vary depending on a city’s land use, spatial form, development level, and state of urbanisation (i.e., whether it is an established city with existing infrastructure, a rapidly growing city with new infrastructure, or an emerging city with infrastructure buildup) (high confidence).  New and emerging cities

will have significant infrastructure development needs to achieve high quality of life, which can be met through energy-efficient infrastructures and services, and people-centred urban design (high confidence). The long lifespan of urban infrastructures locks in behaviour and committed emissions. Urban  infrastructures  and urban form can enable sociocultural and lifestyle changes that can significantly reduce carbon footprints. Rapidly growing cities can  avoid higher future emissions through urban planning to co-locate jobs and housing to achieve compact urban form, and by leapfrogging to low-carbon technologies. Established cities will achieve the largest GHG emissions savings by replacing, repurposing, or retrofitting the building stock, targeted infilling and densifying, as well as through modal shift and the electrification of the urban energy system. New and emerging cities

98
TSTechnical Summaryhave unparalleled potential to become low or net zero GHG emissions 
while achieving high quality of life by creating compact, co-located, and walkable urban areas with mixed land use and transit-oriented design, that also preserve existing green and blue assets. {8.2, 8.4, 8.6} 
With over 880 million people living in informal settlements, there 
are opportunities to harness and enable informal practices and institutions in cities related to housing, waste, energy, water, and sanitation to reduce resource use and mitigate climate change (low evidence, medium agreement). The upgrading of 
informal settlements and inadequate housing to improve resilience and well-being offers a  chance to create a  low-carbon transition. However, there is limited quantifiable data on these practices and their cumulative impacts on GHG emissions. {8.1.4, 8.2.2, Cross-Working Group Box 2 in Chapter 8, 8.3.2, 8.4, 8.6, 8.7}
Achieving transformational changes in cities for climate

change mitigation and adaptation will require engaging multiple scales of governance, including governments and non-state actors, and in connection with substantial financing beyond sectoral approaches (very high confidence).  Large 
and complex infrastructure projects for urban mitigation are often beyond the capacity of local municipality budgets, jurisdictions, and institutions. Partnerships between cities and international institutions, national and regional governments, transnational networks, and local stakeholders play a  pivotal role in mobilising global climate finance resources for a  range of infrastructure projects with low-carbon emissions and related spatial planning programs across key sectors. {8.4, 8.5}
TS.5.3 Transport
Meeting climate mitigation goals would require transformative changes in the transport sector.  In 2019, direct GHG emissions 
from the transport sector were 8.7 GtCO
2-eq (up from 5.0 GtCO 2-eq in

1990) and accounted for 23% of global energy-related CO 2 emissions. 
Road vehicles accounted for 70% of direct transport emissions, while 1%, 11%, and 12% of direct emissions came from rail, shipping, and aviation, respectively. Emissions from shipping and aviation continue to grow rapidly. Transport-related emissions in developing regions of the world have increased more rapidly than in Europe or North America, a  trend that is expected to continue in coming decades (high confidence). {10.1, 10.5, 10.6}
Since AR5 there has been a  growing awareness of the need 
for demand management solutions combined with new technologies, such as the rapidly growing use of electromobility for land transport and the emerging options in advanced biofuels and hydrogen-based fuels for shipping and aviation and in other specific land-based contexts (high confidence).

There is a growing need for systemic infrastructure changes that enable behavioural modifications and reductions in demand for transport services that can in turn reduce energy demand. The response to the COVID-19 pandemic has also shown that behavioural interventions can 
25 ‘Active travel’ is travel that requires physical effort, for example journeys made by walking or cycling.reduce transport-related GHG emissions. For example, COVID-19-based lockdowns have confirmed the transformative value of telecommuting replacing significant numbers of work and personal journeys as well as promoting local active transport. There are growing opportunities to implement strategies that drive behavioural change and support the adoption of new transport technology options. {Chapter 5, 10.2, 10.3, 10.4, 10.8}
Changes in urban form, behaviour programs, the circular

economy, the shared economy, and digitalisation trends can support systemic changes that lead to reductions in demand for transport services or expand the use of more efficient transport modes (high confidence). Cities can reduce their transport-related fuel consumption by around 25% through combinations of more compact land use and the provision of less car-dependent transport infrastructure. Appropriate infrastructure, including protected pedestrian and bike pathways, can also support much greater localised active travel.
25 Transport demand

management incentives are expected to be necessary to support these systemic changes. There is mixed evidence of the effect of circular economy initiatives, shared economy initiatives, and digitalisation on demand for transport services (Box TS.14). For example, while dematerialisation can reduce the amount of material that needs to be transported to manufacturing facilities, an increase in online shopping with priority delivery can increase demand for freight transport. Similarly, while teleworking could reduce travel demand, increased ride-sharing could increase vehicle kilometres travelled (VKT). {Chapters 1 and 5, 10.2, 10.8}
Battery electric vehicles (BEVs) have lower lifecycle greenhouse 
gas (GHG) emissions than internal combustion engine vehicles (ICEVs) when BEVs are charged with low-carbon electricity (high confidence).  Electromobility is being rapidly implemented

in micro-mobility (e-autorickshaws, e-scooters, e-bikes), in transit systems, especially buses, and to a  lesser degree, in personal vehicles. BEVs could also have the added benefit of supporting grid operations. The commercial availability of mature lithium-ion batteries (LIBs) has underpinned this growth in electromobility. As global battery production increases, unit costs are declining. Further efforts to reduce the GHG footprint of battery production, however, are essential for maximising the mitigation potential of BEVs. The continued growth of electromobility for land transport would entail investments in electric charging and related grid infrastructure. Electromobility powered by low-carbon electricity has the potential to rapidly reduce transport GHG and can be applied with multiple co-benefits, especially in developing countries. {10.3, 10.4, 10.8}
Land-based, long-range, heavy-duty trucks can be decarbonised

through battery-electric haulage (including the use of electric road systems), complemented by hydrogen- and biofuel-based fuels in some contexts. These same technologies and expanded use of available electric rail systems can support rail decarbonisation (medium confidence). Initial deployments of battery-electric, hydrogen- and bio-based haulage are underway, and commercial operations of some of these technologies are considered

99TS
Technical Summaryfeasible by 2030 (medium confidence). These technologies 
nevertheless face challenges regarding driving range, capital and operating costs, and infrastructure availability. In particular, fuel-cell durability, high energy consumption, and costs continue to challenge the commercialisation of hydrogen-based fuel-cell vehicles. Increased capacity for low-carbon hydrogen production would also be essential for hydrogen-based fuels to serve as an emissions reduction strategy (high confidence). (Box TS.15) {10.3, 10.4, 10.8}
R&D, though advanced biofuels, ammonia, and synthetic fuels are emerging as viable options (medium confidence). Increased efficiency has been insufficient to limit the emissions from shipping and aviation, and natural gas-based fuels are expected to be inadequate to meet stringent decarbonisation goals for these segments (high confidence). High-energy density, low-carbon fuels

are required, but they have not yet reached commercial scale. Advanced biofuels could provide low-carbon jet fuel (medium confidence). The production of synthetic fuels using low-carbon 
hydrogen with CO
2 captured through DACCS/BECCS could provide 
jet and marine fuels but these options still require demonstration at scale (low confidence). Ammonia produced with low-carbon hydrogen could also serve as a  marine fuel (medium confidence). Deployment of these fuels requires reductions in production costs. (Figure TS.14) {10.2, 10.3, 10.4, 10.5, 10.6, 10.8}
Scenarios from bottom-up and top-down models indicate 
that, without intervention, CO
2 emissions from transport

could grow in the range of 16% and 50% by 2050 (medium confidence). The scenarios literature projects continued growth in demand for freight and passenger services, particularly in developing countries in Africa and Asia (high confidence). This growth is projected to take place across all transport modes. Increases in demand notwithstanding, scenarios that limit warming to 1.5°C degree with no or limited overshoot suggest that a 59% reduction (42–68% interquartile range) in transport-related CO
2 emissions by 
2050, compared to modelled 2020 levels is required. While many global scenarios place greater reliance on emissions reduction in sectors other than transport, a  quarter of the 1.5°C scenarios describe transport-related CO
2 emissions reductions in excess of 68%

(relative to modelled 2020 levels) (medium confidence). Illustrative Mitigation Pathways IMP-Ren and IMP-LD (TS 4.2) describe emission reductions of 80% and 90% in the transport sector, respectively, by 2050. Transport-related emission reductions, however, may not happen uniformly across regions. For example, transport emissions from the Developed Countries, and Eastern Europe and West Central Asia countries decrease from 2020 levels by 2050 across all scenarios limiting global warming to 1.5°C by 2100, but could increase in Africa, Asia and Pacific (APC), Latin America and Caribbean, and the Middle East in some of these scenarios. {10.7}
The scenarios literature indicates that fuel and technology shifts

are crucial in reducing carbon emissions to meet temperature goals (high confidence). In general terms, electrification tends to play the key role in land-based transport, but biofuels and hydrogen (and derivatives) could play a role in decarbonisation of freight in some contexts. Biofuels and hydrogen (and derivatives) are expected to be more prominent in shipping and aviation. The shifts towards these alternative fuels must occur alongside shifts towards clean technologies in other sectors. {10.7}
There is a  growing awareness of the need to plan for the

significant expansion of low-carbon energy infrastructure, including low-carbon power generation and hydrogen production, to support emissions reductions in the transport sector (high confidence). Integrated energy planning and operations that take into account energy demand and system constraints across all sectors (transport, buildings, and industry) offer the opportunity to leverage sectoral synergies and avoid inefficient allocation of energy resources. Integrated planning of transport and power infrastructure would be particularly useful in developing countries where ‘greenfield’ development doesn’t suffer from constraints imposed by legacy systems. {10.3, 10.4, 10.8}
that support decarbonisation of the transport sector could require changes to national and international governance structures (medium confidence).  The UNFCCC does not specifically

cover emissions from international shipping and aviation. Reporting emissions from international transport is at the discretion of each country. While the International Civil Aviation Organization (ICAO) and International Maritime Organization (IMO) have established emissions reductions targets, only strategies to improve fuel efficiency and demand reductions have been pursued, and there has been minimal commitment to new technologies. {10.5, 10.6, 10.7}
There are growing concerns about resource availability, 
labour rights, non-climate environmental impacts, and costs of critical minerals needed for lithium-ion batteries (medium confidence).  Emerging national strategies on critical minerals and

the requirements from major vehicle manufacturers are leading to new, more geographically diverse mines. The standardisation of battery modules and packaging within and across vehicle platforms, as well as increased focus on design for recyclability are important. Given the high degree of potential recyclability of lithium-ion batteries, a  nearly closed-loop system in the future could mitigate concerns about critical mineral issues (medium confidence). {10.3, 10.8}
Legislated climate strategies are emerging at all levels of 
government, and together with pledges for personal choices, could spur the deployment of demand- and supply-side transport mitigation strategies (medium confidence). At the 
local level, legislation can support local transport plans that include

commitments or pledges from local institutions to encourage behaviour change by adopting an organisational culture that motivates sustainable behaviour with inputs from the creative arts. Such institution-led mechanisms could include bike-to-work campaigns, free transport passes, parking charges, or eliminating car benefits. Community-based solutions such as solar sharing, community charging, and mobility as a  service can generate new 
opportunities to facilitate low-carbon transport futures. At the regional and national levels, legislation can include vehicle and fuel efficiency standards, R&D support, and large-scale investments in low-carbon transport infrastructure. (Figure TS.14) {10.8, Chapter 15}

100
TSTechnical SummaryTS.5.4 Buildings
Global GHG emissions from buildings were 12 GtCO 2-eq in 
2019, equivalent to 21% of global GHG emissions. Of this, 
57% (6.8 GtCO 2-eq) were indirect emissions from off-site 
generation of electricity and heat, 24% (2.9 GtCO 2-eq) were 
direct emissions produced on-site and 18% (2.2 GtCO 2-eq) 
were embodied emissions from the production of cement and steel used in buildings (high confidence). Most building-sector emissions are CO
2. Final energy demand from buildings reached 
128 EJ globally in 2019 (around 31% of global final energy demand), and electricity demand from buildings was slightly above 43 EJ globally (around 18% of global electricity demand). Residential buildings consumed 70% (90 EJ) of the global final energy demand from buildings. Over the period 1990–2019, global CO

from buildings increased by 50%, global final energy demand from buildings grew by 38%, and global final electricity demand increased by 161%. {9.3}In most regions, historical improvements in efficiency have been approximately matched by growth in floor area per capita (high confidence).  At the global level, building-specific drivers of

GHG emissions include: (i) population growth, especially in developing countries; (ii) increasing floor area per capita, driven by the increasing size of dwellings while the size of households kept decreasing, especially in developed countries; (iii) the inefficiency of newly constructed buildings, especially in developing countries, and the low renovation rates and low ambition level in developed countries when existing buildings are renovated; (iv) the increase in use, number and size of appliances and equipment, especially information and communication technologies (ICT) and cooling, driven by income; and, (v) the continued reliance on carbon-intensive electricity and heat. These factors taken together are projected to continue driving increased GHG emissions in the building sector in the future. {9.3, 9.6, 9.9}Regime scale
Medium
Small

5 yearsLowHigh• Integrated 
strategies for net zero transport, land use and energy
• Regulatory 
reviews on vehicles, land development and energy
• Procurement 
partnerships on government ﬂeets, buses, EVs, taxis, tuk-tuks, share bikes
• City and regional 
engagement and education programmes• ASI regulations for 
each mode
• ASI strategies for each 
settlement fabric (central, inner, outer, rural, remote)
• ASI engagement and 
• R&D funds from ports 
• Demonstrations in major 
ports
• Local engagement and 
education programmes 
Market 
electro-
mobility
Travel 
demand 
efﬁciency
All modes 
Heavy 

Figure TS.14 | Mitigation options and enabling conditions for transport. ‘Niche’ scale includes strategies that still require innovation.  {Figure 10.22} ASI: 
Avoid-Shift-Improve; TRL: technology readiness level.

101TS
Technical SummaryBuilding-sector GHG emissions were assessed using the 
Sufficiency, Efficiency, Renewable (SER) framework. Sufficiency measures tackle the causes of GHG emissions by limiting the demand for energy and materials over the lifecycle of buildings and appliances (high confidence). In Chapter  9 of this report, sufficiency differs from efficiency: sufficiency is about long-term 
actions driven by non-technological solutions, which consume less energy in absolute terms; efficiency, in contrast is about continuous short-term marginal technological improvements. Sufficiency policies are a  set of measures and daily practices that avoid demand for energy, materials, land and water while delivering human well-being-for-all within planetary boundaries. Use of the SER framework aims to reduce the cost of constructing and using buildings without reducing occupants’ well-being and comfort. {9.1, 9.4, 9.5, 9.9}
Sufficiency interventions do not consume energy during

the use phase of buildings and do not require maintenance nor replacement over the lifetime of buildings. Density, compacity, bioclimatic design to optimise the use of nature-based solutions, multi-functionality of space through shared space and to allow for adjusting the size of buildings to the evolving needs of households, circular use of materials and repurposing unused existing buildings to avoid using virgin materials, optimisation of the use of buildings through lifestyle changes, use of the thermal mass of buildings to reduce thermal needs, and moving from ownership to usership of appliances, are among the sufficiency interventions implemented in leading municipalities (high confidence). At a global level, up to 17% of the mitigation potential in the buildings sector could be captured by 2050 through sufficiency interventions (medium confidence). (Figure TS.15) {9.2, 9.3, 9.4, 9.5, 9.9}The potential associated with sufficiency measures, as well as the replacement of

appliances, equipment and lights by efficient ones, is below zero cost (high confidence). The construction of high-performance buildings is expected to become a  business-as-usual technology by 2050 with costs below USD20 tCO

2–1 in developed 
countries and below USD100 tCO 2–1 in developing countries (medium 
confidence). For existing buildings, there have been many examples of deep retrofits where additional costs per CO
2 abated are not 
significantly higher than those of shallow retrofits. However, for the whole building stock they tend to be in cost intervals of USD–200 tCO
2–1 and >USD200 tCO 2–1 (medium confidence). Literature 
emphasises the critical role of the 2020–2030 decade in accelerating the learning of know-how and skills to reduce the costs and remove feasibility constraints for achieving high-efficiency buildings at scale and set the sector on the pathway to realise its full potential (high confidence). {9.3, 9.6, 9.9}.
The development, since AR5, of integrated approaches to the 
construction and retrofit of buildings has led to increasing the number of zero-energy or zero-carbon buildings in almost all climate zones.  The complementarity and interdependency of

measures leads to cost reductions, while optimising the mitigation potential achieved and avoiding the lock-in-effect (medium confidence). {9.6, 9.9}
of sufﬁciency measures and the slow penetration of renewableTotal
Population EfﬁciencySufﬁciency Renewables1990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
1990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
Scenario
Renewable
scenario
SSP2
LED and
28%52%
16%14%54%
12%
10%31%
6%
0%11%38%
0%
49%30%
21%
64%14%
85%23% 5%
6%
55%22%
23%
17%
30%
1990
2019
2020
2020
2050
2050
20502050

Renewable
Emissions
2°C policy
▲▲▲
▲▲▲
▲ ▲
▲
▲▲ ▲▲
▼▼
▼
▼
▼▼
▼ ▼
▼
▼▼
▼
▼
▼
▼
0%0%
0%
218%
128%22%
70%65%0%
75% 16%12%
20%65%
57%
3%0%
0% 25%
0%
80%11%21%
16%
62%8%
6%
38%19%29%
6%34%
45%
0%0% 3%19%
0%2%27%
0%41%
24% 0%
37%
14%20%
55%15%
20% 9%
7%
31%49%
9%19%
40%20%
50%44%
33%21%86%
0%0%
16%33%
12%0%
0%14%15%
0%
50%46%3%
17%
65%38% 13%
42%9%
28%14%
35%41%
153%83%
42%40%68%
0%0%
40%21%
0%0%
28%42%
0% 23%28%
30%19%
59%12%
21%15%
22%68%17%
15%29%
47%
05001000
31%38%
13%8%
0%0% 17%3%
0% 42%
32% 21%
25%
24%
55%34%
17%
25%
36%31%
26%53%
82%0%
0%
115%
33%21%

64%73%4%
78%13%8%
25%63%
0%0%
56%53%
83%0%
0%
31%65%52%
22%
▲
▼▲▲▲▲
▼▲▲▲
▼
▼▲▲▲▲▲▲
▼
▼▲▲▲
▼
▼
▼
14%55%
2%38%
0%0%14%
0% 1%9%
0%45%8%
22%
26%19%
71%3%
7%
38%9%
8%53%
29%
28% 14%36%
40%
▲▲
▲▲
▲▲▲
▲▲▲
▲▼
▼▼
▼
▼
▼▼▼
▼▼▼
▼▼
▼ ▼
▼
▼
▲▲▲▲▲
▲▲ ▲
▲▲▲ ▲
▲
▼▼▼▼▼
▼
▼▼▼
▼
▼▼▼
▼
▼▲▲
▲
▲▲▲▲
▲▲▲
▲▼
▼▼▼
▼
▼
▼▼
▼▼▼
▼▼
▼
▼
▼
▼
▲▲▲▲▲
▲▲
▲▲
▲▲▲▲▲
▲
▼▼▼
▼
▼▼▼
▼▼
▼
▼
▼
▼▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▼
▼
▼▲▲▲▲▲
▲▲
▲▲
▲▲
▲▲
▲
▲▲
▲▲
▲▲▲▲
▲
▲▲▲
▲
▲▲▲▲▲▲▲▲
▲▲▲▲▲▲▲▲▲
▼▼▼▼
▼▼
▼▼▼
▼
▼
▼▼▼▼▼
▼▼▼▼
▼▼▼▼▼▼
▲▲▲▲▲▲▲▲
▲▲▲▲▲
▲▲▲ ▲
▼▼▼▼
▼▼
▼
▼▼
▼
▼Figure TS.15 | Decompositions of changes in historical residential energy emissions 1990–2019, changes in emissions projected by baseline scenarios for 
2020–2050, and differences between scenarios in 2050 using scenarios from three models: IEA, IMAGE, and RECC.

102
TSTechnical Summaryb)a)GHG emissions (direct and indirect) MtCO 2-eq GHG emissions (direct and indirect) MtCO 2-eq
2019
2020
2050
2050
2020
2050
2020
2050
20502050
1990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
Scenario
Renewable
scenario
SSP2
LED and
28%52%
16%14%54%
12%
10%31%
6%
0%11%38%
0%
49%30%
21%
64%14%
85%23% 5%
6%
55%22%
23%
17%
30%
1990
2019
2020
2020
2050
2050
20502050

Renewable
Emissions
2°C policy
▲▲▲
▲▲▲
▲ ▲
▲
▲▲ ▲▲
▼▼
▼
▼
▼▼
▼ ▼
▼
▼▼
▼
▼
▼
▼
0%0%
0%
218%
128%22%
70%65%0%
75% 16%12%
20%65%
57%
3%0%
0% 25%
0%
80%11%21%
16%
62%8%
6%
38%19%29%
6%34%
45%
0%0% 3%19%
0%2%27%
0%41%
24% 0%
37%
14%20%
55%15%
20% 9%
7%
31%49%
9%19%
40%20%
50%44%
33%21%86%
0%0%
16%33%
12%0%
0%14%15%
0%
50%46%3%
17%
65%38% 13%
42%9%
28%14%
35%41%
153%83%
42%40%68%
0%0%
40%21%
0%0%
28%42%
0% 23%28%
30%19%
59%12%
21%15%
22%68%17%
15%29%
47%
05001000
31%38%
13%8%
0%0% 17%3%
0% 42%
32% 21%
25%
24%
55%34%
17%
25%
36%31%
26%53%
82%0%
0%
115%
33%21%
64%73%4%
78%13%8%
25%63%
0%0%
56%53%
83%0%
0%
31%65%52%
22%

▲
▼▲▲▲▲
▼▲▲▲
▼
▼▲▲▲▲▲▲
▼
▼▲▲▲
▼
▼
▼
14%55%
2%38%
0%0%14%
0% 1%9%
0%45%8%
22%
26%19%
71%3%
7%
38%9%
8%53%
29%
28% 14%36%
40%
▲▲
▲▲
▲▲▲
▲▲▲
▲▼
▼▼
▼
▼
▼▼▼
▼▼▼
▼▼
▼ ▼
▼
▼
▲▲▲▲▲
▲▲ ▲
▲▲▲ ▲
▲
▼▼▼▼▼
▼
▼▼▼
▼
▼▼▼
▼
▼▲▲
▲
▲▲▲▲
▲▲▲
▲▼
▼▼▼
▼
▼
▼▼
▼▼▼
▼▼
▼
▼
▼
▼
▲▲▲▲▲
▲▲
▲▲
▲▲▲▲▲
▲
▼▼▼
▼
▼▼▼
▼▼
▼
▼
▼
▼▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▼
▼
▼▲▲▲▲▲
▲▲
▲▲
▲▲
▲▲
▲
▲▲
▲▲
▲▲▲▲
▲
▲▲▲
▲
▲▲▲▲▲▲▲▲
▲▲▲▲▲▲▲▲▲
▼▼▼▼
▼▼
▼▼▼
▼
▼
▼▼▼▼▼
▼▼▼▼
▼▼▼▼▼▼
▲▲▲▲▲▲▲▲
▲▲▲▲▲
▲▲▲ ▲
▼▼▼▼
▼▼
▼
▼▼
▼
▼
Figure TS.15 (continued): Decompositions of changes in historical residential energy emissions 1990–2019, changes in emissions projected by baseline

scenarios for 2020–2050, and differences between scenarios in 2050 using scenarios from three models: IEA, IMAGE, and RECC. RECC-LED data for (a) global, and (b) for nine world regions, include only space heating and cooling and water heating in residential buildings. Emissions are decomposed using the equation,

which shows changes in driver variables of population, sufficiency (floor area per capita), efficiency (final energy per floor area), and renewables (GHG emissions per final energy). ‘Renewables’ is a summary term describing changes in GHG intensity of energy supply. Emission projections to 2050, and differences between scenarios in 2050, demonstrate mitigation potentials from the dimensions of the SER framework realised in each model scenario. In most regions, historical improvements in efficiency have been approximately matched by growth in floor area per capita. Implementing sufficiency measures that limit growth in floor area per capita, particularly in developed regions, reduces the dependence of climate mitigation on technological solutions. {Figure 9.5, Box 9.2}

103TS
Technical SummaryThe decarbonisation of buildings is constrained by multiple 
barriers and obstacles as well as limited finance flows (high confidence). The lack of institutional capacity, especially in developing countries, and appropriate governance structures slow down the decarbonisation of the global building stock (medium confidence).  The building sector is highly heterogenous 
with many different building types, sizes, and operational uses. The sub-segment representing rented property faces principal/agent problems where the tenant benefits from the decarbonisation’s investment made by the landlord. The organisational context and the governance structure could trigger or hinder the decarbonisation of buildings. Global investment in the decarbonisation of buildings was estimated at USD164 billion in 2020. However, this is not enough by far to close the investment gap (high confidence). {9.9}
Policy packages could grasp the full mitigation potential of

the global building stock. Building energy codes represent the main regulatory instrument to reduce emissions from both new and existing buildings (high confidence).  The most 
advanced building energy codes include requirements on each of the three pillars of the SER framework in the use and construction phase of buildings. Building energy codes have proven to be effective if compulsory and combined with other regulatory instruments such as minimum energy performance standard for appliances and equipment, if the performance level is set at the level of the best available technologies in the market (high confidence). Market-based instruments such as carbon taxes with recycling of the revenues and personal or building carbon allowances could also contribute to fostering the decarbonisation of the building sector (medium confidence). {9.9}
Adapting buildings to future climate while ensuring well-being

for all requires action. Expected heatwaves will inevitably increase cooling needs to limit the health impacts of climate change (medium confidence).  Global warming will impact cooling 
and heating needs but also the performance, durability and safety of buildings, especially historical and coastal ones, through changes in temperature, humidity, atmospheric concentrations of CO
2 and 
chloride, and sea level rise. Adaptation measures to cope with climate change may increase the demand for energy and materials leading to an increase in GHG emissions if not mitigated. Sufficiency measures which anticipate climate change, and include natural ventilation, white walls, and nature-based solutions (e.g.,  green roofs) will decrease the demand for cooling. Shared cooled spaces with highly efficient cooling solutions are among the mitigation strategies which can limit the effect of the expected heatwaves on people’s health. {9.7, 9.8}
Up to 90% GHG emissions 

Up to 28% higher selling prices for decarbonised building in developed countries
indirect jobs per million USD invested in building retroﬁt or new energy efﬁcient buildings
Key point: Achieving SDG targets requires implementation of ambitious climate mitigation policies which include sufﬁciency measures to align

Figure TS.16 | Contribution of building-sector mitigation policies to meeting Sustainable Development Goals. {Figure 9.18}

104
TSTechnical SummaryWell-designed and effectively implemented mitigation actions 
in the buildings sector have significant potential to help achieve the SDGs (high confidence). As shown in Figure TS.16, the impacts of mitigation actions in the building sector go far beyond the goal of climate action (SDG 13) and contribute to meeting 15 other SDGs. Mitigation actions in the building sector bring health gains through improved indoor air quality and thermal comfort, and have positive significant macro- and micro-economic effects, such as increased productivity of labour, job creation, reduced poverty, especially energy poverty, and improved energy security (high confidence). (Figure TS.29) {9.8}
The COVID-19 pandemic emphasised the importance of

buildings for human well-being and highlighted the inequalities in access for all to suitable, healthy buildings, which provide natural daylight and clean air to their occupants (medium confidence). Recent WHO health recommendations have also emphasise indoor air quality, preventive maintenance of centralised mechanical heating, ventilation, and cooling systems. There are opportunities for repurposing existing non-residential buildings, no longer in use due to the expected spread of teleworking triggered by the health crisis and enabled by digitalisation. (Box TS.14) {9.1}
TS.5.5 Industry
The industry chapter focuses on new developments since AR5 and emphasises the role of the energy-intensive and emissions-intensive basic materials industries in strategies for reaching net zero emissions. The Paris Agreement, the SDGs and the COVID-19 pandemic provide a new context for the evolution of industry and mitigation of industry greenhouse gas (GHG) emissions (high confidence). {11.1.1}

Net zero CO
2 industrial-sector emissions are possible but 
challenging (high confidence). Energy efficiency will continue to 
be important. Reduced materials demand, material efficiency, and circular economy solutions can reduce the need for primary production. Primary production options include switching to new processes that use low-to-zero GHG energy carriers and feedstocks (e.g., electricity, hydrogen, biofuels, and carbon dioxide capture and utilisation (CCU) to provide carbon feedstocks). Carbon capture and  storage (CCS) will be required to mitigate remaining CO
2 emissions {11.3}. 
These options require substantial scaling up of electricity, hydrogen, recycling, CO
2, and other infrastructure, as well as phase-out or

conversion of existing industrial plants. While improvements in the GHG intensities of major basic materials have nearly stagnated over the last 30 years, analysis of historical technology shifts and newly available technologies indicate these intensities can be significantly reduced by mid-century. {11.2, 11.3, 11.4}
Industry-sector emissions have been growing faster since 
2000 than emissions in any other sector, driven by increased basic materials extraction and production (high confidence). GHG emissions attributed to the industrial sector originate from fuel combustion, process emissions, product use and waste, which jointly accounted for 14.1 GtCO
2-eq or 24% of all direct anthropogenic 
emissions in 2019, second behind the energy supply sector. Industry is a leading GHG emitter – 20 GtCO 2-eq or 34% of global emissions

in 2019  – if indirect emissions from power and heat generation are included. The share of emissions originating from direct fuel combustion is decreasing and was 7 GtCO
2-eq, 50% of  direct 
industrial emissions in 2019. {11.2.2}
Global material intensity – the in-use stock of manufactured 
capital in tonnes per unit of GDP  – is increasing (high confidence). In-use stock of manufactured capital per capita has been growing faster than GDP per capita since 2000. Total global in-use stock of manufactured capital grew by 3.4% yr
 –1 in 2000–2019. 
At the same time, per-capita material stocks in several developed countries have stopped growing, showing a  decoupling from GDP per capita. {11.2.1, 11.3.1}
The demand for plastic has been growing most strongly since

1970 (high confidence). The current >99% reliance on fossil feedstock, very low recycling, and high emissions from petrochemical processes is a challenge for reaching net zero emissions. At the same time, plastics are important for reducing emissions elsewhere, for example, light-weighting vehicles. There are as yet no shared visions for fossil-free plastics, but several possibilities. {11.4.1.3}
Scenario analyses show that significant reductions in global 
GHG emissions and even close to net zero emissions from GHG intensive industry (e.g., steel, plastics, ammonia, and cement) can be achieved by 2050 by deploying multiple available and emerging options (medium confidence). Significant reductions in industry emissions require a reorientation from the historic focus on important but incremental improvements (e.g., energy efficiency) to transformational changes in energy and feedstock sourcing, materials efficiency, and more circular material flows. {11.3, 11.4}

Key mitigation options such as materials efficiency, circular 
material flows and emerging primary processes, are not well represented in climate change scenario modelling and integrated assessment models (IAMs), albeit with some progress in recent years (high confidence). The character of these interventions (e.g., appearing in many forms across complex value chains, making cost estimates difficult) combined with the limited data on new fossil-free primary processes help explain why they are less represented in models than, for example, CCS. As a result, overall mitigation costs and the need for CCS may be overestimated. {11.4.2.1}
Electrification is emerging as a  key mitigation option for

industry (high confidence). Using electricity directly, or indirectly via hydrogen from electrolysis for high temperature and chemical feedstock requirements, offers many options to reduce emissions. It also can provide substantial grid-balancing services, for example, through electrolysis and storage of hydrogen for chemical process use or demand response. (Box TS.9) {11.3.5}
materials and will remain important (high confidence). In order to reach net zero CO
(e.g., plastics, wood, aviation fuels, solvents, etc.), it is important to

105TS
Technical Summary0.00.10.20.30.40.50.6
2020 2030 2040 2050 2050 2060 2070t CO2-eq/t cement(a) Cement Costs rise: cement – 35–115%; house < 1%
0.11
0.15
EE
ME
Circlularity
FeedCI
FSW+El
CCUSUS$/tCO2-eq<30$/tCO 2
FeedCIME
CCUSCircularity FSW+ElCIEI
20
2020 2030 2040 2050 2050 2060 2070t CO2-eq/t steel(b) Steel Costs rise: steel – 10–50%; home or car <1% 
20502070
0.14
0.10
0.17
0.180.16
CIEl
EE
ME
FeedCI
FSW+El
CCUS0.00.40.81.21.62.0Direct and
indirectDirect andindirect
Direct Direct <20$/tCO 2
<20$/tCO 2
60–100$/tCO 2
2020 2030 2040 2050 2050 2060 2070t CO2-eq/t chemicals(c) Primary chemicalsCosts rise: primary chemicals – 15–115%;plastic bottle <1% 
20502070
0.08
0.21
0.32

1.240.20
EE
ME
FeedCI
FSW+El
2020 2030 2040 2050 2050 2060 2070t CO2-eq(d) Industry (waste excluded)
048121620
20502070
13%
10%
15%40%
35%17%
55%15%Indirect
Chemicals
Metals
DirectDirect<20$/tCO 2
<20$/tCO 2
25–150$/tCO 2CementCement
EE
ME
FeedCI
FSW+El
CCUS
Figure TS.17 | Potentials and costs for zero-carbon mitigation options for industry and basic materials.

106
TSTechnical Summaryclose the use loops for carbon and carbon dioxide through increased 
circularity with mechanical and chemical recycling, more efficient use of biomass feedstock with addition of low-GHG hydrogen to increase product yields (e.g., for biomethane and methanol), and potentially direct air capture of CO
2 as a new carbon source. {11.3, 11.4.1} 
Production costs for very low to zero emissions basic materials may be high but the cost for final consumers and the general economy will be low (medium confidence). Costs and emissions reductions potential in industry, and especially heavy industry, are highly contingent on innovation, commercialisation, and market-uptake policies. Technologies exist to take all industry sectors to very low or zero emissions, but require five to fifteen years of intensive innovation, commercialisation, and policy to ensure uptake. Mitigation costs are in the rough range of USD50–150  tCO
2-eq–1,

with wide variation within and outside this band. This affects competitiveness and requires supporting policy. Although production cost increases can be significant, they translate to very small increases in the costs for final products, typically less than a  few percent depending on product, assumptions, and system boundaries. (Figure TS.17) {11.4.1.5} 
Several technological options exist for very low to zero

emissions steel, but their uptake will require integrated material efficiency, recycling, and production decarbonisation policies (high confidence). Material efficiency can potentially reduce steel demand by up to 40% based on design for less steel use, long life, reuse, constructability, and low-contamination recycling. Secondary production through high-quality recycling must be maximised. Production decarbonisation will also be required, starting with the retrofitting of existing facilities for partial fuel switching (e.g., to biomass or hydrogen), CCU and CCS, followed by very low and zero emissions production based on high-capture CCS or direct hydrogen, or electrolytic iron-ore reduction followed by an electric arc furnace. {11.3.2, 11.4.1.1}
Several current and emerging options can significantly reduce

cement and concrete emissions. Producer, user, and regulator education, as well as innovation and commercialisation policy are needed (medium confidence). Cement and concrete are currently overused because they are inexpensive, durable, and ubiquitous, and consumption decisions typically do not give weight to their production emissions. Basic material efficiency efforts to use only well-made concrete thoughtfully and only where needed (e.g.,  using right-sized, prefabricated components) could reduce emissions by 24–50% through lower demand for clinker. Cementitious material substitution with various materials (e.g., ground limestone and calcined clays) can reduce process calcination emissions by up to 50% and occasionally much more. Until a very low GHG emissions alternative binder to Portland cement is commercialised – which is not anticipated in the near to mid-term  – CCS will be essential for eliminating the limestone calcination process emissions for making clinker, which

currently represent 60% of GHG emissions in best-available technology plants. {11.3.2, 11.3.6, 11.4.1.2}

the main industrial feedstock chemicals and their derivatives, the  costs vary widely (high confidence). Fossil fuel-based feedstocks are inexpensive and still without carbon pricing, and their biomass- and electricity-based replacements are expected to be more expensive. The chemical industry consumes large amounts of hydrogen, ammonia, methanol, carbon monoxide, ethylene, propylene, benzene, toluene, and mixed xylenes and aromatics from fossil feedstock, and from these basic chemicals produces tens of thousands of derivative end-use chemicals. Hydrogen, biogenic or air-capture carbon, and collected plastic waste for the primary feedstocks can greatly reduce total emissions. Biogenic carbon feedstock is expected to be limited due to competing land uses. {11.4.1}

through switching to low-GHG fuels (e.g.,  biofuels and hydrogen) and electricity (e.g.,  for electrothermal heating and heat pumps) (high confidence). Most of these technologies are already mature, for example for low-temperature heat, but a major challenge is the current low cost of fossil CH
4 and coal relative 
to low- and zero-GHG electricity, hydrogen, and biofuels. {11.4.1}
The pulp and paper industry has significant biogenic carbon 
emissions but relatively small fossil carbon emissions. Pulp mills have access to biomass residues and by-products and in paper mills the use of process heat at low to medium temperatures allows for electrification (high confidence). Competition for feedstock will increase if wood substitutes for building materials and petrochemicals feedstock. The pulp and paper industry can also be a source of biogenic carbon dioxide, carbon for organic chemicals feedstock, and for CDR using CCS. {11.4.1}
The geographical distribution of renewable resources has

implications for industry (medium confidence).  The potential 
for zero-emission electricity and low-cost hydrogen from electrolysis powered by solar and wind, or hydrogen from other very low emission sources, may reshape where currently energy- and emissions-intensive basic materials production is located, how value chains are organised, trade patterns, and what gets transported in international shipping. Regions with bountiful solar and wind resources, or low fugitive CH
4 
co-located with CCS geology, may become exporters of hydrogen or hydrogen carriers such as methanol and ammonia, or home to the production of iron and steel, organic platform chemicals, and other energy-intensive basic materials. {11.2, 11.4, Box 11.1}Figure TS.17 (continued): Potentials and costs for zero-carbon mitigation options for industry and basic materials.  CIEl – carbon intensity of electricity for

indirect emissions; EE – energy efficiency; ME – material efficiency; Circularity – material flows (clinker substituted by coal fly ash, blast furnace slag or other by-products and

waste, steel scrap, plastic recycling, etc.); FeedCI – feedstock carbon intensity (hydrogen, biomass, novel cement, natural clinker substitutes); FSW+El – fuel switch and processes electrification with low-carbon electricity. Ranges for mitigation options are shown based on bottom-up studies for grouped technologies packages, not for single technologies. In circles, contribution to mitigation from technologies based on their readiness are shown for 2050 (2040) and 2070. Direct emissions include fuel combustion and process emissions. Indirect emissions include emissions attributed to consumed electricity and purchased heat. For basic chemicals, only methanol, ammonia and high-value chemicals are considered. Total for industry does not include emissions from waste. Negative mitigation costs for some options such as Circularity are not reflected. {Figure 11.13}

107TS
Technical SummaryThe level of policy maturity and experience varies widely across 
the mitigation options (high confidence).  Energy efficiency is 
a  well-established policy field with decades of experience from voluntary and negotiated agreements, regulations, energy auditing and demand-side management (DSM) programmes. In contrast, materials demand management and efficiency are not well understood and addressed from a  policy perspective. Barriers to recycling that policy could address are often specific to the different material loops (e.g., copper contamination for steel and lack of technologies or poor economics for plastics) or waste-management systems. For electrification and fuel switching the focus has so far been mainly on innovation and developing technical supply-side solutions rather than creating market demand. {11.5.2, 11.6}
Industry has so far largely been sheltered from the impacts of 
climate policy and carbon pricing due to concerns about carbon leakage

26 and reducing competitiveness (high confidence). 
New approaches to industrial development policy are emerging for a transition to net zero GHG emissions. The transition requires a clear direction towards net zero, technology development, market demand for low-carbon materials and products, governance capacity and learning, socially inclusive phase-out plans, as well as international coordination of climate and trade policies (see also TS.6.5). It requires comprehensive and sequential industrial policy strategies leading to immediate action as well as preparedness for future decarbonisation, governance at different levels (from international to local) and integration with other policy domains. {11.6}
TS.5.6 Agriculture, Forestry, Other Land Uses, 
and Food Systems
TS.5.6.1 Agricultre, Forestry, and Other Land Use (AFOLU)
The agriculture, forestry and other land use (AFOLU)27 sector 
encompasses managed ecosystems and offers significant

mitigation opportunities while providing food, wood and other renewable resources as well as biodiversity conservation, provided the sector adapts to climate change. Land-based mitigation measures can reduce GHG emissions within the AFOLU sector, deliver CDR and provide biomass thereby enabling emission reductions in other sectors.
28 The rapid deployment of 
AFOLU measures features in all pathways that limit global warming to 1.5°C. Where carefully and appropriately implemented, AFOLU mitigation measures are positioned to deliver substantial co-benefits and help address many of the wider challenges associated with land management. If AFOLU measures are deployed badly, when taken together with the increasing need to produce sufficient food, feed, fuel and wood, they may exacerbate trade-offs with the conservation of habitats, adaptation, biodiversity and other services. 
26 See section TS.5.9.

27 AFOLU is a sector in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. AFOLU anthropogenic greenhouse gas emissions and removals by sinks reported 
by governments under the UNFCCC are defined as all those occurring on ‘managed land’. Managed land is land where human interventions and practices have been applied to perform production, ecological or social functions.
28 For example: in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, CO
2 emissions from biomass used for energy are reported in the AFOLU sector, 
calculated as an implicit component of carbon stock changes. In the energy sector, CO 2 emissions from biomass combustion for energy are recorded as an information item that is not included in 
the sectoral total emissions for the that sector.At the same time the capacity of the land to support these functions

may be threatened by climate change (high confidence). {AR6 WGI Figure SPM.7; AR6 WGII, 7.1, 7.6}
The AFOLU sector, on average, accounted for 13–21% of 
global total anthropogenic GHG emissions in the period 2010–2019. At the same time managed and natural terrestrial ecosystems were a carbon sink, absorbing around one third of anthropogenic CO
2 emissions (medium confidence). 
Estimated anthropogenic net CO 2 emissions from AFOLU (based on 
bookkeeping models) result in a net source of +5.9 ± 4.1 GtCO 2 yr –1 
between 2010 and 2019 with an unclear trend. Based on FAOSTAT or national GHG inventories, the net CO
2 emissions from AFOLU were 
0.0 to +0.8 GtCO 2 yr –1 over the same period. There is a discrepancy 
in the reported CO 2 AFOLU emissions magnitude because alternative

methodological approaches that incorporate different assumptions are used {7.2.2}. If the responses of all managed and natural land to both anthropogenic environmental change and natural climate variability, estimated to be a gross sink of –12.5 ± 3.2 GtCO
2 yr –1 for 
the period 2010–2019, are added to land-use emissions, then land overall constituted a net sink of –6.6 ± 5.2 GtCO
2 yr –1 in terms of CO 2 
emissions (medium confidence). (Table TS.4) {7.2, Table 7.1} 
Land-use change drives net AFOLU CO 2 emission fluxes. The 
rate of deforestation, which accounts for 45% of total AFOLU 
emissions, has generally declined, while global tree cover and global forest-growing stock levels are likely increasing (medium confidence). There are substantial regional differences, with losses of carbon generally observed in tropical regions and gains in temperate and boreal regions. Agricultural CH
4 and N 2O 
emissions are estimated to average 157 ± 47.1 MtCH 4 yr –1 and

6.6 ± 4.0 MtN 2O yr –1 or 4.2 ± 1.3 and 1.8 ± 1.1 GtCO 2-eq yr –1 (using 
IPCC AR6 GWP100 values for CH 4 and N 2O) respectively between 
2010 and 2019 {7.2.1, 7.2.3}. AFOLU CH 4 emissions continue to 
increase, the main source of which is enteric fermentation from ruminant animals. Similarly, AFOLU N
2O emissions are increasing, 
dominated by agriculture, notably from manure application, nitrogen deposition, and nitrogen fertiliser use (high confidence). In addition to being a net carbon sink and source of GHG emissions, land plays an important role in climate through albedo effects, evapotranspiration, and aerosol loading through emissions of volatile organic compounds (VOCs). The combined role of CH
4, N 2O and aerosols in total climate 
forcing, however, is unclear and varies strongly with bioclimatic region and management practice. {2.4.2.5, 7.2, 7.3}

108
TSTechnical SummaryThe AFOLU sector offers significant near-term mitigation 
potential at relatively low cost and can provide 20–30% of the 2050 emissions reduction described in scenarios that limit warming to 2°C  (>67%) or lower (high evidence, medium agreement). The AFOLU sector can provide 20–30% (interquartile range) of the global mitigation needed for a 1.5°C or 2°C pathway towards 2050, though there are highly variable mitigation strategies for how AFOLU potential can be deployed for achieving climate targets {Illustrative Mitigation Pathways in 7.5}. The estimated economic (<USD100 tCO
2-eq–1) AFOLU sector mitigation potential 
is 8 to 14 GtCO 2-eq yr –1 between 2020–2050, with the bottom 
end of this range representing the mean from IAMs and the upper end  representing the mean estimate from global sectoral studies. The economic potential is about half of the technical potential from AFOLU, and about 30–50% could be achieved under USD20 tCO
2-eq–1

{7.4}. The implementation of robust measurement, reporting and verification processes is paramount to improving the transparency of changes in land carbon stocks and this can help prevent misleading assumptions or claims on mitigation. {7.1, 7.4, 7.5} 
Between 2020 and 2050, mitigation measures in forests 
and other natural ecosystems provide the largest share of the AFOLU mitigation potential (up to USD100 tCO
2-eq–1), 
followed by agriculture and demand-side measures (high confidence).  In the global sectoral studies, the protection, improved 
management, and restoration of forests, peatlands, coastal wetlands, savannas and grasslands have the potential to reduce emissions and/or sequester 7.3 mean (3.9–13.1) GtCO
2-eq yr –1. Agriculture 
provides the second largest share of the mitigation potential, with 4.1 (1.7–6.7) GtCO
2-eq yr –1 (up to USD100 tCO 2-eq–1) from cropland

and grassland soil carbon management, agroforestry, use of biochar, improved rice cultivation, and livestock and nutrient management. Demand-side measures including shifting to sustainable healthy diets, reducing food waste, building with wood, biochemicals, and bio-textiles, have a mitigation potential of 2.2 (1.1–3.6) GtCO
2-eq yr –1. 
Most mitigation options are available and ready to deploy. Emissions reductions can be achieved relatively quickly, whereas CDR needs upfront investment. Sustainable intensification in agriculture, shifting diets, and reducing food waste could enhance efficiencies and reduce agricultural land needs, and are therefore critical for enabling supply-side measures such as reforestation, restoration, as well as decreasing CH
4 and N 2O emissions from agricultural production. In 
addition, emerging technologies (e.g.,  vaccines or CH 4 inhibitors) 
have the potential to substantially increase the CH 4 mitigation

potential beyond current estimates. AFOLU mitigation is not only relevant in countries with large land areas. Many smaller countries and regions, particularly with wetlands, have disproportionately high levels of AFOLU mitigation potential density. {7.4, 7.5}
The economic and political feasibility of implementing AFOLU 
mitigation measures is hampered by persistent barriers. Assisting countries to overcome barriers will help to achieve Table TS.4 | Net anthropogenic emissions (annual averages for 2010–2019a) from agriculture, forestry and other land use (AFOLU). For context, the net flux 
due to the natural response of land to climate and environmental change is also shown for CO 2 in column E. Positive values represent emissions, negative values represent 
removals. Due to different approaches to estimate anthropogenic fluxes, AFOLU CO 2 estimates in the table below are not directly comparable to LULUCF in national greenhouse 
gas inventories (NGHGIs).

Gas UnitsAFOLU net 
emissions Non-AFOLU 
GHG emissions Total net 
and non-AFOLU) 
by gasAFOLU as a % 
of total net 
including natural 
response of land 
to anthropogenic 
and climate variability Net-land 
AFOLU and natural 
fluxes across entire 
land surface)
A B C = A + B D = (A/C) *100 E F = A + E
CO 2 GtCO 2-eq yr –15.9 ± 4.1 
(bookkeeping
models, managed
0 to 0.8 (NGHGI/
CH 4 MtCH 4 yr –1157.0 ± 47.1 207.5 ± 62.2 364.4 ± 109.3
GtCO 2-eq yr –14.2 ± 1.3 5.9 ± 1.8 10.2 ± 3.0 41%
N2OMtN 2O yr –16.6 ± 4.0 2.8 ± 1.7 9.4 ± 5.6
GtCO 2-eq yr –11.8 ± 1.1 0.8 ± 0.5 2.6 ± 1.5 69%
Total GtCO 2-eq yr –111.9 ± 4.4
considers 
bookkeeping

models only) 44 ± 3.4 55.9 ± 6.1 21%
a Estimates are given for 2019 as this is the latest date when data are available for all gases, consistent with Chapter 2 of this report. Positive fluxes are emission from land 
to the atmosphere. Negative fluxes are removals. For all Table footnotes see Table 7.1. {Table 7.1}

109TS
Technical Summarysignificant short-term mitigation (medium confidence).  Finance 
forms a critical barrier to achieving these gains as currently mitigation 
efforts rely principally on government sources and funding mechanisms which do not provide sufficient resources to enable the economic potential to be realised. Differences in cultural values, governance, accountability and institutional capacity are also important barriers. Climate change itself could reduce the mitigation potential from the AFOLU sector, although an increase in the capacity of natural sinks could occur despite changes in climate (medium confidence) {AR6 
WGI Figure  SPM.7 and Sections 7.4 and 7.6}. The continued loss of biodiversity makes ecosystems less resilient to climate change extremes and this may further jeopardise the achievement of the AFOLU mitigation potentials indicated in this chapter (high confidence). (Box TS.15) {7.6}
The provision of biomass for bioenergy (with/without BECCS)

and other bio-based products represents an important share of the total mitigation potential associated with the AFOLU sector, though these mitigation effects accrue to other sectors (high confidence). Recent estimates of the technical bioenergy potential, when constrained by food security and environmental considerations, are within the ranges 5–50 and 50–250 EJ yr
 –1 
by 2050 for residues and dedicated biomass production systems, respectively.
29 (TS.5.7) {7.4, 12.3}

Bioenergy is the most land-intensive energy option, but total land occupation of other renewable energy options can also become significant in high deployment scenarios. While not as closely connected to the AFOLU sector as bioenergy, other renewable energy options can influence AFOLU activities in both synergistic and detrimental ways (high confidence). The character of land occupation, and associated impacts, vary considerably among mitigation options and also for the same option depending on geographic location, scale, system design and deployment strategy. Land occupation can be large uniform areas, for example, reservoir hydropower dams and tree plantations, and more distributed occupation that is integrated with other land uses, for example, wind turbines and agroforestry in agriculture landscapes. Deployment can be partly decoupled from additional land use, for example, use of organic waste and residues and integration of solar PV into buildings and other infrastructure (high

confidence). Wind and solar power can coexist with agriculture in beneficial ways (medium confidence). Indirect land occupation includes new agriculture areas following displacement of food production with bioenergy plantations and expansion of mining activities providing minerals required for manufacture of EV batteries, PV, and wind power. {7.4, 12.5}

The deployment of land-based mitigation measures can 
provide co-benefits, but there are also risks and trade-offs from inappropriate land management (high confidence). Such risks can best be managed if AFOLU mitigation is pursued in response to the needs and perspectives of multiple stakeholders to achieve outcomes that maximise synergies 
29 These potentials do not include avoided emissions resulting from bioenergy use associated with BECCS, which depends on energy substitution patterns, conversion efficiencies, and supply chain

emissions for both the BECCS and substituted energy systems. Estimates of substitution effects of bioenergy indicate that this additional mitigation would be of the same magnitude as provided through CDR using BECCS. Bio-based products with long service life, for example, construction timber, can also provide mitigation through substitution of steel, concrete, and other products, and through carbon storage in the bio-based product pool. See section TS.5.7 for the CDR potential of BECCS. {7.4, 12.3}while limiting trade-offs (medium confidence). The results of

implementing AFOLU measures are often variable and highly context-specific. Depending on local conditions (e.g., ecosystem, climate, food system, land ownership) and management strategies (e.g.,  scale, method), mitigation measures can positively or negatively affect biodiversity, ecosystem functioning, air quality, water availability and quality, soil productivity, rights infringements, food security, and human well-being. The agriculture and forestry sectors can devise management approaches that enable biomass production and use for energy in conjunction with the production of food and timber, thereby reducing the conversion pressure on natural ecosystems (medium confidence). Mitigation measures addressing GHGs may also affect other climate forcers such as albedo and evapotranspiration. Integrated responses that contribute to mitigation, adaptation, and other land challenges will have greater likelihood of being successful (high confidence); measures which provide additional

benefits to biodiversity and human well-being are sometimes described as ‘Nature-based Solutions’. {7.1, 7.4, 7.6, 12.4, 12.5}

AFOLU mitigation measures have been well understood 
for decades but deployment remains slow, and emissions trends indicate unsatisfactory progress despite beneficial contributions to global emissions reduction from forest-related options (high confidence).  Globally, the AFOLU sector has 
so far contributed modestly to net mitigation, as past policies have delivered about 0.65 GtCO
2 yr –1 of mitigation during 2010–2019 or

1.4% of global gross emissions. The majority (>80%) of emission reduction resulted from forestry measures. Although the mitigation potential of AFOLU measures is large from a biophysical and ecological perspective, its feasibility is hampered by lack of institutional support, uncertainty over long-term additionality and trade-offs, weak governance, fragmented land ownership, and uncertain permanence effects. Despite these impediments to change, AFOLU mitigation options are demonstrably effective and with appropriate support can enable rapid emission reductions in most countries. {7.4, 7.6}
policymakers and investors to land owners and managers is a  pre-requisite for achieving high levels of mitigation in the AFOLU sector (high confidence).  To date USD0.7 billion yr
 –1 is 
estimated to have been spent on AFOLU mitigation. This is well short of the more than USD400 billion yr
 –1 that is estimated to be necessary

to deliver the up to 30% of global mitigation effort envisaged in deep mitigation scenarios (medium confidence). This estimate of the global funding requirement is smaller than current subsidies provided to agriculture and forestry. A gradual redirection of existing agriculture and forestry subsidies would greatly advance mitigation. Effective policy interventions and national (investment) plans as part of NDCs, specific to local circumstances and needs, are urgently needed to accelerate the deployment of AFOLU mitigation options. These interventions are effective when they include funding schemes and long-term consistent support for implementation with governments taking the initiative together with private funders and non-state actors. {7.6}

110
TSTechnical SummaryRealising the mitigation potential of the AFOLU sector 
depends strongly on policies that directly address emissions and drive the deployment of land-based mitigation options, consistent with carbon prices in deep mitigation scenarios (high confidence).  Examples of successful policies and measures

include establishing and respecting tenure rights and community forestry, improved agricultural management and sustainable intensification, biodiversity conservation, payments for ecosystem services, improved forest management and wood-chain usage, bioenergy, voluntary supply chain management efforts, consumer behaviour campaigns, private funding and joint regulatory efforts to avoid, for example, leakage. The efficacy of different policies, however, will depend on numerous region-specific factors. In addition to funding, these factors include governance, institutions, long-term consistent execution of measures, and the specific policy setting. While the governance of land-based mitigation can draw on lessons from previous experience with regulating biofuels and forest carbon, integrating these insights requires governance that goes beyond project-level approaches emphasising integrated land-use planning and management within the frame of the Sustainable Development Goals. {7.4,

Box 7.2, 7.6}Addressing the many knowledge gaps in the development and testing of AFOLU mitigation options can rapidly advance the likelihood of achieving sustained mitigation (high confidence). Research priorities include improved quantification of anthropogenic and natural GHG fluxes and emissions modelling, better understanding of the impacts of climate change on the mitigation potential, permanence and additionality of estimated mitigation  actions, and improved (real-time and cheap) measurement, reporting and verification. There is a need to include a greater suite of mitigation measures in IAMs, informed by more realistic assessments that take into account local circumstances and socio-economic factors and cross-sector synergies and trade-offs. Finally, there is a critical need for more targeted research to develop appropriate country-level, locally specific, policy and land-management response options. These options could support more specific NDCs with AFOLU measures that

enable mitigation while also contributing to biodiversity conservation, ecosystem functioning, livelihoods for millions of farmers and foresters, and many other SDGs. {7.7, Figure 17.1}

Figure TS.18 | Food-system GHG emissions from the agriculture, and land use, land-use change and forestry (LULUCF), waste, and energy and industry 
sectors.  {Figure 12.5}Waste
F-gases
05101520
300
200100
0CH
4 [MtCH4 yr–1] N2O [MtN2O yr–1]
10.0

111TS
Technical SummaryTS.5.6.2 Food Systems
requires change at all stages from producer to consumer and waste management, which can be facilitated through integrated policy packages (high confidence).  Food systems 
are associated with 23–42% of global GHG emissions, while there is still widespread food insecurity and malnutrition. Absolute GHG emissions from food systems increased from 14 to 17 GtCO
2-eq yr –1 in 
the period 1990–2018. Both supply- and demand-side measures are important to reduce the GHG intensity of food systems. Integrated food policy packages based on a  combination of market-based, administrative, informative, and behavioural policies  can reduce cost compared to uncoordinated interventions, address multiple sustainability goals, and increase acceptance across stakeholders and civil society (limited evidence, medium agreement). Food

systems governance may be pioneered through local food policy initiatives complemented by national and international initiatives, but governance on the national level tends to be fragmented, and thus has limited capacity to address structural issues like inequities in access. (Figure TS.18, Table TS.5, Table TS.6) {7.2, 7.4, 12.4}
associated with lower GHG emissions (high confidence). Ruminant 
meat shows the highest GHG intensity. Beef from dairy systems has lower emissions intensity than beef from beef  herds  (8–23 and 17–94 kgCO
2-eq (100 g protein)–1, respectively) when some emissions

are allocated to dairy products. The wide variation in emissions reflects differences in production systems, which range from intensive feedlots with stock raised largely on grains through to rangeland and transhumance production systems. Where appropriate, a shift to diets with a higher share of plant protein, moderate intake of animal-source foods and reduced intake of saturated fats could lead to substantial decreases in GHG emissions. Benefits would also include reduced land 
020%
10%40%30%60%90%80%70%
Child and maternal malnutrition Dietary risksHigh body-mass index

Countries with high share of food imports have lower territorial GHG emissions (seen speciﬁcally in the Asia-Pasiﬁc developed region).Countries with high share of food exports have increased territorial GHG emissions, and a low to moderate share of energy food system emissions. Some countries with low/medium industrialisation (low share of energy food system emissions) have high per capita territorial GHG emissions because of land-use change emissions that are partly ‘exported’ to industrialised importing consumers. Often this is associated with emerging dietary risks and challenges of high BMI.
system emissions) the whole sale cost for food tends to increase and health problems due to overweight dominate.Low per capita territorial GHG emissions are found in countries with low consumption rates of meat.North and 
West EuropeNorth and West EuropeSouth and 

South AmericaSouth AmericaMeso AmericaMeso AmericaCaribbeanCaribbean
South and West and Middle AfricaSouth and West and Middle Africa
East AfricaEast AfricaRest of 
Sri Lanka
South-East AsiaSouth-East AsiaDeveloping PaciﬁcDeveloping Paciﬁc
Australia and New ZealandAustralia and New ZealandEast AsiaEast Asia USA and CanadaUSA and CanadaMiddle EastMiddle East
Asia Paciﬁc 
Developed (others)Asia Paciﬁc 
1 5 10
Figure TS.19 | Regional differences in health outcome, territorial per-capita GHG emissions from national food systems, and share of food system GHG 
emission from energy use.  GHG emissions are calculated according to the IPCC Tier 1 approach and are assigned to the country where they occur, not necessarily where the

food is consumed. Health outcome is expressed as relative contribution of each of the following risk factors to their combined risk for deaths: Child and maternal malnutrition (red), Dietary risks (yellow) or High body-mass index (blue). {Figure 12.7}

112
TSTechnical SummaryTable TS.5 | Food system mitigation opportunities.
Food system mitigation options  
(I: incremental; T: transformative)Direct and indirect effect on GHG 
mitigation  (+/0/–) a Co-benefits/adverse effects b
Food from 
agriculture, aquaculture and fisheries(I)   Dietary shift, in particular increased share of plant-based protein sourcesD+  ↓ GHG footprintA+   Animal welfare 
L+   Land sparing
H+   Good nutritional properties, potentially ↓ risk from 
zoonotic diseases, pesticides and antibiotics
(I/T)   Digital agriculture D+  ↑ logisticsL+   Land sparing
R+   ↑ resource-use efficiencies
(T)   Gene technology D+  ↑ productivity or efficiencyH+   ↑ nutritional quality
E0   ↓ use of agrochemicals; ↑ probability of off-target 
impacts 
(I)   Sustainable intensification Land-use optimisationD+  ↓ GHG footprint
E0   Mixed effectsL+   Land sparing 
R–   Might ↑ pollution/biodiversity loss
(I)   AgroecologyD+  ↓ GHG/area, positive micro-climatic effects

E+   ↓ energy, possibly ↓ transport  FL+ Circular 
approachesE+   Focus on co-benefits/ecosystem services
R+  Circular, ↑ nutrient and water use efficiencies
Controlled environment agriculture(T)   Soil-less agricultureD+  ↑ productivity, weather independent
FL+  Harvest on demand
E–   Currently ↑ energy demand, but ↓ transport, 
building spaces can be used for renewable energyR+   Controlled loops ↑ nutrient- and water-use efficiency
H+   Crop breeding can be optimised for taste and/or nutritional quality
Emerging food production technologies(T)   InsectsD0  Good feed conversion efficiency
FW+  Can be fed on food wasteH0   Good nutritional qualities but attention to allergies and food safety issues required
(I/T)   Algae and bivalves D+  ↓ GHG footprintsA+   Animal welfare
H+   Good nutritional qualities; risk of heavy-metal and pathogen contamination
R+   Biofiltration of nutrient-polluted waters

(I/T)   Plant-based alternatives to animal-based food productsD+  No emissions from animals, ↓ inputs for feedA+   Animal welfare
H+   Potentially ↓ risk from zoonotic diseases, pesticides 
and antibiotics; but ↑ processing demand 
(T)   Cellular agriculture (including cultured meat, microbial protein)D+   No emissions from animals, high protein conversion efficiency
E–  ↑ energy need
FLW+ ↓ food loss and wasteA+   Animal welfare
R+   ↓ emissions of reactive nitrogen or other pollutants
H0   Potentially ↓ risk from zoonotic diseases, pesticides 
and antibiotics; ↑ research on safety aspects needed
Food processing and packaging(I)   Valorisation of by-products, FLW logistics and managementM+  Substitution of bio-based materials
FL+  ↓ of food losses
(I)   Food conservationFW+  ↓ of food waste
(e.g., refrigeration, transport)
(I)   Smart packaging and other technologies FW+  ↓ of food waste

M0  ↑ material demand and ↑ material efficiency
E0  ↑ energy demand; energy savings possibleH+   Possibly ↑ freshness/reduced food safety risks 
(I)   Energy efficiency E+  ↓ energy 
Storage and distribution(I)   Improved logistics D+  ↓ transport emissions
FL+  ↓ losses in transport
FW–  Easier access to food could ↑ food waste 
(I)   Specific measures to reduce food waste in retail and food catering FW+  ↓ of food waste
E+  ↓ downstream energy demand
M+  ↓ downstream material demand
(I)   Alternative fuels/transport modes D+  ↓ emissions from transport
(I)   Energy efficiency E+  ↓ energy in refrigeration, lightening, climatisation
(I)   Replacing refrigerants D+  ↓ emissions from the cold chain
a Direct and indirect GHG effects: D – direct emissions except emissions from energy use, E – energy demand, M – material demand, FL – food losses, FW – food waste; 
direction of effect on GHG mitigation: (+) increased mitigation, (0) neutral, (–) decreased mitigation.

b Co-benefits/adverse effects: H – health aspects, A – animal welfare, R – resource use, L – land demand, E – ecosystem services; (+) co-benefits, (–) adverse effects. 
{Table 12.8}

113TS
Technical Summaryoccupation and nutrient losses to the surrounding environment, while 
at the same time providing health benefits and reducing mortality from 
diet-related non-communicable diseases. (Figure TS.19) {7.4.5, 12.4}
cultured meat, plant-based alternatives to animal-based food products, and controlled environment agriculture, can bring substantial reduction in direct GHG emissions from food production (limited evidence, high agreement). These technologies have lower land, water, and nutrient footprints, and address concerns over animal welfare. Realising the full mitigation potential depends on access to low-carbon energy as some emerging technologies are relatively more energy intensive. This also holds for deployment of cold-chain and packaging technologies, which can help reduce food loss and waste, but increase energy and materials use in the food system. (Table TS.5) {11.4.1.3, 12.4}

TS.5.7 Carbon Dioxide Removal (CDR)
CDR is a  key element in scenarios that limit warming to 2°C (>67%) or 1.5°C (>50%) by 2100 (high confidence).  
Implementation strategies need to reflect that CDR methods differ in terms of removal process, timescale of carbon storage, technological maturity, mitigation potential, cost, co-benefits, adverse side effects, and governance requirements. (Box TS.10) 
Table TS.6 | Assessment of food system policies targeting (post-farm gate) food-chain actors and consumers.
 
Level  
G: global/multinational;  N: national; L: local
Cost
Co-benefits
a and adverse 
Implications for coordina-
tion, coherence and consist-ency in policy package
b

Integrated food policy packagesNL    can be controlled cost efficient+ balanced, addresses multi-ple sustainability goalsReduces cost of uncoordinated interventions; increases acceptance across stakeholders and civil society (robust evidence, high agreement)
Taxes on food productsGN     regressive low
# 1– unintended  substitution effectsHigh enforcing effect on other food policies; higher acceptance if compensation or hypothecated taxes (medium evidence, high agreement)
GHG taxes on foodGN     regressive low
# 2– unintended  substitution effectsSupportive, enabling effect on other food policies, agricultural/fishery policies; requires changes in power distribution and trade agreements (medium evidence, medium agreement) + high spillover effect
Trade policies G    impacts global distributioncomplex effects+ counters leakage effects

Investment into research and innovationGN  none  medium+ high spillover effect + converging with digital societyCan fill targeted gaps for coordinated policy pack-ages (e.g., monitoring methods) (robust evidence, high agreement)
Food and marketing regulationsN      low  Can be supportive; might be supportive to realise innova-tion; voluntary standards might be less effective (medium evidence, medium agreement)
Organisational-level procure-ment policiesNL      low+ can address multiple sustain-ability goalsEnabling effect on other food policies; reaches large share of population (medium evidence, high agreement)
Sustainable food-based dietary guidelinesGNL      none  low+ can address multiple sustain-ability goalsLittle attention so far on environmental aspects; can serve as benchmark for other policies (labels, food formulation standards, etc.) (medium evidence, medium agreement)

Food labels/ informationGNL    education level relevantlow + empowers citizens+ increases awareness+ multiple objectivesEffective mainly as part of a policy package; incorpo-ration of other objectives (e.g., animal welfare, fair trade); higher effect if mandatory (medium evidence, medium agreement)
Nudges NL      none  low+ possibly counteracting infor -
mation deficits in population subgroupsHigh enabling effect on other food policies (medium evidence, high agreement)
Effect of measures:   negative  none/unclear  slightly positive  positive  
Notes: #1 Minimum level to be effective 20% price increase; #2 Minimum level to be effective USD50–80 tCO 2-eq. a In addition, all interventions are assumed to address health 

114
TSTechnical SummaryAll the illustrative mitigation pathways (IMPs) assessed 
in this report use land-based biological CDR (primarily afforestation/reforestation (A/R)) and/or bioenergy with carbon capture  and storage (BECCS). Some also include direct air CO
2 capture and storage (DACCS) (high confidence). 
Across the scenarios limiting warming to 2°C (>67%) or below, cumulative volumes
30 of BECCS reach 328 (168–763) GtCO 2, CO 2 
removal from AFOLU (mainly A/R) reaches 252 (20–418) GtCO 2, 
and DACCS reaches 29 (0–339) GtCO 2, for the 2020–2100 period. 
Annual volumes in 2050 are 2.75 (0.52–9.45) GtCO 2 yr –1 for BECCS, 
2.98 (0.23–6.38)  GtCO 2  yr –1 for the CO 2 removal from AFOLU 
(mainly A/R), and 0.02 (0–1.74) GtCO 2 yr –1 for DACCS. (Box TS.10) 
{12.3, Cross-Chapter Box 8 in Chapter 12} 
Despite limited current deployment, estimated mitigation

potentials for DACCS, enhanced weathering (EW) and ocean-based CDR methods (including ocean alkalinity enhancement and ocean  fertilisation) are moderate to large 
30 As a median value [5–95th percentile range].(medium confidence). The potential for DACCS (5–40 GtCO 2 yr –1) 
is limited mainly by requirements for low-carbon energy and by cost (100–300 (full range: 84–386) USD tCO
2–1). DACCS is currently 
at a  medium technology readiness level. EW has the potential to remove  2–4 (full range: <1 to around 100) GtCO
2  yr –1, at costs 
ranging from 50 to 200 (full range: 24–578) USD tCO 2–1. Ocean-based 
methods have a combined potential to remove 1–100 GtCO 2 yr –1 at 
possible side effects on the marine environment. EW and ocean-based methods are currently at a low technology readiness level. {12.3}
CDR governance and policymaking can draw on widespread

experience with emissions reduction measures (high confidence).  
Additionally, to accelerate research, development, and demonstration, and to incentivise CDR deployment, a political commitment to formal integration into existing climate policy frameworks is required, including reliable measurement, reporting and verification (MRV) of carbon flows. {12.3.3, 12.4, 12.5}
Box TS.10 | Carbon Dioxide Removal (CDR)
Carbon Dioxide Removal (CDR) is necessary to achieve net zero CO 2 and GHG emissions both globally and nationally, counterbalancing

‘hard-to-abate’ residual emissions. CDR is also an essential element of scenarios that limit warming to 1.5°C or below 2°C (>67%) by 2100, regardless of whether global emissions reach near zero, net zero or net negative levels. While national mitigation portfolios aiming at net zero emissions or lower will need to include some level of CDR, the choice of methods and the scale and timing of their deployment will depend on the achievement of gross emission reductions, and managing multiple sustainability and feasibility constraints, including political preferences and social acceptability.
CDR refers to anthropogenic activities removing CO
2 from the atmosphere and durably storing it in geological, terrestrial, or ocean 
reservoirs, or in products. It includes existing and potential anthropogenic enhancement of biological, geochemical or chemical CO 2 
sinks, but excludes natural CO 2 uptake not directly caused by human activities (Annex I). Carbon Capture and Storage (CCS) and

Carbon Capture and Utilisation (CCU) applied to fossil CO 2 do not count as removal technologies. CCS and CCU can only be part of 
CDR methods if the CO 2 is biogenic or directly captured from ambient air, and stored durably in geological reservoirs or products. {12.3}
There is a great variety of CDR methods and respective implementation options {Cross-Chapter Box 8, Figure 1 in Chapter 12}. Some

of these methods (like afforestation and soil carbon sequestration) have been practiced for decades to millennia, although not necessarily with the intention to remove carbon from the atmosphere. Conversely, for methods such as DACCS and BECCS, experience is growing but still limited in scale. A categorisation of CDR methods can be based on several criteria, depending on the highlighted characteristics. In this report, the categorisation is focused on the role of CDR methods in the carbon cycle, that is on the removal process (land-based biological; ocean-based biological; geochemical; chemical) and on the time scale of storage (decades to centuries; centuries to millennia; 10,000 years or longer), the latter being closely linked to different carbon storage media. Within one category (e.g., ocean-based biological CDR) options often differ with respect to other dynamic or context-specific dimensions such as mitigation potential, cost, potential for co-benefits and adverse side effects,

and technology readiness level. (Table TS.7, TS.5.6, TS. 5.7) {12.3}

It is useful to distinguish between CO
2 removal from the atmosphere as the outcome of deliberate activities implementing CDR 
options, and the net emissions outcome achieved with the help of CDR deployment (i.e., gross emissions minus gross removals). 
As part of ambitious mitigation strategies at global or national levels, gross CDR can fulfil three different roles in complementing emissions abatement: (i) lowering net CO
2 or GHG emissions in the near term; (ii) counterbalancing ‘hard-to-abate’ residual emissions 
such as CO 2 from industrial activities and long-distance transport, or CH 4 and nitrous oxide from agriculture, in order to help reach net 
zero CO 2 or GHG emissions in the mid-term; (iii) achieving net negative CO 2 or GHG emissions in the long term if deployed at levels 
exceeding annual residual emissions {2.7, 3.3, 3.4, 3.5}. These roles of CDR are not mutually exclusive: for example, achieving net zero CO

2 or GHG emissions globally might involve individual developed countries attaining net negative CO 2 emissions at the time of global 
net zero, thereby allowing developing countries a smoother transition. {Cross-Chapter Box 8, Figure 2 in Chapter 12}

115TS
Technical SummaryTable TS.7 | Summary of status, costs, potentials, risk and impacts, co-benefits, trade-offs and spillover effects and the role in mitigation pathways for CDR methods {12.3.2, 7.4}.  (TRL = technology readiness level.)
CDR method Status 
(TRL) Cost1 
(USD 
tCO 2–1) Mitigation 
potential1 
(GtCO 2 yr –1) Risk and impacts  Co-benefits  Trade-offs and spillover effects  Role in mitigation pathways  Section 

reforestation  8–9  0–240  0.5–10 Reversal of carbon removal through wildfire, disease, pests may occur.  Reduced catchment water yield and lower groundwater level if species and biome are inappropriate. Enhanced employment and local livelihoods, improved biodiversity, improved renewable wood products provision, soil carbon and nutrient cycling. Possibly less pressure on primary forest. Inappropriate deployment at large scale can lead to competition for land with biodiversity conservation and food production.Substantial contribution in IAMs and also in bottom-up sectoral studies. {7.4}

Soil carbon sequestration in croplands and grasslands8–9  –45–100  0.6–9.3 Risk of increased nitrous oxide emissions due to higher levels of organic nitrogen in the soil; risk of reversal of carbon sequestration. Improved soil quality, resilience and agricultural productivity. Attempts to increase carbon sequestration potential at the expense of production. Net addition per hectare is very small; hard to monitor.In development – not yet in global mitigation pathways simulated by IAMs in bottom-up studies: with medium contribution. {7.4}
Peatland and coastal wetland restoration 8–9 Insufficient 
data 0.5–2.1 Reversal of carbon removal in drought or future disturbance. Risk of increased CH

4 emissions. Enhanced employment and local livelihoods, increased productivity of fisheries, improved biodiversity, soil carbon and nutrient cycling. Competition for land for food production on some peatlands used for food production. Not in IAMs but some bottom-up studies with medium contribution.{7.4}
Agroforestry  8–9 Insufficient 
data0.3–9.4Risk that some land area lost from food production; requires very high skills. Enhanced employment and local livelihoods, variety of products improved soil quality, more resilient systems. Some trade-off with agricultural crop production, but enhanced biodiversity, and resilience of system.No data from IAMs, but in bottom-up sectoral studies with medium contribution.{7.4}
Improved forest management 8–9 Insufficient

data0.1–2.1 If improved management is understood as merely intensification involving increased fertiliser use and introduced species, then it could reduce biodiversity and increase eutrophication. In case of sustainable forest management, it leads to enhanced employment and local livelihoods, enhanced biodiversity, improved productivity.If it involves increased fertiliser use and introduced species it could reduce biodiversity and increase eutrophication and upstream GHG emissions. No data from IAMs, but in bottom-up sectoral studies with medium contribution.{7.4}
Biochar 6–7  10–345  0.3–6.6 Particulate and GHG emissions from production; biodiversity and carbon stock loss from unsustainable biomass harvest. Increased crop yields and reduced non-CO
2 emissions from soil; and 
resilience to drought. Environmental impacts associated particulate matter; competition for biomass resource. In development – not yet in global mitigation pathways simulated by IAMs. {7.4}

Direct air carbon capture and storage (DACCS) 6 100–300 
(84–386)  5–40  Increased energy and water use. Water produced (solid sorbent DAC designs only). Potentially increased emissions from water supply and energy generation. In a few IAMs; DACCS complements other CDR methods. {12.3} 
Bioenergy with carbon capture and storage (BECCS)5–6  15–400  0.5–11 Inappropriate deployment at very large scale leads to additional land and water use to grow biomass feedstock. Biodiversity and carbon stock loss if from unsustainable biomass harvest. Reduction of air pollutants, fuel security, optimal use of residues, additional income, health benefits, and if implemented well, it can enhance biodiversity. Competition for land with biodiversity conservation and food production.Substantial contribution in IAMs and bottom-up sectoral studies. Note – mitigation through avoided GHG emissions resulting from bioenergy use is of the same magnitude as the mitigation from CDR (TS.5.6). {7.4}

Enhanced weathering (EW) 3–4 50–200 
(24–578) 2–4 (<1–95) Mining impacts; air quality impacts of rock dust when spreading on soil. Enhanced plant growth, reduced erosion, enhanced soil carbon, reduced soil acidity, enhanced soil water retention. Potentially increased emissions from water supply and energy generation. In a few IAMs; EW complements other CDR methods.{12.3}

116
TSTechnical Summary
(USD 
‘Blue carbon 
management’ in coastal wetlands 2–3 Insufficient

data <1 If degraded or lost, coastal blue carbon ecosystems are expected to release most of their carbon back to the atmosphere; potential for sediment contaminants, toxicity, bioaccumulation and biomagnification in organisms; issues related to altering degradability of coastal plants; use of sub-tidal areas for tidal wetland carbon removal; effect of shoreline modifications on sediment redeposition and  natural marsh accretion; abusive use of coastal blue carbon as means to reclaim land for purposes that degrade capacity for carbon removal. Provide many non-climatic benefits and can contribute to ecosystem-based adaptation, coastal protection, increased biodiversity, reduced upper ocean acidification; could potentially benefit human nutrition or produce fertiliser for terrestrial agriculture, anti-methanogenic feed additive, or as an industrial or materials feedstock. If degraded or lost, coastal blue carbon ecosystems are likely to release most of their carbon back to the

atmosphere. The full delivery of the benefits at their maximum global capacity will require years to decades to be achieved.Not incorporated in IAMs, but in some bottom-up studies: small contribution. {7.4, 12.3.1}

Ocean fertilisation 1–2  50–500  1–3 Nutrient redistribution, restructuring of the ecosystem, enhanced oxygen consumption and acidification in deeper waters, potential for decadal-to-millennial-scale return to the atmosphere of nearly all the extra carbon removed, risks of unintended side effects.Increased productivity and fisheries, reduced upper-ocean acidification. Sub-surface ocean acidification, deoxygenation; altered meridional supply of macro-nutrients as they are utilised in the iron-fertilised region and become unavailable for transport to, and utilisation in other regions, fundamental alteration of food webs, biodiversity.No data.  {12.3.1} 
Ocean alkalinity enhancement (OAE)1–2  40–260   1–100  Increased seawater pH and saturation states and may impact marine biota. Possible release of nutritive or toxic elements and compounds. Mining impacts.Limiting ocean acidification. Potentially increased emissions of CO
2

and dust from mining, transport and deployment operations.No data.  {12.3.1} 
1 Range based on authors’ estimates (as assessed from literature) are shown, with full literature ranges shown in ( ) brackets.Table TS.7 (continued):

117TS
Technical SummaryTS.5.8 Demand-side Aspects of Mitigation
The assessment of the social science literature and regional case 
studies reveals how social norms, culture, and individual choices interact with infrastructure and other structural changes over time. This provides new insight into climate change mitigation strategies, and how economic and social activity might be organised across sectors to support emission reductions. To enhance well-being, people demand services and not primary energy and physical resources per se. Focusing on demand for services and the different social and political roles people play broadens the participation in climate action. (Box TS.11)
can help Avoid and Shift final service demands and Improve service delivery. Rapid and deep changes in demand make it easier for every sector to reduce GHG emissions in the near and mid-term (high confidence).  {5.2, 5.3}

The indicative potential of demand-side strategies to reduce emissions of direct and indirect CO
2 and non-CO 2 GHG emissions 
in three end-use sectors (buildings, land transport, and food) is 40–70% globally by 2050 (high confidence). Technical mitigation 
potentials compared to the 2050 emissions projection of two scenarios consistent with policies announced by national governments until 2020 amount to 6.8 GtCO
2 for building use and construction, 4.6 GtCO 2 for 
land transport and 8.0  GtCO 2-eq for food demand,  and amount to 
Shift-Improve (ASI) options, that reflect opportunities for socio-cultural, infrastructural, and technological change. The greatest Avoid potential comes from reducing long-haul aviation and providing short-distance low-carbon urban infrastructures. The greatest Shift potential would 
come from switching to plant-based diets. The greatest Improve

potential comes from within the building sector, and in particular increased use of energy-efficient end-use technologies and passive housing. (Figures TS.20 and TS.21) {5.3.1, 5.3.2, Figures 5.7 and 5.8, Table 5.1 and Table SM.5.2}
Socio-cultural and lifestyle changes can accelerate climate 
change mitigation (medium confidence). Among 60 identified actions that could change individual consumption, individual mobility choices have the largest potential to reduce carbon footprints.  
Prioritising car-free mobility by walking and cycling and adoption of 
electric mobility could save 2 tCO
2-eq cap–1 yr –1. Other options with 
high mitigation potential include reducing air travel, cooling setpoint adjustments, reduced appliance use, shifts to public transit, and shifting consumption towards plant-based diets. {5.3.1, 5.3.1.2, Figure 5.8}
Box TS.11 | A New Chapter in AR6 WGIII Focusing on the Social Science of Demand,  
and Social Aspects of Mitigation

The WGIII contribution to the Sixth Assessment Report of the IPCC (AR6) features a distinct chapter on demand, services and social 
aspects of mitigation {5}. The scope, theories, and evidence for such an assessment are addressed in Sections 5.1 and 5.4 within Chapter 5 and a Social Science Primer as an Appendix to Chapter 5. 
The literature on social science – from sociology, psychology, gender studies and political science for example – and climate change 
mitigation is growing rapidly. A bibliometric search of the literature identified 99,065 peer-reviewed academic papers, based on 34 search queries with content relevant to Chapter 5. This literature is expanding by 15% per year, with twice as many publications in the AR6 period (2014–2020) as in all previous years. 
The models of stakeholders’ decisions assessed by IPCC have continuously evolved. From AR1 to AR4, rational choice was the implicit

assumption: agents with perfect information and unlimited processing capacity maximising self-focused expected utility and differing only in wealth, risk attitude, and time discount rate. The AR5 introduced a broader range of goals (material, social, and psychological) and decision processes (calculation-based, affect-based, and rule-based processes). However, its perspective was still individual- and agency-focused, neglecting structural, cultural, and institutional constraints and the influence of physical and social context.
A social science perspective is important in two ways. By adding new actors and perspectives, it (i) provides more options for climate mitigation;

and (ii) helps to identify and address important social and cultural barriers and opportunities to socio-economic, technological, and institutional change. Demand-side mitigation involves five sets of social actors: individuals (e.g.,  consumption choices, habits), groups and collectives (e.g., social movements, values), corporate actors (e.g., investments, advertising), institutions (e.g., political agency, regulations), and infrastructure actors (e.g., very long-term investments and financing). Actors either contribute to the status-quo of global high-carbon consumption, and a GDP growth-oriented economy, or help generate the desired change to a low-carbon energy-services, well-being, and equity-oriented economy. Each set of actors has novel implications for the design and implementation of both demand- and supply-side mitigation policies. They show important synergies, making energy demand mitigation a dynamic problem where the packaging and/or sequencing of different policies play

a role in their effectiveness {5.5, 5.6}. Incremental interventions change social practices, simultaneously affecting emissions and well-being. The transformative change requires coordinated action across all five sets of actors (Table 5.4), using social science insights about intersection of behaviour, culture, institutional and infrastructural changes for policy design and implementation. Avoid, Shift, and Improve choices by

individuals, households and communities support mitigation {5.3.1.1, Table 5.1}. They are instigated by role models, changing social norms driven by policies and social movements. They also require appropriate infrastructures designed by urban planners and building and transport professionals, corresponding investments, and a political culture supportive of demand-side mitigation action.

118
Improve
AvoidA: Providing decent living standards through low-carbon infrastructures SI: reduce primary demand via efﬁcient technology adoption
SI: additional demand  reduction/waste reduction
different income groupsConsumption of bottom 90% 48%
Consumption of top 10% (excluding top 1%) 37%
Consumer
Role model

Plan infrastructures(a) Tilting the balance towards less resource intensive service provisioning
(b) Using wide range of demand-side options (c) Achieving a Low Demand scenario by 2050
No packing
Telecommuting
One less long-haul ﬂight
Live car-free
Shift
ImproveAvoid
Walking and cycling
Vegetarian diet
Vegan diet
Public transit
Heat pump
Refurbishment and
renovation
Renewable electricity
Battery electric vehicle Utilities, 
equipment 
demand growth due to transport electriﬁcation* Primary energy demand growth due to transport electriﬁcation
End users, 
transport
welfare policies)0100

4200300400500EJ world energy demand
Tonnes CO 2e cap–12020 primary
Energy Final EnergyUsefulEnergyServicesDelivered
2050 LED 
transport choice, Behavioural changeBuilding shells, transport choice, Behavioural change
Increased services (to meet SDGs) m
2 
dwellings, public services, pass-km, Policies, behavioursIncreased services (to meet SDGs) m
2 
dwellings, public services, pass-km, Policies, behaviours
2 0 6 -2
Low-carbon lifestyle transition can be classiﬁed into Avoid, Shift, and Improve options. Individual potential to reduce emissions is highest in mobility systems.Improved service provisioning systems enable increases in service levels and at the same time a reduction in upstream energy demand by 45%.Transport
Building FoodGlobal south Global north
Vehicles, appliances, Adoption decisionsVehicles, appliances, Adoption decisionsEfﬁciency Improvements & decisions:
Power plants,

fuel production, Investments decisionsEfﬁciency Improvements & decisions:
fuel production, Investments decisions
Figure TS.20 | Demand-side strategies for mitigation.  Demand-side mitigation is about more than behavioural change and transformation happens through societal, 
technological and institutional changes. {Figure 5.10, Figure 5.14}

119TS
Technical SummaryDemand-side mitigation can be achieved through changes in socio-cultural factors, infrastructure 
15
10
End-use
sectors
2 Load management refers to demand-side ﬂexibility that cuts across all sectors and can be achieved through incentive design like time of use pricing/monitoring by artiﬁcial intelligence, diversiﬁcation of storage facilities, etc.

End-use technology adoptionIndustryAdd. electriﬁcation
Industry
Buildings
Reduced emissions through demand-side mitigation options (in end-use sectors: buildings, industry and land transport) which has potential to reduce electricity demand
3Demand-side measures –73%a. Nutrition 
Dietary shift (shifting to balanced, sustainable healthy diets), avoidance of food wasteand over-consumption
information to guide dietary choices; ﬁnancial incentives; waste management; recycling infrastructure

Currently estimates are not available (for lab-based meat and similar options – no quantitative literature available, overall potential considered in socio-cultural factors)b. Manufactured products, mobility, shelter 
Shift in demand towards sustainable consumption, such as intensive use of longer-lived repairable productsTeleworking or telecommuting; active mobility through walking and cyclingAvoid long-haul ﬂights; shift to trains wherever possibleSocial practices resulting in energy saving; lifestyle and behavioural changesSocio-cultural factors

Networks established for recycling, repurposing, remanufacturing and reuse of metals, plastics and glass; labelling low-emissions materials and productsPublic transport; shared mobility; compact cities; spatial planningCompact cities; rationalisation of living ﬂoor space; architectural design; urban planning (e.g., green roof, cool roof, urban green spaces etc.)Infrastructure use
Green procurement to access material-efﬁcient products and services; access to energy-efﬁcient and CO
2 neutral materialsElectric vehicles; shift to more efﬁcient vehiclesEnergy-efﬁcient building envelopes and appliances; shift to renewablesEnd-use technology adoption
Total emissions 2050: Mean IEA-STEPS IP_ModActCurrently not applicableCurrently not applicableCurrently not applicable
Adoption ofenergy-efﬁcient technologies; technologies with improved aerodynamics    Adoption of energy-efﬁcient technology/systems

Figure TS.21 | Demand-side mitigation can be achieved through changes in socio-cultural factors, infrastructure design and use, and technology adoption.

Mitigation response options related to demand for services have been categorised into three domains: ‘socio-cultural factors’, related to social norms, culture, and individual choices and behaviour; ‘infrastructure use’, related to the provision and use of supporting infrastructure that enables individual choices and behaviour; and ‘technology adoption’, which refers to the uptake of technologies by end users. Potentials in 2050 are estimated using the International Energy Agency’s 2020 World Energy Outlook STEPS (Stated Policy Scenarios) as a baseline. This scenario is based on a sector-by-sector assessment of specific policies in place, as well as those that have been announced by countries by mid-2020. This scenario was selected due to the detailed representation of options across sectors and sub-sectors. The heights of the coloured columns represent the potentials on which there is a high level of agreement in the literature, based on a range of case studies. The range shown by

the dots connected by dotted lines represents the highest and lowest potentials reported in the literature which have low to medium levels of agreement. The demand-side potential of socio-cultural factors in the food system has two parts. The economic potential of direct emissions (mostly non-CO

2) demand reduction through socio-cultural factors alone is 1.9 GtCO 2-eq without considering land-use 
change by diversion of agricultural land from food production to carbon sequestration. If further changes in land use enabled by this change in demand are considered, the indicative potential could reach 7 GtCO
2-eq. The electricity panel presents separately the mitigation potential from changes in electricity demand and changes associated with

enhanced electrification in end-use sectors. Electrification increases electricity demand, while it is avoided though demand-side mitigation strategies. Load management refers to demand-side flexibility that can be achieved through incentive design such as time-of-use pricing/monitoring by artificial intelligence, diversification of storage facilities, and so on. NZE (IEA Net-Zero Emissions by 2050 scenario) is used to compute the impact of end-use sector electrification, while the impact of demand-side response options is based on bottom-up assessments. Dark grey columns show the emissions that cannot be avoided through demand-side mitigation options. The table indicates which demand-side mitigation options are included. Options are categorised according to: socio-cultural factors, infrastructure use, and technology adoption. Figure SPM.7 covers potential of demand-side options for the year 2050. Figure SPM.8 covers both supply- and demand-side options and their potentials for the

120
TSTechnical SummaryLeveraging improvements in end-use service delivery through 
behavioural and technological innovations, and innovations in market organisation, leads to large reductions in upstream resource use (high confidence). Analysis of indicative potentials range from a  factor 10- to 20-fold improvement in the case of available energy (exergy) analysis, with the highest improvement potentials at the end-user and service-provisioning levels. Realisable service level efficiency improvements could reduce upstream energy demand by 45% in 2050. (Figure TS.20) {5.3.2, Figure 5.10}
Decent living standards (DLS) and well-being for all (SDG 3) are 
achievable if high-efficiency low-demand mitigation pathways are followed (medium confidence).  Minimum requirements of 
energy use consistent with enabling well-being for all is between 20 
and 50 GJ cap
–1 yr –1 depending on the context. (Figure TS.22) {5.2.2.1,

5.2.2.2, Box 5.3}Alternative service provision systems, for example, those enabled through digitalisation, sharing economy initiatives and circular economy initiatives, have to date made a limited contribution to climate change mitigation (medium confidence).  While digitalisation through specific new products 
and applications holds potential for improvement in service-level efficiencies, without public policies and regulations, it also has the potential to increase consumption and energy use. Reducing the energy use of data centres, networks, and connected devices is possible in managing low-carbon digitalisation. Claims on the benefits of the circular economy for sustainability and climate change mitigation have limited evidence. (Box TS.12, Box TS.14) {5.3.4, Figures 5.12 and 5.13}
Box TS.12 | Circular Economy (CE)

In AR6, the circular economy (CE) concept {Annex I} is highlighted as an increasingly important mitigation approach that can help deliver human well-being by minimising waste of energy and resources. While definitions of CE vary, its essence is to shift away from linear ‘make and dispose’ economic models to those that emphasise product longevity, reuse, refurbishment, recycling, and material efficiency, thereby enabling more circular material systems that reduce embodied energy and emissions. {5.3.4, 8.4, 8.5, 9.5, 11.3.3} 
Whereas IPCC AR4 {WGIII, Chapter 10} included a separate chapter on waste-sector emissions and waste-management practices,

and AR5 {WGIII, Chapter 10} reviewed the importance of ‘reduce, reuse, recycle’ and related policies, AR6 focuses on how CE can reduce waste in materials production and consumption by optimising materials’ end-use service utility. Specific examples of CE implementations, policies, and mitigation potentials are included in Chapters 5, 8, 9, 11 and 12. {5.3, 8.4, 9.5, 11.3, 12.6}
CE is shown to empower new social actors in mitigation actions, given that it relies on the synergistic actions of producers, sellers,

and consumers {11.3.3}. As an energy and resource demand-reduction strategy, it is consistent with high levels of human well-being {5.3.4.3} and ensures better environmental quality (Figure TS.22) {5.2.1}. It also creates jobs through increased sharing, reuse, refurbishment, and recycling activities. Therefore, CE contributes to several SDGs, including clean water and sanitation (SDG 6), affordable energy and clean energy (SDG 7), decent work and economic growth (SDG 8), responsible production and consumption (SDG 12) and climate action (SDG 13). {11.5.3.2}
Emissions savings derive from reduced primary material production and transport. For example, in buildings, lifetime extension,

material efficiency, and reusable components reduce embodied emissions by avoiding demand for structural materials {9.3, 9.5}. At regional scales, urban/industrial symbiosis reduce primary material demand through by-product exchange networks {11.3.3}. CE strategies also exhibit enabling effects, such as material-efficient and circular vehicle designs that also improve fuel economy {10.2.2.2}. There is growing interest in ‘circular bioeconomy’ concepts applied to bio-based materials {Box 12.2} and even a ‘circular carbon economy’, wherein carbon captured via CCU {11.3.6} or CDR {3.4.6} is converted into reusable materials, which is especially relevant for the transitions of economies dependent on fossil fuel revenue. {12.6}
While there are many recycling policies, CE-oriented policies for more efficient material use with higher value retention are

comparatively far fewer; these policy gaps have been attributed to institutional failures, lack of coordination, and lack of strong advocates {5.3, 9.5.3.6, Boxes 11.5 and 12.2}. Reviews of mitigation potentials reveal unevenness in the savings of CE applications and potential risks of rebound effects {5.3}. Therefore, CE policies that identify system determinants maximise potential emissions reductions, which vary by material, location, and application. 
There are knowledge gaps for assessing CE opportunities within mitigation models due to CE’s many cross-sectoral linkages and data 
gaps related to its nascent state {3.4.4}. Opportunity exists to bridge knowledge from the industrial ecology field, which has historically studied CE, to the mitigation modelling community for improved analysis of interventions and policies for AR7. For instance, a global CE knowledge-sharing platform is helpful for CE performance measurement, reporting and accounting. {5.3, 9.5, 11.7}

121TS
Technical SummaryProviding better services with less energy and resource input 
has high technical potential and is consistent with providing well-being for all (medium confidence). The assessment of 19  demand-side mitigation options and 18 different constituents of well-being showed that positive impacts on well-being outweigh negative ones by a factor of 11. {5.2, 5.2.3, Figure 5.6}
Demand-side mitigation options bring multiple interacting

benefits (high confidence). Energy services to meet human needs for nutrition, shelter, health, and so on, are met in many different ways with different emissions implications that depend on local contexts, cultures, geography, available technologies, and social preferences. In the near term, many less-developed countries, and poor people everywhere, require better access to safe and low-emissions energy sources to ensure decent living standards and increase energy savings from service improvements by about 20–25%. (Figure  TS.22) {5.2, 5.4.5, Figures 5.3, 5.4, 5.5 and 5.6, Boxes 5.2 and 5.3}
Granular technologies and decentralised energy end-use,

characterised by modularity, small unit sizes and small unit costs, diffuse faster into markets and are associated with faster technological learning benefits, greater efficiency, more opportunities to escape technological lock-in, and greater employment (high confidence). Examples include solar PV systems, batteries, and thermal heat pumps. {5.3, 5.5, 5.5.3} 
emissions and have a high potential for emissions reductions while maintaining decent living standards and well-being (high confidence).  Individuals with high socio-economic status 
are capable of reducing their GHG emissions by becoming role models of low-carbon lifestyles, investing in low-carbon businesses, and advocating for stringent climate policies. {5.4.1, 5.4.3, 5.4.4, Figure 5.14}
for change (high confidence).  Motivation by individuals or

households worldwide to change energy consumption behaviour is generally low. Individual behavioural change is insufficient for climate change mitigation unless embedded in structural and cultural change. Different factors influence individual motivation and capacity for change in different demographics and geographies. These factors go beyond traditional socio-demographic and economic predictors and include psychological variables such as awareness, perceived risk, subjective and social norms, values, and perceived behavioural control. Behavioural nudges promote easy behaviour change, for example, ‘Improve’ actions such as making investments in energy efficiency, but fail to motivate harder lifestyle changes (high confidence). {5.4}
Behavioural interventions, including the way choices are 
presented to end users (an intervention practice known as choice architecture), work synergistically with price signals, making the combination more effective (medium confidence).

Behavioural interventions through nudges, and alternative ways of redesigning and motivating decisions, alone provide small to medium contributions to reduce energy consumption and GHG emissions. Green defaults, such as automatic enrolment in ‘green 
energy’ provision, are highly effective. Judicious labelling, framing, and communication of social norms can also increase the effect of mandates, subsidies, or taxes. {5.4, 5.4.1, Table 5.3, 5.3}
infrastructure, is necessary to enable and realise many Avoid and Shift options (medium confidence). By drawing support from 
diverse actors, narratives of change can enable coalitions to form, providing the basis for social movements to campaign in favour of (or against) societal transformations. People act and contribute to climate change mitigation in their diverse capacities as consumers, citizens, professionals, role models, investors, and policymakers. {5.4, 5.5, 5.6}

Collective action as part of social or lifestyle movements 
underpins system change (high confidence).  Collective action 
and social organising are crucial to shift the possibility space of public policy on climate change mitigation. For example, climate strikes have given voice to youth in more than 180 countries. In other instances, mitigation policies allow the active participation of all stakeholders, resulting in building social trust, new coalitions, legitimising change, and thus initiate a positive cycle in climate governance capacity and policies. {5.4.2, Figure 5.14}

with pilot experiments led by dedicated individuals and niche groups (high confidence). Collectively, such initiatives can find entry points to prompt policy, infrastructure, and policy reconfigurations, supporting the further uptake of technological and lifestyle innovations. Individuals’ agency is central as social change agents and narrators of meaning. These bottom-up socio-cultural forces catalyse a  supportive policy environment, which enables changes. {5.5.2}
The current effects of climate change, as well as some

mitigation strategies, are threatening the viability of existing business practices, while some corporate efforts also delay mitigation action (medium confidence). Policy packages that include job creation programmes can help to preserve social trust, livelihoods, respect, and dignity of all workers and employees involved. Business models that protect rent-extracting behaviour may sometimes delay political action. Corporate advertisement and brand-building strategies may also attempt to deflect corporate responsibility to individuals or aim to appropriate climate-care sentiments in their own brand–building. {5.4.3, 5.6.4}
Middle actors – professionals, experts, and regulators – play

a  crucial, albeit underestimated and underutilised, role in establishing low-carbon standards and practices (medium confidence). Building managers, landlords, energy-efficiency advisers, technology installers, and car dealers influence patterns of mobility and energy consumption by acting as middle actors or intermediaries in the provision of building or mobility services and need greater capacity and motivation to play this role. (Figure TS.20a) {5.4.3}

122
Food
WaterAirHealthSanitationEnergyShelterMobilityEducation Communication 
Social 
Participation Personal 
Security 
Social cohesionPolitical 
stability
Economic 
stability
Material 
provision 
Lower carbon and renewable energy[+2/–1] [+2/–1] [+3] [+3] [+3] [+1] [+1] [+1] [+2] [+1] [+1] [+2/–1] [+2/–1] [+2]

Teleworking and online education system[+1] [+3] [+2] [+2] [+1] [+2] [–1] [+2] [+1] [+2] [+1/–1] [+2] [+2] [+2] 

Systems approach in urban policy and practice[+1] [+2] [+2] [+3] [+1] [+3] [+2] [+3] [+1] [–1] [+1] [+2] [+1] [+1] [+3] 
Nature-Based Solutions[+2] [+1/–1] [+3/–1] [+3] [+1] [+3] 
[+1/–1] [+1] [+2] [+2] [+3] [+1] [+2/–2] [+3] [+1] 
Product life extension[+2] [+2] [+3] [+2] [+2] [+3] [+2] [+2] [+1] [+2] [+1] [–1] [+1] [+1] [+1] [+2] [+3]

••• ••• ••• •• ••• •••• •••• •••• •• ••• •• ••• •• •• ••• ••Figure TS.22 | Demand-side mitigation options, well-being and SDGs.  {Figure 5.6}

123TS
Technical Summary0 2 4 6Potential contribution to net emission reduction, 2030 (GtCO2-eq yr–1)
0 2 4 6
GtCO2-eq yr–1Mitigation optionsEnergyWind energy

Figure TS.23 | Overview of emission mitigation options and their cost and potential for the year 2030. The mitigation potential of each option is the quantity

of net greenhouse gas emission reductions that can be achieved by a given mitigation option relative to specified emission baselines that reflects what would be considered current policies in the period 2015–2019. Mitigation options may overlap or interact and cannot simply be summed together. The potential for each option is broken down into cost categories (see legend). Only monetary costs and revenues are considered. If costs are less than zero, lifetime monetary revenues are higher than lifetime monetary costs. For wind energy, for example, negative cost indicates that the cost is lower than that of fossil-based electricity production. The error bars refer to the total potential for each option. The breakdown into cost categories is subject to uncertainty. Where a smooth colour transition is shown, the breakdown of the potential into cost categories is not well researched, and the colours indicate only into which cost category the potential can predominantly be found in the

literature. {Figure SPM.8, 6.4, Table 7.3, Supplementary Material Table 9.SM.2, Supplementary Material Table 9.SM.3, 10.6, 11.4, Figure 11.13, 12.2, Supplementary Material 12.SM.1.2.3}

124
TSTechnical SummarySocial influencers and thought leaders can increase the 
adoption of low-carbon technologies, behaviours, and lifestyles (high confidence).  Preferences are malleable and can align with 
a cultural shift. The modelling of such shifts by salient and respected community members can help bring about changes in different service provisioning systems. Between 10% and 30% of committed individuals are required to set new social norms. {5.2.1, 5.4} 
TS.5.9 Mitigation Potential Across Sectors and Systems
The total emission mitigation potential achievable by the year 2030, calculated based on sectoral assessments, is sufficient to reduce global greenhouse gas (GHG) emissions to half of the current (2019) level or less (high confidence). This potential – 31–44 GtCO
2-eq – requires the implementation of 
a  wide range of mitigation options. Options with mitigation costs lower than USD20 tCO
2–1 make up more than half of this potential

and are available for all sectors. The market benefits of some options exceed their costs. (Figure TS.23) {12.2, Table 12.3}
Cross-sectoral considerations in mitigation finance are 
critical for the effectiveness of mitigation action as well as for balancing the often conflicting social, developmental, and environmental policy goals at the sectoral level (medium confidence).  True resource mobilisation plans that properly address 
mitigation costs and benefits at sectoral level cannot be developed in isolation of their cross-sectoral implications. There is an urgent need for multilateral financing institutions to align their frameworks and delivery mechanisms, including the use of blended financing to facilitate cross-sectoral solutions as opposed to causing competition for resources among sectors. {12.6.4}
Carbon leakage is a  cross-sectoral and cross-country

consequence of differentiated climate policy (robust evidence, medium agreement). Carbon leakage occurs when mitigation measures implemented in one country/sector leads to increased emissions in other countries/sectors. Global commodity value chains and associated international transport are important mechanisms through which carbon leakage occurs. Reducing emissions from the value chain and transportation can offer opportunities to mitigate three elements of cross-sectoral spillovers and related leakage: (i) domestic cross-sectoral spillovers within the same country; (ii) international spillovers within a  single sector resulting from substitution of domestic production of carbon-intensive goods with their imports from abroad; and (iii) international cross-sectoral spillovers among sectors in different countries. {12.6.3}

125TS
Technical SummaryTS.6 Implementation and Enabling Conditions
Chapters 13 to 16 address the enabling conditions that can accelerate 
or impede rapid progress on mitigation. Chapters 13 and 14 focus on policy, governance and institutional capacity, and international 
cooperation, respectively taking a national and international perspective; Chapter 15 focuses on investment and finance;  and Chapter 16 focuses 
on innovation and technology.  The assessment of social aspects of 
mitigation draws on material assessed in Chapter 5. 
TS.6.1 Policy and Institutions

Long-term deep emission reductions, including the reduction of emissions to net zero, is best achieved through institutions and governance that nurture new mitigation policies, while at the same time reconsidering existing policies that support the continued emission of GHGs (high confidence). To do so effectively, the scope of climate governance needs to include both direct efforts to target GHG emissions and indirect opportunities to tackle GHG emissions that result from efforts directed towards other policy objectives. {13.2, 13.5, 13.6, 13.7, 13.9} 
the legal basis for action. This includes setting up implementing organisations and the frameworks through which diverse actors interact (medium evidence, high agreement).  Institutions 
can create mitigation and sectoral policy instruments; policy packages for low-carbon system transition; and economy-wide measures for systemic restructuring. {13.2, 13.7, 13.9}

specific countries, sectors, and technologies (high confidence), avoiding emissions of several GtCO
2-eq yr –1 (medium 
confidence).  Both market-based and regulatory policies have distinct 
but complementary roles. The share of global GHG emissions subject to mitigation policy has increased rapidly in recent years, but big gaps remain in policy coverage, and the stringency of many policies falls short of what is needed to achieve the desired mitigation outcomes. (Box TS.13) {13.6, Cross-Chapter Box 10 in Chapter 14}
direction of travel, setting targets, mainstreaming mitigation into sector policies, enhancing regulatory certainty, creating law-backed agencies, creating focal points for social mobilisation, and attracting international finance (medium evidence, high agreement).  By 2020, ‘direct’ climate

laws primarily focused on GHG reductions were present in 56 countries covering 53% of global emissions (Figure TS.24). More than 690  laws, including ‘indirect’ laws, however, may also have an  effect on mitigation. Among direct laws, ‘framework’ laws set an overarching legal basis for mitigation either by pursuing a target and implementation approach, or by seeking to mainstream climate objectives through sectoral plans and integrative institutions. (Figure TS.24) {13.2}Institutions can enable improved governance by coordinating across sectors, scales and actors, building consensus for action, and setting strategies (medium evidence, high agreement).

Institutions are more stable and effective when they are congruent with national contexts, leading to mitigation-focused institutions in some countries and the pursuit of multiple objectives in others. Sub-national institutions play a  complementary role to national institutions by developing locally relevant visions and plans, addressing policy gaps or limits in national institutions, building local administrative structures and convening actors for place-based decarbonisation. {13.2}
dominant ideas, values and belief systems within a  country or within a sector are more easily adopted and implemented (medium confidence).  Ideas, values and beliefs may change over 
time. Policies that bring perceived direct benefits, such as subsidies, usually receive greater support. The awareness of co-benefits for the public increases support of climate policies (high confidence). {13.2, 13.3, 13.4}

structural factors, but it is still possible for actors to make substantial changes (medium evidence, high agreement). Key structural factors are domestic material endowments (such as fossil fuels and land-based resources); domestic political systems; and prevalent ideas, values and belief systems. Developing Countries face additional material constraints in climate governance due to development challenges and scarce economic or natural resources. A broad group of actors influence how climate governance develop over time, including a  range of civic organisations, encompassing both pro- and anti-climate action groups. {13.3, 13.4}
Sub-national actors are important for mitigation because

municipalities and regional governments have jurisdiction over climate-relevant sectors such as land use, waste and urban policy. They are able to experiment with climate solutions and can forge partnerships with the private sector and internationally to leverage enhanced climate action (high confidence).  More than 10,500 cities and nearly 
250 regions representing more than 2 billion people have pledged largely voluntary action to reduce emissions. Indirect gains include innovation, establishing norms and developing capacity. However, sub-national actors often lack national support, funding, and capacity to mobilise finance and human resources, and create new institutional competences. {13.5}
Climate litigation is growing and can affect the outcome

and ambition of climate governance (medium evidence, high agreement). Since 2015, at least 37 systemic cases have been initiated against states that challenge the overall effort of a  state to mitigate or adapt to climate change. If successful, such cases can lead to an increase in a  country’s overall ambition to tackle climate change. Climate litigation has also successfully challenged governments’ authorisations of high-emitting projects, setting precedents in favour of climate action. Climate litigation against private sector and financial institutions is also on the rise. {13.4}

126
TSTechnical SummaryClimate legislation in force No climate legislation0510152025
2010 2015 2020GHG emissions coverage (GtCO 2-eq)No climate legislation
Global
Executive GHG emissions target No GHG emissions target Legislative GHG emissions target(b)
Figure TS.24 | Prevalence of legislation and emissions targets across regions. Panel (a): shares of global GHG emissions under national climate change legislations –

in 2010, 2015 and 2020. Climate legislation is defined as an act passed by a parliament that includes the reduction of GHGs in its title or objectives. Panel (b): shares of global GHG emissions under national climate emission targets – in 2010, 2015 and 2020. Emissions reductions targets were taken into account as a legislative target when they were defined in a law or as part of a country’s submission under the Kyoto Protocol, or as an executive target when they were included in a national policy or official submissions under the UNFCCC. Targets were included if they were economy-wide or included at least the energy sector. The proportion of national emissions covered are scaled to reflect coverage and whether targets are in GHG or CO
2 terms. Emissions data used are for 2019. 2020 data was excluded as emissions shares across regions deviated from past

patterns due to COVID-19. AR6 regions: DEV = Developed countries; APC = Asia and Pacific; EEA = Eastern Europe and West Central Asia; AFR = Africa; LAM = Latin America and the Caribbean; ME = Middle East. {Figure 13.1 and 13.2}

127TS
Technical SummaryThe media shapes the public discourse about climate 
mitigation. This can usefully build public support to accelerate mitigation action but may also be used to impede decarbonisation (medium evidence, high agreement).  Global 
media coverage (across a study of 59 countries) has been growing, from about 47,000 articles in 2016–17 to about 87,000 in 2020–21. Generally, the media representation of climate science has increased and become more accurate over time. On occasion, the propagation of scientifically misleading information by organised counter-movements has fuelled polarisation, with negative implications for climate policy. {13.4}
Explicit attention to equity and justice is salient to both social

acceptance and fair and effective policymaking for mitigation (high confidence). Distributional implications of alternative climate policy choices can be usefully evaluated at city, local and national scales as an input to policymaking. It is anticipated that institutions and governance frameworks that enable consideration of justice and Just Transitions can build broader support for climate policymaking. {13.2, 13.6, 13.8, 13.9}
Carbon pricing is effective in promoting implementation of low-
cost emissions reductions (high confidence). While the coverage of emissions trading and carbon taxes has risen to over 20 percent of global CO
2 emissions, both coverage and price are lower than is

needed for deep reductions. Market mechanisms ideally are designed to be effective as well as efficient, balance distributional goals and find social acceptance. Practical experience has driven progress in market mechanism design, especially of emissions trading schemes. Carbon pricing is limited in its effect on adoption of higher-cost mitigation options, and where decisions are often not sensitive to price incentives, such as in energy efficiency, urban planning, and infrastructure (robust evidence,  medium agreement). Subsidies have been used to improve 
energy efficiency, encourage the uptake of renewable energy and other sector-specific emissions-saving options. {13.6}
Carbon pricing is most effective if revenues are redistributed or used impartially (high confidence).  A carbon levy earmarked 
for green infrastructures or saliently returned to taxpayers corresponding to widely accepted notions of fairness increases the political acceptability of carbon pricing. {5.6, Box 5.11}

Removing fossil fuel subsidies would reduce emissions, 
improve public revenue and macroeconomic performance, and yield other environmental and sustainable development benefits. Subsidy removal may have adverse distributional impacts 
especially on the most economically vulnerable groups which, in some cases can be mitigated by measures such as redistributing revenue saved, all of which depend on national circumstances (high confidence); fossil fuel subsidy removal is projected by various studies (using alternative methodologies) to reduce global CO
2 emissions by

1–4%, and GHG emissions by up to 10% by 2030, varying across regions (medium confidence). {6.3, 13.6} {13.6}Regulatory instruments play an important role in achieving specific mitigation outcomes in sectoral applications (high confidence). Regulation is effective in particular applications and often enjoys greater political support, but tends to be more economically costly than pricing instruments (robust evidence, medium agreement). Flexible forms of regulation (e.g., performance standards) have achieved aggregate goals for renewable energy generation, vehicle efficiency and fuel standards, and energy efficiency in buildings and industry. Infrastructure investment decisions are significant for mitigation because they lock-in high- or low-emissions trajectories over long periods. Information and voluntary programs can contribute to overall mitigation outcomes (medium evidence, high agreement). Designing for overlap and interactions among mitigation policies enhances their

effects that both support and hinder mitigation action (medium evidence, high agreement). Reductions in demand for 
fossil fuels tend to negatively affect fossil fuel-exporting countries. Creation of markets for emission reduction credits tends to benefit countries able to supply credits. Policies to support technology development and diffusion tend to have positive spillover effects. There is no consistent evidence of significant emissions leakage or competitiveness effects between countries, including for emissions-intensive trade-exposed industries covered by emission-trading systems (medium confidence). {13.6}
transitions and shifts in development pathways toward low-carbon futures than are individual policies (high confidence).  For best effect, they need to be harnessed to a clear

vision for change and designed with attention to local governance context. Comprehensiveness in coverage, coherence to ensure 
complementarity, and consistency of policies with the overarching vision and its objectives are important design criteria. Integration across objectives occurs when a policy package is informed by a clear problem framing and identification of the full range of relevant policy subsystems. The climate policy landscape is outlined in Table TS.8, which maps framings of desired national policy outcomes to policymaking approaches. {13.7, Figure 13.6}

128
TSTechnical SummaryThe co-benefits and trade-offs of integrating adaptation and 
mitigation are most usefully identified and assessed prior to policymaking rather than being accidentally discovered (high confidence). This requires strengthening relevant national institutions to reduce silos and overlaps, increasing knowledge exchange at the country and regional levels, and supporting engagement with bilateral and multilateral funding partners. Local governments are well placed to develop policies that generate social and environmental co-benefits but to do so require legal backing and adequate capacity and resources. {13.8}
Climate change mitigation is accelerated when attention is 
given to integrated policy and economy-wide approaches, and when enabling conditions (governance, institutions, behaviour and lifestyle, innovation, policy, and finance), are present (robust evidence, medium agreement).  Accelerating

climate mitigation includes simultaneously weakening high-carbon systems and encouraging low-carbon systems; ensuring interaction between adjacent systems (e.g., energy and agriculture); overcoming resistance to policies (e.g.,  from incumbents in high-carbon-emitting industries), including by providing transitional support to the vulnerable and negatively affected by distributional impacts; inducing changes in consumer practices and routines; providing transition support; and addressing coordination challenges in policy and governance. Table TS.9 elucidates the complexity of policymaking in driving sectoral transitions by summarising case studies of sectoral transitions from Chapters 5 to 12. These real-world sectoral transitions reinforce critical lessons on policy integration. (Table TS.9) {13.7, 13.9}Economy-wide packages, including economic-stimulus packages, can contribute to shifting sustainable development pathways and achieving net zero outcomes whilst meeting short-term

2008–9 global recession showed that policies for sustained economic recovery go beyond short-term fiscal stimulus to include long-term commitments of public spending on the low-carbon economy, pricing reform, addressing affordability, and minimising distributional impacts. COVID-19 spurred stimulus packages and multi-objective recovery policies may also have potential to meet short-term economic goals while enabling longer-term sustainability goals. (Table TS.8) {13.9}Table TS.8 | Mapping the landscape of climate policy.  {Figure 13.6}
Approach to 
policymakingFraming of outcome
Enhancing mitigation Addressing multiple objectives of mitigation and development
Shifting incentives ‘Direct mitigation focus’

{2.8, 13.6}Objective: reduce GHG emissions now.Literature: how to design and implement policy instruments, with attention to distributional and other concerns.Examples: carbon tax, cap and trade, border carbon adjustment (BCA), disclosure policies.‘Co-benefits’{5.6.2, 12.4.4, 17.3}Objective: synergies between mitigation and development.Literature: scope for and policies to realise synergies and avoid trade-offs across climate and development objectives.Examples: appliance standards, fuel taxes, community forest management, sustainable dietary guidelines, green building codes, packages for air pollution, packages for public transport.
Enabling transition ‘Socio-technical transitions’{1.7.3, 5.5, 6.7, 10.8, Cross-Chapter Box 12 in Chapter 16}Objective:  accelerate low-carbon shifts in socio-technical systems.

Literature: understand socio-technical transition processes, integrated policies for different stages of a technology ‘S curve’ and explore structural, social and political elements of transitions.Examples: packages for renewable-energy transition and coal phase-out; diffusion of electric vehicles, process and fuel switching in key industries.‘System transitions to shift development pathways’{7.4.5, 11.6.6, 13.9, 17.3.3, Cross-Chapter Box 5 in Chapter 4, Cross-Chapter Box 12 in Chapter 16} Objective: accelerate system transitions and shift development pathways to expand mitigation options and meet other development goals. Literature: examines how structural development patterns and broad cross-sector and economy-wide measures drive ability to mitigate while achieving development goals through integrated policies and aligning enabling conditions. Examples: packages for sustainable urbanisation, land-energy-water nexus approaches, green industrial policy, regional Just Transition plans.

129TS
Technical SummaryTable TS.9 | Case studies of integrated policymaking for sectoral transitions. Real-world sectoral transitions reinforce critical lessons on policy integration: a high-level 
strategic goal (column A), the need for a clear sectoral outcome framing (column B), a carefully coordinated mix of policy instruments and governance actions (column C), and the importance of context-specific governance factors (column D). Illustrative examples, drawn from sectors, help elucidate the complexity of policymaking in driving sectoral transitions. {Cross-Chapter Box 9 in Chapter 13, Table 1}
A. Illustrative case B. Objective C. Policy mixD. Governance context
Enablers Barriers
Shift in mobility service 
provision in Kolkata, India {Box 5.8}–  Improve system 
efficiency, sustainability and comfort
–  Shift public perceptions of public transport–  Strengthen coordination 
between modes 
–  Formalise and green auto-rickshaws
–  Procure fuel-efficient, comfortable low-floor AC buses

–  Ban cycling on busy roads
–  Deploy policy actors as change-agents, mediating between interest groups–  Cultural norms around informal transport-sharing, linked to high levels of social trust 
–  Historically crucial role of buses in transit
–  App-cab companies shifting norms and formalising mobility-sharing
–  Digitalisation and safety on board–  Complexity: multiple modes with separate networks and meanings
–  Accommodating and addressing legitimate concerns from social movements about the exclusionary effects of ‘premium’ fares, cycling bans on busy roads 
LPG subsidy (‘Zero Kero’) programme, Indonesia {Box 6.3}–  Decrease fiscal 
expenditures on kerosene subsidies for cooking–  Subsidise provision of liquefied petroleum gas (LPG) cylinders and initial equipment
–  Convert existing kerosene suppliers to LPG suppliers–  Provincial government and industry support in targeting beneficiaries and implementation

–  Synergies in kerosene and LPG distribution infrastructures–  Continued user preference for traditional solid fuels
–  Reduced GHG benefits as subsidy shifted between fossil fuels
Action Plan for Prevention and Control of Deforestation in the Legal Amazon, Brazil {Box 7.9}–  Control deforestation 
and promote sustainable development–  Expand protected areas; homologation of indigenous lands
–  Improve inspections, 
satellite-based monitoring
–  Restrict public credit for enterprises and municipalities with high deforestation rates
–  Set up a REDD+ mechanism (Amazon Fund)–  Participatory agenda-setting 
process
–  Cross-sectoral consultations 
on conservation guidelines
–  Mainstreaming of deforestation in government programmes and projects–  Political polarisation 
leading to erosion of environmental governance
–  Reduced representation and independence of civil society in decision-making bodies
–  Lack of clarity around land ownership

Climate smart cocoa (CSC) production, Ghana {Box 7.12}–  Promote sustainable 
intensification of cocoa production
–  Reduce deforestation –  Enhance incomes and adaptive capacities–  Distribute shade tree seedlings 
–  Provide access to agronomic information and agrochemical inputs
–  Design a multi-stakeholder 
program including MNCs, farmers and NGOs–  Local resource governance mechanisms ensuring voice for smallholders
–  Community governance allowed adapting to local context
–  Private-sector role in popularising CSC–  Lack of secure tenure (tree rights)
–  Bureaucratic and legal hurdles to register trees
–  State monopoly on cocoa marketing, export
Coordination mechanism for joining fragmented urban policymaking in Shanghai, China {Box 8.3}– Integrate policymaking across objectives, towards low-carbon urban development– Combine central targets and evaluation with local flexibility for initiating varied policy experiments

– Establish a local leadership team for coordinating cross-sectoral policies involving multiple institutions
– Create a direct programme fund for implementation and capacity-building– Strong vertical linkages between central and local levels
– Mandate for policy learning to inform national policy 
– Experience with mainstreaming mitigation in related areas (e.g., air pollution)– Challenging starting point – low share of renewable energy, high dependency on fossil fuels
– Continued need for high investments in a developing context
Policy package for building energy efficiency, EU {Box SM.9.1}– Reduce energy consumption, integrating renewable energy and mitigating GHG emissions from buildings– Energy performance standards, set at nearly zero energy for new buildings
– Energy performance standards for appliances
– Energy performance certificates shown during sale
– Long-term renovation strategies– Binding EU-level targets, directives and sectoral effort-sharing regulations

– Supportive urban policies, coordinated through city partnerships
– Funds raised from allowances auctioned under the Emissions Trading Scheme (ETS)– Inadequate local technical capacity to implement multiple instruments
– Complex governance structure leading to uneven stringency

130
TSTechnical SummaryBox TS.13 | Policy Attribution: Methodologies For – and Estimations of –  
the Macro-level Impact of Mitigation Policies on Indices of GHG Mitigation
Policy attribution examines the extent to which GHG emission reductions, the proximate drivers of emissions, and the deployment of 
technologies that reduce emissions may be reasonably attributed to policies implemented prior to the observed changes. Such policies include regulatory instruments such as energy-efficiency programmes or technical standards and codes, carbon pricing, financial support for low-carbon energy technologies and efficiency, voluntary agreements, and regulation of land-use practices.
The vast majority of literature reviewed for this report examines the effect of particular instruments in particular contexts {13.6, 14.3,

16.4}, and only a small number directly or plausibly infer global impacts of policies. Policies also differ in design, scope, and stringency, may change over time as they require amendments or new laws, and often partially overlap with other instruments. These factors complicate analysis, because they give rise to the potential for double counting emissions reductions that have been observed. These lines of evidence on the impact of polices include: 
• GHG Emissions. Evidence from econometric assessments of the impact of policies in countries which took on Kyoto Protocol 
targets; decomposition analyses that identify policy-related, absolute reductions from historical levels in particular countries. {13.6.2, 14.3.3, Cross-Chapter Box 10 in Chapter 14}
• Proximate emission drivers. Trends in the factors that drive emissions including reduced rates of deforestation {7.6.2}, industrial

energy efficiency {Box 16.3}, buildings energy efficiency {Figure 2.22}, and the policy-driven displacement of fossil fuel combustion by renewable energy. (Box TS.13, Table 1; Box TS.13, Figure 1) {Chapters 2 and 6, Cross-Chapter Box 10 in Chapter 14} 
• Technologies. The literature indicates unambiguously that the rapid expansion of low-carbon energy technologies is substantially attributable to policy. {6.7.5, 16.5}
As illustrated in Box TS.13, Figure 1, these multiple lines of evidence point to policies having had a discernible impact on mitigation for specific countries, sectors, and technologies (high confidence), avoiding emissions of several GtCO
2-eq yr –1 globally (medium confidence).A. Illustrative case B. Objective C. Policy mixD. Governance context
African 
electromobility – trackless trams with solar in Bulawayo and e-motorbikes in Kampala {Box 10.4}– Leapfrog into a decarbonised transport future

– Achieve multiple social benefits beyond mobility provision– Develop urban centres with solar at station precincts 
– Public-private partnerships for financing
– Sanction demonstration projects for new electric transit and new electric motorbikes (for freight)– ‘Achieving SDGs’ was an enabling policy framing
– Multi-objective policy process for mobility, mitigation and manufacturing
– Potential for funding through climate finance
– Co-benefits such as local employment generation – Economic decline in the first decade of the 21st century
– Limited fiscal capacity for public funding of infrastructure
– Inadequate charging infrastructure for e-motorbikes
Initiative for a climate-friendly industry in North Rhine Westphalia (NRW), Germany {Box 11.3}– Collaboratively develop innovative strategies towards a net zero GHG industrial sector, while securing competitiveness– Build platform to bring together industry, scientists and government in self-organised innovation teams

– Intensive cross-branch cooperation to articulate policy/infrastructure needs– NRW is Germany’s industrial heartland, with an export-oriented industrial base
– Established government-industry ties
– Active discourse between industry and public– Compliance rules preventing in-depth co-operation 
Food2030 strategy, Finland {Box 12.2}– Local, organic and climate-friendly food production
– Responsible and healthy food consumption
– A competitive food supply chain– Target funding and knowledge support for innovations
– Apply administrative means (legislation, guidance) to increase organic food production and procurement
– Use education and information instruments to shift behaviour (media campaigns, websites) – Year-long deliberative stakeholder engagement process across sectors 
– Institutional structures for agenda-setting, guiding policy implementation and reflexive discussions– Weak role of integrated impact assessments (IAMs) to inform agenda-setting

– Monitoring and evaluation close to ministry in charge
– Lack of standardised indicators of food system sustainability
Table TS.9 (continued):

131TS
Technical SummaryBox TS.13 (continued)
Box TS.13, Table 1 | The effects of policy on GHG emissions, drivers of emissions, and technology deployment.
Sector Effects on emissions Effects on immediate drivers Effects on low-carbon technology
Energy supply 
{Chapter 6}Carbon pricing, emissions standards, and technology support have led to declining emissions associated with the supply of energy.Carbon pricing and technology support have led to improvements in the efficiency of energy conversion.A variety of market-based instruments, especially technology-support policies have led to high diffusion rates and cost reductions for renewable energy technologies. 
AFOLU{Chapter 7}Regulation of land-use rights and practices have led to falling aggregate AFOLU-sector emissions.Regulation of land-use rights and practices, payments for ecosystem service, and offsets, have led to decreasing rates of deforestation (medium confidence).

Buildings{Chapter 9}Regulatory standards have led to reduced emissions from new buildings.Regulatory standards, financial support for building renovation and market-based instruments have led to improvements in building and building-system efficiencies.Technology support and regulatory standards have led to adoption of low-carbon heating systems and high-efficiency appliances.
Transport{Chapter 10}Vehicle standards, land-use planning, and carbon pricing have led to avoided emissions in ground transportation.Vehicle standard, carbon pricing, and support for electrification have led to automobile efficiency improvements.Technology support and emissions standards have increased diffusion rates and cost reductions for electric vehicles.
Industry{Chapter 11}Carbon pricing has led to efficiency improvements in industrial facilities.

Note: statements describe the effects of policies across those countries where policies are in place. Unless otherwise noted, all findings are of high confidence.
 
Policies
in and diffusion of low-carbon technologies , 
especially for wind and solar energy, electric vehicles, energy-efﬁcient appliances and low-carbon heating.
Decline in costs 
of low-carbon technologies, e.g., solar PV, battery technology. e.g., RD&D funding, technology
Reductions in energy intensity globally and in all but one world region.
intensity in Europe, Eurasia, the Middle East, North America; and globally.
Reductions in the rate

of deforestation in several countries, especially developing countries.Reductions in average annual GHG emissions growth (2.3% in 2000–10; 1.3% in 2010–18). Sustained emissions reductions in 24 countries, relative decoupling in 58 countries by 2015.
4–5 Gt yr–1Cumulative impact of policies on 2019 emissions, fromemission trends reported in Chapter 2, assumingincremental policy-related avoided emissions of  0.1 GtCO
2-eq, annually from 2010Chapter 1,Section 1.4.8
3.81 Gt yr–1Projected by Annex I countries for 2020 in their BR4s, from 2811 Policies and Measures, impacts reported for 38% of themUNFCCC (2020)1.3 Gt yr
–1in Annex B country as a result of the Kyoto Protocol (or –7% yr
–1 on average over 2005–12)Maamoun (2019)

1.8–3 Gt yr–1as the result of various policy instruments withdemonstrable impact. Includes at least 500 MtCO
2-eq yr–1
from renewables diffusionAggregation of multiple sources
Box TS.13, Figure 1 | Policy impacts on key outcome indices: GHG emissions, proximate emission drivers, and technologies, including several 
lines of evidence on GHG abatement attributable to policies.  {Cross-Chapter Box 10, Figure 1 in Chapter 14}

132
TSTechnical SummaryTS.6.2 International Cooperation
results (high confidence).  The Kyoto Protocol led to measurable and 
substantial avoided emissions, including in 20 countries with Kyoto first-commitment period targets that have experienced a decade of declining absolute emissions. It also built national capacity for GHG accounting, catalysed the creation of GHG markets, and increased investments in low-carbon technologies. Other international agreements and institutions have led to avoided CO
2 emissions from 
land-use practices, as well as avoided emissions of some non-CO 2 
greenhouse gases (medium confidence). {14.3, 14.5, 14.6}
New forms of international cooperation have emerged since

AR5 in line with an evolving understanding of effective mitigation policies, processes, and institutions. Both new and pre-existing forms of cooperation are vital for achieving climate mitigation goals in the context of sustainable development (high confidence). While previous IPCC assessments have noted important synergies between the outcomes of climate mitigation and achieving sustainable development objectives, there now appear to be synergies between the two processes themselves (medium confidence). Since AR5, international cooperation has shifted towards facilitating national-level mitigation action through numerous channels, including though processes established under the UNFCCC regime and through regional and sectoral agreements and organisations. {14.2, 14.3, 14.5, 14.6}
Participation in international agreements and transboundary

networks is associated with the adoption of climate policies at the national and sub-national levels, as well as by non-state actors (high confidence).  International cooperation helps countries 
achieve long-term mitigation targets when it supports development and diffusion of low-carbon technologies, often at the level of individual sectors, which can simultaneously lead to significant benefits in the areas of sustainable development and equity (medium confidence). {14.2, 14.3, 14.5, 14.6}
International cooperation under the UN climate regime took

an important new direction with the entry into force of the 2015 Paris Agreement, which strengthened the objective of the UN climate regime, including its long-term temperature goal, while adopting a  different architecture to that of the Kyoto Protocol (high confidence). The core national commitments under the Kyoto Protocol were legally binding quantified emission targets for developed countries tied to well-defined mechanisms for monitoring and enforcement. By contrast, the commitments under the Paris Agreement are primarily procedural, extend to all parties, and are designed to trigger domestic policies and measures, enhance transparency, and stimulate climate investments, particularly in developing countries, and to lead iteratively to rising levels of ambition across all countries. Issues of equity remain of central importance in the UN climate regime, notwithstanding shifts in the operationalisation of ‘common but differentiated responsibilities and respective capabilities’

commitments and mechanisms will lead to the attainment of its stated goals (medium confidence).  Arguments in support of the

Paris Agreement are that the processes it initiates and supports will in multiple ways lead, and indeed have already led, to rising levels of ambition over time. The recent proliferation of national mid-century net zero GHG targets can be attributed in part to the Paris Agreement. Moreover, its processes and commitments will enhance countries’ abilities to achieve their stated level of ambition, particularly among developing countries. Arguments against the Paris Agreement are that it lacks a mechanism to review the adequacy of individual Parties’ Nationally Determined Contributions (NDCs), that collectively current NDCs are inconsistent in their level of ambition with achieving the Paris Agreement’s long-term temperature goal, that its processes will not lead to sufficiently rising levels of ambition in the NDCs, and that NDCs will not be achieved because the targets, policies and measures they contain are not legally binding at the international level. To some extent, arguments on

both sides are aligned with different analytic frameworks, including assumptions about the main barriers to mitigation that international cooperation can help overcome. The extent to which countries increase the ambition of their NDCs and ensure they are effectively implemented will depend in part on the successful implementation of the support mechanisms in the Paris Agreement, and in turn will determine whether the goals of the Paris Agreement are met (high confidence). {14.2, 14.3, 14.4}

and agreements provides critical support for mitigation in particular regions, sectors and industries, for particular types of emissions, and at the sub- and trans-national levels (high confidence).  Agreements addressing ozone depletion, transboundary

air pollution, and release of mercury are all leading to reductions in the emissions of specific greenhouse gases. Cooperation is occurring at multiple governance levels including cities. Transnational partnerships and alliances involving non-state and sub-national actors are also playing a  growing role in stimulating low-carbon technology diffusion and emissions reductions (medium confidence). Such transnational efforts include those focused on climate litigation; the impacts of these are unclear but promising. Climate change is being addressed in a growing number of international agreements operating at sectoral levels, as well as within the practices of many multilateral organisations and institutions. Sub-global and regional cooperation, often described as climate clubs, can play an important role in accelerating mitigation, including the potential for reducing mitigation costs through linking national carbon markets, although actual examples of these remain limited. {14.2, 14.4,

International cooperation will need to be strengthened in several 
key respects in order to support mitigation action consistent with limiting temperature rise to well below 2°C in the context of sustainable development and equity (high confidence).  
Many developing countries’ NDCs have components or additional actions that are conditional on receiving assistance with respect to finance, technology development and transfer, and capacity-building, greater than what has been provided to date. Sectoral and sub-global cooperation is providing critical support, and yet there is room for further progress. In some cases, notably with respect to aviation and shipping, sectoral agreements have adopted climate mitigation goals that fall far short of what would be required to achieve the long-term

133TS
Technical Summarytemperature goal of the Paris Agreement. Moreover, there are cases 
where international cooperation may be hindering mitigation efforts, namely evidence that trade and investment agreements, as well as agreements within the energy sector, impede national mitigation efforts (medium confidence). International cooperation is emerging but so far fails to fully address transboundary issues associated with solar radiation modification (SRM) and carbon dioxide removal (CDR). {14.2, 14.3, 14.4, 14.5, 14.6, Cross-Working Group Box 4 in Chapter 14}
TS.6.3 Societal Aspects of Mitigation
Social equity reinforces capacity and motivation for mitigating climate change (medium confidence).  Impartial governance

such as fair treatment by law-and-order institutions, fair treatment by gender, and income equity, increases social trust, thus enabling demand-side climate policies. High-status (often high-carbon) item consumption may be reduced by taxing absolute wealth without compromising well-being. {5.2, 5.4.2, 5.6}
of women, racialised, and marginalised groups, increase the democratic impetus for climate action (high confidence).  
Including more differently situated knowledge and diverse perspectives makes climate mitigation policies more effective. {5.2, 5.6}
better addresses the challenges of rapid transitions towards zero-carbon systems (high confidence).  Larger systems take

more time to evolve, grow, and change compared to smaller ones. Creating and scaling up entirely new systems takes longer than replacing existing technologies and practices. Late adopters tend to 
adopt faster than early pioneers. Obstacles and feasibility barriers are high in the early transition phases. Barriers decrease as a result of technical and social learning processes, network building, scale economies, cultural debates, and institutional adjustments. {5.5, 5.6}
values people hold are more successful (high confidence).  
Values differ between cultures. Measures that support autonomy, energy security and safety, equity and environmental protection, and fairness resonate well in many communities and social groups. Changing from a  commercialised, individualised, entrepreneurial

training model to an education cognisant of planetary health and human well-being can accelerate climate change awareness and action. {5.4.1, 5.4.2}
Changes in consumption choices that are supported by structural 
changes and political action enable the uptake of low-carbon choices (high confidence). Policy instruments applied in coordination can help to accelerate change in a consistent desired direction. Targeted technological change, regulation, and public policy can help in steering digitalisation, the sharing economy, and circular economy towards climate change mitigation. (Boxes TS.12 and TS.14) {5.3, 5.6}
31 Most of climate finance stays within national borders, especially private climate flows (over 90%). The reasons for this range from national policy support, differences in regulatory standards, 
exchange rate, political and governance risks, to information market failures.Complementarity in policies helps in the design of an optimal

demand-side policy mix (medium confidence).  In the case of 
TS.6.4 Investment and Finance
Finance to reduce net GHG emissions and enhance resilience to climate impacts is a critical enabling factor for the low-carbon transition. Fundamental inequities in access to finance as well as finance terms and conditions, and countries’ exposure to physical impacts of climate change overall, result in a worsening outlook for a global Just Transition (high confidence). Decarbonising the

economy requires global action to address fundamental economic inequities and overcome the climate investment trap that exists for many developing countries. For these countries the costs and risks of financing often represent a  significant challenge for stakeholders at all levels. This challenge is exacerbated by these countries’ general economic vulnerability and indebtedness. The rising public fiscal costs of mitigation, and of adapting to climate shocks, is affecting many countries and worsening public indebtedness and country credit ratings at a time when there were already significant stresses on public finances. The COVID-19 pandemic has made these stresses worse and tightened public finances still further. Other major challenges for commercial climate finance include: the mismatch between capital and investment needs, home bias
31 considerations, differences in risk

perceptions for regions, as well as limited institutional capacity to ensure safeguards are effective (high confidence). {15.2, 15.6.3}
increased awareness of climate risk. This increased awareness can support climate policy development and implementation (high confidence) {15.2, 15.6}.  Climate-related financial risks arise

from physical impacts of climate change (already relevant in the short term), and from a  disorderly transition to a  low-carbon economy. Awareness of these risks is increasing, leading also to concerns about financial stability. Financial regulators and institutions have responded with multiple regulatory and voluntary initiatives to assess and address these risks. Yet despite these initiatives, climate-related financial risks remain greatly underestimated by financial institutions and markets, limiting the capital reallocation needed for the low-carbon transition. Moreover, risks relating to national and international inequity – which act as a barrier to the transformation – are not yet reflected in decisions by the financial community. Stronger steering by regulators and policymakers has the potential to close this gap. Despite the increasing attention of investors to climate change, there is limited evidence that this attention has directly impacted emission reductions. This

leaves high uncertainty, both near term (2021–30) and longer term (2021–50), on the feasibility of an alignment of financial flows with the Paris Agreement goals (high confidence). {15.2, 15.6}

Progress on the alignment of financial flows with low-GHG 
emissions pathways remains slow. There is a climate financing

134
TSTechnical Summarygap which reflects a persistent misallocation of global capital 
(high confidence) {15.2, 15.3}. Persistently high levels of both public and private fossil fuel-related financing continue to be of major concern despite promising recent commitments. This reflects policy misalignment, the current perceived risk-return profile of fossil fuel-related investments, and political economy constraints 
32 Climate finance flows refers to local, national, or transnational financing from public, private, and alternative sources, to support mitigation and adaptation actions addressing climate change.(high confidence). Estimates of climate finance flows32 exhibit highly

divergent patterns across regions and sectors and a slowing growth {15.3}. When the perceived risks are too high, the misallocation of abundant savings persists and investors refrain from investing in infrastructure and industry in search of safer financial assets, even earning low or negative real returns (high confidence). {15.2, 15.3}
By sector
By region
Europe
Africa
Lower
range
x3
x5
x6x7 x14
x12
x4
x7 x7
x2 x4x2
x8
x7
Multiplication factors indicate the x-fold

increase between yearly mitigation ﬂows to average yearly mitigation investment needs. Globally, current mitigation ﬁnancial ﬂows are a factor of three to six below the average levels up to 2030.2017
2018
20192020
Annual mitigation investment needs (averaged until 2030) IEA data mean 2017–2020Average ﬂows Yearly mitigation investment ﬂows (USD2015 yr 
–1) in:*Actual yearly ﬂows compared to average annual needs (billion USD 2015 yr –1)
Figure TS.25 | Breakdown of recent average (downstream) mitigation investments and model-based investment requirements for 2020–2030 (USD billion) 
in scenarios that likely limit warming to 2°C or lower. Mitigation investment flows and model-based investment requirements by sector / segment (energy efficiency in buildings and industry, transport including efficiency, electricity generation, transmission and distribution including electrification, and agriculture, forestry and other land

use), by type of economy, and by region (see Annex II Part I Section 1: By region is based on intermediate level (R10) classification scheme. By type of economy is based on intermediate level (R10) classification scheme, which considers ‘North America’, ‘Europe’, and ’Australia, Japan and New Zealand’ as developed countries, and the other seven regions as developing countries). Breakdown by sector / segment may differ slightly from sectoral analysis in other contexts due to the availability of investment needs data. The granularity of the models assessed in Chapter 3, and other studies, do not allow for a robust assessment of the specific investment needs of LDCs or SIDSs. Investment requirements in developing countries might be underestimated due to missing data points as well as underestimated technology costs. In modelled pathways, regional investments are projected to occur when and where they are cost cost-effective to limit global warming. The model quantifications help to

identify high-priority areas for cost-effective investments, but do not provide any indication on who would finance the regional investments. Investment requirements and flows covering downstream / mitigation technology deployment only. Data includes investments with a direct mitigation effect, and in the case of electricity, additional transmission and distribution investments. See section 15.4.2 Quantitative assessment of financing needs for detailed data on investment requirements. Data on mitigation investment flows are based on a single series of reports (Climate Policy Initiative, CPI) which assembles data from multiple sources. Investment flows for energy efficiency are adjusted based on data from the International Energy Agency (IEA). Data on mitigation investments do not include technical assistance (i.e., policy and national budget support or capacity building), other non-technology deployment financing. Adaptation only flows are also excluded. Data on mitigation investment

requirements for electricity are based on emission pathways C1, C2 and C3 (Table SPM.1). For electricity investment requirements, the upper end refers to the mean of C1 pathways and the lower end to the mean of C3 pathways. Data points for energy efficiency, transport and AFOLU cannot always be linked to C1–C3 scenarios. Data do not include needs for adaptation or general infrastructure investment or investment related to meeting the SDGs other than mitigation, which may be at least partially required to facilitate mitigation. The multiplication factors show the ratio of average annual model-based mitigation investment requirements (2020–2030) and most recent annual mitigation investments (averaged for 2017–2020). The lower and upper multiplication factors refer to the lower and upper ends of the range of investment needs.

Given the multiple sources and lack of harmonised methodologies, the data can only be indicative of the size and pattern of investment gaps. The gap between most recent 
flows and required investments is only a single indicator. A more comprehensive (and qualitative) assessment is required in order to understand the magnitude of the challenge of scaling up investment in sectors and regions. The analysis also does not consider the effects of misaligned flows. {15.3, 15.4, 15.5, Table 15.2, Table 15.3, Table 15.4}

135TS
Technical SummaryGlobal climate finance is heavily focused on mitigation (more

than 90% on average between 2017–2020) (high confidence) {15.4, 15.5}. This is despite the significant economic effects of climate change’s expected physical impacts, and the increasing awareness of these effects on financial stability. To meet the needs for rapid deployment of mitigation options, global mitigation investments are expected to need to increase by the factor of three to six (high confidence). The gaps represent a  major challenge for developing countries, especially Least-Developed Countries (LDCs), where flows have to increase by the factor of four to seven for specific sectors such as AFOLU, and for specific groups with limited access to, and high costs of, climate finance (high confidence) (Figure TS.25) {15.4, 15.5}. The actual size of sectoral and regional climate financing gaps is only one component driving the magnitude of the challenge. Financial and economic viability, access to capital markets, appropriate regulatory frameworks, and institutional capacity to

attract and facilitate investments and ensure safeguards are decisive to scaling-up funding. Soft costs for regulatory environment and institutional capacity, upstream funding needs as well as R&D and venture capital for development of new technologies and business models are often overlooked despite their critical role to facilitate the deployment of scaled-up climate finance (high confidence). {15.4.1, 15.5.2}

The relatively slow implementation of commitments by countries 
and stakeholders in the financial sector to scale up climate  finance reflects neither the urgent need for ambitious climate action, nor the economic rationale for ambitious climate action (high confidence). Delayed climate investments and financing – and limited 
alignment of investment activity with the Paris Agreement – will result in significant carbon lock-ins, stranded assets, and other additional costs. This will particularly impact urban infrastructure and the energy and transport sectors (high confidence). A common understanding of debt sustainability and debt transparency, including negative implications of deferred climate investments on future GDP , and how stranded assets and resources may be compensated, has not yet been developed (medium confidence). {15.6}
There is a  mismatch between capital availability in the

developed world and the future emissions expected in developing countries (high confidence). This emphasises the need to recognise the explicit and positive social value of global cross-border mitigation financing. A significant push for international climate finance access for vulnerable and poor countries is particularly important given these countries’ high costs of financing, debt stress and the impacts of ongoing climate change (high confidence). {15.2, 15.3.2.3, 15.5.2, 15.6.1, 15.6.7}
Innovative financing approaches could help reduce the

systemic under-pricing of climate risk in markets and foster demand for investment opportunities aligned with the Paris Agreement goals. Approaches include de-risking investments, robust ‘green’ labelling and disclosure schemes, in addition to a  regulatory focus on transparency and reforming international monetary system financial sector regulations (medium confidence). Green bond markets and markets for sustainable finance products have grown significantly since AR5 and the landscape continues to evolve. Underpinning this evolution is investors’ preference for scalable and identifiable low-carbon investment opportunities. These relatively new labelled financial products will help by allowing a smooth integration into existing asset allocation models (high confidence). Green bond markets and markets for sustainable finance products have also increased significantly since AR5, but challenges nevertheless remain, in particular, there are concerns about ‘greenwashing’ and the limited

application of these markets to developing countries (high confidence). {15.6.2, 15.6.6}

the aggregation of small-scale financing needs and provide scalable investment opportunities with more attractive risk-return profiles (high confidence). Support and guidance for enhancing transparency can promote capital markets’ climate financing by providing quality information to price climate risks and opportunities. Examples include SDG and environmental, social and governance (ESG) disclosure, scenario analysis and climate risk assessments, including the Task Force on Climate-related Financial Disclosures (TCFD). The outcome of these market-correcting approaches on capital flows cannot be taken for granted, however, without appropriate fiscal, monetary and financial policies. Mitigation policies will be required to enhance the risk-weighted return of low-emission and climate-resilient options, accelerate the emergence and support for financial products based on real projects, such as green bonds, and phase-out fossil fuel subsidies. Greater public-private cooperation can also

encourage the private sector to increase and broaden investments, within a context of safeguards and standards, and this can be integrated into national climate change policies and plans (high confidence). {15.1, 15.2.4, 15.3.1, 15.3.2, 15.3.3, 15.5.2, 15.6.1, 15.6.2, 15.6.6, 15.6.7, 15.6.8}

public climate financing over the next decade (2021–2030) can help redirect capital markets and overcome challenges relating to the need for parallel investments in mitigation. It can also help address macroeconomic uncertainty and alleviate developing countries’ debt burden post-COVID-19 (high confidence). Providing strong climate policy signals helps 
guide investment decisions. Credible signalling by governments and the international community can reduce uncertainty for financial decision-makers and help reduce transition risk. In addition to indirect and direct subsidies, the public sector’s role in addressing market failures, barriers, provision of information, and risk-sharing can encourage the efficient mobilisation of private sector finance (high confidence) {15.2, 15.6.1, 15.6.2}. The mutual benefits of coordinated

support for climate mitigation and adaptation in the next decade for both developed and developing regions could potentially be very high in the post-COVID era. Climate-compatible stimulus packages could significantly reduce the macro-financial uncertainty generated by the pandemic and increase the sustainability of the world economic recovery {15.2, 15.3.2.3, 15.5.2, 15.6.1, 15.6.7}. Political 
leadership and intervention remain central to addressing uncertainty, which is a fundamental barrier for the redirection of financial flows. Existing policy misalignments – for example, in fossil fuel subsidies – undermine the credibility of public commitments, reduce perceived transition risks and limit financial sector action (high confidence). {15.2, 15.3.3, 15.6.1, 15.6.2, 15.6.3}

136
TSTechnical SummaryThe greater the urgency of action to remain on a  1.5°C 
pathway, the greater need for parallel investment decisions in upstream and downstream parts of the value chain (high confidence). Greater urgency also reduces the lead times to build trust in regulatory frameworks. Consequently, many investment decisions will need to be made based on the long-term global goals. This highlights the importance of trust in political leadership which, in turn, affects risk perception and ultimately financing costs (high confidence). {15.6.1, 15.6.2}
Accelerated international cooperation on finance is a critical

enabler of a  low-carbon and Just Transition (very high confidence). Scaled-up public grants for adaptation and mitigation, and funding for low-income and vulnerable regions, especially in Sub-Saharan Africa, may have the highest returns. Key options include: increased public finance flows from developed to developing countries beyond USD100 billion a year; shifting from a direct lending modality towards public guarantees to reduce risks and greatly leverage private flows at lower cost; local capital markets development; and, changing the enabling operational definitions. A coordinated effort to green the post-pandemic recovery is also essential in countries facing much higher debt costs (high confidence). {15.2, 15.6}
TS.6.5 Innovation, Technology Development 
and Transfer
Innovation in climate mitigation technologies has seen

enormous activity and significant progress in recent years. Innovation has also led to, and exacerbated, trade-offs in relation to sustainable development.  Innovation can leverage action to mitigate climate change by reinforcing other interventions. In conjunction with other enabling conditions, innovation can support system transitions to limit warming and help shift development pathways. The currently widespread implementation of solar PV and LED lighting, for instance, could not have happened without technological innovation.  Technological innovation can also bring

about new and improved ways of delivering services that are essential to human well-being (high confidence) {16.1, 16.3, 16.4, 16.6}. At the same time as delivering benefits, innovation can result in trade-offs that undermine both progress on mitigation and progress towards other Sustainable Development Goals (SDGs). Trade-offs include negative externalities’ – for instance, greater environmental pollution and social inequalities – rebound effects leading to lower net emission reductions or even increases in emissions, and increased dependency on foreign knowledge and providers (high confidence). Effective governance and policy have the potential to avoid and minimise such misalignments (medium evidence,  high agreement). {16.2, 16.3, 16.4, 16.5.1, 16.6}

A systemic view of innovation to direct and organise the processes has grown over the last decade. This systemic view of innovation takes into account the role of actors, institutions, and their interactions, and can inform how innovation systems that vary across technologies, sectors and countries, can be strengthened (high confidence) {16.2, 16.3, 16.5}. Where 
a systemic view of innovation has been taken, it has enabled the 
development and implementation of indicators that are better able to provide insights in innovation processes. This, in turn, has enabled the analysis and strengthening of innovation systems. Traditional quantitative innovation indicators mainly include R&D investments and patents. Figure TS.26 illustrates that energy-related research, development and demonstration (RD&D) has risen slowly in the last 
technologies/researchOther power and storage technologies Hydrogen and fuel cells

1974
1976197819801982198419861988199019921994199619982000200220042006200820102012201420162018Figure is same as Box 16.3 Figure 1
Figure TS.26 | Fraction of public energy research, development and demonstration (RD&D) spending by technology over time for IEA (largely OECD) 
countries between 1974 and 2018.  {Box 16.3, Figure 1}

137TS
two decades, and that there has been a reorientation of the portfolio 
of funded energy technologies. Systemic indicators of innovation, however, go well beyond these approaches. They include structural innovation system elements including actors and networks, as well as indicators for how innovation systems function, such as access to finance, employment in relevant sectors, and lobbying activities {16.3.4, Table  16.7}. For example, in Latin America, monitoring systemic innovation indicators for the effectiveness of agroecological mitigation approaches has provided insights on the appropriateness and social alignment of new technologies and practices {Box 16.5}. Climate-energy-economy models, including integrated assessment models (IAMs), generally employ a  stylised and necessarily incomplete view of innovation, and have yet to incorporate a systemic representation of innovation systems. {16.2.4, Box 16.1}

insights to policymakers supporting their selection of effective innovation policy instruments (high confidence) {16.4, 16.5}.  
A combination of scaled-up innovation investments with demand-pull interventions can achieve faster technology unit cost reductions and more rapid scale-up than either approach in isolation. These 
innovation policy instruments would nonetheless have to be tailored to local development priorities, to the specific context of different countries, and to the technology being supported. The timing of interventions and any trade-offs with sustainable development also need to be addressed. Public R&D funding and support, as well as

innovation procurement, have shown to be valuable for fostering innovation in small-to-medium clean-tech firms (Figure  TS.27) {16.4.4.3}. Innovation outcomes of policy instruments not necessarily aimed at innovation, such as feed-in tariffs, auctions, emissions trading schemes, taxes and renewable portfolio standards, vary from negligible to positive for climate change mitigation. Some specific designs of environmental taxation can also result in negative distributional outcomes {16.4.4}. Most of the available literature and evidence on innovation systems come from industrialised countries and larger developing countries. However, there is a growing body of evidence from developing countries and Small Island Developing States (SIDS). {16.4, 16.5, 16.7}
Experience and analyses show that technological change is

inhibited if technological innovation system functions are not adequately fulfilled; this inhibition occurs more often in developing countries (high confidence).  Examples of such

functions are knowledge development, resource mobilisation, and activities that shape the needs, requirements and expectations of actors within the innovation system (guidance of the search). Capabilities play a key role in these functions, the buildup of which can be enhanced by domestic measures, but also by international cooperation. For instance, innovation cooperation on wind energy has contributed to the accelerated global spread of this technology. As another example, the policy guidance by the Indian government, which also promoted development of data, testing capabilities and knowledge within the private sector, has been a key determinant of the success of an energy-efficiency programme for air conditioners and refrigerators in India. {16.3, 16.5, 16.6, Cross-Chapter Box 12 in Chapter 16, Box 16.3}Demonstration
Prototype
Research
(Fundamental science and technology exploration)Early adoptionMarket adoption and scale-upMaturityPublic policy instruments supporting innovation

(learning from early ideas)Second generation(learning from early ideas)Dead endDead endFirst commercial-scale exampleFirst commercial-scale exampleNiche marketsNiche marketsEconomies of scaleEconomies of scaleContinuous incremental 
Potential future disruptorPotential future disruptorKnowledge transferKnowledge transfer
Third generation(new ideas arising from experience)Third generation(new ideas arising from experience)Market creation 
(e.g., feed-in tariffs, tax subsidies, loans, procurement, carbon pricing)
standards
Building codesPerformance 
standards
provision; labelling; mandated purchasesRegulatory and soft instrumentsEconomic and ﬁnancial policy instruments

Figure TS.27 | Technology innovation process and the (illustrative) roles of different public policy instruments (on the right-hand side).  {Figure 16.1} Note 
that demand-pull instruments in the regulatory instrument category, for instance, can also shape the early stages of the innovation process. Their position in the latter stages is 
highlighted in this figure because typically these instruments have been introduced in latter stages of the development of the technology.  {16.4.4}

138
TSTechnical SummaryConsistent with innovation system approaches, the sharing 
of knowledge and experiences between developed and developing countries can contribute to addressing global climate and the SDGs. The effectiveness of such international cooperation arrangements, however, depends on the way they are developed and implemented (high confidence). The effectiveness and sustainable development benefits of technology sharing under market conditions appears to be determined primarily by the complexity of technologies, local capabilities and the policy regime. This suggests that the development of planning and innovation capabilities remains necessary, especially in Least-Developed Countries (LDCs) and SIDS. International diffusion of low-emission technologies is also facilitated by knowledge spillovers from regions engaged in clean R&D (medium confidence). {16.2} 
The evidence on the role of intellectual property rights (IPR)

in innovation is mixed. Some literature suggests that it is a  barrier while other sources suggests that it is an enabler to the diffusion of climate-related technologies (medium confidence). There is agreement that countries with well-developed institutional capacity may benefit from a strengthened IPR regime, but that countries with limited capabilities might face greater barriers to innovation as a consequence. This enhances the continued need for capacity-building. Ideas to improve the alignment of the global IPR regime and addressing climate change include specific arrangements for LDCs, case-by-case decision-making and patent-pooling institutions. {16.2.3, 16.5, Box 16.10} 
Although some initiatives have mobilised investments in

developing countries, gaps in innovation cooperation remain, including in the Paris Agreement instruments. These gaps could be filled by enhancing financial support for international technology cooperation, by strengthening cooperative approaches, and by helping build suitable capacity in developing countries across all technological innovation system functions (high confidence). The implementation of current arrangements of international cooperation for technology development and transfer, as well as capacity-building, are insufficient to meet climate objectives and contribute to sustainable development. For example, despite building a  large market for mitigation technologies in developing countries, the lack of a systemic perspective in the implementation of the Clean Development Mechanism (CDM), operational since the mid-2000s, has only led to some technology transfer, especially to larger developing countries, but limited capacity building and minimal technology development

(medium confidence). In the current climate regime, a  more systemic approach to innovation cooperation could be introduced by linking technology institutions, such as the Technology Mechanism, and financial actors, such as the Financial Mechanism. {16.5.3}

in parallel with the challenges that relate to climate change. Addressing both sets of challenges simultaneously presents multiple and recurrent obstacles that systemic approaches to technological change could help resolve, provided they are well managed (high confidence).  Obstacles include both

entrenched power relations dominated by vested interests that control and benefit from existing technologies, and governance structures that continue to reproduce unsustainable patterns of production and consumption (medium confidence). Studies also highlight the potential of cultural factors to strongly influence the pace and direction of technological change. Sustainable solutions require adoption and mainstreaming of locally novel technologies that can meet local needs, and simultaneously address the SDGs. Acknowledging the systemic nature of technological innovation  – which involve many levels of actors, stages of innovation and scales – can lead to new opportunities to shift development pathways towards sustainability. {16.4, 16.5, 16.6}
Strategies for climate change mitigation can be most effective

in accelerating transformative change when actions taken to strengthen one set of enabling conditions also reinforce and strengthen the effectiveness of other enabling conditions (medium confidence). Applying transition or system dynamics to decisions can help policymakers take advantage of such high-leverage intervention points, address the specific characteristics of technological stages, and respond to societal dynamics. Inspiration can be drawn from the global unit-cost reductions of solar PV, which were accelerated by a  combination of factors interacting in a  mutually reinforcing way across a  limited group of countries (high confidence) {Box 16.2, Cross-Chapter Box 12 in Chapter 16}. Transitions can be accelerated by policies appropriately targeted, which may be grouped in different ‘pillars of policy’. The relative importance of different ‘pillars’ differs according to the stage of the transition. (Figure TS.28) {1.2.3}

can provide timely insights for policymakers and policy design locally, nationally and internationally, especially for developing countries, where such insights are often missing. Data needed include those that can show the strength of technological, sectoral and national innovation systems. It is also necessary to validate current results and generate insights from theoretical frameworks and empirical studies for developing countries’ contexts. Innovation studies on adaptation and mitigation other than energy and ex-post assessments of the effectiveness of various innovation-related policies and interventions, including R&D, would also provide benefits. Furthermore, methodological developments to improve the ability of IAMs to capture energy innovation system dynamics and the relevant institutions and policies (including design and implementation), would allow for more realistic assessment. {16.2, 16.3, 16.7}

139TS
MARKET
MACRO
MESO
MICRO
LANDSCAPE
SOCIO-
TECHINCAL
REGIME
NICHE
Stages of
Time
Figure TS.28 | Transition dynamics: levels, policies and processes. {Figure 1.7} The relative importance of different ‘pillars of policy’ differs according to the stage of 
the transition. The lower panel illustrates growth of innovative technologies or practices, which if successful, emerge from niches into an S-shaped dynamic of exponential

growth. The diffusion stage often involves new infrastructure and reconfiguration of existing market and regulatory structures. During the phase of more widespread diffusion, growth levels off to linear, then slows as the industry and market matures. The processes displace incumbent technologies/practices which decline, initially slowly, but then at an accelerating pace. Many related literatures identify three main levels with different characteristics, most generally termed micro, meso and macro.

140
TSTechnical SummaryBox TS.14 | Digitalisation
Digital technologies can promote large increases in energy efficiency through coordination and an economic shift to services, but they 
can also greatly increase energy demand because of the energy used in digital devices (high confidence). {Cross-Chapter Box 11 in Chapter 16, 16.2}
Digital devices, including servers, increase pressure on the environment due to the demand for rare metals and end-of-life disposal. The

absence of adequate governance in many countries can lead to harsh working conditions and unregulated disposal of electronic waste. Digitalisation also affects firms’ competitiveness, the demand for skills, and the distribution of, and access to resources. The existing digital divide, especially in developing countries, and the lack of appropriate governance of the digital revolution can hamper the role that digitalisation could play in supporting the achievement of stringent mitigation targets. At present, the understanding of both the direct and indirect impacts of digitalisation on energy use, carbon emissions and potential mitigation is limited (medium confidence). 
The digital transformation is a megatrend that is fundamentally changing all economies and societies, albeit in very different ways

depending on the level of development of a given country and on the nature of its economic system. Digital technologies have significant potential to contribute to decarbonisation due to their ability to increase energy and material efficiency, make transport and building systems less wasteful, and improve the access to services for consumers and citizens. Yet, if left unmanaged, the digital transformation will probably increase energy demand, exacerbate inequities and the concentration of power, leaving developing economies with less access to digital technologies behind, raise ethical issues, reduce labour demand and compromise citizens’ welfare. Appropriate governance of the digital transformation can ensure that digitalisation works as an enabler, rather than as a barrier and further strain in decarbonisation pathways. Governance can ensure that digitalisation not only reduces GHG emissions intensity but also contributes to reducing absolute GHG emission, constraining run-away

Digital technologies have the potential to reduce energy demand in all end-use sectors through steep improvements in energy 
efficiency. This includes material input savings and increased coordination as they allow the use of fewer inputs to perform a given task. Smart appliances and energy management, supported by choice architectures, economic incentives and social norms, effectively reduce energy demand and associated GHG emissions by 5–10% while maintaining equal service levels. Data centres can also play a role in energy-system management, for example by waste-heat utilisation where district heat systems are close by; temporal and spatial scheduling of electricity demand can provide about 6% of the total potential demand response. {5.5, Cross-Chapter Box 11, Table 1 in Chapter 16}
Digital technologies, analytics and connectivity consume large amounts of energy, implying higher direct energy demand and related

carbon emissions. Global energy demand from digital appliances reached 7.14 EJ in 2018. The demand for computing services increased by 550% between 2010 and 2018 and is now estimated at 1% of global electricity consumption. Due to efficiency improvements, the associated energy demand increased only modestly, by about 6% from 2000 to 2018. {Box 9.5}
System-wide effects endanger energy and GHG-emission savings. Rising demand can diminish energy savings, and also produce

run-away effects associated with additional consumption and GHG emissions if left unregulated. Savings are varied in smart and shared mobility systems, as ride-hailing increases GHG emissions due to deadheading, whereas shared pooled mobility and shared cycling reduce GHG emissions, as occupancy levels and/or weight per person kilometre transported improve. Systemic effects have wider boundaries of analysis and are more difficult to quantify and investigate but are nonetheless very relevant. Systemic effects tend to have negative impacts, but policies and adequate infrastructures and choice architectures can help manage and contain these. {5.3, 5.4, 5.6}

141TS
Technical SummaryTS.7 Mitigation in the Context 
of Sustainable Development
Accelerating climate mitigation in the context of sustainable 
development  involves not only expediting the pace of change but 
also addressing the underlying drivers of vulnerability and emissions. Addressing these drivers can enable diverse communities, sectors, stakeholders, regions and cultures to participate in just, equitable and inclusive processes that improve the health and well-being of people and the planet. Looking at climate change from a justice perspective also means placing the emphasis on: (i) the protection of vulnerable populations from the impacts of climate change, (ii) mitigating the  effects of low-carbon transformations, and (iii)  ensuring an equitable decarbonised world (high confidence). {17.1}
The SDG framework
33 can serve as a  template to evaluate 
the long-term implications of mitigation on sustainable

development and vice versa (high confidence). Understanding the co-benefits and trade-offs associated with mitigation is key to understanding how societies prioritise among the various sectoral policy options (medium confidence). Areas with anticipated trade-offs include food and biodiversity, energy affordability/access, and mineral-resource extraction. Areas with anticipated co-benefits include health, especially regarding air pollution, clean energy access and water availability. The possible implementation of the different sectoral mitigation options therefore depends on how societies prioritise mitigation versus other products and services: not least, how societies prioritise food, material well-being, nature conservation and biodiversity protection, as well as considerations such as their future dependence on CDR. Figure TS.29 summarises the assessment of where key synergies and trade-offs exist between mitigation options and the SDGs. (Figures TS.29 and TS.31, Table TS.7)

The beneficial and adverse impacts of deploying climate-
change mitigation and adaptation responses are highly context-specific and scale-dependent. There are synergies and trade-offs between adaptation and mitigation as well as synergies and trade-offs with sustainable development (high confidence). Strong links also exist between sustainable development, vulnerability and climate risks, as limited economic, social and institutional resources often result in low adaptive capacities and high vulnerability, especially in developing countries. Resource limitations in these countries can similarly weaken the capacity for climate mitigation and adaptation. The move towards climate-resilient societies requires transformational or deep systemic change. This has important implications for countries’ sustainable development pathways (medium evidence, high agreement). (Box TS.3, Figure TS.29) {4.5, Figure 4.9, 17.3.3}
Many of the potential trade-offs between mitigation and

other sustainable development outcomes depend on policy design and can be compensated or avoided with additional policies and investments, or through policies that integrate mitigation with other SDGs (high confidence). Targeted SDG 
33 The 17 SDGs are at the heart of the UN 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015.policies and investments, for example, in the areas of healthy nutrition, sustainable consumption and production, and international collaboration, can support climate change mitigation policies and resolve or alleviate trade-offs. Trade-offs can also be addressed by

complementary policies and investments, as well as through the design of cross-sectoral policies integrating mitigation with the SDGs, and in particular: good health and well-being (SDG 3), zero hunger and nutrition (SDG 2), responsible consumption and production (SDG 12), reduced inequalities (SDG 10), and life on land (SDG 15). (Figures TS.29 and TS.30) {3.7}
Decent living standards, which encompasses many SDG

dimensions, are achievable at lower energy use than previously thought (high confidence). Mitigation strategies that focus on lowering demand for energy and land-based resources exhibit reduced trade-offs and negative consequences for sustainable development relative to pathways involving either high emissions and climate impacts or pathways with high consumption and emissions that are ultimately compensated by large quantities of BECCS. Figure  TS.30 illustrates how, in the case of pathways limiting warming to 1.5°C (>67%), sustainable development policies can lead to overall benefits compared to mitigation policies alone. (Figures TS.22 and TS.30) {3.7, 5.2} 
will have significant consequences for broader sustainable development outcomes in the longer term (high confidence). Ambitious mitigation can be considered a precondition for achieving the SDGs. {3.7}
Adopting coordinated cross-sectoral approaches to climate

mitigation can target synergies and minimise trade-offs, both between sectors and between sustainable development objectives (high confidence).  This requires integrated planning 
using multiple-objective-multiple-impact policy frameworks. Strong inter-dependencies and cross-sectoral linkages create both opportunities for synergies and need to address trade-offs related to mitigation options and technologies. This can only be done if coordinated sectoral approaches  to climate change mitigation policies are adopted that mainstream these interactions and ensure

local people are involved in the development of new products, as well as production and consumption practices. For instance, there can be many synergies in urban areas between mitigation policies and the SDGs but capturing these depends on the overall planning of urban structures and on local integrated policies such as combining affordable housing and spatial planning with walkable urban areas, green electrification and clean renewable energy (medium confidence). Integrated planning and cross-sectoral alignment of climate change policies are also particularly evident in developing countries’ NDCs under the Paris Agreement, where key priority sectors such as agriculture and energy are closely aligned with the proposed mitigation and adaptation actions and the SDGs. {12.6.2, Supplementary Material Table 17.SM.1, 17.3.3}

142
TSTechnical SummaryType of relations:
Lower of the two conﬁdence levels has been reported4Timber, biomass, agri. feedstock3Related Sustainable Development Goals:
Not assessed due to limited literature5Sectoral and system mitigation options Chapter sourceRelation with Sustainable Development Goals

Synergies
Figure TS.29 | Mitigation options have synergies with many Sustainable Development Goals (SDGs), but there are trade-offs associated with some 
options especially when implemented at scale.

143TS
Technical SummaryFigure TS.29 (continued): Mitigation options have synergies with many Sustainable Development Goals (SDGs), but there are trade-offs associated with 
some options especially when implemented at scale.  The synergies and trade-offs vary widely and depend on the context. Figure presents a summary of the chapter-

level qualitative assessment of the synergies and trade-offs for selected mitigation options. Overlaps may exist in the mitigation options assessed and presented by sector and system, and interlinkages with the SDGs might differ depending on the application of that option by sector. Interactions of mitigation options with the SDGs are context-specific and dependent on the scale of implementation. For some mitigation options, these scaling and context-specific issues imply that there are both synergies and trade-offs in relation to specific SDGs. The SDGs are displayed as coloured squares. They indicate whether a synergy, trade-off, or both synergies and trade-offs exist between the SDG and the mitigation option. Confidence levels are indicated through the solidity of the squares. A solid square indicates high confidence, a partially filled square indicates medium confidence, and an outlined square indicates low confidence. The final column in the figure provides a line of sight to the

chapters that provide details on context-specificity and scale of implementation. {6.3, 6.4, 6.7, 7.3, 7.4, 7.5, 7.6, 8.2, 8.4, 8.6, 9.4, 9.5, 9.8, Table 9.5, 10.3, 10.4, 10.5, 10.6, 10.8, 11.5, Table 10.3, 17.3, Figure 17.1, Supplementary Material Table 17.SM.1, Annex II.IV .12}

–100%
–50%
0%
50%
100%
150%
200%
400%
600%
PM 2.5
F
–100%
–80%
–60%
–40%
–20%
0%
Cooling 
Risk to 
Habitat 
–100%
–50%
0%
Avoided climate impacts
Effects of narrow 
Effects of sustainable 
Change relative to 
current policies
Hunger
Health
Water
Energy
 (land)
 (water)
Res’
C&P
Beneﬁt
Harm
PM 2.5

Food waste
Figure TS.30 | Impacts on SDGs of mitigation limiting warming to 1.5°C (>50%) with narrow mitigation policies vs broader sustainable development policies.  Left: benefits of mitigation from avoided impacts. Middle: sustainability co-benefits and trade-offs of narrow mitigation policies (averaged over multiple models). 
Right: sustainability co-benefits and trade-offs of mitigation policies integrating Sustainable Development Goals. Scale: 0% means no change compared to 3°C (left) or current policies (middle and right). Green values correspond to proportional improvements, red values to proportional worsening. Note: only the left panel considers climate impacts on sustainable development; the middle and right panels do not. ‘Res’ C&P’ stands for Responsible Consumption and Production (SDG 12). {Figure 3.39}

144
TSTechnical SummaryThe feasibility of deploying response options is shaped

by barriers and enabling conditions across geophysical, environmental-ecological, technological, economic, socio-cultural, and institutional dimensions (high confidence). Accelerating the deployment of response options depends on  reducing or removing barriers across these dimensions, as well on establishing and strengthening enabling conditions. Feasibility is context-dependent, and also depends on the scale and the speed of implementation. For example: the institutional, legal and administrative capacity to support deployment varies across countries; the feasibility of options that involve large-scale land-use changes is highly context-dependent; spatial planning has a higher potential in early stages of urban development; the geophysical potential of geothermal is site-specific; and cultural and local conditions may either inhibit or enable demand-side responses. Figure TS.31 summarises the assessment of barriers and enablers for a broad range of sector-specific, and cross-sectoral

Alternative mitigation pathways are also associated with 
different feasibility challenges (high confidence). These challenges are multi-dimensional, context-dependent, malleable to policy and to technological and societal trends. They can also be reduced by putting in place appropriate enabling conditions. Figure TS.32 highlights the dynamic and transient nature of feasibility risks. These risks are transient and concentrated in the decades before mid-century. Figure TS.32 also illustrates how different EnergyGeophysical Mitigation response options Environmental-
ecologicalTechnological Economic Socio-cultural InstitutionalUrban TransportCross
 sectoral BuildingsNuclear
Integrating sectors, strategies and innovationsSolar energy

Enablers Barriers 0% 100% Limited or no evidenceDirect air carbon capture and storageElectriﬁcation of the urban energy systemSystem integrationDemand-side mitigationEnergy storage for low-carbon gridsFossil fuel phaseoutBioenergy
Hydrogen fuel cell vehicles for land transport
Blue carbonCarbon dioxide capture, utilisation, and storage
Some indicators not applicableElectric vehicles for land transportSynthetic fuels for heavy-duty land transport, aviation and shippingDemand reduction and modal shift
Ammonia for shippingChange in construction methods and circular economy 
Biofuels for land transport, aviation and shippingBuilding design and performance
Change in construction materials Envelope improvement
Heating, ventilation and air conditioning (HVAC) 
Demand-side management 

Figure TS.31 | Geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors can enable or act as barriers to 
the deployment of response options.  Chapter-level assessment for selected mitigation options. Overlaps may exist in the mitigation options assessed and presented 
by sector and system, and feasibility might differ depending on the demarcation of that option in each sector. Chapters 6, 8, 9, 10, and 12 assess mitigation response

options across six  feasibility dimensions: geophysical, environmental-ecological, technological, economic, socio-cultural and institutional. AFOLU (Chapter  7) and industry (Chapter 11) are not included because of the heterogeneity of options in these sectors. For each dimension, a set of feasibility indicators was identified. Examples of indicators include impacts on land use, air pollution, economic costs, technology scalability, public acceptance and political acceptance (see Box TS.15, and Annex II.IV .11 for a detailed explanation). An indicator could refer to a barrier or an enabler to implementation, or could refer to both a barrier or an enabler, depending on the context, speed, and scale of implementation. Dark blue bars indicate the extent of enablers to deployment within each dimension. This is shown relative to the maximum number of possible enablers, as indicated by the light blue shading. Dark orange bars indicate the extent of barriers to deployment within each

dimension. This is shown relative to the maximum number of possible barriers, as indicated by light orange shading. A light grey dot indicates that there is limited or no evidence to assess the option. A dark grey dot indicates that one of the feasibility indicators within that dimension is not relevant for the deployment of the option. The relevant sections in the underlying chapters include references to the literature on which the assessment is based and indicate whether the feasibility of an option varies depending on context (e.g., region), scale (e.g., small, medium or full scale), speed (e.g., implementation in 2030 versus 2050) and warming level (e.g., 1.5°C versus 2°C). {6.4, 8.5, 9.10, 10.8, 12.3, Annex II.IV .11}

145TS
Figure TS.32 | The feasibility of mitigation scenarios.  Figure TS.32 shows the proportion of scenarios in the AR6 scenarios database – falling within the warming 
level classifications C1 and C3 (C1:  below 1.5°C (>50%), no or limited overshoot; C3:  below 2°C (>67%)) – that exceed threshold values in 2030, 2050 and 2100 for five 
dimensions of feasibility (Boxes TS.5 and TS.15). The feasibility dimensions shown are: geophysical, technological, economic, socio-cultural and institutional. The thresholds 
shown are: (i)  plausible – range of values based on past historical trends or other peer reviewed assessments; (ii) best-case scenario – range of values assuming major political

support or technological breakthrough; (iii) unprecedented – values going beyond those observed or reported in peer-reviewed assessments. Overlayed are the Illustrative Mitigation Pathways consistent with SSP2 (LD, SP , Ren: C1 category; Neg, GS: C3 category). The positioning of the illustrative pathways is simply indicative of the general trade-offs over time and across the feasibility dimensions, it is not determined mathematically. (Box TS.5) {3.8}

146
TSTechnical Summaryfeasibility dimensions pose differentiated challenges: for example, 
institutional feasibility challenges are shown as unprecedented for a high proportion of scenarios, in line with the qualitative literature, but moving from 2030 to 2050 and 2100 these challenges decrease. 
The feasibility challenges associated with mitigation pathways 
are predominantly institutional and economic rather than 
technological and geophysical (medium confidence). The rapid 
pace of technological development and deployment in mitigation scenarios is not incompatible with historical records, but rather, institutional capacity is a  key limiting factor for a  successful transition. Emerging economies appear to have highest feasibility challenges in the near to mid-term. This suggests a key role of policy and technology as enabling factors. (Figure TS.32) {3.8} 
Pathways relying on a broad portfolio of mitigation strategies

are more robust and resilient (high confidence). Portfolios of technological solutions reduce the feasibility risks associated with the low-carbon transition. (Figures TS.31 and TS.32, Box TS.15) {3.8}
Box TS.15 | A Harmonised Approach to Assessing Feasibility
The assessment of feasibility in this report aims to identify barriers and enablers to the deployment of mitigation options and pathways. The assessment organises evidence to support policy decisions, and decisions on actions, that would improve the feasibility of mitigation options and pathways by removing relevant barriers and by strengthening enablers of change.
The feasibility of mitigation response options
Mitigation response options are assessed against six dimensions of feasibility. Each dimension comprises a key set of indicators that can be evaluated by combining various strands of literature. {Annex II.IV.11, Table 6.1}

The assessment – undertaken by the sectoral chapters in this report – evaluates to what extent each indicator (listed in Box TS.15, 
Table.1) would be an enabler or barrier to implementation using a scoring methodology (described in detail in Annex II.IV.11). When appropriate, it is also indicated whether the feasibility of an option varies across context, scale, time and temperature goal. The resulting scores provide insight into the extent to which each feasibility dimension enables or inhibits the deployment of the relevant option. It also provides insight into the nature of the effort needed to reduce or remove barriers, thereby improving the feasibility of individual options. {Annex II.IV.11}
Box TS.15, Table.1 | Feasibility dimensions and indicators to assess the barriers and enablers of implementing mitigation options.
Feasibility dimension Indicators 
Geophysical feasibilityAvailability of required geophysical resources:

– Physical potential– Geophysical resource availability – Land use 
Environmental-ecological feasibilityImpacts on environment:– Air pollution – Toxic waste, ecotoxicity and eutrophication– Water quantity and quality – Biodiversity
Technological feasibilityExtent to which the technology can be implemented at scale soon:– Simplicity– Technology scalability– Maturity and technology readiness
Economic feasibilityFinancial costs and economic effects:– Costs now, in 2030 and in the long term– Employment effects and economic growth
Socio-cultural feasibilityPublic engagement and support, and social impacts:– Public acceptance– Effects on health and well-being– Distributional effects 
Institutional feasibilityInstitutional conditions that affect the implementation of the response option:– Political acceptance – Institutional capacity and governance, cross-sectoral coordination– Legal and administrative capacity

147TS
Technical SummaryBox TS.15 (continued)
The feasibility of mitigation scenarios
Scenarios provide internally consistent projections of emission-reduction drivers and help contextualise the scale of deployment and 
interactions of mitigation strategies. Recent research has proposed and operationalised frameworks for the feasibility assessment of mitigation scenarios. In this report the feasibility assessment of scenarios uses an approach that involves developing a set of multi-dimensional metrics capturing the timing, disruptiveness and the scale of the transformative change within five dimensions: 
geophysical, technological, economic, socio-cultural and institutional, as illustrated in Box TS.15, Figure 1.
More than 20 indicators were chosen to represent feasibility dimensions that could be related to scenario metrics. Thresholds of

feasibility risks of different intensity were obtained through empirical analysis of historical data and assessed literature. Details of indicators, thresholds, and how they were applied is reported in Annex II.IV.11. {3.8}
Box TS.15, Figure 1 | Steps involved in evaluating the feasibility of scenarios.  {Figure 3.41} Note: in this approach the environmental-ecological dimension 
is captured through different scenarios’ categories. 
A wide range of factors have been found to enable sustainability transitions, ranging from technological innovations to shifts in markets, and from policies and governance arrangements to shifts in belief systems and market forces (high confidence).

Many of these factors have come together in a co-evolutionary process that has unfolded globally, internationally and locally over several decades (low evidence, high agreement). Those same conditions that may serve to impede the transition (i.e., organisational structure, behaviour, technological lock-in) can also ‘flip’ to enable both the transition and the framing of sustainable development policies to create a  stronger basis for policy support (high confidence). It is important to note that strong shocks to these systems, including accelerating climate change impacts, economic crises and political changes, may provide crucial openings for accelerated transitions to sustainable systems. For example, rebuilding more sustainably after an extreme event, or renewed public debate about the drivers of social and economic vulnerability to multiple stressors (medium confidence). {17.4} 
While transition pathways will vary across countries it is

anticipated that they will be challenging in many contexts (high  confidence). Climate change is the result of decades of unsustainable production and consumption patterns, as well as governance arrangements and political economic institutions that lock-in resource-intensive development patterns (high confidence). Resource shortages, social divisions, inequitable distributions of wealth, poor infrastructure and limited access to advanced technologies and skilled human resources can constrain the options and capacity of developing countries to achieve sustainable and Just Transitions (medium evidence, high agreement) {17.1.1}. Reframing development objectives and shifting development pathways towards sustainability can help transform these patterns and practices, allowing space to transform unsustainable systems (medium evidence, high agreement). {1.6, Cross-Chapter Box 5 in Chapter 4, 17.1, 17.3} 
The landscape of transitions to sustainable development is

changing rapidly, with multiple transitions already underway. This creates the room to manage these transitions in ways that prioritise the needs of workers in vulnerable sectors (e.g., land, energy) to secure their jobs and maintain secure and healthy lifestyles (medium evidence, high agreement).  
{17.3.2}
Actions aligning sustainable development, climate mitigation 
and partnerships can support transitions. Strengthening different stakeholders’ ‘response capacities’ to mitigate and adapt to a changing climate will be critical for a sustainable transition (high confidence). {17.1}
Accelerating the transition to sustainability will be enabled by 
explicit consideration being given to the principles of justice, equality and fairness (high confidence). {5.2, 5.4, 5.6, 13.2, 13.6, 13.8, 13.9,17.4}Step 1
Feasibility dimensions
TechnologicalEconomicInstitutionalSocio-culturalStep 2
For each dimension,

selection of relevant indicators measuring decadal changes (among indicators available or computable based on scenario set)Step 3Thresholds
level of feasibility concern for each indicator in each decade based on thresholds deﬁned based on the literature and available empirical data– 3 high– 2 medium– 1 lowStep 4Aggregation (geometric mean)
dimensions at different points in time
dimensions and across timeallows assessing tradeoffs among feasibility dimensions
the timing and disruptiveness of the transformation
the scale of the transformation

SPM150
FAQThese Frequently Asked Questions have been extracted from the chapters and papers of the underlying report and are 
compiled here. When referencing specific FAQs, please reference the corresponding chapter or paper in the report from where the FAQ originated (e.g., FAQ 3.1 is part of Chapter 3).Frequently Asked Questions

151
Frequently Asked QuestionsFrequently Asked Questions (FAQs)
FAQ 1.1 |  What is climate change mitigation?

FAQ 1.2 |  Which greenhouse gases (GHGs) are relevant to which sectors?

FAQ 1.3 |  What is the difference between ‘net zero emissions’ and ‘carbon neutrality’?

see AR6 WGI Box 1.3 and Cross-Chapter Box 2 in Chapter 2 of this report).FAQ

2
FAQ 2.1 |  Are emissions still increasing or are they falling?
FAQ 2.2 |   Are there countries that have reduced emissions and grown economically 
About 24 countries have reduced territorial CO 2 and GHG emissions for more than 10 years. Uncertainties in emission levels and

changes over time prevent a precise assessment in some country cases. In the short observation period of 2010–2015, 43 out of 
166 countries have achieved absolute decoupling of consumption-based CO 2 emissions from economic growth, which means that 
FAQ 2.3 |   How much time do we have to act to keep global warming below 1.5 degrees?

this chapter.Frequently Asked Questions
FAQ
152

3
FAQ 3.1 | Is it possible to stabilise warming without net negative CO 2 and GHG emissions?
GHGs with atmospheric lifetimes of less than 20 years, this is achieved when residual emissions are reduced to levels that are lower than the natural removal of these gases in the atmosphere. Taken together, mitigation pathways that bring CO
2 emissions to net 

GWP-100. A considerable fraction of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and limit warming to 2°C (>67%), respectively, do not or only marginally (<10 GtCO
2 cumulative until 2100) deploy net negative CO 2 emissions (26% 
and 46%, respectively) and do not reach net zero GHG emissions by the end of the century (48% and 70%, respectively). This is no longer the case in pathways that return warming to 1.5°C (>50%) after a high overshoot (typically >0.1°C). All of these pathways deploy net negative emissions on the order of 360 (60–680) GtCO
2 (median and 5–95th percentile) and 87% achieve net negative 

negative GHG emissions, are only needed to decline, not to stabilise global warming. The deployment of carbon dioxide removal (CDR) is distinct from the deployment of net negative CO
2 emissions, because it is also used to neutralise residual CO 2 emissions to 
FAQ 3.2 |  How can net zero emissions be achieved and what are the implications of net zero 

implications of net zero GHG emissions depend on the bundle of gases that is being considered, and the emissions metric used to calculate aggregated GHG emissions and removals. If reached and sustained, global net zero GHG emissions using the 100-year Global Warming Potential (GWP-100) will lead to gradually declining global temperature.
Not all emissions can be avoided. Achieving net zero CO
2 emissions globally therefore requires deep emissions cuts across all sectors 

respectively; instead, positive emissions in one sector or region can be compensated by net negative emissions from another sector or region. The time each sector or region reaches net zero CO
2 or GHG emissions depends on the mitigation options available, the 
cost of those options, and the policies implemented (including any consideration of equity or fairness). Most modelled pathways that likely limit warming to 2°C (>67%) above pre-industrial levels and below use land-based CO
2 removal such as afforestation/

to occur. Pathways with more demand-side interventions that limit the amount of energy we use, or where the diet that we consume is changed, can achieve net zero CO
2, or net zero GHG emissions with less carbon dioxide removal (CDR). All available studies 
require at least some kind of carbon dioxide removal to reach net zero; that is, there are no studies where absolute zero GHG or even CO
2 emissions are reached by deep emissions reductions alone.
several decades, than net zero GHG emissions in all modelled pathways. In most modelled pathways that likely limit warming to 2°C (>67%) above pre-industrial levels and below in the most cost-effective way, the agriculture, forestry and other land-use (AFOLU) and energy supply sectors reach net zero CO
2 emissions several decades earlier than other sectors; however, many pathways show

much reduced, but still positive, net GHG emissions in the AFOLU sector in 2100.Frequently Asked Questions
FAQ
153

3
FAQ 3.3 | How plausible are high emissions scenarios, and how do they inform policy?

‘business-as-usual’ projections and should therefore not be presented as such.Frequently Asked Questions

4Frequently Asked Questions (FAQs)
FAQ 4.1 |   What is to be done over and above countries’ existing pledges under the Paris 
23 GtCO 2-eq (Cross-Chapter Box 4 in this Chapter). There is a further shortfall of about 4 to 7 GtCO 2-eq in 2030 if the conditions

are not fulfilled for those Parties that have made their pledges with conditions for support (Section 4.2.2.3). To cover up for these shortfalls will require taking actions across all sectors that can substantially reduce GHG emissions. Examples of such actions include shifting to low- or zero-emission power generation, such as renewables; changing food systems, such as diet changes away from land-intensive animal products; electrifying transport and developing ‘green infrastructure’, such as building green roofs, or improving energy efficiency by smart urban planning, which will change the layout of many cities. Because these different actions are connected, it means all relevant companies, industries and stakeholders would need to be involved to increase the support and chance of successful implementation (Section 4.2.5). The deployment of low-emission technology depends upon economic conditions (e.g., employment generation or capacity to mobilise investment), but also on

social/cultural conditions (e.g., awareness and acceptability) and institutional conditions (e.g., political support and understanding), and the provision of relevant enabling conditions (Section  4.4.1). Encouraging stronger and more ambitious climate action by non-government and sub-national stakeholders, as well as international cooperative initiatives (ICIs) could make significant contributions to emissions reduction (Section 4.2.3).

FAQ 4.2 |   What is to be done in the near term to accelerate mitigation and shift 
important obstacles, that are rooted in the underlying structure of societies (Section 4.2.7). Addressing these obstacles amounts to shifting away from existing developmental trends (i.e., shifting development pathways, Cross-Chapter Box 5). This can be done by strengthening governance and institutional capacity, aligning technology and innovation systems with low-carbon development, facilitating behaviour change and providing adequate finance within the context of multi-objective policy packages and sequences (Section  4.4.1). Shifting development pathways towards sustainability broadens the scope for, and is thus a  complement to, accelerated mitigation (Section 4.3).

FAQ 4.3 |   Is it possible to accelerate mitigation in the near term while there are so many other 
It is possible to accelerate mitigation while addressing other developmental priorities by implementing measures that simultaneously

address both climate and development goals. Casting mitigation in the broader context of development pathways provides additional opportunities to articulate both (Section 4.3.1.4). Policies such as progressive taxation, investment in public transport, regulatory transparency, commitment to multilateral environmental governance, fiscal incentives for private investments, international technology development and transfer initiatives, and risk disclosure and efforts to improve underlying enabling conditions (improving governance and institutional capacity, fostering behavioural change and technological innovation, and provision of finance) address multiple objectives beyond mitigation, such as job creation, macroeconomic stability, economic growth, public health and welfare, providing energy access, providing formal housing, and providing mobility. How we manage our land and agriculture, growing cities, transport needs, our industries, and the way people are trained and employed all

impact on GHG emissions and the options we have to reduce them. In turn, reducing GHG emissions can also contribute to reducing poverty, preventing hunger, improving health and wellbeing, and providing clean water and clean energy. Implementing right policies and investments can help to address the challenges of how to reduce emissions without constraining development. For example, in land use, widespread planting of a single tree species or crops for bioenergy (organic matter turned into renewable energy) could affect food and water supplies. Therefore, if bioenergy is to be relied upon to offset emissions, the right policies and investments are needed (see also Chapter 17).Frequently Asked Questions

5Frequently Asked Questions (FAQs)
FAQ 5.1 |  What can every person do to limit warming to 1.5°C?
People can be educated through knowledge transfer so they can act in different roles, and in each role everyone can contribute to

limit global warming to 1.5°C. Citizens with enough knowledge can organise and put political pressure on the system. Role models can set examples to others. Professionals (e.g., engineers, urban planners, teachers, researchers) can change professional standards in consistency with decarbonisation; for example urban planners and architects can design physical infrastructures to facilitate low-carbon mobility and energy use by making walking and cycling safe for children. Rich investors can make strategic plans to divest from fossils and invest in carbon-neutral technologies. Consumers, especially those in the top 10% of the world population in terms of income, can limit consumption, especially in mobility, and explore the good life consistent with sustainable consumption.
Policymakers support individual actions in certain contexts, not only by economic incentives, such as carbon pricing, but also by

interventions that understand complex decision-making processes, habits, and routines. Examples of such interventions include, but are not limited to, choice architectures and nudges that set green options as default, shift away from cheap petrol or gasoline, increasing taxes on carbon-intensive products, or substantially tightening regulations and standards to support shifts in social norms, and thus can be effective beyond the direct economic incentive.
FAQ 5.2 |  How does society perceive transformative change?

Humaninduced global warming, together with other global trends and events, such as digitalisation and automation, and the COVID-19 pandemic, induce changes in labour markets, and bring large uncertainty and ambiguity. History and psychology reveal that societies can thrive in these circumstances if they openly embrace uncertainty on the future and try out ways to improve life. Tolerating ambiguity can be learned, for example by interacting with history, poetry and the arts. Sometimes religion and philosophy also help.
As a key enabler, novel narratives created in a variety of ways, such as by advertising, images and the entertainment industry,

help to break away from the established meanings, values and discourses and the status quo. For example, discourses that frame comfortable public transport services to avoid stress from driving cars on busy, congested roads help avoid car driving as a status symbol and create a  new social norm to shift to public transport. Discourses that portray plant-based protein as healthy and natural promote and stabilise particular diets. Novel narratives and inclusive processes help strategies to overcome multiple barriers. Case studies demonstrate that citizens support transformative changes if participatory processes enable a design that meets local interests and culture. Promising narratives specify that even as speed and capabilities differ, humanity embarks on a joint journey towards well-being for all and a healthy planet.
FAQ 5.3 |  Is demand reduction compatible with growth of human well-being?

There is a growing realisation that mere monetary value of income growth is insufficient to measure national welfare and individual well-being. Hence, any action towards climate change mitigation is best evaluated against a set of indicators that represent a broader variety of needs to define individual well-being, macroeconomic stability, and planetary health. Many solutions that reduce primary material and fossil energy demand, and thus reduce GHG emissions, provide better services to help achieve well-being for all.
Economic growth measured by total or individual income growth is a main driver of GHG emissions. Only a few countries with low

economic growth rates have reduced both territorial and consumption-based GHG emissions, typically by switching from fossil fuels to renewable energy and by reduction in energy use and switching to low/zero carbon fuels, but until now at insufficient rates and levels for stabilising global warming at 1.5°C. High deployment of low/zero carbon fuels and associated rapid reduction in demand for and use of coal, gas, and oil can further reduce the interdependence between economic growth and GHG emissions.Frequently Asked Questions
FAQ
156

6Frequently Asked Questions
FAQ 6.1 |  Will energy systems that emit little or no CO 2 be different than those of today?
heating and cooling homes, travelling to work or on vacation, transporting goods and services, and powering manufacturing. But future energy systems may be different in that people may also demand new services that aren’t foreseen today, just as people now use energy for many information technology uses that were not anticipated 50 years ago. More importantly, low-carbon energy systems will be different in the way that energy is produced, transformed, and used to provide these services. In the future, almost all electricity will be produced from sources that emit little or no CO
2, such as solar power, wind power, nuclear power, bioenergy,

be used in many situations where fossil fuels are used today, for example, in cars or heating homes. And energy is likely to be used more efficiently than today, for example, through more efficient cars, trucks, and appliances, buildings that use very little energy, and greater use or public transportation. All of these changes may require new policies, institutions, and even new ways for people to live their lives. And fundamental to all of these changes is that low-carbon energy systems will use far less fossil fuel than today.
FAQ 6.2 |   Can renewable sources provide all the energy needed for energy systems that emit 

(solar energy), the wind (wind energy), plants (bioenergy), rainfall (hydropower), or even the ocean. The energy from these sources exceeds the world’s current and future energy needs many times. But that does not mean that renewable sources will provide all energy in future low-carbon energy systems. Some countries have a lot of renewable energy, whereas others do not, and other energy sources, such as nuclear power or fossil energy in which CO
2 emissions are captured and stored (carbon dioxide capture

and storage, or CCS) can also contribute to low-carbon energy systems. The energy from sources such as solar energy, wind energy, and hydropower can vary throughout the day or over seasons or years. All low-carbon energy sources have other implications for people and countries, some of which are desirable, for example, reducing air pollution or making it easy to provide electricity in remote locations, and some of which are undesirable, for example decreasing biodiversity or mining of minerals to produce low-emissions technologies. For all of these reasons, it is unlikely that all low-carbon energy systems around the world will rely entirely on renewable energy sources.
FAQ 6.3 |  What are the most important steps to decarbonise the energy system?

To create a low-carbon energy system, emissions must be reduced across all parts of the system, and not just one or two. This means, for example, reducing the emissions from producing electricity, driving cars, hauling freight, heating and cooling buildings, powering data centres, and manufacturing goods. There are more opportunities to reduce emissions over the next decade in some sectors compared to others. For example, it is possible to substantially reduce electricity emissions over the next decade by investing in low-carbon electricity sources, while at the same time halting the construction of new coal-fired power plants, retiring existing coal-fired power plants or retrofitting them with carbon capture and storage (CCS), and limiting the construction of new gas-fired power plants. There are also opportunities to increase the number of electric cars, trucks, and other vehicles on the road, or to use electricity rather than natural gas or coal to heat homes. And across the whole

energy system, emissions can be reduced by using more efficient technologies. While these and other actions will be critical over the coming decade, it is also important to remember that the low-carbon energy transition needs to extend for many decades into the future to limit warming. This means that it is important now to improve and test options that could be useful later on, for example, producing hydrogen from low-carbon sources or producing bioenergy from crops that require less land than today.Frequently Asked Questions

7Frequently Asked Questions (FAQs)
FAQ 7.1 |  Why is the Agriculture, Forestry and Other Land Uses (AFOLU) sector unique when 
a sector in its own right, (ii) remove meaningful quantities of carbon from the atmosphere and relatively cheaply, and (iii) provide raw materials to enable mitigation within other sectors, such as energy, industry or the built environment.
The emissions profile of AFOLU differs from other sectors, with a greater proportion of non-CO
2 gases (N 2O and CH 4). The impacts of 

some critical, wider challenges, as well as contributing to climate change adaptation. AFOLU is inextricably linked with some of the most serious challenges that are suggested to have ever faced humanity, such as large-scale biodiversity loss, environmental degradation and the associated consequences. As AFOLU concerns land management and utilises a considerable portion of the Earth’s terrestrial area, the sector greatly influences soil, water and air quality, biological and social diversity, the provision of natural habitats, and ecosystem functioning, consequently impacting many SDGs.
FAQ 7.2 | What AFOLU measures have the greatest economic mitigation potential?

This cost is deemed the price at which society is willing to pay for mitigation and is used as a proxy to estimate the proportion of technical mitigation potential that could realistically be implemented. Between 2020 and 2050, measures concerning forests and other ecosystem are estimated to have an average annual mitigation potential of 7.3 (3.9–13.1) GtCO
2-eq yr–1 at USD100 tCO 2–1. 
effects of changes in demand (reduced food losses, diet changes and improved and enhanced wood products use), is estimated to have an economic potential of 2.2 (1.1–3.6) GtCO

2-eq yr–1. However, cost forms only one constraint to mitigation, with realisation 
FAQ 7.3 |  What are potential impacts of large-scale establishment of dedicated bioenergy 
The potential of bioenergy with carbon capture and storage (BECCS) remains a focus of debate with several studies evaluating the

level at which BECCS could be sustainably implemented, published since AR5. BECCS involves sequestering carbon through plant growth (i.e., in trees or crops) and capturing the carbon generated when this biomass is processed for power or fuel. The captured carbon then requires long-term storage in for example, geological, terrestrial or ocean reservoirs, or in products. While appearing to create a net removal of carbon from the atmosphere, BECCS requires land, water and energy which can create adverse side effects at scale. Controversy has arisen because some of the models calculating the energy mix required to keep the temperature to 1.5°C have included BECCS at very large scales as a means of both providing energy and removing carbon from the atmosphere to offset emissions from industry, power, transport or heat. For example, studies have calculated that for BECCS to achieve 11.5 GtCO
2-eq

per year of carbon removal in 2100, as envisaged in one scenario, 380–700 Mha or 25–46% of all the world’s arable and cropland would be needed. In such a  situation, competition for agricultural land seriously threatens food production and food security, while also impacting biodiversity, water and soil quality, and landscape aesthetic value. More recently however, the scenarios for BECCS have become much more realistic, though concerns regarding impacts on food security and the environment remain, while the reliability of models is uncertain due to methodological flaws. Improvements to models are required to better capture wider environmental and social impacts of BECCS in order to ascertain its sustainable contribution in emissions pathways. Additionally, the opportunity for other options that could negate very large-scale deployment of BECCS, such as other carbon dioxide removal measures or more stringent emission reductions in other sectors, could be explored within

8
Urban areas also account for a growing proportion of national and global emissions, depending on emissions scope and geographic boundary. These trends are projected to grow in the coming decades; in 2100, some scenarios show the urban share of global emissions above 80%, with 63% being the minimum for any scenario (with the shares being in different contexts of emissions reduction or increase) (Sections 8.3.3 and 8.3.4). As such, urban climate change mitigation considers the majority of the world’s population, as well as some of the key drivers of global emissions. In general, emissions scenarios with limited outward urban land expansion are also associated with a smaller rise in global temperature (Section 8.3.4).

and inertia of committed emissions through the long lifespan of urban layout, infrastructures, and behaviour. As such, urban mitigation efforts that address lock-in can significantly reduce emissions (Section 8.4.1). Electrification of urban energy systems, in tandem with implementing multiple urban-scale mitigation strategies, could reduce urban emissions by 90% by 2050 – thereby significantly reducing global emissions (Section 8.3.4). Urban areas can also act as points of intervention to amplify synergies and co-benefits for accomplishing the Sustainable Development Goals (Section 8.2).
FAQ 8.2 |   What are the most impactful options cities can take to mitigate urban emissions, and 
The most impactful urban mitigation plans reduce urban GHG emissions by considering the long lifespan of urban layout and urban

infrastructures (Sections  8.4.1 and 8.6). Chapter  8  identifies three overarching mitigation strategies with the largest potential to decrease current, and avoid future, urban emissions: (i) reducing or changing urban energy and material use towards more sustainable production and consumption across all sectors including through spatial planning and infrastructure that supports compact, walkable urban form (Section  8.4.2); (ii) decarbonise through electrification of the urban energy system, and switch to net-zero-emissions resources (i.e., low-carbon infrastructure) (Section 8.4.3); and (iii) enhance carbon sequestration through urban green and blue infrastructure (e.g., green roofs, urban forests and street trees), which can also offer multiple co-benefits like reducing ground temperatures and supporting public health and well-being (Section 8.4.4). Integrating these mitigation strategies across sectors, geographic scales, and levels of governance will yield the greatest emissions

as its state and form(s) of development (urban growth typology), can inform the most impactful emissions savings ‘entry points’ and priorities for urban mitigation strategies (Sections 8.4.2 and 8.6). For rapidly growing and emerging urban areas, there is the opportunity to avoid carbon lock-in by focusing on urban form that promotes low-carbon infrastructure and enables low-impact behaviour facilitated by co-located medium to high densities of jobs and housing, walkability, and transit-oriented development (Sections 8.6.2 and 8.6.3). For established cities, strategies include electrification of the grid and transport, and implementing energy efficiency across sectors (Section 8.6.1).

There are two different emissions estimation techniques applied, individually or in combination, to the four frameworks outlined in Section 8.1.6.2 to estimate urban GHG emissions: ‘top-down’ and ‘bottom-up’. The top-down technique uses atmospheric GHG concentrations and atmospheric modelling to estimate direct (scope 1) emissions (see Glossary). The bottom-up technique estimates emissions using local activity data or direct measurements such as in smokestacks, traffic data, energy consumption information, and building use. Bottom-up techniques will often include indirect emissions (see Glossary) from purchased electricity (scope 2) and the urban supply chain (scope 3). Inclusion of supply-chain emissions often requires additional data such as consumer purchasing data and supply chain emission factors. Some researchers also take a hybrid approach combining top-down and bottom-up estimation techniques to quantify territorial emissions. Individual self-reported urban inventories from

cities have shown chronic underestimation when compared to estimates using combined top-down/bottom-up atmospherically calibrated estimation techniques.

to subsets of global cities. Considerable uncertainty remains in estimating urban emissions. However, top-down approaches have somewhat more objective techniques for uncertainty estimation in comparison to bottom-up approaches. Furthermore, supply chain estimation typically has more uncertainty than direct or territorial emission frameworks.Frequently Asked Questions
FAQ
158

9Frequently Asked Questions (FAQs)
FAQ 9.1 |  To which GHG emissions do buildings contribute?
i. direct emissions which are defined as all on-site fossil fuel or biomass-based combustion activities (i.e.,  use of biomass
for cooking, or gas for heating and hot water) and F-gas emissions (i.e., use of heating and cooling systems,  aerosols, fire
ii. indirect emissions which occur off-site and are related to heat and electricity production; and
iii. embodied emissions which are related to extracting, producing, transforming, transporting, and installing the constructionmaterial and goods used in buildings. 
In 2019, global GHG emissions from buildings were at 12 GtCO
2-eq out of which 24% were direct emissions, 57% were indirect 

FAQ 9.2 |  What are the co-benefits and trade-offs of mitigation actions in buildings?

Mitigation actions in buildings generate multiple co-benefits (e.g.,  health benefits due to the improved indoor and outdoor conditions, productivity gains in non-residential buildings, creation of new jobs particularly at local level, improvements in social well-being etc.) beyond their direct impact on reducing energy consumption and GHG emissions. Most studies agree that the value of these multiple benefits is greater than the value of energy savings and their inclusion in economic evaluation of mitigation actions may improve substantially their cost-effectiveness. It is also worth mentioning that in several cases the buildings sector is characterised by strong rebound effects, which could be considered as a co-benefit in cases where the mechanisms involved provide faster access to affordable energy but also a trade-off in cases where the external costs of increased energy consumption exceed the welfare benefits of the increased energy service consumption, thus lowering the

economic performance of mitigation actions. The magnitude of these co-benefits and trade-offs are characterised by several uncertainties, which may be even higher in the future as mitigation actions will be implemented in a changing climate, with changing building operation style and occupant behaviour. Mitigation measures influence the degree of vulnerability of buildings to future climate change. For instance, temperature rise can increase energy consumption, which may lead to higher GHG emissions. Also, sea level rise, increased storms and rainfall under future climate may impact building structure, materials and components, resulting in increased energy consumption and household expenditure from producing and installing new components and making renovations. Well-planned energy efficiency, sufficiency and on-site renewable energy production can help to increase building resilience to climate change impacts and reduce adaptation needs.

FAQ 9.3 |  Which are the most effective policies and measures to decarbonise the building sector?

Several barriers (information, financing, markets, behavioural, etc.) still prevents the decarbonisation of buildings stock, despite the several co-benefits, including large energy savings. Solutions include investments in technological solutions (e.g., insulation, efficient equipment, and low-carbon energies and renewable energies) and lifestyle changes. In addition, the concept of sufficiency is suggested to be promoted and implemented through policies and information, as technological solutions will be not enough to decarbonise the building sector. Due to the different types of buildings, occupants, and development stage there is not a single policy, which alone will reach the building decarbonisation target. A range of policy instruments ranging from regulatory measures such as building energy code for NZEBs and appliance standards, to market-based instruments (carbon tax, personal carbon allowance, renewable portfolio standards, etc.) and information. Financing (grants, loans,

performance base incentives, pays as you save, etc.) is another key enabler for energy efficiency technologies and on-site renewables. Finally, effective governance and strong institutional capacity are key to have an effective and successful implementation of policies and financing.Frequently Asked Questions

10Frequently Asked Questions (FAQs)
provide a  mechanism for major GHG emissions reductions from the largest sources in the transport sectors, including cars, motorbikes, autorickshaws, buses and trucks. The mitigation potential of EVs depends on the decarbonisation of the power system. EVs can be charged by home or business renewable power before or in parallel to the transition to grid-based low-carbon power.

energy density, specific energy, and cost. The expected further improvements in LIBs suggest these chemistries will remain superior to alternative battery technologies in the medium term, and therefore LIBs will continue to dominate the electric vehicle market.
the demand for such metals is much lower than the reserves available, with many new mines starting up in response to the new market, particularly in a diversity of places.
Recycling batteries will significantly reduce long-term resource requirements. The standardisation of battery modules and packaging

within and across vehicle platforms, as well as increased focus on design for recyclability, are important. Many mobility manufacturers and governments are considering battery recycling issues to ensure the process is mainstreamed.
increased efficiency, which so far has not prevented these large vehicles from becoming the fastest-growing source of GHG globally. These vehicles likely need alternative fuels that can be fitted to the present propulsion systems. Emerging demonstrations suggest that ammonia, advanced biofuels, or synthetic fuels could become commercial.

Electric propulsion using hydrogen fuel cells or Li-ion batteries could work with short-haul aviation and shipping, but the large 
6, and 12, there are multiple issues constraining traditional biofuels. Sustainable land management and feedstocks, as well as R&D efforts to improve lignocellulosic conversion routes, are key to maximising the mitigation potential from advanced biofuels.
Synthetic jet and marine fuels can be made using CO
2 captured with DAC/BECCS and low-carbon hydrogen. These fuels may also 

changes to national and international governance structures.Frequently Asked Questions
FAQ
160

161
fuel, parking, and vehicles, or subsidies for alternative low-carbon modes) continue to be evaluated, with mixed results due to the dominance of time savings in an individual’s decision-making.
COVID-19 lockdowns have confirmed the transformative value of telecommuting, replacing significant numbers of work and

personal journeys, as well as promoting local active transport. These changes may not last and impacts on productivity and health are still to be fully evaluated.
reducing fossil fuel consumption, especially in transport, are setting out new approaches for how climate change mitigation can be achieved.Frequently Asked Questions
FAQ

162
11Frequently Asked Questions (FAQs)
FAQ 11.1 |  What are the key options to reduce industrial emissions?

FAQ 11.2 |  How costly is industrial decarbonisation and will there be synergies  
In most cases and in early stages of deployment, decarbonisation through electrification or CCS will make the primary production

of basic materials such as cement, steel, or polyethylene more expensive. However, demand management, energy and materials efficiency, and more circular material flows can dampen the effect of such cost increases. In addition, the cost of energy-intensive materials is typically a very small part of the total price of products, such as an appliance, a bottle of soda or a building, so the effect on consumers is very small. Getting actors to pay more for zero-emission materials is a challenge in supply chains with a strong focus on competitiveness and cutting costs, but it is not a significant problem for the broader economy. Reduced demand for services such as square metres of living space or kilometres of car travel is an option where material living standards are already high. If material living standards are very low, increased material use is often needed for more sustainable development. The options of materials and energy efficiency, and more circular material flows, generally

have synergies with sustainable development. Increased use of electricity, hydrogen, CCU and CCS may have both positive and negative implications for sustainable development and thus require careful assessment and implementation for different contexts.

FAQ 11.3 |  What needs to happen for a low-carbon industry tr ansition?

to make the transition to a carbon neutral industry. International competition means that trade rules must be evolved to not conflict with industrial decarbonisation. Some local and regional economies may be disadvantaged from the transition which can motivate re-education and other support.Frequently Asked Questions

12Frequently Asked Questions (FAQs)
Some biological methods for achieving CDR, like afforestation/reforestation or wetland restoration, have long been practised. If implemented well, these practices can provide a  range of co-benefits, but they can also have adverse side effects such as biodiversity loss or food price increases. Other chemical and geochemical approaches to CDR include direct air carbon capture and storage (DACCS), enhanced weathering or ocean alkalinity enhancement. They are generally less vulnerable to reversal than biological methods.

DACCS uses chemicals that bind to CO
2 directly from the air; the CO 2 is then removed from the sorbent and stored underground or 
FAQ 12.2 |   Why is it important to assess mitigation measures fr om a systemic per spective, rather 
than only looking at their potential to reduce gr eenhouse gas (GHG) emissions?

Mitigation measures do not only reduce GHGs, but have wider impacts. They can result in decreases or increases in GHG emissions in another sector or part of the value chain from where they are applied. They can have wider environmental (e.g., air and water pollution, biodiversity), social (e.g., employment creation, health) and economic (e.g., growth, investment) co-benefits or adverse side effects. Mitigation and adaptation can also be linked. Taking these considerations into account can help to enhance the benefits of mitigation action, and avoid unintended consequences, as well as provide a stronger case for achieving political and societal support and raising the finances required for implementation.

Activities associated with the food system caused about one-third of total anthropogenic GHG emissions in 2015, distributed across all sectors. Agriculture and fisheries produce crops and animal-source food, which are partly processed in the food industry, packed, distributed, retailed, cooked, and finally eaten. Each step is associated with resource use, waste generation, and GHG emissions.
side and the demand side of the food system. But complex co-impacts need to be considered and mitigation measures tailored to the specific context. International cooperation and governance of global food trade can support both mitigation and adaptation.
There is large scope for emissions reduction in both cropland and grazing production, and also in food processing, storage and

distribution. Emerging options such as plant-based alternatives to animal food products and food from cellular agriculture are receiving increasing attention, but their mitigation potential is still uncertain and depends on the GHG intensity of associated energy systems due to relatively high energy needs. Diet changes can reduce GHG emissions and also improve health in groups with excess consumption of calories and animal food products, which is mainly prevalent in developed countries. Reductions in food loss and waste can help reduce GHG emissions further.
as trade-offs exist with food waste, GHG footprint at farm gate, and accessibility to diverse healthy diets.Frequently Asked Questions
FAQ
163

13
FAQ 13.1 |   What roles do national play in climate mitigation, and how can they be effective? 
are developed and implemented, and frameworks through which diverse actors interact. Specific organisations, such as expert committees, can inform emission reduction targets, inform the creation of policies and packages, and strengthen accountability. Institutions enable strategic thinking, building consensus among stakeholders and enhanced coordination. 
belief systems, which leads to a variety of country-specific approaches to climate mitigation. 
Countries follow diverse approaches. Some countries focus on greenhouse gases emissions by adopting comprehensive climate laws

and creating dedicated ministries and institutions focused on climate change. Others consider climate change among broader scope of policy objectives, such as poverty alleviation, energy security, economic development and co-benefits of climate actions, with the involvement of existing agencies and ministries. See also FAQ 13.3 on sub-national climate mitigation.
FAQ 13.2 | What policies and strategies can be applied to combat climate change?
Institutions can enable creation of mitigation and sectoral policy instruments; policy packages for low-carbon system transition, and economy-wide measures for systemic restructuring. Policy instruments to reduce greenhouses gas emissions include economic 
instruments, regulatory instruments and other approaches. 
Economic policy instruments directly influence prices to achieve emission reductions through taxes, permit trading, offset systems,

subsidies, and border tax adjustments, and are effective in promoting implementation of low-cost emissions reductions. Regulatory instruments help achieve specific mitigation outcomes particularly in sectoral applications, by establishing technology or performance requirements. Other instruments include information programmes, government provision of goods, services and infrastructure, divestment strategies, and voluntary agreements between governments and private firms.
Climate policy instruments can be sector-specific or economy-wide and could be applied at national, regional, or local levels.

Policymakers may directly target GHG emission reduction or seek to achieve multiple objectives, such as urbanisation or energy security, with the effect of reducing emissions. In practice, climate mitigation policy instruments operate in combination with other policy tools, and require attention to the interaction effects between instruments. At all levels of governance, coverage, stringency and design of climate policies define their efficiency in reducing greenhouse gases emissions.
change, are better able to support socio-technical transitions and shifts in development pathways toward low-carbon futures than individual policies. See also Chapter 14 on international climate governance. 
FAQ 13.3 | How can actions at the sub-national level contribute to climate mitigation?

Sub-national actors (for example individuals, organisations, jurisdictions and networks at regional, local and city levels) often have a remit over areas salient to climate mitigation, such as land-use planning, waste management, infrastructure, housing, and community development. Despite constraints on legal authority and dependence on national policy priorities in many countries, sub-national climate change policies exist in more than 120 countries. However, they often lack national support, funding, and capacity, and adequate coordination with other scales. Sub-national climate action in support of specific goals is more likely to succeed when linked to local issues such as travel congestion alleviation, air pollution control.
The main drivers of climate actions at sub-national levels include high levels of citizen concern, jurisdictional authority and funding,

institutional capacity, national level support and effective linkage to development objectives. Sub-national governments often initiate and implement policy experiments that could be scaled to other levels of governance.Frequently Asked Questions
FAQ
164

165
14Frequently Asked Questions (FAQs)
Yes, to an extent. Countries’ emissions were in line with their internationally agreed targets: the collective greenhouse gas (GHG)

mitigation target for Annex I countries in the UNFCCC to return to their 1990 emissions levels by 2000, and their individual targets in the Kyoto Protocol for 2008–12. Numerous studies suggest that participation in the Kyoto Protocol led to substantial reductions in national GHG emissions, as well increased levels of innovation and investment in low-carbon technologies. In this latter respect, the Kyoto Protocol set in motion some of the transformational changes that will be required to meet the temperature goal of the Paris Agreement. It is too soon to tell whether the processes and commitments embodied in the Paris Agreement will be effective in achieving its stated goals with respect to limiting temperature rise, adaptation, and financial flows. There is, however, evidence that its entry into force has been a contributing factor to many countries’ adopting mid-century targets of net-zero GHG or CO

FAQ 14.2 |   What is the future role of international cooperation in the context  
Continued international cooperation remains critically important both to stimulate countries’ enhanced levels of mitigation ambition,

and through various means of support to increase the likelihood that they achieve these objectives. The latter is particularly the case in developing countries, where mitigation efforts often rely on bilateral and multilateral cooperation on low-carbon finance, technology support, capacity building, and enhanced South-South cooperation. The Paris Agreement is structured around Nationally Determined Contributions that are subject to an international oversight system, and bolstered through international support. The international oversight system is designed to generate transparency and accountability for individual emissions reduction contributions, and regular moments for stock-taking of these efforts towards global goals. Such enhanced transparency may instil confidence and trust, and foster solidarity among nations, with theory-based arguments that this will lead to greater levels of ambition. Together with other cooperative agreements at the sub-global and sectoral levels, as well

as a  growing number of transnational networks and initiatives, the implementation of all of these mechanisms is likely to play an important role in making political, economic, and social conditions more favourable to ambitious mitigation efforts in the context of sustainable development and efforts to eradicate poverty.

filled in or der for countries to achieve the objectives of the Paris Agreement, such 
as holding temper ature incr ease to well below 2°C and pursuing efforts towards 
1.5°C above pr e-industrial levels?
While international cooperation is contributing to global mitigation efforts, its effects are far from uniform. Cooperation has

contributed to setting a global direction of travel, and to falling greenhouse gas emissions in many countries and avoided emissions in others. It remains to be seen whether it can achieve the kind of transformational changes needed to achieve the Paris Agreement’s long-term global goals. There appears to be a large potential role for international cooperation to better address sector-specific technical and infrastructure challenges that are associated with such transformational changes. Finalising the rules to pursue voluntary cooperation, such as through international carbon market mechanisms and public climate finance in the implementation of NDCs, without compromising environmental integrity, may play an important role in accelerating mitigation efforts in developing countries. Finally, there is room for international cooperation to more explicitly address transboundary issues associated with carbon dioxide removal and solar radiation management. Frequently Asked Questions
FAQ

166
FAQ 15.1 |   What’s the r ole of climate finance and the finance sector for a tr ansformation towards 
a sustainable futur e?
The Paris Agreement has widened the scope of all financial flows from climate finance only to the full alignment of finance flows

with the long-term goals of the Paris Agreement. While climate finance relates historically to the financial support of developed countries to developing countries, the Paris Agreement and its Article 2.1(c) have developed a new narrative that goes much beyond traditional flows and relates to all sectors and actors. Finance flows are consistent when the effects are either neutral with or without positive climate co-benefits to climate objectives; or explicitly targeted to climate benefits in adaptation and/or mitigation result areas. Climate-related financial risk is still massively underestimated by financial institutions, financial decision-makers more generally and also among public sector stakeholders, limiting the sector’s potential of being an enabler of the transition. The private sector has started to recognise climate-related risks and consequently redirect investment flows. Dynamics vary across sectors and regions with the financial sector being an enabler of transitions in

only some selected (sub-)sectors and regions. Consistent, credible, timely and forward-looking political leadership remains central to strengthen the financial sector as enabler.

climate change by all public and private actors from global to local scales, including international financial flows to developing countries to assist them in addressing climate change. Total climate finance includes all financial flows whose expected effect aims to reduce net greenhouse gas (GHG) emissions and/or to enhance resilience to the impacts of current and projected climate change. This includes private and public funds, domestic and international flows and expenditures. Tracking of climate finance flows faces limitations, in particular for national climate finance flows.

flows are on an upward trend since the Fifth Assessment Report, according to the Climate Policy Initiative reaching more than USD630 billion in 2019/2020, however, growth has likely slowed down and flows remain significantly below needs. This is driven by barriers within and outside the financial sector. More than 90% of financing is allocated to mitigation activities despite the strong economic rationale of adaptation action. Adjusting for higher estimates on current flows for energy efficiency based on International Energy Agency data, the dominance of mitigation becomes even stronger. Persistently high levels of both public and private fossil-fuel related financing as well as other misaligned flows continue to be of major concern despite recent commitments. Significant progress has been made in the commercial finance sector with regard to the awareness of climate risks resulting from inadequate financial flows and climate action. However, a more consequent investment and policy

decision-making that enables a rapid redirection of financial flows is needed. Regulatory support as a catalyser is an essential driver of such redirections. Dynamics across sectors and regions vary, with some being better positioned to close financing gaps and to benefit from an enabling role of finance in the short-term.

FAQ 15.3 |  What defines a financing gap , and where are the critically identified gaps?
A financing gap is defined as the difference between current flows and average needs to meet the long-term goals of the Paris Agreement. Gaps are driven by various barriers inside (short-termism, information gaps, home bias, limited visibility of future pipelines) and outside (e.g., missing pricing of externalities, missing regulatory frameworks) of the financial sector. Current mitigation financing flows come in significantly below average needs across all regions and sectors despite the availability of sufficient capital on a global basis. Globally, yearly climate finance flows have to increase by a factor between three and six to meet average annual needs between 2020 and 2030.
Gaps are in particular concerning for many developing countries, with COVID-19 exacerbating the macroeconomic outlook and

fiscal space for governments. Also, limited institutional capacity represents a key barrier for many developing countries, burdening risk perceptions and access to appropriately priced financing as well as limiting their ability to actively manage the transformation. Existing fundamental inequities in access to finance, as well as its terms and conditions, and countries’ exposure to physical impacts of climate change, overall result in a worsening outlook for a global just transition.Frequently Asked Questions
FAQ

167
16
and to reduce greenhouse gas emissions. However, innovation and even fast technological change will not be enough to achieve Paris Agreement mitigation objectives. Other changes are necessary across the production and consumption system and the society in general, including behavioural changes.
changing value chains, or banks changing risk profiles. Therefore, technological changes driven by holistic approaches can contribute to accelerate and spread those changes towards the achievement of climate and sustainable development goals.

so that climate-friendly technologies can flourish. Innovation policies can help respond to local priorities and prevent unintended and undesirable consequences of technological change, such as unequal access to new technologies across countries and between income groups, environmental degradation and negative effects on employment.
The speed and success of innovation processes could be enhanced with the involvement of a  wider range of actors from the

industry, research and financial communities working in partnerships at national, regional and international levels. Public policies play a critical role to bring together these different actors and create the necessary enabling conditions, including financial support, through different instruments as well as institutional and human capacities.
the necessary knowledge flow within and between countries and regions. This knowledge flow can take the form of exchanging experiences, ideas, skills, and practices, among others.
Technologies that are currently known but not yet widely used need to be spread around the world, and adapted to local preferences

and conditions. Innovation capabilities are required not only to adapt new technologies for local use, but also to create new markets and business models. International technology cooperation can serve that purpose.
achieve their climate goals more effectively and, if this is done properly, can also help to addressing other sustainable development goals. Many initiatives exist both regionally and globally to help countries in achieving technology development and transfer through partnerships and research collaboration that include developed and developing countries, with a key role for technological institutions and universities. Enhancing current activities would help an effective, long-term global response to climate change, while promoting sustainable development.

for developing countries to advance technology diffusion; however, so far not all countries have benefitted from the globalisation of innovation due to different barriers, such as access to finance and technical capabilities. These asymmetries between countries in the globalisation process can also lead to dependencies on foreign knowledge and providers.
climate technology absorption and deployment in developing countries as well as research, development and demonstration, and knowledge spillovers.
The Paris Agreement also reflects this view by noting that countries shall strengthen cooperative action on technology development

and transfer regarding two main aspects: (i) promoting collaborative approaches to research and development; and (ii) facilitating access to technology to developing country Parties.Frequently Asked Questions
FAQ

168
17
FAQ 17.1 |  Will decarbonisation efforts slow or accelerate sustainable development transitions?
development requires the pursuit of synergies and the avoidance of trade-offs between the economic, social and environmental dimensions of development. It can thus provide pathways that accelerate progress towards ambitious climate change mitigation goals. Factoring in equality and distributional effects will be particularly important in the pursuit of sustainable policies and partnerships, and in accelerating the transition to sustainable development. Using climate change as a key conduit can only work if synergies across sectors are exploited and if policy implementation is supported by national and international partnerships.

as the enabling environment consisting of individual behaviour, mindsets, beliefs and actions, social cohesion, governance, policies, institutions, social and technological innovations, and so on. The integration of both climate change mitigation and adaptation policies in sustainable development is also essential in the establishment of fair and robust transformation pathways.
FAQ 17.2 |  What role do considerations of justice and inclusivity play in the transition towards 
Negative economic and social impacts in some regions could emerge as a consequence of ambitious climate change mitigation

policies if these are not aligned with key sustainable development aspirations such as those represented by the Sustainable Development Goals (SDGs) on ‘no poverty, energy-, water- and food access’, and so on, which could in turn slow down the transition process. Nonetheless, many climate change mitigation policies could generate incomes, new jobs and other benefits. Capturing these benefits could require specific policies and investments to be targeted directly towards including all parts of society in the new activities and industries created by the climate change mitigation policies, and that activities that are reduced in the context of transitions to a low-carbon future, including industries and geographical areas, are seeing new opportunities. Poor understanding of how governance at multiple levels can meet these challenges to the transition may fail to make significant progress in relation to national policies and a  global climate agreement. It may therefore either support or

weaken the climate architecture, thus constituting a limiting factor.

FAQ 17.3 |  How critical are the roles of institutions in accelerating the transition and what can 
Institutions are critical in accelerating the transition towards sustainable development: they can help to shape climate change

response strategies in terms of both adaptation and mitigation. Local institutions are the custodians of critical adaptation services, ranging from the mobilisation of resources, skills development and capacity-building to the dissemination of critical strategies. Transitions towards sustainable development are mediated by actors within particular institutions, the governance mechanisms they use as implementing tools and the political coalitions they form to enable action. Patterns of production and consumption have implications for a low-carbon development, and many of these patterns can act as barriers or opportunities towards sustainable development. Trade policies, international economic issues and international financial flows can positively support the speed and scale of the transition; alternatively, they can have negative impacts on policies that may inhibit the process. Nonetheless, contextual factors are a fundamental part of the change process, and institutions and their

governance systems provide pathways that can influence contextual realities on the ground. For instance, politically vested interests may lead powerful lobby groups or coalition networks to influence the direction of the transition, or they could put pressure on a given political elite through the imposition of regulatory standards, taxation, incentives and policies that may speed or delay the transition process. Civil-society institutions, such as NGOs or research centres, can act as effective governance ‘watchdogs’ in the transition process, particularly when they exercise a challenge function and question government actions in respect of transitions related to sustainable development.Frequently Asked Questions

SPM
171Annex I: Glossary
Coordinating Editors: 
Renée van Diemen (the Netherlands/United Kingdom), J.B. Robin Matthews (France/United 
Kingdom), Vincent Möller (Germany), Jan S. Fuglestvedt (Norway), Valérie Masson-Delmotte (France), Carlos Méndez (Venezuela), Andy Reisinger (New Zealand), Sergey Semenov (the Russian Federation)
Editorial Team:

Elin Lerum Boasson (Norway), Antonethe Castaneda (Guatemala), Lazarus Chapungu (Zimbabwe), Lilia Caiado Couto (Brazil), Annette Cowie (Australia), Nandini Das (India), El  Hdaji  Mbaye Madien  Diagne (Senegal), Oliver Geden (Germany), James S. Gerber (the United States of America), Siyue Guo (China), Shan Hu (China), Kiane de Kleijne (the Netherlands), Michael König (Germany), David Simon Lee (United Kingdom), Andreas Löschel (Germany), André F .P . Lucena (Brazil), Eduardo Miranda Casseres (Brazil), Jacqueline Peel (Australia), Patricia E. Perkins (Canada), Priyadarshi Shukla (India), Jim Skea (United Kingdom), Brent Sohngen (the United States of America), Enock Ssekuubwa (Uganda), Linda Steg (the Netherlands)
Note: 
This glossary defines some specific terms as the Lead Authors intend them to be interpreted in the context of this report. Italicised words in definitions indicate that the italicised term is defined in the Glossary.
Subterms appear in italics beneath main terms.

This chapter should be cited as:
IPCC, 2022: Annex I: Glossary [van Diemen, R., J.B.R. Matthews, V . Möller, J.S. Fuglestvedt, V . Masson-Delmotte, C.  Méndez, A. Reisinger, S. Semenov (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change.Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P .R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P . Vyas, R. Fradera,M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY , USA. doi: 10.1017/9781009157926.020AI

172
Annex I  GlossaryAI1.5°C pathway  See Pathways.
Acceptability of policy or system change  The extent to which 
or rejected or supported, by members of the general public (public acceptability) or politicians or governments (political acceptability). Acceptability may vary from totally unacceptable/fully rejected to totally acceptable/fully supported; individuals may differ in how acceptable policies or system changes are believed to be.
Access to modern energy services
 Access to clean,  reliable 
Adaptation  In human systems , the process of adjustment to actual 
or expected climate and its effects, in order to moderate harm or exploit

beneficial opportunities. In natural systems, the process of adjustment to actual climate and its effects; human intervention may facilitate adjustment to expected climate and its effects. See also Adaptation options,  Adaptive capacity,  and Maladaptive actions (Maladaptation).
Adaptation limits The change in climate where adaptation is unable to prevent damaging impacts and further risk. Soft limits occur when additional adaptation may be possible if constraints are able to be overcome. Hard limits occur when no additional adaptation is possible.

Adaptation that maintains the essence and integrity of a system or process at a given scale (Park et al. 2012). In some cases, incremental adaptation can accrue to result in transformational adaptation (Tàbara et al. 2019; Termeer et al. 2017). Incremental adaptations to change in climate are understood as extensions of actions and behaviours that already reduce the losses or enhance the benefits of natural variations in extreme weather/climate events.
Adaptation that changes the fundamental attributes of a  social-ecological system in anticipation of climate change and its impacts.
 The array of strategies and measures that 
include a wide range of actions that can be categorised as structural, institutional, ecological or behavioural.
 See Pathways.
Adaptive capacity  The ability of systems, institutions, humans and

Adaptive governance  See Governance.
Additionality  The property of being additional. Mitigation is 
would not have occurred in the absence of the associated policy intervention or activity. 
environmental integrity of Offsets (in climate change mitigation)]. 
See also Greenhouse gas emission metric.Adverse side-effect  A negative effect that a  policy or measure 
reducing the net benefit to society or the environment. See also Co-benefits, Risk, and Trade-off.
Aerosol
 A suspension of airborne solid or liquid particles , with 
typical particle size in the range of a few nanometres to several

tens of micrometres and atmospheric lifetimes of up to several days in the troposphere and up to years in the stratosphere. The term aerosol, which includes both the particles and the suspending gas, is often used in this report in its plural form to mean ‘aerosol particles’. Aerosols may be of either natural or anthropogenic origin in the troposphere; stratospheric aerosols mostly stem from volcanic eruptions. Aerosols can cause an effective radiative forcing directly through scattering and absorbing radiation (aerosol–radiation interaction), and indirectly by acting as cloud condensation nuclei or ice nucleating particles that affect the properties of clouds (aerosol–cloud interaction), and upon deposition on snow- or ice-covered surfaces. Atmospheric aerosols may be either emitted as primary particulate matter or formed within the atmosphere from gaseous precursors (secondary production). Aerosols may be composed of sea salt, organic carbon, black carbon (BC), mineral species

(mainly desert dust), sulphate, nitrate and ammonium or their mixtures. See also Short-lived climate forcers (SLCFs).

 Conversion to forest of land that historically has not 
and Forest Degradation (REDD+), Reforestation, Anthropogenic Removals, and Carbon dioxide removal (CDR).
Agreement
 In this report, the degree of agreement within the 
multiple lines of evidence (e.g., mechanistic understanding, theory, data, models, expert judgement) and expressed qualitatively (Mastrandrea et al. 2010). See also Confidence,  Likelihood,  and Uncertainty.
Agriculture, Forestry and Other Land Use (AFOLU)

In the context  
Nations Framework Convention on Climate Change (UNFCCC), AFOLU is the sum of the GHG inventory sectors Agriculture and Land Use, Land-Use Change and Forestry (LULUCF); see the 2006 IPCC Guidelines for National GHG Inventories for details. Given the difference in estimating the ‘ anthropogenic’ carbon dioxide (CO
2 ) 
remov als between countries and the global modelling community, the 
land-related net GHG emissions from global models included in this report are not necessarily directly comparable with LULUCF estimates in national GHG Inventories. See also Land use,  Land-use change and 
 The science and practice of applying ecological 
concepts, principles and knowledge (i.e.,  the interactions of, and explanations for, the diversity, abundance and activities of organisms) to the study, design and management of sustainable agroecosystems.

173
the introduction, by natural processes or human activity, into the 
Albedo
 The proportion of sunlight (solar radiation) reflected by 

Anomaly
 The deviation of a variable from its value averaged over 
 Resulting from or produced by human activities .
Anthropogenic emissions  Emissions of greenhouse gases (GHGs), 
 The withdrawal of greenhouse gases 

 The gaseous envelope surrounding the Earth, divided 

GHG w ater vapour (H 2O), whose concentrations are highly variable 
 Reducing greenhouse gas  emissions 

 See Reference period  and Reference scenario.
Baseline period  See Reference period.
Biochar  Relatively stable , carbon-rich material produced by heating 

Bioenergy
 Energy derived from any form of biomass or its metabolic 
  
Carbon dioxide capture and storage (CCS) technology applied to 
Biofuel  A fuel , generally in liquid form, produced from biomass. 
Biogenic carbon emissions  Carbon released as carbon dioxide or 
products.

Biomass  Organic material excluding the material that is fossilised 
or embedded in geological formations. Biomass may refer to the mass 

174
 A relatively pure form of carbon,  also known as 
marine systems that are amenable to management.  Coastal blue 

 See Infrastructure.
Business as usual (BAU)  The term business as usual scenario has 

Refers to two concepts in the literature:  
[Note 1: Net anthropogenic CO 2 emissions are anthropogenic CO 2 emissions

Carbon cycle  The flow of carbon (in various forms, e.g., as carbon 
dioxide (CO 2 ), carbon in biomass, and carbon dissolved in the ocean

as carbonate and bicarbonate) through the atmosphere , hydrosphere, 
 A process in which 

in a  product. The climate effect of CCU depends on the product 
 Anthropogenic activities removing 

Carbon footprint  Measure of the exclusive total amount of 
emissions of carbon dioxide (CO 2 ) that is directly and indirectly 
The carbon footprint of an individual household, inclusive of the 

home energy use , transportation, food provision, and consumption 

175
Glossary Annex IAICarbon intensity  The amount of emissions of carbon dioxide 
product (GDP) , output energy use or transport.
Carbon leakage  See Leakage.
Carbon neutrality  Condition in which anthropogenic carbon 

Carbon price  The price for avoided or released carbon dioxide

 See Sink.
Carbon stock  The quantity of carbon in a carbon pool.
Choice architecture  The presentation of choices to consumers, 
losses of materials and energy through extensive reduce , reuse, 
Cities
 Cities are open systems , continually exchanging resources, 

 A voluntary participation of the public in the collection  
 The areal extent of an individual city’s material

Climate
 Climate in a  narrow sense is usually defined as the average 
 A change in the state of the climate that can be

The unavoidable future climate 

176
Annex I  GlossaryAIClimate extreme (extreme weather or climate event)  The 
observed values of the variable. By definition, the characteristics of what is called extreme weather may vary from place to place in an absolute sense. When a pattern of extreme weather persists for some time, such as a season, it may be classified as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g., high temperature, drought, or heavy rainfall over a season). For simplicity, both extreme weather events and extreme climate events are referred to collectively as ‘climate extremes’.
 There is no agreed definition of climate finance. 
The term ‘climate finance’ is applied to the financial resources

devoted to addressing climate change by all public and private actors from global to local scales, including international financial flows to developing countries to assist them in addressing climate change. Climate finance aims to reduce net greenhouse gas emissions and/or to enhance adaptation and increase resilience to the impacts of current and projected climate change. Finance can come from private and public sources, channelled by various intermediaries, and is delivered by a range of instruments, including grants, concessional and non-concessional debt, and internal budget reallocations.
 See Governance.
Climate justice  See Justice.
the climate  system based on the physical, chemical and biological 
properties of its components, their interactions and feedback

processes and accounting for some of its known properties. The climate system can be represented by models of varying complexity; that is, for any one component or combination of components a spectrum or hierarchy of models can be identified, differing in such aspects as the number of spatial dimensions, the extent to which physical, chemical or biological processes are explicitly represented, or the level at which empirical parametrisations are involved. There is an evolution towards more complex models with interactive chemistry and biology. Climate models are applied as a  research tool to study and simulate the climate and for operational purposes, including monthly, seasonal and interannual climate predictions. See also Simple climate model (SCM) and Emulators.
 Simulated response of the climate system 
gases (GHGs) and aerosols and changes in land use, generally

from climate predictions by their dependence on the emission/concentration/radiative forcing scenario used, which is in turn based 
on assumptions concerning, for example, future socio-economic and technological developments that may or may not be realised.
 The change in the surface temperature 
The surface temperature response for the hypothetical scenario in which atmospheric carbon dioxide (CO
2 ) increases at 1% yr –1 from pre-industrial to the time of a  doubling of atmospheric CO 2 
The transient surface temperature change per unit cumulative carbon dioxide (CO
2 ) emissions, usually 1000 GtC. TCRE combines both

which is determined by carbon cycle processes) and on the transient climate response (TCR).
 Climate services involve the provision of climate 
information in such a  way as to assist decision-making. The service 
on scientifically credible information and expertise, has an effective access mechanism, and responds to user needs (Hewitt et al. 2012).
 The global system consisting of five major 
the lithosphere and the biosphere and the interactions between them. The climate system changes in time under the influence of its own internal dynamics and because of external forcings such as volcanic eruptions, solar variations, orbital forcing, and anthropogenic forcings such as the

 Deviations of climate v ariables from a given 
mean state (including the occurrence of extremes, etc.) at all spatial and temporal scales beyond that of individual weather events. Variability may be intrinsic, due to fluctuations of processes internal to the climate system (internal variability), or extrinsic, due to variations 
in natural or anthropogenic external forcing (forced  variability). See also Climate change.
 A positive effect that a  policy or measure aimed at 
benefit to society or the environment. Co-benefits are also referred to as ancillary benefits. See also Adverse side-effect and Trade-off.
CO
2 equivalent (CO 2-eq) emission  The amount of carbon dioxide 
key measure of climate change, over a  specified time horizon, as

an emitted amount of another greenhouse gas (GHG) or a mixture of other GHGs. For a mix of GHGs, it is obtained by summing the CO
2-equivalent emissions of each gas. There are various ways and 
time horizons to compute such equivalent emissions (see greenhouse gas emission metric). CO
2-equivalent emissions are commonly used 
to compare emissions of different GHGs, but should not be taken to imply that these emissions have an equivalent effect across all key measures of climate change.
parties have agreed to use GWP100 values from the IPCC AR5 or GWP100 values from a  subsequent IPCC Assessment Report to report aggregate emissions and removals of GHGs. In addition, parties may use other metrics to report supplemental information on aggregate emissions and removals of GHGs.]
Concentrations scenario  See Scenario.
Conference of the Parties (COP)  The supreme body of UN

177
Confidence  The robustness of a finding based on the type, amount, 
minimum soil disturbance (e .g.,  by using no till practices), 

by a  certain entity (e.g., a person, firm, country, or region). See also 
Coping capacity  The ability of people, institutions, organisations, 
 A type of economic evaluation that

 A type of economic ev aluation 
 The total amount of emissions released

Decarbonisation  Human actions to reduce carbon dioxide 
essential for achieving basic human well-being including nutrition, 
Decoupling  Decoupling (in relation to climate change) is where  
 Conversion of forest to non-forest.

Deliberative governance  See Governance.
Demand  Disciplinary approaches use the term in different w ays. 

 See Demand-side measures.
Desertification  Land degradation  in arid, semi-arid, and dry 
developing countries)  There is a  diversity of approaches for

178
Annex I  GlossaryAIDiet T he kinds of food that follow a particular pattern that a person 
or community eats (FAO and Alliance of Bioversity International and CIAT, 2021).
Direct air capture (DAC)
 Chemical process by which a  pure 
(CDR) and Direct air carbon dioxide capture and storage (DACCS).Direct air carbon dioxide capture and storage (DACCS)
 Chemical 
capture and storage (DACS). See also Anthropogenic removals, Carbon dioxide removal (CDR) and Direct air capture (DAC).
Direct and indirect services
 Direct Services:  Services 
(e.g., passenger mobility) required by end-users (consumers). Indirect

services: Services required (e.g., goods transport, manufacturing) for provisioning systems of direct services.
Direct emissions
 Emissions that physically arise from activities 
sector, a company, or a process. See also Indirect emissions.Disaster
 A ‘serious disruption of the functioning of a community 
conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts’ (UNGA 2016). See also Exposure, Hazard, Risk 
 Processes for designing, 
improve the understanding of current and future disaster risk, foster

disaster risk reduction and transfer, and promote continuous improvement in disaster preparedness, prevention and protection, response, and recovery practices, with the explicit purpose of increasing human security, well-being, quality of life, and sustainable development (SD).
 See Discounting.
Discounting  A mathematical operation that aims to mak e monetary 
(or other) amounts received or expended at different times (years) comparable across time. If the discount rate is positive, future values are given less weight than those today. The choice of discount rate(s) is debated as it is a judgement based on hidden and/or explicit values.
Disruptive innovation
 Demand-led technological change that 
Distributive equity  See Equity.
Drought  An exceptional period of w ater shortage for existing 

Ecosystem  A functional unit consisting of living organisms, their 
them. The components included in a  given ecosystem and its spatial boundaries depend on the purpose for which the ecosystem is defined: in some cases they are relatively sharp, while in others they are diffuse. Ecosystem boundaries can change over time. Ecosystems are nested within other ecosystems and their scale can range from very small to the entire biosphere. In the current era, most ecosystems either contain people as key organisms, or are influenced by the effects of human activities in their environment. See also Ecosystem services.
 Ecological processes or functions having 
monetary or non-monetary v alue to individuals or society at large. 
These are frequently classified as: (i) supporting services such as

productivity or biodiversity maintenance; (ii) provisioning services such as food or fibre; (iii) regulating services such as climate regulation or carbon sequestration; and (iv) cultural services such as 
tourism or spiritual and aesthetic appreciation. See also Ecosystem and Nature’s Contribution to People.
 The use of ecosystem 
et al. 2009).Embodied (embedded) [emissions, water, land]
 The total 
of goods and services regardless of the location and timing of those

emissions [water use, land use] in the production process. This includes emissions [water use, land use] within the country used to produce goods or services for the country’s own use, but also includes the emissions [water use, land use] related to the production of such goods or services in other countries that are then consumed in another country through imports. Such emissions [water, land] are termed ‘embodied’ or ‘embedded’ emissions, or, in some cases, (particularly with water) as ‘virtual water use’ (Davis and Caldeira 2010; Allan 2005; MacDonald et al. 2015).
Emission and Socioe-conomic Scenario Ensemble
 A set 
a  database. The scenarios can come from a  single multi-model

study with systematic variation of harmonised scenario designs (structured  ensemble) or from multiple studies in the literature (unstructured ensemble). Depending on the scope of the ensemble, variation of the results across the scenarios in the ensemble give an indication of the spread of results in the literature (unstructured ensemble), or an estimate of uncertainties due to different modelling structures and methodologies (structured ensemble).
Emission factor/Emissions intensity
 A coefficient that quantifies 
the emissions or remov als of a gas per unit activity. Emission factors 
are often based on a  sample of measurement data, averaged to 
develop a representative rate of emission for a given activity level under a given set of operating conditions.
Emission trajectories  A projected development in time of the 
emission of a  greenhouse gas (GHG)  or group of GHGs, aerosols, and 
GHG precursors. See also Pathways.Emissions

See Anthropogenic emissions,  Direct emissions,  Cumulative 
based emissions and Embodied (embedded) [emissions, water, land].Emissions scenario
 See Scenario.

179
Glossary Annex IAIEmulation  Reproducing the behaviour of complex,  process-
approaches, using either emulators or simple climate models (SCMs). The computational efficiency of emulating approaches opens new analytical possibilities, given that ESMs take a lot of computational resources for each simulation.
Emulators
 A broad class of heavily parametrised models (‘simple 
algorithms or other artificial intelligence approaches, designed to reproduce the responses of more complex, process-based Earth System Models (ESMs). The main application of emulators is to extrapolate insights from ESMs and observational constraints to a  larger set of emission scenarios. See also Emulation and Simple climate models (SCMs).
  
Energy access  Access to clean,  reliable and affordable energy 
productive uses (with special reference to Sustainable Development Goal 7) (AGECC 2010). See also Traditional biomass.
 The ratio of output or useful energy or energy 
conversion process, transmission or storage activity to the input of energy (measured as kWh kWh
–1, tonnes kWh–1 or any other 
physical measure of useful output like tonne-km transported). Energy efficiency is often described by energy intensity.
Energy poverty
 The absence of sufficient choice in accessing 

benign energy services to support economic and human development (Reddy 2000). See also Fuel poverty.
 The goal of a  given country, or the global 
predictable energy supply. Measures encompass safeguarding the sufficiency of energy resources to meet national energy demand at competitive and stable prices and the resilience of the energy supply; enabling development and deployment of technologies; building sufficient infrastructure to generate, store and transmit energy supplies and ensuring enforceable contracts of delivery.
 A benefit or amenity (e .g., mobility, communication, 
thermal comfort) received as a result of energy or other resources use.Enhanced weathering
 A proposed method to increase the 
natural rate of removal of carbon dioxide (CO 2 ) from the atmosphere  

minerals is increased by grinding, before they are actively added to soil, beaches or the open ocean. See also Anthropogenic removals and Carbon dioxide removal (CDR).
Ensemble
 A collection of comparable datasets that reflect 
of underlying behaviour. Ensemble techniques are used by the observational, reanalysis and modelling communities. See also Emission and Socio-economic Scenario Ensemble and Integrated 
 A natural part of the digestion process in 
ruminant animal species (domesticated and wild),  such as cattle, 
buffalo, sheep, goats, antelope, etc. Microorganisms (bacteria,

archaea, fungi, protozoa and viruses) present in the fore-stomach (reticulorumen or rumen) breakdown plant biomass to produce substrates that can be used by the animal for energy and growth with methane produced as a by-product. Fermentation end-products such as hydrogen, carbon dioxide, formate and methyl-containing compounds are important substrates for the production of methane by the rumen’s methane-forming archaea (known as methanogens).
Equality
 A principle that ascribes equal worth to all human beings , 
Uneven opportunities and social positions, and processes of discrimination within a  group or society, based on gender, class, ethnicity, age, and (dis)ability, often produced by uneven development. Income inequality refers to gaps between highest and lowest income earners within a country and between countries.
Equity

The principle of being fair and impartial, and a  basis for 
including costs and benefits, are distributed in and by society in more or less equal ways. Often aligned with ideas of equality, fairness 
for, and distribution of, climate impacts and policies across society, generations, and gender, and in the sense of who participates and controls the processes of decision-making.
Equity in the consequences, outcomes, costs and benefits of actions or policies. In the case of climate change or climate policies for different people, places and countries, including equity aspects of sharing burdens and benefits for mitigation and adaptation.
Gender equity

Equity between women and men with regard to their rights, resources and opportunities. In the case of climate change, gender equity recognises that women are often more vulnerable to the impacts of climate change and may be disadvantaged in the process and outcomes of climate policy.
Inter-generational equity 
Equity between generations. In the context of climate change, inter-generational equity acknowledges that the effects of past and present emissions, vulnerabilities and policies impose costs and benefits for people in the future and of different age groups.
Evidence
 Data and information used in the scientific process to 
establish findings . In this report, the degree of evidence reflects the 
which the Lead Authors are basing their findings. See also Agreement, Confidence, Likelihood, and Uncertainty.

180
Annex I  GlossaryAIExergy  Capacity of energy flows to perform useful work.  Exergy 
low (e.g., low-temperature heat, biomass) to high (e.g., electricity). Exergy efficiency describes how much useful work can be performed by a  particular energy flow in relation to the thermodynamic maximum possible. It can be determined for all energy flows and energy conversion steps, also including alternative service delivery systems (Grubler et al. 2012).
Exposure
 The presence of people; livelihoods; species or 
infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected.
 An event that is rare at a  particular 
place and time of year. Definitions of ‘rare’ vary, but an extreme

weather event would normally be as rare as, or rarer than, the 10th or 90th percentile of a probability density function estimated from observations. By definition, the characteristics of what is called extreme weather may vary from place to place in an absolute sense. See also Climate extreme (extreme weather or climate event).
Fairness
 Impartial and just treatment without favouritism or 
with equal opportunity.  See also Equality and Equity.
Feasibility  In this report,  feasibility refers to the potential for

a  mitigation or adaptation option to be implemented. Factors influencing feasibility are context-dependent, temporally dynamic, and may vary between different groups and actors. Feasibility depends on geophysical, environmental-ecological, technological, economic, socio-cultural and institutional factors that enable or constrain the implementation of an option. The feasibility of options may change when different options are combined, and increase when enabling conditions are strengthened. See also Enabling conditions (for adaptation and mitigation options).
Final energy
 The energy delivered to final users (firms, individuals, 
Flexibility (demand and supply)  Adjustment of energy load 
Flexible governance  See Governance.

Flood  The overflowing of the normal confines of a stream or other 
normally submerged. Floods can be caused by unusually heavy rain, for example, during storms and cyclones. Floods include river (fluvial) floods, flash floods, urban floods, rain (pluvial) floods, sewer floods, coastal floods, and glacial lake outburst floods (GLOFs).
Food loss and waste
 ‘The decrease in quantity or quality of 
alternative (non-food) use of food that is safe and nutritious for human consumption along the entire food supply chain, from primary production to end household consumer level. Food waste is recognised as a distinct part of food loss because the drivers that generate it and the solutions to it are different from those of food losses (FAO 2015).Food security
 A situation that exists when all people , at all times,

have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life. The four pillars of food security are: availability; access; utilisation; and stability. The nutritional dimension is integral to the concept of food security (FAO 2009, 2018).
Access
Economic and/or physical access to food. Economic access is determined by disposable income, food prices and the provision of and access to social support. Physical access is determined by the availability and quality of land and other infrastructure, property rights or the functioning of markets.
Physical availability of food. Food availability addresses the supply side of food security and is determined by the levels of food production, stocks and net trade.
Stability

The stability of the other three dimensions over time. Even if individuals’ food intake is adequate today, they are still considered food-insecure if periodically they have inadequate access to food, risking deterioration of their nutrition status. Adverse weather conditions, political instability or economic factors (unemployment, rising food prices) may have an impact on individuals’ food security status.
The way in which the body uses the various nutrients in food. Individuals achieve sufficient energy and nutrient intake through good care and feeding practices, food preparation, diet diversity and intra-household distribution of food. Combined with biological utilisation of the food consumed, energy and nutrient intake determine the nutrition status of individuals.
 All the elements (environment,  people, inputs, 
relate to the production, processing, distribution, preparation and

consumption of food, and the output of these activities, including socio-economic and environmental outcomes (HLPE 2017). 
global production and consumption), each location’s food system is unique, being defined by that place’s mix of food produced locally, nationally, regionally or globally.]
Forest  A vegetation type dominated by trees . Many definitions 
wide differences in biogeophysical conditions, social structure and economics. 
inventories, see the 2006 IPCC Guidelines for National GHG Inventories and their 2019 Refinement, and information provided by the United Nations Framework Convention on Climate Change (IPCC 2006, 2019; UNFCCC 2021a,b).]
Fossil fuels  Carbon-based fuels from fossil hydrocarbon deposits , 

Fuel poverty  A condition in which a  household is unable to 

181
Fugitive emissions (oil and natural gas systems)  The release 
Gender equity  See Equity.
Geothermal energy  Accessible thermal energy stored in the 
(hydrothermal resources), which may be used to generate electric energy in a thermal power plant, or to supply heat to any process requiring it. The main sources of geothermal energy are the residual energy available from planet formation and the energy continuously generated from radionuclide decay. See also Renewable energy.
Gini coefficient

A statistical measure of dispersion in a  distribution 
used for measuring inequality in income, wealth, carbon emissions, and access to well-being defining services. The dimensionless GINI coefficient ranges between 0 (absolute equality) and 1 (absolute inequality).
Global carbon budget
 See Carbon budget.
Global mean surface air temperature (GSAT)  Global average of 
Global mean surface temperature (GMST)  Estimated global 
surface temperature (SST) over ice-free ocean regions, with changes normally expressed as departures from a  value over a  specified reference period. See also Global mean surface air temperature (GSAT).

 Global w arming refers to the increase in 
(e.g., 20 or 30 years). A common choice for the baseline is 1850–1900 (the earliest period of reliable observations with sufficient geographic coverage), with more modern baselines used depending upon the application. See also Climate change and Climate variability.
Global warming potential (GWP)
 An index measuring the 
radiative forcing  following an emission of a  unit mass of a  given 
represents the combined effect of the differing times these substances remain in the atmosphere , and their effectiveness in causing radiative 
 The structures, processes, and actions through which

includes formal and informal institutions and the associated norms, rules, laws and procedures for deciding, managing, implementing and monitoring policies and measures at any geographic or political scale, from global to local.Adaptive governance Adjusting to changing conditions, such as climate change, through governance interactions that seek to maintain a  desired state in a social-ecological system.
The structures, processes, and actions through which private and public actors seek to mitigate and adapt to climate change.
Deliberative governance involves decision-making through inclusive

public conversation which allows opportunity for developing policy options through public discussion rather than collating individual preferences through voting or referenda (although the latter governance mechanisms can also be preceded and legitimated by public deliberation processes).
Strategies of governance at various levels, which prioritise the use of social learning and rapid feedback mechanisms in planning and policymaking, often through incremental, experimental and iterative management processes.
The dispersion of governance across multiple levels of jurisdiction and decision-making, including, global, regional, national and local, as well as trans-regional and trans-national levels.
Participatory governance

A governance system that enables direct public engagement in decision-making using a variety of techniques, for example, referenda, community deliberation, citizen juries or participatory budgeting. The approach can be applied in formal and informal institutional contexts from national to local, but is usually associated with devolved decision-making (Fung and Wright 2003; Sarmiento and Tilly 2018).
 The ability of governance institutions, 
implement, evaluate and adjust policies and measures over the short, medium and long term, adjusting for uncertainty, rapid change and wide-ranging impacts and multiple actors and demands. See also Governance.
Grazing land
 The sum of rangelands and pastures not considered 
as cropland, and subject to livestock grazing or hay production. It

includes a  wide range of ecosystems, for example, systems with vegetation that fall below the threshold used in the forest land category, silvo-pastoral systems, as well as natural, managed grasslands and semi-deserts.
 The GCF was established by the 
operating entity of the financial mechanism of the United Nations Framework Convention on Climate Change (UNFCCC), in accordance with Article 11 of the Convention, to support projects, programmes and policies and other activities in developing country Parties. The Fund is governed by a Board and will receive guidance of the COP . See also Climate finance.

182
Annex I  GlossaryAIGreen infrastructure  See Infrastructure.
Greenhouse gas emission metric  A simplified relationship used 
GHG emission metric expresses the effect from one gas relative to

the effect of emitting a unit mass of a reference GHG on the same measure of climate change. There are multiple emission metrics, and the most appropriate metric depends on the application. GHG emission metrics may differ with respect to: (i) the key measure of climate change they consider; (ii) whether they consider climate outcomes for a specified point in time or integrated over a specified time horizon; (iii) the time horizon over which the metric is applied; (iv) whether they apply to a  single emission pulse, emissionssustained over a period of time, or a combination of both; and (v)whether they consider the climate effect from an emission comparedto the absence of that emission or compared to a reference emissions level or climate state.
potential (GWP), global temperature change potential (GTP), global damage potential, and GWP*), use carbon dioxide (CO
2 ) as the reference gas.

referred to as ‘carbon dioxide equivalent’ emissions. A metric that establishes equivalence regarding one key measure of the climate system response to emissions does not imply equivalence regarding other key measures. The choice of a metric, including its time horizon, should reflect the policy objectives for which the metric is applied.]
Greenhouse gas neutrality  Condition in which metric-weighted 
anthropogenic greenhouse gas (GHG)  emissions associated 

GHG removals. The subject can be an entity such as a country, an organisation, a district or a commodity, or an activity such as a service and an event. GHG neutrality is often assessed over the lifecycle, including indirect (‘scope 3’) emissions, but can also be limited to the emissions and removals, over a specified period, for which the subject has direct control, as determined by the relevant scheme. The quantification of GHG emissions and removals depends on the GHG emission metric chosen to compare emissions and removals of different gases, as well as the time horizon chosen for that metric.
[Note 1: Greenhouse gas neutrality and net-zero greenhouse gas emissions

are overlapping concepts. The concepts can be applied at global or sub-global scales (e.g., regional, national and sub-national). At a global scale, the terms greenhouse gas neutrality and net-zero greenhouse gas emissions are equivalent. At sub-global scales, net-zero GHG emissions is generally applied to emissions and removals under direct control or territorial responsibility of the reporting entity, while GHG neutrality generally includes emissions and removals within and beyond the direct control or territorial responsibility of the reporting entity. Accounting rules specified by GHG programmes or schemes can have a significant influence on the quantification of relevant emissions and removals.
Note 2: Under the Paris Rulebook (Decision 18/CMA.1, annex, paragraph 37),

parties have agreed to use GWP100 values from the IPCC AR5 or GWP100 values from a  subsequent IPCC Assessment Report to report aggregate emissions and removals of GHGs. In addition, parties may use other metrics to report supplemental information on aggregate emissions and removals of GHGs.Note 3: In some cases, achieving greenhouse gas neutrality may rely on the supplementary use of offsets to balance emissions that remain after actions by the reporting entity are taken into account.]
Greenhouse gases (GHGs)  Gaseous constituents of the 
atmosphere , both natural and anthropogenic, that absorb and emit 
emitted by the Earth’s surface, by the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapour (H

2O), carbon dioxide (CO 2 ), nitrous oxide (N 2O), methane (CH 4 ) and 
ozone (O3 ) are the primary GHGs in the Earth’s atmosphere. Human-
Grey infrastructure  See Infrastructure.
Gross domestic product (GDP)  The sum of gross value added, 
the economy, plus any taxes and minus any subsidies not included in the  value of the products in a  country or a  geographic region for a  given period, normally one year. GDP is calculated without deducting for depreciation of fabricated assets or depletion and degradation of natural resources.
 A collective term for the group of partially halogenated  
organic species, which includes the chlorofluorocarbons  (CFCs),

hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), halons, methyl chloride and methyl bromide. Many of the halocarbons have large global warming potentials. The chlorine and bromine-containing 
Human behaviour
 The responses of persons or groups to 
behaviour covers the range of actions by individuals, communities, organisations, governments and at the international level.
 Rights that are inherent to all human beings , universal, 
law. They include the right to life, economic, social, and cultural rights, and the right to development and self-determination (OHCHR 2018).
 A condition that is met when the vital core of 
human lives is protected, and when people have the freedom and

capacity to live with dignity. In the context of climate change, the vital core of human lives includes the universal and culturally specific, material and non-material elements necessary for people to act on behalf of their interests and to live with dignity.
 Any system in which human organisations 
and institutions  play a major role. Often, but not always, the term 
agricultural systems, urban systems, political systems, technological systems and economic systems are all human systems in the sense applied in this report.

183
Glossary Annex IAIHydropower  Power harnessed from the flow of water. See also 
Impacts  The consequences of realised risks on natural and human 
vulnerability. Impacts generally refer to effects on lives, livelihoods, health and well-being, ecosystems and species, economic, social 
and cultural assets, services (including ecosystem services), and infrastructure. Impacts may be referred to as consequences or outcomes, and can be adverse or beneficial. See also Adaptation, Loss and Damage, and losses and damages.
Indigenous knowledge
 The understandings, skills and philosophies 
developed by societies with long histories of interaction with their

natural surroundings. For many indigenous peoples, indigenous knowledge informs decision-making about fundamental aspects of life, from day-to-day activities to longer-term actions. This knowledge is integral to cultural complexes, which also encompass language, systems of classification, resource use practices, social interactions, values, ritual and spirituality. These distinctive ways of knowing are important facets of the world’s cultural diversity (UNESCO 2018). See also Local knowledge.
Indirect emissions
 Emissions that are a  consequence of the 
economic sector, a company or process, but which occur outside the specified boundaries. For example, emissions are described as indirect if they relate to the use of heat but physically arise outside the boundaries of the heat user, or to electricity production but physically arise outside of the boundaries of the power supply sector. See also Direct emissions.

 See Land-use change (LUC).
Industrial revolution  A period of rapid industrial growth with 
during the second half of the 18th century and spreading to Europe and later to other countries including the United States. The invention of the steam engine was an important trigger of this development. The industrial revolution marks the beginning of a strong increase in the use of fossil fuels, initially coal, and hence emission of carbon dioxide (CO
2 ).
Inequality  See Equality.
Infrastructure  The designed and built set of physical systems and 
people, their communities, and the broader environment to provide services that support economic growth, health, quality of life, and safety (Chester 2019; Dawson et al. 2018).

Blue infrastructure includes bodies of water, watercourses, ponds, lakes and storm drainage, that provide ecological and hydrological functions including evaporation, transpiration, drainage, infiltration, and temporary storage of runoff and discharge.
The strategically planned interconnected set of natural and constructed ecological systems, green spaces and other landscape features that can provide functions and services including air and water purification, temperature management, floodwater management and coastal defence often with co-benefits for people and biodiversity. Green infrastructure includes planted and remnant native vegetation, soils, wetlands, parks and green open spaces, as well as building and street-level design interventions that incorporate vegetation (Bobbins and Culwick 2016).

Engineered physical components and networks of pipes, wires, roads, tracks that underpin energy, transport, communications (including digital), built form, water and sanitation and solid waste management systems.
Social infrastructure 
The social, cultural, and financial activities and institutions as well as associated property, buildings and artefacts and policy domains such as social protection, health and education that support well-being and public life (Latham and Layton 2019; Frolova et al. 2016).
 Building and strengthening individual 
organisations and people, as well as empowerment, social capital, and an enabling environment, including the culture, values and power relations (Willems and Baumert 2003). See also Governance and Institutions.
 Rules , norms and conventions that guide, constrain

formally  established, for instance through laws and regulations, or informally established, for instance by traditions or customs. Institutions may spur, hinder, strengthen, weaken or distort the emergence, adoption and implementation of climate action and climate governance.
Integrated assessment  A method of analysis that combines 
results and models from the physical , biological, economic and 
a consistent framework to evaluate the status and the consequences of environmental change and the policy responses to it. See also Integrated assessment model (IAM).
 Models that integrate 
knowledge from two or more domains into a  single framework. They 

One class of IAM used with respect to climate change mitigation may include representations of: multiple sectors of the economy, such as energy, land use and land-use change; interactions between sectors; 
the economy as a whole; associated greenhouse gas (GHG) emissions and sinks; and reduced representations of the climate system. This 
class of model is used to assess linkages between economic, social and technological development and the evolution of the climate system. Another class of IAM additionally includes representations of the costs associated with climate change impacts, but includes less 
detailed representations of economic systems. These can be used to assess impacts and mitigation in a cost–benefit framework and have been used to estimate the social cost of carbon. See also Integrated Assessment Scenario Ensemble.

184
Annex I  GlossaryAIIntegrated Assessment Scenario Ensemble  A set of modelled  
Inter-generational equity  See Equity.
Internet of Things (IoT)  The network of computing devices 
Irreversibility  A perturbed state of a  dynamical system is defined 
due to natural processes takes substantially longer than the time scale of interest. See also Tipping point.
 A set of principles , processes and practices that 
aim to ensure that no people, workers, places, sectors, countries or

regions are left behind in the transition from a high-carbon to a low-carbon economy. It stresses the need for targeted and proactive measures from governments, agencies, and authorities to ensure that any negative social, environmental or economic impacts of economy-wide transitions are minimised, whilst benefits are maximised for those disproportionally affected. Key principles of just transitions include: respect and dignity for vulnerable groups; fairness in energy access and use, social dialogue and democratic consultation with relevant stakeholders; the creation of decent jobs; social protection; and rights at work. Just transitions could include fairness in energy, land use and climate planning and decision-making processes; economic diversification based on low-carbon investments; realistic training/retraining programmes that lead to decent work; gender-specific policies that promote equitable outcomes; the fostering of international cooperation and coordinated multilateral

actions; and the eradication of poverty. Lastly, just transitions may embody the redressing of past harms and perceived injustices (ILO 2015; UNFCCC 2016).

Justice
 Justice is concerned with ensuring that people get what 
is due to them,  setting out the moral or legal principles of fairness 
Justice that links development and human rights to achieve a human-centred approach to addressing climate change, safeguarding the rights of the most vulnerable people and sharing the burdens and benefits of climate change and its impacts equitably and fairly (MRFCJ 2018).
Kaya identity
 In this identity,  global emissions are equal to 
product), multiplied by the energy intensity of production, multiplied by the carbon intensity of energy.
Land
 The terrestrial portion of the biosphere that comprises the 

and water), the ecological processes, topography, and human settlements and infrastructure that operate within that system (FAO 2007; UNCCD 1994).
Land cover
 The biophysical coverage of land (e.g., bare soil, rocks, 
but the two represent distinct classification systems. For example, the land cover class woodland can be under various land uses such as livestock grazing, recreation, conservation, or wood harvest.]
Land cover change  Change from one land cover class to another, 
Land degradation  A negative trend in land condition, caused by 

climate change, expressed as long-term reduction or loss of at least one of the following: biological productivity, ecological integrity or value to humans. 
condition resulting solely from natural processes (such as volcanic eruptions) are not considered to be land degradation. Reduction of biological productivity or ecological integrity or value to humans can constitute degradation, but any one of these changes need not necessarily be considered degradation.] 
Land degradation neutrality  A state whereby the amount and  
Land management  Sum of land-use practices (e .g.,  sowing, 

Land potential  The inherent, long-term potential of the land to 
sustainably generate ecosystem services, which reflects the capacity and resilience of the land-based natural capital, in the face of ongoing 
Land rehabilitation
 Direct or indirect actions undertak en with the 
is provision of goods and services rather than ecological restoration (McDonald et al. 2016).
Land restoration
 The process of assisting the recovery of land 
from a degraded state (IPBES 2018; McDonald et al. 2016).Land use
 The total of arrangements, activities and inputs applied 
to a parcel of land. The term land use is also used in the sense of

the social and economic purposes for which land is managed (e.g.,  grazing, timber extraction, conservation and city dwelling). In national greenhouse gas (GHG) inventories, land use is classified 
according to the IPCC land-use categories of forest land, cropland, grassland, wetlands, settlements, other lands (see the 2006 IPCC Guidelines for National GHG Inventories and their 2019 Refinement for details (IPCC 2006, 2019)).
Land use, land-use change and forestry (LULUCF)
 In the 

185
anthropogenic emissions and removals of GHG in managed lands, excluding non-CO
2 agricultural emissions. Following the 2006 IPCC 
Guidelines for National GHG Inventories and their 2019 Refinement, ‘anthropogenic’ land-related GHG fluxes are defined as all those occurring on ‘managed land’, that is, ‘where human interventions and practices have been applied to perform production, ecological or social functions’. Since managed land may include carbon dioxide (CO
2 ), removals not considered as ‘anthropogenic’ in some of the 
emission estimates from global models included in this report are not necessarily directly comparable with LULUCF estimates in National GHG Inventories (IPCC 2006, 2019).

Land-use change (LUC)
 The change from one land use category 
encompasses changes in land-use categories as well as changes in land management. See also Afforestation, Agriculture, Forestry and Other Land Use (AFOLU), Deforestation, Land use, land-use change and forestry (LULUCF), and Reforestation.
Land-use change outside the area of focus that occurs as a consequence of change in use or management of land within the area of focus, such as through market or policy drivers. For example, if agricultural land is diverted to biofuel production, forest clearance may occur elsewhere to replace the former agricultural production.
Latent heat flux
 The turbulent flux of heat from the Earth’s surface to 

Leakage  The effects of policies that result in a displacement of the 
Leapfrogging  The ability of developing countries to bypass 
Lifecycle assessment (LCA)  Compilation and ev aluation of the 
Likelihood  The chance of a  specific outcome occurring, where 
this report using a standard terminology (Mastrandrea et al. 2010). See also Agreement, Confidence, Evidence, and Uncertainty.
 The resources used and the activities undertaken in 
order for people to live. Livelihoods are usually determined by the

entitlements and assets to which people have access. Such assets can be categorised as human, social, natural, physical or financial.
 The understandings and skills developed 
live. Local knowledge informs decision-making about fundamental aspects of life, from day-to-day activities to longer-term actions. This knowledge is a key element of the social and cultural systems which influence observations of and responses to climate change; it also informs governance decisions (UNESCO 2018). See also Indigenous knowledge.
Lock-in
 A situation in which the future development of a system, 
Long-lived greenhouse gases (LLGHGs)  A set of well-mixed 
greenhouse gases with long atmospheric lifetimes. This set of

compounds includes carbon dioxide (CO 2 ) and nitrous oxide (N 2O), 
together with some fluorinated gases. They have a warming effect on climate. These compounds accumulate in the atmosphere at decadal to centennial time scales, and their effect on climate hence persists for decades to centuries after their emission. On time scales of decades to a century already emitted emissions of long-lived climate forcers can only be abated by greenhouse gas removal (GGR).
 Research has tak en 
under the United Nations Framework Convention on Climate Change (UNFCCC) following the establishment of the Warsaw International

Mechanism for Loss and Damage in 2013, which is to ‘address loss and damage associated with impacts of climate change, including extreme events and slow onset events, in developing countries that are particularly vulnerable to the adverse effects of climate change.’ Lowercase letters (losses and damages) have been taken to refer broadly to harm from (observed) impacts and (projected) risks, and can be economic or non-economic (Mechler et al. 2018).
 Actions that may lead 
to increased risk of adverse climate-related outcomes , including via 
Malnutrition  Deficiencies , excesses, or imbalances in a  person’s 
intake of energy and/or nutrients. The term malnutrition addresses

three broad groups of conditions: undernutrition, which includes wasting (low weight-for-height), stunting (low height-for-age) and underweight (low weight-for-age); micronutrient-related malnutrition, which includes micronutrient deficiencies (a lack of important vitamins and minerals) or micronutrient excess; and overweight, obesity and diet-related noncommunicable diseases (such as heart disease, stroke, diabetes and some cancers) (WHO 2018). Micronutrient deficiencies are sometimes termed ‘hidden hunger’ to emphasise that people can be malnourished in the sense of deficient without being deficient in calories. Hidden hunger can apply even where people are obese.
Managed forest
 Forests subject to human interventions (notably 
silvicultural management such as planting, pruning, thinning), timber

and fuelwood harvest, protection (fire suppression, insect suppression) and management for amenity values or conservation, with defined geographical boundaries (Ogle et al. 2018). See also Managed land.
GHG inventories, see the 2006 IPCC Guidelines for National GHG Inventories (IPCC 2006).]
Managed grassland  Grasslands on which human interventions 
are carried out,  such as grazing domestic livestock or hay removal.

186
Annex I  GlossaryAIManaged land  In the context of national greenhouse gas (GHG)  
Climate Change (UNFCCC), the 2006 IPCC Guidelines for National GHG Inventories (IPCC 2006) defines managed land ‘where human interventions and practices have been applied to perform production, ecological or social functions’. IPCC (2006) defines anthropogenic GHG emissions and removals in the LULUCF sector as all those occurring on ‘managed land’. The key rationale for this approach is that the preponderance of anthropogenic effects occurs on managed lands. 
Market failure  When priv ate decisions are based on market prices 
that do not reflect the real scarcity of goods and services but rather

reflect market distortions, they do not generate an efficient allocation of resources but cause welfare losses. A market distortion is any event in which a market reaches a market clearing price that is substantially different from the price that a market would achieve while operating under conditions of perfect competition and state enforcement of legal contracts and the ownership of private property. Examples of factors causing market prices to deviate from real economic scarcity are environmental externalities, public goods, monopoly power, information asymmetry, transaction costs, and non-rational behaviour.
Material substitution
 Replacement of one material (including an 
energy carrier used as a  feedstock) by another, due to scarcity, price, 

‘Processes of data collection over time, providing basic datasets, including associated accuracy and precision, for the range of relevant variables. Possible data sources are field measurements, field observations, detection through remote sensing and interviews’ (UN-REDD 2009).
Reporting
‘The process of formal reporting of assessment results to the UNFCCC, according to predetermined formats and established standards, especially the Intergovernmental Panel on Climate Change (IPCC) Guidelines and GPG (Good Practice Guidance)’ (UN-REDD 2009).
‘The process of formal verification of reports, for example, the established approach to verify national communications and national inventory reports to the UNFCCC’ (UN-REDD 2009).
Megacity
 Urban agglomerations with 10 million inhabitants or 
Methane (CH 4) T he greenhouse gas (GHG) methane is the major 
component of natural gas and associated with all hydrocarbon fuels.

husbandry and paddy rice production. Methane is also produced naturally where organic matter decays under anaerobic conditions, such as in wetlands. Under future global warming, there is potential for increased methane emissions from thawing permafrost, wetlands and sub-sea gas hydrates. See also Short-lived climate forcers (SLCFs).Migrant
 Any person who is moving or has moved across an  
international border or within a State away from his/her habitual place of residence, regardless of: (1) the person’s legal status; (2) whether the movement is voluntary or involuntary; (3) what the causes for the movement are; or (4) what the length of the stay is (IOM 2018).
 Movement of a  person or a group of persons, 
either across an international border, or within a State. It is a population

movement, encompassing any kind of movement of people, whatever its length, composition and causes; it includes migration of refugees, displaced persons, economic migrants, and persons moving for other purposes, including family reunification (IOM 2018).
 A human intervention to reduce 
Mitigation measures  In climate policy,  mitigation measures are 
for example, renewable energy technologies, waste minimisation processes, and public transport commuting practices.
 A technology or practice that reduces greenhouse 
gas emissions or enhances sinks.Mitigation pathways
Mitigation potential  The quantity of net greenhouse gas emission 

The mitigation potential constrained by biological, geophysical and geochemical limits and thermodynamics, without taking into account technical, social, economic and/or environmental considerations.
The portion of the technical potential for which the social benefits exceed the social costs, taking into account a social discount rate and the value of externalities.
The mitigation potential constrained by biogeophysical limits as well as availability of technologies and practices. Quantification of technical potentials takes into account primarily technical considerations, but social, economic and/or environmental considerations are occasionally also included, if these represent strong barriers for the deployment of an option.

Multi-level governance  See Governance.
Narrative  See Storyline.
Nature’s contributions to people (NCP)  All the contributions , 
both positive and negative, of living nature (i.e.,  diversity of 
evolutionary processes) to the quality of life for people. Beneficial contributions from nature include such things as food provision,

187
detrimental contributions include disease transmission and predation that damages people or their assets. Many NCP may be perceived as benefits or detriments depending on the cultural, temporal or spatial context (Díaz et al. 2018). See also Ecosystem services.
Nature-based solutions
 Actions to protect,  sustainably manage 
challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits (IUCN 2016). See also Biodiversity and Ecosystem.
Net negative greenhouse gas emissions
 A situation of net 
weighted anthropogenic greenhouse gas (GHG) removals exceed

metric-weighted anthropogenic GHG emissions. Where multiple GHG are involved, the quantification of net emissions depends on the metric chosen to compare emissions of different gases (such as global warming potential, global temperature change potential, 
dioxide removal (CDR), Greenhouse gas emission metric, Net-zero CO
2 emissions; Net-zero greenhouse gas emissions, and Negative 
Net-zero CO 2 emissions  Condition in which anthropogenic 
2 emissions are equivalent. At sub-global scales, net-zero CO 2

emissions is generally applied to emissions and removals under direct control or territorial responsibility of the reporting entity, while carbon neutrality generally includes emissions and removals within and beyond the direct control or territorial responsibility of the reporting entity. Accounting rules specified by GHG programmes or schemes can have a significant influence on the quantification of relevant CO
2 emissions and removals.]
Net-zero greenhouse gas emissions  Condition in which metric-
period. The quantification of net-zero GHG emissions depends on the GHG emission metric chosen to compare emissions and removals of different gases, as well as the time horizon chosen for that metric.

[Note 1: Greenhouse gas neutrality and net-zero GHG emissions are

overlapping concepts. The concept of net-zero GHG emissions can be applied at global or sub-global scales (e.g.,  regional, national and sub-national). At a global scale, the terms GHG neutrality and net-zero GHG emissions are equivalent. At sub-global scales, net-zero GHG emissions is generally applied to emissions and removals under direct control or territorial responsibility of the reporting entity, while GHG neutrality generally includes anthropogenic emissions and anthropogenic removals within and beyond the direct control or territorial responsibility of the reporting entity. Accounting rules specified by GHG programmes or schemes can have a  significant influence on the quantification of relevant emissions and removals.Note 2: Under the Paris Rulebook (Decision 18/CMA.1, annex, paragraph 37), parties have agreed to use GWP100 values from the IPCC AR5 or GWP100 values from a  subsequent IPCC Assessment Report to report aggregate emissions and removals of GHGs. In addition,

parties may use other metrics to report supplemental information on aggregate emissions and removals of GHGs.]

Nitrous oxide (N 2O) T he main anthropogenic source of N 2O, 
Non-overshoot pathways  See Pathways.
Ocean alkalinisation/Ocean alkalinity enhancement  A proposed  
alkaline minerals or their dissociation products at the ocean surface. This increases surface total alkalinity, and may thus increase ocean carbon dioxide (CO
2 ) uptake and ameliorate surface ocean acidification. 

Ocean fertilisation  A proposed carbon dioxide removal (CDR) 
method that relies on the deliberate increase of nutrient supply to the near-surface ocean with the aim of sequestering additional CO
2 
from the atmosphere through biological production. Methods include direct addition of micro-nutrients or macro-nutrients. To be successful, the additional carbon needs to reach the deep ocean where it has the potential to be sequestered on climatically relevant time scales. See also Anthropogenic removals.
Offset (in climate policy)
 The reduction, avoidance or removal of 
a unit of greenhouse gas (GHG) emissions by one entity, purchased

by another entity to counterbalance a unit of GHG emissions by that other entity. Offsets are commonly subject to rules and environmental integrity criteria intended to ensure that offsets achieve their stated mitigation outcome. Relevant criteria include, but are not limited to, the avoidance of double counting and leakage, use of appropriate baselines, additionality, and permanence or measures to address 
Organic farming
 An agricultural production system that aims 
society. Organic farming is often legally defined and governed by standards, typically guided by principles outlined by the International Federation of Organic Agriculture Movements (IFOAM  – Organics International) (IFOAM – Organics International 2014).

Ozone (O 3) T he triatomic form of oxygen, and a  gaseous 
from human activities (e.g.,  smog). Tropospheric O 3 acts as 

188
Pareto optimum  A state in which no one’ s welfare can be 
increased without reducing someone else’s welfare.Participatory governance
Particulate matter (PM)  Atmospheric aerosols involved in 
air pollution issues . Of greatest concern for health are particles of 
designated as PM10 and particles of diameter less than or equal to 2.5 micrometers, usually designated as PM2.5.
 The generic situation where decisions, events, 
or other actions or options at a later point in time. See also Lock-in.Pathways
 The temporal evolution of natural and/or human 

of quantitative and qualitative scenarios or narratives of potential futures to solution-oriented decision-making processes to achieve desirable societal goals. Pathway approaches typically focus on biophysical, techno-economic, and/or socio-behavioural trajectories and involve various dynamics, goals, and actors across different scales. See also Scenario and Storyline.
A pathway of emissions of greenhouse gases and other climate forcers that provides an approximately one-in-two to two-in-three chance, given current knowledge of the climate response, of global warming either remaining below 1.5°C or returning to 1.5°C by around 2100 following an overshoot.
A series of adaptation choices involving trade-offs between short-term and long-term goals and values. These are processes of deliberation to identify solutions that are meaningful to people in the context of their daily lives and to avoid potential maladaptation.

Iterative processes for managing change within complex systems in order to reduce disruptions and enhance opportunities associated with climate change.
Development pathways evolve as the result of the countless decisions being made and actions being taken at all levels of societal structure, as well due to the emergent dynamics within and between institutions, cultural norms, technological systems and other drivers of behavioural change. 
development pathways to sustainability (SDPS).Emission pathways 
Modelled trajectories of global anthropogenic emissions over the 21st century.
A temporal evolution of a set of mitigation scenario features, such as greenhouse gas emissions and socio-economic development.Non-overshoot pathways Pathways that stay below a specified concentration, forcing, or global warming level during a specified period of time (e.g., until 2100).

global warming level, and then return to or below that level again before the end of a  specified period of time (e.g.,  before 2100). Sometimes the magnitude and likelihood of the overshoot is also characterised. The overshoot duration can vary from one pathway to the next, but in most overshoot pathways in the literature and referred to as overshoot pathways in the AR6, the overshoot occurs over a period of at least one decade and up to several decades.

Scenarios that include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs) and aerosols and chemically active gases, as well as land use/land cover (Moss et al. 2010). The word representative signifies that each RCP provides only one of many possible scenarios that would lead to the specific radiative forcing characteristics. The term pathway emphasises that not only the long-term concentration levels are of interest, but also the trajectory taken over time to reach that outcome (Moss et al. 2010).
RCPs usually refer to the portion of the concentration pathway

extending up to 2100, for which integrated assessment models produced corresponding emission scenarios. Extended concentration pathways describe extensions of the RCPs from 2100 to 2300 that were calculated using simple rules generated by stakeholder consultations, and do not represent fully consistent scenarios. Four  RCPs produced from integrated assessment models were selected from the published literature and used in the Fifth IPCC Assessment, and are also used in this Assessment for comparison, spanning the range from approximately below 2°C warming to high (>4°C) warming best-estimates by the end of the 21st century: RCP2.6, RCP4.5 and RCP6.0 and RCP8.5.
•
RCP2.6:  One pathway where radiative forcing peaks at
approximately 3 W m–2 and then declines to be limited at 2.6 W m–2
in 2100 (the corresponding Extended Concentration Pathway, or
•RCP4.5 and RCP6.0:  Two intermediate stabilisation pathways

in which radiative forcing is limited at approximately 4.5 W m–2
and 6.0 W m–2 in 2100 (the corresponding ECPs have constant
•RCP8.5:  One high pathway which leads to >8.5 W m–2 in 2100 (the 
corresponding ECP has constant emissions after 2100 until 2150
SSPs have been developed to complement the Representative Concentration Pathways (RCPs). By design, the RCP emission and concentration pathways were stripped of their association with a certain socio-economic development. Different levels of emissions 
and climate change along the dimension of the RCPs can hence

be explored against the backdrop of different socio-economic development pathways (SSPs) on the other dimension in a matrix. This integrative SSP-RCP framework is now widely used in the climate impact and policy analysis literature, where climate projections

188
of various SSPs. As several emission updates were due, a  new set of emission scenarios was developed in conjunction with the SSPs. Hence, the abbreviation SSP is now used for two things: On the one hand SSP1, SSP2, …, SSP5 are used to denote the five socio-economic scenario families. On the other hand, the abbreviations SSP1
-1.9, 
SSP1 -2.6, …, SSP5 -8.5 are used to denote the newly developed 
emission scenarios that are the result of an SSP implementation within an integrated assessment model. Those SSP  scenarios are bare of climate policy assumption, but in combination with so-called shared policy assumptions (SPAs), various approximate radiative forcing levels of 1.9, 2.6, …, or 8.5 W m
–2 are reached by the end of 
Transformation pathways

Trajectories describing consistent sets of possible futures of greenhouse gas (GHG) emissions, atmospheric concentrations, or global mean surface temperatures implied from mitigation and 
adaptation actions associated with a  set of broad and irreversible economic, technological, societal, and behavioural changes. This can encompass changes in the way energy and infrastructure are used and produced, natural resources are managed and institutions are set up, and in the pace and direction of technological change.
 Dynamic transition zones that have intense 
Policies (for climate change mitigation and adaptation)   
adaptation and mitigation. Policies include those developed by

national and subnational public agencies, and with the private sector. Policies for adaptation and mitigation often take the form of economic incentives, regulatory instruments, and decision-making and engagement processes.
 The set of interlinked relationships between 
economics, customs and power that determine the outcome of trade and transactions and the distribution of wealth in a country or economy.
Pool, carbon and nitrogen
 A reservoir in the Earth System where  
Poverty  A complex concept with several definitions stemming from

different schools of thought. It can refer to material circumstances (such as need, pattern of deprivation or limited resources), economic conditions (such as standard of living, inequality or economic position) and/or social relationships (such as social class, dependency, exclusion, lack of basic security or lack of entitlement).
Poverty eradication
 A set of measures to end poverty in all its 
forms everywhere. See also Sustainable Development Goals (SDGs).Precursors
 Atmospheric compounds that are not greenhouse 
concentrations by taking part in physical or chemical processes regulating their production or destruction rates.Pre-industrial (period)
 The multi-century period prior to the 
onset of large-scale industrial activity around 1750. The reference period 1850–1900 is used to approximate pre-industrial global mean surface temperature (GMST). See also Industrial revolution.
Primary energy

The energy that is embodied in resources as they 
used in this report is the direct equivalent method, which counts one unit of secondary energy provided from non-combustible sources as one unit of primary energy. For more details on the methodology, see Section 7 in Working Group III Annex II.]
Primary production  The synthesis of organic compounds by plants 
carbon respectively.  It can also occur through chemosynthesis, using 
Private costs  Costs carried by individuals , companies or other 
private entities that undertake an action, whereas social costs include

additionally the external costs on the environment and on society as a whole. Quantitative estimates of both private and social costs may be incomplete, because of difficulties in measuring all relevant effects.
Production-based emissions
 Emissions released to the atmosphere  
Projection  A potential future evolution of a  quantity or set of 
projections are conditional on assumptions concerning, for example, future socio-economic and technological developments that may or may not be realised. See also Climate projection,  Pathways and Scenario.
Prosumers
 A consumer that also produces energy and inputs 
Radiative forcing  The change in the net, downward minus

aerosols  or in the output of the Sun. The stratospherically adjusted 
radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temperatures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once both stratospheric and tropospheric adjustments are accounted for is termed the ‘effective radiative forcing’.
Rebound effect
 Phenomena whereby the reduction in energy 
consumption or emissions (relative to a baseline) associated with the

implementation of mitigation measures in a jurisdiction is offset to some degree through induced changes in consumption, production, and prices within the same jurisdiction. The rebound effect is most typically ascribed to technological energy efficiency improvements.

189
(REDD+)  REDD+ refers to reducing emissions from deforestation; 
reducing emissions from forest degradation; conservation of forest carbon stocks; sustainable management of forests; and enhancement of forest carbon stocks (see UNFCCC decision 1/CP .16, para. 70).
 A time period of interest,  or a period over which 
A time period against which differences are calculated (e.g., expressed as anomalies relative to a baseline).
Reforestation  Conversion to forest of land that has previously 
[Note: For a  discussion of the term forest and related terms such as

afforestation, reforestation and deforestation, see the 2006 IPCC Guidelines for National Greenhouse Gas Inventories and their 2019 Refinement, and information provided by the United Nations Framework Convention on Climate Change (IPCC 2006, 2019; UNFCCC 2021a,b).] 
(REDD+).
Regenerative agriculture  A universally agreed definition of 
this relatively new farming approach has yet to be established,  
of varying combinations of agricultural management practices, to ensure the continued restoration and enhancement of soil health, biodiversity and ecosystem functioning, in conjunction with profitable agricultural production.
Region
 Land  and/or ocean area characterised by specific 
geographical and/or climatological features. The climate of a region

emerges from a multi-scale combination of its own features, remote influences from other regions, and global climate conditions.
Renewable energy (RE)  Any form of energy that is replenished 
by natural processes at a rate that equals or exceeds its rate of use .
Renewable energy sources such as wind and solar energy whose output is determined by weather, in contrast to ‘dispatchable’ generators that adjust their output as a  reaction to economic incentives. Variable renewables have also been termed intermittent, fluctuating, or non-dispatchable (Hirth 2013).
Resilience  The capacity of interconnected social, economic and 
ecological systems to cope with a hazardous event, trend or disturbance,

responding or reorganising in ways that maintain their essential function, identity and structure. Resilience is a positive attribute when it maintains capacity for adaptation, learning and/or transformation (Arctic Council 2016). See also Hazard,  Risk and Vulnerability.Resource cascade
 Tracking resource use (materials, energy, water, 
etc.), efficiency and losses through all conversion steps from primary resource extraction to various conversion steps, all the way to final service delivery.
Risk
 The potential for adverse consequences for human or 
ecological systems, recognising the diversity of values and objectives

associated with such systems. In the context of climate change, risks can arise from potential impacts of climate change as well as human responses to climate change. Relevant adverse consequences include those on lives, livelihoods, health and well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), ecosystems and species.
interactions between climate-related hazards with the exposure and vulnerability of the affected human or ecological system to the 
hazards. Hazards, exposure and vulnerability may each be subject to uncertainty in terms of magnitude and likelihood of occurrence, and each may change over time and space due to socio-economic changes and human decision-making (see also risk management, adaptation and mitigation).
In the context of climate change responses, risks result from the

potential for such responses not achieving the intended objective(s), or from potential trade-offs with, or negative side-effects on, other societal objectives, such as the Sustainable Development Goals (SDGs) (see also risk trade-off). Risks can arise, for example, from 
uncertainty in implementation, effectiveness or outcomes of climate policy, climate-related investments, technology development or adoption, and system transitions.
 The qualitative and/or quantitative scientific 
Risk management  Plans , actions, strategies or policies to reduce 
Risk perception.Risk perception
 The subjective judgement that people make about 
and Risk management.Risk trade-off

The change in the portfolio of risks that occurs when 
Sea surface temperature (SST)  The subsurface bulk temperature 
drifters. From ships, measurements of water samples in buckets were mostly switched in the 1940s to samples from engine intake water. Satellite measurements of skin temperature (uppermost layer; a fraction of a millimetre thick) in the infrared or the top centimetre or so in the microwave are also used, but must be adjusted to be compatible with the bulk temperature.
Scenario
 A plausible description of how the future may develop 

190
forecasts, but are used to provide a  view of the implications of developments and actions.
A plausible representation of the future development of atmospheric concentrations of substances that are radiatively active (e.g.,  greenhouse 
changes that can be radiatively active via albedo changes, and often used as input to a climate model to compute climate projections.
A plausible representation of the future development of emissions of substances that are radiatively active (e.g.,  greenhouse gases 
or aerosols), plus human-induced land-cover changes that can

be radiatively active via albedo changes, based on a coherent and internally consistent set of assumptions about driving forces (such as demographic and socio-economic development, technological change, energy and land use) and their key relationships. Concentration scenarios, derived from emission scenarios, are often used as input to a climate model to compute climate projections.
A plausible description of the future that describes how the (studied) system responds to the implementation of mitigation policies and measures.
Scenario used as starting or reference point for a comparison between two or more scenarios. 
[Note 1: In many types of climate change research, reference scenarios

reflect specific assumptions about patterns of socio-economic development and may represent futures that assume no climate policies or specified climate policies, for example, those in place or planned at the time a study is carried out. Reference scenarios may also represent futures with limited or no climate impacts or adaptation, to serve as a point of comparison for futures with impacts and adaptation. These are also referred to as ‘baseline scenarios’ in the literature. 
which in that case are taken as a  point of comparison to explore the implications of other features, for example, of delay, technological options, policy design and strategy or to explore the effects of additional impacts and adaptation beyond those represented in the reference scenario. 
Note 3: The term business as usual scenario has been used to describe

a  scenario that assumes no additional policies beyond those currently in place, and where patterns of socio-economic development are consistent with recent trends. The term is now used less frequently than in the past. 
scenarios may refer to counterfactual historical scenarios assuming no anthropogenic greenhouse gas (GHG) emissions (climate change attribution) or no climate change (impact attribution).]Socio-economic scenario 
A scenario that describes a plausible future in terms of population, gross domestic product (GDP), and other socio-economic factors relevant to understanding the implications of climate change.
Scenario storyline
 See Storyline.
Sequestration  The process of storing carbon in a carbon pool. See 
Sequestration potential  The quantity of greenhouse gases that

subcategories of sequestration potential. See also Pool, carbon and nitrogen, Sequestration, Sink, Source, and Uptake.
Service provisioning
 Various services (such as illumination and 
materials, and other resources comprising: (i) Resource flows (e.g., energy); (ii) Technologies for resource use and energy conversion (e.g., vehicles and their engines); and (iii) Social/organisational forms of service delivery (e.g.,  publicly owned companies, or privately owned companies, e-commerce).
Services
 Activities that help satisfy human wants or needs. While 
they usually involve relationships between producers and consumers,

services are less tangible and less storable than goods since they represent flows not stocks, and when their regeneration conditions are protected they may be reused over time.
 Places of concentrated human habitation.  Settlements 
Shared policy assumptions (SPAs)  See Shared Socio-economic 
Shared Socio-economic Pathways (SSPs)  See Pathways.
Sharing economy  A system which allows people to share goods 
and services by enabling collaborative use , access or ownership.
Shifting development pathways (SDPs)  In this report, shifting 
development pathways describes transitions aimed at re-directing

existing developmental trends. Societies may put in place enabling conditions to influence their future development pathways, when they endeavour to achieve certain outcomes. Some outcomes may be common, while others may be context-specific, given different starting points. See also Development pathways (under Pathways), 
Shifting development pathways to sustainability
 Shifting 
development pathw ays to sustainability involves transitions aligned 
with a  shared aspiration in the Sustainable Development Goals 
(SDGs) agreed globally, though sustainability may be interpreted differently in various contexts as societies pursue a  variety of sustainable development objectives. See also Development pathways 
Short-lived climate forcers (SLCFs)
 A set of chemically reactive 

191
different physiochemical properties and environmental effects. Their emission or formation has a  significant effect on radiative forcing over a period determined by their respective atmospheric lifetimes. Changes in their emissions can also induce long-term climate effects via, in particular, their interactions with some biogeochemical cycles. SLCFs are classified as direct or indirect, with direct SLCFs exerting climate effects through their radiative forcing and indirect SLCFs being the precursors of other direct climate forcers. Direct SLCFs include 
methane (CH
4 ), ozone (O 3 ), primary aerosols and some halogenated 
species . Indirect SLCFs are precursors of ozone or secondary aerosols.

SLCFs can be cooling or warming through interactions with radiation and clouds. They are also referred to as near-term climate forcers. Many SLCFs are also air pollutants. A subset of exclusively warming SLCFs is also referred to as short-lived climate pollutants (SLCPs), including methane, ozone, and black carbon (BC).
Short-lived climate pollutants (SLCP)
 See Short-lived climate 
Simple climate model (SCM)  A broad class of lower -dimensional 
models of the energy balance, radiative transfer, carbon cycle, or

a combination of such physical components. SCMs are also suitable for performing emulations of climate-mean variables of Earth System Models (ESMs), given that their structural flexibility can capture both the parametric and structural uncertainties across process-oriented ESM responses. They can also be used to test consistency across multiple lines of evidence with regard to climate sensitivity ranges, transient climate responses (TCRs), transient climate response to 
cumulative CO
2 emissions (TCREs) and carbon cycle feedbacks. 
Small Island Developing States (SIDS)  SIDS, as recognised by 
the United Nations OHRLLS (UN Office of the High Representative for

the Least Developed Countries, Landlocked Developing Countries and Small Island Developing States), are a distinct group of developing countries facing specific social, economic and environmental vulnerabilities (UN-OHRLLS 2011). They were recognised as a special case, both for their environment and development, at the Rio Earth Summit in Brazil in 1992. Fifty-eight countries and territories are presently classified as SIDS by the UN OHRLLS, with 38 being UN member states and 20 being non-UN members or associate members of the Regional Commissions (UN-OHRLLS 2018).
Smart grids
 A smart grid uses information and communications 
Through automated responses or the provision of price signals, this information can then be used to improve the efficiency, reliability, economics, and sustainability of the electricity network.
Social cost of carbon (SCC)

The net present value of aggregate 
trajectory over time .
Social costs  The full costs of an action in terms of social welfare 
losses, including external costs associated with the impacts of this action on the environment, the economy (GDP, employment) and on the society as a whole.
Social group
 A collective of people who share similar characteristics 
and collectively may have a sense of unity (F orsyth 2010).
Social identity  The portion of an individual’s self-concept derived 
Social inclusion  A process of improving the terms of participation 
in society,  particularly for people who are disadvantaged, through 

Social infrastructure  See Infrastructure.
Social learning  A process of social interaction through which 
people learn new behaviours , capacities, values, and attitudes.
Social-ecological system  An integrated system that includes 
interdependence of the social and ecological subsystems. The system’s structure is characterised by reciprocal feedbacks, emphasising that humans must be seen as a part of, not apart from, nature (Berkes and Folke 1998; Arctic Council 2016).
Socio-technical transitions  Where technological change is 
associated with social systems and the two are inextricably link ed.
Soil carbon sequestration (SCS)  Land management  changes 
removal of carbon dioxide (CO 2 ) from the atmosphere. See also

Soil organic carbon  Carbon contained in soil organic matter.
Soil organic matter  The organic component of soil, comprising 
plant and animal residue at various stages of decomposition, and soil organisms.
 Energy from the Sun.  Often the phrase is used to 
light that is converted into chemical energy by natural or artificial photosynthesis, or by photovoltaic panels and converted directly into electricity. See also Renewable energy.
 Refers to a  range of 
(GHG) mitigation that seek to limit global warming. Most methods involve reducing the amount of incoming solar radiation reaching the surface, but others also act on the longwave radiation budget by reducing optical thickness and cloud lifetime.
Source

Any process or activity which releases a  greenhouse gas 
(GHG) , an aerosol or a  precursor of a  GHG into the atmosphere

192
Spill-over effect  The effects of domestic or sector mitigation 
Storyline
 A way of making sense of a situation or a series of events 
through the construction of a set of explanatory elements. Usually, 

 Assets exposed to dev aluations or conversion to 
 State/provincial , regional, metropolitan and 
demand for energy,  materials, land and water while delivering human 

A dynamic process that guarantees the persistence 
of natural and human systems in an equitable manner .
Sustainable development (SD)  Development that meets the 
generations to meet their own needs (WCED 1987) and balances social , economic and environmental concerns. See also Development 
 The 17 global goals 

 The stewardship and use 
Sustainable intensification (of agriculture)  Increasing yields

impacts of agricultural production and increasing the provision of environmental services (CGIAR 2019).  
Sustainable land management  The stewardship and use of 
 The set of public and private

 The act of bringing technology into effective 
Technology diffusion  The spread of a technology across different 
Technology transfer  The exchange of knowledge, hardware and 

 Association between climate variables at widely 
 Exceedance of a  specified global warming 

193
Annex I  GlossaryAITipping point  A critical threshold beyond which a  system 
Total carbon budget  See Carbon budget.
Trade-off  A competition between different objectives within 
a decision situation, where pursuing one objective will diminish 
potentially reducing the net benefit to society or the environment. See also Co-benefit.
 A change in the fundamental attributes of natural 
and human systems .
Transformation pathways  See Pathways.
Transient climate response (TCR)  See Climate sensitivity.
Transient climate response to cumulative CO 2 emissions (TCRE)   

Transition  The process of changing from one state or condition to 
firms, cities, regions and nations, and can be based on incremental or transformative change.
 A state of incomplete knowledge that can result from 
in the data to ambiguously defined concepts or terminology, incomplete understanding of critical processes, or  uncertain projections of human behaviour. Uncertainty can therefore be represented by quantitative measures (e.g.,  a  probability density function) or by qualitative statements (e.g., reflecting the judgement of a team of experts) (Moss and Schneider 2000; Mastrandrea et al. 2010). See also Confidence and Likelihood.
  
A legally binding international agreement linking environment and

The Convention’s objective is ‘to combat desertification and mitigate the effects of drought in countries experiencing drought and/or desertification’. The Convention specifically addresses the arid, semi-arid and dry sub-humid areas, known as the drylands, and has a particular focus on Africa. As of September 2020, the UNCCD had 197 Parties. See also Desertification, Drought and Land degradation.
(UNFCCC)
 The UNFCCC was adopted in May 1992 and opened for

signature at the 1992 Earth Summit in Rio de Janeiro. It entered into force in March 1994 and, as of September 2020, had 197 Parties (196 States and the European Union). The Convention’s ultimate objective is the ‘stabilisation of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system’ (UNFCCC 1992). The provisions of the Convention are pursued and implemented by two further treaties: the Kyoto Protocol and the Paris Agreement.
Uptake
 The transfer of substances (such as carbon) or energy 
or to the land. See also Pool, carbon and nitrogen, Sequestration, Sequestration potential, Sink and Source.
Urban
 The categorisation of areas as ‘urban’ by government 
statistical departments is generally based either on population

size, population density, economic base, provision of services, or some combination of the above. Urban systems are networks and nodes of intensive interaction and exchange including capital, culture, and material objects. Urban areas exist on a continuum with rural areas and tend to exhibit higher levels of complexity, higher populations and population density, intensity of capital investment, and a preponderance of secondary (processing) and tertiary (service) sector industries. The extent and intensity of these features varies significantly within and between urban areas. Urban places and systems are open with much movement and exchange between more rural areas as well as other urban regions. Urban areas can be globally interconnected facilitating rapid flows between them – of capital investment, of ideas and culture, human migration, and disease. See also Cities, Peri-urban areas, and Urbanisation.
 The relative warmth of a  city compared with

use, the configuration and design of the built environment, including street layout and building size, the heat-absorbing properties of urban building materials, reduced ventilation, reduced greenery and water features, and domestic and industrial heat emissions generated directly from human activities. See also City region,  Urban, and Urban System.
Urban Systems
 Urban systems refer to two interconnected systems:  
first, the comprehensive collections of city elements with multiple dimensions and characteristics: a) encompass physical, built, socio-economic-technical, political, and ecological subsystems; b) integrate social agent/constituency/processes with physical structure and processes; and c) exist within broader spatial and temporal scales and governance and institutional contexts; and second, the global system of cities and towns. See also City region, and Urban.

Urbanisation is a  multi-dimensional process that 
transformation of formerly rural settlements or natural land into urban settlements; (ii) demographic change: a shift in the spatial distribution of a population from rural to urban areas; and (iii) infrastructure change: an increase in provision of infrastructure services including electricity, sanitation, etc. Urbanisation often includes changes in lifestyle, culture, and behaviour, and thus alters the demographic, economic, and social structure of both urban and rural areas (Stokes and Seto 2019; Seto et al. 2014; UNDESA 2018). See also Urban,  and Urban Systems.
Variable renewable energy (VRE)
 See Renewable energy.
Vulnerability  The propensity or predisposition to be adversely

affected. Vulnerability encompasses a  variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt. See also Exposure, Hazard and Risk.
 A state of existence that fulfils v arious human needs, 
relationships and quality of life, as well as the ability to pursue one’s goals, to thrive, and feel satisfied with one’s life. Ecosystem well-being refers to the ability of ecosystems to maintain their diversity and quality.

194
Hedonic 
heating of the Earth’s surface. The wind’s kinetic energy is converted 
 See Climate change commitment.

1 T he three Special Reports are: Global Warming of 1.5°C: an IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission 
pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (2018); Climate Change and Land: an

IPCC Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (2019); IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).pathways up to 2100 {3}. Collaboration between the IPCC Working 
Groups is reflected in Cross-Working Group boxes which address topics such as the economic benefits from avoided impacts along mitigation pathways {Cross-Working Group Box  1 in Chapter  3}, climate change and urban areas {Cross-Working Group Box  2 in Chapter  8}, mitigation and adaptation through the bioeconomy {Cross-Working Group Box  3 in Chapter  12} and Solar Radiation Modification (SRM) {Cross-Working Group Box  4 in Chapter  14}. This assessment also gives greater attention than AR5 to social, economic and environmental dimensions of mitigation actions, and institutional, legal and financial aspects. {5, 13, 14, 15}

The report draws from literature on broad and diverse analytic 
•
TS Section 
2 (TS.2) sets out how the global context for mitigation
has changed and summarises signs of progress and continuing
challenges. 
•TS Section 
3 (TS.3) evaluates emission trends and drivers including

recent sectoral, financial, technological and policy developments. 
4 (TS.4) identifies mitigation and development
pathways in the near and mid-term to 2050, and in the longer termto 2100. This section includes an assessment of how mitigationpathways deploying different portfolios of mitigation responsesare consistent with limiting global warming to different levels. 
•
TS Section 
5 (TS.5) summarises recent advances in knowledge
across sectors and systems including energy, urban and othersettlements, transport, buildings, industry, and agriculture,forestry and other land-use (AFOLU).
•
TS Section 
6 (TS.6) examines how enabling conditions including
behaviour and lifestyle, policy, governance and institutionalcapacity, international cooperation, finance, and innovationand technology can accelerate mitigation in the context of
•
TS Section 
7 (TS.7) evaluates how mitigation can be achieved

in the context of sustainable development, while maximisingco-benefits and minimising risks.

54
confidence in findings, and cross-references to Technical Summary 
sections, figures and tables are shown in ( ) brackets.2 References to 

as not (33–66%); unlikely (0–33%); very unlikely (0–10%); exceptionally unlikely (0–1%). Additional terms may also be used when appropriate, consistent with the IPCC uncertainty guidance: 
https://www.ipcc.ch/site/assets/uploads/2018/05/uncertainty-guidance-note.pdf .

55TS
changes that have emerged include the specific objectives 
established in the Paris Agreement of 2015 (for temperature, 
adaptation and finance), rising climate impacts, and higher 
levels of societal awareness and support for climate action 
(high confidence). Meeting the long-term temperature goal in the 
Paris Agreement, however, implies a rapid inflection in GHG emission 
trends and accelerating decline towards ‘net zero’. This is implausible 
without urgent and ambitious action at all scales. {1.2, 1.3, 1.5, 1.6, 
Chapters 3 and 4}
Effective and equitable climate policies are largely compatible 
with the broader goal of sustainable development and efforts 
to eradicate poverty as enshrined in the UN 2030 Agenda for 
Sustainable Development and its 17 Sustainable Development

Goals (SDGs), notwithstanding trade-offs in some cases 
(high  confidence). Taking urgent action to combat climate 
change and its impacts is one of the 17 SDGs (SDG 13). However, 
climate change mitigation also has synergies and/or trade-offs with 
many other SDGs. There has been a  strong relationship between 
development and GHG emissions, as historically both per-capita 
and absolute emissions have risen with industrialisation. However, 
recent evidence shows countries can grow their economies while 
reducing emissions. Countries have different priorities in achieving 
the SDGs and reducing emissions as informed by their respective 
national conditions and capabilities. Given the differences in GHG 
emissions contributions, degree of vulnerability and impacts, as well 
as capacities within and between nations, equity and justice are 
important considerations for effective climate policy and for securing 
national and international support for deep decarbonisation.

Achieving sustainable development and eradicating poverty would 
involve effective and equitable climate policies at all levels from local 
to global scale. Failure to address questions of equity and justice 
over time can undermine social cohesion and stability. International 
cooperation can enhance efforts to achieve ambitious global climate 
mitigation in the context of sustainable development. Pathways 
that illustrate movement towards fulfilling the SDGs are shown in 
Figure TS.1. {1.4, 1.6, Chapters 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13 and 17}
a  wide range of closely intertwined drivers and constraints, 
opportunities for deep decarbonisation, and for alternative 
development pathways which could deliver multiple social 
and developmental goals (high confidence). Drivers for, and

constraints on, low-carbon societal transitions comprise economic 
and technological factors (the means by which services such as 
food, heating and shelter are provided and for whom, the emissions 
intensity of traded products, finance and investment), socio-political 
issues (political economy, equity and fairness, social innovation 
and behaviour change), and institutional factors (legal framework and 
institutions, and the quality of international cooperation). In addition 
to being deeply intertwined, all the factors matter to varying degrees, depending on the prevailing social, economic, cultural and political 
context. They often both drive and inhibit transitions at the same time, 
within and across different scales. The development and deployment 
of innovative technologies and systems at scale are important for 
achieving deep decarbonisation, and in recent years, the cost of 
several low-carbon technologies has declined sharply as deployment

has risen rapidly. (Figure TS.7) {1.3, 1.4, Chapters 2, 4, 5, 13,14} 
interference with the climate system will require the integration 
of broadened assessment frameworks and tools that combine 
multiple perspectives, applied in a  context of multi-level 
governance (high confidence).  Analysing a challenge on the scale 
of fully decarbonising our economies entails integration of multiple 
analytic frameworks. Approaches to risk assessment and resilience, 
established across IPCC Working Groups, are complemented by 
frameworks for probing the challenges in implementing mitigation. 
Aggregate frameworks include cost-effectiveness analysis towards 
given objectives, and cost-benefit analysis, both of which have been 
developing to take fuller account of advances in understanding risks 
and innovation, the dynamics of sectors and systems and of climate 
impacts, and welfare economic theory including growing consensus

on long-term discounting. Ethical frameworks consider the fairness 
of processes and outcomes which can help ameliorate distributional 
impacts across income groups, countries and generations. Transition 
and transformation frameworks explain and evaluate the dynamics of 
levels. Psychological, behavioural and political frameworks outline the 
constraints (and opportunities) arising from human psychology and 
the power of incumbent interests. A comprehensive understanding 
of climate mitigation must combine these multiple frameworks. 
Together with established risk frameworks, these collectively help 
to explain potential synergies and trade-offs in mitigation, implying 
a need for a wide portfolio of policies attuned to different actors and 
levels of decision-making, and underpin ‘just transition’ strategies in 
diverse contexts. {1.2.2, 1.7, 1.8, Figure 1.7}

determined by choices in the environmental, technological, 
economic, socio-cultural and institutional realms (high 
confidence).  Transitions in specific systems can be gradual or can 
be rapid and disruptive. The pace of a  transition can be impeded 
social norms. The interaction between politics, economics and power 
relationships is central to explaining why broad commitments do not 
always translate to urgent action. At the same time, attention to, and 
support for, climate policies and low-carbon societal transitions has 
generally increased, as the impacts have become more salient. Both 
public and private financing and financial structures strongly affect 
the scale and balance of high- and low-carbon investments. Societal 
and behavioural norms, regulations and institutions are essential

conditions to accelerate low-carbon transitions in multiple sectors, 
whilst addressing distributional concerns endemic to any major 
transition. The COVID-19 pandemic has also had far-reaching impacts 
on the global economic and social system, and recovery will present 
both challenges and opportunities for climate mitigation. (Box TS.1) 
{1.3, Box 1.1, 1.4, 1.8, Chapters 2, 3, 4, 5, 15, 17}

Paris Agreement (and SDG 13) involve global average per-capita GHG emissions below about 5 tCO 2-eq by 2030. Likewise, to fulfil SDGs 3, 4 and 8, HIHD levels (see footnote 7 
in Chapter 1) need to be at least 0.5 or greater. This suggests a ‘sustainable development zone’ for year 2030 (in pale brown); the in-figure text also suggests a ‘sustainable

development corridor’, where countries limit per-capita GHG emissions while improving levels of human development over time. The emphasis of pathways into the sustainable 

57TS
scales of governance including local, sub-national, national and 
global levels (high confidence). Accelerating mitigation globally 
would imply strengthening policies adopted to date, expanding 
the effort across options, sectors, and countries, and broadening 
responses to include more diverse actors and societal processes at 
multiple – including international – levels. The effective governance 
of climate change entails strong action across multiple jurisdictions 
and decision-making levels, including regular evaluation and learning. 
Choices that cause climate change as well as the processes for making and implementing relevant decisions involve a range of non-nation 
state actors such as cities, businesses, and civil society organisations.

At global, national and sub-national levels, climate change actions are 
interwoven with, and embedded in, the context of much broader social, 
economic and political goals. Therefore, the governance required to 
address climate change has to navigate power, political, economic, 
and social dynamics at all levels of decision-making. Effective climate-
governing institutions, and openness to experimentation on a variety 
of institutional arrangements, policies and programmes can play 
a  vital role in engaging stakeholders and building momentum for 
effective climate action. {1.4, 1.9, Chapters 8, 13, 15, 17} 
2.1% yr –1 between 2000 and 2009, to 1.3% yr –1 in between 2010 and 2019. (TS.3) {2.2}GHG emissions have continued to grow at high absolute rates. Emissions increased

by 8.9 GtCO 2-eq from 2000 to 2009 and by 6.5 GtCO 2-eq from 2010 to 2019, reaching 
A growing number of countries have reduced both territorial carbon dioxide 
(CO 2) and GHG emissions and consumption-based CO 2 emissions in absolute 
terms for at least 10 years. These include mainly European countries, some of which 
have reduced production-based GHG emissions by a third or more since peaking. Some 
countries have achieved several years of rapid sustained CO 2 reduction rates of 4% yr –1. 
(TS.3) {2.2}The combined emissions reductions achieved by some countries have been 
outweighed by rapid emissions growth elsewhere, particularly among developing 
countries that have grown from a much lower base of per-capita emissions. Uncertainties 
in emissions levels and changes over time prevents a precise assessment of reductions 
in some cases. The per-capita emissions of developed countries remain high, particularly

in Australia, Canada, and the United States of America. {2.2}
Lockdown policies in response to COVID-19 led to an estimated global drop of 
5.8% in CO 2 emissions in 2020 relative to 2019. Energy demand reduction occurred 
across sectors, except in residential buildings due to teleworking and homeschooling. 
The transport sector was particularly impacted and international aviation emissions 
declined by 45%. (Box TS.1) {2.2}Atmospheric CO 2 concentrations continued to rise in 2020 and emissions have 
already rebounded as lockdown policies are eased. Economic recovery packages 
currently include support for fossil fuel industries. (Boxes TS.1 and TS.8)
Sectors
Multiple low-carbon electricity generation and storage technologies have made 
rapid progress: costs have reduced, deployment has scaled up, and performance 
has improved. These include solar photovoltaics (PV), onshore and offshore wind, and 
batteries. In many contexts solar PV and onshore wind power are now competitive with

fossil-based generation. (TS.3) {2.5, 6.3}Although deployment is increasing rapidly, low-carbon electricity generation 
deployment levels and rates are currently insufficient to meet stringent climate 
goals. The combined market share of solar PV and wind generation technologies are still 
below 10%. Global low-carbon electricity generation will have to reach 100% by 2050, 
which is challenged by the continuous global increase in electricity demand. The contribution 
of biomass has absolute limits. (TS.5) {2.5} 
The rate of emissions growth from coal slowed since 2010 as coal power plants 
were retired in the US and Europe, fewer new plants were added in China, and a large 
number of planned global plants were scrapped or converted to co-firing with biomass. 
(TS.3) {2.7, 6.3}Global coal emissions may not have peaked yet, and a few countries and international 
development banks continue to fund and develop new coal capacity, especially abroad.

The lifetime emissions of current fossil-based energy infrastructures may already exceed 
the remaining carbon budget for keeping warming below 1.5°C. (TS.3) {2.2, 2.7, 6.7}
Deforestation has declined since 2010 and net forest cover increased. 
Government initiatives and international moratoria were successful in reducing 
deforestation in the Amazon between 2004 and 2015, while regrowth and regeneration 
occurred in Europe, Eurasia and North America. (TS.5.6.1) {7.3.1}The long-term maintenance of low deforestation rates is challenging. Deforestation 
in the Amazon has risen again over the past four years. Other parts of the world also face 
steady, or rapidly increasing, deforestation. {7.3.1} 
Electrification of public transport services is demonstrated as a feasible, 
scalable and affordable mitigation option to decarbonise mass transportation. 
Electric vehicles (e-vehicles) are the fastest growing segment of the automobile industry,

having achieved double-digit market share by 2020 in many countries. When charged 
with low-carbon electricity, these vehicles can significantly reduce emissions. {10.4}Transport emissions have remained roughly constant, growing at an average 
of 2% yr –1 between 2010 and 2019 due to the persistence of high travel demand, heavier 
vehicles, low efficiencies, and car-centric development. The full decarbonisation of e-vehicles 
requires that they are charged with zero-carbon electricity, and that car production, shipping, 
aviation and supply chains are decarbonised. (TS.3) {2.4} 
There has been a significant global transition from coal and biomass use in 
buildings towards modern energy carriers and efficient conversion technologies.  
This led to efficiency improvements and some emissions reductions in developed 
countries, as well as significant gains in health and well-being outcomes in developing 
regions. Nearly zero energy buildings (nZEB) or low-energy buildings are achievable

in all regions and climate zones for both new and existing buildings. {9.3, 9.8}There is a significant lock-in risk in all regions given the long lifespans 
of buildings and the low ambition of building policies. This is the case for both existing 
buildings in developed countries, and also for new buildings in developing countries that 
are also challenged by the lack of technical capacity and effective governance. Emissions 
reductions in developed countries have been outweighed by the increase in population 
growth, floor area per capita and the demand for electricity and heat. {9.3, 9.9}
The decarbonisation of most industrial processes has been demonstrated 
using technologies that include electricity and hydrogen for energy and 
feedstocks, carbon capture and utilisation technologies, and innovation 
in circular material flows. (TS.5.5) {11.2}Industry emissions continue to increase, driven by a strong global demand for

basic materials. Without reductions in material demand growth and a very rapid scale-up 
of low-carbon innovations, the long lifetimes of industrial capital stock risks locking-in 
emissions for decades to come. (TS.5.5) {11.2}

58
stringent climate goals, while avoiding many areas of deadlock that had arisen 
in trying to extend the Kyoto Protocol. (TS.6.3)Current national pledges under the Paris Agreement3 are insufficient to 
limit warming to 1.5°C (>50%) with no or limited overshoot, and would require 
an abrupt acceleration of mitigation efforts after 2030 to limit warming 
to 2°C (>67%). (TS.6.3)
Most wealthy countries, and a growing list of developing countries, have 
signalled an intention to achieve net zero GHG (or net zero CO 2) emissions by 
mid-century. National economy-wide GHG emissions targets covered 90% of global 
emissions in 2020 compared to 49% in 2010. Direct and indirect climate legislation 
has also steadily increased and this is supported by a growing list of financial

investors. (TS.6.2)Many net-zero targets are ambiguously defined, and the policies needed to achieve 
them are not yet in place. Opposition from status quo interests, as well as insufficient 
low-carbon financial flows, act as barriers to establishing and implementing stringent 
climate policies covering all sectors. (Box TS.6) {13.4}
The global coverage of mandatory policies – pricing and regulation – has 
increased, and sectoral coverage of mitigation policies has expanded. Emission 
trading and carbon taxes now cover over 20% of global CO 2 emissions. Allowance prices 
as of 1 April 2021 ranged from just over USD1 to USD50, covering between 9% and 
80% of a jurisdiction’s emissions {13.6.3}. Many countries have introduced sectoral 
regulations that block new investment in fossil fuel technologies. (TS.6)There is incomplete global policy coverage of non-CO 2 gases, CO 2 from industrial 
processes, and emissions outside the energy sector. Few of the world’s carbon prices

are at a level consistent with various estimates of the carbon price needed to limit warming 
to 2°C or 1.5°C. {13.6}
There has been a marked increase in civic and private engagement with climate 
governance. This includes business measures to limit emissions, invest in reforestation 
and develop carbon-neutral value chains such as using wood for construction. There 
is an upsurge in climate activism, and growing engagement of groups such as labour 
unions {1.3.3, 5.2.3}. The media coverage of climate change has also grown steadily 
across platforms and has generally become more accurate over time. (TS.6.2)There is no conclusive evidence that an increase in engagement results in 
overall pro-mitigation outcomes. A broad group of actors influence how climate 
governance develops over time, including a range of civic organisations, encompassing 
both pro-and anti-climate action groups. Accurate transference of the climate science

has been undermined significantly by climate change counter-movements, in both legacy 
and new/social media environments through misinformation. (TS.6.2)
reflected in studies published up to 11 October 2021. Revised NDCs submitted or announced after 11 October 2021 are not included. Intended Nationally Determined Contributions (INDCs) were 
converted to NDCs as countries ratified the Paris Agreement. Original INDCs and NDCs refer to those submitted to the UNFCCC in 2015 and 2016.GHG emissions continued to rise to 2019, although the growth 
of global GHG emissions has slowed over the past decade 
(high confidence). Delivering the updated Nationally Determined 
Contributions (NDCs) to 2030 would turn this into decline, but the 
implied global emissions by 2030, still exceed pathways consistent

with 1.5°C by a large margin and are near the upper end of the range 
of modelled pathways that limit warming to 2°C (>67%) or below. 
In all chapters of this report there is evidence of progress towards 
deeper mitigation, but there remain many obstacles to be overcome. 
Table TS.1 summarises some of the key signs of progress in emission 
trends, sectors, policies and investment, as well as the challenges 
that persist. Table TS.1 (continued):

59TS
2010–2019 were higher than any previous time in human 
history (high confidence).  Since 2010, GHG emissions have 
continued to grow reaching 59 ± 6.6 GtCO 2-eq in 2019,4 but the 
average annual growth in the last decade (1.3%, 2010–2019) 
was lower than in the previous decade (2.1%, 2000–2009) (high 
confidence). Average annual GHG emissions were 56 GtCO 2-eq yr –1 
for 2010–2019 (the highest decadal average on record) growing by 
about 9.1 GtCO 2-eq yr –1 from the previous decade (2000–2009) (high 

literature on climate change mitigation and is required for reporting emissions under the United Nations Framework Convention on Climate Change (UNFCCC). All metrics have limitations and 
uncertainties. {Cross-Chapter Box 2, Annex II.II.8}
5 In 2019, CO 2 from fossil fuel and industry (FFI) was 38 ± 3.0 Gt; CO 2 from net land use, land-use change and forestry (LULUCF) was 6.6 ± 4.6 Gt.
groups of greenhouse gases (high confidence). The average 
annual emission levels of the last decade (2010–2019) were higher 
than in any previous decade for each group of greenhouse gases (high 
confidence). In 2019, CO 2 emissions were 45 ± 5.5 GtCO 2,5 methane 

38Gt
+0.7% yr  –1
+2.1% yr  –1
2019 
emissions 
increase

in 2019, 
relative 
to 1990 (%)
0
40021%
13%
59%2%
5%
20%
12%
61%2%
5%
18%
10%
65%2%
4%
18%
11%
64%1%
5%

gas is shown for 1990, 2000, 2010 and 2019; as well as the aggregate average annual growth rate between these decades. At the right side of Panel a, GHG emissions in 
2019 are broken down into individual components with the associated uncertainties (90% confidence interval) indicated by the error bars: CO 2-FFI ±8%; CO 2-LULUCF ±70%; 

F-gas emissions individually for the period 1990–2019, normalised relative to 100 in 1990. Note the different scale for the included F-gas emissions compared to other gases, 
highlighting its rapid growth from a low base. Shaded areas indicate the uncertainty range. Uncertainty ranges as shown here are specific for individual groups of greenhouse 
gases and cannot be compared. The table shows the central estimate for: absolute emissions in 2019; the absolute change in emissions between 1990 and 2019; and emissions 
in 2019 expressed as a percentage of 1990 emissions. {2.2, Figure 2.5, Supplementary Material 2.2, Figure TS.2}

60
detection of statistically significant trends. F-gases excluded 
from GHG emissions inventories such as chlorofluorocarbons and 
hydrochlorofluorocarbons are about the same size as those included 
(high confidence). (Figure TS.2) {2.2.1, 2.2.2, Table 2.1, Figures 2.2, 
2.3 and 2.5}
8 EDGAR: Emissions Database for Global Atmospheric ResearchGlobally, gross domestic product (GDP) per capita and 
population growth remained the strongest drivers of CO 2 
emissions from fossil fuel combustion in the last decade (high 
confidence). Trends since 1990 continued in the years 2010 to 2019 
with GDP per capita and population growth increasing emissions 
by 2.3% yr –1 and 1.2% yr –1, respectively. This growth outpaced the 

War {2.2.2}. While emissions and most economies rebounded in 2020, some impacts of the pandemic could last well beyond this. 
Owing to the very recent nature of this event, it remains unclear what the exact short- and long-term impacts on global emissions 
drivers, trends, macroeconomics and finance will be. 
response to the pandemic. Overall, global CO 2-FFI emissions are estimated to have declined by 5.8% (5.1–6.3%) in 2020, or about 

(Box TS.1, Figure 1). During periods of economic lockdown, daily emissions, estimated based on activity and power-generation data, 
declined substantially compared to 2019, particularly in April 2020 – as shown in Box TS.1, Figure 1 – but rebounded by the end of 
2020. Impacts were differentiated by sector, with road transport and aviation particularly affected. Different databases estimate 
the total power-sector CO 2 reduction from 2019 to 2020 at 3% (IEA7) and 4.5% (EDGAR8). Approaches that predict near real-time 
estimates of the power-sector reduction are more uncertain and estimates range more widely between 1.8%, 4.1% and 6.8%, the 
latter taking into account the over-proportional reduction of coal generation due to low gas prices and merit order effects.

of collective social change over a longer term towards less resource-intensive lifestyles, however, depends on the social mandate 
for change. This mandate can be built through public participation, discussion and debate, to produce recommendations that inform 
policymaking. {Box 5.2} 
economic growth and may squeeze financial resources for mitigation and relevant investments in the near future. Pandemic responses 
have increased sovereign debt across countries in all income bands and the sharp increase in most developing economies and regions 
has caused debt distress, widening the gap in developing countries’ access to capital. {15.6.3}

future investment decisions by the private sector {15.2.1, 15.3.1, 15.6.1}. Some countries and regions have prioritised green stimulus 
expenditures, for example, as part of a ‘Green New Deal’ {Box 13.1}. This is motivated by assessments that investing in new growth 
industries can boost the macroeconomic effectiveness (‘multipliers’) of public spending, crowd-in and revive private investment, whilst 
also delivering on mitigation commitments. {15.2.3}
and financial capacity away from efforts to accelerate climate change mitigation and shift development pathways to increased 
sustainability. Yet, studies of previous post-shock periods suggest that waves of innovation that are ready to emerge can be accelerated 
by crises, which may prompt new behaviours, weaken incumbent systems, and initiate rapid reform. {1.6.5}

enabling ‘leapfrogging’ investment {10.8}. Given the unambiguous risks of climate change, and consequent stranded asset risks from 
new fossil fuel investments {Box 6.11}, the most robust recoveries may well be those which align with lower carbon and resilient 

61TS
−1%2020 COVID-19 
pandemic
−6.3% (BP)
−6.0% (CM)
10203040
Total

past five decades. The single-year declines in emissions following major economic and geopolitical events are shown, as well as the decline recorded in five different 
datasets for emissions in 2020 compared to 2019. Panel (b) depicts the perturbation of daily carbon emissions in 2020 compared to 2019, showing the impact of 
COVID-19 lockdown policies. {Figure 2.6}
limit warming to 1.5°C (>67%) (medium confidence). 62% of 
total cumulative CO 2 emissions from 1850 to 2019 occurred since 
remaining carbon budget for keeping warming to 1.5°C with a 67% 
(50%) probability is about 400 (500) ± 220 GtCO 2 (Figure TS.3). {2.2.2, 

reductions over periods longer than 10 years – a few at rates 
that are broadly consistent with the global rates described in 
climate change mitigation scenarios that limit warming to 2°C 
(>67%) (high confidence). At least 18 countries have reduced CO 2 
and GHG emissions for longer than 10 years. Reduction rates in a few 
countries have reached 4% in some years, in line with global rates 
observed in pathways that limit warming to 2°C (>67%). However, 
the total reduction in annual GHG emissions of these countries is 
small (about 3.2 GtCO 2-eq yr –1) compared to global emissions growth observed over the last decades. Complementary evidence suggests 
that countries have decoupled territorial CO 2 emissions from GDP , but 
fewer have decoupled consumption-based emissions from GDP . 
Decoupling has mostly occurred in countries with high per-capita 
GDP and high per-capita CO 2 emissions. (Figure TS.4, Box TS.2) {2.2.3,

62
68%34%
13%
49%32%
14%
33%
18%4%
33%
1%
18%
29%
Cement
40
30
20
10
0
3000
2000
1000
0
1850–
19891990–
20092010–
20191.5°C
budget2°C
1400
± 195620
± 60410
± 30500
± 2201200
± 220

63TS
lifetimes and impacts on the climate system. GHG emission metrics provide simplified information about the effect that emissions of 
different gases have on global temperature or other aspects of climate, usually expressed relative to the effect of emitting CO 2.9 This 
The choice of metric can affect the timing and emphasis placed on reducing emissions of short-lived climate forcers (SLCFs) relative to

CO 2 within multi-gas abatement strategies as well as the costs of such strategies. Different metric choices can also alter the time at 
which net zero GHG emissions are calculated to be reached for any given emissions scenario. A wide range of GHG emission metrics 
has been published in the scientific literature, which differ in terms of: (i) the key measure of climate change they consider, (ii) whether 
they consider climate outcomes for a specified point in time or integrated over a specified time horizon, (iii) the time horizon over 
which the metric is applied, (iv) whether they apply to a single emission pulse, to emissions sustained over a period of time, or to 
a combination of both, and (v) whether they consider the climate effect from an emission compared to the absence of that emission, 
or compared to a reference emissions level or climate state. {Annex II}

Parties to the Paris Agreement decided to report aggregated emissions and removals (expressed as CO 2-eq) based on the Global Warming 
The WGIII contribution to AR6 uses updated GWP100 values from AR6 WGI to report aggregate emissions and removals unless stated 
otherwise. These reflect updated scientific understanding of the response of the climate system to emissions of different gases and 
include a  methodological update to incorporate climate-carbon cycle feedbacks associated with the emission of non-CO 2 gases

(see Annex II.II.8 for a list of GWP100 metric values). The choice of GWP100 was made inter alia for consistency with decisions under 
the Rulebook for the Paris Agreement and because it is the dominant metric used in the literature assessed by WGIII. Furthermore, for 
mitigation pathways that limit global warming to 2°C (>67%) or lower, using GWP100 to inform cost-effective abatement choices 
between gases would achieve such long-term temperature goals at close to least global cost within a few percent (high confidence).
warming at specific points in time. This is because the warming caused by an individual SLCF emission pulse is not permanent, and 
hence, unlike CO 2, the warming from successive SLCF emission pulses over multiple decades or centuries depends mostly on their

ongoing rate of emissions rather than cumulative emissions. Recently developed step/pulse metrics such as the CGTP (combined 
global temperature change potential) and GWP* (referred to as GWP-star and indicated by an asterisk) recognise that a sustained 
increase/decrease in the rate of SLCF emissions has indeed a  similar effect on global surface temperature as one-off emission/
removal of CO 2. These metrics use this relationship to calculate the CO 2 emissions or removals that would result in roughly the same 
temperature change as a sustained change in the rate of SLCF emissions (CGTP) over a given time period, or as a varying time series 
of CH 4 emissions (GWP*). IFrom a mitigation perspective, this makes these metrics well-suited in principle to estimate the effect on the 
remaining carbon budget from more, or less, ambitious SLCF mitigation over multiple decades compared to a given reference scenario

(high confidence). However, potential application in wider climate policy (e.g., to inform equitable and ambitious emission targets or 
to support sector-specific mitigation policies) is contested and relevant literature still limited.
emission metrics in reporting of emissions and removals and accounting for Parties’ NDCs.Figure TS.4 (continued): Emissions have grown in most regions, although some countries have achieved sustained emission reductions in line with 
2°C scenarios. Change in regional GHG emissions and rates of change compatible with warming targets. Panel (a): Regional GHG emission trends (in GtCO 2-eq yr –1

short horizontal lines depict the average change by region. Also shown are global rates of reduction over the period 2020–2040 in scenarios assessed in AR6 that limit global 
warming to 1.5°C and 2°C with different probabilities. The 5–95th percentile range of emissions changes for scenarios below 1.5°C with no or limited overshoot (scenario 
category C1) and scenarios below 2°C (>67%) with immediate action (scenario category C3a) are shown as a shaded area with a horizontal line at the mean value. Panel b 
excludes CO 2 LULUCF due to a lack of consistent historical national data, and International Shipping and Aviation, which cannot be allocated to regions. Global rates of reduction

64
to past and future GHG emissions. For this reason, the WGIII contribution to the AR6 reports emissions and mitigation options for 
individual gases where possible; CO 2-equivalent emissions are reported in addition to individual gas emissions where this is judged to 
be policy-relevant. This approach aims to reduce the ambiguity regarding actual climate outcomes over time arising from the use of 
any specific GHG emission metric. {Cross-Chapter Box 2 in Chapter 2, SM.2.3, Annex II.II.8; AR6 WGI Chapter 7.6}
Africa

Japan, 
New 
Europe, 
West
Central 
America 
and 
EastNorth 
Asia and 
Asia0 2000 4000 6000 8000

width shows the population of the region, so that the area of the rectangles refers to the total emissions for each regional. Percentages refer to overall GHG contributions to 
total global emissions in 2019. Emissions from international aviation and shipping are not included. Panel (b)  shows the share of historical net CO 2 emissions per region

Panel (c) shows population, GDP per person, emission indicators by region in 2019 for percentage GHG contributions, total GHG per person, and total GHG emissions intensity, 
together with production-based and consumption-based CO 2-FFI data, which is assessed in this report up to 2018. Consumption-based emissions are emissions released to 
the atmosphere in order to generate the goods and services consumed by a certain entity (e.g., region). Emissions from international aviation and shipping are not included. 
{1.3, Figure 1.2a, 2.2, Figure 2.10}

65TS
regions (high confidence). In Developed Countries, consumption-
based CO 2 emissions peaked at 15 GtCO 2 in 2007, declining to about 
of the current global population, has become a  major contributor 
to consumption-based CO 2 emission growth since 2000 (5.5% yr –1 
for 2000–2018); in 2015 it exceeded the Developed Countries 
region, with 16% of global population, as the largest emitter of 
consumption-based CO 2. {2.3.2, Figure 2.14}
Carbon-intensity improvements in the production of traded 
products has led to a net reduction in CO 2 emissions embodied 
in international trade (high confidence). A decrease in the carbon 
intensity of traded products has offset increased trade volumes 
between 2006 and 2016. Emissions embodied in internationally traded

products depend on the composition of the global supply chain across 
sectors and countries and the respective carbon intensity of production 
processes (emissions per unit of economic output). {2.3, 2.4} 
Developed Countries tend to be net CO 2 emission importers, 
developing to Developed Countries via global supply chains have 
decreased between 2006 and 2016. Between 2004 and 2011, 
CO 2 emissions embodied in trade between developing countries have 
Territorial emissions from developing country regions 
continue to grow, mostly driven by increased consumption 
and investment, albeit starting from a  low base of per-
capita emissions and with a  lower historic contribution  to 
cumulative emissions than developed countries (high

confidence). Average 2019 per-capita CO 2-FFI emissions in three 
between 2010 and 2019 (compared to 260% between 1990 and 
2010). In contrast, in Developed Countries emissions contracted by 
9.9% between 2010 and 2019 and by 9.6% between 1990 and 2010. 
Historically, these three developing regions together contributed 28% 
to cumulative CO 2-FFI emissions between 1850 and 2019, whereas 
Developed Countries contributed 57%, and least developed countries 
contributed 0.4%. (Figure TS.5) {2.2, Figures 2.9 and 2.10}
about 36–45% of global GHG emissions  (robust evidence, 
medium agreement). About two thirds of the top 10% live in

Developed Countries and one third in other economies. The lifestyle 
consumption emissions of the middle income and poorest citizens 
in emerging economies are between five and 50 times below 
their counterparts in high-income countries  (medium confidence). 
Increasing inequality within a country can exacerbate dilemmas of 
11 Bookkeeping models and dynamic global vegetation models.redistribution and social cohesion, and affect the willingness of the 
rich and poor to accept policies to protect the environment, and to 
accept and afford lifestyle changes that favour mitigation (medium 
confidence). {2.6.1, 2.6.2, Figure 2.29}
Globally, GHG emissions continued to rise across all sectors and 
subsectors, and most rapidly in transport and industry (high 

2010–2019 slowed compared to the previous decade in energy supply 
(from 2.3% to 1.0%) and industry (from 3.4% to 1.4%, direct emissions 
only), but remained roughly constant at about 2% yr –1 in the transport 
sector (high confidence). Emission growth in AFOLU is more uncertain 
due to the high share of CO 2-LULUCF emissions (medium confidence). 
assessing collective progress in a  global stocktake (high 
confidence).  The principal accounting approaches are national GHG

inventories (NGHGI) and global modelling11 approaches. NGHGI, 
based on IPCC guidelines, consider a much larger area of forest to be 
under human management than global models. NGHGI consider the 
fluxes due to human-induced environmental change on this area to 
be anthropogenic and are thus reported. Global models, in contrast, 
consider these fluxes to be natural and are excluded from the total 
reported anthropogenic land CO 2 flux. The accounting method used 
will affect the assessment of collective progress in a global stocktake 
(medium confidence) {Cross-Chapter Box  6 in Chapter  7}. In the 
absence of these adjustments, allowing a like-with-like comparison, 
collective progress would appear better than it is. {7.2}
Assessment Models (IAMs), with the consequence that 
anthropogenic land CO 2 fluxes reported in IAM pathways 
cannot be compared directly with those reported in national

GHG inventories (high confidence). Methodologies enabling 
a  more like-for-like comparison between models’ and countries’ 
approaches would support  more accurate assessment of the 
collective progress achieved under the Paris Agreement. {3.4, 7.2.2} 
improvements in energy efficiency and reductions in the carbon 
intensity of energy supply driven by fuel switching from coal to gas, 
reduced expansion of coal capacity, particularly in Eastern Asia, and 
the increased use of renewables (medium confidence). (Figure TS.6) 
{2.2.4, 2.4.2.1, Figure 2.17}

66
of global GHG emissions, or 66% when the emissions from 
electricity and heat production are reallocated as indirect 
emissions (high confidence). This reallocation makes a substantial 
difference to overall industry and buildings emissions as shown in 
Figure TS.6. Industry, buildings, and transport emissions are driven, 
respectively, by the large rise in demand for basic materials and 
manufactured products, a  global trend of increasing floor space 
size and weight. Between 2010 and 2019, aviation grew particularly 
fast on average at about 3.3% per annum. Globally, energy efficiency 
has improved in all three demand sectors, but carbon intensities have 
not. (Figure TS.6) {2.2.4, Figures 2.18, 2.19 and 2.20}
increase global GHG emissions by a few percent at most (high

confidence).  The additional energy demand needed to support decent 
living standards12 for all is estimated to be well below current average 
equitable income distribution could also reduce carbon emissions, 
but the nature of this relationship can vary by level of income and 
development (limited evidence,  medium agreement). {2.4.3}
studies (medium confidence). Emerging evidence since AR5 on 
past energy transitions identifies a  growing number of cases of 
accelerated technology diffusion at sub-global scales and describes

mechanisms by which future energy transitions may occur more 
quickly than those in the past. Important drivers include technology 
transfer and cooperation, international policy and financial support, 
and harnessing synergies among technologies within a sustainable 
energy system perspective (medium confidence). A fast global 
low-carbon energy transition enabled by finance to facilitate 
low-carbon technology adoption in developing and particularly in 
least developed countries can facilitate achieving climate stabilisation 
targets (high confidence). {2.5.2, Table 2.5}Direct emissions by sector (59 GtCO2-eq) 
5.6%Other energy
10%
16%
– Rail 0.4%

1.1%
1.3%
0.1%
Figure TS.6 | Total anthropogenic direct and indirect GHG emissions for the year 2019 (in GtCO 2-eq) by sector and subsector. Direct emissions estimates

More comprehensive conceptualisations of indirect emissions including all products and services (scope 3 reporting) are discussed in Section 2.3. Emissions are converted into 
CO 2-equivalents based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report. Percentages may not add up to 100 across 

67TS
the feasibility of rapid energy transitions (high confidence). The 
rapid deployment and unit cost decrease of modular technologies like 
solar, wind, and batteries have occurred much faster than anticipated 
by experts and modelled in previous mitigation scenarios, as shown 
in Figure TS.7 (high confidence). The political, economic, social, and 
technical feasibility of solar energy, wind energy and electricity 
storage technologies has improved dramatically over the past few 
years. In contrast, the adoption of nuclear energy and CO 2 capture 
and storage (CCS) in the electricity sector has been slower than 
the growth rates anticipated in stabilisation scenarios. Emerging 
evidence since AR5 indicates that small-scale technologies (e.g., solar, 
batteries) tend to improve faster and be adopted more quickly

than large-scale technologies (nuclear, CCS) (medium confidence). 
(Figure TS.7, Box TS.15) {2.5.3, 2.5.4, Figures 2.22 and 2.23}Robust incentives for investment in innovation, especially 
agreements, are central to accelerating low-carbon technological 
change (robust evidence, medium agreement). Policies have driven 
innovation, including instruments for technology push (e.g., scientific 
training, research and development (R&D)) and demand pull 
(e.g., carbon pricing, adoption subsidies), as well as those promoting 
knowledge flows and especially technology transfer. The magnitude 
of the scale-up challenge elevates the importance of rapid technology 
development and adoption. This includes ensuring participation of 
developing countries in an enhanced global flow of knowledge, skills, 
experience, equipment, and technology; which in turn requires strong

financial, institutional, and capacity-building support. {16.4, 16.5}
12001600
800
400

Figure TS.7 | The unit costs of batteries and some forms of renewable energy have fallen significantly, and their adoption continues to increase. The 
top panel shows global costs per unit of energy (USD per MWh) for some rapidly changing mitigation technologies. Solid blue lines indicate average unit cost in each year. Light 
blue shaded areas show the range between the 5th and 95th percentiles in each year. Grey shading indicates the range of unit costs for new fossil fuel (coal and gas) power in 
2020 (corresponding to USD55–148 per MWh). In 2020, the levelised costs of energy (LCOE) of the four renewable energy technologies could compete with fossil fuels in many 
places. For batteries, costs shown are for 1 kWh of battery storage capacity; for the others, costs are LCOE, which includes installation, capital, operations, and maintenance

costs per MWh of electricity produced. The literature uses LCOE because it allows consistent comparisons of cost trends across a diverse set of energy technologies to be made. 
However, it does not include the costs of grid integration or climate impacts. Further, LCOE does not take into account other environmental and social externalities that may 
modify the overall (monetary and non-monetary) costs of technologies and alter their deployment. The bottom panel shows cumulative global adoption for each technology, 
in GW of installed capacity for renewable energy and in millions of vehicles for battery-electric vehicles. A vertical dashed line is placed in 2010 to indicate the change since AR5. 
Shares of electricity produced and share of passenger vehicle fleet are indicated in text for 2020 based on provisional data, i.e., percentage of total electricity production (for

PV , onshore wind, offshore wind, CSP) and of total stock of passenger vehicles (for EVs). The electricity production share reflects different capacity factors; for example, for the 
same amount of installed capacity, wind produces about twice as much electricity as solar PV . {2.5, 6.4} Renewable energy and battery technologies were selected as illustrative 
examples because they have recently shown rapid changes in costs and adoption, and because consistent data are available. Other mitigation options assessed in the report 
are not included as they do not meet these criteria.

68
in historic patterns of use and decommissioning, estimated future 
CO 2 emissions from existing fossil fuel infrastructure alone are 
in pathways that limit warming to 2°C (>67%) (high confidence). 
While most future CO 2 emissions from existing and currently 
planned fossil fuel infrastructure are situated in the power sector, 
most remaining fossil fuel CO 2 emissions in pathways that limit

warming to 2°C (>67%) and below are from non-electric energy – 
most importantly from the industry and transportation sectors (high 
confidence). Decommissioning and reduced utilisation of existing 
fossil fuel installations in the power sector as well as cancellation of 
new installations are required to align future CO 2 emissions from the 
power sector with projections in these pathways (high confidence). 
(Figure TS.8) {2.7.2, 2.7.3, Figure 2.26, Tables 2.6 and 2.7}92
800
600
400
200
0
gas and oil
5101200
LAMEIT
Asia
97

69TS
pathways, since AR5 there has been an increasing emphasis in 
the literature on development pathways, and in particular at 
the national scale. Chapter  4 assesses near-term (2019–2030) 
to mid-term (2030–2050) pathways, complementing Chapter  3 
which focuses on long-term pathways (up to 2100). While there 
is considerable literature on country-level mitigation pathways, 
including but not limited to NDCs, the country distribution of this 
literature is very unequal (high confidence). {4.2.1, Cross-Chapter 
Box 4 in Chapter 4}TS.4.1 Mitigation and Development Pathways 
gap, despite mitigation efforts including those in Nationally 
Determined Contributions (NDCs). In this report the emissions 
gap is understood as the difference between projected global

emissions with Nationally Determined Contributions (NDCs) in 2030, 
and emissions in 2030 if mitigation pathways consistent with the 
Paris temperature goals were achieved. The term implementation gap 
refers to the gap between NDC mitigation pledges and the expected 
outcome of existing policies. 
102030
0
102030
95th
Median
5th75th
25th
Policy 
for 2030

88
Figure TS.9 | Aggregate greenhouse gas (GHG) emissions of global mitigation pathways (coloured funnels and bars) and projected emission outcomes 
from current policies and emissions implied by unconditional and conditional elements of NDCs, based on updates available by 11 October 2021 
(grey bars). Shaded areas show GHG emission medians and 25–75th percentiles over 2020–2050 for four types of pathways in the AR6 scenario database: (i) pathways 
with near-term emissions developments in line with current policies and extended with comparable ambition levels beyond 2030; (ii) pathways likely to limit warming to 
2°C with near-term emissions developments reflecting 2030 emissions implied by current NDCs followed by accelerated emissions reductions; (iii) pathways likely to limit 
warming to 2°C based on immediate actions from 2020 onwards; (iv) pathways that limit warming to 1.5°C with no or limited overshoot. Right-hand panels show two

snapshots of the 2030 and 2050 emission ranges of the pathways in detail (median, 25–75th and 5–95th percentiles). The 2030 snapshot includes the projected emissions 
from the implementation of the NDCs as assessed in Section 4.2 (Table 4.1; median and full range). Historic GHG emissions trends as used in model studies are shown for 
2010–2015. GHG emissions are in CO 2-equivalent using GWP100 values from AR6. {3.5, Table 4.1, Cross-Chapter Box 4 in Chapter 4}

70
2050, leading to a median global warming of 2.4°C to 3.5°C 
by 2100 (medium confidence). NDCs with unconditional and 
conditional elements14 lead to 53 (50–57) and 50 (47–55) GtCO 2-eq, 
respectively (medium confidence) {Table  4.1}. This leaves median 
estimated emissions gaps of 14–23 GtCO 2-eq to limit warming to 2°C 
Projected global emissions from aggregated NDCs place 
limiting global warming to 1.5°C beyond reach and make it 
harder after 2030 to limit warming to 2°C (high confidence). 
Pathways following NDCs until 2030 show a  smaller reduction 
in fossil fuel use, slower deployment of low-carbon alternatives,

and a smaller reduction in CO 2, CH 4 and overall GHG emissions in 
2030 compared to immediate action scenarios. This is followed by 
a  much faster reduction of emissions and fossil fuels after 2030, 
and a larger increase in the deployment of low-carbon alternatives 
immediate action pathways in 2050. Those pathways also deploy 
a larger amount of carbon dioxide removal (CDR) to compensate for 
higher emissions before 2030. The faster transition during 2030 to 
2050 entails greater investment in fossil fuel infrastructure and lower 
deployment of low-carbon alternatives in 2030, which adds to the 
socio-economic challenges in realising the higher transition rates. 
(TS.4.2) {3.5}
Studies evaluating up to 105 updated NDCs15 indicate that 
gaps by about one third to 2°C and about 20% to 1.5°C compared

to the original NDCs submitted in 2015/16 (medium confidence) 
{4.2.2, Cross-Chapter Box 4 in Chapter 4}. An implementation gap 
also exists between the projected emissions with ‘current policies’ 
and the projected emissions resulting from the implementation 
of the unconditional and conditional elements of NDCs; this is 
estimated to be around 4 and 7 GtCO 2-eq in 2030, respectively 
(medium confidence) {4.2.2}. Many countries would therefore require 
additional policies and associated action on climate change to meet 
their autonomously determined mitigation targets as specified 
under the first NDCs (limited evidence). The disruptions triggered 
by the COVID-19 pandemic increase uncertainty over the range 
of projections relative to pre-COVID-19 literature. As indicated by 
a growing number of studies at the national and global level, how 
large near- to mid-term emissions implications of the COVID-19 
pandemic are, to a large degree depends on how stimulus or recovery

packages are designed. {4.2}
to NDCs and current policies – could close both emission gaps 
and implementation gaps.  There is increasing understanding of the 
technical content of accelerated mitigation pathways, differentiated 
by national circumstances, with considerable, though uneven, 
literature at country-level (medium evidence, high agreement). 
Transformative technological and institutional changes for the near

term include demand reductions through efficiency and  reduced 
activity, rapid decarbonisation of the electricity sector and 
low-carbon electrification of buildings, industry and transport (robust 
evidence, medium agreement). A focus on energy use and supply is 
essential, but not sufficient on its own – the land sector and food 
systems deserve attention. The literature does not adequately include 
demand-side options and systems analysis, and captures the impact 
from non-CO 2 GHGs (medium confidence). {4.2.5} 
If obstacles to accelerated mitigation are rooted in underlying 
structural features of society, then transforming such structures 
can support emission reductions {4.2.6}. Countries and regions 
will have different starting points for transition pathways. Some critical 
differences between countries include climate conditions resulting 
in different heating and cooling needs, endowments with different 
energy resources, patterns of spatial development, and political and

economic conditions {4.2.5}. The way countries develop determines 
development objectives simultaneously (medium confidence) 
{4.3.1,  4.3.2}. Yet meeting ambitious mitigation and development 
goals cannot be achieved through incremental change (robust 
evidence, medium agreement). Though development pathways result 
from the actions of a  wide range of actors, it is possible to shift 
development pathways through policies and enhancing enabling 
conditions (limited evidence, medium agreement). 
ways to broaden the range of levers and enablers that a society 
can use to accelerate mitigation and increases the likelihood 
of making progress simultaneously on climate action and 
other development goals (Box TS.3) {Cross-Chapter Box 5 in 
Chapter  4, Figure  4.7, 4.3}.  There are practical options to shift

development pathways in ways that advance mitigation and other 
sustainable development objectives, support political feasibility, 
increase resources to meet multiple goals, and reduce emissions 
(limited evidence, high agreement). Concrete examples, assessed in 
Chapter 4 of this report, include high-employment and low-emissions 
structural change; fiscal reforms for mitigation and social contract, 
combining housing policies to deliver both housing and transport 
mitigation; and changed economic, social and spatial patterns of 
agriculture sector development, providing the basis for sustained 
reductions in emissions from deforestation. {4.4.1, 4.4, 1.10}

71TS
(>50%) with no or limited overshoot (C1 in Table TS.3; 97 scenarios), (ii) near term action following the NDCs until 2030 and returning warming to 1.5°C (> 50%) by 2100 
after a high overshoot (subset of 42 scenarios following the NDCs until 2030 in C2), (iii) immediate action to limit warming to 2°C (>67%), (C3a in Table TS.3; 204 scenarios), 
(iv) near term action following the NDCs until 2030 followed by post-2030 action to limit warming to 2°C (>67%) (C3b in Table TS.3; 97 scenarios). Also shown are the

characteristics for (v) the combined class of all scenarios that limit warming to 2°C (>67%). The groups (i), (iii), and the combination of (ii) and (iv) are depicted in Figure TS.9. 
Reported are median and interquartile ranges (in brackets) for selected global indicators. Numbers are rounded to the nearest five, with the exception of cumulative net negative 
CO 2 emissions rounded to the nearest 10. Changes from 2019 are relative to modelled 2019 values. Emissions reductions are based on harmonised model emissions used for 

in 2050 (% rel to 2019) –95 (–100,–80) –90 (–100,–85) –85 (–100,–65) –80 (–90,–70) –85 (–95,–65)
Change in primary energy from oil in 2030 
(% rel to 2019)–10 (–25,0) 5 (5,10) 0 (–10,10) 10 (5,10) 5 (0,10)
Change in primary energy from gas in 2030 
(% rel to 2019)–10 (–30,0) 15 (10,25) 10 (0,15) 15 (10,15) 10 (0,15)
Change in primary energy from nuclear in 2030 
(% rel to 2019)40 (10,70) 10 (0,25) 35 (5,50) 10 (0,30) 25 (0,45)
Change in primary energy from modern biomass 
in 2030 (% rel to 2019)75 (55,130) 45 (20,75) 60 (35,105) 45 (20,80) 55 (35,105)
Change in primary energy from non-biomass

renewables in 2030 (% rel to 2019)225 (155,270) 100 (85,145) 150 (115,190) 115 (85,130) 130 (90,170)
Change in carbon intensity of electricity in 2030 
(% rel to 2019)–75 (–80,–70) –30 (–40,–30) –60 (–70,–50) –35 (–40,–30) –50 (–65,–35)

72
actors in the national and global contexts. Each society whether in developing or developed regions follows its own pattern of growth 
(Figure TS.13). Development pathways can also be described at smaller scales (e.g., for regions or cities) and for sectoral systems.
existing development pathways will not achieve rapid and deep emission reductions. In the absence of shifts in development pathways, 
conventional mitigation policy instruments may not be able to limit global emissions to a degree sufficient to meet ambitious mitigation

goals or they may only be able to do so at very high economic and social costs. 
broadens the scope for synergies between sustainable development objectives and mitigation. Development pathways also determine 
the enablers and levers available for adaptation {AR6 WGII TS.E.1.2} and for achieving other SDGs.
can go hand in hand {Chapter 3, Figure 3.33, Chapters 6–12, and 17}. Integrated policies can support the creation of synergies 
between action to combat climate change and its impacts (SDG 13 – climate action) and other SDGs. For example, when measures 
promoting walkable urban areas are combined with electrification and clean renewable energy, there are several co-benefits to be

attained. These include reduced pressures on agricultural land from reduced urban growth, health co-benefits from cleaner air, and 
benefits from enhanced mobility {8.2, 8.4, 4.4.1}. Energy efficiency in buildings and energy poverty alleviation through improved 
access to clean fuels also deliver significant health benefits. {9.8.1 and 9.8.2}
However, decisions about mitigation actions, and their timing and scale, may entail trade-offs with the achievement of other national 
development objectives in the near, mid- and long term {Chapter 12}. In the near term, for example, regulations may ban vehicles from 
city centres to reduce congestion and local air pollution but reduce mobility and choice. Increasing green spaces within cities without 
caps on housing prices may involve trade-offs with affordable housing and push low-income residents outside the city {8.2.2}. In the

mid- and long term, large-scale deployment of biomass energy raises concerns about food security and biodiversity conservation 
{3.7.1, 3.7.5, 7.4.4, 9.8.1, 12.5.2, 12.5.3}. Prioritising is one way to manage these trade-offs, addressing some national development 
objectives earlier than others. Another way is to adopt policy packages aimed at shifting development pathways towards increased 
sustainability (SDPS) as they expand the range of tools available to simultaneously achieve multiple development objectives and 
accelerate mitigation. (Box TS.3, Figure 1) 
Shifting development pathways towards increased sustainability implies making transformative changes that disrupt existing 
developmental trends. Such choices would not be marginal, but include technological, systemic and socio-behavioural changes

{4.4}. Decision points also arise with new infrastructure, sustainable supply chains, institutional capacities for evidence-based and 
integrated decision-making, financial alignment towards low-carbon socially responsible investments, just transitions and shifts in 
behaviour and norms to support shifts away from fossil fuel consumption. Adopting multi-level governance modes, tackling corruption 
where it inhibits shifts to sustainability, and improving social and political trust are also key for aligning and supporting long-term 
environmentally just policies and processes. {4.4, Cross-Chapter Box 5 in Chapter 4} 
Shifting development paths is complex. Changes that involve ‘dissimilar, unfamiliar and more complex science-based components’ 
take more time, acceptance and legitimation and involve complex social learning, even when they promise large gains. Despite the

complexities of the interactions that result in patterns of development, history also shows that societies can influence the direction 
of development pathways based on choices made by decision-makers, citizens, the private sector, and social stakeholders. Shifts in 
development pathways result from both sustained political interventions and bottom-up changes in public opinion. Collective action 
by individuals as part of social movements or lifestyle changes underpins system change. {5.2.3, 5.4.1, 5.4.5}
Sectoral transitions that aim to shift development pathways often have multiple objectives and deploy a diverse mix of policies and 
institutional measures. Context-specific governance conditions can significantly enable or disable sectoral transitions. {Cross-Chapter 
Box 12 in Chapter 16}

73TS
and society within which they take place and may enable a new social contract to address a complex set of interlinkages across 
sectors, classes, and the whole economy. Taking advantage of windows of opportunity and disruptions to mindsets and socio-technical 
systems could advance deeper transformations. 
How can shifts in development pathways be implemented by actors in different contexts? 
Shifting development pathways to increased sustainability is a shared aspiration. Yet since countries differ in starting points (e.g., social, 
economic, cultural, political) and historical backgrounds, they have different urgent needs in terms of facilitating the economic, 
social, and environmental dimensions of sustainable development and, therefore, give different priorities {4.3.2, 17.1}. The appropriate

set of policies to shift development pathways thus depends on national circumstances and capacities. 
Shifting development pathways towards sustainability needs to be supported by multilateral partnerships to strengthen suitable 
capacity, technological innovation (TS.6.5), and financial flows (TS.6.4). The international community can play a particularly key role 
by helping ensure the necessary broad participation in climate-mitigation efforts, including by countries at different development 
levels, through sustained support for policies and partnerships that support shifting development pathways towards sustainability 
while promoting equity and being mindful of different transition capacities. {4.3, 16.5, 16.6}
Box TS.3, Figure  1 | Shifting development pathways to increased sustainability: choices by a  wide range of actors at key decision points on

Most SDGs
Many SDGs
Few SDGs
Time

74
of policies implemented in the pursuit of overall societal 
development objectives, such as job creation, macroeconomic 
stability, economic growth, and public health and welfare.  
Transition (Box TS.3), however, they can have major influence on 
mitigative capacity, and hence can be seen as tools to broaden 
mitigation options (medium confidence) {4.3.3}. Coordinated policy 
mixes would need to orchestrate multiple actors – individuals, groups 
and collectives, corporate actors, institutions and infrastructure 
actors  – to deepen decarbonisation and shift pathways towards 
sustainability. Shifts in one country may spill over to other countries. 
Shifting development pathways can jointly support mitigation and 
adaptation {4.4.2}. Some studies explore the risks of high complexity

and potential delay attached to shifting development pathways. 
(Box TS.4, Figure TS.11) {4.4.3}
(mid-term) have been developed by various actors. A growing 
number of such strategies aim at net zero GHG or CO 2 
emissions, but it is not yet possible to draw global implications 
due to the limited size of sample (medium evidence, low 
agreement) {4.2.4}.  Non-state actors are also engaging in a wide 
range of mitigation initiatives. When adding up emission reduction 
potentials, sub-national and non-state international cooperative 
initiatives could reduce emissions by up to about 20 GtCO 2-eq in 
2030 (limited evidence, medium agreement) {4.2.3}. Yet perceived or 
real conflicts between mitigation and other SDGs can impede such 
found to have significant implications for development objectives

and macroeconomic costs at country level. The literature shows that 
the employment effect of mitigation policies tends to be limited on 
aggregate but can be significant at sectoral level (limited evidence, 
medium agreement). Detailed design of mitigation policies is critical 
for distributional impacts and avoiding lock-in (high confidence), 
though further research is needed in that direction. {4.2.6}
The literature identifies a  broad set of enabling conditions 
that can both foster shifting development pathways and 
accelerated mitigation (medium evidence, high agreement). 
Policy integration is a necessary component of shifting development 
pathways, addressing multiple objectives. To this aim, mobilising 
a range of policies is preferable to single policy instruments (high 
confidence). {4.4.1}. Governance for climate mitigation and shifting 
development pathways is enhanced when tailored to national and 
local contexts. Improved institutions and effective governance enable

ambitious action on climate and can help bridge implementation 
institutions may be a long-term endeavour, it needs attention in the 
near term {4.4.1}. Accelerated mitigation and shifting development 
high- to low-emissions technologies and systems, and providing 
additional resources to overcome current financial barriers (high 
confidence) {4.4.1}. Opportunities exist in the near term to close the 
finance gap {15.2.2}. At the national level, public finance for actions 
promoting sustainable development helps broaden the scope of 
mitigation (medium confidence). Changes in behaviour and lifestyles are important to move beyond mitigation as incremental change, and 
when supporting shifts to more sustainable development pathways 
will broaden the scope of mitigation (medium confidence). {4.4.1, 
Figure 4.8}

while some others may take time to establish underscoring 
the importance of early action (high confidence). Depending on 
context, some enabling conditions such as promoting innovation may 
take time to establish. Other enabling conditions, such as improved 
access to financing, can be put in place in a  relatively short time 
frame, and can yield rapid results {4.4, Figure 5.14, 13.9, 14.5, 15.6, 
16.3, 16.4, 16.5, Cross-Chapter Box 12 in Chapter 16}. Focusing on 
development pathways and considering how to shift them may also 
yield rapid results by providing tools to accelerate mitigation and 
achieve other sustainable development goals {4.4.1}. Charting just 
can help achieve (Boxes TS.4 and TS.8). 
Equity can be an important enabler, increasing the level of 
ambition for accelerated mitigation (high confidence) {4.5}.

Equity deals with the distribution of costs and benefits and how these 
are shared, as per social contracts, national policy and international 
agreements. Transition pathways have distributional consequences 
such as large changes in employment and economic structure (high 
confidence). The Just Transition concept has become an international 
focal point tying together social movements, trade unions, and other 
key stakeholders to ensure equity is better accounted for in low-carbon 
transitions (Box TS.4). The effectiveness of cooperative action and the 
perception of fairness of such arrangements are closely related in 
that pathways that prioritise equity and allow broad stakeholder 
participation can enable broader consensus for the transformational 
change implicit in the need for deeper mitigation (robust evidence, 
medium agreement). (Box TS.4) {4.5, Figure 4.9}

75TS
sectors, countries or regions are left behind in the move from a high-carbon to a low-carbon economy. It includes respect and dignity 
for vulnerable groups; creation of decent jobs; social protection; employment rights; fairness in energy access and use and social 
dialogue and democratic consultation with relevant stakeholders. 
in job losses from highly polluting industries. Traced from a purely labour movement, trade union space, the Just Transition framework 
emphasises that decent work and environmental protection are not incompatible. During COP 24, with the Just Transition Silesia

Declaration, the concept gained in recognition and was signed by 56 heads of state.
and deliberate effort may be required to ensure communities dependent on fossil-fuel based economies and industries do not suffer 
disproportionately {Chapter 4}. ‘Just Transitions’ are integral to the European Union as mentioned in the EU Green Deal, the Scottish 
Government’s development plans and other national low-carbon transition strategies. The US Green New Deal Resolution puts 
structural inequality, poverty mitigation, and ‘Just Transitions’ at its centre. There is a growing awareness of the need for shifting 
finance towards Just Transition in the context of COVID-19, in particular, public finance and governance have a major role in allowing 
a Just Transition more broadly {Chapter 15}.

In the immediate aftermath of the COVID-19 pandemic, low oil prices created additional financial problems for fossil fuel producer 
countries faced with loss of revenue and reduced fiscal latitude and space. Public spending and social safety nets associated with the 
proceeds from producer economies can be affected as assets become stranded and spending on strategic sustainable development 
goals such as free education and health-care services are neglected. Fiscal challenges are intricately linked to ‘Just Transitions’ and 
the management associated with sustainable energy transition. There is no certainty on how energy systems will recover post-
COVID-19. However, ‘Just Transitions’ will have equity implications if stimulus packages are implemented without due regard for the 
differentiated scales and speeds and national and regional contexts, especially in the context of developing countries.

A Just Transition entails targeted and proactive measures from governments, agencies, and other non-state authorities to ensure that 
any negative social, environmental, or economic impacts of economy-wide transitions are minimised, whilst benefits are maximised 
for those disproportionally affected. These proactive measures include eradication of poverty, regulating prosperity and creating jobs 
in ‘green’ sectors. In addition, governments, polluting industries, corporations, and those more able to pay higher associated taxes, 
can pay for transition costs by providing a welfare safety net and adequate compensation to people, communities, and regions that 
have been impacted by pollution, or are marginalised, or are negatively impacted by a transition from a high- to low-carbon economy 
and society. There is, nonetheless, increased recognition that resources that can enable the transition, international development

institutions, as well as other transitional drivers such as tools, strategies and finance, are scarce. A sample of global efforts is 
summarised in Box TS.4, Figure 1.

76
Workers
programme
Year Plan for Coal Industry

Revitalisation Plan 
2018–2050
Society
CA
US
CR
SACH
AUS
PO
GRIT
GHUKIE
Authority

77TS
pathways, their common elements and differences are 
assessed in Chapter 3. Differences between pathways typically 
represent choices that can steer the system in alternative 
directions through the selection of different combinations of 
response options (high confidence). More than 2000 quantitative 
emissions pathways were submitted to the AR6 scenarios database, 
of which more than 1200 pathways included sufficient information 
for the associated warming to be assessed (consistent with AR6 WGI 
methods). (Box TS.5) {3.2, 3.3}
warming to 2°C (>67%) with no overshoot or to limit 
warming to 1.5°C (>50%) with limited overshoot compared to 
1850–1900. The likelihood of limiting warming to 1.5°C with 
no or limited overshoot has dropped in AR6 WGIII compared

to AR6 SR1.5 because global GHG emissions have risen since 
2017, leading to higher near-term emissions (2030) and higher 
cumulative CO 2 emissions until the time of net zero (medium 
confidence). Only a  small number of published pathways limit global warming to 1.5°C without overshoot over the course of the 
21st century. {3.3, Annex III.II.3}
limited overshoot reach 50% CO 2 reductions in the 2030s, 
relative to 2019, then reduce emissions further to reach net 
zero CO 2 emissions in the 2050s. Pathways limiting warming 
to 2°C (>67%) reach 50% reductions in the 2040s and net 
zero CO 2 by the 2070s (medium confidence). (Figure TS.10, 
action to limit warming to 2°C (>67%) are associated with 
net global GHG emissions of 30–49 GtCO 2-eq yr –1 by 2030 
and 14–27 GtCO 2-eq yr –1 by 2050 (medium confidence). This

corresponds to reductions, relative to 2019 levels, of 13–45% by 2030 
and 52–76% by 2050. Pathways that limit global warming to below 
1.5°C with no or limited overshoot require a further acceleration in 
the pace of transformation, with net GHG emissions typically around 
21–36 GtCO 2-eq yr –1 by 2030 and 1–15 GtCO 2-eq yr –1 by 2050; this 
corresponds to reductions of 34–60% by 2030 and 73–98% by 2050 
relative to 2019 levels. {3.3}
The over 2500 model-based pathways submitted to the AR6 scenarios database pathways explore different possible evolutions of 
future energy and land use (with and without climate policy) and the consequences for greenhouse gas emissions. 

were selected to illustrate key mitigation-strategy themes that flow through several chapters in this report. A further two pathways 
illustrative of high emissions assuming continuation of current policies or moderately increased action were selected to show the 
consequences of current policies and pledges. Together these pathways provide illustrations of potential future developments that 
can be shaped by human choices, including: Where are current policies and pledges leading us? What is needed to reach specific 
temperature goals? What are the consequences of using different strategies to meet these goals? What are the consequences of 
delay? How can we shift development from current practices to give higher priority to sustainability and the SDGs? 

numerically. The five Illustrative Mitigation Pathways (IMPs) each emphasise a different scenario element as its defining feature, and 
are named accordingly: heavy reliance on renewables (IMP-Ren), strong emphasis on low demand for energy (IMP-LD), extensive use 
of carbon dioxide removal (CDR) in the energy and the industry sectors to achieve net negative emissions (IMP-Neg), mitigation in the 
context of broader sustainable development and shifting development pathways (IMP-SP), and the implications of a less rapid and 
gradual strengthening of near-term mitigation actions (IMP-GS). In some cases, sectoral chapters may use different quantifications 
that follow the same storyline narrative but contain data that better exemplify the chapter’s assessment. Some IMP variants are also

used to explore the sensitivity around alternative temperature goals. {3.2, 3.3}
The two additional pathways illustrative of higher emissions are current policies (CurPol) and moderate action (ModAct). 
This framework is summarised in Box TS.5, Table.1 below, which also shows where the IMPs are situated with respect to the classification 

(middle of the road), SSP3 (regional rivalry), SSP4 (inequality) and SSP5 (rapid growth). The SSPs were subsequently quantified in terms 
of energy, land-use change, and emission pathways for both (i) no-climate-policy reference scenarios and (ii) mitigation scenarios that 
follow similar radiative forcing pathways as the representative concentration pathways (RCPs) assessed in AR5 WGI. {3.2.3}
Most of the scenarios in the AR6 database are SSP-based. The majority of the assessed scenarios are consistent with SSP2. Using 
the SSPs permits a more systematic assessment of future GHG emissions and their uncertainties than was possible in AR5. The main 
emissions drivers across the SSPs include growth in population reaching 8.5–9.7 billion by 2050, and an increase in global GDP of

2.7–4.1% per year between 2015 and 2050. Final energy demand in the absence of any new climate policies is projected to grow to 
around 480 to 750 EJ yr –1 in 2050 (compared to around 390 EJ yr –1 in 2015) (medium confidence). The highest emissions scenarios in 
the literature result in global warming of >5°C by 2100, based on assumptions of rapid economic growth and pervasive climate policy 
failures (high confidence). {3.3}

likelihood definitions are provided in SPM Box 1.  
square brackets. Percentage of

brackets. 
between 
the year of 
results in 
greater 
declines 
for the median value

[21–36]17
[6–23]9
[1–15]43
[34–60]69
[58–90]84
[73–98]
(100%)
(52%)
[–210 to 
570]–220
[33–58]90
[99–100]
SP
LD33
[22–37]18
[6–24]8
[0–15]41
[31–59]66
[58–89]85
(100%)
[–220 to 
620]–360
[–680 to 
–140]1.6
[34–60]90
[99–100]
GHGsRen29
[21–36]16
[7–21]9
[4–13]48
[35–61]70
[62–87]84
[33–56]89

[31–55]25
[17–34]14
[5–21]23
[0–44]55
[40–71]75
(100%)
(87%)
[–90 to 
620]–360
[15–42]82
[32–55]29
[20–36]20
[13–26]21
[1–42]46
[34–63]64
(93%)
[13–41]76
[68–91]99
[30–49]29
[21–36]20
[14–27]27
[13–45]47
[35–63]63
(91%)
[14–42]78
[98–100]

80
p50  
from 2019  
CO 2 
emissions 
& WGIII 
IPs/IMPs 
emissions  
(% peak 
emissions  
(% peak 
(% net zero 
GHGs  
(% net zero 
net zero 

(97%)
[2440–
3520]
does not 
peak by 
21002.7
[0–0]8
[2–18]71
[53–88]
CurPol62
[53–69]67
[56–76]70
[–41 to 
(56%)4220
[3160–
5000]3.5
[0–0]0
[0–2]22
[69–81]80
[78–96]88
[–34 to 
–17]–35
[–65 to 
–29]–46
[–92 to 
4.2
[0–0]0
[0–0]4
7450]

81TS
assessment of the resulting warming of the pathways. For the ‘Temperature change’ and ‘Likelihood’ columns, the single upper-row values represent the 50th percentile 

IPs and IMPs, and Chapter 3 for full descriptions. {3.2, 3.3, Annex III.II.4}

h Rates of global emission reduction in mitigation pathways are reported on a pathway-by-pathway basis relative to harmonised modelled global emissions in 2019 rather 
than the global emissions reported in SPM Section B and Chapter 2; this ensures internal consistency in assumptions about emission sources and activities, as well as 
consistency with temperature projections based on the physical climate science assessment by WGI. {Annex III.II.2.5}. Negative values (e.g., in C7, C8) represent an increase 
in emissions.

warming for each pathway assessed using the probabilistic climate model emulators calibrated to the AR6 WGI assessment. (See also Box SPM.1) {Annex III.II.2.5; 

82
impossible to limit warming to 1.5°C (>50%) with no or limited 
overshoot and strongly increasing the challenge of limiting 
warming to 2°C (>67%) (high confidence). A high overshoot 
of 1.5°C increases the risks from climate impacts and increases 
dependence on large-scale carbon dioxide removal (CDR) from the 
atmosphere. A future consistent with current NDCs implies higher fossil 
fuel deployment and lower reliance on low-carbon alternatives until 
2030, compared to mitigation pathways describing immediate action 
that limits warming to 1.5°C (>50%) with no or limited overshoot, or 
limits warming to 2°C (>67%) and below. After following the NDCs to 
2030, to limit warming to 2°C (>67%) the pace of global GHG emission 
reductions would need to abruptly increase from 2030 onward to an

average of 1.3–2.1 GtCO 2-eq per year between 2030 and 2050. This 
due to the COVID-19 pandemic lockdowns, and around 70% faster 
than in pathways where immediate action is taken to limit warming 
to 2°C (>67%). Accelerating emission reductions after following an 
NDC pathway to 2030 would also be particularly challenging because 
of the continued buildup of fossil fuel infrastructure that would take 
place between now and 2030. (TS4.1, Table TS.3) {3.5, 4.2}
that reduce annual GHG emissions to 47 (38–51) GtCO 2-eq by 
2030 (which is 3–9 GtCO 2-eq below projected emissions from 
fully implementing current NDCs) – make it less challenging to 
limit warming to 2°C (>67%) after 2030 (medium confidence). 
The accelerated action pathways are characterised by a global, but 
regionally differentiated, roll-out of regulatory and pricing policies.

Compared to current NDCs, they describe less fossil fuel use and 
more low-carbon fuel use until 2030; they narrow, but do not close 
the gap to pathways that assume immediate global action using all 
available least-cost abatement options. All delayed or accelerated 
action pathways limiting warming to below 2°C (>67%) converge 
to a global mitigation regime at some point after 2030 by putting 
a significant value on reducing carbon and other GHG emissions in 
all sectors and regions. {3.5}
cumulative net CO 2 emissions until the time of net zero CO 2 
together with the warming contribution of other GHGs and 
climate forcers at that time (high confidence).  Cumulative 
net CO 2 emissions from 2020 to the time of net zero CO 2 are 510 
with no or limited overshoot and 890 (640–1160) GtCO 2 in pathways

limiting warming to 2°C (>67%). These estimates are consistent with 
the AR6 WGI assessment of remaining carbon budgets adjusting for 
methodological differences and non-CO 2 warming. {3.3, Box 3.4}
16 Reductions greater than 100% in energy supply and AFOLU indicate that these sectors would become carbon sinks.50% (26–64%) in 2050, relative to 2019. CH 4 emission reductions in 
pathways limiting warming to 1.5°C with no or limited overshoot are 
substantially higher by 2030, 33% (19–57%), but only moderately so 
by 2050, 50% (33–69%). CH 4 emissions reductions are thus attainable 
at comparatively low costs, but, at the same time, reductions are limited

in scope in most 1.5°C–2°C pathways. Deeper CH 4 emissions reductions 
for more stringent climate goals. The emissions of cooling aerosols in 
mitigation pathways decrease as fossil fuels use is reduced. The overall 
impact on non-CO 2-related warming combines all these factors. {3.3}
a  level that compensates for residual non-CO 2 emissions. 
Only 30% of the pathways limiting warming to 2°C (>67%) 
or below reach net zero GHG emissions in the 21st  century 
(high confidence). In those pathways reaching net zero GHGs, net 
zero GHGs is achieved around 10–20 years later than net zero CO 2 
is achieved (medium confidence). The reported quantity of residual 
non-CO 2 emissions depends on accounting choices, and in particular

the choice of GHG metric (Box TS.2). Reaching and sustaining global 
net zero GHG emissions – when emissions are measured and reported 
in terms of GWP100 – results in a gradual decline in temperature 
(high confidence). (Box TS.6) {3.3}
confidence). Pathways that limit warming to 1.5°C (>50%) with no 
or limited overshoot entail CO 2 emissions reductions between 2019 
and 2050 of around 77% (31–96%) for energy demand, around 
115% (90–167%) for energy supply, and around 148% (94–387%) 
for AFOLU.16 In pathways that limit warming to 2°C (>67%), projected 
for energy demand, 97% for energy supply, and 136% for AFOLU 
(medium confidence). {3.4}
emissions reductions in one sector or region necessitates

compensating reductions in other sectors or regions (high 
confidence). Mitigation pathways show differences in the timing 
of decarbonisation and when net zero CO 2 emissions are achieved 
emissions, emissions in some sectors and regions are positive while 
others are negative; whether specific sectors and regions are positive 
or negative depends on the availability and cost of mitigation options 
in those regions, and the policies implemented. In cost-effective 
mitigation pathways, the energy supply sector typically reaches net 
Pathways limiting warming to 2°C (>67%) or 1.5°C involve 
substantial reductions in fossil fuel consumption and a  near 
elimination of coal use without CCS (high confidence). These 
pathways show an increase in low-carbon energy, with 88% (69–97%)

83TS
MtN2O yr  –1
C1C3
C1
95th
Median
5th75th
25th

CO2 
051015
023
NDC range

limit warming to 1.5°C (>50%) with no or limited overshoot are shown in light blue (category C1) and pathways that limit warming to 2°C (>67%) are shown in light purple 
(category C3). The grey range comprises all assessed pathways (C1–C8) from the 5th percentile of the lowest warming category (C1) to the 95th percentile of the highest 
warming category (C8). The modelled pathway ranges are compared to the emissions from two pathways illustrative of high emissions (CurPol and ModAct) and five IMPs: 
IMP-LD, IMP-Ren, IMP-SP , IMP-Neg and IMP-GS. Emissions are harmonised to the same 2015 base year. The vertical error bars in 2015 show the 5–95th percentile uncertainty 
range of the non-harmonised emissions across the pathways, and the uncertainty range, and median value, in emission estimates for 2015 and 2019. The vertical error bars

in 2030 (panel a) depict the assessed range of the NDCs, as announced prior to COP26.17 Panel (e) shows the sectoral contributions of CO 2 and non-CO 2 emissions sources 
year 2019. Energy supply (neg.) includes BECCS and DACCS. DACCS features in only two of the five IMPs (IMP-REN and IMP-GS) and contributes <1% and 64%, respectively, 
to the net negative emissions in Energy Supply (neg.). Panel (f) shows the contribution of different sectors and sources to the emissions reductions from a 2019 baseline for 
reaching net zero GHG emissions. Bars denote the median emissions reductions for all pathways that reach net zero GHG emissions. The whiskers indicate the p5–p95 range.

The contributions of the service sectors (transport, buildings, industry) are split into direct (demand-side) as well as indirect (supply-side) CO 2 emissions reductions. Direct 
emissions represent demand-side emissions due to the fuel use in the respective demand sector. Indirect emissions represent upstream emissions due to industrial processes 
and energy conversion, transmission and distribution. In addition, the contributions from the LULUCF sector and reductions from non-CO 2 emissions sources (green and grey 
revised NDCs announced by China, Japan and the Republic of Korea prior to October 2021 but only submitted thereafter. 25 NDC updates were submitted between 12 October 2021 and the start

of COP26.Stringent emissions reductions at the level required for 2°C 
or 1.5°C are achieved through the increased electrification of 
buildings, transport, and industry, consequently all pathways 
entail increased electricity generation (high confidence).  
Nearly all electricity in pathways limiting warming to 2°C (>67%) 
or 1.5°C (>50%) is also from low- or no-carbon technologies, with 
different shares across pathways of: nuclear, biomass, non-biomass 
renewables, and fossil fuels in combination with CCS. {3.4}Measures required to limit warming to 2°C (>67%) or below 
can result in large-scale transformation of the land surface 
(high confidence).  These pathways are projected to reach net zero 
CO 2 emissions in the AFOLU sector between the 2020s and 2070. GtCO2-eq yr  –1
0%  20%  40%  60%  80%  100%  Net zero CO 2 and net zero GHG emissions are possible through different modelled mitigation pathways.

% of modelled 2019 emissions(e) Sector al GHG emissions at the time of net-zer o 
IMP-Neg
IMP-LD
IMP-SP
IMP-Ren
Sources
Sinks
Indirect
Direct:

85TS
overshoot show an increase in forest cover of about 322 
(–67  to 890) million ha in 2050 (high confidence).  In these 
pathways the cropland area to supply biomass for bioenergy 
(including bioenergy with carbon capture and storage (BECCS)) is 
around 199 (56–482) million ha in 2050. The use of bioenergy can 
lead to either increased or reduced emissions, depending on the scale 
of deployment, conversion technology, fuel displaced, and how, and 
where, the biomass is produced (high confidence). {3.4}
require some amount of CDR to compensate for residual 
GHG emissions, even alongside substantial direct emissions 
reductions are achieved in all sectors and regions (high 
confidence). CDR deployment in pathways serves multiple purposes: 
accelerating the pace of emissions reductions, offsetting residual

emissions, and creating the option for net negative CO 2 emissions 
in case temperature reductions need to be achieved in the long term 
(high confidence). CDR options in pathways are mostly limited to 
BECCS, afforestation and direct air CO 2 capture and storage (DACCS). 
CDR through some measures in AFOLU can be maintained for 
decades but not over the very long term because these sinks will 
ultimately saturate (high confidence). {3.4} 
relative to reference scenarios that assume continuation 
of current policies, through a  diverse set of demand-side 
interventions (high confidence). Bottom-up and non-IAM studies 
show significant potential for demand-side mitigation. A stronger 
emphasis on demand-side mitigation implies less dependence on 
CDR and, consequently, reduced pressure on land and biodiversity. 
{3.4, 3.7}

confidence).  The bulk of investments are needed in medium- and 
low-income regions. Investment needs in the electricity sector are 
on average 2.3 trillion USD2015 yr –1 over 2023–2052 for pathways 
limiting temperature to 1.5°C (>50%) with no or limited overshoot, 
and 1.7 trillion USD2015 yr –1 for pathways limiting warming to 2°C 
higher upfront transition costs, but at the same time bring 
long-term gains for the economy as well as earlier benefits 
in avoided climate change impacts (high confidence).  This 
conclusion is independent of the discount rate applied, though the 
modelled cost-optimal balance of mitigation action over time does 
depend on the discount rate. Lower discount rates favour earlier 
mitigation, reducing reliance on CDR and temperature overshoot. 
{3.6.1, 3.8}

18 Numbers in parentheses represent he interquartile range of the scenario samples.Mitigation pathways that limit warming to 2°C (>67%) entail 
of between 1.3% and 2.7% in 2050. In pathways limiting 
warming to 1.5°C (>50%) with no or limited overshoot, 
losses are between 2.6% and 4.2%. These estimates do not 
account for the economic benefits of avoided climate change 
impacts (medium confidence).  In mitigation pathways limiting 
warming to 2°C (>67%), marginal abatement costs of carbon are 
about 90 (60–120) USD2015 tCO 2 in 2030 and about 210 (140–340) 
in 205018 in pathways that limit warming to 1.5°C (>50%) with 
no or limited overshoot. Reference scenarios, in the AR6 scenarios 
database, describe possible emission trajectories in the absence of

new stringent climate policies. Reference scenarios have a  broad 
range depending on socio-economic assumptions and model 
characteristics. {3.2.1, 3.6.1}
(>67%) outweigh global mitigation costs over the 21st century, 
over the long term. This holds true even without accounting 
for benefits in other sustainable development dimensions or 
non-market damages from climate change (medium confidence).  
The aggregate global economic repercussions of mitigation pathways 
include: the macroeconomic impacts of investments in low-carbon 
solutions and structural changes away from emitting activities; 
co-benefits and adverse side effects of mitigation; avoided climate 
change impacts; and reduced adaptation costs. Existing quantifications

of the global aggregate economic impacts show a strong dependence 
on socio-economic development conditions, as these shape exposure 
and vulnerability and adaptation opportunities and responses. 
Avoided impacts for poorer households and poorer countries represent 
a  smaller share in aggregate economic quantifications expressed in 
GDP or monetary terms, whereas their well-being and welfare effects 
are comparatively larger. When aggregate economic benefits from 
avoided climate change impacts are accounted for, mitigation is 
a welfare-enhancing strategy (high confidence) . {3.6.2}
even larger (medium confidence).  {3.6.3}
Differences in aggregate employment between mitigation 
pathways and reference scenarios are relatively small, although 
there may be substantial reallocations across sectors, with job

creation in some sectors and job losses in others (medium 
confidence).  The net employment effect (and whether employment 
increases or decreases) depends on the scenario assumptions, 
modelling framework, and modelled policy design. Mitigation has 
implications for employment through multiple channels, each of which 
impacts geographies, sectors and skill categories differently. {3.6.4}

86
regions and households, depending on policy design and the 
level of international cooperation (high confidence). Delayed 
global cooperation increases policy costs across regions, especially in 
those that are relatively carbon intensive at present (high confidence). 
in more carbon-intensive regions, in fossil fuel-exporting regions, 
19 In this assessment the terms net zero CO 2 emissions and carbon neutrality have different meanings and are only equivalent at the global scale. At the scale of regions, or sectors, each term applies 
different system boundaries. This is also the case for the related terms net zero GHG and GHG neutrality. {Cross-Chapter Box 3 in Chapter 3}expressed in GDP or monetary terms undervalue the economic effects

below implies deep economic and structural changes, thereby raising 
classes, and sectors (high confidence). (Box TS.7) {3.6.1, 3.6.4}
level. At the point of net zero, the amount of CO 2 human activity is putting into the atmosphere equals the amount of CO 2 human 
warming. Moving to net negative CO 2 emissions globally would reduce peak cumulative net CO 2 emissions – which occurs at the time

In the AR6 scenario database, global emissions pathways limi warming to 1.5°C (>50%) with no or limited overshoot reach net zero 
CO 2 emissions between 2050–2055 (2035–2070) (median and 5–95th percentile ranges; 100% of pathways); pathways limiting 
warming to 2°C (>67%) reach net zero CO 2 emissions between 2070–2075 (2055–…) (median and 5–95th percentile ranges; 90% of

variation. The SR1.5 median value of 2050 is still close to the middle of the current range. If emissions are reduced less rapidly in the 
period up to 2030, an earlier net zero year is needed.
Reaching net zero GHG emissions requires net negative CO 2 emissions to balance residual CH 4, N 2O and F-gas emissions. If achieved 
globally, net zero GHG emissions would reduce global warming from an earlier peak. Around half global emission pathways limiting

warming to 1.5°C (>50%), and a  third of pathways limiting warming to 2°C (>67%), reach net zero GHG emissions (based on 
GWP100) in the second half of the century, around 10 to 40 years later than net zero CO 2 emissions. They show warming being halted 
at some peak value followed by a gradual decline towards the end of the century. The remainder of the pathways do not reach net 
zero GHG emissions during the 21st century and show little decline of warming after it stabilised. 
Global net zero CO 2 or GHG emissions can be achieved even while some sectors and regions continue to be net emitters, provided that 
others achieve net GHG removal. Sectors and regions have different potentials and costs to achieve net zero or even net GHG removal. 
The adoption and implementation of net zero emission targets by countries and regions depends on multiple factors, including equity

and capacity criteria and international and cross-sectoral mechanisms to balance emissions and removals. The formulation of net zero 
pathways by countries will benefit from clarity on scope, plans of action, and fairness. Achieving net zero emission targets relies on 
policies, institutions and milestones against which to track progress.

87TS
near-term emissions reductions (in 2030 and 2040) if warming is to be limited to the same level. Funnels show pathways limiting warming to 1.5°C (>50%) with no 
or limited overshoot (light blue) and limiting warming to 2°C (>67%) (beige).Net global CO 2 emissions Median global warming from 1850–1990

reaching net zero CO 2 at a later point in time
Integrated studies use either a cost-effectiveness analysis (CEA) approach (minimising the total mitigation costs of achieving a given 
policy goal) or a cost-benefit analysis (CBA) approach (balancing the cost and benefits of climate action). In the majority of studies 
that have produced the body of work on the cost of mitigation assessed in this report, a CEA approach is adopted, and the feedbacks 
of climate change impacts on the economic development pathways are not accounted for. This omission of climate impacts leads to

overly optimistic economic projections in the reference scenarios, in particular in reference scenarios with no or limited mitigation 
action where the extent of global warming is the greatest. Mitigation cost estimates computed against no or limited policy reference 
scenarios therefore omit economic benefits brought by avoided climate change impact along mitigation pathways. {1.7, 3.6.1}
The difference in aggregate economic impacts from climate change between two given temperature levels represents the aggregate 
economic benefits arising from avoided climate change impacts due to mitigation action. Estimates of these benefits vary widely, 
depending on the methodology used and impacts included, as well as on assumed socio-economic development conditions, which 
shape exposure and vulnerability. The aggregate economic benefits of avoiding climate impacts increase with the stringency of

the mitigation. Global economic impact studies with regional estimates find large differences across regions, with developing and 
transitional economies typically more vulnerable. Furthermore, avoided impacts for poorer households and poorer countries represent 
a smaller share in aggregate quantifications expressed in GDP terms or monetary terms, compared to their influence on well-being 
and welfare (high confidence). {3.6.2, Cross-Working Group Box 1 in Chapter 3}

88
CBA analysis and CBA integrated assessment models (IAMs) remain limited in their ability to represent all damages from climate 
change, including non-monetary damages, and capture the uncertain and heterogeneous nature of damages and the risk of catastrophic 
damages, such that other lines of evidence should be considered in decision-making. However, emerging evidence suggests that, even 
without accounting for co-benefits of mitigation on other sustainable development dimensions, the global benefits of pathways 
limiting warming to 2°C (>67%) outweigh global mitigation costs over the 21st century (medium confidence). Depending on the 
study, the reason for this result lies in assumptions of economic damages from climate change in the higher end of available estimates, 
in the consideration of risks of tipping points or damages to natural capital and non-market goods, or in the combination of updated

representations of carbon cycle and climate modules, updated damage estimates and updated representations of economic and 
mitigation dynamics. In the studies that perform a sensitivity analysis, this result is found to be robust to a wide range of assumptions 
on social preferences (in particular on inequality aversion and pure rate of time preference), and holds except if assumptions of 
economic damages from climate change are in the lower end of available estimates and the pure rate of time preference is in the 
higher range of values usually considered (typically above 1.5%). However, although such pathways bring overall net benefits over time 

89TS
Chapter 9), Industry (Chapter 11), and Agriculture, Forestry and Other 
Land Use (AFOLU) (Chapter  7)  – correspond broadly to the IPCC 
National Greenhouse Gas Inventory reporting categories and build 
on similar chapters in previous WGIII reports. Chapters 5 and 12 tie 
together the cross-sectoral aspects of this group of chapters including 
the assessment of costs and potentials, demand-side aspects of 
mitigation, and carbon dioxide removal (CDR). 
A broad-based approach to deploying energy-sector mitigation 
options can reduce emissions over the next ten years and set the 
stage for still deeper reductions beyond 2030 (high confidence).

There are substantial, cost-effective opportunities to reduce emissions 
rapidly, including in electricity generation, but near-term reductions 
will not be sufficient to limit warming to 2°C (>67%) or limit warming 
to 1.5°C (>50%) with no or limited overshoot. {6.4, 6.6, 6.7}
deep reductions in energy system CO 2 and GHG emissions 
with no or limited overshoot (likely below 2°C), net energy system 
CO 2 emissions fall by 87–97% (interquartile range 60–79%) in 
2050. In 2030, in scenarios limiting warming to 1.5°C with no or 
limited overshoot, net CO 2 and GHG emissions fall by 35–51% and 
38–52% respectively. In scenarios limiting warming to 1.5°C with 
no or limited overshoot (likely below 2°C), net electricity sector CO 2 
emissions reach zero globally between 2045 and 2055 (2050 and 
2080) (high confidence). {6.7}

energy system changes over the next 30 years. This includes 
reduced fossil fuel consumption, increased production from 
low- and zero-carbon energy sources, and increased use of 
electricity and alternative energy carriers (high confidence).  Coal 
consumption without CCS falls by 67–82% (interquartile range) in 2030 
in scenarios limiting warming to 1.5°C with no or limited overshoot. Oil 
and gas consumption fall more slowly. Low-carbon sources produce 
93–97% of global electricity by 2050 in scenarios that limit warming 
to 2°C (>67%) or below. In scenarios limiting warming to 1.5°C with 
no or limited overshoot (likely below 2°C), electricity supplies 48–58% 
(36–47%) of final energy in 2050, up from 20% in 2019. {6.7}
Net zero energy systems will share common characteristics, 
but the approach in every country will depend on national 
circumstances (high confidence). Common characteristics of

net-zero energy systems will include: (i) electricity systems that produce 
no net CO 2 or remove CO 2 from the atmosphere; (ii)  widespread 
electrification of end uses, including light-duty transport, space 
heating, and cooking; (iii) substantially lower use of fossil fuels than today; (iv) use of alternative energy carriers such as hydrogen, 
bioenergy, and ammonia to substitute for fossil fuels in sectors less 
amenable to electrification; (v) more efficient use of energy than 
today; (vi) greater energy system integration across regions and 
across components of the energy system; and (vii) use of CO 2 removal 
to rise (high confidence). From 2015 to 2019, global final 
energy consumption grew by 6.6%, CO 2 emissions from the global 
energy system grew by 4.6%, and total GHG emissions from energy

supply rose by 2.7%. Fugitive CH 4 emissions from oil, gas, and coal, 
accounted for 18% of GHG emissions in 2019. Coal electricity 
capacity grew by 7.6% between 2015 and 2019, as new builds in 
some countries offset declines in others. Total consumption of oil and 
oil products increased by 5%, and natural gas consumption grew 
by 15%. Declining energy intensity in almost all regions has been 
balanced by increased energy consumption. {6.3}
options have dropped rapidly over the last five years, notably 
solar PV, wind power, and batteries (high confidence).  From 
2015 to 2020, the costs of electricity from PV and wind dropped 56% 
and 45%, respectively, and battery prices dropped by 64%. Electricity 
from PV and wind is now cheaper than electricity from fossil sources 
in many regions, electric vehicles are increasingly competitive with 
internal combustion engines, and large-scale battery storage on

electricity grids is increasingly viable. (Figure TS.7) {6.3, 6.4}
increased rapidly driven by policy, societal pressure to limit 
fossil generation, low interest rates, and cost reductions (high 
confidence).  Solar PV grew by 170% (to 680 TWh); wind grew by 
70% (to 1420 TWh) from 2015 to 2019. Solar PV and wind together 
accounted for 21% of total low-carbon electricity generation and 
8% of total electricity generation in 2019. Nuclear generation 
grew 9% between 2015 and 2019 and accounted for 10% of total 
generation in 2019 (2790 TWh); hydro-electric power grew by 10% 
and accounted for 16% (4290 TWh) of total generation. In total, low- 
and zero-carbon electricity generation technologies produced 37% of 
global electricity in 2019. {6.3, 6.4}
energy systems will be locked-in to higher emissions, making it

harder to limit warming to 2°C or 1.5°C (high confidence). Many 
aspects of the energy system – physical infrastructure; institutions, laws, 
and regulations; and behaviour – are resistant to change or take many 
years to change. New investments in coal-fired electricity without CCS 
are inconsistent with limiting warming to well below 2°C. {6.3, 6.7} 
assets, including fossil infrastructure and unburned fossil fuel 
resources (high confidence).  The economic impacts of stranded 
assets could amount to trillions of dollars. Coal assets are most 
vulnerable over the coming decade; oil and gas assets are more 
vulnerable toward mid-century. CCS can allow fossil fuels to be used 
longer, reducing potential stranded assets. (Box TS.8) {6.7}

90
defined as assets which ‘suffer from unanticipated or premature write-offs, downward revaluations or conversion to liabilities’. Climate 
policies, other policies and regulations, innovation in competing technologies, and shifts in fuel prices could all lead to stranded assets. The 
loss of wealth from stranded assets would create risks for financial market stabilityand reduce fiscal revenue for hydrocarbon-dependent 
economies, which in turn could affect macroeconomic stability and the prospects for a Just Transition. (Box TS.4) {6.7, 15.6, Chapter 17} 

stranding under climate policy – depending on their current age and expected lifetimes. Scenario evidence suggests that without 
carbon capture, the worldwide fleet of coal- and gas power plants would need to retire about 23 and 17 years earlier than expected 
lifetimes, respectively, in order to limit global warming to 1.5°C and 2°C {2.7}. Blast furnaces and cement factories without CCS {11.4}, 
new fleets of airplanes and internal combustion engine vehicles {10.4, 10.5}, and new urban infrastructures adapted to sprawl and 
motorisation may also be stranded. {Chapter 8; Box 10.1}

therefore desire to keep assets in operation even if financial, social, or environmental concerns call for retirement. This creates political 
economic risks, including actions by asset owners to hinder climate policy reform {6.7; Box 6.11}. It will be easier to retire these 
assets if the risks are communicated, if sustainability reporting is mandated and enforced, and if corporations are protected with 
arrangements that shield them from short-term shareholder value maximisation. 
Without early retirements, or reductions in utilisation, the current fossil infrastructure will emit more GHGs than is compatible with 
limiting warming to 1.5°C {2.7}. Including the pipeline of planned investments would push these future emissions into the uncertainty 
range of 2°C carbon budgets {2.7}. Continuing to build new coal-fired power plants and other fossil infrastructure will increase future

transition costs and may jeopardise efforts to limit warming to 2°C (>67%) or 1.5°C with no or limited overshoot. One study has 
estimated that USD11.8 trillion in current assets will need to be stranded by 2050 for a 2°C world; further delaying action for another 
10 years would result in an additional USD7.7 trillion in stranded assets by 2050. {15.5.2}  
and create new economic opportunities (high confidence). Total 
energy investment needs will rise, relative to today, over the next 
decades, if warming is limited to 2°C or lower (>67%), or if warming 
is limited to 1.5°C (>50%) with no or limited overshoot. These

increases will be far less pronounced, however, than the reallocations 
of investment flows that are anticipated across subsectors, namely 
from fossil fuels (extraction, conversion, and electricity generation) 
without CCS and toward renewables, nuclear power, CCS, electricity 
networks and storage, and end-use energy efficiency. A significant 
and growing share of investments between now and 2050 will be 
made in emerging economies, particularly in Asia. {6.7} 
particularly at the regional scale (high confidence). Climate 
change will alter hydropower production, bioenergy and agricultural 
yields, thermal power plant efficiencies, and demands for heating 
and cooling, and it will directly impact power system infrastructure. 
Climate change will not affect wind and solar resources to the extent

that it would compromise their ability to reduce emissions. {6.5}Electricity systems powered predominantly by renewables 
will be increasingly viable over the coming decades, but 
it will be challenging to supply the entire energy system with 
renewable energy (high confidence). Large shares of variable 
solar PV and wind power can be incorporated in electricity grids 
through batteries, hydrogen, and other forms of storage; transmission; 
flexible non-renewable generation; advanced controls; and greater 
demand-side responses. Because some applications (e.g.,  aviation) 
are not currently amenable to electrification, it is anticipated that 
100% renewable energy systems will need to include alternative 
fuels such as hydrogen or biofuels. Economic, regulatory, social, and 
operational challenges increase with higher shares of renewable 
electricity and energy. The ability to overcome these challenges in 
practice is not fully understood. (Box TS.9) {6.6}

91TS
and electricity produced from these fossil fuels to energy carriers with little or no carbon footprint. An important question is which new 
energy carriers will emerge to support low-carbon transitions. 
Low-carbon energy systems are expected to rely heavily on end-use electrification, where electricity produced with low GHG emissions 
is used for building and industrial heating, transport and other applications that rely heavily on fossil fuels at present. But not all 
end-uses are expected to be commercially electrifiable in the short to medium term {11.3.5}, and many will require low GHG liquid and

gaseous fuels, that is, hydrogen, ammonia, and biogenic and synthetic low GHG hydrocarbons made from low GHG hydrogen, oxygen 
and carbon sources (the latter from CCU,20 biomass, or direct air capture {11.3.6}). The future role of hydrogen and hydrogen derivatives 
will depend on how quickly and how far production technology improves, that is, from electrolysis (‘green’), biogasification, and fossil 
fuel reforming with CCS (‘blue’) sources. As a general rule, and across all sectors, it is more efficient to use electricity directly and avoid 
the progressively larger conversion losses from producing hydrogen, ammonia, or constructed low GHG hydrocarbons. What hydrogen 
does do, however, is add time and space option value to electricity produced using variable clean sources, for use as hydrogen, as 
stored future electricity via a fuel cell or turbine, or as an industrial feedstock. Furthermore, electrification and hydrogen involve

a symbiotic range of general-purpose technologies, such as electric motors, power electronics, heat pumps, batteries, electrolysis, fuel 
cells, and so on, that have different applications across sectors but cumulative economies of innovation and production scale benefits. 
Finally, neither electrification nor hydrogen produce local air pollutants at point of end-use.
electricity generators. When switching to low-carbon energy sources – renewable sources, nuclear power, and fossil or bioenergy 
with CCS – electricity is expected to become a more pervasive energy carrier. Electricity is a versatile energy carrier, with much higher 
end-use efficiencies than fuels, and it can be used directly to avoid conversion losses. 

many existing guidelines obsolete for power system planning, for example, that specific generation types are needed for baseload, 
intermediate load, and peak load to follow and meet demand. In future power systems with high shares of variable electricity from 
renewable sources, system planning and markets will focus more on demand flexibility, grid infrastructure and interconnections, 
storage on various timelines (on the minute, hourly, overnight and seasonal scale), and increased coupling between the energy sector 
and the building, transport and industrial sectors. This shifts the focus to energy systems that can handle variable supply rather than 
always follow demand. Hydrogen may prove valuable to improve the resilience of electricity systems with high penetration of variable 
renewable electricity. Flexible hydrogen electrolysis, hydrogen power plants and long-duration hydrogen storage may all improve

resilience. Electricity-to-hydrogen-to-electricity round-trip efficiencies are projected to reach up to 50% by 2030. {6.4.3}
Electrification is expected to be the dominant strategy in buildings as electricity is increasingly used for heating and for cooking. 
Electricity will help to integrate renewable energy into buildings and will also lead to more flexible demand for heating, cooling, and 
electricity. District heating and cooling offers potential for demand flexibility through energy storage and supply flexibility through 
cogeneration. Heat pumps are increasingly used in buildings and industry for heating and cooling {9.3.3, Box  9.3}. The ease of 
switching to electricity means that hydrogen is not expected to be a dominant pathway for buildings {Box 9.6}. Using electricity 
directly for heating, cooling and other building energy demand is more efficient than using hydrogen as a fuel, for example, in boilers

or fuel cells. In addition, electricity distribution is already well developed in many regions compared to essentially non-existent 
hydrogen infrastructure, except for a few chemicals industry pipelines. At the same time, hydrogen could potentially be used for on-site 
storage should technology advance sufficiently.

92
have been extensively explored in the literature and small-scale pilots. The role of hydrogen in transport depends on how far technology 
develops. Batteries are currently a more attractive option than hydrogen and fuel cells for light-duty vehicles. Hydrogen and hydrogen-
derived synthetic fuels, such as ammonia and methanol, may have a more important role in heavy vehicles, shipping, and aviation {10.3}. 
Current transport of fossil fuels may be replaced by future transport of hydrogen and hydrogen carriers such as ammonia and methanol, 
or energy-intensive basic materials processed with hydrogen (e.g., reduced iron) in regions with bountiful renewable resources. {Box 11.1}

pumps in light industry) and for feedstocks (e.g., hydrogen for steel-making and chemicals). For example, industrial process heat demand, 
ranging from below 100°C to above 1000°C, can be met through a wide range of electrically powered technologies instead of using fuels. 
Future demand for hydrogen (e.g., for nitrogen fertiliser or as a reduction agent in steel production) also offers electricity-demand flexibility 
for electrolysis through hydrogen storage and flexible production cycles {11.3.5}. The main use of hydrogen and hydrogen carriers in industry 
is expected to be as feedstock (e.g., for ammonia and organic chemicals) rather than for energy as industrial electrification increases.
emissions over the next decade (high confidence).  Nuclear

power and hydropower are already established technologies. Solar 
PV and wind are now cheaper than fossil-generated electricity 
in many locations. Bioenergy accounts for about a tenth of global 
primary energy. Carbon capture is widely used in the oil and gas 
industry, with early applications in electricity production and biofuels. 
It will not be possible to widely deploy all of these and other options 
without efforts to address the geophysical, environmental-ecological, 
economic, technological, socio-cultural, and institutional factors that can facilitate or hinder their implementation (high confidence). 
(Figures TS.11 and TS.31) {6.4}
scales will lower costs and facilitate low-carbon energy system 
transitions (high confidence). Greater integration between the 
electricity sector and end-use sectors can facilitate integration of 
variable renewable energy options. Energy systems can be integrated

across district, regional, national, and international scales (high 
confidence). {6.4, 6.6}
Coal
162
Transport
121Oil
191
140
and 
Heat 6 EJ
Hydro
15
Biomass
57Solar
4Wind
5
10
102
4943
46
244422532
19 4182
29
4112
2912
1116
329
8
5

93TS
2
Transport
99Oil
28
and 
Heat 76 EJ
20
Hydro
24
Biomass
101Solar
261
Wind
– other 10
Nuclear
2Power
plants 
and heat
Reﬁnery
100
3939
395
16 6 727
7
218 2
6
Coal
82
Transport
139Oil
124
and 
Heat 24 EJ
100
Hydro
30
Biomass
156
Solar
91Wind
28
Power
plants 
and heat
Reﬁnery
15418
64
9
7175
26
3
41
14
5
479714
1
13
11

Figure TS.11 continued: Global energy flows within the 2019 global energy system (top panel) and within two illustrative future, net-zero CO 2 emissions 

94
transition will depend on how well it can support SDGs and 
other societal objectives (high confidence). Energy systems 
are linked to a range of societal objectives, including energy access, 
air and water pollution, health, energy security, water security, 
food security, economic prosperity, international competitiveness, 
and employment. These linkages and their importance vary among 
regions. Energy-sector mitigation and efforts to achieve SDGs 
generally support one another, though there are important region-
specific exceptions (high confidence). (Figure TS.29) {6.1, 6.7}
sectors and regions may be on par with, or superior to those of 
an emissions-intensive future (high confidence). Cost reductions 
in key technologies, particularly in electricity and light-duty transport, 
have increased the economic attractiveness of near-term low-carbon

ammonia would substantially improve the economics of net zero 
energy systems (medium confidence). {6.4, 6.7}
variations in per-capita urban carbon emissions. For most regions, 
per-capita urban emissions are lower than per-capita national 
emissions (excluding aviation, shipping and biogenic sources)

(very high confidence). {8.1.4, 8.3.3, 8.4, Box 8.1}
countries, where per-capita emissions are currently low, but 
are expected to increase with the construction and use of 
new infrastructure, and the built environment, and changes 
in incomes and lifestyles (very high confidence).  The drivers 
of urban GHG emissions are complex and include an interplay of 
population size, income, state of urbanisation, and how cities are 
laid out (i.e., urban form). How new cities and towns are designed, 
constructed, managed, and powered will lock-in behaviour, lifestyles, 
and future urban GHG emissions. Urban strategies can improve 
well-being while minimising impact on GHG emissions. However, 
urbanisation can result in increased global GHG emissions through 
emissions outside the city’s boundaries (very high confidence). {8.1.4, 
8.3, Box 8.1, 8.4, 8.6}

of electricity, goods, and services consumed in cities. Estimates include all CO 2 and CH 4 emission categories except for aviation and marine bunker fuels, land-use change, forestry, and agriculture. 
account for approximately 18% of the global carbon footprint (high 
confidence). {8.1, 8.3}
2000 and 2015, with much inter-regional variation in the

share of national emissions increased six percentage points, from 
56% in 2000 to 62% in 2015. For 2000 to 2015, the urban emissions 
share increased from 28% to 38% in Africa, from 46% to 54% in Asia 
and Pacific, from 62% to 72% in Developed Countries, from 57% to 
62% in Eastern Europe and West Central Asia, from 55% to 66% in 
Latin America and Caribbean, and from 68% to 69% in the Middle 
East (high confidence). {8.1.6, 8.3.3}
2015, with cities in developed countries accounting for nearly 
seven times more per capita than the lowest emitting region 
(medium confidence). From 2000 to 2015, global urban GHG 
emissions per capita increased from 5.5 to 6.2 tCO 2-eq per person 
(an increase of 11.8%). Emissions in Africa increased from 1.3 to 
1.5 tCO 2-eq per person (22.6%); in Asia and Pacific from 3.0 to 5.1

Albeit starting from the highest level, developed countries showed 
a  modest decline of 11.4 to 10.7 tCO 2-eq per  person (–6.5%). 

95TS
a)
b)70%15.0
13.0
11.0
9.0
7.0
5.0
3.0
1.0
15.0
13.0
11.0
9.0
7.0
5.0
3.0
1.0
–1.0
40%
30%
20%
10%
0%
70%Africa
Africa
40%
30%
20%
10%

96
increase through 2050 with moderate to low mitigation efforts 
SSP2-4.5), and up to 40 GtCO 2-eq in 2050 with low mitigation efforts 
(high GHG emissions, SSP 3-7.0). With aggressive and immediate 
mitigation efforts to limit global warming to 1.5°C (>50%) with no 

23 These scenarios have been assessed by WGI to correspond to intermediate, high, and very low GHG emissions.SSP1-1.9), including high levels of electrification, energy and material 
efficiency, renewable energy preferences, and socio-behavioural 
a maximum of 3 GtCO 2-eq in 2050. Under a scenario with aggressive 
to 2°C (>67%) (low emissions, SSP1-2.6), urban emissions could 
reach 17 GtCO 2-eq in 2050.23 (Figure TS.13) {8.3.4}
significant implications for future carbon lock-in (medium 
confidence). There is a  large range in the forecasts of urban land 
expansion across scenarios and models, which highlights an opportunity 
to shape future urban development towards low- or net zero GHG

Europe
American
and
EastTotal
Reduction
in 20309.8
3.4
Developed
Europe
American
and
EastTotal
Increase
12
10
8
6
4
2
0

97TS
global urban extent, with the median projected increase ranging from 
43% to 106%. While the largest absolute amount of new urban land is 
forecasted to occur in Asia and Pacific, and in Developed Countries, the 
highest rate of urban land growth is projected to occur in Africa, Eastern 
Europe and West Central Asia, and in the Middle East. Given past trends, 
the expansion of urban areas is expected to take place on agricultural 
lands and forests, with implications for the loss of carbon stocks. The 
infrastructure that will be constructed concomitant with urban land 
expansion will lock-in patterns of energy consumption that will persist 
for decades. {8.3.1, 8.3.4, 8.4.1, 8.6}
infrastructure through 2030 will add to emissions (medium 
evidence, high agreement). The construction of new and upgrading

of existing urban infrastructure using conventional practices and 
technologies can result in a  significant increase in CO 2 emissions, 
90 billion tonnes per year by 2050, up from 40 billion tonnes in 2010. 
{8.4.1, 8.6}
(very high confidence). Mitigation strategies can enhance 
resilience against climate change impacts while contributing to 
social equity, public health, and human well-being. Urban mitigation 
actions that facilitate economic decoupling can have positive 
impacts on employment and local economic competitiveness. {8.2, 
Cross-Working Group Box 2 in Chapter 8, 8.4}

infrastructure; (ii) electrification and switching to low-carbon energy 
sources; and (iii) enhancing carbon uptake and storage in the urban 
environment (high confidence). Given the regional and global reach 
of urban supply chains, cities can achieve net-zero emissions only if 
emissions are reduced both within and outside of their administrative 
boundaries through supply chains. {8.1.6, 8.3.4, 8.4, 8.6}
Packages of mitigation policies that implement multiple 
urban-scale interventions can have cascading effects across

sectors, reduce GHG emissions outside a city’s administrative 
boundaries, and reduce emissions more than the net sum 
24 These examples are considered to be a subset of ‘nature-based solutions’ or ‘ecosystem-based approaches’.based on spatial planning, electrification of the urban energy system, 

development could reduce urban energy use between 23% 
(high confidence). Compact cities with shortened distances between 
housing and jobs, and interventions that support a modal shift away 
from private motor vehicles towards walking, cycling, and low-
emissions shared, or public, transportation, passive energy comfort in 
buildings, and urban green infrastructure can deliver significant public 
health benefits and lower GHG emissions. {8.2, 8.3.4, 8.4, 8.6}
change through carbon sinks, avoided emissions, and 
reduced energy use while offering multiple co-benefits (high 
confidence). Urban green and blue infrastructure, including urban 
forests and street trees, permeable surfaces, and green roofs24 offer 
potentials to mitigate climate change directly through storing carbon,

and indirectly by inducing a cooling effect that both reduces energy 
demand and reduces energy use for water treatment. Globally, urban 
trees store approximately 7.4 billion tonnes of carbon, and sequester 
approximately 217 million tonnes of carbon annually, although 
carbon storage is highly dependent on biome. Among the multiple 
improving air quality, and improving the mental and physical health 
of urban dwellers. Many of these options also provide benefits to 
climate adaptation. (high agreement, robust evidence) {8.2, 8.4.4}
The potential and sequencing of mitigation strategies to reduce 
GHG emissions will vary depending on a city’s land use, spatial 
form, development level, and state of urbanisation (i.e., whether 
it is an established city with existing infrastructure, a rapidly

growing city with new infrastructure, or an emerging city with 
infrastructure buildup) (high confidence).  New and emerging cities 
will have significant infrastructure development needs to achieve high 
quality of life, which can be met through energy-efficient infrastructures 
and services, and people-centred urban design (high confidence). 
The long lifespan of urban infrastructures locks in behaviour and 
committed emissions. Urban  infrastructures  and urban form can 
enable sociocultural and lifestyle changes that can significantly reduce 
carbon footprints. Rapidly growing cities can  avoid higher future 
emissions through urban planning to co-locate jobs and housing to 
achieve compact urban form, and by leapfrogging to low-carbon 
technologies. Established cities will achieve the largest GHG emissions 
savings by replacing, repurposing, or retrofitting the building stock, 
targeted infilling and densifying, as well as through modal shift and

the electrification of the urban energy system. New and emerging cities

98
while achieving high quality of life by creating compact, co-located, 
and walkable urban areas with mixed land use and transit-oriented 
design, that also preserve existing green and blue assets. {8.2, 8.4, 8.6} 
are opportunities to harness and enable informal practices and 
institutions in cities related to housing, waste, energy, water, 
and sanitation to reduce resource use and mitigate climate 
change (low evidence, medium agreement). The upgrading of 
informal settlements and inadequate housing to improve resilience 
and well-being offers a  chance to create a  low-carbon transition. 
However, there is limited quantifiable data on these practices and their 
cumulative impacts on GHG emissions. {8.1.4, 8.2.2, Cross-Working 
Group Box 2 in Chapter 8, 8.3.2, 8.4, 8.6, 8.7}

non-state actors, and in connection with substantial financing 
beyond sectoral approaches (very high confidence).  Large 
carbon emissions and related spatial planning programs across key 
sectors. {8.4, 8.5}
changes in the transport sector.  In 2019, direct GHG emissions

from the transport sector were 8.7 GtCO 2-eq (up from 5.0 GtCO 2-eq in 
Road vehicles accounted for 70% of direct transport emissions, while 
1%, 11%, and 12% of direct emissions came from rail, shipping, and 
aviation, respectively. Emissions from shipping and aviation continue 
to grow rapidly. Transport-related emissions in developing regions 
of the world have increased more rapidly than in Europe or North 
America, a  trend that is expected to continue in coming decades 
(high confidence). {10.1, 10.5, 10.6}
for demand management solutions combined with new 
technologies, such as the rapidly growing use of electromobility 
for land transport and the emerging options in advanced 
biofuels and hydrogen-based fuels for shipping and aviation 
and in other specific land-based contexts (high confidence).

There is a growing need for systemic infrastructure changes that enable 
behavioural modifications and reductions in demand for transport 
services that can in turn reduce energy demand. The response to the 
COVID-19 pandemic has also shown that behavioural interventions can 
25 ‘Active travel’ is travel that requires physical effort, for example journeys made by walking or cycling.reduce transport-related GHG emissions. For example, COVID-19-based 
replacing significant numbers of work and personal journeys as well as 
promoting local active transport. There are growing opportunities to 
implement strategies that drive behavioural change and support the 
adoption of new transport technology options. {Chapter 5, 10.2, 10.3, 
10.4, 10.8}
Changes in urban form, behaviour programs, the circular 
economy, the shared economy, and digitalisation trends 
can support systemic changes that lead to reductions in

efficient transport modes (high confidence). Cities can reduce 
including protected pedestrian and bike pathways, can also 
support much greater localised active travel.25 Transport demand 
management incentives are expected to be necessary to support 
these systemic changes. There is mixed evidence of the effect 
digitalisation on demand for transport services (Box TS.14). For 
example, while dematerialisation can reduce the amount of material 
that needs to be transported to manufacturing facilities, an increase 
in online shopping with priority delivery can increase demand for 
freight transport. Similarly, while teleworking could reduce travel

demand, increased ride-sharing could increase vehicle kilometres 
travelled (VKT). {Chapters 1 and 5, 10.2, 10.8}
gas (GHG) emissions than internal combustion engine vehicles 
(high confidence).  Electromobility is being rapidly implemented 
in micro-mobility (e-autorickshaws, e-scooters, e-bikes), in transit 
systems, especially buses, and to a  lesser degree, in personal 
vehicles. BEVs could also have the added benefit of supporting 
grid operations. The commercial availability of mature lithium-ion 
batteries (LIBs) has underpinned this growth in electromobility. As 
global battery production increases, unit costs are declining. Further 
continued growth of electromobility for land transport would entail

investments in electric charging and related grid infrastructure. 
Electromobility powered by low-carbon electricity has the potential 
to rapidly reduce transport GHG and can be applied with multiple 
co-benefits, especially in developing countries. {10.3, 10.4, 10.8}
through battery-electric haulage (including the use of electric 
based fuels in some contexts. These same technologies and 
expanded use of available electric rail systems can support 
rail decarbonisation (medium confidence). Initial deployments of 
battery-electric, hydrogen- and bio-based haulage are underway, and 
commercial operations of some of these technologies are considered

99TS
nevertheless face challenges regarding driving range, capital and 
operating costs, and infrastructure availability. In particular, fuel-cell 
durability, high energy consumption, and costs continue to challenge 
the commercialisation of hydrogen-based fuel-cell vehicles. Increased 
capacity for low-carbon hydrogen production would also be essential 
for hydrogen-based fuels to serve as an emissions reduction strategy 
(high confidence). (Box TS.15) {10.3, 10.4, 10.8}
R&D, though advanced biofuels, ammonia, and synthetic 
fuels are emerging as viable options (medium confidence). 
Increased efficiency has been insufficient to limit the emissions from 
shipping and aviation, and natural gas-based fuels are expected to 
be inadequate to meet stringent decarbonisation goals for these

segments (high confidence). High-energy density, low-carbon fuels 
are required, but they have not yet reached commercial scale. 
Advanced biofuels could provide low-carbon jet fuel (medium 
confidence). The production of synthetic fuels using low-carbon 
hydrogen with CO 2 captured through DACCS/BECCS could provide 
jet and marine fuels but these options still require demonstration 
at scale (low confidence). Ammonia produced with low-carbon 
hydrogen could also serve as a  marine fuel (medium confidence). 
Deployment of these fuels requires reductions in production costs. 
(Figure TS.14) {10.2, 10.3, 10.4, 10.5, 10.6, 10.8}
that, without intervention, CO 2 emissions from transport 
could grow in the range of 16% and 50% by 2050 (medium 
confidence). The scenarios literature projects continued growth in 
demand for freight and passenger services, particularly in developing

countries in Africa and Asia (high confidence). This growth is 
projected to take place across all transport modes. Increases in 
demand notwithstanding, scenarios that limit warming to 1.5°C 
degree with no or limited overshoot suggest that a 59% reduction 
(42–68% interquartile range) in transport-related CO 2 emissions by 
2050, compared to modelled 2020 levels is required. While many 
global scenarios place greater reliance on emissions reduction 
in sectors other than transport, a  quarter of the 1.5°C scenarios 
describe transport-related CO 2 emissions reductions in excess of 68% 
(relative to modelled 2020 levels) (medium confidence). Illustrative 
Mitigation Pathways IMP-Ren and IMP-LD (TS 4.2) describe emission 
from the Developed Countries, and Eastern Europe and West Central

Asia countries decrease from 2020 levels by 2050 across all scenarios 
limiting global warming to 1.5°C by 2100, but could increase in 
Africa, Asia and Pacific (APC), Latin America and Caribbean, and the 
Middle East in some of these scenarios. {10.7}
The scenarios literature indicates that fuel and technology shifts 
are crucial in reducing carbon emissions to meet temperature 
goals (high confidence). In general terms, electrification tends to 
play the key role in land-based transport, but biofuels and hydrogen 
(and derivatives) could play a role in decarbonisation of freight in 
some contexts. Biofuels and hydrogen (and derivatives) are expected to be more prominent in shipping and aviation. The shifts towards 
these alternative fuels must occur alongside shifts towards clean 
technologies in other sectors. {10.7}
significant expansion of low-carbon energy infrastructure,

countries where ‘greenfield’ development doesn’t suffer from 
structures (medium confidence).  The UNFCCC does not specifically 
cover emissions from international shipping and aviation. Reporting

emissions from international transport is at the discretion of each 
country. While the International Civil Aviation Organization (ICAO) 
and International Maritime Organization (IMO) have established 
emissions reductions targets, only strategies to improve fuel 
efficiency and demand reductions have been pursued, and there has 
been minimal commitment to new technologies. {10.5, 10.6, 10.7}
labour rights, non-climate environmental impacts, and costs 
of critical minerals needed for lithium-ion batteries (medium 
confidence).  Emerging national strategies on critical minerals and 
the requirements from major vehicle manufacturers are leading to 
new, more geographically diverse mines. The standardisation of 
battery modules and packaging within and across vehicle platforms, 
as well as increased focus on design for recyclability are important. 
Given the high degree of potential recyclability of lithium-ion batteries,

a  nearly closed-loop system in the future could mitigate concerns 
about critical mineral issues (medium confidence). {10.3, 10.8}
government, and together with pledges for personal choices, 
could spur the deployment of demand- and supply-side 
transport mitigation strategies (medium confidence). At the 
local level, legislation can support local transport plans that include 
commitments or pledges from local institutions to encourage 
behaviour change by adopting an organisational culture that 
motivates sustainable behaviour with inputs from the creative 
campaigns, free transport passes, parking charges, or eliminating 
car benefits. Community-based solutions such as solar sharing, 
community charging, and mobility as a  service can generate new 
opportunities to facilitate low-carbon transport futures. At the

regional and national levels, legislation can include vehicle and fuel 
efficiency standards, R&D support, and large-scale investments in 
low-carbon transport infrastructure. (Figure TS.14) {10.8, Chapter 15}

100
were embodied emissions from the production of cement and 
steel used in buildings (high confidence). Most building-sector 
emissions are CO 2. Final energy demand from buildings reached 
128 EJ globally in 2019 (around 31% of global final energy demand), 
and electricity demand from buildings was slightly above 43 EJ 
globally (around 18% of global electricity demand). Residential 
buildings consumed 70% (90 EJ) of the global final energy demand 
from buildings. Over the period 1990–2019, global CO 2 emissions 
from buildings increased by 50%, global final energy demand from 
buildings grew by 38%, and global final electricity demand increased

by 161%. {9.3}In most regions, historical improvements in efficiency have 
been approximately matched by growth in floor area per capita 
(high confidence).  At the global level, building-specific drivers of 
GHG emissions include: (i) population growth, especially in developing 
countries; (ii) increasing floor area per capita, driven by the increasing 
size of dwellings while the size of households kept decreasing, especially 
in developed countries; (iii) the inefficiency of newly constructed 
buildings, especially in developing countries, and the low renovation 
rates and low ambition level in developed countries when existing 
buildings are renovated; (iv) the increase in use, number and size of 
appliances and equipment, especially information and communication 
technologies (ICT) and cooling, driven by income; and, (v) the continued 
reliance on carbon-intensive electricity and heat. These factors taken 
together are projected to continue driving increased GHG emissions in

the building sector in the future. {9.3, 9.6, 9.9}Regime scale
Niche scale
R&D demonstrationsLandscape scale
Remove barriers to mainstream
Medium
Small
strategies for 
net zero 
transport, land 
use and energy
reviews on 
vehicles, land 
and energy
partnerships 
on government 
ﬂeets, buses, 
EVs, taxis, 
tuk-tuks, 
share bikes
engagement 
programmes• ASI regulations for 
each mode
settlement fabric 
(central, inner, outer, 
rural, remote)
ports
Market 
electro-
mobility
Travel

demand 
efﬁciency
All modes 
Heavy 

101TS
Sufficiency, Efficiency, Renewable (SER) framework. Sufficiency 
measures tackle the causes of GHG emissions by limiting the 
demand for energy and materials over the lifecycle of buildings 
and appliances (high confidence). In Chapter  9 of this report, 
sufficiency differs from efficiency: sufficiency is about long-term 
actions driven by non-technological solutions, which consume less 
energy in absolute terms; efficiency, in contrast is about continuous 
short-term marginal technological improvements. Sufficiency policies 
are a  set of measures and daily practices that avoid demand for 
energy, materials, land and water while delivering human well-
being-for-all within planetary boundaries. Use of the SER framework 
aims to reduce the cost of constructing and using buildings without 
reducing occupants’ well-being and comfort. {9.1, 9.4, 9.5, 9.9}

the use phase of buildings and do not require maintenance 
nor replacement over the lifetime of buildings. Density, 
compacity, bioclimatic design to optimise the use of nature-based 
solutions, multi-functionality of space through shared space and 
to allow for adjusting the size of buildings to the evolving needs 
of households, circular use of materials and repurposing unused 
existing buildings to avoid using virgin materials, optimisation of the 
use of buildings through lifestyle changes, use of the thermal mass 
of buildings to reduce thermal needs, and moving from ownership 
to usership of appliances, are among the sufficiency interventions 
implemented in leading municipalities (high confidence). At a global 
level, up to 17% of the mitigation potential in the buildings sector 
could be captured by 2050 through sufficiency interventions (medium 
confidence). (Figure TS.15) {9.2, 9.3, 9.4, 9.5, 9.9}The potential associated with sufficiency measures, as well as

the replacement of appliances, equipment and lights by efficient 
ones, is below zero cost (high confidence). The construction of 
high-performance buildings is expected to become a  business-as-
usual technology by 2050 with costs below USD20 tCO 2–1 in developed 
confidence). For existing buildings, there have been many examples 
of deep retrofits where additional costs per CO 2 abated are not 
significantly higher than those of shallow retrofits. However, 
for the whole building stock they tend to be in cost intervals of 
USD–200 tCO 2–1 and >USD200 tCO 2–1 (medium confidence). Literature 
emphasises the critical role of the 2020–2030 decade in accelerating 
the learning of know-how and skills to reduce the costs and remove 
feasibility constraints for achieving high-efficiency buildings at scale 
and set the sector on the pathway to realise its full potential (high 
confidence). {9.3, 9.6, 9.9}.

construction and retrofit of buildings has led to increasing 
the number of zero-energy or zero-carbon buildings in almost 
all climate zones.  The complementarity and interdependency of 
measures leads to cost reductions, while optimising the mitigation 
potential achieved and avoiding the lock-in-effect (medium confidence). 
{9.6, 9.9}
2019
2020
2050
2050
2020
2050
2020
2050
20502050
1990
2019
2020
2050
2050
2020
2050
2020
2050
20502050
Scenario
Renewable
scenario
SSP2
LED and

0%0%
40%21%
0%0%
28%42%
0% 23%28%
30%19%
59%12%
21%15%
22%68%17%
15%29%
47%
05001000
31%38%
13%8%
0%0% 17%3%
0% 42%
32% 21%
25%
24%
55%34%
17%
25%
36%31%
26%53%
82%0%
0%
115%
33%21%
64%73%4%
78%13%8%
25%63%
0%0%
56%53%
83%0%
0%
31%65%52%
22%
▲
▼▲▲▲▲
▼▲▲▲
▼
▼▲▲▲▲▲▲
▼
▼▲▲▲
▼
▼
▼
14%55%
2%38%
0%0%14%
0% 1%9%
0%45%8%
22%
26%19%
71%3%
7%
38%9%
8%53%
29%
28% 14%36%
40%
▲▲
▲▲
▲▲▲
▲▲▲
▲▼
▼▼
▼
▼
▼▼▼
▼▼▼
▼▼
▼ ▼
▼
▼
▲▲▲▲▲
▲▲ ▲
▲▲▲ ▲
▲
▼▼▼▼▼
▼
▼▼▼
▼
▼▼▼
▼
▼▲▲
▲
▲▲▲▲
▲▲▲
▲▼
▼▼▼
▼
▼
▼▼
▼▼▼
▼▼
▼
▼
▼
▼
▲▲▲▲▲
▲▲
▲▲
▲▲▲▲▲
▲
▼▼▼
▼
▼▼▼
▼▼
▼
▼
▼
▼▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▼
▼
▼▲▲▲▲▲
▲▲
▲▲
▲▲
▲▲
▲
▲▲
▲▲
▲▲▲▲
▲
▲▲▲
▲
▲▲▲▲▲▲▲▲
▲▲▲▲▲▲▲▲▲
▼▼▼▼
▼▼
▼▼▼
▼
▼
▼▼▼▼▼
▼▼▼▼
▼▼▼▼▼▼
▲▲▲▲▲▲▲▲
▲▲▲▲▲
▲▲▲ ▲
▼▼▼▼
▼▼
▼
▼▼
▼

▲
▼▲▲▲▲
▼▲▲▲
▼
▼▲▲▲▲▲▲
▼
▼▲▲▲
▼
▼
▼
14%55%
2%38%
0%0%14%
0% 1%9%
0%45%8%
22%
26%19%
71%3%
7%
38%9%
8%53%
29%
28% 14%36%
40%
▲▲
▲▲
▲▲▲
▲▲▲
▲▼
▼▼
▼
▼
▼▼▼
▼▼▼
▼▼
▼ ▼
▼
▼
▲▲▲▲▲
▲▲ ▲
▲▲▲ ▲
▲
▼▼▼▼▼
▼
▼▼▼
▼
▼▼▼
▼
▼▲▲
▲
▲▲▲▲
▲▲▲
▲▼
▼▼▼
▼
▼
▼▼
▼▼▼
▼▼
▼
▼
▼
▼
▲▲▲▲▲
▲▲
▲▲
▲▲▲▲▲
▲
▼▼▼
▼
▼▼▼
▼▼
▼
▼
▼
▼▼▼
▼
▼
▼▼
▼
▼
▼
▼▼▼
▼
▼▲▲▲▲▲
▲▲
▲▲
▲▲
▲▲
▲
▲▲
▲▲
▲▲▲▲
▲
▲▲▲
▲
▲▲▲▲▲▲▲▲
▲▲▲▲▲▲▲▲▲
▼▼▼▼
▼▼
▼▼▼
▼
▼
▼▼▼▼▼
▼▼▼▼
▼▼▼▼▼▼
▲▲▲▲▲▲▲▲
▲▲▲▲▲
▲▲▲ ▲
▼▼▼▼
▼▼
▼
▼▼
▼
▼
Figure TS.15 (continued): Decompositions of changes in historical residential energy emissions 1990–2019, changes in emissions projected by baseline 
scenarios for 2020–2050, and differences between scenarios in 2050 using scenarios from three models: IEA, IMAGE, and RECC. RECC-LED data for

(a) global, and (b) for nine world regions, include only space heating and cooling and water heating in residential buildings. Emissions are decomposed using the equation, 
which shows changes in driver variables of population, sufficiency (floor area per capita), efficiency (final energy per floor area), and renewables (GHG emissions per final 
energy). ‘Renewables’ is a summary term describing changes in GHG intensity of energy supply. Emission projections to 2050, and differences between scenarios in 2050, 
demonstrate mitigation potentials from the dimensions of the SER framework realised in each model scenario. In most regions, historical improvements in efficiency have been 
approximately matched by growth in floor area per capita. Implementing sufficiency measures that limit growth in floor area per capita, particularly in developed regions, 
reduces the dependence of climate mitigation on technological solutions. {Figure 9.5, Box 9.2}

103TS
barriers and obstacles as well as limited finance flows (high 
confidence). The lack of institutional capacity, especially in 
developing countries, and appropriate governance structures 
slow down the decarbonisation of the global building stock 
(medium confidence).  The building sector is highly heterogenous 
with many different building types, sizes, and operational uses. The 
sub-segment representing rented property faces principal/agent 
problems where the tenant benefits from the decarbonisation’s 
investment made by the landlord. The organisational context and the 
governance structure could trigger or hinder the decarbonisation of 
buildings. Global investment in the decarbonisation of buildings was 
estimated at USD164 billion in 2020. However, this is not enough by 
far to close the investment gap (high confidence). {9.9}

the global building stock. Building energy codes represent 
the main regulatory instrument to reduce emissions from 
both new and existing buildings (high confidence).  The most 
advanced building energy codes include requirements on each of 
the three pillars of the SER framework in the use and construction 
phase of buildings. Building energy codes have proven to be effective 
equipment, if the performance level is set at the level of the best available technologies in the market (high confidence). Market-based 
instruments such as carbon taxes with recycling of the revenues 
and personal or building carbon allowances could also contribute 
to fostering the decarbonisation of the building sector (medium 
confidence). {9.9}
Adapting buildings to future climate while ensuring well-being 
for all requires action. Expected heatwaves will inevitably

increase cooling needs to limit the health impacts of climate 
change (medium confidence).  Global warming will impact cooling 
and heating needs but also the performance, durability and safety 
of buildings, especially historical and coastal ones, through changes 
in temperature, humidity, atmospheric concentrations of CO 2 and 
chloride, and sea level rise. Adaptation measures to cope with climate 
change may increase the demand for energy and materials leading to 
an increase in GHG emissions if not mitigated. Sufficiency measures 
which anticipate climate change, and include natural ventilation, 
white walls, and nature-based solutions (e.g.,  green roofs) will 
decrease the demand for cooling. Shared cooled spaces with highly 
efficient cooling solutions are among the mitigation strategies which 
can limit the effect of the expected heatwaves on people’s health. 
{9.7, 9.8}
Up to 80% of GHG emissions

countries

104
in the buildings sector have significant potential to help 
achieve the SDGs (high confidence). As shown in Figure TS.16, 
the impacts of mitigation actions in the building sector go far beyond 
the goal of climate action (SDG 13) and contribute to meeting 15 
gains through improved indoor air quality and thermal comfort, and 
have positive significant macro- and micro-economic effects, such 
as increased productivity of labour, job creation, reduced poverty, 
especially energy poverty, and improved energy security (high 
confidence). (Figure TS.29) {9.8}
The COVID-19 pandemic emphasised the importance of 
buildings for human well-being and highlighted the inequalities 
in access for all to suitable, healthy buildings, which provide 
natural daylight and clean air to their occupants (medium

confidence). Recent WHO health recommendations have also 
emphasise indoor air quality, preventive maintenance of centralised 
mechanical heating, ventilation, and cooling systems. There are 
opportunities for repurposing existing non-residential buildings, no 
the health crisis and enabled by digitalisation. (Box TS.14) {9.1}
The industry chapter focuses on new developments since AR5 and 
emphasises the role of the energy-intensive and emissions-intensive 
basic materials industries in strategies for reaching net zero emissions. 
The Paris Agreement, the SDGs and the COVID-19 pandemic provide 
a new context for the evolution of industry and mitigation of industry 
greenhouse gas (GHG) emissions (high confidence). {11.1.1}
Net zero CO 2 industrial-sector emissions are possible but 
challenging (high confidence). Energy efficiency will continue to

be important. Reduced materials demand, material efficiency, and 
circular economy solutions can reduce the need for primary production. 
Primary production options include switching to new processes that 
use low-to-zero GHG energy carriers and feedstocks (e.g., electricity, 
hydrogen, biofuels, and carbon dioxide capture and utilisation 
(CCU) to provide carbon feedstocks). Carbon capture and  storage 
(CCS) will be required to mitigate remaining CO 2 emissions {11.3}. 
These options require substantial scaling up of electricity, hydrogen, 
recycling, CO 2, and other infrastructure, as well as phase-out or 
conversion of existing industrial plants. While improvements in the 
GHG intensities of major basic materials have nearly stagnated over 
the last 30 years, analysis of historical technology shifts and newly 
available technologies indicate these intensities can be significantly 
reduced by mid-century. {11.2, 11.3, 11.4}
Industry-sector emissions have been growing faster since

2000 than emissions in any other sector, driven by increased 
basic materials extraction and production (high confidence). 
GHG emissions attributed to the industrial sector originate from fuel 
combustion, process emissions, product use and waste, which jointly 
accounted for 14.1 GtCO 2-eq or 24% of all direct anthropogenic 
emissions in 2019, second behind the energy supply sector. Industry is a leading GHG emitter – 20 GtCO 2-eq or 34% of global emissions 
in 2019  – if indirect emissions from power and heat generation 
are included. The share of emissions originating from direct fuel 
combustion is decreasing and was 7 GtCO 2-eq, 50% of  direct 
capital in tonnes per unit of GDP  – is increasing (high 
confidence). In-use stock of manufactured capital per capita has 
been growing faster than GDP per capita since 2000. Total global

in-use stock of manufactured capital grew by 3.4% yr –1 in 2000–2019. 
At the same time, per-capita material stocks in several developed 
countries have stopped growing, showing a  decoupling from GDP 
per capita. {11.2.1, 11.3.1}
The demand for plastic has been growing most strongly since 
1970 (high confidence). The current >99% reliance on fossil 
feedstock, very low recycling, and high emissions from petrochemical 
processes is a challenge for reaching net zero emissions. At the same 
time, plastics are important for reducing emissions elsewhere, for 
example, light-weighting vehicles. There are as yet no shared visions 
for fossil-free plastics, but several possibilities. {11.4.1.3}
GHG emissions and even close to net zero emissions from GHG 
intensive industry (e.g., steel, plastics, ammonia, and cement) 
can be achieved by 2050 by deploying multiple available and

emerging options (medium confidence). Significant reductions in 
industry emissions require a reorientation from the historic focus on 
important but incremental improvements (e.g., energy efficiency) to 
transformational changes in energy and feedstock sourcing, materials 
efficiency, and more circular material flows. {11.3, 11.4}
material flows and emerging primary processes, are not 
well represented in climate change scenario modelling and 
integrated assessment models (IAMs), albeit with some 
progress in recent years (high confidence). The character of these 
interventions (e.g., appearing in many forms across complex value 
chains, making cost estimates difficult) combined with the limited 
data on new fossil-free primary processes help explain why they 
are less represented in models than, for example, CCS. As a result, 
overall mitigation costs and the need for CCS may be overestimated. 
{11.4.2.1}

industry (high confidence). Using electricity directly, or indirectly 
via hydrogen from electrolysis for high temperature and chemical 
feedstock requirements, offers many options to reduce emissions. 
It also can provide substantial grid-balancing services, for example, 
through electrolysis and storage of hydrogen for chemical process 
use or demand response. (Box TS.9) {11.3.5}
materials and will remain important (high confidence). In order 
to reach net zero CO 2 emissions for the carbon needed in society 

105TS
0.11
0.15
EE
ME
FeedCI
FSW+El
FeedCIME
20
20502070
0.14
0.10
0.17
0.180.16
CIEl
EE
ME
FeedCI
FSW+El
indirectDirect and
indirect
<20$/tCO 2
2020 2030 2040 2050 2050 2060 2070t CO2-eq/t chemicals(c) Primary chemicalsCosts rise: primary chemicals – 15–115%;
20502070
0.08
0.21
0.32

106
circularity with mechanical and chemical recycling, more efficient use 
of biomass feedstock with addition of low-GHG hydrogen to increase 
product yields (e.g., for biomethane and methanol), and potentially 
direct air capture of CO 2 as a new carbon source. {11.3, 11.4.1} 
Production costs for very low to zero emissions basic 
materials may be high but the cost for final consumers and 
the general economy will be low (medium confidence). Costs 
and emissions reductions potential in industry, and especially heavy 
industry, are highly contingent on innovation, commercialisation, and 
market-uptake policies. Technologies exist to take all industry sectors 
to very low or zero emissions, but require five to fifteen years of 
intensive innovation, commercialisation, and policy to ensure uptake. 
Mitigation costs are in the rough range of USD50–150  tCO 2-eq–1,

with wide variation within and outside this band. This affects 
competitiveness and requires supporting policy. Although production 
cost increases can be significant, they translate to very small 
increases in the costs for final products, typically less than a  few 
percent depending on product, assumptions, and system boundaries. 
(Figure TS.17) {11.4.1.5} 
Several technological options exist for very low to zero 
long life, reuse, constructability, and low-contamination recycling. 
Secondary production through high-quality recycling must be 
with the retrofitting of existing facilities for partial fuel switching

hydrogen, or electrolytic iron-ore reduction followed by an electric 
arc furnace. {11.3.2, 11.4.1.1}
Several current and emerging options can significantly reduce 
cement and concrete emissions. Producer, user, and regulator 
education, as well as innovation and commercialisation 
policy are needed (medium confidence). Cement and concrete 
are currently overused because they are inexpensive, durable, and 
ubiquitous, and consumption decisions typically do not give weight 
to their production emissions. Basic material efficiency efforts to 
use only well-made concrete thoughtfully and only where needed 
(e.g.,  using right-sized, prefabricated components) could reduce 
emissions by 24–50% through lower demand for clinker. Cementitious 
material substitution with various materials (e.g., ground limestone

and calcined clays) can reduce process calcination emissions by up to 
50% and occasionally much more. Until a very low GHG emissions 
alternative binder to Portland cement is commercialised – which is not anticipated in the near to mid-term  – CCS will be essential 
for eliminating the limestone calcination process emissions for 
making clinker, which currently represent 60% of GHG emissions in 
best-available technology plants. {11.3.2, 11.3.6, 11.4.1.2}
the main industrial feedstock chemicals and their derivatives, 
the  costs vary widely (high confidence). Fossil fuel-based 
feedstocks are inexpensive and still without carbon pricing, and 
their biomass- and electricity-based replacements are expected to 
propylene, benzene, toluene, and mixed xylenes and aromatics from

thousands of derivative end-use chemicals. Hydrogen, biogenic 
or air-capture carbon, and collected plastic waste for the primary 
feedstocks can greatly reduce total emissions. Biogenic carbon 
feedstock is expected to be limited due to competing land uses. 
{11.4.1}
Light industry and manufacturing can be largely decarbonised 
through switching to low-GHG fuels (e.g.,  biofuels and 
and heat pumps) (high confidence). Most of these technologies 
are already mature, for example for low-temperature heat, but 
a major challenge is the current low cost of fossil CH 4 and coal relative 
mills have access to biomass residues and by-products and

industry can also be a source of biogenic carbon dioxide, carbon for 
organic chemicals feedstock, and for CDR using CCS. {11.4.1}
for zero-emission electricity and low-cost hydrogen from electrolysis 
sources, may reshape where currently energy- and emissions-intensive 
Regions with bountiful solar and wind resources, or low fugitive CH 4 
co-located with CCS geology, may become exporters of hydrogen or

hydrogen carriers such as methanol and ammonia, or home to the 
production of iron and steel, organic platform chemicals, and other 
energy-intensive basic materials. {11.2, 11.4, Box 11.1}Figure TS.17 (continued): Potentials and costs for zero-carbon mitigation options for industry and basic materials.  CIEl – carbon intensity of electricity for 
indirect emissions; EE – energy efficiency; ME – material efficiency; Circularity – material flows (clinker substituted by coal fly ash, blast furnace slag or other by-products and 
waste, steel scrap, plastic recycling, etc.); FeedCI – feedstock carbon intensity (hydrogen, biomass, novel cement, natural clinker substitutes); FSW+El – fuel switch and processes 
electrification with low-carbon electricity. Ranges for mitigation options are shown based on bottom-up studies for grouped technologies packages, not for single technologies.

In circles, contribution to mitigation from technologies based on their readiness are shown for 2050 (2040) and 2070. Direct emissions include fuel combustion and process 
emissions. Indirect emissions include emissions attributed to consumed electricity and purchased heat. For basic chemicals, only methanol, ammonia and high-value chemicals 
are considered. Total for industry does not include emissions from waste. Negative mitigation costs for some options such as Circularity are not reflected. {Figure 11.13}

107TS
a  well-established policy field with decades of experience from 
and demand-side management (DSM) programmes. In contrast, 
materials demand management and efficiency are not well understood 
and addressed from a  policy perspective. Barriers to recycling that 
policy could address are often specific to the different material 
or poor economics for plastics) or waste-management systems. For 
electrification and fuel switching the focus has so far been mainly on 
innovation and developing technical supply-side solutions rather than 
creating market demand. {11.5.2, 11.6}
climate policy and carbon pricing due to concerns about carbon

leakage26 and reducing competitiveness (high confidence). 
New approaches to industrial development policy are emerging for 
a transition to net zero GHG emissions. The transition requires a clear 
direction towards net zero, technology development, market demand 
for low-carbon materials and products, governance capacity and 
learning, socially inclusive phase-out plans, as well as international 
coordination of climate and trade policies (see also TS.6.5). It requires 
governance at different levels (from international to local) and 

mitigation opportunities while providing food, wood 
and other renewable resources as well as biodiversity 
conservation, provided the sector adapts to climate change. 
Land-based mitigation measures can reduce GHG emissions within 
the AFOLU sector, deliver CDR and provide biomass thereby enabling 
emission reductions in other sectors.28 The rapid deployment of 
AFOLU measures features in all pathways that limit global warming 
to 1.5°C. Where carefully and appropriately implemented, AFOLU 
mitigation measures are positioned to deliver substantial co-benefits 
and help address many of the wider challenges associated with 
land management. If AFOLU measures are deployed badly, when 
taken together with the increasing need to produce sufficient 
food, feed, fuel and wood, they may exacerbate trade-offs with the 
conservation of habitats, adaptation, biodiversity and other services. 

by governments under the UNFCCC are defined as all those occurring on ‘managed land’. Managed land is land where human interventions and practices have been applied to perform production, 
ecological or social functions.
28 For example: in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, CO 2 emissions from biomass used for energy are reported in the AFOLU sector, 
the sectoral total emissions for the that sector.At the same time the capacity of the land to support these functions 
may be threatened by climate change (high confidence). {AR6 WGI

Figure SPM.7; AR6 WGII, 7.1, 7.6}
global total anthropogenic GHG emissions in the period 
2010–2019. At the same time managed and natural terrestrial 
ecosystems were a carbon sink, absorbing around one third 
of anthropogenic CO 2 emissions (medium confidence). 
between 2010 and 2019 with an unclear trend. Based on FAOSTAT 
or national GHG inventories, the net CO 2 emissions from AFOLU were 
in the reported CO 2 AFOLU emissions magnitude because alternative 
methodological approaches that incorporate different assumptions 
are used {7.2.2}. If the responses of all managed and natural land 
to both anthropogenic environmental change and natural climate 
variability, estimated to be a gross sink of –12.5 ± 3.2 GtCO 2 yr –1 for

the period 2010–2019, are added to land-use emissions, then land 
overall constituted a net sink of –6.6 ± 5.2 GtCO 2 yr –1 in terms of CO 2 
emissions, has generally declined, while global tree cover 
and global forest-growing stock levels are likely increasing 
(medium confidence). There are substantial regional differences, 
with losses of carbon generally observed in tropical regions and 
gains in temperate and boreal regions. Agricultural CH 4 and N 2O 
emissions are estimated to average 157 ± 47.1 MtCH 4 yr –1 and 
increase, the main source of which is enteric fermentation from

ruminant animals. Similarly, AFOLU N 2O emissions are increasing, 
dominated by agriculture, notably from manure application, nitrogen 
deposition, and nitrogen fertiliser use (high confidence). In addition 
to being a net carbon sink and source of GHG emissions, land plays an 
important role in climate through albedo effects, evapotranspiration, 
and aerosol loading through emissions of volatile organic compounds 
(VOCs). The combined role of CH 4, N 2O and aerosols in total climate 
forcing, however, is unclear and varies strongly with bioclimatic 
region and management practice. {2.4.2.5, 7.2, 7.3}

108
potential at relatively low cost and can provide 20–30% of 
the 2050 emissions reduction described in scenarios that limit 
warming to 2°C  (>67%) or lower (high evidence, medium 
agreement). The AFOLU sector can provide 20–30% (interquartile 
range) of the global mitigation needed for a 1.5°C or 2°C pathway 
towards 2050, though there are highly variable mitigation strategies 
targets {Illustrative Mitigation Pathways in 7.5}. The estimated 
economic (<USD100 tCO 2-eq–1) AFOLU sector mitigation potential 
end of this range representing the mean from IAMs and the upper 
end  representing the mean estimate from global sectoral studies. 
The economic potential is about half of the technical potential from 
AFOLU, and about 30–50% could be achieved under USD20 tCO 2-eq–1

{7.4}. The implementation of robust measurement, reporting and 
verification processes is paramount to improving the transparency of 
changes in land carbon stocks and this can help prevent misleading 
assumptions or claims on mitigation. {7.1, 7.4, 7.5} 
and other natural ecosystems provide the largest share of 
the AFOLU mitigation potential (up to USD100 tCO 2-eq–1), 
confidence).  In the global sectoral studies, the protection, improved 
management, and restoration of forests, peatlands, coastal wetlands, savannas and grasslands have the potential to reduce emissions 
and/or sequester 7.3 mean (3.9–13.1) GtCO 2-eq yr –1. Agriculture 
provides the second largest share of the mitigation potential, with 
4.1 (1.7–6.7) GtCO 2-eq yr –1 (up to USD100 tCO 2-eq–1) from cropland 
and grassland soil carbon management, agroforestry, use of biochar,

Demand-side measures including shifting to sustainable healthy 
diets, reducing food waste, building with wood, biochemicals, and 
bio-textiles, have a mitigation potential of 2.2 (1.1–3.6) GtCO 2-eq yr –1. 
Most mitigation options are available and ready to deploy. Emissions 
reductions can be achieved relatively quickly, whereas CDR needs 
have the potential to substantially increase the CH 4 mitigation 
potential beyond current estimates. AFOLU mitigation is not only

relevant in countries with large land areas. Many smaller countries 
and regions, particularly with wetlands, have disproportionately high 
levels of AFOLU mitigation potential density. {7.4, 7.5}
Assisting countries to overcome barriers will help to achieve Table TS.4 | Net anthropogenic emissions (annual averages for 2010–2019a) from agriculture, forestry and other land use (AFOLU). For context, the net flux 

considers 
bookkeeping 

109TS
efforts rely principally on government sources and funding mechanisms 
which do not provide sufficient resources to enable the economic 
potential to be realised. Differences in cultural values, governance, 
accountability and institutional capacity are also important barriers. 
Climate change itself could reduce the mitigation potential from the 
AFOLU sector, although an increase in the capacity of natural sinks 
could occur despite changes in climate (medium confidence) {AR6 
WGI Figure  SPM.7 and Sections 7.4 and 7.6}. The continued loss of 
biodiversity makes ecosystems less resilient to climate change extremes 
and this may further jeopardise the achievement of the AFOLU 
mitigation potentials indicated in this chapter (high confidence). (Box 
TS.15) {7.6}

and other bio-based products represents an important share 
of the total mitigation potential associated with the AFOLU 
sector, though these mitigation effects accrue to other sectors 
(high confidence). Recent estimates of the technical bioenergy 
potential, when constrained by food security and environmental 
considerations, are within the ranges 5–50 and 50–250 EJ yr –1 
by 2050 for residues and dedicated biomass production systems, 
respectively.29 (TS.5.7) {7.4, 12.3} 
Bioenergy is the most land-intensive energy option, but total 
land occupation of other renewable energy options can also 
become significant in high deployment scenarios. While not 
as closely connected to the AFOLU sector as bioenergy, other 
renewable energy options can influence AFOLU activities 
in both synergistic and detrimental ways (high confidence). 
The character of land occupation, and associated impacts, vary 
considerably among mitigation options and also for the same

option depending on geographic location, scale, system design and 
deployment strategy. Land occupation can be large uniform areas, 
for example, reservoir hydropower dams and tree plantations, and 
more distributed occupation that is integrated with other land uses, 
for example, wind turbines and agroforestry in agriculture landscapes. 
Deployment can be partly decoupled from additional land use, for 
example, use of organic waste and residues and integration of solar 
PV into buildings and other infrastructure (high confidence). Wind and 
solar power can coexist with agriculture in beneficial ways (medium 
confidence). Indirect land occupation includes new agriculture areas 
following displacement of food production with bioenergy plantations 
and expansion of mining activities providing minerals required for 
manufacture of EV batteries, PV, and wind power. {7.4, 12.5}
The deployment of land-based mitigation measures can

provide co-benefits, but there are also risks and trade-offs 
from inappropriate land management (high confidence). Such 
risks can best be managed if AFOLU mitigation is pursued 
in response to the needs and perspectives of multiple 
stakeholders to achieve outcomes that maximise synergies 
29 These potentials do not include avoided emissions resulting from bioenergy use associated with BECCS, which depends on energy substitution patterns, conversion efficiencies, and supply chain 
emissions for both the BECCS and substituted energy systems. Estimates of substitution effects of bioenergy indicate that this additional mitigation would be of the same magnitude as provided 
through CDR using BECCS. Bio-based products with long service life, for example, construction timber, can also provide mitigation through substitution of steel, concrete, and other products, and

through carbon storage in the bio-based product pool. See section TS.5.7 for the CDR potential of BECCS. {7.4, 12.3}while limiting trade-offs (medium confidence). The results of 
implementing AFOLU measures are often variable and highly context-
specific. Depending on local conditions (e.g., ecosystem, climate, food 
system, land ownership) and management strategies (e.g.,  scale, 
method), mitigation measures can positively or negatively affect 
human well-being. The agriculture and forestry sectors can devise 
management approaches that enable biomass production and use 
for energy in conjunction with the production of food and timber, 
thereby reducing the conversion pressure on natural ecosystems 
(medium confidence). Mitigation measures addressing GHGs may also 
affect other climate forcers such as albedo and evapotranspiration.

‘Nature-based Solutions’. {7.1, 7.4, 7.6, 12.4, 12.5}
for decades but deployment remains slow, and emissions 
related options (high confidence).  Globally, the AFOLU sector has 
delivered about 0.65 GtCO 2 yr –1 of mitigation during 2010–2019 or 
1.4% of global gross emissions. The majority (>80%) of emission 
reduction resulted from forestry measures. Although the mitigation 
potential of AFOLU measures is large from a biophysical and ecological

perspective, its feasibility is hampered by lack of institutional support, 
uncertainty over long-term additionality and trade-offs, weak 
governance, fragmented land ownership, and uncertain permanence 
effects. Despite these impediments to change, AFOLU mitigation 
options are demonstrably effective and with appropriate support can 
enable rapid emission reductions in most countries. {7.4, 7.6}
policymakers and investors to land owners and managers is 
a  pre-requisite for achieving high levels of mitigation in the 
AFOLU sector (high confidence).  To date USD0.7 billion yr –1 is 
of the more than USD400 billion yr –1 that is estimated to be necessary 
to deliver the up to 30% of global mitigation effort envisaged in deep 
mitigation scenarios (medium confidence). This estimate of the global

funding requirement is smaller than current subsidies provided to 
agriculture and forestry. A gradual redirection of existing agriculture 
and forestry subsidies would greatly advance mitigation. Effective policy 
interventions and national (investment) plans as part of NDCs, specific 
to local circumstances and needs, are urgently needed to accelerate 
the deployment of AFOLU mitigation options. These interventions are 
effective when they include funding schemes and long-term consistent 
support for implementation with governments taking the initiative 
together with private funders and non-state actors. {7.6}

110
depends strongly on policies that directly address emissions 
and drive the deployment of land-based mitigation options, 
consistent with carbon prices in deep mitigation scenarios 
(high confidence).  Examples of successful policies and measures 
include establishing and respecting tenure rights and community 
forestry, improved agricultural management and sustainable 
intensification, biodiversity conservation, payments for ecosystem 
services, improved forest management and wood-chain usage, 
bioenergy, voluntary supply chain management efforts, consumer 
behaviour campaigns, private funding and joint regulatory efforts 
to avoid, for example, leakage. The efficacy of different policies, 
however, will depend on numerous region-specific factors. In addition 
to funding, these factors include governance, institutions, long-term 
consistent execution of measures, and the specific policy setting.

While the governance of land-based mitigation can draw on lessons 
from previous experience with regulating biofuels and forest carbon, 
integrating these insights requires governance that goes beyond 
project-level approaches emphasising integrated land-use planning 
and management within the frame of the Sustainable Development 
Goals. {7.4, Box 7.2, 7.6}Addressing the many knowledge gaps in the development and 
testing of AFOLU mitigation options can rapidly advance the 
likelihood of achieving sustained mitigation (high confidence). 
Research priorities include improved quantification of anthropogenic 
and natural GHG fluxes and emissions modelling, better understanding 
of the impacts of climate change on the mitigation potential, 
permanence and additionality of estimated mitigation  actions, 
and improved (real-time and cheap) measurement, reporting and 
verification. There is a need to include a greater suite of mitigation

measures in IAMs, informed by more realistic assessments that take 
into account local circumstances and socio-economic factors and 
cross-sector synergies and trade-offs. Finally, there is a critical need 
for more targeted research to develop appropriate country-level, 
locally specific, policy and land-management response options. These 
options could support more specific NDCs with AFOLU measures 
that enable mitigation while also contributing to biodiversity 
conservation, ecosystem functioning, livelihoods for millions of 
farmers and foresters, and many other SDGs. {7.7, Figure 17.1} 
F-gases
05101520

300
200
100
10.0
48%

111TS
integrated policy packages (high confidence).  Food systems 
are associated with 23–42% of global GHG emissions, while there 
is still widespread food insecurity and malnutrition. Absolute GHG 
emissions from food systems increased from 14 to 17 GtCO 2-eq yr –1 in 
the period 1990–2018. Both supply- and demand-side measures are 
important to reduce the GHG intensity of food systems. Integrated 
food policy packages based on a  combination of market-based, 
administrative, informative, and behavioural policies  can reduce 
cost compared to uncoordinated interventions, address multiple 
sustainability goals, and increase acceptance across stakeholders 
and civil society (limited evidence, medium agreement). Food

systems governance may be pioneered through local food policy initiatives complemented by national and international initiatives, 
but governance on the national level tends to be fragmented, and 
thus has limited capacity to address structural issues like inequities in 
access. (Figure TS.18, Table TS.5, Table TS.6) {7.2, 7.4, 12.4}
meat shows the highest GHG intensity. Beef from dairy systems 
has lower emissions intensity than beef from beef  herds  (8–23 and 
17–94 kgCO 2-eq (100 g protein)–1, respectively) when some emissions 
are allocated to dairy products. The wide variation in emissions reflects 
differences in production systems, which range from intensive 
feedlots with stock raised largely on grains through to rangeland and 
transhumance production systems. Where appropriate, a shift to diets

with a higher share of plant protein, moderate intake of animal-source 
foods and reduced intake of saturated fats could lead to substantial 
decreases in GHG emissions. Benefits would also include reduced land 
020%
10%40%
30%60%90%
80%
70%
index
in the Asia-Pasiﬁc 
developed region).Countries with high share 
territorial GHG emissions because of land-use change

importing consumers. Often this is associated with emerging 
system emissions) the whole sale cost for food tends to 
consumption rates of 
meat.North and 
South and 
West and 
West and 
Sri Lanka
AsiaEast 
East

1 5 10
emission from energy use.  GHG emissions are calculated according to the IPCC Tier 1 approach and are assigned to the country where they occur, not necessarily where the 
food is consumed. Health outcome is expressed as relative contribution of each of the following risk factors to their combined risk for deaths: Child and maternal malnutrition 
(red), Dietary risks (yellow) or High body-mass index (blue). {Figure 12.7}

112
Food from 
and fisheries(I)   Dietary shift, in particular 
increased share of plant-based 
protein sourcesD+  ↓ GHG footprintA+   Animal welfare 
impacts 
(I)   Sustainable intensification 

Controlled 
agriculture(T)   Soil-less agricultureD+  ↑ productivity, weather independent
H+   Crop breeding can be optimised for taste and/or 
nutritional quality
technologies(T)   InsectsD0  Good feed conversion efficiency
FW+  Can be fed on food wasteH0   Good nutritional qualities but attention to allergies 
and food safety issues required
H+   Good nutritional qualities; risk of heavy-metal 
and pathogen contamination
(I/T)   Plant-based alternatives to

animal-based food productsD+  No emissions from animals, ↓ inputs for feedA+   Animal welfare
(T)   Cellular agriculture (including 
cultured meat, microbial protein)D+   No emissions from animals, high protein conversion 
efficiency
Food 
and packaging(I)   Valorisation of by-products, 
FLW logistics and managementM+  Substitution of bio-based materials
(I)   Smart packaging and 
other technologies FW+  ↓ of food waste

distribution(I)   Improved logistics D+  ↓ transport emissions
(I)   Specific measures to reduce food 
waste in retail and food catering FW+  ↓ of food waste

113TS
food products, and controlled environment agriculture, can 
bring substantial reduction in direct GHG emissions from 
address concerns over animal welfare. Realising the full mitigation 
potential depends on access to low-carbon energy as some emerging 
technologies are relatively more energy intensive. This also holds for deployment of cold-chain and packaging technologies, which can 
help reduce food loss and waste, but increase energy and materials 
use in the food system. (Table TS.5) {11.4.1.3, 12.4}

CDR is a  key element in scenarios that limit warming to 
2°C (>67%) or 1.5°C (>50%) by 2100 (high confidence).  
Implementation strategies need to reflect that CDR methods differ in 
terms of removal process, timescale of carbon storage, technological 
maturity, mitigation potential, cost, co-benefits, adverse side effects, 
and governance requirements. (Box TS.10) 
 
Level  
Cost
tion, coherence and consist-
ency in policy packageb
food policy 
packagesNL    can be 
controlled cost 
efficient+ balanced, addresses multi-
ple sustainability goalsReduces cost of uncoordinated interventions; increases

acceptance across stakeholders and civil society (robust 
evidence, high agreement)
Taxes on 
food productsGN     regressive low# 1– unintended  
substitution effectsHigh enforcing effect on other food policies; higher 
acceptance if compensation or hypothecated taxes 
(medium evidence, high agreement)
GHG taxes 
on foodGN     regressive low# 2– unintended  
substitution effectsSupportive, enabling effect on other food policies, 
agricultural/fishery policies; requires changes in power 
distribution and trade agreements (medium evidence, 
medium agreement) + high spillover effect
Trade policies G    impacts global 
distributioncomplex 
effects+ counters leakage effects
Requires changes in existing trade agreements 
(medium evidence, high agreement) +/– effects on market structure 
and jobs
Investment into 
research and 
innovationGN  none  medium+ high spillover effect 
+ converging with digital societyCan fill targeted gaps for coordinated policy pack-

ages (e.g., monitoring methods) (robust evidence, 
high agreement)
Food and 
marketing 
regulationsN      low  Can be supportive; might be supportive to realise innova-
tion; voluntary standards might be less effective (medium 
evidence, medium agreement)
Organisational-
level procure-
ment policiesNL      low+ can address multiple sustain-
ability goalsEnabling effect on other food policies; reaches large share 
of population (medium evidence, high agreement)
Sustainable food-
based dietary 
guidelinesGNL      none  low+ can address multiple sustain-
ability goalsLittle attention so far on environmental aspects; can serve 
as benchmark for other policies (labels, food formulation 
standards, etc.) (medium evidence, medium agreement)
Food labels/ 
informationGNL    education 
level relevantlow + empowers citizens
+ increases awareness
+ multiple objectivesEffective mainly as part of a policy package; incorpo-
ration of other objectives (e.g., animal welfare, fair

trade); higher effect if mandatory (medium evidence, 
medium agreement)
mation deficits in population 
subgroupsHigh enabling effect on other food policies 
(medium evidence, high agreement)
and climate change mitigation. b Requires coordination between policy areas, participation of stakeholders, transparent methods and indicators to manage trade-offs and 

114
in this report use land-based biological CDR (primarily 
afforestation/reforestation (A/R)) and/or bioenergy with 
carbon capture  and storage (BECCS). Some also include 
direct air CO 2 capture and storage (DACCS) (high confidence). 
Across the scenarios limiting warming to 2°C (>67%) or below, 
cumulative volumes30 of BECCS reach 328 (168–763) GtCO 2, CO 2 
Despite limited current deployment, estimated mitigation 
potentials for DACCS, enhanced weathering (EW) and 
ocean-based CDR methods (including ocean alkalinity

enhancement and ocean  fertilisation) are moderate to large 
is limited mainly by requirements for low-carbon energy and by 
cost (100–300 (full range: 84–386) USD tCO 2–1). DACCS is currently 
at a  medium technology readiness level. EW has the potential 
to remove  2–4 (full range: <1 to around 100) GtCO 2  yr –1, at costs 
possible side effects on the marine environment. EW and ocean-based 
CDR governance and policymaking can draw on widespread 
Additionally, to accelerate research, development, and demonstration,

and to incentivise CDR deployment, a political commitment to formal 
integration into existing climate policy frameworks is required, 
including reliable measurement, reporting and verification (MRV) of 
carbon flows. {12.3.3, 12.4, 12.5}
Carbon Dioxide Removal (CDR) is necessary to achieve net zero CO 2 and GHG emissions both globally and nationally, counterbalancing 
‘hard-to-abate’ residual emissions. CDR is also an essential element of scenarios that limit warming to 1.5°C or below 2°C (>67%) 
by 2100, regardless of whether global emissions reach near zero, net zero or net negative levels. While national mitigation portfolios 
aiming at net zero emissions or lower will need to include some level of CDR, the choice of methods and the scale and timing of 
their deployment will depend on the achievement of gross emission reductions, and managing multiple sustainability and feasibility

constraints, including political preferences and social acceptability.
CDR refers to anthropogenic activities removing CO 2 from the atmosphere and durably storing it in geological, terrestrial, or ocean 
sinks, but excludes natural CO 2 uptake not directly caused by human activities (Annex I). Carbon Capture and Storage (CCS) and 
There is a great variety of CDR methods and respective implementation options {Cross-Chapter Box 8, Figure 1 in Chapter 12}. Some 
of these methods (like afforestation and soil carbon sequestration) have been practiced for decades to millennia, although not

necessarily with the intention to remove carbon from the atmosphere. Conversely, for methods such as DACCS and BECCS, experience 
is growing but still limited in scale. A categorisation of CDR methods can be based on several criteria, depending on the highlighted 
characteristics. In this report, the categorisation is focused on the role of CDR methods in the carbon cycle, that is on the removal 
process (land-based biological; ocean-based biological; geochemical; chemical) and on the time scale of storage (decades to centuries; 
centuries to millennia; 10,000 years or longer), the latter being closely linked to different carbon storage media. Within one category 
(e.g., ocean-based biological CDR) options often differ with respect to other dynamic or context-specific dimensions such as mitigation 
potential, cost, potential for co-benefits and adverse side effects, and technology readiness level. (Table TS.7, TS.5.6, TS. 5.7) {12.3}

It is useful to distinguish between CO 2 removal from the atmosphere as the outcome of deliberate activities implementing CDR 
As part of ambitious mitigation strategies at global or national levels, gross CDR can fulfil three different roles in complementing 
emissions abatement: (i) lowering net CO 2 or GHG emissions in the near term; (ii) counterbalancing ‘hard-to-abate’ residual emissions 
exceeding annual residual emissions {2.7, 3.3, 3.4, 3.5}. These roles of CDR are not mutually exclusive: for example, achieving net zero

CO 2 or GHG emissions globally might involve individual developed countries attaining net negative CO 2 emissions at the time of global 

115TS
(USD 
reforestation  8–9  0–240  0.5–10 Reversal of carbon removal 
through wildfire, disease, pests may 
occur.  Reduced catchment water yield 
and lower groundwater level if species 
and biome are inappropriate. Enhanced employment and local 
livelihoods, improved biodiversity, 
improved renewable wood products 
provision, soil carbon and nutrient 
cycling. Possibly less pressure on 
primary forest. Inappropriate deployment at large 
scale can lead to competition for land 
with biodiversity conservation and

food production.Substantial contribution in IAMs and 
also in bottom-up sectoral studies. {7.4}
sequestration in 
grasslands8–9  –45–100  0.6–9.3 Risk of increased nitrous oxide 
emissions due to higher levels of 
organic nitrogen in the soil; risk of 
reversal of carbon sequestration. Improved soil quality, resilience 
and agricultural productivity. Attempts to increase carbon 
sequestration potential at the expense 
of production. Net addition per hectare 
is very small; hard to monitor.In development – not yet in global 
mitigation pathways simulated by 
IAMs in bottom-up studies: with 
medium contribution. {7.4}
restoration 8–9 Insufficient 
data 0.5–2.1 Reversal of carbon removal in drought 
CH 4 emissions. Enhanced employment and local 
livelihoods, increased productivity of 
fisheries, improved biodiversity, soil 
carbon and nutrient cycling. Competition for land for food

production on some peatlands 
used for food production. Not in IAMs but some bottom-up 
studies with medium contribution.{7.4}
data0.3–9.4Risk that some land area lost 
from food production; requires 
very high skills. Enhanced employment and local 
livelihoods, variety of products 
improved soil quality, more 
resilient systems. Some trade-off with agricultural crop 
production, but enhanced biodiversity, 
and resilience of system.No data from IAMs, but in 
bottom-up sectoral studies with 
medium contribution.{7.4}
data0.1–2.1 If improved management is 
understood as merely intensification 
involving increased fertiliser use 
and introduced species, then it 
could reduce biodiversity and 
increase eutrophication. In case of sustainable forest 
management, it leads to 
enhanced employment and local 
livelihoods, enhanced biodiversity, 
improved productivity.If it involves increased fertiliser

use and introduced species it could 
reduce biodiversity and increase 
eutrophication and upstream 
GHG emissions. No data from IAMs, but in 
Biochar 6–7  10–345  0.3–6.6 Particulate and GHG emissions 
from production; biodiversity and 
carbon stock loss from unsustainable 
biomass harvest. Increased crop yields and reduced 
non-CO 2 emissions from soil; and 
resilience to drought. Environmental impacts associated 
particulate matter; competition for 
biomass resource. In development – not yet in global 
mitigation pathways simulated 
by IAMs. {7.4}
Direct air carbon 
capture and 
storage (DACCS) 6 100–300 
(84–386)  5–40  Increased energy and water use. Water produced (solid sorbent 
DAC designs only). Potentially increased emissions from 
water supply and energy generation. In a few IAMs; DACCS complements 
other CDR methods. {12.3} 
Bioenergy with 
carbon capture 
and storage

(BECCS)5–6  15–400  0.5–11 Inappropriate deployment at very large 
scale leads to additional land and 
water use to grow biomass feedstock. 
Biodiversity and carbon stock loss if 
from unsustainable biomass harvest. Reduction of air pollutants, fuel 
security, optimal use of residues, 
additional income, health benefits, 
and if implemented well, it can 
enhance biodiversity. Competition for land with biodiversity 
conservation and food production.Substantial contribution in IAMs and 
bottom-up sectoral studies. Note – 
mitigation through avoided GHG 
emissions resulting from bioenergy 
use is of the same magnitude as the 
mitigation from CDR (TS.5.6). {7.4}
Enhanced 
weathering (EW) 3–4 50–200 
(24–578) 2–4 (<1–95) Mining impacts; air quality impacts 
of rock dust when spreading on soil. Enhanced plant growth, 
reduced erosion, enhanced soil carbon, 
reduced soil acidity, enhanced soil 
water retention. Potentially increased emissions from

water supply and energy generation. In a few IAMs; EW complements other 
CDR methods.{12.3}

116
(USD 
management’ in 
coastal wetlands 2–3 Insufficient 
data <1 If degraded or lost, coastal blue 
carbon ecosystems are expected 
to release most of their carbon back 
to the atmosphere; potential for 
sediment contaminants, toxicity, 
in organisms; issues related to 
altering degradability of coastal 
plants; use of sub-tidal areas for tidal 
wetland carbon removal; effect of 
shoreline modifications on sediment 
redeposition and  natural marsh 
accretion; abusive use of coastal blue 
carbon as means to reclaim land 
for purposes that degrade capacity 
for carbon removal. Provide many non-climatic benefits 
and can contribute to ecosystem-
based adaptation, coastal protection, 
increased biodiversity, reduced upper

ocean acidification; could potentially 
benefit human nutrition or produce 
fertiliser for terrestrial agriculture, 
anti-methanogenic feed additive, or 
as an industrial or materials feedstock. If degraded or lost, coastal blue 
carbon ecosystems are likely to 
release most of their carbon back to 
the atmosphere. The full delivery of 
the benefits at their maximum global 
capacity will require years to decades 
to be achieved.Not incorporated in IAMs, but in some 
bottom-up studies: small contribution. {7.4, 12.3.1} 
Ocean 
fertilisation 1–2  50–500  1–3 Nutrient redistribution, restructuring 
of the ecosystem, enhanced oxygen 
consumption and acidification in 
deeper waters, potential for decadal-
to-millennial-scale return to the 
atmosphere of nearly all the extra 
carbon removed, risks of unintended 
side effects.Increased productivity and fisheries, 
reduced upper-ocean acidification. Sub-surface ocean acidification, 
deoxygenation; altered meridional

supply of macro-nutrients as they are 
utilised in the iron-fertilised region 
and become unavailable for transport 
to, and utilisation in other regions, 
fundamental alteration of food 
webs, biodiversity.No data.  {12.3.1} 
enhancement 
(OAE)1–2  40–260   1–100  Increased seawater pH and 
saturation states and may impact 
marine biota. Possible release of 
nutritive or toxic elements and 
compounds. Mining impacts.Limiting ocean acidification. Potentially increased emissions of CO 2 
and dust from mining, transport and 
deployment operations.No data.  {12.3.1} 

117TS
studies reveals how social norms, culture, and individual choices 
interact with infrastructure and other structural changes over time. This 
provides new insight into climate change mitigation strategies, and 
how economic and social activity might be organised across sectors to 
services and not primary energy and physical resources per se. Focusing 
on demand for services and the different social and political roles 
people play broadens the participation in climate action. (Box TS.11)
can help Avoid and Shift final service demands and Improve 
service delivery. Rapid and deep changes in demand make it 
easier for every sector to reduce GHG emissions in the near 
and mid-term (high confidence).  {5.2, 5.3}

potentials compared to the 2050 emissions projection of two scenarios consistent with policies announced by national governments until 2020 
infrastructural, and technological change. The greatest Avoid potential 
comes from reducing long-haul aviation and providing short-distance 
low-carbon urban infrastructures. The greatest Shift potential would 

housing. (Figures TS.20 and TS.21) {5.3.1, 5.3.2, Figures 5.7 and 5.8, 
Table 5.1 and Table SM.5.2}
change mitigation (medium confidence). Among 60 identified 
actions that could change individual consumption, individual mobility 
choices have the largest potential to reduce carbon footprints.  
electric mobility could save 2 tCO 2-eq cap–1 yr –1. Other options with 
high mitigation potential include reducing air travel, cooling setpoint 
adjustments, reduced appliance use, shifts to public transit, and shifting 
consumption towards plant-based diets. {5.3.1, 5.3.1.2, Figure 5.8}

aspects of mitigation {5}. The scope, theories, and evidence for such an assessment are addressed in Sections 5.1 and 5.4 within 
Chapter 5 and a Social Science Primer as an Appendix to Chapter 5. 
mitigation is growing rapidly. A bibliometric search of the literature identified 99,065 peer-reviewed academic papers, based on 
34 search queries with content relevant to Chapter 5. This literature is expanding by 15% per year, with twice as many publications in 
the AR6 period (2014–2020) as in all previous years. 

assumption: agents with perfect information and unlimited processing capacity maximising self-focused expected utility and differing 
only in wealth, risk attitude, and time discount rate. The AR5 introduced a broader range of goals (material, social, and psychological) 
and decision processes (calculation-based, affect-based, and rule-based processes). However, its perspective was still individual- and 
agency-focused, neglecting structural, cultural, and institutional constraints and the influence of physical and social context.
A social science perspective is important in two ways. By adding new actors and perspectives, it (i) provides more options for climate mitigation; 
and (ii) helps to identify and address important social and cultural barriers and opportunities to socio-economic, technological, and institutional 
change. Demand-side mitigation involves five sets of social actors: individuals (e.g.,  consumption choices, habits), groups and collectives

(e.g., social movements, values), corporate actors (e.g., investments, advertising), institutions (e.g., political agency, regulations), and infrastructure 
actors (e.g., very long-term investments and financing). Actors either contribute to the status-quo of global high-carbon consumption, and a GDP 
growth-oriented economy, or help generate the desired change to a low-carbon energy-services, well-being, and equity-oriented economy. 
Each set of actors has novel implications for the design and implementation of both demand- and supply-side mitigation policies. They show 
important synergies, making energy demand mitigation a dynamic problem where the packaging and/or sequencing of different policies play 
a role in their effectiveness {5.5, 5.6}. Incremental interventions change social practices, simultaneously affecting emissions and well-being.

The transformative change requires coordinated action across all five sets of actors (Table 5.4), using social science insights about intersection 
of behaviour, culture, institutional and infrastructural changes for policy design and implementation. Avoid, Shift, and Improve choices by 
individuals, households and communities support mitigation {5.3.1.1, Table 5.1}. They are instigated by role models, changing social norms 
driven by policies and social movements. They also require appropriate infrastructures designed by urban planners and building and transport 
professionals, corresponding investments, and a political culture supportive of demand-side mitigation action.

118
Improve
AvoidA: Providing decent living 
Consumption of top 10% 
(excluding top 1%)37%
Consumer
Role model

No packing
Shift
Vegan diet
Heat pump
equipment 
End users, 
transport
operators, public

Delivered
2050 LED 
Policies, 
Policies, 
2 0 6 -2
Low-carbon lifestyle transition can be classiﬁed into Avoid, Shift, 
and Improve options. Individual potential to reduce emissions is 
highest in mobility systems.Improved service provisioning systems enable increases in 
service levels and at the same time a reduction in upstream 
energy demand by 45%.Transport Building FoodGlobal south Global north
Vehicles, 
Adoption

Adoption 
decisions

119TS
15
10
5
End-use
sectors

Industry
Buildings
measures 
information to guide dietary 
waste management;

compact cities; 
spatial planningCompact cities;

systems 

Mitigation response options related to demand for services have been categorised into three domains: ‘socio-cultural factors’, related to social norms, culture, and individual 
choices and behaviour; ‘infrastructure use’, related to the provision and use of supporting infrastructure that enables individual choices and behaviour; and ‘technology adoption’, 
which refers to the uptake of technologies by end users. Potentials in 2050 are estimated using the International Energy Agency’s 2020 World Energy Outlook STEPS (Stated 
Policy Scenarios) as a baseline. This scenario is based on a sector-by-sector assessment of specific policies in place, as well as those that have been announced by countries by 
mid-2020. This scenario was selected due to the detailed representation of options across sectors and sub-sectors. The heights of the coloured columns represent the potentials

on which there is a high level of agreement in the literature, based on a range of case studies. The range shown by the dots connected by dotted lines represents the highest 
and lowest potentials reported in the literature which have low to medium levels of agreement. The demand-side potential of socio-cultural factors in the food system has 
two parts. The economic potential of direct emissions (mostly non-CO 2) demand reduction through socio-cultural factors alone is 1.9 GtCO 2-eq without considering land-use 
change by diversion of agricultural land from food production to carbon sequestration. If further changes in land use enabled by this change in demand are considered, the 
indicative potential could reach 7 GtCO 2-eq. The electricity panel presents separately the mitigation potential from changes in electricity demand and changes associated with

enhanced electrification in end-use sectors. Electrification increases electricity demand, while it is avoided though demand-side mitigation strategies. Load management refers 
to demand-side flexibility that can be achieved through incentive design such as time-of-use pricing/monitoring by artificial intelligence, diversification of storage facilities, and 
so on. NZE (IEA Net-Zero Emissions by 2050 scenario) is used to compute the impact of end-use sector electrification, while the impact of demand-side response options is 
based on bottom-up assessments. Dark grey columns show the emissions that cannot be avoided through demand-side mitigation options. The table indicates which demand-
side mitigation options are included. Options are categorised according to: socio-cultural factors, infrastructure use, and technology adoption. Figure SPM.7 covers potential of

demand-side options for the year 2050. Figure SPM.8 covers both supply- and demand-side options and their potentials for the year 2030. {5.3, Figure 5.7, 5.SM.II}

120
range from a  factor 10- to 20-fold improvement in the case of 
available energy (exergy) analysis, with the highest improvement 
potentials at the end-user and service-provisioning levels. Realisable 
service level efficiency improvements could reduce upstream energy 
demand by 45% in 2050. (Figure TS.20) {5.3.2, Figure 5.10}
achievable if high-efficiency low-demand mitigation pathways 
are followed (medium confidence).  Minimum requirements of 
and 50 GJ cap–1 yr –1 depending on the context. (Figure TS.22) {5.2.2.1,

5.2.2.2, Box 5.3}Alternative service provision systems, for example, those 
and circular economy initiatives, have to date made 
a limited contribution to climate change mitigation (medium 
confidence).  While digitalisation through specific new products 
and applications holds potential for improvement in service-level 
efficiencies, without public policies and regulations, it also has 
the potential to increase consumption and energy use. Reducing 
the energy use of data centres, networks, and connected devices 
is possible in managing low-carbon digitalisation. Claims on the 
benefits of the circular economy for sustainability and climate 
change mitigation have limited evidence. (Box TS.12, Box TS.14) 
{5.3.4, Figures 5.12 and 5.13}
In AR6, the circular economy (CE) concept {Annex I} is highlighted as an increasingly important mitigation approach that can help

deliver human well-being by minimising waste of energy and resources. While definitions of CE vary, its essence is to shift away from 
linear ‘make and dispose’ economic models to those that emphasise product longevity, reuse, refurbishment, recycling, and material 
efficiency, thereby enabling more circular material systems that reduce embodied energy and emissions. {5.3.4, 8.4, 8.5, 9.5, 11.3.3} 
Whereas IPCC AR4 {WGIII, Chapter 10} included a separate chapter on waste-sector emissions and waste-management practices, 
and AR5 {WGIII, Chapter 10} reviewed the importance of ‘reduce, reuse, recycle’ and related policies, AR6 focuses on how CE can 
reduce waste in materials production and consumption by optimising materials’ end-use service utility. Specific examples of CE 
implementations, policies, and mitigation potentials are included in Chapters 5, 8, 9, 11 and 12. {5.3, 8.4, 9.5, 11.3, 12.6}

CE is shown to empower new social actors in mitigation actions, given that it relies on the synergistic actions of producers, sellers, 
and consumers {11.3.3}. As an energy and resource demand-reduction strategy, it is consistent with high levels of human well-being 
{5.3.4.3} and ensures better environmental quality (Figure TS.22) {5.2.1}. It also creates jobs through increased sharing, reuse, 
refurbishment, and recycling activities. Therefore, CE contributes to several SDGs, including clean water and sanitation (SDG 6), 
affordable energy and clean energy (SDG 7), decent work and economic growth (SDG 8), responsible production and consumption 
(SDG 12) and climate action (SDG 13). {11.5.3.2}
Emissions savings derive from reduced primary material production and transport. For example, in buildings, lifetime extension, 
material efficiency, and reusable components reduce embodied emissions by avoiding demand for structural materials {9.3, 9.5}.

At regional scales, urban/industrial symbiosis reduce primary material demand through by-product exchange networks {11.3.3}. 
CE strategies also exhibit enabling effects, such as material-efficient and circular vehicle designs that also improve fuel economy 
{10.2.2.2}. There is growing interest in ‘circular bioeconomy’ concepts applied to bio-based materials {Box 12.2} and even a ‘circular 
carbon economy’, wherein carbon captured via CCU {11.3.6} or CDR {3.4.6} is converted into reusable materials, which is especially 
relevant for the transitions of economies dependent on fossil fuel revenue. {12.6}
While there are many recycling policies, CE-oriented policies for more efficient material use with higher value retention are 
comparatively far fewer; these policy gaps have been attributed to institutional failures, lack of coordination, and lack of strong

advocates {5.3, 9.5.3.6, Boxes 11.5 and 12.2}. Reviews of mitigation potentials reveal unevenness in the savings of CE applications 
and potential risks of rebound effects {5.3}. Therefore, CE policies that identify system determinants maximise potential emissions 
reductions, which vary by material, location, and application. 
gaps related to its nascent state {3.4.4}. Opportunity exists to bridge knowledge from the industrial ecology field, which has historically 
studied CE, to the mitigation modelling community for improved analysis of interventions and policies for AR7. For instance, a global 
CE knowledge-sharing platform is helpful for CE performance measurement, reporting and accounting. {5.3, 9.5, 11.7}

121TS
has high technical potential and is consistent with providing 
well-being for all (medium confidence). The assessment of 
19  demand-side mitigation options and 18 different constituents 
of well-being showed that positive impacts on well-being outweigh 
for nutrition, shelter, health, and so on, are met in many different ways 
In the near term, many less-developed countries, and poor people 
everywhere, require better access to safe and low-emissions energy 

from service improvements by about 20–25%. (Figure  TS.22) 
{5.2, 5.4.5, Figures 5.3, 5.4, 5.5 and 5.6, Boxes 5.2 and 5.3}
Granular technologies and decentralised energy end-use, 
faster technological learning benefits, greater efficiency, 
more opportunities to escape technological lock-in, and 
greater employment (high confidence). Examples include solar 
PV systems, batteries, and thermal heat pumps. {5.3, 5.5, 5.5.3} 
emissions and have a high potential for emissions reductions 
while maintaining decent living standards and well-being 
(high confidence).  Individuals with high socio-economic status 
and advocating for stringent climate policies. {5.4.1, 5.4.3, 5.4.4,

for change (high confidence).  Motivation by individuals or 
households worldwide to change energy consumption behaviour 
is generally low. Individual behavioural change is insufficient for 
climate change mitigation unless embedded in structural and cultural 
change. Different factors influence individual motivation and capacity 
for change in different demographics and geographies. These factors 
go beyond traditional socio-demographic and economic predictors 
and include psychological variables such as awareness, perceived 
risk, subjective and social norms, values, and perceived behavioural 
control. Behavioural nudges promote easy behaviour change, for 
example, ‘Improve’ actions such as making investments in energy 
efficiency, but fail to motivate harder lifestyle changes (high 
confidence). {5.4}
Behavioural interventions, including the way choices are

presented to end users (an intervention practice known as 
choice architecture), work synergistically with price signals, 
making the combination more effective (medium confidence).  
Behavioural interventions through nudges, and alternative ways 
of redesigning and motivating decisions, alone provide small to 
medium contributions to reduce energy consumption and GHG emissions. Green defaults, such as automatic enrolment in ‘green 
energy’ provision, are highly effective. Judicious labelling, framing, 
and communication of social norms can also increase the effect of 
mandates, subsidies, or taxes. {5.4, 5.4.1, Table 5.3, 5.3}
infrastructure, is necessary to enable and realise many Avoid 
and Shift options (medium confidence). By drawing support from 
diverse actors, narratives of change can enable coalitions to form, 
providing the basis for social movements to campaign in favour of

(or against) societal transformations. People act and contribute to 
Transition pathways and changes in social norms often start 
niche groups (high confidence). Collectively, such initiatives

The current effects of climate change, as well as some 
mitigation strategies, are threatening the viability of existing 
business practices, while some corporate efforts also delay 
mitigation action (medium confidence). Policy packages that 
include job creation programmes can help to preserve social trust, 
livelihoods, respect, and dignity of all workers and employees 
involved. Business models that protect rent-extracting behaviour 
may sometimes delay political action. Corporate advertisement and 
brand-building strategies may also attempt to deflect corporate 
responsibility to individuals or aim to appropriate climate-care

sentiments in their own brand–building. {5.4.3, 5.6.4}
confidence). Building managers, landlords, energy-efficiency 
need greater capacity and motivation to play this role. (Figure TS.20a) 
{5.4.3}

122
Food
Water
Air
Health
Energy
Shelter
Mobility
Education 
Social 
Personal 
Security 
Political 
stability
Economic 
stability
Material 
provision 

Figure TS.23 | Overview of emission mitigation options and their cost and potential for the year 2030. The mitigation potential of each option is the quantity 
of net greenhouse gas emission reductions that can be achieved by a given mitigation option relative to specified emission baselines that reflects what would be considered 
current policies in the period 2015–2019. Mitigation options may overlap or interact and cannot simply be summed together. The potential for each option is broken down into 
cost categories (see legend). Only monetary costs and revenues are considered. If costs are less than zero, lifetime monetary revenues are higher than lifetime monetary costs. 
For wind energy, for example, negative cost indicates that the cost is lower than that of fossil-based electricity production. The error bars refer to the total potential for each

option. The breakdown into cost categories is subject to uncertainty. Where a smooth colour transition is shown, the breakdown of the potential into cost categories is not 
well researched, and the colours indicate only into which cost category the potential can predominantly be found in the literature. {Figure SPM.8, 6.4, Table 7.3, Supplementary 
Material Table 9.SM.2, Supplementary Material Table 9.SM.3, 10.6, 11.4, Figure 11.13, 12.2, Supplementary Material 12.SM.1.2.3}

124
(high confidence).  Preferences are malleable and can align with 
individuals are required to set new social norms. {5.2.1, 5.4} 
The total emission mitigation potential achievable by the 
year 2030, calculated based on sectoral assessments, is 
sufficient to reduce global greenhouse gas (GHG) emissions 
to half of the current (2019) level or less (high confidence). 
This potential – 31–44 GtCO 2-eq – requires the implementation of 
a  wide range of mitigation options. Options with mitigation costs 
lower than USD20 tCO 2–1 make up more than half of this potential

and are available for all sectors. The market benefits of some options 
exceed their costs. (Figure TS.23) {12.2, Table 12.3}
critical for the effectiveness of mitigation action as well as 
for balancing the often conflicting social, developmental, and 
environmental policy goals at the sectoral level (medium 
confidence).  True resource mobilisation plans that properly address 
in isolation of their cross-sectoral implications. There is an urgent 
and delivery mechanisms, including the use of blended financing to 
for resources among sectors. {12.6.4}
Carbon leakage is a  cross-sectoral and cross-country 
consequence of differentiated climate policy (robust evidence,

medium agreement). Carbon leakage occurs when mitigation 
measures implemented in one country/sector leads to increased 
emissions in other countries/sectors. Global commodity value chains 
and associated international transport are important mechanisms 
through which carbon leakage occurs. Reducing emissions from the 
value chain and transportation can offer opportunities to mitigate 
three elements of cross-sectoral spillovers and related leakage: 
(i) domestic cross-sectoral spillovers within the same country; 
(ii) international spillovers within a  single sector resulting from 
substitution of domestic production of carbon-intensive goods with 
their imports from abroad; and (iii) international cross-sectoral 
spillovers among sectors in different countries. {12.6.3}

125TS
or impede rapid progress on mitigation. Chapters 13 and 14 focus 
on policy, governance and institutional capacity, and international 
cooperation, respectively taking a national and international perspective; 
Chapter 15 focuses on investment and finance;  and Chapter 16 focuses 
and governance that nurture new mitigation policies, while 
at the same time reconsidering existing policies that support 
the continued emission of GHGs (high confidence). To do so 
effectively, the scope of climate governance needs to include both

direct efforts to target GHG emissions and indirect opportunities to 
tackle GHG emissions that result from efforts directed towards other 
policy objectives. {13.2, 13.5, 13.6, 13.7, 13.9} 
actors interact (medium evidence, high agreement).  Institutions 
specific countries, sectors, and technologies (high confidence), 
avoiding emissions of several GtCO 2-eq yr –1 (medium 
but complementary roles. The share of global GHG emissions subject

to mitigation policy has increased rapidly in recent years, but big gaps 
remain in policy coverage, and the stringency of many policies falls 
short of what is needed to achieve the desired mitigation outcomes. 
(Box TS.13) {13.6, Cross-Chapter Box 10 in Chapter 14}
into sector policies, enhancing regulatory certainty, 
creating law-backed agencies, creating focal points for 
social mobilisation, and attracting international finance 
(medium evidence, high agreement).  By 2020, ‘direct’ climate 
laws primarily focused on GHG reductions were present in 
56 countries covering 53% of global emissions (Figure TS.24). More 
than 690  laws, including ‘indirect’ laws, however, may also have 
an  effect on mitigation. Among direct laws, ‘framework’ laws set 
an overarching legal basis for mitigation either by pursuing a target

and implementation approach, or by seeking to mainstream climate 
objectives through sectoral plans and integrative institutions. 
(Figure TS.24) {13.2}Institutions can enable improved governance by coordinating 
across sectors, scales and actors, building consensus for action, 
and setting strategies (medium evidence, high agreement).  
Institutions are more stable and effective when they are congruent with 
national contexts, leading to mitigation-focused institutions in some 
countries and the pursuit of multiple objectives in others. Sub-national 
institutions play a  complementary role to national institutions by 
developing locally relevant visions and plans, addressing policy gaps or 
limits in national institutions, building local administrative structures 
and convening actors for place-based decarbonisation. {13.2}
dominant ideas, values and belief systems within a  country

or within a sector are more easily adopted and implemented 
(medium confidence).  Ideas, values and beliefs may change over 
time. Policies that bring perceived direct benefits, such as subsidies, 
usually receive greater support. The awareness of co-benefits for the 
public increases support of climate policies (high confidence). {13.2, 
13.3, 13.4}
substantial changes (medium evidence, high agreement). Key 
structural factors are domestic material endowments (such as fossil 
fuels and land-based resources); domestic political systems; and 
prevalent ideas, values and belief systems. Developing Countries 
A broad group of actors influence how climate governance develop

both pro- and anti-climate action groups. {13.3, 13.4}
Sub-national actors are important for mitigation because 
over climate-relevant sectors such as land use, waste and 
urban policy. They are able to experiment with climate 
solutions and can forge partnerships with the private 
sector and internationally to leverage enhanced climate 
action (high confidence).  More than 10,500 cities and nearly 
sub-national actors often lack national support, funding, and 

governments’ authorisations of high-emitting projects, setting 

in 2010, 2015 and 2020. Climate legislation is defined as an act passed by a parliament that includes the reduction of GHGs in its title or objectives. Panel (b): shares of global 
GHG emissions under national climate emission targets – in 2010, 2015 and 2020. Emissions reductions targets were taken into account as a legislative target when they were 
under the UNFCCC. Targets were included if they were economy-wide or included at least the energy sector. The proportion of national emissions covered are scaled to reflect 
coverage and whether targets are in GHG or CO 2 terms. Emissions data used are for 2019. 2020 data was excluded as emissions shares across regions deviated from past

patterns due to COVID-19. AR6 regions: DEV = Developed countries; APC = Asia and Pacific; EEA = Eastern Europe and West Central Asia; AFR = Africa; LAM = Latin America 
and the Caribbean; ME = Middle East. {Figure 13.1 and 13.2}

127TS
mitigation. This can usefully build public support to 
accelerate mitigation action but may also be used to impede 
decarbonisation (medium evidence, high agreement).  Global 
from about 47,000 articles in 2016–17 to about 87,000 in 2020–21. 
Generally, the media representation of climate science has increased 
and become more accurate over time. On occasion, the propagation 
of scientifically misleading information by organised counter-
movements has fuelled polarisation, with negative implications for 
climate policy. {13.4}
(high confidence). Distributional implications of alternative climate 
policy choices can be usefully evaluated at city, local and national

scales as an input to policymaking. It is anticipated that institutions 
and governance frameworks that enable consideration of justice and 
Just Transitions can build broader support for climate policymaking. 
{13.2, 13.6, 13.8, 13.9}
cost emissions reductions (high confidence). While the coverage 
of emissions trading and carbon taxes has risen to over 20 percent 
of global CO 2 emissions, both coverage and price are lower than is 
needed for deep reductions. Market mechanisms ideally are designed 
to be effective as well as efficient, balance distributional goals and find 
social acceptance. Practical experience has driven progress in market 
mechanism design, especially of emissions trading schemes. Carbon 
pricing is limited in its effect on adoption of higher-cost mitigation 
options, and where decisions are often not sensitive to price incentives,

such as in energy efficiency, urban planning, and infrastructure (robust 
evidence,  medium agreement). Subsidies have been used to improve 
energy efficiency, encourage the uptake of renewable energy and other 
sector-specific emissions-saving options. {13.6}
or used impartially (high confidence).  A carbon levy earmarked 
corresponding to widely accepted notions of fairness increases the 
political acceptability of carbon pricing. {5.6, Box 5.11} 
improve public revenue and macroeconomic performance, 
and yield other environmental and sustainable development 
benefits. Subsidy removal may have adverse distributional impacts 
especially on the most economically vulnerable groups which, in 
some cases can be mitigated by measures such as redistributing

revenue saved, all of which depend on national circumstances (high 
confidence); fossil fuel subsidy removal is projected by various studies 
(using alternative methodologies) to reduce global CO 2 emissions by 
1–4%, and GHG emissions by up to 10% by 2030, varying across 
regions (medium confidence). {6.3, 13.6} {13.6}Regulatory instruments play an important role in achieving 
specific mitigation outcomes in sectoral applications (high 
confidence). Regulation is effective in particular applications 
and often enjoys greater political support, but tends to be more 
economically costly than pricing instruments (robust evidence, 
medium agreement). Flexible forms of regulation (e.g., performance 
standards) have achieved aggregate goals for renewable energy 
generation, vehicle efficiency and fuel standards, and energy efficiency 
in buildings and industry. Infrastructure investment decisions are 
significant for mitigation because they lock-in high- or low-emissions

trajectories over long periods. Information and voluntary programs 
can contribute to overall mitigation outcomes (medium evidence, 
high agreement). Designing for overlap and interactions among 
mitigation policies enhances their effectiveness. {13.6}
fossil fuels tend to negatively affect fossil fuel-exporting countries. 
Creation of markets for emission reduction credits tends to benefit 
countries able to supply credits. Policies to support technology 
development and diffusion tend to have positive spillover effects. 
There is no consistent evidence of significant emissions leakage or 
intensive trade-exposed industries covered by emission-trading 
systems (medium confidence). {13.6}

transitions and shifts in development pathways toward 
low-carbon futures than are individual policies (high 
confidence).  For best effect, they need to be harnessed to a clear 
vision for change and designed with attention to local governance 
context. Comprehensiveness in coverage, coherence to ensure 
complementarity, and consistency of policies with the overarching 
vision and its objectives are important design criteria. Integration 
across objectives occurs when a policy package is informed by a clear 
problem framing and identification of the full range of relevant policy 
subsystems. The climate policy landscape is outlined in Table TS.8, 
which maps framings of desired national policy outcomes to 
policymaking approaches. {13.7, Figure 13.6}

128
mitigation are most usefully identified and assessed prior 
to policymaking rather than being accidentally discovered 
(high confidence). This requires strengthening relevant national 
institutions to reduce silos and overlaps, increasing knowledge 
exchange at the country and regional levels, and supporting 
engagement with bilateral and multilateral funding partners. Local 
governments are well placed to develop policies that generate social 
and environmental co-benefits but to do so require legal backing and 
adequate capacity and resources. {13.8}
given to integrated policy and economy-wide approaches, 
and when enabling conditions (governance, institutions, 
behaviour and lifestyle, innovation, policy, and finance), are 
present (robust evidence, medium agreement).  Accelerating

climate mitigation includes simultaneously weakening high-carbon 
systems and encouraging low-carbon systems; ensuring interaction 
between adjacent systems (e.g., energy and agriculture); overcoming 
resistance to policies (e.g.,  from incumbents in high-carbon-
emitting industries), including by providing transitional support to 
the vulnerable and negatively affected by distributional impacts; 
inducing changes in consumer practices and routines; providing 
transition support; and addressing coordination challenges in policy 
and governance. Table TS.9 elucidates the complexity of policymaking 
in driving sectoral transitions by summarising case studies of 
sectoral transitions from Chapters 5 to 12. These real-world sectoral 
transitions reinforce critical lessons on policy integration. (Table TS.9) 
{13.7, 13.9}Economy-wide packages, including economic-stimulus 
packages, can contribute to shifting sustainable development

pathways and achieving net zero outcomes whilst meeting short-
term economic goals (medium evidence, high agreement).  The 
2008–9 global recession showed that policies for sustained economic 
recovery go beyond short-term fiscal stimulus to include long-term 
commitments of public spending on the low-carbon economy, pricing 
reform, addressing affordability, and minimising distributional impacts. 
COVID-19 spurred stimulus packages and multi-objective recovery 
policies may also have potential to meet short-term economic goals 
while enabling longer-term sustainability goals. (Table TS.8) {13.9}Table TS.8 | Mapping the landscape of climate policy.  {Figure 13.6}
{2.8, 13.6}
Objective: reduce GHG emissions now.
Literature: how to design and implement policy instruments,

with attention to distributional and other concerns.
Examples: carbon tax, cap and trade, border carbon adjustment 
(BCA), disclosure policies.‘Co-benefits’
{5.6.2, 12.4.4, 17.3}
Objective: synergies between mitigation and development.
Literature: scope for and policies to realise synergies and avoid trade-offs across 
climate and development objectives.
Examples: appliance standards, fuel taxes, community forest management, 
sustainable dietary guidelines, green building codes, packages for air pollution, 
packages for public transport.
Enabling transition ‘Socio-technical transitions’
{1.7.3, 5.5, 6.7, 10.8, Cross-Chapter Box 12 in Chapter 16}
Objective:  accelerate low-carbon shifts in socio-technical systems.
Literature: understand socio-technical transition processes, 
integrated policies for different stages of a technology ‘S curve’ 
and explore structural, social and political elements of transitions.
Examples: packages for renewable-energy transition and

coal phase-out; diffusion of electric vehicles, process and fuel 
switching in key industries.‘System transitions to shift development pathways’
{7.4.5, 11.6.6, 13.9, 17.3.3, Cross-Chapter Box 5 in Chapter 4, Cross-Chapter Box 12 
in Chapter 16} 
Objective: accelerate system transitions and shift development pathways to expand 
mitigation options and meet other development goals. 
Literature: examines how structural development patterns and broad cross-sector 
and economy-wide measures drive ability to mitigate while achieving development 
goals through integrated policies and aligning enabling conditions. 
Examples: packages for sustainable urbanisation, land-energy-water nexus 
approaches, green industrial policy, regional Just Transition plans.

129TS
strategic goal (column A), the need for a clear sectoral outcome framing (column B), a carefully coordinated mix of policy instruments and governance actions (column C), 
and the importance of context-specific governance factors (column D). Illustrative examples, drawn from sectors, help elucidate the complexity of policymaking in driving 
sectoral transitions. {Cross-Chapter Box 9 in Chapter 13, Table 1}
provision in Kolkata, 
India {Box 5.8}–  Improve system 
efficiency, sustainability 
and comfort
–  Shift public perceptions 
of public transport–  Strengthen coordination 
–  Procure fuel-efficient, comfortable

low-floor AC buses 
–  Deploy policy actors as 
change-agents, mediating 
between interest groups–  Cultural norms around informal 
transport-sharing, linked to high 
levels of social trust 
–  Historically crucial role of buses 
in transit
–  App-cab companies shifting norms 
and formalising mobility-sharing
–  Digitalisation and safety on board–  Complexity: multiple modes with 
–  Accommodating and addressing 
legitimate concerns from social 
movements about the exclusionary 
effects of ‘premium’ fares, cycling 
bans on busy roads 
LPG subsidy (‘Zero 
Kero’) programme, 
Indonesia {Box 6.3}–  Decrease fiscal 
expenditures on kerosene 
subsidies for cooking–  Subsidise provision of liquefied 
petroleum gas (LPG) cylinders 
and initial equipment
–  Convert existing kerosene 
suppliers to LPG suppliers–  Provincial government and 
industry support in targeting 
beneficiaries and implementation
–  Synergies in kerosene and LPG

distribution infrastructures–  Continued user preference 
for traditional solid fuels
–  Reduced GHG benefits as subsidy 
Action Plan for 
Prevention and Control 
of Deforestation in the 
Legal Amazon, Brazil 
{Box 7.9}–  Control deforestation 
and promote sustainable 
development–  Expand protected areas; 
homologation of indigenous lands
–  Restrict public credit for 
enterprises and municipalities 
with high deforestation rates
–  Set up a REDD+ mechanism 
(Amazon Fund)–  Participatory agenda-setting 
process
–  Mainstreaming of deforestation 
and projects–  Political polarisation 
leading to erosion of 
environmental governance
–  Reduced representation and 
–  Lack of clarity around 
Climate smart cocoa 
(CSC) production,

Ghana {Box 7.12}–  Promote sustainable 
cocoa production
–  Reduce deforestation 
–  Enhance incomes and 
adaptive capacities–  Distribute shade tree seedlings 
–  Provide access to 
agronomic information 
and agrochemical inputs
program including MNCs, 
farmers and NGOs–  Local resource governance 
–  Community governance allowed 
–  Private-sector role in 
popularising CSC–  Lack of secure tenure (tree rights)
–  Bureaucratic and legal hurdles 
to register trees
–  State monopoly on cocoa 
mechanism for 
joining fragmented 
urban policymaking 
in Shanghai, China 
{Box 8.3}– Integrate policymaking 
towards low-carbon 
urban development– Combine central targets and 
evaluation with local flexibility for 
initiating varied policy experiments
– Establish a local leadership 
team for coordinating cross-

sectoral policies involving 
multiple institutions
– Create a direct programme 
fund for implementation and 
capacity-building– Strong vertical linkages between 
central and local levels
– Mandate for policy learning 
– Experience with mainstreaming 
mitigation in related areas 
(e.g., air pollution)– Challenging starting point – 
low share of renewable energy, 
high dependency on fossil fuels
– Continued need for 
high investments in 
a developing context
Policy package for 
building energy 
efficiency, EU 
{Box SM.9.1}– Reduce energy 
consumption, integrating 
renewable energy and 
mitigating GHG emissions 
from buildings– Energy performance standards, 
set at nearly zero energy for 
new buildings
– Energy performance standards 
– Energy performance certificates 
shown during sale
– Long-term renovation strategies– Binding EU-level targets, 
directives and sectoral 
effort-sharing regulations
– Supportive urban policies,

– Funds raised from allowances 
auctioned under the Emissions 
Trading Scheme (ETS)– Inadequate local technical capacity 
to implement multiple instruments
– Complex governance structure 

130
technologies that reduce emissions may be reasonably attributed to policies implemented prior to the observed changes. Such policies 
include regulatory instruments such as energy-efficiency programmes or technical standards and codes, carbon pricing, financial 
support for low-carbon energy technologies and efficiency, voluntary agreements, and regulation of land-use practices.
The vast majority of literature reviewed for this report examines the effect of particular instruments in particular contexts {13.6, 14.3, 
16.4}, and only a small number directly or plausibly infer global impacts of policies. Policies also differ in design, scope, and stringency,

may change over time as they require amendments or new laws, and often partially overlap with other instruments. These factors 
complicate analysis, because they give rise to the potential for double counting emissions reductions that have been observed. These 
lines of evidence on the impact of polices include: 
targets; decomposition analyses that identify policy-related, absolute reductions from historical levels in particular countries. 
{13.6.2, 14.3.3, Cross-Chapter Box 10 in Chapter 14}
• Proximate emission drivers. Trends in the factors that drive emissions including reduced rates of deforestation {7.6.2}, industrial 
energy efficiency {Box 16.3}, buildings energy efficiency {Figure 2.22}, and the policy-driven displacement of fossil fuel combustion

by renewable energy. (Box TS.13, Table 1; Box TS.13, Figure 1) {Chapters 2 and 6, Cross-Chapter Box 10 in Chapter 14} 
• Technologies. The literature indicates unambiguously that the rapid expansion of low-carbon energy technologies is substantially 
attributable to policy. {6.7.5, 16.5}
As illustrated in Box TS.13, Figure 1, these multiple lines of evidence point to policies having had a discernible impact on mitigation for 
specific countries, sectors, and technologies (high confidence), avoiding emissions of several GtCO 2-eq yr –1 globally (medium confidence).A. Illustrative case B. Objective C. Policy mixD. Governance context
African 
trackless trams with 
solar in Bulawayo 
and e-motorbikes in 
Kampala {Box 10.4}– Leapfrog into 
transport future
– Achieve multiple 
social benefits beyond 
mobility provision– Develop urban centres with 
solar at station precincts 
– Public-private partnerships 
for financing

– Sanction demonstration projects 
for new electric transit and new 
electric motorbikes (for freight)– ‘Achieving SDGs’ was an 
enabling policy framing
– Multi-objective policy 
process for mobility, mitigation 
and manufacturing
– Potential for funding through 
– Co-benefits such as local 
employment generation – Economic decline in the first 
decade of the 21st century
– Limited fiscal capacity for public 
– Inadequate charging infrastructure 
for e-motorbikes
Initiative for a climate-
friendly industry 
in North Rhine 
Westphalia (NRW), 
Germany {Box 11.3}– Collaboratively develop 
innovative strategies 
towards a net zero GHG 
industrial sector, while 
securing competitiveness– Build platform to bring 
together industry, scientists 
and government in self-organised 
innovation teams
– Intensive cross-branch 
cooperation to articulate 
policy/infrastructure needs– NRW is Germany’s industrial 
heartland, with an export-oriented

– Established government-
industry ties
– Active discourse between 
industry and public– Compliance rules preventing 
in-depth co-operation 
Food2030 strategy, 
Finland {Box 12.2}– Local, organic and 
climate-friendly 
– Responsible and healthy 
food consumption
– A competitive food 
supply chain– Target funding and knowledge 
support for innovations
– Apply administrative means 
(legislation, guidance) to 
increase organic food production 
and procurement
– Use education and information 
instruments to shift behaviour 
(media campaigns, websites) – Year-long deliberative 
stakeholder engagement 
process across sectors 
– Institutional structures 
for agenda-setting, guiding 
reflexive discussions– Weak role of integrated impact 
assessments (IAMs) to inform 
– Monitoring and evaluation close 
to ministry in charge
– Lack of standardised indicators 
of food system sustainabilityTable TS.9 (continued):

131TS
{Chapter 6}Carbon pricing, emissions standards, and 
technology support have led to declining 
emissions associated with the supply of energy.Carbon pricing and technology support have 
led to improvements in the efficiency of 
energy conversion.A variety of market-based instruments, 
especially technology-support policies have 
led to high diffusion rates and cost reductions 
for renewable energy technologies. 
AFOLU
{Chapter 7}Regulation of land-use rights and practices 
have led to falling aggregate AFOLU-sector 
emissions.Regulation of land-use rights and practices, 
payments for ecosystem service, and offsets, 
have led to decreasing rates of deforestation 
Buildings

{Chapter 9}Regulatory standards have led to reduced 
emissions from new buildings.Regulatory standards, financial support 
for building renovation and market-based 
instruments have led to improvements in 
building and building-system efficiencies.Technology support and regulatory standards 
have led to adoption of low-carbon heating 
systems and high-efficiency appliances.
Transport
{Chapter 10}Vehicle standards, land-use planning, and 
carbon pricing have led to avoided emissions 
in ground transportation.Vehicle standard, carbon pricing, and support 
for electrification have led to automobile 
efficiency improvements.Technology support and emissions standards 
have increased diffusion rates and cost 
reductions for electric vehicles.
Industry
{Chapter 11}Carbon pricing has led to efficiency 
improvements in industrial facilities.

Policies
of low-carbon 
North America; 
and globally.
several countries,

especially developing 

132
substantial avoided emissions, including in 20 countries with Kyoto 
first-commitment period targets that have experienced a decade of 
declining absolute emissions. It also built national capacity for GHG 
accounting, catalysed the creation of GHG markets, and increased 
investments in low-carbon technologies. Other international 
agreements and institutions have led to avoided CO 2 emissions from 
New forms of international cooperation have emerged since 
AR5 in line with an evolving understanding of effective 
mitigation policies, processes, and institutions. Both new and 
pre-existing forms of cooperation are vital for achieving climate

mitigation goals in the context of sustainable development (high 
confidence). While previous IPCC assessments have noted important 
synergies between the outcomes of climate mitigation and achieving 
sustainable development objectives, there now appear to be synergies 
between the two processes themselves (medium confidence). Since 
AR5, international cooperation has shifted towards facilitating national-
level mitigation action through numerous channels, including though 
processes established under the UNFCCC regime and through regional 
and sectoral agreements and organisations. {14.2, 14.3, 14.5, 14.6}
networks is associated with the adoption of climate policies 
actors (high confidence).  International cooperation helps countries 
achieve long-term mitigation targets when it supports development

International cooperation under the UN climate regime took 
an important new direction with the entry into force of the 
2015 Paris Agreement, which strengthened the objective of 
the UN climate regime, including its long-term temperature 
goal, while adopting a  different architecture to that of the 
Kyoto Protocol (high confidence). The core national commitments 
under the Kyoto Protocol were legally binding quantified emission 
targets for developed countries tied to well-defined mechanisms for 
monitoring and enforcement. By contrast, the commitments under 
the Paris Agreement are primarily procedural, extend to all parties, 
and are designed to trigger domestic policies and measures, enhance

transparency, and stimulate climate investments, particularly in 
developing countries, and to lead iteratively to rising levels of 
ambition across all countries. Issues of equity remain of central 
importance in the UN climate regime, notwithstanding shifts in the 
operationalisation of ‘common but differentiated responsibilities and 
respective capabilities’ from Kyoto to Paris. {14.3} 
commitments and mechanisms will lead to the attainment of its stated goals (medium confidence).  Arguments in support of the 
Paris Agreement are that the processes it initiates and supports will 
in multiple ways lead, and indeed have already led, to rising levels of 
ambition over time. The recent proliferation of national mid-century 
net zero GHG targets can be attributed in part to the Paris Agreement. 
Moreover, its processes and commitments will enhance countries’

developing countries. Arguments against the Paris Agreement are that 
it lacks a mechanism to review the adequacy of individual Parties’ 
Nationally Determined Contributions (NDCs), that collectively current 
NDCs are inconsistent in their level of ambition with achieving the 
Paris Agreement’s long-term temperature goal, that its processes will 
not lead to sufficiently rising levels of ambition in the NDCs, and that 
NDCs will not be achieved because the targets, policies and measures 
they contain are not legally binding at the international level. To 
some extent, arguments on both sides are aligned with different 
analytic frameworks, including assumptions about the main barriers 
to mitigation that international cooperation can help overcome. The 
extent to which countries increase the ambition of their NDCs and 
ensure they are effectively implemented will depend in part on the

successful implementation of the support mechanisms in the Paris 
Agreement, and in turn will determine whether the goals of the 
Paris Agreement are met (high confidence). {14.2, 14.3, 14.4} 
and agreements provides critical support for mitigation in 
particular regions, sectors and industries, for particular types 
of emissions, and at the sub- and trans-national levels (high 
confidence).  Agreements addressing ozone depletion, transboundary 
air pollution, and release of mercury are all leading to reductions 
in the emissions of specific greenhouse gases. Cooperation is 
occurring at multiple governance levels including cities. Transnational 
partnerships and alliances involving non-state and sub-national 
actors are also playing a  growing role in stimulating low-carbon 
technology diffusion and emissions reductions (medium confidence). 
Such transnational efforts include those focused on climate litigation;

the impacts of these are unclear but promising. Climate change is 
being addressed in a growing number of international agreements 
operating at sectoral levels, as well as within the practices of many 
multilateral organisations and institutions. Sub-global and regional 
actual examples of these remain limited. {14.2, 14.4, 14.5, 14.6} 
key respects in order to support mitigation action consistent 
with limiting temperature rise to well below 2°C in the context 
of sustainable development and equity (high confidence).  
Many developing countries’ NDCs have components or additional 
actions that are conditional on receiving assistance with respect to

finance, technology development and transfer, and capacity-building, 
greater than what has been provided to date. Sectoral and sub-global 
cooperation is providing critical support, and yet there is room for 
further progress. In some cases, notably with respect to aviation and 
shipping, sectoral agreements have adopted climate mitigation goals 
that fall far short of what would be required to achieve the long-term

133TS
where international cooperation may be hindering mitigation efforts, 
namely evidence that trade and investment agreements, as well as 
efforts (medium confidence). International cooperation is emerging but 
so far fails to fully address transboundary issues associated with solar 
radiation modification (SRM) and carbon dioxide removal (CDR). {14.2, 
14.3, 14.4, 14.5, 14.6, Cross-Working Group Box 4 in Chapter 14}
climate change (medium confidence).  Impartial governance 
such as fair treatment by law-and-order institutions, fair treatment 
demand-side climate policies. High-status (often high-carbon) item

of women, racialised, and marginalised groups, increase the 
democratic impetus for climate action (high confidence).  
makes climate mitigation policies more effective. {5.2, 5.6}
zero-carbon systems (high confidence).  Larger systems take 
are high in the early transition phases. Barriers decrease as a result

Values differ between cultures. Measures that support autonomy, 
energy security and safety, equity and environmental protection, 
and fairness resonate well in many communities and social groups. 
Changing from a  commercialised, individualised, entrepreneurial 
training model to an education cognisant of planetary health 
and human well-being can accelerate climate change awareness 
and action. {5.4.1, 5.4.2}
changes and political action enable the uptake of low-carbon 
choices (high confidence). Policy instruments applied in coordination 
can help to accelerate change in a consistent desired direction. Targeted

technological change, regulation, and public policy can help in steering 
digitalisation, the sharing economy, and circular economy towards 
climate change mitigation. (Boxes TS.12 and TS.14) {5.3, 5.6}
exchange rate, political and governance risks, to information market failures.Complementarity in policies helps in the design of an optimal 
Finance to reduce net GHG emissions and enhance resilience to 
climate impacts is a critical enabling factor for the low-carbon 
transition. Fundamental inequities in access to finance as well as

finance terms and conditions, and countries’ exposure to physical 
impacts of climate change overall, result in a worsening outlook 
for a global Just Transition (high confidence). Decarbonising the 
many developing countries. For these countries the costs and risks of 
financing often represent a  significant challenge for stakeholders at 
all levels. This challenge is exacerbated by these countries’ general 
economic vulnerability and indebtedness. The rising public fiscal costs 
of mitigation, and of adapting to climate shocks, is affecting many 
countries and worsening public indebtedness and country credit 
ratings at a time when there were already significant stresses on public 
finances. The COVID-19 pandemic has made these stresses worse and 
tightened public finances still further. Other major challenges for

commercial climate finance include: the mismatch between capital 
and investment needs, home bias31 considerations, differences in risk 
perceptions for regions, as well as limited institutional capacity to 
ensure safeguards are effective (high confidence). {15.2, 15.6.3}
(high confidence) {15.2, 15.6}.  Climate-related financial risks arise 
from physical impacts of climate change (already relevant in the short 
term), and from a  disorderly transition to a  low-carbon economy. 
Awareness of these risks is increasing, leading also to concerns about 
financial stability. Financial regulators and institutions have responded 
with multiple regulatory and voluntary initiatives to assess and address 
these risks. Yet despite these initiatives, climate-related financial risks

remain greatly underestimated by financial institutions and markets, 
Moreover, risks relating to national and international inequity – which 
act as a barrier to the transformation – are not yet reflected in decisions 
by the financial community. Stronger steering by regulators and 
policymakers has the potential to close this gap. Despite the increasing 
attention of investors to climate change, there is limited evidence that 
this attention has directly impacted emission reductions. This leaves 
high uncertainty, both near term (2021–30) and longer term (2021–
50), on the feasibility of an alignment of financial flows with the Paris 
Agreement goals (high confidence). {15.2, 15.6}

134
(high confidence) {15.2, 15.3}. Persistently high levels of both 
public and private fossil fuel-related financing continue to be of 
major concern despite promising recent commitments. This reflects 
policy misalignment, the current perceived risk-return profile of 
fossil fuel-related investments, and political economy constraints 
32 Climate finance flows refers to local, national, or transnational financing from public, private, and alternative sources, to support mitigation and adaptation actions addressing climate change.(high confidence). Estimates of climate finance flows32 exhibit highly 
divergent patterns across regions and sectors and a slowing growth 
{15.3}. When the perceived risks are too high, the misallocation of 
abundant savings persists and investors refrain from investing in 
infrastructure and industry in search of safer financial assets, even

earning low or negative real returns (high confidence). {15.2, 15.3}
By sector
By region
Europe
Africa
Lower
range
x3
x5
x6x7 x14
x12
x4
x7 x7
x2 x4x2
x8
x7
2018
20192020

in scenarios that likely limit warming to 2°C or lower. Mitigation investment flows and model-based investment requirements by sector / segment (energy efficiency 
in buildings and industry, transport including efficiency, electricity generation, transmission and distribution including electrification, and agriculture, forestry and other land 
use), by type of economy, and by region (see Annex II Part I Section 1: By region is based on intermediate level (R10) classification scheme. By type of economy is based on 
intermediate level (R10) classification scheme, which considers ‘North America’, ‘Europe’, and ’Australia, Japan and New Zealand’ as developed countries, and the other seven

regions as developing countries). Breakdown by sector / segment may differ slightly from sectoral analysis in other contexts due to the availability of investment needs data. 
The granularity of the models assessed in Chapter 3, and other studies, do not allow for a robust assessment of the specific investment needs of LDCs or SIDSs. Investment 
requirements in developing countries might be underestimated due to missing data points as well as underestimated technology costs. In modelled pathways, regional 
investments are projected to occur when and where they are cost cost-effective to limit global warming. The model quantifications help to identify high-priority areas for cost-
effective investments, but do not provide any indication on who would finance the regional investments. Investment requirements and flows covering downstream / mitigation

technology deployment only. Data includes investments with a direct mitigation effect, and in the case of electricity, additional transmission and distribution investments. See 
section 15.4.2 Quantitative assessment of financing needs for detailed data on investment requirements. Data on mitigation investment flows are based on a single series of 
reports (Climate Policy Initiative, CPI) which assembles data from multiple sources. Investment flows for energy efficiency are adjusted based on data from the International 
Energy Agency (IEA). Data on mitigation investments do not include technical assistance (i.e., policy and national budget support or capacity building), other non-technology 
deployment financing. Adaptation only flows are also excluded. Data on mitigation investment requirements for electricity are based on emission pathways C1, C2 and C3

(Table SPM.1). For electricity investment requirements, the upper end refers to the mean of C1 pathways and the lower end to the mean of C3 pathways. Data points for energy 
efficiency, transport and AFOLU cannot always be linked to C1–C3 scenarios. Data do not include needs for adaptation or general infrastructure investment or investment 
related to meeting the SDGs other than mitigation, which may be at least partially required to facilitate mitigation. The multiplication factors show the ratio of average annual 
model-based mitigation investment requirements (2020–2030) and most recent annual mitigation investments (averaged for 2017–2020). The lower and upper multiplication 
factors refer to the lower and upper ends of the range of investment needs.

135TS
Technical SummaryGlobal climate finance is heavily focused on mitigation (more 
than 90% on average between 2017–2020) (high confidence) 
{15.4, 15.5}. This is despite the significant economic effects of climate 
change’s expected physical impacts, and the increasing awareness 
of these effects on financial stability. To meet the needs for rapid 
deployment of mitigation options, global mitigation investments 
are expected to need to increase by the factor of three to six (high 
confidence). The gaps represent a  major challenge for developing 
countries, especially Least-Developed Countries (LDCs), where flows 
have to increase by the factor of four to seven for specific sectors such 
as AFOLU, and for specific groups with limited access to, and high 
costs of, climate finance (high confidence) (Figure TS.25) {15.4, 15.5}. 
The actual size of sectoral and regional climate financing gaps is only 
one component driving the magnitude of the challenge. Financial

and economic viability, access to capital markets, appropriate 
regulatory frameworks, and institutional capacity to attract and 
facilitate investments and ensure safeguards are decisive to scaling-
up funding. Soft costs for regulatory environment and institutional 
capacity, upstream funding needs as well as R&D and venture capital 
for development of new technologies and business models are often 
overlooked despite their critical role to facilitate the deployment of 
scaled-up climate finance (high confidence). {15.4.1, 15.5.2}
and stakeholders in the financial sector to scale up climate  finance 
reflects neither the urgent need for ambitious climate action, 
nor the economic rationale for ambitious climate action (high 
confidence). Delayed climate investments and financing – and limited 
alignment of investment activity with the Paris Agreement – will result

in significant carbon lock-ins, stranded assets, and other additional 
costs. This will particularly impact urban infrastructure and the energy 
and transport sectors (high confidence). A common understanding 
of debt sustainability and debt transparency, including negative 
implications of deferred climate investments on future GDP , and how 
stranded assets and resources may be compensated, has not yet been 
There is a  mismatch between capital availability in the 
developed world and the future emissions expected in 
developing countries (high confidence). This emphasises the 
need to recognise the explicit and positive social value of global 
cross-border mitigation financing. A significant push for international 
climate finance access for vulnerable and poor countries is particularly 
important given these countries’ high costs of financing, debt stress 
and the impacts of ongoing climate change (high confidence). {15.2,

15.3.2.3, 15.5.2, 15.6.1, 15.6.7}
Innovative financing approaches could help reduce the 
systemic under-pricing of climate risk in markets and foster 
demand for investment opportunities aligned with the Paris 
Agreement goals. Approaches include de-risking investments, 
robust ‘green’ labelling and disclosure schemes, in addition 
to a  regulatory focus on transparency and reforming 
international monetary system financial sector regulations 
(medium confidence). Green bond markets and markets for 
sustainable finance products have grown significantly since AR5 
and the landscape continues to evolve. Underpinning this evolution is investors’ preference for scalable and identifiable low-carbon 
products will help by allowing a smooth integration into existing asset 
allocation models (high confidence). Green bond markets and markets 
for sustainable finance products have also increased significantly

since AR5, but challenges nevertheless remain, in particular, there are 
concerns about ‘greenwashing’ and the limited application of these 
markets to developing countries (high confidence). {15.6.2, 15.6.6}
New business models (e.g.,  pay-as-you-go) can facilitate 
the aggregation of small-scale financing needs and provide 
scalable investment opportunities with more attractive 
risk-return profiles (high confidence). Support and guidance 
for enhancing transparency can promote capital markets’ climate 
financing by providing quality information to price climate risks 
and opportunities. Examples include SDG and environmental, social 
and governance (ESG) disclosure, scenario analysis and climate risk 
assessments, including the Task Force on Climate-related Financial 
Disclosures (TCFD). The outcome of these market-correcting 
without appropriate fiscal, monetary and financial policies. Mitigation

emission and climate-resilient options, accelerate the emergence and 
support for financial products based on real projects, such as green 
bonds, and phase-out fossil fuel subsidies. Greater public-private 
cooperation can also encourage the private sector to increase and 
broaden investments, within a context of safeguards and standards, 
and this can be integrated into national climate change policies and 
plans (high confidence). {15.1, 15.2.4, 15.3.1, 15.3.2, 15.3.3, 15.5.2, 
15.6.1, 15.6.2, 15.6.6, 15.6.7, 15.6.8} 
public climate financing over the next decade (2021–2030) 
can help redirect capital markets and overcome challenges 
relating to the need for parallel investments in mitigation. 
It can also help address macroeconomic uncertainty and 
alleviate developing countries’ debt burden post-COVID-19

(high confidence). Providing strong climate policy signals helps 
guide investment decisions. Credible signalling by governments and 
the international community can reduce uncertainty for financial 
decision-makers and help reduce transition risk. In addition to indirect 
and direct subsidies, the public sector’s role in addressing market 
failures, barriers, provision of information, and risk-sharing can 
encourage the efficient mobilisation of private sector finance (high 
confidence) {15.2, 15.6.1, 15.6.2}. The mutual benefits of coordinated 
support for climate mitigation and adaptation in the next decade 
for both developed and developing regions could potentially be very 
high in the post-COVID era. Climate-compatible stimulus packages 
could significantly reduce the macro-financial uncertainty generated 
by the pandemic and increase the sustainability of the world 
economic recovery {15.2, 15.3.2.3, 15.5.2, 15.6.1, 15.6.7}. Political

leadership and intervention remain central to addressing uncertainty, 
which is a fundamental barrier for the redirection of financial flows. 
Existing policy misalignments – for example, in fossil fuel subsidies – 
undermine the credibility of public commitments, reduce perceived 
transition risks and limit financial sector action (high confidence). 
{15.2, 15.3.3, 15.6.1, 15.6.2, 15.6.3}

136
pathway, the greater need for parallel investment decisions 
in upstream and downstream parts of the value chain (high 
confidence). Greater urgency also reduces the lead times to build 
trust in regulatory frameworks. Consequently, many investment 
decisions will need to be made based on the long-term global 
goals. This highlights the importance of trust in political leadership 
which, in turn, affects risk perception and ultimately financing costs 
(high confidence). {15.6.1, 15.6.2}
Accelerated international cooperation on finance is a critical 
enabler of a  low-carbon and Just Transition (very high 
confidence). Scaled-up public grants for adaptation and mitigation, 
and funding for low-income and vulnerable regions, especially in 
Sub-Saharan Africa, may have the highest returns. Key options include: 
increased public finance flows from developed to developing countries

beyond USD100 billion a year; shifting from a direct lending modality 
towards public guarantees to reduce risks and greatly leverage private 
flows at lower cost; local capital markets development; and, changing 
the enabling operational definitions. A coordinated effort to green 
the post-pandemic recovery is also essential in countries facing much 
higher debt costs (high confidence). {15.2, 15.6}
Innovation has also led to, and exacerbated, trade-offs in 
relation to sustainable development.  Innovation can leverage action to mitigate climate change by reinforcing other interventions. 
In conjunction with other enabling conditions, innovation can support 
system transitions to limit warming and help shift development 
pathways. The currently widespread implementation of solar PV

and LED lighting, for instance, could not have happened without 
technological innovation.  Technological innovation can also bring 
about new and improved ways of delivering services that are essential 
to human well-being (high confidence) {16.1, 16.3, 16.4, 16.6}. At the 
same time as delivering benefits, innovation can result in trade-offs that 
externalities’ – for instance, greater environmental pollution and social 
inequalities – rebound effects leading to lower net emission reductions 
or even increases in emissions, and increased dependency on foreign 
knowledge and providers (high confidence). Effective governance and 
policy have the potential to avoid and minimise such misalignments 
(medium evidence,  high agreement). {16.2, 16.3, 16.4, 16.5.1, 16.6} 
A systemic view of innovation to direct and organise the

of innovation takes into account the role of actors, institutions, 
that vary across technologies, sectors and countries, can be 
strengthened (high confidence) {16.2, 16.3, 16.5}. Where 
development and implementation of indicators that are better able 
to provide insights in innovation processes. This, in turn, has enabled 
the analysis and strengthening of innovation systems. Traditional 
quantitative innovation indicators mainly include R&D investments 
and patents. Figure TS.26 illustrates that energy-related research, 
development and demonstration (RD&D) has risen slowly in the last 
fuel cells

1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018Figure is same as Box 16.3 Figure 1

137TS
of funded energy technologies. Systemic indicators of innovation, 
as indicators for how innovation systems function, such as access 
to finance, employment in relevant sectors, and lobbying activities 
{16.3.4, Table  16.7}. For example, in Latin America, monitoring 
systemic innovation indicators for the effectiveness of agroecological 
mitigation approaches has provided insights on the appropriateness 
and social alignment of new technologies and practices {Box 16.5}. 
Climate-energy-economy models, including integrated assessment 
models (IAMs), generally employ a  stylised and necessarily 
incomplete view of innovation, and have yet to incorporate a systemic 
representation of innovation systems. {16.2.4, Box 16.1}

innovation policy instruments (high confidence) {16.4, 16.5}.  
A combination of scaled-up innovation investments with demand-
pull interventions can achieve faster technology unit cost reductions 
and more rapid scale-up than either approach in isolation. These 
innovation policy instruments would nonetheless have to be tailored 
interventions and any trade-offs with sustainable development also 
need to be addressed. Public R&D funding and support, as well as 
innovation procurement, have shown to be valuable for fostering 
innovation in small-to-medium clean-tech firms (Figure  TS.27) 
{16.4.4.3}. Innovation outcomes of policy instruments not necessarily

aimed at innovation, such as feed-in tariffs, auctions, emissions trading schemes, taxes and renewable portfolio standards, vary 
from negligible to positive for climate change mitigation. Some 
specific designs of environmental taxation can also result in negative 
distributional outcomes {16.4.4}. Most of the available literature and 
evidence on innovation systems come from industrialised countries 
and larger developing countries. However, there is a growing body 
of evidence from developing countries and Small Island Developing 
States (SIDS). {16.4, 16.5, 16.7}
not adequately fulfilled; this inhibition occurs more often 
in developing countries (high confidence).  Examples of such 
functions are knowledge development, resource mobilisation, and 
activities that shape the needs, requirements and expectations

Capabilities play a key role in these functions, the buildup of which 
cooperation. For instance, innovation cooperation on wind energy 
has contributed to the accelerated global spread of this technology. 
As another example, the policy guidance by the Indian government, 
which also promoted development of data, testing capabilities and 
knowledge within the private sector, has been a key determinant of 
the success of an energy-efficiency programme for air conditioners 
and refrigerators in India. {16.3, 16.5, 16.6, Cross-Chapter Box 12 in 
Chapter 16, Box 16.3}Demonstration
Prototype
Research
(learning from early

transfer
procurement, carbon 
pricing)
standards
standards
Figure TS.27 | Technology innovation process and the (illustrative) roles of different public policy instruments (on the right-hand side).  {Figure 16.1} Note

138
developing countries can contribute to addressing global 
climate and the SDGs. The effectiveness of such international 
sharing under market conditions appears to be determined 
primarily by the complexity of technologies, local capabilities and 
the policy regime. This suggests that the development of planning 
and innovation capabilities remains necessary, especially in Least-
Developed Countries (LDCs) and SIDS. International diffusion of 
low-emission technologies is also facilitated by knowledge spillovers 
from regions engaged in clean R&D (medium confidence). {16.2} 

a  barrier while other sources suggests that it is an enabler 
to the diffusion of climate-related technologies (medium 
institutional capacity may benefit from a strengthened IPR regime, 
but that countries with limited capabilities might face greater 
barriers to innovation as a consequence. This enhances the continued 
need for capacity-building. Ideas to improve the alignment of the 
global IPR regime and addressing climate change include specific 
arrangements for LDCs, case-by-case decision-making and patent-
pooling institutions. {16.2.3, 16.5, Box 16.10} 
technology cooperation, by strengthening cooperative

(high confidence). The implementation of current arrangements of 
as well as capacity-building, are insufficient to meet climate objectives 
of the Clean Development Mechanism (CDM), operational since 
the mid-2000s, has only led to some technology transfer, especially 
to larger developing countries, but limited capacity building and 
minimal technology development (medium confidence). In the 
current climate regime, a  more systemic approach to innovation 
cooperation could be introduced by linking technology institutions,

such as the Technology Mechanism, and financial actors, such as the 
Financial Mechanism. {16.5.3} 
in parallel with the challenges that relate to climate change. 
are well managed (high confidence).  Obstacles include both 
entrenched power relations dominated by vested interests that control and benefit from existing technologies, and governance 
highlight the potential of cultural factors to strongly influence the 
that can meet local needs, and simultaneously address the SDGs.

Acknowledging the systemic nature of technological innovation  – 
which involve many levels of actors, stages of innovation and 
scales – can lead to new opportunities to shift development pathways 
to decisions can help policymakers take advantage of such 
high-leverage intervention points, address the specific characteristics 
of technological stages, and respond to societal dynamics. Inspiration 
can be drawn from the global unit-cost reductions of solar PV, 
which were accelerated by a  combination of factors interacting 
in a  mutually reinforcing way across a  limited group of countries

(high confidence) {Box 16.2, Cross-Chapter Box 12 in Chapter 16}. 
Transitions can be accelerated by policies appropriately targeted, 
which may be grouped in different ‘pillars of policy’. The relative 
importance of different ‘pillars’ differs according to the stage of the 
transition. (Figure TS.28) {1.2.3}
countries, where such insights are often missing. Data needed 
include those that can show the strength of technological, sectoral 
and national innovation systems. It is also necessary to validate 
current results and generate insights from theoretical frameworks 
and empirical studies for developing countries’ contexts. Innovation 
studies on adaptation and mitigation other than energy and ex-post 
assessments of the effectiveness of various innovation-related

policies and interventions, including R&D, would also provide benefits. 
Furthermore, methodological developments to improve the ability of 
IAMs to capture energy innovation system dynamics and the relevant 
institutions and policies (including design and implementation), 
would allow for more realistic assessment. {16.2, 16.3, 16.7}

growth. The diffusion stage often involves new infrastructure and reconfiguration of existing market and regulatory structures. During the phase of more widespread diffusion, 
growth levels off to linear, then slows as the industry and market matures. The processes displace incumbent technologies/practices which decline, initially slowly, but then at an 
accelerating pace. Many related literatures identify three main levels with different characteristics, most generally termed micro, meso and macro.

140
can also greatly increase energy demand because of the energy used in digital devices (high confidence). {Cross-Chapter Box 11 in 
Chapter 16, 16.2}
Digital devices, including servers, increase pressure on the environment due to the demand for rare metals and end-of-life disposal. The 
absence of adequate governance in many countries can lead to harsh working conditions and unregulated disposal of electronic waste. 
Digitalisation also affects firms’ competitiveness, the demand for skills, and the distribution of, and access to resources. The existing 
digital divide, especially in developing countries, and the lack of appropriate governance of the digital revolution can hamper the role

that digitalisation could play in supporting the achievement of stringent mitigation targets. At present, the understanding of both the 
direct and indirect impacts of digitalisation on energy use, carbon emissions and potential mitigation is limited (medium confidence). 
The digital transformation is a megatrend that is fundamentally changing all economies and societies, albeit in very different ways 
depending on the level of development of a given country and on the nature of its economic system. Digital technologies have 
significant potential to contribute to decarbonisation due to their ability to increase energy and material efficiency, make transport 
and building systems less wasteful, and improve the access to services for consumers and citizens. Yet, if left unmanaged, the digital 
transformation will probably increase energy demand, exacerbate inequities and the concentration of power, leaving developing

economies with less access to digital technologies behind, raise ethical issues, reduce labour demand and compromise citizens’ 
welfare. Appropriate governance of the digital transformation can ensure that digitalisation works as an enabler, rather than as 
a barrier and further strain in decarbonisation pathways. Governance can ensure that digitalisation not only reduces GHG emissions 
intensity but also contributes to reducing absolute GHG emission, constraining run-away consumption. {Cross-Chapter Box 11 in 
Chapter 16, 16.2} 
efficiency. This includes material input savings and increased coordination as they allow the use of fewer inputs to perform a given 
task. Smart appliances and energy management, supported by choice architectures, economic incentives and social norms, effectively

reduce energy demand and associated GHG emissions by 5–10% while maintaining equal service levels. Data centres can also play 
a role in energy-system management, for example by waste-heat utilisation where district heat systems are close by; temporal and 
spatial scheduling of electricity demand can provide about 6% of the total potential demand response. {5.5, Cross-Chapter Box 11, 
Table 1 in Chapter 16}
Digital technologies, analytics and connectivity consume large amounts of energy, implying higher direct energy demand and related 
carbon emissions. Global energy demand from digital appliances reached 7.14 EJ in 2018. The demand for computing services increased 
by 550% between 2010 and 2018 and is now estimated at 1% of global electricity consumption. Due to efficiency improvements, the 
associated energy demand increased only modestly, by about 6% from 2000 to 2018. {Box 9.5}

System-wide effects endanger energy and GHG-emission savings. Rising demand can diminish energy savings, and also produce 
run-away effects associated with additional consumption and GHG emissions if left unregulated. Savings are varied in smart and 
shared mobility systems, as ride-hailing increases GHG emissions due to deadheading, whereas shared pooled mobility and shared 
cycling reduce GHG emissions, as occupancy levels and/or weight per person kilometre transported improve. Systemic effects have 
wider boundaries of analysis and are more difficult to quantify and investigate but are nonetheless very relevant. Systemic effects 
tend to have negative impacts, but policies and adequate infrastructures and choice architectures can help manage and contain these. 
{5.3, 5.4, 5.6}

141TS
also addressing the underlying drivers of vulnerability and emissions. 
Addressing these drivers can enable diverse communities, sectors, 
stakeholders, regions and cultures to participate in just, equitable and 
inclusive processes that improve the health and well-being of people 
and the planet. Looking at climate change from a justice perspective 
also means placing the emphasis on: (i) the protection of vulnerable 
populations from the impacts of climate change, (ii) mitigating 
the  effects of low-carbon transformations, and (iii)  ensuring an 
equitable decarbonised world (high confidence). {17.1}
The SDG framework33 can serve as a  template to evaluate 
the long-term implications of mitigation on sustainable 
development and vice versa (high confidence). Understanding

the co-benefits and trade-offs associated with mitigation 
is key to understanding how societies prioritise among the 
various sectoral policy options (medium confidence). Areas 
with anticipated trade-offs include food and biodiversity, energy 
affordability/access, and mineral-resource extraction. Areas with 
anticipated co-benefits include health, especially regarding air 
pollution, clean energy access and water availability. The possible 
implementation of the different sectoral mitigation options therefore 
depends on how societies prioritise mitigation versus other products 
and services: not least, how societies prioritise food, material well-
being, nature conservation and biodiversity protection, as well as 
considerations such as their future dependence on CDR. Figure TS.29 
summarises the assessment of where key synergies and trade-offs 
exist between mitigation options and the SDGs. (Figures TS.29 and 
TS.31, Table TS.7) {12.3, 12.4, 12.5, 12.6.1, Figures 3.39 and 17.1}

change mitigation and adaptation responses are highly 
context-specific and scale-dependent. There are synergies 
and trade-offs between adaptation and mitigation as well 
as synergies and trade-offs with sustainable development 
(high confidence). Strong links also exist between sustainable 
development, vulnerability and climate risks, as limited economic, 
social and institutional resources often result in low adaptive 
capacities and high vulnerability, especially in developing countries. 
Resource limitations in these countries can similarly weaken the 
capacity for climate mitigation and adaptation. The move towards 
climate-resilient societies requires transformational or deep systemic 
change. This has important implications for countries’ sustainable 
development pathways (medium evidence, high agreement). 
(Box TS.3, Figure TS.29) {4.5, Figure 4.9, 17.3.3}

other sustainable development outcomes depend on policy 
design and can be compensated or avoided with additional 
policies and investments, or through policies that integrate 
mitigation with other SDGs (high confidence). Targeted SDG 
33 The 17 SDGs are at the heart of the UN 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015.policies and investments, for example, in the areas of healthy 
nutrition, sustainable consumption and production, and international 
collaboration, can support climate change mitigation policies and 
resolve or alleviate trade-offs. Trade-offs can also be addressed by 
complementary policies and investments, as well as through the 
design of cross-sectoral policies integrating mitigation with the SDGs, 
and in particular: good health and well-being (SDG 3), zero hunger 
and nutrition (SDG 2), responsible consumption and production 
(SDG 12), reduced inequalities (SDG 10), and life on land (SDG 15).

(Figures TS.29 and TS.30) {3.7}
Decent living standards, which encompasses many SDG 
dimensions, are achievable at lower energy use than 
previously thought (high confidence). Mitigation strategies 
that focus on lowering demand for energy and land-based 
resources exhibit reduced trade-offs and negative consequences for 
sustainable development relative to pathways involving either high 
emissions and climate impacts or pathways with high consumption 
and emissions that are ultimately compensated by large quantities 
of BECCS. Figure  TS.30 illustrates how, in the case of pathways 
limiting warming to 1.5°C (>67%), sustainable development policies 
can lead to overall benefits compared to mitigation policies alone. 
(Figures TS.22 and TS.30) {3.7, 5.2} 
development outcomes in the longer term (high confidence).

the SDGs. {3.7}
Adopting coordinated cross-sectoral approaches to climate 
mitigation can target synergies and minimise trade-offs, 
both between sectors and between sustainable development 
objectives (high confidence).  This requires integrated planning 
using multiple-objective-multiple-impact policy frameworks. 
Strong inter-dependencies and cross-sectoral linkages create both 
opportunities for synergies and need to address trade-offs related 
to mitigation options and technologies. This can only be done if 
coordinated sectoral approaches  to climate change mitigation 
policies are adopted that mainstream these interactions and ensure 
local people are involved in the development of new products, as 
well as production and consumption practices. For instance, there can 
be many synergies in urban areas between mitigation policies and 
the SDGs but capturing these depends on the overall planning of

urban structures and on local integrated policies such as combining 
affordable housing and spatial planning with walkable urban 
areas, green electrification and clean renewable energy (medium 
confidence). Integrated planning and cross-sectoral alignment of 
climate change policies are also particularly evident in developing 
countries’ NDCs under the Paris Agreement, where key priority 
sectors such as agriculture and energy are closely aligned with the 
proposed mitigation and adaptation actions and the SDGs. {12.6.2, 
Supplementary Material Table 17.SM.1, 17.3.3}

142

143TS
level qualitative assessment of the synergies and trade-offs for selected mitigation options. Overlaps may exist in the mitigation options assessed and presented by sector and 
system, and interlinkages with the SDGs might differ depending on the application of that option by sector. Interactions of mitigation options with the SDGs are context-specific 
and dependent on the scale of implementation. For some mitigation options, these scaling and context-specific issues imply that there are both synergies and trade-offs in

relation to specific SDGs. The SDGs are displayed as coloured squares. They indicate whether a synergy, trade-off, or both synergies and trade-offs exist between the SDG and 
the mitigation option. Confidence levels are indicated through the solidity of the squares. A solid square indicates high confidence, a partially filled square indicates medium 
confidence, and an outlined square indicates low confidence. The final column in the figure provides a line of sight to the chapters that provide details on context-specificity and 
scale of implementation. {6.3, 6.4, 6.7, 7.3, 7.4, 7.5, 7.6, 8.2, 8.4, 8.6, 9.4, 9.5, 9.8, Table 9.5, 10.3, 10.4, 10.5, 10.6, 10.8, 11.5, Table 10.3, 17.3, Figure 17.1, Supplementary 
Material Table 17.SM.1, Annex II.IV .12}
–100%
–50%
0%
50%
100%
150%
200%
400%
600%
PM 2.5

F
–100%
–80%
–60%
–40%
–20%
0%
Cooling 
Risk to 
Habitat 
–100%
–50%
0%
Hunger
Health
Water
Energy
 (land)
 (water)
Res’
C&P
Beneﬁt
Harm
PM 2.5
Food waste
Figure TS.30 | Impacts on SDGs of mitigation limiting warming to 1.5°C (>50%) with narrow mitigation policies vs broader sustainable development

policies.  Left: benefits of mitigation from avoided impacts. Middle: sustainability co-benefits and trade-offs of narrow mitigation policies (averaged over multiple models). 
Right: sustainability co-benefits and trade-offs of mitigation policies integrating Sustainable Development Goals. Scale: 0% means no change compared to 3°C (left) or current 
policies (middle and right). Green values correspond to proportional improvements, red values to proportional worsening. Note: only the left panel considers climate impacts on 
sustainable development; the middle and right panels do not. ‘Res’ C&P’ stands for Responsible Consumption and Production (SDG 12). {Figure 3.39}

144
TSTechnical SummaryThe feasibility of deploying response options is shaped 
by barriers and enabling conditions across geophysical, 
environmental-ecological, technological, economic, socio-
cultural, and institutional dimensions (high confidence). 
Accelerating the deployment of response options depends 
on  reducing or removing barriers across these dimensions, as well 
on establishing and strengthening enabling conditions. Feasibility is 
context-dependent, and also depends on the scale and the speed of 
implementation. For example: the institutional, legal and administrative 
capacity to support deployment varies across countries; the feasibility 
of options that involve large-scale land-use changes is highly context-
dependent; spatial planning has a higher potential in early stages of 
urban development; the geophysical potential of geothermal is site-specific; and cultural and local conditions may either inhibit or enable

demand-side responses. Figure TS.31 summarises the assessment of 
barriers and enablers for a broad range of sector-specific, and cross-
sectoral response options. (Box TS.15) {6.4, 7.4, 8.5, 9.10, 10.8, 12.3}
different feasibility challenges (high confidence). These 
challenges are multi-dimensional, context-dependent, malleable 
to policy and to technological and societal trends. They can also 
be reduced by putting in place appropriate enabling conditions. 
Figure TS.32 highlights the dynamic and transient nature of feasibility 
risks. These risks are transient and concentrated in the decades 
before mid-century. Figure TS.32 also illustrates how different EnergyGeophysical Mitigation response options Environmental-

by sector and system, and feasibility might differ depending on the demarcation of that option in each sector. Chapters 6, 8, 9, 10, and 12 assess mitigation response 
options across six  feasibility dimensions: geophysical, environmental-ecological, technological, economic, socio-cultural and institutional. AFOLU (Chapter  7) and industry 
(Chapter 11) are not included because of the heterogeneity of options in these sectors. For each dimension, a set of feasibility indicators was identified. Examples of indicators

include impacts on land use, air pollution, economic costs, technology scalability, public acceptance and political acceptance (see Box TS.15, and Annex II.IV .11 for a detailed 
explanation). An indicator could refer to a barrier or an enabler to implementation, or could refer to both a barrier or an enabler, depending on the context, speed, and scale 
of implementation. Dark blue bars indicate the extent of enablers to deployment within each dimension. This is shown relative to the maximum number of possible enablers, 
as indicated by the light blue shading. Dark orange bars indicate the extent of barriers to deployment within each dimension. This is shown relative to the maximum number 
of possible barriers, as indicated by light orange shading. A light grey dot indicates that there is limited or no evidence to assess the option. A dark grey dot indicates that one

of the feasibility indicators within that dimension is not relevant for the deployment of the option. The relevant sections in the underlying chapters include references to the 
literature on which the assessment is based and indicate whether the feasibility of an option varies depending on context (e.g., region), scale (e.g., small, medium or full scale), 
speed (e.g., implementation in 2030 versus 2050) and warming level (e.g., 1.5°C versus 2°C). {6.4, 8.5, 9.10, 10.8, 12.3, Annex II.IV .11}

145TS
shown are: (i)  plausible – range of values based on past historical trends or other peer reviewed assessments; (ii) best-case scenario – range of values assuming major political 
support or technological breakthrough; (iii) unprecedented – values going beyond those observed or reported in peer-reviewed assessments. Overlayed are the Illustrative

Mitigation Pathways consistent with SSP2 (LD, SP , Ren: C1 category; Neg, GS: C3 category). The positioning of the illustrative pathways is simply indicative of the general trade-
offs over time and across the feasibility dimensions, it is not determined mathematically. (Box TS.5) {3.8}

146
institutional feasibility challenges are shown as unprecedented for 
a high proportion of scenarios, in line with the qualitative literature, 
but moving from 2030 to 2050 and 2100 these challenges decrease. 
pace of technological development and deployment in mitigation 
scenarios is not incompatible with historical records, but rather, institutional capacity is a  key limiting factor for a  successful 
transition. Emerging economies appear to have highest feasibility 
challenges in the near to mid-term. This suggests a key role of policy 
and technology as enabling factors. (Figure TS.32) {3.8} 

the low-carbon transition. (Figures TS.31 and TS.32, Box TS.15) {3.8}
The assessment of feasibility in this report aims to identify barriers and enablers to the deployment of mitigation options and 
pathways. The assessment organises evidence to support policy decisions, and decisions on actions, that would improve the feasibility 
of mitigation options and pathways by removing relevant barriers and by strengthening enablers of change.
Mitigation response options are assessed against six dimensions of feasibility. Each dimension comprises a key set of indicators that 
can be evaluated by combining various strands of literature. {Annex II.IV.11, Table 6.1}

Table.1) would be an enabler or barrier to implementation using a scoring methodology (described in detail in Annex II.IV.11). When 
appropriate, it is also indicated whether the feasibility of an option varies across context, scale, time and temperature goal. The resulting 
scores provide insight into the extent to which each feasibility dimension enables or inhibits the deployment of the relevant option. 
It also provides insight into the nature of the effort needed to reduce or remove barriers, thereby improving the feasibility of individual 
options. {Annex II.IV.11}
– Physical potential

– Geophysical resource availability 
– Land use 
Environmental-ecological feasibilityImpacts on environment:
– Air pollution 
– Toxic waste, ecotoxicity and eutrophication
– Water quantity and quality 
– Biodiversity
Technological feasibilityExtent to which the technology can be implemented at scale soon:
– Simplicity
– Technology scalability
– Maturity and technology readiness
Economic feasibilityFinancial costs and economic effects:
– Costs now, in 2030 and in the long term
– Employment effects and economic growth
Socio-cultural feasibilityPublic engagement and support, and social impacts:
– Public acceptance
– Effects on health and well-being
– Distributional effects 
Institutional feasibilityInstitutional conditions that affect the implementation of the response option:
– Political acceptance 
– Institutional capacity and governance, cross-sectoral coordination
– Legal and administrative capacity

147TS
interactions of mitigation strategies. Recent research has proposed and operationalised frameworks for the feasibility assessment 
of mitigation scenarios. In this report the feasibility assessment of scenarios uses an approach that involves developing a set of 
multi-dimensional metrics capturing the timing, disruptiveness and the scale of the transformative change within five dimensions: 
More than 20 indicators were chosen to represent feasibility dimensions that could be related to scenario metrics. Thresholds of 
feasibility risks of different intensity were obtained through empirical analysis of historical data and assessed literature. Details

of indicators, thresholds, and how they were applied is reported in Annex II.IV.11. {3.8}
shifts in belief systems and market forces (high confidence).  
Many of these factors have come together in a co-evolutionary process 
that has unfolded globally, internationally and locally over several 
decades (low evidence, high agreement). Those same conditions that 
may serve to impede the transition (i.e., organisational structure, 
behaviour, technological lock-in) can also ‘flip’ to enable both the 
transition and the framing of sustainable development policies to

create a  stronger basis for policy support (high confidence). It is 
important to note that strong shocks to these systems, including 
accelerating climate change impacts, economic crises and political 
changes, may provide crucial openings for accelerated transitions 
to sustainable systems. For example, rebuilding more sustainably 
after an extreme event, or renewed public debate about the drivers 
of social and economic vulnerability to multiple stressors (medium 
confidence). {17.4} 
While transition pathways will vary across countries it is 
anticipated that they will be challenging in many contexts 
(high  confidence). Climate change is the result of decades of 
unsustainable production and consumption patterns, as well as 
governance arrangements and political economic institutions that 
lock-in resource-intensive development patterns (high confidence). 
Resource shortages, social divisions, inequitable distributions

of wealth, poor infrastructure and limited access to advanced 
technologies and skilled human resources can constrain the options and capacity of developing countries to achieve sustainable and Just 
Transitions (medium evidence, high agreement) {17.1.1}. Reframing 
development objectives and shifting development pathways towards 
sustainability can help transform these patterns and practices, 
allowing space to transform unsustainable systems (medium 
evidence, high agreement). {1.6, Cross-Chapter Box 5 in Chapter 4, 
17.1, 17.3} 
that prioritise the needs of workers in vulnerable sectors 
(e.g., land, energy) to secure their jobs and maintain secure 
and healthy lifestyles (medium evidence, high agreement).  
{17.3.2}
Actions aligning sustainable development, climate mitigation

and partnerships can support transitions. Strengthening 
different stakeholders’ ‘response capacities’ to mitigate and 
adapt to a changing climate will be critical for a sustainable 
transition (high confidence). {17.1}
explicit consideration being given to the principles of justice, 
equality and fairness (high confidence). {5.2, 5.4, 5.6, 13.2, 13.6, 
13.8, 13.9,17.4}Step 1
Economic
Socio-culturalStep 2
computable based 
on scenario set)Step 3
– 3 high
– 2 medium